List[Dict[str, object]]:\n\n # Validate user first\n if not self.validate(username=username, token=token, check_privilege='instructor'):\n raise RuntimeError(\"User not verified!\")\n\n # Get UID from user's username\n uid = self.get_uid(username=username)\n\n # Query database for courses instructed by a user with this UID\n cursor = self._db_connection.cursor()\n cursor.execute(\n '''\n SELECT \n course_id,\n course_abbreviation,\n course_name, \n time,\n seats \n FROM \n courses\n WHERE \n instructor_id = ?\n ;\n ''', (uid,))\n\n db_results = cursor.fetchall()\n\n if db_results is None:\n print(\"No associated courses found!\")\n return []\n\n # Build information dicts for every course this user is instructing\n courses = []\n for result in db_results:\n # Get the number of students enrolled in this course already\n cursor.execute('''SELECT COUNT(*) FROM enrollment_records WHERE course_id = ?;''', (result[0],))\n students_enrolled = cursor.fetchone()[0]\n if students_enrolled is None:\n students_enrolled = 0\n\n # Build a course dict from the data\n courses.append({\n \"course_abbreviation\": result[1],\n \"course_name\": result[2],\n \"time\": result[3],\n \"students_enrolled\": students_enrolled,\n \"capacity\": result[4],\n })\n\n return courses","def _parse_education(self):\n education = [\n h for h in self.right_column.find_all(\"h4\") if h.text == \"Bildungsweg\"\n ]\n if education:\n education = education[0].nextSibling.nextSibling.find_all(\"li\")\n return {\"education\": [e.text.strip() for e in education]}\n return {}","def generate_section_report(self):\n summary_line = [\"No Students in Section\",0,0,0,0,0,0,0.0]\n day_type_list, dates_list = \\\n load_daytypes_lists(self.start_date, self.total_days,\n self.section, self.total_days)\n self.num_schooldays = len(dates_list)\n if len(self.students):\n summary_line[0] = \"Averages\"\n for i in xrange(0,self.total_days):\n if (((day_type_list[i][0] & \\\n SchoolDB.models.StudentAttendanceRecord.school_day)) or\n ((day_type_list[i][1] & \\\n SchoolDB.models.StudentAttendanceRecord.school_day))):\n #skip non school days\n self.keys_list.append(\"\")\n self.table_data.append(\n self._generate_section_report_day(\n dates_list[i], summary_line)[0])\n if (len(self.table_data)):\n for i in range(1,8):\n summary_line[i] = \\\n round((float(summary_line[i])/ len(self.table_data)),1)\n self.table_data.append(summary_line)\n self.table_descriptor = \\\n [('date','string','Date'),\n ('m_en', 'number', 'Male En'),\n ('f_en', 'number', 'Female En'),\n ('m_morn', 'number', 'Male Morn'),\n ('f_morn', 'number', 'Female Morn'),\n ('m_aft', 'number', 'Male Aft'),\n ('f_aft', 'number', 'Female Aft'),\n ('percent', 'number', '% Present')]","def _fill_section_dict(self, dictionary, section, keys=[], streams=1):\n\t\t# if there is no stream, we just place a note in the dictionary\n\t\tif streams == 0:\n\t\t\tdictionary[section] = 'No stream'\n\t\t\treturn dictionary\n\n\t\t# otherwise we have to fill one subdictionary per stream\t\t\n\t\tfor i in range(streams):\n\t\t\tdictionary[section][i] = {}\n\t\t\t# lookup the information for every key requested and fill the dictionary accordingly\n\t\t\tfor key in keys:\n\t\t\t\tif (section + '_' + str(i) + '_' + key) in self.full_info.keys():\n\t\t\t\t\ttry:\n\t\t\t\t\t\tdictionary[section][i][key] = int(self.full_info[section + '_' + str(i) + '_' + key])\n\t\t\t\t\texcept ValueError:\n\t\t\t\t\t\ttry:\n\t\t\t\t\t\t\tdictionary[section][i][key] = float(self.full_info[section + '_' + str(i) + '_' + key])\n\t\t\t\t\t\texcept ValueError:\n\t\t\t\t\t\t\tdictionary[section][i][key] = self.full_info[section + '_' + str(i) + '_' + key]\n\t\t\n\t\treturn dictionary","def canvas_api_sections(state, course_id):\n\n api = state.canvas_api()\n for section in api.list_sections(course_id):\n click.echo(str(section))","def format_sections(self, sections: SectionDict) -> str:","def dump(self):\n course = {\n \"course_id\": self.course_id,\n \"title\": self.title,\n \"chapters\": {}\n }\n for chapter_num in self.chapters:\n chapter = self.chapters[chapter_num]\n course[\"chapters\"][chapter.num] = {\n \"name\": chapter.name,\n \"lessons\": {lesson_num: lesson_data.name for lesson_num,\n lesson_data in chapter.lessons.items()}\n }\n with open(_JSON_PATH_FORMAT % self.course_id, \"w+\") as fp:\n _logger.debug(\"Dumping the data into a JSON file so that it can \"\n \"be accessed at a later time quickly and without \"\n \"need to scrape LearnItFirst.com, saving time and \"\n \"unnecessary requests.\")\n json.dump(course, fp)","def create_courses():\n\n\t# create list for courses\n\tallcourses = []\n\n\t# load courses as classes in allcourses-list\n\twith open(\"../data/vakken.csv\", \"rt\") as coursefile:\n\n\t\t# clean text\n\t\tcourses = csv.reader(coursefile)\n\t\tfor row in courses:\n\t\t\tfor text in row:\n\t\t\t\tcourse_info = text.split(\";\")\n\n\t\t\t\t# add course name\n\t\t\t\tcourse_name = course_info[0]\n\n\t\t\t\t# add amount of lectures\n\t\t\t\tcourse_lectures = course_info[1]\n\n\t\t\t\t# add amount of seminars\n\t\t\t\tcourse_seminars = course_info[2]\n\n\t\t\t\t# add max amount seminars\n\t\t\t\tcourse_max_sem = course_info[3]\n\t\t\t\tif course_max_sem == \"nvt\":\n\t\t\t\t\tcourse_max_sem = 0\n\n\t\t\t\t# add amount of practicals\n\t\t\t\tcourse_practicals = course_info[4]\n\n\t\t\t\t# add max amount practicals\n\t\t\t\tcourse_max_prac = course_info[5]\n\t\t\t\tif course_max_prac == \"nvt\":\n\t\t\t\t\tcourse_max_prac = 0\n\n\t\t\t\t# add course to list\n\t\t\t\tallcourses.append(Course(course_name, course_lectures, course_seminars, course_max_sem, course_practicals, course_max_prac))\n\n\treturn allcourses","def _get_information(self):\n pros_cons = []\n pros_cons_dict = {}\n\n for i, div in enumerate(self._tab.find_all(\"div\")):\n for p in div.find_all(\"p\"):\n pro_con = p.get_text(strip=True)\n pros_cons.append(pro_con)\n pros_cons_dict.update({self._keys_dict[i]: pros_cons})\n pros_cons = []\n\n return pros_cons_dict","def get_data(contents):\n\n header = {}\n copy = contents.split(\"\\n\")\n\n header_contents = dict(\n start_date=\"Start Date\",\n end_date=\" End Date\",\n subject=\" Subject\",\n experiment=\" Experiment\",\n group=\" Group\",\n box=\" Box\",\n start_time=\" Start Time\",\n end_time=\" End Time\",\n program=\" Program\",\n msn=\" MSN\",\n )\n\n for line in copy:\n for key in header_contents:\n heading = line.split(\":\")\n if heading[0] == header_contents[key]:\n if key == \"start_time\" or key == \"end_time\":\n header[key] = (\n heading[1].lstrip() + \":\" + heading[2] + \":\" + heading[3]\n )\n else:\n header[key] = heading[1].lstrip()\n\n data = {}\n copy = contents.split()\n\n uppercase = string.ascii_uppercase\n\n idx = []\n for i, val in enumerate(copy):\n if val[0] in uppercase and val[1] == \":\":\n idx.append(i)\n\n for i, j in zip(idx[:-1], idx[1:]):\n data[copy[i].lower()[0]] = [\n timestamp for timestamp in copy[i + 1 : j] if timestamp[-1] != \":\"\n ]\n\n return header, data","def cert_info(user, course):\r\n if not course.may_certify():\r\n return {}\r\n\r\n return _cert_info(user, course, certificate_status_for_student(user, course.id))","def convertToDict(self): \n out = dict() \n out[\"Title\"] = self.title \n editions = []\n for edition in self.editions.values(): \n editions.append(edition.convertToDict(withTitle = False))\n out[\"Editions\"] = editions\n return out","def to_dict(self, key, errors):\n c = course_list.CourseListDAO.get_course_for_namespace(key)\n if not c:\n errors.append('Course %s not found' % key)\n return\n entity = {\n 'id': c.namespace,\n 'title': c.title,\n\t\t'category': c.category, }\n json_entity = dict()\n self.registration().convert_entity_to_json_entity(entity, json_entity)\n return json_entity","def get_courses(self, depth=0):\r\n return self.courses.values()","def make_gradebook(roster, grades, sub_info):\n gradebook = []\n for student in roster.keys():\n s = {}\n # fill student file with evaluation grades\n for day, score in zip(sub_info.keys(), grades):\n s[str(day)] = score[student]\n s['total'] = sum(s.values())\n s['username'] = student\n gradebook.append(s)\n return gradebook","def _populate_section(self, algo_group, result_template):\n if algo_group.module == CONNECTIVITY_MODULE:\n result_template[KEY_SECTION] = 'connectivity'\n result_template[KEY_SUB_SECTION] = 'connectivity'\n result_template[KEY_SUBMENU_LIST] = self.connectivity_submenu\n elif algo_group.group_category.display:\n ### Visualizers on the Burst Page\n result_template[KEY_SECTION] = 'burst'\n result_template[KEY_SUB_SECTION] = 'view_' + algo_group.subsection_name\n\n elif algo_group.group_category.rawinput:\n ### Upload algorithms\n result_template[KEY_SECTION] = 'project'\n result_template[KEY_SUB_SECTION] = 'data'\n elif 'RAW_DATA' in algo_group.group_category.defaultdatastate:\n ### Creators\n result_template[KEY_SECTION] = 'stimulus'\n result_template[KEY_SUB_SECTION] = 'stimulus'\n else:\n ### Analyzers\n result_template[KEY_SECTION] = algo_group.group_category.displayname.lower()\n result_template[KEY_SUB_SECTION] = algo_group.subsection_name\n result_template[KEY_SUBMENU_LIST] = self.analyze_adapters","def _write_section_values(section_data, fobj):\n\n # Order is significant.\n section_dict = OrderedDict()\n section_dict['Armor'] = section_data.get('armor')\n section_dict['Internals'] = section_data.get('internals')\n section_dict['Rear'] = section_data.get('rear')\n section_dict['Config'] = section_data.get('config')\n\n for name, value in section_dict.items():\n if not value:\n continue\n val_str = \" {name:<14} {{ {value} }}\\n\".format(\n name=name, value=value)\n fobj.write(val_str)","def populate_course(self):\r\n def descend(parent, stack):\r\n xblock_type = stack.pop(0)\r\n for _ in range(2):\r\n child = ItemFactory.create(category=xblock_type, parent_location=parent.location)\r\n if stack:\r\n descend(child, stack)\r\n\r\n descend(self.course, ['chapter', 'sequential', 'vertical', 'problem'])","def __init__(self, test_num):\n if test_num == 1:\n self.degree_chosen = \"Science\"\n self.is_double_major = True\n self.major = \"Computer Science\"\n self.major2 = \"Mathematics\"\n self.user_courses_dict = {\n \"COSC121\": UniCourse(\"COSC121\", \"Introduction to Computer Science\"),\n \"MATH120\": UniCourse(\"MATH120\", \"Discrete Mathematics\")\n }\n self.all_courses_dict = {\n \"COSC121\": UniCourse(\"COSC121\", \"Introduction to Computer Science\"),\n \"MATH120\": UniCourse(\"MATH120\", \"Discrete Mathematics\"),\n \"MATH101\": UniCourse(\"MATH101\", \"Methods of Mathematics\")\n }","def createDictionnary():\n dic = {}\n root = \"https://www.airlinequality.com\"\n url_page = root+\"/review-pages/a-z-airport-reviews/\"\n\n headers = {\n 'User-Agent': 'Mozilla/5.0 (Windows NT 6.1) AppleWebKit/537.36 (KHTML, like Gecko) Chrome/41.0.2228.0 Safari/537.3'}\n req = Request(url_page, headers=headers)\n webpage = urlopen(req).read()\n soup = bs(webpage, 'html.parser')\n\n r = soup.find_all('li')\n list_text = [str(val) for val in r if \"href=\\\"/airport-reviews/\" in str(val)\n and \"article\" not in str(val)]\n for texte in list_text:\n airport, link = getAirportLinks(texte)\n dic[airport.rstrip()] = root+link\n\n return dic","def create_tag_sections(self, rule=None):\n tag_names = ['h{}'.format(i) for i in range(1, 6)]\n for tag_name in tag_names:\n tags = self.soup.find_all(tag_name) # Tags corresponded to headings\n for each_tag in tags:\n inside_tags = [item for item in itertools.takewhile(\n lambda t: t.name not in [each_tag.name, 'script'],\n each_tag.next_siblings)]\n section = self.soup.new_tag('section_{}'.format(tag_name))\n each_tag.wrap(section)\n for tag in inside_tags:\n section.append(tag)","def proc_education_summary( summary: Tag ) -> Dict[str, str]:\n edu_record = dict()\n edu_record['school'] = summary.find('h3').text.strip()\n edu_record['is_abroad_school'] = _is_abroad_school( edu_record['school'] )\n\n for parag in summary.find_all('p'):\n spans = [span.text.strip() for span in parag.find_all('span')]\n if len( spans ) == 2:\n fld_name, value = spans\n value = value.strip()\n elif len(spans) == 0:\n # print( 'education parag: ', parag )\n edu_record['description'] = parag.text.strip()\n continue\n else:\n print( 'education spans: ', spans )\n continue\n\n if fld_name == 'Nombre de la titulación':\n edu_record['degree_raw'] = value\n edu_record['degree'] = _classify_degree( value )\n # print( 'degree: ', value, _classify_degree(value) )\n elif fld_name == 'Disciplina académica':\n edu_record['field_raw'] = value\n elif fld_name == 'Nota':\n edu_record['grade_raw'] = value\n elif fld_name.startswith('Fechas de estudios'):\n edu_record['period_raw'] = value\n elif fld_name.startswith('Actividades y asociaciones'):\n edu_record['activities_raw'] = value\n else:\n print(\"proc_education_summary: \", fld_name, ' :: ', value)\n\n if edu_record.get('degree', 'Unknown') == 'Unknown':\n if re.search( 'Ingenier|Engineering', edu_record.get('field_raw', '') ):\n edu_record['degree'] = 'University'\n\n return edu_record\n # %%","def convert_heading_tree_to_dict(heading_tree_tuples):\n return [convert_heading_node_to_dict(heading_node) for heading_node in heading_tree_tuples]"],"string":"[\n \"def _section_course_info(course_key, access):\\r\\n course = get_course_by_id(course_key, depth=None)\\r\\n\\r\\n section_data = {\\r\\n 'section_key': 'course_info',\\r\\n 'section_display_name': _('Course Info'),\\r\\n 'access': access,\\r\\n 'course_id': course_key,\\r\\n 'course_display_name': course.display_name,\\r\\n 'enrollment_count': CourseEnrollment.num_enrolled_in(course_key),\\r\\n 'has_started': course.has_started(),\\r\\n 'has_ended': course.has_ended(),\\r\\n 'list_instructor_tasks_url': reverse('list_instructor_tasks', kwargs={'course_id': course_key.to_deprecated_string()}),\\r\\n }\\r\\n\\r\\n try:\\r\\n advance = lambda memo, (letter, score): \\\"{}: {}, \\\".format(letter, score) + memo\\r\\n section_data['grade_cutoffs'] = reduce(advance, course.grade_cutoffs.items(), \\\"\\\")[:-2]\\r\\n except Exception:\\r\\n section_data['grade_cutoffs'] = \\\"Not Available\\\"\\r\\n # section_data['offline_grades'] = offline_grades_available(course_key)\\r\\n\\r\\n try:\\r\\n section_data['course_errors'] = [(escape(a), '') for (a, _unused) in modulestore().get_course_errors(course.id)]\\r\\n except Exception:\\r\\n section_data['course_errors'] = [('Error fetching errors', '')]\\r\\n\\r\\n return section_data\",\n \"def export_courses():\\n courses = Course.query().fetch()\\n dictionary = {}\\n\\n for course in courses:\\n dictionary[course.department + \\\"\\\" + course.number] = course.to_dict()\\n\\n return dictionary\",\n \"def make_datatable(self):\\r\\n\\r\\n data = []\\r\\n\\r\\n for course in self.get_courses():\\r\\n gdir = course.id.course\\r\\n data.append([course.display_name, course.id.to_deprecated_string()]\\r\\n + self.git_info_for_course(gdir))\\r\\n\\r\\n return dict(header=[_('Course Name'), _('Directory/ID'),\\r\\n _('Git Commit'), _('Last Change'),\\r\\n _('Last Editor')],\\r\\n title=_('Information about all courses'),\\r\\n data=data)\",\n \"def _create_sections(self):\\n self._SECTIONS = {}\",\n \"def get_course(data):\\n\\n return {item['course'] for item in data}\",\n \"def build_course(self):\\n courses = []\\n aprovacao_d = {}\\n # semestral\\n for rate_it in self.__semestral_rate:\\n # pega uma lista no qual o primeiro elemento é a taxa, o segundo\\n # e o terceiro são quantidades\\n rate_data = self.analysis[\\\"semestral_rate\\\"][rate_it.name]\\n for i in rate_data[0].index:\\n if i[0] not in aprovacao_d:\\n aprovacao_d[i[0]] = {}\\n\\n periodo = str(i[1]) + \\\"/\\\" + str(i[2])\\n aprovacao_d[i[0]][periodo] = [\\n float(rate_data[0][i]),\\n int(rate_data[1][i]),\\n int(rate_data[2][i]),]\\n\\n note = self.analysis[\\\"general_note_statistic\\\"]\\n note_last_year = self.analysis[\\\"last_year_statistic\\\"]\\n for course in self.analysis[\\\"courses\\\"].index:\\n course_dict = {}\\n course_dict[\\\"disciplina_codigo\\\"] = course\\n course_dict[\\\"disciplina_nome\\\"] = self.analysis[\\\"courses\\\"][course]\\n \\n # If the course code is related to more than one name,\\n # concatenate these names into an unique string\\n if type(course_dict[\\\"disciplina_nome\\\"]) != str:\\n new_course_name = \\\" | \\\".join(list(course_dict[\\\"disciplina_nome\\\"]))\\n course_dict[\\\"disciplina_nome\\\"] = new_course_name\\n # quantidade de matriculas\\n count = self.analysis[\\\"general_count_submission\\\"][course]\\n course_dict[\\\"qtd_alunos\\\"] = count\\n # notas\\n course_dict[\\\"qtd_cursada_aprov\\\"] = self.analysis[\\\"coursed_ratio\\\"][course]\\n course_dict[\\\"nota\\\"] = [note[0][course], note[1][course]]\\n course_dict[\\\"nota_ultimo_ano\\\"] = [\\n note_last_year[0][course],\\n note_last_year[1][course]\\n ]\\n # taxas\\n for rate_it in self.__rates:\\n rate_data = self.analysis[\\\"general_rates\\\"][rate_it.name]\\n course_dict[rate_it.name] = float(rate_data[0][course])\\n course_str = rate_it.name.replace(\\\"taxa\\\", \\\"qtd\\\")\\n # count_sel define qual quantidade vai para o json, a especifica\\n # ou geral\\n course_dict[course_str] = int(\\n rate_data[rate_it.count_sel][course])\\n # rate_calc = self.analysis[\\\"general_rates\\\"][rate_it.name][0]\\n\\n # taxas do ultimo anos\\n course_dict[\\\"taxa_reprovacao_ultimo_absoluto\\\"] = self.analysis[\\\"last_year_taxa_reprovacao_absoluta\\\"][course]\\n course_dict[\\\"taxa_reprovacao_ultimo_frequencia\\\"] = self.analysis[\\\"last_year_taxa_reprovacao_frequencia\\\"][course]\\n\\n course_dict[\\\"grafico_qtd_cursada_aprov\\\"] = self.analysis[\\\"coursed_count\\\"][course]\\n course_dict[\\\"aprovacao_semestral\\\"] = aprovacao_d[course]\\n courses.append(course_dict)\\n return courses\",\n \"def build_course_dictionary(title_result_set, desc_result_set) -> Dict[str, List[str]]:\\n\\n course_dictionary = {} # placeholder dictionary\\n\\n for (tagged_title, tagged_description) in zip(title_result_set, desc_result_set): # iterate through multiple result sets\\n full_title_desc_list = {}\\n full_title_desc_list = [str(tagged_title.text)] + str(tagged_description.text).strip().splitlines() # remove trailing whitespace, then get list of lines\\n course_dictionary[str(tagged_title.text)[:8]] = full_title_desc_list\\n\\n return course_dictionary\",\n \"def sections(self):\\r\\n # Dict to store the result\\r\\n nav_dict = dict()\\r\\n\\r\\n section_titles = self._section_titles()\\r\\n\\r\\n # Get the section titles for each chapter\\r\\n for sec_index, sec_title in enumerate(section_titles):\\r\\n\\r\\n if len(section_titles) < 1:\\r\\n self.warning(\\\"Could not find subsections for '{0}'\\\".format(sec_title))\\r\\n else:\\r\\n # Add one to convert list index (starts at 0) to CSS index (starts at 1)\\r\\n nav_dict[sec_title] = self._subsection_titles(sec_index + 1)\\r\\n\\r\\n return nav_dict\",\n \"def parse_courses():\\n\\n subjects = collections.OrderedDict()\\n name = '' # the most recent course name acronym (ex. 'COMP')\\n\\n courses = re.sub(r'\\\\([^)]*\\\\)', '', COURSES).split() # Remove parens and their contents\\n\\n for course in courses:\\n if course == 'OR':\\n continue\\n\\n if course[0].isalpha():\\n\\n index = 0 # the upper bound character index of the subject name\\n for char in course:\\n if char.isalpha():\\n index += 1\\n else:\\n break\\n\\n name = course[:index]\\n number = course[index:index+4]\\n else:\\n number = course[:4]\\n\\n try:\\n subjects[name].append(number)\\n except KeyError:\\n subjects[name] = [number]\\n\\n return subjects\",\n \"def parse_create_section(xml_course):\\n\\n attrs = [\\n \\\"section\\\",\\n 'crn',\\n \\\"start-time\\\",\\n \\\"end-time\\\",\\n \\\"meeting-days\\\",\\n \\\"location\\\",\\n \\\"section-number\\\",\\n \\\"instructor\\\"\\n ]\\n\\n section = pull_attributes_from_xml(xml_course, attrs)\\n\\n section[\\\"places\\\"] = []\\n\\n # Create Place attribute pointer based on location string\\n # Get places from Parse\\n places = get_places()[\\\"results\\\"]\\n # Get location info from section (of form [\\\"BRK 101\\\", \\\"TBA\\\"])\\n all_locations = section[\\\"location\\\"].split(\\\", \\\")\\n # Filter out TBA\\n # TODO Maybe do something else with them\\n locations = [location for location in all_locations if location != \\\"TBA\\\"]\\n\\n for location in locations:\\n building_code = location.split(\\\" \\\")[0]\\n for place in places:\\n if place.get(\\\"symbol\\\") and place[\\\"symbol\\\"] == building_code:\\n section[\\\"places\\\"].append(place[\\\"objectId\\\"])\\n break;\\n\\n\\n return section\",\n \"def get_sections(pages):\\n sections = {}\\n section_pages = {}\\n current_section_name = None\\n current_section = []\\n\\n for page_num, page in enumerate(pages):\\n clean_page = [re.sub(\\\"\\\\s+\\\", \\\" \\\", i.strip()) for i in page.split(\\\"\\\\n\\\")]\\n\\n for ind, i in enumerate(clean_page):\\n\\n if (\\n re.findall(\\\"^Section \\\\d+\\\", i)\\n and \\\"page\\\" not in i\\n or (re.sub(\\\"\\\\d+ [\\\\w+\\\\s+]+\\\", \\\"\\\", i) == \\\"\\\" and ind == 0 and len(i) > 6)\\n ):\\n if current_section_name is not None:\\n sections[current_section_name] = current_section\\n current_section = []\\n current_section_name = i\\n break\\n\\n section_pages[page_num + 1] = current_section_name or \\\"No Section\\\"\\n\\n current_section.extend(clean_page)\\n return sections, section_pages\",\n \"def build_general_course(self):\\n\\n courses = {}\\n\\n if self.__build_analyze is False:\\n self.build_analysis()\\n\\n courses[\\\"taxa_conhecimento\\\"] = self.analysis[\\\"taxa_conhecimento\\\"]\\n courses[\\\"taxa_reprovacao\\\"] = self.analysis[\\\"taxa_reprovacao_absoluta\\\"]\\n courses[\\\"taxa_trancamento\\\"] = self.analysis[\\\"taxa_trancamento\\\"]\\n\\n # cria cache\\n cache = {}\\n note = self.analysis[\\\"general_note_statistic\\\"]\\n for rate_it in self.__rates:\\n rate_calc = self.analysis[\\\"general_rates\\\"][rate_it.name][0]\\n for course in self.analysis[\\\"courses\\\"].index:\\n if course not in cache:\\n cache[course] = {}\\n cache[course][rate_it.name] = rate_calc[course]\\n cache[course][\\\"nota\\\"] = [note[0][course], note[1][course]]\\n\\n courses[\\\"cache\\\"] = cache\\n\\n # cria o campo compara_aprov\\n courses[\\\"compara_aprov\\\"] = self.analysis[\\\"graph_course\\\"]\\n\\n # cria o campo courses\\n courses[\\\"disciplinas\\\"] = self.analysis[\\\"courses\\\"].to_dict()\\n\\n return courses\",\n \"def parse_create_course(xml_course):\\n attrs = [\\n \\\"term-code\\\",\\n \\\"term-description\\\",\\n 'subject',\\n \\\"course-number\\\",\\n \\\"school\\\",\\n \\\"department\\\",\\n \\\"title\\\",\\n \\\"description\\\",\\n \\\"credit-hours\\\",\\n \\\"distribution-group\\\"\\n ]\\n course = pull_attributes_from_xml(xml_course, attrs)\\n course[\\\"sections\\\"] = []\\n\\n return course\",\n \"def all_courses(records):\\n \\n course_and_id_dict = {} #This creates an empty dictionary\\n for all_tuples in records:\\n course_info_tuple = all_tuples[0] #Extracts all course information\\n course_id = course_info_tuple[0]\\n course_name = course_info_tuple[1]\\n \\n course_and_id_dict[course_id] = course_name\\n \\n return course_and_id_dict\",\n \"def _section_membership(course_key, access):\\r\\n section_data = {\\r\\n 'section_key': 'membership',\\r\\n 'section_display_name': _('Membership'),\\r\\n 'access': access,\\r\\n 'enroll_button_url': reverse('students_update_enrollment', kwargs={'course_id': course_key.to_deprecated_string()}),\\r\\n 'unenroll_button_url': reverse('students_update_enrollment', kwargs={'course_id': course_key.to_deprecated_string()}),\\r\\n 'modify_beta_testers_button_url': reverse('bulk_beta_modify_access', kwargs={'course_id': course_key.to_deprecated_string()}),\\r\\n 'list_course_role_members_url': reverse('list_course_role_members', kwargs={'course_id': course_key.to_deprecated_string()}),\\r\\n 'modify_access_url': reverse('modify_access', kwargs={'course_id': course_key.to_deprecated_string()}),\\r\\n 'list_forum_members_url': reverse('list_forum_members', kwargs={'course_id': course_key.to_deprecated_string()}),\\r\\n 'update_forum_role_membership_url': reverse('update_forum_role_membership', kwargs={'course_id': course_key.to_deprecated_string()}),\\r\\n }\\r\\n return section_data\",\n \"def _section_metrics(course_key, access):\\r\\n section_data = {\\r\\n 'section_key': 'metrics',\\r\\n 'section_display_name': ('Metrics'),\\r\\n 'access': access,\\r\\n 'course_id': course_key.to_deprecated_string(),\\r\\n 'sub_section_display_name': get_section_display_name(course_key),\\r\\n 'section_has_problem': get_array_section_has_problem(course_key),\\r\\n 'get_students_opened_subsection_url': reverse('get_students_opened_subsection'),\\r\\n 'get_students_problem_grades_url': reverse('get_students_problem_grades'),\\r\\n 'post_metrics_data_csv_url': reverse('post_metrics_data_csv'),\\r\\n }\\r\\n return section_data\",\n \"def create_variables(self, courses):\\n has_sections = isinstance(courses, dict)\\n for course in courses:\\n self.p.add_variable(course, courses.get(course, []) if has_sections else self.get_sections(course))\",\n \"def build_course_map(course_content):\\n course_blocks = []\\n\\n for key, course_data in course_content.items():\\n block_data = {}\\n block_data['id'] = parse_id(key)\\n block_data['type'] = course_data['category']\\n\\n try:\\n block_data['name'] = course_data['metadata']['display_name']\\n except KeyError:\\n block_data['name'] = block_data['id']\\n\\n customize_by_type(course_data, block_data)\\n add_children(course_data, block_data)\\n\\n course_blocks.append(block_data)\\n\\n return course_blocks\",\n \"def parseSections(data):\\n pro = _sectionSplit.split(data)\\n sections = {}\\n for x in xrange(1, len(pro), 2):\\n sections[pro[x]] = pro[x+1]\\n return sections\",\n \"def extract_course_summary(course):\\n return {\\n field: course.fields[field]\\n for field in CourseSummary.course_info_fields\\n if field in course.fields\\n }\",\n \"def create_dict(rows, tag, tag_id=None, start=0, enroll=False):\\n enrollment_info_map = {\\n 'Enrollment Requirement': 'requirements',\\n 'Add Consent': 'add_consent',\\n 'Drop Consent': 'drop_consent',\\n }\\n\\n data = {}\\n\\n for row in rows:\\n name_raw, desc_raw = row.find_all(tag, id=tag_id)[start:]\\n name = name_raw.text.strip()\\n desc = desc_raw.text.encode('ascii', 'ignore').decode().strip()\\n\\n if enroll:\\n name = enrollment_info_map[name]\\n else:\\n name = name.lower().replace(' / ', '_')\\n\\n data.update({name: desc})\\n\\n return data\",\n \"def get_sections(h):\\n secnames = {}\\n resec = re.compile('(\\\\w+)\\\\[(\\\\d*)\\\\]')\\n for sec in h.allsec():\\n g = resec.match(sec.name())\\n if g.group(1) not in secnames.keys():\\n secnames[g.group(1)] = [int(g.group(2))]\\n else:\\n secnames[g.group(1)].append(int(g.group(2)))\\n return secnames\",\n \"def get_courses_info(url, headers):\\n dash = get_page_contents(url, headers)\\n soup = BeautifulSoup(dash)\\n courses_soup = soup.find_all('article', 'course')\\n courses = []\\n for course_soup in courses_soup:\\n course_id = None\\n course_name = course_soup.h3.text.strip()\\n course_url = None\\n course_state = 'Not yet'\\n try:\\n # started courses include the course link in the href attribute\\n course_url = BASE_URL + course_soup.a['href']\\n if course_url.endswith('info') or course_url.endswith('info/'):\\n course_state = 'Started'\\n # The id of a course in edX is composed by the path\\n # {organization}/{course_number}/{course_run]\\n course_id = course_soup.a['href'][9:-5]\\n except KeyError:\\n pass\\n courses.append(Course(id=course_id,\\n name=course_name,\\n url=course_url,\\n state=course_state))\\n return courses\",\n \"def section(data):\\n if len(data['index']) == 2 and data['index'][1][0].isdigit():\\n element = {}\\n element['is_section'] = True\\n element['section_id'] = '-'.join(data['index'])\\n if u\\\"§§ \\\" == data['title'][:3]:\\n element['is_section_span'] = True\\n else:\\n element['is_section_span'] = False\\n match = SECTION_TITLE_REGEX.match(data['title'])\\n element['label'] = match.group(1)\\n element['sub_label'] = match.group(2)\\n return element\",\n \"def get_all(): \\n cdict = {}\\n for section in rcp.sections():\\n cdict[section] = get_section(section)\\n return cdict\",\n \"def build_course_sections(self, course_section_data):\\n return [self.build_course_section(**row) for row in course_section_data]\",\n \"def spamHeaders(self) -> Tuple[List[str], Dict[str, str]]:\\n sections = [\\\"STATUS\\\", \\\"TITLE\\\", \\\"PROJECT\\\", \\\"FILE\\\", \\\"SITE\\\", \\\"CHANNAME\\\", \\\"DATA\\\"]\\n sectionHeaders = {}\\n sectionHeaders[\\\"STATUS\\\"] = [\\\"STATUS\\\"]\\n sectionHeaders[\\\"TITLE\\\"] = [\\\"AUTHOR\\\", \\\"VERSION\\\", \\\"DATE\\\", \\\"COMMENT\\\"]\\n sectionHeaders[\\\"FILE\\\"] = [\\\"NAME\\\", \\\"FREQBAND\\\", \\\"DATE\\\"]\\n sectionHeaders[\\\"CHANNAME\\\"] = [\\\"ITEMS\\\", \\\"NAME\\\"]\\n sectionHeaders[\\\"DATA\\\"] = [\\\"ITEMS\\\", \\\"CHAN\\\"]\\n return sections, sectionHeaders\",\n \"def csc():\\n endcaps = [1,2]\\n disks = [1,2,3,4]\\n rings = {1:[1,2,3], # different rings for different disks\\n 2:[1,2], \\n 3:[1,2],\\n 4:[1,2]}\\n\\n csc_info = {\\n \\\"endcaps\\\":endcaps,\\n \\\"disks\\\": disks,\\n \\\"rings\\\": rings}\\n\\n return csc_info\",\n \"def reformat_course(courses):\\n\\n if isinstance(courses, Courses): # if it's just one course object\\n reformatted_data = {\\n 'startDate': reformat_date(courses.startDate),\\n 'organizingMeetingDate': reformat_date(courses.organizingMeetingDate),\\n 'startTime': reformat_time(courses.startTime),\\n 'organizingMeetingTime': reformat_time(courses.organizingMeetingTime)\\n }\\n else:\\n reformatted_data = []\\n for course in courses:\\n if isinstance(course, Courses):\\n reformatted_data.append(\\n {\\n 'startDate': reformat_date(course.startDate),\\n 'organizingMeetingDate': reformat_date(course.organizingMeetingDate),\\n 'startTime': reformat_time(course.startTime),\\n 'organizingMeetingTime': reformat_time(course.organizingMeetingTime),\\n 'studentLimit': course.studentLimit,\\n 'studentCount': len(get_students_by_course(course.id)),\\n 'additionalData': course.additionalData\\n }\\n )\\n\\n return reformatted_data\",\n \"def parseSection(self, response):\\n sel = Selector(response)\\n sections = sel.xpath('//table[@class=\\\"sections responsive\\\"]//tr[not(@class=\\\"headers\\\")]')\\n for s in sections:\\n item = CourseItem(response.request.meta[\\\"item\\\"])\\n item['section'] = s.xpath('@data-section-id').get().strip()\\n item['instructors'] = s.css('.instructor::text').get()\\n if item['instructors'] != None:\\n item['instructors'].strip()\\n item['instructors'] = [x.strip() for x in re.split(',', item['instructors'])]\\n item['syllabus'] = s.css('.syllabus a::attr(href)').get()\\n if item['syllabus'] != None:\\n item['syllabus'].strip()\\n return item\\n \\n\\n \\\"\\\"\\\"\\n Ignore the code below this. I was trying to get\\n the times, days, and number registered from the class sections\\n \\\"\\\"\\\"\\n #times = s.xpath('//td[@class=\\\"time\\\"]/text()').get().strip()\\n #times = re.split('-', times)\\n #starttime = times[0]\\n #endtime = times[1]\\n #endt = dt.datetime.strptime(endtime, '%H:%M%p')\\n # TODO: Check if \\\"am\\\"/\\\"pm\\\" from endt, & if endt hour is greater/less than startt \\n #startt = dt.datetime.strptime(starttime, '%H:%M')\\n #days = s.xpath('//td[@class=\\\"days\\\"]/text()').get().strip()\\n #days = re.split(',', days)\\n #numdays = len(days]\\n \\n #cap = s.xpath('//td[@class=\\\"registered\\\"]//a/text()').get().strip()\\n #cap = re.split(' of ', cap.strip())\\n #item['capacity'] = cap[1]\",\n \"def prepare_hw_dict(dictData):\\n\\tresult = defaultdict(list)\\n\\tfor (hw, meanings, verse, verseNumDetails, pageNumDetails) in dictData:\\n\\t\\tallHeadWords = [hw] + meanings\\n\\t\\tfor itm in allHeadWords:\\n\\t\\t\\tresult[itm].append((hw, meanings, verse, verseNumDetails, pageNumDetails))\\n\\treturn result\",\n \"def _parse(self, course: NavigableString) -> ParseType:\\n\\n info = {\\\"link\\\": \\\"\\\", \\\"icon\\\": \\\"\\\", \\\"title\\\": \\\"\\\", \\\"description\\\": \\\"\\\",\\n \\\"counts\\\": {}}\\n\\n info[\\\"link\\\"] = course.a[\\\"href\\\"]\\n info[\\\"icon\\\"] = course.a.img[\\\"src\\\"]\\n\\n description: NavigableString = course.a.div\\n info[\\\"title\\\"] = description.div.get_text()\\n info[\\\"description\\\"] = description.p.get_text()\\n\\n counts: NavigableString = course.find(\\\"div\\\", {\\\"class\\\": \\\"courseCounts\\\"})\\n counts_data: ResultSet = counts.find_all(\\\"li\\\")\\n for data in counts_data:\\n name: str = data.span.get_text().lower()\\n val: str = data.find(\\\"p\\\").get_text()\\n info[\\\"counts\\\"][name] = int(val.replace(\\\",\\\", \\\"\\\"))\\n\\n return info\",\n \"def get_info_list(info_string, course):\\n info_list = []\\n split_on_newline = info_string.split(\\\"\\\\n\\\")\\n for elem in split_on_newline:\\n split = elem.split(\\\": \\\")\\n for s in split:\\n info_list.append(s)\\n info_list = info_list[1:-1]\\n info_tags = [\\n 'session', 'school', 'credits', 'level', 'offered',\\n 'visiting_students', 'erasmus_students',\\n ]\\n i = 0\\n for info_tag in info_tags:\\n course[info_tag] = new_dict(\\n info_list[i] + ': ', info_list[i + 1])\\n i += 2\\n return course\",\n \"def get_available_sections(url, headers):\\n def _make_url(section_soup): # FIXME: Extract from here and test\\n return BASE_URL + section_soup.ul.find('a')['href']\\n\\n def _get_section_name(section_soup): # FIXME: Extract from here and test\\n return section_soup.h3.a.string.strip()\\n\\n def _make_subsections(section_soup):\\n subsections_soup = section_soup.ul.find_all(\\\"li\\\")\\n # FIXME correct extraction of subsection.name (unicode)\\n subsections = [SubSection(position=i,\\n url=BASE_URL + s.a['href'],\\n name=s.p.string)\\n for i, s in enumerate(subsections_soup, 1)]\\n return subsections\\n\\n courseware = get_page_contents(url, headers)\\n soup = BeautifulSoup(courseware)\\n sections_soup = soup.find_all('div', attrs={'class': 'chapter'})\\n\\n sections = [Section(position=i,\\n name=_get_section_name(section_soup),\\n url=_make_url(section_soup),\\n subsections=_make_subsections(section_soup))\\n for i, section_soup in enumerate(sections_soup, 1)]\\n return sections\",\n \"def add_s_courses(student_courses, resume_output):\\n # creates a list to represent each line of the html code for courses section\\n html_courses_section = []\\n\\n # opens the html section with a div tag\\n html_courses_section.append(\\\"<div>\\\")\\n\\n # adds a courses section with header3 format to html section\\n html_courses_section.append(surround_block(\\\"h3\\\", \\\"Courses\\\"))\\n\\n # makes a string of comma-separated (\\\", \\\") student courses\\n student_courses_str = \\\"\\\" # creates a list of student courses, and assigns the first course\\n for course in student_courses: # goes second course to last course... for each course\\n\\n # debugging\\n # print(\\\"idl\\\" + course)\\n\\n # adds the student course to the string of courses\\n student_courses_str += course\\n\\n # adds a comma after the course, unless it is the last course in the list\\n if course is not student_courses[-1]:\\n student_courses_str += \\\", \\\"\\n\\n # print(\\\"student_courses_str:\\\" + student_courses_str)\\n\\n # adds a line with str list of courses in span tag\\n html_courses_section.append(surround_block(\\\"span\\\", student_courses_str))\\n\\n # closes the courses section\\n html_courses_section.append(\\\"</div>\\\")\\n # print(\\\"html courses section\\\", html_courses_section)\\n\\n # adds the courses section to the html code to be printed\\n resume_output.extend(html_courses_section)\\n # print(resume_output)\\n\\n # returns output code\\n return resume_output\",\n \"def get_sections(self, start_year, start_semester, end_year, end_semester, course, section_goal=False):\\n SECTION_GRADES = \\\"\\\"\\\"SELECT section_id, num_students, comment1, comment2 FROM Section WHERE semester = %s AND year = %s AND course_name = %s\\\"\\\"\\\"\\n\\n spring = 'R'\\n fall = 'F'\\n summer = 'S'\\n winter = 'W'\\n\\n sem1 = {1 : 'F', 2 : 'W', 3 : 'R', 4 : 'S'}\\n sem2 = {'F' : 1, 'W' : 2, 'R' : 3,'S' : 4}\\n start = sem2[start_semester]\\n end = sem2[end_semester]\\n\\n semester_list = []\\n year_list = []\\n if start_year != end_year:\\n num_years = end_year-start_year\\n for i in range(0, num_years+1):\\n year_list.append(start_year+i)\\n ctr = start\\n\\n # todo: very jank way of doing this but idk what else to do rn\\n # basically, for each year, we put on 4 semesters in order of when the start semester occurs\\n # we should be putting on one of each\\n for i in range(1,5):\\n semester_list.append(sem1[ctr%5])\\n ctr+=1\\n if ctr%5 == 0:\\n ctr+=1\\n # when we're done with each year, we pop off the end of semester_list until we have the right end semester\\n if semester_list:\\n while semester_list[len(semester_list)-1] != sem1[end]:\\n semester_list.pop(len(semester_list)-1)\\n\\n else:\\n year_list.append(start_year)\\n for i in range(start, end+1):\\n year_list.append(i)\\n\\n\\n ret = None\\n section_list1 = []\\n section_list2 = []\\n current_section = Section()\\n #try:\\n for i in year_list:\\n for j in semester_list:\\n self.db_cursor.execute(SECTION_GRADES, (j, i, course.name))\\n section_list1 = self.db_cursor.fetchall()\\n for k in section_list1:\\n current_section.course_name = course.name\\n current_section.section_id = k[0]\\n current_section.num_students = k[1]\\n current_section.comment1 = k[2]\\n current_section.comment2 = k[3]\\n current_section.year = i\\n current_section.semester = j\\n section_list2.append(current_section)\\n if section_list2:\\n ret = section_list2\\n else:\\n ret = None\\n\\n #except:\\n #logging.warning(\\\"DBAdapter: Error- cannot retrieve sections: \\\" + str(id))\\n\\n return ret\",\n \"def Content():\\n\\tTOPIC_DICT = {\\\"Basics\\\": [[\\\"Introduction to Python\\\", \\\"/introduction-to-python/\\\"], \\n\\t\\t\\t\\t\\t\\t\\t\\t\\t\\t\\t\\t\\t [\\\"Print functions\\\", \\\"/python-print-function/\\\"],\\n\\t\\t\\t\\t\\t\\t\\t\\t\\t\\t\\t\\t\\t [\\\"Math basics with Python 3\\\", \\\"/math-basics-python-3/\\\"\\n\\t\\t\\t\\t\\t\\t\\t\\t\\t\\t\\t\\t\\t ]],\\n\\t\\t\\t\\t\\t\\t\\t\\t\\\"Web Dev\\\": [[\\\"wd1\\\"]]} #Web dev is now the second topic\\n\\t\\t\\t\\t\\t\\t\\t\\t#Introduction to Python\\n\\t#will be the title and the link to that is immediately after. Each inner array will be a specific sub section of the basics\\n\\t#section\\n\\n\\treturn TOPIC_DICT\",\n \"def _parse_sections(cls, content: dict) -> Dict[str, Any]:\\n sections = {}\\n for name, section_cls in cls.classes.items():\\n section = content.get(name)\\n if section:\\n try:\\n sections[name] = section_cls.parse(section)\\n except AttributeError:\\n sections[name] = section_cls(section)\\n else:\\n sections[name] = section_cls()\\n return sections\",\n \"def preprocess_courses_corpus():\\n soup = None\\n with open('courses_corpus.html', 'r') as infile:\\n content = infile.read()\\n\\n soup = BeautifulSoup(content, 'html.parser')\\n\\n docid = 0\\n data = {}\\n data['documents'] = []\\n\\n main_table = soup.find_all(\\\"div\\\", attrs={'class': 'courseblock'})\\n for course in main_table:\\n docid += 1\\n title = course.find_all('p', attrs={'class':'courseblocktitle noindent'})[0].text.lstrip('\\\\n') if len(course.find_all('p', attrs={'class':'courseblocktitle noindent'}))!=0 else ''\\n description = (course.find_all('p', attrs={'class':'courseblockdesc noindent'})[0].text.lstrip('\\\\n') if len(course.find_all('p', attrs={'class':'courseblockdesc noindent'}))!=0 else '') + ' ' + (course.find_all('p', attrs={'class':'courseblockextra noindent'})[0].text if len(course.find_all('p', attrs={'class':'courseblockextra noindent'}))!=0 else '')\\n\\n data['documents'].append({\\n 'docId' : docid,\\n 'title' : title.strip(),\\n 'description' : description.strip()\\n })\\n\\n with open('courses_data.json', 'w') as outfile:\\n json.dump(data, outfile)\",\n \"def get_course_table(self, table):\\n json_result = {}\\n row_list = table.xpath('.//table[@id = \\\"s_course\\\"]/tr[position() > 1]')\\n for row in row_list:\\n session = row.xpath('./td[1]/text()')\\n course_full_code_list = row.xpath('.//a[starts-with(@href, \\\"javascript:course_popup\\\")]/text()')\\n course_name_list = row.xpath('.//font[@style = \\\"font-size:7pt;\\\"]/text()')\\n course_list = []\\n if len(course_full_code_list) != len(course_name_list):\\n # year course design project would be count twice\\n if (\\\"Design Project\\\" == course_name_list[0]) & \\\\\\n (len(course_full_code_list) + 1 == len(course_name_list)):\\n course_name_list = course_name_list[1:]\\n else:\\n raise ProfileException(\\n \\\"Error: unmatched lists. course code list:\\\",\\n course_full_code_list, \\\"\\\\n course name list:\\\", course_name_list)\\n for i, full_code in enumerate(course_full_code_list):\\n if re.match(re.compile('\\\\w{3}\\\\d{3}[YH]1\\\\s+[SFY]'), full_code) is None:\\n raise ProfileException(\\\"Illegal course code!:\\\" + full_code)\\n course_list.append({\\n \\\"courseName\\\": course_name_list[i],\\n \\\"courseCode\\\": full_code[0:6],\\n \\\"courseTime\\\": full_code[-1],\\n \\\"courseLength\\\": full_code[6:8]\\n })\\n # there is a empty session\\n if session:\\n json_result.update({session[0]: course_list})\\n if json_result:\\n return json_result\\n else:\\n raise ProfileException(\\\"Failed to get course_table table(row list is empty)\\\")\",\n \"def _transform_learning_resource_course(course):\\n return {\\n \\\"readable_id\\\": course[\\\"readable_id\\\"],\\n \\\"platform\\\": PlatformType.xpro.value,\\n \\\"title\\\": course[\\\"title\\\"],\\n \\\"image\\\": {\\\"url\\\": course[\\\"thumbnail_url\\\"]},\\n \\\"offered_by\\\": copy.deepcopy(OFFERED_BY),\\n \\\"description\\\": course[\\\"description\\\"],\\n \\\"url\\\": course.get(\\\"url\\\"),\\n \\\"published\\\": any(\\n map(\\n lambda course_run: course_run.get(\\\"current_price\\\", None),\\n course[\\\"courseruns\\\"],\\n )\\n ),\\n \\\"topics\\\": transform_topics(course.get(\\\"topics\\\", [])),\\n \\\"runs\\\": [_transform_run(course_run) for course_run in course[\\\"courseruns\\\"]],\\n \\\"resource_type\\\": LearningResourceType.course.value,\\n }\",\n \"def metadata(self):\\n metadata = dict([(key,{}) for key in self.keys])\\n for day in self.days:\\n metadata[\\\"Days\\\"].append(day.attrs)\\n for period in day.period:\\n metadata[\\\"Periods\\\"].append(period.attrs)\\n for course in period.courses:\\n metadata[\\\"Courses\\\"].append(course.attrs)\\n for instructor in course.instructor:\\n metadata[\\\"Instructors\\\"].append(instructor.attrs)\\n return metadata\",\n \"def get_courses(self) -> Tuple[Dict[int, float], Dict[float, List[int]]]:\\n\\n loop_ids_to_course = {}\\n course_to_loop_ids = {0: []}\\n current_course_id = 0\\n\\n # Iterate through the loops\\n for current_loop_id in range(self.last_loop_id + 1):\\n # Check if there are any parent loops, if there are, then iterate through them\\n for parent in self.loops[current_loop_id].parent_loops:\\n # If the parent loop is in the course below the current course, there is a course change\\n if loop_ids_to_course[parent.loop_id] != current_course_id-1:\\n # Course change! Increment course id and create an empty course.\\n current_course_id += 1\\n course_to_loop_ids[current_course_id] = []\\n # Add values to dictionaries\\n loop_ids_to_course[current_loop_id] = current_course_id\\n course_to_loop_ids[current_course_id].append(current_loop_id)\\n\\n return(loop_ids_to_course, course_to_loop_ids)\",\n \"def fill_header_section():\\n section = _SectionData(\\\"Header\\\")\\n section.props.append((\\\"FormatVersion\\\", 1))\\n section.props.append((\\\"Source\\\", get_combined_ver_str()))\\n section.props.append((\\\"Type\\\", \\\"Configuration\\\"))\\n section.props.append((\\\"Note\\\", \\\"User settings of SCS Blender Tools\\\"))\\n author = bpy.context.user_preferences.system.author\\n if author:\\n section.props.append((\\\"Author\\\", str(author)))\\n section.props.append((\\\"ConfigStoragePlace\\\", _property_utils.get_by_type(bpy.types.GlobalSCSProps.config_storage_place)))\\n section.props.append((\\\"DumpLevel\\\", _property_utils.get_by_type(bpy.types.GlobalSCSProps.dump_level)))\\n return section\",\n \"def to_dict(self, section):\\n\\t\\t\\n\\t\\tdct = {}\\n\\t\\t\\n\\t\\tfor name, value in self.items(section):\\n\\t\\t\\tdct[name] = self.parse_value(value)\\n\\t\\t\\n\\t\\treturn dct\",\n \"def create_info_json(csv_contents):\\n students, assignments = {}, {}\\n questions = settings['questions']\\n\\n # For loop to process the students information dictionary\\n for sid in csv_contents:\\n elems = csv_contents[sid]\\n students[sid] = {\\n 'consent' : elems[0]['consent'],\\n 'experiment group' : elems[0]['exp_group']\\n }\\n\\n # For loop to process the assignments dictionary\\n for name in questions:\\n assignments[questions[name]] = {}\\n\\n return {'students' : students, 'assignments' : assignments}\",\n \"def get_courses(std):\\n return std[2]\",\n \"def _section_analytics(course_key, access):\\r\\n section_data = {\\r\\n 'section_key': 'analytics',\\r\\n 'section_display_name': _('Analytics'),\\r\\n 'access': access,\\r\\n 'get_distribution_url': reverse('get_distribution', kwargs={'course_id': course_key.to_deprecated_string()}),\\r\\n 'proxy_legacy_analytics_url': reverse('proxy_legacy_analytics', kwargs={'course_id': course_key.to_deprecated_string()}),\\r\\n }\\r\\n return section_data\",\n \"def generate_page_sections_dict(self, project_page_data: dict):\\n wiki_obj = WikiService()\\n\\n short_description = (\\n f\\\"\\\\n{project_page_data['project']['shortDescription']}\\\\n\\\"\\n )\\n\\n created_date = wiki_obj.format_date_text(\\n project_page_data['project']['created']\\n )\\n # created_date_text = f\\\"\\\\n{created_date}\\\\n\\\" \\n # due_date = wiki_obj.format_date_text(\\n # project_page_data['project']['due_date']\\n # )\\n timeframe = (\\n f\\\"\\\\n* '''Start Date:''' {created_date}\\\\n\\\"\\n # f\\\"\\\\n* '''End Date:''' Estimate {due_date}\\\\n\\\"\\n )\\n\\n\\n project_url = (\\n f\\\"\\\\n{project_page_data['project']['url']}\\\\n\\\"\\n )\\n\\n hashtag = (\\n project_page_data['project']['changesetComment']\\n )\\n hashtag = (\\n project_page_data['project']['changesetComment'].replace(\\n \\\"#\\\", \\\"<nowiki>#</nowiki>\\\"\\n )\\n )\\n hashtag_text = (\\n f\\\"\\\\n{hashtag}\\\\n\\\"\\n )\\n\\n instructions_text = (\\n project_page_data['project']\\n ['externalSource']\\n ['instructions']\\n )\\n instructions = (\\n f\\\"\\\\n{instructions_text}\\\\n\\\"\\n )\\n\\n per_task_instructions_text = (\\n project_page_data['project']\\n ['externalSource']\\n ['perTaskInstructions']\\n )\\n per_task_instructions = (\\n f\\\"\\\\n{per_task_instructions_text}\\\\n\\\"\\n )\\n\\n imagery_text = (\\n project_page_data['project']\\n ['externalSource']\\n ['imagery']\\n )\\n imagery = (\\n f\\\"\\\\n{imagery_text}\\\\n\\\"\\n )\\n\\n license_text = (\\n project_page_data['project']\\n ['externalSource']\\n ['license']\\n )\\n license = (\\n f\\\"\\\\n{license_text}\\\\n\\\"\\n )\\n\\n # metrics = (\\n # f\\\"\\\\n* {project_page_data.instructions}\\\\n\\\"\\n # )\\n # quality_assurance = (\\n # f\\\"\\\\n* {project_page_data.quality_assurance}\\\\n\\\"\\n # )\\n\\n users = project_page_data['project'][\\\"users\\\"]\\n project_users = \\\"\\\"\\n for user in users:\\n project_users += (\\n f\\\"\\\\n| {user['userId']}\\\\n| {user['userName']}\\\\n|-\\\"\\n )\\n\\n \\n\\n project_page_sections = {\\n self.short_description_section: short_description,\\n self.timeframe_section: timeframe,\\n # self.timeframe_section: {\\n # self.created_section: created_date_text\\n # }, # if choose use subsection for timeframe \\n self.url_section: project_url,\\n self.external_sources_section: {\\n self.instructions_section: instructions,\\n self.per_task_instructions_section: per_task_instructions,\\n self.imagery_section: imagery,\\n self.license_section: license\\n },\\n self.hashtag_section: hashtag_text,\\n # self.instructions_section: instructions,\\n # self.metrics_section: metrics,\\n # self.quality_assurance_section: quality_assurance,\\n self.team_user_section: {\\n self.users_list_section: project_users\\n }\\n }\\n return project_page_sections\",\n \"def bsoup(coursepage):\\n soup = BeautifulSoup(coursepage.content, 'lxml')\\n h1 = soup.find_all('h1')[2]\\n html = h1.find_next_siblings()\\n all_strings = [h1.string]\\n for div in html:\\n try:\\n text = div.get_text()\\n except:\\n text = div.string\\n if text is not None:\\n all_strings.append(text)\\n course = {'title': all_strings[0]}\\n course = get_info_list(all_strings[1], course)\\n course['description'] = new_dict(all_strings[2], all_strings[3])\\n course['timetable'] = new_dict(all_strings[4], all_strings[5])\\n course['requirements_of_entry'] = new_dict(all_strings[6], all_strings[7])\\n course['excluded_courses'] = new_dict(all_strings[8], all_strings[9])\\n course['co_requisites'] = new_dict(all_strings[10], all_strings[11])\\n course['assessment_weighting'] = new_dict(all_strings[12], all_strings[13])\\n course['aims'] = new_dict(all_strings[17], all_strings[18])\\n date = all_strings[14].split(': ')\\n course['assessment_date'] = new_dict(date[0] + \\\": \\\", date[1])\\n course['learning_outcomes'] = new_dict(all_strings[19], all_strings[20])\\n # TODO Doesn't parse Minimum Requirement for Award of Credit or\\n # Reassessment Options\\n return course\",\n \"def filter_sections(courses, selected_sections):\\n for c in courses:\\n c_key = f\\\"{c.name} {c.num}\\\"\\n\\n lab_section = selected_sections[c_key][\\\"lab\\\"]\\n lecture_section = selected_sections[c_key][\\\"lecture\\\"]\\n tutorial_section = selected_sections[c_key][\\\"tutorial\\\"]\\n\\n c.labs = [s for s in c.labs if s.section == lab_section]\\n c.lectures = [s for s in c.lectures if s.section == lecture_section]\\n c.tutorials = [s for s in c.tutorials if s.section == tutorial_section]\",\n \"def prep_hd(header,phi_c,lambda_c,nx,ny,dx,dy):\\n header_out = {}\\n\\n # Keywords to get from original header\\n keys_hd = ['TELESCOP', 'INSTRUME', 'WAVELNTH', 'CAMERA','DATE',\\n 'DATE_S','DATE-OBS','T_OBS','T_REC','TRECEPOC',\\n 'TRECSTEP','TRECUNIT','HARPNUM','DSUN_OBS','DSUN_REF',\\n 'RSUN_REF','CRLN_OBS','CRLT_OBS','CAR_ROT','OBS_VR',\\n 'OBS_VW','OBS_VN','RSUN_OBS','QUALITY','QUAL_S','QUALLEV1']\\n\\n for key in keys_hd:\\n header_out[key] = header[key]\\n\\n # Add new keywords\\n header_out['NAXIS'] = 2\\n header_out['NAXIS1'] = nx\\n header_out['NAXIS2'] = ny\\n\\n header_out['CUNIT1'] = 'degree'\\n header_out['CUNIT2'] = 'degree'\\n\\n header_out['CRPIX1'] = (nx - 1) / 2 + 1\\n header_out['CRPIX2'] = (ny - 1) / 2 + 1\\n header_out['CRVAL1'] = phi_c\\n header_out['CRVAL2'] = lambda_c\\n header_out['CDELT1'] = dx\\n header_out['CDELT2'] = dy\\n header_out['CTYPE1'] = 'CRLN-CEA'\\n header_out['CTYPE2'] = 'CRLT-CEA'\\n header_out['CROTA2'] = 0.0\\n\\n header_out['WCSNAME'] = 'Carrington Heliographic'\\n header_out['BUNIT'] = 'Mx/cm^2'\\n\\n return header_out\",\n \"def _get_courses(self) -> None:\\n\\n courses_content: NavigableString = self.soup.find(\\\"div\\\", \\n {\\\"class\\\": \\\"coursesContent\\\"})\\n course_items: ResultSet = courses_content.find_all(\\\"div\\\", \\n {\\\"class\\\": \\\"courseItem\\\"})\\n\\n for item in course_items:\\n course_name: str = item.a[\\\"href\\\"].split(\\\"/\\\")[-2].lower()\\n course_data: ParseType = self._parse(item)\\n self._update(course_name, course_data)\",\n \"def _section_extensions(course):\\r\\n section_data = {\\r\\n 'section_key': 'extensions',\\r\\n 'section_display_name': _('Extensions'),\\r\\n 'units_with_due_dates': [(title_or_url(unit), unit.location.to_deprecated_string())\\r\\n for unit in get_units_with_due_date(course)],\\r\\n 'change_due_date_url': reverse('change_due_date', kwargs={'course_id': course.id.to_deprecated_string()}),\\r\\n 'reset_due_date_url': reverse('reset_due_date', kwargs={'course_id': course.id.to_deprecated_string()}),\\r\\n 'show_unit_extensions_url': reverse('show_unit_extensions', kwargs={'course_id': course.id.to_deprecated_string()}),\\r\\n 'show_student_extensions_url': reverse('show_student_extensions', kwargs={'course_id': course.id.to_deprecated_string()}),\\r\\n }\\r\\n return section_data\",\n \"def _generate_hcs_meta(self):\\n self.hcs_meta = {'plate': self.plate_meta}\\n\\n well_metas = []\\n for well in self.wells:\\n meta = self.store[well].attrs.get('well')\\n well_metas.append(meta)\\n\\n self.hcs_meta['well'] = well_metas\",\n \"def _get_catalog_data_for_course(course_key):\\n course_certificate_settings = CertificateGenerationCourseSetting.get(course_key)\\n if not course_certificate_settings:\\n return {}\\n\\n catalog_data = {}\\n course_run_fields = []\\n if course_certificate_settings.language_specific_templates_enabled:\\n course_run_fields.append('content_language')\\n if course_certificate_settings.include_hours_of_effort:\\n course_run_fields.extend(['weeks_to_complete', 'max_effort'])\\n\\n if course_run_fields:\\n course_run_data = get_course_run_details(course_key, course_run_fields)\\n if course_run_data.get('weeks_to_complete') and course_run_data.get('max_effort'):\\n try:\\n weeks_to_complete = int(course_run_data['weeks_to_complete'])\\n max_effort = int(course_run_data['max_effort'])\\n catalog_data['hours_of_effort'] = weeks_to_complete * max_effort\\n except ValueError:\\n log.exception('Error occurred while parsing course run details')\\n catalog_data['content_language'] = course_run_data.get('content_language')\\n log.info(\\n \\\"catalog data received for course: %s is : %s\\\",\\n course_key,\\n catalog_data,\\n )\\n return catalog_data\",\n \"def completed_course()->str:\\r\\n try:\\r\\n db: sqlite3.Connection = sqlite3.connect(DB_FILE)\\r\\n query: str = \\\"SELECT s.Name, s.CWID, g.Course, g.Grade, i.Name AS 'Instructor' \\\" \\\\\\r\\n \\\"FROM grades2 g JOIN students2 s ON g.StudentCWID = s.CWID \\\" \\\\\\r\\n \\\"JOIN instructors2 i ON g.InstructorCWID = i.CWID ORDER BY s.Name\\\" \\r\\n except sqlite3.OperationalError as e:\\r\\n print(e)\\r\\n\\r\\n data: Dict[str,str]=\\\\\\r\\n [{\\\"name\\\": name,\\\"cwid\\\": cwid,\\\"course\\\": course , \\\"grade\\\":grade, \\\"instructor\\\": instructor}\\r\\n for name, cwid, course, grade, instructor in db.execute(query)]\\r\\n db.close()\\r\\n \\\"\\\"\\\"Render the template from templates folder\\\"\\\"\\\"\\r\\n return render_template(\\\"student_summary.html\\\",\\r\\n title=\\\"Stevens Repository\\\",\\r\\n table_title=\\\"Student, Course, Grade and Instructor\\\",\\r\\n students=data )\",\n \"def course_index(request, course_key):\\r\\n course_module = _get_course_module(course_key, request.user, depth=3)\\r\\n lms_link = get_lms_link_for_item(course_module.location)\\r\\n sections = course_module.get_children()\\r\\n\\r\\n\\r\\n return render_to_response('overview.html', {\\r\\n 'context_course': course_module,\\r\\n 'lms_link': lms_link,\\r\\n 'sections': sections,\\r\\n 'course_graders': json.dumps(\\r\\n CourseGradingModel.fetch(course_key).graders\\r\\n ),\\r\\n 'new_section_category': 'chapter',\\r\\n 'new_subsection_category': 'sequential',\\r\\n 'new_unit_category': 'vertical',\\r\\n 'category': 'vertical'\\r\\n })\",\n \"def parse_section(section):\\n data = {}\\n for line in section.splitlines(False):\\n if not line:\\n continue\\n if not line.startswith(' '):\\n # new key/value\\n key, _, value = line.partition(': ')\\n data[key] = value\\n else:\\n # continuation of the previous value\\n data[key] += line[1:]\\n return data\",\n \"def get_sections_of_a_course(self, course_name, year, semester_name):\\n if semester_name not in SEMESTER_NAME_MAP.keys():\\n logging.warning(\\\"DBAdapter: Error- invalid semester name.\\\")\\n return None\\n semester = SEMESTER_NAME_MAP[semester_name]\\n return_list = []\\n\\n GET_SECTIONS = \\\"\\\"\\\"SELECT section_id, num_students, comment1, comment2 FROM Section WHERE course_name = %s AND semester = %s AND year = %s\\\"\\\"\\\"\\n\\n #try:\\n self.db_cursor.execute(GET_SECTIONS, (course_name, semester, year))\\n tups = self.db_cursor.fetchall()\\n for t in tups:\\n new_section = Section()\\n new_section.semester = semester\\n new_section.year = year\\n new_section.course_name = course_name\\n new_section.section_id = t[0]\\n new_section.num_students = t[1]\\n new_section.comment1 = t[2]\\n new_section.comment2 = t[3]\\n return_list.append(new_section)\\n\\n return return_list\\n\\n \\\"\\\"\\\"\\n except:\\n logging.warning(\\\"DBAdapter: Error- failed to retrieve all sections of a course.\\\")\\n return []\\n \\\"\\\"\\\"\",\n \"def _parse_lesson(self, lesson_link: str):\\n\\n lesson_page: Soup = self._get_soup(lesson_link)\\n lesson_content: NavigableString = lesson_page.find(\\\"div\\\", \\n {\\\"class\\\": \\\"moduleContent\\\"})\\n course_divs: ResultSet = lesson_content.find_all(\\\"div\\\")\\n\\n data = {}\\n module = \\\"\\\"\\n for div in course_divs:\\n if div[\\\"class\\\"][0] == \\\"courseModule\\\":\\n module = div.p.get_text().split(\\\":\\\")[-1].strip()\\n data[module] = []\\n elif div[\\\"class\\\"][0] == \\\"courseLesson\\\":\\n lesson_title = div.find(\\\"span\\\", {\\\"class\\\": \\\"courseLessonTitle\\\"})\\n data[module].append(lesson_title.get_text().strip())\\n \\n return data\",\n \"def get_courses_metadata(self):\\n return Metadata(**settings.METADATA['course_ids'])\",\n \"def parseCourses(self, response):\\n sel = Selector(response)\\n courses = sel.xpath('//div[@class=\\\"course-info expandable\\\"]')\\n for c in courses:\\n item = CourseItem(response.request.meta[\\\"item\\\"])\\n item['code'] += '-' + c.xpath('@id').get().strip()\\n item['name'] = c.xpath('//a[@class=\\\"courselink\\\"]/text()').get().strip()\\n # everything works up to here #\\n href = c.xpath('div/h3/a/@href').get()\\n url = urljoin('https://web-app.usc.edu', href)\\n yield Request(url=url,callback=self.parseSection,meta={'item':item})\",\n \"def course_rolls(records):\\n \\n dict_map_course_and_stud_num = {}\\n \\n for each_tuple in records:\\n \\n course_info = each_tuple[0]\\n student_info = each_tuple[1]\\n course_id = course_info[0]\\n \\n \\n if course_id in dict_map_course_and_stud_num:\\n dict_map_course_and_stud_num[course_id].add(student_info)\\n \\n else:\\n dict_map_course_and_stud_num[course_id] = set()\\n dict_map_course_and_stud_num[course_id].add(student_info)\\n \\n return(dict_map_course_and_stud_num)\",\n \"def get_sections(data):\\n print \\\" * Extracting sections\\\"\\n sections = OrderedDict()\\n\\n results = re.finditer(r\\\"^([A-Z][A-Z]+)([ ]+.*)?$\\\", data, re.M)\\n data_start = None\\n data_end = None\\n prev_section = None\\n cur_section = None\\n for res in results:\\n print \\\" * Found\\\", res.groups()[0]\\n data_end = res.start()\\n if prev_section is not None:\\n # Get rid of potential comments at the end of a line.\\n _data = re.sub(r\\\"\\\\s*#.*\\\", \\\"\\\", data[data_start:data_end])\\n sections[prev_section][\\\"data\\\"] = filter(None, _data.splitlines())\\n data_start = res.end()\\n cur_section = res.groups()[0]\\n sections[cur_section] = {\\\"arguments\\\": res.groups()[1], \\\"data\\\": \\\"\\\"}\\n prev_section = \\\"%s\\\" % cur_section # Only to be sure we get a brand new string...\\n\\n return sections\",\n \"def get_sections(self, s, base=None,\\n sections=['Parameters', 'Other Parameters']):\\n params = self.params\\n # Remove the summary and dedent the rest\\n s = self._remove_summary(s)\\n\\n ret = {}\\n\\n for section in sections:\\n ret[section] = section_doc = self._get_section(s, section)\\n if base:\\n key = '%s.%s' % (base, section.lower().replace(' ', '_'))\\n params[key] = section_doc\\n return s\",\n \"def get_sections(self, course):\\n return course.available_sections if self.free_sections_only else course.sections\",\n \"def _get_page_sections(self, sectionNum=None, sectionName=None):\\n self.section = {}\\n self.sections = [] # list maintains order\\n content = self.page.content\\n lines = content.split(\\\"\\\\n\\\")\\n currentSection = None\\n for line in lines:\\n if \\\"==\\\" in line:\\n line = line.replace(\\\"Edit =\\\",\\\"\\\")\\n line = line.replace(\\\"=\\\",\\\"\\\").lstrip().rstrip()\\n self.section[line] = []\\n currentSection = line\\n self.sections.append(currentSection)\\n elif currentSection is not None:\\n line = line.lstrip().rstrip()\\n self.section[currentSection].append(line)\\n else:\\n pass\\n logger.info(\\\"Sections in page: \\\"+str(self.sections))\\n # and return some section:\\n if sectionNum is not None:\\n if sectionNum > len(self.sections) or sectionNum < 0:\\n sectionNum = 0\\n return self.section[self.sections[sectionNum]]\\n elif sectionName is not None:\\n pass\",\n \"def get_course_about_section(course, section_key):\\r\\n\\r\\n # Many of these are stored as html files instead of some semantic\\r\\n # markup. This can change without effecting this interface when we find a\\r\\n # good format for defining so many snippets of text/html.\\r\\n\\r\\n # TODO: Remove number, instructors from this list\\r\\n if section_key in ['short_description', 'description', 'key_dates', 'video',\\r\\n 'course_staff_short', 'course_staff_extended',\\r\\n 'requirements', 'syllabus', 'textbook', 'faq', 'more_info',\\r\\n 'number', 'instructors', 'overview',\\r\\n 'effort', 'end_date', 'prerequisites', 'ocw_links']:\\r\\n\\r\\n try:\\r\\n\\r\\n request = get_request_for_thread()\\r\\n\\r\\n loc = course.location.replace(category='about', name=section_key)\\r\\n\\r\\n # Use an empty cache\\r\\n field_data_cache = FieldDataCache([], course.id, request.user)\\r\\n about_module = get_module(\\r\\n request.user,\\r\\n request,\\r\\n loc,\\r\\n field_data_cache,\\r\\n course.id,\\r\\n not_found_ok=True,\\r\\n wrap_xmodule_display=False,\\r\\n static_asset_path=course.static_asset_path\\r\\n )\\r\\n\\r\\n html = ''\\r\\n\\r\\n if about_module is not None:\\r\\n try:\\r\\n html = about_module.render('student_view').content\\r\\n except Exception: # pylint: disable=broad-except\\r\\n html = render_to_string('courseware/error-message.html', None)\\r\\n log.exception(\\r\\n u\\\"Error rendering course={course}, section_key={section_key}\\\".format(\\r\\n course=course, section_key=section_key\\r\\n ))\\r\\n return html\\r\\n\\r\\n except ItemNotFoundError:\\r\\n log.warning(\\r\\n u\\\"Missing about section {key} in course {url}\\\".format(key=section_key, url=course.location.to_deprecated_string())\\r\\n )\\r\\n return None\\r\\n elif section_key == \\\"title\\\":\\r\\n return course.display_name_with_default\\r\\n elif section_key == \\\"university\\\":\\r\\n return course.display_org_with_default\\r\\n elif section_key == \\\"number\\\":\\r\\n return course.display_number_with_default\\r\\n\\r\\n raise KeyError(\\\"Invalid about key \\\" + str(section_key))\",\n \"def create_enrollment(context: dict) -> dict:\\n enrollment = Enrollment()\\n\\n for attr in context.keys():\\n setattr(enrollment, attr, context[attr])\\n\\n enrollment.save()\\n return enrollment.asdict()\",\n \"def list_sections(self, sections):\\n # issue_data['sections'][0]['titles'][0][0=idioma, 1=titulo]\\n # no entanto, deveria ser\\n # issue_data['sections'][0]['titles'][0][idioma] = titulo\\n titles = []\\n for section in sections:\\n for lang, sectitle in section['titles']:\\n titles.append(sectitle)\\n return ' | '.join(titles)\",\n \"def get_structure(self):\\n main = {}\\n for line in self.load():\\n match = re.match('^\\\\s*([A-Za-z0-9_]+)(\\\\((\\\\d+)\\\\))?=(.*)$', line)\\n if match:\\n key = match.group(1)\\n index = match.group(3)\\n value = match.group(4)\\n if index is None:\\n main[key] = self.parse_data_value(value)\\n else:\\n if key not in main:\\n main[key] = []\\n main[key].append(self.parse_data_value(value))\\n #else:\\n # print(line)\\n return main\",\n \"def _serialize_course(cls, course, modes=None, verification_deadline=None):\\n modes = modes or []\\n verification_deadline = verification_deadline or VerificationDeadline.deadline_for_course(course.id)\\n\\n return {\\n 'id': str(course.id),\\n 'name': str(course.display_name),\\n 'verification_deadline': cls._serialize_datetime(verification_deadline),\\n 'modes': [cls._serialize_course_mode(mode) for mode in modes]\\n }\",\n \"def _save_section_scores(self, section_scores):\\n l = []\\n for section in section_scores:\\n d = {\\n 'name': section['name'],\\n 'score': section['auto_score'],\\n 'max_score': section['max_score'],\\n\\t\\t'failed_tests': section['failed_tests']\\n }\\n l.append(d)\\n self.student_section_scores = l\",\n \"def get_errored_courses(self):\\r\\n return {}\",\n \"def get_courseware_with_tabs(course_id):\\r\\n\\r\\n course = get_course_by_id(course_id)\\r\\n chapters = [chapter for chapter in course.get_children() if not chapter.hide_from_toc]\\r\\n courseware = [{'chapter_name': c.display_name_with_default,\\r\\n 'sections': [{'section_name': s.display_name_with_default,\\r\\n 'clickable_tab_count': len(s.get_children()) if (type(s) == seq_module.SequenceDescriptor) else 0,\\r\\n 'tabs': [{'children_count': len(t.get_children()) if (type(t) == vertical_module.VerticalDescriptor) else 0,\\r\\n 'class': t.__class__.__name__}\\r\\n for t in s.get_children()]}\\r\\n for s in c.get_children() if not s.hide_from_toc]}\\r\\n for c in chapters]\\r\\n\\r\\n return courseware\",\n \"def gen_dict():\\n lines = [line for line in csv.reader(open(__ppath__ + \\\"/data/occupations.csv\\\"))] # uses a csv.reader to parse the file, converts the generic iterable to a list\\n lines = [(line[0],float(line[1])) for line in lines[1:-2]]# removes the column names and \\\"Total\\\" row, re-expresses as a list of tuples to enable dictionary conversion\\n lines.append((\\\"Unemployed\\\",0.2)) # accounts for missing 0.2% of jobs\\n return dict(lines) # converts to dictionary\",\n \"def see_teaching_courses(self, username: str, token: str) -> List[Dict[str, object]]:\\n\\n # Validate user first\\n if not self.validate(username=username, token=token, check_privilege='instructor'):\\n raise RuntimeError(\\\"User not verified!\\\")\\n\\n # Get UID from user's username\\n uid = self.get_uid(username=username)\\n\\n # Query database for courses instructed by a user with this UID\\n cursor = self._db_connection.cursor()\\n cursor.execute(\\n '''\\n SELECT \\n course_id,\\n course_abbreviation,\\n course_name, \\n time,\\n seats \\n FROM \\n courses\\n WHERE \\n instructor_id = ?\\n ;\\n ''', (uid,))\\n\\n db_results = cursor.fetchall()\\n\\n if db_results is None:\\n print(\\\"No associated courses found!\\\")\\n return []\\n\\n # Build information dicts for every course this user is instructing\\n courses = []\\n for result in db_results:\\n # Get the number of students enrolled in this course already\\n cursor.execute('''SELECT COUNT(*) FROM enrollment_records WHERE course_id = ?;''', (result[0],))\\n students_enrolled = cursor.fetchone()[0]\\n if students_enrolled is None:\\n students_enrolled = 0\\n\\n # Build a course dict from the data\\n courses.append({\\n \\\"course_abbreviation\\\": result[1],\\n \\\"course_name\\\": result[2],\\n \\\"time\\\": result[3],\\n \\\"students_enrolled\\\": students_enrolled,\\n \\\"capacity\\\": result[4],\\n })\\n\\n return courses\",\n \"def _parse_education(self):\\n education = [\\n h for h in self.right_column.find_all(\\\"h4\\\") if h.text == \\\"Bildungsweg\\\"\\n ]\\n if education:\\n education = education[0].nextSibling.nextSibling.find_all(\\\"li\\\")\\n return {\\\"education\\\": [e.text.strip() for e in education]}\\n return {}\",\n \"def generate_section_report(self):\\n summary_line = [\\\"No Students in Section\\\",0,0,0,0,0,0,0.0]\\n day_type_list, dates_list = \\\\\\n load_daytypes_lists(self.start_date, self.total_days,\\n self.section, self.total_days)\\n self.num_schooldays = len(dates_list)\\n if len(self.students):\\n summary_line[0] = \\\"Averages\\\"\\n for i in xrange(0,self.total_days):\\n if (((day_type_list[i][0] & \\\\\\n SchoolDB.models.StudentAttendanceRecord.school_day)) or\\n ((day_type_list[i][1] & \\\\\\n SchoolDB.models.StudentAttendanceRecord.school_day))):\\n #skip non school days\\n self.keys_list.append(\\\"\\\")\\n self.table_data.append(\\n self._generate_section_report_day(\\n dates_list[i], summary_line)[0])\\n if (len(self.table_data)):\\n for i in range(1,8):\\n summary_line[i] = \\\\\\n round((float(summary_line[i])/ len(self.table_data)),1)\\n self.table_data.append(summary_line)\\n self.table_descriptor = \\\\\\n [('date','string','Date'),\\n ('m_en', 'number', 'Male En'),\\n ('f_en', 'number', 'Female En'),\\n ('m_morn', 'number', 'Male Morn'),\\n ('f_morn', 'number', 'Female Morn'),\\n ('m_aft', 'number', 'Male Aft'),\\n ('f_aft', 'number', 'Female Aft'),\\n ('percent', 'number', '% Present')]\",\n \"def _fill_section_dict(self, dictionary, section, keys=[], streams=1):\\n\\t\\t# if there is no stream, we just place a note in the dictionary\\n\\t\\tif streams == 0:\\n\\t\\t\\tdictionary[section] = 'No stream'\\n\\t\\t\\treturn dictionary\\n\\n\\t\\t# otherwise we have to fill one subdictionary per stream\\t\\t\\n\\t\\tfor i in range(streams):\\n\\t\\t\\tdictionary[section][i] = {}\\n\\t\\t\\t# lookup the information for every key requested and fill the dictionary accordingly\\n\\t\\t\\tfor key in keys:\\n\\t\\t\\t\\tif (section + '_' + str(i) + '_' + key) in self.full_info.keys():\\n\\t\\t\\t\\t\\ttry:\\n\\t\\t\\t\\t\\t\\tdictionary[section][i][key] = int(self.full_info[section + '_' + str(i) + '_' + key])\\n\\t\\t\\t\\t\\texcept ValueError:\\n\\t\\t\\t\\t\\t\\ttry:\\n\\t\\t\\t\\t\\t\\t\\tdictionary[section][i][key] = float(self.full_info[section + '_' + str(i) + '_' + key])\\n\\t\\t\\t\\t\\t\\texcept ValueError:\\n\\t\\t\\t\\t\\t\\t\\tdictionary[section][i][key] = self.full_info[section + '_' + str(i) + '_' + key]\\n\\t\\t\\n\\t\\treturn dictionary\",\n \"def canvas_api_sections(state, course_id):\\n\\n api = state.canvas_api()\\n for section in api.list_sections(course_id):\\n click.echo(str(section))\",\n \"def format_sections(self, sections: SectionDict) -> str:\",\n \"def dump(self):\\n course = {\\n \\\"course_id\\\": self.course_id,\\n \\\"title\\\": self.title,\\n \\\"chapters\\\": {}\\n }\\n for chapter_num in self.chapters:\\n chapter = self.chapters[chapter_num]\\n course[\\\"chapters\\\"][chapter.num] = {\\n \\\"name\\\": chapter.name,\\n \\\"lessons\\\": {lesson_num: lesson_data.name for lesson_num,\\n lesson_data in chapter.lessons.items()}\\n }\\n with open(_JSON_PATH_FORMAT % self.course_id, \\\"w+\\\") as fp:\\n _logger.debug(\\\"Dumping the data into a JSON file so that it can \\\"\\n \\\"be accessed at a later time quickly and without \\\"\\n \\\"need to scrape LearnItFirst.com, saving time and \\\"\\n \\\"unnecessary requests.\\\")\\n json.dump(course, fp)\",\n \"def create_courses():\\n\\n\\t# create list for courses\\n\\tallcourses = []\\n\\n\\t# load courses as classes in allcourses-list\\n\\twith open(\\\"../data/vakken.csv\\\", \\\"rt\\\") as coursefile:\\n\\n\\t\\t# clean text\\n\\t\\tcourses = csv.reader(coursefile)\\n\\t\\tfor row in courses:\\n\\t\\t\\tfor text in row:\\n\\t\\t\\t\\tcourse_info = text.split(\\\";\\\")\\n\\n\\t\\t\\t\\t# add course name\\n\\t\\t\\t\\tcourse_name = course_info[0]\\n\\n\\t\\t\\t\\t# add amount of lectures\\n\\t\\t\\t\\tcourse_lectures = course_info[1]\\n\\n\\t\\t\\t\\t# add amount of seminars\\n\\t\\t\\t\\tcourse_seminars = course_info[2]\\n\\n\\t\\t\\t\\t# add max amount seminars\\n\\t\\t\\t\\tcourse_max_sem = course_info[3]\\n\\t\\t\\t\\tif course_max_sem == \\\"nvt\\\":\\n\\t\\t\\t\\t\\tcourse_max_sem = 0\\n\\n\\t\\t\\t\\t# add amount of practicals\\n\\t\\t\\t\\tcourse_practicals = course_info[4]\\n\\n\\t\\t\\t\\t# add max amount practicals\\n\\t\\t\\t\\tcourse_max_prac = course_info[5]\\n\\t\\t\\t\\tif course_max_prac == \\\"nvt\\\":\\n\\t\\t\\t\\t\\tcourse_max_prac = 0\\n\\n\\t\\t\\t\\t# add course to list\\n\\t\\t\\t\\tallcourses.append(Course(course_name, course_lectures, course_seminars, course_max_sem, course_practicals, course_max_prac))\\n\\n\\treturn allcourses\",\n \"def _get_information(self):\\n pros_cons = []\\n pros_cons_dict = {}\\n\\n for i, div in enumerate(self._tab.find_all(\\\"div\\\")):\\n for p in div.find_all(\\\"p\\\"):\\n pro_con = p.get_text(strip=True)\\n pros_cons.append(pro_con)\\n pros_cons_dict.update({self._keys_dict[i]: pros_cons})\\n pros_cons = []\\n\\n return pros_cons_dict\",\n \"def get_data(contents):\\n\\n header = {}\\n copy = contents.split(\\\"\\\\n\\\")\\n\\n header_contents = dict(\\n start_date=\\\"Start Date\\\",\\n end_date=\\\" End Date\\\",\\n subject=\\\" Subject\\\",\\n experiment=\\\" Experiment\\\",\\n group=\\\" Group\\\",\\n box=\\\" Box\\\",\\n start_time=\\\" Start Time\\\",\\n end_time=\\\" End Time\\\",\\n program=\\\" Program\\\",\\n msn=\\\" MSN\\\",\\n )\\n\\n for line in copy:\\n for key in header_contents:\\n heading = line.split(\\\":\\\")\\n if heading[0] == header_contents[key]:\\n if key == \\\"start_time\\\" or key == \\\"end_time\\\":\\n header[key] = (\\n heading[1].lstrip() + \\\":\\\" + heading[2] + \\\":\\\" + heading[3]\\n )\\n else:\\n header[key] = heading[1].lstrip()\\n\\n data = {}\\n copy = contents.split()\\n\\n uppercase = string.ascii_uppercase\\n\\n idx = []\\n for i, val in enumerate(copy):\\n if val[0] in uppercase and val[1] == \\\":\\\":\\n idx.append(i)\\n\\n for i, j in zip(idx[:-1], idx[1:]):\\n data[copy[i].lower()[0]] = [\\n timestamp for timestamp in copy[i + 1 : j] if timestamp[-1] != \\\":\\\"\\n ]\\n\\n return header, data\",\n \"def cert_info(user, course):\\r\\n if not course.may_certify():\\r\\n return {}\\r\\n\\r\\n return _cert_info(user, course, certificate_status_for_student(user, course.id))\",\n \"def convertToDict(self): \\n out = dict() \\n out[\\\"Title\\\"] = self.title \\n editions = []\\n for edition in self.editions.values(): \\n editions.append(edition.convertToDict(withTitle = False))\\n out[\\\"Editions\\\"] = editions\\n return out\",\n \"def to_dict(self, key, errors):\\n c = course_list.CourseListDAO.get_course_for_namespace(key)\\n if not c:\\n errors.append('Course %s not found' % key)\\n return\\n entity = {\\n 'id': c.namespace,\\n 'title': c.title,\\n\\t\\t'category': c.category, }\\n json_entity = dict()\\n self.registration().convert_entity_to_json_entity(entity, json_entity)\\n return json_entity\",\n \"def get_courses(self, depth=0):\\r\\n return self.courses.values()\",\n \"def make_gradebook(roster, grades, sub_info):\\n gradebook = []\\n for student in roster.keys():\\n s = {}\\n # fill student file with evaluation grades\\n for day, score in zip(sub_info.keys(), grades):\\n s[str(day)] = score[student]\\n s['total'] = sum(s.values())\\n s['username'] = student\\n gradebook.append(s)\\n return gradebook\",\n \"def _populate_section(self, algo_group, result_template):\\n if algo_group.module == CONNECTIVITY_MODULE:\\n result_template[KEY_SECTION] = 'connectivity'\\n result_template[KEY_SUB_SECTION] = 'connectivity'\\n result_template[KEY_SUBMENU_LIST] = self.connectivity_submenu\\n elif algo_group.group_category.display:\\n ### Visualizers on the Burst Page\\n result_template[KEY_SECTION] = 'burst'\\n result_template[KEY_SUB_SECTION] = 'view_' + algo_group.subsection_name\\n\\n elif algo_group.group_category.rawinput:\\n ### Upload algorithms\\n result_template[KEY_SECTION] = 'project'\\n result_template[KEY_SUB_SECTION] = 'data'\\n elif 'RAW_DATA' in algo_group.group_category.defaultdatastate:\\n ### Creators\\n result_template[KEY_SECTION] = 'stimulus'\\n result_template[KEY_SUB_SECTION] = 'stimulus'\\n else:\\n ### Analyzers\\n result_template[KEY_SECTION] = algo_group.group_category.displayname.lower()\\n result_template[KEY_SUB_SECTION] = algo_group.subsection_name\\n result_template[KEY_SUBMENU_LIST] = self.analyze_adapters\",\n \"def _write_section_values(section_data, fobj):\\n\\n # Order is significant.\\n section_dict = OrderedDict()\\n section_dict['Armor'] = section_data.get('armor')\\n section_dict['Internals'] = section_data.get('internals')\\n section_dict['Rear'] = section_data.get('rear')\\n section_dict['Config'] = section_data.get('config')\\n\\n for name, value in section_dict.items():\\n if not value:\\n continue\\n val_str = \\\" {name:<14} {{ {value} }}\\\\n\\\".format(\\n name=name, value=value)\\n fobj.write(val_str)\",\n \"def populate_course(self):\\r\\n def descend(parent, stack):\\r\\n xblock_type = stack.pop(0)\\r\\n for _ in range(2):\\r\\n child = ItemFactory.create(category=xblock_type, parent_location=parent.location)\\r\\n if stack:\\r\\n descend(child, stack)\\r\\n\\r\\n descend(self.course, ['chapter', 'sequential', 'vertical', 'problem'])\",\n \"def __init__(self, test_num):\\n if test_num == 1:\\n self.degree_chosen = \\\"Science\\\"\\n self.is_double_major = True\\n self.major = \\\"Computer Science\\\"\\n self.major2 = \\\"Mathematics\\\"\\n self.user_courses_dict = {\\n \\\"COSC121\\\": UniCourse(\\\"COSC121\\\", \\\"Introduction to Computer Science\\\"),\\n \\\"MATH120\\\": UniCourse(\\\"MATH120\\\", \\\"Discrete Mathematics\\\")\\n }\\n self.all_courses_dict = {\\n \\\"COSC121\\\": UniCourse(\\\"COSC121\\\", \\\"Introduction to Computer Science\\\"),\\n \\\"MATH120\\\": UniCourse(\\\"MATH120\\\", \\\"Discrete Mathematics\\\"),\\n \\\"MATH101\\\": UniCourse(\\\"MATH101\\\", \\\"Methods of Mathematics\\\")\\n }\",\n \"def createDictionnary():\\n dic = {}\\n root = \\\"https://www.airlinequality.com\\\"\\n url_page = root+\\\"/review-pages/a-z-airport-reviews/\\\"\\n\\n headers = {\\n 'User-Agent': 'Mozilla/5.0 (Windows NT 6.1) AppleWebKit/537.36 (KHTML, like Gecko) Chrome/41.0.2228.0 Safari/537.3'}\\n req = Request(url_page, headers=headers)\\n webpage = urlopen(req).read()\\n soup = bs(webpage, 'html.parser')\\n\\n r = soup.find_all('li')\\n list_text = [str(val) for val in r if \\\"href=\\\\\\\"/airport-reviews/\\\" in str(val)\\n and \\\"article\\\" not in str(val)]\\n for texte in list_text:\\n airport, link = getAirportLinks(texte)\\n dic[airport.rstrip()] = root+link\\n\\n return dic\",\n \"def create_tag_sections(self, rule=None):\\n tag_names = ['h{}'.format(i) for i in range(1, 6)]\\n for tag_name in tag_names:\\n tags = self.soup.find_all(tag_name) # Tags corresponded to headings\\n for each_tag in tags:\\n inside_tags = [item for item in itertools.takewhile(\\n lambda t: t.name not in [each_tag.name, 'script'],\\n each_tag.next_siblings)]\\n section = self.soup.new_tag('section_{}'.format(tag_name))\\n each_tag.wrap(section)\\n for tag in inside_tags:\\n section.append(tag)\",\n \"def proc_education_summary( summary: Tag ) -> Dict[str, str]:\\n edu_record = dict()\\n edu_record['school'] = summary.find('h3').text.strip()\\n edu_record['is_abroad_school'] = _is_abroad_school( edu_record['school'] )\\n\\n for parag in summary.find_all('p'):\\n spans = [span.text.strip() for span in parag.find_all('span')]\\n if len( spans ) == 2:\\n fld_name, value = spans\\n value = value.strip()\\n elif len(spans) == 0:\\n # print( 'education parag: ', parag )\\n edu_record['description'] = parag.text.strip()\\n continue\\n else:\\n print( 'education spans: ', spans )\\n continue\\n\\n if fld_name == 'Nombre de la titulación':\\n edu_record['degree_raw'] = value\\n edu_record['degree'] = _classify_degree( value )\\n # print( 'degree: ', value, _classify_degree(value) )\\n elif fld_name == 'Disciplina académica':\\n edu_record['field_raw'] = value\\n elif fld_name == 'Nota':\\n edu_record['grade_raw'] = value\\n elif fld_name.startswith('Fechas de estudios'):\\n edu_record['period_raw'] = value\\n elif fld_name.startswith('Actividades y asociaciones'):\\n edu_record['activities_raw'] = value\\n else:\\n print(\\\"proc_education_summary: \\\", fld_name, ' :: ', value)\\n\\n if edu_record.get('degree', 'Unknown') == 'Unknown':\\n if re.search( 'Ingenier|Engineering', edu_record.get('field_raw', '') ):\\n edu_record['degree'] = 'University'\\n\\n return edu_record\\n # %%\",\n \"def convert_heading_tree_to_dict(heading_tree_tuples):\\n return [convert_heading_node_to_dict(heading_node) for heading_node in heading_tree_tuples]\"\n]","isConversational":false},"negative_scores":{"kind":"list like","value":["0.7070789","0.60182756","0.6016982","0.5990312","0.5969372","0.59540147","0.59495693","0.59283185","0.58511907","0.58379775","0.58187705","0.5808631","0.5805773","0.58040214","0.5761286","0.57347447","0.5731346","0.5700729","0.5679609","0.5641057","0.5584008","0.5567756","0.55592006","0.55542386","0.55371946","0.55106723","0.5503354","0.54758847","0.5440823","0.54243755","0.5397898","0.5356291","0.5356231","0.53540844","0.53448766","0.5329744","0.5325272","0.5319968","0.53189385","0.5317076","0.53108186","0.5303226","0.52961457","0.5292468","0.528416","0.5280988","0.52807957","0.52789986","0.5273152","0.524586","0.524312","0.5241708","0.5238632","0.52285767","0.5226827","0.519365","0.5185909","0.51606196","0.5160426","0.5150561","0.5147921","0.51323515","0.5132153","0.51231396","0.5095527","0.5094483","0.50944304","0.507717","0.50634074","0.5058349","0.50540817","0.50416476","0.50412256","0.5038434","0.50268525","0.50248885","0.5014087","0.5010122","0.5009602","0.5004245","0.49988768","0.49984312","0.499343","0.49827087","0.49811795","0.4977245","0.4960149","0.49558565","0.49493966","0.49436268","0.49372852","0.493042","0.4918335","0.49086455","0.48901382","0.4889831","0.488185","0.48816475","0.48770234","0.48720437"],"string":"[\n \"0.7070789\",\n \"0.60182756\",\n \"0.6016982\",\n \"0.5990312\",\n \"0.5969372\",\n \"0.59540147\",\n \"0.59495693\",\n \"0.59283185\",\n \"0.58511907\",\n \"0.58379775\",\n \"0.58187705\",\n \"0.5808631\",\n \"0.5805773\",\n \"0.58040214\",\n \"0.5761286\",\n \"0.57347447\",\n \"0.5731346\",\n \"0.5700729\",\n \"0.5679609\",\n \"0.5641057\",\n \"0.5584008\",\n \"0.5567756\",\n \"0.55592006\",\n \"0.55542386\",\n \"0.55371946\",\n \"0.55106723\",\n \"0.5503354\",\n \"0.54758847\",\n \"0.5440823\",\n \"0.54243755\",\n \"0.5397898\",\n \"0.5356291\",\n \"0.5356231\",\n \"0.53540844\",\n \"0.53448766\",\n \"0.5329744\",\n \"0.5325272\",\n \"0.5319968\",\n \"0.53189385\",\n \"0.5317076\",\n \"0.53108186\",\n \"0.5303226\",\n \"0.52961457\",\n \"0.5292468\",\n \"0.528416\",\n \"0.5280988\",\n \"0.52807957\",\n \"0.52789986\",\n \"0.5273152\",\n \"0.524586\",\n \"0.524312\",\n \"0.5241708\",\n \"0.5238632\",\n \"0.52285767\",\n \"0.5226827\",\n \"0.519365\",\n \"0.5185909\",\n \"0.51606196\",\n \"0.5160426\",\n \"0.5150561\",\n \"0.5147921\",\n \"0.51323515\",\n \"0.5132153\",\n \"0.51231396\",\n \"0.5095527\",\n \"0.5094483\",\n \"0.50944304\",\n \"0.507717\",\n \"0.50634074\",\n \"0.5058349\",\n \"0.50540817\",\n \"0.50416476\",\n \"0.50412256\",\n \"0.5038434\",\n \"0.50268525\",\n \"0.50248885\",\n \"0.5014087\",\n \"0.5010122\",\n \"0.5009602\",\n \"0.5004245\",\n \"0.49988768\",\n \"0.49984312\",\n \"0.499343\",\n \"0.49827087\",\n \"0.49811795\",\n \"0.4977245\",\n \"0.4960149\",\n \"0.49558565\",\n \"0.49493966\",\n \"0.49436268\",\n \"0.49372852\",\n \"0.493042\",\n \"0.4918335\",\n \"0.49086455\",\n \"0.48901382\",\n \"0.4889831\",\n \"0.488185\",\n \"0.48816475\",\n \"0.48770234\",\n \"0.48720437\"\n]","isConversational":false},"document_score":{"kind":"string","value":"0.64286023"},"document_rank":{"kind":"string","value":"1"}}},{"rowIdx":22664,"cells":{"query":{"kind":"string","value":"Each course page has a small info section at the beginning, which I had to extract and formulate in a different way to the main sections. This function constructs the dictionary entries for he course when given a string with all the details required for the info section"},"document":{"kind":"string","value":"def get_info_list(info_string, course):\n info_list = []\n split_on_newline = info_string.split(\"\\n\")\n for elem in split_on_newline:\n split = elem.split(\": \")\n for s in split:\n info_list.append(s)\n info_list = info_list[1:-1]\n info_tags = [\n 'session', 'school', 'credits', 'level', 'offered',\n 'visiting_students', 'erasmus_students',\n ]\n i = 0\n for info_tag in info_tags:\n course[info_tag] = new_dict(\n info_list[i] + ': ', info_list[i + 1])\n i += 2\n return course"},"metadata":{"kind":"string","value":"{\n \"objective\": {\n \"self\": [],\n \"paired\": [],\n \"triplet\": [\n [\n \"query\",\n \"document\",\n \"negatives\"\n ]\n ]\n }\n}"},"negatives":{"kind":"list like","value":["def _section_course_info(course_key, access):\r\n course = get_course_by_id(course_key, depth=None)\r\n\r\n section_data = {\r\n 'section_key': 'course_info',\r\n 'section_display_name': _('Course Info'),\r\n 'access': access,\r\n 'course_id': course_key,\r\n 'course_display_name': course.display_name,\r\n 'enrollment_count': CourseEnrollment.num_enrolled_in(course_key),\r\n 'has_started': course.has_started(),\r\n 'has_ended': course.has_ended(),\r\n 'list_instructor_tasks_url': reverse('list_instructor_tasks', kwargs={'course_id': course_key.to_deprecated_string()}),\r\n }\r\n\r\n try:\r\n advance = lambda memo, (letter, score): \"{}: {}, \".format(letter, score) + memo\r\n section_data['grade_cutoffs'] = reduce(advance, course.grade_cutoffs.items(), \"\")[:-2]\r\n except Exception:\r\n section_data['grade_cutoffs'] = \"Not Available\"\r\n # section_data['offline_grades'] = offline_grades_available(course_key)\r\n\r\n try:\r\n section_data['course_errors'] = [(escape(a), '') for (a, _unused) in modulestore().get_course_errors(course.id)]\r\n except Exception:\r\n section_data['course_errors'] = [('Error fetching errors', '')]\r\n\r\n return section_data","def parse_create_section(xml_course):\n\n attrs = [\n \"section\",\n 'crn',\n \"start-time\",\n \"end-time\",\n \"meeting-days\",\n \"location\",\n \"section-number\",\n \"instructor\"\n ]\n\n section = pull_attributes_from_xml(xml_course, attrs)\n\n section[\"places\"] = []\n\n # Create Place attribute pointer based on location string\n # Get places from Parse\n places = get_places()[\"results\"]\n # Get location info from section (of form [\"BRK 101\", \"TBA\"])\n all_locations = section[\"location\"].split(\", \")\n # Filter out TBA\n # TODO Maybe do something else with them\n locations = [location for location in all_locations if location != \"TBA\"]\n\n for location in locations:\n building_code = location.split(\" \")[0]\n for place in places:\n if place.get(\"symbol\") and place[\"symbol\"] == building_code:\n section[\"places\"].append(place[\"objectId\"])\n break;\n\n\n return section","def _parse(self, course: NavigableString) -> ParseType:\n\n info = {\"link\": \"\", \"icon\": \"\", \"title\": \"\", \"description\": \"\",\n \"counts\": {}}\n\n info[\"link\"] = course.a[\"href\"]\n info[\"icon\"] = course.a.img[\"src\"]\n\n description: NavigableString = course.a.div\n info[\"title\"] = description.div.get_text()\n info[\"description\"] = description.p.get_text()\n\n counts: NavigableString = course.find(\"div\", {\"class\": \"courseCounts\"})\n counts_data: ResultSet = counts.find_all(\"li\")\n for data in counts_data:\n name: str = data.span.get_text().lower()\n val: str = data.find(\"p\").get_text()\n info[\"counts\"][name] = int(val.replace(\",\", \"\"))\n\n return info","def get_sections(pages):\n sections = {}\n section_pages = {}\n current_section_name = None\n current_section = []\n\n for page_num, page in enumerate(pages):\n clean_page = [re.sub(\"\\s+\", \" \", i.strip()) for i in page.split(\"\\n\")]\n\n for ind, i in enumerate(clean_page):\n\n if (\n re.findall(\"^Section \\d+\", i)\n and \"page\" not in i\n or (re.sub(\"\\d+ [\\w+\\s+]+\", \"\", i) == \"\" and ind == 0 and len(i) > 6)\n ):\n if current_section_name is not None:\n sections[current_section_name] = current_section\n current_section = []\n current_section_name = i\n break\n\n section_pages[page_num + 1] = current_section_name or \"No Section\"\n\n current_section.extend(clean_page)\n return sections, section_pages","def about_course(command):\n\n response = {'code': None, 'title': None,\\\n 'prof': None, 'section1': None,\\\n 'timing1': None, 'room1' : None,\\\n 'section2': None, 'timing2': None,\\\n 'room2': None, 'description': None,\\\n 'prereq': None, 'perm': None}\n \n if re.search('ENPM611', command):\n response = {'code': 'ENPM 611', 'title': 'Software Engineering',\\\n 'prof': 'Christopher Ackermann', 'section1': '0101',\\\n 'timing1': 'Monday 4:00 - 6:40 PM', 'room1': 'JMP 2121',\\\n 'description': 'Software engineering concepts, methods, and practices important to both the theorist and the practitioner will be covered. The entire range of responsibilities expected of a software engineer are presented. The fundamental areas of requirements development, software design, programming languages, and testing are covered extensively. Sessions on supporting areas such as systems engineering, project management, and software estimation are also included.', 'prereq': 'Competency in one programming language; and must have completed an undergraduate software engineering course. Or permission of instructor.', 'perm': 'Permission of ENGR-CDL-Office of Advanced Engineering Education.'\n}\n elif re.search('ENPM613', command):\n response = {'code': 'ENPM 613', 'title': 'Software Design and Implementation', 'prof': 'Ioana Rus', 'section1': '0101', 'timing1': 'Wednesday 7:00 - 9:40 PM', 'room1': 'TBA', 'description': 'Software design concepts and practices within the field important to both the practitioner and the theorist will be covered. Architectural and detailed designs are included for batch, client/server, and real-time systems. Design considerations for structured, object-oriented, and Web-based systems are covered. Design of databases, user interfaces, forms, and reports are also included. Implementation issues that affect the design, including error handling, performance, and inter-process communication, are presented.', 'perm': 'Permission of ENGR-CDL-Office of Advanced Engineering Education.'}\n elif re.search('ENPM631', command):\n response = {'code': 'ENPM631', 'title': 'TCP/IP Networking', 'prof': 'Pedram Fard', 'section1': '0101', 'timing1': 'Tuesday 7:00 - 9:40 PM', 'room1': 'TBA' , 'description': 'Describe how IP datagram travels through the internet and are routed from the source to the destination. Introduce the two transport protocols: UDP and TCP, the proper context to use each one, and related parameters and issues. Cover some other protocols, closely related to the TCP/IP that are responsible for the seamless operation of the Internet.', 'perm': 'ENPM602; or permission of instructor. And permission of ENGR-CDL-Office of Advanced Engineering Education.'}\n elif re.search('ENPM687', command):\n response = {'code': 'ENPM687', 'title': 'Digital Forensics and Incidence Response', 'prof': 'Jonas Amoonarquah', 'section1': 'Online', 'timing1': 'NA', 'room1': 'NA', 'description': 'Students will implement a robust incident response methodology, including proper forensic handling of evidence, and cover legal aspects of national and international law regarding forensics. The bulk of the course covers evidence acquisition, preservation, analysis and reporting on multiple platforms.', 'perm':'None'}\n elif re.search('ENPM691', command):\n response = {'code': 'ENPM691', 'title': 'Hacking of C Programs and Unix Binaries', 'prof': 'Dharmalingam Ganesan', 'section1': '0101', 'timing1': 'Thursday 7:00 - 9:40 PM', 'room1': 'JMP 3201', 'description': 'Teaches the fundamentals of secure programming in C. An in depth discussion on various security vulnerabilities (e.g., buffer overflows) in C applications will be taught with hands-on demo of concepts during the class. Students will learn how a C program runs \"under-the-hood\". The course will teach nitty-gritty of C programs by analyzing at the assembly level. The course discusses best practices (e.g., coding standards) and design principles for secure programming so that security can be built-in during design time. In addition to assignments, students are required to present papers related to this course.', 'perm': None, 'prereq': 'ENEE150; or students who have taken courses with comparable content may contact the department.'}\n elif re.search('ENPM693', command):\n response = {'code': 'ENPM693', 'title': 'Network Security', 'prof': 'Sohraab Soltani', 'section1': '0101', 'timing1': 'Tuesday 7:00 - 9:40 PM', 'room1' : 'JMP 3201', 'description': 'Introduction to various approaches to design; specify and verify security protocols used in large systems and networks; familiarization with some current technologies. Security threats and countermeasures, communication security and basic encryption techniques, authentication protocols, data confidentiality and integrity, analysis of cryptographic protocols, and access control in large systems and networks.', 'perm': None, 'prereq': 'An operating systems and/or network protocol course or equivalent.'}\n elif re.search('ENPM694', command):\n response = {'code': 'ENPM694', 'title': 'Networks and Protocols', 'prof': 'Sohraab Soltani', 'section1': '0101', 'timing1': 'Wednesday 7:00 - 9:40 PM', 'room1': 'JMP 3201', 'description': 'Provides a deep understanding of TCP/IP protocol suit and routing in the internet. The course topics are: overview of TCP/IP, basics of IP protocol, basics of TCP protocol, Network Address Translation (NAT), Dynamic Host Configuration Protocol (DHCP), Internet Protocol Security (IPsec), Internet Control Message Protocol (ICMP), Simple Mail Transfer Protocol (SMTP), Domain Name Service (DNS), IPv6, Concepts of routing (Bellman-Ford and Dijkstra algorithms), Routing Information Protocol (RIP), Open Shortest Path First (OSPF), Interior Gateway Routing Protocol (IGRP), Enhance Gateway Routing Protocol (EIGRP), and Border Gateway Protocol (BGP).'}\n elif re.search('ENPM696', command):\n response = {'code': 'ENPM696', 'title': 'Reverse Software Engineering', 'prof': 'Allen Hazelton', 'section1': '0101', 'timing1': 'Tuesday 4:00 - 6:40 PM', 'room1': 'TBA', 'description': 'An in-depth understanding of software reverse engineering concepts and hands-on training with reverse engineering tools, including disassemblers, decompilers, and code analyzers. Students will become familiar with both low-level software and the x86 instruction set through binary reversing sessions. This course also provides insights into many subjects such as system security, source code analysis, software design, and program understanding that will be beneficial in a variety of fields.', 'prereq': 'ENPM691 and CMSC106; or permission of instructor. And permission of ENGR-CDL-Office of Advanced Engineering Education.'}\n elif re.search('ENPM809J', command):\n response = {'code': 'ENPM809J', 'title': 'Cloud Security', 'prof': 'Kevin Shivers', 'section1': '0101', 'timing1': 'Monday 7:00 - 9:40 PM', 'room1': 'TBA', 'section2': '0201', 'timing2': 'Thursday 4:00 - 6:40 PM', 'room2': 'TBA', 'description': 'NA'}\n elif re.search('ENPM809R', command):\n response = {'code': 'ENPM809R', 'title': 'Software Defined Networking', 'prof': 'Emre Gunduzhan', 'section1': '0101', 'timing1': 'Monday 4:00 - 6:40 PM', 'room1': 'TBA', 'description': 'NA'}\n elif re.search('ENPM809W', command):\n response = {'code': 'ENPM809W', 'title': 'Security and Software', 'prof': 'Mikael Lindvall', 'section1': '0101', 'timing1': 'Thursday 7:00 - 9:40 PM', 'room1': 'TBA', 'description': 'NA'}\n\n return response","def section(data):\n if len(data['index']) == 2 and data['index'][1][0].isdigit():\n element = {}\n element['is_section'] = True\n element['section_id'] = '-'.join(data['index'])\n if u\"§§ \" == data['title'][:3]:\n element['is_section_span'] = True\n else:\n element['is_section_span'] = False\n match = SECTION_TITLE_REGEX.match(data['title'])\n element['label'] = match.group(1)\n element['sub_label'] = match.group(2)\n return element","def bsoup(coursepage):\n soup = BeautifulSoup(coursepage.content, 'lxml')\n h1 = soup.find_all('h1')[2]\n html = h1.find_next_siblings()\n all_strings = [h1.string]\n for div in html:\n try:\n text = div.get_text()\n except:\n text = div.string\n if text is not None:\n all_strings.append(text)\n course = {'title': all_strings[0]}\n course = get_info_list(all_strings[1], course)\n course['description'] = new_dict(all_strings[2], all_strings[3])\n course['timetable'] = new_dict(all_strings[4], all_strings[5])\n course['requirements_of_entry'] = new_dict(all_strings[6], all_strings[7])\n course['excluded_courses'] = new_dict(all_strings[8], all_strings[9])\n course['co_requisites'] = new_dict(all_strings[10], all_strings[11])\n course['assessment_weighting'] = new_dict(all_strings[12], all_strings[13])\n course['aims'] = new_dict(all_strings[17], all_strings[18])\n date = all_strings[14].split(': ')\n course['assessment_date'] = new_dict(date[0] + \": \", date[1])\n course['learning_outcomes'] = new_dict(all_strings[19], all_strings[20])\n # TODO Doesn't parse Minimum Requirement for Award of Credit or\n # Reassessment Options\n return course","def parseSection(self, response):\n sel = Selector(response)\n sections = sel.xpath('//table[@class=\"sections responsive\"]//tr[not(@class=\"headers\")]')\n for s in sections:\n item = CourseItem(response.request.meta[\"item\"])\n item['section'] = s.xpath('@data-section-id').get().strip()\n item['instructors'] = s.css('.instructor::text').get()\n if item['instructors'] != None:\n item['instructors'].strip()\n item['instructors'] = [x.strip() for x in re.split(',', item['instructors'])]\n item['syllabus'] = s.css('.syllabus a::attr(href)').get()\n if item['syllabus'] != None:\n item['syllabus'].strip()\n return item\n \n\n \"\"\"\n Ignore the code below this. I was trying to get\n the times, days, and number registered from the class sections\n \"\"\"\n #times = s.xpath('//td[@class=\"time\"]/text()').get().strip()\n #times = re.split('-', times)\n #starttime = times[0]\n #endtime = times[1]\n #endt = dt.datetime.strptime(endtime, '%H:%M%p')\n # TODO: Check if \"am\"/\"pm\" from endt, & if endt hour is greater/less than startt \n #startt = dt.datetime.strptime(starttime, '%H:%M')\n #days = s.xpath('//td[@class=\"days\"]/text()').get().strip()\n #days = re.split(',', days)\n #numdays = len(days]\n \n #cap = s.xpath('//td[@class=\"registered\"]//a/text()').get().strip()\n #cap = re.split(' of ', cap.strip())\n #item['capacity'] = cap[1]","def get_sections(data):\n print \" * Extracting sections\"\n sections = OrderedDict()\n\n results = re.finditer(r\"^([A-Z][A-Z]+)([ ]+.*)?$\", data, re.M)\n data_start = None\n data_end = None\n prev_section = None\n cur_section = None\n for res in results:\n print \" * Found\", res.groups()[0]\n data_end = res.start()\n if prev_section is not None:\n # Get rid of potential comments at the end of a line.\n _data = re.sub(r\"\\s*#.*\", \"\", data[data_start:data_end])\n sections[prev_section][\"data\"] = filter(None, _data.splitlines())\n data_start = res.end()\n cur_section = res.groups()[0]\n sections[cur_section] = {\"arguments\": res.groups()[1], \"data\": \"\"}\n prev_section = \"%s\" % cur_section # Only to be sure we get a brand new string...\n\n return sections","def parse(u):\n rec = {}\n\n try:\n r = requests.get(u, headers=headers)\n\n if r.status_code == 200:\n html = r.text\n soup = BeautifulSoup(html, 'lxml')\n overview_section = soup.select('.Raw-s14xcvr1-0 gXqFYO')\n full_name_section = soup.select('.sc-iwsKbI kjxnCg')\n years_of_practice_section = soup.select('.DataField__Data-c3wc7f-1 gLHSHx')\n language_section = soup.select('.DataField__Data-c3wc7f-1 gLHSHx')\n office_location_section = soup.select('.Paragraph-fqygwe-0 cojhks')\n hospital_affiliation_section = soup.select('.Paragraph-fqygwe-0 fwayNy')\n specialties = soup.select('.DataField__Data-c3wc7f-1 gLHSHx')\n education_and_medical_training_section = soup.select('.EducationAndExperience__Item-xn5fll-0 bzYYRk')\n certification_and_licensure_section = soup.select('.Paragraph-fqygwe-0 bQPwuv')\n\n if overview_section:\n overview = overview_section[0].text.replace('\"', '')\n if full_name_section:\n full_name = full_name_section[0].text\n if years_of_practice_section:\n years_of_practice = years_of_practice_section[0].text.strip().replace('\"', '')\n if language_section:\n language = language_section[0].text.strip().replace('\"', '')\n if office_location_section:\n office_location = office_location_section[0].text\n if hospital_affiliation_section:\n hospital_affiliation = hospital_affiliation_section[0].text.strip().replace('\"', '')\n if specialties_section:\n specialties = specialties_section[0].text.replace('\"', '')\n if education_and_medical_training_section:\n education_and_medical_training = education_and_medical_training_section[0].text\n if certification_and_licensure_section:\n certification_and_licensure = certification_and_licensure_section[0].text\n\n\n rec = {'overview': overview, 'full_name': full_name, 'years_of_practice': years_of_practice, 'language': language,\n 'office_location': office_location, 'hospital_affiliation': hospital_affiliation, 'specialties':specialties,\n 'education_and_medical_training': education_and_medical_training,\n 'certification_and_licensure': certification_and_licensure}\n except Exception as ex:\n print('Exception while parsing')\n print(str(ex))\n finally:\n return json.dumps(rec)","def get_course_about_section(course, section_key):\r\n\r\n # Many of these are stored as html files instead of some semantic\r\n # markup. This can change without effecting this interface when we find a\r\n # good format for defining so many snippets of text/html.\r\n\r\n # TODO: Remove number, instructors from this list\r\n if section_key in ['short_description', 'description', 'key_dates', 'video',\r\n 'course_staff_short', 'course_staff_extended',\r\n 'requirements', 'syllabus', 'textbook', 'faq', 'more_info',\r\n 'number', 'instructors', 'overview',\r\n 'effort', 'end_date', 'prerequisites', 'ocw_links']:\r\n\r\n try:\r\n\r\n request = get_request_for_thread()\r\n\r\n loc = course.location.replace(category='about', name=section_key)\r\n\r\n # Use an empty cache\r\n field_data_cache = FieldDataCache([], course.id, request.user)\r\n about_module = get_module(\r\n request.user,\r\n request,\r\n loc,\r\n field_data_cache,\r\n course.id,\r\n not_found_ok=True,\r\n wrap_xmodule_display=False,\r\n static_asset_path=course.static_asset_path\r\n )\r\n\r\n html = ''\r\n\r\n if about_module is not None:\r\n try:\r\n html = about_module.render('student_view').content\r\n except Exception: # pylint: disable=broad-except\r\n html = render_to_string('courseware/error-message.html', None)\r\n log.exception(\r\n u\"Error rendering course={course}, section_key={section_key}\".format(\r\n course=course, section_key=section_key\r\n ))\r\n return html\r\n\r\n except ItemNotFoundError:\r\n log.warning(\r\n u\"Missing about section {key} in course {url}\".format(key=section_key, url=course.location.to_deprecated_string())\r\n )\r\n return None\r\n elif section_key == \"title\":\r\n return course.display_name_with_default\r\n elif section_key == \"university\":\r\n return course.display_org_with_default\r\n elif section_key == \"number\":\r\n return course.display_number_with_default\r\n\r\n raise KeyError(\"Invalid about key \" + str(section_key))","def parseLesson(lesson_text):\n lesson_parsed = dict()\n\n lesson_parsed['section_starts'] = list()\n lesson_parsed['sections'] = list()\n lesson_parsed['end_of_lesson'] = None\n lesson_parsed['source'] = None\n lesson_parsed['title'] = None\n lesson_parsed['number'] = None\n\n end_of_lesson_markers = [\n r'^\\s*\\(End\\s+of\\s+lesson',\n r'^\\s*\\(End\\s+of\\s+lecture',\n r'^\\(End\\s+of\\s+recording',\n r'^\\[End\\s+of\\s+recording',\n r'^\\[End\\s+of\\s+the\\s+recording',\n r'^\\(End of the recording',\n r'^\\[End\\s+of\\s+the\\s+tape',\n r'^\\(End\\s+of\\s+audible\\s+portion\\s+of\\s+the\\s+lesson',\n r'^\\[Note\\:\\s+This\\s+is\\s+the\\s+end\\s+of\\s+the\\s+recording',\n ]\n\n # Do a parsing pass of the overall lesson text. This information will be used\n # to compile the lesson into a form useful for display and use markup.\n line_number = 0\n for line in lesson_text:\n mobj = re.search(r'^Source:\\s+(.+)$', line)\n if mobj:\n # WORKING HERE\n pass\n\n if re.search(r'^\\d+[a-z]?\\.', line):\n lesson_parsed['section_starts'].append(line_number)\n line_number += 1\n continue\n\n found_end_of_lesson = False \n for end_of_lesson_marker in end_of_lesson_markers:\n if re.search(end_of_lesson_marker, line):\n lesson_parsed['end_of_lesson'] = line_number\n line_number += 1\n found_end_of_lesson = True\n break\n\n if found_end_of_lesson:\n continue\n\n for end_of_lesson_marker in end_of_lesson_markers:\n if re.search(end_of_lesson_marker, line):\n lesson_parsed['end_of_lesson'] = line_number\n line_number += 1\n break\n\n if lesson_parsed['end_of_lesson'] is not None:\n continue\n\n # Remove tabs from the start of a line.\n # Replace tabs between the number and the start of text with a space.\n # Skip \"This page is included for printing and binding purposes\"\n # Footnotes \"[fnX]\" will need to be linked.\n\n line_number += 1\n\n\n # Now compile the lesson instructions into their own things.\n\n idx = 0\n\n new_section = list()\n for section_line in xrange(lesson_parsed['section_starts'][idx], lesson_parsed['section_starts'][idx+1]:\n section_start_text = lesson_text[section_line]\n lesson_parsed['sections'].append(lesson_text[section_line])\n\n\n\n return lesson_parsed","def print_section( secName, dictName ):\n\n\t# Number of entries in IA dictionary.\n\tmaxitem = len(dictName)\n\n\toutFile.write('\\n\\\\begin{enumerate}\\n')\n\n\tfor m in xrange(0,maxitem):\n\n\t\toutFile.write('\\\\item ' + dictName[m])\n\n\t\tbeans = [bean for bean in secName if int(bean[1]) == (m+1)]\n\t\t\n \t\tif beans[0][-1] == 'N/A':\n \t\t\toutFile.write('\\n\\t\\\\begin{description}[font=\\\\normalfont]\\n')\n \t\t\toutFile.write('\\t\\t\\\\item[N/A]\\n')\n \t\t\toutFile.write('\\t\\\\end{description}\\n\\n')\n\t\telse:\n\t\t\tl = len(beans)\n\t\t\tif beans[0][2].startswith('AY'):\n\t\t\t\toutFile.write('\\t\\\\begin{description}[leftmargin=1.75cm, font=\\\\normalfont]\\n')\n\t\t\t\tbeans[0][2] = string.replace(beans[0][2],'AY','\\\\textsc{ay}')\n\t\t\telse:\n\t\t\t\toutFile.write('\\t\\\\begin{description}[font=\\\\normalfont]\\n')\n\t\t\tfor i in xrange(0,l):\n\t\t\t\tif beans[i][2].startswith('AY'):\n\t\t\t\t\tbeans[i][2] = string.replace(beans[i][2],'AY','\\\\textsc{ay}')\n\t\t\t\toutFile.write('\\t\\t\\\\item[\\\\small ' + beans[i][2] + '] ' + beans[i][3] + '\\n')\n\n\t\t\t\tif len(beans[i]) == 5:\n\t\t\t\t\toutFile.write('\\n\\n\\t\\t' + beans[i][4] + '\\n')\n\n\t\t\toutFile.write('\\t\\\\end{description}\\n\\n')\n\n\toutFile.write('\\\\end{enumerate}\\n')","def Content():\n\tTOPIC_DICT = {\"Basics\": [[\"Introduction to Python\", \"/introduction-to-python/\"], \n\t\t\t\t\t\t\t\t\t\t\t\t\t [\"Print functions\", \"/python-print-function/\"],\n\t\t\t\t\t\t\t\t\t\t\t\t\t [\"Math basics with Python 3\", \"/math-basics-python-3/\"\n\t\t\t\t\t\t\t\t\t\t\t\t\t ]],\n\t\t\t\t\t\t\t\t\"Web Dev\": [[\"wd1\"]]} #Web dev is now the second topic\n\t\t\t\t\t\t\t\t#Introduction to Python\n\t#will be the title and the link to that is immediately after. Each inner array will be a specific sub section of the basics\n\t#section\n\n\treturn TOPIC_DICT","def get_course_info_section(request, course, section_key):\r\n usage_key = course.id.make_usage_key('course_info', section_key)\r\n\r\n # Use an empty cache\r\n field_data_cache = FieldDataCache([], course.id, request.user)\r\n info_module = get_module(\r\n request.user,\r\n request,\r\n usage_key,\r\n field_data_cache,\r\n course.id,\r\n wrap_xmodule_display=False,\r\n static_asset_path=course.static_asset_path\r\n )\r\n\r\n html = ''\r\n\r\n if info_module is not None:\r\n try:\r\n html = info_module.render('student_view').content\r\n except Exception: # pylint: disable=broad-except\r\n html = render_to_string('courseware/error-message.html', None)\r\n log.exception(\r\n u\"Error rendering course={course}, section_key={section_key}\".format(\r\n course=course, section_key=section_key\r\n ))\r\n\r\n return html","def parseCourses(self, response):\n sel = Selector(response)\n courses = sel.xpath('//div[@class=\"course-info expandable\"]')\n for c in courses:\n item = CourseItem(response.request.meta[\"item\"])\n item['code'] += '-' + c.xpath('@id').get().strip()\n item['name'] = c.xpath('//a[@class=\"courselink\"]/text()').get().strip()\n # everything works up to here #\n href = c.xpath('div/h3/a/@href').get()\n url = urljoin('https://web-app.usc.edu', href)\n yield Request(url=url,callback=self.parseSection,meta={'item':item})","def generate_page_sections_dict(self, project_page_data: dict):\n wiki_obj = WikiService()\n\n short_description = (\n f\"\\n{project_page_data['project']['shortDescription']}\\n\"\n )\n\n created_date = wiki_obj.format_date_text(\n project_page_data['project']['created']\n )\n # created_date_text = f\"\\n{created_date}\\n\" \n # due_date = wiki_obj.format_date_text(\n # project_page_data['project']['due_date']\n # )\n timeframe = (\n f\"\\n* '''Start Date:''' {created_date}\\n\"\n # f\"\\n* '''End Date:''' Estimate {due_date}\\n\"\n )\n\n\n project_url = (\n f\"\\n{project_page_data['project']['url']}\\n\"\n )\n\n hashtag = (\n project_page_data['project']['changesetComment']\n )\n hashtag = (\n project_page_data['project']['changesetComment'].replace(\n \"#\", \"<nowiki>#</nowiki>\"\n )\n )\n hashtag_text = (\n f\"\\n{hashtag}\\n\"\n )\n\n instructions_text = (\n project_page_data['project']\n ['externalSource']\n ['instructions']\n )\n instructions = (\n f\"\\n{instructions_text}\\n\"\n )\n\n per_task_instructions_text = (\n project_page_data['project']\n ['externalSource']\n ['perTaskInstructions']\n )\n per_task_instructions = (\n f\"\\n{per_task_instructions_text}\\n\"\n )\n\n imagery_text = (\n project_page_data['project']\n ['externalSource']\n ['imagery']\n )\n imagery = (\n f\"\\n{imagery_text}\\n\"\n )\n\n license_text = (\n project_page_data['project']\n ['externalSource']\n ['license']\n )\n license = (\n f\"\\n{license_text}\\n\"\n )\n\n # metrics = (\n # f\"\\n* {project_page_data.instructions}\\n\"\n # )\n # quality_assurance = (\n # f\"\\n* {project_page_data.quality_assurance}\\n\"\n # )\n\n users = project_page_data['project'][\"users\"]\n project_users = \"\"\n for user in users:\n project_users += (\n f\"\\n| {user['userId']}\\n| {user['userName']}\\n|-\"\n )\n\n \n\n project_page_sections = {\n self.short_description_section: short_description,\n self.timeframe_section: timeframe,\n # self.timeframe_section: {\n # self.created_section: created_date_text\n # }, # if choose use subsection for timeframe \n self.url_section: project_url,\n self.external_sources_section: {\n self.instructions_section: instructions,\n self.per_task_instructions_section: per_task_instructions,\n self.imagery_section: imagery,\n self.license_section: license\n },\n self.hashtag_section: hashtag_text,\n # self.instructions_section: instructions,\n # self.metrics_section: metrics,\n # self.quality_assurance_section: quality_assurance,\n self.team_user_section: {\n self.users_list_section: project_users\n }\n }\n return project_page_sections","def parse_create_course(xml_course):\n attrs = [\n \"term-code\",\n \"term-description\",\n 'subject',\n \"course-number\",\n \"school\",\n \"department\",\n \"title\",\n \"description\",\n \"credit-hours\",\n \"distribution-group\"\n ]\n course = pull_attributes_from_xml(xml_course, attrs)\n course[\"sections\"] = []\n\n return course","def insert_course(dept, num, text):\n\n # Course Title \n m = re.search(\"[\\d\\w]{5} - ([\\w ]*)\", text)\n title = m.group(1) if m else \"nomatch\"\n\n # Course Description\n m = re.search(\"\\.\\s(.*)\\sTypically\",text)\n des = m.group(1) if m else \"nomatch\"\n\n # Credit hours aren't fixed for every course\n # Credit Hours: 2.00\n # Credit Hours: 2.00 or 3.00. \n # Credit Hours: 1.00 to 18.00. \n m = re.search(\"Credit Hours: (\\d+\\.\\d+)\",text, flags=re.IGNORECASE)\n m = re.search(\"(\\d+\\.\\d+)(.*?)Credit hours\",text, flags=re.IGNORECASE) if not m else m\n cr = m.group(1) if m else \"-1\"\n\n # Semesters Offered\n m = re.search(\"Typically offered (.*?)\\.\", text)\n sem = m.group(1).split() if m else [\"nomatch\"]\n\n # Course Type: Lecture, Recitation, Lab, Seminar, etc.\n m = re.search(\"Schedule Types:\\s((?:[\\w ]+)(?:,[\\w ]+)*) \\s+\", text)\n form = m.group(1).split(\", \") if m else [\"nomatch\"]\n\n # Learning objectives will not necessarily follow campuses\n m = re.search(\"campuses:(\\s+([\\w\\s])+\\n)\", text)\n campus = m.group(1).strip().split(\"\\n\\n\") if m else [\"nomatch\"]\n campus = [camp.strip() for camp in campus]\n\n # prereq regex and decomosition of prereqs into lists of AND conditions (works for most classes, not 477 and similar)\n # re.DOTALL matches all characters, including \"\\n\"\n idx = text.find(\"campuses:\")\n m = re.search(\"Prerequisites:(.*)\",text[idx:],flags=re.DOTALL)\n if m:\n allReqs = []\n prereqText = m.group(1).strip()\n prereqText = prereqText.encode('ascii', 'ignore') \n for i in PrereqParser.parseprereq(prereqText):\n reqArr = []\n for j in i.split():\n if j.find(\"-C\") != -1:\n j = j.replace(\"-C\",\"\")\n reqArr.append(Requisite(course=j,reqType=False))\n else:\n reqArr.append(Requisite(course=j,reqType=True)) \n allReqs.append(RequisiteList(courses=reqArr))\n\n else:\n allReqs = []\n\n # create course entity\n course = Course(number=num, title=title, department=dept, form=form,\n description=des, credits=float(cr), semesters=sem,\n campuses=campus,requisites=allReqs, id=dept + num)\n # store course \n course.put()","def get_course(data):\n\n return {item['course'] for item in data}","def _recipe_details_generator(self, converted_content, overview_recipe):\n def get_cooking_shop_strings(lines):\n ret = []\n buf = None\n is_recipe_step_area = False\n for l in lines:\n if re.search(\"軒目\", l.strip()) or re.match(r\"^[①-⑳＊].*『.*』\", l.strip()) or re.match(r\"^[①-⑳＊].*「.*」\", l.strip()):\n if buf:\n ret.append(buf)\n buf = l.strip()\n continue\n\n if re.search(\"^(料理|万能調味料)\", l.strip()):\n is_recipe_step_area = False\n\n if re.search(\"^材料\", l.strip()):\n title, materials = re.search(\"(材料)(.*)\", l.strip()).groups()\n # buf += \"\\n\" + \"\\n\".join(l.strip().split(None, 1))\n buf += \"\\n\" + title + \"\\n\" + materials.strip()\n continue\n\n if re.search(\"^作り方\", l.strip()):\n is_recipe_step_area = True\n title, recipe_steps = re.search(\"(作り方)(.*)\", l.strip()).groups()\n # buf += \"\\n\" + \"\\n\".join(l.strip().split(None, 1))\n buf += \"\\n\" + title + \"\\n\" + recipe_steps.strip()\n continue\n \n if buf:\n if is_recipe_step_area:\n if re.match(r\"^[①-⑳＊]\", l.strip()):\n buf += \"\\n\" + l.strip()\n else:\n buf += l.strip()\n else:\n buf += \"\\n\" + l.strip()\n if buf:\n ret.append(buf)\n\n return ret\n \n \n for ii, l in enumerate(converted_content.splitlines()):\n if ii == 1:\n overview_recipe.cooking_name_sub = l.strip()\n continue\n \n if -1 < l.find(\"初回放送\"):\n overview_recipe.program_date = dateutil.parser.parse(\"/\".join(re.search(r\"(\\d+)\\D+(\\d+)\\D+(\\d+)\\D+\", l).groups()))\n break\n\n cooking_shop_strings = get_cooking_shop_strings(converted_content.splitlines())\n\n logger.debug(\"-\" * 20)\n logger.debug(cooking_shop_strings)\n for shop_string in cooking_shop_strings:\n recipe_shop = None\n recipe = None\n is_material_area = False\n is_recipe_step_area = False\n for l in shop_string.splitlines():\n if len(l.strip()) == 0:\n continue\n \n if is_material_area == False and is_recipe_step_area == False:\n if re.search(\"軒目\", l.strip()) or re.match(r\"^[①-⑳＊].*『.*』\", l.strip()) or re.match(r\"^[①-⑳＊].*「.*」\", l.strip()):\n recipe_shop = copy.deepcopy(overview_recipe)\n recipe = None\n \n m = re.search(r\"「(.*)」\", l)\n if m:\n recipe_shop.cooking_name_sub += \"/\" + m.group(1)\n else:\n m2 = re.search(r\"『(.*)』\", l)\n if m2:\n recipe_shop.cooking_name_sub += \"/\" + m2.group(1)\n \n continue\n \n if re.search(\"^(料理|万能調味料)\", l.strip()):\n is_material_area = False\n is_recipe_step_area = False\n if recipe:\n yield recipe\n\n if recipe_shop:\n recipe = copy.deepcopy(recipe_shop)\n else:\n recipe = copy.deepcopy(overview_recipe)\n \n if -1 < l.find(\":\"):\n recipe.cooking_name = l.split(\":\")[1].strip()\n elif -1 < l.find(\"：\"):\n recipe.cooking_name = l.split(\"：\")[1].strip()\n elif re.search(r\"^(料理|万能調味料)[①-⑳]\", l.strip()):\n # https://www.nhk.or.jp/program/manpuku/recipe/dg0_200115.pdf\n # 料理①カルパッチョ\n recipe.cooking_name = l.strip()[3:].strip()\n else:\n recipe.cooking_name = l.split(None, 1)[1].strip()\n continue\n \n if re.search(\"^材料\", l.strip()):\n is_material_area = True\n is_recipe_step_area = False\n if l.strip() == \"材料\":\n continue\n \n if re.search(\"^作り方\", l.strip()):\n is_material_area = False\n is_recipe_step_area = True\n if l.strip() == \"作り方\":\n pass\n else:\n l = l.replace(\"作り方\", \"\", 1)\n # recipeがNoneの場合はエラーとして検出したい\n recipe.recipe_steps.append(RecipeText(l.strip()))\n continue\n \n \n if is_material_area:\n for material in l.strip().split(\"、\"):\n material = material.strip()\n if len(material):\n if material.startswith(\"(\"):\n recipe.materials.append(RecipeText(material))\n else:\n recipe.materials.append(RecipeText(material.replace(\"（\", \": \").replace(\"）\", \"\")))\n \n if is_recipe_step_area:\n recipe.recipe_steps.append(RecipeText(l.strip()))\n if recipe:\n yield recipe","def get_courses_info(url, headers):\n dash = get_page_contents(url, headers)\n soup = BeautifulSoup(dash)\n courses_soup = soup.find_all('article', 'course')\n courses = []\n for course_soup in courses_soup:\n course_id = None\n course_name = course_soup.h3.text.strip()\n course_url = None\n course_state = 'Not yet'\n try:\n # started courses include the course link in the href attribute\n course_url = BASE_URL + course_soup.a['href']\n if course_url.endswith('info') or course_url.endswith('info/'):\n course_state = 'Started'\n # The id of a course in edX is composed by the path\n # {organization}/{course_number}/{course_run]\n course_id = course_soup.a['href'][9:-5]\n except KeyError:\n pass\n courses.append(Course(id=course_id,\n name=course_name,\n url=course_url,\n state=course_state))\n return courses","def __init__(self, course_id, name, content):\n # course id and name need to go over the string_correct function\n self.ID = self.id_correct(str(course_id))\n self.name = self.string_correct(str(name))\n\n self.content = str(content)\n\n # uninitialized variables\n self.prere = {}\n self.postre = {}\n self.description = None\n self.department_title = None\n self.course_level = None\n\n # parse description and prerequisite raw data from content var\n self.seperate_content()","def test_extract_info():\n string = \"Mika Hakkinen Mclaren-Mercedes 79694 1\"\n assert type(race.extract_info(string)) == dict\n assert race.extract_info(string) == {\"Name\": \"Mika Hakkinen\", \"Team\": \"Mclaren-Mercedes\", \"Time\": 79694, \"Diff\": \"\",\n \"Race\": 1}\n assert \"Hello, world!\" not in race.extract_info(string)","def extract_course_summary(course):\n return {\n field: course.fields[field]\n for field in CourseSummary.course_info_fields\n if field in course.fields\n }","def build_course(self):\n courses = []\n aprovacao_d = {}\n # semestral\n for rate_it in self.__semestral_rate:\n # pega uma lista no qual o primeiro elemento é a taxa, o segundo\n # e o terceiro são quantidades\n rate_data = self.analysis[\"semestral_rate\"][rate_it.name]\n for i in rate_data[0].index:\n if i[0] not in aprovacao_d:\n aprovacao_d[i[0]] = {}\n\n periodo = str(i[1]) + \"/\" + str(i[2])\n aprovacao_d[i[0]][periodo] = [\n float(rate_data[0][i]),\n int(rate_data[1][i]),\n int(rate_data[2][i]),]\n\n note = self.analysis[\"general_note_statistic\"]\n note_last_year = self.analysis[\"last_year_statistic\"]\n for course in self.analysis[\"courses\"].index:\n course_dict = {}\n course_dict[\"disciplina_codigo\"] = course\n course_dict[\"disciplina_nome\"] = self.analysis[\"courses\"][course]\n \n # If the course code is related to more than one name,\n # concatenate these names into an unique string\n if type(course_dict[\"disciplina_nome\"]) != str:\n new_course_name = \" | \".join(list(course_dict[\"disciplina_nome\"]))\n course_dict[\"disciplina_nome\"] = new_course_name\n # quantidade de matriculas\n count = self.analysis[\"general_count_submission\"][course]\n course_dict[\"qtd_alunos\"] = count\n # notas\n course_dict[\"qtd_cursada_aprov\"] = self.analysis[\"coursed_ratio\"][course]\n course_dict[\"nota\"] = [note[0][course], note[1][course]]\n course_dict[\"nota_ultimo_ano\"] = [\n note_last_year[0][course],\n note_last_year[1][course]\n ]\n # taxas\n for rate_it in self.__rates:\n rate_data = self.analysis[\"general_rates\"][rate_it.name]\n course_dict[rate_it.name] = float(rate_data[0][course])\n course_str = rate_it.name.replace(\"taxa\", \"qtd\")\n # count_sel define qual quantidade vai para o json, a especifica\n # ou geral\n course_dict[course_str] = int(\n rate_data[rate_it.count_sel][course])\n # rate_calc = self.analysis[\"general_rates\"][rate_it.name][0]\n\n # taxas do ultimo anos\n course_dict[\"taxa_reprovacao_ultimo_absoluto\"] = self.analysis[\"last_year_taxa_reprovacao_absoluta\"][course]\n course_dict[\"taxa_reprovacao_ultimo_frequencia\"] = self.analysis[\"last_year_taxa_reprovacao_frequencia\"][course]\n\n course_dict[\"grafico_qtd_cursada_aprov\"] = self.analysis[\"coursed_count\"][course]\n course_dict[\"aprovacao_semestral\"] = aprovacao_d[course]\n courses.append(course_dict)\n return courses","def _get_page_sections(self, sectionNum=None, sectionName=None):\n self.section = {}\n self.sections = [] # list maintains order\n content = self.page.content\n lines = content.split(\"\\n\")\n currentSection = None\n for line in lines:\n if \"==\" in line:\n line = line.replace(\"Edit =\",\"\")\n line = line.replace(\"=\",\"\").lstrip().rstrip()\n self.section[line] = []\n currentSection = line\n self.sections.append(currentSection)\n elif currentSection is not None:\n line = line.lstrip().rstrip()\n self.section[currentSection].append(line)\n else:\n pass\n logger.info(\"Sections in page: \"+str(self.sections))\n # and return some section:\n if sectionNum is not None:\n if sectionNum > len(self.sections) or sectionNum < 0:\n sectionNum = 0\n return self.section[self.sections[sectionNum]]\n elif sectionName is not None:\n pass","def parse_text(self, text: str) -> SectionDict:","def get_doctor_info(url, html):\n \n # create a dictionary to save data\n doctor_info = {}\n \n # parsed html by using lxml\n # In the following processing\n # the one ending in org is the raw data in the HTML\n # the one ending in inf is the processed data by extracting from raw data\n select = etree.HTML(html)\n \n # part 1: get basic information about your doctor\n # attribute: Name, Title, Hospital, Department\n name_org = select.xpath('//div[@class=\"profile-text\"]//h1[@class=\"doctor-name\"]//text()')\n name_inf = name_org[0].strip()\n \n title_org = select.xpath('//div[@class=\"profile-text\"]//span[@class=\"positon\"]//text()')\n title_inf = [i.strip() for i in title_org if len(i.strip()) > 0]\n title_inf = ' '.join(title_inf)\n \n hospital_department_org = select.xpath('//div[@class=\"profile-text\"]//p[@class=\"doctor-faculty\"]//text()')\n hospital_department_inf = [i.strip() for i in hospital_department_org if len(i.strip()) > 0]\n hospital_inf = hospital_department_inf[0]\n department_inf = hospital_department_inf[1]\n \n doctor_info['姓名'] = name_inf\n doctor_info['职称'] = title_inf\n doctor_info['医院'] = hospital_inf\n doctor_info['科室'] = department_inf\n \n # part2: get header format data\n org = select.xpath('//div[@class=\"profile-sta\"]//text()')\n inf = [i.strip() for i in org if len(i.strip()) > 0 and i.strip() != '%']\n for i in range(len(inf)//2):\n doctor_info[inf[2*i]] = inf[2*i + 1]\n \n \n # part3: get sidebar format data\n org_1 = select.xpath('//div[@class=\"item-body\"]//div[@class=\"clearfix\"]//div[@class=\"per-sta-label\"]//text()')\n org_2 = select.xpath('//div[@class=\"item-body\"]//div[@class=\"clearfix\"]//div[@class=\"per-sta-data\"]//text()')\n for i in range(len(org_1)):\n doctor_info[org_1[i][:-1]] = org_2[i]\n \n # part4: get body format data\n honour_org = select.xpath('//div[@class=\"honour-header\"]//text()')\n honour_inf = ''.join([i.strip() for i in honour_org])\n \n honour_detail_org = select.xpath('//li[@class=\"honour-title\"]//text()')\n honour_detail_inf = [i.strip()[:4] for i in honour_detail_org if len(i.strip()) > 0]\n honour_detail_inf = ' '.join(honour_detail_inf)\n \n satisfaction_org = select.xpath('//div[@class=\"item-body\"]//div[@class=\"satisfaction clearfix\"]//i[@class=\"sta-num\"]//text()')\n satisfaction_inf = [i.strip() for i in satisfaction_org if len(i.strip()) > 0 and i.strip() != '%']\n \n resume_org = select.xpath('//div[@class=\"good-at-text\"]//text()')\n resume_inf = [i.strip() for i in resume_org]\n if len(resume_inf) <= 20:\n resume_inf = ''.join(resume_inf)\n resume_inf = ''.join(resume_inf[:20])\n \n star_org = select.xpath('//div[@class=\"experience-row clearfix\"]//span[@class=\"experience-label\"]//text()')\n star_inf = 1 if len(star_org) >= 1 else 0\n\n doctor_info['好大夫届数'] = honour_inf\n doctor_info['好大夫具体年份'] = honour_detail_inf\n doctor_info['简历'] = resume_inf \n doctor_info['诊后服务星'] = star_inf\n try:\n doctor_info['疗效满意度'] = satisfaction_inf[0]\n doctor_info['态度满意度'] = satisfaction_inf[1]\n except:\n pass\n \n # part5: personal url\n personal_url = url\n doctor_info['医生个人链接'] = personal_url\n \n return doctor_info","def create_detail(l):\n parsed_detail = create_soup(l)\n kv_pair = \"\"\n details = dict()\n for i in parsed_detail.find_all('tbody'):\n for j in i.find_all('tr'):\n for k in j.find_all('td'):\n cln = clean_string(k.text)\n if cln.find(\":\")<0:\n #if re.search(r'[A-Za-z]:', cln):\n kv_pair += \" \" + cln\n else:\n if len(kv_pair) > 0:\n kv_process = kv_pair.split(\":\")\n det_key = re.sub(\n r'\\s', '_', kv_process[0].strip()\n ).lower()\n details[det_key] = kv_process[1].strip()\n kv_pair = cln\n return details","def parse_courses():\n\n subjects = collections.OrderedDict()\n name = '' # the most recent course name acronym (ex. 'COMP')\n\n courses = re.sub(r'\\([^)]*\\)', '', COURSES).split() # Remove parens and their contents\n\n for course in courses:\n if course == 'OR':\n continue\n\n if course[0].isalpha():\n\n index = 0 # the upper bound character index of the subject name\n for char in course:\n if char.isalpha():\n index += 1\n else:\n break\n\n name = course[:index]\n number = course[index:index+4]\n else:\n number = course[:4]\n\n try:\n subjects[name].append(number)\n except KeyError:\n subjects[name] = [number]\n\n return subjects","def parseSections(data):\n pro = _sectionSplit.split(data)\n sections = {}\n for x in xrange(1, len(pro), 2):\n sections[pro[x]] = pro[x+1]\n return sections","def info():\n # -------- Task 1 -------------------------\n # Please complete the following information\n\n return {\"agent name\": \"?\", # COMPLETE HERE\n \"student name\": [\"?\"], # COMPLETE HERE\n \"student number\": [\"?\"]} # COMPLETE HERE","def build_general_course(self):\n\n courses = {}\n\n if self.__build_analyze is False:\n self.build_analysis()\n\n courses[\"taxa_conhecimento\"] = self.analysis[\"taxa_conhecimento\"]\n courses[\"taxa_reprovacao\"] = self.analysis[\"taxa_reprovacao_absoluta\"]\n courses[\"taxa_trancamento\"] = self.analysis[\"taxa_trancamento\"]\n\n # cria cache\n cache = {}\n note = self.analysis[\"general_note_statistic\"]\n for rate_it in self.__rates:\n rate_calc = self.analysis[\"general_rates\"][rate_it.name][0]\n for course in self.analysis[\"courses\"].index:\n if course not in cache:\n cache[course] = {}\n cache[course][rate_it.name] = rate_calc[course]\n cache[course][\"nota\"] = [note[0][course], note[1][course]]\n\n courses[\"cache\"] = cache\n\n # cria o campo compara_aprov\n courses[\"compara_aprov\"] = self.analysis[\"graph_course\"]\n\n # cria o campo courses\n courses[\"disciplinas\"] = self.analysis[\"courses\"].to_dict()\n\n return courses","def preprocess_courses_corpus():\n soup = None\n with open('courses_corpus.html', 'r') as infile:\n content = infile.read()\n\n soup = BeautifulSoup(content, 'html.parser')\n\n docid = 0\n data = {}\n data['documents'] = []\n\n main_table = soup.find_all(\"div\", attrs={'class': 'courseblock'})\n for course in main_table:\n docid += 1\n title = course.find_all('p', attrs={'class':'courseblocktitle noindent'})[0].text.lstrip('\\n') if len(course.find_all('p', attrs={'class':'courseblocktitle noindent'}))!=0 else ''\n description = (course.find_all('p', attrs={'class':'courseblockdesc noindent'})[0].text.lstrip('\\n') if len(course.find_all('p', attrs={'class':'courseblockdesc noindent'}))!=0 else '') + ' ' + (course.find_all('p', attrs={'class':'courseblockextra noindent'})[0].text if len(course.find_all('p', attrs={'class':'courseblockextra noindent'}))!=0 else '')\n\n data['documents'].append({\n 'docId' : docid,\n 'title' : title.strip(),\n 'description' : description.strip()\n })\n\n with open('courses_data.json', 'w') as outfile:\n json.dump(data, outfile)","def parse_catalogs(campuses=['Seattle', 'Bothell', 'Tacoma'], struct='df', \n show_progress=False):\n assert type(campuses) == list or type(campuses) == dict, 'Type of \"campuses\" must be list or dict'\n if type(campuses) == dict:\n for key, value in campuses.items():\n if type(key) != str or type(value) != list:\n raise ValueError('''\"campuses\" dict must have keys of type str and\n values of type list''')\n # Check if all campuses in 'campuses' are valid\n assert all([c in ['Seattle', 'Bothell', 'Tacoma'] for c in list(map(str.title, campuses))])\n assert type(struct) == str, 'Type of \"struct\" must be str'\n assert type(show_progress) == bool, 'Type of \"show_progress\" must be bool'\n assert struct in ['df', 'dict'], f'{struct} is an invalid argument for \"struct\"'\n\n # Progress bar for Course Schedule Parsing\n if show_progress:\n progress_bar = tqdm()\n\n def parse_campus(department_data, campus):\n \"\"\"\n Parses all courses from a UW Campus\n\n @params\n\n 'department_data': BeautifulSoup object with the department list website source\n for the given 'campus'\n\n 'campus': The campus to get courses from\n\n Returns\n\n A pandas DataFrame with all courses in the given campus\n \"\"\"\n\n def extract_data(department_link):\n \"\"\"\n Extracts all course information from a UW Department\n\n @params:\n\n 'department_link': The url to the UW Department to get course\n information from\n\n Returns\n\n A list of lists. Each nested list represents one course section with the\n following values (in this order):\n\n 'Campus', 'Department Name', 'Course Number', 'Course Name', 'Credits',\n 'Areas of Knowledge', 'Quarters Offered', 'Offered with', \n 'Prerequisites', 'Co-Requisites', 'Description'\n \"\"\"\n # Update the progress bar\n if show_progress:\n progress_bar.update()\n\n # Regular expressions for searching course descriptions stored in local variables\n # for better peformance\n local_course_re = course_re\n local_course_name_re = course_name_re\n local_credits_re = credits_re\n local_credits_num_re = credits_num_re\n local_offered_jointly_re = offered_jointly_re\n local_CAMPUSES = CAMPUSES\n\n # Method used in extracting data from course descriptions found in the local scope\n # are stored in local variables for better performance\n local_complete_description = complete_description\n local_get_offered = get_offered\n local_get_requisites = get_requisites\n\n # All the courses in the department\n courses = []\n dep_file = department_link.get('href')\n\n # If the user entered a dict as the 'campuse' parameter, departments\n # are checked here\n try:\n # The String in the conditional is the abbreviated Department Name i.e EE\n # for Electrical Engineering\n if normalize('NFKD', department_link.text).rsplit('(', 1) \\\n [-1].replace(' ', '')[:-1] not in campuses[campus]:\n return None\n except TypeError:\n pass\n \n # The only links that are used for finding departments are those\n # of the format [a-z]+.html\n if '/' not in dep_file and dep_file.endswith('.html') \\\n and dep_file not in parsed_departments:\n parsed_departments.add(dep_file)\n department = BeautifulSoup(requests.get( \\\n f'{local_CAMPUSES[campus]}{dep_file}').text, features='lxml')\n for course in department.find_all('a'):\n course_ID = course.get('name') \n if course_ID:\n course_ID = course_ID.upper()\n course_title = course.find('b').text\n # The Course Description\n description = course.get_text().replace(course_title, '', 1) \n instructors = course.find('i')\n if instructors:\n description = description.replace(str(instructors.get_text()), '', 1)\n del instructors\n course_text = local_complete_description( \\\n description.rsplit('View course details in MyPlan', 1)[0])\n # Course Number i.e 351\n course_number = re.sub(local_course_re, '', course_ID)\n match_name = re.search(local_course_name_re, course_title)\n match_credit_num = re.search(local_credits_num_re, course_title)\n match_credit_types = re.findall(local_credits_re, course_title)\n # Jointly offered course with the given course\n if 'jointly with' in course_text: \n offered_jointly = course_text.rsplit('jointly with ', 1)[-1].rsplit(';', 1)[0] \n offered_jointly = ','.join(re.findall( \\\n local_offered_jointly_re, offered_jointly)).replace(' ', '') \n else:\n offered_jointly = ''\n courses.append(\n # Campus, Department Name and Course Number\n [campus, course_ID[:-3], course_number, \n # Course Name\n match_name.group(0).split(course_number, 1)[-1].strip() \\\n if match_name else '',\n # Number of credits for the course\n match_credit_num.group(0)[1:-1] \\\n if match_credit_num else '', \n # Course Credit Types (I&S, DIV, NW, VLPA, QSR, C)\n ','.join([list(filter(('').__ne__, x))[0] for x in match_credit_types]) \\\n if match_credit_types else '', \n local_get_offered(course_text),\n offered_jointly, local_get_requisites(course_text, 'Prerequisite:'), \n local_get_requisites(course_text, 'Co-requisite'), course_text]\n )\n return courses\n\n # In the course catalog website, several department links appear multiple times\n # To prevent parsing the same department more than once, parsed departments\n # are tracked in 'parsed_departments'\n parsed_departments = set()\n local_extract_data = extract_data\n department_data = BeautifulSoup(requests.get(department_data).text, features='lxml')\n\n campus_catalog = []\n # Extract data from department websites in parallel to reduce idle time\n with cf.ThreadPoolExecutor() as executor:\n results = [executor.submit(local_extract_data, department_link) \n for department_link in department_data.find_all('a')]\n for result in cf.as_completed(results):\n dptmnt = result.result()\n if dptmnt:\n campus_catalog.append(dptmnt)\n\n # DataFrame with all courses in the campus\n return pd.DataFrame(\n [course for department in campus_catalog for course in department], \n columns=COLUMN_NAMES\n )\n\n # The pandas DataFrame to store the entire course catalog for each UW Campus entered\n # by the user\n course_catalog = pd.DataFrame()\n\n # Parse all three campuses in parallel for faster run time as well\n # as get the departments dictionary from the 'get_departments' method\n # to add a 'Colleges' column to categorize all courses in their College.\n with cf.ThreadPoolExecutor() as executor:\n results = []\n campuses_for_dict = campuses\n if type(campuses) == dict:\n campuses_for_dict = list(campuses.keys())\n results.append(executor.submit(get_departments, campuses=campuses_for_dict, struct='dict'))\n for campus, link in CAMPUSES.items():\n if campus.title() in campuses: \n results.append(executor.submit(parse_campus, link, campus.title()))\n for result in cf.as_completed(results):\n returned = result.result()\n if type(returned) == dict:\n # Departments dict used to create the 'College' column in the main DataFrame\n departments = returned\n else:\n course_catalog = pd.concat([course_catalog, returned])\n\n # Add Course ID as the index of the DataFrame to allow for easy course searching\n # Course ID = Department Name + Course Number\n # Example: EE235 = EE + 235\n course_catalog['Course ID'] = course_catalog['Department Name'] + course_catalog['Course Number']\n course_catalog['College'] = course_catalog['Department Name'].apply(check_campus, args=(departments, 'College'))\n course_catalog.set_index('Course ID', inplace=True)\n # Re-order indices to place 'College' right after the 'Department Name'\n course_catalog = course_catalog[['Campus', 'Department Name', 'College', 'Course Number', 'Course Name', 'Credits',\n 'Areas of Knowledge', 'Quarters Offered', 'Offered with', \n 'Prerequisites', 'Co-Requisites', 'Description']]\n \n if struct == 'df':\n return course_catalog\n elif struct == 'dict':\n return course_catalog.to_dict(orient='index')","def infotodict(seqinfo):\n\n # data = create_key('run{item:03d}')\n # info = {data: []}\n # last_run = len(seqinfo)\n\n \"\"\"\n The namedtuple `s` contains the following fields:\n\n * total_files_till_now\n * example_dcm_file\n * series_id\n * dcm_dir_name\n * unspecified2\n * unspecified3\n * dim1\n * dim2\n * dim3\n * dim4\n * TR\n * TE\n * protocol_name\n * is_motion_corrected\n * is_derived\n * patient_id\n * study_description\n * referring_physician_name\n * series_description\n * image_type\n \"\"\"\n\n t1w = create_key('sub-{subject}/{session}/anat/sub-{subject}_{session}_T1w')\n t2w = create_key('sub-{subject}/{session}/anat/sub-{subject}_{session}_T2w')\n func_rest = create_key('sub-{subject}/{session}/func/sub-{subject}_{session}_task-rest_bold')\n dwi_ap = create_key('sub-{subject}/{session}/dwi/sub-{subject}_{session}_acq-AP_dwi')\n dwi_pa = create_key('sub-{subject}/{session}/dwi/sub-{subject}_{session}_acq-PA_dwi')\n t2star = create_key('sub-{subject}/{session}/dwi/sub-{subject}_{session}_T2star')\n t2w_fatsat = create_key('sub-{subject}/{session}/anat/sub-{subject}_{session}_acq-fatsat_T2w')\n \n info = {t1w: [], t2w: [], func_rest: [], dwi_ap: [], dwi_pa: [], t2star: [], t2w_fatsat: []}\n\n for idx, s in enumerate(seqinfo):\n if (s.example_dcm_file == 'mp_rage_1_mm-00001.dcm'):\n info[t1w].append(s.series_id)\n if ('edti_2mm_cdif45_AP' in s.series_description):\n info[dwi_ap].append(s.series_id)\n if ('edti_2mm_cdif45_PA' in s.series_description):\n info[dwi_pa].append(s.series_id)\n if (s.series_description == 'Sag CUBE T2'):\n info[t2w].append(s.series_id)\n if (s.series_description == 'ORIG Sag CUBE T2'):\n info[t2w_orig].append(s.series_id)\n if ('T2_1.7mm_fat_sat' in s.series_description): \n info[t2w_fatsat].append(s.series_id)\n if (s.series_description == 'Reverse blip EPI 3mm iso'):\n info[t2star].append(s.series_id) \n if (s.series_description == 'Resting EPI 3mm iso RS') and (s.dim3 == 12300):\n info[func_rest].append(s.series_id)\n return info","def process_key_items(html_soup):\r\n\r\n info_dict = {}\r\n all_tag_a = html_soup.find_all('a')\r\n\r\n numeric_info2 = all_tag_a[2].get('href').split('=')\r\n info_dict['id2'] = numeric_info2[4].split('&')[0]\r\n info_dict['ord2'] = numeric_info2[5]\r\n\r\n numeric_info3 = all_tag_a[3].get('href').split('=')\r\n info_dict['id3'] = numeric_info3[4].split('&')[0]\r\n info_dict['ord3'] = numeric_info3[5]\r\n\r\n numeric_info4 = all_tag_a[4].get('href').split('=')\r\n info_dict['id4'] = numeric_info4[4].split('&')[0]\r\n info_dict['ord4'] = numeric_info4[5]\r\n\r\n info_dict['id'] = all_tag_a[6].get('href').split('=')[4].split('&')[0]\r\n\r\n pagenum_afr = None\r\n pagenum_pre = None\r\n\r\n if len(all_tag_a) == 10:\r\n\r\n\r\n info_dict['multipage'] = False\r\n info_dict['pagenum'] = 1\r\n info_dict['pagenum_pre'] = pagenum_pre\r\n info_dict['pagenum_afr'] = pagenum_afr\r\n\r\n if len(all_tag_a) == 11:\r\n #print()\r\n\r\n #print(all_tag_a[7].text)\r\n #print(all_tag_a[7].get('href'))\r\n #print(all_tag_a[8].text)\r\n #print(all_tag_a[8].get('href'))\r\n\r\n if all_tag_a[7].text == 'صفحهی قبل':\r\n splitted = all_tag_a[7].get('href').split('=')\r\n if len(splitted) == 7:\r\n pagenum_pre = int(all_tag_a[7].get('href').split('=')[-1])\r\n else:\r\n pagenum_pre = 1\r\n\r\n pagenum = pagenum_pre + 1\r\n elif all_tag_a[7].text == 'صفحهی بعد':\r\n splitted = all_tag_a[7].get('href').split('=')\r\n if len(splitted) == 7:\r\n pagenum_afr = int(all_tag_a[7].get('href').split('=')[-1])\r\n else:\r\n pagenum_afr = None\r\n\r\n pagenum = pagenum_afr - 1\r\n else:\r\n pagenum = None\r\n\r\n info_dict['multipage'] = True\r\n info_dict['pagenum'] = pagenum\r\n info_dict['pagenum_pre'] = pagenum_afr\r\n info_dict['pagenum_afr'] = pagenum_pre\r\n\r\n if len(all_tag_a) == 12:\r\n\r\n pagenum_pre = None\r\n pagenum = None\r\n pagenum_afr = None\r\n\r\n\r\n\r\n if all_tag_a[7].text == 'صفحهی قبل' :\r\n if len(all_tag_a[7].get('href').split('=')) > 5:\r\n pagenum_pre = int(all_tag_a[7].get('href').split('=')[-1])\r\n else:\r\n pagenum_pre = 1\r\n\r\n pagenum = pagenum_pre + 1\r\n if all_tag_a[8].text == 'صفحهی بعد' :\r\n\r\n if len(all_tag_a[8].get('href').split('=')) > 5:\r\n pagenum_afr = int(all_tag_a[8].get('href').split('=')[-1])\r\n pagenum = pagenum_afr - 1\r\n else:\r\n pagenum_afr = None\r\n pagenum = pagenum_pre + 1\r\n\r\n info_dict['multipage'] = True\r\n info_dict['pagenum_pre'] = pagenum_pre\r\n info_dict['pagenum_afr'] = pagenum_afr\r\n info_dict['pagenum'] = pagenum\r\n\r\n catgo = all_tag_a[1].text\r\n shaer = all_tag_a[2].text\r\n ketab = all_tag_a[3].text\r\n bakhsh= all_tag_a[4].text\r\n title = all_tag_a[5].text\r\n\r\n info_dict['title'] = title\r\n info_dict['bakhsh'] = bakhsh\r\n info_dict['ketab'] = ketab\r\n info_dict['shaer'] = shaer\r\n info_dict['catgo'] = catgo\r\n return info_dict","def extract_data(department_link):\n # Update the progress bar\n if show_progress:\n progress_bar.update()\n\n # Regular expressions for searching course descriptions stored in local variables\n # for better peformance\n local_course_re = course_re\n local_course_name_re = course_name_re\n local_credits_re = credits_re\n local_credits_num_re = credits_num_re\n local_offered_jointly_re = offered_jointly_re\n local_CAMPUSES = CAMPUSES\n\n # Method used in extracting data from course descriptions found in the local scope\n # are stored in local variables for better performance\n local_complete_description = complete_description\n local_get_offered = get_offered\n local_get_requisites = get_requisites\n\n # All the courses in the department\n courses = []\n dep_file = department_link.get('href')\n\n # If the user entered a dict as the 'campuse' parameter, departments\n # are checked here\n try:\n # The String in the conditional is the abbreviated Department Name i.e EE\n # for Electrical Engineering\n if normalize('NFKD', department_link.text).rsplit('(', 1) \\\n [-1].replace(' ', '')[:-1] not in campuses[campus]:\n return None\n except TypeError:\n pass\n \n # The only links that are used for finding departments are those\n # of the format [a-z]+.html\n if '/' not in dep_file and dep_file.endswith('.html') \\\n and dep_file not in parsed_departments:\n parsed_departments.add(dep_file)\n department = BeautifulSoup(requests.get( \\\n f'{local_CAMPUSES[campus]}{dep_file}').text, features='lxml')\n for course in department.find_all('a'):\n course_ID = course.get('name') \n if course_ID:\n course_ID = course_ID.upper()\n course_title = course.find('b').text\n # The Course Description\n description = course.get_text().replace(course_title, '', 1) \n instructors = course.find('i')\n if instructors:\n description = description.replace(str(instructors.get_text()), '', 1)\n del instructors\n course_text = local_complete_description( \\\n description.rsplit('View course details in MyPlan', 1)[0])\n # Course Number i.e 351\n course_number = re.sub(local_course_re, '', course_ID)\n match_name = re.search(local_course_name_re, course_title)\n match_credit_num = re.search(local_credits_num_re, course_title)\n match_credit_types = re.findall(local_credits_re, course_title)\n # Jointly offered course with the given course\n if 'jointly with' in course_text: \n offered_jointly = course_text.rsplit('jointly with ', 1)[-1].rsplit(';', 1)[0] \n offered_jointly = ','.join(re.findall( \\\n local_offered_jointly_re, offered_jointly)).replace(' ', '') \n else:\n offered_jointly = ''\n courses.append(\n # Campus, Department Name and Course Number\n [campus, course_ID[:-3], course_number, \n # Course Name\n match_name.group(0).split(course_number, 1)[-1].strip() \\\n if match_name else '',\n # Number of credits for the course\n match_credit_num.group(0)[1:-1] \\\n if match_credit_num else '', \n # Course Credit Types (I&S, DIV, NW, VLPA, QSR, C)\n ','.join([list(filter(('').__ne__, x))[0] for x in match_credit_types]) \\\n if match_credit_types else '', \n local_get_offered(course_text),\n offered_jointly, local_get_requisites(course_text, 'Prerequisite:'), \n local_get_requisites(course_text, 'Co-requisite'), course_text]\n )\n return courses","def _get_information(self):\n pros_cons = []\n pros_cons_dict = {}\n\n for i, div in enumerate(self._tab.find_all(\"div\")):\n for p in div.find_all(\"p\"):\n pro_con = p.get_text(strip=True)\n pros_cons.append(pro_con)\n pros_cons_dict.update({self._keys_dict[i]: pros_cons})\n pros_cons = []\n\n return pros_cons_dict","def _parse_lesson(self, lesson_link: str):\n\n lesson_page: Soup = self._get_soup(lesson_link)\n lesson_content: NavigableString = lesson_page.find(\"div\", \n {\"class\": \"moduleContent\"})\n course_divs: ResultSet = lesson_content.find_all(\"div\")\n\n data = {}\n module = \"\"\n for div in course_divs:\n if div[\"class\"][0] == \"courseModule\":\n module = div.p.get_text().split(\":\")[-1].strip()\n data[module] = []\n elif div[\"class\"][0] == \"courseLesson\":\n lesson_title = div.find(\"span\", {\"class\": \"courseLessonTitle\"})\n data[module].append(lesson_title.get_text().strip())\n \n return data","def general_scraper(section_url):\n\n prefix = \"http://mesva.univaq.it\"\n\n request = []\n news = []\n\n for i, url in enumerate(section_url):\n request.append(requests.get(url))\n news_division = BeautifulSoup(request[i].text, \"html.parser\").find(class_=\"view-content\")\n\n discab_news = news_division.find_all(\"div\", recursive=False)[0:5]\n\n for single_news in discab_news:\n news.append({\n 'description': '',\n 'title': single_news.a.string,\n 'link': prefix + single_news.a['href']\n })\n\n return news","def __init__(self, browser, course_org, course_num, course_run):\r\n super(CoursePage, self).__init__(browser)\r\n self.course_info = {\r\n 'course_org': course_org,\r\n 'course_num': course_num,\r\n 'course_run': course_run\r\n }","def parse_sections(soup, report, baseUrl):\n parse_sections = False # To parse section wise set it to True else full content is parsed\n overview = False\n config = False\n usecase = False\n overview_content = \"\"\n config_content = \"\"\n usecases_content = \"\"\n isFullContent = False\n full_content = \"\"\n updateImgUrl(baseUrl, soup)\n for e in soup.contents:\n if not parse_sections:\n if 'h1' == str(e.name).lower():\n isFullContent = True\n if isFullContent:\n full_content += \"\\n\" + str(e)\n else:\n content_value = e.next\n if content_value == 'Overview':\n overview = True\n if content_value == 'Configuration':\n config = True\n overview = False\n if content_value == 'Use Cases':\n usecase = True\n config = False\n if overview == True and config == False and usecase == False:\n overview_content += \"\\n\" + str(e)\n if overview == False and config == True and usecase == False:\n config_content += \"\\n\" + str(e)\n if overview == False and config == False and usecase == True:\n usecases_content += \"\\n\" + str(e)\n\n if not parse_sections:\n report[\"content\"] = convert_to_base64(full_content)\n else:\n if overview_content:\n report[\"overview\"] = convert_to_base64(overview_content)\n if config_content:\n report[\"configuration\"] = convert_to_base64(config_content)\n if usecases_content:\n report[\"use_cases\"] = convert_to_base64(usecases_content)","def build_course_map(course_content):\n course_blocks = []\n\n for key, course_data in course_content.items():\n block_data = {}\n block_data['id'] = parse_id(key)\n block_data['type'] = course_data['category']\n\n try:\n block_data['name'] = course_data['metadata']['display_name']\n except KeyError:\n block_data['name'] = block_data['id']\n\n customize_by_type(course_data, block_data)\n add_children(course_data, block_data)\n\n course_blocks.append(block_data)\n\n return course_blocks","def infotodict(seqinfo):\n\n t1 = create_key('anat/sub-{subject}_run-{item:02d}_T1w')\n rest_fmri_ap = create_key('func/sub-{subject}_dir-ap_task-rest_run-{item:02d}_bold')\n rest_topup_ap = create_key('func/sub-{subject}_dir-ap_run-{item:02d}_bold')\n rest_topup_pa = create_key('func/sub-{subject}_dir-pa_run-{item:02d}_bold')\n fmap_rest_magnitude1 = create_key('fmap/sub-{subject}_run-{item:02d}_magnitude1')\n fmap_rest_phasediff = create_key('fmap/sub-{subject}_run-{item:02d}_phasediff')\n\n # Create an empty dictionary called info for each key\n\n info = {t1: [],\n rest_fmri_ap: [],\n rest_topup_ap: [],\n rest_topup_pa: [],\n fmap_rest_magnitude1: [],\n fmap_rest_phasediff: [],\n }\n\n # Loop over each sequence. Use if statements to determine which sequences should be linked to which key\n\n for idx, s in enumerate(seqinfo):\n\n if (('MPRAGE_GRAPPA2' in s.series_id) and\n ('tfl3d1_16ns' in s.sequence_name) and\n (s.dim3 == 192) and\n (s.dim4 == 1)):\n info[t1] = [s.series_id]\n\n if (('BOLD_resting 4X4X4 A>>P' in s.series_id) and\n ('epfid2d1_64' in s.sequence_name) and\n (s.dim3 == 35) and\n (s.dim4 == 190)):\n info[rest_fmri_ap] = [s.series_id]\n\n if (('rest_topup_A>>P' in s.series_id) and\n ('epse2d1_64' in s.sequence_name) and\n (s.dim3 == 140) and\n (s.dim4 == 1)):\n info[rest_topup_ap] = [s.series_id]\n\n if (('rest_topup_P>>A' in s.series_id) and\n ('epse2d1_64' in s.sequence_name) and\n (s.dim3 == 140) and\n (s.dim4 == 1)):\n info[rest_topup_pa] = [s.series_id]\n\n if (('Field_mapping 4X4X4 A>>P' in s.series_id) and\n ('fm2d2r' in s.sequence_name) and\n (s.dim3 == 35) and\n (s.dim4 == 1) and\n (s.TE == 4.92)):\n info[fmap_rest_magnitude1] = [s.series_id]\n\n if (('Field_mapping 4X4X4 A>>P' in s.series_id) and\n ('fm2d2r' in s.sequence_name) and\n (s.dim3 == 35) and\n (s.dim4 == 1) and\n (s.TE == 7.38)):\n info[fmap_rest_phasediff] = [s.series_id]\n\n return info","def scrape_all_course_names(filename, verbose):\n \n\tsoup =\tBeautifulSoup(open(filename, 'r'), 'html.parser')\n\t#print(soup)\n\tcourses = []\n\t# all the courses are stored in a div w/class=view-content\n\t\t\n\th4_field_content = soup.find('h4', 'field-content')\n\t#print(h4_field_content)\n\tall_course_content = soup.find(\"div\", \"view-content\") # this contains ALL the classes...\n\tif all_course_content == None:\n\t\tprint(\"There are no courses on this page. Try a smaller page number!\")\n\t\treturn []\n\t\n\tcandidate_classes = all_course_content.find_all('a') # we want all the 'a' tags within\n\tclass_list=[]\n course_code_and_number = {}\n\tfor c in candidate_classes:\n\t\t#print(c.text, \"\\n\")\n\t\ttext = c.text # ex. AEMA 611 Experimental Designs 1 (3 credits)\n\t\ttext = text.split(\" \")# split on the space\n\t\tcourse_id = \" \".join(text[:2]).replace('\\n', '') # the first 2 are the course id\n\t\tcourse_name = \" \".join(text[2:-2]).replace('\\n', '')\n\t\tnum_credits = text[-2].replace(\"(\", \"\")# just get the course number, replace the ( with nothing \n\t\n\t\n\t\t#print(f\"{course_id}\\n{course_name}\\n{num_credits}\\n\")\n\t\ttry:# Check that the course number is a digit bc sometimes it is something weird\n\t\t\tfloat(num_credits)\n\t\texcept ValueError:\n\t\t\t#print(f\"Wrong course format. Ignoring {c}\")\n\t\t\tif(verbose):\n\t\t\t\tprint(f\"Wrong course format. Ignoring course: {c.text}\")\n\t\t\tcontinue\n\t\tclass_list.append(course_id)\n\treturn class_list","def get_available_sections(url, headers):\n def _make_url(section_soup): # FIXME: Extract from here and test\n return BASE_URL + section_soup.ul.find('a')['href']\n\n def _get_section_name(section_soup): # FIXME: Extract from here and test\n return section_soup.h3.a.string.strip()\n\n def _make_subsections(section_soup):\n subsections_soup = section_soup.ul.find_all(\"li\")\n # FIXME correct extraction of subsection.name (unicode)\n subsections = [SubSection(position=i,\n url=BASE_URL + s.a['href'],\n name=s.p.string)\n for i, s in enumerate(subsections_soup, 1)]\n return subsections\n\n courseware = get_page_contents(url, headers)\n soup = BeautifulSoup(courseware)\n sections_soup = soup.find_all('div', attrs={'class': 'chapter'})\n\n sections = [Section(position=i,\n name=_get_section_name(section_soup),\n url=_make_url(section_soup),\n subsections=_make_subsections(section_soup))\n for i, section_soup in enumerate(sections_soup, 1)]\n return sections","def separateSource(self,compInfo):\n sourceInfo = {}\n source = []\n for eachline in compInfo:\n words = eachline.split() ##This line need to be confirmed with Manas\n if eachline[0] in ['f', 'h']:\n source.append(words[3])\n if len(source) > 0:\n for eachline in compInfo:\n words_s = eachline.split()\n if words_s[0] in source:\n sourceInfo[words_s[0]] = words_s[1:3]\n return sourceInfo","def define_info_dict():\n\n d = {\n \"PRED\": {\n \"COLUMN\": [\"predicted_class\"],\n \"Number\": \"1\",\n \"Type\": \"String\",\n \"Description\": \"Predicted class: somatic, germline, artifact\",\n },\n \"PROB\": {\n \"COLUMN\": [\"prob_s\", \"prob_g\", \"prob_a\"],\n \"Number\": \"3\",\n \"Type\": \"Float\",\n \"Description\": \"Prediction probability of \"\n \"being somatic, germline, artifact in this order\",\n },\n \"SNP\": {\n \"COLUMN\": [\"is_on_db\"],\n \"Number\": \"0\",\n \"Type\": \"Flag\",\n \"Description\": \"Present on SNP database (modified dbSNP/gnomAD (default) or user-provided database)\",\n },\n \"ANNO\": {\n \"COLUMN\": [\"annotation\"],\n \"Number\": \".\",\n \"Type\": \"String\",\n \"Description\": \"Indel annotation formatted as \"\n \"GeneSymbol|RefSeqAccession|CodonPos|IndelEffect\"\n \"Delimited by comma for multiple isoforms\",\n },\n \"COSMIC_CNT\": {\n \"COLUMN\": [\"cosmic_cnt\"],\n \"Number\": \"1\",\n \"Type\": \"Integer\",\n \"Description\": \"COSMIC count in v89\",\n },\n \"MAXMAF\": {\n \"COLUMN\": [\"max_maf\"],\n \"Number\": \"1\",\n \"Type\": \"Float\",\n \"Description\": \"Maximum minor allele frequency (MAF) \"\n \"reported in dbSNP, ClinVar and gnomAD non-cancer population\",\n },\n \"COMMON\": {\n \"COLUMN\": [\"is_common\"],\n \"Number\": \"0\",\n \"Type\": \"Flag\",\n \"Description\": \"Common in dbSNP or MAXMAF > 0.01\",\n },\n \"CLIN\": {\n \"COLUMN\": [\"clin_info\"],\n \"Number\": \"1\",\n \"Type\": \"String\",\n \"Description\": \"ClinVar annotation formatted as ClinicalSignificance|Condition\",\n },\n \"ICP\": {\n \"COLUMN\": [\"indel_complexity\"],\n \"Number\": \"1\",\n \"Type\": \"Integer\",\n \"Description\": \"Indel complexity: mismatches around the indel measured by edit distance\",\n },\n \"DSM\": {\n \"COLUMN\": [\"dissimilarity\"],\n \"Number\": \"1\",\n \"Type\": \"Float\",\n \"Description\": \"Dissimilarity: edit distance between indel and flanking sequences\",\n },\n \"ISZ\": {\n \"COLUMN\": [\"indel_size\"],\n \"Number\": \"1\",\n \"Type\": \"Integer\",\n \"Description\": \"Indel size\",\n },\n \"REP\": {\n \"COLUMN\": [\"repeat\"],\n \"Number\": \"1\",\n \"Type\": \"Integer\",\n \"Description\": \"Repeat: count of the indel-sequence repeats in flanking region\",\n },\n \"UQM\": {\n \"COLUMN\": [\"is_uniq_mapped\"],\n \"Number\": \"0\",\n \"Type\": \"Flag\",\n \"Description\": \"Supported by uniquely mapped reads\",\n },\n \"NEB\": {\n \"COLUMN\": [\"is_near_boundary\"],\n \"Number\": \"0\",\n \"Type\": \"Flag\",\n \"Description\": \"Near exon boundary\",\n },\n \"EQX\": {\n \"COLUMN\": [\"equivalence_exists\"],\n \"Number\": \"0\",\n \"Type\": \"Flag\",\n \"Description\": \"Equivalent alignments exist for the indel\",\n },\n \"BID\": {\n \"COLUMN\": [\"is_bidirectional\"],\n \"Number\": \"0\",\n \"Type\": \"Flag\",\n \"Description\": \"Supported by forward and reverse reads\",\n },\n \"MTA\": {\n \"COLUMN\": [\"is_multiallelic\"],\n \"Number\": \"0\",\n \"Type\": \"Flag\",\n \"Description\": \"Multialleleic\",\n },\n \"FRM\": {\n \"COLUMN\": [\"is_inframe\"],\n \"Number\": \"0\",\n \"Type\": \"Flag\",\n \"Description\": \"In-frame indel\",\n },\n \"SPL\": {\n \"COLUMN\": [\"is_splice\"],\n \"Number\": \"0\",\n \"Type\": \"Flag\",\n \"Description\": \"Located in splice region\",\n },\n \"TRN\": {\n \"COLUMN\": [\"is_truncating\"],\n \"Number\": \"0\",\n \"Type\": \"Flag\",\n \"Description\": \"Truncating indel\",\n },\n \"CDD\": {\n \"COLUMN\": [\"is_in_cdd\"],\n \"Number\": \"0\",\n \"Type\": \"Flag\",\n \"Description\": \"Located in conserved domain\",\n },\n \"LOC\": {\n \"COLUMN\": [\"indel_location\"],\n \"Number\": \"1\",\n \"Type\": \"Float\",\n \"Description\": \"Relative indel location within the transcript coding region\",\n },\n \"NMD\": {\n \"COLUMN\": [\"is_nmd_insensitive\"],\n \"Number\": \"0\",\n \"Type\": \"Flag\",\n \"Description\": \"Insensitive to nonsense mediated decay\",\n },\n \"IPG\": {\n \"COLUMN\": [\"ipg\"],\n \"Number\": \"1\",\n \"Type\": \"Float\",\n \"Description\": \"Indels per gene\",\n },\n \"LEN\": {\n \"COLUMN\": [\"cds_length\"],\n \"Number\": \"1\",\n \"Type\": \"Float\",\n \"Description\": \"Coding sequence length. Median value if multiple isoforms exist\",\n },\n \"LC\": {\n \"COLUMN\": [\"lc\"],\n \"Number\": \"1\",\n \"Type\": \"Float\",\n \"Description\": \"Linguistic complexity: diversity of k-mers in flanking 50-bp region\",\n },\n \"LLC\": {\n \"COLUMN\": [\"local_lc\"],\n \"Number\": \"1\",\n \"Type\": \"Float\",\n \"Description\": \"Local linguistic complexity: diversity of k-mers in flanking 6-bp region\",\n },\n \"GC\": {\n \"COLUMN\": [\"gc\"],\n \"Number\": \"1\",\n \"Type\": \"Float\",\n \"Description\": \"GC-content in flanking 50-bp region\",\n },\n \"LGC\": {\n \"COLUMN\": [\"local_gc\"],\n \"Number\": \"1\",\n \"Type\": \"Float\",\n \"Description\": \"Local GC-content in flanking 6-bp region\",\n },\n \"SG\": {\n \"COLUMN\": [\"strength\"],\n \"Number\": \"1\",\n \"Type\": \"Float\",\n \"Description\": \"DNA bond strength of 2-mers in flanking 50-bp region\",\n },\n \"LSG\": {\n \"COLUMN\": [\"local_strength\"],\n \"Number\": \"1\",\n \"Type\": \"Float\",\n \"Description\": \"Local DNA bond strength of 2-mers in flanking 6-bp region\",\n },\n \"INS\": {\n \"COLUMN\": [\"is_ins\"],\n \"Number\": \"0\",\n \"Type\": \"Flag\",\n \"Description\": \"Insertion\",\n },\n \"ATI\": {\n \"COLUMN\": [\"is_at_ins\"],\n \"Number\": \"0\",\n \"Type\": \"Flag\",\n \"Description\": \"Single insertion of A or T\",\n },\n \"ATD\": {\n \"COLUMN\": [\"is_at_del\"],\n \"Number\": \"0\",\n \"Type\": \"Flag\",\n \"Description\": \"Single deletion of A or T\",\n },\n \"GCI\": {\n \"COLUMN\": [\"is_gc_ins\"],\n \"Number\": \"0\",\n \"Type\": \"Flag\",\n \"Description\": \"Single insertion of G or C\",\n },\n \"GCD\": {\n \"COLUMN\": [\"is_gc_del\"],\n \"Number\": \"0\",\n \"Type\": \"Flag\",\n \"Description\": \"Single deletion of G or C\",\n },\n \"ALTC\": {\n \"COLUMN\": [\"alt_count\"],\n \"Number\": \"1\",\n \"Type\": \"Integer\",\n \"Description\": \"Alt count: count of unique reads supporting ALT allele\",\n },\n \"REFC\": {\n \"COLUMN\": [\"ref_count\"],\n \"Number\": \"1\",\n \"Type\": \"Integer\",\n \"Description\": \"Ref count: count of unique reads supporting REF allele\",\n },\n \"RCF\": {\n \"COLUMN\": [\"reclassified\"],\n \"Number\": \"0\",\n \"Type\": \"Flag\",\n \"Description\": \"Reclassification applied\",\n },\n \"RQB\": {\n \"COLUMN\": [\"filtered\", \"rescued\"],\n \"Number\": \"1\",\n \"Type\": \"String\",\n \"Description\": \"Indel used to rescue this entry formatted as CHROM:POS:REF:ALT\",\n },\n }\n\n return d","def createDictionnary():\n dic = {}\n root = \"https://www.airlinequality.com\"\n url_page = root+\"/review-pages/a-z-airport-reviews/\"\n\n headers = {\n 'User-Agent': 'Mozilla/5.0 (Windows NT 6.1) AppleWebKit/537.36 (KHTML, like Gecko) Chrome/41.0.2228.0 Safari/537.3'}\n req = Request(url_page, headers=headers)\n webpage = urlopen(req).read()\n soup = bs(webpage, 'html.parser')\n\n r = soup.find_all('li')\n list_text = [str(val) for val in r if \"href=\\\"/airport-reviews/\" in str(val)\n and \"article\" not in str(val)]\n for texte in list_text:\n airport, link = getAirportLinks(texte)\n dic[airport.rstrip()] = root+link\n\n return dic","def get_chapter_text(location):\n chapter_output_dictionary = {}\n chapter_contents_list = []\n\n soup = Ripper(location, parser=\"html5lib\", save_path=save_path).soup\n text = soup.find(\"table\", class_=\"texttable\")\n\n for each in text.find_all(\"p\"):\n attributes = each.attrs\n if attributes:\n if \"desc\" in attributes[\"class\"]:\n pass\n elif \"note\" in attributes[\"class\"]:\n pass\n else:\n new_cont = each.contents\n chapter_contents_list.extend(new_cont)\n return join_chapter_text(chapter_contents_list)","def _create_sections(self):\n self._SECTIONS = {}","def parse_details(ep_news):\n\n # instances that might be able to get from ep_news\n information = {\n 'price': None, # 价格：\n 'area': None, # 地区：*\n 'address': None, # 地址：\n 'contact': None, # 联系人：\n 'telephone': None, # 电话：*\n 'wechat': None, # 微信：\n 'qq': None, # QQ：\n }\n\n # price and location\n left = ep_news.find('div', {'class': 'l'})\n\n # loop through all left information\n for info in left.find_all('dl'):\n # get info name and value\n name, value = info.find('dt').get_text().encode('utf-8'), info.find('dd').contents[0].encode('utf-8')\n\n # match values\n if name == u'价格：'.encode('utf-8'):\n information['price'] = value if value != '面议' else None\n elif name == u'地区：'.encode('utf-8'):\n information['area'] = value\n elif name == u'地址：'.encode('utf-8'):\n information['address'] = value\n\n # contact information\n right = ep_news.find('div', {'class': 'r'})\n\n # loop through all right information\n for info in right.find_all('dl'):\n # get info name and value\n name, value = info.find('dt').get_text().encode('utf-8'), info.find('dd').contents[0].encode('utf-8')\n\n # match values\n if name == u'联系人：'.encode('utf-8'):\n information['contact'] = value\n elif name == u'电话：'.encode('utf-8'):\n information['telephone'] = value\n elif name == u'微信：'.encode('utf-8'):\n information['wechat'] = value\n elif name == u'QQ：'.encode('utf-8'):\n information['qq'] = value\n\n return information","def get_general(soup):\n \n general_info = {}\n general_info.update(get_route_name(soup))\n general_info.update(get_box_data(soup))\n general_info.update(get_description(soup))\n general_info.update(get_hierarchy(soup))\n general_info.update(get_first_img_source(soup))\n\n return general_info","def _populate(self):\n\n # Assume the first word is what we want, and we can find well formed years\n # This sucks, but will work for these ones.\n # Roll on bibtex for citations in the CIM.\n\n citation_detail = self.doc.citation_detail\n author = citation_detail.split(',')[0]\n match = '([^\\w])19|20\\d\\d([^\\w])*?'\n m = re.search(match, citation_detail)\n if m:\n year = m.group(0)\n else:\n year = None\n\n # one error in existing es-doc content to be fixed:\n if 'van Vuuren DP' in author:\n author = 'van Vuuren'\n print 'applying vv fix'\n\n self.year = int(year)\n\n # We assume that this table will have entries which ne\n\n # I use the first three letters of a an authors name, and for\n # three or more authors, EA, and then the year for my bibtex citation string\n self.citeguess = author[0:3] + 'EA' + year[2:]\n # This is what will appear in the table:\n self.citestring = '%s et al. (%s)' % (author, year)\n # Keep this for a reference list for checking against the eventual bibtex reference list.\n self.text = citation_detail","def get_structure(self):\n main = {}\n for line in self.load():\n match = re.match('^\\s*([A-Za-z0-9_]+)(\\((\\d+)\\))?=(.*)$', line)\n if match:\n key = match.group(1)\n index = match.group(3)\n value = match.group(4)\n if index is None:\n main[key] = self.parse_data_value(value)\n else:\n if key not in main:\n main[key] = []\n main[key].append(self.parse_data_value(value))\n #else:\n # print(line)\n return main","def _course_info_content(html_parsed):\n if len(html_parsed) == 1:\n # could enforce that update[0].tag == 'h2'\n content = html_parsed[0].tail\n else:\n content = html_parsed[0].tail if html_parsed[0].tail is not None else \"\"\n content += \"\\n\".join([html.tostring(ele).decode('utf-8') for ele in html_parsed[1:]])\n return content","def get_commentary_for_chapter(location):\n chapter_commentary_dictionary = {}\n\n soup = Ripper(location, parser=\"html5lib\", save_path=save_path).soup\n text = soup.find(\"table\", class_=\"texttable\")\n\n for each in text.find_all(\"p\"):\n attributes = each.attrs\n if attributes:\n if \"desc\" in attributes[\"class\"]:\n pass\n elif \"note\" in attributes[\"class\"]:\n new_content = each.contents\n verse_header, text = export_commentary_text_as_dictionary(new_content)\n chapter_commentary_dictionary[verse_header] = text\n else:\n continue\n return chapter_commentary_dictionary","def parse_campus(department_data, campus):\n\n def extract_data(department_link):\n \"\"\"\n Extracts all course information from a UW Department\n\n @params:\n\n 'department_link': The url to the UW Department to get course\n information from\n\n Returns\n\n A list of lists. Each nested list represents one course section with the\n following values (in this order):\n\n 'Campus', 'Department Name', 'Course Number', 'Course Name', 'Credits',\n 'Areas of Knowledge', 'Quarters Offered', 'Offered with', \n 'Prerequisites', 'Co-Requisites', 'Description'\n \"\"\"\n # Update the progress bar\n if show_progress:\n progress_bar.update()\n\n # Regular expressions for searching course descriptions stored in local variables\n # for better peformance\n local_course_re = course_re\n local_course_name_re = course_name_re\n local_credits_re = credits_re\n local_credits_num_re = credits_num_re\n local_offered_jointly_re = offered_jointly_re\n local_CAMPUSES = CAMPUSES\n\n # Method used in extracting data from course descriptions found in the local scope\n # are stored in local variables for better performance\n local_complete_description = complete_description\n local_get_offered = get_offered\n local_get_requisites = get_requisites\n\n # All the courses in the department\n courses = []\n dep_file = department_link.get('href')\n\n # If the user entered a dict as the 'campuse' parameter, departments\n # are checked here\n try:\n # The String in the conditional is the abbreviated Department Name i.e EE\n # for Electrical Engineering\n if normalize('NFKD', department_link.text).rsplit('(', 1) \\\n [-1].replace(' ', '')[:-1] not in campuses[campus]:\n return None\n except TypeError:\n pass\n \n # The only links that are used for finding departments are those\n # of the format [a-z]+.html\n if '/' not in dep_file and dep_file.endswith('.html') \\\n and dep_file not in parsed_departments:\n parsed_departments.add(dep_file)\n department = BeautifulSoup(requests.get( \\\n f'{local_CAMPUSES[campus]}{dep_file}').text, features='lxml')\n for course in department.find_all('a'):\n course_ID = course.get('name') \n if course_ID:\n course_ID = course_ID.upper()\n course_title = course.find('b').text\n # The Course Description\n description = course.get_text().replace(course_title, '', 1) \n instructors = course.find('i')\n if instructors:\n description = description.replace(str(instructors.get_text()), '', 1)\n del instructors\n course_text = local_complete_description( \\\n description.rsplit('View course details in MyPlan', 1)[0])\n # Course Number i.e 351\n course_number = re.sub(local_course_re, '', course_ID)\n match_name = re.search(local_course_name_re, course_title)\n match_credit_num = re.search(local_credits_num_re, course_title)\n match_credit_types = re.findall(local_credits_re, course_title)\n # Jointly offered course with the given course\n if 'jointly with' in course_text: \n offered_jointly = course_text.rsplit('jointly with ', 1)[-1].rsplit(';', 1)[0] \n offered_jointly = ','.join(re.findall( \\\n local_offered_jointly_re, offered_jointly)).replace(' ', '') \n else:\n offered_jointly = ''\n courses.append(\n # Campus, Department Name and Course Number\n [campus, course_ID[:-3], course_number, \n # Course Name\n match_name.group(0).split(course_number, 1)[-1].strip() \\\n if match_name else '',\n # Number of credits for the course\n match_credit_num.group(0)[1:-1] \\\n if match_credit_num else '', \n # Course Credit Types (I&S, DIV, NW, VLPA, QSR, C)\n ','.join([list(filter(('').__ne__, x))[0] for x in match_credit_types]) \\\n if match_credit_types else '', \n local_get_offered(course_text),\n offered_jointly, local_get_requisites(course_text, 'Prerequisite:'), \n local_get_requisites(course_text, 'Co-requisite'), course_text]\n )\n return courses\n\n # In the course catalog website, several department links appear multiple times\n # To prevent parsing the same department more than once, parsed departments\n # are tracked in 'parsed_departments'\n parsed_departments = set()\n local_extract_data = extract_data\n department_data = BeautifulSoup(requests.get(department_data).text, features='lxml')\n\n campus_catalog = []\n # Extract data from department websites in parallel to reduce idle time\n with cf.ThreadPoolExecutor() as executor:\n results = [executor.submit(local_extract_data, department_link) \n for department_link in department_data.find_all('a')]\n for result in cf.as_completed(results):\n dptmnt = result.result()\n if dptmnt:\n campus_catalog.append(dptmnt)\n\n # DataFrame with all courses in the campus\n return pd.DataFrame(\n [course for department in campus_catalog for course in department], \n columns=COLUMN_NAMES\n )","def test_course_info(self):\r\n # Regex of first 3 columns of course information table row for\r\n # test course loaded from git. Would not have sha1 if\r\n # git_info_for_course failed.\r\n table_re = re.compile(r\"\"\"\r\n <tr>\\s+\r\n <td>edX\\sAuthor\\sCourse</td>\\s+ # expected test git course name\r\n <td>MITx/edx4edx/edx4edx</td>\\s+ # expected test git course_id\r\n <td>[a-fA-F\\d]{40}</td> # git sha1 hash\r\n \"\"\", re.VERBOSE)\r\n\r\n self._setstaff_login()\r\n self._mkdir(getattr(settings, 'GIT_REPO_DIR'))\r\n\r\n # Make sure we don't have any git hashes on the page\r\n response = self.client.get(reverse('sysadmin_courses'))\r\n self.assertNotRegexpMatches(response.content, table_re)\r\n\r\n # Now add the course and make sure it does match\r\n response = self._add_edx4edx()\r\n self.assertRegexpMatches(response.content, table_re)","def extractInfo(article):\n headline = article['headline']['main']\n url = article['web_url']\n keywords = article['keywords']\n locations = []\n for k in keywords:\n if k['name'] == \"glocations\":\n locations += [k['value']]\n return {'headline':headline, 'url':url, 'locations':locations}","def _extract_page_info(article: dict, url: str) -> dict:\n\n if not article:\n return {}\n language = detect(article.get('content_text'))\n if len(language) > 2 and len(language[2]) > 1:\n language_code = language[2][0][1]\n else:\n language_code = None\n return {'url': url, 'language': language_code}","def get_semester_course_data(self, url, semester):\n print(f\"Obtaining and indexing information for {semester}\")\n html = self.call_url_and_get_html_object(url)\n tables = html.findall(\".//table[@class='courseListing basicTable courseListingSetWidths']\")\n\n # Parse html to get course offering data\n for table in tables:\n fields = table.findall(\".//td\")\n spans = table.findall(\".//span\")\n course_number = str(spans[1].text.strip())\n title = str(fields[4].text).strip()\n professor = str(fields[6].text).strip()\n status = str(fields[0].text)\n crn = str(fields[1].text)\n\n # Add course offering data to dictionary of course classes\n if course_number not in self.course_dict.keys():\n # If course doesn't already exist in dictionary keys, instantiate class of it\n self.course_dict[course_number] = Course(\n title=title, semester=semester,\n professor=professor, crn=crn, status=status)\n else:\n self.course_dict[course_number].add_instance_of_course(\n semester, professor, crn, status)","def _course_info_content(html_parsed):\r\n if len(html_parsed) == 1:\r\n # could enforce that update[0].tag == 'h2'\r\n content = html_parsed[0].tail\r\n else:\r\n content = html_parsed[0].tail if html_parsed[0].tail is not None else \"\"\r\n content += \"\\n\".join([html.tostring(ele) for ele in html_parsed[1:]])\r\n return content","def fetch(cls, course_key):\r\n descriptor = modulestore('direct').get_course(course_key)\r\n course_details = cls(course_key.org, course_key.course, course_key.run)\r\n\r\n course_details.start_date = descriptor.start\r\n course_details.end_date = descriptor.end\r\n course_details.enrollment_start = descriptor.enrollment_start\r\n course_details.enrollment_end = descriptor.enrollment_end\r\n course_details.course_image_name = descriptor.course_image\r\n course_details.course_image_asset_path = course_image_url(descriptor)\r\n\r\n temploc = course_key.make_usage_key('about', 'syllabus')\r\n try:\r\n course_details.syllabus = get_modulestore(temploc).get_item(temploc).data\r\n except ItemNotFoundError:\r\n pass\r\n\r\n temploc = course_key.make_usage_key('about', 'short_description')\r\n try:\r\n course_details.short_description = get_modulestore(temploc).get_item(temploc).data\r\n except ItemNotFoundError:\r\n pass\r\n\r\n temploc = course_key.make_usage_key('about', 'overview')\r\n try:\r\n course_details.overview = get_modulestore(temploc).get_item(temploc).data\r\n except ItemNotFoundError:\r\n pass\r\n\r\n temploc = course_key.make_usage_key('about', 'effort')\r\n try:\r\n course_details.effort = get_modulestore(temploc).get_item(temploc).data\r\n except ItemNotFoundError:\r\n pass\r\n\r\n temploc = course_key.make_usage_key('about', 'video')\r\n try:\r\n raw_video = get_modulestore(temploc).get_item(temploc).data\r\n course_details.intro_video = CourseDetails.parse_video_tag(raw_video)\r\n except ItemNotFoundError:\r\n pass\r\n\r\n return course_details","def _section_extensions(course):\r\n section_data = {\r\n 'section_key': 'extensions',\r\n 'section_display_name': _('Extensions'),\r\n 'units_with_due_dates': [(title_or_url(unit), unit.location.to_deprecated_string())\r\n for unit in get_units_with_due_date(course)],\r\n 'change_due_date_url': reverse('change_due_date', kwargs={'course_id': course.id.to_deprecated_string()}),\r\n 'reset_due_date_url': reverse('reset_due_date', kwargs={'course_id': course.id.to_deprecated_string()}),\r\n 'show_unit_extensions_url': reverse('show_unit_extensions', kwargs={'course_id': course.id.to_deprecated_string()}),\r\n 'show_student_extensions_url': reverse('show_student_extensions', kwargs={'course_id': course.id.to_deprecated_string()}),\r\n }\r\n return section_data","def split_full_text(self, full_text, headers_list):\n\n sectioned_text = {}\n indices = {}\n no_abstr = False\n\n for i, hd in enumerate(headers_list):\n #need to replace special regex characters before matching substrings\n if '(' in hd:\n hd = hd.replace('(', '\\(')\n\n if ')' in hd:\n hd = hd.replace(')', '\\)')\n\n if '[' in hd:\n hd = hd.replace('[', '\\[')\n\n if ']' in hd:\n hd = hd.replace(']', '\\]')\n\n if '{' in hd:\n hd = hd.replace('{', '\\{')\n\n if '}' in hd:\n hd = hd.replace('}', '\\}')\n\n if '+' in hd:\n hd = hd.replace('+', '\\+')\n\n if '*' in hd:\n hd = hd.replace('*', '\\*')\n\n if ':' in hd:\n hd = hd.replace(':', '\\:')\n\n if i == 0: # meta-data has no substring-matching to do\n\n inds = [m.start() for m in re.finditer(hd, full_text)]\n #Abstract can appear in text, but isn't listed w/ headers\n #Only use first instance\n if len(inds) > 0:\n indices[hd] = inds[0]\n\n else: #if there is no abstract, use figures to remove meta-data\n fig_text = [m.start() for m in re.finditer('Figure', full_text)]\n indices[hd] = fig_text[0]\n no_abstr = True\n\n else:\n inds = [m.start() for m in re.finditer(hd, full_text)]\n #assume final instance of substring match corresponds\n #to the correct header text instance\n indices[hd] = inds[-1]\n\n\n for i, hd in enumerate(headers_list):\n\n if i == 0:\n if no_abstr == True:\n\n #get meta-data, which has no keyword matching\n sectioned_text['Section Headers'] = headers_list\n end_ind = indices[' Abstract ']\n sectioned_text['Meta-data'] = full_text[:end_ind]\n\n #indicate there is no abstract\n start_id = indices[' Abstract ']\n end_id = indices[list(indices.keys())[1]]\n sectioned_text[' Abstract '] = ''\n\n\n if no_abstr == False:\n #get meta-data, which has no keyword matching\n sectioned_text['Section Headers'] = headers_list\n end_ind = indices[' Abstract ']\n sectioned_text['Meta-data'] = full_text[:end_ind]\n\n #get abstract\n start_id = indices[' Abstract ']\n end_id = indices[list(indices.keys())[1]]\n sectioned_text[hd] = full_text[start_id : end_id]\n\n if i > 0 and i < len(headers_list)-1: #all setions but final section\n if i == 1:\n if no_abstr == True:\n start_id = indices[' Abstract ']\n end_id = indices[list(indices.keys())[i+1]]\n sectioned_text[hd] = full_text[start_id:end_id]\n\n else:\n start_id = indices[list(indices.keys())[i]]\n end_id = indices[list(indices.keys())[i+1]]\n sectioned_text[hd] = full_text[start_id:end_id]\n\n else:\n start_id = indices[list(indices.keys())[i]]\n end_id = indices[list(indices.keys())[i+1]]\n sectioned_text[hd] = full_text[start_id:end_id]\n\n if i == len(headers_list) - 1: #final header\n start_id = indices[list(indices.keys())[i]]\n sectioned_text[hd] = full_text[start_id:]\n\n return sectioned_text","def parse_question_data(self):\n section = ''\n subsection = ''\n quest = ''\n # The data falls into 4 cases\n # 1. Sections\n # 2. subsections\n # 3. questions\n # 4. answers.\n\n for line in self.question_data: \n\n if \":\" in line: # case #2\n subsection = line.split(\":\")[1] # split the line on the : into an array but only take the [1] element\n debug(\"Subsection: %s\" % subsection)\n \n elif \".\" in line: # this is either a question or an answer?\n \n if line.split(\".\")[0].isdigit(): # case #3 it's a question, split on . into an array and take the element to the left and ask if it's a digit.\n quest = line # Since we know it's something like \"3. Are you a warlock?\" we stick that in the quest varable.\n debug(\"Question: %s\" % quest)\n # Create a question object and stick it in the dictonary with the key being the question (since we know it'll be unique)\n self.questions[quest] = question(section, subsection, quest) # I know it's redundant to have the key and have a value.\n \n elif line.startswith(\".\"): # case #4 answer All the answers startswith \".\" \n debug(\"Answer: %s\" % line)\n # take the question and append it to the answers array in the question object.\n self.questions[quest].answers.append(line[2:]) # Trim the first two characters off the answer since it's \". the answer\"\n \n else: # case #1 # This is section like AMERICAN DEMOCRACY\n section = line # load the line from the file into the section variable\n debug(\"Section = %s\" % section)","def proc_education_summary( summary: Tag ) -> Dict[str, str]:\n edu_record = dict()\n edu_record['school'] = summary.find('h3').text.strip()\n edu_record['is_abroad_school'] = _is_abroad_school( edu_record['school'] )\n\n for parag in summary.find_all('p'):\n spans = [span.text.strip() for span in parag.find_all('span')]\n if len( spans ) == 2:\n fld_name, value = spans\n value = value.strip()\n elif len(spans) == 0:\n # print( 'education parag: ', parag )\n edu_record['description'] = parag.text.strip()\n continue\n else:\n print( 'education spans: ', spans )\n continue\n\n if fld_name == 'Nombre de la titulación':\n edu_record['degree_raw'] = value\n edu_record['degree'] = _classify_degree( value )\n # print( 'degree: ', value, _classify_degree(value) )\n elif fld_name == 'Disciplina académica':\n edu_record['field_raw'] = value\n elif fld_name == 'Nota':\n edu_record['grade_raw'] = value\n elif fld_name.startswith('Fechas de estudios'):\n edu_record['period_raw'] = value\n elif fld_name.startswith('Actividades y asociaciones'):\n edu_record['activities_raw'] = value\n else:\n print(\"proc_education_summary: \", fld_name, ' :: ', value)\n\n if edu_record.get('degree', 'Unknown') == 'Unknown':\n if re.search( 'Ingenier|Engineering', edu_record.get('field_raw', '') ):\n edu_record['degree'] = 'University'\n\n return edu_record\n # %%","def parse_data(filename):\n ta = {\n \"name\": \"\", # TA full name\n \"total \": 0, # total contract hours\n \"detailed\": [], # materials for the first page\n \"summary\": {} # materials for the second page\n }\n category = None\n total_hours = 0\n\n for line in open(filename):\n line = line.strip()\n if not line or line.startswith(\"====\") or line.startswith(\"TA INFO\"):\n # skip these lines. they are not meaningful\n continue\n\n key, info = line.split(\":\")\n key = key.strip()\n\n if key in DDAH_CATEGORIES:\n category = key\n\n if key.startswith(\"Full Name\"):\n ta[\"name\"] = info.strip()\n elif key.startswith(\"Total contract\"):\n ta[\"total\"] = float(info.strip().strip(\"_\"))\n else:\n # convert info -> hours, filter out zero hours:\n info = info.strip(\"_\")\n if not info:\n continue\n hours = float(info)\n if hours < 0.1:\n continue\n\n # detailed hours:\n ta[\"detailed\"].append((key, category, hours),)\n # summary hours:\n assert category is not None\n if category not in ta[\"summary\"]:\n ta[\"summary\"][category] = 0\n ta[\"summary\"][category] += hours\n # total hours:\n total_hours += hours\n\n # verify that the hours add up\n if total_hours != ta[\"total\"]:\n raise ValueError(\"Total contract hours is {0} but {1} hours are assigned\".format(ta[\"total\"], total_hours))\n # verify that there are at most 12 rows in ta[\"detailed\"]\n if len(ta[\"detailed\"]) > 12:\n raise ValueError(\"DDAH form supports at most 12 rows of detailed activity, but there are {0}\".format(len(ta[\"detailed\"])))\n\n return ta","def build_course_dictionary(title_result_set, desc_result_set) -> Dict[str, List[str]]:\n\n course_dictionary = {} # placeholder dictionary\n\n for (tagged_title, tagged_description) in zip(title_result_set, desc_result_set): # iterate through multiple result sets\n full_title_desc_list = {}\n full_title_desc_list = [str(tagged_title.text)] + str(tagged_description.text).strip().splitlines() # remove trailing whitespace, then get list of lines\n course_dictionary[str(tagged_title.text)[:8]] = full_title_desc_list\n\n return course_dictionary","def meta_cv_scraper(self, type):\n sections = self.filter_sections(filter_string=type, sections=self.cv_sections)\n if len(sections) > 1:\n raise Exception(f'Multiple {type} sections found')\n self.data[type] = []\n if len(sections) == 1:\n section = sections[0]\n asides = section.findAll('aside')\n for item in asides:\n for rowline in item.text.strip().replace('\\xa0', ' ').replace('\\r\\n', '\\n').split('\\n'):\n if rowline.strip() != '':\n self.data[type].append({'type': 'aside', 'data' : rowline.strip()})\n ps = section.findAll('p')\n for item in ps:\n for rowline in item.text.strip().replace('\\xa0', ' ').replace('\\r\\n', '\\n').split('\\n'):\n if rowline.strip() != '':\n self.data[type].append({'type' : 'p', 'data': rowline.strip()})","def _parse_biography(self):\n data = {}\n self.right_column = self.content.find(\"div\", class_=\"rechteSpalte60\")\n heading = self.right_column.find(\"h3\")\n # The page of the second president hides the details information\n # and displays a biography instead. By selecting the second div,\n # we get the hidden div containing the MPs details.\n if not heading:\n self.right_column = self.content.find_all(\"div\", class_=\"rechteSpalte60\")[1]\n data.update(self._parse_dob_job())\n data.update(self._parse_political_mandates())\n data.update(self._parse_political_posts())\n data.update(self._parse_work_history())\n data.update(self._parse_education())\n return data","def scrape_detail_pages(h, s):\n n = list()\n for i, header in enumerate(h):\n notice = dict()\n notice['header'] = header\n print notice['header']['link']\n notice['detail'] = create_detail(notice['header']['link'])\n notice['detail']['postcode'] = (\n extract_postcode(notice['detail']['address'])\n )\n notice['search_details'] = s\n n.append(notice)\n return n","def test_tax_court_citation_extractor(self):\n\n test_pairs = (\n (\n \"\"\" 1 UNITED STATES TAX COURT REPORT (2018)\n\n\n\n UNITED STATES TAX COURT\n\n\n\n BENTLEY COURT II LIMITED PARTNERSHIP, B.F. BENTLEY, INC., TAX\n MATTERS PARTNER, Petitioner v.\n COMMISSIONER OF INTERNAL REVENUE, Respondent\n\n\n\n Docket No. 5393-04. Filed May 31, 2006.\n\n\n\n Nancy Ortmeyer Kuhn, for petitioner.\n \"\"\",\n \"1 T.C. 2018\",\n ),\n (\n \"\"\" T.C. Memo. 2003-150\n\n\n\n UNITED STATES TAX COURT\n\n\n\n RIVER CITY RANCHES #1 LTD., LEON SHEPARD,\n TAX MATTERS PARTNER,\n RIVER CITY RANCHES #2 LTD., LEON SHEPARD,\n TAX MATTERS PARTNER,\n RIVER CITY RANCHES #3 LTD., LEON SHEPARD,\n TAX MATTERS PARTNER,\n\n\n \"\"\",\n \"2003 T.C. Memo. 150\",\n ),\n (\n \"\"\" T.C. Summary Opinion 2003-150\n\n\n\n UNITED STATES TAX COURT\n\n\n\n RIVER CITY RANCHES #1 LTD., LEON SHEPARD,\n TAX MATTERS PARTNER,\n RIVER CITY RANCHES #2 LTD., LEON SHEPARD,\n TAX MATTERS PARTNER,\n RIVER CITY RANCHES #3 LTD., LEON SHEPARD,\n TAX MATTERS PARTNER,\n\n\n \"\"\",\n \"2003 T.C. Summary Opinion 150\",\n ),\n (\n \"\"\"\n MICHAEL KEITH SHENK, PETITIONER v. COMMISSIONER\n OF INTERNAL REVENUE, RESPONDENT\n\n Docket No. 5706–12. Filed May 6, 2013.\n\n P was divorced from his wife, and their 2003 ‘‘Judgment of\n Absolute Divorce’’ provided that his ex-wife would have pri-\n mary residential custody of their three minor children. The\n judgment provided that the dependency exemption deductions\n for the three children would be divided between the two ex-\n spouses according to various conditions but did not provide\n that the ex-wife must execute in P’s favor a Form 8332,\n ‘‘Release of Claim to Exemption for Child of Divorced or Sepa-\n rated Parents’’. The children resided with P’s ex-wife for more\n than half of 2009, and P’s ex-wife did not execute in P’s favor\n any Form 8332 or equivalent document for any year. For 2009\n P timely filed a Federal income tax return on which he\n claimed dependency exemption deductions and the child tax\n credit for two of the children, consistent with his under-\n standing of the terms of the judgment, but he did not attach\n any Form 8332 to his return. He also claimed head-of-house-\n hold filing status. His ex-wife, the custodial parent, timely\n filed a Federal income tax return for 2009 on which she also\n\n 200\n\n\n\n\nVerDate Nov 24 2008 10:59 Jul 11, 2014 Jkt 372897 PO 20012 Frm 00001 Fmt 3857 Sfmt 3857 V:\\FILES\\BOUND VOL. WITHOUT CROP MARKS\\B.V.140\\SHENK JAMIE\n\f (200) SHENK v. COMMISSIONER 201\n\n\n claimed two dependency exemption deductions, so that one\n child was claimed on both parents’ returns. R allowed to P the\n dependency exemption deduction for one of the children but\n disallowed his claim for the dependency exemption deduction\n for the child who had also been claimed by the custodial\n parent. At trial P contended he is entitled to a dependency\n exemption deduction for all three children. Held: Since the\n custodial parent did not execute, and P could not and did not\n attach to his return, any Form 8332 or equivalent release, P\n is not entitled under I.R.C. sec. 152(e)(2)(A) to claim the\n dependency exemption deduction or the child tax credit. Held,\n further, where both the custodial parent and the noncustodial\n parent have claimed for the same year a dependency exemp-\n tion deduction for the same child, a declaration signed by the\n custodial parent after the period of limitations for assess-\n ments has expired as to the custodial parent could not qualify\n under I.R.C. sec. 152(e)(2)(A), and therefore there is no reason\n to grant P’s request to leave the record open so that he may\n obtain and proffer such a declaration. Held, further, P is not\n entitled to head-of-household filing status under I.R.C. sec.\n 2(b)(1) nor to the child tax credit under I.R.C. sec. 24.\n\n Michael Keith Shenk, for himself.\n Shari Salu, for respondent.\n GUSTAFSON, Judge: The Internal Revenue Service (IRS)\n determined a deficiency of $3,136 in the 2009 Federal income\n tax of petitioner Michael Keith Shenk. Mr. Shenk petitioned\n this Court, pursuant to section 6213(a), 1 for redetermination\n of the deficiency. After Mr. Shenk’s concession that he\n received but did not report $254 in dividend income, the\n issue for decision is whether Mr. Shenk is entitled to a\n dependency exemption deduction for one of his children\n under section 151(c), a child tax credit for that child under\n section 24(a), and head-of-household filing status under sec-\n tion 2(b)(1). On these issues, we hold for the IRS.\n FINDINGS OF FACT\n\n The judgment of divorce\n Mr. Shenk was married to Julie Phillips, and they have\n three minor children—M.S., W.S., and L.S. They divorced in\n 2003. The family court’s ‘‘Judgment of Absolute Divorce’’ pro-\n 1 Unless otherwise indicated, all citations of sections refer to the Internal\n\n Revenue Code (26 U.S.C.) in effect for the tax year at issue, and all cita-\n tions of Rules refer to the Tax Court Rules of Practice and Procedure.\n\n\n\n\nVerDate Nov 24 2008 10:59 Jul 11, 2014 Jkt 372897 PO 20012 Frm 00002 Fmt 3857 Sfmt 3857 V:\\FILES\\BOUND VOL. WITHOUT CROP MARKS\\B.V.140\\SHENK JAMIE\n\f 202 140 UNITED STATES TAX COURT REPORTS (200)\n\n\n vided: that Ms. Phillips was ‘‘awarded primary residential\n custody’’ of the parties’ three children; and that Mr. Shenk\n would be liable for child support payments; but that, as to\n dependency exemptions—\"\"\",\n \"140 T.C. 200\",\n ),\n )\n site = tax.Site()\n for q, a in test_pairs:\n results = site.extract_from_text(q)\n cite_string = \"%s %s %s\" % (\n results[\"Citation\"][\"volume\"],\n results[\"Citation\"][\"reporter\"],\n results[\"Citation\"][\"page\"],\n )\n\n self.assertEqual(cite_string, a)\n print \"✓\", cite_string","def get_bare_file(filename):\n \"\"\" for a given entry, finds all of the info we want to display \"\"\"\n f = open(filename, 'r')\n str = f.read()\n str = str.decode('utf-8')\n e = {}\n try: e['title'] = re.search('(?<=title:)(.)*', str).group()\n except: pass\n try: e['slug'] = re.search('(?<=slug:)(.)*', str).group()\n except: pass\n try: e['summary'] = re.search('(?<=summary:)(.)*', str).group()\n except: pass\n try:\n e['content'] =re.search('(?<=content:)((?!category:)(?!published:)(.)|(\\n))*', str).group()\n if e['content'] == None:\n e['content'] = re.search('(?<=content:)((.)|(\\n))*$', str).group()\n except:\n pass\n try:\n e['published'] = re.search('(?<=published:)(.)*', str).group()\n except: pass\n try: e['author'] = re.search('(?<=author:)(.)*', str).group()\n except: pass\n try: e['category'] = re.search('(?<=category:)(.)*', str).group()\n except: pass\n try: e['url'] = re.search('(?<=url:)(.)*', str).group()\n except: pass\n try:\n e['uid'] = re.search('(?<=u-uid:)(.)*', str)\n if e['uid']:\n e['uid'] = e['uid'].group()\n else:\n e['uid'] = re.search('(?<=u-uid)(.)*', str).group()\n except: pass\n try: e['time-zone'] = re.search('(?<=time-zone:)(.)*', str).group()\n except: pass\n try: e['location'] = re.search('(?<=location:)(.)*', str).group()\n except: pass\n try: e['syndication'] = re.search('(?<=syndication:)(.)*', str).group()\n except: pass\n try: e['location_name'] = re.search('(?<=location-name:)(.)*', str).group()\n except: pass\n try: e['in_reply_to'] = re.search('(?<=in-reply-to:)(.)*', str).group()\n except:pass\n return e","def _get_information(self):\n info_dict = {}\n table = self._tab.find('table', class_='details')\n for row in table.find_all('tr'):\n row_key, row_value = row.find(class_='key'), row.find(class_='value')\n key_with_emoji, value = self._get_key(row_key), self._get_value(row_value)\n split_key = key_with_emoji.split(\" \", 1)\n\n if len(split_key):\n key = split_key[-1]\n info_dict.update({key: value})\n\n return info_dict","def get_course_table(self, table):\n json_result = {}\n row_list = table.xpath('.//table[@id = \"s_course\"]/tr[position() > 1]')\n for row in row_list:\n session = row.xpath('./td[1]/text()')\n course_full_code_list = row.xpath('.//a[starts-with(@href, \"javascript:course_popup\")]/text()')\n course_name_list = row.xpath('.//font[@style = \"font-size:7pt;\"]/text()')\n course_list = []\n if len(course_full_code_list) != len(course_name_list):\n # year course design project would be count twice\n if (\"Design Project\" == course_name_list[0]) & \\\n (len(course_full_code_list) + 1 == len(course_name_list)):\n course_name_list = course_name_list[1:]\n else:\n raise ProfileException(\n \"Error: unmatched lists. course code list:\",\n course_full_code_list, \"\\n course name list:\", course_name_list)\n for i, full_code in enumerate(course_full_code_list):\n if re.match(re.compile('\\w{3}\\d{3}[YH]1\\s+[SFY]'), full_code) is None:\n raise ProfileException(\"Illegal course code!:\" + full_code)\n course_list.append({\n \"courseName\": course_name_list[i],\n \"courseCode\": full_code[0:6],\n \"courseTime\": full_code[-1],\n \"courseLength\": full_code[6:8]\n })\n # there is a empty session\n if session:\n json_result.update({session[0]: course_list})\n if json_result:\n return json_result\n else:\n raise ProfileException(\"Failed to get course_table table(row list is empty)\")","def _get_courses(self) -> None:\n\n courses_content: NavigableString = self.soup.find(\"div\", \n {\"class\": \"coursesContent\"})\n course_items: ResultSet = courses_content.find_all(\"div\", \n {\"class\": \"courseItem\"})\n\n for item in course_items:\n course_name: str = item.a[\"href\"].split(\"/\")[-2].lower()\n course_data: ParseType = self._parse(item)\n self._update(course_name, course_data)","def get_annotated_content_infos(course_id, thread, user, user_info):\r\n infos = {}\r\n\r\n def annotate(content):\r\n infos[str(content['id'])] = get_annotated_content_info(course_id, content, user, user_info)\r\n for child in content.get('children', []):\r\n annotate(child)\r\n annotate(thread)\r\n return infos","def parse_info(s:str) -> dict:\n d = {}\n d[\"SVTYPE\"] = re.search(r'(?<=SVTYPE=)\\w+',s).group(0)\n d[\"SUPPORT\"] = re.search(r'(?<=SUPPORT=)\\d+',s).group(0)\n if d[\"SVTYPE\"] in [\"BND\"]:\n return d\n d[\"END\"] = re.search(r'(?<=END=)\\d+',s).group(0)\n if d[\"SVTYPE\"] in [\"INV\"]:\n return d\n d[\"SVLEN\"] = re.search(r'(?<=SVLEN=)(.*?)(?=;)',s).group(0)\n d[\"READS\"] = re.search(r'(?<=READS=)(.*?)(?=$)',s).group(0).split(\",\")\n if d[\"SVTYPE\"] == \"INS\":\n d[\"SEQS\"] = re.search(r'(?<=SEQS=)(.*?)(?=;)',s).group(0).split(\",\")\n return d","def get_description(soup):\n\n standard_head = ['Description', 'Getting There', 'Protection', 'Location']\n\n # grab all h3 orange header sections on the page\n detail = {}\n other_text = []\n for h3 in soup.find_all('h3', { 'class': \"dkorange\" }):\n \n # text is the element after the h3\n body = h3.next_sibling\n \n if isinstance(body, NavigableString):\n # ignore sections from here on like 'Climbing Season' and such\n break\n else:\n # these are the valuable text sections\n body = body.get_text()\n body = body.encode('utf-8', errors = 'ignore')\n head = h3.get_text().encode('utf-8', errors = 'ignore')\n head = head.strip('\\xc2\\xa0')\n\n if head in standard_head:\n head = head.replace(' ','_').lower()\n detail[head] = body\n else:\n other_text.append(body)\n\n # combine text into a full description\n if len(other_text) > 0:\n if 'description' in detail:\n # combine description with other text -- questionable but appropriate\n detail['description'] = detail['description'] + '\\n'.join(other_text)\n else:\n detail['description'] = '\\n'.join(other_text)\n\n # blank if there is no text at all\n if 'description' not in detail:\n detail['description'] = ''\n\n return detail","def all_courses(records):\n \n course_and_id_dict = {} #This creates an empty dictionary\n for all_tuples in records:\n course_info_tuple = all_tuples[0] #Extracts all course information\n course_id = course_info_tuple[0]\n course_name = course_info_tuple[1]\n \n course_and_id_dict[course_id] = course_name\n \n return course_and_id_dict","def course_info(request, course_id):\r\n course_key = SlashSeparatedCourseKey.from_deprecated_string(course_id)\r\n course = get_course_with_access(request.user, 'load', course_key)\r\n staff_access = has_access(request.user, 'staff', course)\r\n masq = setup_masquerade(request, staff_access) # allow staff to toggle masquerade on info page\r\n reverifications = fetch_reverify_banner_info(request, course_key)\r\n studio_url = get_studio_url(course_key, 'course_info')\r\n\r\n context = {\r\n 'request': request,\r\n 'course_id': course_key.to_deprecated_string(),\r\n 'cache': None,\r\n 'course': course,\r\n 'staff_access': staff_access,\r\n 'masquerade': masq,\r\n 'studio_url': studio_url,\r\n 'reverifications': reverifications,\r\n }\r\n\r\n return render_to_response('courseware/info.html', context)","def main_dictionary():\n for lyric in lyrics:\n for line in lyric.split(\"\\n\"):\n dictionary(line.split(\" \"))","def scrape_compInfo(data, comp):\n\tc_data = comp # id_org, mex_ciudad y nombre de xml\n\tplatino = False #Determina si es necesario crear complejo platino\n\tsoup = BeautifulSoup(data, convertEntities=BeautifulStoneSoup.HTML_ENTITIES)\n\t\n\tc_data['cadena']='Cinemex'\n\t\n\t#direccion soup.find('table', cellspacing='3').findAll('td',colspan='2')[0].p.findNextSiblings(text=True)\n\t#telefono soup.find('table', cellspacing='3').findAll('td',colspan='2')[1]\n\tdatos = soup.find('table', cellspacing='3').findAll('td',colspan='2')\n\t\n\tc_data['nombre'] = str(soup.find('span', 'texto_6').string)\n\tc_data['direccion'] = ''.join(datos[0].p.findAll(text=True)).strip()\n\tc_data['telefonos_string'] = ''.join(datos[1].findAll(text=True)).strip()\n\t\n\t#Checa si existe salas platino\n\tprecios = soup.find('td', width='179')\n\tprecios_str = ''.join(precios.findAll(text=True))\n\tif precios_str.find('Platino') > -1:\n\t\tplatino = True\n\t\n\treturn c_data, platino","def _parse_education(self):\n education = [\n h for h in self.right_column.find_all(\"h4\") if h.text == \"Bildungsweg\"\n ]\n if education:\n education = education[0].nextSibling.nextSibling.find_all(\"li\")\n return {\"education\": [e.text.strip() for e in education]}\n return {}","def parse_details_more_cves(self, content):\n result = {}\n detail = ''\n header_appeared = False\n vuln_headers = content.xpath('.//*[self::strong or self::h3]/text()')\n details_list = content.xpath('.//div[@id=\"detailfield\"]/span//text()')\n for item in details_list:\n item = normalize_string(item)\n if item == '':\n continue\n if item in vuln_headers:\n header_appeared = True\n detail = ''\n elif header_appeared:\n cve_match = self.cve_match(item)\n if cve_match == '':\n detail += item\n else:\n result[cve_match] = detail\n detail = ''\n return result","def infotodict(seqinfo):\n \n \"\"\"\n MCF Pilot Protocol acquired on Friday April 13th\n \n >>> hdc_look.py -s mfc001 -ss 1\n series_id sequence_name series_description dim1 dim2 dim3 dim4 TR TE is_derived is_motion_corrected\n 0 1-localizer *fl2d1 localizer 192 192 3 1 0.020 5.00 False False\n 1 2-pre_Neutral1_A>>P Resting 4X4X4 *epfid2d1_64 pre_Neutral1_A>>P Resting 4X4X4 64 64 35 148 2.000 25.00 False False\n 2 3-pre_topup_A>>P *epse2d1_64 pre_topup_A>>P 64 64 140 1 2.400 38.00 False False\n 3 4-pre_topup_P>>A *epse2d1_64 pre_topup_P>>A 64 64 140 1 2.400 38.00 False False\n 4 5-Field_mapping 4X4X4 A>>P *fm2d2r Field_mapping 4X4X4 A>>P 64 64 35 1 0.488 4.92 False False\n 5 6-Field_mapping 4X4X4 A>>P *fm2d2r Field_mapping 4X4X4 A>>P 64 64 35 1 0.488 7.38 False False\n 6 7-pre+heat1_A>>P 4X4X4 *epfid2d1_64 pre+heat1_A>>P 4X4X4 64 64 35 148 2.000 25.00 False False\n 7 8-pre_Neutral2_A>>P Resting 4X4X4 *epfid2d1_64 pre_Neutral2_A>>P Resting 4X4X4 64 64 35 148 2.000 25.00 False False\n 8 9-pre+heat2_A>>P 4X4X4 *epfid2d1_64 pre+heat2_A>>P 4X4X4 64 64 35 148 2.000 25.00 False False\n 9 10-MPRAGE_GRAPPA2 *tfl3d1_16ns MPRAGE_GRAPPA2 256 240 192 1 2.300 2.98 False False\n 10 11-post_Neutral3_A>>P Resting 4X4X4 *epfid2d1_64 post_Neutral3_A>>P Resting 4X4X4 64 64 35 148 2.000 25.00 False False\n 11 12-post+heat3_A>>P 4X4X4 *epfid2d1_64 post+heat3_A>>P 4X4X4 64 64 35 148 2.000 25.00 False False\n 12 13-post_Neutral4_A>>P Resting 4X4X4 *epfid2d1_64 post_Neutral4_A>>P Resting 4X4X4 64 64 35 148 2.000 25.00 False False\n 13 14-post+heat4_A>>P 4X4X4 *epfid2d1_64 post+heat4_A>>P 4X4X4 64 64 35 148 2.000 25.00 False False\n 14 15-post_topup_A>>P *epse2d1_64 post_topup_A>>P 64 64 140 1 2.400 38.00 False False\n 15 16-post_topup_P>>A *epse2d1_64 post_topup_P>>A 64 64 140 1 2.400 38.00 False False\n \n \"\"\"\n\n bids_prefix = 'sub-{subject}/{session}/'\n\n pre_neutral1_ap_fmap = create_key(bids_prefix + 'func/sub-{subject}_{session}_task-preNeutral1_acq-epi_rec-fmap_bold.{item:01d}')\n pre_heat1_ap_fmap = create_key(bids_prefix + 'func/sub-{subject}_{session}_task-preHeat1_acq-epi_rec-fmap_bold.{item:01d}')\n pre_heat2_ap_fmap = create_key(bids_prefix + 'func/sub-{subject}_{session}_task-preHeat2_acq-epi_rec-fmap_bold.{item:01d}')\n pre_neutral2_ap_fmap = create_key(bids_prefix + 'func/sub-{subject}_{session}_task-preNeutral2_acq-epi_rec-fmap_bold.{item:01d}')\n\n pre_neutral1_ap_topup = create_key(bids_prefix + 'func/sub-{subject}_{session}_task-preNeutral1_acq-epi_rec-topup_bold.{item:01d}')\n pre_heat1_ap_topup = create_key(bids_prefix + 'func/sub-{subject}_{session}_task-preHeat1_acq-epi_rec-topup_bold.{item:01d}')\n pre_heat2_ap_topup = create_key(bids_prefix + 'func/sub-{subject}_{session}_task-preHeat2_acq-epi_rec-topup_bold.{item:01d}')\n pre_neutral2_ap_topup = create_key(bids_prefix + 'func/sub-{subject}_{session}_task-preNeutral2_acq-epi_rec-topup_bold.{item:01d}')\n\n pre_topup_ap = create_key(bids_prefix + 'fmap/sub-{subject}_{session}_acq-preEpi_dir-ap_epi.{item:01d}')\n pre_topup_pa = create_key(bids_prefix + 'fmap/sub-{subject}_{session}_acq-preEpi_dir-pa_epi.{item:01d}')\n\n # The item was commented out for Phase Difference field maps. Conversion did not work correctly. I removed the item number to try to\n # isolate the problem.\n\n pre_fmap_magnitude1 = create_key(bids_prefix + 'fmap/sub-{subject}_{session}_acq-pre_magnitude1.{item:01d}')\n pre_fmap_phasediff = create_key(bids_prefix + 'fmap/sub-{subject}_{session}_acq-pre_phasediff.{item:01d}')\n\n t1w = create_key(bids_prefix + 'anat/sub-{subject}_{session}_T1w')\n\n post_neutral3_ap_fmap = create_key(bids_prefix + 'func/sub-{subject}_{session}_task-postNeutral3_acq-epi_rec-fmap_bold.{item:01d}')\n post_heat3_ap_fmap = create_key(bids_prefix + 'func/sub-{subject}_{session}_task-postHeat3_acq-epi_rec-fmap_bold.{item:01d}')\n post_heat4_ap_fmap = create_key(bids_prefix + 'func/sub-{subject}_{session}_task-postHeat4_acq-epi_rec-fmap_bold.{item:01d}')\n post_neutral4_ap_fmap = create_key(bids_prefix + 'func/sub-{subject}_{session}_task-postNeutral4_acq-epi_rec-fmap_bold.{item:01d}')\n\n post_neutral3_ap_topup = create_key(bids_prefix + 'func/sub-{subject}_{session}_task-postNeutral3_acq-epi_rec-topup_bold.{item:01d}')\n post_heat3_ap_topup = create_key(bids_prefix + 'func/sub-{subject}_{session}_task-postHeat3_acq-epi_rec-topup_bold.{item:01d}')\n post_heat4_ap_topup = create_key(bids_prefix + 'func/sub-{subject}_{session}_task-postHeat4_acq-epi_rec-topup_bold.{item:01d}')\n post_neutral4_ap_topup = create_key(bids_prefix + 'func/sub-{subject}_{session}_task-postNeutral4_acq-epi_rec-topup_bold.{item:01d}')\n\n post_topup_ap = create_key(bids_prefix + 'fmap/sub-{subject}_{session}_acq-postEpi_dir-ap_epi.{item:01d}')\n post_topup_pa = create_key(bids_prefix + 'fmap/sub-{subject}_{session}_acq-postEpi_dir-pa_epi.{item:01d}')\n\n # Create an empty dictionary called info for each key\n\n info = {pre_neutral1_ap_fmap: [],\n pre_heat1_ap_fmap: [],\n pre_heat2_ap_fmap: [],\n pre_neutral2_ap_fmap: [],\n\n pre_neutral1_ap_topup: [],\n pre_heat1_ap_topup: [],\n pre_heat2_ap_topup: [],\n pre_neutral2_ap_topup: [],\n\n pre_topup_ap: [],\n pre_topup_pa: [],\n\n pre_fmap_magnitude1: [],\n pre_fmap_phasediff: [],\n\n t1w: [],\n\n post_neutral3_ap_fmap: [],\n post_heat3_ap_fmap: [],\n post_heat4_ap_fmap: [],\n post_neutral4_ap_fmap: [],\n\n post_neutral3_ap_topup: [],\n post_heat3_ap_topup: [],\n post_heat4_ap_topup: [],\n post_neutral4_ap_topup: [],\n\n post_topup_ap: [],\n post_topup_pa: [],\n\n }\n\n # Loop over each sequence. Use if statements to determine which sequences should be linked to which key\n\n for idx, s in enumerate(seqinfo):\n\n if 'pre_Neutral1' in s.series_id:\n info[pre_neutral1_ap_fmap].append([s.series_id])\n info[pre_neutral1_ap_topup].append([s.series_id])\n\n if 'pre+heat1' in s.series_id:\n info[pre_heat1_ap_fmap].append([s.series_id])\n info[pre_heat1_ap_topup].append([s.series_id])\n\n if 'pre+heat2' in s.series_id:\n info[pre_heat2_ap_fmap].append([s.series_id])\n info[pre_heat2_ap_topup].append([s.series_id])\n\n if 'pre_Neutral2' in s.series_id:\n info[pre_neutral2_ap_fmap].append([s.series_id])\n info[pre_neutral2_ap_topup].append([s.series_id])\n\n if 'pre_topup_A>>P' in s.series_id:\n info[pre_topup_ap].append([s.series_id])\n\n if 'pre_topup_P>>A' in s.series_id:\n info[pre_topup_pa].append([s.series_id])\n\n if (('Field_mapping 4X4X4 A>>P' in s.series_id) and\n (s.TE == 4.92)):\n info[pre_fmap_magnitude1].append([s.series_id])\n \n if (('Field_mapping 4X4X4 A>>P' in s.series_id) and\n (s.TE == 7.38)):\n info[pre_fmap_phasediff].append([s.series_id])\n\n if 'MPRAGE_GRAPPA2' in s.series_id:\n info[t1w].append([s.series_id])\n\n if 'post_Neutral3' in s.series_id:\n info[post_neutral3_ap_fmap].append([s.series_id])\n info[post_neutral3_ap_topup].append([s.series_id])\n\n if 'post+heat3' in s.series_id:\n info[post_heat3_ap_fmap].append([s.series_id])\n info[post_heat3_ap_topup].append([s.series_id])\n\n if 'post+heat4' in s.series_id:\n info[post_heat4_ap_fmap].append([s.series_id])\n info[post_heat4_ap_topup].append([s.series_id])\n\n if 'post_Neutral4' in s.series_id:\n info[post_neutral4_ap_fmap].append([s.series_id])\n info[post_neutral4_ap_topup].append([s.series_id])\n\n if 'post_topup_A>>P' in s.series_id:\n info[post_topup_ap].append([s.series_id])\n\n if 'post_topup_P>>A' in s.series_id:\n info[post_topup_pa].append([s.series_id])\n\n return info","def parse(self, is_president):\n mp = {}\n self.content = self.page.find(id=\"content\")\n\n salutation = self.content.find(id=\"inhalt\").text.strip()\n if is_president:\n salutation = salutation[: salutation.rfind(\" - \")]\n mp[\"is_president\"] = is_president\n mp[\"salutation\"] = salutation\n mp[\"in_committees\"] = self.page.find(\"a\", href=\"#tab-Ausschuesse\") is not None\n mp.update(self._parse_picture())\n mp.update(self._parse_contact_information())\n mp.update(self._parse_biography())\n return mp","def select_course_detail(self, course_id, course_section_id):\n conn = sqlite3.connect(self.db_path)\n cursor = conn.cursor()\n with conn:\n cursor.execute(\n \"\"\"SELECT \n c.subject, c.course_num, c.course_title,\n cs.course_section_id, cs.schedule_days, cs.start_time, cs.end_time,\n i.first_name || ' ' || i.last_name AS 'Instructor Name', c.course_units\n FROM courses c\n JOIN course_sections cs\n ON c.course_id = cs.course_id\n JOIN instructors i\n ON cs.instructor_id = i.instructor_id\n WHERE c.course_id = ? AND cs.course_section_id = ?\"\"\",\n (course_id, course_section_id),\n )\n return cursor.fetchone()","def _get_new_data(self, page_url, soup):\n data = {}\n data['url'] = page_url\n title = soup.find('dd', class_='lemmaWgt-lemmaTitle-title').find('h1')\n data['title'] = title.get_text()\n summary = soup.find('div', class_='lemma-summary')\n data['summary'] = summary.get_text()\n return data","def parse_programme(lines):\n lines = [i.decode('utf-8') for i in lines]\n header = parse_header_block(lines)\n footer = parse_footer_block(lines)\n programme = find_programme_block(lines)\n programme = strip_paging(programme)\n items = parse_items(programme)\n return {'header':header, 'footer':footer, 'items':items}","def get_course_info(self, course):\n course_preview_url = course.slug\n if course.slug == '/':\n course_preview_url = '/course'\n course.course_preview_url = course_preview_url\n course.is_registered = self.is_enrolled(course)\n course.is_completed = self.is_completed(course)\n\n\n return course","def _getsec(self, line):\n m = sectionheader_re.match(line)\n if not m:\n return\n self.anchors = anchors = []\n self.d[m.group(1)] = anchors\n self.getline = self._getanchor","def olive_parser(text: str) -> dict:\n soup = BeautifulSoup(text, \"lxml\")\n root = soup.find(\"xmd-entity\")\n page_no = root['page_no']\n identifier = root['id']\n language = root['language']\n title = soup.meta['name']\n entity_type = root['entity_type']\n issue_date = soup.meta['issue_date']\n\n out = {\n \"meta\": {\n \"language\": None,\n \"type\": {}\n },\n \"r\": [],\n \"stats\": {},\n \"legacy\": {\"continuation_from\": None, \"continuation_to\": None},\n }\n out[\"meta\"][\"title\"] = title\n out[\"meta\"][\"page_no\"] = [int(page_no)]\n out[\"meta\"][\"language\"] = normalize_language(language)\n out[\"meta\"][\"type\"][\"raw\"] = entity_type\n out[\"meta\"][\"issue_date\"] = issue_date\n\n new_region = {\n \"c\": [],\n \"p\": []\n }\n\n new_paragraph = {\n \"l\": []\n }\n\n new_line = {\n \"c\": [],\n \"t\": []\n }\n\n new_token = {\n \"c\": [],\n \"tx\": \"\"\n }\n\n for primitive in soup.find_all(\"primitive\"):\n\n # store coordinate of text areas (boxes) by page\n # 1) page number, 2) coordinate list\n region = copy.deepcopy(new_region)\n region[\"c\"] = [int(i) for i in primitive.get('box').split(\" \")]\n\n para = None\n line = None\n line_counter = 0\n\n for tag in primitive.find_all(recursive=False):\n\n if tag.name == \"l\":\n\n if para is None and line is None:\n para = copy.deepcopy(new_paragraph)\n line = copy.deepcopy(new_line)\n\n if line_counter > 0 and line is not None:\n line = normalize_line(line, out[\"meta\"][\"language\"])\n para[\"l\"].append(line)\n\n if tag.get(\"p\") in [\"S\", \"SA\"] and line_counter > 0:\n region[\"p\"].append(para)\n para = copy.deepcopy(new_paragraph)\n\n line = copy.deepcopy(new_line)\n line[\"c\"] = [\n int(i)\n for i in tag.get('box').split(\" \")\n ]\n line_counter += 1\n\n if tag.name in [\"w\", \"q\"]:\n\n # store coordinates of each token\n # 1) token, 2) page number, 3) coordinate list\n t = copy.deepcopy(new_token)\n t[\"c\"] = [int(i) for i in tag.get('box').split(\" \")]\n t[\"tx\"] = tag.string\n t[\"s\"] = int(tag.get('style_ref'))\n\n if tag.name == \"q\" and tag.get('qid') is not None:\n qid = tag.get('qid')\n normalized_form = soup.find('qw', qid=qid).text\n t[\"nf\"] = normalized_form\n t[\"qid\"] = qid\n\n # append the token to the line\n line[\"t\"].append(t)\n\n # append orphan lines\n if line is not None:\n line = normalize_line(line, out[\"meta\"][\"language\"])\n para[\"l\"].append(line)\n\n region[\"p\"].append(para)\n\n if para is not None:\n out[\"r\"].append(region)\n\n out[\"legacy\"][\"id\"] = identifier\n out[\"legacy\"][\"source\"] = soup.link['source']\n \"\"\"\n # I suspect this could be deleted\n out[\"legacy\"][\"word_count\"] = int(soup.meta['wordcnt'])\n out[\"legacy\"][\"chars_count\"] = int(soup.meta['total_chars_count'])\n suspicious_chars_count = int(soup.meta['suspicious_chars_count'])\n out[\"legacy\"][\"suspicious_chars_count\"] = int(suspicious_chars_count)\n \"\"\"\n out[\"legacy\"][\"first_id\"] = soup.link['first_id']\n out[\"legacy\"][\"last_id\"] = soup.link['last_id']\n out[\"legacy\"][\"next_id\"] = soup.link['next_id']\n out[\"legacy\"][\"prev_id\"] = soup.link['prev_id']\n\n if root.has_attr('continuation_from'):\n out[\"legacy\"][\"continuation_from\"] = root['continuation_from']\n\n if root.has_attr('continuation_to'):\n out[\"legacy\"][\"continuation_to\"] = root['continuation_to']\n\n return out","def _parse_sections(cls, content: dict) -> Dict[str, Any]:\n sections = {}\n for name, section_cls in cls.classes.items():\n section = content.get(name)\n if section:\n try:\n sections[name] = section_cls.parse(section)\n except AttributeError:\n sections[name] = section_cls(section)\n else:\n sections[name] = section_cls()\n return sections","def get_meta_information() -> Dict:\n return {'name': 'NAS-Bench-201',\n 'references': ['Xuanyi Dong, Yi Yang',\n 'NAS-Bench-201: Extending the Scope of Reproducible Neural Architecture Search',\n 'https://openreview.net/forum?id=HJxyZkBKDr',\n 'https://github.com/D-X-Y/AutoDL-Projects'],\n }","def get_sample_info(lines):\r\n mapping_data, header, comments = parse_mapping_file(lines)\r\n labels = [\"from\", \"to\", \"eweight\", \"consensus_lin\"]\r\n node_labels = [\"node_name\", \"node_disp_name\", \"ntype\", \"degree\",\r\n \"weighted_degree\", \"consensus_lin\"]\r\n cat_by_sample = {}\r\n sample_by_cat = defaultdict(list)\r\n meta_dict = {}\r\n category_labels = header[1:-1]\r\n labels.extend(category_labels)\r\n node_labels.extend(category_labels)\r\n label_list = [[] for c in category_labels]\r\n for r in mapping_data:\r\n categories = r[0:len(category_labels) + 1]\r\n sample = categories[0]\r\n meta_dict[sample] = ['\\t'.join(categories[1:]), 0]\r\n\r\n cat_by_sample[sample] = [(l.strip(), c.strip())\r\n for l, c in zip(category_labels, categories[1:])]\r\n\r\n cat_list = []\r\n for i, (l, c) in enumerate(zip(category_labels, categories[1:])):\r\n if c not in label_list[i]:\r\n label_list[i].append(c)\r\n l = l.strip()\r\n c = c.strip()\r\n cat_list.append((l, c))\r\n sample_by_cat[(l, c)].append(sample)\r\n\r\n cat_by_sample[sample] = cat_list\r\n\r\n return cat_by_sample, sample_by_cat, len(category_labels), meta_dict,\\\r\n labels, node_labels, label_list"],"string":"[\n \"def _section_course_info(course_key, access):\\r\\n course = get_course_by_id(course_key, depth=None)\\r\\n\\r\\n section_data = {\\r\\n 'section_key': 'course_info',\\r\\n 'section_display_name': _('Course Info'),\\r\\n 'access': access,\\r\\n 'course_id': course_key,\\r\\n 'course_display_name': course.display_name,\\r\\n 'enrollment_count': CourseEnrollment.num_enrolled_in(course_key),\\r\\n 'has_started': course.has_started(),\\r\\n 'has_ended': course.has_ended(),\\r\\n 'list_instructor_tasks_url': reverse('list_instructor_tasks', kwargs={'course_id': course_key.to_deprecated_string()}),\\r\\n }\\r\\n\\r\\n try:\\r\\n advance = lambda memo, (letter, score): \\\"{}: {}, \\\".format(letter, score) + memo\\r\\n section_data['grade_cutoffs'] = reduce(advance, course.grade_cutoffs.items(), \\\"\\\")[:-2]\\r\\n except Exception:\\r\\n section_data['grade_cutoffs'] = \\\"Not Available\\\"\\r\\n # section_data['offline_grades'] = offline_grades_available(course_key)\\r\\n\\r\\n try:\\r\\n section_data['course_errors'] = [(escape(a), '') for (a, _unused) in modulestore().get_course_errors(course.id)]\\r\\n except Exception:\\r\\n section_data['course_errors'] = [('Error fetching errors', '')]\\r\\n\\r\\n return section_data\",\n \"def parse_create_section(xml_course):\\n\\n attrs = [\\n \\\"section\\\",\\n 'crn',\\n \\\"start-time\\\",\\n \\\"end-time\\\",\\n \\\"meeting-days\\\",\\n \\\"location\\\",\\n \\\"section-number\\\",\\n \\\"instructor\\\"\\n ]\\n\\n section = pull_attributes_from_xml(xml_course, attrs)\\n\\n section[\\\"places\\\"] = []\\n\\n # Create Place attribute pointer based on location string\\n # Get places from Parse\\n places = get_places()[\\\"results\\\"]\\n # Get location info from section (of form [\\\"BRK 101\\\", \\\"TBA\\\"])\\n all_locations = section[\\\"location\\\"].split(\\\", \\\")\\n # Filter out TBA\\n # TODO Maybe do something else with them\\n locations = [location for location in all_locations if location != \\\"TBA\\\"]\\n\\n for location in locations:\\n building_code = location.split(\\\" \\\")[0]\\n for place in places:\\n if place.get(\\\"symbol\\\") and place[\\\"symbol\\\"] == building_code:\\n section[\\\"places\\\"].append(place[\\\"objectId\\\"])\\n break;\\n\\n\\n return section\",\n \"def _parse(self, course: NavigableString) -> ParseType:\\n\\n info = {\\\"link\\\": \\\"\\\", \\\"icon\\\": \\\"\\\", \\\"title\\\": \\\"\\\", \\\"description\\\": \\\"\\\",\\n \\\"counts\\\": {}}\\n\\n info[\\\"link\\\"] = course.a[\\\"href\\\"]\\n info[\\\"icon\\\"] = course.a.img[\\\"src\\\"]\\n\\n description: NavigableString = course.a.div\\n info[\\\"title\\\"] = description.div.get_text()\\n info[\\\"description\\\"] = description.p.get_text()\\n\\n counts: NavigableString = course.find(\\\"div\\\", {\\\"class\\\": \\\"courseCounts\\\"})\\n counts_data: ResultSet = counts.find_all(\\\"li\\\")\\n for data in counts_data:\\n name: str = data.span.get_text().lower()\\n val: str = data.find(\\\"p\\\").get_text()\\n info[\\\"counts\\\"][name] = int(val.replace(\\\",\\\", \\\"\\\"))\\n\\n return info\",\n \"def get_sections(pages):\\n sections = {}\\n section_pages = {}\\n current_section_name = None\\n current_section = []\\n\\n for page_num, page in enumerate(pages):\\n clean_page = [re.sub(\\\"\\\\s+\\\", \\\" \\\", i.strip()) for i in page.split(\\\"\\\\n\\\")]\\n\\n for ind, i in enumerate(clean_page):\\n\\n if (\\n re.findall(\\\"^Section \\\\d+\\\", i)\\n and \\\"page\\\" not in i\\n or (re.sub(\\\"\\\\d+ [\\\\w+\\\\s+]+\\\", \\\"\\\", i) == \\\"\\\" and ind == 0 and len(i) > 6)\\n ):\\n if current_section_name is not None:\\n sections[current_section_name] = current_section\\n current_section = []\\n current_section_name = i\\n break\\n\\n section_pages[page_num + 1] = current_section_name or \\\"No Section\\\"\\n\\n current_section.extend(clean_page)\\n return sections, section_pages\",\n \"def about_course(command):\\n\\n response = {'code': None, 'title': None,\\\\\\n 'prof': None, 'section1': None,\\\\\\n 'timing1': None, 'room1' : None,\\\\\\n 'section2': None, 'timing2': None,\\\\\\n 'room2': None, 'description': None,\\\\\\n 'prereq': None, 'perm': None}\\n \\n if re.search('ENPM611', command):\\n response = {'code': 'ENPM 611', 'title': 'Software Engineering',\\\\\\n 'prof': 'Christopher Ackermann', 'section1': '0101',\\\\\\n 'timing1': 'Monday 4:00 - 6:40 PM', 'room1': 'JMP 2121',\\\\\\n 'description': 'Software engineering concepts, methods, and practices important to both the theorist and the practitioner will be covered. The entire range of responsibilities expected of a software engineer are presented. The fundamental areas of requirements development, software design, programming languages, and testing are covered extensively. Sessions on supporting areas such as systems engineering, project management, and software estimation are also included.', 'prereq': 'Competency in one programming language; and must have completed an undergraduate software engineering course. Or permission of instructor.', 'perm': 'Permission of ENGR-CDL-Office of Advanced Engineering Education.'\\n}\\n elif re.search('ENPM613', command):\\n response = {'code': 'ENPM 613', 'title': 'Software Design and Implementation', 'prof': 'Ioana Rus', 'section1': '0101', 'timing1': 'Wednesday 7:00 - 9:40 PM', 'room1': 'TBA', 'description': 'Software design concepts and practices within the field important to both the practitioner and the theorist will be covered. Architectural and detailed designs are included for batch, client/server, and real-time systems. Design considerations for structured, object-oriented, and Web-based systems are covered. Design of databases, user interfaces, forms, and reports are also included. Implementation issues that affect the design, including error handling, performance, and inter-process communication, are presented.', 'perm': 'Permission of ENGR-CDL-Office of Advanced Engineering Education.'}\\n elif re.search('ENPM631', command):\\n response = {'code': 'ENPM631', 'title': 'TCP/IP Networking', 'prof': 'Pedram Fard', 'section1': '0101', 'timing1': 'Tuesday 7:00 - 9:40 PM', 'room1': 'TBA' , 'description': 'Describe how IP datagram travels through the internet and are routed from the source to the destination. Introduce the two transport protocols: UDP and TCP, the proper context to use each one, and related parameters and issues. Cover some other protocols, closely related to the TCP/IP that are responsible for the seamless operation of the Internet.', 'perm': 'ENPM602; or permission of instructor. And permission of ENGR-CDL-Office of Advanced Engineering Education.'}\\n elif re.search('ENPM687', command):\\n response = {'code': 'ENPM687', 'title': 'Digital Forensics and Incidence Response', 'prof': 'Jonas Amoonarquah', 'section1': 'Online', 'timing1': 'NA', 'room1': 'NA', 'description': 'Students will implement a robust incident response methodology, including proper forensic handling of evidence, and cover legal aspects of national and international law regarding forensics. The bulk of the course covers evidence acquisition, preservation, analysis and reporting on multiple platforms.', 'perm':'None'}\\n elif re.search('ENPM691', command):\\n response = {'code': 'ENPM691', 'title': 'Hacking of C Programs and Unix Binaries', 'prof': 'Dharmalingam Ganesan', 'section1': '0101', 'timing1': 'Thursday 7:00 - 9:40 PM', 'room1': 'JMP 3201', 'description': 'Teaches the fundamentals of secure programming in C. An in depth discussion on various security vulnerabilities (e.g., buffer overflows) in C applications will be taught with hands-on demo of concepts during the class. Students will learn how a C program runs \\\"under-the-hood\\\". The course will teach nitty-gritty of C programs by analyzing at the assembly level. The course discusses best practices (e.g., coding standards) and design principles for secure programming so that security can be built-in during design time. In addition to assignments, students are required to present papers related to this course.', 'perm': None, 'prereq': 'ENEE150; or students who have taken courses with comparable content may contact the department.'}\\n elif re.search('ENPM693', command):\\n response = {'code': 'ENPM693', 'title': 'Network Security', 'prof': 'Sohraab Soltani', 'section1': '0101', 'timing1': 'Tuesday 7:00 - 9:40 PM', 'room1' : 'JMP 3201', 'description': 'Introduction to various approaches to design; specify and verify security protocols used in large systems and networks; familiarization with some current technologies. Security threats and countermeasures, communication security and basic encryption techniques, authentication protocols, data confidentiality and integrity, analysis of cryptographic protocols, and access control in large systems and networks.', 'perm': None, 'prereq': 'An operating systems and/or network protocol course or equivalent.'}\\n elif re.search('ENPM694', command):\\n response = {'code': 'ENPM694', 'title': 'Networks and Protocols', 'prof': 'Sohraab Soltani', 'section1': '0101', 'timing1': 'Wednesday 7:00 - 9:40 PM', 'room1': 'JMP 3201', 'description': 'Provides a deep understanding of TCP/IP protocol suit and routing in the internet. The course topics are: overview of TCP/IP, basics of IP protocol, basics of TCP protocol, Network Address Translation (NAT), Dynamic Host Configuration Protocol (DHCP), Internet Protocol Security (IPsec), Internet Control Message Protocol (ICMP), Simple Mail Transfer Protocol (SMTP), Domain Name Service (DNS), IPv6, Concepts of routing (Bellman-Ford and Dijkstra algorithms), Routing Information Protocol (RIP), Open Shortest Path First (OSPF), Interior Gateway Routing Protocol (IGRP), Enhance Gateway Routing Protocol (EIGRP), and Border Gateway Protocol (BGP).'}\\n elif re.search('ENPM696', command):\\n response = {'code': 'ENPM696', 'title': 'Reverse Software Engineering', 'prof': 'Allen Hazelton', 'section1': '0101', 'timing1': 'Tuesday 4:00 - 6:40 PM', 'room1': 'TBA', 'description': 'An in-depth understanding of software reverse engineering concepts and hands-on training with reverse engineering tools, including disassemblers, decompilers, and code analyzers. Students will become familiar with both low-level software and the x86 instruction set through binary reversing sessions. This course also provides insights into many subjects such as system security, source code analysis, software design, and program understanding that will be beneficial in a variety of fields.', 'prereq': 'ENPM691 and CMSC106; or permission of instructor. And permission of ENGR-CDL-Office of Advanced Engineering Education.'}\\n elif re.search('ENPM809J', command):\\n response = {'code': 'ENPM809J', 'title': 'Cloud Security', 'prof': 'Kevin Shivers', 'section1': '0101', 'timing1': 'Monday 7:00 - 9:40 PM', 'room1': 'TBA', 'section2': '0201', 'timing2': 'Thursday 4:00 - 6:40 PM', 'room2': 'TBA', 'description': 'NA'}\\n elif re.search('ENPM809R', command):\\n response = {'code': 'ENPM809R', 'title': 'Software Defined Networking', 'prof': 'Emre Gunduzhan', 'section1': '0101', 'timing1': 'Monday 4:00 - 6:40 PM', 'room1': 'TBA', 'description': 'NA'}\\n elif re.search('ENPM809W', command):\\n response = {'code': 'ENPM809W', 'title': 'Security and Software', 'prof': 'Mikael Lindvall', 'section1': '0101', 'timing1': 'Thursday 7:00 - 9:40 PM', 'room1': 'TBA', 'description': 'NA'}\\n\\n return response\",\n \"def section(data):\\n if len(data['index']) == 2 and data['index'][1][0].isdigit():\\n element = {}\\n element['is_section'] = True\\n element['section_id'] = '-'.join(data['index'])\\n if u\\\"§§ \\\" == data['title'][:3]:\\n element['is_section_span'] = True\\n else:\\n element['is_section_span'] = False\\n match = SECTION_TITLE_REGEX.match(data['title'])\\n element['label'] = match.group(1)\\n element['sub_label'] = match.group(2)\\n return element\",\n \"def bsoup(coursepage):\\n soup = BeautifulSoup(coursepage.content, 'lxml')\\n h1 = soup.find_all('h1')[2]\\n html = h1.find_next_siblings()\\n all_strings = [h1.string]\\n for div in html:\\n try:\\n text = div.get_text()\\n except:\\n text = div.string\\n if text is not None:\\n all_strings.append(text)\\n course = {'title': all_strings[0]}\\n course = get_info_list(all_strings[1], course)\\n course['description'] = new_dict(all_strings[2], all_strings[3])\\n course['timetable'] = new_dict(all_strings[4], all_strings[5])\\n course['requirements_of_entry'] = new_dict(all_strings[6], all_strings[7])\\n course['excluded_courses'] = new_dict(all_strings[8], all_strings[9])\\n course['co_requisites'] = new_dict(all_strings[10], all_strings[11])\\n course['assessment_weighting'] = new_dict(all_strings[12], all_strings[13])\\n course['aims'] = new_dict(all_strings[17], all_strings[18])\\n date = all_strings[14].split(': ')\\n course['assessment_date'] = new_dict(date[0] + \\\": \\\", date[1])\\n course['learning_outcomes'] = new_dict(all_strings[19], all_strings[20])\\n # TODO Doesn't parse Minimum Requirement for Award of Credit or\\n # Reassessment Options\\n return course\",\n \"def parseSection(self, response):\\n sel = Selector(response)\\n sections = sel.xpath('//table[@class=\\\"sections responsive\\\"]//tr[not(@class=\\\"headers\\\")]')\\n for s in sections:\\n item = CourseItem(response.request.meta[\\\"item\\\"])\\n item['section'] = s.xpath('@data-section-id').get().strip()\\n item['instructors'] = s.css('.instructor::text').get()\\n if item['instructors'] != None:\\n item['instructors'].strip()\\n item['instructors'] = [x.strip() for x in re.split(',', item['instructors'])]\\n item['syllabus'] = s.css('.syllabus a::attr(href)').get()\\n if item['syllabus'] != None:\\n item['syllabus'].strip()\\n return item\\n \\n\\n \\\"\\\"\\\"\\n Ignore the code below this. I was trying to get\\n the times, days, and number registered from the class sections\\n \\\"\\\"\\\"\\n #times = s.xpath('//td[@class=\\\"time\\\"]/text()').get().strip()\\n #times = re.split('-', times)\\n #starttime = times[0]\\n #endtime = times[1]\\n #endt = dt.datetime.strptime(endtime, '%H:%M%p')\\n # TODO: Check if \\\"am\\\"/\\\"pm\\\" from endt, & if endt hour is greater/less than startt \\n #startt = dt.datetime.strptime(starttime, '%H:%M')\\n #days = s.xpath('//td[@class=\\\"days\\\"]/text()').get().strip()\\n #days = re.split(',', days)\\n #numdays = len(days]\\n \\n #cap = s.xpath('//td[@class=\\\"registered\\\"]//a/text()').get().strip()\\n #cap = re.split(' of ', cap.strip())\\n #item['capacity'] = cap[1]\",\n \"def get_sections(data):\\n print \\\" * Extracting sections\\\"\\n sections = OrderedDict()\\n\\n results = re.finditer(r\\\"^([A-Z][A-Z]+)([ ]+.*)?$\\\", data, re.M)\\n data_start = None\\n data_end = None\\n prev_section = None\\n cur_section = None\\n for res in results:\\n print \\\" * Found\\\", res.groups()[0]\\n data_end = res.start()\\n if prev_section is not None:\\n # Get rid of potential comments at the end of a line.\\n _data = re.sub(r\\\"\\\\s*#.*\\\", \\\"\\\", data[data_start:data_end])\\n sections[prev_section][\\\"data\\\"] = filter(None, _data.splitlines())\\n data_start = res.end()\\n cur_section = res.groups()[0]\\n sections[cur_section] = {\\\"arguments\\\": res.groups()[1], \\\"data\\\": \\\"\\\"}\\n prev_section = \\\"%s\\\" % cur_section # Only to be sure we get a brand new string...\\n\\n return sections\",\n \"def parse(u):\\n rec = {}\\n\\n try:\\n r = requests.get(u, headers=headers)\\n\\n if r.status_code == 200:\\n html = r.text\\n soup = BeautifulSoup(html, 'lxml')\\n overview_section = soup.select('.Raw-s14xcvr1-0 gXqFYO')\\n full_name_section = soup.select('.sc-iwsKbI kjxnCg')\\n years_of_practice_section = soup.select('.DataField__Data-c3wc7f-1 gLHSHx')\\n language_section = soup.select('.DataField__Data-c3wc7f-1 gLHSHx')\\n office_location_section = soup.select('.Paragraph-fqygwe-0 cojhks')\\n hospital_affiliation_section = soup.select('.Paragraph-fqygwe-0 fwayNy')\\n specialties = soup.select('.DataField__Data-c3wc7f-1 gLHSHx')\\n education_and_medical_training_section = soup.select('.EducationAndExperience__Item-xn5fll-0 bzYYRk')\\n certification_and_licensure_section = soup.select('.Paragraph-fqygwe-0 bQPwuv')\\n\\n if overview_section:\\n overview = overview_section[0].text.replace('\\\"', '')\\n if full_name_section:\\n full_name = full_name_section[0].text\\n if years_of_practice_section:\\n years_of_practice = years_of_practice_section[0].text.strip().replace('\\\"', '')\\n if language_section:\\n language = language_section[0].text.strip().replace('\\\"', '')\\n if office_location_section:\\n office_location = office_location_section[0].text\\n if hospital_affiliation_section:\\n hospital_affiliation = hospital_affiliation_section[0].text.strip().replace('\\\"', '')\\n if specialties_section:\\n specialties = specialties_section[0].text.replace('\\\"', '')\\n if education_and_medical_training_section:\\n education_and_medical_training = education_and_medical_training_section[0].text\\n if certification_and_licensure_section:\\n certification_and_licensure = certification_and_licensure_section[0].text\\n\\n\\n rec = {'overview': overview, 'full_name': full_name, 'years_of_practice': years_of_practice, 'language': language,\\n 'office_location': office_location, 'hospital_affiliation': hospital_affiliation, 'specialties':specialties,\\n 'education_and_medical_training': education_and_medical_training,\\n 'certification_and_licensure': certification_and_licensure}\\n except Exception as ex:\\n print('Exception while parsing')\\n print(str(ex))\\n finally:\\n return json.dumps(rec)\",\n \"def get_course_about_section(course, section_key):\\r\\n\\r\\n # Many of these are stored as html files instead of some semantic\\r\\n # markup. This can change without effecting this interface when we find a\\r\\n # good format for defining so many snippets of text/html.\\r\\n\\r\\n # TODO: Remove number, instructors from this list\\r\\n if section_key in ['short_description', 'description', 'key_dates', 'video',\\r\\n 'course_staff_short', 'course_staff_extended',\\r\\n 'requirements', 'syllabus', 'textbook', 'faq', 'more_info',\\r\\n 'number', 'instructors', 'overview',\\r\\n 'effort', 'end_date', 'prerequisites', 'ocw_links']:\\r\\n\\r\\n try:\\r\\n\\r\\n request = get_request_for_thread()\\r\\n\\r\\n loc = course.location.replace(category='about', name=section_key)\\r\\n\\r\\n # Use an empty cache\\r\\n field_data_cache = FieldDataCache([], course.id, request.user)\\r\\n about_module = get_module(\\r\\n request.user,\\r\\n request,\\r\\n loc,\\r\\n field_data_cache,\\r\\n course.id,\\r\\n not_found_ok=True,\\r\\n wrap_xmodule_display=False,\\r\\n static_asset_path=course.static_asset_path\\r\\n )\\r\\n\\r\\n html = ''\\r\\n\\r\\n if about_module is not None:\\r\\n try:\\r\\n html = about_module.render('student_view').content\\r\\n except Exception: # pylint: disable=broad-except\\r\\n html = render_to_string('courseware/error-message.html', None)\\r\\n log.exception(\\r\\n u\\\"Error rendering course={course}, section_key={section_key}\\\".format(\\r\\n course=course, section_key=section_key\\r\\n ))\\r\\n return html\\r\\n\\r\\n except ItemNotFoundError:\\r\\n log.warning(\\r\\n u\\\"Missing about section {key} in course {url}\\\".format(key=section_key, url=course.location.to_deprecated_string())\\r\\n )\\r\\n return None\\r\\n elif section_key == \\\"title\\\":\\r\\n return course.display_name_with_default\\r\\n elif section_key == \\\"university\\\":\\r\\n return course.display_org_with_default\\r\\n elif section_key == \\\"number\\\":\\r\\n return course.display_number_with_default\\r\\n\\r\\n raise KeyError(\\\"Invalid about key \\\" + str(section_key))\",\n \"def parseLesson(lesson_text):\\n lesson_parsed = dict()\\n\\n lesson_parsed['section_starts'] = list()\\n lesson_parsed['sections'] = list()\\n lesson_parsed['end_of_lesson'] = None\\n lesson_parsed['source'] = None\\n lesson_parsed['title'] = None\\n lesson_parsed['number'] = None\\n\\n end_of_lesson_markers = [\\n r'^\\\\s*\\\\(End\\\\s+of\\\\s+lesson',\\n r'^\\\\s*\\\\(End\\\\s+of\\\\s+lecture',\\n r'^\\\\(End\\\\s+of\\\\s+recording',\\n r'^\\\\[End\\\\s+of\\\\s+recording',\\n r'^\\\\[End\\\\s+of\\\\s+the\\\\s+recording',\\n r'^\\\\(End of the recording',\\n r'^\\\\[End\\\\s+of\\\\s+the\\\\s+tape',\\n r'^\\\\(End\\\\s+of\\\\s+audible\\\\s+portion\\\\s+of\\\\s+the\\\\s+lesson',\\n r'^\\\\[Note\\\\:\\\\s+This\\\\s+is\\\\s+the\\\\s+end\\\\s+of\\\\s+the\\\\s+recording',\\n ]\\n\\n # Do a parsing pass of the overall lesson text. This information will be used\\n # to compile the lesson into a form useful for display and use markup.\\n line_number = 0\\n for line in lesson_text:\\n mobj = re.search(r'^Source:\\\\s+(.+)$', line)\\n if mobj:\\n # WORKING HERE\\n pass\\n\\n if re.search(r'^\\\\d+[a-z]?\\\\.', line):\\n lesson_parsed['section_starts'].append(line_number)\\n line_number += 1\\n continue\\n\\n found_end_of_lesson = False \\n for end_of_lesson_marker in end_of_lesson_markers:\\n if re.search(end_of_lesson_marker, line):\\n lesson_parsed['end_of_lesson'] = line_number\\n line_number += 1\\n found_end_of_lesson = True\\n break\\n\\n if found_end_of_lesson:\\n continue\\n\\n for end_of_lesson_marker in end_of_lesson_markers:\\n if re.search(end_of_lesson_marker, line):\\n lesson_parsed['end_of_lesson'] = line_number\\n line_number += 1\\n break\\n\\n if lesson_parsed['end_of_lesson'] is not None:\\n continue\\n\\n # Remove tabs from the start of a line.\\n # Replace tabs between the number and the start of text with a space.\\n # Skip \\\"This page is included for printing and binding purposes\\\"\\n # Footnotes \\\"[fnX]\\\" will need to be linked.\\n\\n line_number += 1\\n\\n\\n # Now compile the lesson instructions into their own things.\\n\\n idx = 0\\n\\n new_section = list()\\n for section_line in xrange(lesson_parsed['section_starts'][idx], lesson_parsed['section_starts'][idx+1]:\\n section_start_text = lesson_text[section_line]\\n lesson_parsed['sections'].append(lesson_text[section_line])\\n\\n\\n\\n return lesson_parsed\",\n \"def print_section( secName, dictName ):\\n\\n\\t# Number of entries in IA dictionary.\\n\\tmaxitem = len(dictName)\\n\\n\\toutFile.write('\\\\n\\\\\\\\begin{enumerate}\\\\n')\\n\\n\\tfor m in xrange(0,maxitem):\\n\\n\\t\\toutFile.write('\\\\\\\\item ' + dictName[m])\\n\\n\\t\\tbeans = [bean for bean in secName if int(bean[1]) == (m+1)]\\n\\t\\t\\n \\t\\tif beans[0][-1] == 'N/A':\\n \\t\\t\\toutFile.write('\\\\n\\\\t\\\\\\\\begin{description}[font=\\\\\\\\normalfont]\\\\n')\\n \\t\\t\\toutFile.write('\\\\t\\\\t\\\\\\\\item[N/A]\\\\n')\\n \\t\\t\\toutFile.write('\\\\t\\\\\\\\end{description}\\\\n\\\\n')\\n\\t\\telse:\\n\\t\\t\\tl = len(beans)\\n\\t\\t\\tif beans[0][2].startswith('AY'):\\n\\t\\t\\t\\toutFile.write('\\\\t\\\\\\\\begin{description}[leftmargin=1.75cm, font=\\\\\\\\normalfont]\\\\n')\\n\\t\\t\\t\\tbeans[0][2] = string.replace(beans[0][2],'AY','\\\\\\\\textsc{ay}')\\n\\t\\t\\telse:\\n\\t\\t\\t\\toutFile.write('\\\\t\\\\\\\\begin{description}[font=\\\\\\\\normalfont]\\\\n')\\n\\t\\t\\tfor i in xrange(0,l):\\n\\t\\t\\t\\tif beans[i][2].startswith('AY'):\\n\\t\\t\\t\\t\\tbeans[i][2] = string.replace(beans[i][2],'AY','\\\\\\\\textsc{ay}')\\n\\t\\t\\t\\toutFile.write('\\\\t\\\\t\\\\\\\\item[\\\\\\\\small ' + beans[i][2] + '] ' + beans[i][3] + '\\\\n')\\n\\n\\t\\t\\t\\tif len(beans[i]) == 5:\\n\\t\\t\\t\\t\\toutFile.write('\\\\n\\\\n\\\\t\\\\t' + beans[i][4] + '\\\\n')\\n\\n\\t\\t\\toutFile.write('\\\\t\\\\\\\\end{description}\\\\n\\\\n')\\n\\n\\toutFile.write('\\\\\\\\end{enumerate}\\\\n')\",\n \"def Content():\\n\\tTOPIC_DICT = {\\\"Basics\\\": [[\\\"Introduction to Python\\\", \\\"/introduction-to-python/\\\"], \\n\\t\\t\\t\\t\\t\\t\\t\\t\\t\\t\\t\\t\\t [\\\"Print functions\\\", \\\"/python-print-function/\\\"],\\n\\t\\t\\t\\t\\t\\t\\t\\t\\t\\t\\t\\t\\t [\\\"Math basics with Python 3\\\", \\\"/math-basics-python-3/\\\"\\n\\t\\t\\t\\t\\t\\t\\t\\t\\t\\t\\t\\t\\t ]],\\n\\t\\t\\t\\t\\t\\t\\t\\t\\\"Web Dev\\\": [[\\\"wd1\\\"]]} #Web dev is now the second topic\\n\\t\\t\\t\\t\\t\\t\\t\\t#Introduction to Python\\n\\t#will be the title and the link to that is immediately after. Each inner array will be a specific sub section of the basics\\n\\t#section\\n\\n\\treturn TOPIC_DICT\",\n \"def get_course_info_section(request, course, section_key):\\r\\n usage_key = course.id.make_usage_key('course_info', section_key)\\r\\n\\r\\n # Use an empty cache\\r\\n field_data_cache = FieldDataCache([], course.id, request.user)\\r\\n info_module = get_module(\\r\\n request.user,\\r\\n request,\\r\\n usage_key,\\r\\n field_data_cache,\\r\\n course.id,\\r\\n wrap_xmodule_display=False,\\r\\n static_asset_path=course.static_asset_path\\r\\n )\\r\\n\\r\\n html = ''\\r\\n\\r\\n if info_module is not None:\\r\\n try:\\r\\n html = info_module.render('student_view').content\\r\\n except Exception: # pylint: disable=broad-except\\r\\n html = render_to_string('courseware/error-message.html', None)\\r\\n log.exception(\\r\\n u\\\"Error rendering course={course}, section_key={section_key}\\\".format(\\r\\n course=course, section_key=section_key\\r\\n ))\\r\\n\\r\\n return html\",\n \"def parseCourses(self, response):\\n sel = Selector(response)\\n courses = sel.xpath('//div[@class=\\\"course-info expandable\\\"]')\\n for c in courses:\\n item = CourseItem(response.request.meta[\\\"item\\\"])\\n item['code'] += '-' + c.xpath('@id').get().strip()\\n item['name'] = c.xpath('//a[@class=\\\"courselink\\\"]/text()').get().strip()\\n # everything works up to here #\\n href = c.xpath('div/h3/a/@href').get()\\n url = urljoin('https://web-app.usc.edu', href)\\n yield Request(url=url,callback=self.parseSection,meta={'item':item})\",\n \"def generate_page_sections_dict(self, project_page_data: dict):\\n wiki_obj = WikiService()\\n\\n short_description = (\\n f\\\"\\\\n{project_page_data['project']['shortDescription']}\\\\n\\\"\\n )\\n\\n created_date = wiki_obj.format_date_text(\\n project_page_data['project']['created']\\n )\\n # created_date_text = f\\\"\\\\n{created_date}\\\\n\\\" \\n # due_date = wiki_obj.format_date_text(\\n # project_page_data['project']['due_date']\\n # )\\n timeframe = (\\n f\\\"\\\\n* '''Start Date:''' {created_date}\\\\n\\\"\\n # f\\\"\\\\n* '''End Date:''' Estimate {due_date}\\\\n\\\"\\n )\\n\\n\\n project_url = (\\n f\\\"\\\\n{project_page_data['project']['url']}\\\\n\\\"\\n )\\n\\n hashtag = (\\n project_page_data['project']['changesetComment']\\n )\\n hashtag = (\\n project_page_data['project']['changesetComment'].replace(\\n \\\"#\\\", \\\"<nowiki>#</nowiki>\\\"\\n )\\n )\\n hashtag_text = (\\n f\\\"\\\\n{hashtag}\\\\n\\\"\\n )\\n\\n instructions_text = (\\n project_page_data['project']\\n ['externalSource']\\n ['instructions']\\n )\\n instructions = (\\n f\\\"\\\\n{instructions_text}\\\\n\\\"\\n )\\n\\n per_task_instructions_text = (\\n project_page_data['project']\\n ['externalSource']\\n ['perTaskInstructions']\\n )\\n per_task_instructions = (\\n f\\\"\\\\n{per_task_instructions_text}\\\\n\\\"\\n )\\n\\n imagery_text = (\\n project_page_data['project']\\n ['externalSource']\\n ['imagery']\\n )\\n imagery = (\\n f\\\"\\\\n{imagery_text}\\\\n\\\"\\n )\\n\\n license_text = (\\n project_page_data['project']\\n ['externalSource']\\n ['license']\\n )\\n license = (\\n f\\\"\\\\n{license_text}\\\\n\\\"\\n )\\n\\n # metrics = (\\n # f\\\"\\\\n* {project_page_data.instructions}\\\\n\\\"\\n # )\\n # quality_assurance = (\\n # f\\\"\\\\n* {project_page_data.quality_assurance}\\\\n\\\"\\n # )\\n\\n users = project_page_data['project'][\\\"users\\\"]\\n project_users = \\\"\\\"\\n for user in users:\\n project_users += (\\n f\\\"\\\\n| {user['userId']}\\\\n| {user['userName']}\\\\n|-\\\"\\n )\\n\\n \\n\\n project_page_sections = {\\n self.short_description_section: short_description,\\n self.timeframe_section: timeframe,\\n # self.timeframe_section: {\\n # self.created_section: created_date_text\\n # }, # if choose use subsection for timeframe \\n self.url_section: project_url,\\n self.external_sources_section: {\\n self.instructions_section: instructions,\\n self.per_task_instructions_section: per_task_instructions,\\n self.imagery_section: imagery,\\n self.license_section: license\\n },\\n self.hashtag_section: hashtag_text,\\n # self.instructions_section: instructions,\\n # self.metrics_section: metrics,\\n # self.quality_assurance_section: quality_assurance,\\n self.team_user_section: {\\n self.users_list_section: project_users\\n }\\n }\\n return project_page_sections\",\n \"def parse_create_course(xml_course):\\n attrs = [\\n \\\"term-code\\\",\\n \\\"term-description\\\",\\n 'subject',\\n \\\"course-number\\\",\\n \\\"school\\\",\\n \\\"department\\\",\\n \\\"title\\\",\\n \\\"description\\\",\\n \\\"credit-hours\\\",\\n \\\"distribution-group\\\"\\n ]\\n course = pull_attributes_from_xml(xml_course, attrs)\\n course[\\\"sections\\\"] = []\\n\\n return course\",\n \"def insert_course(dept, num, text):\\n\\n # Course Title \\n m = re.search(\\\"[\\\\d\\\\w]{5} - ([\\\\w ]*)\\\", text)\\n title = m.group(1) if m else \\\"nomatch\\\"\\n\\n # Course Description\\n m = re.search(\\\"\\\\.\\\\s(.*)\\\\sTypically\\\",text)\\n des = m.group(1) if m else \\\"nomatch\\\"\\n\\n # Credit hours aren't fixed for every course\\n # Credit Hours: 2.00\\n # Credit Hours: 2.00 or 3.00. \\n # Credit Hours: 1.00 to 18.00. \\n m = re.search(\\\"Credit Hours: (\\\\d+\\\\.\\\\d+)\\\",text, flags=re.IGNORECASE)\\n m = re.search(\\\"(\\\\d+\\\\.\\\\d+)(.*?)Credit hours\\\",text, flags=re.IGNORECASE) if not m else m\\n cr = m.group(1) if m else \\\"-1\\\"\\n\\n # Semesters Offered\\n m = re.search(\\\"Typically offered (.*?)\\\\.\\\", text)\\n sem = m.group(1).split() if m else [\\\"nomatch\\\"]\\n\\n # Course Type: Lecture, Recitation, Lab, Seminar, etc.\\n m = re.search(\\\"Schedule Types:\\\\s((?:[\\\\w ]+)(?:,[\\\\w ]+)*) \\\\s+\\\", text)\\n form = m.group(1).split(\\\", \\\") if m else [\\\"nomatch\\\"]\\n\\n # Learning objectives will not necessarily follow campuses\\n m = re.search(\\\"campuses:(\\\\s+([\\\\w\\\\s])+\\\\n)\\\", text)\\n campus = m.group(1).strip().split(\\\"\\\\n\\\\n\\\") if m else [\\\"nomatch\\\"]\\n campus = [camp.strip() for camp in campus]\\n\\n # prereq regex and decomosition of prereqs into lists of AND conditions (works for most classes, not 477 and similar)\\n # re.DOTALL matches all characters, including \\\"\\\\n\\\"\\n idx = text.find(\\\"campuses:\\\")\\n m = re.search(\\\"Prerequisites:(.*)\\\",text[idx:],flags=re.DOTALL)\\n if m:\\n allReqs = []\\n prereqText = m.group(1).strip()\\n prereqText = prereqText.encode('ascii', 'ignore') \\n for i in PrereqParser.parseprereq(prereqText):\\n reqArr = []\\n for j in i.split():\\n if j.find(\\\"-C\\\") != -1:\\n j = j.replace(\\\"-C\\\",\\\"\\\")\\n reqArr.append(Requisite(course=j,reqType=False))\\n else:\\n reqArr.append(Requisite(course=j,reqType=True)) \\n allReqs.append(RequisiteList(courses=reqArr))\\n\\n else:\\n allReqs = []\\n\\n # create course entity\\n course = Course(number=num, title=title, department=dept, form=form,\\n description=des, credits=float(cr), semesters=sem,\\n campuses=campus,requisites=allReqs, id=dept + num)\\n # store course \\n course.put()\",\n \"def get_course(data):\\n\\n return {item['course'] for item in data}\",\n \"def _recipe_details_generator(self, converted_content, overview_recipe):\\n def get_cooking_shop_strings(lines):\\n ret = []\\n buf = None\\n is_recipe_step_area = False\\n for l in lines:\\n if re.search(\\\"軒目\\\", l.strip()) or re.match(r\\\"^[①-⑳＊].*『.*』\\\", l.strip()) or re.match(r\\\"^[①-⑳＊].*「.*」\\\", l.strip()):\\n if buf:\\n ret.append(buf)\\n buf = l.strip()\\n continue\\n\\n if re.search(\\\"^(料理|万能調味料)\\\", l.strip()):\\n is_recipe_step_area = False\\n\\n if re.search(\\\"^材料\\\", l.strip()):\\n title, materials = re.search(\\\"(材料)(.*)\\\", l.strip()).groups()\\n # buf += \\\"\\\\n\\\" + \\\"\\\\n\\\".join(l.strip().split(None, 1))\\n buf += \\\"\\\\n\\\" + title + \\\"\\\\n\\\" + materials.strip()\\n continue\\n\\n if re.search(\\\"^作り方\\\", l.strip()):\\n is_recipe_step_area = True\\n title, recipe_steps = re.search(\\\"(作り方)(.*)\\\", l.strip()).groups()\\n # buf += \\\"\\\\n\\\" + \\\"\\\\n\\\".join(l.strip().split(None, 1))\\n buf += \\\"\\\\n\\\" + title + \\\"\\\\n\\\" + recipe_steps.strip()\\n continue\\n \\n if buf:\\n if is_recipe_step_area:\\n if re.match(r\\\"^[①-⑳＊]\\\", l.strip()):\\n buf += \\\"\\\\n\\\" + l.strip()\\n else:\\n buf += l.strip()\\n else:\\n buf += \\\"\\\\n\\\" + l.strip()\\n if buf:\\n ret.append(buf)\\n\\n return ret\\n \\n \\n for ii, l in enumerate(converted_content.splitlines()):\\n if ii == 1:\\n overview_recipe.cooking_name_sub = l.strip()\\n continue\\n \\n if -1 < l.find(\\\"初回放送\\\"):\\n overview_recipe.program_date = dateutil.parser.parse(\\\"/\\\".join(re.search(r\\\"(\\\\d+)\\\\D+(\\\\d+)\\\\D+(\\\\d+)\\\\D+\\\", l).groups()))\\n break\\n\\n cooking_shop_strings = get_cooking_shop_strings(converted_content.splitlines())\\n\\n logger.debug(\\\"-\\\" * 20)\\n logger.debug(cooking_shop_strings)\\n for shop_string in cooking_shop_strings:\\n recipe_shop = None\\n recipe = None\\n is_material_area = False\\n is_recipe_step_area = False\\n for l in shop_string.splitlines():\\n if len(l.strip()) == 0:\\n continue\\n \\n if is_material_area == False and is_recipe_step_area == False:\\n if re.search(\\\"軒目\\\", l.strip()) or re.match(r\\\"^[①-⑳＊].*『.*』\\\", l.strip()) or re.match(r\\\"^[①-⑳＊].*「.*」\\\", l.strip()):\\n recipe_shop = copy.deepcopy(overview_recipe)\\n recipe = None\\n \\n m = re.search(r\\\"「(.*)」\\\", l)\\n if m:\\n recipe_shop.cooking_name_sub += \\\"/\\\" + m.group(1)\\n else:\\n m2 = re.search(r\\\"『(.*)』\\\", l)\\n if m2:\\n recipe_shop.cooking_name_sub += \\\"/\\\" + m2.group(1)\\n \\n continue\\n \\n if re.search(\\\"^(料理|万能調味料)\\\", l.strip()):\\n is_material_area = False\\n is_recipe_step_area = False\\n if recipe:\\n yield recipe\\n\\n if recipe_shop:\\n recipe = copy.deepcopy(recipe_shop)\\n else:\\n recipe = copy.deepcopy(overview_recipe)\\n \\n if -1 < l.find(\\\":\\\"):\\n recipe.cooking_name = l.split(\\\":\\\")[1].strip()\\n elif -1 < l.find(\\\"：\\\"):\\n recipe.cooking_name = l.split(\\\"：\\\")[1].strip()\\n elif re.search(r\\\"^(料理|万能調味料)[①-⑳]\\\", l.strip()):\\n # https://www.nhk.or.jp/program/manpuku/recipe/dg0_200115.pdf\\n # 料理①カルパッチョ\\n recipe.cooking_name = l.strip()[3:].strip()\\n else:\\n recipe.cooking_name = l.split(None, 1)[1].strip()\\n continue\\n \\n if re.search(\\\"^材料\\\", l.strip()):\\n is_material_area = True\\n is_recipe_step_area = False\\n if l.strip() == \\\"材料\\\":\\n continue\\n \\n if re.search(\\\"^作り方\\\", l.strip()):\\n is_material_area = False\\n is_recipe_step_area = True\\n if l.strip() == \\\"作り方\\\":\\n pass\\n else:\\n l = l.replace(\\\"作り方\\\", \\\"\\\", 1)\\n # recipeがNoneの場合はエラーとして検出したい\\n recipe.recipe_steps.append(RecipeText(l.strip()))\\n continue\\n \\n \\n if is_material_area:\\n for material in l.strip().split(\\\"、\\\"):\\n material = material.strip()\\n if len(material):\\n if material.startswith(\\\"(\\\"):\\n recipe.materials.append(RecipeText(material))\\n else:\\n recipe.materials.append(RecipeText(material.replace(\\\"（\\\", \\\": \\\").replace(\\\"）\\\", \\\"\\\")))\\n \\n if is_recipe_step_area:\\n recipe.recipe_steps.append(RecipeText(l.strip()))\\n if recipe:\\n yield recipe\",\n \"def get_courses_info(url, headers):\\n dash = get_page_contents(url, headers)\\n soup = BeautifulSoup(dash)\\n courses_soup = soup.find_all('article', 'course')\\n courses = []\\n for course_soup in courses_soup:\\n course_id = None\\n course_name = course_soup.h3.text.strip()\\n course_url = None\\n course_state = 'Not yet'\\n try:\\n # started courses include the course link in the href attribute\\n course_url = BASE_URL + course_soup.a['href']\\n if course_url.endswith('info') or course_url.endswith('info/'):\\n course_state = 'Started'\\n # The id of a course in edX is composed by the path\\n # {organization}/{course_number}/{course_run]\\n course_id = course_soup.a['href'][9:-5]\\n except KeyError:\\n pass\\n courses.append(Course(id=course_id,\\n name=course_name,\\n url=course_url,\\n state=course_state))\\n return courses\",\n \"def __init__(self, course_id, name, content):\\n # course id and name need to go over the string_correct function\\n self.ID = self.id_correct(str(course_id))\\n self.name = self.string_correct(str(name))\\n\\n self.content = str(content)\\n\\n # uninitialized variables\\n self.prere = {}\\n self.postre = {}\\n self.description = None\\n self.department_title = None\\n self.course_level = None\\n\\n # parse description and prerequisite raw data from content var\\n self.seperate_content()\",\n \"def test_extract_info():\\n string = \\\"Mika Hakkinen Mclaren-Mercedes 79694 1\\\"\\n assert type(race.extract_info(string)) == dict\\n assert race.extract_info(string) == {\\\"Name\\\": \\\"Mika Hakkinen\\\", \\\"Team\\\": \\\"Mclaren-Mercedes\\\", \\\"Time\\\": 79694, \\\"Diff\\\": \\\"\\\",\\n \\\"Race\\\": 1}\\n assert \\\"Hello, world!\\\" not in race.extract_info(string)\",\n \"def extract_course_summary(course):\\n return {\\n field: course.fields[field]\\n for field in CourseSummary.course_info_fields\\n if field in course.fields\\n }\",\n \"def build_course(self):\\n courses = []\\n aprovacao_d = {}\\n # semestral\\n for rate_it in self.__semestral_rate:\\n # pega uma lista no qual o primeiro elemento é a taxa, o segundo\\n # e o terceiro são quantidades\\n rate_data = self.analysis[\\\"semestral_rate\\\"][rate_it.name]\\n for i in rate_data[0].index:\\n if i[0] not in aprovacao_d:\\n aprovacao_d[i[0]] = {}\\n\\n periodo = str(i[1]) + \\\"/\\\" + str(i[2])\\n aprovacao_d[i[0]][periodo] = [\\n float(rate_data[0][i]),\\n int(rate_data[1][i]),\\n int(rate_data[2][i]),]\\n\\n note = self.analysis[\\\"general_note_statistic\\\"]\\n note_last_year = self.analysis[\\\"last_year_statistic\\\"]\\n for course in self.analysis[\\\"courses\\\"].index:\\n course_dict = {}\\n course_dict[\\\"disciplina_codigo\\\"] = course\\n course_dict[\\\"disciplina_nome\\\"] = self.analysis[\\\"courses\\\"][course]\\n \\n # If the course code is related to more than one name,\\n # concatenate these names into an unique string\\n if type(course_dict[\\\"disciplina_nome\\\"]) != str:\\n new_course_name = \\\" | \\\".join(list(course_dict[\\\"disciplina_nome\\\"]))\\n course_dict[\\\"disciplina_nome\\\"] = new_course_name\\n # quantidade de matriculas\\n count = self.analysis[\\\"general_count_submission\\\"][course]\\n course_dict[\\\"qtd_alunos\\\"] = count\\n # notas\\n course_dict[\\\"qtd_cursada_aprov\\\"] = self.analysis[\\\"coursed_ratio\\\"][course]\\n course_dict[\\\"nota\\\"] = [note[0][course], note[1][course]]\\n course_dict[\\\"nota_ultimo_ano\\\"] = [\\n note_last_year[0][course],\\n note_last_year[1][course]\\n ]\\n # taxas\\n for rate_it in self.__rates:\\n rate_data = self.analysis[\\\"general_rates\\\"][rate_it.name]\\n course_dict[rate_it.name] = float(rate_data[0][course])\\n course_str = rate_it.name.replace(\\\"taxa\\\", \\\"qtd\\\")\\n # count_sel define qual quantidade vai para o json, a especifica\\n # ou geral\\n course_dict[course_str] = int(\\n rate_data[rate_it.count_sel][course])\\n # rate_calc = self.analysis[\\\"general_rates\\\"][rate_it.name][0]\\n\\n # taxas do ultimo anos\\n course_dict[\\\"taxa_reprovacao_ultimo_absoluto\\\"] = self.analysis[\\\"last_year_taxa_reprovacao_absoluta\\\"][course]\\n course_dict[\\\"taxa_reprovacao_ultimo_frequencia\\\"] = self.analysis[\\\"last_year_taxa_reprovacao_frequencia\\\"][course]\\n\\n course_dict[\\\"grafico_qtd_cursada_aprov\\\"] = self.analysis[\\\"coursed_count\\\"][course]\\n course_dict[\\\"aprovacao_semestral\\\"] = aprovacao_d[course]\\n courses.append(course_dict)\\n return courses\",\n \"def _get_page_sections(self, sectionNum=None, sectionName=None):\\n self.section = {}\\n self.sections = [] # list maintains order\\n content = self.page.content\\n lines = content.split(\\\"\\\\n\\\")\\n currentSection = None\\n for line in lines:\\n if \\\"==\\\" in line:\\n line = line.replace(\\\"Edit =\\\",\\\"\\\")\\n line = line.replace(\\\"=\\\",\\\"\\\").lstrip().rstrip()\\n self.section[line] = []\\n currentSection = line\\n self.sections.append(currentSection)\\n elif currentSection is not None:\\n line = line.lstrip().rstrip()\\n self.section[currentSection].append(line)\\n else:\\n pass\\n logger.info(\\\"Sections in page: \\\"+str(self.sections))\\n # and return some section:\\n if sectionNum is not None:\\n if sectionNum > len(self.sections) or sectionNum < 0:\\n sectionNum = 0\\n return self.section[self.sections[sectionNum]]\\n elif sectionName is not None:\\n pass\",\n \"def parse_text(self, text: str) -> SectionDict:\",\n \"def get_doctor_info(url, html):\\n \\n # create a dictionary to save data\\n doctor_info = {}\\n \\n # parsed html by using lxml\\n # In the following processing\\n # the one ending in org is the raw data in the HTML\\n # the one ending in inf is the processed data by extracting from raw data\\n select = etree.HTML(html)\\n \\n # part 1: get basic information about your doctor\\n # attribute: Name, Title, Hospital, Department\\n name_org = select.xpath('//div[@class=\\\"profile-text\\\"]//h1[@class=\\\"doctor-name\\\"]//text()')\\n name_inf = name_org[0].strip()\\n \\n title_org = select.xpath('//div[@class=\\\"profile-text\\\"]//span[@class=\\\"positon\\\"]//text()')\\n title_inf = [i.strip() for i in title_org if len(i.strip()) > 0]\\n title_inf = ' '.join(title_inf)\\n \\n hospital_department_org = select.xpath('//div[@class=\\\"profile-text\\\"]//p[@class=\\\"doctor-faculty\\\"]//text()')\\n hospital_department_inf = [i.strip() for i in hospital_department_org if len(i.strip()) > 0]\\n hospital_inf = hospital_department_inf[0]\\n department_inf = hospital_department_inf[1]\\n \\n doctor_info['姓名'] = name_inf\\n doctor_info['职称'] = title_inf\\n doctor_info['医院'] = hospital_inf\\n doctor_info['科室'] = department_inf\\n \\n # part2: get header format data\\n org = select.xpath('//div[@class=\\\"profile-sta\\\"]//text()')\\n inf = [i.strip() for i in org if len(i.strip()) > 0 and i.strip() != '%']\\n for i in range(len(inf)//2):\\n doctor_info[inf[2*i]] = inf[2*i + 1]\\n \\n \\n # part3: get sidebar format data\\n org_1 = select.xpath('//div[@class=\\\"item-body\\\"]//div[@class=\\\"clearfix\\\"]//div[@class=\\\"per-sta-label\\\"]//text()')\\n org_2 = select.xpath('//div[@class=\\\"item-body\\\"]//div[@class=\\\"clearfix\\\"]//div[@class=\\\"per-sta-data\\\"]//text()')\\n for i in range(len(org_1)):\\n doctor_info[org_1[i][:-1]] = org_2[i]\\n \\n # part4: get body format data\\n honour_org = select.xpath('//div[@class=\\\"honour-header\\\"]//text()')\\n honour_inf = ''.join([i.strip() for i in honour_org])\\n \\n honour_detail_org = select.xpath('//li[@class=\\\"honour-title\\\"]//text()')\\n honour_detail_inf = [i.strip()[:4] for i in honour_detail_org if len(i.strip()) > 0]\\n honour_detail_inf = ' '.join(honour_detail_inf)\\n \\n satisfaction_org = select.xpath('//div[@class=\\\"item-body\\\"]//div[@class=\\\"satisfaction clearfix\\\"]//i[@class=\\\"sta-num\\\"]//text()')\\n satisfaction_inf = [i.strip() for i in satisfaction_org if len(i.strip()) > 0 and i.strip() != '%']\\n \\n resume_org = select.xpath('//div[@class=\\\"good-at-text\\\"]//text()')\\n resume_inf = [i.strip() for i in resume_org]\\n if len(resume_inf) <= 20:\\n resume_inf = ''.join(resume_inf)\\n resume_inf = ''.join(resume_inf[:20])\\n \\n star_org = select.xpath('//div[@class=\\\"experience-row clearfix\\\"]//span[@class=\\\"experience-label\\\"]//text()')\\n star_inf = 1 if len(star_org) >= 1 else 0\\n\\n doctor_info['好大夫届数'] = honour_inf\\n doctor_info['好大夫具体年份'] = honour_detail_inf\\n doctor_info['简历'] = resume_inf \\n doctor_info['诊后服务星'] = star_inf\\n try:\\n doctor_info['疗效满意度'] = satisfaction_inf[0]\\n doctor_info['态度满意度'] = satisfaction_inf[1]\\n except:\\n pass\\n \\n # part5: personal url\\n personal_url = url\\n doctor_info['医生个人链接'] = personal_url\\n \\n return doctor_info\",\n \"def create_detail(l):\\n parsed_detail = create_soup(l)\\n kv_pair = \\\"\\\"\\n details = dict()\\n for i in parsed_detail.find_all('tbody'):\\n for j in i.find_all('tr'):\\n for k in j.find_all('td'):\\n cln = clean_string(k.text)\\n if cln.find(\\\":\\\")<0:\\n #if re.search(r'[A-Za-z]:', cln):\\n kv_pair += \\\" \\\" + cln\\n else:\\n if len(kv_pair) > 0:\\n kv_process = kv_pair.split(\\\":\\\")\\n det_key = re.sub(\\n r'\\\\s', '_', kv_process[0].strip()\\n ).lower()\\n details[det_key] = kv_process[1].strip()\\n kv_pair = cln\\n return details\",\n \"def parse_courses():\\n\\n subjects = collections.OrderedDict()\\n name = '' # the most recent course name acronym (ex. 'COMP')\\n\\n courses = re.sub(r'\\\\([^)]*\\\\)', '', COURSES).split() # Remove parens and their contents\\n\\n for course in courses:\\n if course == 'OR':\\n continue\\n\\n if course[0].isalpha():\\n\\n index = 0 # the upper bound character index of the subject name\\n for char in course:\\n if char.isalpha():\\n index += 1\\n else:\\n break\\n\\n name = course[:index]\\n number = course[index:index+4]\\n else:\\n number = course[:4]\\n\\n try:\\n subjects[name].append(number)\\n except KeyError:\\n subjects[name] = [number]\\n\\n return subjects\",\n \"def parseSections(data):\\n pro = _sectionSplit.split(data)\\n sections = {}\\n for x in xrange(1, len(pro), 2):\\n sections[pro[x]] = pro[x+1]\\n return sections\",\n \"def info():\\n # -------- Task 1 -------------------------\\n # Please complete the following information\\n\\n return {\\\"agent name\\\": \\\"?\\\", # COMPLETE HERE\\n \\\"student name\\\": [\\\"?\\\"], # COMPLETE HERE\\n \\\"student number\\\": [\\\"?\\\"]} # COMPLETE HERE\",\n \"def build_general_course(self):\\n\\n courses = {}\\n\\n if self.__build_analyze is False:\\n self.build_analysis()\\n\\n courses[\\\"taxa_conhecimento\\\"] = self.analysis[\\\"taxa_conhecimento\\\"]\\n courses[\\\"taxa_reprovacao\\\"] = self.analysis[\\\"taxa_reprovacao_absoluta\\\"]\\n courses[\\\"taxa_trancamento\\\"] = self.analysis[\\\"taxa_trancamento\\\"]\\n\\n # cria cache\\n cache = {}\\n note = self.analysis[\\\"general_note_statistic\\\"]\\n for rate_it in self.__rates:\\n rate_calc = self.analysis[\\\"general_rates\\\"][rate_it.name][0]\\n for course in self.analysis[\\\"courses\\\"].index:\\n if course not in cache:\\n cache[course] = {}\\n cache[course][rate_it.name] = rate_calc[course]\\n cache[course][\\\"nota\\\"] = [note[0][course], note[1][course]]\\n\\n courses[\\\"cache\\\"] = cache\\n\\n # cria o campo compara_aprov\\n courses[\\\"compara_aprov\\\"] = self.analysis[\\\"graph_course\\\"]\\n\\n # cria o campo courses\\n courses[\\\"disciplinas\\\"] = self.analysis[\\\"courses\\\"].to_dict()\\n\\n return courses\",\n \"def preprocess_courses_corpus():\\n soup = None\\n with open('courses_corpus.html', 'r') as infile:\\n content = infile.read()\\n\\n soup = BeautifulSoup(content, 'html.parser')\\n\\n docid = 0\\n data = {}\\n data['documents'] = []\\n\\n main_table = soup.find_all(\\\"div\\\", attrs={'class': 'courseblock'})\\n for course in main_table:\\n docid += 1\\n title = course.find_all('p', attrs={'class':'courseblocktitle noindent'})[0].text.lstrip('\\\\n') if len(course.find_all('p', attrs={'class':'courseblocktitle noindent'}))!=0 else ''\\n description = (course.find_all('p', attrs={'class':'courseblockdesc noindent'})[0].text.lstrip('\\\\n') if len(course.find_all('p', attrs={'class':'courseblockdesc noindent'}))!=0 else '') + ' ' + (course.find_all('p', attrs={'class':'courseblockextra noindent'})[0].text if len(course.find_all('p', attrs={'class':'courseblockextra noindent'}))!=0 else '')\\n\\n data['documents'].append({\\n 'docId' : docid,\\n 'title' : title.strip(),\\n 'description' : description.strip()\\n })\\n\\n with open('courses_data.json', 'w') as outfile:\\n json.dump(data, outfile)\",\n \"def parse_catalogs(campuses=['Seattle', 'Bothell', 'Tacoma'], struct='df', \\n show_progress=False):\\n assert type(campuses) == list or type(campuses) == dict, 'Type of \\\"campuses\\\" must be list or dict'\\n if type(campuses) == dict:\\n for key, value in campuses.items():\\n if type(key) != str or type(value) != list:\\n raise ValueError('''\\\"campuses\\\" dict must have keys of type str and\\n values of type list''')\\n # Check if all campuses in 'campuses' are valid\\n assert all([c in ['Seattle', 'Bothell', 'Tacoma'] for c in list(map(str.title, campuses))])\\n assert type(struct) == str, 'Type of \\\"struct\\\" must be str'\\n assert type(show_progress) == bool, 'Type of \\\"show_progress\\\" must be bool'\\n assert struct in ['df', 'dict'], f'{struct} is an invalid argument for \\\"struct\\\"'\\n\\n # Progress bar for Course Schedule Parsing\\n if show_progress:\\n progress_bar = tqdm()\\n\\n def parse_campus(department_data, campus):\\n \\\"\\\"\\\"\\n Parses all courses from a UW Campus\\n\\n @params\\n\\n 'department_data': BeautifulSoup object with the department list website source\\n for the given 'campus'\\n\\n 'campus': The campus to get courses from\\n\\n Returns\\n\\n A pandas DataFrame with all courses in the given campus\\n \\\"\\\"\\\"\\n\\n def extract_data(department_link):\\n \\\"\\\"\\\"\\n Extracts all course information from a UW Department\\n\\n @params:\\n\\n 'department_link': The url to the UW Department to get course\\n information from\\n\\n Returns\\n\\n A list of lists. Each nested list represents one course section with the\\n following values (in this order):\\n\\n 'Campus', 'Department Name', 'Course Number', 'Course Name', 'Credits',\\n 'Areas of Knowledge', 'Quarters Offered', 'Offered with', \\n 'Prerequisites', 'Co-Requisites', 'Description'\\n \\\"\\\"\\\"\\n # Update the progress bar\\n if show_progress:\\n progress_bar.update()\\n\\n # Regular expressions for searching course descriptions stored in local variables\\n # for better peformance\\n local_course_re = course_re\\n local_course_name_re = course_name_re\\n local_credits_re = credits_re\\n local_credits_num_re = credits_num_re\\n local_offered_jointly_re = offered_jointly_re\\n local_CAMPUSES = CAMPUSES\\n\\n # Method used in extracting data from course descriptions found in the local scope\\n # are stored in local variables for better performance\\n local_complete_description = complete_description\\n local_get_offered = get_offered\\n local_get_requisites = get_requisites\\n\\n # All the courses in the department\\n courses = []\\n dep_file = department_link.get('href')\\n\\n # If the user entered a dict as the 'campuse' parameter, departments\\n # are checked here\\n try:\\n # The String in the conditional is the abbreviated Department Name i.e EE\\n # for Electrical Engineering\\n if normalize('NFKD', department_link.text).rsplit('(', 1) \\\\\\n [-1].replace(' ', '')[:-1] not in campuses[campus]:\\n return None\\n except TypeError:\\n pass\\n \\n # The only links that are used for finding departments are those\\n # of the format [a-z]+.html\\n if '/' not in dep_file and dep_file.endswith('.html') \\\\\\n and dep_file not in parsed_departments:\\n parsed_departments.add(dep_file)\\n department = BeautifulSoup(requests.get( \\\\\\n f'{local_CAMPUSES[campus]}{dep_file}').text, features='lxml')\\n for course in department.find_all('a'):\\n course_ID = course.get('name') \\n if course_ID:\\n course_ID = course_ID.upper()\\n course_title = course.find('b').text\\n # The Course Description\\n description = course.get_text().replace(course_title, '', 1) \\n instructors = course.find('i')\\n if instructors:\\n description = description.replace(str(instructors.get_text()), '', 1)\\n del instructors\\n course_text = local_complete_description( \\\\\\n description.rsplit('View course details in MyPlan', 1)[0])\\n # Course Number i.e 351\\n course_number = re.sub(local_course_re, '', course_ID)\\n match_name = re.search(local_course_name_re, course_title)\\n match_credit_num = re.search(local_credits_num_re, course_title)\\n match_credit_types = re.findall(local_credits_re, course_title)\\n # Jointly offered course with the given course\\n if 'jointly with' in course_text: \\n offered_jointly = course_text.rsplit('jointly with ', 1)[-1].rsplit(';', 1)[0] \\n offered_jointly = ','.join(re.findall( \\\\\\n local_offered_jointly_re, offered_jointly)).replace(' ', '') \\n else:\\n offered_jointly = ''\\n courses.append(\\n # Campus, Department Name and Course Number\\n [campus, course_ID[:-3], course_number, \\n # Course Name\\n match_name.group(0).split(course_number, 1)[-1].strip() \\\\\\n if match_name else '',\\n # Number of credits for the course\\n match_credit_num.group(0)[1:-1] \\\\\\n if match_credit_num else '', \\n # Course Credit Types (I&S, DIV, NW, VLPA, QSR, C)\\n ','.join([list(filter(('').__ne__, x))[0] for x in match_credit_types]) \\\\\\n if match_credit_types else '', \\n local_get_offered(course_text),\\n offered_jointly, local_get_requisites(course_text, 'Prerequisite:'), \\n local_get_requisites(course_text, 'Co-requisite'), course_text]\\n )\\n return courses\\n\\n # In the course catalog website, several department links appear multiple times\\n # To prevent parsing the same department more than once, parsed departments\\n # are tracked in 'parsed_departments'\\n parsed_departments = set()\\n local_extract_data = extract_data\\n department_data = BeautifulSoup(requests.get(department_data).text, features='lxml')\\n\\n campus_catalog = []\\n # Extract data from department websites in parallel to reduce idle time\\n with cf.ThreadPoolExecutor() as executor:\\n results = [executor.submit(local_extract_data, department_link) \\n for department_link in department_data.find_all('a')]\\n for result in cf.as_completed(results):\\n dptmnt = result.result()\\n if dptmnt:\\n campus_catalog.append(dptmnt)\\n\\n # DataFrame with all courses in the campus\\n return pd.DataFrame(\\n [course for department in campus_catalog for course in department], \\n columns=COLUMN_NAMES\\n )\\n\\n # The pandas DataFrame to store the entire course catalog for each UW Campus entered\\n # by the user\\n course_catalog = pd.DataFrame()\\n\\n # Parse all three campuses in parallel for faster run time as well\\n # as get the departments dictionary from the 'get_departments' method\\n # to add a 'Colleges' column to categorize all courses in their College.\\n with cf.ThreadPoolExecutor() as executor:\\n results = []\\n campuses_for_dict = campuses\\n if type(campuses) == dict:\\n campuses_for_dict = list(campuses.keys())\\n results.append(executor.submit(get_departments, campuses=campuses_for_dict, struct='dict'))\\n for campus, link in CAMPUSES.items():\\n if campus.title() in campuses: \\n results.append(executor.submit(parse_campus, link, campus.title()))\\n for result in cf.as_completed(results):\\n returned = result.result()\\n if type(returned) == dict:\\n # Departments dict used to create the 'College' column in the main DataFrame\\n departments = returned\\n else:\\n course_catalog = pd.concat([course_catalog, returned])\\n\\n # Add Course ID as the index of the DataFrame to allow for easy course searching\\n # Course ID = Department Name + Course Number\\n # Example: EE235 = EE + 235\\n course_catalog['Course ID'] = course_catalog['Department Name'] + course_catalog['Course Number']\\n course_catalog['College'] = course_catalog['Department Name'].apply(check_campus, args=(departments, 'College'))\\n course_catalog.set_index('Course ID', inplace=True)\\n # Re-order indices to place 'College' right after the 'Department Name'\\n course_catalog = course_catalog[['Campus', 'Department Name', 'College', 'Course Number', 'Course Name', 'Credits',\\n 'Areas of Knowledge', 'Quarters Offered', 'Offered with', \\n 'Prerequisites', 'Co-Requisites', 'Description']]\\n \\n if struct == 'df':\\n return course_catalog\\n elif struct == 'dict':\\n return course_catalog.to_dict(orient='index')\",\n \"def infotodict(seqinfo):\\n\\n # data = create_key('run{item:03d}')\\n # info = {data: []}\\n # last_run = len(seqinfo)\\n\\n \\\"\\\"\\\"\\n The namedtuple `s` contains the following fields:\\n\\n * total_files_till_now\\n * example_dcm_file\\n * series_id\\n * dcm_dir_name\\n * unspecified2\\n * unspecified3\\n * dim1\\n * dim2\\n * dim3\\n * dim4\\n * TR\\n * TE\\n * protocol_name\\n * is_motion_corrected\\n * is_derived\\n * patient_id\\n * study_description\\n * referring_physician_name\\n * series_description\\n * image_type\\n \\\"\\\"\\\"\\n\\n t1w = create_key('sub-{subject}/{session}/anat/sub-{subject}_{session}_T1w')\\n t2w = create_key('sub-{subject}/{session}/anat/sub-{subject}_{session}_T2w')\\n func_rest = create_key('sub-{subject}/{session}/func/sub-{subject}_{session}_task-rest_bold')\\n dwi_ap = create_key('sub-{subject}/{session}/dwi/sub-{subject}_{session}_acq-AP_dwi')\\n dwi_pa = create_key('sub-{subject}/{session}/dwi/sub-{subject}_{session}_acq-PA_dwi')\\n t2star = create_key('sub-{subject}/{session}/dwi/sub-{subject}_{session}_T2star')\\n t2w_fatsat = create_key('sub-{subject}/{session}/anat/sub-{subject}_{session}_acq-fatsat_T2w')\\n \\n info = {t1w: [], t2w: [], func_rest: [], dwi_ap: [], dwi_pa: [], t2star: [], t2w_fatsat: []}\\n\\n for idx, s in enumerate(seqinfo):\\n if (s.example_dcm_file == 'mp_rage_1_mm-00001.dcm'):\\n info[t1w].append(s.series_id)\\n if ('edti_2mm_cdif45_AP' in s.series_description):\\n info[dwi_ap].append(s.series_id)\\n if ('edti_2mm_cdif45_PA' in s.series_description):\\n info[dwi_pa].append(s.series_id)\\n if (s.series_description == 'Sag CUBE T2'):\\n info[t2w].append(s.series_id)\\n if (s.series_description == 'ORIG Sag CUBE T2'):\\n info[t2w_orig].append(s.series_id)\\n if ('T2_1.7mm_fat_sat' in s.series_description): \\n info[t2w_fatsat].append(s.series_id)\\n if (s.series_description == 'Reverse blip EPI 3mm iso'):\\n info[t2star].append(s.series_id) \\n if (s.series_description == 'Resting EPI 3mm iso RS') and (s.dim3 == 12300):\\n info[func_rest].append(s.series_id)\\n return info\",\n \"def process_key_items(html_soup):\\r\\n\\r\\n info_dict = {}\\r\\n all_tag_a = html_soup.find_all('a')\\r\\n\\r\\n numeric_info2 = all_tag_a[2].get('href').split('=')\\r\\n info_dict['id2'] = numeric_info2[4].split('&')[0]\\r\\n info_dict['ord2'] = numeric_info2[5]\\r\\n\\r\\n numeric_info3 = all_tag_a[3].get('href').split('=')\\r\\n info_dict['id3'] = numeric_info3[4].split('&')[0]\\r\\n info_dict['ord3'] = numeric_info3[5]\\r\\n\\r\\n numeric_info4 = all_tag_a[4].get('href').split('=')\\r\\n info_dict['id4'] = numeric_info4[4].split('&')[0]\\r\\n info_dict['ord4'] = numeric_info4[5]\\r\\n\\r\\n info_dict['id'] = all_tag_a[6].get('href').split('=')[4].split('&')[0]\\r\\n\\r\\n pagenum_afr = None\\r\\n pagenum_pre = None\\r\\n\\r\\n if len(all_tag_a) == 10:\\r\\n\\r\\n\\r\\n info_dict['multipage'] = False\\r\\n info_dict['pagenum'] = 1\\r\\n info_dict['pagenum_pre'] = pagenum_pre\\r\\n info_dict['pagenum_afr'] = pagenum_afr\\r\\n\\r\\n if len(all_tag_a) == 11:\\r\\n #print()\\r\\n\\r\\n #print(all_tag_a[7].text)\\r\\n #print(all_tag_a[7].get('href'))\\r\\n #print(all_tag_a[8].text)\\r\\n #print(all_tag_a[8].get('href'))\\r\\n\\r\\n if all_tag_a[7].text == 'صفحهی قبل':\\r\\n splitted = all_tag_a[7].get('href').split('=')\\r\\n if len(splitted) == 7:\\r\\n pagenum_pre = int(all_tag_a[7].get('href').split('=')[-1])\\r\\n else:\\r\\n pagenum_pre = 1\\r\\n\\r\\n pagenum = pagenum_pre + 1\\r\\n elif all_tag_a[7].text == 'صفحهی بعد':\\r\\n splitted = all_tag_a[7].get('href').split('=')\\r\\n if len(splitted) == 7:\\r\\n pagenum_afr = int(all_tag_a[7].get('href').split('=')[-1])\\r\\n else:\\r\\n pagenum_afr = None\\r\\n\\r\\n pagenum = pagenum_afr - 1\\r\\n else:\\r\\n pagenum = None\\r\\n\\r\\n info_dict['multipage'] = True\\r\\n info_dict['pagenum'] = pagenum\\r\\n info_dict['pagenum_pre'] = pagenum_afr\\r\\n info_dict['pagenum_afr'] = pagenum_pre\\r\\n\\r\\n if len(all_tag_a) == 12:\\r\\n\\r\\n pagenum_pre = None\\r\\n pagenum = None\\r\\n pagenum_afr = None\\r\\n\\r\\n\\r\\n\\r\\n if all_tag_a[7].text == 'صفحهی قبل' :\\r\\n if len(all_tag_a[7].get('href').split('=')) > 5:\\r\\n pagenum_pre = int(all_tag_a[7].get('href').split('=')[-1])\\r\\n else:\\r\\n pagenum_pre = 1\\r\\n\\r\\n pagenum = pagenum_pre + 1\\r\\n if all_tag_a[8].text == 'صفحهی بعد' :\\r\\n\\r\\n if len(all_tag_a[8].get('href').split('=')) > 5:\\r\\n pagenum_afr = int(all_tag_a[8].get('href').split('=')[-1])\\r\\n pagenum = pagenum_afr - 1\\r\\n else:\\r\\n pagenum_afr = None\\r\\n pagenum = pagenum_pre + 1\\r\\n\\r\\n info_dict['multipage'] = True\\r\\n info_dict['pagenum_pre'] = pagenum_pre\\r\\n info_dict['pagenum_afr'] = pagenum_afr\\r\\n info_dict['pagenum'] = pagenum\\r\\n\\r\\n catgo = all_tag_a[1].text\\r\\n shaer = all_tag_a[2].text\\r\\n ketab = all_tag_a[3].text\\r\\n bakhsh= all_tag_a[4].text\\r\\n title = all_tag_a[5].text\\r\\n\\r\\n info_dict['title'] = title\\r\\n info_dict['bakhsh'] = bakhsh\\r\\n info_dict['ketab'] = ketab\\r\\n info_dict['shaer'] = shaer\\r\\n info_dict['catgo'] = catgo\\r\\n return info_dict\",\n \"def extract_data(department_link):\\n # Update the progress bar\\n if show_progress:\\n progress_bar.update()\\n\\n # Regular expressions for searching course descriptions stored in local variables\\n # for better peformance\\n local_course_re = course_re\\n local_course_name_re = course_name_re\\n local_credits_re = credits_re\\n local_credits_num_re = credits_num_re\\n local_offered_jointly_re = offered_jointly_re\\n local_CAMPUSES = CAMPUSES\\n\\n # Method used in extracting data from course descriptions found in the local scope\\n # are stored in local variables for better performance\\n local_complete_description = complete_description\\n local_get_offered = get_offered\\n local_get_requisites = get_requisites\\n\\n # All the courses in the department\\n courses = []\\n dep_file = department_link.get('href')\\n\\n # If the user entered a dict as the 'campuse' parameter, departments\\n # are checked here\\n try:\\n # The String in the conditional is the abbreviated Department Name i.e EE\\n # for Electrical Engineering\\n if normalize('NFKD', department_link.text).rsplit('(', 1) \\\\\\n [-1].replace(' ', '')[:-1] not in campuses[campus]:\\n return None\\n except TypeError:\\n pass\\n \\n # The only links that are used for finding departments are those\\n # of the format [a-z]+.html\\n if '/' not in dep_file and dep_file.endswith('.html') \\\\\\n and dep_file not in parsed_departments:\\n parsed_departments.add(dep_file)\\n department = BeautifulSoup(requests.get( \\\\\\n f'{local_CAMPUSES[campus]}{dep_file}').text, features='lxml')\\n for course in department.find_all('a'):\\n course_ID = course.get('name') \\n if course_ID:\\n course_ID = course_ID.upper()\\n course_title = course.find('b').text\\n # The Course Description\\n description = course.get_text().replace(course_title, '', 1) \\n instructors = course.find('i')\\n if instructors:\\n description = description.replace(str(instructors.get_text()), '', 1)\\n del instructors\\n course_text = local_complete_description( \\\\\\n description.rsplit('View course details in MyPlan', 1)[0])\\n # Course Number i.e 351\\n course_number = re.sub(local_course_re, '', course_ID)\\n match_name = re.search(local_course_name_re, course_title)\\n match_credit_num = re.search(local_credits_num_re, course_title)\\n match_credit_types = re.findall(local_credits_re, course_title)\\n # Jointly offered course with the given course\\n if 'jointly with' in course_text: \\n offered_jointly = course_text.rsplit('jointly with ', 1)[-1].rsplit(';', 1)[0] \\n offered_jointly = ','.join(re.findall( \\\\\\n local_offered_jointly_re, offered_jointly)).replace(' ', '') \\n else:\\n offered_jointly = ''\\n courses.append(\\n # Campus, Department Name and Course Number\\n [campus, course_ID[:-3], course_number, \\n # Course Name\\n match_name.group(0).split(course_number, 1)[-1].strip() \\\\\\n if match_name else '',\\n # Number of credits for the course\\n match_credit_num.group(0)[1:-1] \\\\\\n if match_credit_num else '', \\n # Course Credit Types (I&S, DIV, NW, VLPA, QSR, C)\\n ','.join([list(filter(('').__ne__, x))[0] for x in match_credit_types]) \\\\\\n if match_credit_types else '', \\n local_get_offered(course_text),\\n offered_jointly, local_get_requisites(course_text, 'Prerequisite:'), \\n local_get_requisites(course_text, 'Co-requisite'), course_text]\\n )\\n return courses\",\n \"def _get_information(self):\\n pros_cons = []\\n pros_cons_dict = {}\\n\\n for i, div in enumerate(self._tab.find_all(\\\"div\\\")):\\n for p in div.find_all(\\\"p\\\"):\\n pro_con = p.get_text(strip=True)\\n pros_cons.append(pro_con)\\n pros_cons_dict.update({self._keys_dict[i]: pros_cons})\\n pros_cons = []\\n\\n return pros_cons_dict\",\n \"def _parse_lesson(self, lesson_link: str):\\n\\n lesson_page: Soup = self._get_soup(lesson_link)\\n lesson_content: NavigableString = lesson_page.find(\\\"div\\\", \\n {\\\"class\\\": \\\"moduleContent\\\"})\\n course_divs: ResultSet = lesson_content.find_all(\\\"div\\\")\\n\\n data = {}\\n module = \\\"\\\"\\n for div in course_divs:\\n if div[\\\"class\\\"][0] == \\\"courseModule\\\":\\n module = div.p.get_text().split(\\\":\\\")[-1].strip()\\n data[module] = []\\n elif div[\\\"class\\\"][0] == \\\"courseLesson\\\":\\n lesson_title = div.find(\\\"span\\\", {\\\"class\\\": \\\"courseLessonTitle\\\"})\\n data[module].append(lesson_title.get_text().strip())\\n \\n return data\",\n \"def general_scraper(section_url):\\n\\n prefix = \\\"http://mesva.univaq.it\\\"\\n\\n request = []\\n news = []\\n\\n for i, url in enumerate(section_url):\\n request.append(requests.get(url))\\n news_division = BeautifulSoup(request[i].text, \\\"html.parser\\\").find(class_=\\\"view-content\\\")\\n\\n discab_news = news_division.find_all(\\\"div\\\", recursive=False)[0:5]\\n\\n for single_news in discab_news:\\n news.append({\\n 'description': '',\\n 'title': single_news.a.string,\\n 'link': prefix + single_news.a['href']\\n })\\n\\n return news\",\n \"def __init__(self, browser, course_org, course_num, course_run):\\r\\n super(CoursePage, self).__init__(browser)\\r\\n self.course_info = {\\r\\n 'course_org': course_org,\\r\\n 'course_num': course_num,\\r\\n 'course_run': course_run\\r\\n }\",\n \"def parse_sections(soup, report, baseUrl):\\n parse_sections = False # To parse section wise set it to True else full content is parsed\\n overview = False\\n config = False\\n usecase = False\\n overview_content = \\\"\\\"\\n config_content = \\\"\\\"\\n usecases_content = \\\"\\\"\\n isFullContent = False\\n full_content = \\\"\\\"\\n updateImgUrl(baseUrl, soup)\\n for e in soup.contents:\\n if not parse_sections:\\n if 'h1' == str(e.name).lower():\\n isFullContent = True\\n if isFullContent:\\n full_content += \\\"\\\\n\\\" + str(e)\\n else:\\n content_value = e.next\\n if content_value == 'Overview':\\n overview = True\\n if content_value == 'Configuration':\\n config = True\\n overview = False\\n if content_value == 'Use Cases':\\n usecase = True\\n config = False\\n if overview == True and config == False and usecase == False:\\n overview_content += \\\"\\\\n\\\" + str(e)\\n if overview == False and config == True and usecase == False:\\n config_content += \\\"\\\\n\\\" + str(e)\\n if overview == False and config == False and usecase == True:\\n usecases_content += \\\"\\\\n\\\" + str(e)\\n\\n if not parse_sections:\\n report[\\\"content\\\"] = convert_to_base64(full_content)\\n else:\\n if overview_content:\\n report[\\\"overview\\\"] = convert_to_base64(overview_content)\\n if config_content:\\n report[\\\"configuration\\\"] = convert_to_base64(config_content)\\n if usecases_content:\\n report[\\\"use_cases\\\"] = convert_to_base64(usecases_content)\",\n \"def build_course_map(course_content):\\n course_blocks = []\\n\\n for key, course_data in course_content.items():\\n block_data = {}\\n block_data['id'] = parse_id(key)\\n block_data['type'] = course_data['category']\\n\\n try:\\n block_data['name'] = course_data['metadata']['display_name']\\n except KeyError:\\n block_data['name'] = block_data['id']\\n\\n customize_by_type(course_data, block_data)\\n add_children(course_data, block_data)\\n\\n course_blocks.append(block_data)\\n\\n return course_blocks\",\n \"def infotodict(seqinfo):\\n\\n t1 = create_key('anat/sub-{subject}_run-{item:02d}_T1w')\\n rest_fmri_ap = create_key('func/sub-{subject}_dir-ap_task-rest_run-{item:02d}_bold')\\n rest_topup_ap = create_key('func/sub-{subject}_dir-ap_run-{item:02d}_bold')\\n rest_topup_pa = create_key('func/sub-{subject}_dir-pa_run-{item:02d}_bold')\\n fmap_rest_magnitude1 = create_key('fmap/sub-{subject}_run-{item:02d}_magnitude1')\\n fmap_rest_phasediff = create_key('fmap/sub-{subject}_run-{item:02d}_phasediff')\\n\\n # Create an empty dictionary called info for each key\\n\\n info = {t1: [],\\n rest_fmri_ap: [],\\n rest_topup_ap: [],\\n rest_topup_pa: [],\\n fmap_rest_magnitude1: [],\\n fmap_rest_phasediff: [],\\n }\\n\\n # Loop over each sequence. Use if statements to determine which sequences should be linked to which key\\n\\n for idx, s in enumerate(seqinfo):\\n\\n if (('MPRAGE_GRAPPA2' in s.series_id) and\\n ('tfl3d1_16ns' in s.sequence_name) and\\n (s.dim3 == 192) and\\n (s.dim4 == 1)):\\n info[t1] = [s.series_id]\\n\\n if (('BOLD_resting 4X4X4 A>>P' in s.series_id) and\\n ('epfid2d1_64' in s.sequence_name) and\\n (s.dim3 == 35) and\\n (s.dim4 == 190)):\\n info[rest_fmri_ap] = [s.series_id]\\n\\n if (('rest_topup_A>>P' in s.series_id) and\\n ('epse2d1_64' in s.sequence_name) and\\n (s.dim3 == 140) and\\n (s.dim4 == 1)):\\n info[rest_topup_ap] = [s.series_id]\\n\\n if (('rest_topup_P>>A' in s.series_id) and\\n ('epse2d1_64' in s.sequence_name) and\\n (s.dim3 == 140) and\\n (s.dim4 == 1)):\\n info[rest_topup_pa] = [s.series_id]\\n\\n if (('Field_mapping 4X4X4 A>>P' in s.series_id) and\\n ('fm2d2r' in s.sequence_name) and\\n (s.dim3 == 35) and\\n (s.dim4 == 1) and\\n (s.TE == 4.92)):\\n info[fmap_rest_magnitude1] = [s.series_id]\\n\\n if (('Field_mapping 4X4X4 A>>P' in s.series_id) and\\n ('fm2d2r' in s.sequence_name) and\\n (s.dim3 == 35) and\\n (s.dim4 == 1) and\\n (s.TE == 7.38)):\\n info[fmap_rest_phasediff] = [s.series_id]\\n\\n return info\",\n \"def scrape_all_course_names(filename, verbose):\\n \\n\\tsoup =\\tBeautifulSoup(open(filename, 'r'), 'html.parser')\\n\\t#print(soup)\\n\\tcourses = []\\n\\t# all the courses are stored in a div w/class=view-content\\n\\t\\t\\n\\th4_field_content = soup.find('h4', 'field-content')\\n\\t#print(h4_field_content)\\n\\tall_course_content = soup.find(\\\"div\\\", \\\"view-content\\\") # this contains ALL the classes...\\n\\tif all_course_content == None:\\n\\t\\tprint(\\\"There are no courses on this page. Try a smaller page number!\\\")\\n\\t\\treturn []\\n\\t\\n\\tcandidate_classes = all_course_content.find_all('a') # we want all the 'a' tags within\\n\\tclass_list=[]\\n course_code_and_number = {}\\n\\tfor c in candidate_classes:\\n\\t\\t#print(c.text, \\\"\\\\n\\\")\\n\\t\\ttext = c.text # ex. AEMA 611 Experimental Designs 1 (3 credits)\\n\\t\\ttext = text.split(\\\" \\\")# split on the space\\n\\t\\tcourse_id = \\\" \\\".join(text[:2]).replace('\\\\n', '') # the first 2 are the course id\\n\\t\\tcourse_name = \\\" \\\".join(text[2:-2]).replace('\\\\n', '')\\n\\t\\tnum_credits = text[-2].replace(\\\"(\\\", \\\"\\\")# just get the course number, replace the ( with nothing \\n\\t\\n\\t\\n\\t\\t#print(f\\\"{course_id}\\\\n{course_name}\\\\n{num_credits}\\\\n\\\")\\n\\t\\ttry:# Check that the course number is a digit bc sometimes it is something weird\\n\\t\\t\\tfloat(num_credits)\\n\\t\\texcept ValueError:\\n\\t\\t\\t#print(f\\\"Wrong course format. Ignoring {c}\\\")\\n\\t\\t\\tif(verbose):\\n\\t\\t\\t\\tprint(f\\\"Wrong course format. Ignoring course: {c.text}\\\")\\n\\t\\t\\tcontinue\\n\\t\\tclass_list.append(course_id)\\n\\treturn class_list\",\n \"def get_available_sections(url, headers):\\n def _make_url(section_soup): # FIXME: Extract from here and test\\n return BASE_URL + section_soup.ul.find('a')['href']\\n\\n def _get_section_name(section_soup): # FIXME: Extract from here and test\\n return section_soup.h3.a.string.strip()\\n\\n def _make_subsections(section_soup):\\n subsections_soup = section_soup.ul.find_all(\\\"li\\\")\\n # FIXME correct extraction of subsection.name (unicode)\\n subsections = [SubSection(position=i,\\n url=BASE_URL + s.a['href'],\\n name=s.p.string)\\n for i, s in enumerate(subsections_soup, 1)]\\n return subsections\\n\\n courseware = get_page_contents(url, headers)\\n soup = BeautifulSoup(courseware)\\n sections_soup = soup.find_all('div', attrs={'class': 'chapter'})\\n\\n sections = [Section(position=i,\\n name=_get_section_name(section_soup),\\n url=_make_url(section_soup),\\n subsections=_make_subsections(section_soup))\\n for i, section_soup in enumerate(sections_soup, 1)]\\n return sections\",\n \"def separateSource(self,compInfo):\\n sourceInfo = {}\\n source = []\\n for eachline in compInfo:\\n words = eachline.split() ##This line need to be confirmed with Manas\\n if eachline[0] in ['f', 'h']:\\n source.append(words[3])\\n if len(source) > 0:\\n for eachline in compInfo:\\n words_s = eachline.split()\\n if words_s[0] in source:\\n sourceInfo[words_s[0]] = words_s[1:3]\\n return sourceInfo\",\n \"def define_info_dict():\\n\\n d = {\\n \\\"PRED\\\": {\\n \\\"COLUMN\\\": [\\\"predicted_class\\\"],\\n \\\"Number\\\": \\\"1\\\",\\n \\\"Type\\\": \\\"String\\\",\\n \\\"Description\\\": \\\"Predicted class: somatic, germline, artifact\\\",\\n },\\n \\\"PROB\\\": {\\n \\\"COLUMN\\\": [\\\"prob_s\\\", \\\"prob_g\\\", \\\"prob_a\\\"],\\n \\\"Number\\\": \\\"3\\\",\\n \\\"Type\\\": \\\"Float\\\",\\n \\\"Description\\\": \\\"Prediction probability of \\\"\\n \\\"being somatic, germline, artifact in this order\\\",\\n },\\n \\\"SNP\\\": {\\n \\\"COLUMN\\\": [\\\"is_on_db\\\"],\\n \\\"Number\\\": \\\"0\\\",\\n \\\"Type\\\": \\\"Flag\\\",\\n \\\"Description\\\": \\\"Present on SNP database (modified dbSNP/gnomAD (default) or user-provided database)\\\",\\n },\\n \\\"ANNO\\\": {\\n \\\"COLUMN\\\": [\\\"annotation\\\"],\\n \\\"Number\\\": \\\".\\\",\\n \\\"Type\\\": \\\"String\\\",\\n \\\"Description\\\": \\\"Indel annotation formatted as \\\"\\n \\\"GeneSymbol|RefSeqAccession|CodonPos|IndelEffect\\\"\\n \\\"Delimited by comma for multiple isoforms\\\",\\n },\\n \\\"COSMIC_CNT\\\": {\\n \\\"COLUMN\\\": [\\\"cosmic_cnt\\\"],\\n \\\"Number\\\": \\\"1\\\",\\n \\\"Type\\\": \\\"Integer\\\",\\n \\\"Description\\\": \\\"COSMIC count in v89\\\",\\n },\\n \\\"MAXMAF\\\": {\\n \\\"COLUMN\\\": [\\\"max_maf\\\"],\\n \\\"Number\\\": \\\"1\\\",\\n \\\"Type\\\": \\\"Float\\\",\\n \\\"Description\\\": \\\"Maximum minor allele frequency (MAF) \\\"\\n \\\"reported in dbSNP, ClinVar and gnomAD non-cancer population\\\",\\n },\\n \\\"COMMON\\\": {\\n \\\"COLUMN\\\": [\\\"is_common\\\"],\\n \\\"Number\\\": \\\"0\\\",\\n \\\"Type\\\": \\\"Flag\\\",\\n \\\"Description\\\": \\\"Common in dbSNP or MAXMAF > 0.01\\\",\\n },\\n \\\"CLIN\\\": {\\n \\\"COLUMN\\\": [\\\"clin_info\\\"],\\n \\\"Number\\\": \\\"1\\\",\\n \\\"Type\\\": \\\"String\\\",\\n \\\"Description\\\": \\\"ClinVar annotation formatted as ClinicalSignificance|Condition\\\",\\n },\\n \\\"ICP\\\": {\\n \\\"COLUMN\\\": [\\\"indel_complexity\\\"],\\n \\\"Number\\\": \\\"1\\\",\\n \\\"Type\\\": \\\"Integer\\\",\\n \\\"Description\\\": \\\"Indel complexity: mismatches around the indel measured by edit distance\\\",\\n },\\n \\\"DSM\\\": {\\n \\\"COLUMN\\\": [\\\"dissimilarity\\\"],\\n \\\"Number\\\": \\\"1\\\",\\n \\\"Type\\\": \\\"Float\\\",\\n \\\"Description\\\": \\\"Dissimilarity: edit distance between indel and flanking sequences\\\",\\n },\\n \\\"ISZ\\\": {\\n \\\"COLUMN\\\": [\\\"indel_size\\\"],\\n \\\"Number\\\": \\\"1\\\",\\n \\\"Type\\\": \\\"Integer\\\",\\n \\\"Description\\\": \\\"Indel size\\\",\\n },\\n \\\"REP\\\": {\\n \\\"COLUMN\\\": [\\\"repeat\\\"],\\n \\\"Number\\\": \\\"1\\\",\\n \\\"Type\\\": \\\"Integer\\\",\\n \\\"Description\\\": \\\"Repeat: count of the indel-sequence repeats in flanking region\\\",\\n },\\n \\\"UQM\\\": {\\n \\\"COLUMN\\\": [\\\"is_uniq_mapped\\\"],\\n \\\"Number\\\": \\\"0\\\",\\n \\\"Type\\\": \\\"Flag\\\",\\n \\\"Description\\\": \\\"Supported by uniquely mapped reads\\\",\\n },\\n \\\"NEB\\\": {\\n \\\"COLUMN\\\": [\\\"is_near_boundary\\\"],\\n \\\"Number\\\": \\\"0\\\",\\n \\\"Type\\\": \\\"Flag\\\",\\n \\\"Description\\\": \\\"Near exon boundary\\\",\\n },\\n \\\"EQX\\\": {\\n \\\"COLUMN\\\": [\\\"equivalence_exists\\\"],\\n \\\"Number\\\": \\\"0\\\",\\n \\\"Type\\\": \\\"Flag\\\",\\n \\\"Description\\\": \\\"Equivalent alignments exist for the indel\\\",\\n },\\n \\\"BID\\\": {\\n \\\"COLUMN\\\": [\\\"is_bidirectional\\\"],\\n \\\"Number\\\": \\\"0\\\",\\n \\\"Type\\\": \\\"Flag\\\",\\n \\\"Description\\\": \\\"Supported by forward and reverse reads\\\",\\n },\\n \\\"MTA\\\": {\\n \\\"COLUMN\\\": [\\\"is_multiallelic\\\"],\\n \\\"Number\\\": \\\"0\\\",\\n \\\"Type\\\": \\\"Flag\\\",\\n \\\"Description\\\": \\\"Multialleleic\\\",\\n },\\n \\\"FRM\\\": {\\n \\\"COLUMN\\\": [\\\"is_inframe\\\"],\\n \\\"Number\\\": \\\"0\\\",\\n \\\"Type\\\": \\\"Flag\\\",\\n \\\"Description\\\": \\\"In-frame indel\\\",\\n },\\n \\\"SPL\\\": {\\n \\\"COLUMN\\\": [\\\"is_splice\\\"],\\n \\\"Number\\\": \\\"0\\\",\\n \\\"Type\\\": \\\"Flag\\\",\\n \\\"Description\\\": \\\"Located in splice region\\\",\\n },\\n \\\"TRN\\\": {\\n \\\"COLUMN\\\": [\\\"is_truncating\\\"],\\n \\\"Number\\\": \\\"0\\\",\\n \\\"Type\\\": \\\"Flag\\\",\\n \\\"Description\\\": \\\"Truncating indel\\\",\\n },\\n \\\"CDD\\\": {\\n \\\"COLUMN\\\": [\\\"is_in_cdd\\\"],\\n \\\"Number\\\": \\\"0\\\",\\n \\\"Type\\\": \\\"Flag\\\",\\n \\\"Description\\\": \\\"Located in conserved domain\\\",\\n },\\n \\\"LOC\\\": {\\n \\\"COLUMN\\\": [\\\"indel_location\\\"],\\n \\\"Number\\\": \\\"1\\\",\\n \\\"Type\\\": \\\"Float\\\",\\n \\\"Description\\\": \\\"Relative indel location within the transcript coding region\\\",\\n },\\n \\\"NMD\\\": {\\n \\\"COLUMN\\\": [\\\"is_nmd_insensitive\\\"],\\n \\\"Number\\\": \\\"0\\\",\\n \\\"Type\\\": \\\"Flag\\\",\\n \\\"Description\\\": \\\"Insensitive to nonsense mediated decay\\\",\\n },\\n \\\"IPG\\\": {\\n \\\"COLUMN\\\": [\\\"ipg\\\"],\\n \\\"Number\\\": \\\"1\\\",\\n \\\"Type\\\": \\\"Float\\\",\\n \\\"Description\\\": \\\"Indels per gene\\\",\\n },\\n \\\"LEN\\\": {\\n \\\"COLUMN\\\": [\\\"cds_length\\\"],\\n \\\"Number\\\": \\\"1\\\",\\n \\\"Type\\\": \\\"Float\\\",\\n \\\"Description\\\": \\\"Coding sequence length. Median value if multiple isoforms exist\\\",\\n },\\n \\\"LC\\\": {\\n \\\"COLUMN\\\": [\\\"lc\\\"],\\n \\\"Number\\\": \\\"1\\\",\\n \\\"Type\\\": \\\"Float\\\",\\n \\\"Description\\\": \\\"Linguistic complexity: diversity of k-mers in flanking 50-bp region\\\",\\n },\\n \\\"LLC\\\": {\\n \\\"COLUMN\\\": [\\\"local_lc\\\"],\\n \\\"Number\\\": \\\"1\\\",\\n \\\"Type\\\": \\\"Float\\\",\\n \\\"Description\\\": \\\"Local linguistic complexity: diversity of k-mers in flanking 6-bp region\\\",\\n },\\n \\\"GC\\\": {\\n \\\"COLUMN\\\": [\\\"gc\\\"],\\n \\\"Number\\\": \\\"1\\\",\\n \\\"Type\\\": \\\"Float\\\",\\n \\\"Description\\\": \\\"GC-content in flanking 50-bp region\\\",\\n },\\n \\\"LGC\\\": {\\n \\\"COLUMN\\\": [\\\"local_gc\\\"],\\n \\\"Number\\\": \\\"1\\\",\\n \\\"Type\\\": \\\"Float\\\",\\n \\\"Description\\\": \\\"Local GC-content in flanking 6-bp region\\\",\\n },\\n \\\"SG\\\": {\\n \\\"COLUMN\\\": [\\\"strength\\\"],\\n \\\"Number\\\": \\\"1\\\",\\n \\\"Type\\\": \\\"Float\\\",\\n \\\"Description\\\": \\\"DNA bond strength of 2-mers in flanking 50-bp region\\\",\\n },\\n \\\"LSG\\\": {\\n \\\"COLUMN\\\": [\\\"local_strength\\\"],\\n \\\"Number\\\": \\\"1\\\",\\n \\\"Type\\\": \\\"Float\\\",\\n \\\"Description\\\": \\\"Local DNA bond strength of 2-mers in flanking 6-bp region\\\",\\n },\\n \\\"INS\\\": {\\n \\\"COLUMN\\\": [\\\"is_ins\\\"],\\n \\\"Number\\\": \\\"0\\\",\\n \\\"Type\\\": \\\"Flag\\\",\\n \\\"Description\\\": \\\"Insertion\\\",\\n },\\n \\\"ATI\\\": {\\n \\\"COLUMN\\\": [\\\"is_at_ins\\\"],\\n \\\"Number\\\": \\\"0\\\",\\n \\\"Type\\\": \\\"Flag\\\",\\n \\\"Description\\\": \\\"Single insertion of A or T\\\",\\n },\\n \\\"ATD\\\": {\\n \\\"COLUMN\\\": [\\\"is_at_del\\\"],\\n \\\"Number\\\": \\\"0\\\",\\n \\\"Type\\\": \\\"Flag\\\",\\n \\\"Description\\\": \\\"Single deletion of A or T\\\",\\n },\\n \\\"GCI\\\": {\\n \\\"COLUMN\\\": [\\\"is_gc_ins\\\"],\\n \\\"Number\\\": \\\"0\\\",\\n \\\"Type\\\": \\\"Flag\\\",\\n \\\"Description\\\": \\\"Single insertion of G or C\\\",\\n },\\n \\\"GCD\\\": {\\n \\\"COLUMN\\\": [\\\"is_gc_del\\\"],\\n \\\"Number\\\": \\\"0\\\",\\n \\\"Type\\\": \\\"Flag\\\",\\n \\\"Description\\\": \\\"Single deletion of G or C\\\",\\n },\\n \\\"ALTC\\\": {\\n \\\"COLUMN\\\": [\\\"alt_count\\\"],\\n \\\"Number\\\": \\\"1\\\",\\n \\\"Type\\\": \\\"Integer\\\",\\n \\\"Description\\\": \\\"Alt count: count of unique reads supporting ALT allele\\\",\\n },\\n \\\"REFC\\\": {\\n \\\"COLUMN\\\": [\\\"ref_count\\\"],\\n \\\"Number\\\": \\\"1\\\",\\n \\\"Type\\\": \\\"Integer\\\",\\n \\\"Description\\\": \\\"Ref count: count of unique reads supporting REF allele\\\",\\n },\\n \\\"RCF\\\": {\\n \\\"COLUMN\\\": [\\\"reclassified\\\"],\\n \\\"Number\\\": \\\"0\\\",\\n \\\"Type\\\": \\\"Flag\\\",\\n \\\"Description\\\": \\\"Reclassification applied\\\",\\n },\\n \\\"RQB\\\": {\\n \\\"COLUMN\\\": [\\\"filtered\\\", \\\"rescued\\\"],\\n \\\"Number\\\": \\\"1\\\",\\n \\\"Type\\\": \\\"String\\\",\\n \\\"Description\\\": \\\"Indel used to rescue this entry formatted as CHROM:POS:REF:ALT\\\",\\n },\\n }\\n\\n return d\",\n \"def createDictionnary():\\n dic = {}\\n root = \\\"https://www.airlinequality.com\\\"\\n url_page = root+\\\"/review-pages/a-z-airport-reviews/\\\"\\n\\n headers = {\\n 'User-Agent': 'Mozilla/5.0 (Windows NT 6.1) AppleWebKit/537.36 (KHTML, like Gecko) Chrome/41.0.2228.0 Safari/537.3'}\\n req = Request(url_page, headers=headers)\\n webpage = urlopen(req).read()\\n soup = bs(webpage, 'html.parser')\\n\\n r = soup.find_all('li')\\n list_text = [str(val) for val in r if \\\"href=\\\\\\\"/airport-reviews/\\\" in str(val)\\n and \\\"article\\\" not in str(val)]\\n for texte in list_text:\\n airport, link = getAirportLinks(texte)\\n dic[airport.rstrip()] = root+link\\n\\n return dic\",\n \"def get_chapter_text(location):\\n chapter_output_dictionary = {}\\n chapter_contents_list = []\\n\\n soup = Ripper(location, parser=\\\"html5lib\\\", save_path=save_path).soup\\n text = soup.find(\\\"table\\\", class_=\\\"texttable\\\")\\n\\n for each in text.find_all(\\\"p\\\"):\\n attributes = each.attrs\\n if attributes:\\n if \\\"desc\\\" in attributes[\\\"class\\\"]:\\n pass\\n elif \\\"note\\\" in attributes[\\\"class\\\"]:\\n pass\\n else:\\n new_cont = each.contents\\n chapter_contents_list.extend(new_cont)\\n return join_chapter_text(chapter_contents_list)\",\n \"def _create_sections(self):\\n self._SECTIONS = {}\",\n \"def parse_details(ep_news):\\n\\n # instances that might be able to get from ep_news\\n information = {\\n 'price': None, # 价格：\\n 'area': None, # 地区：*\\n 'address': None, # 地址：\\n 'contact': None, # 联系人：\\n 'telephone': None, # 电话：*\\n 'wechat': None, # 微信：\\n 'qq': None, # QQ：\\n }\\n\\n # price and location\\n left = ep_news.find('div', {'class': 'l'})\\n\\n # loop through all left information\\n for info in left.find_all('dl'):\\n # get info name and value\\n name, value = info.find('dt').get_text().encode('utf-8'), info.find('dd').contents[0].encode('utf-8')\\n\\n # match values\\n if name == u'价格：'.encode('utf-8'):\\n information['price'] = value if value != '面议' else None\\n elif name == u'地区：'.encode('utf-8'):\\n information['area'] = value\\n elif name == u'地址：'.encode('utf-8'):\\n information['address'] = value\\n\\n # contact information\\n right = ep_news.find('div', {'class': 'r'})\\n\\n # loop through all right information\\n for info in right.find_all('dl'):\\n # get info name and value\\n name, value = info.find('dt').get_text().encode('utf-8'), info.find('dd').contents[0].encode('utf-8')\\n\\n # match values\\n if name == u'联系人：'.encode('utf-8'):\\n information['contact'] = value\\n elif name == u'电话：'.encode('utf-8'):\\n information['telephone'] = value\\n elif name == u'微信：'.encode('utf-8'):\\n information['wechat'] = value\\n elif name == u'QQ：'.encode('utf-8'):\\n information['qq'] = value\\n\\n return information\",\n \"def get_general(soup):\\n \\n general_info = {}\\n general_info.update(get_route_name(soup))\\n general_info.update(get_box_data(soup))\\n general_info.update(get_description(soup))\\n general_info.update(get_hierarchy(soup))\\n general_info.update(get_first_img_source(soup))\\n\\n return general_info\",\n \"def _populate(self):\\n\\n # Assume the first word is what we want, and we can find well formed years\\n # This sucks, but will work for these ones.\\n # Roll on bibtex for citations in the CIM.\\n\\n citation_detail = self.doc.citation_detail\\n author = citation_detail.split(',')[0]\\n match = '([^\\\\w])19|20\\\\d\\\\d([^\\\\w])*?'\\n m = re.search(match, citation_detail)\\n if m:\\n year = m.group(0)\\n else:\\n year = None\\n\\n # one error in existing es-doc content to be fixed:\\n if 'van Vuuren DP' in author:\\n author = 'van Vuuren'\\n print 'applying vv fix'\\n\\n self.year = int(year)\\n\\n # We assume that this table will have entries which ne\\n\\n # I use the first three letters of a an authors name, and for\\n # three or more authors, EA, and then the year for my bibtex citation string\\n self.citeguess = author[0:3] + 'EA' + year[2:]\\n # This is what will appear in the table:\\n self.citestring = '%s et al. (%s)' % (author, year)\\n # Keep this for a reference list for checking against the eventual bibtex reference list.\\n self.text = citation_detail\",\n \"def get_structure(self):\\n main = {}\\n for line in self.load():\\n match = re.match('^\\\\s*([A-Za-z0-9_]+)(\\\\((\\\\d+)\\\\))?=(.*)$', line)\\n if match:\\n key = match.group(1)\\n index = match.group(3)\\n value = match.group(4)\\n if index is None:\\n main[key] = self.parse_data_value(value)\\n else:\\n if key not in main:\\n main[key] = []\\n main[key].append(self.parse_data_value(value))\\n #else:\\n # print(line)\\n return main\",\n \"def _course_info_content(html_parsed):\\n if len(html_parsed) == 1:\\n # could enforce that update[0].tag == 'h2'\\n content = html_parsed[0].tail\\n else:\\n content = html_parsed[0].tail if html_parsed[0].tail is not None else \\\"\\\"\\n content += \\\"\\\\n\\\".join([html.tostring(ele).decode('utf-8') for ele in html_parsed[1:]])\\n return content\",\n \"def get_commentary_for_chapter(location):\\n chapter_commentary_dictionary = {}\\n\\n soup = Ripper(location, parser=\\\"html5lib\\\", save_path=save_path).soup\\n text = soup.find(\\\"table\\\", class_=\\\"texttable\\\")\\n\\n for each in text.find_all(\\\"p\\\"):\\n attributes = each.attrs\\n if attributes:\\n if \\\"desc\\\" in attributes[\\\"class\\\"]:\\n pass\\n elif \\\"note\\\" in attributes[\\\"class\\\"]:\\n new_content = each.contents\\n verse_header, text = export_commentary_text_as_dictionary(new_content)\\n chapter_commentary_dictionary[verse_header] = text\\n else:\\n continue\\n return chapter_commentary_dictionary\",\n \"def parse_campus(department_data, campus):\\n\\n def extract_data(department_link):\\n \\\"\\\"\\\"\\n Extracts all course information from a UW Department\\n\\n @params:\\n\\n 'department_link': The url to the UW Department to get course\\n information from\\n\\n Returns\\n\\n A list of lists. Each nested list represents one course section with the\\n following values (in this order):\\n\\n 'Campus', 'Department Name', 'Course Number', 'Course Name', 'Credits',\\n 'Areas of Knowledge', 'Quarters Offered', 'Offered with', \\n 'Prerequisites', 'Co-Requisites', 'Description'\\n \\\"\\\"\\\"\\n # Update the progress bar\\n if show_progress:\\n progress_bar.update()\\n\\n # Regular expressions for searching course descriptions stored in local variables\\n # for better peformance\\n local_course_re = course_re\\n local_course_name_re = course_name_re\\n local_credits_re = credits_re\\n local_credits_num_re = credits_num_re\\n local_offered_jointly_re = offered_jointly_re\\n local_CAMPUSES = CAMPUSES\\n\\n # Method used in extracting data from course descriptions found in the local scope\\n # are stored in local variables for better performance\\n local_complete_description = complete_description\\n local_get_offered = get_offered\\n local_get_requisites = get_requisites\\n\\n # All the courses in the department\\n courses = []\\n dep_file = department_link.get('href')\\n\\n # If the user entered a dict as the 'campuse' parameter, departments\\n # are checked here\\n try:\\n # The String in the conditional is the abbreviated Department Name i.e EE\\n # for Electrical Engineering\\n if normalize('NFKD', department_link.text).rsplit('(', 1) \\\\\\n [-1].replace(' ', '')[:-1] not in campuses[campus]:\\n return None\\n except TypeError:\\n pass\\n \\n # The only links that are used for finding departments are those\\n # of the format [a-z]+.html\\n if '/' not in dep_file and dep_file.endswith('.html') \\\\\\n and dep_file not in parsed_departments:\\n parsed_departments.add(dep_file)\\n department = BeautifulSoup(requests.get( \\\\\\n f'{local_CAMPUSES[campus]}{dep_file}').text, features='lxml')\\n for course in department.find_all('a'):\\n course_ID = course.get('name') \\n if course_ID:\\n course_ID = course_ID.upper()\\n course_title = course.find('b').text\\n # The Course Description\\n description = course.get_text().replace(course_title, '', 1) \\n instructors = course.find('i')\\n if instructors:\\n description = description.replace(str(instructors.get_text()), '', 1)\\n del instructors\\n course_text = local_complete_description( \\\\\\n description.rsplit('View course details in MyPlan', 1)[0])\\n # Course Number i.e 351\\n course_number = re.sub(local_course_re, '', course_ID)\\n match_name = re.search(local_course_name_re, course_title)\\n match_credit_num = re.search(local_credits_num_re, course_title)\\n match_credit_types = re.findall(local_credits_re, course_title)\\n # Jointly offered course with the given course\\n if 'jointly with' in course_text: \\n offered_jointly = course_text.rsplit('jointly with ', 1)[-1].rsplit(';', 1)[0] \\n offered_jointly = ','.join(re.findall( \\\\\\n local_offered_jointly_re, offered_jointly)).replace(' ', '') \\n else:\\n offered_jointly = ''\\n courses.append(\\n # Campus, Department Name and Course Number\\n [campus, course_ID[:-3], course_number, \\n # Course Name\\n match_name.group(0).split(course_number, 1)[-1].strip() \\\\\\n if match_name else '',\\n # Number of credits for the course\\n match_credit_num.group(0)[1:-1] \\\\\\n if match_credit_num else '', \\n # Course Credit Types (I&S, DIV, NW, VLPA, QSR, C)\\n ','.join([list(filter(('').__ne__, x))[0] for x in match_credit_types]) \\\\\\n if match_credit_types else '', \\n local_get_offered(course_text),\\n offered_jointly, local_get_requisites(course_text, 'Prerequisite:'), \\n local_get_requisites(course_text, 'Co-requisite'), course_text]\\n )\\n return courses\\n\\n # In the course catalog website, several department links appear multiple times\\n # To prevent parsing the same department more than once, parsed departments\\n # are tracked in 'parsed_departments'\\n parsed_departments = set()\\n local_extract_data = extract_data\\n department_data = BeautifulSoup(requests.get(department_data).text, features='lxml')\\n\\n campus_catalog = []\\n # Extract data from department websites in parallel to reduce idle time\\n with cf.ThreadPoolExecutor() as executor:\\n results = [executor.submit(local_extract_data, department_link) \\n for department_link in department_data.find_all('a')]\\n for result in cf.as_completed(results):\\n dptmnt = result.result()\\n if dptmnt:\\n campus_catalog.append(dptmnt)\\n\\n # DataFrame with all courses in the campus\\n return pd.DataFrame(\\n [course for department in campus_catalog for course in department], \\n columns=COLUMN_NAMES\\n )\",\n \"def test_course_info(self):\\r\\n # Regex of first 3 columns of course information table row for\\r\\n # test course loaded from git. Would not have sha1 if\\r\\n # git_info_for_course failed.\\r\\n table_re = re.compile(r\\\"\\\"\\\"\\r\\n <tr>\\\\s+\\r\\n <td>edX\\\\sAuthor\\\\sCourse</td>\\\\s+ # expected test git course name\\r\\n <td>MITx/edx4edx/edx4edx</td>\\\\s+ # expected test git course_id\\r\\n <td>[a-fA-F\\\\d]{40}</td> # git sha1 hash\\r\\n \\\"\\\"\\\", re.VERBOSE)\\r\\n\\r\\n self._setstaff_login()\\r\\n self._mkdir(getattr(settings, 'GIT_REPO_DIR'))\\r\\n\\r\\n # Make sure we don't have any git hashes on the page\\r\\n response = self.client.get(reverse('sysadmin_courses'))\\r\\n self.assertNotRegexpMatches(response.content, table_re)\\r\\n\\r\\n # Now add the course and make sure it does match\\r\\n response = self._add_edx4edx()\\r\\n self.assertRegexpMatches(response.content, table_re)\",\n \"def extractInfo(article):\\n headline = article['headline']['main']\\n url = article['web_url']\\n keywords = article['keywords']\\n locations = []\\n for k in keywords:\\n if k['name'] == \\\"glocations\\\":\\n locations += [k['value']]\\n return {'headline':headline, 'url':url, 'locations':locations}\",\n \"def _extract_page_info(article: dict, url: str) -> dict:\\n\\n if not article:\\n return {}\\n language = detect(article.get('content_text'))\\n if len(language) > 2 and len(language[2]) > 1:\\n language_code = language[2][0][1]\\n else:\\n language_code = None\\n return {'url': url, 'language': language_code}\",\n \"def get_semester_course_data(self, url, semester):\\n print(f\\\"Obtaining and indexing information for {semester}\\\")\\n html = self.call_url_and_get_html_object(url)\\n tables = html.findall(\\\".//table[@class='courseListing basicTable courseListingSetWidths']\\\")\\n\\n # Parse html to get course offering data\\n for table in tables:\\n fields = table.findall(\\\".//td\\\")\\n spans = table.findall(\\\".//span\\\")\\n course_number = str(spans[1].text.strip())\\n title = str(fields[4].text).strip()\\n professor = str(fields[6].text).strip()\\n status = str(fields[0].text)\\n crn = str(fields[1].text)\\n\\n # Add course offering data to dictionary of course classes\\n if course_number not in self.course_dict.keys():\\n # If course doesn't already exist in dictionary keys, instantiate class of it\\n self.course_dict[course_number] = Course(\\n title=title, semester=semester,\\n professor=professor, crn=crn, status=status)\\n else:\\n self.course_dict[course_number].add_instance_of_course(\\n semester, professor, crn, status)\",\n \"def _course_info_content(html_parsed):\\r\\n if len(html_parsed) == 1:\\r\\n # could enforce that update[0].tag == 'h2'\\r\\n content = html_parsed[0].tail\\r\\n else:\\r\\n content = html_parsed[0].tail if html_parsed[0].tail is not None else \\\"\\\"\\r\\n content += \\\"\\\\n\\\".join([html.tostring(ele) for ele in html_parsed[1:]])\\r\\n return content\",\n \"def fetch(cls, course_key):\\r\\n descriptor = modulestore('direct').get_course(course_key)\\r\\n course_details = cls(course_key.org, course_key.course, course_key.run)\\r\\n\\r\\n course_details.start_date = descriptor.start\\r\\n course_details.end_date = descriptor.end\\r\\n course_details.enrollment_start = descriptor.enrollment_start\\r\\n course_details.enrollment_end = descriptor.enrollment_end\\r\\n course_details.course_image_name = descriptor.course_image\\r\\n course_details.course_image_asset_path = course_image_url(descriptor)\\r\\n\\r\\n temploc = course_key.make_usage_key('about', 'syllabus')\\r\\n try:\\r\\n course_details.syllabus = get_modulestore(temploc).get_item(temploc).data\\r\\n except ItemNotFoundError:\\r\\n pass\\r\\n\\r\\n temploc = course_key.make_usage_key('about', 'short_description')\\r\\n try:\\r\\n course_details.short_description = get_modulestore(temploc).get_item(temploc).data\\r\\n except ItemNotFoundError:\\r\\n pass\\r\\n\\r\\n temploc = course_key.make_usage_key('about', 'overview')\\r\\n try:\\r\\n course_details.overview = get_modulestore(temploc).get_item(temploc).data\\r\\n except ItemNotFoundError:\\r\\n pass\\r\\n\\r\\n temploc = course_key.make_usage_key('about', 'effort')\\r\\n try:\\r\\n course_details.effort = get_modulestore(temploc).get_item(temploc).data\\r\\n except ItemNotFoundError:\\r\\n pass\\r\\n\\r\\n temploc = course_key.make_usage_key('about', 'video')\\r\\n try:\\r\\n raw_video = get_modulestore(temploc).get_item(temploc).data\\r\\n course_details.intro_video = CourseDetails.parse_video_tag(raw_video)\\r\\n except ItemNotFoundError:\\r\\n pass\\r\\n\\r\\n return course_details\",\n \"def _section_extensions(course):\\r\\n section_data = {\\r\\n 'section_key': 'extensions',\\r\\n 'section_display_name': _('Extensions'),\\r\\n 'units_with_due_dates': [(title_or_url(unit), unit.location.to_deprecated_string())\\r\\n for unit in get_units_with_due_date(course)],\\r\\n 'change_due_date_url': reverse('change_due_date', kwargs={'course_id': course.id.to_deprecated_string()}),\\r\\n 'reset_due_date_url': reverse('reset_due_date', kwargs={'course_id': course.id.to_deprecated_string()}),\\r\\n 'show_unit_extensions_url': reverse('show_unit_extensions', kwargs={'course_id': course.id.to_deprecated_string()}),\\r\\n 'show_student_extensions_url': reverse('show_student_extensions', kwargs={'course_id': course.id.to_deprecated_string()}),\\r\\n }\\r\\n return section_data\",\n \"def split_full_text(self, full_text, headers_list):\\n\\n sectioned_text = {}\\n indices = {}\\n no_abstr = False\\n\\n for i, hd in enumerate(headers_list):\\n #need to replace special regex characters before matching substrings\\n if '(' in hd:\\n hd = hd.replace('(', '\\\\(')\\n\\n if ')' in hd:\\n hd = hd.replace(')', '\\\\)')\\n\\n if '[' in hd:\\n hd = hd.replace('[', '\\\\[')\\n\\n if ']' in hd:\\n hd = hd.replace(']', '\\\\]')\\n\\n if '{' in hd:\\n hd = hd.replace('{', '\\\\{')\\n\\n if '}' in hd:\\n hd = hd.replace('}', '\\\\}')\\n\\n if '+' in hd:\\n hd = hd.replace('+', '\\\\+')\\n\\n if '*' in hd:\\n hd = hd.replace('*', '\\\\*')\\n\\n if ':' in hd:\\n hd = hd.replace(':', '\\\\:')\\n\\n if i == 0: # meta-data has no substring-matching to do\\n\\n inds = [m.start() for m in re.finditer(hd, full_text)]\\n #Abstract can appear in text, but isn't listed w/ headers\\n #Only use first instance\\n if len(inds) > 0:\\n indices[hd] = inds[0]\\n\\n else: #if there is no abstract, use figures to remove meta-data\\n fig_text = [m.start() for m in re.finditer('Figure', full_text)]\\n indices[hd] = fig_text[0]\\n no_abstr = True\\n\\n else:\\n inds = [m.start() for m in re.finditer(hd, full_text)]\\n #assume final instance of substring match corresponds\\n #to the correct header text instance\\n indices[hd] = inds[-1]\\n\\n\\n for i, hd in enumerate(headers_list):\\n\\n if i == 0:\\n if no_abstr == True:\\n\\n #get meta-data, which has no keyword matching\\n sectioned_text['Section Headers'] = headers_list\\n end_ind = indices[' Abstract ']\\n sectioned_text['Meta-data'] = full_text[:end_ind]\\n\\n #indicate there is no abstract\\n start_id = indices[' Abstract ']\\n end_id = indices[list(indices.keys())[1]]\\n sectioned_text[' Abstract '] = ''\\n\\n\\n if no_abstr == False:\\n #get meta-data, which has no keyword matching\\n sectioned_text['Section Headers'] = headers_list\\n end_ind = indices[' Abstract ']\\n sectioned_text['Meta-data'] = full_text[:end_ind]\\n\\n #get abstract\\n start_id = indices[' Abstract ']\\n end_id = indices[list(indices.keys())[1]]\\n sectioned_text[hd] = full_text[start_id : end_id]\\n\\n if i > 0 and i < len(headers_list)-1: #all setions but final section\\n if i == 1:\\n if no_abstr == True:\\n start_id = indices[' Abstract ']\\n end_id = indices[list(indices.keys())[i+1]]\\n sectioned_text[hd] = full_text[start_id:end_id]\\n\\n else:\\n start_id = indices[list(indices.keys())[i]]\\n end_id = indices[list(indices.keys())[i+1]]\\n sectioned_text[hd] = full_text[start_id:end_id]\\n\\n else:\\n start_id = indices[list(indices.keys())[i]]\\n end_id = indices[list(indices.keys())[i+1]]\\n sectioned_text[hd] = full_text[start_id:end_id]\\n\\n if i == len(headers_list) - 1: #final header\\n start_id = indices[list(indices.keys())[i]]\\n sectioned_text[hd] = full_text[start_id:]\\n\\n return sectioned_text\",\n \"def parse_question_data(self):\\n section = ''\\n subsection = ''\\n quest = ''\\n # The data falls into 4 cases\\n # 1. Sections\\n # 2. subsections\\n # 3. questions\\n # 4. answers.\\n\\n for line in self.question_data: \\n\\n if \\\":\\\" in line: # case #2\\n subsection = line.split(\\\":\\\")[1] # split the line on the : into an array but only take the [1] element\\n debug(\\\"Subsection: %s\\\" % subsection)\\n \\n elif \\\".\\\" in line: # this is either a question or an answer?\\n \\n if line.split(\\\".\\\")[0].isdigit(): # case #3 it's a question, split on . into an array and take the element to the left and ask if it's a digit.\\n quest = line # Since we know it's something like \\\"3. Are you a warlock?\\\" we stick that in the quest varable.\\n debug(\\\"Question: %s\\\" % quest)\\n # Create a question object and stick it in the dictonary with the key being the question (since we know it'll be unique)\\n self.questions[quest] = question(section, subsection, quest) # I know it's redundant to have the key and have a value.\\n \\n elif line.startswith(\\\".\\\"): # case #4 answer All the answers startswith \\\".\\\" \\n debug(\\\"Answer: %s\\\" % line)\\n # take the question and append it to the answers array in the question object.\\n self.questions[quest].answers.append(line[2:]) # Trim the first two characters off the answer since it's \\\". the answer\\\"\\n \\n else: # case #1 # This is section like AMERICAN DEMOCRACY\\n section = line # load the line from the file into the section variable\\n debug(\\\"Section = %s\\\" % section)\",\n \"def proc_education_summary( summary: Tag ) -> Dict[str, str]:\\n edu_record = dict()\\n edu_record['school'] = summary.find('h3').text.strip()\\n edu_record['is_abroad_school'] = _is_abroad_school( edu_record['school'] )\\n\\n for parag in summary.find_all('p'):\\n spans = [span.text.strip() for span in parag.find_all('span')]\\n if len( spans ) == 2:\\n fld_name, value = spans\\n value = value.strip()\\n elif len(spans) == 0:\\n # print( 'education parag: ', parag )\\n edu_record['description'] = parag.text.strip()\\n continue\\n else:\\n print( 'education spans: ', spans )\\n continue\\n\\n if fld_name == 'Nombre de la titulación':\\n edu_record['degree_raw'] = value\\n edu_record['degree'] = _classify_degree( value )\\n # print( 'degree: ', value, _classify_degree(value) )\\n elif fld_name == 'Disciplina académica':\\n edu_record['field_raw'] = value\\n elif fld_name == 'Nota':\\n edu_record['grade_raw'] = value\\n elif fld_name.startswith('Fechas de estudios'):\\n edu_record['period_raw'] = value\\n elif fld_name.startswith('Actividades y asociaciones'):\\n edu_record['activities_raw'] = value\\n else:\\n print(\\\"proc_education_summary: \\\", fld_name, ' :: ', value)\\n\\n if edu_record.get('degree', 'Unknown') == 'Unknown':\\n if re.search( 'Ingenier|Engineering', edu_record.get('field_raw', '') ):\\n edu_record['degree'] = 'University'\\n\\n return edu_record\\n # %%\",\n \"def parse_data(filename):\\n ta = {\\n \\\"name\\\": \\\"\\\", # TA full name\\n \\\"total \\\": 0, # total contract hours\\n \\\"detailed\\\": [], # materials for the first page\\n \\\"summary\\\": {} # materials for the second page\\n }\\n category = None\\n total_hours = 0\\n\\n for line in open(filename):\\n line = line.strip()\\n if not line or line.startswith(\\\"====\\\") or line.startswith(\\\"TA INFO\\\"):\\n # skip these lines. they are not meaningful\\n continue\\n\\n key, info = line.split(\\\":\\\")\\n key = key.strip()\\n\\n if key in DDAH_CATEGORIES:\\n category = key\\n\\n if key.startswith(\\\"Full Name\\\"):\\n ta[\\\"name\\\"] = info.strip()\\n elif key.startswith(\\\"Total contract\\\"):\\n ta[\\\"total\\\"] = float(info.strip().strip(\\\"_\\\"))\\n else:\\n # convert info -> hours, filter out zero hours:\\n info = info.strip(\\\"_\\\")\\n if not info:\\n continue\\n hours = float(info)\\n if hours < 0.1:\\n continue\\n\\n # detailed hours:\\n ta[\\\"detailed\\\"].append((key, category, hours),)\\n # summary hours:\\n assert category is not None\\n if category not in ta[\\\"summary\\\"]:\\n ta[\\\"summary\\\"][category] = 0\\n ta[\\\"summary\\\"][category] += hours\\n # total hours:\\n total_hours += hours\\n\\n # verify that the hours add up\\n if total_hours != ta[\\\"total\\\"]:\\n raise ValueError(\\\"Total contract hours is {0} but {1} hours are assigned\\\".format(ta[\\\"total\\\"], total_hours))\\n # verify that there are at most 12 rows in ta[\\\"detailed\\\"]\\n if len(ta[\\\"detailed\\\"]) > 12:\\n raise ValueError(\\\"DDAH form supports at most 12 rows of detailed activity, but there are {0}\\\".format(len(ta[\\\"detailed\\\"])))\\n\\n return ta\",\n \"def build_course_dictionary(title_result_set, desc_result_set) -> Dict[str, List[str]]:\\n\\n course_dictionary = {} # placeholder dictionary\\n\\n for (tagged_title, tagged_description) in zip(title_result_set, desc_result_set): # iterate through multiple result sets\\n full_title_desc_list = {}\\n full_title_desc_list = [str(tagged_title.text)] + str(tagged_description.text).strip().splitlines() # remove trailing whitespace, then get list of lines\\n course_dictionary[str(tagged_title.text)[:8]] = full_title_desc_list\\n\\n return course_dictionary\",\n \"def meta_cv_scraper(self, type):\\n sections = self.filter_sections(filter_string=type, sections=self.cv_sections)\\n if len(sections) > 1:\\n raise Exception(f'Multiple {type} sections found')\\n self.data[type] = []\\n if len(sections) == 1:\\n section = sections[0]\\n asides = section.findAll('aside')\\n for item in asides:\\n for rowline in item.text.strip().replace('\\\\xa0', ' ').replace('\\\\r\\\\n', '\\\\n').split('\\\\n'):\\n if rowline.strip() != '':\\n self.data[type].append({'type': 'aside', 'data' : rowline.strip()})\\n ps = section.findAll('p')\\n for item in ps:\\n for rowline in item.text.strip().replace('\\\\xa0', ' ').replace('\\\\r\\\\n', '\\\\n').split('\\\\n'):\\n if rowline.strip() != '':\\n self.data[type].append({'type' : 'p', 'data': rowline.strip()})\",\n \"def _parse_biography(self):\\n data = {}\\n self.right_column = self.content.find(\\\"div\\\", class_=\\\"rechteSpalte60\\\")\\n heading = self.right_column.find(\\\"h3\\\")\\n # The page of the second president hides the details information\\n # and displays a biography instead. By selecting the second div,\\n # we get the hidden div containing the MPs details.\\n if not heading:\\n self.right_column = self.content.find_all(\\\"div\\\", class_=\\\"rechteSpalte60\\\")[1]\\n data.update(self._parse_dob_job())\\n data.update(self._parse_political_mandates())\\n data.update(self._parse_political_posts())\\n data.update(self._parse_work_history())\\n data.update(self._parse_education())\\n return data\",\n \"def scrape_detail_pages(h, s):\\n n = list()\\n for i, header in enumerate(h):\\n notice = dict()\\n notice['header'] = header\\n print notice['header']['link']\\n notice['detail'] = create_detail(notice['header']['link'])\\n notice['detail']['postcode'] = (\\n extract_postcode(notice['detail']['address'])\\n )\\n notice['search_details'] = s\\n n.append(notice)\\n return n\",\n \"def test_tax_court_citation_extractor(self):\\n\\n test_pairs = (\\n (\\n \\\"\\\"\\\" 1 UNITED STATES TAX COURT REPORT (2018)\\n\\n\\n\\n UNITED STATES TAX COURT\\n\\n\\n\\n BENTLEY COURT II LIMITED PARTNERSHIP, B.F. BENTLEY, INC., TAX\\n MATTERS PARTNER, Petitioner v.\\n COMMISSIONER OF INTERNAL REVENUE, Respondent\\n\\n\\n\\n Docket No. 5393-04. Filed May 31, 2006.\\n\\n\\n\\n Nancy Ortmeyer Kuhn, for petitioner.\\n \\\"\\\"\\\",\\n \\\"1 T.C. 2018\\\",\\n ),\\n (\\n \\\"\\\"\\\" T.C. Memo. 2003-150\\n\\n\\n\\n UNITED STATES TAX COURT\\n\\n\\n\\n RIVER CITY RANCHES #1 LTD., LEON SHEPARD,\\n TAX MATTERS PARTNER,\\n RIVER CITY RANCHES #2 LTD., LEON SHEPARD,\\n TAX MATTERS PARTNER,\\n RIVER CITY RANCHES #3 LTD., LEON SHEPARD,\\n TAX MATTERS PARTNER,\\n\\n\\n \\\"\\\"\\\",\\n \\\"2003 T.C. Memo. 150\\\",\\n ),\\n (\\n \\\"\\\"\\\" T.C. Summary Opinion 2003-150\\n\\n\\n\\n UNITED STATES TAX COURT\\n\\n\\n\\n RIVER CITY RANCHES #1 LTD., LEON SHEPARD,\\n TAX MATTERS PARTNER,\\n RIVER CITY RANCHES #2 LTD., LEON SHEPARD,\\n TAX MATTERS PARTNER,\\n RIVER CITY RANCHES #3 LTD., LEON SHEPARD,\\n TAX MATTERS PARTNER,\\n\\n\\n \\\"\\\"\\\",\\n \\\"2003 T.C. Summary Opinion 150\\\",\\n ),\\n (\\n \\\"\\\"\\\"\\n MICHAEL KEITH SHENK, PETITIONER v. COMMISSIONER\\n OF INTERNAL REVENUE, RESPONDENT\\n\\n Docket No. 5706–12. Filed May 6, 2013.\\n\\n P was divorced from his wife, and their 2003 ‘‘Judgment of\\n Absolute Divorce’’ provided that his ex-wife would have pri-\\n mary residential custody of their three minor children. The\\n judgment provided that the dependency exemption deductions\\n for the three children would be divided between the two ex-\\n spouses according to various conditions but did not provide\\n that the ex-wife must execute in P’s favor a Form 8332,\\n ‘‘Release of Claim to Exemption for Child of Divorced or Sepa-\\n rated Parents’’. The children resided with P’s ex-wife for more\\n than half of 2009, and P’s ex-wife did not execute in P’s favor\\n any Form 8332 or equivalent document for any year. For 2009\\n P timely filed a Federal income tax return on which he\\n claimed dependency exemption deductions and the child tax\\n credit for two of the children, consistent with his under-\\n standing of the terms of the judgment, but he did not attach\\n any Form 8332 to his return. He also claimed head-of-house-\\n hold filing status. His ex-wife, the custodial parent, timely\\n filed a Federal income tax return for 2009 on which she also\\n\\n 200\\n\\n\\n\\n\\nVerDate Nov 24 2008 10:59 Jul 11, 2014 Jkt 372897 PO 20012 Frm 00001 Fmt 3857 Sfmt 3857 V:\\\\FILES\\\\BOUND VOL. WITHOUT CROP MARKS\\\\B.V.140\\\\SHENK JAMIE\\n\\f (200) SHENK v. COMMISSIONER 201\\n\\n\\n claimed two dependency exemption deductions, so that one\\n child was claimed on both parents’ returns. R allowed to P the\\n dependency exemption deduction for one of the children but\\n disallowed his claim for the dependency exemption deduction\\n for the child who had also been claimed by the custodial\\n parent. At trial P contended he is entitled to a dependency\\n exemption deduction for all three children. Held: Since the\\n custodial parent did not execute, and P could not and did not\\n attach to his return, any Form 8332 or equivalent release, P\\n is not entitled under I.R.C. sec. 152(e)(2)(A) to claim the\\n dependency exemption deduction or the child tax credit. Held,\\n further, where both the custodial parent and the noncustodial\\n parent have claimed for the same year a dependency exemp-\\n tion deduction for the same child, a declaration signed by the\\n custodial parent after the period of limitations for assess-\\n ments has expired as to the custodial parent could not qualify\\n under I.R.C. sec. 152(e)(2)(A), and therefore there is no reason\\n to grant P’s request to leave the record open so that he may\\n obtain and proffer such a declaration. Held, further, P is not\\n entitled to head-of-household filing status under I.R.C. sec.\\n 2(b)(1) nor to the child tax credit under I.R.C. sec. 24.\\n\\n Michael Keith Shenk, for himself.\\n Shari Salu, for respondent.\\n GUSTAFSON, Judge: The Internal Revenue Service (IRS)\\n determined a deficiency of $3,136 in the 2009 Federal income\\n tax of petitioner Michael Keith Shenk. Mr. Shenk petitioned\\n this Court, pursuant to section 6213(a), 1 for redetermination\\n of the deficiency. After Mr. Shenk’s concession that he\\n received but did not report $254 in dividend income, the\\n issue for decision is whether Mr. Shenk is entitled to a\\n dependency exemption deduction for one of his children\\n under section 151(c), a child tax credit for that child under\\n section 24(a), and head-of-household filing status under sec-\\n tion 2(b)(1). On these issues, we hold for the IRS.\\n FINDINGS OF FACT\\n\\n The judgment of divorce\\n Mr. Shenk was married to Julie Phillips, and they have\\n three minor children—M.S., W.S., and L.S. They divorced in\\n 2003. The family court’s ‘‘Judgment of Absolute Divorce’’ pro-\\n 1 Unless otherwise indicated, all citations of sections refer to the Internal\\n\\n Revenue Code (26 U.S.C.) in effect for the tax year at issue, and all cita-\\n tions of Rules refer to the Tax Court Rules of Practice and Procedure.\\n\\n\\n\\n\\nVerDate Nov 24 2008 10:59 Jul 11, 2014 Jkt 372897 PO 20012 Frm 00002 Fmt 3857 Sfmt 3857 V:\\\\FILES\\\\BOUND VOL. WITHOUT CROP MARKS\\\\B.V.140\\\\SHENK JAMIE\\n\\f 202 140 UNITED STATES TAX COURT REPORTS (200)\\n\\n\\n vided: that Ms. Phillips was ‘‘awarded primary residential\\n custody’’ of the parties’ three children; and that Mr. Shenk\\n would be liable for child support payments; but that, as to\\n dependency exemptions—\\\"\\\"\\\",\\n \\\"140 T.C. 200\\\",\\n ),\\n )\\n site = tax.Site()\\n for q, a in test_pairs:\\n results = site.extract_from_text(q)\\n cite_string = \\\"%s %s %s\\\" % (\\n results[\\\"Citation\\\"][\\\"volume\\\"],\\n results[\\\"Citation\\\"][\\\"reporter\\\"],\\n results[\\\"Citation\\\"][\\\"page\\\"],\\n )\\n\\n self.assertEqual(cite_string, a)\\n print \\\"✓\\\", cite_string\",\n \"def get_bare_file(filename):\\n \\\"\\\"\\\" for a given entry, finds all of the info we want to display \\\"\\\"\\\"\\n f = open(filename, 'r')\\n str = f.read()\\n str = str.decode('utf-8')\\n e = {}\\n try: e['title'] = re.search('(?<=title:)(.)*', str).group()\\n except: pass\\n try: e['slug'] = re.search('(?<=slug:)(.)*', str).group()\\n except: pass\\n try: e['summary'] = re.search('(?<=summary:)(.)*', str).group()\\n except: pass\\n try:\\n e['content'] =re.search('(?<=content:)((?!category:)(?!published:)(.)|(\\\\n))*', str).group()\\n if e['content'] == None:\\n e['content'] = re.search('(?<=content:)((.)|(\\\\n))*$', str).group()\\n except:\\n pass\\n try:\\n e['published'] = re.search('(?<=published:)(.)*', str).group()\\n except: pass\\n try: e['author'] = re.search('(?<=author:)(.)*', str).group()\\n except: pass\\n try: e['category'] = re.search('(?<=category:)(.)*', str).group()\\n except: pass\\n try: e['url'] = re.search('(?<=url:)(.)*', str).group()\\n except: pass\\n try:\\n e['uid'] = re.search('(?<=u-uid:)(.)*', str)\\n if e['uid']:\\n e['uid'] = e['uid'].group()\\n else:\\n e['uid'] = re.search('(?<=u-uid)(.)*', str).group()\\n except: pass\\n try: e['time-zone'] = re.search('(?<=time-zone:)(.)*', str).group()\\n except: pass\\n try: e['location'] = re.search('(?<=location:)(.)*', str).group()\\n except: pass\\n try: e['syndication'] = re.search('(?<=syndication:)(.)*', str).group()\\n except: pass\\n try: e['location_name'] = re.search('(?<=location-name:)(.)*', str).group()\\n except: pass\\n try: e['in_reply_to'] = re.search('(?<=in-reply-to:)(.)*', str).group()\\n except:pass\\n return e\",\n \"def _get_information(self):\\n info_dict = {}\\n table = self._tab.find('table', class_='details')\\n for row in table.find_all('tr'):\\n row_key, row_value = row.find(class_='key'), row.find(class_='value')\\n key_with_emoji, value = self._get_key(row_key), self._get_value(row_value)\\n split_key = key_with_emoji.split(\\\" \\\", 1)\\n\\n if len(split_key):\\n key = split_key[-1]\\n info_dict.update({key: value})\\n\\n return info_dict\",\n \"def get_course_table(self, table):\\n json_result = {}\\n row_list = table.xpath('.//table[@id = \\\"s_course\\\"]/tr[position() > 1]')\\n for row in row_list:\\n session = row.xpath('./td[1]/text()')\\n course_full_code_list = row.xpath('.//a[starts-with(@href, \\\"javascript:course_popup\\\")]/text()')\\n course_name_list = row.xpath('.//font[@style = \\\"font-size:7pt;\\\"]/text()')\\n course_list = []\\n if len(course_full_code_list) != len(course_name_list):\\n # year course design project would be count twice\\n if (\\\"Design Project\\\" == course_name_list[0]) & \\\\\\n (len(course_full_code_list) + 1 == len(course_name_list)):\\n course_name_list = course_name_list[1:]\\n else:\\n raise ProfileException(\\n \\\"Error: unmatched lists. course code list:\\\",\\n course_full_code_list, \\\"\\\\n course name list:\\\", course_name_list)\\n for i, full_code in enumerate(course_full_code_list):\\n if re.match(re.compile('\\\\w{3}\\\\d{3}[YH]1\\\\s+[SFY]'), full_code) is None:\\n raise ProfileException(\\\"Illegal course code!:\\\" + full_code)\\n course_list.append({\\n \\\"courseName\\\": course_name_list[i],\\n \\\"courseCode\\\": full_code[0:6],\\n \\\"courseTime\\\": full_code[-1],\\n \\\"courseLength\\\": full_code[6:8]\\n })\\n # there is a empty session\\n if session:\\n json_result.update({session[0]: course_list})\\n if json_result:\\n return json_result\\n else:\\n raise ProfileException(\\\"Failed to get course_table table(row list is empty)\\\")\",\n \"def _get_courses(self) -> None:\\n\\n courses_content: NavigableString = self.soup.find(\\\"div\\\", \\n {\\\"class\\\": \\\"coursesContent\\\"})\\n course_items: ResultSet = courses_content.find_all(\\\"div\\\", \\n {\\\"class\\\": \\\"courseItem\\\"})\\n\\n for item in course_items:\\n course_name: str = item.a[\\\"href\\\"].split(\\\"/\\\")[-2].lower()\\n course_data: ParseType = self._parse(item)\\n self._update(course_name, course_data)\",\n \"def get_annotated_content_infos(course_id, thread, user, user_info):\\r\\n infos = {}\\r\\n\\r\\n def annotate(content):\\r\\n infos[str(content['id'])] = get_annotated_content_info(course_id, content, user, user_info)\\r\\n for child in content.get('children', []):\\r\\n annotate(child)\\r\\n annotate(thread)\\r\\n return infos\",\n \"def parse_info(s:str) -> dict:\\n d = {}\\n d[\\\"SVTYPE\\\"] = re.search(r'(?<=SVTYPE=)\\\\w+',s).group(0)\\n d[\\\"SUPPORT\\\"] = re.search(r'(?<=SUPPORT=)\\\\d+',s).group(0)\\n if d[\\\"SVTYPE\\\"] in [\\\"BND\\\"]:\\n return d\\n d[\\\"END\\\"] = re.search(r'(?<=END=)\\\\d+',s).group(0)\\n if d[\\\"SVTYPE\\\"] in [\\\"INV\\\"]:\\n return d\\n d[\\\"SVLEN\\\"] = re.search(r'(?<=SVLEN=)(.*?)(?=;)',s).group(0)\\n d[\\\"READS\\\"] = re.search(r'(?<=READS=)(.*?)(?=$)',s).group(0).split(\\\",\\\")\\n if d[\\\"SVTYPE\\\"] == \\\"INS\\\":\\n d[\\\"SEQS\\\"] = re.search(r'(?<=SEQS=)(.*?)(?=;)',s).group(0).split(\\\",\\\")\\n return d\",\n \"def get_description(soup):\\n\\n standard_head = ['Description', 'Getting There', 'Protection', 'Location']\\n\\n # grab all h3 orange header sections on the page\\n detail = {}\\n other_text = []\\n for h3 in soup.find_all('h3', { 'class': \\\"dkorange\\\" }):\\n \\n # text is the element after the h3\\n body = h3.next_sibling\\n \\n if isinstance(body, NavigableString):\\n # ignore sections from here on like 'Climbing Season' and such\\n break\\n else:\\n # these are the valuable text sections\\n body = body.get_text()\\n body = body.encode('utf-8', errors = 'ignore')\\n head = h3.get_text().encode('utf-8', errors = 'ignore')\\n head = head.strip('\\\\xc2\\\\xa0')\\n\\n if head in standard_head:\\n head = head.replace(' ','_').lower()\\n detail[head] = body\\n else:\\n other_text.append(body)\\n\\n # combine text into a full description\\n if len(other_text) > 0:\\n if 'description' in detail:\\n # combine description with other text -- questionable but appropriate\\n detail['description'] = detail['description'] + '\\\\n'.join(other_text)\\n else:\\n detail['description'] = '\\\\n'.join(other_text)\\n\\n # blank if there is no text at all\\n if 'description' not in detail:\\n detail['description'] = ''\\n\\n return detail\",\n \"def all_courses(records):\\n \\n course_and_id_dict = {} #This creates an empty dictionary\\n for all_tuples in records:\\n course_info_tuple = all_tuples[0] #Extracts all course information\\n course_id = course_info_tuple[0]\\n course_name = course_info_tuple[1]\\n \\n course_and_id_dict[course_id] = course_name\\n \\n return course_and_id_dict\",\n \"def course_info(request, course_id):\\r\\n course_key = SlashSeparatedCourseKey.from_deprecated_string(course_id)\\r\\n course = get_course_with_access(request.user, 'load', course_key)\\r\\n staff_access = has_access(request.user, 'staff', course)\\r\\n masq = setup_masquerade(request, staff_access) # allow staff to toggle masquerade on info page\\r\\n reverifications = fetch_reverify_banner_info(request, course_key)\\r\\n studio_url = get_studio_url(course_key, 'course_info')\\r\\n\\r\\n context = {\\r\\n 'request': request,\\r\\n 'course_id': course_key.to_deprecated_string(),\\r\\n 'cache': None,\\r\\n 'course': course,\\r\\n 'staff_access': staff_access,\\r\\n 'masquerade': masq,\\r\\n 'studio_url': studio_url,\\r\\n 'reverifications': reverifications,\\r\\n }\\r\\n\\r\\n return render_to_response('courseware/info.html', context)\",\n \"def main_dictionary():\\n for lyric in lyrics:\\n for line in lyric.split(\\\"\\\\n\\\"):\\n dictionary(line.split(\\\" \\\"))\",\n \"def scrape_compInfo(data, comp):\\n\\tc_data = comp # id_org, mex_ciudad y nombre de xml\\n\\tplatino = False #Determina si es necesario crear complejo platino\\n\\tsoup = BeautifulSoup(data, convertEntities=BeautifulStoneSoup.HTML_ENTITIES)\\n\\t\\n\\tc_data['cadena']='Cinemex'\\n\\t\\n\\t#direccion soup.find('table', cellspacing='3').findAll('td',colspan='2')[0].p.findNextSiblings(text=True)\\n\\t#telefono soup.find('table', cellspacing='3').findAll('td',colspan='2')[1]\\n\\tdatos = soup.find('table', cellspacing='3').findAll('td',colspan='2')\\n\\t\\n\\tc_data['nombre'] = str(soup.find('span', 'texto_6').string)\\n\\tc_data['direccion'] = ''.join(datos[0].p.findAll(text=True)).strip()\\n\\tc_data['telefonos_string'] = ''.join(datos[1].findAll(text=True)).strip()\\n\\t\\n\\t#Checa si existe salas platino\\n\\tprecios = soup.find('td', width='179')\\n\\tprecios_str = ''.join(precios.findAll(text=True))\\n\\tif precios_str.find('Platino') > -1:\\n\\t\\tplatino = True\\n\\t\\n\\treturn c_data, platino\",\n \"def _parse_education(self):\\n education = [\\n h for h in self.right_column.find_all(\\\"h4\\\") if h.text == \\\"Bildungsweg\\\"\\n ]\\n if education:\\n education = education[0].nextSibling.nextSibling.find_all(\\\"li\\\")\\n return {\\\"education\\\": [e.text.strip() for e in education]}\\n return {}\",\n \"def parse_details_more_cves(self, content):\\n result = {}\\n detail = ''\\n header_appeared = False\\n vuln_headers = content.xpath('.//*[self::strong or self::h3]/text()')\\n details_list = content.xpath('.//div[@id=\\\"detailfield\\\"]/span//text()')\\n for item in details_list:\\n item = normalize_string(item)\\n if item == '':\\n continue\\n if item in vuln_headers:\\n header_appeared = True\\n detail = ''\\n elif header_appeared:\\n cve_match = self.cve_match(item)\\n if cve_match == '':\\n detail += item\\n else:\\n result[cve_match] = detail\\n detail = ''\\n return result\",\n \"def infotodict(seqinfo):\\n \\n \\\"\\\"\\\"\\n MCF Pilot Protocol acquired on Friday April 13th\\n \\n >>> hdc_look.py -s mfc001 -ss 1\\n series_id sequence_name series_description dim1 dim2 dim3 dim4 TR TE is_derived is_motion_corrected\\n 0 1-localizer *fl2d1 localizer 192 192 3 1 0.020 5.00 False False\\n 1 2-pre_Neutral1_A>>P Resting 4X4X4 *epfid2d1_64 pre_Neutral1_A>>P Resting 4X4X4 64 64 35 148 2.000 25.00 False False\\n 2 3-pre_topup_A>>P *epse2d1_64 pre_topup_A>>P 64 64 140 1 2.400 38.00 False False\\n 3 4-pre_topup_P>>A *epse2d1_64 pre_topup_P>>A 64 64 140 1 2.400 38.00 False False\\n 4 5-Field_mapping 4X4X4 A>>P *fm2d2r Field_mapping 4X4X4 A>>P 64 64 35 1 0.488 4.92 False False\\n 5 6-Field_mapping 4X4X4 A>>P *fm2d2r Field_mapping 4X4X4 A>>P 64 64 35 1 0.488 7.38 False False\\n 6 7-pre+heat1_A>>P 4X4X4 *epfid2d1_64 pre+heat1_A>>P 4X4X4 64 64 35 148 2.000 25.00 False False\\n 7 8-pre_Neutral2_A>>P Resting 4X4X4 *epfid2d1_64 pre_Neutral2_A>>P Resting 4X4X4 64 64 35 148 2.000 25.00 False False\\n 8 9-pre+heat2_A>>P 4X4X4 *epfid2d1_64 pre+heat2_A>>P 4X4X4 64 64 35 148 2.000 25.00 False False\\n 9 10-MPRAGE_GRAPPA2 *tfl3d1_16ns MPRAGE_GRAPPA2 256 240 192 1 2.300 2.98 False False\\n 10 11-post_Neutral3_A>>P Resting 4X4X4 *epfid2d1_64 post_Neutral3_A>>P Resting 4X4X4 64 64 35 148 2.000 25.00 False False\\n 11 12-post+heat3_A>>P 4X4X4 *epfid2d1_64 post+heat3_A>>P 4X4X4 64 64 35 148 2.000 25.00 False False\\n 12 13-post_Neutral4_A>>P Resting 4X4X4 *epfid2d1_64 post_Neutral4_A>>P Resting 4X4X4 64 64 35 148 2.000 25.00 False False\\n 13 14-post+heat4_A>>P 4X4X4 *epfid2d1_64 post+heat4_A>>P 4X4X4 64 64 35 148 2.000 25.00 False False\\n 14 15-post_topup_A>>P *epse2d1_64 post_topup_A>>P 64 64 140 1 2.400 38.00 False False\\n 15 16-post_topup_P>>A *epse2d1_64 post_topup_P>>A 64 64 140 1 2.400 38.00 False False\\n \\n \\\"\\\"\\\"\\n\\n bids_prefix = 'sub-{subject}/{session}/'\\n\\n pre_neutral1_ap_fmap = create_key(bids_prefix + 'func/sub-{subject}_{session}_task-preNeutral1_acq-epi_rec-fmap_bold.{item:01d}')\\n pre_heat1_ap_fmap = create_key(bids_prefix + 'func/sub-{subject}_{session}_task-preHeat1_acq-epi_rec-fmap_bold.{item:01d}')\\n pre_heat2_ap_fmap = create_key(bids_prefix + 'func/sub-{subject}_{session}_task-preHeat2_acq-epi_rec-fmap_bold.{item:01d}')\\n pre_neutral2_ap_fmap = create_key(bids_prefix + 'func/sub-{subject}_{session}_task-preNeutral2_acq-epi_rec-fmap_bold.{item:01d}')\\n\\n pre_neutral1_ap_topup = create_key(bids_prefix + 'func/sub-{subject}_{session}_task-preNeutral1_acq-epi_rec-topup_bold.{item:01d}')\\n pre_heat1_ap_topup = create_key(bids_prefix + 'func/sub-{subject}_{session}_task-preHeat1_acq-epi_rec-topup_bold.{item:01d}')\\n pre_heat2_ap_topup = create_key(bids_prefix + 'func/sub-{subject}_{session}_task-preHeat2_acq-epi_rec-topup_bold.{item:01d}')\\n pre_neutral2_ap_topup = create_key(bids_prefix + 'func/sub-{subject}_{session}_task-preNeutral2_acq-epi_rec-topup_bold.{item:01d}')\\n\\n pre_topup_ap = create_key(bids_prefix + 'fmap/sub-{subject}_{session}_acq-preEpi_dir-ap_epi.{item:01d}')\\n pre_topup_pa = create_key(bids_prefix + 'fmap/sub-{subject}_{session}_acq-preEpi_dir-pa_epi.{item:01d}')\\n\\n # The item was commented out for Phase Difference field maps. Conversion did not work correctly. I removed the item number to try to\\n # isolate the problem.\\n\\n pre_fmap_magnitude1 = create_key(bids_prefix + 'fmap/sub-{subject}_{session}_acq-pre_magnitude1.{item:01d}')\\n pre_fmap_phasediff = create_key(bids_prefix + 'fmap/sub-{subject}_{session}_acq-pre_phasediff.{item:01d}')\\n\\n t1w = create_key(bids_prefix + 'anat/sub-{subject}_{session}_T1w')\\n\\n post_neutral3_ap_fmap = create_key(bids_prefix + 'func/sub-{subject}_{session}_task-postNeutral3_acq-epi_rec-fmap_bold.{item:01d}')\\n post_heat3_ap_fmap = create_key(bids_prefix + 'func/sub-{subject}_{session}_task-postHeat3_acq-epi_rec-fmap_bold.{item:01d}')\\n post_heat4_ap_fmap = create_key(bids_prefix + 'func/sub-{subject}_{session}_task-postHeat4_acq-epi_rec-fmap_bold.{item:01d}')\\n post_neutral4_ap_fmap = create_key(bids_prefix + 'func/sub-{subject}_{session}_task-postNeutral4_acq-epi_rec-fmap_bold.{item:01d}')\\n\\n post_neutral3_ap_topup = create_key(bids_prefix + 'func/sub-{subject}_{session}_task-postNeutral3_acq-epi_rec-topup_bold.{item:01d}')\\n post_heat3_ap_topup = create_key(bids_prefix + 'func/sub-{subject}_{session}_task-postHeat3_acq-epi_rec-topup_bold.{item:01d}')\\n post_heat4_ap_topup = create_key(bids_prefix + 'func/sub-{subject}_{session}_task-postHeat4_acq-epi_rec-topup_bold.{item:01d}')\\n post_neutral4_ap_topup = create_key(bids_prefix + 'func/sub-{subject}_{session}_task-postNeutral4_acq-epi_rec-topup_bold.{item:01d}')\\n\\n post_topup_ap = create_key(bids_prefix + 'fmap/sub-{subject}_{session}_acq-postEpi_dir-ap_epi.{item:01d}')\\n post_topup_pa = create_key(bids_prefix + 'fmap/sub-{subject}_{session}_acq-postEpi_dir-pa_epi.{item:01d}')\\n\\n # Create an empty dictionary called info for each key\\n\\n info = {pre_neutral1_ap_fmap: [],\\n pre_heat1_ap_fmap: [],\\n pre_heat2_ap_fmap: [],\\n pre_neutral2_ap_fmap: [],\\n\\n pre_neutral1_ap_topup: [],\\n pre_heat1_ap_topup: [],\\n pre_heat2_ap_topup: [],\\n pre_neutral2_ap_topup: [],\\n\\n pre_topup_ap: [],\\n pre_topup_pa: [],\\n\\n pre_fmap_magnitude1: [],\\n pre_fmap_phasediff: [],\\n\\n t1w: [],\\n\\n post_neutral3_ap_fmap: [],\\n post_heat3_ap_fmap: [],\\n post_heat4_ap_fmap: [],\\n post_neutral4_ap_fmap: [],\\n\\n post_neutral3_ap_topup: [],\\n post_heat3_ap_topup: [],\\n post_heat4_ap_topup: [],\\n post_neutral4_ap_topup: [],\\n\\n post_topup_ap: [],\\n post_topup_pa: [],\\n\\n }\\n\\n # Loop over each sequence. Use if statements to determine which sequences should be linked to which key\\n\\n for idx, s in enumerate(seqinfo):\\n\\n if 'pre_Neutral1' in s.series_id:\\n info[pre_neutral1_ap_fmap].append([s.series_id])\\n info[pre_neutral1_ap_topup].append([s.series_id])\\n\\n if 'pre+heat1' in s.series_id:\\n info[pre_heat1_ap_fmap].append([s.series_id])\\n info[pre_heat1_ap_topup].append([s.series_id])\\n\\n if 'pre+heat2' in s.series_id:\\n info[pre_heat2_ap_fmap].append([s.series_id])\\n info[pre_heat2_ap_topup].append([s.series_id])\\n\\n if 'pre_Neutral2' in s.series_id:\\n info[pre_neutral2_ap_fmap].append([s.series_id])\\n info[pre_neutral2_ap_topup].append([s.series_id])\\n\\n if 'pre_topup_A>>P' in s.series_id:\\n info[pre_topup_ap].append([s.series_id])\\n\\n if 'pre_topup_P>>A' in s.series_id:\\n info[pre_topup_pa].append([s.series_id])\\n\\n if (('Field_mapping 4X4X4 A>>P' in s.series_id) and\\n (s.TE == 4.92)):\\n info[pre_fmap_magnitude1].append([s.series_id])\\n \\n if (('Field_mapping 4X4X4 A>>P' in s.series_id) and\\n (s.TE == 7.38)):\\n info[pre_fmap_phasediff].append([s.series_id])\\n\\n if 'MPRAGE_GRAPPA2' in s.series_id:\\n info[t1w].append([s.series_id])\\n\\n if 'post_Neutral3' in s.series_id:\\n info[post_neutral3_ap_fmap].append([s.series_id])\\n info[post_neutral3_ap_topup].append([s.series_id])\\n\\n if 'post+heat3' in s.series_id:\\n info[post_heat3_ap_fmap].append([s.series_id])\\n info[post_heat3_ap_topup].append([s.series_id])\\n\\n if 'post+heat4' in s.series_id:\\n info[post_heat4_ap_fmap].append([s.series_id])\\n info[post_heat4_ap_topup].append([s.series_id])\\n\\n if 'post_Neutral4' in s.series_id:\\n info[post_neutral4_ap_fmap].append([s.series_id])\\n info[post_neutral4_ap_topup].append([s.series_id])\\n\\n if 'post_topup_A>>P' in s.series_id:\\n info[post_topup_ap].append([s.series_id])\\n\\n if 'post_topup_P>>A' in s.series_id:\\n info[post_topup_pa].append([s.series_id])\\n\\n return info\",\n \"def parse(self, is_president):\\n mp = {}\\n self.content = self.page.find(id=\\\"content\\\")\\n\\n salutation = self.content.find(id=\\\"inhalt\\\").text.strip()\\n if is_president:\\n salutation = salutation[: salutation.rfind(\\\" - \\\")]\\n mp[\\\"is_president\\\"] = is_president\\n mp[\\\"salutation\\\"] = salutation\\n mp[\\\"in_committees\\\"] = self.page.find(\\\"a\\\", href=\\\"#tab-Ausschuesse\\\") is not None\\n mp.update(self._parse_picture())\\n mp.update(self._parse_contact_information())\\n mp.update(self._parse_biography())\\n return mp\",\n \"def select_course_detail(self, course_id, course_section_id):\\n conn = sqlite3.connect(self.db_path)\\n cursor = conn.cursor()\\n with conn:\\n cursor.execute(\\n \\\"\\\"\\\"SELECT \\n c.subject, c.course_num, c.course_title,\\n cs.course_section_id, cs.schedule_days, cs.start_time, cs.end_time,\\n i.first_name || ' ' || i.last_name AS 'Instructor Name', c.course_units\\n FROM courses c\\n JOIN course_sections cs\\n ON c.course_id = cs.course_id\\n JOIN instructors i\\n ON cs.instructor_id = i.instructor_id\\n WHERE c.course_id = ? AND cs.course_section_id = ?\\\"\\\"\\\",\\n (course_id, course_section_id),\\n )\\n return cursor.fetchone()\",\n \"def _get_new_data(self, page_url, soup):\\n data = {}\\n data['url'] = page_url\\n title = soup.find('dd', class_='lemmaWgt-lemmaTitle-title').find('h1')\\n data['title'] = title.get_text()\\n summary = soup.find('div', class_='lemma-summary')\\n data['summary'] = summary.get_text()\\n return data\",\n \"def parse_programme(lines):\\n lines = [i.decode('utf-8') for i in lines]\\n header = parse_header_block(lines)\\n footer = parse_footer_block(lines)\\n programme = find_programme_block(lines)\\n programme = strip_paging(programme)\\n items = parse_items(programme)\\n return {'header':header, 'footer':footer, 'items':items}\",\n \"def get_course_info(self, course):\\n course_preview_url = course.slug\\n if course.slug == '/':\\n course_preview_url = '/course'\\n course.course_preview_url = course_preview_url\\n course.is_registered = self.is_enrolled(course)\\n course.is_completed = self.is_completed(course)\\n\\n\\n return course\",\n \"def _getsec(self, line):\\n m = sectionheader_re.match(line)\\n if not m:\\n return\\n self.anchors = anchors = []\\n self.d[m.group(1)] = anchors\\n self.getline = self._getanchor\",\n \"def olive_parser(text: str) -> dict:\\n soup = BeautifulSoup(text, \\\"lxml\\\")\\n root = soup.find(\\\"xmd-entity\\\")\\n page_no = root['page_no']\\n identifier = root['id']\\n language = root['language']\\n title = soup.meta['name']\\n entity_type = root['entity_type']\\n issue_date = soup.meta['issue_date']\\n\\n out = {\\n \\\"meta\\\": {\\n \\\"language\\\": None,\\n \\\"type\\\": {}\\n },\\n \\\"r\\\": [],\\n \\\"stats\\\": {},\\n \\\"legacy\\\": {\\\"continuation_from\\\": None, \\\"continuation_to\\\": None},\\n }\\n out[\\\"meta\\\"][\\\"title\\\"] = title\\n out[\\\"meta\\\"][\\\"page_no\\\"] = [int(page_no)]\\n out[\\\"meta\\\"][\\\"language\\\"] = normalize_language(language)\\n out[\\\"meta\\\"][\\\"type\\\"][\\\"raw\\\"] = entity_type\\n out[\\\"meta\\\"][\\\"issue_date\\\"] = issue_date\\n\\n new_region = {\\n \\\"c\\\": [],\\n \\\"p\\\": []\\n }\\n\\n new_paragraph = {\\n \\\"l\\\": []\\n }\\n\\n new_line = {\\n \\\"c\\\": [],\\n \\\"t\\\": []\\n }\\n\\n new_token = {\\n \\\"c\\\": [],\\n \\\"tx\\\": \\\"\\\"\\n }\\n\\n for primitive in soup.find_all(\\\"primitive\\\"):\\n\\n # store coordinate of text areas (boxes) by page\\n # 1) page number, 2) coordinate list\\n region = copy.deepcopy(new_region)\\n region[\\\"c\\\"] = [int(i) for i in primitive.get('box').split(\\\" \\\")]\\n\\n para = None\\n line = None\\n line_counter = 0\\n\\n for tag in primitive.find_all(recursive=False):\\n\\n if tag.name == \\\"l\\\":\\n\\n if para is None and line is None:\\n para = copy.deepcopy(new_paragraph)\\n line = copy.deepcopy(new_line)\\n\\n if line_counter > 0 and line is not None:\\n line = normalize_line(line, out[\\\"meta\\\"][\\\"language\\\"])\\n para[\\\"l\\\"].append(line)\\n\\n if tag.get(\\\"p\\\") in [\\\"S\\\", \\\"SA\\\"] and line_counter > 0:\\n region[\\\"p\\\"].append(para)\\n para = copy.deepcopy(new_paragraph)\\n\\n line = copy.deepcopy(new_line)\\n line[\\\"c\\\"] = [\\n int(i)\\n for i in tag.get('box').split(\\\" \\\")\\n ]\\n line_counter += 1\\n\\n if tag.name in [\\\"w\\\", \\\"q\\\"]:\\n\\n # store coordinates of each token\\n # 1) token, 2) page number, 3) coordinate list\\n t = copy.deepcopy(new_token)\\n t[\\\"c\\\"] = [int(i) for i in tag.get('box').split(\\\" \\\")]\\n t[\\\"tx\\\"] = tag.string\\n t[\\\"s\\\"] = int(tag.get('style_ref'))\\n\\n if tag.name == \\\"q\\\" and tag.get('qid') is not None:\\n qid = tag.get('qid')\\n normalized_form = soup.find('qw', qid=qid).text\\n t[\\\"nf\\\"] = normalized_form\\n t[\\\"qid\\\"] = qid\\n\\n # append the token to the line\\n line[\\\"t\\\"].append(t)\\n\\n # append orphan lines\\n if line is not None:\\n line = normalize_line(line, out[\\\"meta\\\"][\\\"language\\\"])\\n para[\\\"l\\\"].append(line)\\n\\n region[\\\"p\\\"].append(para)\\n\\n if para is not None:\\n out[\\\"r\\\"].append(region)\\n\\n out[\\\"legacy\\\"][\\\"id\\\"] = identifier\\n out[\\\"legacy\\\"][\\\"source\\\"] = soup.link['source']\\n \\\"\\\"\\\"\\n # I suspect this could be deleted\\n out[\\\"legacy\\\"][\\\"word_count\\\"] = int(soup.meta['wordcnt'])\\n out[\\\"legacy\\\"][\\\"chars_count\\\"] = int(soup.meta['total_chars_count'])\\n suspicious_chars_count = int(soup.meta['suspicious_chars_count'])\\n out[\\\"legacy\\\"][\\\"suspicious_chars_count\\\"] = int(suspicious_chars_count)\\n \\\"\\\"\\\"\\n out[\\\"legacy\\\"][\\\"first_id\\\"] = soup.link['first_id']\\n out[\\\"legacy\\\"][\\\"last_id\\\"] = soup.link['last_id']\\n out[\\\"legacy\\\"][\\\"next_id\\\"] = soup.link['next_id']\\n out[\\\"legacy\\\"][\\\"prev_id\\\"] = soup.link['prev_id']\\n\\n if root.has_attr('continuation_from'):\\n out[\\\"legacy\\\"][\\\"continuation_from\\\"] = root['continuation_from']\\n\\n if root.has_attr('continuation_to'):\\n out[\\\"legacy\\\"][\\\"continuation_to\\\"] = root['continuation_to']\\n\\n return out\",\n \"def _parse_sections(cls, content: dict) -> Dict[str, Any]:\\n sections = {}\\n for name, section_cls in cls.classes.items():\\n section = content.get(name)\\n if section:\\n try:\\n sections[name] = section_cls.parse(section)\\n except AttributeError:\\n sections[name] = section_cls(section)\\n else:\\n sections[name] = section_cls()\\n return sections\",\n \"def get_meta_information() -> Dict:\\n return {'name': 'NAS-Bench-201',\\n 'references': ['Xuanyi Dong, Yi Yang',\\n 'NAS-Bench-201: Extending the Scope of Reproducible Neural Architecture Search',\\n 'https://openreview.net/forum?id=HJxyZkBKDr',\\n 'https://github.com/D-X-Y/AutoDL-Projects'],\\n }\",\n \"def get_sample_info(lines):\\r\\n mapping_data, header, comments = parse_mapping_file(lines)\\r\\n labels = [\\\"from\\\", \\\"to\\\", \\\"eweight\\\", \\\"consensus_lin\\\"]\\r\\n node_labels = [\\\"node_name\\\", \\\"node_disp_name\\\", \\\"ntype\\\", \\\"degree\\\",\\r\\n \\\"weighted_degree\\\", \\\"consensus_lin\\\"]\\r\\n cat_by_sample = {}\\r\\n sample_by_cat = defaultdict(list)\\r\\n meta_dict = {}\\r\\n category_labels = header[1:-1]\\r\\n labels.extend(category_labels)\\r\\n node_labels.extend(category_labels)\\r\\n label_list = [[] for c in category_labels]\\r\\n for r in mapping_data:\\r\\n categories = r[0:len(category_labels) + 1]\\r\\n sample = categories[0]\\r\\n meta_dict[sample] = ['\\\\t'.join(categories[1:]), 0]\\r\\n\\r\\n cat_by_sample[sample] = [(l.strip(), c.strip())\\r\\n for l, c in zip(category_labels, categories[1:])]\\r\\n\\r\\n cat_list = []\\r\\n for i, (l, c) in enumerate(zip(category_labels, categories[1:])):\\r\\n if c not in label_list[i]:\\r\\n label_list[i].append(c)\\r\\n l = l.strip()\\r\\n c = c.strip()\\r\\n cat_list.append((l, c))\\r\\n sample_by_cat[(l, c)].append(sample)\\r\\n\\r\\n cat_by_sample[sample] = cat_list\\r\\n\\r\\n return cat_by_sample, sample_by_cat, len(category_labels), meta_dict,\\\\\\r\\n labels, node_labels, label_list\"\n]","isConversational":false},"negative_scores":{"kind":"list like","value":["0.698141","0.6433248","0.61811566","0.608286","0.6082416","0.6056426","0.6050662","0.6001302","0.5982004","0.59207666","0.58908296","0.5850639","0.5830395","0.5818191","0.5790374","0.577954","0.57718724","0.5770274","0.57416594","0.57249576","0.57086146","0.56785774","0.5651648","0.56339157","0.56309235","0.56044084","0.55505025","0.5549235","0.5538389","0.55373746","0.55342275","0.55321366","0.5523454","0.5465843","0.54581684","0.5438516","0.5435774","0.5421909","0.541754","0.54094183","0.5400432","0.53772","0.5363403","0.5357415","0.5354454","0.5343212","0.53409636","0.53180903","0.5318077","0.531261","0.53043705","0.53033096","0.5295012","0.52885985","0.5285505","0.5285406","0.5281129","0.5277634","0.52698046","0.5269219","0.5259235","0.5256093","0.5248214","0.5243447","0.52414316","0.52354944","0.5221914","0.5221775","0.5221605","0.5216271","0.5211633","0.5209064","0.5200239","0.5183509","0.51824117","0.5180805","0.5156553","0.51564574","0.5156114","0.51483154","0.51459557","0.5141684","0.51372457","0.51358664","0.51219547","0.511078","0.51052666","0.5103269","0.51026857","0.509914","0.5092504","0.5090355","0.5081241","0.5077029","0.50735444","0.5067621","0.50673383","0.50618804","0.5061333","0.50554866"],"string":"[\n \"0.698141\",\n \"0.6433248\",\n \"0.61811566\",\n \"0.608286\",\n \"0.6082416\",\n \"0.6056426\",\n \"0.6050662\",\n \"0.6001302\",\n \"0.5982004\",\n \"0.59207666\",\n \"0.58908296\",\n \"0.5850639\",\n \"0.5830395\",\n \"0.5818191\",\n \"0.5790374\",\n \"0.577954\",\n \"0.57718724\",\n \"0.5770274\",\n \"0.57416594\",\n \"0.57249576\",\n \"0.57086146\",\n \"0.56785774\",\n \"0.5651648\",\n \"0.56339157\",\n \"0.56309235\",\n \"0.56044084\",\n \"0.55505025\",\n \"0.5549235\",\n \"0.5538389\",\n \"0.55373746\",\n \"0.55342275\",\n \"0.55321366\",\n \"0.5523454\",\n \"0.5465843\",\n \"0.54581684\",\n \"0.5438516\",\n \"0.5435774\",\n \"0.5421909\",\n \"0.541754\",\n \"0.54094183\",\n \"0.5400432\",\n \"0.53772\",\n \"0.5363403\",\n \"0.5357415\",\n \"0.5354454\",\n \"0.5343212\",\n \"0.53409636\",\n \"0.53180903\",\n \"0.5318077\",\n \"0.531261\",\n \"0.53043705\",\n \"0.53033096\",\n \"0.5295012\",\n \"0.52885985\",\n \"0.5285505\",\n \"0.5285406\",\n \"0.5281129\",\n \"0.5277634\",\n \"0.52698046\",\n \"0.5269219\",\n \"0.5259235\",\n \"0.5256093\",\n \"0.5248214\",\n \"0.5243447\",\n \"0.52414316\",\n \"0.52354944\",\n \"0.5221914\",\n \"0.5221775\",\n \"0.5221605\",\n \"0.5216271\",\n \"0.5211633\",\n \"0.5209064\",\n \"0.5200239\",\n \"0.5183509\",\n \"0.51824117\",\n \"0.5180805\",\n \"0.5156553\",\n \"0.51564574\",\n \"0.5156114\",\n \"0.51483154\",\n \"0.51459557\",\n \"0.5141684\",\n \"0.51372457\",\n \"0.51358664\",\n \"0.51219547\",\n \"0.511078\",\n \"0.51052666\",\n \"0.5103269\",\n \"0.51026857\",\n \"0.509914\",\n \"0.5092504\",\n \"0.5090355\",\n \"0.5081241\",\n \"0.5077029\",\n \"0.50735444\",\n \"0.5067621\",\n \"0.50673383\",\n \"0.50618804\",\n \"0.5061333\",\n \"0.50554866\"\n]","isConversational":false},"document_score":{"kind":"string","value":"0.7118217"},"document_rank":{"kind":"string","value":"0"}}},{"rowIdx":22665,"cells":{"query":{"kind":"string","value":"Given a course page, takes the context and parses it to extract all the useful information and construct a dictionary with the information corresponding to assigned names ready to be written into the TeX file"},"document":{"kind":"string","value":"def bsoup(coursepage):\n soup = BeautifulSoup(coursepage.content, 'lxml')\n h1 = soup.find_all('h1')[2]\n html = h1.find_next_siblings()\n all_strings = [h1.string]\n for div in html:\n try:\n text = div.get_text()\n except:\n text = div.string\n if text is not None:\n all_strings.append(text)\n course = {'title': all_strings[0]}\n course = get_info_list(all_strings[1], course)\n course['description'] = new_dict(all_strings[2], all_strings[3])\n course['timetable'] = new_dict(all_strings[4], all_strings[5])\n course['requirements_of_entry'] = new_dict(all_strings[6], all_strings[7])\n course['excluded_courses'] = new_dict(all_strings[8], all_strings[9])\n course['co_requisites'] = new_dict(all_strings[10], all_strings[11])\n course['assessment_weighting'] = new_dict(all_strings[12], all_strings[13])\n course['aims'] = new_dict(all_strings[17], all_strings[18])\n date = all_strings[14].split(': ')\n course['assessment_date'] = new_dict(date[0] + \": \", date[1])\n course['learning_outcomes'] = new_dict(all_strings[19], all_strings[20])\n # TODO Doesn't parse Minimum Requirement for Award of Credit or\n # Reassessment Options\n return course"},"metadata":{"kind":"string","value":"{\n \"objective\": {\n \"self\": [],\n \"paired\": [],\n \"triplet\": [\n [\n \"query\",\n \"document\",\n \"negatives\"\n ]\n ]\n }\n}"},"negatives":{"kind":"list like","value":["def __title_context_dict(data):\n d = {}\n if isinstance(data, str):\n raw_data = __load_data(data)['data']\n else:\n raw_data = data['data']\n for i in range(len(raw_data)):\n title = raw_data[i]['title']\n paragraphs = raw_data[i]['paragraphs']\n text = []\n for j in range(len(paragraphs)):\n text.append(paragraphs[j]['context'])\n context = ' '.join(text)\n d[title] = context\n return d","def scrape_all_course_names(filename, verbose):\n \n\tsoup =\tBeautifulSoup(open(filename, 'r'), 'html.parser')\n\t#print(soup)\n\tcourses = []\n\t# all the courses are stored in a div w/class=view-content\n\t\t\n\th4_field_content = soup.find('h4', 'field-content')\n\t#print(h4_field_content)\n\tall_course_content = soup.find(\"div\", \"view-content\") # this contains ALL the classes...\n\tif all_course_content == None:\n\t\tprint(\"There are no courses on this page. Try a smaller page number!\")\n\t\treturn []\n\t\n\tcandidate_classes = all_course_content.find_all('a') # we want all the 'a' tags within\n\tclass_list=[]\n course_code_and_number = {}\n\tfor c in candidate_classes:\n\t\t#print(c.text, \"\\n\")\n\t\ttext = c.text # ex. AEMA 611 Experimental Designs 1 (3 credits)\n\t\ttext = text.split(\" \")# split on the space\n\t\tcourse_id = \" \".join(text[:2]).replace('\\n', '') # the first 2 are the course id\n\t\tcourse_name = \" \".join(text[2:-2]).replace('\\n', '')\n\t\tnum_credits = text[-2].replace(\"(\", \"\")# just get the course number, replace the ( with nothing \n\t\n\t\n\t\t#print(f\"{course_id}\\n{course_name}\\n{num_credits}\\n\")\n\t\ttry:# Check that the course number is a digit bc sometimes it is something weird\n\t\t\tfloat(num_credits)\n\t\texcept ValueError:\n\t\t\t#print(f\"Wrong course format. Ignoring {c}\")\n\t\t\tif(verbose):\n\t\t\t\tprint(f\"Wrong course format. Ignoring course: {c.text}\")\n\t\t\tcontinue\n\t\tclass_list.append(course_id)\n\treturn class_list","def _section_course_info(course_key, access):\r\n course = get_course_by_id(course_key, depth=None)\r\n\r\n section_data = {\r\n 'section_key': 'course_info',\r\n 'section_display_name': _('Course Info'),\r\n 'access': access,\r\n 'course_id': course_key,\r\n 'course_display_name': course.display_name,\r\n 'enrollment_count': CourseEnrollment.num_enrolled_in(course_key),\r\n 'has_started': course.has_started(),\r\n 'has_ended': course.has_ended(),\r\n 'list_instructor_tasks_url': reverse('list_instructor_tasks', kwargs={'course_id': course_key.to_deprecated_string()}),\r\n }\r\n\r\n try:\r\n advance = lambda memo, (letter, score): \"{}: {}, \".format(letter, score) + memo\r\n section_data['grade_cutoffs'] = reduce(advance, course.grade_cutoffs.items(), \"\")[:-2]\r\n except Exception:\r\n section_data['grade_cutoffs'] = \"Not Available\"\r\n # section_data['offline_grades'] = offline_grades_available(course_key)\r\n\r\n try:\r\n section_data['course_errors'] = [(escape(a), '') for (a, _unused) in modulestore().get_course_errors(course.id)]\r\n except Exception:\r\n section_data['course_errors'] = [('Error fetching errors', '')]\r\n\r\n return section_data","def parse_create_course(xml_course):\n attrs = [\n \"term-code\",\n \"term-description\",\n 'subject',\n \"course-number\",\n \"school\",\n \"department\",\n \"title\",\n \"description\",\n \"credit-hours\",\n \"distribution-group\"\n ]\n course = pull_attributes_from_xml(xml_course, attrs)\n course[\"sections\"] = []\n\n return course","def _parse(self, course: NavigableString) -> ParseType:\n\n info = {\"link\": \"\", \"icon\": \"\", \"title\": \"\", \"description\": \"\",\n \"counts\": {}}\n\n info[\"link\"] = course.a[\"href\"]\n info[\"icon\"] = course.a.img[\"src\"]\n\n description: NavigableString = course.a.div\n info[\"title\"] = description.div.get_text()\n info[\"description\"] = description.p.get_text()\n\n counts: NavigableString = course.find(\"div\", {\"class\": \"courseCounts\"})\n counts_data: ResultSet = counts.find_all(\"li\")\n for data in counts_data:\n name: str = data.span.get_text().lower()\n val: str = data.find(\"p\").get_text()\n info[\"counts\"][name] = int(val.replace(\",\", \"\"))\n\n return info","def parseCourses(self, response):\n sel = Selector(response)\n courses = sel.xpath('//div[@class=\"course-info expandable\"]')\n for c in courses:\n item = CourseItem(response.request.meta[\"item\"])\n item['code'] += '-' + c.xpath('@id').get().strip()\n item['name'] = c.xpath('//a[@class=\"courselink\"]/text()').get().strip()\n # everything works up to here #\n href = c.xpath('div/h3/a/@href').get()\n url = urljoin('https://web-app.usc.edu', href)\n yield Request(url=url,callback=self.parseSection,meta={'item':item})","def about_course(command):\n\n response = {'code': None, 'title': None,\\\n 'prof': None, 'section1': None,\\\n 'timing1': None, 'room1' : None,\\\n 'section2': None, 'timing2': None,\\\n 'room2': None, 'description': None,\\\n 'prereq': None, 'perm': None}\n \n if re.search('ENPM611', command):\n response = {'code': 'ENPM 611', 'title': 'Software Engineering',\\\n 'prof': 'Christopher Ackermann', 'section1': '0101',\\\n 'timing1': 'Monday 4:00 - 6:40 PM', 'room1': 'JMP 2121',\\\n 'description': 'Software engineering concepts, methods, and practices important to both the theorist and the practitioner will be covered. The entire range of responsibilities expected of a software engineer are presented. The fundamental areas of requirements development, software design, programming languages, and testing are covered extensively. Sessions on supporting areas such as systems engineering, project management, and software estimation are also included.', 'prereq': 'Competency in one programming language; and must have completed an undergraduate software engineering course. Or permission of instructor.', 'perm': 'Permission of ENGR-CDL-Office of Advanced Engineering Education.'\n}\n elif re.search('ENPM613', command):\n response = {'code': 'ENPM 613', 'title': 'Software Design and Implementation', 'prof': 'Ioana Rus', 'section1': '0101', 'timing1': 'Wednesday 7:00 - 9:40 PM', 'room1': 'TBA', 'description': 'Software design concepts and practices within the field important to both the practitioner and the theorist will be covered. Architectural and detailed designs are included for batch, client/server, and real-time systems. Design considerations for structured, object-oriented, and Web-based systems are covered. Design of databases, user interfaces, forms, and reports are also included. Implementation issues that affect the design, including error handling, performance, and inter-process communication, are presented.', 'perm': 'Permission of ENGR-CDL-Office of Advanced Engineering Education.'}\n elif re.search('ENPM631', command):\n response = {'code': 'ENPM631', 'title': 'TCP/IP Networking', 'prof': 'Pedram Fard', 'section1': '0101', 'timing1': 'Tuesday 7:00 - 9:40 PM', 'room1': 'TBA' , 'description': 'Describe how IP datagram travels through the internet and are routed from the source to the destination. Introduce the two transport protocols: UDP and TCP, the proper context to use each one, and related parameters and issues. Cover some other protocols, closely related to the TCP/IP that are responsible for the seamless operation of the Internet.', 'perm': 'ENPM602; or permission of instructor. And permission of ENGR-CDL-Office of Advanced Engineering Education.'}\n elif re.search('ENPM687', command):\n response = {'code': 'ENPM687', 'title': 'Digital Forensics and Incidence Response', 'prof': 'Jonas Amoonarquah', 'section1': 'Online', 'timing1': 'NA', 'room1': 'NA', 'description': 'Students will implement a robust incident response methodology, including proper forensic handling of evidence, and cover legal aspects of national and international law regarding forensics. The bulk of the course covers evidence acquisition, preservation, analysis and reporting on multiple platforms.', 'perm':'None'}\n elif re.search('ENPM691', command):\n response = {'code': 'ENPM691', 'title': 'Hacking of C Programs and Unix Binaries', 'prof': 'Dharmalingam Ganesan', 'section1': '0101', 'timing1': 'Thursday 7:00 - 9:40 PM', 'room1': 'JMP 3201', 'description': 'Teaches the fundamentals of secure programming in C. An in depth discussion on various security vulnerabilities (e.g., buffer overflows) in C applications will be taught with hands-on demo of concepts during the class. Students will learn how a C program runs \"under-the-hood\". The course will teach nitty-gritty of C programs by analyzing at the assembly level. The course discusses best practices (e.g., coding standards) and design principles for secure programming so that security can be built-in during design time. In addition to assignments, students are required to present papers related to this course.', 'perm': None, 'prereq': 'ENEE150; or students who have taken courses with comparable content may contact the department.'}\n elif re.search('ENPM693', command):\n response = {'code': 'ENPM693', 'title': 'Network Security', 'prof': 'Sohraab Soltani', 'section1': '0101', 'timing1': 'Tuesday 7:00 - 9:40 PM', 'room1' : 'JMP 3201', 'description': 'Introduction to various approaches to design; specify and verify security protocols used in large systems and networks; familiarization with some current technologies. Security threats and countermeasures, communication security and basic encryption techniques, authentication protocols, data confidentiality and integrity, analysis of cryptographic protocols, and access control in large systems and networks.', 'perm': None, 'prereq': 'An operating systems and/or network protocol course or equivalent.'}\n elif re.search('ENPM694', command):\n response = {'code': 'ENPM694', 'title': 'Networks and Protocols', 'prof': 'Sohraab Soltani', 'section1': '0101', 'timing1': 'Wednesday 7:00 - 9:40 PM', 'room1': 'JMP 3201', 'description': 'Provides a deep understanding of TCP/IP protocol suit and routing in the internet. The course topics are: overview of TCP/IP, basics of IP protocol, basics of TCP protocol, Network Address Translation (NAT), Dynamic Host Configuration Protocol (DHCP), Internet Protocol Security (IPsec), Internet Control Message Protocol (ICMP), Simple Mail Transfer Protocol (SMTP), Domain Name Service (DNS), IPv6, Concepts of routing (Bellman-Ford and Dijkstra algorithms), Routing Information Protocol (RIP), Open Shortest Path First (OSPF), Interior Gateway Routing Protocol (IGRP), Enhance Gateway Routing Protocol (EIGRP), and Border Gateway Protocol (BGP).'}\n elif re.search('ENPM696', command):\n response = {'code': 'ENPM696', 'title': 'Reverse Software Engineering', 'prof': 'Allen Hazelton', 'section1': '0101', 'timing1': 'Tuesday 4:00 - 6:40 PM', 'room1': 'TBA', 'description': 'An in-depth understanding of software reverse engineering concepts and hands-on training with reverse engineering tools, including disassemblers, decompilers, and code analyzers. Students will become familiar with both low-level software and the x86 instruction set through binary reversing sessions. This course also provides insights into many subjects such as system security, source code analysis, software design, and program understanding that will be beneficial in a variety of fields.', 'prereq': 'ENPM691 and CMSC106; or permission of instructor. And permission of ENGR-CDL-Office of Advanced Engineering Education.'}\n elif re.search('ENPM809J', command):\n response = {'code': 'ENPM809J', 'title': 'Cloud Security', 'prof': 'Kevin Shivers', 'section1': '0101', 'timing1': 'Monday 7:00 - 9:40 PM', 'room1': 'TBA', 'section2': '0201', 'timing2': 'Thursday 4:00 - 6:40 PM', 'room2': 'TBA', 'description': 'NA'}\n elif re.search('ENPM809R', command):\n response = {'code': 'ENPM809R', 'title': 'Software Defined Networking', 'prof': 'Emre Gunduzhan', 'section1': '0101', 'timing1': 'Monday 4:00 - 6:40 PM', 'room1': 'TBA', 'description': 'NA'}\n elif re.search('ENPM809W', command):\n response = {'code': 'ENPM809W', 'title': 'Security and Software', 'prof': 'Mikael Lindvall', 'section1': '0101', 'timing1': 'Thursday 7:00 - 9:40 PM', 'room1': 'TBA', 'description': 'NA'}\n\n return response","def get_courses_info(url, headers):\n dash = get_page_contents(url, headers)\n soup = BeautifulSoup(dash)\n courses_soup = soup.find_all('article', 'course')\n courses = []\n for course_soup in courses_soup:\n course_id = None\n course_name = course_soup.h3.text.strip()\n course_url = None\n course_state = 'Not yet'\n try:\n # started courses include the course link in the href attribute\n course_url = BASE_URL + course_soup.a['href']\n if course_url.endswith('info') or course_url.endswith('info/'):\n course_state = 'Started'\n # The id of a course in edX is composed by the path\n # {organization}/{course_number}/{course_run]\n course_id = course_soup.a['href'][9:-5]\n except KeyError:\n pass\n courses.append(Course(id=course_id,\n name=course_name,\n url=course_url,\n state=course_state))\n return courses","def __init__(self, browser, course_org, course_num, course_run):\r\n super(CoursePage, self).__init__(browser)\r\n self.course_info = {\r\n 'course_org': course_org,\r\n 'course_num': course_num,\r\n 'course_run': course_run\r\n }","def process_request(self, request):\r\n match = COURSE_REGEX.match(request.build_absolute_uri())\r\n course_id = None\r\n if match:\r\n course_id = match.group('course_id')\r\n course_key = SlashSeparatedCourseKey.from_deprecated_string(course_id)\r\n\r\n context = {}\r\n\r\n if course_id:\r\n context['course_id'] = course_id\r\n\r\n if request.user.is_authenticated():\r\n context['course_user_tags'] = dict(\r\n UserCourseTag.objects.filter(\r\n user=request.user.pk,\r\n course_id=course_key,\r\n ).values_list('key', 'value')\r\n )\r\n else:\r\n context['course_user_tags'] = {}\r\n\r\n tracker.get_tracker().enter_context(\r\n self.CONTEXT_NAME,\r\n context\r\n )","def context(template):\n\n return {\n v.key: v.read()\n for v in [Variable(name) for name in extract_variables(template)]\n }","def codrspace_contexts(request):\n contexts = {}\n contexts.update({\n 'TAGLINE': \"Why you no write tutorial?\"\n })\n\n return contexts","def get_course(data):\n\n return {item['course'] for item in data}","def process_course(xml_course):\n\n parse_course = parse_get_course(xml_course)\n if not parse_course: # Does not already exist, create\n parse_course = parse_create_course(xml_course)\n else:\n # TODO: Update existing attributes of the course here and upload the changes\n None\n parse_course_id = upload_course(parse_course)\n\n print (\"Processed Course: {0} {1}\"\n .format(parse_course[\"subject\"], parse_course[\"courseNumber\"]))\n\n # TODO: Implement the functions for this stuff\n parse_section = parse_get_section(xml_course)\n if not parse_section: # Does not already exist, create\n parse_section = parse_create_section(xml_course)\n else:\n # TODO: Update existing attributes of the section here and upload the changes\n None\n parse_section_id = upload_section(parse_section)\n\n put_child(parse_course_id, parse_section_id)","def get_info_list(info_string, course):\n info_list = []\n split_on_newline = info_string.split(\"\\n\")\n for elem in split_on_newline:\n split = elem.split(\": \")\n for s in split:\n info_list.append(s)\n info_list = info_list[1:-1]\n info_tags = [\n 'session', 'school', 'credits', 'level', 'offered',\n 'visiting_students', 'erasmus_students',\n ]\n i = 0\n for info_tag in info_tags:\n course[info_tag] = new_dict(\n info_list[i] + ': ', info_list[i + 1])\n i += 2\n return course","def _update_context_with_user_info(context, user, user_certificate):\n user_fullname = get_preferred_certificate_name(user)\n\n context['username'] = user.username\n context['course_mode'] = user_certificate.mode\n context['accomplishment_user_id'] = user.id\n context['accomplishment_copy_name'] = user_fullname\n context['accomplishment_copy_username'] = user.username\n\n context['accomplishment_more_title'] = _(\"More Information About {user_name}'s Certificate:\").format(\n user_name=user_fullname\n )\n # Translators: This line is displayed to a user who has completed a course and achieved a certification\n context['accomplishment_banner_opening'] = _(\"{fullname}, you earned a certificate!\").format(\n fullname=user_fullname\n )\n\n # Translators: This line congratulates the user and instructs them to share their accomplishment on social networks\n context['accomplishment_banner_congrats'] = _(\"Congratulations! This page summarizes what \"\n \"you accomplished. Show it off to family, friends, and colleagues \"\n \"in your social and professional networks.\")\n\n # Translators: This line leads the reader to understand more about the certificate that a student has been awarded\n context['accomplishment_copy_more_about'] = _(\"More about {fullname}'s accomplishment\").format(\n fullname=user_fullname\n )","def context(self) -> dict:\n\n try:\n context = cfr.train.find(self.train_number)\n\n meta_description = self.request.locale.gettext((\n 'Real-time updates for {rank}'\n '{number}, on the route \\'{route}\\'.'\n )).format(**context)\n\n context.update({\n 'meta': {\n 'page': '%s%s' % (context['rank'], context['number']),\n 'description': meta_description\n }\n })\n\n return context\n except cfr.exceptions.TrainNotFound:\n raise web.HTTPNotFound(reason='We couldn\\'t find that train')","def _get_sample_plain_context(self):\r\n context = {\r\n 'course_title': \"Bogus Course Title\",\r\n 'course_url': \"/location/of/course/url\",\r\n 'account_settings_url': \"/location/of/account/settings/url\",\r\n 'platform_name': 'edX',\r\n 'email': 'your-email@test.com',\r\n }\r\n return context","def _update_course_context(request, context, course, platform_name):\n context['full_course_image_url'] = request.build_absolute_uri(course_image_url(course))\n course_title_from_cert = context['certificate_data'].get('course_title', '')\n accomplishment_copy_course_name = course_title_from_cert if course_title_from_cert else course.display_name\n context['accomplishment_copy_course_name'] = accomplishment_copy_course_name\n course_number = course.display_coursenumber if course.display_coursenumber else course.number\n context['course_number'] = course_number\n if context['organization_long_name']:\n # Translators: This text represents the description of course\n context['accomplishment_copy_course_description'] = _('a course of study offered by {partner_short_name}, '\n 'an online learning initiative of '\n '{partner_long_name}.').format(\n partner_short_name=context['organization_short_name'],\n partner_long_name=context['organization_long_name'],\n platform_name=platform_name)\n else:\n # Translators: This text represents the description of course\n context['accomplishment_copy_course_description'] = _('a course of study offered by '\n '{partner_short_name}.').format(\n partner_short_name=context['organization_short_name'],\n platform_name=platform_name)","def all_courses(records):\n \n course_and_id_dict = {} #This creates an empty dictionary\n for all_tuples in records:\n course_info_tuple = all_tuples[0] #Extracts all course information\n course_id = course_info_tuple[0]\n course_name = course_info_tuple[1]\n \n course_and_id_dict[course_id] = course_name\n \n return course_and_id_dict","def insert_course(dept, num, text):\n\n # Course Title \n m = re.search(\"[\\d\\w]{5} - ([\\w ]*)\", text)\n title = m.group(1) if m else \"nomatch\"\n\n # Course Description\n m = re.search(\"\\.\\s(.*)\\sTypically\",text)\n des = m.group(1) if m else \"nomatch\"\n\n # Credit hours aren't fixed for every course\n # Credit Hours: 2.00\n # Credit Hours: 2.00 or 3.00. \n # Credit Hours: 1.00 to 18.00. \n m = re.search(\"Credit Hours: (\\d+\\.\\d+)\",text, flags=re.IGNORECASE)\n m = re.search(\"(\\d+\\.\\d+)(.*?)Credit hours\",text, flags=re.IGNORECASE) if not m else m\n cr = m.group(1) if m else \"-1\"\n\n # Semesters Offered\n m = re.search(\"Typically offered (.*?)\\.\", text)\n sem = m.group(1).split() if m else [\"nomatch\"]\n\n # Course Type: Lecture, Recitation, Lab, Seminar, etc.\n m = re.search(\"Schedule Types:\\s((?:[\\w ]+)(?:,[\\w ]+)*) \\s+\", text)\n form = m.group(1).split(\", \") if m else [\"nomatch\"]\n\n # Learning objectives will not necessarily follow campuses\n m = re.search(\"campuses:(\\s+([\\w\\s])+\\n)\", text)\n campus = m.group(1).strip().split(\"\\n\\n\") if m else [\"nomatch\"]\n campus = [camp.strip() for camp in campus]\n\n # prereq regex and decomosition of prereqs into lists of AND conditions (works for most classes, not 477 and similar)\n # re.DOTALL matches all characters, including \"\\n\"\n idx = text.find(\"campuses:\")\n m = re.search(\"Prerequisites:(.*)\",text[idx:],flags=re.DOTALL)\n if m:\n allReqs = []\n prereqText = m.group(1).strip()\n prereqText = prereqText.encode('ascii', 'ignore') \n for i in PrereqParser.parseprereq(prereqText):\n reqArr = []\n for j in i.split():\n if j.find(\"-C\") != -1:\n j = j.replace(\"-C\",\"\")\n reqArr.append(Requisite(course=j,reqType=False))\n else:\n reqArr.append(Requisite(course=j,reqType=True)) \n allReqs.append(RequisiteList(courses=reqArr))\n\n else:\n allReqs = []\n\n # create course entity\n course = Course(number=num, title=title, department=dept, form=form,\n description=des, credits=float(cr), semesters=sem,\n campuses=campus,requisites=allReqs, id=dept + num)\n # store course \n course.put()","def preprocess_courses_corpus():\n soup = None\n with open('courses_corpus.html', 'r') as infile:\n content = infile.read()\n\n soup = BeautifulSoup(content, 'html.parser')\n\n docid = 0\n data = {}\n data['documents'] = []\n\n main_table = soup.find_all(\"div\", attrs={'class': 'courseblock'})\n for course in main_table:\n docid += 1\n title = course.find_all('p', attrs={'class':'courseblocktitle noindent'})[0].text.lstrip('\\n') if len(course.find_all('p', attrs={'class':'courseblocktitle noindent'}))!=0 else ''\n description = (course.find_all('p', attrs={'class':'courseblockdesc noindent'})[0].text.lstrip('\\n') if len(course.find_all('p', attrs={'class':'courseblockdesc noindent'}))!=0 else '') + ' ' + (course.find_all('p', attrs={'class':'courseblockextra noindent'})[0].text if len(course.find_all('p', attrs={'class':'courseblockextra noindent'}))!=0 else '')\n\n data['documents'].append({\n 'docId' : docid,\n 'title' : title.strip(),\n 'description' : description.strip()\n })\n\n with open('courses_data.json', 'w') as outfile:\n json.dump(data, outfile)","def test_course_info(self):\r\n # Regex of first 3 columns of course information table row for\r\n # test course loaded from git. Would not have sha1 if\r\n # git_info_for_course failed.\r\n table_re = re.compile(r\"\"\"\r\n <tr>\\s+\r\n <td>edX\\sAuthor\\sCourse</td>\\s+ # expected test git course name\r\n <td>MITx/edx4edx/edx4edx</td>\\s+ # expected test git course_id\r\n <td>[a-fA-F\\d]{40}</td> # git sha1 hash\r\n \"\"\", re.VERBOSE)\r\n\r\n self._setstaff_login()\r\n self._mkdir(getattr(settings, 'GIT_REPO_DIR'))\r\n\r\n # Make sure we don't have any git hashes on the page\r\n response = self.client.get(reverse('sysadmin_courses'))\r\n self.assertNotRegexpMatches(response.content, table_re)\r\n\r\n # Now add the course and make sure it does match\r\n response = self._add_edx4edx()\r\n self.assertRegexpMatches(response.content, table_re)","def _get_sample_html_context(self):\r\n context = self._get_sample_plain_context()\r\n context['course_image_url'] = \"/location/of/course/image/url\"\r\n return context","def generate_page_sections_dict(self, project_page_data: dict):\n wiki_obj = WikiService()\n\n short_description = (\n f\"\\n{project_page_data['project']['shortDescription']}\\n\"\n )\n\n created_date = wiki_obj.format_date_text(\n project_page_data['project']['created']\n )\n # created_date_text = f\"\\n{created_date}\\n\" \n # due_date = wiki_obj.format_date_text(\n # project_page_data['project']['due_date']\n # )\n timeframe = (\n f\"\\n* '''Start Date:''' {created_date}\\n\"\n # f\"\\n* '''End Date:''' Estimate {due_date}\\n\"\n )\n\n\n project_url = (\n f\"\\n{project_page_data['project']['url']}\\n\"\n )\n\n hashtag = (\n project_page_data['project']['changesetComment']\n )\n hashtag = (\n project_page_data['project']['changesetComment'].replace(\n \"#\", \"<nowiki>#</nowiki>\"\n )\n )\n hashtag_text = (\n f\"\\n{hashtag}\\n\"\n )\n\n instructions_text = (\n project_page_data['project']\n ['externalSource']\n ['instructions']\n )\n instructions = (\n f\"\\n{instructions_text}\\n\"\n )\n\n per_task_instructions_text = (\n project_page_data['project']\n ['externalSource']\n ['perTaskInstructions']\n )\n per_task_instructions = (\n f\"\\n{per_task_instructions_text}\\n\"\n )\n\n imagery_text = (\n project_page_data['project']\n ['externalSource']\n ['imagery']\n )\n imagery = (\n f\"\\n{imagery_text}\\n\"\n )\n\n license_text = (\n project_page_data['project']\n ['externalSource']\n ['license']\n )\n license = (\n f\"\\n{license_text}\\n\"\n )\n\n # metrics = (\n # f\"\\n* {project_page_data.instructions}\\n\"\n # )\n # quality_assurance = (\n # f\"\\n* {project_page_data.quality_assurance}\\n\"\n # )\n\n users = project_page_data['project'][\"users\"]\n project_users = \"\"\n for user in users:\n project_users += (\n f\"\\n| {user['userId']}\\n| {user['userName']}\\n|-\"\n )\n\n \n\n project_page_sections = {\n self.short_description_section: short_description,\n self.timeframe_section: timeframe,\n # self.timeframe_section: {\n # self.created_section: created_date_text\n # }, # if choose use subsection for timeframe \n self.url_section: project_url,\n self.external_sources_section: {\n self.instructions_section: instructions,\n self.per_task_instructions_section: per_task_instructions,\n self.imagery_section: imagery,\n self.license_section: license\n },\n self.hashtag_section: hashtag_text,\n # self.instructions_section: instructions,\n # self.metrics_section: metrics,\n # self.quality_assurance_section: quality_assurance,\n self.team_user_section: {\n self.users_list_section: project_users\n }\n }\n return project_page_sections","def __init__(self, course_id, name, content):\n # course id and name need to go over the string_correct function\n self.ID = self.id_correct(str(course_id))\n self.name = self.string_correct(str(name))\n\n self.content = str(content)\n\n # uninitialized variables\n self.prere = {}\n self.postre = {}\n self.description = None\n self.department_title = None\n self.course_level = None\n\n # parse description and prerequisite raw data from content var\n self.seperate_content()","def populate_course(self):\r\n def descend(parent, stack):\r\n xblock_type = stack.pop(0)\r\n for _ in range(2):\r\n child = ItemFactory.create(category=xblock_type, parent_location=parent.location)\r\n if stack:\r\n descend(child, stack)\r\n\r\n descend(self.course, ['chapter', 'sequential', 'vertical', 'problem'])","def completed_course()->str:\r\n try:\r\n db: sqlite3.Connection = sqlite3.connect(DB_FILE)\r\n query: str = \"SELECT s.Name, s.CWID, g.Course, g.Grade, i.Name AS 'Instructor' \" \\\r\n \"FROM grades2 g JOIN students2 s ON g.StudentCWID = s.CWID \" \\\r\n \"JOIN instructors2 i ON g.InstructorCWID = i.CWID ORDER BY s.Name\" \r\n except sqlite3.OperationalError as e:\r\n print(e)\r\n\r\n data: Dict[str,str]=\\\r\n [{\"name\": name,\"cwid\": cwid,\"course\": course , \"grade\":grade, \"instructor\": instructor}\r\n for name, cwid, course, grade, instructor in db.execute(query)]\r\n db.close()\r\n \"\"\"Render the template from templates folder\"\"\"\r\n return render_template(\"student_summary.html\",\r\n title=\"Stevens Repository\",\r\n table_title=\"Student, Course, Grade and Instructor\",\r\n students=data )","def parse_course(browser, college, course_node):\n # open the course details\n course_handle = course_node.find_element_by_class_name('course')\n course_handle.click()\n wait_for_load(browser)\n\n title = course_node.find_element_by_class_name('courseID').text\n m = re.match(r'([A-Z&]+) *(\\d+)', title)\n if not m:\n logging.warning('Unable to parse title: %s', title)\n return\n\n dept = m.group(1)\n code = m.group(2)\n name = titlecase.titlecase(\n course_node.find_element_by_class_name('courseTitle').text)\n creds = float(course_node.find_element_by_class_name('courseCredits').text)\n tags = parse_tags(course_node)\n prerequisites = parse_prerequisites(course_node)\n\n # close the course details\n course_handle.click()\n wait_for_load(browser)\n\n return Course(\n titlecase.titlecase(college), dept, code, name, creds, tags,\n prerequisites)","def common_context(request):\n c = {\n 'lessons': get_lesson_numbers(),\n }\n return c","def compute_course_stats(course):\r\n\r\n # walk the course by using get_children() until we come to the leaves; count the\r\n # number of different leaf types\r\n\r\n counts = defaultdict(int)\r\n\r\n def walk(module):\r\n children = module.get_children()\r\n category = module.__class__.__name__ # HtmlDescriptor, CapaDescriptor, ...\r\n counts[category] += 1\r\n for c in children:\r\n walk(c)\r\n\r\n walk(course)\r\n stats = dict(counts) # number of each kind of module\r\n return stats","def setup_xml_course(self):\r\n course_key = SlashSeparatedCourseKey('edX', 'toy', '2012_Fall')\r\n location = course_key.make_usage_key('chapter', 'Overview')\r\n descriptor = modulestore().get_item(location)\r\n\r\n self.module = self._get_module(course_key, descriptor, location)","def test_tax_court_citation_extractor(self):\n\n test_pairs = (\n (\n \"\"\" 1 UNITED STATES TAX COURT REPORT (2018)\n\n\n\n UNITED STATES TAX COURT\n\n\n\n BENTLEY COURT II LIMITED PARTNERSHIP, B.F. BENTLEY, INC., TAX\n MATTERS PARTNER, Petitioner v.\n COMMISSIONER OF INTERNAL REVENUE, Respondent\n\n\n\n Docket No. 5393-04. Filed May 31, 2006.\n\n\n\n Nancy Ortmeyer Kuhn, for petitioner.\n \"\"\",\n \"1 T.C. 2018\",\n ),\n (\n \"\"\" T.C. Memo. 2003-150\n\n\n\n UNITED STATES TAX COURT\n\n\n\n RIVER CITY RANCHES #1 LTD., LEON SHEPARD,\n TAX MATTERS PARTNER,\n RIVER CITY RANCHES #2 LTD., LEON SHEPARD,\n TAX MATTERS PARTNER,\n RIVER CITY RANCHES #3 LTD., LEON SHEPARD,\n TAX MATTERS PARTNER,\n\n\n \"\"\",\n \"2003 T.C. Memo. 150\",\n ),\n (\n \"\"\" T.C. Summary Opinion 2003-150\n\n\n\n UNITED STATES TAX COURT\n\n\n\n RIVER CITY RANCHES #1 LTD., LEON SHEPARD,\n TAX MATTERS PARTNER,\n RIVER CITY RANCHES #2 LTD., LEON SHEPARD,\n TAX MATTERS PARTNER,\n RIVER CITY RANCHES #3 LTD., LEON SHEPARD,\n TAX MATTERS PARTNER,\n\n\n \"\"\",\n \"2003 T.C. Summary Opinion 150\",\n ),\n (\n \"\"\"\n MICHAEL KEITH SHENK, PETITIONER v. COMMISSIONER\n OF INTERNAL REVENUE, RESPONDENT\n\n Docket No. 5706–12. Filed May 6, 2013.\n\n P was divorced from his wife, and their 2003 ‘‘Judgment of\n Absolute Divorce’’ provided that his ex-wife would have pri-\n mary residential custody of their three minor children. The\n judgment provided that the dependency exemption deductions\n for the three children would be divided between the two ex-\n spouses according to various conditions but did not provide\n that the ex-wife must execute in P’s favor a Form 8332,\n ‘‘Release of Claim to Exemption for Child of Divorced or Sepa-\n rated Parents’’. The children resided with P’s ex-wife for more\n than half of 2009, and P’s ex-wife did not execute in P’s favor\n any Form 8332 or equivalent document for any year. For 2009\n P timely filed a Federal income tax return on which he\n claimed dependency exemption deductions and the child tax\n credit for two of the children, consistent with his under-\n standing of the terms of the judgment, but he did not attach\n any Form 8332 to his return. He also claimed head-of-house-\n hold filing status. His ex-wife, the custodial parent, timely\n filed a Federal income tax return for 2009 on which she also\n\n 200\n\n\n\n\nVerDate Nov 24 2008 10:59 Jul 11, 2014 Jkt 372897 PO 20012 Frm 00001 Fmt 3857 Sfmt 3857 V:\\FILES\\BOUND VOL. WITHOUT CROP MARKS\\B.V.140\\SHENK JAMIE\n\f (200) SHENK v. COMMISSIONER 201\n\n\n claimed two dependency exemption deductions, so that one\n child was claimed on both parents’ returns. R allowed to P the\n dependency exemption deduction for one of the children but\n disallowed his claim for the dependency exemption deduction\n for the child who had also been claimed by the custodial\n parent. At trial P contended he is entitled to a dependency\n exemption deduction for all three children. Held: Since the\n custodial parent did not execute, and P could not and did not\n attach to his return, any Form 8332 or equivalent release, P\n is not entitled under I.R.C. sec. 152(e)(2)(A) to claim the\n dependency exemption deduction or the child tax credit. Held,\n further, where both the custodial parent and the noncustodial\n parent have claimed for the same year a dependency exemp-\n tion deduction for the same child, a declaration signed by the\n custodial parent after the period of limitations for assess-\n ments has expired as to the custodial parent could not qualify\n under I.R.C. sec. 152(e)(2)(A), and therefore there is no reason\n to grant P’s request to leave the record open so that he may\n obtain and proffer such a declaration. Held, further, P is not\n entitled to head-of-household filing status under I.R.C. sec.\n 2(b)(1) nor to the child tax credit under I.R.C. sec. 24.\n\n Michael Keith Shenk, for himself.\n Shari Salu, for respondent.\n GUSTAFSON, Judge: The Internal Revenue Service (IRS)\n determined a deficiency of $3,136 in the 2009 Federal income\n tax of petitioner Michael Keith Shenk. Mr. Shenk petitioned\n this Court, pursuant to section 6213(a), 1 for redetermination\n of the deficiency. After Mr. Shenk’s concession that he\n received but did not report $254 in dividend income, the\n issue for decision is whether Mr. Shenk is entitled to a\n dependency exemption deduction for one of his children\n under section 151(c), a child tax credit for that child under\n section 24(a), and head-of-household filing status under sec-\n tion 2(b)(1). On these issues, we hold for the IRS.\n FINDINGS OF FACT\n\n The judgment of divorce\n Mr. Shenk was married to Julie Phillips, and they have\n three minor children—M.S., W.S., and L.S. They divorced in\n 2003. The family court’s ‘‘Judgment of Absolute Divorce’’ pro-\n 1 Unless otherwise indicated, all citations of sections refer to the Internal\n\n Revenue Code (26 U.S.C.) in effect for the tax year at issue, and all cita-\n tions of Rules refer to the Tax Court Rules of Practice and Procedure.\n\n\n\n\nVerDate Nov 24 2008 10:59 Jul 11, 2014 Jkt 372897 PO 20012 Frm 00002 Fmt 3857 Sfmt 3857 V:\\FILES\\BOUND VOL. WITHOUT CROP MARKS\\B.V.140\\SHENK JAMIE\n\f 202 140 UNITED STATES TAX COURT REPORTS (200)\n\n\n vided: that Ms. Phillips was ‘‘awarded primary residential\n custody’’ of the parties’ three children; and that Mr. Shenk\n would be liable for child support payments; but that, as to\n dependency exemptions—\"\"\",\n \"140 T.C. 200\",\n ),\n )\n site = tax.Site()\n for q, a in test_pairs:\n results = site.extract_from_text(q)\n cite_string = \"%s %s %s\" % (\n results[\"Citation\"][\"volume\"],\n results[\"Citation\"][\"reporter\"],\n results[\"Citation\"][\"page\"],\n )\n\n self.assertEqual(cite_string, a)\n print \"✓\", cite_string","def scrape_features(self, page_url):\n\n\t\t# create the soup with a certain page URL\n\t\tcourse_list_page = requests.get(page_url)\n\t\tcourse_list_soup = BeautifulSoup(course_list_page.content,\n\t\t\t\t\t\t\t\t\t\t'html.parser')\n\n\t\t# pick course name\n\t\tcnames = course_list_soup.select(\".headline-1-text\")\n\t\tfor i in range(10):\n\t\t\tself.courses.append(cnames[i].text)\n\n\t\t# pick partner name\n\t\tpnames = course_list_soup.select(\".horizontal-box > .partner-name\")\n\t\tfor i in range(10):\n\t\t\tself.organizations.append(pnames[i].text)\n\n\t\t# pick URLs\n\t\troot = \"https://www.coursera.org\"\n\t\tlinks = course_list_soup.select(\n\t\t\t\".ais-InfiniteHits > .ais-InfiniteHits-list > .ais-InfiniteHits-item\" \n\t\t)\n\t\tfor i in range(10):\n\t\t\tself.urls.append(root+links[i].a[\"href\"])\n\n\t\t# pick learning product\n\t\tfor i in range(10):\n\t\t\tlearn_pdcts = course_list_soup.find_all('div', '_jen3vs _1d8rgfy3')\n\t\t\tself.learning_products.append(learn_pdcts[i].text)\n\n\t\t# pick course rating and number of people who rated\n\t\tratings = []\n\t\tnum_ratings = []\n\t\tcratings = course_list_soup.select(\n\t\t\t\".ratings-text\")\n\t\tcnumratings = course_list_soup.select(\n\t\t\t\".ratings-count\")\n\t\tfor i in range(10):\n\t\t\ttry:\n\t\t\t\tself.ratings.append(float(cratings[i].text))\n\t\t\texcept:\n\t\t\t\tself.ratings.append(\"Missing\")\n\t\t\ttry:\n\t\t\t\tself.num_rated.append(int(cnumratings[i].text.\\\n\t\t\t\t\treplace(',','').\\\n\t\t\t\t\treplace('(','').\\\n\t\t\t\t\treplace(')','')))\n\t\t\texcept:\n\t\t\t\tself.num_rated.append(\"Missing\")\n\n\t\t# pick enrollment number\n\t\tenrollers = course_list_soup.select(\".enrollment-number\")\n\t\tfor i in range(10):\n\t\t\ttry:\n\t\t\t\tself.enrolled.append(enrollers[i].text)\n\t\t\texcept:\n\t\t\t\tself.enrolled.append(\"Missing\")\n\n\t\t# pick difficulty\n\t\tdifficulty = course_list_soup.select(\".difficulty\")\n\t\tfor i in range(10):\n\t\t\tself.difficulty.append(difficulty[i].text)","def extract_data(department_link):\n # Update the progress bar\n if show_progress:\n progress_bar.update()\n\n # Regular expressions for searching course descriptions stored in local variables\n # for better peformance\n local_course_re = course_re\n local_course_name_re = course_name_re\n local_credits_re = credits_re\n local_credits_num_re = credits_num_re\n local_offered_jointly_re = offered_jointly_re\n local_CAMPUSES = CAMPUSES\n\n # Method used in extracting data from course descriptions found in the local scope\n # are stored in local variables for better performance\n local_complete_description = complete_description\n local_get_offered = get_offered\n local_get_requisites = get_requisites\n\n # All the courses in the department\n courses = []\n dep_file = department_link.get('href')\n\n # If the user entered a dict as the 'campuse' parameter, departments\n # are checked here\n try:\n # The String in the conditional is the abbreviated Department Name i.e EE\n # for Electrical Engineering\n if normalize('NFKD', department_link.text).rsplit('(', 1) \\\n [-1].replace(' ', '')[:-1] not in campuses[campus]:\n return None\n except TypeError:\n pass\n \n # The only links that are used for finding departments are those\n # of the format [a-z]+.html\n if '/' not in dep_file and dep_file.endswith('.html') \\\n and dep_file not in parsed_departments:\n parsed_departments.add(dep_file)\n department = BeautifulSoup(requests.get( \\\n f'{local_CAMPUSES[campus]}{dep_file}').text, features='lxml')\n for course in department.find_all('a'):\n course_ID = course.get('name') \n if course_ID:\n course_ID = course_ID.upper()\n course_title = course.find('b').text\n # The Course Description\n description = course.get_text().replace(course_title, '', 1) \n instructors = course.find('i')\n if instructors:\n description = description.replace(str(instructors.get_text()), '', 1)\n del instructors\n course_text = local_complete_description( \\\n description.rsplit('View course details in MyPlan', 1)[0])\n # Course Number i.e 351\n course_number = re.sub(local_course_re, '', course_ID)\n match_name = re.search(local_course_name_re, course_title)\n match_credit_num = re.search(local_credits_num_re, course_title)\n match_credit_types = re.findall(local_credits_re, course_title)\n # Jointly offered course with the given course\n if 'jointly with' in course_text: \n offered_jointly = course_text.rsplit('jointly with ', 1)[-1].rsplit(';', 1)[0] \n offered_jointly = ','.join(re.findall( \\\n local_offered_jointly_re, offered_jointly)).replace(' ', '') \n else:\n offered_jointly = ''\n courses.append(\n # Campus, Department Name and Course Number\n [campus, course_ID[:-3], course_number, \n # Course Name\n match_name.group(0).split(course_number, 1)[-1].strip() \\\n if match_name else '',\n # Number of credits for the course\n match_credit_num.group(0)[1:-1] \\\n if match_credit_num else '', \n # Course Credit Types (I&S, DIV, NW, VLPA, QSR, C)\n ','.join([list(filter(('').__ne__, x))[0] for x in match_credit_types]) \\\n if match_credit_types else '', \n local_get_offered(course_text),\n offered_jointly, local_get_requisites(course_text, 'Prerequisite:'), \n local_get_requisites(course_text, 'Co-requisite'), course_text]\n )\n return courses","def course_info(request, course_id):\r\n course_key = SlashSeparatedCourseKey.from_deprecated_string(course_id)\r\n course = get_course_with_access(request.user, 'load', course_key)\r\n staff_access = has_access(request.user, 'staff', course)\r\n masq = setup_masquerade(request, staff_access) # allow staff to toggle masquerade on info page\r\n reverifications = fetch_reverify_banner_info(request, course_key)\r\n studio_url = get_studio_url(course_key, 'course_info')\r\n\r\n context = {\r\n 'request': request,\r\n 'course_id': course_key.to_deprecated_string(),\r\n 'cache': None,\r\n 'course': course,\r\n 'staff_access': staff_access,\r\n 'masquerade': masq,\r\n 'studio_url': studio_url,\r\n 'reverifications': reverifications,\r\n }\r\n\r\n return render_to_response('courseware/info.html', context)","def ContextFromURL( url ):\n url = TidyURL(url)\n context = {\n 'srcurl': url,\n 'permalink': url,\n 'srcorgname': 'dailymail', \n 'lastseen': datetime.now(),\n }\n return context","def get_annotated_content_infos(course_id, thread, user, user_info):\r\n infos = {}\r\n\r\n def annotate(content):\r\n infos[str(content['id'])] = get_annotated_content_info(course_id, content, user, user_info)\r\n for child in content.get('children', []):\r\n annotate(child)\r\n annotate(thread)\r\n return infos","def cert_info(user, course):\r\n if not course.may_certify():\r\n return {}\r\n\r\n return _cert_info(user, course, certificate_status_for_student(user, course.id))","def create_tex(unwanted_courses, wanted_courses=None):\n page = requests.get(\n 'http://gla.ac.uk/coursecatalogue/courselist/' +\n '?code=REG30200000&name=School+of+Computing+Science')\n tree = html.fromstring(page.content)\n spans = tree.xpath('//span/text()')\n codes = []\n if wanted_courses is None:\n for s in spans:\n if s[0:4] == \"COMP\" and s[7] == '4' and s not in unwanted_courses:\n codes.append(s)\n else:\n for s in wanted_courses:\n codes.append(s)\n write_to_latex(codes, unwanted_courses)\n return None","def isolate_relevant_information(self):\n\n def get_formula(oncat_formula):\n \"\"\"will need to go from something like\n \"${value/10e11}`\"\n to something more pythonic\n \"{value/10e11}\"\"\"\n regular_expression = r'\\$(?P<formula>.+)\\`'\n m = re.search(regular_expression, oncat_formula)\n if m:\n return m.group('formula')\n else:\n return \"\"\n\n template_information = {}\n for _index, _element in enumerate(self._oncat_default_template):\n _title = _element[\"name\"]\n _path = _element[\"path\"]\n if \"units\" in _element:\n _units = _element[\"units\"]\n else:\n _units = \"\"\n if \"transform\" in _element:\n _formula = get_formula(_element[\"transform\"])\n else:\n _formula = \"\"\n template_information[_index] = {'title': _title,\n 'path': _path,\n 'units': _units,\n 'formula': _formula}\n self.template_information = template_information","def _update_context_with_basic_info(context, course_id, platform_name, configuration):\n context['platform_name'] = platform_name\n context['course_id'] = course_id\n\n # Update the view context with the default ConfigurationModel settings\n context.update(configuration.get('default', {}))\n\n # Translators: 'All rights reserved' is a legal term used in copyrighting to protect published content\n reserved = _(\"All rights reserved\")\n context['copyright_text'] = '© {year} {platform_name}. {reserved}.'.format(\n year=datetime.now(pytz.timezone(settings.TIME_ZONE)).year,\n platform_name=platform_name,\n reserved=reserved\n )\n\n # Translators: This text is bound to the HTML 'title' element of the page and appears\n # in the browser title bar when a requested certificate is not found or recognized\n context['document_title'] = _(\"Invalid Certificate\")\n\n context['company_tos_urltext'] = _(\"Terms of Service & Honor Code\")\n\n # Translators: A 'Privacy Policy' is a legal document/statement describing a website's use of personal information\n context['company_privacy_urltext'] = _(\"Privacy Policy\")\n\n # Translators: This line appears as a byline to a header image and describes the purpose of the page\n context['logo_subtitle'] = _(\"Certificate Validation\")\n\n # Translators: Accomplishments describe the awards/certifications obtained by students on this platform\n context['accomplishment_copy_about'] = _('About {platform_name} Accomplishments').format(\n platform_name=platform_name\n )\n\n # Translators: This line appears on the page just before the generation date for the certificate\n context['certificate_date_issued_title'] = _(\"Issued On:\")\n\n # Translators: The Certificate ID Number is an alphanumeric value unique to each individual certificate\n context['certificate_id_number_title'] = _('Certificate ID Number')\n\n context['certificate_info_title'] = _('About {platform_name} Certificates').format(\n platform_name=platform_name\n )\n\n context['certificate_verify_title'] = _(\"How {platform_name} Validates Student Certificates\").format(\n platform_name=platform_name\n )\n\n # Translators: This text describes the validation mechanism for a certificate file (known as GPG security)\n context['certificate_verify_description'] = _('Certificates issued by {platform_name} are signed by a gpg key so '\n 'that they can be validated independently by anyone with the '\n '{platform_name} public key. For independent verification, '\n '{platform_name} uses what is called a '\n '\"detached signature\""\".').format(platform_name=platform_name)\n\n context['certificate_verify_urltext'] = _(\"Validate this certificate for yourself\")\n\n # Translators: This text describes (at a high level) the mission and charter the edX platform and organization\n context['company_about_description'] = _(\"{platform_name} offers interactive online classes and MOOCs.\").format(\n platform_name=platform_name)\n\n context['company_about_title'] = _(\"About {platform_name}\").format(platform_name=platform_name)\n\n context['company_about_urltext'] = _(\"Learn more about {platform_name}\").format(platform_name=platform_name)\n\n context['company_courselist_urltext'] = _(\"Learn with {platform_name}\").format(platform_name=platform_name)\n\n context['company_careers_urltext'] = _(\"Work at {platform_name}\").format(platform_name=platform_name)\n\n context['company_contact_urltext'] = _(\"Contact {platform_name}\").format(platform_name=platform_name)\n\n # Translators: This text appears near the top of the certificate and describes the guarantee provided by edX\n context['document_banner'] = _(\"{platform_name} acknowledges the following student accomplishment\").format(\n platform_name=platform_name\n )","def get_course_info_section(request, course, section_key):\r\n usage_key = course.id.make_usage_key('course_info', section_key)\r\n\r\n # Use an empty cache\r\n field_data_cache = FieldDataCache([], course.id, request.user)\r\n info_module = get_module(\r\n request.user,\r\n request,\r\n usage_key,\r\n field_data_cache,\r\n course.id,\r\n wrap_xmodule_display=False,\r\n static_asset_path=course.static_asset_path\r\n )\r\n\r\n html = ''\r\n\r\n if info_module is not None:\r\n try:\r\n html = info_module.render('student_view').content\r\n except Exception: # pylint: disable=broad-except\r\n html = render_to_string('courseware/error-message.html', None)\r\n log.exception(\r\n u\"Error rendering course={course}, section_key={section_key}\".format(\r\n course=course, section_key=section_key\r\n ))\r\n\r\n return html","def get_course_info(self, course):\n course_preview_url = course.slug\n if course.slug == '/':\n course_preview_url = '/course'\n course.course_preview_url = course_preview_url\n course.is_registered = self.is_enrolled(course)\n course.is_completed = self.is_completed(course)\n\n\n return course","def latex_course(course):\n basic_info_list = [\n 'session', 'school', 'credits', 'level', 'offered',\n 'visiting_students', 'erasmus_students'\n ]\n generic_subsection_list = [\n 'description', 'timetable', 'requirements_of_entry',\n 'excluded_courses', 'co_requisites', 'assessment_weighting'\n ]\n string = '\\\\subsection{' + course[\"title\"] + '}\\n'\n for info in basic_info_list:\n string += latex_info(course[info])\n for subsection in generic_subsection_list:\n string += latex_subsection(course[subsection])\n string += '\\\\break \\\\textbf{' + course['assessment_date'][\n 'heading'] + '}' + course['assessment_date']['value'] + '\\n'\n string += latex_subsection(course['aims'])\n string += '\\\\subsubsection*{' + \\\n course['learning_outcomes']['heading'] + '}\\n'\n outcome_list = re.split(\n '\\d+\\. ', course['learning_outcomes']['value'])\n string += outcome_list[0] + '\\n'\n string += '\\\\begin{enumerate}\\n'\n for i in outcome_list[1:-1]:\n string += '\\\\item ' + i + '\\n'\n string += '\\\\end{enumerate}\\n'\n return string","def index(request, course_id, chapter=None, section=None,\r\n position=None):\r\n course_key = SlashSeparatedCourseKey.from_deprecated_string(course_id)\r\n user = User.objects.prefetch_related(\"groups\").get(id=request.user.id)\r\n request.user = user # keep just one instance of User\r\n course = get_course_with_access(user, 'load', course_key, depth=2)\r\n staff_access = has_access(user, 'staff', course)\r\n registered = registered_for_course(course, user)\r\n if not registered:\r\n # TODO (vshnayder): do course instructors need to be registered to see course?\r\n log.debug(u'User %s tried to view course %s but is not enrolled', user, course.location.to_deprecated_string())\r\n return redirect(reverse('about_course', args=[course_key.to_deprecated_string()]))\r\n\r\n masq = setup_masquerade(request, staff_access)\r\n\r\n try:\r\n field_data_cache = FieldDataCache.cache_for_descriptor_descendents(\r\n course_key, user, course, depth=2)\r\n\r\n course_module = get_module_for_descriptor(user, request, course, field_data_cache, course_key)\r\n if course_module is None:\r\n log.warning(u'If you see this, something went wrong: if we got this'\r\n u' far, should have gotten a course module for this user')\r\n return redirect(reverse('about_course', args=[course_key.to_deprecated_string()]))\r\n\r\n studio_url = get_studio_url(course_key, 'course')\r\n\r\n if chapter is None:\r\n return redirect_to_course_position(course_module)\r\n\r\n context = {\r\n 'csrf': csrf(request)['csrf_token'],\r\n 'accordion': render_accordion(request, course, chapter, section, field_data_cache),\r\n 'COURSE_TITLE': course.display_name_with_default,\r\n 'course': course,\r\n 'init': '',\r\n 'fragment': Fragment(),\r\n 'staff_access': staff_access,\r\n 'studio_url': studio_url,\r\n 'masquerade': masq,\r\n 'xqa_server': settings.FEATURES.get('USE_XQA_SERVER', 'http://xqa:server@content-qa.mitx.mit.edu/xqa'),\r\n 'reverifications': fetch_reverify_banner_info(request, course_key),\r\n }\r\n\r\n # Only show the chat if it's enabled by the course and in the\r\n # settings.\r\n show_chat = course.show_chat and settings.FEATURES['ENABLE_CHAT']\r\n if show_chat:\r\n context['chat'] = chat_settings(course, user)\r\n # If we couldn't load the chat settings, then don't show\r\n # the widget in the courseware.\r\n if context['chat'] is None:\r\n show_chat = False\r\n\r\n context['show_chat'] = show_chat\r\n\r\n chapter_descriptor = course.get_child_by(lambda m: m.location.name == chapter)\r\n if chapter_descriptor is not None:\r\n save_child_position(course_module, chapter)\r\n else:\r\n raise Http404('No chapter descriptor found with name {}'.format(chapter))\r\n\r\n chapter_module = course_module.get_child_by(lambda m: m.location.name == chapter)\r\n if chapter_module is None:\r\n # User may be trying to access a chapter that isn't live yet\r\n if masq == 'student': # if staff is masquerading as student be kinder, don't 404\r\n log.debug('staff masq as student: no chapter %s' % chapter)\r\n return redirect(reverse('courseware', args=[course.id.to_deprecated_string()]))\r\n raise Http404\r\n\r\n if section is not None:\r\n section_descriptor = chapter_descriptor.get_child_by(lambda m: m.location.name == section)\r\n if section_descriptor is None:\r\n # Specifically asked-for section doesn't exist\r\n if masq == 'student': # if staff is masquerading as student be kinder, don't 404\r\n log.debug('staff masq as student: no section %s' % section)\r\n return redirect(reverse('courseware', args=[course.id.to_deprecated_string()]))\r\n raise Http404\r\n\r\n # cdodge: this looks silly, but let's refetch the section_descriptor with depth=None\r\n # which will prefetch the children more efficiently than doing a recursive load\r\n section_descriptor = modulestore().get_item(section_descriptor.location, depth=None)\r\n\r\n # Load all descendants of the section, because we're going to display its\r\n # html, which in general will need all of its children\r\n section_field_data_cache = FieldDataCache.cache_for_descriptor_descendents(\r\n course_key, user, section_descriptor, depth=None)\r\n\r\n section_module = get_module_for_descriptor(\r\n request.user,\r\n request,\r\n section_descriptor,\r\n section_field_data_cache,\r\n course_key,\r\n position\r\n )\r\n\r\n if section_module is None:\r\n # User may be trying to be clever and access something\r\n # they don't have access to.\r\n raise Http404\r\n\r\n # Save where we are in the chapter\r\n save_child_position(chapter_module, section)\r\n context['fragment'] = section_module.render('student_view')\r\n context['section_title'] = section_descriptor.display_name_with_default\r\n else:\r\n # section is none, so display a message\r\n studio_url = get_studio_url(course_key, 'course')\r\n prev_section = get_current_child(chapter_module)\r\n if prev_section is None:\r\n # Something went wrong -- perhaps this chapter has no sections visible to the user\r\n raise Http404\r\n prev_section_url = reverse('courseware_section', kwargs={\r\n 'course_id': course_key.to_deprecated_string(),\r\n 'chapter': chapter_descriptor.url_name,\r\n 'section': prev_section.url_name\r\n })\r\n context['fragment'] = Fragment(content=render_to_string(\r\n 'courseware/welcome-back.html',\r\n {\r\n 'course': course,\r\n 'studio_url': studio_url,\r\n 'chapter_module': chapter_module,\r\n 'prev_section': prev_section,\r\n 'prev_section_url': prev_section_url\r\n }\r\n ))\r\n\r\n result = render_to_response('courseware/courseware.html', context)\r\n except Exception as e:\r\n if isinstance(e, Http404):\r\n # let it propagate\r\n raise\r\n\r\n # In production, don't want to let a 500 out for any reason\r\n if settings.DEBUG:\r\n raise\r\n else:\r\n log.exception(\r\n u\"Error in index view: user={user}, course={course}, chapter={chapter}\"\r\n u\" section={section} position={position}\".format(\r\n user=user,\r\n course=course,\r\n chapter=chapter,\r\n section=section,\r\n position=position\r\n ))\r\n try:\r\n result = render_to_response('courseware/courseware-error.html', {\r\n 'staff_access': staff_access,\r\n 'course': course\r\n })\r\n except:\r\n # Let the exception propagate, relying on global config to at\r\n # at least return a nice error message\r\n log.exception(\"Error while rendering courseware-error page\")\r\n raise\r\n\r\n return result","def _get_courses(self) -> None:\n\n courses_content: NavigableString = self.soup.find(\"div\", \n {\"class\": \"coursesContent\"})\n course_items: ResultSet = courses_content.find_all(\"div\", \n {\"class\": \"courseItem\"})\n\n for item in course_items:\n course_name: str = item.a[\"href\"].split(\"/\")[-2].lower()\n course_data: ParseType = self._parse(item)\n self._update(course_name, course_data)","def parse_courses():\n\n subjects = collections.OrderedDict()\n name = '' # the most recent course name acronym (ex. 'COMP')\n\n courses = re.sub(r'\\([^)]*\\)', '', COURSES).split() # Remove parens and their contents\n\n for course in courses:\n if course == 'OR':\n continue\n\n if course[0].isalpha():\n\n index = 0 # the upper bound character index of the subject name\n for char in course:\n if char.isalpha():\n index += 1\n else:\n break\n\n name = course[:index]\n number = course[index:index+4]\n else:\n number = course[:4]\n\n try:\n subjects[name].append(number)\n except KeyError:\n subjects[name] = [number]\n\n return subjects","def event_html_page_context(app, pagename, templatename, context, doctree):\n\n reURL = re.compile('^https?://\\S+/$')\n current_builder = app.builder.name\n if current_builder == 'html' or current_builder == 'readthedocs':\n if doctree and doctree.traverse(DiscourseNode):\n if not app.config.discourse_url:\n raise DiscourseError('::discourse: directive found, but discourse_url is not set in conf.py')\n if not reURL.match(app.config.discourse_url):\n raise DiscourseError('::discourse: directive found, but discourse_url is not set properly in conf.py (must be a valid URL starting with http:// or https:// and ending with trailing slash)')\n for discourse_node in doctree.traverse(DiscourseNode):\n context['body'] += \"\"\"<script type='text/javascript'>\n DiscourseEmbed = { discourseUrl: '%s',\n topicId: '%s' };\n (function() {\n var d = document.createElement('script'); d.type = 'text/javascript'; d.async = true;\n d.src = DiscourseEmbed.discourseUrl + 'javascripts/embed.js';\n (document.getElementsByTagName('head')[0] || document.getElementsByTagName('body')[0]).appendChild(d);\n })();\n </script>\"\"\" % (app.config.discourse_url, discourse_node.get_topic_identifier())","def extract_course_summary(course):\n return {\n field: course.fields[field]\n for field in CourseSummary.course_info_fields\n if field in course.fields\n }","def get_semester_course_data(self, url, semester):\n print(f\"Obtaining and indexing information for {semester}\")\n html = self.call_url_and_get_html_object(url)\n tables = html.findall(\".//table[@class='courseListing basicTable courseListingSetWidths']\")\n\n # Parse html to get course offering data\n for table in tables:\n fields = table.findall(\".//td\")\n spans = table.findall(\".//span\")\n course_number = str(spans[1].text.strip())\n title = str(fields[4].text).strip()\n professor = str(fields[6].text).strip()\n status = str(fields[0].text)\n crn = str(fields[1].text)\n\n # Add course offering data to dictionary of course classes\n if course_number not in self.course_dict.keys():\n # If course doesn't already exist in dictionary keys, instantiate class of it\n self.course_dict[course_number] = Course(\n title=title, semester=semester,\n professor=professor, crn=crn, status=status)\n else:\n self.course_dict[course_number].add_instance_of_course(\n semester, professor, crn, status)","def build_general_course(self):\n\n courses = {}\n\n if self.__build_analyze is False:\n self.build_analysis()\n\n courses[\"taxa_conhecimento\"] = self.analysis[\"taxa_conhecimento\"]\n courses[\"taxa_reprovacao\"] = self.analysis[\"taxa_reprovacao_absoluta\"]\n courses[\"taxa_trancamento\"] = self.analysis[\"taxa_trancamento\"]\n\n # cria cache\n cache = {}\n note = self.analysis[\"general_note_statistic\"]\n for rate_it in self.__rates:\n rate_calc = self.analysis[\"general_rates\"][rate_it.name][0]\n for course in self.analysis[\"courses\"].index:\n if course not in cache:\n cache[course] = {}\n cache[course][rate_it.name] = rate_calc[course]\n cache[course][\"nota\"] = [note[0][course], note[1][course]]\n\n courses[\"cache\"] = cache\n\n # cria o campo compara_aprov\n courses[\"compara_aprov\"] = self.analysis[\"graph_course\"]\n\n # cria o campo courses\n courses[\"disciplinas\"] = self.analysis[\"courses\"].to_dict()\n\n return courses","def parse_create_section(xml_course):\n\n attrs = [\n \"section\",\n 'crn',\n \"start-time\",\n \"end-time\",\n \"meeting-days\",\n \"location\",\n \"section-number\",\n \"instructor\"\n ]\n\n section = pull_attributes_from_xml(xml_course, attrs)\n\n section[\"places\"] = []\n\n # Create Place attribute pointer based on location string\n # Get places from Parse\n places = get_places()[\"results\"]\n # Get location info from section (of form [\"BRK 101\", \"TBA\"])\n all_locations = section[\"location\"].split(\", \")\n # Filter out TBA\n # TODO Maybe do something else with them\n locations = [location for location in all_locations if location != \"TBA\"]\n\n for location in locations:\n building_code = location.split(\" \")[0]\n for place in places:\n if place.get(\"symbol\") and place[\"symbol\"] == building_code:\n section[\"places\"].append(place[\"objectId\"])\n break;\n\n\n return section","def _get_course_email_context(course):\r\n course_id = course.id.to_deprecated_string()\r\n course_title = course.display_name\r\n course_url = 'https://{}{}'.format(\r\n settings.SITE_NAME,\r\n reverse('course_root', kwargs={'course_id': course_id})\r\n )\r\n image_url = 'https://{}{}'.format(settings.SITE_NAME, course_image_url(course))\r\n email_context = {\r\n 'course_title': course_title,\r\n 'course_url': course_url,\r\n 'course_image_url': image_url,\r\n 'account_settings_url': 'https://{}{}'.format(settings.SITE_NAME, reverse('dashboard')),\r\n 'platform_name': settings.PLATFORM_NAME,\r\n }\r\n return email_context","def get_context(nav, config, page=None):\n\n if nav is None:\n return {'page', page}\n\n extra_javascript = utils.create_media_urls(nav, config['extra_javascript'])\n\n extra_css = utils.create_media_urls(nav, config['extra_css'])\n\n # Support SOURCE_DATE_EPOCH environment variable for \"reproducible\" builds.\n # See https://reproducible-builds.org/specs/source-date-epoch/\n timestamp = int(os.environ.get('SOURCE_DATE_EPOCH', timegm(datetime.utcnow().utctimetuple())))\n\n return {\n 'nav': nav,\n # base_url should never end with a slash.\n 'base_url': nav.url_context.make_relative('/').rstrip('/'),\n\n 'extra_css': extra_css,\n 'extra_javascript': extra_javascript,\n\n 'mkdocs_version': mkdocs.__version__,\n 'build_date_utc': datetime.utcfromtimestamp(timestamp),\n\n 'config': config,\n 'page': page,\n }","def get_text(self, course): # pylint: disable=unused-argument\r\n raise NotImplementedError","def get_course_about_section(course, section_key):\r\n\r\n # Many of these are stored as html files instead of some semantic\r\n # markup. This can change without effecting this interface when we find a\r\n # good format for defining so many snippets of text/html.\r\n\r\n # TODO: Remove number, instructors from this list\r\n if section_key in ['short_description', 'description', 'key_dates', 'video',\r\n 'course_staff_short', 'course_staff_extended',\r\n 'requirements', 'syllabus', 'textbook', 'faq', 'more_info',\r\n 'number', 'instructors', 'overview',\r\n 'effort', 'end_date', 'prerequisites', 'ocw_links']:\r\n\r\n try:\r\n\r\n request = get_request_for_thread()\r\n\r\n loc = course.location.replace(category='about', name=section_key)\r\n\r\n # Use an empty cache\r\n field_data_cache = FieldDataCache([], course.id, request.user)\r\n about_module = get_module(\r\n request.user,\r\n request,\r\n loc,\r\n field_data_cache,\r\n course.id,\r\n not_found_ok=True,\r\n wrap_xmodule_display=False,\r\n static_asset_path=course.static_asset_path\r\n )\r\n\r\n html = ''\r\n\r\n if about_module is not None:\r\n try:\r\n html = about_module.render('student_view').content\r\n except Exception: # pylint: disable=broad-except\r\n html = render_to_string('courseware/error-message.html', None)\r\n log.exception(\r\n u\"Error rendering course={course}, section_key={section_key}\".format(\r\n course=course, section_key=section_key\r\n ))\r\n return html\r\n\r\n except ItemNotFoundError:\r\n log.warning(\r\n u\"Missing about section {key} in course {url}\".format(key=section_key, url=course.location.to_deprecated_string())\r\n )\r\n return None\r\n elif section_key == \"title\":\r\n return course.display_name_with_default\r\n elif section_key == \"university\":\r\n return course.display_org_with_default\r\n elif section_key == \"number\":\r\n return course.display_number_with_default\r\n\r\n raise KeyError(\"Invalid about key \" + str(section_key))","def get_course_page(self):\n\n print(\"Course URL: {}\".format(self.course_url))\n try:\n self.course_page = BeautifulSoup(requests.get(self.course_url).text, \"lxml\")\n except requests.ConnectionError as e:\n print(\"Error Connecting!\\n\", e)\n sys.exit(1)\n except requests.exceptions.HTTPError as errh:\n print(\"Http Error:\", errh)\n sys.exit(1)\n except requests.exceptions.ConnectionError as errc:\n print(\"Error Connecting:\", errc)\n sys.exit(1)\n except requests.exceptions.Timeout as errt:\n print(\"Timeout Error:\", errt)\n sys.exit(1)\n except requests.exceptions.RequestException as err:\n print(\"Oops: Something Else\", err)\n sys.exit(1)","def export_courses():\n courses = Course.query().fetch()\n dictionary = {}\n\n for course in courses:\n dictionary[course.department + \"\" + course.number] = course.to_dict()\n\n return dictionary","def build_item_context(tree: HtmlElement) -> Dict:\n data = {\"description\": \"\"}\n\n jsonld_elem = tree.find('./head/script[@type=\"application/ld+json\"]')\n if jsonld_elem is not None:\n jsonld = json.loads(jsonld_elem.text)\n if \"description\" not in jsonld:\n description_meta = tree.find('./head/meta[@name=\"description\"]')\n jsonld[\"description\"] = description_meta.get(\"content\")\n data[\"description\"] = DESCRIPTION_FMT(**jsonld)\n\n return data","def _transform_learning_resource_course(course):\n return {\n \"readable_id\": course[\"readable_id\"],\n \"platform\": PlatformType.xpro.value,\n \"title\": course[\"title\"],\n \"image\": {\"url\": course[\"thumbnail_url\"]},\n \"offered_by\": copy.deepcopy(OFFERED_BY),\n \"description\": course[\"description\"],\n \"url\": course.get(\"url\"),\n \"published\": any(\n map(\n lambda course_run: course_run.get(\"current_price\", None),\n course[\"courseruns\"],\n )\n ),\n \"topics\": transform_topics(course.get(\"topics\", [])),\n \"runs\": [_transform_run(course_run) for course_run in course[\"courseruns\"]],\n \"resource_type\": LearningResourceType.course.value,\n }","def build_course_dictionary(title_result_set, desc_result_set) -> Dict[str, List[str]]:\n\n course_dictionary = {} # placeholder dictionary\n\n for (tagged_title, tagged_description) in zip(title_result_set, desc_result_set): # iterate through multiple result sets\n full_title_desc_list = {}\n full_title_desc_list = [str(tagged_title.text)] + str(tagged_description.text).strip().splitlines() # remove trailing whitespace, then get list of lines\n course_dictionary[str(tagged_title.text)[:8]] = full_title_desc_list\n\n return course_dictionary","def get_context(self, request, *args, **kwargs):\n program_page_qset = (\n ProgramPage.objects.live()\n .filter(program__live=True)\n .order_by(\"id\")\n .select_related(\"program\", \"thumbnail_image\")\n .prefetch_related(\"program__courses__courseruns\")\n )\n course_page_qset = (\n CoursePage.objects.live()\n .filter(course__live=True)\n .order_by(\"id\")\n .select_related(\"course\", \"thumbnail_image\")\n .prefetch_related(\"course__courseruns\")\n )\n external_course_qset = (\n ExternalCoursePage.objects.live()\n .order_by(\"title\")\n .select_related(\"thumbnail_image\")\n )\n\n external_program_qset = (\n ExternalProgramPage.objects.live()\n .order_by(\"title\")\n .select_related(\"thumbnail_image\")\n )\n\n featured_product = program_page_qset.filter(\n featured=True\n ) or course_page_qset.filter(featured=True)\n all_pages, program_pages, course_pages = filter_and_sort_catalog_pages(\n program_page_qset,\n course_page_qset,\n external_course_qset,\n external_program_qset,\n )\n return dict(\n **super().get_context(request),\n **get_base_context(request),\n all_pages=all_pages,\n program_pages=program_pages,\n course_pages=course_pages,\n featured_product=featured_product.first(),\n default_image_path=DEFAULT_COURSE_IMG_PATH,\n hubspot_portal_id=settings.HUBSPOT_CONFIG.get(\"HUBSPOT_PORTAL_ID\"),\n hubspot_new_courses_form_guid=settings.HUBSPOT_CONFIG.get(\n \"HUBSPOT_NEW_COURSES_FORM_GUID\"\n ),\n )","def GenerateMapper(t):\n context_token_limit = 2000\n\n url, corpus, story_html = t\n\n if not story_html:\n return None\n\n # Extract title\n raw_story = RawStory(url, story_html)\n story_title = ParseHtml(raw_story, corpus).getstory_title()\n # print story_title\n\n # Extract Introduction\n raw_story = RawStory(url, story_html)\n story_introduction = ParseHtml(raw_story, corpus).getstory_introduction()\n # print story_introduction\n\n # Extract rest of the content\n raw_story = RawStory(url, story_html)\n story_restcontent = ParseHtml(raw_story, corpus).getstory_restcontent()\n # print story_restcontent\n\n return story_title, story_introduction, story_restcontent","def get_chapter_text(location):\n chapter_output_dictionary = {}\n chapter_contents_list = []\n\n soup = Ripper(location, parser=\"html5lib\", save_path=save_path).soup\n text = soup.find(\"table\", class_=\"texttable\")\n\n for each in text.find_all(\"p\"):\n attributes = each.attrs\n if attributes:\n if \"desc\" in attributes[\"class\"]:\n pass\n elif \"note\" in attributes[\"class\"]:\n pass\n else:\n new_cont = each.contents\n chapter_contents_list.extend(new_cont)\n return join_chapter_text(chapter_contents_list)","def get_doctor_info(url, html):\n \n # create a dictionary to save data\n doctor_info = {}\n \n # parsed html by using lxml\n # In the following processing\n # the one ending in org is the raw data in the HTML\n # the one ending in inf is the processed data by extracting from raw data\n select = etree.HTML(html)\n \n # part 1: get basic information about your doctor\n # attribute: Name, Title, Hospital, Department\n name_org = select.xpath('//div[@class=\"profile-text\"]//h1[@class=\"doctor-name\"]//text()')\n name_inf = name_org[0].strip()\n \n title_org = select.xpath('//div[@class=\"profile-text\"]//span[@class=\"positon\"]//text()')\n title_inf = [i.strip() for i in title_org if len(i.strip()) > 0]\n title_inf = ' '.join(title_inf)\n \n hospital_department_org = select.xpath('//div[@class=\"profile-text\"]//p[@class=\"doctor-faculty\"]//text()')\n hospital_department_inf = [i.strip() for i in hospital_department_org if len(i.strip()) > 0]\n hospital_inf = hospital_department_inf[0]\n department_inf = hospital_department_inf[1]\n \n doctor_info['姓名'] = name_inf\n doctor_info['职称'] = title_inf\n doctor_info['医院'] = hospital_inf\n doctor_info['科室'] = department_inf\n \n # part2: get header format data\n org = select.xpath('//div[@class=\"profile-sta\"]//text()')\n inf = [i.strip() for i in org if len(i.strip()) > 0 and i.strip() != '%']\n for i in range(len(inf)//2):\n doctor_info[inf[2*i]] = inf[2*i + 1]\n \n \n # part3: get sidebar format data\n org_1 = select.xpath('//div[@class=\"item-body\"]//div[@class=\"clearfix\"]//div[@class=\"per-sta-label\"]//text()')\n org_2 = select.xpath('//div[@class=\"item-body\"]//div[@class=\"clearfix\"]//div[@class=\"per-sta-data\"]//text()')\n for i in range(len(org_1)):\n doctor_info[org_1[i][:-1]] = org_2[i]\n \n # part4: get body format data\n honour_org = select.xpath('//div[@class=\"honour-header\"]//text()')\n honour_inf = ''.join([i.strip() for i in honour_org])\n \n honour_detail_org = select.xpath('//li[@class=\"honour-title\"]//text()')\n honour_detail_inf = [i.strip()[:4] for i in honour_detail_org if len(i.strip()) > 0]\n honour_detail_inf = ' '.join(honour_detail_inf)\n \n satisfaction_org = select.xpath('//div[@class=\"item-body\"]//div[@class=\"satisfaction clearfix\"]//i[@class=\"sta-num\"]//text()')\n satisfaction_inf = [i.strip() for i in satisfaction_org if len(i.strip()) > 0 and i.strip() != '%']\n \n resume_org = select.xpath('//div[@class=\"good-at-text\"]//text()')\n resume_inf = [i.strip() for i in resume_org]\n if len(resume_inf) <= 20:\n resume_inf = ''.join(resume_inf)\n resume_inf = ''.join(resume_inf[:20])\n \n star_org = select.xpath('//div[@class=\"experience-row clearfix\"]//span[@class=\"experience-label\"]//text()')\n star_inf = 1 if len(star_org) >= 1 else 0\n\n doctor_info['好大夫届数'] = honour_inf\n doctor_info['好大夫具体年份'] = honour_detail_inf\n doctor_info['简历'] = resume_inf \n doctor_info['诊后服务星'] = star_inf\n try:\n doctor_info['疗效满意度'] = satisfaction_inf[0]\n doctor_info['态度满意度'] = satisfaction_inf[1]\n except:\n pass\n \n # part5: personal url\n personal_url = url\n doctor_info['医生个人链接'] = personal_url\n \n return doctor_info","def _extract_page_info(article: dict, url: str) -> dict:\n\n if not article:\n return {}\n language = detect(article.get('content_text'))\n if len(language) > 2 and len(language[2]) > 1:\n language_code = language[2][0][1]\n else:\n language_code = None\n return {'url': url, 'language': language_code}","def __init__(self, xmlstore, course_id, course_dir,\r\n error_tracker, parent_tracker,\r\n load_error_modules=True, **kwargs):\r\n self.unnamed = defaultdict(int) # category -> num of new url_names for that category\r\n self.used_names = defaultdict(set) # category -> set of used url_names\r\n id_generator = CourseLocationGenerator(course_id)\r\n\r\n # cdodge: adding the course_id as passed in for later reference rather than having to recomine the org/course/url_name\r\n self.course_id = course_id\r\n self.load_error_modules = load_error_modules\r\n\r\n def process_xml(xml):\r\n \"\"\"Takes an xml string, and returns a XBlock created from\r\n that xml.\r\n \"\"\"\r\n\r\n def make_name_unique(xml_data):\r\n \"\"\"\r\n Make sure that the url_name of xml_data is unique. If a previously loaded\r\n unnamed descriptor stole this element's url_name, create a new one.\r\n\r\n Removes 'slug' attribute if present, and adds or overwrites the 'url_name' attribute.\r\n \"\"\"\r\n # VS[compat]. Take this out once course conversion is done (perhaps leave the uniqueness check)\r\n\r\n # tags that really need unique names--they store (or should store) state.\r\n need_uniq_names = ('problem', 'sequential', 'video', 'course', 'chapter',\r\n 'videosequence', 'poll_question', 'vertical')\r\n\r\n attr = xml_data.attrib\r\n tag = xml_data.tag\r\n id = lambda x: x\r\n # Things to try to get a name, in order (key, cleaning function, remove key after reading?)\r\n lookups = [('url_name', id, False),\r\n ('slug', id, True),\r\n ('name', Location.clean, False),\r\n ('display_name', Location.clean, False)]\r\n\r\n url_name = None\r\n for key, clean, remove in lookups:\r\n if key in attr:\r\n url_name = clean(attr[key])\r\n if remove:\r\n del attr[key]\r\n break\r\n\r\n def looks_like_fallback(url_name):\r\n \"\"\"Does this look like something that came from fallback_name()?\"\"\"\r\n return (url_name is not None\r\n and url_name.startswith(tag)\r\n and re.search('[0-9a-fA-F]{12}$', url_name))\r\n\r\n def fallback_name(orig_name=None):\r\n \"\"\"Return the fallback name for this module. This is a function instead of a variable\r\n because we want it to be lazy.\"\"\"\r\n if looks_like_fallback(orig_name):\r\n # We're about to re-hash, in case something changed, so get rid of the tag_ and hash\r\n orig_name = orig_name[len(tag) + 1:-12]\r\n # append the hash of the content--the first 12 bytes should be plenty.\r\n orig_name = \"_\" + orig_name if orig_name not in (None, \"\") else \"\"\r\n xml_bytes = xml.encode('utf8')\r\n return tag + orig_name + \"_\" + hashlib.sha1(xml_bytes).hexdigest()[:12]\r\n\r\n # Fallback if there was nothing we could use:\r\n if url_name is None or url_name == \"\":\r\n url_name = fallback_name()\r\n # Don't log a warning--we don't need this in the log. Do\r\n # put it in the error tracker--content folks need to see it.\r\n\r\n if tag in need_uniq_names:\r\n error_tracker(\"PROBLEM: no name of any kind specified for {tag}. Student \"\r\n \"state will not be properly tracked for this module. Problem xml:\"\r\n \" '{xml}...'\".format(tag=tag, xml=xml[:100]))\r\n else:\r\n # TODO (vshnayder): We may want to enable this once course repos are cleaned up.\r\n # (or we may want to give up on the requirement for non-state-relevant issues...)\r\n # error_tracker(\"WARNING: no name specified for module. xml='{0}...'\".format(xml[:100]))\r\n pass\r\n\r\n # Make sure everything is unique\r\n if url_name in self.used_names[tag]:\r\n # Always complain about modules that store state. If it\r\n # doesn't store state, don't complain about things that are\r\n # hashed.\r\n if tag in need_uniq_names:\r\n msg = (\"Non-unique url_name in xml. This may break state tracking for content.\"\r\n \" url_name={0}. Content={1}\".format(url_name, xml[:100]))\r\n error_tracker(\"PROBLEM: \" + msg)\r\n log.warning(msg)\r\n # Just set name to fallback_name--if there are multiple things with the same fallback name,\r\n # they are actually identical, so it's fragile, but not immediately broken.\r\n\r\n # TODO (vshnayder): if the tag is a pointer tag, this will\r\n # break the content because we won't have the right link.\r\n # That's also a legitimate attempt to reuse the same content\r\n # from multiple places. Once we actually allow that, we'll\r\n # need to update this to complain about non-unique names for\r\n # definitions, but allow multiple uses.\r\n url_name = fallback_name(url_name)\r\n\r\n self.used_names[tag].add(url_name)\r\n xml_data.set('url_name', url_name)\r\n\r\n try:\r\n # VS[compat]\r\n # TODO (cpennington): Remove this once all fall 2012 courses\r\n # have been imported into the cms from xml\r\n xml = clean_out_mako_templating(xml)\r\n xml_data = etree.fromstring(xml)\r\n\r\n make_name_unique(xml_data)\r\n\r\n descriptor = create_block_from_xml(\r\n etree.tostring(xml_data, encoding='unicode'),\r\n self,\r\n id_generator,\r\n )\r\n except Exception as err: # pylint: disable=broad-except\r\n if not self.load_error_modules:\r\n raise\r\n\r\n # Didn't load properly. Fall back on loading as an error\r\n # descriptor. This should never error due to formatting.\r\n\r\n msg = \"Error loading from xml. %s\"\r\n log.warning(\r\n msg,\r\n unicode(err)[:200],\r\n # Normally, we don't want lots of exception traces in our logs from common\r\n # content problems. But if you're debugging the xml loading code itself,\r\n # uncomment the next line.\r\n # exc_info=True\r\n )\r\n\r\n msg = msg % (unicode(err)[:200])\r\n\r\n self.error_tracker(msg)\r\n err_msg = msg + \"\\n\" + exc_info_to_str(sys.exc_info())\r\n descriptor = ErrorDescriptor.from_xml(\r\n xml,\r\n self,\r\n id_generator,\r\n err_msg\r\n )\r\n\r\n descriptor.data_dir = course_dir\r\n\r\n xmlstore.modules[course_id][descriptor.scope_ids.usage_id] = descriptor\r\n\r\n if descriptor.has_children:\r\n for child in descriptor.get_children():\r\n parent_tracker.add_parent(child.scope_ids.usage_id, descriptor.scope_ids.usage_id)\r\n\r\n # After setting up the descriptor, save any changes that we have\r\n # made to attributes on the descriptor to the underlying KeyValueStore.\r\n descriptor.save()\r\n return descriptor\r\n\r\n render_template = lambda template, context: u''\r\n\r\n # TODO (vshnayder): we are somewhat architecturally confused in the loading code:\r\n # load_item should actually be get_instance, because it expects the course-specific\r\n # policy to be loaded. For now, just add the course_id here...\r\n def load_item(usage_key):\r\n \"\"\"Return the XBlock for the specified location\"\"\"\r\n return xmlstore.get_item(usage_key)\r\n\r\n resources_fs = OSFS(xmlstore.data_dir / course_dir)\r\n\r\n super(ImportSystem, self).__init__(\r\n load_item=load_item,\r\n resources_fs=resources_fs,\r\n render_template=render_template,\r\n error_tracker=error_tracker,\r\n process_xml=process_xml,\r\n **kwargs\r\n )","def course_info(self):\n print(\"Course name: {}\".format(self._course_name))\n print(\"Lead teacher: {}\".format(self._teacher))\n\n if len(self._students) == 0:\n print(\"Course does not enrolled by any student\")\n else:\n print(\"Enrolled: {}/{}\".format(len(self._students), self._total_place))","def etymologies(pages):\n etymologies = defaultdict(list)\n print('Building etymology dictionary...')\n with ProgressBar(maxval=len(pages)) as progress:\n for i, page in enumerate(pages):\n entries = (Entry(*e) for e in load_page(page))\n for entry in entries:\n etymologies[entry.text].insert(entry.index, entry)\n progress.update(i)\n return etymologies","def create_template_dict(name, cat, boilerplate_name=None, is_common=False):\r\n return {\r\n \"display_name\": name,\r\n \"category\": cat,\r\n \"boilerplate_name\": boilerplate_name,\r\n \"is_common\": is_common\r\n }","def olive_parser(text: str) -> dict:\n soup = BeautifulSoup(text, \"lxml\")\n root = soup.find(\"xmd-entity\")\n page_no = root['page_no']\n identifier = root['id']\n language = root['language']\n title = soup.meta['name']\n entity_type = root['entity_type']\n issue_date = soup.meta['issue_date']\n\n out = {\n \"meta\": {\n \"language\": None,\n \"type\": {}\n },\n \"r\": [],\n \"stats\": {},\n \"legacy\": {\"continuation_from\": None, \"continuation_to\": None},\n }\n out[\"meta\"][\"title\"] = title\n out[\"meta\"][\"page_no\"] = [int(page_no)]\n out[\"meta\"][\"language\"] = normalize_language(language)\n out[\"meta\"][\"type\"][\"raw\"] = entity_type\n out[\"meta\"][\"issue_date\"] = issue_date\n\n new_region = {\n \"c\": [],\n \"p\": []\n }\n\n new_paragraph = {\n \"l\": []\n }\n\n new_line = {\n \"c\": [],\n \"t\": []\n }\n\n new_token = {\n \"c\": [],\n \"tx\": \"\"\n }\n\n for primitive in soup.find_all(\"primitive\"):\n\n # store coordinate of text areas (boxes) by page\n # 1) page number, 2) coordinate list\n region = copy.deepcopy(new_region)\n region[\"c\"] = [int(i) for i in primitive.get('box').split(\" \")]\n\n para = None\n line = None\n line_counter = 0\n\n for tag in primitive.find_all(recursive=False):\n\n if tag.name == \"l\":\n\n if para is None and line is None:\n para = copy.deepcopy(new_paragraph)\n line = copy.deepcopy(new_line)\n\n if line_counter > 0 and line is not None:\n line = normalize_line(line, out[\"meta\"][\"language\"])\n para[\"l\"].append(line)\n\n if tag.get(\"p\") in [\"S\", \"SA\"] and line_counter > 0:\n region[\"p\"].append(para)\n para = copy.deepcopy(new_paragraph)\n\n line = copy.deepcopy(new_line)\n line[\"c\"] = [\n int(i)\n for i in tag.get('box').split(\" \")\n ]\n line_counter += 1\n\n if tag.name in [\"w\", \"q\"]:\n\n # store coordinates of each token\n # 1) token, 2) page number, 3) coordinate list\n t = copy.deepcopy(new_token)\n t[\"c\"] = [int(i) for i in tag.get('box').split(\" \")]\n t[\"tx\"] = tag.string\n t[\"s\"] = int(tag.get('style_ref'))\n\n if tag.name == \"q\" and tag.get('qid') is not None:\n qid = tag.get('qid')\n normalized_form = soup.find('qw', qid=qid).text\n t[\"nf\"] = normalized_form\n t[\"qid\"] = qid\n\n # append the token to the line\n line[\"t\"].append(t)\n\n # append orphan lines\n if line is not None:\n line = normalize_line(line, out[\"meta\"][\"language\"])\n para[\"l\"].append(line)\n\n region[\"p\"].append(para)\n\n if para is not None:\n out[\"r\"].append(region)\n\n out[\"legacy\"][\"id\"] = identifier\n out[\"legacy\"][\"source\"] = soup.link['source']\n \"\"\"\n # I suspect this could be deleted\n out[\"legacy\"][\"word_count\"] = int(soup.meta['wordcnt'])\n out[\"legacy\"][\"chars_count\"] = int(soup.meta['total_chars_count'])\n suspicious_chars_count = int(soup.meta['suspicious_chars_count'])\n out[\"legacy\"][\"suspicious_chars_count\"] = int(suspicious_chars_count)\n \"\"\"\n out[\"legacy\"][\"first_id\"] = soup.link['first_id']\n out[\"legacy\"][\"last_id\"] = soup.link['last_id']\n out[\"legacy\"][\"next_id\"] = soup.link['next_id']\n out[\"legacy\"][\"prev_id\"] = soup.link['prev_id']\n\n if root.has_attr('continuation_from'):\n out[\"legacy\"][\"continuation_from\"] = root['continuation_from']\n\n if root.has_attr('continuation_to'):\n out[\"legacy\"][\"continuation_to\"] = root['continuation_to']\n\n return out","def parse_text(self):\n self.text={}\n for i, lang in enumerate(LANGS):\n text=file(self.src).read()\n self.text[lang]=\"\"\n extracted, finish = \"\", 0\n start_string, stop_string = r\"<!--%s-->\" % lang, r\"<!--/%s-->\" % lang\n # Iterates to check multiple blocks of text within the file!\n # Pay attention to infinite loops!\n # AttributeError exception raised when no more blocks to extract exist\n while True:\n try:\n start=re.compile(start_string, re.IGNORECASE).search(text).span()[1]\n finish=re.compile(stop_string, re.IGNORECASE).search(text).span()[0]\n extracted+=text[start:finish]\n text=text[finish+1:]\n except AttributeError:\n break\n self.text[lang]+=extracted","def extract(self):\n self.logger.info(f'Opening {self.file}')\n try:\n f = open(self.file, 'r')\n except FileNotFoundError:\n self.logger.info('Could not open the .vrt file')\n return\n\n result = {}\n self.logger.info('Extracting')\n for line in f:\n # Only attempt to extract if line is correct\n if self.is_target(line):\n # Add the closing text tag to make the line have proper XML\n xml_attributes = XML.fromstring(line.rstrip()+'</text>').attrib\n # Extract wanted parameters\n month = xml_attributes['date'][5:7] # Extract month\n # Add month to the keys\n if month not in result.keys():\n result[month] = []\n result[month].append({\n 'title': xml_attributes['title'], # Extract thread title\n 'thread_id': xml_attributes['thread_id'], # Extract thread id\n 'datetime': xml_attributes['datetime'] # Extract datetime\n })\n self.logger.info('Extraction complete')\n self.db = result\n # self.db[self.year] = result # Assign results to year\n self.save_result() # Save the results","def html_collect_pages(app):\n if not hasattr(app.builder.env, \"categories\"):\n return # nothing to do\n\n for name, context, template in create_category_pages(app):\n yield (name, context, template)","def info():\n # -------- Task 1 -------------------------\n # Please complete the following information\n\n return {\"agent name\": \"?\", # COMPLETE HERE\n \"student name\": [\"?\"], # COMPLETE HERE\n \"student number\": [\"?\"]} # COMPLETE HERE","def Content():\n\tTOPIC_DICT = {\"Basics\": [[\"Introduction to Python\", \"/introduction-to-python/\"], \n\t\t\t\t\t\t\t\t\t\t\t\t\t [\"Print functions\", \"/python-print-function/\"],\n\t\t\t\t\t\t\t\t\t\t\t\t\t [\"Math basics with Python 3\", \"/math-basics-python-3/\"\n\t\t\t\t\t\t\t\t\t\t\t\t\t ]],\n\t\t\t\t\t\t\t\t\"Web Dev\": [[\"wd1\"]]} #Web dev is now the second topic\n\t\t\t\t\t\t\t\t#Introduction to Python\n\t#will be the title and the link to that is immediately after. Each inner array will be a specific sub section of the basics\n\t#section\n\n\treturn TOPIC_DICT","def _update_certificate_context(context, course, course_overview, user_certificate, platform_name):\n # Populate dynamic output values using the course/certificate data loaded above\n certificate_type = context.get('certificate_type')\n\n # Override the defaults with any mode-specific static values\n context['certificate_id_number'] = user_certificate.verify_uuid\n context['certificate_verify_url'] = \"{prefix}{uuid}{suffix}\".format(\n prefix=context.get('certificate_verify_url_prefix'),\n uuid=user_certificate.verify_uuid,\n suffix=context.get('certificate_verify_url_suffix')\n )\n\n # We prefer a CourseOverview for this function because it validates and corrects certificate_available_date\n # and certificates_display_behavior values. However, not all certificates are guaranteed to have a CourseOverview\n # associated with them, so we fall back on the course in that case. This shouldn't cause a problem because courses\n # that are missing CourseOverviews are generally old courses, and thus their display values are no longer relevant\n if course_overview:\n date = display_date_for_certificate(course_overview, user_certificate)\n else:\n date = display_date_for_certificate(course, user_certificate)\n # Translators: The format of the date includes the full name of the month\n context['certificate_date_issued'] = strftime_localized(date, settings.CERTIFICATE_DATE_FORMAT)\n\n # Translators: This text represents the verification of the certificate\n context['document_meta_description'] = _('This is a valid {platform_name} certificate for {user_name}, '\n 'who participated in {partner_short_name} {course_number}').format(\n platform_name=platform_name,\n user_name=context['accomplishment_copy_name'],\n partner_short_name=context['organization_short_name'],\n course_number=context['course_number']\n )\n\n # Translators: This text is bound to the HTML 'title' element of the page and appears in the browser title bar\n context['document_title'] = _(\"{partner_short_name} {course_number} Certificate | {platform_name}\").format(\n partner_short_name=context['organization_short_name'],\n course_number=context['course_number'],\n platform_name=platform_name\n )\n\n # Translators: This text fragment appears after the student's name (displayed in a large font) on the certificate\n # screen. The text describes the accomplishment represented by the certificate information displayed to the user\n context['accomplishment_copy_description_full'] = _(\"successfully completed, received a passing grade, and was \"\n \"awarded this {platform_name} {certificate_type} \"\n \"Certificate of Completion in \").format(\n platform_name=platform_name,\n certificate_type=context.get(\"certificate_type\"))\n\n certificate_type_description = get_certificate_description(user_certificate.mode, certificate_type, platform_name)\n if certificate_type_description:\n context['certificate_type_description'] = certificate_type_description\n\n # Translators: This text describes the purpose (and therefore, value) of a course certificate\n context['certificate_info_description'] = _(\"{platform_name} acknowledges achievements through \"\n \"certificates, which are awarded for course activities \"\n \"that {platform_name} students complete.\").format(\n platform_name=platform_name,\n )","def build_course_map(course_content):\n course_blocks = []\n\n for key, course_data in course_content.items():\n block_data = {}\n block_data['id'] = parse_id(key)\n block_data['type'] = course_data['category']\n\n try:\n block_data['name'] = course_data['metadata']['display_name']\n except KeyError:\n block_data['name'] = block_data['id']\n\n customize_by_type(course_data, block_data)\n add_children(course_data, block_data)\n\n course_blocks.append(block_data)\n\n return course_blocks","def html_collect_pages(app):\n\n if not hasattr(app.builder.env, \"categories\"):\n return # nothing to do\n\n for name, context, template in create_category_pages(app):\n yield (name, context, template)","def enter_request_context(self, request):\r\n context = {\r\n 'session': self.get_session_key(request),\r\n 'user_id': self.get_user_primary_key(request),\r\n 'username': self.get_username(request),\r\n }\r\n for header_name, context_key in META_KEY_TO_CONTEXT_KEY.iteritems():\r\n context[context_key] = request.META.get(header_name, '')\r\n\r\n context.update(contexts.course_context_from_url(request.build_absolute_uri()))\r\n\r\n tracker.get_tracker().enter_context(\r\n CONTEXT_NAME,\r\n context\r\n )","def parseLesson(lesson_text):\n lesson_parsed = dict()\n\n lesson_parsed['section_starts'] = list()\n lesson_parsed['sections'] = list()\n lesson_parsed['end_of_lesson'] = None\n lesson_parsed['source'] = None\n lesson_parsed['title'] = None\n lesson_parsed['number'] = None\n\n end_of_lesson_markers = [\n r'^\\s*\\(End\\s+of\\s+lesson',\n r'^\\s*\\(End\\s+of\\s+lecture',\n r'^\\(End\\s+of\\s+recording',\n r'^\\[End\\s+of\\s+recording',\n r'^\\[End\\s+of\\s+the\\s+recording',\n r'^\\(End of the recording',\n r'^\\[End\\s+of\\s+the\\s+tape',\n r'^\\(End\\s+of\\s+audible\\s+portion\\s+of\\s+the\\s+lesson',\n r'^\\[Note\\:\\s+This\\s+is\\s+the\\s+end\\s+of\\s+the\\s+recording',\n ]\n\n # Do a parsing pass of the overall lesson text. This information will be used\n # to compile the lesson into a form useful for display and use markup.\n line_number = 0\n for line in lesson_text:\n mobj = re.search(r'^Source:\\s+(.+)$', line)\n if mobj:\n # WORKING HERE\n pass\n\n if re.search(r'^\\d+[a-z]?\\.', line):\n lesson_parsed['section_starts'].append(line_number)\n line_number += 1\n continue\n\n found_end_of_lesson = False \n for end_of_lesson_marker in end_of_lesson_markers:\n if re.search(end_of_lesson_marker, line):\n lesson_parsed['end_of_lesson'] = line_number\n line_number += 1\n found_end_of_lesson = True\n break\n\n if found_end_of_lesson:\n continue\n\n for end_of_lesson_marker in end_of_lesson_markers:\n if re.search(end_of_lesson_marker, line):\n lesson_parsed['end_of_lesson'] = line_number\n line_number += 1\n break\n\n if lesson_parsed['end_of_lesson'] is not None:\n continue\n\n # Remove tabs from the start of a line.\n # Replace tabs between the number and the start of text with a space.\n # Skip \"This page is included for printing and binding purposes\"\n # Footnotes \"[fnX]\" will need to be linked.\n\n line_number += 1\n\n\n # Now compile the lesson instructions into their own things.\n\n idx = 0\n\n new_section = list()\n for section_line in xrange(lesson_parsed['section_starts'][idx], lesson_parsed['section_starts'][idx+1]:\n section_start_text = lesson_text[section_line]\n lesson_parsed['sections'].append(lesson_text[section_line])\n\n\n\n return lesson_parsed","def treat_page(self):\n # let's define some basic variables\n urtext = self.current_page.text\n urlang = self.current_page.site.code\n urtitle = self.current_page.title()\n urcat = []\n eng_site = pywikibot.Site('en')\n eng_title = ''\n \n interDict = {}\n try:\n site = pywikibot.Site('ur', 'wikipedia')\n urpage = pywikibot.Page(site, urtitle)\n langlst = urpage.iterlanglinks()\n\n \n for i in langlst:\n lang = str(i.site).split(':')[1]\n interDict[lang] = i.title\n \n eng_title = interDict['en']\n except:\n pywikibot.output(u'\\03{lightred}Unable to fetch interwiki links!\\03{default}')\n return False\n \n site = pywikibot.Site('en', 'wikipedia')\n enpage = pywikibot.Page(site, eng_title)\n\n wikitext = enpage.get() \n wikicode = mwp.parse(wikitext)\n\n # Extracting sfn templates and converting them in REF tags\n sfnlist = []\n for template in wikicode.filter_templates():\n if template.name in ('sfn', 'sfn'):\n sfnlist.append(template)\n templ_rep = '<ref>' + str(template) + '</ref>'\n wikicode.replace(template , templ_rep)\n\n alltags = wikicode.filter_tags() \n reftags = {}\n \n def search(myDict, search1):\n for key, value in myDict.items():\n if search1 in value: \n return key \n \n i=1\n for tag in alltags:\n if tag.tag=='ref':\n if tag.attributes == []: # check if attributes list is empty\n refval='NoRefName' # Reference has no name so assigning \"NoRefName\"\n else:\n name = tag.attributes[0]\n refval = name.value\n \n if tag.contents is None:\n #conval = search(reftags,refval)\n #reftags[i] = (refval,reftags[conval][1])\n pass\n else: \n reftags[i] = (refval,tag.contents)\n i += 1\n\n dlinks = {}\n for k,v in reftags.items():\n dkey = 'و' + str(k) + 'و'\n dlinks[dkey] = '<ref>' + str(v[1]) + '</ref>'\n\n urtext = urpage.text\n for r in tuple(dlinks.items()):\n urtext = urtext.replace(*r)\n\n # newln = '\\n'\n # Using noreferences to add Reference template if not present\n self.norefbot = noreferences.NoReferencesBot(None)\n if self.norefbot.lacksReferences(urtext):\n urtext = self.norefbot.addReferences(urtext)\n else:\n urpage.text = urtext + '\\n'\n\n print(urpage.text)\n \n # save the page \n urpage.save(summary=self.summary, minor=False)\n #self.put_current(urpage.text, summary=self.summary)","def parse_catalogs(campuses=['Seattle', 'Bothell', 'Tacoma'], struct='df', \n show_progress=False):\n assert type(campuses) == list or type(campuses) == dict, 'Type of \"campuses\" must be list or dict'\n if type(campuses) == dict:\n for key, value in campuses.items():\n if type(key) != str or type(value) != list:\n raise ValueError('''\"campuses\" dict must have keys of type str and\n values of type list''')\n # Check if all campuses in 'campuses' are valid\n assert all([c in ['Seattle', 'Bothell', 'Tacoma'] for c in list(map(str.title, campuses))])\n assert type(struct) == str, 'Type of \"struct\" must be str'\n assert type(show_progress) == bool, 'Type of \"show_progress\" must be bool'\n assert struct in ['df', 'dict'], f'{struct} is an invalid argument for \"struct\"'\n\n # Progress bar for Course Schedule Parsing\n if show_progress:\n progress_bar = tqdm()\n\n def parse_campus(department_data, campus):\n \"\"\"\n Parses all courses from a UW Campus\n\n @params\n\n 'department_data': BeautifulSoup object with the department list website source\n for the given 'campus'\n\n 'campus': The campus to get courses from\n\n Returns\n\n A pandas DataFrame with all courses in the given campus\n \"\"\"\n\n def extract_data(department_link):\n \"\"\"\n Extracts all course information from a UW Department\n\n @params:\n\n 'department_link': The url to the UW Department to get course\n information from\n\n Returns\n\n A list of lists. Each nested list represents one course section with the\n following values (in this order):\n\n 'Campus', 'Department Name', 'Course Number', 'Course Name', 'Credits',\n 'Areas of Knowledge', 'Quarters Offered', 'Offered with', \n 'Prerequisites', 'Co-Requisites', 'Description'\n \"\"\"\n # Update the progress bar\n if show_progress:\n progress_bar.update()\n\n # Regular expressions for searching course descriptions stored in local variables\n # for better peformance\n local_course_re = course_re\n local_course_name_re = course_name_re\n local_credits_re = credits_re\n local_credits_num_re = credits_num_re\n local_offered_jointly_re = offered_jointly_re\n local_CAMPUSES = CAMPUSES\n\n # Method used in extracting data from course descriptions found in the local scope\n # are stored in local variables for better performance\n local_complete_description = complete_description\n local_get_offered = get_offered\n local_get_requisites = get_requisites\n\n # All the courses in the department\n courses = []\n dep_file = department_link.get('href')\n\n # If the user entered a dict as the 'campuse' parameter, departments\n # are checked here\n try:\n # The String in the conditional is the abbreviated Department Name i.e EE\n # for Electrical Engineering\n if normalize('NFKD', department_link.text).rsplit('(', 1) \\\n [-1].replace(' ', '')[:-1] not in campuses[campus]:\n return None\n except TypeError:\n pass\n \n # The only links that are used for finding departments are those\n # of the format [a-z]+.html\n if '/' not in dep_file and dep_file.endswith('.html') \\\n and dep_file not in parsed_departments:\n parsed_departments.add(dep_file)\n department = BeautifulSoup(requests.get( \\\n f'{local_CAMPUSES[campus]}{dep_file}').text, features='lxml')\n for course in department.find_all('a'):\n course_ID = course.get('name') \n if course_ID:\n course_ID = course_ID.upper()\n course_title = course.find('b').text\n # The Course Description\n description = course.get_text().replace(course_title, '', 1) \n instructors = course.find('i')\n if instructors:\n description = description.replace(str(instructors.get_text()), '', 1)\n del instructors\n course_text = local_complete_description( \\\n description.rsplit('View course details in MyPlan', 1)[0])\n # Course Number i.e 351\n course_number = re.sub(local_course_re, '', course_ID)\n match_name = re.search(local_course_name_re, course_title)\n match_credit_num = re.search(local_credits_num_re, course_title)\n match_credit_types = re.findall(local_credits_re, course_title)\n # Jointly offered course with the given course\n if 'jointly with' in course_text: \n offered_jointly = course_text.rsplit('jointly with ', 1)[-1].rsplit(';', 1)[0] \n offered_jointly = ','.join(re.findall( \\\n local_offered_jointly_re, offered_jointly)).replace(' ', '') \n else:\n offered_jointly = ''\n courses.append(\n # Campus, Department Name and Course Number\n [campus, course_ID[:-3], course_number, \n # Course Name\n match_name.group(0).split(course_number, 1)[-1].strip() \\\n if match_name else '',\n # Number of credits for the course\n match_credit_num.group(0)[1:-1] \\\n if match_credit_num else '', \n # Course Credit Types (I&S, DIV, NW, VLPA, QSR, C)\n ','.join([list(filter(('').__ne__, x))[0] for x in match_credit_types]) \\\n if match_credit_types else '', \n local_get_offered(course_text),\n offered_jointly, local_get_requisites(course_text, 'Prerequisite:'), \n local_get_requisites(course_text, 'Co-requisite'), course_text]\n )\n return courses\n\n # In the course catalog website, several department links appear multiple times\n # To prevent parsing the same department more than once, parsed departments\n # are tracked in 'parsed_departments'\n parsed_departments = set()\n local_extract_data = extract_data\n department_data = BeautifulSoup(requests.get(department_data).text, features='lxml')\n\n campus_catalog = []\n # Extract data from department websites in parallel to reduce idle time\n with cf.ThreadPoolExecutor() as executor:\n results = [executor.submit(local_extract_data, department_link) \n for department_link in department_data.find_all('a')]\n for result in cf.as_completed(results):\n dptmnt = result.result()\n if dptmnt:\n campus_catalog.append(dptmnt)\n\n # DataFrame with all courses in the campus\n return pd.DataFrame(\n [course for department in campus_catalog for course in department], \n columns=COLUMN_NAMES\n )\n\n # The pandas DataFrame to store the entire course catalog for each UW Campus entered\n # by the user\n course_catalog = pd.DataFrame()\n\n # Parse all three campuses in parallel for faster run time as well\n # as get the departments dictionary from the 'get_departments' method\n # to add a 'Colleges' column to categorize all courses in their College.\n with cf.ThreadPoolExecutor() as executor:\n results = []\n campuses_for_dict = campuses\n if type(campuses) == dict:\n campuses_for_dict = list(campuses.keys())\n results.append(executor.submit(get_departments, campuses=campuses_for_dict, struct='dict'))\n for campus, link in CAMPUSES.items():\n if campus.title() in campuses: \n results.append(executor.submit(parse_campus, link, campus.title()))\n for result in cf.as_completed(results):\n returned = result.result()\n if type(returned) == dict:\n # Departments dict used to create the 'College' column in the main DataFrame\n departments = returned\n else:\n course_catalog = pd.concat([course_catalog, returned])\n\n # Add Course ID as the index of the DataFrame to allow for easy course searching\n # Course ID = Department Name + Course Number\n # Example: EE235 = EE + 235\n course_catalog['Course ID'] = course_catalog['Department Name'] + course_catalog['Course Number']\n course_catalog['College'] = course_catalog['Department Name'].apply(check_campus, args=(departments, 'College'))\n course_catalog.set_index('Course ID', inplace=True)\n # Re-order indices to place 'College' right after the 'Department Name'\n course_catalog = course_catalog[['Campus', 'Department Name', 'College', 'Course Number', 'Course Name', 'Credits',\n 'Areas of Knowledge', 'Quarters Offered', 'Offered with', \n 'Prerequisites', 'Co-Requisites', 'Description']]\n \n if struct == 'df':\n return course_catalog\n elif struct == 'dict':\n return course_catalog.to_dict(orient='index')","def _parse_lesson(self, lesson_link: str):\n\n lesson_page: Soup = self._get_soup(lesson_link)\n lesson_content: NavigableString = lesson_page.find(\"div\", \n {\"class\": \"moduleContent\"})\n course_divs: ResultSet = lesson_content.find_all(\"div\")\n\n data = {}\n module = \"\"\n for div in course_divs:\n if div[\"class\"][0] == \"courseModule\":\n module = div.p.get_text().split(\":\")[-1].strip()\n data[module] = []\n elif div[\"class\"][0] == \"courseLesson\":\n lesson_title = div.find(\"span\", {\"class\": \"courseLessonTitle\"})\n data[module].append(lesson_title.get_text().strip())\n \n return data","def available_courses(self):\r\n def _get_course_name(el):\r\n # The first component in the link text is the course number\r\n _, course_name = el.text.split(' ', 1)\r\n return course_name\r\n\r\n return self.q(css='section.info > hgroup > h3 > a').map(_get_course_name).results","def getCourseData(self, course):\n\t\tif course == None:\n\t\t\treturn None\n\t\tcommand = \"SELECT name, description, author_id FROM courses WHERE id=?;\"\n\t\tparams = (course,)\n\n\t\tdata = self._run_command(command, params)\n\n\t\tif not data:\n\t\t\treturn None\n\n\t\tdata = data[0]\n\t\tresult = {\"name\": data[0] if data[0] else \"\", \n\t\t\"description\": data[1] if data[1] else \"\", \n\t\t\"author_id\": data[2]}\n\n\t\treturn result","def dump_grading_context(course):\r\n hbar = \"{}\\n\".format(\"-\" * 77)\r\n msg = hbar\r\n msg += \"Course grader:\\n\"\r\n\r\n msg += '%s\\n' % course.grader.__class__\r\n graders = {}\r\n if isinstance(course.grader, xmgraders.WeightedSubsectionsGrader):\r\n msg += '\\n'\r\n msg += \"Graded sections:\\n\"\r\n for subgrader, category, weight in course.grader.sections:\r\n msg += \" subgrader=%s, type=%s, category=%s, weight=%s\\n\"\\\r\n % (subgrader.__class__, subgrader.type, category, weight)\r\n subgrader.index = 1\r\n graders[subgrader.type] = subgrader\r\n msg += hbar\r\n msg += \"Listing grading context for course %s\\n\" % course.id.to_deprecated_string()\r\n\r\n gcontext = course.grading_context\r\n msg += \"graded sections:\\n\"\r\n\r\n msg += '%s\\n' % gcontext['graded_sections'].keys()\r\n for (gsomething, gsvals) in gcontext['graded_sections'].items():\r\n msg += \"--> Section %s:\\n\" % (gsomething)\r\n for sec in gsvals:\r\n sdesc = sec['section_descriptor']\r\n frmat = getattr(sdesc, 'format', None)\r\n aname = ''\r\n if frmat in graders:\r\n gform = graders[frmat]\r\n aname = '%s %02d' % (gform.short_label, gform.index)\r\n gform.index += 1\r\n elif sdesc.display_name in graders:\r\n gform = graders[sdesc.display_name]\r\n aname = '%s' % gform.short_label\r\n notes = ''\r\n if getattr(sdesc, 'score_by_attempt', False):\r\n notes = ', score by attempt!'\r\n msg += \" %s (format=%s, Assignment=%s%s)\\n\"\\\r\n % (sdesc.display_name, frmat, aname, notes)\r\n msg += \"all descriptors:\\n\"\r\n msg += \"length=%d\\n\" % len(gcontext['all_descriptors'])\r\n msg = '<pre>%s</pre>' % msg.replace('<', '<')\r\n return msg","def extract_names(pages: Iterable[tuple[int, list[str]]]) -> DataT:\n found_first = False\n current_name: dict[str, Any] | None = None\n current_label: str | None = None\n current_lines: list[str] = []\n in_headings = True\n\n def start_label(label: str, line: str) -> None:\n nonlocal current_label, current_lines\n assert current_name is not None\n assert current_label is not None\n if label in current_name:\n if label in (\"Syntype\", \"Type Locality\"):\n label = f\"Syntype {line}\"\n assert (\n label not in current_name\n ), f\"duplicate label {label} in {current_name}\"\n current_name[current_label] = current_lines\n current_label = label\n current_lines = [line]\n\n for page, lines in pages:\n if current_name is not None:\n current_name[\"pages\"].append(page)\n for line in lines:\n if not found_first:\n if line.strip() in (\"TYPE SPECIMENS\", \"SPECIMENS\"):\n found_first = True\n continue\n # ignore family/genus headers\n if re.match(\n (\n r\"^\\s*(Genus|Family|Subfamily|Suborder|Order) [A-Z][a-zA-Z]+\"\n r\" [a-zA-Z\\.’, \\-]+(, \\d{4})?$\"\n ),\n line,\n ):\n in_headings = True\n continue\n # ignore blank lines\n if not line:\n continue\n if in_headings:\n if line.startswith(\" \"):\n continue\n else:\n in_headings = False\n if line.startswith(\" \"):\n current_lines.append(line)\n elif re.match(r\"^[A-Z][A-Z a-z-]+: \", line):\n start_label(line.split(\":\")[0], line)\n elif line.startswith(\"Lectotype as designated\"):\n start_label(\"Lectotype\", line)\n elif line.startswith(\"Neotype as designated\"):\n start_label(\"Neotype\", line)\n elif line.startswith(\n (\n \"This specimen\",\n \"Type \",\n \"No type\",\n \"There are\",\n \"No additional\",\n \"All \",\n \"Subspecies of \",\n \"Neotype designated \",\n \"Padre Island\",\n )\n ):\n start_label(\"comments\", line)\n elif line.startswith(\n (\"Secondary junior\", \"Primary junior\", \"Junior primary\")\n ):\n start_label(\"homonymy\", line)\n elif re.match(r\"^[\\d/]+\\. \", line):\n start_label(line.split(\".\")[0], line)\n elif line.startswith(\"USNM\"):\n start_label(line.split(\".\")[0], line)\n elif (\n current_label not in (\"name\", \"verbatim_citation\", \"homonymy\")\n and \":\" not in line\n ):\n # new name\n if current_name is not None:\n assert current_label is not None\n current_name[current_label] = current_lines\n assert any(\n field in current_name\n for field in (\n \"Holotype\",\n \"Type Locality\",\n \"Lectotype\",\n \"Syntype\",\n \"Syntypes\",\n \"No name-bearing status\",\n \"Neotype\",\n )\n ), current_name\n yield current_name\n current_name = {\"pages\": [page]}\n current_label = \"name\"\n current_lines = [line]\n elif current_label == \"name\":\n if re.search(\n r\"\\d|\\b[A-Z][a-z]+\\.|\\baus\\b|\\bDas\\b|\\bPreliminary\\b|\\., \", line\n ):\n start_label(\"verbatim_citation\", line)\n else:\n # probably continuation of the author\n current_lines.append(line)\n elif (\n current_label == \"verbatim_citation\"\n or current_label == \"homonymy\"\n or line.startswith(\"= \")\n ):\n start_label(\"synonymy\", line)\n else:\n assert False, f\"{line!r} with label {current_label}\"\n assert current_label is not None\n assert current_name is not None\n current_name[current_label] = current_lines\n yield current_name","def process_article(self, title, text, template = 'Infobox person'):\n \n # Create a parsing object\n wikicode = mwparserfromhell.parse(text)\n \n # Search through templates for the template\n matches = wikicode.filter_templates(matches = template)\n raw_year_string = 'EMPTY'\n birth_year = 'EMPTY'\n infobox = ''\n if len(matches) >= 1:\n # Extract information from infobox\n for match in matches:\n infobox = str(match)\n for param in match.params:\n if param.name.strip_code().strip() == 'birth_date':\n raw_year_string = str(param.value)\n birth_year = self.get_birth_year(raw_year_string)\n summary = self.get_summary(wikicode.strip_code().strip())\n return (title, birth_year, summary, raw_year_string, infobox)","def print_catalog(self):\n # first download the json for the catalog\n self.download_json()\n\n # open the saved json file and load the json\n with self.file.open(\"r\") as catalog_file:\n pages = json.load(catalog_file)\n\n # the catalog json is just a list of pages\n # so we begin by iterating through the pages\n for page_num in range(len(pages)):\n # get each page\n page = pages[page_num]\n\n # get the threads on each page\n threads = page[\"threads\"]\n\n # print the page heading\n print(\"*** PAGE \", page_num + 1, \"***\")\n\n # iterate through the threads on each page\n for thread_num in range(len(threads)):\n # get each thread\n thread = threads[thread_num]\n\n # print the thread number\n num = thread[\"no\"]\n print(\"---\", \"Thread:\", num, \"---\")\n\n # not all threads have a subject or comment\n try:\n subject = thread[\"sub\"]\n comment = thread[\"com\"]\n\n print(\"Sub:\", subject)\n print(\"Comment:\", comment)\n except KeyError:\n print(\"N/A\")","def course_about(request, course_id):\r\n\r\n if microsite.get_value(\r\n 'ENABLE_MKTG_SITE',\r\n settings.FEATURES.get('ENABLE_MKTG_SITE', False)\r\n ):\r\n raise Http404\r\n course_key = SlashSeparatedCourseKey.from_deprecated_string(course_id)\r\n course = get_course_with_access(request.user, 'see_exists', course_key)\r\n registered = registered_for_course(course, request.user)\r\n staff_access = has_access(request.user, 'staff', course)\r\n studio_url = get_studio_url(course_key, 'settings/details')\r\n\r\n if has_access(request.user, 'load', course):\r\n course_target = reverse('info', args=[course.id.to_deprecated_string()])\r\n else:\r\n course_target = reverse('about_course', args=[course.id.to_deprecated_string()])\r\n\r\n show_courseware_link = (has_access(request.user, 'load', course) or\r\n settings.FEATURES.get('ENABLE_LMS_MIGRATION'))\r\n\r\n # Note: this is a flow for payment for course registration, not the Verified Certificate flow.\r\n registration_price = 0\r\n in_cart = False\r\n reg_then_add_to_cart_link = \"\"\r\n if (settings.FEATURES.get('ENABLE_SHOPPING_CART') and\r\n settings.FEATURES.get('ENABLE_PAID_COURSE_REGISTRATION')):\r\n registration_price = CourseMode.min_course_price_for_currency(course_key,\r\n settings.PAID_COURSE_REGISTRATION_CURRENCY[0])\r\n if request.user.is_authenticated():\r\n cart = shoppingcart.models.Order.get_cart_for_user(request.user)\r\n in_cart = shoppingcart.models.PaidCourseRegistration.contained_in_order(cart, course_key)\r\n\r\n reg_then_add_to_cart_link = \"{reg_url}?course_id={course_id}&enrollment_action=add_to_cart\".format(\r\n reg_url=reverse('register_user'), course_id=course.id.to_deprecated_string())\r\n\r\n # see if we have already filled up all allowed enrollments\r\n is_course_full = CourseEnrollment.is_course_full(course)\r\n\r\n return render_to_response('courseware/course_about.html', {\r\n 'course': course,\r\n 'staff_access': staff_access,\r\n 'studio_url': studio_url,\r\n 'registered': registered,\r\n 'course_target': course_target,\r\n 'registration_price': registration_price,\r\n 'in_cart': in_cart,\r\n 'reg_then_add_to_cart_link': reg_then_add_to_cart_link,\r\n 'show_courseware_link': show_courseware_link,\r\n 'is_course_full': is_course_full\r\n })","def get_sections(pages):\n sections = {}\n section_pages = {}\n current_section_name = None\n current_section = []\n\n for page_num, page in enumerate(pages):\n clean_page = [re.sub(\"\\s+\", \" \", i.strip()) for i in page.split(\"\\n\")]\n\n for ind, i in enumerate(clean_page):\n\n if (\n re.findall(\"^Section \\d+\", i)\n and \"page\" not in i\n or (re.sub(\"\\d+ [\\w+\\s+]+\", \"\", i) == \"\" and ind == 0 and len(i) > 6)\n ):\n if current_section_name is not None:\n sections[current_section_name] = current_section\n current_section = []\n current_section_name = i\n break\n\n section_pages[page_num + 1] = current_section_name or \"No Section\"\n\n current_section.extend(clean_page)\n return sections, section_pages","def _populate(self):\n\n # Assume the first word is what we want, and we can find well formed years\n # This sucks, but will work for these ones.\n # Roll on bibtex for citations in the CIM.\n\n citation_detail = self.doc.citation_detail\n author = citation_detail.split(',')[0]\n match = '([^\\w])19|20\\d\\d([^\\w])*?'\n m = re.search(match, citation_detail)\n if m:\n year = m.group(0)\n else:\n year = None\n\n # one error in existing es-doc content to be fixed:\n if 'van Vuuren DP' in author:\n author = 'van Vuuren'\n print 'applying vv fix'\n\n self.year = int(year)\n\n # We assume that this table will have entries which ne\n\n # I use the first three letters of a an authors name, and for\n # three or more authors, EA, and then the year for my bibtex citation string\n self.citeguess = author[0:3] + 'EA' + year[2:]\n # This is what will appear in the table:\n self.citestring = '%s et al. (%s)' % (author, year)\n # Keep this for a reference list for checking against the eventual bibtex reference list.\n self.text = citation_detail","def scrape(self, letter):\n soup = self._request_page()\n departments = self._get_departments(soup, letter)\n\n self.logger.debug('Letter %s has %s depts.', letter, len(departments))\n\n # For each department under a certain letter search.\n for department in departments:\n try:\n dept_data = self._parse_department_data(department)\n save_department_data(dept_data, self.scraper, self.location)\n\n courses = department.find_all(\n 'tr', id=re.compile('trCOURSE_LIST'))\n\n # For each course under a certain department.\n for course in courses:\n return_state = 'DERIVED_SAA_CRS_RETURN_PB$163$'\n\n try:\n course_number = course.find(\n 'a', id=re.compile(r'CRSE_NBR\\$'))['id']\n course_name = course.find(\n 'span', id=re.compile(r'CRSE_TITLE\\$')).text\n\n if not course_number:\n self.logger.debug('Skipping non-existent course')\n continue\n\n if 'unspecified' in course_name.lower():\n self.logger.debug('Skipping unspecified course')\n continue\n\n # Note: Selecting course only takes one parameter,\n # which is the ICAction.\n ic_action = {'ICAction': course_number}\n soup = self._request_page(ic_action)\n\n # Some courses have multiple offerings of the same\n # course, E.g: MATH121 offered on campus and online.\n # Check if table representing academic levels exists.\n if not self._has_multiple_course_offerings(soup):\n\n title = ''\n title_temp = soup.find(\n 'span', id='DERIVED_CRSECAT_DESCR200'\n )\n\n if title_temp:\n title = title_temp.text.strip()\n\n self.logger.debug('Course title: %s', title)\n\n self._navigate_and_parse_course(soup)\n else:\n return_state = 'DERIVED_SSS_SEL_RETURN_PB$181$'\n title = soup.find(\n 'span', id='DERIVED_SSS_SEL_DESCR200'\n ).text.strip()\n self.logger.debug('Course title: %s', title)\n self.logger.debug('Multiple offerings found')\n\n academic_levels = self._get_academic_levels(soup)\n\n for academic_level in academic_levels:\n try:\n cr_number = academic_level['id']\n cr_name = academic_level.text.strip()\n self.logger.debug('Career: %s', cr_name)\n\n # Go from a certain academic level to basic\n # course page.\n ic_action = {'ICAction': cr_number}\n\n soup = self._request_page(ic_action)\n self._navigate_and_parse_course(soup)\n\n except Exception:\n self.scraper.handle_error()\n\n self.logger.debug('Done careers.')\n\n except Exception:\n self.scraper.handle_error()\n\n # Go back to course listing.\n self.logger.debug('Returning to course list')\n ic_action = {'ICAction': return_state}\n self._request_page(ic_action)\n\n self.logger.debug('Done department')\n\n except Exception:\n self.scraper.handle_error()\n\n self.logger.debug('Done letter %s', letter)","def see_teaching_courses(self, username: str, token: str) -> List[Dict[str, object]]:\n\n # Validate user first\n if not self.validate(username=username, token=token, check_privilege='instructor'):\n raise RuntimeError(\"User not verified!\")\n\n # Get UID from user's username\n uid = self.get_uid(username=username)\n\n # Query database for courses instructed by a user with this UID\n cursor = self._db_connection.cursor()\n cursor.execute(\n '''\n SELECT \n course_id,\n course_abbreviation,\n course_name, \n time,\n seats \n FROM \n courses\n WHERE \n instructor_id = ?\n ;\n ''', (uid,))\n\n db_results = cursor.fetchall()\n\n if db_results is None:\n print(\"No associated courses found!\")\n return []\n\n # Build information dicts for every course this user is instructing\n courses = []\n for result in db_results:\n # Get the number of students enrolled in this course already\n cursor.execute('''SELECT COUNT(*) FROM enrollment_records WHERE course_id = ?;''', (result[0],))\n students_enrolled = cursor.fetchone()[0]\n if students_enrolled is None:\n students_enrolled = 0\n\n # Build a course dict from the data\n courses.append({\n \"course_abbreviation\": result[1],\n \"course_name\": result[2],\n \"time\": result[3],\n \"students_enrolled\": students_enrolled,\n \"capacity\": result[4],\n })\n\n return courses","def html_page_context(app, pagename, templatename, context, doctree):\n site = context[\"SITEMAP_BASE_URL\"]\n version = context[\"version\"]\n app.sitemap_links.add(f\"{site}{version}/{pagename}.html\")","def scrape(url, filename):\n courselist = []\n headers = ['title', 'description', 'course number', 'duration', 'difficulty', 'instructors', 'course url']\n with open(filename, 'wb') as outfile:\n wr = csv.writer(outfile)\n wr.writerow(headers)\n courses = json.load(urllib2.urlopen(url))\n for course in courses['courses']:\n c = Course()\n c.title = course['title']\n c.desc = course['summary']\n c.course_number = course['key']\n c.duration = str(course['expected_duration']) + ' ' + str(course['expected_duration_unit'])\n c.difficulty = course['level']\n c.url = 'https://www.udacity.com/course/' + course['slug']\n l = len(course['instructors'])\n for i in xrange(l):\n if(i == 0):\n c.instructors += course['instructors'][i]['name']\n else:\n c.instructors += ';' + course['instructors'][i]['name']\n with open(filename, 'ab') as outfile:\n wr = csv.writer(outfile)\n wr.writerow(c.getaslist())","def get_course_title(self):\n\n course_title = self.course_page.find(attrs=COURSE_TITLE)\n self.course_title = course_title.text.replace(\" \", \"_\")\n logging.debug(\"course_title:{}\".format(self.course_title))\n logging.info(\"Course title retrieved\")","def _course_from_query_response(self, term, response):\n units_low, units_hi = float(response['UNITS_LOW']), float(response['UNITS_HIGH'])\n if units_low > units_hi:\n # Yes, this is an actual response case...\n # Occurs when a course has a constant # of units.\n # I think units_hi should equal units_low when actual units is constant.\n units_hi = units_low\n\n instructor_name = instructor_email = None\n try:\n instructor_meta = next(instr for instr in response['INSTRUCTORS'] if instr['PRIMARY_IND'] == 'Y')\n instructor_name = '{} {}'.format(instructor_meta['FIRST_NAME'], instructor_meta['LAST_NAME'])\n instructor_name = instructor_name.strip()\n instructor_email = instructor_meta['EMAIL']\n except StopIteration:\n # No instructor specified\n pass\n\n ge_areas = list()\n try:\n area_codes = filter(None, response['GE3CREDIT'].split(','))\n ge_areas = [GE_AREA_NAMES_BY_SB_CODE[area_code] for area_code in area_codes]\n except KeyError as e:\n logging.exception('Unrecognized GE code')\n\n meetings = list()\n for meeting in response['COURSEMEETINGDATA']:\n days = meeting['WEEKDAYS'].replace(',', '')\n times = None\n try:\n begin_hour, begin_minutes = meeting['BEGIN_TIME'][:2], meeting['BEGIN_TIME'][2:]\n end_hour, end_minutes = meeting['END_TIME'][:2], meeting['END_TIME'][2:]\n begin = timedelta(hours=int(begin_hour), minutes=int(begin_minutes))\n end = timedelta(hours=int(end_hour), minutes=int(end_minutes))\n times = (begin, end)\n except TypeError:\n # times are None, indicating TBA\n pass\n\n location = meeting['BLDG_DESC']\n if meeting['ROOM']:\n location += ' ' + meeting['ROOM']\n\n meeting = {\n 'days': days,\n 'times': times,\n 'location': location,\n 'type': meeting['MEET_TYPE_DESC_SHORT']\n }\n meetings.append(meeting)\n\n final_exam = None\n try:\n final_exam = datetime.strptime(response['FINALEXAMSTARTDATE'], '%B, %d %Y %H:%M:%S')\n except TypeError:\n # No final exam\n pass\n\n drop_time = response['ALLOWEDDROPDESC']\n drop_days_match = re.match(r'^([0-9]+)', drop_time)\n if drop_days_match:\n drop_time = int(drop_days_match.group(1))\n if response['DESCRIPTION']:\n response['DESCRIPTION'] = response['DESCRIPTION'].replace('\\n', ' ').replace('\\r', '').strip()\n response['TITLE'] = response['TITLE'].strip()\n\n return Course(\n term=term,\n crn=response['PASSEDCRN'],\n subject_code=response['SUBJECT_CODE'],\n name='{} {}'.format(response['SUBJECT_CODE'], response['COURSE_NUMBER']),\n number=response['COURSE_NUMBER'],\n section=response['SEC'],\n title=response['TITLE'].strip(),\n description=response['DESCRIPTION'],\n instructor_consent_required=bool(int(response['CONSENTOFINSRUCTORREQUIRED'])),\n units=(units_low, units_hi),\n instructor=instructor_name,\n instructor_email=instructor_email,\n ge_areas=ge_areas,\n available_seats=response['BLEND_SEATS_AVAIL'],\n wl_length=response['BLEND_WAIT_COUNT'],\n meetings=meetings,\n final_exam=final_exam,\n drop_time=drop_time,\n prerequisites=re.sub(r'\\s+', ' ', response['PREREQUISITES']) if response['PREREQUISITES'] else None)"],"string":"[\n \"def __title_context_dict(data):\\n d = {}\\n if isinstance(data, str):\\n raw_data = __load_data(data)['data']\\n else:\\n raw_data = data['data']\\n for i in range(len(raw_data)):\\n title = raw_data[i]['title']\\n paragraphs = raw_data[i]['paragraphs']\\n text = []\\n for j in range(len(paragraphs)):\\n text.append(paragraphs[j]['context'])\\n context = ' '.join(text)\\n d[title] = context\\n return d\",\n \"def scrape_all_course_names(filename, verbose):\\n \\n\\tsoup =\\tBeautifulSoup(open(filename, 'r'), 'html.parser')\\n\\t#print(soup)\\n\\tcourses = []\\n\\t# all the courses are stored in a div w/class=view-content\\n\\t\\t\\n\\th4_field_content = soup.find('h4', 'field-content')\\n\\t#print(h4_field_content)\\n\\tall_course_content = soup.find(\\\"div\\\", \\\"view-content\\\") # this contains ALL the classes...\\n\\tif all_course_content == None:\\n\\t\\tprint(\\\"There are no courses on this page. Try a smaller page number!\\\")\\n\\t\\treturn []\\n\\t\\n\\tcandidate_classes = all_course_content.find_all('a') # we want all the 'a' tags within\\n\\tclass_list=[]\\n course_code_and_number = {}\\n\\tfor c in candidate_classes:\\n\\t\\t#print(c.text, \\\"\\\\n\\\")\\n\\t\\ttext = c.text # ex. AEMA 611 Experimental Designs 1 (3 credits)\\n\\t\\ttext = text.split(\\\" \\\")# split on the space\\n\\t\\tcourse_id = \\\" \\\".join(text[:2]).replace('\\\\n', '') # the first 2 are the course id\\n\\t\\tcourse_name = \\\" \\\".join(text[2:-2]).replace('\\\\n', '')\\n\\t\\tnum_credits = text[-2].replace(\\\"(\\\", \\\"\\\")# just get the course number, replace the ( with nothing \\n\\t\\n\\t\\n\\t\\t#print(f\\\"{course_id}\\\\n{course_name}\\\\n{num_credits}\\\\n\\\")\\n\\t\\ttry:# Check that the course number is a digit bc sometimes it is something weird\\n\\t\\t\\tfloat(num_credits)\\n\\t\\texcept ValueError:\\n\\t\\t\\t#print(f\\\"Wrong course format. Ignoring {c}\\\")\\n\\t\\t\\tif(verbose):\\n\\t\\t\\t\\tprint(f\\\"Wrong course format. Ignoring course: {c.text}\\\")\\n\\t\\t\\tcontinue\\n\\t\\tclass_list.append(course_id)\\n\\treturn class_list\",\n \"def _section_course_info(course_key, access):\\r\\n course = get_course_by_id(course_key, depth=None)\\r\\n\\r\\n section_data = {\\r\\n 'section_key': 'course_info',\\r\\n 'section_display_name': _('Course Info'),\\r\\n 'access': access,\\r\\n 'course_id': course_key,\\r\\n 'course_display_name': course.display_name,\\r\\n 'enrollment_count': CourseEnrollment.num_enrolled_in(course_key),\\r\\n 'has_started': course.has_started(),\\r\\n 'has_ended': course.has_ended(),\\r\\n 'list_instructor_tasks_url': reverse('list_instructor_tasks', kwargs={'course_id': course_key.to_deprecated_string()}),\\r\\n }\\r\\n\\r\\n try:\\r\\n advance = lambda memo, (letter, score): \\\"{}: {}, \\\".format(letter, score) + memo\\r\\n section_data['grade_cutoffs'] = reduce(advance, course.grade_cutoffs.items(), \\\"\\\")[:-2]\\r\\n except Exception:\\r\\n section_data['grade_cutoffs'] = \\\"Not Available\\\"\\r\\n # section_data['offline_grades'] = offline_grades_available(course_key)\\r\\n\\r\\n try:\\r\\n section_data['course_errors'] = [(escape(a), '') for (a, _unused) in modulestore().get_course_errors(course.id)]\\r\\n except Exception:\\r\\n section_data['course_errors'] = [('Error fetching errors', '')]\\r\\n\\r\\n return section_data\",\n \"def parse_create_course(xml_course):\\n attrs = [\\n \\\"term-code\\\",\\n \\\"term-description\\\",\\n 'subject',\\n \\\"course-number\\\",\\n \\\"school\\\",\\n \\\"department\\\",\\n \\\"title\\\",\\n \\\"description\\\",\\n \\\"credit-hours\\\",\\n \\\"distribution-group\\\"\\n ]\\n course = pull_attributes_from_xml(xml_course, attrs)\\n course[\\\"sections\\\"] = []\\n\\n return course\",\n \"def _parse(self, course: NavigableString) -> ParseType:\\n\\n info = {\\\"link\\\": \\\"\\\", \\\"icon\\\": \\\"\\\", \\\"title\\\": \\\"\\\", \\\"description\\\": \\\"\\\",\\n \\\"counts\\\": {}}\\n\\n info[\\\"link\\\"] = course.a[\\\"href\\\"]\\n info[\\\"icon\\\"] = course.a.img[\\\"src\\\"]\\n\\n description: NavigableString = course.a.div\\n info[\\\"title\\\"] = description.div.get_text()\\n info[\\\"description\\\"] = description.p.get_text()\\n\\n counts: NavigableString = course.find(\\\"div\\\", {\\\"class\\\": \\\"courseCounts\\\"})\\n counts_data: ResultSet = counts.find_all(\\\"li\\\")\\n for data in counts_data:\\n name: str = data.span.get_text().lower()\\n val: str = data.find(\\\"p\\\").get_text()\\n info[\\\"counts\\\"][name] = int(val.replace(\\\",\\\", \\\"\\\"))\\n\\n return info\",\n \"def parseCourses(self, response):\\n sel = Selector(response)\\n courses = sel.xpath('//div[@class=\\\"course-info expandable\\\"]')\\n for c in courses:\\n item = CourseItem(response.request.meta[\\\"item\\\"])\\n item['code'] += '-' + c.xpath('@id').get().strip()\\n item['name'] = c.xpath('//a[@class=\\\"courselink\\\"]/text()').get().strip()\\n # everything works up to here #\\n href = c.xpath('div/h3/a/@href').get()\\n url = urljoin('https://web-app.usc.edu', href)\\n yield Request(url=url,callback=self.parseSection,meta={'item':item})\",\n \"def about_course(command):\\n\\n response = {'code': None, 'title': None,\\\\\\n 'prof': None, 'section1': None,\\\\\\n 'timing1': None, 'room1' : None,\\\\\\n 'section2': None, 'timing2': None,\\\\\\n 'room2': None, 'description': None,\\\\\\n 'prereq': None, 'perm': None}\\n \\n if re.search('ENPM611', command):\\n response = {'code': 'ENPM 611', 'title': 'Software Engineering',\\\\\\n 'prof': 'Christopher Ackermann', 'section1': '0101',\\\\\\n 'timing1': 'Monday 4:00 - 6:40 PM', 'room1': 'JMP 2121',\\\\\\n 'description': 'Software engineering concepts, methods, and practices important to both the theorist and the practitioner will be covered. The entire range of responsibilities expected of a software engineer are presented. The fundamental areas of requirements development, software design, programming languages, and testing are covered extensively. Sessions on supporting areas such as systems engineering, project management, and software estimation are also included.', 'prereq': 'Competency in one programming language; and must have completed an undergraduate software engineering course. Or permission of instructor.', 'perm': 'Permission of ENGR-CDL-Office of Advanced Engineering Education.'\\n}\\n elif re.search('ENPM613', command):\\n response = {'code': 'ENPM 613', 'title': 'Software Design and Implementation', 'prof': 'Ioana Rus', 'section1': '0101', 'timing1': 'Wednesday 7:00 - 9:40 PM', 'room1': 'TBA', 'description': 'Software design concepts and practices within the field important to both the practitioner and the theorist will be covered. Architectural and detailed designs are included for batch, client/server, and real-time systems. Design considerations for structured, object-oriented, and Web-based systems are covered. Design of databases, user interfaces, forms, and reports are also included. Implementation issues that affect the design, including error handling, performance, and inter-process communication, are presented.', 'perm': 'Permission of ENGR-CDL-Office of Advanced Engineering Education.'}\\n elif re.search('ENPM631', command):\\n response = {'code': 'ENPM631', 'title': 'TCP/IP Networking', 'prof': 'Pedram Fard', 'section1': '0101', 'timing1': 'Tuesday 7:00 - 9:40 PM', 'room1': 'TBA' , 'description': 'Describe how IP datagram travels through the internet and are routed from the source to the destination. Introduce the two transport protocols: UDP and TCP, the proper context to use each one, and related parameters and issues. Cover some other protocols, closely related to the TCP/IP that are responsible for the seamless operation of the Internet.', 'perm': 'ENPM602; or permission of instructor. And permission of ENGR-CDL-Office of Advanced Engineering Education.'}\\n elif re.search('ENPM687', command):\\n response = {'code': 'ENPM687', 'title': 'Digital Forensics and Incidence Response', 'prof': 'Jonas Amoonarquah', 'section1': 'Online', 'timing1': 'NA', 'room1': 'NA', 'description': 'Students will implement a robust incident response methodology, including proper forensic handling of evidence, and cover legal aspects of national and international law regarding forensics. The bulk of the course covers evidence acquisition, preservation, analysis and reporting on multiple platforms.', 'perm':'None'}\\n elif re.search('ENPM691', command):\\n response = {'code': 'ENPM691', 'title': 'Hacking of C Programs and Unix Binaries', 'prof': 'Dharmalingam Ganesan', 'section1': '0101', 'timing1': 'Thursday 7:00 - 9:40 PM', 'room1': 'JMP 3201', 'description': 'Teaches the fundamentals of secure programming in C. An in depth discussion on various security vulnerabilities (e.g., buffer overflows) in C applications will be taught with hands-on demo of concepts during the class. Students will learn how a C program runs \\\"under-the-hood\\\". The course will teach nitty-gritty of C programs by analyzing at the assembly level. The course discusses best practices (e.g., coding standards) and design principles for secure programming so that security can be built-in during design time. In addition to assignments, students are required to present papers related to this course.', 'perm': None, 'prereq': 'ENEE150; or students who have taken courses with comparable content may contact the department.'}\\n elif re.search('ENPM693', command):\\n response = {'code': 'ENPM693', 'title': 'Network Security', 'prof': 'Sohraab Soltani', 'section1': '0101', 'timing1': 'Tuesday 7:00 - 9:40 PM', 'room1' : 'JMP 3201', 'description': 'Introduction to various approaches to design; specify and verify security protocols used in large systems and networks; familiarization with some current technologies. Security threats and countermeasures, communication security and basic encryption techniques, authentication protocols, data confidentiality and integrity, analysis of cryptographic protocols, and access control in large systems and networks.', 'perm': None, 'prereq': 'An operating systems and/or network protocol course or equivalent.'}\\n elif re.search('ENPM694', command):\\n response = {'code': 'ENPM694', 'title': 'Networks and Protocols', 'prof': 'Sohraab Soltani', 'section1': '0101', 'timing1': 'Wednesday 7:00 - 9:40 PM', 'room1': 'JMP 3201', 'description': 'Provides a deep understanding of TCP/IP protocol suit and routing in the internet. The course topics are: overview of TCP/IP, basics of IP protocol, basics of TCP protocol, Network Address Translation (NAT), Dynamic Host Configuration Protocol (DHCP), Internet Protocol Security (IPsec), Internet Control Message Protocol (ICMP), Simple Mail Transfer Protocol (SMTP), Domain Name Service (DNS), IPv6, Concepts of routing (Bellman-Ford and Dijkstra algorithms), Routing Information Protocol (RIP), Open Shortest Path First (OSPF), Interior Gateway Routing Protocol (IGRP), Enhance Gateway Routing Protocol (EIGRP), and Border Gateway Protocol (BGP).'}\\n elif re.search('ENPM696', command):\\n response = {'code': 'ENPM696', 'title': 'Reverse Software Engineering', 'prof': 'Allen Hazelton', 'section1': '0101', 'timing1': 'Tuesday 4:00 - 6:40 PM', 'room1': 'TBA', 'description': 'An in-depth understanding of software reverse engineering concepts and hands-on training with reverse engineering tools, including disassemblers, decompilers, and code analyzers. Students will become familiar with both low-level software and the x86 instruction set through binary reversing sessions. This course also provides insights into many subjects such as system security, source code analysis, software design, and program understanding that will be beneficial in a variety of fields.', 'prereq': 'ENPM691 and CMSC106; or permission of instructor. And permission of ENGR-CDL-Office of Advanced Engineering Education.'}\\n elif re.search('ENPM809J', command):\\n response = {'code': 'ENPM809J', 'title': 'Cloud Security', 'prof': 'Kevin Shivers', 'section1': '0101', 'timing1': 'Monday 7:00 - 9:40 PM', 'room1': 'TBA', 'section2': '0201', 'timing2': 'Thursday 4:00 - 6:40 PM', 'room2': 'TBA', 'description': 'NA'}\\n elif re.search('ENPM809R', command):\\n response = {'code': 'ENPM809R', 'title': 'Software Defined Networking', 'prof': 'Emre Gunduzhan', 'section1': '0101', 'timing1': 'Monday 4:00 - 6:40 PM', 'room1': 'TBA', 'description': 'NA'}\\n elif re.search('ENPM809W', command):\\n response = {'code': 'ENPM809W', 'title': 'Security and Software', 'prof': 'Mikael Lindvall', 'section1': '0101', 'timing1': 'Thursday 7:00 - 9:40 PM', 'room1': 'TBA', 'description': 'NA'}\\n\\n return response\",\n \"def get_courses_info(url, headers):\\n dash = get_page_contents(url, headers)\\n soup = BeautifulSoup(dash)\\n courses_soup = soup.find_all('article', 'course')\\n courses = []\\n for course_soup in courses_soup:\\n course_id = None\\n course_name = course_soup.h3.text.strip()\\n course_url = None\\n course_state = 'Not yet'\\n try:\\n # started courses include the course link in the href attribute\\n course_url = BASE_URL + course_soup.a['href']\\n if course_url.endswith('info') or course_url.endswith('info/'):\\n course_state = 'Started'\\n # The id of a course in edX is composed by the path\\n # {organization}/{course_number}/{course_run]\\n course_id = course_soup.a['href'][9:-5]\\n except KeyError:\\n pass\\n courses.append(Course(id=course_id,\\n name=course_name,\\n url=course_url,\\n state=course_state))\\n return courses\",\n \"def __init__(self, browser, course_org, course_num, course_run):\\r\\n super(CoursePage, self).__init__(browser)\\r\\n self.course_info = {\\r\\n 'course_org': course_org,\\r\\n 'course_num': course_num,\\r\\n 'course_run': course_run\\r\\n }\",\n \"def process_request(self, request):\\r\\n match = COURSE_REGEX.match(request.build_absolute_uri())\\r\\n course_id = None\\r\\n if match:\\r\\n course_id = match.group('course_id')\\r\\n course_key = SlashSeparatedCourseKey.from_deprecated_string(course_id)\\r\\n\\r\\n context = {}\\r\\n\\r\\n if course_id:\\r\\n context['course_id'] = course_id\\r\\n\\r\\n if request.user.is_authenticated():\\r\\n context['course_user_tags'] = dict(\\r\\n UserCourseTag.objects.filter(\\r\\n user=request.user.pk,\\r\\n course_id=course_key,\\r\\n ).values_list('key', 'value')\\r\\n )\\r\\n else:\\r\\n context['course_user_tags'] = {}\\r\\n\\r\\n tracker.get_tracker().enter_context(\\r\\n self.CONTEXT_NAME,\\r\\n context\\r\\n )\",\n \"def context(template):\\n\\n return {\\n v.key: v.read()\\n for v in [Variable(name) for name in extract_variables(template)]\\n }\",\n \"def codrspace_contexts(request):\\n contexts = {}\\n contexts.update({\\n 'TAGLINE': \\\"Why you no write tutorial?\\\"\\n })\\n\\n return contexts\",\n \"def get_course(data):\\n\\n return {item['course'] for item in data}\",\n \"def process_course(xml_course):\\n\\n parse_course = parse_get_course(xml_course)\\n if not parse_course: # Does not already exist, create\\n parse_course = parse_create_course(xml_course)\\n else:\\n # TODO: Update existing attributes of the course here and upload the changes\\n None\\n parse_course_id = upload_course(parse_course)\\n\\n print (\\\"Processed Course: {0} {1}\\\"\\n .format(parse_course[\\\"subject\\\"], parse_course[\\\"courseNumber\\\"]))\\n\\n # TODO: Implement the functions for this stuff\\n parse_section = parse_get_section(xml_course)\\n if not parse_section: # Does not already exist, create\\n parse_section = parse_create_section(xml_course)\\n else:\\n # TODO: Update existing attributes of the section here and upload the changes\\n None\\n parse_section_id = upload_section(parse_section)\\n\\n put_child(parse_course_id, parse_section_id)\",\n \"def get_info_list(info_string, course):\\n info_list = []\\n split_on_newline = info_string.split(\\\"\\\\n\\\")\\n for elem in split_on_newline:\\n split = elem.split(\\\": \\\")\\n for s in split:\\n info_list.append(s)\\n info_list = info_list[1:-1]\\n info_tags = [\\n 'session', 'school', 'credits', 'level', 'offered',\\n 'visiting_students', 'erasmus_students',\\n ]\\n i = 0\\n for info_tag in info_tags:\\n course[info_tag] = new_dict(\\n info_list[i] + ': ', info_list[i + 1])\\n i += 2\\n return course\",\n \"def _update_context_with_user_info(context, user, user_certificate):\\n user_fullname = get_preferred_certificate_name(user)\\n\\n context['username'] = user.username\\n context['course_mode'] = user_certificate.mode\\n context['accomplishment_user_id'] = user.id\\n context['accomplishment_copy_name'] = user_fullname\\n context['accomplishment_copy_username'] = user.username\\n\\n context['accomplishment_more_title'] = _(\\\"More Information About {user_name}'s Certificate:\\\").format(\\n user_name=user_fullname\\n )\\n # Translators: This line is displayed to a user who has completed a course and achieved a certification\\n context['accomplishment_banner_opening'] = _(\\\"{fullname}, you earned a certificate!\\\").format(\\n fullname=user_fullname\\n )\\n\\n # Translators: This line congratulates the user and instructs them to share their accomplishment on social networks\\n context['accomplishment_banner_congrats'] = _(\\\"Congratulations! This page summarizes what \\\"\\n \\\"you accomplished. Show it off to family, friends, and colleagues \\\"\\n \\\"in your social and professional networks.\\\")\\n\\n # Translators: This line leads the reader to understand more about the certificate that a student has been awarded\\n context['accomplishment_copy_more_about'] = _(\\\"More about {fullname}'s accomplishment\\\").format(\\n fullname=user_fullname\\n )\",\n \"def context(self) -> dict:\\n\\n try:\\n context = cfr.train.find(self.train_number)\\n\\n meta_description = self.request.locale.gettext((\\n 'Real-time updates for {rank}'\\n '{number}, on the route \\\\'{route}\\\\'.'\\n )).format(**context)\\n\\n context.update({\\n 'meta': {\\n 'page': '%s%s' % (context['rank'], context['number']),\\n 'description': meta_description\\n }\\n })\\n\\n return context\\n except cfr.exceptions.TrainNotFound:\\n raise web.HTTPNotFound(reason='We couldn\\\\'t find that train')\",\n \"def _get_sample_plain_context(self):\\r\\n context = {\\r\\n 'course_title': \\\"Bogus Course Title\\\",\\r\\n 'course_url': \\\"/location/of/course/url\\\",\\r\\n 'account_settings_url': \\\"/location/of/account/settings/url\\\",\\r\\n 'platform_name': 'edX',\\r\\n 'email': 'your-email@test.com',\\r\\n }\\r\\n return context\",\n \"def _update_course_context(request, context, course, platform_name):\\n context['full_course_image_url'] = request.build_absolute_uri(course_image_url(course))\\n course_title_from_cert = context['certificate_data'].get('course_title', '')\\n accomplishment_copy_course_name = course_title_from_cert if course_title_from_cert else course.display_name\\n context['accomplishment_copy_course_name'] = accomplishment_copy_course_name\\n course_number = course.display_coursenumber if course.display_coursenumber else course.number\\n context['course_number'] = course_number\\n if context['organization_long_name']:\\n # Translators: This text represents the description of course\\n context['accomplishment_copy_course_description'] = _('a course of study offered by {partner_short_name}, '\\n 'an online learning initiative of '\\n '{partner_long_name}.').format(\\n partner_short_name=context['organization_short_name'],\\n partner_long_name=context['organization_long_name'],\\n platform_name=platform_name)\\n else:\\n # Translators: This text represents the description of course\\n context['accomplishment_copy_course_description'] = _('a course of study offered by '\\n '{partner_short_name}.').format(\\n partner_short_name=context['organization_short_name'],\\n platform_name=platform_name)\",\n \"def all_courses(records):\\n \\n course_and_id_dict = {} #This creates an empty dictionary\\n for all_tuples in records:\\n course_info_tuple = all_tuples[0] #Extracts all course information\\n course_id = course_info_tuple[0]\\n course_name = course_info_tuple[1]\\n \\n course_and_id_dict[course_id] = course_name\\n \\n return course_and_id_dict\",\n \"def insert_course(dept, num, text):\\n\\n # Course Title \\n m = re.search(\\\"[\\\\d\\\\w]{5} - ([\\\\w ]*)\\\", text)\\n title = m.group(1) if m else \\\"nomatch\\\"\\n\\n # Course Description\\n m = re.search(\\\"\\\\.\\\\s(.*)\\\\sTypically\\\",text)\\n des = m.group(1) if m else \\\"nomatch\\\"\\n\\n # Credit hours aren't fixed for every course\\n # Credit Hours: 2.00\\n # Credit Hours: 2.00 or 3.00. \\n # Credit Hours: 1.00 to 18.00. \\n m = re.search(\\\"Credit Hours: (\\\\d+\\\\.\\\\d+)\\\",text, flags=re.IGNORECASE)\\n m = re.search(\\\"(\\\\d+\\\\.\\\\d+)(.*?)Credit hours\\\",text, flags=re.IGNORECASE) if not m else m\\n cr = m.group(1) if m else \\\"-1\\\"\\n\\n # Semesters Offered\\n m = re.search(\\\"Typically offered (.*?)\\\\.\\\", text)\\n sem = m.group(1).split() if m else [\\\"nomatch\\\"]\\n\\n # Course Type: Lecture, Recitation, Lab, Seminar, etc.\\n m = re.search(\\\"Schedule Types:\\\\s((?:[\\\\w ]+)(?:,[\\\\w ]+)*) \\\\s+\\\", text)\\n form = m.group(1).split(\\\", \\\") if m else [\\\"nomatch\\\"]\\n\\n # Learning objectives will not necessarily follow campuses\\n m = re.search(\\\"campuses:(\\\\s+([\\\\w\\\\s])+\\\\n)\\\", text)\\n campus = m.group(1).strip().split(\\\"\\\\n\\\\n\\\") if m else [\\\"nomatch\\\"]\\n campus = [camp.strip() for camp in campus]\\n\\n # prereq regex and decomosition of prereqs into lists of AND conditions (works for most classes, not 477 and similar)\\n # re.DOTALL matches all characters, including \\\"\\\\n\\\"\\n idx = text.find(\\\"campuses:\\\")\\n m = re.search(\\\"Prerequisites:(.*)\\\",text[idx:],flags=re.DOTALL)\\n if m:\\n allReqs = []\\n prereqText = m.group(1).strip()\\n prereqText = prereqText.encode('ascii', 'ignore') \\n for i in PrereqParser.parseprereq(prereqText):\\n reqArr = []\\n for j in i.split():\\n if j.find(\\\"-C\\\") != -1:\\n j = j.replace(\\\"-C\\\",\\\"\\\")\\n reqArr.append(Requisite(course=j,reqType=False))\\n else:\\n reqArr.append(Requisite(course=j,reqType=True)) \\n allReqs.append(RequisiteList(courses=reqArr))\\n\\n else:\\n allReqs = []\\n\\n # create course entity\\n course = Course(number=num, title=title, department=dept, form=form,\\n description=des, credits=float(cr), semesters=sem,\\n campuses=campus,requisites=allReqs, id=dept + num)\\n # store course \\n course.put()\",\n \"def preprocess_courses_corpus():\\n soup = None\\n with open('courses_corpus.html', 'r') as infile:\\n content = infile.read()\\n\\n soup = BeautifulSoup(content, 'html.parser')\\n\\n docid = 0\\n data = {}\\n data['documents'] = []\\n\\n main_table = soup.find_all(\\\"div\\\", attrs={'class': 'courseblock'})\\n for course in main_table:\\n docid += 1\\n title = course.find_all('p', attrs={'class':'courseblocktitle noindent'})[0].text.lstrip('\\\\n') if len(course.find_all('p', attrs={'class':'courseblocktitle noindent'}))!=0 else ''\\n description = (course.find_all('p', attrs={'class':'courseblockdesc noindent'})[0].text.lstrip('\\\\n') if len(course.find_all('p', attrs={'class':'courseblockdesc noindent'}))!=0 else '') + ' ' + (course.find_all('p', attrs={'class':'courseblockextra noindent'})[0].text if len(course.find_all('p', attrs={'class':'courseblockextra noindent'}))!=0 else '')\\n\\n data['documents'].append({\\n 'docId' : docid,\\n 'title' : title.strip(),\\n 'description' : description.strip()\\n })\\n\\n with open('courses_data.json', 'w') as outfile:\\n json.dump(data, outfile)\",\n \"def test_course_info(self):\\r\\n # Regex of first 3 columns of course information table row for\\r\\n # test course loaded from git. Would not have sha1 if\\r\\n # git_info_for_course failed.\\r\\n table_re = re.compile(r\\\"\\\"\\\"\\r\\n <tr>\\\\s+\\r\\n <td>edX\\\\sAuthor\\\\sCourse</td>\\\\s+ # expected test git course name\\r\\n <td>MITx/edx4edx/edx4edx</td>\\\\s+ # expected test git course_id\\r\\n <td>[a-fA-F\\\\d]{40}</td> # git sha1 hash\\r\\n \\\"\\\"\\\", re.VERBOSE)\\r\\n\\r\\n self._setstaff_login()\\r\\n self._mkdir(getattr(settings, 'GIT_REPO_DIR'))\\r\\n\\r\\n # Make sure we don't have any git hashes on the page\\r\\n response = self.client.get(reverse('sysadmin_courses'))\\r\\n self.assertNotRegexpMatches(response.content, table_re)\\r\\n\\r\\n # Now add the course and make sure it does match\\r\\n response = self._add_edx4edx()\\r\\n self.assertRegexpMatches(response.content, table_re)\",\n \"def _get_sample_html_context(self):\\r\\n context = self._get_sample_plain_context()\\r\\n context['course_image_url'] = \\\"/location/of/course/image/url\\\"\\r\\n return context\",\n \"def generate_page_sections_dict(self, project_page_data: dict):\\n wiki_obj = WikiService()\\n\\n short_description = (\\n f\\\"\\\\n{project_page_data['project']['shortDescription']}\\\\n\\\"\\n )\\n\\n created_date = wiki_obj.format_date_text(\\n project_page_data['project']['created']\\n )\\n # created_date_text = f\\\"\\\\n{created_date}\\\\n\\\" \\n # due_date = wiki_obj.format_date_text(\\n # project_page_data['project']['due_date']\\n # )\\n timeframe = (\\n f\\\"\\\\n* '''Start Date:''' {created_date}\\\\n\\\"\\n # f\\\"\\\\n* '''End Date:''' Estimate {due_date}\\\\n\\\"\\n )\\n\\n\\n project_url = (\\n f\\\"\\\\n{project_page_data['project']['url']}\\\\n\\\"\\n )\\n\\n hashtag = (\\n project_page_data['project']['changesetComment']\\n )\\n hashtag = (\\n project_page_data['project']['changesetComment'].replace(\\n \\\"#\\\", \\\"<nowiki>#</nowiki>\\\"\\n )\\n )\\n hashtag_text = (\\n f\\\"\\\\n{hashtag}\\\\n\\\"\\n )\\n\\n instructions_text = (\\n project_page_data['project']\\n ['externalSource']\\n ['instructions']\\n )\\n instructions = (\\n f\\\"\\\\n{instructions_text}\\\\n\\\"\\n )\\n\\n per_task_instructions_text = (\\n project_page_data['project']\\n ['externalSource']\\n ['perTaskInstructions']\\n )\\n per_task_instructions = (\\n f\\\"\\\\n{per_task_instructions_text}\\\\n\\\"\\n )\\n\\n imagery_text = (\\n project_page_data['project']\\n ['externalSource']\\n ['imagery']\\n )\\n imagery = (\\n f\\\"\\\\n{imagery_text}\\\\n\\\"\\n )\\n\\n license_text = (\\n project_page_data['project']\\n ['externalSource']\\n ['license']\\n )\\n license = (\\n f\\\"\\\\n{license_text}\\\\n\\\"\\n )\\n\\n # metrics = (\\n # f\\\"\\\\n* {project_page_data.instructions}\\\\n\\\"\\n # )\\n # quality_assurance = (\\n # f\\\"\\\\n* {project_page_data.quality_assurance}\\\\n\\\"\\n # )\\n\\n users = project_page_data['project'][\\\"users\\\"]\\n project_users = \\\"\\\"\\n for user in users:\\n project_users += (\\n f\\\"\\\\n| {user['userId']}\\\\n| {user['userName']}\\\\n|-\\\"\\n )\\n\\n \\n\\n project_page_sections = {\\n self.short_description_section: short_description,\\n self.timeframe_section: timeframe,\\n # self.timeframe_section: {\\n # self.created_section: created_date_text\\n # }, # if choose use subsection for timeframe \\n self.url_section: project_url,\\n self.external_sources_section: {\\n self.instructions_section: instructions,\\n self.per_task_instructions_section: per_task_instructions,\\n self.imagery_section: imagery,\\n self.license_section: license\\n },\\n self.hashtag_section: hashtag_text,\\n # self.instructions_section: instructions,\\n # self.metrics_section: metrics,\\n # self.quality_assurance_section: quality_assurance,\\n self.team_user_section: {\\n self.users_list_section: project_users\\n }\\n }\\n return project_page_sections\",\n \"def __init__(self, course_id, name, content):\\n # course id and name need to go over the string_correct function\\n self.ID = self.id_correct(str(course_id))\\n self.name = self.string_correct(str(name))\\n\\n self.content = str(content)\\n\\n # uninitialized variables\\n self.prere = {}\\n self.postre = {}\\n self.description = None\\n self.department_title = None\\n self.course_level = None\\n\\n # parse description and prerequisite raw data from content var\\n self.seperate_content()\",\n \"def populate_course(self):\\r\\n def descend(parent, stack):\\r\\n xblock_type = stack.pop(0)\\r\\n for _ in range(2):\\r\\n child = ItemFactory.create(category=xblock_type, parent_location=parent.location)\\r\\n if stack:\\r\\n descend(child, stack)\\r\\n\\r\\n descend(self.course, ['chapter', 'sequential', 'vertical', 'problem'])\",\n \"def completed_course()->str:\\r\\n try:\\r\\n db: sqlite3.Connection = sqlite3.connect(DB_FILE)\\r\\n query: str = \\\"SELECT s.Name, s.CWID, g.Course, g.Grade, i.Name AS 'Instructor' \\\" \\\\\\r\\n \\\"FROM grades2 g JOIN students2 s ON g.StudentCWID = s.CWID \\\" \\\\\\r\\n \\\"JOIN instructors2 i ON g.InstructorCWID = i.CWID ORDER BY s.Name\\\" \\r\\n except sqlite3.OperationalError as e:\\r\\n print(e)\\r\\n\\r\\n data: Dict[str,str]=\\\\\\r\\n [{\\\"name\\\": name,\\\"cwid\\\": cwid,\\\"course\\\": course , \\\"grade\\\":grade, \\\"instructor\\\": instructor}\\r\\n for name, cwid, course, grade, instructor in db.execute(query)]\\r\\n db.close()\\r\\n \\\"\\\"\\\"Render the template from templates folder\\\"\\\"\\\"\\r\\n return render_template(\\\"student_summary.html\\\",\\r\\n title=\\\"Stevens Repository\\\",\\r\\n table_title=\\\"Student, Course, Grade and Instructor\\\",\\r\\n students=data )\",\n \"def parse_course(browser, college, course_node):\\n # open the course details\\n course_handle = course_node.find_element_by_class_name('course')\\n course_handle.click()\\n wait_for_load(browser)\\n\\n title = course_node.find_element_by_class_name('courseID').text\\n m = re.match(r'([A-Z&]+) *(\\\\d+)', title)\\n if not m:\\n logging.warning('Unable to parse title: %s', title)\\n return\\n\\n dept = m.group(1)\\n code = m.group(2)\\n name = titlecase.titlecase(\\n course_node.find_element_by_class_name('courseTitle').text)\\n creds = float(course_node.find_element_by_class_name('courseCredits').text)\\n tags = parse_tags(course_node)\\n prerequisites = parse_prerequisites(course_node)\\n\\n # close the course details\\n course_handle.click()\\n wait_for_load(browser)\\n\\n return Course(\\n titlecase.titlecase(college), dept, code, name, creds, tags,\\n prerequisites)\",\n \"def common_context(request):\\n c = {\\n 'lessons': get_lesson_numbers(),\\n }\\n return c\",\n \"def compute_course_stats(course):\\r\\n\\r\\n # walk the course by using get_children() until we come to the leaves; count the\\r\\n # number of different leaf types\\r\\n\\r\\n counts = defaultdict(int)\\r\\n\\r\\n def walk(module):\\r\\n children = module.get_children()\\r\\n category = module.__class__.__name__ # HtmlDescriptor, CapaDescriptor, ...\\r\\n counts[category] += 1\\r\\n for c in children:\\r\\n walk(c)\\r\\n\\r\\n walk(course)\\r\\n stats = dict(counts) # number of each kind of module\\r\\n return stats\",\n \"def setup_xml_course(self):\\r\\n course_key = SlashSeparatedCourseKey('edX', 'toy', '2012_Fall')\\r\\n location = course_key.make_usage_key('chapter', 'Overview')\\r\\n descriptor = modulestore().get_item(location)\\r\\n\\r\\n self.module = self._get_module(course_key, descriptor, location)\",\n \"def test_tax_court_citation_extractor(self):\\n\\n test_pairs = (\\n (\\n \\\"\\\"\\\" 1 UNITED STATES TAX COURT REPORT (2018)\\n\\n\\n\\n UNITED STATES TAX COURT\\n\\n\\n\\n BENTLEY COURT II LIMITED PARTNERSHIP, B.F. BENTLEY, INC., TAX\\n MATTERS PARTNER, Petitioner v.\\n COMMISSIONER OF INTERNAL REVENUE, Respondent\\n\\n\\n\\n Docket No. 5393-04. Filed May 31, 2006.\\n\\n\\n\\n Nancy Ortmeyer Kuhn, for petitioner.\\n \\\"\\\"\\\",\\n \\\"1 T.C. 2018\\\",\\n ),\\n (\\n \\\"\\\"\\\" T.C. Memo. 2003-150\\n\\n\\n\\n UNITED STATES TAX COURT\\n\\n\\n\\n RIVER CITY RANCHES #1 LTD., LEON SHEPARD,\\n TAX MATTERS PARTNER,\\n RIVER CITY RANCHES #2 LTD., LEON SHEPARD,\\n TAX MATTERS PARTNER,\\n RIVER CITY RANCHES #3 LTD., LEON SHEPARD,\\n TAX MATTERS PARTNER,\\n\\n\\n \\\"\\\"\\\",\\n \\\"2003 T.C. Memo. 150\\\",\\n ),\\n (\\n \\\"\\\"\\\" T.C. Summary Opinion 2003-150\\n\\n\\n\\n UNITED STATES TAX COURT\\n\\n\\n\\n RIVER CITY RANCHES #1 LTD., LEON SHEPARD,\\n TAX MATTERS PARTNER,\\n RIVER CITY RANCHES #2 LTD., LEON SHEPARD,\\n TAX MATTERS PARTNER,\\n RIVER CITY RANCHES #3 LTD., LEON SHEPARD,\\n TAX MATTERS PARTNER,\\n\\n\\n \\\"\\\"\\\",\\n \\\"2003 T.C. Summary Opinion 150\\\",\\n ),\\n (\\n \\\"\\\"\\\"\\n MICHAEL KEITH SHENK, PETITIONER v. COMMISSIONER\\n OF INTERNAL REVENUE, RESPONDENT\\n\\n Docket No. 5706–12. Filed May 6, 2013.\\n\\n P was divorced from his wife, and their 2003 ‘‘Judgment of\\n Absolute Divorce’’ provided that his ex-wife would have pri-\\n mary residential custody of their three minor children. The\\n judgment provided that the dependency exemption deductions\\n for the three children would be divided between the two ex-\\n spouses according to various conditions but did not provide\\n that the ex-wife must execute in P’s favor a Form 8332,\\n ‘‘Release of Claim to Exemption for Child of Divorced or Sepa-\\n rated Parents’’. The children resided with P’s ex-wife for more\\n than half of 2009, and P’s ex-wife did not execute in P’s favor\\n any Form 8332 or equivalent document for any year. For 2009\\n P timely filed a Federal income tax return on which he\\n claimed dependency exemption deductions and the child tax\\n credit for two of the children, consistent with his under-\\n standing of the terms of the judgment, but he did not attach\\n any Form 8332 to his return. He also claimed head-of-house-\\n hold filing status. His ex-wife, the custodial parent, timely\\n filed a Federal income tax return for 2009 on which she also\\n\\n 200\\n\\n\\n\\n\\nVerDate Nov 24 2008 10:59 Jul 11, 2014 Jkt 372897 PO 20012 Frm 00001 Fmt 3857 Sfmt 3857 V:\\\\FILES\\\\BOUND VOL. WITHOUT CROP MARKS\\\\B.V.140\\\\SHENK JAMIE\\n\\f (200) SHENK v. COMMISSIONER 201\\n\\n\\n claimed two dependency exemption deductions, so that one\\n child was claimed on both parents’ returns. R allowed to P the\\n dependency exemption deduction for one of the children but\\n disallowed his claim for the dependency exemption deduction\\n for the child who had also been claimed by the custodial\\n parent. At trial P contended he is entitled to a dependency\\n exemption deduction for all three children. Held: Since the\\n custodial parent did not execute, and P could not and did not\\n attach to his return, any Form 8332 or equivalent release, P\\n is not entitled under I.R.C. sec. 152(e)(2)(A) to claim the\\n dependency exemption deduction or the child tax credit. Held,\\n further, where both the custodial parent and the noncustodial\\n parent have claimed for the same year a dependency exemp-\\n tion deduction for the same child, a declaration signed by the\\n custodial parent after the period of limitations for assess-\\n ments has expired as to the custodial parent could not qualify\\n under I.R.C. sec. 152(e)(2)(A), and therefore there is no reason\\n to grant P’s request to leave the record open so that he may\\n obtain and proffer such a declaration. Held, further, P is not\\n entitled to head-of-household filing status under I.R.C. sec.\\n 2(b)(1) nor to the child tax credit under I.R.C. sec. 24.\\n\\n Michael Keith Shenk, for himself.\\n Shari Salu, for respondent.\\n GUSTAFSON, Judge: The Internal Revenue Service (IRS)\\n determined a deficiency of $3,136 in the 2009 Federal income\\n tax of petitioner Michael Keith Shenk. Mr. Shenk petitioned\\n this Court, pursuant to section 6213(a), 1 for redetermination\\n of the deficiency. After Mr. Shenk’s concession that he\\n received but did not report $254 in dividend income, the\\n issue for decision is whether Mr. Shenk is entitled to a\\n dependency exemption deduction for one of his children\\n under section 151(c), a child tax credit for that child under\\n section 24(a), and head-of-household filing status under sec-\\n tion 2(b)(1). On these issues, we hold for the IRS.\\n FINDINGS OF FACT\\n\\n The judgment of divorce\\n Mr. Shenk was married to Julie Phillips, and they have\\n three minor children—M.S., W.S., and L.S. They divorced in\\n 2003. The family court’s ‘‘Judgment of Absolute Divorce’’ pro-\\n 1 Unless otherwise indicated, all citations of sections refer to the Internal\\n\\n Revenue Code (26 U.S.C.) in effect for the tax year at issue, and all cita-\\n tions of Rules refer to the Tax Court Rules of Practice and Procedure.\\n\\n\\n\\n\\nVerDate Nov 24 2008 10:59 Jul 11, 2014 Jkt 372897 PO 20012 Frm 00002 Fmt 3857 Sfmt 3857 V:\\\\FILES\\\\BOUND VOL. WITHOUT CROP MARKS\\\\B.V.140\\\\SHENK JAMIE\\n\\f 202 140 UNITED STATES TAX COURT REPORTS (200)\\n\\n\\n vided: that Ms. Phillips was ‘‘awarded primary residential\\n custody’’ of the parties’ three children; and that Mr. Shenk\\n would be liable for child support payments; but that, as to\\n dependency exemptions—\\\"\\\"\\\",\\n \\\"140 T.C. 200\\\",\\n ),\\n )\\n site = tax.Site()\\n for q, a in test_pairs:\\n results = site.extract_from_text(q)\\n cite_string = \\\"%s %s %s\\\" % (\\n results[\\\"Citation\\\"][\\\"volume\\\"],\\n results[\\\"Citation\\\"][\\\"reporter\\\"],\\n results[\\\"Citation\\\"][\\\"page\\\"],\\n )\\n\\n self.assertEqual(cite_string, a)\\n print \\\"✓\\\", cite_string\",\n \"def scrape_features(self, page_url):\\n\\n\\t\\t# create the soup with a certain page URL\\n\\t\\tcourse_list_page = requests.get(page_url)\\n\\t\\tcourse_list_soup = BeautifulSoup(course_list_page.content,\\n\\t\\t\\t\\t\\t\\t\\t\\t\\t\\t'html.parser')\\n\\n\\t\\t# pick course name\\n\\t\\tcnames = course_list_soup.select(\\\".headline-1-text\\\")\\n\\t\\tfor i in range(10):\\n\\t\\t\\tself.courses.append(cnames[i].text)\\n\\n\\t\\t# pick partner name\\n\\t\\tpnames = course_list_soup.select(\\\".horizontal-box > .partner-name\\\")\\n\\t\\tfor i in range(10):\\n\\t\\t\\tself.organizations.append(pnames[i].text)\\n\\n\\t\\t# pick URLs\\n\\t\\troot = \\\"https://www.coursera.org\\\"\\n\\t\\tlinks = course_list_soup.select(\\n\\t\\t\\t\\\".ais-InfiniteHits > .ais-InfiniteHits-list > .ais-InfiniteHits-item\\\" \\n\\t\\t)\\n\\t\\tfor i in range(10):\\n\\t\\t\\tself.urls.append(root+links[i].a[\\\"href\\\"])\\n\\n\\t\\t# pick learning product\\n\\t\\tfor i in range(10):\\n\\t\\t\\tlearn_pdcts = course_list_soup.find_all('div', '_jen3vs _1d8rgfy3')\\n\\t\\t\\tself.learning_products.append(learn_pdcts[i].text)\\n\\n\\t\\t# pick course rating and number of people who rated\\n\\t\\tratings = []\\n\\t\\tnum_ratings = []\\n\\t\\tcratings = course_list_soup.select(\\n\\t\\t\\t\\\".ratings-text\\\")\\n\\t\\tcnumratings = course_list_soup.select(\\n\\t\\t\\t\\\".ratings-count\\\")\\n\\t\\tfor i in range(10):\\n\\t\\t\\ttry:\\n\\t\\t\\t\\tself.ratings.append(float(cratings[i].text))\\n\\t\\t\\texcept:\\n\\t\\t\\t\\tself.ratings.append(\\\"Missing\\\")\\n\\t\\t\\ttry:\\n\\t\\t\\t\\tself.num_rated.append(int(cnumratings[i].text.\\\\\\n\\t\\t\\t\\t\\treplace(',','').\\\\\\n\\t\\t\\t\\t\\treplace('(','').\\\\\\n\\t\\t\\t\\t\\treplace(')','')))\\n\\t\\t\\texcept:\\n\\t\\t\\t\\tself.num_rated.append(\\\"Missing\\\")\\n\\n\\t\\t# pick enrollment number\\n\\t\\tenrollers = course_list_soup.select(\\\".enrollment-number\\\")\\n\\t\\tfor i in range(10):\\n\\t\\t\\ttry:\\n\\t\\t\\t\\tself.enrolled.append(enrollers[i].text)\\n\\t\\t\\texcept:\\n\\t\\t\\t\\tself.enrolled.append(\\\"Missing\\\")\\n\\n\\t\\t# pick difficulty\\n\\t\\tdifficulty = course_list_soup.select(\\\".difficulty\\\")\\n\\t\\tfor i in range(10):\\n\\t\\t\\tself.difficulty.append(difficulty[i].text)\",\n \"def extract_data(department_link):\\n # Update the progress bar\\n if show_progress:\\n progress_bar.update()\\n\\n # Regular expressions for searching course descriptions stored in local variables\\n # for better peformance\\n local_course_re = course_re\\n local_course_name_re = course_name_re\\n local_credits_re = credits_re\\n local_credits_num_re = credits_num_re\\n local_offered_jointly_re = offered_jointly_re\\n local_CAMPUSES = CAMPUSES\\n\\n # Method used in extracting data from course descriptions found in the local scope\\n # are stored in local variables for better performance\\n local_complete_description = complete_description\\n local_get_offered = get_offered\\n local_get_requisites = get_requisites\\n\\n # All the courses in the department\\n courses = []\\n dep_file = department_link.get('href')\\n\\n # If the user entered a dict as the 'campuse' parameter, departments\\n # are checked here\\n try:\\n # The String in the conditional is the abbreviated Department Name i.e EE\\n # for Electrical Engineering\\n if normalize('NFKD', department_link.text).rsplit('(', 1) \\\\\\n [-1].replace(' ', '')[:-1] not in campuses[campus]:\\n return None\\n except TypeError:\\n pass\\n \\n # The only links that are used for finding departments are those\\n # of the format [a-z]+.html\\n if '/' not in dep_file and dep_file.endswith('.html') \\\\\\n and dep_file not in parsed_departments:\\n parsed_departments.add(dep_file)\\n department = BeautifulSoup(requests.get( \\\\\\n f'{local_CAMPUSES[campus]}{dep_file}').text, features='lxml')\\n for course in department.find_all('a'):\\n course_ID = course.get('name') \\n if course_ID:\\n course_ID = course_ID.upper()\\n course_title = course.find('b').text\\n # The Course Description\\n description = course.get_text().replace(course_title, '', 1) \\n instructors = course.find('i')\\n if instructors:\\n description = description.replace(str(instructors.get_text()), '', 1)\\n del instructors\\n course_text = local_complete_description( \\\\\\n description.rsplit('View course details in MyPlan', 1)[0])\\n # Course Number i.e 351\\n course_number = re.sub(local_course_re, '', course_ID)\\n match_name = re.search(local_course_name_re, course_title)\\n match_credit_num = re.search(local_credits_num_re, course_title)\\n match_credit_types = re.findall(local_credits_re, course_title)\\n # Jointly offered course with the given course\\n if 'jointly with' in course_text: \\n offered_jointly = course_text.rsplit('jointly with ', 1)[-1].rsplit(';', 1)[0] \\n offered_jointly = ','.join(re.findall( \\\\\\n local_offered_jointly_re, offered_jointly)).replace(' ', '') \\n else:\\n offered_jointly = ''\\n courses.append(\\n # Campus, Department Name and Course Number\\n [campus, course_ID[:-3], course_number, \\n # Course Name\\n match_name.group(0).split(course_number, 1)[-1].strip() \\\\\\n if match_name else '',\\n # Number of credits for the course\\n match_credit_num.group(0)[1:-1] \\\\\\n if match_credit_num else '', \\n # Course Credit Types (I&S, DIV, NW, VLPA, QSR, C)\\n ','.join([list(filter(('').__ne__, x))[0] for x in match_credit_types]) \\\\\\n if match_credit_types else '', \\n local_get_offered(course_text),\\n offered_jointly, local_get_requisites(course_text, 'Prerequisite:'), \\n local_get_requisites(course_text, 'Co-requisite'), course_text]\\n )\\n return courses\",\n \"def course_info(request, course_id):\\r\\n course_key = SlashSeparatedCourseKey.from_deprecated_string(course_id)\\r\\n course = get_course_with_access(request.user, 'load', course_key)\\r\\n staff_access = has_access(request.user, 'staff', course)\\r\\n masq = setup_masquerade(request, staff_access) # allow staff to toggle masquerade on info page\\r\\n reverifications = fetch_reverify_banner_info(request, course_key)\\r\\n studio_url = get_studio_url(course_key, 'course_info')\\r\\n\\r\\n context = {\\r\\n 'request': request,\\r\\n 'course_id': course_key.to_deprecated_string(),\\r\\n 'cache': None,\\r\\n 'course': course,\\r\\n 'staff_access': staff_access,\\r\\n 'masquerade': masq,\\r\\n 'studio_url': studio_url,\\r\\n 'reverifications': reverifications,\\r\\n }\\r\\n\\r\\n return render_to_response('courseware/info.html', context)\",\n \"def ContextFromURL( url ):\\n url = TidyURL(url)\\n context = {\\n 'srcurl': url,\\n 'permalink': url,\\n 'srcorgname': 'dailymail', \\n 'lastseen': datetime.now(),\\n }\\n return context\",\n \"def get_annotated_content_infos(course_id, thread, user, user_info):\\r\\n infos = {}\\r\\n\\r\\n def annotate(content):\\r\\n infos[str(content['id'])] = get_annotated_content_info(course_id, content, user, user_info)\\r\\n for child in content.get('children', []):\\r\\n annotate(child)\\r\\n annotate(thread)\\r\\n return infos\",\n \"def cert_info(user, course):\\r\\n if not course.may_certify():\\r\\n return {}\\r\\n\\r\\n return _cert_info(user, course, certificate_status_for_student(user, course.id))\",\n \"def create_tex(unwanted_courses, wanted_courses=None):\\n page = requests.get(\\n 'http://gla.ac.uk/coursecatalogue/courselist/' +\\n '?code=REG30200000&name=School+of+Computing+Science')\\n tree = html.fromstring(page.content)\\n spans = tree.xpath('//span/text()')\\n codes = []\\n if wanted_courses is None:\\n for s in spans:\\n if s[0:4] == \\\"COMP\\\" and s[7] == '4' and s not in unwanted_courses:\\n codes.append(s)\\n else:\\n for s in wanted_courses:\\n codes.append(s)\\n write_to_latex(codes, unwanted_courses)\\n return None\",\n \"def isolate_relevant_information(self):\\n\\n def get_formula(oncat_formula):\\n \\\"\\\"\\\"will need to go from something like\\n \\\"${value/10e11}`\\\"\\n to something more pythonic\\n \\\"{value/10e11}\\\"\\\"\\\"\\n regular_expression = r'\\\\$(?P<formula>.+)\\\\`'\\n m = re.search(regular_expression, oncat_formula)\\n if m:\\n return m.group('formula')\\n else:\\n return \\\"\\\"\\n\\n template_information = {}\\n for _index, _element in enumerate(self._oncat_default_template):\\n _title = _element[\\\"name\\\"]\\n _path = _element[\\\"path\\\"]\\n if \\\"units\\\" in _element:\\n _units = _element[\\\"units\\\"]\\n else:\\n _units = \\\"\\\"\\n if \\\"transform\\\" in _element:\\n _formula = get_formula(_element[\\\"transform\\\"])\\n else:\\n _formula = \\\"\\\"\\n template_information[_index] = {'title': _title,\\n 'path': _path,\\n 'units': _units,\\n 'formula': _formula}\\n self.template_information = template_information\",\n \"def _update_context_with_basic_info(context, course_id, platform_name, configuration):\\n context['platform_name'] = platform_name\\n context['course_id'] = course_id\\n\\n # Update the view context with the default ConfigurationModel settings\\n context.update(configuration.get('default', {}))\\n\\n # Translators: 'All rights reserved' is a legal term used in copyrighting to protect published content\\n reserved = _(\\\"All rights reserved\\\")\\n context['copyright_text'] = '© {year} {platform_name}. {reserved}.'.format(\\n year=datetime.now(pytz.timezone(settings.TIME_ZONE)).year,\\n platform_name=platform_name,\\n reserved=reserved\\n )\\n\\n # Translators: This text is bound to the HTML 'title' element of the page and appears\\n # in the browser title bar when a requested certificate is not found or recognized\\n context['document_title'] = _(\\\"Invalid Certificate\\\")\\n\\n context['company_tos_urltext'] = _(\\\"Terms of Service & Honor Code\\\")\\n\\n # Translators: A 'Privacy Policy' is a legal document/statement describing a website's use of personal information\\n context['company_privacy_urltext'] = _(\\\"Privacy Policy\\\")\\n\\n # Translators: This line appears as a byline to a header image and describes the purpose of the page\\n context['logo_subtitle'] = _(\\\"Certificate Validation\\\")\\n\\n # Translators: Accomplishments describe the awards/certifications obtained by students on this platform\\n context['accomplishment_copy_about'] = _('About {platform_name} Accomplishments').format(\\n platform_name=platform_name\\n )\\n\\n # Translators: This line appears on the page just before the generation date for the certificate\\n context['certificate_date_issued_title'] = _(\\\"Issued On:\\\")\\n\\n # Translators: The Certificate ID Number is an alphanumeric value unique to each individual certificate\\n context['certificate_id_number_title'] = _('Certificate ID Number')\\n\\n context['certificate_info_title'] = _('About {platform_name} Certificates').format(\\n platform_name=platform_name\\n )\\n\\n context['certificate_verify_title'] = _(\\\"How {platform_name} Validates Student Certificates\\\").format(\\n platform_name=platform_name\\n )\\n\\n # Translators: This text describes the validation mechanism for a certificate file (known as GPG security)\\n context['certificate_verify_description'] = _('Certificates issued by {platform_name} are signed by a gpg key so '\\n 'that they can be validated independently by anyone with the '\\n '{platform_name} public key. For independent verification, '\\n '{platform_name} uses what is called a '\\n '\\\"detached signature\\\""\\\".').format(platform_name=platform_name)\\n\\n context['certificate_verify_urltext'] = _(\\\"Validate this certificate for yourself\\\")\\n\\n # Translators: This text describes (at a high level) the mission and charter the edX platform and organization\\n context['company_about_description'] = _(\\\"{platform_name} offers interactive online classes and MOOCs.\\\").format(\\n platform_name=platform_name)\\n\\n context['company_about_title'] = _(\\\"About {platform_name}\\\").format(platform_name=platform_name)\\n\\n context['company_about_urltext'] = _(\\\"Learn more about {platform_name}\\\").format(platform_name=platform_name)\\n\\n context['company_courselist_urltext'] = _(\\\"Learn with {platform_name}\\\").format(platform_name=platform_name)\\n\\n context['company_careers_urltext'] = _(\\\"Work at {platform_name}\\\").format(platform_name=platform_name)\\n\\n context['company_contact_urltext'] = _(\\\"Contact {platform_name}\\\").format(platform_name=platform_name)\\n\\n # Translators: This text appears near the top of the certificate and describes the guarantee provided by edX\\n context['document_banner'] = _(\\\"{platform_name} acknowledges the following student accomplishment\\\").format(\\n platform_name=platform_name\\n )\",\n \"def get_course_info_section(request, course, section_key):\\r\\n usage_key = course.id.make_usage_key('course_info', section_key)\\r\\n\\r\\n # Use an empty cache\\r\\n field_data_cache = FieldDataCache([], course.id, request.user)\\r\\n info_module = get_module(\\r\\n request.user,\\r\\n request,\\r\\n usage_key,\\r\\n field_data_cache,\\r\\n course.id,\\r\\n wrap_xmodule_display=False,\\r\\n static_asset_path=course.static_asset_path\\r\\n )\\r\\n\\r\\n html = ''\\r\\n\\r\\n if info_module is not None:\\r\\n try:\\r\\n html = info_module.render('student_view').content\\r\\n except Exception: # pylint: disable=broad-except\\r\\n html = render_to_string('courseware/error-message.html', None)\\r\\n log.exception(\\r\\n u\\\"Error rendering course={course}, section_key={section_key}\\\".format(\\r\\n course=course, section_key=section_key\\r\\n ))\\r\\n\\r\\n return html\",\n \"def get_course_info(self, course):\\n course_preview_url = course.slug\\n if course.slug == '/':\\n course_preview_url = '/course'\\n course.course_preview_url = course_preview_url\\n course.is_registered = self.is_enrolled(course)\\n course.is_completed = self.is_completed(course)\\n\\n\\n return course\",\n \"def latex_course(course):\\n basic_info_list = [\\n 'session', 'school', 'credits', 'level', 'offered',\\n 'visiting_students', 'erasmus_students'\\n ]\\n generic_subsection_list = [\\n 'description', 'timetable', 'requirements_of_entry',\\n 'excluded_courses', 'co_requisites', 'assessment_weighting'\\n ]\\n string = '\\\\\\\\subsection{' + course[\\\"title\\\"] + '}\\\\n'\\n for info in basic_info_list:\\n string += latex_info(course[info])\\n for subsection in generic_subsection_list:\\n string += latex_subsection(course[subsection])\\n string += '\\\\\\\\break \\\\\\\\textbf{' + course['assessment_date'][\\n 'heading'] + '}' + course['assessment_date']['value'] + '\\\\n'\\n string += latex_subsection(course['aims'])\\n string += '\\\\\\\\subsubsection*{' + \\\\\\n course['learning_outcomes']['heading'] + '}\\\\n'\\n outcome_list = re.split(\\n '\\\\d+\\\\. ', course['learning_outcomes']['value'])\\n string += outcome_list[0] + '\\\\n'\\n string += '\\\\\\\\begin{enumerate}\\\\n'\\n for i in outcome_list[1:-1]:\\n string += '\\\\\\\\item ' + i + '\\\\n'\\n string += '\\\\\\\\end{enumerate}\\\\n'\\n return string\",\n \"def index(request, course_id, chapter=None, section=None,\\r\\n position=None):\\r\\n course_key = SlashSeparatedCourseKey.from_deprecated_string(course_id)\\r\\n user = User.objects.prefetch_related(\\\"groups\\\").get(id=request.user.id)\\r\\n request.user = user # keep just one instance of User\\r\\n course = get_course_with_access(user, 'load', course_key, depth=2)\\r\\n staff_access = has_access(user, 'staff', course)\\r\\n registered = registered_for_course(course, user)\\r\\n if not registered:\\r\\n # TODO (vshnayder): do course instructors need to be registered to see course?\\r\\n log.debug(u'User %s tried to view course %s but is not enrolled', user, course.location.to_deprecated_string())\\r\\n return redirect(reverse('about_course', args=[course_key.to_deprecated_string()]))\\r\\n\\r\\n masq = setup_masquerade(request, staff_access)\\r\\n\\r\\n try:\\r\\n field_data_cache = FieldDataCache.cache_for_descriptor_descendents(\\r\\n course_key, user, course, depth=2)\\r\\n\\r\\n course_module = get_module_for_descriptor(user, request, course, field_data_cache, course_key)\\r\\n if course_module is None:\\r\\n log.warning(u'If you see this, something went wrong: if we got this'\\r\\n u' far, should have gotten a course module for this user')\\r\\n return redirect(reverse('about_course', args=[course_key.to_deprecated_string()]))\\r\\n\\r\\n studio_url = get_studio_url(course_key, 'course')\\r\\n\\r\\n if chapter is None:\\r\\n return redirect_to_course_position(course_module)\\r\\n\\r\\n context = {\\r\\n 'csrf': csrf(request)['csrf_token'],\\r\\n 'accordion': render_accordion(request, course, chapter, section, field_data_cache),\\r\\n 'COURSE_TITLE': course.display_name_with_default,\\r\\n 'course': course,\\r\\n 'init': '',\\r\\n 'fragment': Fragment(),\\r\\n 'staff_access': staff_access,\\r\\n 'studio_url': studio_url,\\r\\n 'masquerade': masq,\\r\\n 'xqa_server': settings.FEATURES.get('USE_XQA_SERVER', 'http://xqa:server@content-qa.mitx.mit.edu/xqa'),\\r\\n 'reverifications': fetch_reverify_banner_info(request, course_key),\\r\\n }\\r\\n\\r\\n # Only show the chat if it's enabled by the course and in the\\r\\n # settings.\\r\\n show_chat = course.show_chat and settings.FEATURES['ENABLE_CHAT']\\r\\n if show_chat:\\r\\n context['chat'] = chat_settings(course, user)\\r\\n # If we couldn't load the chat settings, then don't show\\r\\n # the widget in the courseware.\\r\\n if context['chat'] is None:\\r\\n show_chat = False\\r\\n\\r\\n context['show_chat'] = show_chat\\r\\n\\r\\n chapter_descriptor = course.get_child_by(lambda m: m.location.name == chapter)\\r\\n if chapter_descriptor is not None:\\r\\n save_child_position(course_module, chapter)\\r\\n else:\\r\\n raise Http404('No chapter descriptor found with name {}'.format(chapter))\\r\\n\\r\\n chapter_module = course_module.get_child_by(lambda m: m.location.name == chapter)\\r\\n if chapter_module is None:\\r\\n # User may be trying to access a chapter that isn't live yet\\r\\n if masq == 'student': # if staff is masquerading as student be kinder, don't 404\\r\\n log.debug('staff masq as student: no chapter %s' % chapter)\\r\\n return redirect(reverse('courseware', args=[course.id.to_deprecated_string()]))\\r\\n raise Http404\\r\\n\\r\\n if section is not None:\\r\\n section_descriptor = chapter_descriptor.get_child_by(lambda m: m.location.name == section)\\r\\n if section_descriptor is None:\\r\\n # Specifically asked-for section doesn't exist\\r\\n if masq == 'student': # if staff is masquerading as student be kinder, don't 404\\r\\n log.debug('staff masq as student: no section %s' % section)\\r\\n return redirect(reverse('courseware', args=[course.id.to_deprecated_string()]))\\r\\n raise Http404\\r\\n\\r\\n # cdodge: this looks silly, but let's refetch the section_descriptor with depth=None\\r\\n # which will prefetch the children more efficiently than doing a recursive load\\r\\n section_descriptor = modulestore().get_item(section_descriptor.location, depth=None)\\r\\n\\r\\n # Load all descendants of the section, because we're going to display its\\r\\n # html, which in general will need all of its children\\r\\n section_field_data_cache = FieldDataCache.cache_for_descriptor_descendents(\\r\\n course_key, user, section_descriptor, depth=None)\\r\\n\\r\\n section_module = get_module_for_descriptor(\\r\\n request.user,\\r\\n request,\\r\\n section_descriptor,\\r\\n section_field_data_cache,\\r\\n course_key,\\r\\n position\\r\\n )\\r\\n\\r\\n if section_module is None:\\r\\n # User may be trying to be clever and access something\\r\\n # they don't have access to.\\r\\n raise Http404\\r\\n\\r\\n # Save where we are in the chapter\\r\\n save_child_position(chapter_module, section)\\r\\n context['fragment'] = section_module.render('student_view')\\r\\n context['section_title'] = section_descriptor.display_name_with_default\\r\\n else:\\r\\n # section is none, so display a message\\r\\n studio_url = get_studio_url(course_key, 'course')\\r\\n prev_section = get_current_child(chapter_module)\\r\\n if prev_section is None:\\r\\n # Something went wrong -- perhaps this chapter has no sections visible to the user\\r\\n raise Http404\\r\\n prev_section_url = reverse('courseware_section', kwargs={\\r\\n 'course_id': course_key.to_deprecated_string(),\\r\\n 'chapter': chapter_descriptor.url_name,\\r\\n 'section': prev_section.url_name\\r\\n })\\r\\n context['fragment'] = Fragment(content=render_to_string(\\r\\n 'courseware/welcome-back.html',\\r\\n {\\r\\n 'course': course,\\r\\n 'studio_url': studio_url,\\r\\n 'chapter_module': chapter_module,\\r\\n 'prev_section': prev_section,\\r\\n 'prev_section_url': prev_section_url\\r\\n }\\r\\n ))\\r\\n\\r\\n result = render_to_response('courseware/courseware.html', context)\\r\\n except Exception as e:\\r\\n if isinstance(e, Http404):\\r\\n # let it propagate\\r\\n raise\\r\\n\\r\\n # In production, don't want to let a 500 out for any reason\\r\\n if settings.DEBUG:\\r\\n raise\\r\\n else:\\r\\n log.exception(\\r\\n u\\\"Error in index view: user={user}, course={course}, chapter={chapter}\\\"\\r\\n u\\\" section={section} position={position}\\\".format(\\r\\n user=user,\\r\\n course=course,\\r\\n chapter=chapter,\\r\\n section=section,\\r\\n position=position\\r\\n ))\\r\\n try:\\r\\n result = render_to_response('courseware/courseware-error.html', {\\r\\n 'staff_access': staff_access,\\r\\n 'course': course\\r\\n })\\r\\n except:\\r\\n # Let the exception propagate, relying on global config to at\\r\\n # at least return a nice error message\\r\\n log.exception(\\\"Error while rendering courseware-error page\\\")\\r\\n raise\\r\\n\\r\\n return result\",\n \"def _get_courses(self) -> None:\\n\\n courses_content: NavigableString = self.soup.find(\\\"div\\\", \\n {\\\"class\\\": \\\"coursesContent\\\"})\\n course_items: ResultSet = courses_content.find_all(\\\"div\\\", \\n {\\\"class\\\": \\\"courseItem\\\"})\\n\\n for item in course_items:\\n course_name: str = item.a[\\\"href\\\"].split(\\\"/\\\")[-2].lower()\\n course_data: ParseType = self._parse(item)\\n self._update(course_name, course_data)\",\n \"def parse_courses():\\n\\n subjects = collections.OrderedDict()\\n name = '' # the most recent course name acronym (ex. 'COMP')\\n\\n courses = re.sub(r'\\\\([^)]*\\\\)', '', COURSES).split() # Remove parens and their contents\\n\\n for course in courses:\\n if course == 'OR':\\n continue\\n\\n if course[0].isalpha():\\n\\n index = 0 # the upper bound character index of the subject name\\n for char in course:\\n if char.isalpha():\\n index += 1\\n else:\\n break\\n\\n name = course[:index]\\n number = course[index:index+4]\\n else:\\n number = course[:4]\\n\\n try:\\n subjects[name].append(number)\\n except KeyError:\\n subjects[name] = [number]\\n\\n return subjects\",\n \"def event_html_page_context(app, pagename, templatename, context, doctree):\\n\\n reURL = re.compile('^https?://\\\\S+/$')\\n current_builder = app.builder.name\\n if current_builder == 'html' or current_builder == 'readthedocs':\\n if doctree and doctree.traverse(DiscourseNode):\\n if not app.config.discourse_url:\\n raise DiscourseError('::discourse: directive found, but discourse_url is not set in conf.py')\\n if not reURL.match(app.config.discourse_url):\\n raise DiscourseError('::discourse: directive found, but discourse_url is not set properly in conf.py (must be a valid URL starting with http:// or https:// and ending with trailing slash)')\\n for discourse_node in doctree.traverse(DiscourseNode):\\n context['body'] += \\\"\\\"\\\"<script type='text/javascript'>\\n DiscourseEmbed = { discourseUrl: '%s',\\n topicId: '%s' };\\n (function() {\\n var d = document.createElement('script'); d.type = 'text/javascript'; d.async = true;\\n d.src = DiscourseEmbed.discourseUrl + 'javascripts/embed.js';\\n (document.getElementsByTagName('head')[0] || document.getElementsByTagName('body')[0]).appendChild(d);\\n })();\\n </script>\\\"\\\"\\\" % (app.config.discourse_url, discourse_node.get_topic_identifier())\",\n \"def extract_course_summary(course):\\n return {\\n field: course.fields[field]\\n for field in CourseSummary.course_info_fields\\n if field in course.fields\\n }\",\n \"def get_semester_course_data(self, url, semester):\\n print(f\\\"Obtaining and indexing information for {semester}\\\")\\n html = self.call_url_and_get_html_object(url)\\n tables = html.findall(\\\".//table[@class='courseListing basicTable courseListingSetWidths']\\\")\\n\\n # Parse html to get course offering data\\n for table in tables:\\n fields = table.findall(\\\".//td\\\")\\n spans = table.findall(\\\".//span\\\")\\n course_number = str(spans[1].text.strip())\\n title = str(fields[4].text).strip()\\n professor = str(fields[6].text).strip()\\n status = str(fields[0].text)\\n crn = str(fields[1].text)\\n\\n # Add course offering data to dictionary of course classes\\n if course_number not in self.course_dict.keys():\\n # If course doesn't already exist in dictionary keys, instantiate class of it\\n self.course_dict[course_number] = Course(\\n title=title, semester=semester,\\n professor=professor, crn=crn, status=status)\\n else:\\n self.course_dict[course_number].add_instance_of_course(\\n semester, professor, crn, status)\",\n \"def build_general_course(self):\\n\\n courses = {}\\n\\n if self.__build_analyze is False:\\n self.build_analysis()\\n\\n courses[\\\"taxa_conhecimento\\\"] = self.analysis[\\\"taxa_conhecimento\\\"]\\n courses[\\\"taxa_reprovacao\\\"] = self.analysis[\\\"taxa_reprovacao_absoluta\\\"]\\n courses[\\\"taxa_trancamento\\\"] = self.analysis[\\\"taxa_trancamento\\\"]\\n\\n # cria cache\\n cache = {}\\n note = self.analysis[\\\"general_note_statistic\\\"]\\n for rate_it in self.__rates:\\n rate_calc = self.analysis[\\\"general_rates\\\"][rate_it.name][0]\\n for course in self.analysis[\\\"courses\\\"].index:\\n if course not in cache:\\n cache[course] = {}\\n cache[course][rate_it.name] = rate_calc[course]\\n cache[course][\\\"nota\\\"] = [note[0][course], note[1][course]]\\n\\n courses[\\\"cache\\\"] = cache\\n\\n # cria o campo compara_aprov\\n courses[\\\"compara_aprov\\\"] = self.analysis[\\\"graph_course\\\"]\\n\\n # cria o campo courses\\n courses[\\\"disciplinas\\\"] = self.analysis[\\\"courses\\\"].to_dict()\\n\\n return courses\",\n \"def parse_create_section(xml_course):\\n\\n attrs = [\\n \\\"section\\\",\\n 'crn',\\n \\\"start-time\\\",\\n \\\"end-time\\\",\\n \\\"meeting-days\\\",\\n \\\"location\\\",\\n \\\"section-number\\\",\\n \\\"instructor\\\"\\n ]\\n\\n section = pull_attributes_from_xml(xml_course, attrs)\\n\\n section[\\\"places\\\"] = []\\n\\n # Create Place attribute pointer based on location string\\n # Get places from Parse\\n places = get_places()[\\\"results\\\"]\\n # Get location info from section (of form [\\\"BRK 101\\\", \\\"TBA\\\"])\\n all_locations = section[\\\"location\\\"].split(\\\", \\\")\\n # Filter out TBA\\n # TODO Maybe do something else with them\\n locations = [location for location in all_locations if location != \\\"TBA\\\"]\\n\\n for location in locations:\\n building_code = location.split(\\\" \\\")[0]\\n for place in places:\\n if place.get(\\\"symbol\\\") and place[\\\"symbol\\\"] == building_code:\\n section[\\\"places\\\"].append(place[\\\"objectId\\\"])\\n break;\\n\\n\\n return section\",\n \"def _get_course_email_context(course):\\r\\n course_id = course.id.to_deprecated_string()\\r\\n course_title = course.display_name\\r\\n course_url = 'https://{}{}'.format(\\r\\n settings.SITE_NAME,\\r\\n reverse('course_root', kwargs={'course_id': course_id})\\r\\n )\\r\\n image_url = 'https://{}{}'.format(settings.SITE_NAME, course_image_url(course))\\r\\n email_context = {\\r\\n 'course_title': course_title,\\r\\n 'course_url': course_url,\\r\\n 'course_image_url': image_url,\\r\\n 'account_settings_url': 'https://{}{}'.format(settings.SITE_NAME, reverse('dashboard')),\\r\\n 'platform_name': settings.PLATFORM_NAME,\\r\\n }\\r\\n return email_context\",\n \"def get_context(nav, config, page=None):\\n\\n if nav is None:\\n return {'page', page}\\n\\n extra_javascript = utils.create_media_urls(nav, config['extra_javascript'])\\n\\n extra_css = utils.create_media_urls(nav, config['extra_css'])\\n\\n # Support SOURCE_DATE_EPOCH environment variable for \\\"reproducible\\\" builds.\\n # See https://reproducible-builds.org/specs/source-date-epoch/\\n timestamp = int(os.environ.get('SOURCE_DATE_EPOCH', timegm(datetime.utcnow().utctimetuple())))\\n\\n return {\\n 'nav': nav,\\n # base_url should never end with a slash.\\n 'base_url': nav.url_context.make_relative('/').rstrip('/'),\\n\\n 'extra_css': extra_css,\\n 'extra_javascript': extra_javascript,\\n\\n 'mkdocs_version': mkdocs.__version__,\\n 'build_date_utc': datetime.utcfromtimestamp(timestamp),\\n\\n 'config': config,\\n 'page': page,\\n }\",\n \"def get_text(self, course): # pylint: disable=unused-argument\\r\\n raise NotImplementedError\",\n \"def get_course_about_section(course, section_key):\\r\\n\\r\\n # Many of these are stored as html files instead of some semantic\\r\\n # markup. This can change without effecting this interface when we find a\\r\\n # good format for defining so many snippets of text/html.\\r\\n\\r\\n # TODO: Remove number, instructors from this list\\r\\n if section_key in ['short_description', 'description', 'key_dates', 'video',\\r\\n 'course_staff_short', 'course_staff_extended',\\r\\n 'requirements', 'syllabus', 'textbook', 'faq', 'more_info',\\r\\n 'number', 'instructors', 'overview',\\r\\n 'effort', 'end_date', 'prerequisites', 'ocw_links']:\\r\\n\\r\\n try:\\r\\n\\r\\n request = get_request_for_thread()\\r\\n\\r\\n loc = course.location.replace(category='about', name=section_key)\\r\\n\\r\\n # Use an empty cache\\r\\n field_data_cache = FieldDataCache([], course.id, request.user)\\r\\n about_module = get_module(\\r\\n request.user,\\r\\n request,\\r\\n loc,\\r\\n field_data_cache,\\r\\n course.id,\\r\\n not_found_ok=True,\\r\\n wrap_xmodule_display=False,\\r\\n static_asset_path=course.static_asset_path\\r\\n )\\r\\n\\r\\n html = ''\\r\\n\\r\\n if about_module is not None:\\r\\n try:\\r\\n html = about_module.render('student_view').content\\r\\n except Exception: # pylint: disable=broad-except\\r\\n html = render_to_string('courseware/error-message.html', None)\\r\\n log.exception(\\r\\n u\\\"Error rendering course={course}, section_key={section_key}\\\".format(\\r\\n course=course, section_key=section_key\\r\\n ))\\r\\n return html\\r\\n\\r\\n except ItemNotFoundError:\\r\\n log.warning(\\r\\n u\\\"Missing about section {key} in course {url}\\\".format(key=section_key, url=course.location.to_deprecated_string())\\r\\n )\\r\\n return None\\r\\n elif section_key == \\\"title\\\":\\r\\n return course.display_name_with_default\\r\\n elif section_key == \\\"university\\\":\\r\\n return course.display_org_with_default\\r\\n elif section_key == \\\"number\\\":\\r\\n return course.display_number_with_default\\r\\n\\r\\n raise KeyError(\\\"Invalid about key \\\" + str(section_key))\",\n \"def get_course_page(self):\\n\\n print(\\\"Course URL: {}\\\".format(self.course_url))\\n try:\\n self.course_page = BeautifulSoup(requests.get(self.course_url).text, \\\"lxml\\\")\\n except requests.ConnectionError as e:\\n print(\\\"Error Connecting!\\\\n\\\", e)\\n sys.exit(1)\\n except requests.exceptions.HTTPError as errh:\\n print(\\\"Http Error:\\\", errh)\\n sys.exit(1)\\n except requests.exceptions.ConnectionError as errc:\\n print(\\\"Error Connecting:\\\", errc)\\n sys.exit(1)\\n except requests.exceptions.Timeout as errt:\\n print(\\\"Timeout Error:\\\", errt)\\n sys.exit(1)\\n except requests.exceptions.RequestException as err:\\n print(\\\"Oops: Something Else\\\", err)\\n sys.exit(1)\",\n \"def export_courses():\\n courses = Course.query().fetch()\\n dictionary = {}\\n\\n for course in courses:\\n dictionary[course.department + \\\"\\\" + course.number] = course.to_dict()\\n\\n return dictionary\",\n \"def build_item_context(tree: HtmlElement) -> Dict:\\n data = {\\\"description\\\": \\\"\\\"}\\n\\n jsonld_elem = tree.find('./head/script[@type=\\\"application/ld+json\\\"]')\\n if jsonld_elem is not None:\\n jsonld = json.loads(jsonld_elem.text)\\n if \\\"description\\\" not in jsonld:\\n description_meta = tree.find('./head/meta[@name=\\\"description\\\"]')\\n jsonld[\\\"description\\\"] = description_meta.get(\\\"content\\\")\\n data[\\\"description\\\"] = DESCRIPTION_FMT(**jsonld)\\n\\n return data\",\n \"def _transform_learning_resource_course(course):\\n return {\\n \\\"readable_id\\\": course[\\\"readable_id\\\"],\\n \\\"platform\\\": PlatformType.xpro.value,\\n \\\"title\\\": course[\\\"title\\\"],\\n \\\"image\\\": {\\\"url\\\": course[\\\"thumbnail_url\\\"]},\\n \\\"offered_by\\\": copy.deepcopy(OFFERED_BY),\\n \\\"description\\\": course[\\\"description\\\"],\\n \\\"url\\\": course.get(\\\"url\\\"),\\n \\\"published\\\": any(\\n map(\\n lambda course_run: course_run.get(\\\"current_price\\\", None),\\n course[\\\"courseruns\\\"],\\n )\\n ),\\n \\\"topics\\\": transform_topics(course.get(\\\"topics\\\", [])),\\n \\\"runs\\\": [_transform_run(course_run) for course_run in course[\\\"courseruns\\\"]],\\n \\\"resource_type\\\": LearningResourceType.course.value,\\n }\",\n \"def build_course_dictionary(title_result_set, desc_result_set) -> Dict[str, List[str]]:\\n\\n course_dictionary = {} # placeholder dictionary\\n\\n for (tagged_title, tagged_description) in zip(title_result_set, desc_result_set): # iterate through multiple result sets\\n full_title_desc_list = {}\\n full_title_desc_list = [str(tagged_title.text)] + str(tagged_description.text).strip().splitlines() # remove trailing whitespace, then get list of lines\\n course_dictionary[str(tagged_title.text)[:8]] = full_title_desc_list\\n\\n return course_dictionary\",\n \"def get_context(self, request, *args, **kwargs):\\n program_page_qset = (\\n ProgramPage.objects.live()\\n .filter(program__live=True)\\n .order_by(\\\"id\\\")\\n .select_related(\\\"program\\\", \\\"thumbnail_image\\\")\\n .prefetch_related(\\\"program__courses__courseruns\\\")\\n )\\n course_page_qset = (\\n CoursePage.objects.live()\\n .filter(course__live=True)\\n .order_by(\\\"id\\\")\\n .select_related(\\\"course\\\", \\\"thumbnail_image\\\")\\n .prefetch_related(\\\"course__courseruns\\\")\\n )\\n external_course_qset = (\\n ExternalCoursePage.objects.live()\\n .order_by(\\\"title\\\")\\n .select_related(\\\"thumbnail_image\\\")\\n )\\n\\n external_program_qset = (\\n ExternalProgramPage.objects.live()\\n .order_by(\\\"title\\\")\\n .select_related(\\\"thumbnail_image\\\")\\n )\\n\\n featured_product = program_page_qset.filter(\\n featured=True\\n ) or course_page_qset.filter(featured=True)\\n all_pages, program_pages, course_pages = filter_and_sort_catalog_pages(\\n program_page_qset,\\n course_page_qset,\\n external_course_qset,\\n external_program_qset,\\n )\\n return dict(\\n **super().get_context(request),\\n **get_base_context(request),\\n all_pages=all_pages,\\n program_pages=program_pages,\\n course_pages=course_pages,\\n featured_product=featured_product.first(),\\n default_image_path=DEFAULT_COURSE_IMG_PATH,\\n hubspot_portal_id=settings.HUBSPOT_CONFIG.get(\\\"HUBSPOT_PORTAL_ID\\\"),\\n hubspot_new_courses_form_guid=settings.HUBSPOT_CONFIG.get(\\n \\\"HUBSPOT_NEW_COURSES_FORM_GUID\\\"\\n ),\\n )\",\n \"def GenerateMapper(t):\\n context_token_limit = 2000\\n\\n url, corpus, story_html = t\\n\\n if not story_html:\\n return None\\n\\n # Extract title\\n raw_story = RawStory(url, story_html)\\n story_title = ParseHtml(raw_story, corpus).getstory_title()\\n # print story_title\\n\\n # Extract Introduction\\n raw_story = RawStory(url, story_html)\\n story_introduction = ParseHtml(raw_story, corpus).getstory_introduction()\\n # print story_introduction\\n\\n # Extract rest of the content\\n raw_story = RawStory(url, story_html)\\n story_restcontent = ParseHtml(raw_story, corpus).getstory_restcontent()\\n # print story_restcontent\\n\\n return story_title, story_introduction, story_restcontent\",\n \"def get_chapter_text(location):\\n chapter_output_dictionary = {}\\n chapter_contents_list = []\\n\\n soup = Ripper(location, parser=\\\"html5lib\\\", save_path=save_path).soup\\n text = soup.find(\\\"table\\\", class_=\\\"texttable\\\")\\n\\n for each in text.find_all(\\\"p\\\"):\\n attributes = each.attrs\\n if attributes:\\n if \\\"desc\\\" in attributes[\\\"class\\\"]:\\n pass\\n elif \\\"note\\\" in attributes[\\\"class\\\"]:\\n pass\\n else:\\n new_cont = each.contents\\n chapter_contents_list.extend(new_cont)\\n return join_chapter_text(chapter_contents_list)\",\n \"def get_doctor_info(url, html):\\n \\n # create a dictionary to save data\\n doctor_info = {}\\n \\n # parsed html by using lxml\\n # In the following processing\\n # the one ending in org is the raw data in the HTML\\n # the one ending in inf is the processed data by extracting from raw data\\n select = etree.HTML(html)\\n \\n # part 1: get basic information about your doctor\\n # attribute: Name, Title, Hospital, Department\\n name_org = select.xpath('//div[@class=\\\"profile-text\\\"]//h1[@class=\\\"doctor-name\\\"]//text()')\\n name_inf = name_org[0].strip()\\n \\n title_org = select.xpath('//div[@class=\\\"profile-text\\\"]//span[@class=\\\"positon\\\"]//text()')\\n title_inf = [i.strip() for i in title_org if len(i.strip()) > 0]\\n title_inf = ' '.join(title_inf)\\n \\n hospital_department_org = select.xpath('//div[@class=\\\"profile-text\\\"]//p[@class=\\\"doctor-faculty\\\"]//text()')\\n hospital_department_inf = [i.strip() for i in hospital_department_org if len(i.strip()) > 0]\\n hospital_inf = hospital_department_inf[0]\\n department_inf = hospital_department_inf[1]\\n \\n doctor_info['姓名'] = name_inf\\n doctor_info['职称'] = title_inf\\n doctor_info['医院'] = hospital_inf\\n doctor_info['科室'] = department_inf\\n \\n # part2: get header format data\\n org = select.xpath('//div[@class=\\\"profile-sta\\\"]//text()')\\n inf = [i.strip() for i in org if len(i.strip()) > 0 and i.strip() != '%']\\n for i in range(len(inf)//2):\\n doctor_info[inf[2*i]] = inf[2*i + 1]\\n \\n \\n # part3: get sidebar format data\\n org_1 = select.xpath('//div[@class=\\\"item-body\\\"]//div[@class=\\\"clearfix\\\"]//div[@class=\\\"per-sta-label\\\"]//text()')\\n org_2 = select.xpath('//div[@class=\\\"item-body\\\"]//div[@class=\\\"clearfix\\\"]//div[@class=\\\"per-sta-data\\\"]//text()')\\n for i in range(len(org_1)):\\n doctor_info[org_1[i][:-1]] = org_2[i]\\n \\n # part4: get body format data\\n honour_org = select.xpath('//div[@class=\\\"honour-header\\\"]//text()')\\n honour_inf = ''.join([i.strip() for i in honour_org])\\n \\n honour_detail_org = select.xpath('//li[@class=\\\"honour-title\\\"]//text()')\\n honour_detail_inf = [i.strip()[:4] for i in honour_detail_org if len(i.strip()) > 0]\\n honour_detail_inf = ' '.join(honour_detail_inf)\\n \\n satisfaction_org = select.xpath('//div[@class=\\\"item-body\\\"]//div[@class=\\\"satisfaction clearfix\\\"]//i[@class=\\\"sta-num\\\"]//text()')\\n satisfaction_inf = [i.strip() for i in satisfaction_org if len(i.strip()) > 0 and i.strip() != '%']\\n \\n resume_org = select.xpath('//div[@class=\\\"good-at-text\\\"]//text()')\\n resume_inf = [i.strip() for i in resume_org]\\n if len(resume_inf) <= 20:\\n resume_inf = ''.join(resume_inf)\\n resume_inf = ''.join(resume_inf[:20])\\n \\n star_org = select.xpath('//div[@class=\\\"experience-row clearfix\\\"]//span[@class=\\\"experience-label\\\"]//text()')\\n star_inf = 1 if len(star_org) >= 1 else 0\\n\\n doctor_info['好大夫届数'] = honour_inf\\n doctor_info['好大夫具体年份'] = honour_detail_inf\\n doctor_info['简历'] = resume_inf \\n doctor_info['诊后服务星'] = star_inf\\n try:\\n doctor_info['疗效满意度'] = satisfaction_inf[0]\\n doctor_info['态度满意度'] = satisfaction_inf[1]\\n except:\\n pass\\n \\n # part5: personal url\\n personal_url = url\\n doctor_info['医生个人链接'] = personal_url\\n \\n return doctor_info\",\n \"def _extract_page_info(article: dict, url: str) -> dict:\\n\\n if not article:\\n return {}\\n language = detect(article.get('content_text'))\\n if len(language) > 2 and len(language[2]) > 1:\\n language_code = language[2][0][1]\\n else:\\n language_code = None\\n return {'url': url, 'language': language_code}\",\n \"def __init__(self, xmlstore, course_id, course_dir,\\r\\n error_tracker, parent_tracker,\\r\\n load_error_modules=True, **kwargs):\\r\\n self.unnamed = defaultdict(int) # category -> num of new url_names for that category\\r\\n self.used_names = defaultdict(set) # category -> set of used url_names\\r\\n id_generator = CourseLocationGenerator(course_id)\\r\\n\\r\\n # cdodge: adding the course_id as passed in for later reference rather than having to recomine the org/course/url_name\\r\\n self.course_id = course_id\\r\\n self.load_error_modules = load_error_modules\\r\\n\\r\\n def process_xml(xml):\\r\\n \\\"\\\"\\\"Takes an xml string, and returns a XBlock created from\\r\\n that xml.\\r\\n \\\"\\\"\\\"\\r\\n\\r\\n def make_name_unique(xml_data):\\r\\n \\\"\\\"\\\"\\r\\n Make sure that the url_name of xml_data is unique. If a previously loaded\\r\\n unnamed descriptor stole this element's url_name, create a new one.\\r\\n\\r\\n Removes 'slug' attribute if present, and adds or overwrites the 'url_name' attribute.\\r\\n \\\"\\\"\\\"\\r\\n # VS[compat]. Take this out once course conversion is done (perhaps leave the uniqueness check)\\r\\n\\r\\n # tags that really need unique names--they store (or should store) state.\\r\\n need_uniq_names = ('problem', 'sequential', 'video', 'course', 'chapter',\\r\\n 'videosequence', 'poll_question', 'vertical')\\r\\n\\r\\n attr = xml_data.attrib\\r\\n tag = xml_data.tag\\r\\n id = lambda x: x\\r\\n # Things to try to get a name, in order (key, cleaning function, remove key after reading?)\\r\\n lookups = [('url_name', id, False),\\r\\n ('slug', id, True),\\r\\n ('name', Location.clean, False),\\r\\n ('display_name', Location.clean, False)]\\r\\n\\r\\n url_name = None\\r\\n for key, clean, remove in lookups:\\r\\n if key in attr:\\r\\n url_name = clean(attr[key])\\r\\n if remove:\\r\\n del attr[key]\\r\\n break\\r\\n\\r\\n def looks_like_fallback(url_name):\\r\\n \\\"\\\"\\\"Does this look like something that came from fallback_name()?\\\"\\\"\\\"\\r\\n return (url_name is not None\\r\\n and url_name.startswith(tag)\\r\\n and re.search('[0-9a-fA-F]{12}$', url_name))\\r\\n\\r\\n def fallback_name(orig_name=None):\\r\\n \\\"\\\"\\\"Return the fallback name for this module. This is a function instead of a variable\\r\\n because we want it to be lazy.\\\"\\\"\\\"\\r\\n if looks_like_fallback(orig_name):\\r\\n # We're about to re-hash, in case something changed, so get rid of the tag_ and hash\\r\\n orig_name = orig_name[len(tag) + 1:-12]\\r\\n # append the hash of the content--the first 12 bytes should be plenty.\\r\\n orig_name = \\\"_\\\" + orig_name if orig_name not in (None, \\\"\\\") else \\\"\\\"\\r\\n xml_bytes = xml.encode('utf8')\\r\\n return tag + orig_name + \\\"_\\\" + hashlib.sha1(xml_bytes).hexdigest()[:12]\\r\\n\\r\\n # Fallback if there was nothing we could use:\\r\\n if url_name is None or url_name == \\\"\\\":\\r\\n url_name = fallback_name()\\r\\n # Don't log a warning--we don't need this in the log. Do\\r\\n # put it in the error tracker--content folks need to see it.\\r\\n\\r\\n if tag in need_uniq_names:\\r\\n error_tracker(\\\"PROBLEM: no name of any kind specified for {tag}. Student \\\"\\r\\n \\\"state will not be properly tracked for this module. Problem xml:\\\"\\r\\n \\\" '{xml}...'\\\".format(tag=tag, xml=xml[:100]))\\r\\n else:\\r\\n # TODO (vshnayder): We may want to enable this once course repos are cleaned up.\\r\\n # (or we may want to give up on the requirement for non-state-relevant issues...)\\r\\n # error_tracker(\\\"WARNING: no name specified for module. xml='{0}...'\\\".format(xml[:100]))\\r\\n pass\\r\\n\\r\\n # Make sure everything is unique\\r\\n if url_name in self.used_names[tag]:\\r\\n # Always complain about modules that store state. If it\\r\\n # doesn't store state, don't complain about things that are\\r\\n # hashed.\\r\\n if tag in need_uniq_names:\\r\\n msg = (\\\"Non-unique url_name in xml. This may break state tracking for content.\\\"\\r\\n \\\" url_name={0}. Content={1}\\\".format(url_name, xml[:100]))\\r\\n error_tracker(\\\"PROBLEM: \\\" + msg)\\r\\n log.warning(msg)\\r\\n # Just set name to fallback_name--if there are multiple things with the same fallback name,\\r\\n # they are actually identical, so it's fragile, but not immediately broken.\\r\\n\\r\\n # TODO (vshnayder): if the tag is a pointer tag, this will\\r\\n # break the content because we won't have the right link.\\r\\n # That's also a legitimate attempt to reuse the same content\\r\\n # from multiple places. Once we actually allow that, we'll\\r\\n # need to update this to complain about non-unique names for\\r\\n # definitions, but allow multiple uses.\\r\\n url_name = fallback_name(url_name)\\r\\n\\r\\n self.used_names[tag].add(url_name)\\r\\n xml_data.set('url_name', url_name)\\r\\n\\r\\n try:\\r\\n # VS[compat]\\r\\n # TODO (cpennington): Remove this once all fall 2012 courses\\r\\n # have been imported into the cms from xml\\r\\n xml = clean_out_mako_templating(xml)\\r\\n xml_data = etree.fromstring(xml)\\r\\n\\r\\n make_name_unique(xml_data)\\r\\n\\r\\n descriptor = create_block_from_xml(\\r\\n etree.tostring(xml_data, encoding='unicode'),\\r\\n self,\\r\\n id_generator,\\r\\n )\\r\\n except Exception as err: # pylint: disable=broad-except\\r\\n if not self.load_error_modules:\\r\\n raise\\r\\n\\r\\n # Didn't load properly. Fall back on loading as an error\\r\\n # descriptor. This should never error due to formatting.\\r\\n\\r\\n msg = \\\"Error loading from xml. %s\\\"\\r\\n log.warning(\\r\\n msg,\\r\\n unicode(err)[:200],\\r\\n # Normally, we don't want lots of exception traces in our logs from common\\r\\n # content problems. But if you're debugging the xml loading code itself,\\r\\n # uncomment the next line.\\r\\n # exc_info=True\\r\\n )\\r\\n\\r\\n msg = msg % (unicode(err)[:200])\\r\\n\\r\\n self.error_tracker(msg)\\r\\n err_msg = msg + \\\"\\\\n\\\" + exc_info_to_str(sys.exc_info())\\r\\n descriptor = ErrorDescriptor.from_xml(\\r\\n xml,\\r\\n self,\\r\\n id_generator,\\r\\n err_msg\\r\\n )\\r\\n\\r\\n descriptor.data_dir = course_dir\\r\\n\\r\\n xmlstore.modules[course_id][descriptor.scope_ids.usage_id] = descriptor\\r\\n\\r\\n if descriptor.has_children:\\r\\n for child in descriptor.get_children():\\r\\n parent_tracker.add_parent(child.scope_ids.usage_id, descriptor.scope_ids.usage_id)\\r\\n\\r\\n # After setting up the descriptor, save any changes that we have\\r\\n # made to attributes on the descriptor to the underlying KeyValueStore.\\r\\n descriptor.save()\\r\\n return descriptor\\r\\n\\r\\n render_template = lambda template, context: u''\\r\\n\\r\\n # TODO (vshnayder): we are somewhat architecturally confused in the loading code:\\r\\n # load_item should actually be get_instance, because it expects the course-specific\\r\\n # policy to be loaded. For now, just add the course_id here...\\r\\n def load_item(usage_key):\\r\\n \\\"\\\"\\\"Return the XBlock for the specified location\\\"\\\"\\\"\\r\\n return xmlstore.get_item(usage_key)\\r\\n\\r\\n resources_fs = OSFS(xmlstore.data_dir / course_dir)\\r\\n\\r\\n super(ImportSystem, self).__init__(\\r\\n load_item=load_item,\\r\\n resources_fs=resources_fs,\\r\\n render_template=render_template,\\r\\n error_tracker=error_tracker,\\r\\n process_xml=process_xml,\\r\\n **kwargs\\r\\n )\",\n \"def course_info(self):\\n print(\\\"Course name: {}\\\".format(self._course_name))\\n print(\\\"Lead teacher: {}\\\".format(self._teacher))\\n\\n if len(self._students) == 0:\\n print(\\\"Course does not enrolled by any student\\\")\\n else:\\n print(\\\"Enrolled: {}/{}\\\".format(len(self._students), self._total_place))\",\n \"def etymologies(pages):\\n etymologies = defaultdict(list)\\n print('Building etymology dictionary...')\\n with ProgressBar(maxval=len(pages)) as progress:\\n for i, page in enumerate(pages):\\n entries = (Entry(*e) for e in load_page(page))\\n for entry in entries:\\n etymologies[entry.text].insert(entry.index, entry)\\n progress.update(i)\\n return etymologies\",\n \"def create_template_dict(name, cat, boilerplate_name=None, is_common=False):\\r\\n return {\\r\\n \\\"display_name\\\": name,\\r\\n \\\"category\\\": cat,\\r\\n \\\"boilerplate_name\\\": boilerplate_name,\\r\\n \\\"is_common\\\": is_common\\r\\n }\",\n \"def olive_parser(text: str) -> dict:\\n soup = BeautifulSoup(text, \\\"lxml\\\")\\n root = soup.find(\\\"xmd-entity\\\")\\n page_no = root['page_no']\\n identifier = root['id']\\n language = root['language']\\n title = soup.meta['name']\\n entity_type = root['entity_type']\\n issue_date = soup.meta['issue_date']\\n\\n out = {\\n \\\"meta\\\": {\\n \\\"language\\\": None,\\n \\\"type\\\": {}\\n },\\n \\\"r\\\": [],\\n \\\"stats\\\": {},\\n \\\"legacy\\\": {\\\"continuation_from\\\": None, \\\"continuation_to\\\": None},\\n }\\n out[\\\"meta\\\"][\\\"title\\\"] = title\\n out[\\\"meta\\\"][\\\"page_no\\\"] = [int(page_no)]\\n out[\\\"meta\\\"][\\\"language\\\"] = normalize_language(language)\\n out[\\\"meta\\\"][\\\"type\\\"][\\\"raw\\\"] = entity_type\\n out[\\\"meta\\\"][\\\"issue_date\\\"] = issue_date\\n\\n new_region = {\\n \\\"c\\\": [],\\n \\\"p\\\": []\\n }\\n\\n new_paragraph = {\\n \\\"l\\\": []\\n }\\n\\n new_line = {\\n \\\"c\\\": [],\\n \\\"t\\\": []\\n }\\n\\n new_token = {\\n \\\"c\\\": [],\\n \\\"tx\\\": \\\"\\\"\\n }\\n\\n for primitive in soup.find_all(\\\"primitive\\\"):\\n\\n # store coordinate of text areas (boxes) by page\\n # 1) page number, 2) coordinate list\\n region = copy.deepcopy(new_region)\\n region[\\\"c\\\"] = [int(i) for i in primitive.get('box').split(\\\" \\\")]\\n\\n para = None\\n line = None\\n line_counter = 0\\n\\n for tag in primitive.find_all(recursive=False):\\n\\n if tag.name == \\\"l\\\":\\n\\n if para is None and line is None:\\n para = copy.deepcopy(new_paragraph)\\n line = copy.deepcopy(new_line)\\n\\n if line_counter > 0 and line is not None:\\n line = normalize_line(line, out[\\\"meta\\\"][\\\"language\\\"])\\n para[\\\"l\\\"].append(line)\\n\\n if tag.get(\\\"p\\\") in [\\\"S\\\", \\\"SA\\\"] and line_counter > 0:\\n region[\\\"p\\\"].append(para)\\n para = copy.deepcopy(new_paragraph)\\n\\n line = copy.deepcopy(new_line)\\n line[\\\"c\\\"] = [\\n int(i)\\n for i in tag.get('box').split(\\\" \\\")\\n ]\\n line_counter += 1\\n\\n if tag.name in [\\\"w\\\", \\\"q\\\"]:\\n\\n # store coordinates of each token\\n # 1) token, 2) page number, 3) coordinate list\\n t = copy.deepcopy(new_token)\\n t[\\\"c\\\"] = [int(i) for i in tag.get('box').split(\\\" \\\")]\\n t[\\\"tx\\\"] = tag.string\\n t[\\\"s\\\"] = int(tag.get('style_ref'))\\n\\n if tag.name == \\\"q\\\" and tag.get('qid') is not None:\\n qid = tag.get('qid')\\n normalized_form = soup.find('qw', qid=qid).text\\n t[\\\"nf\\\"] = normalized_form\\n t[\\\"qid\\\"] = qid\\n\\n # append the token to the line\\n line[\\\"t\\\"].append(t)\\n\\n # append orphan lines\\n if line is not None:\\n line = normalize_line(line, out[\\\"meta\\\"][\\\"language\\\"])\\n para[\\\"l\\\"].append(line)\\n\\n region[\\\"p\\\"].append(para)\\n\\n if para is not None:\\n out[\\\"r\\\"].append(region)\\n\\n out[\\\"legacy\\\"][\\\"id\\\"] = identifier\\n out[\\\"legacy\\\"][\\\"source\\\"] = soup.link['source']\\n \\\"\\\"\\\"\\n # I suspect this could be deleted\\n out[\\\"legacy\\\"][\\\"word_count\\\"] = int(soup.meta['wordcnt'])\\n out[\\\"legacy\\\"][\\\"chars_count\\\"] = int(soup.meta['total_chars_count'])\\n suspicious_chars_count = int(soup.meta['suspicious_chars_count'])\\n out[\\\"legacy\\\"][\\\"suspicious_chars_count\\\"] = int(suspicious_chars_count)\\n \\\"\\\"\\\"\\n out[\\\"legacy\\\"][\\\"first_id\\\"] = soup.link['first_id']\\n out[\\\"legacy\\\"][\\\"last_id\\\"] = soup.link['last_id']\\n out[\\\"legacy\\\"][\\\"next_id\\\"] = soup.link['next_id']\\n out[\\\"legacy\\\"][\\\"prev_id\\\"] = soup.link['prev_id']\\n\\n if root.has_attr('continuation_from'):\\n out[\\\"legacy\\\"][\\\"continuation_from\\\"] = root['continuation_from']\\n\\n if root.has_attr('continuation_to'):\\n out[\\\"legacy\\\"][\\\"continuation_to\\\"] = root['continuation_to']\\n\\n return out\",\n \"def parse_text(self):\\n self.text={}\\n for i, lang in enumerate(LANGS):\\n text=file(self.src).read()\\n self.text[lang]=\\\"\\\"\\n extracted, finish = \\\"\\\", 0\\n start_string, stop_string = r\\\"<!--%s-->\\\" % lang, r\\\"<!--/%s-->\\\" % lang\\n # Iterates to check multiple blocks of text within the file!\\n # Pay attention to infinite loops!\\n # AttributeError exception raised when no more blocks to extract exist\\n while True:\\n try:\\n start=re.compile(start_string, re.IGNORECASE).search(text).span()[1]\\n finish=re.compile(stop_string, re.IGNORECASE).search(text).span()[0]\\n extracted+=text[start:finish]\\n text=text[finish+1:]\\n except AttributeError:\\n break\\n self.text[lang]+=extracted\",\n \"def extract(self):\\n self.logger.info(f'Opening {self.file}')\\n try:\\n f = open(self.file, 'r')\\n except FileNotFoundError:\\n self.logger.info('Could not open the .vrt file')\\n return\\n\\n result = {}\\n self.logger.info('Extracting')\\n for line in f:\\n # Only attempt to extract if line is correct\\n if self.is_target(line):\\n # Add the closing text tag to make the line have proper XML\\n xml_attributes = XML.fromstring(line.rstrip()+'</text>').attrib\\n # Extract wanted parameters\\n month = xml_attributes['date'][5:7] # Extract month\\n # Add month to the keys\\n if month not in result.keys():\\n result[month] = []\\n result[month].append({\\n 'title': xml_attributes['title'], # Extract thread title\\n 'thread_id': xml_attributes['thread_id'], # Extract thread id\\n 'datetime': xml_attributes['datetime'] # Extract datetime\\n })\\n self.logger.info('Extraction complete')\\n self.db = result\\n # self.db[self.year] = result # Assign results to year\\n self.save_result() # Save the results\",\n \"def html_collect_pages(app):\\n if not hasattr(app.builder.env, \\\"categories\\\"):\\n return # nothing to do\\n\\n for name, context, template in create_category_pages(app):\\n yield (name, context, template)\",\n \"def info():\\n # -------- Task 1 -------------------------\\n # Please complete the following information\\n\\n return {\\\"agent name\\\": \\\"?\\\", # COMPLETE HERE\\n \\\"student name\\\": [\\\"?\\\"], # COMPLETE HERE\\n \\\"student number\\\": [\\\"?\\\"]} # COMPLETE HERE\",\n \"def Content():\\n\\tTOPIC_DICT = {\\\"Basics\\\": [[\\\"Introduction to Python\\\", \\\"/introduction-to-python/\\\"], \\n\\t\\t\\t\\t\\t\\t\\t\\t\\t\\t\\t\\t\\t [\\\"Print functions\\\", \\\"/python-print-function/\\\"],\\n\\t\\t\\t\\t\\t\\t\\t\\t\\t\\t\\t\\t\\t [\\\"Math basics with Python 3\\\", \\\"/math-basics-python-3/\\\"\\n\\t\\t\\t\\t\\t\\t\\t\\t\\t\\t\\t\\t\\t ]],\\n\\t\\t\\t\\t\\t\\t\\t\\t\\\"Web Dev\\\": [[\\\"wd1\\\"]]} #Web dev is now the second topic\\n\\t\\t\\t\\t\\t\\t\\t\\t#Introduction to Python\\n\\t#will be the title and the link to that is immediately after. Each inner array will be a specific sub section of the basics\\n\\t#section\\n\\n\\treturn TOPIC_DICT\",\n \"def _update_certificate_context(context, course, course_overview, user_certificate, platform_name):\\n # Populate dynamic output values using the course/certificate data loaded above\\n certificate_type = context.get('certificate_type')\\n\\n # Override the defaults with any mode-specific static values\\n context['certificate_id_number'] = user_certificate.verify_uuid\\n context['certificate_verify_url'] = \\\"{prefix}{uuid}{suffix}\\\".format(\\n prefix=context.get('certificate_verify_url_prefix'),\\n uuid=user_certificate.verify_uuid,\\n suffix=context.get('certificate_verify_url_suffix')\\n )\\n\\n # We prefer a CourseOverview for this function because it validates and corrects certificate_available_date\\n # and certificates_display_behavior values. However, not all certificates are guaranteed to have a CourseOverview\\n # associated with them, so we fall back on the course in that case. This shouldn't cause a problem because courses\\n # that are missing CourseOverviews are generally old courses, and thus their display values are no longer relevant\\n if course_overview:\\n date = display_date_for_certificate(course_overview, user_certificate)\\n else:\\n date = display_date_for_certificate(course, user_certificate)\\n # Translators: The format of the date includes the full name of the month\\n context['certificate_date_issued'] = strftime_localized(date, settings.CERTIFICATE_DATE_FORMAT)\\n\\n # Translators: This text represents the verification of the certificate\\n context['document_meta_description'] = _('This is a valid {platform_name} certificate for {user_name}, '\\n 'who participated in {partner_short_name} {course_number}').format(\\n platform_name=platform_name,\\n user_name=context['accomplishment_copy_name'],\\n partner_short_name=context['organization_short_name'],\\n course_number=context['course_number']\\n )\\n\\n # Translators: This text is bound to the HTML 'title' element of the page and appears in the browser title bar\\n context['document_title'] = _(\\\"{partner_short_name} {course_number} Certificate | {platform_name}\\\").format(\\n partner_short_name=context['organization_short_name'],\\n course_number=context['course_number'],\\n platform_name=platform_name\\n )\\n\\n # Translators: This text fragment appears after the student's name (displayed in a large font) on the certificate\\n # screen. The text describes the accomplishment represented by the certificate information displayed to the user\\n context['accomplishment_copy_description_full'] = _(\\\"successfully completed, received a passing grade, and was \\\"\\n \\\"awarded this {platform_name} {certificate_type} \\\"\\n \\\"Certificate of Completion in \\\").format(\\n platform_name=platform_name,\\n certificate_type=context.get(\\\"certificate_type\\\"))\\n\\n certificate_type_description = get_certificate_description(user_certificate.mode, certificate_type, platform_name)\\n if certificate_type_description:\\n context['certificate_type_description'] = certificate_type_description\\n\\n # Translators: This text describes the purpose (and therefore, value) of a course certificate\\n context['certificate_info_description'] = _(\\\"{platform_name} acknowledges achievements through \\\"\\n \\\"certificates, which are awarded for course activities \\\"\\n \\\"that {platform_name} students complete.\\\").format(\\n platform_name=platform_name,\\n )\",\n \"def build_course_map(course_content):\\n course_blocks = []\\n\\n for key, course_data in course_content.items():\\n block_data = {}\\n block_data['id'] = parse_id(key)\\n block_data['type'] = course_data['category']\\n\\n try:\\n block_data['name'] = course_data['metadata']['display_name']\\n except KeyError:\\n block_data['name'] = block_data['id']\\n\\n customize_by_type(course_data, block_data)\\n add_children(course_data, block_data)\\n\\n course_blocks.append(block_data)\\n\\n return course_blocks\",\n \"def html_collect_pages(app):\\n\\n if not hasattr(app.builder.env, \\\"categories\\\"):\\n return # nothing to do\\n\\n for name, context, template in create_category_pages(app):\\n yield (name, context, template)\",\n \"def enter_request_context(self, request):\\r\\n context = {\\r\\n 'session': self.get_session_key(request),\\r\\n 'user_id': self.get_user_primary_key(request),\\r\\n 'username': self.get_username(request),\\r\\n }\\r\\n for header_name, context_key in META_KEY_TO_CONTEXT_KEY.iteritems():\\r\\n context[context_key] = request.META.get(header_name, '')\\r\\n\\r\\n context.update(contexts.course_context_from_url(request.build_absolute_uri()))\\r\\n\\r\\n tracker.get_tracker().enter_context(\\r\\n CONTEXT_NAME,\\r\\n context\\r\\n )\",\n \"def parseLesson(lesson_text):\\n lesson_parsed = dict()\\n\\n lesson_parsed['section_starts'] = list()\\n lesson_parsed['sections'] = list()\\n lesson_parsed['end_of_lesson'] = None\\n lesson_parsed['source'] = None\\n lesson_parsed['title'] = None\\n lesson_parsed['number'] = None\\n\\n end_of_lesson_markers = [\\n r'^\\\\s*\\\\(End\\\\s+of\\\\s+lesson',\\n r'^\\\\s*\\\\(End\\\\s+of\\\\s+lecture',\\n r'^\\\\(End\\\\s+of\\\\s+recording',\\n r'^\\\\[End\\\\s+of\\\\s+recording',\\n r'^\\\\[End\\\\s+of\\\\s+the\\\\s+recording',\\n r'^\\\\(End of the recording',\\n r'^\\\\[End\\\\s+of\\\\s+the\\\\s+tape',\\n r'^\\\\(End\\\\s+of\\\\s+audible\\\\s+portion\\\\s+of\\\\s+the\\\\s+lesson',\\n r'^\\\\[Note\\\\:\\\\s+This\\\\s+is\\\\s+the\\\\s+end\\\\s+of\\\\s+the\\\\s+recording',\\n ]\\n\\n # Do a parsing pass of the overall lesson text. This information will be used\\n # to compile the lesson into a form useful for display and use markup.\\n line_number = 0\\n for line in lesson_text:\\n mobj = re.search(r'^Source:\\\\s+(.+)$', line)\\n if mobj:\\n # WORKING HERE\\n pass\\n\\n if re.search(r'^\\\\d+[a-z]?\\\\.', line):\\n lesson_parsed['section_starts'].append(line_number)\\n line_number += 1\\n continue\\n\\n found_end_of_lesson = False \\n for end_of_lesson_marker in end_of_lesson_markers:\\n if re.search(end_of_lesson_marker, line):\\n lesson_parsed['end_of_lesson'] = line_number\\n line_number += 1\\n found_end_of_lesson = True\\n break\\n\\n if found_end_of_lesson:\\n continue\\n\\n for end_of_lesson_marker in end_of_lesson_markers:\\n if re.search(end_of_lesson_marker, line):\\n lesson_parsed['end_of_lesson'] = line_number\\n line_number += 1\\n break\\n\\n if lesson_parsed['end_of_lesson'] is not None:\\n continue\\n\\n # Remove tabs from the start of a line.\\n # Replace tabs between the number and the start of text with a space.\\n # Skip \\\"This page is included for printing and binding purposes\\\"\\n # Footnotes \\\"[fnX]\\\" will need to be linked.\\n\\n line_number += 1\\n\\n\\n # Now compile the lesson instructions into their own things.\\n\\n idx = 0\\n\\n new_section = list()\\n for section_line in xrange(lesson_parsed['section_starts'][idx], lesson_parsed['section_starts'][idx+1]:\\n section_start_text = lesson_text[section_line]\\n lesson_parsed['sections'].append(lesson_text[section_line])\\n\\n\\n\\n return lesson_parsed\",\n \"def treat_page(self):\\n # let's define some basic variables\\n urtext = self.current_page.text\\n urlang = self.current_page.site.code\\n urtitle = self.current_page.title()\\n urcat = []\\n eng_site = pywikibot.Site('en')\\n eng_title = ''\\n \\n interDict = {}\\n try:\\n site = pywikibot.Site('ur', 'wikipedia')\\n urpage = pywikibot.Page(site, urtitle)\\n langlst = urpage.iterlanglinks()\\n\\n \\n for i in langlst:\\n lang = str(i.site).split(':')[1]\\n interDict[lang] = i.title\\n \\n eng_title = interDict['en']\\n except:\\n pywikibot.output(u'\\\\03{lightred}Unable to fetch interwiki links!\\\\03{default}')\\n return False\\n \\n site = pywikibot.Site('en', 'wikipedia')\\n enpage = pywikibot.Page(site, eng_title)\\n\\n wikitext = enpage.get() \\n wikicode = mwp.parse(wikitext)\\n\\n # Extracting sfn templates and converting them in REF tags\\n sfnlist = []\\n for template in wikicode.filter_templates():\\n if template.name in ('sfn', 'sfn'):\\n sfnlist.append(template)\\n templ_rep = '<ref>' + str(template) + '</ref>'\\n wikicode.replace(template , templ_rep)\\n\\n alltags = wikicode.filter_tags() \\n reftags = {}\\n \\n def search(myDict, search1):\\n for key, value in myDict.items():\\n if search1 in value: \\n return key \\n \\n i=1\\n for tag in alltags:\\n if tag.tag=='ref':\\n if tag.attributes == []: # check if attributes list is empty\\n refval='NoRefName' # Reference has no name so assigning \\\"NoRefName\\\"\\n else:\\n name = tag.attributes[0]\\n refval = name.value\\n \\n if tag.contents is None:\\n #conval = search(reftags,refval)\\n #reftags[i] = (refval,reftags[conval][1])\\n pass\\n else: \\n reftags[i] = (refval,tag.contents)\\n i += 1\\n\\n dlinks = {}\\n for k,v in reftags.items():\\n dkey = 'و' + str(k) + 'و'\\n dlinks[dkey] = '<ref>' + str(v[1]) + '</ref>'\\n\\n urtext = urpage.text\\n for r in tuple(dlinks.items()):\\n urtext = urtext.replace(*r)\\n\\n # newln = '\\\\n'\\n # Using noreferences to add Reference template if not present\\n self.norefbot = noreferences.NoReferencesBot(None)\\n if self.norefbot.lacksReferences(urtext):\\n urtext = self.norefbot.addReferences(urtext)\\n else:\\n urpage.text = urtext + '\\\\n'\\n\\n print(urpage.text)\\n \\n # save the page \\n urpage.save(summary=self.summary, minor=False)\\n #self.put_current(urpage.text, summary=self.summary)\",\n \"def parse_catalogs(campuses=['Seattle', 'Bothell', 'Tacoma'], struct='df', \\n show_progress=False):\\n assert type(campuses) == list or type(campuses) == dict, 'Type of \\\"campuses\\\" must be list or dict'\\n if type(campuses) == dict:\\n for key, value in campuses.items():\\n if type(key) != str or type(value) != list:\\n raise ValueError('''\\\"campuses\\\" dict must have keys of type str and\\n values of type list''')\\n # Check if all campuses in 'campuses' are valid\\n assert all([c in ['Seattle', 'Bothell', 'Tacoma'] for c in list(map(str.title, campuses))])\\n assert type(struct) == str, 'Type of \\\"struct\\\" must be str'\\n assert type(show_progress) == bool, 'Type of \\\"show_progress\\\" must be bool'\\n assert struct in ['df', 'dict'], f'{struct} is an invalid argument for \\\"struct\\\"'\\n\\n # Progress bar for Course Schedule Parsing\\n if show_progress:\\n progress_bar = tqdm()\\n\\n def parse_campus(department_data, campus):\\n \\\"\\\"\\\"\\n Parses all courses from a UW Campus\\n\\n @params\\n\\n 'department_data': BeautifulSoup object with the department list website source\\n for the given 'campus'\\n\\n 'campus': The campus to get courses from\\n\\n Returns\\n\\n A pandas DataFrame with all courses in the given campus\\n \\\"\\\"\\\"\\n\\n def extract_data(department_link):\\n \\\"\\\"\\\"\\n Extracts all course information from a UW Department\\n\\n @params:\\n\\n 'department_link': The url to the UW Department to get course\\n information from\\n\\n Returns\\n\\n A list of lists. Each nested list represents one course section with the\\n following values (in this order):\\n\\n 'Campus', 'Department Name', 'Course Number', 'Course Name', 'Credits',\\n 'Areas of Knowledge', 'Quarters Offered', 'Offered with', \\n 'Prerequisites', 'Co-Requisites', 'Description'\\n \\\"\\\"\\\"\\n # Update the progress bar\\n if show_progress:\\n progress_bar.update()\\n\\n # Regular expressions for searching course descriptions stored in local variables\\n # for better peformance\\n local_course_re = course_re\\n local_course_name_re = course_name_re\\n local_credits_re = credits_re\\n local_credits_num_re = credits_num_re\\n local_offered_jointly_re = offered_jointly_re\\n local_CAMPUSES = CAMPUSES\\n\\n # Method used in extracting data from course descriptions found in the local scope\\n # are stored in local variables for better performance\\n local_complete_description = complete_description\\n local_get_offered = get_offered\\n local_get_requisites = get_requisites\\n\\n # All the courses in the department\\n courses = []\\n dep_file = department_link.get('href')\\n\\n # If the user entered a dict as the 'campuse' parameter, departments\\n # are checked here\\n try:\\n # The String in the conditional is the abbreviated Department Name i.e EE\\n # for Electrical Engineering\\n if normalize('NFKD', department_link.text).rsplit('(', 1) \\\\\\n [-1].replace(' ', '')[:-1] not in campuses[campus]:\\n return None\\n except TypeError:\\n pass\\n \\n # The only links that are used for finding departments are those\\n # of the format [a-z]+.html\\n if '/' not in dep_file and dep_file.endswith('.html') \\\\\\n and dep_file not in parsed_departments:\\n parsed_departments.add(dep_file)\\n department = BeautifulSoup(requests.get( \\\\\\n f'{local_CAMPUSES[campus]}{dep_file}').text, features='lxml')\\n for course in department.find_all('a'):\\n course_ID = course.get('name') \\n if course_ID:\\n course_ID = course_ID.upper()\\n course_title = course.find('b').text\\n # The Course Description\\n description = course.get_text().replace(course_title, '', 1) \\n instructors = course.find('i')\\n if instructors:\\n description = description.replace(str(instructors.get_text()), '', 1)\\n del instructors\\n course_text = local_complete_description( \\\\\\n description.rsplit('View course details in MyPlan', 1)[0])\\n # Course Number i.e 351\\n course_number = re.sub(local_course_re, '', course_ID)\\n match_name = re.search(local_course_name_re, course_title)\\n match_credit_num = re.search(local_credits_num_re, course_title)\\n match_credit_types = re.findall(local_credits_re, course_title)\\n # Jointly offered course with the given course\\n if 'jointly with' in course_text: \\n offered_jointly = course_text.rsplit('jointly with ', 1)[-1].rsplit(';', 1)[0] \\n offered_jointly = ','.join(re.findall( \\\\\\n local_offered_jointly_re, offered_jointly)).replace(' ', '') \\n else:\\n offered_jointly = ''\\n courses.append(\\n # Campus, Department Name and Course Number\\n [campus, course_ID[:-3], course_number, \\n # Course Name\\n match_name.group(0).split(course_number, 1)[-1].strip() \\\\\\n if match_name else '',\\n # Number of credits for the course\\n match_credit_num.group(0)[1:-1] \\\\\\n if match_credit_num else '', \\n # Course Credit Types (I&S, DIV, NW, VLPA, QSR, C)\\n ','.join([list(filter(('').__ne__, x))[0] for x in match_credit_types]) \\\\\\n if match_credit_types else '', \\n local_get_offered(course_text),\\n offered_jointly, local_get_requisites(course_text, 'Prerequisite:'), \\n local_get_requisites(course_text, 'Co-requisite'), course_text]\\n )\\n return courses\\n\\n # In the course catalog website, several department links appear multiple times\\n # To prevent parsing the same department more than once, parsed departments\\n # are tracked in 'parsed_departments'\\n parsed_departments = set()\\n local_extract_data = extract_data\\n department_data = BeautifulSoup(requests.get(department_data).text, features='lxml')\\n\\n campus_catalog = []\\n # Extract data from department websites in parallel to reduce idle time\\n with cf.ThreadPoolExecutor() as executor:\\n results = [executor.submit(local_extract_data, department_link) \\n for department_link in department_data.find_all('a')]\\n for result in cf.as_completed(results):\\n dptmnt = result.result()\\n if dptmnt:\\n campus_catalog.append(dptmnt)\\n\\n # DataFrame with all courses in the campus\\n return pd.DataFrame(\\n [course for department in campus_catalog for course in department], \\n columns=COLUMN_NAMES\\n )\\n\\n # The pandas DataFrame to store the entire course catalog for each UW Campus entered\\n # by the user\\n course_catalog = pd.DataFrame()\\n\\n # Parse all three campuses in parallel for faster run time as well\\n # as get the departments dictionary from the 'get_departments' method\\n # to add a 'Colleges' column to categorize all courses in their College.\\n with cf.ThreadPoolExecutor() as executor:\\n results = []\\n campuses_for_dict = campuses\\n if type(campuses) == dict:\\n campuses_for_dict = list(campuses.keys())\\n results.append(executor.submit(get_departments, campuses=campuses_for_dict, struct='dict'))\\n for campus, link in CAMPUSES.items():\\n if campus.title() in campuses: \\n results.append(executor.submit(parse_campus, link, campus.title()))\\n for result in cf.as_completed(results):\\n returned = result.result()\\n if type(returned) == dict:\\n # Departments dict used to create the 'College' column in the main DataFrame\\n departments = returned\\n else:\\n course_catalog = pd.concat([course_catalog, returned])\\n\\n # Add Course ID as the index of the DataFrame to allow for easy course searching\\n # Course ID = Department Name + Course Number\\n # Example: EE235 = EE + 235\\n course_catalog['Course ID'] = course_catalog['Department Name'] + course_catalog['Course Number']\\n course_catalog['College'] = course_catalog['Department Name'].apply(check_campus, args=(departments, 'College'))\\n course_catalog.set_index('Course ID', inplace=True)\\n # Re-order indices to place 'College' right after the 'Department Name'\\n course_catalog = course_catalog[['Campus', 'Department Name', 'College', 'Course Number', 'Course Name', 'Credits',\\n 'Areas of Knowledge', 'Quarters Offered', 'Offered with', \\n 'Prerequisites', 'Co-Requisites', 'Description']]\\n \\n if struct == 'df':\\n return course_catalog\\n elif struct == 'dict':\\n return course_catalog.to_dict(orient='index')\",\n \"def _parse_lesson(self, lesson_link: str):\\n\\n lesson_page: Soup = self._get_soup(lesson_link)\\n lesson_content: NavigableString = lesson_page.find(\\\"div\\\", \\n {\\\"class\\\": \\\"moduleContent\\\"})\\n course_divs: ResultSet = lesson_content.find_all(\\\"div\\\")\\n\\n data = {}\\n module = \\\"\\\"\\n for div in course_divs:\\n if div[\\\"class\\\"][0] == \\\"courseModule\\\":\\n module = div.p.get_text().split(\\\":\\\")[-1].strip()\\n data[module] = []\\n elif div[\\\"class\\\"][0] == \\\"courseLesson\\\":\\n lesson_title = div.find(\\\"span\\\", {\\\"class\\\": \\\"courseLessonTitle\\\"})\\n data[module].append(lesson_title.get_text().strip())\\n \\n return data\",\n \"def available_courses(self):\\r\\n def _get_course_name(el):\\r\\n # The first component in the link text is the course number\\r\\n _, course_name = el.text.split(' ', 1)\\r\\n return course_name\\r\\n\\r\\n return self.q(css='section.info > hgroup > h3 > a').map(_get_course_name).results\",\n \"def getCourseData(self, course):\\n\\t\\tif course == None:\\n\\t\\t\\treturn None\\n\\t\\tcommand = \\\"SELECT name, description, author_id FROM courses WHERE id=?;\\\"\\n\\t\\tparams = (course,)\\n\\n\\t\\tdata = self._run_command(command, params)\\n\\n\\t\\tif not data:\\n\\t\\t\\treturn None\\n\\n\\t\\tdata = data[0]\\n\\t\\tresult = {\\\"name\\\": data[0] if data[0] else \\\"\\\", \\n\\t\\t\\\"description\\\": data[1] if data[1] else \\\"\\\", \\n\\t\\t\\\"author_id\\\": data[2]}\\n\\n\\t\\treturn result\",\n \"def dump_grading_context(course):\\r\\n hbar = \\\"{}\\\\n\\\".format(\\\"-\\\" * 77)\\r\\n msg = hbar\\r\\n msg += \\\"Course grader:\\\\n\\\"\\r\\n\\r\\n msg += '%s\\\\n' % course.grader.__class__\\r\\n graders = {}\\r\\n if isinstance(course.grader, xmgraders.WeightedSubsectionsGrader):\\r\\n msg += '\\\\n'\\r\\n msg += \\\"Graded sections:\\\\n\\\"\\r\\n for subgrader, category, weight in course.grader.sections:\\r\\n msg += \\\" subgrader=%s, type=%s, category=%s, weight=%s\\\\n\\\"\\\\\\r\\n % (subgrader.__class__, subgrader.type, category, weight)\\r\\n subgrader.index = 1\\r\\n graders[subgrader.type] = subgrader\\r\\n msg += hbar\\r\\n msg += \\\"Listing grading context for course %s\\\\n\\\" % course.id.to_deprecated_string()\\r\\n\\r\\n gcontext = course.grading_context\\r\\n msg += \\\"graded sections:\\\\n\\\"\\r\\n\\r\\n msg += '%s\\\\n' % gcontext['graded_sections'].keys()\\r\\n for (gsomething, gsvals) in gcontext['graded_sections'].items():\\r\\n msg += \\\"--> Section %s:\\\\n\\\" % (gsomething)\\r\\n for sec in gsvals:\\r\\n sdesc = sec['section_descriptor']\\r\\n frmat = getattr(sdesc, 'format', None)\\r\\n aname = ''\\r\\n if frmat in graders:\\r\\n gform = graders[frmat]\\r\\n aname = '%s %02d' % (gform.short_label, gform.index)\\r\\n gform.index += 1\\r\\n elif sdesc.display_name in graders:\\r\\n gform = graders[sdesc.display_name]\\r\\n aname = '%s' % gform.short_label\\r\\n notes = ''\\r\\n if getattr(sdesc, 'score_by_attempt', False):\\r\\n notes = ', score by attempt!'\\r\\n msg += \\\" %s (format=%s, Assignment=%s%s)\\\\n\\\"\\\\\\r\\n % (sdesc.display_name, frmat, aname, notes)\\r\\n msg += \\\"all descriptors:\\\\n\\\"\\r\\n msg += \\\"length=%d\\\\n\\\" % len(gcontext['all_descriptors'])\\r\\n msg = '<pre>%s</pre>' % msg.replace('<', '<')\\r\\n return msg\",\n \"def extract_names(pages: Iterable[tuple[int, list[str]]]) -> DataT:\\n found_first = False\\n current_name: dict[str, Any] | None = None\\n current_label: str | None = None\\n current_lines: list[str] = []\\n in_headings = True\\n\\n def start_label(label: str, line: str) -> None:\\n nonlocal current_label, current_lines\\n assert current_name is not None\\n assert current_label is not None\\n if label in current_name:\\n if label in (\\\"Syntype\\\", \\\"Type Locality\\\"):\\n label = f\\\"Syntype {line}\\\"\\n assert (\\n label not in current_name\\n ), f\\\"duplicate label {label} in {current_name}\\\"\\n current_name[current_label] = current_lines\\n current_label = label\\n current_lines = [line]\\n\\n for page, lines in pages:\\n if current_name is not None:\\n current_name[\\\"pages\\\"].append(page)\\n for line in lines:\\n if not found_first:\\n if line.strip() in (\\\"TYPE SPECIMENS\\\", \\\"SPECIMENS\\\"):\\n found_first = True\\n continue\\n # ignore family/genus headers\\n if re.match(\\n (\\n r\\\"^\\\\s*(Genus|Family|Subfamily|Suborder|Order) [A-Z][a-zA-Z]+\\\"\\n r\\\" [a-zA-Z\\\\.’, \\\\-]+(, \\\\d{4})?$\\\"\\n ),\\n line,\\n ):\\n in_headings = True\\n continue\\n # ignore blank lines\\n if not line:\\n continue\\n if in_headings:\\n if line.startswith(\\\" \\\"):\\n continue\\n else:\\n in_headings = False\\n if line.startswith(\\\" \\\"):\\n current_lines.append(line)\\n elif re.match(r\\\"^[A-Z][A-Z a-z-]+: \\\", line):\\n start_label(line.split(\\\":\\\")[0], line)\\n elif line.startswith(\\\"Lectotype as designated\\\"):\\n start_label(\\\"Lectotype\\\", line)\\n elif line.startswith(\\\"Neotype as designated\\\"):\\n start_label(\\\"Neotype\\\", line)\\n elif line.startswith(\\n (\\n \\\"This specimen\\\",\\n \\\"Type \\\",\\n \\\"No type\\\",\\n \\\"There are\\\",\\n \\\"No additional\\\",\\n \\\"All \\\",\\n \\\"Subspecies of \\\",\\n \\\"Neotype designated \\\",\\n \\\"Padre Island\\\",\\n )\\n ):\\n start_label(\\\"comments\\\", line)\\n elif line.startswith(\\n (\\\"Secondary junior\\\", \\\"Primary junior\\\", \\\"Junior primary\\\")\\n ):\\n start_label(\\\"homonymy\\\", line)\\n elif re.match(r\\\"^[\\\\d/]+\\\\. \\\", line):\\n start_label(line.split(\\\".\\\")[0], line)\\n elif line.startswith(\\\"USNM\\\"):\\n start_label(line.split(\\\".\\\")[0], line)\\n elif (\\n current_label not in (\\\"name\\\", \\\"verbatim_citation\\\", \\\"homonymy\\\")\\n and \\\":\\\" not in line\\n ):\\n # new name\\n if current_name is not None:\\n assert current_label is not None\\n current_name[current_label] = current_lines\\n assert any(\\n field in current_name\\n for field in (\\n \\\"Holotype\\\",\\n \\\"Type Locality\\\",\\n \\\"Lectotype\\\",\\n \\\"Syntype\\\",\\n \\\"Syntypes\\\",\\n \\\"No name-bearing status\\\",\\n \\\"Neotype\\\",\\n )\\n ), current_name\\n yield current_name\\n current_name = {\\\"pages\\\": [page]}\\n current_label = \\\"name\\\"\\n current_lines = [line]\\n elif current_label == \\\"name\\\":\\n if re.search(\\n r\\\"\\\\d|\\\\b[A-Z][a-z]+\\\\.|\\\\baus\\\\b|\\\\bDas\\\\b|\\\\bPreliminary\\\\b|\\\\., \\\", line\\n ):\\n start_label(\\\"verbatim_citation\\\", line)\\n else:\\n # probably continuation of the author\\n current_lines.append(line)\\n elif (\\n current_label == \\\"verbatim_citation\\\"\\n or current_label == \\\"homonymy\\\"\\n or line.startswith(\\\"= \\\")\\n ):\\n start_label(\\\"synonymy\\\", line)\\n else:\\n assert False, f\\\"{line!r} with label {current_label}\\\"\\n assert current_label is not None\\n assert current_name is not None\\n current_name[current_label] = current_lines\\n yield current_name\",\n \"def process_article(self, title, text, template = 'Infobox person'):\\n \\n # Create a parsing object\\n wikicode = mwparserfromhell.parse(text)\\n \\n # Search through templates for the template\\n matches = wikicode.filter_templates(matches = template)\\n raw_year_string = 'EMPTY'\\n birth_year = 'EMPTY'\\n infobox = ''\\n if len(matches) >= 1:\\n # Extract information from infobox\\n for match in matches:\\n infobox = str(match)\\n for param in match.params:\\n if param.name.strip_code().strip() == 'birth_date':\\n raw_year_string = str(param.value)\\n birth_year = self.get_birth_year(raw_year_string)\\n summary = self.get_summary(wikicode.strip_code().strip())\\n return (title, birth_year, summary, raw_year_string, infobox)\",\n \"def print_catalog(self):\\n # first download the json for the catalog\\n self.download_json()\\n\\n # open the saved json file and load the json\\n with self.file.open(\\\"r\\\") as catalog_file:\\n pages = json.load(catalog_file)\\n\\n # the catalog json is just a list of pages\\n # so we begin by iterating through the pages\\n for page_num in range(len(pages)):\\n # get each page\\n page = pages[page_num]\\n\\n # get the threads on each page\\n threads = page[\\\"threads\\\"]\\n\\n # print the page heading\\n print(\\\"*** PAGE \\\", page_num + 1, \\\"***\\\")\\n\\n # iterate through the threads on each page\\n for thread_num in range(len(threads)):\\n # get each thread\\n thread = threads[thread_num]\\n\\n # print the thread number\\n num = thread[\\\"no\\\"]\\n print(\\\"---\\\", \\\"Thread:\\\", num, \\\"---\\\")\\n\\n # not all threads have a subject or comment\\n try:\\n subject = thread[\\\"sub\\\"]\\n comment = thread[\\\"com\\\"]\\n\\n print(\\\"Sub:\\\", subject)\\n print(\\\"Comment:\\\", comment)\\n except KeyError:\\n print(\\\"N/A\\\")\",\n \"def course_about(request, course_id):\\r\\n\\r\\n if microsite.get_value(\\r\\n 'ENABLE_MKTG_SITE',\\r\\n settings.FEATURES.get('ENABLE_MKTG_SITE', False)\\r\\n ):\\r\\n raise Http404\\r\\n course_key = SlashSeparatedCourseKey.from_deprecated_string(course_id)\\r\\n course = get_course_with_access(request.user, 'see_exists', course_key)\\r\\n registered = registered_for_course(course, request.user)\\r\\n staff_access = has_access(request.user, 'staff', course)\\r\\n studio_url = get_studio_url(course_key, 'settings/details')\\r\\n\\r\\n if has_access(request.user, 'load', course):\\r\\n course_target = reverse('info', args=[course.id.to_deprecated_string()])\\r\\n else:\\r\\n course_target = reverse('about_course', args=[course.id.to_deprecated_string()])\\r\\n\\r\\n show_courseware_link = (has_access(request.user, 'load', course) or\\r\\n settings.FEATURES.get('ENABLE_LMS_MIGRATION'))\\r\\n\\r\\n # Note: this is a flow for payment for course registration, not the Verified Certificate flow.\\r\\n registration_price = 0\\r\\n in_cart = False\\r\\n reg_then_add_to_cart_link = \\\"\\\"\\r\\n if (settings.FEATURES.get('ENABLE_SHOPPING_CART') and\\r\\n settings.FEATURES.get('ENABLE_PAID_COURSE_REGISTRATION')):\\r\\n registration_price = CourseMode.min_course_price_for_currency(course_key,\\r\\n settings.PAID_COURSE_REGISTRATION_CURRENCY[0])\\r\\n if request.user.is_authenticated():\\r\\n cart = shoppingcart.models.Order.get_cart_for_user(request.user)\\r\\n in_cart = shoppingcart.models.PaidCourseRegistration.contained_in_order(cart, course_key)\\r\\n\\r\\n reg_then_add_to_cart_link = \\\"{reg_url}?course_id={course_id}&enrollment_action=add_to_cart\\\".format(\\r\\n reg_url=reverse('register_user'), course_id=course.id.to_deprecated_string())\\r\\n\\r\\n # see if we have already filled up all allowed enrollments\\r\\n is_course_full = CourseEnrollment.is_course_full(course)\\r\\n\\r\\n return render_to_response('courseware/course_about.html', {\\r\\n 'course': course,\\r\\n 'staff_access': staff_access,\\r\\n 'studio_url': studio_url,\\r\\n 'registered': registered,\\r\\n 'course_target': course_target,\\r\\n 'registration_price': registration_price,\\r\\n 'in_cart': in_cart,\\r\\n 'reg_then_add_to_cart_link': reg_then_add_to_cart_link,\\r\\n 'show_courseware_link': show_courseware_link,\\r\\n 'is_course_full': is_course_full\\r\\n })\",\n \"def get_sections(pages):\\n sections = {}\\n section_pages = {}\\n current_section_name = None\\n current_section = []\\n\\n for page_num, page in enumerate(pages):\\n clean_page = [re.sub(\\\"\\\\s+\\\", \\\" \\\", i.strip()) for i in page.split(\\\"\\\\n\\\")]\\n\\n for ind, i in enumerate(clean_page):\\n\\n if (\\n re.findall(\\\"^Section \\\\d+\\\", i)\\n and \\\"page\\\" not in i\\n or (re.sub(\\\"\\\\d+ [\\\\w+\\\\s+]+\\\", \\\"\\\", i) == \\\"\\\" and ind == 0 and len(i) > 6)\\n ):\\n if current_section_name is not None:\\n sections[current_section_name] = current_section\\n current_section = []\\n current_section_name = i\\n break\\n\\n section_pages[page_num + 1] = current_section_name or \\\"No Section\\\"\\n\\n current_section.extend(clean_page)\\n return sections, section_pages\",\n \"def _populate(self):\\n\\n # Assume the first word is what we want, and we can find well formed years\\n # This sucks, but will work for these ones.\\n # Roll on bibtex for citations in the CIM.\\n\\n citation_detail = self.doc.citation_detail\\n author = citation_detail.split(',')[0]\\n match = '([^\\\\w])19|20\\\\d\\\\d([^\\\\w])*?'\\n m = re.search(match, citation_detail)\\n if m:\\n year = m.group(0)\\n else:\\n year = None\\n\\n # one error in existing es-doc content to be fixed:\\n if 'van Vuuren DP' in author:\\n author = 'van Vuuren'\\n print 'applying vv fix'\\n\\n self.year = int(year)\\n\\n # We assume that this table will have entries which ne\\n\\n # I use the first three letters of a an authors name, and for\\n # three or more authors, EA, and then the year for my bibtex citation string\\n self.citeguess = author[0:3] + 'EA' + year[2:]\\n # This is what will appear in the table:\\n self.citestring = '%s et al. (%s)' % (author, year)\\n # Keep this for a reference list for checking against the eventual bibtex reference list.\\n self.text = citation_detail\",\n \"def scrape(self, letter):\\n soup = self._request_page()\\n departments = self._get_departments(soup, letter)\\n\\n self.logger.debug('Letter %s has %s depts.', letter, len(departments))\\n\\n # For each department under a certain letter search.\\n for department in departments:\\n try:\\n dept_data = self._parse_department_data(department)\\n save_department_data(dept_data, self.scraper, self.location)\\n\\n courses = department.find_all(\\n 'tr', id=re.compile('trCOURSE_LIST'))\\n\\n # For each course under a certain department.\\n for course in courses:\\n return_state = 'DERIVED_SAA_CRS_RETURN_PB$163$'\\n\\n try:\\n course_number = course.find(\\n 'a', id=re.compile(r'CRSE_NBR\\\\$'))['id']\\n course_name = course.find(\\n 'span', id=re.compile(r'CRSE_TITLE\\\\$')).text\\n\\n if not course_number:\\n self.logger.debug('Skipping non-existent course')\\n continue\\n\\n if 'unspecified' in course_name.lower():\\n self.logger.debug('Skipping unspecified course')\\n continue\\n\\n # Note: Selecting course only takes one parameter,\\n # which is the ICAction.\\n ic_action = {'ICAction': course_number}\\n soup = self._request_page(ic_action)\\n\\n # Some courses have multiple offerings of the same\\n # course, E.g: MATH121 offered on campus and online.\\n # Check if table representing academic levels exists.\\n if not self._has_multiple_course_offerings(soup):\\n\\n title = ''\\n title_temp = soup.find(\\n 'span', id='DERIVED_CRSECAT_DESCR200'\\n )\\n\\n if title_temp:\\n title = title_temp.text.strip()\\n\\n self.logger.debug('Course title: %s', title)\\n\\n self._navigate_and_parse_course(soup)\\n else:\\n return_state = 'DERIVED_SSS_SEL_RETURN_PB$181$'\\n title = soup.find(\\n 'span', id='DERIVED_SSS_SEL_DESCR200'\\n ).text.strip()\\n self.logger.debug('Course title: %s', title)\\n self.logger.debug('Multiple offerings found')\\n\\n academic_levels = self._get_academic_levels(soup)\\n\\n for academic_level in academic_levels:\\n try:\\n cr_number = academic_level['id']\\n cr_name = academic_level.text.strip()\\n self.logger.debug('Career: %s', cr_name)\\n\\n # Go from a certain academic level to basic\\n # course page.\\n ic_action = {'ICAction': cr_number}\\n\\n soup = self._request_page(ic_action)\\n self._navigate_and_parse_course(soup)\\n\\n except Exception:\\n self.scraper.handle_error()\\n\\n self.logger.debug('Done careers.')\\n\\n except Exception:\\n self.scraper.handle_error()\\n\\n # Go back to course listing.\\n self.logger.debug('Returning to course list')\\n ic_action = {'ICAction': return_state}\\n self._request_page(ic_action)\\n\\n self.logger.debug('Done department')\\n\\n except Exception:\\n self.scraper.handle_error()\\n\\n self.logger.debug('Done letter %s', letter)\",\n \"def see_teaching_courses(self, username: str, token: str) -> List[Dict[str, object]]:\\n\\n # Validate user first\\n if not self.validate(username=username, token=token, check_privilege='instructor'):\\n raise RuntimeError(\\\"User not verified!\\\")\\n\\n # Get UID from user's username\\n uid = self.get_uid(username=username)\\n\\n # Query database for courses instructed by a user with this UID\\n cursor = self._db_connection.cursor()\\n cursor.execute(\\n '''\\n SELECT \\n course_id,\\n course_abbreviation,\\n course_name, \\n time,\\n seats \\n FROM \\n courses\\n WHERE \\n instructor_id = ?\\n ;\\n ''', (uid,))\\n\\n db_results = cursor.fetchall()\\n\\n if db_results is None:\\n print(\\\"No associated courses found!\\\")\\n return []\\n\\n # Build information dicts for every course this user is instructing\\n courses = []\\n for result in db_results:\\n # Get the number of students enrolled in this course already\\n cursor.execute('''SELECT COUNT(*) FROM enrollment_records WHERE course_id = ?;''', (result[0],))\\n students_enrolled = cursor.fetchone()[0]\\n if students_enrolled is None:\\n students_enrolled = 0\\n\\n # Build a course dict from the data\\n courses.append({\\n \\\"course_abbreviation\\\": result[1],\\n \\\"course_name\\\": result[2],\\n \\\"time\\\": result[3],\\n \\\"students_enrolled\\\": students_enrolled,\\n \\\"capacity\\\": result[4],\\n })\\n\\n return courses\",\n \"def html_page_context(app, pagename, templatename, context, doctree):\\n site = context[\\\"SITEMAP_BASE_URL\\\"]\\n version = context[\\\"version\\\"]\\n app.sitemap_links.add(f\\\"{site}{version}/{pagename}.html\\\")\",\n \"def scrape(url, filename):\\n courselist = []\\n headers = ['title', 'description', 'course number', 'duration', 'difficulty', 'instructors', 'course url']\\n with open(filename, 'wb') as outfile:\\n wr = csv.writer(outfile)\\n wr.writerow(headers)\\n courses = json.load(urllib2.urlopen(url))\\n for course in courses['courses']:\\n c = Course()\\n c.title = course['title']\\n c.desc = course['summary']\\n c.course_number = course['key']\\n c.duration = str(course['expected_duration']) + ' ' + str(course['expected_duration_unit'])\\n c.difficulty = course['level']\\n c.url = 'https://www.udacity.com/course/' + course['slug']\\n l = len(course['instructors'])\\n for i in xrange(l):\\n if(i == 0):\\n c.instructors += course['instructors'][i]['name']\\n else:\\n c.instructors += ';' + course['instructors'][i]['name']\\n with open(filename, 'ab') as outfile:\\n wr = csv.writer(outfile)\\n wr.writerow(c.getaslist())\",\n \"def get_course_title(self):\\n\\n course_title = self.course_page.find(attrs=COURSE_TITLE)\\n self.course_title = course_title.text.replace(\\\" \\\", \\\"_\\\")\\n logging.debug(\\\"course_title:{}\\\".format(self.course_title))\\n logging.info(\\\"Course title retrieved\\\")\",\n \"def _course_from_query_response(self, term, response):\\n units_low, units_hi = float(response['UNITS_LOW']), float(response['UNITS_HIGH'])\\n if units_low > units_hi:\\n # Yes, this is an actual response case...\\n # Occurs when a course has a constant # of units.\\n # I think units_hi should equal units_low when actual units is constant.\\n units_hi = units_low\\n\\n instructor_name = instructor_email = None\\n try:\\n instructor_meta = next(instr for instr in response['INSTRUCTORS'] if instr['PRIMARY_IND'] == 'Y')\\n instructor_name = '{} {}'.format(instructor_meta['FIRST_NAME'], instructor_meta['LAST_NAME'])\\n instructor_name = instructor_name.strip()\\n instructor_email = instructor_meta['EMAIL']\\n except StopIteration:\\n # No instructor specified\\n pass\\n\\n ge_areas = list()\\n try:\\n area_codes = filter(None, response['GE3CREDIT'].split(','))\\n ge_areas = [GE_AREA_NAMES_BY_SB_CODE[area_code] for area_code in area_codes]\\n except KeyError as e:\\n logging.exception('Unrecognized GE code')\\n\\n meetings = list()\\n for meeting in response['COURSEMEETINGDATA']:\\n days = meeting['WEEKDAYS'].replace(',', '')\\n times = None\\n try:\\n begin_hour, begin_minutes = meeting['BEGIN_TIME'][:2], meeting['BEGIN_TIME'][2:]\\n end_hour, end_minutes = meeting['END_TIME'][:2], meeting['END_TIME'][2:]\\n begin = timedelta(hours=int(begin_hour), minutes=int(begin_minutes))\\n end = timedelta(hours=int(end_hour), minutes=int(end_minutes))\\n times = (begin, end)\\n except TypeError:\\n # times are None, indicating TBA\\n pass\\n\\n location = meeting['BLDG_DESC']\\n if meeting['ROOM']:\\n location += ' ' + meeting['ROOM']\\n\\n meeting = {\\n 'days': days,\\n 'times': times,\\n 'location': location,\\n 'type': meeting['MEET_TYPE_DESC_SHORT']\\n }\\n meetings.append(meeting)\\n\\n final_exam = None\\n try:\\n final_exam = datetime.strptime(response['FINALEXAMSTARTDATE'], '%B, %d %Y %H:%M:%S')\\n except TypeError:\\n # No final exam\\n pass\\n\\n drop_time = response['ALLOWEDDROPDESC']\\n drop_days_match = re.match(r'^([0-9]+)', drop_time)\\n if drop_days_match:\\n drop_time = int(drop_days_match.group(1))\\n if response['DESCRIPTION']:\\n response['DESCRIPTION'] = response['DESCRIPTION'].replace('\\\\n', ' ').replace('\\\\r', '').strip()\\n response['TITLE'] = response['TITLE'].strip()\\n\\n return Course(\\n term=term,\\n crn=response['PASSEDCRN'],\\n subject_code=response['SUBJECT_CODE'],\\n name='{} {}'.format(response['SUBJECT_CODE'], response['COURSE_NUMBER']),\\n number=response['COURSE_NUMBER'],\\n section=response['SEC'],\\n title=response['TITLE'].strip(),\\n description=response['DESCRIPTION'],\\n instructor_consent_required=bool(int(response['CONSENTOFINSRUCTORREQUIRED'])),\\n units=(units_low, units_hi),\\n instructor=instructor_name,\\n instructor_email=instructor_email,\\n ge_areas=ge_areas,\\n available_seats=response['BLEND_SEATS_AVAIL'],\\n wl_length=response['BLEND_WAIT_COUNT'],\\n meetings=meetings,\\n final_exam=final_exam,\\n drop_time=drop_time,\\n prerequisites=re.sub(r'\\\\s+', ' ', response['PREREQUISITES']) if response['PREREQUISITES'] else None)\"\n]","isConversational":false},"negative_scores":{"kind":"list like","value":["0.56877637","0.56606907","0.5630893","0.53998905","0.53966093","0.5361915","0.5360787","0.5359631","0.5314254","0.52851605","0.5281027","0.5259862","0.52557594","0.52467847","0.5240469","0.5206612","0.5163807","0.5151245","0.51210815","0.5102797","0.51018554","0.50949013","0.5088406","0.50583214","0.5048805","0.5030471","0.5029336","0.5027226","0.5027094","0.5024275","0.5015125","0.50124973","0.5002242","0.4994496","0.49933723","0.4985937","0.49817353","0.49735904","0.49710888","0.49679402","0.49650875","0.496432","0.49420363","0.49374506","0.49339715","0.49328235","0.49178654","0.49137926","0.49089244","0.48978767","0.48958275","0.48938543","0.48921305","0.48798388","0.48761603","0.4875049","0.48697996","0.48639318","0.48619357","0.4857372","0.48489258","0.48413968","0.48227468","0.48225856","0.48181868","0.48152554","0.4811406","0.48087755","0.4785411","0.47853884","0.477454","0.4770695","0.47597837","0.47560856","0.4728653","0.47267887","0.472367","0.47133276","0.4712501","0.47111565","0.4710372","0.4710217","0.47092062","0.470156","0.46876794","0.46850762","0.46848935","0.4682123","0.4669752","0.46653625","0.4661258","0.4658807","0.4654839","0.46481496","0.4640489","0.4635139","0.46313575","0.46313015","0.46299118","0.46294206"],"string":"[\n \"0.56877637\",\n \"0.56606907\",\n \"0.5630893\",\n \"0.53998905\",\n \"0.53966093\",\n \"0.5361915\",\n \"0.5360787\",\n \"0.5359631\",\n \"0.5314254\",\n \"0.52851605\",\n \"0.5281027\",\n \"0.5259862\",\n \"0.52557594\",\n \"0.52467847\",\n \"0.5240469\",\n \"0.5206612\",\n \"0.5163807\",\n \"0.5151245\",\n \"0.51210815\",\n \"0.5102797\",\n \"0.51018554\",\n \"0.50949013\",\n \"0.5088406\",\n \"0.50583214\",\n \"0.5048805\",\n \"0.5030471\",\n \"0.5029336\",\n \"0.5027226\",\n \"0.5027094\",\n \"0.5024275\",\n \"0.5015125\",\n \"0.50124973\",\n \"0.5002242\",\n \"0.4994496\",\n \"0.49933723\",\n \"0.4985937\",\n \"0.49817353\",\n \"0.49735904\",\n \"0.49710888\",\n \"0.49679402\",\n \"0.49650875\",\n \"0.496432\",\n \"0.49420363\",\n \"0.49374506\",\n \"0.49339715\",\n \"0.49328235\",\n \"0.49178654\",\n \"0.49137926\",\n \"0.49089244\",\n \"0.48978767\",\n \"0.48958275\",\n \"0.48938543\",\n \"0.48921305\",\n \"0.48798388\",\n \"0.48761603\",\n \"0.4875049\",\n \"0.48697996\",\n \"0.48639318\",\n \"0.48619357\",\n \"0.4857372\",\n \"0.48489258\",\n \"0.48413968\",\n \"0.48227468\",\n \"0.48225856\",\n \"0.48181868\",\n \"0.48152554\",\n \"0.4811406\",\n \"0.48087755\",\n \"0.4785411\",\n \"0.47853884\",\n \"0.477454\",\n \"0.4770695\",\n \"0.47597837\",\n \"0.47560856\",\n \"0.4728653\",\n \"0.47267887\",\n \"0.472367\",\n \"0.47133276\",\n \"0.4712501\",\n \"0.47111565\",\n \"0.4710372\",\n \"0.4710217\",\n \"0.47092062\",\n \"0.470156\",\n \"0.46876794\",\n \"0.46850762\",\n \"0.46848935\",\n \"0.4682123\",\n \"0.4669752\",\n \"0.46653625\",\n \"0.4661258\",\n \"0.4658807\",\n \"0.4654839\",\n \"0.46481496\",\n \"0.4640489\",\n \"0.4635139\",\n \"0.46313575\",\n \"0.46313015\",\n \"0.46299118\",\n \"0.46294206\"\n]","isConversational":false},"document_score":{"kind":"string","value":"0.52513224"},"document_rank":{"kind":"string","value":"13"}}},{"rowIdx":22666,"cells":{"query":{"kind":"string","value":"Given a list of course codes, ge5t their corresponding titles and format them in a bulletted TeX list. This is used to indicate in the abstract which courses have been deliberately discluded from the document"},"document":{"kind":"string","value":"def create_not_included_list(codes):\n string = '\\\\begin{itemize}\\n'\n for code in codes:\n title = get_course_title_only(code)\n string += '\\\\item{' + title + '}\\n'\n string += '\\\\end{itemize}\\n'\n return string"},"metadata":{"kind":"string","value":"{\n \"objective\": {\n \"self\": [],\n \"paired\": [],\n \"triplet\": [\n [\n \"query\",\n \"document\",\n \"negatives\"\n ]\n ]\n }\n}"},"negatives":{"kind":"list like","value":["def write_to_latex(codelist, unwanted_courses):\n # TODO: investigate a way to add large amounts of text outside of the\n # function\n abstract01 = \"I created this document to practice parsing html and using\\\n tools like Beautiful Soup which I've previously had little experience\\\n in. As a result, it's not perfect.\\\\newline\\\n It is also a slightly condensed all-in-one-place look at a selection\\\n of courses that are available for fourth year computer science\\\n students at the University of Glasgow. For the purposes of clarity I\\\n have removed several courses from this selection. The following\\\n courses have been omitted:\"\n abstract02 = \"For more insight into the project, to report issues or to\\\n inspect the code, have a look at the GitHub:\\\n \\\\url{https://github.com/IS0metric/course-ripper}\"\n unincluded = create_not_included_list(unwanted_courses)\n with open('courses.tex', 'w') as f:\n # TODO Try and move all this to a separate function?\n # TODO: Check if it's more efficient to write a single, massive string\n # to file\n f.write('\\\\documentclass{hitec}\\n')\n f.write('\\\\usepackage[document]{ragged2e}\\n')\n f.write('\\\\usepackage{url}\\n')\n f.write('\\\\usepackage{hyperref}\\n')\n f.write('\\\\setcounter{tocdepth}{4}\\n')\n f.write('\\\\begin{document}\\n')\n f.write('\\\\title{Fourth Year (2016-17) Courses}\\n')\n f.write('\\\\author{Jack Parkinson}\\n')\n f.write('\\\\date{August 2016}\\n')\n f.write('\\\\maketitle\\n')\n f.write('\\\\abstract{' + abstract01 + unincluded + abstract02 + '}\\n')\n f.write('\\\\newpage\\n\\n')\n f.write('\\\\tableofcontents\\n')\n f.write('\\\\newpage\\n\\n')\n # TODO: Look into alternatives to the three lists\n all_courses = []\n sem1_courses = []\n sem2_courses = []\n for code in codelist:\n course = bsoup(get_coursepage(code))\n if course['offered']['value'] == 'Runs Throughout Semesters 1 and 2':\n all_courses.append(course)\n elif \"1\" in course['offered']['value']:\n sem1_courses.append(course)\n elif \"2\" in course['offered']['value']:\n sem2_courses.append(course)\n f.write('\\\\section{Semester 1 and 2 Courses}\\n\\n')\n for course in all_courses:\n f.write(latex_course(course))\n f.write('\\\\section{Semester 1 Only Courses}\\n\\n')\n for course in sem1_courses:\n f.write(latex_course(course))\n f.write('\\\\section{Semester 2 Only Courses}\\n\\n')\n for course in sem2_courses:\n f.write(latex_course(course))\n f.write('\\\\end{document}')\n return None","def create_tex(unwanted_courses, wanted_courses=None):\n page = requests.get(\n 'http://gla.ac.uk/coursecatalogue/courselist/' +\n '?code=REG30200000&name=School+of+Computing+Science')\n tree = html.fromstring(page.content)\n spans = tree.xpath('//span/text()')\n codes = []\n if wanted_courses is None:\n for s in spans:\n if s[0:4] == \"COMP\" and s[7] == '4' and s not in unwanted_courses:\n codes.append(s)\n else:\n for s in wanted_courses:\n codes.append(s)\n write_to_latex(codes, unwanted_courses)\n return None","def get_course_name(self,soup):\n\t\tcourse_title = []\n\t\tfor title in soup.find_all(\"div\", class_=\"views-field views-field-title\"):\n\t\t\tcourse_title.append(''.join(title.findAll(text=True)))\n\t\t\tcourse_title.append(' \\n')\t\n\t\t\n\t\tself.new_list.append(course_title)\n\t\treturn course_title","def scrape_all_course_names(filename, verbose):\n \n\tsoup =\tBeautifulSoup(open(filename, 'r'), 'html.parser')\n\t#print(soup)\n\tcourses = []\n\t# all the courses are stored in a div w/class=view-content\n\t\t\n\th4_field_content = soup.find('h4', 'field-content')\n\t#print(h4_field_content)\n\tall_course_content = soup.find(\"div\", \"view-content\") # this contains ALL the classes...\n\tif all_course_content == None:\n\t\tprint(\"There are no courses on this page. Try a smaller page number!\")\n\t\treturn []\n\t\n\tcandidate_classes = all_course_content.find_all('a') # we want all the 'a' tags within\n\tclass_list=[]\n course_code_and_number = {}\n\tfor c in candidate_classes:\n\t\t#print(c.text, \"\\n\")\n\t\ttext = c.text # ex. AEMA 611 Experimental Designs 1 (3 credits)\n\t\ttext = text.split(\" \")# split on the space\n\t\tcourse_id = \" \".join(text[:2]).replace('\\n', '') # the first 2 are the course id\n\t\tcourse_name = \" \".join(text[2:-2]).replace('\\n', '')\n\t\tnum_credits = text[-2].replace(\"(\", \"\")# just get the course number, replace the ( with nothing \n\t\n\t\n\t\t#print(f\"{course_id}\\n{course_name}\\n{num_credits}\\n\")\n\t\ttry:# Check that the course number is a digit bc sometimes it is something weird\n\t\t\tfloat(num_credits)\n\t\texcept ValueError:\n\t\t\t#print(f\"Wrong course format. Ignoring {c}\")\n\t\t\tif(verbose):\n\t\t\t\tprint(f\"Wrong course format. Ignoring course: {c.text}\")\n\t\t\tcontinue\n\t\tclass_list.append(course_id)\n\treturn class_list","def create_menu_text(list_of_items):\n ret = \"\"\n for item in list_of_items:\n item = clean(item)\n ret += item + \"\\n\"\n # translate = gs.translate(item, 'en', 'de')\n # ret += \"_\" + translate.replace(\" , \", \", \") + \"_\\n\"\n # ret += \"\\n\"\n return ret[:-1] # ignore last newline","def print_output(filtered_courses_list):\n\n if not filtered_courses_list:\n print(\"No courses matched the query.\")\n return\n\n max_name_length = max([len(course.name) for course in filtered_courses_list])\n\n print(\" Sem | Course ID | Pts | \" +\n \" \" * ((max_name_length - 11) // 2 + (max_name_length - 1) % 2) + \"Course Name\" + \" \" * ((max_name_length - 11) // 2) +\n \" | Grade\")\n print(\"______|___________|_____|\" + \"_\" * (max_name_length + 2) + \"|______\")\n\n for course in filtered_courses_list:\n print(str(course.year) + course.semester, end=\" | \")\n print(course.cid, end=\" | \")\n print(course.creditpts, end=\" | \")\n print(course.name, end=\" \" * (max_name_length - len(course.name) + 1) + \"| \")\n\n print(course.grade)\n\n print(\"\\nGPA: \" + (\"%.3f\" % calculate_weighted_average(filtered_courses_list)))","def presidente(lst_presidente):\n tmp=''\n tmp+='\\t\\t<para style=\"P3\">\\n'\n tmp+='\\t\\t\\t<font color=\"white\"> </font>\\n'\n tmp+='\\t\\t</para>\\n'\n tmp+='\\t\\t<para style=\"P3\">\\n'\n tmp+='\\t\\t\\t<font color=\"white\"> </font>\\n'\n tmp+='\\t\\t</para>\\n'\n tmp+='\\t\\t<para style=\"P3\" spaceAfter=\"40\">\\n'\n tmp+='\\t\\t\\t<font color=\"white\"> </font>\\n'\n tmp+='\\t\\t</para>\\n'\n tmp+='\\t\\t<para style=\"P1\"><b>' + str(lst_presidente) + '</b></para>\\n'\n tmp+='\\t\\t<para style=\"P4\">Presidente </para>\\n'\n tmp+='\\t</story>\\n'\n return tmp","def createtext(lst):\n newlst = []\n for item in lst:\n item = item.replace(\"_!\",\"\")\n newlst.append(item)\n text = ' '.join(newlst)\n # Lower-casing\n return text.lower()","def insert_course(dept, num, text):\n\n # Course Title \n m = re.search(\"[\\d\\w]{5} - ([\\w ]*)\", text)\n title = m.group(1) if m else \"nomatch\"\n\n # Course Description\n m = re.search(\"\\.\\s(.*)\\sTypically\",text)\n des = m.group(1) if m else \"nomatch\"\n\n # Credit hours aren't fixed for every course\n # Credit Hours: 2.00\n # Credit Hours: 2.00 or 3.00. \n # Credit Hours: 1.00 to 18.00. \n m = re.search(\"Credit Hours: (\\d+\\.\\d+)\",text, flags=re.IGNORECASE)\n m = re.search(\"(\\d+\\.\\d+)(.*?)Credit hours\",text, flags=re.IGNORECASE) if not m else m\n cr = m.group(1) if m else \"-1\"\n\n # Semesters Offered\n m = re.search(\"Typically offered (.*?)\\.\", text)\n sem = m.group(1).split() if m else [\"nomatch\"]\n\n # Course Type: Lecture, Recitation, Lab, Seminar, etc.\n m = re.search(\"Schedule Types:\\s((?:[\\w ]+)(?:,[\\w ]+)*) \\s+\", text)\n form = m.group(1).split(\", \") if m else [\"nomatch\"]\n\n # Learning objectives will not necessarily follow campuses\n m = re.search(\"campuses:(\\s+([\\w\\s])+\\n)\", text)\n campus = m.group(1).strip().split(\"\\n\\n\") if m else [\"nomatch\"]\n campus = [camp.strip() for camp in campus]\n\n # prereq regex and decomosition of prereqs into lists of AND conditions (works for most classes, not 477 and similar)\n # re.DOTALL matches all characters, including \"\\n\"\n idx = text.find(\"campuses:\")\n m = re.search(\"Prerequisites:(.*)\",text[idx:],flags=re.DOTALL)\n if m:\n allReqs = []\n prereqText = m.group(1).strip()\n prereqText = prereqText.encode('ascii', 'ignore') \n for i in PrereqParser.parseprereq(prereqText):\n reqArr = []\n for j in i.split():\n if j.find(\"-C\") != -1:\n j = j.replace(\"-C\",\"\")\n reqArr.append(Requisite(course=j,reqType=False))\n else:\n reqArr.append(Requisite(course=j,reqType=True)) \n allReqs.append(RequisiteList(courses=reqArr))\n\n else:\n allReqs = []\n\n # create course entity\n course = Course(number=num, title=title, department=dept, form=form,\n description=des, credits=float(cr), semesters=sem,\n campuses=campus,requisites=allReqs, id=dept + num)\n # store course \n course.put()","def upcoming_courses(aud):\n \n courses = [c for c in aud.all_courses() if c.grade == u\"*\"]\n return [c.number.replace(\"-\", \"\") for c in courses]","def complete_description(description):\n local_find_all_re = find_all_re\n local_find_one_re = find_one_re\n local_find_series_2_re = find_series_2_re\n local_find_series_3_re = find_series_3_re\n local_course_re = course_re\n local_three_digits = three_digits\n\n find_all = re.findall(local_find_all_re, description)\n find_one = re.findall(local_find_one_re, description)\n \n def remove_in(a, b):\n remove = [y for x in a for y in b if y in x]\n a.extend(b)\n for x in remove: a.remove(x)\n return a\n \n if find_one:\n find_all = remove_in(find_all, find_one)\n for i, x in enumerate(find_all):\n completed = []\n find_all[i] = x.replace(' ', '')\n number = re.search(local_three_digits, find_all[i]).group(0)\n for crs in re.findall(local_course_re, find_all[i]):\n completed.append(f'{crs}{number}')\n description = description.replace(x, ' {}'.format('/'.join(completed)), 1)\n\n find_series_2 = re.findall(local_find_series_2_re, description)\n find_series_3 = re.findall(local_find_series_3_re, description)\n if find_series_2 or find_series_3:\n find_series_2 = remove_in(find_series_3, find_series_2)\n for i, series in enumerate(find_series_2):\n find_series_2[i] = series.replace(' ', '')\n course_dep = re.search(local_course_re, find_series_2[i])\n if course_dep: \n depmnt = course_dep.group(0)\n description = description.replace(series, f'{depmnt}{find_series_2[i][-3:]}', 1)\n return description","def description(soup: str, nb:int):\n desc = []\n for span in soup.findAll('article', attrs={'itemprop': 'review'}):\n dat = str(recovTextBetweenTags(str(span.findAll('time', attrs={\n 'itemprop': 'datePublished'})), ',')).replace(\"['[\", '').replace(\"]']\", '')\n dat = (format_date(dat))\n if (dat) > (datetime.now() - timedelta(nb)):\n top = span.findAll('div', attrs={'class': 'text_content'})\n desc.append(translate(recovTextBetweenTags(str(top), ',')))\n\n return desc","def title_comm(soup: str, nb:int):\n title = []\n for span in soup.findAll('article', attrs={'itemprop': 'review'}):\n dat = str(recovTextBetweenTags(str(span.findAll('time', attrs={\n 'itemprop': 'datePublished'})), ',')).replace(\"['[\", '').replace(\"]']\", '')\n dat = (format_date(dat))\n if (dat) > (datetime.now() - timedelta(nb)):\n top = span.findAll('h2', attrs={'class': 'text_header'})\n top = translate(recovTextBetweenTags(str(top), 'non'))\n title.append(top[0][1:len(top[0])])\n\n return title","def display_words(word_list,specifier):\n \n if specifier.lower() == 'score':\n print(\"{:>6s} - {:s}\".format(\"Score\", \"Word\"))\n if len(word_list) < 5:\n for tup in word_list:\n print(\"{:>6d} - {:s}\".format(tup[1], tup[0]))\n else:\n \n for tup in word_list[:5]:\n print(\"{:>6d} - {:s}\".format(tup[1], tup[0]))\n \n \n elif specifier.lower() == 'length':\n print(\"{:>6s} - {:s}\".format(\"Length\", \"Word\"))\n if len(word_list) < 5:\n for tup in word_list:\n print(\"{:>6d} - {:s}\".format(tup[2], tup[0]))\n else:\n \n for tup in word_list[:5]:\n print(\"{:>6d} - {:s}\".format(tup[2], tup[0]))","def test_reformat_paragraph_list_5_of_5(self):\n before_b = \"\"\"\\\n This paragraph leads of this test. It is\n the \"lead\" paragraph.\n\n 1. This is item number 1. It is the\n first item in the list.\n\n 2. This is item number 2. It is the\n second item in the list.\n\n 3. This is item number 3. It is the\n third item in the list.\n\n This paragraph ends the test. It is the \"final\"\n paragraph.\n \"\"\"\n after_b = \"\"\"\\\n This paragraph leads of this test. It is\n the \"lead\" paragraph.\n\n 1. This is item number 1. It is the\n first item in the list.\n\n 2. This is item number 2. It is the\n second item in the list.\n\n 3. This is item number 3. It is the\n third item in the list.\n\n This paragraph ends the test. It is the\n \"final\" paragraph.\n \"\"\"\n self.run_test(\n before_b=before_b,\n after_b=after_b,\n before_sel=(\"13.0\", \"13.0\"),\n after_sel=(\"15.1\", \"15.1\"),\n command_name=\"reformat-paragraph\",\n directives=\"@language plain\\n@pagewidth 40\\n@tabwidth 8\",\n )","def cv_list(self):\n\n mystr = \"\"\n for p in self.mypapers:\n mystr += f\"{p.title[0]}\\n\"\n if len(p.author) > 12:\n a = f\"{p.author[0]} et al. \"\n elif len(p.author) > 2:\n a = \", \".join(p.author[:-1]) + f\" & {p.author[-1]} \"\n elif len(p.author) == 2:\n a = f\"{p.author[0]} & {p.author[1]} \"\n else:\n a = f\"{p.author[0]} \"\n\n mystr += f\"{a}\"\n mystr += f\"{p.year}, {p.pub}\"\n if p.volume is not None:\n mystr += f\", {p.volume}\"\n if p.issue is not None:\n mystr += f\", {p.issue}\"\n if p.page is not None:\n mystr += f\", {p.page[0]}\"\n mystr += \"\\n\\n\"\n return mystr","def presidente(lst_presidente):\n tmp=''\n tmp+='\\t\\t<para style=\"P3\" spaceAfter=\"35\">\\n'\n tmp+='\\t\\t\\t<font color=\"white\"> </font>\\n'\n tmp+='\\t\\t</para>\\n'\n tmp+='\\t\\t<para style=\"P4\"><b>' + str(lst_presidente) + '</b></para>\\n'\n tmp+='\\t\\t<para style=\"P4\">Presidente </para>\\n'\n return tmp","def create_formatted_accomplishments(accomplishments):\n output = ''\n\n for index, accomplishment in enumerate(accomplishments):\n output += f\"* {accomplishment}\"\n if index + 1 != len(accomplishments):\n output += '\\n'\n\n return output","def display_courses(courses):\n\n _print('You can access %d courses' % len(courses))\n for i, course in enumerate(courses, 1):\n _print('%d - [%s] - %s' % (i, course.state, course.name))","def output_labelled_ranked_list(sysdoclab):\n for did, grade in sysdoclab:\n print(did + (\" L\" + str(grade) if grade is not None else \"\"))","def latex_course(course):\n basic_info_list = [\n 'session', 'school', 'credits', 'level', 'offered',\n 'visiting_students', 'erasmus_students'\n ]\n generic_subsection_list = [\n 'description', 'timetable', 'requirements_of_entry',\n 'excluded_courses', 'co_requisites', 'assessment_weighting'\n ]\n string = '\\\\subsection{' + course[\"title\"] + '}\\n'\n for info in basic_info_list:\n string += latex_info(course[info])\n for subsection in generic_subsection_list:\n string += latex_subsection(course[subsection])\n string += '\\\\break \\\\textbf{' + course['assessment_date'][\n 'heading'] + '}' + course['assessment_date']['value'] + '\\n'\n string += latex_subsection(course['aims'])\n string += '\\\\subsubsection*{' + \\\n course['learning_outcomes']['heading'] + '}\\n'\n outcome_list = re.split(\n '\\d+\\. ', course['learning_outcomes']['value'])\n string += outcome_list[0] + '\\n'\n string += '\\\\begin{enumerate}\\n'\n for i in outcome_list[1:-1]:\n string += '\\\\item ' + i + '\\n'\n string += '\\\\end{enumerate}\\n'\n return string","def get_course_title_only(code):\n coursepage = get_coursepage(code)\n soup = BeautifulSoup(coursepage.content, 'lxml')\n title = [soup.find_all('h1')[2].string][0]\n return title","def main():\n\n #Courses\n years = [2016, 2017, 2018, 2019, 2020]\n with sqlite3.connect(\"determined.db\") as conn:\n c = conn.cursor()\n c.execute(\n \"\"\"\n CREATE TABLE \"course\" (\n \"course_number\"\tint NOT NULL,\n \"dept_id\"\tvarchar(4) NOT NULL,\n \"title\"\tvarchar(100) NOT NULL,\n \"instructor_fname\"\tvarchar(35) DEFAULT NULL,\n \"instructor_lname\"\tvarchar(35) DEFAULT NULL,\n \"student_work_products\"\tjson DEFAULT NULL,\n `term` varchar(7) NOT NULL,\n `year` int NOT NULL,\n PRIMARY KEY(\"course_number\", \"term\", \"year\")) \n \"\"\"\n )\n conn.commit()\n courses = [\n (1370, \"CPSC\", \"Computer Literacy\", names.get_first_name(), names.get_last_name(), json.dumps({\"swp\": [\"Midterm\", \"Final Exam\", \"Project 1\"]}), \"Fall\"),\n (1375, \"CPSC\", \"Programming I\", names.get_first_name(), names.get_last_name(), json.dumps({\"swp\": [\"Midterm\", \"Final Exam\", \"Project 1\"]}), \"Fall\"),\n (2376, \"CPSC\", \"Intro to Game Programming\", names.get_first_name(), names.get_last_name(), json.dumps({\"swp\": [\"Midterm\", \"Final Exam\", \"Project 1\"]}), \"Fall\"),\n (2380, \"CPSC\", \"Algorithms\", names.get_first_name(), names.get_last_name(), json.dumps({\"swp\": [\"Midterm\", \"Final Exam\", \"Project 1\"]}), \"Fall\"),\n (2482, \"CPSC\", \"Computer Organization\", names.get_first_name(), names.get_last_name(), json.dumps({\"swp\": [\"Midterm\", \"Final Exam\", \"Project 1\"]}), \"Spring\"),\n (3377, \"CPSC\", \"Advanced Game Programming\", names.get_first_name(), names.get_last_name(), json.dumps({\"swp\": [\"Midterm\", \"Final Exam\", \"Project 1\"]}), \"Spring\"),\n (3380, \"CPSC\", \"Operating Systems\", names.get_first_name(), names.get_last_name(), json.dumps({\"swp\": [\"Midterm\", \"Final Exam\", \"Project 1\"]}), \"Spring\"),\n (3383, \"CPSC\", \"Programming Languages\", names.get_first_name(), names.get_last_name(), json.dumps({\"swp\": [\"Midterm\", \"Final Exam\", \"Project 1\"]}), \"Spring\"),\n (3384, \"CPSC\", \"Computer Networks\", names.get_first_name(), names.get_last_name(), json.dumps({\"swp\": [\"Midterm\", \"Final Exam\", \"Project 1\"]}), \"Summer\"),\n (4360, \"CPSC\", \"Computer Security\", names.get_first_name(), names.get_last_name(), json.dumps({\"swp\": [\"Midterm\", \"Final Exam\", \"Project 1\"]}), \"Summer\")\n ]\n #Adding years\n upload_courses = []\n for year in years:\n upload_courses += [x + (year,) for x in courses]\n #Making a few instructors teach multiple course\n new_courses = [\n (4557, \"CPSC\", \"Natural Language Processing\", ),\n (2375, \"CPSC\", \"Programming II\",),\n (2776, \"CPSC\", \"Data Structures and Algorithms\",),\n (4862, \"CPSC\", \"Image Recognition\", ),\n ]\n for i in range(0,len(new_courses)):\n year = choice(years)\n for y in range(0,2): #Number of times new course is taught\n c = upload_courses[i]\n new_data = (c[3], c[4], c[5], choice([\"Fall\", \"Spring\", \"Summer\"]), year+y)\n data = new_courses[i] + new_data\n upload_courses.append(data)\n #Adding solo instructors and solo courses\n upload_courses += [\n (4672, \"CPSC\", \"Programming Memes\", names.get_first_name(), names.get_last_name(), json.dumps({\"swp\": [\"Midterm\", \"Final Exam\", \"Project 1\"]}), \"Spring\", choice(years)),\n (1872, \"CPSC\", \"Information Systems\", names.get_first_name(), names.get_last_name(), json.dumps({\"swp\": [\"Midterm\", \"Final Exam\", \"Project 1\"]}), \"Summer\", choice(years)),\n (1123, \"CPSC\", \"Microsoft Office\", names.get_first_name(), names.get_last_name(), json.dumps({\"swp\": [\"Midterm\", \"Final Exam\", \"Project 1\"]}), \"Fall\", choice(years))\n ]\n\n with sqlite3.connect(\"determined.db\") as conn:\n c = conn.cursor()\n c.executemany('''INSERT INTO course (course_number, dept_id, title, instructor_fname, instructor_lname, student_work_products, term, year)\n VALUES ( ?, ?, ?, ?, ?, ?, ?, ?)''', upload_courses)\n conn.commit()\n\n #SWP\n with sqlite3.connect(\"determined.db\") as conn:\n c = conn.cursor()\n c.execute(\n \"\"\"\n CREATE TABLE `student_work_product` (\n `id` INTEGER PRIMARY KEY,\n `product` varchar(250) NOT NULL,\n `course_id` int NOT NULL,\n `dept_id` int NOT NULL,\n `student_fname` varchar(35) NOT NULL,\n `student_lname` varchar(35) NOT NULL,\n `student_outcome` int DEFAULT NULL,\n `score` int DEFAULT NULL,\n `term` varchar(7) NOT NULL,\n `year` int NOT NULL,\n CONSTRAINT `course` FOREIGN KEY (`course_id`) REFERENCES `course` (`course_number`)\n CONSTRAINT `course` FOREIGN KEY (`dept_id`) REFERENCES `course` (`dept_id`)\n )\n \"\"\"\n )\n conn.commit()\n \n swps = []\n with sqlite3.connect(\"determined.db\") as conn:\n conn.row_factory = sqlite3.Row\n c = conn.cursor()\n c.execute (\"Select * from course\")\n records = [dict(x) for x in c.fetchall()]\n #Generating 20 student records for each swp in each course\n for i, course in enumerate(records):\n student_names = []\n for _ in range(20):\n student_names.append({'fname': names.get_first_name(),\n 'lname': names.get_last_name()})\n for product in json.loads(course['student_work_products'])['swp']:\n for student in student_names:\n if i%7 == 0:\n score = int(triangular(50, 85))\n else:\n score = int(triangular(50, 100))\n if score >= 90: outcome = 4\n elif score >= 80: outcome = 3\n elif score >= 70: outcome = 2\n elif score >= 60: outcome = 1\n else: outcome = 0 \n swps.append((\n product,\n course['course_number'],\n \"CPSC\",\n student['fname'],\n student['lname'],\n outcome,\n score, \n course['term'], \n course['year']\n ))\n \n c.executemany('''INSERT INTO student_work_product (product, course_id, dept_id, student_fname, student_lname, student_outcome, score, term, year)\n VALUES ( ?, ?, ?, ?, ?, ?, ?, ?, ?)''', swps)\n conn.commit()","def parse_course_pre_to_list(self):\n prere_courses = []\n\n # convert non-word to spaces except \"-\"\n self.prere_raw = re.sub(\"[^\\w-]\", \" \", self.prere_raw)\n\n # split the string by spaces\n words = self.prere_raw.split()\n\n # check if the string contains number, if True then the string is of the form: \"140A\"\n def append_to_list(word, previous_word):\n try:\n if word[0].isdigit():\n toappend = None\n # course abbs = words[i-1]\n try:\n toappend = \"{} {}\".format(previous_word.upper(), word.upper())\n except AttributeError:\n #TODO check this error for HIGR 216A-B\n print(\"previous word is {}, word is {}\".format(previous_word, word))\n if toappend not in prere_courses:\n prere_courses.append(toappend)\n except IndexError:\n #TODO why this would occur?\n print(\"word is {}, previous word is {}\".format(word, previous_word))\n\n # iterate through words to find numbers\n for i in range(len(words)):\n\n previous_word = None\n if i is not 0:\n # define the previous word like MATH\n previous_word = words[i-1]\n\n if \"-\" in words[i]:\n num = re.split(\"[A-Z]\", words[i])[0]\n letters = re.split(\"-\", words[i])\n new_words = []\n for i in range(len(letters)):\n if i is 0:\n new_words.append(letters[0])\n else:\n new_words.append(num + letters[i])\n for word in new_words:\n if word is not None and previous_word is not None:\n append_to_list(word, previous_word)\n else:\n #TODO: what if the word is None?\n pass\n else:\n append_to_list(words[i], previous_word)\n\n return prere_courses","def group_list(self, group_number=1):\n text = []\n group = self.varexercise_numbers[group_number-1]\n group_name = _('Group %s') % group[0]\n text.append('\\n\\\\subsection*{%s}\\n' % group_name)\n for number in range(1, self.number_of_variations+1):\n print(\"---------\", number) # !!!\n variation = '\\n\\n\\\\textbf{%s}\\\\\\\\\\n' %\\\n _('Variation %d') % number\n text.append(variation)\n exercise_number = 1\n for item in group[1:]:\n # print(' '*5, item) # !!!\n if not isinstance(item, str):\n latex_plain = item.latex_plain(number)\n if latex_plain:\n text.append('%d. ' % exercise_number)\n # print(' '*5, number) # !!!\n # print(' '*5, latex_plain) # !!!\n text.append(latex_plain + '\\n')\n exercise_number += 1\n # if with_solution:\n # text.extend(self.one_group(group_number=group_number))\n # text.append(\"\\\\newpage\\n\")\n return text","def courses_string(self):\n course_string = \"\"\n for c in self.courses[0]:\n course_string += c + \", \"\n course_string = course_string[:-2]\n return course_string","def snippetList(requeset, format = None):","def clean_text_clues(texts):\n\n clean_clues = []\n for clues in texts:\n clue = remove_hyphens(clues)\n clue = tokenize(clue)\n clue = remove_characters(clue)\n clue = lowercase(clue)\n clue = remove_stopwords(clue)\n clue = lemmatized_words(clue)\n clue = remove_short_tokens(clue)\n clue = remove_non_wordnet(clue)\n clue = apply_lemmatize(clue)\n clean_clues.append(clue)\n return [' '.join(item) for item in clean_clues]","def build_latex(file_list):\n eingabe=[]\n anhang_count=0\n anhaenge=[]\n anhaenge_file=[]\n for file in file_list:\n x=load_file(file)[1]\n eingabe.append(\"\\section{%s}\" %(x[2]))\n eingabe.append(\"\\subsection{Infos}\")\n eingabe.append(\"\\\\begin{tabularx}{\\linewidth}{@{}lX}\")\n eingabe.append(r\"\\textbf{Datum} & %s\\\\\" %(x[0]))\n eingabe.append(r\"\\textbf{Gremium} & %s\\\\\" %(x[1]))\n eingabe.append(r\"\\textbf{Anatrag/Beschluss wurde} & %s\\\\\" %(x[9]))\n x[11]=x[11].replace(\" \",\"\")\n kw=x[11].split(\",\")\n for i in range(0,len(kw)):\n if i==0:\n eingabe.append(r\"\\textbf{Keyword:} & %s\\\\\" %(kw[i]))\n else:\n eingabe.append(r\" & %s\\\\\" %(kw[i]))\n eingabe.append(\"\\end{tabularx}\")\n eingabe.append(\"\\\\begin{tabularx}{\\linewidth}{@{}XXX}\")\n eingabe.append(r\"\\textbf{Abstimmungsergebniss:}&&\\\\\")\n eingabe.append(r\"Zustimmung & Ablehnung & Enthaltungen \\\\\")\n eingabe.append(r\"{} & {} & {} \\\\\".format(x[6],x[7],x[8]))\n eingabe.append(\"\\end{tabularx}\")\n eingabe.append(\"\\subsection{Antrags/Beschlusstext}\")\n line_text=len(eingabe)\n eingabe.append(x[3])\n eingabe.append(\"\\subsection{Begründung}\")\n eingabe.append(x[4])\n if x[23]==\"Ja\" and x[24]!=\"\":\n delta=7\n anzahl=int((len(x)-23)/delta)\n if anzahl==1:\n eingabe.append(\"\\subsection{Änderungsantrag}\")\n eingabe.append(\"\\subsubsection*{Vorschlag}\")\n eingabe.append(x[24])\n eingabe.append(\"\\subsubsection*{Begründung}\")\n eingabe.append(x[25]+\"\\\\vspace{1.5ex} \\\\\\\\\")\n eingabe.append(\"\\\\begin{tabularx}{\\linewidth}{@{}XXX}\")\n eingabe.append(r\"\\textbf{Abstimmungsergebniss:}&&\\\\\")\n eingabe.append(r\"Zustimmung & Ablehnung & Enthaltungen \\\\\")\n eingabe.append(r\"{} & {} & {} \\\\\".format(x[26],x[27],x[28]))\n eingabe.append(r\"\\multicolumn{2}{@{}l}{\\textbf{Änderungsantrag wurde:}} & %s \\\\\" %(x[29]))\n eingabe.append(\"\\\\end{tabularx}\")\n else:\n eingabe.append(\"\\subsection{Änderungsanträge}\")\n for i in range(0,anzahl):\n eingabe.append(\"\\subsubsection{Änderungsvorschlag %s}\" %(i+1))\n eingabe.append(\"\\\\paragraph*{Vorschlag}\")\n eingabe.append(x[24+(delta*i)])\n eingabe.append(\"\\\\paragraph*{Begründung}\")\n eingabe.append(x[25+(delta*i)]+\"\\\\vspace{1.5ex} \\\\\\\\\")\n eingabe.append(\"\\\\begin{tabularx}{\\linewidth}{@{}XXX}\")\n eingabe.append(r\"\\textbf{Abstimmungsergebniss:}&&\\\\\")\n eingabe.append(r\"Zustimmung & Ablehnung & Enthaltungen \\\\\")\n eingabe.append(r\"{} & {} & {} \\\\\".format(x[26+(delta*i)],x[27+(delta*i)],x[28+(delta*i)]))\n eingabe.append(r\"\\multicolumn{2}{@{}l}{\\textbf{Änderungsantrag wurde:}} & %s \\\\\" %(x[29+(delta*i)]))\n eingabe.append(\"\\\\end{tabularx}\")\n if x[10]!=\"\":\n anhang=x[10].split(\",\")\n bennenung=x[11].split(\",\")\n eingabe[line_text]=eingabe[line_text]+\"\\\\\\\\ \\n Dieser Antrag enthält %s Anhänge: \" %(len(anhang))\n for i in range(0,len(anhang)):\n anhang_count=anhang_count+1\n anhaenge.append(\"\\section{%s - %s} \\label{An:%s}\" % (x[2],bennenung[i],str(anhang_count)))\n anhaenge.append(\"\\includepdf[pages=-]{%s}\" %(anhang[i]))\n anhaenge_file.append(anhang[i])\n if i!=len(anhang)-1:\n eingabe[line_text]=eingabe[line_text]+\"\\\\nameref{An:%s}, \" % (str(anhang_count))\n else:\n eingabe[line_text]=eingabe[line_text]+\"\\\\nameref{An:%s} \" % (str(anhang_count)) \n \n eingabe.append(\"\\\\newpage\") \n eingabe.append(\"\\\\appendix\") \n eingabe.append(\"\\\\pagenumbering{Roman}\") \n ausgabe=\"\"\n for i in range(0,len(eingabe)):\n ausgabe=ausgabe+eingabe[i]+\"\\n\"\n \n for i in range(0,len(anhaenge)):\n ausgabe=ausgabe+anhaenge[i]+\"\\n\"\n \n return ausgabe,anhaenge_file","def completed_course()->str:\r\n try:\r\n db: sqlite3.Connection = sqlite3.connect(DB_FILE)\r\n query: str = \"SELECT s.Name, s.CWID, g.Course, g.Grade, i.Name AS 'Instructor' \" \\\r\n \"FROM grades2 g JOIN students2 s ON g.StudentCWID = s.CWID \" \\\r\n \"JOIN instructors2 i ON g.InstructorCWID = i.CWID ORDER BY s.Name\" \r\n except sqlite3.OperationalError as e:\r\n print(e)\r\n\r\n data: Dict[str,str]=\\\r\n [{\"name\": name,\"cwid\": cwid,\"course\": course , \"grade\":grade, \"instructor\": instructor}\r\n for name, cwid, course, grade, instructor in db.execute(query)]\r\n db.close()\r\n \"\"\"Render the template from templates folder\"\"\"\r\n return render_template(\"student_summary.html\",\r\n title=\"Stevens Repository\",\r\n table_title=\"Student, Course, Grade and Instructor\",\r\n students=data )","def test_reformat_paragraph_list_4_of_5(self):\n before_b = \"\"\"\\\n This paragraph leads of this test. It is\n the \"lead\" paragraph.\n\n 1. This is item number 1. It is the\n first item in the list.\n\n 2. This is item number 2. It is the\n second item in the list.\n\n 3. This is item \n number 3. It is the third item in the list.\n\n This paragraph ends the test. It is the \"final\"\n paragraph.\n \"\"\"\n after_b = \"\"\"\\\n This paragraph leads of this test. It is\n the \"lead\" paragraph.\n\n 1. This is item number 1. It is the\n first item in the list.\n\n 2. This is item number 2. It is the\n second item in the list.\n\n 3. This is item number 3. It is the\n third item in the list.\n\n This paragraph ends the test. It is the \"final\"\n paragraph.\n \"\"\"\n self.run_test(\n before_b=before_b,\n after_b=after_b,\n before_sel=(\"10.0\", \"10.0\"),\n after_sel=(\"13.0\", \"13.0\"),\n command_name=\"reformat-paragraph\",\n directives=\"@language plain\\n@pagewidth 40\\n@tabwidth 8\",\n )","def filter_course_name(soup):\n course_title = soup.find('span', id=regex_title).text.strip()\n name_idx = course_title.find('-')\n\n dept_raw, course_code_raw = course_title[:name_idx - 1].split(' ')\n course_name = course_title[name_idx + 1:].strip()\n\n dept = dept_raw.encode('ascii', 'ignore').decode().strip()\n course_code = course_code_raw.encode(\n 'ascii', 'ignore').decode().strip()\n\n return dept, course_code, course_name","def categorize(book: TextIO) -> list:\n chunks = get_texts(book)\n texts = []\n for t in chunks:\n level = difficulty(complexity(t))\n texts.append((t, level, keywords(t)))\n return texts","def list(self, frame=0):\n text = []\n if not self.number_of_variations:\n return \"\"\n for group_number in range(1, len(self.varexercise_numbers)+1):\n text.extend(\n self.group_list(group_number))\n return text","def html_sequence(seq_esc: \"Sequence\") -> str:\n items = (f\"<li>{htmlize(item)}</li>\" for item in seq_esc)\n return \"<ul>\\n\" + \"\\n\".join(items) + \"\\n</ul>\"","def get_wordlists():\n\n\tCS = {'ACM', 'IEEE', 'Computer Science', 'Artificial Intelligence',\n\t\t'Pattern Recognition', 'Computer Vision', 'Machine Learning',\n\t\t'Signal Processing', 'Electrical Engineering', 'Image Processing',\n\t\t'Data Mining', 'Neural Networks', 'Computer Graphics', 'Graphics',\n\t\t'Language Processing', 'Internet', 'Intelligent Systems',\n\t\t'Robotic','Data','Software', 'Machine Vision', 'Image Analysis',\n\t\t'Scientific Computing', 'SIAM', 'Malware','World Wide Web', \n\t\t'Computational Intelligence', 'Computational Linguistics',\n\t\t'Computational linguistics','Algorithm','Computer','ITiCSE',\n\t\t'ITICSE','Machine learning','Learning','learning',\n\t\t'Artificial intelligence','CIVR','Document Analysis'}\n\n\tbio = {'Biology', 'Microbiology', 'Molecular', 'Medical', 'Biological',\n\t\t'Cancer', 'Genome', 'Bioinformatics', 'Protein', 'Biocomputing',\n\t\t'Biomedical', 'biology', 'Medicine', 'Biosystems', 'Virology',\n\t\t'Brain', 'Psychology', 'Genetics', 'Bioengineering', 'Cell',\n\t\t'Cardiology', 'Metabolic', 'Biotechnology', 'Pathogens',\n\t\t'Pathology', 'Plant', 'PLANT', 'Virus', 'Drug','Medicinal',\n\t\t'Neuro','Psych',\n\t\t'Genomic','Diseases','Endocrinology', 'Epidemiology',\n\t\t'Proteom','Biochem', 'DNA', 'Pharma', 'Biomedic', 'biomedica',\n\t\t'Neurobiological'}\n\n\tmath = {'Mathemati','Markov','Probability','Algebra','Network',\n\t\t'Topology','Optimization', 'Geometr','Statistic','Algorithm',\n\t\t'Graph ','Graphs','Combinatori','Riemann Surfaces','Permutation Groups',\n\t\t'Functional Analysis', 'SIAM','Fixed Point','Wavelet','Statistics',\n\t\t'Linear Regression','Fractal','geometry','Multivariate','Chaos',\n\t\t'mathemati','Kernel'}\n\n\tlinguistics = {}\n\n\tcomputer_vision = {}\n\n\tchemistry = {}\n\n\tphysics = {}\n\n\t# Rename \"Computer Vision\" to \"Image Processing\"?\n\ttopic_names = ['Computer Science','Biology','Mathematics','Chemistry',\n\t\t'Physics','Computer Vision','Natural Language Processing']\n\ttopics = [CS, bio, math]#, linguistics, computer_vision, chemistry, physics]\n\n\treturn {topic_names[i]:topics[i] for i in range(len(topics))}","def print_all_courses(filename): \n records = records_from_file(filename)\n course_code_and_name = all_courses(records)\n print(\"All courses:\")\n for course_code, course_name in sorted(course_code_and_name.items()):\n print((\" {}: {}\").format(course_code, course_name))\n print()","def coursecode(el):\n txt = text(el)\n return re.sub(r\"\\s*\\[\\d+\\]$\", \"\", txt, re.UNICODE)","def lists(self, text):\n\n #Replace line-initial bullets with asterisks\n bullet_pattern = re.compile(u'^•', re.U | re.M)\n\n pattern = re.compile(r'^((?:[*;:]+|[*;:#]*#(?:_|\\d+)?)%s[ .].*)$(?![^#*;:])'\n % self.lc, re.U | re.M | re.S)\n return pattern.sub(self.fList, bullet_pattern.sub('*', text))","def create_hn_text(self):\n text_list = [f\"Top {STORIES_NUMBER} from HackerNews:\"]\n sorted_stories = self.get_top_stories()\n # Format slack text\n for story in sorted_stories:\n text_list.append(\n \"*<{}|{}>* - <{}|{}>\".format(\n \"{}/item?id={}\".format(HN_URL, story[\"id\"]),\n story[\"score\"],\n # Ask HN type posts do not have 'url' key, so using get to return None\n story.get('url'),\n story[\"title\"],\n )\n )\n self.logger.debug(text_list)\n return \"\\n>\".join(text_list)","def longwords_Li_Comp(strings):\n return [string for string in strings if len(string)>4 ]","def get_kindle_sentences() -> List[str]:\n reviews = dl.load_json_from_file(\"../data/kindle_500.json\")\n return [sent[1] for review in reviews for sent in enumerate(review)]","def longwords_Li_Comp(strings):\n # write your code here\n return [string for string in strings if len(string)>4]","def get_text(downgrade_titles=False):","def tags_tocl(d, tag_list, title):\r\n filtered_anchors = []\r\n for anc in sorted(d.keys(), key=str.lower):\r\n entry = d[anc]\r\n if not \"tags\" in entry: continue\r\n found = [t for t in tag_list if t in entry[\"tags\"]]\r\n if not found: continue\r\n filtered_anchors.append(anc)\r\n return TemplateData(t=title, e=filtered_anchors)","def test_get_passage_with_list(self):\n simple = self.TEI.getPassage([\"1\", \"pr\", \"2\"])\n self.assertEqual(\n simple.text().strip(),\n \"tum, ut de illis queri non possit quisquis de se bene\",\n \"Ensure passage finding with context is fully TEI / Capitains compliant (Different level range Passage)\"\n )","def test_list_to_string_display(self): \n test1 = list_as_text(['a', 'b', 'c', 'd', 'e'])\n self.assertEqual(test1, 'a, b, c, d and e')\n test2 = list_as_text(['Atlanta, GA', 'New York City, NY',\n 'Miami, FL'])\n self.assertEqual(test2, 'Atlanta, GA, New York City, NY and Miami, FL')\n test3 = list_as_text(['Apple a day...'])\n self.assertEqual(test3, 'Apple a day...')\n test4 = list_as_text(['love', 'hate'])\n self.assertEqual(test4, 'love and hate') \n sites = Site.objects.filter(id__in=[2, 3, 4])\n test5 = list_as_text(sites)\n self.assertEqual(test5, 'Hudson Valley, Triangle and Capital Area')","def get_listings_pre_text(self, items_length):\n raise NotImplementedError","def test_reformat_paragraph_list_3_of_5(self):\n before_b = \"\"\"\\\n This paragraph leads of this test. It is\n the \"lead\" paragraph.\n\n 1. This is item number 1. It is the\n first item in the list.\n\n 2. This is item \n number 2. It is the second item in the list.\n\n 3. This is item \n number 3. It is the third item in the list.\n\n This paragraph ends the test. It is the \"final\"\n paragraph.\n \"\"\"\n after_b = \"\"\"\\\n This paragraph leads of this test. It is\n the \"lead\" paragraph.\n\n 1. This is item number 1. It is the\n first item in the list.\n\n 2. This is item number 2. It is the\n second item in the list.\n\n 3. This is item \n number 3. It is the third item in the list.\n\n This paragraph ends the test. It is the \"final\"\n paragraph.\n \"\"\"\n self.run_test(\n before_b=before_b,\n after_b=after_b,\n before_sel=(\"7.0\", \"7.0\"),\n after_sel=(\"10.0\", \"10.0\"),\n command_name=\"reformat-paragraph\",\n directives=\"@language plain\\n@pagewidth 40\\n@tabwidth 8\",\n )","def fname_tsk5(lst):\n lst[0] = del_lst(lst[0])\n lst[2] = del_lst(lst[2])\n body = \"The weight of an {0} is {1} and the weight of a \" \\\n \"{2} is {3}\"\n return body.format(*lst)","def get_texts(book: TextIO) -> list:\n content = book.read()\n chars_limit = 970\n texts = [content[i:i + chars_limit] for i in range(0, len(content), chars_limit)]\n return [\"...\" + t + \"...\" if t != texts[0] else t + \"...\" for t in texts]","def test_with_4_items(self):\n self.assertEqual(humanize_list(['a', 'b', 'c', 'd']),\n 'a, b, c, and d')","def test_reformat_paragraph_list_2_of_5(self):\n before_b = \"\"\"\\\n This paragraph leads of this test. It is\n the \"lead\" paragraph.\n\n 1. This is item number 1. It is the\n first item in the list.\n\n 2. This is item \n number 2. It is the second item in the list.\n\n 3. This is item \n number 3. It is the third item in the list.\n\n This paragraph ends the test. It is the \"final\"\n paragraph.\n \"\"\"\n after_b = \"\"\"\\\n This paragraph leads of this test. It is\n the \"lead\" paragraph.\n\n 1. This is item number 1. It is the\n first item in the list.\n\n 2. This is item \n number 2. It is the second item in the list.\n\n 3. This is item \n number 3. It is the third item in the list.\n\n This paragraph ends the test. It is the \"final\"\n paragraph.\n \"\"\"\n self.run_test(\n before_b=before_b,\n after_b=after_b,\n before_sel=(\"4.0\", \"4.0\"),\n after_sel=(\"7.0\", \"7.0\"),\n command_name=\"reformat-paragraph\",\n directives=\"@language plain\\n@pagewidth 40\\n@tabwidth 8\",\n )","def checklist_line(line, day, checklist):\n\n output_lines = f'<b>Checklist items due before class on {day}</b>'\n output_lines += f'<ul class=\"checklist\" id=\"check-list-{day}\">'\n for item in line[2:].split(' | '):\n item = item.strip()\n item_without_markdown_link = re.sub(r\"\\[(.+)\\]\\(.+\\)\", r\"\\1\", item)\n if day in checklist and item_without_markdown_link in checklist[day]:\n output_lines += f'<li class=\"unchecked checked\">{item}</li>'\n else:\n output_lines += f'<li class=\"unchecked\">{item}</li>'\n if item_without_markdown_link == 'Practice (no points) worksheet demo.':\n print(output_lines)\n output_lines += '</ul>'\n return output_lines","def prepare_courses_for_radio():\n\n three_closest_courses = get_four_future_courses()\n radio_courses = []\n\n for course in three_closest_courses:\n radio_courses.append(\n (int(course.id), str(reformat_date(course.startDate) + ' r.'))\n )\n return radio_courses","def convert_examples_to_features(examples, label_list, max_seq_length, tokenizer):\n\n label_map = {label : i for i, label in enumerate(label_list)}\n\n features = []\n for (ex_index, example) in enumerate(examples):\n tokens = example.text\n\n# # Account for [CLS] and [SEP] with \"- 2\"\n# if len(tokens) > max_seq_length - 2:\n# tokens = tokens[:(max_seq_length - 2)]\n\n bert_tokens = []\n orig_to_tok_map = []\n\n bert_tokens.append(\"[CLS]\")\n for token in tokens:\n new_tokens = tokenizer.tokenize(token)\n if len(bert_tokens) + len(new_tokens) > max_seq_length - 1:\n # print(\"You shouldn't see this since the test set is already pre-separated.\")\n break\n else:\n orig_to_tok_map.append(len(bert_tokens))\n bert_tokens.extend(new_tokens)\n bert_tokens.append(\"[SEP]\")\n\n if len(bert_tokens) == 2: # edge case\n continue\n\n # The convention in BERT is:\n # (a) For sequence pairs:\n # tokens: [CLS] is this jack ##son ##ville ? [SEP] no it is not . [SEP]\n # type_ids: 0 0 0 0 0 0 0 0 1 1 1 1 1 1\n # (b) For single sequences:\n # tokens: [CLS] the dog is hairy . [SEP]\n # type_ids: 0 0 0 0 0 0 0\n #\n # Where \"type_ids\" are used to indicate whether this is the first\n # sequence or the second sequence. The embedding vectors for `type=0` and\n # `type=1` were learned during pre-training and are added to the wordpiece\n # embedding vector (and position vector). This is not *strictly* necessary\n # since the [SEP] token unambigiously separates the sequences, but it makes\n # it easier for the model to learn the concept of sequences.\n #\n # For classification tasks, the first vector (corresponding to [CLS]) is\n # used as as the \"sentence vector\". Note that this only makes sense because\n # the entire model is fine-tuned.\n\n input_ids = tokenizer.convert_tokens_to_ids(bert_tokens)\n\n # The mask has 1 for real tokens and 0 for padding tokens. Only real\n # tokens are attended to.\n input_mask = [1] * len(input_ids)\n\n # Zero-pad up to the sequence length.\n padding = [0] * (max_seq_length - len(input_ids))\n input_ids += padding\n input_mask += padding\n\n assert len(input_ids) == max_seq_length\n assert len(input_mask) == max_seq_length\n\n segment_ids = [0] * max_seq_length # no use for our problem\n\n labels = example.label\n label_ids = [0] * max_seq_length\n label_mask = [0] * max_seq_length\n\n for label, target_index in zip(labels, orig_to_tok_map):\n label_ids[target_index] = label_map[label]\n label_mask[target_index] = 1\n\n assert len(segment_ids) == max_seq_length\n assert len(label_ids) == max_seq_length\n assert len(label_mask) == max_seq_length\n\n features.append(\n InputFeatures(input_ids=input_ids,\n input_mask=input_mask,\n segment_ids=segment_ids,\n label_ids=label_ids,\n label_mask=label_mask))\n return features","def __gettextinfo(edudict, eduspan):\n # text = lnode.text + \" \" + rnode.text\n text = []\n for idx in range(eduspan[0], eduspan[1]+1, 1):\n text += edudict[idx]\n # Return: A list of token indices\n return text","def print_table(table, title_list):\n\n # your goes code\n \n table.insert(0, title_list)\n # title listet 0.helyre teszi\n # your code\n\n lenght_list = [] # tartalmazza az összes szót\n for lines in table:\n for items in lines:\n lenght_list.append(items)\n\n longest_words_length = len(max(lenght_list, key=len))\n multiplier = len(title_list)*(longest_words_length+1)\n\n for sublist in table:\n print(\"|\\n|\", \"-\"*multiplier, \"|\")\n\n for j in sublist:\n print(\"|\", j, end = \" \"*(longest_words_length-len(j)))\n\n print(\"|\\n|\",\"-\"*multiplier, \"|\")","def news_template_flex(news_list, events, key, id, isMore, alt_text='推薦新聞'):\n news_box_components = []\n for i, news in enumerate(news_list):\n # img = \"https://cdn.pixabay.com/photo/2015/12/03/22/16/newspaper-1075795_1280.jpg\"\n date = news['date']\n date = re.sub(\"T.*Z\", \"\", date)\n url = news['url']\n\n # Add separators between news.\n if i > 0:\n news_box_components.append(SeparatorComponent(margin='lg'))\n # related_news_uri = f\"{RELATED_NEWS_LIFF_URI}?cid={news['cid']}&nid={news['nid']}\"\n\n TextComponents = []\n TextComponents.append(TextComponent(\n text=news['title'],\n wrap=True,\n size='md',\n weight='bold',\n action=URIAction(uri=url)\n ))\n TextComponents.append(TextComponent(\n text='{} - {}'.format(news['source'], date),\n wrap=True,\n size='xxs',\n color='#6c757d'\n ))\n words = []\n event_v, event_o = events.split()\n regex = \"({}|{})\".format(event_v, event_o)\n tokenized_sentence = re.split(regex, news['sentences'][1])\n tokenized_sentence = [t for t in tokenized_sentence if t != \"\"]\n tokenized_sentence = [news['sentences'][0] + \" \"] + tokenized_sentence + [\" \" + news['sentences'][2]]\n for i in tokenized_sentence:\n if i == event_v or i == event_o:\n words.append(SpanComponent(\n text=i,\n color=\"#e86d48\"\n ))\n else:\n words.append(SpanComponent(\n text=i,\n ))\n new_text_componet = TextComponent2(type=\"text\", text=\"hello, world\", contents=words, wrap=True)\n TextComponents.append(new_text_componet)\n TextComponents.append(BoxComponent(\n layout='horizontal',\n spacing='md',\n contents=[\n TextComponent(\n text='查看這則新聞',\n size='md',\n color='#6E7DAB',\n flex=1,\n action=URIAction(uri=url),\n ),\n ]\n ))\n\n news_box_components.append(\n BoxComponent(\n layout='horizontal',\n spacing='md',\n contents=[\n BoxComponent(\n layout='vertical',\n flex=2,\n spacing='sm',\n contents=TextComponents\n )\n ]\n )\n )\n if isMore:\n news_box_components.append(\n ButtonComponent(style=\"link\",\n # margin=\"xs\",\n action=MessageTemplateAction(\n label=\"more\",\n text=\"more:\" + str(id) + \":\" + key + \":\" + events\n )))\n\n bubble = BubbleContainer(\n body=BoxComponent(\n layout='vertical',\n spacing='lg',\n contents=news_box_components\n )\n )\n return FlexSendMessage(alt_text=alt_text, contents=bubble)","def gen_text_list(ids):\n return [[tag.lemma for tag in para if (tag.pos not in TT_EXCLUDED_TAGS)]\n for dm in dm_from_id(ids) for para in dm.get_text_tags()]","def show_list(site, titles): # type: (str, List[str]) -> None\n print(u\"The latest tutorials from {}\".format(site))\n for article_id, title in enumerate(titles):\n print(u\"{:>3} {}\".format(article_id, title))","def getFullCourseTitle(self, brain):\n full_title = ''\n\n id = brain.getCourseId\n if id:\n full_title = '%s - ' %id\n full_title += brain.Title\n term = brain.getTerm\n if term:\n full_title += ', %s' %term\n\n return full_title","def print_tod_accomplishments(accomplishments):\n print(Colors.BLUE + \"Accomplishments from Tod:\" + Colors.NORMAL)\n for accomplishment in accomplishments:\n print(Colors.CYAN + '* ' + Colors.NORMAL + accomplishment)","def escape_list(l):\n return [_escape_harlowe_html(item) if isinstance(item, text_type) else str(item) for item in l]","def test_tax_court_citation_extractor(self):\n\n test_pairs = (\n (\n \"\"\" 1 UNITED STATES TAX COURT REPORT (2018)\n\n\n\n UNITED STATES TAX COURT\n\n\n\n BENTLEY COURT II LIMITED PARTNERSHIP, B.F. BENTLEY, INC., TAX\n MATTERS PARTNER, Petitioner v.\n COMMISSIONER OF INTERNAL REVENUE, Respondent\n\n\n\n Docket No. 5393-04. Filed May 31, 2006.\n\n\n\n Nancy Ortmeyer Kuhn, for petitioner.\n \"\"\",\n \"1 T.C. 2018\",\n ),\n (\n \"\"\" T.C. Memo. 2003-150\n\n\n\n UNITED STATES TAX COURT\n\n\n\n RIVER CITY RANCHES #1 LTD., LEON SHEPARD,\n TAX MATTERS PARTNER,\n RIVER CITY RANCHES #2 LTD., LEON SHEPARD,\n TAX MATTERS PARTNER,\n RIVER CITY RANCHES #3 LTD., LEON SHEPARD,\n TAX MATTERS PARTNER,\n\n\n \"\"\",\n \"2003 T.C. Memo. 150\",\n ),\n (\n \"\"\" T.C. Summary Opinion 2003-150\n\n\n\n UNITED STATES TAX COURT\n\n\n\n RIVER CITY RANCHES #1 LTD., LEON SHEPARD,\n TAX MATTERS PARTNER,\n RIVER CITY RANCHES #2 LTD., LEON SHEPARD,\n TAX MATTERS PARTNER,\n RIVER CITY RANCHES #3 LTD., LEON SHEPARD,\n TAX MATTERS PARTNER,\n\n\n \"\"\",\n \"2003 T.C. Summary Opinion 150\",\n ),\n (\n \"\"\"\n MICHAEL KEITH SHENK, PETITIONER v. COMMISSIONER\n OF INTERNAL REVENUE, RESPONDENT\n\n Docket No. 5706–12. Filed May 6, 2013.\n\n P was divorced from his wife, and their 2003 ‘‘Judgment of\n Absolute Divorce’’ provided that his ex-wife would have pri-\n mary residential custody of their three minor children. The\n judgment provided that the dependency exemption deductions\n for the three children would be divided between the two ex-\n spouses according to various conditions but did not provide\n that the ex-wife must execute in P’s favor a Form 8332,\n ‘‘Release of Claim to Exemption for Child of Divorced or Sepa-\n rated Parents’’. The children resided with P’s ex-wife for more\n than half of 2009, and P’s ex-wife did not execute in P’s favor\n any Form 8332 or equivalent document for any year. For 2009\n P timely filed a Federal income tax return on which he\n claimed dependency exemption deductions and the child tax\n credit for two of the children, consistent with his under-\n standing of the terms of the judgment, but he did not attach\n any Form 8332 to his return. He also claimed head-of-house-\n hold filing status. His ex-wife, the custodial parent, timely\n filed a Federal income tax return for 2009 on which she also\n\n 200\n\n\n\n\nVerDate Nov 24 2008 10:59 Jul 11, 2014 Jkt 372897 PO 20012 Frm 00001 Fmt 3857 Sfmt 3857 V:\\FILES\\BOUND VOL. WITHOUT CROP MARKS\\B.V.140\\SHENK JAMIE\n\f (200) SHENK v. COMMISSIONER 201\n\n\n claimed two dependency exemption deductions, so that one\n child was claimed on both parents’ returns. R allowed to P the\n dependency exemption deduction for one of the children but\n disallowed his claim for the dependency exemption deduction\n for the child who had also been claimed by the custodial\n parent. At trial P contended he is entitled to a dependency\n exemption deduction for all three children. Held: Since the\n custodial parent did not execute, and P could not and did not\n attach to his return, any Form 8332 or equivalent release, P\n is not entitled under I.R.C. sec. 152(e)(2)(A) to claim the\n dependency exemption deduction or the child tax credit. Held,\n further, where both the custodial parent and the noncustodial\n parent have claimed for the same year a dependency exemp-\n tion deduction for the same child, a declaration signed by the\n custodial parent after the period of limitations for assess-\n ments has expired as to the custodial parent could not qualify\n under I.R.C. sec. 152(e)(2)(A), and therefore there is no reason\n to grant P’s request to leave the record open so that he may\n obtain and proffer such a declaration. Held, further, P is not\n entitled to head-of-household filing status under I.R.C. sec.\n 2(b)(1) nor to the child tax credit under I.R.C. sec. 24.\n\n Michael Keith Shenk, for himself.\n Shari Salu, for respondent.\n GUSTAFSON, Judge: The Internal Revenue Service (IRS)\n determined a deficiency of $3,136 in the 2009 Federal income\n tax of petitioner Michael Keith Shenk. Mr. Shenk petitioned\n this Court, pursuant to section 6213(a), 1 for redetermination\n of the deficiency. After Mr. Shenk’s concession that he\n received but did not report $254 in dividend income, the\n issue for decision is whether Mr. Shenk is entitled to a\n dependency exemption deduction for one of his children\n under section 151(c), a child tax credit for that child under\n section 24(a), and head-of-household filing status under sec-\n tion 2(b)(1). On these issues, we hold for the IRS.\n FINDINGS OF FACT\n\n The judgment of divorce\n Mr. Shenk was married to Julie Phillips, and they have\n three minor children—M.S., W.S., and L.S. They divorced in\n 2003. The family court’s ‘‘Judgment of Absolute Divorce’’ pro-\n 1 Unless otherwise indicated, all citations of sections refer to the Internal\n\n Revenue Code (26 U.S.C.) in effect for the tax year at issue, and all cita-\n tions of Rules refer to the Tax Court Rules of Practice and Procedure.\n\n\n\n\nVerDate Nov 24 2008 10:59 Jul 11, 2014 Jkt 372897 PO 20012 Frm 00002 Fmt 3857 Sfmt 3857 V:\\FILES\\BOUND VOL. WITHOUT CROP MARKS\\B.V.140\\SHENK JAMIE\n\f 202 140 UNITED STATES TAX COURT REPORTS (200)\n\n\n vided: that Ms. Phillips was ‘‘awarded primary residential\n custody’’ of the parties’ three children; and that Mr. Shenk\n would be liable for child support payments; but that, as to\n dependency exemptions—\"\"\",\n \"140 T.C. 200\",\n ),\n )\n site = tax.Site()\n for q, a in test_pairs:\n results = site.extract_from_text(q)\n cite_string = \"%s %s %s\" % (\n results[\"Citation\"][\"volume\"],\n results[\"Citation\"][\"reporter\"],\n results[\"Citation\"][\"page\"],\n )\n\n self.assertEqual(cite_string, a)\n print \"✓\", cite_string","def make_title(words):","def test_reformat_paragraph_list_1_of_5(self):\n before_b = \"\"\"\\\n This paragraph leads of this test. It is the \"lead\"\n paragraph.\n\n 1. This is item \n number 1. It is the first item in the list.\n\n 2. This is item \n number 2. It is the second item in the list.\n\n 3. This is item \n number 3. It is the third item in the list.\n\n This paragraph ends the test. It is the \"final\"\n paragraph.\n \"\"\"\n after_b = \"\"\"\\\n This paragraph leads of this test. It is\n the \"lead\" paragraph.\n\n 1. This is item \n number 1. It is the first item in the list.\n\n 2. This is item \n number 2. It is the second item in the list.\n\n 3. This is item \n number 3. It is the third item in the list.\n\n This paragraph ends the test. It is the \"final\"\n paragraph.\n \"\"\"\n self.run_test(\n before_b=before_b,\n after_b=after_b,\n before_sel=(\"1.0\", \"1.0\"),\n after_sel=(\"4.0\", \"4.0\"),\n command_name=\"reformat-paragraph\",\n directives=\"@language plain\\n@pagewidth 40\\n@tabwidth 8\",\n )","def courses(request):\r\n courses = get_courses(request.user, request.META.get('HTTP_HOST'))\r\n courses = sort_by_announcement(courses)\r\n\r\n return render_to_response(\"courseware/courses.html\", {'courses': courses})","def __repr__(self): \n raw = self.body.split()\n i = 0\n for words in raw:\n if i%15 == 0:\n raw.insert(i, \"\\n\")\n i = i + 1\n raw.append(\"\\n Different Centuries meet System\\n\")\n return ' '.join(raw)","def get_list_html(self, items):\n html = \"\"\"\n <html>\n\t\t\t<head>\n\t\t\t\t<title>OpenFDA Cool App</title>\n\t\t\t</head>\n\t\t\t<body>\n <ol>\n \"\"\"\n\n for item in items:\n html += \"<li>\" + item + \"</li>\\n\"\n\n html += \"\"\"\n </ol>\n\t\t\t</body>\n </html>\n \"\"\"\n\n return html","def convert_to_t5_format(nlp, texts):\n\n inputs = []\n outputs = []\n original_texts = []\n\n for text, doc in zip(texts, nlp.pipe(texts, n_process=-1)):\n\n pairs = set()\n\n for chunk in doc.noun_chunks:\n if chunk.text == text:\n continue\n input_ = text[0 : chunk.start_char] + \"<extra_id_0> \" + text[chunk.end_char + 1 :]\n output = \"<extra_id_0> \" + chunk.text + \" <extra_id_1> </s>\"\n\n pairs.add((input_.strip(), output))\n\n for token in doc:\n left_edge_i = token.left_edge.i\n right_edge_i = token.right_edge.i\n chunk_length = right_edge_i - left_edge_i + 1\n if chunk_length / len(doc) > 0.5 or chunk_length > 10: # if chunk is too long, just skip it\n continue\n\n input_ = str(doc[:left_edge_i]) + \" <extra_id_0> \" + str(doc[right_edge_i + 1 :])\n output = \"<extra_id_0> \" + str(doc[left_edge_i : right_edge_i + 1]) + \" <extra_id_1> </s>\"\n\n pairs.add((input_.strip(), output))\n\n for token in doc:\n if token.pos_ in [\"NOUN\", \"PRON\", \"PROPN\"]: # we don't want to mask parts of noun chunks\n continue\n input_ = str(doc[: token.i]) + \" <extra_id_0> \" + str(doc[token.i + 1 :])\n output = \"<extra_id_0> \" + token.text + \" <extra_id_1> </s>\"\n\n pairs.add((input_.strip(), output))\n\n for (input_, output) in pairs:\n inputs.append(input_)\n outputs.append(output)\n original_texts.append(text)\n\n return inputs, outputs, original_texts","def generatePerWordHTML(testDataWithFeatures, \n allLabels, classifications, colors):\n \n assert (len(testDataWithFeatures)-1) % len(allLabels) == 0\n numWords = (len(testDataWithFeatures) - 1) / len(allLabels) + 1 \n \n allLabelsDict = dict((label,index) for index,label in enumerate(allLabels)) \n \n perWordHTML = []\n for i in xrange(numWords):\n if classifications:\n word, correctClass, guessClass = classifications[i]\n color = colors[(correctClass, guessClass)]\n else:\n datum = getDatum(testDataWithFeatures, \n allLabelsDict, i, allLabels[0])\n word = datum.word\n color = colors[datum.label, datum.label]\n html = [('<a href=\"#\" onmouseover=\"a({0});\" '\n + 'onmouseout=\"b({0});\">').format(i), \n '<span style=\"color:{}\">{}</span>'.format(\n color, escape(word)),\n '</a>']\n \n if word in \".!?\":\n html.append(\"<br/>\")\n \n perWordHTML.append(\"\".join(html)) \n return perWordHTML","def course_reviews(request, course_code):\n try:\n course = (\n Course.objects.filter(course_filters_pcr_allow_xlist, full_code=course_code)\n .order_by(\"-semester\")[:1]\n .select_related(\n \"topic\",\n \"topic__most_recent\",\n \"topic__branched_from\",\n \"topic__branched_from__most_recent\",\n )\n .prefetch_related(\"topic__courses\")\n .get()\n )\n except Course.DoesNotExist:\n raise Http404()\n\n topic = course.topic\n course = topic.most_recent\n course_code = course.full_code\n aliases = course.crosslistings.values_list(\"full_code\", flat=True)\n\n instructor_reviews = review_averages(\n Review.objects.filter(review_filters_pcr, section__course__topic=topic),\n reviewbit_subfilters=Q(review_id=OuterRef(\"id\")),\n section_subfilters=Q(id=OuterRef(\"section_id\")),\n fields=ALL_FIELD_SLUGS,\n prefix=\"bit_\",\n extra_metrics=True,\n ).annotate(instructor_name=F(\"instructor__name\"), semester=F(\"section__course__semester\"))\n recent_instructors = list(\n Instructor.objects.filter(\n id__in=Subquery(\n Section.objects.filter(section_filters_pcr, course__topic=topic).values(\n \"instructors__id\"\n )\n )\n )\n .distinct()\n .annotate(\n most_recent_sem=Subquery(\n Section.objects.filter(instructors__id=OuterRef(\"id\"), course__topic=topic)\n .annotate(common=Value(1))\n .values(\"common\")\n .annotate(max_sem=Max(\"course__semester\"))\n .values(\"max_sem\")\n )\n )\n .values(instructor_id=F(\"id\"), instructor_name=F(\"name\"), semester=F(\"most_recent_sem\"))\n )\n for instructor in recent_instructors:\n instructor[\"exclude_from_recent\"] = True\n all_instructors = list(instructor_reviews.values()) + recent_instructors\n instructors = aggregate_reviews(all_instructors, \"instructor_id\", name=\"instructor_name\")\n\n course_qs = annotate_average_and_recent(\n Course.objects.filter(course_filters_pcr, topic=topic).order_by(\"-semester\")[:1],\n match_review_on=Q(section__course__topic=topic) & review_filters_pcr,\n match_section_on=Q(course__topic=topic) & section_filters_pcr,\n extra_metrics=True,\n )\n course = get_single_dict_from_qs(course_qs)\n\n num_registration_metrics = Section.objects.filter(\n extra_metrics_section_filters_pcr(),\n course__topic=topic,\n ).count()\n\n num_sections, num_sections_recent = get_num_sections(\n section_filters_pcr,\n course__topic=topic,\n )\n\n return Response(\n {\n \"code\": course[\"full_code\"],\n \"name\": course[\"title\"],\n \"description\": course[\"description\"],\n \"aliases\": aliases,\n \"historical_codes\": get_historical_codes(\n topic, exclude_codes=set(aliases) | {course[\"full_code\"]}\n ),\n \"latest_semester\": course[\"semester\"],\n \"num_sections\": num_sections,\n \"num_sections_recent\": num_sections_recent,\n \"instructors\": instructors,\n \"registration_metrics\": num_registration_metrics > 0,\n **get_average_and_recent_dict_single(course),\n }\n )","def print_cities() -> None:\n city_list = get_cities()\n i = 1\n for a,b,c,d,e in zip(city_list[::5], city_list[1::5], city_list[2::5], city_list[3::5], city_list[4::5]):\n a = str(i) + f\".{a}\"\n i += 1\n b = str(i) + f\".{b}\"\n i += 1\n c = str(i) + f\".{c}\"\n i += 1\n d = str(i) + f\".{d}\"\n i += 1\n e = str(i) + f\".{e}\"\n i += 1\n if e == '410.youngstown':\n print(\"{:<20}{:<20}{:<20}{:<20}{:<}\".format(a,b,c,d,e))\n a = str(i) + f\".{city_list[i-1]}\"\n b = str(i+1) + f\".{city_list[i]}\"\n i += 1\n c = c = str(i+1) + f\".{city_list[i]}\"\n print(\"{:<20}{:<20}{:<}\".format(a,b,c))\n else:\n print(\"{:<20}{:<20}{:<20}{:<20}{:<}\".format(a,b,c,d,e))","def create_list_html(array):\n if not array:\n return ''\n msg = ''\n for item in array:\n msg += '<li>' + item + '</li>'\n return '<ul>' + msg + '</ul>'","def help_text(command):\n\n courses_list = ('ENPM611', 'ENPM613', 'ENPM631', 'ENPM687',\\\n 'ENPM691', 'ENPM693', 'ENPM694', 'ENPM696',\\\n 'ENPM809J','ENPM809R', 'ENPM809W')\n\n response = 'I have course descriptions for: '\n for course_name in courses_list:\n response = response + course_name + ' '\n\n response = response + '\\nTo get the course description, execute command: about ENPM<course_number>'\n\n return response","def display_abstract( id, highlights=[]):\n a = Abstracts[ id ]\n for h in highlights:\n a = re.sub(r'\\b(%s)\\b'%h,'<mark>\\\\1</mark>',a, flags=re.IGNORECASE)\n print ('<blockquote>%s</blockquote>' % a)","def remove_html_tags(text):\r\n global count\r\n clean = re.compile('<.*?>')\r\n var = re.sub(clean, '', str(item))\r\n # FOR ADDING NUMBERS ON SAME LINE\r\n if count < 2:\r\n print(var,end=' ')\r\n count+=1\r\n if count > 1:\r\n count = 0\r\n print(\"\\n\")\r\n if var.isnumeric()==False:\r\n college_names.append(var)","def ROOM_DESCRIPTIONS(adventurer):\n rooms = (\n f\"{text_color.MAGENTA_TEXT}{adventurer}{text_color.FINISH_COLOR} is inside a wine cellar, most of it is filled with cracked, empty bottles.\\n\"\n \"Perhaps it is better to not go looking for an intact bottle.\\n\"\n f\"{text_color.MAGENTA_TEXT}{adventurer}{text_color.FINISH_COLOR} suspects it will be very detrimental to their health to fight monsters while drunk.\",\n f\"{text_color.MAGENTA_TEXT}{adventurer}{text_color.FINISH_COLOR} is inside a gaol, the cells are mostly filled with the skeletons of prisoners long dead.\\n\"\n \"Most of the cell doors are open with the exception of one in the far corner.\\n\"\n \"There seems to be a person in there but they are crouched in the corner muttering to themself.\\n\"\n \"The cleaver they are holding looks very dangerous so perhaps it is best to leave them alone.\\n\",\n f\"{text_color.MAGENTA_TEXT}{adventurer}{text_color.FINISH_COLOR}is inside a dusty old library, piles and piles of books are scattered amongst the dirty floor.\\n\"\n f\"{text_color.MAGENTA_TEXT}{adventurer}{text_color.FINISH_COLOR} remembers the last time they perused books inside of that nast vampire\\'s castle.\\n\"\n f\"The tentacles that came out of the pages almost strangled {text_color.MAGENTA_TEXT}{adventurer}{text_color.FINISH_COLOR}.\\n\"\n f\"Perhaps it is better to finish what {text_color.MAGENTA_TEXT}{adventurer}{text_color.FINISH_COLOR} came here to do instead of doing some light reading.\",\n f\"{text_color.MAGENTA_TEXT}{adventurer}{text_color.FINISH_COLOR} is inside what appears to be a ritual room.\\n\"\n \"The only things inside a room are black candles mounted on the walls and a pentagram in the middle of the floor.\\n\"\n f\"The pentagram appears to be made from dried blood, {text_color.MAGENTA_TEXT}{adventurer}{text_color.FINISH_COLOR} would rather not think of who or what provided that blood.\\n\"\n \"On each corner of the star there appears to be a random assortment of objects.\\n\"\n \"For some reason one of them looks like a phallic object.\\n\"\n f\"{text_color.MAGENTA_TEXT}{adventurer}{text_color.FINISH_COLOR} decides they don\\'t want to stay here any longer than they have to.\",\n f\"{text_color.MAGENTA_TEXT}{adventurer}{text_color.FINISH_COLOR} is inside their childhood bedroom.\\n\"\n f\"The bed and drawer is just like how they remember it.\\n\"\n f\"There's even that empty aquarium {adventurer} left inside their room even though the fish all died within a day.\\n\"\n f\"{text_color.MAGENTA_TEXT}{adventurer}{text_color.FINISH_COLOR} would rather not think too hard about why their room is inside of a dungeon.\\n\",\n f\"{text_color.MAGENTA_TEXT}{adventurer}{text_color.FINISH_COLOR} is inside of a sewer, the smell feels like its burning away their nostril hairs.\\n\"\n f\"{text_color.MAGENTA_TEXT}{adventurer}{text_color.FINISH_COLOR} wonders how there could even be a sewer when the doors connecting it lead into different rooms.\\n\"\n f\"As {text_color.MAGENTA_TEXT}{adventurer}{text_color.FINISH_COLOR} stands there they see a figure in their peripheral vision.\\n\"\n \"What looks like a large bipedal rat is standing there 4 smaller bipedal turtles.\\n\"\n f\"Before {text_color.MAGENTA_TEXT}{adventurer}{text_color.FINISH_COLOR} can say anything a large cloud of smoke appears and when it disappears they are gone.\\n\"\n )\n return random.choice(rooms)","def nautc(\n text: str,\n no_label: bool = False,\n labels: bool = False,\n dash_length: int = 20,\n) -> None:\n if text:\n for label, ctxt in convert(text):\n print(\"-\" * dash_length)\n ((not no_label) or labels) and print(label) # noqa: WPS428\n not labels and print(ctxt) # noqa: WPS428\n print(\"-\" * dash_length)","def format_list(self, at_char, user, list_name):\r\n return u'<a href=\"http://%s/%s/%s\" data-list=\"\">%s%s/%s</a>' \\\r\n % (self.domain, user, list_name, at_char, user, list_name)","def export_courses(courses, output):\n courses = sorted(courses)\n writer = csv.writer(output)\n writer.writerow([\n 'College', 'Department', 'Code', 'Name', 'Credits', 'Tags',\n 'Prerequisites'\n ])\n\n for course in courses:\n writer.writerow([\n course.college, course.department, course.code, course.name,\n course.credits, ','.join(course.tags), ','.join(course.prerequisites)\n ])","def makeNonsense(part1, part2, part3, n=10):\n #convert to lists\n p1 = part1.split('\\n')\n p2 = part2.split('\\n')\n p3 = part3.split('\\n')\n #shuffle the lists\n [ random.shuffle( p ) for p in [p1,p2,p3] ]\n #concatinate the sentences\n sentence = []\n for k in range(n):\n try:\n s = p1[k] + ' ' + p2[k] + ' ' + p3[k] \n # THE LOUDER YOU YELL, THE RIGHTER YOU ARE!!\n s += '!!!'\n sentence.append(s)\n except IndexError:\n break\n return sentence","def addSemester(inputFile, outputFile):\n\n \n fall_classes = ['cs 121', 'cs 223', 'cs 260', 'cs 215']\n spring_classes = ['cs 122', 'cs 166', 'cs 224', 'cs 251', 'cs 261']\n \n\n with open(inputFile, 'r') as f:\n newlist = f.readlines() \n for i in range(len(newlist)):\n newlist[i] = newlist[i].strip()\n print(newlist)\n for i in newlist:\n print(i) \n print(\"-----------------------\") # this is all taking each line in the text and converting it into a list while also getting rid of any new lines to make it easier to concatanate fall or spring\n\n with open(outputFile, 'w') as f:\n index = 0 # this is a counter that is incrementing once every loop and is being used to index into the list \n for i in newlist: \n if i in fall_classes: \n newlist[index] += \" fall\\n\"\n index += 1\n elif i in spring_classes:\n newlist[index] += \" spring\\n\"\n index += 1\n\n \n original = ''.join(newlist) # this brings together the finalized list into the original format just with the corresponding fall or spring\n print(original)\n f.write(original)","def booksAvailable(books):\n print(\"The following titles are available for purchase: \")\n for book in books:\n print (' -' + book.title())","def print_list(self):\n self.print_avec_separateur(\" \")","def ConstructList(title, items):\n buf = cStringIO.StringIO()\n fmt = 'list[title=\"{title}\",always-display-title]'.format(title=title)\n resource_printer.Print(sorted(set(items)), fmt, out=buf)\n return buf.getvalue()","def get_tagged_titles(ttls_lnks):\n\ttagged_titles = []\n\tfor title, link in ttls_lnks:\n\t\t# get the html tree for the paper's page\n\t\tpaper_tree = get_tree(link)\n\t\tpath = '//table/tr/th[text() = \"Subjects:\"]'\n\t\t# Check if html contains the table header \"Subjects:\"\n\t\tsubject_th = paper_tree.xpath(path)\n\t\t# If it does, this means paper is tagged so add to the list to be returned\n\t\tif subject_th:\n\t\t\ttagged_titles.append(title)\n\n\treturn tagged_titles","def get_courses(std):\n return std[2]","def clean_5_words(self):\n self.first_word.configure(text=\"\")\n self.second_word.configure(text=\"\")\n self.third_word.configure(text=\"\")\n self.fourth_word.configure(text=\"\")\n self.fifth_word.configure(text=\"\")","def get_text(self, course):\r\n return views.render_accordion(\r\n self.request, course, course.get_children()[0].scope_ids.usage_id.to_deprecated_string(), None, None\r\n )","def passages_labels (passages):\n\n def group_by (s):\n return s[:3]\n\n def title (s):\n return s[1:3]\n\n group = None\n ticks = []\n labels = []\n\n for i, passage in enumerate (passages):\n g = group_by (passage)\n if g != group:\n ticks.append (i)\n labels.append (title (passage))\n group = g\n\n return ticks, labels","def typoglycemia(sequence):\n words = sequence.split()\n\t\n for i in range(len(words)):\n if len(words[i]) > 4:\n word_partial = list(words[i][1:-1])\n random.shuffle(word_partial)\n word_shuffle = \"\".join(word_partial)\n words[i] = words[i][0] + word_shuffle + words[i][-1]\n \n return ' '.join(words)","def get_subtitle_print(subs: List[Track]) -> List[str]:\n data = []\n if not subs:\n data.append(\"--\")\n for sub in subs:\n line_items = []\n\n # following sub.title tree checks and supports three different language and title scenarios\n # The second scenario is the recommended option to choose if you are open to choosing any\n # The third scenario should be used if you have nothing unique to state about the track\n # | Language | Track Title | Output |\n # | ------------ | ----------------------------- | --------------------------------------------- |\n # | es / Spanish | Spanish (Latin American, SDH) | - Spanish (Latin American, SDH), SubRip (SRT) |\n # | es / Spanish | Latin American (SDH) | - Spanish, Latin American (SDH), SubRip (SRT) |\n # | es / Spanish | None | - Spanish, SubRip (SRT) |\n language = pycountry.languages.get(alpha_2=sub.language).name\n if sub.title:\n if language.lower() in sub.title.lower():\n line_items.append(sub.title)\n else:\n line_items.append(f\"{language}, {sub.title}\")\n else:\n line_items.append(language)\n\n line_items.append(sub.format.replace(\"UTF-8\", \"SubRip (SRT)\"))\n\n line = \"- \" + \", \".join(line_items)\n data += [\n (\" \" + x if i > 0 else x)\n for i, x in enumerate(textwrap.wrap(line, 64))\n ]\n return data","def test15():\n\tdef guest_list(guests):\n\t\tfor name, old, job in guests:\n\t\t\tprint('{} is {} years old and works as {}'.format(name, old, job))\n\n\tguest_list([('Ken', 30, \"Chef\"), (\"Pat\", 35, 'Lawyer'), ('Amanda', 25, \"Engineer\")])\n\n\t#Click Run to submit code\n\t\"\"\"\n\tOutput should match:\n\tKen is 30 years old and works as Chef\n\tPat is 35 years old and works as Lawyer\n\tAmanda is 25 years old and works as Engineer\n\t\"\"\"","def make_text(chains):\n words = []\n not_end_of_list = True\n # your code goes here\n \n # starts with a capital lettered word from source text\n capitalized_ngrams = []\n for key in chains.keys():\n # check if the first tuple in key[0][0]\n if key[0][0].isupper():\n capitalized_ngrams.append(key)\n \n \n\n selected_keys = list(capitalized_ngrams)\n count = 0\n while not_end_of_list:\n choice_n = choice(selected_keys)\n\n if count == 0:\n words.extend(choice_n)\n \n if chains[choice_n] and count <= 150:\n # as long as there is an option, picks a random element from dict list\n choose_next = choice(chains[choice_n])\n # adds new word to list\n words.append(choose_next)\n # creates a list of keys whose last item in tuple is item from list\n selected_keys = [x for x in chains.keys() if x == tuple([*choice_n[1:], choose_next])]\n # it is possible continues\n if selected_keys:\n pass\n else:\n not_end_of_list = False\n\n count += 1\n \n else:\n not_end_of_list = False\n\n return \" \".join(words)","def _make_song_list_html(song_list):\n return '<p class=\"song_name\">' + '<br>'.join([f'{song[\"title\"]} <span class=\"artist_album\">{song[\"artist\"]} - {song[\"album\"]}</span>' for song in song_list]) + '</p>'","def demonstrate_list_comprehension():\n\n from array import array\n a = array('i', [4, 2, 8, 9])\n l = [i for i in a]\n print(l)\n l = [str(i) for i in a]\n print(l)\n print()\n\n john = ['John', 'Lennon', 'Liverpool']\n l = [s for s in john]\n print(l)\n print(', '.join(s for s in john))\n print(', '.join(john))\n print()\n\n songs = ['Imagine a Man', 'There\\'s a Place', 'No Expectations', 'Heaven is a Place on Earth']\n\n first_words = [words[0] for words in [title.split() for title in songs]]\n print(first_words)\n print(' '.join(first_words))\n lyric = [first_words[0]] + [word.lower() for word in first_words[1:]]\n print(' '.join(lyric))\n print()\n\n songs = ['No Expectations', 'Imagine a Man', 'There\\'s a Place', 'No Expectations', 'Heaven is a Place on Earth',\n 'No Expectations', 'No Expectations', ]\n print([i for i, k in enumerate(songs) if k == 'No Expectations'])","def decorate(self, content: StringList) -> None:\n super().decorate(content)\n for line in content[:20]:\n if line.startswith(TITLE_MARKER):\n title = line[len(TITLE_MARKER) :].strip()\n fence = \"=\" * len(title)\n content.insert(0, \"\", \"<generated>\", 0)\n content.insert(0, fence, \"<generated>\", 0)\n content.insert(0, title, \"<generated>\", 0)\n content.insert(0, fence, \"<generated>\", 0)","def list(self, option: str = \"\", short=False, **kwargs):\n courses = self.get_sorted_courses()\n\n if option == \"plain\":\n if short:\n for course in sorted(courses, key=lambda x: x.name + x.type):\n print(f\"{course.name} ({course.type})\")\n else:\n for course in sorted(courses, key=lambda x: x.abbreviation + x.type):\n print(f\"{course.abbreviation}-{course.type[0]}\")\n quit()\n\n current_day = datetime.today()\n current_weekday = current_day.weekday()\n\n # split to scheduled and non-scheduled\n unscheduled = [c for c in courses if c.time is None]\n courses = [c for c in courses if c not in unscheduled]\n\n table = []\n option = option.lower()\n\n for i, course in enumerate(courses):\n # lambda functions to test for various options\n # a is current weekday and b is the course's weekday\n options = {\n \"\": lambda _, __: True, # all of them\n \"t\": lambda a, b: a == b, # today\n \"tm\": lambda a, b: (a + 1) % 7 == b, # tomorrow\n \"mo\": lambda a, b: b == 0,\n \"tu\": lambda a, b: b == 1,\n \"we\": lambda a, b: b == 2,\n \"th\": lambda a, b: b == 3,\n \"fr\": lambda a, b: b == 4,\n \"sa\": lambda a, b: b == 5,\n \"su\": lambda a, b: b == 6,\n }\n\n if option not in options:\n exit_with_error(\"Invalid course-listing option!\")\n\n if options[option](current_weekday, course.weekday()):\n # include the name of the day before first day's course\n if courses[i - 1].time.day != courses[i].time.day:\n weekday = course.time.day.capitalize()\n\n # calculate the next occurrence\n date = (\n current_day\n + timedelta(days=(course.weekday() - current_weekday) % 7)\n ).strftime(\"%-d. %-m.\")\n\n table.append([f\"{weekday if not short else weekday[:3]} / {date}\"])\n\n # for possibly surrounding the name with chars if it's ongoing\n name_surround_char = \"•\" if course.is_ongoing() else \"\"\n\n row = [\n f\"{name_surround_char}{course.name if not short else course.abbreviation}{name_surround_char}\",\n f\"{minutes_to_HHMM(course.time.start)} -\"\n f\" {minutes_to_HHMM(course.time.end)}\"\n + (\n \"\"\n if course.time.weeks is None\n else (\n f\" ({course.time.weeks if not short else course.time.weeks[0]})\"\n )\n ),\n \"-\" if course.classroom is None else course.classroom.number,\n ]\n\n # color the course name the appropriate color, depending on its type\n row[0] = Ansi.color(row[0], course_types[course.type].color)\n\n # append useful information\n table.append(row)\n\n # list unscheduled courses only when no options are specified\n if option == \"\" and len(unscheduled) != 0:\n table.append([\"Unscheduled\"])\n for course in unscheduled:\n table.append(\n [\n course.name if not short else course.abbreviation,\n course.type[0],\n \"-\",\n \"-\",\n ]\n )\n\n if len(table) == 0:\n exit_with_error(\"No courses matching the criteria found!\")\n\n print_table(table)"],"string":"[\n \"def write_to_latex(codelist, unwanted_courses):\\n # TODO: investigate a way to add large amounts of text outside of the\\n # function\\n abstract01 = \\\"I created this document to practice parsing html and using\\\\\\n tools like Beautiful Soup which I've previously had little experience\\\\\\n in. As a result, it's not perfect.\\\\\\\\newline\\\\\\n It is also a slightly condensed all-in-one-place look at a selection\\\\\\n of courses that are available for fourth year computer science\\\\\\n students at the University of Glasgow. For the purposes of clarity I\\\\\\n have removed several courses from this selection. The following\\\\\\n courses have been omitted:\\\"\\n abstract02 = \\\"For more insight into the project, to report issues or to\\\\\\n inspect the code, have a look at the GitHub:\\\\\\n \\\\\\\\url{https://github.com/IS0metric/course-ripper}\\\"\\n unincluded = create_not_included_list(unwanted_courses)\\n with open('courses.tex', 'w') as f:\\n # TODO Try and move all this to a separate function?\\n # TODO: Check if it's more efficient to write a single, massive string\\n # to file\\n f.write('\\\\\\\\documentclass{hitec}\\\\n')\\n f.write('\\\\\\\\usepackage[document]{ragged2e}\\\\n')\\n f.write('\\\\\\\\usepackage{url}\\\\n')\\n f.write('\\\\\\\\usepackage{hyperref}\\\\n')\\n f.write('\\\\\\\\setcounter{tocdepth}{4}\\\\n')\\n f.write('\\\\\\\\begin{document}\\\\n')\\n f.write('\\\\\\\\title{Fourth Year (2016-17) Courses}\\\\n')\\n f.write('\\\\\\\\author{Jack Parkinson}\\\\n')\\n f.write('\\\\\\\\date{August 2016}\\\\n')\\n f.write('\\\\\\\\maketitle\\\\n')\\n f.write('\\\\\\\\abstract{' + abstract01 + unincluded + abstract02 + '}\\\\n')\\n f.write('\\\\\\\\newpage\\\\n\\\\n')\\n f.write('\\\\\\\\tableofcontents\\\\n')\\n f.write('\\\\\\\\newpage\\\\n\\\\n')\\n # TODO: Look into alternatives to the three lists\\n all_courses = []\\n sem1_courses = []\\n sem2_courses = []\\n for code in codelist:\\n course = bsoup(get_coursepage(code))\\n if course['offered']['value'] == 'Runs Throughout Semesters 1 and 2':\\n all_courses.append(course)\\n elif \\\"1\\\" in course['offered']['value']:\\n sem1_courses.append(course)\\n elif \\\"2\\\" in course['offered']['value']:\\n sem2_courses.append(course)\\n f.write('\\\\\\\\section{Semester 1 and 2 Courses}\\\\n\\\\n')\\n for course in all_courses:\\n f.write(latex_course(course))\\n f.write('\\\\\\\\section{Semester 1 Only Courses}\\\\n\\\\n')\\n for course in sem1_courses:\\n f.write(latex_course(course))\\n f.write('\\\\\\\\section{Semester 2 Only Courses}\\\\n\\\\n')\\n for course in sem2_courses:\\n f.write(latex_course(course))\\n f.write('\\\\\\\\end{document}')\\n return None\",\n \"def create_tex(unwanted_courses, wanted_courses=None):\\n page = requests.get(\\n 'http://gla.ac.uk/coursecatalogue/courselist/' +\\n '?code=REG30200000&name=School+of+Computing+Science')\\n tree = html.fromstring(page.content)\\n spans = tree.xpath('//span/text()')\\n codes = []\\n if wanted_courses is None:\\n for s in spans:\\n if s[0:4] == \\\"COMP\\\" and s[7] == '4' and s not in unwanted_courses:\\n codes.append(s)\\n else:\\n for s in wanted_courses:\\n codes.append(s)\\n write_to_latex(codes, unwanted_courses)\\n return None\",\n \"def get_course_name(self,soup):\\n\\t\\tcourse_title = []\\n\\t\\tfor title in soup.find_all(\\\"div\\\", class_=\\\"views-field views-field-title\\\"):\\n\\t\\t\\tcourse_title.append(''.join(title.findAll(text=True)))\\n\\t\\t\\tcourse_title.append(' \\\\n')\\t\\n\\t\\t\\n\\t\\tself.new_list.append(course_title)\\n\\t\\treturn course_title\",\n \"def scrape_all_course_names(filename, verbose):\\n \\n\\tsoup =\\tBeautifulSoup(open(filename, 'r'), 'html.parser')\\n\\t#print(soup)\\n\\tcourses = []\\n\\t# all the courses are stored in a div w/class=view-content\\n\\t\\t\\n\\th4_field_content = soup.find('h4', 'field-content')\\n\\t#print(h4_field_content)\\n\\tall_course_content = soup.find(\\\"div\\\", \\\"view-content\\\") # this contains ALL the classes...\\n\\tif all_course_content == None:\\n\\t\\tprint(\\\"There are no courses on this page. Try a smaller page number!\\\")\\n\\t\\treturn []\\n\\t\\n\\tcandidate_classes = all_course_content.find_all('a') # we want all the 'a' tags within\\n\\tclass_list=[]\\n course_code_and_number = {}\\n\\tfor c in candidate_classes:\\n\\t\\t#print(c.text, \\\"\\\\n\\\")\\n\\t\\ttext = c.text # ex. AEMA 611 Experimental Designs 1 (3 credits)\\n\\t\\ttext = text.split(\\\" \\\")# split on the space\\n\\t\\tcourse_id = \\\" \\\".join(text[:2]).replace('\\\\n', '') # the first 2 are the course id\\n\\t\\tcourse_name = \\\" \\\".join(text[2:-2]).replace('\\\\n', '')\\n\\t\\tnum_credits = text[-2].replace(\\\"(\\\", \\\"\\\")# just get the course number, replace the ( with nothing \\n\\t\\n\\t\\n\\t\\t#print(f\\\"{course_id}\\\\n{course_name}\\\\n{num_credits}\\\\n\\\")\\n\\t\\ttry:# Check that the course number is a digit bc sometimes it is something weird\\n\\t\\t\\tfloat(num_credits)\\n\\t\\texcept ValueError:\\n\\t\\t\\t#print(f\\\"Wrong course format. Ignoring {c}\\\")\\n\\t\\t\\tif(verbose):\\n\\t\\t\\t\\tprint(f\\\"Wrong course format. Ignoring course: {c.text}\\\")\\n\\t\\t\\tcontinue\\n\\t\\tclass_list.append(course_id)\\n\\treturn class_list\",\n \"def create_menu_text(list_of_items):\\n ret = \\\"\\\"\\n for item in list_of_items:\\n item = clean(item)\\n ret += item + \\\"\\\\n\\\"\\n # translate = gs.translate(item, 'en', 'de')\\n # ret += \\\"_\\\" + translate.replace(\\\" , \\\", \\\", \\\") + \\\"_\\\\n\\\"\\n # ret += \\\"\\\\n\\\"\\n return ret[:-1] # ignore last newline\",\n \"def print_output(filtered_courses_list):\\n\\n if not filtered_courses_list:\\n print(\\\"No courses matched the query.\\\")\\n return\\n\\n max_name_length = max([len(course.name) for course in filtered_courses_list])\\n\\n print(\\\" Sem | Course ID | Pts | \\\" +\\n \\\" \\\" * ((max_name_length - 11) // 2 + (max_name_length - 1) % 2) + \\\"Course Name\\\" + \\\" \\\" * ((max_name_length - 11) // 2) +\\n \\\" | Grade\\\")\\n print(\\\"______|___________|_____|\\\" + \\\"_\\\" * (max_name_length + 2) + \\\"|______\\\")\\n\\n for course in filtered_courses_list:\\n print(str(course.year) + course.semester, end=\\\" | \\\")\\n print(course.cid, end=\\\" | \\\")\\n print(course.creditpts, end=\\\" | \\\")\\n print(course.name, end=\\\" \\\" * (max_name_length - len(course.name) + 1) + \\\"| \\\")\\n\\n print(course.grade)\\n\\n print(\\\"\\\\nGPA: \\\" + (\\\"%.3f\\\" % calculate_weighted_average(filtered_courses_list)))\",\n \"def presidente(lst_presidente):\\n tmp=''\\n tmp+='\\\\t\\\\t<para style=\\\"P3\\\">\\\\n'\\n tmp+='\\\\t\\\\t\\\\t<font color=\\\"white\\\"> </font>\\\\n'\\n tmp+='\\\\t\\\\t</para>\\\\n'\\n tmp+='\\\\t\\\\t<para style=\\\"P3\\\">\\\\n'\\n tmp+='\\\\t\\\\t\\\\t<font color=\\\"white\\\"> </font>\\\\n'\\n tmp+='\\\\t\\\\t</para>\\\\n'\\n tmp+='\\\\t\\\\t<para style=\\\"P3\\\" spaceAfter=\\\"40\\\">\\\\n'\\n tmp+='\\\\t\\\\t\\\\t<font color=\\\"white\\\"> </font>\\\\n'\\n tmp+='\\\\t\\\\t</para>\\\\n'\\n tmp+='\\\\t\\\\t<para style=\\\"P1\\\"><b>' + str(lst_presidente) + '</b></para>\\\\n'\\n tmp+='\\\\t\\\\t<para style=\\\"P4\\\">Presidente </para>\\\\n'\\n tmp+='\\\\t</story>\\\\n'\\n return tmp\",\n \"def createtext(lst):\\n newlst = []\\n for item in lst:\\n item = item.replace(\\\"_!\\\",\\\"\\\")\\n newlst.append(item)\\n text = ' '.join(newlst)\\n # Lower-casing\\n return text.lower()\",\n \"def insert_course(dept, num, text):\\n\\n # Course Title \\n m = re.search(\\\"[\\\\d\\\\w]{5} - ([\\\\w ]*)\\\", text)\\n title = m.group(1) if m else \\\"nomatch\\\"\\n\\n # Course Description\\n m = re.search(\\\"\\\\.\\\\s(.*)\\\\sTypically\\\",text)\\n des = m.group(1) if m else \\\"nomatch\\\"\\n\\n # Credit hours aren't fixed for every course\\n # Credit Hours: 2.00\\n # Credit Hours: 2.00 or 3.00. \\n # Credit Hours: 1.00 to 18.00. \\n m = re.search(\\\"Credit Hours: (\\\\d+\\\\.\\\\d+)\\\",text, flags=re.IGNORECASE)\\n m = re.search(\\\"(\\\\d+\\\\.\\\\d+)(.*?)Credit hours\\\",text, flags=re.IGNORECASE) if not m else m\\n cr = m.group(1) if m else \\\"-1\\\"\\n\\n # Semesters Offered\\n m = re.search(\\\"Typically offered (.*?)\\\\.\\\", text)\\n sem = m.group(1).split() if m else [\\\"nomatch\\\"]\\n\\n # Course Type: Lecture, Recitation, Lab, Seminar, etc.\\n m = re.search(\\\"Schedule Types:\\\\s((?:[\\\\w ]+)(?:,[\\\\w ]+)*) \\\\s+\\\", text)\\n form = m.group(1).split(\\\", \\\") if m else [\\\"nomatch\\\"]\\n\\n # Learning objectives will not necessarily follow campuses\\n m = re.search(\\\"campuses:(\\\\s+([\\\\w\\\\s])+\\\\n)\\\", text)\\n campus = m.group(1).strip().split(\\\"\\\\n\\\\n\\\") if m else [\\\"nomatch\\\"]\\n campus = [camp.strip() for camp in campus]\\n\\n # prereq regex and decomosition of prereqs into lists of AND conditions (works for most classes, not 477 and similar)\\n # re.DOTALL matches all characters, including \\\"\\\\n\\\"\\n idx = text.find(\\\"campuses:\\\")\\n m = re.search(\\\"Prerequisites:(.*)\\\",text[idx:],flags=re.DOTALL)\\n if m:\\n allReqs = []\\n prereqText = m.group(1).strip()\\n prereqText = prereqText.encode('ascii', 'ignore') \\n for i in PrereqParser.parseprereq(prereqText):\\n reqArr = []\\n for j in i.split():\\n if j.find(\\\"-C\\\") != -1:\\n j = j.replace(\\\"-C\\\",\\\"\\\")\\n reqArr.append(Requisite(course=j,reqType=False))\\n else:\\n reqArr.append(Requisite(course=j,reqType=True)) \\n allReqs.append(RequisiteList(courses=reqArr))\\n\\n else:\\n allReqs = []\\n\\n # create course entity\\n course = Course(number=num, title=title, department=dept, form=form,\\n description=des, credits=float(cr), semesters=sem,\\n campuses=campus,requisites=allReqs, id=dept + num)\\n # store course \\n course.put()\",\n \"def upcoming_courses(aud):\\n \\n courses = [c for c in aud.all_courses() if c.grade == u\\\"*\\\"]\\n return [c.number.replace(\\\"-\\\", \\\"\\\") for c in courses]\",\n \"def complete_description(description):\\n local_find_all_re = find_all_re\\n local_find_one_re = find_one_re\\n local_find_series_2_re = find_series_2_re\\n local_find_series_3_re = find_series_3_re\\n local_course_re = course_re\\n local_three_digits = three_digits\\n\\n find_all = re.findall(local_find_all_re, description)\\n find_one = re.findall(local_find_one_re, description)\\n \\n def remove_in(a, b):\\n remove = [y for x in a for y in b if y in x]\\n a.extend(b)\\n for x in remove: a.remove(x)\\n return a\\n \\n if find_one:\\n find_all = remove_in(find_all, find_one)\\n for i, x in enumerate(find_all):\\n completed = []\\n find_all[i] = x.replace(' ', '')\\n number = re.search(local_three_digits, find_all[i]).group(0)\\n for crs in re.findall(local_course_re, find_all[i]):\\n completed.append(f'{crs}{number}')\\n description = description.replace(x, ' {}'.format('/'.join(completed)), 1)\\n\\n find_series_2 = re.findall(local_find_series_2_re, description)\\n find_series_3 = re.findall(local_find_series_3_re, description)\\n if find_series_2 or find_series_3:\\n find_series_2 = remove_in(find_series_3, find_series_2)\\n for i, series in enumerate(find_series_2):\\n find_series_2[i] = series.replace(' ', '')\\n course_dep = re.search(local_course_re, find_series_2[i])\\n if course_dep: \\n depmnt = course_dep.group(0)\\n description = description.replace(series, f'{depmnt}{find_series_2[i][-3:]}', 1)\\n return description\",\n \"def description(soup: str, nb:int):\\n desc = []\\n for span in soup.findAll('article', attrs={'itemprop': 'review'}):\\n dat = str(recovTextBetweenTags(str(span.findAll('time', attrs={\\n 'itemprop': 'datePublished'})), ',')).replace(\\\"['[\\\", '').replace(\\\"]']\\\", '')\\n dat = (format_date(dat))\\n if (dat) > (datetime.now() - timedelta(nb)):\\n top = span.findAll('div', attrs={'class': 'text_content'})\\n desc.append(translate(recovTextBetweenTags(str(top), ',')))\\n\\n return desc\",\n \"def title_comm(soup: str, nb:int):\\n title = []\\n for span in soup.findAll('article', attrs={'itemprop': 'review'}):\\n dat = str(recovTextBetweenTags(str(span.findAll('time', attrs={\\n 'itemprop': 'datePublished'})), ',')).replace(\\\"['[\\\", '').replace(\\\"]']\\\", '')\\n dat = (format_date(dat))\\n if (dat) > (datetime.now() - timedelta(nb)):\\n top = span.findAll('h2', attrs={'class': 'text_header'})\\n top = translate(recovTextBetweenTags(str(top), 'non'))\\n title.append(top[0][1:len(top[0])])\\n\\n return title\",\n \"def display_words(word_list,specifier):\\n \\n if specifier.lower() == 'score':\\n print(\\\"{:>6s} - {:s}\\\".format(\\\"Score\\\", \\\"Word\\\"))\\n if len(word_list) < 5:\\n for tup in word_list:\\n print(\\\"{:>6d} - {:s}\\\".format(tup[1], tup[0]))\\n else:\\n \\n for tup in word_list[:5]:\\n print(\\\"{:>6d} - {:s}\\\".format(tup[1], tup[0]))\\n \\n \\n elif specifier.lower() == 'length':\\n print(\\\"{:>6s} - {:s}\\\".format(\\\"Length\\\", \\\"Word\\\"))\\n if len(word_list) < 5:\\n for tup in word_list:\\n print(\\\"{:>6d} - {:s}\\\".format(tup[2], tup[0]))\\n else:\\n \\n for tup in word_list[:5]:\\n print(\\\"{:>6d} - {:s}\\\".format(tup[2], tup[0]))\",\n \"def test_reformat_paragraph_list_5_of_5(self):\\n before_b = \\\"\\\"\\\"\\\\\\n This paragraph leads of this test. It is\\n the \\\"lead\\\" paragraph.\\n\\n 1. This is item number 1. It is the\\n first item in the list.\\n\\n 2. This is item number 2. It is the\\n second item in the list.\\n\\n 3. This is item number 3. It is the\\n third item in the list.\\n\\n This paragraph ends the test. It is the \\\"final\\\"\\n paragraph.\\n \\\"\\\"\\\"\\n after_b = \\\"\\\"\\\"\\\\\\n This paragraph leads of this test. It is\\n the \\\"lead\\\" paragraph.\\n\\n 1. This is item number 1. It is the\\n first item in the list.\\n\\n 2. This is item number 2. It is the\\n second item in the list.\\n\\n 3. This is item number 3. It is the\\n third item in the list.\\n\\n This paragraph ends the test. It is the\\n \\\"final\\\" paragraph.\\n \\\"\\\"\\\"\\n self.run_test(\\n before_b=before_b,\\n after_b=after_b,\\n before_sel=(\\\"13.0\\\", \\\"13.0\\\"),\\n after_sel=(\\\"15.1\\\", \\\"15.1\\\"),\\n command_name=\\\"reformat-paragraph\\\",\\n directives=\\\"@language plain\\\\n@pagewidth 40\\\\n@tabwidth 8\\\",\\n )\",\n \"def cv_list(self):\\n\\n mystr = \\\"\\\"\\n for p in self.mypapers:\\n mystr += f\\\"{p.title[0]}\\\\n\\\"\\n if len(p.author) > 12:\\n a = f\\\"{p.author[0]} et al. \\\"\\n elif len(p.author) > 2:\\n a = \\\", \\\".join(p.author[:-1]) + f\\\" & {p.author[-1]} \\\"\\n elif len(p.author) == 2:\\n a = f\\\"{p.author[0]} & {p.author[1]} \\\"\\n else:\\n a = f\\\"{p.author[0]} \\\"\\n\\n mystr += f\\\"{a}\\\"\\n mystr += f\\\"{p.year}, {p.pub}\\\"\\n if p.volume is not None:\\n mystr += f\\\", {p.volume}\\\"\\n if p.issue is not None:\\n mystr += f\\\", {p.issue}\\\"\\n if p.page is not None:\\n mystr += f\\\", {p.page[0]}\\\"\\n mystr += \\\"\\\\n\\\\n\\\"\\n return mystr\",\n \"def presidente(lst_presidente):\\n tmp=''\\n tmp+='\\\\t\\\\t<para style=\\\"P3\\\" spaceAfter=\\\"35\\\">\\\\n'\\n tmp+='\\\\t\\\\t\\\\t<font color=\\\"white\\\"> </font>\\\\n'\\n tmp+='\\\\t\\\\t</para>\\\\n'\\n tmp+='\\\\t\\\\t<para style=\\\"P4\\\"><b>' + str(lst_presidente) + '</b></para>\\\\n'\\n tmp+='\\\\t\\\\t<para style=\\\"P4\\\">Presidente </para>\\\\n'\\n return tmp\",\n \"def create_formatted_accomplishments(accomplishments):\\n output = ''\\n\\n for index, accomplishment in enumerate(accomplishments):\\n output += f\\\"* {accomplishment}\\\"\\n if index + 1 != len(accomplishments):\\n output += '\\\\n'\\n\\n return output\",\n \"def display_courses(courses):\\n\\n _print('You can access %d courses' % len(courses))\\n for i, course in enumerate(courses, 1):\\n _print('%d - [%s] - %s' % (i, course.state, course.name))\",\n \"def output_labelled_ranked_list(sysdoclab):\\n for did, grade in sysdoclab:\\n print(did + (\\\" L\\\" + str(grade) if grade is not None else \\\"\\\"))\",\n \"def latex_course(course):\\n basic_info_list = [\\n 'session', 'school', 'credits', 'level', 'offered',\\n 'visiting_students', 'erasmus_students'\\n ]\\n generic_subsection_list = [\\n 'description', 'timetable', 'requirements_of_entry',\\n 'excluded_courses', 'co_requisites', 'assessment_weighting'\\n ]\\n string = '\\\\\\\\subsection{' + course[\\\"title\\\"] + '}\\\\n'\\n for info in basic_info_list:\\n string += latex_info(course[info])\\n for subsection in generic_subsection_list:\\n string += latex_subsection(course[subsection])\\n string += '\\\\\\\\break \\\\\\\\textbf{' + course['assessment_date'][\\n 'heading'] + '}' + course['assessment_date']['value'] + '\\\\n'\\n string += latex_subsection(course['aims'])\\n string += '\\\\\\\\subsubsection*{' + \\\\\\n course['learning_outcomes']['heading'] + '}\\\\n'\\n outcome_list = re.split(\\n '\\\\d+\\\\. ', course['learning_outcomes']['value'])\\n string += outcome_list[0] + '\\\\n'\\n string += '\\\\\\\\begin{enumerate}\\\\n'\\n for i in outcome_list[1:-1]:\\n string += '\\\\\\\\item ' + i + '\\\\n'\\n string += '\\\\\\\\end{enumerate}\\\\n'\\n return string\",\n \"def get_course_title_only(code):\\n coursepage = get_coursepage(code)\\n soup = BeautifulSoup(coursepage.content, 'lxml')\\n title = [soup.find_all('h1')[2].string][0]\\n return title\",\n \"def main():\\n\\n #Courses\\n years = [2016, 2017, 2018, 2019, 2020]\\n with sqlite3.connect(\\\"determined.db\\\") as conn:\\n c = conn.cursor()\\n c.execute(\\n \\\"\\\"\\\"\\n CREATE TABLE \\\"course\\\" (\\n \\\"course_number\\\"\\tint NOT NULL,\\n \\\"dept_id\\\"\\tvarchar(4) NOT NULL,\\n \\\"title\\\"\\tvarchar(100) NOT NULL,\\n \\\"instructor_fname\\\"\\tvarchar(35) DEFAULT NULL,\\n \\\"instructor_lname\\\"\\tvarchar(35) DEFAULT NULL,\\n \\\"student_work_products\\\"\\tjson DEFAULT NULL,\\n `term` varchar(7) NOT NULL,\\n `year` int NOT NULL,\\n PRIMARY KEY(\\\"course_number\\\", \\\"term\\\", \\\"year\\\")) \\n \\\"\\\"\\\"\\n )\\n conn.commit()\\n courses = [\\n (1370, \\\"CPSC\\\", \\\"Computer Literacy\\\", names.get_first_name(), names.get_last_name(), json.dumps({\\\"swp\\\": [\\\"Midterm\\\", \\\"Final Exam\\\", \\\"Project 1\\\"]}), \\\"Fall\\\"),\\n (1375, \\\"CPSC\\\", \\\"Programming I\\\", names.get_first_name(), names.get_last_name(), json.dumps({\\\"swp\\\": [\\\"Midterm\\\", \\\"Final Exam\\\", \\\"Project 1\\\"]}), \\\"Fall\\\"),\\n (2376, \\\"CPSC\\\", \\\"Intro to Game Programming\\\", names.get_first_name(), names.get_last_name(), json.dumps({\\\"swp\\\": [\\\"Midterm\\\", \\\"Final Exam\\\", \\\"Project 1\\\"]}), \\\"Fall\\\"),\\n (2380, \\\"CPSC\\\", \\\"Algorithms\\\", names.get_first_name(), names.get_last_name(), json.dumps({\\\"swp\\\": [\\\"Midterm\\\", \\\"Final Exam\\\", \\\"Project 1\\\"]}), \\\"Fall\\\"),\\n (2482, \\\"CPSC\\\", \\\"Computer Organization\\\", names.get_first_name(), names.get_last_name(), json.dumps({\\\"swp\\\": [\\\"Midterm\\\", \\\"Final Exam\\\", \\\"Project 1\\\"]}), \\\"Spring\\\"),\\n (3377, \\\"CPSC\\\", \\\"Advanced Game Programming\\\", names.get_first_name(), names.get_last_name(), json.dumps({\\\"swp\\\": [\\\"Midterm\\\", \\\"Final Exam\\\", \\\"Project 1\\\"]}), \\\"Spring\\\"),\\n (3380, \\\"CPSC\\\", \\\"Operating Systems\\\", names.get_first_name(), names.get_last_name(), json.dumps({\\\"swp\\\": [\\\"Midterm\\\", \\\"Final Exam\\\", \\\"Project 1\\\"]}), \\\"Spring\\\"),\\n (3383, \\\"CPSC\\\", \\\"Programming Languages\\\", names.get_first_name(), names.get_last_name(), json.dumps({\\\"swp\\\": [\\\"Midterm\\\", \\\"Final Exam\\\", \\\"Project 1\\\"]}), \\\"Spring\\\"),\\n (3384, \\\"CPSC\\\", \\\"Computer Networks\\\", names.get_first_name(), names.get_last_name(), json.dumps({\\\"swp\\\": [\\\"Midterm\\\", \\\"Final Exam\\\", \\\"Project 1\\\"]}), \\\"Summer\\\"),\\n (4360, \\\"CPSC\\\", \\\"Computer Security\\\", names.get_first_name(), names.get_last_name(), json.dumps({\\\"swp\\\": [\\\"Midterm\\\", \\\"Final Exam\\\", \\\"Project 1\\\"]}), \\\"Summer\\\")\\n ]\\n #Adding years\\n upload_courses = []\\n for year in years:\\n upload_courses += [x + (year,) for x in courses]\\n #Making a few instructors teach multiple course\\n new_courses = [\\n (4557, \\\"CPSC\\\", \\\"Natural Language Processing\\\", ),\\n (2375, \\\"CPSC\\\", \\\"Programming II\\\",),\\n (2776, \\\"CPSC\\\", \\\"Data Structures and Algorithms\\\",),\\n (4862, \\\"CPSC\\\", \\\"Image Recognition\\\", ),\\n ]\\n for i in range(0,len(new_courses)):\\n year = choice(years)\\n for y in range(0,2): #Number of times new course is taught\\n c = upload_courses[i]\\n new_data = (c[3], c[4], c[5], choice([\\\"Fall\\\", \\\"Spring\\\", \\\"Summer\\\"]), year+y)\\n data = new_courses[i] + new_data\\n upload_courses.append(data)\\n #Adding solo instructors and solo courses\\n upload_courses += [\\n (4672, \\\"CPSC\\\", \\\"Programming Memes\\\", names.get_first_name(), names.get_last_name(), json.dumps({\\\"swp\\\": [\\\"Midterm\\\", \\\"Final Exam\\\", \\\"Project 1\\\"]}), \\\"Spring\\\", choice(years)),\\n (1872, \\\"CPSC\\\", \\\"Information Systems\\\", names.get_first_name(), names.get_last_name(), json.dumps({\\\"swp\\\": [\\\"Midterm\\\", \\\"Final Exam\\\", \\\"Project 1\\\"]}), \\\"Summer\\\", choice(years)),\\n (1123, \\\"CPSC\\\", \\\"Microsoft Office\\\", names.get_first_name(), names.get_last_name(), json.dumps({\\\"swp\\\": [\\\"Midterm\\\", \\\"Final Exam\\\", \\\"Project 1\\\"]}), \\\"Fall\\\", choice(years))\\n ]\\n\\n with sqlite3.connect(\\\"determined.db\\\") as conn:\\n c = conn.cursor()\\n c.executemany('''INSERT INTO course (course_number, dept_id, title, instructor_fname, instructor_lname, student_work_products, term, year)\\n VALUES ( ?, ?, ?, ?, ?, ?, ?, ?)''', upload_courses)\\n conn.commit()\\n\\n #SWP\\n with sqlite3.connect(\\\"determined.db\\\") as conn:\\n c = conn.cursor()\\n c.execute(\\n \\\"\\\"\\\"\\n CREATE TABLE `student_work_product` (\\n `id` INTEGER PRIMARY KEY,\\n `product` varchar(250) NOT NULL,\\n `course_id` int NOT NULL,\\n `dept_id` int NOT NULL,\\n `student_fname` varchar(35) NOT NULL,\\n `student_lname` varchar(35) NOT NULL,\\n `student_outcome` int DEFAULT NULL,\\n `score` int DEFAULT NULL,\\n `term` varchar(7) NOT NULL,\\n `year` int NOT NULL,\\n CONSTRAINT `course` FOREIGN KEY (`course_id`) REFERENCES `course` (`course_number`)\\n CONSTRAINT `course` FOREIGN KEY (`dept_id`) REFERENCES `course` (`dept_id`)\\n )\\n \\\"\\\"\\\"\\n )\\n conn.commit()\\n \\n swps = []\\n with sqlite3.connect(\\\"determined.db\\\") as conn:\\n conn.row_factory = sqlite3.Row\\n c = conn.cursor()\\n c.execute (\\\"Select * from course\\\")\\n records = [dict(x) for x in c.fetchall()]\\n #Generating 20 student records for each swp in each course\\n for i, course in enumerate(records):\\n student_names = []\\n for _ in range(20):\\n student_names.append({'fname': names.get_first_name(),\\n 'lname': names.get_last_name()})\\n for product in json.loads(course['student_work_products'])['swp']:\\n for student in student_names:\\n if i%7 == 0:\\n score = int(triangular(50, 85))\\n else:\\n score = int(triangular(50, 100))\\n if score >= 90: outcome = 4\\n elif score >= 80: outcome = 3\\n elif score >= 70: outcome = 2\\n elif score >= 60: outcome = 1\\n else: outcome = 0 \\n swps.append((\\n product,\\n course['course_number'],\\n \\\"CPSC\\\",\\n student['fname'],\\n student['lname'],\\n outcome,\\n score, \\n course['term'], \\n course['year']\\n ))\\n \\n c.executemany('''INSERT INTO student_work_product (product, course_id, dept_id, student_fname, student_lname, student_outcome, score, term, year)\\n VALUES ( ?, ?, ?, ?, ?, ?, ?, ?, ?)''', swps)\\n conn.commit()\",\n \"def parse_course_pre_to_list(self):\\n prere_courses = []\\n\\n # convert non-word to spaces except \\\"-\\\"\\n self.prere_raw = re.sub(\\\"[^\\\\w-]\\\", \\\" \\\", self.prere_raw)\\n\\n # split the string by spaces\\n words = self.prere_raw.split()\\n\\n # check if the string contains number, if True then the string is of the form: \\\"140A\\\"\\n def append_to_list(word, previous_word):\\n try:\\n if word[0].isdigit():\\n toappend = None\\n # course abbs = words[i-1]\\n try:\\n toappend = \\\"{} {}\\\".format(previous_word.upper(), word.upper())\\n except AttributeError:\\n #TODO check this error for HIGR 216A-B\\n print(\\\"previous word is {}, word is {}\\\".format(previous_word, word))\\n if toappend not in prere_courses:\\n prere_courses.append(toappend)\\n except IndexError:\\n #TODO why this would occur?\\n print(\\\"word is {}, previous word is {}\\\".format(word, previous_word))\\n\\n # iterate through words to find numbers\\n for i in range(len(words)):\\n\\n previous_word = None\\n if i is not 0:\\n # define the previous word like MATH\\n previous_word = words[i-1]\\n\\n if \\\"-\\\" in words[i]:\\n num = re.split(\\\"[A-Z]\\\", words[i])[0]\\n letters = re.split(\\\"-\\\", words[i])\\n new_words = []\\n for i in range(len(letters)):\\n if i is 0:\\n new_words.append(letters[0])\\n else:\\n new_words.append(num + letters[i])\\n for word in new_words:\\n if word is not None and previous_word is not None:\\n append_to_list(word, previous_word)\\n else:\\n #TODO: what if the word is None?\\n pass\\n else:\\n append_to_list(words[i], previous_word)\\n\\n return prere_courses\",\n \"def group_list(self, group_number=1):\\n text = []\\n group = self.varexercise_numbers[group_number-1]\\n group_name = _('Group %s') % group[0]\\n text.append('\\\\n\\\\\\\\subsection*{%s}\\\\n' % group_name)\\n for number in range(1, self.number_of_variations+1):\\n print(\\\"---------\\\", number) # !!!\\n variation = '\\\\n\\\\n\\\\\\\\textbf{%s}\\\\\\\\\\\\\\\\\\\\n' %\\\\\\n _('Variation %d') % number\\n text.append(variation)\\n exercise_number = 1\\n for item in group[1:]:\\n # print(' '*5, item) # !!!\\n if not isinstance(item, str):\\n latex_plain = item.latex_plain(number)\\n if latex_plain:\\n text.append('%d. ' % exercise_number)\\n # print(' '*5, number) # !!!\\n # print(' '*5, latex_plain) # !!!\\n text.append(latex_plain + '\\\\n')\\n exercise_number += 1\\n # if with_solution:\\n # text.extend(self.one_group(group_number=group_number))\\n # text.append(\\\"\\\\\\\\newpage\\\\n\\\")\\n return text\",\n \"def courses_string(self):\\n course_string = \\\"\\\"\\n for c in self.courses[0]:\\n course_string += c + \\\", \\\"\\n course_string = course_string[:-2]\\n return course_string\",\n \"def snippetList(requeset, format = None):\",\n \"def clean_text_clues(texts):\\n\\n clean_clues = []\\n for clues in texts:\\n clue = remove_hyphens(clues)\\n clue = tokenize(clue)\\n clue = remove_characters(clue)\\n clue = lowercase(clue)\\n clue = remove_stopwords(clue)\\n clue = lemmatized_words(clue)\\n clue = remove_short_tokens(clue)\\n clue = remove_non_wordnet(clue)\\n clue = apply_lemmatize(clue)\\n clean_clues.append(clue)\\n return [' '.join(item) for item in clean_clues]\",\n \"def build_latex(file_list):\\n eingabe=[]\\n anhang_count=0\\n anhaenge=[]\\n anhaenge_file=[]\\n for file in file_list:\\n x=load_file(file)[1]\\n eingabe.append(\\\"\\\\section{%s}\\\" %(x[2]))\\n eingabe.append(\\\"\\\\subsection{Infos}\\\")\\n eingabe.append(\\\"\\\\\\\\begin{tabularx}{\\\\linewidth}{@{}lX}\\\")\\n eingabe.append(r\\\"\\\\textbf{Datum} & %s\\\\\\\\\\\" %(x[0]))\\n eingabe.append(r\\\"\\\\textbf{Gremium} & %s\\\\\\\\\\\" %(x[1]))\\n eingabe.append(r\\\"\\\\textbf{Anatrag/Beschluss wurde} & %s\\\\\\\\\\\" %(x[9]))\\n x[11]=x[11].replace(\\\" \\\",\\\"\\\")\\n kw=x[11].split(\\\",\\\")\\n for i in range(0,len(kw)):\\n if i==0:\\n eingabe.append(r\\\"\\\\textbf{Keyword:} & %s\\\\\\\\\\\" %(kw[i]))\\n else:\\n eingabe.append(r\\\" & %s\\\\\\\\\\\" %(kw[i]))\\n eingabe.append(\\\"\\\\end{tabularx}\\\")\\n eingabe.append(\\\"\\\\\\\\begin{tabularx}{\\\\linewidth}{@{}XXX}\\\")\\n eingabe.append(r\\\"\\\\textbf{Abstimmungsergebniss:}&&\\\\\\\\\\\")\\n eingabe.append(r\\\"Zustimmung & Ablehnung & Enthaltungen \\\\\\\\\\\")\\n eingabe.append(r\\\"{} & {} & {} \\\\\\\\\\\".format(x[6],x[7],x[8]))\\n eingabe.append(\\\"\\\\end{tabularx}\\\")\\n eingabe.append(\\\"\\\\subsection{Antrags/Beschlusstext}\\\")\\n line_text=len(eingabe)\\n eingabe.append(x[3])\\n eingabe.append(\\\"\\\\subsection{Begründung}\\\")\\n eingabe.append(x[4])\\n if x[23]==\\\"Ja\\\" and x[24]!=\\\"\\\":\\n delta=7\\n anzahl=int((len(x)-23)/delta)\\n if anzahl==1:\\n eingabe.append(\\\"\\\\subsection{Änderungsantrag}\\\")\\n eingabe.append(\\\"\\\\subsubsection*{Vorschlag}\\\")\\n eingabe.append(x[24])\\n eingabe.append(\\\"\\\\subsubsection*{Begründung}\\\")\\n eingabe.append(x[25]+\\\"\\\\\\\\vspace{1.5ex} \\\\\\\\\\\\\\\\\\\")\\n eingabe.append(\\\"\\\\\\\\begin{tabularx}{\\\\linewidth}{@{}XXX}\\\")\\n eingabe.append(r\\\"\\\\textbf{Abstimmungsergebniss:}&&\\\\\\\\\\\")\\n eingabe.append(r\\\"Zustimmung & Ablehnung & Enthaltungen \\\\\\\\\\\")\\n eingabe.append(r\\\"{} & {} & {} \\\\\\\\\\\".format(x[26],x[27],x[28]))\\n eingabe.append(r\\\"\\\\multicolumn{2}{@{}l}{\\\\textbf{Änderungsantrag wurde:}} & %s \\\\\\\\\\\" %(x[29]))\\n eingabe.append(\\\"\\\\\\\\end{tabularx}\\\")\\n else:\\n eingabe.append(\\\"\\\\subsection{Änderungsanträge}\\\")\\n for i in range(0,anzahl):\\n eingabe.append(\\\"\\\\subsubsection{Änderungsvorschlag %s}\\\" %(i+1))\\n eingabe.append(\\\"\\\\\\\\paragraph*{Vorschlag}\\\")\\n eingabe.append(x[24+(delta*i)])\\n eingabe.append(\\\"\\\\\\\\paragraph*{Begründung}\\\")\\n eingabe.append(x[25+(delta*i)]+\\\"\\\\\\\\vspace{1.5ex} \\\\\\\\\\\\\\\\\\\")\\n eingabe.append(\\\"\\\\\\\\begin{tabularx}{\\\\linewidth}{@{}XXX}\\\")\\n eingabe.append(r\\\"\\\\textbf{Abstimmungsergebniss:}&&\\\\\\\\\\\")\\n eingabe.append(r\\\"Zustimmung & Ablehnung & Enthaltungen \\\\\\\\\\\")\\n eingabe.append(r\\\"{} & {} & {} \\\\\\\\\\\".format(x[26+(delta*i)],x[27+(delta*i)],x[28+(delta*i)]))\\n eingabe.append(r\\\"\\\\multicolumn{2}{@{}l}{\\\\textbf{Änderungsantrag wurde:}} & %s \\\\\\\\\\\" %(x[29+(delta*i)]))\\n eingabe.append(\\\"\\\\\\\\end{tabularx}\\\")\\n if x[10]!=\\\"\\\":\\n anhang=x[10].split(\\\",\\\")\\n bennenung=x[11].split(\\\",\\\")\\n eingabe[line_text]=eingabe[line_text]+\\\"\\\\\\\\\\\\\\\\ \\\\n Dieser Antrag enthält %s Anhänge: \\\" %(len(anhang))\\n for i in range(0,len(anhang)):\\n anhang_count=anhang_count+1\\n anhaenge.append(\\\"\\\\section{%s - %s} \\\\label{An:%s}\\\" % (x[2],bennenung[i],str(anhang_count)))\\n anhaenge.append(\\\"\\\\includepdf[pages=-]{%s}\\\" %(anhang[i]))\\n anhaenge_file.append(anhang[i])\\n if i!=len(anhang)-1:\\n eingabe[line_text]=eingabe[line_text]+\\\"\\\\\\\\nameref{An:%s}, \\\" % (str(anhang_count))\\n else:\\n eingabe[line_text]=eingabe[line_text]+\\\"\\\\\\\\nameref{An:%s} \\\" % (str(anhang_count)) \\n \\n eingabe.append(\\\"\\\\\\\\newpage\\\") \\n eingabe.append(\\\"\\\\\\\\appendix\\\") \\n eingabe.append(\\\"\\\\\\\\pagenumbering{Roman}\\\") \\n ausgabe=\\\"\\\"\\n for i in range(0,len(eingabe)):\\n ausgabe=ausgabe+eingabe[i]+\\\"\\\\n\\\"\\n \\n for i in range(0,len(anhaenge)):\\n ausgabe=ausgabe+anhaenge[i]+\\\"\\\\n\\\"\\n \\n return ausgabe,anhaenge_file\",\n \"def completed_course()->str:\\r\\n try:\\r\\n db: sqlite3.Connection = sqlite3.connect(DB_FILE)\\r\\n query: str = \\\"SELECT s.Name, s.CWID, g.Course, g.Grade, i.Name AS 'Instructor' \\\" \\\\\\r\\n \\\"FROM grades2 g JOIN students2 s ON g.StudentCWID = s.CWID \\\" \\\\\\r\\n \\\"JOIN instructors2 i ON g.InstructorCWID = i.CWID ORDER BY s.Name\\\" \\r\\n except sqlite3.OperationalError as e:\\r\\n print(e)\\r\\n\\r\\n data: Dict[str,str]=\\\\\\r\\n [{\\\"name\\\": name,\\\"cwid\\\": cwid,\\\"course\\\": course , \\\"grade\\\":grade, \\\"instructor\\\": instructor}\\r\\n for name, cwid, course, grade, instructor in db.execute(query)]\\r\\n db.close()\\r\\n \\\"\\\"\\\"Render the template from templates folder\\\"\\\"\\\"\\r\\n return render_template(\\\"student_summary.html\\\",\\r\\n title=\\\"Stevens Repository\\\",\\r\\n table_title=\\\"Student, Course, Grade and Instructor\\\",\\r\\n students=data )\",\n \"def test_reformat_paragraph_list_4_of_5(self):\\n before_b = \\\"\\\"\\\"\\\\\\n This paragraph leads of this test. It is\\n the \\\"lead\\\" paragraph.\\n\\n 1. This is item number 1. It is the\\n first item in the list.\\n\\n 2. This is item number 2. It is the\\n second item in the list.\\n\\n 3. This is item \\n number 3. It is the third item in the list.\\n\\n This paragraph ends the test. It is the \\\"final\\\"\\n paragraph.\\n \\\"\\\"\\\"\\n after_b = \\\"\\\"\\\"\\\\\\n This paragraph leads of this test. It is\\n the \\\"lead\\\" paragraph.\\n\\n 1. This is item number 1. It is the\\n first item in the list.\\n\\n 2. This is item number 2. It is the\\n second item in the list.\\n\\n 3. This is item number 3. It is the\\n third item in the list.\\n\\n This paragraph ends the test. It is the \\\"final\\\"\\n paragraph.\\n \\\"\\\"\\\"\\n self.run_test(\\n before_b=before_b,\\n after_b=after_b,\\n before_sel=(\\\"10.0\\\", \\\"10.0\\\"),\\n after_sel=(\\\"13.0\\\", \\\"13.0\\\"),\\n command_name=\\\"reformat-paragraph\\\",\\n directives=\\\"@language plain\\\\n@pagewidth 40\\\\n@tabwidth 8\\\",\\n )\",\n \"def filter_course_name(soup):\\n course_title = soup.find('span', id=regex_title).text.strip()\\n name_idx = course_title.find('-')\\n\\n dept_raw, course_code_raw = course_title[:name_idx - 1].split(' ')\\n course_name = course_title[name_idx + 1:].strip()\\n\\n dept = dept_raw.encode('ascii', 'ignore').decode().strip()\\n course_code = course_code_raw.encode(\\n 'ascii', 'ignore').decode().strip()\\n\\n return dept, course_code, course_name\",\n \"def categorize(book: TextIO) -> list:\\n chunks = get_texts(book)\\n texts = []\\n for t in chunks:\\n level = difficulty(complexity(t))\\n texts.append((t, level, keywords(t)))\\n return texts\",\n \"def list(self, frame=0):\\n text = []\\n if not self.number_of_variations:\\n return \\\"\\\"\\n for group_number in range(1, len(self.varexercise_numbers)+1):\\n text.extend(\\n self.group_list(group_number))\\n return text\",\n \"def html_sequence(seq_esc: \\\"Sequence\\\") -> str:\\n items = (f\\\"<li>{htmlize(item)}</li>\\\" for item in seq_esc)\\n return \\\"<ul>\\\\n\\\" + \\\"\\\\n\\\".join(items) + \\\"\\\\n</ul>\\\"\",\n \"def get_wordlists():\\n\\n\\tCS = {'ACM', 'IEEE', 'Computer Science', 'Artificial Intelligence',\\n\\t\\t'Pattern Recognition', 'Computer Vision', 'Machine Learning',\\n\\t\\t'Signal Processing', 'Electrical Engineering', 'Image Processing',\\n\\t\\t'Data Mining', 'Neural Networks', 'Computer Graphics', 'Graphics',\\n\\t\\t'Language Processing', 'Internet', 'Intelligent Systems',\\n\\t\\t'Robotic','Data','Software', 'Machine Vision', 'Image Analysis',\\n\\t\\t'Scientific Computing', 'SIAM', 'Malware','World Wide Web', \\n\\t\\t'Computational Intelligence', 'Computational Linguistics',\\n\\t\\t'Computational linguistics','Algorithm','Computer','ITiCSE',\\n\\t\\t'ITICSE','Machine learning','Learning','learning',\\n\\t\\t'Artificial intelligence','CIVR','Document Analysis'}\\n\\n\\tbio = {'Biology', 'Microbiology', 'Molecular', 'Medical', 'Biological',\\n\\t\\t'Cancer', 'Genome', 'Bioinformatics', 'Protein', 'Biocomputing',\\n\\t\\t'Biomedical', 'biology', 'Medicine', 'Biosystems', 'Virology',\\n\\t\\t'Brain', 'Psychology', 'Genetics', 'Bioengineering', 'Cell',\\n\\t\\t'Cardiology', 'Metabolic', 'Biotechnology', 'Pathogens',\\n\\t\\t'Pathology', 'Plant', 'PLANT', 'Virus', 'Drug','Medicinal',\\n\\t\\t'Neuro','Psych',\\n\\t\\t'Genomic','Diseases','Endocrinology', 'Epidemiology',\\n\\t\\t'Proteom','Biochem', 'DNA', 'Pharma', 'Biomedic', 'biomedica',\\n\\t\\t'Neurobiological'}\\n\\n\\tmath = {'Mathemati','Markov','Probability','Algebra','Network',\\n\\t\\t'Topology','Optimization', 'Geometr','Statistic','Algorithm',\\n\\t\\t'Graph ','Graphs','Combinatori','Riemann Surfaces','Permutation Groups',\\n\\t\\t'Functional Analysis', 'SIAM','Fixed Point','Wavelet','Statistics',\\n\\t\\t'Linear Regression','Fractal','geometry','Multivariate','Chaos',\\n\\t\\t'mathemati','Kernel'}\\n\\n\\tlinguistics = {}\\n\\n\\tcomputer_vision = {}\\n\\n\\tchemistry = {}\\n\\n\\tphysics = {}\\n\\n\\t# Rename \\\"Computer Vision\\\" to \\\"Image Processing\\\"?\\n\\ttopic_names = ['Computer Science','Biology','Mathematics','Chemistry',\\n\\t\\t'Physics','Computer Vision','Natural Language Processing']\\n\\ttopics = [CS, bio, math]#, linguistics, computer_vision, chemistry, physics]\\n\\n\\treturn {topic_names[i]:topics[i] for i in range(len(topics))}\",\n \"def print_all_courses(filename): \\n records = records_from_file(filename)\\n course_code_and_name = all_courses(records)\\n print(\\\"All courses:\\\")\\n for course_code, course_name in sorted(course_code_and_name.items()):\\n print((\\\" {}: {}\\\").format(course_code, course_name))\\n print()\",\n \"def coursecode(el):\\n txt = text(el)\\n return re.sub(r\\\"\\\\s*\\\\[\\\\d+\\\\]$\\\", \\\"\\\", txt, re.UNICODE)\",\n \"def lists(self, text):\\n\\n #Replace line-initial bullets with asterisks\\n bullet_pattern = re.compile(u'^•', re.U | re.M)\\n\\n pattern = re.compile(r'^((?:[*;:]+|[*;:#]*#(?:_|\\\\d+)?)%s[ .].*)$(?![^#*;:])'\\n % self.lc, re.U | re.M | re.S)\\n return pattern.sub(self.fList, bullet_pattern.sub('*', text))\",\n \"def create_hn_text(self):\\n text_list = [f\\\"Top {STORIES_NUMBER} from HackerNews:\\\"]\\n sorted_stories = self.get_top_stories()\\n # Format slack text\\n for story in sorted_stories:\\n text_list.append(\\n \\\"*<{}|{}>* - <{}|{}>\\\".format(\\n \\\"{}/item?id={}\\\".format(HN_URL, story[\\\"id\\\"]),\\n story[\\\"score\\\"],\\n # Ask HN type posts do not have 'url' key, so using get to return None\\n story.get('url'),\\n story[\\\"title\\\"],\\n )\\n )\\n self.logger.debug(text_list)\\n return \\\"\\\\n>\\\".join(text_list)\",\n \"def longwords_Li_Comp(strings):\\n return [string for string in strings if len(string)>4 ]\",\n \"def get_kindle_sentences() -> List[str]:\\n reviews = dl.load_json_from_file(\\\"../data/kindle_500.json\\\")\\n return [sent[1] for review in reviews for sent in enumerate(review)]\",\n \"def longwords_Li_Comp(strings):\\n # write your code here\\n return [string for string in strings if len(string)>4]\",\n \"def get_text(downgrade_titles=False):\",\n \"def tags_tocl(d, tag_list, title):\\r\\n filtered_anchors = []\\r\\n for anc in sorted(d.keys(), key=str.lower):\\r\\n entry = d[anc]\\r\\n if not \\\"tags\\\" in entry: continue\\r\\n found = [t for t in tag_list if t in entry[\\\"tags\\\"]]\\r\\n if not found: continue\\r\\n filtered_anchors.append(anc)\\r\\n return TemplateData(t=title, e=filtered_anchors)\",\n \"def test_get_passage_with_list(self):\\n simple = self.TEI.getPassage([\\\"1\\\", \\\"pr\\\", \\\"2\\\"])\\n self.assertEqual(\\n simple.text().strip(),\\n \\\"tum, ut de illis queri non possit quisquis de se bene\\\",\\n \\\"Ensure passage finding with context is fully TEI / Capitains compliant (Different level range Passage)\\\"\\n )\",\n \"def test_list_to_string_display(self): \\n test1 = list_as_text(['a', 'b', 'c', 'd', 'e'])\\n self.assertEqual(test1, 'a, b, c, d and e')\\n test2 = list_as_text(['Atlanta, GA', 'New York City, NY',\\n 'Miami, FL'])\\n self.assertEqual(test2, 'Atlanta, GA, New York City, NY and Miami, FL')\\n test3 = list_as_text(['Apple a day...'])\\n self.assertEqual(test3, 'Apple a day...')\\n test4 = list_as_text(['love', 'hate'])\\n self.assertEqual(test4, 'love and hate') \\n sites = Site.objects.filter(id__in=[2, 3, 4])\\n test5 = list_as_text(sites)\\n self.assertEqual(test5, 'Hudson Valley, Triangle and Capital Area')\",\n \"def get_listings_pre_text(self, items_length):\\n raise NotImplementedError\",\n \"def test_reformat_paragraph_list_3_of_5(self):\\n before_b = \\\"\\\"\\\"\\\\\\n This paragraph leads of this test. It is\\n the \\\"lead\\\" paragraph.\\n\\n 1. This is item number 1. It is the\\n first item in the list.\\n\\n 2. This is item \\n number 2. It is the second item in the list.\\n\\n 3. This is item \\n number 3. It is the third item in the list.\\n\\n This paragraph ends the test. It is the \\\"final\\\"\\n paragraph.\\n \\\"\\\"\\\"\\n after_b = \\\"\\\"\\\"\\\\\\n This paragraph leads of this test. It is\\n the \\\"lead\\\" paragraph.\\n\\n 1. This is item number 1. It is the\\n first item in the list.\\n\\n 2. This is item number 2. It is the\\n second item in the list.\\n\\n 3. This is item \\n number 3. It is the third item in the list.\\n\\n This paragraph ends the test. It is the \\\"final\\\"\\n paragraph.\\n \\\"\\\"\\\"\\n self.run_test(\\n before_b=before_b,\\n after_b=after_b,\\n before_sel=(\\\"7.0\\\", \\\"7.0\\\"),\\n after_sel=(\\\"10.0\\\", \\\"10.0\\\"),\\n command_name=\\\"reformat-paragraph\\\",\\n directives=\\\"@language plain\\\\n@pagewidth 40\\\\n@tabwidth 8\\\",\\n )\",\n \"def fname_tsk5(lst):\\n lst[0] = del_lst(lst[0])\\n lst[2] = del_lst(lst[2])\\n body = \\\"The weight of an {0} is {1} and the weight of a \\\" \\\\\\n \\\"{2} is {3}\\\"\\n return body.format(*lst)\",\n \"def get_texts(book: TextIO) -> list:\\n content = book.read()\\n chars_limit = 970\\n texts = [content[i:i + chars_limit] for i in range(0, len(content), chars_limit)]\\n return [\\\"...\\\" + t + \\\"...\\\" if t != texts[0] else t + \\\"...\\\" for t in texts]\",\n \"def test_with_4_items(self):\\n self.assertEqual(humanize_list(['a', 'b', 'c', 'd']),\\n 'a, b, c, and d')\",\n \"def test_reformat_paragraph_list_2_of_5(self):\\n before_b = \\\"\\\"\\\"\\\\\\n This paragraph leads of this test. It is\\n the \\\"lead\\\" paragraph.\\n\\n 1. This is item number 1. It is the\\n first item in the list.\\n\\n 2. This is item \\n number 2. It is the second item in the list.\\n\\n 3. This is item \\n number 3. It is the third item in the list.\\n\\n This paragraph ends the test. It is the \\\"final\\\"\\n paragraph.\\n \\\"\\\"\\\"\\n after_b = \\\"\\\"\\\"\\\\\\n This paragraph leads of this test. It is\\n the \\\"lead\\\" paragraph.\\n\\n 1. This is item number 1. It is the\\n first item in the list.\\n\\n 2. This is item \\n number 2. It is the second item in the list.\\n\\n 3. This is item \\n number 3. It is the third item in the list.\\n\\n This paragraph ends the test. It is the \\\"final\\\"\\n paragraph.\\n \\\"\\\"\\\"\\n self.run_test(\\n before_b=before_b,\\n after_b=after_b,\\n before_sel=(\\\"4.0\\\", \\\"4.0\\\"),\\n after_sel=(\\\"7.0\\\", \\\"7.0\\\"),\\n command_name=\\\"reformat-paragraph\\\",\\n directives=\\\"@language plain\\\\n@pagewidth 40\\\\n@tabwidth 8\\\",\\n )\",\n \"def checklist_line(line, day, checklist):\\n\\n output_lines = f'<b>Checklist items due before class on {day}</b>'\\n output_lines += f'<ul class=\\\"checklist\\\" id=\\\"check-list-{day}\\\">'\\n for item in line[2:].split(' | '):\\n item = item.strip()\\n item_without_markdown_link = re.sub(r\\\"\\\\[(.+)\\\\]\\\\(.+\\\\)\\\", r\\\"\\\\1\\\", item)\\n if day in checklist and item_without_markdown_link in checklist[day]:\\n output_lines += f'<li class=\\\"unchecked checked\\\">{item}</li>'\\n else:\\n output_lines += f'<li class=\\\"unchecked\\\">{item}</li>'\\n if item_without_markdown_link == 'Practice (no points) worksheet demo.':\\n print(output_lines)\\n output_lines += '</ul>'\\n return output_lines\",\n \"def prepare_courses_for_radio():\\n\\n three_closest_courses = get_four_future_courses()\\n radio_courses = []\\n\\n for course in three_closest_courses:\\n radio_courses.append(\\n (int(course.id), str(reformat_date(course.startDate) + ' r.'))\\n )\\n return radio_courses\",\n \"def convert_examples_to_features(examples, label_list, max_seq_length, tokenizer):\\n\\n label_map = {label : i for i, label in enumerate(label_list)}\\n\\n features = []\\n for (ex_index, example) in enumerate(examples):\\n tokens = example.text\\n\\n# # Account for [CLS] and [SEP] with \\\"- 2\\\"\\n# if len(tokens) > max_seq_length - 2:\\n# tokens = tokens[:(max_seq_length - 2)]\\n\\n bert_tokens = []\\n orig_to_tok_map = []\\n\\n bert_tokens.append(\\\"[CLS]\\\")\\n for token in tokens:\\n new_tokens = tokenizer.tokenize(token)\\n if len(bert_tokens) + len(new_tokens) > max_seq_length - 1:\\n # print(\\\"You shouldn't see this since the test set is already pre-separated.\\\")\\n break\\n else:\\n orig_to_tok_map.append(len(bert_tokens))\\n bert_tokens.extend(new_tokens)\\n bert_tokens.append(\\\"[SEP]\\\")\\n\\n if len(bert_tokens) == 2: # edge case\\n continue\\n\\n # The convention in BERT is:\\n # (a) For sequence pairs:\\n # tokens: [CLS] is this jack ##son ##ville ? [SEP] no it is not . [SEP]\\n # type_ids: 0 0 0 0 0 0 0 0 1 1 1 1 1 1\\n # (b) For single sequences:\\n # tokens: [CLS] the dog is hairy . [SEP]\\n # type_ids: 0 0 0 0 0 0 0\\n #\\n # Where \\\"type_ids\\\" are used to indicate whether this is the first\\n # sequence or the second sequence. The embedding vectors for `type=0` and\\n # `type=1` were learned during pre-training and are added to the wordpiece\\n # embedding vector (and position vector). This is not *strictly* necessary\\n # since the [SEP] token unambigiously separates the sequences, but it makes\\n # it easier for the model to learn the concept of sequences.\\n #\\n # For classification tasks, the first vector (corresponding to [CLS]) is\\n # used as as the \\\"sentence vector\\\". Note that this only makes sense because\\n # the entire model is fine-tuned.\\n\\n input_ids = tokenizer.convert_tokens_to_ids(bert_tokens)\\n\\n # The mask has 1 for real tokens and 0 for padding tokens. Only real\\n # tokens are attended to.\\n input_mask = [1] * len(input_ids)\\n\\n # Zero-pad up to the sequence length.\\n padding = [0] * (max_seq_length - len(input_ids))\\n input_ids += padding\\n input_mask += padding\\n\\n assert len(input_ids) == max_seq_length\\n assert len(input_mask) == max_seq_length\\n\\n segment_ids = [0] * max_seq_length # no use for our problem\\n\\n labels = example.label\\n label_ids = [0] * max_seq_length\\n label_mask = [0] * max_seq_length\\n\\n for label, target_index in zip(labels, orig_to_tok_map):\\n label_ids[target_index] = label_map[label]\\n label_mask[target_index] = 1\\n\\n assert len(segment_ids) == max_seq_length\\n assert len(label_ids) == max_seq_length\\n assert len(label_mask) == max_seq_length\\n\\n features.append(\\n InputFeatures(input_ids=input_ids,\\n input_mask=input_mask,\\n segment_ids=segment_ids,\\n label_ids=label_ids,\\n label_mask=label_mask))\\n return features\",\n \"def __gettextinfo(edudict, eduspan):\\n # text = lnode.text + \\\" \\\" + rnode.text\\n text = []\\n for idx in range(eduspan[0], eduspan[1]+1, 1):\\n text += edudict[idx]\\n # Return: A list of token indices\\n return text\",\n \"def print_table(table, title_list):\\n\\n # your goes code\\n \\n table.insert(0, title_list)\\n # title listet 0.helyre teszi\\n # your code\\n\\n lenght_list = [] # tartalmazza az összes szót\\n for lines in table:\\n for items in lines:\\n lenght_list.append(items)\\n\\n longest_words_length = len(max(lenght_list, key=len))\\n multiplier = len(title_list)*(longest_words_length+1)\\n\\n for sublist in table:\\n print(\\\"|\\\\n|\\\", \\\"-\\\"*multiplier, \\\"|\\\")\\n\\n for j in sublist:\\n print(\\\"|\\\", j, end = \\\" \\\"*(longest_words_length-len(j)))\\n\\n print(\\\"|\\\\n|\\\",\\\"-\\\"*multiplier, \\\"|\\\")\",\n \"def news_template_flex(news_list, events, key, id, isMore, alt_text='推薦新聞'):\\n news_box_components = []\\n for i, news in enumerate(news_list):\\n # img = \\\"https://cdn.pixabay.com/photo/2015/12/03/22/16/newspaper-1075795_1280.jpg\\\"\\n date = news['date']\\n date = re.sub(\\\"T.*Z\\\", \\\"\\\", date)\\n url = news['url']\\n\\n # Add separators between news.\\n if i > 0:\\n news_box_components.append(SeparatorComponent(margin='lg'))\\n # related_news_uri = f\\\"{RELATED_NEWS_LIFF_URI}?cid={news['cid']}&nid={news['nid']}\\\"\\n\\n TextComponents = []\\n TextComponents.append(TextComponent(\\n text=news['title'],\\n wrap=True,\\n size='md',\\n weight='bold',\\n action=URIAction(uri=url)\\n ))\\n TextComponents.append(TextComponent(\\n text='{} - {}'.format(news['source'], date),\\n wrap=True,\\n size='xxs',\\n color='#6c757d'\\n ))\\n words = []\\n event_v, event_o = events.split()\\n regex = \\\"({}|{})\\\".format(event_v, event_o)\\n tokenized_sentence = re.split(regex, news['sentences'][1])\\n tokenized_sentence = [t for t in tokenized_sentence if t != \\\"\\\"]\\n tokenized_sentence = [news['sentences'][0] + \\\" \\\"] + tokenized_sentence + [\\\" \\\" + news['sentences'][2]]\\n for i in tokenized_sentence:\\n if i == event_v or i == event_o:\\n words.append(SpanComponent(\\n text=i,\\n color=\\\"#e86d48\\\"\\n ))\\n else:\\n words.append(SpanComponent(\\n text=i,\\n ))\\n new_text_componet = TextComponent2(type=\\\"text\\\", text=\\\"hello, world\\\", contents=words, wrap=True)\\n TextComponents.append(new_text_componet)\\n TextComponents.append(BoxComponent(\\n layout='horizontal',\\n spacing='md',\\n contents=[\\n TextComponent(\\n text='查看這則新聞',\\n size='md',\\n color='#6E7DAB',\\n flex=1,\\n action=URIAction(uri=url),\\n ),\\n ]\\n ))\\n\\n news_box_components.append(\\n BoxComponent(\\n layout='horizontal',\\n spacing='md',\\n contents=[\\n BoxComponent(\\n layout='vertical',\\n flex=2,\\n spacing='sm',\\n contents=TextComponents\\n )\\n ]\\n )\\n )\\n if isMore:\\n news_box_components.append(\\n ButtonComponent(style=\\\"link\\\",\\n # margin=\\\"xs\\\",\\n action=MessageTemplateAction(\\n label=\\\"more\\\",\\n text=\\\"more:\\\" + str(id) + \\\":\\\" + key + \\\":\\\" + events\\n )))\\n\\n bubble = BubbleContainer(\\n body=BoxComponent(\\n layout='vertical',\\n spacing='lg',\\n contents=news_box_components\\n )\\n )\\n return FlexSendMessage(alt_text=alt_text, contents=bubble)\",\n \"def gen_text_list(ids):\\n return [[tag.lemma for tag in para if (tag.pos not in TT_EXCLUDED_TAGS)]\\n for dm in dm_from_id(ids) for para in dm.get_text_tags()]\",\n \"def show_list(site, titles): # type: (str, List[str]) -> None\\n print(u\\\"The latest tutorials from {}\\\".format(site))\\n for article_id, title in enumerate(titles):\\n print(u\\\"{:>3} {}\\\".format(article_id, title))\",\n \"def getFullCourseTitle(self, brain):\\n full_title = ''\\n\\n id = brain.getCourseId\\n if id:\\n full_title = '%s - ' %id\\n full_title += brain.Title\\n term = brain.getTerm\\n if term:\\n full_title += ', %s' %term\\n\\n return full_title\",\n \"def print_tod_accomplishments(accomplishments):\\n print(Colors.BLUE + \\\"Accomplishments from Tod:\\\" + Colors.NORMAL)\\n for accomplishment in accomplishments:\\n print(Colors.CYAN + '* ' + Colors.NORMAL + accomplishment)\",\n \"def escape_list(l):\\n return [_escape_harlowe_html(item) if isinstance(item, text_type) else str(item) for item in l]\",\n \"def test_tax_court_citation_extractor(self):\\n\\n test_pairs = (\\n (\\n \\\"\\\"\\\" 1 UNITED STATES TAX COURT REPORT (2018)\\n\\n\\n\\n UNITED STATES TAX COURT\\n\\n\\n\\n BENTLEY COURT II LIMITED PARTNERSHIP, B.F. BENTLEY, INC., TAX\\n MATTERS PARTNER, Petitioner v.\\n COMMISSIONER OF INTERNAL REVENUE, Respondent\\n\\n\\n\\n Docket No. 5393-04. Filed May 31, 2006.\\n\\n\\n\\n Nancy Ortmeyer Kuhn, for petitioner.\\n \\\"\\\"\\\",\\n \\\"1 T.C. 2018\\\",\\n ),\\n (\\n \\\"\\\"\\\" T.C. Memo. 2003-150\\n\\n\\n\\n UNITED STATES TAX COURT\\n\\n\\n\\n RIVER CITY RANCHES #1 LTD., LEON SHEPARD,\\n TAX MATTERS PARTNER,\\n RIVER CITY RANCHES #2 LTD., LEON SHEPARD,\\n TAX MATTERS PARTNER,\\n RIVER CITY RANCHES #3 LTD., LEON SHEPARD,\\n TAX MATTERS PARTNER,\\n\\n\\n \\\"\\\"\\\",\\n \\\"2003 T.C. Memo. 150\\\",\\n ),\\n (\\n \\\"\\\"\\\" T.C. Summary Opinion 2003-150\\n\\n\\n\\n UNITED STATES TAX COURT\\n\\n\\n\\n RIVER CITY RANCHES #1 LTD., LEON SHEPARD,\\n TAX MATTERS PARTNER,\\n RIVER CITY RANCHES #2 LTD., LEON SHEPARD,\\n TAX MATTERS PARTNER,\\n RIVER CITY RANCHES #3 LTD., LEON SHEPARD,\\n TAX MATTERS PARTNER,\\n\\n\\n \\\"\\\"\\\",\\n \\\"2003 T.C. Summary Opinion 150\\\",\\n ),\\n (\\n \\\"\\\"\\\"\\n MICHAEL KEITH SHENK, PETITIONER v. COMMISSIONER\\n OF INTERNAL REVENUE, RESPONDENT\\n\\n Docket No. 5706–12. Filed May 6, 2013.\\n\\n P was divorced from his wife, and their 2003 ‘‘Judgment of\\n Absolute Divorce’’ provided that his ex-wife would have pri-\\n mary residential custody of their three minor children. The\\n judgment provided that the dependency exemption deductions\\n for the three children would be divided between the two ex-\\n spouses according to various conditions but did not provide\\n that the ex-wife must execute in P’s favor a Form 8332,\\n ‘‘Release of Claim to Exemption for Child of Divorced or Sepa-\\n rated Parents’’. The children resided with P’s ex-wife for more\\n than half of 2009, and P’s ex-wife did not execute in P’s favor\\n any Form 8332 or equivalent document for any year. For 2009\\n P timely filed a Federal income tax return on which he\\n claimed dependency exemption deductions and the child tax\\n credit for two of the children, consistent with his under-\\n standing of the terms of the judgment, but he did not attach\\n any Form 8332 to his return. He also claimed head-of-house-\\n hold filing status. His ex-wife, the custodial parent, timely\\n filed a Federal income tax return for 2009 on which she also\\n\\n 200\\n\\n\\n\\n\\nVerDate Nov 24 2008 10:59 Jul 11, 2014 Jkt 372897 PO 20012 Frm 00001 Fmt 3857 Sfmt 3857 V:\\\\FILES\\\\BOUND VOL. WITHOUT CROP MARKS\\\\B.V.140\\\\SHENK JAMIE\\n\\f (200) SHENK v. COMMISSIONER 201\\n\\n\\n claimed two dependency exemption deductions, so that one\\n child was claimed on both parents’ returns. R allowed to P the\\n dependency exemption deduction for one of the children but\\n disallowed his claim for the dependency exemption deduction\\n for the child who had also been claimed by the custodial\\n parent. At trial P contended he is entitled to a dependency\\n exemption deduction for all three children. Held: Since the\\n custodial parent did not execute, and P could not and did not\\n attach to his return, any Form 8332 or equivalent release, P\\n is not entitled under I.R.C. sec. 152(e)(2)(A) to claim the\\n dependency exemption deduction or the child tax credit. Held,\\n further, where both the custodial parent and the noncustodial\\n parent have claimed for the same year a dependency exemp-\\n tion deduction for the same child, a declaration signed by the\\n custodial parent after the period of limitations for assess-\\n ments has expired as to the custodial parent could not qualify\\n under I.R.C. sec. 152(e)(2)(A), and therefore there is no reason\\n to grant P’s request to leave the record open so that he may\\n obtain and proffer such a declaration. Held, further, P is not\\n entitled to head-of-household filing status under I.R.C. sec.\\n 2(b)(1) nor to the child tax credit under I.R.C. sec. 24.\\n\\n Michael Keith Shenk, for himself.\\n Shari Salu, for respondent.\\n GUSTAFSON, Judge: The Internal Revenue Service (IRS)\\n determined a deficiency of $3,136 in the 2009 Federal income\\n tax of petitioner Michael Keith Shenk. Mr. Shenk petitioned\\n this Court, pursuant to section 6213(a), 1 for redetermination\\n of the deficiency. After Mr. Shenk’s concession that he\\n received but did not report $254 in dividend income, the\\n issue for decision is whether Mr. Shenk is entitled to a\\n dependency exemption deduction for one of his children\\n under section 151(c), a child tax credit for that child under\\n section 24(a), and head-of-household filing status under sec-\\n tion 2(b)(1). On these issues, we hold for the IRS.\\n FINDINGS OF FACT\\n\\n The judgment of divorce\\n Mr. Shenk was married to Julie Phillips, and they have\\n three minor children—M.S., W.S., and L.S. They divorced in\\n 2003. The family court’s ‘‘Judgment of Absolute Divorce’’ pro-\\n 1 Unless otherwise indicated, all citations of sections refer to the Internal\\n\\n Revenue Code (26 U.S.C.) in effect for the tax year at issue, and all cita-\\n tions of Rules refer to the Tax Court Rules of Practice and Procedure.\\n\\n\\n\\n\\nVerDate Nov 24 2008 10:59 Jul 11, 2014 Jkt 372897 PO 20012 Frm 00002 Fmt 3857 Sfmt 3857 V:\\\\FILES\\\\BOUND VOL. WITHOUT CROP MARKS\\\\B.V.140\\\\SHENK JAMIE\\n\\f 202 140 UNITED STATES TAX COURT REPORTS (200)\\n\\n\\n vided: that Ms. Phillips was ‘‘awarded primary residential\\n custody’’ of the parties’ three children; and that Mr. Shenk\\n would be liable for child support payments; but that, as to\\n dependency exemptions—\\\"\\\"\\\",\\n \\\"140 T.C. 200\\\",\\n ),\\n )\\n site = tax.Site()\\n for q, a in test_pairs:\\n results = site.extract_from_text(q)\\n cite_string = \\\"%s %s %s\\\" % (\\n results[\\\"Citation\\\"][\\\"volume\\\"],\\n results[\\\"Citation\\\"][\\\"reporter\\\"],\\n results[\\\"Citation\\\"][\\\"page\\\"],\\n )\\n\\n self.assertEqual(cite_string, a)\\n print \\\"✓\\\", cite_string\",\n \"def make_title(words):\",\n \"def test_reformat_paragraph_list_1_of_5(self):\\n before_b = \\\"\\\"\\\"\\\\\\n This paragraph leads of this test. It is the \\\"lead\\\"\\n paragraph.\\n\\n 1. This is item \\n number 1. It is the first item in the list.\\n\\n 2. This is item \\n number 2. It is the second item in the list.\\n\\n 3. This is item \\n number 3. It is the third item in the list.\\n\\n This paragraph ends the test. It is the \\\"final\\\"\\n paragraph.\\n \\\"\\\"\\\"\\n after_b = \\\"\\\"\\\"\\\\\\n This paragraph leads of this test. It is\\n the \\\"lead\\\" paragraph.\\n\\n 1. This is item \\n number 1. It is the first item in the list.\\n\\n 2. This is item \\n number 2. It is the second item in the list.\\n\\n 3. This is item \\n number 3. It is the third item in the list.\\n\\n This paragraph ends the test. It is the \\\"final\\\"\\n paragraph.\\n \\\"\\\"\\\"\\n self.run_test(\\n before_b=before_b,\\n after_b=after_b,\\n before_sel=(\\\"1.0\\\", \\\"1.0\\\"),\\n after_sel=(\\\"4.0\\\", \\\"4.0\\\"),\\n command_name=\\\"reformat-paragraph\\\",\\n directives=\\\"@language plain\\\\n@pagewidth 40\\\\n@tabwidth 8\\\",\\n )\",\n \"def courses(request):\\r\\n courses = get_courses(request.user, request.META.get('HTTP_HOST'))\\r\\n courses = sort_by_announcement(courses)\\r\\n\\r\\n return render_to_response(\\\"courseware/courses.html\\\", {'courses': courses})\",\n \"def __repr__(self): \\n raw = self.body.split()\\n i = 0\\n for words in raw:\\n if i%15 == 0:\\n raw.insert(i, \\\"\\\\n\\\")\\n i = i + 1\\n raw.append(\\\"\\\\n Different Centuries meet System\\\\n\\\")\\n return ' '.join(raw)\",\n \"def get_list_html(self, items):\\n html = \\\"\\\"\\\"\\n <html>\\n\\t\\t\\t<head>\\n\\t\\t\\t\\t<title>OpenFDA Cool App</title>\\n\\t\\t\\t</head>\\n\\t\\t\\t<body>\\n <ol>\\n \\\"\\\"\\\"\\n\\n for item in items:\\n html += \\\"<li>\\\" + item + \\\"</li>\\\\n\\\"\\n\\n html += \\\"\\\"\\\"\\n </ol>\\n\\t\\t\\t</body>\\n </html>\\n \\\"\\\"\\\"\\n\\n return html\",\n \"def convert_to_t5_format(nlp, texts):\\n\\n inputs = []\\n outputs = []\\n original_texts = []\\n\\n for text, doc in zip(texts, nlp.pipe(texts, n_process=-1)):\\n\\n pairs = set()\\n\\n for chunk in doc.noun_chunks:\\n if chunk.text == text:\\n continue\\n input_ = text[0 : chunk.start_char] + \\\"<extra_id_0> \\\" + text[chunk.end_char + 1 :]\\n output = \\\"<extra_id_0> \\\" + chunk.text + \\\" <extra_id_1> </s>\\\"\\n\\n pairs.add((input_.strip(), output))\\n\\n for token in doc:\\n left_edge_i = token.left_edge.i\\n right_edge_i = token.right_edge.i\\n chunk_length = right_edge_i - left_edge_i + 1\\n if chunk_length / len(doc) > 0.5 or chunk_length > 10: # if chunk is too long, just skip it\\n continue\\n\\n input_ = str(doc[:left_edge_i]) + \\\" <extra_id_0> \\\" + str(doc[right_edge_i + 1 :])\\n output = \\\"<extra_id_0> \\\" + str(doc[left_edge_i : right_edge_i + 1]) + \\\" <extra_id_1> </s>\\\"\\n\\n pairs.add((input_.strip(), output))\\n\\n for token in doc:\\n if token.pos_ in [\\\"NOUN\\\", \\\"PRON\\\", \\\"PROPN\\\"]: # we don't want to mask parts of noun chunks\\n continue\\n input_ = str(doc[: token.i]) + \\\" <extra_id_0> \\\" + str(doc[token.i + 1 :])\\n output = \\\"<extra_id_0> \\\" + token.text + \\\" <extra_id_1> </s>\\\"\\n\\n pairs.add((input_.strip(), output))\\n\\n for (input_, output) in pairs:\\n inputs.append(input_)\\n outputs.append(output)\\n original_texts.append(text)\\n\\n return inputs, outputs, original_texts\",\n \"def generatePerWordHTML(testDataWithFeatures, \\n allLabels, classifications, colors):\\n \\n assert (len(testDataWithFeatures)-1) % len(allLabels) == 0\\n numWords = (len(testDataWithFeatures) - 1) / len(allLabels) + 1 \\n \\n allLabelsDict = dict((label,index) for index,label in enumerate(allLabels)) \\n \\n perWordHTML = []\\n for i in xrange(numWords):\\n if classifications:\\n word, correctClass, guessClass = classifications[i]\\n color = colors[(correctClass, guessClass)]\\n else:\\n datum = getDatum(testDataWithFeatures, \\n allLabelsDict, i, allLabels[0])\\n word = datum.word\\n color = colors[datum.label, datum.label]\\n html = [('<a href=\\\"#\\\" onmouseover=\\\"a({0});\\\" '\\n + 'onmouseout=\\\"b({0});\\\">').format(i), \\n '<span style=\\\"color:{}\\\">{}</span>'.format(\\n color, escape(word)),\\n '</a>']\\n \\n if word in \\\".!?\\\":\\n html.append(\\\"<br/>\\\")\\n \\n perWordHTML.append(\\\"\\\".join(html)) \\n return perWordHTML\",\n \"def course_reviews(request, course_code):\\n try:\\n course = (\\n Course.objects.filter(course_filters_pcr_allow_xlist, full_code=course_code)\\n .order_by(\\\"-semester\\\")[:1]\\n .select_related(\\n \\\"topic\\\",\\n \\\"topic__most_recent\\\",\\n \\\"topic__branched_from\\\",\\n \\\"topic__branched_from__most_recent\\\",\\n )\\n .prefetch_related(\\\"topic__courses\\\")\\n .get()\\n )\\n except Course.DoesNotExist:\\n raise Http404()\\n\\n topic = course.topic\\n course = topic.most_recent\\n course_code = course.full_code\\n aliases = course.crosslistings.values_list(\\\"full_code\\\", flat=True)\\n\\n instructor_reviews = review_averages(\\n Review.objects.filter(review_filters_pcr, section__course__topic=topic),\\n reviewbit_subfilters=Q(review_id=OuterRef(\\\"id\\\")),\\n section_subfilters=Q(id=OuterRef(\\\"section_id\\\")),\\n fields=ALL_FIELD_SLUGS,\\n prefix=\\\"bit_\\\",\\n extra_metrics=True,\\n ).annotate(instructor_name=F(\\\"instructor__name\\\"), semester=F(\\\"section__course__semester\\\"))\\n recent_instructors = list(\\n Instructor.objects.filter(\\n id__in=Subquery(\\n Section.objects.filter(section_filters_pcr, course__topic=topic).values(\\n \\\"instructors__id\\\"\\n )\\n )\\n )\\n .distinct()\\n .annotate(\\n most_recent_sem=Subquery(\\n Section.objects.filter(instructors__id=OuterRef(\\\"id\\\"), course__topic=topic)\\n .annotate(common=Value(1))\\n .values(\\\"common\\\")\\n .annotate(max_sem=Max(\\\"course__semester\\\"))\\n .values(\\\"max_sem\\\")\\n )\\n )\\n .values(instructor_id=F(\\\"id\\\"), instructor_name=F(\\\"name\\\"), semester=F(\\\"most_recent_sem\\\"))\\n )\\n for instructor in recent_instructors:\\n instructor[\\\"exclude_from_recent\\\"] = True\\n all_instructors = list(instructor_reviews.values()) + recent_instructors\\n instructors = aggregate_reviews(all_instructors, \\\"instructor_id\\\", name=\\\"instructor_name\\\")\\n\\n course_qs = annotate_average_and_recent(\\n Course.objects.filter(course_filters_pcr, topic=topic).order_by(\\\"-semester\\\")[:1],\\n match_review_on=Q(section__course__topic=topic) & review_filters_pcr,\\n match_section_on=Q(course__topic=topic) & section_filters_pcr,\\n extra_metrics=True,\\n )\\n course = get_single_dict_from_qs(course_qs)\\n\\n num_registration_metrics = Section.objects.filter(\\n extra_metrics_section_filters_pcr(),\\n course__topic=topic,\\n ).count()\\n\\n num_sections, num_sections_recent = get_num_sections(\\n section_filters_pcr,\\n course__topic=topic,\\n )\\n\\n return Response(\\n {\\n \\\"code\\\": course[\\\"full_code\\\"],\\n \\\"name\\\": course[\\\"title\\\"],\\n \\\"description\\\": course[\\\"description\\\"],\\n \\\"aliases\\\": aliases,\\n \\\"historical_codes\\\": get_historical_codes(\\n topic, exclude_codes=set(aliases) | {course[\\\"full_code\\\"]}\\n ),\\n \\\"latest_semester\\\": course[\\\"semester\\\"],\\n \\\"num_sections\\\": num_sections,\\n \\\"num_sections_recent\\\": num_sections_recent,\\n \\\"instructors\\\": instructors,\\n \\\"registration_metrics\\\": num_registration_metrics > 0,\\n **get_average_and_recent_dict_single(course),\\n }\\n )\",\n \"def print_cities() -> None:\\n city_list = get_cities()\\n i = 1\\n for a,b,c,d,e in zip(city_list[::5], city_list[1::5], city_list[2::5], city_list[3::5], city_list[4::5]):\\n a = str(i) + f\\\".{a}\\\"\\n i += 1\\n b = str(i) + f\\\".{b}\\\"\\n i += 1\\n c = str(i) + f\\\".{c}\\\"\\n i += 1\\n d = str(i) + f\\\".{d}\\\"\\n i += 1\\n e = str(i) + f\\\".{e}\\\"\\n i += 1\\n if e == '410.youngstown':\\n print(\\\"{:<20}{:<20}{:<20}{:<20}{:<}\\\".format(a,b,c,d,e))\\n a = str(i) + f\\\".{city_list[i-1]}\\\"\\n b = str(i+1) + f\\\".{city_list[i]}\\\"\\n i += 1\\n c = c = str(i+1) + f\\\".{city_list[i]}\\\"\\n print(\\\"{:<20}{:<20}{:<}\\\".format(a,b,c))\\n else:\\n print(\\\"{:<20}{:<20}{:<20}{:<20}{:<}\\\".format(a,b,c,d,e))\",\n \"def create_list_html(array):\\n if not array:\\n return ''\\n msg = ''\\n for item in array:\\n msg += '<li>' + item + '</li>'\\n return '<ul>' + msg + '</ul>'\",\n \"def help_text(command):\\n\\n courses_list = ('ENPM611', 'ENPM613', 'ENPM631', 'ENPM687',\\\\\\n 'ENPM691', 'ENPM693', 'ENPM694', 'ENPM696',\\\\\\n 'ENPM809J','ENPM809R', 'ENPM809W')\\n\\n response = 'I have course descriptions for: '\\n for course_name in courses_list:\\n response = response + course_name + ' '\\n\\n response = response + '\\\\nTo get the course description, execute command: about ENPM<course_number>'\\n\\n return response\",\n \"def display_abstract( id, highlights=[]):\\n a = Abstracts[ id ]\\n for h in highlights:\\n a = re.sub(r'\\\\b(%s)\\\\b'%h,'<mark>\\\\\\\\1</mark>',a, flags=re.IGNORECASE)\\n print ('<blockquote>%s</blockquote>' % a)\",\n \"def remove_html_tags(text):\\r\\n global count\\r\\n clean = re.compile('<.*?>')\\r\\n var = re.sub(clean, '', str(item))\\r\\n # FOR ADDING NUMBERS ON SAME LINE\\r\\n if count < 2:\\r\\n print(var,end=' ')\\r\\n count+=1\\r\\n if count > 1:\\r\\n count = 0\\r\\n print(\\\"\\\\n\\\")\\r\\n if var.isnumeric()==False:\\r\\n college_names.append(var)\",\n \"def ROOM_DESCRIPTIONS(adventurer):\\n rooms = (\\n f\\\"{text_color.MAGENTA_TEXT}{adventurer}{text_color.FINISH_COLOR} is inside a wine cellar, most of it is filled with cracked, empty bottles.\\\\n\\\"\\n \\\"Perhaps it is better to not go looking for an intact bottle.\\\\n\\\"\\n f\\\"{text_color.MAGENTA_TEXT}{adventurer}{text_color.FINISH_COLOR} suspects it will be very detrimental to their health to fight monsters while drunk.\\\",\\n f\\\"{text_color.MAGENTA_TEXT}{adventurer}{text_color.FINISH_COLOR} is inside a gaol, the cells are mostly filled with the skeletons of prisoners long dead.\\\\n\\\"\\n \\\"Most of the cell doors are open with the exception of one in the far corner.\\\\n\\\"\\n \\\"There seems to be a person in there but they are crouched in the corner muttering to themself.\\\\n\\\"\\n \\\"The cleaver they are holding looks very dangerous so perhaps it is best to leave them alone.\\\\n\\\",\\n f\\\"{text_color.MAGENTA_TEXT}{adventurer}{text_color.FINISH_COLOR}is inside a dusty old library, piles and piles of books are scattered amongst the dirty floor.\\\\n\\\"\\n f\\\"{text_color.MAGENTA_TEXT}{adventurer}{text_color.FINISH_COLOR} remembers the last time they perused books inside of that nast vampire\\\\'s castle.\\\\n\\\"\\n f\\\"The tentacles that came out of the pages almost strangled {text_color.MAGENTA_TEXT}{adventurer}{text_color.FINISH_COLOR}.\\\\n\\\"\\n f\\\"Perhaps it is better to finish what {text_color.MAGENTA_TEXT}{adventurer}{text_color.FINISH_COLOR} came here to do instead of doing some light reading.\\\",\\n f\\\"{text_color.MAGENTA_TEXT}{adventurer}{text_color.FINISH_COLOR} is inside what appears to be a ritual room.\\\\n\\\"\\n \\\"The only things inside a room are black candles mounted on the walls and a pentagram in the middle of the floor.\\\\n\\\"\\n f\\\"The pentagram appears to be made from dried blood, {text_color.MAGENTA_TEXT}{adventurer}{text_color.FINISH_COLOR} would rather not think of who or what provided that blood.\\\\n\\\"\\n \\\"On each corner of the star there appears to be a random assortment of objects.\\\\n\\\"\\n \\\"For some reason one of them looks like a phallic object.\\\\n\\\"\\n f\\\"{text_color.MAGENTA_TEXT}{adventurer}{text_color.FINISH_COLOR} decides they don\\\\'t want to stay here any longer than they have to.\\\",\\n f\\\"{text_color.MAGENTA_TEXT}{adventurer}{text_color.FINISH_COLOR} is inside their childhood bedroom.\\\\n\\\"\\n f\\\"The bed and drawer is just like how they remember it.\\\\n\\\"\\n f\\\"There's even that empty aquarium {adventurer} left inside their room even though the fish all died within a day.\\\\n\\\"\\n f\\\"{text_color.MAGENTA_TEXT}{adventurer}{text_color.FINISH_COLOR} would rather not think too hard about why their room is inside of a dungeon.\\\\n\\\",\\n f\\\"{text_color.MAGENTA_TEXT}{adventurer}{text_color.FINISH_COLOR} is inside of a sewer, the smell feels like its burning away their nostril hairs.\\\\n\\\"\\n f\\\"{text_color.MAGENTA_TEXT}{adventurer}{text_color.FINISH_COLOR} wonders how there could even be a sewer when the doors connecting it lead into different rooms.\\\\n\\\"\\n f\\\"As {text_color.MAGENTA_TEXT}{adventurer}{text_color.FINISH_COLOR} stands there they see a figure in their peripheral vision.\\\\n\\\"\\n \\\"What looks like a large bipedal rat is standing there 4 smaller bipedal turtles.\\\\n\\\"\\n f\\\"Before {text_color.MAGENTA_TEXT}{adventurer}{text_color.FINISH_COLOR} can say anything a large cloud of smoke appears and when it disappears they are gone.\\\\n\\\"\\n )\\n return random.choice(rooms)\",\n \"def nautc(\\n text: str,\\n no_label: bool = False,\\n labels: bool = False,\\n dash_length: int = 20,\\n) -> None:\\n if text:\\n for label, ctxt in convert(text):\\n print(\\\"-\\\" * dash_length)\\n ((not no_label) or labels) and print(label) # noqa: WPS428\\n not labels and print(ctxt) # noqa: WPS428\\n print(\\\"-\\\" * dash_length)\",\n \"def format_list(self, at_char, user, list_name):\\r\\n return u'<a href=\\\"http://%s/%s/%s\\\" data-list=\\\"\\\">%s%s/%s</a>' \\\\\\r\\n % (self.domain, user, list_name, at_char, user, list_name)\",\n \"def export_courses(courses, output):\\n courses = sorted(courses)\\n writer = csv.writer(output)\\n writer.writerow([\\n 'College', 'Department', 'Code', 'Name', 'Credits', 'Tags',\\n 'Prerequisites'\\n ])\\n\\n for course in courses:\\n writer.writerow([\\n course.college, course.department, course.code, course.name,\\n course.credits, ','.join(course.tags), ','.join(course.prerequisites)\\n ])\",\n \"def makeNonsense(part1, part2, part3, n=10):\\n #convert to lists\\n p1 = part1.split('\\\\n')\\n p2 = part2.split('\\\\n')\\n p3 = part3.split('\\\\n')\\n #shuffle the lists\\n [ random.shuffle( p ) for p in [p1,p2,p3] ]\\n #concatinate the sentences\\n sentence = []\\n for k in range(n):\\n try:\\n s = p1[k] + ' ' + p2[k] + ' ' + p3[k] \\n # THE LOUDER YOU YELL, THE RIGHTER YOU ARE!!\\n s += '!!!'\\n sentence.append(s)\\n except IndexError:\\n break\\n return sentence\",\n \"def addSemester(inputFile, outputFile):\\n\\n \\n fall_classes = ['cs 121', 'cs 223', 'cs 260', 'cs 215']\\n spring_classes = ['cs 122', 'cs 166', 'cs 224', 'cs 251', 'cs 261']\\n \\n\\n with open(inputFile, 'r') as f:\\n newlist = f.readlines() \\n for i in range(len(newlist)):\\n newlist[i] = newlist[i].strip()\\n print(newlist)\\n for i in newlist:\\n print(i) \\n print(\\\"-----------------------\\\") # this is all taking each line in the text and converting it into a list while also getting rid of any new lines to make it easier to concatanate fall or spring\\n\\n with open(outputFile, 'w') as f:\\n index = 0 # this is a counter that is incrementing once every loop and is being used to index into the list \\n for i in newlist: \\n if i in fall_classes: \\n newlist[index] += \\\" fall\\\\n\\\"\\n index += 1\\n elif i in spring_classes:\\n newlist[index] += \\\" spring\\\\n\\\"\\n index += 1\\n\\n \\n original = ''.join(newlist) # this brings together the finalized list into the original format just with the corresponding fall or spring\\n print(original)\\n f.write(original)\",\n \"def booksAvailable(books):\\n print(\\\"The following titles are available for purchase: \\\")\\n for book in books:\\n print (' -' + book.title())\",\n \"def print_list(self):\\n self.print_avec_separateur(\\\" \\\")\",\n \"def ConstructList(title, items):\\n buf = cStringIO.StringIO()\\n fmt = 'list[title=\\\"{title}\\\",always-display-title]'.format(title=title)\\n resource_printer.Print(sorted(set(items)), fmt, out=buf)\\n return buf.getvalue()\",\n \"def get_tagged_titles(ttls_lnks):\\n\\ttagged_titles = []\\n\\tfor title, link in ttls_lnks:\\n\\t\\t# get the html tree for the paper's page\\n\\t\\tpaper_tree = get_tree(link)\\n\\t\\tpath = '//table/tr/th[text() = \\\"Subjects:\\\"]'\\n\\t\\t# Check if html contains the table header \\\"Subjects:\\\"\\n\\t\\tsubject_th = paper_tree.xpath(path)\\n\\t\\t# If it does, this means paper is tagged so add to the list to be returned\\n\\t\\tif subject_th:\\n\\t\\t\\ttagged_titles.append(title)\\n\\n\\treturn tagged_titles\",\n \"def get_courses(std):\\n return std[2]\",\n \"def clean_5_words(self):\\n self.first_word.configure(text=\\\"\\\")\\n self.second_word.configure(text=\\\"\\\")\\n self.third_word.configure(text=\\\"\\\")\\n self.fourth_word.configure(text=\\\"\\\")\\n self.fifth_word.configure(text=\\\"\\\")\",\n \"def get_text(self, course):\\r\\n return views.render_accordion(\\r\\n self.request, course, course.get_children()[0].scope_ids.usage_id.to_deprecated_string(), None, None\\r\\n )\",\n \"def passages_labels (passages):\\n\\n def group_by (s):\\n return s[:3]\\n\\n def title (s):\\n return s[1:3]\\n\\n group = None\\n ticks = []\\n labels = []\\n\\n for i, passage in enumerate (passages):\\n g = group_by (passage)\\n if g != group:\\n ticks.append (i)\\n labels.append (title (passage))\\n group = g\\n\\n return ticks, labels\",\n \"def typoglycemia(sequence):\\n words = sequence.split()\\n\\t\\n for i in range(len(words)):\\n if len(words[i]) > 4:\\n word_partial = list(words[i][1:-1])\\n random.shuffle(word_partial)\\n word_shuffle = \\\"\\\".join(word_partial)\\n words[i] = words[i][0] + word_shuffle + words[i][-1]\\n \\n return ' '.join(words)\",\n \"def get_subtitle_print(subs: List[Track]) -> List[str]:\\n data = []\\n if not subs:\\n data.append(\\\"--\\\")\\n for sub in subs:\\n line_items = []\\n\\n # following sub.title tree checks and supports three different language and title scenarios\\n # The second scenario is the recommended option to choose if you are open to choosing any\\n # The third scenario should be used if you have nothing unique to state about the track\\n # | Language | Track Title | Output |\\n # | ------------ | ----------------------------- | --------------------------------------------- |\\n # | es / Spanish | Spanish (Latin American, SDH) | - Spanish (Latin American, SDH), SubRip (SRT) |\\n # | es / Spanish | Latin American (SDH) | - Spanish, Latin American (SDH), SubRip (SRT) |\\n # | es / Spanish | None | - Spanish, SubRip (SRT) |\\n language = pycountry.languages.get(alpha_2=sub.language).name\\n if sub.title:\\n if language.lower() in sub.title.lower():\\n line_items.append(sub.title)\\n else:\\n line_items.append(f\\\"{language}, {sub.title}\\\")\\n else:\\n line_items.append(language)\\n\\n line_items.append(sub.format.replace(\\\"UTF-8\\\", \\\"SubRip (SRT)\\\"))\\n\\n line = \\\"- \\\" + \\\", \\\".join(line_items)\\n data += [\\n (\\\" \\\" + x if i > 0 else x)\\n for i, x in enumerate(textwrap.wrap(line, 64))\\n ]\\n return data\",\n \"def test15():\\n\\tdef guest_list(guests):\\n\\t\\tfor name, old, job in guests:\\n\\t\\t\\tprint('{} is {} years old and works as {}'.format(name, old, job))\\n\\n\\tguest_list([('Ken', 30, \\\"Chef\\\"), (\\\"Pat\\\", 35, 'Lawyer'), ('Amanda', 25, \\\"Engineer\\\")])\\n\\n\\t#Click Run to submit code\\n\\t\\\"\\\"\\\"\\n\\tOutput should match:\\n\\tKen is 30 years old and works as Chef\\n\\tPat is 35 years old and works as Lawyer\\n\\tAmanda is 25 years old and works as Engineer\\n\\t\\\"\\\"\\\"\",\n \"def make_text(chains):\\n words = []\\n not_end_of_list = True\\n # your code goes here\\n \\n # starts with a capital lettered word from source text\\n capitalized_ngrams = []\\n for key in chains.keys():\\n # check if the first tuple in key[0][0]\\n if key[0][0].isupper():\\n capitalized_ngrams.append(key)\\n \\n \\n\\n selected_keys = list(capitalized_ngrams)\\n count = 0\\n while not_end_of_list:\\n choice_n = choice(selected_keys)\\n\\n if count == 0:\\n words.extend(choice_n)\\n \\n if chains[choice_n] and count <= 150:\\n # as long as there is an option, picks a random element from dict list\\n choose_next = choice(chains[choice_n])\\n # adds new word to list\\n words.append(choose_next)\\n # creates a list of keys whose last item in tuple is item from list\\n selected_keys = [x for x in chains.keys() if x == tuple([*choice_n[1:], choose_next])]\\n # it is possible continues\\n if selected_keys:\\n pass\\n else:\\n not_end_of_list = False\\n\\n count += 1\\n \\n else:\\n not_end_of_list = False\\n\\n return \\\" \\\".join(words)\",\n \"def _make_song_list_html(song_list):\\n return '<p class=\\\"song_name\\\">' + '<br>'.join([f'{song[\\\"title\\\"]} <span class=\\\"artist_album\\\">{song[\\\"artist\\\"]} - {song[\\\"album\\\"]}</span>' for song in song_list]) + '</p>'\",\n \"def demonstrate_list_comprehension():\\n\\n from array import array\\n a = array('i', [4, 2, 8, 9])\\n l = [i for i in a]\\n print(l)\\n l = [str(i) for i in a]\\n print(l)\\n print()\\n\\n john = ['John', 'Lennon', 'Liverpool']\\n l = [s for s in john]\\n print(l)\\n print(', '.join(s for s in john))\\n print(', '.join(john))\\n print()\\n\\n songs = ['Imagine a Man', 'There\\\\'s a Place', 'No Expectations', 'Heaven is a Place on Earth']\\n\\n first_words = [words[0] for words in [title.split() for title in songs]]\\n print(first_words)\\n print(' '.join(first_words))\\n lyric = [first_words[0]] + [word.lower() for word in first_words[1:]]\\n print(' '.join(lyric))\\n print()\\n\\n songs = ['No Expectations', 'Imagine a Man', 'There\\\\'s a Place', 'No Expectations', 'Heaven is a Place on Earth',\\n 'No Expectations', 'No Expectations', ]\\n print([i for i, k in enumerate(songs) if k == 'No Expectations'])\",\n \"def decorate(self, content: StringList) -> None:\\n super().decorate(content)\\n for line in content[:20]:\\n if line.startswith(TITLE_MARKER):\\n title = line[len(TITLE_MARKER) :].strip()\\n fence = \\\"=\\\" * len(title)\\n content.insert(0, \\\"\\\", \\\"<generated>\\\", 0)\\n content.insert(0, fence, \\\"<generated>\\\", 0)\\n content.insert(0, title, \\\"<generated>\\\", 0)\\n content.insert(0, fence, \\\"<generated>\\\", 0)\",\n \"def list(self, option: str = \\\"\\\", short=False, **kwargs):\\n courses = self.get_sorted_courses()\\n\\n if option == \\\"plain\\\":\\n if short:\\n for course in sorted(courses, key=lambda x: x.name + x.type):\\n print(f\\\"{course.name} ({course.type})\\\")\\n else:\\n for course in sorted(courses, key=lambda x: x.abbreviation + x.type):\\n print(f\\\"{course.abbreviation}-{course.type[0]}\\\")\\n quit()\\n\\n current_day = datetime.today()\\n current_weekday = current_day.weekday()\\n\\n # split to scheduled and non-scheduled\\n unscheduled = [c for c in courses if c.time is None]\\n courses = [c for c in courses if c not in unscheduled]\\n\\n table = []\\n option = option.lower()\\n\\n for i, course in enumerate(courses):\\n # lambda functions to test for various options\\n # a is current weekday and b is the course's weekday\\n options = {\\n \\\"\\\": lambda _, __: True, # all of them\\n \\\"t\\\": lambda a, b: a == b, # today\\n \\\"tm\\\": lambda a, b: (a + 1) % 7 == b, # tomorrow\\n \\\"mo\\\": lambda a, b: b == 0,\\n \\\"tu\\\": lambda a, b: b == 1,\\n \\\"we\\\": lambda a, b: b == 2,\\n \\\"th\\\": lambda a, b: b == 3,\\n \\\"fr\\\": lambda a, b: b == 4,\\n \\\"sa\\\": lambda a, b: b == 5,\\n \\\"su\\\": lambda a, b: b == 6,\\n }\\n\\n if option not in options:\\n exit_with_error(\\\"Invalid course-listing option!\\\")\\n\\n if options[option](current_weekday, course.weekday()):\\n # include the name of the day before first day's course\\n if courses[i - 1].time.day != courses[i].time.day:\\n weekday = course.time.day.capitalize()\\n\\n # calculate the next occurrence\\n date = (\\n current_day\\n + timedelta(days=(course.weekday() - current_weekday) % 7)\\n ).strftime(\\\"%-d. %-m.\\\")\\n\\n table.append([f\\\"{weekday if not short else weekday[:3]} / {date}\\\"])\\n\\n # for possibly surrounding the name with chars if it's ongoing\\n name_surround_char = \\\"•\\\" if course.is_ongoing() else \\\"\\\"\\n\\n row = [\\n f\\\"{name_surround_char}{course.name if not short else course.abbreviation}{name_surround_char}\\\",\\n f\\\"{minutes_to_HHMM(course.time.start)} -\\\"\\n f\\\" {minutes_to_HHMM(course.time.end)}\\\"\\n + (\\n \\\"\\\"\\n if course.time.weeks is None\\n else (\\n f\\\" ({course.time.weeks if not short else course.time.weeks[0]})\\\"\\n )\\n ),\\n \\\"-\\\" if course.classroom is None else course.classroom.number,\\n ]\\n\\n # color the course name the appropriate color, depending on its type\\n row[0] = Ansi.color(row[0], course_types[course.type].color)\\n\\n # append useful information\\n table.append(row)\\n\\n # list unscheduled courses only when no options are specified\\n if option == \\\"\\\" and len(unscheduled) != 0:\\n table.append([\\\"Unscheduled\\\"])\\n for course in unscheduled:\\n table.append(\\n [\\n course.name if not short else course.abbreviation,\\n course.type[0],\\n \\\"-\\\",\\n \\\"-\\\",\\n ]\\n )\\n\\n if len(table) == 0:\\n exit_with_error(\\\"No courses matching the criteria found!\\\")\\n\\n print_table(table)\"\n]","isConversational":false},"negative_scores":{"kind":"list like","value":["0.61776614","0.61535215","0.5460157","0.53928626","0.5347271","0.5328471","0.5140035","0.5117348","0.51146877","0.5056962","0.5048898","0.50328934","0.50134706","0.50019485","0.49917555","0.49796224","0.49568665","0.4950176","0.49257633","0.49147525","0.48917115","0.4876727","0.48721468","0.48712775","0.48478454","0.48441556","0.48265794","0.48217246","0.47843155","0.47706375","0.4767975","0.47606608","0.47477517","0.47425175","0.47395515","0.47171417","0.46958214","0.4693171","0.46876085","0.46811336","0.46754268","0.4671579","0.46674898","0.46598497","0.4658901","0.46574724","0.4650284","0.4646608","0.46456885","0.46416155","0.46405825","0.46396026","0.46327963","0.46322495","0.46321365","0.46311197","0.46274915","0.46256238","0.46229622","0.4613891","0.46131974","0.46057093","0.4596404","0.45894527","0.45756522","0.45740652","0.45662993","0.45637223","0.45493412","0.45491812","0.45457178","0.45396352","0.45373094","0.45367992","0.45367754","0.45356694","0.45299333","0.45200604","0.45176935","0.450448","0.45000306","0.4492431","0.4488791","0.44833162","0.44758233","0.44749668","0.4471958","0.4467824","0.4465533","0.44632465","0.44620666","0.44578376","0.44512907","0.4447331","0.444697","0.44465473","0.44456998","0.44444123","0.44435117","0.4429275"],"string":"[\n \"0.61776614\",\n \"0.61535215\",\n \"0.5460157\",\n \"0.53928626\",\n \"0.5347271\",\n \"0.5328471\",\n \"0.5140035\",\n \"0.5117348\",\n \"0.51146877\",\n \"0.5056962\",\n \"0.5048898\",\n \"0.50328934\",\n \"0.50134706\",\n \"0.50019485\",\n \"0.49917555\",\n \"0.49796224\",\n \"0.49568665\",\n \"0.4950176\",\n \"0.49257633\",\n \"0.49147525\",\n \"0.48917115\",\n \"0.4876727\",\n \"0.48721468\",\n \"0.48712775\",\n \"0.48478454\",\n \"0.48441556\",\n \"0.48265794\",\n \"0.48217246\",\n \"0.47843155\",\n \"0.47706375\",\n \"0.4767975\",\n \"0.47606608\",\n \"0.47477517\",\n \"0.47425175\",\n \"0.47395515\",\n \"0.47171417\",\n \"0.46958214\",\n \"0.4693171\",\n \"0.46876085\",\n \"0.46811336\",\n \"0.46754268\",\n \"0.4671579\",\n \"0.46674898\",\n \"0.46598497\",\n \"0.4658901\",\n \"0.46574724\",\n \"0.4650284\",\n \"0.4646608\",\n \"0.46456885\",\n \"0.46416155\",\n \"0.46405825\",\n \"0.46396026\",\n \"0.46327963\",\n \"0.46322495\",\n \"0.46321365\",\n \"0.46311197\",\n \"0.46274915\",\n \"0.46256238\",\n \"0.46229622\",\n \"0.4613891\",\n \"0.46131974\",\n \"0.46057093\",\n \"0.4596404\",\n \"0.45894527\",\n \"0.45756522\",\n \"0.45740652\",\n \"0.45662993\",\n \"0.45637223\",\n \"0.45493412\",\n \"0.45491812\",\n \"0.45457178\",\n \"0.45396352\",\n \"0.45373094\",\n \"0.45367992\",\n \"0.45367754\",\n \"0.45356694\",\n \"0.45299333\",\n \"0.45200604\",\n \"0.45176935\",\n \"0.450448\",\n \"0.45000306\",\n \"0.4492431\",\n \"0.4488791\",\n \"0.44833162\",\n \"0.44758233\",\n \"0.44749668\",\n \"0.4471958\",\n \"0.4467824\",\n \"0.4465533\",\n \"0.44632465\",\n \"0.44620666\",\n \"0.44578376\",\n \"0.44512907\",\n \"0.4447331\",\n \"0.444697\",\n \"0.44465473\",\n \"0.44456998\",\n \"0.44444123\",\n \"0.44435117\",\n \"0.4429275\"\n]","isConversational":false},"document_score":{"kind":"string","value":"0.66535985"},"document_rank":{"kind":"string","value":"0"}}},{"rowIdx":22667,"cells":{"query":{"kind":"string","value":"Constructs the entire TeX document from all the courses with key document details (like author and table of contents)"},"document":{"kind":"string","value":"def write_to_latex(codelist, unwanted_courses):\n # TODO: investigate a way to add large amounts of text outside of the\n # function\n abstract01 = \"I created this document to practice parsing html and using\\\n tools like Beautiful Soup which I've previously had little experience\\\n in. As a result, it's not perfect.\\\\newline\\\n It is also a slightly condensed all-in-one-place look at a selection\\\n of courses that are available for fourth year computer science\\\n students at the University of Glasgow. For the purposes of clarity I\\\n have removed several courses from this selection. The following\\\n courses have been omitted:\"\n abstract02 = \"For more insight into the project, to report issues or to\\\n inspect the code, have a look at the GitHub:\\\n \\\\url{https://github.com/IS0metric/course-ripper}\"\n unincluded = create_not_included_list(unwanted_courses)\n with open('courses.tex', 'w') as f:\n # TODO Try and move all this to a separate function?\n # TODO: Check if it's more efficient to write a single, massive string\n # to file\n f.write('\\\\documentclass{hitec}\\n')\n f.write('\\\\usepackage[document]{ragged2e}\\n')\n f.write('\\\\usepackage{url}\\n')\n f.write('\\\\usepackage{hyperref}\\n')\n f.write('\\\\setcounter{tocdepth}{4}\\n')\n f.write('\\\\begin{document}\\n')\n f.write('\\\\title{Fourth Year (2016-17) Courses}\\n')\n f.write('\\\\author{Jack Parkinson}\\n')\n f.write('\\\\date{August 2016}\\n')\n f.write('\\\\maketitle\\n')\n f.write('\\\\abstract{' + abstract01 + unincluded + abstract02 + '}\\n')\n f.write('\\\\newpage\\n\\n')\n f.write('\\\\tableofcontents\\n')\n f.write('\\\\newpage\\n\\n')\n # TODO: Look into alternatives to the three lists\n all_courses = []\n sem1_courses = []\n sem2_courses = []\n for code in codelist:\n course = bsoup(get_coursepage(code))\n if course['offered']['value'] == 'Runs Throughout Semesters 1 and 2':\n all_courses.append(course)\n elif \"1\" in course['offered']['value']:\n sem1_courses.append(course)\n elif \"2\" in course['offered']['value']:\n sem2_courses.append(course)\n f.write('\\\\section{Semester 1 and 2 Courses}\\n\\n')\n for course in all_courses:\n f.write(latex_course(course))\n f.write('\\\\section{Semester 1 Only Courses}\\n\\n')\n for course in sem1_courses:\n f.write(latex_course(course))\n f.write('\\\\section{Semester 2 Only Courses}\\n\\n')\n for course in sem2_courses:\n f.write(latex_course(course))\n f.write('\\\\end{document}')\n return None"},"metadata":{"kind":"string","value":"{\n \"objective\": {\n \"self\": [],\n \"paired\": [],\n \"triplet\": [\n [\n \"query\",\n \"document\",\n \"negatives\"\n ]\n ]\n }\n}"},"negatives":{"kind":"list like","value":["def preprocess_courses_corpus():\n soup = None\n with open('courses_corpus.html', 'r') as infile:\n content = infile.read()\n\n soup = BeautifulSoup(content, 'html.parser')\n\n docid = 0\n data = {}\n data['documents'] = []\n\n main_table = soup.find_all(\"div\", attrs={'class': 'courseblock'})\n for course in main_table:\n docid += 1\n title = course.find_all('p', attrs={'class':'courseblocktitle noindent'})[0].text.lstrip('\\n') if len(course.find_all('p', attrs={'class':'courseblocktitle noindent'}))!=0 else ''\n description = (course.find_all('p', attrs={'class':'courseblockdesc noindent'})[0].text.lstrip('\\n') if len(course.find_all('p', attrs={'class':'courseblockdesc noindent'}))!=0 else '') + ' ' + (course.find_all('p', attrs={'class':'courseblockextra noindent'})[0].text if len(course.find_all('p', attrs={'class':'courseblockextra noindent'}))!=0 else '')\n\n data['documents'].append({\n 'docId' : docid,\n 'title' : title.strip(),\n 'description' : description.strip()\n })\n\n with open('courses_data.json', 'w') as outfile:\n json.dump(data, outfile)","def create_tex(unwanted_courses, wanted_courses=None):\n page = requests.get(\n 'http://gla.ac.uk/coursecatalogue/courselist/' +\n '?code=REG30200000&name=School+of+Computing+Science')\n tree = html.fromstring(page.content)\n spans = tree.xpath('//span/text()')\n codes = []\n if wanted_courses is None:\n for s in spans:\n if s[0:4] == \"COMP\" and s[7] == '4' and s not in unwanted_courses:\n codes.append(s)\n else:\n for s in wanted_courses:\n codes.append(s)\n write_to_latex(codes, unwanted_courses)\n return None","def parse_create_course(xml_course):\n attrs = [\n \"term-code\",\n \"term-description\",\n 'subject',\n \"course-number\",\n \"school\",\n \"department\",\n \"title\",\n \"description\",\n \"credit-hours\",\n \"distribution-group\"\n ]\n course = pull_attributes_from_xml(xml_course, attrs)\n course[\"sections\"] = []\n\n return course","def _createCoursesTable(self):\n\t\tcommand = \"\"\"CREATE TABLE courses (ID INTEGER PRIMARY KEY,\n\t\t\tname TEXT,\n\t\t\tauthor_id INTEGER,\n\t\t\tdescription TEXT\n\t\t\t);\n\"\"\"\n\n\t\tself._run_command(command)","def create_courses():\n\n\t# create list for courses\n\tallcourses = []\n\n\t# load courses as classes in allcourses-list\n\twith open(\"../data/vakken.csv\", \"rt\") as coursefile:\n\n\t\t# clean text\n\t\tcourses = csv.reader(coursefile)\n\t\tfor row in courses:\n\t\t\tfor text in row:\n\t\t\t\tcourse_info = text.split(\";\")\n\n\t\t\t\t# add course name\n\t\t\t\tcourse_name = course_info[0]\n\n\t\t\t\t# add amount of lectures\n\t\t\t\tcourse_lectures = course_info[1]\n\n\t\t\t\t# add amount of seminars\n\t\t\t\tcourse_seminars = course_info[2]\n\n\t\t\t\t# add max amount seminars\n\t\t\t\tcourse_max_sem = course_info[3]\n\t\t\t\tif course_max_sem == \"nvt\":\n\t\t\t\t\tcourse_max_sem = 0\n\n\t\t\t\t# add amount of practicals\n\t\t\t\tcourse_practicals = course_info[4]\n\n\t\t\t\t# add max amount practicals\n\t\t\t\tcourse_max_prac = course_info[5]\n\t\t\t\tif course_max_prac == \"nvt\":\n\t\t\t\t\tcourse_max_prac = 0\n\n\t\t\t\t# add course to list\n\t\t\t\tallcourses.append(Course(course_name, course_lectures, course_seminars, course_max_sem, course_practicals, course_max_prac))\n\n\treturn allcourses","def makeCourse( xmlFile, genPath, importPaths, commonFiles, rendererContent=True):\n\ttry:\n\n\t\t# parse the command line\n\t\tConfig.add_option('--verbose', help='Set verbosity to maximum', dest='verbosity', default=0, action='store_const', const=2)\n\t\tConfig.add_option('-v', '--verbosity', help='Set the verbosity level (0: quiet, 1: display the command lines, 2: display command lines and their outputs', dest='verbosity', default=0, type=int)\n\t\tConfig.add_option('-d', '--debug', help='Create the files in the debug/ folder, instead of in a temporary one', dest='debug', action='store_true', default=False)\n\t\tConfig.add_option('-f', '--force', help='Force the generation of the documents, even if nothing changes from last run', dest='force', action='store_true', default=False)\n\t\tConfig.add_option('-q', '--quick', help='Quick pdf generation (do not compile twice the latex, do not produce handout, etc.)', dest='quick', action='store_true', default=False)\n\t\tConfig.add_option('-w', '--wordpress', help='Publish to wordpress', dest='wordpress', default=False, action='store_true')\n\t\tConfig.add_option('-c', '--HTMLcorrection', help='Display an HTML correction', dest='HTMLcorrection', default=False, action='store_true')\n\t\tConfig.add_option('-s', '--shared', help='Copy the required files to the <shared> path (via ssh)', default=False,\taction='store_true')\n\t\tConfig.parse()\n\t\targs = Config.args\n\t\toptions = Config.options\n\t\tConfig.importPaths = importPaths \n\t\tConfig.commonFiles = commonFiles\n\t\tConfig.allSessions = { x.__name__:x for x in Session.__subclasses__()}\t# list of the created session classes\n\t\tConfig.rendererContent = rendererContent\n\t\t\n\t\t# clean the debug directory in debug mode\n\t\tbasePath = os.path.abspath('.')+'/'\t\t\t# base path (from where the script is run, because the path are relative)\n\t\tif options.debug:\n\t\t\tif os.path.exists('debug/'):\n\t\t\t\trunCommand(['rm','-rf','debug/'])\n\n\t\t# open and parse the course file\n\t\twith codecs.open(xmlFile, encoding='utf-8') as f:\n\t\t\tbs = BeautifulSoup(f, features=\"xml\")\n\n\n\t\t# build the recursively the sessions\n\t\ttop = createTagSession( bs, father=None )\t\t# bs.contents[0]\n\t\tsessionsToBuild = Session.sessionsToBuild\t\t# get the list of the sessions object\n\t\t\n\n\t\t\"\"\"\n\t\timportFiles( bs.contents[0], importPaths)\n\n\t\t# get the list of sessions we can build (with a 'make' method)\n\t\tbuildableSessions = { x.__name__:x for x in Session.__subclasses__() if 'make' in x.__dict__ }\n\n\t\t#This set the PATH for PyDev only...\n\t\tos.environ['PATH'] = os.environ['PATH']+':'+os.getenv('PATH')\n\n\n\t\t# build the list of Sessions to build\n\t\tsessionsToBuild = []\n\t\tfor name,session in buildableSessions.items():\n\t\t\tsessionsToBuild.extend( session(tag, commonFiles) for tag in bs(name) )\n\t\t\"\"\"\n\t\t\n\t\t\n\n\t\t# if possible, load the previous xml file, and look for the differences\n\t\tdirName,baseName = split(xmlFile) \n\t\ttry:\n\t\t\twith open(dirName+\"/.\"+baseName+\".makeCourse\", \"rb\") as f:\n\t\t\t\tdata = load( f )\n\t\t\t\tfor s in sessionsToBuild:\n\t\t\t\t\tif s.name in data:\n\t\t\t\t\t\ts.checkDifferences( data[s.name] )\n\t\texcept IOError:\n\t\t\tpass\n\n\n\t\t# build every argument in the command line arguments\n\t\tsomethingHasBeDone = False\n\t\tfor s in sessionsToBuild:\n\t\t\tif (not args) or (\"all\" in args) or (s.name in args) or (s.type in args):\n\t\t\t\t\n\t\t\t\tcd( basePath)\n\t\t\t\t\n\t\t\t\t# check if something has to be done\n\t\t\t\tif s.shouldBeMake(basePath+'/'+genPath, options) or options.force:\n\t\t\t\t\tsomethingHasBeDone = True\n\n\t\t\t\t\t#Make one build (TP, course, etc.)\n\t\t\t\t\tprint ( Fore.BLUE+\"*) Make \"+Style.BRIGHT+s.name+Fore.RESET+Style.NORMAL)\n\n\t\t\t\t\t# make temp directory and copy all the file in resources dir\n\t\t\t\t\tif options.debug:\n\t\t\t\t\t\ttmp = \"debug/\"+s.name+'/'\n\t\t\t\t\t\tcreateDirectory(tmp)\n\t\t\t\t\telse:\n\t\t\t\t\t\ttmp = mkdtemp()\n\n\t\t\t\t\ts.prepareResources(tmp )\n\t\t\t\t\tcd( tmp)\n\n\t\t\t\t\t# call the custom function associated with the type, to produce the documents\n\t\t\t\t\ts.make(options)\n\n\t\t\t\t\t# then move the files in the right place\n\t\t\t\t\tfor f in s.files(options):\n\t\t\t\t\t\tcreateDirectory( basePath+'/'+genPath.format( **s.dict ) )\n\t\t\t\t\t\tnewFile = basePath+'/'+genPath.format( **s.dict )+f\n\t\t\t\t\t\tif not os.path.exists(f):\n\t\t\t\t\t\t\tprint( Fore.YELLOW+'The file '+f+' has not been created by '+s.type+' function !'+Fore.RESET)\n\t\t\t\t\t\trunCommand( ['cp', f, newFile])\n\n\t\t\t\t\t# del the temporary directory or clean debug directory\n\t\t\t\t\tif not options.debug:\n\t\t\t\t\t\trunCommand( ['rm', '-rf', tmp])\n\t\t\t\telse:\n\t\t\t\t\tif options.verbosity>0:\n\t\t\t\t\t\tprint( Fore.BLUE + \"*) Nothing changed for \"+Style.BRIGHT+s.name+Style.NORMAL+\", skipped\"+Fore.RESET)\n\n\n\n\t\tif not somethingHasBeDone:\n\t\t\tprint( Fore.BLUE + \"Nothing has changed, nothing to do, so nothing has been done...\" + Fore.RESET)\n\n\n\t\t# save the data file\n\t\tdata = {L.name: {key:md5(str(val).encode('utf-8')).hexdigest() for key,val in L.dict.items()} for L in sessionsToBuild }\n\t\tcd( basePath)\n\t\twith open(dirName+\"/.\"+baseName+\".makeCourse\", 'wb') as f:\n\t\t\tdump( data, f)\n\n\n\n\n\n\n\n\texcept mkcException as err:\n\t\tprint( err )","def _get_courses(self) -> None:\n\n courses_content: NavigableString = self.soup.find(\"div\", \n {\"class\": \"coursesContent\"})\n course_items: ResultSet = courses_content.find_all(\"div\", \n {\"class\": \"courseItem\"})\n\n for item in course_items:\n course_name: str = item.a[\"href\"].split(\"/\")[-2].lower()\n course_data: ParseType = self._parse(item)\n self._update(course_name, course_data)","def latex_course(course):\n basic_info_list = [\n 'session', 'school', 'credits', 'level', 'offered',\n 'visiting_students', 'erasmus_students'\n ]\n generic_subsection_list = [\n 'description', 'timetable', 'requirements_of_entry',\n 'excluded_courses', 'co_requisites', 'assessment_weighting'\n ]\n string = '\\\\subsection{' + course[\"title\"] + '}\\n'\n for info in basic_info_list:\n string += latex_info(course[info])\n for subsection in generic_subsection_list:\n string += latex_subsection(course[subsection])\n string += '\\\\break \\\\textbf{' + course['assessment_date'][\n 'heading'] + '}' + course['assessment_date']['value'] + '\\n'\n string += latex_subsection(course['aims'])\n string += '\\\\subsubsection*{' + \\\n course['learning_outcomes']['heading'] + '}\\n'\n outcome_list = re.split(\n '\\d+\\. ', course['learning_outcomes']['value'])\n string += outcome_list[0] + '\\n'\n string += '\\\\begin{enumerate}\\n'\n for i in outcome_list[1:-1]:\n string += '\\\\item ' + i + '\\n'\n string += '\\\\end{enumerate}\\n'\n return string","def _create_course(self):\r\n super(TestOrphan, self)._create_course()\r\n\r\n self._create_item('chapter', 'Chapter1', {}, {'display_name': 'Chapter 1'}, 'course', 'runid')\r\n self._create_item('chapter', 'Chapter2', {}, {'display_name': 'Chapter 2'}, 'course', 'runid')\r\n self._create_item('chapter', 'OrphanChapter', {}, {'display_name': 'Orphan Chapter'}, None, None)\r\n self._create_item('vertical', 'Vert1', {}, {'display_name': 'Vertical 1'}, 'chapter', 'Chapter1')\r\n self._create_item('vertical', 'OrphanVert', {}, {'display_name': 'Orphan Vertical'}, None, None)\r\n self._create_item('html', 'Html1', \"<p>Goodbye</p>\", {'display_name': 'Parented Html'}, 'vertical', 'Vert1')\r\n self._create_item('html', 'OrphanHtml', \"<p>Hello</p>\", {'display_name': 'Orphan html'}, None, None)\r\n self._create_item('static_tab', 'staticuno', \"<p>tab</p>\", {'display_name': 'Tab uno'}, None, None)\r\n self._create_item('about', 'overview', \"<p>overview</p>\", {}, None, None)\r\n self._create_item('course_info', 'updates', \"<ol><li><h2>Sep 22</h2><p>test</p></li></ol>\", {}, None, None)","def completed_course()->str:\r\n try:\r\n db: sqlite3.Connection = sqlite3.connect(DB_FILE)\r\n query: str = \"SELECT s.Name, s.CWID, g.Course, g.Grade, i.Name AS 'Instructor' \" \\\r\n \"FROM grades2 g JOIN students2 s ON g.StudentCWID = s.CWID \" \\\r\n \"JOIN instructors2 i ON g.InstructorCWID = i.CWID ORDER BY s.Name\" \r\n except sqlite3.OperationalError as e:\r\n print(e)\r\n\r\n data: Dict[str,str]=\\\r\n [{\"name\": name,\"cwid\": cwid,\"course\": course , \"grade\":grade, \"instructor\": instructor}\r\n for name, cwid, course, grade, instructor in db.execute(query)]\r\n db.close()\r\n \"\"\"Render the template from templates folder\"\"\"\r\n return render_template(\"student_summary.html\",\r\n title=\"Stevens Repository\",\r\n table_title=\"Student, Course, Grade and Instructor\",\r\n students=data )","def _create_course(self):\r\n super(TestPublish, self)._create_course(split=False)\r\n\r\n self._create_item('chapter', 'Chapter1', {}, {'display_name': 'Chapter 1'}, 'course', 'runid', split=False)\r\n self._create_item('chapter', 'Chapter2', {}, {'display_name': 'Chapter 2'}, 'course', 'runid', split=False)\r\n self._create_item('vertical', 'Vert1', {}, {'display_name': 'Vertical 1'}, 'chapter', 'Chapter1', split=False)\r\n self._create_item('vertical', 'Vert2', {}, {'display_name': 'Vertical 2'}, 'chapter', 'Chapter1', split=False)\r\n self._create_item('html', 'Html1', \"<p>Goodbye</p>\", {'display_name': 'Parented Html'}, 'vertical', 'Vert1', split=False)\r\n self._create_item(\r\n 'discussion', 'Discussion1',\r\n \"discussion discussion_category=\\\"Lecture 1\\\" discussion_id=\\\"a08bfd89b2aa40fa81f2c650a9332846\\\" discussion_target=\\\"Lecture 1\\\"/>\\n\",\r\n {\r\n \"discussion_category\": \"Lecture 1\",\r\n \"discussion_target\": \"Lecture 1\",\r\n \"display_name\": \"Lecture 1 Discussion\",\r\n \"discussion_id\": \"a08bfd89b2aa40fa81f2c650a9332846\"\r\n },\r\n 'vertical', 'Vert1',\r\n split=False\r\n )\r\n self._create_item('html', 'Html2', \"<p>Hellow</p>\", {'display_name': 'Hollow Html'}, 'vertical', 'Vert1', split=False)\r\n self._create_item(\r\n 'discussion', 'Discussion2',\r\n \"discussion discussion_category=\\\"Lecture 2\\\" discussion_id=\\\"b08bfd89b2aa40fa81f2c650a9332846\\\" discussion_target=\\\"Lecture 2\\\"/>\\n\",\r\n {\r\n \"discussion_category\": \"Lecture 2\",\r\n \"discussion_target\": \"Lecture 2\",\r\n \"display_name\": \"Lecture 2 Discussion\",\r\n \"discussion_id\": \"b08bfd89b2aa40fa81f2c650a9332846\"\r\n },\r\n 'vertical', 'Vert2',\r\n split=False\r\n )\r\n self._create_item('static_tab', 'staticuno', \"<p>tab</p>\", {'display_name': 'Tab uno'}, None, None, split=False)\r\n self._create_item('about', 'overview', \"<p>overview</p>\", {}, None, None, split=False)\r\n self._create_item('course_info', 'updates', \"<ol><li><h2>Sep 22</h2><p>test</p></li></ol>\", {}, None, None, split=False)","def get_courses_info(url, headers):\n dash = get_page_contents(url, headers)\n soup = BeautifulSoup(dash)\n courses_soup = soup.find_all('article', 'course')\n courses = []\n for course_soup in courses_soup:\n course_id = None\n course_name = course_soup.h3.text.strip()\n course_url = None\n course_state = 'Not yet'\n try:\n # started courses include the course link in the href attribute\n course_url = BASE_URL + course_soup.a['href']\n if course_url.endswith('info') or course_url.endswith('info/'):\n course_state = 'Started'\n # The id of a course in edX is composed by the path\n # {organization}/{course_number}/{course_run]\n course_id = course_soup.a['href'][9:-5]\n except KeyError:\n pass\n courses.append(Course(id=course_id,\n name=course_name,\n url=course_url,\n state=course_state))\n return courses","def get_courses(self):\n\n self.search([]).unlink()\n token = self.env['odoo.moodle'].search([('create_uid', '=', self.env.user.id)]).token\n domain = \"http://localhost:8888\"\n webservice_url = \"/webservice/rest/server.php?\"\n parameters = {\n \"wstoken\":token,\n 'wsfunction': 'core_course_get_courses',\n 'moodlewsrestformat': 'json'\n }\n request = requests.get(url=domain+webservice_url, params=parameters)\n request = request.json()\n print(request)\n\n for req in request:\n try:\n if req['id']==1:\n pass\n else:\n self.create({\n 'course_id': req['id'], \n 'category':req['categoryid'],\n 'fullname':req['fullname'], \n 'shortname':req['shortname'],\n 'summary': req['summary']\n }\n )\n except Exception:\n print('Course not created')","def _construct_course_son(self, course_key):\r\n assert(isinstance(course_key, SlashSeparatedCourseKey))\r\n return bson.son.SON([\r\n ('org', course_key.org),\r\n ('course', course_key.course),\r\n ('name', course_key.run)\r\n ])","def main():\n\n #Courses\n years = [2016, 2017, 2018, 2019, 2020]\n with sqlite3.connect(\"determined.db\") as conn:\n c = conn.cursor()\n c.execute(\n \"\"\"\n CREATE TABLE \"course\" (\n \"course_number\"\tint NOT NULL,\n \"dept_id\"\tvarchar(4) NOT NULL,\n \"title\"\tvarchar(100) NOT NULL,\n \"instructor_fname\"\tvarchar(35) DEFAULT NULL,\n \"instructor_lname\"\tvarchar(35) DEFAULT NULL,\n \"student_work_products\"\tjson DEFAULT NULL,\n `term` varchar(7) NOT NULL,\n `year` int NOT NULL,\n PRIMARY KEY(\"course_number\", \"term\", \"year\")) \n \"\"\"\n )\n conn.commit()\n courses = [\n (1370, \"CPSC\", \"Computer Literacy\", names.get_first_name(), names.get_last_name(), json.dumps({\"swp\": [\"Midterm\", \"Final Exam\", \"Project 1\"]}), \"Fall\"),\n (1375, \"CPSC\", \"Programming I\", names.get_first_name(), names.get_last_name(), json.dumps({\"swp\": [\"Midterm\", \"Final Exam\", \"Project 1\"]}), \"Fall\"),\n (2376, \"CPSC\", \"Intro to Game Programming\", names.get_first_name(), names.get_last_name(), json.dumps({\"swp\": [\"Midterm\", \"Final Exam\", \"Project 1\"]}), \"Fall\"),\n (2380, \"CPSC\", \"Algorithms\", names.get_first_name(), names.get_last_name(), json.dumps({\"swp\": [\"Midterm\", \"Final Exam\", \"Project 1\"]}), \"Fall\"),\n (2482, \"CPSC\", \"Computer Organization\", names.get_first_name(), names.get_last_name(), json.dumps({\"swp\": [\"Midterm\", \"Final Exam\", \"Project 1\"]}), \"Spring\"),\n (3377, \"CPSC\", \"Advanced Game Programming\", names.get_first_name(), names.get_last_name(), json.dumps({\"swp\": [\"Midterm\", \"Final Exam\", \"Project 1\"]}), \"Spring\"),\n (3380, \"CPSC\", \"Operating Systems\", names.get_first_name(), names.get_last_name(), json.dumps({\"swp\": [\"Midterm\", \"Final Exam\", \"Project 1\"]}), \"Spring\"),\n (3383, \"CPSC\", \"Programming Languages\", names.get_first_name(), names.get_last_name(), json.dumps({\"swp\": [\"Midterm\", \"Final Exam\", \"Project 1\"]}), \"Spring\"),\n (3384, \"CPSC\", \"Computer Networks\", names.get_first_name(), names.get_last_name(), json.dumps({\"swp\": [\"Midterm\", \"Final Exam\", \"Project 1\"]}), \"Summer\"),\n (4360, \"CPSC\", \"Computer Security\", names.get_first_name(), names.get_last_name(), json.dumps({\"swp\": [\"Midterm\", \"Final Exam\", \"Project 1\"]}), \"Summer\")\n ]\n #Adding years\n upload_courses = []\n for year in years:\n upload_courses += [x + (year,) for x in courses]\n #Making a few instructors teach multiple course\n new_courses = [\n (4557, \"CPSC\", \"Natural Language Processing\", ),\n (2375, \"CPSC\", \"Programming II\",),\n (2776, \"CPSC\", \"Data Structures and Algorithms\",),\n (4862, \"CPSC\", \"Image Recognition\", ),\n ]\n for i in range(0,len(new_courses)):\n year = choice(years)\n for y in range(0,2): #Number of times new course is taught\n c = upload_courses[i]\n new_data = (c[3], c[4], c[5], choice([\"Fall\", \"Spring\", \"Summer\"]), year+y)\n data = new_courses[i] + new_data\n upload_courses.append(data)\n #Adding solo instructors and solo courses\n upload_courses += [\n (4672, \"CPSC\", \"Programming Memes\", names.get_first_name(), names.get_last_name(), json.dumps({\"swp\": [\"Midterm\", \"Final Exam\", \"Project 1\"]}), \"Spring\", choice(years)),\n (1872, \"CPSC\", \"Information Systems\", names.get_first_name(), names.get_last_name(), json.dumps({\"swp\": [\"Midterm\", \"Final Exam\", \"Project 1\"]}), \"Summer\", choice(years)),\n (1123, \"CPSC\", \"Microsoft Office\", names.get_first_name(), names.get_last_name(), json.dumps({\"swp\": [\"Midterm\", \"Final Exam\", \"Project 1\"]}), \"Fall\", choice(years))\n ]\n\n with sqlite3.connect(\"determined.db\") as conn:\n c = conn.cursor()\n c.executemany('''INSERT INTO course (course_number, dept_id, title, instructor_fname, instructor_lname, student_work_products, term, year)\n VALUES ( ?, ?, ?, ?, ?, ?, ?, ?)''', upload_courses)\n conn.commit()\n\n #SWP\n with sqlite3.connect(\"determined.db\") as conn:\n c = conn.cursor()\n c.execute(\n \"\"\"\n CREATE TABLE `student_work_product` (\n `id` INTEGER PRIMARY KEY,\n `product` varchar(250) NOT NULL,\n `course_id` int NOT NULL,\n `dept_id` int NOT NULL,\n `student_fname` varchar(35) NOT NULL,\n `student_lname` varchar(35) NOT NULL,\n `student_outcome` int DEFAULT NULL,\n `score` int DEFAULT NULL,\n `term` varchar(7) NOT NULL,\n `year` int NOT NULL,\n CONSTRAINT `course` FOREIGN KEY (`course_id`) REFERENCES `course` (`course_number`)\n CONSTRAINT `course` FOREIGN KEY (`dept_id`) REFERENCES `course` (`dept_id`)\n )\n \"\"\"\n )\n conn.commit()\n \n swps = []\n with sqlite3.connect(\"determined.db\") as conn:\n conn.row_factory = sqlite3.Row\n c = conn.cursor()\n c.execute (\"Select * from course\")\n records = [dict(x) for x in c.fetchall()]\n #Generating 20 student records for each swp in each course\n for i, course in enumerate(records):\n student_names = []\n for _ in range(20):\n student_names.append({'fname': names.get_first_name(),\n 'lname': names.get_last_name()})\n for product in json.loads(course['student_work_products'])['swp']:\n for student in student_names:\n if i%7 == 0:\n score = int(triangular(50, 85))\n else:\n score = int(triangular(50, 100))\n if score >= 90: outcome = 4\n elif score >= 80: outcome = 3\n elif score >= 70: outcome = 2\n elif score >= 60: outcome = 1\n else: outcome = 0 \n swps.append((\n product,\n course['course_number'],\n \"CPSC\",\n student['fname'],\n student['lname'],\n outcome,\n score, \n course['term'], \n course['year']\n ))\n \n c.executemany('''INSERT INTO student_work_product (product, course_id, dept_id, student_fname, student_lname, student_outcome, score, term, year)\n VALUES ( ?, ?, ?, ?, ?, ?, ?, ?, ?)''', swps)\n conn.commit()","def test_get_toc_text(self) -> None:\n extraction = docx2python(RESOURCES / \"zen_of_python.docx\")\n assert extraction.document_runs == [\n [\n [[[\"Contents\"], [\"\\t\", \"Beautiful is better than ugly.\\t1\"], []]],\n [\n [\n [],\n [],\n [\"Beautiful is better than ugly.\"],\n [\"Explicit is better than implicit.\"],\n [\"Simple is better than complex.\"],\n [\"Complex is better than complicated.\"],\n [\"Flat is better than nested.\"],\n [\"Sparse is better than dense.\"],\n [\"Readability counts.\"],\n [\"Special cases aren't special enough to break the rules.\"],\n [\"Although practicality beats purity.\"],\n [\"Errors should never pass silently.\"],\n [\"Unless explicitly silenced.\"],\n [\"In the face of ambiguity, refuse the temptation to guess.\"],\n [\n \"There should be one-- and preferably only one --obvious way to do it.\"\n ],\n [\n \"Although that way may not be obvious at first unless you're Dutch.\"\n ],\n [\"Now is better than never.\"],\n [\"Although never is often better than *right* now.\"],\n [\"If the implementation is hard to explain, it's a bad idea.\"],\n [\n \"If the implementation is easy to explain, it may be a good idea.\"\n ],\n [\n \"Namespaces are one honking great idea -- let's do more of those!\"\n ],\n ]\n ],\n ]\n ]\n extraction.close()","def build_general_course(self):\n\n courses = {}\n\n if self.__build_analyze is False:\n self.build_analysis()\n\n courses[\"taxa_conhecimento\"] = self.analysis[\"taxa_conhecimento\"]\n courses[\"taxa_reprovacao\"] = self.analysis[\"taxa_reprovacao_absoluta\"]\n courses[\"taxa_trancamento\"] = self.analysis[\"taxa_trancamento\"]\n\n # cria cache\n cache = {}\n note = self.analysis[\"general_note_statistic\"]\n for rate_it in self.__rates:\n rate_calc = self.analysis[\"general_rates\"][rate_it.name][0]\n for course in self.analysis[\"courses\"].index:\n if course not in cache:\n cache[course] = {}\n cache[course][rate_it.name] = rate_calc[course]\n cache[course][\"nota\"] = [note[0][course], note[1][course]]\n\n courses[\"cache\"] = cache\n\n # cria o campo compara_aprov\n courses[\"compara_aprov\"] = self.analysis[\"graph_course\"]\n\n # cria o campo courses\n courses[\"disciplinas\"] = self.analysis[\"courses\"].to_dict()\n\n return courses","def build_course(self):\n courses = []\n aprovacao_d = {}\n # semestral\n for rate_it in self.__semestral_rate:\n # pega uma lista no qual o primeiro elemento é a taxa, o segundo\n # e o terceiro são quantidades\n rate_data = self.analysis[\"semestral_rate\"][rate_it.name]\n for i in rate_data[0].index:\n if i[0] not in aprovacao_d:\n aprovacao_d[i[0]] = {}\n\n periodo = str(i[1]) + \"/\" + str(i[2])\n aprovacao_d[i[0]][periodo] = [\n float(rate_data[0][i]),\n int(rate_data[1][i]),\n int(rate_data[2][i]),]\n\n note = self.analysis[\"general_note_statistic\"]\n note_last_year = self.analysis[\"last_year_statistic\"]\n for course in self.analysis[\"courses\"].index:\n course_dict = {}\n course_dict[\"disciplina_codigo\"] = course\n course_dict[\"disciplina_nome\"] = self.analysis[\"courses\"][course]\n \n # If the course code is related to more than one name,\n # concatenate these names into an unique string\n if type(course_dict[\"disciplina_nome\"]) != str:\n new_course_name = \" | \".join(list(course_dict[\"disciplina_nome\"]))\n course_dict[\"disciplina_nome\"] = new_course_name\n # quantidade de matriculas\n count = self.analysis[\"general_count_submission\"][course]\n course_dict[\"qtd_alunos\"] = count\n # notas\n course_dict[\"qtd_cursada_aprov\"] = self.analysis[\"coursed_ratio\"][course]\n course_dict[\"nota\"] = [note[0][course], note[1][course]]\n course_dict[\"nota_ultimo_ano\"] = [\n note_last_year[0][course],\n note_last_year[1][course]\n ]\n # taxas\n for rate_it in self.__rates:\n rate_data = self.analysis[\"general_rates\"][rate_it.name]\n course_dict[rate_it.name] = float(rate_data[0][course])\n course_str = rate_it.name.replace(\"taxa\", \"qtd\")\n # count_sel define qual quantidade vai para o json, a especifica\n # ou geral\n course_dict[course_str] = int(\n rate_data[rate_it.count_sel][course])\n # rate_calc = self.analysis[\"general_rates\"][rate_it.name][0]\n\n # taxas do ultimo anos\n course_dict[\"taxa_reprovacao_ultimo_absoluto\"] = self.analysis[\"last_year_taxa_reprovacao_absoluta\"][course]\n course_dict[\"taxa_reprovacao_ultimo_frequencia\"] = self.analysis[\"last_year_taxa_reprovacao_frequencia\"][course]\n\n course_dict[\"grafico_qtd_cursada_aprov\"] = self.analysis[\"coursed_count\"][course]\n course_dict[\"aprovacao_semestral\"] = aprovacao_d[course]\n courses.append(course_dict)\n return courses","def course_index(request, course_key):\r\n course_module = _get_course_module(course_key, request.user, depth=3)\r\n lms_link = get_lms_link_for_item(course_module.location)\r\n sections = course_module.get_children()\r\n\r\n\r\n return render_to_response('overview.html', {\r\n 'context_course': course_module,\r\n 'lms_link': lms_link,\r\n 'sections': sections,\r\n 'course_graders': json.dumps(\r\n CourseGradingModel.fetch(course_key).graders\r\n ),\r\n 'new_section_category': 'chapter',\r\n 'new_subsection_category': 'sequential',\r\n 'new_unit_category': 'vertical',\r\n 'category': 'vertical'\r\n })","def test_course_info(self):\r\n # Regex of first 3 columns of course information table row for\r\n # test course loaded from git. Would not have sha1 if\r\n # git_info_for_course failed.\r\n table_re = re.compile(r\"\"\"\r\n <tr>\\s+\r\n <td>edX\\sAuthor\\sCourse</td>\\s+ # expected test git course name\r\n <td>MITx/edx4edx/edx4edx</td>\\s+ # expected test git course_id\r\n <td>[a-fA-F\\d]{40}</td> # git sha1 hash\r\n \"\"\", re.VERBOSE)\r\n\r\n self._setstaff_login()\r\n self._mkdir(getattr(settings, 'GIT_REPO_DIR'))\r\n\r\n # Make sure we don't have any git hashes on the page\r\n response = self.client.get(reverse('sysadmin_courses'))\r\n self.assertNotRegexpMatches(response.content, table_re)\r\n\r\n # Now add the course and make sure it does match\r\n response = self._add_edx4edx()\r\n self.assertRegexpMatches(response.content, table_re)","def pdflatex(unwanted_courses):\n create_tex(unwanted_courses)\n cmd = ['pdflatex', '-interaction', 'nonstopmode', 'courses.tex']\n proc = subprocess.Popen(cmd)\n proc.communicate()\n return None","def make_datatable(self):\r\n\r\n data = []\r\n\r\n for course in self.get_courses():\r\n gdir = course.id.course\r\n data.append([course.display_name, course.id.to_deprecated_string()]\r\n + self.git_info_for_course(gdir))\r\n\r\n return dict(header=[_('Course Name'), _('Directory/ID'),\r\n _('Git Commit'), _('Last Change'),\r\n _('Last Editor')],\r\n title=_('Information about all courses'),\r\n data=data)","def populate_course(self):\r\n def descend(parent, stack):\r\n xblock_type = stack.pop(0)\r\n for _ in range(2):\r\n child = ItemFactory.create(category=xblock_type, parent_location=parent.location)\r\n if stack:\r\n descend(child, stack)\r\n\r\n descend(self.course, ['chapter', 'sequential', 'vertical', 'problem'])","def generate_courses():\r\n for category in CourseCategory.objects.all():\r\n Course.objects.create(name=category.name, category=category, is_active=True,\r\n is_featured=True)","def get_course_by_key_words(input):","def print_all_courses(filename): \n records = records_from_file(filename)\n course_code_and_name = all_courses(records)\n print(\"All courses:\")\n for course_code, course_name in sorted(course_code_and_name.items()):\n print((\" {}: {}\").format(course_code, course_name))\n print()","def Content():\n\tTOPIC_DICT = {\"Basics\": [[\"Introduction to Python\", \"/introduction-to-python/\"], \n\t\t\t\t\t\t\t\t\t\t\t\t\t [\"Print functions\", \"/python-print-function/\"],\n\t\t\t\t\t\t\t\t\t\t\t\t\t [\"Math basics with Python 3\", \"/math-basics-python-3/\"\n\t\t\t\t\t\t\t\t\t\t\t\t\t ]],\n\t\t\t\t\t\t\t\t\"Web Dev\": [[\"wd1\"]]} #Web dev is now the second topic\n\t\t\t\t\t\t\t\t#Introduction to Python\n\t#will be the title and the link to that is immediately after. Each inner array will be a specific sub section of the basics\n\t#section\n\n\treturn TOPIC_DICT","def __init__(self, tutorial):\n self._tex = \"\"\n\n self.texPreamble(tutorial)\n self.texTutorialHeader(tutorial)\n\n for lecture in self.children(tutorial):\n self.texLectureHeader(lecture)\n\n for slide in self.children(lecture):\n self.texSlideHeader(slide)\n for section in slide.sections:\n self.texSlideSection(section)\n self.texSlideFooter(slide)\n\n self.texLectureFooter(lecture)\n\n self.texTutorialFooter(tutorial)\n self.texPostamble(tutorial)","def __call__(self, filename, manuscript=\"\", journal=\"\", author=[], \n publisher=\"\"):\n\n # check the parameters\n while len(author) < 4:\n author.append(\"\")\n\n doc = getDocument(filename)\n\n Story = []\n\n # Section 1\n Story.append(\n Paragraph(\n \"\"\"<seqreset id=\"main\" /><seq id=\"main\">. THIS Amendment hereby \n modifies and supplements the attached Publication Agreement \n concerning the following Article:\"\"\", styles['outer_style'])\n )\n\n journal_info_table = Table([\n [fillInRow(manuscript, \"(manuscript title)\", width=inch*5)],\n [fillInRow(journal, \"(journal name)\", width=inch*5)],\n ],\n )\n journal_info_table.hAlign = 'LEFT'\n Story.append(journal_info_table)\n\n # Section 2\n Story.append(\n Paragraph(\n \"\"\"<seq id=\"main\">. The parties to the Publication Agreement and\n to this Amendment are:\"\"\", styles['outer_style'])\n )\n\n journal_info_table = Table([\n [fillInRow(author[0], \"(corresponding author)\", width=inch*5)],\n [Paragraph(\"and\", styles['outer_style'])],\n [fillInRow(journal, \"(the Publisher)\", width=inch*5)],\n ],\n )\n journal_info_table.hAlign = 'LEFT'\n Story.append(journal_info_table)\n\n # Section 3\n Story.append(\n Paragraph(\n \"\"\"<seq id=\"main\">. The parties agree that wherever there is any\n conflict between the Amendment and the Publication Agreement, \n the provisions of this Amendment are paramount and the \n Publication Agreement shall be construed accordingly.\"\"\",\n styles['outer_style'])\n )\n\n # Section 4\n Story.append(\n Paragraph(\n \"\"\"<seq id=\"main\">. Notwithstanding any terms in the Publication\nAgreement to the contrary and in addition to the rights retained by Author \nor licensed by Published to Author in the Publication Agreement and any fair \nuse rights of Author, Author and Publisher agree that the Author shall also \nretain the following rights:\"\"\",\n styles['outer_style'])\n )\n\n # 4a\n Story.append(\n Paragraph(\n \"\"\"a. The Author shall, without limitation, have the non-exclusive right to use, reproduce, distribute, create derivative works including update, perform, and display publicly, the Article in electronic, digital or print form in connection with the Author's teaching, conference presentations, lectures, other scholarly works, and for all of Author's academic and professional activities. \"\"\",\n styles['inner_style'])\n )\n\n # 4b\n Story.append(\n Paragraph(\n \"\"\"b. Once the Article has been published by Publisher, the Author shall also have all the non-exclusive rights necessary to make, or to authorize others to make, the final published version of the Article available in digital form over the Internet, including but not limited to a website under the control of the Author or the Author's employer or through any digital repository, such as MIT's DSpace or the National Library of Medicine's PubMed Central database.\"\"\",\n styles['inner_style'])\n )\n\n #4c\n Story.append(\n Paragraph(\n \"\"\"c. The Author further retains all non-exclusive rights necessary to grant to the Author's employing institution the non-exclusive right to use, reproduce, distribute, display, publicly perform, and make copies of the work in electronic, digital or in print form in connection with teaching, digital repositories, conference presentations, lectures, other scholarly works, and all academic and professional activities conducted at the Author's employing institution. \"\"\",\n styles['inner_style'])\n )\n\n # Section 5\n Story.append(\n Paragraph(\n \"\"\"<seq id=\"main\" />. <b>Final Agreement.</b> This Amendment and the Publication Agreement, taken together, constitute the final agreement between the Author and the Publisher with respect to the publication of the Article and allocation of rights under copyright in the Article. Any modification of or additions to the terms of this Amendment or to the Publication Agreement must be in writing and executed by both Publisher and Author in order to be effective.\"\"\",\n styles['outer_style'])\n )\n\n # Signature\n journal_info_table = Table([\n [\"AUTHOR\", \" \", \"PUBLISHER\"],\n [fillInRow(\"\", \"(corresponding author on behalf of all authors)\"),\n \"\", fillInRow(\"\", \"\")],\n [fillInRow(\"\", \"Date\"),\n \"\",\n fillInRow(\"\", \"Date\")]\n ],\n colWidths=[inch*3, inch*.25, inch*3],\n )\n\n journal_info_table.hAlign = 'LEFT'\n Story.append(journal_info_table)\n\n # MIT Directive\n Story.append(\n Paragraph(\"<b>MIT Authors:</b>\", styles['outer_style'])\n )\n Story.append(\n Paragraph(\"Please fax a copy of the agreement to 617-253-8894. Direct any questions to amend-cip@mit.edu\",\n styles['inner_style'])\n )\n\n\n agreement = \"%s %s\" % (self.NAME, self.VERSION)\n doc.build(Story, \n onFirstPage=mit_pageInfo, onLaterPages=mit_pageInfo)","def insert_course(dept, num, text):\n\n # Course Title \n m = re.search(\"[\\d\\w]{5} - ([\\w ]*)\", text)\n title = m.group(1) if m else \"nomatch\"\n\n # Course Description\n m = re.search(\"\\.\\s(.*)\\sTypically\",text)\n des = m.group(1) if m else \"nomatch\"\n\n # Credit hours aren't fixed for every course\n # Credit Hours: 2.00\n # Credit Hours: 2.00 or 3.00. \n # Credit Hours: 1.00 to 18.00. \n m = re.search(\"Credit Hours: (\\d+\\.\\d+)\",text, flags=re.IGNORECASE)\n m = re.search(\"(\\d+\\.\\d+)(.*?)Credit hours\",text, flags=re.IGNORECASE) if not m else m\n cr = m.group(1) if m else \"-1\"\n\n # Semesters Offered\n m = re.search(\"Typically offered (.*?)\\.\", text)\n sem = m.group(1).split() if m else [\"nomatch\"]\n\n # Course Type: Lecture, Recitation, Lab, Seminar, etc.\n m = re.search(\"Schedule Types:\\s((?:[\\w ]+)(?:,[\\w ]+)*) \\s+\", text)\n form = m.group(1).split(\", \") if m else [\"nomatch\"]\n\n # Learning objectives will not necessarily follow campuses\n m = re.search(\"campuses:(\\s+([\\w\\s])+\\n)\", text)\n campus = m.group(1).strip().split(\"\\n\\n\") if m else [\"nomatch\"]\n campus = [camp.strip() for camp in campus]\n\n # prereq regex and decomosition of prereqs into lists of AND conditions (works for most classes, not 477 and similar)\n # re.DOTALL matches all characters, including \"\\n\"\n idx = text.find(\"campuses:\")\n m = re.search(\"Prerequisites:(.*)\",text[idx:],flags=re.DOTALL)\n if m:\n allReqs = []\n prereqText = m.group(1).strip()\n prereqText = prereqText.encode('ascii', 'ignore') \n for i in PrereqParser.parseprereq(prereqText):\n reqArr = []\n for j in i.split():\n if j.find(\"-C\") != -1:\n j = j.replace(\"-C\",\"\")\n reqArr.append(Requisite(course=j,reqType=False))\n else:\n reqArr.append(Requisite(course=j,reqType=True)) \n allReqs.append(RequisiteList(courses=reqArr))\n\n else:\n allReqs = []\n\n # create course entity\n course = Course(number=num, title=title, department=dept, form=form,\n description=des, credits=float(cr), semesters=sem,\n campuses=campus,requisites=allReqs, id=dept + num)\n # store course \n course.put()","def export_courses():\n courses = Course.query().fetch()\n dictionary = {}\n\n for course in courses:\n dictionary[course.department + \"\" + course.number] = course.to_dict()\n\n return dictionary","def setup_xml_course(self):\r\n course_key = SlashSeparatedCourseKey('edX', 'toy', '2012_Fall')\r\n location = course_key.make_usage_key('chapter', 'Overview')\r\n descriptor = modulestore().get_item(location)\r\n\r\n self.module = self._get_module(course_key, descriptor, location)","def parse_courses():\n\n subjects = collections.OrderedDict()\n name = '' # the most recent course name acronym (ex. 'COMP')\n\n courses = re.sub(r'\\([^)]*\\)', '', COURSES).split() # Remove parens and their contents\n\n for course in courses:\n if course == 'OR':\n continue\n\n if course[0].isalpha():\n\n index = 0 # the upper bound character index of the subject name\n for char in course:\n if char.isalpha():\n index += 1\n else:\n break\n\n name = course[:index]\n number = course[index:index+4]\n else:\n number = course[:4]\n\n try:\n subjects[name].append(number)\n except KeyError:\n subjects[name] = [number]\n\n return subjects","def start(self) -> None:\n\n self.doc = self.doc + r'''\n \\documentclass[\n 10pt, % Main document font size\n a4paper, % Paper type, use 'letterpaper' for US Letter paper\n ]{scrartcl}\n\n \\usepackage{graphicx}\n \\usepackage{epstopdf}\n \\usepackage{float}\n \\usepackage[scale=0.75]{geometry} % Reduce document margins\n \\usepackage{hyperref}\n \\usepackage{longtable}\n\n \\begin{document}\n\n \\title{Automatic Exploratory Data Analysis} % The article title\n\n \\subtitle{Study Case} % Uncomment to display a subtitle\n\n \\author{Jacob} % The article author(s) - author affiliations need to be specified in the AUTHOR AFFILIATIONS block\\\n\n \\maketitle % Print the title/author/date block\n\n \\newpage\n \\tableofcontents % Print the table of contents\n\n \\newpage\n \\listoffigures % Print the list of figures\n\n \\newpage\n \\listoftables % Print the list of tables\n '''","def check_courses(self):\n course_list = list(self.json_parsed_file['course'].items())\n course_found = False\n index = 0\n\n course_id = \"\"\n version = \"\"\n\n for courses in course_list:\n course_id = courses[0]\n version = courses[1]\n\n if Course.objects.filter(CourseID=course_id, Version=version).exists():\n course_found = True\n self.json_parsed_file['course'] = course_list[index]\n continue\n\n index += 1\n\n if course_list is []:\n self.is_parsed_pdf_valid = False\n self.output_message += \"No parsed course information found\\n\"\n\n elif not course_found:\n self.is_parsed_pdf_valid = False\n self.output_message += 'Courses: ' + str(course_id) + ', Version: ' + str(version) + \\\n ' Does not exist in the database.\\n'","def get_about_text(self, course_key):\r\n text = views.course_about(self.request, course_key.to_deprecated_string()).content\r\n return text","def test_get_single_topic_courses(self):\r\n course_id = None # Change me!!\r\n topic_id = None # Change me!!\r\n\r\n r = self.client.get_single_topic_courses(topic_id, course_id)","def GetCourses(firebase: firebase) -> None:\n\n global courses\n obj_key_list = []\n\n result = firebase.get('/course', None)\n\n if result is None:\n return\n\n for i in result.keys():\n obj_key_list.append(i)\n\n for i in obj_key_list:\n course = Course()\n course.setId(i)\n course.setKnowledgeAreaId(result[i]['knowledgeareaid'])\n course.setCatalogId(result[i]['catalogid'])\n course.setTitle(result[i]['name'])\n course.setDescription(result[i]['description'])\n course.setInstructor(result[i]['instructor'])\n course.setFee(result[i]['fee'])\n courses.append(course)","def index(request, course_id, chapter=None, section=None,\r\n position=None):\r\n course_key = SlashSeparatedCourseKey.from_deprecated_string(course_id)\r\n user = User.objects.prefetch_related(\"groups\").get(id=request.user.id)\r\n request.user = user # keep just one instance of User\r\n course = get_course_with_access(user, 'load', course_key, depth=2)\r\n staff_access = has_access(user, 'staff', course)\r\n registered = registered_for_course(course, user)\r\n if not registered:\r\n # TODO (vshnayder): do course instructors need to be registered to see course?\r\n log.debug(u'User %s tried to view course %s but is not enrolled', user, course.location.to_deprecated_string())\r\n return redirect(reverse('about_course', args=[course_key.to_deprecated_string()]))\r\n\r\n masq = setup_masquerade(request, staff_access)\r\n\r\n try:\r\n field_data_cache = FieldDataCache.cache_for_descriptor_descendents(\r\n course_key, user, course, depth=2)\r\n\r\n course_module = get_module_for_descriptor(user, request, course, field_data_cache, course_key)\r\n if course_module is None:\r\n log.warning(u'If you see this, something went wrong: if we got this'\r\n u' far, should have gotten a course module for this user')\r\n return redirect(reverse('about_course', args=[course_key.to_deprecated_string()]))\r\n\r\n studio_url = get_studio_url(course_key, 'course')\r\n\r\n if chapter is None:\r\n return redirect_to_course_position(course_module)\r\n\r\n context = {\r\n 'csrf': csrf(request)['csrf_token'],\r\n 'accordion': render_accordion(request, course, chapter, section, field_data_cache),\r\n 'COURSE_TITLE': course.display_name_with_default,\r\n 'course': course,\r\n 'init': '',\r\n 'fragment': Fragment(),\r\n 'staff_access': staff_access,\r\n 'studio_url': studio_url,\r\n 'masquerade': masq,\r\n 'xqa_server': settings.FEATURES.get('USE_XQA_SERVER', 'http://xqa:server@content-qa.mitx.mit.edu/xqa'),\r\n 'reverifications': fetch_reverify_banner_info(request, course_key),\r\n }\r\n\r\n # Only show the chat if it's enabled by the course and in the\r\n # settings.\r\n show_chat = course.show_chat and settings.FEATURES['ENABLE_CHAT']\r\n if show_chat:\r\n context['chat'] = chat_settings(course, user)\r\n # If we couldn't load the chat settings, then don't show\r\n # the widget in the courseware.\r\n if context['chat'] is None:\r\n show_chat = False\r\n\r\n context['show_chat'] = show_chat\r\n\r\n chapter_descriptor = course.get_child_by(lambda m: m.location.name == chapter)\r\n if chapter_descriptor is not None:\r\n save_child_position(course_module, chapter)\r\n else:\r\n raise Http404('No chapter descriptor found with name {}'.format(chapter))\r\n\r\n chapter_module = course_module.get_child_by(lambda m: m.location.name == chapter)\r\n if chapter_module is None:\r\n # User may be trying to access a chapter that isn't live yet\r\n if masq == 'student': # if staff is masquerading as student be kinder, don't 404\r\n log.debug('staff masq as student: no chapter %s' % chapter)\r\n return redirect(reverse('courseware', args=[course.id.to_deprecated_string()]))\r\n raise Http404\r\n\r\n if section is not None:\r\n section_descriptor = chapter_descriptor.get_child_by(lambda m: m.location.name == section)\r\n if section_descriptor is None:\r\n # Specifically asked-for section doesn't exist\r\n if masq == 'student': # if staff is masquerading as student be kinder, don't 404\r\n log.debug('staff masq as student: no section %s' % section)\r\n return redirect(reverse('courseware', args=[course.id.to_deprecated_string()]))\r\n raise Http404\r\n\r\n # cdodge: this looks silly, but let's refetch the section_descriptor with depth=None\r\n # which will prefetch the children more efficiently than doing a recursive load\r\n section_descriptor = modulestore().get_item(section_descriptor.location, depth=None)\r\n\r\n # Load all descendants of the section, because we're going to display its\r\n # html, which in general will need all of its children\r\n section_field_data_cache = FieldDataCache.cache_for_descriptor_descendents(\r\n course_key, user, section_descriptor, depth=None)\r\n\r\n section_module = get_module_for_descriptor(\r\n request.user,\r\n request,\r\n section_descriptor,\r\n section_field_data_cache,\r\n course_key,\r\n position\r\n )\r\n\r\n if section_module is None:\r\n # User may be trying to be clever and access something\r\n # they don't have access to.\r\n raise Http404\r\n\r\n # Save where we are in the chapter\r\n save_child_position(chapter_module, section)\r\n context['fragment'] = section_module.render('student_view')\r\n context['section_title'] = section_descriptor.display_name_with_default\r\n else:\r\n # section is none, so display a message\r\n studio_url = get_studio_url(course_key, 'course')\r\n prev_section = get_current_child(chapter_module)\r\n if prev_section is None:\r\n # Something went wrong -- perhaps this chapter has no sections visible to the user\r\n raise Http404\r\n prev_section_url = reverse('courseware_section', kwargs={\r\n 'course_id': course_key.to_deprecated_string(),\r\n 'chapter': chapter_descriptor.url_name,\r\n 'section': prev_section.url_name\r\n })\r\n context['fragment'] = Fragment(content=render_to_string(\r\n 'courseware/welcome-back.html',\r\n {\r\n 'course': course,\r\n 'studio_url': studio_url,\r\n 'chapter_module': chapter_module,\r\n 'prev_section': prev_section,\r\n 'prev_section_url': prev_section_url\r\n }\r\n ))\r\n\r\n result = render_to_response('courseware/courseware.html', context)\r\n except Exception as e:\r\n if isinstance(e, Http404):\r\n # let it propagate\r\n raise\r\n\r\n # In production, don't want to let a 500 out for any reason\r\n if settings.DEBUG:\r\n raise\r\n else:\r\n log.exception(\r\n u\"Error in index view: user={user}, course={course}, chapter={chapter}\"\r\n u\" section={section} position={position}\".format(\r\n user=user,\r\n course=course,\r\n chapter=chapter,\r\n section=section,\r\n position=position\r\n ))\r\n try:\r\n result = render_to_response('courseware/courseware-error.html', {\r\n 'staff_access': staff_access,\r\n 'course': course\r\n })\r\n except:\r\n # Let the exception propagate, relying on global config to at\r\n # at least return a nice error message\r\n log.exception(\"Error while rendering courseware-error page\")\r\n raise\r\n\r\n return result","def __init__(self, course_id, name, content):\n # course id and name need to go over the string_correct function\n self.ID = self.id_correct(str(course_id))\n self.name = self.string_correct(str(name))\n\n self.content = str(content)\n\n # uninitialized variables\n self.prere = {}\n self.postre = {}\n self.description = None\n self.department_title = None\n self.course_level = None\n\n # parse description and prerequisite raw data from content var\n self.seperate_content()","def test_list_topic_entries_courses(self):\r\n course_id = None # Change me!!\r\n topic_id = None # Change me!!\r\n\r\n r = self.client.list_topic_entries_courses(topic_id, course_id)","def courses(request):\r\n courses = get_courses(request.user, request.META.get('HTTP_HOST'))\r\n courses = sort_by_announcement(courses)\r\n\r\n return render_to_response(\"courseware/courses.html\", {'courses': courses})","def scrape_all_course_names(filename, verbose):\n \n\tsoup =\tBeautifulSoup(open(filename, 'r'), 'html.parser')\n\t#print(soup)\n\tcourses = []\n\t# all the courses are stored in a div w/class=view-content\n\t\t\n\th4_field_content = soup.find('h4', 'field-content')\n\t#print(h4_field_content)\n\tall_course_content = soup.find(\"div\", \"view-content\") # this contains ALL the classes...\n\tif all_course_content == None:\n\t\tprint(\"There are no courses on this page. Try a smaller page number!\")\n\t\treturn []\n\t\n\tcandidate_classes = all_course_content.find_all('a') # we want all the 'a' tags within\n\tclass_list=[]\n course_code_and_number = {}\n\tfor c in candidate_classes:\n\t\t#print(c.text, \"\\n\")\n\t\ttext = c.text # ex. AEMA 611 Experimental Designs 1 (3 credits)\n\t\ttext = text.split(\" \")# split on the space\n\t\tcourse_id = \" \".join(text[:2]).replace('\\n', '') # the first 2 are the course id\n\t\tcourse_name = \" \".join(text[2:-2]).replace('\\n', '')\n\t\tnum_credits = text[-2].replace(\"(\", \"\")# just get the course number, replace the ( with nothing \n\t\n\t\n\t\t#print(f\"{course_id}\\n{course_name}\\n{num_credits}\\n\")\n\t\ttry:# Check that the course number is a digit bc sometimes it is something weird\n\t\t\tfloat(num_credits)\n\t\texcept ValueError:\n\t\t\t#print(f\"Wrong course format. Ignoring {c}\")\n\t\t\tif(verbose):\n\t\t\t\tprint(f\"Wrong course format. Ignoring course: {c.text}\")\n\t\t\tcontinue\n\t\tclass_list.append(course_id)\n\treturn class_list","def _create_courselike( # lint-amnesty, pylint: disable=too-many-statements\n self, locator, user_id, master_branch, fields=None,\n versions_dict=None, search_targets=None, root_category='course',\n root_block_id=None, **kwargs\n ):\n index = self.get_course_index(locator, ignore_case=True)\n if index is not None:\n raise DuplicateCourseError(locator, index)\n\n partitioned_fields = self.partition_fields_by_scope(root_category, fields)\n block_fields = partitioned_fields[Scope.settings]\n if Scope.children in partitioned_fields:\n block_fields.update(partitioned_fields[Scope.children])\n definition_fields = self._serialize_fields(root_category, partitioned_fields.get(Scope.content, {}))\n\n # build from inside out: definition, structure, index entry\n # if building a wholly new structure\n if versions_dict is None or master_branch not in versions_dict:\n # create new definition and structure\n definition_id = self.create_definition_from_data(locator, definition_fields, root_category, user_id).definition_id # lint-amnesty, pylint: disable=line-too-long\n\n draft_structure = self._new_structure(\n user_id,\n BlockKey(\n root_category,\n root_block_id or SplitMongoModuleStore.DEFAULT_ROOT_COURSE_BLOCK_ID,\n ),\n block_fields,\n definition_id\n )\n new_id = draft_structure['_id']\n\n if versions_dict is None:\n versions_dict = {master_branch: new_id}\n else:\n versions_dict[master_branch] = new_id\n\n elif block_fields or definition_fields: # pointing to existing course w/ some overrides\n # just get the draft_version structure\n draft_version = CourseLocator(version_guid=versions_dict[master_branch])\n draft_structure = self._lookup_course(draft_version).structure\n draft_structure = self.version_structure(locator, draft_structure, user_id)\n new_id = draft_structure['_id']\n root_block = draft_structure['blocks'][draft_structure['root']]\n if block_fields is not None:\n root_block.fields.update(self._serialize_fields(root_category, block_fields))\n if definition_fields is not None:\n old_def = self.get_definition(locator, root_block.definition)\n new_fields = old_def['fields']\n new_fields.update(definition_fields)\n definition_id = self._update_definition_from_data(locator, old_def, new_fields, user_id).definition_id\n root_block.definition = definition_id\n root_block.edit_info.edited_on = datetime.datetime.now(UTC)\n root_block.edit_info.edited_by = user_id\n root_block.edit_info.previous_version = root_block.edit_info.update_version\n root_block.edit_info.update_version = new_id\n\n versions_dict[master_branch] = new_id\n else: # Pointing to an existing course structure\n new_id = versions_dict[master_branch]\n draft_version = CourseLocator(version_guid=new_id)\n draft_structure = self._lookup_course(draft_version).structure\n\n locator = locator.replace(version_guid=new_id)\n with self.bulk_operations(locator):\n self.update_structure(locator, draft_structure)\n index_entry = {\n '_id': ObjectId(),\n 'org': locator.org,\n 'course': locator.course,\n 'run': locator.run,\n 'edited_by': user_id,\n 'edited_on': datetime.datetime.now(UTC),\n 'versions': versions_dict,\n 'schema_version': self.SCHEMA_VERSION,\n 'search_targets': search_targets or {},\n }\n if fields is not None:\n self._update_search_targets(index_entry, fields)\n self.insert_course_index(locator, index_entry)\n\n # expensive hack to persist default field values set in __init__ method (e.g., wiki_slug)\n if isinstance(locator, LibraryLocator):\n course = self.get_library(locator, **kwargs)\n else:\n course = self.get_course(locator, **kwargs)\n return self.update_item(course, user_id, **kwargs)","def get_course_about_section(course, section_key):\r\n\r\n # Many of these are stored as html files instead of some semantic\r\n # markup. This can change without effecting this interface when we find a\r\n # good format for defining so many snippets of text/html.\r\n\r\n # TODO: Remove number, instructors from this list\r\n if section_key in ['short_description', 'description', 'key_dates', 'video',\r\n 'course_staff_short', 'course_staff_extended',\r\n 'requirements', 'syllabus', 'textbook', 'faq', 'more_info',\r\n 'number', 'instructors', 'overview',\r\n 'effort', 'end_date', 'prerequisites', 'ocw_links']:\r\n\r\n try:\r\n\r\n request = get_request_for_thread()\r\n\r\n loc = course.location.replace(category='about', name=section_key)\r\n\r\n # Use an empty cache\r\n field_data_cache = FieldDataCache([], course.id, request.user)\r\n about_module = get_module(\r\n request.user,\r\n request,\r\n loc,\r\n field_data_cache,\r\n course.id,\r\n not_found_ok=True,\r\n wrap_xmodule_display=False,\r\n static_asset_path=course.static_asset_path\r\n )\r\n\r\n html = ''\r\n\r\n if about_module is not None:\r\n try:\r\n html = about_module.render('student_view').content\r\n except Exception: # pylint: disable=broad-except\r\n html = render_to_string('courseware/error-message.html', None)\r\n log.exception(\r\n u\"Error rendering course={course}, section_key={section_key}\".format(\r\n course=course, section_key=section_key\r\n ))\r\n return html\r\n\r\n except ItemNotFoundError:\r\n log.warning(\r\n u\"Missing about section {key} in course {url}\".format(key=section_key, url=course.location.to_deprecated_string())\r\n )\r\n return None\r\n elif section_key == \"title\":\r\n return course.display_name_with_default\r\n elif section_key == \"university\":\r\n return course.display_org_with_default\r\n elif section_key == \"number\":\r\n return course.display_number_with_default\r\n\r\n raise KeyError(\"Invalid about key \" + str(section_key))","def test_get_full_topic_courses(self):\r\n course_id = None # Change me!!\r\n topic_id = None # Change me!!\r\n\r\n r = self.client.get_full_topic_courses(topic_id, course_id)","def _create_certificate_form(self, user, department, data=None, index=1, key=None):\n key = key or time.time()\n form = CertificateForm(user, department, data, prefix='gc{}'.format(key), index=index)\n return key, form","def get_courses_html():\r\n r = requests.get(URL_CS_ALL_REQ)\r\n if r.status_code == 200:\r\n return r.text\r\n else:\r\n return None","def all_courses(records):\n \n course_and_id_dict = {} #This creates an empty dictionary\n for all_tuples in records:\n course_info_tuple = all_tuples[0] #Extracts all course information\n course_id = course_info_tuple[0]\n course_name = course_info_tuple[1]\n \n course_and_id_dict[course_id] = course_name\n \n return course_and_id_dict","def build_corpus(self):\n print(\"Inside the build_corpus >>>>>\")\n documentsCount = 0\n documents = self.documents\n\t\t\n with open(self.documents_path) as file:\n for documents in file.readlines():\n documents = documents.rstrip('}\\n ').strip('0\\t').strip('1\\t').split(' ')\n documentsCount = documentsCount +1\n self.documents.append(documents)\n\t\t\t\n self.number_of_documents = documentsCount","def test_course_overview_view_with_course(self):\r\n course = CourseFactory.create(org='MITx', course='999', display_name='Robot Super Course')\r\n resp = self._show_course_overview(course.id)\r\n self.assertContains(\r\n resp,\r\n '<article class=\"courseware-overview\" data-locator=\"location:MITx+999+Robot_Super_Course+course+Robot_Super_Course\" data-course-key=\"slashes:MITx+999+Robot_Super_Course\">',\r\n status_code=200,\r\n html=True\r\n )","def _section_course_info(course_key, access):\r\n course = get_course_by_id(course_key, depth=None)\r\n\r\n section_data = {\r\n 'section_key': 'course_info',\r\n 'section_display_name': _('Course Info'),\r\n 'access': access,\r\n 'course_id': course_key,\r\n 'course_display_name': course.display_name,\r\n 'enrollment_count': CourseEnrollment.num_enrolled_in(course_key),\r\n 'has_started': course.has_started(),\r\n 'has_ended': course.has_ended(),\r\n 'list_instructor_tasks_url': reverse('list_instructor_tasks', kwargs={'course_id': course_key.to_deprecated_string()}),\r\n }\r\n\r\n try:\r\n advance = lambda memo, (letter, score): \"{}: {}, \".format(letter, score) + memo\r\n section_data['grade_cutoffs'] = reduce(advance, course.grade_cutoffs.items(), \"\")[:-2]\r\n except Exception:\r\n section_data['grade_cutoffs'] = \"Not Available\"\r\n # section_data['offline_grades'] = offline_grades_available(course_key)\r\n\r\n try:\r\n section_data['course_errors'] = [(escape(a), '') for (a, _unused) in modulestore().get_course_errors(course.id)]\r\n except Exception:\r\n section_data['course_errors'] = [('Error fetching errors', '')]\r\n\r\n return section_data","def get_courses(std):\n return std[2]","def _populate(self):\n\n # Assume the first word is what we want, and we can find well formed years\n # This sucks, but will work for these ones.\n # Roll on bibtex for citations in the CIM.\n\n citation_detail = self.doc.citation_detail\n author = citation_detail.split(',')[0]\n match = '([^\\w])19|20\\d\\d([^\\w])*?'\n m = re.search(match, citation_detail)\n if m:\n year = m.group(0)\n else:\n year = None\n\n # one error in existing es-doc content to be fixed:\n if 'van Vuuren DP' in author:\n author = 'van Vuuren'\n print 'applying vv fix'\n\n self.year = int(year)\n\n # We assume that this table will have entries which ne\n\n # I use the first three letters of a an authors name, and for\n # three or more authors, EA, and then the year for my bibtex citation string\n self.citeguess = author[0:3] + 'EA' + year[2:]\n # This is what will appear in the table:\n self.citestring = '%s et al. (%s)' % (author, year)\n # Keep this for a reference list for checking against the eventual bibtex reference list.\n self.text = citation_detail","def load2TexAS(data):\n # State global variable\n global cache_stanza, cache_spacy, cache_udpipe, cache_trankit\n\n # Collect the data\n string = data['text']\n lang = data['lang']\n packages = data['packages']\n\n hash_string = hashlib.sha1(string.encode()).hexdigest()\n\n final_HTML = \"\"\n message_HTML = \"<div class=\\'message\\'>\"\n isMessage = False\n header_input = []\n log_row = [datetime.now().strftime('%Y-%m-%d %H:%M:%S'), lang]\n\n if \"stanza\" in packages:\n # Initialize the TexAS document\n mydoc = tx.Document(string)\n mydoc.meta().set(\"authors\",\"hegler,yiwen,celine,yuqian\")\n mydoc.date().setTimestamp(\"2021-01-19T14:44\")\n\n ## If cache is full, reload the cache.\n if cache.count(cache_stanza) > 100:\n cache.write(cache_stanza, \"stanza\")\n cache_stanza = cache.load(\"stanza\")\n \n ## Check text whether is already in cache\n if hash_string in cache_stanza[lang].keys():\n tokens, end_pos, lemma, pos, nlpWordsList, hasCompoundWords, cache_stanza = cache.read(\"stanza\", cache_stanza, lang, string) #The output cache_stanza has 'count' been updated.\n else:\n tokens, end_pos, lemma, pos, nlpWordsList, hasCompoundWords, cache_stanza = cache.add(\"stanza\", cache_stanza, lang, string, get_services_stanza)\n \n mydoc.setTokenList(tokens, indexed=True)\n mydoc.views().get(\"TOKENS\").meta().set(\"generator\", \"stanza\")\n mydoc.views().get(\"TOKENS\").meta().set(\"model\", \"stanza\" + \"-\" + lang)\n mydoc.setSentenceList(end_pos)\n\n if hasCompoundWords:\n mydoc.addTokenView( \"WORDS\", nlpWordsList )\n mydoc.addTokenView(\"LEMMA\", lemma)\n mydoc.addTokenView(\"POS\", pos)\n \n # Extract HTML View\n myTabView = tx.UITabularView(mydoc)\n if hasCompoundWords:\n myTabView.showView(\"WORDS\")\n myTabView.showView(\"LEMMA\", labelCSS=False)\n myTabView.showView(\"POS\")\n\n # concatenate the myTabView.HTML()\n header_input.append((\"Stanza\", str(len(end_pos)) , str(len(tokens)), str(get_tokens_per_sents(end_pos))))\n final_HTML += \"<div class='subtitle'>Stanza</div> <br>\" + myTabView.HTML().replace(\"\\n\", \"\") + \"<br>\"\n log_row.append(\"stanza\")\n \n else:\n log_row.append(\"\")\n\n if \"spacy\" in packages:\n # SpaCy does not support Arabic and Russian\n if lang == 'ara' or lang == 'rus':\n message_HTML += \"SpaCy does not support Arabic or Russian. <br>\"\n isMessage = True\n\n else:\n mydoc = tx.Document(string)\n mydoc.meta().set(\"authors\",\"hegler,yiwen,celine,yuqian\")\n mydoc.date().setTimestamp(\"2021-01-19T14:44\")\n \n ## If cache is full, reload the cache.\n if cache.count(cache_spacy) > 100:\n cache.write(cache_spacy, \"spacy\")\n cache_spacy = cache.load(\"spacy\")\n \n ## Check text whether is already in cache\n if hash_string in cache_spacy[lang].keys():\n tokens, end_pos, lemma, pos, cache_spacy = cache.read(\"spacy\", cache_spacy, lang, string)\n else:\n tokens, end_pos, lemma, pos, cache_spacy = cache.add(\"spacy\", cache_spacy, lang, string, get_services_spacy)\n \n mydoc.setTokenList(tokens, indexed=True)\n mydoc.views().get(\"TOKENS\").meta().set(\"generator\", \"spacy\")\n mydoc.views().get(\"TOKENS\").meta().set(\"model\", \"spacy\" + \"-\" + lang )\n mydoc.setSentenceList(end_pos)\n mydoc.addTokenView(\"LEMMA\", lemma)\n mydoc.addTokenView(\"POS\", pos)\n \n # Extract HTML View\n myTabView = tx.UITabularView(mydoc)\n myTabView.showView(\"LEMMA\", labelCSS=False)\n myTabView.showView(\"POS\")\n\n # concatenate the myTabView.HTML()\n header_input.append((\"SpaCy\", str(len(end_pos)) , str(len(tokens)), str(get_tokens_per_sents(end_pos))))\n final_HTML += \"<div class='subtitle'>\" + \"SpaCy\" + \"</div><br>\" + myTabView.HTML().replace(\"\\n\", \"\") + \"<br>\"\n log_row.append(\"spacy\")\n \n else:\n log_row.append(\"\")\n\n if \"udpipe\" in packages: \n ## If cache is full, reload the cache.\n if cache.count(cache_udpipe) > 100:\n cache.write(cache_udpipe, \"udpipe\")\n cache_udpipe = cache.load(\"udpipe\")\n \n ## Check text whether is already in cache\n if hash_string in cache_udpipe[lang].keys():\n tokens, end_pos, lemma, pos, cache_udpipe = cache.read(\"udpipe\", cache_udpipe, lang, string)\n else:\n tokens, end_pos, lemma, pos, cache_udpipe = cache.add(\"udpipe\", cache_udpipe, lang, string, get_services_udpipe)\n \n string_udpipe = \" \".join(tokens)\n\n # Initialize the TexAS document\n mydoc = tx.Document(string_udpipe)\n mydoc.meta().set(\"authors\",\"hegler,yiwen,celine,yuqian\")\n mydoc.date().setTimestamp(\"2021-01-19T14:44\")\n\n mydoc.setTokenList(tokens, indexed=True)\n mydoc.views().get(\"TOKENS\").meta().set(\"generator\", \"udpipe\")\n mydoc.views().get(\"TOKENS\").meta().set(\"model\", \"udpipe\" + \"-\" + lang )\n mydoc.setSentenceList(end_pos)\n mydoc.addTokenView(\"LEMMA\", lemma)\n mydoc.addTokenView(\"POS\", pos)\n \n # Extract HTML View\n myTabView = tx.UITabularView(mydoc)\n myTabView.showView(\"LEMMA\", labelCSS=False)\n myTabView.showView(\"POS\")\n\n # concatenate the myTabView.HTML()\n header_input.append((\"UDpipe\", str(len(end_pos)) , str(len(tokens)), str(get_tokens_per_sents(end_pos))))\n final_HTML += \"<div class='subtitle'>UDpipe</div> <br>\" + myTabView.HTML().replace(\"\\n\", \"\") + \"<br>\"\n log_row.append(\"udpipe\")\n \n else:\n log_row.append(\"\")\n \n if \"trankit\" in packages:\n # trankit temporarily only support english\n if lang == 'eng':\n mydoc = tx.Document(string)\n mydoc.meta().set(\"authors\",\"hegler,yiwen,celine,yuqian\")\n mydoc.date().setTimestamp(\"2021-01-19T14:44\")\n \n ## If cache is full, reload the cache.\n if cache.count(cache_trankit) > 100:\n cache.write(cache_trankit, \"trankit\")\n cache_trankit = cache.load(\"trankit\")\n \n ## Check text whether is already in cache\n if hash_string in cache_trankit[lang].keys():\n tokens, end_pos, lemma, pos, cache_trankit = cache.read(\"trankit\", cache_trankit, lang, string)\n else:\n tokens, end_pos, lemma, pos, cache_trankit = cache.add(\"trankit\", cache_trankit, lang, string, get_services_trankit)\n \n mydoc.setTokenList(tokens, indexed=True)\n mydoc.views().get(\"TOKENS\").meta().set(\"generator\", \"spacy\")\n mydoc.views().get(\"TOKENS\").meta().set(\"model\", \"spacy\" + \"-\" + lang )\n mydoc.setSentenceList(end_pos)\n mydoc.addTokenView(\"LEMMA\", lemma)\n mydoc.addTokenView(\"POS\", pos)\n \n # Extract HTML View\n myTabView = tx.UITabularView(mydoc)\n myTabView.showView(\"LEMMA\", labelCSS=False)\n myTabView.showView(\"POS\")\n\n # concatenate the myTabView.HTML()\n header_input.append((\"Trankit\", str(len(end_pos)) , str(len(tokens)), str(get_tokens_per_sents(end_pos))))\n final_HTML += \"<div class='subtitle'>\" + \"Trankit\" + \"</div><br>\" + myTabView.HTML().replace(\"\\n\", \"\") + \"<br>\"\n log_row.append(\"trankit\")\n\n else:\n message_HTML += \"Trankit temporarily only supports English. <br>\"\n isMessage = True \n \n else:\n log_row.append(\"\")\n\n message_HTML += \"</div>\"\n if isMessage:\n return message_HTML + get_header_table(header_input) + \"<br><br>\" + final_HTML\n\n writeLog(log_row)\n return get_header_table(header_input) + \"<br><br>\" + final_HTML","def test_get_courses_for_wiki(self):\r\n store = XMLModuleStore(DATA_DIR, course_dirs=['toy', 'simple'])\r\n for course in store.get_courses():\r\n course_locations = store.get_courses_for_wiki(course.wiki_slug)\r\n self.assertEqual(len(course_locations), 1)\r\n self.assertIn(course.location, course_locations)\r\n\r\n course_locations = store.get_courses_for_wiki('no_such_wiki')\r\n self.assertEqual(len(course_locations), 0)\r\n\r\n # now set toy course to share the wiki with simple course\r\n toy_course = store.get_course(SlashSeparatedCourseKey('edX', 'toy', '2012_Fall'))\r\n toy_course.wiki_slug = 'simple'\r\n\r\n course_locations = store.get_courses_for_wiki('toy')\r\n self.assertEqual(len(course_locations), 0)\r\n\r\n course_locations = store.get_courses_for_wiki('simple')\r\n self.assertEqual(len(course_locations), 2)\r\n for course_number in ['toy', 'simple']:\r\n self.assertIn(Location('edX', course_number, '2012_Fall', 'course', '2012_Fall'), course_locations)","def extract_courses():\n if settings.XPRO_COURSES_API_URL:\n return requests.get(settings.XPRO_COURSES_API_URL, timeout=20).json()\n return []","def fetch(cls, course_key):\r\n descriptor = modulestore('direct').get_course(course_key)\r\n course_details = cls(course_key.org, course_key.course, course_key.run)\r\n\r\n course_details.start_date = descriptor.start\r\n course_details.end_date = descriptor.end\r\n course_details.enrollment_start = descriptor.enrollment_start\r\n course_details.enrollment_end = descriptor.enrollment_end\r\n course_details.course_image_name = descriptor.course_image\r\n course_details.course_image_asset_path = course_image_url(descriptor)\r\n\r\n temploc = course_key.make_usage_key('about', 'syllabus')\r\n try:\r\n course_details.syllabus = get_modulestore(temploc).get_item(temploc).data\r\n except ItemNotFoundError:\r\n pass\r\n\r\n temploc = course_key.make_usage_key('about', 'short_description')\r\n try:\r\n course_details.short_description = get_modulestore(temploc).get_item(temploc).data\r\n except ItemNotFoundError:\r\n pass\r\n\r\n temploc = course_key.make_usage_key('about', 'overview')\r\n try:\r\n course_details.overview = get_modulestore(temploc).get_item(temploc).data\r\n except ItemNotFoundError:\r\n pass\r\n\r\n temploc = course_key.make_usage_key('about', 'effort')\r\n try:\r\n course_details.effort = get_modulestore(temploc).get_item(temploc).data\r\n except ItemNotFoundError:\r\n pass\r\n\r\n temploc = course_key.make_usage_key('about', 'video')\r\n try:\r\n raw_video = get_modulestore(temploc).get_item(temploc).data\r\n course_details.intro_video = CourseDetails.parse_video_tag(raw_video)\r\n except ItemNotFoundError:\r\n pass\r\n\r\n return course_details","def test_citations_and_toc_with_valid_defaults(self):\n\n pandoc_default_files = [\n os.path.join(\n TEST_DEFAULT_FILES_PATH,\n \"valid_defaults_with_toc_and_citations.yaml\",\n )\n ]\n\n settings = get_settings(\n PANDOC_DEFAULT_FILES=pandoc_default_files,\n FORMATTED_FIELDS=FORMATTED_FIELDS,\n )\n pandoc_reader = PandocReader(settings)\n\n source_path = os.path.join(\n TEST_CONTENT_PATH, \"valid_content_with_citation.md\"\n )\n output, metadata = pandoc_reader.read(source_path)\n self.maxDiff = None # pylint: disable=invalid-name\n\n self.assertEqual(\n (\n '<h2 id=\"string-theory\">String Theory</h2>\\n'\n \"<p>But this foundational principle of science has\"\n \" now been called into question by\"\n ' <a href=\"https://www.britannica.com/science/'\n 'string-theory\">String Theory</a>,'\n \" which is a relative newcomer to theoretical physics, but one\"\n \" that has captured the common imagination, judging by\"\n \" the popular explanations that abound on the Web\"\n ' <span class=\"citation\" data-cites=\"mann2019 wood2019'\n ' jones2020\">[1]–[3]</span>.'\n \" And whether string theory is or is not science, Popper\"\n \" notwithstanding, is an issue that is still up for debate\"\n \" <span\"\n ' class=\"citation\" data-cites=\"siegel2015 castelvecchi2016'\n ' alves2017 francis2019\">[4]–[7]</span>.</p>\\n'\n '<h1 class=\"unnumbered\" id=\"bibliography\">References</h1>\\n'\n '<div id=\"refs\" class=\"references csl-bib-body\"'\n ' role=\"doc-bibliography\">\\n'\n '<div id=\"ref-mann2019\" class=\"csl-entry\"'\n ' role=\"doc-biblioentry\">\\n'\n '<div class=\"csl-left-margin\">[1]'\n ' </div><div class=\"csl-right-inline\">A. Mann,'\n \" <span>“<span>What Is String Theory?</span>”</span>\"\n \" 20-Mar-2019. [Online].\"\n ' Available: <a href=\"https://www.livescience.com/'\n '65033-what-is-string-theory.html\">'\n \"https://www.livescience.com/\"\n \"65033-what-is-string-theory.html</a>.\"\n \" [Accessed: 12-Nov-2020]</div>\\n\"\n \"</div>\\n\"\n '<div id=\"ref-wood2019\" class=\"csl-entry\"'\n ' role=\"doc-biblioentry\">\\n'\n '<div class=\"csl-left-margin\">[2] </div>'\n '<div class=\"csl-right-inline\">'\n \"C. Wood, <span>“<span>What Is String Theory?</span>.\"\n \" Reference article:\"\n \" A simplified explanation and brief history of string\"\n \" theory,”</span> 11-Jul-2019.\"\n ' [Online]. Available: <a href=\"https://www.space.com/'\n '17594-string-theory.html\">'\n \"https://www.space.com/17594-string-theory.html</a>.\"\n \" [Accessed: 12-Nov-2020]</div>\\n\"\n \"</div>\\n\"\n '<div id=\"ref-jones2020\" class=\"csl-entry\"'\n ' role=\"doc-biblioentry\">\\n'\n '<div class=\"csl-left-margin\">[3]'\n ' </div><div class=\"csl-right-inline\">'\n 'A. Z. Jones, <span>“<span class=\"nocase\">The Basics of String'\n \" Theory</span>,”</span> 02-Mar-2019. [Online]. Available:\"\n ' <a href=\"https://www.thoughtco.com/'\n 'what-is-string-theory-2699363\">'\n \"https://www.thoughtco.com/what-is-string-theory-2699363</a>.\"\n \" [Accessed: 12-Nov-2020]</div>\\n\"\n \"</div>\\n\"\n '<div id=\"ref-siegel2015\" class=\"csl-entry\"'\n ' role=\"doc-biblioentry\">\\n'\n '<div class=\"csl-left-margin\">[4]'\n ' </div><div class=\"csl-right-inline\">'\n \"E. Siegel, <span>“<span>Why String Theory Is Not A Scientific\"\n \" Theory</span>,”</span> 23-Dec-2015. [Online]. Available:\"\n \" <a\"\n ' href=\"https://www.forbes.com/sites/'\n \"startswithabang/2015/12/23/\"\n 'why-string-theory-is-not-science/\">https://www.forbes.com/'\n \"sites/startswithabang/2015/12/23/\"\n \"why-string-theory-is-not-science/</a>.\"\n \" [Accessed: 12-Nov-2020]</div>\\n\"\n \"</div>\\n\"\n '<div id=\"ref-castelvecchi2016\" class=\"csl-entry\"'\n ' role=\"doc-biblioentry\">\\n'\n '<div class=\"csl-left-margin\">[5]'\n ' </div><div class=\"csl-right-inline\">'\n 'D. Castelvecchi, <span>“<span class=\"nocase\">'\n \"Feuding physicists turn\"\n \" to philosophy for help</span>. String theory is at the\"\n \" heart of a debate over the integrity of the scientific\"\n \" method itself,”</span> 05-Jan-2016. [Online]. Available:\"\n ' <a href=\"https://www.nature.com/news/'\n 'feuding-physicists-turn-to-philosophy-for-help-1.19076\">'\n \"https://www.nature.com/news/\"\n \"feuding-physicists-turn-to-philosophy-for-help-1.19076</a>.\"\n \" [Accessed: 12-Nov-2020]</div>\\n\"\n \"</div>\\n\"\n '<div id=\"ref-alves2017\" class=\"csl-entry\"'\n ' role=\"doc-biblioentry\">\\n'\n '<div class=\"csl-left-margin\">[6] </div>'\n '<div class=\"csl-right-inline\">'\n 'R. A. Batista and J. Primack, <span>“<span class=\"nocase\">'\n \"Is String theory falsifiable?</span>. Can a theory that isn’t\"\n \" completely testable still be useful to physics?”</span>\"\n \" [Online].\"\n ' Available: <a href=\"https://metafact.io/factchecks/'\n '30-is-string-theory-falsifiable\">'\n \"https://metafact.io/factchecks/\"\n \"30-is-string-theory-falsifiable</a>.\"\n \" [Accessed: 12-Nov-2020]</div>\\n\"\n \"</div>\\n\"\n '<div id=\"ref-francis2019\" class=\"csl-entry\"'\n ' role=\"doc-biblioentry\">\\n'\n '<div class=\"csl-left-margin\">[7]'\n ' </div><div class=\"csl-right-inline\">'\n 'M. R. Francis, <span>“<span class=\"nocase\">Falsifiability and'\n \" physics</span>. Can a theory that isn’t completely testable\"\n \" still be useful to physics?”</span> 23-Apr-2019.\"\n \" [Online]. Available:\"\n ' <a href=\"https://www.scientificamerican.com/'\n 'article/is-string-theory-science/\">'\n \"https://www.scientificamerican.com/article/is-\"\n \"string-theory-science/</a>. [Accessed: 12-Nov-2020]</div>\\n\"\n \"</div>\\n\"\n \"</div>\\n\"\n ),\n output,\n )\n\n self.assertEqual(\"Valid Content With Citation\", str(metadata[\"title\"]))\n self.assertEqual(\"My Author\", str(metadata[\"author\"]))\n self.assertEqual(\"2020-10-16 00:00:00\", str(metadata[\"date\"]))\n self.assertEqual(\n (\n '<nav class=\"toc\" role=\"doc-toc\">\\n'\n \"<ul>\\n\"\n '<li><a href=\"#string-theory\">String Theory</a></li>\\n'\n '<li><a href=\"#bibliography\">References</a></li>\\n'\n \"</ul>\\n</nav>\\n\"\n ),\n str(metadata[\"toc\"]),\n )","def parseCourses(self, response):\n sel = Selector(response)\n courses = sel.xpath('//div[@class=\"course-info expandable\"]')\n for c in courses:\n item = CourseItem(response.request.meta[\"item\"])\n item['code'] += '-' + c.xpath('@id').get().strip()\n item['name'] = c.xpath('//a[@class=\"courselink\"]/text()').get().strip()\n # everything works up to here #\n href = c.xpath('div/h3/a/@href').get()\n url = urljoin('https://web-app.usc.edu', href)\n yield Request(url=url,callback=self.parseSection,meta={'item':item})","def test_course_index_view_with_course(self):\r\n CourseFactory.create(display_name='Robot Super Educational Course')\r\n resp = self.client.get_html('/course/')\r\n self.assertContains(\r\n resp,\r\n '<h3 class=\"course-title\">Robot Super Educational Course</h3>',\r\n status_code=200,\r\n html=True\r\n )\r\n _test_no_locations(self, resp)","def _construct_course_son(org, course, run):\r\n return bson.son.SON([\r\n ('org', org),\r\n ('course', course),\r\n ('name', run)\r\n ])","def build_document(self, values: dict):\n doc = Document(self.config.get('template_file'))\n for section in doc.sections:\n # First Header\n header = section.header\n for p in header.paragraphs:\n for key, field in self.config.get('fields').items():\n # Format key\n formatted_key = f\"<{key.upper()}>\"\n if formatted_key in p.text:\n inline = p.runs\n # Loop added to work with runs (strings with same style)\n for i in range(len(inline)):\n if formatted_key in inline[i].text:\n text = inline[i].text.replace(\n formatted_key, values[key])\n inline[i].text = text\n # Second Footer\n footer = section.footer\n for p in footer.paragraphs:\n for key, field in self.config.get(\"fields\").items():\n # Format key\n formatted_key = f\"<{key.upper()}>\"\n if formatted_key in p.text:\n inline = p.runs\n # Loop added to work with runs (strings with same style)\n for i in range(len(inline)):\n if formatted_key in inline[i].text:\n text = inline[i].text.replace(\n formatted_key, values[key])\n inline[i].text = text\n\n # Go by tables\n for table in doc.tables:\n for row in table.rows:\n for cell in row.cells:\n for p in cell.paragraphs:\n for key, field in self.config.get(\"fields\").items():\n # Format key\n formatted_key = f\"<{key.upper()}>\"\n if formatted_key in p.text:\n inline = p.runs\n # Loop added to work with runs (strings with same style)\n for i in range(len(inline)):\n if formatted_key in inline[i].text:\n text = inline[i].text.replace(formatted_key, values[key])\n inline[i].text = text\n\n # Go by the rest of the document\n for p in doc.paragraphs:\n for key, field in self.config.get(\"fields\").items():\n # Format key\n formatted_key = f\"<{key.upper()}>\"\n if formatted_key in p.text:\n inline = p.runs\n # Loop added to work with runs (strings with same style)\n for i in range(len(inline)):\n if formatted_key in inline[i].text:\n text = inline[i].text.replace(formatted_key, values[key])\n inline[i].text = text\n\n # By default filename will be the template filename with copy_ before\n filename = f\"copy_{self.config.get('template_file')}\"\n if \"filename\" in self.config:\n if \"type\" in self.config.get(\"filename\"):\n # We can have 2 types static value or based in a field\n if self.config.get(\"filename\").get(\"type\") == \"static\" and \"value\" in self.config.get(\"filename\"):\n filename = self.config.get(\"filename\").get(\"value\")\n elif self.config[\"filename\"][\"type\"] == \"field\" and \"value\" in self.config[\"filename\"]:\n filename = values.get(self.config.get(\"filename\").get(\"value\"))\n\n # Make sure we have a prefix populated\n if \"file_prefix\" not in self.config:\n self.config.update({\"file_prefix\": \"\"})\n\n # Make sure we have a posfix populated\n if \"file_posfix\" not in self.config:\n self.config.update({\"file_posfix\": \"\"})\n\n filename = self.config.get(\"file_prefix\") + filename + self.config.get(\"file_posfix\")\n\n doc.save(f\"{filename}.docx\")\n\n return f\"{filename}.docx\"","def _get_sample_plain_context(self):\r\n context = {\r\n 'course_title': \"Bogus Course Title\",\r\n 'course_url': \"/location/of/course/url\",\r\n 'account_settings_url': \"/location/of/account/settings/url\",\r\n 'platform_name': 'edX',\r\n 'email': 'your-email@test.com',\r\n }\r\n return context","def test_list_entries_courses(self):\r\n course_id = None # Change me!!\r\n topic_id = None # Change me!!\r\n\r\n r = self.client.list_entries_courses(topic_id, course_id, ids=None)","def get_courses_for_wiki(self, wiki_slug):\r\n courses = []\r\n return courses","def download_course_interactive(self):\n self.domain_prompt()\n self.course_prompt()\n self.get_course_page()\n self.get_course_title()\n self.get_course_unit_titles()\n self.get_course_unit_slugs()\n self.get_course_unit_urls()\n\n print(\"\\nGenerating Path Slugs...\\n\")\n self.get_course_all_slugs()\n self.get_course_youtube_ids()\n self.download_course_videos()","def index():\n user, user_id = get_user()\n # Get this user's course with their roles\n my_courses = []\n if user:\n my_courses = user.get_courses()\n # Get all public courses\n public_courses = Course.get_public()\n\n return render_template('courses/index.html',\n user=user,\n my_courses=my_courses,\n public_courses=public_courses)","def build(self) -> None:\n\n print(\"Genereting files..\")\n self.doc = self.doc + r'\\end{document}'\n\n f = open(\"latex\\\\\" + self.report_name + '.tex', 'w')\n f.write(self.doc)\n f.close()\n\n os.chdir('latex')\n\n cmd = ['pdflatex', '-interaction', 'nonstopmode', self.report_name + '.tex']\n #cmd = ['pdflatex', '-interaction', self.report_name + '.tex']\n\n for i in range(2):\n proc = subprocess.Popen(cmd)\n proc.communicate()\n retcode = proc.returncode\n if not retcode == 0:\n os.chdir('..')\n raise ValueError('Error {} executing command: {}'.format(retcode, ' '.join(cmd)))\n\n os.unlink(self.report_name + '.aux')\n os.unlink(self.report_name + '.lof')\n os.unlink(self.report_name + '.log')\n os.unlink(self.report_name + '.lot')\n os.unlink(self.report_name + '.out')\n os.unlink(self.report_name + '.toc')\n\n os.chdir('..')","def createTrecTextForCurrentDocuments(baseDir):\n pathToFolder = baseDir + 'Collections/'\n if not os.path.exists(pathToFolder):\n os.makedirs(pathToFolder)\n currentTime = str(datetime.datetime.now()).replace(\":\", \"-\").replace(\" \", \"-\").replace(\".\", \"-\")\n pathToTrecText = pathToFolder+\"TrecText/\"\n if not os.path.exists(pathToTrecText):\n os.makedirs(pathToTrecText)\n filename = pathToTrecText + currentTime\n client = MongoClient('asr2.iem.technion.ac.il',27017)\n db = client.asr16\n documents = db.documents.find({}).sort('query_id',1)\n queryToDocnos= {}\n current_users = retrieve_users()\n f = open(filename, 'w')\n for document in documents:\n if document['username'] in current_users:\n print(document['query_id'], document['username'])\n f.write('<DOC>\\n')\n docno = str(document['query_id']).zfill(3) + '-' + str(document['username'])\n f.write('<DOCNO>' + docno + '</DOCNO>\\n')\n docnos = queryToDocnos.get(str(document['query_id']).zfill(3), [])\n docnos.append(docno)\n queryToDocnos[str(document['query_id']).zfill(3)] = docnos\n f.write('<TEXT>\\n')\n f.write(unicodedata.normalize('NFKD', document['current_document']).encode('cp1252', \"ignore\").decode('utf-8', 'replace').replace(u'\\uFFFD', ' ').rstrip())\n f.write('\\n</TEXT>\\n')\n f.write('</DOC>\\n')\n f.close()\n pathToWorkingSet = pathToFolder+ 'WorkingSets/'\n if not os.path.exists(pathToWorkingSet):\n os.makedirs(pathToWorkingSet)\n workingSetFilename = pathToWorkingSet + currentTime\n f = open(workingSetFilename, 'w')\n for query, docnos in queryToDocnos.items():\n i = 1\n for docid in docnos:\n f.write(query.zfill(3) + ' Q0 ' + docid + ' ' + str(i) + ' -' + str(i) + ' indri\\n')\n i +=1\n f.close()\n return filename, workingSetFilename, currentTime","def get_courses(db: Session = Depends(get_db)): # , _: models.User = Depends(get_current_user))\n return crud.course.get_multi(db, skip=0, limit=100)","def __init__(self, test_num):\n if test_num == 1:\n self.degree_chosen = \"Science\"\n self.is_double_major = True\n self.major = \"Computer Science\"\n self.major2 = \"Mathematics\"\n self.user_courses_dict = {\n \"COSC121\": UniCourse(\"COSC121\", \"Introduction to Computer Science\"),\n \"MATH120\": UniCourse(\"MATH120\", \"Discrete Mathematics\")\n }\n self.all_courses_dict = {\n \"COSC121\": UniCourse(\"COSC121\", \"Introduction to Computer Science\"),\n \"MATH120\": UniCourse(\"MATH120\", \"Discrete Mathematics\"),\n \"MATH101\": UniCourse(\"MATH101\", \"Methods of Mathematics\")\n }","def make_bibtex(self):\n\n\t\t# bib = requests.request('GET', 'http://dx.doi.org/' + self.doi, ","def generate_latex_table(dictionary,filename,location=\".\"):\n if type(filename) != str:\n raise TypeError('filename should be string')\n if type(dictionary) != dict:\n raise TypeError('dictionary should be dictionary')\n\n head_code = \"\"\"\\\\documentclass{article}\n%In the preamble section include the arabtex and utf8 packages\n\\\\usepackage{arabtex}\n\\\\usepackage{utf8}\n\\\\usepackage{longtable}\n\\\\usepackage{color, colortbl}\n\\\\usepackage{supertabular}\n\\\\usepackage{multicol}\n\\\\usepackage{geometry}\n\\\\geometry{left=.1in, right=.1in, top=.1in, bottom=.1in}\n\n\\\\begin{document}\n\\\\begin{multicols}{6}\n\\\\setcode{utf8}\n\n\\\\begin{center}\"\"\"\n\n tail_code = \"\"\"\\\\end{center}\n\\\\end{multicols}\n\\\\end{document}\"\"\"\n\n begin_table = \"\"\"\\\\begin{tabular}{ P{2cm} P{1cm}}\n\\\\textbf{words} & \\\\textbf{\\\\#} \\\\\\\\\n\\\\hline\n\\\\\\\\[0.01cm]\"\"\"\n end_table= \"\"\"\\\\end{tabular}\"\"\"\n rows_num = 40\n if location != '.':\n filename = location +\"/\"+ filename\n\n try:\n file = open(filename+'.tex', 'w', encoding='utf8')\n file.write(head_code+'\\n')\n n= int(len(dictionary)/rows_num)\n words = [(\"\\\\<\"+word+\"> & \"+str(frequancy)+' \\\\\\\\ \\n') for word, frequancy in dictionary.items()]\n start=0\n end=rows_num\n new_words = []\n for i in range(n):\n new_words = new_words+ [begin_table+'\\n'] +words[start:end] +[end_table+\" \\n\"]\n start=end\n end+=rows_num\n remain_words = len(dictionary) - rows_num*n\n if remain_words > 0:\n new_words += [begin_table+\" \\n\"]+ words[-1*remain_words:]+[end_table+\" \\n\"]\n for word in new_words:\n file.write(word)\n file.write(tail_code)\n file.close()\n return True\n except:\n return False","def test_list_discussion_topics_courses(self):\r\n course_id = None # Change me!!\r\n\r\n r = self.client.list_discussion_topics_courses(course_id, exclude_context_module_locked_topics=None, include=None, only_announcements=None, order_by=None, scope=None, search_term=None)","def test_buildTeX(self):\n version = \"3.2.1\"\n input1, output1 = self.getArbitraryLoreInputAndOutput(version)\n input2, output2 = self.getArbitraryLoreInputAndOutput(version)\n\n # Filenames are chosen by getArbitraryOutput to match the counter used\n # by getArbitraryLoreInputAndOutput.\n self.howtoDir.child(\"1.xhtml\").setContent(input1)\n self.howtoDir.child(\"2.xhtml\").setContent(input2)\n\n builder = BookBuilder()\n builder.buildTeX(self.howtoDir)\n self.assertEqual(self.howtoDir.child(\"1.tex\").getContent(), output1)\n self.assertEqual(self.howtoDir.child(\"2.tex\").getContent(), output2)","def see_teaching_courses(self, username: str, token: str) -> List[Dict[str, object]]:\n\n # Validate user first\n if not self.validate(username=username, token=token, check_privilege='instructor'):\n raise RuntimeError(\"User not verified!\")\n\n # Get UID from user's username\n uid = self.get_uid(username=username)\n\n # Query database for courses instructed by a user with this UID\n cursor = self._db_connection.cursor()\n cursor.execute(\n '''\n SELECT \n course_id,\n course_abbreviation,\n course_name, \n time,\n seats \n FROM \n courses\n WHERE \n instructor_id = ?\n ;\n ''', (uid,))\n\n db_results = cursor.fetchall()\n\n if db_results is None:\n print(\"No associated courses found!\")\n return []\n\n # Build information dicts for every course this user is instructing\n courses = []\n for result in db_results:\n # Get the number of students enrolled in this course already\n cursor.execute('''SELECT COUNT(*) FROM enrollment_records WHERE course_id = ?;''', (result[0],))\n students_enrolled = cursor.fetchone()[0]\n if students_enrolled is None:\n students_enrolled = 0\n\n # Build a course dict from the data\n courses.append({\n \"course_abbreviation\": result[1],\n \"course_name\": result[2],\n \"time\": result[3],\n \"students_enrolled\": students_enrolled,\n \"capacity\": result[4],\n })\n\n return courses","def test_tax_court_citation_extractor(self):\n\n test_pairs = (\n (\n \"\"\" 1 UNITED STATES TAX COURT REPORT (2018)\n\n\n\n UNITED STATES TAX COURT\n\n\n\n BENTLEY COURT II LIMITED PARTNERSHIP, B.F. BENTLEY, INC., TAX\n MATTERS PARTNER, Petitioner v.\n COMMISSIONER OF INTERNAL REVENUE, Respondent\n\n\n\n Docket No. 5393-04. Filed May 31, 2006.\n\n\n\n Nancy Ortmeyer Kuhn, for petitioner.\n \"\"\",\n \"1 T.C. 2018\",\n ),\n (\n \"\"\" T.C. Memo. 2003-150\n\n\n\n UNITED STATES TAX COURT\n\n\n\n RIVER CITY RANCHES #1 LTD., LEON SHEPARD,\n TAX MATTERS PARTNER,\n RIVER CITY RANCHES #2 LTD., LEON SHEPARD,\n TAX MATTERS PARTNER,\n RIVER CITY RANCHES #3 LTD., LEON SHEPARD,\n TAX MATTERS PARTNER,\n\n\n \"\"\",\n \"2003 T.C. Memo. 150\",\n ),\n (\n \"\"\" T.C. Summary Opinion 2003-150\n\n\n\n UNITED STATES TAX COURT\n\n\n\n RIVER CITY RANCHES #1 LTD., LEON SHEPARD,\n TAX MATTERS PARTNER,\n RIVER CITY RANCHES #2 LTD., LEON SHEPARD,\n TAX MATTERS PARTNER,\n RIVER CITY RANCHES #3 LTD., LEON SHEPARD,\n TAX MATTERS PARTNER,\n\n\n \"\"\",\n \"2003 T.C. Summary Opinion 150\",\n ),\n (\n \"\"\"\n MICHAEL KEITH SHENK, PETITIONER v. COMMISSIONER\n OF INTERNAL REVENUE, RESPONDENT\n\n Docket No. 5706–12. Filed May 6, 2013.\n\n P was divorced from his wife, and their 2003 ‘‘Judgment of\n Absolute Divorce’’ provided that his ex-wife would have pri-\n mary residential custody of their three minor children. The\n judgment provided that the dependency exemption deductions\n for the three children would be divided between the two ex-\n spouses according to various conditions but did not provide\n that the ex-wife must execute in P’s favor a Form 8332,\n ‘‘Release of Claim to Exemption for Child of Divorced or Sepa-\n rated Parents’’. The children resided with P’s ex-wife for more\n than half of 2009, and P’s ex-wife did not execute in P’s favor\n any Form 8332 or equivalent document for any year. For 2009\n P timely filed a Federal income tax return on which he\n claimed dependency exemption deductions and the child tax\n credit for two of the children, consistent with his under-\n standing of the terms of the judgment, but he did not attach\n any Form 8332 to his return. He also claimed head-of-house-\n hold filing status. His ex-wife, the custodial parent, timely\n filed a Federal income tax return for 2009 on which she also\n\n 200\n\n\n\n\nVerDate Nov 24 2008 10:59 Jul 11, 2014 Jkt 372897 PO 20012 Frm 00001 Fmt 3857 Sfmt 3857 V:\\FILES\\BOUND VOL. WITHOUT CROP MARKS\\B.V.140\\SHENK JAMIE\n\f (200) SHENK v. COMMISSIONER 201\n\n\n claimed two dependency exemption deductions, so that one\n child was claimed on both parents’ returns. R allowed to P the\n dependency exemption deduction for one of the children but\n disallowed his claim for the dependency exemption deduction\n for the child who had also been claimed by the custodial\n parent. At trial P contended he is entitled to a dependency\n exemption deduction for all three children. Held: Since the\n custodial parent did not execute, and P could not and did not\n attach to his return, any Form 8332 or equivalent release, P\n is not entitled under I.R.C. sec. 152(e)(2)(A) to claim the\n dependency exemption deduction or the child tax credit. Held,\n further, where both the custodial parent and the noncustodial\n parent have claimed for the same year a dependency exemp-\n tion deduction for the same child, a declaration signed by the\n custodial parent after the period of limitations for assess-\n ments has expired as to the custodial parent could not qualify\n under I.R.C. sec. 152(e)(2)(A), and therefore there is no reason\n to grant P’s request to leave the record open so that he may\n obtain and proffer such a declaration. Held, further, P is not\n entitled to head-of-household filing status under I.R.C. sec.\n 2(b)(1) nor to the child tax credit under I.R.C. sec. 24.\n\n Michael Keith Shenk, for himself.\n Shari Salu, for respondent.\n GUSTAFSON, Judge: The Internal Revenue Service (IRS)\n determined a deficiency of $3,136 in the 2009 Federal income\n tax of petitioner Michael Keith Shenk. Mr. Shenk petitioned\n this Court, pursuant to section 6213(a), 1 for redetermination\n of the deficiency. After Mr. Shenk’s concession that he\n received but did not report $254 in dividend income, the\n issue for decision is whether Mr. Shenk is entitled to a\n dependency exemption deduction for one of his children\n under section 151(c), a child tax credit for that child under\n section 24(a), and head-of-household filing status under sec-\n tion 2(b)(1). On these issues, we hold for the IRS.\n FINDINGS OF FACT\n\n The judgment of divorce\n Mr. Shenk was married to Julie Phillips, and they have\n three minor children—M.S., W.S., and L.S. They divorced in\n 2003. The family court’s ‘‘Judgment of Absolute Divorce’’ pro-\n 1 Unless otherwise indicated, all citations of sections refer to the Internal\n\n Revenue Code (26 U.S.C.) in effect for the tax year at issue, and all cita-\n tions of Rules refer to the Tax Court Rules of Practice and Procedure.\n\n\n\n\nVerDate Nov 24 2008 10:59 Jul 11, 2014 Jkt 372897 PO 20012 Frm 00002 Fmt 3857 Sfmt 3857 V:\\FILES\\BOUND VOL. WITHOUT CROP MARKS\\B.V.140\\SHENK JAMIE\n\f 202 140 UNITED STATES TAX COURT REPORTS (200)\n\n\n vided: that Ms. Phillips was ‘‘awarded primary residential\n custody’’ of the parties’ three children; and that Mr. Shenk\n would be liable for child support payments; but that, as to\n dependency exemptions—\"\"\",\n \"140 T.C. 200\",\n ),\n )\n site = tax.Site()\n for q, a in test_pairs:\n results = site.extract_from_text(q)\n cite_string = \"%s %s %s\" % (\n results[\"Citation\"][\"volume\"],\n results[\"Citation\"][\"reporter\"],\n results[\"Citation\"][\"page\"],\n )\n\n self.assertEqual(cite_string, a)\n print \"✓\", cite_string","def _create_course(self, default, course_key):\r\n if default == 'split':\r\n offering = course_key.offering.replace('/', '.')\r\n else:\r\n offering = course_key.offering\r\n course = self.store.create_course(course_key.org, offering, store_name=default)\r\n category = self.import_chapter_location.category\r\n block_id = self.import_chapter_location.name\r\n chapter = self.store.create_item(\r\n # don't use course_location as it may not be the repr\r\n course.location, category, location=self.import_chapter_location, block_id=block_id\r\n )\r\n if isinstance(course.id, CourseLocator):\r\n self.course_locations[self.MONGO_COURSEID] = course.location.version_agnostic()\r\n self.import_chapter_location = chapter.location.version_agnostic()\r\n else:\r\n self.assertEqual(course.id, course_key)\r\n self.assertEqual(chapter.location, self.import_chapter_location)","def generate_tex(title, author, txt, indices=None):\n with open('head', 'r') as head,\\\n open('stegano.tex', 'w') as file_tex:\n\n # First lines are\n # - Title\n # - Author\n\n file_tex.write(\"\".join(head.readlines())) # Write heading\n file_tex.write(\"\\\\title{{{}}}\\n\".format(title))\n file_tex.write(\"\\\\author{{{}}}\\n\".format(author))\n file_tex.write(\"\\\\date{{}}\\n\")\n\n file_tex.write(\"\\\\begin{document}\\n\\\\maketitle\\n\")\n\n # Write char by char, adding decoration when indices is reach\n i = 0\n while i < len(txt):\n if i in indices:\n file_tex.write(secret_char(txt[i]))\n else:\n file_tex.write(non_secret_char(txt[i]))\n i += 1\n\n file_tex.write(\"\\\\end{document}\")","def available_courses(self):\r\n def _get_course_name(el):\r\n # The first component in the link text is the course number\r\n _, course_name = el.text.split(' ', 1)\r\n return course_name\r\n\r\n return self.q(css='section.info > hgroup > h3 > a').map(_get_course_name).results","def get_courses(self, *args):\n courses = []\n user = self.context['user']\n modules = user.profile.purchased_modules.all()\n for module in modules:\n course_id = self.course_in_courses(module.course.mnemo, courses)\n if course_id:\n courses[course_id[0]]['modules'].append({'mnemo': module.mnemo})\n else:\n courses.append({\n 'mnemo': module.course.mnemo,\n 'modules': [{'mnemo': module.mnemo}]\n })\n return courses","def list_courses_subjects(self, all=False):\n q = {'facet': 'true',\n 'facet.field': 'course_subject',\n 'facet.mincount': '1',\n 'facet.sort': 'index', # Sort alphabetically\n 'rows': '0', # we don't need any actual document\n }\n if all:\n q['q'] = '*:*'\n else:\n q['q'] = 'NOT presentation_start:[* TO NOW]'\n results = searcher.search(q, start=0, count=1000) # Do not paginate\n subjects = subjects_facet_to_subjects_domain(results)\n return subjects","def test_citations_and_toc(self):\n settings = get_settings(\n PANDOC_EXTENSIONS=PANDOC_EXTENSIONS + [\"+citations\"],\n PANDOC_ARGS=PANDOC_ARGS\n + [\n \"--toc\",\n \"-C\",\n \"--csl=https://www.zotero.org/styles/ieee-with-url\",\n \"--metadata=link-citations:false\",\n \"--metadata=reference-section-title:References\",\n ],\n FORMATTED_FIELDS=FORMATTED_FIELDS,\n )\n\n pandoc_reader = PandocReader(settings)\n source_path = os.path.join(\n TEST_CONTENT_PATH, \"valid_content_with_citation.md\"\n )\n output, metadata = pandoc_reader.read(source_path)\n self.maxDiff = None\n\n self.assertEqual(\n (\n '<h2 id=\"string-theory\">String Theory</h2>\\n'\n \"<p>But this foundational principle of science has\"\n \" now been called into question by\"\n ' <a href=\"https://www.britannica.com/science/'\n 'string-theory\">String Theory</a>,'\n \" which is a relative newcomer to theoretical physics, but one\"\n \" that has captured the common imagination, judging by\"\n \" the popular explanations that abound on the Web\"\n ' <span class=\"citation\" data-cites=\"mann2019 wood2019'\n ' jones2020\">[1]–[3]</span>.'\n \" And whether string theory is or is not science, Popper\"\n \" notwithstanding, is an issue that is still up for debate\"\n \" <span\"\n ' class=\"citation\" data-cites=\"siegel2015 castelvecchi2016'\n ' alves2017 francis2019\">[4]–[7]</span>.</p>\\n'\n '<h1 class=\"unnumbered\" id=\"bibliography\">References</h1>\\n'\n '<div id=\"refs\" class=\"references csl-bib-body\"'\n ' role=\"doc-bibliography\">\\n'\n '<div id=\"ref-mann2019\" class=\"csl-entry\"'\n ' role=\"doc-biblioentry\">\\n'\n '<div class=\"csl-left-margin\">[1]'\n ' </div><div class=\"csl-right-inline\">A. Mann,'\n \" <span>“<span>What Is String Theory?</span>”</span>\"\n \" 20-Mar-2019. [Online].\"\n ' Available: <a href=\"https://www.livescience.com/'\n '65033-what-is-string-theory.html\">'\n \"https://www.livescience.com/\"\n \"65033-what-is-string-theory.html</a>.\"\n \" [Accessed: 12-Nov-2020]</div>\\n\"\n \"</div>\\n\"\n '<div id=\"ref-wood2019\" class=\"csl-entry\"'\n ' role=\"doc-biblioentry\">\\n'\n '<div class=\"csl-left-margin\">[2] </div>'\n '<div class=\"csl-right-inline\">'\n \"C. Wood, <span>“<span>What Is String Theory?</span>.\"\n \" Reference article:\"\n \" A simplified explanation and brief history of string\"\n \" theory,”</span> 11-Jul-2019.\"\n ' [Online]. Available: <a href=\"https://www.space.com/'\n '17594-string-theory.html\">'\n \"https://www.space.com/17594-string-theory.html</a>.\"\n \" [Accessed: 12-Nov-2020]</div>\\n\"\n \"</div>\\n\"\n '<div id=\"ref-jones2020\" class=\"csl-entry\"'\n ' role=\"doc-biblioentry\">\\n'\n '<div class=\"csl-left-margin\">[3]'\n ' </div><div class=\"csl-right-inline\">'\n 'A. Z. Jones, <span>“<span class=\"nocase\">The Basics of String'\n \" Theory</span>,”</span> 02-Mar-2019. [Online]. Available:\"\n ' <a href=\"https://www.thoughtco.com/'\n 'what-is-string-theory-2699363\">'\n \"https://www.thoughtco.com/what-is-string-theory-2699363</a>.\"\n \" [Accessed: 12-Nov-2020]</div>\\n\"\n \"</div>\\n\"\n '<div id=\"ref-siegel2015\" class=\"csl-entry\"'\n ' role=\"doc-biblioentry\">\\n'\n '<div class=\"csl-left-margin\">[4]'\n ' </div><div class=\"csl-right-inline\">'\n \"E. Siegel, <span>“<span>Why String Theory Is Not A Scientific\"\n \" Theory</span>,”</span> 23-Dec-2015. [Online]. Available:\"\n \" <a\"\n ' href=\"https://www.forbes.com/sites/'\n \"startswithabang/2015/12/23/\"\n 'why-string-theory-is-not-science/\">https://www.forbes.com/'\n \"sites/startswithabang/2015/12/23/\"\n \"why-string-theory-is-not-science/</a>.\"\n \" [Accessed: 12-Nov-2020]</div>\\n\"\n \"</div>\\n\"\n '<div id=\"ref-castelvecchi2016\" class=\"csl-entry\"'\n ' role=\"doc-biblioentry\">\\n'\n '<div class=\"csl-left-margin\">[5]'\n ' </div><div class=\"csl-right-inline\">'\n 'D. Castelvecchi, <span>“<span class=\"nocase\">'\n \"Feuding physicists turn\"\n \" to philosophy for help</span>. String theory is at the\"\n \" heart of a debate over the integrity of the scientific\"\n \" method itself,”</span> 05-Jan-2016. [Online]. Available:\"\n ' <a href=\"https://www.nature.com/news/'\n 'feuding-physicists-turn-to-philosophy-for-help-1.19076\">'\n \"https://www.nature.com/news/\"\n \"feuding-physicists-turn-to-philosophy-for-help-1.19076</a>.\"\n \" [Accessed: 12-Nov-2020]</div>\\n\"\n \"</div>\\n\"\n '<div id=\"ref-alves2017\" class=\"csl-entry\"'\n ' role=\"doc-biblioentry\">\\n'\n '<div class=\"csl-left-margin\">[6] </div>'\n '<div class=\"csl-right-inline\">'\n 'R. A. Batista and J. Primack, <span>“<span class=\"nocase\">'\n \"Is String theory falsifiable?</span>. Can a theory that isn’t\"\n \" completely testable still be useful to physics?”</span>\"\n \" [Online].\"\n ' Available: <a href=\"https://metafact.io/factchecks/'\n '30-is-string-theory-falsifiable\">'\n \"https://metafact.io/factchecks/\"\n \"30-is-string-theory-falsifiable</a>.\"\n \" [Accessed: 12-Nov-2020]</div>\\n\"\n \"</div>\\n\"\n '<div id=\"ref-francis2019\" class=\"csl-entry\"'\n ' role=\"doc-biblioentry\">\\n'\n '<div class=\"csl-left-margin\">[7]'\n ' </div><div class=\"csl-right-inline\">'\n 'M. R. Francis, <span>“<span class=\"nocase\">Falsifiability and'\n \" physics</span>. Can a theory that isn’t completely testable\"\n \" still be useful to physics?”</span> 23-Apr-2019.\"\n \" [Online]. Available:\"\n ' <a href=\"https://www.scientificamerican.com/'\n 'article/is-string-theory-science/\">'\n \"https://www.scientificamerican.com/article/is-\"\n \"string-theory-science/</a>. [Accessed: 12-Nov-2020]</div>\\n\"\n \"</div>\\n\"\n \"</div>\\n\"\n ),\n output,\n )\n\n self.assertEqual(\"Valid Content With Citation\", str(metadata[\"title\"]))\n self.assertEqual(\"My Author\", str(metadata[\"author\"]))\n self.assertEqual(\"2020-10-16 00:00:00\", str(metadata[\"date\"]))\n self.assertEqual(\n (\n '<nav class=\"toc\" role=\"doc-toc\">\\n'\n \"<ul>\\n\"\n '<li><a href=\"#string-theory\">String Theory</a></li>\\n'\n '<li><a href=\"#bibliography\">References</a></li>\\n'\n \"</ul>\\n</nav>\\n\"\n ),\n str(metadata[\"toc\"]),\n )\n self.assertEqual(\n (\n \"<p>But this foundational principle of science has now been\"\n \" called into question by\"\n ' <a href=\"https://www.britannica.com/science/string-theory\">'\n \"String Theory</a>.</p>\\n\"\n ),\n str(metadata[\"summary\"]),\n )","def print_catalog(self):\n # first download the json for the catalog\n self.download_json()\n\n # open the saved json file and load the json\n with self.file.open(\"r\") as catalog_file:\n pages = json.load(catalog_file)\n\n # the catalog json is just a list of pages\n # so we begin by iterating through the pages\n for page_num in range(len(pages)):\n # get each page\n page = pages[page_num]\n\n # get the threads on each page\n threads = page[\"threads\"]\n\n # print the page heading\n print(\"*** PAGE \", page_num + 1, \"***\")\n\n # iterate through the threads on each page\n for thread_num in range(len(threads)):\n # get each thread\n thread = threads[thread_num]\n\n # print the thread number\n num = thread[\"no\"]\n print(\"---\", \"Thread:\", num, \"---\")\n\n # not all threads have a subject or comment\n try:\n subject = thread[\"sub\"]\n comment = thread[\"com\"]\n\n print(\"Sub:\", subject)\n print(\"Comment:\", comment)\n except KeyError:\n print(\"N/A\")","def read_data(term):\n course_url = '/admweb/!SWKSECX.main?term='+ term + '&title=&course=&crn=&coll=&dept=&subj='\n connection = httplib.HTTPSConnection('courses.rice.edu', 443)\n connection.connect()\n connection.request(\n 'GET',\n course_url\n )\n courses_xml = connection.getresponse()\n return courses_xml","def create_course(self, modules_count, module_store, topics):\n course = CourseFactory.create(\n org=\"a\",\n course=\"b\",\n run=\"c\",\n start=datetime.now(UTC),\n default_store=module_store,\n discussion_topics=topics\n )\n CourseEnrollmentFactory.create(user=self.user, course_id=course.id)\n course_url = reverse(\"course_topics\", kwargs={\"course_id\": str(course.id)})\n # add some discussion xblocks\n for i in range(modules_count):\n ItemFactory.create(\n parent_location=course.location,\n category='discussion',\n discussion_id=f'id_module_{i}',\n discussion_category=f'Category {i}',\n discussion_target=f'Discussion {i}',\n publish_item=False,\n )\n return course_url","def load_courses(cls, config: DefaultConfig):\n lecture_halls = cls.load_course_resource(config.CoursePaths.LECTURER_HALLS.value, LectureHall.create)\n course_units = cls.load_course_resource(config.CoursePaths.COURSE_UNITS.value, CourseUnit.create(lecture_halls))\n lecturers = cls.load_course_resource(config.CoursePaths.LECTURER.value, Lecturer.create(course_units))\n teaching_assistants = cls.load_course_resource(\n config.CoursePaths.TEACHING_ASSISTANTS.value, Lecturer.create(course_units))\n\n return cls(\n course_units=course_units,\n lecturers=lecturers,\n teaching_assistants=teaching_assistants\n )","def get_courses():\n courses = []\n courses_recs = Course._file.read_db()\n for course in courses_recs[\"courses\"]:\n courses.append(Course(**course))\n return courses","def initialize(self, cwd: str, option: str = \"\", **kwargs):\n # TODO: make the times when the courses start prettier\n path = os.path.join(cwd, option)\n\n def recursive_dictionary_clear(d):\n \"\"\"Recursively clear dictionary keys with empty values.\"\"\"\n for key in list(d):\n if isinstance(d[key], dict):\n recursive_dictionary_clear(d[key])\n\n if d[key] == \"\" or d[key] == {}:\n del d[key]\n\n def format_teacher(teacher):\n \"\"\"An ungly, hard-coded way to format the names of the teachers. Couldn't\n find something more solid, so this will have to do for now.\"\"\"\n l = split(\n \"|\".join(\n [\n \"doc\\.\",\n \"Ing\\.\",\n \"Ph.D\\.\",\n \"CSc\\.\",\n \"PhDr\\.\",\n \"DrSc\\.\",\n \"Mgr\\.\",\n \"RNDr\\.\",\n \"M\\.Sc\\.\",\n \"Bc\\.\",\n \"Dr\\.\",\n \"D\\.Phil\\.\",\n \"Ph\\.\",\n \"r\\.\",\n ]\n ),\n teacher,\n )\n l = [i.strip().strip(\",\").strip() for i in l]\n l = [i for i in l if i not in (\",\", \"\")]\n return \" / \".join([\" \".join(list(reversed(i.split()))) for i in l])\n\n if option == \"\":\n exit_with_error(\"No CSV to initialize from specified.\")\n\n if not os.path.exists(path):\n exit_with_error(\"Specified file doesn't exist.\")\n\n with open(path, \"rb\") as f:\n # SIS uses cp1250 :(\n contents = f.read().decode(\"cp1250\")\n\n course_count = 0\n for l in list(csv.reader(contents.splitlines(), delimiter=\";\"))[1:]:\n uid, _, code, name, day, start, self, dur, _, _, _, weeks, teacher = l\n\n teacher = format_teacher(teacher)\n\n # ATTENTION: watch out for 'and's here\n # in order for the code not to crash, they do the following:\n # '' and x -> ''\n # 'something' and x -> x\n out = {\n \"teacher\": {\"name\": teacher},\n \"classroom\": {\"number\": self},\n \"time\": {\n \"day\": day and WD_EN[int(day) - 1].capitalize(),\n \"start\": start and int(start), # TODO HH:MM formatting\n \"end\": start and int(start) + int(dur), # TODO HH:MM formatting\n \"weeks\": \"even\"\n if weeks == \"sude\"\n else \"odd\"\n if weeks == \"liche\"\n else \"\",\n },\n \"code\": code,\n }\n\n # don't print empty dictionary parts\n recursive_dictionary_clear(out)\n\n # create a basic abbreviation from taking first letters of each word\n abbreviation = \"\".join(\n [\n word[0].upper()\n if word[0].isalpha() or word[0].isdigit()\n else \"\"\n for word in name.split()\n ]\n )\n\n # create the directory with the name of the course\n course_dir = os.path.join(cwd, f\"{name} ({abbreviation})\")\n if not os.path.exists(course_dir):\n os.mkdir(course_dir)\n\n # lecture / lab\n # based on the ID of the SIS ticket - labs end with x** and lectures with p*\n course_type = \"přednáška\" if uid[:-1].endswith(\"p\") else \"cvičení\"\n\n if not os.path.exists(os.path.join(course_dir, course_type)):\n os.mkdir(os.path.join(course_dir, course_type))\n\n with open(os.path.join(course_dir, course_type, \"info.yaml\"), \"w\") as f:\n yaml.dump(out, stream=f, allow_unicode=True)\n\n course_count += 1\n\n exit_with_success(f\"New semester with {course_count} courses initialized.\")","def process_course(xml_course):\n\n parse_course = parse_get_course(xml_course)\n if not parse_course: # Does not already exist, create\n parse_course = parse_create_course(xml_course)\n else:\n # TODO: Update existing attributes of the course here and upload the changes\n None\n parse_course_id = upload_course(parse_course)\n\n print (\"Processed Course: {0} {1}\"\n .format(parse_course[\"subject\"], parse_course[\"courseNumber\"]))\n\n # TODO: Implement the functions for this stuff\n parse_section = parse_get_section(xml_course)\n if not parse_section: # Does not already exist, create\n parse_section = parse_create_section(xml_course)\n else:\n # TODO: Update existing attributes of the section here and upload the changes\n None\n parse_section_id = upload_section(parse_section)\n\n put_child(parse_course_id, parse_section_id)","def get_courseware_with_tabs(course_id):\r\n\r\n course = get_course_by_id(course_id)\r\n chapters = [chapter for chapter in course.get_children() if not chapter.hide_from_toc]\r\n courseware = [{'chapter_name': c.display_name_with_default,\r\n 'sections': [{'section_name': s.display_name_with_default,\r\n 'clickable_tab_count': len(s.get_children()) if (type(s) == seq_module.SequenceDescriptor) else 0,\r\n 'tabs': [{'children_count': len(t.get_children()) if (type(t) == vertical_module.VerticalDescriptor) else 0,\r\n 'class': t.__class__.__name__}\r\n for t in s.get_children()]}\r\n for s in c.get_children() if not s.hide_from_toc]}\r\n for c in chapters]\r\n\r\n return courseware","def get_courses(self, depth=0):\r\n return self.courses.values()","def getUserCoursesList(self, chat_id):\n\t\tcommand = \"SELECT ID, name, description FROM courses WHERE author_id=?;\"\n\t\tparams = (chat_id,)\n\n\t\tdata = self._run_command(command, params)\n\n\t\tif not data:\n\t\t\treturn None\n\n\t\tresult = []\n\t\tfor i in data:\n\t\t\tresult.append({\"ID\": i[0], \n\t\t\t\t\"name\": i[1],\n\t\t\t\t\"description\": i[2] if i[2] else \"\",\n\t\t\t\t})\n\n\t\treturn result","def build_corpus(self):\n # #############################\n\n doc = metapy.index.Document()\n tok = metapy.analyzers.ICUTokenizer(suppress_tags=True)\n tok = metapy.analyzers.LowercaseFilter(tok)\n tok = metapy.analyzers.LengthFilter(tok, min=3, max=1000)\n tok = metapy.analyzers.Porter2Filter(tok)\n tok = metapy.analyzers.ListFilter(tok, \"lemur-stopwords.txt\", metapy.analyzers.ListFilter.Type.Reject)\n collection = -1\n\n with open(self.documents_path) as file:\n for num, line in enumerate(file):\n l = line.strip()\n c = int(l[0])\n l = l[2:]\n doc.content(l)\n tok.set_content(doc.content())\n if c != collection:\n self.documents.append([])\n collection = c\n self.documents[c].append([token for token in tok])\n self.number_of_collections = len(self.documents)\n self.number_of_documents = len(self.documents[0])\n #print(self.number_of_collections)\n #print(self.number_of_documents)\n #print(self.documents[0])","def test_xml_get_courses(self):\r\n self.initdb('direct')\r\n courses = self.store.modulestores['xml'].get_courses()\r\n self.assertEqual(len(courses), 2)\r\n course_ids = [course.id for course in courses]\r\n self.assertIn(self.course_locations[self.XML_COURSEID1].course_key, course_ids)\r\n self.assertIn(self.course_locations[self.XML_COURSEID2].course_key, course_ids)\r\n # this course is in the directory from which we loaded courses but not in the map\r\n self.assertNotIn(\"edX/toy/TT_2012_Fall\", course_ids)","def get_courses_metadata(self):\n return Metadata(**settings.METADATA['course_ids'])","def build_course_dictionary(title_result_set, desc_result_set) -> Dict[str, List[str]]:\n\n course_dictionary = {} # placeholder dictionary\n\n for (tagged_title, tagged_description) in zip(title_result_set, desc_result_set): # iterate through multiple result sets\n full_title_desc_list = {}\n full_title_desc_list = [str(tagged_title.text)] + str(tagged_description.text).strip().splitlines() # remove trailing whitespace, then get list of lines\n course_dictionary[str(tagged_title.text)[:8]] = full_title_desc_list\n\n return course_dictionary","def show_courses_and_profs(request):\r\n\tcourses = course_views.all_courses(request)\r\n\treturn render(request, 'templates/browse.html', {'courses': courses})","def Generar_Claves():\n salida=Keypp()\n savekey(salida)\n savecomp(salida)"],"string":"[\n \"def preprocess_courses_corpus():\\n soup = None\\n with open('courses_corpus.html', 'r') as infile:\\n content = infile.read()\\n\\n soup = BeautifulSoup(content, 'html.parser')\\n\\n docid = 0\\n data = {}\\n data['documents'] = []\\n\\n main_table = soup.find_all(\\\"div\\\", attrs={'class': 'courseblock'})\\n for course in main_table:\\n docid += 1\\n title = course.find_all('p', attrs={'class':'courseblocktitle noindent'})[0].text.lstrip('\\\\n') if len(course.find_all('p', attrs={'class':'courseblocktitle noindent'}))!=0 else ''\\n description = (course.find_all('p', attrs={'class':'courseblockdesc noindent'})[0].text.lstrip('\\\\n') if len(course.find_all('p', attrs={'class':'courseblockdesc noindent'}))!=0 else '') + ' ' + (course.find_all('p', attrs={'class':'courseblockextra noindent'})[0].text if len(course.find_all('p', attrs={'class':'courseblockextra noindent'}))!=0 else '')\\n\\n data['documents'].append({\\n 'docId' : docid,\\n 'title' : title.strip(),\\n 'description' : description.strip()\\n })\\n\\n with open('courses_data.json', 'w') as outfile:\\n json.dump(data, outfile)\",\n \"def create_tex(unwanted_courses, wanted_courses=None):\\n page = requests.get(\\n 'http://gla.ac.uk/coursecatalogue/courselist/' +\\n '?code=REG30200000&name=School+of+Computing+Science')\\n tree = html.fromstring(page.content)\\n spans = tree.xpath('//span/text()')\\n codes = []\\n if wanted_courses is None:\\n for s in spans:\\n if s[0:4] == \\\"COMP\\\" and s[7] == '4' and s not in unwanted_courses:\\n codes.append(s)\\n else:\\n for s in wanted_courses:\\n codes.append(s)\\n write_to_latex(codes, unwanted_courses)\\n return None\",\n \"def parse_create_course(xml_course):\\n attrs = [\\n \\\"term-code\\\",\\n \\\"term-description\\\",\\n 'subject',\\n \\\"course-number\\\",\\n \\\"school\\\",\\n \\\"department\\\",\\n \\\"title\\\",\\n \\\"description\\\",\\n \\\"credit-hours\\\",\\n \\\"distribution-group\\\"\\n ]\\n course = pull_attributes_from_xml(xml_course, attrs)\\n course[\\\"sections\\\"] = []\\n\\n return course\",\n \"def _createCoursesTable(self):\\n\\t\\tcommand = \\\"\\\"\\\"CREATE TABLE courses (ID INTEGER PRIMARY KEY,\\n\\t\\t\\tname TEXT,\\n\\t\\t\\tauthor_id INTEGER,\\n\\t\\t\\tdescription TEXT\\n\\t\\t\\t);\\n\\\"\\\"\\\"\\n\\n\\t\\tself._run_command(command)\",\n \"def create_courses():\\n\\n\\t# create list for courses\\n\\tallcourses = []\\n\\n\\t# load courses as classes in allcourses-list\\n\\twith open(\\\"../data/vakken.csv\\\", \\\"rt\\\") as coursefile:\\n\\n\\t\\t# clean text\\n\\t\\tcourses = csv.reader(coursefile)\\n\\t\\tfor row in courses:\\n\\t\\t\\tfor text in row:\\n\\t\\t\\t\\tcourse_info = text.split(\\\";\\\")\\n\\n\\t\\t\\t\\t# add course name\\n\\t\\t\\t\\tcourse_name = course_info[0]\\n\\n\\t\\t\\t\\t# add amount of lectures\\n\\t\\t\\t\\tcourse_lectures = course_info[1]\\n\\n\\t\\t\\t\\t# add amount of seminars\\n\\t\\t\\t\\tcourse_seminars = course_info[2]\\n\\n\\t\\t\\t\\t# add max amount seminars\\n\\t\\t\\t\\tcourse_max_sem = course_info[3]\\n\\t\\t\\t\\tif course_max_sem == \\\"nvt\\\":\\n\\t\\t\\t\\t\\tcourse_max_sem = 0\\n\\n\\t\\t\\t\\t# add amount of practicals\\n\\t\\t\\t\\tcourse_practicals = course_info[4]\\n\\n\\t\\t\\t\\t# add max amount practicals\\n\\t\\t\\t\\tcourse_max_prac = course_info[5]\\n\\t\\t\\t\\tif course_max_prac == \\\"nvt\\\":\\n\\t\\t\\t\\t\\tcourse_max_prac = 0\\n\\n\\t\\t\\t\\t# add course to list\\n\\t\\t\\t\\tallcourses.append(Course(course_name, course_lectures, course_seminars, course_max_sem, course_practicals, course_max_prac))\\n\\n\\treturn allcourses\",\n \"def makeCourse( xmlFile, genPath, importPaths, commonFiles, rendererContent=True):\\n\\ttry:\\n\\n\\t\\t# parse the command line\\n\\t\\tConfig.add_option('--verbose', help='Set verbosity to maximum', dest='verbosity', default=0, action='store_const', const=2)\\n\\t\\tConfig.add_option('-v', '--verbosity', help='Set the verbosity level (0: quiet, 1: display the command lines, 2: display command lines and their outputs', dest='verbosity', default=0, type=int)\\n\\t\\tConfig.add_option('-d', '--debug', help='Create the files in the debug/ folder, instead of in a temporary one', dest='debug', action='store_true', default=False)\\n\\t\\tConfig.add_option('-f', '--force', help='Force the generation of the documents, even if nothing changes from last run', dest='force', action='store_true', default=False)\\n\\t\\tConfig.add_option('-q', '--quick', help='Quick pdf generation (do not compile twice the latex, do not produce handout, etc.)', dest='quick', action='store_true', default=False)\\n\\t\\tConfig.add_option('-w', '--wordpress', help='Publish to wordpress', dest='wordpress', default=False, action='store_true')\\n\\t\\tConfig.add_option('-c', '--HTMLcorrection', help='Display an HTML correction', dest='HTMLcorrection', default=False, action='store_true')\\n\\t\\tConfig.add_option('-s', '--shared', help='Copy the required files to the <shared> path (via ssh)', default=False,\\taction='store_true')\\n\\t\\tConfig.parse()\\n\\t\\targs = Config.args\\n\\t\\toptions = Config.options\\n\\t\\tConfig.importPaths = importPaths \\n\\t\\tConfig.commonFiles = commonFiles\\n\\t\\tConfig.allSessions = { x.__name__:x for x in Session.__subclasses__()}\\t# list of the created session classes\\n\\t\\tConfig.rendererContent = rendererContent\\n\\t\\t\\n\\t\\t# clean the debug directory in debug mode\\n\\t\\tbasePath = os.path.abspath('.')+'/'\\t\\t\\t# base path (from where the script is run, because the path are relative)\\n\\t\\tif options.debug:\\n\\t\\t\\tif os.path.exists('debug/'):\\n\\t\\t\\t\\trunCommand(['rm','-rf','debug/'])\\n\\n\\t\\t# open and parse the course file\\n\\t\\twith codecs.open(xmlFile, encoding='utf-8') as f:\\n\\t\\t\\tbs = BeautifulSoup(f, features=\\\"xml\\\")\\n\\n\\n\\t\\t# build the recursively the sessions\\n\\t\\ttop = createTagSession( bs, father=None )\\t\\t# bs.contents[0]\\n\\t\\tsessionsToBuild = Session.sessionsToBuild\\t\\t# get the list of the sessions object\\n\\t\\t\\n\\n\\t\\t\\\"\\\"\\\"\\n\\t\\timportFiles( bs.contents[0], importPaths)\\n\\n\\t\\t# get the list of sessions we can build (with a 'make' method)\\n\\t\\tbuildableSessions = { x.__name__:x for x in Session.__subclasses__() if 'make' in x.__dict__ }\\n\\n\\t\\t#This set the PATH for PyDev only...\\n\\t\\tos.environ['PATH'] = os.environ['PATH']+':'+os.getenv('PATH')\\n\\n\\n\\t\\t# build the list of Sessions to build\\n\\t\\tsessionsToBuild = []\\n\\t\\tfor name,session in buildableSessions.items():\\n\\t\\t\\tsessionsToBuild.extend( session(tag, commonFiles) for tag in bs(name) )\\n\\t\\t\\\"\\\"\\\"\\n\\t\\t\\n\\t\\t\\n\\n\\t\\t# if possible, load the previous xml file, and look for the differences\\n\\t\\tdirName,baseName = split(xmlFile) \\n\\t\\ttry:\\n\\t\\t\\twith open(dirName+\\\"/.\\\"+baseName+\\\".makeCourse\\\", \\\"rb\\\") as f:\\n\\t\\t\\t\\tdata = load( f )\\n\\t\\t\\t\\tfor s in sessionsToBuild:\\n\\t\\t\\t\\t\\tif s.name in data:\\n\\t\\t\\t\\t\\t\\ts.checkDifferences( data[s.name] )\\n\\t\\texcept IOError:\\n\\t\\t\\tpass\\n\\n\\n\\t\\t# build every argument in the command line arguments\\n\\t\\tsomethingHasBeDone = False\\n\\t\\tfor s in sessionsToBuild:\\n\\t\\t\\tif (not args) or (\\\"all\\\" in args) or (s.name in args) or (s.type in args):\\n\\t\\t\\t\\t\\n\\t\\t\\t\\tcd( basePath)\\n\\t\\t\\t\\t\\n\\t\\t\\t\\t# check if something has to be done\\n\\t\\t\\t\\tif s.shouldBeMake(basePath+'/'+genPath, options) or options.force:\\n\\t\\t\\t\\t\\tsomethingHasBeDone = True\\n\\n\\t\\t\\t\\t\\t#Make one build (TP, course, etc.)\\n\\t\\t\\t\\t\\tprint ( Fore.BLUE+\\\"*) Make \\\"+Style.BRIGHT+s.name+Fore.RESET+Style.NORMAL)\\n\\n\\t\\t\\t\\t\\t# make temp directory and copy all the file in resources dir\\n\\t\\t\\t\\t\\tif options.debug:\\n\\t\\t\\t\\t\\t\\ttmp = \\\"debug/\\\"+s.name+'/'\\n\\t\\t\\t\\t\\t\\tcreateDirectory(tmp)\\n\\t\\t\\t\\t\\telse:\\n\\t\\t\\t\\t\\t\\ttmp = mkdtemp()\\n\\n\\t\\t\\t\\t\\ts.prepareResources(tmp )\\n\\t\\t\\t\\t\\tcd( tmp)\\n\\n\\t\\t\\t\\t\\t# call the custom function associated with the type, to produce the documents\\n\\t\\t\\t\\t\\ts.make(options)\\n\\n\\t\\t\\t\\t\\t# then move the files in the right place\\n\\t\\t\\t\\t\\tfor f in s.files(options):\\n\\t\\t\\t\\t\\t\\tcreateDirectory( basePath+'/'+genPath.format( **s.dict ) )\\n\\t\\t\\t\\t\\t\\tnewFile = basePath+'/'+genPath.format( **s.dict )+f\\n\\t\\t\\t\\t\\t\\tif not os.path.exists(f):\\n\\t\\t\\t\\t\\t\\t\\tprint( Fore.YELLOW+'The file '+f+' has not been created by '+s.type+' function !'+Fore.RESET)\\n\\t\\t\\t\\t\\t\\trunCommand( ['cp', f, newFile])\\n\\n\\t\\t\\t\\t\\t# del the temporary directory or clean debug directory\\n\\t\\t\\t\\t\\tif not options.debug:\\n\\t\\t\\t\\t\\t\\trunCommand( ['rm', '-rf', tmp])\\n\\t\\t\\t\\telse:\\n\\t\\t\\t\\t\\tif options.verbosity>0:\\n\\t\\t\\t\\t\\t\\tprint( Fore.BLUE + \\\"*) Nothing changed for \\\"+Style.BRIGHT+s.name+Style.NORMAL+\\\", skipped\\\"+Fore.RESET)\\n\\n\\n\\n\\t\\tif not somethingHasBeDone:\\n\\t\\t\\tprint( Fore.BLUE + \\\"Nothing has changed, nothing to do, so nothing has been done...\\\" + Fore.RESET)\\n\\n\\n\\t\\t# save the data file\\n\\t\\tdata = {L.name: {key:md5(str(val).encode('utf-8')).hexdigest() for key,val in L.dict.items()} for L in sessionsToBuild }\\n\\t\\tcd( basePath)\\n\\t\\twith open(dirName+\\\"/.\\\"+baseName+\\\".makeCourse\\\", 'wb') as f:\\n\\t\\t\\tdump( data, f)\\n\\n\\n\\n\\n\\n\\n\\n\\texcept mkcException as err:\\n\\t\\tprint( err )\",\n \"def _get_courses(self) -> None:\\n\\n courses_content: NavigableString = self.soup.find(\\\"div\\\", \\n {\\\"class\\\": \\\"coursesContent\\\"})\\n course_items: ResultSet = courses_content.find_all(\\\"div\\\", \\n {\\\"class\\\": \\\"courseItem\\\"})\\n\\n for item in course_items:\\n course_name: str = item.a[\\\"href\\\"].split(\\\"/\\\")[-2].lower()\\n course_data: ParseType = self._parse(item)\\n self._update(course_name, course_data)\",\n \"def latex_course(course):\\n basic_info_list = [\\n 'session', 'school', 'credits', 'level', 'offered',\\n 'visiting_students', 'erasmus_students'\\n ]\\n generic_subsection_list = [\\n 'description', 'timetable', 'requirements_of_entry',\\n 'excluded_courses', 'co_requisites', 'assessment_weighting'\\n ]\\n string = '\\\\\\\\subsection{' + course[\\\"title\\\"] + '}\\\\n'\\n for info in basic_info_list:\\n string += latex_info(course[info])\\n for subsection in generic_subsection_list:\\n string += latex_subsection(course[subsection])\\n string += '\\\\\\\\break \\\\\\\\textbf{' + course['assessment_date'][\\n 'heading'] + '}' + course['assessment_date']['value'] + '\\\\n'\\n string += latex_subsection(course['aims'])\\n string += '\\\\\\\\subsubsection*{' + \\\\\\n course['learning_outcomes']['heading'] + '}\\\\n'\\n outcome_list = re.split(\\n '\\\\d+\\\\. ', course['learning_outcomes']['value'])\\n string += outcome_list[0] + '\\\\n'\\n string += '\\\\\\\\begin{enumerate}\\\\n'\\n for i in outcome_list[1:-1]:\\n string += '\\\\\\\\item ' + i + '\\\\n'\\n string += '\\\\\\\\end{enumerate}\\\\n'\\n return string\",\n \"def _create_course(self):\\r\\n super(TestOrphan, self)._create_course()\\r\\n\\r\\n self._create_item('chapter', 'Chapter1', {}, {'display_name': 'Chapter 1'}, 'course', 'runid')\\r\\n self._create_item('chapter', 'Chapter2', {}, {'display_name': 'Chapter 2'}, 'course', 'runid')\\r\\n self._create_item('chapter', 'OrphanChapter', {}, {'display_name': 'Orphan Chapter'}, None, None)\\r\\n self._create_item('vertical', 'Vert1', {}, {'display_name': 'Vertical 1'}, 'chapter', 'Chapter1')\\r\\n self._create_item('vertical', 'OrphanVert', {}, {'display_name': 'Orphan Vertical'}, None, None)\\r\\n self._create_item('html', 'Html1', \\\"<p>Goodbye</p>\\\", {'display_name': 'Parented Html'}, 'vertical', 'Vert1')\\r\\n self._create_item('html', 'OrphanHtml', \\\"<p>Hello</p>\\\", {'display_name': 'Orphan html'}, None, None)\\r\\n self._create_item('static_tab', 'staticuno', \\\"<p>tab</p>\\\", {'display_name': 'Tab uno'}, None, None)\\r\\n self._create_item('about', 'overview', \\\"<p>overview</p>\\\", {}, None, None)\\r\\n self._create_item('course_info', 'updates', \\\"<ol><li><h2>Sep 22</h2><p>test</p></li></ol>\\\", {}, None, None)\",\n \"def completed_course()->str:\\r\\n try:\\r\\n db: sqlite3.Connection = sqlite3.connect(DB_FILE)\\r\\n query: str = \\\"SELECT s.Name, s.CWID, g.Course, g.Grade, i.Name AS 'Instructor' \\\" \\\\\\r\\n \\\"FROM grades2 g JOIN students2 s ON g.StudentCWID = s.CWID \\\" \\\\\\r\\n \\\"JOIN instructors2 i ON g.InstructorCWID = i.CWID ORDER BY s.Name\\\" \\r\\n except sqlite3.OperationalError as e:\\r\\n print(e)\\r\\n\\r\\n data: Dict[str,str]=\\\\\\r\\n [{\\\"name\\\": name,\\\"cwid\\\": cwid,\\\"course\\\": course , \\\"grade\\\":grade, \\\"instructor\\\": instructor}\\r\\n for name, cwid, course, grade, instructor in db.execute(query)]\\r\\n db.close()\\r\\n \\\"\\\"\\\"Render the template from templates folder\\\"\\\"\\\"\\r\\n return render_template(\\\"student_summary.html\\\",\\r\\n title=\\\"Stevens Repository\\\",\\r\\n table_title=\\\"Student, Course, Grade and Instructor\\\",\\r\\n students=data )\",\n \"def _create_course(self):\\r\\n super(TestPublish, self)._create_course(split=False)\\r\\n\\r\\n self._create_item('chapter', 'Chapter1', {}, {'display_name': 'Chapter 1'}, 'course', 'runid', split=False)\\r\\n self._create_item('chapter', 'Chapter2', {}, {'display_name': 'Chapter 2'}, 'course', 'runid', split=False)\\r\\n self._create_item('vertical', 'Vert1', {}, {'display_name': 'Vertical 1'}, 'chapter', 'Chapter1', split=False)\\r\\n self._create_item('vertical', 'Vert2', {}, {'display_name': 'Vertical 2'}, 'chapter', 'Chapter1', split=False)\\r\\n self._create_item('html', 'Html1', \\\"<p>Goodbye</p>\\\", {'display_name': 'Parented Html'}, 'vertical', 'Vert1', split=False)\\r\\n self._create_item(\\r\\n 'discussion', 'Discussion1',\\r\\n \\\"discussion discussion_category=\\\\\\\"Lecture 1\\\\\\\" discussion_id=\\\\\\\"a08bfd89b2aa40fa81f2c650a9332846\\\\\\\" discussion_target=\\\\\\\"Lecture 1\\\\\\\"/>\\\\n\\\",\\r\\n {\\r\\n \\\"discussion_category\\\": \\\"Lecture 1\\\",\\r\\n \\\"discussion_target\\\": \\\"Lecture 1\\\",\\r\\n \\\"display_name\\\": \\\"Lecture 1 Discussion\\\",\\r\\n \\\"discussion_id\\\": \\\"a08bfd89b2aa40fa81f2c650a9332846\\\"\\r\\n },\\r\\n 'vertical', 'Vert1',\\r\\n split=False\\r\\n )\\r\\n self._create_item('html', 'Html2', \\\"<p>Hellow</p>\\\", {'display_name': 'Hollow Html'}, 'vertical', 'Vert1', split=False)\\r\\n self._create_item(\\r\\n 'discussion', 'Discussion2',\\r\\n \\\"discussion discussion_category=\\\\\\\"Lecture 2\\\\\\\" discussion_id=\\\\\\\"b08bfd89b2aa40fa81f2c650a9332846\\\\\\\" discussion_target=\\\\\\\"Lecture 2\\\\\\\"/>\\\\n\\\",\\r\\n {\\r\\n \\\"discussion_category\\\": \\\"Lecture 2\\\",\\r\\n \\\"discussion_target\\\": \\\"Lecture 2\\\",\\r\\n \\\"display_name\\\": \\\"Lecture 2 Discussion\\\",\\r\\n \\\"discussion_id\\\": \\\"b08bfd89b2aa40fa81f2c650a9332846\\\"\\r\\n },\\r\\n 'vertical', 'Vert2',\\r\\n split=False\\r\\n )\\r\\n self._create_item('static_tab', 'staticuno', \\\"<p>tab</p>\\\", {'display_name': 'Tab uno'}, None, None, split=False)\\r\\n self._create_item('about', 'overview', \\\"<p>overview</p>\\\", {}, None, None, split=False)\\r\\n self._create_item('course_info', 'updates', \\\"<ol><li><h2>Sep 22</h2><p>test</p></li></ol>\\\", {}, None, None, split=False)\",\n \"def get_courses_info(url, headers):\\n dash = get_page_contents(url, headers)\\n soup = BeautifulSoup(dash)\\n courses_soup = soup.find_all('article', 'course')\\n courses = []\\n for course_soup in courses_soup:\\n course_id = None\\n course_name = course_soup.h3.text.strip()\\n course_url = None\\n course_state = 'Not yet'\\n try:\\n # started courses include the course link in the href attribute\\n course_url = BASE_URL + course_soup.a['href']\\n if course_url.endswith('info') or course_url.endswith('info/'):\\n course_state = 'Started'\\n # The id of a course in edX is composed by the path\\n # {organization}/{course_number}/{course_run]\\n course_id = course_soup.a['href'][9:-5]\\n except KeyError:\\n pass\\n courses.append(Course(id=course_id,\\n name=course_name,\\n url=course_url,\\n state=course_state))\\n return courses\",\n \"def get_courses(self):\\n\\n self.search([]).unlink()\\n token = self.env['odoo.moodle'].search([('create_uid', '=', self.env.user.id)]).token\\n domain = \\\"http://localhost:8888\\\"\\n webservice_url = \\\"/webservice/rest/server.php?\\\"\\n parameters = {\\n \\\"wstoken\\\":token,\\n 'wsfunction': 'core_course_get_courses',\\n 'moodlewsrestformat': 'json'\\n }\\n request = requests.get(url=domain+webservice_url, params=parameters)\\n request = request.json()\\n print(request)\\n\\n for req in request:\\n try:\\n if req['id']==1:\\n pass\\n else:\\n self.create({\\n 'course_id': req['id'], \\n 'category':req['categoryid'],\\n 'fullname':req['fullname'], \\n 'shortname':req['shortname'],\\n 'summary': req['summary']\\n }\\n )\\n except Exception:\\n print('Course not created')\",\n \"def _construct_course_son(self, course_key):\\r\\n assert(isinstance(course_key, SlashSeparatedCourseKey))\\r\\n return bson.son.SON([\\r\\n ('org', course_key.org),\\r\\n ('course', course_key.course),\\r\\n ('name', course_key.run)\\r\\n ])\",\n \"def main():\\n\\n #Courses\\n years = [2016, 2017, 2018, 2019, 2020]\\n with sqlite3.connect(\\\"determined.db\\\") as conn:\\n c = conn.cursor()\\n c.execute(\\n \\\"\\\"\\\"\\n CREATE TABLE \\\"course\\\" (\\n \\\"course_number\\\"\\tint NOT NULL,\\n \\\"dept_id\\\"\\tvarchar(4) NOT NULL,\\n \\\"title\\\"\\tvarchar(100) NOT NULL,\\n \\\"instructor_fname\\\"\\tvarchar(35) DEFAULT NULL,\\n \\\"instructor_lname\\\"\\tvarchar(35) DEFAULT NULL,\\n \\\"student_work_products\\\"\\tjson DEFAULT NULL,\\n `term` varchar(7) NOT NULL,\\n `year` int NOT NULL,\\n PRIMARY KEY(\\\"course_number\\\", \\\"term\\\", \\\"year\\\")) \\n \\\"\\\"\\\"\\n )\\n conn.commit()\\n courses = [\\n (1370, \\\"CPSC\\\", \\\"Computer Literacy\\\", names.get_first_name(), names.get_last_name(), json.dumps({\\\"swp\\\": [\\\"Midterm\\\", \\\"Final Exam\\\", \\\"Project 1\\\"]}), \\\"Fall\\\"),\\n (1375, \\\"CPSC\\\", \\\"Programming I\\\", names.get_first_name(), names.get_last_name(), json.dumps({\\\"swp\\\": [\\\"Midterm\\\", \\\"Final Exam\\\", \\\"Project 1\\\"]}), \\\"Fall\\\"),\\n (2376, \\\"CPSC\\\", \\\"Intro to Game Programming\\\", names.get_first_name(), names.get_last_name(), json.dumps({\\\"swp\\\": [\\\"Midterm\\\", \\\"Final Exam\\\", \\\"Project 1\\\"]}), \\\"Fall\\\"),\\n (2380, \\\"CPSC\\\", \\\"Algorithms\\\", names.get_first_name(), names.get_last_name(), json.dumps({\\\"swp\\\": [\\\"Midterm\\\", \\\"Final Exam\\\", \\\"Project 1\\\"]}), \\\"Fall\\\"),\\n (2482, \\\"CPSC\\\", \\\"Computer Organization\\\", names.get_first_name(), names.get_last_name(), json.dumps({\\\"swp\\\": [\\\"Midterm\\\", \\\"Final Exam\\\", \\\"Project 1\\\"]}), \\\"Spring\\\"),\\n (3377, \\\"CPSC\\\", \\\"Advanced Game Programming\\\", names.get_first_name(), names.get_last_name(), json.dumps({\\\"swp\\\": [\\\"Midterm\\\", \\\"Final Exam\\\", \\\"Project 1\\\"]}), \\\"Spring\\\"),\\n (3380, \\\"CPSC\\\", \\\"Operating Systems\\\", names.get_first_name(), names.get_last_name(), json.dumps({\\\"swp\\\": [\\\"Midterm\\\", \\\"Final Exam\\\", \\\"Project 1\\\"]}), \\\"Spring\\\"),\\n (3383, \\\"CPSC\\\", \\\"Programming Languages\\\", names.get_first_name(), names.get_last_name(), json.dumps({\\\"swp\\\": [\\\"Midterm\\\", \\\"Final Exam\\\", \\\"Project 1\\\"]}), \\\"Spring\\\"),\\n (3384, \\\"CPSC\\\", \\\"Computer Networks\\\", names.get_first_name(), names.get_last_name(), json.dumps({\\\"swp\\\": [\\\"Midterm\\\", \\\"Final Exam\\\", \\\"Project 1\\\"]}), \\\"Summer\\\"),\\n (4360, \\\"CPSC\\\", \\\"Computer Security\\\", names.get_first_name(), names.get_last_name(), json.dumps({\\\"swp\\\": [\\\"Midterm\\\", \\\"Final Exam\\\", \\\"Project 1\\\"]}), \\\"Summer\\\")\\n ]\\n #Adding years\\n upload_courses = []\\n for year in years:\\n upload_courses += [x + (year,) for x in courses]\\n #Making a few instructors teach multiple course\\n new_courses = [\\n (4557, \\\"CPSC\\\", \\\"Natural Language Processing\\\", ),\\n (2375, \\\"CPSC\\\", \\\"Programming II\\\",),\\n (2776, \\\"CPSC\\\", \\\"Data Structures and Algorithms\\\",),\\n (4862, \\\"CPSC\\\", \\\"Image Recognition\\\", ),\\n ]\\n for i in range(0,len(new_courses)):\\n year = choice(years)\\n for y in range(0,2): #Number of times new course is taught\\n c = upload_courses[i]\\n new_data = (c[3], c[4], c[5], choice([\\\"Fall\\\", \\\"Spring\\\", \\\"Summer\\\"]), year+y)\\n data = new_courses[i] + new_data\\n upload_courses.append(data)\\n #Adding solo instructors and solo courses\\n upload_courses += [\\n (4672, \\\"CPSC\\\", \\\"Programming Memes\\\", names.get_first_name(), names.get_last_name(), json.dumps({\\\"swp\\\": [\\\"Midterm\\\", \\\"Final Exam\\\", \\\"Project 1\\\"]}), \\\"Spring\\\", choice(years)),\\n (1872, \\\"CPSC\\\", \\\"Information Systems\\\", names.get_first_name(), names.get_last_name(), json.dumps({\\\"swp\\\": [\\\"Midterm\\\", \\\"Final Exam\\\", \\\"Project 1\\\"]}), \\\"Summer\\\", choice(years)),\\n (1123, \\\"CPSC\\\", \\\"Microsoft Office\\\", names.get_first_name(), names.get_last_name(), json.dumps({\\\"swp\\\": [\\\"Midterm\\\", \\\"Final Exam\\\", \\\"Project 1\\\"]}), \\\"Fall\\\", choice(years))\\n ]\\n\\n with sqlite3.connect(\\\"determined.db\\\") as conn:\\n c = conn.cursor()\\n c.executemany('''INSERT INTO course (course_number, dept_id, title, instructor_fname, instructor_lname, student_work_products, term, year)\\n VALUES ( ?, ?, ?, ?, ?, ?, ?, ?)''', upload_courses)\\n conn.commit()\\n\\n #SWP\\n with sqlite3.connect(\\\"determined.db\\\") as conn:\\n c = conn.cursor()\\n c.execute(\\n \\\"\\\"\\\"\\n CREATE TABLE `student_work_product` (\\n `id` INTEGER PRIMARY KEY,\\n `product` varchar(250) NOT NULL,\\n `course_id` int NOT NULL,\\n `dept_id` int NOT NULL,\\n `student_fname` varchar(35) NOT NULL,\\n `student_lname` varchar(35) NOT NULL,\\n `student_outcome` int DEFAULT NULL,\\n `score` int DEFAULT NULL,\\n `term` varchar(7) NOT NULL,\\n `year` int NOT NULL,\\n CONSTRAINT `course` FOREIGN KEY (`course_id`) REFERENCES `course` (`course_number`)\\n CONSTRAINT `course` FOREIGN KEY (`dept_id`) REFERENCES `course` (`dept_id`)\\n )\\n \\\"\\\"\\\"\\n )\\n conn.commit()\\n \\n swps = []\\n with sqlite3.connect(\\\"determined.db\\\") as conn:\\n conn.row_factory = sqlite3.Row\\n c = conn.cursor()\\n c.execute (\\\"Select * from course\\\")\\n records = [dict(x) for x in c.fetchall()]\\n #Generating 20 student records for each swp in each course\\n for i, course in enumerate(records):\\n student_names = []\\n for _ in range(20):\\n student_names.append({'fname': names.get_first_name(),\\n 'lname': names.get_last_name()})\\n for product in json.loads(course['student_work_products'])['swp']:\\n for student in student_names:\\n if i%7 == 0:\\n score = int(triangular(50, 85))\\n else:\\n score = int(triangular(50, 100))\\n if score >= 90: outcome = 4\\n elif score >= 80: outcome = 3\\n elif score >= 70: outcome = 2\\n elif score >= 60: outcome = 1\\n else: outcome = 0 \\n swps.append((\\n product,\\n course['course_number'],\\n \\\"CPSC\\\",\\n student['fname'],\\n student['lname'],\\n outcome,\\n score, \\n course['term'], \\n course['year']\\n ))\\n \\n c.executemany('''INSERT INTO student_work_product (product, course_id, dept_id, student_fname, student_lname, student_outcome, score, term, year)\\n VALUES ( ?, ?, ?, ?, ?, ?, ?, ?, ?)''', swps)\\n conn.commit()\",\n \"def test_get_toc_text(self) -> None:\\n extraction = docx2python(RESOURCES / \\\"zen_of_python.docx\\\")\\n assert extraction.document_runs == [\\n [\\n [[[\\\"Contents\\\"], [\\\"\\\\t\\\", \\\"Beautiful is better than ugly.\\\\t1\\\"], []]],\\n [\\n [\\n [],\\n [],\\n [\\\"Beautiful is better than ugly.\\\"],\\n [\\\"Explicit is better than implicit.\\\"],\\n [\\\"Simple is better than complex.\\\"],\\n [\\\"Complex is better than complicated.\\\"],\\n [\\\"Flat is better than nested.\\\"],\\n [\\\"Sparse is better than dense.\\\"],\\n [\\\"Readability counts.\\\"],\\n [\\\"Special cases aren't special enough to break the rules.\\\"],\\n [\\\"Although practicality beats purity.\\\"],\\n [\\\"Errors should never pass silently.\\\"],\\n [\\\"Unless explicitly silenced.\\\"],\\n [\\\"In the face of ambiguity, refuse the temptation to guess.\\\"],\\n [\\n \\\"There should be one-- and preferably only one --obvious way to do it.\\\"\\n ],\\n [\\n \\\"Although that way may not be obvious at first unless you're Dutch.\\\"\\n ],\\n [\\\"Now is better than never.\\\"],\\n [\\\"Although never is often better than *right* now.\\\"],\\n [\\\"If the implementation is hard to explain, it's a bad idea.\\\"],\\n [\\n \\\"If the implementation is easy to explain, it may be a good idea.\\\"\\n ],\\n [\\n \\\"Namespaces are one honking great idea -- let's do more of those!\\\"\\n ],\\n ]\\n ],\\n ]\\n ]\\n extraction.close()\",\n \"def build_general_course(self):\\n\\n courses = {}\\n\\n if self.__build_analyze is False:\\n self.build_analysis()\\n\\n courses[\\\"taxa_conhecimento\\\"] = self.analysis[\\\"taxa_conhecimento\\\"]\\n courses[\\\"taxa_reprovacao\\\"] = self.analysis[\\\"taxa_reprovacao_absoluta\\\"]\\n courses[\\\"taxa_trancamento\\\"] = self.analysis[\\\"taxa_trancamento\\\"]\\n\\n # cria cache\\n cache = {}\\n note = self.analysis[\\\"general_note_statistic\\\"]\\n for rate_it in self.__rates:\\n rate_calc = self.analysis[\\\"general_rates\\\"][rate_it.name][0]\\n for course in self.analysis[\\\"courses\\\"].index:\\n if course not in cache:\\n cache[course] = {}\\n cache[course][rate_it.name] = rate_calc[course]\\n cache[course][\\\"nota\\\"] = [note[0][course], note[1][course]]\\n\\n courses[\\\"cache\\\"] = cache\\n\\n # cria o campo compara_aprov\\n courses[\\\"compara_aprov\\\"] = self.analysis[\\\"graph_course\\\"]\\n\\n # cria o campo courses\\n courses[\\\"disciplinas\\\"] = self.analysis[\\\"courses\\\"].to_dict()\\n\\n return courses\",\n \"def build_course(self):\\n courses = []\\n aprovacao_d = {}\\n # semestral\\n for rate_it in self.__semestral_rate:\\n # pega uma lista no qual o primeiro elemento é a taxa, o segundo\\n # e o terceiro são quantidades\\n rate_data = self.analysis[\\\"semestral_rate\\\"][rate_it.name]\\n for i in rate_data[0].index:\\n if i[0] not in aprovacao_d:\\n aprovacao_d[i[0]] = {}\\n\\n periodo = str(i[1]) + \\\"/\\\" + str(i[2])\\n aprovacao_d[i[0]][periodo] = [\\n float(rate_data[0][i]),\\n int(rate_data[1][i]),\\n int(rate_data[2][i]),]\\n\\n note = self.analysis[\\\"general_note_statistic\\\"]\\n note_last_year = self.analysis[\\\"last_year_statistic\\\"]\\n for course in self.analysis[\\\"courses\\\"].index:\\n course_dict = {}\\n course_dict[\\\"disciplina_codigo\\\"] = course\\n course_dict[\\\"disciplina_nome\\\"] = self.analysis[\\\"courses\\\"][course]\\n \\n # If the course code is related to more than one name,\\n # concatenate these names into an unique string\\n if type(course_dict[\\\"disciplina_nome\\\"]) != str:\\n new_course_name = \\\" | \\\".join(list(course_dict[\\\"disciplina_nome\\\"]))\\n course_dict[\\\"disciplina_nome\\\"] = new_course_name\\n # quantidade de matriculas\\n count = self.analysis[\\\"general_count_submission\\\"][course]\\n course_dict[\\\"qtd_alunos\\\"] = count\\n # notas\\n course_dict[\\\"qtd_cursada_aprov\\\"] = self.analysis[\\\"coursed_ratio\\\"][course]\\n course_dict[\\\"nota\\\"] = [note[0][course], note[1][course]]\\n course_dict[\\\"nota_ultimo_ano\\\"] = [\\n note_last_year[0][course],\\n note_last_year[1][course]\\n ]\\n # taxas\\n for rate_it in self.__rates:\\n rate_data = self.analysis[\\\"general_rates\\\"][rate_it.name]\\n course_dict[rate_it.name] = float(rate_data[0][course])\\n course_str = rate_it.name.replace(\\\"taxa\\\", \\\"qtd\\\")\\n # count_sel define qual quantidade vai para o json, a especifica\\n # ou geral\\n course_dict[course_str] = int(\\n rate_data[rate_it.count_sel][course])\\n # rate_calc = self.analysis[\\\"general_rates\\\"][rate_it.name][0]\\n\\n # taxas do ultimo anos\\n course_dict[\\\"taxa_reprovacao_ultimo_absoluto\\\"] = self.analysis[\\\"last_year_taxa_reprovacao_absoluta\\\"][course]\\n course_dict[\\\"taxa_reprovacao_ultimo_frequencia\\\"] = self.analysis[\\\"last_year_taxa_reprovacao_frequencia\\\"][course]\\n\\n course_dict[\\\"grafico_qtd_cursada_aprov\\\"] = self.analysis[\\\"coursed_count\\\"][course]\\n course_dict[\\\"aprovacao_semestral\\\"] = aprovacao_d[course]\\n courses.append(course_dict)\\n return courses\",\n \"def course_index(request, course_key):\\r\\n course_module = _get_course_module(course_key, request.user, depth=3)\\r\\n lms_link = get_lms_link_for_item(course_module.location)\\r\\n sections = course_module.get_children()\\r\\n\\r\\n\\r\\n return render_to_response('overview.html', {\\r\\n 'context_course': course_module,\\r\\n 'lms_link': lms_link,\\r\\n 'sections': sections,\\r\\n 'course_graders': json.dumps(\\r\\n CourseGradingModel.fetch(course_key).graders\\r\\n ),\\r\\n 'new_section_category': 'chapter',\\r\\n 'new_subsection_category': 'sequential',\\r\\n 'new_unit_category': 'vertical',\\r\\n 'category': 'vertical'\\r\\n })\",\n \"def test_course_info(self):\\r\\n # Regex of first 3 columns of course information table row for\\r\\n # test course loaded from git. Would not have sha1 if\\r\\n # git_info_for_course failed.\\r\\n table_re = re.compile(r\\\"\\\"\\\"\\r\\n <tr>\\\\s+\\r\\n <td>edX\\\\sAuthor\\\\sCourse</td>\\\\s+ # expected test git course name\\r\\n <td>MITx/edx4edx/edx4edx</td>\\\\s+ # expected test git course_id\\r\\n <td>[a-fA-F\\\\d]{40}</td> # git sha1 hash\\r\\n \\\"\\\"\\\", re.VERBOSE)\\r\\n\\r\\n self._setstaff_login()\\r\\n self._mkdir(getattr(settings, 'GIT_REPO_DIR'))\\r\\n\\r\\n # Make sure we don't have any git hashes on the page\\r\\n response = self.client.get(reverse('sysadmin_courses'))\\r\\n self.assertNotRegexpMatches(response.content, table_re)\\r\\n\\r\\n # Now add the course and make sure it does match\\r\\n response = self._add_edx4edx()\\r\\n self.assertRegexpMatches(response.content, table_re)\",\n \"def pdflatex(unwanted_courses):\\n create_tex(unwanted_courses)\\n cmd = ['pdflatex', '-interaction', 'nonstopmode', 'courses.tex']\\n proc = subprocess.Popen(cmd)\\n proc.communicate()\\n return None\",\n \"def make_datatable(self):\\r\\n\\r\\n data = []\\r\\n\\r\\n for course in self.get_courses():\\r\\n gdir = course.id.course\\r\\n data.append([course.display_name, course.id.to_deprecated_string()]\\r\\n + self.git_info_for_course(gdir))\\r\\n\\r\\n return dict(header=[_('Course Name'), _('Directory/ID'),\\r\\n _('Git Commit'), _('Last Change'),\\r\\n _('Last Editor')],\\r\\n title=_('Information about all courses'),\\r\\n data=data)\",\n \"def populate_course(self):\\r\\n def descend(parent, stack):\\r\\n xblock_type = stack.pop(0)\\r\\n for _ in range(2):\\r\\n child = ItemFactory.create(category=xblock_type, parent_location=parent.location)\\r\\n if stack:\\r\\n descend(child, stack)\\r\\n\\r\\n descend(self.course, ['chapter', 'sequential', 'vertical', 'problem'])\",\n \"def generate_courses():\\r\\n for category in CourseCategory.objects.all():\\r\\n Course.objects.create(name=category.name, category=category, is_active=True,\\r\\n is_featured=True)\",\n \"def get_course_by_key_words(input):\",\n \"def print_all_courses(filename): \\n records = records_from_file(filename)\\n course_code_and_name = all_courses(records)\\n print(\\\"All courses:\\\")\\n for course_code, course_name in sorted(course_code_and_name.items()):\\n print((\\\" {}: {}\\\").format(course_code, course_name))\\n print()\",\n \"def Content():\\n\\tTOPIC_DICT = {\\\"Basics\\\": [[\\\"Introduction to Python\\\", \\\"/introduction-to-python/\\\"], \\n\\t\\t\\t\\t\\t\\t\\t\\t\\t\\t\\t\\t\\t [\\\"Print functions\\\", \\\"/python-print-function/\\\"],\\n\\t\\t\\t\\t\\t\\t\\t\\t\\t\\t\\t\\t\\t [\\\"Math basics with Python 3\\\", \\\"/math-basics-python-3/\\\"\\n\\t\\t\\t\\t\\t\\t\\t\\t\\t\\t\\t\\t\\t ]],\\n\\t\\t\\t\\t\\t\\t\\t\\t\\\"Web Dev\\\": [[\\\"wd1\\\"]]} #Web dev is now the second topic\\n\\t\\t\\t\\t\\t\\t\\t\\t#Introduction to Python\\n\\t#will be the title and the link to that is immediately after. Each inner array will be a specific sub section of the basics\\n\\t#section\\n\\n\\treturn TOPIC_DICT\",\n \"def __init__(self, tutorial):\\n self._tex = \\\"\\\"\\n\\n self.texPreamble(tutorial)\\n self.texTutorialHeader(tutorial)\\n\\n for lecture in self.children(tutorial):\\n self.texLectureHeader(lecture)\\n\\n for slide in self.children(lecture):\\n self.texSlideHeader(slide)\\n for section in slide.sections:\\n self.texSlideSection(section)\\n self.texSlideFooter(slide)\\n\\n self.texLectureFooter(lecture)\\n\\n self.texTutorialFooter(tutorial)\\n self.texPostamble(tutorial)\",\n \"def __call__(self, filename, manuscript=\\\"\\\", journal=\\\"\\\", author=[], \\n publisher=\\\"\\\"):\\n\\n # check the parameters\\n while len(author) < 4:\\n author.append(\\\"\\\")\\n\\n doc = getDocument(filename)\\n\\n Story = []\\n\\n # Section 1\\n Story.append(\\n Paragraph(\\n \\\"\\\"\\\"<seqreset id=\\\"main\\\" /><seq id=\\\"main\\\">. THIS Amendment hereby \\n modifies and supplements the attached Publication Agreement \\n concerning the following Article:\\\"\\\"\\\", styles['outer_style'])\\n )\\n\\n journal_info_table = Table([\\n [fillInRow(manuscript, \\\"(manuscript title)\\\", width=inch*5)],\\n [fillInRow(journal, \\\"(journal name)\\\", width=inch*5)],\\n ],\\n )\\n journal_info_table.hAlign = 'LEFT'\\n Story.append(journal_info_table)\\n\\n # Section 2\\n Story.append(\\n Paragraph(\\n \\\"\\\"\\\"<seq id=\\\"main\\\">. The parties to the Publication Agreement and\\n to this Amendment are:\\\"\\\"\\\", styles['outer_style'])\\n )\\n\\n journal_info_table = Table([\\n [fillInRow(author[0], \\\"(corresponding author)\\\", width=inch*5)],\\n [Paragraph(\\\"and\\\", styles['outer_style'])],\\n [fillInRow(journal, \\\"(the Publisher)\\\", width=inch*5)],\\n ],\\n )\\n journal_info_table.hAlign = 'LEFT'\\n Story.append(journal_info_table)\\n\\n # Section 3\\n Story.append(\\n Paragraph(\\n \\\"\\\"\\\"<seq id=\\\"main\\\">. The parties agree that wherever there is any\\n conflict between the Amendment and the Publication Agreement, \\n the provisions of this Amendment are paramount and the \\n Publication Agreement shall be construed accordingly.\\\"\\\"\\\",\\n styles['outer_style'])\\n )\\n\\n # Section 4\\n Story.append(\\n Paragraph(\\n \\\"\\\"\\\"<seq id=\\\"main\\\">. Notwithstanding any terms in the Publication\\nAgreement to the contrary and in addition to the rights retained by Author \\nor licensed by Published to Author in the Publication Agreement and any fair \\nuse rights of Author, Author and Publisher agree that the Author shall also \\nretain the following rights:\\\"\\\"\\\",\\n styles['outer_style'])\\n )\\n\\n # 4a\\n Story.append(\\n Paragraph(\\n \\\"\\\"\\\"a. The Author shall, without limitation, have the non-exclusive right to use, reproduce, distribute, create derivative works including update, perform, and display publicly, the Article in electronic, digital or print form in connection with the Author's teaching, conference presentations, lectures, other scholarly works, and for all of Author's academic and professional activities. \\\"\\\"\\\",\\n styles['inner_style'])\\n )\\n\\n # 4b\\n Story.append(\\n Paragraph(\\n \\\"\\\"\\\"b. Once the Article has been published by Publisher, the Author shall also have all the non-exclusive rights necessary to make, or to authorize others to make, the final published version of the Article available in digital form over the Internet, including but not limited to a website under the control of the Author or the Author's employer or through any digital repository, such as MIT's DSpace or the National Library of Medicine's PubMed Central database.\\\"\\\"\\\",\\n styles['inner_style'])\\n )\\n\\n #4c\\n Story.append(\\n Paragraph(\\n \\\"\\\"\\\"c. The Author further retains all non-exclusive rights necessary to grant to the Author's employing institution the non-exclusive right to use, reproduce, distribute, display, publicly perform, and make copies of the work in electronic, digital or in print form in connection with teaching, digital repositories, conference presentations, lectures, other scholarly works, and all academic and professional activities conducted at the Author's employing institution. \\\"\\\"\\\",\\n styles['inner_style'])\\n )\\n\\n # Section 5\\n Story.append(\\n Paragraph(\\n \\\"\\\"\\\"<seq id=\\\"main\\\" />. <b>Final Agreement.</b> This Amendment and the Publication Agreement, taken together, constitute the final agreement between the Author and the Publisher with respect to the publication of the Article and allocation of rights under copyright in the Article. Any modification of or additions to the terms of this Amendment or to the Publication Agreement must be in writing and executed by both Publisher and Author in order to be effective.\\\"\\\"\\\",\\n styles['outer_style'])\\n )\\n\\n # Signature\\n journal_info_table = Table([\\n [\\\"AUTHOR\\\", \\\" \\\", \\\"PUBLISHER\\\"],\\n [fillInRow(\\\"\\\", \\\"(corresponding author on behalf of all authors)\\\"),\\n \\\"\\\", fillInRow(\\\"\\\", \\\"\\\")],\\n [fillInRow(\\\"\\\", \\\"Date\\\"),\\n \\\"\\\",\\n fillInRow(\\\"\\\", \\\"Date\\\")]\\n ],\\n colWidths=[inch*3, inch*.25, inch*3],\\n )\\n\\n journal_info_table.hAlign = 'LEFT'\\n Story.append(journal_info_table)\\n\\n # MIT Directive\\n Story.append(\\n Paragraph(\\\"<b>MIT Authors:</b>\\\", styles['outer_style'])\\n )\\n Story.append(\\n Paragraph(\\\"Please fax a copy of the agreement to 617-253-8894. Direct any questions to amend-cip@mit.edu\\\",\\n styles['inner_style'])\\n )\\n\\n\\n agreement = \\\"%s %s\\\" % (self.NAME, self.VERSION)\\n doc.build(Story, \\n onFirstPage=mit_pageInfo, onLaterPages=mit_pageInfo)\",\n \"def insert_course(dept, num, text):\\n\\n # Course Title \\n m = re.search(\\\"[\\\\d\\\\w]{5} - ([\\\\w ]*)\\\", text)\\n title = m.group(1) if m else \\\"nomatch\\\"\\n\\n # Course Description\\n m = re.search(\\\"\\\\.\\\\s(.*)\\\\sTypically\\\",text)\\n des = m.group(1) if m else \\\"nomatch\\\"\\n\\n # Credit hours aren't fixed for every course\\n # Credit Hours: 2.00\\n # Credit Hours: 2.00 or 3.00. \\n # Credit Hours: 1.00 to 18.00. \\n m = re.search(\\\"Credit Hours: (\\\\d+\\\\.\\\\d+)\\\",text, flags=re.IGNORECASE)\\n m = re.search(\\\"(\\\\d+\\\\.\\\\d+)(.*?)Credit hours\\\",text, flags=re.IGNORECASE) if not m else m\\n cr = m.group(1) if m else \\\"-1\\\"\\n\\n # Semesters Offered\\n m = re.search(\\\"Typically offered (.*?)\\\\.\\\", text)\\n sem = m.group(1).split() if m else [\\\"nomatch\\\"]\\n\\n # Course Type: Lecture, Recitation, Lab, Seminar, etc.\\n m = re.search(\\\"Schedule Types:\\\\s((?:[\\\\w ]+)(?:,[\\\\w ]+)*) \\\\s+\\\", text)\\n form = m.group(1).split(\\\", \\\") if m else [\\\"nomatch\\\"]\\n\\n # Learning objectives will not necessarily follow campuses\\n m = re.search(\\\"campuses:(\\\\s+([\\\\w\\\\s])+\\\\n)\\\", text)\\n campus = m.group(1).strip().split(\\\"\\\\n\\\\n\\\") if m else [\\\"nomatch\\\"]\\n campus = [camp.strip() for camp in campus]\\n\\n # prereq regex and decomosition of prereqs into lists of AND conditions (works for most classes, not 477 and similar)\\n # re.DOTALL matches all characters, including \\\"\\\\n\\\"\\n idx = text.find(\\\"campuses:\\\")\\n m = re.search(\\\"Prerequisites:(.*)\\\",text[idx:],flags=re.DOTALL)\\n if m:\\n allReqs = []\\n prereqText = m.group(1).strip()\\n prereqText = prereqText.encode('ascii', 'ignore') \\n for i in PrereqParser.parseprereq(prereqText):\\n reqArr = []\\n for j in i.split():\\n if j.find(\\\"-C\\\") != -1:\\n j = j.replace(\\\"-C\\\",\\\"\\\")\\n reqArr.append(Requisite(course=j,reqType=False))\\n else:\\n reqArr.append(Requisite(course=j,reqType=True)) \\n allReqs.append(RequisiteList(courses=reqArr))\\n\\n else:\\n allReqs = []\\n\\n # create course entity\\n course = Course(number=num, title=title, department=dept, form=form,\\n description=des, credits=float(cr), semesters=sem,\\n campuses=campus,requisites=allReqs, id=dept + num)\\n # store course \\n course.put()\",\n \"def export_courses():\\n courses = Course.query().fetch()\\n dictionary = {}\\n\\n for course in courses:\\n dictionary[course.department + \\\"\\\" + course.number] = course.to_dict()\\n\\n return dictionary\",\n \"def setup_xml_course(self):\\r\\n course_key = SlashSeparatedCourseKey('edX', 'toy', '2012_Fall')\\r\\n location = course_key.make_usage_key('chapter', 'Overview')\\r\\n descriptor = modulestore().get_item(location)\\r\\n\\r\\n self.module = self._get_module(course_key, descriptor, location)\",\n \"def parse_courses():\\n\\n subjects = collections.OrderedDict()\\n name = '' # the most recent course name acronym (ex. 'COMP')\\n\\n courses = re.sub(r'\\\\([^)]*\\\\)', '', COURSES).split() # Remove parens and their contents\\n\\n for course in courses:\\n if course == 'OR':\\n continue\\n\\n if course[0].isalpha():\\n\\n index = 0 # the upper bound character index of the subject name\\n for char in course:\\n if char.isalpha():\\n index += 1\\n else:\\n break\\n\\n name = course[:index]\\n number = course[index:index+4]\\n else:\\n number = course[:4]\\n\\n try:\\n subjects[name].append(number)\\n except KeyError:\\n subjects[name] = [number]\\n\\n return subjects\",\n \"def start(self) -> None:\\n\\n self.doc = self.doc + r'''\\n \\\\documentclass[\\n 10pt, % Main document font size\\n a4paper, % Paper type, use 'letterpaper' for US Letter paper\\n ]{scrartcl}\\n\\n \\\\usepackage{graphicx}\\n \\\\usepackage{epstopdf}\\n \\\\usepackage{float}\\n \\\\usepackage[scale=0.75]{geometry} % Reduce document margins\\n \\\\usepackage{hyperref}\\n \\\\usepackage{longtable}\\n\\n \\\\begin{document}\\n\\n \\\\title{Automatic Exploratory Data Analysis} % The article title\\n\\n \\\\subtitle{Study Case} % Uncomment to display a subtitle\\n\\n \\\\author{Jacob} % The article author(s) - author affiliations need to be specified in the AUTHOR AFFILIATIONS block\\\\\\n\\n \\\\maketitle % Print the title/author/date block\\n\\n \\\\newpage\\n \\\\tableofcontents % Print the table of contents\\n\\n \\\\newpage\\n \\\\listoffigures % Print the list of figures\\n\\n \\\\newpage\\n \\\\listoftables % Print the list of tables\\n '''\",\n \"def check_courses(self):\\n course_list = list(self.json_parsed_file['course'].items())\\n course_found = False\\n index = 0\\n\\n course_id = \\\"\\\"\\n version = \\\"\\\"\\n\\n for courses in course_list:\\n course_id = courses[0]\\n version = courses[1]\\n\\n if Course.objects.filter(CourseID=course_id, Version=version).exists():\\n course_found = True\\n self.json_parsed_file['course'] = course_list[index]\\n continue\\n\\n index += 1\\n\\n if course_list is []:\\n self.is_parsed_pdf_valid = False\\n self.output_message += \\\"No parsed course information found\\\\n\\\"\\n\\n elif not course_found:\\n self.is_parsed_pdf_valid = False\\n self.output_message += 'Courses: ' + str(course_id) + ', Version: ' + str(version) + \\\\\\n ' Does not exist in the database.\\\\n'\",\n \"def get_about_text(self, course_key):\\r\\n text = views.course_about(self.request, course_key.to_deprecated_string()).content\\r\\n return text\",\n \"def test_get_single_topic_courses(self):\\r\\n course_id = None # Change me!!\\r\\n topic_id = None # Change me!!\\r\\n\\r\\n r = self.client.get_single_topic_courses(topic_id, course_id)\",\n \"def GetCourses(firebase: firebase) -> None:\\n\\n global courses\\n obj_key_list = []\\n\\n result = firebase.get('/course', None)\\n\\n if result is None:\\n return\\n\\n for i in result.keys():\\n obj_key_list.append(i)\\n\\n for i in obj_key_list:\\n course = Course()\\n course.setId(i)\\n course.setKnowledgeAreaId(result[i]['knowledgeareaid'])\\n course.setCatalogId(result[i]['catalogid'])\\n course.setTitle(result[i]['name'])\\n course.setDescription(result[i]['description'])\\n course.setInstructor(result[i]['instructor'])\\n course.setFee(result[i]['fee'])\\n courses.append(course)\",\n \"def index(request, course_id, chapter=None, section=None,\\r\\n position=None):\\r\\n course_key = SlashSeparatedCourseKey.from_deprecated_string(course_id)\\r\\n user = User.objects.prefetch_related(\\\"groups\\\").get(id=request.user.id)\\r\\n request.user = user # keep just one instance of User\\r\\n course = get_course_with_access(user, 'load', course_key, depth=2)\\r\\n staff_access = has_access(user, 'staff', course)\\r\\n registered = registered_for_course(course, user)\\r\\n if not registered:\\r\\n # TODO (vshnayder): do course instructors need to be registered to see course?\\r\\n log.debug(u'User %s tried to view course %s but is not enrolled', user, course.location.to_deprecated_string())\\r\\n return redirect(reverse('about_course', args=[course_key.to_deprecated_string()]))\\r\\n\\r\\n masq = setup_masquerade(request, staff_access)\\r\\n\\r\\n try:\\r\\n field_data_cache = FieldDataCache.cache_for_descriptor_descendents(\\r\\n course_key, user, course, depth=2)\\r\\n\\r\\n course_module = get_module_for_descriptor(user, request, course, field_data_cache, course_key)\\r\\n if course_module is None:\\r\\n log.warning(u'If you see this, something went wrong: if we got this'\\r\\n u' far, should have gotten a course module for this user')\\r\\n return redirect(reverse('about_course', args=[course_key.to_deprecated_string()]))\\r\\n\\r\\n studio_url = get_studio_url(course_key, 'course')\\r\\n\\r\\n if chapter is None:\\r\\n return redirect_to_course_position(course_module)\\r\\n\\r\\n context = {\\r\\n 'csrf': csrf(request)['csrf_token'],\\r\\n 'accordion': render_accordion(request, course, chapter, section, field_data_cache),\\r\\n 'COURSE_TITLE': course.display_name_with_default,\\r\\n 'course': course,\\r\\n 'init': '',\\r\\n 'fragment': Fragment(),\\r\\n 'staff_access': staff_access,\\r\\n 'studio_url': studio_url,\\r\\n 'masquerade': masq,\\r\\n 'xqa_server': settings.FEATURES.get('USE_XQA_SERVER', 'http://xqa:server@content-qa.mitx.mit.edu/xqa'),\\r\\n 'reverifications': fetch_reverify_banner_info(request, course_key),\\r\\n }\\r\\n\\r\\n # Only show the chat if it's enabled by the course and in the\\r\\n # settings.\\r\\n show_chat = course.show_chat and settings.FEATURES['ENABLE_CHAT']\\r\\n if show_chat:\\r\\n context['chat'] = chat_settings(course, user)\\r\\n # If we couldn't load the chat settings, then don't show\\r\\n # the widget in the courseware.\\r\\n if context['chat'] is None:\\r\\n show_chat = False\\r\\n\\r\\n context['show_chat'] = show_chat\\r\\n\\r\\n chapter_descriptor = course.get_child_by(lambda m: m.location.name == chapter)\\r\\n if chapter_descriptor is not None:\\r\\n save_child_position(course_module, chapter)\\r\\n else:\\r\\n raise Http404('No chapter descriptor found with name {}'.format(chapter))\\r\\n\\r\\n chapter_module = course_module.get_child_by(lambda m: m.location.name == chapter)\\r\\n if chapter_module is None:\\r\\n # User may be trying to access a chapter that isn't live yet\\r\\n if masq == 'student': # if staff is masquerading as student be kinder, don't 404\\r\\n log.debug('staff masq as student: no chapter %s' % chapter)\\r\\n return redirect(reverse('courseware', args=[course.id.to_deprecated_string()]))\\r\\n raise Http404\\r\\n\\r\\n if section is not None:\\r\\n section_descriptor = chapter_descriptor.get_child_by(lambda m: m.location.name == section)\\r\\n if section_descriptor is None:\\r\\n # Specifically asked-for section doesn't exist\\r\\n if masq == 'student': # if staff is masquerading as student be kinder, don't 404\\r\\n log.debug('staff masq as student: no section %s' % section)\\r\\n return redirect(reverse('courseware', args=[course.id.to_deprecated_string()]))\\r\\n raise Http404\\r\\n\\r\\n # cdodge: this looks silly, but let's refetch the section_descriptor with depth=None\\r\\n # which will prefetch the children more efficiently than doing a recursive load\\r\\n section_descriptor = modulestore().get_item(section_descriptor.location, depth=None)\\r\\n\\r\\n # Load all descendants of the section, because we're going to display its\\r\\n # html, which in general will need all of its children\\r\\n section_field_data_cache = FieldDataCache.cache_for_descriptor_descendents(\\r\\n course_key, user, section_descriptor, depth=None)\\r\\n\\r\\n section_module = get_module_for_descriptor(\\r\\n request.user,\\r\\n request,\\r\\n section_descriptor,\\r\\n section_field_data_cache,\\r\\n course_key,\\r\\n position\\r\\n )\\r\\n\\r\\n if section_module is None:\\r\\n # User may be trying to be clever and access something\\r\\n # they don't have access to.\\r\\n raise Http404\\r\\n\\r\\n # Save where we are in the chapter\\r\\n save_child_position(chapter_module, section)\\r\\n context['fragment'] = section_module.render('student_view')\\r\\n context['section_title'] = section_descriptor.display_name_with_default\\r\\n else:\\r\\n # section is none, so display a message\\r\\n studio_url = get_studio_url(course_key, 'course')\\r\\n prev_section = get_current_child(chapter_module)\\r\\n if prev_section is None:\\r\\n # Something went wrong -- perhaps this chapter has no sections visible to the user\\r\\n raise Http404\\r\\n prev_section_url = reverse('courseware_section', kwargs={\\r\\n 'course_id': course_key.to_deprecated_string(),\\r\\n 'chapter': chapter_descriptor.url_name,\\r\\n 'section': prev_section.url_name\\r\\n })\\r\\n context['fragment'] = Fragment(content=render_to_string(\\r\\n 'courseware/welcome-back.html',\\r\\n {\\r\\n 'course': course,\\r\\n 'studio_url': studio_url,\\r\\n 'chapter_module': chapter_module,\\r\\n 'prev_section': prev_section,\\r\\n 'prev_section_url': prev_section_url\\r\\n }\\r\\n ))\\r\\n\\r\\n result = render_to_response('courseware/courseware.html', context)\\r\\n except Exception as e:\\r\\n if isinstance(e, Http404):\\r\\n # let it propagate\\r\\n raise\\r\\n\\r\\n # In production, don't want to let a 500 out for any reason\\r\\n if settings.DEBUG:\\r\\n raise\\r\\n else:\\r\\n log.exception(\\r\\n u\\\"Error in index view: user={user}, course={course}, chapter={chapter}\\\"\\r\\n u\\\" section={section} position={position}\\\".format(\\r\\n user=user,\\r\\n course=course,\\r\\n chapter=chapter,\\r\\n section=section,\\r\\n position=position\\r\\n ))\\r\\n try:\\r\\n result = render_to_response('courseware/courseware-error.html', {\\r\\n 'staff_access': staff_access,\\r\\n 'course': course\\r\\n })\\r\\n except:\\r\\n # Let the exception propagate, relying on global config to at\\r\\n # at least return a nice error message\\r\\n log.exception(\\\"Error while rendering courseware-error page\\\")\\r\\n raise\\r\\n\\r\\n return result\",\n \"def __init__(self, course_id, name, content):\\n # course id and name need to go over the string_correct function\\n self.ID = self.id_correct(str(course_id))\\n self.name = self.string_correct(str(name))\\n\\n self.content = str(content)\\n\\n # uninitialized variables\\n self.prere = {}\\n self.postre = {}\\n self.description = None\\n self.department_title = None\\n self.course_level = None\\n\\n # parse description and prerequisite raw data from content var\\n self.seperate_content()\",\n \"def test_list_topic_entries_courses(self):\\r\\n course_id = None # Change me!!\\r\\n topic_id = None # Change me!!\\r\\n\\r\\n r = self.client.list_topic_entries_courses(topic_id, course_id)\",\n \"def courses(request):\\r\\n courses = get_courses(request.user, request.META.get('HTTP_HOST'))\\r\\n courses = sort_by_announcement(courses)\\r\\n\\r\\n return render_to_response(\\\"courseware/courses.html\\\", {'courses': courses})\",\n \"def scrape_all_course_names(filename, verbose):\\n \\n\\tsoup =\\tBeautifulSoup(open(filename, 'r'), 'html.parser')\\n\\t#print(soup)\\n\\tcourses = []\\n\\t# all the courses are stored in a div w/class=view-content\\n\\t\\t\\n\\th4_field_content = soup.find('h4', 'field-content')\\n\\t#print(h4_field_content)\\n\\tall_course_content = soup.find(\\\"div\\\", \\\"view-content\\\") # this contains ALL the classes...\\n\\tif all_course_content == None:\\n\\t\\tprint(\\\"There are no courses on this page. Try a smaller page number!\\\")\\n\\t\\treturn []\\n\\t\\n\\tcandidate_classes = all_course_content.find_all('a') # we want all the 'a' tags within\\n\\tclass_list=[]\\n course_code_and_number = {}\\n\\tfor c in candidate_classes:\\n\\t\\t#print(c.text, \\\"\\\\n\\\")\\n\\t\\ttext = c.text # ex. AEMA 611 Experimental Designs 1 (3 credits)\\n\\t\\ttext = text.split(\\\" \\\")# split on the space\\n\\t\\tcourse_id = \\\" \\\".join(text[:2]).replace('\\\\n', '') # the first 2 are the course id\\n\\t\\tcourse_name = \\\" \\\".join(text[2:-2]).replace('\\\\n', '')\\n\\t\\tnum_credits = text[-2].replace(\\\"(\\\", \\\"\\\")# just get the course number, replace the ( with nothing \\n\\t\\n\\t\\n\\t\\t#print(f\\\"{course_id}\\\\n{course_name}\\\\n{num_credits}\\\\n\\\")\\n\\t\\ttry:# Check that the course number is a digit bc sometimes it is something weird\\n\\t\\t\\tfloat(num_credits)\\n\\t\\texcept ValueError:\\n\\t\\t\\t#print(f\\\"Wrong course format. Ignoring {c}\\\")\\n\\t\\t\\tif(verbose):\\n\\t\\t\\t\\tprint(f\\\"Wrong course format. Ignoring course: {c.text}\\\")\\n\\t\\t\\tcontinue\\n\\t\\tclass_list.append(course_id)\\n\\treturn class_list\",\n \"def _create_courselike( # lint-amnesty, pylint: disable=too-many-statements\\n self, locator, user_id, master_branch, fields=None,\\n versions_dict=None, search_targets=None, root_category='course',\\n root_block_id=None, **kwargs\\n ):\\n index = self.get_course_index(locator, ignore_case=True)\\n if index is not None:\\n raise DuplicateCourseError(locator, index)\\n\\n partitioned_fields = self.partition_fields_by_scope(root_category, fields)\\n block_fields = partitioned_fields[Scope.settings]\\n if Scope.children in partitioned_fields:\\n block_fields.update(partitioned_fields[Scope.children])\\n definition_fields = self._serialize_fields(root_category, partitioned_fields.get(Scope.content, {}))\\n\\n # build from inside out: definition, structure, index entry\\n # if building a wholly new structure\\n if versions_dict is None or master_branch not in versions_dict:\\n # create new definition and structure\\n definition_id = self.create_definition_from_data(locator, definition_fields, root_category, user_id).definition_id # lint-amnesty, pylint: disable=line-too-long\\n\\n draft_structure = self._new_structure(\\n user_id,\\n BlockKey(\\n root_category,\\n root_block_id or SplitMongoModuleStore.DEFAULT_ROOT_COURSE_BLOCK_ID,\\n ),\\n block_fields,\\n definition_id\\n )\\n new_id = draft_structure['_id']\\n\\n if versions_dict is None:\\n versions_dict = {master_branch: new_id}\\n else:\\n versions_dict[master_branch] = new_id\\n\\n elif block_fields or definition_fields: # pointing to existing course w/ some overrides\\n # just get the draft_version structure\\n draft_version = CourseLocator(version_guid=versions_dict[master_branch])\\n draft_structure = self._lookup_course(draft_version).structure\\n draft_structure = self.version_structure(locator, draft_structure, user_id)\\n new_id = draft_structure['_id']\\n root_block = draft_structure['blocks'][draft_structure['root']]\\n if block_fields is not None:\\n root_block.fields.update(self._serialize_fields(root_category, block_fields))\\n if definition_fields is not None:\\n old_def = self.get_definition(locator, root_block.definition)\\n new_fields = old_def['fields']\\n new_fields.update(definition_fields)\\n definition_id = self._update_definition_from_data(locator, old_def, new_fields, user_id).definition_id\\n root_block.definition = definition_id\\n root_block.edit_info.edited_on = datetime.datetime.now(UTC)\\n root_block.edit_info.edited_by = user_id\\n root_block.edit_info.previous_version = root_block.edit_info.update_version\\n root_block.edit_info.update_version = new_id\\n\\n versions_dict[master_branch] = new_id\\n else: # Pointing to an existing course structure\\n new_id = versions_dict[master_branch]\\n draft_version = CourseLocator(version_guid=new_id)\\n draft_structure = self._lookup_course(draft_version).structure\\n\\n locator = locator.replace(version_guid=new_id)\\n with self.bulk_operations(locator):\\n self.update_structure(locator, draft_structure)\\n index_entry = {\\n '_id': ObjectId(),\\n 'org': locator.org,\\n 'course': locator.course,\\n 'run': locator.run,\\n 'edited_by': user_id,\\n 'edited_on': datetime.datetime.now(UTC),\\n 'versions': versions_dict,\\n 'schema_version': self.SCHEMA_VERSION,\\n 'search_targets': search_targets or {},\\n }\\n if fields is not None:\\n self._update_search_targets(index_entry, fields)\\n self.insert_course_index(locator, index_entry)\\n\\n # expensive hack to persist default field values set in __init__ method (e.g., wiki_slug)\\n if isinstance(locator, LibraryLocator):\\n course = self.get_library(locator, **kwargs)\\n else:\\n course = self.get_course(locator, **kwargs)\\n return self.update_item(course, user_id, **kwargs)\",\n \"def get_course_about_section(course, section_key):\\r\\n\\r\\n # Many of these are stored as html files instead of some semantic\\r\\n # markup. This can change without effecting this interface when we find a\\r\\n # good format for defining so many snippets of text/html.\\r\\n\\r\\n # TODO: Remove number, instructors from this list\\r\\n if section_key in ['short_description', 'description', 'key_dates', 'video',\\r\\n 'course_staff_short', 'course_staff_extended',\\r\\n 'requirements', 'syllabus', 'textbook', 'faq', 'more_info',\\r\\n 'number', 'instructors', 'overview',\\r\\n 'effort', 'end_date', 'prerequisites', 'ocw_links']:\\r\\n\\r\\n try:\\r\\n\\r\\n request = get_request_for_thread()\\r\\n\\r\\n loc = course.location.replace(category='about', name=section_key)\\r\\n\\r\\n # Use an empty cache\\r\\n field_data_cache = FieldDataCache([], course.id, request.user)\\r\\n about_module = get_module(\\r\\n request.user,\\r\\n request,\\r\\n loc,\\r\\n field_data_cache,\\r\\n course.id,\\r\\n not_found_ok=True,\\r\\n wrap_xmodule_display=False,\\r\\n static_asset_path=course.static_asset_path\\r\\n )\\r\\n\\r\\n html = ''\\r\\n\\r\\n if about_module is not None:\\r\\n try:\\r\\n html = about_module.render('student_view').content\\r\\n except Exception: # pylint: disable=broad-except\\r\\n html = render_to_string('courseware/error-message.html', None)\\r\\n log.exception(\\r\\n u\\\"Error rendering course={course}, section_key={section_key}\\\".format(\\r\\n course=course, section_key=section_key\\r\\n ))\\r\\n return html\\r\\n\\r\\n except ItemNotFoundError:\\r\\n log.warning(\\r\\n u\\\"Missing about section {key} in course {url}\\\".format(key=section_key, url=course.location.to_deprecated_string())\\r\\n )\\r\\n return None\\r\\n elif section_key == \\\"title\\\":\\r\\n return course.display_name_with_default\\r\\n elif section_key == \\\"university\\\":\\r\\n return course.display_org_with_default\\r\\n elif section_key == \\\"number\\\":\\r\\n return course.display_number_with_default\\r\\n\\r\\n raise KeyError(\\\"Invalid about key \\\" + str(section_key))\",\n \"def test_get_full_topic_courses(self):\\r\\n course_id = None # Change me!!\\r\\n topic_id = None # Change me!!\\r\\n\\r\\n r = self.client.get_full_topic_courses(topic_id, course_id)\",\n \"def _create_certificate_form(self, user, department, data=None, index=1, key=None):\\n key = key or time.time()\\n form = CertificateForm(user, department, data, prefix='gc{}'.format(key), index=index)\\n return key, form\",\n \"def get_courses_html():\\r\\n r = requests.get(URL_CS_ALL_REQ)\\r\\n if r.status_code == 200:\\r\\n return r.text\\r\\n else:\\r\\n return None\",\n \"def all_courses(records):\\n \\n course_and_id_dict = {} #This creates an empty dictionary\\n for all_tuples in records:\\n course_info_tuple = all_tuples[0] #Extracts all course information\\n course_id = course_info_tuple[0]\\n course_name = course_info_tuple[1]\\n \\n course_and_id_dict[course_id] = course_name\\n \\n return course_and_id_dict\",\n \"def build_corpus(self):\\n print(\\\"Inside the build_corpus >>>>>\\\")\\n documentsCount = 0\\n documents = self.documents\\n\\t\\t\\n with open(self.documents_path) as file:\\n for documents in file.readlines():\\n documents = documents.rstrip('}\\\\n ').strip('0\\\\t').strip('1\\\\t').split(' ')\\n documentsCount = documentsCount +1\\n self.documents.append(documents)\\n\\t\\t\\t\\n self.number_of_documents = documentsCount\",\n \"def test_course_overview_view_with_course(self):\\r\\n course = CourseFactory.create(org='MITx', course='999', display_name='Robot Super Course')\\r\\n resp = self._show_course_overview(course.id)\\r\\n self.assertContains(\\r\\n resp,\\r\\n '<article class=\\\"courseware-overview\\\" data-locator=\\\"location:MITx+999+Robot_Super_Course+course+Robot_Super_Course\\\" data-course-key=\\\"slashes:MITx+999+Robot_Super_Course\\\">',\\r\\n status_code=200,\\r\\n html=True\\r\\n )\",\n \"def _section_course_info(course_key, access):\\r\\n course = get_course_by_id(course_key, depth=None)\\r\\n\\r\\n section_data = {\\r\\n 'section_key': 'course_info',\\r\\n 'section_display_name': _('Course Info'),\\r\\n 'access': access,\\r\\n 'course_id': course_key,\\r\\n 'course_display_name': course.display_name,\\r\\n 'enrollment_count': CourseEnrollment.num_enrolled_in(course_key),\\r\\n 'has_started': course.has_started(),\\r\\n 'has_ended': course.has_ended(),\\r\\n 'list_instructor_tasks_url': reverse('list_instructor_tasks', kwargs={'course_id': course_key.to_deprecated_string()}),\\r\\n }\\r\\n\\r\\n try:\\r\\n advance = lambda memo, (letter, score): \\\"{}: {}, \\\".format(letter, score) + memo\\r\\n section_data['grade_cutoffs'] = reduce(advance, course.grade_cutoffs.items(), \\\"\\\")[:-2]\\r\\n except Exception:\\r\\n section_data['grade_cutoffs'] = \\\"Not Available\\\"\\r\\n # section_data['offline_grades'] = offline_grades_available(course_key)\\r\\n\\r\\n try:\\r\\n section_data['course_errors'] = [(escape(a), '') for (a, _unused) in modulestore().get_course_errors(course.id)]\\r\\n except Exception:\\r\\n section_data['course_errors'] = [('Error fetching errors', '')]\\r\\n\\r\\n return section_data\",\n \"def get_courses(std):\\n return std[2]\",\n \"def _populate(self):\\n\\n # Assume the first word is what we want, and we can find well formed years\\n # This sucks, but will work for these ones.\\n # Roll on bibtex for citations in the CIM.\\n\\n citation_detail = self.doc.citation_detail\\n author = citation_detail.split(',')[0]\\n match = '([^\\\\w])19|20\\\\d\\\\d([^\\\\w])*?'\\n m = re.search(match, citation_detail)\\n if m:\\n year = m.group(0)\\n else:\\n year = None\\n\\n # one error in existing es-doc content to be fixed:\\n if 'van Vuuren DP' in author:\\n author = 'van Vuuren'\\n print 'applying vv fix'\\n\\n self.year = int(year)\\n\\n # We assume that this table will have entries which ne\\n\\n # I use the first three letters of a an authors name, and for\\n # three or more authors, EA, and then the year for my bibtex citation string\\n self.citeguess = author[0:3] + 'EA' + year[2:]\\n # This is what will appear in the table:\\n self.citestring = '%s et al. (%s)' % (author, year)\\n # Keep this for a reference list for checking against the eventual bibtex reference list.\\n self.text = citation_detail\",\n \"def load2TexAS(data):\\n # State global variable\\n global cache_stanza, cache_spacy, cache_udpipe, cache_trankit\\n\\n # Collect the data\\n string = data['text']\\n lang = data['lang']\\n packages = data['packages']\\n\\n hash_string = hashlib.sha1(string.encode()).hexdigest()\\n\\n final_HTML = \\\"\\\"\\n message_HTML = \\\"<div class=\\\\'message\\\\'>\\\"\\n isMessage = False\\n header_input = []\\n log_row = [datetime.now().strftime('%Y-%m-%d %H:%M:%S'), lang]\\n\\n if \\\"stanza\\\" in packages:\\n # Initialize the TexAS document\\n mydoc = tx.Document(string)\\n mydoc.meta().set(\\\"authors\\\",\\\"hegler,yiwen,celine,yuqian\\\")\\n mydoc.date().setTimestamp(\\\"2021-01-19T14:44\\\")\\n\\n ## If cache is full, reload the cache.\\n if cache.count(cache_stanza) > 100:\\n cache.write(cache_stanza, \\\"stanza\\\")\\n cache_stanza = cache.load(\\\"stanza\\\")\\n \\n ## Check text whether is already in cache\\n if hash_string in cache_stanza[lang].keys():\\n tokens, end_pos, lemma, pos, nlpWordsList, hasCompoundWords, cache_stanza = cache.read(\\\"stanza\\\", cache_stanza, lang, string) #The output cache_stanza has 'count' been updated.\\n else:\\n tokens, end_pos, lemma, pos, nlpWordsList, hasCompoundWords, cache_stanza = cache.add(\\\"stanza\\\", cache_stanza, lang, string, get_services_stanza)\\n \\n mydoc.setTokenList(tokens, indexed=True)\\n mydoc.views().get(\\\"TOKENS\\\").meta().set(\\\"generator\\\", \\\"stanza\\\")\\n mydoc.views().get(\\\"TOKENS\\\").meta().set(\\\"model\\\", \\\"stanza\\\" + \\\"-\\\" + lang)\\n mydoc.setSentenceList(end_pos)\\n\\n if hasCompoundWords:\\n mydoc.addTokenView( \\\"WORDS\\\", nlpWordsList )\\n mydoc.addTokenView(\\\"LEMMA\\\", lemma)\\n mydoc.addTokenView(\\\"POS\\\", pos)\\n \\n # Extract HTML View\\n myTabView = tx.UITabularView(mydoc)\\n if hasCompoundWords:\\n myTabView.showView(\\\"WORDS\\\")\\n myTabView.showView(\\\"LEMMA\\\", labelCSS=False)\\n myTabView.showView(\\\"POS\\\")\\n\\n # concatenate the myTabView.HTML()\\n header_input.append((\\\"Stanza\\\", str(len(end_pos)) , str(len(tokens)), str(get_tokens_per_sents(end_pos))))\\n final_HTML += \\\"<div class='subtitle'>Stanza</div> <br>\\\" + myTabView.HTML().replace(\\\"\\\\n\\\", \\\"\\\") + \\\"<br>\\\"\\n log_row.append(\\\"stanza\\\")\\n \\n else:\\n log_row.append(\\\"\\\")\\n\\n if \\\"spacy\\\" in packages:\\n # SpaCy does not support Arabic and Russian\\n if lang == 'ara' or lang == 'rus':\\n message_HTML += \\\"SpaCy does not support Arabic or Russian. <br>\\\"\\n isMessage = True\\n\\n else:\\n mydoc = tx.Document(string)\\n mydoc.meta().set(\\\"authors\\\",\\\"hegler,yiwen,celine,yuqian\\\")\\n mydoc.date().setTimestamp(\\\"2021-01-19T14:44\\\")\\n \\n ## If cache is full, reload the cache.\\n if cache.count(cache_spacy) > 100:\\n cache.write(cache_spacy, \\\"spacy\\\")\\n cache_spacy = cache.load(\\\"spacy\\\")\\n \\n ## Check text whether is already in cache\\n if hash_string in cache_spacy[lang].keys():\\n tokens, end_pos, lemma, pos, cache_spacy = cache.read(\\\"spacy\\\", cache_spacy, lang, string)\\n else:\\n tokens, end_pos, lemma, pos, cache_spacy = cache.add(\\\"spacy\\\", cache_spacy, lang, string, get_services_spacy)\\n \\n mydoc.setTokenList(tokens, indexed=True)\\n mydoc.views().get(\\\"TOKENS\\\").meta().set(\\\"generator\\\", \\\"spacy\\\")\\n mydoc.views().get(\\\"TOKENS\\\").meta().set(\\\"model\\\", \\\"spacy\\\" + \\\"-\\\" + lang )\\n mydoc.setSentenceList(end_pos)\\n mydoc.addTokenView(\\\"LEMMA\\\", lemma)\\n mydoc.addTokenView(\\\"POS\\\", pos)\\n \\n # Extract HTML View\\n myTabView = tx.UITabularView(mydoc)\\n myTabView.showView(\\\"LEMMA\\\", labelCSS=False)\\n myTabView.showView(\\\"POS\\\")\\n\\n # concatenate the myTabView.HTML()\\n header_input.append((\\\"SpaCy\\\", str(len(end_pos)) , str(len(tokens)), str(get_tokens_per_sents(end_pos))))\\n final_HTML += \\\"<div class='subtitle'>\\\" + \\\"SpaCy\\\" + \\\"</div><br>\\\" + myTabView.HTML().replace(\\\"\\\\n\\\", \\\"\\\") + \\\"<br>\\\"\\n log_row.append(\\\"spacy\\\")\\n \\n else:\\n log_row.append(\\\"\\\")\\n\\n if \\\"udpipe\\\" in packages: \\n ## If cache is full, reload the cache.\\n if cache.count(cache_udpipe) > 100:\\n cache.write(cache_udpipe, \\\"udpipe\\\")\\n cache_udpipe = cache.load(\\\"udpipe\\\")\\n \\n ## Check text whether is already in cache\\n if hash_string in cache_udpipe[lang].keys():\\n tokens, end_pos, lemma, pos, cache_udpipe = cache.read(\\\"udpipe\\\", cache_udpipe, lang, string)\\n else:\\n tokens, end_pos, lemma, pos, cache_udpipe = cache.add(\\\"udpipe\\\", cache_udpipe, lang, string, get_services_udpipe)\\n \\n string_udpipe = \\\" \\\".join(tokens)\\n\\n # Initialize the TexAS document\\n mydoc = tx.Document(string_udpipe)\\n mydoc.meta().set(\\\"authors\\\",\\\"hegler,yiwen,celine,yuqian\\\")\\n mydoc.date().setTimestamp(\\\"2021-01-19T14:44\\\")\\n\\n mydoc.setTokenList(tokens, indexed=True)\\n mydoc.views().get(\\\"TOKENS\\\").meta().set(\\\"generator\\\", \\\"udpipe\\\")\\n mydoc.views().get(\\\"TOKENS\\\").meta().set(\\\"model\\\", \\\"udpipe\\\" + \\\"-\\\" + lang )\\n mydoc.setSentenceList(end_pos)\\n mydoc.addTokenView(\\\"LEMMA\\\", lemma)\\n mydoc.addTokenView(\\\"POS\\\", pos)\\n \\n # Extract HTML View\\n myTabView = tx.UITabularView(mydoc)\\n myTabView.showView(\\\"LEMMA\\\", labelCSS=False)\\n myTabView.showView(\\\"POS\\\")\\n\\n # concatenate the myTabView.HTML()\\n header_input.append((\\\"UDpipe\\\", str(len(end_pos)) , str(len(tokens)), str(get_tokens_per_sents(end_pos))))\\n final_HTML += \\\"<div class='subtitle'>UDpipe</div> <br>\\\" + myTabView.HTML().replace(\\\"\\\\n\\\", \\\"\\\") + \\\"<br>\\\"\\n log_row.append(\\\"udpipe\\\")\\n \\n else:\\n log_row.append(\\\"\\\")\\n \\n if \\\"trankit\\\" in packages:\\n # trankit temporarily only support english\\n if lang == 'eng':\\n mydoc = tx.Document(string)\\n mydoc.meta().set(\\\"authors\\\",\\\"hegler,yiwen,celine,yuqian\\\")\\n mydoc.date().setTimestamp(\\\"2021-01-19T14:44\\\")\\n \\n ## If cache is full, reload the cache.\\n if cache.count(cache_trankit) > 100:\\n cache.write(cache_trankit, \\\"trankit\\\")\\n cache_trankit = cache.load(\\\"trankit\\\")\\n \\n ## Check text whether is already in cache\\n if hash_string in cache_trankit[lang].keys():\\n tokens, end_pos, lemma, pos, cache_trankit = cache.read(\\\"trankit\\\", cache_trankit, lang, string)\\n else:\\n tokens, end_pos, lemma, pos, cache_trankit = cache.add(\\\"trankit\\\", cache_trankit, lang, string, get_services_trankit)\\n \\n mydoc.setTokenList(tokens, indexed=True)\\n mydoc.views().get(\\\"TOKENS\\\").meta().set(\\\"generator\\\", \\\"spacy\\\")\\n mydoc.views().get(\\\"TOKENS\\\").meta().set(\\\"model\\\", \\\"spacy\\\" + \\\"-\\\" + lang )\\n mydoc.setSentenceList(end_pos)\\n mydoc.addTokenView(\\\"LEMMA\\\", lemma)\\n mydoc.addTokenView(\\\"POS\\\", pos)\\n \\n # Extract HTML View\\n myTabView = tx.UITabularView(mydoc)\\n myTabView.showView(\\\"LEMMA\\\", labelCSS=False)\\n myTabView.showView(\\\"POS\\\")\\n\\n # concatenate the myTabView.HTML()\\n header_input.append((\\\"Trankit\\\", str(len(end_pos)) , str(len(tokens)), str(get_tokens_per_sents(end_pos))))\\n final_HTML += \\\"<div class='subtitle'>\\\" + \\\"Trankit\\\" + \\\"</div><br>\\\" + myTabView.HTML().replace(\\\"\\\\n\\\", \\\"\\\") + \\\"<br>\\\"\\n log_row.append(\\\"trankit\\\")\\n\\n else:\\n message_HTML += \\\"Trankit temporarily only supports English. <br>\\\"\\n isMessage = True \\n \\n else:\\n log_row.append(\\\"\\\")\\n\\n message_HTML += \\\"</div>\\\"\\n if isMessage:\\n return message_HTML + get_header_table(header_input) + \\\"<br><br>\\\" + final_HTML\\n\\n writeLog(log_row)\\n return get_header_table(header_input) + \\\"<br><br>\\\" + final_HTML\",\n \"def test_get_courses_for_wiki(self):\\r\\n store = XMLModuleStore(DATA_DIR, course_dirs=['toy', 'simple'])\\r\\n for course in store.get_courses():\\r\\n course_locations = store.get_courses_for_wiki(course.wiki_slug)\\r\\n self.assertEqual(len(course_locations), 1)\\r\\n self.assertIn(course.location, course_locations)\\r\\n\\r\\n course_locations = store.get_courses_for_wiki('no_such_wiki')\\r\\n self.assertEqual(len(course_locations), 0)\\r\\n\\r\\n # now set toy course to share the wiki with simple course\\r\\n toy_course = store.get_course(SlashSeparatedCourseKey('edX', 'toy', '2012_Fall'))\\r\\n toy_course.wiki_slug = 'simple'\\r\\n\\r\\n course_locations = store.get_courses_for_wiki('toy')\\r\\n self.assertEqual(len(course_locations), 0)\\r\\n\\r\\n course_locations = store.get_courses_for_wiki('simple')\\r\\n self.assertEqual(len(course_locations), 2)\\r\\n for course_number in ['toy', 'simple']:\\r\\n self.assertIn(Location('edX', course_number, '2012_Fall', 'course', '2012_Fall'), course_locations)\",\n \"def extract_courses():\\n if settings.XPRO_COURSES_API_URL:\\n return requests.get(settings.XPRO_COURSES_API_URL, timeout=20).json()\\n return []\",\n \"def fetch(cls, course_key):\\r\\n descriptor = modulestore('direct').get_course(course_key)\\r\\n course_details = cls(course_key.org, course_key.course, course_key.run)\\r\\n\\r\\n course_details.start_date = descriptor.start\\r\\n course_details.end_date = descriptor.end\\r\\n course_details.enrollment_start = descriptor.enrollment_start\\r\\n course_details.enrollment_end = descriptor.enrollment_end\\r\\n course_details.course_image_name = descriptor.course_image\\r\\n course_details.course_image_asset_path = course_image_url(descriptor)\\r\\n\\r\\n temploc = course_key.make_usage_key('about', 'syllabus')\\r\\n try:\\r\\n course_details.syllabus = get_modulestore(temploc).get_item(temploc).data\\r\\n except ItemNotFoundError:\\r\\n pass\\r\\n\\r\\n temploc = course_key.make_usage_key('about', 'short_description')\\r\\n try:\\r\\n course_details.short_description = get_modulestore(temploc).get_item(temploc).data\\r\\n except ItemNotFoundError:\\r\\n pass\\r\\n\\r\\n temploc = course_key.make_usage_key('about', 'overview')\\r\\n try:\\r\\n course_details.overview = get_modulestore(temploc).get_item(temploc).data\\r\\n except ItemNotFoundError:\\r\\n pass\\r\\n\\r\\n temploc = course_key.make_usage_key('about', 'effort')\\r\\n try:\\r\\n course_details.effort = get_modulestore(temploc).get_item(temploc).data\\r\\n except ItemNotFoundError:\\r\\n pass\\r\\n\\r\\n temploc = course_key.make_usage_key('about', 'video')\\r\\n try:\\r\\n raw_video = get_modulestore(temploc).get_item(temploc).data\\r\\n course_details.intro_video = CourseDetails.parse_video_tag(raw_video)\\r\\n except ItemNotFoundError:\\r\\n pass\\r\\n\\r\\n return course_details\",\n \"def test_citations_and_toc_with_valid_defaults(self):\\n\\n pandoc_default_files = [\\n os.path.join(\\n TEST_DEFAULT_FILES_PATH,\\n \\\"valid_defaults_with_toc_and_citations.yaml\\\",\\n )\\n ]\\n\\n settings = get_settings(\\n PANDOC_DEFAULT_FILES=pandoc_default_files,\\n FORMATTED_FIELDS=FORMATTED_FIELDS,\\n )\\n pandoc_reader = PandocReader(settings)\\n\\n source_path = os.path.join(\\n TEST_CONTENT_PATH, \\\"valid_content_with_citation.md\\\"\\n )\\n output, metadata = pandoc_reader.read(source_path)\\n self.maxDiff = None # pylint: disable=invalid-name\\n\\n self.assertEqual(\\n (\\n '<h2 id=\\\"string-theory\\\">String Theory</h2>\\\\n'\\n \\\"<p>But this foundational principle of science has\\\"\\n \\\" now been called into question by\\\"\\n ' <a href=\\\"https://www.britannica.com/science/'\\n 'string-theory\\\">String Theory</a>,'\\n \\\" which is a relative newcomer to theoretical physics, but one\\\"\\n \\\" that has captured the common imagination, judging by\\\"\\n \\\" the popular explanations that abound on the Web\\\"\\n ' <span class=\\\"citation\\\" data-cites=\\\"mann2019 wood2019'\\n ' jones2020\\\">[1]–[3]</span>.'\\n \\\" And whether string theory is or is not science, Popper\\\"\\n \\\" notwithstanding, is an issue that is still up for debate\\\"\\n \\\" <span\\\"\\n ' class=\\\"citation\\\" data-cites=\\\"siegel2015 castelvecchi2016'\\n ' alves2017 francis2019\\\">[4]–[7]</span>.</p>\\\\n'\\n '<h1 class=\\\"unnumbered\\\" id=\\\"bibliography\\\">References</h1>\\\\n'\\n '<div id=\\\"refs\\\" class=\\\"references csl-bib-body\\\"'\\n ' role=\\\"doc-bibliography\\\">\\\\n'\\n '<div id=\\\"ref-mann2019\\\" class=\\\"csl-entry\\\"'\\n ' role=\\\"doc-biblioentry\\\">\\\\n'\\n '<div class=\\\"csl-left-margin\\\">[1]'\\n ' </div><div class=\\\"csl-right-inline\\\">A. Mann,'\\n \\\" <span>“<span>What Is String Theory?</span>”</span>\\\"\\n \\\" 20-Mar-2019. [Online].\\\"\\n ' Available: <a href=\\\"https://www.livescience.com/'\\n '65033-what-is-string-theory.html\\\">'\\n \\\"https://www.livescience.com/\\\"\\n \\\"65033-what-is-string-theory.html</a>.\\\"\\n \\\" [Accessed: 12-Nov-2020]</div>\\\\n\\\"\\n \\\"</div>\\\\n\\\"\\n '<div id=\\\"ref-wood2019\\\" class=\\\"csl-entry\\\"'\\n ' role=\\\"doc-biblioentry\\\">\\\\n'\\n '<div class=\\\"csl-left-margin\\\">[2] </div>'\\n '<div class=\\\"csl-right-inline\\\">'\\n \\\"C. Wood, <span>“<span>What Is String Theory?</span>.\\\"\\n \\\" Reference article:\\\"\\n \\\" A simplified explanation and brief history of string\\\"\\n \\\" theory,”</span> 11-Jul-2019.\\\"\\n ' [Online]. Available: <a href=\\\"https://www.space.com/'\\n '17594-string-theory.html\\\">'\\n \\\"https://www.space.com/17594-string-theory.html</a>.\\\"\\n \\\" [Accessed: 12-Nov-2020]</div>\\\\n\\\"\\n \\\"</div>\\\\n\\\"\\n '<div id=\\\"ref-jones2020\\\" class=\\\"csl-entry\\\"'\\n ' role=\\\"doc-biblioentry\\\">\\\\n'\\n '<div class=\\\"csl-left-margin\\\">[3]'\\n ' </div><div class=\\\"csl-right-inline\\\">'\\n 'A. Z. Jones, <span>“<span class=\\\"nocase\\\">The Basics of String'\\n \\\" Theory</span>,”</span> 02-Mar-2019. [Online]. Available:\\\"\\n ' <a href=\\\"https://www.thoughtco.com/'\\n 'what-is-string-theory-2699363\\\">'\\n \\\"https://www.thoughtco.com/what-is-string-theory-2699363</a>.\\\"\\n \\\" [Accessed: 12-Nov-2020]</div>\\\\n\\\"\\n \\\"</div>\\\\n\\\"\\n '<div id=\\\"ref-siegel2015\\\" class=\\\"csl-entry\\\"'\\n ' role=\\\"doc-biblioentry\\\">\\\\n'\\n '<div class=\\\"csl-left-margin\\\">[4]'\\n ' </div><div class=\\\"csl-right-inline\\\">'\\n \\\"E. Siegel, <span>“<span>Why String Theory Is Not A Scientific\\\"\\n \\\" Theory</span>,”</span> 23-Dec-2015. [Online]. Available:\\\"\\n \\\" <a\\\"\\n ' href=\\\"https://www.forbes.com/sites/'\\n \\\"startswithabang/2015/12/23/\\\"\\n 'why-string-theory-is-not-science/\\\">https://www.forbes.com/'\\n \\\"sites/startswithabang/2015/12/23/\\\"\\n \\\"why-string-theory-is-not-science/</a>.\\\"\\n \\\" [Accessed: 12-Nov-2020]</div>\\\\n\\\"\\n \\\"</div>\\\\n\\\"\\n '<div id=\\\"ref-castelvecchi2016\\\" class=\\\"csl-entry\\\"'\\n ' role=\\\"doc-biblioentry\\\">\\\\n'\\n '<div class=\\\"csl-left-margin\\\">[5]'\\n ' </div><div class=\\\"csl-right-inline\\\">'\\n 'D. Castelvecchi, <span>“<span class=\\\"nocase\\\">'\\n \\\"Feuding physicists turn\\\"\\n \\\" to philosophy for help</span>. String theory is at the\\\"\\n \\\" heart of a debate over the integrity of the scientific\\\"\\n \\\" method itself,”</span> 05-Jan-2016. [Online]. Available:\\\"\\n ' <a href=\\\"https://www.nature.com/news/'\\n 'feuding-physicists-turn-to-philosophy-for-help-1.19076\\\">'\\n \\\"https://www.nature.com/news/\\\"\\n \\\"feuding-physicists-turn-to-philosophy-for-help-1.19076</a>.\\\"\\n \\\" [Accessed: 12-Nov-2020]</div>\\\\n\\\"\\n \\\"</div>\\\\n\\\"\\n '<div id=\\\"ref-alves2017\\\" class=\\\"csl-entry\\\"'\\n ' role=\\\"doc-biblioentry\\\">\\\\n'\\n '<div class=\\\"csl-left-margin\\\">[6] </div>'\\n '<div class=\\\"csl-right-inline\\\">'\\n 'R. A. Batista and J. Primack, <span>“<span class=\\\"nocase\\\">'\\n \\\"Is String theory falsifiable?</span>. Can a theory that isn’t\\\"\\n \\\" completely testable still be useful to physics?”</span>\\\"\\n \\\" [Online].\\\"\\n ' Available: <a href=\\\"https://metafact.io/factchecks/'\\n '30-is-string-theory-falsifiable\\\">'\\n \\\"https://metafact.io/factchecks/\\\"\\n \\\"30-is-string-theory-falsifiable</a>.\\\"\\n \\\" [Accessed: 12-Nov-2020]</div>\\\\n\\\"\\n \\\"</div>\\\\n\\\"\\n '<div id=\\\"ref-francis2019\\\" class=\\\"csl-entry\\\"'\\n ' role=\\\"doc-biblioentry\\\">\\\\n'\\n '<div class=\\\"csl-left-margin\\\">[7]'\\n ' </div><div class=\\\"csl-right-inline\\\">'\\n 'M. R. Francis, <span>“<span class=\\\"nocase\\\">Falsifiability and'\\n \\\" physics</span>. Can a theory that isn’t completely testable\\\"\\n \\\" still be useful to physics?”</span> 23-Apr-2019.\\\"\\n \\\" [Online]. Available:\\\"\\n ' <a href=\\\"https://www.scientificamerican.com/'\\n 'article/is-string-theory-science/\\\">'\\n \\\"https://www.scientificamerican.com/article/is-\\\"\\n \\\"string-theory-science/</a>. [Accessed: 12-Nov-2020]</div>\\\\n\\\"\\n \\\"</div>\\\\n\\\"\\n \\\"</div>\\\\n\\\"\\n ),\\n output,\\n )\\n\\n self.assertEqual(\\\"Valid Content With Citation\\\", str(metadata[\\\"title\\\"]))\\n self.assertEqual(\\\"My Author\\\", str(metadata[\\\"author\\\"]))\\n self.assertEqual(\\\"2020-10-16 00:00:00\\\", str(metadata[\\\"date\\\"]))\\n self.assertEqual(\\n (\\n '<nav class=\\\"toc\\\" role=\\\"doc-toc\\\">\\\\n'\\n \\\"<ul>\\\\n\\\"\\n '<li><a href=\\\"#string-theory\\\">String Theory</a></li>\\\\n'\\n '<li><a href=\\\"#bibliography\\\">References</a></li>\\\\n'\\n \\\"</ul>\\\\n</nav>\\\\n\\\"\\n ),\\n str(metadata[\\\"toc\\\"]),\\n )\",\n \"def parseCourses(self, response):\\n sel = Selector(response)\\n courses = sel.xpath('//div[@class=\\\"course-info expandable\\\"]')\\n for c in courses:\\n item = CourseItem(response.request.meta[\\\"item\\\"])\\n item['code'] += '-' + c.xpath('@id').get().strip()\\n item['name'] = c.xpath('//a[@class=\\\"courselink\\\"]/text()').get().strip()\\n # everything works up to here #\\n href = c.xpath('div/h3/a/@href').get()\\n url = urljoin('https://web-app.usc.edu', href)\\n yield Request(url=url,callback=self.parseSection,meta={'item':item})\",\n \"def test_course_index_view_with_course(self):\\r\\n CourseFactory.create(display_name='Robot Super Educational Course')\\r\\n resp = self.client.get_html('/course/')\\r\\n self.assertContains(\\r\\n resp,\\r\\n '<h3 class=\\\"course-title\\\">Robot Super Educational Course</h3>',\\r\\n status_code=200,\\r\\n html=True\\r\\n )\\r\\n _test_no_locations(self, resp)\",\n \"def _construct_course_son(org, course, run):\\r\\n return bson.son.SON([\\r\\n ('org', org),\\r\\n ('course', course),\\r\\n ('name', run)\\r\\n ])\",\n \"def build_document(self, values: dict):\\n doc = Document(self.config.get('template_file'))\\n for section in doc.sections:\\n # First Header\\n header = section.header\\n for p in header.paragraphs:\\n for key, field in self.config.get('fields').items():\\n # Format key\\n formatted_key = f\\\"<{key.upper()}>\\\"\\n if formatted_key in p.text:\\n inline = p.runs\\n # Loop added to work with runs (strings with same style)\\n for i in range(len(inline)):\\n if formatted_key in inline[i].text:\\n text = inline[i].text.replace(\\n formatted_key, values[key])\\n inline[i].text = text\\n # Second Footer\\n footer = section.footer\\n for p in footer.paragraphs:\\n for key, field in self.config.get(\\\"fields\\\").items():\\n # Format key\\n formatted_key = f\\\"<{key.upper()}>\\\"\\n if formatted_key in p.text:\\n inline = p.runs\\n # Loop added to work with runs (strings with same style)\\n for i in range(len(inline)):\\n if formatted_key in inline[i].text:\\n text = inline[i].text.replace(\\n formatted_key, values[key])\\n inline[i].text = text\\n\\n # Go by tables\\n for table in doc.tables:\\n for row in table.rows:\\n for cell in row.cells:\\n for p in cell.paragraphs:\\n for key, field in self.config.get(\\\"fields\\\").items():\\n # Format key\\n formatted_key = f\\\"<{key.upper()}>\\\"\\n if formatted_key in p.text:\\n inline = p.runs\\n # Loop added to work with runs (strings with same style)\\n for i in range(len(inline)):\\n if formatted_key in inline[i].text:\\n text = inline[i].text.replace(formatted_key, values[key])\\n inline[i].text = text\\n\\n # Go by the rest of the document\\n for p in doc.paragraphs:\\n for key, field in self.config.get(\\\"fields\\\").items():\\n # Format key\\n formatted_key = f\\\"<{key.upper()}>\\\"\\n if formatted_key in p.text:\\n inline = p.runs\\n # Loop added to work with runs (strings with same style)\\n for i in range(len(inline)):\\n if formatted_key in inline[i].text:\\n text = inline[i].text.replace(formatted_key, values[key])\\n inline[i].text = text\\n\\n # By default filename will be the template filename with copy_ before\\n filename = f\\\"copy_{self.config.get('template_file')}\\\"\\n if \\\"filename\\\" in self.config:\\n if \\\"type\\\" in self.config.get(\\\"filename\\\"):\\n # We can have 2 types static value or based in a field\\n if self.config.get(\\\"filename\\\").get(\\\"type\\\") == \\\"static\\\" and \\\"value\\\" in self.config.get(\\\"filename\\\"):\\n filename = self.config.get(\\\"filename\\\").get(\\\"value\\\")\\n elif self.config[\\\"filename\\\"][\\\"type\\\"] == \\\"field\\\" and \\\"value\\\" in self.config[\\\"filename\\\"]:\\n filename = values.get(self.config.get(\\\"filename\\\").get(\\\"value\\\"))\\n\\n # Make sure we have a prefix populated\\n if \\\"file_prefix\\\" not in self.config:\\n self.config.update({\\\"file_prefix\\\": \\\"\\\"})\\n\\n # Make sure we have a posfix populated\\n if \\\"file_posfix\\\" not in self.config:\\n self.config.update({\\\"file_posfix\\\": \\\"\\\"})\\n\\n filename = self.config.get(\\\"file_prefix\\\") + filename + self.config.get(\\\"file_posfix\\\")\\n\\n doc.save(f\\\"{filename}.docx\\\")\\n\\n return f\\\"{filename}.docx\\\"\",\n \"def _get_sample_plain_context(self):\\r\\n context = {\\r\\n 'course_title': \\\"Bogus Course Title\\\",\\r\\n 'course_url': \\\"/location/of/course/url\\\",\\r\\n 'account_settings_url': \\\"/location/of/account/settings/url\\\",\\r\\n 'platform_name': 'edX',\\r\\n 'email': 'your-email@test.com',\\r\\n }\\r\\n return context\",\n \"def test_list_entries_courses(self):\\r\\n course_id = None # Change me!!\\r\\n topic_id = None # Change me!!\\r\\n\\r\\n r = self.client.list_entries_courses(topic_id, course_id, ids=None)\",\n \"def get_courses_for_wiki(self, wiki_slug):\\r\\n courses = []\\r\\n return courses\",\n \"def download_course_interactive(self):\\n self.domain_prompt()\\n self.course_prompt()\\n self.get_course_page()\\n self.get_course_title()\\n self.get_course_unit_titles()\\n self.get_course_unit_slugs()\\n self.get_course_unit_urls()\\n\\n print(\\\"\\\\nGenerating Path Slugs...\\\\n\\\")\\n self.get_course_all_slugs()\\n self.get_course_youtube_ids()\\n self.download_course_videos()\",\n \"def index():\\n user, user_id = get_user()\\n # Get this user's course with their roles\\n my_courses = []\\n if user:\\n my_courses = user.get_courses()\\n # Get all public courses\\n public_courses = Course.get_public()\\n\\n return render_template('courses/index.html',\\n user=user,\\n my_courses=my_courses,\\n public_courses=public_courses)\",\n \"def build(self) -> None:\\n\\n print(\\\"Genereting files..\\\")\\n self.doc = self.doc + r'\\\\end{document}'\\n\\n f = open(\\\"latex\\\\\\\\\\\" + self.report_name + '.tex', 'w')\\n f.write(self.doc)\\n f.close()\\n\\n os.chdir('latex')\\n\\n cmd = ['pdflatex', '-interaction', 'nonstopmode', self.report_name + '.tex']\\n #cmd = ['pdflatex', '-interaction', self.report_name + '.tex']\\n\\n for i in range(2):\\n proc = subprocess.Popen(cmd)\\n proc.communicate()\\n retcode = proc.returncode\\n if not retcode == 0:\\n os.chdir('..')\\n raise ValueError('Error {} executing command: {}'.format(retcode, ' '.join(cmd)))\\n\\n os.unlink(self.report_name + '.aux')\\n os.unlink(self.report_name + '.lof')\\n os.unlink(self.report_name + '.log')\\n os.unlink(self.report_name + '.lot')\\n os.unlink(self.report_name + '.out')\\n os.unlink(self.report_name + '.toc')\\n\\n os.chdir('..')\",\n \"def createTrecTextForCurrentDocuments(baseDir):\\n pathToFolder = baseDir + 'Collections/'\\n if not os.path.exists(pathToFolder):\\n os.makedirs(pathToFolder)\\n currentTime = str(datetime.datetime.now()).replace(\\\":\\\", \\\"-\\\").replace(\\\" \\\", \\\"-\\\").replace(\\\".\\\", \\\"-\\\")\\n pathToTrecText = pathToFolder+\\\"TrecText/\\\"\\n if not os.path.exists(pathToTrecText):\\n os.makedirs(pathToTrecText)\\n filename = pathToTrecText + currentTime\\n client = MongoClient('asr2.iem.technion.ac.il',27017)\\n db = client.asr16\\n documents = db.documents.find({}).sort('query_id',1)\\n queryToDocnos= {}\\n current_users = retrieve_users()\\n f = open(filename, 'w')\\n for document in documents:\\n if document['username'] in current_users:\\n print(document['query_id'], document['username'])\\n f.write('<DOC>\\\\n')\\n docno = str(document['query_id']).zfill(3) + '-' + str(document['username'])\\n f.write('<DOCNO>' + docno + '</DOCNO>\\\\n')\\n docnos = queryToDocnos.get(str(document['query_id']).zfill(3), [])\\n docnos.append(docno)\\n queryToDocnos[str(document['query_id']).zfill(3)] = docnos\\n f.write('<TEXT>\\\\n')\\n f.write(unicodedata.normalize('NFKD', document['current_document']).encode('cp1252', \\\"ignore\\\").decode('utf-8', 'replace').replace(u'\\\\uFFFD', ' ').rstrip())\\n f.write('\\\\n</TEXT>\\\\n')\\n f.write('</DOC>\\\\n')\\n f.close()\\n pathToWorkingSet = pathToFolder+ 'WorkingSets/'\\n if not os.path.exists(pathToWorkingSet):\\n os.makedirs(pathToWorkingSet)\\n workingSetFilename = pathToWorkingSet + currentTime\\n f = open(workingSetFilename, 'w')\\n for query, docnos in queryToDocnos.items():\\n i = 1\\n for docid in docnos:\\n f.write(query.zfill(3) + ' Q0 ' + docid + ' ' + str(i) + ' -' + str(i) + ' indri\\\\n')\\n i +=1\\n f.close()\\n return filename, workingSetFilename, currentTime\",\n \"def get_courses(db: Session = Depends(get_db)): # , _: models.User = Depends(get_current_user))\\n return crud.course.get_multi(db, skip=0, limit=100)\",\n \"def __init__(self, test_num):\\n if test_num == 1:\\n self.degree_chosen = \\\"Science\\\"\\n self.is_double_major = True\\n self.major = \\\"Computer Science\\\"\\n self.major2 = \\\"Mathematics\\\"\\n self.user_courses_dict = {\\n \\\"COSC121\\\": UniCourse(\\\"COSC121\\\", \\\"Introduction to Computer Science\\\"),\\n \\\"MATH120\\\": UniCourse(\\\"MATH120\\\", \\\"Discrete Mathematics\\\")\\n }\\n self.all_courses_dict = {\\n \\\"COSC121\\\": UniCourse(\\\"COSC121\\\", \\\"Introduction to Computer Science\\\"),\\n \\\"MATH120\\\": UniCourse(\\\"MATH120\\\", \\\"Discrete Mathematics\\\"),\\n \\\"MATH101\\\": UniCourse(\\\"MATH101\\\", \\\"Methods of Mathematics\\\")\\n }\",\n \"def make_bibtex(self):\\n\\n\\t\\t# bib = requests.request('GET', 'http://dx.doi.org/' + self.doi, \",\n \"def generate_latex_table(dictionary,filename,location=\\\".\\\"):\\n if type(filename) != str:\\n raise TypeError('filename should be string')\\n if type(dictionary) != dict:\\n raise TypeError('dictionary should be dictionary')\\n\\n head_code = \\\"\\\"\\\"\\\\\\\\documentclass{article}\\n%In the preamble section include the arabtex and utf8 packages\\n\\\\\\\\usepackage{arabtex}\\n\\\\\\\\usepackage{utf8}\\n\\\\\\\\usepackage{longtable}\\n\\\\\\\\usepackage{color, colortbl}\\n\\\\\\\\usepackage{supertabular}\\n\\\\\\\\usepackage{multicol}\\n\\\\\\\\usepackage{geometry}\\n\\\\\\\\geometry{left=.1in, right=.1in, top=.1in, bottom=.1in}\\n\\n\\\\\\\\begin{document}\\n\\\\\\\\begin{multicols}{6}\\n\\\\\\\\setcode{utf8}\\n\\n\\\\\\\\begin{center}\\\"\\\"\\\"\\n\\n tail_code = \\\"\\\"\\\"\\\\\\\\end{center}\\n\\\\\\\\end{multicols}\\n\\\\\\\\end{document}\\\"\\\"\\\"\\n\\n begin_table = \\\"\\\"\\\"\\\\\\\\begin{tabular}{ P{2cm} P{1cm}}\\n\\\\\\\\textbf{words} & \\\\\\\\textbf{\\\\\\\\#} \\\\\\\\\\\\\\\\\\n\\\\\\\\hline\\n\\\\\\\\\\\\\\\\[0.01cm]\\\"\\\"\\\"\\n end_table= \\\"\\\"\\\"\\\\\\\\end{tabular}\\\"\\\"\\\"\\n rows_num = 40\\n if location != '.':\\n filename = location +\\\"/\\\"+ filename\\n\\n try:\\n file = open(filename+'.tex', 'w', encoding='utf8')\\n file.write(head_code+'\\\\n')\\n n= int(len(dictionary)/rows_num)\\n words = [(\\\"\\\\\\\\<\\\"+word+\\\"> & \\\"+str(frequancy)+' \\\\\\\\\\\\\\\\ \\\\n') for word, frequancy in dictionary.items()]\\n start=0\\n end=rows_num\\n new_words = []\\n for i in range(n):\\n new_words = new_words+ [begin_table+'\\\\n'] +words[start:end] +[end_table+\\\" \\\\n\\\"]\\n start=end\\n end+=rows_num\\n remain_words = len(dictionary) - rows_num*n\\n if remain_words > 0:\\n new_words += [begin_table+\\\" \\\\n\\\"]+ words[-1*remain_words:]+[end_table+\\\" \\\\n\\\"]\\n for word in new_words:\\n file.write(word)\\n file.write(tail_code)\\n file.close()\\n return True\\n except:\\n return False\",\n \"def test_list_discussion_topics_courses(self):\\r\\n course_id = None # Change me!!\\r\\n\\r\\n r = self.client.list_discussion_topics_courses(course_id, exclude_context_module_locked_topics=None, include=None, only_announcements=None, order_by=None, scope=None, search_term=None)\",\n \"def test_buildTeX(self):\\n version = \\\"3.2.1\\\"\\n input1, output1 = self.getArbitraryLoreInputAndOutput(version)\\n input2, output2 = self.getArbitraryLoreInputAndOutput(version)\\n\\n # Filenames are chosen by getArbitraryOutput to match the counter used\\n # by getArbitraryLoreInputAndOutput.\\n self.howtoDir.child(\\\"1.xhtml\\\").setContent(input1)\\n self.howtoDir.child(\\\"2.xhtml\\\").setContent(input2)\\n\\n builder = BookBuilder()\\n builder.buildTeX(self.howtoDir)\\n self.assertEqual(self.howtoDir.child(\\\"1.tex\\\").getContent(), output1)\\n self.assertEqual(self.howtoDir.child(\\\"2.tex\\\").getContent(), output2)\",\n \"def see_teaching_courses(self, username: str, token: str) -> List[Dict[str, object]]:\\n\\n # Validate user first\\n if not self.validate(username=username, token=token, check_privilege='instructor'):\\n raise RuntimeError(\\\"User not verified!\\\")\\n\\n # Get UID from user's username\\n uid = self.get_uid(username=username)\\n\\n # Query database for courses instructed by a user with this UID\\n cursor = self._db_connection.cursor()\\n cursor.execute(\\n '''\\n SELECT \\n course_id,\\n course_abbreviation,\\n course_name, \\n time,\\n seats \\n FROM \\n courses\\n WHERE \\n instructor_id = ?\\n ;\\n ''', (uid,))\\n\\n db_results = cursor.fetchall()\\n\\n if db_results is None:\\n print(\\\"No associated courses found!\\\")\\n return []\\n\\n # Build information dicts for every course this user is instructing\\n courses = []\\n for result in db_results:\\n # Get the number of students enrolled in this course already\\n cursor.execute('''SELECT COUNT(*) FROM enrollment_records WHERE course_id = ?;''', (result[0],))\\n students_enrolled = cursor.fetchone()[0]\\n if students_enrolled is None:\\n students_enrolled = 0\\n\\n # Build a course dict from the data\\n courses.append({\\n \\\"course_abbreviation\\\": result[1],\\n \\\"course_name\\\": result[2],\\n \\\"time\\\": result[3],\\n \\\"students_enrolled\\\": students_enrolled,\\n \\\"capacity\\\": result[4],\\n })\\n\\n return courses\",\n \"def test_tax_court_citation_extractor(self):\\n\\n test_pairs = (\\n (\\n \\\"\\\"\\\" 1 UNITED STATES TAX COURT REPORT (2018)\\n\\n\\n\\n UNITED STATES TAX COURT\\n\\n\\n\\n BENTLEY COURT II LIMITED PARTNERSHIP, B.F. BENTLEY, INC., TAX\\n MATTERS PARTNER, Petitioner v.\\n COMMISSIONER OF INTERNAL REVENUE, Respondent\\n\\n\\n\\n Docket No. 5393-04. Filed May 31, 2006.\\n\\n\\n\\n Nancy Ortmeyer Kuhn, for petitioner.\\n \\\"\\\"\\\",\\n \\\"1 T.C. 2018\\\",\\n ),\\n (\\n \\\"\\\"\\\" T.C. Memo. 2003-150\\n\\n\\n\\n UNITED STATES TAX COURT\\n\\n\\n\\n RIVER CITY RANCHES #1 LTD., LEON SHEPARD,\\n TAX MATTERS PARTNER,\\n RIVER CITY RANCHES #2 LTD., LEON SHEPARD,\\n TAX MATTERS PARTNER,\\n RIVER CITY RANCHES #3 LTD., LEON SHEPARD,\\n TAX MATTERS PARTNER,\\n\\n\\n \\\"\\\"\\\",\\n \\\"2003 T.C. Memo. 150\\\",\\n ),\\n (\\n \\\"\\\"\\\" T.C. Summary Opinion 2003-150\\n\\n\\n\\n UNITED STATES TAX COURT\\n\\n\\n\\n RIVER CITY RANCHES #1 LTD., LEON SHEPARD,\\n TAX MATTERS PARTNER,\\n RIVER CITY RANCHES #2 LTD., LEON SHEPARD,\\n TAX MATTERS PARTNER,\\n RIVER CITY RANCHES #3 LTD., LEON SHEPARD,\\n TAX MATTERS PARTNER,\\n\\n\\n \\\"\\\"\\\",\\n \\\"2003 T.C. Summary Opinion 150\\\",\\n ),\\n (\\n \\\"\\\"\\\"\\n MICHAEL KEITH SHENK, PETITIONER v. COMMISSIONER\\n OF INTERNAL REVENUE, RESPONDENT\\n\\n Docket No. 5706–12. Filed May 6, 2013.\\n\\n P was divorced from his wife, and their 2003 ‘‘Judgment of\\n Absolute Divorce’’ provided that his ex-wife would have pri-\\n mary residential custody of their three minor children. The\\n judgment provided that the dependency exemption deductions\\n for the three children would be divided between the two ex-\\n spouses according to various conditions but did not provide\\n that the ex-wife must execute in P’s favor a Form 8332,\\n ‘‘Release of Claim to Exemption for Child of Divorced or Sepa-\\n rated Parents’’. The children resided with P’s ex-wife for more\\n than half of 2009, and P’s ex-wife did not execute in P’s favor\\n any Form 8332 or equivalent document for any year. For 2009\\n P timely filed a Federal income tax return on which he\\n claimed dependency exemption deductions and the child tax\\n credit for two of the children, consistent with his under-\\n standing of the terms of the judgment, but he did not attach\\n any Form 8332 to his return. He also claimed head-of-house-\\n hold filing status. His ex-wife, the custodial parent, timely\\n filed a Federal income tax return for 2009 on which she also\\n\\n 200\\n\\n\\n\\n\\nVerDate Nov 24 2008 10:59 Jul 11, 2014 Jkt 372897 PO 20012 Frm 00001 Fmt 3857 Sfmt 3857 V:\\\\FILES\\\\BOUND VOL. WITHOUT CROP MARKS\\\\B.V.140\\\\SHENK JAMIE\\n\\f (200) SHENK v. COMMISSIONER 201\\n\\n\\n claimed two dependency exemption deductions, so that one\\n child was claimed on both parents’ returns. R allowed to P the\\n dependency exemption deduction for one of the children but\\n disallowed his claim for the dependency exemption deduction\\n for the child who had also been claimed by the custodial\\n parent. At trial P contended he is entitled to a dependency\\n exemption deduction for all three children. Held: Since the\\n custodial parent did not execute, and P could not and did not\\n attach to his return, any Form 8332 or equivalent release, P\\n is not entitled under I.R.C. sec. 152(e)(2)(A) to claim the\\n dependency exemption deduction or the child tax credit. Held,\\n further, where both the custodial parent and the noncustodial\\n parent have claimed for the same year a dependency exemp-\\n tion deduction for the same child, a declaration signed by the\\n custodial parent after the period of limitations for assess-\\n ments has expired as to the custodial parent could not qualify\\n under I.R.C. sec. 152(e)(2)(A), and therefore there is no reason\\n to grant P’s request to leave the record open so that he may\\n obtain and proffer such a declaration. Held, further, P is not\\n entitled to head-of-household filing status under I.R.C. sec.\\n 2(b)(1) nor to the child tax credit under I.R.C. sec. 24.\\n\\n Michael Keith Shenk, for himself.\\n Shari Salu, for respondent.\\n GUSTAFSON, Judge: The Internal Revenue Service (IRS)\\n determined a deficiency of $3,136 in the 2009 Federal income\\n tax of petitioner Michael Keith Shenk. Mr. Shenk petitioned\\n this Court, pursuant to section 6213(a), 1 for redetermination\\n of the deficiency. After Mr. Shenk’s concession that he\\n received but did not report $254 in dividend income, the\\n issue for decision is whether Mr. Shenk is entitled to a\\n dependency exemption deduction for one of his children\\n under section 151(c), a child tax credit for that child under\\n section 24(a), and head-of-household filing status under sec-\\n tion 2(b)(1). On these issues, we hold for the IRS.\\n FINDINGS OF FACT\\n\\n The judgment of divorce\\n Mr. Shenk was married to Julie Phillips, and they have\\n three minor children—M.S., W.S., and L.S. They divorced in\\n 2003. The family court’s ‘‘Judgment of Absolute Divorce’’ pro-\\n 1 Unless otherwise indicated, all citations of sections refer to the Internal\\n\\n Revenue Code (26 U.S.C.) in effect for the tax year at issue, and all cita-\\n tions of Rules refer to the Tax Court Rules of Practice and Procedure.\\n\\n\\n\\n\\nVerDate Nov 24 2008 10:59 Jul 11, 2014 Jkt 372897 PO 20012 Frm 00002 Fmt 3857 Sfmt 3857 V:\\\\FILES\\\\BOUND VOL. WITHOUT CROP MARKS\\\\B.V.140\\\\SHENK JAMIE\\n\\f 202 140 UNITED STATES TAX COURT REPORTS (200)\\n\\n\\n vided: that Ms. Phillips was ‘‘awarded primary residential\\n custody’’ of the parties’ three children; and that Mr. Shenk\\n would be liable for child support payments; but that, as to\\n dependency exemptions—\\\"\\\"\\\",\\n \\\"140 T.C. 200\\\",\\n ),\\n )\\n site = tax.Site()\\n for q, a in test_pairs:\\n results = site.extract_from_text(q)\\n cite_string = \\\"%s %s %s\\\" % (\\n results[\\\"Citation\\\"][\\\"volume\\\"],\\n results[\\\"Citation\\\"][\\\"reporter\\\"],\\n results[\\\"Citation\\\"][\\\"page\\\"],\\n )\\n\\n self.assertEqual(cite_string, a)\\n print \\\"✓\\\", cite_string\",\n \"def _create_course(self, default, course_key):\\r\\n if default == 'split':\\r\\n offering = course_key.offering.replace('/', '.')\\r\\n else:\\r\\n offering = course_key.offering\\r\\n course = self.store.create_course(course_key.org, offering, store_name=default)\\r\\n category = self.import_chapter_location.category\\r\\n block_id = self.import_chapter_location.name\\r\\n chapter = self.store.create_item(\\r\\n # don't use course_location as it may not be the repr\\r\\n course.location, category, location=self.import_chapter_location, block_id=block_id\\r\\n )\\r\\n if isinstance(course.id, CourseLocator):\\r\\n self.course_locations[self.MONGO_COURSEID] = course.location.version_agnostic()\\r\\n self.import_chapter_location = chapter.location.version_agnostic()\\r\\n else:\\r\\n self.assertEqual(course.id, course_key)\\r\\n self.assertEqual(chapter.location, self.import_chapter_location)\",\n \"def generate_tex(title, author, txt, indices=None):\\n with open('head', 'r') as head,\\\\\\n open('stegano.tex', 'w') as file_tex:\\n\\n # First lines are\\n # - Title\\n # - Author\\n\\n file_tex.write(\\\"\\\".join(head.readlines())) # Write heading\\n file_tex.write(\\\"\\\\\\\\title{{{}}}\\\\n\\\".format(title))\\n file_tex.write(\\\"\\\\\\\\author{{{}}}\\\\n\\\".format(author))\\n file_tex.write(\\\"\\\\\\\\date{{}}\\\\n\\\")\\n\\n file_tex.write(\\\"\\\\\\\\begin{document}\\\\n\\\\\\\\maketitle\\\\n\\\")\\n\\n # Write char by char, adding decoration when indices is reach\\n i = 0\\n while i < len(txt):\\n if i in indices:\\n file_tex.write(secret_char(txt[i]))\\n else:\\n file_tex.write(non_secret_char(txt[i]))\\n i += 1\\n\\n file_tex.write(\\\"\\\\\\\\end{document}\\\")\",\n \"def available_courses(self):\\r\\n def _get_course_name(el):\\r\\n # The first component in the link text is the course number\\r\\n _, course_name = el.text.split(' ', 1)\\r\\n return course_name\\r\\n\\r\\n return self.q(css='section.info > hgroup > h3 > a').map(_get_course_name).results\",\n \"def get_courses(self, *args):\\n courses = []\\n user = self.context['user']\\n modules = user.profile.purchased_modules.all()\\n for module in modules:\\n course_id = self.course_in_courses(module.course.mnemo, courses)\\n if course_id:\\n courses[course_id[0]]['modules'].append({'mnemo': module.mnemo})\\n else:\\n courses.append({\\n 'mnemo': module.course.mnemo,\\n 'modules': [{'mnemo': module.mnemo}]\\n })\\n return courses\",\n \"def list_courses_subjects(self, all=False):\\n q = {'facet': 'true',\\n 'facet.field': 'course_subject',\\n 'facet.mincount': '1',\\n 'facet.sort': 'index', # Sort alphabetically\\n 'rows': '0', # we don't need any actual document\\n }\\n if all:\\n q['q'] = '*:*'\\n else:\\n q['q'] = 'NOT presentation_start:[* TO NOW]'\\n results = searcher.search(q, start=0, count=1000) # Do not paginate\\n subjects = subjects_facet_to_subjects_domain(results)\\n return subjects\",\n \"def test_citations_and_toc(self):\\n settings = get_settings(\\n PANDOC_EXTENSIONS=PANDOC_EXTENSIONS + [\\\"+citations\\\"],\\n PANDOC_ARGS=PANDOC_ARGS\\n + [\\n \\\"--toc\\\",\\n \\\"-C\\\",\\n \\\"--csl=https://www.zotero.org/styles/ieee-with-url\\\",\\n \\\"--metadata=link-citations:false\\\",\\n \\\"--metadata=reference-section-title:References\\\",\\n ],\\n FORMATTED_FIELDS=FORMATTED_FIELDS,\\n )\\n\\n pandoc_reader = PandocReader(settings)\\n source_path = os.path.join(\\n TEST_CONTENT_PATH, \\\"valid_content_with_citation.md\\\"\\n )\\n output, metadata = pandoc_reader.read(source_path)\\n self.maxDiff = None\\n\\n self.assertEqual(\\n (\\n '<h2 id=\\\"string-theory\\\">String Theory</h2>\\\\n'\\n \\\"<p>But this foundational principle of science has\\\"\\n \\\" now been called into question by\\\"\\n ' <a href=\\\"https://www.britannica.com/science/'\\n 'string-theory\\\">String Theory</a>,'\\n \\\" which is a relative newcomer to theoretical physics, but one\\\"\\n \\\" that has captured the common imagination, judging by\\\"\\n \\\" the popular explanations that abound on the Web\\\"\\n ' <span class=\\\"citation\\\" data-cites=\\\"mann2019 wood2019'\\n ' jones2020\\\">[1]–[3]</span>.'\\n \\\" And whether string theory is or is not science, Popper\\\"\\n \\\" notwithstanding, is an issue that is still up for debate\\\"\\n \\\" <span\\\"\\n ' class=\\\"citation\\\" data-cites=\\\"siegel2015 castelvecchi2016'\\n ' alves2017 francis2019\\\">[4]–[7]</span>.</p>\\\\n'\\n '<h1 class=\\\"unnumbered\\\" id=\\\"bibliography\\\">References</h1>\\\\n'\\n '<div id=\\\"refs\\\" class=\\\"references csl-bib-body\\\"'\\n ' role=\\\"doc-bibliography\\\">\\\\n'\\n '<div id=\\\"ref-mann2019\\\" class=\\\"csl-entry\\\"'\\n ' role=\\\"doc-biblioentry\\\">\\\\n'\\n '<div class=\\\"csl-left-margin\\\">[1]'\\n ' </div><div class=\\\"csl-right-inline\\\">A. Mann,'\\n \\\" <span>“<span>What Is String Theory?</span>”</span>\\\"\\n \\\" 20-Mar-2019. [Online].\\\"\\n ' Available: <a href=\\\"https://www.livescience.com/'\\n '65033-what-is-string-theory.html\\\">'\\n \\\"https://www.livescience.com/\\\"\\n \\\"65033-what-is-string-theory.html</a>.\\\"\\n \\\" [Accessed: 12-Nov-2020]</div>\\\\n\\\"\\n \\\"</div>\\\\n\\\"\\n '<div id=\\\"ref-wood2019\\\" class=\\\"csl-entry\\\"'\\n ' role=\\\"doc-biblioentry\\\">\\\\n'\\n '<div class=\\\"csl-left-margin\\\">[2] </div>'\\n '<div class=\\\"csl-right-inline\\\">'\\n \\\"C. Wood, <span>“<span>What Is String Theory?</span>.\\\"\\n \\\" Reference article:\\\"\\n \\\" A simplified explanation and brief history of string\\\"\\n \\\" theory,”</span> 11-Jul-2019.\\\"\\n ' [Online]. Available: <a href=\\\"https://www.space.com/'\\n '17594-string-theory.html\\\">'\\n \\\"https://www.space.com/17594-string-theory.html</a>.\\\"\\n \\\" [Accessed: 12-Nov-2020]</div>\\\\n\\\"\\n \\\"</div>\\\\n\\\"\\n '<div id=\\\"ref-jones2020\\\" class=\\\"csl-entry\\\"'\\n ' role=\\\"doc-biblioentry\\\">\\\\n'\\n '<div class=\\\"csl-left-margin\\\">[3]'\\n ' </div><div class=\\\"csl-right-inline\\\">'\\n 'A. Z. Jones, <span>“<span class=\\\"nocase\\\">The Basics of String'\\n \\\" Theory</span>,”</span> 02-Mar-2019. [Online]. Available:\\\"\\n ' <a href=\\\"https://www.thoughtco.com/'\\n 'what-is-string-theory-2699363\\\">'\\n \\\"https://www.thoughtco.com/what-is-string-theory-2699363</a>.\\\"\\n \\\" [Accessed: 12-Nov-2020]</div>\\\\n\\\"\\n \\\"</div>\\\\n\\\"\\n '<div id=\\\"ref-siegel2015\\\" class=\\\"csl-entry\\\"'\\n ' role=\\\"doc-biblioentry\\\">\\\\n'\\n '<div class=\\\"csl-left-margin\\\">[4]'\\n ' </div><div class=\\\"csl-right-inline\\\">'\\n \\\"E. Siegel, <span>“<span>Why String Theory Is Not A Scientific\\\"\\n \\\" Theory</span>,”</span> 23-Dec-2015. [Online]. Available:\\\"\\n \\\" <a\\\"\\n ' href=\\\"https://www.forbes.com/sites/'\\n \\\"startswithabang/2015/12/23/\\\"\\n 'why-string-theory-is-not-science/\\\">https://www.forbes.com/'\\n \\\"sites/startswithabang/2015/12/23/\\\"\\n \\\"why-string-theory-is-not-science/</a>.\\\"\\n \\\" [Accessed: 12-Nov-2020]</div>\\\\n\\\"\\n \\\"</div>\\\\n\\\"\\n '<div id=\\\"ref-castelvecchi2016\\\" class=\\\"csl-entry\\\"'\\n ' role=\\\"doc-biblioentry\\\">\\\\n'\\n '<div class=\\\"csl-left-margin\\\">[5]'\\n ' </div><div class=\\\"csl-right-inline\\\">'\\n 'D. Castelvecchi, <span>“<span class=\\\"nocase\\\">'\\n \\\"Feuding physicists turn\\\"\\n \\\" to philosophy for help</span>. String theory is at the\\\"\\n \\\" heart of a debate over the integrity of the scientific\\\"\\n \\\" method itself,”</span> 05-Jan-2016. [Online]. Available:\\\"\\n ' <a href=\\\"https://www.nature.com/news/'\\n 'feuding-physicists-turn-to-philosophy-for-help-1.19076\\\">'\\n \\\"https://www.nature.com/news/\\\"\\n \\\"feuding-physicists-turn-to-philosophy-for-help-1.19076</a>.\\\"\\n \\\" [Accessed: 12-Nov-2020]</div>\\\\n\\\"\\n \\\"</div>\\\\n\\\"\\n '<div id=\\\"ref-alves2017\\\" class=\\\"csl-entry\\\"'\\n ' role=\\\"doc-biblioentry\\\">\\\\n'\\n '<div class=\\\"csl-left-margin\\\">[6] </div>'\\n '<div class=\\\"csl-right-inline\\\">'\\n 'R. A. Batista and J. Primack, <span>“<span class=\\\"nocase\\\">'\\n \\\"Is String theory falsifiable?</span>. Can a theory that isn’t\\\"\\n \\\" completely testable still be useful to physics?”</span>\\\"\\n \\\" [Online].\\\"\\n ' Available: <a href=\\\"https://metafact.io/factchecks/'\\n '30-is-string-theory-falsifiable\\\">'\\n \\\"https://metafact.io/factchecks/\\\"\\n \\\"30-is-string-theory-falsifiable</a>.\\\"\\n \\\" [Accessed: 12-Nov-2020]</div>\\\\n\\\"\\n \\\"</div>\\\\n\\\"\\n '<div id=\\\"ref-francis2019\\\" class=\\\"csl-entry\\\"'\\n ' role=\\\"doc-biblioentry\\\">\\\\n'\\n '<div class=\\\"csl-left-margin\\\">[7]'\\n ' </div><div class=\\\"csl-right-inline\\\">'\\n 'M. R. Francis, <span>“<span class=\\\"nocase\\\">Falsifiability and'\\n \\\" physics</span>. Can a theory that isn’t completely testable\\\"\\n \\\" still be useful to physics?”</span> 23-Apr-2019.\\\"\\n \\\" [Online]. Available:\\\"\\n ' <a href=\\\"https://www.scientificamerican.com/'\\n 'article/is-string-theory-science/\\\">'\\n \\\"https://www.scientificamerican.com/article/is-\\\"\\n \\\"string-theory-science/</a>. [Accessed: 12-Nov-2020]</div>\\\\n\\\"\\n \\\"</div>\\\\n\\\"\\n \\\"</div>\\\\n\\\"\\n ),\\n output,\\n )\\n\\n self.assertEqual(\\\"Valid Content With Citation\\\", str(metadata[\\\"title\\\"]))\\n self.assertEqual(\\\"My Author\\\", str(metadata[\\\"author\\\"]))\\n self.assertEqual(\\\"2020-10-16 00:00:00\\\", str(metadata[\\\"date\\\"]))\\n self.assertEqual(\\n (\\n '<nav class=\\\"toc\\\" role=\\\"doc-toc\\\">\\\\n'\\n \\\"<ul>\\\\n\\\"\\n '<li><a href=\\\"#string-theory\\\">String Theory</a></li>\\\\n'\\n '<li><a href=\\\"#bibliography\\\">References</a></li>\\\\n'\\n \\\"</ul>\\\\n</nav>\\\\n\\\"\\n ),\\n str(metadata[\\\"toc\\\"]),\\n )\\n self.assertEqual(\\n (\\n \\\"<p>But this foundational principle of science has now been\\\"\\n \\\" called into question by\\\"\\n ' <a href=\\\"https://www.britannica.com/science/string-theory\\\">'\\n \\\"String Theory</a>.</p>\\\\n\\\"\\n ),\\n str(metadata[\\\"summary\\\"]),\\n )\",\n \"def print_catalog(self):\\n # first download the json for the catalog\\n self.download_json()\\n\\n # open the saved json file and load the json\\n with self.file.open(\\\"r\\\") as catalog_file:\\n pages = json.load(catalog_file)\\n\\n # the catalog json is just a list of pages\\n # so we begin by iterating through the pages\\n for page_num in range(len(pages)):\\n # get each page\\n page = pages[page_num]\\n\\n # get the threads on each page\\n threads = page[\\\"threads\\\"]\\n\\n # print the page heading\\n print(\\\"*** PAGE \\\", page_num + 1, \\\"***\\\")\\n\\n # iterate through the threads on each page\\n for thread_num in range(len(threads)):\\n # get each thread\\n thread = threads[thread_num]\\n\\n # print the thread number\\n num = thread[\\\"no\\\"]\\n print(\\\"---\\\", \\\"Thread:\\\", num, \\\"---\\\")\\n\\n # not all threads have a subject or comment\\n try:\\n subject = thread[\\\"sub\\\"]\\n comment = thread[\\\"com\\\"]\\n\\n print(\\\"Sub:\\\", subject)\\n print(\\\"Comment:\\\", comment)\\n except KeyError:\\n print(\\\"N/A\\\")\",\n \"def read_data(term):\\n course_url = '/admweb/!SWKSECX.main?term='+ term + '&title=&course=&crn=&coll=&dept=&subj='\\n connection = httplib.HTTPSConnection('courses.rice.edu', 443)\\n connection.connect()\\n connection.request(\\n 'GET',\\n course_url\\n )\\n courses_xml = connection.getresponse()\\n return courses_xml\",\n \"def create_course(self, modules_count, module_store, topics):\\n course = CourseFactory.create(\\n org=\\\"a\\\",\\n course=\\\"b\\\",\\n run=\\\"c\\\",\\n start=datetime.now(UTC),\\n default_store=module_store,\\n discussion_topics=topics\\n )\\n CourseEnrollmentFactory.create(user=self.user, course_id=course.id)\\n course_url = reverse(\\\"course_topics\\\", kwargs={\\\"course_id\\\": str(course.id)})\\n # add some discussion xblocks\\n for i in range(modules_count):\\n ItemFactory.create(\\n parent_location=course.location,\\n category='discussion',\\n discussion_id=f'id_module_{i}',\\n discussion_category=f'Category {i}',\\n discussion_target=f'Discussion {i}',\\n publish_item=False,\\n )\\n return course_url\",\n \"def load_courses(cls, config: DefaultConfig):\\n lecture_halls = cls.load_course_resource(config.CoursePaths.LECTURER_HALLS.value, LectureHall.create)\\n course_units = cls.load_course_resource(config.CoursePaths.COURSE_UNITS.value, CourseUnit.create(lecture_halls))\\n lecturers = cls.load_course_resource(config.CoursePaths.LECTURER.value, Lecturer.create(course_units))\\n teaching_assistants = cls.load_course_resource(\\n config.CoursePaths.TEACHING_ASSISTANTS.value, Lecturer.create(course_units))\\n\\n return cls(\\n course_units=course_units,\\n lecturers=lecturers,\\n teaching_assistants=teaching_assistants\\n )\",\n \"def get_courses():\\n courses = []\\n courses_recs = Course._file.read_db()\\n for course in courses_recs[\\\"courses\\\"]:\\n courses.append(Course(**course))\\n return courses\",\n \"def initialize(self, cwd: str, option: str = \\\"\\\", **kwargs):\\n # TODO: make the times when the courses start prettier\\n path = os.path.join(cwd, option)\\n\\n def recursive_dictionary_clear(d):\\n \\\"\\\"\\\"Recursively clear dictionary keys with empty values.\\\"\\\"\\\"\\n for key in list(d):\\n if isinstance(d[key], dict):\\n recursive_dictionary_clear(d[key])\\n\\n if d[key] == \\\"\\\" or d[key] == {}:\\n del d[key]\\n\\n def format_teacher(teacher):\\n \\\"\\\"\\\"An ungly, hard-coded way to format the names of the teachers. Couldn't\\n find something more solid, so this will have to do for now.\\\"\\\"\\\"\\n l = split(\\n \\\"|\\\".join(\\n [\\n \\\"doc\\\\.\\\",\\n \\\"Ing\\\\.\\\",\\n \\\"Ph.D\\\\.\\\",\\n \\\"CSc\\\\.\\\",\\n \\\"PhDr\\\\.\\\",\\n \\\"DrSc\\\\.\\\",\\n \\\"Mgr\\\\.\\\",\\n \\\"RNDr\\\\.\\\",\\n \\\"M\\\\.Sc\\\\.\\\",\\n \\\"Bc\\\\.\\\",\\n \\\"Dr\\\\.\\\",\\n \\\"D\\\\.Phil\\\\.\\\",\\n \\\"Ph\\\\.\\\",\\n \\\"r\\\\.\\\",\\n ]\\n ),\\n teacher,\\n )\\n l = [i.strip().strip(\\\",\\\").strip() for i in l]\\n l = [i for i in l if i not in (\\\",\\\", \\\"\\\")]\\n return \\\" / \\\".join([\\\" \\\".join(list(reversed(i.split()))) for i in l])\\n\\n if option == \\\"\\\":\\n exit_with_error(\\\"No CSV to initialize from specified.\\\")\\n\\n if not os.path.exists(path):\\n exit_with_error(\\\"Specified file doesn't exist.\\\")\\n\\n with open(path, \\\"rb\\\") as f:\\n # SIS uses cp1250 :(\\n contents = f.read().decode(\\\"cp1250\\\")\\n\\n course_count = 0\\n for l in list(csv.reader(contents.splitlines(), delimiter=\\\";\\\"))[1:]:\\n uid, _, code, name, day, start, self, dur, _, _, _, weeks, teacher = l\\n\\n teacher = format_teacher(teacher)\\n\\n # ATTENTION: watch out for 'and's here\\n # in order for the code not to crash, they do the following:\\n # '' and x -> ''\\n # 'something' and x -> x\\n out = {\\n \\\"teacher\\\": {\\\"name\\\": teacher},\\n \\\"classroom\\\": {\\\"number\\\": self},\\n \\\"time\\\": {\\n \\\"day\\\": day and WD_EN[int(day) - 1].capitalize(),\\n \\\"start\\\": start and int(start), # TODO HH:MM formatting\\n \\\"end\\\": start and int(start) + int(dur), # TODO HH:MM formatting\\n \\\"weeks\\\": \\\"even\\\"\\n if weeks == \\\"sude\\\"\\n else \\\"odd\\\"\\n if weeks == \\\"liche\\\"\\n else \\\"\\\",\\n },\\n \\\"code\\\": code,\\n }\\n\\n # don't print empty dictionary parts\\n recursive_dictionary_clear(out)\\n\\n # create a basic abbreviation from taking first letters of each word\\n abbreviation = \\\"\\\".join(\\n [\\n word[0].upper()\\n if word[0].isalpha() or word[0].isdigit()\\n else \\\"\\\"\\n for word in name.split()\\n ]\\n )\\n\\n # create the directory with the name of the course\\n course_dir = os.path.join(cwd, f\\\"{name} ({abbreviation})\\\")\\n if not os.path.exists(course_dir):\\n os.mkdir(course_dir)\\n\\n # lecture / lab\\n # based on the ID of the SIS ticket - labs end with x** and lectures with p*\\n course_type = \\\"přednáška\\\" if uid[:-1].endswith(\\\"p\\\") else \\\"cvičení\\\"\\n\\n if not os.path.exists(os.path.join(course_dir, course_type)):\\n os.mkdir(os.path.join(course_dir, course_type))\\n\\n with open(os.path.join(course_dir, course_type, \\\"info.yaml\\\"), \\\"w\\\") as f:\\n yaml.dump(out, stream=f, allow_unicode=True)\\n\\n course_count += 1\\n\\n exit_with_success(f\\\"New semester with {course_count} courses initialized.\\\")\",\n \"def process_course(xml_course):\\n\\n parse_course = parse_get_course(xml_course)\\n if not parse_course: # Does not already exist, create\\n parse_course = parse_create_course(xml_course)\\n else:\\n # TODO: Update existing attributes of the course here and upload the changes\\n None\\n parse_course_id = upload_course(parse_course)\\n\\n print (\\\"Processed Course: {0} {1}\\\"\\n .format(parse_course[\\\"subject\\\"], parse_course[\\\"courseNumber\\\"]))\\n\\n # TODO: Implement the functions for this stuff\\n parse_section = parse_get_section(xml_course)\\n if not parse_section: # Does not already exist, create\\n parse_section = parse_create_section(xml_course)\\n else:\\n # TODO: Update existing attributes of the section here and upload the changes\\n None\\n parse_section_id = upload_section(parse_section)\\n\\n put_child(parse_course_id, parse_section_id)\",\n \"def get_courseware_with_tabs(course_id):\\r\\n\\r\\n course = get_course_by_id(course_id)\\r\\n chapters = [chapter for chapter in course.get_children() if not chapter.hide_from_toc]\\r\\n courseware = [{'chapter_name': c.display_name_with_default,\\r\\n 'sections': [{'section_name': s.display_name_with_default,\\r\\n 'clickable_tab_count': len(s.get_children()) if (type(s) == seq_module.SequenceDescriptor) else 0,\\r\\n 'tabs': [{'children_count': len(t.get_children()) if (type(t) == vertical_module.VerticalDescriptor) else 0,\\r\\n 'class': t.__class__.__name__}\\r\\n for t in s.get_children()]}\\r\\n for s in c.get_children() if not s.hide_from_toc]}\\r\\n for c in chapters]\\r\\n\\r\\n return courseware\",\n \"def get_courses(self, depth=0):\\r\\n return self.courses.values()\",\n \"def getUserCoursesList(self, chat_id):\\n\\t\\tcommand = \\\"SELECT ID, name, description FROM courses WHERE author_id=?;\\\"\\n\\t\\tparams = (chat_id,)\\n\\n\\t\\tdata = self._run_command(command, params)\\n\\n\\t\\tif not data:\\n\\t\\t\\treturn None\\n\\n\\t\\tresult = []\\n\\t\\tfor i in data:\\n\\t\\t\\tresult.append({\\\"ID\\\": i[0], \\n\\t\\t\\t\\t\\\"name\\\": i[1],\\n\\t\\t\\t\\t\\\"description\\\": i[2] if i[2] else \\\"\\\",\\n\\t\\t\\t\\t})\\n\\n\\t\\treturn result\",\n \"def build_corpus(self):\\n # #############################\\n\\n doc = metapy.index.Document()\\n tok = metapy.analyzers.ICUTokenizer(suppress_tags=True)\\n tok = metapy.analyzers.LowercaseFilter(tok)\\n tok = metapy.analyzers.LengthFilter(tok, min=3, max=1000)\\n tok = metapy.analyzers.Porter2Filter(tok)\\n tok = metapy.analyzers.ListFilter(tok, \\\"lemur-stopwords.txt\\\", metapy.analyzers.ListFilter.Type.Reject)\\n collection = -1\\n\\n with open(self.documents_path) as file:\\n for num, line in enumerate(file):\\n l = line.strip()\\n c = int(l[0])\\n l = l[2:]\\n doc.content(l)\\n tok.set_content(doc.content())\\n if c != collection:\\n self.documents.append([])\\n collection = c\\n self.documents[c].append([token for token in tok])\\n self.number_of_collections = len(self.documents)\\n self.number_of_documents = len(self.documents[0])\\n #print(self.number_of_collections)\\n #print(self.number_of_documents)\\n #print(self.documents[0])\",\n \"def test_xml_get_courses(self):\\r\\n self.initdb('direct')\\r\\n courses = self.store.modulestores['xml'].get_courses()\\r\\n self.assertEqual(len(courses), 2)\\r\\n course_ids = [course.id for course in courses]\\r\\n self.assertIn(self.course_locations[self.XML_COURSEID1].course_key, course_ids)\\r\\n self.assertIn(self.course_locations[self.XML_COURSEID2].course_key, course_ids)\\r\\n # this course is in the directory from which we loaded courses but not in the map\\r\\n self.assertNotIn(\\\"edX/toy/TT_2012_Fall\\\", course_ids)\",\n \"def get_courses_metadata(self):\\n return Metadata(**settings.METADATA['course_ids'])\",\n \"def build_course_dictionary(title_result_set, desc_result_set) -> Dict[str, List[str]]:\\n\\n course_dictionary = {} # placeholder dictionary\\n\\n for (tagged_title, tagged_description) in zip(title_result_set, desc_result_set): # iterate through multiple result sets\\n full_title_desc_list = {}\\n full_title_desc_list = [str(tagged_title.text)] + str(tagged_description.text).strip().splitlines() # remove trailing whitespace, then get list of lines\\n course_dictionary[str(tagged_title.text)[:8]] = full_title_desc_list\\n\\n return course_dictionary\",\n \"def show_courses_and_profs(request):\\r\\n\\tcourses = course_views.all_courses(request)\\r\\n\\treturn render(request, 'templates/browse.html', {'courses': courses})\",\n \"def Generar_Claves():\\n salida=Keypp()\\n savekey(salida)\\n savecomp(salida)\"\n]","isConversational":false},"negative_scores":{"kind":"list like","value":["0.67228544","0.64569527","0.5929274","0.5925268","0.5916192","0.564219","0.5635638","0.5593578","0.55859154","0.5570419","0.5569982","0.55561346","0.55053335","0.5473243","0.5456045","0.54416245","0.5440941","0.5430982","0.54077095","0.5400782","0.53955734","0.5390833","0.5386149","0.53727466","0.53718007","0.53714395","0.5303233","0.52861136","0.5268244","0.52542925","0.5230265","0.52230114","0.52177894","0.5211493","0.52084595","0.5184168","0.5164817","0.5159505","0.5144329","0.5137261","0.5134224","0.5116418","0.5109806","0.51087844","0.5107539","0.5107052","0.51051545","0.51045746","0.5101843","0.5096655","0.5095641","0.50857466","0.50842285","0.5082668","0.507239","0.5072296","0.5067598","0.50628906","0.5061387","0.5059249","0.50570387","0.5053261","0.50311184","0.5025785","0.50241065","0.5022448","0.50173146","0.50089854","0.5007971","0.50015527","0.5001203","0.5000912","0.49984762","0.49975094","0.49959284","0.4994409","0.49915257","0.4984035","0.49804136","0.4977103","0.49751338","0.49727854","0.4972189","0.4971014","0.49630466","0.49629572","0.4935845","0.4933986","0.4931819","0.49307337","0.49274898","0.49269035","0.4923285","0.49221388","0.49207214","0.49121338","0.49092314","0.4909201","0.49037206","0.49022186"],"string":"[\n \"0.67228544\",\n \"0.64569527\",\n \"0.5929274\",\n \"0.5925268\",\n \"0.5916192\",\n \"0.564219\",\n \"0.5635638\",\n \"0.5593578\",\n \"0.55859154\",\n \"0.5570419\",\n \"0.5569982\",\n \"0.55561346\",\n \"0.55053335\",\n \"0.5473243\",\n \"0.5456045\",\n \"0.54416245\",\n \"0.5440941\",\n \"0.5430982\",\n \"0.54077095\",\n \"0.5400782\",\n \"0.53955734\",\n \"0.5390833\",\n \"0.5386149\",\n \"0.53727466\",\n \"0.53718007\",\n \"0.53714395\",\n \"0.5303233\",\n \"0.52861136\",\n \"0.5268244\",\n \"0.52542925\",\n \"0.5230265\",\n \"0.52230114\",\n \"0.52177894\",\n \"0.5211493\",\n \"0.52084595\",\n \"0.5184168\",\n \"0.5164817\",\n \"0.5159505\",\n \"0.5144329\",\n \"0.5137261\",\n \"0.5134224\",\n \"0.5116418\",\n \"0.5109806\",\n \"0.51087844\",\n \"0.5107539\",\n \"0.5107052\",\n \"0.51051545\",\n \"0.51045746\",\n \"0.5101843\",\n \"0.5096655\",\n \"0.5095641\",\n \"0.50857466\",\n \"0.50842285\",\n \"0.5082668\",\n \"0.507239\",\n \"0.5072296\",\n \"0.5067598\",\n \"0.50628906\",\n \"0.5061387\",\n \"0.5059249\",\n \"0.50570387\",\n \"0.5053261\",\n \"0.50311184\",\n \"0.5025785\",\n \"0.50241065\",\n \"0.5022448\",\n \"0.50173146\",\n \"0.50089854\",\n \"0.5007971\",\n \"0.50015527\",\n \"0.5001203\",\n \"0.5000912\",\n \"0.49984762\",\n \"0.49975094\",\n \"0.49959284\",\n \"0.4994409\",\n \"0.49915257\",\n \"0.4984035\",\n \"0.49804136\",\n \"0.4977103\",\n \"0.49751338\",\n \"0.49727854\",\n \"0.4972189\",\n \"0.4971014\",\n \"0.49630466\",\n \"0.49629572\",\n \"0.4935845\",\n \"0.4933986\",\n \"0.4931819\",\n \"0.49307337\",\n \"0.49274898\",\n \"0.49269035\",\n \"0.4923285\",\n \"0.49221388\",\n \"0.49207214\",\n \"0.49121338\",\n \"0.49092314\",\n \"0.4909201\",\n \"0.49037206\",\n \"0.49022186\"\n]","isConversational":false},"document_score":{"kind":"string","value":"0.5822862"},"document_rank":{"kind":"string","value":"5"}}},{"rowIdx":22668,"cells":{"query":{"kind":"string","value":"Provides the special treatment that the info section requires"},"document":{"kind":"string","value":"def latex_info(info):\n return '\\\\textbf{' + info['heading'] + '} ' + info['value'] + ' \\\\break\\n'"},"metadata":{"kind":"string","value":"{\n \"objective\": {\n \"self\": [],\n \"paired\": [],\n \"triplet\": [\n [\n \"query\",\n \"document\",\n \"negatives\"\n ]\n ]\n }\n}"},"negatives":{"kind":"list like","value":["def get_info(self, info):\r\n pass","def ExtraInfo(self) -> object:","def manage_info():","def _get_information(self):\n pass","def process_info(self, info):\n return info","def info() -> None:","def info(self):","def info(self):","def info(self, *args, **kwargs):","def info(self) -> dict:","def getInfo():","def info(): # noqa: E501\n return 'do some magic!'","def get_info(self):\n return \"TODO !\"","def extract_info(self, docstring):\n pass","def information(self, params):\n # TODO: If the docstring is right, then why not just implement this?\n raise NotImplementedError # pragma: no cover","def add_info(transmogrifier, category, section, info):\n try:\n a = transmogrifier._collected_info\n except AttributeError:\n a = []\n transmogrifier._collected_info = a\n a.append({'category': category,\n 'section': section,\n 'info': info,\n })","def getInfo(self,item):\r\n return ''","def get_info(self):\n pass","def get_info(self):\n pass","def process_app_info(self):\n pass","def get_info(self) -> Optional[Dict[str, Any]]:","def get_info(self):\n return None","def getInfo(self):\n return {\"title\": \"Not Implemented\",\n \"category\": \"contextual\", \"priority\": 2}\n raise NotImplementedError(\"Shouldn't be called\")","def ui_additional_info(self) -> Optional[str]:\n return None","def register_info(self, info):\n\n self.info = info\n\n # Add description from module __doc__ if missing\n self.info['description'] = (\n self.info.get('description')\n if self.info.get('description')\n else self.__doc__.strip()\n )\n if not self.info['description']:\n raise DevException(messages.module.error_module_missing_description)","def from_info(self, info: Dict[str, Any]) -> None:","def info(self) -> _InfoType:\n return {}","def additional_info(self) -> Optional[Mapping[str, str]]:\n return pulumi.get(self, \"additional_info\")","def info() -> Dict[str, Any]:","def setInfo(*args):","def info():\n return r\"\"\"None\"\"\"","def define_info_dict():\n\n d = {\n \"PRED\": {\n \"COLUMN\": [\"predicted_class\"],\n \"Number\": \"1\",\n \"Type\": \"String\",\n \"Description\": \"Predicted class: somatic, germline, artifact\",\n },\n \"PROB\": {\n \"COLUMN\": [\"prob_s\", \"prob_g\", \"prob_a\"],\n \"Number\": \"3\",\n \"Type\": \"Float\",\n \"Description\": \"Prediction probability of \"\n \"being somatic, germline, artifact in this order\",\n },\n \"SNP\": {\n \"COLUMN\": [\"is_on_db\"],\n \"Number\": \"0\",\n \"Type\": \"Flag\",\n \"Description\": \"Present on SNP database (modified dbSNP/gnomAD (default) or user-provided database)\",\n },\n \"ANNO\": {\n \"COLUMN\": [\"annotation\"],\n \"Number\": \".\",\n \"Type\": \"String\",\n \"Description\": \"Indel annotation formatted as \"\n \"GeneSymbol|RefSeqAccession|CodonPos|IndelEffect\"\n \"Delimited by comma for multiple isoforms\",\n },\n \"COSMIC_CNT\": {\n \"COLUMN\": [\"cosmic_cnt\"],\n \"Number\": \"1\",\n \"Type\": \"Integer\",\n \"Description\": \"COSMIC count in v89\",\n },\n \"MAXMAF\": {\n \"COLUMN\": [\"max_maf\"],\n \"Number\": \"1\",\n \"Type\": \"Float\",\n \"Description\": \"Maximum minor allele frequency (MAF) \"\n \"reported in dbSNP, ClinVar and gnomAD non-cancer population\",\n },\n \"COMMON\": {\n \"COLUMN\": [\"is_common\"],\n \"Number\": \"0\",\n \"Type\": \"Flag\",\n \"Description\": \"Common in dbSNP or MAXMAF > 0.01\",\n },\n \"CLIN\": {\n \"COLUMN\": [\"clin_info\"],\n \"Number\": \"1\",\n \"Type\": \"String\",\n \"Description\": \"ClinVar annotation formatted as ClinicalSignificance|Condition\",\n },\n \"ICP\": {\n \"COLUMN\": [\"indel_complexity\"],\n \"Number\": \"1\",\n \"Type\": \"Integer\",\n \"Description\": \"Indel complexity: mismatches around the indel measured by edit distance\",\n },\n \"DSM\": {\n \"COLUMN\": [\"dissimilarity\"],\n \"Number\": \"1\",\n \"Type\": \"Float\",\n \"Description\": \"Dissimilarity: edit distance between indel and flanking sequences\",\n },\n \"ISZ\": {\n \"COLUMN\": [\"indel_size\"],\n \"Number\": \"1\",\n \"Type\": \"Integer\",\n \"Description\": \"Indel size\",\n },\n \"REP\": {\n \"COLUMN\": [\"repeat\"],\n \"Number\": \"1\",\n \"Type\": \"Integer\",\n \"Description\": \"Repeat: count of the indel-sequence repeats in flanking region\",\n },\n \"UQM\": {\n \"COLUMN\": [\"is_uniq_mapped\"],\n \"Number\": \"0\",\n \"Type\": \"Flag\",\n \"Description\": \"Supported by uniquely mapped reads\",\n },\n \"NEB\": {\n \"COLUMN\": [\"is_near_boundary\"],\n \"Number\": \"0\",\n \"Type\": \"Flag\",\n \"Description\": \"Near exon boundary\",\n },\n \"EQX\": {\n \"COLUMN\": [\"equivalence_exists\"],\n \"Number\": \"0\",\n \"Type\": \"Flag\",\n \"Description\": \"Equivalent alignments exist for the indel\",\n },\n \"BID\": {\n \"COLUMN\": [\"is_bidirectional\"],\n \"Number\": \"0\",\n \"Type\": \"Flag\",\n \"Description\": \"Supported by forward and reverse reads\",\n },\n \"MTA\": {\n \"COLUMN\": [\"is_multiallelic\"],\n \"Number\": \"0\",\n \"Type\": \"Flag\",\n \"Description\": \"Multialleleic\",\n },\n \"FRM\": {\n \"COLUMN\": [\"is_inframe\"],\n \"Number\": \"0\",\n \"Type\": \"Flag\",\n \"Description\": \"In-frame indel\",\n },\n \"SPL\": {\n \"COLUMN\": [\"is_splice\"],\n \"Number\": \"0\",\n \"Type\": \"Flag\",\n \"Description\": \"Located in splice region\",\n },\n \"TRN\": {\n \"COLUMN\": [\"is_truncating\"],\n \"Number\": \"0\",\n \"Type\": \"Flag\",\n \"Description\": \"Truncating indel\",\n },\n \"CDD\": {\n \"COLUMN\": [\"is_in_cdd\"],\n \"Number\": \"0\",\n \"Type\": \"Flag\",\n \"Description\": \"Located in conserved domain\",\n },\n \"LOC\": {\n \"COLUMN\": [\"indel_location\"],\n \"Number\": \"1\",\n \"Type\": \"Float\",\n \"Description\": \"Relative indel location within the transcript coding region\",\n },\n \"NMD\": {\n \"COLUMN\": [\"is_nmd_insensitive\"],\n \"Number\": \"0\",\n \"Type\": \"Flag\",\n \"Description\": \"Insensitive to nonsense mediated decay\",\n },\n \"IPG\": {\n \"COLUMN\": [\"ipg\"],\n \"Number\": \"1\",\n \"Type\": \"Float\",\n \"Description\": \"Indels per gene\",\n },\n \"LEN\": {\n \"COLUMN\": [\"cds_length\"],\n \"Number\": \"1\",\n \"Type\": \"Float\",\n \"Description\": \"Coding sequence length. Median value if multiple isoforms exist\",\n },\n \"LC\": {\n \"COLUMN\": [\"lc\"],\n \"Number\": \"1\",\n \"Type\": \"Float\",\n \"Description\": \"Linguistic complexity: diversity of k-mers in flanking 50-bp region\",\n },\n \"LLC\": {\n \"COLUMN\": [\"local_lc\"],\n \"Number\": \"1\",\n \"Type\": \"Float\",\n \"Description\": \"Local linguistic complexity: diversity of k-mers in flanking 6-bp region\",\n },\n \"GC\": {\n \"COLUMN\": [\"gc\"],\n \"Number\": \"1\",\n \"Type\": \"Float\",\n \"Description\": \"GC-content in flanking 50-bp region\",\n },\n \"LGC\": {\n \"COLUMN\": [\"local_gc\"],\n \"Number\": \"1\",\n \"Type\": \"Float\",\n \"Description\": \"Local GC-content in flanking 6-bp region\",\n },\n \"SG\": {\n \"COLUMN\": [\"strength\"],\n \"Number\": \"1\",\n \"Type\": \"Float\",\n \"Description\": \"DNA bond strength of 2-mers in flanking 50-bp region\",\n },\n \"LSG\": {\n \"COLUMN\": [\"local_strength\"],\n \"Number\": \"1\",\n \"Type\": \"Float\",\n \"Description\": \"Local DNA bond strength of 2-mers in flanking 6-bp region\",\n },\n \"INS\": {\n \"COLUMN\": [\"is_ins\"],\n \"Number\": \"0\",\n \"Type\": \"Flag\",\n \"Description\": \"Insertion\",\n },\n \"ATI\": {\n \"COLUMN\": [\"is_at_ins\"],\n \"Number\": \"0\",\n \"Type\": \"Flag\",\n \"Description\": \"Single insertion of A or T\",\n },\n \"ATD\": {\n \"COLUMN\": [\"is_at_del\"],\n \"Number\": \"0\",\n \"Type\": \"Flag\",\n \"Description\": \"Single deletion of A or T\",\n },\n \"GCI\": {\n \"COLUMN\": [\"is_gc_ins\"],\n \"Number\": \"0\",\n \"Type\": \"Flag\",\n \"Description\": \"Single insertion of G or C\",\n },\n \"GCD\": {\n \"COLUMN\": [\"is_gc_del\"],\n \"Number\": \"0\",\n \"Type\": \"Flag\",\n \"Description\": \"Single deletion of G or C\",\n },\n \"ALTC\": {\n \"COLUMN\": [\"alt_count\"],\n \"Number\": \"1\",\n \"Type\": \"Integer\",\n \"Description\": \"Alt count: count of unique reads supporting ALT allele\",\n },\n \"REFC\": {\n \"COLUMN\": [\"ref_count\"],\n \"Number\": \"1\",\n \"Type\": \"Integer\",\n \"Description\": \"Ref count: count of unique reads supporting REF allele\",\n },\n \"RCF\": {\n \"COLUMN\": [\"reclassified\"],\n \"Number\": \"0\",\n \"Type\": \"Flag\",\n \"Description\": \"Reclassification applied\",\n },\n \"RQB\": {\n \"COLUMN\": [\"filtered\", \"rescued\"],\n \"Number\": \"1\",\n \"Type\": \"String\",\n \"Description\": \"Indel used to rescue this entry formatted as CHROM:POS:REF:ALT\",\n },\n }\n\n return d","def _add_info(self, infos: dict, info: dict, env_num: int) -> dict:\n for k in info.keys():\n if k not in infos:\n info_array, array_mask = self._init_info_arrays(type(info[k]))\n else:\n info_array, array_mask = infos[k], infos[f\"_{k}\"]\n\n info_array[env_num], array_mask[env_num] = info[k], True\n infos[k], infos[f\"_{k}\"] = info_array, array_mask\n return infos","def get_definition(self, info):\r\n pass","def info_from_entry(self, entry):\n info = super().info_from_entry(entry)\n return info","def get_info_string(self) -> str:\n return \"Not implemented\"","def return_info(self):\n\t\treturn self.info","def info(self):\n return self.info_text","def _add_details(self, info):\n for (key, val) in six.iteritems(info):\n if key == \"nodes\":\n val = [Node(parent=self, **nd) for nd in val]\n elif key == \"sessionPersistence\":\n val = val['persistenceType']\n elif key == \"cluster\":\n val = val['name']\n elif key == \"virtualIps\":\n key = \"virtual_ips\"\n val = [VirtualIP(parent=self, **vip) for vip in val]\n setattr(self, key, val)","def meta_info(environ, start_response, logger, handle):\n pass","def details(self):\n pass","def additionalInfo(request):\n # Test Comment\n assert isinstance(request, HttpRequest)\n return render(\n request,\n 'additionalInfo.html',\n context_instance=RequestContext(request, {})\n )","def info(self, msg, *args, **kwargs):\n pass","def get_infos(self):\n infos = dict()\n infos[\"dataset\"] = self.dataset_name\n infos[\"task\"] = \"separate_noisy\"\n infos[\"licenses\"] = [librispeech_license, tac_license]\n return infos","def get_details(self):","def _handle_info_response(self, resp, info, prev_info):\r\n if info.line_num != prev_info.line_num:\r\n return\r\n\r\n if resp['calltip']:\r\n info.editor.show_calltip('Arguments', resp['calltip'],\r\n signature=True,\r\n at_position=prev_info.position)\r\n\r\n if resp['name']:\r\n self.send_to_inspector.emit(\r\n resp['name'], resp['argspec'],\r\n resp['note'], resp['docstring'],\r\n not prev_info.auto)","def buildSimpleInfoDeclaration(self):\n return \"\"","def info(self):\r\n return self._get('info', {})","def missing_information(self, info, field):\n raise NoData","def get_info(transmogrifier, category=None, section=None, short=True):\n res = []\n try:\n rows = transmogrifier._collected_info\n except AttributeError:\n return res\n else:\n for dic in rows:\n if category is not None and dic['category'] != category:\n continue\n if section is not None and dic['section'] != section:\n continue\n if short:\n res.append(dic['info'])\n else:\n res.append(dic)\n return res","def do_info(self, args):\n if self.exploit is None:\n eprint(colorize('No exploit set; nothing to describe. Select an exploit with the \\'use\\' command',\n 'cyan'))\n else:\n eprint(colorize('\\n ' + self.exploit.DESCRIPTION + '\\n', 'green'))","def test_get_info(self):\n pass","def _make_information_storable( self, data ):\n\t\tpass","def do_info(self, line):\n args = filter(None, line.strip().split())\n if len(args) != 1 or args[0] not in self.router.app_order:\n self.help_info()\n else:\n self.router.print_path_help(args[0])","def _EpiInfo(self, info, path):\n\n epi_vals = {'tdim':self.hdr['tdim'], 'plane':self.hdr['plane'], \\\n 'SeriesNumber':self.hdr['subhdr']['SeriesNumber']}\n for key in self.epi_keys.keys():\n if self.epi_keys[key] != str(epi_vals[key]):\n# Return None, which will cause these data to be ignored.\n return None\n\n# Early versions of the EPIC software saved p-files for the setup epis.\n# Don't process these (or any epi with fewer than eight useable frames).\n if self.hdr['tdim'] < (8 + self.skip):\n return None\n\n info['slice_order'] = self.shdr.get('SliceOrder', 'altplus')\n if self.shdr['EffEchoSpacing'] is not None:\n info['echo_spacing'] = self.shdr['EffEchoSpacing']/1000.\n else:\n info['echo_spacing'] = 0.\n if info['data_filetype'] == 'dicom':\n# Entry is name of dirctory for dicom images.\n if not os.path.isdir(path):\n entry = os.path.dirname(path)\n else:\n entry = path\n else:\n# Otherwise it is the name of a directory containing p-files.\n entry = path\n\n if info['data_filetype'] == 'ge_data' and info['type'] is not None:\n# Found a pfile. Add it to the list.\n if entry not in self.pfiles and info['tdim'] > 2:\n self.pfiles.append(entry)\n self.entry_map['epi'].append(entry)\n if info['series'] not in self.epi_series:\n self.epi_series.append(info['series'])\n elif info['data_filetype'] == 'dicom' and \\\n info['psdname'] == 'epibold':\n# This is the initial EPI done during setup.\n info['outdir'] = self.episetup_dir\n info['type'] = 'first_epi'\n self.entry_map['first_epi'].append(entry)\n info['imgfile'] = '%s/first_epi_%d' % \\\n (self.episetup_dir, len(self.entry_map['first_epi']))\n elif ('epirt' in info['psdname'] or info['psdname'] == 'epi' or \\\n info['psdname'] == '*epfid2d1_64') and info['tdim'] > 2:\n# This is an epi reconstructed on the scanner.\n self.epi_series.append(info['series'])\n self.entry_map['epi'].append(entry)\n if not os.path.isdir(path):\n tmp_path = os.path.dirname(path)\n else:\n tmp_path = path\n self.epirt_paths.append(tmp_path)\n\n if self.fsl_flip:\n info['filetype'] = 'brik'\n else:\n info['filetype'] = self.tmplt['epi_file_format']\n\n info['TR'] = self.hdr['tsize']\n if self.tmplt['acq_tr'] is None:\n info['acq_tr'] = float(info['TR'])\n else:\n info['acq_tr'] = float(self.tmplt['acq_tr'])\n return OK","def get_info(self) -> str:\n raise NotImplementedError()","def info(self, id):","def _augment_info(self, header):\n # Information on carriers\n header.add_info_line(\n vcfpy.OrderedDict(\n [\n (\"ID\", \"AFFECTED_CARRIERS\"),\n (\"Number\", \"1\"),\n (\"Type\", \"Integer\"),\n (\"Description\", \"Number of affected samples from pedigree that are carriers\"),\n ]\n )\n )\n header.add_info_line(\n vcfpy.OrderedDict(\n [\n (\"ID\", \"UNAFFECTED_CARRIERS\"),\n (\"Number\", \"1\"),\n (\"Type\", \"Integer\"),\n (\"Description\", \"Number of unaffected samples from pedigree that are carriers\"),\n ]\n )\n )\n header.add_info_line(\n vcfpy.OrderedDict(\n [\n (\"ID\", \"BACKGROUND_CARRIERS\"),\n (\"Number\", \"1\"),\n (\"Type\", \"Integer\"),\n (\"Description\", \"Number of background samples that are carriers\"),\n ]\n )\n )\n for anno_args in self.args.annotation_beds:\n header.add_info_line(\n vcfpy.OrderedDict(\n [\n (\"ID\", anno_args[\"info\"]),\n (\"Number\", \".\"),\n (\"Type\", \"String\"),\n (\"Description\", anno_args[\"description\"]),\n ]\n )\n )\n return header","def setInfo(self,item,text):\r\n raise AbstractError","def info(self) -> Optional[Dict[str, Any]]:\n return self._state.get(\"info\", None)","def setInfoData(self):\n self.infoData['id'] = str(self.id)\n self.infoData['name'] = str(self.name)\n self.infoData['space'] = str(self.space.id)\n self.infoData['size'] = str(self.size)\n self.infoData['ndof'] = str(self.ndof)\n self.infoData['type'] = str(self.type)\n self.infoData['operator'] = str(self.operator)\n self.infoData['operande_id'] = str(self.operande_id)\n self.infoData['func_arguments'] = str(self.func_arguments)\n self.infoData['nderiv'] = str(self.nderiv)\n self.infoData['parameval'] = str(self.parameval)\n self.infoData['paramevalfunc'] = str(self.paramevalfunc)\n self.infoData['loc_id'] = str(self.loc_id)","def report_finding(self, info_inst):\n if INFORMATION == info_inst.get_severity():\n self.information(info_inst.get_desc())\n\n else:\n self.vulnerability(info_inst.get_desc(),\n severity=info_inst.get_severity())","def pre_process_information(self):\n self.logger.debug(\n colorama.Fore.BLUE\n + \"jsnapy.cfg file location used : %s\" % get_config_location(),\n extra=self.log_detail,\n )\n self.logger.debug(\n colorama.Fore.BLUE\n + \"Configuration file location used : %s\"\n % get_path(\"DEFAULT\", \"config_file_path\"),\n extra=self.log_detail,\n )","def log_info(info_dict):\n pass","def showInfo(parent,message,title=_('Information')):\r\n return askStyled(parent,message,title,wx.OK|wx.ICON_INFORMATION)","def _get_spec_info(self):\n raise NotImplementedError()","def handle_info_request(model_name, component_name):\n if model_name in app.models:\n pipeline = app.models[model_name][\"object\"]\n info = None\n if component_name == \"preprocessor\":\n if pipeline.preprocessor is not None:\n info = pipeline.preprocessor.info()\n info[\"interface\"] = pipeline.preprocessor.__class__.__name__\n else:\n return _respond_not_found(\n \"Model does not contain a preprocessor\")\n elif component_name == \"embedder\":\n if pipeline.embedder is not None:\n info = pipeline.embedder.info()\n else:\n return _respond_not_found(\n \"Model does not contain an embedder\")\n elif component_name == \"similarity-processor\":\n info = pipeline.similarity_processor.info()\n info[\"interface\"] = pipeline.similarity_processor.__class__.__name__\n\n # Convert all the values to str\n for k in info.keys():\n info[k] = str(info[k])\n\n return (\n json.dumps(info), 200,\n {'ContentType': 'application/json'}\n )\n else:\n return _respond_not_found()","def get_info(self):\n return {}","def info(self):\n return {}","def details(self):\n raise NotImplementedError()","def wms_getfeatureinfo_plain(self, feature_info, permission):\n \"\"\"\n GetFeatureInfo results\n\n Layer 'Grundstuecke'\n Feature 1\n t_id = '1234'\n nbident = 'SO0123456789'\n nummer = '1234'\n ...\n \"\"\"\n if feature_info.startswith('GetFeatureInfo'):\n lines = []\n\n layer_pattern = re.compile(\"^Layer '(.+)'$\")\n attr_pattern = re.compile(\"^(.+) = .+$\")\n permitted_attributes = {}\n\n # filter feature attributes by permission\n for line in feature_info.splitlines():\n m = attr_pattern.match(line)\n if m is not None:\n # attribute line\n # check if layer attribute is permitted\n attr = m.group(1)\n if attr not in permitted_attributes:\n # skip not permitted attribute\n continue\n else:\n m = layer_pattern.match(line)\n if m is not None:\n # layer line\n # get permitted attributes for layer\n current_layer = m.group(1)\n permitted_attributes = self.permitted_info_attributes(\n current_layer, permission\n )\n\n # keep line\n lines.append(line)\n\n # join filtered lines\n feature_info = '\\n'.join(lines)\n\n return feature_info","def information(self, bot, update):\n update.message.reply_markdown(\"Nun werden einige Informationen zu Alfred angezeigt.\",\n reply_markup=self.option_markup)","def do_info (self, line) :\n\t\tprint\n\t\tprint get_info_string( self.__image )\n\t\tprint","def exclude_info_types(self) -> Optional[bool]:\n return pulumi.get(self, \"exclude_info_types\")","def get_details(self):\n raise Exception(\"bad details\")","def get_infos(self):\n infos = dict()\n infos['dataset'] = self._dataset_name()\n infos['task'] = self.task\n if self.task == 'sep_clean':\n data_license = [librispeech_license]\n else:\n data_license = [librispeech_license, wham_noise_license]\n infos['licenses'] = data_license\n return infos","def readInfoElement(self, infoElement, instanceObject):\n infoLocation = self.locationFromElement(infoElement)\n instanceObject.addInfo(infoLocation, copySourceName=self.infoSource)","def check_info(self):\n #define convertissor\n def pass_in_integer(value):\n return int(value)\n def pass_in_logical(value):\n if value in ['1','t','T','.true.']:\n return 1\n else:\n return 0\n def pass_in_float(value):\n return float(value)\n\n for line in open('./Source/MadWeight_File/Python/MW_param_default.inc'):\n line=line.split('#')[0] #remove comment\n splitline=line.split() #split the data\n if len(splitline)!=4:\n continue\n #assign element\n block=splitline[0].lower()\n tag=splitline[1].lower()\n type=splitline[2].lower()\n value=splitline[3]\n #check if exist -> default\n try:\n self[block][tag]\n except:\n try:\n self[block][tag]=value\n except:\n self[block]={tag:value}\n #change type\n if type in ['integer','logical','float']:\n self[block][tag]=eval('pass_in_'+type+'(self[block][tag])')","def get_info(self) -> str:\n return self.info","def _AnatInfo(self, info, path):\n if info['data_filetype'] == 'ge_data':\n return ERROR\n outdir = '%s/%s' % (self.procdir, self.tmplt['anat']['outdir'])\n info['InversionTime'] = self.hdr['native_header']['InversionTime']\n\n if info['psdname'] == 'efgre3d' or info['psdname'] == 'bravo':\n# Structural scans are 3d inversion-recovery.\n if self.hdr['native_header']['InversionTime'] < 1.:\n# Only inversion recovery used for anatomy. Must be calibration.\n return None\n elif self.hdr['zsize'] > 1.25:\n# Only one slab acquired. Assume thick slices.\n name = 'T1Low_%d' % self.n_t1low\n self.n_t1low += 1\n else:\n if self.n_t1high == 0:\n name = 'T1High'\n else:\n name = 'T1High_%d' % self.n_t1high\n self.n_t1high += 1\n else:\n psdname = info['psdname']\n name = self.imgtype.get(psdname, info['psdname'])\n if self.ntype.has_key(psdname):\n self.ntype[psdname] += 1\n name = '%s_%0d' % (name, self.ntype[psdname])\n else:\n self.ntype[psdname] = 1\n info['norm_src'] = False\n info['outdir'] = outdir\n info['filetype'] = self.tmplt['anat']['format']\n info['imgfile'] = '%s/%s' % (info['outdir'], name)\n\n self.entry_map['anat'].append(self.current_entry)\n return OK","def info(self, text):\n\n debug_text = self._get_debug_text(text)\n super().info(debug_text)","def buildSimpleInfoDeclaration(self):\n if self.info == \"\":\n raise Exception(\"Info block empty in symbol: \"+self.name)\n # buid some html to show\n dec = \" <tr>\\n\"\n dec += \" <td><a href=\\\"#\"+self.link+\"\\\">\"+self.name+\"</a></td>\\n\"\n dec += \" <td>\"+self.getPrettyType()\n dec += \"</td>\\n\"\n dec += \" <td>\"+self.info+\"</td>\\n\"\n dec += \" </tr>\\n\"\n return dec","def bioinfo():\n\n pass","def output_debug_info(self):","def show_info(title, message):\n\n pass","def add_info(info):\n self._info[get_test_name(request)].append(info)","def info(self) -> int:","def full_info(self, object, name, value):\n return self.info()","def info(self):\n pp = pprint.PrettyPrinter(indent=4)\n print_text_box('Info')\n pp.pprint(self.manager.data[\"info\"])\n print('')","def prepare_info(self, infoxml_file):\n\n self.info = InfoXML(infoxml_file)\n\n if self.info.name:\n self.LOGGER << f\"Mod Name from info.xml: {self.info.name}\"\n\n self._install_dirname = self.info.name.lower()","def put_info(self, info):\n info = [\"{0:{1}s}\".format(x, MAX_LINE_LENGTH)[:MAX_LINE_LENGTH]\n for x in info]\n ierr = exolib.py_expinf(self.exoid, info)\n if ierr:\n raise ExodusIIWriterError(\"Error putting information record\")","def handle_info(self, api, command):\n return self.handle_log(api, command, level=logging.INFO)","def parse_item_page_info(self, id, body):\n info = {}\n info['title'] = self.__re_search(body, *self.regx['title'])\n if info['title'] == 'Suggested Products':\n return None\n info['model'] = self.__re_search(body, *self.regx['model'])\n if self.__re_search(body, *self.regx['deactivated']):\n info['deactivated'] = True\n return info\n free_shipping = self.__re_search(body, *self.regx['free_shipping'])\n cart = self.__re_search(body, *self.regx['cart'])\n if free_shipping and not cart:\n info.update(self.parse_item_page_price(id, body))\n return info","def info():\n # -------- Task 1 -------------------------\n # Please complete the following information\n\n return {\"agent name\": \"?\", # COMPLETE HERE\n \"student name\": [\"?\"], # COMPLETE HERE\n \"student number\": [\"?\"]} # COMPLETE HERE","def info(self, info: str):\n\n self._info = info","def get_info_about(attr):\n return getattr(get_info_about, attr)","def match_info(info_dict):\n try:\n return info_dict['title']==\"An Extra Slice\"\n except KeyError:\n return False","def _describe(self) -> Dict[str, Any]:","def isolate_relevant_information(self):\n\n def get_formula(oncat_formula):\n \"\"\"will need to go from something like\n \"${value/10e11}`\"\n to something more pythonic\n \"{value/10e11}\"\"\"\n regular_expression = r'\\$(?P<formula>.+)\\`'\n m = re.search(regular_expression, oncat_formula)\n if m:\n return m.group('formula')\n else:\n return \"\"\n\n template_information = {}\n for _index, _element in enumerate(self._oncat_default_template):\n _title = _element[\"name\"]\n _path = _element[\"path\"]\n if \"units\" in _element:\n _units = _element[\"units\"]\n else:\n _units = \"\"\n if \"transform\" in _element:\n _formula = get_formula(_element[\"transform\"])\n else:\n _formula = \"\"\n template_information[_index] = {'title': _title,\n 'path': _path,\n 'units': _units,\n 'formula': _formula}\n self.template_information = template_information","def info(self):\n return self._info","def DebugInfo( self, request_data ):\n pass"],"string":"[\n \"def get_info(self, info):\\r\\n pass\",\n \"def ExtraInfo(self) -> object:\",\n \"def manage_info():\",\n \"def _get_information(self):\\n pass\",\n \"def process_info(self, info):\\n return info\",\n \"def info() -> None:\",\n \"def info(self):\",\n \"def info(self):\",\n \"def info(self, *args, **kwargs):\",\n \"def info(self) -> dict:\",\n \"def getInfo():\",\n \"def info(): # noqa: E501\\n return 'do some magic!'\",\n \"def get_info(self):\\n return \\\"TODO !\\\"\",\n \"def extract_info(self, docstring):\\n pass\",\n \"def information(self, params):\\n # TODO: If the docstring is right, then why not just implement this?\\n raise NotImplementedError # pragma: no cover\",\n \"def add_info(transmogrifier, category, section, info):\\n try:\\n a = transmogrifier._collected_info\\n except AttributeError:\\n a = []\\n transmogrifier._collected_info = a\\n a.append({'category': category,\\n 'section': section,\\n 'info': info,\\n })\",\n \"def getInfo(self,item):\\r\\n return ''\",\n \"def get_info(self):\\n pass\",\n \"def get_info(self):\\n pass\",\n \"def process_app_info(self):\\n pass\",\n \"def get_info(self) -> Optional[Dict[str, Any]]:\",\n \"def get_info(self):\\n return None\",\n \"def getInfo(self):\\n return {\\\"title\\\": \\\"Not Implemented\\\",\\n \\\"category\\\": \\\"contextual\\\", \\\"priority\\\": 2}\\n raise NotImplementedError(\\\"Shouldn't be called\\\")\",\n \"def ui_additional_info(self) -> Optional[str]:\\n return None\",\n \"def register_info(self, info):\\n\\n self.info = info\\n\\n # Add description from module __doc__ if missing\\n self.info['description'] = (\\n self.info.get('description')\\n if self.info.get('description')\\n else self.__doc__.strip()\\n )\\n if not self.info['description']:\\n raise DevException(messages.module.error_module_missing_description)\",\n \"def from_info(self, info: Dict[str, Any]) -> None:\",\n \"def info(self) -> _InfoType:\\n return {}\",\n \"def additional_info(self) -> Optional[Mapping[str, str]]:\\n return pulumi.get(self, \\\"additional_info\\\")\",\n \"def info() -> Dict[str, Any]:\",\n \"def setInfo(*args):\",\n \"def info():\\n return r\\\"\\\"\\\"None\\\"\\\"\\\"\",\n \"def define_info_dict():\\n\\n d = {\\n \\\"PRED\\\": {\\n \\\"COLUMN\\\": [\\\"predicted_class\\\"],\\n \\\"Number\\\": \\\"1\\\",\\n \\\"Type\\\": \\\"String\\\",\\n \\\"Description\\\": \\\"Predicted class: somatic, germline, artifact\\\",\\n },\\n \\\"PROB\\\": {\\n \\\"COLUMN\\\": [\\\"prob_s\\\", \\\"prob_g\\\", \\\"prob_a\\\"],\\n \\\"Number\\\": \\\"3\\\",\\n \\\"Type\\\": \\\"Float\\\",\\n \\\"Description\\\": \\\"Prediction probability of \\\"\\n \\\"being somatic, germline, artifact in this order\\\",\\n },\\n \\\"SNP\\\": {\\n \\\"COLUMN\\\": [\\\"is_on_db\\\"],\\n \\\"Number\\\": \\\"0\\\",\\n \\\"Type\\\": \\\"Flag\\\",\\n \\\"Description\\\": \\\"Present on SNP database (modified dbSNP/gnomAD (default) or user-provided database)\\\",\\n },\\n \\\"ANNO\\\": {\\n \\\"COLUMN\\\": [\\\"annotation\\\"],\\n \\\"Number\\\": \\\".\\\",\\n \\\"Type\\\": \\\"String\\\",\\n \\\"Description\\\": \\\"Indel annotation formatted as \\\"\\n \\\"GeneSymbol|RefSeqAccession|CodonPos|IndelEffect\\\"\\n \\\"Delimited by comma for multiple isoforms\\\",\\n },\\n \\\"COSMIC_CNT\\\": {\\n \\\"COLUMN\\\": [\\\"cosmic_cnt\\\"],\\n \\\"Number\\\": \\\"1\\\",\\n \\\"Type\\\": \\\"Integer\\\",\\n \\\"Description\\\": \\\"COSMIC count in v89\\\",\\n },\\n \\\"MAXMAF\\\": {\\n \\\"COLUMN\\\": [\\\"max_maf\\\"],\\n \\\"Number\\\": \\\"1\\\",\\n \\\"Type\\\": \\\"Float\\\",\\n \\\"Description\\\": \\\"Maximum minor allele frequency (MAF) \\\"\\n \\\"reported in dbSNP, ClinVar and gnomAD non-cancer population\\\",\\n },\\n \\\"COMMON\\\": {\\n \\\"COLUMN\\\": [\\\"is_common\\\"],\\n \\\"Number\\\": \\\"0\\\",\\n \\\"Type\\\": \\\"Flag\\\",\\n \\\"Description\\\": \\\"Common in dbSNP or MAXMAF > 0.01\\\",\\n },\\n \\\"CLIN\\\": {\\n \\\"COLUMN\\\": [\\\"clin_info\\\"],\\n \\\"Number\\\": \\\"1\\\",\\n \\\"Type\\\": \\\"String\\\",\\n \\\"Description\\\": \\\"ClinVar annotation formatted as ClinicalSignificance|Condition\\\",\\n },\\n \\\"ICP\\\": {\\n \\\"COLUMN\\\": [\\\"indel_complexity\\\"],\\n \\\"Number\\\": \\\"1\\\",\\n \\\"Type\\\": \\\"Integer\\\",\\n \\\"Description\\\": \\\"Indel complexity: mismatches around the indel measured by edit distance\\\",\\n },\\n \\\"DSM\\\": {\\n \\\"COLUMN\\\": [\\\"dissimilarity\\\"],\\n \\\"Number\\\": \\\"1\\\",\\n \\\"Type\\\": \\\"Float\\\",\\n \\\"Description\\\": \\\"Dissimilarity: edit distance between indel and flanking sequences\\\",\\n },\\n \\\"ISZ\\\": {\\n \\\"COLUMN\\\": [\\\"indel_size\\\"],\\n \\\"Number\\\": \\\"1\\\",\\n \\\"Type\\\": \\\"Integer\\\",\\n \\\"Description\\\": \\\"Indel size\\\",\\n },\\n \\\"REP\\\": {\\n \\\"COLUMN\\\": [\\\"repeat\\\"],\\n \\\"Number\\\": \\\"1\\\",\\n \\\"Type\\\": \\\"Integer\\\",\\n \\\"Description\\\": \\\"Repeat: count of the indel-sequence repeats in flanking region\\\",\\n },\\n \\\"UQM\\\": {\\n \\\"COLUMN\\\": [\\\"is_uniq_mapped\\\"],\\n \\\"Number\\\": \\\"0\\\",\\n \\\"Type\\\": \\\"Flag\\\",\\n \\\"Description\\\": \\\"Supported by uniquely mapped reads\\\",\\n },\\n \\\"NEB\\\": {\\n \\\"COLUMN\\\": [\\\"is_near_boundary\\\"],\\n \\\"Number\\\": \\\"0\\\",\\n \\\"Type\\\": \\\"Flag\\\",\\n \\\"Description\\\": \\\"Near exon boundary\\\",\\n },\\n \\\"EQX\\\": {\\n \\\"COLUMN\\\": [\\\"equivalence_exists\\\"],\\n \\\"Number\\\": \\\"0\\\",\\n \\\"Type\\\": \\\"Flag\\\",\\n \\\"Description\\\": \\\"Equivalent alignments exist for the indel\\\",\\n },\\n \\\"BID\\\": {\\n \\\"COLUMN\\\": [\\\"is_bidirectional\\\"],\\n \\\"Number\\\": \\\"0\\\",\\n \\\"Type\\\": \\\"Flag\\\",\\n \\\"Description\\\": \\\"Supported by forward and reverse reads\\\",\\n },\\n \\\"MTA\\\": {\\n \\\"COLUMN\\\": [\\\"is_multiallelic\\\"],\\n \\\"Number\\\": \\\"0\\\",\\n \\\"Type\\\": \\\"Flag\\\",\\n \\\"Description\\\": \\\"Multialleleic\\\",\\n },\\n \\\"FRM\\\": {\\n \\\"COLUMN\\\": [\\\"is_inframe\\\"],\\n \\\"Number\\\": \\\"0\\\",\\n \\\"Type\\\": \\\"Flag\\\",\\n \\\"Description\\\": \\\"In-frame indel\\\",\\n },\\n \\\"SPL\\\": {\\n \\\"COLUMN\\\": [\\\"is_splice\\\"],\\n \\\"Number\\\": \\\"0\\\",\\n \\\"Type\\\": \\\"Flag\\\",\\n \\\"Description\\\": \\\"Located in splice region\\\",\\n },\\n \\\"TRN\\\": {\\n \\\"COLUMN\\\": [\\\"is_truncating\\\"],\\n \\\"Number\\\": \\\"0\\\",\\n \\\"Type\\\": \\\"Flag\\\",\\n \\\"Description\\\": \\\"Truncating indel\\\",\\n },\\n \\\"CDD\\\": {\\n \\\"COLUMN\\\": [\\\"is_in_cdd\\\"],\\n \\\"Number\\\": \\\"0\\\",\\n \\\"Type\\\": \\\"Flag\\\",\\n \\\"Description\\\": \\\"Located in conserved domain\\\",\\n },\\n \\\"LOC\\\": {\\n \\\"COLUMN\\\": [\\\"indel_location\\\"],\\n \\\"Number\\\": \\\"1\\\",\\n \\\"Type\\\": \\\"Float\\\",\\n \\\"Description\\\": \\\"Relative indel location within the transcript coding region\\\",\\n },\\n \\\"NMD\\\": {\\n \\\"COLUMN\\\": [\\\"is_nmd_insensitive\\\"],\\n \\\"Number\\\": \\\"0\\\",\\n \\\"Type\\\": \\\"Flag\\\",\\n \\\"Description\\\": \\\"Insensitive to nonsense mediated decay\\\",\\n },\\n \\\"IPG\\\": {\\n \\\"COLUMN\\\": [\\\"ipg\\\"],\\n \\\"Number\\\": \\\"1\\\",\\n \\\"Type\\\": \\\"Float\\\",\\n \\\"Description\\\": \\\"Indels per gene\\\",\\n },\\n \\\"LEN\\\": {\\n \\\"COLUMN\\\": [\\\"cds_length\\\"],\\n \\\"Number\\\": \\\"1\\\",\\n \\\"Type\\\": \\\"Float\\\",\\n \\\"Description\\\": \\\"Coding sequence length. Median value if multiple isoforms exist\\\",\\n },\\n \\\"LC\\\": {\\n \\\"COLUMN\\\": [\\\"lc\\\"],\\n \\\"Number\\\": \\\"1\\\",\\n \\\"Type\\\": \\\"Float\\\",\\n \\\"Description\\\": \\\"Linguistic complexity: diversity of k-mers in flanking 50-bp region\\\",\\n },\\n \\\"LLC\\\": {\\n \\\"COLUMN\\\": [\\\"local_lc\\\"],\\n \\\"Number\\\": \\\"1\\\",\\n \\\"Type\\\": \\\"Float\\\",\\n \\\"Description\\\": \\\"Local linguistic complexity: diversity of k-mers in flanking 6-bp region\\\",\\n },\\n \\\"GC\\\": {\\n \\\"COLUMN\\\": [\\\"gc\\\"],\\n \\\"Number\\\": \\\"1\\\",\\n \\\"Type\\\": \\\"Float\\\",\\n \\\"Description\\\": \\\"GC-content in flanking 50-bp region\\\",\\n },\\n \\\"LGC\\\": {\\n \\\"COLUMN\\\": [\\\"local_gc\\\"],\\n \\\"Number\\\": \\\"1\\\",\\n \\\"Type\\\": \\\"Float\\\",\\n \\\"Description\\\": \\\"Local GC-content in flanking 6-bp region\\\",\\n },\\n \\\"SG\\\": {\\n \\\"COLUMN\\\": [\\\"strength\\\"],\\n \\\"Number\\\": \\\"1\\\",\\n \\\"Type\\\": \\\"Float\\\",\\n \\\"Description\\\": \\\"DNA bond strength of 2-mers in flanking 50-bp region\\\",\\n },\\n \\\"LSG\\\": {\\n \\\"COLUMN\\\": [\\\"local_strength\\\"],\\n \\\"Number\\\": \\\"1\\\",\\n \\\"Type\\\": \\\"Float\\\",\\n \\\"Description\\\": \\\"Local DNA bond strength of 2-mers in flanking 6-bp region\\\",\\n },\\n \\\"INS\\\": {\\n \\\"COLUMN\\\": [\\\"is_ins\\\"],\\n \\\"Number\\\": \\\"0\\\",\\n \\\"Type\\\": \\\"Flag\\\",\\n \\\"Description\\\": \\\"Insertion\\\",\\n },\\n \\\"ATI\\\": {\\n \\\"COLUMN\\\": [\\\"is_at_ins\\\"],\\n \\\"Number\\\": \\\"0\\\",\\n \\\"Type\\\": \\\"Flag\\\",\\n \\\"Description\\\": \\\"Single insertion of A or T\\\",\\n },\\n \\\"ATD\\\": {\\n \\\"COLUMN\\\": [\\\"is_at_del\\\"],\\n \\\"Number\\\": \\\"0\\\",\\n \\\"Type\\\": \\\"Flag\\\",\\n \\\"Description\\\": \\\"Single deletion of A or T\\\",\\n },\\n \\\"GCI\\\": {\\n \\\"COLUMN\\\": [\\\"is_gc_ins\\\"],\\n \\\"Number\\\": \\\"0\\\",\\n \\\"Type\\\": \\\"Flag\\\",\\n \\\"Description\\\": \\\"Single insertion of G or C\\\",\\n },\\n \\\"GCD\\\": {\\n \\\"COLUMN\\\": [\\\"is_gc_del\\\"],\\n \\\"Number\\\": \\\"0\\\",\\n \\\"Type\\\": \\\"Flag\\\",\\n \\\"Description\\\": \\\"Single deletion of G or C\\\",\\n },\\n \\\"ALTC\\\": {\\n \\\"COLUMN\\\": [\\\"alt_count\\\"],\\n \\\"Number\\\": \\\"1\\\",\\n \\\"Type\\\": \\\"Integer\\\",\\n \\\"Description\\\": \\\"Alt count: count of unique reads supporting ALT allele\\\",\\n },\\n \\\"REFC\\\": {\\n \\\"COLUMN\\\": [\\\"ref_count\\\"],\\n \\\"Number\\\": \\\"1\\\",\\n \\\"Type\\\": \\\"Integer\\\",\\n \\\"Description\\\": \\\"Ref count: count of unique reads supporting REF allele\\\",\\n },\\n \\\"RCF\\\": {\\n \\\"COLUMN\\\": [\\\"reclassified\\\"],\\n \\\"Number\\\": \\\"0\\\",\\n \\\"Type\\\": \\\"Flag\\\",\\n \\\"Description\\\": \\\"Reclassification applied\\\",\\n },\\n \\\"RQB\\\": {\\n \\\"COLUMN\\\": [\\\"filtered\\\", \\\"rescued\\\"],\\n \\\"Number\\\": \\\"1\\\",\\n \\\"Type\\\": \\\"String\\\",\\n \\\"Description\\\": \\\"Indel used to rescue this entry formatted as CHROM:POS:REF:ALT\\\",\\n },\\n }\\n\\n return d\",\n \"def _add_info(self, infos: dict, info: dict, env_num: int) -> dict:\\n for k in info.keys():\\n if k not in infos:\\n info_array, array_mask = self._init_info_arrays(type(info[k]))\\n else:\\n info_array, array_mask = infos[k], infos[f\\\"_{k}\\\"]\\n\\n info_array[env_num], array_mask[env_num] = info[k], True\\n infos[k], infos[f\\\"_{k}\\\"] = info_array, array_mask\\n return infos\",\n \"def get_definition(self, info):\\r\\n pass\",\n \"def info_from_entry(self, entry):\\n info = super().info_from_entry(entry)\\n return info\",\n \"def get_info_string(self) -> str:\\n return \\\"Not implemented\\\"\",\n \"def return_info(self):\\n\\t\\treturn self.info\",\n \"def info(self):\\n return self.info_text\",\n \"def _add_details(self, info):\\n for (key, val) in six.iteritems(info):\\n if key == \\\"nodes\\\":\\n val = [Node(parent=self, **nd) for nd in val]\\n elif key == \\\"sessionPersistence\\\":\\n val = val['persistenceType']\\n elif key == \\\"cluster\\\":\\n val = val['name']\\n elif key == \\\"virtualIps\\\":\\n key = \\\"virtual_ips\\\"\\n val = [VirtualIP(parent=self, **vip) for vip in val]\\n setattr(self, key, val)\",\n \"def meta_info(environ, start_response, logger, handle):\\n pass\",\n \"def details(self):\\n pass\",\n \"def additionalInfo(request):\\n # Test Comment\\n assert isinstance(request, HttpRequest)\\n return render(\\n request,\\n 'additionalInfo.html',\\n context_instance=RequestContext(request, {})\\n )\",\n \"def info(self, msg, *args, **kwargs):\\n pass\",\n \"def get_infos(self):\\n infos = dict()\\n infos[\\\"dataset\\\"] = self.dataset_name\\n infos[\\\"task\\\"] = \\\"separate_noisy\\\"\\n infos[\\\"licenses\\\"] = [librispeech_license, tac_license]\\n return infos\",\n \"def get_details(self):\",\n \"def _handle_info_response(self, resp, info, prev_info):\\r\\n if info.line_num != prev_info.line_num:\\r\\n return\\r\\n\\r\\n if resp['calltip']:\\r\\n info.editor.show_calltip('Arguments', resp['calltip'],\\r\\n signature=True,\\r\\n at_position=prev_info.position)\\r\\n\\r\\n if resp['name']:\\r\\n self.send_to_inspector.emit(\\r\\n resp['name'], resp['argspec'],\\r\\n resp['note'], resp['docstring'],\\r\\n not prev_info.auto)\",\n \"def buildSimpleInfoDeclaration(self):\\n return \\\"\\\"\",\n \"def info(self):\\r\\n return self._get('info', {})\",\n \"def missing_information(self, info, field):\\n raise NoData\",\n \"def get_info(transmogrifier, category=None, section=None, short=True):\\n res = []\\n try:\\n rows = transmogrifier._collected_info\\n except AttributeError:\\n return res\\n else:\\n for dic in rows:\\n if category is not None and dic['category'] != category:\\n continue\\n if section is not None and dic['section'] != section:\\n continue\\n if short:\\n res.append(dic['info'])\\n else:\\n res.append(dic)\\n return res\",\n \"def do_info(self, args):\\n if self.exploit is None:\\n eprint(colorize('No exploit set; nothing to describe. Select an exploit with the \\\\'use\\\\' command',\\n 'cyan'))\\n else:\\n eprint(colorize('\\\\n ' + self.exploit.DESCRIPTION + '\\\\n', 'green'))\",\n \"def test_get_info(self):\\n pass\",\n \"def _make_information_storable( self, data ):\\n\\t\\tpass\",\n \"def do_info(self, line):\\n args = filter(None, line.strip().split())\\n if len(args) != 1 or args[0] not in self.router.app_order:\\n self.help_info()\\n else:\\n self.router.print_path_help(args[0])\",\n \"def _EpiInfo(self, info, path):\\n\\n epi_vals = {'tdim':self.hdr['tdim'], 'plane':self.hdr['plane'], \\\\\\n 'SeriesNumber':self.hdr['subhdr']['SeriesNumber']}\\n for key in self.epi_keys.keys():\\n if self.epi_keys[key] != str(epi_vals[key]):\\n# Return None, which will cause these data to be ignored.\\n return None\\n\\n# Early versions of the EPIC software saved p-files for the setup epis.\\n# Don't process these (or any epi with fewer than eight useable frames).\\n if self.hdr['tdim'] < (8 + self.skip):\\n return None\\n\\n info['slice_order'] = self.shdr.get('SliceOrder', 'altplus')\\n if self.shdr['EffEchoSpacing'] is not None:\\n info['echo_spacing'] = self.shdr['EffEchoSpacing']/1000.\\n else:\\n info['echo_spacing'] = 0.\\n if info['data_filetype'] == 'dicom':\\n# Entry is name of dirctory for dicom images.\\n if not os.path.isdir(path):\\n entry = os.path.dirname(path)\\n else:\\n entry = path\\n else:\\n# Otherwise it is the name of a directory containing p-files.\\n entry = path\\n\\n if info['data_filetype'] == 'ge_data' and info['type'] is not None:\\n# Found a pfile. Add it to the list.\\n if entry not in self.pfiles and info['tdim'] > 2:\\n self.pfiles.append(entry)\\n self.entry_map['epi'].append(entry)\\n if info['series'] not in self.epi_series:\\n self.epi_series.append(info['series'])\\n elif info['data_filetype'] == 'dicom' and \\\\\\n info['psdname'] == 'epibold':\\n# This is the initial EPI done during setup.\\n info['outdir'] = self.episetup_dir\\n info['type'] = 'first_epi'\\n self.entry_map['first_epi'].append(entry)\\n info['imgfile'] = '%s/first_epi_%d' % \\\\\\n (self.episetup_dir, len(self.entry_map['first_epi']))\\n elif ('epirt' in info['psdname'] or info['psdname'] == 'epi' or \\\\\\n info['psdname'] == '*epfid2d1_64') and info['tdim'] > 2:\\n# This is an epi reconstructed on the scanner.\\n self.epi_series.append(info['series'])\\n self.entry_map['epi'].append(entry)\\n if not os.path.isdir(path):\\n tmp_path = os.path.dirname(path)\\n else:\\n tmp_path = path\\n self.epirt_paths.append(tmp_path)\\n\\n if self.fsl_flip:\\n info['filetype'] = 'brik'\\n else:\\n info['filetype'] = self.tmplt['epi_file_format']\\n\\n info['TR'] = self.hdr['tsize']\\n if self.tmplt['acq_tr'] is None:\\n info['acq_tr'] = float(info['TR'])\\n else:\\n info['acq_tr'] = float(self.tmplt['acq_tr'])\\n return OK\",\n \"def get_info(self) -> str:\\n raise NotImplementedError()\",\n \"def info(self, id):\",\n \"def _augment_info(self, header):\\n # Information on carriers\\n header.add_info_line(\\n vcfpy.OrderedDict(\\n [\\n (\\\"ID\\\", \\\"AFFECTED_CARRIERS\\\"),\\n (\\\"Number\\\", \\\"1\\\"),\\n (\\\"Type\\\", \\\"Integer\\\"),\\n (\\\"Description\\\", \\\"Number of affected samples from pedigree that are carriers\\\"),\\n ]\\n )\\n )\\n header.add_info_line(\\n vcfpy.OrderedDict(\\n [\\n (\\\"ID\\\", \\\"UNAFFECTED_CARRIERS\\\"),\\n (\\\"Number\\\", \\\"1\\\"),\\n (\\\"Type\\\", \\\"Integer\\\"),\\n (\\\"Description\\\", \\\"Number of unaffected samples from pedigree that are carriers\\\"),\\n ]\\n )\\n )\\n header.add_info_line(\\n vcfpy.OrderedDict(\\n [\\n (\\\"ID\\\", \\\"BACKGROUND_CARRIERS\\\"),\\n (\\\"Number\\\", \\\"1\\\"),\\n (\\\"Type\\\", \\\"Integer\\\"),\\n (\\\"Description\\\", \\\"Number of background samples that are carriers\\\"),\\n ]\\n )\\n )\\n for anno_args in self.args.annotation_beds:\\n header.add_info_line(\\n vcfpy.OrderedDict(\\n [\\n (\\\"ID\\\", anno_args[\\\"info\\\"]),\\n (\\\"Number\\\", \\\".\\\"),\\n (\\\"Type\\\", \\\"String\\\"),\\n (\\\"Description\\\", anno_args[\\\"description\\\"]),\\n ]\\n )\\n )\\n return header\",\n \"def setInfo(self,item,text):\\r\\n raise AbstractError\",\n \"def info(self) -> Optional[Dict[str, Any]]:\\n return self._state.get(\\\"info\\\", None)\",\n \"def setInfoData(self):\\n self.infoData['id'] = str(self.id)\\n self.infoData['name'] = str(self.name)\\n self.infoData['space'] = str(self.space.id)\\n self.infoData['size'] = str(self.size)\\n self.infoData['ndof'] = str(self.ndof)\\n self.infoData['type'] = str(self.type)\\n self.infoData['operator'] = str(self.operator)\\n self.infoData['operande_id'] = str(self.operande_id)\\n self.infoData['func_arguments'] = str(self.func_arguments)\\n self.infoData['nderiv'] = str(self.nderiv)\\n self.infoData['parameval'] = str(self.parameval)\\n self.infoData['paramevalfunc'] = str(self.paramevalfunc)\\n self.infoData['loc_id'] = str(self.loc_id)\",\n \"def report_finding(self, info_inst):\\n if INFORMATION == info_inst.get_severity():\\n self.information(info_inst.get_desc())\\n\\n else:\\n self.vulnerability(info_inst.get_desc(),\\n severity=info_inst.get_severity())\",\n \"def pre_process_information(self):\\n self.logger.debug(\\n colorama.Fore.BLUE\\n + \\\"jsnapy.cfg file location used : %s\\\" % get_config_location(),\\n extra=self.log_detail,\\n )\\n self.logger.debug(\\n colorama.Fore.BLUE\\n + \\\"Configuration file location used : %s\\\"\\n % get_path(\\\"DEFAULT\\\", \\\"config_file_path\\\"),\\n extra=self.log_detail,\\n )\",\n \"def log_info(info_dict):\\n pass\",\n \"def showInfo(parent,message,title=_('Information')):\\r\\n return askStyled(parent,message,title,wx.OK|wx.ICON_INFORMATION)\",\n \"def _get_spec_info(self):\\n raise NotImplementedError()\",\n \"def handle_info_request(model_name, component_name):\\n if model_name in app.models:\\n pipeline = app.models[model_name][\\\"object\\\"]\\n info = None\\n if component_name == \\\"preprocessor\\\":\\n if pipeline.preprocessor is not None:\\n info = pipeline.preprocessor.info()\\n info[\\\"interface\\\"] = pipeline.preprocessor.__class__.__name__\\n else:\\n return _respond_not_found(\\n \\\"Model does not contain a preprocessor\\\")\\n elif component_name == \\\"embedder\\\":\\n if pipeline.embedder is not None:\\n info = pipeline.embedder.info()\\n else:\\n return _respond_not_found(\\n \\\"Model does not contain an embedder\\\")\\n elif component_name == \\\"similarity-processor\\\":\\n info = pipeline.similarity_processor.info()\\n info[\\\"interface\\\"] = pipeline.similarity_processor.__class__.__name__\\n\\n # Convert all the values to str\\n for k in info.keys():\\n info[k] = str(info[k])\\n\\n return (\\n json.dumps(info), 200,\\n {'ContentType': 'application/json'}\\n )\\n else:\\n return _respond_not_found()\",\n \"def get_info(self):\\n return {}\",\n \"def info(self):\\n return {}\",\n \"def details(self):\\n raise NotImplementedError()\",\n \"def wms_getfeatureinfo_plain(self, feature_info, permission):\\n \\\"\\\"\\\"\\n GetFeatureInfo results\\n\\n Layer 'Grundstuecke'\\n Feature 1\\n t_id = '1234'\\n nbident = 'SO0123456789'\\n nummer = '1234'\\n ...\\n \\\"\\\"\\\"\\n if feature_info.startswith('GetFeatureInfo'):\\n lines = []\\n\\n layer_pattern = re.compile(\\\"^Layer '(.+)'$\\\")\\n attr_pattern = re.compile(\\\"^(.+) = .+$\\\")\\n permitted_attributes = {}\\n\\n # filter feature attributes by permission\\n for line in feature_info.splitlines():\\n m = attr_pattern.match(line)\\n if m is not None:\\n # attribute line\\n # check if layer attribute is permitted\\n attr = m.group(1)\\n if attr not in permitted_attributes:\\n # skip not permitted attribute\\n continue\\n else:\\n m = layer_pattern.match(line)\\n if m is not None:\\n # layer line\\n # get permitted attributes for layer\\n current_layer = m.group(1)\\n permitted_attributes = self.permitted_info_attributes(\\n current_layer, permission\\n )\\n\\n # keep line\\n lines.append(line)\\n\\n # join filtered lines\\n feature_info = '\\\\n'.join(lines)\\n\\n return feature_info\",\n \"def information(self, bot, update):\\n update.message.reply_markdown(\\\"Nun werden einige Informationen zu Alfred angezeigt.\\\",\\n reply_markup=self.option_markup)\",\n \"def do_info (self, line) :\\n\\t\\tprint\\n\\t\\tprint get_info_string( self.__image )\\n\\t\\tprint\",\n \"def exclude_info_types(self) -> Optional[bool]:\\n return pulumi.get(self, \\\"exclude_info_types\\\")\",\n \"def get_details(self):\\n raise Exception(\\\"bad details\\\")\",\n \"def get_infos(self):\\n infos = dict()\\n infos['dataset'] = self._dataset_name()\\n infos['task'] = self.task\\n if self.task == 'sep_clean':\\n data_license = [librispeech_license]\\n else:\\n data_license = [librispeech_license, wham_noise_license]\\n infos['licenses'] = data_license\\n return infos\",\n \"def readInfoElement(self, infoElement, instanceObject):\\n infoLocation = self.locationFromElement(infoElement)\\n instanceObject.addInfo(infoLocation, copySourceName=self.infoSource)\",\n \"def check_info(self):\\n #define convertissor\\n def pass_in_integer(value):\\n return int(value)\\n def pass_in_logical(value):\\n if value in ['1','t','T','.true.']:\\n return 1\\n else:\\n return 0\\n def pass_in_float(value):\\n return float(value)\\n\\n for line in open('./Source/MadWeight_File/Python/MW_param_default.inc'):\\n line=line.split('#')[0] #remove comment\\n splitline=line.split() #split the data\\n if len(splitline)!=4:\\n continue\\n #assign element\\n block=splitline[0].lower()\\n tag=splitline[1].lower()\\n type=splitline[2].lower()\\n value=splitline[3]\\n #check if exist -> default\\n try:\\n self[block][tag]\\n except:\\n try:\\n self[block][tag]=value\\n except:\\n self[block]={tag:value}\\n #change type\\n if type in ['integer','logical','float']:\\n self[block][tag]=eval('pass_in_'+type+'(self[block][tag])')\",\n \"def get_info(self) -> str:\\n return self.info\",\n \"def _AnatInfo(self, info, path):\\n if info['data_filetype'] == 'ge_data':\\n return ERROR\\n outdir = '%s/%s' % (self.procdir, self.tmplt['anat']['outdir'])\\n info['InversionTime'] = self.hdr['native_header']['InversionTime']\\n\\n if info['psdname'] == 'efgre3d' or info['psdname'] == 'bravo':\\n# Structural scans are 3d inversion-recovery.\\n if self.hdr['native_header']['InversionTime'] < 1.:\\n# Only inversion recovery used for anatomy. Must be calibration.\\n return None\\n elif self.hdr['zsize'] > 1.25:\\n# Only one slab acquired. Assume thick slices.\\n name = 'T1Low_%d' % self.n_t1low\\n self.n_t1low += 1\\n else:\\n if self.n_t1high == 0:\\n name = 'T1High'\\n else:\\n name = 'T1High_%d' % self.n_t1high\\n self.n_t1high += 1\\n else:\\n psdname = info['psdname']\\n name = self.imgtype.get(psdname, info['psdname'])\\n if self.ntype.has_key(psdname):\\n self.ntype[psdname] += 1\\n name = '%s_%0d' % (name, self.ntype[psdname])\\n else:\\n self.ntype[psdname] = 1\\n info['norm_src'] = False\\n info['outdir'] = outdir\\n info['filetype'] = self.tmplt['anat']['format']\\n info['imgfile'] = '%s/%s' % (info['outdir'], name)\\n\\n self.entry_map['anat'].append(self.current_entry)\\n return OK\",\n \"def info(self, text):\\n\\n debug_text = self._get_debug_text(text)\\n super().info(debug_text)\",\n \"def buildSimpleInfoDeclaration(self):\\n if self.info == \\\"\\\":\\n raise Exception(\\\"Info block empty in symbol: \\\"+self.name)\\n # buid some html to show\\n dec = \\\" <tr>\\\\n\\\"\\n dec += \\\" <td><a href=\\\\\\\"#\\\"+self.link+\\\"\\\\\\\">\\\"+self.name+\\\"</a></td>\\\\n\\\"\\n dec += \\\" <td>\\\"+self.getPrettyType()\\n dec += \\\"</td>\\\\n\\\"\\n dec += \\\" <td>\\\"+self.info+\\\"</td>\\\\n\\\"\\n dec += \\\" </tr>\\\\n\\\"\\n return dec\",\n \"def bioinfo():\\n\\n pass\",\n \"def output_debug_info(self):\",\n \"def show_info(title, message):\\n\\n pass\",\n \"def add_info(info):\\n self._info[get_test_name(request)].append(info)\",\n \"def info(self) -> int:\",\n \"def full_info(self, object, name, value):\\n return self.info()\",\n \"def info(self):\\n pp = pprint.PrettyPrinter(indent=4)\\n print_text_box('Info')\\n pp.pprint(self.manager.data[\\\"info\\\"])\\n print('')\",\n \"def prepare_info(self, infoxml_file):\\n\\n self.info = InfoXML(infoxml_file)\\n\\n if self.info.name:\\n self.LOGGER << f\\\"Mod Name from info.xml: {self.info.name}\\\"\\n\\n self._install_dirname = self.info.name.lower()\",\n \"def put_info(self, info):\\n info = [\\\"{0:{1}s}\\\".format(x, MAX_LINE_LENGTH)[:MAX_LINE_LENGTH]\\n for x in info]\\n ierr = exolib.py_expinf(self.exoid, info)\\n if ierr:\\n raise ExodusIIWriterError(\\\"Error putting information record\\\")\",\n \"def handle_info(self, api, command):\\n return self.handle_log(api, command, level=logging.INFO)\",\n \"def parse_item_page_info(self, id, body):\\n info = {}\\n info['title'] = self.__re_search(body, *self.regx['title'])\\n if info['title'] == 'Suggested Products':\\n return None\\n info['model'] = self.__re_search(body, *self.regx['model'])\\n if self.__re_search(body, *self.regx['deactivated']):\\n info['deactivated'] = True\\n return info\\n free_shipping = self.__re_search(body, *self.regx['free_shipping'])\\n cart = self.__re_search(body, *self.regx['cart'])\\n if free_shipping and not cart:\\n info.update(self.parse_item_page_price(id, body))\\n return info\",\n \"def info():\\n # -------- Task 1 -------------------------\\n # Please complete the following information\\n\\n return {\\\"agent name\\\": \\\"?\\\", # COMPLETE HERE\\n \\\"student name\\\": [\\\"?\\\"], # COMPLETE HERE\\n \\\"student number\\\": [\\\"?\\\"]} # COMPLETE HERE\",\n \"def info(self, info: str):\\n\\n self._info = info\",\n \"def get_info_about(attr):\\n return getattr(get_info_about, attr)\",\n \"def match_info(info_dict):\\n try:\\n return info_dict['title']==\\\"An Extra Slice\\\"\\n except KeyError:\\n return False\",\n \"def _describe(self) -> Dict[str, Any]:\",\n \"def isolate_relevant_information(self):\\n\\n def get_formula(oncat_formula):\\n \\\"\\\"\\\"will need to go from something like\\n \\\"${value/10e11}`\\\"\\n to something more pythonic\\n \\\"{value/10e11}\\\"\\\"\\\"\\n regular_expression = r'\\\\$(?P<formula>.+)\\\\`'\\n m = re.search(regular_expression, oncat_formula)\\n if m:\\n return m.group('formula')\\n else:\\n return \\\"\\\"\\n\\n template_information = {}\\n for _index, _element in enumerate(self._oncat_default_template):\\n _title = _element[\\\"name\\\"]\\n _path = _element[\\\"path\\\"]\\n if \\\"units\\\" in _element:\\n _units = _element[\\\"units\\\"]\\n else:\\n _units = \\\"\\\"\\n if \\\"transform\\\" in _element:\\n _formula = get_formula(_element[\\\"transform\\\"])\\n else:\\n _formula = \\\"\\\"\\n template_information[_index] = {'title': _title,\\n 'path': _path,\\n 'units': _units,\\n 'formula': _formula}\\n self.template_information = template_information\",\n \"def info(self):\\n return self._info\",\n \"def DebugInfo( self, request_data ):\\n pass\"\n]","isConversational":false},"negative_scores":{"kind":"list like","value":["0.7103027","0.69999164","0.6913003","0.66993517","0.6696078","0.649746","0.6492575","0.6492575","0.6453869","0.64022076","0.6401089","0.6384137","0.63549614","0.6325025","0.6280559","0.62133276","0.62043655","0.6174107","0.6174107","0.6089512","0.6064964","0.604914","0.6017706","0.60116833","0.59628224","0.59308094","0.59097856","0.58810174","0.5875787","0.5872436","0.58617127","0.5847513","0.58371437","0.5825238","0.58060586","0.57760423","0.57582897","0.57559717","0.5741378","0.57357967","0.57244027","0.57207346","0.57052666","0.57026696","0.5699567","0.5691737","0.5674839","0.56698745","0.56507474","0.56489575","0.5647272","0.56436497","0.56394905","0.5629451","0.56042945","0.5586043","0.5575358","0.5564507","0.5560274","0.5559202","0.55566984","0.5551199","0.5545818","0.55355006","0.55347306","0.5532357","0.5530181","0.5528539","0.5509141","0.55047363","0.5498427","0.5487555","0.5478177","0.5471607","0.54585004","0.54517007","0.5451064","0.54461145","0.54454315","0.54394543","0.54364944","0.5430838","0.54263145","0.54211265","0.54192626","0.54138917","0.53921056","0.5391449","0.53893536","0.5388354","0.5388113","0.538079","0.537628","0.5375829","0.5363433","0.5354562","0.5343961","0.5339366","0.53313524","0.5327705","0.53269"],"string":"[\n \"0.7103027\",\n \"0.69999164\",\n \"0.6913003\",\n \"0.66993517\",\n \"0.6696078\",\n \"0.649746\",\n \"0.6492575\",\n \"0.6492575\",\n \"0.6453869\",\n \"0.64022076\",\n \"0.6401089\",\n \"0.6384137\",\n \"0.63549614\",\n \"0.6325025\",\n \"0.6280559\",\n \"0.62133276\",\n \"0.62043655\",\n \"0.6174107\",\n \"0.6174107\",\n \"0.6089512\",\n \"0.6064964\",\n \"0.604914\",\n \"0.6017706\",\n \"0.60116833\",\n \"0.59628224\",\n \"0.59308094\",\n \"0.59097856\",\n \"0.58810174\",\n \"0.5875787\",\n \"0.5872436\",\n \"0.58617127\",\n \"0.5847513\",\n \"0.58371437\",\n \"0.5825238\",\n \"0.58060586\",\n \"0.57760423\",\n \"0.57582897\",\n \"0.57559717\",\n \"0.5741378\",\n \"0.57357967\",\n \"0.57244027\",\n \"0.57207346\",\n \"0.57052666\",\n \"0.57026696\",\n \"0.5699567\",\n \"0.5691737\",\n \"0.5674839\",\n \"0.56698745\",\n \"0.56507474\",\n \"0.56489575\",\n \"0.5647272\",\n \"0.56436497\",\n \"0.56394905\",\n \"0.5629451\",\n \"0.56042945\",\n \"0.5586043\",\n \"0.5575358\",\n \"0.5564507\",\n \"0.5560274\",\n \"0.5559202\",\n \"0.55566984\",\n \"0.5551199\",\n \"0.5545818\",\n \"0.55355006\",\n \"0.55347306\",\n \"0.5532357\",\n \"0.5530181\",\n \"0.5528539\",\n \"0.5509141\",\n \"0.55047363\",\n \"0.5498427\",\n \"0.5487555\",\n \"0.5478177\",\n \"0.5471607\",\n \"0.54585004\",\n \"0.54517007\",\n \"0.5451064\",\n \"0.54461145\",\n \"0.54454315\",\n \"0.54394543\",\n \"0.54364944\",\n \"0.5430838\",\n \"0.54263145\",\n \"0.54211265\",\n \"0.54192626\",\n \"0.54138917\",\n \"0.53921056\",\n \"0.5391449\",\n \"0.53893536\",\n \"0.5388354\",\n \"0.5388113\",\n \"0.538079\",\n \"0.537628\",\n \"0.5375829\",\n \"0.5363433\",\n \"0.5354562\",\n \"0.5343961\",\n \"0.5339366\",\n \"0.53313524\",\n \"0.5327705\",\n \"0.53269\"\n]","isConversational":false},"document_score":{"kind":"string","value":"0.0"},"document_rank":{"kind":"string","value":"-1"}}},{"rowIdx":22669,"cells":{"query":{"kind":"string","value":"Creates a TeX formatted string for a given subsubsection"},"document":{"kind":"string","value":"def latex_subsection(section):\n string = '\\\\subsubsection*{' + section['heading'] + '}\\n'\n string += section['value'] + '\\n'\n return string"},"metadata":{"kind":"string","value":"{\n \"objective\": {\n \"self\": [],\n \"paired\": [],\n \"triplet\": [\n [\n \"query\",\n \"document\",\n \"negatives\"\n ]\n ]\n }\n}"},"negatives":{"kind":"list like","value":["def sub(string, subscript):\n return string + \"<sub>\" + subscript + \"</sub>\"","def _build_sub(self) -> str:\n return dedent(\n \"\"\"\n @SP\n M=M-1\n A=M\n D=M\n @SP\n M=M-1\n A=M\n M=M-D\n @SP\n M=M+1\n \"\"\"\n )","def print_sub_section(self, s, level=0):\n section = s.capitalize()\n\n self.print_newline()\n self._write('%s+ %s\\n' % ('-' * level, section))\n self.print_newline()","def subtitle(self, txt):\n num = len(txt)\n ticks = \"-\" * num\n print(txt)\n print(ticks)","def subtitle(string):\n print(\"{}\\n{}\\n\".format(bold(string), underline(string, \"-\")))","def sub_section(title, lines, pad_len=4, sep='-'):\n pad = pad_len * \" \"\n msg = \"{pad}{}\\n{pad}{}\\n\".format(title, sep * len(title), pad=pad)\n msg += pad + (\"\\n\" + pad).join(lines) + \"\\n\"\n\n return msg","def add_subsection(self, text: str) -> None:\n\n tag = r'\\subsection{%s}' % (text)\n self.doc = self.doc + tag","def get_subtitle_print(subs: List[Track]) -> List[str]:\n data = []\n if not subs:\n data.append(\"--\")\n for sub in subs:\n line_items = []\n\n # following sub.title tree checks and supports three different language and title scenarios\n # The second scenario is the recommended option to choose if you are open to choosing any\n # The third scenario should be used if you have nothing unique to state about the track\n # | Language | Track Title | Output |\n # | ------------ | ----------------------------- | --------------------------------------------- |\n # | es / Spanish | Spanish (Latin American, SDH) | - Spanish (Latin American, SDH), SubRip (SRT) |\n # | es / Spanish | Latin American (SDH) | - Spanish, Latin American (SDH), SubRip (SRT) |\n # | es / Spanish | None | - Spanish, SubRip (SRT) |\n language = pycountry.languages.get(alpha_2=sub.language).name\n if sub.title:\n if language.lower() in sub.title.lower():\n line_items.append(sub.title)\n else:\n line_items.append(f\"{language}, {sub.title}\")\n else:\n line_items.append(language)\n\n line_items.append(sub.format.replace(\"UTF-8\", \"SubRip (SRT)\"))\n\n line = \"- \" + \", \".join(line_items)\n data += [\n (\" \" + x if i > 0 else x)\n for i, x in enumerate(textwrap.wrap(line, 64))\n ]\n return data","def makeTexInput(self, tabletitle, subsection=True):\r\n tablestring = \"\"\r\n\r\n # if there's enough effluent data\r\n if self.ds.effluent.include:\r\n if subsection:\r\n tablestring += r\"\\subsection{%s}\" % (self.bmp,)\r\n\r\n # caption for the stats plot\r\n prob_caption = \"Box and Probability Plots of {} at {} BMPs\".format(\r\n self.parameter.name, self.bmp\r\n )\r\n\r\n # caption for the scatter plot\r\n scatter_caption = \"Influent vs. Effluent Plots of {} at {} BMPs\".format(\r\n self.parameter.name, self.bmp\r\n )\r\n\r\n # warning about having a lot of non-detects\r\n warning = \"\"\"\r\n Warning: there is a very high percentage of non-detects in\r\n this data set. The hypothesis test results and other\r\n statistics reported in this table may not be valid.\r\n \"\"\"\r\n\r\n # make the table and write it to the output file\r\n tablestring += self._make_tex_table(tabletitle)\r\n\r\n # if less than 80% of the data is ND\r\n if self.ds.effluent.ND / self.ds.effluent.N <= 0.8:\r\n\r\n # make the stat plot string\r\n statfig = self._make_tex_figure(\r\n self.stat_fig_name, prob_caption, clearpage=False\r\n )\r\n\r\n # make the scatter plot string\r\n scatterfig = self._make_tex_figure(\r\n self.scatter_fig_name, scatter_caption, clearpage=True\r\n )\r\n\r\n # write the strings to the file\r\n tablestring += statfig\r\n tablestring += scatterfig\r\n\r\n else:\r\n # if there are too many non-detect,\r\n # issue the warning\r\n tablestring += warning\r\n\r\n return tablestring","def subtitle(\n pdf, text, indent=10, border=BORDER, font_size=12, font_style=\"B\"\n): # pylint: disable = too-many-arguments\n pdf.cell(indent, border=border)\n pdf.set_font(\"arial\", font_style, font_size)\n pdf.cell(75, 10, text, border, 1)","def create_caption(section, superscript, text):\n section.append('\\n')\n\n # Superscript\n section.append(bold(pylatex.NoEscape(r'{\\footnotesize \\textsuperscript {' + superscript + '}}')))\n\n # Text\n section.append(italic(pylatex.NoEscape(r'{\\footnotesize {' + text + '}}')))","def _parse_sub(self, parsetree, text, fpos=0):\r\n curr = 0\r\n for match in self._reSubstitution.finditer(text):\r\n start = match.start()\r\n if start > curr:\r\n parsetree.append((\"str\", self._reComment.sub('', text[curr:start])))\r\n\r\n if match.group(\"sub\") is not None:\r\n if not match.group(\"end\"):\r\n raise TemplateSyntaxError(\"Missing closing tag '%s' for '%s'.\" \r\n % (self._sub_end, match.group()), self._errpos(fpos+start))\r\n if len(match.group(\"sub\")) > 0:\r\n self._testexpr(match.group(\"sub\"), fpos+start)\r\n parsetree.append((\"sub\", match.group(\"sub\")))\r\n else:\r\n assert(match.group(\"escsub\") is not None)\r\n if not match.group(\"escend\"):\r\n raise TemplateSyntaxError(\"Missing closing tag '%s' for '%s'.\"\r\n % (self._subesc_end, match.group()), self._errpos(fpos+start))\r\n if len(match.group(\"escsub\")) > 0:\r\n self._testexpr(match.group(\"escsub\"), fpos+start)\r\n parsetree.append((\"esc\", self.escape, match.group(\"escsub\")))\r\n\r\n curr = match.end()\r\n\r\n if len(text) > curr:\r\n parsetree.append((\"str\", self._reComment.sub('', text[curr:])))","def latex_course(course):\n basic_info_list = [\n 'session', 'school', 'credits', 'level', 'offered',\n 'visiting_students', 'erasmus_students'\n ]\n generic_subsection_list = [\n 'description', 'timetable', 'requirements_of_entry',\n 'excluded_courses', 'co_requisites', 'assessment_weighting'\n ]\n string = '\\\\subsection{' + course[\"title\"] + '}\\n'\n for info in basic_info_list:\n string += latex_info(course[info])\n for subsection in generic_subsection_list:\n string += latex_subsection(course[subsection])\n string += '\\\\break \\\\textbf{' + course['assessment_date'][\n 'heading'] + '}' + course['assessment_date']['value'] + '\\n'\n string += latex_subsection(course['aims'])\n string += '\\\\subsubsection*{' + \\\n course['learning_outcomes']['heading'] + '}\\n'\n outcome_list = re.split(\n '\\d+\\. ', course['learning_outcomes']['value'])\n string += outcome_list[0] + '\\n'\n string += '\\\\begin{enumerate}\\n'\n for i in outcome_list[1:-1]:\n string += '\\\\item ' + i + '\\n'\n string += '\\\\end{enumerate}\\n'\n return string","def print_help():\n \n print(\"\"\"\n catsub - substitutes every value for each variable in each word of a template file.\n\n Usage:\n\n catsub [--help] [-s] [-u] [-D|-dSTR] [TEMPLATEFILES] [%VARNAME VALUE1 VALUE2 ... ]*\n\n Arguments:\n\n TEMPLATEFILES Name(s) of file(s) containg the template with\n variables of the from %VARNAME; If no file name\n is given, or the name is '-', catsub will read\n from standard input;\n %VARNAME Variable name to substitute;\n VALUE1 VALUE2 ... Values to substitute for the variable;\n -s Print statistics to stderr on resolved and unresolved variables.\n -u Escaped percentage in template are returned unescaped;\n -dSTR Use STR to divide multiple substituted values\n -D Use newline to divide multiple substituted values\n --help Show this help page.\n\n Notes: \n\n - The names of the template files may not start with a percent sign. \n\n - All variables must start with a percent sign and cannot contain\n whitespace.\n\n - Substituted values cannot start with a percent sign.\n\n - Substitution happens only once per variable, i.e., substituted\n values do not undergo subsequent substitutions.\n\n - When substituting several values, those values are separated by a\n space, by a newline if the -D argument was given, or by STR if\n the -dSTR argument was given.\n\n - When a variable has been given several values to substitute and\n the variable occurs in a substring of a word in the template,\n that word get repeated. E.g. \"echo un%X | catsub %X kind tidy\"\n gives \"unkind untidy\"\n\n - Substitution happens combinatorically within a word. E.g. a word\n \"%X,%Y\" in the template, when processed with \"catsub %X a b %Y c d\"\n becomes \"a,c a,d b,c b,d\". Combinatorics can be circumvented by\n quoting the replacement values, i.e. \"catsub %X 'a b' %Y 'c d'\"\n gives \"a b,c d\".\n\n - Substitution uses the longest possible variable name. E.g. in\n \"%HELLOWORLD\", both %HELLO and %HELLOWORLD could be substituted\n if values for both are specified on the catsub command, but it is\n the longer %HELLOWORLD that gets used.\n \n - Percentage signs in the template can escape substitution by\n prepeding them with a slash, i.e., '\\%'. Every '\\%' in the\n template will be remain a '\\%' unless the -u argument is used in\n the catsub command, in which case, they are replaced by '%'.\n\n - The template cannot use the unicode character '％'.\n\n Examples:\n\n $ echo %HELLO %UNIVERSE | catsub %HELLO Hi %UNIVERSE world\n Hi world\n\n $ echo %HELLO %UNIVERSE > example.tmpl\n $ catsub example.tmpl %HELLO Greetings %UNIVERSE universe!\n Greetings universe!\n\"\"\")","def get_as_subtext_field(field, field_title=None) -> str:\n s = \"\"\n if field:\n s = f\"{field} | \"\n else:\n return \"\"\n\n if field_title:\n s = f\"{field_title}: \" + s\n\n return s","def get_as_subtext_field(field, field_title=None) -> str:\n s = \"\"\n if field:\n s = f\"{field} | \"\n else:\n return \"\"\n\n if field_title:\n s = f\"{field_title}: \" + s\n\n return s","def repr_subfigure(self):\n default_kwargs = {'placement': self.subfig_placement,\n 'width': self.subfig_width,\n 'caption': self.caption,\n 'label': self.label}\n\n myfig = self.extension_mapping[self.extension]()\n\n return self.subfig_str.format(myfig=myfig, **default_kwargs)","def format_subrange(start, end, step):\n \n if start == end:\n return str(start)\n elif step == 1:\n return \"%d-%d\" % (start, end)\n else:\n return \"%d-%dx%d\" % (start, end, step)","def format_sub_part(cls, field, length):\n try:\n if not length:\n raise ValueError\n\n length = int(length)\n return \"`%s`(%d)\" % (field, length)\n\n except ValueError:\n return \"`%s`\" % (field,)","def sub(abbreviation: str, alias: str) -> str:\n return f'<sub alias=\"{alias}\">{abbreviation}</sub>'","def format_substitutions(subs: Union[SubstituteTerm, List[SubstituteTerm]]):\n text = \"\"\n if isinstance(subs, SubstituteTerm):\n term_str = str(subs)\n for line in term_str.split('\\n'):\n text += Markup.escape(line) + Markup('<br />')\n text += Markup('<br />')\n return text\n for term in subs:\n term_str = str(term)\n for line in term_str.split('\\n'):\n text += Markup.escape(line) + Markup('<br />')\n text += Markup('<br />')\n return text","def write_subtitle(self, subtitle: str, break_page: bool, class_txt: str) -> str:\n if break_page:\n str_title = \"\"\"<h2 class=\"break-before\">\"\"\" + subtitle + \"\"\"</h2>\\n\"\"\"\n else:\n str_title = \"\"\"<h2 class=\\\"\"\"\" + class_txt + \"\"\"\\\">\"\"\" + subtitle + \"\"\"</h2>\\n\"\"\"\n self.html_doc = self.html_doc + str_title\n return self.html_doc","def _Subscript(self, t):\n self.dispatch(t.value)\n self.write(\"[\")\n self.dispatch(t.slice)\n self.write(\"]\")","def sub(proto, *args):\n try:\n text = proto.format(*args)\n except:\n text = \"--\"\n #print sub(\"WARNING: Couldn't sub {} with {}\", proto, args)\n return text","def get_as_subtext_field(field, field_title=None) -> str:\n s = \"\"\n if field:\n s = f\"{field} | \"\n else:\n return \"\"\n\n if field_title:\n s = f\"{field_title} :\" + s\n\n return s","def Subtlety(self):\n s = self.subtlety\n assert s in range(1,6), \"Subtlety score out of bounds.\"\n return _char_to_word_[::-1][ s-1 ] + ' Subtlety'","def _var_quote_sub(self, text, VARS):\n ## No need to live on class. Can be moved to tools. - Add assert test.\n qvars = map(lambda x: \"\\{ \" + x + \" \\}\", VARS)\n return text % tuple(qvars)","def print_substep(text, style=\"\"):\n console.print(text, style=style)","def test__render_inline_section_into(field_added, section_title, section_content):\n into, field_added = render_inline_section_into([], field_added, section_title, section_content)\n return ''.join(into), field_added","def format_sections(self, sections: SectionDict) -> str:","def _plainTextSFinal(\n app,\n browseNavLevel,\n finalSecType,\n sNode,\n opened,\n secFinal,\n **options,\n):\n\n display = app.display\n dContext = display.distill(options)\n fmt = dContext.fmt\n\n api = app.api\n T = api.T\n\n aContext = app.context\n formatCls = aContext.formatCls\n\n secStr = str(T.sectionFromNode(sNode)[browseNavLevel])\n isOpened = secStr in opened\n tCls = \"\" if fmt is None else formatCls[fmt or DEFAULT_FORMAT].lower()\n\n prettyRep = (\n prettyTuple(\n app,\n (sNode,),\n seq=secStr,\n condensed=False,\n condenseType=finalSecType,\n **display.consume(options, \"condensed\", \"condenseType\"),\n )\n if isOpened\n else \"\"\n )\n current = \" focus\" if secStr == str(secFinal) else \"\"\n attOpen = \" open \" if isOpened else \"\"\n\n textRep = plain(\n app,\n sNode,\n withPassage=False,\n **display.consume(options, \"withPassage\"),\n )\n html = f\"\"\"\\\n<details\n class=\"pretty{current}\"\n seq=\"{secStr}\"\n {attOpen}\n>\n <summary class=\"{tCls} ubd\">{app._sectionLink(sNode, text=secStr)} {textRep}</summary>\n <div class=\"pretty\">{prettyRep}</div>\n</details>\\\n\"\"\"\n return html","def _var_id_sub(self, sprintf):\n id_list = map(lambda x: self.cdict[x][1], sprintf[\"vars\"] )\n return sprintf[\"text\"] % tuple(id_list)","def to_srt(self, subtitles):\n \n srt_data = ''\n subtitle_num = self.start_index\n for subtitle in subtitles:\n subtitle_num += 1\n \n offset = self.start_time\n \n start_time = self._ms_to_time(subtitle['start_time'] + offset)\n end_time = self._ms_to_time(subtitle['end_time'] + offset)\n \n content = subtitle['content'].replace('<br>', ' ')\n \n srt_data += str(subtitle_num) + '\\r\\n'\n srt_data += '%s --> %s' % (start_time, end_time) + '\\r\\n'\n srt_data += content + '\\r\\n'\n srt_data += '\\r\\n'\n \n self.end_index = subtitle_num\n \n return srt_data","def do_text(name, param, level):\n text = [\n f'{name}',\n f'{level * len(name)}',\n '',\n f'| *default*: {param[\"default\"]!r}',\n f'| *type*: {param[\"type\"]}',\n f'| *title*: {param[\"title\"]}',\n f'| *short description*: {param[\"short\"]}',\n f'| *long description*: {param[\"long\"]}',\n f'| *group*: {param.get(\"group\", \"No group assigned\")}',\n f'| *explevel*: {param[\"explevel\"]}',\n '',\n ]\n\n return os.linesep.join(text)","def repr_sub_module(self, class_name: str):\n s = 'py::module {sub} = {base}.def_submodule(\"{name}\", \"Submodule {name}\")'\n data = {\n \"sub\": self.sub_module_name(class_name),\n \"base\": BASE_SUB_MODULE_NAME,\n \"name\": class_name\n }\n return s.format(**data)","def _latex_(self):\n old = s = \"\\\\begin{verbatim}\\\\end{verbatim}\"\n new = \"\"\n s = \"\\\\begin{verbatim}\\\\end{verbatim}\"\n s += \"Element of \" + self._space._latex_().replace(old, new) + \" With principal part \"\n WR = self._space.multiplier()\n # If we have more than one non-zero element in the principal part we have to\n # add a + between terms\n sp = \"\"\n for (b, m) in self._principal_part:\n a = self._principal_part[(b, m)]\n if a != 0:\n x = QQ(m + WR.Qv[WR.D.index(b)])\n if a != 1:\n if a > 0 and len(sp) > 0:\n ast = \"+\" + str(a)\n else:\n ast = str(a)\n sp = sp + ast + \"q^{\" + str(x) + \"}\"\n else:\n sp = sp + \"q^{\" + str(x) + \"}\"\n s = s + sp + \"$.\"\n return s","def format_yaml_tag(subcls):\n return f'!{subcls.__name__}Class'","def subtitle(self):\n worksheet_type = self.options[\"worksheet_type\"].value\n return \"{} - {}\".format(WORKSHEET_OPTIONS[worksheet_type], super().subtitle)","def generate_cross_section_title(grid, field, start, end):\n\n # Grab information about the grid\n time_str = generate_grid_time_begin(grid).strftime(\"%Y-%m-%dT%H:%M:%SZ\")\n field_name = generate_field_name(grid, field)\n\n # Deal with the latitude/longitude part\n start_lat, start_lon = start\n end_lat, end_lon = end\n\n degree_symbol = \"\\N{DEGREE SIGN}\"\n\n # Add degrees latitude/longitude labels\n\n if start_lat < 0:\n start_lat_string = f\"{-start_lat}{degree_symbol}S\"\n else:\n start_lat_string = f\"{start_lat}{degree_symbol}N\"\n\n if end_lat < 0:\n end_lat_string = f\"{-end_lat}{degree_symbol}S\"\n else:\n end_lat_string = f\"{end_lat}{degree_symbol}N\"\n\n if start_lon < 0:\n start_lon_string = f\"{-start_lon}{degree_symbol}W\"\n else:\n start_lon_string = f\"{start_lon}{degree_symbol}E\"\n\n if end_lon < 0:\n end_lon_string = f\"{-end_lon}{degree_symbol}W\"\n else:\n end_lon_string = f\"{end_lon}{degree_symbol}E\"\n\n return (\n f\"({start_lat_string}, {start_lon_string})\"\n f\" to ({end_lat_string}, {end_lon_string}) \\n {time_str} \\n {field_name}\"\n )","def create_subtitle(self):\n label_subtitle = Label(self.frame, text=\"Projet Python 2020\", font=(\"Arial\", 25), bg='light blue',\n fg='white')\n label_subtitle.pack()","def _subsection_titles(self, section_index):\r\n # Retrieve the subsection title for the section\r\n # Add one to the list index to get the CSS index, which starts at one\r\n subsection_css = 'nav>div.chapter:nth-of-type({0})>ul>li>a>p:nth-of-type(1)'.format(section_index)\r\n\r\n # If the element is visible, we can get its text directly\r\n # Otherwise, we need to get the HTML\r\n # It *would* make sense to always get the HTML, but unfortunately\r\n # the open tab has some child <span> tags that we don't want.\r\n return self.q(\r\n css=subsection_css).map(\r\n lambda el: el.text.strip().split('\\n')[0] if el.is_displayed() else el.get_attribute('innerHTML').strip()\r\n ).results","def __str__(self):\n t = Template(\n \"\"\"\n <h4>$title</h4>\n $imgs\n $footnotes\n <hr/>\"\"\")\n # Return result.\n return t.substitute({\n \"title\": self.title,\n \"imgs\": self.render_images(),\n \"footnotes\": self.render_footnotes()\n })","def assemble_subpeak_record(subpeak, celltype_activations, sequence):\n # make the header\n header ='\\t'.join([s for s in subpeak])\n header = '>' + header\n\n # make the activation string\n activation = ';'.join([ct+' '+str(score) for (ct,score) in celltype_activations])\n\n # append the sequence\n seq_string = str(sequence)\n return header, activation, seq_string","def add_flaux(self, identifier, sub, expression, doc=''):\n # todo: why does the no-sub condition give [''] as the argument?\n # todo: evaluate if we should instead use syntax [['a1','pears'],['a2','pears']]\n # todo: build a test case to test\n # todo: clean up this function\n # todo: add docstring handling\n\n docstring = ''\n\n if sub[0]!='': #todo: consider factoring this out if it is useful for the multiple flows\n directory, size = get_array_info(sub, self.dictofsubs)\n\n funcset = 'loc_dimension_dir = %s.dimension_dir \\n'%identifier\n funcset += ' output = np.ndarray((%s))\\n'%','.join(map(str,size)) #lines which encode the expressions for partially defined subscript pieces\n for expr, subi in zip(expression, sub):\n expr = expr.replace('\\n','').replace('\\t','').strip() # todo: pull out\n indices = ','.join(map(str,getelempos(subi, self.dictofsubs)))\n if re.search(';',expr): # if 2d array, format for numpy\n expr = 'np.'+np.array(np.mat(expr.strip(';'))).__repr__()\n funcset += ' output[%s] = %s\\n'%(indices, expr)\n else:\n funcset = 'loc_dimension_dir = 0 \\n'\n funcset += ' output = %s\\n'%expression[0]\n\n funcstr = templates['flaux'].substitute(identifier=identifier,\n expression=funcset,\n docstring=docstring)\n\n if sub[0] != '': # todo: make less brittle\n funcstr += '%s.dimension_dir = '%identifier+directory.__repr__()+'\\n' # todo: do we like 'dimension_dictionary' as a name?\n self.body.append(funcstr)\n\n return identifier","def get_subtitle(annotation, sub_duration, video_clip, seen_annotations):\n if len(annotation[\"text\"]) == 0:\n return None\n\n annotation_txt = calculate_needed_subtitle_height(annotation, seen_annotations, video_clip)\n\n txt_clip = TextClip(annotation_txt, color=\"white\", fontsize=70, font='Sans Serif')\n txt_clip = txt_clip.set_position((\"center\", get_subtitle_offset(annotation, seen_annotations, video_clip)))\n txt_clip = txt_clip.set_start(float(annotation[\"time\"]) / 1000.0)\n txt_clip = txt_clip.set_duration(sub_duration)\n\n return txt_clip","def _get_section(registry, section, title, hdg_level1=\"#\", hdg_level2=\"=\",\n output_dir=None):\n file_per_topic = output_dir is not None\n lines = [title, hdg_level1 * len(title), \"\"]\n if file_per_topic:\n lines.extend([\".. toctree::\", \" :maxdepth: 1\", \"\"])\n\n topics = sorted(registry.get_topics_for_section(section))\n for topic in topics:\n help = registry.get_detail(topic)\n heading, text = help.split(\"\\n\", 1)\n if not text.startswith(hdg_level2):\n underline = hdg_level2 * len(heading)\n help = \"%s\\n%s\\n\\n%s\\n\\n\" % (heading, underline, text)\n else:\n help = \"%s\\n%s\\n\\n\" % (heading, text)\n if file_per_topic:\n topic_id = _dump_text(output_dir, topic, help)\n lines.append(\" %s\" % topic_id)\n else:\n lines.append(help)\n\n return \"\\n\" + \"\\n\".join(lines) + \"\\n\"","def subsection(self, title):\n section = self.__class__(title, self, self.level + 1, proc=self.proc)\n self.subsections.append(section)\n return section","def fn_sub(self, value):\n\n if isinstance(value, list):\n value, variables = value\n else:\n # only template parameter names, resource logical IDs, and resource attributes, will be parsed\n value, variables = value, {}\n\n for name, target in variables.items():\n value = value.replace('${{{}}}'.format(name), target)\n\n return Functions.SUB_VARIABLE_PATTERN.sub(self._sub_variable, value)","def doc_sub(*sub):\n def dec(obj):\n obj.__doc__ = obj.__doc__.format(*sub)\n return obj\n return dec","def buildGSUB(self):\n\t\t# Construct GSUB table bottom-up.\n\t\tli_fi = Ligature()\n\t\tli_fi.LigGlyph = 'f_i'\n\t\tli_fi.Component = ['i']\n\t\tli_fi.CompCount = 2\n\n\t\tliSubst = LigatureSubst()\n\t\tliSubst.ligatures = {'f': li_fi}\n\t\tliSubst.Format = 1\n\t\tliSubst.LookupType = 4\n\n\t\tlookup = Lookup()\n\t\tlookup.LookupType = 4 # Ligature\n\t\tlookup.LookupFlag = 0\n\t\tlookup.SubTable = [liSubst]\n\t\tlookup.SubTableCount = len(lookup.SubTable)\n\n\t\tlookupList = LookupList()\n\t\tlookupList.Lookup = [lookup]\n\t\tlookupList.LookupCount = len(lookupList.Lookup)\n\n\t\tfea = Feature()\n\t\tfea.FeatureParams = None\n\t\tfea.LookupCount = 1\n\t\tfea.LookupListIndex = [0]\n\n\t\tfeaRecord = FeatureRecord()\n\t\tfeaRecord.FeatureTag = 'liga'\n\t\tfeaRecord.Feature = fea\n\n\t\tfeaList = FeatureList()\n\t\tfeaList.FeatureRecord = [feaRecord]\n\t\tfeaList.FeatureCount = len(feaList.FeatureRecord)\n\n\t\tlangSys = LangSys()\n\t\tlangSys.LookupOrder = None\n\t\tlangSys.ReqFeatureIndex = 0xFFFF\n\t\tlangSys.FeatureIndex = [0]\n\t\tlangSys.FeatureCount = len(langSys.FeatureIndex)\n\n\t\tsct = Script()\n\t\tsct.DefaultLangSys = langSys\n\t\tsct.LangSysRecord = []\n\t\tsct.LangSysCount = len(sct.LangSysRecord)\n\n\t\tsctRec = ScriptRecord()\n\t\tsctRec.ScriptTag = 'tag1'\n\t\tsctRec.Script = sct\n\n\t\tsctList = ScriptList()\n\t\tsctList.ScriptRecord = [sctRec]\n\t\tsctList.ScriptCount = len(sctList.ScriptRecord)\n\n\t\tgsub = GSUB()\n\t\tgsub.LookupList = lookupList\n\t\tgsub.FeatureList = feaList\n\t\tgsub.ScriptList = sctList\n\n\t\ttable = ttLib.newTable('GSUB')\n\t\ttable.table = gsub\n\t\treturn table","def section(sect_name, width=TERMINAL_CHARS):\n\n true_length = width - len(sect_name) - 1\n print(\"\\n_%s\" % (sect_name.upper() + \"_\" * true_length))","def astext(self):\n self.elements.update({\n 'body': u''.join(self.body),\n 'indices': self.generate_indices()\n })\n return self.render('beamer.tex_t', self.elements)","def group_list(self, group_number=1):\n text = []\n group = self.varexercise_numbers[group_number-1]\n group_name = _('Group %s') % group[0]\n text.append('\\n\\\\subsection*{%s}\\n' % group_name)\n for number in range(1, self.number_of_variations+1):\n print(\"---------\", number) # !!!\n variation = '\\n\\n\\\\textbf{%s}\\\\\\\\\\n' %\\\n _('Variation %d') % number\n text.append(variation)\n exercise_number = 1\n for item in group[1:]:\n # print(' '*5, item) # !!!\n if not isinstance(item, str):\n latex_plain = item.latex_plain(number)\n if latex_plain:\n text.append('%d. ' % exercise_number)\n # print(' '*5, number) # !!!\n # print(' '*5, latex_plain) # !!!\n text.append(latex_plain + '\\n')\n exercise_number += 1\n # if with_solution:\n # text.extend(self.one_group(group_number=group_number))\n # text.append(\"\\\\newpage\\n\")\n return text","def section_header(text):\n\n print \"---- %s ----\" % text","def _create_formatted_string(self):\n string = NALSyntax.StatementSyntax.Start.value + \\\n self.get_subject_term().get_formatted_string()\n\n string += \" \" + self.get_copula_string() + \" \"\n\n string += self.get_predicate_term().get_formatted_string() + \\\n NALSyntax.StatementSyntax.End.value\n\n return string","def __str__(self):\n outs = str(self.cluster_subspace).split(\"\\n\")[:6]\n\n if self.regression_data is not None:\n # This might need to be redefined to take \"expectation\" using measure\n feature_avg = np.average(self.feature_matrix, axis=0)\n feature_std = np.std(self.feature_matrix, axis=0)\n outs += [\n f\"Regression Data : estimator={self.regression_data.estimator_name}\",\n f\" module={self.regression_data.module}\",\n f\" parameters={self.regression_data.parameters}\",\n f\"Target Property : \"\n f\"mean={np.mean(self.regression_data.property_vector):0.4f} \"\n f\"std={np.std(self.regression_data.property_vector):0.4f}\",\n ]\n fit_var = sum(\n self._subspace.function_total_multiplicities[1:] * self.eci[1:] ** 2\n )\n outs += [\n f\"ECI-based Property : mean={self.eci[0]:0.4f}\"\n f\" std={np.sqrt(fit_var):0.4f}\",\n \"Fit Summary\",\n ]\n\n for i, term in enumerate(self._subspace.external_terms):\n outs.append(f\"{repr(term)}={self.coefs[len(self.eci) + i]:0.3f}\")\n\n if self.regression_data is not None:\n outs += [\n \" ---------------------------------------------------------------------\"\n \"-------------------------------\",\n \" | ID Orbit ID Degree Cluster Diameter ECI Feature AVG\"\n \" Feature STD ECI * STD |\",\n f\" | 0 0 0 NA \"\n f\"{self.eci[0]:^7.3f}{feature_avg[0]:^15.3f}\"\n f\"{feature_std[0]:^15.3f}{feature_std[0] * self.eci[0]:^13.3f}|\",\n ]\n else:\n outs += [\n \" ---------------------------------------------------------\",\n \" | ID Orbit ID Degree Cluster Diameter ECI |\",\n f\" | 0 0 0 NA \"\n f\"{self.eci[0]:^7.3f} |\",\n ]\n\n for degree, orbits in self.cluster_subspace.orbits_by_size.items():\n for orbit in orbits:\n for i, bits in enumerate(orbit.bit_combos):\n line = (\n f\" |{orbit.bit_id + i:^6}{orbit.id:^12}{degree:^10}\"\n f\"{orbit.base_cluster.diameter:^20.4f}\"\n f\"{self.eci[orbit.bit_id + i]:^7.3f}\"\n )\n if self.regression_data is not None:\n line += (\n f\"{feature_avg[orbit.bit_id + i]:^15.3f}\"\n f\"{feature_std[orbit.bit_id + i]:^15.3f}\"\n f\"{feature_std[orbit.bit_id + i] * self.eci[orbit.bit_id + i]:^13.3f}\" # noqa\n )\n line += \"|\"\n outs.append(line)\n outs.append(\" \" + (len(outs[-1]) - 1) * \"-\")\n return \"\\n\".join(outs)","def make_section_str(str=\"\", width=60, symbol='-'):\n strlen = len(str)\n dash_len = width - strlen if strlen < width else 0\n side_dash = symbol * (dash_len // 2)\n section_str = \"{}{}{}\".format(side_dash, str, side_dash)\n return section_str","def getSubtitleTable(date) -> str:\n return \"\"\"| Start of the day | Weeks until NIMCET |\n| ---------------- | -----------------: |\n| {time} | {weeks} weeks |\"\"\".format(time=formattedTimeNow(), weeks=round((datetime(2021, 5, 21) - date).days/7, 1))","def subsection_handler(request, usage_key_string):\r\n if 'text/html' in request.META.get('HTTP_ACCEPT', 'text/html'):\r\n usage_key = UsageKey.from_string(usage_key_string)\r\n try:\r\n course, item, lms_link = _get_item_in_course(request, usage_key)\r\n except ItemNotFoundError:\r\n return HttpResponseBadRequest()\r\n\r\n preview_link = get_lms_link_for_item(usage_key, preview=True)\r\n\r\n # make sure that location references a 'sequential', otherwise return\r\n # BadRequest\r\n if item.location.category != 'sequential':\r\n return HttpResponseBadRequest()\r\n\r\n parent = get_parent_xblock(item)\r\n\r\n # remove all metadata from the generic dictionary that is presented in a\r\n # more normalized UI. We only want to display the XBlocks fields, not\r\n # the fields from any mixins that have been added\r\n fields = getattr(item, 'unmixed_class', item.__class__).fields\r\n\r\n policy_metadata = dict(\r\n (field.name, field.read_from(item))\r\n for field\r\n in fields.values()\r\n if field.name not in ['display_name', 'start', 'due', 'format'] and field.scope == Scope.settings\r\n )\r\n\r\n can_view_live = False\r\n subsection_units = item.get_children()\r\n for unit in subsection_units:\r\n state = compute_publish_state(unit)\r\n if state in (PublishState.public, PublishState.draft):\r\n can_view_live = True\r\n break\r\n\r\n return render_to_response(\r\n 'edit_subsection.html',\r\n {\r\n 'subsection': item,\r\n 'context_course': course,\r\n 'new_unit_category': 'vertical',\r\n 'lms_link': lms_link,\r\n 'preview_link': preview_link,\r\n 'course_graders': json.dumps(CourseGradingModel.fetch(usage_key.course_key).graders),\r\n 'parent_item': parent,\r\n 'locator': usage_key,\r\n 'policy_metadata': policy_metadata,\r\n 'subsection_units': subsection_units,\r\n 'can_view_live': can_view_live\r\n }\r\n )\r\n else:\r\n return HttpResponseBadRequest(\"Only supports html requests\")","def format_template(template, *args):\n return textwrap.dedent(template % args).strip()","def outputText(self, item, titleMode, internal=False):\n for num in range(self.parentLevel):\n item = item.parent\n if not item:\n return ''\n field = item.nodeFormat().findField(self.name)\n if not field:\n return ''\n return field.outputText(item, titleMode, internal)","def __str__(self):\n\n # can't use backslash in f-string\n newline = \"\\n\"\n\n options = self.options\n kwoptions = self.kwoptions\n\n # add background to options\n if self.background:\n options.append(\"show background rectangle\")\n kwoptions.update({\"background rectangle/.style\": f\"{{fill={self.background}}}\"})\n\n return rf\"\"\"\\begin{{tikzpicture}}{wrap(fmt_options(self.options, self.kwoptions))}\n{newline.join(define_colours(self._used_colours))}\n{newline.join(self.defines)}\n{newline.join(self._commands)}\n\\end{{tikzpicture}}\"\"\"","def printable(title, subtitle=None, resp=None):\n title = getfirst(title)\n subtitle = getfirst(subtitle)\n resp = getfirst(resp)\n if subtitle:\n title += \" : \" + subtitle\n if resp:\n title += \" / \" + resp\n return title","def eng_with_sub(self, eng: list, subword: list) -> list:\n subwords = subword + eng[0]\n while [] in subwords:\n subwords.remove([])\n out = \" \".join('%s' % id for id in subwords).split()\n return out","def add_subsection(self):\n self.add_child() # lint-amnesty, pylint: disable=no-member","def render_subtypes(spec_catalog, data_type, prefix=None):\n subtypes = spec_catalog.get_subtypes(data_type)\n if len(subtypes) == 0:\n return None\n re = prefix if prefix is not None else \"\"\n re += \", \".join([RSTDocument.get_reference(RSTSectionLabelHelper.get_section_label(ct), ct)\n for ct in subtypes])\n return re","def print_section(section_name, width=120):\n section_name = ' ' + section_name + ' '\n print('{:=^{ }}'.format(section_name, width))","def syntax_text():","def latex_info(info):\n return '\\\\textbf{' + info['heading'] + '} ' + info['value'] + ' \\\\break\\n'","def title(text, level=0):\n return '\\n' + text + '\\n' + '=-~_#%^' [level] * len(text) + '\\n\\n'","def print_sentence_voice(self, final_subj, final_obj, verb, v_aux, v_tense, subj_tag, subj_word, final_mod2=None, final_root=None):\n new_verb = ''\n s_sentence1 = s_sentence2 = ''\n\n new_verb = gl.verb_conjugate(verb, v_aux, v_tense) + \" \"\n\n if new_verb.strip() == \"\":\n new_verb = gl.verb_conjugate(verb, v_aux, \"VMIS3S0\") + \" \"\n\n\n for k in sorted(final_subj.keys()):\n s_sentence1 += final_subj[k] + \" \"\n\n for k in sorted(final_obj.keys()):\n s_sentence2 += final_obj[k] + \" \"\n\n if final_mod2 != None:\n for k in sorted(final_mod2.keys()):\n s_sentence2 += final_mod2[k] + \" \"\n if final_root != None:\n for k in sorted(final_root.keys()):\n s_sentence2 += final_root[k] + \" \"\n\n\n #removing errors in punctuation\n s_sentence1 = s_sentence1.replace(\", .\", \".\").replace(\"; .\", \".\").replace(\": .\", \".\").replace(\", ?\", \"?\").replace(\"; ?\", \"?\").replace(\": ?\", \"?\").replace(\", !\", \"!\").replace(\"; !\", \"!\").replace(\": !\", \"!\").replace(\". .\", \".\")\n s_sentence2 = s_sentence2.replace(\", .\", \".\").replace(\"; .\", \".\").replace(\": .\", \".\").replace(\", ?\", \"?\").replace(\"; ?\", \"?\").replace(\": ?\", \"?\").replace(\", !\", \"!\").replace(\"; !\", \"!\").replace(\": !\", \"!\").replace(\". .\", \".\")\n\n\n return self.runTrueCaser(s_sentence1 + new_verb + s_sentence2)","def generate_srt_from_sjson(sjson_subs, speed):\r\n\r\n output = ''\r\n\r\n equal_len = len(sjson_subs['start']) == len(sjson_subs['end']) == len(sjson_subs['text'])\r\n if not equal_len:\r\n return output\r\n\r\n sjson_speed_1 = generate_subs(speed, 1, sjson_subs)\r\n\r\n for i in range(len(sjson_speed_1['start'])):\r\n item = SubRipItem(\r\n index=i,\r\n start=SubRipTime(milliseconds=sjson_speed_1['start'][i]),\r\n end=SubRipTime(milliseconds=sjson_speed_1['end'][i]),\r\n text=sjson_speed_1['text'][i]\r\n )\r\n output += (unicode(item))\r\n output += '\\n'\r\n return output","def template(title, steps, loops):\n return \"%-8s %-20s %20d\" % (title, format_power(steps), loops)","def build_scatter_subplot(fig, x, y, row_num, text, xaxis_text, yaxis_text):\n\n # Place provided data\n fig.add_trace(go.Scatter(x=x, y=y, mode=\"markers\", marker=dict(size=10)), row=row_num, col=1)\n # Approximate linear function\n res = sm.OLS(y, sm.add_constant(x)).fit().fittedvalues\n # Place trendline\n fig.add_trace(go.Scatter(x=x, y=res, mode=\"lines\"), row=row_num, col=1)\n # Place subplot (for the first subplot, we need to refer to the title of the figure itself) title\n if row_num == 1:\n fig.update_layout(title={\"text\": text})\n else:\n fig.layout.annotations[row_num - 2].update(text=text)\n # Place axis text\n fig.update_xaxes(title_text=xaxis_text, row=row_num, col=1)\n fig.update_yaxes(title_text=yaxis_text, row=row_num, col=1)","def __repr__(self) -> str:\n return f\"{self.title} that starts at {self.start} and ends at {self.end} and is taught by {self.instructor}\"","def print_submenu_opc1():\n\n sub_m = '==' * 18 + '\\n|--> 1. Carga Manual' + '\\n|' \\\n + '\\n|--> 2. Generación Automática' + '\\n|'\n\n print(sub_m)","def test_export_failure_subsection_level(self):\r\n vertical = ItemFactory.create(parent_location=self.course.location, category='vertical', display_name='foo')\r\n ItemFactory.create(\r\n parent_location=vertical.location,\r\n category='aawefawef'\r\n )\r\n\r\n self._verify_export_failure(u'/unit/location:MITx+999+Robot_Super_Course+vertical+foo')","def latex_plain(self, num):\n if not self.list:\n return \"\"\n values, erased_elements = self.list[num - 1]\n string_list = self.latex_string_list(values, erased_elements)\n if not string_list:\n raise ValueError('There is not values for the variation')\n # return '-\\;'\n\n for i in range(len(string_list)):\n unit = self.unit_list[i] or ''\n name = self.variable_list[i]\n value_string = string_list[i]\n string_list[i] = '\\\\detokenize{%s} = %s~%s' % (name, value_string, unit)\n\n str = ',\\\\hspace{10pt plus 1cm} '.join(string_list)\n str = str + ';\\\\\\\\'\n return str","def make_shirt(size, text):\n\tprint(f\"\\nThe size of the shirt is {size} and the printed text reads: {text}.\")","def substitute(self, line, system = 'output'):\r\n if type(line) not in (str, unicode):\r\n line = str(line)\r\n setattr(self, '%sSub' % system, line)","def _build_compose_section(ctx, template, name, sls_function):\n\n def get_vol(include):\n name = include[include.rindex('/') + 1:]\n return f' - {include}:/var/task/common/{name}'\n\n output_dir = ctx.get('package', {}).get('output', DEFAULT_OUT_DIR)\n volumes = [\n f' - {output_dir}:/var/task/dist',\n ' - ./.juni/bin:/var/task/bin',\n ] + [\n get_vol(include)\n for include in sls_function.get('include', [])\n ]\n\n reqs_path = sls_function.get('requirements')\n if reqs_path:\n volumes.append(f' - {reqs_path}:/var/task/common/requirements.txt')\n\n return template.format(name=name, volumes='\\n'.join(volumes))","def format_course_output(self, course, schedule):\n\t\t\n\t\t# handle schedule\n\t\tstr_list = schedule.schedule.strip().split(' ')\n\t\tsch_res = ''\n\t\tif (len(str_list) >= 6):\n\t\t\tval_list = str_list[:6]\n\t\t\tval_list[5] = val_list[5][:3]\n\t\t\t\n\t\t\t# time\n\t\t\ttime = val_list[0] + ' ' + val_list[1] + ' ' + val_list[2] + ' ' + val_list[3]\n\t\t\tlocation = val_list[4] + ' ' + val_list[5]\n\t\t\tsch_res = str(time + '\\t' + location)\n\t\t\n\t\tres = \"\"\n\t\tres += 'Course code: ' + course.course_code + '\\n'\n\t\tres += 'Title: ' + course.title + '\\n'\n\t\tres += 'Department: ' + course.department + '\\n'\n\t\tres += 'Credit Hours: ' + course.credit_hours + '\\n'\n\t\tres += 'Description: ' + course.description + '\\n'\n\t\tif (sch_res != ''):\n\t\t\tres += 'Schedule: ' + sch_res + '\\n'\n\t\tres += 'Instructor: ' + schedule.instructor + '\\n'\n\t\treturn res","def gen_rst(txt):\n res = \"\"\n title_block = get_title_block(txt)\n title_rst = gen_title_rst(title_block)\n res += title_rst\n # add a blank line just to be sure\n res += \"\\n\"\n fun_blocks = get_fun_blocks(txt)\n for (name, fun_block) in fun_blocks:\n fun_rst = gen_fun_rst(name, fun_block)\n res += fun_rst\n return res","def __str__(self):\n return \"(%s)\" % ' '.join(map(str, self.__subrules))","def sub_response():\n a = int(request.args[\"a\"])\n b = int(request.args[\"b\"])\n\n result = sub(a,b)\n return f\"<h1>{result}</h1>\"","def visit_Subscript(self, node):\n self.generic_visit(node)\n return node","def __str__(self) -> str:\n\n assert self.data is not None\n title = \"{} ({})\".format(self.name, self.data[\"role\"][\"name\"])\n name_border = \"-\" * len(title)\n lines = [name_border, title, name_border]\n\n # build the talent-tree \"table\"\n for row_idx, row_data in self.talent_rows.items():\n lines.append(\"{}:\".format(row_idx))\n for talent_idx, talent_data in row_data.items():\n lines.append(\n \" {}: {}\".format(talent_idx, talent_data[\"text\"])\n )\n macro = build_row_macro(row_idx, row_data)\n if macro is not None:\n lines.append(\"macro:\")\n lines.append(macro[0])\n\n return os.linesep.join(lines)","def _deal_with_super_sub_expanded(self, string, style=\"plain\"):\n if \"{\" in string:\n name, supers, subs = string, [], []\n else:\n name, supers, subs = split_super_sub(string)\n\n names = [translate(name) for name in name.split(\" \")]\n supers = [translate(sup) for sup in supers]\n subs = [translate(sub) for sub in subs]\n\n name = \" \".join(names)\n\n # apply the style only to the name\n if style == \"bold\":\n name = \"\\\\mathbf{{{}}}\".format(name)\n\n # glue all items together:\n if supers:\n name += \"^{%s}\" % \" \".join(supers)\n if subs:\n name += \"_{%s}\" % \" \".join(subs)\n\n return name","def subtitle_header(slide, title: str):\n top = Inches(0.61)\n left = Inches(0.42)\n height = Inches(0.5)\n width = Inches(0.5)\n txt_box = slide.shapes.add_textbox(left, top, width, height)\n text_frame = txt_box.text_frame\n\n paragraph = text_frame.paragraphs[0]\n paragraph.text = title\n paragraph.font.bold = False\n paragraph.font.size = Pt(22)\n paragraph.font.name = 'Times New Roman'\n\n return slide","def __str__(self, level=0):\n pt1 = ' join '.join([str(video.video_metadata.file) for video in self.videos])\n pt2 = ' = '.join([str(id) for id in self._join_ids])\n join_cols_str = '{}__{}'.format(pt1, pt2)\n out_string = \"\\t\" * level + join_cols_str + \"\\n\"\n for child in self.children:\n out_string += child.__str__(level + 1)\n return out_string","def vartext(self, num, full=0):\n if not self.is_interval() and not self.is_ecChoose():\n return self.text\n values, erased_elements = self.list[num]\n vtext = self.text[:]\n space = \"\\spacer\" # It is at the place of an erased element.\n\n element_list = (self.interval_list + self.compute_list\n + self.ecChoose_list)\n for element in element_list:\n element['shift'] = 0\n\n for element in element_list:\n start = element['start'] + element['shift']\n end = element['end'] + element['shift']\n row = element['row']\n line = vtext[row - 1]\n if element['name']:\n value = values[element['name']]\n else:\n # It is just a transient solution\n value = interval_.random(element['element'])\n ltx = latex_number(value)\n if element['unit']:\n ltx += '~' + element['unit']\n if element['name'] in erased_elements:\n ltx = space\n line = line[:start] + \\\n ltx + \\\n line[end:]\n vtext[row - 1] = line\n\n shift = len(ltx) - (end - start)\n for element2 in element_list:\n if row == element2['row'] and element2['start'] >= end:\n element2['shift'] += shift\n return vtext","def title(string: str, level=H1) -> str:\n appended = []\n for lvl in range(0, level):\n appended.append(\"#\")\n return f\"{''.join(appended)} {string}\"","def __unicode__(self):\n if self.level == 0:\n return u'Wurzel: %s' % (self.courseevent)\n elif self.level == 1:\n return u'Block: %s' % (self.title)\n elif self.level == 2:\n return u'Block: %s, Lektion: %s. %s' % (self.parent.title, self.lesson_nr, self.title)\n elif self.level == 3:\n return u'%s %s %s' % (self.courseevent, self.lesson_nr, self.title)","def formatter(in_tuple):\n length = len(in_tuple)\n form_string = (\"the {} numbers are: \" + \", \".join([\"{}\"]*length)).format(length, *in_tuple)\n return form_string.format(in_tuple)","def make_shirt(text='I love Python', size='large'):\t\n\tprint(f\"\\nThe size of the shirt is {size} and the printed text reads: {text}.\")","def build_tex(self,signs='-',request_parens=False):\n return self._build_tex(signs,request_parens)","def formatOutput(self, storedText, titleMode, internal=False):\n prefix = self.prefix\n suffix = self.suffix\n if titleMode:\n if self.html:\n storedText = self.removeMarkup(storedText)\n if globalref.docRef.formHtml:\n prefix = self.removeMarkup(prefix)\n suffix = self.removeMarkup(suffix)\n else:\n if not self.html:\n storedText = escape(storedText).replace('\\n', '<br />')\n if not globalref.docRef.formHtml:\n prefix = escape(prefix)\n suffix = escape(suffix)\n return u'%s%s%s' % (prefix, storedText, suffix)","def print_section(self, s):\n section = s.upper()\n\n self.print_newline()\n self.print_newline()\n self._write('%s\\n' % section)\n self._write('%s\\n' % ('-' * len(section)))\n self.print_newline()","def __str__(self):\n ret_str = \"\"\n for element_type in ('nodes', 'edges', 'layers'):\n elements = getattr(self, element_type)\n subtype_counts = defaultdict(int)\n ret_str += \"{0} {1}:\\n\".format(len(elements), element_type)\n for element in elements:\n subtype_counts[type(element).__name__] += 1\n for subtype in subtype_counts:\n ret_str += \"\\t{0}: {1}\\n\".format(subtype,\n subtype_counts[subtype])\n ret_str += \"\\n\"\n ret_str += \"primary text:\\n{0}\".format(self.text.encode('utf-8'))\n return ret_str","def _tstep_msg(fem: BDF,\n subcase: Subcase,\n tstep_id: int,\n tstep_type: str='') -> str:\n msg = (\n 'tstep%s_id=%s\\n'\n 'tsteps=%s\\n'\n 'tstepnls=%s\\n'\n #'tstep1s=%s\\n'\n 'subcase:\\n%s' % (\n tstep_type, tstep_id,\n str(fem.tsteps),\n str(fem.tstepnls),\n #str(fem.tstep1s),\n str(subcase))\n )\n return msg"],"string":"[\n \"def sub(string, subscript):\\n return string + \\\"<sub>\\\" + subscript + \\\"</sub>\\\"\",\n \"def _build_sub(self) -> str:\\n return dedent(\\n \\\"\\\"\\\"\\n @SP\\n M=M-1\\n A=M\\n D=M\\n @SP\\n M=M-1\\n A=M\\n M=M-D\\n @SP\\n M=M+1\\n \\\"\\\"\\\"\\n )\",\n \"def print_sub_section(self, s, level=0):\\n section = s.capitalize()\\n\\n self.print_newline()\\n self._write('%s+ %s\\\\n' % ('-' * level, section))\\n self.print_newline()\",\n \"def subtitle(self, txt):\\n num = len(txt)\\n ticks = \\\"-\\\" * num\\n print(txt)\\n print(ticks)\",\n \"def subtitle(string):\\n print(\\\"{}\\\\n{}\\\\n\\\".format(bold(string), underline(string, \\\"-\\\")))\",\n \"def sub_section(title, lines, pad_len=4, sep='-'):\\n pad = pad_len * \\\" \\\"\\n msg = \\\"{pad}{}\\\\n{pad}{}\\\\n\\\".format(title, sep * len(title), pad=pad)\\n msg += pad + (\\\"\\\\n\\\" + pad).join(lines) + \\\"\\\\n\\\"\\n\\n return msg\",\n \"def add_subsection(self, text: str) -> None:\\n\\n tag = r'\\\\subsection{%s}' % (text)\\n self.doc = self.doc + tag\",\n \"def get_subtitle_print(subs: List[Track]) -> List[str]:\\n data = []\\n if not subs:\\n data.append(\\\"--\\\")\\n for sub in subs:\\n line_items = []\\n\\n # following sub.title tree checks and supports three different language and title scenarios\\n # The second scenario is the recommended option to choose if you are open to choosing any\\n # The third scenario should be used if you have nothing unique to state about the track\\n # | Language | Track Title | Output |\\n # | ------------ | ----------------------------- | --------------------------------------------- |\\n # | es / Spanish | Spanish (Latin American, SDH) | - Spanish (Latin American, SDH), SubRip (SRT) |\\n # | es / Spanish | Latin American (SDH) | - Spanish, Latin American (SDH), SubRip (SRT) |\\n # | es / Spanish | None | - Spanish, SubRip (SRT) |\\n language = pycountry.languages.get(alpha_2=sub.language).name\\n if sub.title:\\n if language.lower() in sub.title.lower():\\n line_items.append(sub.title)\\n else:\\n line_items.append(f\\\"{language}, {sub.title}\\\")\\n else:\\n line_items.append(language)\\n\\n line_items.append(sub.format.replace(\\\"UTF-8\\\", \\\"SubRip (SRT)\\\"))\\n\\n line = \\\"- \\\" + \\\", \\\".join(line_items)\\n data += [\\n (\\\" \\\" + x if i > 0 else x)\\n for i, x in enumerate(textwrap.wrap(line, 64))\\n ]\\n return data\",\n \"def makeTexInput(self, tabletitle, subsection=True):\\r\\n tablestring = \\\"\\\"\\r\\n\\r\\n # if there's enough effluent data\\r\\n if self.ds.effluent.include:\\r\\n if subsection:\\r\\n tablestring += r\\\"\\\\subsection{%s}\\\" % (self.bmp,)\\r\\n\\r\\n # caption for the stats plot\\r\\n prob_caption = \\\"Box and Probability Plots of {} at {} BMPs\\\".format(\\r\\n self.parameter.name, self.bmp\\r\\n )\\r\\n\\r\\n # caption for the scatter plot\\r\\n scatter_caption = \\\"Influent vs. Effluent Plots of {} at {} BMPs\\\".format(\\r\\n self.parameter.name, self.bmp\\r\\n )\\r\\n\\r\\n # warning about having a lot of non-detects\\r\\n warning = \\\"\\\"\\\"\\r\\n Warning: there is a very high percentage of non-detects in\\r\\n this data set. The hypothesis test results and other\\r\\n statistics reported in this table may not be valid.\\r\\n \\\"\\\"\\\"\\r\\n\\r\\n # make the table and write it to the output file\\r\\n tablestring += self._make_tex_table(tabletitle)\\r\\n\\r\\n # if less than 80% of the data is ND\\r\\n if self.ds.effluent.ND / self.ds.effluent.N <= 0.8:\\r\\n\\r\\n # make the stat plot string\\r\\n statfig = self._make_tex_figure(\\r\\n self.stat_fig_name, prob_caption, clearpage=False\\r\\n )\\r\\n\\r\\n # make the scatter plot string\\r\\n scatterfig = self._make_tex_figure(\\r\\n self.scatter_fig_name, scatter_caption, clearpage=True\\r\\n )\\r\\n\\r\\n # write the strings to the file\\r\\n tablestring += statfig\\r\\n tablestring += scatterfig\\r\\n\\r\\n else:\\r\\n # if there are too many non-detect,\\r\\n # issue the warning\\r\\n tablestring += warning\\r\\n\\r\\n return tablestring\",\n \"def subtitle(\\n pdf, text, indent=10, border=BORDER, font_size=12, font_style=\\\"B\\\"\\n): # pylint: disable = too-many-arguments\\n pdf.cell(indent, border=border)\\n pdf.set_font(\\\"arial\\\", font_style, font_size)\\n pdf.cell(75, 10, text, border, 1)\",\n \"def create_caption(section, superscript, text):\\n section.append('\\\\n')\\n\\n # Superscript\\n section.append(bold(pylatex.NoEscape(r'{\\\\footnotesize \\\\textsuperscript {' + superscript + '}}')))\\n\\n # Text\\n section.append(italic(pylatex.NoEscape(r'{\\\\footnotesize {' + text + '}}')))\",\n \"def _parse_sub(self, parsetree, text, fpos=0):\\r\\n curr = 0\\r\\n for match in self._reSubstitution.finditer(text):\\r\\n start = match.start()\\r\\n if start > curr:\\r\\n parsetree.append((\\\"str\\\", self._reComment.sub('', text[curr:start])))\\r\\n\\r\\n if match.group(\\\"sub\\\") is not None:\\r\\n if not match.group(\\\"end\\\"):\\r\\n raise TemplateSyntaxError(\\\"Missing closing tag '%s' for '%s'.\\\" \\r\\n % (self._sub_end, match.group()), self._errpos(fpos+start))\\r\\n if len(match.group(\\\"sub\\\")) > 0:\\r\\n self._testexpr(match.group(\\\"sub\\\"), fpos+start)\\r\\n parsetree.append((\\\"sub\\\", match.group(\\\"sub\\\")))\\r\\n else:\\r\\n assert(match.group(\\\"escsub\\\") is not None)\\r\\n if not match.group(\\\"escend\\\"):\\r\\n raise TemplateSyntaxError(\\\"Missing closing tag '%s' for '%s'.\\\"\\r\\n % (self._subesc_end, match.group()), self._errpos(fpos+start))\\r\\n if len(match.group(\\\"escsub\\\")) > 0:\\r\\n self._testexpr(match.group(\\\"escsub\\\"), fpos+start)\\r\\n parsetree.append((\\\"esc\\\", self.escape, match.group(\\\"escsub\\\")))\\r\\n\\r\\n curr = match.end()\\r\\n\\r\\n if len(text) > curr:\\r\\n parsetree.append((\\\"str\\\", self._reComment.sub('', text[curr:])))\",\n \"def latex_course(course):\\n basic_info_list = [\\n 'session', 'school', 'credits', 'level', 'offered',\\n 'visiting_students', 'erasmus_students'\\n ]\\n generic_subsection_list = [\\n 'description', 'timetable', 'requirements_of_entry',\\n 'excluded_courses', 'co_requisites', 'assessment_weighting'\\n ]\\n string = '\\\\\\\\subsection{' + course[\\\"title\\\"] + '}\\\\n'\\n for info in basic_info_list:\\n string += latex_info(course[info])\\n for subsection in generic_subsection_list:\\n string += latex_subsection(course[subsection])\\n string += '\\\\\\\\break \\\\\\\\textbf{' + course['assessment_date'][\\n 'heading'] + '}' + course['assessment_date']['value'] + '\\\\n'\\n string += latex_subsection(course['aims'])\\n string += '\\\\\\\\subsubsection*{' + \\\\\\n course['learning_outcomes']['heading'] + '}\\\\n'\\n outcome_list = re.split(\\n '\\\\d+\\\\. ', course['learning_outcomes']['value'])\\n string += outcome_list[0] + '\\\\n'\\n string += '\\\\\\\\begin{enumerate}\\\\n'\\n for i in outcome_list[1:-1]:\\n string += '\\\\\\\\item ' + i + '\\\\n'\\n string += '\\\\\\\\end{enumerate}\\\\n'\\n return string\",\n \"def print_help():\\n \\n print(\\\"\\\"\\\"\\n catsub - substitutes every value for each variable in each word of a template file.\\n\\n Usage:\\n\\n catsub [--help] [-s] [-u] [-D|-dSTR] [TEMPLATEFILES] [%VARNAME VALUE1 VALUE2 ... ]*\\n\\n Arguments:\\n\\n TEMPLATEFILES Name(s) of file(s) containg the template with\\n variables of the from %VARNAME; If no file name\\n is given, or the name is '-', catsub will read\\n from standard input;\\n %VARNAME Variable name to substitute;\\n VALUE1 VALUE2 ... Values to substitute for the variable;\\n -s Print statistics to stderr on resolved and unresolved variables.\\n -u Escaped percentage in template are returned unescaped;\\n -dSTR Use STR to divide multiple substituted values\\n -D Use newline to divide multiple substituted values\\n --help Show this help page.\\n\\n Notes: \\n\\n - The names of the template files may not start with a percent sign. \\n\\n - All variables must start with a percent sign and cannot contain\\n whitespace.\\n\\n - Substituted values cannot start with a percent sign.\\n\\n - Substitution happens only once per variable, i.e., substituted\\n values do not undergo subsequent substitutions.\\n\\n - When substituting several values, those values are separated by a\\n space, by a newline if the -D argument was given, or by STR if\\n the -dSTR argument was given.\\n\\n - When a variable has been given several values to substitute and\\n the variable occurs in a substring of a word in the template,\\n that word get repeated. E.g. \\\"echo un%X | catsub %X kind tidy\\\"\\n gives \\\"unkind untidy\\\"\\n\\n - Substitution happens combinatorically within a word. E.g. a word\\n \\\"%X,%Y\\\" in the template, when processed with \\\"catsub %X a b %Y c d\\\"\\n becomes \\\"a,c a,d b,c b,d\\\". Combinatorics can be circumvented by\\n quoting the replacement values, i.e. \\\"catsub %X 'a b' %Y 'c d'\\\"\\n gives \\\"a b,c d\\\".\\n\\n - Substitution uses the longest possible variable name. E.g. in\\n \\\"%HELLOWORLD\\\", both %HELLO and %HELLOWORLD could be substituted\\n if values for both are specified on the catsub command, but it is\\n the longer %HELLOWORLD that gets used.\\n \\n - Percentage signs in the template can escape substitution by\\n prepeding them with a slash, i.e., '\\\\%'. Every '\\\\%' in the\\n template will be remain a '\\\\%' unless the -u argument is used in\\n the catsub command, in which case, they are replaced by '%'.\\n\\n - The template cannot use the unicode character '％'.\\n\\n Examples:\\n\\n $ echo %HELLO %UNIVERSE | catsub %HELLO Hi %UNIVERSE world\\n Hi world\\n\\n $ echo %HELLO %UNIVERSE > example.tmpl\\n $ catsub example.tmpl %HELLO Greetings %UNIVERSE universe!\\n Greetings universe!\\n\\\"\\\"\\\")\",\n \"def get_as_subtext_field(field, field_title=None) -> str:\\n s = \\\"\\\"\\n if field:\\n s = f\\\"{field} | \\\"\\n else:\\n return \\\"\\\"\\n\\n if field_title:\\n s = f\\\"{field_title}: \\\" + s\\n\\n return s\",\n \"def get_as_subtext_field(field, field_title=None) -> str:\\n s = \\\"\\\"\\n if field:\\n s = f\\\"{field} | \\\"\\n else:\\n return \\\"\\\"\\n\\n if field_title:\\n s = f\\\"{field_title}: \\\" + s\\n\\n return s\",\n \"def repr_subfigure(self):\\n default_kwargs = {'placement': self.subfig_placement,\\n 'width': self.subfig_width,\\n 'caption': self.caption,\\n 'label': self.label}\\n\\n myfig = self.extension_mapping[self.extension]()\\n\\n return self.subfig_str.format(myfig=myfig, **default_kwargs)\",\n \"def format_subrange(start, end, step):\\n \\n if start == end:\\n return str(start)\\n elif step == 1:\\n return \\\"%d-%d\\\" % (start, end)\\n else:\\n return \\\"%d-%dx%d\\\" % (start, end, step)\",\n \"def format_sub_part(cls, field, length):\\n try:\\n if not length:\\n raise ValueError\\n\\n length = int(length)\\n return \\\"`%s`(%d)\\\" % (field, length)\\n\\n except ValueError:\\n return \\\"`%s`\\\" % (field,)\",\n \"def sub(abbreviation: str, alias: str) -> str:\\n return f'<sub alias=\\\"{alias}\\\">{abbreviation}</sub>'\",\n \"def format_substitutions(subs: Union[SubstituteTerm, List[SubstituteTerm]]):\\n text = \\\"\\\"\\n if isinstance(subs, SubstituteTerm):\\n term_str = str(subs)\\n for line in term_str.split('\\\\n'):\\n text += Markup.escape(line) + Markup('<br />')\\n text += Markup('<br />')\\n return text\\n for term in subs:\\n term_str = str(term)\\n for line in term_str.split('\\\\n'):\\n text += Markup.escape(line) + Markup('<br />')\\n text += Markup('<br />')\\n return text\",\n \"def write_subtitle(self, subtitle: str, break_page: bool, class_txt: str) -> str:\\n if break_page:\\n str_title = \\\"\\\"\\\"<h2 class=\\\"break-before\\\">\\\"\\\"\\\" + subtitle + \\\"\\\"\\\"</h2>\\\\n\\\"\\\"\\\"\\n else:\\n str_title = \\\"\\\"\\\"<h2 class=\\\\\\\"\\\"\\\"\\\" + class_txt + \\\"\\\"\\\"\\\\\\\">\\\"\\\"\\\" + subtitle + \\\"\\\"\\\"</h2>\\\\n\\\"\\\"\\\"\\n self.html_doc = self.html_doc + str_title\\n return self.html_doc\",\n \"def _Subscript(self, t):\\n self.dispatch(t.value)\\n self.write(\\\"[\\\")\\n self.dispatch(t.slice)\\n self.write(\\\"]\\\")\",\n \"def sub(proto, *args):\\n try:\\n text = proto.format(*args)\\n except:\\n text = \\\"--\\\"\\n #print sub(\\\"WARNING: Couldn't sub {} with {}\\\", proto, args)\\n return text\",\n \"def get_as_subtext_field(field, field_title=None) -> str:\\n s = \\\"\\\"\\n if field:\\n s = f\\\"{field} | \\\"\\n else:\\n return \\\"\\\"\\n\\n if field_title:\\n s = f\\\"{field_title} :\\\" + s\\n\\n return s\",\n \"def Subtlety(self):\\n s = self.subtlety\\n assert s in range(1,6), \\\"Subtlety score out of bounds.\\\"\\n return _char_to_word_[::-1][ s-1 ] + ' Subtlety'\",\n \"def _var_quote_sub(self, text, VARS):\\n ## No need to live on class. Can be moved to tools. - Add assert test.\\n qvars = map(lambda x: \\\"\\\\{ \\\" + x + \\\" \\\\}\\\", VARS)\\n return text % tuple(qvars)\",\n \"def print_substep(text, style=\\\"\\\"):\\n console.print(text, style=style)\",\n \"def test__render_inline_section_into(field_added, section_title, section_content):\\n into, field_added = render_inline_section_into([], field_added, section_title, section_content)\\n return ''.join(into), field_added\",\n \"def format_sections(self, sections: SectionDict) -> str:\",\n \"def _plainTextSFinal(\\n app,\\n browseNavLevel,\\n finalSecType,\\n sNode,\\n opened,\\n secFinal,\\n **options,\\n):\\n\\n display = app.display\\n dContext = display.distill(options)\\n fmt = dContext.fmt\\n\\n api = app.api\\n T = api.T\\n\\n aContext = app.context\\n formatCls = aContext.formatCls\\n\\n secStr = str(T.sectionFromNode(sNode)[browseNavLevel])\\n isOpened = secStr in opened\\n tCls = \\\"\\\" if fmt is None else formatCls[fmt or DEFAULT_FORMAT].lower()\\n\\n prettyRep = (\\n prettyTuple(\\n app,\\n (sNode,),\\n seq=secStr,\\n condensed=False,\\n condenseType=finalSecType,\\n **display.consume(options, \\\"condensed\\\", \\\"condenseType\\\"),\\n )\\n if isOpened\\n else \\\"\\\"\\n )\\n current = \\\" focus\\\" if secStr == str(secFinal) else \\\"\\\"\\n attOpen = \\\" open \\\" if isOpened else \\\"\\\"\\n\\n textRep = plain(\\n app,\\n sNode,\\n withPassage=False,\\n **display.consume(options, \\\"withPassage\\\"),\\n )\\n html = f\\\"\\\"\\\"\\\\\\n<details\\n class=\\\"pretty{current}\\\"\\n seq=\\\"{secStr}\\\"\\n {attOpen}\\n>\\n <summary class=\\\"{tCls} ubd\\\">{app._sectionLink(sNode, text=secStr)} {textRep}</summary>\\n <div class=\\\"pretty\\\">{prettyRep}</div>\\n</details>\\\\\\n\\\"\\\"\\\"\\n return html\",\n \"def _var_id_sub(self, sprintf):\\n id_list = map(lambda x: self.cdict[x][1], sprintf[\\\"vars\\\"] )\\n return sprintf[\\\"text\\\"] % tuple(id_list)\",\n \"def to_srt(self, subtitles):\\n \\n srt_data = ''\\n subtitle_num = self.start_index\\n for subtitle in subtitles:\\n subtitle_num += 1\\n \\n offset = self.start_time\\n \\n start_time = self._ms_to_time(subtitle['start_time'] + offset)\\n end_time = self._ms_to_time(subtitle['end_time'] + offset)\\n \\n content = subtitle['content'].replace('<br>', ' ')\\n \\n srt_data += str(subtitle_num) + '\\\\r\\\\n'\\n srt_data += '%s --> %s' % (start_time, end_time) + '\\\\r\\\\n'\\n srt_data += content + '\\\\r\\\\n'\\n srt_data += '\\\\r\\\\n'\\n \\n self.end_index = subtitle_num\\n \\n return srt_data\",\n \"def do_text(name, param, level):\\n text = [\\n f'{name}',\\n f'{level * len(name)}',\\n '',\\n f'| *default*: {param[\\\"default\\\"]!r}',\\n f'| *type*: {param[\\\"type\\\"]}',\\n f'| *title*: {param[\\\"title\\\"]}',\\n f'| *short description*: {param[\\\"short\\\"]}',\\n f'| *long description*: {param[\\\"long\\\"]}',\\n f'| *group*: {param.get(\\\"group\\\", \\\"No group assigned\\\")}',\\n f'| *explevel*: {param[\\\"explevel\\\"]}',\\n '',\\n ]\\n\\n return os.linesep.join(text)\",\n \"def repr_sub_module(self, class_name: str):\\n s = 'py::module {sub} = {base}.def_submodule(\\\"{name}\\\", \\\"Submodule {name}\\\")'\\n data = {\\n \\\"sub\\\": self.sub_module_name(class_name),\\n \\\"base\\\": BASE_SUB_MODULE_NAME,\\n \\\"name\\\": class_name\\n }\\n return s.format(**data)\",\n \"def _latex_(self):\\n old = s = \\\"\\\\\\\\begin{verbatim}\\\\\\\\end{verbatim}\\\"\\n new = \\\"\\\"\\n s = \\\"\\\\\\\\begin{verbatim}\\\\\\\\end{verbatim}\\\"\\n s += \\\"Element of \\\" + self._space._latex_().replace(old, new) + \\\" With principal part \\\"\\n WR = self._space.multiplier()\\n # If we have more than one non-zero element in the principal part we have to\\n # add a + between terms\\n sp = \\\"\\\"\\n for (b, m) in self._principal_part:\\n a = self._principal_part[(b, m)]\\n if a != 0:\\n x = QQ(m + WR.Qv[WR.D.index(b)])\\n if a != 1:\\n if a > 0 and len(sp) > 0:\\n ast = \\\"+\\\" + str(a)\\n else:\\n ast = str(a)\\n sp = sp + ast + \\\"q^{\\\" + str(x) + \\\"}\\\"\\n else:\\n sp = sp + \\\"q^{\\\" + str(x) + \\\"}\\\"\\n s = s + sp + \\\"$.\\\"\\n return s\",\n \"def format_yaml_tag(subcls):\\n return f'!{subcls.__name__}Class'\",\n \"def subtitle(self):\\n worksheet_type = self.options[\\\"worksheet_type\\\"].value\\n return \\\"{} - {}\\\".format(WORKSHEET_OPTIONS[worksheet_type], super().subtitle)\",\n \"def generate_cross_section_title(grid, field, start, end):\\n\\n # Grab information about the grid\\n time_str = generate_grid_time_begin(grid).strftime(\\\"%Y-%m-%dT%H:%M:%SZ\\\")\\n field_name = generate_field_name(grid, field)\\n\\n # Deal with the latitude/longitude part\\n start_lat, start_lon = start\\n end_lat, end_lon = end\\n\\n degree_symbol = \\\"\\\\N{DEGREE SIGN}\\\"\\n\\n # Add degrees latitude/longitude labels\\n\\n if start_lat < 0:\\n start_lat_string = f\\\"{-start_lat}{degree_symbol}S\\\"\\n else:\\n start_lat_string = f\\\"{start_lat}{degree_symbol}N\\\"\\n\\n if end_lat < 0:\\n end_lat_string = f\\\"{-end_lat}{degree_symbol}S\\\"\\n else:\\n end_lat_string = f\\\"{end_lat}{degree_symbol}N\\\"\\n\\n if start_lon < 0:\\n start_lon_string = f\\\"{-start_lon}{degree_symbol}W\\\"\\n else:\\n start_lon_string = f\\\"{start_lon}{degree_symbol}E\\\"\\n\\n if end_lon < 0:\\n end_lon_string = f\\\"{-end_lon}{degree_symbol}W\\\"\\n else:\\n end_lon_string = f\\\"{end_lon}{degree_symbol}E\\\"\\n\\n return (\\n f\\\"({start_lat_string}, {start_lon_string})\\\"\\n f\\\" to ({end_lat_string}, {end_lon_string}) \\\\n {time_str} \\\\n {field_name}\\\"\\n )\",\n \"def create_subtitle(self):\\n label_subtitle = Label(self.frame, text=\\\"Projet Python 2020\\\", font=(\\\"Arial\\\", 25), bg='light blue',\\n fg='white')\\n label_subtitle.pack()\",\n \"def _subsection_titles(self, section_index):\\r\\n # Retrieve the subsection title for the section\\r\\n # Add one to the list index to get the CSS index, which starts at one\\r\\n subsection_css = 'nav>div.chapter:nth-of-type({0})>ul>li>a>p:nth-of-type(1)'.format(section_index)\\r\\n\\r\\n # If the element is visible, we can get its text directly\\r\\n # Otherwise, we need to get the HTML\\r\\n # It *would* make sense to always get the HTML, but unfortunately\\r\\n # the open tab has some child <span> tags that we don't want.\\r\\n return self.q(\\r\\n css=subsection_css).map(\\r\\n lambda el: el.text.strip().split('\\\\n')[0] if el.is_displayed() else el.get_attribute('innerHTML').strip()\\r\\n ).results\",\n \"def __str__(self):\\n t = Template(\\n \\\"\\\"\\\"\\n <h4>$title</h4>\\n $imgs\\n $footnotes\\n <hr/>\\\"\\\"\\\")\\n # Return result.\\n return t.substitute({\\n \\\"title\\\": self.title,\\n \\\"imgs\\\": self.render_images(),\\n \\\"footnotes\\\": self.render_footnotes()\\n })\",\n \"def assemble_subpeak_record(subpeak, celltype_activations, sequence):\\n # make the header\\n header ='\\\\t'.join([s for s in subpeak])\\n header = '>' + header\\n\\n # make the activation string\\n activation = ';'.join([ct+' '+str(score) for (ct,score) in celltype_activations])\\n\\n # append the sequence\\n seq_string = str(sequence)\\n return header, activation, seq_string\",\n \"def add_flaux(self, identifier, sub, expression, doc=''):\\n # todo: why does the no-sub condition give [''] as the argument?\\n # todo: evaluate if we should instead use syntax [['a1','pears'],['a2','pears']]\\n # todo: build a test case to test\\n # todo: clean up this function\\n # todo: add docstring handling\\n\\n docstring = ''\\n\\n if sub[0]!='': #todo: consider factoring this out if it is useful for the multiple flows\\n directory, size = get_array_info(sub, self.dictofsubs)\\n\\n funcset = 'loc_dimension_dir = %s.dimension_dir \\\\n'%identifier\\n funcset += ' output = np.ndarray((%s))\\\\n'%','.join(map(str,size)) #lines which encode the expressions for partially defined subscript pieces\\n for expr, subi in zip(expression, sub):\\n expr = expr.replace('\\\\n','').replace('\\\\t','').strip() # todo: pull out\\n indices = ','.join(map(str,getelempos(subi, self.dictofsubs)))\\n if re.search(';',expr): # if 2d array, format for numpy\\n expr = 'np.'+np.array(np.mat(expr.strip(';'))).__repr__()\\n funcset += ' output[%s] = %s\\\\n'%(indices, expr)\\n else:\\n funcset = 'loc_dimension_dir = 0 \\\\n'\\n funcset += ' output = %s\\\\n'%expression[0]\\n\\n funcstr = templates['flaux'].substitute(identifier=identifier,\\n expression=funcset,\\n docstring=docstring)\\n\\n if sub[0] != '': # todo: make less brittle\\n funcstr += '%s.dimension_dir = '%identifier+directory.__repr__()+'\\\\n' # todo: do we like 'dimension_dictionary' as a name?\\n self.body.append(funcstr)\\n\\n return identifier\",\n \"def get_subtitle(annotation, sub_duration, video_clip, seen_annotations):\\n if len(annotation[\\\"text\\\"]) == 0:\\n return None\\n\\n annotation_txt = calculate_needed_subtitle_height(annotation, seen_annotations, video_clip)\\n\\n txt_clip = TextClip(annotation_txt, color=\\\"white\\\", fontsize=70, font='Sans Serif')\\n txt_clip = txt_clip.set_position((\\\"center\\\", get_subtitle_offset(annotation, seen_annotations, video_clip)))\\n txt_clip = txt_clip.set_start(float(annotation[\\\"time\\\"]) / 1000.0)\\n txt_clip = txt_clip.set_duration(sub_duration)\\n\\n return txt_clip\",\n \"def _get_section(registry, section, title, hdg_level1=\\\"#\\\", hdg_level2=\\\"=\\\",\\n output_dir=None):\\n file_per_topic = output_dir is not None\\n lines = [title, hdg_level1 * len(title), \\\"\\\"]\\n if file_per_topic:\\n lines.extend([\\\".. toctree::\\\", \\\" :maxdepth: 1\\\", \\\"\\\"])\\n\\n topics = sorted(registry.get_topics_for_section(section))\\n for topic in topics:\\n help = registry.get_detail(topic)\\n heading, text = help.split(\\\"\\\\n\\\", 1)\\n if not text.startswith(hdg_level2):\\n underline = hdg_level2 * len(heading)\\n help = \\\"%s\\\\n%s\\\\n\\\\n%s\\\\n\\\\n\\\" % (heading, underline, text)\\n else:\\n help = \\\"%s\\\\n%s\\\\n\\\\n\\\" % (heading, text)\\n if file_per_topic:\\n topic_id = _dump_text(output_dir, topic, help)\\n lines.append(\\\" %s\\\" % topic_id)\\n else:\\n lines.append(help)\\n\\n return \\\"\\\\n\\\" + \\\"\\\\n\\\".join(lines) + \\\"\\\\n\\\"\",\n \"def subsection(self, title):\\n section = self.__class__(title, self, self.level + 1, proc=self.proc)\\n self.subsections.append(section)\\n return section\",\n \"def fn_sub(self, value):\\n\\n if isinstance(value, list):\\n value, variables = value\\n else:\\n # only template parameter names, resource logical IDs, and resource attributes, will be parsed\\n value, variables = value, {}\\n\\n for name, target in variables.items():\\n value = value.replace('${{{}}}'.format(name), target)\\n\\n return Functions.SUB_VARIABLE_PATTERN.sub(self._sub_variable, value)\",\n \"def doc_sub(*sub):\\n def dec(obj):\\n obj.__doc__ = obj.__doc__.format(*sub)\\n return obj\\n return dec\",\n \"def buildGSUB(self):\\n\\t\\t# Construct GSUB table bottom-up.\\n\\t\\tli_fi = Ligature()\\n\\t\\tli_fi.LigGlyph = 'f_i'\\n\\t\\tli_fi.Component = ['i']\\n\\t\\tli_fi.CompCount = 2\\n\\n\\t\\tliSubst = LigatureSubst()\\n\\t\\tliSubst.ligatures = {'f': li_fi}\\n\\t\\tliSubst.Format = 1\\n\\t\\tliSubst.LookupType = 4\\n\\n\\t\\tlookup = Lookup()\\n\\t\\tlookup.LookupType = 4 # Ligature\\n\\t\\tlookup.LookupFlag = 0\\n\\t\\tlookup.SubTable = [liSubst]\\n\\t\\tlookup.SubTableCount = len(lookup.SubTable)\\n\\n\\t\\tlookupList = LookupList()\\n\\t\\tlookupList.Lookup = [lookup]\\n\\t\\tlookupList.LookupCount = len(lookupList.Lookup)\\n\\n\\t\\tfea = Feature()\\n\\t\\tfea.FeatureParams = None\\n\\t\\tfea.LookupCount = 1\\n\\t\\tfea.LookupListIndex = [0]\\n\\n\\t\\tfeaRecord = FeatureRecord()\\n\\t\\tfeaRecord.FeatureTag = 'liga'\\n\\t\\tfeaRecord.Feature = fea\\n\\n\\t\\tfeaList = FeatureList()\\n\\t\\tfeaList.FeatureRecord = [feaRecord]\\n\\t\\tfeaList.FeatureCount = len(feaList.FeatureRecord)\\n\\n\\t\\tlangSys = LangSys()\\n\\t\\tlangSys.LookupOrder = None\\n\\t\\tlangSys.ReqFeatureIndex = 0xFFFF\\n\\t\\tlangSys.FeatureIndex = [0]\\n\\t\\tlangSys.FeatureCount = len(langSys.FeatureIndex)\\n\\n\\t\\tsct = Script()\\n\\t\\tsct.DefaultLangSys = langSys\\n\\t\\tsct.LangSysRecord = []\\n\\t\\tsct.LangSysCount = len(sct.LangSysRecord)\\n\\n\\t\\tsctRec = ScriptRecord()\\n\\t\\tsctRec.ScriptTag = 'tag1'\\n\\t\\tsctRec.Script = sct\\n\\n\\t\\tsctList = ScriptList()\\n\\t\\tsctList.ScriptRecord = [sctRec]\\n\\t\\tsctList.ScriptCount = len(sctList.ScriptRecord)\\n\\n\\t\\tgsub = GSUB()\\n\\t\\tgsub.LookupList = lookupList\\n\\t\\tgsub.FeatureList = feaList\\n\\t\\tgsub.ScriptList = sctList\\n\\n\\t\\ttable = ttLib.newTable('GSUB')\\n\\t\\ttable.table = gsub\\n\\t\\treturn table\",\n \"def section(sect_name, width=TERMINAL_CHARS):\\n\\n true_length = width - len(sect_name) - 1\\n print(\\\"\\\\n_%s\\\" % (sect_name.upper() + \\\"_\\\" * true_length))\",\n \"def astext(self):\\n self.elements.update({\\n 'body': u''.join(self.body),\\n 'indices': self.generate_indices()\\n })\\n return self.render('beamer.tex_t', self.elements)\",\n \"def group_list(self, group_number=1):\\n text = []\\n group = self.varexercise_numbers[group_number-1]\\n group_name = _('Group %s') % group[0]\\n text.append('\\\\n\\\\\\\\subsection*{%s}\\\\n' % group_name)\\n for number in range(1, self.number_of_variations+1):\\n print(\\\"---------\\\", number) # !!!\\n variation = '\\\\n\\\\n\\\\\\\\textbf{%s}\\\\\\\\\\\\\\\\\\\\n' %\\\\\\n _('Variation %d') % number\\n text.append(variation)\\n exercise_number = 1\\n for item in group[1:]:\\n # print(' '*5, item) # !!!\\n if not isinstance(item, str):\\n latex_plain = item.latex_plain(number)\\n if latex_plain:\\n text.append('%d. ' % exercise_number)\\n # print(' '*5, number) # !!!\\n # print(' '*5, latex_plain) # !!!\\n text.append(latex_plain + '\\\\n')\\n exercise_number += 1\\n # if with_solution:\\n # text.extend(self.one_group(group_number=group_number))\\n # text.append(\\\"\\\\\\\\newpage\\\\n\\\")\\n return text\",\n \"def section_header(text):\\n\\n print \\\"---- %s ----\\\" % text\",\n \"def _create_formatted_string(self):\\n string = NALSyntax.StatementSyntax.Start.value + \\\\\\n self.get_subject_term().get_formatted_string()\\n\\n string += \\\" \\\" + self.get_copula_string() + \\\" \\\"\\n\\n string += self.get_predicate_term().get_formatted_string() + \\\\\\n NALSyntax.StatementSyntax.End.value\\n\\n return string\",\n \"def __str__(self):\\n outs = str(self.cluster_subspace).split(\\\"\\\\n\\\")[:6]\\n\\n if self.regression_data is not None:\\n # This might need to be redefined to take \\\"expectation\\\" using measure\\n feature_avg = np.average(self.feature_matrix, axis=0)\\n feature_std = np.std(self.feature_matrix, axis=0)\\n outs += [\\n f\\\"Regression Data : estimator={self.regression_data.estimator_name}\\\",\\n f\\\" module={self.regression_data.module}\\\",\\n f\\\" parameters={self.regression_data.parameters}\\\",\\n f\\\"Target Property : \\\"\\n f\\\"mean={np.mean(self.regression_data.property_vector):0.4f} \\\"\\n f\\\"std={np.std(self.regression_data.property_vector):0.4f}\\\",\\n ]\\n fit_var = sum(\\n self._subspace.function_total_multiplicities[1:] * self.eci[1:] ** 2\\n )\\n outs += [\\n f\\\"ECI-based Property : mean={self.eci[0]:0.4f}\\\"\\n f\\\" std={np.sqrt(fit_var):0.4f}\\\",\\n \\\"Fit Summary\\\",\\n ]\\n\\n for i, term in enumerate(self._subspace.external_terms):\\n outs.append(f\\\"{repr(term)}={self.coefs[len(self.eci) + i]:0.3f}\\\")\\n\\n if self.regression_data is not None:\\n outs += [\\n \\\" ---------------------------------------------------------------------\\\"\\n \\\"-------------------------------\\\",\\n \\\" | ID Orbit ID Degree Cluster Diameter ECI Feature AVG\\\"\\n \\\" Feature STD ECI * STD |\\\",\\n f\\\" | 0 0 0 NA \\\"\\n f\\\"{self.eci[0]:^7.3f}{feature_avg[0]:^15.3f}\\\"\\n f\\\"{feature_std[0]:^15.3f}{feature_std[0] * self.eci[0]:^13.3f}|\\\",\\n ]\\n else:\\n outs += [\\n \\\" ---------------------------------------------------------\\\",\\n \\\" | ID Orbit ID Degree Cluster Diameter ECI |\\\",\\n f\\\" | 0 0 0 NA \\\"\\n f\\\"{self.eci[0]:^7.3f} |\\\",\\n ]\\n\\n for degree, orbits in self.cluster_subspace.orbits_by_size.items():\\n for orbit in orbits:\\n for i, bits in enumerate(orbit.bit_combos):\\n line = (\\n f\\\" |{orbit.bit_id + i:^6}{orbit.id:^12}{degree:^10}\\\"\\n f\\\"{orbit.base_cluster.diameter:^20.4f}\\\"\\n f\\\"{self.eci[orbit.bit_id + i]:^7.3f}\\\"\\n )\\n if self.regression_data is not None:\\n line += (\\n f\\\"{feature_avg[orbit.bit_id + i]:^15.3f}\\\"\\n f\\\"{feature_std[orbit.bit_id + i]:^15.3f}\\\"\\n f\\\"{feature_std[orbit.bit_id + i] * self.eci[orbit.bit_id + i]:^13.3f}\\\" # noqa\\n )\\n line += \\\"|\\\"\\n outs.append(line)\\n outs.append(\\\" \\\" + (len(outs[-1]) - 1) * \\\"-\\\")\\n return \\\"\\\\n\\\".join(outs)\",\n \"def make_section_str(str=\\\"\\\", width=60, symbol='-'):\\n strlen = len(str)\\n dash_len = width - strlen if strlen < width else 0\\n side_dash = symbol * (dash_len // 2)\\n section_str = \\\"{}{}{}\\\".format(side_dash, str, side_dash)\\n return section_str\",\n \"def getSubtitleTable(date) -> str:\\n return \\\"\\\"\\\"| Start of the day | Weeks until NIMCET |\\n| ---------------- | -----------------: |\\n| {time} | {weeks} weeks |\\\"\\\"\\\".format(time=formattedTimeNow(), weeks=round((datetime(2021, 5, 21) - date).days/7, 1))\",\n \"def subsection_handler(request, usage_key_string):\\r\\n if 'text/html' in request.META.get('HTTP_ACCEPT', 'text/html'):\\r\\n usage_key = UsageKey.from_string(usage_key_string)\\r\\n try:\\r\\n course, item, lms_link = _get_item_in_course(request, usage_key)\\r\\n except ItemNotFoundError:\\r\\n return HttpResponseBadRequest()\\r\\n\\r\\n preview_link = get_lms_link_for_item(usage_key, preview=True)\\r\\n\\r\\n # make sure that location references a 'sequential', otherwise return\\r\\n # BadRequest\\r\\n if item.location.category != 'sequential':\\r\\n return HttpResponseBadRequest()\\r\\n\\r\\n parent = get_parent_xblock(item)\\r\\n\\r\\n # remove all metadata from the generic dictionary that is presented in a\\r\\n # more normalized UI. We only want to display the XBlocks fields, not\\r\\n # the fields from any mixins that have been added\\r\\n fields = getattr(item, 'unmixed_class', item.__class__).fields\\r\\n\\r\\n policy_metadata = dict(\\r\\n (field.name, field.read_from(item))\\r\\n for field\\r\\n in fields.values()\\r\\n if field.name not in ['display_name', 'start', 'due', 'format'] and field.scope == Scope.settings\\r\\n )\\r\\n\\r\\n can_view_live = False\\r\\n subsection_units = item.get_children()\\r\\n for unit in subsection_units:\\r\\n state = compute_publish_state(unit)\\r\\n if state in (PublishState.public, PublishState.draft):\\r\\n can_view_live = True\\r\\n break\\r\\n\\r\\n return render_to_response(\\r\\n 'edit_subsection.html',\\r\\n {\\r\\n 'subsection': item,\\r\\n 'context_course': course,\\r\\n 'new_unit_category': 'vertical',\\r\\n 'lms_link': lms_link,\\r\\n 'preview_link': preview_link,\\r\\n 'course_graders': json.dumps(CourseGradingModel.fetch(usage_key.course_key).graders),\\r\\n 'parent_item': parent,\\r\\n 'locator': usage_key,\\r\\n 'policy_metadata': policy_metadata,\\r\\n 'subsection_units': subsection_units,\\r\\n 'can_view_live': can_view_live\\r\\n }\\r\\n )\\r\\n else:\\r\\n return HttpResponseBadRequest(\\\"Only supports html requests\\\")\",\n \"def format_template(template, *args):\\n return textwrap.dedent(template % args).strip()\",\n \"def outputText(self, item, titleMode, internal=False):\\n for num in range(self.parentLevel):\\n item = item.parent\\n if not item:\\n return ''\\n field = item.nodeFormat().findField(self.name)\\n if not field:\\n return ''\\n return field.outputText(item, titleMode, internal)\",\n \"def __str__(self):\\n\\n # can't use backslash in f-string\\n newline = \\\"\\\\n\\\"\\n\\n options = self.options\\n kwoptions = self.kwoptions\\n\\n # add background to options\\n if self.background:\\n options.append(\\\"show background rectangle\\\")\\n kwoptions.update({\\\"background rectangle/.style\\\": f\\\"{{fill={self.background}}}\\\"})\\n\\n return rf\\\"\\\"\\\"\\\\begin{{tikzpicture}}{wrap(fmt_options(self.options, self.kwoptions))}\\n{newline.join(define_colours(self._used_colours))}\\n{newline.join(self.defines)}\\n{newline.join(self._commands)}\\n\\\\end{{tikzpicture}}\\\"\\\"\\\"\",\n \"def printable(title, subtitle=None, resp=None):\\n title = getfirst(title)\\n subtitle = getfirst(subtitle)\\n resp = getfirst(resp)\\n if subtitle:\\n title += \\\" : \\\" + subtitle\\n if resp:\\n title += \\\" / \\\" + resp\\n return title\",\n \"def eng_with_sub(self, eng: list, subword: list) -> list:\\n subwords = subword + eng[0]\\n while [] in subwords:\\n subwords.remove([])\\n out = \\\" \\\".join('%s' % id for id in subwords).split()\\n return out\",\n \"def add_subsection(self):\\n self.add_child() # lint-amnesty, pylint: disable=no-member\",\n \"def render_subtypes(spec_catalog, data_type, prefix=None):\\n subtypes = spec_catalog.get_subtypes(data_type)\\n if len(subtypes) == 0:\\n return None\\n re = prefix if prefix is not None else \\\"\\\"\\n re += \\\", \\\".join([RSTDocument.get_reference(RSTSectionLabelHelper.get_section_label(ct), ct)\\n for ct in subtypes])\\n return re\",\n \"def print_section(section_name, width=120):\\n section_name = ' ' + section_name + ' '\\n print('{:=^{ }}'.format(section_name, width))\",\n \"def syntax_text():\",\n \"def latex_info(info):\\n return '\\\\\\\\textbf{' + info['heading'] + '} ' + info['value'] + ' \\\\\\\\break\\\\n'\",\n \"def title(text, level=0):\\n return '\\\\n' + text + '\\\\n' + '=-~_#%^' [level] * len(text) + '\\\\n\\\\n'\",\n \"def print_sentence_voice(self, final_subj, final_obj, verb, v_aux, v_tense, subj_tag, subj_word, final_mod2=None, final_root=None):\\n new_verb = ''\\n s_sentence1 = s_sentence2 = ''\\n\\n new_verb = gl.verb_conjugate(verb, v_aux, v_tense) + \\\" \\\"\\n\\n if new_verb.strip() == \\\"\\\":\\n new_verb = gl.verb_conjugate(verb, v_aux, \\\"VMIS3S0\\\") + \\\" \\\"\\n\\n\\n for k in sorted(final_subj.keys()):\\n s_sentence1 += final_subj[k] + \\\" \\\"\\n\\n for k in sorted(final_obj.keys()):\\n s_sentence2 += final_obj[k] + \\\" \\\"\\n\\n if final_mod2 != None:\\n for k in sorted(final_mod2.keys()):\\n s_sentence2 += final_mod2[k] + \\\" \\\"\\n if final_root != None:\\n for k in sorted(final_root.keys()):\\n s_sentence2 += final_root[k] + \\\" \\\"\\n\\n\\n #removing errors in punctuation\\n s_sentence1 = s_sentence1.replace(\\\", .\\\", \\\".\\\").replace(\\\"; .\\\", \\\".\\\").replace(\\\": .\\\", \\\".\\\").replace(\\\", ?\\\", \\\"?\\\").replace(\\\"; ?\\\", \\\"?\\\").replace(\\\": ?\\\", \\\"?\\\").replace(\\\", !\\\", \\\"!\\\").replace(\\\"; !\\\", \\\"!\\\").replace(\\\": !\\\", \\\"!\\\").replace(\\\". .\\\", \\\".\\\")\\n s_sentence2 = s_sentence2.replace(\\\", .\\\", \\\".\\\").replace(\\\"; .\\\", \\\".\\\").replace(\\\": .\\\", \\\".\\\").replace(\\\", ?\\\", \\\"?\\\").replace(\\\"; ?\\\", \\\"?\\\").replace(\\\": ?\\\", \\\"?\\\").replace(\\\", !\\\", \\\"!\\\").replace(\\\"; !\\\", \\\"!\\\").replace(\\\": !\\\", \\\"!\\\").replace(\\\". .\\\", \\\".\\\")\\n\\n\\n return self.runTrueCaser(s_sentence1 + new_verb + s_sentence2)\",\n \"def generate_srt_from_sjson(sjson_subs, speed):\\r\\n\\r\\n output = ''\\r\\n\\r\\n equal_len = len(sjson_subs['start']) == len(sjson_subs['end']) == len(sjson_subs['text'])\\r\\n if not equal_len:\\r\\n return output\\r\\n\\r\\n sjson_speed_1 = generate_subs(speed, 1, sjson_subs)\\r\\n\\r\\n for i in range(len(sjson_speed_1['start'])):\\r\\n item = SubRipItem(\\r\\n index=i,\\r\\n start=SubRipTime(milliseconds=sjson_speed_1['start'][i]),\\r\\n end=SubRipTime(milliseconds=sjson_speed_1['end'][i]),\\r\\n text=sjson_speed_1['text'][i]\\r\\n )\\r\\n output += (unicode(item))\\r\\n output += '\\\\n'\\r\\n return output\",\n \"def template(title, steps, loops):\\n return \\\"%-8s %-20s %20d\\\" % (title, format_power(steps), loops)\",\n \"def build_scatter_subplot(fig, x, y, row_num, text, xaxis_text, yaxis_text):\\n\\n # Place provided data\\n fig.add_trace(go.Scatter(x=x, y=y, mode=\\\"markers\\\", marker=dict(size=10)), row=row_num, col=1)\\n # Approximate linear function\\n res = sm.OLS(y, sm.add_constant(x)).fit().fittedvalues\\n # Place trendline\\n fig.add_trace(go.Scatter(x=x, y=res, mode=\\\"lines\\\"), row=row_num, col=1)\\n # Place subplot (for the first subplot, we need to refer to the title of the figure itself) title\\n if row_num == 1:\\n fig.update_layout(title={\\\"text\\\": text})\\n else:\\n fig.layout.annotations[row_num - 2].update(text=text)\\n # Place axis text\\n fig.update_xaxes(title_text=xaxis_text, row=row_num, col=1)\\n fig.update_yaxes(title_text=yaxis_text, row=row_num, col=1)\",\n \"def __repr__(self) -> str:\\n return f\\\"{self.title} that starts at {self.start} and ends at {self.end} and is taught by {self.instructor}\\\"\",\n \"def print_submenu_opc1():\\n\\n sub_m = '==' * 18 + '\\\\n|--> 1. Carga Manual' + '\\\\n|' \\\\\\n + '\\\\n|--> 2. Generación Automática' + '\\\\n|'\\n\\n print(sub_m)\",\n \"def test_export_failure_subsection_level(self):\\r\\n vertical = ItemFactory.create(parent_location=self.course.location, category='vertical', display_name='foo')\\r\\n ItemFactory.create(\\r\\n parent_location=vertical.location,\\r\\n category='aawefawef'\\r\\n )\\r\\n\\r\\n self._verify_export_failure(u'/unit/location:MITx+999+Robot_Super_Course+vertical+foo')\",\n \"def latex_plain(self, num):\\n if not self.list:\\n return \\\"\\\"\\n values, erased_elements = self.list[num - 1]\\n string_list = self.latex_string_list(values, erased_elements)\\n if not string_list:\\n raise ValueError('There is not values for the variation')\\n # return '-\\\\;'\\n\\n for i in range(len(string_list)):\\n unit = self.unit_list[i] or ''\\n name = self.variable_list[i]\\n value_string = string_list[i]\\n string_list[i] = '\\\\\\\\detokenize{%s} = %s~%s' % (name, value_string, unit)\\n\\n str = ',\\\\\\\\hspace{10pt plus 1cm} '.join(string_list)\\n str = str + ';\\\\\\\\\\\\\\\\'\\n return str\",\n \"def make_shirt(size, text):\\n\\tprint(f\\\"\\\\nThe size of the shirt is {size} and the printed text reads: {text}.\\\")\",\n \"def substitute(self, line, system = 'output'):\\r\\n if type(line) not in (str, unicode):\\r\\n line = str(line)\\r\\n setattr(self, '%sSub' % system, line)\",\n \"def _build_compose_section(ctx, template, name, sls_function):\\n\\n def get_vol(include):\\n name = include[include.rindex('/') + 1:]\\n return f' - {include}:/var/task/common/{name}'\\n\\n output_dir = ctx.get('package', {}).get('output', DEFAULT_OUT_DIR)\\n volumes = [\\n f' - {output_dir}:/var/task/dist',\\n ' - ./.juni/bin:/var/task/bin',\\n ] + [\\n get_vol(include)\\n for include in sls_function.get('include', [])\\n ]\\n\\n reqs_path = sls_function.get('requirements')\\n if reqs_path:\\n volumes.append(f' - {reqs_path}:/var/task/common/requirements.txt')\\n\\n return template.format(name=name, volumes='\\\\n'.join(volumes))\",\n \"def format_course_output(self, course, schedule):\\n\\t\\t\\n\\t\\t# handle schedule\\n\\t\\tstr_list = schedule.schedule.strip().split(' ')\\n\\t\\tsch_res = ''\\n\\t\\tif (len(str_list) >= 6):\\n\\t\\t\\tval_list = str_list[:6]\\n\\t\\t\\tval_list[5] = val_list[5][:3]\\n\\t\\t\\t\\n\\t\\t\\t# time\\n\\t\\t\\ttime = val_list[0] + ' ' + val_list[1] + ' ' + val_list[2] + ' ' + val_list[3]\\n\\t\\t\\tlocation = val_list[4] + ' ' + val_list[5]\\n\\t\\t\\tsch_res = str(time + '\\\\t' + location)\\n\\t\\t\\n\\t\\tres = \\\"\\\"\\n\\t\\tres += 'Course code: ' + course.course_code + '\\\\n'\\n\\t\\tres += 'Title: ' + course.title + '\\\\n'\\n\\t\\tres += 'Department: ' + course.department + '\\\\n'\\n\\t\\tres += 'Credit Hours: ' + course.credit_hours + '\\\\n'\\n\\t\\tres += 'Description: ' + course.description + '\\\\n'\\n\\t\\tif (sch_res != ''):\\n\\t\\t\\tres += 'Schedule: ' + sch_res + '\\\\n'\\n\\t\\tres += 'Instructor: ' + schedule.instructor + '\\\\n'\\n\\t\\treturn res\",\n \"def gen_rst(txt):\\n res = \\\"\\\"\\n title_block = get_title_block(txt)\\n title_rst = gen_title_rst(title_block)\\n res += title_rst\\n # add a blank line just to be sure\\n res += \\\"\\\\n\\\"\\n fun_blocks = get_fun_blocks(txt)\\n for (name, fun_block) in fun_blocks:\\n fun_rst = gen_fun_rst(name, fun_block)\\n res += fun_rst\\n return res\",\n \"def __str__(self):\\n return \\\"(%s)\\\" % ' '.join(map(str, self.__subrules))\",\n \"def sub_response():\\n a = int(request.args[\\\"a\\\"])\\n b = int(request.args[\\\"b\\\"])\\n\\n result = sub(a,b)\\n return f\\\"<h1>{result}</h1>\\\"\",\n \"def visit_Subscript(self, node):\\n self.generic_visit(node)\\n return node\",\n \"def __str__(self) -> str:\\n\\n assert self.data is not None\\n title = \\\"{} ({})\\\".format(self.name, self.data[\\\"role\\\"][\\\"name\\\"])\\n name_border = \\\"-\\\" * len(title)\\n lines = [name_border, title, name_border]\\n\\n # build the talent-tree \\\"table\\\"\\n for row_idx, row_data in self.talent_rows.items():\\n lines.append(\\\"{}:\\\".format(row_idx))\\n for talent_idx, talent_data in row_data.items():\\n lines.append(\\n \\\" {}: {}\\\".format(talent_idx, talent_data[\\\"text\\\"])\\n )\\n macro = build_row_macro(row_idx, row_data)\\n if macro is not None:\\n lines.append(\\\"macro:\\\")\\n lines.append(macro[0])\\n\\n return os.linesep.join(lines)\",\n \"def _deal_with_super_sub_expanded(self, string, style=\\\"plain\\\"):\\n if \\\"{\\\" in string:\\n name, supers, subs = string, [], []\\n else:\\n name, supers, subs = split_super_sub(string)\\n\\n names = [translate(name) for name in name.split(\\\" \\\")]\\n supers = [translate(sup) for sup in supers]\\n subs = [translate(sub) for sub in subs]\\n\\n name = \\\" \\\".join(names)\\n\\n # apply the style only to the name\\n if style == \\\"bold\\\":\\n name = \\\"\\\\\\\\mathbf{{{}}}\\\".format(name)\\n\\n # glue all items together:\\n if supers:\\n name += \\\"^{%s}\\\" % \\\" \\\".join(supers)\\n if subs:\\n name += \\\"_{%s}\\\" % \\\" \\\".join(subs)\\n\\n return name\",\n \"def subtitle_header(slide, title: str):\\n top = Inches(0.61)\\n left = Inches(0.42)\\n height = Inches(0.5)\\n width = Inches(0.5)\\n txt_box = slide.shapes.add_textbox(left, top, width, height)\\n text_frame = txt_box.text_frame\\n\\n paragraph = text_frame.paragraphs[0]\\n paragraph.text = title\\n paragraph.font.bold = False\\n paragraph.font.size = Pt(22)\\n paragraph.font.name = 'Times New Roman'\\n\\n return slide\",\n \"def __str__(self, level=0):\\n pt1 = ' join '.join([str(video.video_metadata.file) for video in self.videos])\\n pt2 = ' = '.join([str(id) for id in self._join_ids])\\n join_cols_str = '{}__{}'.format(pt1, pt2)\\n out_string = \\\"\\\\t\\\" * level + join_cols_str + \\\"\\\\n\\\"\\n for child in self.children:\\n out_string += child.__str__(level + 1)\\n return out_string\",\n \"def vartext(self, num, full=0):\\n if not self.is_interval() and not self.is_ecChoose():\\n return self.text\\n values, erased_elements = self.list[num]\\n vtext = self.text[:]\\n space = \\\"\\\\spacer\\\" # It is at the place of an erased element.\\n\\n element_list = (self.interval_list + self.compute_list\\n + self.ecChoose_list)\\n for element in element_list:\\n element['shift'] = 0\\n\\n for element in element_list:\\n start = element['start'] + element['shift']\\n end = element['end'] + element['shift']\\n row = element['row']\\n line = vtext[row - 1]\\n if element['name']:\\n value = values[element['name']]\\n else:\\n # It is just a transient solution\\n value = interval_.random(element['element'])\\n ltx = latex_number(value)\\n if element['unit']:\\n ltx += '~' + element['unit']\\n if element['name'] in erased_elements:\\n ltx = space\\n line = line[:start] + \\\\\\n ltx + \\\\\\n line[end:]\\n vtext[row - 1] = line\\n\\n shift = len(ltx) - (end - start)\\n for element2 in element_list:\\n if row == element2['row'] and element2['start'] >= end:\\n element2['shift'] += shift\\n return vtext\",\n \"def title(string: str, level=H1) -> str:\\n appended = []\\n for lvl in range(0, level):\\n appended.append(\\\"#\\\")\\n return f\\\"{''.join(appended)} {string}\\\"\",\n \"def __unicode__(self):\\n if self.level == 0:\\n return u'Wurzel: %s' % (self.courseevent)\\n elif self.level == 1:\\n return u'Block: %s' % (self.title)\\n elif self.level == 2:\\n return u'Block: %s, Lektion: %s. %s' % (self.parent.title, self.lesson_nr, self.title)\\n elif self.level == 3:\\n return u'%s %s %s' % (self.courseevent, self.lesson_nr, self.title)\",\n \"def formatter(in_tuple):\\n length = len(in_tuple)\\n form_string = (\\\"the {} numbers are: \\\" + \\\", \\\".join([\\\"{}\\\"]*length)).format(length, *in_tuple)\\n return form_string.format(in_tuple)\",\n \"def make_shirt(text='I love Python', size='large'):\\t\\n\\tprint(f\\\"\\\\nThe size of the shirt is {size} and the printed text reads: {text}.\\\")\",\n \"def build_tex(self,signs='-',request_parens=False):\\n return self._build_tex(signs,request_parens)\",\n \"def formatOutput(self, storedText, titleMode, internal=False):\\n prefix = self.prefix\\n suffix = self.suffix\\n if titleMode:\\n if self.html:\\n storedText = self.removeMarkup(storedText)\\n if globalref.docRef.formHtml:\\n prefix = self.removeMarkup(prefix)\\n suffix = self.removeMarkup(suffix)\\n else:\\n if not self.html:\\n storedText = escape(storedText).replace('\\\\n', '<br />')\\n if not globalref.docRef.formHtml:\\n prefix = escape(prefix)\\n suffix = escape(suffix)\\n return u'%s%s%s' % (prefix, storedText, suffix)\",\n \"def print_section(self, s):\\n section = s.upper()\\n\\n self.print_newline()\\n self.print_newline()\\n self._write('%s\\\\n' % section)\\n self._write('%s\\\\n' % ('-' * len(section)))\\n self.print_newline()\",\n \"def __str__(self):\\n ret_str = \\\"\\\"\\n for element_type in ('nodes', 'edges', 'layers'):\\n elements = getattr(self, element_type)\\n subtype_counts = defaultdict(int)\\n ret_str += \\\"{0} {1}:\\\\n\\\".format(len(elements), element_type)\\n for element in elements:\\n subtype_counts[type(element).__name__] += 1\\n for subtype in subtype_counts:\\n ret_str += \\\"\\\\t{0}: {1}\\\\n\\\".format(subtype,\\n subtype_counts[subtype])\\n ret_str += \\\"\\\\n\\\"\\n ret_str += \\\"primary text:\\\\n{0}\\\".format(self.text.encode('utf-8'))\\n return ret_str\",\n \"def _tstep_msg(fem: BDF,\\n subcase: Subcase,\\n tstep_id: int,\\n tstep_type: str='') -> str:\\n msg = (\\n 'tstep%s_id=%s\\\\n'\\n 'tsteps=%s\\\\n'\\n 'tstepnls=%s\\\\n'\\n #'tstep1s=%s\\\\n'\\n 'subcase:\\\\n%s' % (\\n tstep_type, tstep_id,\\n str(fem.tsteps),\\n str(fem.tstepnls),\\n #str(fem.tstep1s),\\n str(subcase))\\n )\\n return msg\"\n]","isConversational":false},"negative_scores":{"kind":"list like","value":["0.65773475","0.63128084","0.62126887","0.6208371","0.61632997","0.6140767","0.6039124","0.6012975","0.5978812","0.5934507","0.58059335","0.5795126","0.569796","0.5690162","0.5618066","0.5618066","0.5604984","0.5596953","0.5590398","0.55688125","0.55626035","0.5555129","0.5532533","0.55151886","0.55123144","0.5450941","0.54002756","0.5374349","0.532257","0.5321723","0.53026885","0.52717155","0.51799834","0.5171706","0.5164233","0.51560086","0.51468277","0.51343465","0.51208043","0.5115898","0.50805926","0.5078588","0.50776464","0.5075747","0.5071453","0.5066403","0.50485575","0.50390625","0.5037364","0.5033364","0.5017483","0.5013747","0.5006383","0.49965066","0.49962258","0.49911487","0.49857542","0.49798954","0.49794197","0.49739507","0.49542546","0.4953127","0.49413323","0.49273193","0.49214822","0.4903098","0.4895781","0.4893218","0.48903978","0.48827693","0.4879801","0.4877985","0.4871362","0.48681006","0.48679528","0.48611462","0.48610196","0.48533398","0.4844514","0.48395702","0.48384026","0.48354462","0.48155212","0.4812809","0.48114684","0.48082566","0.48074454","0.48061338","0.48023197","0.48019406","0.47996613","0.47994807","0.4797489","0.47916412","0.47870955","0.47755167","0.477531","0.47721112","0.47698697","0.4766535"],"string":"[\n \"0.65773475\",\n \"0.63128084\",\n \"0.62126887\",\n \"0.6208371\",\n \"0.61632997\",\n \"0.6140767\",\n \"0.6039124\",\n \"0.6012975\",\n \"0.5978812\",\n \"0.5934507\",\n \"0.58059335\",\n \"0.5795126\",\n \"0.569796\",\n \"0.5690162\",\n \"0.5618066\",\n \"0.5618066\",\n \"0.5604984\",\n \"0.5596953\",\n \"0.5590398\",\n \"0.55688125\",\n \"0.55626035\",\n \"0.5555129\",\n \"0.5532533\",\n \"0.55151886\",\n \"0.55123144\",\n \"0.5450941\",\n \"0.54002756\",\n \"0.5374349\",\n \"0.532257\",\n \"0.5321723\",\n \"0.53026885\",\n \"0.52717155\",\n \"0.51799834\",\n \"0.5171706\",\n \"0.5164233\",\n \"0.51560086\",\n \"0.51468277\",\n \"0.51343465\",\n \"0.51208043\",\n \"0.5115898\",\n \"0.50805926\",\n \"0.5078588\",\n \"0.50776464\",\n \"0.5075747\",\n \"0.5071453\",\n \"0.5066403\",\n \"0.50485575\",\n \"0.50390625\",\n \"0.5037364\",\n \"0.5033364\",\n \"0.5017483\",\n \"0.5013747\",\n \"0.5006383\",\n \"0.49965066\",\n \"0.49962258\",\n \"0.49911487\",\n \"0.49857542\",\n \"0.49798954\",\n \"0.49794197\",\n \"0.49739507\",\n \"0.49542546\",\n \"0.4953127\",\n \"0.49413323\",\n \"0.49273193\",\n \"0.49214822\",\n \"0.4903098\",\n \"0.4895781\",\n \"0.4893218\",\n \"0.48903978\",\n \"0.48827693\",\n \"0.4879801\",\n \"0.4877985\",\n \"0.4871362\",\n \"0.48681006\",\n \"0.48679528\",\n \"0.48611462\",\n \"0.48610196\",\n \"0.48533398\",\n \"0.4844514\",\n \"0.48395702\",\n \"0.48384026\",\n \"0.48354462\",\n \"0.48155212\",\n \"0.4812809\",\n \"0.48114684\",\n \"0.48082566\",\n \"0.48074454\",\n \"0.48061338\",\n \"0.48023197\",\n \"0.48019406\",\n \"0.47996613\",\n \"0.47994807\",\n \"0.4797489\",\n \"0.47916412\",\n \"0.47870955\",\n \"0.47755167\",\n \"0.477531\",\n \"0.47721112\",\n \"0.47698697\",\n \"0.4766535\"\n]","isConversational":false},"document_score":{"kind":"string","value":"0.75560915"},"document_rank":{"kind":"string","value":"0"}}},{"rowIdx":22670,"cells":{"query":{"kind":"string","value":"Creates a TeX formatted string for a course"},"document":{"kind":"string","value":"def latex_course(course):\n basic_info_list = [\n 'session', 'school', 'credits', 'level', 'offered',\n 'visiting_students', 'erasmus_students'\n ]\n generic_subsection_list = [\n 'description', 'timetable', 'requirements_of_entry',\n 'excluded_courses', 'co_requisites', 'assessment_weighting'\n ]\n string = '\\\\subsection{' + course[\"title\"] + '}\\n'\n for info in basic_info_list:\n string += latex_info(course[info])\n for subsection in generic_subsection_list:\n string += latex_subsection(course[subsection])\n string += '\\\\break \\\\textbf{' + course['assessment_date'][\n 'heading'] + '}' + course['assessment_date']['value'] + '\\n'\n string += latex_subsection(course['aims'])\n string += '\\\\subsubsection*{' + \\\n course['learning_outcomes']['heading'] + '}\\n'\n outcome_list = re.split(\n '\\d+\\. ', course['learning_outcomes']['value'])\n string += outcome_list[0] + '\\n'\n string += '\\\\begin{enumerate}\\n'\n for i in outcome_list[1:-1]:\n string += '\\\\item ' + i + '\\n'\n string += '\\\\end{enumerate}\\n'\n return string"},"metadata":{"kind":"string","value":"{\n \"objective\": {\n \"self\": [],\n \"paired\": [],\n \"triplet\": [\n [\n \"query\",\n \"document\",\n \"negatives\"\n ]\n ]\n }\n}"},"negatives":{"kind":"list like","value":["def create_tex(unwanted_courses, wanted_courses=None):\n page = requests.get(\n 'http://gla.ac.uk/coursecatalogue/courselist/' +\n '?code=REG30200000&name=School+of+Computing+Science')\n tree = html.fromstring(page.content)\n spans = tree.xpath('//span/text()')\n codes = []\n if wanted_courses is None:\n for s in spans:\n if s[0:4] == \"COMP\" and s[7] == '4' and s not in unwanted_courses:\n codes.append(s)\n else:\n for s in wanted_courses:\n codes.append(s)\n write_to_latex(codes, unwanted_courses)\n return None","def format_course_output(self, course, schedule):\n\t\t\n\t\t# handle schedule\n\t\tstr_list = schedule.schedule.strip().split(' ')\n\t\tsch_res = ''\n\t\tif (len(str_list) >= 6):\n\t\t\tval_list = str_list[:6]\n\t\t\tval_list[5] = val_list[5][:3]\n\t\t\t\n\t\t\t# time\n\t\t\ttime = val_list[0] + ' ' + val_list[1] + ' ' + val_list[2] + ' ' + val_list[3]\n\t\t\tlocation = val_list[4] + ' ' + val_list[5]\n\t\t\tsch_res = str(time + '\\t' + location)\n\t\t\n\t\tres = \"\"\n\t\tres += 'Course code: ' + course.course_code + '\\n'\n\t\tres += 'Title: ' + course.title + '\\n'\n\t\tres += 'Department: ' + course.department + '\\n'\n\t\tres += 'Credit Hours: ' + course.credit_hours + '\\n'\n\t\tres += 'Description: ' + course.description + '\\n'\n\t\tif (sch_res != ''):\n\t\t\tres += 'Schedule: ' + sch_res + '\\n'\n\t\tres += 'Instructor: ' + schedule.instructor + '\\n'\n\t\treturn res","def completed_course()->str:\r\n try:\r\n db: sqlite3.Connection = sqlite3.connect(DB_FILE)\r\n query: str = \"SELECT s.Name, s.CWID, g.Course, g.Grade, i.Name AS 'Instructor' \" \\\r\n \"FROM grades2 g JOIN students2 s ON g.StudentCWID = s.CWID \" \\\r\n \"JOIN instructors2 i ON g.InstructorCWID = i.CWID ORDER BY s.Name\" \r\n except sqlite3.OperationalError as e:\r\n print(e)\r\n\r\n data: Dict[str,str]=\\\r\n [{\"name\": name,\"cwid\": cwid,\"course\": course , \"grade\":grade, \"instructor\": instructor}\r\n for name, cwid, course, grade, instructor in db.execute(query)]\r\n db.close()\r\n \"\"\"Render the template from templates folder\"\"\"\r\n return render_template(\"student_summary.html\",\r\n title=\"Stevens Repository\",\r\n table_title=\"Student, Course, Grade and Instructor\",\r\n students=data )","def __str__(self):\r\n return \"<CourseFixture: org='{org}', number='{number}', run='{run}'>\".format(**self._course_dict)","def to_string(self):\n return \"{base_msg} Courses: {courses}\".format(\n base_msg=super().to_string(),\n courses=self.courses_string()\n )","def get_text(self, course):\r\n return views.render_accordion(\r\n self.request, course, course.get_children()[0].scope_ids.usage_id.to_deprecated_string(), None, None\r\n )","def get_text(self, course): # pylint: disable=unused-argument\r\n raise NotImplementedError","def render_plaintext(self, plaintext, context):\r\n return CourseEmailTemplate._render(self.plain_template, plaintext, context)","def format_course_for_view(course):\r\n return (\r\n course.display_name,\r\n reverse_course_url('course_handler', course.id),\r\n get_lms_link_for_item(course.location),\r\n course.display_org_with_default,\r\n course.display_number_with_default,\r\n course.location.name\r\n )","def courses_string(self):\n course_string = \"\"\n for c in self.courses[0]:\n course_string += c + \", \"\n course_string = course_string[:-2]\n return course_string","def write_to_latex(codelist, unwanted_courses):\n # TODO: investigate a way to add large amounts of text outside of the\n # function\n abstract01 = \"I created this document to practice parsing html and using\\\n tools like Beautiful Soup which I've previously had little experience\\\n in. As a result, it's not perfect.\\\\newline\\\n It is also a slightly condensed all-in-one-place look at a selection\\\n of courses that are available for fourth year computer science\\\n students at the University of Glasgow. For the purposes of clarity I\\\n have removed several courses from this selection. The following\\\n courses have been omitted:\"\n abstract02 = \"For more insight into the project, to report issues or to\\\n inspect the code, have a look at the GitHub:\\\n \\\\url{https://github.com/IS0metric/course-ripper}\"\n unincluded = create_not_included_list(unwanted_courses)\n with open('courses.tex', 'w') as f:\n # TODO Try and move all this to a separate function?\n # TODO: Check if it's more efficient to write a single, massive string\n # to file\n f.write('\\\\documentclass{hitec}\\n')\n f.write('\\\\usepackage[document]{ragged2e}\\n')\n f.write('\\\\usepackage{url}\\n')\n f.write('\\\\usepackage{hyperref}\\n')\n f.write('\\\\setcounter{tocdepth}{4}\\n')\n f.write('\\\\begin{document}\\n')\n f.write('\\\\title{Fourth Year (2016-17) Courses}\\n')\n f.write('\\\\author{Jack Parkinson}\\n')\n f.write('\\\\date{August 2016}\\n')\n f.write('\\\\maketitle\\n')\n f.write('\\\\abstract{' + abstract01 + unincluded + abstract02 + '}\\n')\n f.write('\\\\newpage\\n\\n')\n f.write('\\\\tableofcontents\\n')\n f.write('\\\\newpage\\n\\n')\n # TODO: Look into alternatives to the three lists\n all_courses = []\n sem1_courses = []\n sem2_courses = []\n for code in codelist:\n course = bsoup(get_coursepage(code))\n if course['offered']['value'] == 'Runs Throughout Semesters 1 and 2':\n all_courses.append(course)\n elif \"1\" in course['offered']['value']:\n sem1_courses.append(course)\n elif \"2\" in course['offered']['value']:\n sem2_courses.append(course)\n f.write('\\\\section{Semester 1 and 2 Courses}\\n\\n')\n for course in all_courses:\n f.write(latex_course(course))\n f.write('\\\\section{Semester 1 Only Courses}\\n\\n')\n for course in sem1_courses:\n f.write(latex_course(course))\n f.write('\\\\section{Semester 2 Only Courses}\\n\\n')\n for course in sem2_courses:\n f.write(latex_course(course))\n f.write('\\\\end{document}')\n return None","def get_about_text(self, course_key):\r\n text = views.course_about(self.request, course_key.to_deprecated_string()).content\r\n return text","def __str__(self):\n # Use 'Unknown' if the course instance does not have a term\n if self.course_instance.term:\n term = self.course_instance.term.verbose_name()\n else:\n term = 'Unknown'\n\n exam_unicode = '{term} {number} {type} for {course}'.format(\n term=term,\n number=self.get_exam_number_display(),\n type=self.get_exam_type_display(),\n course=self.course_instance.course)\n if self.instructors:\n instructors = ', '.join([i.last_name for i in self.instructors])\n return '{}, taught by {}'.format(exam_unicode, instructors)\n else:\n return '{} (Instructors Unknown)'.format(exam_unicode)","def course_info(self):\n print(\"Course name: {}\".format(self._course_name))\n print(\"Lead teacher: {}\".format(self._teacher))\n\n if len(self._students) == 0:\n print(\"Course does not enrolled by any student\")\n else:\n print(\"Enrolled: {}/{}\".format(len(self._students), self._total_place))","def getFullCourseTitle(self, brain):\n full_title = ''\n\n id = brain.getCourseId\n if id:\n full_title = '%s - ' %id\n full_title += brain.Title\n term = brain.getTerm\n if term:\n full_title += ', %s' %term\n\n return full_title","def _create_formatted_string(self):\n string = NALSyntax.StatementSyntax.Start.value + \\\n self.get_subject_term().get_formatted_string()\n\n string += \" \" + self.get_copula_string() + \" \"\n\n string += self.get_predicate_term().get_formatted_string() + \\\n NALSyntax.StatementSyntax.End.value\n\n return string","def help_text(command):\n\n courses_list = ('ENPM611', 'ENPM613', 'ENPM631', 'ENPM687',\\\n 'ENPM691', 'ENPM693', 'ENPM694', 'ENPM696',\\\n 'ENPM809J','ENPM809R', 'ENPM809W')\n\n response = 'I have course descriptions for: '\n for course_name in courses_list:\n response = response + course_name + ' '\n\n response = response + '\\nTo get the course description, execute command: about ENPM<course_number>'\n\n return response","def render_htmltext(self, htmltext, context):\r\n return CourseEmailTemplate._render(self.html_template, htmltext, context)","def MakeTexStr(self):\n if self._latex_str == '':\n self._latex_str += bmr_hdr.format(\n self._filename.replace('.tex', ''))\n else:\n # Get rid of ftr so new images maybe added\n self._latex_str = self._latex_str.split(bmr_ftr)[0]\n self.AddImageFrames()\n # print self._latex_str\n self._latex_str += bmr_ftr","def display_courses(courses):\n\n _print('You can access %d courses' % len(courses))\n for i, course in enumerate(courses, 1):\n _print('%d - [%s] - %s' % (i, course.state, course.name))","def pdflatex(unwanted_courses):\n create_tex(unwanted_courses)\n cmd = ['pdflatex', '-interaction', 'nonstopmode', 'courses.tex']\n proc = subprocess.Popen(cmd)\n proc.communicate()\n return None","def get_description(self):\n text = \"is a student's t distribution; characterised by its degrees of freedom, which here is\"+str(self.dofs)+\".\"\n return text","def _get_course_email_context(course):\r\n course_id = course.id.to_deprecated_string()\r\n course_title = course.display_name\r\n course_url = 'https://{}{}'.format(\r\n settings.SITE_NAME,\r\n reverse('course_root', kwargs={'course_id': course_id})\r\n )\r\n image_url = 'https://{}{}'.format(settings.SITE_NAME, course_image_url(course))\r\n email_context = {\r\n 'course_title': course_title,\r\n 'course_url': course_url,\r\n 'course_image_url': image_url,\r\n 'account_settings_url': 'https://{}{}'.format(settings.SITE_NAME, reverse('dashboard')),\r\n 'platform_name': settings.PLATFORM_NAME,\r\n }\r\n return email_context","def insert_course(dept, num, text):\n\n # Course Title \n m = re.search(\"[\\d\\w]{5} - ([\\w ]*)\", text)\n title = m.group(1) if m else \"nomatch\"\n\n # Course Description\n m = re.search(\"\\.\\s(.*)\\sTypically\",text)\n des = m.group(1) if m else \"nomatch\"\n\n # Credit hours aren't fixed for every course\n # Credit Hours: 2.00\n # Credit Hours: 2.00 or 3.00. \n # Credit Hours: 1.00 to 18.00. \n m = re.search(\"Credit Hours: (\\d+\\.\\d+)\",text, flags=re.IGNORECASE)\n m = re.search(\"(\\d+\\.\\d+)(.*?)Credit hours\",text, flags=re.IGNORECASE) if not m else m\n cr = m.group(1) if m else \"-1\"\n\n # Semesters Offered\n m = re.search(\"Typically offered (.*?)\\.\", text)\n sem = m.group(1).split() if m else [\"nomatch\"]\n\n # Course Type: Lecture, Recitation, Lab, Seminar, etc.\n m = re.search(\"Schedule Types:\\s((?:[\\w ]+)(?:,[\\w ]+)*) \\s+\", text)\n form = m.group(1).split(\", \") if m else [\"nomatch\"]\n\n # Learning objectives will not necessarily follow campuses\n m = re.search(\"campuses:(\\s+([\\w\\s])+\\n)\", text)\n campus = m.group(1).strip().split(\"\\n\\n\") if m else [\"nomatch\"]\n campus = [camp.strip() for camp in campus]\n\n # prereq regex and decomosition of prereqs into lists of AND conditions (works for most classes, not 477 and similar)\n # re.DOTALL matches all characters, including \"\\n\"\n idx = text.find(\"campuses:\")\n m = re.search(\"Prerequisites:(.*)\",text[idx:],flags=re.DOTALL)\n if m:\n allReqs = []\n prereqText = m.group(1).strip()\n prereqText = prereqText.encode('ascii', 'ignore') \n for i in PrereqParser.parseprereq(prereqText):\n reqArr = []\n for j in i.split():\n if j.find(\"-C\") != -1:\n j = j.replace(\"-C\",\"\")\n reqArr.append(Requisite(course=j,reqType=False))\n else:\n reqArr.append(Requisite(course=j,reqType=True)) \n allReqs.append(RequisiteList(courses=reqArr))\n\n else:\n allReqs = []\n\n # create course entity\n course = Course(number=num, title=title, department=dept, form=form,\n description=des, credits=float(cr), semesters=sem,\n campuses=campus,requisites=allReqs, id=dept + num)\n # store course \n course.put()","def toStudentString(self):\r\n return \"{0}th year, section {1}, {2} {3}\".format(self.batch, self.batch_id, self.batch, self.batch_id)","def create_course_with_unit():\r\n world.clear_courses()\r\n course = world.CourseFactory.create()\r\n world.scenario_dict['COURSE'] = course\r\n section = world.ItemFactory.create(parent_location=course.location)\r\n world.ItemFactory.create(\r\n parent_location=section.location,\r\n category='sequential',\r\n display_name='Subsection One',\r\n )\r\n user = create_studio_user(is_staff=False)\r\n add_course_author(user, course)\r\n\r\n log_into_studio()\r\n world.css_click('a.course-link')\r\n\r\n world.wait_for_js_to_load()\r\n css_selectors = [\r\n 'div.section-item a.expand-collapse', 'a.new-unit-item'\r\n ]\r\n for selector in css_selectors:\r\n world.css_click(selector)\r\n\r\n world.wait_for_mathjax()\r\n world.wait_for_xmodule()\r\n\r\n assert world.is_css_present('ul.new-component-type')","def parse_create_course(xml_course):\n attrs = [\n \"term-code\",\n \"term-description\",\n 'subject',\n \"course-number\",\n \"school\",\n \"department\",\n \"title\",\n \"description\",\n \"credit-hours\",\n \"distribution-group\"\n ]\n course = pull_attributes_from_xml(xml_course, attrs)\n course[\"sections\"] = []\n\n return course","def add_s_courses(student_courses, resume_output):\n # creates a list to represent each line of the html code for courses section\n html_courses_section = []\n\n # opens the html section with a div tag\n html_courses_section.append(\"<div>\")\n\n # adds a courses section with header3 format to html section\n html_courses_section.append(surround_block(\"h3\", \"Courses\"))\n\n # makes a string of comma-separated (\", \") student courses\n student_courses_str = \"\" # creates a list of student courses, and assigns the first course\n for course in student_courses: # goes second course to last course... for each course\n\n # debugging\n # print(\"idl\" + course)\n\n # adds the student course to the string of courses\n student_courses_str += course\n\n # adds a comma after the course, unless it is the last course in the list\n if course is not student_courses[-1]:\n student_courses_str += \", \"\n\n # print(\"student_courses_str:\" + student_courses_str)\n\n # adds a line with str list of courses in span tag\n html_courses_section.append(surround_block(\"span\", student_courses_str))\n\n # closes the courses section\n html_courses_section.append(\"</div>\")\n # print(\"html courses section\", html_courses_section)\n\n # adds the courses section to the html code to be printed\n resume_output.extend(html_courses_section)\n # print(resume_output)\n\n # returns output code\n return resume_output","def createCourse():\n\tif request.method == 'POST':\n\t\tcname = request.form['cname']\n\t\tcourseterm = request.form['courseterm']\n\t\tcoursepoint = request.form['coursepoint']\n\t\tcoursetype = request.form['coursetype']\t\n\t\tcourseyear = request.form['courseyear']\t\n\t\ttname = request.form['tname']\t\n\t\terror = None\n\n\t\tif not cname:\n\t\t\terror = 'Course name is required.'\n\t\telif not courseterm:\n\t\t\terror = 'Course term is required'\n\t\telif not courseterm:\n\t\t\terror = 'Course point is required'\n\n\t\tif error is not None:\n\t\t\tflash(error)\n\t\telse:\n\t\t\tdb = get_db()\n\t\t\tcur = db.cursor()\n\t\t\tcur.execute(\n\t\t\t\t'INSERT INTO course (cname, courseyear, coursetype, courseterm, coursepoint, tname)'\n\t\t\t\t' VALUES (%s, %s, %s, %s, %s, %s)',\n\t\t\t\t(cname, courseyear, coursetype, courseterm, coursepoint, tname)\n\t\t\t)\n\t\t\tdb.commit()\n\t\t\treturn redirect(url_for('info.index'))\n\n\treturn render_template('info/createCourse.html')","def render_preview_certificate(request, course_id):\n return render_html_view(request, str(course_id))","def __str__(self):\n start = f\"{self.start:%y/%m/%d %H:%M} - \" if self.start else \"\"\n return f\"Course run {self.id!s} starting {start:s}\"","def _createCoursesTable(self):\n\t\tcommand = \"\"\"CREATE TABLE courses (ID INTEGER PRIMARY KEY,\n\t\t\tname TEXT,\n\t\t\tauthor_id INTEGER,\n\t\t\tdescription TEXT\n\t\t\t);\n\"\"\"\n\n\t\tself._run_command(command)","def get_template(self):\n return self.sep.join([self.htmls[html] for html in self.lang]).format(**self.fields)","def mktg_course_about(request, course_id):\r\n\r\n course_key = SlashSeparatedCourseKey.from_deprecated_string(course_id)\r\n try:\r\n course = get_course_with_access(request.user, 'see_exists', course_key)\r\n except (ValueError, Http404) as e:\r\n # if a course does not exist yet, display a coming\r\n # soon button\r\n return render_to_response(\r\n 'courseware/mktg_coming_soon.html', {'course_id': course_key.to_deprecated_string()}\r\n )\r\n\r\n registered = registered_for_course(course, request.user)\r\n\r\n if has_access(request.user, 'load', course):\r\n course_target = reverse('info', args=[course.id.to_deprecated_string()])\r\n else:\r\n course_target = reverse('about_course', args=[course.id.to_deprecated_string()])\r\n\r\n allow_registration = has_access(request.user, 'enroll', course)\r\n\r\n show_courseware_link = (has_access(request.user, 'load', course) or\r\n settings.FEATURES.get('ENABLE_LMS_MIGRATION'))\r\n course_modes = CourseMode.modes_for_course(course.id)\r\n\r\n return render_to_response('courseware/mktg_course_about.html', {\r\n 'course': course,\r\n 'registered': registered,\r\n 'allow_registration': allow_registration,\r\n 'course_target': course_target,\r\n 'show_courseware_link': show_courseware_link,\r\n 'course_modes': course_modes,\r\n })","def get_text(self, course):\r\n return views.progress(self.request, course.id.to_deprecated_string(), self.user.id).content","def description(self):\n return ('According to Wikipedia, the Affine cipher is '\n 'a \"monoalphabetic substitution cipher, wherein each letter in an alphabet '\n 'is mapped to its numeric equivalent, '\n 'encrypted using the function [E(x)=(ax+b) mod m], '\n 'and converted back to a letter.\"\\n'\n 'The characters supported by the implementation in this program include:\\n'\n '-- uppercase letters: {}\\n'\n '-- lowercase letters: {}\\n'\n '-- digits: {}\\n'\n '-- punctuation: {}\\n'\n '-- blank space: \" \"\\n'\n .format(string.ascii_uppercase,\n string.ascii_lowercase,\n string.digits,\n string.punctuation)\n )","def __str__(self):\n\n rep = 'Generalized Syllogism:\\n'\n rep += '\\ttask: {}\\n'.format(self.task)\n rep += '\\tencoded_task: {}\\n'.format(self.encoded_task)\n rep += '\\tp1: {}\\n'.format(self.p1)\n rep += '\\tp2: {}\\n'.format(self.p2)\n rep += '\\tquantifier_p1: {}\\n'.format(self.quantifier_p1)\n rep += '\\tquantifier_p2: {}\\n'.format(self.quantifier_p2)\n rep += '\\tfigure: {}\\n'.format(self.figure)\n rep += '\\tTerms:\\n'\n rep += '\\t\\tA: {}\\n'.format(self.A)\n rep += '\\t\\tB: {}\\n'.format(self.B)\n rep += '\\t\\tC: {}\\n'.format(self.C)\n return rep","def astext(self):\n self.elements.update({\n 'body': u''.join(self.body),\n 'indices': self.generate_indices()\n })\n return self.render('beamer.tex_t', self.elements)","def __repr__(self) -> str:\n # start the textual representation off with a newline\n weekly_schedule = \"\\n\"\n # iterate through each day of the week in the schedule\n for day in self.schedule.keys():\n # add the name of the current day of the week\n weekly_schedule += day + \":\\n\\n\\t\"\n # access the schedule for the current day of the week\n schedule_for_day = self.schedule[day]\n # add all of the classes of the current day of the week\n weekly_schedule += \"\\n\\t\".join(map(str, schedule_for_day))\n # for course in schedule_for_day:\n # weekly_schedule += str(course) + \"\\n\"\n # if not processing the last day of the week, add two newlines\n if day != list(self.schedule.keys())[-1]:\n weekly_schedule += \"\\n\\n\"\n # if processing the last day of the week, add one newline\n else:\n weekly_schedule += \"\\n\"\n # return a string that displays all courses taught in a week\n return weekly_schedule","def subject(template_name):\n subject = loader.render_to_string(template_name,\n self.get_context())\n return ''.join(subject.splitlines())","def get_rendered_text(self, context):\n missing = set()\n for required in utils.get_variable_names_from_template(self):\n if required not in context:\n missing.add(required)\n if missing:\n raise MissingContext(missing)\n tmpl = utils.PyratempTemplate(self.text)\n context = context.copy()\n context[\"locale\"] = self.language.iso_code\n return tmpl.render(context)","def _course_factory_create_course():\r\n return CourseFactory.create(org='MITx', course='999', display_name='Robot Super Course')","def __str__(self):\n table_data = [\n ['', 'C', 'G', 'A', 'T'],\n ['total', str(self.total['C']), str(self.total['G']), str(self.total['A']), str(self.total['T'])],\n ['reverse half strand', str(self.reverse['C']), str(self.reverse['G']), str(self.reverse['A']),\n str(self.reverse['T'])],\n ['forward half strand', str(self.forward['C']), str(self.forward['G']), str(self.forward['A']),\n str(self.forward['T'])]\n ]\n table = AsciiTable(table_data)\n return \"Generation #{}\\n\".format(self.epoch) + table.table","def top_students_table(course):\n\n return 'COURSE {}\\n'.format(course) + \\\n '\\n'.join('{:10} --- {:4}'.format(*rate)\n for rate in itertools.islice(get_rates(students_data(), course), 3))","def makeTexInput(self, tabletitle, subsection=True):\r\n tablestring = \"\"\r\n\r\n # if there's enough effluent data\r\n if self.ds.effluent.include:\r\n if subsection:\r\n tablestring += r\"\\subsection{%s}\" % (self.bmp,)\r\n\r\n # caption for the stats plot\r\n prob_caption = \"Box and Probability Plots of {} at {} BMPs\".format(\r\n self.parameter.name, self.bmp\r\n )\r\n\r\n # caption for the scatter plot\r\n scatter_caption = \"Influent vs. Effluent Plots of {} at {} BMPs\".format(\r\n self.parameter.name, self.bmp\r\n )\r\n\r\n # warning about having a lot of non-detects\r\n warning = \"\"\"\r\n Warning: there is a very high percentage of non-detects in\r\n this data set. The hypothesis test results and other\r\n statistics reported in this table may not be valid.\r\n \"\"\"\r\n\r\n # make the table and write it to the output file\r\n tablestring += self._make_tex_table(tabletitle)\r\n\r\n # if less than 80% of the data is ND\r\n if self.ds.effluent.ND / self.ds.effluent.N <= 0.8:\r\n\r\n # make the stat plot string\r\n statfig = self._make_tex_figure(\r\n self.stat_fig_name, prob_caption, clearpage=False\r\n )\r\n\r\n # make the scatter plot string\r\n scatterfig = self._make_tex_figure(\r\n self.scatter_fig_name, scatter_caption, clearpage=True\r\n )\r\n\r\n # write the strings to the file\r\n tablestring += statfig\r\n tablestring += scatterfig\r\n\r\n else:\r\n # if there are too many non-detect,\r\n # issue the warning\r\n tablestring += warning\r\n\r\n return tablestring","def __str__(self):\n t = Template(\n \"\"\"\n <h4>$title</h4>\n $imgs\n $footnotes\n <hr/>\"\"\")\n # Return result.\n return t.substitute({\n \"title\": self.title,\n \"imgs\": self.render_images(),\n \"footnotes\": self.render_footnotes()\n })","def generate_tex(title, author, txt, indices=None):\n with open('head', 'r') as head,\\\n open('stegano.tex', 'w') as file_tex:\n\n # First lines are\n # - Title\n # - Author\n\n file_tex.write(\"\".join(head.readlines())) # Write heading\n file_tex.write(\"\\\\title{{{}}}\\n\".format(title))\n file_tex.write(\"\\\\author{{{}}}\\n\".format(author))\n file_tex.write(\"\\\\date{{}}\\n\")\n\n file_tex.write(\"\\\\begin{document}\\n\\\\maketitle\\n\")\n\n # Write char by char, adding decoration when indices is reach\n i = 0\n while i < len(txt):\n if i in indices:\n file_tex.write(secret_char(txt[i]))\n else:\n file_tex.write(non_secret_char(txt[i]))\n i += 1\n\n file_tex.write(\"\\\\end{document}\")","def _create_course(self):\r\n super(TestOrphan, self)._create_course()\r\n\r\n self._create_item('chapter', 'Chapter1', {}, {'display_name': 'Chapter 1'}, 'course', 'runid')\r\n self._create_item('chapter', 'Chapter2', {}, {'display_name': 'Chapter 2'}, 'course', 'runid')\r\n self._create_item('chapter', 'OrphanChapter', {}, {'display_name': 'Orphan Chapter'}, None, None)\r\n self._create_item('vertical', 'Vert1', {}, {'display_name': 'Vertical 1'}, 'chapter', 'Chapter1')\r\n self._create_item('vertical', 'OrphanVert', {}, {'display_name': 'Orphan Vertical'}, None, None)\r\n self._create_item('html', 'Html1', \"<p>Goodbye</p>\", {'display_name': 'Parented Html'}, 'vertical', 'Vert1')\r\n self._create_item('html', 'OrphanHtml', \"<p>Hello</p>\", {'display_name': 'Orphan html'}, None, None)\r\n self._create_item('static_tab', 'staticuno', \"<p>tab</p>\", {'display_name': 'Tab uno'}, None, None)\r\n self._create_item('about', 'overview', \"<p>overview</p>\", {}, None, None)\r\n self._create_item('course_info', 'updates', \"<ol><li><h2>Sep 22</h2><p>test</p></li></ol>\", {}, None, None)","def dump_grading_context(course):\r\n hbar = \"{}\\n\".format(\"-\" * 77)\r\n msg = hbar\r\n msg += \"Course grader:\\n\"\r\n\r\n msg += '%s\\n' % course.grader.__class__\r\n graders = {}\r\n if isinstance(course.grader, xmgraders.WeightedSubsectionsGrader):\r\n msg += '\\n'\r\n msg += \"Graded sections:\\n\"\r\n for subgrader, category, weight in course.grader.sections:\r\n msg += \" subgrader=%s, type=%s, category=%s, weight=%s\\n\"\\\r\n % (subgrader.__class__, subgrader.type, category, weight)\r\n subgrader.index = 1\r\n graders[subgrader.type] = subgrader\r\n msg += hbar\r\n msg += \"Listing grading context for course %s\\n\" % course.id.to_deprecated_string()\r\n\r\n gcontext = course.grading_context\r\n msg += \"graded sections:\\n\"\r\n\r\n msg += '%s\\n' % gcontext['graded_sections'].keys()\r\n for (gsomething, gsvals) in gcontext['graded_sections'].items():\r\n msg += \"--> Section %s:\\n\" % (gsomething)\r\n for sec in gsvals:\r\n sdesc = sec['section_descriptor']\r\n frmat = getattr(sdesc, 'format', None)\r\n aname = ''\r\n if frmat in graders:\r\n gform = graders[frmat]\r\n aname = '%s %02d' % (gform.short_label, gform.index)\r\n gform.index += 1\r\n elif sdesc.display_name in graders:\r\n gform = graders[sdesc.display_name]\r\n aname = '%s' % gform.short_label\r\n notes = ''\r\n if getattr(sdesc, 'score_by_attempt', False):\r\n notes = ', score by attempt!'\r\n msg += \" %s (format=%s, Assignment=%s%s)\\n\"\\\r\n % (sdesc.display_name, frmat, aname, notes)\r\n msg += \"all descriptors:\\n\"\r\n msg += \"length=%d\\n\" % len(gcontext['all_descriptors'])\r\n msg = '<pre>%s</pre>' % msg.replace('<', '<')\r\n return msg","def makeCourse( xmlFile, genPath, importPaths, commonFiles, rendererContent=True):\n\ttry:\n\n\t\t# parse the command line\n\t\tConfig.add_option('--verbose', help='Set verbosity to maximum', dest='verbosity', default=0, action='store_const', const=2)\n\t\tConfig.add_option('-v', '--verbosity', help='Set the verbosity level (0: quiet, 1: display the command lines, 2: display command lines and their outputs', dest='verbosity', default=0, type=int)\n\t\tConfig.add_option('-d', '--debug', help='Create the files in the debug/ folder, instead of in a temporary one', dest='debug', action='store_true', default=False)\n\t\tConfig.add_option('-f', '--force', help='Force the generation of the documents, even if nothing changes from last run', dest='force', action='store_true', default=False)\n\t\tConfig.add_option('-q', '--quick', help='Quick pdf generation (do not compile twice the latex, do not produce handout, etc.)', dest='quick', action='store_true', default=False)\n\t\tConfig.add_option('-w', '--wordpress', help='Publish to wordpress', dest='wordpress', default=False, action='store_true')\n\t\tConfig.add_option('-c', '--HTMLcorrection', help='Display an HTML correction', dest='HTMLcorrection', default=False, action='store_true')\n\t\tConfig.add_option('-s', '--shared', help='Copy the required files to the <shared> path (via ssh)', default=False,\taction='store_true')\n\t\tConfig.parse()\n\t\targs = Config.args\n\t\toptions = Config.options\n\t\tConfig.importPaths = importPaths \n\t\tConfig.commonFiles = commonFiles\n\t\tConfig.allSessions = { x.__name__:x for x in Session.__subclasses__()}\t# list of the created session classes\n\t\tConfig.rendererContent = rendererContent\n\t\t\n\t\t# clean the debug directory in debug mode\n\t\tbasePath = os.path.abspath('.')+'/'\t\t\t# base path (from where the script is run, because the path are relative)\n\t\tif options.debug:\n\t\t\tif os.path.exists('debug/'):\n\t\t\t\trunCommand(['rm','-rf','debug/'])\n\n\t\t# open and parse the course file\n\t\twith codecs.open(xmlFile, encoding='utf-8') as f:\n\t\t\tbs = BeautifulSoup(f, features=\"xml\")\n\n\n\t\t# build the recursively the sessions\n\t\ttop = createTagSession( bs, father=None )\t\t# bs.contents[0]\n\t\tsessionsToBuild = Session.sessionsToBuild\t\t# get the list of the sessions object\n\t\t\n\n\t\t\"\"\"\n\t\timportFiles( bs.contents[0], importPaths)\n\n\t\t# get the list of sessions we can build (with a 'make' method)\n\t\tbuildableSessions = { x.__name__:x for x in Session.__subclasses__() if 'make' in x.__dict__ }\n\n\t\t#This set the PATH for PyDev only...\n\t\tos.environ['PATH'] = os.environ['PATH']+':'+os.getenv('PATH')\n\n\n\t\t# build the list of Sessions to build\n\t\tsessionsToBuild = []\n\t\tfor name,session in buildableSessions.items():\n\t\t\tsessionsToBuild.extend( session(tag, commonFiles) for tag in bs(name) )\n\t\t\"\"\"\n\t\t\n\t\t\n\n\t\t# if possible, load the previous xml file, and look for the differences\n\t\tdirName,baseName = split(xmlFile) \n\t\ttry:\n\t\t\twith open(dirName+\"/.\"+baseName+\".makeCourse\", \"rb\") as f:\n\t\t\t\tdata = load( f )\n\t\t\t\tfor s in sessionsToBuild:\n\t\t\t\t\tif s.name in data:\n\t\t\t\t\t\ts.checkDifferences( data[s.name] )\n\t\texcept IOError:\n\t\t\tpass\n\n\n\t\t# build every argument in the command line arguments\n\t\tsomethingHasBeDone = False\n\t\tfor s in sessionsToBuild:\n\t\t\tif (not args) or (\"all\" in args) or (s.name in args) or (s.type in args):\n\t\t\t\t\n\t\t\t\tcd( basePath)\n\t\t\t\t\n\t\t\t\t# check if something has to be done\n\t\t\t\tif s.shouldBeMake(basePath+'/'+genPath, options) or options.force:\n\t\t\t\t\tsomethingHasBeDone = True\n\n\t\t\t\t\t#Make one build (TP, course, etc.)\n\t\t\t\t\tprint ( Fore.BLUE+\"*) Make \"+Style.BRIGHT+s.name+Fore.RESET+Style.NORMAL)\n\n\t\t\t\t\t# make temp directory and copy all the file in resources dir\n\t\t\t\t\tif options.debug:\n\t\t\t\t\t\ttmp = \"debug/\"+s.name+'/'\n\t\t\t\t\t\tcreateDirectory(tmp)\n\t\t\t\t\telse:\n\t\t\t\t\t\ttmp = mkdtemp()\n\n\t\t\t\t\ts.prepareResources(tmp )\n\t\t\t\t\tcd( tmp)\n\n\t\t\t\t\t# call the custom function associated with the type, to produce the documents\n\t\t\t\t\ts.make(options)\n\n\t\t\t\t\t# then move the files in the right place\n\t\t\t\t\tfor f in s.files(options):\n\t\t\t\t\t\tcreateDirectory( basePath+'/'+genPath.format( **s.dict ) )\n\t\t\t\t\t\tnewFile = basePath+'/'+genPath.format( **s.dict )+f\n\t\t\t\t\t\tif not os.path.exists(f):\n\t\t\t\t\t\t\tprint( Fore.YELLOW+'The file '+f+' has not been created by '+s.type+' function !'+Fore.RESET)\n\t\t\t\t\t\trunCommand( ['cp', f, newFile])\n\n\t\t\t\t\t# del the temporary directory or clean debug directory\n\t\t\t\t\tif not options.debug:\n\t\t\t\t\t\trunCommand( ['rm', '-rf', tmp])\n\t\t\t\telse:\n\t\t\t\t\tif options.verbosity>0:\n\t\t\t\t\t\tprint( Fore.BLUE + \"*) Nothing changed for \"+Style.BRIGHT+s.name+Style.NORMAL+\", skipped\"+Fore.RESET)\n\n\n\n\t\tif not somethingHasBeDone:\n\t\t\tprint( Fore.BLUE + \"Nothing has changed, nothing to do, so nothing has been done...\" + Fore.RESET)\n\n\n\t\t# save the data file\n\t\tdata = {L.name: {key:md5(str(val).encode('utf-8')).hexdigest() for key,val in L.dict.items()} for L in sessionsToBuild }\n\t\tcd( basePath)\n\t\twith open(dirName+\"/.\"+baseName+\".makeCourse\", 'wb') as f:\n\t\t\tdump( data, f)\n\n\n\n\n\n\n\n\texcept mkcException as err:\n\t\tprint( err )","def __str__(self):\n return \"%s %s\" % (self.course.code, self.code)","def __repr__(self):\r\n return \"Lesson(id='%i', courseName='%s', teachers='%s', semester_groups='%s')\" % \\\r\n (self.id, self.course.name,\r\n str(list(map(lambda t: t.id, self.teachers))),\r\n str(list(map(lambda s: s.id, self.course.semester_groups))))","def syntax_text():","def subtitle(string):\n print(\"{}\\n{}\\n\".format(bold(string), underline(string, \"-\")))","def __repr__(self) -> str:\n return f\"{self.title} that starts at {self.start} and ends at {self.end} and is taught by {self.instructor}\"","def subject(self):\n subject = loader.render_to_string(self.subject_template_name,\n self.get_context())\n return ''.join(subject.splitlines())","def __str__(self):\n return \"\"\"translate([{0!s}, {1!s}, {2!s}]) {{\n {3!s}\n}}\"\"\".format(self.vector[0], self.vector[1], self.vector[2], self.body)","def gt(text, font=DEFAULT_FONT, color=\"magenta\",\n on_color=None, attr=None, width=80,\n justify=\"center\"):\n\n f = Figlet(\n font, width=width,\n justify=justify\n )\n r = f.renderText(text)\n return colored(r, color, on_color, attr)","def to_str(self):\n return self.template.format(\n text_color=self.color.to_str(),\n font_type=self.font_type.to_str(),\n font_weight=self.font_weight.to_str(),\n font_size=self.font_size.to_str()\n )","def draw(self):\n drawing = \"\"\n \n if self.get_gene().get_choice_at(1) is 'c':\n drawing += 'o'\n else:\n drawing += 'x'\n \n if self.is_tft():\n drawing += \"tft\"\n return drawing\n elif self.is_t2t():\n drawing += \"t2t\"\n return drawing\n elif self.is_ftf():\n drawing += \"ftf\"\n return drawing\n\n rule = self.get_gene().get_defect_fraction()\n fraction_display = 0.166\n\n if rule >= 1.0:\n drawing += \"ddd\"\n elif rule > (5*fraction_display):\n drawing += \"ddc\"\n elif rule > (4*fraction_display):\n drawing += \"dcd\"\n elif rule > (3*fraction_display):\n drawing += \"dcc\"\n elif rule > (2*fraction_display):\n drawing += \"cdd\"\n elif rule > (1*fraction_display):\n drawing += \"cdc\"\n elif rule > (0*fraction_display):\n drawing += \"ccd\"\n else:\n drawing += \"ccc\"\n\n return drawing","def test_course_overview_view_with_course(self):\r\n course = CourseFactory.create(org='MITx', course='999', display_name='Robot Super Course')\r\n resp = self._show_course_overview(course.id)\r\n self.assertContains(\r\n resp,\r\n '<article class=\"courseware-overview\" data-locator=\"location:MITx+999+Robot_Super_Course+course+Robot_Super_Course\" data-course-key=\"slashes:MITx+999+Robot_Super_Course\">',\r\n status_code=200,\r\n html=True\r\n )","def generate_cross_section_title(grid, field, start, end):\n\n # Grab information about the grid\n time_str = generate_grid_time_begin(grid).strftime(\"%Y-%m-%dT%H:%M:%SZ\")\n field_name = generate_field_name(grid, field)\n\n # Deal with the latitude/longitude part\n start_lat, start_lon = start\n end_lat, end_lon = end\n\n degree_symbol = \"\\N{DEGREE SIGN}\"\n\n # Add degrees latitude/longitude labels\n\n if start_lat < 0:\n start_lat_string = f\"{-start_lat}{degree_symbol}S\"\n else:\n start_lat_string = f\"{start_lat}{degree_symbol}N\"\n\n if end_lat < 0:\n end_lat_string = f\"{-end_lat}{degree_symbol}S\"\n else:\n end_lat_string = f\"{end_lat}{degree_symbol}N\"\n\n if start_lon < 0:\n start_lon_string = f\"{-start_lon}{degree_symbol}W\"\n else:\n start_lon_string = f\"{start_lon}{degree_symbol}E\"\n\n if end_lon < 0:\n end_lon_string = f\"{-end_lon}{degree_symbol}W\"\n else:\n end_lon_string = f\"{end_lon}{degree_symbol}E\"\n\n return (\n f\"({start_lat_string}, {start_lon_string})\"\n f\" to ({end_lat_string}, {end_lon_string}) \\n {time_str} \\n {field_name}\"\n )","def dump_grading_context(course):\r\n msg = \"-----------------------------------------------------------------------------\\n\"\r\n msg += \"Course grader:\\n\"\r\n\r\n msg += '%s\\n' % course.grader.__class__\r\n graders = {}\r\n if isinstance(course.grader, xmgraders.WeightedSubsectionsGrader):\r\n msg += '\\n'\r\n msg += \"Graded sections:\\n\"\r\n for subgrader, category, weight in course.grader.sections:\r\n msg += \" subgrader=%s, type=%s, category=%s, weight=%s\\n\" % (subgrader.__class__, subgrader.type, category, weight)\r\n subgrader.index = 1\r\n graders[subgrader.type] = subgrader\r\n msg += \"-----------------------------------------------------------------------------\\n\"\r\n msg += \"Listing grading context for course %s\\n\" % course.id\r\n\r\n gcontext = course.grading_context\r\n msg += \"graded sections:\\n\"\r\n\r\n msg += '%s\\n' % gcontext['graded_sections'].keys()\r\n for (gsections, gsvals) in gcontext['graded_sections'].items():\r\n msg += \"--> Section %s:\\n\" % (gsections)\r\n for sec in gsvals:\r\n sdesc = sec['section_descriptor']\r\n grade_format = getattr(sdesc, 'grade_format', None)\r\n aname = ''\r\n if grade_format in graders:\r\n gfmt = graders[grade_format]\r\n aname = '%s %02d' % (gfmt.short_label, gfmt.index)\r\n gfmt.index += 1\r\n elif sdesc.display_name in graders:\r\n gfmt = graders[sdesc.display_name]\r\n aname = '%s' % gfmt.short_label\r\n notes = ''\r\n if getattr(sdesc, 'score_by_attempt', False):\r\n notes = ', score by attempt!'\r\n msg += \" %s (grade_format=%s, Assignment=%s%s)\\n\" % (s.display_name, grade_format, aname, notes)\r\n msg += \"all descriptors:\\n\"\r\n msg += \"length=%d\\n\" % len(gcontext['all_descriptors'])\r\n msg = '<pre>%s</pre>' % msg.replace('<', '<')\r\n return msg","def _course_key(self):\r\n return \"slashes:{org}+{number}+{run}\".format(**self._course_dict)","def __str__(self):\n description = \"Object class Critter.\"\n description += \"\\nName: \" + self.name + \"\\nMood: \" + self.mood + \"\\nHunger: \" + str(self.hunger) + \"\\nBoredom: \" + str(self.boredom) + \"\\n\"\n return description","def get_courses(std):\n return std[2]","def CreateFigletString(text: str, font: str = \"standard\", **kwargs) -> pyfiglet.FigletString:\n return pyfiglet.figlet_format(text=text, font=font, **kwargs)","def edit_course(self, course):\n EDIT_COURSE = \"\"\"UPDATE Course SET subject_code = %s, credit_hours = %s, description = %s WHERE name = %s\"\"\"\n\n self.db_cursor.execute(EDIT_COURSE, (\n course.subject_code, course.credit_hours, course.description, course.name))\n self.db_connection.commit()\n\n DELETE_COURSE_TOPICS = \"\"\"DELETE FROM CourseTopics WHERE course_name = %s\"\"\"\n self.db_cursor.execute(DELETE_COURSE_TOPICS, (course.name,))\n self.db_connection.commit()\n INSERT_COURSE_TOPICS = \"\"\"INSERT INTO CourseTopics (course_name, topic_id) VALUES (%s, %s)\"\"\"\n for ct in course.topics:\n self.db_cursor.execute(INSERT_COURSE_TOPICS, (course.name,ct))\n self.db_connection.commit()\n\n DELETE_COURSE_GOALS = \"\"\"DELETE FROM CourseGoals WHERE course_name = %s\"\"\"\n self.db_cursor.execute(DELETE_COURSE_GOALS, (course.name,))\n self.db_connection.commit()\n INSERT_COURSE_GOALS = \"\"\"INSERT INTO CourseGoals (course_name, goal_id) VALUES (%s, %s)\"\"\"\n for cg in course.goals:\n self.db_cursor.execute(INSERT_COURSE_GOALS, (course.name, cg))\n self.db_connection.commit()","def degree_creator():\n return render_template(\"degree_creator.html\")","def create_course(self, modules_count, module_store, topics):\n course = CourseFactory.create(\n org=\"a\",\n course=\"b\",\n run=\"c\",\n start=datetime.now(UTC),\n default_store=module_store,\n discussion_topics=topics\n )\n CourseEnrollmentFactory.create(user=self.user, course_id=course.id)\n course_url = reverse(\"course_topics\", kwargs={\"course_id\": str(course.id)})\n # add some discussion xblocks\n for i in range(modules_count):\n ItemFactory.create(\n parent_location=course.location,\n category='discussion',\n discussion_id=f'id_module_{i}',\n discussion_category=f'Category {i}',\n discussion_target=f'Discussion {i}',\n publish_item=False,\n )\n return course_url","def generate_html(self):\n html_text_1 = \"\"\"\n <div class=\"concept\">\n\n \t\t<div class=\"concept-title\">\n\n \t\t\t\t\"\"\" + self.title\n\n html_text_2 = \"\"\"\n \t\t</div>\n\n \t\t<div class=\"concept-description\">\n\n\t\t <p>\n\t\t\t\n \t\t \t\t\"\"\" + self.description + \"\"\" \n \n </p>\"\"\"\n\n html_text_3 = '''\n\n \t\t</div>\n\n </div>'''\n\n return html_text_1 + html_text_2 + html_text_3","def _latex_(self):\n old = s = \"\\\\begin{verbatim}\\\\end{verbatim}\"\n new = \"\"\n s = \"\\\\begin{verbatim}\\\\end{verbatim}\"\n s += \"Element of \" + self._space._latex_().replace(old, new) + \" With principal part \"\n WR = self._space.multiplier()\n # If we have more than one non-zero element in the principal part we have to\n # add a + between terms\n sp = \"\"\n for (b, m) in self._principal_part:\n a = self._principal_part[(b, m)]\n if a != 0:\n x = QQ(m + WR.Qv[WR.D.index(b)])\n if a != 1:\n if a > 0 and len(sp) > 0:\n ast = \"+\" + str(a)\n else:\n ast = str(a)\n sp = sp + ast + \"q^{\" + str(x) + \"}\"\n else:\n sp = sp + \"q^{\" + str(x) + \"}\"\n s = s + sp + \"$.\"\n return s","def __str__(self) -> str:\n\n assert self.data is not None\n title = \"{} ({})\".format(self.name, self.data[\"role\"][\"name\"])\n name_border = \"-\" * len(title)\n lines = [name_border, title, name_border]\n\n # build the talent-tree \"table\"\n for row_idx, row_data in self.talent_rows.items():\n lines.append(\"{}:\".format(row_idx))\n for talent_idx, talent_data in row_data.items():\n lines.append(\n \" {}: {}\".format(talent_idx, talent_data[\"text\"])\n )\n macro = build_row_macro(row_idx, row_data)\n if macro is not None:\n lines.append(\"macro:\")\n lines.append(macro[0])\n\n return os.linesep.join(lines)","def get_next_course_message(i: int, courses: list) -> str:\n course = (\n None\n if i + 1 >= len(courses)\n or courses[i].time.day != courses[i + 1].time.day\n else courses[i + 1]\n )\n\n return (\n notify_no_more_courses\n if course is None\n else notify_next_course_message.format(\n course.name,\n course.type,\n course.time.start - courses[i].time.end,\n \"?\" if course.classroom is None else course.classroom.number,\n )\n )","def prescription(self):\n prescription = \"\\n{0:>10}\\t{1:>10}\\t{2:>10}\\t{3:>10}\\n\".format(\"R\",\"Material\",\"d\",\"diameter\")\n for surface in self.lensSurfaces():\n prescription += \"{0:>10.2f}\\t{1:>10}\\t{2:>10.2f}\\t{3:>10.2f}\\n\".format(surface.R, str(surface.mat), surface.spacing, surface.diameter)\n return prescription","def print_output(filtered_courses_list):\n\n if not filtered_courses_list:\n print(\"No courses matched the query.\")\n return\n\n max_name_length = max([len(course.name) for course in filtered_courses_list])\n\n print(\" Sem | Course ID | Pts | \" +\n \" \" * ((max_name_length - 11) // 2 + (max_name_length - 1) % 2) + \"Course Name\" + \" \" * ((max_name_length - 11) // 2) +\n \" | Grade\")\n print(\"______|___________|_____|\" + \"_\" * (max_name_length + 2) + \"|______\")\n\n for course in filtered_courses_list:\n print(str(course.year) + course.semester, end=\" | \")\n print(course.cid, end=\" | \")\n print(course.creditpts, end=\" | \")\n print(course.name, end=\" \" * (max_name_length - len(course.name) + 1) + \"| \")\n\n print(course.grade)\n\n print(\"\\nGPA: \" + (\"%.3f\" % calculate_weighted_average(filtered_courses_list)))","def tex(self):\n self.cardinality = self.question.sorted_answers_cardinality(\n self.min_cardinality,\n self.group_together,\n self.group_by_letter_case,\n self.group_by_slugify,\n self.filter,\n self.sort_answer,\n other_question=self.other_question,\n )\n q1 = []\n q2 = []\n for answer_to_q1, cardinality_to_q2 in list(self.cardinality.items()):\n for answer_to_q2, number_of_time in list(cardinality_to_q2.items()):\n q1 += [answer_to_q1] * number_of_time\n q2 += [answer_to_q2] * number_of_time\n df = DataFrame(data={self.question.text: q1, self.other_question.text: q2})\n name = \"tex/q{}_vs_q{}\".format(self.question.pk, self.other_question.pk)\n sankey(df[self.question.text], df[self.other_question.text], aspect=20, fontsize=10, figureName=name)\n return Question2TexSankey.TEX_SKELETON % (\n name[4:],\n self.question.pk,\n self.other_question.pk,\n self.get_caption(),\n )","def build_tex(self,signs='-',request_parens=False):\n return self._build_tex(signs,request_parens)","def create_courses():\n\n\t# create list for courses\n\tallcourses = []\n\n\t# load courses as classes in allcourses-list\n\twith open(\"../data/vakken.csv\", \"rt\") as coursefile:\n\n\t\t# clean text\n\t\tcourses = csv.reader(coursefile)\n\t\tfor row in courses:\n\t\t\tfor text in row:\n\t\t\t\tcourse_info = text.split(\";\")\n\n\t\t\t\t# add course name\n\t\t\t\tcourse_name = course_info[0]\n\n\t\t\t\t# add amount of lectures\n\t\t\t\tcourse_lectures = course_info[1]\n\n\t\t\t\t# add amount of seminars\n\t\t\t\tcourse_seminars = course_info[2]\n\n\t\t\t\t# add max amount seminars\n\t\t\t\tcourse_max_sem = course_info[3]\n\t\t\t\tif course_max_sem == \"nvt\":\n\t\t\t\t\tcourse_max_sem = 0\n\n\t\t\t\t# add amount of practicals\n\t\t\t\tcourse_practicals = course_info[4]\n\n\t\t\t\t# add max amount practicals\n\t\t\t\tcourse_max_prac = course_info[5]\n\t\t\t\tif course_max_prac == \"nvt\":\n\t\t\t\t\tcourse_max_prac = 0\n\n\t\t\t\t# add course to list\n\t\t\t\tallcourses.append(Course(course_name, course_lectures, course_seminars, course_max_sem, course_practicals, course_max_prac))\n\n\treturn allcourses","def detail_course(request, pk, template=\"core/detail_course.html\"):\n response = {\n 'course': get_object_or_404(Course, pk=pk)\n }\n return direct_to_template(request, template, response)","def make_lexicon_txt(self):\n raise NotImplementedError","def template(title, steps, loops):\n return \"%-8s %-20s %20d\" % (title, format_power(steps), loops)","def Template(Fenetre_largeur,Fenetre_hauteur):\r\n li= Select_ligne(\"Nombre de lignes: \",Fenetre_largeur,Fenetre_hauteur)\r\n nom=\"Template\"\r\n fich=\"Template\"\r\n version=0\r\n while Path(\"stages/\"+fich+\".txt\").is_file() == True:\r\n version+=1\r\n fich=nom+str(version)\r\n fichier=open(\"stages/\"+fich+\".txt\",'w')\r\n fichier.write(str(li))\r\n fichier.write(\"\\n\")\r\n fichier.write(\"\\n\")\r\n for i in range(li):\r\n for j in range(10):\r\n fichier.write(\"0,0|\")\r\n fichier.write(\"\\n\")\r\n fichier.write(\"\\n\")\r\n fichier.write(\"gauche: resistance, droite: bonus\")\r\n fichier.write(\"\\n\")\r\n fichier.write(\"resistance max: 3\")\r\n fichier.write(\"\\n\")\r\n fichier.write(\"6=barre+\")\r\n fichier.write(\"\\n\")\r\n fichier.write(\"7=score+\")\r\n fichier.close()","def course(self):\n return self.section.course","def make_final_text(text: str) -> str:\n baseline_text_template = \"\"\"Use this thread to discuss anything (within the rules of the subreddit):\n\n* What you didn't think was worthy of its own post\n* What club game you're most excited for\n* Where you're staying to watch a friendly\n* Which players should be called in\n{}\n* What the mods told you to re-post here\n* Etc\n\n### Schedules\n{}\n\"\"\"\n\n with open('random_dumb_questions.txt', 'r') as files:\n list_of_questions = files.readlines()\n\n question = list_of_questions[randint(0, len(list_of_questions))].replace('\\n', '')\n\n return baseline_text_template.format(question, text)","def _create_course(self):\r\n super(TestPublish, self)._create_course(split=False)\r\n\r\n self._create_item('chapter', 'Chapter1', {}, {'display_name': 'Chapter 1'}, 'course', 'runid', split=False)\r\n self._create_item('chapter', 'Chapter2', {}, {'display_name': 'Chapter 2'}, 'course', 'runid', split=False)\r\n self._create_item('vertical', 'Vert1', {}, {'display_name': 'Vertical 1'}, 'chapter', 'Chapter1', split=False)\r\n self._create_item('vertical', 'Vert2', {}, {'display_name': 'Vertical 2'}, 'chapter', 'Chapter1', split=False)\r\n self._create_item('html', 'Html1', \"<p>Goodbye</p>\", {'display_name': 'Parented Html'}, 'vertical', 'Vert1', split=False)\r\n self._create_item(\r\n 'discussion', 'Discussion1',\r\n \"discussion discussion_category=\\\"Lecture 1\\\" discussion_id=\\\"a08bfd89b2aa40fa81f2c650a9332846\\\" discussion_target=\\\"Lecture 1\\\"/>\\n\",\r\n {\r\n \"discussion_category\": \"Lecture 1\",\r\n \"discussion_target\": \"Lecture 1\",\r\n \"display_name\": \"Lecture 1 Discussion\",\r\n \"discussion_id\": \"a08bfd89b2aa40fa81f2c650a9332846\"\r\n },\r\n 'vertical', 'Vert1',\r\n split=False\r\n )\r\n self._create_item('html', 'Html2', \"<p>Hellow</p>\", {'display_name': 'Hollow Html'}, 'vertical', 'Vert1', split=False)\r\n self._create_item(\r\n 'discussion', 'Discussion2',\r\n \"discussion discussion_category=\\\"Lecture 2\\\" discussion_id=\\\"b08bfd89b2aa40fa81f2c650a9332846\\\" discussion_target=\\\"Lecture 2\\\"/>\\n\",\r\n {\r\n \"discussion_category\": \"Lecture 2\",\r\n \"discussion_target\": \"Lecture 2\",\r\n \"display_name\": \"Lecture 2 Discussion\",\r\n \"discussion_id\": \"b08bfd89b2aa40fa81f2c650a9332846\"\r\n },\r\n 'vertical', 'Vert2',\r\n split=False\r\n )\r\n self._create_item('static_tab', 'staticuno', \"<p>tab</p>\", {'display_name': 'Tab uno'}, None, None, split=False)\r\n self._create_item('about', 'overview', \"<p>overview</p>\", {}, None, None, split=False)\r\n self._create_item('course_info', 'updates', \"<ol><li><h2>Sep 22</h2><p>test</p></li></ol>\", {}, None, None, split=False)","def __init__(self, tutorial):\n self._tex = \"\"\n\n self.texPreamble(tutorial)\n self.texTutorialHeader(tutorial)\n\n for lecture in self.children(tutorial):\n self.texLectureHeader(lecture)\n\n for slide in self.children(lecture):\n self.texSlideHeader(slide)\n for section in slide.sections:\n self.texSlideSection(section)\n self.texSlideFooter(slide)\n\n self.texLectureFooter(lecture)\n\n self.texTutorialFooter(tutorial)\n self.texPostamble(tutorial)","def __str__(self):\n if self.tabletitle:\n s = [\"<a name='%s'>%s</a><P>\" % (self.tabletitle, self.tabletitle)]\n else:\n s = []\n\n s.append('<TABLE border=%s cellpadding=%s cellspacing=%s width=\"%s\">\\n' % \\\n (self.border, self.cell_padding, self.cell_spacing, self.width))\n if self.tabletitle:\n s.append('<CAPTION align=%s><STRONG>%s</STRONG></CAPTION>\\n' % \\\n (self.caption_align, self.tabletitle))\n\n for i in range(len(self.body)):\n for j in range(len(self.body[i])):\n if type(self.body[i][j]) == type(''):\n #process cell contents to insert breaks for \\n char.\n if self.cell_line_breaks:\n self.body[i][j] = self.body[i][j].replace('\\n','<br>')\n else:\n self.body[i][j] = Text(self.body[i][j])\n\n # Initialize colspan property to 1 for each\n # heading column if user doesn't provide it.\n if self.heading:\n if not self.colspan:\n if type(self.heading[0]) == list:\n self.colspan = [1]*len(self.heading[0])\n else:\n self.colspan = [1]*len(self.heading)\n # Construct heading spec\n # can handle multi-row headings. colspan is a list specifying how many\n # columns the i-th element should span. Spanning only applies to the first\n # or only heading line.\n if self.heading:\n prefix = '<TR Align=' + self.heading_align + '> '\n postfix = '</TR>\\n'\n middle = ''\n if type(self.heading[0]) == type([]):\n for i in range(len(self.heading[0])):\n middle = middle + '<TH ColSpan=%s%s>' % \\\n (self.colspan[i], \\\n self.get_body_color(self.heading_color,i)) \\\n + str(self.heading[0][i]) +'</TH>'\n s.append(prefix + middle + postfix)\n for i in range(len(self.heading[1])):\n middle = middle + '<TH>' + str(self.heading[i]) +'</TH>'\n for heading_row in self.heading[1:]:\n for i in range(len(self.heading[1])):\n middle = middle + '<TH>' + heading_row[i] +'</TH>'\n s.append(prefix + middle + postfix)\n else:\n for i in range(len(self.heading)):\n middle = middle + '<TH ColSpan=%s%s>' % \\\n (self.colspan[i], \\\n self.get_body_color(self.heading_color,i)) \\\n + str(self.heading[i]) +'</TH>'\n s.append(prefix + middle + postfix)\n # construct the rows themselves\n stmp = '<TD Align=%s %s>'\n for row in self.body:\n s.append('<TR>')\n for i in range(len(row)):\n if i == 0 :\n ss1 = self.column1_align\n else:\n ss1 = self.cell_align\n s.append(stmp % (ss1, self.get_body_color(self.body_color,i)))\n s.append(str(row[i]))\n s.append('</TD>\\n')\n s.append('</TR>\\n')\n #close table\n s.append('</TABLE><P>\\n')\n return ''.join(s)","def create_opl_mod_output_text(tdf):\n return _create_opl_mod_text(tdf, True)","def create_course(self, org, offering, user_id=None, fields=None, **kwargs):\r\n\r\n course, _, run = offering.partition('/')\r\n course_id = SlashSeparatedCourseKey(org, course, run)\r\n\r\n # Check if a course with this org/course has been defined before (case-insensitive)\r\n course_search_location = SON([\r\n ('_id.tag', 'i4x'),\r\n ('_id.org', re.compile(u'^{}$'.format(course_id.org), re.IGNORECASE)),\r\n ('_id.course', re.compile(u'^{}$'.format(course_id.course), re.IGNORECASE)),\r\n ('_id.category', 'course'),\r\n ])\r\n courses = self.collection.find(course_search_location, fields=('_id'))\r\n if courses.count() > 0:\r\n raise InvalidLocationError(\r\n \"There are already courses with the given org and course id: {}\".format([\r\n course['_id'] for course in courses\r\n ]))\r\n\r\n location = course_id.make_usage_key('course', course_id.run)\r\n course = self.create_and_save_xmodule(location, fields=fields, **kwargs)\r\n\r\n # clone a default 'about' overview module as well\r\n about_location = location.replace(\r\n category='about',\r\n name='overview'\r\n )\r\n overview_template = AboutDescriptor.get_template('overview.yaml')\r\n self.create_and_save_xmodule(\r\n about_location,\r\n system=course.system,\r\n definition_data=overview_template.get('data')\r\n )\r\n\r\n return course","def to_string(self, t = None, title = None):\n if title:\n print title\n column_width = self.max_cell_print_len()\n cell_hline = [ '-' for i in range(column_width + 2) ] + ['|']\n cell_hline = ''.join(cell_hline)\n hline = ['|'] + [ cell_hline for i in range(self.width + 1) ] + ['\\n']\n hline = ''.join(hline)\n slist = []\n if len(self.agents) > 0:\n slist += ['Scores:']\n for agent in self.agents:\n slist.append(' {0}={1}'.format(agent, agent.performance_measure))\n\n if len(self.agents) > 0:\n slist.append('\\n')\n for c in range(0, self.width + 1):\n spacer = ''.join([ ' ' for i in range(column_width - 1) ])\n slist.append(' {0}{1} '.format(c, spacer))\n\n slist.append(' time_step={0}'.format(t if t else self.time_step))\n slist.append('\\n')\n slist.append(hline)\n for r in range(self.height, -1, -1):\n for c in range(0, self.width + 1):\n things_at = self.list_things_at((c, r))\n cell_width = 0\n for thing_at in things_at:\n cell_width += len(thing_at.to_string())\n\n spacer = ''.join([ ' ' for i in range(column_width - cell_width) ])\n slist.append('| ')\n for thing in things_at:\n slist.append(thing.to_string())\n\n slist.append(spacer + ' ')\n\n slist.append('| {0}\\n'.format(r))\n slist.append(hline)\n\n return ''.join(slist)","def test_custom_decorator_displaytex_ok(self):\n\n tex = \"\\left.\\frac{x^3}{3}\\right|_0^1\" # noqa: W605\n\n self.assertEqual(\n DOM.render(\n DOM.create_element(\n ashley_render_children,\n {\n \"block\": {\n \"key\": \"a215p\",\n \"text\": \"\",\n \"type\": \"atomic\",\n \"data\": {\"tex\": tex, \"type\": \"TEXBLOCK\"},\n }\n },\n )\n ),\n f'<span class=\"ashley-latex-display\">{tex}</span>',\n )","def _define_course_email(self):\r\n course_email = CourseEmail.create(self.course.id, self.instructor, SEND_TO_ALL, \"Test Subject\", \"<p>This is a test message</p>\")\r\n return course_email.id # pylint: disable=E1101\r","def tikzcode(self):\n tex = \"\"\n tex += r\"\\draw\"\n if len(self.options):\n options = ', '.join(self.options)\n tex += \"[{options}] \".format(options=options)\n tex += \"({a.xpos:.4f},{a.ypos:.4f}) \".format(a=self.node_a)\n tex += \"to\"\n # if the nodes are arranged, then they have angle in/out\n inout = []\n inout.append('out={angle!s}'.format(angle=self.node_a.angle_inout))\n inout.append('in={angle!s}'.format(angle=self.node_b.angle_inout))\n if inout:\n tex += \"[\" + \", \".join(inout) + \"] \"\n tex += \"({b.xpos:.4f},{b.ypos:.4f})\".format(b=self.node_b)\n tex += \";\\n\"\n return tex","def latex_info(info):\n return '\\\\textbf{' + info['heading'] + '} ' + info['value'] + ' \\\\break\\n'","def __str__(self):\n\n # can't use backslash in f-string\n newline = \"\\n\"\n\n options = self.options\n kwoptions = self.kwoptions\n\n # add background to options\n if self.background:\n options.append(\"show background rectangle\")\n kwoptions.update({\"background rectangle/.style\": f\"{{fill={self.background}}}\"})\n\n return rf\"\"\"\\begin{{tikzpicture}}{wrap(fmt_options(self.options, self.kwoptions))}\n{newline.join(define_colours(self._used_colours))}\n{newline.join(self.defines)}\n{newline.join(self._commands)}\n\\end{{tikzpicture}}\"\"\"","def _create_course(self):\r\n # If the course already exists, this will respond\r\n # with a 200 and an error message, which we ignore.\r\n response = self.session.post(\r\n STUDIO_BASE_URL + '/course/',\r\n data=self._encode_post_dict(self._course_dict),\r\n headers=self.headers\r\n )\r\n\r\n try:\r\n err = response.json().get('ErrMsg')\r\n\r\n except ValueError:\r\n raise CourseFixtureError(\r\n \"Could not parse response from course request as JSON: '{0}'\".format(\r\n response.content))\r\n\r\n # This will occur if the course identifier is not unique\r\n if err is not None:\r\n raise CourseFixtureError(\"Could not create course {0}. Error message: '{1}'\".format(self, err))\r\n\r\n if not response.ok:\r\n raise CourseFixtureError(\r\n \"Could not create course {0}. Status was {1}\".format(\r\n self._course_dict, response.status_code))","def create_opl_mod_text(tdf):\n return _create_opl_mod_text(tdf, False)","def __str__(self):\n print('=' * 20, \"Subject Information\", '=' * 20)\n print(\"Subject Name: {}\".format(self.name))\n print(\"Pulse Data Length for general questions\")\n print(self.pulse_length[0:20])\n print(\"Number of general Questions: {}\".format(\n len(self.pulse_data[0])))\n print(\"Pulse Data Length for video 1\")\n print(\"Number of questions for video 1: {}\".format(\n len(self.pulse_data[1])))\n print(self.pulse_length[20:40])\n print(\"Pulse Data Length for video 2\")\n print(\"Number of questions for video 2: {}\".format(\n len(self.pulse_data[0])))\n print(self.pulse_length[40:60])\n print('Label Data')\n print(self.label_data)\n print('Label Data shape: {}'.format(self.label_data.shape))\n\n return ''","def subtitle(self, txt):\n num = len(txt)\n ticks = \"-\" * num\n print(txt)\n print(ticks)"],"string":"[\n \"def create_tex(unwanted_courses, wanted_courses=None):\\n page = requests.get(\\n 'http://gla.ac.uk/coursecatalogue/courselist/' +\\n '?code=REG30200000&name=School+of+Computing+Science')\\n tree = html.fromstring(page.content)\\n spans = tree.xpath('//span/text()')\\n codes = []\\n if wanted_courses is None:\\n for s in spans:\\n if s[0:4] == \\\"COMP\\\" and s[7] == '4' and s not in unwanted_courses:\\n codes.append(s)\\n else:\\n for s in wanted_courses:\\n codes.append(s)\\n write_to_latex(codes, unwanted_courses)\\n return None\",\n \"def format_course_output(self, course, schedule):\\n\\t\\t\\n\\t\\t# handle schedule\\n\\t\\tstr_list = schedule.schedule.strip().split(' ')\\n\\t\\tsch_res = ''\\n\\t\\tif (len(str_list) >= 6):\\n\\t\\t\\tval_list = str_list[:6]\\n\\t\\t\\tval_list[5] = val_list[5][:3]\\n\\t\\t\\t\\n\\t\\t\\t# time\\n\\t\\t\\ttime = val_list[0] + ' ' + val_list[1] + ' ' + val_list[2] + ' ' + val_list[3]\\n\\t\\t\\tlocation = val_list[4] + ' ' + val_list[5]\\n\\t\\t\\tsch_res = str(time + '\\\\t' + location)\\n\\t\\t\\n\\t\\tres = \\\"\\\"\\n\\t\\tres += 'Course code: ' + course.course_code + '\\\\n'\\n\\t\\tres += 'Title: ' + course.title + '\\\\n'\\n\\t\\tres += 'Department: ' + course.department + '\\\\n'\\n\\t\\tres += 'Credit Hours: ' + course.credit_hours + '\\\\n'\\n\\t\\tres += 'Description: ' + course.description + '\\\\n'\\n\\t\\tif (sch_res != ''):\\n\\t\\t\\tres += 'Schedule: ' + sch_res + '\\\\n'\\n\\t\\tres += 'Instructor: ' + schedule.instructor + '\\\\n'\\n\\t\\treturn res\",\n \"def completed_course()->str:\\r\\n try:\\r\\n db: sqlite3.Connection = sqlite3.connect(DB_FILE)\\r\\n query: str = \\\"SELECT s.Name, s.CWID, g.Course, g.Grade, i.Name AS 'Instructor' \\\" \\\\\\r\\n \\\"FROM grades2 g JOIN students2 s ON g.StudentCWID = s.CWID \\\" \\\\\\r\\n \\\"JOIN instructors2 i ON g.InstructorCWID = i.CWID ORDER BY s.Name\\\" \\r\\n except sqlite3.OperationalError as e:\\r\\n print(e)\\r\\n\\r\\n data: Dict[str,str]=\\\\\\r\\n [{\\\"name\\\": name,\\\"cwid\\\": cwid,\\\"course\\\": course , \\\"grade\\\":grade, \\\"instructor\\\": instructor}\\r\\n for name, cwid, course, grade, instructor in db.execute(query)]\\r\\n db.close()\\r\\n \\\"\\\"\\\"Render the template from templates folder\\\"\\\"\\\"\\r\\n return render_template(\\\"student_summary.html\\\",\\r\\n title=\\\"Stevens Repository\\\",\\r\\n table_title=\\\"Student, Course, Grade and Instructor\\\",\\r\\n students=data )\",\n \"def __str__(self):\\r\\n return \\\"<CourseFixture: org='{org}', number='{number}', run='{run}'>\\\".format(**self._course_dict)\",\n \"def to_string(self):\\n return \\\"{base_msg} Courses: {courses}\\\".format(\\n base_msg=super().to_string(),\\n courses=self.courses_string()\\n )\",\n \"def get_text(self, course):\\r\\n return views.render_accordion(\\r\\n self.request, course, course.get_children()[0].scope_ids.usage_id.to_deprecated_string(), None, None\\r\\n )\",\n \"def get_text(self, course): # pylint: disable=unused-argument\\r\\n raise NotImplementedError\",\n \"def render_plaintext(self, plaintext, context):\\r\\n return CourseEmailTemplate._render(self.plain_template, plaintext, context)\",\n \"def format_course_for_view(course):\\r\\n return (\\r\\n course.display_name,\\r\\n reverse_course_url('course_handler', course.id),\\r\\n get_lms_link_for_item(course.location),\\r\\n course.display_org_with_default,\\r\\n course.display_number_with_default,\\r\\n course.location.name\\r\\n )\",\n \"def courses_string(self):\\n course_string = \\\"\\\"\\n for c in self.courses[0]:\\n course_string += c + \\\", \\\"\\n course_string = course_string[:-2]\\n return course_string\",\n \"def write_to_latex(codelist, unwanted_courses):\\n # TODO: investigate a way to add large amounts of text outside of the\\n # function\\n abstract01 = \\\"I created this document to practice parsing html and using\\\\\\n tools like Beautiful Soup which I've previously had little experience\\\\\\n in. As a result, it's not perfect.\\\\\\\\newline\\\\\\n It is also a slightly condensed all-in-one-place look at a selection\\\\\\n of courses that are available for fourth year computer science\\\\\\n students at the University of Glasgow. For the purposes of clarity I\\\\\\n have removed several courses from this selection. The following\\\\\\n courses have been omitted:\\\"\\n abstract02 = \\\"For more insight into the project, to report issues or to\\\\\\n inspect the code, have a look at the GitHub:\\\\\\n \\\\\\\\url{https://github.com/IS0metric/course-ripper}\\\"\\n unincluded = create_not_included_list(unwanted_courses)\\n with open('courses.tex', 'w') as f:\\n # TODO Try and move all this to a separate function?\\n # TODO: Check if it's more efficient to write a single, massive string\\n # to file\\n f.write('\\\\\\\\documentclass{hitec}\\\\n')\\n f.write('\\\\\\\\usepackage[document]{ragged2e}\\\\n')\\n f.write('\\\\\\\\usepackage{url}\\\\n')\\n f.write('\\\\\\\\usepackage{hyperref}\\\\n')\\n f.write('\\\\\\\\setcounter{tocdepth}{4}\\\\n')\\n f.write('\\\\\\\\begin{document}\\\\n')\\n f.write('\\\\\\\\title{Fourth Year (2016-17) Courses}\\\\n')\\n f.write('\\\\\\\\author{Jack Parkinson}\\\\n')\\n f.write('\\\\\\\\date{August 2016}\\\\n')\\n f.write('\\\\\\\\maketitle\\\\n')\\n f.write('\\\\\\\\abstract{' + abstract01 + unincluded + abstract02 + '}\\\\n')\\n f.write('\\\\\\\\newpage\\\\n\\\\n')\\n f.write('\\\\\\\\tableofcontents\\\\n')\\n f.write('\\\\\\\\newpage\\\\n\\\\n')\\n # TODO: Look into alternatives to the three lists\\n all_courses = []\\n sem1_courses = []\\n sem2_courses = []\\n for code in codelist:\\n course = bsoup(get_coursepage(code))\\n if course['offered']['value'] == 'Runs Throughout Semesters 1 and 2':\\n all_courses.append(course)\\n elif \\\"1\\\" in course['offered']['value']:\\n sem1_courses.append(course)\\n elif \\\"2\\\" in course['offered']['value']:\\n sem2_courses.append(course)\\n f.write('\\\\\\\\section{Semester 1 and 2 Courses}\\\\n\\\\n')\\n for course in all_courses:\\n f.write(latex_course(course))\\n f.write('\\\\\\\\section{Semester 1 Only Courses}\\\\n\\\\n')\\n for course in sem1_courses:\\n f.write(latex_course(course))\\n f.write('\\\\\\\\section{Semester 2 Only Courses}\\\\n\\\\n')\\n for course in sem2_courses:\\n f.write(latex_course(course))\\n f.write('\\\\\\\\end{document}')\\n return None\",\n \"def get_about_text(self, course_key):\\r\\n text = views.course_about(self.request, course_key.to_deprecated_string()).content\\r\\n return text\",\n \"def __str__(self):\\n # Use 'Unknown' if the course instance does not have a term\\n if self.course_instance.term:\\n term = self.course_instance.term.verbose_name()\\n else:\\n term = 'Unknown'\\n\\n exam_unicode = '{term} {number} {type} for {course}'.format(\\n term=term,\\n number=self.get_exam_number_display(),\\n type=self.get_exam_type_display(),\\n course=self.course_instance.course)\\n if self.instructors:\\n instructors = ', '.join([i.last_name for i in self.instructors])\\n return '{}, taught by {}'.format(exam_unicode, instructors)\\n else:\\n return '{} (Instructors Unknown)'.format(exam_unicode)\",\n \"def course_info(self):\\n print(\\\"Course name: {}\\\".format(self._course_name))\\n print(\\\"Lead teacher: {}\\\".format(self._teacher))\\n\\n if len(self._students) == 0:\\n print(\\\"Course does not enrolled by any student\\\")\\n else:\\n print(\\\"Enrolled: {}/{}\\\".format(len(self._students), self._total_place))\",\n \"def getFullCourseTitle(self, brain):\\n full_title = ''\\n\\n id = brain.getCourseId\\n if id:\\n full_title = '%s - ' %id\\n full_title += brain.Title\\n term = brain.getTerm\\n if term:\\n full_title += ', %s' %term\\n\\n return full_title\",\n \"def _create_formatted_string(self):\\n string = NALSyntax.StatementSyntax.Start.value + \\\\\\n self.get_subject_term().get_formatted_string()\\n\\n string += \\\" \\\" + self.get_copula_string() + \\\" \\\"\\n\\n string += self.get_predicate_term().get_formatted_string() + \\\\\\n NALSyntax.StatementSyntax.End.value\\n\\n return string\",\n \"def help_text(command):\\n\\n courses_list = ('ENPM611', 'ENPM613', 'ENPM631', 'ENPM687',\\\\\\n 'ENPM691', 'ENPM693', 'ENPM694', 'ENPM696',\\\\\\n 'ENPM809J','ENPM809R', 'ENPM809W')\\n\\n response = 'I have course descriptions for: '\\n for course_name in courses_list:\\n response = response + course_name + ' '\\n\\n response = response + '\\\\nTo get the course description, execute command: about ENPM<course_number>'\\n\\n return response\",\n \"def render_htmltext(self, htmltext, context):\\r\\n return CourseEmailTemplate._render(self.html_template, htmltext, context)\",\n \"def MakeTexStr(self):\\n if self._latex_str == '':\\n self._latex_str += bmr_hdr.format(\\n self._filename.replace('.tex', ''))\\n else:\\n # Get rid of ftr so new images maybe added\\n self._latex_str = self._latex_str.split(bmr_ftr)[0]\\n self.AddImageFrames()\\n # print self._latex_str\\n self._latex_str += bmr_ftr\",\n \"def display_courses(courses):\\n\\n _print('You can access %d courses' % len(courses))\\n for i, course in enumerate(courses, 1):\\n _print('%d - [%s] - %s' % (i, course.state, course.name))\",\n \"def pdflatex(unwanted_courses):\\n create_tex(unwanted_courses)\\n cmd = ['pdflatex', '-interaction', 'nonstopmode', 'courses.tex']\\n proc = subprocess.Popen(cmd)\\n proc.communicate()\\n return None\",\n \"def get_description(self):\\n text = \\\"is a student's t distribution; characterised by its degrees of freedom, which here is\\\"+str(self.dofs)+\\\".\\\"\\n return text\",\n \"def _get_course_email_context(course):\\r\\n course_id = course.id.to_deprecated_string()\\r\\n course_title = course.display_name\\r\\n course_url = 'https://{}{}'.format(\\r\\n settings.SITE_NAME,\\r\\n reverse('course_root', kwargs={'course_id': course_id})\\r\\n )\\r\\n image_url = 'https://{}{}'.format(settings.SITE_NAME, course_image_url(course))\\r\\n email_context = {\\r\\n 'course_title': course_title,\\r\\n 'course_url': course_url,\\r\\n 'course_image_url': image_url,\\r\\n 'account_settings_url': 'https://{}{}'.format(settings.SITE_NAME, reverse('dashboard')),\\r\\n 'platform_name': settings.PLATFORM_NAME,\\r\\n }\\r\\n return email_context\",\n \"def insert_course(dept, num, text):\\n\\n # Course Title \\n m = re.search(\\\"[\\\\d\\\\w]{5} - ([\\\\w ]*)\\\", text)\\n title = m.group(1) if m else \\\"nomatch\\\"\\n\\n # Course Description\\n m = re.search(\\\"\\\\.\\\\s(.*)\\\\sTypically\\\",text)\\n des = m.group(1) if m else \\\"nomatch\\\"\\n\\n # Credit hours aren't fixed for every course\\n # Credit Hours: 2.00\\n # Credit Hours: 2.00 or 3.00. \\n # Credit Hours: 1.00 to 18.00. \\n m = re.search(\\\"Credit Hours: (\\\\d+\\\\.\\\\d+)\\\",text, flags=re.IGNORECASE)\\n m = re.search(\\\"(\\\\d+\\\\.\\\\d+)(.*?)Credit hours\\\",text, flags=re.IGNORECASE) if not m else m\\n cr = m.group(1) if m else \\\"-1\\\"\\n\\n # Semesters Offered\\n m = re.search(\\\"Typically offered (.*?)\\\\.\\\", text)\\n sem = m.group(1).split() if m else [\\\"nomatch\\\"]\\n\\n # Course Type: Lecture, Recitation, Lab, Seminar, etc.\\n m = re.search(\\\"Schedule Types:\\\\s((?:[\\\\w ]+)(?:,[\\\\w ]+)*) \\\\s+\\\", text)\\n form = m.group(1).split(\\\", \\\") if m else [\\\"nomatch\\\"]\\n\\n # Learning objectives will not necessarily follow campuses\\n m = re.search(\\\"campuses:(\\\\s+([\\\\w\\\\s])+\\\\n)\\\", text)\\n campus = m.group(1).strip().split(\\\"\\\\n\\\\n\\\") if m else [\\\"nomatch\\\"]\\n campus = [camp.strip() for camp in campus]\\n\\n # prereq regex and decomosition of prereqs into lists of AND conditions (works for most classes, not 477 and similar)\\n # re.DOTALL matches all characters, including \\\"\\\\n\\\"\\n idx = text.find(\\\"campuses:\\\")\\n m = re.search(\\\"Prerequisites:(.*)\\\",text[idx:],flags=re.DOTALL)\\n if m:\\n allReqs = []\\n prereqText = m.group(1).strip()\\n prereqText = prereqText.encode('ascii', 'ignore') \\n for i in PrereqParser.parseprereq(prereqText):\\n reqArr = []\\n for j in i.split():\\n if j.find(\\\"-C\\\") != -1:\\n j = j.replace(\\\"-C\\\",\\\"\\\")\\n reqArr.append(Requisite(course=j,reqType=False))\\n else:\\n reqArr.append(Requisite(course=j,reqType=True)) \\n allReqs.append(RequisiteList(courses=reqArr))\\n\\n else:\\n allReqs = []\\n\\n # create course entity\\n course = Course(number=num, title=title, department=dept, form=form,\\n description=des, credits=float(cr), semesters=sem,\\n campuses=campus,requisites=allReqs, id=dept + num)\\n # store course \\n course.put()\",\n \"def toStudentString(self):\\r\\n return \\\"{0}th year, section {1}, {2} {3}\\\".format(self.batch, self.batch_id, self.batch, self.batch_id)\",\n \"def create_course_with_unit():\\r\\n world.clear_courses()\\r\\n course = world.CourseFactory.create()\\r\\n world.scenario_dict['COURSE'] = course\\r\\n section = world.ItemFactory.create(parent_location=course.location)\\r\\n world.ItemFactory.create(\\r\\n parent_location=section.location,\\r\\n category='sequential',\\r\\n display_name='Subsection One',\\r\\n )\\r\\n user = create_studio_user(is_staff=False)\\r\\n add_course_author(user, course)\\r\\n\\r\\n log_into_studio()\\r\\n world.css_click('a.course-link')\\r\\n\\r\\n world.wait_for_js_to_load()\\r\\n css_selectors = [\\r\\n 'div.section-item a.expand-collapse', 'a.new-unit-item'\\r\\n ]\\r\\n for selector in css_selectors:\\r\\n world.css_click(selector)\\r\\n\\r\\n world.wait_for_mathjax()\\r\\n world.wait_for_xmodule()\\r\\n\\r\\n assert world.is_css_present('ul.new-component-type')\",\n \"def parse_create_course(xml_course):\\n attrs = [\\n \\\"term-code\\\",\\n \\\"term-description\\\",\\n 'subject',\\n \\\"course-number\\\",\\n \\\"school\\\",\\n \\\"department\\\",\\n \\\"title\\\",\\n \\\"description\\\",\\n \\\"credit-hours\\\",\\n \\\"distribution-group\\\"\\n ]\\n course = pull_attributes_from_xml(xml_course, attrs)\\n course[\\\"sections\\\"] = []\\n\\n return course\",\n \"def add_s_courses(student_courses, resume_output):\\n # creates a list to represent each line of the html code for courses section\\n html_courses_section = []\\n\\n # opens the html section with a div tag\\n html_courses_section.append(\\\"<div>\\\")\\n\\n # adds a courses section with header3 format to html section\\n html_courses_section.append(surround_block(\\\"h3\\\", \\\"Courses\\\"))\\n\\n # makes a string of comma-separated (\\\", \\\") student courses\\n student_courses_str = \\\"\\\" # creates a list of student courses, and assigns the first course\\n for course in student_courses: # goes second course to last course... for each course\\n\\n # debugging\\n # print(\\\"idl\\\" + course)\\n\\n # adds the student course to the string of courses\\n student_courses_str += course\\n\\n # adds a comma after the course, unless it is the last course in the list\\n if course is not student_courses[-1]:\\n student_courses_str += \\\", \\\"\\n\\n # print(\\\"student_courses_str:\\\" + student_courses_str)\\n\\n # adds a line with str list of courses in span tag\\n html_courses_section.append(surround_block(\\\"span\\\", student_courses_str))\\n\\n # closes the courses section\\n html_courses_section.append(\\\"</div>\\\")\\n # print(\\\"html courses section\\\", html_courses_section)\\n\\n # adds the courses section to the html code to be printed\\n resume_output.extend(html_courses_section)\\n # print(resume_output)\\n\\n # returns output code\\n return resume_output\",\n \"def createCourse():\\n\\tif request.method == 'POST':\\n\\t\\tcname = request.form['cname']\\n\\t\\tcourseterm = request.form['courseterm']\\n\\t\\tcoursepoint = request.form['coursepoint']\\n\\t\\tcoursetype = request.form['coursetype']\\t\\n\\t\\tcourseyear = request.form['courseyear']\\t\\n\\t\\ttname = request.form['tname']\\t\\n\\t\\terror = None\\n\\n\\t\\tif not cname:\\n\\t\\t\\terror = 'Course name is required.'\\n\\t\\telif not courseterm:\\n\\t\\t\\terror = 'Course term is required'\\n\\t\\telif not courseterm:\\n\\t\\t\\terror = 'Course point is required'\\n\\n\\t\\tif error is not None:\\n\\t\\t\\tflash(error)\\n\\t\\telse:\\n\\t\\t\\tdb = get_db()\\n\\t\\t\\tcur = db.cursor()\\n\\t\\t\\tcur.execute(\\n\\t\\t\\t\\t'INSERT INTO course (cname, courseyear, coursetype, courseterm, coursepoint, tname)'\\n\\t\\t\\t\\t' VALUES (%s, %s, %s, %s, %s, %s)',\\n\\t\\t\\t\\t(cname, courseyear, coursetype, courseterm, coursepoint, tname)\\n\\t\\t\\t)\\n\\t\\t\\tdb.commit()\\n\\t\\t\\treturn redirect(url_for('info.index'))\\n\\n\\treturn render_template('info/createCourse.html')\",\n \"def render_preview_certificate(request, course_id):\\n return render_html_view(request, str(course_id))\",\n \"def __str__(self):\\n start = f\\\"{self.start:%y/%m/%d %H:%M} - \\\" if self.start else \\\"\\\"\\n return f\\\"Course run {self.id!s} starting {start:s}\\\"\",\n \"def _createCoursesTable(self):\\n\\t\\tcommand = \\\"\\\"\\\"CREATE TABLE courses (ID INTEGER PRIMARY KEY,\\n\\t\\t\\tname TEXT,\\n\\t\\t\\tauthor_id INTEGER,\\n\\t\\t\\tdescription TEXT\\n\\t\\t\\t);\\n\\\"\\\"\\\"\\n\\n\\t\\tself._run_command(command)\",\n \"def get_template(self):\\n return self.sep.join([self.htmls[html] for html in self.lang]).format(**self.fields)\",\n \"def mktg_course_about(request, course_id):\\r\\n\\r\\n course_key = SlashSeparatedCourseKey.from_deprecated_string(course_id)\\r\\n try:\\r\\n course = get_course_with_access(request.user, 'see_exists', course_key)\\r\\n except (ValueError, Http404) as e:\\r\\n # if a course does not exist yet, display a coming\\r\\n # soon button\\r\\n return render_to_response(\\r\\n 'courseware/mktg_coming_soon.html', {'course_id': course_key.to_deprecated_string()}\\r\\n )\\r\\n\\r\\n registered = registered_for_course(course, request.user)\\r\\n\\r\\n if has_access(request.user, 'load', course):\\r\\n course_target = reverse('info', args=[course.id.to_deprecated_string()])\\r\\n else:\\r\\n course_target = reverse('about_course', args=[course.id.to_deprecated_string()])\\r\\n\\r\\n allow_registration = has_access(request.user, 'enroll', course)\\r\\n\\r\\n show_courseware_link = (has_access(request.user, 'load', course) or\\r\\n settings.FEATURES.get('ENABLE_LMS_MIGRATION'))\\r\\n course_modes = CourseMode.modes_for_course(course.id)\\r\\n\\r\\n return render_to_response('courseware/mktg_course_about.html', {\\r\\n 'course': course,\\r\\n 'registered': registered,\\r\\n 'allow_registration': allow_registration,\\r\\n 'course_target': course_target,\\r\\n 'show_courseware_link': show_courseware_link,\\r\\n 'course_modes': course_modes,\\r\\n })\",\n \"def get_text(self, course):\\r\\n return views.progress(self.request, course.id.to_deprecated_string(), self.user.id).content\",\n \"def description(self):\\n return ('According to Wikipedia, the Affine cipher is '\\n 'a \\\"monoalphabetic substitution cipher, wherein each letter in an alphabet '\\n 'is mapped to its numeric equivalent, '\\n 'encrypted using the function [E(x)=(ax+b) mod m], '\\n 'and converted back to a letter.\\\"\\\\n'\\n 'The characters supported by the implementation in this program include:\\\\n'\\n '-- uppercase letters: {}\\\\n'\\n '-- lowercase letters: {}\\\\n'\\n '-- digits: {}\\\\n'\\n '-- punctuation: {}\\\\n'\\n '-- blank space: \\\" \\\"\\\\n'\\n .format(string.ascii_uppercase,\\n string.ascii_lowercase,\\n string.digits,\\n string.punctuation)\\n )\",\n \"def __str__(self):\\n\\n rep = 'Generalized Syllogism:\\\\n'\\n rep += '\\\\ttask: {}\\\\n'.format(self.task)\\n rep += '\\\\tencoded_task: {}\\\\n'.format(self.encoded_task)\\n rep += '\\\\tp1: {}\\\\n'.format(self.p1)\\n rep += '\\\\tp2: {}\\\\n'.format(self.p2)\\n rep += '\\\\tquantifier_p1: {}\\\\n'.format(self.quantifier_p1)\\n rep += '\\\\tquantifier_p2: {}\\\\n'.format(self.quantifier_p2)\\n rep += '\\\\tfigure: {}\\\\n'.format(self.figure)\\n rep += '\\\\tTerms:\\\\n'\\n rep += '\\\\t\\\\tA: {}\\\\n'.format(self.A)\\n rep += '\\\\t\\\\tB: {}\\\\n'.format(self.B)\\n rep += '\\\\t\\\\tC: {}\\\\n'.format(self.C)\\n return rep\",\n \"def astext(self):\\n self.elements.update({\\n 'body': u''.join(self.body),\\n 'indices': self.generate_indices()\\n })\\n return self.render('beamer.tex_t', self.elements)\",\n \"def __repr__(self) -> str:\\n # start the textual representation off with a newline\\n weekly_schedule = \\\"\\\\n\\\"\\n # iterate through each day of the week in the schedule\\n for day in self.schedule.keys():\\n # add the name of the current day of the week\\n weekly_schedule += day + \\\":\\\\n\\\\n\\\\t\\\"\\n # access the schedule for the current day of the week\\n schedule_for_day = self.schedule[day]\\n # add all of the classes of the current day of the week\\n weekly_schedule += \\\"\\\\n\\\\t\\\".join(map(str, schedule_for_day))\\n # for course in schedule_for_day:\\n # weekly_schedule += str(course) + \\\"\\\\n\\\"\\n # if not processing the last day of the week, add two newlines\\n if day != list(self.schedule.keys())[-1]:\\n weekly_schedule += \\\"\\\\n\\\\n\\\"\\n # if processing the last day of the week, add one newline\\n else:\\n weekly_schedule += \\\"\\\\n\\\"\\n # return a string that displays all courses taught in a week\\n return weekly_schedule\",\n \"def subject(template_name):\\n subject = loader.render_to_string(template_name,\\n self.get_context())\\n return ''.join(subject.splitlines())\",\n \"def get_rendered_text(self, context):\\n missing = set()\\n for required in utils.get_variable_names_from_template(self):\\n if required not in context:\\n missing.add(required)\\n if missing:\\n raise MissingContext(missing)\\n tmpl = utils.PyratempTemplate(self.text)\\n context = context.copy()\\n context[\\\"locale\\\"] = self.language.iso_code\\n return tmpl.render(context)\",\n \"def _course_factory_create_course():\\r\\n return CourseFactory.create(org='MITx', course='999', display_name='Robot Super Course')\",\n \"def __str__(self):\\n table_data = [\\n ['', 'C', 'G', 'A', 'T'],\\n ['total', str(self.total['C']), str(self.total['G']), str(self.total['A']), str(self.total['T'])],\\n ['reverse half strand', str(self.reverse['C']), str(self.reverse['G']), str(self.reverse['A']),\\n str(self.reverse['T'])],\\n ['forward half strand', str(self.forward['C']), str(self.forward['G']), str(self.forward['A']),\\n str(self.forward['T'])]\\n ]\\n table = AsciiTable(table_data)\\n return \\\"Generation #{}\\\\n\\\".format(self.epoch) + table.table\",\n \"def top_students_table(course):\\n\\n return 'COURSE {}\\\\n'.format(course) + \\\\\\n '\\\\n'.join('{:10} --- {:4}'.format(*rate)\\n for rate in itertools.islice(get_rates(students_data(), course), 3))\",\n \"def makeTexInput(self, tabletitle, subsection=True):\\r\\n tablestring = \\\"\\\"\\r\\n\\r\\n # if there's enough effluent data\\r\\n if self.ds.effluent.include:\\r\\n if subsection:\\r\\n tablestring += r\\\"\\\\subsection{%s}\\\" % (self.bmp,)\\r\\n\\r\\n # caption for the stats plot\\r\\n prob_caption = \\\"Box and Probability Plots of {} at {} BMPs\\\".format(\\r\\n self.parameter.name, self.bmp\\r\\n )\\r\\n\\r\\n # caption for the scatter plot\\r\\n scatter_caption = \\\"Influent vs. Effluent Plots of {} at {} BMPs\\\".format(\\r\\n self.parameter.name, self.bmp\\r\\n )\\r\\n\\r\\n # warning about having a lot of non-detects\\r\\n warning = \\\"\\\"\\\"\\r\\n Warning: there is a very high percentage of non-detects in\\r\\n this data set. The hypothesis test results and other\\r\\n statistics reported in this table may not be valid.\\r\\n \\\"\\\"\\\"\\r\\n\\r\\n # make the table and write it to the output file\\r\\n tablestring += self._make_tex_table(tabletitle)\\r\\n\\r\\n # if less than 80% of the data is ND\\r\\n if self.ds.effluent.ND / self.ds.effluent.N <= 0.8:\\r\\n\\r\\n # make the stat plot string\\r\\n statfig = self._make_tex_figure(\\r\\n self.stat_fig_name, prob_caption, clearpage=False\\r\\n )\\r\\n\\r\\n # make the scatter plot string\\r\\n scatterfig = self._make_tex_figure(\\r\\n self.scatter_fig_name, scatter_caption, clearpage=True\\r\\n )\\r\\n\\r\\n # write the strings to the file\\r\\n tablestring += statfig\\r\\n tablestring += scatterfig\\r\\n\\r\\n else:\\r\\n # if there are too many non-detect,\\r\\n # issue the warning\\r\\n tablestring += warning\\r\\n\\r\\n return tablestring\",\n \"def __str__(self):\\n t = Template(\\n \\\"\\\"\\\"\\n <h4>$title</h4>\\n $imgs\\n $footnotes\\n <hr/>\\\"\\\"\\\")\\n # Return result.\\n return t.substitute({\\n \\\"title\\\": self.title,\\n \\\"imgs\\\": self.render_images(),\\n \\\"footnotes\\\": self.render_footnotes()\\n })\",\n \"def generate_tex(title, author, txt, indices=None):\\n with open('head', 'r') as head,\\\\\\n open('stegano.tex', 'w') as file_tex:\\n\\n # First lines are\\n # - Title\\n # - Author\\n\\n file_tex.write(\\\"\\\".join(head.readlines())) # Write heading\\n file_tex.write(\\\"\\\\\\\\title{{{}}}\\\\n\\\".format(title))\\n file_tex.write(\\\"\\\\\\\\author{{{}}}\\\\n\\\".format(author))\\n file_tex.write(\\\"\\\\\\\\date{{}}\\\\n\\\")\\n\\n file_tex.write(\\\"\\\\\\\\begin{document}\\\\n\\\\\\\\maketitle\\\\n\\\")\\n\\n # Write char by char, adding decoration when indices is reach\\n i = 0\\n while i < len(txt):\\n if i in indices:\\n file_tex.write(secret_char(txt[i]))\\n else:\\n file_tex.write(non_secret_char(txt[i]))\\n i += 1\\n\\n file_tex.write(\\\"\\\\\\\\end{document}\\\")\",\n \"def _create_course(self):\\r\\n super(TestOrphan, self)._create_course()\\r\\n\\r\\n self._create_item('chapter', 'Chapter1', {}, {'display_name': 'Chapter 1'}, 'course', 'runid')\\r\\n self._create_item('chapter', 'Chapter2', {}, {'display_name': 'Chapter 2'}, 'course', 'runid')\\r\\n self._create_item('chapter', 'OrphanChapter', {}, {'display_name': 'Orphan Chapter'}, None, None)\\r\\n self._create_item('vertical', 'Vert1', {}, {'display_name': 'Vertical 1'}, 'chapter', 'Chapter1')\\r\\n self._create_item('vertical', 'OrphanVert', {}, {'display_name': 'Orphan Vertical'}, None, None)\\r\\n self._create_item('html', 'Html1', \\\"<p>Goodbye</p>\\\", {'display_name': 'Parented Html'}, 'vertical', 'Vert1')\\r\\n self._create_item('html', 'OrphanHtml', \\\"<p>Hello</p>\\\", {'display_name': 'Orphan html'}, None, None)\\r\\n self._create_item('static_tab', 'staticuno', \\\"<p>tab</p>\\\", {'display_name': 'Tab uno'}, None, None)\\r\\n self._create_item('about', 'overview', \\\"<p>overview</p>\\\", {}, None, None)\\r\\n self._create_item('course_info', 'updates', \\\"<ol><li><h2>Sep 22</h2><p>test</p></li></ol>\\\", {}, None, None)\",\n \"def dump_grading_context(course):\\r\\n hbar = \\\"{}\\\\n\\\".format(\\\"-\\\" * 77)\\r\\n msg = hbar\\r\\n msg += \\\"Course grader:\\\\n\\\"\\r\\n\\r\\n msg += '%s\\\\n' % course.grader.__class__\\r\\n graders = {}\\r\\n if isinstance(course.grader, xmgraders.WeightedSubsectionsGrader):\\r\\n msg += '\\\\n'\\r\\n msg += \\\"Graded sections:\\\\n\\\"\\r\\n for subgrader, category, weight in course.grader.sections:\\r\\n msg += \\\" subgrader=%s, type=%s, category=%s, weight=%s\\\\n\\\"\\\\\\r\\n % (subgrader.__class__, subgrader.type, category, weight)\\r\\n subgrader.index = 1\\r\\n graders[subgrader.type] = subgrader\\r\\n msg += hbar\\r\\n msg += \\\"Listing grading context for course %s\\\\n\\\" % course.id.to_deprecated_string()\\r\\n\\r\\n gcontext = course.grading_context\\r\\n msg += \\\"graded sections:\\\\n\\\"\\r\\n\\r\\n msg += '%s\\\\n' % gcontext['graded_sections'].keys()\\r\\n for (gsomething, gsvals) in gcontext['graded_sections'].items():\\r\\n msg += \\\"--> Section %s:\\\\n\\\" % (gsomething)\\r\\n for sec in gsvals:\\r\\n sdesc = sec['section_descriptor']\\r\\n frmat = getattr(sdesc, 'format', None)\\r\\n aname = ''\\r\\n if frmat in graders:\\r\\n gform = graders[frmat]\\r\\n aname = '%s %02d' % (gform.short_label, gform.index)\\r\\n gform.index += 1\\r\\n elif sdesc.display_name in graders:\\r\\n gform = graders[sdesc.display_name]\\r\\n aname = '%s' % gform.short_label\\r\\n notes = ''\\r\\n if getattr(sdesc, 'score_by_attempt', False):\\r\\n notes = ', score by attempt!'\\r\\n msg += \\\" %s (format=%s, Assignment=%s%s)\\\\n\\\"\\\\\\r\\n % (sdesc.display_name, frmat, aname, notes)\\r\\n msg += \\\"all descriptors:\\\\n\\\"\\r\\n msg += \\\"length=%d\\\\n\\\" % len(gcontext['all_descriptors'])\\r\\n msg = '<pre>%s</pre>' % msg.replace('<', '<')\\r\\n return msg\",\n \"def makeCourse( xmlFile, genPath, importPaths, commonFiles, rendererContent=True):\\n\\ttry:\\n\\n\\t\\t# parse the command line\\n\\t\\tConfig.add_option('--verbose', help='Set verbosity to maximum', dest='verbosity', default=0, action='store_const', const=2)\\n\\t\\tConfig.add_option('-v', '--verbosity', help='Set the verbosity level (0: quiet, 1: display the command lines, 2: display command lines and their outputs', dest='verbosity', default=0, type=int)\\n\\t\\tConfig.add_option('-d', '--debug', help='Create the files in the debug/ folder, instead of in a temporary one', dest='debug', action='store_true', default=False)\\n\\t\\tConfig.add_option('-f', '--force', help='Force the generation of the documents, even if nothing changes from last run', dest='force', action='store_true', default=False)\\n\\t\\tConfig.add_option('-q', '--quick', help='Quick pdf generation (do not compile twice the latex, do not produce handout, etc.)', dest='quick', action='store_true', default=False)\\n\\t\\tConfig.add_option('-w', '--wordpress', help='Publish to wordpress', dest='wordpress', default=False, action='store_true')\\n\\t\\tConfig.add_option('-c', '--HTMLcorrection', help='Display an HTML correction', dest='HTMLcorrection', default=False, action='store_true')\\n\\t\\tConfig.add_option('-s', '--shared', help='Copy the required files to the <shared> path (via ssh)', default=False,\\taction='store_true')\\n\\t\\tConfig.parse()\\n\\t\\targs = Config.args\\n\\t\\toptions = Config.options\\n\\t\\tConfig.importPaths = importPaths \\n\\t\\tConfig.commonFiles = commonFiles\\n\\t\\tConfig.allSessions = { x.__name__:x for x in Session.__subclasses__()}\\t# list of the created session classes\\n\\t\\tConfig.rendererContent = rendererContent\\n\\t\\t\\n\\t\\t# clean the debug directory in debug mode\\n\\t\\tbasePath = os.path.abspath('.')+'/'\\t\\t\\t# base path (from where the script is run, because the path are relative)\\n\\t\\tif options.debug:\\n\\t\\t\\tif os.path.exists('debug/'):\\n\\t\\t\\t\\trunCommand(['rm','-rf','debug/'])\\n\\n\\t\\t# open and parse the course file\\n\\t\\twith codecs.open(xmlFile, encoding='utf-8') as f:\\n\\t\\t\\tbs = BeautifulSoup(f, features=\\\"xml\\\")\\n\\n\\n\\t\\t# build the recursively the sessions\\n\\t\\ttop = createTagSession( bs, father=None )\\t\\t# bs.contents[0]\\n\\t\\tsessionsToBuild = Session.sessionsToBuild\\t\\t# get the list of the sessions object\\n\\t\\t\\n\\n\\t\\t\\\"\\\"\\\"\\n\\t\\timportFiles( bs.contents[0], importPaths)\\n\\n\\t\\t# get the list of sessions we can build (with a 'make' method)\\n\\t\\tbuildableSessions = { x.__name__:x for x in Session.__subclasses__() if 'make' in x.__dict__ }\\n\\n\\t\\t#This set the PATH for PyDev only...\\n\\t\\tos.environ['PATH'] = os.environ['PATH']+':'+os.getenv('PATH')\\n\\n\\n\\t\\t# build the list of Sessions to build\\n\\t\\tsessionsToBuild = []\\n\\t\\tfor name,session in buildableSessions.items():\\n\\t\\t\\tsessionsToBuild.extend( session(tag, commonFiles) for tag in bs(name) )\\n\\t\\t\\\"\\\"\\\"\\n\\t\\t\\n\\t\\t\\n\\n\\t\\t# if possible, load the previous xml file, and look for the differences\\n\\t\\tdirName,baseName = split(xmlFile) \\n\\t\\ttry:\\n\\t\\t\\twith open(dirName+\\\"/.\\\"+baseName+\\\".makeCourse\\\", \\\"rb\\\") as f:\\n\\t\\t\\t\\tdata = load( f )\\n\\t\\t\\t\\tfor s in sessionsToBuild:\\n\\t\\t\\t\\t\\tif s.name in data:\\n\\t\\t\\t\\t\\t\\ts.checkDifferences( data[s.name] )\\n\\t\\texcept IOError:\\n\\t\\t\\tpass\\n\\n\\n\\t\\t# build every argument in the command line arguments\\n\\t\\tsomethingHasBeDone = False\\n\\t\\tfor s in sessionsToBuild:\\n\\t\\t\\tif (not args) or (\\\"all\\\" in args) or (s.name in args) or (s.type in args):\\n\\t\\t\\t\\t\\n\\t\\t\\t\\tcd( basePath)\\n\\t\\t\\t\\t\\n\\t\\t\\t\\t# check if something has to be done\\n\\t\\t\\t\\tif s.shouldBeMake(basePath+'/'+genPath, options) or options.force:\\n\\t\\t\\t\\t\\tsomethingHasBeDone = True\\n\\n\\t\\t\\t\\t\\t#Make one build (TP, course, etc.)\\n\\t\\t\\t\\t\\tprint ( Fore.BLUE+\\\"*) Make \\\"+Style.BRIGHT+s.name+Fore.RESET+Style.NORMAL)\\n\\n\\t\\t\\t\\t\\t# make temp directory and copy all the file in resources dir\\n\\t\\t\\t\\t\\tif options.debug:\\n\\t\\t\\t\\t\\t\\ttmp = \\\"debug/\\\"+s.name+'/'\\n\\t\\t\\t\\t\\t\\tcreateDirectory(tmp)\\n\\t\\t\\t\\t\\telse:\\n\\t\\t\\t\\t\\t\\ttmp = mkdtemp()\\n\\n\\t\\t\\t\\t\\ts.prepareResources(tmp )\\n\\t\\t\\t\\t\\tcd( tmp)\\n\\n\\t\\t\\t\\t\\t# call the custom function associated with the type, to produce the documents\\n\\t\\t\\t\\t\\ts.make(options)\\n\\n\\t\\t\\t\\t\\t# then move the files in the right place\\n\\t\\t\\t\\t\\tfor f in s.files(options):\\n\\t\\t\\t\\t\\t\\tcreateDirectory( basePath+'/'+genPath.format( **s.dict ) )\\n\\t\\t\\t\\t\\t\\tnewFile = basePath+'/'+genPath.format( **s.dict )+f\\n\\t\\t\\t\\t\\t\\tif not os.path.exists(f):\\n\\t\\t\\t\\t\\t\\t\\tprint( Fore.YELLOW+'The file '+f+' has not been created by '+s.type+' function !'+Fore.RESET)\\n\\t\\t\\t\\t\\t\\trunCommand( ['cp', f, newFile])\\n\\n\\t\\t\\t\\t\\t# del the temporary directory or clean debug directory\\n\\t\\t\\t\\t\\tif not options.debug:\\n\\t\\t\\t\\t\\t\\trunCommand( ['rm', '-rf', tmp])\\n\\t\\t\\t\\telse:\\n\\t\\t\\t\\t\\tif options.verbosity>0:\\n\\t\\t\\t\\t\\t\\tprint( Fore.BLUE + \\\"*) Nothing changed for \\\"+Style.BRIGHT+s.name+Style.NORMAL+\\\", skipped\\\"+Fore.RESET)\\n\\n\\n\\n\\t\\tif not somethingHasBeDone:\\n\\t\\t\\tprint( Fore.BLUE + \\\"Nothing has changed, nothing to do, so nothing has been done...\\\" + Fore.RESET)\\n\\n\\n\\t\\t# save the data file\\n\\t\\tdata = {L.name: {key:md5(str(val).encode('utf-8')).hexdigest() for key,val in L.dict.items()} for L in sessionsToBuild }\\n\\t\\tcd( basePath)\\n\\t\\twith open(dirName+\\\"/.\\\"+baseName+\\\".makeCourse\\\", 'wb') as f:\\n\\t\\t\\tdump( data, f)\\n\\n\\n\\n\\n\\n\\n\\n\\texcept mkcException as err:\\n\\t\\tprint( err )\",\n \"def __str__(self):\\n return \\\"%s %s\\\" % (self.course.code, self.code)\",\n \"def __repr__(self):\\r\\n return \\\"Lesson(id='%i', courseName='%s', teachers='%s', semester_groups='%s')\\\" % \\\\\\r\\n (self.id, self.course.name,\\r\\n str(list(map(lambda t: t.id, self.teachers))),\\r\\n str(list(map(lambda s: s.id, self.course.semester_groups))))\",\n \"def syntax_text():\",\n \"def subtitle(string):\\n print(\\\"{}\\\\n{}\\\\n\\\".format(bold(string), underline(string, \\\"-\\\")))\",\n \"def __repr__(self) -> str:\\n return f\\\"{self.title} that starts at {self.start} and ends at {self.end} and is taught by {self.instructor}\\\"\",\n \"def subject(self):\\n subject = loader.render_to_string(self.subject_template_name,\\n self.get_context())\\n return ''.join(subject.splitlines())\",\n \"def __str__(self):\\n return \\\"\\\"\\\"translate([{0!s}, {1!s}, {2!s}]) {{\\n {3!s}\\n}}\\\"\\\"\\\".format(self.vector[0], self.vector[1], self.vector[2], self.body)\",\n \"def gt(text, font=DEFAULT_FONT, color=\\\"magenta\\\",\\n on_color=None, attr=None, width=80,\\n justify=\\\"center\\\"):\\n\\n f = Figlet(\\n font, width=width,\\n justify=justify\\n )\\n r = f.renderText(text)\\n return colored(r, color, on_color, attr)\",\n \"def to_str(self):\\n return self.template.format(\\n text_color=self.color.to_str(),\\n font_type=self.font_type.to_str(),\\n font_weight=self.font_weight.to_str(),\\n font_size=self.font_size.to_str()\\n )\",\n \"def draw(self):\\n drawing = \\\"\\\"\\n \\n if self.get_gene().get_choice_at(1) is 'c':\\n drawing += 'o'\\n else:\\n drawing += 'x'\\n \\n if self.is_tft():\\n drawing += \\\"tft\\\"\\n return drawing\\n elif self.is_t2t():\\n drawing += \\\"t2t\\\"\\n return drawing\\n elif self.is_ftf():\\n drawing += \\\"ftf\\\"\\n return drawing\\n\\n rule = self.get_gene().get_defect_fraction()\\n fraction_display = 0.166\\n\\n if rule >= 1.0:\\n drawing += \\\"ddd\\\"\\n elif rule > (5*fraction_display):\\n drawing += \\\"ddc\\\"\\n elif rule > (4*fraction_display):\\n drawing += \\\"dcd\\\"\\n elif rule > (3*fraction_display):\\n drawing += \\\"dcc\\\"\\n elif rule > (2*fraction_display):\\n drawing += \\\"cdd\\\"\\n elif rule > (1*fraction_display):\\n drawing += \\\"cdc\\\"\\n elif rule > (0*fraction_display):\\n drawing += \\\"ccd\\\"\\n else:\\n drawing += \\\"ccc\\\"\\n\\n return drawing\",\n \"def test_course_overview_view_with_course(self):\\r\\n course = CourseFactory.create(org='MITx', course='999', display_name='Robot Super Course')\\r\\n resp = self._show_course_overview(course.id)\\r\\n self.assertContains(\\r\\n resp,\\r\\n '<article class=\\\"courseware-overview\\\" data-locator=\\\"location:MITx+999+Robot_Super_Course+course+Robot_Super_Course\\\" data-course-key=\\\"slashes:MITx+999+Robot_Super_Course\\\">',\\r\\n status_code=200,\\r\\n html=True\\r\\n )\",\n \"def generate_cross_section_title(grid, field, start, end):\\n\\n # Grab information about the grid\\n time_str = generate_grid_time_begin(grid).strftime(\\\"%Y-%m-%dT%H:%M:%SZ\\\")\\n field_name = generate_field_name(grid, field)\\n\\n # Deal with the latitude/longitude part\\n start_lat, start_lon = start\\n end_lat, end_lon = end\\n\\n degree_symbol = \\\"\\\\N{DEGREE SIGN}\\\"\\n\\n # Add degrees latitude/longitude labels\\n\\n if start_lat < 0:\\n start_lat_string = f\\\"{-start_lat}{degree_symbol}S\\\"\\n else:\\n start_lat_string = f\\\"{start_lat}{degree_symbol}N\\\"\\n\\n if end_lat < 0:\\n end_lat_string = f\\\"{-end_lat}{degree_symbol}S\\\"\\n else:\\n end_lat_string = f\\\"{end_lat}{degree_symbol}N\\\"\\n\\n if start_lon < 0:\\n start_lon_string = f\\\"{-start_lon}{degree_symbol}W\\\"\\n else:\\n start_lon_string = f\\\"{start_lon}{degree_symbol}E\\\"\\n\\n if end_lon < 0:\\n end_lon_string = f\\\"{-end_lon}{degree_symbol}W\\\"\\n else:\\n end_lon_string = f\\\"{end_lon}{degree_symbol}E\\\"\\n\\n return (\\n f\\\"({start_lat_string}, {start_lon_string})\\\"\\n f\\\" to ({end_lat_string}, {end_lon_string}) \\\\n {time_str} \\\\n {field_name}\\\"\\n )\",\n \"def dump_grading_context(course):\\r\\n msg = \\\"-----------------------------------------------------------------------------\\\\n\\\"\\r\\n msg += \\\"Course grader:\\\\n\\\"\\r\\n\\r\\n msg += '%s\\\\n' % course.grader.__class__\\r\\n graders = {}\\r\\n if isinstance(course.grader, xmgraders.WeightedSubsectionsGrader):\\r\\n msg += '\\\\n'\\r\\n msg += \\\"Graded sections:\\\\n\\\"\\r\\n for subgrader, category, weight in course.grader.sections:\\r\\n msg += \\\" subgrader=%s, type=%s, category=%s, weight=%s\\\\n\\\" % (subgrader.__class__, subgrader.type, category, weight)\\r\\n subgrader.index = 1\\r\\n graders[subgrader.type] = subgrader\\r\\n msg += \\\"-----------------------------------------------------------------------------\\\\n\\\"\\r\\n msg += \\\"Listing grading context for course %s\\\\n\\\" % course.id\\r\\n\\r\\n gcontext = course.grading_context\\r\\n msg += \\\"graded sections:\\\\n\\\"\\r\\n\\r\\n msg += '%s\\\\n' % gcontext['graded_sections'].keys()\\r\\n for (gsections, gsvals) in gcontext['graded_sections'].items():\\r\\n msg += \\\"--> Section %s:\\\\n\\\" % (gsections)\\r\\n for sec in gsvals:\\r\\n sdesc = sec['section_descriptor']\\r\\n grade_format = getattr(sdesc, 'grade_format', None)\\r\\n aname = ''\\r\\n if grade_format in graders:\\r\\n gfmt = graders[grade_format]\\r\\n aname = '%s %02d' % (gfmt.short_label, gfmt.index)\\r\\n gfmt.index += 1\\r\\n elif sdesc.display_name in graders:\\r\\n gfmt = graders[sdesc.display_name]\\r\\n aname = '%s' % gfmt.short_label\\r\\n notes = ''\\r\\n if getattr(sdesc, 'score_by_attempt', False):\\r\\n notes = ', score by attempt!'\\r\\n msg += \\\" %s (grade_format=%s, Assignment=%s%s)\\\\n\\\" % (s.display_name, grade_format, aname, notes)\\r\\n msg += \\\"all descriptors:\\\\n\\\"\\r\\n msg += \\\"length=%d\\\\n\\\" % len(gcontext['all_descriptors'])\\r\\n msg = '<pre>%s</pre>' % msg.replace('<', '<')\\r\\n return msg\",\n \"def _course_key(self):\\r\\n return \\\"slashes:{org}+{number}+{run}\\\".format(**self._course_dict)\",\n \"def __str__(self):\\n description = \\\"Object class Critter.\\\"\\n description += \\\"\\\\nName: \\\" + self.name + \\\"\\\\nMood: \\\" + self.mood + \\\"\\\\nHunger: \\\" + str(self.hunger) + \\\"\\\\nBoredom: \\\" + str(self.boredom) + \\\"\\\\n\\\"\\n return description\",\n \"def get_courses(std):\\n return std[2]\",\n \"def CreateFigletString(text: str, font: str = \\\"standard\\\", **kwargs) -> pyfiglet.FigletString:\\n return pyfiglet.figlet_format(text=text, font=font, **kwargs)\",\n \"def edit_course(self, course):\\n EDIT_COURSE = \\\"\\\"\\\"UPDATE Course SET subject_code = %s, credit_hours = %s, description = %s WHERE name = %s\\\"\\\"\\\"\\n\\n self.db_cursor.execute(EDIT_COURSE, (\\n course.subject_code, course.credit_hours, course.description, course.name))\\n self.db_connection.commit()\\n\\n DELETE_COURSE_TOPICS = \\\"\\\"\\\"DELETE FROM CourseTopics WHERE course_name = %s\\\"\\\"\\\"\\n self.db_cursor.execute(DELETE_COURSE_TOPICS, (course.name,))\\n self.db_connection.commit()\\n INSERT_COURSE_TOPICS = \\\"\\\"\\\"INSERT INTO CourseTopics (course_name, topic_id) VALUES (%s, %s)\\\"\\\"\\\"\\n for ct in course.topics:\\n self.db_cursor.execute(INSERT_COURSE_TOPICS, (course.name,ct))\\n self.db_connection.commit()\\n\\n DELETE_COURSE_GOALS = \\\"\\\"\\\"DELETE FROM CourseGoals WHERE course_name = %s\\\"\\\"\\\"\\n self.db_cursor.execute(DELETE_COURSE_GOALS, (course.name,))\\n self.db_connection.commit()\\n INSERT_COURSE_GOALS = \\\"\\\"\\\"INSERT INTO CourseGoals (course_name, goal_id) VALUES (%s, %s)\\\"\\\"\\\"\\n for cg in course.goals:\\n self.db_cursor.execute(INSERT_COURSE_GOALS, (course.name, cg))\\n self.db_connection.commit()\",\n \"def degree_creator():\\n return render_template(\\\"degree_creator.html\\\")\",\n \"def create_course(self, modules_count, module_store, topics):\\n course = CourseFactory.create(\\n org=\\\"a\\\",\\n course=\\\"b\\\",\\n run=\\\"c\\\",\\n start=datetime.now(UTC),\\n default_store=module_store,\\n discussion_topics=topics\\n )\\n CourseEnrollmentFactory.create(user=self.user, course_id=course.id)\\n course_url = reverse(\\\"course_topics\\\", kwargs={\\\"course_id\\\": str(course.id)})\\n # add some discussion xblocks\\n for i in range(modules_count):\\n ItemFactory.create(\\n parent_location=course.location,\\n category='discussion',\\n discussion_id=f'id_module_{i}',\\n discussion_category=f'Category {i}',\\n discussion_target=f'Discussion {i}',\\n publish_item=False,\\n )\\n return course_url\",\n \"def generate_html(self):\\n html_text_1 = \\\"\\\"\\\"\\n <div class=\\\"concept\\\">\\n\\n \\t\\t<div class=\\\"concept-title\\\">\\n\\n \\t\\t\\t\\t\\\"\\\"\\\" + self.title\\n\\n html_text_2 = \\\"\\\"\\\"\\n \\t\\t</div>\\n\\n \\t\\t<div class=\\\"concept-description\\\">\\n\\n\\t\\t <p>\\n\\t\\t\\t\\n \\t\\t \\t\\t\\\"\\\"\\\" + self.description + \\\"\\\"\\\" \\n \\n </p>\\\"\\\"\\\"\\n\\n html_text_3 = '''\\n\\n \\t\\t</div>\\n\\n </div>'''\\n\\n return html_text_1 + html_text_2 + html_text_3\",\n \"def _latex_(self):\\n old = s = \\\"\\\\\\\\begin{verbatim}\\\\\\\\end{verbatim}\\\"\\n new = \\\"\\\"\\n s = \\\"\\\\\\\\begin{verbatim}\\\\\\\\end{verbatim}\\\"\\n s += \\\"Element of \\\" + self._space._latex_().replace(old, new) + \\\" With principal part \\\"\\n WR = self._space.multiplier()\\n # If we have more than one non-zero element in the principal part we have to\\n # add a + between terms\\n sp = \\\"\\\"\\n for (b, m) in self._principal_part:\\n a = self._principal_part[(b, m)]\\n if a != 0:\\n x = QQ(m + WR.Qv[WR.D.index(b)])\\n if a != 1:\\n if a > 0 and len(sp) > 0:\\n ast = \\\"+\\\" + str(a)\\n else:\\n ast = str(a)\\n sp = sp + ast + \\\"q^{\\\" + str(x) + \\\"}\\\"\\n else:\\n sp = sp + \\\"q^{\\\" + str(x) + \\\"}\\\"\\n s = s + sp + \\\"$.\\\"\\n return s\",\n \"def __str__(self) -> str:\\n\\n assert self.data is not None\\n title = \\\"{} ({})\\\".format(self.name, self.data[\\\"role\\\"][\\\"name\\\"])\\n name_border = \\\"-\\\" * len(title)\\n lines = [name_border, title, name_border]\\n\\n # build the talent-tree \\\"table\\\"\\n for row_idx, row_data in self.talent_rows.items():\\n lines.append(\\\"{}:\\\".format(row_idx))\\n for talent_idx, talent_data in row_data.items():\\n lines.append(\\n \\\" {}: {}\\\".format(talent_idx, talent_data[\\\"text\\\"])\\n )\\n macro = build_row_macro(row_idx, row_data)\\n if macro is not None:\\n lines.append(\\\"macro:\\\")\\n lines.append(macro[0])\\n\\n return os.linesep.join(lines)\",\n \"def get_next_course_message(i: int, courses: list) -> str:\\n course = (\\n None\\n if i + 1 >= len(courses)\\n or courses[i].time.day != courses[i + 1].time.day\\n else courses[i + 1]\\n )\\n\\n return (\\n notify_no_more_courses\\n if course is None\\n else notify_next_course_message.format(\\n course.name,\\n course.type,\\n course.time.start - courses[i].time.end,\\n \\\"?\\\" if course.classroom is None else course.classroom.number,\\n )\\n )\",\n \"def prescription(self):\\n prescription = \\\"\\\\n{0:>10}\\\\t{1:>10}\\\\t{2:>10}\\\\t{3:>10}\\\\n\\\".format(\\\"R\\\",\\\"Material\\\",\\\"d\\\",\\\"diameter\\\")\\n for surface in self.lensSurfaces():\\n prescription += \\\"{0:>10.2f}\\\\t{1:>10}\\\\t{2:>10.2f}\\\\t{3:>10.2f}\\\\n\\\".format(surface.R, str(surface.mat), surface.spacing, surface.diameter)\\n return prescription\",\n \"def print_output(filtered_courses_list):\\n\\n if not filtered_courses_list:\\n print(\\\"No courses matched the query.\\\")\\n return\\n\\n max_name_length = max([len(course.name) for course in filtered_courses_list])\\n\\n print(\\\" Sem | Course ID | Pts | \\\" +\\n \\\" \\\" * ((max_name_length - 11) // 2 + (max_name_length - 1) % 2) + \\\"Course Name\\\" + \\\" \\\" * ((max_name_length - 11) // 2) +\\n \\\" | Grade\\\")\\n print(\\\"______|___________|_____|\\\" + \\\"_\\\" * (max_name_length + 2) + \\\"|______\\\")\\n\\n for course in filtered_courses_list:\\n print(str(course.year) + course.semester, end=\\\" | \\\")\\n print(course.cid, end=\\\" | \\\")\\n print(course.creditpts, end=\\\" | \\\")\\n print(course.name, end=\\\" \\\" * (max_name_length - len(course.name) + 1) + \\\"| \\\")\\n\\n print(course.grade)\\n\\n print(\\\"\\\\nGPA: \\\" + (\\\"%.3f\\\" % calculate_weighted_average(filtered_courses_list)))\",\n \"def tex(self):\\n self.cardinality = self.question.sorted_answers_cardinality(\\n self.min_cardinality,\\n self.group_together,\\n self.group_by_letter_case,\\n self.group_by_slugify,\\n self.filter,\\n self.sort_answer,\\n other_question=self.other_question,\\n )\\n q1 = []\\n q2 = []\\n for answer_to_q1, cardinality_to_q2 in list(self.cardinality.items()):\\n for answer_to_q2, number_of_time in list(cardinality_to_q2.items()):\\n q1 += [answer_to_q1] * number_of_time\\n q2 += [answer_to_q2] * number_of_time\\n df = DataFrame(data={self.question.text: q1, self.other_question.text: q2})\\n name = \\\"tex/q{}_vs_q{}\\\".format(self.question.pk, self.other_question.pk)\\n sankey(df[self.question.text], df[self.other_question.text], aspect=20, fontsize=10, figureName=name)\\n return Question2TexSankey.TEX_SKELETON % (\\n name[4:],\\n self.question.pk,\\n self.other_question.pk,\\n self.get_caption(),\\n )\",\n \"def build_tex(self,signs='-',request_parens=False):\\n return self._build_tex(signs,request_parens)\",\n \"def create_courses():\\n\\n\\t# create list for courses\\n\\tallcourses = []\\n\\n\\t# load courses as classes in allcourses-list\\n\\twith open(\\\"../data/vakken.csv\\\", \\\"rt\\\") as coursefile:\\n\\n\\t\\t# clean text\\n\\t\\tcourses = csv.reader(coursefile)\\n\\t\\tfor row in courses:\\n\\t\\t\\tfor text in row:\\n\\t\\t\\t\\tcourse_info = text.split(\\\";\\\")\\n\\n\\t\\t\\t\\t# add course name\\n\\t\\t\\t\\tcourse_name = course_info[0]\\n\\n\\t\\t\\t\\t# add amount of lectures\\n\\t\\t\\t\\tcourse_lectures = course_info[1]\\n\\n\\t\\t\\t\\t# add amount of seminars\\n\\t\\t\\t\\tcourse_seminars = course_info[2]\\n\\n\\t\\t\\t\\t# add max amount seminars\\n\\t\\t\\t\\tcourse_max_sem = course_info[3]\\n\\t\\t\\t\\tif course_max_sem == \\\"nvt\\\":\\n\\t\\t\\t\\t\\tcourse_max_sem = 0\\n\\n\\t\\t\\t\\t# add amount of practicals\\n\\t\\t\\t\\tcourse_practicals = course_info[4]\\n\\n\\t\\t\\t\\t# add max amount practicals\\n\\t\\t\\t\\tcourse_max_prac = course_info[5]\\n\\t\\t\\t\\tif course_max_prac == \\\"nvt\\\":\\n\\t\\t\\t\\t\\tcourse_max_prac = 0\\n\\n\\t\\t\\t\\t# add course to list\\n\\t\\t\\t\\tallcourses.append(Course(course_name, course_lectures, course_seminars, course_max_sem, course_practicals, course_max_prac))\\n\\n\\treturn allcourses\",\n \"def detail_course(request, pk, template=\\\"core/detail_course.html\\\"):\\n response = {\\n 'course': get_object_or_404(Course, pk=pk)\\n }\\n return direct_to_template(request, template, response)\",\n \"def make_lexicon_txt(self):\\n raise NotImplementedError\",\n \"def template(title, steps, loops):\\n return \\\"%-8s %-20s %20d\\\" % (title, format_power(steps), loops)\",\n \"def Template(Fenetre_largeur,Fenetre_hauteur):\\r\\n li= Select_ligne(\\\"Nombre de lignes: \\\",Fenetre_largeur,Fenetre_hauteur)\\r\\n nom=\\\"Template\\\"\\r\\n fich=\\\"Template\\\"\\r\\n version=0\\r\\n while Path(\\\"stages/\\\"+fich+\\\".txt\\\").is_file() == True:\\r\\n version+=1\\r\\n fich=nom+str(version)\\r\\n fichier=open(\\\"stages/\\\"+fich+\\\".txt\\\",'w')\\r\\n fichier.write(str(li))\\r\\n fichier.write(\\\"\\\\n\\\")\\r\\n fichier.write(\\\"\\\\n\\\")\\r\\n for i in range(li):\\r\\n for j in range(10):\\r\\n fichier.write(\\\"0,0|\\\")\\r\\n fichier.write(\\\"\\\\n\\\")\\r\\n fichier.write(\\\"\\\\n\\\")\\r\\n fichier.write(\\\"gauche: resistance, droite: bonus\\\")\\r\\n fichier.write(\\\"\\\\n\\\")\\r\\n fichier.write(\\\"resistance max: 3\\\")\\r\\n fichier.write(\\\"\\\\n\\\")\\r\\n fichier.write(\\\"6=barre+\\\")\\r\\n fichier.write(\\\"\\\\n\\\")\\r\\n fichier.write(\\\"7=score+\\\")\\r\\n fichier.close()\",\n \"def course(self):\\n return self.section.course\",\n \"def make_final_text(text: str) -> str:\\n baseline_text_template = \\\"\\\"\\\"Use this thread to discuss anything (within the rules of the subreddit):\\n\\n* What you didn't think was worthy of its own post\\n* What club game you're most excited for\\n* Where you're staying to watch a friendly\\n* Which players should be called in\\n{}\\n* What the mods told you to re-post here\\n* Etc\\n\\n### Schedules\\n{}\\n\\\"\\\"\\\"\\n\\n with open('random_dumb_questions.txt', 'r') as files:\\n list_of_questions = files.readlines()\\n\\n question = list_of_questions[randint(0, len(list_of_questions))].replace('\\\\n', '')\\n\\n return baseline_text_template.format(question, text)\",\n \"def _create_course(self):\\r\\n super(TestPublish, self)._create_course(split=False)\\r\\n\\r\\n self._create_item('chapter', 'Chapter1', {}, {'display_name': 'Chapter 1'}, 'course', 'runid', split=False)\\r\\n self._create_item('chapter', 'Chapter2', {}, {'display_name': 'Chapter 2'}, 'course', 'runid', split=False)\\r\\n self._create_item('vertical', 'Vert1', {}, {'display_name': 'Vertical 1'}, 'chapter', 'Chapter1', split=False)\\r\\n self._create_item('vertical', 'Vert2', {}, {'display_name': 'Vertical 2'}, 'chapter', 'Chapter1', split=False)\\r\\n self._create_item('html', 'Html1', \\\"<p>Goodbye</p>\\\", {'display_name': 'Parented Html'}, 'vertical', 'Vert1', split=False)\\r\\n self._create_item(\\r\\n 'discussion', 'Discussion1',\\r\\n \\\"discussion discussion_category=\\\\\\\"Lecture 1\\\\\\\" discussion_id=\\\\\\\"a08bfd89b2aa40fa81f2c650a9332846\\\\\\\" discussion_target=\\\\\\\"Lecture 1\\\\\\\"/>\\\\n\\\",\\r\\n {\\r\\n \\\"discussion_category\\\": \\\"Lecture 1\\\",\\r\\n \\\"discussion_target\\\": \\\"Lecture 1\\\",\\r\\n \\\"display_name\\\": \\\"Lecture 1 Discussion\\\",\\r\\n \\\"discussion_id\\\": \\\"a08bfd89b2aa40fa81f2c650a9332846\\\"\\r\\n },\\r\\n 'vertical', 'Vert1',\\r\\n split=False\\r\\n )\\r\\n self._create_item('html', 'Html2', \\\"<p>Hellow</p>\\\", {'display_name': 'Hollow Html'}, 'vertical', 'Vert1', split=False)\\r\\n self._create_item(\\r\\n 'discussion', 'Discussion2',\\r\\n \\\"discussion discussion_category=\\\\\\\"Lecture 2\\\\\\\" discussion_id=\\\\\\\"b08bfd89b2aa40fa81f2c650a9332846\\\\\\\" discussion_target=\\\\\\\"Lecture 2\\\\\\\"/>\\\\n\\\",\\r\\n {\\r\\n \\\"discussion_category\\\": \\\"Lecture 2\\\",\\r\\n \\\"discussion_target\\\": \\\"Lecture 2\\\",\\r\\n \\\"display_name\\\": \\\"Lecture 2 Discussion\\\",\\r\\n \\\"discussion_id\\\": \\\"b08bfd89b2aa40fa81f2c650a9332846\\\"\\r\\n },\\r\\n 'vertical', 'Vert2',\\r\\n split=False\\r\\n )\\r\\n self._create_item('static_tab', 'staticuno', \\\"<p>tab</p>\\\", {'display_name': 'Tab uno'}, None, None, split=False)\\r\\n self._create_item('about', 'overview', \\\"<p>overview</p>\\\", {}, None, None, split=False)\\r\\n self._create_item('course_info', 'updates', \\\"<ol><li><h2>Sep 22</h2><p>test</p></li></ol>\\\", {}, None, None, split=False)\",\n \"def __init__(self, tutorial):\\n self._tex = \\\"\\\"\\n\\n self.texPreamble(tutorial)\\n self.texTutorialHeader(tutorial)\\n\\n for lecture in self.children(tutorial):\\n self.texLectureHeader(lecture)\\n\\n for slide in self.children(lecture):\\n self.texSlideHeader(slide)\\n for section in slide.sections:\\n self.texSlideSection(section)\\n self.texSlideFooter(slide)\\n\\n self.texLectureFooter(lecture)\\n\\n self.texTutorialFooter(tutorial)\\n self.texPostamble(tutorial)\",\n \"def __str__(self):\\n if self.tabletitle:\\n s = [\\\"<a name='%s'>%s</a><P>\\\" % (self.tabletitle, self.tabletitle)]\\n else:\\n s = []\\n\\n s.append('<TABLE border=%s cellpadding=%s cellspacing=%s width=\\\"%s\\\">\\\\n' % \\\\\\n (self.border, self.cell_padding, self.cell_spacing, self.width))\\n if self.tabletitle:\\n s.append('<CAPTION align=%s><STRONG>%s</STRONG></CAPTION>\\\\n' % \\\\\\n (self.caption_align, self.tabletitle))\\n\\n for i in range(len(self.body)):\\n for j in range(len(self.body[i])):\\n if type(self.body[i][j]) == type(''):\\n #process cell contents to insert breaks for \\\\n char.\\n if self.cell_line_breaks:\\n self.body[i][j] = self.body[i][j].replace('\\\\n','<br>')\\n else:\\n self.body[i][j] = Text(self.body[i][j])\\n\\n # Initialize colspan property to 1 for each\\n # heading column if user doesn't provide it.\\n if self.heading:\\n if not self.colspan:\\n if type(self.heading[0]) == list:\\n self.colspan = [1]*len(self.heading[0])\\n else:\\n self.colspan = [1]*len(self.heading)\\n # Construct heading spec\\n # can handle multi-row headings. colspan is a list specifying how many\\n # columns the i-th element should span. Spanning only applies to the first\\n # or only heading line.\\n if self.heading:\\n prefix = '<TR Align=' + self.heading_align + '> '\\n postfix = '</TR>\\\\n'\\n middle = ''\\n if type(self.heading[0]) == type([]):\\n for i in range(len(self.heading[0])):\\n middle = middle + '<TH ColSpan=%s%s>' % \\\\\\n (self.colspan[i], \\\\\\n self.get_body_color(self.heading_color,i)) \\\\\\n + str(self.heading[0][i]) +'</TH>'\\n s.append(prefix + middle + postfix)\\n for i in range(len(self.heading[1])):\\n middle = middle + '<TH>' + str(self.heading[i]) +'</TH>'\\n for heading_row in self.heading[1:]:\\n for i in range(len(self.heading[1])):\\n middle = middle + '<TH>' + heading_row[i] +'</TH>'\\n s.append(prefix + middle + postfix)\\n else:\\n for i in range(len(self.heading)):\\n middle = middle + '<TH ColSpan=%s%s>' % \\\\\\n (self.colspan[i], \\\\\\n self.get_body_color(self.heading_color,i)) \\\\\\n + str(self.heading[i]) +'</TH>'\\n s.append(prefix + middle + postfix)\\n # construct the rows themselves\\n stmp = '<TD Align=%s %s>'\\n for row in self.body:\\n s.append('<TR>')\\n for i in range(len(row)):\\n if i == 0 :\\n ss1 = self.column1_align\\n else:\\n ss1 = self.cell_align\\n s.append(stmp % (ss1, self.get_body_color(self.body_color,i)))\\n s.append(str(row[i]))\\n s.append('</TD>\\\\n')\\n s.append('</TR>\\\\n')\\n #close table\\n s.append('</TABLE><P>\\\\n')\\n return ''.join(s)\",\n \"def create_opl_mod_output_text(tdf):\\n return _create_opl_mod_text(tdf, True)\",\n \"def create_course(self, org, offering, user_id=None, fields=None, **kwargs):\\r\\n\\r\\n course, _, run = offering.partition('/')\\r\\n course_id = SlashSeparatedCourseKey(org, course, run)\\r\\n\\r\\n # Check if a course with this org/course has been defined before (case-insensitive)\\r\\n course_search_location = SON([\\r\\n ('_id.tag', 'i4x'),\\r\\n ('_id.org', re.compile(u'^{}$'.format(course_id.org), re.IGNORECASE)),\\r\\n ('_id.course', re.compile(u'^{}$'.format(course_id.course), re.IGNORECASE)),\\r\\n ('_id.category', 'course'),\\r\\n ])\\r\\n courses = self.collection.find(course_search_location, fields=('_id'))\\r\\n if courses.count() > 0:\\r\\n raise InvalidLocationError(\\r\\n \\\"There are already courses with the given org and course id: {}\\\".format([\\r\\n course['_id'] for course in courses\\r\\n ]))\\r\\n\\r\\n location = course_id.make_usage_key('course', course_id.run)\\r\\n course = self.create_and_save_xmodule(location, fields=fields, **kwargs)\\r\\n\\r\\n # clone a default 'about' overview module as well\\r\\n about_location = location.replace(\\r\\n category='about',\\r\\n name='overview'\\r\\n )\\r\\n overview_template = AboutDescriptor.get_template('overview.yaml')\\r\\n self.create_and_save_xmodule(\\r\\n about_location,\\r\\n system=course.system,\\r\\n definition_data=overview_template.get('data')\\r\\n )\\r\\n\\r\\n return course\",\n \"def to_string(self, t = None, title = None):\\n if title:\\n print title\\n column_width = self.max_cell_print_len()\\n cell_hline = [ '-' for i in range(column_width + 2) ] + ['|']\\n cell_hline = ''.join(cell_hline)\\n hline = ['|'] + [ cell_hline for i in range(self.width + 1) ] + ['\\\\n']\\n hline = ''.join(hline)\\n slist = []\\n if len(self.agents) > 0:\\n slist += ['Scores:']\\n for agent in self.agents:\\n slist.append(' {0}={1}'.format(agent, agent.performance_measure))\\n\\n if len(self.agents) > 0:\\n slist.append('\\\\n')\\n for c in range(0, self.width + 1):\\n spacer = ''.join([ ' ' for i in range(column_width - 1) ])\\n slist.append(' {0}{1} '.format(c, spacer))\\n\\n slist.append(' time_step={0}'.format(t if t else self.time_step))\\n slist.append('\\\\n')\\n slist.append(hline)\\n for r in range(self.height, -1, -1):\\n for c in range(0, self.width + 1):\\n things_at = self.list_things_at((c, r))\\n cell_width = 0\\n for thing_at in things_at:\\n cell_width += len(thing_at.to_string())\\n\\n spacer = ''.join([ ' ' for i in range(column_width - cell_width) ])\\n slist.append('| ')\\n for thing in things_at:\\n slist.append(thing.to_string())\\n\\n slist.append(spacer + ' ')\\n\\n slist.append('| {0}\\\\n'.format(r))\\n slist.append(hline)\\n\\n return ''.join(slist)\",\n \"def test_custom_decorator_displaytex_ok(self):\\n\\n tex = \\\"\\\\left.\\\\frac{x^3}{3}\\\\right|_0^1\\\" # noqa: W605\\n\\n self.assertEqual(\\n DOM.render(\\n DOM.create_element(\\n ashley_render_children,\\n {\\n \\\"block\\\": {\\n \\\"key\\\": \\\"a215p\\\",\\n \\\"text\\\": \\\"\\\",\\n \\\"type\\\": \\\"atomic\\\",\\n \\\"data\\\": {\\\"tex\\\": tex, \\\"type\\\": \\\"TEXBLOCK\\\"},\\n }\\n },\\n )\\n ),\\n f'<span class=\\\"ashley-latex-display\\\">{tex}</span>',\\n )\",\n \"def _define_course_email(self):\\r\\n course_email = CourseEmail.create(self.course.id, self.instructor, SEND_TO_ALL, \\\"Test Subject\\\", \\\"<p>This is a test message</p>\\\")\\r\\n return course_email.id # pylint: disable=E1101\\r\",\n \"def tikzcode(self):\\n tex = \\\"\\\"\\n tex += r\\\"\\\\draw\\\"\\n if len(self.options):\\n options = ', '.join(self.options)\\n tex += \\\"[{options}] \\\".format(options=options)\\n tex += \\\"({a.xpos:.4f},{a.ypos:.4f}) \\\".format(a=self.node_a)\\n tex += \\\"to\\\"\\n # if the nodes are arranged, then they have angle in/out\\n inout = []\\n inout.append('out={angle!s}'.format(angle=self.node_a.angle_inout))\\n inout.append('in={angle!s}'.format(angle=self.node_b.angle_inout))\\n if inout:\\n tex += \\\"[\\\" + \\\", \\\".join(inout) + \\\"] \\\"\\n tex += \\\"({b.xpos:.4f},{b.ypos:.4f})\\\".format(b=self.node_b)\\n tex += \\\";\\\\n\\\"\\n return tex\",\n \"def latex_info(info):\\n return '\\\\\\\\textbf{' + info['heading'] + '} ' + info['value'] + ' \\\\\\\\break\\\\n'\",\n \"def __str__(self):\\n\\n # can't use backslash in f-string\\n newline = \\\"\\\\n\\\"\\n\\n options = self.options\\n kwoptions = self.kwoptions\\n\\n # add background to options\\n if self.background:\\n options.append(\\\"show background rectangle\\\")\\n kwoptions.update({\\\"background rectangle/.style\\\": f\\\"{{fill={self.background}}}\\\"})\\n\\n return rf\\\"\\\"\\\"\\\\begin{{tikzpicture}}{wrap(fmt_options(self.options, self.kwoptions))}\\n{newline.join(define_colours(self._used_colours))}\\n{newline.join(self.defines)}\\n{newline.join(self._commands)}\\n\\\\end{{tikzpicture}}\\\"\\\"\\\"\",\n \"def _create_course(self):\\r\\n # If the course already exists, this will respond\\r\\n # with a 200 and an error message, which we ignore.\\r\\n response = self.session.post(\\r\\n STUDIO_BASE_URL + '/course/',\\r\\n data=self._encode_post_dict(self._course_dict),\\r\\n headers=self.headers\\r\\n )\\r\\n\\r\\n try:\\r\\n err = response.json().get('ErrMsg')\\r\\n\\r\\n except ValueError:\\r\\n raise CourseFixtureError(\\r\\n \\\"Could not parse response from course request as JSON: '{0}'\\\".format(\\r\\n response.content))\\r\\n\\r\\n # This will occur if the course identifier is not unique\\r\\n if err is not None:\\r\\n raise CourseFixtureError(\\\"Could not create course {0}. Error message: '{1}'\\\".format(self, err))\\r\\n\\r\\n if not response.ok:\\r\\n raise CourseFixtureError(\\r\\n \\\"Could not create course {0}. Status was {1}\\\".format(\\r\\n self._course_dict, response.status_code))\",\n \"def create_opl_mod_text(tdf):\\n return _create_opl_mod_text(tdf, False)\",\n \"def __str__(self):\\n print('=' * 20, \\\"Subject Information\\\", '=' * 20)\\n print(\\\"Subject Name: {}\\\".format(self.name))\\n print(\\\"Pulse Data Length for general questions\\\")\\n print(self.pulse_length[0:20])\\n print(\\\"Number of general Questions: {}\\\".format(\\n len(self.pulse_data[0])))\\n print(\\\"Pulse Data Length for video 1\\\")\\n print(\\\"Number of questions for video 1: {}\\\".format(\\n len(self.pulse_data[1])))\\n print(self.pulse_length[20:40])\\n print(\\\"Pulse Data Length for video 2\\\")\\n print(\\\"Number of questions for video 2: {}\\\".format(\\n len(self.pulse_data[0])))\\n print(self.pulse_length[40:60])\\n print('Label Data')\\n print(self.label_data)\\n print('Label Data shape: {}'.format(self.label_data.shape))\\n\\n return ''\",\n \"def subtitle(self, txt):\\n num = len(txt)\\n ticks = \\\"-\\\" * num\\n print(txt)\\n print(ticks)\"\n]","isConversational":false},"negative_scores":{"kind":"list like","value":["0.6946371","0.6540668","0.63720375","0.62898386","0.6107234","0.6071788","0.5923579","0.585324","0.5844796","0.58082366","0.5749922","0.5556818","0.54912657","0.53892386","0.5379833","0.53704685","0.53665537","0.5356205","0.53560764","0.5349088","0.5335669","0.53247696","0.5314801","0.5314547","0.5305893","0.52961653","0.5255212","0.52499","0.52436703","0.5241185","0.52302986","0.52282137","0.5227981","0.5177225","0.51455003","0.51416457","0.5136286","0.51354676","0.51343274","0.5130823","0.5124687","0.51240605","0.5117245","0.5089371","0.508763","0.50844175","0.5081672","0.50529116","0.50489855","0.50472677","0.50421655","0.5039125","0.50300723","0.50278103","0.49971277","0.4991185","0.49851674","0.49803826","0.49744385","0.4962428","0.49529245","0.49522823","0.49412742","0.49323118","0.4929941","0.49271306","0.49176642","0.4911273","0.49070954","0.4899071","0.489436","0.48884952","0.488122","0.48805118","0.4874955","0.48747256","0.48744667","0.48730305","0.48730174","0.48724917","0.48670527","0.48654544","0.48589754","0.48482275","0.48475227","0.48377782","0.48363918","0.48361707","0.4830752","0.48294994","0.4829002","0.48263046","0.4823971","0.48200837","0.48181868","0.48175782","0.48157057","0.48134056","0.48132303","0.4812984"],"string":"[\n \"0.6946371\",\n \"0.6540668\",\n \"0.63720375\",\n \"0.62898386\",\n \"0.6107234\",\n \"0.6071788\",\n \"0.5923579\",\n \"0.585324\",\n \"0.5844796\",\n \"0.58082366\",\n \"0.5749922\",\n \"0.5556818\",\n \"0.54912657\",\n \"0.53892386\",\n \"0.5379833\",\n \"0.53704685\",\n \"0.53665537\",\n \"0.5356205\",\n \"0.53560764\",\n \"0.5349088\",\n \"0.5335669\",\n \"0.53247696\",\n \"0.5314801\",\n \"0.5314547\",\n \"0.5305893\",\n \"0.52961653\",\n \"0.5255212\",\n \"0.52499\",\n \"0.52436703\",\n \"0.5241185\",\n \"0.52302986\",\n \"0.52282137\",\n \"0.5227981\",\n \"0.5177225\",\n \"0.51455003\",\n \"0.51416457\",\n \"0.5136286\",\n \"0.51354676\",\n \"0.51343274\",\n \"0.5130823\",\n \"0.5124687\",\n \"0.51240605\",\n \"0.5117245\",\n \"0.5089371\",\n \"0.508763\",\n \"0.50844175\",\n \"0.5081672\",\n \"0.50529116\",\n \"0.50489855\",\n \"0.50472677\",\n \"0.50421655\",\n \"0.5039125\",\n \"0.50300723\",\n \"0.50278103\",\n \"0.49971277\",\n \"0.4991185\",\n \"0.49851674\",\n \"0.49803826\",\n \"0.49744385\",\n \"0.4962428\",\n \"0.49529245\",\n \"0.49522823\",\n \"0.49412742\",\n \"0.49323118\",\n \"0.4929941\",\n \"0.49271306\",\n \"0.49176642\",\n \"0.4911273\",\n \"0.49070954\",\n \"0.4899071\",\n \"0.489436\",\n \"0.48884952\",\n \"0.488122\",\n \"0.48805118\",\n \"0.4874955\",\n \"0.48747256\",\n \"0.48744667\",\n \"0.48730305\",\n \"0.48730174\",\n \"0.48724917\",\n \"0.48670527\",\n \"0.48654544\",\n \"0.48589754\",\n \"0.48482275\",\n \"0.48475227\",\n \"0.48377782\",\n \"0.48363918\",\n \"0.48361707\",\n \"0.4830752\",\n \"0.48294994\",\n \"0.4829002\",\n \"0.48263046\",\n \"0.4823971\",\n \"0.48200837\",\n \"0.48181868\",\n \"0.48175782\",\n \"0.48157057\",\n \"0.48134056\",\n \"0.48132303\",\n \"0.4812984\"\n]","isConversational":false},"document_score":{"kind":"string","value":"0.7629145"},"document_rank":{"kind":"string","value":"0"}}},{"rowIdx":22671,"cells":{"query":{"kind":"string","value":"Creates the TeX document from the Computer Science Course Catalog"},"document":{"kind":"string","value":"def create_tex(unwanted_courses, wanted_courses=None):\n page = requests.get(\n 'http://gla.ac.uk/coursecatalogue/courselist/' +\n '?code=REG30200000&name=School+of+Computing+Science')\n tree = html.fromstring(page.content)\n spans = tree.xpath('//span/text()')\n codes = []\n if wanted_courses is None:\n for s in spans:\n if s[0:4] == \"COMP\" and s[7] == '4' and s not in unwanted_courses:\n codes.append(s)\n else:\n for s in wanted_courses:\n codes.append(s)\n write_to_latex(codes, unwanted_courses)\n return None"},"metadata":{"kind":"string","value":"{\n \"objective\": {\n \"self\": [],\n \"paired\": [],\n \"triplet\": [\n [\n \"query\",\n \"document\",\n \"negatives\"\n ]\n ]\n }\n}"},"negatives":{"kind":"list like","value":["def makeCourse( xmlFile, genPath, importPaths, commonFiles, rendererContent=True):\n\ttry:\n\n\t\t# parse the command line\n\t\tConfig.add_option('--verbose', help='Set verbosity to maximum', dest='verbosity', default=0, action='store_const', const=2)\n\t\tConfig.add_option('-v', '--verbosity', help='Set the verbosity level (0: quiet, 1: display the command lines, 2: display command lines and their outputs', dest='verbosity', default=0, type=int)\n\t\tConfig.add_option('-d', '--debug', help='Create the files in the debug/ folder, instead of in a temporary one', dest='debug', action='store_true', default=False)\n\t\tConfig.add_option('-f', '--force', help='Force the generation of the documents, even if nothing changes from last run', dest='force', action='store_true', default=False)\n\t\tConfig.add_option('-q', '--quick', help='Quick pdf generation (do not compile twice the latex, do not produce handout, etc.)', dest='quick', action='store_true', default=False)\n\t\tConfig.add_option('-w', '--wordpress', help='Publish to wordpress', dest='wordpress', default=False, action='store_true')\n\t\tConfig.add_option('-c', '--HTMLcorrection', help='Display an HTML correction', dest='HTMLcorrection', default=False, action='store_true')\n\t\tConfig.add_option('-s', '--shared', help='Copy the required files to the <shared> path (via ssh)', default=False,\taction='store_true')\n\t\tConfig.parse()\n\t\targs = Config.args\n\t\toptions = Config.options\n\t\tConfig.importPaths = importPaths \n\t\tConfig.commonFiles = commonFiles\n\t\tConfig.allSessions = { x.__name__:x for x in Session.__subclasses__()}\t# list of the created session classes\n\t\tConfig.rendererContent = rendererContent\n\t\t\n\t\t# clean the debug directory in debug mode\n\t\tbasePath = os.path.abspath('.')+'/'\t\t\t# base path (from where the script is run, because the path are relative)\n\t\tif options.debug:\n\t\t\tif os.path.exists('debug/'):\n\t\t\t\trunCommand(['rm','-rf','debug/'])\n\n\t\t# open and parse the course file\n\t\twith codecs.open(xmlFile, encoding='utf-8') as f:\n\t\t\tbs = BeautifulSoup(f, features=\"xml\")\n\n\n\t\t# build the recursively the sessions\n\t\ttop = createTagSession( bs, father=None )\t\t# bs.contents[0]\n\t\tsessionsToBuild = Session.sessionsToBuild\t\t# get the list of the sessions object\n\t\t\n\n\t\t\"\"\"\n\t\timportFiles( bs.contents[0], importPaths)\n\n\t\t# get the list of sessions we can build (with a 'make' method)\n\t\tbuildableSessions = { x.__name__:x for x in Session.__subclasses__() if 'make' in x.__dict__ }\n\n\t\t#This set the PATH for PyDev only...\n\t\tos.environ['PATH'] = os.environ['PATH']+':'+os.getenv('PATH')\n\n\n\t\t# build the list of Sessions to build\n\t\tsessionsToBuild = []\n\t\tfor name,session in buildableSessions.items():\n\t\t\tsessionsToBuild.extend( session(tag, commonFiles) for tag in bs(name) )\n\t\t\"\"\"\n\t\t\n\t\t\n\n\t\t# if possible, load the previous xml file, and look for the differences\n\t\tdirName,baseName = split(xmlFile) \n\t\ttry:\n\t\t\twith open(dirName+\"/.\"+baseName+\".makeCourse\", \"rb\") as f:\n\t\t\t\tdata = load( f )\n\t\t\t\tfor s in sessionsToBuild:\n\t\t\t\t\tif s.name in data:\n\t\t\t\t\t\ts.checkDifferences( data[s.name] )\n\t\texcept IOError:\n\t\t\tpass\n\n\n\t\t# build every argument in the command line arguments\n\t\tsomethingHasBeDone = False\n\t\tfor s in sessionsToBuild:\n\t\t\tif (not args) or (\"all\" in args) or (s.name in args) or (s.type in args):\n\t\t\t\t\n\t\t\t\tcd( basePath)\n\t\t\t\t\n\t\t\t\t# check if something has to be done\n\t\t\t\tif s.shouldBeMake(basePath+'/'+genPath, options) or options.force:\n\t\t\t\t\tsomethingHasBeDone = True\n\n\t\t\t\t\t#Make one build (TP, course, etc.)\n\t\t\t\t\tprint ( Fore.BLUE+\"*) Make \"+Style.BRIGHT+s.name+Fore.RESET+Style.NORMAL)\n\n\t\t\t\t\t# make temp directory and copy all the file in resources dir\n\t\t\t\t\tif options.debug:\n\t\t\t\t\t\ttmp = \"debug/\"+s.name+'/'\n\t\t\t\t\t\tcreateDirectory(tmp)\n\t\t\t\t\telse:\n\t\t\t\t\t\ttmp = mkdtemp()\n\n\t\t\t\t\ts.prepareResources(tmp )\n\t\t\t\t\tcd( tmp)\n\n\t\t\t\t\t# call the custom function associated with the type, to produce the documents\n\t\t\t\t\ts.make(options)\n\n\t\t\t\t\t# then move the files in the right place\n\t\t\t\t\tfor f in s.files(options):\n\t\t\t\t\t\tcreateDirectory( basePath+'/'+genPath.format( **s.dict ) )\n\t\t\t\t\t\tnewFile = basePath+'/'+genPath.format( **s.dict )+f\n\t\t\t\t\t\tif not os.path.exists(f):\n\t\t\t\t\t\t\tprint( Fore.YELLOW+'The file '+f+' has not been created by '+s.type+' function !'+Fore.RESET)\n\t\t\t\t\t\trunCommand( ['cp', f, newFile])\n\n\t\t\t\t\t# del the temporary directory or clean debug directory\n\t\t\t\t\tif not options.debug:\n\t\t\t\t\t\trunCommand( ['rm', '-rf', tmp])\n\t\t\t\telse:\n\t\t\t\t\tif options.verbosity>0:\n\t\t\t\t\t\tprint( Fore.BLUE + \"*) Nothing changed for \"+Style.BRIGHT+s.name+Style.NORMAL+\", skipped\"+Fore.RESET)\n\n\n\n\t\tif not somethingHasBeDone:\n\t\t\tprint( Fore.BLUE + \"Nothing has changed, nothing to do, so nothing has been done...\" + Fore.RESET)\n\n\n\t\t# save the data file\n\t\tdata = {L.name: {key:md5(str(val).encode('utf-8')).hexdigest() for key,val in L.dict.items()} for L in sessionsToBuild }\n\t\tcd( basePath)\n\t\twith open(dirName+\"/.\"+baseName+\".makeCourse\", 'wb') as f:\n\t\t\tdump( data, f)\n\n\n\n\n\n\n\n\texcept mkcException as err:\n\t\tprint( err )","def parse_create_course(xml_course):\n attrs = [\n \"term-code\",\n \"term-description\",\n 'subject',\n \"course-number\",\n \"school\",\n \"department\",\n \"title\",\n \"description\",\n \"credit-hours\",\n \"distribution-group\"\n ]\n course = pull_attributes_from_xml(xml_course, attrs)\n course[\"sections\"] = []\n\n return course","def latex_course(course):\n basic_info_list = [\n 'session', 'school', 'credits', 'level', 'offered',\n 'visiting_students', 'erasmus_students'\n ]\n generic_subsection_list = [\n 'description', 'timetable', 'requirements_of_entry',\n 'excluded_courses', 'co_requisites', 'assessment_weighting'\n ]\n string = '\\\\subsection{' + course[\"title\"] + '}\\n'\n for info in basic_info_list:\n string += latex_info(course[info])\n for subsection in generic_subsection_list:\n string += latex_subsection(course[subsection])\n string += '\\\\break \\\\textbf{' + course['assessment_date'][\n 'heading'] + '}' + course['assessment_date']['value'] + '\\n'\n string += latex_subsection(course['aims'])\n string += '\\\\subsubsection*{' + \\\n course['learning_outcomes']['heading'] + '}\\n'\n outcome_list = re.split(\n '\\d+\\. ', course['learning_outcomes']['value'])\n string += outcome_list[0] + '\\n'\n string += '\\\\begin{enumerate}\\n'\n for i in outcome_list[1:-1]:\n string += '\\\\item ' + i + '\\n'\n string += '\\\\end{enumerate}\\n'\n return string","def write_to_latex(codelist, unwanted_courses):\n # TODO: investigate a way to add large amounts of text outside of the\n # function\n abstract01 = \"I created this document to practice parsing html and using\\\n tools like Beautiful Soup which I've previously had little experience\\\n in. As a result, it's not perfect.\\\\newline\\\n It is also a slightly condensed all-in-one-place look at a selection\\\n of courses that are available for fourth year computer science\\\n students at the University of Glasgow. For the purposes of clarity I\\\n have removed several courses from this selection. The following\\\n courses have been omitted:\"\n abstract02 = \"For more insight into the project, to report issues or to\\\n inspect the code, have a look at the GitHub:\\\n \\\\url{https://github.com/IS0metric/course-ripper}\"\n unincluded = create_not_included_list(unwanted_courses)\n with open('courses.tex', 'w') as f:\n # TODO Try and move all this to a separate function?\n # TODO: Check if it's more efficient to write a single, massive string\n # to file\n f.write('\\\\documentclass{hitec}\\n')\n f.write('\\\\usepackage[document]{ragged2e}\\n')\n f.write('\\\\usepackage{url}\\n')\n f.write('\\\\usepackage{hyperref}\\n')\n f.write('\\\\setcounter{tocdepth}{4}\\n')\n f.write('\\\\begin{document}\\n')\n f.write('\\\\title{Fourth Year (2016-17) Courses}\\n')\n f.write('\\\\author{Jack Parkinson}\\n')\n f.write('\\\\date{August 2016}\\n')\n f.write('\\\\maketitle\\n')\n f.write('\\\\abstract{' + abstract01 + unincluded + abstract02 + '}\\n')\n f.write('\\\\newpage\\n\\n')\n f.write('\\\\tableofcontents\\n')\n f.write('\\\\newpage\\n\\n')\n # TODO: Look into alternatives to the three lists\n all_courses = []\n sem1_courses = []\n sem2_courses = []\n for code in codelist:\n course = bsoup(get_coursepage(code))\n if course['offered']['value'] == 'Runs Throughout Semesters 1 and 2':\n all_courses.append(course)\n elif \"1\" in course['offered']['value']:\n sem1_courses.append(course)\n elif \"2\" in course['offered']['value']:\n sem2_courses.append(course)\n f.write('\\\\section{Semester 1 and 2 Courses}\\n\\n')\n for course in all_courses:\n f.write(latex_course(course))\n f.write('\\\\section{Semester 1 Only Courses}\\n\\n')\n for course in sem1_courses:\n f.write(latex_course(course))\n f.write('\\\\section{Semester 2 Only Courses}\\n\\n')\n for course in sem2_courses:\n f.write(latex_course(course))\n f.write('\\\\end{document}')\n return None","def createCourse():\n\tif request.method == 'POST':\n\t\tcname = request.form['cname']\n\t\tcourseterm = request.form['courseterm']\n\t\tcoursepoint = request.form['coursepoint']\n\t\tcoursetype = request.form['coursetype']\t\n\t\tcourseyear = request.form['courseyear']\t\n\t\ttname = request.form['tname']\t\n\t\terror = None\n\n\t\tif not cname:\n\t\t\terror = 'Course name is required.'\n\t\telif not courseterm:\n\t\t\terror = 'Course term is required'\n\t\telif not courseterm:\n\t\t\terror = 'Course point is required'\n\n\t\tif error is not None:\n\t\t\tflash(error)\n\t\telse:\n\t\t\tdb = get_db()\n\t\t\tcur = db.cursor()\n\t\t\tcur.execute(\n\t\t\t\t'INSERT INTO course (cname, courseyear, coursetype, courseterm, coursepoint, tname)'\n\t\t\t\t' VALUES (%s, %s, %s, %s, %s, %s)',\n\t\t\t\t(cname, courseyear, coursetype, courseterm, coursepoint, tname)\n\t\t\t)\n\t\t\tdb.commit()\n\t\t\treturn redirect(url_for('info.index'))\n\n\treturn render_template('info/createCourse.html')","def _create_course(self):\r\n super(TestOrphan, self)._create_course()\r\n\r\n self._create_item('chapter', 'Chapter1', {}, {'display_name': 'Chapter 1'}, 'course', 'runid')\r\n self._create_item('chapter', 'Chapter2', {}, {'display_name': 'Chapter 2'}, 'course', 'runid')\r\n self._create_item('chapter', 'OrphanChapter', {}, {'display_name': 'Orphan Chapter'}, None, None)\r\n self._create_item('vertical', 'Vert1', {}, {'display_name': 'Vertical 1'}, 'chapter', 'Chapter1')\r\n self._create_item('vertical', 'OrphanVert', {}, {'display_name': 'Orphan Vertical'}, None, None)\r\n self._create_item('html', 'Html1', \"<p>Goodbye</p>\", {'display_name': 'Parented Html'}, 'vertical', 'Vert1')\r\n self._create_item('html', 'OrphanHtml', \"<p>Hello</p>\", {'display_name': 'Orphan html'}, None, None)\r\n self._create_item('static_tab', 'staticuno', \"<p>tab</p>\", {'display_name': 'Tab uno'}, None, None)\r\n self._create_item('about', 'overview', \"<p>overview</p>\", {}, None, None)\r\n self._create_item('course_info', 'updates', \"<ol><li><h2>Sep 22</h2><p>test</p></li></ol>\", {}, None, None)","def _createCoursesTable(self):\n\t\tcommand = \"\"\"CREATE TABLE courses (ID INTEGER PRIMARY KEY,\n\t\t\tname TEXT,\n\t\t\tauthor_id INTEGER,\n\t\t\tdescription TEXT\n\t\t\t);\n\"\"\"\n\n\t\tself._run_command(command)","def pdflatex(unwanted_courses):\n create_tex(unwanted_courses)\n cmd = ['pdflatex', '-interaction', 'nonstopmode', 'courses.tex']\n proc = subprocess.Popen(cmd)\n proc.communicate()\n return None","def create_courses():\n\n\t# create list for courses\n\tallcourses = []\n\n\t# load courses as classes in allcourses-list\n\twith open(\"../data/vakken.csv\", \"rt\") as coursefile:\n\n\t\t# clean text\n\t\tcourses = csv.reader(coursefile)\n\t\tfor row in courses:\n\t\t\tfor text in row:\n\t\t\t\tcourse_info = text.split(\";\")\n\n\t\t\t\t# add course name\n\t\t\t\tcourse_name = course_info[0]\n\n\t\t\t\t# add amount of lectures\n\t\t\t\tcourse_lectures = course_info[1]\n\n\t\t\t\t# add amount of seminars\n\t\t\t\tcourse_seminars = course_info[2]\n\n\t\t\t\t# add max amount seminars\n\t\t\t\tcourse_max_sem = course_info[3]\n\t\t\t\tif course_max_sem == \"nvt\":\n\t\t\t\t\tcourse_max_sem = 0\n\n\t\t\t\t# add amount of practicals\n\t\t\t\tcourse_practicals = course_info[4]\n\n\t\t\t\t# add max amount practicals\n\t\t\t\tcourse_max_prac = course_info[5]\n\t\t\t\tif course_max_prac == \"nvt\":\n\t\t\t\t\tcourse_max_prac = 0\n\n\t\t\t\t# add course to list\n\t\t\t\tallcourses.append(Course(course_name, course_lectures, course_seminars, course_max_sem, course_practicals, course_max_prac))\n\n\treturn allcourses","def _course_factory_create_course():\r\n return CourseFactory.create(org='MITx', course='999', display_name='Robot Super Course')","def main():\n\n #Courses\n years = [2016, 2017, 2018, 2019, 2020]\n with sqlite3.connect(\"determined.db\") as conn:\n c = conn.cursor()\n c.execute(\n \"\"\"\n CREATE TABLE \"course\" (\n \"course_number\"\tint NOT NULL,\n \"dept_id\"\tvarchar(4) NOT NULL,\n \"title\"\tvarchar(100) NOT NULL,\n \"instructor_fname\"\tvarchar(35) DEFAULT NULL,\n \"instructor_lname\"\tvarchar(35) DEFAULT NULL,\n \"student_work_products\"\tjson DEFAULT NULL,\n `term` varchar(7) NOT NULL,\n `year` int NOT NULL,\n PRIMARY KEY(\"course_number\", \"term\", \"year\")) \n \"\"\"\n )\n conn.commit()\n courses = [\n (1370, \"CPSC\", \"Computer Literacy\", names.get_first_name(), names.get_last_name(), json.dumps({\"swp\": [\"Midterm\", \"Final Exam\", \"Project 1\"]}), \"Fall\"),\n (1375, \"CPSC\", \"Programming I\", names.get_first_name(), names.get_last_name(), json.dumps({\"swp\": [\"Midterm\", \"Final Exam\", \"Project 1\"]}), \"Fall\"),\n (2376, \"CPSC\", \"Intro to Game Programming\", names.get_first_name(), names.get_last_name(), json.dumps({\"swp\": [\"Midterm\", \"Final Exam\", \"Project 1\"]}), \"Fall\"),\n (2380, \"CPSC\", \"Algorithms\", names.get_first_name(), names.get_last_name(), json.dumps({\"swp\": [\"Midterm\", \"Final Exam\", \"Project 1\"]}), \"Fall\"),\n (2482, \"CPSC\", \"Computer Organization\", names.get_first_name(), names.get_last_name(), json.dumps({\"swp\": [\"Midterm\", \"Final Exam\", \"Project 1\"]}), \"Spring\"),\n (3377, \"CPSC\", \"Advanced Game Programming\", names.get_first_name(), names.get_last_name(), json.dumps({\"swp\": [\"Midterm\", \"Final Exam\", \"Project 1\"]}), \"Spring\"),\n (3380, \"CPSC\", \"Operating Systems\", names.get_first_name(), names.get_last_name(), json.dumps({\"swp\": [\"Midterm\", \"Final Exam\", \"Project 1\"]}), \"Spring\"),\n (3383, \"CPSC\", \"Programming Languages\", names.get_first_name(), names.get_last_name(), json.dumps({\"swp\": [\"Midterm\", \"Final Exam\", \"Project 1\"]}), \"Spring\"),\n (3384, \"CPSC\", \"Computer Networks\", names.get_first_name(), names.get_last_name(), json.dumps({\"swp\": [\"Midterm\", \"Final Exam\", \"Project 1\"]}), \"Summer\"),\n (4360, \"CPSC\", \"Computer Security\", names.get_first_name(), names.get_last_name(), json.dumps({\"swp\": [\"Midterm\", \"Final Exam\", \"Project 1\"]}), \"Summer\")\n ]\n #Adding years\n upload_courses = []\n for year in years:\n upload_courses += [x + (year,) for x in courses]\n #Making a few instructors teach multiple course\n new_courses = [\n (4557, \"CPSC\", \"Natural Language Processing\", ),\n (2375, \"CPSC\", \"Programming II\",),\n (2776, \"CPSC\", \"Data Structures and Algorithms\",),\n (4862, \"CPSC\", \"Image Recognition\", ),\n ]\n for i in range(0,len(new_courses)):\n year = choice(years)\n for y in range(0,2): #Number of times new course is taught\n c = upload_courses[i]\n new_data = (c[3], c[4], c[5], choice([\"Fall\", \"Spring\", \"Summer\"]), year+y)\n data = new_courses[i] + new_data\n upload_courses.append(data)\n #Adding solo instructors and solo courses\n upload_courses += [\n (4672, \"CPSC\", \"Programming Memes\", names.get_first_name(), names.get_last_name(), json.dumps({\"swp\": [\"Midterm\", \"Final Exam\", \"Project 1\"]}), \"Spring\", choice(years)),\n (1872, \"CPSC\", \"Information Systems\", names.get_first_name(), names.get_last_name(), json.dumps({\"swp\": [\"Midterm\", \"Final Exam\", \"Project 1\"]}), \"Summer\", choice(years)),\n (1123, \"CPSC\", \"Microsoft Office\", names.get_first_name(), names.get_last_name(), json.dumps({\"swp\": [\"Midterm\", \"Final Exam\", \"Project 1\"]}), \"Fall\", choice(years))\n ]\n\n with sqlite3.connect(\"determined.db\") as conn:\n c = conn.cursor()\n c.executemany('''INSERT INTO course (course_number, dept_id, title, instructor_fname, instructor_lname, student_work_products, term, year)\n VALUES ( ?, ?, ?, ?, ?, ?, ?, ?)''', upload_courses)\n conn.commit()\n\n #SWP\n with sqlite3.connect(\"determined.db\") as conn:\n c = conn.cursor()\n c.execute(\n \"\"\"\n CREATE TABLE `student_work_product` (\n `id` INTEGER PRIMARY KEY,\n `product` varchar(250) NOT NULL,\n `course_id` int NOT NULL,\n `dept_id` int NOT NULL,\n `student_fname` varchar(35) NOT NULL,\n `student_lname` varchar(35) NOT NULL,\n `student_outcome` int DEFAULT NULL,\n `score` int DEFAULT NULL,\n `term` varchar(7) NOT NULL,\n `year` int NOT NULL,\n CONSTRAINT `course` FOREIGN KEY (`course_id`) REFERENCES `course` (`course_number`)\n CONSTRAINT `course` FOREIGN KEY (`dept_id`) REFERENCES `course` (`dept_id`)\n )\n \"\"\"\n )\n conn.commit()\n \n swps = []\n with sqlite3.connect(\"determined.db\") as conn:\n conn.row_factory = sqlite3.Row\n c = conn.cursor()\n c.execute (\"Select * from course\")\n records = [dict(x) for x in c.fetchall()]\n #Generating 20 student records for each swp in each course\n for i, course in enumerate(records):\n student_names = []\n for _ in range(20):\n student_names.append({'fname': names.get_first_name(),\n 'lname': names.get_last_name()})\n for product in json.loads(course['student_work_products'])['swp']:\n for student in student_names:\n if i%7 == 0:\n score = int(triangular(50, 85))\n else:\n score = int(triangular(50, 100))\n if score >= 90: outcome = 4\n elif score >= 80: outcome = 3\n elif score >= 70: outcome = 2\n elif score >= 60: outcome = 1\n else: outcome = 0 \n swps.append((\n product,\n course['course_number'],\n \"CPSC\",\n student['fname'],\n student['lname'],\n outcome,\n score, \n course['term'], \n course['year']\n ))\n \n c.executemany('''INSERT INTO student_work_product (product, course_id, dept_id, student_fname, student_lname, student_outcome, score, term, year)\n VALUES ( ?, ?, ?, ?, ?, ?, ?, ?, ?)''', swps)\n conn.commit()","def preprocess_courses_corpus():\n soup = None\n with open('courses_corpus.html', 'r') as infile:\n content = infile.read()\n\n soup = BeautifulSoup(content, 'html.parser')\n\n docid = 0\n data = {}\n data['documents'] = []\n\n main_table = soup.find_all(\"div\", attrs={'class': 'courseblock'})\n for course in main_table:\n docid += 1\n title = course.find_all('p', attrs={'class':'courseblocktitle noindent'})[0].text.lstrip('\\n') if len(course.find_all('p', attrs={'class':'courseblocktitle noindent'}))!=0 else ''\n description = (course.find_all('p', attrs={'class':'courseblockdesc noindent'})[0].text.lstrip('\\n') if len(course.find_all('p', attrs={'class':'courseblockdesc noindent'}))!=0 else '') + ' ' + (course.find_all('p', attrs={'class':'courseblockextra noindent'})[0].text if len(course.find_all('p', attrs={'class':'courseblockextra noindent'}))!=0 else '')\n\n data['documents'].append({\n 'docId' : docid,\n 'title' : title.strip(),\n 'description' : description.strip()\n })\n\n with open('courses_data.json', 'w') as outfile:\n json.dump(data, outfile)","def print_catalog(self):\n # first download the json for the catalog\n self.download_json()\n\n # open the saved json file and load the json\n with self.file.open(\"r\") as catalog_file:\n pages = json.load(catalog_file)\n\n # the catalog json is just a list of pages\n # so we begin by iterating through the pages\n for page_num in range(len(pages)):\n # get each page\n page = pages[page_num]\n\n # get the threads on each page\n threads = page[\"threads\"]\n\n # print the page heading\n print(\"*** PAGE \", page_num + 1, \"***\")\n\n # iterate through the threads on each page\n for thread_num in range(len(threads)):\n # get each thread\n thread = threads[thread_num]\n\n # print the thread number\n num = thread[\"no\"]\n print(\"---\", \"Thread:\", num, \"---\")\n\n # not all threads have a subject or comment\n try:\n subject = thread[\"sub\"]\n comment = thread[\"com\"]\n\n print(\"Sub:\", subject)\n print(\"Comment:\", comment)\n except KeyError:\n print(\"N/A\")","def _create_course(self):\r\n super(TestPublish, self)._create_course(split=False)\r\n\r\n self._create_item('chapter', 'Chapter1', {}, {'display_name': 'Chapter 1'}, 'course', 'runid', split=False)\r\n self._create_item('chapter', 'Chapter2', {}, {'display_name': 'Chapter 2'}, 'course', 'runid', split=False)\r\n self._create_item('vertical', 'Vert1', {}, {'display_name': 'Vertical 1'}, 'chapter', 'Chapter1', split=False)\r\n self._create_item('vertical', 'Vert2', {}, {'display_name': 'Vertical 2'}, 'chapter', 'Chapter1', split=False)\r\n self._create_item('html', 'Html1', \"<p>Goodbye</p>\", {'display_name': 'Parented Html'}, 'vertical', 'Vert1', split=False)\r\n self._create_item(\r\n 'discussion', 'Discussion1',\r\n \"discussion discussion_category=\\\"Lecture 1\\\" discussion_id=\\\"a08bfd89b2aa40fa81f2c650a9332846\\\" discussion_target=\\\"Lecture 1\\\"/>\\n\",\r\n {\r\n \"discussion_category\": \"Lecture 1\",\r\n \"discussion_target\": \"Lecture 1\",\r\n \"display_name\": \"Lecture 1 Discussion\",\r\n \"discussion_id\": \"a08bfd89b2aa40fa81f2c650a9332846\"\r\n },\r\n 'vertical', 'Vert1',\r\n split=False\r\n )\r\n self._create_item('html', 'Html2', \"<p>Hellow</p>\", {'display_name': 'Hollow Html'}, 'vertical', 'Vert1', split=False)\r\n self._create_item(\r\n 'discussion', 'Discussion2',\r\n \"discussion discussion_category=\\\"Lecture 2\\\" discussion_id=\\\"b08bfd89b2aa40fa81f2c650a9332846\\\" discussion_target=\\\"Lecture 2\\\"/>\\n\",\r\n {\r\n \"discussion_category\": \"Lecture 2\",\r\n \"discussion_target\": \"Lecture 2\",\r\n \"display_name\": \"Lecture 2 Discussion\",\r\n \"discussion_id\": \"b08bfd89b2aa40fa81f2c650a9332846\"\r\n },\r\n 'vertical', 'Vert2',\r\n split=False\r\n )\r\n self._create_item('static_tab', 'staticuno', \"<p>tab</p>\", {'display_name': 'Tab uno'}, None, None, split=False)\r\n self._create_item('about', 'overview', \"<p>overview</p>\", {}, None, None, split=False)\r\n self._create_item('course_info', 'updates', \"<ol><li><h2>Sep 22</h2><p>test</p></li></ol>\", {}, None, None, split=False)","def generate_courses():\r\n for category in CourseCategory.objects.all():\r\n Course.objects.create(name=category.name, category=category, is_active=True,\r\n is_featured=True)","def testCosmologyCatalog(self):\n dbObj = myTestGals(database=self.dbName)\n cat = cosmologicalGalaxyCatalog(dbObj)\n cat.write_catalog(self.catName)","def createStructuredTranscript_Non_Core_Doc():\n\n #create a temporary folder that will hold the data transformed from doc to docx\n os.system('mkdir ' + INPUT_FOLDER+'temp')\n\n core_doc_asset = []\n missing_count = 0\n missing_files=[]\n # get all the docx files that are part of the core asset\n for file in glob.glob(INPUT_FOLDER+\"*.doc\"):\n\n # RG numbers for the core asset\n if (\"RG-50.030\" not in file and\n \"RG-50.106\" not in file and\n \"RG-50.549\" not in file):\n \n\n \n # convert file to docx, storing it in an untracked folder called temp\n file_docx = file + 'x'\n command = 'textutil -convert docx ' + file + ' -output ' + INPUT_FOLDER+'temp/'+ file_docx.split('/')[-1]\n call(command, shell=True)\n\n # append to the array\n core_doc_asset.append(file_docx)\n \n\n \n\n # get the units for each file, store them and update tracker\n core_doc_asset=create_dictionary_of_file_list(core_doc_asset)\n \n not_processed=0\n processed_doc=0\n \n # get the units for each file, store them and update tracker \n for mongo_rg in core_doc_asset:\n # get text units for this entry\n processed=[]\n result=[]\n \n for file in core_doc_asset[mongo_rg]:\n \n \n \n units = getTextUnits(INPUT_FOLDER+'temp/'+file.split('/')[-1])\n \n if units:\n #replace white spaces\n for i,element in enumerate(units):\n units[i]['unit']=' '.join(element['unit'].split())\n result.extend(units)\n \n processed.append(True)\n else:\n #check if processed\n processed.append(False)\n\n #set the method used to transform the transcript\n h.update_field(DB, TRACKER, \"rg_number\", mongo_rg, \"method\", \"transcribe_non_core_doc\")\n\n not_processed=not_processed+1\n\n if False in processed:\n\n h.update_field(DB, TRACKER, \"rg_number\", mongo_rg, \"status\", \"Unprocessed\")\n not_processed=not_processed+1\n missing_files.append(' '.join(core_doc_asset[mongo_rg]))\n else:\n # insert units on the output collection\n h.update_field(DB, OUTPUT, \"shelfmark\", 'USHMM '+mongo_rg, \"structured_transcript\", result)\n\n \n # update status on the stracker\n \n h.update_field(DB, TRACKER, \"rg_number\", mongo_rg, \"status\", \"Processed\")\n processed_doc=processed_doc+1\n \n\n #delete the temporary folder\n os.system('rm -r ' + INPUT_FOLDER+'temp')\n\n \n #write the missing files to text file\n file = open(OUTPUT_FOLDER_USHMM_PROCESSING_LOGS+'transcribe_non_core_doc_failed.txt','w')\n file.write('\\n'.join(missing_files))\n\n \n # success\n pprint.pprint(\"Non-core doc files were successfully processed, but there are \" + str(missing_count) + \" missing\")","def process_course(xml_course):\n\n parse_course = parse_get_course(xml_course)\n if not parse_course: # Does not already exist, create\n parse_course = parse_create_course(xml_course)\n else:\n # TODO: Update existing attributes of the course here and upload the changes\n None\n parse_course_id = upload_course(parse_course)\n\n print (\"Processed Course: {0} {1}\"\n .format(parse_course[\"subject\"], parse_course[\"courseNumber\"]))\n\n # TODO: Implement the functions for this stuff\n parse_section = parse_get_section(xml_course)\n if not parse_section: # Does not already exist, create\n parse_section = parse_create_section(xml_course)\n else:\n # TODO: Update existing attributes of the section here and upload the changes\n None\n parse_section_id = upload_section(parse_section)\n\n put_child(parse_course_id, parse_section_id)","def setup_xml_course(self):\r\n course_key = SlashSeparatedCourseKey('edX', 'toy', '2012_Fall')\r\n location = course_key.make_usage_key('chapter', 'Overview')\r\n descriptor = modulestore().get_item(location)\r\n\r\n self.module = self._get_module(course_key, descriptor, location)","def create_course( # lint-amnesty, pylint: disable=arguments-differ\n self, org, course, run, user_id, master_branch=None, fields=None,\n versions_dict=None, search_targets=None, root_category='course',\n root_block_id=None, **kwargs\n ):\n # either need to assert this or have a default\n assert master_branch is not None\n # check course and run's uniqueness\n locator = CourseLocator(org=org, course=course, run=run, branch=master_branch)\n return self._create_courselike(\n locator, user_id, master_branch, fields, versions_dict,\n search_targets, root_category, root_block_id, **kwargs\n )","def _create_course(self):\r\n # If the course already exists, this will respond\r\n # with a 200 and an error message, which we ignore.\r\n response = self.session.post(\r\n STUDIO_BASE_URL + '/course/',\r\n data=self._encode_post_dict(self._course_dict),\r\n headers=self.headers\r\n )\r\n\r\n try:\r\n err = response.json().get('ErrMsg')\r\n\r\n except ValueError:\r\n raise CourseFixtureError(\r\n \"Could not parse response from course request as JSON: '{0}'\".format(\r\n response.content))\r\n\r\n # This will occur if the course identifier is not unique\r\n if err is not None:\r\n raise CourseFixtureError(\"Could not create course {0}. Error message: '{1}'\".format(self, err))\r\n\r\n if not response.ok:\r\n raise CourseFixtureError(\r\n \"Could not create course {0}. Status was {1}\".format(\r\n self._course_dict, response.status_code))","def start(self) -> None:\n\n self.doc = self.doc + r'''\n \\documentclass[\n 10pt, % Main document font size\n a4paper, % Paper type, use 'letterpaper' for US Letter paper\n ]{scrartcl}\n\n \\usepackage{graphicx}\n \\usepackage{epstopdf}\n \\usepackage{float}\n \\usepackage[scale=0.75]{geometry} % Reduce document margins\n \\usepackage{hyperref}\n \\usepackage{longtable}\n\n \\begin{document}\n\n \\title{Automatic Exploratory Data Analysis} % The article title\n\n \\subtitle{Study Case} % Uncomment to display a subtitle\n\n \\author{Jacob} % The article author(s) - author affiliations need to be specified in the AUTHOR AFFILIATIONS block\\\n\n \\maketitle % Print the title/author/date block\n\n \\newpage\n \\tableofcontents % Print the table of contents\n\n \\newpage\n \\listoffigures % Print the list of figures\n\n \\newpage\n \\listoftables % Print the list of tables\n '''","def newCatalog():\n catalog = {'videosContext': None,\n 'caraContenido': None,\n 'musicalGenero': None,\n 'fechaMusica': None}\n\n catalog['videosContext'] = lt.newList('ARRAY_LIST')\n catalog['caraContenido'] = mp.newMap(30,\n maptype='PROBING',\n loadfactor=0.4)\n catalog['musicaGenero'] = mp.newMap(30,\n maptype='PROBING',\n loadfactor=0.4)\n catalog['fechaMusica'] = om.newMap('RBT')\n\n return catalog","def create_course(self, org, offering, user_id=None, fields=None, **kwargs):\r\n\r\n course, _, run = offering.partition('/')\r\n course_id = SlashSeparatedCourseKey(org, course, run)\r\n\r\n # Check if a course with this org/course has been defined before (case-insensitive)\r\n course_search_location = SON([\r\n ('_id.tag', 'i4x'),\r\n ('_id.org', re.compile(u'^{}$'.format(course_id.org), re.IGNORECASE)),\r\n ('_id.course', re.compile(u'^{}$'.format(course_id.course), re.IGNORECASE)),\r\n ('_id.category', 'course'),\r\n ])\r\n courses = self.collection.find(course_search_location, fields=('_id'))\r\n if courses.count() > 0:\r\n raise InvalidLocationError(\r\n \"There are already courses with the given org and course id: {}\".format([\r\n course['_id'] for course in courses\r\n ]))\r\n\r\n location = course_id.make_usage_key('course', course_id.run)\r\n course = self.create_and_save_xmodule(location, fields=fields, **kwargs)\r\n\r\n # clone a default 'about' overview module as well\r\n about_location = location.replace(\r\n category='about',\r\n name='overview'\r\n )\r\n overview_template = AboutDescriptor.get_template('overview.yaml')\r\n self.create_and_save_xmodule(\r\n about_location,\r\n system=course.system,\r\n definition_data=overview_template.get('data')\r\n )\r\n\r\n return course","def _create_course(self, split=True):\r\n metadata = {\r\n 'start': datetime.datetime(2000, 3, 13, 4),\r\n 'display_name': 'Migration test course',\r\n }\r\n data = {\r\n 'wiki_slug': 'test_course_slug'\r\n }\r\n fields = metadata.copy()\r\n fields.update(data)\r\n if split:\r\n # split requires the course to be created separately from creating items\r\n self.split_mongo.create_course(\r\n self.split_course_key.org, self.split_course_key.offering, self.userid, fields=fields, root_block_id='runid'\r\n )\r\n old_course = self.old_mongo.create_course(self.split_course_key.org, 'test_course/runid', fields=fields)\r\n self.old_course_key = old_course.id\r\n self.runtime = old_course.runtime","def create_document(self, data):\n command = CreateDocumentFromOneOffixxTemplateCommand(self.context, data['title'], data['template'])\n return command.execute()","def insert_course(dept, num, text):\n\n # Course Title \n m = re.search(\"[\\d\\w]{5} - ([\\w ]*)\", text)\n title = m.group(1) if m else \"nomatch\"\n\n # Course Description\n m = re.search(\"\\.\\s(.*)\\sTypically\",text)\n des = m.group(1) if m else \"nomatch\"\n\n # Credit hours aren't fixed for every course\n # Credit Hours: 2.00\n # Credit Hours: 2.00 or 3.00. \n # Credit Hours: 1.00 to 18.00. \n m = re.search(\"Credit Hours: (\\d+\\.\\d+)\",text, flags=re.IGNORECASE)\n m = re.search(\"(\\d+\\.\\d+)(.*?)Credit hours\",text, flags=re.IGNORECASE) if not m else m\n cr = m.group(1) if m else \"-1\"\n\n # Semesters Offered\n m = re.search(\"Typically offered (.*?)\\.\", text)\n sem = m.group(1).split() if m else [\"nomatch\"]\n\n # Course Type: Lecture, Recitation, Lab, Seminar, etc.\n m = re.search(\"Schedule Types:\\s((?:[\\w ]+)(?:,[\\w ]+)*) \\s+\", text)\n form = m.group(1).split(\", \") if m else [\"nomatch\"]\n\n # Learning objectives will not necessarily follow campuses\n m = re.search(\"campuses:(\\s+([\\w\\s])+\\n)\", text)\n campus = m.group(1).strip().split(\"\\n\\n\") if m else [\"nomatch\"]\n campus = [camp.strip() for camp in campus]\n\n # prereq regex and decomosition of prereqs into lists of AND conditions (works for most classes, not 477 and similar)\n # re.DOTALL matches all characters, including \"\\n\"\n idx = text.find(\"campuses:\")\n m = re.search(\"Prerequisites:(.*)\",text[idx:],flags=re.DOTALL)\n if m:\n allReqs = []\n prereqText = m.group(1).strip()\n prereqText = prereqText.encode('ascii', 'ignore') \n for i in PrereqParser.parseprereq(prereqText):\n reqArr = []\n for j in i.split():\n if j.find(\"-C\") != -1:\n j = j.replace(\"-C\",\"\")\n reqArr.append(Requisite(course=j,reqType=False))\n else:\n reqArr.append(Requisite(course=j,reqType=True)) \n allReqs.append(RequisiteList(courses=reqArr))\n\n else:\n allReqs = []\n\n # create course entity\n course = Course(number=num, title=title, department=dept, form=form,\n description=des, credits=float(cr), semesters=sem,\n campuses=campus,requisites=allReqs, id=dept + num)\n # store course \n course.put()","def load2TexAS(data):\n # State global variable\n global cache_stanza, cache_spacy, cache_udpipe, cache_trankit\n\n # Collect the data\n string = data['text']\n lang = data['lang']\n packages = data['packages']\n\n hash_string = hashlib.sha1(string.encode()).hexdigest()\n\n final_HTML = \"\"\n message_HTML = \"<div class=\\'message\\'>\"\n isMessage = False\n header_input = []\n log_row = [datetime.now().strftime('%Y-%m-%d %H:%M:%S'), lang]\n\n if \"stanza\" in packages:\n # Initialize the TexAS document\n mydoc = tx.Document(string)\n mydoc.meta().set(\"authors\",\"hegler,yiwen,celine,yuqian\")\n mydoc.date().setTimestamp(\"2021-01-19T14:44\")\n\n ## If cache is full, reload the cache.\n if cache.count(cache_stanza) > 100:\n cache.write(cache_stanza, \"stanza\")\n cache_stanza = cache.load(\"stanza\")\n \n ## Check text whether is already in cache\n if hash_string in cache_stanza[lang].keys():\n tokens, end_pos, lemma, pos, nlpWordsList, hasCompoundWords, cache_stanza = cache.read(\"stanza\", cache_stanza, lang, string) #The output cache_stanza has 'count' been updated.\n else:\n tokens, end_pos, lemma, pos, nlpWordsList, hasCompoundWords, cache_stanza = cache.add(\"stanza\", cache_stanza, lang, string, get_services_stanza)\n \n mydoc.setTokenList(tokens, indexed=True)\n mydoc.views().get(\"TOKENS\").meta().set(\"generator\", \"stanza\")\n mydoc.views().get(\"TOKENS\").meta().set(\"model\", \"stanza\" + \"-\" + lang)\n mydoc.setSentenceList(end_pos)\n\n if hasCompoundWords:\n mydoc.addTokenView( \"WORDS\", nlpWordsList )\n mydoc.addTokenView(\"LEMMA\", lemma)\n mydoc.addTokenView(\"POS\", pos)\n \n # Extract HTML View\n myTabView = tx.UITabularView(mydoc)\n if hasCompoundWords:\n myTabView.showView(\"WORDS\")\n myTabView.showView(\"LEMMA\", labelCSS=False)\n myTabView.showView(\"POS\")\n\n # concatenate the myTabView.HTML()\n header_input.append((\"Stanza\", str(len(end_pos)) , str(len(tokens)), str(get_tokens_per_sents(end_pos))))\n final_HTML += \"<div class='subtitle'>Stanza</div> <br>\" + myTabView.HTML().replace(\"\\n\", \"\") + \"<br>\"\n log_row.append(\"stanza\")\n \n else:\n log_row.append(\"\")\n\n if \"spacy\" in packages:\n # SpaCy does not support Arabic and Russian\n if lang == 'ara' or lang == 'rus':\n message_HTML += \"SpaCy does not support Arabic or Russian. <br>\"\n isMessage = True\n\n else:\n mydoc = tx.Document(string)\n mydoc.meta().set(\"authors\",\"hegler,yiwen,celine,yuqian\")\n mydoc.date().setTimestamp(\"2021-01-19T14:44\")\n \n ## If cache is full, reload the cache.\n if cache.count(cache_spacy) > 100:\n cache.write(cache_spacy, \"spacy\")\n cache_spacy = cache.load(\"spacy\")\n \n ## Check text whether is already in cache\n if hash_string in cache_spacy[lang].keys():\n tokens, end_pos, lemma, pos, cache_spacy = cache.read(\"spacy\", cache_spacy, lang, string)\n else:\n tokens, end_pos, lemma, pos, cache_spacy = cache.add(\"spacy\", cache_spacy, lang, string, get_services_spacy)\n \n mydoc.setTokenList(tokens, indexed=True)\n mydoc.views().get(\"TOKENS\").meta().set(\"generator\", \"spacy\")\n mydoc.views().get(\"TOKENS\").meta().set(\"model\", \"spacy\" + \"-\" + lang )\n mydoc.setSentenceList(end_pos)\n mydoc.addTokenView(\"LEMMA\", lemma)\n mydoc.addTokenView(\"POS\", pos)\n \n # Extract HTML View\n myTabView = tx.UITabularView(mydoc)\n myTabView.showView(\"LEMMA\", labelCSS=False)\n myTabView.showView(\"POS\")\n\n # concatenate the myTabView.HTML()\n header_input.append((\"SpaCy\", str(len(end_pos)) , str(len(tokens)), str(get_tokens_per_sents(end_pos))))\n final_HTML += \"<div class='subtitle'>\" + \"SpaCy\" + \"</div><br>\" + myTabView.HTML().replace(\"\\n\", \"\") + \"<br>\"\n log_row.append(\"spacy\")\n \n else:\n log_row.append(\"\")\n\n if \"udpipe\" in packages: \n ## If cache is full, reload the cache.\n if cache.count(cache_udpipe) > 100:\n cache.write(cache_udpipe, \"udpipe\")\n cache_udpipe = cache.load(\"udpipe\")\n \n ## Check text whether is already in cache\n if hash_string in cache_udpipe[lang].keys():\n tokens, end_pos, lemma, pos, cache_udpipe = cache.read(\"udpipe\", cache_udpipe, lang, string)\n else:\n tokens, end_pos, lemma, pos, cache_udpipe = cache.add(\"udpipe\", cache_udpipe, lang, string, get_services_udpipe)\n \n string_udpipe = \" \".join(tokens)\n\n # Initialize the TexAS document\n mydoc = tx.Document(string_udpipe)\n mydoc.meta().set(\"authors\",\"hegler,yiwen,celine,yuqian\")\n mydoc.date().setTimestamp(\"2021-01-19T14:44\")\n\n mydoc.setTokenList(tokens, indexed=True)\n mydoc.views().get(\"TOKENS\").meta().set(\"generator\", \"udpipe\")\n mydoc.views().get(\"TOKENS\").meta().set(\"model\", \"udpipe\" + \"-\" + lang )\n mydoc.setSentenceList(end_pos)\n mydoc.addTokenView(\"LEMMA\", lemma)\n mydoc.addTokenView(\"POS\", pos)\n \n # Extract HTML View\n myTabView = tx.UITabularView(mydoc)\n myTabView.showView(\"LEMMA\", labelCSS=False)\n myTabView.showView(\"POS\")\n\n # concatenate the myTabView.HTML()\n header_input.append((\"UDpipe\", str(len(end_pos)) , str(len(tokens)), str(get_tokens_per_sents(end_pos))))\n final_HTML += \"<div class='subtitle'>UDpipe</div> <br>\" + myTabView.HTML().replace(\"\\n\", \"\") + \"<br>\"\n log_row.append(\"udpipe\")\n \n else:\n log_row.append(\"\")\n \n if \"trankit\" in packages:\n # trankit temporarily only support english\n if lang == 'eng':\n mydoc = tx.Document(string)\n mydoc.meta().set(\"authors\",\"hegler,yiwen,celine,yuqian\")\n mydoc.date().setTimestamp(\"2021-01-19T14:44\")\n \n ## If cache is full, reload the cache.\n if cache.count(cache_trankit) > 100:\n cache.write(cache_trankit, \"trankit\")\n cache_trankit = cache.load(\"trankit\")\n \n ## Check text whether is already in cache\n if hash_string in cache_trankit[lang].keys():\n tokens, end_pos, lemma, pos, cache_trankit = cache.read(\"trankit\", cache_trankit, lang, string)\n else:\n tokens, end_pos, lemma, pos, cache_trankit = cache.add(\"trankit\", cache_trankit, lang, string, get_services_trankit)\n \n mydoc.setTokenList(tokens, indexed=True)\n mydoc.views().get(\"TOKENS\").meta().set(\"generator\", \"spacy\")\n mydoc.views().get(\"TOKENS\").meta().set(\"model\", \"spacy\" + \"-\" + lang )\n mydoc.setSentenceList(end_pos)\n mydoc.addTokenView(\"LEMMA\", lemma)\n mydoc.addTokenView(\"POS\", pos)\n \n # Extract HTML View\n myTabView = tx.UITabularView(mydoc)\n myTabView.showView(\"LEMMA\", labelCSS=False)\n myTabView.showView(\"POS\")\n\n # concatenate the myTabView.HTML()\n header_input.append((\"Trankit\", str(len(end_pos)) , str(len(tokens)), str(get_tokens_per_sents(end_pos))))\n final_HTML += \"<div class='subtitle'>\" + \"Trankit\" + \"</div><br>\" + myTabView.HTML().replace(\"\\n\", \"\") + \"<br>\"\n log_row.append(\"trankit\")\n\n else:\n message_HTML += \"Trankit temporarily only supports English. <br>\"\n isMessage = True \n \n else:\n log_row.append(\"\")\n\n message_HTML += \"</div>\"\n if isMessage:\n return message_HTML + get_header_table(header_input) + \"<br><br>\" + final_HTML\n\n writeLog(log_row)\n return get_header_table(header_input) + \"<br><br>\" + final_HTML","def test_get_toc_text(self) -> None:\n extraction = docx2python(RESOURCES / \"zen_of_python.docx\")\n assert extraction.document_runs == [\n [\n [[[\"Contents\"], [\"\\t\", \"Beautiful is better than ugly.\\t1\"], []]],\n [\n [\n [],\n [],\n [\"Beautiful is better than ugly.\"],\n [\"Explicit is better than implicit.\"],\n [\"Simple is better than complex.\"],\n [\"Complex is better than complicated.\"],\n [\"Flat is better than nested.\"],\n [\"Sparse is better than dense.\"],\n [\"Readability counts.\"],\n [\"Special cases aren't special enough to break the rules.\"],\n [\"Although practicality beats purity.\"],\n [\"Errors should never pass silently.\"],\n [\"Unless explicitly silenced.\"],\n [\"In the face of ambiguity, refuse the temptation to guess.\"],\n [\n \"There should be one-- and preferably only one --obvious way to do it.\"\n ],\n [\n \"Although that way may not be obvious at first unless you're Dutch.\"\n ],\n [\"Now is better than never.\"],\n [\"Although never is often better than *right* now.\"],\n [\"If the implementation is hard to explain, it's a bad idea.\"],\n [\n \"If the implementation is easy to explain, it may be a good idea.\"\n ],\n [\n \"Namespaces are one honking great idea -- let's do more of those!\"\n ],\n ]\n ],\n ]\n ]\n extraction.close()","def test_course_index_view_with_course(self):\r\n CourseFactory.create(display_name='Robot Super Educational Course')\r\n resp = self.client.get_html('/course/')\r\n self.assertContains(\r\n resp,\r\n '<h3 class=\"course-title\">Robot Super Educational Course</h3>',\r\n status_code=200,\r\n html=True\r\n )\r\n _test_no_locations(self, resp)","def get_text(self, course): # pylint: disable=unused-argument\r\n raise NotImplementedError","def populate_course(self):\r\n def descend(parent, stack):\r\n xblock_type = stack.pop(0)\r\n for _ in range(2):\r\n child = ItemFactory.create(category=xblock_type, parent_location=parent.location)\r\n if stack:\r\n descend(child, stack)\r\n\r\n descend(self.course, ['chapter', 'sequential', 'vertical', 'problem'])","def create_course(course_request: schemas.CourseRequest, db: Session = Depends(get_db)):\n\n try:\n course = crud.course.create(db, obj_in=course_request)\n\n except Exception as error:\n logger.error(f'Error when creating a course: {error}')\n raise HTTPException(status_code=status.HTTP_500_INTERNAL_SERVER_ERROR, detail=f'{error}')\n\n return course","def create_tutorial(cls, *filenames):\n return cls.create_input_document(cls.doc_type_tutorial, *filenames)","def read_data(term):\n course_url = '/admweb/!SWKSECX.main?term='+ term + '&title=&course=&crn=&coll=&dept=&subj='\n connection = httplib.HTTPSConnection('courses.rice.edu', 443)\n connection.connect()\n connection.request(\n 'GET',\n course_url\n )\n courses_xml = connection.getresponse()\n return courses_xml","def completed_course()->str:\r\n try:\r\n db: sqlite3.Connection = sqlite3.connect(DB_FILE)\r\n query: str = \"SELECT s.Name, s.CWID, g.Course, g.Grade, i.Name AS 'Instructor' \" \\\r\n \"FROM grades2 g JOIN students2 s ON g.StudentCWID = s.CWID \" \\\r\n \"JOIN instructors2 i ON g.InstructorCWID = i.CWID ORDER BY s.Name\" \r\n except sqlite3.OperationalError as e:\r\n print(e)\r\n\r\n data: Dict[str,str]=\\\r\n [{\"name\": name,\"cwid\": cwid,\"course\": course , \"grade\":grade, \"instructor\": instructor}\r\n for name, cwid, course, grade, instructor in db.execute(query)]\r\n db.close()\r\n \"\"\"Render the template from templates folder\"\"\"\r\n return render_template(\"student_summary.html\",\r\n title=\"Stevens Repository\",\r\n table_title=\"Student, Course, Grade and Instructor\",\r\n students=data )","def thredds_catalog(self, tmpdir_factory):\n input_dir = os.path.abspath(\"esacci_esgf/test_input_catalogs\")\n output_dir = str(tmpdir_factory.mktemp(\"output\", numbered=True))\n # Process all catalogs in input dir and create aggregations with WMS\n test_files = glob(\"{}/*.xml\".format(input_dir))\n assert test_files, \"No test catalogs found\"\n\n pb = ProcessBatch([\"-aw\", \"-o\", output_dir] + test_files)\n pb.do_all()\n tree = ET.ElementTree()\n tree.parse(os.path.join(output_dir, os.listdir(input_dir)[0]))\n return tree.getroot()","def build(self) -> None:\n\n print(\"Genereting files..\")\n self.doc = self.doc + r'\\end{document}'\n\n f = open(\"latex\\\\\" + self.report_name + '.tex', 'w')\n f.write(self.doc)\n f.close()\n\n os.chdir('latex')\n\n cmd = ['pdflatex', '-interaction', 'nonstopmode', self.report_name + '.tex']\n #cmd = ['pdflatex', '-interaction', self.report_name + '.tex']\n\n for i in range(2):\n proc = subprocess.Popen(cmd)\n proc.communicate()\n retcode = proc.returncode\n if not retcode == 0:\n os.chdir('..')\n raise ValueError('Error {} executing command: {}'.format(retcode, ' '.join(cmd)))\n\n os.unlink(self.report_name + '.aux')\n os.unlink(self.report_name + '.lof')\n os.unlink(self.report_name + '.log')\n os.unlink(self.report_name + '.lot')\n os.unlink(self.report_name + '.out')\n os.unlink(self.report_name + '.toc')\n\n os.chdir('..')","def goto_create_course(self):\n\n self.create.click()","def get_courses(self):\n\n self.search([]).unlink()\n token = self.env['odoo.moodle'].search([('create_uid', '=', self.env.user.id)]).token\n domain = \"http://localhost:8888\"\n webservice_url = \"/webservice/rest/server.php?\"\n parameters = {\n \"wstoken\":token,\n 'wsfunction': 'core_course_get_courses',\n 'moodlewsrestformat': 'json'\n }\n request = requests.get(url=domain+webservice_url, params=parameters)\n request = request.json()\n print(request)\n\n for req in request:\n try:\n if req['id']==1:\n pass\n else:\n self.create({\n 'course_id': req['id'], \n 'category':req['categoryid'],\n 'fullname':req['fullname'], \n 'shortname':req['shortname'],\n 'summary': req['summary']\n }\n )\n except Exception:\n print('Course not created')","def test_tax_court_citation_extractor(self):\n\n test_pairs = (\n (\n \"\"\" 1 UNITED STATES TAX COURT REPORT (2018)\n\n\n\n UNITED STATES TAX COURT\n\n\n\n BENTLEY COURT II LIMITED PARTNERSHIP, B.F. BENTLEY, INC., TAX\n MATTERS PARTNER, Petitioner v.\n COMMISSIONER OF INTERNAL REVENUE, Respondent\n\n\n\n Docket No. 5393-04. Filed May 31, 2006.\n\n\n\n Nancy Ortmeyer Kuhn, for petitioner.\n \"\"\",\n \"1 T.C. 2018\",\n ),\n (\n \"\"\" T.C. Memo. 2003-150\n\n\n\n UNITED STATES TAX COURT\n\n\n\n RIVER CITY RANCHES #1 LTD., LEON SHEPARD,\n TAX MATTERS PARTNER,\n RIVER CITY RANCHES #2 LTD., LEON SHEPARD,\n TAX MATTERS PARTNER,\n RIVER CITY RANCHES #3 LTD., LEON SHEPARD,\n TAX MATTERS PARTNER,\n\n\n \"\"\",\n \"2003 T.C. Memo. 150\",\n ),\n (\n \"\"\" T.C. Summary Opinion 2003-150\n\n\n\n UNITED STATES TAX COURT\n\n\n\n RIVER CITY RANCHES #1 LTD., LEON SHEPARD,\n TAX MATTERS PARTNER,\n RIVER CITY RANCHES #2 LTD., LEON SHEPARD,\n TAX MATTERS PARTNER,\n RIVER CITY RANCHES #3 LTD., LEON SHEPARD,\n TAX MATTERS PARTNER,\n\n\n \"\"\",\n \"2003 T.C. Summary Opinion 150\",\n ),\n (\n \"\"\"\n MICHAEL KEITH SHENK, PETITIONER v. COMMISSIONER\n OF INTERNAL REVENUE, RESPONDENT\n\n Docket No. 5706–12. Filed May 6, 2013.\n\n P was divorced from his wife, and their 2003 ‘‘Judgment of\n Absolute Divorce’’ provided that his ex-wife would have pri-\n mary residential custody of their three minor children. The\n judgment provided that the dependency exemption deductions\n for the three children would be divided between the two ex-\n spouses according to various conditions but did not provide\n that the ex-wife must execute in P’s favor a Form 8332,\n ‘‘Release of Claim to Exemption for Child of Divorced or Sepa-\n rated Parents’’. The children resided with P’s ex-wife for more\n than half of 2009, and P’s ex-wife did not execute in P’s favor\n any Form 8332 or equivalent document for any year. For 2009\n P timely filed a Federal income tax return on which he\n claimed dependency exemption deductions and the child tax\n credit for two of the children, consistent with his under-\n standing of the terms of the judgment, but he did not attach\n any Form 8332 to his return. He also claimed head-of-house-\n hold filing status. His ex-wife, the custodial parent, timely\n filed a Federal income tax return for 2009 on which she also\n\n 200\n\n\n\n\nVerDate Nov 24 2008 10:59 Jul 11, 2014 Jkt 372897 PO 20012 Frm 00001 Fmt 3857 Sfmt 3857 V:\\FILES\\BOUND VOL. WITHOUT CROP MARKS\\B.V.140\\SHENK JAMIE\n\f (200) SHENK v. COMMISSIONER 201\n\n\n claimed two dependency exemption deductions, so that one\n child was claimed on both parents’ returns. R allowed to P the\n dependency exemption deduction for one of the children but\n disallowed his claim for the dependency exemption deduction\n for the child who had also been claimed by the custodial\n parent. At trial P contended he is entitled to a dependency\n exemption deduction for all three children. Held: Since the\n custodial parent did not execute, and P could not and did not\n attach to his return, any Form 8332 or equivalent release, P\n is not entitled under I.R.C. sec. 152(e)(2)(A) to claim the\n dependency exemption deduction or the child tax credit. Held,\n further, where both the custodial parent and the noncustodial\n parent have claimed for the same year a dependency exemp-\n tion deduction for the same child, a declaration signed by the\n custodial parent after the period of limitations for assess-\n ments has expired as to the custodial parent could not qualify\n under I.R.C. sec. 152(e)(2)(A), and therefore there is no reason\n to grant P’s request to leave the record open so that he may\n obtain and proffer such a declaration. Held, further, P is not\n entitled to head-of-household filing status under I.R.C. sec.\n 2(b)(1) nor to the child tax credit under I.R.C. sec. 24.\n\n Michael Keith Shenk, for himself.\n Shari Salu, for respondent.\n GUSTAFSON, Judge: The Internal Revenue Service (IRS)\n determined a deficiency of $3,136 in the 2009 Federal income\n tax of petitioner Michael Keith Shenk. Mr. Shenk petitioned\n this Court, pursuant to section 6213(a), 1 for redetermination\n of the deficiency. After Mr. Shenk’s concession that he\n received but did not report $254 in dividend income, the\n issue for decision is whether Mr. Shenk is entitled to a\n dependency exemption deduction for one of his children\n under section 151(c), a child tax credit for that child under\n section 24(a), and head-of-household filing status under sec-\n tion 2(b)(1). On these issues, we hold for the IRS.\n FINDINGS OF FACT\n\n The judgment of divorce\n Mr. Shenk was married to Julie Phillips, and they have\n three minor children—M.S., W.S., and L.S. They divorced in\n 2003. The family court’s ‘‘Judgment of Absolute Divorce’’ pro-\n 1 Unless otherwise indicated, all citations of sections refer to the Internal\n\n Revenue Code (26 U.S.C.) in effect for the tax year at issue, and all cita-\n tions of Rules refer to the Tax Court Rules of Practice and Procedure.\n\n\n\n\nVerDate Nov 24 2008 10:59 Jul 11, 2014 Jkt 372897 PO 20012 Frm 00002 Fmt 3857 Sfmt 3857 V:\\FILES\\BOUND VOL. WITHOUT CROP MARKS\\B.V.140\\SHENK JAMIE\n\f 202 140 UNITED STATES TAX COURT REPORTS (200)\n\n\n vided: that Ms. Phillips was ‘‘awarded primary residential\n custody’’ of the parties’ three children; and that Mr. Shenk\n would be liable for child support payments; but that, as to\n dependency exemptions—\"\"\",\n \"140 T.C. 200\",\n ),\n )\n site = tax.Site()\n for q, a in test_pairs:\n results = site.extract_from_text(q)\n cite_string = \"%s %s %s\" % (\n results[\"Citation\"][\"volume\"],\n results[\"Citation\"][\"reporter\"],\n results[\"Citation\"][\"page\"],\n )\n\n self.assertEqual(cite_string, a)\n print \"✓\", cite_string","def initialize_course(self):\r\n self.module_store = editable_modulestore()\r\n\r\n # Create the course\r\n self.course = CourseFactory.create(org=TEST_COURSE_ORG,\r\n number=TEST_COURSE_NUMBER,\r\n display_name=TEST_COURSE_NAME)\r\n\r\n # Add a chapter to the course\r\n chapter = ItemFactory.create(parent_location=self.course.location,\r\n display_name=TEST_SECTION_NAME)\r\n\r\n # add a sequence to the course to which the problems can be added\r\n self.problem_section = ItemFactory.create(parent_location=chapter.location,\r\n category='sequential',\r\n metadata={'graded': True, 'format': 'Homework'},\r\n display_name=TEST_SECTION_NAME)","def set_up_course(self):\r\n course = CourseFactory(start=datetime(2013, 9, 16, 7, 17, 28))\r\n course = modulestore().get_course(course.id) # pylint: disable=no-member\r\n return course","def makeDocument(fontPath):\n\n f = Font(fontPath) # Get PageBot Font instance of Variable font.\n \n W = H = PageSize\n\n # Create a new document, default to the defined page size. \n doc = Document(w=W, h=H, originTop=False, title='Text Flow', autoPages=1)\n \n view = doc.getView()\n view.padding = 0 # Aboid showing of crop marks, etc.\n view.showPageCropMarks = True\n view.showPageRegistrationMarks = True\n view.showPageFrame = True\n view.showPagePadding = True\n view.showElementOrigin = False\n view.showElementDimensions = False\n \n # Get list of pages with equal y, then equal x. \n #page = doc[0][0] # Get the single page from te document.\n page = doc.getPage(0) # Get page on pageNumber, first in row (this is only one now).\n page.name = 'Page 1'\n page.padding = PagePadding\n \n fs = newFS(f.info.familyName + ' ' + f.info.styleName, \n style=dict(font=f.name, fontSize=18, textFill=0))\n _, th = textSize(fs)\n title = newTextBox(fs, conditions=[Top2Top(), Fit2Width()],\n parent=page, h=th*1.2)\n \n circle = VariableCircle(f, s=GLYPH_NAME, name='VariableCircleSpeciment',\n parent=page, padding=4, x=100, fontSize=64,\n maxW=W-2*PagePadding, minW=100, showAxisName=True, \n # Conditions make the element move to top-left of the page.\n # And the condition that there should be no overflow, otherwise the text box\n # will try to solve it. \n conditions=[Float2Top(), Fit2Bottom(), Center2Center()],\n # Position of the origin of the element. Just to show where it is.\n # Has no effect on the position conditions. \n yAlign=BOTTOM, xAlign=LEFT, fill=CIRCLE_ELEMENT_FILL, borders=0,\n )\n \n score = doc.solve() # Try to solve all pages.\n if score.fails:\n print score.fails\n\n # To avoid circular dependent conditions, we correct the position of the title\n # on left to that the position of the circle has become.\n title.pl = circle.x - page.pl\n \n return doc # Answer the doc for further doing.","def create_course_page(self, course):\r\n\r\n course_wiki_home = reverse('course_wiki', kwargs={'course_id': course.id.to_deprecated_string()})\r\n referer = reverse(\"progress\", kwargs={'course_id': self.toy.id.to_deprecated_string()})\r\n\r\n resp = self.client.get(course_wiki_home, follow=True, HTTP_REFERER=referer)\r\n\r\n course_wiki_page = referer.replace('progress', 'wiki/' + self.toy.wiki_slug + \"/\")\r\n\r\n ending_location = resp.redirect_chain[-1][0]\r\n ending_status = resp.redirect_chain[-1][1]\r\n\r\n self.assertEquals(ending_location, 'http://testserver' + course_wiki_page)\r\n self.assertEquals(resp.status_code, 200)\r\n\r\n self.has_course_navigator(resp)","def create_course(self, modules_count, module_store, topics):\n course = CourseFactory.create(\n org=\"a\",\n course=\"b\",\n run=\"c\",\n start=datetime.now(UTC),\n default_store=module_store,\n discussion_topics=topics\n )\n CourseEnrollmentFactory.create(user=self.user, course_id=course.id)\n course_url = reverse(\"course_topics\", kwargs={\"course_id\": str(course.id)})\n # add some discussion xblocks\n for i in range(modules_count):\n ItemFactory.create(\n parent_location=course.location,\n category='discussion',\n discussion_id=f'id_module_{i}',\n discussion_category=f'Category {i}',\n discussion_target=f'Discussion {i}',\n publish_item=False,\n )\n return course_url","def test_buildTeX(self):\n version = \"3.2.1\"\n input1, output1 = self.getArbitraryLoreInputAndOutput(version)\n input2, output2 = self.getArbitraryLoreInputAndOutput(version)\n\n # Filenames are chosen by getArbitraryOutput to match the counter used\n # by getArbitraryLoreInputAndOutput.\n self.howtoDir.child(\"1.xhtml\").setContent(input1)\n self.howtoDir.child(\"2.xhtml\").setContent(input2)\n\n builder = BookBuilder()\n builder.buildTeX(self.howtoDir)\n self.assertEqual(self.howtoDir.child(\"1.tex\").getContent(), output1)\n self.assertEqual(self.howtoDir.child(\"2.tex\").getContent(), output2)","def create_course_with_unit():\r\n world.clear_courses()\r\n course = world.CourseFactory.create()\r\n world.scenario_dict['COURSE'] = course\r\n section = world.ItemFactory.create(parent_location=course.location)\r\n world.ItemFactory.create(\r\n parent_location=section.location,\r\n category='sequential',\r\n display_name='Subsection One',\r\n )\r\n user = create_studio_user(is_staff=False)\r\n add_course_author(user, course)\r\n\r\n log_into_studio()\r\n world.css_click('a.course-link')\r\n\r\n world.wait_for_js_to_load()\r\n css_selectors = [\r\n 'div.section-item a.expand-collapse', 'a.new-unit-item'\r\n ]\r\n for selector in css_selectors:\r\n world.css_click(selector)\r\n\r\n world.wait_for_mathjax()\r\n world.wait_for_xmodule()\r\n\r\n assert world.is_css_present('ul.new-component-type')","def test_create_course(self):\r\n self.assert_created_course()","def cmd_makecldf(self, args):\n wl = lingpy.Wordlist(self.raw_dir.joinpath(\"GEM-CNL.csv\").as_posix())\n concepts = args.writer.add_concepts(\n id_factory=lambda x: x.id.split(\"-\")[-1] + \"_\" + slug(x.english), lookup_factory=\"Name\"\n )\n for concept in self.conceptlists[0].concepts.values():\n for cis in concept.attributes[\"lexibank_gloss\"]:\n if cis not in concepts:\n concepts[cis] = concepts[concept.english]\n\n languages = args.writer.add_languages(lookup_factory=\"STEDT_Name\")\n args.writer.add_sources()\n\n for idx, language, concept, value, pos in wl.iter_rows(\n \"doculect\", \"concept\", \"reflex\", \"gfn\"\n ):\n # Fix for 251479\n if concept == \"top (i.e. highest point\":\n concept = \"top (i.e. highest point)\"\n\n if concept not in concepts:\n args.log.warning(concept)\n else:\n args.writer.add_forms_from_value(\n Language_ID=languages[language],\n Parameter_ID=concepts[concept],\n Value=value,\n Source=[\"Marrison1967\"],\n )","def __init__(self, tutorial):\n self._tex = \"\"\n\n self.texPreamble(tutorial)\n self.texTutorialHeader(tutorial)\n\n for lecture in self.children(tutorial):\n self.texLectureHeader(lecture)\n\n for slide in self.children(lecture):\n self.texSlideHeader(slide)\n for section in slide.sections:\n self.texSlideSection(section)\n self.texSlideFooter(slide)\n\n self.texLectureFooter(lecture)\n\n self.texTutorialFooter(tutorial)\n self.texPostamble(tutorial)","def do_ascii(catalog):\n task_str = catalog.get_current_task_str()\n\n\n # Howerton Catalog\n datafile = os.path.join(catalog.get_current_task_repo(), 'ASCII',\n 'vizier_J_MNRAS_441_1186_table1 J_MNRAS_441_1186_table3_20200403.csv')\n data = read(datafile, format='csv')\n for rrow in pbar(data, task_str):\n row = dict((x, str(rrow[x])) for x in rrow.columns)\n# if any(x in row['Notes'].lower() for x in ['artifact']):\n# continue\n# ctypes = row['Type'].split('/')\n# nonsne = False\n# for ct in ctypes:\n# if ct.replace('?', '') in catalog.nonsnetypes:\n# nonsne = True\n# else:\n# nonsne = False\n# break\n# if nonsne:\n# continue\n name, source = catalog.new_entry(\n row['CRTS'],\n srcname='CRTS',\n bibcode='2014MNRAS.441.1186D')\n# if row['IAU des.'] != '--':\n# catalog.entries[name].add_quantity(SUPERNOVA.ALIAS,\n# row['IAU des.'], source)\n# for ct in ctypes:\n# catalog.entries[name].add_quantity(SUPERNOVA.CLAIMED_TYPE, ct,\n# source)\n# catalog.entries[name].add_quantity(SUPERNOVA.DISCOVERER,\n# row['Discoverer'], source)\n# date = row['Discovery'].split('/')\n# date = '/'.join([date[-1].zfill(2), date[0].zfill(2), date[1]])\n# catalog.entries[name].add_quantity(SUPERNOVA.DISCOVER_DATE, date,\n# source)\n catalog.entries[name].add_quantity(CATACLYSMIC.VISUAL_MAG, row['Vmax'],\n source)\n catalog.entries[name].add_quantity(CATACLYSMIC.RA, row['RAJ2000'], source)\n catalog.entries[name].add_quantity(CATACLYSMIC.DEC, row['DEJ2000'], source)\n catalog.journal_entries()\n\n # Howerton Catalog","def createchore():\n return render_template(\"newchore.html\")","def __init__(self,\n title=\"[CDR Form Letter]\",\n subject=None,\n template=None,\n pngName=\"dhhslogo.png\",\n author=\"cdr\",\n company=\"CIPS\",\n binImage=False,\n invitePara=\"\"):\n\n Document.__init__(self, title, author, company, subject)\n self.generator = \"CDR RTF Writer\"\n self.pngName = pngName # location of bitmap file for logo\n self.margL = 1080 # 3/4-inch left margin\n self.margR = 720 # 1/2-inch right margin\n self.margT = 1080 # 5/8-inch top margin\n self.margB = 1260 # 7/8-inch bottom margin\n self.fSize = 21 # 10.5-point default font size\n self.baskervilleFont = self.addFont(\"Baskerville Old Face\",\n \"roman\",\n \"Times New Roman\")\n self.__addLetterHead(binImage)\n if template:\n try:\n with open(template) as fp:\n body = fp.read()\n except Exception as e:\n raise Exception(\"loading %s: %s\" % (template, str(e)))\n if invitePara:\n replacement = \"@@MISCDOC:%s@@\" % invitePara\n body = body.replace(\"@@INVITATION@@\", replacement)\n pattern = re.compile(\"@@MISCDOC:(.*?)@@\")\n body = pattern.sub(lambda p: self.__loadMiscDoc(p), body)\n self.addRawContent(body)","def create_course_for_deletion(self):\r\n course = modulestore().create_course('nihilx', 'deletion', 'deleting_user')\r\n root = course.location.version_agnostic().for_branch('draft')\r\n for _ in range(4):\r\n self.create_subtree_for_deletion(root, ['chapter', 'vertical', 'problem'])\r\n return modulestore().get_item(root)","def testCourses(self):\n self.person.invokeFactory(type_name=\"FSDCourse\", id=\"test-course\")\n self.failUnless('test-course' in self.person.contentIds())\n self.failUnless('test-course' in [c.id for c in self.person.getCourses()])","def test_course_plain_english(self):\r\n self.client = AjaxEnabledTestClient()\r\n self.client.login(username=self.uname, password=self.password)\r\n\r\n resp = self.client.get_html('/course/')\r\n self.assertContains(resp,\r\n '<h1 class=\"page-header\">My Courses</h1>',\r\n status_code=200,\r\n html=True)","def CrearLlaveMusicaContext(catalog):\n Lista = ['instrumentalness' , 'liveness' , 'speechiness' , 'danceability' , 'valence' ,\n 'loudness' , 'tempo' , 'acousticness' , 'energy']\n \n for contenido in Lista:\n mp.put(catalog['caraContenido'], contenido, om.newMap('RBT'))","def test_with_coursexml(self):\r\n with open(self.good_tar) as gtar:\r\n args = {\"name\": self.good_tar, \"course-data\": [gtar]}\r\n resp = self.client.post(self.url, args)\r\n\r\n self.assertEquals(resp.status_code, 200)","def homepage():\n \n return render_template(\"coursePage.html\",courseName = \"Welcome\", Courses = COURSES, blank = 1)","def catalogConvert():\n ret = libxml2mod.xmlCatalogConvert()\n return ret","def catalogs(env):\n envs = environments()\n check_env(env, envs)\n\n if app.config['ENABLE_CATALOG']:\n nodenames = []\n catalog_list = []\n query = AndOperator()\n\n if env != '*':\n query.add(EqualsOperator(\"catalog_environment\", env))\n\n query.add(NullOperator(\"catalog_timestamp\", False))\n\n order_by_str = '[{\"field\": \"certname\", \"order\": \"asc\"}]'\n nodes = get_or_abort(puppetdb.nodes,\n query=query,\n with_status=False,\n order_by=order_by_str)\n nodes, temp = tee(nodes)\n\n for node in temp:\n nodenames.append(node.name)\n\n for node in nodes:\n table_row = {\n 'name': node.name,\n 'catalog_timestamp': node.catalog_timestamp\n }\n\n if len(nodenames) > 1:\n form = CatalogForm()\n\n form.compare.data = node.name\n form.against.choices = [(x, x) for x in nodenames\n if x != node.name]\n table_row['form'] = form\n else:\n table_row['form'] = None\n\n catalog_list.append(table_row)\n\n return render_template(\n 'catalogs.html',\n nodes=catalog_list,\n envs=envs,\n current_env=env)\n else:\n log.warn('Access to catalog interface disabled by administrator')\n abort(403)","def test_course_overview_view_with_course(self):\r\n course = CourseFactory.create(org='MITx', course='999', display_name='Robot Super Course')\r\n resp = self._show_course_overview(course.id)\r\n self.assertContains(\r\n resp,\r\n '<article class=\"courseware-overview\" data-locator=\"location:MITx+999+Robot_Super_Course+course+Robot_Super_Course\" data-course-key=\"slashes:MITx+999+Robot_Super_Course\">',\r\n status_code=200,\r\n html=True\r\n )","def compile(self,**attrs):\n\n\n\t\tpath_to_txt = os.path.join(self.path_raw,'COCA Text')\n\t\tpath_to_sources = os.path.join(self.path_raw,'coca-sources_2017_12.txt')\n\n\t\tif not os.path.exists(path_to_txt) or not os.path.exists(path_to_sources):\n\t\t\tprint(f'Place in {self.path_raw} the following files:\\n * COCA Text\\n * coca-sources_2017_12.txt')\n\t\t\treturn\n\n\t\t#txt\n\t\tself.compile_txt()\n\t\t# metadata\n\t\tself.compile_metadata()","def generate_tex(title, author, txt, indices=None):\n with open('head', 'r') as head,\\\n open('stegano.tex', 'w') as file_tex:\n\n # First lines are\n # - Title\n # - Author\n\n file_tex.write(\"\".join(head.readlines())) # Write heading\n file_tex.write(\"\\\\title{{{}}}\\n\".format(title))\n file_tex.write(\"\\\\author{{{}}}\\n\".format(author))\n file_tex.write(\"\\\\date{{}}\\n\")\n\n file_tex.write(\"\\\\begin{document}\\n\\\\maketitle\\n\")\n\n # Write char by char, adding decoration when indices is reach\n i = 0\n while i < len(txt):\n if i in indices:\n file_tex.write(secret_char(txt[i]))\n else:\n file_tex.write(non_secret_char(txt[i]))\n i += 1\n\n file_tex.write(\"\\\\end{document}\")","def _create_course(self, default, course_key):\r\n if default == 'split':\r\n offering = course_key.offering.replace('/', '.')\r\n else:\r\n offering = course_key.offering\r\n course = self.store.create_course(course_key.org, offering, store_name=default)\r\n category = self.import_chapter_location.category\r\n block_id = self.import_chapter_location.name\r\n chapter = self.store.create_item(\r\n # don't use course_location as it may not be the repr\r\n course.location, category, location=self.import_chapter_location, block_id=block_id\r\n )\r\n if isinstance(course.id, CourseLocator):\r\n self.course_locations[self.MONGO_COURSEID] = course.location.version_agnostic()\r\n self.import_chapter_location = chapter.location.version_agnostic()\r\n else:\r\n self.assertEqual(course.id, course_key)\r\n self.assertEqual(chapter.location, self.import_chapter_location)","def main():\n init_latex()","def _DocSim(self,df,a):\r\n #Obtain the descriptions of the two input courses.\r\n textA = df['description'][a]\r\n #Obtain the document embedding vector for each description.\r\n vectorA = self.DocVecModel.infer_vector([textA], alpha=0.1, min_alpha=0.0001, steps=300)\r\n return vectorA","def generate_trivia(titles, output, lang):\n\n click.echo('Language: {0}'.format(lang))\n\n # Use the sample articles if the user didn't supply any\n if len(titles) == 0:\n titles = SAMPLE_ARTICLES\n\n # Retrieve the trivia sentences\n questions = []\n for article in titles:\n click.echo('Analyzing \\'{0}\\''.format(article))\n article = Article(title=article, lang=lang)\n questions = questions + article.generate_trivia_sentences(lang=lang)\n\n # Output to stdout or JSON\n if output:\n output_file = output.open()\n json.dump(questions, output_file, sort_keys=True, indent=4)\n click.echo('Output stored in {0}'.format(output.name))\n else:\n click.echo(json.dumps(questions, sort_keys=True, indent=4))","def test_get_tosca_template(self):\n pass","def test_render_templates():\n water_mol = Atoms(cclib.bridge.cclib2openbabel.readfile(\"data/water.xyz\", \"xyz\"))\n if not water_mol.name:\n water_mol.name = \"data/water.xyz\"\n\n main([\"-g\", \"/tmp/foo.ADF.in\"])\n main([\"/tmp/foo.ADF.in\", \"data/water.xyz\"])\n assert_equals(\n open(\"data/water.in\").read().strip(),\n \"\"\"TITLE data/water.xyz\n\nCHARGE 0 0\n\nNumber of atoms\n 3\n\nATOMS Cartesian\nO 0.0584027061 0.0584027059 0.0000000000\nH 1.0096135406 -0.0680162466 0.0000000000\nH -0.0680162466 1.0096135407 0.0000000000\nEnd\n\nBasis\nEnd\n\nGeometry\nEnd\"\"\",\n )\n\n main([\"-g\", \"/tmp/test.GAMESS.inp\"])\n main([\"/tmp/test.GAMESS.inp\", \"data/water.xyz\"])\n assert_equals(\n open(\"data/water.inp\").read(),\n \"\"\" $CONTRL COORD=CART UNITS=ANGS $END\n\n $DATA\ndata/water.xyz\nC1\nO 8.0 0.0584027061 0.0584027059 0.0000000000\nH 1.0 1.0096135406 -0.0680162466 0.0000000000\nH 1.0 -0.0680162466 1.0096135407 0.0000000000\n $END\n\n\n\"\"\",\n )\n\n main([\"-g\", \"/tmp/hello.GAMESSUK.inp\"])\n main([\"/tmp/hello.GAMESSUK.inp\", \"data/water.xyz\"])\n assert_equals(open(\"data/water.inp\").read(), water_mol.to_string(\"gukin\"))\n\n main([\"-g\", \"/tmp/hello.world.Gaussian.gjf\"])\n main([\"/tmp/hello.world.Gaussian.gjf\", \"data/water.xyz\"])\n assert_equals(\n open(\"data/water.gjf\").read(),\n \"\"\"#Put Keywords Here, check Charge and Multiplicity.\n\n data/water.xyz\n\n0 1\nO 0.0584027061 0.0584027059 0.0000000000\nH 1.0096135406 -0.0680162466 0.0000000000\nH -0.0680162466 1.0096135407 0.0000000000\n\n\"\"\",\n )\n\n main([\"-g\", \"/tmp/bar.Jaguar.in\"])\n main([\"/tmp/bar.Jaguar.in\", \"data/water.xyz\"])\n assert_equals(open(\"data/water.in\").read(), water_mol.to_string(\"jin\"))\n\n main([\"-g\", \"/tmp/foo.Molpro.inp\"])\n main([\"/tmp/foo.Molpro.inp\", \"data/water.xyz\"])\n assert_equals(open(\"data/water.inp\").read(), water_mol.to_string(\"mp\"))\n\n main([\"-g\", \"/tmp/example.MOPAC.mop\"])\n main([\"/tmp/example.MOPAC.mop\", \"data/water.xyz\"])\n assert_equals(\n open(\"data/water.mop\").read(),\n \"\"\"CHARGE=0 MS=0.0\ndata/water.xyz\n\nO 0.05840 1 0.05840 1 0.00000 1\nH 1.00961 1 -0.06802 1 0.00000 1\nH -0.06802 1 1.00961 1 0.00000 1\n\"\"\",\n )\n\n main([\"-g\", \"/tmp/bar.MPQC.in\"])\n main([\"/tmp/bar.MPQC.in\", \"data/water.xyz\"])\n assert_equals(open(\"data/water.in\").read(), water_mol.to_string(\"mpqcin\"))\n\n main([\"-g\", \"/tmp/foo.NWChem.nw\"])\n main([\"/tmp/foo.NWChem.nw\", \"data/water.xyz\"])\n assert_equals(\n open(\"data/water.nw\").read(),\n \"\"\"start molecule\n\ntitle data/water.xyz\n\ngeometry units angstroms print xyz autosym\nO 0.0584027061 0.0584027059 0.0000000000\nH 1.0096135406 -0.0680162466 0.0000000000\nH -0.0680162466 1.0096135407 0.0000000000\nend\n\"\"\",\n )\n\n main([\"-g\", \"/tmp/example.ORCA.inp\"])\n main([\"/tmp/example.ORCA.inp\", \"data/water.xyz\"])\n assert_equals(\n open(\"data/water.inp\").read(),\n \"\"\"# data/water.xyz\n! Opt\n\n* xyz 0 1\nO 0.0584027061 0.0584027059 0.0000000000\nH 1.0096135406 -0.0680162466 0.0000000000\nH -0.0680162466 1.0096135407 0.0000000000\n*\n\"\"\",\n )\n\n main([\"-g\", \"/tmp/bar.Psi.dat\"])\n main([\"/tmp/bar.Psi.dat\", \"data/water.xyz\"])\n assert_equals(\n open(\"data/water.dat\").read(),\n \"\"\"# data/water.xyz\n\nmolecule {\n0 1\nO 0.0584027061 0.0584027059 0.0000000000\nH 1.0096135406 -0.0680162466 0.0000000000\nH -0.0680162466 1.0096135407 0.0000000000\nunits angstrom\n}\n\noptimize('scf')\n\"\"\",\n )\n\n main([\"-g\", \"/tmp/example.QChem.in\"])\n main([\"/tmp/example.QChem.in\", \"data/water.xyz\"])\n assert_equals(open(\"data/water.in\").read(), water_mol.to_string(\"qcin\"))\n\n main([\"-g\", \"/tmp/foo.ZINDO.input\"])\n main([\"/tmp/foo.ZINDO.input\", \"data/water.xyz\"])\n assert_equals(open(\"data/water.input\").read(), water_mol.to_string(\"zin\"))","def build_general_course(self):\n\n courses = {}\n\n if self.__build_analyze is False:\n self.build_analysis()\n\n courses[\"taxa_conhecimento\"] = self.analysis[\"taxa_conhecimento\"]\n courses[\"taxa_reprovacao\"] = self.analysis[\"taxa_reprovacao_absoluta\"]\n courses[\"taxa_trancamento\"] = self.analysis[\"taxa_trancamento\"]\n\n # cria cache\n cache = {}\n note = self.analysis[\"general_note_statistic\"]\n for rate_it in self.__rates:\n rate_calc = self.analysis[\"general_rates\"][rate_it.name][0]\n for course in self.analysis[\"courses\"].index:\n if course not in cache:\n cache[course] = {}\n cache[course][rate_it.name] = rate_calc[course]\n cache[course][\"nota\"] = [note[0][course], note[1][course]]\n\n courses[\"cache\"] = cache\n\n # cria o campo compara_aprov\n courses[\"compara_aprov\"] = self.analysis[\"graph_course\"]\n\n # cria o campo courses\n courses[\"disciplinas\"] = self.analysis[\"courses\"].to_dict()\n\n return courses","def parse_file(self, file_lines):\n # separate the file into chunks of text\n chunks, chunk = [], []\n # check to see what format the corpus is in, we assume that the headers are the same for all\n # texts in the file... (maybe not safe?)\n if re.match('Primary publication:', file_lines[0]):\n header = re.compile('Primary publication:')\n else:\n header = re.compile(r'&?P\\d{6}')\n for line in file_lines:\n if header.match(line):\n if len(chunk) > 0: # pylint: disable=len-as-condition\n chunks.append(chunk)\n chunk = [line]\n else:\n if len(line) > 0: # pylint: disable=len-as-condition\n chunk.append(line)\n chunks.append(chunk)\n self.chunks = chunks\n # create a rich catalog from the chunks\n re_translit = re.compile(r'(\\d+\\'?\\.) ?(.*)')\n re_normaliz = re.compile(r'(#tr\\.ts:) ?(.*)')\n re_translat = re.compile(r'(#tr\\.en:) ?(.*)')\n for chunk in self.chunks:\n text = chunk\n if chunk[0].startswith('Primary publication:'):\n # we've got full metadata, add additional parsing later\n metadata = chunk[:25]\n text = chunk[26:]\n else: # no metadata\n metadata = []\n pnum = ''.join([c for c in text[0].split('=')[0] if c != '&']).rstrip()\n edition = text[0].split('=')[1].lstrip()\n text = text[3:]\n translit = []\n normaliz = []\n translat = []\n for line in text:\n if re.match(r'\\d+\\'?\\.', line):\n translit.append(re_translit.match(line).groups()[1])\n if line.startswith('#tr.ts:'):\n normaliz.append(re_normaliz.match(line).groups()[1])\n if line.startswith('#tr.en:'):\n translat.append(re_translat.match(line).groups()[1])\n self.catalog[pnum] = {'metadata': metadata,\n 'pnum': pnum,\n 'edition': edition,\n 'raw_text': text,\n 'transliteration': translit,\n 'normalization': normaliz,\n 'translation': translat}","def index():\n user, user_id = get_user()\n # Get this user's course with their roles\n my_courses = []\n if user:\n my_courses = user.get_courses()\n # Get all public courses\n public_courses = Course.get_public()\n\n return render_template('courses/index.html',\n user=user,\n my_courses=my_courses,\n public_courses=public_courses)","def make_bibtex(self):\n\n\t\t# bib = requests.request('GET', 'http://dx.doi.org/' + self.doi, ","def _create_design(self, state: Dict[str, Any], new_fname: str) -> Tuple[str, str]:\n output = self.template.render(**state)\n design_folder = os.path.join(self.gen_dir, new_fname)\n os.makedirs(design_folder, exist_ok=True)\n fpath = os.path.join(design_folder, new_fname + '.scs')\n with open(fpath, 'w') as f:\n f.write(output)\n f.close()\n return design_folder, fpath","def _construct_course_son(org, course, run):\r\n return bson.son.SON([\r\n ('org', org),\r\n ('course', course),\r\n ('name', run)\r\n ])","def test_course_info(self):\r\n # Regex of first 3 columns of course information table row for\r\n # test course loaded from git. Would not have sha1 if\r\n # git_info_for_course failed.\r\n table_re = re.compile(r\"\"\"\r\n <tr>\\s+\r\n <td>edX\\sAuthor\\sCourse</td>\\s+ # expected test git course name\r\n <td>MITx/edx4edx/edx4edx</td>\\s+ # expected test git course_id\r\n <td>[a-fA-F\\d]{40}</td> # git sha1 hash\r\n \"\"\", re.VERBOSE)\r\n\r\n self._setstaff_login()\r\n self._mkdir(getattr(settings, 'GIT_REPO_DIR'))\r\n\r\n # Make sure we don't have any git hashes on the page\r\n response = self.client.get(reverse('sysadmin_courses'))\r\n self.assertNotRegexpMatches(response.content, table_re)\r\n\r\n # Now add the course and make sure it does match\r\n response = self._add_edx4edx()\r\n self.assertRegexpMatches(response.content, table_re)","def create_course_with_orphans(self, default_store):\n course = CourseFactory.create(default_store=default_store)\n\n # create chapters and add them to course tree\n chapter1 = self.store.create_child(self.user.id, course.location, 'chapter', \"Chapter1\")\n self.store.publish(chapter1.location, self.user.id)\n\n chapter2 = self.store.create_child(self.user.id, course.location, 'chapter', \"Chapter2\")\n self.store.publish(chapter2.location, self.user.id)\n\n # orphan chapter\n orphan_chapter = self.store.create_item(self.user.id, course.id, 'chapter', \"OrphanChapter\")\n self.store.publish(orphan_chapter.location, self.user.id)\n\n # create vertical and add it as child to chapter1\n vertical1 = self.store.create_child(self.user.id, chapter1.location, 'vertical', \"Vertical1\")\n self.store.publish(vertical1.location, self.user.id)\n\n # create orphan vertical\n orphan_vertical = self.store.create_item(self.user.id, course.id, 'vertical', \"OrphanVert\")\n self.store.publish(orphan_vertical.location, self.user.id)\n\n # create component and add it to vertical1\n html1 = self.store.create_child(self.user.id, vertical1.location, 'html', \"Html1\")\n self.store.publish(html1.location, self.user.id)\n\n # create component and add it as a child to vertical1 and orphan_vertical\n multi_parent_html = self.store.create_child(self.user.id, vertical1.location, 'html', \"multi_parent_html\")\n self.store.publish(multi_parent_html.location, self.user.id)\n\n orphan_vertical.children.append(multi_parent_html.location)\n self.store.update_item(orphan_vertical, self.user.id)\n\n # create an orphaned html module\n orphan_html = self.store.create_item(self.user.id, course.id, 'html', \"OrphanHtml\")\n self.store.publish(orphan_html.location, self.user.id)\n\n self.store.create_child(self.user.id, course.location, 'static_tab', \"staticuno\")\n self.store.create_child(self.user.id, course.location, 'course_info', \"updates\")\n\n return course","def get_import_course(self):\n\n template_values = {}\n template_values['page_title'] = self.format_title('Import Course')\n\n annotations = ImportCourseRESTHandler.SCHEMA_ANNOTATIONS_DICT()\n if not annotations:\n template_values['main_content'] = 'No courses to import from.'\n self.render_page(template_values)\n return\n\n exit_url = self.canonicalize_url('/dashboard')\n rest_url = self.canonicalize_url(ImportCourseRESTHandler.URI)\n form_html = oeditor.ObjectEditor.get_html_for(\n self,\n ImportCourseRESTHandler.SCHEMA_JSON,\n annotations,\n None, rest_url, exit_url,\n auto_return=True,\n save_button_caption='Import',\n required_modules=ImportCourseRESTHandler.REQUIRED_MODULES)\n\n template_values = {}\n template_values['page_title'] = self.format_title('Import Course')\n template_values['page_description'] = messages.IMPORT_COURSE_DESCRIPTION\n template_values['main_content'] = form_html\n self.render_page(template_values)","def __init__(self, txt='', unicodeEncoding='utf-8'):\n # __document capture the document level structure\n # for each sentence and then put in the archives when the next sentence\n # is processed\n super(ConTextMarkup, self).__init__(__txt=None,\n __rawtxt=txt,\n __scope=None,\n __SCOPEUPDATED=False)\n self.__document = nx.DiGraph()\n self.__document.add_node(\"top\", category=\"document\")\n self.__VERBOSE = False\n self.__tagID = 0\n self.__unicodeEncoding = unicodeEncoding","def create_course(self, org, offering, user_id=None, fields=None, store_name='default', **kwargs):\r\n store = self.modulestores[store_name]\r\n\r\n if not hasattr(store, 'create_course'):\r\n raise NotImplementedError(u\"Cannot create a course on store %s\" % store_name)\r\n\r\n return store.create_course(org, offering, user_id, fields, **kwargs)","def test_course_with_accents(self):\r\n self.client = AjaxEnabledTestClient()\r\n self.client.login(username=self.uname, password=self.password)\r\n\r\n resp = self.client.get_html(\r\n '/course/',\r\n {},\r\n HTTP_ACCEPT_LANGUAGE='eo'\r\n )\r\n\r\n TEST_STRING = (\r\n u'<h1 class=\"title-1\">'\r\n u'My \\xc7\\xf6\\xfcrs\\xe9s L#'\r\n u'</h1>'\r\n )\r\n\r\n self.assertContains(resp,\r\n TEST_STRING,\r\n status_code=200,\r\n html=True)","def set_up_course(self, **course_kwargs):\r\n course = CourseFactory(**course_kwargs)\r\n chapter = ItemFactory(category='chapter', parent_location=course.location) # pylint: disable=no-member\r\n section = ItemFactory(category='sequential', parent_location=chapter.location, due=datetime(2013, 9, 18, 11, 30, 00))\r\n vertical = ItemFactory(category='vertical', parent_location=section.location)\r\n ItemFactory(category='problem', parent_location=vertical.location)\r\n\r\n course = modulestore().get_course(course.id) # pylint: disable=no-member\r\n self.assertIsNotNone(course.get_children()[0].get_children()[0].due)\r\n return course","def build_course(self):\n courses = []\n aprovacao_d = {}\n # semestral\n for rate_it in self.__semestral_rate:\n # pega uma lista no qual o primeiro elemento é a taxa, o segundo\n # e o terceiro são quantidades\n rate_data = self.analysis[\"semestral_rate\"][rate_it.name]\n for i in rate_data[0].index:\n if i[0] not in aprovacao_d:\n aprovacao_d[i[0]] = {}\n\n periodo = str(i[1]) + \"/\" + str(i[2])\n aprovacao_d[i[0]][periodo] = [\n float(rate_data[0][i]),\n int(rate_data[1][i]),\n int(rate_data[2][i]),]\n\n note = self.analysis[\"general_note_statistic\"]\n note_last_year = self.analysis[\"last_year_statistic\"]\n for course in self.analysis[\"courses\"].index:\n course_dict = {}\n course_dict[\"disciplina_codigo\"] = course\n course_dict[\"disciplina_nome\"] = self.analysis[\"courses\"][course]\n \n # If the course code is related to more than one name,\n # concatenate these names into an unique string\n if type(course_dict[\"disciplina_nome\"]) != str:\n new_course_name = \" | \".join(list(course_dict[\"disciplina_nome\"]))\n course_dict[\"disciplina_nome\"] = new_course_name\n # quantidade de matriculas\n count = self.analysis[\"general_count_submission\"][course]\n course_dict[\"qtd_alunos\"] = count\n # notas\n course_dict[\"qtd_cursada_aprov\"] = self.analysis[\"coursed_ratio\"][course]\n course_dict[\"nota\"] = [note[0][course], note[1][course]]\n course_dict[\"nota_ultimo_ano\"] = [\n note_last_year[0][course],\n note_last_year[1][course]\n ]\n # taxas\n for rate_it in self.__rates:\n rate_data = self.analysis[\"general_rates\"][rate_it.name]\n course_dict[rate_it.name] = float(rate_data[0][course])\n course_str = rate_it.name.replace(\"taxa\", \"qtd\")\n # count_sel define qual quantidade vai para o json, a especifica\n # ou geral\n course_dict[course_str] = int(\n rate_data[rate_it.count_sel][course])\n # rate_calc = self.analysis[\"general_rates\"][rate_it.name][0]\n\n # taxas do ultimo anos\n course_dict[\"taxa_reprovacao_ultimo_absoluto\"] = self.analysis[\"last_year_taxa_reprovacao_absoluta\"][course]\n course_dict[\"taxa_reprovacao_ultimo_frequencia\"] = self.analysis[\"last_year_taxa_reprovacao_frequencia\"][course]\n\n course_dict[\"grafico_qtd_cursada_aprov\"] = self.analysis[\"coursed_count\"][course]\n course_dict[\"aprovacao_semestral\"] = aprovacao_d[course]\n courses.append(course_dict)\n return courses","def download_course_interactive(self):\n self.domain_prompt()\n self.course_prompt()\n self.get_course_page()\n self.get_course_title()\n self.get_course_unit_titles()\n self.get_course_unit_slugs()\n self.get_course_unit_urls()\n\n print(\"\\nGenerating Path Slugs...\\n\")\n self.get_course_all_slugs()\n self.get_course_youtube_ids()\n self.download_course_videos()","def test_unicode(self):\r\n\r\n print(\"Starting import\")\r\n modulestore = XMLModuleStore(DATA_DIR, course_dirs=['test_unicode'])\r\n courses = modulestore.get_courses()\r\n self.assertEquals(len(courses), 1)\r\n course = courses[0]\r\n\r\n print(\"course errors:\")\r\n\r\n # Expect to find an error/exception about characters in \"®esources\"\r\n expect = \"Invalid characters\"\r\n errors = [\r\n (msg.encode(\"utf-8\"), err.encode(\"utf-8\"))\r\n for msg, err\r\n in modulestore.get_course_errors(course.id)\r\n ]\r\n\r\n self.assertTrue(any(\r\n expect in msg or expect in err\r\n for msg, err in errors\r\n ))\r\n chapters = course.get_children()\r\n self.assertEqual(len(chapters), 4)","def create_corpus_for_artist(artist):\n raise NotImplementedError","def newCatalog(sgml):\n ret = libxml2mod.xmlNewCatalog(sgml)\n if ret is None:raise treeError('xmlNewCatalog() failed')\n return catalog(_obj=ret)","def create_new_course(request):\r\n if not auth.has_access(request.user, CourseCreatorRole()):\r\n raise PermissionDenied()\r\n\r\n org = request.json.get('org')\r\n number = request.json.get('number')\r\n display_name = request.json.get('display_name')\r\n run = request.json.get('run')\r\n\r\n # allow/disable unicode characters in course_id according to settings\r\n if not settings.FEATURES.get('ALLOW_UNICODE_COURSE_ID'):\r\n if _has_non_ascii_characters(org) or _has_non_ascii_characters(number) or _has_non_ascii_characters(run):\r\n return JsonResponse(\r\n {'error': _('Special characters not allowed in organization, course number, and course run.')},\r\n status=400\r\n )\r\n\r\n try:\r\n course_key = SlashSeparatedCourseKey(org, number, run)\r\n\r\n # instantiate the CourseDescriptor and then persist it\r\n # note: no system to pass\r\n if display_name is None:\r\n metadata = {}\r\n else:\r\n metadata = {'display_name': display_name}\r\n\r\n # Set a unique wiki_slug for newly created courses. To maintain active wiki_slugs for\r\n # existing xml courses this cannot be changed in CourseDescriptor.\r\n # # TODO get rid of defining wiki slug in this org/course/run specific way and reconcile\r\n # w/ xmodule.course_module.CourseDescriptor.__init__\r\n wiki_slug = u\"{0}.{1}.{2}\".format(course_key.org, course_key.course, course_key.run)\r\n definition_data = {'wiki_slug': wiki_slug}\r\n\r\n # Create the course then fetch it from the modulestore\r\n # Check if role permissions group for a course named like this already exists\r\n # Important because role groups are case insensitive\r\n if CourseRole.course_group_already_exists(course_key):\r\n raise InvalidLocationError()\r\n\r\n fields = {}\r\n fields.update(definition_data)\r\n fields.update(metadata)\r\n\r\n # Creating the course raises InvalidLocationError if an existing course with this org/name is found\r\n new_course = modulestore('direct').create_course(\r\n course_key.org,\r\n course_key.offering,\r\n fields=fields,\r\n )\r\n\r\n # can't use auth.add_users here b/c it requires request.user to already have Instructor perms in this course\r\n # however, we can assume that b/c this user had authority to create the course, the user can add themselves\r\n CourseInstructorRole(new_course.id).add_users(request.user)\r\n auth.add_users(request.user, CourseStaffRole(new_course.id), request.user)\r\n\r\n # seed the forums\r\n seed_permissions_roles(new_course.id)\r\n\r\n # auto-enroll the course creator in the course so that \"View Live\" will\r\n # work.\r\n CourseEnrollment.enroll(request.user, new_course.id)\r\n _users_assign_default_role(new_course.id)\r\n\r\n return JsonResponse({\r\n 'url': reverse_course_url('course_handler', new_course.id)\r\n })\r\n\r\n except InvalidLocationError:\r\n return JsonResponse({\r\n 'ErrMsg': _(\r\n 'There is already a course defined with the same '\r\n 'organization, course number, and course run. Please '\r\n 'change either organization or course number to be unique.'\r\n ),\r\n 'OrgErrMsg': _(\r\n 'Please change either the organization or '\r\n 'course number so that it is unique.'),\r\n 'CourseErrMsg': _(\r\n 'Please change either the organization or '\r\n 'course number so that it is unique.'),\r\n })\r\n except InvalidKeyError as error:\r\n return JsonResponse({\r\n \"ErrMsg\": _(\"Unable to create course '{name}'.\\n\\n{err}\").format(name=display_name, err=error.message)}\r\n )","def create_file(date, title, text, n):\r\n \"\"\"with date as file name and text as content\"\"\"\r\n filename = \"%s_%s.txt\" % (date, n)\r\n with io.open(filename, \"w+\", encoding=\"UTF8\") as newfile:\r\n text = text.replace(\" \", \"\") #remove all spaces\r\n sentences= re.sub(\"，|。\", \"\\n\", text) #one sentence per line\r\n newfile.write(title+\"\\n\")\r\n newfile.write(date+\"\\n\")\r\n newfile.write(sentences)\r\n print(filename)","def create_coll(request):\n form = CollForm(request)\n c = {}\n return render(request, \"browse/curate.html\", c)","def create_citation(cit):\n if cit is not None:\n if cit['citation-type'] == \"BIBTEX\":\n return pybtex.database.parse_string(cit['citation-value'], \"bibtex\")\n return None","def catalogue(self, fix_author=True):\n response = requests.get(CATALOGUE_URL)\n\n flags = DOTALL | UNICODE\n tables = findall(r'<table.*?</table>', response.text, flags=flags)\n rows = findall(r'<tr.*?</tr>', tables[1], flags=flags)\n\n books = []\n for row in rows[1:]:\n elements = findall('<td.*?</td>', row, flags=flags)\n book_id, title = findall(r'/(.*?)/\">(.*?)<',\n elements[4], flags=flags)[0]\n try:\n author_id, author = findall(r'/authors/(.*?).html\">(.*?)<',\n elements[6], flags=flags)[0]\n except:\n author_id, author = '', ''\n if fix_author:\n # fix_author name collision. TODO\n author = globals()['fix_author'](author)\n book = {\n 'type': findall(r'alt=\"(.*?)\">', elements[0], flags=flags)[0],\n 'book_id': book_id,\n 'title': title,\n 'author_id': author_id,\n 'author': author,\n 'year': elements[8][15:-5],\n 'language': elements[10][-9:-7]\n }\n books.append(book)\n return DataFrame(books)","def generate_latex_table(dictionary,filename,location=\".\"):\n if type(filename) != str:\n raise TypeError('filename should be string')\n if type(dictionary) != dict:\n raise TypeError('dictionary should be dictionary')\n\n head_code = \"\"\"\\\\documentclass{article}\n%In the preamble section include the arabtex and utf8 packages\n\\\\usepackage{arabtex}\n\\\\usepackage{utf8}\n\\\\usepackage{longtable}\n\\\\usepackage{color, colortbl}\n\\\\usepackage{supertabular}\n\\\\usepackage{multicol}\n\\\\usepackage{geometry}\n\\\\geometry{left=.1in, right=.1in, top=.1in, bottom=.1in}\n\n\\\\begin{document}\n\\\\begin{multicols}{6}\n\\\\setcode{utf8}\n\n\\\\begin{center}\"\"\"\n\n tail_code = \"\"\"\\\\end{center}\n\\\\end{multicols}\n\\\\end{document}\"\"\"\n\n begin_table = \"\"\"\\\\begin{tabular}{ P{2cm} P{1cm}}\n\\\\textbf{words} & \\\\textbf{\\\\#} \\\\\\\\\n\\\\hline\n\\\\\\\\[0.01cm]\"\"\"\n end_table= \"\"\"\\\\end{tabular}\"\"\"\n rows_num = 40\n if location != '.':\n filename = location +\"/\"+ filename\n\n try:\n file = open(filename+'.tex', 'w', encoding='utf8')\n file.write(head_code+'\\n')\n n= int(len(dictionary)/rows_num)\n words = [(\"\\\\<\"+word+\"> & \"+str(frequancy)+' \\\\\\\\ \\n') for word, frequancy in dictionary.items()]\n start=0\n end=rows_num\n new_words = []\n for i in range(n):\n new_words = new_words+ [begin_table+'\\n'] +words[start:end] +[end_table+\" \\n\"]\n start=end\n end+=rows_num\n remain_words = len(dictionary) - rows_num*n\n if remain_words > 0:\n new_words += [begin_table+\" \\n\"]+ words[-1*remain_words:]+[end_table+\" \\n\"]\n for word in new_words:\n file.write(word)\n file.write(tail_code)\n file.close()\n return True\n except:\n return False","def createTrecTextForCurrentDocuments(baseDir):\n pathToFolder = baseDir + 'Collections/'\n if not os.path.exists(pathToFolder):\n os.makedirs(pathToFolder)\n currentTime = str(datetime.datetime.now()).replace(\":\", \"-\").replace(\" \", \"-\").replace(\".\", \"-\")\n pathToTrecText = pathToFolder+\"TrecText/\"\n if not os.path.exists(pathToTrecText):\n os.makedirs(pathToTrecText)\n filename = pathToTrecText + currentTime\n client = MongoClient('asr2.iem.technion.ac.il',27017)\n db = client.asr16\n documents = db.documents.find({}).sort('query_id',1)\n queryToDocnos= {}\n current_users = retrieve_users()\n f = open(filename, 'w')\n for document in documents:\n if document['username'] in current_users:\n print(document['query_id'], document['username'])\n f.write('<DOC>\\n')\n docno = str(document['query_id']).zfill(3) + '-' + str(document['username'])\n f.write('<DOCNO>' + docno + '</DOCNO>\\n')\n docnos = queryToDocnos.get(str(document['query_id']).zfill(3), [])\n docnos.append(docno)\n queryToDocnos[str(document['query_id']).zfill(3)] = docnos\n f.write('<TEXT>\\n')\n f.write(unicodedata.normalize('NFKD', document['current_document']).encode('cp1252', \"ignore\").decode('utf-8', 'replace').replace(u'\\uFFFD', ' ').rstrip())\n f.write('\\n</TEXT>\\n')\n f.write('</DOC>\\n')\n f.close()\n pathToWorkingSet = pathToFolder+ 'WorkingSets/'\n if not os.path.exists(pathToWorkingSet):\n os.makedirs(pathToWorkingSet)\n workingSetFilename = pathToWorkingSet + currentTime\n f = open(workingSetFilename, 'w')\n for query, docnos in queryToDocnos.items():\n i = 1\n for docid in docnos:\n f.write(query.zfill(3) + ' Q0 ' + docid + ' ' + str(i) + ' -' + str(i) + ' indri\\n')\n i +=1\n f.close()\n return filename, workingSetFilename, currentTime","def CreateNewFile(self):\n\t\tself.acad.Documents.Add()","def initCatalog(tipolista : str):\n catalog = model.newCatalog(tipolista)\n return catalog","def create(cls, title=None, ca_type=None, definition_type=None,\n helptext=\"\", placeholder=None, multi_choice_options=None,\n is_mandatory=False, ca_global=True):\n ca_entity = cls._create_random_ca()\n ca_entity = cls._fill_ca_entity_fields(\n ca_entity, title=title, ca_type=ca_type,\n definition_type=definition_type,\n helptext=helptext, placeholder=placeholder,\n multi_choice_options=multi_choice_options, is_mandatory=is_mandatory,\n ca_global=ca_global\n )\n ca_entity = cls._normalize_ca_definition_type(ca_entity)\n return ca_entity","def initCatalog():\n catalog = model.newCatalog()\n return catalog"],"string":"[\n \"def makeCourse( xmlFile, genPath, importPaths, commonFiles, rendererContent=True):\\n\\ttry:\\n\\n\\t\\t# parse the command line\\n\\t\\tConfig.add_option('--verbose', help='Set verbosity to maximum', dest='verbosity', default=0, action='store_const', const=2)\\n\\t\\tConfig.add_option('-v', '--verbosity', help='Set the verbosity level (0: quiet, 1: display the command lines, 2: display command lines and their outputs', dest='verbosity', default=0, type=int)\\n\\t\\tConfig.add_option('-d', '--debug', help='Create the files in the debug/ folder, instead of in a temporary one', dest='debug', action='store_true', default=False)\\n\\t\\tConfig.add_option('-f', '--force', help='Force the generation of the documents, even if nothing changes from last run', dest='force', action='store_true', default=False)\\n\\t\\tConfig.add_option('-q', '--quick', help='Quick pdf generation (do not compile twice the latex, do not produce handout, etc.)', dest='quick', action='store_true', default=False)\\n\\t\\tConfig.add_option('-w', '--wordpress', help='Publish to wordpress', dest='wordpress', default=False, action='store_true')\\n\\t\\tConfig.add_option('-c', '--HTMLcorrection', help='Display an HTML correction', dest='HTMLcorrection', default=False, action='store_true')\\n\\t\\tConfig.add_option('-s', '--shared', help='Copy the required files to the <shared> path (via ssh)', default=False,\\taction='store_true')\\n\\t\\tConfig.parse()\\n\\t\\targs = Config.args\\n\\t\\toptions = Config.options\\n\\t\\tConfig.importPaths = importPaths \\n\\t\\tConfig.commonFiles = commonFiles\\n\\t\\tConfig.allSessions = { x.__name__:x for x in Session.__subclasses__()}\\t# list of the created session classes\\n\\t\\tConfig.rendererContent = rendererContent\\n\\t\\t\\n\\t\\t# clean the debug directory in debug mode\\n\\t\\tbasePath = os.path.abspath('.')+'/'\\t\\t\\t# base path (from where the script is run, because the path are relative)\\n\\t\\tif options.debug:\\n\\t\\t\\tif os.path.exists('debug/'):\\n\\t\\t\\t\\trunCommand(['rm','-rf','debug/'])\\n\\n\\t\\t# open and parse the course file\\n\\t\\twith codecs.open(xmlFile, encoding='utf-8') as f:\\n\\t\\t\\tbs = BeautifulSoup(f, features=\\\"xml\\\")\\n\\n\\n\\t\\t# build the recursively the sessions\\n\\t\\ttop = createTagSession( bs, father=None )\\t\\t# bs.contents[0]\\n\\t\\tsessionsToBuild = Session.sessionsToBuild\\t\\t# get the list of the sessions object\\n\\t\\t\\n\\n\\t\\t\\\"\\\"\\\"\\n\\t\\timportFiles( bs.contents[0], importPaths)\\n\\n\\t\\t# get the list of sessions we can build (with a 'make' method)\\n\\t\\tbuildableSessions = { x.__name__:x for x in Session.__subclasses__() if 'make' in x.__dict__ }\\n\\n\\t\\t#This set the PATH for PyDev only...\\n\\t\\tos.environ['PATH'] = os.environ['PATH']+':'+os.getenv('PATH')\\n\\n\\n\\t\\t# build the list of Sessions to build\\n\\t\\tsessionsToBuild = []\\n\\t\\tfor name,session in buildableSessions.items():\\n\\t\\t\\tsessionsToBuild.extend( session(tag, commonFiles) for tag in bs(name) )\\n\\t\\t\\\"\\\"\\\"\\n\\t\\t\\n\\t\\t\\n\\n\\t\\t# if possible, load the previous xml file, and look for the differences\\n\\t\\tdirName,baseName = split(xmlFile) \\n\\t\\ttry:\\n\\t\\t\\twith open(dirName+\\\"/.\\\"+baseName+\\\".makeCourse\\\", \\\"rb\\\") as f:\\n\\t\\t\\t\\tdata = load( f )\\n\\t\\t\\t\\tfor s in sessionsToBuild:\\n\\t\\t\\t\\t\\tif s.name in data:\\n\\t\\t\\t\\t\\t\\ts.checkDifferences( data[s.name] )\\n\\t\\texcept IOError:\\n\\t\\t\\tpass\\n\\n\\n\\t\\t# build every argument in the command line arguments\\n\\t\\tsomethingHasBeDone = False\\n\\t\\tfor s in sessionsToBuild:\\n\\t\\t\\tif (not args) or (\\\"all\\\" in args) or (s.name in args) or (s.type in args):\\n\\t\\t\\t\\t\\n\\t\\t\\t\\tcd( basePath)\\n\\t\\t\\t\\t\\n\\t\\t\\t\\t# check if something has to be done\\n\\t\\t\\t\\tif s.shouldBeMake(basePath+'/'+genPath, options) or options.force:\\n\\t\\t\\t\\t\\tsomethingHasBeDone = True\\n\\n\\t\\t\\t\\t\\t#Make one build (TP, course, etc.)\\n\\t\\t\\t\\t\\tprint ( Fore.BLUE+\\\"*) Make \\\"+Style.BRIGHT+s.name+Fore.RESET+Style.NORMAL)\\n\\n\\t\\t\\t\\t\\t# make temp directory and copy all the file in resources dir\\n\\t\\t\\t\\t\\tif options.debug:\\n\\t\\t\\t\\t\\t\\ttmp = \\\"debug/\\\"+s.name+'/'\\n\\t\\t\\t\\t\\t\\tcreateDirectory(tmp)\\n\\t\\t\\t\\t\\telse:\\n\\t\\t\\t\\t\\t\\ttmp = mkdtemp()\\n\\n\\t\\t\\t\\t\\ts.prepareResources(tmp )\\n\\t\\t\\t\\t\\tcd( tmp)\\n\\n\\t\\t\\t\\t\\t# call the custom function associated with the type, to produce the documents\\n\\t\\t\\t\\t\\ts.make(options)\\n\\n\\t\\t\\t\\t\\t# then move the files in the right place\\n\\t\\t\\t\\t\\tfor f in s.files(options):\\n\\t\\t\\t\\t\\t\\tcreateDirectory( basePath+'/'+genPath.format( **s.dict ) )\\n\\t\\t\\t\\t\\t\\tnewFile = basePath+'/'+genPath.format( **s.dict )+f\\n\\t\\t\\t\\t\\t\\tif not os.path.exists(f):\\n\\t\\t\\t\\t\\t\\t\\tprint( Fore.YELLOW+'The file '+f+' has not been created by '+s.type+' function !'+Fore.RESET)\\n\\t\\t\\t\\t\\t\\trunCommand( ['cp', f, newFile])\\n\\n\\t\\t\\t\\t\\t# del the temporary directory or clean debug directory\\n\\t\\t\\t\\t\\tif not options.debug:\\n\\t\\t\\t\\t\\t\\trunCommand( ['rm', '-rf', tmp])\\n\\t\\t\\t\\telse:\\n\\t\\t\\t\\t\\tif options.verbosity>0:\\n\\t\\t\\t\\t\\t\\tprint( Fore.BLUE + \\\"*) Nothing changed for \\\"+Style.BRIGHT+s.name+Style.NORMAL+\\\", skipped\\\"+Fore.RESET)\\n\\n\\n\\n\\t\\tif not somethingHasBeDone:\\n\\t\\t\\tprint( Fore.BLUE + \\\"Nothing has changed, nothing to do, so nothing has been done...\\\" + Fore.RESET)\\n\\n\\n\\t\\t# save the data file\\n\\t\\tdata = {L.name: {key:md5(str(val).encode('utf-8')).hexdigest() for key,val in L.dict.items()} for L in sessionsToBuild }\\n\\t\\tcd( basePath)\\n\\t\\twith open(dirName+\\\"/.\\\"+baseName+\\\".makeCourse\\\", 'wb') as f:\\n\\t\\t\\tdump( data, f)\\n\\n\\n\\n\\n\\n\\n\\n\\texcept mkcException as err:\\n\\t\\tprint( err )\",\n \"def parse_create_course(xml_course):\\n attrs = [\\n \\\"term-code\\\",\\n \\\"term-description\\\",\\n 'subject',\\n \\\"course-number\\\",\\n \\\"school\\\",\\n \\\"department\\\",\\n \\\"title\\\",\\n \\\"description\\\",\\n \\\"credit-hours\\\",\\n \\\"distribution-group\\\"\\n ]\\n course = pull_attributes_from_xml(xml_course, attrs)\\n course[\\\"sections\\\"] = []\\n\\n return course\",\n \"def latex_course(course):\\n basic_info_list = [\\n 'session', 'school', 'credits', 'level', 'offered',\\n 'visiting_students', 'erasmus_students'\\n ]\\n generic_subsection_list = [\\n 'description', 'timetable', 'requirements_of_entry',\\n 'excluded_courses', 'co_requisites', 'assessment_weighting'\\n ]\\n string = '\\\\\\\\subsection{' + course[\\\"title\\\"] + '}\\\\n'\\n for info in basic_info_list:\\n string += latex_info(course[info])\\n for subsection in generic_subsection_list:\\n string += latex_subsection(course[subsection])\\n string += '\\\\\\\\break \\\\\\\\textbf{' + course['assessment_date'][\\n 'heading'] + '}' + course['assessment_date']['value'] + '\\\\n'\\n string += latex_subsection(course['aims'])\\n string += '\\\\\\\\subsubsection*{' + \\\\\\n course['learning_outcomes']['heading'] + '}\\\\n'\\n outcome_list = re.split(\\n '\\\\d+\\\\. ', course['learning_outcomes']['value'])\\n string += outcome_list[0] + '\\\\n'\\n string += '\\\\\\\\begin{enumerate}\\\\n'\\n for i in outcome_list[1:-1]:\\n string += '\\\\\\\\item ' + i + '\\\\n'\\n string += '\\\\\\\\end{enumerate}\\\\n'\\n return string\",\n \"def write_to_latex(codelist, unwanted_courses):\\n # TODO: investigate a way to add large amounts of text outside of the\\n # function\\n abstract01 = \\\"I created this document to practice parsing html and using\\\\\\n tools like Beautiful Soup which I've previously had little experience\\\\\\n in. As a result, it's not perfect.\\\\\\\\newline\\\\\\n It is also a slightly condensed all-in-one-place look at a selection\\\\\\n of courses that are available for fourth year computer science\\\\\\n students at the University of Glasgow. For the purposes of clarity I\\\\\\n have removed several courses from this selection. The following\\\\\\n courses have been omitted:\\\"\\n abstract02 = \\\"For more insight into the project, to report issues or to\\\\\\n inspect the code, have a look at the GitHub:\\\\\\n \\\\\\\\url{https://github.com/IS0metric/course-ripper}\\\"\\n unincluded = create_not_included_list(unwanted_courses)\\n with open('courses.tex', 'w') as f:\\n # TODO Try and move all this to a separate function?\\n # TODO: Check if it's more efficient to write a single, massive string\\n # to file\\n f.write('\\\\\\\\documentclass{hitec}\\\\n')\\n f.write('\\\\\\\\usepackage[document]{ragged2e}\\\\n')\\n f.write('\\\\\\\\usepackage{url}\\\\n')\\n f.write('\\\\\\\\usepackage{hyperref}\\\\n')\\n f.write('\\\\\\\\setcounter{tocdepth}{4}\\\\n')\\n f.write('\\\\\\\\begin{document}\\\\n')\\n f.write('\\\\\\\\title{Fourth Year (2016-17) Courses}\\\\n')\\n f.write('\\\\\\\\author{Jack Parkinson}\\\\n')\\n f.write('\\\\\\\\date{August 2016}\\\\n')\\n f.write('\\\\\\\\maketitle\\\\n')\\n f.write('\\\\\\\\abstract{' + abstract01 + unincluded + abstract02 + '}\\\\n')\\n f.write('\\\\\\\\newpage\\\\n\\\\n')\\n f.write('\\\\\\\\tableofcontents\\\\n')\\n f.write('\\\\\\\\newpage\\\\n\\\\n')\\n # TODO: Look into alternatives to the three lists\\n all_courses = []\\n sem1_courses = []\\n sem2_courses = []\\n for code in codelist:\\n course = bsoup(get_coursepage(code))\\n if course['offered']['value'] == 'Runs Throughout Semesters 1 and 2':\\n all_courses.append(course)\\n elif \\\"1\\\" in course['offered']['value']:\\n sem1_courses.append(course)\\n elif \\\"2\\\" in course['offered']['value']:\\n sem2_courses.append(course)\\n f.write('\\\\\\\\section{Semester 1 and 2 Courses}\\\\n\\\\n')\\n for course in all_courses:\\n f.write(latex_course(course))\\n f.write('\\\\\\\\section{Semester 1 Only Courses}\\\\n\\\\n')\\n for course in sem1_courses:\\n f.write(latex_course(course))\\n f.write('\\\\\\\\section{Semester 2 Only Courses}\\\\n\\\\n')\\n for course in sem2_courses:\\n f.write(latex_course(course))\\n f.write('\\\\\\\\end{document}')\\n return None\",\n \"def createCourse():\\n\\tif request.method == 'POST':\\n\\t\\tcname = request.form['cname']\\n\\t\\tcourseterm = request.form['courseterm']\\n\\t\\tcoursepoint = request.form['coursepoint']\\n\\t\\tcoursetype = request.form['coursetype']\\t\\n\\t\\tcourseyear = request.form['courseyear']\\t\\n\\t\\ttname = request.form['tname']\\t\\n\\t\\terror = None\\n\\n\\t\\tif not cname:\\n\\t\\t\\terror = 'Course name is required.'\\n\\t\\telif not courseterm:\\n\\t\\t\\terror = 'Course term is required'\\n\\t\\telif not courseterm:\\n\\t\\t\\terror = 'Course point is required'\\n\\n\\t\\tif error is not None:\\n\\t\\t\\tflash(error)\\n\\t\\telse:\\n\\t\\t\\tdb = get_db()\\n\\t\\t\\tcur = db.cursor()\\n\\t\\t\\tcur.execute(\\n\\t\\t\\t\\t'INSERT INTO course (cname, courseyear, coursetype, courseterm, coursepoint, tname)'\\n\\t\\t\\t\\t' VALUES (%s, %s, %s, %s, %s, %s)',\\n\\t\\t\\t\\t(cname, courseyear, coursetype, courseterm, coursepoint, tname)\\n\\t\\t\\t)\\n\\t\\t\\tdb.commit()\\n\\t\\t\\treturn redirect(url_for('info.index'))\\n\\n\\treturn render_template('info/createCourse.html')\",\n \"def _create_course(self):\\r\\n super(TestOrphan, self)._create_course()\\r\\n\\r\\n self._create_item('chapter', 'Chapter1', {}, {'display_name': 'Chapter 1'}, 'course', 'runid')\\r\\n self._create_item('chapter', 'Chapter2', {}, {'display_name': 'Chapter 2'}, 'course', 'runid')\\r\\n self._create_item('chapter', 'OrphanChapter', {}, {'display_name': 'Orphan Chapter'}, None, None)\\r\\n self._create_item('vertical', 'Vert1', {}, {'display_name': 'Vertical 1'}, 'chapter', 'Chapter1')\\r\\n self._create_item('vertical', 'OrphanVert', {}, {'display_name': 'Orphan Vertical'}, None, None)\\r\\n self._create_item('html', 'Html1', \\\"<p>Goodbye</p>\\\", {'display_name': 'Parented Html'}, 'vertical', 'Vert1')\\r\\n self._create_item('html', 'OrphanHtml', \\\"<p>Hello</p>\\\", {'display_name': 'Orphan html'}, None, None)\\r\\n self._create_item('static_tab', 'staticuno', \\\"<p>tab</p>\\\", {'display_name': 'Tab uno'}, None, None)\\r\\n self._create_item('about', 'overview', \\\"<p>overview</p>\\\", {}, None, None)\\r\\n self._create_item('course_info', 'updates', \\\"<ol><li><h2>Sep 22</h2><p>test</p></li></ol>\\\", {}, None, None)\",\n \"def _createCoursesTable(self):\\n\\t\\tcommand = \\\"\\\"\\\"CREATE TABLE courses (ID INTEGER PRIMARY KEY,\\n\\t\\t\\tname TEXT,\\n\\t\\t\\tauthor_id INTEGER,\\n\\t\\t\\tdescription TEXT\\n\\t\\t\\t);\\n\\\"\\\"\\\"\\n\\n\\t\\tself._run_command(command)\",\n \"def pdflatex(unwanted_courses):\\n create_tex(unwanted_courses)\\n cmd = ['pdflatex', '-interaction', 'nonstopmode', 'courses.tex']\\n proc = subprocess.Popen(cmd)\\n proc.communicate()\\n return None\",\n \"def create_courses():\\n\\n\\t# create list for courses\\n\\tallcourses = []\\n\\n\\t# load courses as classes in allcourses-list\\n\\twith open(\\\"../data/vakken.csv\\\", \\\"rt\\\") as coursefile:\\n\\n\\t\\t# clean text\\n\\t\\tcourses = csv.reader(coursefile)\\n\\t\\tfor row in courses:\\n\\t\\t\\tfor text in row:\\n\\t\\t\\t\\tcourse_info = text.split(\\\";\\\")\\n\\n\\t\\t\\t\\t# add course name\\n\\t\\t\\t\\tcourse_name = course_info[0]\\n\\n\\t\\t\\t\\t# add amount of lectures\\n\\t\\t\\t\\tcourse_lectures = course_info[1]\\n\\n\\t\\t\\t\\t# add amount of seminars\\n\\t\\t\\t\\tcourse_seminars = course_info[2]\\n\\n\\t\\t\\t\\t# add max amount seminars\\n\\t\\t\\t\\tcourse_max_sem = course_info[3]\\n\\t\\t\\t\\tif course_max_sem == \\\"nvt\\\":\\n\\t\\t\\t\\t\\tcourse_max_sem = 0\\n\\n\\t\\t\\t\\t# add amount of practicals\\n\\t\\t\\t\\tcourse_practicals = course_info[4]\\n\\n\\t\\t\\t\\t# add max amount practicals\\n\\t\\t\\t\\tcourse_max_prac = course_info[5]\\n\\t\\t\\t\\tif course_max_prac == \\\"nvt\\\":\\n\\t\\t\\t\\t\\tcourse_max_prac = 0\\n\\n\\t\\t\\t\\t# add course to list\\n\\t\\t\\t\\tallcourses.append(Course(course_name, course_lectures, course_seminars, course_max_sem, course_practicals, course_max_prac))\\n\\n\\treturn allcourses\",\n \"def _course_factory_create_course():\\r\\n return CourseFactory.create(org='MITx', course='999', display_name='Robot Super Course')\",\n \"def main():\\n\\n #Courses\\n years = [2016, 2017, 2018, 2019, 2020]\\n with sqlite3.connect(\\\"determined.db\\\") as conn:\\n c = conn.cursor()\\n c.execute(\\n \\\"\\\"\\\"\\n CREATE TABLE \\\"course\\\" (\\n \\\"course_number\\\"\\tint NOT NULL,\\n \\\"dept_id\\\"\\tvarchar(4) NOT NULL,\\n \\\"title\\\"\\tvarchar(100) NOT NULL,\\n \\\"instructor_fname\\\"\\tvarchar(35) DEFAULT NULL,\\n \\\"instructor_lname\\\"\\tvarchar(35) DEFAULT NULL,\\n \\\"student_work_products\\\"\\tjson DEFAULT NULL,\\n `term` varchar(7) NOT NULL,\\n `year` int NOT NULL,\\n PRIMARY KEY(\\\"course_number\\\", \\\"term\\\", \\\"year\\\")) \\n \\\"\\\"\\\"\\n )\\n conn.commit()\\n courses = [\\n (1370, \\\"CPSC\\\", \\\"Computer Literacy\\\", names.get_first_name(), names.get_last_name(), json.dumps({\\\"swp\\\": [\\\"Midterm\\\", \\\"Final Exam\\\", \\\"Project 1\\\"]}), \\\"Fall\\\"),\\n (1375, \\\"CPSC\\\", \\\"Programming I\\\", names.get_first_name(), names.get_last_name(), json.dumps({\\\"swp\\\": [\\\"Midterm\\\", \\\"Final Exam\\\", \\\"Project 1\\\"]}), \\\"Fall\\\"),\\n (2376, \\\"CPSC\\\", \\\"Intro to Game Programming\\\", names.get_first_name(), names.get_last_name(), json.dumps({\\\"swp\\\": [\\\"Midterm\\\", \\\"Final Exam\\\", \\\"Project 1\\\"]}), \\\"Fall\\\"),\\n (2380, \\\"CPSC\\\", \\\"Algorithms\\\", names.get_first_name(), names.get_last_name(), json.dumps({\\\"swp\\\": [\\\"Midterm\\\", \\\"Final Exam\\\", \\\"Project 1\\\"]}), \\\"Fall\\\"),\\n (2482, \\\"CPSC\\\", \\\"Computer Organization\\\", names.get_first_name(), names.get_last_name(), json.dumps({\\\"swp\\\": [\\\"Midterm\\\", \\\"Final Exam\\\", \\\"Project 1\\\"]}), \\\"Spring\\\"),\\n (3377, \\\"CPSC\\\", \\\"Advanced Game Programming\\\", names.get_first_name(), names.get_last_name(), json.dumps({\\\"swp\\\": [\\\"Midterm\\\", \\\"Final Exam\\\", \\\"Project 1\\\"]}), \\\"Spring\\\"),\\n (3380, \\\"CPSC\\\", \\\"Operating Systems\\\", names.get_first_name(), names.get_last_name(), json.dumps({\\\"swp\\\": [\\\"Midterm\\\", \\\"Final Exam\\\", \\\"Project 1\\\"]}), \\\"Spring\\\"),\\n (3383, \\\"CPSC\\\", \\\"Programming Languages\\\", names.get_first_name(), names.get_last_name(), json.dumps({\\\"swp\\\": [\\\"Midterm\\\", \\\"Final Exam\\\", \\\"Project 1\\\"]}), \\\"Spring\\\"),\\n (3384, \\\"CPSC\\\", \\\"Computer Networks\\\", names.get_first_name(), names.get_last_name(), json.dumps({\\\"swp\\\": [\\\"Midterm\\\", \\\"Final Exam\\\", \\\"Project 1\\\"]}), \\\"Summer\\\"),\\n (4360, \\\"CPSC\\\", \\\"Computer Security\\\", names.get_first_name(), names.get_last_name(), json.dumps({\\\"swp\\\": [\\\"Midterm\\\", \\\"Final Exam\\\", \\\"Project 1\\\"]}), \\\"Summer\\\")\\n ]\\n #Adding years\\n upload_courses = []\\n for year in years:\\n upload_courses += [x + (year,) for x in courses]\\n #Making a few instructors teach multiple course\\n new_courses = [\\n (4557, \\\"CPSC\\\", \\\"Natural Language Processing\\\", ),\\n (2375, \\\"CPSC\\\", \\\"Programming II\\\",),\\n (2776, \\\"CPSC\\\", \\\"Data Structures and Algorithms\\\",),\\n (4862, \\\"CPSC\\\", \\\"Image Recognition\\\", ),\\n ]\\n for i in range(0,len(new_courses)):\\n year = choice(years)\\n for y in range(0,2): #Number of times new course is taught\\n c = upload_courses[i]\\n new_data = (c[3], c[4], c[5], choice([\\\"Fall\\\", \\\"Spring\\\", \\\"Summer\\\"]), year+y)\\n data = new_courses[i] + new_data\\n upload_courses.append(data)\\n #Adding solo instructors and solo courses\\n upload_courses += [\\n (4672, \\\"CPSC\\\", \\\"Programming Memes\\\", names.get_first_name(), names.get_last_name(), json.dumps({\\\"swp\\\": [\\\"Midterm\\\", \\\"Final Exam\\\", \\\"Project 1\\\"]}), \\\"Spring\\\", choice(years)),\\n (1872, \\\"CPSC\\\", \\\"Information Systems\\\", names.get_first_name(), names.get_last_name(), json.dumps({\\\"swp\\\": [\\\"Midterm\\\", \\\"Final Exam\\\", \\\"Project 1\\\"]}), \\\"Summer\\\", choice(years)),\\n (1123, \\\"CPSC\\\", \\\"Microsoft Office\\\", names.get_first_name(), names.get_last_name(), json.dumps({\\\"swp\\\": [\\\"Midterm\\\", \\\"Final Exam\\\", \\\"Project 1\\\"]}), \\\"Fall\\\", choice(years))\\n ]\\n\\n with sqlite3.connect(\\\"determined.db\\\") as conn:\\n c = conn.cursor()\\n c.executemany('''INSERT INTO course (course_number, dept_id, title, instructor_fname, instructor_lname, student_work_products, term, year)\\n VALUES ( ?, ?, ?, ?, ?, ?, ?, ?)''', upload_courses)\\n conn.commit()\\n\\n #SWP\\n with sqlite3.connect(\\\"determined.db\\\") as conn:\\n c = conn.cursor()\\n c.execute(\\n \\\"\\\"\\\"\\n CREATE TABLE `student_work_product` (\\n `id` INTEGER PRIMARY KEY,\\n `product` varchar(250) NOT NULL,\\n `course_id` int NOT NULL,\\n `dept_id` int NOT NULL,\\n `student_fname` varchar(35) NOT NULL,\\n `student_lname` varchar(35) NOT NULL,\\n `student_outcome` int DEFAULT NULL,\\n `score` int DEFAULT NULL,\\n `term` varchar(7) NOT NULL,\\n `year` int NOT NULL,\\n CONSTRAINT `course` FOREIGN KEY (`course_id`) REFERENCES `course` (`course_number`)\\n CONSTRAINT `course` FOREIGN KEY (`dept_id`) REFERENCES `course` (`dept_id`)\\n )\\n \\\"\\\"\\\"\\n )\\n conn.commit()\\n \\n swps = []\\n with sqlite3.connect(\\\"determined.db\\\") as conn:\\n conn.row_factory = sqlite3.Row\\n c = conn.cursor()\\n c.execute (\\\"Select * from course\\\")\\n records = [dict(x) for x in c.fetchall()]\\n #Generating 20 student records for each swp in each course\\n for i, course in enumerate(records):\\n student_names = []\\n for _ in range(20):\\n student_names.append({'fname': names.get_first_name(),\\n 'lname': names.get_last_name()})\\n for product in json.loads(course['student_work_products'])['swp']:\\n for student in student_names:\\n if i%7 == 0:\\n score = int(triangular(50, 85))\\n else:\\n score = int(triangular(50, 100))\\n if score >= 90: outcome = 4\\n elif score >= 80: outcome = 3\\n elif score >= 70: outcome = 2\\n elif score >= 60: outcome = 1\\n else: outcome = 0 \\n swps.append((\\n product,\\n course['course_number'],\\n \\\"CPSC\\\",\\n student['fname'],\\n student['lname'],\\n outcome,\\n score, \\n course['term'], \\n course['year']\\n ))\\n \\n c.executemany('''INSERT INTO student_work_product (product, course_id, dept_id, student_fname, student_lname, student_outcome, score, term, year)\\n VALUES ( ?, ?, ?, ?, ?, ?, ?, ?, ?)''', swps)\\n conn.commit()\",\n \"def preprocess_courses_corpus():\\n soup = None\\n with open('courses_corpus.html', 'r') as infile:\\n content = infile.read()\\n\\n soup = BeautifulSoup(content, 'html.parser')\\n\\n docid = 0\\n data = {}\\n data['documents'] = []\\n\\n main_table = soup.find_all(\\\"div\\\", attrs={'class': 'courseblock'})\\n for course in main_table:\\n docid += 1\\n title = course.find_all('p', attrs={'class':'courseblocktitle noindent'})[0].text.lstrip('\\\\n') if len(course.find_all('p', attrs={'class':'courseblocktitle noindent'}))!=0 else ''\\n description = (course.find_all('p', attrs={'class':'courseblockdesc noindent'})[0].text.lstrip('\\\\n') if len(course.find_all('p', attrs={'class':'courseblockdesc noindent'}))!=0 else '') + ' ' + (course.find_all('p', attrs={'class':'courseblockextra noindent'})[0].text if len(course.find_all('p', attrs={'class':'courseblockextra noindent'}))!=0 else '')\\n\\n data['documents'].append({\\n 'docId' : docid,\\n 'title' : title.strip(),\\n 'description' : description.strip()\\n })\\n\\n with open('courses_data.json', 'w') as outfile:\\n json.dump(data, outfile)\",\n \"def print_catalog(self):\\n # first download the json for the catalog\\n self.download_json()\\n\\n # open the saved json file and load the json\\n with self.file.open(\\\"r\\\") as catalog_file:\\n pages = json.load(catalog_file)\\n\\n # the catalog json is just a list of pages\\n # so we begin by iterating through the pages\\n for page_num in range(len(pages)):\\n # get each page\\n page = pages[page_num]\\n\\n # get the threads on each page\\n threads = page[\\\"threads\\\"]\\n\\n # print the page heading\\n print(\\\"*** PAGE \\\", page_num + 1, \\\"***\\\")\\n\\n # iterate through the threads on each page\\n for thread_num in range(len(threads)):\\n # get each thread\\n thread = threads[thread_num]\\n\\n # print the thread number\\n num = thread[\\\"no\\\"]\\n print(\\\"---\\\", \\\"Thread:\\\", num, \\\"---\\\")\\n\\n # not all threads have a subject or comment\\n try:\\n subject = thread[\\\"sub\\\"]\\n comment = thread[\\\"com\\\"]\\n\\n print(\\\"Sub:\\\", subject)\\n print(\\\"Comment:\\\", comment)\\n except KeyError:\\n print(\\\"N/A\\\")\",\n \"def _create_course(self):\\r\\n super(TestPublish, self)._create_course(split=False)\\r\\n\\r\\n self._create_item('chapter', 'Chapter1', {}, {'display_name': 'Chapter 1'}, 'course', 'runid', split=False)\\r\\n self._create_item('chapter', 'Chapter2', {}, {'display_name': 'Chapter 2'}, 'course', 'runid', split=False)\\r\\n self._create_item('vertical', 'Vert1', {}, {'display_name': 'Vertical 1'}, 'chapter', 'Chapter1', split=False)\\r\\n self._create_item('vertical', 'Vert2', {}, {'display_name': 'Vertical 2'}, 'chapter', 'Chapter1', split=False)\\r\\n self._create_item('html', 'Html1', \\\"<p>Goodbye</p>\\\", {'display_name': 'Parented Html'}, 'vertical', 'Vert1', split=False)\\r\\n self._create_item(\\r\\n 'discussion', 'Discussion1',\\r\\n \\\"discussion discussion_category=\\\\\\\"Lecture 1\\\\\\\" discussion_id=\\\\\\\"a08bfd89b2aa40fa81f2c650a9332846\\\\\\\" discussion_target=\\\\\\\"Lecture 1\\\\\\\"/>\\\\n\\\",\\r\\n {\\r\\n \\\"discussion_category\\\": \\\"Lecture 1\\\",\\r\\n \\\"discussion_target\\\": \\\"Lecture 1\\\",\\r\\n \\\"display_name\\\": \\\"Lecture 1 Discussion\\\",\\r\\n \\\"discussion_id\\\": \\\"a08bfd89b2aa40fa81f2c650a9332846\\\"\\r\\n },\\r\\n 'vertical', 'Vert1',\\r\\n split=False\\r\\n )\\r\\n self._create_item('html', 'Html2', \\\"<p>Hellow</p>\\\", {'display_name': 'Hollow Html'}, 'vertical', 'Vert1', split=False)\\r\\n self._create_item(\\r\\n 'discussion', 'Discussion2',\\r\\n \\\"discussion discussion_category=\\\\\\\"Lecture 2\\\\\\\" discussion_id=\\\\\\\"b08bfd89b2aa40fa81f2c650a9332846\\\\\\\" discussion_target=\\\\\\\"Lecture 2\\\\\\\"/>\\\\n\\\",\\r\\n {\\r\\n \\\"discussion_category\\\": \\\"Lecture 2\\\",\\r\\n \\\"discussion_target\\\": \\\"Lecture 2\\\",\\r\\n \\\"display_name\\\": \\\"Lecture 2 Discussion\\\",\\r\\n \\\"discussion_id\\\": \\\"b08bfd89b2aa40fa81f2c650a9332846\\\"\\r\\n },\\r\\n 'vertical', 'Vert2',\\r\\n split=False\\r\\n )\\r\\n self._create_item('static_tab', 'staticuno', \\\"<p>tab</p>\\\", {'display_name': 'Tab uno'}, None, None, split=False)\\r\\n self._create_item('about', 'overview', \\\"<p>overview</p>\\\", {}, None, None, split=False)\\r\\n self._create_item('course_info', 'updates', \\\"<ol><li><h2>Sep 22</h2><p>test</p></li></ol>\\\", {}, None, None, split=False)\",\n \"def generate_courses():\\r\\n for category in CourseCategory.objects.all():\\r\\n Course.objects.create(name=category.name, category=category, is_active=True,\\r\\n is_featured=True)\",\n \"def testCosmologyCatalog(self):\\n dbObj = myTestGals(database=self.dbName)\\n cat = cosmologicalGalaxyCatalog(dbObj)\\n cat.write_catalog(self.catName)\",\n \"def createStructuredTranscript_Non_Core_Doc():\\n\\n #create a temporary folder that will hold the data transformed from doc to docx\\n os.system('mkdir ' + INPUT_FOLDER+'temp')\\n\\n core_doc_asset = []\\n missing_count = 0\\n missing_files=[]\\n # get all the docx files that are part of the core asset\\n for file in glob.glob(INPUT_FOLDER+\\\"*.doc\\\"):\\n\\n # RG numbers for the core asset\\n if (\\\"RG-50.030\\\" not in file and\\n \\\"RG-50.106\\\" not in file and\\n \\\"RG-50.549\\\" not in file):\\n \\n\\n \\n # convert file to docx, storing it in an untracked folder called temp\\n file_docx = file + 'x'\\n command = 'textutil -convert docx ' + file + ' -output ' + INPUT_FOLDER+'temp/'+ file_docx.split('/')[-1]\\n call(command, shell=True)\\n\\n # append to the array\\n core_doc_asset.append(file_docx)\\n \\n\\n \\n\\n # get the units for each file, store them and update tracker\\n core_doc_asset=create_dictionary_of_file_list(core_doc_asset)\\n \\n not_processed=0\\n processed_doc=0\\n \\n # get the units for each file, store them and update tracker \\n for mongo_rg in core_doc_asset:\\n # get text units for this entry\\n processed=[]\\n result=[]\\n \\n for file in core_doc_asset[mongo_rg]:\\n \\n \\n \\n units = getTextUnits(INPUT_FOLDER+'temp/'+file.split('/')[-1])\\n \\n if units:\\n #replace white spaces\\n for i,element in enumerate(units):\\n units[i]['unit']=' '.join(element['unit'].split())\\n result.extend(units)\\n \\n processed.append(True)\\n else:\\n #check if processed\\n processed.append(False)\\n\\n #set the method used to transform the transcript\\n h.update_field(DB, TRACKER, \\\"rg_number\\\", mongo_rg, \\\"method\\\", \\\"transcribe_non_core_doc\\\")\\n\\n not_processed=not_processed+1\\n\\n if False in processed:\\n\\n h.update_field(DB, TRACKER, \\\"rg_number\\\", mongo_rg, \\\"status\\\", \\\"Unprocessed\\\")\\n not_processed=not_processed+1\\n missing_files.append(' '.join(core_doc_asset[mongo_rg]))\\n else:\\n # insert units on the output collection\\n h.update_field(DB, OUTPUT, \\\"shelfmark\\\", 'USHMM '+mongo_rg, \\\"structured_transcript\\\", result)\\n\\n \\n # update status on the stracker\\n \\n h.update_field(DB, TRACKER, \\\"rg_number\\\", mongo_rg, \\\"status\\\", \\\"Processed\\\")\\n processed_doc=processed_doc+1\\n \\n\\n #delete the temporary folder\\n os.system('rm -r ' + INPUT_FOLDER+'temp')\\n\\n \\n #write the missing files to text file\\n file = open(OUTPUT_FOLDER_USHMM_PROCESSING_LOGS+'transcribe_non_core_doc_failed.txt','w')\\n file.write('\\\\n'.join(missing_files))\\n\\n \\n # success\\n pprint.pprint(\\\"Non-core doc files were successfully processed, but there are \\\" + str(missing_count) + \\\" missing\\\")\",\n \"def process_course(xml_course):\\n\\n parse_course = parse_get_course(xml_course)\\n if not parse_course: # Does not already exist, create\\n parse_course = parse_create_course(xml_course)\\n else:\\n # TODO: Update existing attributes of the course here and upload the changes\\n None\\n parse_course_id = upload_course(parse_course)\\n\\n print (\\\"Processed Course: {0} {1}\\\"\\n .format(parse_course[\\\"subject\\\"], parse_course[\\\"courseNumber\\\"]))\\n\\n # TODO: Implement the functions for this stuff\\n parse_section = parse_get_section(xml_course)\\n if not parse_section: # Does not already exist, create\\n parse_section = parse_create_section(xml_course)\\n else:\\n # TODO: Update existing attributes of the section here and upload the changes\\n None\\n parse_section_id = upload_section(parse_section)\\n\\n put_child(parse_course_id, parse_section_id)\",\n \"def setup_xml_course(self):\\r\\n course_key = SlashSeparatedCourseKey('edX', 'toy', '2012_Fall')\\r\\n location = course_key.make_usage_key('chapter', 'Overview')\\r\\n descriptor = modulestore().get_item(location)\\r\\n\\r\\n self.module = self._get_module(course_key, descriptor, location)\",\n \"def create_course( # lint-amnesty, pylint: disable=arguments-differ\\n self, org, course, run, user_id, master_branch=None, fields=None,\\n versions_dict=None, search_targets=None, root_category='course',\\n root_block_id=None, **kwargs\\n ):\\n # either need to assert this or have a default\\n assert master_branch is not None\\n # check course and run's uniqueness\\n locator = CourseLocator(org=org, course=course, run=run, branch=master_branch)\\n return self._create_courselike(\\n locator, user_id, master_branch, fields, versions_dict,\\n search_targets, root_category, root_block_id, **kwargs\\n )\",\n \"def _create_course(self):\\r\\n # If the course already exists, this will respond\\r\\n # with a 200 and an error message, which we ignore.\\r\\n response = self.session.post(\\r\\n STUDIO_BASE_URL + '/course/',\\r\\n data=self._encode_post_dict(self._course_dict),\\r\\n headers=self.headers\\r\\n )\\r\\n\\r\\n try:\\r\\n err = response.json().get('ErrMsg')\\r\\n\\r\\n except ValueError:\\r\\n raise CourseFixtureError(\\r\\n \\\"Could not parse response from course request as JSON: '{0}'\\\".format(\\r\\n response.content))\\r\\n\\r\\n # This will occur if the course identifier is not unique\\r\\n if err is not None:\\r\\n raise CourseFixtureError(\\\"Could not create course {0}. Error message: '{1}'\\\".format(self, err))\\r\\n\\r\\n if not response.ok:\\r\\n raise CourseFixtureError(\\r\\n \\\"Could not create course {0}. Status was {1}\\\".format(\\r\\n self._course_dict, response.status_code))\",\n \"def start(self) -> None:\\n\\n self.doc = self.doc + r'''\\n \\\\documentclass[\\n 10pt, % Main document font size\\n a4paper, % Paper type, use 'letterpaper' for US Letter paper\\n ]{scrartcl}\\n\\n \\\\usepackage{graphicx}\\n \\\\usepackage{epstopdf}\\n \\\\usepackage{float}\\n \\\\usepackage[scale=0.75]{geometry} % Reduce document margins\\n \\\\usepackage{hyperref}\\n \\\\usepackage{longtable}\\n\\n \\\\begin{document}\\n\\n \\\\title{Automatic Exploratory Data Analysis} % The article title\\n\\n \\\\subtitle{Study Case} % Uncomment to display a subtitle\\n\\n \\\\author{Jacob} % The article author(s) - author affiliations need to be specified in the AUTHOR AFFILIATIONS block\\\\\\n\\n \\\\maketitle % Print the title/author/date block\\n\\n \\\\newpage\\n \\\\tableofcontents % Print the table of contents\\n\\n \\\\newpage\\n \\\\listoffigures % Print the list of figures\\n\\n \\\\newpage\\n \\\\listoftables % Print the list of tables\\n '''\",\n \"def newCatalog():\\n catalog = {'videosContext': None,\\n 'caraContenido': None,\\n 'musicalGenero': None,\\n 'fechaMusica': None}\\n\\n catalog['videosContext'] = lt.newList('ARRAY_LIST')\\n catalog['caraContenido'] = mp.newMap(30,\\n maptype='PROBING',\\n loadfactor=0.4)\\n catalog['musicaGenero'] = mp.newMap(30,\\n maptype='PROBING',\\n loadfactor=0.4)\\n catalog['fechaMusica'] = om.newMap('RBT')\\n\\n return catalog\",\n \"def create_course(self, org, offering, user_id=None, fields=None, **kwargs):\\r\\n\\r\\n course, _, run = offering.partition('/')\\r\\n course_id = SlashSeparatedCourseKey(org, course, run)\\r\\n\\r\\n # Check if a course with this org/course has been defined before (case-insensitive)\\r\\n course_search_location = SON([\\r\\n ('_id.tag', 'i4x'),\\r\\n ('_id.org', re.compile(u'^{}$'.format(course_id.org), re.IGNORECASE)),\\r\\n ('_id.course', re.compile(u'^{}$'.format(course_id.course), re.IGNORECASE)),\\r\\n ('_id.category', 'course'),\\r\\n ])\\r\\n courses = self.collection.find(course_search_location, fields=('_id'))\\r\\n if courses.count() > 0:\\r\\n raise InvalidLocationError(\\r\\n \\\"There are already courses with the given org and course id: {}\\\".format([\\r\\n course['_id'] for course in courses\\r\\n ]))\\r\\n\\r\\n location = course_id.make_usage_key('course', course_id.run)\\r\\n course = self.create_and_save_xmodule(location, fields=fields, **kwargs)\\r\\n\\r\\n # clone a default 'about' overview module as well\\r\\n about_location = location.replace(\\r\\n category='about',\\r\\n name='overview'\\r\\n )\\r\\n overview_template = AboutDescriptor.get_template('overview.yaml')\\r\\n self.create_and_save_xmodule(\\r\\n about_location,\\r\\n system=course.system,\\r\\n definition_data=overview_template.get('data')\\r\\n )\\r\\n\\r\\n return course\",\n \"def _create_course(self, split=True):\\r\\n metadata = {\\r\\n 'start': datetime.datetime(2000, 3, 13, 4),\\r\\n 'display_name': 'Migration test course',\\r\\n }\\r\\n data = {\\r\\n 'wiki_slug': 'test_course_slug'\\r\\n }\\r\\n fields = metadata.copy()\\r\\n fields.update(data)\\r\\n if split:\\r\\n # split requires the course to be created separately from creating items\\r\\n self.split_mongo.create_course(\\r\\n self.split_course_key.org, self.split_course_key.offering, self.userid, fields=fields, root_block_id='runid'\\r\\n )\\r\\n old_course = self.old_mongo.create_course(self.split_course_key.org, 'test_course/runid', fields=fields)\\r\\n self.old_course_key = old_course.id\\r\\n self.runtime = old_course.runtime\",\n \"def create_document(self, data):\\n command = CreateDocumentFromOneOffixxTemplateCommand(self.context, data['title'], data['template'])\\n return command.execute()\",\n \"def insert_course(dept, num, text):\\n\\n # Course Title \\n m = re.search(\\\"[\\\\d\\\\w]{5} - ([\\\\w ]*)\\\", text)\\n title = m.group(1) if m else \\\"nomatch\\\"\\n\\n # Course Description\\n m = re.search(\\\"\\\\.\\\\s(.*)\\\\sTypically\\\",text)\\n des = m.group(1) if m else \\\"nomatch\\\"\\n\\n # Credit hours aren't fixed for every course\\n # Credit Hours: 2.00\\n # Credit Hours: 2.00 or 3.00. \\n # Credit Hours: 1.00 to 18.00. \\n m = re.search(\\\"Credit Hours: (\\\\d+\\\\.\\\\d+)\\\",text, flags=re.IGNORECASE)\\n m = re.search(\\\"(\\\\d+\\\\.\\\\d+)(.*?)Credit hours\\\",text, flags=re.IGNORECASE) if not m else m\\n cr = m.group(1) if m else \\\"-1\\\"\\n\\n # Semesters Offered\\n m = re.search(\\\"Typically offered (.*?)\\\\.\\\", text)\\n sem = m.group(1).split() if m else [\\\"nomatch\\\"]\\n\\n # Course Type: Lecture, Recitation, Lab, Seminar, etc.\\n m = re.search(\\\"Schedule Types:\\\\s((?:[\\\\w ]+)(?:,[\\\\w ]+)*) \\\\s+\\\", text)\\n form = m.group(1).split(\\\", \\\") if m else [\\\"nomatch\\\"]\\n\\n # Learning objectives will not necessarily follow campuses\\n m = re.search(\\\"campuses:(\\\\s+([\\\\w\\\\s])+\\\\n)\\\", text)\\n campus = m.group(1).strip().split(\\\"\\\\n\\\\n\\\") if m else [\\\"nomatch\\\"]\\n campus = [camp.strip() for camp in campus]\\n\\n # prereq regex and decomosition of prereqs into lists of AND conditions (works for most classes, not 477 and similar)\\n # re.DOTALL matches all characters, including \\\"\\\\n\\\"\\n idx = text.find(\\\"campuses:\\\")\\n m = re.search(\\\"Prerequisites:(.*)\\\",text[idx:],flags=re.DOTALL)\\n if m:\\n allReqs = []\\n prereqText = m.group(1).strip()\\n prereqText = prereqText.encode('ascii', 'ignore') \\n for i in PrereqParser.parseprereq(prereqText):\\n reqArr = []\\n for j in i.split():\\n if j.find(\\\"-C\\\") != -1:\\n j = j.replace(\\\"-C\\\",\\\"\\\")\\n reqArr.append(Requisite(course=j,reqType=False))\\n else:\\n reqArr.append(Requisite(course=j,reqType=True)) \\n allReqs.append(RequisiteList(courses=reqArr))\\n\\n else:\\n allReqs = []\\n\\n # create course entity\\n course = Course(number=num, title=title, department=dept, form=form,\\n description=des, credits=float(cr), semesters=sem,\\n campuses=campus,requisites=allReqs, id=dept + num)\\n # store course \\n course.put()\",\n \"def load2TexAS(data):\\n # State global variable\\n global cache_stanza, cache_spacy, cache_udpipe, cache_trankit\\n\\n # Collect the data\\n string = data['text']\\n lang = data['lang']\\n packages = data['packages']\\n\\n hash_string = hashlib.sha1(string.encode()).hexdigest()\\n\\n final_HTML = \\\"\\\"\\n message_HTML = \\\"<div class=\\\\'message\\\\'>\\\"\\n isMessage = False\\n header_input = []\\n log_row = [datetime.now().strftime('%Y-%m-%d %H:%M:%S'), lang]\\n\\n if \\\"stanza\\\" in packages:\\n # Initialize the TexAS document\\n mydoc = tx.Document(string)\\n mydoc.meta().set(\\\"authors\\\",\\\"hegler,yiwen,celine,yuqian\\\")\\n mydoc.date().setTimestamp(\\\"2021-01-19T14:44\\\")\\n\\n ## If cache is full, reload the cache.\\n if cache.count(cache_stanza) > 100:\\n cache.write(cache_stanza, \\\"stanza\\\")\\n cache_stanza = cache.load(\\\"stanza\\\")\\n \\n ## Check text whether is already in cache\\n if hash_string in cache_stanza[lang].keys():\\n tokens, end_pos, lemma, pos, nlpWordsList, hasCompoundWords, cache_stanza = cache.read(\\\"stanza\\\", cache_stanza, lang, string) #The output cache_stanza has 'count' been updated.\\n else:\\n tokens, end_pos, lemma, pos, nlpWordsList, hasCompoundWords, cache_stanza = cache.add(\\\"stanza\\\", cache_stanza, lang, string, get_services_stanza)\\n \\n mydoc.setTokenList(tokens, indexed=True)\\n mydoc.views().get(\\\"TOKENS\\\").meta().set(\\\"generator\\\", \\\"stanza\\\")\\n mydoc.views().get(\\\"TOKENS\\\").meta().set(\\\"model\\\", \\\"stanza\\\" + \\\"-\\\" + lang)\\n mydoc.setSentenceList(end_pos)\\n\\n if hasCompoundWords:\\n mydoc.addTokenView( \\\"WORDS\\\", nlpWordsList )\\n mydoc.addTokenView(\\\"LEMMA\\\", lemma)\\n mydoc.addTokenView(\\\"POS\\\", pos)\\n \\n # Extract HTML View\\n myTabView = tx.UITabularView(mydoc)\\n if hasCompoundWords:\\n myTabView.showView(\\\"WORDS\\\")\\n myTabView.showView(\\\"LEMMA\\\", labelCSS=False)\\n myTabView.showView(\\\"POS\\\")\\n\\n # concatenate the myTabView.HTML()\\n header_input.append((\\\"Stanza\\\", str(len(end_pos)) , str(len(tokens)), str(get_tokens_per_sents(end_pos))))\\n final_HTML += \\\"<div class='subtitle'>Stanza</div> <br>\\\" + myTabView.HTML().replace(\\\"\\\\n\\\", \\\"\\\") + \\\"<br>\\\"\\n log_row.append(\\\"stanza\\\")\\n \\n else:\\n log_row.append(\\\"\\\")\\n\\n if \\\"spacy\\\" in packages:\\n # SpaCy does not support Arabic and Russian\\n if lang == 'ara' or lang == 'rus':\\n message_HTML += \\\"SpaCy does not support Arabic or Russian. <br>\\\"\\n isMessage = True\\n\\n else:\\n mydoc = tx.Document(string)\\n mydoc.meta().set(\\\"authors\\\",\\\"hegler,yiwen,celine,yuqian\\\")\\n mydoc.date().setTimestamp(\\\"2021-01-19T14:44\\\")\\n \\n ## If cache is full, reload the cache.\\n if cache.count(cache_spacy) > 100:\\n cache.write(cache_spacy, \\\"spacy\\\")\\n cache_spacy = cache.load(\\\"spacy\\\")\\n \\n ## Check text whether is already in cache\\n if hash_string in cache_spacy[lang].keys():\\n tokens, end_pos, lemma, pos, cache_spacy = cache.read(\\\"spacy\\\", cache_spacy, lang, string)\\n else:\\n tokens, end_pos, lemma, pos, cache_spacy = cache.add(\\\"spacy\\\", cache_spacy, lang, string, get_services_spacy)\\n \\n mydoc.setTokenList(tokens, indexed=True)\\n mydoc.views().get(\\\"TOKENS\\\").meta().set(\\\"generator\\\", \\\"spacy\\\")\\n mydoc.views().get(\\\"TOKENS\\\").meta().set(\\\"model\\\", \\\"spacy\\\" + \\\"-\\\" + lang )\\n mydoc.setSentenceList(end_pos)\\n mydoc.addTokenView(\\\"LEMMA\\\", lemma)\\n mydoc.addTokenView(\\\"POS\\\", pos)\\n \\n # Extract HTML View\\n myTabView = tx.UITabularView(mydoc)\\n myTabView.showView(\\\"LEMMA\\\", labelCSS=False)\\n myTabView.showView(\\\"POS\\\")\\n\\n # concatenate the myTabView.HTML()\\n header_input.append((\\\"SpaCy\\\", str(len(end_pos)) , str(len(tokens)), str(get_tokens_per_sents(end_pos))))\\n final_HTML += \\\"<div class='subtitle'>\\\" + \\\"SpaCy\\\" + \\\"</div><br>\\\" + myTabView.HTML().replace(\\\"\\\\n\\\", \\\"\\\") + \\\"<br>\\\"\\n log_row.append(\\\"spacy\\\")\\n \\n else:\\n log_row.append(\\\"\\\")\\n\\n if \\\"udpipe\\\" in packages: \\n ## If cache is full, reload the cache.\\n if cache.count(cache_udpipe) > 100:\\n cache.write(cache_udpipe, \\\"udpipe\\\")\\n cache_udpipe = cache.load(\\\"udpipe\\\")\\n \\n ## Check text whether is already in cache\\n if hash_string in cache_udpipe[lang].keys():\\n tokens, end_pos, lemma, pos, cache_udpipe = cache.read(\\\"udpipe\\\", cache_udpipe, lang, string)\\n else:\\n tokens, end_pos, lemma, pos, cache_udpipe = cache.add(\\\"udpipe\\\", cache_udpipe, lang, string, get_services_udpipe)\\n \\n string_udpipe = \\\" \\\".join(tokens)\\n\\n # Initialize the TexAS document\\n mydoc = tx.Document(string_udpipe)\\n mydoc.meta().set(\\\"authors\\\",\\\"hegler,yiwen,celine,yuqian\\\")\\n mydoc.date().setTimestamp(\\\"2021-01-19T14:44\\\")\\n\\n mydoc.setTokenList(tokens, indexed=True)\\n mydoc.views().get(\\\"TOKENS\\\").meta().set(\\\"generator\\\", \\\"udpipe\\\")\\n mydoc.views().get(\\\"TOKENS\\\").meta().set(\\\"model\\\", \\\"udpipe\\\" + \\\"-\\\" + lang )\\n mydoc.setSentenceList(end_pos)\\n mydoc.addTokenView(\\\"LEMMA\\\", lemma)\\n mydoc.addTokenView(\\\"POS\\\", pos)\\n \\n # Extract HTML View\\n myTabView = tx.UITabularView(mydoc)\\n myTabView.showView(\\\"LEMMA\\\", labelCSS=False)\\n myTabView.showView(\\\"POS\\\")\\n\\n # concatenate the myTabView.HTML()\\n header_input.append((\\\"UDpipe\\\", str(len(end_pos)) , str(len(tokens)), str(get_tokens_per_sents(end_pos))))\\n final_HTML += \\\"<div class='subtitle'>UDpipe</div> <br>\\\" + myTabView.HTML().replace(\\\"\\\\n\\\", \\\"\\\") + \\\"<br>\\\"\\n log_row.append(\\\"udpipe\\\")\\n \\n else:\\n log_row.append(\\\"\\\")\\n \\n if \\\"trankit\\\" in packages:\\n # trankit temporarily only support english\\n if lang == 'eng':\\n mydoc = tx.Document(string)\\n mydoc.meta().set(\\\"authors\\\",\\\"hegler,yiwen,celine,yuqian\\\")\\n mydoc.date().setTimestamp(\\\"2021-01-19T14:44\\\")\\n \\n ## If cache is full, reload the cache.\\n if cache.count(cache_trankit) > 100:\\n cache.write(cache_trankit, \\\"trankit\\\")\\n cache_trankit = cache.load(\\\"trankit\\\")\\n \\n ## Check text whether is already in cache\\n if hash_string in cache_trankit[lang].keys():\\n tokens, end_pos, lemma, pos, cache_trankit = cache.read(\\\"trankit\\\", cache_trankit, lang, string)\\n else:\\n tokens, end_pos, lemma, pos, cache_trankit = cache.add(\\\"trankit\\\", cache_trankit, lang, string, get_services_trankit)\\n \\n mydoc.setTokenList(tokens, indexed=True)\\n mydoc.views().get(\\\"TOKENS\\\").meta().set(\\\"generator\\\", \\\"spacy\\\")\\n mydoc.views().get(\\\"TOKENS\\\").meta().set(\\\"model\\\", \\\"spacy\\\" + \\\"-\\\" + lang )\\n mydoc.setSentenceList(end_pos)\\n mydoc.addTokenView(\\\"LEMMA\\\", lemma)\\n mydoc.addTokenView(\\\"POS\\\", pos)\\n \\n # Extract HTML View\\n myTabView = tx.UITabularView(mydoc)\\n myTabView.showView(\\\"LEMMA\\\", labelCSS=False)\\n myTabView.showView(\\\"POS\\\")\\n\\n # concatenate the myTabView.HTML()\\n header_input.append((\\\"Trankit\\\", str(len(end_pos)) , str(len(tokens)), str(get_tokens_per_sents(end_pos))))\\n final_HTML += \\\"<div class='subtitle'>\\\" + \\\"Trankit\\\" + \\\"</div><br>\\\" + myTabView.HTML().replace(\\\"\\\\n\\\", \\\"\\\") + \\\"<br>\\\"\\n log_row.append(\\\"trankit\\\")\\n\\n else:\\n message_HTML += \\\"Trankit temporarily only supports English. <br>\\\"\\n isMessage = True \\n \\n else:\\n log_row.append(\\\"\\\")\\n\\n message_HTML += \\\"</div>\\\"\\n if isMessage:\\n return message_HTML + get_header_table(header_input) + \\\"<br><br>\\\" + final_HTML\\n\\n writeLog(log_row)\\n return get_header_table(header_input) + \\\"<br><br>\\\" + final_HTML\",\n \"def test_get_toc_text(self) -> None:\\n extraction = docx2python(RESOURCES / \\\"zen_of_python.docx\\\")\\n assert extraction.document_runs == [\\n [\\n [[[\\\"Contents\\\"], [\\\"\\\\t\\\", \\\"Beautiful is better than ugly.\\\\t1\\\"], []]],\\n [\\n [\\n [],\\n [],\\n [\\\"Beautiful is better than ugly.\\\"],\\n [\\\"Explicit is better than implicit.\\\"],\\n [\\\"Simple is better than complex.\\\"],\\n [\\\"Complex is better than complicated.\\\"],\\n [\\\"Flat is better than nested.\\\"],\\n [\\\"Sparse is better than dense.\\\"],\\n [\\\"Readability counts.\\\"],\\n [\\\"Special cases aren't special enough to break the rules.\\\"],\\n [\\\"Although practicality beats purity.\\\"],\\n [\\\"Errors should never pass silently.\\\"],\\n [\\\"Unless explicitly silenced.\\\"],\\n [\\\"In the face of ambiguity, refuse the temptation to guess.\\\"],\\n [\\n \\\"There should be one-- and preferably only one --obvious way to do it.\\\"\\n ],\\n [\\n \\\"Although that way may not be obvious at first unless you're Dutch.\\\"\\n ],\\n [\\\"Now is better than never.\\\"],\\n [\\\"Although never is often better than *right* now.\\\"],\\n [\\\"If the implementation is hard to explain, it's a bad idea.\\\"],\\n [\\n \\\"If the implementation is easy to explain, it may be a good idea.\\\"\\n ],\\n [\\n \\\"Namespaces are one honking great idea -- let's do more of those!\\\"\\n ],\\n ]\\n ],\\n ]\\n ]\\n extraction.close()\",\n \"def test_course_index_view_with_course(self):\\r\\n CourseFactory.create(display_name='Robot Super Educational Course')\\r\\n resp = self.client.get_html('/course/')\\r\\n self.assertContains(\\r\\n resp,\\r\\n '<h3 class=\\\"course-title\\\">Robot Super Educational Course</h3>',\\r\\n status_code=200,\\r\\n html=True\\r\\n )\\r\\n _test_no_locations(self, resp)\",\n \"def get_text(self, course): # pylint: disable=unused-argument\\r\\n raise NotImplementedError\",\n \"def populate_course(self):\\r\\n def descend(parent, stack):\\r\\n xblock_type = stack.pop(0)\\r\\n for _ in range(2):\\r\\n child = ItemFactory.create(category=xblock_type, parent_location=parent.location)\\r\\n if stack:\\r\\n descend(child, stack)\\r\\n\\r\\n descend(self.course, ['chapter', 'sequential', 'vertical', 'problem'])\",\n \"def create_course(course_request: schemas.CourseRequest, db: Session = Depends(get_db)):\\n\\n try:\\n course = crud.course.create(db, obj_in=course_request)\\n\\n except Exception as error:\\n logger.error(f'Error when creating a course: {error}')\\n raise HTTPException(status_code=status.HTTP_500_INTERNAL_SERVER_ERROR, detail=f'{error}')\\n\\n return course\",\n \"def create_tutorial(cls, *filenames):\\n return cls.create_input_document(cls.doc_type_tutorial, *filenames)\",\n \"def read_data(term):\\n course_url = '/admweb/!SWKSECX.main?term='+ term + '&title=&course=&crn=&coll=&dept=&subj='\\n connection = httplib.HTTPSConnection('courses.rice.edu', 443)\\n connection.connect()\\n connection.request(\\n 'GET',\\n course_url\\n )\\n courses_xml = connection.getresponse()\\n return courses_xml\",\n \"def completed_course()->str:\\r\\n try:\\r\\n db: sqlite3.Connection = sqlite3.connect(DB_FILE)\\r\\n query: str = \\\"SELECT s.Name, s.CWID, g.Course, g.Grade, i.Name AS 'Instructor' \\\" \\\\\\r\\n \\\"FROM grades2 g JOIN students2 s ON g.StudentCWID = s.CWID \\\" \\\\\\r\\n \\\"JOIN instructors2 i ON g.InstructorCWID = i.CWID ORDER BY s.Name\\\" \\r\\n except sqlite3.OperationalError as e:\\r\\n print(e)\\r\\n\\r\\n data: Dict[str,str]=\\\\\\r\\n [{\\\"name\\\": name,\\\"cwid\\\": cwid,\\\"course\\\": course , \\\"grade\\\":grade, \\\"instructor\\\": instructor}\\r\\n for name, cwid, course, grade, instructor in db.execute(query)]\\r\\n db.close()\\r\\n \\\"\\\"\\\"Render the template from templates folder\\\"\\\"\\\"\\r\\n return render_template(\\\"student_summary.html\\\",\\r\\n title=\\\"Stevens Repository\\\",\\r\\n table_title=\\\"Student, Course, Grade and Instructor\\\",\\r\\n students=data )\",\n \"def thredds_catalog(self, tmpdir_factory):\\n input_dir = os.path.abspath(\\\"esacci_esgf/test_input_catalogs\\\")\\n output_dir = str(tmpdir_factory.mktemp(\\\"output\\\", numbered=True))\\n # Process all catalogs in input dir and create aggregations with WMS\\n test_files = glob(\\\"{}/*.xml\\\".format(input_dir))\\n assert test_files, \\\"No test catalogs found\\\"\\n\\n pb = ProcessBatch([\\\"-aw\\\", \\\"-o\\\", output_dir] + test_files)\\n pb.do_all()\\n tree = ET.ElementTree()\\n tree.parse(os.path.join(output_dir, os.listdir(input_dir)[0]))\\n return tree.getroot()\",\n \"def build(self) -> None:\\n\\n print(\\\"Genereting files..\\\")\\n self.doc = self.doc + r'\\\\end{document}'\\n\\n f = open(\\\"latex\\\\\\\\\\\" + self.report_name + '.tex', 'w')\\n f.write(self.doc)\\n f.close()\\n\\n os.chdir('latex')\\n\\n cmd = ['pdflatex', '-interaction', 'nonstopmode', self.report_name + '.tex']\\n #cmd = ['pdflatex', '-interaction', self.report_name + '.tex']\\n\\n for i in range(2):\\n proc = subprocess.Popen(cmd)\\n proc.communicate()\\n retcode = proc.returncode\\n if not retcode == 0:\\n os.chdir('..')\\n raise ValueError('Error {} executing command: {}'.format(retcode, ' '.join(cmd)))\\n\\n os.unlink(self.report_name + '.aux')\\n os.unlink(self.report_name + '.lof')\\n os.unlink(self.report_name + '.log')\\n os.unlink(self.report_name + '.lot')\\n os.unlink(self.report_name + '.out')\\n os.unlink(self.report_name + '.toc')\\n\\n os.chdir('..')\",\n \"def goto_create_course(self):\\n\\n self.create.click()\",\n \"def get_courses(self):\\n\\n self.search([]).unlink()\\n token = self.env['odoo.moodle'].search([('create_uid', '=', self.env.user.id)]).token\\n domain = \\\"http://localhost:8888\\\"\\n webservice_url = \\\"/webservice/rest/server.php?\\\"\\n parameters = {\\n \\\"wstoken\\\":token,\\n 'wsfunction': 'core_course_get_courses',\\n 'moodlewsrestformat': 'json'\\n }\\n request = requests.get(url=domain+webservice_url, params=parameters)\\n request = request.json()\\n print(request)\\n\\n for req in request:\\n try:\\n if req['id']==1:\\n pass\\n else:\\n self.create({\\n 'course_id': req['id'], \\n 'category':req['categoryid'],\\n 'fullname':req['fullname'], \\n 'shortname':req['shortname'],\\n 'summary': req['summary']\\n }\\n )\\n except Exception:\\n print('Course not created')\",\n \"def test_tax_court_citation_extractor(self):\\n\\n test_pairs = (\\n (\\n \\\"\\\"\\\" 1 UNITED STATES TAX COURT REPORT (2018)\\n\\n\\n\\n UNITED STATES TAX COURT\\n\\n\\n\\n BENTLEY COURT II LIMITED PARTNERSHIP, B.F. BENTLEY, INC., TAX\\n MATTERS PARTNER, Petitioner v.\\n COMMISSIONER OF INTERNAL REVENUE, Respondent\\n\\n\\n\\n Docket No. 5393-04. Filed May 31, 2006.\\n\\n\\n\\n Nancy Ortmeyer Kuhn, for petitioner.\\n \\\"\\\"\\\",\\n \\\"1 T.C. 2018\\\",\\n ),\\n (\\n \\\"\\\"\\\" T.C. Memo. 2003-150\\n\\n\\n\\n UNITED STATES TAX COURT\\n\\n\\n\\n RIVER CITY RANCHES #1 LTD., LEON SHEPARD,\\n TAX MATTERS PARTNER,\\n RIVER CITY RANCHES #2 LTD., LEON SHEPARD,\\n TAX MATTERS PARTNER,\\n RIVER CITY RANCHES #3 LTD., LEON SHEPARD,\\n TAX MATTERS PARTNER,\\n\\n\\n \\\"\\\"\\\",\\n \\\"2003 T.C. Memo. 150\\\",\\n ),\\n (\\n \\\"\\\"\\\" T.C. Summary Opinion 2003-150\\n\\n\\n\\n UNITED STATES TAX COURT\\n\\n\\n\\n RIVER CITY RANCHES #1 LTD., LEON SHEPARD,\\n TAX MATTERS PARTNER,\\n RIVER CITY RANCHES #2 LTD., LEON SHEPARD,\\n TAX MATTERS PARTNER,\\n RIVER CITY RANCHES #3 LTD., LEON SHEPARD,\\n TAX MATTERS PARTNER,\\n\\n\\n \\\"\\\"\\\",\\n \\\"2003 T.C. Summary Opinion 150\\\",\\n ),\\n (\\n \\\"\\\"\\\"\\n MICHAEL KEITH SHENK, PETITIONER v. COMMISSIONER\\n OF INTERNAL REVENUE, RESPONDENT\\n\\n Docket No. 5706–12. Filed May 6, 2013.\\n\\n P was divorced from his wife, and their 2003 ‘‘Judgment of\\n Absolute Divorce’’ provided that his ex-wife would have pri-\\n mary residential custody of their three minor children. The\\n judgment provided that the dependency exemption deductions\\n for the three children would be divided between the two ex-\\n spouses according to various conditions but did not provide\\n that the ex-wife must execute in P’s favor a Form 8332,\\n ‘‘Release of Claim to Exemption for Child of Divorced or Sepa-\\n rated Parents’’. The children resided with P’s ex-wife for more\\n than half of 2009, and P’s ex-wife did not execute in P’s favor\\n any Form 8332 or equivalent document for any year. For 2009\\n P timely filed a Federal income tax return on which he\\n claimed dependency exemption deductions and the child tax\\n credit for two of the children, consistent with his under-\\n standing of the terms of the judgment, but he did not attach\\n any Form 8332 to his return. He also claimed head-of-house-\\n hold filing status. His ex-wife, the custodial parent, timely\\n filed a Federal income tax return for 2009 on which she also\\n\\n 200\\n\\n\\n\\n\\nVerDate Nov 24 2008 10:59 Jul 11, 2014 Jkt 372897 PO 20012 Frm 00001 Fmt 3857 Sfmt 3857 V:\\\\FILES\\\\BOUND VOL. WITHOUT CROP MARKS\\\\B.V.140\\\\SHENK JAMIE\\n\\f (200) SHENK v. COMMISSIONER 201\\n\\n\\n claimed two dependency exemption deductions, so that one\\n child was claimed on both parents’ returns. R allowed to P the\\n dependency exemption deduction for one of the children but\\n disallowed his claim for the dependency exemption deduction\\n for the child who had also been claimed by the custodial\\n parent. At trial P contended he is entitled to a dependency\\n exemption deduction for all three children. Held: Since the\\n custodial parent did not execute, and P could not and did not\\n attach to his return, any Form 8332 or equivalent release, P\\n is not entitled under I.R.C. sec. 152(e)(2)(A) to claim the\\n dependency exemption deduction or the child tax credit. Held,\\n further, where both the custodial parent and the noncustodial\\n parent have claimed for the same year a dependency exemp-\\n tion deduction for the same child, a declaration signed by the\\n custodial parent after the period of limitations for assess-\\n ments has expired as to the custodial parent could not qualify\\n under I.R.C. sec. 152(e)(2)(A), and therefore there is no reason\\n to grant P’s request to leave the record open so that he may\\n obtain and proffer such a declaration. Held, further, P is not\\n entitled to head-of-household filing status under I.R.C. sec.\\n 2(b)(1) nor to the child tax credit under I.R.C. sec. 24.\\n\\n Michael Keith Shenk, for himself.\\n Shari Salu, for respondent.\\n GUSTAFSON, Judge: The Internal Revenue Service (IRS)\\n determined a deficiency of $3,136 in the 2009 Federal income\\n tax of petitioner Michael Keith Shenk. Mr. Shenk petitioned\\n this Court, pursuant to section 6213(a), 1 for redetermination\\n of the deficiency. After Mr. Shenk’s concession that he\\n received but did not report $254 in dividend income, the\\n issue for decision is whether Mr. Shenk is entitled to a\\n dependency exemption deduction for one of his children\\n under section 151(c), a child tax credit for that child under\\n section 24(a), and head-of-household filing status under sec-\\n tion 2(b)(1). On these issues, we hold for the IRS.\\n FINDINGS OF FACT\\n\\n The judgment of divorce\\n Mr. Shenk was married to Julie Phillips, and they have\\n three minor children—M.S., W.S., and L.S. They divorced in\\n 2003. The family court’s ‘‘Judgment of Absolute Divorce’’ pro-\\n 1 Unless otherwise indicated, all citations of sections refer to the Internal\\n\\n Revenue Code (26 U.S.C.) in effect for the tax year at issue, and all cita-\\n tions of Rules refer to the Tax Court Rules of Practice and Procedure.\\n\\n\\n\\n\\nVerDate Nov 24 2008 10:59 Jul 11, 2014 Jkt 372897 PO 20012 Frm 00002 Fmt 3857 Sfmt 3857 V:\\\\FILES\\\\BOUND VOL. WITHOUT CROP MARKS\\\\B.V.140\\\\SHENK JAMIE\\n\\f 202 140 UNITED STATES TAX COURT REPORTS (200)\\n\\n\\n vided: that Ms. Phillips was ‘‘awarded primary residential\\n custody’’ of the parties’ three children; and that Mr. Shenk\\n would be liable for child support payments; but that, as to\\n dependency exemptions—\\\"\\\"\\\",\\n \\\"140 T.C. 200\\\",\\n ),\\n )\\n site = tax.Site()\\n for q, a in test_pairs:\\n results = site.extract_from_text(q)\\n cite_string = \\\"%s %s %s\\\" % (\\n results[\\\"Citation\\\"][\\\"volume\\\"],\\n results[\\\"Citation\\\"][\\\"reporter\\\"],\\n results[\\\"Citation\\\"][\\\"page\\\"],\\n )\\n\\n self.assertEqual(cite_string, a)\\n print \\\"✓\\\", cite_string\",\n \"def initialize_course(self):\\r\\n self.module_store = editable_modulestore()\\r\\n\\r\\n # Create the course\\r\\n self.course = CourseFactory.create(org=TEST_COURSE_ORG,\\r\\n number=TEST_COURSE_NUMBER,\\r\\n display_name=TEST_COURSE_NAME)\\r\\n\\r\\n # Add a chapter to the course\\r\\n chapter = ItemFactory.create(parent_location=self.course.location,\\r\\n display_name=TEST_SECTION_NAME)\\r\\n\\r\\n # add a sequence to the course to which the problems can be added\\r\\n self.problem_section = ItemFactory.create(parent_location=chapter.location,\\r\\n category='sequential',\\r\\n metadata={'graded': True, 'format': 'Homework'},\\r\\n display_name=TEST_SECTION_NAME)\",\n \"def set_up_course(self):\\r\\n course = CourseFactory(start=datetime(2013, 9, 16, 7, 17, 28))\\r\\n course = modulestore().get_course(course.id) # pylint: disable=no-member\\r\\n return course\",\n \"def makeDocument(fontPath):\\n\\n f = Font(fontPath) # Get PageBot Font instance of Variable font.\\n \\n W = H = PageSize\\n\\n # Create a new document, default to the defined page size. \\n doc = Document(w=W, h=H, originTop=False, title='Text Flow', autoPages=1)\\n \\n view = doc.getView()\\n view.padding = 0 # Aboid showing of crop marks, etc.\\n view.showPageCropMarks = True\\n view.showPageRegistrationMarks = True\\n view.showPageFrame = True\\n view.showPagePadding = True\\n view.showElementOrigin = False\\n view.showElementDimensions = False\\n \\n # Get list of pages with equal y, then equal x. \\n #page = doc[0][0] # Get the single page from te document.\\n page = doc.getPage(0) # Get page on pageNumber, first in row (this is only one now).\\n page.name = 'Page 1'\\n page.padding = PagePadding\\n \\n fs = newFS(f.info.familyName + ' ' + f.info.styleName, \\n style=dict(font=f.name, fontSize=18, textFill=0))\\n _, th = textSize(fs)\\n title = newTextBox(fs, conditions=[Top2Top(), Fit2Width()],\\n parent=page, h=th*1.2)\\n \\n circle = VariableCircle(f, s=GLYPH_NAME, name='VariableCircleSpeciment',\\n parent=page, padding=4, x=100, fontSize=64,\\n maxW=W-2*PagePadding, minW=100, showAxisName=True, \\n # Conditions make the element move to top-left of the page.\\n # And the condition that there should be no overflow, otherwise the text box\\n # will try to solve it. \\n conditions=[Float2Top(), Fit2Bottom(), Center2Center()],\\n # Position of the origin of the element. Just to show where it is.\\n # Has no effect on the position conditions. \\n yAlign=BOTTOM, xAlign=LEFT, fill=CIRCLE_ELEMENT_FILL, borders=0,\\n )\\n \\n score = doc.solve() # Try to solve all pages.\\n if score.fails:\\n print score.fails\\n\\n # To avoid circular dependent conditions, we correct the position of the title\\n # on left to that the position of the circle has become.\\n title.pl = circle.x - page.pl\\n \\n return doc # Answer the doc for further doing.\",\n \"def create_course_page(self, course):\\r\\n\\r\\n course_wiki_home = reverse('course_wiki', kwargs={'course_id': course.id.to_deprecated_string()})\\r\\n referer = reverse(\\\"progress\\\", kwargs={'course_id': self.toy.id.to_deprecated_string()})\\r\\n\\r\\n resp = self.client.get(course_wiki_home, follow=True, HTTP_REFERER=referer)\\r\\n\\r\\n course_wiki_page = referer.replace('progress', 'wiki/' + self.toy.wiki_slug + \\\"/\\\")\\r\\n\\r\\n ending_location = resp.redirect_chain[-1][0]\\r\\n ending_status = resp.redirect_chain[-1][1]\\r\\n\\r\\n self.assertEquals(ending_location, 'http://testserver' + course_wiki_page)\\r\\n self.assertEquals(resp.status_code, 200)\\r\\n\\r\\n self.has_course_navigator(resp)\",\n \"def create_course(self, modules_count, module_store, topics):\\n course = CourseFactory.create(\\n org=\\\"a\\\",\\n course=\\\"b\\\",\\n run=\\\"c\\\",\\n start=datetime.now(UTC),\\n default_store=module_store,\\n discussion_topics=topics\\n )\\n CourseEnrollmentFactory.create(user=self.user, course_id=course.id)\\n course_url = reverse(\\\"course_topics\\\", kwargs={\\\"course_id\\\": str(course.id)})\\n # add some discussion xblocks\\n for i in range(modules_count):\\n ItemFactory.create(\\n parent_location=course.location,\\n category='discussion',\\n discussion_id=f'id_module_{i}',\\n discussion_category=f'Category {i}',\\n discussion_target=f'Discussion {i}',\\n publish_item=False,\\n )\\n return course_url\",\n \"def test_buildTeX(self):\\n version = \\\"3.2.1\\\"\\n input1, output1 = self.getArbitraryLoreInputAndOutput(version)\\n input2, output2 = self.getArbitraryLoreInputAndOutput(version)\\n\\n # Filenames are chosen by getArbitraryOutput to match the counter used\\n # by getArbitraryLoreInputAndOutput.\\n self.howtoDir.child(\\\"1.xhtml\\\").setContent(input1)\\n self.howtoDir.child(\\\"2.xhtml\\\").setContent(input2)\\n\\n builder = BookBuilder()\\n builder.buildTeX(self.howtoDir)\\n self.assertEqual(self.howtoDir.child(\\\"1.tex\\\").getContent(), output1)\\n self.assertEqual(self.howtoDir.child(\\\"2.tex\\\").getContent(), output2)\",\n \"def create_course_with_unit():\\r\\n world.clear_courses()\\r\\n course = world.CourseFactory.create()\\r\\n world.scenario_dict['COURSE'] = course\\r\\n section = world.ItemFactory.create(parent_location=course.location)\\r\\n world.ItemFactory.create(\\r\\n parent_location=section.location,\\r\\n category='sequential',\\r\\n display_name='Subsection One',\\r\\n )\\r\\n user = create_studio_user(is_staff=False)\\r\\n add_course_author(user, course)\\r\\n\\r\\n log_into_studio()\\r\\n world.css_click('a.course-link')\\r\\n\\r\\n world.wait_for_js_to_load()\\r\\n css_selectors = [\\r\\n 'div.section-item a.expand-collapse', 'a.new-unit-item'\\r\\n ]\\r\\n for selector in css_selectors:\\r\\n world.css_click(selector)\\r\\n\\r\\n world.wait_for_mathjax()\\r\\n world.wait_for_xmodule()\\r\\n\\r\\n assert world.is_css_present('ul.new-component-type')\",\n \"def test_create_course(self):\\r\\n self.assert_created_course()\",\n \"def cmd_makecldf(self, args):\\n wl = lingpy.Wordlist(self.raw_dir.joinpath(\\\"GEM-CNL.csv\\\").as_posix())\\n concepts = args.writer.add_concepts(\\n id_factory=lambda x: x.id.split(\\\"-\\\")[-1] + \\\"_\\\" + slug(x.english), lookup_factory=\\\"Name\\\"\\n )\\n for concept in self.conceptlists[0].concepts.values():\\n for cis in concept.attributes[\\\"lexibank_gloss\\\"]:\\n if cis not in concepts:\\n concepts[cis] = concepts[concept.english]\\n\\n languages = args.writer.add_languages(lookup_factory=\\\"STEDT_Name\\\")\\n args.writer.add_sources()\\n\\n for idx, language, concept, value, pos in wl.iter_rows(\\n \\\"doculect\\\", \\\"concept\\\", \\\"reflex\\\", \\\"gfn\\\"\\n ):\\n # Fix for 251479\\n if concept == \\\"top (i.e. highest point\\\":\\n concept = \\\"top (i.e. highest point)\\\"\\n\\n if concept not in concepts:\\n args.log.warning(concept)\\n else:\\n args.writer.add_forms_from_value(\\n Language_ID=languages[language],\\n Parameter_ID=concepts[concept],\\n Value=value,\\n Source=[\\\"Marrison1967\\\"],\\n )\",\n \"def __init__(self, tutorial):\\n self._tex = \\\"\\\"\\n\\n self.texPreamble(tutorial)\\n self.texTutorialHeader(tutorial)\\n\\n for lecture in self.children(tutorial):\\n self.texLectureHeader(lecture)\\n\\n for slide in self.children(lecture):\\n self.texSlideHeader(slide)\\n for section in slide.sections:\\n self.texSlideSection(section)\\n self.texSlideFooter(slide)\\n\\n self.texLectureFooter(lecture)\\n\\n self.texTutorialFooter(tutorial)\\n self.texPostamble(tutorial)\",\n \"def do_ascii(catalog):\\n task_str = catalog.get_current_task_str()\\n\\n\\n # Howerton Catalog\\n datafile = os.path.join(catalog.get_current_task_repo(), 'ASCII',\\n 'vizier_J_MNRAS_441_1186_table1 J_MNRAS_441_1186_table3_20200403.csv')\\n data = read(datafile, format='csv')\\n for rrow in pbar(data, task_str):\\n row = dict((x, str(rrow[x])) for x in rrow.columns)\\n# if any(x in row['Notes'].lower() for x in ['artifact']):\\n# continue\\n# ctypes = row['Type'].split('/')\\n# nonsne = False\\n# for ct in ctypes:\\n# if ct.replace('?', '') in catalog.nonsnetypes:\\n# nonsne = True\\n# else:\\n# nonsne = False\\n# break\\n# if nonsne:\\n# continue\\n name, source = catalog.new_entry(\\n row['CRTS'],\\n srcname='CRTS',\\n bibcode='2014MNRAS.441.1186D')\\n# if row['IAU des.'] != '--':\\n# catalog.entries[name].add_quantity(SUPERNOVA.ALIAS,\\n# row['IAU des.'], source)\\n# for ct in ctypes:\\n# catalog.entries[name].add_quantity(SUPERNOVA.CLAIMED_TYPE, ct,\\n# source)\\n# catalog.entries[name].add_quantity(SUPERNOVA.DISCOVERER,\\n# row['Discoverer'], source)\\n# date = row['Discovery'].split('/')\\n# date = '/'.join([date[-1].zfill(2), date[0].zfill(2), date[1]])\\n# catalog.entries[name].add_quantity(SUPERNOVA.DISCOVER_DATE, date,\\n# source)\\n catalog.entries[name].add_quantity(CATACLYSMIC.VISUAL_MAG, row['Vmax'],\\n source)\\n catalog.entries[name].add_quantity(CATACLYSMIC.RA, row['RAJ2000'], source)\\n catalog.entries[name].add_quantity(CATACLYSMIC.DEC, row['DEJ2000'], source)\\n catalog.journal_entries()\\n\\n # Howerton Catalog\",\n \"def createchore():\\n return render_template(\\\"newchore.html\\\")\",\n \"def __init__(self,\\n title=\\\"[CDR Form Letter]\\\",\\n subject=None,\\n template=None,\\n pngName=\\\"dhhslogo.png\\\",\\n author=\\\"cdr\\\",\\n company=\\\"CIPS\\\",\\n binImage=False,\\n invitePara=\\\"\\\"):\\n\\n Document.__init__(self, title, author, company, subject)\\n self.generator = \\\"CDR RTF Writer\\\"\\n self.pngName = pngName # location of bitmap file for logo\\n self.margL = 1080 # 3/4-inch left margin\\n self.margR = 720 # 1/2-inch right margin\\n self.margT = 1080 # 5/8-inch top margin\\n self.margB = 1260 # 7/8-inch bottom margin\\n self.fSize = 21 # 10.5-point default font size\\n self.baskervilleFont = self.addFont(\\\"Baskerville Old Face\\\",\\n \\\"roman\\\",\\n \\\"Times New Roman\\\")\\n self.__addLetterHead(binImage)\\n if template:\\n try:\\n with open(template) as fp:\\n body = fp.read()\\n except Exception as e:\\n raise Exception(\\\"loading %s: %s\\\" % (template, str(e)))\\n if invitePara:\\n replacement = \\\"@@MISCDOC:%s@@\\\" % invitePara\\n body = body.replace(\\\"@@INVITATION@@\\\", replacement)\\n pattern = re.compile(\\\"@@MISCDOC:(.*?)@@\\\")\\n body = pattern.sub(lambda p: self.__loadMiscDoc(p), body)\\n self.addRawContent(body)\",\n \"def create_course_for_deletion(self):\\r\\n course = modulestore().create_course('nihilx', 'deletion', 'deleting_user')\\r\\n root = course.location.version_agnostic().for_branch('draft')\\r\\n for _ in range(4):\\r\\n self.create_subtree_for_deletion(root, ['chapter', 'vertical', 'problem'])\\r\\n return modulestore().get_item(root)\",\n \"def testCourses(self):\\n self.person.invokeFactory(type_name=\\\"FSDCourse\\\", id=\\\"test-course\\\")\\n self.failUnless('test-course' in self.person.contentIds())\\n self.failUnless('test-course' in [c.id for c in self.person.getCourses()])\",\n \"def test_course_plain_english(self):\\r\\n self.client = AjaxEnabledTestClient()\\r\\n self.client.login(username=self.uname, password=self.password)\\r\\n\\r\\n resp = self.client.get_html('/course/')\\r\\n self.assertContains(resp,\\r\\n '<h1 class=\\\"page-header\\\">My Courses</h1>',\\r\\n status_code=200,\\r\\n html=True)\",\n \"def CrearLlaveMusicaContext(catalog):\\n Lista = ['instrumentalness' , 'liveness' , 'speechiness' , 'danceability' , 'valence' ,\\n 'loudness' , 'tempo' , 'acousticness' , 'energy']\\n \\n for contenido in Lista:\\n mp.put(catalog['caraContenido'], contenido, om.newMap('RBT'))\",\n \"def test_with_coursexml(self):\\r\\n with open(self.good_tar) as gtar:\\r\\n args = {\\\"name\\\": self.good_tar, \\\"course-data\\\": [gtar]}\\r\\n resp = self.client.post(self.url, args)\\r\\n\\r\\n self.assertEquals(resp.status_code, 200)\",\n \"def homepage():\\n \\n return render_template(\\\"coursePage.html\\\",courseName = \\\"Welcome\\\", Courses = COURSES, blank = 1)\",\n \"def catalogConvert():\\n ret = libxml2mod.xmlCatalogConvert()\\n return ret\",\n \"def catalogs(env):\\n envs = environments()\\n check_env(env, envs)\\n\\n if app.config['ENABLE_CATALOG']:\\n nodenames = []\\n catalog_list = []\\n query = AndOperator()\\n\\n if env != '*':\\n query.add(EqualsOperator(\\\"catalog_environment\\\", env))\\n\\n query.add(NullOperator(\\\"catalog_timestamp\\\", False))\\n\\n order_by_str = '[{\\\"field\\\": \\\"certname\\\", \\\"order\\\": \\\"asc\\\"}]'\\n nodes = get_or_abort(puppetdb.nodes,\\n query=query,\\n with_status=False,\\n order_by=order_by_str)\\n nodes, temp = tee(nodes)\\n\\n for node in temp:\\n nodenames.append(node.name)\\n\\n for node in nodes:\\n table_row = {\\n 'name': node.name,\\n 'catalog_timestamp': node.catalog_timestamp\\n }\\n\\n if len(nodenames) > 1:\\n form = CatalogForm()\\n\\n form.compare.data = node.name\\n form.against.choices = [(x, x) for x in nodenames\\n if x != node.name]\\n table_row['form'] = form\\n else:\\n table_row['form'] = None\\n\\n catalog_list.append(table_row)\\n\\n return render_template(\\n 'catalogs.html',\\n nodes=catalog_list,\\n envs=envs,\\n current_env=env)\\n else:\\n log.warn('Access to catalog interface disabled by administrator')\\n abort(403)\",\n \"def test_course_overview_view_with_course(self):\\r\\n course = CourseFactory.create(org='MITx', course='999', display_name='Robot Super Course')\\r\\n resp = self._show_course_overview(course.id)\\r\\n self.assertContains(\\r\\n resp,\\r\\n '<article class=\\\"courseware-overview\\\" data-locator=\\\"location:MITx+999+Robot_Super_Course+course+Robot_Super_Course\\\" data-course-key=\\\"slashes:MITx+999+Robot_Super_Course\\\">',\\r\\n status_code=200,\\r\\n html=True\\r\\n )\",\n \"def compile(self,**attrs):\\n\\n\\n\\t\\tpath_to_txt = os.path.join(self.path_raw,'COCA Text')\\n\\t\\tpath_to_sources = os.path.join(self.path_raw,'coca-sources_2017_12.txt')\\n\\n\\t\\tif not os.path.exists(path_to_txt) or not os.path.exists(path_to_sources):\\n\\t\\t\\tprint(f'Place in {self.path_raw} the following files:\\\\n * COCA Text\\\\n * coca-sources_2017_12.txt')\\n\\t\\t\\treturn\\n\\n\\t\\t#txt\\n\\t\\tself.compile_txt()\\n\\t\\t# metadata\\n\\t\\tself.compile_metadata()\",\n \"def generate_tex(title, author, txt, indices=None):\\n with open('head', 'r') as head,\\\\\\n open('stegano.tex', 'w') as file_tex:\\n\\n # First lines are\\n # - Title\\n # - Author\\n\\n file_tex.write(\\\"\\\".join(head.readlines())) # Write heading\\n file_tex.write(\\\"\\\\\\\\title{{{}}}\\\\n\\\".format(title))\\n file_tex.write(\\\"\\\\\\\\author{{{}}}\\\\n\\\".format(author))\\n file_tex.write(\\\"\\\\\\\\date{{}}\\\\n\\\")\\n\\n file_tex.write(\\\"\\\\\\\\begin{document}\\\\n\\\\\\\\maketitle\\\\n\\\")\\n\\n # Write char by char, adding decoration when indices is reach\\n i = 0\\n while i < len(txt):\\n if i in indices:\\n file_tex.write(secret_char(txt[i]))\\n else:\\n file_tex.write(non_secret_char(txt[i]))\\n i += 1\\n\\n file_tex.write(\\\"\\\\\\\\end{document}\\\")\",\n \"def _create_course(self, default, course_key):\\r\\n if default == 'split':\\r\\n offering = course_key.offering.replace('/', '.')\\r\\n else:\\r\\n offering = course_key.offering\\r\\n course = self.store.create_course(course_key.org, offering, store_name=default)\\r\\n category = self.import_chapter_location.category\\r\\n block_id = self.import_chapter_location.name\\r\\n chapter = self.store.create_item(\\r\\n # don't use course_location as it may not be the repr\\r\\n course.location, category, location=self.import_chapter_location, block_id=block_id\\r\\n )\\r\\n if isinstance(course.id, CourseLocator):\\r\\n self.course_locations[self.MONGO_COURSEID] = course.location.version_agnostic()\\r\\n self.import_chapter_location = chapter.location.version_agnostic()\\r\\n else:\\r\\n self.assertEqual(course.id, course_key)\\r\\n self.assertEqual(chapter.location, self.import_chapter_location)\",\n \"def main():\\n init_latex()\",\n \"def _DocSim(self,df,a):\\r\\n #Obtain the descriptions of the two input courses.\\r\\n textA = df['description'][a]\\r\\n #Obtain the document embedding vector for each description.\\r\\n vectorA = self.DocVecModel.infer_vector([textA], alpha=0.1, min_alpha=0.0001, steps=300)\\r\\n return vectorA\",\n \"def generate_trivia(titles, output, lang):\\n\\n click.echo('Language: {0}'.format(lang))\\n\\n # Use the sample articles if the user didn't supply any\\n if len(titles) == 0:\\n titles = SAMPLE_ARTICLES\\n\\n # Retrieve the trivia sentences\\n questions = []\\n for article in titles:\\n click.echo('Analyzing \\\\'{0}\\\\''.format(article))\\n article = Article(title=article, lang=lang)\\n questions = questions + article.generate_trivia_sentences(lang=lang)\\n\\n # Output to stdout or JSON\\n if output:\\n output_file = output.open()\\n json.dump(questions, output_file, sort_keys=True, indent=4)\\n click.echo('Output stored in {0}'.format(output.name))\\n else:\\n click.echo(json.dumps(questions, sort_keys=True, indent=4))\",\n \"def test_get_tosca_template(self):\\n pass\",\n \"def test_render_templates():\\n water_mol = Atoms(cclib.bridge.cclib2openbabel.readfile(\\\"data/water.xyz\\\", \\\"xyz\\\"))\\n if not water_mol.name:\\n water_mol.name = \\\"data/water.xyz\\\"\\n\\n main([\\\"-g\\\", \\\"/tmp/foo.ADF.in\\\"])\\n main([\\\"/tmp/foo.ADF.in\\\", \\\"data/water.xyz\\\"])\\n assert_equals(\\n open(\\\"data/water.in\\\").read().strip(),\\n \\\"\\\"\\\"TITLE data/water.xyz\\n\\nCHARGE 0 0\\n\\nNumber of atoms\\n 3\\n\\nATOMS Cartesian\\nO 0.0584027061 0.0584027059 0.0000000000\\nH 1.0096135406 -0.0680162466 0.0000000000\\nH -0.0680162466 1.0096135407 0.0000000000\\nEnd\\n\\nBasis\\nEnd\\n\\nGeometry\\nEnd\\\"\\\"\\\",\\n )\\n\\n main([\\\"-g\\\", \\\"/tmp/test.GAMESS.inp\\\"])\\n main([\\\"/tmp/test.GAMESS.inp\\\", \\\"data/water.xyz\\\"])\\n assert_equals(\\n open(\\\"data/water.inp\\\").read(),\\n \\\"\\\"\\\" $CONTRL COORD=CART UNITS=ANGS $END\\n\\n $DATA\\ndata/water.xyz\\nC1\\nO 8.0 0.0584027061 0.0584027059 0.0000000000\\nH 1.0 1.0096135406 -0.0680162466 0.0000000000\\nH 1.0 -0.0680162466 1.0096135407 0.0000000000\\n $END\\n\\n\\n\\\"\\\"\\\",\\n )\\n\\n main([\\\"-g\\\", \\\"/tmp/hello.GAMESSUK.inp\\\"])\\n main([\\\"/tmp/hello.GAMESSUK.inp\\\", \\\"data/water.xyz\\\"])\\n assert_equals(open(\\\"data/water.inp\\\").read(), water_mol.to_string(\\\"gukin\\\"))\\n\\n main([\\\"-g\\\", \\\"/tmp/hello.world.Gaussian.gjf\\\"])\\n main([\\\"/tmp/hello.world.Gaussian.gjf\\\", \\\"data/water.xyz\\\"])\\n assert_equals(\\n open(\\\"data/water.gjf\\\").read(),\\n \\\"\\\"\\\"#Put Keywords Here, check Charge and Multiplicity.\\n\\n data/water.xyz\\n\\n0 1\\nO 0.0584027061 0.0584027059 0.0000000000\\nH 1.0096135406 -0.0680162466 0.0000000000\\nH -0.0680162466 1.0096135407 0.0000000000\\n\\n\\\"\\\"\\\",\\n )\\n\\n main([\\\"-g\\\", \\\"/tmp/bar.Jaguar.in\\\"])\\n main([\\\"/tmp/bar.Jaguar.in\\\", \\\"data/water.xyz\\\"])\\n assert_equals(open(\\\"data/water.in\\\").read(), water_mol.to_string(\\\"jin\\\"))\\n\\n main([\\\"-g\\\", \\\"/tmp/foo.Molpro.inp\\\"])\\n main([\\\"/tmp/foo.Molpro.inp\\\", \\\"data/water.xyz\\\"])\\n assert_equals(open(\\\"data/water.inp\\\").read(), water_mol.to_string(\\\"mp\\\"))\\n\\n main([\\\"-g\\\", \\\"/tmp/example.MOPAC.mop\\\"])\\n main([\\\"/tmp/example.MOPAC.mop\\\", \\\"data/water.xyz\\\"])\\n assert_equals(\\n open(\\\"data/water.mop\\\").read(),\\n \\\"\\\"\\\"CHARGE=0 MS=0.0\\ndata/water.xyz\\n\\nO 0.05840 1 0.05840 1 0.00000 1\\nH 1.00961 1 -0.06802 1 0.00000 1\\nH -0.06802 1 1.00961 1 0.00000 1\\n\\\"\\\"\\\",\\n )\\n\\n main([\\\"-g\\\", \\\"/tmp/bar.MPQC.in\\\"])\\n main([\\\"/tmp/bar.MPQC.in\\\", \\\"data/water.xyz\\\"])\\n assert_equals(open(\\\"data/water.in\\\").read(), water_mol.to_string(\\\"mpqcin\\\"))\\n\\n main([\\\"-g\\\", \\\"/tmp/foo.NWChem.nw\\\"])\\n main([\\\"/tmp/foo.NWChem.nw\\\", \\\"data/water.xyz\\\"])\\n assert_equals(\\n open(\\\"data/water.nw\\\").read(),\\n \\\"\\\"\\\"start molecule\\n\\ntitle data/water.xyz\\n\\ngeometry units angstroms print xyz autosym\\nO 0.0584027061 0.0584027059 0.0000000000\\nH 1.0096135406 -0.0680162466 0.0000000000\\nH -0.0680162466 1.0096135407 0.0000000000\\nend\\n\\\"\\\"\\\",\\n )\\n\\n main([\\\"-g\\\", \\\"/tmp/example.ORCA.inp\\\"])\\n main([\\\"/tmp/example.ORCA.inp\\\", \\\"data/water.xyz\\\"])\\n assert_equals(\\n open(\\\"data/water.inp\\\").read(),\\n \\\"\\\"\\\"# data/water.xyz\\n! Opt\\n\\n* xyz 0 1\\nO 0.0584027061 0.0584027059 0.0000000000\\nH 1.0096135406 -0.0680162466 0.0000000000\\nH -0.0680162466 1.0096135407 0.0000000000\\n*\\n\\\"\\\"\\\",\\n )\\n\\n main([\\\"-g\\\", \\\"/tmp/bar.Psi.dat\\\"])\\n main([\\\"/tmp/bar.Psi.dat\\\", \\\"data/water.xyz\\\"])\\n assert_equals(\\n open(\\\"data/water.dat\\\").read(),\\n \\\"\\\"\\\"# data/water.xyz\\n\\nmolecule {\\n0 1\\nO 0.0584027061 0.0584027059 0.0000000000\\nH 1.0096135406 -0.0680162466 0.0000000000\\nH -0.0680162466 1.0096135407 0.0000000000\\nunits angstrom\\n}\\n\\noptimize('scf')\\n\\\"\\\"\\\",\\n )\\n\\n main([\\\"-g\\\", \\\"/tmp/example.QChem.in\\\"])\\n main([\\\"/tmp/example.QChem.in\\\", \\\"data/water.xyz\\\"])\\n assert_equals(open(\\\"data/water.in\\\").read(), water_mol.to_string(\\\"qcin\\\"))\\n\\n main([\\\"-g\\\", \\\"/tmp/foo.ZINDO.input\\\"])\\n main([\\\"/tmp/foo.ZINDO.input\\\", \\\"data/water.xyz\\\"])\\n assert_equals(open(\\\"data/water.input\\\").read(), water_mol.to_string(\\\"zin\\\"))\",\n \"def build_general_course(self):\\n\\n courses = {}\\n\\n if self.__build_analyze is False:\\n self.build_analysis()\\n\\n courses[\\\"taxa_conhecimento\\\"] = self.analysis[\\\"taxa_conhecimento\\\"]\\n courses[\\\"taxa_reprovacao\\\"] = self.analysis[\\\"taxa_reprovacao_absoluta\\\"]\\n courses[\\\"taxa_trancamento\\\"] = self.analysis[\\\"taxa_trancamento\\\"]\\n\\n # cria cache\\n cache = {}\\n note = self.analysis[\\\"general_note_statistic\\\"]\\n for rate_it in self.__rates:\\n rate_calc = self.analysis[\\\"general_rates\\\"][rate_it.name][0]\\n for course in self.analysis[\\\"courses\\\"].index:\\n if course not in cache:\\n cache[course] = {}\\n cache[course][rate_it.name] = rate_calc[course]\\n cache[course][\\\"nota\\\"] = [note[0][course], note[1][course]]\\n\\n courses[\\\"cache\\\"] = cache\\n\\n # cria o campo compara_aprov\\n courses[\\\"compara_aprov\\\"] = self.analysis[\\\"graph_course\\\"]\\n\\n # cria o campo courses\\n courses[\\\"disciplinas\\\"] = self.analysis[\\\"courses\\\"].to_dict()\\n\\n return courses\",\n \"def parse_file(self, file_lines):\\n # separate the file into chunks of text\\n chunks, chunk = [], []\\n # check to see what format the corpus is in, we assume that the headers are the same for all\\n # texts in the file... (maybe not safe?)\\n if re.match('Primary publication:', file_lines[0]):\\n header = re.compile('Primary publication:')\\n else:\\n header = re.compile(r'&?P\\\\d{6}')\\n for line in file_lines:\\n if header.match(line):\\n if len(chunk) > 0: # pylint: disable=len-as-condition\\n chunks.append(chunk)\\n chunk = [line]\\n else:\\n if len(line) > 0: # pylint: disable=len-as-condition\\n chunk.append(line)\\n chunks.append(chunk)\\n self.chunks = chunks\\n # create a rich catalog from the chunks\\n re_translit = re.compile(r'(\\\\d+\\\\'?\\\\.) ?(.*)')\\n re_normaliz = re.compile(r'(#tr\\\\.ts:) ?(.*)')\\n re_translat = re.compile(r'(#tr\\\\.en:) ?(.*)')\\n for chunk in self.chunks:\\n text = chunk\\n if chunk[0].startswith('Primary publication:'):\\n # we've got full metadata, add additional parsing later\\n metadata = chunk[:25]\\n text = chunk[26:]\\n else: # no metadata\\n metadata = []\\n pnum = ''.join([c for c in text[0].split('=')[0] if c != '&']).rstrip()\\n edition = text[0].split('=')[1].lstrip()\\n text = text[3:]\\n translit = []\\n normaliz = []\\n translat = []\\n for line in text:\\n if re.match(r'\\\\d+\\\\'?\\\\.', line):\\n translit.append(re_translit.match(line).groups()[1])\\n if line.startswith('#tr.ts:'):\\n normaliz.append(re_normaliz.match(line).groups()[1])\\n if line.startswith('#tr.en:'):\\n translat.append(re_translat.match(line).groups()[1])\\n self.catalog[pnum] = {'metadata': metadata,\\n 'pnum': pnum,\\n 'edition': edition,\\n 'raw_text': text,\\n 'transliteration': translit,\\n 'normalization': normaliz,\\n 'translation': translat}\",\n \"def index():\\n user, user_id = get_user()\\n # Get this user's course with their roles\\n my_courses = []\\n if user:\\n my_courses = user.get_courses()\\n # Get all public courses\\n public_courses = Course.get_public()\\n\\n return render_template('courses/index.html',\\n user=user,\\n my_courses=my_courses,\\n public_courses=public_courses)\",\n \"def make_bibtex(self):\\n\\n\\t\\t# bib = requests.request('GET', 'http://dx.doi.org/' + self.doi, \",\n \"def _create_design(self, state: Dict[str, Any], new_fname: str) -> Tuple[str, str]:\\n output = self.template.render(**state)\\n design_folder = os.path.join(self.gen_dir, new_fname)\\n os.makedirs(design_folder, exist_ok=True)\\n fpath = os.path.join(design_folder, new_fname + '.scs')\\n with open(fpath, 'w') as f:\\n f.write(output)\\n f.close()\\n return design_folder, fpath\",\n \"def _construct_course_son(org, course, run):\\r\\n return bson.son.SON([\\r\\n ('org', org),\\r\\n ('course', course),\\r\\n ('name', run)\\r\\n ])\",\n \"def test_course_info(self):\\r\\n # Regex of first 3 columns of course information table row for\\r\\n # test course loaded from git. Would not have sha1 if\\r\\n # git_info_for_course failed.\\r\\n table_re = re.compile(r\\\"\\\"\\\"\\r\\n <tr>\\\\s+\\r\\n <td>edX\\\\sAuthor\\\\sCourse</td>\\\\s+ # expected test git course name\\r\\n <td>MITx/edx4edx/edx4edx</td>\\\\s+ # expected test git course_id\\r\\n <td>[a-fA-F\\\\d]{40}</td> # git sha1 hash\\r\\n \\\"\\\"\\\", re.VERBOSE)\\r\\n\\r\\n self._setstaff_login()\\r\\n self._mkdir(getattr(settings, 'GIT_REPO_DIR'))\\r\\n\\r\\n # Make sure we don't have any git hashes on the page\\r\\n response = self.client.get(reverse('sysadmin_courses'))\\r\\n self.assertNotRegexpMatches(response.content, table_re)\\r\\n\\r\\n # Now add the course and make sure it does match\\r\\n response = self._add_edx4edx()\\r\\n self.assertRegexpMatches(response.content, table_re)\",\n \"def create_course_with_orphans(self, default_store):\\n course = CourseFactory.create(default_store=default_store)\\n\\n # create chapters and add them to course tree\\n chapter1 = self.store.create_child(self.user.id, course.location, 'chapter', \\\"Chapter1\\\")\\n self.store.publish(chapter1.location, self.user.id)\\n\\n chapter2 = self.store.create_child(self.user.id, course.location, 'chapter', \\\"Chapter2\\\")\\n self.store.publish(chapter2.location, self.user.id)\\n\\n # orphan chapter\\n orphan_chapter = self.store.create_item(self.user.id, course.id, 'chapter', \\\"OrphanChapter\\\")\\n self.store.publish(orphan_chapter.location, self.user.id)\\n\\n # create vertical and add it as child to chapter1\\n vertical1 = self.store.create_child(self.user.id, chapter1.location, 'vertical', \\\"Vertical1\\\")\\n self.store.publish(vertical1.location, self.user.id)\\n\\n # create orphan vertical\\n orphan_vertical = self.store.create_item(self.user.id, course.id, 'vertical', \\\"OrphanVert\\\")\\n self.store.publish(orphan_vertical.location, self.user.id)\\n\\n # create component and add it to vertical1\\n html1 = self.store.create_child(self.user.id, vertical1.location, 'html', \\\"Html1\\\")\\n self.store.publish(html1.location, self.user.id)\\n\\n # create component and add it as a child to vertical1 and orphan_vertical\\n multi_parent_html = self.store.create_child(self.user.id, vertical1.location, 'html', \\\"multi_parent_html\\\")\\n self.store.publish(multi_parent_html.location, self.user.id)\\n\\n orphan_vertical.children.append(multi_parent_html.location)\\n self.store.update_item(orphan_vertical, self.user.id)\\n\\n # create an orphaned html module\\n orphan_html = self.store.create_item(self.user.id, course.id, 'html', \\\"OrphanHtml\\\")\\n self.store.publish(orphan_html.location, self.user.id)\\n\\n self.store.create_child(self.user.id, course.location, 'static_tab', \\\"staticuno\\\")\\n self.store.create_child(self.user.id, course.location, 'course_info', \\\"updates\\\")\\n\\n return course\",\n \"def get_import_course(self):\\n\\n template_values = {}\\n template_values['page_title'] = self.format_title('Import Course')\\n\\n annotations = ImportCourseRESTHandler.SCHEMA_ANNOTATIONS_DICT()\\n if not annotations:\\n template_values['main_content'] = 'No courses to import from.'\\n self.render_page(template_values)\\n return\\n\\n exit_url = self.canonicalize_url('/dashboard')\\n rest_url = self.canonicalize_url(ImportCourseRESTHandler.URI)\\n form_html = oeditor.ObjectEditor.get_html_for(\\n self,\\n ImportCourseRESTHandler.SCHEMA_JSON,\\n annotations,\\n None, rest_url, exit_url,\\n auto_return=True,\\n save_button_caption='Import',\\n required_modules=ImportCourseRESTHandler.REQUIRED_MODULES)\\n\\n template_values = {}\\n template_values['page_title'] = self.format_title('Import Course')\\n template_values['page_description'] = messages.IMPORT_COURSE_DESCRIPTION\\n template_values['main_content'] = form_html\\n self.render_page(template_values)\",\n \"def __init__(self, txt='', unicodeEncoding='utf-8'):\\n # __document capture the document level structure\\n # for each sentence and then put in the archives when the next sentence\\n # is processed\\n super(ConTextMarkup, self).__init__(__txt=None,\\n __rawtxt=txt,\\n __scope=None,\\n __SCOPEUPDATED=False)\\n self.__document = nx.DiGraph()\\n self.__document.add_node(\\\"top\\\", category=\\\"document\\\")\\n self.__VERBOSE = False\\n self.__tagID = 0\\n self.__unicodeEncoding = unicodeEncoding\",\n \"def create_course(self, org, offering, user_id=None, fields=None, store_name='default', **kwargs):\\r\\n store = self.modulestores[store_name]\\r\\n\\r\\n if not hasattr(store, 'create_course'):\\r\\n raise NotImplementedError(u\\\"Cannot create a course on store %s\\\" % store_name)\\r\\n\\r\\n return store.create_course(org, offering, user_id, fields, **kwargs)\",\n \"def test_course_with_accents(self):\\r\\n self.client = AjaxEnabledTestClient()\\r\\n self.client.login(username=self.uname, password=self.password)\\r\\n\\r\\n resp = self.client.get_html(\\r\\n '/course/',\\r\\n {},\\r\\n HTTP_ACCEPT_LANGUAGE='eo'\\r\\n )\\r\\n\\r\\n TEST_STRING = (\\r\\n u'<h1 class=\\\"title-1\\\">'\\r\\n u'My \\\\xc7\\\\xf6\\\\xfcrs\\\\xe9s L#'\\r\\n u'</h1>'\\r\\n )\\r\\n\\r\\n self.assertContains(resp,\\r\\n TEST_STRING,\\r\\n status_code=200,\\r\\n html=True)\",\n \"def set_up_course(self, **course_kwargs):\\r\\n course = CourseFactory(**course_kwargs)\\r\\n chapter = ItemFactory(category='chapter', parent_location=course.location) # pylint: disable=no-member\\r\\n section = ItemFactory(category='sequential', parent_location=chapter.location, due=datetime(2013, 9, 18, 11, 30, 00))\\r\\n vertical = ItemFactory(category='vertical', parent_location=section.location)\\r\\n ItemFactory(category='problem', parent_location=vertical.location)\\r\\n\\r\\n course = modulestore().get_course(course.id) # pylint: disable=no-member\\r\\n self.assertIsNotNone(course.get_children()[0].get_children()[0].due)\\r\\n return course\",\n \"def build_course(self):\\n courses = []\\n aprovacao_d = {}\\n # semestral\\n for rate_it in self.__semestral_rate:\\n # pega uma lista no qual o primeiro elemento é a taxa, o segundo\\n # e o terceiro são quantidades\\n rate_data = self.analysis[\\\"semestral_rate\\\"][rate_it.name]\\n for i in rate_data[0].index:\\n if i[0] not in aprovacao_d:\\n aprovacao_d[i[0]] = {}\\n\\n periodo = str(i[1]) + \\\"/\\\" + str(i[2])\\n aprovacao_d[i[0]][periodo] = [\\n float(rate_data[0][i]),\\n int(rate_data[1][i]),\\n int(rate_data[2][i]),]\\n\\n note = self.analysis[\\\"general_note_statistic\\\"]\\n note_last_year = self.analysis[\\\"last_year_statistic\\\"]\\n for course in self.analysis[\\\"courses\\\"].index:\\n course_dict = {}\\n course_dict[\\\"disciplina_codigo\\\"] = course\\n course_dict[\\\"disciplina_nome\\\"] = self.analysis[\\\"courses\\\"][course]\\n \\n # If the course code is related to more than one name,\\n # concatenate these names into an unique string\\n if type(course_dict[\\\"disciplina_nome\\\"]) != str:\\n new_course_name = \\\" | \\\".join(list(course_dict[\\\"disciplina_nome\\\"]))\\n course_dict[\\\"disciplina_nome\\\"] = new_course_name\\n # quantidade de matriculas\\n count = self.analysis[\\\"general_count_submission\\\"][course]\\n course_dict[\\\"qtd_alunos\\\"] = count\\n # notas\\n course_dict[\\\"qtd_cursada_aprov\\\"] = self.analysis[\\\"coursed_ratio\\\"][course]\\n course_dict[\\\"nota\\\"] = [note[0][course], note[1][course]]\\n course_dict[\\\"nota_ultimo_ano\\\"] = [\\n note_last_year[0][course],\\n note_last_year[1][course]\\n ]\\n # taxas\\n for rate_it in self.__rates:\\n rate_data = self.analysis[\\\"general_rates\\\"][rate_it.name]\\n course_dict[rate_it.name] = float(rate_data[0][course])\\n course_str = rate_it.name.replace(\\\"taxa\\\", \\\"qtd\\\")\\n # count_sel define qual quantidade vai para o json, a especifica\\n # ou geral\\n course_dict[course_str] = int(\\n rate_data[rate_it.count_sel][course])\\n # rate_calc = self.analysis[\\\"general_rates\\\"][rate_it.name][0]\\n\\n # taxas do ultimo anos\\n course_dict[\\\"taxa_reprovacao_ultimo_absoluto\\\"] = self.analysis[\\\"last_year_taxa_reprovacao_absoluta\\\"][course]\\n course_dict[\\\"taxa_reprovacao_ultimo_frequencia\\\"] = self.analysis[\\\"last_year_taxa_reprovacao_frequencia\\\"][course]\\n\\n course_dict[\\\"grafico_qtd_cursada_aprov\\\"] = self.analysis[\\\"coursed_count\\\"][course]\\n course_dict[\\\"aprovacao_semestral\\\"] = aprovacao_d[course]\\n courses.append(course_dict)\\n return courses\",\n \"def download_course_interactive(self):\\n self.domain_prompt()\\n self.course_prompt()\\n self.get_course_page()\\n self.get_course_title()\\n self.get_course_unit_titles()\\n self.get_course_unit_slugs()\\n self.get_course_unit_urls()\\n\\n print(\\\"\\\\nGenerating Path Slugs...\\\\n\\\")\\n self.get_course_all_slugs()\\n self.get_course_youtube_ids()\\n self.download_course_videos()\",\n \"def test_unicode(self):\\r\\n\\r\\n print(\\\"Starting import\\\")\\r\\n modulestore = XMLModuleStore(DATA_DIR, course_dirs=['test_unicode'])\\r\\n courses = modulestore.get_courses()\\r\\n self.assertEquals(len(courses), 1)\\r\\n course = courses[0]\\r\\n\\r\\n print(\\\"course errors:\\\")\\r\\n\\r\\n # Expect to find an error/exception about characters in \\\"®esources\\\"\\r\\n expect = \\\"Invalid characters\\\"\\r\\n errors = [\\r\\n (msg.encode(\\\"utf-8\\\"), err.encode(\\\"utf-8\\\"))\\r\\n for msg, err\\r\\n in modulestore.get_course_errors(course.id)\\r\\n ]\\r\\n\\r\\n self.assertTrue(any(\\r\\n expect in msg or expect in err\\r\\n for msg, err in errors\\r\\n ))\\r\\n chapters = course.get_children()\\r\\n self.assertEqual(len(chapters), 4)\",\n \"def create_corpus_for_artist(artist):\\n raise NotImplementedError\",\n \"def newCatalog(sgml):\\n ret = libxml2mod.xmlNewCatalog(sgml)\\n if ret is None:raise treeError('xmlNewCatalog() failed')\\n return catalog(_obj=ret)\",\n \"def create_new_course(request):\\r\\n if not auth.has_access(request.user, CourseCreatorRole()):\\r\\n raise PermissionDenied()\\r\\n\\r\\n org = request.json.get('org')\\r\\n number = request.json.get('number')\\r\\n display_name = request.json.get('display_name')\\r\\n run = request.json.get('run')\\r\\n\\r\\n # allow/disable unicode characters in course_id according to settings\\r\\n if not settings.FEATURES.get('ALLOW_UNICODE_COURSE_ID'):\\r\\n if _has_non_ascii_characters(org) or _has_non_ascii_characters(number) or _has_non_ascii_characters(run):\\r\\n return JsonResponse(\\r\\n {'error': _('Special characters not allowed in organization, course number, and course run.')},\\r\\n status=400\\r\\n )\\r\\n\\r\\n try:\\r\\n course_key = SlashSeparatedCourseKey(org, number, run)\\r\\n\\r\\n # instantiate the CourseDescriptor and then persist it\\r\\n # note: no system to pass\\r\\n if display_name is None:\\r\\n metadata = {}\\r\\n else:\\r\\n metadata = {'display_name': display_name}\\r\\n\\r\\n # Set a unique wiki_slug for newly created courses. To maintain active wiki_slugs for\\r\\n # existing xml courses this cannot be changed in CourseDescriptor.\\r\\n # # TODO get rid of defining wiki slug in this org/course/run specific way and reconcile\\r\\n # w/ xmodule.course_module.CourseDescriptor.__init__\\r\\n wiki_slug = u\\\"{0}.{1}.{2}\\\".format(course_key.org, course_key.course, course_key.run)\\r\\n definition_data = {'wiki_slug': wiki_slug}\\r\\n\\r\\n # Create the course then fetch it from the modulestore\\r\\n # Check if role permissions group for a course named like this already exists\\r\\n # Important because role groups are case insensitive\\r\\n if CourseRole.course_group_already_exists(course_key):\\r\\n raise InvalidLocationError()\\r\\n\\r\\n fields = {}\\r\\n fields.update(definition_data)\\r\\n fields.update(metadata)\\r\\n\\r\\n # Creating the course raises InvalidLocationError if an existing course with this org/name is found\\r\\n new_course = modulestore('direct').create_course(\\r\\n course_key.org,\\r\\n course_key.offering,\\r\\n fields=fields,\\r\\n )\\r\\n\\r\\n # can't use auth.add_users here b/c it requires request.user to already have Instructor perms in this course\\r\\n # however, we can assume that b/c this user had authority to create the course, the user can add themselves\\r\\n CourseInstructorRole(new_course.id).add_users(request.user)\\r\\n auth.add_users(request.user, CourseStaffRole(new_course.id), request.user)\\r\\n\\r\\n # seed the forums\\r\\n seed_permissions_roles(new_course.id)\\r\\n\\r\\n # auto-enroll the course creator in the course so that \\\"View Live\\\" will\\r\\n # work.\\r\\n CourseEnrollment.enroll(request.user, new_course.id)\\r\\n _users_assign_default_role(new_course.id)\\r\\n\\r\\n return JsonResponse({\\r\\n 'url': reverse_course_url('course_handler', new_course.id)\\r\\n })\\r\\n\\r\\n except InvalidLocationError:\\r\\n return JsonResponse({\\r\\n 'ErrMsg': _(\\r\\n 'There is already a course defined with the same '\\r\\n 'organization, course number, and course run. Please '\\r\\n 'change either organization or course number to be unique.'\\r\\n ),\\r\\n 'OrgErrMsg': _(\\r\\n 'Please change either the organization or '\\r\\n 'course number so that it is unique.'),\\r\\n 'CourseErrMsg': _(\\r\\n 'Please change either the organization or '\\r\\n 'course number so that it is unique.'),\\r\\n })\\r\\n except InvalidKeyError as error:\\r\\n return JsonResponse({\\r\\n \\\"ErrMsg\\\": _(\\\"Unable to create course '{name}'.\\\\n\\\\n{err}\\\").format(name=display_name, err=error.message)}\\r\\n )\",\n \"def create_file(date, title, text, n):\\r\\n \\\"\\\"\\\"with date as file name and text as content\\\"\\\"\\\"\\r\\n filename = \\\"%s_%s.txt\\\" % (date, n)\\r\\n with io.open(filename, \\\"w+\\\", encoding=\\\"UTF8\\\") as newfile:\\r\\n text = text.replace(\\\" \\\", \\\"\\\") #remove all spaces\\r\\n sentences= re.sub(\\\"，|。\\\", \\\"\\\\n\\\", text) #one sentence per line\\r\\n newfile.write(title+\\\"\\\\n\\\")\\r\\n newfile.write(date+\\\"\\\\n\\\")\\r\\n newfile.write(sentences)\\r\\n print(filename)\",\n \"def create_coll(request):\\n form = CollForm(request)\\n c = {}\\n return render(request, \\\"browse/curate.html\\\", c)\",\n \"def create_citation(cit):\\n if cit is not None:\\n if cit['citation-type'] == \\\"BIBTEX\\\":\\n return pybtex.database.parse_string(cit['citation-value'], \\\"bibtex\\\")\\n return None\",\n \"def catalogue(self, fix_author=True):\\n response = requests.get(CATALOGUE_URL)\\n\\n flags = DOTALL | UNICODE\\n tables = findall(r'<table.*?</table>', response.text, flags=flags)\\n rows = findall(r'<tr.*?</tr>', tables[1], flags=flags)\\n\\n books = []\\n for row in rows[1:]:\\n elements = findall('<td.*?</td>', row, flags=flags)\\n book_id, title = findall(r'/(.*?)/\\\">(.*?)<',\\n elements[4], flags=flags)[0]\\n try:\\n author_id, author = findall(r'/authors/(.*?).html\\\">(.*?)<',\\n elements[6], flags=flags)[0]\\n except:\\n author_id, author = '', ''\\n if fix_author:\\n # fix_author name collision. TODO\\n author = globals()['fix_author'](author)\\n book = {\\n 'type': findall(r'alt=\\\"(.*?)\\\">', elements[0], flags=flags)[0],\\n 'book_id': book_id,\\n 'title': title,\\n 'author_id': author_id,\\n 'author': author,\\n 'year': elements[8][15:-5],\\n 'language': elements[10][-9:-7]\\n }\\n books.append(book)\\n return DataFrame(books)\",\n \"def generate_latex_table(dictionary,filename,location=\\\".\\\"):\\n if type(filename) != str:\\n raise TypeError('filename should be string')\\n if type(dictionary) != dict:\\n raise TypeError('dictionary should be dictionary')\\n\\n head_code = \\\"\\\"\\\"\\\\\\\\documentclass{article}\\n%In the preamble section include the arabtex and utf8 packages\\n\\\\\\\\usepackage{arabtex}\\n\\\\\\\\usepackage{utf8}\\n\\\\\\\\usepackage{longtable}\\n\\\\\\\\usepackage{color, colortbl}\\n\\\\\\\\usepackage{supertabular}\\n\\\\\\\\usepackage{multicol}\\n\\\\\\\\usepackage{geometry}\\n\\\\\\\\geometry{left=.1in, right=.1in, top=.1in, bottom=.1in}\\n\\n\\\\\\\\begin{document}\\n\\\\\\\\begin{multicols}{6}\\n\\\\\\\\setcode{utf8}\\n\\n\\\\\\\\begin{center}\\\"\\\"\\\"\\n\\n tail_code = \\\"\\\"\\\"\\\\\\\\end{center}\\n\\\\\\\\end{multicols}\\n\\\\\\\\end{document}\\\"\\\"\\\"\\n\\n begin_table = \\\"\\\"\\\"\\\\\\\\begin{tabular}{ P{2cm} P{1cm}}\\n\\\\\\\\textbf{words} & \\\\\\\\textbf{\\\\\\\\#} \\\\\\\\\\\\\\\\\\n\\\\\\\\hline\\n\\\\\\\\\\\\\\\\[0.01cm]\\\"\\\"\\\"\\n end_table= \\\"\\\"\\\"\\\\\\\\end{tabular}\\\"\\\"\\\"\\n rows_num = 40\\n if location != '.':\\n filename = location +\\\"/\\\"+ filename\\n\\n try:\\n file = open(filename+'.tex', 'w', encoding='utf8')\\n file.write(head_code+'\\\\n')\\n n= int(len(dictionary)/rows_num)\\n words = [(\\\"\\\\\\\\<\\\"+word+\\\"> & \\\"+str(frequancy)+' \\\\\\\\\\\\\\\\ \\\\n') for word, frequancy in dictionary.items()]\\n start=0\\n end=rows_num\\n new_words = []\\n for i in range(n):\\n new_words = new_words+ [begin_table+'\\\\n'] +words[start:end] +[end_table+\\\" \\\\n\\\"]\\n start=end\\n end+=rows_num\\n remain_words = len(dictionary) - rows_num*n\\n if remain_words > 0:\\n new_words += [begin_table+\\\" \\\\n\\\"]+ words[-1*remain_words:]+[end_table+\\\" \\\\n\\\"]\\n for word in new_words:\\n file.write(word)\\n file.write(tail_code)\\n file.close()\\n return True\\n except:\\n return False\",\n \"def createTrecTextForCurrentDocuments(baseDir):\\n pathToFolder = baseDir + 'Collections/'\\n if not os.path.exists(pathToFolder):\\n os.makedirs(pathToFolder)\\n currentTime = str(datetime.datetime.now()).replace(\\\":\\\", \\\"-\\\").replace(\\\" \\\", \\\"-\\\").replace(\\\".\\\", \\\"-\\\")\\n pathToTrecText = pathToFolder+\\\"TrecText/\\\"\\n if not os.path.exists(pathToTrecText):\\n os.makedirs(pathToTrecText)\\n filename = pathToTrecText + currentTime\\n client = MongoClient('asr2.iem.technion.ac.il',27017)\\n db = client.asr16\\n documents = db.documents.find({}).sort('query_id',1)\\n queryToDocnos= {}\\n current_users = retrieve_users()\\n f = open(filename, 'w')\\n for document in documents:\\n if document['username'] in current_users:\\n print(document['query_id'], document['username'])\\n f.write('<DOC>\\\\n')\\n docno = str(document['query_id']).zfill(3) + '-' + str(document['username'])\\n f.write('<DOCNO>' + docno + '</DOCNO>\\\\n')\\n docnos = queryToDocnos.get(str(document['query_id']).zfill(3), [])\\n docnos.append(docno)\\n queryToDocnos[str(document['query_id']).zfill(3)] = docnos\\n f.write('<TEXT>\\\\n')\\n f.write(unicodedata.normalize('NFKD', document['current_document']).encode('cp1252', \\\"ignore\\\").decode('utf-8', 'replace').replace(u'\\\\uFFFD', ' ').rstrip())\\n f.write('\\\\n</TEXT>\\\\n')\\n f.write('</DOC>\\\\n')\\n f.close()\\n pathToWorkingSet = pathToFolder+ 'WorkingSets/'\\n if not os.path.exists(pathToWorkingSet):\\n os.makedirs(pathToWorkingSet)\\n workingSetFilename = pathToWorkingSet + currentTime\\n f = open(workingSetFilename, 'w')\\n for query, docnos in queryToDocnos.items():\\n i = 1\\n for docid in docnos:\\n f.write(query.zfill(3) + ' Q0 ' + docid + ' ' + str(i) + ' -' + str(i) + ' indri\\\\n')\\n i +=1\\n f.close()\\n return filename, workingSetFilename, currentTime\",\n \"def CreateNewFile(self):\\n\\t\\tself.acad.Documents.Add()\",\n \"def initCatalog(tipolista : str):\\n catalog = model.newCatalog(tipolista)\\n return catalog\",\n \"def create(cls, title=None, ca_type=None, definition_type=None,\\n helptext=\\\"\\\", placeholder=None, multi_choice_options=None,\\n is_mandatory=False, ca_global=True):\\n ca_entity = cls._create_random_ca()\\n ca_entity = cls._fill_ca_entity_fields(\\n ca_entity, title=title, ca_type=ca_type,\\n definition_type=definition_type,\\n helptext=helptext, placeholder=placeholder,\\n multi_choice_options=multi_choice_options, is_mandatory=is_mandatory,\\n ca_global=ca_global\\n )\\n ca_entity = cls._normalize_ca_definition_type(ca_entity)\\n return ca_entity\",\n \"def initCatalog():\\n catalog = model.newCatalog()\\n return catalog\"\n]","isConversational":false},"negative_scores":{"kind":"list like","value":["0.6260808","0.6079598","0.5850458","0.5665877","0.55810404","0.55767274","0.5510529","0.55077094","0.550131","0.5462771","0.53828937","0.537524","0.53713846","0.5369341","0.533082","0.52978146","0.52650625","0.52579224","0.5252961","0.52399004","0.5219007","0.5206727","0.5205716","0.5194098","0.5182053","0.51784045","0.5156206","0.51533103","0.51482713","0.5148134","0.51283216","0.51232076","0.5115963","0.5109188","0.5069184","0.50569427","0.5053359","0.5049629","0.5049073","0.50467783","0.5004568","0.49977782","0.4996365","0.4984107","0.4976046","0.4958953","0.4950014","0.49459141","0.49361363","0.49357095","0.49321073","0.49255177","0.49230924","0.4913173","0.49063426","0.4903186","0.48996451","0.48887542","0.48865247","0.4870907","0.48673403","0.4860945","0.4857696","0.48568666","0.48488125","0.48351955","0.48334214","0.48331252","0.48242423","0.48199764","0.4811163","0.48108718","0.48080316","0.4796496","0.4796238","0.47942656","0.47907978","0.4787865","0.47824693","0.4773942","0.47651145","0.4760113","0.47556922","0.47472858","0.4738487","0.47360876","0.47356248","0.4733161","0.4721519","0.47176906","0.47174114","0.47138935","0.47089106","0.47042245","0.4700538","0.46994898","0.46993256","0.4698844","0.46925268","0.4687988"],"string":"[\n \"0.6260808\",\n \"0.6079598\",\n \"0.5850458\",\n \"0.5665877\",\n \"0.55810404\",\n \"0.55767274\",\n \"0.5510529\",\n \"0.55077094\",\n \"0.550131\",\n \"0.5462771\",\n \"0.53828937\",\n \"0.537524\",\n \"0.53713846\",\n \"0.5369341\",\n \"0.533082\",\n \"0.52978146\",\n \"0.52650625\",\n \"0.52579224\",\n \"0.5252961\",\n \"0.52399004\",\n \"0.5219007\",\n \"0.5206727\",\n \"0.5205716\",\n \"0.5194098\",\n \"0.5182053\",\n \"0.51784045\",\n \"0.5156206\",\n \"0.51533103\",\n \"0.51482713\",\n \"0.5148134\",\n \"0.51283216\",\n \"0.51232076\",\n \"0.5115963\",\n \"0.5109188\",\n \"0.5069184\",\n \"0.50569427\",\n \"0.5053359\",\n \"0.5049629\",\n \"0.5049073\",\n \"0.50467783\",\n \"0.5004568\",\n \"0.49977782\",\n \"0.4996365\",\n \"0.4984107\",\n \"0.4976046\",\n \"0.4958953\",\n \"0.4950014\",\n \"0.49459141\",\n \"0.49361363\",\n \"0.49357095\",\n \"0.49321073\",\n \"0.49255177\",\n \"0.49230924\",\n \"0.4913173\",\n \"0.49063426\",\n \"0.4903186\",\n \"0.48996451\",\n \"0.48887542\",\n \"0.48865247\",\n \"0.4870907\",\n \"0.48673403\",\n \"0.4860945\",\n \"0.4857696\",\n \"0.48568666\",\n \"0.48488125\",\n \"0.48351955\",\n \"0.48334214\",\n \"0.48331252\",\n \"0.48242423\",\n \"0.48199764\",\n \"0.4811163\",\n \"0.48108718\",\n \"0.48080316\",\n \"0.4796496\",\n \"0.4796238\",\n \"0.47942656\",\n \"0.47907978\",\n \"0.4787865\",\n \"0.47824693\",\n \"0.4773942\",\n \"0.47651145\",\n \"0.4760113\",\n \"0.47556922\",\n \"0.47472858\",\n \"0.4738487\",\n \"0.47360876\",\n \"0.47356248\",\n \"0.4733161\",\n \"0.4721519\",\n \"0.47176906\",\n \"0.47174114\",\n \"0.47138935\",\n \"0.47089106\",\n \"0.47042245\",\n \"0.4700538\",\n \"0.46994898\",\n \"0.46993256\",\n \"0.4698844\",\n \"0.46925268\",\n \"0.4687988\"\n]","isConversational":false},"document_score":{"kind":"string","value":"0.64500177"},"document_rank":{"kind":"string","value":"0"}}},{"rowIdx":22672,"cells":{"query":{"kind":"string","value":"Generates a TeX document and then runs the pdflatex command to create a PDF from the TeX"},"document":{"kind":"string","value":"def pdflatex(unwanted_courses):\n create_tex(unwanted_courses)\n cmd = ['pdflatex', '-interaction', 'nonstopmode', 'courses.tex']\n proc = subprocess.Popen(cmd)\n proc.communicate()\n return None"},"metadata":{"kind":"string","value":"{\n \"objective\": {\n \"self\": [],\n \"paired\": [],\n \"triplet\": [\n [\n \"query\",\n \"document\",\n \"negatives\"\n ]\n ]\n }\n}"},"negatives":{"kind":"list like","value":["def pdf():\n env.file_ext = \".pdf\"\n local(\"pandoc {input_files} -o {output_file}{file_ext} -H {preamble_file} --template {template_file} --bibliography={bib_file} --csl={csl_file} -V fontsize=12pt -V papersize=a4paper -V documentclass:report -N --latex-engine=xelatex\".format(**env))","def compile_latex(filepath_to_latex, workdir):\n proc = subprocess.Popen(['pdflatex', filepath_to_latex], cwd=workdir)\n proc.communicate()","def tex():\n env.file_ext = \".tex\"\n local(\"pandoc {input_files} -o {output_file}{file_ext} -H {preamble_file} --template {template_file} --bibliography={bib_file} --csl={csl_file} -V fontsize=12pt -V papersize=a4paper -V documentclass:report -N --latex-engine=xelatex\".format(**env))","def createPDF(self, tex):\n for b in [TIMEOUT_BINARY, RUBBER_BINARY]:\n if not os.path.isfile(b):\n raise ValueError(\"Binary %s not available\" % b)\n\n self.explodeTeX(tex)\n\n p = subprocess.Popen(\n [\n TIMEOUT_BINARY, TIMEOUT,\n RUBBER_BINARY, '--inplace', '--ps', '-ops2pdf', '-Wall', '--maxerr=100', '--force',\n os.path.join(self.dir, 'exploded.tex'),\n ],\n stdin=subprocess.PIPE,\n stdout=subprocess.PIPE,\n stderr=subprocess.PIPE,\n )\n (processOut, processErr) = p.communicate(\"\")\n exitCode = p.wait()\n if exitCode != 0:\n return processOut + processErr + \"\\nCompilation of document failed!\"\n\n self._outputFile = os.path.join(self.dir, 'exploded.pdf')\n return processOut + processErr","def createPDFDoc(self, filepath):\n print(\"Starting pdf creation\")\n strMD=\"\"\n for fileMD,data in self.graph.nodes(data=True):\n if not os.path.isfile(fileMD):\n sys.exit(\"Error: \" + fileMD + \" does not exist\")\n if not fileMD.endswith(\"md\" or \"markdown\"):\n sys.exit(fileMD + \" is not a markdown file\");\n print(\"Found file: \" + fileMD)\n strMD = strMD + \" \" + fileMD\n cmd = \"pandoc --latex-engine=xelatex -s -o \" + filepath + strMD\t\n print(\"Starting file conversion.\")\n if subprocess.call(cmd) != 0:\n print(\"Conversion failed\")\n else:\n print(\"Saving pdf file to: \" + filepath)\n print(\"Conversion successfull\")","def compile_latex_str(latex: str, filename: str):\n document = pylatex.Document()\n document.packages.append(pylatex.Package('amsfonts'))\n document.packages.append(pylatex.Package('amsmath'))\n document.append(pylatex.NoEscape(r'\\thispagestyle{empty}'))\n document.append(pylatex.NoEscape(' $' + latex + '$ '))\n document.generate_pdf(filename, compiler='pdflatex')","def build(self) -> None:\n\n print(\"Genereting files..\")\n self.doc = self.doc + r'\\end{document}'\n\n f = open(\"latex\\\\\" + self.report_name + '.tex', 'w')\n f.write(self.doc)\n f.close()\n\n os.chdir('latex')\n\n cmd = ['pdflatex', '-interaction', 'nonstopmode', self.report_name + '.tex']\n #cmd = ['pdflatex', '-interaction', self.report_name + '.tex']\n\n for i in range(2):\n proc = subprocess.Popen(cmd)\n proc.communicate()\n retcode = proc.returncode\n if not retcode == 0:\n os.chdir('..')\n raise ValueError('Error {} executing command: {}'.format(retcode, ' '.join(cmd)))\n\n os.unlink(self.report_name + '.aux')\n os.unlink(self.report_name + '.lof')\n os.unlink(self.report_name + '.log')\n os.unlink(self.report_name + '.lot')\n os.unlink(self.report_name + '.out')\n os.unlink(self.report_name + '.toc')\n\n os.chdir('..')","def buildPDF(self):\n\n # TODO: get this working\n # TODO: make this configurable via a dialog\n os.chdir(self.file_path.parent)\n proc = subprocess.Popen(\n [\"make\", \"latexpdf\"],\n stdin=subprocess.PIPE,\n stdout=subprocess.PIPE,\n stderr=subprocess.STDOUT\n )\n proc.wait()\n for line in proc.stdout:\n print(\"stdout: \" + line.rstrip())","def compile_pdf_from_template(template, insert_variables, out_path, pdflatex_options=\"-quiet\"):\n\n rendered_template = template.render(**insert_variables)\n build_d = os.path.join(os.path.dirname(os.path.realpath(out_path)), '.build')\n\n if not os.path.exists(build_d): # create the build directory if not exisiting\n os.makedirs(build_d)\n\n temp_out = os.path.join(build_d, \"tmp\")\n print(\"Writing output to {}.pdf\".format(temp_out))\n with open(temp_out + '.tex', \"w\") as f: # saves tex_code to output file\n f.write(rendered_template)\n\n os.system('pdflatex {} -output-directory {} {}'.format(pdflatex_options, build_d, temp_out + '.tex'))\n # shutil.copy2(temp_out+\".pdf\", os.path.relpath(out_path))","def generate_pdf (tmp_folder, filename, pdf_page_size):\n\n shell_cmd = PDF_CONVERT_CMD.substitute(wkhtmltox_path=WKHTMLTOX_PATH, folder=tmp_folder, article_id=filename, page_size=pdf_page_size)\n proc = subprocess.Popen(shell_cmd, shell=True, stdout=subprocess.PIPE, stderr=subprocess.PIPE)\n stdout_value, stderr_value = proc.communicate()\n\n print u'\\n'.join(filter(None, [shell_cmd, stdout_value, stderr_value]))","def render_latex(self, latex_snippet, output_png_file, output_pdf_file):\n\t\ttmp_dir = tempfile.mkdtemp(prefix = \"mdx_latex_\")\n\t\ttex_file = os.path.join(tmp_dir, \"file.tex\")\n\t\tpdf_file = os.path.join(tmp_dir, \"file.pdf\")\n\t\t\n\t\ttry:\n\t\t\t# Generate the tex file\n\t\t\ttex = LaTeXBlockProcessor.LATEX_TEMPLATE.render(\n\t\t\t\tpreamble = self.preamble,\n\t\t\t\tdocument = latex_snippet,\n\t\t\t)\n\t\t\ttex_hash = hashlib.sha1(tex).hexdigest()\n\t\t\t\n\t\t\t# Check if anything needs to be done\n\t\t\ttry:\n\t\t\t\tpng_meta = self.get_png_metadata(output_png_file)\n\t\t\t\tif tex_hash == png_meta.get(\"tex_hash\", None):\n\t\t\t\t\t# Already got the latest version in the PNG, do nothing!\n\t\t\t\t\treturn\n\t\t\texcept IOError:\n\t\t\t\t# No file exists yet so we definately should build it\n\t\t\t\tpass\n\t\t\t\n\t\t\t\n\t\t\t# Store the tex in a file to be compiled\n\t\t\twith open(tex_file, \"w\") as f:\n\t\t\t\tf.write(tex)\n\t\t\t\n\t\t\t# Try and build the file\n\t\t\tfor build_num in range(LaTeXBlockProcessor.LATEX_BUILDS):\n\t\t\t\tp = Popen( [ \"pdflatex\"\n\t\t\t\t , \"-shell-escape\"\n\t\t\t\t , \"-halt-on-error\"\n\t\t\t\t , \"-output-directory\", tmp_dir\n\t\t\t\t , tex_file]\n\t\t\t\t , cwd = os.path.realpath(self.configs.get(\"input_path\", \"./\"))\n\t\t\t\t , stdin = None\n\t\t\t\t , stdout = sys.stderr\n\t\t\t\t , stderr = sys.stderr\n\t\t\t\t )\n\t\t\t\tif p.wait() != 0:\n\t\t\t\t\traise Exception(\"LaTeX Compilation Failed for:\\n%s\"%latex_snippet)\n\t\t\t\n\t\t\t# Make a copy of required\n\t\t\tif output_pdf_file is not None:\n\t\t\t\tshutil.copy(pdf_file, output_pdf_file)\n\t\t\t\n\t\t\t# Convert to PNG\n\t\t\tp = Popen( [\"convert\", \"-density\", \"440\", pdf_file, \"-resize\", \"25%\", output_png_file]\n\t\t\t , stdin = None\n\t\t\t , stdout = sys.stderr\n\t\t\t , stderr = sys.stderr\n\t\t\t )\n\t\t\tif p.wait() != 0:\n\t\t\t\traise Exception(\"Converting PDF to PNG failed for:\\n%s\"%latex_snippet)\n\t\t\t\n\t\t\t# Add the hash of the source to the metadata \n\t\t\tself.set_png_metadata(output_png_file, {\"tex_hash\":tex_hash})\n\t\t\t\n\t\tfinally:\n\t\t\tshutil.rmtree(tmp_dir)","def latex(name,\n latex_program='pdflatex', # or 'latex'\n options='--latex_code_style=vrb',\n version='paper', # or 'screen', '2up', 'A4', 'A4-2up'\n postfix='', # or 'auto'\n ptex2tex=None, # only for ptex2tex step\n ):\n if name.endswith('.do.txt'):\n name = name.replace('.do.txt', '')\n system('rm -f %(name)s.aux' % vars())\n\n if version in ('paper', 'A4', '2up', 'A4-2up'):\n if not '--device=paper' in options:\n options += ' --device=paper'\n elif version == 'screen' and '--device=paper' in options:\n options = options.replace('--device=paper', '')\n if version in ('A4', 'A4-2up'):\n if not '--latex_papersize=a4' in options:\n options += ' --latex_papersize=a4'\n if postfix == 'auto':\n if version == 'paper':\n postfix = '4print'\n elif version == 'screen':\n postfix = '4screen'\n else:\n postfix = version\n\n # Compile source\n cmd = 'doconce format %(latex_program)s %(name)s %(options)s ' % vars()\n system(cmd)\n\n cmd = r\"doconce replace '%% insert custom LaTeX commands...' '\\usepackage[russian]{babel} \\usepackage{titlesec} \\titleformat{\\subsubsection}[runin] {\\normalfont\\normalsize\\bfseries}{\\thesubsubsection.}{1em}{} \\let\\paragraph=\\subsubsection' %(name)s.tex\" % vars()\n system(cmd)\n\n cmd = r\"doconce replace '\\usepackage{lmodern}' '%%\\usepackage{lmodern}' %(name)s.tex\" % vars()\n system(cmd)\n # Transform .p.tex to .tex?\n if ptex2tex is not None:\n cmd = ptex2tex\n system(cmd)\n\n # Load latex file into string for examination\n dofile = open(name + '.tex', 'r')\n text = dofile.read()\n dofile.close()\n\n latex_options = ''\n if latex_program == 'pdflatex':\n latex_options = '-file-line-error -interaction nonstopmode -halt-on-error'\n\n # Run latex\n shell_escape = ' -shell-escape' if 'begin{minted}' in text else ''\n cmd_latex = '%(latex_program)s%(shell_escape)s %(latex_options)s %(name)s' % vars()\n system(cmd_latex)\n\n if 'idx{' in text:\n cmd = 'makeindex %(name)s' % vars()\n system(cmd)\n\n if 'BIBFILE:' in text:\n cmd = 'bibtex %(name)s' % vars()\n system(cmd)\n system(cmd_latex)\n system(cmd_latex)\n if latex_program == 'latex':\n cmd = 'dvipdf %(name)s' % vars()\n system(cmd)\n # Could instead of dvipdf run the old-fashioned dvips and ps2pdf\n\n if version in ('2up', 'A4-2up'):\n # Use pdfnup to make two pages per sheet\n cmd = 'pdfnup --frame true --outfile %(name)s.pdf %(name)s.pdf' % vars()\n system(cmd)\n if postfix:\n shutil.copy(name + '.pdf', name + '-' + postfix + '.pdf')","def generate_problems_pdf(args):\n contents = generate_table(start_int=args.start_int, end_int=args.end_int, table_type=args.table_type)\n convert_latex_to_pdf(args.filename, contents=contents, view=True)\n remove_temporary_files(args.filename)","def make_pdf(self):\n source = self.get_page_source()\n if not source:\n self.errors.append('no_source')\n if not self.errors:\n self.generate_pdf_file(source)","def compile_latex(filename):\n working_directory, texfile = os.path.split(filename)\n try:\n run_command([\"pdflatex\", \"-draftmode\", \"-interaction=batchmode\", texfile], working_directory)\n run_command([\"pdflatex\", \"-interaction=batchmode\", texfile], working_directory)\n return True\n except subprocess.TimeoutExpired:\n return False","def main():\n init_latex()","def compile_pdf(directory, filename, move_result_to):\n cmd = ['latexmk', '-pdf', '-dvi-', '-interaction=nonstopmode', filename]\n proc = subprocess.Popen(cmd, cwd=directory)\n proc.communicate()\n\n retcode = proc.returncode\n if not retcode == 0:\n raise ValueError('Error {} executing command: {}'.format(retcode, ' '.join(cmd)))\n\n pdf_name = filename.replace('.tex', '.pdf')\n from_file = os.path.join(directory, pdf_name)\n to_file = os.path.join(move_result_to, pdf_name)\n \n if not os.path.exists(move_result_to):\n os.makedirs(move_result_to)\n \n os.rename(from_file, to_file)","def compile(lines, name):\n with open(\"{}.tex\".format(name), \"w\") as f:\n for line in lines:\n f.write(line+\"\\n\")\n os.system(\"pdflatex {}.tex\".format(name))\n os.system(\"xdg-open {}.pdf\".format(name))\n os.system(\"rm {}.log\".format(name))\n os.system(\"rm {}.aux\".format(name))","def generate_pdf(self):\n x = 100\n y = 100\n buffer = BytesIO()\n p = canvas.Canvas(buffer, pagesize=\"A4\")\n p.drawString(x, y, \"TO DO\")\n p.showPage()\n p.save()\n pdf = buffer.getvalue()\n buffer.close()\n return pdf","def build(self,\n save_to_disk=True,\n compile_to_pdf=True,\n show_pdf=True,\n delete_files=list(),\n build_from_dir='cwd'):\n tex = super().build()\n\n tex = build(self.doc_class) + '\\n' + self.build_preamble() + '\\n' + tex\n if save_to_disk:\n self.file.save(tex)\n\n if compile_to_pdf:\n self.file.compile_to_pdf(build_from_dir=build_from_dir)\n\n if show_pdf:\n open_file_with_default_program(self.filename, self.filepath)\n\n if isinstance(delete_files, str):\n if delete_files == 'all':\n delete_files = ['tex', 'aux', 'log']\n else:\n delete_files = [delete_files]\n\n for ext in delete_files:\n if ext in ['tex', 'aux', 'log', 'pdf']:\n os.remove(f'{self.filepath}/{self.filename}.{ext}')\n\n return tex","def build_pdf(branch):\n os.chdir(os.path.join(gitdname,'statsmodels','docs'))\n sphinx_dir = os.path.join(virtual_dir,'bin')\n retcode = subprocess.call(\" \".join(['make','latexpdf',\n 'SPHINXBUILD='+sphinx_dir+'/sphinx-build']), shell=True)\n if retcode != 0:\n os.chdir(old_cwd)\n msg = \"\"\"Could not build the pdf docs for branch %s\"\"\" % branch\n raise Exception(msg)\n os.chdir(dname)","def test_buildPDF(self):\n bookPath = self._setupTeXFiles()\n outputPath = FilePath(self.mktemp())\n\n builder = BookBuilder()\n builder.buildPDF(bookPath, self.howtoDir, outputPath)\n\n self.assertTrue(outputPath.exists())","def generate_envelope_pdf(document):\n compiler = LatexCompiler()\n\n return compiler.compile(document)","def generate_pdf(pdf_data):\n\n html = HTML(string=pdf_data)\n f = html.write_pdf()\n\n return f","def convert_to_latex(self, builder, filename, latex_metadata):\n relative_path = ''\n tex_data = ''\n tex_build_path = self.texdir + relative_path\n pdf_build_path = self.pdfdir + relative_path\n template_folder = builder.config['jupyter_template_path']\n\n\n ensuredir(tex_build_path)\n ensuredir(pdf_build_path)\n\n ## setting the working directory\n os.chdir(self.texdir)\n\n ## copies all theme folder images to static folder\n if os.path.exists(builder.confdir + \"/theme/static/img\"):\n copy_tree(builder.confdir + \"/theme/static/img\", self.texdir + \"/_static/img/\", preserve_symlinks=1)\n else:\n self.logger.warning(\"Image folder not present inside the theme folder\")\n\n fl_ipynb = self.texdir + \"/\" + \"{}.ipynb\".format(filename)\n fl_tex = self.texdir + \"/\" + \"{}.tex\".format(filename)\n fl_tex_template = builder.confdir + \"/\" + template_folder + \"/\" + builder.config['jupyter_latex_template']\n\n ## do not convert excluded patterns to latex\n excluded_files = [x in filename for x in builder.config['jupyter_pdf_excludepatterns']]\n\n if not True in excluded_files: \n ## --output-dir - forms a directory in the same path as fl_ipynb - need a way to specify properly?\n ### converting to pdf using xelatex subprocess\n if sys.version_info[0] < 3:\n subprocess.call([\"jupyter\", \"nbconvert\",\"--to\",\"latex\",\"--template\",fl_tex_template,\"from\", fl_ipynb])\n else:\n subprocess.run([\"jupyter\", \"nbconvert\",\"--to\",\"latex\",\"--template\",fl_tex_template,\"from\", fl_ipynb])\n\n ### check if subdirectory\n subdirectory = \"\"\n index = filename.rfind('/')\n if index > 0:\n subdirectory = filename[0:index]\n filename = filename[index + 1:]\n\n ### set working directory for xelatex processing\n os.chdir(self.texdir + \"/\" + subdirectory)\n\n try:\n self.subprocess_xelatex(fl_tex, filename)\n if 'bib_include' in latex_metadata:\n self.subprocess_bibtex(filename)\n self.subprocess_xelatex(fl_tex, filename)\n self.subprocess_xelatex(fl_tex, filename)\n except OSError as e:\n print(e)\n except AssertionError as e:\n pass\n # exit() - to be used when we want the execution to stop on error","def main(\n files: List[Path] = typer.Argument(default=None, dir_okay=False, exists=True),\n template: Optional[str] = typer.Option(\n None, '--template', help='Name of template file'\n ),\n logo: Optional[str] = typer.Option(None, '--logo', help='Name of logo file'),\n logo_width: Optional[str] = typer.Option(\n None, '--logo-width', help='Logo width (default 35mm)'\n ),\n highlight_style: Optional[str] = typer.Option(None, '--highlight-style',\n help='Specify coloring style to be used in highlighting source code'),\n syntax_definition: Optional[str] = typer.Option(None, '--syntax-definition',\n help='Specify a directory which contains syntax definition files'),\n no_toc: bool = typer.Option(\n False, '--no-toc', help='table of contents in PDF document'\n ),\n no_number_sections: bool = typer.Option(False, '--no-number-sections', help='no section numbering'),\n\n no_titlepage: bool = typer.Option(False, '--no-titlepage', help='title in PDF document'),\n tex_file: bool = typer.Option(\n False, '--tex', help='create TeX file instead of PDF document'\n ),\n email: Optional[str] = typer.Option(None, '--email', help='Author email'),\n company: Optional[str] = typer.Option(None, '--company', help='Name of company'),\n department: Optional[str] = typer.Option(\n None, '--department', help='Name of department'\n ),\n confidential: bool = typer.Option(\n False, '--confidential', help='indicate confidential'\n ),\n debug: bool = typer.Option(False, '--debug', help='turns debugging on'),\n pdf_engine: str = typer.Option(\n 'xelatex',\n '--pdf-engine',\n help='Specify pdf engine, one of lualatex, xelatex or tectonic ',\n ),\n _version: bool = typer.Option(\n None, '-V', '--version', callback=version_callback, help='Show version and exit'\n ),\n):\n\n if not files:\n typer.echo('Error: Must specify at least one .md file.')\n raise typer.Abort()\n\n mdfiles: List[str] = [str(md) for md in files]\n\n template = template or os.environ.get('MD2PDF_TEMPLATE')\n if template is None:\n print('No template specified')\n sys.exit(1)\n\n email = email or os.environ.get('MD2PDF_AUTHOR_EMAIL')\n footer_center = ''\n\n # command line overwrites `MD2PDF_PDF_ENGINE`. if both are not given\n # then `xelatex` is the default\n pdf_engine = pdf_engine or os.environ.get('MD2PDF_PDF_ENGINE') or 'xelatex'\n # check that pdf-engine is one of the following\n if pdf_engine not in ['xelatex', 'lualatex', 'tectonic']:\n print('--pdf-engine must be one of \"xelatex\", \"lualatex\", \"tectonic\"')\n sys.exit(1)\n\n ext = '.pdf'\n if tex_file:\n ext = '.tex'\n\n if len(mdfiles) == 1:\n toml_file = os.path.splitext(mdfiles[0])[0] + '.toml'\n\n if os.path.exists(toml_file):\n print(f'TOML file {toml_file} found')\n parsed_toml = toml.load(toml_file)\n default_val = parsed_toml.get('default')\n if default_val is None:\n print(f'No file names found in {toml_file}')\n else:\n mdfiles = default_val.get('files')\n\n for mdf in mdfiles:\n print(f'Compiling {mdf}')\n\n main_mdfile = os.path.realpath(mdfiles[0])\n\n outfile = Path(main_mdfile).stem + ext\n\n year = date.today().year\n\n company = company or os.environ.get('MD2PDF_COMPANY')\n department = department or os.environ.get('MD2PDF_DEPARTMENT')\n\n if company:\n if confidential:\n footer_center = f'© Copyright {year} {company}'\n else:\n footer_center = f'{year} {company}'\n\n pdcmd = PandocCmd(outfile)\n pdcmd.append(f'--template={template}')\n pdcmd.append(f'--pdf-engine={pdf_engine}')\n\n pdcmd.set_v('footer-center', footer_center)\n pdcmd.set_v('company', company)\n pdcmd.set_v('department', department)\n\n syntax_definition = syntax_definition or os.environ.get('MD2PDF_SYNTAX_DEFINITION_DIR')\n if syntax_definition is not None:\n add_syntax_definition(pdcmd, syntax_definition)\n\n pdcmd.append('--highlight-style')\n highlight_style = highlight_style or os.environ.get('MD2PDF_HIGHLIGHT_STYLE')\n if highlight_style is None:\n pdcmd.append('pygments')\n else:\n check_highlight_style(highlight_style)\n pdcmd.append(highlight_style)\n\n if not no_number_sections:\n pdcmd.append('--number-sections')\n\n if no_titlepage:\n pdcmd.set_m('titlepage', 'false')\n\n logo = logo or os.environ.get('MD2PDF_LOGO')\n pdcmd.set_v('logo', logo)\n\n logo_width = logo_width or os.environ.get('MD2PDF_LOGO_WIDTH')\n pdcmd.set_v('logo-width', logo_width)\n\n pdcmd.set_m('email', email)\n\n if not no_toc:\n pdcmd.append('--toc')\n\n pdcmd.extend(mdfiles)\n\n if debug:\n print(' '.join(pdcmd.pandoc))\n\n\n pdcmd.run()","def render_to_pdf(template_src, context_dict={}):\n template = get_template(template_src)\n html = template.render(context_dict)\n result = BytesIO()\n pdf = pisa.pisaDocument(BytesIO(html.encode(\"ISO-8859-1\")), result)\n if not pdf.err:\n return HttpResponse(result.getvalue(), content_type='application/pdf')\n return None","def convert_to_latex(filename):\n basename, ext = os.path.splitext(filename)\n print(\"Converting '{0:s}' to LaTeX...\".format(filename))\n filename1 = basename + \".tex\"\n call_pandoc(filename, filename1, \"markdown\", \"latex\")\n print(\"Done!\")","def generate_command(ctx, template):\n config_extension = '.py'\n template_extension = '.html'\n output_extension = '.pdf'\n\n context = Context(\n config=path.join(\n ctx.obj.get(\"config_path\"),\n template + config_extension\n ),\n template_path=path.join(\n ctx.obj.get(\"template_path\"),\n template + template_extension\n ),\n variables=ctx.obj.get(\"varibales\"),\n output_path=path.join(\n ctx.obj.get(\"output_path\"),\n template, output_extension),\n )\n\n generator = Generator(context)\n generator.execute()\n exit(0)","def main():\n f_name = sys.argv[1]\n file_contents = open(f_name).read()\n C = CAST([], \"python\")\n C2 = C.from_json_str(file_contents)\n\n V = CASTToAGraphVisitor(C2)\n last_slash_idx = f_name.rfind(\"/\")\n file_ending_idx = f_name.rfind(\".\")\n pdf_file_name = f\"{f_name[last_slash_idx + 1 : file_ending_idx]}.pdf\"\n V.to_pdf(pdf_file_name)","def run(self, cmd=\"\", file_regex=\"\", path=\"\", file=None, debug=False):\n # Handling process killing\n if self.proc:\n self.output(\"\\n\\n### Got request to terminate compilation ###\")\n # Not sure if this is needed, but to avoid race conditions\n proc = self.proc\n self.proc = None\n proc.kill()\n return\n\n view = self.window.active_view()\n \n # Save if there are unchanged edits\n if view.is_dirty():\n print(\"Saving...\")\n view.run_command('save')\n\n # Getting the proper file name\n # Using file switch for testing only\n if file:\n file_name = file\n else:\n file_name = get_tex_root(view)\n\n if not os.path.isfile(file_name):\n sublime.error_message(file_name + \": file not found.\")\n return\n\n # Check for the tex extension\n _, tex_ext = os.path.splitext(file_name)\n tex_dir = os.path.dirname(file_name)\n \n if tex_ext.lower() != \".tex\":\n sublime.error_message(\"%s is not a Tex source file: cannot compile\" % os.path.basename(view.file_name()))\n return\n\n print(cmd, file_name)\n\n # Creating the output view\n if not self.output_view:\n self.output_view = self.window.get_output_panel(\"subtexexec\")\n \n # Showing the output\n self.window.run_command(\"show_panel\", {\"panel\": \"output.subtexexec\"})\n # A bit crazy\n t_kwargs = {\n \"file_name\": file_name,\n \"cmd\": cmd + [\"-outdir={}\".format(tex_dir)],\n \"path\": path,\n \"debug\": debug\n }\n\n # Need to change dir here\n threading.Thread(target=self.thread, kwargs=t_kwargs).start()\n print(\"[make_pdf] Number of active threads: {}\".format(threading.active_count()))","def create_pdf(html, options):\n\n # TODO: we will change this path, or use an other library for converting PDF!\n # TODO: otherwise just say that wkhtmltopdf needs to be pre-installed (and how) and added to windows path\n path_wkthmltopdf = \"C:/Program Files/wkhtmltopdf/bin/wkhtmltopdf.exe\"\n config = pdfkit.configuration(wkhtmltopdf=path_wkthmltopdf)\n\n pdfkit.from_file(html, html.replace(\".html\", \".pdf\"), configuration=config, options=options)\n return html + \".pdf\"","def compile(self, texdoc, clean=False):\n\n if helpers.checkdep_tex() is not None:\n # use ``pdflatex`` to compile the document\n tex = subprocess.call(\n [\"pdflatex\", texdoc, \"--quiet\"],\n stdout=subprocess.PIPE,\n stderr=subprocess.PIPE,\n shell=False,\n )\n\n if clean:\n extensions = [\"aux\", \"log\", \"nav\", \"out\", \"snm\", \"toc\"]\n for ext in extensions:\n junkfiles = glob.glob(\"*.{}\".format(ext))\n for junk in junkfiles:\n os.remove(junk)\n\n else:\n tex = None\n\n return tex","def generate_pdf():\n uu_id = uuid.uuid4().hex\n current_app.logger.debug(uu_id)\n current_app.logger.debug(request.form)\n tmp_pdf_filename = \"{}_tmp.pdf\".format(uu_id)\n pdf_filename = \"{}.pdf\".format(uu_id)\n tmp_out_pdf_path = os.path.join(\n current_app.config[\"UPLOAD_FOLDER\"], tmp_pdf_filename\n )\n out_pdf_path = os.path.join(\n current_app.config[\"UPLOAD_FOLDER\"], pdf_filename\n )\n fill_template_from_input(\n request.form,\n current_app.config[\"PDF_TEMPLATE_PATH\"],\n tmp_out_pdf_path,\n INPUT_FORM_MAP\n )\n fdf_tmp = os.path.join(\n current_app.config[\"UPLOAD_FOLDER\"], 'tmp.fdf'\n )\n os.system('pdftk ' + tmp_out_pdf_path + ' generate_fdf output ' + fdf_tmp)\n os.system(\n 'pdftk ' + tmp_out_pdf_path + ' fill_form ' + fdf_tmp +\n ' output ' + out_pdf_path + ' flatten'\n )\n os.remove(tmp_out_pdf_path)\n return pdf_filename","def generate_pdf(list,id):\n\n doc = SimpleDocTemplate(settings.STATIC_ROOT+\"/tests/\"+str(id)+\"/\"+str(id)+\".pdf\")\n\n Story = [Spacer(1,2*inch)]\n styles = stylesheet()\n global Title\n\n # Add 10 questions with boxes below\n for i in list:\n if not i[0] in \"skills-scan\" and not i[0] in \"csrfmiddlewaretoken\" and not i[0] in \"titre\" and not i[0] in \"custom\":\n tmp = int(i[0])+1\n bogustext = (str(tmp)+\". %s\" % i[1])\n p = Paragraph(bogustext, styles['default'])\n # Write the paragraph\n\n draw = Drawing()\n # rect(x1,y1,width,height)\n rec = Rect(0, 100, 450, 150)\n rec.fillColor = colors.white\n # draw the rect under each paragraph\n draw.add(rec)\n p.keepWithNext = True\n Story.append(p)\n Story.append(draw)\n Story.append(Spacer(1,-0.9 * inch))\n elif i[0] in \"titre\":\n Title = i[1]\n # build the document by inserting the whole story\n doc.build(Story, onFirstPage=myFirstPage, onLaterPages=myLaterPages)\n return str(id)+\".pdf\"","def create_pdf(f,s1,s2='',s3=''):\n # does not need reportlab!\n if s1 == 'White Ballot': s1 = '\"'+'_'*10+'\"'\n cod = zlib.compress('BT /F1 16 Tf ET\\r\\nBT 300 270 Td (%s) Tj ET\\r\\nBT /F1 48 Tf ET\\r\\nBT 5 180 Td (%16s) Tj ET\\r\\nBT /F1 12 Tf ET\\r\\nBT 10 50 Td (%s) Tj ET'%(s3,s1,s2))\n open(f,'w').write(create_pdf.__doc__ + '/Length %d>>\\nstream\\n'%len(cod) + cod + 'endstream endobj\\ntrailer<</Root 4 0 R>>')","def latex2wp():\n parser = argparse.ArgumentParser()\n parser.add_argument('input_path')\n #parser.add_argument('destination')\n args = parser.parse_args()\n\n source = None\n blocks = None\n paragraphs = []\n\n source = from_file(args.input_path)\n source = document(source)\n source = split_inline(source)\n blocks = split_blocks(source)\n fragments = process(blocks)\n\n html = refs('\\n'.join(fragments))\n print(html)","def main():\n argparser = argparse.ArgumentParser(description=\"Convert plot to table\")\n\n argparser.add_argument(\"pdf\", action=\"store\", help=\"pdf file\",\n default=None, nargs=\"*\")\n\n args = argparser.parse_args()\n\n if len(args.pdf) == 0:\n open_gui()\n else:\n process_pdf(args.pdf[0])\n\n generate_data()","def export(bill, template_dir=None, pdf_dir=None):\n # if template_dir not provided,\n # look for the template directory of this script's location\n if not template_dir:\n template_dir = os.path.join(os.path.dirname(os.path.abspath(__file__)), 'template')\n # If the user-defined or default template directories don't exist, raise an error\n if not os.path.exists(template_dir):\n raise OSError('Could not find the template directory')\n\n # If no user-defined pdf output directory, put it in a folder where this script lives\n if not pdf_dir:\n basedir = os.path.dirname(os.path.abspath(__file__))\n pdf_dir = os.path.join(basedir, 'pdfs')\n # if the default pdf output directory doesn't exist, make it\n if not os.path.exists(pdf_dir):\n os.makedirs(pdf_dir)\n\n # if the user-defined pdf_dir does not exist, raise an error\n if not os.path.exists(pdf_dir):\n raise IOError('Could not find a directory to output pdfs')\n\n # get the path to the template\n template_path = os.path.join(template_dir, 'templates', 'template.html')\n # read the template\n template = open(template_path).read()\n\n # Replace relative imports of images and CSS with the full path to the files\n # Note: I'm including the '/' in the replacement so that\n # it doesn't replace other uses for '..' such as in regular text (i.e. an ellipsis)\n template = template.replace('../', os.path.join(path2url(template_dir), ''))\n\n # Insert billing data using find/replace\n # Sort by field length longest to shortest\n # This prevents values from fields that are substrings of other fields from going in the wrong place\n # e.g. the value of \"rebate\" would be inserted into the field \"rebate_closing_balance\"\n for key, value in sorted(bill.items(), key=lambda t: len(t[0]), reverse=True):\n template = template.replace(\"__\"+key, format_value(value))\n\n # Now create the pdf\n try:\n # options = {'encoding': 'utf-8'}\n report_name = make_report_name(bill)\n output_file = os.path.join(pdf_dir, report_name)\n pdfkit.from_string(template, output_file)\n except:\n typ, value, tb = sys.exc_info()\n traceback.print_exc()\n pdb.post_mortem(tb)","def template2pdf(template=None, **kwargs):\n str = render_to_string(template, kwargs)\n output = kwargs.get('output', False)\n ret = pdfkit.from_string(str, output, settings.WKHTMLTOPDF_OPTIONS or default_options)\n return (ret, str)","def generate_pdf_report(release, spec, report_week):\n\n logging.info(u\" Generating the pdf report, give me a few minutes, please \"\n u\"...\")\n\n working_dir = spec.environment[u\"paths\"][u\"DIR[WORKING,SRC]\"]\n\n execute_command(f\"cd {working_dir} && mv -f index.pdf.template index.rst\")\n\n _convert_all_svg_to_pdf(spec.environment[u\"paths\"][u\"DIR[WORKING,SRC]\"])\n\n # Convert PyPLOT graphs in HTML format to PDF.\n convert_plots = u\"xvfb-run -a wkhtmltopdf {html} {pdf}\"\n plots = get_files(spec.environment[u\"paths\"][u\"DIR[STATIC,VPP]\"], u\"html\")\n plots.extend(\n get_files(spec.environment[u\"paths\"][u\"DIR[STATIC,DPDK]\"], u\"html\")\n )\n for plot in plots:\n file_name = f\"{plot.rsplit(u'.', 1)[0]}.pdf\"\n logging.info(f\"Converting {plot} to {file_name}\")\n execute_command(convert_plots.format(html=plot, pdf=file_name))\n\n # Generate the LaTeX documentation\n build_dir = spec.environment[u\"paths\"][u\"DIR[BUILD,LATEX]\"]\n cmd = PDF_BUILDER.format(\n release=release,\n date=datetime.datetime.utcnow().strftime(u'%Y-%m-%d %H:%M UTC'),\n working_dir=working_dir,\n build_dir=build_dir)\n execute_command(cmd)\n\n # Build pdf documentation\n archive_dir = spec.environment[u\"paths\"][u\"DIR[STATIC,ARCH]\"]\n cmds = [\n f'cd {build_dir} && '\n f'pdflatex -shell-escape -interaction nonstopmode csit.tex || true',\n f'cd {build_dir} && '\n f'pdflatex -interaction nonstopmode csit.tex || true',\n f'cd {build_dir} && '\n f'cp csit.pdf ../{archive_dir}/csit_{release}.{report_week}.pdf &&'\n f'cp csit.pdf ../{archive_dir}/csit_{release}.pdf'\n ]\n\n for cmd in cmds:\n execute_command(cmd)\n\n logging.info(u\" Done.\")","def generate_pdf_file(self, source):\n filename = self.generate_temp_filename()\n if not filename:\n self.errors.append('filename_generation_failed')\n return\n\n try:\n transform_module = getattr(transforms, self.pdf_generator)\n except AttributeError:\n self.errors.append('wrong_generator_configuration')\n return\n\n self.filename = filename\n url = self.context.absolute_url()\n\n print_css = (self.pdf_tool.always_print_css or\n self.context.portal_type in self.pdf_tool.print_css_types)\n\n # When the source is sent through Ajax, it's already encoded\n # as a utf-8 string. When using it without javascript, the\n # source comes from a view, which always returns unicode. In\n # that case we need to encode it.\n if isinstance(source, unicode):\n source = source.encode('utf-8')\n export_file, err = transform_module.html_to_pdf(\n source,\n self.tempdir,\n filename,\n url,\n print_css,\n self.get_extra_options())\n\n if err:\n self.errors.append('pdf_creation_failed')\n return\n\n self.pdf_tool.registerPDF(filename)\n self.pdf_file = export_file\n self.pdf_file.close()","def main():\n mip = parametros()\n mir = Reporte(CURRENT_PATH, mip.debug, mip.overwrite)\n pdfs = mir.obtener()\n if pdfs:\n print(\"Obteniendo nuevos pdf:\")\n for pdf in pdfs:\n print(f\"* {pdf}\")\n\n for file in glob.glob(f\"{CURRENT_PATH}/resources/pdf/*.pdf\"):\n data = mir.parser(file)\n mir.escribir(data)","def task_render_report():\n target = 'report.pdf'\n dep = 'report.md'\n return {\n 'file_dep': [dep],\n 'targets': [target],\n 'actions': [\n f\"pandoc -t latex -o {target} {dep}\"\n ],\n 'clean': True\n }","def print_latex(self):\n\n pdf = pylatex.Document(\n \"default\"\n )\n\n with pdf.create(pylatex.Section(\n \"Equações Diofantinas\"\n )) as section:\n\n section.append(\"Equação:\")\n ultimo = self.numbers[-1]\n eq = []\n cont = 1\n for i in self.numbers:\n simbolo = \"+\"\n if i == ultimo:\n simbolo = \"= 1\"\n eq.append(\n pylatex.NoEscape(\n \" {}x_{} {}\".format(i, cont, simbolo)\n )\n )\n cont = cont + 1\n\n section.append(pylatex.Math(data=eq))\n\n text = \"n = {}\".format(self.order)\n section.append(text)\n\n m = pylatex.Matrix(self.take_vec(), mtype='b')\n matrix = pylatex.Math(data=['b = ', m])\n section.append(matrix)\n\n m = pylatex.Matrix(self.take_matrix(), mtype='b')\n matrix = pylatex.Math(data=['A = ', m])\n section.append(matrix)\n\n section.append(\"Resposta = {}\".format(self.cofactor_matrix()))\n\n section.append(pylatex.LineBreak())\n section.append(\"Confirmando:\")\n section.append(pylatex.LineBreak())\n s = 0\n for i in range(len(self.numbers)):\n r = self.numbers[i] * self.cofactor_matrix()[i]\n s = s + r\n resp = \"\\t {}\\t{} \\t* \\t{} \\t= \\t{} \\t({})\\n\".format(\n i,\n self.numbers[i],\n self.cofactor_matrix()[i],\n r,\n s\n )\n section.append(resp)\n\n if self.create_pdf:\n pdf.generate_pdf()\n\n pdf.generate_tex()","def generate_pdf(request):\n reg_no = request.user.username\n user = get_object_or_404(User, username=reg_no)\n user_profile = user.get_profile()\n user_application = user_profile.application\n np = user_application.np\n \n response = HttpResponse(mimetype='application/pdf')\n response['Content-Disposition'] = 'attachment; filename=JAM2012_Allottment.pdf'\n \n elements = []\n doc = SimpleDocTemplate(response)\n \n formatted_time = time.ctime()\n styles = getSampleStyleSheet()\n styles.add(ParagraphStyle(name='Justify', alignment=TA_JUSTIFY))\n \n ptext = '<font size=15>JAM 2012 - Admissions.</font>' \n elements.append(Paragraph(ptext, styles[\"Justify\"]))\n elements.append(Spacer(4, 20))\n \n ptext = '<font size=12>Registration Number: %s</font>' % reg_no \n elements.append(Paragraph(ptext, styles[\"Normal\"]))\n elements.append(Spacer(1, 12))\n \n data = [] \n options = get_chosen_options(user) ##Put a check to show when the options chosen is empty\n \n if not(options):\n ptext = '<font size=12>No choices were selected.</font>' \n elements.append(Paragraph(ptext, styles[\"Normal\"]))\n elements.append(Spacer(1, 12))\n doc.build(elements) \n return response \n \n ptext = '<font size=12>The choices selected by me are as follows: </font>' \n elements.append(Paragraph(ptext, styles[\"Normal\"]))\n elements.append(Spacer(4, 30))\n \n counter = 1\n for opt in options:\n data.append([counter, opt.opt_code, opt.opt_location, opt.opt_name])\n counter = counter + 1\n \n t = Table(data)\n t.setStyle(TableStyle([('GRID',(0,0),(3,len(options)),1,colors.black),\n ('TEXTCOLOR',(0,0),(0,-1),colors.green)]))\n \n elements.append(t) \n \n elements.append(Spacer(4, 30))\n \n ptext = '<font size=12>I hereby declare that the order of preference given by me for my eligible programmes is final. </font>' \n elements.append(Paragraph(ptext, styles[\"Normal\"]))\n elements.append(Spacer(4, 25))\n \n ptext = '<font size=12>Signature of the Candidate</font>' \n elements.append(Paragraph(ptext, styles[\"Normal\"]))\n elements.append(Spacer(4, 20))\n \n ptext = '<font size=12>%s</font>' % formatted_time\n elements.append(Paragraph(ptext, styles[\"Normal\"]))\n elements.append(Spacer(1, 12))\n \n doc.build(elements)\n \n return response","def create_pdf(file_path: Path) -> None:\n pdf = FPDF()\n pdf.add_page()\n pdf.set_font(\"Arial\", size=12)\n with open(file_path, \"r\") as file:\n for line in file:\n pdf.cell(200, 10, txt=line, ln=1)\n pdf_dir_path: Path = Path(calculate_path(file_path))\n Path(pdf_dir_path.parent).mkdir(parents=True, exist_ok=True)\n pdf_path: str = f\"{str(pdf_dir_path)}.pdf\"\n pdf.output(pdf_path)","def _create_pdfdoc(pdfdoc, story):\n pdf_doc = BaseDocTemplate(\n pdfdoc, pagesize=letter,\n leftMargin=inch, rightMargin=inch,\n topMargin=inch, bottomMargin=inch)\n main_frame = Frame(\n inch, inch,\n letter[0] - 2 * inch, letter[1] - 2 * inch,\n leftPadding=0, rightPadding=0, bottomPadding=0,\n topPadding=0, id='main_frame')\n main_template = PageTemplate(id='main_template', frames=[main_frame])\n pdf_doc.addPageTemplates([main_template])\n\n pdf_doc.build(story)","def pdf_gen(report, summary=None):\n with open(\"report_content.yaml\", \"r\") as stream:\n docs = yaml.safe_load(stream)\n\n style = g_stylesheet.get(\"styles\")\n elems = [] # elements array used to build pdf structure\n pdf = SimpleDocTemplate(\n f\"{report.replay_id}_report.pdf\",\n pagesize=letter,\n leftMargin=0.75 * inch,\n rightMargin=0.75 * inch,\n topMargin=0.75 * inch,\n bottomMargin=0.75 * inch,\n )\n\n # title and subtitle and cluster info table\n elems.append(Paragraph(docs[\"title\"], style[\"Title\"]))\n elems.append(\n Paragraph(sub_yaml_vars(report, docs[\"subtitle\"]), style[\"Heading4\"])\n )\n cluster_info = pd.DataFrame.from_dict(report.cluster_details, orient=\"index\")\n elems.append(\n Table(\n df_to_np(report.cluster_details.keys(), cluster_info.transpose()),\n hAlign=\"LEFT\",\n style=g_stylesheet.get(\"table_style\"),\n )\n )\n # replay summary\n if summary is not None:\n elems.append(Paragraph(f\"Replay Summary\", style[\"Heading4\"]))\n elems.append(\n ListFlowable(\n [ListItem(Paragraph(x, style[\"Normal\"])) for x in summary],\n bulletType=\"bullet\",\n )\n )\n elems.append(Spacer(0, 5))\n\n elems.append(Paragraph(docs[\"report_paragraph\"], style[\"Normal\"]))\n\n # glossary section\n elems.append(Paragraph(docs[\"glossary_header\"], style[\"Heading4\"]))\n elems.append(Paragraph(docs[\"glossary_paragraph\"], style[\"Normal\"]))\n elems.append(\n ListFlowable(\n [ListItem(Paragraph(x, style[\"Normal\"])) for x in docs[\"glossary\"]],\n bulletType=\"bullet\",\n )\n )\n elems.append(Spacer(0, 5))\n\n # access data section\n elems.append(Paragraph(docs[\"data_header\"], style[\"Heading4\"]))\n elems.append(\n Paragraph(sub_yaml_vars(report, docs[\"data_paragraph\"]), style[\"Normal\"])\n )\n elems.append(\n ListFlowable(\n [ListItem(Paragraph(x, style[\"Normal\"])) for x in docs[\"raw_data\"]],\n bulletType=\"bullet\",\n )\n )\n elems.append(Spacer(0, 5))\n elems.append(\n Paragraph(\n sub_yaml_vars(report, docs[\"agg_data_paragraph\"]), style[\"Normal\"]\n )\n )\n\n # notes section\n elems.append(Paragraph(docs[\"notes_header\"], style[\"Heading4\"]))\n elems.append(Paragraph(docs[\"notes_paragraph\"], style[\"Normal\"]))\n elems.append(\n ListFlowable(\n [ListItem(Paragraph(x, style[\"Normal\"])) for x in docs[\"notes\"]],\n bulletType=\"bullet\",\n )\n )\n\n elems.append(PageBreak()) # page 2: cluster details\n\n # query breakdown\n build_pdf_tables(elems, docs[\"query_breakdown\"], report)\n elems.append(Spacer(0, 5))\n\n # histogram and description\n image_path = hist_gen(\n x_data=report.feature_graph[\"sec_start\"],\n y_data=report.feature_graph[\"count\"],\n title=docs[\"graph\"].get(\"title\"),\n x_label=\"Average Elapsed Time (s)\",\n )\n\n desc = Paragraph(docs[\"graph\"].get(\"paragraph\"), style[\"Normal\"])\n data = [[Image(image_path, width=300, height=200, hAlign=\"LEFT\"), desc]]\n elems.append(\n Table(data, style=TableStyle([(\"VALIGN\", (0, 0), (-1, -1), \"MIDDLE\")]))\n )\n elems.append(Spacer(0, 5))\n\n # cluster metrics table\n build_pdf_tables(elems, docs[\"cluster_metrics\"], report)\n\n elems.append(PageBreak()) # page 3+ measure tables\n\n build_pdf_tables(\n elems, docs[\"measure_tables\"], report\n ) # build 5 measure tables all at once\n\n # build pdf\n pdf.build(\n elems,\n onFirstPage=partial(first_page, report=report),\n onLaterPages=partial(later_pages, report=report),\n )\n os.remove(image_path)\n\n return pdf.filename","def make_pdf(self, htmlbody, html_only=False):\n # wrap htmlbody with provided HTML template\n template = self.context.auto_template\n template = template.replace(u'${body}', htmlbody)\n if html_only:\n return template\n try:\n tempdir = tempfile.mkdtemp()\n # attachemnts saved. Let's save generated HTML\n fullpath = os.path.join(tempdir, 'issue.html')\n fp = open(fullpath, 'w')\n fp.write(template.encode('utf-8'))\n fp.close()\n # Run wkhtmltopdf and generate the PDF\n targetpath = os.path.join(tempdir, 'issue.pdf')\n result = subprocess.call([\"wkhtmltopdf\", '-q', 'file://%s' % fullpath, '%s' % targetpath])\n if result == 0:\n return open(targetpath, 'rb').read()\n else:\n return ''\n finally:\n shutil.rmtree(tempdir, ignore_errors=True)","def test_buildPDFRunsLaTeXThreeTimes(self):\n class InspectableBookBuilder(BookBuilder):\n def __init__(self):\n BookBuilder.__init__(self)\n self.commands = []\n\n def run(self, command):\n \"\"\"\n Record the command and then execute it.\n \"\"\"\n self.commands.append(command)\n return BookBuilder.run(self, command)\n\n bookPath = self._setupTeXFiles()\n outputPath = FilePath(self.mktemp())\n\n builder = InspectableBookBuilder()\n builder.buildPDF(bookPath, self.howtoDir, outputPath)\n\n # These string comparisons are very fragile. It would be better to\n # have a test which asserted the correctness of the contents of the\n # output files. I don't know how one could do that, though. -exarkun\n latex1, latex2, latex3, dvips, ps2pdf13 = builder.commands\n self.assertEquals(latex1, latex2)\n self.assertEquals(latex2, latex3)\n self.assertEquals(\n latex1[:1], [\"latex\"],\n \"LaTeX command %r does not seem right.\" % (latex1,))\n self.assertEquals(\n latex1[-1:], [bookPath.path],\n \"LaTeX command %r does not end with the book path (%r).\" % (\n latex1, bookPath.path))\n\n self.assertEquals(\n dvips[:1], [\"dvips\"],\n \"dvips command %r does not seem right.\" % (dvips,))\n self.assertEquals(\n ps2pdf13[:1], [\"ps2pdf13\"],\n \"ps2pdf13 command %r does not seem right.\" % (ps2pdf13,))","def toPDF(Infos):\n\n\n #returnPDF = PDFDocument(\"output\")\n #returnPDF.Infos.get(\"name\")\n returnPDF = PDF(\"Courier\", Infos.get(\"name\"))\n if Infos.get('contact'):\n returnPDF.contact(Infos.get(\"contact\"))\n if Infos.get('Current position'):\n returnPDF.currentposition(Infos.get(\"Current position\"))\n if Infos.get('Education'):\n returnPDF.currentposition(Infos.get(\"Education\"))\n if Infos.get('Langue'):\n returnPDF.currentposition(Infos.get(\"Langue\"))\n returnPDF.output(\"result.pdf\", 'F')","def create_pdf():\n\n fig = plt.gcf()\n fig.set_size_inches(OutFileParameter.width, OutFileParameter.height)\n fig.savefig(OutFileParameter.name + '.' + OutFileParameter.ext, dpi=OutFileParameter.dpi)\n\n return None","def docx():\n env.file_ext = \".docx\"\n local(\"pandoc {input_files} -o {output_file}{file_ext} --bibliography={bib_file} --csl={csl_file} --toc\".format(**env))","def build_pdf(filename=\"bids-spec.pdf\", logfile=\"bids-spec_pandoc_log.json\"):\n\n def _flatten_values(lst):\n \"\"\"Flatten a list of dicts of lists to a list of values.\"\"\"\n for obj in lst:\n for val in obj.values():\n if isinstance(val, str):\n yield val\n else:\n yield from _flatten_values(val)\n\n fname_mkdocs_yml = _find(HERE, \"mkdocs.yml\")\n\n with open(fname_mkdocs_yml, \"r\") as stream:\n mkdocs_yml = yaml.safe_load(stream)\n\n sections = mkdocs_yml[\"nav\"][0][\"The BIDS Specification\"]\n\n # special files\n index_page = \"./index.md\"\n pandoc_metadata = _find(HERE, \"metadata.yml\")\n\n # Prepare the command options\n cmd = [\n \"pandoc\",\n \"--from=markdown_github+yaml_metadata_block\",\n \"--include-before-body=./cover.tex\",\n \"--include-in-header=./header.tex\",\n \"--include-in-header=./header_setup.tex\",\n \"--pdf-engine=xelatex\",\n f\"--log={logfile}\",\n f\"--output={filename}\",\n ]\n\n # Resources are searched relative to the working directory, but\n # we can add additional search paths using <path>:<another path>, ...\n # When in one of the appendices/ files there is a reference to\n # \"../04-modality-specific-files/images/...\", then we need to use\n # appendices/ as a resource-path so that the relative files can\n # be found.\n build_root = HERE.parent\n cmd += [f'--resource-path=.:{build_root / \"appendices\"}']\n\n # Add input files to command\n # The filenames in `markdown_list` will ensure correct order when sorted\n cmd += [str(build_root / index_page)]\n cmd += [str(pandoc_metadata)]\n cmd += [str(build_root / md) for md in _flatten_values(sections)]\n\n # print and run\n print(\"pandoc command being run: \\n\\n\" + \"\\n\".join(cmd))\n subprocess.run(cmd)","def compile_single(is_update):\n for task in ['pdflatex', 'bibtex', 'pdflatex', 'pdflatex']:\n subprocess.check_call(task + ' main', shell=True)\n shutil.move(\"main.pdf\", \"slides.pdf\")","def book(**kwargs):\n print(\"pdf created\")","def run(self):\n make_sure_path_exists(OUT_FOLDER)\n\n if self.config['type'] == 'website':\n make_sure_path_exists(self.config['out_folder'])\n\n\n for file in self.config['bodies']:\n if file['type'] == 'content':\n self.pandoc_file(file)\n if self.config['type'] == 'website':\n shutil.copyfile(file['generated'], os.path.join(self.config['out_folder'], os.path.basename(file['source'])))\n\n if self.config['type'] == 'website':\n return\n\n for file in self.config['abstract']:\n self.pandoc_file(file)\n for file in self.config['summary']:\n self.pandoc_file(file)\n\n template = LATEX_JINJA_ENV.get_template(self.config['template_file'])\n\n logging.info('Rendering template')\n out = template.render(**self.config)\n with open(self.config['name'] + self.get_file_extension(), 'w') as file:\n file.write(out)\n\n if not self.args.pandoc:\n logging.info('Rendering latex')\n self.write()\n if not self.args.fast:\n logging.info('Rendering latex, again')\n self.write() # twice for the toc\n\n logging.info('Done!')","def tex(filename):\n entries = [entry for entry in parse_listing(filename)]\n export_to_latex('listing.tex', entries)","def create_pdfdoc(pdfdoc, story):\n pdf_doc = BaseDocTemplate(pdfdoc, pagesize = letter,\n leftMargin = MARGIN_SIZE, rightMargin = MARGIN_SIZE,\n topMargin = 24, bottomMargin = MARGIN_SIZE)\n\n second_frame = main_frame = Frame(MARGIN_SIZE, MARGIN_SIZE,\n width - 2 * MARGIN_SIZE, height - (24 + 72),\n leftPadding = 0, rightPadding = 0, bottomPadding = 0,\n topPadding = 0, id = 'main_frame', showBoundary=0)\n second_template = PageTemplate(id = 'second_template', frames=[second_frame], onPage=header)\n\n main_frame = Frame(MARGIN_SIZE, MARGIN_SIZE,\n width - 2 * MARGIN_SIZE, height - (24 + MARGIN_SIZE),\n leftPadding = 0, rightPadding = 0, bottomPadding = 0,\n topPadding = 0, id = 'main_frame', showBoundary=0)\n main_template = PageTemplate(id = 'main_template', frames = [main_frame], onPage=header)\n\n \n \n pdf_doc.addPageTemplates([main_template, second_template])\n\n pdf_doc.build(story)","def xelatex_document(doc_args, template_file, field_name, output_dir='.'):\n # input data\n input_data = doc_args\n\n # template doc\n template_doc = XeLateXDocument(template_file)\n\n # output file name\n field_val = input_data[field_name].replace(' ', '')\n\n file_extension = get_extension(template_file)\n basename = path.basename(template_file).replace(file_extension, '')\n\n file_name = basename + '_' + field_val\n file_path = path.join(output_dir, file_name + '.pdf')\n\n # make output folder\n if not os.path.exists(output_dir):\n os.mkdir(outdir)\n\n # fill the template\n template_doc.fill(doc_args)\n\n # save into PDF\n template_doc.render(file_path)\n\n # clean up LateX mess\n cleanup_docstamp_output(output_dir)\n\n return file_path","def generate_contract_de_fdf_pdf(user):\n if DEBUG: # pragma: no cover\n print \"===== this is generate_fdf_pdf\"\n from fdfgen import forge_fdf\n fields = [\n ('surname', user.surname),\n ('lastname', user.lastname),\n ('street', user.street),\n ('number', user.number),\n ('postcode', user.postcode),\n ('city', user.city),\n ('email', user.email),\n ('user_id', user.id),\n ('username', user.username),\n ('date_registered', user.date_registered),\n ('date_generated', datetime.now()),\n ]\n #generate fdf string\n fdf = forge_fdf(\"\", fields, [], [], [])\n # write to file\n my_fdf_filename = \"fdf\" + str(user.id) + \".fdf\"\n\n fdf_file = open(my_fdf_filename, \"w\")\n fdf_file.write(fdf)\n fdf_file.close()\n if DEBUG: # pragma: no cover\n print \"fdf file written.\"\n\n res = os.popen(\n 'pdftk pdftk/berechtigungsvertrag-2.2.pdf fill_form %s output'\n ' formoutput.pdf flatten' % my_fdf_filename).read()\n\n if DEBUG: # pragma: no cover\n print res\n print \"done: put data into form and finalized it\"\n\n # delete the fdf file\n res = os.popen('rm %s' % my_fdf_filename)\n if DEBUG: # pragma: no cover\n print res\n print \"combining with bank account form\"\n # combine\n res = os.popen(\n 'pdftk formoutput.pdf pdftk/bankaccount.pdf output output.pdf').read()\n if DEBUG: # pragma: no cover\n print res\n print \"combined personal form and bank form\"\n\n # delete the fdf file\n os.popen('rm formoutput.pdf').read()\n\n # return a pdf file\n from pyramid.response import Response\n response = Response(content_type='application/pdf')\n response.app_iter = open(\"output.pdf\", \"r\")\n return response","def create_pdf(self):\n\n my_datetime = datetime.now()\n self.pdf_name = (\n self.pdf_name + \"_\" + my_datetime.strftime(\"%H%M_%d%m%Y\") + \".pdf\"\n )\n fig_width = aW * self.column_ratio[0]\n\n clm_width_meta = (aW * self.column_ratio[1]) / len(self.fields)\n\n c = canvas.Canvas(os.path.join(self.pdf_folder, self.pdf_name), pagesize=A4)\n\n for qc_run_id, fig_file in sorted(self._files.items()):\n (param_values, feature_values) = get_param_values(\n qc_run_id, self.db_name, return_meta_add_on=True\n )\n\n comment = self.subject + \"<br/>\"\n # c.saveState()\n title = \"Dataset \" + qc_run_id\n\n # Prepare header\n header = Paragraph(title, title_style)\n h_w, h_h = header.wrap(aW, aH)\n\n # Prepare image\n img = ImageReader(fig_file)\n im_width, im_height = img.getSize()\n aspect = im_height / float(im_width)\n fig_height = fig_width * aspect\n\n # Prepare metadata section\n\n meta_table = Table(\n param_values,\n colWidths=[clm_width_meta] * len(self.fields),\n hAlign=\"CENTER\",\n rowHeights=0.22 * inch,\n )\n meta_table.setStyle(\n TableStyle(\n [\n (\"FONT\", (0, 0), (-1, 0), \"Helvetica-Bold\"),\n (\"FONT\", (0, 1), (-1, -1), \"Helvetica\"),\n (\"LINEBELOW\", (0, 0), (1, 0), 0.08, colors.black),\n (\"SIZE\", (0, 0), (-1, -1), 8),\n (\"VALIGN\", (0, 0), (-1, -1), \"BOTTOM\"),\n # ('ALIGN', (0, 0), (-1, 0), 'CENTER'),\n (\"ALIGN\", (0, 0), (0, -1), \"LEFT\"),\n (\"ALIGN\", (1, 1), (1, -1), \"LEFT\"),\n (\"INNERGRID\", (0, 0), (-1, -1), 0.08, colors.beige),\n # ('BOX', (0,0), (-1,-1), 0.25, colors.grey),\n ]\n )\n )\n\n meta_width, meta_height = meta_table.wrap(aW - im_width, aH / 2)\n\n # Prepare comments header\n comments_header = Paragraph(\"Comments:\", title_style)\n avail_height = aH - fig_height - v_padding\n comm_h_width, comm_h_height = comments_header.wrap(\n im_width, avail_height # aW - meta_width,\n )\n # Prepare comments\n my_datetime = datetime.now()\n ts = \"Printed on \" + my_datetime.strftime(\"%c\")\n\n try:\n data_specific_comment = self.comments[int(qc_run_id)]\n comment += data_specific_comment + \"<br/>\"\n comment += self.comments[\"general\"] + \"<br/>\"\n\n comment += self.smalltalk + \"<br/>\"\n except Exception:\n logger.warning(\n \"Unable to summarize result of \" + \"dataset {}\".format(qc_run_id)\n )\n comment_ts = comment + ts\n comment_ts = textwrap.fill(comment_ts, 70)\n comment_ts = comment_ts.replace(\"\\n\", \"<br/>\")\n\n comments_p = Paragraph(comment_ts, body_style)\n\n avail_height = aH - fig_height - v_padding - comm_h_height\n\n comm_width, comm_height = comments_p.wrap(im_width, avail_height) # aW,\n\n line_widths = comments_p.getActualLineWidths0()\n number_of_lines = len(line_widths)\n if number_of_lines > 1:\n pass\n if number_of_lines == 1:\n min(line_widths)\n comm_width, comm_height = comments_p.wrap(im_width, avail_height)\n\n # Prepare features\n feat_table = Table(\n feature_values,\n colWidths=[clm_width_meta] * len(self.fields),\n hAlign=\"CENTER\",\n rowHeights=0.22 * inch,\n )\n feat_table.setStyle(\n TableStyle(\n [\n (\"FONT\", (0, 0), (-1, 0), \"Helvetica-Bold\"),\n (\"FONT\", (0, 1), (-1, -1), \"Helvetica\"),\n (\"LINEBELOW\", (0, 0), (1, 0), 0.08, colors.black),\n (\"SIZE\", (0, 0), (-1, -1), 8),\n (\"VALIGN\", (0, 0), (-1, -1), \"BOTTOM\"),\n # ('ALIGN', (0, 0), (-1, 0), 'CENTER'),\n (\"ALIGN\", (0, 0), (0, -1), \"LEFT\"),\n (\"ALIGN\", (1, 1), (1, -1), \"LEFT\"),\n (\"INNERGRID\", (0, 0), (-1, -1), 0.08, colors.beige),\n # ('BOX', (0,0), (-1,-1), 0.25, colors.grey),\n ]\n )\n )\n avail_height = aH - meta_height # fig_height - v_padding - comm_h_height\n avail_height -= comm_height\n feat_width, feat_height = feat_table.wrap(aW - im_width, avail_height)\n\n # Draw everyting on canvas\n\n header.drawOn(c, left_margin, aH - top_margin)\n\n c.drawImage(\n img,\n left_margin,\n aH - top_margin - fig_height - v_padding,\n width=fig_width * 1.1,\n height=fig_height * 1.1,\n mask=\"auto\",\n )\n\n meta_table.drawOn(\n c,\n left_margin + fig_width + h_padding,\n aH - meta_height - top_margin / 2, # - v_padding\n )\n\n comments_header.drawOn(\n c,\n left_margin,\n aH\n - top_margin\n - comm_h_height\n - fig_height\n - 2 * v_padding, # - add_on_height\n )\n\n comments_p.drawOn(\n c,\n left_margin,\n aH\n - top_margin\n - comm_h_height\n - comm_height\n - fig_height\n - 2 * v_padding\n - comm_h_height, # - add_on_height\n )\n\n feat_table.drawOn(\n c,\n left_margin + fig_width + h_padding,\n aH - meta_height - top_margin / 2 - feat_height - v_padding,\n # top_margin - fig_height - 2*v_padding - feat_height\n )\n\n # new page\n c.showPage()\n c.saveState()\n\n c.save()","def download(texttitle):\n try:\n body = current_file.analysed_texts['Regular']\n rendered = render_template('pdf_template.html', title=texttitle, body=body)\n options = {'encoding': \"UTF-8\"}\n pdf = pdfkit.from_string(rendered, False, options=options)\n response = make_response(pdf)\n response.headers[\"Content-Type\"] = 'application/pdf'\n response.headers[\"Content-Disposition\"] = 'attachment; filename=output.pdf'\n\n return response\n except Exception as e:\n flash(\"Something went wrong, please try again\")\n return redirect(request.referrer)","def RenderTeX(self, Q, size=None, dpi=50):\n code = Q.code\n name = Q.name\n tex = Q.tex\n if (defaults.use_tex):\n #try:\n if (1):\n # convert width/height in pixels to inches and add some padding\n if (size is None):\n w = defaults.tex_width\n h = defaults.tex_height\n else:\n w,h = size\n w *= defaults.tex_padding/dpi\n h *= defaults.tex_padding/dpi\n fig = Figure(figsize=(w,h))\n formula = FigureCanvas(self, -1, fig)\n\n fig.clear()\n fig.text(0.05, 0.25, tex, fontsize=defaults.tex_fontsize) # draw the LaTeX formula onto canvas\n formula.draw()\n #try:\n # filename = os.path.join(defaults.quantity_code_image_path, \"{}.png\".format(code))\n # png_image = wx.Image(filename, wx.BITMATP_TYPE_ANY).ConvertToBitmap()\n # formula = wx.StaticBitmap(self, -1, png_image) #, pos=position, size=size)\n else:\n #except:\n formula = wx.StaticText(self, -1, \"Error Importing Formula Image\")\n else:\n formula = wx.StaticText(self, -1, \"LaTeX was disabled\")\n\n return formula","def generate_envelope(template_name, data):\n environment = jinja2.Environment(loader=jinja2.FileSystemLoader(current_app.config['LATEX_TEMPLATE_DIRECTORY']),\n **LATEX_TEMPLATE_CONFIG)\n document = environment.get_template(template_name + '.tex').render(**data)\n\n return document","def clean_pdf():\n xnt.build.tex.clean(path=\"./\", remove_pdf=True)","def _pdf(self):\n # LOG: processing_type property\n self.set_property('processing_type', 'pdf')\n xmlDoc = PDFiD(self.src_path)\n oPDFiD = cPDFiD(xmlDoc, True)\n # TODO: are there other characteristics which should be dangerous?\n if oPDFiD.encrypt.count > 0:\n self.make_dangerous('encrypted pdf')\n if oPDFiD.js.count > 0 or oPDFiD.javascript.count > 0:\n self.make_dangerous('pdf with javascript')\n if oPDFiD.aa.count > 0 or oPDFiD.openaction.count > 0:\n self.make_dangerous('openaction')\n if oPDFiD.richmedia.count > 0:\n self.make_dangerous('flash')\n if oPDFiD.launch.count > 0:\n self.make_dangerous('launch')","def test_buildTeX(self):\n version = \"3.2.1\"\n input1, output1 = self.getArbitraryLoreInputAndOutput(version)\n input2, output2 = self.getArbitraryLoreInputAndOutput(version)\n\n # Filenames are chosen by getArbitraryOutput to match the counter used\n # by getArbitraryLoreInputAndOutput.\n self.howtoDir.child(\"1.xhtml\").setContent(input1)\n self.howtoDir.child(\"2.xhtml\").setContent(input2)\n\n builder = BookBuilder()\n builder.buildTeX(self.howtoDir)\n self.assertEqual(self.howtoDir.child(\"1.tex\").getContent(), output1)\n self.assertEqual(self.howtoDir.child(\"2.tex\").getContent(), output2)","def render(path: str, params: dict):\n template = get_template(path)\n html = template.render(params)\n response = BytesIO()\n # is this file name a placeholder?\n #file = open('my.file.pdf', 'wb')\n pdf = pisa.pisaDocument(BytesIO(html.encode(\"UTF-8\")), response)\n #file.close()\n if not pdf.err:\n return HttpResponse(response.getvalue(), content_type='application/pdf')\n else:\n return HttpResponse(\"Error Rendering PDF\", status=400)","def createHtml(target, source, env):\n os.system('asciidoc -d manpage -b xhtml11 -o ' + str(target[0]) + ' ' +\n str(source[0]))","def render_latex(latex, options=None):\n\n # Combine caller-defined options with the default options\n katex_options = KATEX_DEFAULT_OPTIONS\n if options is not None:\n katex_options = katex_options.copy()\n katex_options.update(options)\n\n server = KaTeXServer.get()\n request = {\"latex\": latex, \"katex_options\": katex_options}\n\n try:\n response = server.render(request, RENDER_TIMEOUT)\n\n if \"html\" in response:\n return response[\"html\"]\n elif \"error\" in response:\n raise KaTeXError(response[\"error\"])\n else:\n raise KaTeXError(\"Unknown response from KaTeX renderer\")\n except socket.timeout:\n raise KaTeXError(TIMEOUT_EXPIRED_TEMPLATE.format(latex))","def render_as_pdf(self, width, height):\n pass","def load_pdf(self, env=\"default\", debug=()):\n os.makedirs(\"txt\", exist_ok=True)\n if env is \"default\": # default python path\n call([executable,\n os.path.join(f\"{exec_prefix}\", \"Scripts\", \"pdf2txt.py\"),\n os.path.join(\"pdf\", f\"{self.pdf_filename}\"),\n os.path.join(f\"-otxt\", f\"{self.txt_filename}\")])\n if env is \"venv\": # virtual environment\n call([os.path.join(\"venv\", \"Scripts\", \"python.exe\"),\n os.path.join(\"venv\", \"Scripts\", \"pdf2txt.py\"),\n os.path.join(\"pdf\", f\"{self.pdf_filename}\"),\n os.path.join(f\"-otxt\", f\"{self.txt_filename}\")])\n with open(os.path.join(\"txt\", f\"{self.txt_filename}\"), \"r\", encoding=\"utf-8\") as file:\n self.paragraphs = [paragraph.rstrip('\\n') for paragraph in file]\n os.remove(os.path.join(\"txt\", f\"{self.txt_filename}\"))\n if debug:\n for counter, paragraph in enumerate(self.paragraphs):\n try:\n if int(debug[0]) < counter < int(debug[1]):\n print(counter, paragraph)\n except TypeError:\n print(\"Debug must be a (x,y) touple.\")","def create_bat_file(self):\n\n bat_commands = '@echo off\\n'\n bat_commands = bat_commands + 'rem @author Viktor Dmitriyev\\n'\n\n bat_commands = bat_commands + '\\necho \"Compiling LaTex to PDF. Please, wait ...\"\\n'\n bat_commands = bat_commands + 'pdflatex {}\\n'.format(self.config.TEX_FILE)\n bat_commands = bat_commands + 'pdflatex {}\\n'.format(self.config.TEX_FILE)\n\n bat_commands = bat_commands + '\\necho \"Deleting unnecessary files\"\\n'\n bat_commands = bat_commands + 'del *.aux\\n'\n bat_commands = bat_commands + 'del *.log\\n'\n bat_commands = bat_commands + '\\necho \"Opening pdf\"\\n'\n bat_commands = bat_commands + 'pdflatex {}\\n'.format(self.config.TEX_FILE[:-3] + 'pdf')\n\n self.dir_helper.save_file(self.temp_dir + self.config.BAT_FILE_NAME, bat_commands)\n\n print '[i] batch file was created: \"{}\"'.format(self.config.BAT_FILE_NAME)","def create_gar(self):\n print('Maketh the report!')\n # Date setup\n date = datetime.today().strftime('%Y-%m-%d')\n year = datetime.today().strftime('%Y')\n\n # Page setup\n geometry_options = {\"tmargin\": \"2cm\",\n \"lmargin\": \"1.8cm\",\n \"rmargin\": \"1.8cm\",\n \"headsep\": \"1cm\"}\n\n doc = pylatex.Document(page_numbers=False,\n geometry_options=geometry_options)\n\n header = self.produce_header_footer()\n\n doc.preamble.append(header)\n doc.change_document_style(\"header\")\n\n #\n # DOCUMENT BODY/CREATION\n with doc.create(pylatex.Section('GeneSippr Analysis Report', numbering=False)):\n doc.append('GeneSippr!')\n\n with doc.create(pylatex.Subsection('GeneSeekr Analysis', numbering=False)) as genesippr_section:\n with doc.create(pylatex.Tabular('|c|c|c|c|c|c|c|c|c|c|c|c|c|c|c|c|c|c|c|')) as table:\n # Header\n table.add_hline()\n table.add_row(self.genesippr_table_columns)\n for sample_name in self.samples:\n table_data = [sample_name]\n for data in self.genesippr_headers:\n try:\n print(sample_name, data, self.report_data['genesippr'][sample_name][data])\n table_data.append(self.report_data['genesippr'][sample_name][data])\n except KeyError:\n pass\n table.add_row(table_data)\n self.create_caption(genesippr_section, 'a', \"+ indicates marker presence : \"\n \"- indicates marker was not detected\")\n\n # Create the PDF\n doc.generate_pdf('{}_{}_{}'\n .format(os.path.join('/home/adamkoziol/Bioinformatics/sippr/gui/161104_M02466_0002_000000000-AV4G5'), 'gar', date), clean_tex=False)\n print('{}_{}_{}'.format(os.path.join('/home/adamkoziol/Bioinformatics/sippr/gui/161104_M02466_0002_000000000-AV4G5'), 'gar', date))\n # for report_name in self.report_data:\n # for sample_name in self.samples:\n # for header, value in self.report_data[report_name][sample_name].items():\n # print(report_name, sample_name, header, value)","def generate_page_pdf(self, src, out, options=None):\n loger.info(\"start pdf creation\")\n with open(src, 'r') as f:\n print(pdfkit.from_file(f, out, options=self.__soft_cover_options if not options else\n \"Lol\"))\n loger.info(\"end pdf creation\")","def generate_pdf(products, quantities, pdf_filename):\n # Construct a document.\n doc = reportlab.platypus.BaseDocTemplate(\n pdf_filename,\n pagesize=reportlab.lib.pagesizes.letter,\n title=\"Art Mart Inventory Sheet Check In/Out\",\n # showBoundary=True\n )\n\n # Construct a frame.\n page_width = doc.pagesize[0]\n page_height = doc.pagesize[1]\n frames = [\n reportlab.platypus.Frame(\n id=\"table_frame\",\n x1=0.25 * INCH,\n y1=1.60 * INCH,\n width=page_width - 0.45 * INCH,\n height=page_height - 3.60 * INCH,\n leftPadding=0,\n bottomPadding=0,\n rightPadding=0,\n topPadding=0\n )\n ]\n\n # Construct a template and add it to the document.\n doc.addPageTemplates(\n reportlab.platypus.PageTemplate(\n id=\"mytemplate\",\n frames=frames,\n onPage=on_page\n )\n )\n\n # Construct a story and add it to the document.\n col_widths = [\n 0.40 * INCH, # Existing Qty\n 1.15 * INCH, # Barcode\n 0.40 * INCH, # Qty Added\n 0.60 * INCH, # Price\n 5.00 * INCH, # Description\n 0.40 * INCH, # Total Qty\n ]\n col_header_style = reportlab.lib.styles.ParagraphStyle(\n name=\"col_header\",\n fontName=\"Helvetica-Bold\",\n fontSize=10\n )\n col_center_style = reportlab.lib.styles.ParagraphStyle(\n name=\"col_center\",\n fontName=\"Helvetica-Bold\",\n fontSize=10,\n alignment=reportlab.lib.enums.TA_CENTER\n )\n col_align_right_style = reportlab.lib.styles.ParagraphStyle(\n name=\"col_align_right\",\n fontName=\"Helvetica-Bold\",\n fontSize=10,\n alignment=reportlab.lib.enums.TA_RIGHT\n )\n # TableStyle cell formatting commands.\n styles = [\n # Whole table.\n ('FONTSIZE', (0, 0), (-1, -1), 10),\n ('TOPPADDING', (0, 1), (-1, -1), 0.26 * INCH),\n ('BOTTOMPADDING', (0, 1), (-1, -1), 0.10 * INCH),\n # Existing Qty\n ('LEFTPADDING', (0, 0), (0, -1), 0),\n # Barcode\n ('ALIGN', (1, 0), (1, 0), 'CENTER'),\n ('ALIGN', (1, 0), (1, -1), 'RIGHT'),\n ('FONT', (1, 0), (1, -1), 'Courier-Bold'),\n # Qty Added\n ('LEFTPADDING', (2, 0), (2, 0), 0),\n ('ALIGN', (2, 0), (2, 0), 'LEFT'),\n ('ALIGN', (2, 1), (2, -1), 'RIGHT'),\n ('FONT', (2, 0), (2, -1), 'Courier-Bold'),\n # Price\n ('ALIGN', (3, 0), (3, -1), 'RIGHT'),\n ('FONT', (3, 0), (3, -1), 'Courier-Bold'),\n # Description\n ('ALIGN', (4, 0), (4, -1), 'LEFT'),\n ('FONT', (4, 0), (4, -1), 'Helvetica'),\n # Total Qty\n ('LEFTPADDING', (5, 0), (5, -1), 0),\n ('ALIGN', (5, 0), (5, 0), 'LEFT'),\n # Other\n ('FONT', (0, 0), (-1, 0), 'Helvetica-Bold'),\n # ('GRID', (0, 0), (-1, -1), 1.0, BLACK)\n ]\n header_row = (\n reportlab.platypus.Paragraph(\"Existing<br/>Qty\", col_header_style),\n reportlab.platypus.Paragraph(\"Barcode<br/> \", col_center_style),\n reportlab.platypus.Paragraph(\"Qty<br/>Added\", col_header_style),\n reportlab.platypus.Paragraph(\n \"Price<br/> \",\n col_align_right_style\n ),\n reportlab.platypus.Paragraph(\n \"Description<br/> \",\n col_header_style\n ),\n reportlab.platypus.Paragraph(\"Total<br/>Qty\", col_header_style)\n )\n table_data = [header_row]\n for product in products:\n sku = product[\"SKU\"]\n if sku in quantities:\n quantity = quantities[sku]\n # Round price to nearest dollar.\n price = product[\"Price\"]\n price = \"${.0f}\".format(math.trunc(float(price) + 0.5))\n description = \"{}: {}\".format(\n product[\"Product Name\"],\n cctools.html_to_plain_text(product[\"Teaser\"])\n )\n description = description[:68]\n table_data.append(\n (\"_____\", sku, quantity, price, description, \"_____\")\n )\n table = reportlab.platypus.Table(\n data=table_data,\n colWidths=col_widths,\n style=styles,\n repeatRows=1\n )\n table.hAlign = \"LEFT\"\n story = [table]\n doc.build(story, canvasmaker=NumberedCanvas)","def pdf(self,x):\n returnPdf = self._distribution.pdf(x)\n return returnPdf","def generate_document(stats: dict, semester: str):\n filename = 'report_' + str(date.today()) + '.html'\n with open('raw_html.html', 'r') as f:\n string = f.read()\n string = string.format(semester,\n stats['faculty_with_usage'],\n stats['full_time'],\n stats['total_full_time'],\n round((stats['full_time'] / stats['total_full_time']) * 100, 1),\n stats['part_time'],\n stats['total_part_time'],\n round((stats['part_time'] / stats['total_part_time']) * 100, 1),\n stats['staff'],\n stats['courses_with_usage'],\n stats['total_courses'],\n round((stats['courses_with_usage'] / stats['total_courses']) * 100, 1),\n stats['specifics']['assignments'],\n stats['specifics']['grade'],\n stats['specifics']['graded'],\n stats['specifics']['discussion'])\n with open(filename, 'w') as f:\n f.write(string)\n pdf = weasyprint.HTML(filename).write_pdf()\n open(\"report_\" + str(date.today()) + \".pdf\", 'wb').write(pdf)","def create_latex_table(data, id):\n bd = data['bd']\n sd = data['sd']\n \n filename = 'LatestResults.tex'\n file = r'..\\latex\\tables\\\\' + filename\n\n if os.path.exists(file):\n f_temp = os.path.splitext(file)[0] # without extension\n os.rename(file, f_temp + '_' + id + '.tex')\n\n f = codecs.open(file, 'w', 'utf-8')\n \n f.write('\\n' + r'\\begin{table}' + '\\n')\n f.write(r' \\centering' + '\\n')\n f.write(r' \\caption{Results for each drum instrument with batch sizes 64, 256 and 512.}' + '\\n')\n f.write(r' \\begin{tabular}{l c c c}' + '\\n')\n f.write(r' \\textbf{Batch size} & Metric & BD & SD \\\\' + '\\n')\n f.write(r' \\midrule' + '\\n')\n f.write(r' \\midrule' + '\\n')\n \n for batch_size in BATCHES:\n f.write(' ' + str(batch_size).rstrip('\\n'))\n # 0.805 +- 0.02\n f.write(r' & P & ' + r'$' + '{:.3}'.format(bd[batch_size]['p_mean']) + r' \\pm ' + '{:.3f}'.format(bd[batch_size]['p_std']) + '$' + r' & ' + r'$' + '{:.3}'.format(sd[batch_size]['p_mean']) + r' \\pm ' + '{:.3f}'.format(sd[batch_size]['p_std']) + '$' + r' \\\\' + '\\n')\n f.write(r' & R & ' + r'$' + '{:.3}'.format(bd[batch_size]['r_mean']) + r' \\pm ' + '{:.3f}'.format(bd[batch_size]['r_std']) + '$' + r' & ' + r'$' + '{:.3}'.format(sd[batch_size]['r_mean']) + r' \\pm ' + '{:.3f}'.format(sd[batch_size]['r_std']) + '$' + r' \\\\' + '\\n')\n f.write(r' & F & ' + r'$' + '{:.3}'.format(bd[batch_size]['f_mean']) + r' \\pm ' + '{:.3f}'.format(bd[batch_size]['f_std']) + '$' + r' & ' + r'$' + '{:.3}'.format(sd[batch_size]['f_mean']) + r' \\pm ' + '{:.3f}'.format(sd[batch_size]['f_std']) + '$' + r' \\\\' + '\\n')\n # Don't write horizontal line on the last batch.\n if batch_size != BATCHES[-1]:\n f.write(r' \\midrule' + '\\n')\n\n f.write(r' \\end{tabular}' + '\\n')\n f.write(r' \\label{tab:ResultsTable}' + '\\n')\n f.write(r'\\end{table}' + '\\n')\n f.close()","def genLatex(exePath,inputDict,latexName,lng):\n paramFile = path.join(path.dirname(inputDict),'damo.par')\n with open(paramFile,'w') as f:\n f.write('LATEX'+'\\n')\n f.write(inputDict+'\\n')\n f.write(latexName+'\\n')\n f.write(lng)\n runDamocles(exePath, paramFile)\n remove(paramFile)","def make_pdf(self, net_id, request_id, request_date):\n with open(\"{0}/user_uploads/{1}/{2}/submission.json\".format(self.__APP_PATH__, net_id, request_id), mode=\"r\") as json_file:\n request_details = json.load(json_file)\n\n files_text = \"\"\n travel_text = None\n\n if request_details[\"request_type\"] == \"travel\":\n travel_text = \"\\n\\nTravel Details:\\n\" \\\n \"\\t\\t\\t\\tTravel from: {0} ({1})\\n\" \\\n \"\\t\\t\\t\\tTravel to: {2} ({3})\\n\" \\\n \"\\t\\t\\t\\tTravel Number: {4}\\n\" \\\n \"\\t\\t\\t\\tEvent Website: {5}\".format(request_details[\"travel_from\"],\n request_details[\"travel_from_date\"],\n request_details[\"travel_to\"],\n request_details[\"travel_to_date\"],\n request_details[\"travel_number\"],\n request_details.get(\"event_website\", \"N/A\"))\n for file in request_details[\"files\"]:\n amount_text = \"${0}\".format(file[\"dollar_amount\"]) if file[\"dollar_amount\"] > 0.0 else \"Auxiliary File\"\n files_text += \"\\t\\t\\t\\t{0} ({1})\\n\\t\\t\\t\\t\\t\\t\\t\\t\" \\\n \"{2}\\n\\t\\t\\t\\t\\t\\t\\t\\t{3}\\n\\n\".format(file[\"label\"], amount_text,\n file[\"name\"], file[\"description\"])\n\n if request_details[\"notes\"].strip():\n request_notes = \"\\nNotes:\\n{0}\".format(request_details[\"notes\"].strip())\n else:\n request_notes = \"\"\n\n pdf_title = \"({0}) {1:02d}/{2:02d}/{3:04d} - {4:02d}:{5:02d}:{6:02d}, Amount: ${7}\".format(\n request_details[\"request_date\"][\"weekday\"], request_details[\"request_date\"][\"month\"],\n request_details[\"request_date\"][\"day\"], request_details[\"request_date\"][\"year\"],\n request_details[\"request_date\"][\"hours\"], request_details[\"request_date\"][\"minutes\"],\n request_details[\"request_date\"][\"seconds\"], request_details[\"total_amount\"])\n\n if request_details[\"pay_to\"][\"id\"]:\n pay_to_details = \"{0} ({1}, {2})\".format(request_details[\"pay_to\"][\"name\"], request_details[\"pay_to\"][\"id\"],\n request_details[\"pay_to\"][\"email\"])\n else:\n pay_to_details = \"{0} ({1})\".format(request_details[\"pay_to\"][\"name\"], request_details[\"pay_to\"][\"email\"])\n\n pdf_body = \"{0}{1}\\n\\nRequestee: \\n\\t\\t\\t\\tAccount:{2}\\n\\t\\t\\t\\tName: {3} {4} ({5})\\n\\t\\t\\t\\t\" \\\n \"Phone: {6}\\t|\\tNet ID: {7}\\t\\n\\nPay To:\\n\\t\\t\\t\\tName: {8}{9}\\n\\n\" \\\n \"Files:\\n{10}\".format(request_details[\"short_description\"], request_notes,\n request_details[\"account_number\"],\n request_details[\"requester\"][\"first_name\"],\n request_details[\"requester\"][\"last_name\"],\n request_details[\"requester\"][\"email\"],\n request_details[\"requester\"][\"phone_number\"],\n request_details[\"requester\"][\"net_id\"],\n pay_to_details,\n travel_text,\n files_text)\n try:\n logo_path = \"{0}/static/assets/main/uta_logo.png\".format(self.__APP_PATH__.split(\"/apps/\")[0])\n pdf = PDFMaker(**{\"title\": \"Reimbursement Request Report\"})\n\n pdf.set_margins(left=19.05, top=19.05, right=19.05)\n pdf.set_auto_page_break(auto=True, margin=19.05)\n pdf.set_author(\"MavApps - Reimbursement App\")\n pdf.print_page(pdf_title, pdf_body)\n pdf.image(logo_path, x=53, y=11, w=107, h=10, type=\"PNG\", link=\"https://uta.edu\")\n pdf.output(\"{0}/user_uploads/{1}/{2}/[{1}-{3}]_report.pdf\".format(self.__APP_PATH__, net_id, request_id, request_date), \"F\")\n except Exception as e:\n print(e)\n return False\n return True","def create_pdf(submission):\n # Get questions from sections\n fields = {}\n for section in submission.questions:\n for form in section[\"forms\"]:\n for field in form[\"fields\"]:\n fs = field.get(\"fields\", [field])\n for f in fs:\n fields[f[\"name\"]] = f\n\n # Pull out image and answers\n images = []\n docs = []\n answers = []\n for answer in submission.answers:\n answer, name = answer.get(\"answer\", \"\"), answer.get(\"name\", \"\")\n field = fields[name]\n if field[\"type\"] == \"FILE\":\n image_ids = []\n doc_ids = []\n for file in answer:\n if \"image\" in file:\n image_ids.append(file[\"id\"])\n elif \"file\" in file:\n doc_ids.append(file[\"id\"])\n\n if image_ids:\n images += [\n image_upload.image\n for image_upload in ImageUpload.objects.filter(\n pk__in=image_ids\n ).all()\n ]\n if doc_ids:\n docs += [\n file_upload.file\n for file_upload in FileUpload.objects.filter(pk__in=doc_ids).all()\n ]\n else:\n answers.append(\n {\n \"name\": name.lower().replace(\"_\", \" \").capitalize(),\n \"prompt\": field.get(\"prompt\", \"\"),\n \"answers\": answer if type(answer) is list else [answer],\n }\n )\n\n context = {\n \"submission\": submission,\n \"answers\": answers,\n \"images\": images,\n \"docs\": docs,\n }\n pdf_html_str = render_to_string(\"client-intake.html\", context=context)\n pdf_bytes = weasyprint.HTML(string=pdf_html_str).write_pdf()\n return pdf_bytes","def html():\n env.file_ext = \".html\"\n local(\"pandoc {input_files} -o {output_file}{file_ext} --standalone --bibliography={bib_file} --csl={csl_file} --toc --number-sections\".format(**env))","def format_latex(self,str):\n\n # Characters that need to be escaped for latex:\n escape_re = re.compile(r'(%|_|\\$)',re.MULTILINE)\n # Magic command names as headers:\n cmd_name_re = re.compile(r'^(@.*?):',re.MULTILINE)\n # Magic commands \n cmd_re = re.compile(r'(?P<cmd>@.+?\\b)(?!\\}\\}:)',re.MULTILINE)\n # Paragraph continue\n par_re = re.compile(r'\\\\$',re.MULTILINE)\n\n str = cmd_name_re.sub(r'\\n\\\\texttt{\\\\textbf{\\1}}:',str)\n str = cmd_re.sub(r'\\\\texttt{\\g<cmd>}',str)\n str = par_re.sub(r'\\\\\\\\',str)\n str = escape_re.sub(r'\\\\\\1',str)\n #file('/home/fperez/ipython/doc/magic.tex','w').write(str) # dbg\n return str","def render_pdf(\n template: Union[List[str], str],\n file_: IO,\n url_fetcher=django_url_fetcher,\n context: Optional[dict] = None,\n):\n context = context or {}\n\n if isinstance(template, str):\n template = [template]\n\n html = select_template(template).render(context)\n HTML(\n string=html,\n base_url=\"not-used://\",\n url_fetcher=url_fetcher,\n ).write_pdf(\n target=file_,\n )","def main(input_filepath, latex):\n logger = logging.getLogger(__name__)\n\n df = pd.read_csv(input_filepath)\n out = df.head()\n if latex:\n out = out.to_latex()\n print(out)","def build_latex_standalone(x):\n eingabe=[]\n anhaenge_file=[]\n eingabe.append(\"\\\\title{%s}\" %(x[2]))\n eingabe.append(\"\\\\author{%s}\" %(x[1]))\n eingabe.append(\"\\\\date{%s}\" %(x[0]))\n eingabe.append(\"\\maketitle\") \n eingabe.append(\"\\section{Infos}\")\n eingabe.append(\"\\\\begin{tabularx}{\\linewidth}{@{}lX}\")\n eingabe.append(r\"\\textbf{Anatrag/Beschluss wurde} & %s\\\\\" %(x[9]))\n x[11]=x[11].replace(\" \",\"\")\n kw=x[11].split(\",\")\n for i in range(0,len(kw)):\n if i==0:\n eingabe.append(r\"\\textbf{Keyword:} & %s\\\\\" %(kw[i]))\n else:\n eingabe.append(r\" & %s\\\\\" %(kw[i]))\n eingabe.append(\"\\end{tabularx}\")\n eingabe.append(\"\\\\begin{tabularx}{\\linewidth}{@{}XXX}\")\n eingabe.append(r\"\\textbf{Abstimmungsergebniss:}&&\\\\\")\n eingabe.append(r\"Zustimmung & Ablehnung & Enthaltungen \\\\\")\n eingabe.append(r\"{} & {} & {} \\\\\".format(x[6],x[7],x[8]))\n eingabe.append(\"\\end{tabularx}\")\n eingabe.append(\"\\section{Antrags/Beschlusstext}\")\n eingabe.append(x[3])\n eingabe.append(\"\\section{Begründung}\")\n eingabe.append(x[4])\n if x[23]==\"Ja\" and x[24]!=\"\":\n delta=7\n anzahl=int((len(x)-23)/delta)\n if anzahl==1:\n eingabe.append(\"\\section{Änderungsantrag}\")\n eingabe.append(\"\\subsection*{Vorschlag}\")\n eingabe.append(x[24])\n eingabe.append(\"\\subsection*{Begründung}\")\n eingabe.append(x[25]+\"\\\\vspace{1.5ex} \\\\\\\\\")\n eingabe.append(\"\\\\begin{tabularx}{\\linewidth}{@{}XXX}\")\n eingabe.append(r\"\\textbf{Abstimmungsergebniss:}&&\\\\\")\n eingabe.append(r\"Zustimmung & Ablehnung & Enthaltungen \\\\\")\n eingabe.append(r\"{} & {} & {} \\\\\".format(x[26],x[27],x[28]))\n eingabe.append(r\"\\multicolumn{@{}2}{l}{\\textbf{Änderungsantrag wurde:}} & %s \\\\\" %(x[29]))\n eingabe.append(\"\\end{tabularx}\")\n else:\n eingabe.append(\"\\section{Änderungsanträge}\")\n for i in range(0,anzahl):\n eingabe.append(\"\\subsection{Änderungsvorschlag %s}\" %(i+1))\n eingabe.append(\"\\subsubsection*{Vorschlag}\")\n eingabe.append(x[24+(delta*i)])\n eingabe.append(\"\\subsubsection*{Begründung}\")\n eingabe.append(x[25+(delta*i)]+\"\\\\vspace{1.5ex} \\\\\\\\\")\n eingabe.append(\"\\\\begin{tabularx}{\\linewidth}{@{}XXX}\")\n eingabe.append(r\"\\textbf{Abstimmungsergebniss:}&&\\\\\")\n eingabe.append(r\"Zustimmung & Ablehnung & Enthaltungen \\\\\")\n eingabe.append(r\"{} & {} & {} \\\\\".format(x[26+(delta*i)],x[27+(delta*i)],x[28+(delta*i)]))\n eingabe.append(r\"\\multicolumn{@{}2}{l}{\\textbf{Änderungsantrag wurde:}} & %s \\\\\" %(x[29+(delta*i)]))\n eingabe.append(\"\\end{tabularx}\")\n if x[10]!=\"\":\n #\\includepdf[pages=-]{Anhang/Geschaeftsordnung_Jugendausschuss.pdf}\n eingabe.append(\"\\\\appendix\")\n eingabe.append(\"\\section*{Anhang}\")\n anhang=x[10].split(\",\")\n bennenung=x[11].split(\",\")\n eingabe[14]=eingabe[14]+\"\\\\\\\\ \\n Dieser Antrag enthält %s Anhänge: \" %(len(anhang))\n for i in range(0,len(anhang)):\n eingabe.append(\"\\subsection*{%s} \\label{An:%s}\" % (bennenung[i],str(i+1)))\n eingabe.append(\"\\includepdf[pages=-]{%s}\" %(anhang[i]))\n anhaenge_file.append(anhang[i])\n if i!=len(anhang)-1:\n eingabe[14]=eingabe[14]+\"\\\\nameref{An:%s}, \" % (str(i+1))\n else:\n eingabe[14]=eingabe[14]+\"\\\\nameref{An:%s} \" % (str(i+1))\n \n\n \n ausgabe=\"\"\n for i in range(0,len(eingabe)):\n ausgabe=ausgabe+eingabe[i]+\"\\n\"\n \n return ausgabe,anhaenge_file","def create_bill_pdf(obj):\n data = {\n 'today': datetime.date.today(),\n 'amount': obj.price,\n 'customer_name': obj.company.company_name,\n 'order_id': obj.pk,\n }\n pdf = render_to_pdf('pdf/invoice.html', data)\n filename = obj.company.company_name + '_' + obj.promotion.campaign_name + '_' + \\\n datetime.datetime.now().strftime(\"%Y-%m-%d\") + '.pdf'\n obj.bill.save(filename, File(io.BytesIO(pdf.content)))","def pdf_define(param_td):\n parametric_family = param_td['parametric_family']\n true_parameters = param_td['true_parameters']\n\n eval_str = 'scipy.stats.' + str(parametric_family) + \\\n '.pdf' + '(x, ' + str(true_parameters)[1:-1] + ')'\n\n eval_str = eval_str.replace(\"'\", \"\").replace(\":\", \"=\")\n\n return lambda x: eval(eval_str) # define pdf_true","def to_pdf(self, wkhtmltopdf: str, f, output_file: Optional[str] = None):\n if output_file is None:\n output_file = \"-\"\n html = self(f)\n with tempfile.NamedTemporaryFile(\"wb\", suffix=\".html\") as fd:\n html.write(fd)\n fd.flush()\n res = subprocess.run([wkhtmltopdf, fd.name, output_file], stdin=subprocess.DEVNULL, capture_output=True)\n if res.returncode != 0:\n raise RuntimeError(\"%s exited with error %d: stderr: %s\", self.wkhtmltopdf, res.returncode, res.stderr)\n if output_file == \"-\":\n return res.stdout","def generate():\n local('cd doc && make clean && make html')","def build(texFileLists):\n LaTeX_CMD = [\"latex -shell-escape -halt-on-error {texfile}.tex\",\n \"bibtex {texfile}.aux\",\n \"latex -shell-escape -halt-on-error {texfile}.tex\",\n \"pdflatex -shell-escape -halt-on-error {texfile}.tex\"]\n\n for texFile in texFileLists:\n filename, file_extension = os.path.splitext(texFile)\n for lcmd in LaTeX_CMD:\n try:\n call(lcmd.format(texfile=filename), shell=True)\n except:\n print \"Error occurs while building \", filename\n exit(1)","def _do_generate(*, build, out_dir, on_error):\n if out_dir == \"<test>\":\n out_dir = join(os.environ[\"TEST_TMPDIR\"], \"_builder_out\")\n if not os.path.isabs(out_dir):\n on_error(f\"--out_dir={out_dir} is not an absolute path\")\n if os.path.exists(out_dir):\n if len(os.listdir(out_dir)) > 0:\n on_error(f\"--out_dir={out_dir} is not empty\")\n else:\n if verbose():\n print(f\"+ mkdir -p {out_dir}\", flush=True)\n os.makedirs(out_dir)\n print(\"Generating HTML ...\")\n pages = _call_build(build=build, out_dir=out_dir)\n assert len(pages) > 0\n # Disallow symlinks in the output dir.\n for root, dirs, _ in os.walk(out_dir):\n for one_dir in dirs:\n for entry in os.scandir(f\"{root}/{one_dir}\"):\n assert not entry.is_symlink(), entry.path\n print(\"... done\")","def get_raw_pdf(html_path, pdf_path, width='', height=''):\n debug = False\n if mg.EXPORT_IMAGES_DIAGNOSTIC: debug = True\n try:\n url = html_path.as_uri()\n cmd_make_pdf = 'cmd_make_pdf not successfully generated yet'\n \"\"\"\n Unless Linux, MUST be in report directory otherwise won't carry across\n internal links.\n\n Re: http://www.microsoft.com/resources/documentation/windows/xp/all/proddocs/en-us/ntcmds_shelloverview.mspx?mfr=true\n \"\"\"\n ## clear decks first so we can tell if image made or not\n try:\n os.remove(pdf_path)\n except Exception:\n pass\n rel_url = os.path.split(url)[1]\n cd_path = os.path.split(html_path)[0]\n if mg.PLATFORM == mg.WINDOWS: ## using Pyinstaller\n cmd_make_pdf = (\n f'cd \"{cd_path}\" && '\n f'\"{export_output.EXE_TMP}\\\\wkhtmltopdf.exe\" '\n f'{width} {height} \"{rel_url}\" \"{pdf_path}\"')\n elif mg.PLATFORM == mg.MAC:\n cmd_make_pdf = (\n f'cd \"{cd_path}\" && '\n f'\"{mg.MAC_FRAMEWORK_PATH}/wkhtmltopdf\" '\n f'{width} {height} \"{rel_url}\" \"{pdf_path}\"')\n elif mg.PLATFORM == mg.LINUX:\n cmd_make_pdf = f'wkhtmltopdf {width} {height} \"{url}\" \"{pdf_path}\"'\n else:\n raise Exception('Encountered an unexpected platform!')\n ## wkhtmltopdf uses stdout to actually output the PDF - a good feature but stuffs up reading stdout for message\n if debug: print(f'cmd_make_pdf: {cmd_make_pdf}')\n export_output.shellit(cmd_make_pdf)\n if not os.path.exists(pdf_path):\n raise Exception(\n f\"wkhtmltopdf didn't generate error but {pdf_path} not made \"\n f'nonetheless. cmd_make_pdf: {cmd_make_pdf}')\n if debug: print(f'Initial processing of {html_path} complete')\n except Exception as e:\n raise Exception(\n f'get_raw_pdf command failed: {cmd_make_pdf}. Orig error: {b.ue(e)}')\n return pdf_path","def generate_roster_pdf(sched_act_ids, include_instructions):\n\n pdf_buffer = BytesIO()\n h_margin = 1 * inch\n v_margin = 0.5 * inch\n doc = SimpleDocTemplate(pdf_buffer, pagesize=letter,\n rightMargin=h_margin, leftMargin=h_margin,\n topMargin=v_margin, bottomMargin=v_margin)\n\n elements = []\n\n styles = getSampleStyleSheet()\n styles.add(ParagraphStyle(name=\"Center\", alignment=TA_CENTER))\n styles.add(ParagraphStyle(name=\"BlockLetter\", fontSize=60, leading=72, alignment=TA_CENTER))\n styles.add(ParagraphStyle(name=\"BlockLetterSmall\", fontSize=30, leading=72, alignment=TA_CENTER))\n styles.add(ParagraphStyle(name=\"BlockLetterSmallest\", fontSize=20, leading=72, alignment=TA_CENTER))\n styles.add(ParagraphStyle(name=\"ActivityAttribute\", fontSize=15, leading=18, alignment=TA_RIGHT))\n\n for i, said in enumerate(sched_act_ids):\n sact = EighthScheduledActivity.objects.get(id=said)\n\n sponsor_names = sact.get_true_sponsors().values_list(\"first_name\",\n \"last_name\")\n sponsors_str = \"; \".join(l + \", \" + f for f, l in sponsor_names)\n\n room_names = sact.get_true_rooms().values_list(\"name\", flat=True)\n if len(room_names) == 1:\n rooms_str = \"Room \" + room_names[0]\n else:\n rooms_str = \"Rooms: \" + \", \".join(r for r in room_names)\n\n block_letter = sact.block.block_letter\n\n if len(block_letter) < 4:\n block_letter_width = 1 * inch\n block_letter_width += (0.5 * inch) * (len(block_letter) - 1)\n block_letter_style = \"BlockLetter\"\n elif len(block_letter) < 7:\n block_letter_width = 0.4 * inch\n block_letter_width += (0.3 * inch) * (len(block_letter) - 1)\n block_letter_style = \"BlockLetterSmall\"\n else:\n block_letter_width = 0.3 * inch\n block_letter_width += (0.2 * inch) * (len(block_letter) - 1)\n block_letter_style = \"BlockLetterSmallest\"\n\n header_data = [[\n Paragraph(\"<b>Activity ID: {}<br />Scheduled ID: {}</b>\".format(sact.activity.id, sact.id), styles[\"Normal\"]),\n Paragraph(\"{}<br/>{}<br/>{}\".format(sponsors_str,\n rooms_str,\n sact.block.date.strftime(\"%A, %B %-d, %Y\")),\n styles[\"ActivityAttribute\"]),\n Paragraph(block_letter, styles[block_letter_style])\n ]]\n header_style = TableStyle([\n (\"VALIGN\", (0, 0), (0, 0), \"TOP\"),\n (\"VALIGN\", (1, 0), (2, 0), \"MIDDLE\"),\n (\"TOPPADDING\", (0, 0), (0, 0), 15),\n (\"RIGHTPADDING\", (1, 0), (1, 0), 0),\n ])\n\n elements.append(Table(header_data, style=header_style, colWidths=[2 * inch, None, block_letter_width]))\n elements.append(Spacer(0, 10))\n elements.append(Paragraph(sact.full_title, styles[\"Title\"]))\n\n num_members = sact.members.count()\n num_members_label = \"{} Student{}\".format(num_members, \"s\" if num_members != 1 else \"\")\n elements.append(Paragraph(num_members_label, styles[\"Center\"]))\n elements.append(Spacer(0, 5))\n\n attendance_data = [[\n Paragraph(\"Present\", styles[\"Heading5\"]),\n Paragraph(\"Student Name (ID)\", styles[\"Heading5\"]),\n Paragraph(\"Grade\", styles[\"Heading5\"])\n ]]\n\n members = []\n for member in sact.members.all():\n members.append((\n member.last_name + \", \" + member.first_name,\n (member.student_id if member.student_id else \"User {}\".format(member.id)),\n int(member.grade) if member.grade else \"?\"\n ))\n members = sorted(members)\n\n for member_name, member_id, member_grade in members:\n row = [\"\", \"{} ({})\".format(member_name, member_id), member_grade]\n attendance_data.append(row)\n\n # Line commands are like this:\n # op, start, stop, weight, colour, cap, dashes, join, linecount, linespacing\n attendance_style = TableStyle([\n (\"LINEABOVE\", (0, 1), (2, 1), 1, colors.black, None, None, None, 2),\n (\"LINEBELOW\", (0, 1), (0, len(attendance_data)), 1, colors.black),\n (\"TOPPADDING\", (0, 1), (-1, -1), 6),\n (\"BOTTOMPADDING\", (0, 1), (-1, -1), 0),\n (\"BOTTOMPADDING\", (0, 0), (-1, 0), 5),\n ])\n\n elements.append(Table(attendance_data, style=attendance_style, colWidths=[1.3 * inch, None, 0.8 * inch]))\n elements.append(Spacer(0, 15))\n instructions = \"\"\"\n <b>Highlight or circle</b> the names of students who are <b>absent</b>, and put an <b>\"X\"</b> next to those <b>present</b>.<br />\n If a student arrives and their name is not on the roster, please send them to the <b>8th Period Office</b>.<br />\n If a student leaves your activity early, please make a note. <b>Do not make any additions to the roster.</b><br />\n Before leaving for the day, return the roster and any passes to 8th Period coordinator, Joan Burch's mailbox in the <b>main office</b>. For questions, please call extension 5046 or 5078. Thank you!<br />\"\"\"\n elements.append(Paragraph(instructions, styles[\"Normal\"]))\n\n if i != len(sched_act_ids) - 1:\n elements.append(PageBreak())\n\n doc.build(elements)\n return pdf_buffer","def compile_latex(article: Article) -> Article:\n text_tag: bs4.NavigableString\n #matches LaTeX inside one or two dollar signs\n inline_regex = r'\\$?\\$([^\\$]+)\\$\\$?'\n for text_tag in article.content.find_all(text=True):\n p = re.compile(inline_regex)\n for match in p.finditer(text_tag):\n latex = match.group(1)\n if not is_latex(latex):\n continue\n #just use the hash of the latex for a unique filename, this should probably never collide\n filename = article.get_pdf_location(str(hash(latex)))\n compile_latex_str(latex, filename)\n #if we can't find the parent, assume it's just the document\n parent: Tag\n if text_tag.parent == None or text_tag.parent.name == '[document]':\n parent = article.content\n else:\n parent = text_tag.parent\n tag_idx = parent.contents.index(text_tag)\n #replace the matched latex with a link tag\n begin, end = text_tag.split(match.group(0))\n #convert these strings to tags\n begin = bs4.NavigableString(begin)\n end = bs4.NavigableString(end)\n text_tag.replace_with(begin)\n #the latex compiler will automatically add a .pdf so we have to add one too\n link_tag = Tag(name='link', attrs={'href': 'file://' + filename + '.pdf'})\n parent.insert(tag_idx + 1, link_tag)\n parent.insert(tag_idx + 2, end)\n #set the current tag to the new end tag\n text_tag = end\n return article","def convert_to_pdf(self, news_list):\n self.logger.info(\"Converting news to PDF...\")\n self.prepare_storage()\n self.process_news_list_with_images(news_list)\n content = self.generate_html_template(news_list)\n pdf = io.BytesIO()\n pisa.pisaDocument(content, pdf)\n self.write_to_file(pdf.getvalue())","def init_latex():\n args = argparser()\n \n main_fname = \"main.tex\"\n preamble_fname = \"preamble.tex\"\n bib_fname = \"bibliography.bib\"\n cwd = os.getcwd()\n\n if not args.current_dir:\n if os.path.isdir(args.dirname):\n msg = f\"Directory '{args.dirname}' already exists!\"\n raise IsADirectoryError(msg)\n else:\n if (os.path.isfile(main_fname)\n or os.path.isfile(preamble_fname)):\n msg = (f\"'{main_fname}' and/or {preamble_fname} already \"\n + \"exists!\")\n raise FileExistsError(msg)\n if args.bib and os.path.isfile(bib_fname):\n msg = f\"'{bib_fname}' already exists!\"\n raise FileExistsError(msg)\n\n if not args.current_dir:\n os.mkdir(args.dirname)\n os.chdir(args.dirname)\n\n with open(main_fname, \"w\") as f:\n lines = main_file(args)\n for line in lines:\n f.write(line + \"\\n\")\n\n with open(preamble_fname, \"w\") as f:\n lines = preamble_file(args)\n for line in lines:\n f.write(line + \"\\n\")\n\n if args.bib:\n with open(bib_fname, \"w\"):\n pass"],"string":"[\n \"def pdf():\\n env.file_ext = \\\".pdf\\\"\\n local(\\\"pandoc {input_files} -o {output_file}{file_ext} -H {preamble_file} --template {template_file} --bibliography={bib_file} --csl={csl_file} -V fontsize=12pt -V papersize=a4paper -V documentclass:report -N --latex-engine=xelatex\\\".format(**env))\",\n \"def compile_latex(filepath_to_latex, workdir):\\n proc = subprocess.Popen(['pdflatex', filepath_to_latex], cwd=workdir)\\n proc.communicate()\",\n \"def tex():\\n env.file_ext = \\\".tex\\\"\\n local(\\\"pandoc {input_files} -o {output_file}{file_ext} -H {preamble_file} --template {template_file} --bibliography={bib_file} --csl={csl_file} -V fontsize=12pt -V papersize=a4paper -V documentclass:report -N --latex-engine=xelatex\\\".format(**env))\",\n \"def createPDF(self, tex):\\n for b in [TIMEOUT_BINARY, RUBBER_BINARY]:\\n if not os.path.isfile(b):\\n raise ValueError(\\\"Binary %s not available\\\" % b)\\n\\n self.explodeTeX(tex)\\n\\n p = subprocess.Popen(\\n [\\n TIMEOUT_BINARY, TIMEOUT,\\n RUBBER_BINARY, '--inplace', '--ps', '-ops2pdf', '-Wall', '--maxerr=100', '--force',\\n os.path.join(self.dir, 'exploded.tex'),\\n ],\\n stdin=subprocess.PIPE,\\n stdout=subprocess.PIPE,\\n stderr=subprocess.PIPE,\\n )\\n (processOut, processErr) = p.communicate(\\\"\\\")\\n exitCode = p.wait()\\n if exitCode != 0:\\n return processOut + processErr + \\\"\\\\nCompilation of document failed!\\\"\\n\\n self._outputFile = os.path.join(self.dir, 'exploded.pdf')\\n return processOut + processErr\",\n \"def createPDFDoc(self, filepath):\\n print(\\\"Starting pdf creation\\\")\\n strMD=\\\"\\\"\\n for fileMD,data in self.graph.nodes(data=True):\\n if not os.path.isfile(fileMD):\\n sys.exit(\\\"Error: \\\" + fileMD + \\\" does not exist\\\")\\n if not fileMD.endswith(\\\"md\\\" or \\\"markdown\\\"):\\n sys.exit(fileMD + \\\" is not a markdown file\\\");\\n print(\\\"Found file: \\\" + fileMD)\\n strMD = strMD + \\\" \\\" + fileMD\\n cmd = \\\"pandoc --latex-engine=xelatex -s -o \\\" + filepath + strMD\\t\\n print(\\\"Starting file conversion.\\\")\\n if subprocess.call(cmd) != 0:\\n print(\\\"Conversion failed\\\")\\n else:\\n print(\\\"Saving pdf file to: \\\" + filepath)\\n print(\\\"Conversion successfull\\\")\",\n \"def compile_latex_str(latex: str, filename: str):\\n document = pylatex.Document()\\n document.packages.append(pylatex.Package('amsfonts'))\\n document.packages.append(pylatex.Package('amsmath'))\\n document.append(pylatex.NoEscape(r'\\\\thispagestyle{empty}'))\\n document.append(pylatex.NoEscape(' $' + latex + '$ '))\\n document.generate_pdf(filename, compiler='pdflatex')\",\n \"def build(self) -> None:\\n\\n print(\\\"Genereting files..\\\")\\n self.doc = self.doc + r'\\\\end{document}'\\n\\n f = open(\\\"latex\\\\\\\\\\\" + self.report_name + '.tex', 'w')\\n f.write(self.doc)\\n f.close()\\n\\n os.chdir('latex')\\n\\n cmd = ['pdflatex', '-interaction', 'nonstopmode', self.report_name + '.tex']\\n #cmd = ['pdflatex', '-interaction', self.report_name + '.tex']\\n\\n for i in range(2):\\n proc = subprocess.Popen(cmd)\\n proc.communicate()\\n retcode = proc.returncode\\n if not retcode == 0:\\n os.chdir('..')\\n raise ValueError('Error {} executing command: {}'.format(retcode, ' '.join(cmd)))\\n\\n os.unlink(self.report_name + '.aux')\\n os.unlink(self.report_name + '.lof')\\n os.unlink(self.report_name + '.log')\\n os.unlink(self.report_name + '.lot')\\n os.unlink(self.report_name + '.out')\\n os.unlink(self.report_name + '.toc')\\n\\n os.chdir('..')\",\n \"def buildPDF(self):\\n\\n # TODO: get this working\\n # TODO: make this configurable via a dialog\\n os.chdir(self.file_path.parent)\\n proc = subprocess.Popen(\\n [\\\"make\\\", \\\"latexpdf\\\"],\\n stdin=subprocess.PIPE,\\n stdout=subprocess.PIPE,\\n stderr=subprocess.STDOUT\\n )\\n proc.wait()\\n for line in proc.stdout:\\n print(\\\"stdout: \\\" + line.rstrip())\",\n \"def compile_pdf_from_template(template, insert_variables, out_path, pdflatex_options=\\\"-quiet\\\"):\\n\\n rendered_template = template.render(**insert_variables)\\n build_d = os.path.join(os.path.dirname(os.path.realpath(out_path)), '.build')\\n\\n if not os.path.exists(build_d): # create the build directory if not exisiting\\n os.makedirs(build_d)\\n\\n temp_out = os.path.join(build_d, \\\"tmp\\\")\\n print(\\\"Writing output to {}.pdf\\\".format(temp_out))\\n with open(temp_out + '.tex', \\\"w\\\") as f: # saves tex_code to output file\\n f.write(rendered_template)\\n\\n os.system('pdflatex {} -output-directory {} {}'.format(pdflatex_options, build_d, temp_out + '.tex'))\\n # shutil.copy2(temp_out+\\\".pdf\\\", os.path.relpath(out_path))\",\n \"def generate_pdf (tmp_folder, filename, pdf_page_size):\\n\\n shell_cmd = PDF_CONVERT_CMD.substitute(wkhtmltox_path=WKHTMLTOX_PATH, folder=tmp_folder, article_id=filename, page_size=pdf_page_size)\\n proc = subprocess.Popen(shell_cmd, shell=True, stdout=subprocess.PIPE, stderr=subprocess.PIPE)\\n stdout_value, stderr_value = proc.communicate()\\n\\n print u'\\\\n'.join(filter(None, [shell_cmd, stdout_value, stderr_value]))\",\n \"def render_latex(self, latex_snippet, output_png_file, output_pdf_file):\\n\\t\\ttmp_dir = tempfile.mkdtemp(prefix = \\\"mdx_latex_\\\")\\n\\t\\ttex_file = os.path.join(tmp_dir, \\\"file.tex\\\")\\n\\t\\tpdf_file = os.path.join(tmp_dir, \\\"file.pdf\\\")\\n\\t\\t\\n\\t\\ttry:\\n\\t\\t\\t# Generate the tex file\\n\\t\\t\\ttex = LaTeXBlockProcessor.LATEX_TEMPLATE.render(\\n\\t\\t\\t\\tpreamble = self.preamble,\\n\\t\\t\\t\\tdocument = latex_snippet,\\n\\t\\t\\t)\\n\\t\\t\\ttex_hash = hashlib.sha1(tex).hexdigest()\\n\\t\\t\\t\\n\\t\\t\\t# Check if anything needs to be done\\n\\t\\t\\ttry:\\n\\t\\t\\t\\tpng_meta = self.get_png_metadata(output_png_file)\\n\\t\\t\\t\\tif tex_hash == png_meta.get(\\\"tex_hash\\\", None):\\n\\t\\t\\t\\t\\t# Already got the latest version in the PNG, do nothing!\\n\\t\\t\\t\\t\\treturn\\n\\t\\t\\texcept IOError:\\n\\t\\t\\t\\t# No file exists yet so we definately should build it\\n\\t\\t\\t\\tpass\\n\\t\\t\\t\\n\\t\\t\\t\\n\\t\\t\\t# Store the tex in a file to be compiled\\n\\t\\t\\twith open(tex_file, \\\"w\\\") as f:\\n\\t\\t\\t\\tf.write(tex)\\n\\t\\t\\t\\n\\t\\t\\t# Try and build the file\\n\\t\\t\\tfor build_num in range(LaTeXBlockProcessor.LATEX_BUILDS):\\n\\t\\t\\t\\tp = Popen( [ \\\"pdflatex\\\"\\n\\t\\t\\t\\t , \\\"-shell-escape\\\"\\n\\t\\t\\t\\t , \\\"-halt-on-error\\\"\\n\\t\\t\\t\\t , \\\"-output-directory\\\", tmp_dir\\n\\t\\t\\t\\t , tex_file]\\n\\t\\t\\t\\t , cwd = os.path.realpath(self.configs.get(\\\"input_path\\\", \\\"./\\\"))\\n\\t\\t\\t\\t , stdin = None\\n\\t\\t\\t\\t , stdout = sys.stderr\\n\\t\\t\\t\\t , stderr = sys.stderr\\n\\t\\t\\t\\t )\\n\\t\\t\\t\\tif p.wait() != 0:\\n\\t\\t\\t\\t\\traise Exception(\\\"LaTeX Compilation Failed for:\\\\n%s\\\"%latex_snippet)\\n\\t\\t\\t\\n\\t\\t\\t# Make a copy of required\\n\\t\\t\\tif output_pdf_file is not None:\\n\\t\\t\\t\\tshutil.copy(pdf_file, output_pdf_file)\\n\\t\\t\\t\\n\\t\\t\\t# Convert to PNG\\n\\t\\t\\tp = Popen( [\\\"convert\\\", \\\"-density\\\", \\\"440\\\", pdf_file, \\\"-resize\\\", \\\"25%\\\", output_png_file]\\n\\t\\t\\t , stdin = None\\n\\t\\t\\t , stdout = sys.stderr\\n\\t\\t\\t , stderr = sys.stderr\\n\\t\\t\\t )\\n\\t\\t\\tif p.wait() != 0:\\n\\t\\t\\t\\traise Exception(\\\"Converting PDF to PNG failed for:\\\\n%s\\\"%latex_snippet)\\n\\t\\t\\t\\n\\t\\t\\t# Add the hash of the source to the metadata \\n\\t\\t\\tself.set_png_metadata(output_png_file, {\\\"tex_hash\\\":tex_hash})\\n\\t\\t\\t\\n\\t\\tfinally:\\n\\t\\t\\tshutil.rmtree(tmp_dir)\",\n \"def latex(name,\\n latex_program='pdflatex', # or 'latex'\\n options='--latex_code_style=vrb',\\n version='paper', # or 'screen', '2up', 'A4', 'A4-2up'\\n postfix='', # or 'auto'\\n ptex2tex=None, # only for ptex2tex step\\n ):\\n if name.endswith('.do.txt'):\\n name = name.replace('.do.txt', '')\\n system('rm -f %(name)s.aux' % vars())\\n\\n if version in ('paper', 'A4', '2up', 'A4-2up'):\\n if not '--device=paper' in options:\\n options += ' --device=paper'\\n elif version == 'screen' and '--device=paper' in options:\\n options = options.replace('--device=paper', '')\\n if version in ('A4', 'A4-2up'):\\n if not '--latex_papersize=a4' in options:\\n options += ' --latex_papersize=a4'\\n if postfix == 'auto':\\n if version == 'paper':\\n postfix = '4print'\\n elif version == 'screen':\\n postfix = '4screen'\\n else:\\n postfix = version\\n\\n # Compile source\\n cmd = 'doconce format %(latex_program)s %(name)s %(options)s ' % vars()\\n system(cmd)\\n\\n cmd = r\\\"doconce replace '%% insert custom LaTeX commands...' '\\\\usepackage[russian]{babel} \\\\usepackage{titlesec} \\\\titleformat{\\\\subsubsection}[runin] {\\\\normalfont\\\\normalsize\\\\bfseries}{\\\\thesubsubsection.}{1em}{} \\\\let\\\\paragraph=\\\\subsubsection' %(name)s.tex\\\" % vars()\\n system(cmd)\\n\\n cmd = r\\\"doconce replace '\\\\usepackage{lmodern}' '%%\\\\usepackage{lmodern}' %(name)s.tex\\\" % vars()\\n system(cmd)\\n # Transform .p.tex to .tex?\\n if ptex2tex is not None:\\n cmd = ptex2tex\\n system(cmd)\\n\\n # Load latex file into string for examination\\n dofile = open(name + '.tex', 'r')\\n text = dofile.read()\\n dofile.close()\\n\\n latex_options = ''\\n if latex_program == 'pdflatex':\\n latex_options = '-file-line-error -interaction nonstopmode -halt-on-error'\\n\\n # Run latex\\n shell_escape = ' -shell-escape' if 'begin{minted}' in text else ''\\n cmd_latex = '%(latex_program)s%(shell_escape)s %(latex_options)s %(name)s' % vars()\\n system(cmd_latex)\\n\\n if 'idx{' in text:\\n cmd = 'makeindex %(name)s' % vars()\\n system(cmd)\\n\\n if 'BIBFILE:' in text:\\n cmd = 'bibtex %(name)s' % vars()\\n system(cmd)\\n system(cmd_latex)\\n system(cmd_latex)\\n if latex_program == 'latex':\\n cmd = 'dvipdf %(name)s' % vars()\\n system(cmd)\\n # Could instead of dvipdf run the old-fashioned dvips and ps2pdf\\n\\n if version in ('2up', 'A4-2up'):\\n # Use pdfnup to make two pages per sheet\\n cmd = 'pdfnup --frame true --outfile %(name)s.pdf %(name)s.pdf' % vars()\\n system(cmd)\\n if postfix:\\n shutil.copy(name + '.pdf', name + '-' + postfix + '.pdf')\",\n \"def generate_problems_pdf(args):\\n contents = generate_table(start_int=args.start_int, end_int=args.end_int, table_type=args.table_type)\\n convert_latex_to_pdf(args.filename, contents=contents, view=True)\\n remove_temporary_files(args.filename)\",\n \"def make_pdf(self):\\n source = self.get_page_source()\\n if not source:\\n self.errors.append('no_source')\\n if not self.errors:\\n self.generate_pdf_file(source)\",\n \"def compile_latex(filename):\\n working_directory, texfile = os.path.split(filename)\\n try:\\n run_command([\\\"pdflatex\\\", \\\"-draftmode\\\", \\\"-interaction=batchmode\\\", texfile], working_directory)\\n run_command([\\\"pdflatex\\\", \\\"-interaction=batchmode\\\", texfile], working_directory)\\n return True\\n except subprocess.TimeoutExpired:\\n return False\",\n \"def main():\\n init_latex()\",\n \"def compile_pdf(directory, filename, move_result_to):\\n cmd = ['latexmk', '-pdf', '-dvi-', '-interaction=nonstopmode', filename]\\n proc = subprocess.Popen(cmd, cwd=directory)\\n proc.communicate()\\n\\n retcode = proc.returncode\\n if not retcode == 0:\\n raise ValueError('Error {} executing command: {}'.format(retcode, ' '.join(cmd)))\\n\\n pdf_name = filename.replace('.tex', '.pdf')\\n from_file = os.path.join(directory, pdf_name)\\n to_file = os.path.join(move_result_to, pdf_name)\\n \\n if not os.path.exists(move_result_to):\\n os.makedirs(move_result_to)\\n \\n os.rename(from_file, to_file)\",\n \"def compile(lines, name):\\n with open(\\\"{}.tex\\\".format(name), \\\"w\\\") as f:\\n for line in lines:\\n f.write(line+\\\"\\\\n\\\")\\n os.system(\\\"pdflatex {}.tex\\\".format(name))\\n os.system(\\\"xdg-open {}.pdf\\\".format(name))\\n os.system(\\\"rm {}.log\\\".format(name))\\n os.system(\\\"rm {}.aux\\\".format(name))\",\n \"def generate_pdf(self):\\n x = 100\\n y = 100\\n buffer = BytesIO()\\n p = canvas.Canvas(buffer, pagesize=\\\"A4\\\")\\n p.drawString(x, y, \\\"TO DO\\\")\\n p.showPage()\\n p.save()\\n pdf = buffer.getvalue()\\n buffer.close()\\n return pdf\",\n \"def build(self,\\n save_to_disk=True,\\n compile_to_pdf=True,\\n show_pdf=True,\\n delete_files=list(),\\n build_from_dir='cwd'):\\n tex = super().build()\\n\\n tex = build(self.doc_class) + '\\\\n' + self.build_preamble() + '\\\\n' + tex\\n if save_to_disk:\\n self.file.save(tex)\\n\\n if compile_to_pdf:\\n self.file.compile_to_pdf(build_from_dir=build_from_dir)\\n\\n if show_pdf:\\n open_file_with_default_program(self.filename, self.filepath)\\n\\n if isinstance(delete_files, str):\\n if delete_files == 'all':\\n delete_files = ['tex', 'aux', 'log']\\n else:\\n delete_files = [delete_files]\\n\\n for ext in delete_files:\\n if ext in ['tex', 'aux', 'log', 'pdf']:\\n os.remove(f'{self.filepath}/{self.filename}.{ext}')\\n\\n return tex\",\n \"def build_pdf(branch):\\n os.chdir(os.path.join(gitdname,'statsmodels','docs'))\\n sphinx_dir = os.path.join(virtual_dir,'bin')\\n retcode = subprocess.call(\\\" \\\".join(['make','latexpdf',\\n 'SPHINXBUILD='+sphinx_dir+'/sphinx-build']), shell=True)\\n if retcode != 0:\\n os.chdir(old_cwd)\\n msg = \\\"\\\"\\\"Could not build the pdf docs for branch %s\\\"\\\"\\\" % branch\\n raise Exception(msg)\\n os.chdir(dname)\",\n \"def test_buildPDF(self):\\n bookPath = self._setupTeXFiles()\\n outputPath = FilePath(self.mktemp())\\n\\n builder = BookBuilder()\\n builder.buildPDF(bookPath, self.howtoDir, outputPath)\\n\\n self.assertTrue(outputPath.exists())\",\n \"def generate_envelope_pdf(document):\\n compiler = LatexCompiler()\\n\\n return compiler.compile(document)\",\n \"def generate_pdf(pdf_data):\\n\\n html = HTML(string=pdf_data)\\n f = html.write_pdf()\\n\\n return f\",\n \"def convert_to_latex(self, builder, filename, latex_metadata):\\n relative_path = ''\\n tex_data = ''\\n tex_build_path = self.texdir + relative_path\\n pdf_build_path = self.pdfdir + relative_path\\n template_folder = builder.config['jupyter_template_path']\\n\\n\\n ensuredir(tex_build_path)\\n ensuredir(pdf_build_path)\\n\\n ## setting the working directory\\n os.chdir(self.texdir)\\n\\n ## copies all theme folder images to static folder\\n if os.path.exists(builder.confdir + \\\"/theme/static/img\\\"):\\n copy_tree(builder.confdir + \\\"/theme/static/img\\\", self.texdir + \\\"/_static/img/\\\", preserve_symlinks=1)\\n else:\\n self.logger.warning(\\\"Image folder not present inside the theme folder\\\")\\n\\n fl_ipynb = self.texdir + \\\"/\\\" + \\\"{}.ipynb\\\".format(filename)\\n fl_tex = self.texdir + \\\"/\\\" + \\\"{}.tex\\\".format(filename)\\n fl_tex_template = builder.confdir + \\\"/\\\" + template_folder + \\\"/\\\" + builder.config['jupyter_latex_template']\\n\\n ## do not convert excluded patterns to latex\\n excluded_files = [x in filename for x in builder.config['jupyter_pdf_excludepatterns']]\\n\\n if not True in excluded_files: \\n ## --output-dir - forms a directory in the same path as fl_ipynb - need a way to specify properly?\\n ### converting to pdf using xelatex subprocess\\n if sys.version_info[0] < 3:\\n subprocess.call([\\\"jupyter\\\", \\\"nbconvert\\\",\\\"--to\\\",\\\"latex\\\",\\\"--template\\\",fl_tex_template,\\\"from\\\", fl_ipynb])\\n else:\\n subprocess.run([\\\"jupyter\\\", \\\"nbconvert\\\",\\\"--to\\\",\\\"latex\\\",\\\"--template\\\",fl_tex_template,\\\"from\\\", fl_ipynb])\\n\\n ### check if subdirectory\\n subdirectory = \\\"\\\"\\n index = filename.rfind('/')\\n if index > 0:\\n subdirectory = filename[0:index]\\n filename = filename[index + 1:]\\n\\n ### set working directory for xelatex processing\\n os.chdir(self.texdir + \\\"/\\\" + subdirectory)\\n\\n try:\\n self.subprocess_xelatex(fl_tex, filename)\\n if 'bib_include' in latex_metadata:\\n self.subprocess_bibtex(filename)\\n self.subprocess_xelatex(fl_tex, filename)\\n self.subprocess_xelatex(fl_tex, filename)\\n except OSError as e:\\n print(e)\\n except AssertionError as e:\\n pass\\n # exit() - to be used when we want the execution to stop on error\",\n \"def main(\\n files: List[Path] = typer.Argument(default=None, dir_okay=False, exists=True),\\n template: Optional[str] = typer.Option(\\n None, '--template', help='Name of template file'\\n ),\\n logo: Optional[str] = typer.Option(None, '--logo', help='Name of logo file'),\\n logo_width: Optional[str] = typer.Option(\\n None, '--logo-width', help='Logo width (default 35mm)'\\n ),\\n highlight_style: Optional[str] = typer.Option(None, '--highlight-style',\\n help='Specify coloring style to be used in highlighting source code'),\\n syntax_definition: Optional[str] = typer.Option(None, '--syntax-definition',\\n help='Specify a directory which contains syntax definition files'),\\n no_toc: bool = typer.Option(\\n False, '--no-toc', help='table of contents in PDF document'\\n ),\\n no_number_sections: bool = typer.Option(False, '--no-number-sections', help='no section numbering'),\\n\\n no_titlepage: bool = typer.Option(False, '--no-titlepage', help='title in PDF document'),\\n tex_file: bool = typer.Option(\\n False, '--tex', help='create TeX file instead of PDF document'\\n ),\\n email: Optional[str] = typer.Option(None, '--email', help='Author email'),\\n company: Optional[str] = typer.Option(None, '--company', help='Name of company'),\\n department: Optional[str] = typer.Option(\\n None, '--department', help='Name of department'\\n ),\\n confidential: bool = typer.Option(\\n False, '--confidential', help='indicate confidential'\\n ),\\n debug: bool = typer.Option(False, '--debug', help='turns debugging on'),\\n pdf_engine: str = typer.Option(\\n 'xelatex',\\n '--pdf-engine',\\n help='Specify pdf engine, one of lualatex, xelatex or tectonic ',\\n ),\\n _version: bool = typer.Option(\\n None, '-V', '--version', callback=version_callback, help='Show version and exit'\\n ),\\n):\\n\\n if not files:\\n typer.echo('Error: Must specify at least one .md file.')\\n raise typer.Abort()\\n\\n mdfiles: List[str] = [str(md) for md in files]\\n\\n template = template or os.environ.get('MD2PDF_TEMPLATE')\\n if template is None:\\n print('No template specified')\\n sys.exit(1)\\n\\n email = email or os.environ.get('MD2PDF_AUTHOR_EMAIL')\\n footer_center = ''\\n\\n # command line overwrites `MD2PDF_PDF_ENGINE`. if both are not given\\n # then `xelatex` is the default\\n pdf_engine = pdf_engine or os.environ.get('MD2PDF_PDF_ENGINE') or 'xelatex'\\n # check that pdf-engine is one of the following\\n if pdf_engine not in ['xelatex', 'lualatex', 'tectonic']:\\n print('--pdf-engine must be one of \\\"xelatex\\\", \\\"lualatex\\\", \\\"tectonic\\\"')\\n sys.exit(1)\\n\\n ext = '.pdf'\\n if tex_file:\\n ext = '.tex'\\n\\n if len(mdfiles) == 1:\\n toml_file = os.path.splitext(mdfiles[0])[0] + '.toml'\\n\\n if os.path.exists(toml_file):\\n print(f'TOML file {toml_file} found')\\n parsed_toml = toml.load(toml_file)\\n default_val = parsed_toml.get('default')\\n if default_val is None:\\n print(f'No file names found in {toml_file}')\\n else:\\n mdfiles = default_val.get('files')\\n\\n for mdf in mdfiles:\\n print(f'Compiling {mdf}')\\n\\n main_mdfile = os.path.realpath(mdfiles[0])\\n\\n outfile = Path(main_mdfile).stem + ext\\n\\n year = date.today().year\\n\\n company = company or os.environ.get('MD2PDF_COMPANY')\\n department = department or os.environ.get('MD2PDF_DEPARTMENT')\\n\\n if company:\\n if confidential:\\n footer_center = f'© Copyright {year} {company}'\\n else:\\n footer_center = f'{year} {company}'\\n\\n pdcmd = PandocCmd(outfile)\\n pdcmd.append(f'--template={template}')\\n pdcmd.append(f'--pdf-engine={pdf_engine}')\\n\\n pdcmd.set_v('footer-center', footer_center)\\n pdcmd.set_v('company', company)\\n pdcmd.set_v('department', department)\\n\\n syntax_definition = syntax_definition or os.environ.get('MD2PDF_SYNTAX_DEFINITION_DIR')\\n if syntax_definition is not None:\\n add_syntax_definition(pdcmd, syntax_definition)\\n\\n pdcmd.append('--highlight-style')\\n highlight_style = highlight_style or os.environ.get('MD2PDF_HIGHLIGHT_STYLE')\\n if highlight_style is None:\\n pdcmd.append('pygments')\\n else:\\n check_highlight_style(highlight_style)\\n pdcmd.append(highlight_style)\\n\\n if not no_number_sections:\\n pdcmd.append('--number-sections')\\n\\n if no_titlepage:\\n pdcmd.set_m('titlepage', 'false')\\n\\n logo = logo or os.environ.get('MD2PDF_LOGO')\\n pdcmd.set_v('logo', logo)\\n\\n logo_width = logo_width or os.environ.get('MD2PDF_LOGO_WIDTH')\\n pdcmd.set_v('logo-width', logo_width)\\n\\n pdcmd.set_m('email', email)\\n\\n if not no_toc:\\n pdcmd.append('--toc')\\n\\n pdcmd.extend(mdfiles)\\n\\n if debug:\\n print(' '.join(pdcmd.pandoc))\\n\\n\\n pdcmd.run()\",\n \"def render_to_pdf(template_src, context_dict={}):\\n template = get_template(template_src)\\n html = template.render(context_dict)\\n result = BytesIO()\\n pdf = pisa.pisaDocument(BytesIO(html.encode(\\\"ISO-8859-1\\\")), result)\\n if not pdf.err:\\n return HttpResponse(result.getvalue(), content_type='application/pdf')\\n return None\",\n \"def convert_to_latex(filename):\\n basename, ext = os.path.splitext(filename)\\n print(\\\"Converting '{0:s}' to LaTeX...\\\".format(filename))\\n filename1 = basename + \\\".tex\\\"\\n call_pandoc(filename, filename1, \\\"markdown\\\", \\\"latex\\\")\\n print(\\\"Done!\\\")\",\n \"def generate_command(ctx, template):\\n config_extension = '.py'\\n template_extension = '.html'\\n output_extension = '.pdf'\\n\\n context = Context(\\n config=path.join(\\n ctx.obj.get(\\\"config_path\\\"),\\n template + config_extension\\n ),\\n template_path=path.join(\\n ctx.obj.get(\\\"template_path\\\"),\\n template + template_extension\\n ),\\n variables=ctx.obj.get(\\\"varibales\\\"),\\n output_path=path.join(\\n ctx.obj.get(\\\"output_path\\\"),\\n template, output_extension),\\n )\\n\\n generator = Generator(context)\\n generator.execute()\\n exit(0)\",\n \"def main():\\n f_name = sys.argv[1]\\n file_contents = open(f_name).read()\\n C = CAST([], \\\"python\\\")\\n C2 = C.from_json_str(file_contents)\\n\\n V = CASTToAGraphVisitor(C2)\\n last_slash_idx = f_name.rfind(\\\"/\\\")\\n file_ending_idx = f_name.rfind(\\\".\\\")\\n pdf_file_name = f\\\"{f_name[last_slash_idx + 1 : file_ending_idx]}.pdf\\\"\\n V.to_pdf(pdf_file_name)\",\n \"def run(self, cmd=\\\"\\\", file_regex=\\\"\\\", path=\\\"\\\", file=None, debug=False):\\n # Handling process killing\\n if self.proc:\\n self.output(\\\"\\\\n\\\\n### Got request to terminate compilation ###\\\")\\n # Not sure if this is needed, but to avoid race conditions\\n proc = self.proc\\n self.proc = None\\n proc.kill()\\n return\\n\\n view = self.window.active_view()\\n \\n # Save if there are unchanged edits\\n if view.is_dirty():\\n print(\\\"Saving...\\\")\\n view.run_command('save')\\n\\n # Getting the proper file name\\n # Using file switch for testing only\\n if file:\\n file_name = file\\n else:\\n file_name = get_tex_root(view)\\n\\n if not os.path.isfile(file_name):\\n sublime.error_message(file_name + \\\": file not found.\\\")\\n return\\n\\n # Check for the tex extension\\n _, tex_ext = os.path.splitext(file_name)\\n tex_dir = os.path.dirname(file_name)\\n \\n if tex_ext.lower() != \\\".tex\\\":\\n sublime.error_message(\\\"%s is not a Tex source file: cannot compile\\\" % os.path.basename(view.file_name()))\\n return\\n\\n print(cmd, file_name)\\n\\n # Creating the output view\\n if not self.output_view:\\n self.output_view = self.window.get_output_panel(\\\"subtexexec\\\")\\n \\n # Showing the output\\n self.window.run_command(\\\"show_panel\\\", {\\\"panel\\\": \\\"output.subtexexec\\\"})\\n # A bit crazy\\n t_kwargs = {\\n \\\"file_name\\\": file_name,\\n \\\"cmd\\\": cmd + [\\\"-outdir={}\\\".format(tex_dir)],\\n \\\"path\\\": path,\\n \\\"debug\\\": debug\\n }\\n\\n # Need to change dir here\\n threading.Thread(target=self.thread, kwargs=t_kwargs).start()\\n print(\\\"[make_pdf] Number of active threads: {}\\\".format(threading.active_count()))\",\n \"def create_pdf(html, options):\\n\\n # TODO: we will change this path, or use an other library for converting PDF!\\n # TODO: otherwise just say that wkhtmltopdf needs to be pre-installed (and how) and added to windows path\\n path_wkthmltopdf = \\\"C:/Program Files/wkhtmltopdf/bin/wkhtmltopdf.exe\\\"\\n config = pdfkit.configuration(wkhtmltopdf=path_wkthmltopdf)\\n\\n pdfkit.from_file(html, html.replace(\\\".html\\\", \\\".pdf\\\"), configuration=config, options=options)\\n return html + \\\".pdf\\\"\",\n \"def compile(self, texdoc, clean=False):\\n\\n if helpers.checkdep_tex() is not None:\\n # use ``pdflatex`` to compile the document\\n tex = subprocess.call(\\n [\\\"pdflatex\\\", texdoc, \\\"--quiet\\\"],\\n stdout=subprocess.PIPE,\\n stderr=subprocess.PIPE,\\n shell=False,\\n )\\n\\n if clean:\\n extensions = [\\\"aux\\\", \\\"log\\\", \\\"nav\\\", \\\"out\\\", \\\"snm\\\", \\\"toc\\\"]\\n for ext in extensions:\\n junkfiles = glob.glob(\\\"*.{}\\\".format(ext))\\n for junk in junkfiles:\\n os.remove(junk)\\n\\n else:\\n tex = None\\n\\n return tex\",\n \"def generate_pdf():\\n uu_id = uuid.uuid4().hex\\n current_app.logger.debug(uu_id)\\n current_app.logger.debug(request.form)\\n tmp_pdf_filename = \\\"{}_tmp.pdf\\\".format(uu_id)\\n pdf_filename = \\\"{}.pdf\\\".format(uu_id)\\n tmp_out_pdf_path = os.path.join(\\n current_app.config[\\\"UPLOAD_FOLDER\\\"], tmp_pdf_filename\\n )\\n out_pdf_path = os.path.join(\\n current_app.config[\\\"UPLOAD_FOLDER\\\"], pdf_filename\\n )\\n fill_template_from_input(\\n request.form,\\n current_app.config[\\\"PDF_TEMPLATE_PATH\\\"],\\n tmp_out_pdf_path,\\n INPUT_FORM_MAP\\n )\\n fdf_tmp = os.path.join(\\n current_app.config[\\\"UPLOAD_FOLDER\\\"], 'tmp.fdf'\\n )\\n os.system('pdftk ' + tmp_out_pdf_path + ' generate_fdf output ' + fdf_tmp)\\n os.system(\\n 'pdftk ' + tmp_out_pdf_path + ' fill_form ' + fdf_tmp +\\n ' output ' + out_pdf_path + ' flatten'\\n )\\n os.remove(tmp_out_pdf_path)\\n return pdf_filename\",\n \"def generate_pdf(list,id):\\n\\n doc = SimpleDocTemplate(settings.STATIC_ROOT+\\\"/tests/\\\"+str(id)+\\\"/\\\"+str(id)+\\\".pdf\\\")\\n\\n Story = [Spacer(1,2*inch)]\\n styles = stylesheet()\\n global Title\\n\\n # Add 10 questions with boxes below\\n for i in list:\\n if not i[0] in \\\"skills-scan\\\" and not i[0] in \\\"csrfmiddlewaretoken\\\" and not i[0] in \\\"titre\\\" and not i[0] in \\\"custom\\\":\\n tmp = int(i[0])+1\\n bogustext = (str(tmp)+\\\". %s\\\" % i[1])\\n p = Paragraph(bogustext, styles['default'])\\n # Write the paragraph\\n\\n draw = Drawing()\\n # rect(x1,y1,width,height)\\n rec = Rect(0, 100, 450, 150)\\n rec.fillColor = colors.white\\n # draw the rect under each paragraph\\n draw.add(rec)\\n p.keepWithNext = True\\n Story.append(p)\\n Story.append(draw)\\n Story.append(Spacer(1,-0.9 * inch))\\n elif i[0] in \\\"titre\\\":\\n Title = i[1]\\n # build the document by inserting the whole story\\n doc.build(Story, onFirstPage=myFirstPage, onLaterPages=myLaterPages)\\n return str(id)+\\\".pdf\\\"\",\n \"def create_pdf(f,s1,s2='',s3=''):\\n # does not need reportlab!\\n if s1 == 'White Ballot': s1 = '\\\"'+'_'*10+'\\\"'\\n cod = zlib.compress('BT /F1 16 Tf ET\\\\r\\\\nBT 300 270 Td (%s) Tj ET\\\\r\\\\nBT /F1 48 Tf ET\\\\r\\\\nBT 5 180 Td (%16s) Tj ET\\\\r\\\\nBT /F1 12 Tf ET\\\\r\\\\nBT 10 50 Td (%s) Tj ET'%(s3,s1,s2))\\n open(f,'w').write(create_pdf.__doc__ + '/Length %d>>\\\\nstream\\\\n'%len(cod) + cod + 'endstream endobj\\\\ntrailer<</Root 4 0 R>>')\",\n \"def latex2wp():\\n parser = argparse.ArgumentParser()\\n parser.add_argument('input_path')\\n #parser.add_argument('destination')\\n args = parser.parse_args()\\n\\n source = None\\n blocks = None\\n paragraphs = []\\n\\n source = from_file(args.input_path)\\n source = document(source)\\n source = split_inline(source)\\n blocks = split_blocks(source)\\n fragments = process(blocks)\\n\\n html = refs('\\\\n'.join(fragments))\\n print(html)\",\n \"def main():\\n argparser = argparse.ArgumentParser(description=\\\"Convert plot to table\\\")\\n\\n argparser.add_argument(\\\"pdf\\\", action=\\\"store\\\", help=\\\"pdf file\\\",\\n default=None, nargs=\\\"*\\\")\\n\\n args = argparser.parse_args()\\n\\n if len(args.pdf) == 0:\\n open_gui()\\n else:\\n process_pdf(args.pdf[0])\\n\\n generate_data()\",\n \"def export(bill, template_dir=None, pdf_dir=None):\\n # if template_dir not provided,\\n # look for the template directory of this script's location\\n if not template_dir:\\n template_dir = os.path.join(os.path.dirname(os.path.abspath(__file__)), 'template')\\n # If the user-defined or default template directories don't exist, raise an error\\n if not os.path.exists(template_dir):\\n raise OSError('Could not find the template directory')\\n\\n # If no user-defined pdf output directory, put it in a folder where this script lives\\n if not pdf_dir:\\n basedir = os.path.dirname(os.path.abspath(__file__))\\n pdf_dir = os.path.join(basedir, 'pdfs')\\n # if the default pdf output directory doesn't exist, make it\\n if not os.path.exists(pdf_dir):\\n os.makedirs(pdf_dir)\\n\\n # if the user-defined pdf_dir does not exist, raise an error\\n if not os.path.exists(pdf_dir):\\n raise IOError('Could not find a directory to output pdfs')\\n\\n # get the path to the template\\n template_path = os.path.join(template_dir, 'templates', 'template.html')\\n # read the template\\n template = open(template_path).read()\\n\\n # Replace relative imports of images and CSS with the full path to the files\\n # Note: I'm including the '/' in the replacement so that\\n # it doesn't replace other uses for '..' such as in regular text (i.e. an ellipsis)\\n template = template.replace('../', os.path.join(path2url(template_dir), ''))\\n\\n # Insert billing data using find/replace\\n # Sort by field length longest to shortest\\n # This prevents values from fields that are substrings of other fields from going in the wrong place\\n # e.g. the value of \\\"rebate\\\" would be inserted into the field \\\"rebate_closing_balance\\\"\\n for key, value in sorted(bill.items(), key=lambda t: len(t[0]), reverse=True):\\n template = template.replace(\\\"__\\\"+key, format_value(value))\\n\\n # Now create the pdf\\n try:\\n # options = {'encoding': 'utf-8'}\\n report_name = make_report_name(bill)\\n output_file = os.path.join(pdf_dir, report_name)\\n pdfkit.from_string(template, output_file)\\n except:\\n typ, value, tb = sys.exc_info()\\n traceback.print_exc()\\n pdb.post_mortem(tb)\",\n \"def template2pdf(template=None, **kwargs):\\n str = render_to_string(template, kwargs)\\n output = kwargs.get('output', False)\\n ret = pdfkit.from_string(str, output, settings.WKHTMLTOPDF_OPTIONS or default_options)\\n return (ret, str)\",\n \"def generate_pdf_report(release, spec, report_week):\\n\\n logging.info(u\\\" Generating the pdf report, give me a few minutes, please \\\"\\n u\\\"...\\\")\\n\\n working_dir = spec.environment[u\\\"paths\\\"][u\\\"DIR[WORKING,SRC]\\\"]\\n\\n execute_command(f\\\"cd {working_dir} && mv -f index.pdf.template index.rst\\\")\\n\\n _convert_all_svg_to_pdf(spec.environment[u\\\"paths\\\"][u\\\"DIR[WORKING,SRC]\\\"])\\n\\n # Convert PyPLOT graphs in HTML format to PDF.\\n convert_plots = u\\\"xvfb-run -a wkhtmltopdf {html} {pdf}\\\"\\n plots = get_files(spec.environment[u\\\"paths\\\"][u\\\"DIR[STATIC,VPP]\\\"], u\\\"html\\\")\\n plots.extend(\\n get_files(spec.environment[u\\\"paths\\\"][u\\\"DIR[STATIC,DPDK]\\\"], u\\\"html\\\")\\n )\\n for plot in plots:\\n file_name = f\\\"{plot.rsplit(u'.', 1)[0]}.pdf\\\"\\n logging.info(f\\\"Converting {plot} to {file_name}\\\")\\n execute_command(convert_plots.format(html=plot, pdf=file_name))\\n\\n # Generate the LaTeX documentation\\n build_dir = spec.environment[u\\\"paths\\\"][u\\\"DIR[BUILD,LATEX]\\\"]\\n cmd = PDF_BUILDER.format(\\n release=release,\\n date=datetime.datetime.utcnow().strftime(u'%Y-%m-%d %H:%M UTC'),\\n working_dir=working_dir,\\n build_dir=build_dir)\\n execute_command(cmd)\\n\\n # Build pdf documentation\\n archive_dir = spec.environment[u\\\"paths\\\"][u\\\"DIR[STATIC,ARCH]\\\"]\\n cmds = [\\n f'cd {build_dir} && '\\n f'pdflatex -shell-escape -interaction nonstopmode csit.tex || true',\\n f'cd {build_dir} && '\\n f'pdflatex -interaction nonstopmode csit.tex || true',\\n f'cd {build_dir} && '\\n f'cp csit.pdf ../{archive_dir}/csit_{release}.{report_week}.pdf &&'\\n f'cp csit.pdf ../{archive_dir}/csit_{release}.pdf'\\n ]\\n\\n for cmd in cmds:\\n execute_command(cmd)\\n\\n logging.info(u\\\" Done.\\\")\",\n \"def generate_pdf_file(self, source):\\n filename = self.generate_temp_filename()\\n if not filename:\\n self.errors.append('filename_generation_failed')\\n return\\n\\n try:\\n transform_module = getattr(transforms, self.pdf_generator)\\n except AttributeError:\\n self.errors.append('wrong_generator_configuration')\\n return\\n\\n self.filename = filename\\n url = self.context.absolute_url()\\n\\n print_css = (self.pdf_tool.always_print_css or\\n self.context.portal_type in self.pdf_tool.print_css_types)\\n\\n # When the source is sent through Ajax, it's already encoded\\n # as a utf-8 string. When using it without javascript, the\\n # source comes from a view, which always returns unicode. In\\n # that case we need to encode it.\\n if isinstance(source, unicode):\\n source = source.encode('utf-8')\\n export_file, err = transform_module.html_to_pdf(\\n source,\\n self.tempdir,\\n filename,\\n url,\\n print_css,\\n self.get_extra_options())\\n\\n if err:\\n self.errors.append('pdf_creation_failed')\\n return\\n\\n self.pdf_tool.registerPDF(filename)\\n self.pdf_file = export_file\\n self.pdf_file.close()\",\n \"def main():\\n mip = parametros()\\n mir = Reporte(CURRENT_PATH, mip.debug, mip.overwrite)\\n pdfs = mir.obtener()\\n if pdfs:\\n print(\\\"Obteniendo nuevos pdf:\\\")\\n for pdf in pdfs:\\n print(f\\\"* {pdf}\\\")\\n\\n for file in glob.glob(f\\\"{CURRENT_PATH}/resources/pdf/*.pdf\\\"):\\n data = mir.parser(file)\\n mir.escribir(data)\",\n \"def task_render_report():\\n target = 'report.pdf'\\n dep = 'report.md'\\n return {\\n 'file_dep': [dep],\\n 'targets': [target],\\n 'actions': [\\n f\\\"pandoc -t latex -o {target} {dep}\\\"\\n ],\\n 'clean': True\\n }\",\n \"def print_latex(self):\\n\\n pdf = pylatex.Document(\\n \\\"default\\\"\\n )\\n\\n with pdf.create(pylatex.Section(\\n \\\"Equações Diofantinas\\\"\\n )) as section:\\n\\n section.append(\\\"Equação:\\\")\\n ultimo = self.numbers[-1]\\n eq = []\\n cont = 1\\n for i in self.numbers:\\n simbolo = \\\"+\\\"\\n if i == ultimo:\\n simbolo = \\\"= 1\\\"\\n eq.append(\\n pylatex.NoEscape(\\n \\\" {}x_{} {}\\\".format(i, cont, simbolo)\\n )\\n )\\n cont = cont + 1\\n\\n section.append(pylatex.Math(data=eq))\\n\\n text = \\\"n = {}\\\".format(self.order)\\n section.append(text)\\n\\n m = pylatex.Matrix(self.take_vec(), mtype='b')\\n matrix = pylatex.Math(data=['b = ', m])\\n section.append(matrix)\\n\\n m = pylatex.Matrix(self.take_matrix(), mtype='b')\\n matrix = pylatex.Math(data=['A = ', m])\\n section.append(matrix)\\n\\n section.append(\\\"Resposta = {}\\\".format(self.cofactor_matrix()))\\n\\n section.append(pylatex.LineBreak())\\n section.append(\\\"Confirmando:\\\")\\n section.append(pylatex.LineBreak())\\n s = 0\\n for i in range(len(self.numbers)):\\n r = self.numbers[i] * self.cofactor_matrix()[i]\\n s = s + r\\n resp = \\\"\\\\t {}\\\\t{} \\\\t* \\\\t{} \\\\t= \\\\t{} \\\\t({})\\\\n\\\".format(\\n i,\\n self.numbers[i],\\n self.cofactor_matrix()[i],\\n r,\\n s\\n )\\n section.append(resp)\\n\\n if self.create_pdf:\\n pdf.generate_pdf()\\n\\n pdf.generate_tex()\",\n \"def generate_pdf(request):\\n reg_no = request.user.username\\n user = get_object_or_404(User, username=reg_no)\\n user_profile = user.get_profile()\\n user_application = user_profile.application\\n np = user_application.np\\n \\n response = HttpResponse(mimetype='application/pdf')\\n response['Content-Disposition'] = 'attachment; filename=JAM2012_Allottment.pdf'\\n \\n elements = []\\n doc = SimpleDocTemplate(response)\\n \\n formatted_time = time.ctime()\\n styles = getSampleStyleSheet()\\n styles.add(ParagraphStyle(name='Justify', alignment=TA_JUSTIFY))\\n \\n ptext = '<font size=15>JAM 2012 - Admissions.</font>' \\n elements.append(Paragraph(ptext, styles[\\\"Justify\\\"]))\\n elements.append(Spacer(4, 20))\\n \\n ptext = '<font size=12>Registration Number: %s</font>' % reg_no \\n elements.append(Paragraph(ptext, styles[\\\"Normal\\\"]))\\n elements.append(Spacer(1, 12))\\n \\n data = [] \\n options = get_chosen_options(user) ##Put a check to show when the options chosen is empty\\n \\n if not(options):\\n ptext = '<font size=12>No choices were selected.</font>' \\n elements.append(Paragraph(ptext, styles[\\\"Normal\\\"]))\\n elements.append(Spacer(1, 12))\\n doc.build(elements) \\n return response \\n \\n ptext = '<font size=12>The choices selected by me are as follows: </font>' \\n elements.append(Paragraph(ptext, styles[\\\"Normal\\\"]))\\n elements.append(Spacer(4, 30))\\n \\n counter = 1\\n for opt in options:\\n data.append([counter, opt.opt_code, opt.opt_location, opt.opt_name])\\n counter = counter + 1\\n \\n t = Table(data)\\n t.setStyle(TableStyle([('GRID',(0,0),(3,len(options)),1,colors.black),\\n ('TEXTCOLOR',(0,0),(0,-1),colors.green)]))\\n \\n elements.append(t) \\n \\n elements.append(Spacer(4, 30))\\n \\n ptext = '<font size=12>I hereby declare that the order of preference given by me for my eligible programmes is final. </font>' \\n elements.append(Paragraph(ptext, styles[\\\"Normal\\\"]))\\n elements.append(Spacer(4, 25))\\n \\n ptext = '<font size=12>Signature of the Candidate</font>' \\n elements.append(Paragraph(ptext, styles[\\\"Normal\\\"]))\\n elements.append(Spacer(4, 20))\\n \\n ptext = '<font size=12>%s</font>' % formatted_time\\n elements.append(Paragraph(ptext, styles[\\\"Normal\\\"]))\\n elements.append(Spacer(1, 12))\\n \\n doc.build(elements)\\n \\n return response\",\n \"def create_pdf(file_path: Path) -> None:\\n pdf = FPDF()\\n pdf.add_page()\\n pdf.set_font(\\\"Arial\\\", size=12)\\n with open(file_path, \\\"r\\\") as file:\\n for line in file:\\n pdf.cell(200, 10, txt=line, ln=1)\\n pdf_dir_path: Path = Path(calculate_path(file_path))\\n Path(pdf_dir_path.parent).mkdir(parents=True, exist_ok=True)\\n pdf_path: str = f\\\"{str(pdf_dir_path)}.pdf\\\"\\n pdf.output(pdf_path)\",\n \"def _create_pdfdoc(pdfdoc, story):\\n pdf_doc = BaseDocTemplate(\\n pdfdoc, pagesize=letter,\\n leftMargin=inch, rightMargin=inch,\\n topMargin=inch, bottomMargin=inch)\\n main_frame = Frame(\\n inch, inch,\\n letter[0] - 2 * inch, letter[1] - 2 * inch,\\n leftPadding=0, rightPadding=0, bottomPadding=0,\\n topPadding=0, id='main_frame')\\n main_template = PageTemplate(id='main_template', frames=[main_frame])\\n pdf_doc.addPageTemplates([main_template])\\n\\n pdf_doc.build(story)\",\n \"def pdf_gen(report, summary=None):\\n with open(\\\"report_content.yaml\\\", \\\"r\\\") as stream:\\n docs = yaml.safe_load(stream)\\n\\n style = g_stylesheet.get(\\\"styles\\\")\\n elems = [] # elements array used to build pdf structure\\n pdf = SimpleDocTemplate(\\n f\\\"{report.replay_id}_report.pdf\\\",\\n pagesize=letter,\\n leftMargin=0.75 * inch,\\n rightMargin=0.75 * inch,\\n topMargin=0.75 * inch,\\n bottomMargin=0.75 * inch,\\n )\\n\\n # title and subtitle and cluster info table\\n elems.append(Paragraph(docs[\\\"title\\\"], style[\\\"Title\\\"]))\\n elems.append(\\n Paragraph(sub_yaml_vars(report, docs[\\\"subtitle\\\"]), style[\\\"Heading4\\\"])\\n )\\n cluster_info = pd.DataFrame.from_dict(report.cluster_details, orient=\\\"index\\\")\\n elems.append(\\n Table(\\n df_to_np(report.cluster_details.keys(), cluster_info.transpose()),\\n hAlign=\\\"LEFT\\\",\\n style=g_stylesheet.get(\\\"table_style\\\"),\\n )\\n )\\n # replay summary\\n if summary is not None:\\n elems.append(Paragraph(f\\\"Replay Summary\\\", style[\\\"Heading4\\\"]))\\n elems.append(\\n ListFlowable(\\n [ListItem(Paragraph(x, style[\\\"Normal\\\"])) for x in summary],\\n bulletType=\\\"bullet\\\",\\n )\\n )\\n elems.append(Spacer(0, 5))\\n\\n elems.append(Paragraph(docs[\\\"report_paragraph\\\"], style[\\\"Normal\\\"]))\\n\\n # glossary section\\n elems.append(Paragraph(docs[\\\"glossary_header\\\"], style[\\\"Heading4\\\"]))\\n elems.append(Paragraph(docs[\\\"glossary_paragraph\\\"], style[\\\"Normal\\\"]))\\n elems.append(\\n ListFlowable(\\n [ListItem(Paragraph(x, style[\\\"Normal\\\"])) for x in docs[\\\"glossary\\\"]],\\n bulletType=\\\"bullet\\\",\\n )\\n )\\n elems.append(Spacer(0, 5))\\n\\n # access data section\\n elems.append(Paragraph(docs[\\\"data_header\\\"], style[\\\"Heading4\\\"]))\\n elems.append(\\n Paragraph(sub_yaml_vars(report, docs[\\\"data_paragraph\\\"]), style[\\\"Normal\\\"])\\n )\\n elems.append(\\n ListFlowable(\\n [ListItem(Paragraph(x, style[\\\"Normal\\\"])) for x in docs[\\\"raw_data\\\"]],\\n bulletType=\\\"bullet\\\",\\n )\\n )\\n elems.append(Spacer(0, 5))\\n elems.append(\\n Paragraph(\\n sub_yaml_vars(report, docs[\\\"agg_data_paragraph\\\"]), style[\\\"Normal\\\"]\\n )\\n )\\n\\n # notes section\\n elems.append(Paragraph(docs[\\\"notes_header\\\"], style[\\\"Heading4\\\"]))\\n elems.append(Paragraph(docs[\\\"notes_paragraph\\\"], style[\\\"Normal\\\"]))\\n elems.append(\\n ListFlowable(\\n [ListItem(Paragraph(x, style[\\\"Normal\\\"])) for x in docs[\\\"notes\\\"]],\\n bulletType=\\\"bullet\\\",\\n )\\n )\\n\\n elems.append(PageBreak()) # page 2: cluster details\\n\\n # query breakdown\\n build_pdf_tables(elems, docs[\\\"query_breakdown\\\"], report)\\n elems.append(Spacer(0, 5))\\n\\n # histogram and description\\n image_path = hist_gen(\\n x_data=report.feature_graph[\\\"sec_start\\\"],\\n y_data=report.feature_graph[\\\"count\\\"],\\n title=docs[\\\"graph\\\"].get(\\\"title\\\"),\\n x_label=\\\"Average Elapsed Time (s)\\\",\\n )\\n\\n desc = Paragraph(docs[\\\"graph\\\"].get(\\\"paragraph\\\"), style[\\\"Normal\\\"])\\n data = [[Image(image_path, width=300, height=200, hAlign=\\\"LEFT\\\"), desc]]\\n elems.append(\\n Table(data, style=TableStyle([(\\\"VALIGN\\\", (0, 0), (-1, -1), \\\"MIDDLE\\\")]))\\n )\\n elems.append(Spacer(0, 5))\\n\\n # cluster metrics table\\n build_pdf_tables(elems, docs[\\\"cluster_metrics\\\"], report)\\n\\n elems.append(PageBreak()) # page 3+ measure tables\\n\\n build_pdf_tables(\\n elems, docs[\\\"measure_tables\\\"], report\\n ) # build 5 measure tables all at once\\n\\n # build pdf\\n pdf.build(\\n elems,\\n onFirstPage=partial(first_page, report=report),\\n onLaterPages=partial(later_pages, report=report),\\n )\\n os.remove(image_path)\\n\\n return pdf.filename\",\n \"def make_pdf(self, htmlbody, html_only=False):\\n # wrap htmlbody with provided HTML template\\n template = self.context.auto_template\\n template = template.replace(u'${body}', htmlbody)\\n if html_only:\\n return template\\n try:\\n tempdir = tempfile.mkdtemp()\\n # attachemnts saved. Let's save generated HTML\\n fullpath = os.path.join(tempdir, 'issue.html')\\n fp = open(fullpath, 'w')\\n fp.write(template.encode('utf-8'))\\n fp.close()\\n # Run wkhtmltopdf and generate the PDF\\n targetpath = os.path.join(tempdir, 'issue.pdf')\\n result = subprocess.call([\\\"wkhtmltopdf\\\", '-q', 'file://%s' % fullpath, '%s' % targetpath])\\n if result == 0:\\n return open(targetpath, 'rb').read()\\n else:\\n return ''\\n finally:\\n shutil.rmtree(tempdir, ignore_errors=True)\",\n \"def test_buildPDFRunsLaTeXThreeTimes(self):\\n class InspectableBookBuilder(BookBuilder):\\n def __init__(self):\\n BookBuilder.__init__(self)\\n self.commands = []\\n\\n def run(self, command):\\n \\\"\\\"\\\"\\n Record the command and then execute it.\\n \\\"\\\"\\\"\\n self.commands.append(command)\\n return BookBuilder.run(self, command)\\n\\n bookPath = self._setupTeXFiles()\\n outputPath = FilePath(self.mktemp())\\n\\n builder = InspectableBookBuilder()\\n builder.buildPDF(bookPath, self.howtoDir, outputPath)\\n\\n # These string comparisons are very fragile. It would be better to\\n # have a test which asserted the correctness of the contents of the\\n # output files. I don't know how one could do that, though. -exarkun\\n latex1, latex2, latex3, dvips, ps2pdf13 = builder.commands\\n self.assertEquals(latex1, latex2)\\n self.assertEquals(latex2, latex3)\\n self.assertEquals(\\n latex1[:1], [\\\"latex\\\"],\\n \\\"LaTeX command %r does not seem right.\\\" % (latex1,))\\n self.assertEquals(\\n latex1[-1:], [bookPath.path],\\n \\\"LaTeX command %r does not end with the book path (%r).\\\" % (\\n latex1, bookPath.path))\\n\\n self.assertEquals(\\n dvips[:1], [\\\"dvips\\\"],\\n \\\"dvips command %r does not seem right.\\\" % (dvips,))\\n self.assertEquals(\\n ps2pdf13[:1], [\\\"ps2pdf13\\\"],\\n \\\"ps2pdf13 command %r does not seem right.\\\" % (ps2pdf13,))\",\n \"def toPDF(Infos):\\n\\n\\n #returnPDF = PDFDocument(\\\"output\\\")\\n #returnPDF.Infos.get(\\\"name\\\")\\n returnPDF = PDF(\\\"Courier\\\", Infos.get(\\\"name\\\"))\\n if Infos.get('contact'):\\n returnPDF.contact(Infos.get(\\\"contact\\\"))\\n if Infos.get('Current position'):\\n returnPDF.currentposition(Infos.get(\\\"Current position\\\"))\\n if Infos.get('Education'):\\n returnPDF.currentposition(Infos.get(\\\"Education\\\"))\\n if Infos.get('Langue'):\\n returnPDF.currentposition(Infos.get(\\\"Langue\\\"))\\n returnPDF.output(\\\"result.pdf\\\", 'F')\",\n \"def create_pdf():\\n\\n fig = plt.gcf()\\n fig.set_size_inches(OutFileParameter.width, OutFileParameter.height)\\n fig.savefig(OutFileParameter.name + '.' + OutFileParameter.ext, dpi=OutFileParameter.dpi)\\n\\n return None\",\n \"def docx():\\n env.file_ext = \\\".docx\\\"\\n local(\\\"pandoc {input_files} -o {output_file}{file_ext} --bibliography={bib_file} --csl={csl_file} --toc\\\".format(**env))\",\n \"def build_pdf(filename=\\\"bids-spec.pdf\\\", logfile=\\\"bids-spec_pandoc_log.json\\\"):\\n\\n def _flatten_values(lst):\\n \\\"\\\"\\\"Flatten a list of dicts of lists to a list of values.\\\"\\\"\\\"\\n for obj in lst:\\n for val in obj.values():\\n if isinstance(val, str):\\n yield val\\n else:\\n yield from _flatten_values(val)\\n\\n fname_mkdocs_yml = _find(HERE, \\\"mkdocs.yml\\\")\\n\\n with open(fname_mkdocs_yml, \\\"r\\\") as stream:\\n mkdocs_yml = yaml.safe_load(stream)\\n\\n sections = mkdocs_yml[\\\"nav\\\"][0][\\\"The BIDS Specification\\\"]\\n\\n # special files\\n index_page = \\\"./index.md\\\"\\n pandoc_metadata = _find(HERE, \\\"metadata.yml\\\")\\n\\n # Prepare the command options\\n cmd = [\\n \\\"pandoc\\\",\\n \\\"--from=markdown_github+yaml_metadata_block\\\",\\n \\\"--include-before-body=./cover.tex\\\",\\n \\\"--include-in-header=./header.tex\\\",\\n \\\"--include-in-header=./header_setup.tex\\\",\\n \\\"--pdf-engine=xelatex\\\",\\n f\\\"--log={logfile}\\\",\\n f\\\"--output={filename}\\\",\\n ]\\n\\n # Resources are searched relative to the working directory, but\\n # we can add additional search paths using <path>:<another path>, ...\\n # When in one of the appendices/ files there is a reference to\\n # \\\"../04-modality-specific-files/images/...\\\", then we need to use\\n # appendices/ as a resource-path so that the relative files can\\n # be found.\\n build_root = HERE.parent\\n cmd += [f'--resource-path=.:{build_root / \\\"appendices\\\"}']\\n\\n # Add input files to command\\n # The filenames in `markdown_list` will ensure correct order when sorted\\n cmd += [str(build_root / index_page)]\\n cmd += [str(pandoc_metadata)]\\n cmd += [str(build_root / md) for md in _flatten_values(sections)]\\n\\n # print and run\\n print(\\\"pandoc command being run: \\\\n\\\\n\\\" + \\\"\\\\n\\\".join(cmd))\\n subprocess.run(cmd)\",\n \"def compile_single(is_update):\\n for task in ['pdflatex', 'bibtex', 'pdflatex', 'pdflatex']:\\n subprocess.check_call(task + ' main', shell=True)\\n shutil.move(\\\"main.pdf\\\", \\\"slides.pdf\\\")\",\n \"def book(**kwargs):\\n print(\\\"pdf created\\\")\",\n \"def run(self):\\n make_sure_path_exists(OUT_FOLDER)\\n\\n if self.config['type'] == 'website':\\n make_sure_path_exists(self.config['out_folder'])\\n\\n\\n for file in self.config['bodies']:\\n if file['type'] == 'content':\\n self.pandoc_file(file)\\n if self.config['type'] == 'website':\\n shutil.copyfile(file['generated'], os.path.join(self.config['out_folder'], os.path.basename(file['source'])))\\n\\n if self.config['type'] == 'website':\\n return\\n\\n for file in self.config['abstract']:\\n self.pandoc_file(file)\\n for file in self.config['summary']:\\n self.pandoc_file(file)\\n\\n template = LATEX_JINJA_ENV.get_template(self.config['template_file'])\\n\\n logging.info('Rendering template')\\n out = template.render(**self.config)\\n with open(self.config['name'] + self.get_file_extension(), 'w') as file:\\n file.write(out)\\n\\n if not self.args.pandoc:\\n logging.info('Rendering latex')\\n self.write()\\n if not self.args.fast:\\n logging.info('Rendering latex, again')\\n self.write() # twice for the toc\\n\\n logging.info('Done!')\",\n \"def tex(filename):\\n entries = [entry for entry in parse_listing(filename)]\\n export_to_latex('listing.tex', entries)\",\n \"def create_pdfdoc(pdfdoc, story):\\n pdf_doc = BaseDocTemplate(pdfdoc, pagesize = letter,\\n leftMargin = MARGIN_SIZE, rightMargin = MARGIN_SIZE,\\n topMargin = 24, bottomMargin = MARGIN_SIZE)\\n\\n second_frame = main_frame = Frame(MARGIN_SIZE, MARGIN_SIZE,\\n width - 2 * MARGIN_SIZE, height - (24 + 72),\\n leftPadding = 0, rightPadding = 0, bottomPadding = 0,\\n topPadding = 0, id = 'main_frame', showBoundary=0)\\n second_template = PageTemplate(id = 'second_template', frames=[second_frame], onPage=header)\\n\\n main_frame = Frame(MARGIN_SIZE, MARGIN_SIZE,\\n width - 2 * MARGIN_SIZE, height - (24 + MARGIN_SIZE),\\n leftPadding = 0, rightPadding = 0, bottomPadding = 0,\\n topPadding = 0, id = 'main_frame', showBoundary=0)\\n main_template = PageTemplate(id = 'main_template', frames = [main_frame], onPage=header)\\n\\n \\n \\n pdf_doc.addPageTemplates([main_template, second_template])\\n\\n pdf_doc.build(story)\",\n \"def xelatex_document(doc_args, template_file, field_name, output_dir='.'):\\n # input data\\n input_data = doc_args\\n\\n # template doc\\n template_doc = XeLateXDocument(template_file)\\n\\n # output file name\\n field_val = input_data[field_name].replace(' ', '')\\n\\n file_extension = get_extension(template_file)\\n basename = path.basename(template_file).replace(file_extension, '')\\n\\n file_name = basename + '_' + field_val\\n file_path = path.join(output_dir, file_name + '.pdf')\\n\\n # make output folder\\n if not os.path.exists(output_dir):\\n os.mkdir(outdir)\\n\\n # fill the template\\n template_doc.fill(doc_args)\\n\\n # save into PDF\\n template_doc.render(file_path)\\n\\n # clean up LateX mess\\n cleanup_docstamp_output(output_dir)\\n\\n return file_path\",\n \"def generate_contract_de_fdf_pdf(user):\\n if DEBUG: # pragma: no cover\\n print \\\"===== this is generate_fdf_pdf\\\"\\n from fdfgen import forge_fdf\\n fields = [\\n ('surname', user.surname),\\n ('lastname', user.lastname),\\n ('street', user.street),\\n ('number', user.number),\\n ('postcode', user.postcode),\\n ('city', user.city),\\n ('email', user.email),\\n ('user_id', user.id),\\n ('username', user.username),\\n ('date_registered', user.date_registered),\\n ('date_generated', datetime.now()),\\n ]\\n #generate fdf string\\n fdf = forge_fdf(\\\"\\\", fields, [], [], [])\\n # write to file\\n my_fdf_filename = \\\"fdf\\\" + str(user.id) + \\\".fdf\\\"\\n\\n fdf_file = open(my_fdf_filename, \\\"w\\\")\\n fdf_file.write(fdf)\\n fdf_file.close()\\n if DEBUG: # pragma: no cover\\n print \\\"fdf file written.\\\"\\n\\n res = os.popen(\\n 'pdftk pdftk/berechtigungsvertrag-2.2.pdf fill_form %s output'\\n ' formoutput.pdf flatten' % my_fdf_filename).read()\\n\\n if DEBUG: # pragma: no cover\\n print res\\n print \\\"done: put data into form and finalized it\\\"\\n\\n # delete the fdf file\\n res = os.popen('rm %s' % my_fdf_filename)\\n if DEBUG: # pragma: no cover\\n print res\\n print \\\"combining with bank account form\\\"\\n # combine\\n res = os.popen(\\n 'pdftk formoutput.pdf pdftk/bankaccount.pdf output output.pdf').read()\\n if DEBUG: # pragma: no cover\\n print res\\n print \\\"combined personal form and bank form\\\"\\n\\n # delete the fdf file\\n os.popen('rm formoutput.pdf').read()\\n\\n # return a pdf file\\n from pyramid.response import Response\\n response = Response(content_type='application/pdf')\\n response.app_iter = open(\\\"output.pdf\\\", \\\"r\\\")\\n return response\",\n \"def create_pdf(self):\\n\\n my_datetime = datetime.now()\\n self.pdf_name = (\\n self.pdf_name + \\\"_\\\" + my_datetime.strftime(\\\"%H%M_%d%m%Y\\\") + \\\".pdf\\\"\\n )\\n fig_width = aW * self.column_ratio[0]\\n\\n clm_width_meta = (aW * self.column_ratio[1]) / len(self.fields)\\n\\n c = canvas.Canvas(os.path.join(self.pdf_folder, self.pdf_name), pagesize=A4)\\n\\n for qc_run_id, fig_file in sorted(self._files.items()):\\n (param_values, feature_values) = get_param_values(\\n qc_run_id, self.db_name, return_meta_add_on=True\\n )\\n\\n comment = self.subject + \\\"<br/>\\\"\\n # c.saveState()\\n title = \\\"Dataset \\\" + qc_run_id\\n\\n # Prepare header\\n header = Paragraph(title, title_style)\\n h_w, h_h = header.wrap(aW, aH)\\n\\n # Prepare image\\n img = ImageReader(fig_file)\\n im_width, im_height = img.getSize()\\n aspect = im_height / float(im_width)\\n fig_height = fig_width * aspect\\n\\n # Prepare metadata section\\n\\n meta_table = Table(\\n param_values,\\n colWidths=[clm_width_meta] * len(self.fields),\\n hAlign=\\\"CENTER\\\",\\n rowHeights=0.22 * inch,\\n )\\n meta_table.setStyle(\\n TableStyle(\\n [\\n (\\\"FONT\\\", (0, 0), (-1, 0), \\\"Helvetica-Bold\\\"),\\n (\\\"FONT\\\", (0, 1), (-1, -1), \\\"Helvetica\\\"),\\n (\\\"LINEBELOW\\\", (0, 0), (1, 0), 0.08, colors.black),\\n (\\\"SIZE\\\", (0, 0), (-1, -1), 8),\\n (\\\"VALIGN\\\", (0, 0), (-1, -1), \\\"BOTTOM\\\"),\\n # ('ALIGN', (0, 0), (-1, 0), 'CENTER'),\\n (\\\"ALIGN\\\", (0, 0), (0, -1), \\\"LEFT\\\"),\\n (\\\"ALIGN\\\", (1, 1), (1, -1), \\\"LEFT\\\"),\\n (\\\"INNERGRID\\\", (0, 0), (-1, -1), 0.08, colors.beige),\\n # ('BOX', (0,0), (-1,-1), 0.25, colors.grey),\\n ]\\n )\\n )\\n\\n meta_width, meta_height = meta_table.wrap(aW - im_width, aH / 2)\\n\\n # Prepare comments header\\n comments_header = Paragraph(\\\"Comments:\\\", title_style)\\n avail_height = aH - fig_height - v_padding\\n comm_h_width, comm_h_height = comments_header.wrap(\\n im_width, avail_height # aW - meta_width,\\n )\\n # Prepare comments\\n my_datetime = datetime.now()\\n ts = \\\"Printed on \\\" + my_datetime.strftime(\\\"%c\\\")\\n\\n try:\\n data_specific_comment = self.comments[int(qc_run_id)]\\n comment += data_specific_comment + \\\"<br/>\\\"\\n comment += self.comments[\\\"general\\\"] + \\\"<br/>\\\"\\n\\n comment += self.smalltalk + \\\"<br/>\\\"\\n except Exception:\\n logger.warning(\\n \\\"Unable to summarize result of \\\" + \\\"dataset {}\\\".format(qc_run_id)\\n )\\n comment_ts = comment + ts\\n comment_ts = textwrap.fill(comment_ts, 70)\\n comment_ts = comment_ts.replace(\\\"\\\\n\\\", \\\"<br/>\\\")\\n\\n comments_p = Paragraph(comment_ts, body_style)\\n\\n avail_height = aH - fig_height - v_padding - comm_h_height\\n\\n comm_width, comm_height = comments_p.wrap(im_width, avail_height) # aW,\\n\\n line_widths = comments_p.getActualLineWidths0()\\n number_of_lines = len(line_widths)\\n if number_of_lines > 1:\\n pass\\n if number_of_lines == 1:\\n min(line_widths)\\n comm_width, comm_height = comments_p.wrap(im_width, avail_height)\\n\\n # Prepare features\\n feat_table = Table(\\n feature_values,\\n colWidths=[clm_width_meta] * len(self.fields),\\n hAlign=\\\"CENTER\\\",\\n rowHeights=0.22 * inch,\\n )\\n feat_table.setStyle(\\n TableStyle(\\n [\\n (\\\"FONT\\\", (0, 0), (-1, 0), \\\"Helvetica-Bold\\\"),\\n (\\\"FONT\\\", (0, 1), (-1, -1), \\\"Helvetica\\\"),\\n (\\\"LINEBELOW\\\", (0, 0), (1, 0), 0.08, colors.black),\\n (\\\"SIZE\\\", (0, 0), (-1, -1), 8),\\n (\\\"VALIGN\\\", (0, 0), (-1, -1), \\\"BOTTOM\\\"),\\n # ('ALIGN', (0, 0), (-1, 0), 'CENTER'),\\n (\\\"ALIGN\\\", (0, 0), (0, -1), \\\"LEFT\\\"),\\n (\\\"ALIGN\\\", (1, 1), (1, -1), \\\"LEFT\\\"),\\n (\\\"INNERGRID\\\", (0, 0), (-1, -1), 0.08, colors.beige),\\n # ('BOX', (0,0), (-1,-1), 0.25, colors.grey),\\n ]\\n )\\n )\\n avail_height = aH - meta_height # fig_height - v_padding - comm_h_height\\n avail_height -= comm_height\\n feat_width, feat_height = feat_table.wrap(aW - im_width, avail_height)\\n\\n # Draw everyting on canvas\\n\\n header.drawOn(c, left_margin, aH - top_margin)\\n\\n c.drawImage(\\n img,\\n left_margin,\\n aH - top_margin - fig_height - v_padding,\\n width=fig_width * 1.1,\\n height=fig_height * 1.1,\\n mask=\\\"auto\\\",\\n )\\n\\n meta_table.drawOn(\\n c,\\n left_margin + fig_width + h_padding,\\n aH - meta_height - top_margin / 2, # - v_padding\\n )\\n\\n comments_header.drawOn(\\n c,\\n left_margin,\\n aH\\n - top_margin\\n - comm_h_height\\n - fig_height\\n - 2 * v_padding, # - add_on_height\\n )\\n\\n comments_p.drawOn(\\n c,\\n left_margin,\\n aH\\n - top_margin\\n - comm_h_height\\n - comm_height\\n - fig_height\\n - 2 * v_padding\\n - comm_h_height, # - add_on_height\\n )\\n\\n feat_table.drawOn(\\n c,\\n left_margin + fig_width + h_padding,\\n aH - meta_height - top_margin / 2 - feat_height - v_padding,\\n # top_margin - fig_height - 2*v_padding - feat_height\\n )\\n\\n # new page\\n c.showPage()\\n c.saveState()\\n\\n c.save()\",\n \"def download(texttitle):\\n try:\\n body = current_file.analysed_texts['Regular']\\n rendered = render_template('pdf_template.html', title=texttitle, body=body)\\n options = {'encoding': \\\"UTF-8\\\"}\\n pdf = pdfkit.from_string(rendered, False, options=options)\\n response = make_response(pdf)\\n response.headers[\\\"Content-Type\\\"] = 'application/pdf'\\n response.headers[\\\"Content-Disposition\\\"] = 'attachment; filename=output.pdf'\\n\\n return response\\n except Exception as e:\\n flash(\\\"Something went wrong, please try again\\\")\\n return redirect(request.referrer)\",\n \"def RenderTeX(self, Q, size=None, dpi=50):\\n code = Q.code\\n name = Q.name\\n tex = Q.tex\\n if (defaults.use_tex):\\n #try:\\n if (1):\\n # convert width/height in pixels to inches and add some padding\\n if (size is None):\\n w = defaults.tex_width\\n h = defaults.tex_height\\n else:\\n w,h = size\\n w *= defaults.tex_padding/dpi\\n h *= defaults.tex_padding/dpi\\n fig = Figure(figsize=(w,h))\\n formula = FigureCanvas(self, -1, fig)\\n\\n fig.clear()\\n fig.text(0.05, 0.25, tex, fontsize=defaults.tex_fontsize) # draw the LaTeX formula onto canvas\\n formula.draw()\\n #try:\\n # filename = os.path.join(defaults.quantity_code_image_path, \\\"{}.png\\\".format(code))\\n # png_image = wx.Image(filename, wx.BITMATP_TYPE_ANY).ConvertToBitmap()\\n # formula = wx.StaticBitmap(self, -1, png_image) #, pos=position, size=size)\\n else:\\n #except:\\n formula = wx.StaticText(self, -1, \\\"Error Importing Formula Image\\\")\\n else:\\n formula = wx.StaticText(self, -1, \\\"LaTeX was disabled\\\")\\n\\n return formula\",\n \"def generate_envelope(template_name, data):\\n environment = jinja2.Environment(loader=jinja2.FileSystemLoader(current_app.config['LATEX_TEMPLATE_DIRECTORY']),\\n **LATEX_TEMPLATE_CONFIG)\\n document = environment.get_template(template_name + '.tex').render(**data)\\n\\n return document\",\n \"def clean_pdf():\\n xnt.build.tex.clean(path=\\\"./\\\", remove_pdf=True)\",\n \"def _pdf(self):\\n # LOG: processing_type property\\n self.set_property('processing_type', 'pdf')\\n xmlDoc = PDFiD(self.src_path)\\n oPDFiD = cPDFiD(xmlDoc, True)\\n # TODO: are there other characteristics which should be dangerous?\\n if oPDFiD.encrypt.count > 0:\\n self.make_dangerous('encrypted pdf')\\n if oPDFiD.js.count > 0 or oPDFiD.javascript.count > 0:\\n self.make_dangerous('pdf with javascript')\\n if oPDFiD.aa.count > 0 or oPDFiD.openaction.count > 0:\\n self.make_dangerous('openaction')\\n if oPDFiD.richmedia.count > 0:\\n self.make_dangerous('flash')\\n if oPDFiD.launch.count > 0:\\n self.make_dangerous('launch')\",\n \"def test_buildTeX(self):\\n version = \\\"3.2.1\\\"\\n input1, output1 = self.getArbitraryLoreInputAndOutput(version)\\n input2, output2 = self.getArbitraryLoreInputAndOutput(version)\\n\\n # Filenames are chosen by getArbitraryOutput to match the counter used\\n # by getArbitraryLoreInputAndOutput.\\n self.howtoDir.child(\\\"1.xhtml\\\").setContent(input1)\\n self.howtoDir.child(\\\"2.xhtml\\\").setContent(input2)\\n\\n builder = BookBuilder()\\n builder.buildTeX(self.howtoDir)\\n self.assertEqual(self.howtoDir.child(\\\"1.tex\\\").getContent(), output1)\\n self.assertEqual(self.howtoDir.child(\\\"2.tex\\\").getContent(), output2)\",\n \"def render(path: str, params: dict):\\n template = get_template(path)\\n html = template.render(params)\\n response = BytesIO()\\n # is this file name a placeholder?\\n #file = open('my.file.pdf', 'wb')\\n pdf = pisa.pisaDocument(BytesIO(html.encode(\\\"UTF-8\\\")), response)\\n #file.close()\\n if not pdf.err:\\n return HttpResponse(response.getvalue(), content_type='application/pdf')\\n else:\\n return HttpResponse(\\\"Error Rendering PDF\\\", status=400)\",\n \"def createHtml(target, source, env):\\n os.system('asciidoc -d manpage -b xhtml11 -o ' + str(target[0]) + ' ' +\\n str(source[0]))\",\n \"def render_latex(latex, options=None):\\n\\n # Combine caller-defined options with the default options\\n katex_options = KATEX_DEFAULT_OPTIONS\\n if options is not None:\\n katex_options = katex_options.copy()\\n katex_options.update(options)\\n\\n server = KaTeXServer.get()\\n request = {\\\"latex\\\": latex, \\\"katex_options\\\": katex_options}\\n\\n try:\\n response = server.render(request, RENDER_TIMEOUT)\\n\\n if \\\"html\\\" in response:\\n return response[\\\"html\\\"]\\n elif \\\"error\\\" in response:\\n raise KaTeXError(response[\\\"error\\\"])\\n else:\\n raise KaTeXError(\\\"Unknown response from KaTeX renderer\\\")\\n except socket.timeout:\\n raise KaTeXError(TIMEOUT_EXPIRED_TEMPLATE.format(latex))\",\n \"def render_as_pdf(self, width, height):\\n pass\",\n \"def load_pdf(self, env=\\\"default\\\", debug=()):\\n os.makedirs(\\\"txt\\\", exist_ok=True)\\n if env is \\\"default\\\": # default python path\\n call([executable,\\n os.path.join(f\\\"{exec_prefix}\\\", \\\"Scripts\\\", \\\"pdf2txt.py\\\"),\\n os.path.join(\\\"pdf\\\", f\\\"{self.pdf_filename}\\\"),\\n os.path.join(f\\\"-otxt\\\", f\\\"{self.txt_filename}\\\")])\\n if env is \\\"venv\\\": # virtual environment\\n call([os.path.join(\\\"venv\\\", \\\"Scripts\\\", \\\"python.exe\\\"),\\n os.path.join(\\\"venv\\\", \\\"Scripts\\\", \\\"pdf2txt.py\\\"),\\n os.path.join(\\\"pdf\\\", f\\\"{self.pdf_filename}\\\"),\\n os.path.join(f\\\"-otxt\\\", f\\\"{self.txt_filename}\\\")])\\n with open(os.path.join(\\\"txt\\\", f\\\"{self.txt_filename}\\\"), \\\"r\\\", encoding=\\\"utf-8\\\") as file:\\n self.paragraphs = [paragraph.rstrip('\\\\n') for paragraph in file]\\n os.remove(os.path.join(\\\"txt\\\", f\\\"{self.txt_filename}\\\"))\\n if debug:\\n for counter, paragraph in enumerate(self.paragraphs):\\n try:\\n if int(debug[0]) < counter < int(debug[1]):\\n print(counter, paragraph)\\n except TypeError:\\n print(\\\"Debug must be a (x,y) touple.\\\")\",\n \"def create_bat_file(self):\\n\\n bat_commands = '@echo off\\\\n'\\n bat_commands = bat_commands + 'rem @author Viktor Dmitriyev\\\\n'\\n\\n bat_commands = bat_commands + '\\\\necho \\\"Compiling LaTex to PDF. Please, wait ...\\\"\\\\n'\\n bat_commands = bat_commands + 'pdflatex {}\\\\n'.format(self.config.TEX_FILE)\\n bat_commands = bat_commands + 'pdflatex {}\\\\n'.format(self.config.TEX_FILE)\\n\\n bat_commands = bat_commands + '\\\\necho \\\"Deleting unnecessary files\\\"\\\\n'\\n bat_commands = bat_commands + 'del *.aux\\\\n'\\n bat_commands = bat_commands + 'del *.log\\\\n'\\n bat_commands = bat_commands + '\\\\necho \\\"Opening pdf\\\"\\\\n'\\n bat_commands = bat_commands + 'pdflatex {}\\\\n'.format(self.config.TEX_FILE[:-3] + 'pdf')\\n\\n self.dir_helper.save_file(self.temp_dir + self.config.BAT_FILE_NAME, bat_commands)\\n\\n print '[i] batch file was created: \\\"{}\\\"'.format(self.config.BAT_FILE_NAME)\",\n \"def create_gar(self):\\n print('Maketh the report!')\\n # Date setup\\n date = datetime.today().strftime('%Y-%m-%d')\\n year = datetime.today().strftime('%Y')\\n\\n # Page setup\\n geometry_options = {\\\"tmargin\\\": \\\"2cm\\\",\\n \\\"lmargin\\\": \\\"1.8cm\\\",\\n \\\"rmargin\\\": \\\"1.8cm\\\",\\n \\\"headsep\\\": \\\"1cm\\\"}\\n\\n doc = pylatex.Document(page_numbers=False,\\n geometry_options=geometry_options)\\n\\n header = self.produce_header_footer()\\n\\n doc.preamble.append(header)\\n doc.change_document_style(\\\"header\\\")\\n\\n #\\n # DOCUMENT BODY/CREATION\\n with doc.create(pylatex.Section('GeneSippr Analysis Report', numbering=False)):\\n doc.append('GeneSippr!')\\n\\n with doc.create(pylatex.Subsection('GeneSeekr Analysis', numbering=False)) as genesippr_section:\\n with doc.create(pylatex.Tabular('|c|c|c|c|c|c|c|c|c|c|c|c|c|c|c|c|c|c|c|')) as table:\\n # Header\\n table.add_hline()\\n table.add_row(self.genesippr_table_columns)\\n for sample_name in self.samples:\\n table_data = [sample_name]\\n for data in self.genesippr_headers:\\n try:\\n print(sample_name, data, self.report_data['genesippr'][sample_name][data])\\n table_data.append(self.report_data['genesippr'][sample_name][data])\\n except KeyError:\\n pass\\n table.add_row(table_data)\\n self.create_caption(genesippr_section, 'a', \\\"+ indicates marker presence : \\\"\\n \\\"- indicates marker was not detected\\\")\\n\\n # Create the PDF\\n doc.generate_pdf('{}_{}_{}'\\n .format(os.path.join('/home/adamkoziol/Bioinformatics/sippr/gui/161104_M02466_0002_000000000-AV4G5'), 'gar', date), clean_tex=False)\\n print('{}_{}_{}'.format(os.path.join('/home/adamkoziol/Bioinformatics/sippr/gui/161104_M02466_0002_000000000-AV4G5'), 'gar', date))\\n # for report_name in self.report_data:\\n # for sample_name in self.samples:\\n # for header, value in self.report_data[report_name][sample_name].items():\\n # print(report_name, sample_name, header, value)\",\n \"def generate_page_pdf(self, src, out, options=None):\\n loger.info(\\\"start pdf creation\\\")\\n with open(src, 'r') as f:\\n print(pdfkit.from_file(f, out, options=self.__soft_cover_options if not options else\\n \\\"Lol\\\"))\\n loger.info(\\\"end pdf creation\\\")\",\n \"def generate_pdf(products, quantities, pdf_filename):\\n # Construct a document.\\n doc = reportlab.platypus.BaseDocTemplate(\\n pdf_filename,\\n pagesize=reportlab.lib.pagesizes.letter,\\n title=\\\"Art Mart Inventory Sheet Check In/Out\\\",\\n # showBoundary=True\\n )\\n\\n # Construct a frame.\\n page_width = doc.pagesize[0]\\n page_height = doc.pagesize[1]\\n frames = [\\n reportlab.platypus.Frame(\\n id=\\\"table_frame\\\",\\n x1=0.25 * INCH,\\n y1=1.60 * INCH,\\n width=page_width - 0.45 * INCH,\\n height=page_height - 3.60 * INCH,\\n leftPadding=0,\\n bottomPadding=0,\\n rightPadding=0,\\n topPadding=0\\n )\\n ]\\n\\n # Construct a template and add it to the document.\\n doc.addPageTemplates(\\n reportlab.platypus.PageTemplate(\\n id=\\\"mytemplate\\\",\\n frames=frames,\\n onPage=on_page\\n )\\n )\\n\\n # Construct a story and add it to the document.\\n col_widths = [\\n 0.40 * INCH, # Existing Qty\\n 1.15 * INCH, # Barcode\\n 0.40 * INCH, # Qty Added\\n 0.60 * INCH, # Price\\n 5.00 * INCH, # Description\\n 0.40 * INCH, # Total Qty\\n ]\\n col_header_style = reportlab.lib.styles.ParagraphStyle(\\n name=\\\"col_header\\\",\\n fontName=\\\"Helvetica-Bold\\\",\\n fontSize=10\\n )\\n col_center_style = reportlab.lib.styles.ParagraphStyle(\\n name=\\\"col_center\\\",\\n fontName=\\\"Helvetica-Bold\\\",\\n fontSize=10,\\n alignment=reportlab.lib.enums.TA_CENTER\\n )\\n col_align_right_style = reportlab.lib.styles.ParagraphStyle(\\n name=\\\"col_align_right\\\",\\n fontName=\\\"Helvetica-Bold\\\",\\n fontSize=10,\\n alignment=reportlab.lib.enums.TA_RIGHT\\n )\\n # TableStyle cell formatting commands.\\n styles = [\\n # Whole table.\\n ('FONTSIZE', (0, 0), (-1, -1), 10),\\n ('TOPPADDING', (0, 1), (-1, -1), 0.26 * INCH),\\n ('BOTTOMPADDING', (0, 1), (-1, -1), 0.10 * INCH),\\n # Existing Qty\\n ('LEFTPADDING', (0, 0), (0, -1), 0),\\n # Barcode\\n ('ALIGN', (1, 0), (1, 0), 'CENTER'),\\n ('ALIGN', (1, 0), (1, -1), 'RIGHT'),\\n ('FONT', (1, 0), (1, -1), 'Courier-Bold'),\\n # Qty Added\\n ('LEFTPADDING', (2, 0), (2, 0), 0),\\n ('ALIGN', (2, 0), (2, 0), 'LEFT'),\\n ('ALIGN', (2, 1), (2, -1), 'RIGHT'),\\n ('FONT', (2, 0), (2, -1), 'Courier-Bold'),\\n # Price\\n ('ALIGN', (3, 0), (3, -1), 'RIGHT'),\\n ('FONT', (3, 0), (3, -1), 'Courier-Bold'),\\n # Description\\n ('ALIGN', (4, 0), (4, -1), 'LEFT'),\\n ('FONT', (4, 0), (4, -1), 'Helvetica'),\\n # Total Qty\\n ('LEFTPADDING', (5, 0), (5, -1), 0),\\n ('ALIGN', (5, 0), (5, 0), 'LEFT'),\\n # Other\\n ('FONT', (0, 0), (-1, 0), 'Helvetica-Bold'),\\n # ('GRID', (0, 0), (-1, -1), 1.0, BLACK)\\n ]\\n header_row = (\\n reportlab.platypus.Paragraph(\\\"Existing<br/>Qty\\\", col_header_style),\\n reportlab.platypus.Paragraph(\\\"Barcode<br/> \\\", col_center_style),\\n reportlab.platypus.Paragraph(\\\"Qty<br/>Added\\\", col_header_style),\\n reportlab.platypus.Paragraph(\\n \\\"Price<br/> \\\",\\n col_align_right_style\\n ),\\n reportlab.platypus.Paragraph(\\n \\\"Description<br/> \\\",\\n col_header_style\\n ),\\n reportlab.platypus.Paragraph(\\\"Total<br/>Qty\\\", col_header_style)\\n )\\n table_data = [header_row]\\n for product in products:\\n sku = product[\\\"SKU\\\"]\\n if sku in quantities:\\n quantity = quantities[sku]\\n # Round price to nearest dollar.\\n price = product[\\\"Price\\\"]\\n price = \\\"${.0f}\\\".format(math.trunc(float(price) + 0.5))\\n description = \\\"{}: {}\\\".format(\\n product[\\\"Product Name\\\"],\\n cctools.html_to_plain_text(product[\\\"Teaser\\\"])\\n )\\n description = description[:68]\\n table_data.append(\\n (\\\"_____\\\", sku, quantity, price, description, \\\"_____\\\")\\n )\\n table = reportlab.platypus.Table(\\n data=table_data,\\n colWidths=col_widths,\\n style=styles,\\n repeatRows=1\\n )\\n table.hAlign = \\\"LEFT\\\"\\n story = [table]\\n doc.build(story, canvasmaker=NumberedCanvas)\",\n \"def pdf(self,x):\\n returnPdf = self._distribution.pdf(x)\\n return returnPdf\",\n \"def generate_document(stats: dict, semester: str):\\n filename = 'report_' + str(date.today()) + '.html'\\n with open('raw_html.html', 'r') as f:\\n string = f.read()\\n string = string.format(semester,\\n stats['faculty_with_usage'],\\n stats['full_time'],\\n stats['total_full_time'],\\n round((stats['full_time'] / stats['total_full_time']) * 100, 1),\\n stats['part_time'],\\n stats['total_part_time'],\\n round((stats['part_time'] / stats['total_part_time']) * 100, 1),\\n stats['staff'],\\n stats['courses_with_usage'],\\n stats['total_courses'],\\n round((stats['courses_with_usage'] / stats['total_courses']) * 100, 1),\\n stats['specifics']['assignments'],\\n stats['specifics']['grade'],\\n stats['specifics']['graded'],\\n stats['specifics']['discussion'])\\n with open(filename, 'w') as f:\\n f.write(string)\\n pdf = weasyprint.HTML(filename).write_pdf()\\n open(\\\"report_\\\" + str(date.today()) + \\\".pdf\\\", 'wb').write(pdf)\",\n \"def create_latex_table(data, id):\\n bd = data['bd']\\n sd = data['sd']\\n \\n filename = 'LatestResults.tex'\\n file = r'..\\\\latex\\\\tables\\\\\\\\' + filename\\n\\n if os.path.exists(file):\\n f_temp = os.path.splitext(file)[0] # without extension\\n os.rename(file, f_temp + '_' + id + '.tex')\\n\\n f = codecs.open(file, 'w', 'utf-8')\\n \\n f.write('\\\\n' + r'\\\\begin{table}' + '\\\\n')\\n f.write(r' \\\\centering' + '\\\\n')\\n f.write(r' \\\\caption{Results for each drum instrument with batch sizes 64, 256 and 512.}' + '\\\\n')\\n f.write(r' \\\\begin{tabular}{l c c c}' + '\\\\n')\\n f.write(r' \\\\textbf{Batch size} & Metric & BD & SD \\\\\\\\' + '\\\\n')\\n f.write(r' \\\\midrule' + '\\\\n')\\n f.write(r' \\\\midrule' + '\\\\n')\\n \\n for batch_size in BATCHES:\\n f.write(' ' + str(batch_size).rstrip('\\\\n'))\\n # 0.805 +- 0.02\\n f.write(r' & P & ' + r'$' + '{:.3}'.format(bd[batch_size]['p_mean']) + r' \\\\pm ' + '{:.3f}'.format(bd[batch_size]['p_std']) + '$' + r' & ' + r'$' + '{:.3}'.format(sd[batch_size]['p_mean']) + r' \\\\pm ' + '{:.3f}'.format(sd[batch_size]['p_std']) + '$' + r' \\\\\\\\' + '\\\\n')\\n f.write(r' & R & ' + r'$' + '{:.3}'.format(bd[batch_size]['r_mean']) + r' \\\\pm ' + '{:.3f}'.format(bd[batch_size]['r_std']) + '$' + r' & ' + r'$' + '{:.3}'.format(sd[batch_size]['r_mean']) + r' \\\\pm ' + '{:.3f}'.format(sd[batch_size]['r_std']) + '$' + r' \\\\\\\\' + '\\\\n')\\n f.write(r' & F & ' + r'$' + '{:.3}'.format(bd[batch_size]['f_mean']) + r' \\\\pm ' + '{:.3f}'.format(bd[batch_size]['f_std']) + '$' + r' & ' + r'$' + '{:.3}'.format(sd[batch_size]['f_mean']) + r' \\\\pm ' + '{:.3f}'.format(sd[batch_size]['f_std']) + '$' + r' \\\\\\\\' + '\\\\n')\\n # Don't write horizontal line on the last batch.\\n if batch_size != BATCHES[-1]:\\n f.write(r' \\\\midrule' + '\\\\n')\\n\\n f.write(r' \\\\end{tabular}' + '\\\\n')\\n f.write(r' \\\\label{tab:ResultsTable}' + '\\\\n')\\n f.write(r'\\\\end{table}' + '\\\\n')\\n f.close()\",\n \"def genLatex(exePath,inputDict,latexName,lng):\\n paramFile = path.join(path.dirname(inputDict),'damo.par')\\n with open(paramFile,'w') as f:\\n f.write('LATEX'+'\\\\n')\\n f.write(inputDict+'\\\\n')\\n f.write(latexName+'\\\\n')\\n f.write(lng)\\n runDamocles(exePath, paramFile)\\n remove(paramFile)\",\n \"def make_pdf(self, net_id, request_id, request_date):\\n with open(\\\"{0}/user_uploads/{1}/{2}/submission.json\\\".format(self.__APP_PATH__, net_id, request_id), mode=\\\"r\\\") as json_file:\\n request_details = json.load(json_file)\\n\\n files_text = \\\"\\\"\\n travel_text = None\\n\\n if request_details[\\\"request_type\\\"] == \\\"travel\\\":\\n travel_text = \\\"\\\\n\\\\nTravel Details:\\\\n\\\" \\\\\\n \\\"\\\\t\\\\t\\\\t\\\\tTravel from: {0} ({1})\\\\n\\\" \\\\\\n \\\"\\\\t\\\\t\\\\t\\\\tTravel to: {2} ({3})\\\\n\\\" \\\\\\n \\\"\\\\t\\\\t\\\\t\\\\tTravel Number: {4}\\\\n\\\" \\\\\\n \\\"\\\\t\\\\t\\\\t\\\\tEvent Website: {5}\\\".format(request_details[\\\"travel_from\\\"],\\n request_details[\\\"travel_from_date\\\"],\\n request_details[\\\"travel_to\\\"],\\n request_details[\\\"travel_to_date\\\"],\\n request_details[\\\"travel_number\\\"],\\n request_details.get(\\\"event_website\\\", \\\"N/A\\\"))\\n for file in request_details[\\\"files\\\"]:\\n amount_text = \\\"${0}\\\".format(file[\\\"dollar_amount\\\"]) if file[\\\"dollar_amount\\\"] > 0.0 else \\\"Auxiliary File\\\"\\n files_text += \\\"\\\\t\\\\t\\\\t\\\\t{0} ({1})\\\\n\\\\t\\\\t\\\\t\\\\t\\\\t\\\\t\\\\t\\\\t\\\" \\\\\\n \\\"{2}\\\\n\\\\t\\\\t\\\\t\\\\t\\\\t\\\\t\\\\t\\\\t{3}\\\\n\\\\n\\\".format(file[\\\"label\\\"], amount_text,\\n file[\\\"name\\\"], file[\\\"description\\\"])\\n\\n if request_details[\\\"notes\\\"].strip():\\n request_notes = \\\"\\\\nNotes:\\\\n{0}\\\".format(request_details[\\\"notes\\\"].strip())\\n else:\\n request_notes = \\\"\\\"\\n\\n pdf_title = \\\"({0}) {1:02d}/{2:02d}/{3:04d} - {4:02d}:{5:02d}:{6:02d}, Amount: ${7}\\\".format(\\n request_details[\\\"request_date\\\"][\\\"weekday\\\"], request_details[\\\"request_date\\\"][\\\"month\\\"],\\n request_details[\\\"request_date\\\"][\\\"day\\\"], request_details[\\\"request_date\\\"][\\\"year\\\"],\\n request_details[\\\"request_date\\\"][\\\"hours\\\"], request_details[\\\"request_date\\\"][\\\"minutes\\\"],\\n request_details[\\\"request_date\\\"][\\\"seconds\\\"], request_details[\\\"total_amount\\\"])\\n\\n if request_details[\\\"pay_to\\\"][\\\"id\\\"]:\\n pay_to_details = \\\"{0} ({1}, {2})\\\".format(request_details[\\\"pay_to\\\"][\\\"name\\\"], request_details[\\\"pay_to\\\"][\\\"id\\\"],\\n request_details[\\\"pay_to\\\"][\\\"email\\\"])\\n else:\\n pay_to_details = \\\"{0} ({1})\\\".format(request_details[\\\"pay_to\\\"][\\\"name\\\"], request_details[\\\"pay_to\\\"][\\\"email\\\"])\\n\\n pdf_body = \\\"{0}{1}\\\\n\\\\nRequestee: \\\\n\\\\t\\\\t\\\\t\\\\tAccount:{2}\\\\n\\\\t\\\\t\\\\t\\\\tName: {3} {4} ({5})\\\\n\\\\t\\\\t\\\\t\\\\t\\\" \\\\\\n \\\"Phone: {6}\\\\t|\\\\tNet ID: {7}\\\\t\\\\n\\\\nPay To:\\\\n\\\\t\\\\t\\\\t\\\\tName: {8}{9}\\\\n\\\\n\\\" \\\\\\n \\\"Files:\\\\n{10}\\\".format(request_details[\\\"short_description\\\"], request_notes,\\n request_details[\\\"account_number\\\"],\\n request_details[\\\"requester\\\"][\\\"first_name\\\"],\\n request_details[\\\"requester\\\"][\\\"last_name\\\"],\\n request_details[\\\"requester\\\"][\\\"email\\\"],\\n request_details[\\\"requester\\\"][\\\"phone_number\\\"],\\n request_details[\\\"requester\\\"][\\\"net_id\\\"],\\n pay_to_details,\\n travel_text,\\n files_text)\\n try:\\n logo_path = \\\"{0}/static/assets/main/uta_logo.png\\\".format(self.__APP_PATH__.split(\\\"/apps/\\\")[0])\\n pdf = PDFMaker(**{\\\"title\\\": \\\"Reimbursement Request Report\\\"})\\n\\n pdf.set_margins(left=19.05, top=19.05, right=19.05)\\n pdf.set_auto_page_break(auto=True, margin=19.05)\\n pdf.set_author(\\\"MavApps - Reimbursement App\\\")\\n pdf.print_page(pdf_title, pdf_body)\\n pdf.image(logo_path, x=53, y=11, w=107, h=10, type=\\\"PNG\\\", link=\\\"https://uta.edu\\\")\\n pdf.output(\\\"{0}/user_uploads/{1}/{2}/[{1}-{3}]_report.pdf\\\".format(self.__APP_PATH__, net_id, request_id, request_date), \\\"F\\\")\\n except Exception as e:\\n print(e)\\n return False\\n return True\",\n \"def create_pdf(submission):\\n # Get questions from sections\\n fields = {}\\n for section in submission.questions:\\n for form in section[\\\"forms\\\"]:\\n for field in form[\\\"fields\\\"]:\\n fs = field.get(\\\"fields\\\", [field])\\n for f in fs:\\n fields[f[\\\"name\\\"]] = f\\n\\n # Pull out image and answers\\n images = []\\n docs = []\\n answers = []\\n for answer in submission.answers:\\n answer, name = answer.get(\\\"answer\\\", \\\"\\\"), answer.get(\\\"name\\\", \\\"\\\")\\n field = fields[name]\\n if field[\\\"type\\\"] == \\\"FILE\\\":\\n image_ids = []\\n doc_ids = []\\n for file in answer:\\n if \\\"image\\\" in file:\\n image_ids.append(file[\\\"id\\\"])\\n elif \\\"file\\\" in file:\\n doc_ids.append(file[\\\"id\\\"])\\n\\n if image_ids:\\n images += [\\n image_upload.image\\n for image_upload in ImageUpload.objects.filter(\\n pk__in=image_ids\\n ).all()\\n ]\\n if doc_ids:\\n docs += [\\n file_upload.file\\n for file_upload in FileUpload.objects.filter(pk__in=doc_ids).all()\\n ]\\n else:\\n answers.append(\\n {\\n \\\"name\\\": name.lower().replace(\\\"_\\\", \\\" \\\").capitalize(),\\n \\\"prompt\\\": field.get(\\\"prompt\\\", \\\"\\\"),\\n \\\"answers\\\": answer if type(answer) is list else [answer],\\n }\\n )\\n\\n context = {\\n \\\"submission\\\": submission,\\n \\\"answers\\\": answers,\\n \\\"images\\\": images,\\n \\\"docs\\\": docs,\\n }\\n pdf_html_str = render_to_string(\\\"client-intake.html\\\", context=context)\\n pdf_bytes = weasyprint.HTML(string=pdf_html_str).write_pdf()\\n return pdf_bytes\",\n \"def html():\\n env.file_ext = \\\".html\\\"\\n local(\\\"pandoc {input_files} -o {output_file}{file_ext} --standalone --bibliography={bib_file} --csl={csl_file} --toc --number-sections\\\".format(**env))\",\n \"def format_latex(self,str):\\n\\n # Characters that need to be escaped for latex:\\n escape_re = re.compile(r'(%|_|\\\\$)',re.MULTILINE)\\n # Magic command names as headers:\\n cmd_name_re = re.compile(r'^(@.*?):',re.MULTILINE)\\n # Magic commands \\n cmd_re = re.compile(r'(?P<cmd>@.+?\\\\b)(?!\\\\}\\\\}:)',re.MULTILINE)\\n # Paragraph continue\\n par_re = re.compile(r'\\\\\\\\$',re.MULTILINE)\\n\\n str = cmd_name_re.sub(r'\\\\n\\\\\\\\texttt{\\\\\\\\textbf{\\\\1}}:',str)\\n str = cmd_re.sub(r'\\\\\\\\texttt{\\\\g<cmd>}',str)\\n str = par_re.sub(r'\\\\\\\\\\\\\\\\',str)\\n str = escape_re.sub(r'\\\\\\\\\\\\1',str)\\n #file('/home/fperez/ipython/doc/magic.tex','w').write(str) # dbg\\n return str\",\n \"def render_pdf(\\n template: Union[List[str], str],\\n file_: IO,\\n url_fetcher=django_url_fetcher,\\n context: Optional[dict] = None,\\n):\\n context = context or {}\\n\\n if isinstance(template, str):\\n template = [template]\\n\\n html = select_template(template).render(context)\\n HTML(\\n string=html,\\n base_url=\\\"not-used://\\\",\\n url_fetcher=url_fetcher,\\n ).write_pdf(\\n target=file_,\\n )\",\n \"def main(input_filepath, latex):\\n logger = logging.getLogger(__name__)\\n\\n df = pd.read_csv(input_filepath)\\n out = df.head()\\n if latex:\\n out = out.to_latex()\\n print(out)\",\n \"def build_latex_standalone(x):\\n eingabe=[]\\n anhaenge_file=[]\\n eingabe.append(\\\"\\\\\\\\title{%s}\\\" %(x[2]))\\n eingabe.append(\\\"\\\\\\\\author{%s}\\\" %(x[1]))\\n eingabe.append(\\\"\\\\\\\\date{%s}\\\" %(x[0]))\\n eingabe.append(\\\"\\\\maketitle\\\") \\n eingabe.append(\\\"\\\\section{Infos}\\\")\\n eingabe.append(\\\"\\\\\\\\begin{tabularx}{\\\\linewidth}{@{}lX}\\\")\\n eingabe.append(r\\\"\\\\textbf{Anatrag/Beschluss wurde} & %s\\\\\\\\\\\" %(x[9]))\\n x[11]=x[11].replace(\\\" \\\",\\\"\\\")\\n kw=x[11].split(\\\",\\\")\\n for i in range(0,len(kw)):\\n if i==0:\\n eingabe.append(r\\\"\\\\textbf{Keyword:} & %s\\\\\\\\\\\" %(kw[i]))\\n else:\\n eingabe.append(r\\\" & %s\\\\\\\\\\\" %(kw[i]))\\n eingabe.append(\\\"\\\\end{tabularx}\\\")\\n eingabe.append(\\\"\\\\\\\\begin{tabularx}{\\\\linewidth}{@{}XXX}\\\")\\n eingabe.append(r\\\"\\\\textbf{Abstimmungsergebniss:}&&\\\\\\\\\\\")\\n eingabe.append(r\\\"Zustimmung & Ablehnung & Enthaltungen \\\\\\\\\\\")\\n eingabe.append(r\\\"{} & {} & {} \\\\\\\\\\\".format(x[6],x[7],x[8]))\\n eingabe.append(\\\"\\\\end{tabularx}\\\")\\n eingabe.append(\\\"\\\\section{Antrags/Beschlusstext}\\\")\\n eingabe.append(x[3])\\n eingabe.append(\\\"\\\\section{Begründung}\\\")\\n eingabe.append(x[4])\\n if x[23]==\\\"Ja\\\" and x[24]!=\\\"\\\":\\n delta=7\\n anzahl=int((len(x)-23)/delta)\\n if anzahl==1:\\n eingabe.append(\\\"\\\\section{Änderungsantrag}\\\")\\n eingabe.append(\\\"\\\\subsection*{Vorschlag}\\\")\\n eingabe.append(x[24])\\n eingabe.append(\\\"\\\\subsection*{Begründung}\\\")\\n eingabe.append(x[25]+\\\"\\\\\\\\vspace{1.5ex} \\\\\\\\\\\\\\\\\\\")\\n eingabe.append(\\\"\\\\\\\\begin{tabularx}{\\\\linewidth}{@{}XXX}\\\")\\n eingabe.append(r\\\"\\\\textbf{Abstimmungsergebniss:}&&\\\\\\\\\\\")\\n eingabe.append(r\\\"Zustimmung & Ablehnung & Enthaltungen \\\\\\\\\\\")\\n eingabe.append(r\\\"{} & {} & {} \\\\\\\\\\\".format(x[26],x[27],x[28]))\\n eingabe.append(r\\\"\\\\multicolumn{@{}2}{l}{\\\\textbf{Änderungsantrag wurde:}} & %s \\\\\\\\\\\" %(x[29]))\\n eingabe.append(\\\"\\\\end{tabularx}\\\")\\n else:\\n eingabe.append(\\\"\\\\section{Änderungsanträge}\\\")\\n for i in range(0,anzahl):\\n eingabe.append(\\\"\\\\subsection{Änderungsvorschlag %s}\\\" %(i+1))\\n eingabe.append(\\\"\\\\subsubsection*{Vorschlag}\\\")\\n eingabe.append(x[24+(delta*i)])\\n eingabe.append(\\\"\\\\subsubsection*{Begründung}\\\")\\n eingabe.append(x[25+(delta*i)]+\\\"\\\\\\\\vspace{1.5ex} \\\\\\\\\\\\\\\\\\\")\\n eingabe.append(\\\"\\\\\\\\begin{tabularx}{\\\\linewidth}{@{}XXX}\\\")\\n eingabe.append(r\\\"\\\\textbf{Abstimmungsergebniss:}&&\\\\\\\\\\\")\\n eingabe.append(r\\\"Zustimmung & Ablehnung & Enthaltungen \\\\\\\\\\\")\\n eingabe.append(r\\\"{} & {} & {} \\\\\\\\\\\".format(x[26+(delta*i)],x[27+(delta*i)],x[28+(delta*i)]))\\n eingabe.append(r\\\"\\\\multicolumn{@{}2}{l}{\\\\textbf{Änderungsantrag wurde:}} & %s \\\\\\\\\\\" %(x[29+(delta*i)]))\\n eingabe.append(\\\"\\\\end{tabularx}\\\")\\n if x[10]!=\\\"\\\":\\n #\\\\includepdf[pages=-]{Anhang/Geschaeftsordnung_Jugendausschuss.pdf}\\n eingabe.append(\\\"\\\\\\\\appendix\\\")\\n eingabe.append(\\\"\\\\section*{Anhang}\\\")\\n anhang=x[10].split(\\\",\\\")\\n bennenung=x[11].split(\\\",\\\")\\n eingabe[14]=eingabe[14]+\\\"\\\\\\\\\\\\\\\\ \\\\n Dieser Antrag enthält %s Anhänge: \\\" %(len(anhang))\\n for i in range(0,len(anhang)):\\n eingabe.append(\\\"\\\\subsection*{%s} \\\\label{An:%s}\\\" % (bennenung[i],str(i+1)))\\n eingabe.append(\\\"\\\\includepdf[pages=-]{%s}\\\" %(anhang[i]))\\n anhaenge_file.append(anhang[i])\\n if i!=len(anhang)-1:\\n eingabe[14]=eingabe[14]+\\\"\\\\\\\\nameref{An:%s}, \\\" % (str(i+1))\\n else:\\n eingabe[14]=eingabe[14]+\\\"\\\\\\\\nameref{An:%s} \\\" % (str(i+1))\\n \\n\\n \\n ausgabe=\\\"\\\"\\n for i in range(0,len(eingabe)):\\n ausgabe=ausgabe+eingabe[i]+\\\"\\\\n\\\"\\n \\n return ausgabe,anhaenge_file\",\n \"def create_bill_pdf(obj):\\n data = {\\n 'today': datetime.date.today(),\\n 'amount': obj.price,\\n 'customer_name': obj.company.company_name,\\n 'order_id': obj.pk,\\n }\\n pdf = render_to_pdf('pdf/invoice.html', data)\\n filename = obj.company.company_name + '_' + obj.promotion.campaign_name + '_' + \\\\\\n datetime.datetime.now().strftime(\\\"%Y-%m-%d\\\") + '.pdf'\\n obj.bill.save(filename, File(io.BytesIO(pdf.content)))\",\n \"def pdf_define(param_td):\\n parametric_family = param_td['parametric_family']\\n true_parameters = param_td['true_parameters']\\n\\n eval_str = 'scipy.stats.' + str(parametric_family) + \\\\\\n '.pdf' + '(x, ' + str(true_parameters)[1:-1] + ')'\\n\\n eval_str = eval_str.replace(\\\"'\\\", \\\"\\\").replace(\\\":\\\", \\\"=\\\")\\n\\n return lambda x: eval(eval_str) # define pdf_true\",\n \"def to_pdf(self, wkhtmltopdf: str, f, output_file: Optional[str] = None):\\n if output_file is None:\\n output_file = \\\"-\\\"\\n html = self(f)\\n with tempfile.NamedTemporaryFile(\\\"wb\\\", suffix=\\\".html\\\") as fd:\\n html.write(fd)\\n fd.flush()\\n res = subprocess.run([wkhtmltopdf, fd.name, output_file], stdin=subprocess.DEVNULL, capture_output=True)\\n if res.returncode != 0:\\n raise RuntimeError(\\\"%s exited with error %d: stderr: %s\\\", self.wkhtmltopdf, res.returncode, res.stderr)\\n if output_file == \\\"-\\\":\\n return res.stdout\",\n \"def generate():\\n local('cd doc && make clean && make html')\",\n \"def build(texFileLists):\\n LaTeX_CMD = [\\\"latex -shell-escape -halt-on-error {texfile}.tex\\\",\\n \\\"bibtex {texfile}.aux\\\",\\n \\\"latex -shell-escape -halt-on-error {texfile}.tex\\\",\\n \\\"pdflatex -shell-escape -halt-on-error {texfile}.tex\\\"]\\n\\n for texFile in texFileLists:\\n filename, file_extension = os.path.splitext(texFile)\\n for lcmd in LaTeX_CMD:\\n try:\\n call(lcmd.format(texfile=filename), shell=True)\\n except:\\n print \\\"Error occurs while building \\\", filename\\n exit(1)\",\n \"def _do_generate(*, build, out_dir, on_error):\\n if out_dir == \\\"<test>\\\":\\n out_dir = join(os.environ[\\\"TEST_TMPDIR\\\"], \\\"_builder_out\\\")\\n if not os.path.isabs(out_dir):\\n on_error(f\\\"--out_dir={out_dir} is not an absolute path\\\")\\n if os.path.exists(out_dir):\\n if len(os.listdir(out_dir)) > 0:\\n on_error(f\\\"--out_dir={out_dir} is not empty\\\")\\n else:\\n if verbose():\\n print(f\\\"+ mkdir -p {out_dir}\\\", flush=True)\\n os.makedirs(out_dir)\\n print(\\\"Generating HTML ...\\\")\\n pages = _call_build(build=build, out_dir=out_dir)\\n assert len(pages) > 0\\n # Disallow symlinks in the output dir.\\n for root, dirs, _ in os.walk(out_dir):\\n for one_dir in dirs:\\n for entry in os.scandir(f\\\"{root}/{one_dir}\\\"):\\n assert not entry.is_symlink(), entry.path\\n print(\\\"... done\\\")\",\n \"def get_raw_pdf(html_path, pdf_path, width='', height=''):\\n debug = False\\n if mg.EXPORT_IMAGES_DIAGNOSTIC: debug = True\\n try:\\n url = html_path.as_uri()\\n cmd_make_pdf = 'cmd_make_pdf not successfully generated yet'\\n \\\"\\\"\\\"\\n Unless Linux, MUST be in report directory otherwise won't carry across\\n internal links.\\n\\n Re: http://www.microsoft.com/resources/documentation/windows/xp/all/proddocs/en-us/ntcmds_shelloverview.mspx?mfr=true\\n \\\"\\\"\\\"\\n ## clear decks first so we can tell if image made or not\\n try:\\n os.remove(pdf_path)\\n except Exception:\\n pass\\n rel_url = os.path.split(url)[1]\\n cd_path = os.path.split(html_path)[0]\\n if mg.PLATFORM == mg.WINDOWS: ## using Pyinstaller\\n cmd_make_pdf = (\\n f'cd \\\"{cd_path}\\\" && '\\n f'\\\"{export_output.EXE_TMP}\\\\\\\\wkhtmltopdf.exe\\\" '\\n f'{width} {height} \\\"{rel_url}\\\" \\\"{pdf_path}\\\"')\\n elif mg.PLATFORM == mg.MAC:\\n cmd_make_pdf = (\\n f'cd \\\"{cd_path}\\\" && '\\n f'\\\"{mg.MAC_FRAMEWORK_PATH}/wkhtmltopdf\\\" '\\n f'{width} {height} \\\"{rel_url}\\\" \\\"{pdf_path}\\\"')\\n elif mg.PLATFORM == mg.LINUX:\\n cmd_make_pdf = f'wkhtmltopdf {width} {height} \\\"{url}\\\" \\\"{pdf_path}\\\"'\\n else:\\n raise Exception('Encountered an unexpected platform!')\\n ## wkhtmltopdf uses stdout to actually output the PDF - a good feature but stuffs up reading stdout for message\\n if debug: print(f'cmd_make_pdf: {cmd_make_pdf}')\\n export_output.shellit(cmd_make_pdf)\\n if not os.path.exists(pdf_path):\\n raise Exception(\\n f\\\"wkhtmltopdf didn't generate error but {pdf_path} not made \\\"\\n f'nonetheless. cmd_make_pdf: {cmd_make_pdf}')\\n if debug: print(f'Initial processing of {html_path} complete')\\n except Exception as e:\\n raise Exception(\\n f'get_raw_pdf command failed: {cmd_make_pdf}. Orig error: {b.ue(e)}')\\n return pdf_path\",\n \"def generate_roster_pdf(sched_act_ids, include_instructions):\\n\\n pdf_buffer = BytesIO()\\n h_margin = 1 * inch\\n v_margin = 0.5 * inch\\n doc = SimpleDocTemplate(pdf_buffer, pagesize=letter,\\n rightMargin=h_margin, leftMargin=h_margin,\\n topMargin=v_margin, bottomMargin=v_margin)\\n\\n elements = []\\n\\n styles = getSampleStyleSheet()\\n styles.add(ParagraphStyle(name=\\\"Center\\\", alignment=TA_CENTER))\\n styles.add(ParagraphStyle(name=\\\"BlockLetter\\\", fontSize=60, leading=72, alignment=TA_CENTER))\\n styles.add(ParagraphStyle(name=\\\"BlockLetterSmall\\\", fontSize=30, leading=72, alignment=TA_CENTER))\\n styles.add(ParagraphStyle(name=\\\"BlockLetterSmallest\\\", fontSize=20, leading=72, alignment=TA_CENTER))\\n styles.add(ParagraphStyle(name=\\\"ActivityAttribute\\\", fontSize=15, leading=18, alignment=TA_RIGHT))\\n\\n for i, said in enumerate(sched_act_ids):\\n sact = EighthScheduledActivity.objects.get(id=said)\\n\\n sponsor_names = sact.get_true_sponsors().values_list(\\\"first_name\\\",\\n \\\"last_name\\\")\\n sponsors_str = \\\"; \\\".join(l + \\\", \\\" + f for f, l in sponsor_names)\\n\\n room_names = sact.get_true_rooms().values_list(\\\"name\\\", flat=True)\\n if len(room_names) == 1:\\n rooms_str = \\\"Room \\\" + room_names[0]\\n else:\\n rooms_str = \\\"Rooms: \\\" + \\\", \\\".join(r for r in room_names)\\n\\n block_letter = sact.block.block_letter\\n\\n if len(block_letter) < 4:\\n block_letter_width = 1 * inch\\n block_letter_width += (0.5 * inch) * (len(block_letter) - 1)\\n block_letter_style = \\\"BlockLetter\\\"\\n elif len(block_letter) < 7:\\n block_letter_width = 0.4 * inch\\n block_letter_width += (0.3 * inch) * (len(block_letter) - 1)\\n block_letter_style = \\\"BlockLetterSmall\\\"\\n else:\\n block_letter_width = 0.3 * inch\\n block_letter_width += (0.2 * inch) * (len(block_letter) - 1)\\n block_letter_style = \\\"BlockLetterSmallest\\\"\\n\\n header_data = [[\\n Paragraph(\\\"<b>Activity ID: {}<br />Scheduled ID: {}</b>\\\".format(sact.activity.id, sact.id), styles[\\\"Normal\\\"]),\\n Paragraph(\\\"{}<br/>{}<br/>{}\\\".format(sponsors_str,\\n rooms_str,\\n sact.block.date.strftime(\\\"%A, %B %-d, %Y\\\")),\\n styles[\\\"ActivityAttribute\\\"]),\\n Paragraph(block_letter, styles[block_letter_style])\\n ]]\\n header_style = TableStyle([\\n (\\\"VALIGN\\\", (0, 0), (0, 0), \\\"TOP\\\"),\\n (\\\"VALIGN\\\", (1, 0), (2, 0), \\\"MIDDLE\\\"),\\n (\\\"TOPPADDING\\\", (0, 0), (0, 0), 15),\\n (\\\"RIGHTPADDING\\\", (1, 0), (1, 0), 0),\\n ])\\n\\n elements.append(Table(header_data, style=header_style, colWidths=[2 * inch, None, block_letter_width]))\\n elements.append(Spacer(0, 10))\\n elements.append(Paragraph(sact.full_title, styles[\\\"Title\\\"]))\\n\\n num_members = sact.members.count()\\n num_members_label = \\\"{} Student{}\\\".format(num_members, \\\"s\\\" if num_members != 1 else \\\"\\\")\\n elements.append(Paragraph(num_members_label, styles[\\\"Center\\\"]))\\n elements.append(Spacer(0, 5))\\n\\n attendance_data = [[\\n Paragraph(\\\"Present\\\", styles[\\\"Heading5\\\"]),\\n Paragraph(\\\"Student Name (ID)\\\", styles[\\\"Heading5\\\"]),\\n Paragraph(\\\"Grade\\\", styles[\\\"Heading5\\\"])\\n ]]\\n\\n members = []\\n for member in sact.members.all():\\n members.append((\\n member.last_name + \\\", \\\" + member.first_name,\\n (member.student_id if member.student_id else \\\"User {}\\\".format(member.id)),\\n int(member.grade) if member.grade else \\\"?\\\"\\n ))\\n members = sorted(members)\\n\\n for member_name, member_id, member_grade in members:\\n row = [\\\"\\\", \\\"{} ({})\\\".format(member_name, member_id), member_grade]\\n attendance_data.append(row)\\n\\n # Line commands are like this:\\n # op, start, stop, weight, colour, cap, dashes, join, linecount, linespacing\\n attendance_style = TableStyle([\\n (\\\"LINEABOVE\\\", (0, 1), (2, 1), 1, colors.black, None, None, None, 2),\\n (\\\"LINEBELOW\\\", (0, 1), (0, len(attendance_data)), 1, colors.black),\\n (\\\"TOPPADDING\\\", (0, 1), (-1, -1), 6),\\n (\\\"BOTTOMPADDING\\\", (0, 1), (-1, -1), 0),\\n (\\\"BOTTOMPADDING\\\", (0, 0), (-1, 0), 5),\\n ])\\n\\n elements.append(Table(attendance_data, style=attendance_style, colWidths=[1.3 * inch, None, 0.8 * inch]))\\n elements.append(Spacer(0, 15))\\n instructions = \\\"\\\"\\\"\\n <b>Highlight or circle</b> the names of students who are <b>absent</b>, and put an <b>\\\"X\\\"</b> next to those <b>present</b>.<br />\\n If a student arrives and their name is not on the roster, please send them to the <b>8th Period Office</b>.<br />\\n If a student leaves your activity early, please make a note. <b>Do not make any additions to the roster.</b><br />\\n Before leaving for the day, return the roster and any passes to 8th Period coordinator, Joan Burch's mailbox in the <b>main office</b>. For questions, please call extension 5046 or 5078. Thank you!<br />\\\"\\\"\\\"\\n elements.append(Paragraph(instructions, styles[\\\"Normal\\\"]))\\n\\n if i != len(sched_act_ids) - 1:\\n elements.append(PageBreak())\\n\\n doc.build(elements)\\n return pdf_buffer\",\n \"def compile_latex(article: Article) -> Article:\\n text_tag: bs4.NavigableString\\n #matches LaTeX inside one or two dollar signs\\n inline_regex = r'\\\\$?\\\\$([^\\\\$]+)\\\\$\\\\$?'\\n for text_tag in article.content.find_all(text=True):\\n p = re.compile(inline_regex)\\n for match in p.finditer(text_tag):\\n latex = match.group(1)\\n if not is_latex(latex):\\n continue\\n #just use the hash of the latex for a unique filename, this should probably never collide\\n filename = article.get_pdf_location(str(hash(latex)))\\n compile_latex_str(latex, filename)\\n #if we can't find the parent, assume it's just the document\\n parent: Tag\\n if text_tag.parent == None or text_tag.parent.name == '[document]':\\n parent = article.content\\n else:\\n parent = text_tag.parent\\n tag_idx = parent.contents.index(text_tag)\\n #replace the matched latex with a link tag\\n begin, end = text_tag.split(match.group(0))\\n #convert these strings to tags\\n begin = bs4.NavigableString(begin)\\n end = bs4.NavigableString(end)\\n text_tag.replace_with(begin)\\n #the latex compiler will automatically add a .pdf so we have to add one too\\n link_tag = Tag(name='link', attrs={'href': 'file://' + filename + '.pdf'})\\n parent.insert(tag_idx + 1, link_tag)\\n parent.insert(tag_idx + 2, end)\\n #set the current tag to the new end tag\\n text_tag = end\\n return article\",\n \"def convert_to_pdf(self, news_list):\\n self.logger.info(\\\"Converting news to PDF...\\\")\\n self.prepare_storage()\\n self.process_news_list_with_images(news_list)\\n content = self.generate_html_template(news_list)\\n pdf = io.BytesIO()\\n pisa.pisaDocument(content, pdf)\\n self.write_to_file(pdf.getvalue())\",\n \"def init_latex():\\n args = argparser()\\n \\n main_fname = \\\"main.tex\\\"\\n preamble_fname = \\\"preamble.tex\\\"\\n bib_fname = \\\"bibliography.bib\\\"\\n cwd = os.getcwd()\\n\\n if not args.current_dir:\\n if os.path.isdir(args.dirname):\\n msg = f\\\"Directory '{args.dirname}' already exists!\\\"\\n raise IsADirectoryError(msg)\\n else:\\n if (os.path.isfile(main_fname)\\n or os.path.isfile(preamble_fname)):\\n msg = (f\\\"'{main_fname}' and/or {preamble_fname} already \\\"\\n + \\\"exists!\\\")\\n raise FileExistsError(msg)\\n if args.bib and os.path.isfile(bib_fname):\\n msg = f\\\"'{bib_fname}' already exists!\\\"\\n raise FileExistsError(msg)\\n\\n if not args.current_dir:\\n os.mkdir(args.dirname)\\n os.chdir(args.dirname)\\n\\n with open(main_fname, \\\"w\\\") as f:\\n lines = main_file(args)\\n for line in lines:\\n f.write(line + \\\"\\\\n\\\")\\n\\n with open(preamble_fname, \\\"w\\\") as f:\\n lines = preamble_file(args)\\n for line in lines:\\n f.write(line + \\\"\\\\n\\\")\\n\\n if args.bib:\\n with open(bib_fname, \\\"w\\\"):\\n pass\"\n]","isConversational":false},"negative_scores":{"kind":"list like","value":["0.7378825","0.71747476","0.7139049","0.70846206","0.6956496","0.6915404","0.69058365","0.68944186","0.67462385","0.66899025","0.66854495","0.66612506","0.65569717","0.6552778","0.65360767","0.64799696","0.6461581","0.6451979","0.6432288","0.63957024","0.6367583","0.63053197","0.628626","0.6249333","0.62437063","0.61355364","0.612611","0.6104166","0.6089054","0.6032495","0.6030254","0.6027495","0.59927106","0.597556","0.59445024","0.59405875","0.59292346","0.58983856","0.5875737","0.58684295","0.5848187","0.5847334","0.5846002","0.5837648","0.58147025","0.5813958","0.5801162","0.57245356","0.5708322","0.56717753","0.5668422","0.5662749","0.56586444","0.56482","0.5637728","0.5633497","0.56043494","0.55782044","0.5572456","0.557093","0.55652183","0.5561755","0.55191296","0.55170465","0.5516486","0.55143404","0.5510923","0.55088264","0.5505039","0.55028015","0.5495503","0.5495097","0.5486298","0.5474421","0.54717654","0.5465448","0.5464849","0.54633355","0.54578876","0.5446475","0.5446157","0.54433686","0.5435654","0.5434859","0.5432154","0.542278","0.53970146","0.53935677","0.5386349","0.5371921","0.5367932","0.5365142","0.5356426","0.53507745","0.533552","0.5328145","0.5326822","0.53116965","0.5308033","0.5304391"],"string":"[\n \"0.7378825\",\n \"0.71747476\",\n \"0.7139049\",\n \"0.70846206\",\n \"0.6956496\",\n \"0.6915404\",\n \"0.69058365\",\n \"0.68944186\",\n \"0.67462385\",\n \"0.66899025\",\n \"0.66854495\",\n \"0.66612506\",\n \"0.65569717\",\n \"0.6552778\",\n \"0.65360767\",\n \"0.64799696\",\n \"0.6461581\",\n \"0.6451979\",\n \"0.6432288\",\n \"0.63957024\",\n \"0.6367583\",\n \"0.63053197\",\n \"0.628626\",\n \"0.6249333\",\n \"0.62437063\",\n \"0.61355364\",\n \"0.612611\",\n \"0.6104166\",\n \"0.6089054\",\n \"0.6032495\",\n \"0.6030254\",\n \"0.6027495\",\n \"0.59927106\",\n \"0.597556\",\n \"0.59445024\",\n \"0.59405875\",\n \"0.59292346\",\n \"0.58983856\",\n \"0.5875737\",\n \"0.58684295\",\n \"0.5848187\",\n \"0.5847334\",\n \"0.5846002\",\n \"0.5837648\",\n \"0.58147025\",\n \"0.5813958\",\n \"0.5801162\",\n \"0.57245356\",\n \"0.5708322\",\n \"0.56717753\",\n \"0.5668422\",\n \"0.5662749\",\n \"0.56586444\",\n \"0.56482\",\n \"0.5637728\",\n \"0.5633497\",\n \"0.56043494\",\n \"0.55782044\",\n \"0.5572456\",\n \"0.557093\",\n \"0.55652183\",\n \"0.5561755\",\n \"0.55191296\",\n \"0.55170465\",\n \"0.5516486\",\n \"0.55143404\",\n \"0.5510923\",\n \"0.55088264\",\n \"0.5505039\",\n \"0.55028015\",\n \"0.5495503\",\n \"0.5495097\",\n \"0.5486298\",\n \"0.5474421\",\n \"0.54717654\",\n \"0.5465448\",\n \"0.5464849\",\n \"0.54633355\",\n \"0.54578876\",\n \"0.5446475\",\n \"0.5446157\",\n \"0.54433686\",\n \"0.5435654\",\n \"0.5434859\",\n \"0.5432154\",\n \"0.542278\",\n \"0.53970146\",\n \"0.53935677\",\n \"0.5386349\",\n \"0.5371921\",\n \"0.5367932\",\n \"0.5365142\",\n \"0.5356426\",\n \"0.53507745\",\n \"0.533552\",\n \"0.5328145\",\n \"0.5326822\",\n \"0.53116965\",\n \"0.5308033\",\n \"0.5304391\"\n]","isConversational":false},"document_score":{"kind":"string","value":"0.7367564"},"document_rank":{"kind":"string","value":"1"}}},{"rowIdx":22673,"cells":{"query":{"kind":"string","value":"Propagate changes made to models into database schema."},"document":{"kind":"string","value":"def migrate_db():\n Base.metadata.create_all(ENGINE)"},"metadata":{"kind":"string","value":"{\n \"objective\": {\n \"self\": [],\n \"paired\": [],\n \"triplet\": [\n [\n \"query\",\n \"document\",\n \"negatives\"\n ]\n ]\n }\n}"},"negatives":{"kind":"list like","value":["def migrate(self):\n\tpass","def setup_schema(self):\n models.Base.metadata.create_all(self.session.bind)","def update_model(self):\n pass","def model_update(self, db):\n db.session.commit()","def post_migrations(self):","def update_model(self):\n pass # TODO: Implement this.","async def upgradeSchema(self) -> None:","def apply_model(engine):\n Base.metadata.create_all(engine)","def updateModel(self):\n pass","def migration():","def update_model_fields(self, model, payload):\n fk_fields = model.fk_field_names()\n models_fields = {\n i: payload[i]\n for i, mapping in model.fields.items()\n if i not in fk_fields\n }\n for i, mapping in model.fields.items():\n if i in fk_fields:\n try:\n models_fields[i] = self.render_relation_field(\n mapping, payload[i]\n )\n except SkipField:\n models_fields.pop(i, None)\n model.update(models_fields)\n model.remote_instance = self.create_remote_instance(payload)\n return model","def update(self):\n current = LazyRegister(self.db)\n current.render()\n cur = self.db.cursor()\n for table in self.tables:\n if table in current.tables:\n additions, removals = current.tables[table].migrate(self.tables[table])\n for addition in additions:\n cur.execute(\"\"\"ALTER TABLE %s ADD COLUMN %s\"\"\" % (\n table, addition[1].get_sql()\n ))\n print('Added column: ', addition[0])\n for removal in removals:\n #cur.execute(\"\"\"ALTER TABLE %s DROP COLUMN %s\"\"\" % (\n # table, removal[0]\n #))\n #print('Removed column: ', removal[0])\n print('Did not removed column: ', removal[0])\n else:\n schema = self.tables[table].get_create_table_sql()\n cur.execute(schema)\n print('Added table %s' % table)","def start(self):\n \n self.db.session.add(self.sql_model_instance)\n self.db.session.commit()","def migrate_database(self):\n\n self.db.migrate_database()","def _on_post_syncdb(app, verbosity=2, db=DEFAULT_DB_ALIAS, **kwargs):\n app_models = [m for m in get_models(app) if issubclass(m, ContentItem)]\n for model in app_models:\n update_model_prefix(model, verbosity=verbosity, db=db)","def upgrade():\n op.execute(\n \"\"\"\n insert ignore into relationships (\n modified_by_id,\n created_at,\n updated_at,\n source_id,\n source_type,\n destination_id,\n destination_type\n )\n select\n modified_by_id,\n created_at,\n updated_at,\n parent_id as source_id,\n parent_type as source_type,\n child_id as destination_id,\n child_type as destination_type\n from snapshots\n \"\"\"\n )","def migrate(cls)->None:\n pass","def apply_migration (self, migration) :\n scope = self.home_scope\n for k in (\"Account\", \"Group\", \"Person\", \"links\") :\n for epk, db_attrs in sorted (pyk.iteritems (migration [k])) :\n ET = scope [epk [-1]]\n obj = ET.instance (* epk, raw = True)\n if obj is None :\n obj = ET (* epk, raw = True, ** dict (db_attrs))\n elif k == \"Account\" :\n obj.set_raw (** dict (db_attrs))","def setup_before_migration(self, apps):","def run_migration_checks():\n check_model_state()\n check_migration_state()","def _clean_database(self):\n # noinspection PyUnresolvedReferences\n env = self.env\n cr = env.cr\n modules_to_resolve = [\n 'ch_vendor_info',\n 'API_PDA_receiver',\n 'delivery_report_custom',\n 'myevo_base',\n 'myevo_nobutton_sending_email',\n 'myevo_web',\n 'purchase_order_custom']\n\n # Rename model module ch_vendor_info\n cr.execute(\"\"\"UPDATE ir_model_data SET module = 'soupese_base' WHERE module = 'ch_vendor_info'\"\"\")\n # Delete module soupese_base models that exists in old models\n cr.execute(\"\"\"DELETE FROM ir_model_data WHERE module = 'soupese_base' AND name = 'model_res_users'\"\"\")\n # Rename\n cr.execute(\"\"\"UPDATE ir_model_data SET module = 'soupese_base' WHERE module = 'API_PDA_receiver'\"\"\")\n\n # Rename module\n cr.execute(\"\"\"UPDATE ir_model_data SET module = 'soupese_base' WHERE module = 'delivery_report_custom'\"\"\")\n cr.execute(\"\"\"UPDATE ir_model_data SET module = 'soupese_base' WHERE module = 'myevo_base'\"\"\")\n cr.execute(\n \"\"\"UPDATE ir_model_data SET module = 'soupese_base' WHERE module = 'myevo_nobutton_sending_email'\"\"\")\n cr.execute(\"\"\"UPDATE ir_model_data SET module = 'soupese_base' WHERE module = 'myevo_web'\"\"\")\n\n # Delete module soupese_base models that exists in old models\n cr.execute(\"\"\"DELETE FROM ir_model_data WHERE module = 'soupese_base' AND name = 'model_measure_scale'\"\"\")\n cr.execute(\"\"\"DELETE FROM ir_model_data WHERE module = 'soupese_base' AND name = 'model_pda_operation'\"\"\")\n cr.execute(\"\"\"DELETE FROM ir_model_data WHERE module = 'soupese_base' AND name = 'model_res_partner'\"\"\")\n\n # Rename module\n cr.execute(\"\"\"UPDATE ir_model_data SET module = 'soupese_base' WHERE module = 'purchase_order_custom'\"\"\")\n\n # Rename module_ in base\n for x in modules_to_resolve:\n cr.execute(\"\"\"\n DELETE FROM ir_model_data\n WHERE name = 'module_%s' AND module = 'base' AND model = 'ir.module.module'\"\"\", (x,))\n\n # Uninstall modules\n cr.execute(\"\"\"UPDATE ir_module_module SET state = 'uninstalled' WHERE name = '%s'\"\"\", (x,))\n\n # Remove vendor.information.scale table\n cr.execute(\"DROP TABLE vendor_information_scale\")\n\n # Commit finally\n cr.commit()","def recreate_db(self, run=False):\n if run:\n db_schema = open(self.db_schema_file).read().splitlines()\n for s in db_schema:\n t = s.strip()\n if len(t):\n self.cur.execute(t)","def model_post_migrate(*args, **kwargs):\n global IN_MIGRATIONS\n IN_MIGRATIONS = False","def forwards_func(apps, schema_editor):\n judge_file = apps.get_model(\"judges\", \"JudgeFile\")\n db_alias = schema_editor.connection.alias\n judge_file.objects.using(db_alias).filter(\n file_type='ruling').update(file_type='rulings')\n judge_file.objects.using(db_alias).filter(\n file_type='verdict').update(file_type='verdicts')\n judge_file.objects.using(db_alias).filter(\n file_type='transcript').update(file_type='transcripts')\n judge_file.objects.using(db_alias).filter(\n file_type='image').update(file_type='campaigns')","def presavemodel(self):\n # one thing we need to do here is handle any lazy serialization helpers.\"\"\"\n self.presavemodel_serializationhelpers_updatefields()","def upgrade():\n\n conn = op.get_bind()\n invalid_acr = get_invalid_acrs(conn, models_names)\n\n if invalid_acr:\n invalid_acr_ids = [x.id for x in invalid_acr]\n add_to_objects_without_revisions_bulk(conn,\n invalid_acr_ids,\n acr,\n \"deleted\")\n delete_invalid_acr(conn, models_names)","def init_models(self):\n from ron import Application\n from ron.models.basemodel import BaseModel\n if self.models == None or not Application().db:\n return\n models_namespace = self.__namespace + \".models\" # TODO: allow customize this\n try:\n models_package = import_module(models_namespace)\n except:\n models_package = None\n if models_package:\n models_modules = self._get_package_modules(models_package)\n for model_name in models_modules:\n imported_model = import_module('.' + model_name, package=models_namespace)\n for i in dir(imported_model):\n attribute = getattr(imported_model, i)\n if inspect.isclass(attribute) and issubclass(attribute, BaseModel):\n self.models.append(attribute)\n Application().db().database.create_tables(self.models)","def db_for_write(self, model, **hints):\n return None","def create_models( self ):","def generate_altered_fields(self):\n result = super(MigrationAutodetector, self).generate_altered_fields()\n self.generate_sql_changes()\n return result","def _add_to_schema(self, new: dict):\n self._defaults.update(new)\n self._migrate()","def model_pre_migrate(*args, **kwargs):\n global IN_MIGRATIONS\n IN_MIGRATIONS = True","def apply_model_to_query(self, query):\n pass","def db_changed(self):\n self.dbstate.db.connect('person-add', self.update)\n self.dbstate.db.connect('person-delete', self.update)\n self.dbstate.db.connect('person-update', self.update)\n self.dbstate.db.connect('family-add', self.update)\n self.dbstate.db.connect('family-delete', self.update)\n self.dbstate.db.connect('family-update', self.update)","def populate_db():\n\n populate_table(db, models.Department, departments_data)\n populate_table(db, models.Employee, employees_data)","def set(\n self,\n *,\n crosswalk: CrosswalkModel | dict | Path | str | None = None,\n schema_source: SchemaDefinition | SchemaModel | dict | Path | str | None = None,\n schema_destination: SchemaDefinition | SchemaModel | dict | Path | str | None = None,\n ) -> None:\n self.model = self.core.create_or_update_model(modelType=CrosswalkModel, source=crosswalk, model=self.model)\n # And update the original data\n # https://fastapi.tiangolo.com/tutorial/body-updates/#partial-updates-with-patch\n if self.model.schemaSource and not schema_source:\n self.schema_source.set(schema=self.model.schemaSource)\n elif schema_source:\n if isinstance(schema_source, SchemaDefinition):\n self.schema_source = schema_source\n else:\n self.schema_source.set(schema=schema_source)\n self.model.schemaSource = self.schema_source.get\n if self.model.schemaDestination and not schema_destination:\n self.schema_destination.set(schema=self.model.schemaDestination)\n elif schema_destination:\n if isinstance(schema_destination, SchemaDefinition):\n self.schema_destination = schema_destination\n else:\n self.schema_destination.set(schema=schema_destination)\n self.model.schemaDestination = self.schema_destination.get\n if self.model.schemaSource and self.model.schemaDestination:\n self.crud.set_schema(schema_source=self.schema_source, schema_destination=self.schema_destination)\n self._refresh_model_fields()","def setup_db():\n\n engine = config['tg.app_globals'].sa_engine\n # model.init_model(engine)\n # model.metadata.create_all(engine)","def schema(self):","def save_model(self):\n pass","def update_model(\n self, content: Dict[str, Any], model_name: str, columns_on_db: Sequence[str]\n ) -> Dict[str, Any]:\n logger.info(f\"The model '{model_name}' already exists, updating its documentation.\")\n for model in content.get(\"models\", []):\n if model[\"name\"] == model_name:\n for column in columns_on_db:\n # Check whether the model has columns already documented in schema yaml\n if not model.get(\"columns\", None):\n model[\"columns\"] = []\n\n columns = model.get(\"columns\", [])\n columns_names = [column[\"name\"] for column in columns]\n if column not in columns_names:\n description = self.get_column_description_from_dbt_definitions(column)\n logger.info(f\"Updating column '{column.lower()}'\")\n columns.append({\"name\": column, \"description\": description})\n return content","def migrate(env, dry_run=False):\n registry = env['registry']\n settings = registry.settings\n readonly_backends = ('storage', 'permission')\n readonly_mode = asbool(settings.get('readonly', False))\n\n for backend in ('cache', 'storage', 'permission'):\n if hasattr(registry, backend):\n if readonly_mode and backend in readonly_backends:\n message = ('Cannot migrate the %s backend while '\n 'in readonly mode.' % backend)\n logger.error(message)\n else:\n getattr(registry, backend).initialize_schema(dry_run=dry_run)","def allow_syncdb(self, db, model):\n\n return True","def smart_syncdb_migrate(self):\n local('python manage.py syncdb')\n local('python manage.py migrate')\n local('python manage.py syncdb --all')","def prepare_model(self, **kwargs):\n pass","def migrate(env):\n registry = env['registry']\n settings = registry.settings\n readonly_backends = ('storage', 'permission')\n readonly_mode = asbool(settings.get('readonly', False))\n\n for backend in ('cache', 'storage', 'permission'):\n if hasattr(registry, backend):\n if readonly_mode and backend in readonly_backends:\n message = ('Cannot migrate the %s backend while '\n 'in readonly mode.' % backend)\n warnings.warn(message)\n else:\n getattr(registry, backend).initialize_schema()","def update(self, force=False):\n if (self.__fields is None) or force:\n self.__fields = [f for f in self.dbms.fields(self.table, self.db)]","def _prepare(cls):\n # the dbmodel is either the proxy base or ourselves\n dbmodel = cls._meta.concrete_model if cls._meta.proxy else cls\n cls.__dbclass__ = dbmodel\n if not hasattr(dbmodel, \"__instance_cache__\"):\n # we store __instance_cache__ only on the dbmodel base\n dbmodel.__instance_cache__ = {}\n super()._prepare()","def upgrade_schema():\n\n db_version = get_db_version()\n try:\n while db_version < CURRENT_DATABASE_VERSION:\n db_version += 1\n upgrade_script = 'upgrade_to_'+str(db_version)\n globals()[upgrade_script]()\n except KeyError as e:\n logging.exception('Attempted to upgrade using script that does not exist: {}'.format(e))\n sys.exit(1)\n except Exception as e:\n logging.exception('Incremental upgrade of db failed')\n sys.exit(1)\n else:\n config.db.singletons.update_one({'_id': 'version'}, {'$set': {'database': CURRENT_DATABASE_VERSION}})\n sys.exit(0)","def _refresh_model_fields(self) -> None:\n if self.model and self.model.actions:\n self.crud.add_multi(terms=self.model.actions)\n if self.model and not self.model.actions:\n self.crud.reset()","def bootstrap():\n Base.metadata.create_all(engine)","def sync_tables():\n sync_table(ShoppingList)\n sync_table(User)\n sync_table(Category)\n sync_table(Feed)\n sync_table(News)\n sync_table(Photo)\n sync_table(Profile)\n sync_table(Video)\n sync_type(FeedPhoto)\n sync_type(NewsPhoto)","def teardown_schema(self):\n models.Base.metadata.drop_all(self.session.bind)","def set_version_db(apps, schema_editor):\n Version = apps.get_model(\"reversion\", \"Version\")\n content_types = Version.objects.values_list(\"content_type\", flat=True).distinct()\n for content_type in content_types:\n model_class = content_type.model_class()\n db = router.db_for_write(model_class)\n Version.objects.filter(content_type=content_type).update(db=db)","def install(drop_all=False):\n\n print \"Connecting to database...\"\n engine = create_engine(config.DATABASE_URI)\n Base.metadata.bind = engine\n\n if drop_all:\n print \"Dropping old tables...\"\n Base.metadata.drop_all()\n \n print \"Installing new schema...\"\n Base.metadata.create_all(engine)","def setUp(self):\n self.model = ModelBase(\n '__TestModel__' + self.mixin.__name__,\n (self.mixin,),\n {'__module__': self.mixin.__module__}\n )\n\n with connection.schema_editor() as schema_editor:\n schema_editor.create_model(self.model)","def save_model( self, request, obj, form, change ):\n obj.save()","def save_model(self, request, obj, form, change):\n obj.revise()","def _sync_foreign_model(self, instance, data, cls, manager_name):\n # Remove all foreign instances that are not featured in data\n data_ids = [item[\"id\"] for item in data if \"id\" in item]\n for existing_foreigns in getattr(instance, manager_name).all():\n if existing_foreigns.id not in data_ids:\n existing_foreigns.delete()\n\n # Update all foreign instances using data\n for item in data:\n new_foreign = cls(**item, profile=instance)\n new_foreign.save()","def execute_upgrade():\n\n def migrate_review_policy(community_record):\n review_policy = community_record[\"access\"].get(\n \"review_policy\", ReviewPolicyEnum.CLOSED.value\n )\n community_record[\"access\"][\"review_policy\"] = review_policy\n\n secho(\"Starting data migration...\", fg=\"green\")\n communities = Community.model_cls.query.all()\n\n for community_data in communities:\n community = Community(community_data.data, model=community_data)\n migrate_review_policy(community)\n community.commit()\n\n secho(f\"Committing to DB\", fg=\"green\")\n db.session.commit()\n secho(\"Data migration completed.\", fg=\"green\")","def migrate(ctx):\n connecter = ScalingoInterface(ctx.obj)\n connecter.manage_py(\"migrate\")","def allow_syncdb(self, db, model):\n return True","def allow_syncdb(self, db, model):\n return True","def ProcessSchemaUpdate(ref, args, request):\n table = request.table\n relaxed_columns = args.relax_columns\n if not table.schema and not relaxed_columns: # if not updating schema,\n return request # then just return.\n\n original_schema = _TryGetCurrentSchema(ref.Parent().Name(),\n ref.Name(),\n ref.projectId)\n\n new_schema_columns = table.schema\n updated_fields = _GetUpdatedSchema(original_schema,\n new_schema_columns,\n relaxed_columns)\n\n table_schema_type = GetApiMessage('TableSchema')\n request.table.schema = table_schema_type(fields=updated_fields)\n\n return request","def auto_populate(self, model):\n for name, val in self._fields.iteritems():\n setattr(model, name, val.data)","def populate_database(self):\n self.insert_products()\n self.insert_categories()\n self.insert_products_categories()\n self.insert_stores()\n self.insert_products_stores()","def ensure_internal_schema_updated(self):\n if self._internal_schema_updated:\n return\n if internalmigrations.needs_upgrading(self):\n assert not self._in_transaction\n with self.lock():\n internalmigrations.upgrade(self)\n self.connection.commit()\n self._internal_schema_updated = True","def test_make_update_script_for_model(self):\n\n self.setup_model_params()\n self.write_file(self.first_model_path, self.base_source)\n self.write_file(self.second_model_path, self.base_source + self.model_source)\n\n source_script = self.pyscript.make_update_script_for_model(\n engine=self.engine,\n oldmodel=load_model('testmodel_first:meta'),\n model=load_model('testmodel_second:meta'),\n repository=self.repo_path,\n )\n\n self.assertTrue(\"['User'].create()\" in source_script)\n self.assertTrue(\"['User'].drop()\" in source_script)","def repair(self):\n # self.add_cons_vars([x.constraint for x in self._cons_dict.values()])\n # self.add_cons_vars([x.variable for x in self._var_dict.values()])\n self._push_queue()\n Model.repair(self)\n self.regenerate_constraints()\n self.regenerate_variables()","def save_model(self, request, instance, form, change):\n pass","def tearDown(self):\n with connection.schema_editor() as schema_editor:\n schema_editor.delete_model(self.model)","def southify(app):\n managepy('migrate %s 0001 --fake' % app)\n managepy('migrate %s' % app)","def forwards_func(apps, schema_editor):\n Project = apps.get_model(\"projects\", \"Project\")\n Feature = apps.get_model(\"projects\", \"Feature\")\n\n feature = Feature.objects.create(feature_id=\"allow_version_warning_banner\")\n for project in Project.objects.filter(show_version_warning=True).iterator():\n feature.projects.add(project)","def init_db(ctx: click.core.Context, force: bool) -> None:\n engine = ctx.obj['engine']\n if force:\n contract.Base.metadata.drop_all(engine)\n contract.Base.metadata.create_all(engine)","def rebuild_db():\n delete_db()\n create_db()\n insert_db()","def recreate_db():\n db.session.execute('SET FOREIGN_KEY_CHECKS=0;')\n db.session.execute('DROP TABLE IF EXISTS logs;')\n db.session.execute('DROP TABLE IF EXISTS employees;')\n db.session.execute('DROP TABLE IF EXISTS sales;')\n db.session.execute('DROP TABLE IF EXISTS plants;')\n db.session.execute('DROP TABLE IF EXISTS products;')\n db.session.execute('DROP TABLE IF EXISTS suppliers;')\n db.session.execute('DROP TABLE IF EXISTS orders;')\n db.session.execute('DROP TABLE IF EXISTS contacts;')\n db.session.execute('DROP TABLE IF EXISTS varieties;')\n db.session.execute('DROP TABLE IF EXISTS species;')\n db.session.execute('DROP TABLE IF EXISTS genera;')\n db.session.execute('DROP TABLE IF EXISTS families;')\n db.drop_all()\n db.create_all()\n db.session.commit()\n fakePlant = Plant(living = True)\n db.session.add(fakePlant)\n db.session.commit()\n db.session.delete(fakePlant)\n db.session.execute('SET FOREIGN_KEY_CHECKS=1;')\n db.session.commit()","def postCommitHook(self, datamodel=None):\n self.updateProperties()","def update_view(self):\n for row in self.view.obj_list:\n for obj in row:\n obj._update(self.model)","def clean_all_db():\n for model in [\n Component, Arch, AutoCase, AutoCaseFailure, Bug, Linkage, WorkItem,\n Document, Project, Framework]:\n model.objects.all().delete()","def _on_model_change(self, change):\n\n if (\n not self.hydraulic_conductivity.needs_model\n and not self.water_retention.needs_model\n ):\n warnings.warn(\"There is no model to set.\")\n return\n\n model = change[\"value\"]\n\n if self.hydraulic_conductivity.needs_model:\n self.hydraulic_conductivity.model = model\n\n if self.water_retention.needs_model:\n self.water_retention.model = model","def setup_models(self):\n pass","def init_model(connection):\n db = connection\n\n for obj in common.__dict__.itervalues():\n if type(obj) == type and issubclass(obj, common.Model) and hasattr(obj, '__tablename__'):\n tablename = getattr(obj, '__tablename__')\n obj._object_store = Domain(db, tablename)\n collection_to_class[obj._object_store] = obj","def schema(self):\n pass","def reset_model(self):\n raise NotImplementedError","def reset_model(self):\n raise NotImplementedError","def reset_model(self):\n raise NotImplementedError","def install(cls):\n return cls.interface.set_table(cls.schema)","def _migrate_if_necessary(self, entries):\r\n entries = [\r\n self._migrate[entry.get('schema', 0)](self, entry)\r\n for entry in entries\r\n ]\r\n return entries","def test_migrating_old_and_new_with_new_only_post_migrate_function(self):\n yield self.mk_simple_models_old(1)\n yield self.mk_simple_models_new(1, start=1)\n yield self.mk_simple_models_old(1, start=2)\n model_migrator = self.make_migrator(\n post_migrate_function=fqpn(post_migrate_function_new_only))\n loads, stores = self.recorded_loads_and_stores(model_migrator)\n\n yield model_migrator.run()\n self.assertEqual(model_migrator.output, [\n \"Migrating ...\",\n \"Done, 3 objects migrated.\",\n ])\n self.assertEqual(sorted(loads), [u\"key-0\", u\"key-1\", u\"key-2\"])\n self.assertEqual(sorted(stores), [u\"key-0\", u\"key-2\"])\n\n obj_0 = yield self.model.load(u\"key-0\")\n self.assertEqual(obj_0.a, u\"value-0-modified\")\n obj_1 = yield self.model.load(u\"key-1\")\n self.assertEqual(obj_1.a, u\"value-1\")\n obj_2 = yield self.model.load(u\"key-2\")\n self.assertEqual(obj_2.a, u\"value-2-modified\")","def execute_migrations(self):\n response = self._connection.session.post(\n self._connection.url + \"object_manager_attributes_execute_migrations\"\n )\n return self._raise_or_return_json(response)","def save(self):\r\n if self.table == \"Category\":\r\n Category(self.db).insert_query(self.elements)\r\n elif self.table == \"Brand\":\r\n Brand(self.db).insert_query(self.elements)\r\n elif self.table == \"Store\":\r\n Store(self.db).insert_query(self.elements)\r\n elif self.table == \"Product\":\r\n Product(self.db).insert_query(self.elements)","def db_create_models():\n # db_createall doesn't work if the models aren't imported\n import_string('models', silent=True)\n for blueprint_name, blueprint in app.blueprints.items():\n import_string('%s.models' % blueprint.import_name, silent=True)\n db.create_all()","def recreate_db():\n\n print(\"will reinit db - FAKE\")\n db.create_tables([Message, Instance])\n\n # no need to prepare a sample record.\n # use http to create init request instead.\n #inject_record()","def initialize_schema(self, dry_run=False):\n if not dry_run:\n self.flush()","def dummy():\n\t\t\tself.save()","def db_create_all(self):\n with self.app_context():\n self.db.create_all()","def migrate_to_db(self, source, items_per_cat):\n data = self.fetch(source, items_per_cat)\n data = self.__attach_source(data, source)\n logger.debug('Migrating %d items to DB', len(data))\n self.repository.write(data)","def _apply(self):\n s = [(iptables_save, iptables_restore, self.ipv4)]\n if self.use_ipv6:\n s += [(ip6tables_save, ip6tables_restore, self.ipv6)]\n\n for save, restore, tables in s:\n all_tables, _err = save()\n all_lines = all_tables.split('\\n')\n for table_name, table in six.iteritems(tables):\n start, end = self._find_table(all_lines, table_name)\n all_lines[start:end] = self._modify_rules(\n all_lines[start:end], table, table_name)\n table.dirty = False\n restore('\\n'.join(all_lines))","def init_db():\n import cerbereapp.models\n Base.metadata.create_all(bind=engine)","def recreate_db():\n drop_db()\n create_db()\n populate_db()","def update(self):\n self.getDbRecord().update()","def backwards(apps, schema_editor):\n pass"],"string":"[\n \"def migrate(self):\\n\\tpass\",\n \"def setup_schema(self):\\n models.Base.metadata.create_all(self.session.bind)\",\n \"def update_model(self):\\n pass\",\n \"def model_update(self, db):\\n db.session.commit()\",\n \"def post_migrations(self):\",\n \"def update_model(self):\\n pass # TODO: Implement this.\",\n \"async def upgradeSchema(self) -> None:\",\n \"def apply_model(engine):\\n Base.metadata.create_all(engine)\",\n \"def updateModel(self):\\n pass\",\n \"def migration():\",\n \"def update_model_fields(self, model, payload):\\n fk_fields = model.fk_field_names()\\n models_fields = {\\n i: payload[i]\\n for i, mapping in model.fields.items()\\n if i not in fk_fields\\n }\\n for i, mapping in model.fields.items():\\n if i in fk_fields:\\n try:\\n models_fields[i] = self.render_relation_field(\\n mapping, payload[i]\\n )\\n except SkipField:\\n models_fields.pop(i, None)\\n model.update(models_fields)\\n model.remote_instance = self.create_remote_instance(payload)\\n return model\",\n \"def update(self):\\n current = LazyRegister(self.db)\\n current.render()\\n cur = self.db.cursor()\\n for table in self.tables:\\n if table in current.tables:\\n additions, removals = current.tables[table].migrate(self.tables[table])\\n for addition in additions:\\n cur.execute(\\\"\\\"\\\"ALTER TABLE %s ADD COLUMN %s\\\"\\\"\\\" % (\\n table, addition[1].get_sql()\\n ))\\n print('Added column: ', addition[0])\\n for removal in removals:\\n #cur.execute(\\\"\\\"\\\"ALTER TABLE %s DROP COLUMN %s\\\"\\\"\\\" % (\\n # table, removal[0]\\n #))\\n #print('Removed column: ', removal[0])\\n print('Did not removed column: ', removal[0])\\n else:\\n schema = self.tables[table].get_create_table_sql()\\n cur.execute(schema)\\n print('Added table %s' % table)\",\n \"def start(self):\\n \\n self.db.session.add(self.sql_model_instance)\\n self.db.session.commit()\",\n \"def migrate_database(self):\\n\\n self.db.migrate_database()\",\n \"def _on_post_syncdb(app, verbosity=2, db=DEFAULT_DB_ALIAS, **kwargs):\\n app_models = [m for m in get_models(app) if issubclass(m, ContentItem)]\\n for model in app_models:\\n update_model_prefix(model, verbosity=verbosity, db=db)\",\n \"def upgrade():\\n op.execute(\\n \\\"\\\"\\\"\\n insert ignore into relationships (\\n modified_by_id,\\n created_at,\\n updated_at,\\n source_id,\\n source_type,\\n destination_id,\\n destination_type\\n )\\n select\\n modified_by_id,\\n created_at,\\n updated_at,\\n parent_id as source_id,\\n parent_type as source_type,\\n child_id as destination_id,\\n child_type as destination_type\\n from snapshots\\n \\\"\\\"\\\"\\n )\",\n \"def migrate(cls)->None:\\n pass\",\n \"def apply_migration (self, migration) :\\n scope = self.home_scope\\n for k in (\\\"Account\\\", \\\"Group\\\", \\\"Person\\\", \\\"links\\\") :\\n for epk, db_attrs in sorted (pyk.iteritems (migration [k])) :\\n ET = scope [epk [-1]]\\n obj = ET.instance (* epk, raw = True)\\n if obj is None :\\n obj = ET (* epk, raw = True, ** dict (db_attrs))\\n elif k == \\\"Account\\\" :\\n obj.set_raw (** dict (db_attrs))\",\n \"def setup_before_migration(self, apps):\",\n \"def run_migration_checks():\\n check_model_state()\\n check_migration_state()\",\n \"def _clean_database(self):\\n # noinspection PyUnresolvedReferences\\n env = self.env\\n cr = env.cr\\n modules_to_resolve = [\\n 'ch_vendor_info',\\n 'API_PDA_receiver',\\n 'delivery_report_custom',\\n 'myevo_base',\\n 'myevo_nobutton_sending_email',\\n 'myevo_web',\\n 'purchase_order_custom']\\n\\n # Rename model module ch_vendor_info\\n cr.execute(\\\"\\\"\\\"UPDATE ir_model_data SET module = 'soupese_base' WHERE module = 'ch_vendor_info'\\\"\\\"\\\")\\n # Delete module soupese_base models that exists in old models\\n cr.execute(\\\"\\\"\\\"DELETE FROM ir_model_data WHERE module = 'soupese_base' AND name = 'model_res_users'\\\"\\\"\\\")\\n # Rename\\n cr.execute(\\\"\\\"\\\"UPDATE ir_model_data SET module = 'soupese_base' WHERE module = 'API_PDA_receiver'\\\"\\\"\\\")\\n\\n # Rename module\\n cr.execute(\\\"\\\"\\\"UPDATE ir_model_data SET module = 'soupese_base' WHERE module = 'delivery_report_custom'\\\"\\\"\\\")\\n cr.execute(\\\"\\\"\\\"UPDATE ir_model_data SET module = 'soupese_base' WHERE module = 'myevo_base'\\\"\\\"\\\")\\n cr.execute(\\n \\\"\\\"\\\"UPDATE ir_model_data SET module = 'soupese_base' WHERE module = 'myevo_nobutton_sending_email'\\\"\\\"\\\")\\n cr.execute(\\\"\\\"\\\"UPDATE ir_model_data SET module = 'soupese_base' WHERE module = 'myevo_web'\\\"\\\"\\\")\\n\\n # Delete module soupese_base models that exists in old models\\n cr.execute(\\\"\\\"\\\"DELETE FROM ir_model_data WHERE module = 'soupese_base' AND name = 'model_measure_scale'\\\"\\\"\\\")\\n cr.execute(\\\"\\\"\\\"DELETE FROM ir_model_data WHERE module = 'soupese_base' AND name = 'model_pda_operation'\\\"\\\"\\\")\\n cr.execute(\\\"\\\"\\\"DELETE FROM ir_model_data WHERE module = 'soupese_base' AND name = 'model_res_partner'\\\"\\\"\\\")\\n\\n # Rename module\\n cr.execute(\\\"\\\"\\\"UPDATE ir_model_data SET module = 'soupese_base' WHERE module = 'purchase_order_custom'\\\"\\\"\\\")\\n\\n # Rename module_ in base\\n for x in modules_to_resolve:\\n cr.execute(\\\"\\\"\\\"\\n DELETE FROM ir_model_data\\n WHERE name = 'module_%s' AND module = 'base' AND model = 'ir.module.module'\\\"\\\"\\\", (x,))\\n\\n # Uninstall modules\\n cr.execute(\\\"\\\"\\\"UPDATE ir_module_module SET state = 'uninstalled' WHERE name = '%s'\\\"\\\"\\\", (x,))\\n\\n # Remove vendor.information.scale table\\n cr.execute(\\\"DROP TABLE vendor_information_scale\\\")\\n\\n # Commit finally\\n cr.commit()\",\n \"def recreate_db(self, run=False):\\n if run:\\n db_schema = open(self.db_schema_file).read().splitlines()\\n for s in db_schema:\\n t = s.strip()\\n if len(t):\\n self.cur.execute(t)\",\n \"def model_post_migrate(*args, **kwargs):\\n global IN_MIGRATIONS\\n IN_MIGRATIONS = False\",\n \"def forwards_func(apps, schema_editor):\\n judge_file = apps.get_model(\\\"judges\\\", \\\"JudgeFile\\\")\\n db_alias = schema_editor.connection.alias\\n judge_file.objects.using(db_alias).filter(\\n file_type='ruling').update(file_type='rulings')\\n judge_file.objects.using(db_alias).filter(\\n file_type='verdict').update(file_type='verdicts')\\n judge_file.objects.using(db_alias).filter(\\n file_type='transcript').update(file_type='transcripts')\\n judge_file.objects.using(db_alias).filter(\\n file_type='image').update(file_type='campaigns')\",\n \"def presavemodel(self):\\n # one thing we need to do here is handle any lazy serialization helpers.\\\"\\\"\\\"\\n self.presavemodel_serializationhelpers_updatefields()\",\n \"def upgrade():\\n\\n conn = op.get_bind()\\n invalid_acr = get_invalid_acrs(conn, models_names)\\n\\n if invalid_acr:\\n invalid_acr_ids = [x.id for x in invalid_acr]\\n add_to_objects_without_revisions_bulk(conn,\\n invalid_acr_ids,\\n acr,\\n \\\"deleted\\\")\\n delete_invalid_acr(conn, models_names)\",\n \"def init_models(self):\\n from ron import Application\\n from ron.models.basemodel import BaseModel\\n if self.models == None or not Application().db:\\n return\\n models_namespace = self.__namespace + \\\".models\\\" # TODO: allow customize this\\n try:\\n models_package = import_module(models_namespace)\\n except:\\n models_package = None\\n if models_package:\\n models_modules = self._get_package_modules(models_package)\\n for model_name in models_modules:\\n imported_model = import_module('.' + model_name, package=models_namespace)\\n for i in dir(imported_model):\\n attribute = getattr(imported_model, i)\\n if inspect.isclass(attribute) and issubclass(attribute, BaseModel):\\n self.models.append(attribute)\\n Application().db().database.create_tables(self.models)\",\n \"def db_for_write(self, model, **hints):\\n return None\",\n \"def create_models( self ):\",\n \"def generate_altered_fields(self):\\n result = super(MigrationAutodetector, self).generate_altered_fields()\\n self.generate_sql_changes()\\n return result\",\n \"def _add_to_schema(self, new: dict):\\n self._defaults.update(new)\\n self._migrate()\",\n \"def model_pre_migrate(*args, **kwargs):\\n global IN_MIGRATIONS\\n IN_MIGRATIONS = True\",\n \"def apply_model_to_query(self, query):\\n pass\",\n \"def db_changed(self):\\n self.dbstate.db.connect('person-add', self.update)\\n self.dbstate.db.connect('person-delete', self.update)\\n self.dbstate.db.connect('person-update', self.update)\\n self.dbstate.db.connect('family-add', self.update)\\n self.dbstate.db.connect('family-delete', self.update)\\n self.dbstate.db.connect('family-update', self.update)\",\n \"def populate_db():\\n\\n populate_table(db, models.Department, departments_data)\\n populate_table(db, models.Employee, employees_data)\",\n \"def set(\\n self,\\n *,\\n crosswalk: CrosswalkModel | dict | Path | str | None = None,\\n schema_source: SchemaDefinition | SchemaModel | dict | Path | str | None = None,\\n schema_destination: SchemaDefinition | SchemaModel | dict | Path | str | None = None,\\n ) -> None:\\n self.model = self.core.create_or_update_model(modelType=CrosswalkModel, source=crosswalk, model=self.model)\\n # And update the original data\\n # https://fastapi.tiangolo.com/tutorial/body-updates/#partial-updates-with-patch\\n if self.model.schemaSource and not schema_source:\\n self.schema_source.set(schema=self.model.schemaSource)\\n elif schema_source:\\n if isinstance(schema_source, SchemaDefinition):\\n self.schema_source = schema_source\\n else:\\n self.schema_source.set(schema=schema_source)\\n self.model.schemaSource = self.schema_source.get\\n if self.model.schemaDestination and not schema_destination:\\n self.schema_destination.set(schema=self.model.schemaDestination)\\n elif schema_destination:\\n if isinstance(schema_destination, SchemaDefinition):\\n self.schema_destination = schema_destination\\n else:\\n self.schema_destination.set(schema=schema_destination)\\n self.model.schemaDestination = self.schema_destination.get\\n if self.model.schemaSource and self.model.schemaDestination:\\n self.crud.set_schema(schema_source=self.schema_source, schema_destination=self.schema_destination)\\n self._refresh_model_fields()\",\n \"def setup_db():\\n\\n engine = config['tg.app_globals'].sa_engine\\n # model.init_model(engine)\\n # model.metadata.create_all(engine)\",\n \"def schema(self):\",\n \"def save_model(self):\\n pass\",\n \"def update_model(\\n self, content: Dict[str, Any], model_name: str, columns_on_db: Sequence[str]\\n ) -> Dict[str, Any]:\\n logger.info(f\\\"The model '{model_name}' already exists, updating its documentation.\\\")\\n for model in content.get(\\\"models\\\", []):\\n if model[\\\"name\\\"] == model_name:\\n for column in columns_on_db:\\n # Check whether the model has columns already documented in schema yaml\\n if not model.get(\\\"columns\\\", None):\\n model[\\\"columns\\\"] = []\\n\\n columns = model.get(\\\"columns\\\", [])\\n columns_names = [column[\\\"name\\\"] for column in columns]\\n if column not in columns_names:\\n description = self.get_column_description_from_dbt_definitions(column)\\n logger.info(f\\\"Updating column '{column.lower()}'\\\")\\n columns.append({\\\"name\\\": column, \\\"description\\\": description})\\n return content\",\n \"def migrate(env, dry_run=False):\\n registry = env['registry']\\n settings = registry.settings\\n readonly_backends = ('storage', 'permission')\\n readonly_mode = asbool(settings.get('readonly', False))\\n\\n for backend in ('cache', 'storage', 'permission'):\\n if hasattr(registry, backend):\\n if readonly_mode and backend in readonly_backends:\\n message = ('Cannot migrate the %s backend while '\\n 'in readonly mode.' % backend)\\n logger.error(message)\\n else:\\n getattr(registry, backend).initialize_schema(dry_run=dry_run)\",\n \"def allow_syncdb(self, db, model):\\n\\n return True\",\n \"def smart_syncdb_migrate(self):\\n local('python manage.py syncdb')\\n local('python manage.py migrate')\\n local('python manage.py syncdb --all')\",\n \"def prepare_model(self, **kwargs):\\n pass\",\n \"def migrate(env):\\n registry = env['registry']\\n settings = registry.settings\\n readonly_backends = ('storage', 'permission')\\n readonly_mode = asbool(settings.get('readonly', False))\\n\\n for backend in ('cache', 'storage', 'permission'):\\n if hasattr(registry, backend):\\n if readonly_mode and backend in readonly_backends:\\n message = ('Cannot migrate the %s backend while '\\n 'in readonly mode.' % backend)\\n warnings.warn(message)\\n else:\\n getattr(registry, backend).initialize_schema()\",\n \"def update(self, force=False):\\n if (self.__fields is None) or force:\\n self.__fields = [f for f in self.dbms.fields(self.table, self.db)]\",\n \"def _prepare(cls):\\n # the dbmodel is either the proxy base or ourselves\\n dbmodel = cls._meta.concrete_model if cls._meta.proxy else cls\\n cls.__dbclass__ = dbmodel\\n if not hasattr(dbmodel, \\\"__instance_cache__\\\"):\\n # we store __instance_cache__ only on the dbmodel base\\n dbmodel.__instance_cache__ = {}\\n super()._prepare()\",\n \"def upgrade_schema():\\n\\n db_version = get_db_version()\\n try:\\n while db_version < CURRENT_DATABASE_VERSION:\\n db_version += 1\\n upgrade_script = 'upgrade_to_'+str(db_version)\\n globals()[upgrade_script]()\\n except KeyError as e:\\n logging.exception('Attempted to upgrade using script that does not exist: {}'.format(e))\\n sys.exit(1)\\n except Exception as e:\\n logging.exception('Incremental upgrade of db failed')\\n sys.exit(1)\\n else:\\n config.db.singletons.update_one({'_id': 'version'}, {'$set': {'database': CURRENT_DATABASE_VERSION}})\\n sys.exit(0)\",\n \"def _refresh_model_fields(self) -> None:\\n if self.model and self.model.actions:\\n self.crud.add_multi(terms=self.model.actions)\\n if self.model and not self.model.actions:\\n self.crud.reset()\",\n \"def bootstrap():\\n Base.metadata.create_all(engine)\",\n \"def sync_tables():\\n sync_table(ShoppingList)\\n sync_table(User)\\n sync_table(Category)\\n sync_table(Feed)\\n sync_table(News)\\n sync_table(Photo)\\n sync_table(Profile)\\n sync_table(Video)\\n sync_type(FeedPhoto)\\n sync_type(NewsPhoto)\",\n \"def teardown_schema(self):\\n models.Base.metadata.drop_all(self.session.bind)\",\n \"def set_version_db(apps, schema_editor):\\n Version = apps.get_model(\\\"reversion\\\", \\\"Version\\\")\\n content_types = Version.objects.values_list(\\\"content_type\\\", flat=True).distinct()\\n for content_type in content_types:\\n model_class = content_type.model_class()\\n db = router.db_for_write(model_class)\\n Version.objects.filter(content_type=content_type).update(db=db)\",\n \"def install(drop_all=False):\\n\\n print \\\"Connecting to database...\\\"\\n engine = create_engine(config.DATABASE_URI)\\n Base.metadata.bind = engine\\n\\n if drop_all:\\n print \\\"Dropping old tables...\\\"\\n Base.metadata.drop_all()\\n \\n print \\\"Installing new schema...\\\"\\n Base.metadata.create_all(engine)\",\n \"def setUp(self):\\n self.model = ModelBase(\\n '__TestModel__' + self.mixin.__name__,\\n (self.mixin,),\\n {'__module__': self.mixin.__module__}\\n )\\n\\n with connection.schema_editor() as schema_editor:\\n schema_editor.create_model(self.model)\",\n \"def save_model( self, request, obj, form, change ):\\n obj.save()\",\n \"def save_model(self, request, obj, form, change):\\n obj.revise()\",\n \"def _sync_foreign_model(self, instance, data, cls, manager_name):\\n # Remove all foreign instances that are not featured in data\\n data_ids = [item[\\\"id\\\"] for item in data if \\\"id\\\" in item]\\n for existing_foreigns in getattr(instance, manager_name).all():\\n if existing_foreigns.id not in data_ids:\\n existing_foreigns.delete()\\n\\n # Update all foreign instances using data\\n for item in data:\\n new_foreign = cls(**item, profile=instance)\\n new_foreign.save()\",\n \"def execute_upgrade():\\n\\n def migrate_review_policy(community_record):\\n review_policy = community_record[\\\"access\\\"].get(\\n \\\"review_policy\\\", ReviewPolicyEnum.CLOSED.value\\n )\\n community_record[\\\"access\\\"][\\\"review_policy\\\"] = review_policy\\n\\n secho(\\\"Starting data migration...\\\", fg=\\\"green\\\")\\n communities = Community.model_cls.query.all()\\n\\n for community_data in communities:\\n community = Community(community_data.data, model=community_data)\\n migrate_review_policy(community)\\n community.commit()\\n\\n secho(f\\\"Committing to DB\\\", fg=\\\"green\\\")\\n db.session.commit()\\n secho(\\\"Data migration completed.\\\", fg=\\\"green\\\")\",\n \"def migrate(ctx):\\n connecter = ScalingoInterface(ctx.obj)\\n connecter.manage_py(\\\"migrate\\\")\",\n \"def allow_syncdb(self, db, model):\\n return True\",\n \"def allow_syncdb(self, db, model):\\n return True\",\n \"def ProcessSchemaUpdate(ref, args, request):\\n table = request.table\\n relaxed_columns = args.relax_columns\\n if not table.schema and not relaxed_columns: # if not updating schema,\\n return request # then just return.\\n\\n original_schema = _TryGetCurrentSchema(ref.Parent().Name(),\\n ref.Name(),\\n ref.projectId)\\n\\n new_schema_columns = table.schema\\n updated_fields = _GetUpdatedSchema(original_schema,\\n new_schema_columns,\\n relaxed_columns)\\n\\n table_schema_type = GetApiMessage('TableSchema')\\n request.table.schema = table_schema_type(fields=updated_fields)\\n\\n return request\",\n \"def auto_populate(self, model):\\n for name, val in self._fields.iteritems():\\n setattr(model, name, val.data)\",\n \"def populate_database(self):\\n self.insert_products()\\n self.insert_categories()\\n self.insert_products_categories()\\n self.insert_stores()\\n self.insert_products_stores()\",\n \"def ensure_internal_schema_updated(self):\\n if self._internal_schema_updated:\\n return\\n if internalmigrations.needs_upgrading(self):\\n assert not self._in_transaction\\n with self.lock():\\n internalmigrations.upgrade(self)\\n self.connection.commit()\\n self._internal_schema_updated = True\",\n \"def test_make_update_script_for_model(self):\\n\\n self.setup_model_params()\\n self.write_file(self.first_model_path, self.base_source)\\n self.write_file(self.second_model_path, self.base_source + self.model_source)\\n\\n source_script = self.pyscript.make_update_script_for_model(\\n engine=self.engine,\\n oldmodel=load_model('testmodel_first:meta'),\\n model=load_model('testmodel_second:meta'),\\n repository=self.repo_path,\\n )\\n\\n self.assertTrue(\\\"['User'].create()\\\" in source_script)\\n self.assertTrue(\\\"['User'].drop()\\\" in source_script)\",\n \"def repair(self):\\n # self.add_cons_vars([x.constraint for x in self._cons_dict.values()])\\n # self.add_cons_vars([x.variable for x in self._var_dict.values()])\\n self._push_queue()\\n Model.repair(self)\\n self.regenerate_constraints()\\n self.regenerate_variables()\",\n \"def save_model(self, request, instance, form, change):\\n pass\",\n \"def tearDown(self):\\n with connection.schema_editor() as schema_editor:\\n schema_editor.delete_model(self.model)\",\n \"def southify(app):\\n managepy('migrate %s 0001 --fake' % app)\\n managepy('migrate %s' % app)\",\n \"def forwards_func(apps, schema_editor):\\n Project = apps.get_model(\\\"projects\\\", \\\"Project\\\")\\n Feature = apps.get_model(\\\"projects\\\", \\\"Feature\\\")\\n\\n feature = Feature.objects.create(feature_id=\\\"allow_version_warning_banner\\\")\\n for project in Project.objects.filter(show_version_warning=True).iterator():\\n feature.projects.add(project)\",\n \"def init_db(ctx: click.core.Context, force: bool) -> None:\\n engine = ctx.obj['engine']\\n if force:\\n contract.Base.metadata.drop_all(engine)\\n contract.Base.metadata.create_all(engine)\",\n \"def rebuild_db():\\n delete_db()\\n create_db()\\n insert_db()\",\n \"def recreate_db():\\n db.session.execute('SET FOREIGN_KEY_CHECKS=0;')\\n db.session.execute('DROP TABLE IF EXISTS logs;')\\n db.session.execute('DROP TABLE IF EXISTS employees;')\\n db.session.execute('DROP TABLE IF EXISTS sales;')\\n db.session.execute('DROP TABLE IF EXISTS plants;')\\n db.session.execute('DROP TABLE IF EXISTS products;')\\n db.session.execute('DROP TABLE IF EXISTS suppliers;')\\n db.session.execute('DROP TABLE IF EXISTS orders;')\\n db.session.execute('DROP TABLE IF EXISTS contacts;')\\n db.session.execute('DROP TABLE IF EXISTS varieties;')\\n db.session.execute('DROP TABLE IF EXISTS species;')\\n db.session.execute('DROP TABLE IF EXISTS genera;')\\n db.session.execute('DROP TABLE IF EXISTS families;')\\n db.drop_all()\\n db.create_all()\\n db.session.commit()\\n fakePlant = Plant(living = True)\\n db.session.add(fakePlant)\\n db.session.commit()\\n db.session.delete(fakePlant)\\n db.session.execute('SET FOREIGN_KEY_CHECKS=1;')\\n db.session.commit()\",\n \"def postCommitHook(self, datamodel=None):\\n self.updateProperties()\",\n \"def update_view(self):\\n for row in self.view.obj_list:\\n for obj in row:\\n obj._update(self.model)\",\n \"def clean_all_db():\\n for model in [\\n Component, Arch, AutoCase, AutoCaseFailure, Bug, Linkage, WorkItem,\\n Document, Project, Framework]:\\n model.objects.all().delete()\",\n \"def _on_model_change(self, change):\\n\\n if (\\n not self.hydraulic_conductivity.needs_model\\n and not self.water_retention.needs_model\\n ):\\n warnings.warn(\\\"There is no model to set.\\\")\\n return\\n\\n model = change[\\\"value\\\"]\\n\\n if self.hydraulic_conductivity.needs_model:\\n self.hydraulic_conductivity.model = model\\n\\n if self.water_retention.needs_model:\\n self.water_retention.model = model\",\n \"def setup_models(self):\\n pass\",\n \"def init_model(connection):\\n db = connection\\n\\n for obj in common.__dict__.itervalues():\\n if type(obj) == type and issubclass(obj, common.Model) and hasattr(obj, '__tablename__'):\\n tablename = getattr(obj, '__tablename__')\\n obj._object_store = Domain(db, tablename)\\n collection_to_class[obj._object_store] = obj\",\n \"def schema(self):\\n pass\",\n \"def reset_model(self):\\n raise NotImplementedError\",\n \"def reset_model(self):\\n raise NotImplementedError\",\n \"def reset_model(self):\\n raise NotImplementedError\",\n \"def install(cls):\\n return cls.interface.set_table(cls.schema)\",\n \"def _migrate_if_necessary(self, entries):\\r\\n entries = [\\r\\n self._migrate[entry.get('schema', 0)](self, entry)\\r\\n for entry in entries\\r\\n ]\\r\\n return entries\",\n \"def test_migrating_old_and_new_with_new_only_post_migrate_function(self):\\n yield self.mk_simple_models_old(1)\\n yield self.mk_simple_models_new(1, start=1)\\n yield self.mk_simple_models_old(1, start=2)\\n model_migrator = self.make_migrator(\\n post_migrate_function=fqpn(post_migrate_function_new_only))\\n loads, stores = self.recorded_loads_and_stores(model_migrator)\\n\\n yield model_migrator.run()\\n self.assertEqual(model_migrator.output, [\\n \\\"Migrating ...\\\",\\n \\\"Done, 3 objects migrated.\\\",\\n ])\\n self.assertEqual(sorted(loads), [u\\\"key-0\\\", u\\\"key-1\\\", u\\\"key-2\\\"])\\n self.assertEqual(sorted(stores), [u\\\"key-0\\\", u\\\"key-2\\\"])\\n\\n obj_0 = yield self.model.load(u\\\"key-0\\\")\\n self.assertEqual(obj_0.a, u\\\"value-0-modified\\\")\\n obj_1 = yield self.model.load(u\\\"key-1\\\")\\n self.assertEqual(obj_1.a, u\\\"value-1\\\")\\n obj_2 = yield self.model.load(u\\\"key-2\\\")\\n self.assertEqual(obj_2.a, u\\\"value-2-modified\\\")\",\n \"def execute_migrations(self):\\n response = self._connection.session.post(\\n self._connection.url + \\\"object_manager_attributes_execute_migrations\\\"\\n )\\n return self._raise_or_return_json(response)\",\n \"def save(self):\\r\\n if self.table == \\\"Category\\\":\\r\\n Category(self.db).insert_query(self.elements)\\r\\n elif self.table == \\\"Brand\\\":\\r\\n Brand(self.db).insert_query(self.elements)\\r\\n elif self.table == \\\"Store\\\":\\r\\n Store(self.db).insert_query(self.elements)\\r\\n elif self.table == \\\"Product\\\":\\r\\n Product(self.db).insert_query(self.elements)\",\n \"def db_create_models():\\n # db_createall doesn't work if the models aren't imported\\n import_string('models', silent=True)\\n for blueprint_name, blueprint in app.blueprints.items():\\n import_string('%s.models' % blueprint.import_name, silent=True)\\n db.create_all()\",\n \"def recreate_db():\\n\\n print(\\\"will reinit db - FAKE\\\")\\n db.create_tables([Message, Instance])\\n\\n # no need to prepare a sample record.\\n # use http to create init request instead.\\n #inject_record()\",\n \"def initialize_schema(self, dry_run=False):\\n if not dry_run:\\n self.flush()\",\n \"def dummy():\\n\\t\\t\\tself.save()\",\n \"def db_create_all(self):\\n with self.app_context():\\n self.db.create_all()\",\n \"def migrate_to_db(self, source, items_per_cat):\\n data = self.fetch(source, items_per_cat)\\n data = self.__attach_source(data, source)\\n logger.debug('Migrating %d items to DB', len(data))\\n self.repository.write(data)\",\n \"def _apply(self):\\n s = [(iptables_save, iptables_restore, self.ipv4)]\\n if self.use_ipv6:\\n s += [(ip6tables_save, ip6tables_restore, self.ipv6)]\\n\\n for save, restore, tables in s:\\n all_tables, _err = save()\\n all_lines = all_tables.split('\\\\n')\\n for table_name, table in six.iteritems(tables):\\n start, end = self._find_table(all_lines, table_name)\\n all_lines[start:end] = self._modify_rules(\\n all_lines[start:end], table, table_name)\\n table.dirty = False\\n restore('\\\\n'.join(all_lines))\",\n \"def init_db():\\n import cerbereapp.models\\n Base.metadata.create_all(bind=engine)\",\n \"def recreate_db():\\n drop_db()\\n create_db()\\n populate_db()\",\n \"def update(self):\\n self.getDbRecord().update()\",\n \"def backwards(apps, schema_editor):\\n pass\"\n]","isConversational":false},"negative_scores":{"kind":"list like","value":["0.6670259","0.6642882","0.66169477","0.6522209","0.6405807","0.6354266","0.62821496","0.6278887","0.6274175","0.6015691","0.59840304","0.5931382","0.5882499","0.58795726","0.58661336","0.58658046","0.5792609","0.57480407","0.57398003","0.57097906","0.56850487","0.5665864","0.5660554","0.56256837","0.56246835","0.5610122","0.5604883","0.55897397","0.5587097","0.5586104","0.5576394","0.556719","0.55617094","0.5555682","0.55505854","0.5539272","0.55221444","0.5498408","0.54952204","0.54933375","0.5492117","0.54877275","0.54807633","0.5478182","0.5477624","0.5472829","0.5472575","0.54680896","0.546124","0.54608876","0.5459257","0.54289246","0.54215956","0.54032695","0.5402617","0.5398621","0.5393895","0.5359785","0.53575045","0.53361446","0.5326526","0.5326526","0.5324291","0.5311551","0.530855","0.5303742","0.52993613","0.52965784","0.5291472","0.52899307","0.5283222","0.52808905","0.52780163","0.52757084","0.52737564","0.5270469","0.5263685","0.5260701","0.52584314","0.52572","0.52536476","0.52501243","0.5245271","0.5245271","0.5245271","0.52301824","0.5218183","0.5213198","0.5212314","0.5211332","0.5207655","0.52030814","0.5201974","0.5198945","0.5193025","0.51928544","0.5191622","0.518757","0.5172823","0.51716924","0.5168909"],"string":"[\n \"0.6670259\",\n \"0.6642882\",\n \"0.66169477\",\n \"0.6522209\",\n \"0.6405807\",\n \"0.6354266\",\n \"0.62821496\",\n \"0.6278887\",\n \"0.6274175\",\n \"0.6015691\",\n \"0.59840304\",\n \"0.5931382\",\n \"0.5882499\",\n \"0.58795726\",\n \"0.58661336\",\n \"0.58658046\",\n \"0.5792609\",\n \"0.57480407\",\n \"0.57398003\",\n \"0.57097906\",\n \"0.56850487\",\n \"0.5665864\",\n \"0.5660554\",\n \"0.56256837\",\n \"0.56246835\",\n \"0.5610122\",\n \"0.5604883\",\n \"0.55897397\",\n \"0.5587097\",\n \"0.5586104\",\n \"0.5576394\",\n \"0.556719\",\n \"0.55617094\",\n \"0.5555682\",\n \"0.55505854\",\n \"0.5539272\",\n \"0.55221444\",\n \"0.5498408\",\n \"0.54952204\",\n \"0.54933375\",\n \"0.5492117\",\n \"0.54877275\",\n \"0.54807633\",\n \"0.5478182\",\n \"0.5477624\",\n \"0.5472829\",\n \"0.5472575\",\n \"0.54680896\",\n \"0.546124\",\n \"0.54608876\",\n \"0.5459257\",\n \"0.54289246\",\n \"0.54215956\",\n \"0.54032695\",\n \"0.5402617\",\n \"0.5398621\",\n \"0.5393895\",\n \"0.5359785\",\n \"0.53575045\",\n \"0.53361446\",\n \"0.5326526\",\n \"0.5326526\",\n \"0.5324291\",\n \"0.5311551\",\n \"0.530855\",\n \"0.5303742\",\n \"0.52993613\",\n \"0.52965784\",\n \"0.5291472\",\n \"0.52899307\",\n \"0.5283222\",\n \"0.52808905\",\n \"0.52780163\",\n \"0.52757084\",\n \"0.52737564\",\n \"0.5270469\",\n \"0.5263685\",\n \"0.5260701\",\n \"0.52584314\",\n \"0.52572\",\n \"0.52536476\",\n \"0.52501243\",\n \"0.5245271\",\n \"0.5245271\",\n \"0.5245271\",\n \"0.52301824\",\n \"0.5218183\",\n \"0.5213198\",\n \"0.5212314\",\n \"0.5211332\",\n \"0.5207655\",\n \"0.52030814\",\n \"0.5201974\",\n \"0.5198945\",\n \"0.5193025\",\n \"0.51928544\",\n \"0.5191622\",\n \"0.518757\",\n \"0.5172823\",\n \"0.51716924\",\n \"0.5168909\"\n]","isConversational":false},"document_score":{"kind":"string","value":"0.0"},"document_rank":{"kind":"string","value":"-1"}}},{"rowIdx":22674,"cells":{"query":{"kind":"string","value":"Database connection context manager."},"document":{"kind":"string","value":"def session_scope(expire=True):\n if not isinstance(expire, bool):\n raise ValueError(f'Expire attr must be bool. Got {type(expire)}')\n\n session = Session()\n session.expire_on_commit = expire\n try:\n yield session\n session.commit()\n except:\n session.rollback()\n raise\n finally:\n session.close()"},"metadata":{"kind":"string","value":"{\n \"objective\": {\n \"self\": [],\n \"paired\": [],\n \"triplet\": [\n [\n \"query\",\n \"document\",\n \"negatives\"\n ]\n ]\n }\n}"},"negatives":{"kind":"list like","value":["def _db_connection(self):\n pass","def obtainDatabaseConnection(self):\n\t\tself.databaseConnector = DatabaseConnector()","def db_session_context(db_name):\n try:\n db = psycopg2.connect(database=db_name)\n yield db\n finally:\n db.close()","def __enter__(self) -> sqlite3.Connection:\n self.conn = sqlite3.connect(self.path_db)\n return self.conn","def __enter__(self) -> 'DBcursor':\n self.conn = connector.connect(**self.dbconfig)\n self.cursor = self.conn.cursor()\n return self.cursor","def __enter__(self):\n try:\n self.conn = sqlite3.connect(self.database_configuration)\n self.cursor = self.conn.cursor()\n\n return self.cursor\n except Exception as error:\n print(f\"DBCM::__enter__::{error}\")","async def database(self, *_):\n config = {'dsn': os.getenv('DATABASE_URL')}\n self.db = await aiopg.sa.create_engine(**config)\n yield\n self.db.close()\n await self.db.wait_closed()","def __enter__(self):\n self.conn = sqlite3.connect(self.db_name)\n return self.conn","def __enter__(self) -> cx_Oracle.connect:\n self.db_connection = cx_Oracle.connect(\n self.config['oracle']['ora_user'],\n self.config['oracle']['ora_pass'],\n f\"{self.config['oracle']['ora_host']}/{self.config['oracle']['ora_sid']}\")\n return self.db_connection","def __init__(self):\r\n self.conn = create_connection(DATABASE_PATH)","def _connect_db():\n try:\n conn = connect()\n cur = conn.cursor() \n yield conn, cur\n\n finally:\n cur.close()\n conn.close()","def getConnection(self):\n\n try:\n if os.path.exists(self.rootdir) is False:\n os.makedirs(self.rootdir)\n db_name = os.path.join(self.rootdir, '{0}.db'.format(self.wfname))\n conn = sqlite3.connect(db_name)\n\n # Enable foreign key support for database\n cur = conn.cursor()\n cur.execute('pragma foreign_keys = on;')\n\n yield conn\n\n except Exception as error:\n conn.rollback()\n raise workflowException('Workflow Database Error: {0}'.format(repr(error)))\n\n else:\n #print('commit transx')\n conn.commit()\n\n return conn","def __init__(self):\n self._connection = get_db_connection()","def db_connection(self):\n try:\n self.connection = connect(host=self.host, user=self.user, password = self.password, db = self.db, cursorclass = self.cursor)\n except MySQLError:\n print(\"DB Error\")","def connect_to_db(cls):\n conn = psycopg2.connect(os.environ['DATABASE_URL'])\n conn.autocommit = True\n cursor = conn.cursor()\n\n return cursor","def conn(self):\n self.cnx = psycopg2.connect(**self.dbConfig) \n self.cur = self.cnx.cursor()","def connect(self) -> ContextManager[Connection]:","def db_connection(engine, tables):\n connection = engine.connect()\n # begin the nested transaction\n transaction = connection.begin()\n\n yield transaction\n\n # roll back the broader transaction\n transaction.rollback()\n # put back the connection to the connection pool\n connection.close()","def __enter__(self):\n fp = os.path.abspath(self.db_name)\n if self.read_only:\n self.conn = sqlite3.connect('file:/{}?mode=ro'.format(fp), uri=True)\n else:\n self.conn = sqlite3.connect(fp)\n return self.conn","def create_connection():\n conn = psycopg2.connect(database=getenv('DB_NAME'), user=getenv('DB_USER'),\n password=getenv('DB_PASSWORD'), port=getenv('DB_PORT'), host=getenv('DB_HOST'))\n try:\n yield conn\n finally:\n conn.commit()\n conn.close()","def connect_db(self) -> sqlite3.Connection:","def db_connect():\n if 'db' not in g:\n g.db = sql.connect(current_app.config[\"DATABASE\"], detect_types=sql.PARSE_DECLTYPES)\n g.db.row_factory = sql.Row\n return g.db","def get_db():\n db = None\n try:\n db = SessionLocal()\n yield db\n finally:\n db.close()","def get_db():\n try:\n db = SessionLocal()\n yield db\n finally:\n db.close()","def connect_db(self) -> sqlite3.Connection:\n self.connection = sqlite3.connect(self.database)\n self.connection.row_factory = sqlite3.Row\n\n self.get_cursor()","def connect_db(self):\n try:\n self.connection = self.engine.connect()\n except Exception:\n self.print_std_error()","def __connect(self):\n session, metadata, connection = db(dbhost=getattr(self, \"host\"),\n dbuser=getattr(self, \"user\"),\n dbpass=getattr(self, \"password\"),\n dbname=getattr(self, \"dbname\"))\n return session, metadata, connection","def setup_db_conn():\n # TODO update so DB does not have to be hard coded\n # Currently DB is hardcoded","def get_db():\n if not hasattr(g, 'db_connection'):\n g.db_connection = connect_db()\n return g.db_connection","def getConnection(self):\n if (not self.initialized):\n logging.error(\"Module is not initialized\")\n \n conn_options = {\n 'user': self.user,\n 'password' : self.password,\n 'host' : self.host,\n 'port' : self.port,\n 'database' : self.dbname,\n 'raise_on_warnings': True\n }\n db = mysql.connector.connect(**conn_options)\n return db","def get_db():\n db = SessionLocal()\n try:\n yield db\n finally:\n db.close()","def __init__(self):\n self.dbcon = DbConnection.get_con()","def get_db_connection():\n db = sqlite3.connect(config.PERSISTENCE_LOCATION, check_same_thread=False)\n db.isolation_level = None\n db.row_factory = sqlite3.Row\n return db","def get_connection():\n con = psycopg2.connect(**DB_CONFIG)\n return con","def connection(self):\n connection = self._try_connect()\n\n try:\n yield connection\n except Exception:\n connection.rollback()\n raise\n else:\n connection.commit()\n finally:\n connection.close()","def get_connection():\n\t# flask.g documentation: http://flask.pocoo.org/docs/0.12/api/#flask.g\n\ttry:\n\t\tconn = flask.g._database_connection\n\texcept AttributeError:\n\t\tconn = flask.g._database_connection = sqlite3.connect(config.PATH_DATABASE,\n\t\t\t\tdetect_types=sqlite3.PARSE_DECLTYPES) # allows storing datetime, etc.\n\t\tconn.row_factory = sqlite3.Row\n\treturn conn","def db( self ):\n #TODO: backoff\n if self._db is None:\n self._db = self._GetNewConnection()\n try:\n self._db.isolation_level\n except (OperationalError, InterfaceError, InternalError):\n l_logger.exception(\"Looks like the db is not responding. Trying to recover.\")\n try:\n self._db.close()\n except ProgrammingError:\n l_logger.info(\"Database is closed, attempting to recover\")\n self._db = self._GetNewConnection()\n return self._db","def getDbConnection(self, **kwargs):\r\n \r\n con = sql.connect(self._filename, **kwargs)\r\n con.row_factory = sql.Row\r\n return con","def get_connection(cls):\n return cls.database.connection","def __init__(self):\n\t\tself.obtainDatabaseConnection()","def open_db_connection():\n database = MySQLdb.connect( host = HOST, db = DATABASE,\n user = DBUSER, passwd = DBPASSWD,\n cursorclass = DictCursor )\n database.cursor().execute( 'SET AUTOCOMMIT = 1' )\n database.cursor().execute( \"SET NAMES 'utf8'\" )\n return database","def open_connection(self):\n self.conn = pymysql.connect(host=self.host, user=self.user, passwd=self.passwd, db=self.db)","def _getConnection():\n db = sqlite.connect(db_path)\n db.row_factory = sqlite.Row\n cursor = db.cursor()\n return cursor, db","def bd_conecta():\n if not hasattr(g, 'sqlite_db'):\n rv = sqlite3.connect(app.config['DATABASE'])\n rv.row_factory = sqlite3.Row\n g.sqlite_db = rv\n return g.sqlite_db","def connect_db():\n conn = psycopg2.connect(\n database = app.config['DATABASE'],\n host = app.config['HOST'],\n port = app.config['PORT'])\n conn.autocommit = True\n return conn","def database():\n db = Database()\n yield db\n db.close()","def connection(self):\n\n ctx = _app_ctx_stack.top\n if ctx is not None:\n if not hasattr(ctx, 'mysql_db'):\n ctx.mysql_db = self.connect\n return ctx.mysql_db","def open_database(self):\n if self._conn is None:\n self._conn = sqlite3.connect(self._db_path)","def con(database):\r\n \r\n conn = sqlite3.connect(database)\r\n db = conn.cursor()\r\n return [conn, db]","def get_db():\r\n con = sqlite3.connect(\"whinge.db\")\r\n con.row_factory = sqlite3.Row # return dict-like rows\r\n return con","def connect(cls):\n cls.conn = MySQLdb.connect(**cls.configs)\n cls.conn.autocommit(cls.autocommit)","def connect_to_db(self):\n\t\t# connection = psycopg2.connect(database=config.database, user=config.user,password = config.password)\n\t\tconnection = psycopg2.connect(database=config.database, user=config.user)\n\t\treturn connection","def get_db() -> Generator:\n db = SessionLocal()\n try:\n yield db\n finally:\n db.close()","def db_connection():\n global dbconnection\n try:\n conn = dbconnection\n except:\n dbconnection = psycopg2.connect(user = dbuser,\n password = dbpass,\n host = dbserver,\n port = \"5432\",\n database = dbname)\n conn = dbconnection\n return conn","def db_open():\n if not hasattr(g, 'sqlite_db'):\n g.sqlite_db = db_connect()\n return g.sqlite_db","def sql_connection():\n return sqlite3.connect('database.db')","def persistence():\n connection = None\n LOGGER.debug(POSTGRES_USER)\n try:\n LOGGER.debug('connecting to postgres at %s:%s', POSTGRES_HOST, POSTGRES_PORT)\n connection = psycopg2.connect(host=POSTGRES_HOST, port=POSTGRES_PORT, dbname=POSTGRES_DB,\n user=POSTGRES_USER, password=POSTGRES_PASSWORD)\n yield connection\n except Exception:\n LOGGER.exception('unable to connect to postgres server')\n raise\n finally:\n if connection is not None:\n connection.close()","def test_context_manager(self):\n with database.Database() as db:\n pass\n self.assertTrue(self.mocked_connection.close.call_count is 1)","def __enter__(self) -> 'cursor':\n self.conn = pymysql.connect(self.configuration)\n #self.conn = pyodbc.connect(self.configuration)\n self.cursor = self.conn.cursor()\n return self.cursor","def database_session():\n if \"CI\" in os.environ:\n con = psycopg2.connect(\n host=os.environ[\"POSTGRES_HOST\"],\n port=os.environ[\"POSTGRES_PORT\"],\n user=os.environ[\"PGUSER\"],\n password=os.environ[\"PGPASSWORD\"],\n )\n else:\n con = psycopg2.connect(host=\"127.0.0.1\", port=\"5432\")\n # Setup\n con.set_isolation_level(ISOLATION_LEVEL_AUTOCOMMIT)\n cursor = con.cursor()\n cursor.execute(f'create database \"{DB}\";')\n session = Meta.init(CONN_STRING).Session()\n yield session\n\n # Teardown\n engine = session.get_bind()\n session.close()\n engine.dispose()\n Meta.engine = None\n\n cursor.execute(f'drop database \"{DB}\";')\n cursor.close()\n con.close()","def connect(self):\n try:\n connection = Connection(host=settings.DATABASE_HOST, port=settings.DATABASE_PORT)\n except ConnectionFailure, error:\n return \"Could not connect to database: %s\" % error\n print \"Could not connect to database: %s \\n\" % error\n if __name__ == \"spider\":\n sys.exit(1)\n self.dbconnection = connection[settings.DATABASE_NAME]","def __open(self):\n\n try:\n cnx = mysql.connector.connect(\n host=self.__host,\n user=self.__user,\n password=self.__password,\n database=self.__database\n )\n self.__connection = cnx\n self.__cursor = cnx.cursor(buffered=True, dictionary=True)\n\n except mysql.connector.Error as err:\n print(\"Something went wrong: {}\".format(err))","def __enter__(self) -> 'cursor':\n self.conn = cx_Oracle.connect(self.configuration)\n self.cursor = self.conn.cursor()\n return self.cursor","def make_db_conn():\n\n global DB_CONN\n DB_CONN = psycopg2.connect(\"dbname=tornadowatch user=postgres host=localhost port=6432\")\n DB_CONN.set_isolation_level(psycopg2.extensions.ISOLATION_LEVEL_AUTOCOMMIT)","def getconn(self):\n #hdbport = int('3%s15' % Settings.hdbinstancenum)\n con = dbapi.connect(address = self.host, \\\n port = self.port, \\\n user = self.username, \\\n password = self.password, \\\n autocommit = True)\n if self.schema:\n cur = con.cursor()\n try:\n cur.execute('ALTER SESSION SET CURRENT_SCHEMA = %s' % self.schema)\n return con\n except dbapi.Error, err:\n cur.close()\n con.close()\n cur = None\n raise err\n finally:\n if cur:\n cur.close()\n else:\n return con","def conn(self):\n try:\n if self._db is None:\n self._db = sqlc.connect(user=self.login,\n password=self.passwd,\n host=self.host,\n database=self.database)\n\n except sqlc.Error as e:\n print (\"MySQL exception #{0} getting connection: {1}\".format(e.errno, e.msg))\n if e.errno == 2003:\n exit(-1)\n except Exception as e:\n print (\"Couldn't get connection property: {0}\".format(e.message))\n finally:\n return self._db","def get_db():\n if not hasattr(g, 'db_conn'):\n g.db_conn = sqlite3.connect(\"pypatch.sqlite\")\n g.db_conn.row_factory = sqlite3.Row\n \n return g.db_conn","def getDbConnection(self):\n return self._oDb;","def get_dbapi20_connection ():\n return cherrypy.engine.pool.connect ()","def get_db(self):\n db = getattr(g, '_database', None)\n if db is None:\n db = g._database = connect(DATABASE)\n return db","async def database():\n db = await Database.connect_pool()\n return db","def db_init(self):\n if self.platform == 'Linux':\n print(self.db)\n conn = sqlite3.connect(self.db)\n # if debug\n conn.set_trace_callback(print)\n # converter = Converter(self.db)\n # converter.mdb2sqlite()\n # return converter.conn\n return conn, conn.cursor()\n elif self.platform == 'Windows':\n # Todo: update database config.\n user = ''\n password = ''\n odbc_conn_str = \"DRIVER={Microsoft Access Driver (*.mdb, *.accdb)};\" \\\n \"DBQ=%s;UID=%s;PWD=%s\" % (self.db, user, password)\n import pypyodbc\n conn = pypyodbc.connect(odbc_conn_str)\n return conn, conn.cursor()\n else:\n raise OSError('Unsupported OS')","def override_get_db():\n try:\n db = TestingSessionLocal()\n yield db\n finally:\n db.close()","def db_conection():\n try:\n\t conn = pymysql.connect(host='127.0.0.1', user='root', passwd='test', db='EMP')\n\t __log(1,\"DB Connection Established\")\n\t return conn\n except Exception as e:\n#\t\t\tprint (e)\n __log(3,\"Database Connection Establishment Error, Fatal Error, Exiting from Code\")\n exit()","def db(self):\n return DbManager(self)","def connect(self):\n\n self._dbcon = psycopg2.connect(\n database=self._dbname, host=self._host, port=self._port, user=self._username, password=self._password)\n self.result_as_dict(self._result_as_dict)","def _connect(self):\n if self.settings.get('ENGINE', 'mysql') == 'mysql':\n self._db = self.dbdriver.connect(self.settings['HOST'],\n self.settings['USER'],\n self.settings['PASSWORD'],\n self.settings['NAME'],\n self.settings.get('PORT', 3306))\n elif self.settings['ENGINE'] == 'postgres':\n self._db = self.dbdriver.connect(database=self.settings['NAME'],\n user=self.settings['USER'],\n password=self.settings['PASSWORD'],\n host=self.settings['HOST'])\n elif self.settings['ENGINE'] == 'sqlite':\n self._db = self.dbdriver.connect(self.settings['NAME'])\n self.cursor = self._db.cursor()","def connect_db():\n logging.info('Connects to the specific database.')\n rv = sqlite3.connect(app.config['DATABASE'])\n rv.row_factory = sqlite3.Row\n g.db = rv\n logging.info(rv)\n return rv","def _connect_to_db(self) -> None:\n self._connection = psycopg2.connect(database=self.dbname,\n user=self.dbuser, password=self.dbpassword,\n host=self.dbhost, port=str(self.dbport))","def get_conn(self):\n conn = sqlite3.connect(self.uri)\n conn.row_factory = sqlite3.Row\n return conn","def __init__(self):\n self.dbconnect = dbConnection.connection","def connect(self):\n self.db = pymysql.connect(self.db_ip, self.uid, self.pwd, self.db_name)\n self.cursor = self.db.cursor()","def logic_db_connection():\n try:\n boto_session = boto3.Session(profile_name='loidsig')\n except:\n boto_session = boto3.Session()\n sm_client = boto_session.client(\n service_name='secretsmanager',\n region_name='us-east-1',\n endpoint_url='https://secretsmanager.us-east-1.amazonaws.com'\n )\n get_secret_value_response = sm_client.get_secret_value(SecretId='Loidsig_DB')\n cred_dict = ast.literal_eval(get_secret_value_response['SecretString'])\n db_user, db_pass = cred_dict['username'], cred_dict['password']\n db_host, db_port, db_name = cred_dict['host'], cred_dict['port'], cred_dict['dbname']\n\n try:\n conn = psycopg2.connect(\n host=db_host,\n port=db_port,\n user=db_user,\n password=db_pass,\n database=db_name,\n )\n except Exception as e:\n print(\"Unable to connect to postgres! Error: {}\".format(e))\n raise\n return conn","def initialize_database():\n\n global DatabaseSession\n from config import config\n\n engine = create_engine(\n config[saq.CONFIG['global']['instance_type']].SQLALCHEMY_DATABASE_URI, \n **config[saq.CONFIG['global']['instance_type']].SQLALCHEMY_DATABASE_OPTIONS)\n\n DatabaseSession = sessionmaker(bind=engine)\n saq.db = scoped_session(DatabaseSession)","def create_connection():\r\n try:\r\n conn = sq.connect(DBClass.db_name)\r\n except sq.Error as e:\r\n raise e\r\n \r\n return conn","def connection_database(self):\n # connection to the database\n self.data_base = mysql.connector.connect(user=self.info[0], password=self.info[1],\n host=self.info[2])\n self.cursor = self.data_base.cursor()\n\n # executed \"use Purbeurre\" request\n self.cursor.execute(\"USE Purbeurre\")","def init_database(self):\n # init_database(self.engine)","def connect_db():\n return hc_db.HCDB(app.config[\"DATABASE\"])","def _connect(self):\n\n assert not self.__is_connected\n\n self._db_engine = create_engine(self._database.url)\n\n from sqlalchemy import event\n\n if self._database.driver == 'sqlite':\n @event.listens_for(self._db_engine, \"connect\")\n def do_connect(dbapi_connection, *args, **kwargs):\n # disable pysqlite's emitting of the BEGIN statement entirely.\n # also stops it from emitting COMMIT before any DDL.\n dbapi_connection.isolation_level = None\n\n @event.listens_for(self._db_engine, \"begin\")\n def do_begin(conn):\n # emit our own BEGIN\n conn.execute(\"BEGIN\")\n\n self._db_connection = self._db_engine.connect()\n self._db_transaction = self._db_connection.begin()\n\n self._orm_session = Session(\n bind=self._db_connection,\n transaction=self,\n )\n self._orm_session_proxy = weakref.proxy(self._orm_session)\n\n self.__is_connected = True","def create_connection(self, context, *, engine):\n try:\n connection = engine.connect()\n except OperationalError as exc:\n raise UnrecoverableError('Could not create SQLAlchemy connection: {}.'.format(str(exc).replace('\\n', ''))\n ) from exc\n\n with connection:\n yield connection","def get(self):\n\n \n\n try:\n db = getDatabase()\n connection = db.connect()\n\n connection.get(self)\n\n except Exception as e:\n raise e\n finally:\n db.dispose()","def get_database(self):\n if self._database is None:\n conn = self.get_connection()\n db = conn[self.database]\n self._database = db\n\n return self._database","def getDb(self):\n return self.db","def get_db():\n # this is a bit of a hack, since it assumes all the models talk to the same\n # db. that said a lot of our code relies on that assumption.\n # this import is here because of annoying dependencies\n return Database(settings.COUCH_DATABASE)","def connect_to_db(self):\n try:\n self.connection = sqlite3.connect('test_database.db')\n self.cursor = sqlite3.connect('test_database.db').cursor()\n return self.connection, self.cursor\n except EnvironmentError:\n print(EnvironmentError)","def database():\n return sqlite3.connect(DATABASE)","def open_database(dbpath=DB_PATH):\n\n # __enter__ (Establish the connection)\n db = sqlite3.connect(dbpath)\n cursor = db.cursor()\n\n yield BlockChainDB(cursor)\n\n # __exit__ (Commit and close the connection)\n db.commit()\n db.close()","def __connect(self):\n self.conn = pymysql.connect(self.opts.DB_HOST, self.opts.DB_USER,\n self.opts.DB_PASSWORD, self.opts.DB_NAME)","def connect_db():\n db = psycopg2.connect(\n dbname=app.config['DBNAME'],\n user=app.config['DBUSER'],\n password=app.config['DBPASSWORD'],\n host=app.config['DBHOST'],\n port=app.config['DBPORT']\n )\n return db","def init_database(self):\n init_database(self.engine)","async def _get_db_connection():\n return await gino.Gino(get_database_dsn())"],"string":"[\n \"def _db_connection(self):\\n pass\",\n \"def obtainDatabaseConnection(self):\\n\\t\\tself.databaseConnector = DatabaseConnector()\",\n \"def db_session_context(db_name):\\n try:\\n db = psycopg2.connect(database=db_name)\\n yield db\\n finally:\\n db.close()\",\n \"def __enter__(self) -> sqlite3.Connection:\\n self.conn = sqlite3.connect(self.path_db)\\n return self.conn\",\n \"def __enter__(self) -> 'DBcursor':\\n self.conn = connector.connect(**self.dbconfig)\\n self.cursor = self.conn.cursor()\\n return self.cursor\",\n \"def __enter__(self):\\n try:\\n self.conn = sqlite3.connect(self.database_configuration)\\n self.cursor = self.conn.cursor()\\n\\n return self.cursor\\n except Exception as error:\\n print(f\\\"DBCM::__enter__::{error}\\\")\",\n \"async def database(self, *_):\\n config = {'dsn': os.getenv('DATABASE_URL')}\\n self.db = await aiopg.sa.create_engine(**config)\\n yield\\n self.db.close()\\n await self.db.wait_closed()\",\n \"def __enter__(self):\\n self.conn = sqlite3.connect(self.db_name)\\n return self.conn\",\n \"def __enter__(self) -> cx_Oracle.connect:\\n self.db_connection = cx_Oracle.connect(\\n self.config['oracle']['ora_user'],\\n self.config['oracle']['ora_pass'],\\n f\\\"{self.config['oracle']['ora_host']}/{self.config['oracle']['ora_sid']}\\\")\\n return self.db_connection\",\n \"def __init__(self):\\r\\n self.conn = create_connection(DATABASE_PATH)\",\n \"def _connect_db():\\n try:\\n conn = connect()\\n cur = conn.cursor() \\n yield conn, cur\\n\\n finally:\\n cur.close()\\n conn.close()\",\n \"def getConnection(self):\\n\\n try:\\n if os.path.exists(self.rootdir) is False:\\n os.makedirs(self.rootdir)\\n db_name = os.path.join(self.rootdir, '{0}.db'.format(self.wfname))\\n conn = sqlite3.connect(db_name)\\n\\n # Enable foreign key support for database\\n cur = conn.cursor()\\n cur.execute('pragma foreign_keys = on;')\\n\\n yield conn\\n\\n except Exception as error:\\n conn.rollback()\\n raise workflowException('Workflow Database Error: {0}'.format(repr(error)))\\n\\n else:\\n #print('commit transx')\\n conn.commit()\\n\\n return conn\",\n \"def __init__(self):\\n self._connection = get_db_connection()\",\n \"def db_connection(self):\\n try:\\n self.connection = connect(host=self.host, user=self.user, password = self.password, db = self.db, cursorclass = self.cursor)\\n except MySQLError:\\n print(\\\"DB Error\\\")\",\n \"def connect_to_db(cls):\\n conn = psycopg2.connect(os.environ['DATABASE_URL'])\\n conn.autocommit = True\\n cursor = conn.cursor()\\n\\n return cursor\",\n \"def conn(self):\\n self.cnx = psycopg2.connect(**self.dbConfig) \\n self.cur = self.cnx.cursor()\",\n \"def connect(self) -> ContextManager[Connection]:\",\n \"def db_connection(engine, tables):\\n connection = engine.connect()\\n # begin the nested transaction\\n transaction = connection.begin()\\n\\n yield transaction\\n\\n # roll back the broader transaction\\n transaction.rollback()\\n # put back the connection to the connection pool\\n connection.close()\",\n \"def __enter__(self):\\n fp = os.path.abspath(self.db_name)\\n if self.read_only:\\n self.conn = sqlite3.connect('file:/{}?mode=ro'.format(fp), uri=True)\\n else:\\n self.conn = sqlite3.connect(fp)\\n return self.conn\",\n \"def create_connection():\\n conn = psycopg2.connect(database=getenv('DB_NAME'), user=getenv('DB_USER'),\\n password=getenv('DB_PASSWORD'), port=getenv('DB_PORT'), host=getenv('DB_HOST'))\\n try:\\n yield conn\\n finally:\\n conn.commit()\\n conn.close()\",\n \"def connect_db(self) -> sqlite3.Connection:\",\n \"def db_connect():\\n if 'db' not in g:\\n g.db = sql.connect(current_app.config[\\\"DATABASE\\\"], detect_types=sql.PARSE_DECLTYPES)\\n g.db.row_factory = sql.Row\\n return g.db\",\n \"def get_db():\\n db = None\\n try:\\n db = SessionLocal()\\n yield db\\n finally:\\n db.close()\",\n \"def get_db():\\n try:\\n db = SessionLocal()\\n yield db\\n finally:\\n db.close()\",\n \"def connect_db(self) -> sqlite3.Connection:\\n self.connection = sqlite3.connect(self.database)\\n self.connection.row_factory = sqlite3.Row\\n\\n self.get_cursor()\",\n \"def connect_db(self):\\n try:\\n self.connection = self.engine.connect()\\n except Exception:\\n self.print_std_error()\",\n \"def __connect(self):\\n session, metadata, connection = db(dbhost=getattr(self, \\\"host\\\"),\\n dbuser=getattr(self, \\\"user\\\"),\\n dbpass=getattr(self, \\\"password\\\"),\\n dbname=getattr(self, \\\"dbname\\\"))\\n return session, metadata, connection\",\n \"def setup_db_conn():\\n # TODO update so DB does not have to be hard coded\\n # Currently DB is hardcoded\",\n \"def get_db():\\n if not hasattr(g, 'db_connection'):\\n g.db_connection = connect_db()\\n return g.db_connection\",\n \"def getConnection(self):\\n if (not self.initialized):\\n logging.error(\\\"Module is not initialized\\\")\\n \\n conn_options = {\\n 'user': self.user,\\n 'password' : self.password,\\n 'host' : self.host,\\n 'port' : self.port,\\n 'database' : self.dbname,\\n 'raise_on_warnings': True\\n }\\n db = mysql.connector.connect(**conn_options)\\n return db\",\n \"def get_db():\\n db = SessionLocal()\\n try:\\n yield db\\n finally:\\n db.close()\",\n \"def __init__(self):\\n self.dbcon = DbConnection.get_con()\",\n \"def get_db_connection():\\n db = sqlite3.connect(config.PERSISTENCE_LOCATION, check_same_thread=False)\\n db.isolation_level = None\\n db.row_factory = sqlite3.Row\\n return db\",\n \"def get_connection():\\n con = psycopg2.connect(**DB_CONFIG)\\n return con\",\n \"def connection(self):\\n connection = self._try_connect()\\n\\n try:\\n yield connection\\n except Exception:\\n connection.rollback()\\n raise\\n else:\\n connection.commit()\\n finally:\\n connection.close()\",\n \"def get_connection():\\n\\t# flask.g documentation: http://flask.pocoo.org/docs/0.12/api/#flask.g\\n\\ttry:\\n\\t\\tconn = flask.g._database_connection\\n\\texcept AttributeError:\\n\\t\\tconn = flask.g._database_connection = sqlite3.connect(config.PATH_DATABASE,\\n\\t\\t\\t\\tdetect_types=sqlite3.PARSE_DECLTYPES) # allows storing datetime, etc.\\n\\t\\tconn.row_factory = sqlite3.Row\\n\\treturn conn\",\n \"def db( self ):\\n #TODO: backoff\\n if self._db is None:\\n self._db = self._GetNewConnection()\\n try:\\n self._db.isolation_level\\n except (OperationalError, InterfaceError, InternalError):\\n l_logger.exception(\\\"Looks like the db is not responding. Trying to recover.\\\")\\n try:\\n self._db.close()\\n except ProgrammingError:\\n l_logger.info(\\\"Database is closed, attempting to recover\\\")\\n self._db = self._GetNewConnection()\\n return self._db\",\n \"def getDbConnection(self, **kwargs):\\r\\n \\r\\n con = sql.connect(self._filename, **kwargs)\\r\\n con.row_factory = sql.Row\\r\\n return con\",\n \"def get_connection(cls):\\n return cls.database.connection\",\n \"def __init__(self):\\n\\t\\tself.obtainDatabaseConnection()\",\n \"def open_db_connection():\\n database = MySQLdb.connect( host = HOST, db = DATABASE,\\n user = DBUSER, passwd = DBPASSWD,\\n cursorclass = DictCursor )\\n database.cursor().execute( 'SET AUTOCOMMIT = 1' )\\n database.cursor().execute( \\\"SET NAMES 'utf8'\\\" )\\n return database\",\n \"def open_connection(self):\\n self.conn = pymysql.connect(host=self.host, user=self.user, passwd=self.passwd, db=self.db)\",\n \"def _getConnection():\\n db = sqlite.connect(db_path)\\n db.row_factory = sqlite.Row\\n cursor = db.cursor()\\n return cursor, db\",\n \"def bd_conecta():\\n if not hasattr(g, 'sqlite_db'):\\n rv = sqlite3.connect(app.config['DATABASE'])\\n rv.row_factory = sqlite3.Row\\n g.sqlite_db = rv\\n return g.sqlite_db\",\n \"def connect_db():\\n conn = psycopg2.connect(\\n database = app.config['DATABASE'],\\n host = app.config['HOST'],\\n port = app.config['PORT'])\\n conn.autocommit = True\\n return conn\",\n \"def database():\\n db = Database()\\n yield db\\n db.close()\",\n \"def connection(self):\\n\\n ctx = _app_ctx_stack.top\\n if ctx is not None:\\n if not hasattr(ctx, 'mysql_db'):\\n ctx.mysql_db = self.connect\\n return ctx.mysql_db\",\n \"def open_database(self):\\n if self._conn is None:\\n self._conn = sqlite3.connect(self._db_path)\",\n \"def con(database):\\r\\n \\r\\n conn = sqlite3.connect(database)\\r\\n db = conn.cursor()\\r\\n return [conn, db]\",\n \"def get_db():\\r\\n con = sqlite3.connect(\\\"whinge.db\\\")\\r\\n con.row_factory = sqlite3.Row # return dict-like rows\\r\\n return con\",\n \"def connect(cls):\\n cls.conn = MySQLdb.connect(**cls.configs)\\n cls.conn.autocommit(cls.autocommit)\",\n \"def connect_to_db(self):\\n\\t\\t# connection = psycopg2.connect(database=config.database, user=config.user,password = config.password)\\n\\t\\tconnection = psycopg2.connect(database=config.database, user=config.user)\\n\\t\\treturn connection\",\n \"def get_db() -> Generator:\\n db = SessionLocal()\\n try:\\n yield db\\n finally:\\n db.close()\",\n \"def db_connection():\\n global dbconnection\\n try:\\n conn = dbconnection\\n except:\\n dbconnection = psycopg2.connect(user = dbuser,\\n password = dbpass,\\n host = dbserver,\\n port = \\\"5432\\\",\\n database = dbname)\\n conn = dbconnection\\n return conn\",\n \"def db_open():\\n if not hasattr(g, 'sqlite_db'):\\n g.sqlite_db = db_connect()\\n return g.sqlite_db\",\n \"def sql_connection():\\n return sqlite3.connect('database.db')\",\n \"def persistence():\\n connection = None\\n LOGGER.debug(POSTGRES_USER)\\n try:\\n LOGGER.debug('connecting to postgres at %s:%s', POSTGRES_HOST, POSTGRES_PORT)\\n connection = psycopg2.connect(host=POSTGRES_HOST, port=POSTGRES_PORT, dbname=POSTGRES_DB,\\n user=POSTGRES_USER, password=POSTGRES_PASSWORD)\\n yield connection\\n except Exception:\\n LOGGER.exception('unable to connect to postgres server')\\n raise\\n finally:\\n if connection is not None:\\n connection.close()\",\n \"def test_context_manager(self):\\n with database.Database() as db:\\n pass\\n self.assertTrue(self.mocked_connection.close.call_count is 1)\",\n \"def __enter__(self) -> 'cursor':\\n self.conn = pymysql.connect(self.configuration)\\n #self.conn = pyodbc.connect(self.configuration)\\n self.cursor = self.conn.cursor()\\n return self.cursor\",\n \"def database_session():\\n if \\\"CI\\\" in os.environ:\\n con = psycopg2.connect(\\n host=os.environ[\\\"POSTGRES_HOST\\\"],\\n port=os.environ[\\\"POSTGRES_PORT\\\"],\\n user=os.environ[\\\"PGUSER\\\"],\\n password=os.environ[\\\"PGPASSWORD\\\"],\\n )\\n else:\\n con = psycopg2.connect(host=\\\"127.0.0.1\\\", port=\\\"5432\\\")\\n # Setup\\n con.set_isolation_level(ISOLATION_LEVEL_AUTOCOMMIT)\\n cursor = con.cursor()\\n cursor.execute(f'create database \\\"{DB}\\\";')\\n session = Meta.init(CONN_STRING).Session()\\n yield session\\n\\n # Teardown\\n engine = session.get_bind()\\n session.close()\\n engine.dispose()\\n Meta.engine = None\\n\\n cursor.execute(f'drop database \\\"{DB}\\\";')\\n cursor.close()\\n con.close()\",\n \"def connect(self):\\n try:\\n connection = Connection(host=settings.DATABASE_HOST, port=settings.DATABASE_PORT)\\n except ConnectionFailure, error:\\n return \\\"Could not connect to database: %s\\\" % error\\n print \\\"Could not connect to database: %s \\\\n\\\" % error\\n if __name__ == \\\"spider\\\":\\n sys.exit(1)\\n self.dbconnection = connection[settings.DATABASE_NAME]\",\n \"def __open(self):\\n\\n try:\\n cnx = mysql.connector.connect(\\n host=self.__host,\\n user=self.__user,\\n password=self.__password,\\n database=self.__database\\n )\\n self.__connection = cnx\\n self.__cursor = cnx.cursor(buffered=True, dictionary=True)\\n\\n except mysql.connector.Error as err:\\n print(\\\"Something went wrong: {}\\\".format(err))\",\n \"def __enter__(self) -> 'cursor':\\n self.conn = cx_Oracle.connect(self.configuration)\\n self.cursor = self.conn.cursor()\\n return self.cursor\",\n \"def make_db_conn():\\n\\n global DB_CONN\\n DB_CONN = psycopg2.connect(\\\"dbname=tornadowatch user=postgres host=localhost port=6432\\\")\\n DB_CONN.set_isolation_level(psycopg2.extensions.ISOLATION_LEVEL_AUTOCOMMIT)\",\n \"def getconn(self):\\n #hdbport = int('3%s15' % Settings.hdbinstancenum)\\n con = dbapi.connect(address = self.host, \\\\\\n port = self.port, \\\\\\n user = self.username, \\\\\\n password = self.password, \\\\\\n autocommit = True)\\n if self.schema:\\n cur = con.cursor()\\n try:\\n cur.execute('ALTER SESSION SET CURRENT_SCHEMA = %s' % self.schema)\\n return con\\n except dbapi.Error, err:\\n cur.close()\\n con.close()\\n cur = None\\n raise err\\n finally:\\n if cur:\\n cur.close()\\n else:\\n return con\",\n \"def conn(self):\\n try:\\n if self._db is None:\\n self._db = sqlc.connect(user=self.login,\\n password=self.passwd,\\n host=self.host,\\n database=self.database)\\n\\n except sqlc.Error as e:\\n print (\\\"MySQL exception #{0} getting connection: {1}\\\".format(e.errno, e.msg))\\n if e.errno == 2003:\\n exit(-1)\\n except Exception as e:\\n print (\\\"Couldn't get connection property: {0}\\\".format(e.message))\\n finally:\\n return self._db\",\n \"def get_db():\\n if not hasattr(g, 'db_conn'):\\n g.db_conn = sqlite3.connect(\\\"pypatch.sqlite\\\")\\n g.db_conn.row_factory = sqlite3.Row\\n \\n return g.db_conn\",\n \"def getDbConnection(self):\\n return self._oDb;\",\n \"def get_dbapi20_connection ():\\n return cherrypy.engine.pool.connect ()\",\n \"def get_db(self):\\n db = getattr(g, '_database', None)\\n if db is None:\\n db = g._database = connect(DATABASE)\\n return db\",\n \"async def database():\\n db = await Database.connect_pool()\\n return db\",\n \"def db_init(self):\\n if self.platform == 'Linux':\\n print(self.db)\\n conn = sqlite3.connect(self.db)\\n # if debug\\n conn.set_trace_callback(print)\\n # converter = Converter(self.db)\\n # converter.mdb2sqlite()\\n # return converter.conn\\n return conn, conn.cursor()\\n elif self.platform == 'Windows':\\n # Todo: update database config.\\n user = ''\\n password = ''\\n odbc_conn_str = \\\"DRIVER={Microsoft Access Driver (*.mdb, *.accdb)};\\\" \\\\\\n \\\"DBQ=%s;UID=%s;PWD=%s\\\" % (self.db, user, password)\\n import pypyodbc\\n conn = pypyodbc.connect(odbc_conn_str)\\n return conn, conn.cursor()\\n else:\\n raise OSError('Unsupported OS')\",\n \"def override_get_db():\\n try:\\n db = TestingSessionLocal()\\n yield db\\n finally:\\n db.close()\",\n \"def db_conection():\\n try:\\n\\t conn = pymysql.connect(host='127.0.0.1', user='root', passwd='test', db='EMP')\\n\\t __log(1,\\\"DB Connection Established\\\")\\n\\t return conn\\n except Exception as e:\\n#\\t\\t\\tprint (e)\\n __log(3,\\\"Database Connection Establishment Error, Fatal Error, Exiting from Code\\\")\\n exit()\",\n \"def db(self):\\n return DbManager(self)\",\n \"def connect(self):\\n\\n self._dbcon = psycopg2.connect(\\n database=self._dbname, host=self._host, port=self._port, user=self._username, password=self._password)\\n self.result_as_dict(self._result_as_dict)\",\n \"def _connect(self):\\n if self.settings.get('ENGINE', 'mysql') == 'mysql':\\n self._db = self.dbdriver.connect(self.settings['HOST'],\\n self.settings['USER'],\\n self.settings['PASSWORD'],\\n self.settings['NAME'],\\n self.settings.get('PORT', 3306))\\n elif self.settings['ENGINE'] == 'postgres':\\n self._db = self.dbdriver.connect(database=self.settings['NAME'],\\n user=self.settings['USER'],\\n password=self.settings['PASSWORD'],\\n host=self.settings['HOST'])\\n elif self.settings['ENGINE'] == 'sqlite':\\n self._db = self.dbdriver.connect(self.settings['NAME'])\\n self.cursor = self._db.cursor()\",\n \"def connect_db():\\n logging.info('Connects to the specific database.')\\n rv = sqlite3.connect(app.config['DATABASE'])\\n rv.row_factory = sqlite3.Row\\n g.db = rv\\n logging.info(rv)\\n return rv\",\n \"def _connect_to_db(self) -> None:\\n self._connection = psycopg2.connect(database=self.dbname,\\n user=self.dbuser, password=self.dbpassword,\\n host=self.dbhost, port=str(self.dbport))\",\n \"def get_conn(self):\\n conn = sqlite3.connect(self.uri)\\n conn.row_factory = sqlite3.Row\\n return conn\",\n \"def __init__(self):\\n self.dbconnect = dbConnection.connection\",\n \"def connect(self):\\n self.db = pymysql.connect(self.db_ip, self.uid, self.pwd, self.db_name)\\n self.cursor = self.db.cursor()\",\n \"def logic_db_connection():\\n try:\\n boto_session = boto3.Session(profile_name='loidsig')\\n except:\\n boto_session = boto3.Session()\\n sm_client = boto_session.client(\\n service_name='secretsmanager',\\n region_name='us-east-1',\\n endpoint_url='https://secretsmanager.us-east-1.amazonaws.com'\\n )\\n get_secret_value_response = sm_client.get_secret_value(SecretId='Loidsig_DB')\\n cred_dict = ast.literal_eval(get_secret_value_response['SecretString'])\\n db_user, db_pass = cred_dict['username'], cred_dict['password']\\n db_host, db_port, db_name = cred_dict['host'], cred_dict['port'], cred_dict['dbname']\\n\\n try:\\n conn = psycopg2.connect(\\n host=db_host,\\n port=db_port,\\n user=db_user,\\n password=db_pass,\\n database=db_name,\\n )\\n except Exception as e:\\n print(\\\"Unable to connect to postgres! Error: {}\\\".format(e))\\n raise\\n return conn\",\n \"def initialize_database():\\n\\n global DatabaseSession\\n from config import config\\n\\n engine = create_engine(\\n config[saq.CONFIG['global']['instance_type']].SQLALCHEMY_DATABASE_URI, \\n **config[saq.CONFIG['global']['instance_type']].SQLALCHEMY_DATABASE_OPTIONS)\\n\\n DatabaseSession = sessionmaker(bind=engine)\\n saq.db = scoped_session(DatabaseSession)\",\n \"def create_connection():\\r\\n try:\\r\\n conn = sq.connect(DBClass.db_name)\\r\\n except sq.Error as e:\\r\\n raise e\\r\\n \\r\\n return conn\",\n \"def connection_database(self):\\n # connection to the database\\n self.data_base = mysql.connector.connect(user=self.info[0], password=self.info[1],\\n host=self.info[2])\\n self.cursor = self.data_base.cursor()\\n\\n # executed \\\"use Purbeurre\\\" request\\n self.cursor.execute(\\\"USE Purbeurre\\\")\",\n \"def init_database(self):\\n # init_database(self.engine)\",\n \"def connect_db():\\n return hc_db.HCDB(app.config[\\\"DATABASE\\\"])\",\n \"def _connect(self):\\n\\n assert not self.__is_connected\\n\\n self._db_engine = create_engine(self._database.url)\\n\\n from sqlalchemy import event\\n\\n if self._database.driver == 'sqlite':\\n @event.listens_for(self._db_engine, \\\"connect\\\")\\n def do_connect(dbapi_connection, *args, **kwargs):\\n # disable pysqlite's emitting of the BEGIN statement entirely.\\n # also stops it from emitting COMMIT before any DDL.\\n dbapi_connection.isolation_level = None\\n\\n @event.listens_for(self._db_engine, \\\"begin\\\")\\n def do_begin(conn):\\n # emit our own BEGIN\\n conn.execute(\\\"BEGIN\\\")\\n\\n self._db_connection = self._db_engine.connect()\\n self._db_transaction = self._db_connection.begin()\\n\\n self._orm_session = Session(\\n bind=self._db_connection,\\n transaction=self,\\n )\\n self._orm_session_proxy = weakref.proxy(self._orm_session)\\n\\n self.__is_connected = True\",\n \"def create_connection(self, context, *, engine):\\n try:\\n connection = engine.connect()\\n except OperationalError as exc:\\n raise UnrecoverableError('Could not create SQLAlchemy connection: {}.'.format(str(exc).replace('\\\\n', ''))\\n ) from exc\\n\\n with connection:\\n yield connection\",\n \"def get(self):\\n\\n \\n\\n try:\\n db = getDatabase()\\n connection = db.connect()\\n\\n connection.get(self)\\n\\n except Exception as e:\\n raise e\\n finally:\\n db.dispose()\",\n \"def get_database(self):\\n if self._database is None:\\n conn = self.get_connection()\\n db = conn[self.database]\\n self._database = db\\n\\n return self._database\",\n \"def getDb(self):\\n return self.db\",\n \"def get_db():\\n # this is a bit of a hack, since it assumes all the models talk to the same\\n # db. that said a lot of our code relies on that assumption.\\n # this import is here because of annoying dependencies\\n return Database(settings.COUCH_DATABASE)\",\n \"def connect_to_db(self):\\n try:\\n self.connection = sqlite3.connect('test_database.db')\\n self.cursor = sqlite3.connect('test_database.db').cursor()\\n return self.connection, self.cursor\\n except EnvironmentError:\\n print(EnvironmentError)\",\n \"def database():\\n return sqlite3.connect(DATABASE)\",\n \"def open_database(dbpath=DB_PATH):\\n\\n # __enter__ (Establish the connection)\\n db = sqlite3.connect(dbpath)\\n cursor = db.cursor()\\n\\n yield BlockChainDB(cursor)\\n\\n # __exit__ (Commit and close the connection)\\n db.commit()\\n db.close()\",\n \"def __connect(self):\\n self.conn = pymysql.connect(self.opts.DB_HOST, self.opts.DB_USER,\\n self.opts.DB_PASSWORD, self.opts.DB_NAME)\",\n \"def connect_db():\\n db = psycopg2.connect(\\n dbname=app.config['DBNAME'],\\n user=app.config['DBUSER'],\\n password=app.config['DBPASSWORD'],\\n host=app.config['DBHOST'],\\n port=app.config['DBPORT']\\n )\\n return db\",\n \"def init_database(self):\\n init_database(self.engine)\",\n \"async def _get_db_connection():\\n return await gino.Gino(get_database_dsn())\"\n]","isConversational":false},"negative_scores":{"kind":"list like","value":["0.7842984","0.77469206","0.7391354","0.7299444","0.7295811","0.7281722","0.7276145","0.7275543","0.72531223","0.72529674","0.72500235","0.7196104","0.71822184","0.71411437","0.71265644","0.7103985","0.70446414","0.7011069","0.69964695","0.6982708","0.6979474","0.69745517","0.6967357","0.69364744","0.6930807","0.6930552","0.6925258","0.69248575","0.6920498","0.69124705","0.6911626","0.6909525","0.6904458","0.6903865","0.68996036","0.6898808","0.68823886","0.68739194","0.687321","0.685345","0.68467677","0.684474","0.6844333","0.6840108","0.68377113","0.68062055","0.6799461","0.6798398","0.6789956","0.67802143","0.6771771","0.67663854","0.67326933","0.6730273","0.67218834","0.67133147","0.67039216","0.66965616","0.6695546","0.6693332","0.6685635","0.6682017","0.6676372","0.66737616","0.6670246","0.66527003","0.66491115","0.66408813","0.6618669","0.661813","0.6613929","0.661176","0.66108954","0.6598134","0.6598067","0.65897405","0.65747356","0.6573169","0.6569819","0.6565107","0.65644544","0.6553537","0.65471387","0.6546284","0.6539713","0.653261","0.65259594","0.6524567","0.6521438","0.6520166","0.6519974","0.6509142","0.65085346","0.6504816","0.6498176","0.6491804","0.6484867","0.64845896","0.64834136","0.6480432","0.64787585"],"string":"[\n \"0.7842984\",\n \"0.77469206\",\n \"0.7391354\",\n \"0.7299444\",\n \"0.7295811\",\n \"0.7281722\",\n \"0.7276145\",\n \"0.7275543\",\n \"0.72531223\",\n \"0.72529674\",\n \"0.72500235\",\n \"0.7196104\",\n \"0.71822184\",\n \"0.71411437\",\n \"0.71265644\",\n \"0.7103985\",\n \"0.70446414\",\n \"0.7011069\",\n \"0.69964695\",\n \"0.6982708\",\n \"0.6979474\",\n \"0.69745517\",\n \"0.6967357\",\n \"0.69364744\",\n \"0.6930807\",\n \"0.6930552\",\n \"0.6925258\",\n \"0.69248575\",\n \"0.6920498\",\n \"0.69124705\",\n \"0.6911626\",\n \"0.6909525\",\n \"0.6904458\",\n \"0.6903865\",\n \"0.68996036\",\n \"0.6898808\",\n \"0.68823886\",\n \"0.68739194\",\n \"0.687321\",\n \"0.685345\",\n \"0.68467677\",\n \"0.684474\",\n \"0.6844333\",\n \"0.6840108\",\n \"0.68377113\",\n \"0.68062055\",\n \"0.6799461\",\n \"0.6798398\",\n \"0.6789956\",\n \"0.67802143\",\n \"0.6771771\",\n \"0.67663854\",\n \"0.67326933\",\n \"0.6730273\",\n \"0.67218834\",\n \"0.67133147\",\n \"0.67039216\",\n \"0.66965616\",\n \"0.6695546\",\n \"0.6693332\",\n \"0.6685635\",\n \"0.6682017\",\n \"0.6676372\",\n \"0.66737616\",\n \"0.6670246\",\n \"0.66527003\",\n \"0.66491115\",\n \"0.66408813\",\n \"0.6618669\",\n \"0.661813\",\n \"0.6613929\",\n \"0.661176\",\n \"0.66108954\",\n \"0.6598134\",\n \"0.6598067\",\n \"0.65897405\",\n \"0.65747356\",\n \"0.6573169\",\n \"0.6569819\",\n \"0.6565107\",\n \"0.65644544\",\n \"0.6553537\",\n \"0.65471387\",\n \"0.6546284\",\n \"0.6539713\",\n \"0.653261\",\n \"0.65259594\",\n \"0.6524567\",\n \"0.6521438\",\n \"0.6520166\",\n \"0.6519974\",\n \"0.6509142\",\n \"0.65085346\",\n \"0.6504816\",\n \"0.6498176\",\n \"0.6491804\",\n \"0.6484867\",\n \"0.64845896\",\n \"0.64834136\",\n \"0.6480432\",\n \"0.64787585\"\n]","isConversational":false},"document_score":{"kind":"string","value":"0.0"},"document_rank":{"kind":"string","value":"-1"}}},{"rowIdx":22675,"cells":{"query":{"kind":"string","value":"Tests http request construction."},"document":{"kind":"string","value":"def test_search(self):\n req = http.make_request('http://xxx', 'GET', None, None)\n self.assertIsNone(req.data)\n\n req = http.make_request('http://xxx', 'GET', 'ignored', None)\n self.assertIsNone(req.data)\n\n req = http.make_request('http://xxx', 'DELETE', None, None)\n self.assertIsNone(req.data)\n\n req = http.make_request('http://xxx', 'DELETE', 'ignored', None)\n self.assertIsNone(req.data)\n\n req = http.make_request('http://xxx', 'POST', '', None)\n self.assertEqual(0, len(req.data))\n\n req = http.make_request('http://xxx', 'POST', 'abc', None)\n self.assertEqual(3, len(req.data))\n\n req = http.make_request('http://xxx', 'POST', '', [('xxx', 'yyy'),\n ('foo',)])\n\n self.assertEqual('yyy', req.get_header('Xxx'))\n self.assertEqual('1', req.get_header('Foo'))"},"metadata":{"kind":"string","value":"{\n \"objective\": {\n \"self\": [],\n \"paired\": [],\n \"triplet\": [\n [\n \"query\",\n \"document\",\n \"negatives\"\n ]\n ]\n }\n}"},"negatives":{"kind":"list like","value":["def test_request():\n response = requests.get('http://jsonplaceholder.typicode.com/todos')\n assert response.ok","def createRequest(test, url, headers=None):\n request = HTTPRequest(url=url)\n if headers: request.headers=headers\n test.record(request, HTTPRequest.getHttpMethodFilter())\n return request","def __init__( request ):","def test_http_request(self):\n\n response = requests.get(self.live_server_url)\n assert response.status_code == 200","def test_create_namespaced_build_request_instantiate(self):\n pass","def test_basic(self):\n request = fake_twisted_request(request_headers={\n b'x-foo': [b'bar'],\n })\n self.assertThat(\n _nevow_request_to_request_map(request),\n ContainsDict({\n 'content_type': Equals(b'application/octet-stream'),\n 'content_length': Equals(0),\n 'character_encoding': Is(None),\n 'headers': Equals({b'Content-Length': [0],\n b'X-Foo': [b'bar'],\n b'Host': [b'example.com']}),\n 'remote_addr': Equals(b'192.168.1.1'),\n 'request_method': Equals(b'GET'),\n 'server_name': Equals(b'example.com'),\n 'server_port': Equals(80),\n 'scheme': Equals(b'http'),\n 'uri': Equals(URL.from_text(u'/one'))}))","def test_orchestrator_http_simple(self):\n pass","def test_request():\n return make_response(\"ok\")","def test_send_http_request_valid(self):\n na_element = zapi_fakes.FAKE_NA_ELEMENT\n self.mock_object(self.root, '_create_request',\n return_value=('abc', zapi_fakes.FAKE_NA_ELEMENT))\n self.mock_object(netapp_api, 'LOG')\n self.root._opener = zapi_fakes.FAKE_HTTP_OPENER\n self.mock_object(self.root, '_build_opener')\n self.mock_object(self.root, '_get_result',\n return_value=zapi_fakes.FAKE_NA_ELEMENT)\n opener_mock = self.mock_object(self.root._opener, 'open')\n opener_mock.read.side_effect = ['resp1', 'resp2']\n\n self.root.send_http_request(na_element)","def test_make_request(self, m_requests, m_sleep):\r\n request = testing.DummyRequest({mut.URL_KEY: SAMPLE_URL})\r\n m_response, response_dict = self.mock_response()\r\n m_requests.get.return_value = m_response\r\n self.assertEqual(response_dict, mut.make_request(request))\r\n m_requests.get.assert_called_with(url=SAMPLE_URL)\r\n m_sleep.assert_called_with(mut.SECURITY_SLEEP)","def test_init(self):\n self.assertEqual(self.request_mgr.f2f_search_url,\n \"https://www.food2fork.com/api/search\")\n self.assertEqual(self.request_mgr.f2f_detail_url,\n \"https://www.food2fork.com/api/get\")","def testBuildRequest(self):\n expected_request = Request1()\n self.protocol.decode_message(Request1,\n 'my body').AndReturn(expected_request)\n\n self.mox.ReplayAll()\n\n mapper = service_handlers.RPCMapper(['POST'],\n 'my-content-type',\n self.protocol)\n\n request = mapper.build_request(self.handler, Request1)\n\n self.assertTrue(expected_request is request)","def test_from_request_works_with_wsgi(self):\n url = \"http://sp.example.com/\"\n\n params = {\n 'oauth_version': \"1.0\",\n 'oauth_nonce': \"4572616e48616d6d65724c61686176\",\n 'oauth_timestamp': \"137131200\",\n 'oauth_consumer_key': \"0685bd9184jfhq22\",\n 'oauth_signature_method': \"HMAC-SHA1\",\n 'oauth_token': \"ad180jjd733klru7\",\n 'oauth_signature': \"wOJIO9A2W5mFwDgiDvZbTSMK%2FPY%3D\",\n }\n\n req = oauth.Request(\"GET\", url, params)\n headers = req.to_header()\n\n # Munge the headers\n headers['HTTP_AUTHORIZATION'] = headers['Authorization']\n del headers['Authorization'] \n\n # Test from the headers\n req = oauth.Request.from_request(\"GET\", url, headers)\n self.assertEqual(req.method, \"GET\")\n self.assertEqual(req.url, url)\n self.assertEqual(params, req.copy())","def basicRequest(self):\n endpoint = \"/foo\"\n\n def verify(request):\n o(request.method).equals(\"GET\")(\"Checking basic request method.\")\n o(request.url).equals(endpoint)(\"Checking basic request url.\")\n request.respond(200)\n self.testServer.respondWith(verify)\n\n server.request(endpoint)\n self.testServer.respond()","def __init__(self, headers=None, body=None, status=None,\n request_headers={}):\n self.headers = headers\n self.body = body\n self.status_code = status\n self.request = mock.Mock()\n self.request.headers = request_headers","def __init__(self, request: Request, response: Response):\n self.request = request\n self.response = response","def test_get(self):\n return self.doRequest(self.url, method=\"GET\", body=self.input)","def testWholeRequest(self):\n body = self.protocol.encode_message(self.request_message)\n self.Reinitialize(input=body,\n content_type=self.content_type)\n self.factory.add_request_mapper(self.mapper())\n self.service_handler.handle('POST', '/my_service', 'method1')\n VerifyResponse(self,\n self.service_handler.response,\n '200',\n 'OK',\n self.protocol.encode_message(self.response_message),\n self.content_type)","def init_request(self):\n\t\tprint(\"Testes\")\n\t\treturn Request(url=self.login_page[0], callback=self.login)","def __init__(self, name, method, url, headers={}, auth=None, body=None, expected_status=None, expected_body=None):\n \n self.name = name\n self.method = method\n self.url = url\n self.headers = headers\n self.auth = auth\n self.body = body\n self.expected_status = expected_status\n self.expected_body = expected_body","def __init__(self, get_request):\n self.get_request = get_request","def dummy_request():\n return DummyRequest()","def __init__(self, request: object) -> None:\n super().__init__({}, request, URL, Api)","def create_request(params={}, path='/', method='POST'):\n request = DummyRequest(path)\n request.method = method\n request.args = params\n return request","def testSimpleRequest(self):\n d = self._getClientConnection()\n self._addRequest(d)\n return d","def testInitializer(self):\n request = http.HttpRequest()\n\n data, check, mutator = initialize.MELANGE_INITIALIZER.initialize(\n request, [], {})\n self.assertEqual(request, data.request)\n self.assertEqual(data, check.data)\n self.assertEqual(data, mutator.data)","def setUp(self):\n self.client = HTTPClient()\n self.method = 'GET'\n self.url = 'http://github.com/ojengwa'\n self.headers = {}","def __init__(self, request: Request):\n self.request = request","def setUp(self):\n self.response = self.s.get(self.url, params=self.params)","def test_make_request_parameters(self, m_requests, m_sleep):\r\n request = testing.DummyRequest({mut.URL_KEY: SAMPLE_URL, \r\n mut.PARAMETERS_KEY: json.dumps(SAMPLE_PARAMETERS)})\r\n m_response, response_dict = self.mock_response()\r\n m_requests.get.return_value = m_response\r\n self.assertEqual(response_dict, mut.make_request(request))\r\n m_requests.get.assert_called_with(url=SAMPLE_URL, \r\n params=SAMPLE_PARAMETERS)\r\n m_sleep.assert_called_with(mut.SECURITY_SLEEP)","def test_basic_generic_request(self, api_instance):\n action = \"BasicGenericRequest\"\n\n # Send a basic payload.\n params = {\n \"ADateTime\": datetime.datetime(2020, 10, 12),\n \"ATrueBool\": True,\n \"AFalseBool\": False,\n \"NoneShouldNotExist\": None,\n }\n\n request_params = api_instance.generic_request(action=action, params=params)\n self.assert_common_params(request_params, action=\"BasicGenericRequest\")\n assert request_params[\"ADateTime\"] == \"2020-10-12T00:00:00\"\n assert request_params[\"ATrueBool\"] == \"true\"\n assert request_params[\"AFalseBool\"] == \"false\"\n assert \"NoneShouldNotExist\" not in request_params","def test_make_request_method(self, m_requests, m_sleep):\r\n request = testing.DummyRequest({mut.URL_KEY: SAMPLE_URL, \r\n mut.METHOD_KEY: SAMPLE_METHOD})\r\n m_response, response_dict = self.mock_response()\r\n m_requests.post.return_value = m_response\r\n self.assertEqual(response_dict, mut.make_request(request))\r\n m_requests.post.assert_called_with(url=SAMPLE_URL)\r\n m_sleep.assert_called_with(mut.SECURITY_SLEEP)","def __init__(self, request, data):\n self.request = request\n self.data = data","def req():\n return Request()","def test_get_request_output(self):\n pass","def setUp(self):\n self.client = RequestsClient()\n self.method = 'GET'\n self.url = 'http://github.com/ojengwa'\n self.headers = {}","def initialize_from_http_request(self, request):\n self.tool_id = request.path.strip('/').split('/')[0]\n self.path = request.path\n self._set_response_format(request)\n self._set_ip_address(request)\n self._set_tool_address_family(request)\n self._set_user_agent(request)\n self._set_path_qs(request)\n self._set_geolocation(request)\n self.metro = request.get(message.METRO, default_value=None)\n self._set_policy(request)","def test_simple_request(self):\n\n\t\tself.n = tracker.decode_request(\"?key=value\")\n\t\tself.assertEqual(self.n, {\"key\":[\"value\"]})","def test_new_user_request(self):\r\n # Send a request\r\n values = {'username': u'user100',\r\n 'external_id': '4', 'email': u'user100@edx.org'}\r\n data = json.dumps(values)\r\n headers = {'Content-Type': 'application/json', 'Content-Length': len(data), 'X-Edx-Api-Key': 'TEST_API_KEY'}\r\n req = urllib2.Request(self.server_url + '/api/v1/users/4', data, headers)\r\n\r\n # Send the request to the mock cs server\r\n response = urllib2.urlopen(req)\r\n\r\n # Receive the reply from the mock cs server\r\n response_dict = json.loads(response.read())\r\n\r\n # You should have received the response specified in the setup above\r\n self.assertEqual(response_dict, self.expected_response)","def test_request(self):\n client = RestClient(host=self.host, username='')\n rest_url = 'some/url/'\n\n # Mock good get response\n with responses.RequestsMock() as rsps:\n rsps.add(responses.GET, f'{self.host}/{rest_url}', status=200,\n json={'value':\"good!\"})\n r = client.request('get', rest_url)\n assert r.status_code == 200\n assert r.json()['value'] == 'good!'\n \n # Mock bad get response\n with responses.RequestsMock() as rsps:\n rsps.add(responses.GET, f'{self.host}/{rest_url}', status=401,\n json={'value':\"bad!\"})\n with raises(requests.HTTPError):\n r = client.request('get', rest_url)\n r = client.request('get', rest_url, checkstatus=False)\n assert r.status_code == 401\n assert r.json()['value'] == 'bad!'","def test_post(self):\n url, port = self.server.address\n\n #couple of basic POSTs\n #request parameters\n r = self.client.get(\"http://{0}:{1}/\".format(url, port))\n self.assertEqual(200, r.status_code)\n r = self.client.get(\"http://{0}:{1}\".format(url, port))\n self.assertEqual(200, r.status_code)\n r = self.client.get(\"http://{0}:{1}/200\".format(url, port))\n self.assertEqual(200, r.status_code)\n r = self.client.get(\"http://{0}:{1}/400\".format(url, port))\n self.assertEqual(400, r.status_code)","def _request(http, project, method, data, base_url, client_info):\n user_agent = client_info.to_user_agent()\n headers = {\n \"Content-Type\": \"application/x-protobuf\",\n \"User-Agent\": user_agent,\n connection_module.CLIENT_INFO_HEADER: user_agent,\n }\n api_url = build_api_url(project, method, base_url)\n\n response = http.request(url=api_url, method=\"POST\", headers=headers, data=data)\n\n if response.status_code != 200:\n error_status = status_pb2.Status.FromString(response.content)\n raise exceptions.from_http_status(\n response.status_code, error_status.message, errors=[error_status]\n )\n\n return response.content","def test_prepare_bearer_request(self):\n self.assertEqual(prepare_bearer_headers(self.token), self.bearer_headers)\n self.assertEqual(prepare_bearer_body(self.token), self.bearer_body)\n self.assertEqual(prepare_bearer_uri(self.token, uri=self.uri), self.bearer_uri)","def test_unauthenticated_request(self):\n http = FakeHttp([(FakeResponse(200), {})])\n self.mock.Http.return_value = http\n response, _ = http_utils.request('https://url/', body='test body')\n\n # Ensure that all expected requests were made.\n self.assertEqual(http.replies, [])\n\n self.assertEqual(http.last_body, '\"test body\"')\n self.assertEqual(http.last_headers, {})\n self.assertEqual(response.status, 200)","def __init__(self, request):\n \n self.request = request","def test_post(self):\n return self.doRequest(self.url, method=\"POST\", body=self.input)","def request(self, **request):\n environ = {\n 'HTTP_COOKIE': self.cookies,\n 'PATH_INFO': '/',\n 'QUERY_STRING': '',\n 'REQUEST_METHOD': 'GET',\n 'SCRIPT_NAME': '',\n 'SERVER_NAME': 'testserver',\n 'SERVER_PORT': 80,\n 'SERVER_PROTOCOL': 'HTTP/1.1',\n 'wsgi.version': (1,0),\n 'wsgi.url_scheme': 'http',\n 'wsgi.input': FakePayload(''),\n 'wsgi.errors': self.errors,\n 'wsgi.multiprocess': True,\n 'wsgi.multithread': False,\n 'wsgi.run_once': False,\n }\n environ.update(self.defaults)\n environ.update(request)\n return WSGIRequest(environ)","def bind_request(**request_data):\n\n class Request(Api):\n \"\"\"Request class. Does the actual API request.\"\"\"\n\n model = request_data.get(constants.ClientConst.MODEL)\n api_path = request_data.get(constants.RequestConst.API_PATH)\n formatter = request_data.get(constants.ClientConst.FORMATTER)\n method = request_data.get(\n constants.RequestConst.METHOD, constants.RequestConst.GET\n )\n query_parameters = request_data.get(\n constants.RequestConst.QUERY_PARAMETERS\n )\n fake_response_path = request_data.get(\n constants.TestConst.FAKE_RESPONSE_PATH\n )\n default_parameters = request_data.get(\n constants.RequestConst.DEFAULT_PARAMETERS, {}\n )\n # immediately determine type of response\n single_model_response = request_data.get(\n constants.ClientConst.FORCE_SINGLE_MODEL_RESPONSE, False\n ) or method in [\n constants.RequestConst.POST,\n constants.RequestConst.PUT,\n constants.RequestConst.DELETE\n ]\n\n def __init__(self, client, debug: 'Debug',\n *path_params, **query_params):\n client.request = self\n\n self.url = None\n self.debug = debug\n self.client = client\n self.parameters = {\n constants.RequestConst.QUERY: {},\n constants.RequestConst.PATH: []\n }\n\n self._timeout = 5\n\n self._set_parameters(*path_params, **query_params)\n\n def _set_parameters(self, *path_params, **query_params):\n \"\"\"\n Prepares the list of query parameters\n :path_params: list of path parameters\n :query_params: dict of query parameters\n :return: None\n \"\"\"\n\n # take timeout\n try:\n self._timeout = int(query_params.get(\n constants.RequestConst.TIMEOUT, self._timeout\n ))\n except ValueError:\n pass\n try:\n del query_params[constants.RequestConst.TIMEOUT]\n except KeyError:\n pass\n\n # set default API call params\n for key, value in self.default_parameters.items():\n self.parameters[constants.RequestConst.QUERY][key] = value\n\n _query_params = self.query_parameters.get_params()\n\n # set API call params defined during the \"call\" invocation\n for key, value in query_params.items():\n if value is None:\n continue\n\n if key in _query_params.values():\n self.parameters[constants.RequestConst.QUERY][key] = value\n\n elif key in _query_params.keys():\n self.parameters[\n constants.RequestConst.QUERY\n ][_query_params[key]] = value\n\n if self.method == constants.RequestConst.GET:\n # transform all True and False param to 1 and 0\n for key, value in self.parameters[\n constants.RequestConst.QUERY\n ].items():\n if value is True:\n self.parameters[constants.RequestConst.QUERY][key] = \\\n constants.BoolConst.TRUE\n if value is False:\n self.parameters[constants.RequestConst.QUERY][key] = \\\n constants.BoolConst.FALSE\n\n # set optional url path params\n for value in path_params:\n self.parameters[constants.RequestConst.PATH].append(value)\n\n def _prepare_url(self):\n \"\"\"\n Prepares url and query parameters for the request\n :return: URL\n \"\"\"\n\n base_url = '{}://{}{}'.format(\n self.client.protocol, self.client.base_url, self.api_path\n )\n url_parts = '/'.join(\n [part for part in self.parameters[constants.RequestConst.PATH]]\n )\n\n if url_parts:\n final_url = '{}/{}'.format(base_url, url_parts)\n else:\n final_url = base_url\n\n if self.method == constants.RequestConst.GET:\n params = self.parameters[constants.RequestConst.QUERY]\n for param, value in params.items():\n if isinstance(value, list):\n params[param] = ','.join(value)\n elif isinstance(value, dict):\n params[param] = ','.join([f'{k}:{v}' for k, v in value])\n\n url_query = '?' + '&'.join([f'{k}={v}' for k, v in params.items()])\n final_url = '{}{}'.format(final_url, url_query)\n\n self.debug.ok('final url', final_url)\n\n return final_url\n\n def _headers(self):\n \"\"\"Construct headers data with authentication part\"\"\"\n\n auth_token = SendbeeAuth(self.client.api_secret).get_auth_token()\n headers = {\n 'X-Auth-Token': auth_token,\n 'X-Api-Key': self.client.api_key,\n 'Accept': 'application/json',\n 'Content-Type': 'application/json',\n 'User-Agent': 'Sendbee Python API Client'\n }\n self.debug.ok('headers', headers)\n\n return headers\n\n def _do_request(self, url: str):\n \"\"\"\n Makes the request to Sendbee Api servers\n :url: Url for the request\n :return: Tuple with two elements, status code and content\n \"\"\"\n\n self.debug.ok('method', self.method)\n\n if self.client.fake_response_path:\n with open(self.client.fake_response_path, 'r') as f:\n return constants.ResponseCode.OK, f.read()\n\n elif self.method == constants.RequestConst.GET:\n response = requests.get(\n url, headers=self._headers(), timeout=self._timeout\n )\n\n self.debug.ok(\n constants.RequestConst.QUERY_PARAMETERS,\n self.parameters[constants.RequestConst.QUERY]\n )\n self.debug.ok(constants.ResponseConst.RESPONSE_OBJECT, response)\n\n return response.status_code, response.text\n\n elif self.method in [\n constants.RequestConst.POST,\n constants.RequestConst.PUT,\n constants.RequestConst.DELETE\n ]:\n if self.method == constants.RequestConst.POST:\n send_request = requests.post\n elif self.method == constants.RequestConst.PUT:\n send_request = requests.put\n elif self.method == constants.RequestConst.DELETE:\n send_request = requests.delete\n\n response = send_request(\n url, json=self.parameters[constants.RequestConst.QUERY],\n headers=self._headers(), timeout=self._timeout\n )\n\n self.debug.ok('payload', self.parameters[\n constants.RequestConst.QUERY\n ])\n self.debug.ok(constants.ResponseConst.RESPONSE_OBJECT, response)\n\n return response.status_code, response.text\n\n else:\n return constants.ResponseCode.NOT_FOUND, {}\n\n def _process_response(self, status_code, response):\n \"\"\"\n Process response using models\n :status_code: Response status code\n :response: Content\n :return: Response object\n \"\"\"\n\n formatter = self.formatter\n if not formatter:\n formatter = FormatterFactory(constants.FormatterConst.JSON)\\\n .get_formatter()\n\n response = Response(response, status_code, formatter, self)\n formatted_data = response.formatted_data\n\n if status_code >= constants.ResponseCode.BAD_REQUEST:\n\n if status_code == constants.ResponseCode.NOT_FOUND:\n error_msg = \\\n constants.ErrorConst.NOT_FOUND\n elif constants.ErrorConst.ERROR not in formatted_data:\n error_msg = \\\n constants.ResponseConst.DEFAULT_ERROR_MESSAGE\n else:\n error_msg = formatted_data.get(\n constants.ErrorConst.ERROR, {}\n ).get(\n constants.ErrorConst.DETAIL,\n constants.ErrorConst.UNRECOGNIZED_ERROR\n )\n\n self.debug.error(\n constants.ResponseConst.STATUS_CODE, status_code\n )\n self.debug.error(\n constants.ResponseConst.RESPONSE, response.formatted_data\n )\n raise SendbeeRequestApiException(error_msg)\n else:\n self.debug.ok(constants.ResponseConst.STATUS_CODE, status_code)\n self.debug.ok(constants.ResponseConst.RESPONSE, response.raw_data)\n\n if response.meta.current_page:\n if response.meta.current_page > 1 and len(response.models) == 0:\n raise PaginationException(\n f'Page {response.meta.current_page} has no data'\n )\n\n if response.warning:\n click.secho(\n constants.WarningConst.MESSAGE + response.warning,\n fg='yellow'\n )\n\n if self.single_model_response:\n if response.models:\n return response.models[0]\n else:\n return None\n else:\n return response\n\n def call(self):\n \"\"\"\n Makes the API call\n :return: Return value from self._process_response()\n \"\"\"\n\n self.url = self._prepare_url()\n status_code, response = self._do_request(self.url)\n return self._process_response(status_code, response)\n\n def call(client, *path_params, **query_params):\n \"\"\"\n Binded method for API calls\n :path_params: list of path parameters\n :query_params: dict of query parameters\n :return: Return value from Request.call()\n \"\"\"\n\n if constants.MiscConst.PRINT_PARAMS in query_params:\n Request.query_parameters.print_params()\n return\n\n with Debug(client=client) as debug:\n request = Request(client, debug, *path_params, **query_params)\n return request.call()\n\n call.__doc__ = request_data.get(constants.ClientConst.DESCRIPTION)\n\n return call","def request(self, **request):\r\n environ = {\r\n 'HTTP_COOKIE': self.cookies,\r\n 'PATH_INFO': '/',\r\n 'QUERY_STRING': '',\r\n 'REQUEST_METHOD': 'GET',\r\n 'SCRIPT_NAME': '',\r\n 'SERVER_NAME': 'testserver',\r\n 'SERVER_PORT': 80,\r\n 'SERVER_PROTOCOL': 'HTTP/1.1',\r\n }\r\n environ.update(self.defaults)\r\n environ.update(request)\r\n request = WSGIRequest(environ)\r\n\r\n handler = BaseHandler()\r\n handler.load_middleware()\r\n for middleware_method in handler._request_middleware:\r\n if middleware_method(request):\r\n raise Exception(\"Couldn't create request object - \"\r\n \"request middleware returned a response\")\r\n\r\n return request","def mock_request(request_path_query=\"\", # type: Str\n method=\"GET\", # type: Str\n params=None, # type: Optional[Dict[Str, Str]]\n body=\"\", # type: Union[Str, JSON]\n content_type=None, # type: Optional[Str]\n headers=None, # type: Optional[AnyHeadersType]\n cookies=None, # type: Optional[AnyCookiesType]\n settings=None, # type: SettingsType\n ): # type: (...) -> Request\n parts = request_path_query.split(\"?\")\n path = parts[0]\n query = dict()\n if len(parts) > 1 and parts[1]:\n for part in parts[1].split(\"&\"):\n kv = part.split(\"=\") # handle trailing keyword query arguments without values\n if kv[0]: # handle invalid keyword missing\n query[kv[0]] = kv[1] if len(kv) > 1 else None\n elif params:\n query = params\n request = DummyRequest(path=path, params=query)\n request.path_qs = request_path_query\n request.method = method\n request.content_type = content_type\n request.headers = headers or {}\n request.cookies = cookies or {}\n request.matched_route = None # cornice method\n if content_type:\n request.headers[\"Content-Type\"] = content_type\n request.body = body\n try:\n if body:\n # set missing DummyRequest.json attribute\n request.json = json_pkg.loads(body)\n except (TypeError, ValueError):\n pass\n request.registry.settings = settings or {}\n return request # noqa # fake type of what is normally expected just to avoid many 'noqa'","def get_request(self, post=None, request_method=\"GET\"):\n if post is None:\n post = {\"csrf_token\": \"irrelevant but required\"}\n request = testing.DummyRequest(post)\n request.client_addr = \"127.0.0.1\"\n request.session = Mock()\n request.method = request_method\n request.repo = self.repo\n request.kerno = self.kerno\n return request","def setUp(self):\n self.factory = RequestFactory()","def setUp(self):\n self.factory = RequestFactory()","def setUp(self):\n self.factory = RequestFactory()","def test_request(self, kasserver, kasapi):\n kasserver._request(self.REQUEST_TYPE, self.REQUEST_PARAMS)\n request = {\n \"KasUser\": USERNAME,\n \"KasAuthType\": \"plain\",\n \"KasAuthData\": PASSWORD,\n \"KasRequestType\": self.REQUEST_TYPE,\n \"KasRequestParams\": self.REQUEST_PARAMS,\n }\n kasapi.assert_called_once_with(json.dumps(request))","def test_get(self):\n response = self.get('/study/create/')\n self.assertEqual(response.code, 200)\n self.assertNotEqual(str(response.body), \"\")","def test_request_parameters(self):\n httpretty.register_uri(httpretty.POST, 'http://somewhere.com/test')\n r = CkanResource('http://somewhere.com/test', None, {'offset': None, 'limit': None, 'carrot': 'cake'})\n s = r._get_response(10, 200)\n self._assert_params_equals(httpretty.last_request().path, {'offset': 2000, 'limit': 200, 'carrot': 'cake'})","def test_requests(self):\n\n text_data = \"\"\"\n BERNARDO: Who's there?\n FRANCISCO: Nay, answer me: stand, and unfold yourself.\n BERNARDO: Long live the king!\n FRANCISCO: Bernardo?\n BERNARDO: He.\n FRANCISCO: You come most carefully upon your hour.\n BERNARDO: 'Tis now struck twelve; get thee to bed, Francisco.\n \"\"\"\n binary_data = text_data.encode()\n file_content = {\"/foo.txt\": binary_data}\n\n # Test all possible combinations of:\n # - whether or not the server supports compression\n # - whether or not the server supports random access\n # - chosen buffering policy\n for allow_gzip in (False, True):\n for allow_range in (False, True):\n with DummyHTTPServer(\n file_content=file_content,\n allow_gzip=allow_gzip,\n allow_range=allow_range,\n ) as server:\n url = server.url(\"/foo.txt\")\n for buffering in (-2, -1, 0, 20):\n self._test_text(url, text_data, buffering)\n self._test_binary(url, binary_data, buffering)","def __init__(self, url, header=None, logger=None):\n\n httpDict = {}\n header = header or {}\n # url is end point\n httpDict['endpoint'] = \"%s/data\" % url\n httpDict['logger'] = logger if logger else logging.getLogger()\n\n # cherrypy converts request.body to params when content type is set\n # application/x-www-form-urlencodeds\n httpDict.setdefault(\"content_type\", 'application/json')\n httpDict.setdefault('cacheduration', 0)\n httpDict.setdefault(\"accept_type\", \"application/json\")\n httpDict.update(header)\n self.encoder = json.dumps\n Service.__init__(self, httpDict)\n # This is only for the unittest: never set it true unless it is unittest\n self._noStale = False","def __init__(self, request):\n self.request = request\n self._is_verified = False","def __init__(self, request: Request) -> None:\n super().__init__(request)","def __init__(self, request: Request) -> None:\n super().__init__(request)","def test_valid_request(self):\n print(\"Testing valid request...\")\n # create a file with random data in the server folder\n self.__create_test_file()\n valid_path = os.path.join(os.path.dirname(self.client_path), \\\n os.path.basename(self.test_file))\n os.chdir(os.path.dirname(self.client_path))\n subprocess.call([self.client_path, \\\n \"{}:{}\".format(self.args.ip, self.args.port), \\\n os.path.basename(valid_path)])\n self.assertTrue(os.path.isfile(valid_path))\n self.__compare_files(valid_path, self.test_file)\n os.remove(valid_path)","def test_create_canonical_req(self):\n\n request = self_signed.create_canonical_req()\n assert \"content-type:\" in request\n assert \"x-amz-date:\" in request\n assert \"host\" in request","def test_setup(self):\n assert self.http_handler.setup() is None\n self.assert_quantity_in_outbox(0)","def test_request_response():\n response = get_todos()\n assert response is not None","def __init__( httpconn, method, uri, uriparts, version, headers ):","def test_request_url(self):\n httpretty.register_uri(httpretty.POST, 'http://somewhere.com/test')\n r = CkanResource('http://somewhere.com/test', None, {'offset': None, 'limit': None})\n s = r._get_response(0, 0)\n assert_equals(httpretty.last_request().path, '/test?limit=0&offset=0')","def test_get_method(self):\n self.getPage('/')\n self.assertStatus('200 OK')\n self.assertHeader('Content-Type', 'application/json')\n self.assertBody('{\"mystring\": \"\"}')","def __init__(self, request, name) :\n self.request = request\n self.name = name","def test_request_basic(self):\n request = Request.objects.first()\n self.assertEqual(request.path, 'request/')\n self.assertEqual(request.viewed, False)","def __init__(self, request):\n self._data = request.get_json(silent=True)","def test_no_parameters(self):\n\n x = self.start_request_tests(service.get_request('POST', None))\n self.assert_response_status(x, 400)\n self.assertTrue('tree' in x.json()[u'message'])","def request_factory(environ):\n request = Request(environ)\n _LOG.debug('trunctated request body: {b}'.format(b=request.body[:1000]))\n return request","def __init__(self):\n self._url = \"\"\n self._verb = VERBS.GET\n self._params = {}\n self._data = {}","def do_request(\n self,\n version: str,\n action: str,\n protocol: str,\n method: str,\n pathname: str,\n request: dict,\n headers: Dict[str, str],\n runtime: util_models.RuntimeOptions,\n ) -> dict:\n runtime.validate()\n _runtime = {\n 'timeouted': 'retry',\n 'readTimeout': UtilClient.default_number(runtime.read_timeout, self._read_timeout),\n 'connectTimeout': UtilClient.default_number(runtime.connect_timeout, self._connect_timeout),\n 'httpProxy': UtilClient.default_string(runtime.http_proxy, self._http_proxy),\n 'httpsProxy': UtilClient.default_string(runtime.https_proxy, self._https_proxy),\n 'noProxy': UtilClient.default_string(runtime.no_proxy, self._no_proxy),\n 'maxIdleConns': UtilClient.default_number(runtime.max_idle_conns, self._max_idle_conns),\n 'maxIdleTimeMillis': self._max_idle_time_millis,\n 'keepAliveDuration': self._keep_alive_duration_millis,\n 'maxRequests': self._max_requests,\n 'maxRequestsPerHost': self._max_requests_per_host,\n 'retry': {\n 'retryable': runtime.autoretry,\n 'maxAttempts': UtilClient.default_number(runtime.max_attempts, 3)\n },\n 'backoff': {\n 'policy': UtilClient.default_string(runtime.backoff_policy, 'no'),\n 'period': UtilClient.default_number(runtime.backoff_period, 1)\n },\n 'ignoreSSL': runtime.ignore_ssl,\n # 创建凭证One\n }\n _last_request = None\n _last_exception = None\n _now = time.time()\n _retry_times = 0\n while TeaCore.allow_retry(_runtime.get('retry'), _retry_times, _now):\n if _retry_times > 0:\n _backoff_time = TeaCore.get_backoff_time(_runtime.get('backoff'), _retry_times)\n if _backoff_time > 0:\n TeaCore.sleep(_backoff_time)\n _retry_times = _retry_times + 1\n try:\n _request = TeaRequest()\n _request.protocol = UtilClient.default_string(self._protocol, protocol)\n _request.method = method\n _request.pathname = pathname\n _request.query = {\n 'method': action,\n 'version': version,\n 'sign_type': 'HmacSHA1',\n 'req_time': AntchainUtils.get_timestamp(),\n 'req_msg_id': AntchainUtils.get_nonce(),\n 'access_key': self._access_key_id,\n 'base_sdk_version': 'TeaSDK-2.0',\n 'sdk_version': '1.6.10',\n '_prod_code': 'SHUZIWULIU',\n '_prod_channel': 'undefined'\n }\n if not UtilClient.empty(self._security_token):\n _request.query['security_token'] = self._security_token\n _request.headers = TeaCore.merge({\n 'host': UtilClient.default_string(self._endpoint, 'openapi.antchain.antgroup.com'),\n 'user-agent': UtilClient.get_user_agent(self._user_agent)\n }, headers)\n tmp = UtilClient.anyify_map_value(RPCUtilClient.query(request))\n _request.body = UtilClient.to_form_string(tmp)\n _request.headers['content-type'] = 'application/x-www-form-urlencoded'\n signed_param = TeaCore.merge(_request.query,\n RPCUtilClient.query(request))\n _request.query['sign'] = AntchainUtils.get_signature(signed_param, self._access_key_secret)\n _last_request = _request\n _response = TeaCore.do_action(_request, _runtime)\n raw = UtilClient.read_as_string(_response.body)\n obj = UtilClient.parse_json(raw)\n res = UtilClient.assert_as_map(obj)\n resp = UtilClient.assert_as_map(res.get('response'))\n if AntchainUtils.has_error(raw, self._access_key_secret):\n raise TeaException({\n 'message': resp.get('result_msg'),\n 'data': resp,\n 'code': resp.get('result_code')\n })\n return resp\n except Exception as e:\n if TeaCore.is_retryable(e):\n _last_exception = e\n continue\n raise e\n raise UnretryableException(_last_request, _last_exception)","def test_client_can_do_get_request(self):\n response = self.httpbin.get_my_headers(headers={'User-agent': 'Fake user agent'})\n self.assertEqual(response.request.method, 'GET')\n self.assertEqual(response.status_code, 200)","def test_init(self):\n assert self.route.route[\"transform\"] == \"transform\"\n assert self.route.route[\"output\"] == \"output\"\n assert \"api\" not in self.route.route","def test_get_requests(self):\n response = self.client.open('/api/provisioning/port',\n method='GET')\n self.assert200(response, \"Response body is : \" + response.data.decode('utf-8'))","def test_create_namespaced_build_request_clone(self):\n pass","def test_GET3(self):\n r = requests.get(self.address)\n self.assertEqual(r.status_code, 400)","def _spy_on_make_request(self):\n data = {}\n\n def _make_request(api, url, body=None, method='GET', headers={},\n content_type=None):\n # We can't actually do any assertions in here, because they'll get\n # swallowed by SignalHook's sandboxing. We therefore record the\n # data we need and assert later.\n data['url'] = url\n data['request'] = json.loads(body)['request']\n return '{}'\n\n self.spy_on(TravisAPI._make_request, owner=TravisAPI,\n call_fake=_make_request)\n\n return data","def __init__(self, request):\n\n self.request = request","def test_send_http_request_normal(self):\n # test body is None\n http_client = bce_http_client.BceHttpClient(None)\n conn = MockHttpConnection()\n method = http_methods.GET\n uri = \"/unknown/unknown\"\n headers = {\"Content-Length\": 15,\n \"Content-Encoding\": \"utf8\",\n }\n body = None\n send_buf_size = -1\n\n http_client._send_http_request(conn,\n method,\n uri,\n headers,\n body,\n send_buf_size)\n self.assertEqual(conn.putrequest_called, 1)\n self.assertEqual(conn.putheader_called, 2)\n self.assertEqual(conn.endheaders_called, 1)\n self.assertEqual(conn.send_called, 0)\n self.assertEqual(conn.content, \"\")\n self.assertEqual(conn.getresponse_called, 1)\n\n # test body is string\n conn = MockHttpConnection()\n method = http_methods.GET\n uri = \"/unknown/unknown\"\n headers = {\"Content-Length\": 15,\n \"Content-Encoding\": \"utf8\",\n }\n body = \"Test with string\"\n send_buf_size = -1\n http_client._send_http_request(conn,\n method,\n uri,\n headers,\n body,\n send_buf_size)\n self.assertEqual(conn.putrequest_called, 1)\n self.assertEqual(conn.putheader_called, 2)\n self.assertEqual(conn.endheaders_called, 1)\n self.assertEqual(conn.send_called, 1)\n self.assertEqual(conn.content, body)\n self.assertEqual(conn.getresponse_called, 1)\n\n # test body is input stream\n conn = MockHttpConnection()\n method = http_methods.GET\n uri = \"/unknown/unknown\"\n headers = {\"Content-Length\": 16,\n \"Content-Encoding\": \"utf8\",\n }\n body = MockInputStream(\"Test with string\")\n send_buf_size = 5\n http_client._send_http_request(conn,\n method,\n uri,\n headers,\n body,\n send_buf_size)\n self.assertEqual(conn.putrequest_called, 1)\n self.assertEqual(conn.putheader_called, 2)\n self.assertEqual(conn.endheaders_called, 1)\n self.assertEqual(conn.send_called, len(\"Test with string\") // 5 + 1)\n self.assertEqual(conn.content, \"Test with string\")\n self.assertEqual(conn.getresponse_called, 1)","def test_parse_invalid_request(self):\n request = self.factory.post(self.url, data={})\n self.assertRaises(RequestParseError, self.parser.parse, request)","def setUp(self):\n h = self.MyTestHandler()\n h.request = Request.blank('/rpc/')\n h.response = Response()\n self.handler = h","def test_request_context_create(self, mock_rc_init):\n mock_request = MagicMock()\n rc = get_request_context(mock_request)\n mock_rc_init.assert_called_with(mock_request)\n self.assertIsInstance(rc, RequestContext)","def __init__(self, **kwargs):\n super(Request, self).__init__(**kwargs)","def create_request(url, headers, attempts, request_type, data=None):\n request_func = getattr(requests, request_type)\n kwargs = {\"url\": url, \"headers\": headers}\n if request_type == \"post\" or request_type == \"patch\":\n kwargs[\"json\"] = data\n try:\n req = request_func(**kwargs)\n status_code = req.status_code\n time.sleep(1)\n while status_code >= 400 and attempts < 5:\n req = request_func(**kwargs)\n status_code = req.status_code\n attempts += 1\n time.sleep(1)\n return req\n except Exception as e:\n print(\"[ERROR] There was an error with the request, details:\")\n print(e)\n return None","def setUp(self):\n\n c = Client()\n self.response = c.get('/')\n self.content = self.response.content","def mock_request(auth_header):\n request = HttpRequest()\n request.META['HTTP_AUTHORIZATION'] = auth_header\n return request","def test_right_request_class(self):\n environ = complete_environ(REQUEST_METHOD=\"GET\", PATH_INFO=\"/\")\n\n def start_response(status, response_headers):\n pass\n\n self.handler(environ, start_response)\n\n ok_(isinstance(self.handler.request, DjangoWSGIRequest))","def test_send_http_request_exception(self):\n # test body is not sufficient\n http_client = bce_http_client.BceHttpClient(None)\n conn = MockHttpConnection()\n method = http_methods.GET\n uri = \"/unknown/unknown\"\n headers = {\"Content-Length\": 100,\n \"Content-Encoding\": \"utf8\",\n }\n body = MockInputStream(\"Test with string\")\n send_buf_size = 5\n self.assertRaises(BceClientError,\n http_client._send_http_request,\n conn,\n method,\n uri,\n headers,\n body,\n send_buf_size)\n self.assertEqual(conn.putrequest_called, 1)\n self.assertEqual(conn.putheader_called, 2)\n self.assertEqual(conn.endheaders_called, 1)\n self.assertEqual(conn.send_called, len(\"Test with string\") // 5 + 1)","def test_GET5(self):\n r = requests.get(self.address + \"/carca\")\n self.assertEqual(r.status_code, 400)","def request(self, **request):\n request = RequestFactory.request(self, **request)\n handler = BaseHandler()\n handler.load_middleware()\n for middleware_method in handler._request_middleware:\n if middleware_method(request):\n raise Exception(\"Couldn't create request mock object - \"\n \"request middleware returned a response\")\n return request","def open(self, *args, **kwargs):\n assert_status = kwargs.pop('assert_status', None)\n\n if \"data\" in kwargs:\n # Parse data\n kwargs['data'] = json.dumps(kwargs['data'])\n # Set header\n kwargs['content_type'] = \"application/json\"\n\n # Add Fake Header if specified in context\n try:\n auth = g.fake_token\n kwargs.setdefault('headers', {})['Authorization'] = auth\n except (RuntimeError, AttributeError):\n # RuntimeError: No g, AttributeError: key in g is missing\n pass # No fake token to set\n\n # Send the actual request\n response = super(TestClient, self).open(*args, **kwargs)\n\n if assert_status is not None:\n assert response.status_code == assert_status, \\\n response.get_data(as_text=True)\n\n return json.loads(response.get_data(as_text=True) or '{}')","def __init__(self, requestor):\n self.requestor = requestor","def test_constructor_is_nt_so_all_args_work(self):\n u1 = url.URL(\n \"somedriver\",\n \"user\",\n \"secret\",\n \"10.20.30.40\",\n 1234,\n \"DB\",\n {\"key\": \"value\"},\n )\n eq_(\n u1,\n url.make_url(\n \"somedriver://user:secret@10.20.30.40:1234/DB?key=value\"\n ),\n )","def test_to_from_bytes(self):\n self.assertIsInstance(\n Request.create_from_bytes(Request().to_bytes()), Request)","def request_factory(environ):\n request = Request(environ)\n if request.is_xhr:\n request.response = Response()\n request.response.headerlist = []\n request.response.headerlist.extend(\n (\n ('Access-Control-Allow-Origin', '*'),\n ('Access-Control-Allow-Credentials', 'true'),\n ('Access-Control-Max-Age', 86400),\n ('Content-Type', 'application/json')\n )\n )\n return request"],"string":"[\n \"def test_request():\\n response = requests.get('http://jsonplaceholder.typicode.com/todos')\\n assert response.ok\",\n \"def createRequest(test, url, headers=None):\\n request = HTTPRequest(url=url)\\n if headers: request.headers=headers\\n test.record(request, HTTPRequest.getHttpMethodFilter())\\n return request\",\n \"def __init__( request ):\",\n \"def test_http_request(self):\\n\\n response = requests.get(self.live_server_url)\\n assert response.status_code == 200\",\n \"def test_create_namespaced_build_request_instantiate(self):\\n pass\",\n \"def test_basic(self):\\n request = fake_twisted_request(request_headers={\\n b'x-foo': [b'bar'],\\n })\\n self.assertThat(\\n _nevow_request_to_request_map(request),\\n ContainsDict({\\n 'content_type': Equals(b'application/octet-stream'),\\n 'content_length': Equals(0),\\n 'character_encoding': Is(None),\\n 'headers': Equals({b'Content-Length': [0],\\n b'X-Foo': [b'bar'],\\n b'Host': [b'example.com']}),\\n 'remote_addr': Equals(b'192.168.1.1'),\\n 'request_method': Equals(b'GET'),\\n 'server_name': Equals(b'example.com'),\\n 'server_port': Equals(80),\\n 'scheme': Equals(b'http'),\\n 'uri': Equals(URL.from_text(u'/one'))}))\",\n \"def test_orchestrator_http_simple(self):\\n pass\",\n \"def test_request():\\n return make_response(\\\"ok\\\")\",\n \"def test_send_http_request_valid(self):\\n na_element = zapi_fakes.FAKE_NA_ELEMENT\\n self.mock_object(self.root, '_create_request',\\n return_value=('abc', zapi_fakes.FAKE_NA_ELEMENT))\\n self.mock_object(netapp_api, 'LOG')\\n self.root._opener = zapi_fakes.FAKE_HTTP_OPENER\\n self.mock_object(self.root, '_build_opener')\\n self.mock_object(self.root, '_get_result',\\n return_value=zapi_fakes.FAKE_NA_ELEMENT)\\n opener_mock = self.mock_object(self.root._opener, 'open')\\n opener_mock.read.side_effect = ['resp1', 'resp2']\\n\\n self.root.send_http_request(na_element)\",\n \"def test_make_request(self, m_requests, m_sleep):\\r\\n request = testing.DummyRequest({mut.URL_KEY: SAMPLE_URL})\\r\\n m_response, response_dict = self.mock_response()\\r\\n m_requests.get.return_value = m_response\\r\\n self.assertEqual(response_dict, mut.make_request(request))\\r\\n m_requests.get.assert_called_with(url=SAMPLE_URL)\\r\\n m_sleep.assert_called_with(mut.SECURITY_SLEEP)\",\n \"def test_init(self):\\n self.assertEqual(self.request_mgr.f2f_search_url,\\n \\\"https://www.food2fork.com/api/search\\\")\\n self.assertEqual(self.request_mgr.f2f_detail_url,\\n \\\"https://www.food2fork.com/api/get\\\")\",\n \"def testBuildRequest(self):\\n expected_request = Request1()\\n self.protocol.decode_message(Request1,\\n 'my body').AndReturn(expected_request)\\n\\n self.mox.ReplayAll()\\n\\n mapper = service_handlers.RPCMapper(['POST'],\\n 'my-content-type',\\n self.protocol)\\n\\n request = mapper.build_request(self.handler, Request1)\\n\\n self.assertTrue(expected_request is request)\",\n \"def test_from_request_works_with_wsgi(self):\\n url = \\\"http://sp.example.com/\\\"\\n\\n params = {\\n 'oauth_version': \\\"1.0\\\",\\n 'oauth_nonce': \\\"4572616e48616d6d65724c61686176\\\",\\n 'oauth_timestamp': \\\"137131200\\\",\\n 'oauth_consumer_key': \\\"0685bd9184jfhq22\\\",\\n 'oauth_signature_method': \\\"HMAC-SHA1\\\",\\n 'oauth_token': \\\"ad180jjd733klru7\\\",\\n 'oauth_signature': \\\"wOJIO9A2W5mFwDgiDvZbTSMK%2FPY%3D\\\",\\n }\\n\\n req = oauth.Request(\\\"GET\\\", url, params)\\n headers = req.to_header()\\n\\n # Munge the headers\\n headers['HTTP_AUTHORIZATION'] = headers['Authorization']\\n del headers['Authorization'] \\n\\n # Test from the headers\\n req = oauth.Request.from_request(\\\"GET\\\", url, headers)\\n self.assertEqual(req.method, \\\"GET\\\")\\n self.assertEqual(req.url, url)\\n self.assertEqual(params, req.copy())\",\n \"def basicRequest(self):\\n endpoint = \\\"/foo\\\"\\n\\n def verify(request):\\n o(request.method).equals(\\\"GET\\\")(\\\"Checking basic request method.\\\")\\n o(request.url).equals(endpoint)(\\\"Checking basic request url.\\\")\\n request.respond(200)\\n self.testServer.respondWith(verify)\\n\\n server.request(endpoint)\\n self.testServer.respond()\",\n \"def __init__(self, headers=None, body=None, status=None,\\n request_headers={}):\\n self.headers = headers\\n self.body = body\\n self.status_code = status\\n self.request = mock.Mock()\\n self.request.headers = request_headers\",\n \"def __init__(self, request: Request, response: Response):\\n self.request = request\\n self.response = response\",\n \"def test_get(self):\\n return self.doRequest(self.url, method=\\\"GET\\\", body=self.input)\",\n \"def testWholeRequest(self):\\n body = self.protocol.encode_message(self.request_message)\\n self.Reinitialize(input=body,\\n content_type=self.content_type)\\n self.factory.add_request_mapper(self.mapper())\\n self.service_handler.handle('POST', '/my_service', 'method1')\\n VerifyResponse(self,\\n self.service_handler.response,\\n '200',\\n 'OK',\\n self.protocol.encode_message(self.response_message),\\n self.content_type)\",\n \"def init_request(self):\\n\\t\\tprint(\\\"Testes\\\")\\n\\t\\treturn Request(url=self.login_page[0], callback=self.login)\",\n \"def __init__(self, name, method, url, headers={}, auth=None, body=None, expected_status=None, expected_body=None):\\n \\n self.name = name\\n self.method = method\\n self.url = url\\n self.headers = headers\\n self.auth = auth\\n self.body = body\\n self.expected_status = expected_status\\n self.expected_body = expected_body\",\n \"def __init__(self, get_request):\\n self.get_request = get_request\",\n \"def dummy_request():\\n return DummyRequest()\",\n \"def __init__(self, request: object) -> None:\\n super().__init__({}, request, URL, Api)\",\n \"def create_request(params={}, path='/', method='POST'):\\n request = DummyRequest(path)\\n request.method = method\\n request.args = params\\n return request\",\n \"def testSimpleRequest(self):\\n d = self._getClientConnection()\\n self._addRequest(d)\\n return d\",\n \"def testInitializer(self):\\n request = http.HttpRequest()\\n\\n data, check, mutator = initialize.MELANGE_INITIALIZER.initialize(\\n request, [], {})\\n self.assertEqual(request, data.request)\\n self.assertEqual(data, check.data)\\n self.assertEqual(data, mutator.data)\",\n \"def setUp(self):\\n self.client = HTTPClient()\\n self.method = 'GET'\\n self.url = 'http://github.com/ojengwa'\\n self.headers = {}\",\n \"def __init__(self, request: Request):\\n self.request = request\",\n \"def setUp(self):\\n self.response = self.s.get(self.url, params=self.params)\",\n \"def test_make_request_parameters(self, m_requests, m_sleep):\\r\\n request = testing.DummyRequest({mut.URL_KEY: SAMPLE_URL, \\r\\n mut.PARAMETERS_KEY: json.dumps(SAMPLE_PARAMETERS)})\\r\\n m_response, response_dict = self.mock_response()\\r\\n m_requests.get.return_value = m_response\\r\\n self.assertEqual(response_dict, mut.make_request(request))\\r\\n m_requests.get.assert_called_with(url=SAMPLE_URL, \\r\\n params=SAMPLE_PARAMETERS)\\r\\n m_sleep.assert_called_with(mut.SECURITY_SLEEP)\",\n \"def test_basic_generic_request(self, api_instance):\\n action = \\\"BasicGenericRequest\\\"\\n\\n # Send a basic payload.\\n params = {\\n \\\"ADateTime\\\": datetime.datetime(2020, 10, 12),\\n \\\"ATrueBool\\\": True,\\n \\\"AFalseBool\\\": False,\\n \\\"NoneShouldNotExist\\\": None,\\n }\\n\\n request_params = api_instance.generic_request(action=action, params=params)\\n self.assert_common_params(request_params, action=\\\"BasicGenericRequest\\\")\\n assert request_params[\\\"ADateTime\\\"] == \\\"2020-10-12T00:00:00\\\"\\n assert request_params[\\\"ATrueBool\\\"] == \\\"true\\\"\\n assert request_params[\\\"AFalseBool\\\"] == \\\"false\\\"\\n assert \\\"NoneShouldNotExist\\\" not in request_params\",\n \"def test_make_request_method(self, m_requests, m_sleep):\\r\\n request = testing.DummyRequest({mut.URL_KEY: SAMPLE_URL, \\r\\n mut.METHOD_KEY: SAMPLE_METHOD})\\r\\n m_response, response_dict = self.mock_response()\\r\\n m_requests.post.return_value = m_response\\r\\n self.assertEqual(response_dict, mut.make_request(request))\\r\\n m_requests.post.assert_called_with(url=SAMPLE_URL)\\r\\n m_sleep.assert_called_with(mut.SECURITY_SLEEP)\",\n \"def __init__(self, request, data):\\n self.request = request\\n self.data = data\",\n \"def req():\\n return Request()\",\n \"def test_get_request_output(self):\\n pass\",\n \"def setUp(self):\\n self.client = RequestsClient()\\n self.method = 'GET'\\n self.url = 'http://github.com/ojengwa'\\n self.headers = {}\",\n \"def initialize_from_http_request(self, request):\\n self.tool_id = request.path.strip('/').split('/')[0]\\n self.path = request.path\\n self._set_response_format(request)\\n self._set_ip_address(request)\\n self._set_tool_address_family(request)\\n self._set_user_agent(request)\\n self._set_path_qs(request)\\n self._set_geolocation(request)\\n self.metro = request.get(message.METRO, default_value=None)\\n self._set_policy(request)\",\n \"def test_simple_request(self):\\n\\n\\t\\tself.n = tracker.decode_request(\\\"?key=value\\\")\\n\\t\\tself.assertEqual(self.n, {\\\"key\\\":[\\\"value\\\"]})\",\n \"def test_new_user_request(self):\\r\\n # Send a request\\r\\n values = {'username': u'user100',\\r\\n 'external_id': '4', 'email': u'user100@edx.org'}\\r\\n data = json.dumps(values)\\r\\n headers = {'Content-Type': 'application/json', 'Content-Length': len(data), 'X-Edx-Api-Key': 'TEST_API_KEY'}\\r\\n req = urllib2.Request(self.server_url + '/api/v1/users/4', data, headers)\\r\\n\\r\\n # Send the request to the mock cs server\\r\\n response = urllib2.urlopen(req)\\r\\n\\r\\n # Receive the reply from the mock cs server\\r\\n response_dict = json.loads(response.read())\\r\\n\\r\\n # You should have received the response specified in the setup above\\r\\n self.assertEqual(response_dict, self.expected_response)\",\n \"def test_request(self):\\n client = RestClient(host=self.host, username='')\\n rest_url = 'some/url/'\\n\\n # Mock good get response\\n with responses.RequestsMock() as rsps:\\n rsps.add(responses.GET, f'{self.host}/{rest_url}', status=200,\\n json={'value':\\\"good!\\\"})\\n r = client.request('get', rest_url)\\n assert r.status_code == 200\\n assert r.json()['value'] == 'good!'\\n \\n # Mock bad get response\\n with responses.RequestsMock() as rsps:\\n rsps.add(responses.GET, f'{self.host}/{rest_url}', status=401,\\n json={'value':\\\"bad!\\\"})\\n with raises(requests.HTTPError):\\n r = client.request('get', rest_url)\\n r = client.request('get', rest_url, checkstatus=False)\\n assert r.status_code == 401\\n assert r.json()['value'] == 'bad!'\",\n \"def test_post(self):\\n url, port = self.server.address\\n\\n #couple of basic POSTs\\n #request parameters\\n r = self.client.get(\\\"http://{0}:{1}/\\\".format(url, port))\\n self.assertEqual(200, r.status_code)\\n r = self.client.get(\\\"http://{0}:{1}\\\".format(url, port))\\n self.assertEqual(200, r.status_code)\\n r = self.client.get(\\\"http://{0}:{1}/200\\\".format(url, port))\\n self.assertEqual(200, r.status_code)\\n r = self.client.get(\\\"http://{0}:{1}/400\\\".format(url, port))\\n self.assertEqual(400, r.status_code)\",\n \"def _request(http, project, method, data, base_url, client_info):\\n user_agent = client_info.to_user_agent()\\n headers = {\\n \\\"Content-Type\\\": \\\"application/x-protobuf\\\",\\n \\\"User-Agent\\\": user_agent,\\n connection_module.CLIENT_INFO_HEADER: user_agent,\\n }\\n api_url = build_api_url(project, method, base_url)\\n\\n response = http.request(url=api_url, method=\\\"POST\\\", headers=headers, data=data)\\n\\n if response.status_code != 200:\\n error_status = status_pb2.Status.FromString(response.content)\\n raise exceptions.from_http_status(\\n response.status_code, error_status.message, errors=[error_status]\\n )\\n\\n return response.content\",\n \"def test_prepare_bearer_request(self):\\n self.assertEqual(prepare_bearer_headers(self.token), self.bearer_headers)\\n self.assertEqual(prepare_bearer_body(self.token), self.bearer_body)\\n self.assertEqual(prepare_bearer_uri(self.token, uri=self.uri), self.bearer_uri)\",\n \"def test_unauthenticated_request(self):\\n http = FakeHttp([(FakeResponse(200), {})])\\n self.mock.Http.return_value = http\\n response, _ = http_utils.request('https://url/', body='test body')\\n\\n # Ensure that all expected requests were made.\\n self.assertEqual(http.replies, [])\\n\\n self.assertEqual(http.last_body, '\\\"test body\\\"')\\n self.assertEqual(http.last_headers, {})\\n self.assertEqual(response.status, 200)\",\n \"def __init__(self, request):\\n \\n self.request = request\",\n \"def test_post(self):\\n return self.doRequest(self.url, method=\\\"POST\\\", body=self.input)\",\n \"def request(self, **request):\\n environ = {\\n 'HTTP_COOKIE': self.cookies,\\n 'PATH_INFO': '/',\\n 'QUERY_STRING': '',\\n 'REQUEST_METHOD': 'GET',\\n 'SCRIPT_NAME': '',\\n 'SERVER_NAME': 'testserver',\\n 'SERVER_PORT': 80,\\n 'SERVER_PROTOCOL': 'HTTP/1.1',\\n 'wsgi.version': (1,0),\\n 'wsgi.url_scheme': 'http',\\n 'wsgi.input': FakePayload(''),\\n 'wsgi.errors': self.errors,\\n 'wsgi.multiprocess': True,\\n 'wsgi.multithread': False,\\n 'wsgi.run_once': False,\\n }\\n environ.update(self.defaults)\\n environ.update(request)\\n return WSGIRequest(environ)\",\n \"def bind_request(**request_data):\\n\\n class Request(Api):\\n \\\"\\\"\\\"Request class. Does the actual API request.\\\"\\\"\\\"\\n\\n model = request_data.get(constants.ClientConst.MODEL)\\n api_path = request_data.get(constants.RequestConst.API_PATH)\\n formatter = request_data.get(constants.ClientConst.FORMATTER)\\n method = request_data.get(\\n constants.RequestConst.METHOD, constants.RequestConst.GET\\n )\\n query_parameters = request_data.get(\\n constants.RequestConst.QUERY_PARAMETERS\\n )\\n fake_response_path = request_data.get(\\n constants.TestConst.FAKE_RESPONSE_PATH\\n )\\n default_parameters = request_data.get(\\n constants.RequestConst.DEFAULT_PARAMETERS, {}\\n )\\n # immediately determine type of response\\n single_model_response = request_data.get(\\n constants.ClientConst.FORCE_SINGLE_MODEL_RESPONSE, False\\n ) or method in [\\n constants.RequestConst.POST,\\n constants.RequestConst.PUT,\\n constants.RequestConst.DELETE\\n ]\\n\\n def __init__(self, client, debug: 'Debug',\\n *path_params, **query_params):\\n client.request = self\\n\\n self.url = None\\n self.debug = debug\\n self.client = client\\n self.parameters = {\\n constants.RequestConst.QUERY: {},\\n constants.RequestConst.PATH: []\\n }\\n\\n self._timeout = 5\\n\\n self._set_parameters(*path_params, **query_params)\\n\\n def _set_parameters(self, *path_params, **query_params):\\n \\\"\\\"\\\"\\n Prepares the list of query parameters\\n :path_params: list of path parameters\\n :query_params: dict of query parameters\\n :return: None\\n \\\"\\\"\\\"\\n\\n # take timeout\\n try:\\n self._timeout = int(query_params.get(\\n constants.RequestConst.TIMEOUT, self._timeout\\n ))\\n except ValueError:\\n pass\\n try:\\n del query_params[constants.RequestConst.TIMEOUT]\\n except KeyError:\\n pass\\n\\n # set default API call params\\n for key, value in self.default_parameters.items():\\n self.parameters[constants.RequestConst.QUERY][key] = value\\n\\n _query_params = self.query_parameters.get_params()\\n\\n # set API call params defined during the \\\"call\\\" invocation\\n for key, value in query_params.items():\\n if value is None:\\n continue\\n\\n if key in _query_params.values():\\n self.parameters[constants.RequestConst.QUERY][key] = value\\n\\n elif key in _query_params.keys():\\n self.parameters[\\n constants.RequestConst.QUERY\\n ][_query_params[key]] = value\\n\\n if self.method == constants.RequestConst.GET:\\n # transform all True and False param to 1 and 0\\n for key, value in self.parameters[\\n constants.RequestConst.QUERY\\n ].items():\\n if value is True:\\n self.parameters[constants.RequestConst.QUERY][key] = \\\\\\n constants.BoolConst.TRUE\\n if value is False:\\n self.parameters[constants.RequestConst.QUERY][key] = \\\\\\n constants.BoolConst.FALSE\\n\\n # set optional url path params\\n for value in path_params:\\n self.parameters[constants.RequestConst.PATH].append(value)\\n\\n def _prepare_url(self):\\n \\\"\\\"\\\"\\n Prepares url and query parameters for the request\\n :return: URL\\n \\\"\\\"\\\"\\n\\n base_url = '{}://{}{}'.format(\\n self.client.protocol, self.client.base_url, self.api_path\\n )\\n url_parts = '/'.join(\\n [part for part in self.parameters[constants.RequestConst.PATH]]\\n )\\n\\n if url_parts:\\n final_url = '{}/{}'.format(base_url, url_parts)\\n else:\\n final_url = base_url\\n\\n if self.method == constants.RequestConst.GET:\\n params = self.parameters[constants.RequestConst.QUERY]\\n for param, value in params.items():\\n if isinstance(value, list):\\n params[param] = ','.join(value)\\n elif isinstance(value, dict):\\n params[param] = ','.join([f'{k}:{v}' for k, v in value])\\n\\n url_query = '?' + '&'.join([f'{k}={v}' for k, v in params.items()])\\n final_url = '{}{}'.format(final_url, url_query)\\n\\n self.debug.ok('final url', final_url)\\n\\n return final_url\\n\\n def _headers(self):\\n \\\"\\\"\\\"Construct headers data with authentication part\\\"\\\"\\\"\\n\\n auth_token = SendbeeAuth(self.client.api_secret).get_auth_token()\\n headers = {\\n 'X-Auth-Token': auth_token,\\n 'X-Api-Key': self.client.api_key,\\n 'Accept': 'application/json',\\n 'Content-Type': 'application/json',\\n 'User-Agent': 'Sendbee Python API Client'\\n }\\n self.debug.ok('headers', headers)\\n\\n return headers\\n\\n def _do_request(self, url: str):\\n \\\"\\\"\\\"\\n Makes the request to Sendbee Api servers\\n :url: Url for the request\\n :return: Tuple with two elements, status code and content\\n \\\"\\\"\\\"\\n\\n self.debug.ok('method', self.method)\\n\\n if self.client.fake_response_path:\\n with open(self.client.fake_response_path, 'r') as f:\\n return constants.ResponseCode.OK, f.read()\\n\\n elif self.method == constants.RequestConst.GET:\\n response = requests.get(\\n url, headers=self._headers(), timeout=self._timeout\\n )\\n\\n self.debug.ok(\\n constants.RequestConst.QUERY_PARAMETERS,\\n self.parameters[constants.RequestConst.QUERY]\\n )\\n self.debug.ok(constants.ResponseConst.RESPONSE_OBJECT, response)\\n\\n return response.status_code, response.text\\n\\n elif self.method in [\\n constants.RequestConst.POST,\\n constants.RequestConst.PUT,\\n constants.RequestConst.DELETE\\n ]:\\n if self.method == constants.RequestConst.POST:\\n send_request = requests.post\\n elif self.method == constants.RequestConst.PUT:\\n send_request = requests.put\\n elif self.method == constants.RequestConst.DELETE:\\n send_request = requests.delete\\n\\n response = send_request(\\n url, json=self.parameters[constants.RequestConst.QUERY],\\n headers=self._headers(), timeout=self._timeout\\n )\\n\\n self.debug.ok('payload', self.parameters[\\n constants.RequestConst.QUERY\\n ])\\n self.debug.ok(constants.ResponseConst.RESPONSE_OBJECT, response)\\n\\n return response.status_code, response.text\\n\\n else:\\n return constants.ResponseCode.NOT_FOUND, {}\\n\\n def _process_response(self, status_code, response):\\n \\\"\\\"\\\"\\n Process response using models\\n :status_code: Response status code\\n :response: Content\\n :return: Response object\\n \\\"\\\"\\\"\\n\\n formatter = self.formatter\\n if not formatter:\\n formatter = FormatterFactory(constants.FormatterConst.JSON)\\\\\\n .get_formatter()\\n\\n response = Response(response, status_code, formatter, self)\\n formatted_data = response.formatted_data\\n\\n if status_code >= constants.ResponseCode.BAD_REQUEST:\\n\\n if status_code == constants.ResponseCode.NOT_FOUND:\\n error_msg = \\\\\\n constants.ErrorConst.NOT_FOUND\\n elif constants.ErrorConst.ERROR not in formatted_data:\\n error_msg = \\\\\\n constants.ResponseConst.DEFAULT_ERROR_MESSAGE\\n else:\\n error_msg = formatted_data.get(\\n constants.ErrorConst.ERROR, {}\\n ).get(\\n constants.ErrorConst.DETAIL,\\n constants.ErrorConst.UNRECOGNIZED_ERROR\\n )\\n\\n self.debug.error(\\n constants.ResponseConst.STATUS_CODE, status_code\\n )\\n self.debug.error(\\n constants.ResponseConst.RESPONSE, response.formatted_data\\n )\\n raise SendbeeRequestApiException(error_msg)\\n else:\\n self.debug.ok(constants.ResponseConst.STATUS_CODE, status_code)\\n self.debug.ok(constants.ResponseConst.RESPONSE, response.raw_data)\\n\\n if response.meta.current_page:\\n if response.meta.current_page > 1 and len(response.models) == 0:\\n raise PaginationException(\\n f'Page {response.meta.current_page} has no data'\\n )\\n\\n if response.warning:\\n click.secho(\\n constants.WarningConst.MESSAGE + response.warning,\\n fg='yellow'\\n )\\n\\n if self.single_model_response:\\n if response.models:\\n return response.models[0]\\n else:\\n return None\\n else:\\n return response\\n\\n def call(self):\\n \\\"\\\"\\\"\\n Makes the API call\\n :return: Return value from self._process_response()\\n \\\"\\\"\\\"\\n\\n self.url = self._prepare_url()\\n status_code, response = self._do_request(self.url)\\n return self._process_response(status_code, response)\\n\\n def call(client, *path_params, **query_params):\\n \\\"\\\"\\\"\\n Binded method for API calls\\n :path_params: list of path parameters\\n :query_params: dict of query parameters\\n :return: Return value from Request.call()\\n \\\"\\\"\\\"\\n\\n if constants.MiscConst.PRINT_PARAMS in query_params:\\n Request.query_parameters.print_params()\\n return\\n\\n with Debug(client=client) as debug:\\n request = Request(client, debug, *path_params, **query_params)\\n return request.call()\\n\\n call.__doc__ = request_data.get(constants.ClientConst.DESCRIPTION)\\n\\n return call\",\n \"def request(self, **request):\\r\\n environ = {\\r\\n 'HTTP_COOKIE': self.cookies,\\r\\n 'PATH_INFO': '/',\\r\\n 'QUERY_STRING': '',\\r\\n 'REQUEST_METHOD': 'GET',\\r\\n 'SCRIPT_NAME': '',\\r\\n 'SERVER_NAME': 'testserver',\\r\\n 'SERVER_PORT': 80,\\r\\n 'SERVER_PROTOCOL': 'HTTP/1.1',\\r\\n }\\r\\n environ.update(self.defaults)\\r\\n environ.update(request)\\r\\n request = WSGIRequest(environ)\\r\\n\\r\\n handler = BaseHandler()\\r\\n handler.load_middleware()\\r\\n for middleware_method in handler._request_middleware:\\r\\n if middleware_method(request):\\r\\n raise Exception(\\\"Couldn't create request object - \\\"\\r\\n \\\"request middleware returned a response\\\")\\r\\n\\r\\n return request\",\n \"def mock_request(request_path_query=\\\"\\\", # type: Str\\n method=\\\"GET\\\", # type: Str\\n params=None, # type: Optional[Dict[Str, Str]]\\n body=\\\"\\\", # type: Union[Str, JSON]\\n content_type=None, # type: Optional[Str]\\n headers=None, # type: Optional[AnyHeadersType]\\n cookies=None, # type: Optional[AnyCookiesType]\\n settings=None, # type: SettingsType\\n ): # type: (...) -> Request\\n parts = request_path_query.split(\\\"?\\\")\\n path = parts[0]\\n query = dict()\\n if len(parts) > 1 and parts[1]:\\n for part in parts[1].split(\\\"&\\\"):\\n kv = part.split(\\\"=\\\") # handle trailing keyword query arguments without values\\n if kv[0]: # handle invalid keyword missing\\n query[kv[0]] = kv[1] if len(kv) > 1 else None\\n elif params:\\n query = params\\n request = DummyRequest(path=path, params=query)\\n request.path_qs = request_path_query\\n request.method = method\\n request.content_type = content_type\\n request.headers = headers or {}\\n request.cookies = cookies or {}\\n request.matched_route = None # cornice method\\n if content_type:\\n request.headers[\\\"Content-Type\\\"] = content_type\\n request.body = body\\n try:\\n if body:\\n # set missing DummyRequest.json attribute\\n request.json = json_pkg.loads(body)\\n except (TypeError, ValueError):\\n pass\\n request.registry.settings = settings or {}\\n return request # noqa # fake type of what is normally expected just to avoid many 'noqa'\",\n \"def get_request(self, post=None, request_method=\\\"GET\\\"):\\n if post is None:\\n post = {\\\"csrf_token\\\": \\\"irrelevant but required\\\"}\\n request = testing.DummyRequest(post)\\n request.client_addr = \\\"127.0.0.1\\\"\\n request.session = Mock()\\n request.method = request_method\\n request.repo = self.repo\\n request.kerno = self.kerno\\n return request\",\n \"def setUp(self):\\n self.factory = RequestFactory()\",\n \"def setUp(self):\\n self.factory = RequestFactory()\",\n \"def setUp(self):\\n self.factory = RequestFactory()\",\n \"def test_request(self, kasserver, kasapi):\\n kasserver._request(self.REQUEST_TYPE, self.REQUEST_PARAMS)\\n request = {\\n \\\"KasUser\\\": USERNAME,\\n \\\"KasAuthType\\\": \\\"plain\\\",\\n \\\"KasAuthData\\\": PASSWORD,\\n \\\"KasRequestType\\\": self.REQUEST_TYPE,\\n \\\"KasRequestParams\\\": self.REQUEST_PARAMS,\\n }\\n kasapi.assert_called_once_with(json.dumps(request))\",\n \"def test_get(self):\\n response = self.get('/study/create/')\\n self.assertEqual(response.code, 200)\\n self.assertNotEqual(str(response.body), \\\"\\\")\",\n \"def test_request_parameters(self):\\n httpretty.register_uri(httpretty.POST, 'http://somewhere.com/test')\\n r = CkanResource('http://somewhere.com/test', None, {'offset': None, 'limit': None, 'carrot': 'cake'})\\n s = r._get_response(10, 200)\\n self._assert_params_equals(httpretty.last_request().path, {'offset': 2000, 'limit': 200, 'carrot': 'cake'})\",\n \"def test_requests(self):\\n\\n text_data = \\\"\\\"\\\"\\n BERNARDO: Who's there?\\n FRANCISCO: Nay, answer me: stand, and unfold yourself.\\n BERNARDO: Long live the king!\\n FRANCISCO: Bernardo?\\n BERNARDO: He.\\n FRANCISCO: You come most carefully upon your hour.\\n BERNARDO: 'Tis now struck twelve; get thee to bed, Francisco.\\n \\\"\\\"\\\"\\n binary_data = text_data.encode()\\n file_content = {\\\"/foo.txt\\\": binary_data}\\n\\n # Test all possible combinations of:\\n # - whether or not the server supports compression\\n # - whether or not the server supports random access\\n # - chosen buffering policy\\n for allow_gzip in (False, True):\\n for allow_range in (False, True):\\n with DummyHTTPServer(\\n file_content=file_content,\\n allow_gzip=allow_gzip,\\n allow_range=allow_range,\\n ) as server:\\n url = server.url(\\\"/foo.txt\\\")\\n for buffering in (-2, -1, 0, 20):\\n self._test_text(url, text_data, buffering)\\n self._test_binary(url, binary_data, buffering)\",\n \"def __init__(self, url, header=None, logger=None):\\n\\n httpDict = {}\\n header = header or {}\\n # url is end point\\n httpDict['endpoint'] = \\\"%s/data\\\" % url\\n httpDict['logger'] = logger if logger else logging.getLogger()\\n\\n # cherrypy converts request.body to params when content type is set\\n # application/x-www-form-urlencodeds\\n httpDict.setdefault(\\\"content_type\\\", 'application/json')\\n httpDict.setdefault('cacheduration', 0)\\n httpDict.setdefault(\\\"accept_type\\\", \\\"application/json\\\")\\n httpDict.update(header)\\n self.encoder = json.dumps\\n Service.__init__(self, httpDict)\\n # This is only for the unittest: never set it true unless it is unittest\\n self._noStale = False\",\n \"def __init__(self, request):\\n self.request = request\\n self._is_verified = False\",\n \"def __init__(self, request: Request) -> None:\\n super().__init__(request)\",\n \"def __init__(self, request: Request) -> None:\\n super().__init__(request)\",\n \"def test_valid_request(self):\\n print(\\\"Testing valid request...\\\")\\n # create a file with random data in the server folder\\n self.__create_test_file()\\n valid_path = os.path.join(os.path.dirname(self.client_path), \\\\\\n os.path.basename(self.test_file))\\n os.chdir(os.path.dirname(self.client_path))\\n subprocess.call([self.client_path, \\\\\\n \\\"{}:{}\\\".format(self.args.ip, self.args.port), \\\\\\n os.path.basename(valid_path)])\\n self.assertTrue(os.path.isfile(valid_path))\\n self.__compare_files(valid_path, self.test_file)\\n os.remove(valid_path)\",\n \"def test_create_canonical_req(self):\\n\\n request = self_signed.create_canonical_req()\\n assert \\\"content-type:\\\" in request\\n assert \\\"x-amz-date:\\\" in request\\n assert \\\"host\\\" in request\",\n \"def test_setup(self):\\n assert self.http_handler.setup() is None\\n self.assert_quantity_in_outbox(0)\",\n \"def test_request_response():\\n response = get_todos()\\n assert response is not None\",\n \"def __init__( httpconn, method, uri, uriparts, version, headers ):\",\n \"def test_request_url(self):\\n httpretty.register_uri(httpretty.POST, 'http://somewhere.com/test')\\n r = CkanResource('http://somewhere.com/test', None, {'offset': None, 'limit': None})\\n s = r._get_response(0, 0)\\n assert_equals(httpretty.last_request().path, '/test?limit=0&offset=0')\",\n \"def test_get_method(self):\\n self.getPage('/')\\n self.assertStatus('200 OK')\\n self.assertHeader('Content-Type', 'application/json')\\n self.assertBody('{\\\"mystring\\\": \\\"\\\"}')\",\n \"def __init__(self, request, name) :\\n self.request = request\\n self.name = name\",\n \"def test_request_basic(self):\\n request = Request.objects.first()\\n self.assertEqual(request.path, 'request/')\\n self.assertEqual(request.viewed, False)\",\n \"def __init__(self, request):\\n self._data = request.get_json(silent=True)\",\n \"def test_no_parameters(self):\\n\\n x = self.start_request_tests(service.get_request('POST', None))\\n self.assert_response_status(x, 400)\\n self.assertTrue('tree' in x.json()[u'message'])\",\n \"def request_factory(environ):\\n request = Request(environ)\\n _LOG.debug('trunctated request body: {b}'.format(b=request.body[:1000]))\\n return request\",\n \"def __init__(self):\\n self._url = \\\"\\\"\\n self._verb = VERBS.GET\\n self._params = {}\\n self._data = {}\",\n \"def do_request(\\n self,\\n version: str,\\n action: str,\\n protocol: str,\\n method: str,\\n pathname: str,\\n request: dict,\\n headers: Dict[str, str],\\n runtime: util_models.RuntimeOptions,\\n ) -> dict:\\n runtime.validate()\\n _runtime = {\\n 'timeouted': 'retry',\\n 'readTimeout': UtilClient.default_number(runtime.read_timeout, self._read_timeout),\\n 'connectTimeout': UtilClient.default_number(runtime.connect_timeout, self._connect_timeout),\\n 'httpProxy': UtilClient.default_string(runtime.http_proxy, self._http_proxy),\\n 'httpsProxy': UtilClient.default_string(runtime.https_proxy, self._https_proxy),\\n 'noProxy': UtilClient.default_string(runtime.no_proxy, self._no_proxy),\\n 'maxIdleConns': UtilClient.default_number(runtime.max_idle_conns, self._max_idle_conns),\\n 'maxIdleTimeMillis': self._max_idle_time_millis,\\n 'keepAliveDuration': self._keep_alive_duration_millis,\\n 'maxRequests': self._max_requests,\\n 'maxRequestsPerHost': self._max_requests_per_host,\\n 'retry': {\\n 'retryable': runtime.autoretry,\\n 'maxAttempts': UtilClient.default_number(runtime.max_attempts, 3)\\n },\\n 'backoff': {\\n 'policy': UtilClient.default_string(runtime.backoff_policy, 'no'),\\n 'period': UtilClient.default_number(runtime.backoff_period, 1)\\n },\\n 'ignoreSSL': runtime.ignore_ssl,\\n # 创建凭证One\\n }\\n _last_request = None\\n _last_exception = None\\n _now = time.time()\\n _retry_times = 0\\n while TeaCore.allow_retry(_runtime.get('retry'), _retry_times, _now):\\n if _retry_times > 0:\\n _backoff_time = TeaCore.get_backoff_time(_runtime.get('backoff'), _retry_times)\\n if _backoff_time > 0:\\n TeaCore.sleep(_backoff_time)\\n _retry_times = _retry_times + 1\\n try:\\n _request = TeaRequest()\\n _request.protocol = UtilClient.default_string(self._protocol, protocol)\\n _request.method = method\\n _request.pathname = pathname\\n _request.query = {\\n 'method': action,\\n 'version': version,\\n 'sign_type': 'HmacSHA1',\\n 'req_time': AntchainUtils.get_timestamp(),\\n 'req_msg_id': AntchainUtils.get_nonce(),\\n 'access_key': self._access_key_id,\\n 'base_sdk_version': 'TeaSDK-2.0',\\n 'sdk_version': '1.6.10',\\n '_prod_code': 'SHUZIWULIU',\\n '_prod_channel': 'undefined'\\n }\\n if not UtilClient.empty(self._security_token):\\n _request.query['security_token'] = self._security_token\\n _request.headers = TeaCore.merge({\\n 'host': UtilClient.default_string(self._endpoint, 'openapi.antchain.antgroup.com'),\\n 'user-agent': UtilClient.get_user_agent(self._user_agent)\\n }, headers)\\n tmp = UtilClient.anyify_map_value(RPCUtilClient.query(request))\\n _request.body = UtilClient.to_form_string(tmp)\\n _request.headers['content-type'] = 'application/x-www-form-urlencoded'\\n signed_param = TeaCore.merge(_request.query,\\n RPCUtilClient.query(request))\\n _request.query['sign'] = AntchainUtils.get_signature(signed_param, self._access_key_secret)\\n _last_request = _request\\n _response = TeaCore.do_action(_request, _runtime)\\n raw = UtilClient.read_as_string(_response.body)\\n obj = UtilClient.parse_json(raw)\\n res = UtilClient.assert_as_map(obj)\\n resp = UtilClient.assert_as_map(res.get('response'))\\n if AntchainUtils.has_error(raw, self._access_key_secret):\\n raise TeaException({\\n 'message': resp.get('result_msg'),\\n 'data': resp,\\n 'code': resp.get('result_code')\\n })\\n return resp\\n except Exception as e:\\n if TeaCore.is_retryable(e):\\n _last_exception = e\\n continue\\n raise e\\n raise UnretryableException(_last_request, _last_exception)\",\n \"def test_client_can_do_get_request(self):\\n response = self.httpbin.get_my_headers(headers={'User-agent': 'Fake user agent'})\\n self.assertEqual(response.request.method, 'GET')\\n self.assertEqual(response.status_code, 200)\",\n \"def test_init(self):\\n assert self.route.route[\\\"transform\\\"] == \\\"transform\\\"\\n assert self.route.route[\\\"output\\\"] == \\\"output\\\"\\n assert \\\"api\\\" not in self.route.route\",\n \"def test_get_requests(self):\\n response = self.client.open('/api/provisioning/port',\\n method='GET')\\n self.assert200(response, \\\"Response body is : \\\" + response.data.decode('utf-8'))\",\n \"def test_create_namespaced_build_request_clone(self):\\n pass\",\n \"def test_GET3(self):\\n r = requests.get(self.address)\\n self.assertEqual(r.status_code, 400)\",\n \"def _spy_on_make_request(self):\\n data = {}\\n\\n def _make_request(api, url, body=None, method='GET', headers={},\\n content_type=None):\\n # We can't actually do any assertions in here, because they'll get\\n # swallowed by SignalHook's sandboxing. We therefore record the\\n # data we need and assert later.\\n data['url'] = url\\n data['request'] = json.loads(body)['request']\\n return '{}'\\n\\n self.spy_on(TravisAPI._make_request, owner=TravisAPI,\\n call_fake=_make_request)\\n\\n return data\",\n \"def __init__(self, request):\\n\\n self.request = request\",\n \"def test_send_http_request_normal(self):\\n # test body is None\\n http_client = bce_http_client.BceHttpClient(None)\\n conn = MockHttpConnection()\\n method = http_methods.GET\\n uri = \\\"/unknown/unknown\\\"\\n headers = {\\\"Content-Length\\\": 15,\\n \\\"Content-Encoding\\\": \\\"utf8\\\",\\n }\\n body = None\\n send_buf_size = -1\\n\\n http_client._send_http_request(conn,\\n method,\\n uri,\\n headers,\\n body,\\n send_buf_size)\\n self.assertEqual(conn.putrequest_called, 1)\\n self.assertEqual(conn.putheader_called, 2)\\n self.assertEqual(conn.endheaders_called, 1)\\n self.assertEqual(conn.send_called, 0)\\n self.assertEqual(conn.content, \\\"\\\")\\n self.assertEqual(conn.getresponse_called, 1)\\n\\n # test body is string\\n conn = MockHttpConnection()\\n method = http_methods.GET\\n uri = \\\"/unknown/unknown\\\"\\n headers = {\\\"Content-Length\\\": 15,\\n \\\"Content-Encoding\\\": \\\"utf8\\\",\\n }\\n body = \\\"Test with string\\\"\\n send_buf_size = -1\\n http_client._send_http_request(conn,\\n method,\\n uri,\\n headers,\\n body,\\n send_buf_size)\\n self.assertEqual(conn.putrequest_called, 1)\\n self.assertEqual(conn.putheader_called, 2)\\n self.assertEqual(conn.endheaders_called, 1)\\n self.assertEqual(conn.send_called, 1)\\n self.assertEqual(conn.content, body)\\n self.assertEqual(conn.getresponse_called, 1)\\n\\n # test body is input stream\\n conn = MockHttpConnection()\\n method = http_methods.GET\\n uri = \\\"/unknown/unknown\\\"\\n headers = {\\\"Content-Length\\\": 16,\\n \\\"Content-Encoding\\\": \\\"utf8\\\",\\n }\\n body = MockInputStream(\\\"Test with string\\\")\\n send_buf_size = 5\\n http_client._send_http_request(conn,\\n method,\\n uri,\\n headers,\\n body,\\n send_buf_size)\\n self.assertEqual(conn.putrequest_called, 1)\\n self.assertEqual(conn.putheader_called, 2)\\n self.assertEqual(conn.endheaders_called, 1)\\n self.assertEqual(conn.send_called, len(\\\"Test with string\\\") // 5 + 1)\\n self.assertEqual(conn.content, \\\"Test with string\\\")\\n self.assertEqual(conn.getresponse_called, 1)\",\n \"def test_parse_invalid_request(self):\\n request = self.factory.post(self.url, data={})\\n self.assertRaises(RequestParseError, self.parser.parse, request)\",\n \"def setUp(self):\\n h = self.MyTestHandler()\\n h.request = Request.blank('/rpc/')\\n h.response = Response()\\n self.handler = h\",\n \"def test_request_context_create(self, mock_rc_init):\\n mock_request = MagicMock()\\n rc = get_request_context(mock_request)\\n mock_rc_init.assert_called_with(mock_request)\\n self.assertIsInstance(rc, RequestContext)\",\n \"def __init__(self, **kwargs):\\n super(Request, self).__init__(**kwargs)\",\n \"def create_request(url, headers, attempts, request_type, data=None):\\n request_func = getattr(requests, request_type)\\n kwargs = {\\\"url\\\": url, \\\"headers\\\": headers}\\n if request_type == \\\"post\\\" or request_type == \\\"patch\\\":\\n kwargs[\\\"json\\\"] = data\\n try:\\n req = request_func(**kwargs)\\n status_code = req.status_code\\n time.sleep(1)\\n while status_code >= 400 and attempts < 5:\\n req = request_func(**kwargs)\\n status_code = req.status_code\\n attempts += 1\\n time.sleep(1)\\n return req\\n except Exception as e:\\n print(\\\"[ERROR] There was an error with the request, details:\\\")\\n print(e)\\n return None\",\n \"def setUp(self):\\n\\n c = Client()\\n self.response = c.get('/')\\n self.content = self.response.content\",\n \"def mock_request(auth_header):\\n request = HttpRequest()\\n request.META['HTTP_AUTHORIZATION'] = auth_header\\n return request\",\n \"def test_right_request_class(self):\\n environ = complete_environ(REQUEST_METHOD=\\\"GET\\\", PATH_INFO=\\\"/\\\")\\n\\n def start_response(status, response_headers):\\n pass\\n\\n self.handler(environ, start_response)\\n\\n ok_(isinstance(self.handler.request, DjangoWSGIRequest))\",\n \"def test_send_http_request_exception(self):\\n # test body is not sufficient\\n http_client = bce_http_client.BceHttpClient(None)\\n conn = MockHttpConnection()\\n method = http_methods.GET\\n uri = \\\"/unknown/unknown\\\"\\n headers = {\\\"Content-Length\\\": 100,\\n \\\"Content-Encoding\\\": \\\"utf8\\\",\\n }\\n body = MockInputStream(\\\"Test with string\\\")\\n send_buf_size = 5\\n self.assertRaises(BceClientError,\\n http_client._send_http_request,\\n conn,\\n method,\\n uri,\\n headers,\\n body,\\n send_buf_size)\\n self.assertEqual(conn.putrequest_called, 1)\\n self.assertEqual(conn.putheader_called, 2)\\n self.assertEqual(conn.endheaders_called, 1)\\n self.assertEqual(conn.send_called, len(\\\"Test with string\\\") // 5 + 1)\",\n \"def test_GET5(self):\\n r = requests.get(self.address + \\\"/carca\\\")\\n self.assertEqual(r.status_code, 400)\",\n \"def request(self, **request):\\n request = RequestFactory.request(self, **request)\\n handler = BaseHandler()\\n handler.load_middleware()\\n for middleware_method in handler._request_middleware:\\n if middleware_method(request):\\n raise Exception(\\\"Couldn't create request mock object - \\\"\\n \\\"request middleware returned a response\\\")\\n return request\",\n \"def open(self, *args, **kwargs):\\n assert_status = kwargs.pop('assert_status', None)\\n\\n if \\\"data\\\" in kwargs:\\n # Parse data\\n kwargs['data'] = json.dumps(kwargs['data'])\\n # Set header\\n kwargs['content_type'] = \\\"application/json\\\"\\n\\n # Add Fake Header if specified in context\\n try:\\n auth = g.fake_token\\n kwargs.setdefault('headers', {})['Authorization'] = auth\\n except (RuntimeError, AttributeError):\\n # RuntimeError: No g, AttributeError: key in g is missing\\n pass # No fake token to set\\n\\n # Send the actual request\\n response = super(TestClient, self).open(*args, **kwargs)\\n\\n if assert_status is not None:\\n assert response.status_code == assert_status, \\\\\\n response.get_data(as_text=True)\\n\\n return json.loads(response.get_data(as_text=True) or '{}')\",\n \"def __init__(self, requestor):\\n self.requestor = requestor\",\n \"def test_constructor_is_nt_so_all_args_work(self):\\n u1 = url.URL(\\n \\\"somedriver\\\",\\n \\\"user\\\",\\n \\\"secret\\\",\\n \\\"10.20.30.40\\\",\\n 1234,\\n \\\"DB\\\",\\n {\\\"key\\\": \\\"value\\\"},\\n )\\n eq_(\\n u1,\\n url.make_url(\\n \\\"somedriver://user:secret@10.20.30.40:1234/DB?key=value\\\"\\n ),\\n )\",\n \"def test_to_from_bytes(self):\\n self.assertIsInstance(\\n Request.create_from_bytes(Request().to_bytes()), Request)\",\n \"def request_factory(environ):\\n request = Request(environ)\\n if request.is_xhr:\\n request.response = Response()\\n request.response.headerlist = []\\n request.response.headerlist.extend(\\n (\\n ('Access-Control-Allow-Origin', '*'),\\n ('Access-Control-Allow-Credentials', 'true'),\\n ('Access-Control-Max-Age', 86400),\\n ('Content-Type', 'application/json')\\n )\\n )\\n return request\"\n]","isConversational":false},"negative_scores":{"kind":"list like","value":["0.69102174","0.6897086","0.67407787","0.67172885","0.668804","0.668527","0.66408455","0.66260934","0.65981567","0.6573342","0.65649724","0.6560071","0.6523077","0.651958","0.65011233","0.6492675","0.6482599","0.64548385","0.64541405","0.6449433","0.64455515","0.64262277","0.6408869","0.6404299","0.6394719","0.63805","0.63752073","0.6330565","0.6329536","0.6328581","0.63263947","0.6314394","0.6301223","0.6286272","0.6276972","0.62665385","0.6265779","0.6251561","0.6248305","0.6247503","0.6244876","0.6208964","0.620702","0.6197238","0.61917424","0.6190837","0.6176905","0.61747193","0.6168067","0.61667085","0.61560416","0.61491424","0.61491424","0.61491424","0.61469907","0.6135638","0.6127558","0.61224693","0.61139524","0.6108441","0.6103606","0.6103606","0.60951203","0.60939616","0.6081892","0.60746956","0.60744965","0.6072137","0.60640454","0.6062576","0.6058259","0.6057682","0.6057188","0.60502476","0.6047167","0.6043569","0.60423833","0.6022365","0.6016997","0.6016445","0.60112596","0.60105884","0.60051125","0.5998272","0.5996492","0.59962076","0.59917814","0.5990347","0.5972692","0.59718466","0.5964329","0.59638816","0.5962091","0.59615326","0.5956742","0.595621","0.59551495","0.5945766","0.5940918","0.59395427"],"string":"[\n \"0.69102174\",\n \"0.6897086\",\n \"0.67407787\",\n \"0.67172885\",\n \"0.668804\",\n \"0.668527\",\n \"0.66408455\",\n \"0.66260934\",\n \"0.65981567\",\n \"0.6573342\",\n \"0.65649724\",\n \"0.6560071\",\n \"0.6523077\",\n \"0.651958\",\n \"0.65011233\",\n \"0.6492675\",\n \"0.6482599\",\n \"0.64548385\",\n \"0.64541405\",\n \"0.6449433\",\n \"0.64455515\",\n \"0.64262277\",\n \"0.6408869\",\n \"0.6404299\",\n \"0.6394719\",\n \"0.63805\",\n \"0.63752073\",\n \"0.6330565\",\n \"0.6329536\",\n \"0.6328581\",\n \"0.63263947\",\n \"0.6314394\",\n \"0.6301223\",\n \"0.6286272\",\n \"0.6276972\",\n \"0.62665385\",\n \"0.6265779\",\n \"0.6251561\",\n \"0.6248305\",\n \"0.6247503\",\n \"0.6244876\",\n \"0.6208964\",\n \"0.620702\",\n \"0.6197238\",\n \"0.61917424\",\n \"0.6190837\",\n \"0.6176905\",\n \"0.61747193\",\n \"0.6168067\",\n \"0.61667085\",\n \"0.61560416\",\n \"0.61491424\",\n \"0.61491424\",\n \"0.61491424\",\n \"0.61469907\",\n \"0.6135638\",\n \"0.6127558\",\n \"0.61224693\",\n \"0.61139524\",\n \"0.6108441\",\n \"0.6103606\",\n \"0.6103606\",\n \"0.60951203\",\n \"0.60939616\",\n \"0.6081892\",\n \"0.60746956\",\n \"0.60744965\",\n \"0.6072137\",\n \"0.60640454\",\n \"0.6062576\",\n \"0.6058259\",\n \"0.6057682\",\n \"0.6057188\",\n \"0.60502476\",\n \"0.6047167\",\n \"0.6043569\",\n \"0.60423833\",\n \"0.6022365\",\n \"0.6016997\",\n \"0.6016445\",\n \"0.60112596\",\n \"0.60105884\",\n \"0.60051125\",\n \"0.5998272\",\n \"0.5996492\",\n \"0.59962076\",\n \"0.59917814\",\n \"0.5990347\",\n \"0.5972692\",\n \"0.59718466\",\n \"0.5964329\",\n \"0.59638816\",\n \"0.5962091\",\n \"0.59615326\",\n \"0.5956742\",\n \"0.595621\",\n \"0.59551495\",\n \"0.5945766\",\n \"0.5940918\",\n \"0.59395427\"\n]","isConversational":false},"document_score":{"kind":"string","value":"0.65178454"},"document_rank":{"kind":"string","value":"14"}}},{"rowIdx":22676,"cells":{"query":{"kind":"string","value":"Given a sparse matrix, set all pixels in missing (invalid) bins to 0."},"document":{"kind":"string","value":"def erase_missing(signal, valid_rows, valid_cols, sym_upper=True):\n if sym_upper and sp.issparse(signal):\n if np.any(valid_rows != valid_cols):\n raise ValueError(\n \"Valid rows and columns must be identical with sym_upper=True\"\n )\n if signal.shape[0] != signal.shape[1]:\n raise ValueError(\n \"Input matrix must be square when using sym_upper=True\"\n )\n # Make a boolean mask from good bins\n good_mask = np.isin(range(signal.shape[0]), valid_rows)\n # Set all pixels in a nondetectable bin to 0\n # For faster masking of bins, mask bins using dot product with an\n # identify matrix where bad bins have been masked on the diagonal\n # E.g. if removing the second bin (row and column):\n # 1 0 0 9 6 5 1 0 0 9 0 5\n # 0 0 0 X 6 8 7 X 0 0 0 = 0 0 0\n # 0 0 1 6 7 8 0 0 1 6 0 8\n mask_mat = sp.eye(signal.shape[0])\n mask_mat.data[0][~good_mask] = 0\n erased = mask_mat.dot(signal).dot(mask_mat)\n else:\n # Get a boolean array of missing (1) and valid (0) rows\n missing_rows = valid_to_missing(valid_rows, signal.shape[0])\n missing_cols = valid_to_missing(valid_cols, signal.shape[1])\n erased = signal.copy()\n erased[missing_rows, :] = 0\n erased[:, missing_cols] = 0\n\n return erased"},"metadata":{"kind":"string","value":"{\n \"objective\": {\n \"self\": [],\n \"paired\": [],\n \"triplet\": [\n [\n \"query\",\n \"document\",\n \"negatives\"\n ]\n ]\n }\n}"},"negatives":{"kind":"list like","value":["def setZeroes(self, matrix: List[List[int]]) -> None:\n m = len(matrix)\n n = len(matrix[0])\n rows = set()\n columns = set()\n for i in range(m):\n for j in range(n):\n if not matrix[i][j]:\n rows.add(i)\n columns.add(j)\n\n for i in rows:\n for j in range(n):\n matrix[i][j] = 0\n for j in columns:\n for i in range(m):\n matrix[i][j] = 0","def zero_matrix(matrix):\n rows = set()\n columns = set()\n m = len(matrix)\n n = len(matrix[0])\n for i in range(m):\n for j in range(n):\n if matrix[i][j] == 0:\n rows.add(i)\n columns.add(j)\n\n for i in range(m):\n for j in range(n):\n if i in rows or j in columns:\n matrix[i][j] = 0\n return matrix","def setZeroes(self, matrix: List[List[int]]) -> None:\n if not matrix:\n return\n row = set()\n col = set()\n rowl = len(matrix[0])\n for i, r in enumerate(matrix):\n for j, c in enumerate(r):\n if c == 0:\n row.add(i)\n col.add(j)\n \n for r in row:\n matrix[r] = [0]*rowl\n for j in col:\n for i in range(len(matrix)):\n matrix[i][j] = 0","def setZeroes(self, matrix: List[List[int]]) -> None:\n m = len(matrix)\n n = len(matrix[0])\n set_rows = set()\n set_cols = set()\n for i in range(m):\n for j in range(n):\n if matrix[i][j] == 0:\n set_rows.add(i)\n set_cols.add(j)\n \n while set_rows:\n temp = set_rows.pop()\n for j in range(n):\n matrix[temp][j] = 0\n \n while set_cols:\n temp = set_cols.pop()\n for i in range(m):\n matrix[i][temp] = 0","def setZeroes(self, matrix: List[List[int]]) -> None:\n m, n = len(matrix), len(matrix[0])\n zero_row, zero_col = set(), set()\n for row in range(m):\n for col in range(n):\n if matrix[row][col] == 0:\n if row not in zero_row:\n zero_row.add(row)\n if col not in zero_col:\n zero_col.add(col)\n for row in zero_row:\n for col in range(n):\n matrix[row][col] = 0\n for col in zero_col:\n for row in range(m):\n matrix[row][col] = 0","def setZeroes(self, matrix: List[List[int]]) -> None:\n m, n = len(matrix), len(matrix[0])\n row, col = [0 for i in range(m)], [0 for j in range(n)]\n for i in range(m):\n for j in range(n):\n if not matrix[i][j]:\n row[i] = col[j] = 1\n for i in range(m):\n if row[i]:\n for j in range(n):\n matrix[i][j] = 0\n for j in range(n):\n if col[j]:\n for i in range(m):\n matrix[i][j] = 0","def setZeroes(self, matrix: List[List[int]]) -> None:\n m = len(matrix)\n n = len(matrix[0])\n \n i = 0\n j = 0\n row_zero = set()\n col_zero = set()\n \n for i in range(m):\n for j in range(n):\n if matrix[i][j] == 0:\n row_zero.update({i})\n col_zero.update({j})\n \n ## update rows with zero\n for t in row_zero:\n matrix[t][0:n] = [0 for i in range(n)]\n \n ## update cols with zero\n for i in range(m):\n for c in col_zero:\n #print(i, c)\n matrix[i][c] = 0","def setZeroes(self, matrix: List[List[int]]) -> None:\r\n import copy\r\n m=len(matrix)\r\n n=len(matrix[0])\r\n m_copy=copy.deepcopy(matrix)\r\n for i in range(m):\r\n for j in range(n):\r\n if m_copy[i][j]==0:\r\n matrix[i]=[0]*n\r\n for x in range(m):\r\n matrix[x][j]=0","def setZeroes(self, matrix):\n for i in range(len(matrix)):\n for j in range(len(matrix[i])):\n if matrix[i][j] == 0 and (i, j) not in self.visited:\n for neighbor in self.setter(matrix, i, j):\n matrix[neighbor[0]][neighbor[1]] = 0\n self.visited.add((neighbor[0], neighbor[1]))\n print(matrix)","def setZeroes(self, matrix: List[List[int]]) -> None:\n row = set()\n column = set()\n \n m = len(matrix)\n n = len(matrix[0])\n \n for i in range(m):\n for j in range(n):\n if matrix[i][j] == 0:\n row.add(i)\n column.add(j)\n \n for i in range(m):\n for j in range(n):\n if i in row or j in column:\n matrix[i][j] = 0","def setZeroes(self, matrix) -> None:\n if not matrix: return\n h, w = len(matrix), len(matrix[0])\n col0 = False\n for i in range(h):\n if matrix[i][0] == 0:\n col0 = True # 第一列是否要全置为0\n for j in range(1, w): # 从第二列开始\n if matrix[i][j] == 0: # (i,j)为0\n matrix[0][j] = 0 # 第i行为0\n matrix[i][0] = 0 # 第j列为0\n for i in range(1, h):\n for j in range(1, w):\n if matrix[i][j] != 0 and (matrix[i][0] == 0 or matrix[0][j] == 0):\n matrix[i][j] = 0 # 需要置为0的位置\n if matrix[0][0] == 0: # 第一行是否要全置为0\n for j in range(w):\n matrix[0][j] = 0\n if col0: # 第一列是否要全置为0\n for i in range(h):\n matrix[i][0] = 0","def setZeroes(self, matrix: List[List[int]]) -> None:\n colsToZero = set()\n rowsToZero = set()\n for rowIdx, row in enumerate(matrix):\n for colIdx, num in enumerate(row): \n if num == 0: \n colsToZero.add(colIdx)\n rowsToZero.add(rowIdx)\n \n for col in colsToZero:\n self.writeZeroCol(col, matrix)\n for row in rowsToZero:\n self.writeZeroRow(row, matrix)","def setZeroes(self, matrix: List[List[int]]) -> None:\n m, n = len(matrix), len(matrix[0])\n temp = [[matrix[i][j] for j in range(n)] for i in range(m)]\n for i in range(m):\n for j in range(n):\n if not temp[i][j]:\n self.setZero(i, j, n, m, matrix)","def setZeroes(self, matrix) -> None:\n m, n = len(matrix), len(matrix[0])\n rows, cols = set(), set()\n # 获取0所在的位置对应的行和列\n for i in range(m):\n for j in range(n):\n if matrix[i][j] == 0:\n rows.add(i)\n cols.add(j)\n # 将该行和列的所有元素置为0\n for row in rows:\n for j in range(n):\n matrix[row][j] = 0\n for col in cols:\n for i in range(m):\n matrix[i][col] = 0","def zero_matrix(matrix):\n n = len(matrix)\n if n == 0:\n return True\n\n m = len(matrix[0])\n if m == 0:\n return True\n\n zero_rows = set()\n zero_columns = set()\n for i in range(n):\n for j in range(m):\n if matrix[i][j] == 0:\n zero_rows.add(i)\n zero_columns.add(j)\n \n for i in zero_rows:\n for j in range(m):\n matrix[i][j] = 0\n\n for i in range(n):\n for j in zero_columns:\n matrix[i][j] = 0\n\n return True","def set_zero(mn_matrix): \r\n \r\n row_flag = [0]*len(mn_matrix)\r\n column_flag = [0]*len(mn_matrix[0])\r\n for i in range(0, len(row_flag)):\r\n for j in range(0, len(column_flag)):\r\n if mn_matrix[i][j] == 0:\r\n row_flag[i] = 1\r\n column_flag[j] = 1\r\n \r\n for i in range(0, len(row_flag)):\r\n for j in range(0, len(column_flag)):\r\n if row_flag[i] == 1 or column_flag[j] == 1: \r\n mn_matrix[i][j] = 0","def setZeroes(self, matrix: List[List[int]]) -> None:\n rows, cols = [], []\n for i in range(len(matrix)):\n for j in range(len(matrix[i])):\n if matrix[i][j] == 0:\n rows.append(i)\n cols.append(j)\n for i in rows:\n for j in range(len(matrix[i])):\n matrix[i][j] = 0\n for j in cols:\n for i in range(len(matrix)):\n matrix[i][j] = 0\n\n return","def setZeroes(self, matrix: List[List[int]]) -> None:\n zero_columns = []\n zero_rows = []\n for i in range(len(matrix)):\n for j in range(len(matrix[0])):\n if matrix[i][j] == 0:\n zero_columns.append(j)\n zero_rows.append(i)\n for zero_column in zero_columns:\n for row in matrix:\n row[zero_column] = 0\n for zero_row in zero_rows:\n matrix[zero_row] = [0] * len(matrix[0])","def setZeroes(self, matrix: List[List[int]]) -> None:\n m, n = len(matrix), len(matrix[0])\n firstRow, firstCol = False, False\n for i in range(m):\n for j in range(n):\n if i == 0 and matrix[i][j] == 0:\n firstRow = True\n if j == 0 and matrix[i][j] == 0:\n firstCol = True\n if i != 0 and j != 0 and matrix[i][j] == 0:\n matrix[i][0] = 0\n matrix[0][j] = 0\n for i in range(1, m):\n for j in range(1, n):\n if matrix[i][0] == 0 or matrix[0][j] == 0:\n matrix[i][j] = 0\n if firstRow:\n for i in range(n):\n matrix[0][i] = 0\n if firstCol:\n for i in range(m):\n matrix[i][0] = 0","def setZeroes(self, matrix: List[List[int]]) -> None:\n new_matrix = [row.copy() for row in matrix]\n for i in range(len(matrix)):\n for j in range(len(matrix[0])):\n if matrix[i][j] == 0 and new_matrix[i][j] == 0:\n self.setZero(matrix, i, j)","def setZeroes(self, matrix: List[List[int]]) -> None:\n row_num, col_num = len(matrix), len(matrix[0])\n # 创建集合set()用于存放需要置零的行和列\n row_set, col_set = set(), set()\n for row in range(row_num):\n for col in range(col_num):\n if matrix[row][col]==0:\n row_set.add(row)\n col_set.add(col)\n # 将记录的行、列中的元素赋值为0\n # 再次遍历赋值\n for row in range(row_num):\n for col in range(col_num):\n if row in row_set or col in col_set:\n matrix[row][col] = 0\n # # 或者行列单独赋值均可\n # for row in row_set:\n # for col in range(col_num):\n # matrix[row][col] = 0\n # for col in col_set:\n # for row in range(row_num):\n # matrix[row][col] = 0","def setZeroes(self, matrix: List[List[int]]) -> None:\n m, n = len(matrix), len(matrix[0]) if matrix else 0\n \n row = [False for _ in range(m)]\n col = [False for _ in range(n)]\n \n for i in range(m):\n for j in range(n):\n if matrix[i][j] == 0:\n row[i] = col[j] = True\n \n for i in range(m):\n for j in range(n):\n if row[i] or col[j]:\n matrix[i][j] = 0","def setZeroes(self, matrix: List[List[int]]) -> None:\n \n m = len(matrix)\n n = len(matrix[0])\n \n i_zeros = []\n j_zeros = []\n \n for i in range(m): ## find the zeros\n for j in range(n):\n \n if matrix[i][j] == 0:\n i_zeros.append(i)\n j_zeros.append(j)\n \n for i in i_zeros:\n matrix[i] = [0]*n\n \n for j in j_zeros:\n for i in range(m):\n matrix[i][j] = 0","def zero_matrix(matrix):\n cols = []\n for row in range(0, len(matrix)):\n for col in range(0, len(matrix[row])):\n if matrix[row][col] == 0:\n cols.append(col)\n matrix[row] = [0 for _ in range(0, len(matrix[row]))]\n break\n for col in cols:\n for row in range(0, len(matrix)):\n matrix[row][col] = 0\n return matrix","def setZeroes(self, matrix: List[List[int]]) -> None:\n row = []\n col = []\n for i in range(len(matrix)):\n for j in range(len(matrix[i])):\n if matrix[i][j] == 0:\n col.append(j)\n row.append(i)\n\n for i in range(len(row)):\n self.col(matrix, col[i])\n for i in range(len(col)):\n self.row(matrix, row[i])","def setZeroes(self, matrix: List[List[int]]) -> None:\n zeros = []\n m, n = len(matrix), len(matrix[0])\n for i in range(m):\n for j in range(n):\n if matrix[i][j] == 0:\n zeros.append([i, j])\n for index in zeros:\n row = index[0]\n col = index[1]\n for r in range(m):\n matrix[r][col] = 0\n for c in range(n):\n matrix[row][c] = 0","def setZeroes(self, matrix: List[List[int]]) -> None:\n n = len(matrix)\n m = len(matrix[0])\n zeroRow = False\n zeroColumn = False\n for i in range(0,n):\n if matrix[i][0] == 0:\n zeroColumn = True\n break\n for j in range(0,m):\n if matrix[0][j] == 0:\n zeroRow = True\n break\n for i in range(1,n):\n for j in range(1,m):\n if matrix[i][j] == 0:\n matrix[0][j] = 0\n matrix[i][0] = 0\n for i in range(1,n):\n if matrix[i][0] == 0:\n for j in range(0,m):\n matrix[i][j] = 0\n for j in range(1,m):\n if matrix[0][j] == 0:\n for i in range(0,n):\n matrix[i][j] = 0\n if zeroColumn:\n for i in range(0,n):\n matrix[i][0] = 0\n if zeroRow:\n for i in range(0,m):\n matrix[0][i] = 0","def setZeroes(self, matrix: List[List[int]]) -> None:\n m = len(matrix)\n n = len(matrix[0])\n # replace zeros by None\n for i in range(m):\n for j in range(n):\n if matrix[i][j] == 0:\n matrix[i][j] = None\n # do the rows\n for i, row in enumerate(matrix):\n if None in row:\n for j, x in enumerate(row):\n if x is not None:\n matrix[i][j] = 0\n # do the columns\n for j in range(n):\n # false if the column had no zeros\n z = False\n for i in range(m):\n if matrix[i][j] is None:\n z = True\n break\n if z:\n for i in range(m):\n if matrix[i][j] is not None:\n matrix[i][j] = 0\n # undo replace zeros by None\n for i in range(m):\n for j in range(n):\n if matrix[i][j] is None:\n matrix[i][j] = 0","def setZeroes(self, matrix: List[List[int]]) -> None:\n m, n = len(matrix), len(matrix[0])\n zero_row, zero_col = False, False\n\n for i in range(m):\n for j in range(n):\n if matrix[i][j] == 0:\n matrix[i][0] = matrix[0][j] = 0\n zero_row = True if i == 0 else zero_row\n zero_col = True if j == 0 else zero_col\n\n for j in range(1, n):\n if matrix[0][j] == 0:\n for i in range(1, m):\n matrix[i][j] = 0\n for i in range(1, m):\n if matrix[i][0] == 0:\n for j in range(1, n):\n matrix[i][j] = 0\n\n if zero_row:\n for j in range(n):\n matrix[0][j] = 0\n if zero_col:\n for i in range(m):\n matrix[i][0] = 0\n return","def setZeroes(self, matrix: List[List[int]]) -> None:\n m = len(matrix)\n if m == 0:\n return\n n = len(matrix[0])\n\n row_zero = False\n for i in range(m):\n if matrix[i][0] == 0:\n row_zero = True\n col_zero = False\n for j in range(n):\n if matrix[0][j] == 0:\n col_zero = True\n\n for i in range(1, m):\n for j in range(1, n):\n if matrix[i][j] == 0:\n matrix[i][0] = 0\n matrix[0][j] = 0\n\n for i in range(1, m):\n if matrix[i][0] == 0:\n for j in range(1, n):\n matrix[i][j] = 0\n\n for j in range(1, n):\n if matrix[0][j] == 0:\n for i in range(1, m):\n matrix[i][j] = 0\n\n if col_zero:\n for j in range(n):\n matrix[0][j] = 0\n if row_zero:\n for i in range(m):\n matrix[i][0] = 0","def setZeroes(self, matrix: List[List[int]]) -> None:\n \n m = len(matrix)\n n = len(matrix[0])\n \n firstrowhaszero = False\n firstcolumnhaszero = False\n for i in range(m):\n if matrix[i][0] == 0:\n firstcolumnhaszero = True\n break\n for j in range(n):\n if matrix[0][j] == 0:\n firstrowhaszero = True\n break\n \n for i in range(1, m):\n for j in range(1, n):\n if matrix[i][j] == 0:\n matrix[i][0] = 0\n matrix[0][j] = 0\n \n for i in range(1, m):\n for j in range(1, n):\n if matrix[i][0] == 0 or matrix[0][j] == 0:\n matrix[i][j] = 0\n \n if firstcolumnhaszero:\n for i in range(m):\n matrix[i][0] = 0\n \n if firstrowhaszero:\n for j in range(n):\n matrix[0][j] = 0","def setZeroes(self, matrix: List[List[int]]) -> None:\n first_col = False\n for i in range(len(matrix)):\n for j in range(len(matrix[0])):\n if matrix[i][j] == 0:\n matrix[i][0] = 0\n if j == 0:\n first_col = True\n else:\n matrix[0][j] = 0\n for i in range(1, len(matrix[0])):\n if matrix[0][i] == 0:\n for j in range(len(matrix)):\n matrix[j][i] = 0\n if matrix[0][0] == 0:\n matrix[0] = [0 for x in range(len(matrix[0]))]\n for i in range(1, len(matrix)):\n if matrix[i][0] == 0:\n for j in range(len(matrix[0])):\n matrix[i][j] = 0\n if first_col:\n for i in range(len(matrix)):\n matrix[i][0] = 0","def setZeroes(self, matrix: List[List[int]]) -> None:\n\n row = len(matrix)\n column = len(matrix[0])\n\n for i in range(row):\n for j in range(column):\n if matrix[i][j] == 0:\n for r in range(row):\n if matrix[r][j] != 0:\n matrix[r][j] = 'c'\n for c in range(column):\n if matrix[i][c] != 0:\n matrix[i][c] = 'c'\n\n for i in range(row):\n for j in range(column):\n if matrix[i][j] == 'c':\n matrix[i][j] = 0","def setZeroes(self, matrix: List[List[int]]) -> None:\r\n \r\n rows, cols = len(matrix), len(matrix[0])\r\n \r\n #This solution has been done in constant space\r\n #using first row and first column for deciding which rows and columns\r\n #should be zero\r\n \r\n #using one extra variable for first row\r\n #because first cell is common in first row and column\r\n first_row = 1\r\n \r\n for i in range(rows):\r\n for j in range(cols):\r\n if matrix[i][j]==0:\r\n #for column\r\n matrix[0][j] = 0\r\n \r\n #for row\r\n if i==0:\r\n first_row = 0\r\n else:\r\n matrix[i][0] = 0\r\n \r\n #checking for rows except first row\r\n for i in range(1,rows):\r\n if matrix[i][0] == 0:\r\n for j in range(cols):\r\n matrix[i][j] = 0\r\n \r\n #checking for columns except first column\r\n for j in range(1, cols):\r\n if matrix[0][j] == 0:\r\n for i in range(rows):\r\n matrix[i][j] = 0\r\n \r\n #for first column\r\n if matrix[0][0] == 0:\r\n for i in range(rows):\r\n matrix[i][0] = 0\r\n \r\n #for first row\r\n if first_row == 0:\r\n for j in range(cols):\r\n matrix[0][j] = 0\r\n \r\n #print(matrix)","def setZeroes(self, matrix: List[List[int]]) -> None:\n m, n = len(matrix), len(matrix[0])\n\n col_0_flag = any(matrix[i][0] == 0 for i in range(m))\n row_0_flag = any(matrix[0][i] == 0 for i in range(n))\n\n # 第 0 列， 第 0 行可以作为 [i,j] 的指示存储\n # 例如，如果 [i,j] 为 0， 那么 [0, j], [i, 0] 可以置 0 \n # 再次遍历矩阵，通过判断 [0, j], [i, 0] 去把该行，该列置 0\n # 这样的话就不需要一个额外的矩阵去 for i,j 遍历是否为 0 了\n\n # 注意是从 1 开始的。 \n for i in range(1, m):\n for j in range(1, n):\n if matrix[i][j] == 0:\n matrix[i][0] = matrix[0][j] = 0 \n\n for i in range(1, m):\n for j in range(1, n):\n if matrix[i][0] == 0 or matrix[0][j] == 0:\n matrix[i][j] = 0\n\n if col_0_flag:\n for i in range(m):\n matrix[i][0] = 0\n\n if row_0_flag:\n for j in range(n):\n matrix[0][j] = 0","def setZeroes(matrix):\r\n \r\n #An average O(n^2) time traversal solution with memoization\r\n #for each 0 we encounter, we update the entire row and column to 0s, but on the condition that the row/column has not been updated yet\r\n \r\n row_cache = {}\r\n column_cache = {}\r\n \r\n for r in range(0,rows := len(matrix)):\r\n for c in range(0,cols := len(matrix[0])):\r\n \r\n if matrix[r][c] == 0:\r\n \r\n if not row_cache.get(r):\r\n for i in range(0,cols):\r\n if matrix[r][i] != 0:\r\n matrix[r][i] = '0' #we use strings so we only consider the initial 0s\r\n row_cache[r] = True\r\n \r\n if not column_cache.get(c):\r\n for i in range(0,rows):\r\n if matrix[i][c] != 0:\r\n matrix[i][c] = '0'\r\n column_cache[c] = True\r\n return","def setZeroes(self, matrix: List[List[int]]) -> None:\n m = len(matrix)\n n = len(matrix[0])\n \n row_1st_0 = False\n col_1st_0 = False\n \n ## check whether 1st row and 1st col contains 0\n for i in range(n):\n if matrix[0][i] == 0:\n row_1st_0 = True\n break\n for j in range(m):\n if matrix[j][0] == 0:\n col_1st_0 = True\n break\n \n ## check row 1:m and col 1:n, store the 0 indicator to 1st row and 1st col\n for i in range(1, m):\n for j in range(1, n):\n if matrix[i][j] == 0:\n matrix[i][0] = 0\n matrix[0][j] = 0\n \n ## update cols to zero except 1st row\n for i in range(1, n):\n if matrix[0][i] == 0:\n for r in range(1, m):\n matrix[r][i] = 0\n \n ## update rows to zero except 1st col\n for j in range(1, m):\n if matrix[j][0] == 0:\n matrix[j][1:n] = [0 for i in range(n-1)]\n \n ## update 1st row and 1st col to 0\n if row_1st_0:\n matrix[0][0:n] = [0 for i in range(n)]\n if col_1st_0:\n for r in range(m):\n matrix[r][0] = 0","def setZeroes(self, matrix: List[List[int]]) -> None:\n m, n = len(matrix), len(matrix[0]) if matrix else 0\n \n # Use first row to store zero-info for each col\n # Use first col to store zero-info for each row\n # matrix[0][0] will store col info\n \n is_first_row = any(x == 0 for x in matrix[0])\n \n for i in range(1, m):\n for j in range(n):\n if matrix[i][j] == 0:\n matrix[0][j] = matrix[i][0] = 0\n \n for i in range(1, m):\n for j in range(1, n):\n if matrix[i][0] * matrix[0][j] == 0:\n matrix[i][j] = 0\n \n if matrix[0][0] == 0:\n for i in range(1, m):\n matrix[i][0] = 0\n \n if is_first_row:\n for j in range(n):\n matrix[0][j] = 0","def setZeroes(self, matrix: List[List[int]]) -> None:\n # 把第一行，第一列当作标志位\n row_flag = False\n col_flag = False # 防止第一行第一列无效\n row = len(matrix)\n col = len(matrix[0]) # 这种表征\n for j in range(col):\n if matrix[0][j] == 0:\n row_flag = True\n break\n for i in range(row):\n if matrix[i][0] == 0:\n col_flag = True\n break\n for i in range(1,row):\n for j in range(1,col):\n if matrix[i][j] == 0:\n matrix[i][0] = matrix[0][j] = 0\n # 检查,第二遍扫描\n for i in range(1,row):\n for j in range(1,col):\n if matrix[i][0]==0 or matrix[0][j] ==0:\n matrix[i][j] = 0\n # 防止标志位本身也是\n if row_flag:\n for j in range(col):\n matrix[0][j] = 0\n if col_flag:\n for i in range(row):\n matrix[i][0] = 0","def zero_matrix(matrix):\n if not matrix:\n return\n\n n_rows = len(matrix)\n n_cols = len(matrix[0])\n\n # First row and first column of the matrix are used to store row and columns to nullify.\n # At the beginning we determine if the first row and/or first column contain a zero.\n first_row_zero = False\n first_col_zero = False\n for col in range(n_cols):\n if not matrix[0][col]:\n first_row_zero = True\n break\n for row in range(n_rows):\n if not matrix[row][0]:\n first_col_zero = True\n break\n\n # Then go through the rest of the matrix and determine which rows and columns must be zeroed.\n for row in range(1, n_rows):\n for col in range(1, n_cols):\n if not matrix[row][col]:\n matrix[0][col] = 0\n matrix[row][0] = 0\n\n # Go through the rest of the matrix again and insert zeros where needed.\n for row in range(1, n_rows):\n for col in range(1, n_cols):\n if matrix[0][col] == 0 or matrix[row][0] == 0:\n matrix[row][col] = 0\n\n # Nullify first row and column if needed.\n if first_row_zero:\n for col in range(n_cols):\n matrix[0][col] = 0\n if first_col_zero:\n for row in range(n_rows):\n matrix[row][0] = 0","def mask_sparse(self, threshold=10):\n self.MaskPrefix = 's' + self.MaskPrefix\n print('Masking pixels that do not have at least {0} coherent values'.format(threshold))\n # each pixel assigned an integer corresponding to # of igrams where coherent\n # NOTE: save coverage map if it doesn't exist already\n coverage = self.get_coverage()\n sparse = ma.masked_less(coverage, threshold)\n for ig in self.Set:\n igram = self.load_ma(ig)\n igram[sparse.mask] = ma.masked\n self.save_ma(ig, igram)\n print('Done')","def make_all_zero(curr_clusters, k, num_of_cords):\r\n for i in range(k):\r\n for j in range(num_of_cords):\r\n curr_clusters[i][j] = 0","def replace_zeros(self):\n\n min_c = np.array(self.map[self.column])\n self.map.loc[self.map[self.column]==0, self.column] = np.min(min_c[np.nonzero(min_c)])","def _zero(array):\n a = array.ravel()\n for i in numba.prange(len(a)):\n a[i] = 0\n return True","def zero_blind_range(data):\n try:\n start_i = data['first_data_bin']\n except:\n start_i = 0\n data['data'][...,:start_i] = 0.0","def updateEmptiesSet(self):\n self.emptiesSet = []\n for i in self.Range:\n if self.get_cell(i) == 0:\n self.emptiesSet.append(i)","def ignore_biasbn(directions):\n for d in directions:\n if d.dim() <= 1:\n d.fill_(0)","def applymask(self,mask):\n self.spec[mask==0]=np.nan","def pfd_clear(n):\n\tglobal matrix\n\tr = []\n\tfor i in xrange(size[0]):\n\t\tif matrix[i][n] == 1:\n\t\t\tmatrix[i][n] = 0\n\t\t\tr.append(i)\n\treturn r","def clean(self):\n self.board_values = np.zeros((self.size, self.size))\n self.tiles_taken[:, :] = False","def makesparse(matrix):\n n = matrix[0].size\n elements = []\n for i in range(n):\n for j in range(n):\n if matrix[i][j] != 0 :\n temp = MatrixElement(i, j, matrix[i][j])\n elements.append(temp)\n return SparseMatrix(n, elements)","def negM(M):\r\n M[np.where(M > 0)] = 0\r\n return M","def filter_zeros(X):\n\tnoNonzeros = np.count_nonzero(X, axis=1)\n\tmask = np.where(noNonzeros > 0)\n\treturn X[mask[0], :]","def nozero(arr):\n vals=sorted(list(set(np.array(arr).flatten())))\n if vals[0]<0:\n print(\"correcting for div/zero by replacing 0 with\",vals[1])\n arr[arr==0]=vals[1]\n return arr","def trim_zeros(x):\n assert len(x.shape) == 2\n return x[~np.all(x == 0, axis=1)]","def test_normalize_matrix_with_only_zeros(self):\n input_matrix = [\n [0, 0],\n [0, 0]\n ]\n\n expected = [\n [0, 0],\n [0, 0]\n ]\n\n result = self.summarizer.normalize_matrix(input_matrix)\n\n self.assertEqual(expected, result)","def clear_exclude_bits(self):\n self.bitcell_array.init_graph_params()","def reset_mask(self):\n\n self.mask = np.ones(self.dispersion.shape, dtype=bool)","def solution2(array):\n if not array:\n return\n\n n_rows = len(array)\n n_cols = len(array[0])\n\n # Since we are using first row and column as space to mark rows and columns to be zeroed,\n # first check whether they have any zeros or not\n first_row_zero = False\n first_column_zero = False\n \n for c in range(n_cols):\n if not array[0][c]:\n first_row_zero=True\n \n for r in range(n_rows):\n if not array[r][0]:\n first_column_zero = True\n\n # Now start from 1,1 and check for 0. If 0 is found in certain row and column, mark first row\n # and column\n for r in range(1, n_rows):\n for c in range(1, n_cols):\n if not array[r][c]:\n array[0][c] = 0\n array[r][0] = 0\n\n # Now iterate through entire matrix again starting from 1,1 and set element to zero if \n # first row or column for that element is zero\n for r in range(1, n_rows):\n for c in range(1, n_cols):\n if array[0][c]==0 or array[r][0]==0:\n array[r][c] = 0\n\n # Go back to the first row and column.\n if first_row_zero:\n for c in range(n_cols):\n array[0][c] = 0\n if first_column_zero:\n for r in range(n_rows):\n array[r][0] = 0\n\n return array","def compress_csr(self):\n _, unique, indices = np.unique(\n self.m*self.rows + self.cols,\n return_index=True, return_inverse=True)\n self.rows = self.rows[unique]\n self.cols = self.cols[unique]\n self.vals = np.bincount(indices, weights=self.vals)","def ensure_sparse_cols(self,max_density,remove_lowest=True):\n if max_density >= 1:\n max_nnz = int(max_density)\n else:\n max_nnz = int(max_density*self.shape[0])\n for j in range(self.shape[1]):\n col = self.fast_get_col(j)\n excess = col.nnz - max_nnz\n if excess > 0:\n if remove_lowest:\n zero_entries = np.argsort(col.data)[:excess]\n else:\n zero_entries = random.sample(range(col.nnz),excess)\n col.data[zero_entries] = 0\n self.fast_update_col(j,col.data)","def trim_zeros(array):\n multislice = []\n for i in range(array.ndim):\n sum_axes = tuple(j for j in range(array.ndim) if j is not i)\n edges = np.where(np.sum(array, axis=sum_axes) > 0)\n if edges[0].size == 0:\n return np.array([], dtype=array.dtype)\n low = edges[0][0]\n high = edges[0][-1]\n multislice.append(slice(low, high+1, 1))\n return array[tuple(multislice)]","def zero_negative_weights(self):\n for k in range(len(self)):\n self[k] *= 0 if self[k] < 0 else 1\n self.finalized = True\n return self","def test_nullspace_blocked(input_matrix):\n mat = sp.Matrix(input_matrix)\n assert sorted(mat.nullspace()) == sorted(nullspace_blocked(mat))","def faca_zero(self):\n n = self.n\n\n for i in range(n):\n if comozero(self[i]):\n self[i] = 0\n\n return self","def convert_fill_zeros_like(g, op, block):\n\n x = g.get_node(op.input(\"X\")[0])\n dtype = op.attr(\"dtype\")\n dtype = _convert_dtype_value(dtype)\n value = _expr.const(0, dtype=dtype)\n out = _op.transform.full_like(x, value).astype(dtype)\n g.add_node(op.output(\"Out\")[0], out)","def remove_bad_cells(self, *dims):\n ranges = [DimRange(d, 0, np.inf) for d in dims]\n return self.gate(*ranges)","def remove_blank_pixels(self,pixels,non_zero=None):\n self.uni2pix= np.unique(pixels).astype(int)\n self.pix2uni = {u:k for k,u in enumerate(self.uni2pix)}\n\n gb, gl = hp.pix2ang(self.nside, self.uni2pix)\n\n self.npix = self.uni2pix.size\n if isinstance(non_zero,type(None)):\n non_zero = np.where(self.wei != 0)[0]\n\n self.output = self.output[self.uni2pix]\n self.sigwei = self.sigwei[self.uni2pix]\n self.wei = self.wei[self.uni2pix]\n\n print('SIZE CHECK', self.wei.size, self.npix)","def clean_zeros(data, cpy = 0):\r\n \r\n if cpy:\r\n edited_data = data.copy()\r\n else:\r\n edited_data = data\r\n \r\n \r\n # run through each stream \r\n for stream in range(edited_data.shape[1]):\r\n \r\n # Get zeroes indexs\r\n zeros_idx = edited_data[:,stream] == 0\r\n for idx, val in enumerate(zeros_idx):\r\n if val:\r\n # Replace with previous value\r\n edited_data[idx,stream] = edited_data[idx-1,stream]\r\n\r\n return edited_data","def clear(self):\n\t\tself.n = 0\n\t\tself.bv = BitVector.BitVector(size = self.m)","def make_missing_mask(\n shape, valid_rows, valid_cols, max_dist=None, sym_upper=False\n):\n # Error if the matrix upper symmetric but shape is rectangle or missing\n # rows and cols are different\n sm, sn = shape\n if sym_upper and (sm != sn or len(valid_rows) != len(valid_cols)):\n raise ValueError(\"Rectangular matrices cannot be upper symmetric\")\n\n # Get a boolean array of missing (1) and valid (0) rows\n missing_rows = valid_to_missing(valid_rows, sm)\n # When matrix is sym., rows and cols are synonym, no need to compute 2x\n if sym_upper:\n missing_cols = missing_rows\n else:\n missing_cols = valid_to_missing(valid_cols, sn)\n # If upper sym., fill only upper diag up to max_dist.\n # E. g. with bins 1 and 3 missing\n # and a max_dist of 1:\n # 0 1 0 0 0\n # 0 1 1 0 0\n # 0 0 0 1 0\n # 0 0 0 1 1\n # 0 0 0 0 0\n # For each missing bin, mask is apply 1 pixel upwards and 1 to the right\n # to fill only the upper triangle up to max_dist\n if sym_upper:\n # If no max dist has been specified, fill the whole upper triangle\n if max_dist is None:\n max_dist = min(shape)\n # Generate matrix of distance shifts by row.\n # Shape is len(missing_rows) x (max_dist + 1)\n # e.g.: 2 missing rows and max dist of 1\n # 0 0\n # 1 1\n row_shifts = np.tile(\n np.array(range(max_dist + 1)), (len(missing_rows), 1)\n ).T\n # Compute row positions upwards to diagonal by subtracting missing rows\n # to the shifts. Following the previous example, if missing rows are\n # bins 1 and 3:\n # 1 3\n # 0 2\n rows_before = (missing_rows - row_shifts).flatten(\"F\")\n # looking at pixels up from the bins, cols remain the same:\n # 1 3\n # 1 3\n cols_before = np.repeat(missing_rows, max_dist+1)\n # Compute col position to the right until diagonal by adding the shift\n # Note: upper symmetric, so row_shifts = col_shift_\n # 1 3\n # 2 4\n cols_after = (missing_cols + row_shifts).flatten(\"F\")\n # This time, rows remain constant since we are computing positions to\n # the right\n rows_after = np.repeat(missing_cols, max_dist+1)\n # Combine positions to the right and upwards\n rows = np.concatenate([rows_before, rows_after])\n cols = np.concatenate([cols_before, cols_after])\n data = np.ones(rows.shape, dtype=bool)\n # Remove entries where rows or cols are negative or larger than shape\n valid = (cols < sm) & (cols >= 0) & (rows < sm) & (rows >= 0)\n # Build mask mat with miss bins up to max scan dist in upper triangle\n mask = sp.coo_matrix(\n (data[valid], (rows[valid], cols[valid])), shape=shape, dtype=bool\n ).tocsr()\n else:\n mask = sp.csr_matrix(shape, dtype=bool)\n mask[missing_rows, :] = 1\n mask[:, missing_cols] = 1\n\n return mask","def zero_like(matrix):\n dtype = numpy.int_ if is_integer(matrix) else numpy.float_\n return numpy.zeros(matrix.shape, dtype=dtype)","def remove_nans(coords):\n s = np.apply_along_axis(sum,1,np.isnan(coords[1])) == 0\n coords[0] = (np.asarray(coords[0])[s]).tolist()\n coords[1] = coords[1][s,:]","def _unmap(data, count, inds, fill=0):\r\n if len(data.shape) == 1:\r\n ret = np.empty((count,), dtype=np.float32)\r\n ret.fill(fill)\r\n ret[inds] = data\r\n else:\r\n ret = np.empty((count,) + data.shape[1:], dtype=np.float32)\r\n ret.fill(fill)\r\n ret[inds, :] = data\r\n return ret","def posM(M):\r\n M[np.where(M < 0)] = 0\r\n return M","def set_step_zero(self):\n self.gcam_landmatrix, self.ixr_ixm_ixg = self.cst.apply_constraints_zero()","def remove_dbledge(img):\n\t(ny,nx) = np.shape(img)\n\tfor i in range(nx):\n\t\tidx = np.array(np.nonzero(img[:,i]))\n\t\tif np.size(idx) == 0:\n\t\t\tcontinue\n\t\tidx = idx[0][-1]\n\t\timg[idx-1::-1,i] = 0\n\treturn img","def _unmap(data, count, inds, fill=0):\n if len(data.shape) == 1:\n ret = np.empty((count,), dtype=np.float32)\n ret.fill(fill)\n ret[inds] = data\n else:\n ret = np.empty((count,) + data.shape[1:], dtype=np.float32)\n ret.fill(fill)\n ret[inds, :] = data\n return ret","def zero_to_nodata(base_raster):\n target_raster = base_raster.copy()\n target_raster[target_raster == 0] = _IC_NODATA\n return target_raster","def remove_blanks(mat):\n ids = []\n for idx, row in enumerate(mat):\n if not 1 in row:\n ids.append(idx)\n mat = np.delete(mat, ids, 0)\n mat = np.delete(mat, ids, 1)\n return mat","def zeros_matrix(self, rows, cols):\r\n M = []\r\n while len(M) < rows:\r\n M.append([])\r\n while len(M[-1]) < cols:\r\n M[-1].append(0.0)\r\n\r\n return M","def removeInfinities(mat):\n isFinite = np.all(np.isfinite(mat))\n \n if not isFinite:\n nrow, ncol = mat.shape\n matCopy = mat.copy()\n matReshaped = matCopy.reshape(-1)\n minVal = np.nanmin(matReshaped[matReshaped != -np.inf])\n maxVal = max(matReshaped)\n \n #count infinities and get their indicies\n #countInfinites(matReshaped) \n \n #replace all infinities with nan\n for i in range(0,len(matReshaped)):\n if matReshaped[i] in [-np.inf, np.inf]:\n matReshaped[i] = minVal\n '''\n #get average of matrix values and replace the nan with averages\n def averageVal(array):\n total = 0\n count = 0\n for elmt in array:\n if elmt != -90001:\n total+=elmt\n count+=1\n return total/count\n\n average = averageVal(matReshaped)\n\n #replace nan with average\n for i in range(0,len(matReshaped)):\n if matReshaped[i] == -90001:\n matReshaped[i] = average\n '''\n #catch any potential errors \n if np.any(np.isinf(matReshaped)):\n raise ValueError\n\n return matReshaped.reshape((nrow, ncol))\n else:\n return mat","def _unmap(data, count, inds, fill=0):\n if len(data.shape) == 1:\n ret = np.empty((count, ), dtype='float32')\n ret.fill(fill)\n ret[inds] = data\n else:\n ret = np.empty((count, ) + data.shape[1:], dtype='float32')\n ret.fill(fill)\n ret[inds, :] = data\n return ret","def _unmap(data, count, inds, fill=0):\n if len(data.shape) == 1:\n ret = np.empty((count, ), dtype=np.float32)\n ret.fill(fill)\n ret[inds] = data\n else:\n ret = np.empty((count, ) + data.shape[1:], dtype=np.float32)\n ret.fill(fill)\n ret[inds, :] = data\n return ret","def zero_mask(self):\n accum = 0\n for i in range(self.data.itemsize):\n accum += (0x55 << (i << 3))\n return accum","def _empty_clusters(clusters):\n for clst in clusters:\n clst.points = []","def clear(self):\n for y in range(len(self.matrix)):\n for x in range(len(self.matrix[0])):\n self.matrix[y-1][x-1] = (0,0,0)","def zeros_like(self):\n raise NotImplementedError","def shrink_mask(self):\n m = self._mask\n if m.ndim and not m.any():\n self._mask = nomask\n return self","def unmap(data, count, inds, fill=0):\n if data.dim() == 1:\n ret = data.new_full((count,), fill)\n ret[inds.type(torch.bool)] = data\n else:\n new_size = (count,) + data.size()[1:]\n ret = data.new_full(new_size, fill)\n ret[inds.type(torch.bool), :] = data\n return ret","def set_empty(self):\n pattern = [[0,0,0,0],\n [0,0,0,0],\n [0,0,0,0],\n [0,0,0,0]]\n self.set_pattern(pattern)","def reset(self):\r\n # replace with your code\r\n for row in range(0, self._grid_height):\r\n for col in range(0, self._grid_width):\r\n self._grid_tile[row][col] = 0\r\n # at this step, all cells should be available\r\n self.new_tile()\r\n self.new_tile()","def unmap(data, count, inds, fill=0):\n if data.dim() == 1:\n ret = data.new_full((count,), fill)\n ret[inds.type(torch.bool)] = data\n else:\n new_size = (count,) + data.size()[1:]\n ret = data.new_full(new_size, fill)\n ret[inds.type(torch.bool), :] = data\n return ret","def fill_blind_pores(im):\n holes = find_disconnected_voxels(im)\n im[holes] = False\n return im","def _unmap(data, count, inds, fill=0):\r\n if len(data.shape) == 1:\r\n ret = np.empty((count,), dtype=data.dtype)\r\n ret.fill(fill)\r\n ret[inds] = data\r\n else:\r\n ret = np.empty((count,) + data.shape[1:], dtype=data.dtype)\r\n ret.fill(fill)\r\n ret[inds, :] = data\r\n return ret","def zeroes(height, width):\n g = [[0.0 for _ in range(width)] for __ in range(height)]\n return Matrix(g)","def zero_safe(self, hist) :\n if hist.Integral() == 0 :\n print \"Warning: histogram \", hist.GetName(), \"is empty!\"\n hist.SetBinContent(1, 10e-6)\n return hist","def filter_zero(points_np):\n # TODO replace with cython or numba\n A = points_np[:, 0]\n B = points_np[:, 1]\n C = points_np[:, 2]\n t0 = time.time()\n mask = A == 0.0\n mask = mask & (B == 0.0)\n mask = mask & (C == 0.0)\n points_np = points_np[~mask]\n # print(f\"Filtering Took {(time.time() - t0) * 1000:.1f} ms\")\n return points_np","def normalize(self,matrix):\n for i in range(self.N):\n matrix[self.N-1][i] = 0\n for i in range(self.n):\n matrix[self.N - 1][self.index(i,i)] = 1\n return matrix","def nonzeros(m, row):\n for index in range(m.indptr[row], m.indptr[row+1]):\n yield m.indices[index], m.data[index]"],"string":"[\n \"def setZeroes(self, matrix: List[List[int]]) -> None:\\n m = len(matrix)\\n n = len(matrix[0])\\n rows = set()\\n columns = set()\\n for i in range(m):\\n for j in range(n):\\n if not matrix[i][j]:\\n rows.add(i)\\n columns.add(j)\\n\\n for i in rows:\\n for j in range(n):\\n matrix[i][j] = 0\\n for j in columns:\\n for i in range(m):\\n matrix[i][j] = 0\",\n \"def zero_matrix(matrix):\\n rows = set()\\n columns = set()\\n m = len(matrix)\\n n = len(matrix[0])\\n for i in range(m):\\n for j in range(n):\\n if matrix[i][j] == 0:\\n rows.add(i)\\n columns.add(j)\\n\\n for i in range(m):\\n for j in range(n):\\n if i in rows or j in columns:\\n matrix[i][j] = 0\\n return matrix\",\n \"def setZeroes(self, matrix: List[List[int]]) -> None:\\n if not matrix:\\n return\\n row = set()\\n col = set()\\n rowl = len(matrix[0])\\n for i, r in enumerate(matrix):\\n for j, c in enumerate(r):\\n if c == 0:\\n row.add(i)\\n col.add(j)\\n \\n for r in row:\\n matrix[r] = [0]*rowl\\n for j in col:\\n for i in range(len(matrix)):\\n matrix[i][j] = 0\",\n \"def setZeroes(self, matrix: List[List[int]]) -> None:\\n m = len(matrix)\\n n = len(matrix[0])\\n set_rows = set()\\n set_cols = set()\\n for i in range(m):\\n for j in range(n):\\n if matrix[i][j] == 0:\\n set_rows.add(i)\\n set_cols.add(j)\\n \\n while set_rows:\\n temp = set_rows.pop()\\n for j in range(n):\\n matrix[temp][j] = 0\\n \\n while set_cols:\\n temp = set_cols.pop()\\n for i in range(m):\\n matrix[i][temp] = 0\",\n \"def setZeroes(self, matrix: List[List[int]]) -> None:\\n m, n = len(matrix), len(matrix[0])\\n zero_row, zero_col = set(), set()\\n for row in range(m):\\n for col in range(n):\\n if matrix[row][col] == 0:\\n if row not in zero_row:\\n zero_row.add(row)\\n if col not in zero_col:\\n zero_col.add(col)\\n for row in zero_row:\\n for col in range(n):\\n matrix[row][col] = 0\\n for col in zero_col:\\n for row in range(m):\\n matrix[row][col] = 0\",\n \"def setZeroes(self, matrix: List[List[int]]) -> None:\\n m, n = len(matrix), len(matrix[0])\\n row, col = [0 for i in range(m)], [0 for j in range(n)]\\n for i in range(m):\\n for j in range(n):\\n if not matrix[i][j]:\\n row[i] = col[j] = 1\\n for i in range(m):\\n if row[i]:\\n for j in range(n):\\n matrix[i][j] = 0\\n for j in range(n):\\n if col[j]:\\n for i in range(m):\\n matrix[i][j] = 0\",\n \"def setZeroes(self, matrix: List[List[int]]) -> None:\\n m = len(matrix)\\n n = len(matrix[0])\\n \\n i = 0\\n j = 0\\n row_zero = set()\\n col_zero = set()\\n \\n for i in range(m):\\n for j in range(n):\\n if matrix[i][j] == 0:\\n row_zero.update({i})\\n col_zero.update({j})\\n \\n ## update rows with zero\\n for t in row_zero:\\n matrix[t][0:n] = [0 for i in range(n)]\\n \\n ## update cols with zero\\n for i in range(m):\\n for c in col_zero:\\n #print(i, c)\\n matrix[i][c] = 0\",\n \"def setZeroes(self, matrix: List[List[int]]) -> None:\\r\\n import copy\\r\\n m=len(matrix)\\r\\n n=len(matrix[0])\\r\\n m_copy=copy.deepcopy(matrix)\\r\\n for i in range(m):\\r\\n for j in range(n):\\r\\n if m_copy[i][j]==0:\\r\\n matrix[i]=[0]*n\\r\\n for x in range(m):\\r\\n matrix[x][j]=0\",\n \"def setZeroes(self, matrix):\\n for i in range(len(matrix)):\\n for j in range(len(matrix[i])):\\n if matrix[i][j] == 0 and (i, j) not in self.visited:\\n for neighbor in self.setter(matrix, i, j):\\n matrix[neighbor[0]][neighbor[1]] = 0\\n self.visited.add((neighbor[0], neighbor[1]))\\n print(matrix)\",\n \"def setZeroes(self, matrix: List[List[int]]) -> None:\\n row = set()\\n column = set()\\n \\n m = len(matrix)\\n n = len(matrix[0])\\n \\n for i in range(m):\\n for j in range(n):\\n if matrix[i][j] == 0:\\n row.add(i)\\n column.add(j)\\n \\n for i in range(m):\\n for j in range(n):\\n if i in row or j in column:\\n matrix[i][j] = 0\",\n \"def setZeroes(self, matrix) -> None:\\n if not matrix: return\\n h, w = len(matrix), len(matrix[0])\\n col0 = False\\n for i in range(h):\\n if matrix[i][0] == 0:\\n col0 = True # 第一列是否要全置为0\\n for j in range(1, w): # 从第二列开始\\n if matrix[i][j] == 0: # (i,j)为0\\n matrix[0][j] = 0 # 第i行为0\\n matrix[i][0] = 0 # 第j列为0\\n for i in range(1, h):\\n for j in range(1, w):\\n if matrix[i][j] != 0 and (matrix[i][0] == 0 or matrix[0][j] == 0):\\n matrix[i][j] = 0 # 需要置为0的位置\\n if matrix[0][0] == 0: # 第一行是否要全置为0\\n for j in range(w):\\n matrix[0][j] = 0\\n if col0: # 第一列是否要全置为0\\n for i in range(h):\\n matrix[i][0] = 0\",\n \"def setZeroes(self, matrix: List[List[int]]) -> None:\\n colsToZero = set()\\n rowsToZero = set()\\n for rowIdx, row in enumerate(matrix):\\n for colIdx, num in enumerate(row): \\n if num == 0: \\n colsToZero.add(colIdx)\\n rowsToZero.add(rowIdx)\\n \\n for col in colsToZero:\\n self.writeZeroCol(col, matrix)\\n for row in rowsToZero:\\n self.writeZeroRow(row, matrix)\",\n \"def setZeroes(self, matrix: List[List[int]]) -> None:\\n m, n = len(matrix), len(matrix[0])\\n temp = [[matrix[i][j] for j in range(n)] for i in range(m)]\\n for i in range(m):\\n for j in range(n):\\n if not temp[i][j]:\\n self.setZero(i, j, n, m, matrix)\",\n \"def setZeroes(self, matrix) -> None:\\n m, n = len(matrix), len(matrix[0])\\n rows, cols = set(), set()\\n # 获取0所在的位置对应的行和列\\n for i in range(m):\\n for j in range(n):\\n if matrix[i][j] == 0:\\n rows.add(i)\\n cols.add(j)\\n # 将该行和列的所有元素置为0\\n for row in rows:\\n for j in range(n):\\n matrix[row][j] = 0\\n for col in cols:\\n for i in range(m):\\n matrix[i][col] = 0\",\n \"def zero_matrix(matrix):\\n n = len(matrix)\\n if n == 0:\\n return True\\n\\n m = len(matrix[0])\\n if m == 0:\\n return True\\n\\n zero_rows = set()\\n zero_columns = set()\\n for i in range(n):\\n for j in range(m):\\n if matrix[i][j] == 0:\\n zero_rows.add(i)\\n zero_columns.add(j)\\n \\n for i in zero_rows:\\n for j in range(m):\\n matrix[i][j] = 0\\n\\n for i in range(n):\\n for j in zero_columns:\\n matrix[i][j] = 0\\n\\n return True\",\n \"def set_zero(mn_matrix): \\r\\n \\r\\n row_flag = [0]*len(mn_matrix)\\r\\n column_flag = [0]*len(mn_matrix[0])\\r\\n for i in range(0, len(row_flag)):\\r\\n for j in range(0, len(column_flag)):\\r\\n if mn_matrix[i][j] == 0:\\r\\n row_flag[i] = 1\\r\\n column_flag[j] = 1\\r\\n \\r\\n for i in range(0, len(row_flag)):\\r\\n for j in range(0, len(column_flag)):\\r\\n if row_flag[i] == 1 or column_flag[j] == 1: \\r\\n mn_matrix[i][j] = 0\",\n \"def setZeroes(self, matrix: List[List[int]]) -> None:\\n rows, cols = [], []\\n for i in range(len(matrix)):\\n for j in range(len(matrix[i])):\\n if matrix[i][j] == 0:\\n rows.append(i)\\n cols.append(j)\\n for i in rows:\\n for j in range(len(matrix[i])):\\n matrix[i][j] = 0\\n for j in cols:\\n for i in range(len(matrix)):\\n matrix[i][j] = 0\\n\\n return\",\n \"def setZeroes(self, matrix: List[List[int]]) -> None:\\n zero_columns = []\\n zero_rows = []\\n for i in range(len(matrix)):\\n for j in range(len(matrix[0])):\\n if matrix[i][j] == 0:\\n zero_columns.append(j)\\n zero_rows.append(i)\\n for zero_column in zero_columns:\\n for row in matrix:\\n row[zero_column] = 0\\n for zero_row in zero_rows:\\n matrix[zero_row] = [0] * len(matrix[0])\",\n \"def setZeroes(self, matrix: List[List[int]]) -> None:\\n m, n = len(matrix), len(matrix[0])\\n firstRow, firstCol = False, False\\n for i in range(m):\\n for j in range(n):\\n if i == 0 and matrix[i][j] == 0:\\n firstRow = True\\n if j == 0 and matrix[i][j] == 0:\\n firstCol = True\\n if i != 0 and j != 0 and matrix[i][j] == 0:\\n matrix[i][0] = 0\\n matrix[0][j] = 0\\n for i in range(1, m):\\n for j in range(1, n):\\n if matrix[i][0] == 0 or matrix[0][j] == 0:\\n matrix[i][j] = 0\\n if firstRow:\\n for i in range(n):\\n matrix[0][i] = 0\\n if firstCol:\\n for i in range(m):\\n matrix[i][0] = 0\",\n \"def setZeroes(self, matrix: List[List[int]]) -> None:\\n new_matrix = [row.copy() for row in matrix]\\n for i in range(len(matrix)):\\n for j in range(len(matrix[0])):\\n if matrix[i][j] == 0 and new_matrix[i][j] == 0:\\n self.setZero(matrix, i, j)\",\n \"def setZeroes(self, matrix: List[List[int]]) -> None:\\n row_num, col_num = len(matrix), len(matrix[0])\\n # 创建集合set()用于存放需要置零的行和列\\n row_set, col_set = set(), set()\\n for row in range(row_num):\\n for col in range(col_num):\\n if matrix[row][col]==0:\\n row_set.add(row)\\n col_set.add(col)\\n # 将记录的行、列中的元素赋值为0\\n # 再次遍历赋值\\n for row in range(row_num):\\n for col in range(col_num):\\n if row in row_set or col in col_set:\\n matrix[row][col] = 0\\n # # 或者行列单独赋值均可\\n # for row in row_set:\\n # for col in range(col_num):\\n # matrix[row][col] = 0\\n # for col in col_set:\\n # for row in range(row_num):\\n # matrix[row][col] = 0\",\n \"def setZeroes(self, matrix: List[List[int]]) -> None:\\n m, n = len(matrix), len(matrix[0]) if matrix else 0\\n \\n row = [False for _ in range(m)]\\n col = [False for _ in range(n)]\\n \\n for i in range(m):\\n for j in range(n):\\n if matrix[i][j] == 0:\\n row[i] = col[j] = True\\n \\n for i in range(m):\\n for j in range(n):\\n if row[i] or col[j]:\\n matrix[i][j] = 0\",\n \"def setZeroes(self, matrix: List[List[int]]) -> None:\\n \\n m = len(matrix)\\n n = len(matrix[0])\\n \\n i_zeros = []\\n j_zeros = []\\n \\n for i in range(m): ## find the zeros\\n for j in range(n):\\n \\n if matrix[i][j] == 0:\\n i_zeros.append(i)\\n j_zeros.append(j)\\n \\n for i in i_zeros:\\n matrix[i] = [0]*n\\n \\n for j in j_zeros:\\n for i in range(m):\\n matrix[i][j] = 0\",\n \"def zero_matrix(matrix):\\n cols = []\\n for row in range(0, len(matrix)):\\n for col in range(0, len(matrix[row])):\\n if matrix[row][col] == 0:\\n cols.append(col)\\n matrix[row] = [0 for _ in range(0, len(matrix[row]))]\\n break\\n for col in cols:\\n for row in range(0, len(matrix)):\\n matrix[row][col] = 0\\n return matrix\",\n \"def setZeroes(self, matrix: List[List[int]]) -> None:\\n row = []\\n col = []\\n for i in range(len(matrix)):\\n for j in range(len(matrix[i])):\\n if matrix[i][j] == 0:\\n col.append(j)\\n row.append(i)\\n\\n for i in range(len(row)):\\n self.col(matrix, col[i])\\n for i in range(len(col)):\\n self.row(matrix, row[i])\",\n \"def setZeroes(self, matrix: List[List[int]]) -> None:\\n zeros = []\\n m, n = len(matrix), len(matrix[0])\\n for i in range(m):\\n for j in range(n):\\n if matrix[i][j] == 0:\\n zeros.append([i, j])\\n for index in zeros:\\n row = index[0]\\n col = index[1]\\n for r in range(m):\\n matrix[r][col] = 0\\n for c in range(n):\\n matrix[row][c] = 0\",\n \"def setZeroes(self, matrix: List[List[int]]) -> None:\\n n = len(matrix)\\n m = len(matrix[0])\\n zeroRow = False\\n zeroColumn = False\\n for i in range(0,n):\\n if matrix[i][0] == 0:\\n zeroColumn = True\\n break\\n for j in range(0,m):\\n if matrix[0][j] == 0:\\n zeroRow = True\\n break\\n for i in range(1,n):\\n for j in range(1,m):\\n if matrix[i][j] == 0:\\n matrix[0][j] = 0\\n matrix[i][0] = 0\\n for i in range(1,n):\\n if matrix[i][0] == 0:\\n for j in range(0,m):\\n matrix[i][j] = 0\\n for j in range(1,m):\\n if matrix[0][j] == 0:\\n for i in range(0,n):\\n matrix[i][j] = 0\\n if zeroColumn:\\n for i in range(0,n):\\n matrix[i][0] = 0\\n if zeroRow:\\n for i in range(0,m):\\n matrix[0][i] = 0\",\n \"def setZeroes(self, matrix: List[List[int]]) -> None:\\n m = len(matrix)\\n n = len(matrix[0])\\n # replace zeros by None\\n for i in range(m):\\n for j in range(n):\\n if matrix[i][j] == 0:\\n matrix[i][j] = None\\n # do the rows\\n for i, row in enumerate(matrix):\\n if None in row:\\n for j, x in enumerate(row):\\n if x is not None:\\n matrix[i][j] = 0\\n # do the columns\\n for j in range(n):\\n # false if the column had no zeros\\n z = False\\n for i in range(m):\\n if matrix[i][j] is None:\\n z = True\\n break\\n if z:\\n for i in range(m):\\n if matrix[i][j] is not None:\\n matrix[i][j] = 0\\n # undo replace zeros by None\\n for i in range(m):\\n for j in range(n):\\n if matrix[i][j] is None:\\n matrix[i][j] = 0\",\n \"def setZeroes(self, matrix: List[List[int]]) -> None:\\n m, n = len(matrix), len(matrix[0])\\n zero_row, zero_col = False, False\\n\\n for i in range(m):\\n for j in range(n):\\n if matrix[i][j] == 0:\\n matrix[i][0] = matrix[0][j] = 0\\n zero_row = True if i == 0 else zero_row\\n zero_col = True if j == 0 else zero_col\\n\\n for j in range(1, n):\\n if matrix[0][j] == 0:\\n for i in range(1, m):\\n matrix[i][j] = 0\\n for i in range(1, m):\\n if matrix[i][0] == 0:\\n for j in range(1, n):\\n matrix[i][j] = 0\\n\\n if zero_row:\\n for j in range(n):\\n matrix[0][j] = 0\\n if zero_col:\\n for i in range(m):\\n matrix[i][0] = 0\\n return\",\n \"def setZeroes(self, matrix: List[List[int]]) -> None:\\n m = len(matrix)\\n if m == 0:\\n return\\n n = len(matrix[0])\\n\\n row_zero = False\\n for i in range(m):\\n if matrix[i][0] == 0:\\n row_zero = True\\n col_zero = False\\n for j in range(n):\\n if matrix[0][j] == 0:\\n col_zero = True\\n\\n for i in range(1, m):\\n for j in range(1, n):\\n if matrix[i][j] == 0:\\n matrix[i][0] = 0\\n matrix[0][j] = 0\\n\\n for i in range(1, m):\\n if matrix[i][0] == 0:\\n for j in range(1, n):\\n matrix[i][j] = 0\\n\\n for j in range(1, n):\\n if matrix[0][j] == 0:\\n for i in range(1, m):\\n matrix[i][j] = 0\\n\\n if col_zero:\\n for j in range(n):\\n matrix[0][j] = 0\\n if row_zero:\\n for i in range(m):\\n matrix[i][0] = 0\",\n \"def setZeroes(self, matrix: List[List[int]]) -> None:\\n \\n m = len(matrix)\\n n = len(matrix[0])\\n \\n firstrowhaszero = False\\n firstcolumnhaszero = False\\n for i in range(m):\\n if matrix[i][0] == 0:\\n firstcolumnhaszero = True\\n break\\n for j in range(n):\\n if matrix[0][j] == 0:\\n firstrowhaszero = True\\n break\\n \\n for i in range(1, m):\\n for j in range(1, n):\\n if matrix[i][j] == 0:\\n matrix[i][0] = 0\\n matrix[0][j] = 0\\n \\n for i in range(1, m):\\n for j in range(1, n):\\n if matrix[i][0] == 0 or matrix[0][j] == 0:\\n matrix[i][j] = 0\\n \\n if firstcolumnhaszero:\\n for i in range(m):\\n matrix[i][0] = 0\\n \\n if firstrowhaszero:\\n for j in range(n):\\n matrix[0][j] = 0\",\n \"def setZeroes(self, matrix: List[List[int]]) -> None:\\n first_col = False\\n for i in range(len(matrix)):\\n for j in range(len(matrix[0])):\\n if matrix[i][j] == 0:\\n matrix[i][0] = 0\\n if j == 0:\\n first_col = True\\n else:\\n matrix[0][j] = 0\\n for i in range(1, len(matrix[0])):\\n if matrix[0][i] == 0:\\n for j in range(len(matrix)):\\n matrix[j][i] = 0\\n if matrix[0][0] == 0:\\n matrix[0] = [0 for x in range(len(matrix[0]))]\\n for i in range(1, len(matrix)):\\n if matrix[i][0] == 0:\\n for j in range(len(matrix[0])):\\n matrix[i][j] = 0\\n if first_col:\\n for i in range(len(matrix)):\\n matrix[i][0] = 0\",\n \"def setZeroes(self, matrix: List[List[int]]) -> None:\\n\\n row = len(matrix)\\n column = len(matrix[0])\\n\\n for i in range(row):\\n for j in range(column):\\n if matrix[i][j] == 0:\\n for r in range(row):\\n if matrix[r][j] != 0:\\n matrix[r][j] = 'c'\\n for c in range(column):\\n if matrix[i][c] != 0:\\n matrix[i][c] = 'c'\\n\\n for i in range(row):\\n for j in range(column):\\n if matrix[i][j] == 'c':\\n matrix[i][j] = 0\",\n \"def setZeroes(self, matrix: List[List[int]]) -> None:\\r\\n \\r\\n rows, cols = len(matrix), len(matrix[0])\\r\\n \\r\\n #This solution has been done in constant space\\r\\n #using first row and first column for deciding which rows and columns\\r\\n #should be zero\\r\\n \\r\\n #using one extra variable for first row\\r\\n #because first cell is common in first row and column\\r\\n first_row = 1\\r\\n \\r\\n for i in range(rows):\\r\\n for j in range(cols):\\r\\n if matrix[i][j]==0:\\r\\n #for column\\r\\n matrix[0][j] = 0\\r\\n \\r\\n #for row\\r\\n if i==0:\\r\\n first_row = 0\\r\\n else:\\r\\n matrix[i][0] = 0\\r\\n \\r\\n #checking for rows except first row\\r\\n for i in range(1,rows):\\r\\n if matrix[i][0] == 0:\\r\\n for j in range(cols):\\r\\n matrix[i][j] = 0\\r\\n \\r\\n #checking for columns except first column\\r\\n for j in range(1, cols):\\r\\n if matrix[0][j] == 0:\\r\\n for i in range(rows):\\r\\n matrix[i][j] = 0\\r\\n \\r\\n #for first column\\r\\n if matrix[0][0] == 0:\\r\\n for i in range(rows):\\r\\n matrix[i][0] = 0\\r\\n \\r\\n #for first row\\r\\n if first_row == 0:\\r\\n for j in range(cols):\\r\\n matrix[0][j] = 0\\r\\n \\r\\n #print(matrix)\",\n \"def setZeroes(self, matrix: List[List[int]]) -> None:\\n m, n = len(matrix), len(matrix[0])\\n\\n col_0_flag = any(matrix[i][0] == 0 for i in range(m))\\n row_0_flag = any(matrix[0][i] == 0 for i in range(n))\\n\\n # 第 0 列， 第 0 行可以作为 [i,j] 的指示存储\\n # 例如，如果 [i,j] 为 0， 那么 [0, j], [i, 0] 可以置 0 \\n # 再次遍历矩阵，通过判断 [0, j], [i, 0] 去把该行，该列置 0\\n # 这样的话就不需要一个额外的矩阵去 for i,j 遍历是否为 0 了\\n\\n # 注意是从 1 开始的。 \\n for i in range(1, m):\\n for j in range(1, n):\\n if matrix[i][j] == 0:\\n matrix[i][0] = matrix[0][j] = 0 \\n\\n for i in range(1, m):\\n for j in range(1, n):\\n if matrix[i][0] == 0 or matrix[0][j] == 0:\\n matrix[i][j] = 0\\n\\n if col_0_flag:\\n for i in range(m):\\n matrix[i][0] = 0\\n\\n if row_0_flag:\\n for j in range(n):\\n matrix[0][j] = 0\",\n \"def setZeroes(matrix):\\r\\n \\r\\n #An average O(n^2) time traversal solution with memoization\\r\\n #for each 0 we encounter, we update the entire row and column to 0s, but on the condition that the row/column has not been updated yet\\r\\n \\r\\n row_cache = {}\\r\\n column_cache = {}\\r\\n \\r\\n for r in range(0,rows := len(matrix)):\\r\\n for c in range(0,cols := len(matrix[0])):\\r\\n \\r\\n if matrix[r][c] == 0:\\r\\n \\r\\n if not row_cache.get(r):\\r\\n for i in range(0,cols):\\r\\n if matrix[r][i] != 0:\\r\\n matrix[r][i] = '0' #we use strings so we only consider the initial 0s\\r\\n row_cache[r] = True\\r\\n \\r\\n if not column_cache.get(c):\\r\\n for i in range(0,rows):\\r\\n if matrix[i][c] != 0:\\r\\n matrix[i][c] = '0'\\r\\n column_cache[c] = True\\r\\n return\",\n \"def setZeroes(self, matrix: List[List[int]]) -> None:\\n m = len(matrix)\\n n = len(matrix[0])\\n \\n row_1st_0 = False\\n col_1st_0 = False\\n \\n ## check whether 1st row and 1st col contains 0\\n for i in range(n):\\n if matrix[0][i] == 0:\\n row_1st_0 = True\\n break\\n for j in range(m):\\n if matrix[j][0] == 0:\\n col_1st_0 = True\\n break\\n \\n ## check row 1:m and col 1:n, store the 0 indicator to 1st row and 1st col\\n for i in range(1, m):\\n for j in range(1, n):\\n if matrix[i][j] == 0:\\n matrix[i][0] = 0\\n matrix[0][j] = 0\\n \\n ## update cols to zero except 1st row\\n for i in range(1, n):\\n if matrix[0][i] == 0:\\n for r in range(1, m):\\n matrix[r][i] = 0\\n \\n ## update rows to zero except 1st col\\n for j in range(1, m):\\n if matrix[j][0] == 0:\\n matrix[j][1:n] = [0 for i in range(n-1)]\\n \\n ## update 1st row and 1st col to 0\\n if row_1st_0:\\n matrix[0][0:n] = [0 for i in range(n)]\\n if col_1st_0:\\n for r in range(m):\\n matrix[r][0] = 0\",\n \"def setZeroes(self, matrix: List[List[int]]) -> None:\\n m, n = len(matrix), len(matrix[0]) if matrix else 0\\n \\n # Use first row to store zero-info for each col\\n # Use first col to store zero-info for each row\\n # matrix[0][0] will store col info\\n \\n is_first_row = any(x == 0 for x in matrix[0])\\n \\n for i in range(1, m):\\n for j in range(n):\\n if matrix[i][j] == 0:\\n matrix[0][j] = matrix[i][0] = 0\\n \\n for i in range(1, m):\\n for j in range(1, n):\\n if matrix[i][0] * matrix[0][j] == 0:\\n matrix[i][j] = 0\\n \\n if matrix[0][0] == 0:\\n for i in range(1, m):\\n matrix[i][0] = 0\\n \\n if is_first_row:\\n for j in range(n):\\n matrix[0][j] = 0\",\n \"def setZeroes(self, matrix: List[List[int]]) -> None:\\n # 把第一行，第一列当作标志位\\n row_flag = False\\n col_flag = False # 防止第一行第一列无效\\n row = len(matrix)\\n col = len(matrix[0]) # 这种表征\\n for j in range(col):\\n if matrix[0][j] == 0:\\n row_flag = True\\n break\\n for i in range(row):\\n if matrix[i][0] == 0:\\n col_flag = True\\n break\\n for i in range(1,row):\\n for j in range(1,col):\\n if matrix[i][j] == 0:\\n matrix[i][0] = matrix[0][j] = 0\\n # 检查,第二遍扫描\\n for i in range(1,row):\\n for j in range(1,col):\\n if matrix[i][0]==0 or matrix[0][j] ==0:\\n matrix[i][j] = 0\\n # 防止标志位本身也是\\n if row_flag:\\n for j in range(col):\\n matrix[0][j] = 0\\n if col_flag:\\n for i in range(row):\\n matrix[i][0] = 0\",\n \"def zero_matrix(matrix):\\n if not matrix:\\n return\\n\\n n_rows = len(matrix)\\n n_cols = len(matrix[0])\\n\\n # First row and first column of the matrix are used to store row and columns to nullify.\\n # At the beginning we determine if the first row and/or first column contain a zero.\\n first_row_zero = False\\n first_col_zero = False\\n for col in range(n_cols):\\n if not matrix[0][col]:\\n first_row_zero = True\\n break\\n for row in range(n_rows):\\n if not matrix[row][0]:\\n first_col_zero = True\\n break\\n\\n # Then go through the rest of the matrix and determine which rows and columns must be zeroed.\\n for row in range(1, n_rows):\\n for col in range(1, n_cols):\\n if not matrix[row][col]:\\n matrix[0][col] = 0\\n matrix[row][0] = 0\\n\\n # Go through the rest of the matrix again and insert zeros where needed.\\n for row in range(1, n_rows):\\n for col in range(1, n_cols):\\n if matrix[0][col] == 0 or matrix[row][0] == 0:\\n matrix[row][col] = 0\\n\\n # Nullify first row and column if needed.\\n if first_row_zero:\\n for col in range(n_cols):\\n matrix[0][col] = 0\\n if first_col_zero:\\n for row in range(n_rows):\\n matrix[row][0] = 0\",\n \"def mask_sparse(self, threshold=10):\\n self.MaskPrefix = 's' + self.MaskPrefix\\n print('Masking pixels that do not have at least {0} coherent values'.format(threshold))\\n # each pixel assigned an integer corresponding to # of igrams where coherent\\n # NOTE: save coverage map if it doesn't exist already\\n coverage = self.get_coverage()\\n sparse = ma.masked_less(coverage, threshold)\\n for ig in self.Set:\\n igram = self.load_ma(ig)\\n igram[sparse.mask] = ma.masked\\n self.save_ma(ig, igram)\\n print('Done')\",\n \"def make_all_zero(curr_clusters, k, num_of_cords):\\r\\n for i in range(k):\\r\\n for j in range(num_of_cords):\\r\\n curr_clusters[i][j] = 0\",\n \"def replace_zeros(self):\\n\\n min_c = np.array(self.map[self.column])\\n self.map.loc[self.map[self.column]==0, self.column] = np.min(min_c[np.nonzero(min_c)])\",\n \"def _zero(array):\\n a = array.ravel()\\n for i in numba.prange(len(a)):\\n a[i] = 0\\n return True\",\n \"def zero_blind_range(data):\\n try:\\n start_i = data['first_data_bin']\\n except:\\n start_i = 0\\n data['data'][...,:start_i] = 0.0\",\n \"def updateEmptiesSet(self):\\n self.emptiesSet = []\\n for i in self.Range:\\n if self.get_cell(i) == 0:\\n self.emptiesSet.append(i)\",\n \"def ignore_biasbn(directions):\\n for d in directions:\\n if d.dim() <= 1:\\n d.fill_(0)\",\n \"def applymask(self,mask):\\n self.spec[mask==0]=np.nan\",\n \"def pfd_clear(n):\\n\\tglobal matrix\\n\\tr = []\\n\\tfor i in xrange(size[0]):\\n\\t\\tif matrix[i][n] == 1:\\n\\t\\t\\tmatrix[i][n] = 0\\n\\t\\t\\tr.append(i)\\n\\treturn r\",\n \"def clean(self):\\n self.board_values = np.zeros((self.size, self.size))\\n self.tiles_taken[:, :] = False\",\n \"def makesparse(matrix):\\n n = matrix[0].size\\n elements = []\\n for i in range(n):\\n for j in range(n):\\n if matrix[i][j] != 0 :\\n temp = MatrixElement(i, j, matrix[i][j])\\n elements.append(temp)\\n return SparseMatrix(n, elements)\",\n \"def negM(M):\\r\\n M[np.where(M > 0)] = 0\\r\\n return M\",\n \"def filter_zeros(X):\\n\\tnoNonzeros = np.count_nonzero(X, axis=1)\\n\\tmask = np.where(noNonzeros > 0)\\n\\treturn X[mask[0], :]\",\n \"def nozero(arr):\\n vals=sorted(list(set(np.array(arr).flatten())))\\n if vals[0]<0:\\n print(\\\"correcting for div/zero by replacing 0 with\\\",vals[1])\\n arr[arr==0]=vals[1]\\n return arr\",\n \"def trim_zeros(x):\\n assert len(x.shape) == 2\\n return x[~np.all(x == 0, axis=1)]\",\n \"def test_normalize_matrix_with_only_zeros(self):\\n input_matrix = [\\n [0, 0],\\n [0, 0]\\n ]\\n\\n expected = [\\n [0, 0],\\n [0, 0]\\n ]\\n\\n result = self.summarizer.normalize_matrix(input_matrix)\\n\\n self.assertEqual(expected, result)\",\n \"def clear_exclude_bits(self):\\n self.bitcell_array.init_graph_params()\",\n \"def reset_mask(self):\\n\\n self.mask = np.ones(self.dispersion.shape, dtype=bool)\",\n \"def solution2(array):\\n if not array:\\n return\\n\\n n_rows = len(array)\\n n_cols = len(array[0])\\n\\n # Since we are using first row and column as space to mark rows and columns to be zeroed,\\n # first check whether they have any zeros or not\\n first_row_zero = False\\n first_column_zero = False\\n \\n for c in range(n_cols):\\n if not array[0][c]:\\n first_row_zero=True\\n \\n for r in range(n_rows):\\n if not array[r][0]:\\n first_column_zero = True\\n\\n # Now start from 1,1 and check for 0. If 0 is found in certain row and column, mark first row\\n # and column\\n for r in range(1, n_rows):\\n for c in range(1, n_cols):\\n if not array[r][c]:\\n array[0][c] = 0\\n array[r][0] = 0\\n\\n # Now iterate through entire matrix again starting from 1,1 and set element to zero if \\n # first row or column for that element is zero\\n for r in range(1, n_rows):\\n for c in range(1, n_cols):\\n if array[0][c]==0 or array[r][0]==0:\\n array[r][c] = 0\\n\\n # Go back to the first row and column.\\n if first_row_zero:\\n for c in range(n_cols):\\n array[0][c] = 0\\n if first_column_zero:\\n for r in range(n_rows):\\n array[r][0] = 0\\n\\n return array\",\n \"def compress_csr(self):\\n _, unique, indices = np.unique(\\n self.m*self.rows + self.cols,\\n return_index=True, return_inverse=True)\\n self.rows = self.rows[unique]\\n self.cols = self.cols[unique]\\n self.vals = np.bincount(indices, weights=self.vals)\",\n \"def ensure_sparse_cols(self,max_density,remove_lowest=True):\\n if max_density >= 1:\\n max_nnz = int(max_density)\\n else:\\n max_nnz = int(max_density*self.shape[0])\\n for j in range(self.shape[1]):\\n col = self.fast_get_col(j)\\n excess = col.nnz - max_nnz\\n if excess > 0:\\n if remove_lowest:\\n zero_entries = np.argsort(col.data)[:excess]\\n else:\\n zero_entries = random.sample(range(col.nnz),excess)\\n col.data[zero_entries] = 0\\n self.fast_update_col(j,col.data)\",\n \"def trim_zeros(array):\\n multislice = []\\n for i in range(array.ndim):\\n sum_axes = tuple(j for j in range(array.ndim) if j is not i)\\n edges = np.where(np.sum(array, axis=sum_axes) > 0)\\n if edges[0].size == 0:\\n return np.array([], dtype=array.dtype)\\n low = edges[0][0]\\n high = edges[0][-1]\\n multislice.append(slice(low, high+1, 1))\\n return array[tuple(multislice)]\",\n \"def zero_negative_weights(self):\\n for k in range(len(self)):\\n self[k] *= 0 if self[k] < 0 else 1\\n self.finalized = True\\n return self\",\n \"def test_nullspace_blocked(input_matrix):\\n mat = sp.Matrix(input_matrix)\\n assert sorted(mat.nullspace()) == sorted(nullspace_blocked(mat))\",\n \"def faca_zero(self):\\n n = self.n\\n\\n for i in range(n):\\n if comozero(self[i]):\\n self[i] = 0\\n\\n return self\",\n \"def convert_fill_zeros_like(g, op, block):\\n\\n x = g.get_node(op.input(\\\"X\\\")[0])\\n dtype = op.attr(\\\"dtype\\\")\\n dtype = _convert_dtype_value(dtype)\\n value = _expr.const(0, dtype=dtype)\\n out = _op.transform.full_like(x, value).astype(dtype)\\n g.add_node(op.output(\\\"Out\\\")[0], out)\",\n \"def remove_bad_cells(self, *dims):\\n ranges = [DimRange(d, 0, np.inf) for d in dims]\\n return self.gate(*ranges)\",\n \"def remove_blank_pixels(self,pixels,non_zero=None):\\n self.uni2pix= np.unique(pixels).astype(int)\\n self.pix2uni = {u:k for k,u in enumerate(self.uni2pix)}\\n\\n gb, gl = hp.pix2ang(self.nside, self.uni2pix)\\n\\n self.npix = self.uni2pix.size\\n if isinstance(non_zero,type(None)):\\n non_zero = np.where(self.wei != 0)[0]\\n\\n self.output = self.output[self.uni2pix]\\n self.sigwei = self.sigwei[self.uni2pix]\\n self.wei = self.wei[self.uni2pix]\\n\\n print('SIZE CHECK', self.wei.size, self.npix)\",\n \"def clean_zeros(data, cpy = 0):\\r\\n \\r\\n if cpy:\\r\\n edited_data = data.copy()\\r\\n else:\\r\\n edited_data = data\\r\\n \\r\\n \\r\\n # run through each stream \\r\\n for stream in range(edited_data.shape[1]):\\r\\n \\r\\n # Get zeroes indexs\\r\\n zeros_idx = edited_data[:,stream] == 0\\r\\n for idx, val in enumerate(zeros_idx):\\r\\n if val:\\r\\n # Replace with previous value\\r\\n edited_data[idx,stream] = edited_data[idx-1,stream]\\r\\n\\r\\n return edited_data\",\n \"def clear(self):\\n\\t\\tself.n = 0\\n\\t\\tself.bv = BitVector.BitVector(size = self.m)\",\n \"def make_missing_mask(\\n shape, valid_rows, valid_cols, max_dist=None, sym_upper=False\\n):\\n # Error if the matrix upper symmetric but shape is rectangle or missing\\n # rows and cols are different\\n sm, sn = shape\\n if sym_upper and (sm != sn or len(valid_rows) != len(valid_cols)):\\n raise ValueError(\\\"Rectangular matrices cannot be upper symmetric\\\")\\n\\n # Get a boolean array of missing (1) and valid (0) rows\\n missing_rows = valid_to_missing(valid_rows, sm)\\n # When matrix is sym., rows and cols are synonym, no need to compute 2x\\n if sym_upper:\\n missing_cols = missing_rows\\n else:\\n missing_cols = valid_to_missing(valid_cols, sn)\\n # If upper sym., fill only upper diag up to max_dist.\\n # E. g. with bins 1 and 3 missing\\n # and a max_dist of 1:\\n # 0 1 0 0 0\\n # 0 1 1 0 0\\n # 0 0 0 1 0\\n # 0 0 0 1 1\\n # 0 0 0 0 0\\n # For each missing bin, mask is apply 1 pixel upwards and 1 to the right\\n # to fill only the upper triangle up to max_dist\\n if sym_upper:\\n # If no max dist has been specified, fill the whole upper triangle\\n if max_dist is None:\\n max_dist = min(shape)\\n # Generate matrix of distance shifts by row.\\n # Shape is len(missing_rows) x (max_dist + 1)\\n # e.g.: 2 missing rows and max dist of 1\\n # 0 0\\n # 1 1\\n row_shifts = np.tile(\\n np.array(range(max_dist + 1)), (len(missing_rows), 1)\\n ).T\\n # Compute row positions upwards to diagonal by subtracting missing rows\\n # to the shifts. Following the previous example, if missing rows are\\n # bins 1 and 3:\\n # 1 3\\n # 0 2\\n rows_before = (missing_rows - row_shifts).flatten(\\\"F\\\")\\n # looking at pixels up from the bins, cols remain the same:\\n # 1 3\\n # 1 3\\n cols_before = np.repeat(missing_rows, max_dist+1)\\n # Compute col position to the right until diagonal by adding the shift\\n # Note: upper symmetric, so row_shifts = col_shift_\\n # 1 3\\n # 2 4\\n cols_after = (missing_cols + row_shifts).flatten(\\\"F\\\")\\n # This time, rows remain constant since we are computing positions to\\n # the right\\n rows_after = np.repeat(missing_cols, max_dist+1)\\n # Combine positions to the right and upwards\\n rows = np.concatenate([rows_before, rows_after])\\n cols = np.concatenate([cols_before, cols_after])\\n data = np.ones(rows.shape, dtype=bool)\\n # Remove entries where rows or cols are negative or larger than shape\\n valid = (cols < sm) & (cols >= 0) & (rows < sm) & (rows >= 0)\\n # Build mask mat with miss bins up to max scan dist in upper triangle\\n mask = sp.coo_matrix(\\n (data[valid], (rows[valid], cols[valid])), shape=shape, dtype=bool\\n ).tocsr()\\n else:\\n mask = sp.csr_matrix(shape, dtype=bool)\\n mask[missing_rows, :] = 1\\n mask[:, missing_cols] = 1\\n\\n return mask\",\n \"def zero_like(matrix):\\n dtype = numpy.int_ if is_integer(matrix) else numpy.float_\\n return numpy.zeros(matrix.shape, dtype=dtype)\",\n \"def remove_nans(coords):\\n s = np.apply_along_axis(sum,1,np.isnan(coords[1])) == 0\\n coords[0] = (np.asarray(coords[0])[s]).tolist()\\n coords[1] = coords[1][s,:]\",\n \"def _unmap(data, count, inds, fill=0):\\r\\n if len(data.shape) == 1:\\r\\n ret = np.empty((count,), dtype=np.float32)\\r\\n ret.fill(fill)\\r\\n ret[inds] = data\\r\\n else:\\r\\n ret = np.empty((count,) + data.shape[1:], dtype=np.float32)\\r\\n ret.fill(fill)\\r\\n ret[inds, :] = data\\r\\n return ret\",\n \"def posM(M):\\r\\n M[np.where(M < 0)] = 0\\r\\n return M\",\n \"def set_step_zero(self):\\n self.gcam_landmatrix, self.ixr_ixm_ixg = self.cst.apply_constraints_zero()\",\n \"def remove_dbledge(img):\\n\\t(ny,nx) = np.shape(img)\\n\\tfor i in range(nx):\\n\\t\\tidx = np.array(np.nonzero(img[:,i]))\\n\\t\\tif np.size(idx) == 0:\\n\\t\\t\\tcontinue\\n\\t\\tidx = idx[0][-1]\\n\\t\\timg[idx-1::-1,i] = 0\\n\\treturn img\",\n \"def _unmap(data, count, inds, fill=0):\\n if len(data.shape) == 1:\\n ret = np.empty((count,), dtype=np.float32)\\n ret.fill(fill)\\n ret[inds] = data\\n else:\\n ret = np.empty((count,) + data.shape[1:], dtype=np.float32)\\n ret.fill(fill)\\n ret[inds, :] = data\\n return ret\",\n \"def zero_to_nodata(base_raster):\\n target_raster = base_raster.copy()\\n target_raster[target_raster == 0] = _IC_NODATA\\n return target_raster\",\n \"def remove_blanks(mat):\\n ids = []\\n for idx, row in enumerate(mat):\\n if not 1 in row:\\n ids.append(idx)\\n mat = np.delete(mat, ids, 0)\\n mat = np.delete(mat, ids, 1)\\n return mat\",\n \"def zeros_matrix(self, rows, cols):\\r\\n M = []\\r\\n while len(M) < rows:\\r\\n M.append([])\\r\\n while len(M[-1]) < cols:\\r\\n M[-1].append(0.0)\\r\\n\\r\\n return M\",\n \"def removeInfinities(mat):\\n isFinite = np.all(np.isfinite(mat))\\n \\n if not isFinite:\\n nrow, ncol = mat.shape\\n matCopy = mat.copy()\\n matReshaped = matCopy.reshape(-1)\\n minVal = np.nanmin(matReshaped[matReshaped != -np.inf])\\n maxVal = max(matReshaped)\\n \\n #count infinities and get their indicies\\n #countInfinites(matReshaped) \\n \\n #replace all infinities with nan\\n for i in range(0,len(matReshaped)):\\n if matReshaped[i] in [-np.inf, np.inf]:\\n matReshaped[i] = minVal\\n '''\\n #get average of matrix values and replace the nan with averages\\n def averageVal(array):\\n total = 0\\n count = 0\\n for elmt in array:\\n if elmt != -90001:\\n total+=elmt\\n count+=1\\n return total/count\\n\\n average = averageVal(matReshaped)\\n\\n #replace nan with average\\n for i in range(0,len(matReshaped)):\\n if matReshaped[i] == -90001:\\n matReshaped[i] = average\\n '''\\n #catch any potential errors \\n if np.any(np.isinf(matReshaped)):\\n raise ValueError\\n\\n return matReshaped.reshape((nrow, ncol))\\n else:\\n return mat\",\n \"def _unmap(data, count, inds, fill=0):\\n if len(data.shape) == 1:\\n ret = np.empty((count, ), dtype='float32')\\n ret.fill(fill)\\n ret[inds] = data\\n else:\\n ret = np.empty((count, ) + data.shape[1:], dtype='float32')\\n ret.fill(fill)\\n ret[inds, :] = data\\n return ret\",\n \"def _unmap(data, count, inds, fill=0):\\n if len(data.shape) == 1:\\n ret = np.empty((count, ), dtype=np.float32)\\n ret.fill(fill)\\n ret[inds] = data\\n else:\\n ret = np.empty((count, ) + data.shape[1:], dtype=np.float32)\\n ret.fill(fill)\\n ret[inds, :] = data\\n return ret\",\n \"def zero_mask(self):\\n accum = 0\\n for i in range(self.data.itemsize):\\n accum += (0x55 << (i << 3))\\n return accum\",\n \"def _empty_clusters(clusters):\\n for clst in clusters:\\n clst.points = []\",\n \"def clear(self):\\n for y in range(len(self.matrix)):\\n for x in range(len(self.matrix[0])):\\n self.matrix[y-1][x-1] = (0,0,0)\",\n \"def zeros_like(self):\\n raise NotImplementedError\",\n \"def shrink_mask(self):\\n m = self._mask\\n if m.ndim and not m.any():\\n self._mask = nomask\\n return self\",\n \"def unmap(data, count, inds, fill=0):\\n if data.dim() == 1:\\n ret = data.new_full((count,), fill)\\n ret[inds.type(torch.bool)] = data\\n else:\\n new_size = (count,) + data.size()[1:]\\n ret = data.new_full(new_size, fill)\\n ret[inds.type(torch.bool), :] = data\\n return ret\",\n \"def set_empty(self):\\n pattern = [[0,0,0,0],\\n [0,0,0,0],\\n [0,0,0,0],\\n [0,0,0,0]]\\n self.set_pattern(pattern)\",\n \"def reset(self):\\r\\n # replace with your code\\r\\n for row in range(0, self._grid_height):\\r\\n for col in range(0, self._grid_width):\\r\\n self._grid_tile[row][col] = 0\\r\\n # at this step, all cells should be available\\r\\n self.new_tile()\\r\\n self.new_tile()\",\n \"def unmap(data, count, inds, fill=0):\\n if data.dim() == 1:\\n ret = data.new_full((count,), fill)\\n ret[inds.type(torch.bool)] = data\\n else:\\n new_size = (count,) + data.size()[1:]\\n ret = data.new_full(new_size, fill)\\n ret[inds.type(torch.bool), :] = data\\n return ret\",\n \"def fill_blind_pores(im):\\n holes = find_disconnected_voxels(im)\\n im[holes] = False\\n return im\",\n \"def _unmap(data, count, inds, fill=0):\\r\\n if len(data.shape) == 1:\\r\\n ret = np.empty((count,), dtype=data.dtype)\\r\\n ret.fill(fill)\\r\\n ret[inds] = data\\r\\n else:\\r\\n ret = np.empty((count,) + data.shape[1:], dtype=data.dtype)\\r\\n ret.fill(fill)\\r\\n ret[inds, :] = data\\r\\n return ret\",\n \"def zeroes(height, width):\\n g = [[0.0 for _ in range(width)] for __ in range(height)]\\n return Matrix(g)\",\n \"def zero_safe(self, hist) :\\n if hist.Integral() == 0 :\\n print \\\"Warning: histogram \\\", hist.GetName(), \\\"is empty!\\\"\\n hist.SetBinContent(1, 10e-6)\\n return hist\",\n \"def filter_zero(points_np):\\n # TODO replace with cython or numba\\n A = points_np[:, 0]\\n B = points_np[:, 1]\\n C = points_np[:, 2]\\n t0 = time.time()\\n mask = A == 0.0\\n mask = mask & (B == 0.0)\\n mask = mask & (C == 0.0)\\n points_np = points_np[~mask]\\n # print(f\\\"Filtering Took {(time.time() - t0) * 1000:.1f} ms\\\")\\n return points_np\",\n \"def normalize(self,matrix):\\n for i in range(self.N):\\n matrix[self.N-1][i] = 0\\n for i in range(self.n):\\n matrix[self.N - 1][self.index(i,i)] = 1\\n return matrix\",\n \"def nonzeros(m, row):\\n for index in range(m.indptr[row], m.indptr[row+1]):\\n yield m.indices[index], m.data[index]\"\n]","isConversational":false},"negative_scores":{"kind":"list like","value":["0.69846714","0.69818574","0.69084895","0.68822354","0.68314415","0.68032897","0.6796617","0.6766385","0.67445296","0.6728862","0.6650272","0.66448796","0.66283685","0.66217226","0.6617057","0.6614921","0.65993714","0.6568216","0.6566557","0.65331614","0.6532012","0.65318686","0.65259814","0.65183306","0.6507791","0.6504317","0.6504177","0.6481897","0.64329404","0.6428977","0.640118","0.6377764","0.6373799","0.6341068","0.6317284","0.6290669","0.6288457","0.62839025","0.6269644","0.6239771","0.6224696","0.60971737","0.6034561","0.59947145","0.5975686","0.5912601","0.5911425","0.58483094","0.5819229","0.5714043","0.57083845","0.5706078","0.5681865","0.56547046","0.5653427","0.5609467","0.55799264","0.5554647","0.5531756","0.55278015","0.5527626","0.5524856","0.5514233","0.54861045","0.5475966","0.5473251","0.54710186","0.54693764","0.5465578","0.54547936","0.5447677","0.54456526","0.54247314","0.54038763","0.5403794","0.54035175","0.53995746","0.5396353","0.53935355","0.53736395","0.53590757","0.53574127","0.5357293","0.5351188","0.5348211","0.53319186","0.5318868","0.53151846","0.53097177","0.5290606","0.52869004","0.52836996","0.5279671","0.5278687","0.525338","0.52398705","0.52325416","0.521068","0.52091324","0.52084625"],"string":"[\n \"0.69846714\",\n \"0.69818574\",\n \"0.69084895\",\n \"0.68822354\",\n \"0.68314415\",\n \"0.68032897\",\n \"0.6796617\",\n \"0.6766385\",\n \"0.67445296\",\n \"0.6728862\",\n \"0.6650272\",\n \"0.66448796\",\n \"0.66283685\",\n \"0.66217226\",\n \"0.6617057\",\n \"0.6614921\",\n \"0.65993714\",\n \"0.6568216\",\n \"0.6566557\",\n \"0.65331614\",\n \"0.6532012\",\n \"0.65318686\",\n \"0.65259814\",\n \"0.65183306\",\n \"0.6507791\",\n \"0.6504317\",\n \"0.6504177\",\n \"0.6481897\",\n \"0.64329404\",\n \"0.6428977\",\n \"0.640118\",\n \"0.6377764\",\n \"0.6373799\",\n \"0.6341068\",\n \"0.6317284\",\n \"0.6290669\",\n \"0.6288457\",\n \"0.62839025\",\n \"0.6269644\",\n \"0.6239771\",\n \"0.6224696\",\n \"0.60971737\",\n \"0.6034561\",\n \"0.59947145\",\n \"0.5975686\",\n \"0.5912601\",\n \"0.5911425\",\n \"0.58483094\",\n \"0.5819229\",\n \"0.5714043\",\n \"0.57083845\",\n \"0.5706078\",\n \"0.5681865\",\n \"0.56547046\",\n \"0.5653427\",\n \"0.5609467\",\n \"0.55799264\",\n \"0.5554647\",\n \"0.5531756\",\n \"0.55278015\",\n \"0.5527626\",\n \"0.5524856\",\n \"0.5514233\",\n \"0.54861045\",\n \"0.5475966\",\n \"0.5473251\",\n \"0.54710186\",\n \"0.54693764\",\n \"0.5465578\",\n \"0.54547936\",\n \"0.5447677\",\n \"0.54456526\",\n \"0.54247314\",\n \"0.54038763\",\n \"0.5403794\",\n \"0.54035175\",\n \"0.53995746\",\n \"0.5396353\",\n \"0.53935355\",\n \"0.53736395\",\n \"0.53590757\",\n \"0.53574127\",\n \"0.5357293\",\n \"0.5351188\",\n \"0.5348211\",\n \"0.53319186\",\n \"0.5318868\",\n \"0.53151846\",\n \"0.53097177\",\n \"0.5290606\",\n \"0.52869004\",\n \"0.52836996\",\n \"0.5279671\",\n \"0.5278687\",\n \"0.525338\",\n \"0.52398705\",\n \"0.52325416\",\n \"0.521068\",\n \"0.52091324\",\n \"0.52084625\"\n]","isConversational":false},"document_score":{"kind":"string","value":"0.6212842"},"document_rank":{"kind":"string","value":"41"}}},{"rowIdx":22677,"cells":{"query":{"kind":"string","value":"Set the nth diagonal of a symmetric 2D numpy array to a fixed value. Operates in place."},"document":{"kind":"string","value":"def set_mat_diag(mat, diag=0, val=0):\n m = mat.shape[0]\n step = m + 1\n start = diag\n end = m ** 2 - diag * m\n mat.flat[start:end:step] = val"},"metadata":{"kind":"string","value":"{\n \"objective\": {\n \"self\": [],\n \"paired\": [],\n \"triplet\": [\n [\n \"query\",\n \"document\",\n \"negatives\"\n ]\n ]\n }\n}"},"negatives":{"kind":"list like","value":["def set_diagonal(self, value = 0):\n for d in range(self.size):\n self.write(d, d, value)","def set_diag(M,d,idx=0):\n n, m = shape_mat(M)\n if idx >= 0:\n for i, di in enumerate( d ):\n M[i][i+idx] = di\n else:\n for i, di in enumerate( d ):\n M[i-idx][i] = di","def fill_diagonal(a, val):\r\n if a.ndim < 2:\r\n raise ValueError(\"array must be at least 2-d\")\r\n if a.ndim == 2:\r\n # Explicit, fast formula for the common case. For 2-d arrays, we\r\n # accept rectangular ones.\r\n step = a.shape[1] + 1\r\n else:\r\n # For more than d=2, the strided formula is only valid for arrays with\r\n # all dimensions equal, so we check first.\r\n if not np.alltrue(np.diff(a.shape) == 0):\r\n raise ValueError(\"All dimensions of input must be of equal length\")\r\n step = np.cumprod((1,) + a.shape[:-1]).sum()\r\n\r\n # Write the value out into the diagonal.\r\n a.flat[::step] = val","def fill_diagonal(a, val):\r\n return fill_diagonal_(a, val)","def set_diag(x, new_diag):\n arr_shape = x.shape\n off_diag = (1 - _torch.eye(arr_shape[-1])) * x\n diag = _torch.einsum(\"ij,...i->...ij\", _torch.eye(new_diag.shape[-1]), new_diag)\n return diag + off_diag","def _set_diag(laplacian, value, norm_laplacian):\n n_nodes = laplacian.shape[0]\n # We need all entries in the diagonal to values\n if not sparse.isspmatrix(laplacian):\n if norm_laplacian:\n laplacian.flat[::n_nodes + 1] = value\n else:\n laplacian = laplacian.tocoo()\n if norm_laplacian:\n diag_idx = (laplacian.row == laplacian.col)\n laplacian.data[diag_idx] = value\n # If the matrix has a small number of diagonals (as in the\n # case of structured matrices coming from images), the\n # dia format might be best suited for matvec products:\n n_diags = np.unique(laplacian.row - laplacian.col).size\n if n_diags <= 7:\n # 3 or less outer diagonals on each side\n laplacian = laplacian.todia()\n else:\n # csr has the fastest matvec and is thus best suited to\n # arpack\n laplacian = laplacian.tocsr()\n return laplacian","def fill_diagonal(x1, val, wrap=False):\n\n x1_desc = dpnp.get_dpnp_descriptor(\n x1, copy_when_strides=False, copy_when_nondefault_queue=False\n )\n if x1_desc:\n if not dpnp.isscalar(val):\n pass\n elif wrap:\n pass\n else:\n return dpnp_fill_diagonal(x1_desc, val)\n\n return call_origin(numpy.fill_diagonal, x1, val, wrap, dpnp_inplace=True)","def matrix_set_diag(input_np, diagonal, k=0, align=\"RIGHT_LEFT\"):\n out = np.array(input_np, copy=True)\n\n cols = input_np.shape[-1]\n rows = input_np.shape[-2]\n\n onlyOneDiagonal = True\n if isinstance(k, (tuple, list)):\n if len(k) < 2 or k[0] == k[1]:\n k = k[0]\n else:\n onlyOneDiagonal = False\n\n if onlyOneDiagonal:\n for i in range(diagonal.shape[-1]):\n if k >= 0:\n out[..., i, i + k] = diagonal[..., i]\n else:\n out[..., i - k, i] = diagonal[..., i]\n else:\n for ki in range(k[0], k[1] + 1):\n diag_len = min(cols - max(ki, 0), rows + min(ki, 0))\n offset = 0\n if ki >= 0:\n if align[:5] == \"RIGHT\":\n offset = diagonal.shape[-1] - diag_len\n else:\n if align[-5:] == \"RIGHT\":\n offset = diagonal.shape[-1] - diag_len\n for i in range(diag_len):\n if ki >= 0:\n out[..., i, i + ki] = diagonal[..., k[1] - ki, i + offset]\n else:\n out[..., i - ki, i] = diagonal[..., k[1] - ki, i + offset]\n return out","def diagonal(nd):\n assert nd.ndim == 2, \"diagonal requires 2 dimensional ndarray\"\n shape_min = hl.min(nd.shape[0], nd.shape[1])\n return hl.nd.array(hl.range(hl.int32(shape_min)).map(lambda i: nd[i, i]))","def putDiagonal(self, vector):\n if type(vector) in [type(1), type(1.)]:\n ids = numerix.arange(self._shape[0])\n tmp = numerix.zeros((self._shape[0],), 'd')\n tmp[:] = vector\n self.put(tmp, ids, ids)\n else:\n ids = numerix.arange(len(vector))\n self.put(vector, ids, ids)","def apply_diagonalO(self, diagO, n):\n for ii in range(len(diagO)):\n self.A[n][:, ii, :] *= diagO[ii]","def diagonal(size, value):\n if not isinstance(size, int):\n raise TypeError(\"int expected, not {}\".format(type(size)))\n\n if not mathutil.is_scalar(value):\n raise TypeError(\n \"int, float or complex expected, not {}\".format(type(value)))\n\n sc = SparkContext.getOrCreate()\n\n shape = (size, size)\n dtype = type(value)\n\n nelem = shape[0]\n\n if value == dtype():\n rdd = sc.emptyRDD()\n else:\n num_partitions = util.get_num_partitions(\n sc,\n util.get_size_of_type(dtype) * nelem\n )\n\n rdd = sc.range(size, numSlices=num_partitions).map(\n lambda m: (m, m, value)\n )\n\n return Matrix(rdd, shape, dtype=dtype, nelem=nelem)","def diagonal(self) -> Float[Array, \" N\"]:\n return self.value * jnp.ones(self.size)","def diag(v, k=0):\n v = wrappers.mpmath.numeric.asmparray(v)\n s = v.shape\n if len(s) == 1:\n n = s[0]+abs(k)\n res = wrappers.mpmath.numeric.zeros((n, n), v.ctx)\n if k >= 0:\n i = k\n else:\n i = (-k) * n\n res[:n-k].flat[i::n+1] = v\n return res\n elif len(s) == 2:\n return np.diagonal(v, k)\n else:\n raise ValueError(\"Input must be 1- or 2-d.\")","def index_condensed_matrix(n, i, j):\n if i == j:\n main_warning(\"Diagonal elements (i=j) are not stored in condensed matrices.\")\n return None\n elif i > j:\n i, j = j, i\n return int(i * (n - (i + 3) * 0.5) + j - 1)","def diagonal(a, offset=0, axis1=0, axis2=1):\n # TODO(okuta): check type\n return a.diagonal(offset, axis1, axis2)","def diagonal_conv(n):\n\n\t# Building bi-dimensional array\n\tlim = int(math.sqrt(n))\n\tconv = np.zeros((lim,lim))\n\n\t# If element on diagonal set to 1/lim\n\tfor i in range(0,lim ):\n\t\tfor j in range(0,lim ):\n\t\t\tif j == i:\n\t\t\t\tconv[i,j]= 1/lim\n\n\t# Reshape to uni-dimensional array\n\tconv = np.reshape(conv,n)\n\n\treturn conv","def Problem2(n):\n diag_entries = np.empty((3,n))\n diag_entries[0] = np.ones(n)*(-1)\n diag_entries[1] = np.ones(n)*2\n diag_entries[2] = np.ones(n)*(-1)\n A = sparse.spdiags(diag_entries, [-1,0,1],n,n,format=\"csr\")\n return A","def diag(v, k=0):\n if isinstance(v, cupy.ndarray):\n if v.ndim == 1:\n size = v.size + abs(k)\n ret = cupy.zeros((size, size), dtype=v.dtype)\n ret.diagonal(k)[:] = v\n return ret\n else:\n return v.diagonal(k)\n else:\n return cupy.array(numpy.diag(v, k))","def diag(self):\n in_diag = (self.rows == self.cols)\n diag = np.zeros(min(self.n, self.n), dtype=np.float64) # default 0.\n diag[self.rows[in_diag]] = self.vals[in_diag]\n return diag","def obter_diagonal(tab, n): \r\n if not eh_tabuleiro(tab) or not (n in [1,2] and type(n)==int):\r\n raise ValueError('obter_diagonal: algum dos argumentos e invalido')\r\n else:\r\n if n==1:\r\n return (tab[0][0],tab[1][1],tab[2][2])\r\n else:\r\n return (tab[2][0],tab[1][1],tab[0][2])","def zero_diag(mat):\n\n return replace_diag(mat, np.zeros(mat.shape[0]))","def Kdiag(self,X,target):\r\n foo = np.zeros((X.shape[0],X.shape[0]))\r\n self.K(X,X,foo)\r\n target += np.diag(foo)","def _modify_diag_with_comm(cov_mat, comm):\n\n modified_cov_mat = np.copy(cov_mat)\n np.fill_diagonal(\n modified_cov_mat,\n comm * np.diag(cov_mat)\n )\n\n return modified_cov_mat","def diag(v, k=0):\n\n if not use_origin_backend(v):\n if not isinstance(v, dparray):\n pass\n else:\n return dpnp_diag(v, k)\n\n return call_origin(numpy.diag, v, k)","def diag_indices(n, ndim=2):\r\n idx = np.arange(n)\r\n return (idx,) * ndim","def diag_indices(n, ndim=2):\n\n if not use_origin_backend():\n return dpnp_diag_indices(n, ndim)\n\n return call_origin(numpy.diag_indices, n, ndim)","def diagonalize(width, height):\n a = create_board(width, height)\n\n for row in range(1, height - 1):\n for col in range(1, width - 1):\n if row == col:\n a[row][col] = 1\n else:\n a[row][col] = 0\n\n return a","def diagonalize(width,height):\r\n A = createBoard(height, width) \r\n \r\n for row in range(height):\r\n for col in range(width):\r\n if row == col:\r\n A[row][col] = 1\r\n else:\r\n A[row][col] = 0 \r\n\r\n return A","def diagonalAtPoint(self, point=..., idx:int=...):\n point, idx = self.resolvePoint(point=point, idx=idx)\n print(idx, type(idx))\n return point.lineToPoint(self.vertices[idx-2])","def implicit_matrix_1d(xsize, centervalue, neighborvalue):\n matrix1d = np.zeros([xsize, xsize])\n np.fill_diagonal(matrix1d, centervalue)\n matrix1d = np.roll(matrix1d, 1, 0)\n np.fill_diagonal(matrix1d, neighborvalue)\n matrix1d = np.roll(matrix1d, -2, 0)\n np.fill_diagonal(matrix1d, neighborvalue)\n matrix1d = np.roll(matrix1d, 1, 0)\n return matrix1d","def apply_diagonal_inplace(self, array: 'Nparray') -> None:\n beta_ptr = 0\n\n if array.size == 2 * self.norb():\n beta_ptr = self.norb()\n\n elif array.size != self.norb():\n raise ValueError('Non-diagonal array passed'\n ' into apply_diagonal_inplace')\n\n if not array.flags['C_CONTIGUOUS']:\n array = numpy.copy(array)\n\n if fqe.settings.use_accelerated_code:\n aarray = array[:self.norb()]\n barray = array[beta_ptr:]\n _apply_diagonal_inplace(self.coeff, aarray, barray,\n self._core.string_alpha_all(),\n self._core.string_beta_all())\n else:\n alpha = numpy.zeros((self._core.lena(),), dtype=numpy.complex128)\n beta = numpy.zeros((self._core.lenb(),), dtype=numpy.complex128)\n\n for alp_cnf in range(self._core.lena()):\n occupation = self._core.string_alpha(alp_cnf)\n diag_ele = 0.0\n for ind in integer_index(occupation):\n diag_ele += array[ind]\n alpha[alp_cnf] = diag_ele\n for bet_cnf in range(self._core.lenb()):\n occupation = self._core.string_beta(bet_cnf)\n diag_ele = 0.0\n for ind in integer_index(occupation):\n diag_ele += array[beta_ptr + ind]\n beta[bet_cnf] = diag_ele\n\n for alp_cnf in range(self._core.lena()):\n for bet_cnf in range(self._core.lenb()):\n self.coeff[alp_cnf,\n bet_cnf] *= alpha[alp_cnf] + beta[bet_cnf]","def identity(n):\r\n I = np.zeros((n, n))\r\n diag = np.ones(n)\r\n np.fill_diagonal(I, diag)\r\n return matrix(I)","def Kdiag(self, X, target):\r\n target+=self._Kdiag(X)","def diagonal_inds(tensor):\n assert tensor.dim() >= 2\n assert tensor.size(0) == tensor.size(1)\n size = tensor.size(0)\n arange_inds = tensor.new(size).long()\n torch.arange(0, tensor.size(0), out=arange_inds)\n return (size + 1) * arange_inds","def create_diagonal(m: NumpyRealArray) -> NumpyRealArray:\n indices = (..., *np.diag_indices(m.shape[-1]))\n retval = np.zeros((*m.shape, m.shape[-1]), dtype=m.dtype)\n retval[indices] = m\n return retval","def symmetrize(a):\n return a + a.T - np.diag(a.diagonal());","def diagonalize(width, height):\n A = createBoard(width, height)\n\n for row in range(1, height - 1):\n for col in range(1, width - 1):\n if row == col:\n A[row][col] = 1\n else:\n A[row][col] = 0\n\n return A","def diagM(l):\r\n dim = len(l)\r\n M = np.zeros((dim, dim))\r\n np.fill_diagonal(M, l)\r\n return matrix(M)","def diag_dom(n, num_entries=None):\n if num_entries is None:\n num_entries = int(n**1.5) - n\n A = np.zeros((n,n))\n rows = np.random.choice(np.arange(0,n), size=num_entries)\n cols = np.random.choice(np.arange(0,n), size=num_entries)\n data = np.random.randint(-4, 4, size=num_entries)\n for i in xrange(num_entries):\n A[rows[i], cols[i]] = data[i]\n for i in xrange(n):\n A[i,i] = np.sum(np.abs(A[i])) + 1\n return A","def diag(M,idx=0):\n n, m = shape_mat(M)\n if idx >= 0:\n return [ M[i][i+idx] for i in xrange( min( n, m-idx ) ) ]\n else:\n return [ M[i-idx][i] for i in xrange( min( n+idx, m ) ) ]","def Kdiag(self,X,target):\r\n target1 = np.zeros(X.shape[0])\r\n target2 = np.zeros(X.shape[0])\r\n self.k1.Kdiag(X[:,self.slice1],target1)\r\n self.k2.Kdiag(X[:,self.slice2],target2)\r\n target += target1 * target2","def diag(diagnoal):\n raise NotImplementedError","def is_diagonal(i, j):\n return 1 if i == j else 0","def is_diagonal(i, j):\n return 1 if i == j else 0","def diagonal(d, axis=0):\n assert d.ndim == 1\n n = d.shape[0]\n times = lambda x: d * x\n trans = lambda x: _hermitian(d) * x\n times, trans = apply_along_axis(times, trans, axis)\n return Operator(times=times, trans=trans, shape=(n,n))","def create_diagonal_image(n):\n alist = []\n for i in range(0, n):\n alist.append([])\n for j in range(0,n):\n if i ==j:\n alist[i].append(1)\n else:\n alist[i].append(0)\n\n ##########\n # img = create_zeroed_image(n)\n # for i in range(n):\n# for j in range(n):\n# if i == j:\n# img[i][j] = 1\n #######\n \n return alist","def diagonal_across(self, current_state):\n # Find the index of the '0' tile\n index = current_state.index('0')\n tile_that_was_swapped = '0'\n\n # If the current state can't perform the 'diagonal' action, then exit the function\n if not self.can_move_diagonally(index):\n return 0, tile_that_was_swapped\n\n # Else, we can move the '0' tile to the other side of the grid\n else:\n # We need to find the diagonal-across position of the '0' tile\n index_to_swap = index\n\n # If the '0' tile is in the top-left corner, then we need to switch it with the bottom-right corner\n if index == 0:\n index_to_swap = self.puzzle_length - 1\n\n # If the '0' tile is in the top-right corner, then we need to switch it with the bottom-left corner\n elif index == self.puzzle_width - 1:\n index_to_swap = self.puzzle_length - self.puzzle_width\n\n # If the '0' tile is in the bottom-left corner, then we need to switch it with the top-right corner\n elif index == self.puzzle_length - self.puzzle_width:\n index_to_swap = self.puzzle_width - 1\n\n # If the '0' tile is in the bottom-right corner, then we need to switch it with the top-left corner\n elif index == self.puzzle_length - 1:\n index_to_swap = 0\n\n # Swap the '0' tile with the other index\n tile_that_was_swapped = current_state[index_to_swap]\n current_state[index] = current_state[index_to_swap]\n current_state[index_to_swap] = '0'\n # end: if-else\n\n return self.cost_of_diagonal_across, tile_that_was_swapped","def replace_diag(mat, newdiag):\n\n if newdiag.ndim>1: \n raise Exception(\"newdiag should be 1-dimensional\")\n if not (mat.shape[0]==mat.shape[1]==newdiag.size):\n raise Exception(\"Incorrect dimensions.\")\n return mat - np.diag(mat.diagonal()) + np.diag(newdiag)","def diagonal(self):\n M = self.rep\n m, n = self.shape\n return [M[i, i] for i in range(min(m, n))]","def diag(x):\r\n xx = as_tensor_variable(x)\r\n if xx.type.ndim == 1:\r\n return alloc_diag(xx)\r\n elif xx.type.ndim == 2:\r\n return extract_diag(xx)\r\n else:\r\n raise TypeError('diag requires vector or matrix argument', x)","def Dmat(numpts, delta=1):\n a = 0.5 / delta * ones(numpts)\n a[0] = 0\n a[-2] = 0\n #b=-2./delta**2*ones(numpts); b[0]=0;b[-1]=0\n c = -0.5 / delta * ones(numpts)\n c[1] = 0\n c[-1] = 0\n return sparse.spdiags([a, c], [-1, 1], numpts, numpts)","def non_clock_spirral_matrix(Matrix, size):\n\tx, y = 0, 0\n\tdx, dy = 1, 0\n\tnx, ny = 0, 0\n\n\tfor i in range(1, size ** 2 + 1):\n\t\tMatrix[y][x] = i\n\n\t\tnx, ny = x + dx, y + dy\n\n\t\tif (0 <= nx < size and 0 <= ny < size and not Matrix[ny][nx]):\n\t\t\tx, y = nx, ny\n\t\telse:\n\t\t\tdx, dy = -dy, dx\n\t\t\tx, y = x + dx, y + dy","def set_value(self, m: int, n: int, value: int) -> None:\n\t\tself.matrix[m][n] = value","def row_col_diag(arr):\n three_sets = np.zeros((8,3), dtype=int)\n for i in range(arr.shape[0]):\n three_sets[i] = arr[i]\n for i in range(arr.shape[1]):\n three_sets[i+3] = arr[:,i]\n three_sets[6] = np.diag(arr)\n three_sets[7] = np.diag(np.flipud(arr))\n return three_sets","def _Kdiag(self, X):\r\n return self.mapping.f(X).flatten()**2","def matDiag(vec):\n ret=matZeros((len(vec),len(vec)))\n for i in range(len(vec)):\n matSet(ret,i,i,vec[i])\n return ret","def get_diagonal_subtensor_view(x, i0, i1):\r\n # We have to cast i0 and i0 to int because python 2.4 (and maybe later)\r\n # do not support indexing with 0-dim, 'int*' ndarrays.\r\n i0 = int(i0)\r\n i1 = int(i1)\r\n if x.shape[i0] < x.shape[i1]:\r\n raise NotImplementedError('is this allowed?')\r\n idx = [slice(None)] * x.ndim\r\n idx[i0] = slice(x.shape[i1] - 1, None, None)\r\n xview = x.__getitem__(tuple(idx))\r\n strides = list(xview.strides)\r\n strides[i1] -= strides[i0]\r\n xview.strides = strides\r\n return xview","def get_reverse_diagonal(self):\n start = 0\n end = 9\n step = 4\n\n return self.grid[start:end:step] # array[start:end:step]","def generate_square_subsequent_mask(sz: int) -> Tensor:\n return torch.triu(torch.ones(sz, sz) * float('-inf'), diagonal=1)","def from_diag(d, context = FloatContext):\n n = len(d)\n S = zeros(n,n,context)\n set_diag(S,d)\n return S","def construct_matrix_A(n):\n n2 = n**2\n D0 = 4*np.ones(n2) # 0th diagonal\n D1 = - np.ones(n2 - 1) # -1st, 1st diagonals\n D1[n-1::n] = 0 # Setting every k*n-1 entries = 0 for k < n\n DN = - np.ones(n2 - n) # -nth, nth diagonals\n return scipy.sparse.diags((DN, D1, D0, D1, DN), (-n, -1, 0, 1, n),\n shape=(n2, n2), format=\"csr\")","def get_forward_diagonal(self):\n start = 2\n end = 7\n step = 2\n\n return self.grid[start:end:step] # array[start:end:step]","def diagonal(step='Metropolis', iters=5000):\n X = mc.Uniform('X', lower=-1., upper=1., value=[0., 0.])\n\n @mc.potential\n def near_diag(X=X):\n if abs(X[0] - X[1]) < .1:\n return 0\n else:\n return -inf\n\n mod = setup_and_sample(vars(), step, iters)\n mod.shape = pl.array([[-1,-1], [-1,-.9], [.9,1], [1,1], [1,.9], [-.9,-1], [-1,-1]])\n mod.true_mean = [0,0]\n mod.true_iqr = ['(-.5,.5)', '(-.5,5)']\n return mod","def spaceDiagonal(self):\n spaceDiagonal = (3**(1/2)) * self.sideLength\n return spaceDiagonal","def diagflat(v, k=0):\n\n if not use_origin_backend(v):\n if not isinstance(v, dparray):\n pass\n else:\n return dpnp_diag(v.ravel(), k)\n\n return call_origin(numpy.diagflat, v, k)","def fill_diagonal(self):\n if not self.is_square():\n raise Exception(u\"Not a square matrix\")\n\n mat = clone_matrix(self.coefficients)\n size = self.get_size()[0]\n permut = list(range(size))\n\n for col in range(size):\n cur_line = col\n best_line = col\n best_value = 0\n for line in range(col, size):\n cur_value = mat[line][col]\n if abs(cur_value) > best_value:\n best_line = line\n best_value = cur_value\n if best_value == 0:\n raise Exception(u\"Singular matrix\")\n permut[cur_line], permut[best_line] = permut[best_line], permut[cur_line]\n for idx in range(size):\n mat[cur_line][idx], mat[best_line][idx] = mat[best_line][idx], mat[cur_line][idx]\n\n return Matrix(mat), permut","def kron_diag(*lts):\n lead_diag = lts[0].diagonal(dim1=-1, dim2=-2)\n if len(lts) == 1: # base case:\n return lead_diag\n trail_diag = kron_diag(*lts[1:])\n diag = lead_diag.unsqueeze(-2) * trail_diag.unsqueeze(-1)\n return diag.mT.reshape(*diag.shape[:-2], -1)","def Dinvmatrix(N):\r\n import numpy as np\r\n D = np.zeros((N,N,2))\r\n D[:,:,0] = np.diag((np.append(np.ones((1,int(N/2))),np.zeros((1,int(N/2))))))\r\n D[:,:,1] = np.diag((np.append(np.zeros((1,int(N/2))),np.ones((1,int(N/2))))))\r\n return D","def DiagonalGate():\n\n def f(x): # pylint: disable=invalid-name\n # x : [batch, 1, length, depth]\n x = jnp.pad(x, [(0, 0), (0, 0), (1, 1), (0, 0)],\n mode='constant', constant_values=0.0)\n depth = x.shape[-1] // 3\n assert 3 * depth == x.shape[-1], ('Depth must be divisible by 3', depth,\n x.shape)\n xs = [\n x[:, :, :-2, :depth], x[:, :, 1:-1, depth:2 * depth],\n x[:, :, 2:, 2 * depth:3 * depth]\n ]\n return jnp.concatenate(xs, axis=3)\n return tl.Fn('DiagonalGate', f)","def col_diag(self):\n rows = self._row_type_2d()\n for i, m in iter_items(self.col_totals):\n rows[i][i] = m\n return self.__class__(rows=rows)","def gen_square_subsequent_mask(sz: int) -> torch.Tensor:\n return torch.triu(torch.ones(sz, sz) * float('-inf'), diagonal=1)","def __init__(self, n):\n self.row = [0]*n\n self.col =[0]*n\n self.diagonal = 0\n self.antidiag = 0\n self.n = n","def Kdiag(self,X,target):\r\n target1 = np.zeros(X.shape[0])\r\n target2 = np.zeros(X.shape[0])\r\n self.k1.Kdiag(X[:,:self.k1.input_dim],target1)\r\n self.k2.Kdiag(X[:,self.k1.input_dim:],target2)\r\n target += target1 * target2","def __init__(self, n):\n self.rows = [0] * n\n self.cols = [0] * n\n self.diagonal1 = 0\n self.diagonal2 = 0\n self.n = n","def Kdiag(self, X, target):\r\n self._K_diag_computations(X)\r\n target+= self.variance*self._K_diag_dvar","def Kdiag(self, X, target):\r\n self._K_diag_computations(X)\r\n target+= self.variance*self._K_diag_dvar","def is_diagonal(row, col):\n return 1 if row == col else 0","def diag(cls, diagonal, domain, shape=None):\n if shape is None:\n N = len(diagonal)\n shape = (N, N)\n return cls.from_rep(SDM.diag(diagonal, domain, shape))","def move_diagonal(self):\r\n if self.movement == \"diagonal\" and self.flag_move:\r\n self.move_ball()\r\n self.canvas.after(50, self.move_diagonal)","def Kdiag(self,X,target):\r\n FX = self._cos(self.basis_alpha[None,:],self.basis_omega[None,:],self.basis_phi[None,:])(X)\r\n np.add(target,np.diag(mdot(FX,self.Gi,FX.T)),target)","def Kdiag(self,X,target):\r\n FX = self._cos(self.basis_alpha[None,:],self.basis_omega[None,:],self.basis_phi[None,:])(X)\r\n np.add(target,np.diag(mdot(FX,self.Gi,FX.T)),target)","def Kdiag(self,X,target):\r\n FX = self._cos(self.basis_alpha[None,:],self.basis_omega[None,:],self.basis_phi[None,:])(X)\r\n np.add(target,np.diag(mdot(FX,self.Gi,FX.T)),target)","def tunneling(N, m=1, *, dtype=None):\n diags = [np.ones(N-m, dtype=int), np.ones(N-m, dtype=int)]\n T = qdiags(diags, [m, -m], dtype=dtype)\n T.isherm = True\n return T","def scale_matrix(N):\n return N * np.identity(2)","def diag(self):\n assert len(self.shape) == 1 or len(self.shape) == 2\n if len(self.shape) == 1:\n dim = self.shape[0]\n qim = self.qhape[0]\n shape = [dim, dim]\n qhape = [qim, qim]\n d = self.dirs[0]\n dirs = [d, -d]\n sects = {}\n for k, v in self.sects.items():\n new_k = (k[0], k[0])\n sects[new_k] = np.diag(v)\n res = type(self)(\n shape,\n qhape=qhape,\n qodulus=self.qodulus,\n sects=sects,\n dirs=dirs,\n dtype=self.dtype,\n )\n return res\n else:\n assert self.invar\n assert self.compatible_indices(self, 0, 1)\n d = self.dirs[0]\n if self.dirs[1] + d != 0:\n warnings.warn(\n \"Automatically flipping dir 1 in diag.\", stacklevel=2\n )\n self = self.flip_dir(1)\n dim = self.shape[0]\n qim = self.qhape[0]\n shape = [dim]\n qhape = [qim]\n dirs = [d]\n sects = {}\n for qnum in qim:\n try:\n diag_block = self[(qnum, qnum)]\n sects[(qnum,)] = np.diag(diag_block)\n except KeyError:\n # The diagonal block was not found, so we move on.\n pass\n res = type(self)(\n shape,\n qhape=qhape,\n qodulus=self.qodulus,\n sects=sects,\n dtype=self.dtype,\n dirs=dirs,\n invar=False,\n )\n return res","def diagonal(t, x, y):\n from math import atan2, sqrt, pi\n angle = atan2(y, x) * 180 / pi\n dist = sqrt(x**2 + y**2)\n lt(t, angle)\n fdbk(t, dist)\n rt(t, angle)","def diag_operator(self, diag_elements, subsystem):\n dim = subsystem.truncated_dim\n index = range(dim)\n diag_matrix = np.zeros((dim, dim), dtype=np.float_)\n diag_matrix[index, index] = diag_elements\n return self.identity_wrap(diag_matrix, subsystem)","def diagonal(matrix):\n if sp.sparse.issparse(matrix):\n diag = np.array(matrix.diagonal())\n else:\n diag = np.diagonal(matrix).copy()\n return diag","def __setitem__(self, idx, value):\n row, col = idx\n\n if row < 0 or row >= self.num_rows:\n raise IndexError(\"Row out of bounds\")\n\n if col < 0 or col >= self.num_cols:\n raise IndexError(\"Col out of bounds\")\n\n if value == self.default:\n del self[row, col]\n return\n\n array_row = self._find_row_before(row)\n\n if (array_row.next_row == None or array_row.next_row.row_number > row):\n new_row = SparseMatrix.MatrixRow()\n new_row.row_number = row\n new_row.next_row = array_row.next_row\n array_row.next_row = new_row\n\n sentinel_entry = SparseMatrix.MatrixEntry()\n new_row.row_sentinel = sentinel_entry\n\n array_row = array_row.next_row\n array_entry = self._find_column_before(array_row, col)\n\n if (array_entry == None or array_entry.next_entry == None or\n array_entry.next_entry.column_number > col):\n new_entry = SparseMatrix.MatrixEntry()\n new_entry.column_number = col\n if array_entry == None:\n new_entry.next_entry = None\n else:\n new_entry.next_entry = array_entry.next_entry\n array_entry.next_entry = new_entry\n\n array_entry = array_entry.next_entry\n array_entry.value = value","def diagonal(self):\n return self.rep.diagonal()","def on_clock_spirral_matrix(Matrix, size):\n\tx, y = 0, 0\n\tdx, dy = 1, 0\n\tnx, ny = 0, 0\n\n\tfor i in range(1, size ** 2 + 1):\n\t\tMatrix[x][y] = i\n\n\t\tnx, ny = x + dx, y + dy\n\n\t\tif (0 <= nx < size and 0 <= ny < size and not Matrix[nx][ny]):\n\t\t\tx, y = nx, ny\n\t\telse:\n\t\t\tdx, dy = -dy, dx\n\t\t\tx, y = x + dx, y + dy","def slidekernelthroughdiagonal(kernel, matrix):\n size_kernel = kernel.shape[0]\n size_matrix = matrix.shape[0]\n result = np.zeros([size_matrix])\n for i in range(size_matrix):\n # Calculate zero padding needed\n padding_b = -min(i - int(size_kernel/2), 0)\n padding_a = -min(size_matrix - int(i + size_kernel/2), 0)\n matrix_selection = matrix[max(0, i-int(size_kernel/2)):min(size_matrix, i+int(size_kernel/2)),max(0, i-int(size_kernel/2)):min(size_matrix, i+int(size_kernel/2))]\n matrix_padded = np.pad(matrix_selection, [(padding_b, padding_a), (padding_b, padding_a)])\n result[i] = np.sum(matrix_padded*kernel)\n return result","def diag(self, X):\n\n raise NotImplementedError(\"base class\")","def right_dimension_pad(array, n):\n if array.ndim >= n:\n return array\n\n nadd = n - array.ndim\n atts = [_new_attribute_label('_dim%i' % i, array) for i in range(nadd)]\n apply_args = [x for item in enumerate(atts) for x in item[::-1]]\n\n ds = array.datashape.copy()\n ds.dim_low = list(ds.dim_low) + ([0] * nadd)\n ds.dim_high = list(ds.dim_high) + ([0] * nadd)\n ds.dim_names = list(ds.dim_names) + atts\n ds.chunk_overlap = list(ds.chunk_overlap) + ([0] * nadd)\n ds.chunk_size = list(ds.chunk_size) + ([1000] * nadd)\n\n return array.apply(*apply_args).redimension(ds.schema)","def block_diagonals(board: Board, pawn_value: int, x: int, y: int) -> None:\n\n for row_index, row in enumerate(board):\n for col_index, _ in enumerate(row):\n if abs(row_index-y) is abs(col_index-x):\n block(board, pawn_value, col_index, row_index)","def sub_major_diags(arr, sub_size):\n rows, cols = arr.shape\n for i in range(rows - sub_size + 1):\n for j in range(cols - sub_size + 1):\n yield arr[range(i, i + sub_size), range(j, j + sub_size)]","def row_diag(self):\n rows = self._row_type_2d()\n for i, m in iter_items(self.row_totals):\n rows[i][i] = m\n return self.__class__(rows=rows)","def eye(size):\n return Matrix.diagonal(size, 1.0)","def pad_csr(a, newshape):\n n, m = a.shape\n a._shape = newshape\n a.indptr = np.pad(a.indptr, (0, newshape[0] - n), 'edge')"],"string":"[\n \"def set_diagonal(self, value = 0):\\n for d in range(self.size):\\n self.write(d, d, value)\",\n \"def set_diag(M,d,idx=0):\\n n, m = shape_mat(M)\\n if idx >= 0:\\n for i, di in enumerate( d ):\\n M[i][i+idx] = di\\n else:\\n for i, di in enumerate( d ):\\n M[i-idx][i] = di\",\n \"def fill_diagonal(a, val):\\r\\n if a.ndim < 2:\\r\\n raise ValueError(\\\"array must be at least 2-d\\\")\\r\\n if a.ndim == 2:\\r\\n # Explicit, fast formula for the common case. For 2-d arrays, we\\r\\n # accept rectangular ones.\\r\\n step = a.shape[1] + 1\\r\\n else:\\r\\n # For more than d=2, the strided formula is only valid for arrays with\\r\\n # all dimensions equal, so we check first.\\r\\n if not np.alltrue(np.diff(a.shape) == 0):\\r\\n raise ValueError(\\\"All dimensions of input must be of equal length\\\")\\r\\n step = np.cumprod((1,) + a.shape[:-1]).sum()\\r\\n\\r\\n # Write the value out into the diagonal.\\r\\n a.flat[::step] = val\",\n \"def fill_diagonal(a, val):\\r\\n return fill_diagonal_(a, val)\",\n \"def set_diag(x, new_diag):\\n arr_shape = x.shape\\n off_diag = (1 - _torch.eye(arr_shape[-1])) * x\\n diag = _torch.einsum(\\\"ij,...i->...ij\\\", _torch.eye(new_diag.shape[-1]), new_diag)\\n return diag + off_diag\",\n \"def _set_diag(laplacian, value, norm_laplacian):\\n n_nodes = laplacian.shape[0]\\n # We need all entries in the diagonal to values\\n if not sparse.isspmatrix(laplacian):\\n if norm_laplacian:\\n laplacian.flat[::n_nodes + 1] = value\\n else:\\n laplacian = laplacian.tocoo()\\n if norm_laplacian:\\n diag_idx = (laplacian.row == laplacian.col)\\n laplacian.data[diag_idx] = value\\n # If the matrix has a small number of diagonals (as in the\\n # case of structured matrices coming from images), the\\n # dia format might be best suited for matvec products:\\n n_diags = np.unique(laplacian.row - laplacian.col).size\\n if n_diags <= 7:\\n # 3 or less outer diagonals on each side\\n laplacian = laplacian.todia()\\n else:\\n # csr has the fastest matvec and is thus best suited to\\n # arpack\\n laplacian = laplacian.tocsr()\\n return laplacian\",\n \"def fill_diagonal(x1, val, wrap=False):\\n\\n x1_desc = dpnp.get_dpnp_descriptor(\\n x1, copy_when_strides=False, copy_when_nondefault_queue=False\\n )\\n if x1_desc:\\n if not dpnp.isscalar(val):\\n pass\\n elif wrap:\\n pass\\n else:\\n return dpnp_fill_diagonal(x1_desc, val)\\n\\n return call_origin(numpy.fill_diagonal, x1, val, wrap, dpnp_inplace=True)\",\n \"def matrix_set_diag(input_np, diagonal, k=0, align=\\\"RIGHT_LEFT\\\"):\\n out = np.array(input_np, copy=True)\\n\\n cols = input_np.shape[-1]\\n rows = input_np.shape[-2]\\n\\n onlyOneDiagonal = True\\n if isinstance(k, (tuple, list)):\\n if len(k) < 2 or k[0] == k[1]:\\n k = k[0]\\n else:\\n onlyOneDiagonal = False\\n\\n if onlyOneDiagonal:\\n for i in range(diagonal.shape[-1]):\\n if k >= 0:\\n out[..., i, i + k] = diagonal[..., i]\\n else:\\n out[..., i - k, i] = diagonal[..., i]\\n else:\\n for ki in range(k[0], k[1] + 1):\\n diag_len = min(cols - max(ki, 0), rows + min(ki, 0))\\n offset = 0\\n if ki >= 0:\\n if align[:5] == \\\"RIGHT\\\":\\n offset = diagonal.shape[-1] - diag_len\\n else:\\n if align[-5:] == \\\"RIGHT\\\":\\n offset = diagonal.shape[-1] - diag_len\\n for i in range(diag_len):\\n if ki >= 0:\\n out[..., i, i + ki] = diagonal[..., k[1] - ki, i + offset]\\n else:\\n out[..., i - ki, i] = diagonal[..., k[1] - ki, i + offset]\\n return out\",\n \"def diagonal(nd):\\n assert nd.ndim == 2, \\\"diagonal requires 2 dimensional ndarray\\\"\\n shape_min = hl.min(nd.shape[0], nd.shape[1])\\n return hl.nd.array(hl.range(hl.int32(shape_min)).map(lambda i: nd[i, i]))\",\n \"def putDiagonal(self, vector):\\n if type(vector) in [type(1), type(1.)]:\\n ids = numerix.arange(self._shape[0])\\n tmp = numerix.zeros((self._shape[0],), 'd')\\n tmp[:] = vector\\n self.put(tmp, ids, ids)\\n else:\\n ids = numerix.arange(len(vector))\\n self.put(vector, ids, ids)\",\n \"def apply_diagonalO(self, diagO, n):\\n for ii in range(len(diagO)):\\n self.A[n][:, ii, :] *= diagO[ii]\",\n \"def diagonal(size, value):\\n if not isinstance(size, int):\\n raise TypeError(\\\"int expected, not {}\\\".format(type(size)))\\n\\n if not mathutil.is_scalar(value):\\n raise TypeError(\\n \\\"int, float or complex expected, not {}\\\".format(type(value)))\\n\\n sc = SparkContext.getOrCreate()\\n\\n shape = (size, size)\\n dtype = type(value)\\n\\n nelem = shape[0]\\n\\n if value == dtype():\\n rdd = sc.emptyRDD()\\n else:\\n num_partitions = util.get_num_partitions(\\n sc,\\n util.get_size_of_type(dtype) * nelem\\n )\\n\\n rdd = sc.range(size, numSlices=num_partitions).map(\\n lambda m: (m, m, value)\\n )\\n\\n return Matrix(rdd, shape, dtype=dtype, nelem=nelem)\",\n \"def diagonal(self) -> Float[Array, \\\" N\\\"]:\\n return self.value * jnp.ones(self.size)\",\n \"def diag(v, k=0):\\n v = wrappers.mpmath.numeric.asmparray(v)\\n s = v.shape\\n if len(s) == 1:\\n n = s[0]+abs(k)\\n res = wrappers.mpmath.numeric.zeros((n, n), v.ctx)\\n if k >= 0:\\n i = k\\n else:\\n i = (-k) * n\\n res[:n-k].flat[i::n+1] = v\\n return res\\n elif len(s) == 2:\\n return np.diagonal(v, k)\\n else:\\n raise ValueError(\\\"Input must be 1- or 2-d.\\\")\",\n \"def index_condensed_matrix(n, i, j):\\n if i == j:\\n main_warning(\\\"Diagonal elements (i=j) are not stored in condensed matrices.\\\")\\n return None\\n elif i > j:\\n i, j = j, i\\n return int(i * (n - (i + 3) * 0.5) + j - 1)\",\n \"def diagonal(a, offset=0, axis1=0, axis2=1):\\n # TODO(okuta): check type\\n return a.diagonal(offset, axis1, axis2)\",\n \"def diagonal_conv(n):\\n\\n\\t# Building bi-dimensional array\\n\\tlim = int(math.sqrt(n))\\n\\tconv = np.zeros((lim,lim))\\n\\n\\t# If element on diagonal set to 1/lim\\n\\tfor i in range(0,lim ):\\n\\t\\tfor j in range(0,lim ):\\n\\t\\t\\tif j == i:\\n\\t\\t\\t\\tconv[i,j]= 1/lim\\n\\n\\t# Reshape to uni-dimensional array\\n\\tconv = np.reshape(conv,n)\\n\\n\\treturn conv\",\n \"def Problem2(n):\\n diag_entries = np.empty((3,n))\\n diag_entries[0] = np.ones(n)*(-1)\\n diag_entries[1] = np.ones(n)*2\\n diag_entries[2] = np.ones(n)*(-1)\\n A = sparse.spdiags(diag_entries, [-1,0,1],n,n,format=\\\"csr\\\")\\n return A\",\n \"def diag(v, k=0):\\n if isinstance(v, cupy.ndarray):\\n if v.ndim == 1:\\n size = v.size + abs(k)\\n ret = cupy.zeros((size, size), dtype=v.dtype)\\n ret.diagonal(k)[:] = v\\n return ret\\n else:\\n return v.diagonal(k)\\n else:\\n return cupy.array(numpy.diag(v, k))\",\n \"def diag(self):\\n in_diag = (self.rows == self.cols)\\n diag = np.zeros(min(self.n, self.n), dtype=np.float64) # default 0.\\n diag[self.rows[in_diag]] = self.vals[in_diag]\\n return diag\",\n \"def obter_diagonal(tab, n): \\r\\n if not eh_tabuleiro(tab) or not (n in [1,2] and type(n)==int):\\r\\n raise ValueError('obter_diagonal: algum dos argumentos e invalido')\\r\\n else:\\r\\n if n==1:\\r\\n return (tab[0][0],tab[1][1],tab[2][2])\\r\\n else:\\r\\n return (tab[2][0],tab[1][1],tab[0][2])\",\n \"def zero_diag(mat):\\n\\n return replace_diag(mat, np.zeros(mat.shape[0]))\",\n \"def Kdiag(self,X,target):\\r\\n foo = np.zeros((X.shape[0],X.shape[0]))\\r\\n self.K(X,X,foo)\\r\\n target += np.diag(foo)\",\n \"def _modify_diag_with_comm(cov_mat, comm):\\n\\n modified_cov_mat = np.copy(cov_mat)\\n np.fill_diagonal(\\n modified_cov_mat,\\n comm * np.diag(cov_mat)\\n )\\n\\n return modified_cov_mat\",\n \"def diag(v, k=0):\\n\\n if not use_origin_backend(v):\\n if not isinstance(v, dparray):\\n pass\\n else:\\n return dpnp_diag(v, k)\\n\\n return call_origin(numpy.diag, v, k)\",\n \"def diag_indices(n, ndim=2):\\r\\n idx = np.arange(n)\\r\\n return (idx,) * ndim\",\n \"def diag_indices(n, ndim=2):\\n\\n if not use_origin_backend():\\n return dpnp_diag_indices(n, ndim)\\n\\n return call_origin(numpy.diag_indices, n, ndim)\",\n \"def diagonalize(width, height):\\n a = create_board(width, height)\\n\\n for row in range(1, height - 1):\\n for col in range(1, width - 1):\\n if row == col:\\n a[row][col] = 1\\n else:\\n a[row][col] = 0\\n\\n return a\",\n \"def diagonalize(width,height):\\r\\n A = createBoard(height, width) \\r\\n \\r\\n for row in range(height):\\r\\n for col in range(width):\\r\\n if row == col:\\r\\n A[row][col] = 1\\r\\n else:\\r\\n A[row][col] = 0 \\r\\n\\r\\n return A\",\n \"def diagonalAtPoint(self, point=..., idx:int=...):\\n point, idx = self.resolvePoint(point=point, idx=idx)\\n print(idx, type(idx))\\n return point.lineToPoint(self.vertices[idx-2])\",\n \"def implicit_matrix_1d(xsize, centervalue, neighborvalue):\\n matrix1d = np.zeros([xsize, xsize])\\n np.fill_diagonal(matrix1d, centervalue)\\n matrix1d = np.roll(matrix1d, 1, 0)\\n np.fill_diagonal(matrix1d, neighborvalue)\\n matrix1d = np.roll(matrix1d, -2, 0)\\n np.fill_diagonal(matrix1d, neighborvalue)\\n matrix1d = np.roll(matrix1d, 1, 0)\\n return matrix1d\",\n \"def apply_diagonal_inplace(self, array: 'Nparray') -> None:\\n beta_ptr = 0\\n\\n if array.size == 2 * self.norb():\\n beta_ptr = self.norb()\\n\\n elif array.size != self.norb():\\n raise ValueError('Non-diagonal array passed'\\n ' into apply_diagonal_inplace')\\n\\n if not array.flags['C_CONTIGUOUS']:\\n array = numpy.copy(array)\\n\\n if fqe.settings.use_accelerated_code:\\n aarray = array[:self.norb()]\\n barray = array[beta_ptr:]\\n _apply_diagonal_inplace(self.coeff, aarray, barray,\\n self._core.string_alpha_all(),\\n self._core.string_beta_all())\\n else:\\n alpha = numpy.zeros((self._core.lena(),), dtype=numpy.complex128)\\n beta = numpy.zeros((self._core.lenb(),), dtype=numpy.complex128)\\n\\n for alp_cnf in range(self._core.lena()):\\n occupation = self._core.string_alpha(alp_cnf)\\n diag_ele = 0.0\\n for ind in integer_index(occupation):\\n diag_ele += array[ind]\\n alpha[alp_cnf] = diag_ele\\n for bet_cnf in range(self._core.lenb()):\\n occupation = self._core.string_beta(bet_cnf)\\n diag_ele = 0.0\\n for ind in integer_index(occupation):\\n diag_ele += array[beta_ptr + ind]\\n beta[bet_cnf] = diag_ele\\n\\n for alp_cnf in range(self._core.lena()):\\n for bet_cnf in range(self._core.lenb()):\\n self.coeff[alp_cnf,\\n bet_cnf] *= alpha[alp_cnf] + beta[bet_cnf]\",\n \"def identity(n):\\r\\n I = np.zeros((n, n))\\r\\n diag = np.ones(n)\\r\\n np.fill_diagonal(I, diag)\\r\\n return matrix(I)\",\n \"def Kdiag(self, X, target):\\r\\n target+=self._Kdiag(X)\",\n \"def diagonal_inds(tensor):\\n assert tensor.dim() >= 2\\n assert tensor.size(0) == tensor.size(1)\\n size = tensor.size(0)\\n arange_inds = tensor.new(size).long()\\n torch.arange(0, tensor.size(0), out=arange_inds)\\n return (size + 1) * arange_inds\",\n \"def create_diagonal(m: NumpyRealArray) -> NumpyRealArray:\\n indices = (..., *np.diag_indices(m.shape[-1]))\\n retval = np.zeros((*m.shape, m.shape[-1]), dtype=m.dtype)\\n retval[indices] = m\\n return retval\",\n \"def symmetrize(a):\\n return a + a.T - np.diag(a.diagonal());\",\n \"def diagonalize(width, height):\\n A = createBoard(width, height)\\n\\n for row in range(1, height - 1):\\n for col in range(1, width - 1):\\n if row == col:\\n A[row][col] = 1\\n else:\\n A[row][col] = 0\\n\\n return A\",\n \"def diagM(l):\\r\\n dim = len(l)\\r\\n M = np.zeros((dim, dim))\\r\\n np.fill_diagonal(M, l)\\r\\n return matrix(M)\",\n \"def diag_dom(n, num_entries=None):\\n if num_entries is None:\\n num_entries = int(n**1.5) - n\\n A = np.zeros((n,n))\\n rows = np.random.choice(np.arange(0,n), size=num_entries)\\n cols = np.random.choice(np.arange(0,n), size=num_entries)\\n data = np.random.randint(-4, 4, size=num_entries)\\n for i in xrange(num_entries):\\n A[rows[i], cols[i]] = data[i]\\n for i in xrange(n):\\n A[i,i] = np.sum(np.abs(A[i])) + 1\\n return A\",\n \"def diag(M,idx=0):\\n n, m = shape_mat(M)\\n if idx >= 0:\\n return [ M[i][i+idx] for i in xrange( min( n, m-idx ) ) ]\\n else:\\n return [ M[i-idx][i] for i in xrange( min( n+idx, m ) ) ]\",\n \"def Kdiag(self,X,target):\\r\\n target1 = np.zeros(X.shape[0])\\r\\n target2 = np.zeros(X.shape[0])\\r\\n self.k1.Kdiag(X[:,self.slice1],target1)\\r\\n self.k2.Kdiag(X[:,self.slice2],target2)\\r\\n target += target1 * target2\",\n \"def diag(diagnoal):\\n raise NotImplementedError\",\n \"def is_diagonal(i, j):\\n return 1 if i == j else 0\",\n \"def is_diagonal(i, j):\\n return 1 if i == j else 0\",\n \"def diagonal(d, axis=0):\\n assert d.ndim == 1\\n n = d.shape[0]\\n times = lambda x: d * x\\n trans = lambda x: _hermitian(d) * x\\n times, trans = apply_along_axis(times, trans, axis)\\n return Operator(times=times, trans=trans, shape=(n,n))\",\n \"def create_diagonal_image(n):\\n alist = []\\n for i in range(0, n):\\n alist.append([])\\n for j in range(0,n):\\n if i ==j:\\n alist[i].append(1)\\n else:\\n alist[i].append(0)\\n\\n ##########\\n # img = create_zeroed_image(n)\\n # for i in range(n):\\n# for j in range(n):\\n# if i == j:\\n# img[i][j] = 1\\n #######\\n \\n return alist\",\n \"def diagonal_across(self, current_state):\\n # Find the index of the '0' tile\\n index = current_state.index('0')\\n tile_that_was_swapped = '0'\\n\\n # If the current state can't perform the 'diagonal' action, then exit the function\\n if not self.can_move_diagonally(index):\\n return 0, tile_that_was_swapped\\n\\n # Else, we can move the '0' tile to the other side of the grid\\n else:\\n # We need to find the diagonal-across position of the '0' tile\\n index_to_swap = index\\n\\n # If the '0' tile is in the top-left corner, then we need to switch it with the bottom-right corner\\n if index == 0:\\n index_to_swap = self.puzzle_length - 1\\n\\n # If the '0' tile is in the top-right corner, then we need to switch it with the bottom-left corner\\n elif index == self.puzzle_width - 1:\\n index_to_swap = self.puzzle_length - self.puzzle_width\\n\\n # If the '0' tile is in the bottom-left corner, then we need to switch it with the top-right corner\\n elif index == self.puzzle_length - self.puzzle_width:\\n index_to_swap = self.puzzle_width - 1\\n\\n # If the '0' tile is in the bottom-right corner, then we need to switch it with the top-left corner\\n elif index == self.puzzle_length - 1:\\n index_to_swap = 0\\n\\n # Swap the '0' tile with the other index\\n tile_that_was_swapped = current_state[index_to_swap]\\n current_state[index] = current_state[index_to_swap]\\n current_state[index_to_swap] = '0'\\n # end: if-else\\n\\n return self.cost_of_diagonal_across, tile_that_was_swapped\",\n \"def replace_diag(mat, newdiag):\\n\\n if newdiag.ndim>1: \\n raise Exception(\\\"newdiag should be 1-dimensional\\\")\\n if not (mat.shape[0]==mat.shape[1]==newdiag.size):\\n raise Exception(\\\"Incorrect dimensions.\\\")\\n return mat - np.diag(mat.diagonal()) + np.diag(newdiag)\",\n \"def diagonal(self):\\n M = self.rep\\n m, n = self.shape\\n return [M[i, i] for i in range(min(m, n))]\",\n \"def diag(x):\\r\\n xx = as_tensor_variable(x)\\r\\n if xx.type.ndim == 1:\\r\\n return alloc_diag(xx)\\r\\n elif xx.type.ndim == 2:\\r\\n return extract_diag(xx)\\r\\n else:\\r\\n raise TypeError('diag requires vector or matrix argument', x)\",\n \"def Dmat(numpts, delta=1):\\n a = 0.5 / delta * ones(numpts)\\n a[0] = 0\\n a[-2] = 0\\n #b=-2./delta**2*ones(numpts); b[0]=0;b[-1]=0\\n c = -0.5 / delta * ones(numpts)\\n c[1] = 0\\n c[-1] = 0\\n return sparse.spdiags([a, c], [-1, 1], numpts, numpts)\",\n \"def non_clock_spirral_matrix(Matrix, size):\\n\\tx, y = 0, 0\\n\\tdx, dy = 1, 0\\n\\tnx, ny = 0, 0\\n\\n\\tfor i in range(1, size ** 2 + 1):\\n\\t\\tMatrix[y][x] = i\\n\\n\\t\\tnx, ny = x + dx, y + dy\\n\\n\\t\\tif (0 <= nx < size and 0 <= ny < size and not Matrix[ny][nx]):\\n\\t\\t\\tx, y = nx, ny\\n\\t\\telse:\\n\\t\\t\\tdx, dy = -dy, dx\\n\\t\\t\\tx, y = x + dx, y + dy\",\n \"def set_value(self, m: int, n: int, value: int) -> None:\\n\\t\\tself.matrix[m][n] = value\",\n \"def row_col_diag(arr):\\n three_sets = np.zeros((8,3), dtype=int)\\n for i in range(arr.shape[0]):\\n three_sets[i] = arr[i]\\n for i in range(arr.shape[1]):\\n three_sets[i+3] = arr[:,i]\\n three_sets[6] = np.diag(arr)\\n three_sets[7] = np.diag(np.flipud(arr))\\n return three_sets\",\n \"def _Kdiag(self, X):\\r\\n return self.mapping.f(X).flatten()**2\",\n \"def matDiag(vec):\\n ret=matZeros((len(vec),len(vec)))\\n for i in range(len(vec)):\\n matSet(ret,i,i,vec[i])\\n return ret\",\n \"def get_diagonal_subtensor_view(x, i0, i1):\\r\\n # We have to cast i0 and i0 to int because python 2.4 (and maybe later)\\r\\n # do not support indexing with 0-dim, 'int*' ndarrays.\\r\\n i0 = int(i0)\\r\\n i1 = int(i1)\\r\\n if x.shape[i0] < x.shape[i1]:\\r\\n raise NotImplementedError('is this allowed?')\\r\\n idx = [slice(None)] * x.ndim\\r\\n idx[i0] = slice(x.shape[i1] - 1, None, None)\\r\\n xview = x.__getitem__(tuple(idx))\\r\\n strides = list(xview.strides)\\r\\n strides[i1] -= strides[i0]\\r\\n xview.strides = strides\\r\\n return xview\",\n \"def get_reverse_diagonal(self):\\n start = 0\\n end = 9\\n step = 4\\n\\n return self.grid[start:end:step] # array[start:end:step]\",\n \"def generate_square_subsequent_mask(sz: int) -> Tensor:\\n return torch.triu(torch.ones(sz, sz) * float('-inf'), diagonal=1)\",\n \"def from_diag(d, context = FloatContext):\\n n = len(d)\\n S = zeros(n,n,context)\\n set_diag(S,d)\\n return S\",\n \"def construct_matrix_A(n):\\n n2 = n**2\\n D0 = 4*np.ones(n2) # 0th diagonal\\n D1 = - np.ones(n2 - 1) # -1st, 1st diagonals\\n D1[n-1::n] = 0 # Setting every k*n-1 entries = 0 for k < n\\n DN = - np.ones(n2 - n) # -nth, nth diagonals\\n return scipy.sparse.diags((DN, D1, D0, D1, DN), (-n, -1, 0, 1, n),\\n shape=(n2, n2), format=\\\"csr\\\")\",\n \"def get_forward_diagonal(self):\\n start = 2\\n end = 7\\n step = 2\\n\\n return self.grid[start:end:step] # array[start:end:step]\",\n \"def diagonal(step='Metropolis', iters=5000):\\n X = mc.Uniform('X', lower=-1., upper=1., value=[0., 0.])\\n\\n @mc.potential\\n def near_diag(X=X):\\n if abs(X[0] - X[1]) < .1:\\n return 0\\n else:\\n return -inf\\n\\n mod = setup_and_sample(vars(), step, iters)\\n mod.shape = pl.array([[-1,-1], [-1,-.9], [.9,1], [1,1], [1,.9], [-.9,-1], [-1,-1]])\\n mod.true_mean = [0,0]\\n mod.true_iqr = ['(-.5,.5)', '(-.5,5)']\\n return mod\",\n \"def spaceDiagonal(self):\\n spaceDiagonal = (3**(1/2)) * self.sideLength\\n return spaceDiagonal\",\n \"def diagflat(v, k=0):\\n\\n if not use_origin_backend(v):\\n if not isinstance(v, dparray):\\n pass\\n else:\\n return dpnp_diag(v.ravel(), k)\\n\\n return call_origin(numpy.diagflat, v, k)\",\n \"def fill_diagonal(self):\\n if not self.is_square():\\n raise Exception(u\\\"Not a square matrix\\\")\\n\\n mat = clone_matrix(self.coefficients)\\n size = self.get_size()[0]\\n permut = list(range(size))\\n\\n for col in range(size):\\n cur_line = col\\n best_line = col\\n best_value = 0\\n for line in range(col, size):\\n cur_value = mat[line][col]\\n if abs(cur_value) > best_value:\\n best_line = line\\n best_value = cur_value\\n if best_value == 0:\\n raise Exception(u\\\"Singular matrix\\\")\\n permut[cur_line], permut[best_line] = permut[best_line], permut[cur_line]\\n for idx in range(size):\\n mat[cur_line][idx], mat[best_line][idx] = mat[best_line][idx], mat[cur_line][idx]\\n\\n return Matrix(mat), permut\",\n \"def kron_diag(*lts):\\n lead_diag = lts[0].diagonal(dim1=-1, dim2=-2)\\n if len(lts) == 1: # base case:\\n return lead_diag\\n trail_diag = kron_diag(*lts[1:])\\n diag = lead_diag.unsqueeze(-2) * trail_diag.unsqueeze(-1)\\n return diag.mT.reshape(*diag.shape[:-2], -1)\",\n \"def Dinvmatrix(N):\\r\\n import numpy as np\\r\\n D = np.zeros((N,N,2))\\r\\n D[:,:,0] = np.diag((np.append(np.ones((1,int(N/2))),np.zeros((1,int(N/2))))))\\r\\n D[:,:,1] = np.diag((np.append(np.zeros((1,int(N/2))),np.ones((1,int(N/2))))))\\r\\n return D\",\n \"def DiagonalGate():\\n\\n def f(x): # pylint: disable=invalid-name\\n # x : [batch, 1, length, depth]\\n x = jnp.pad(x, [(0, 0), (0, 0), (1, 1), (0, 0)],\\n mode='constant', constant_values=0.0)\\n depth = x.shape[-1] // 3\\n assert 3 * depth == x.shape[-1], ('Depth must be divisible by 3', depth,\\n x.shape)\\n xs = [\\n x[:, :, :-2, :depth], x[:, :, 1:-1, depth:2 * depth],\\n x[:, :, 2:, 2 * depth:3 * depth]\\n ]\\n return jnp.concatenate(xs, axis=3)\\n return tl.Fn('DiagonalGate', f)\",\n \"def col_diag(self):\\n rows = self._row_type_2d()\\n for i, m in iter_items(self.col_totals):\\n rows[i][i] = m\\n return self.__class__(rows=rows)\",\n \"def gen_square_subsequent_mask(sz: int) -> torch.Tensor:\\n return torch.triu(torch.ones(sz, sz) * float('-inf'), diagonal=1)\",\n \"def __init__(self, n):\\n self.row = [0]*n\\n self.col =[0]*n\\n self.diagonal = 0\\n self.antidiag = 0\\n self.n = n\",\n \"def Kdiag(self,X,target):\\r\\n target1 = np.zeros(X.shape[0])\\r\\n target2 = np.zeros(X.shape[0])\\r\\n self.k1.Kdiag(X[:,:self.k1.input_dim],target1)\\r\\n self.k2.Kdiag(X[:,self.k1.input_dim:],target2)\\r\\n target += target1 * target2\",\n \"def __init__(self, n):\\n self.rows = [0] * n\\n self.cols = [0] * n\\n self.diagonal1 = 0\\n self.diagonal2 = 0\\n self.n = n\",\n \"def Kdiag(self, X, target):\\r\\n self._K_diag_computations(X)\\r\\n target+= self.variance*self._K_diag_dvar\",\n \"def Kdiag(self, X, target):\\r\\n self._K_diag_computations(X)\\r\\n target+= self.variance*self._K_diag_dvar\",\n \"def is_diagonal(row, col):\\n return 1 if row == col else 0\",\n \"def diag(cls, diagonal, domain, shape=None):\\n if shape is None:\\n N = len(diagonal)\\n shape = (N, N)\\n return cls.from_rep(SDM.diag(diagonal, domain, shape))\",\n \"def move_diagonal(self):\\r\\n if self.movement == \\\"diagonal\\\" and self.flag_move:\\r\\n self.move_ball()\\r\\n self.canvas.after(50, self.move_diagonal)\",\n \"def Kdiag(self,X,target):\\r\\n FX = self._cos(self.basis_alpha[None,:],self.basis_omega[None,:],self.basis_phi[None,:])(X)\\r\\n np.add(target,np.diag(mdot(FX,self.Gi,FX.T)),target)\",\n \"def Kdiag(self,X,target):\\r\\n FX = self._cos(self.basis_alpha[None,:],self.basis_omega[None,:],self.basis_phi[None,:])(X)\\r\\n np.add(target,np.diag(mdot(FX,self.Gi,FX.T)),target)\",\n \"def Kdiag(self,X,target):\\r\\n FX = self._cos(self.basis_alpha[None,:],self.basis_omega[None,:],self.basis_phi[None,:])(X)\\r\\n np.add(target,np.diag(mdot(FX,self.Gi,FX.T)),target)\",\n \"def tunneling(N, m=1, *, dtype=None):\\n diags = [np.ones(N-m, dtype=int), np.ones(N-m, dtype=int)]\\n T = qdiags(diags, [m, -m], dtype=dtype)\\n T.isherm = True\\n return T\",\n \"def scale_matrix(N):\\n return N * np.identity(2)\",\n \"def diag(self):\\n assert len(self.shape) == 1 or len(self.shape) == 2\\n if len(self.shape) == 1:\\n dim = self.shape[0]\\n qim = self.qhape[0]\\n shape = [dim, dim]\\n qhape = [qim, qim]\\n d = self.dirs[0]\\n dirs = [d, -d]\\n sects = {}\\n for k, v in self.sects.items():\\n new_k = (k[0], k[0])\\n sects[new_k] = np.diag(v)\\n res = type(self)(\\n shape,\\n qhape=qhape,\\n qodulus=self.qodulus,\\n sects=sects,\\n dirs=dirs,\\n dtype=self.dtype,\\n )\\n return res\\n else:\\n assert self.invar\\n assert self.compatible_indices(self, 0, 1)\\n d = self.dirs[0]\\n if self.dirs[1] + d != 0:\\n warnings.warn(\\n \\\"Automatically flipping dir 1 in diag.\\\", stacklevel=2\\n )\\n self = self.flip_dir(1)\\n dim = self.shape[0]\\n qim = self.qhape[0]\\n shape = [dim]\\n qhape = [qim]\\n dirs = [d]\\n sects = {}\\n for qnum in qim:\\n try:\\n diag_block = self[(qnum, qnum)]\\n sects[(qnum,)] = np.diag(diag_block)\\n except KeyError:\\n # The diagonal block was not found, so we move on.\\n pass\\n res = type(self)(\\n shape,\\n qhape=qhape,\\n qodulus=self.qodulus,\\n sects=sects,\\n dtype=self.dtype,\\n dirs=dirs,\\n invar=False,\\n )\\n return res\",\n \"def diagonal(t, x, y):\\n from math import atan2, sqrt, pi\\n angle = atan2(y, x) * 180 / pi\\n dist = sqrt(x**2 + y**2)\\n lt(t, angle)\\n fdbk(t, dist)\\n rt(t, angle)\",\n \"def diag_operator(self, diag_elements, subsystem):\\n dim = subsystem.truncated_dim\\n index = range(dim)\\n diag_matrix = np.zeros((dim, dim), dtype=np.float_)\\n diag_matrix[index, index] = diag_elements\\n return self.identity_wrap(diag_matrix, subsystem)\",\n \"def diagonal(matrix):\\n if sp.sparse.issparse(matrix):\\n diag = np.array(matrix.diagonal())\\n else:\\n diag = np.diagonal(matrix).copy()\\n return diag\",\n \"def __setitem__(self, idx, value):\\n row, col = idx\\n\\n if row < 0 or row >= self.num_rows:\\n raise IndexError(\\\"Row out of bounds\\\")\\n\\n if col < 0 or col >= self.num_cols:\\n raise IndexError(\\\"Col out of bounds\\\")\\n\\n if value == self.default:\\n del self[row, col]\\n return\\n\\n array_row = self._find_row_before(row)\\n\\n if (array_row.next_row == None or array_row.next_row.row_number > row):\\n new_row = SparseMatrix.MatrixRow()\\n new_row.row_number = row\\n new_row.next_row = array_row.next_row\\n array_row.next_row = new_row\\n\\n sentinel_entry = SparseMatrix.MatrixEntry()\\n new_row.row_sentinel = sentinel_entry\\n\\n array_row = array_row.next_row\\n array_entry = self._find_column_before(array_row, col)\\n\\n if (array_entry == None or array_entry.next_entry == None or\\n array_entry.next_entry.column_number > col):\\n new_entry = SparseMatrix.MatrixEntry()\\n new_entry.column_number = col\\n if array_entry == None:\\n new_entry.next_entry = None\\n else:\\n new_entry.next_entry = array_entry.next_entry\\n array_entry.next_entry = new_entry\\n\\n array_entry = array_entry.next_entry\\n array_entry.value = value\",\n \"def diagonal(self):\\n return self.rep.diagonal()\",\n \"def on_clock_spirral_matrix(Matrix, size):\\n\\tx, y = 0, 0\\n\\tdx, dy = 1, 0\\n\\tnx, ny = 0, 0\\n\\n\\tfor i in range(1, size ** 2 + 1):\\n\\t\\tMatrix[x][y] = i\\n\\n\\t\\tnx, ny = x + dx, y + dy\\n\\n\\t\\tif (0 <= nx < size and 0 <= ny < size and not Matrix[nx][ny]):\\n\\t\\t\\tx, y = nx, ny\\n\\t\\telse:\\n\\t\\t\\tdx, dy = -dy, dx\\n\\t\\t\\tx, y = x + dx, y + dy\",\n \"def slidekernelthroughdiagonal(kernel, matrix):\\n size_kernel = kernel.shape[0]\\n size_matrix = matrix.shape[0]\\n result = np.zeros([size_matrix])\\n for i in range(size_matrix):\\n # Calculate zero padding needed\\n padding_b = -min(i - int(size_kernel/2), 0)\\n padding_a = -min(size_matrix - int(i + size_kernel/2), 0)\\n matrix_selection = matrix[max(0, i-int(size_kernel/2)):min(size_matrix, i+int(size_kernel/2)),max(0, i-int(size_kernel/2)):min(size_matrix, i+int(size_kernel/2))]\\n matrix_padded = np.pad(matrix_selection, [(padding_b, padding_a), (padding_b, padding_a)])\\n result[i] = np.sum(matrix_padded*kernel)\\n return result\",\n \"def diag(self, X):\\n\\n raise NotImplementedError(\\\"base class\\\")\",\n \"def right_dimension_pad(array, n):\\n if array.ndim >= n:\\n return array\\n\\n nadd = n - array.ndim\\n atts = [_new_attribute_label('_dim%i' % i, array) for i in range(nadd)]\\n apply_args = [x for item in enumerate(atts) for x in item[::-1]]\\n\\n ds = array.datashape.copy()\\n ds.dim_low = list(ds.dim_low) + ([0] * nadd)\\n ds.dim_high = list(ds.dim_high) + ([0] * nadd)\\n ds.dim_names = list(ds.dim_names) + atts\\n ds.chunk_overlap = list(ds.chunk_overlap) + ([0] * nadd)\\n ds.chunk_size = list(ds.chunk_size) + ([1000] * nadd)\\n\\n return array.apply(*apply_args).redimension(ds.schema)\",\n \"def block_diagonals(board: Board, pawn_value: int, x: int, y: int) -> None:\\n\\n for row_index, row in enumerate(board):\\n for col_index, _ in enumerate(row):\\n if abs(row_index-y) is abs(col_index-x):\\n block(board, pawn_value, col_index, row_index)\",\n \"def sub_major_diags(arr, sub_size):\\n rows, cols = arr.shape\\n for i in range(rows - sub_size + 1):\\n for j in range(cols - sub_size + 1):\\n yield arr[range(i, i + sub_size), range(j, j + sub_size)]\",\n \"def row_diag(self):\\n rows = self._row_type_2d()\\n for i, m in iter_items(self.row_totals):\\n rows[i][i] = m\\n return self.__class__(rows=rows)\",\n \"def eye(size):\\n return Matrix.diagonal(size, 1.0)\",\n \"def pad_csr(a, newshape):\\n n, m = a.shape\\n a._shape = newshape\\n a.indptr = np.pad(a.indptr, (0, newshape[0] - n), 'edge')\"\n]","isConversational":false},"negative_scores":{"kind":"list like","value":["0.7125442","0.69381803","0.66931725","0.6662349","0.6404038","0.6235831","0.6171781","0.6152024","0.60630727","0.5889371","0.57974076","0.5756528","0.57523865","0.5675084","0.5653157","0.5643824","0.55920154","0.5581051","0.55746204","0.5569762","0.5548352","0.5507623","0.54771113","0.5475269","0.5457448","0.5415262","0.5409227","0.53881025","0.536449","0.53532225","0.53265125","0.52957654","0.52953","0.5291469","0.5290101","0.52885765","0.5263242","0.5258192","0.52322894","0.5223341","0.5180603","0.5176733","0.51695055","0.5156225","0.5156225","0.5147032","0.5141254","0.5132731","0.5108546","0.5108071","0.5103415","0.51032054","0.5084468","0.5062737","0.5048061","0.5040642","0.5040409","0.50325125","0.50296736","0.50002706","0.49965873","0.49759278","0.4973462","0.49604627","0.49567842","0.49554676","0.493419","0.49289837","0.49242276","0.49214894","0.49208462","0.49113423","0.48914698","0.48896587","0.48731428","0.487028","0.487028","0.48695964","0.48662412","0.4856271","0.48553142","0.48553142","0.48553142","0.48484147","0.48448336","0.48386005","0.48321566","0.48038715","0.47993362","0.4782322","0.47813886","0.4777293","0.47716954","0.47698525","0.47662792","0.4765706","0.47585997","0.4756347","0.47471344","0.47437447"],"string":"[\n \"0.7125442\",\n \"0.69381803\",\n \"0.66931725\",\n \"0.6662349\",\n \"0.6404038\",\n \"0.6235831\",\n \"0.6171781\",\n \"0.6152024\",\n \"0.60630727\",\n \"0.5889371\",\n \"0.57974076\",\n \"0.5756528\",\n \"0.57523865\",\n \"0.5675084\",\n \"0.5653157\",\n \"0.5643824\",\n \"0.55920154\",\n \"0.5581051\",\n \"0.55746204\",\n \"0.5569762\",\n \"0.5548352\",\n \"0.5507623\",\n \"0.54771113\",\n \"0.5475269\",\n \"0.5457448\",\n \"0.5415262\",\n \"0.5409227\",\n \"0.53881025\",\n \"0.536449\",\n \"0.53532225\",\n \"0.53265125\",\n \"0.52957654\",\n \"0.52953\",\n \"0.5291469\",\n \"0.5290101\",\n \"0.52885765\",\n \"0.5263242\",\n \"0.5258192\",\n \"0.52322894\",\n \"0.5223341\",\n \"0.5180603\",\n \"0.5176733\",\n \"0.51695055\",\n \"0.5156225\",\n \"0.5156225\",\n \"0.5147032\",\n \"0.5141254\",\n \"0.5132731\",\n \"0.5108546\",\n \"0.5108071\",\n \"0.5103415\",\n \"0.51032054\",\n \"0.5084468\",\n \"0.5062737\",\n \"0.5048061\",\n \"0.5040642\",\n \"0.5040409\",\n \"0.50325125\",\n \"0.50296736\",\n \"0.50002706\",\n \"0.49965873\",\n \"0.49759278\",\n \"0.4973462\",\n \"0.49604627\",\n \"0.49567842\",\n \"0.49554676\",\n \"0.493419\",\n \"0.49289837\",\n \"0.49242276\",\n \"0.49214894\",\n \"0.49208462\",\n \"0.49113423\",\n \"0.48914698\",\n \"0.48896587\",\n \"0.48731428\",\n \"0.487028\",\n \"0.487028\",\n \"0.48695964\",\n \"0.48662412\",\n \"0.4856271\",\n \"0.48553142\",\n \"0.48553142\",\n \"0.48553142\",\n \"0.48484147\",\n \"0.48448336\",\n \"0.48386005\",\n \"0.48321566\",\n \"0.48038715\",\n \"0.47993362\",\n \"0.4782322\",\n \"0.47813886\",\n \"0.4777293\",\n \"0.47716954\",\n \"0.47698525\",\n \"0.47662792\",\n \"0.4765706\",\n \"0.47585997\",\n \"0.4756347\",\n \"0.47471344\",\n \"0.47437447\"\n]","isConversational":false},"document_score":{"kind":"string","value":"0.6709417"},"document_rank":{"kind":"string","value":"2"}}},{"rowIdx":22678,"cells":{"query":{"kind":"string","value":"Trim an upper triangle sparse matrix so that only the first n diagonals are kept."},"document":{"kind":"string","value":"def diag_trim(mat, n):\n if sp.issparse(mat):\n if mat.format != \"csr\":\n raise ValueError(\"input type must be scipy.sparse.csr_matrix\")\n # Trim diagonals by removing all elements further than n in the\n # upper triangle\n trimmed = sp.tril(mat, n, format=\"csr\")\n trimmed = sp.triu(trimmed, format=\"csr\")\n else:\n trimmed = mat.copy()\n n_diags = trimmed.shape[0]\n for diag in range(n, n_diags):\n set_mat_diag(trimmed, diag, 0)\n return trimmed\n\n return trimmed"},"metadata":{"kind":"string","value":"{\n \"objective\": {\n \"self\": [],\n \"paired\": [],\n \"triplet\": [\n [\n \"query\",\n \"document\",\n \"negatives\"\n ]\n ]\n }\n}"},"negatives":{"kind":"list like","value":["def trim_whitespace(matrix, details, min_gap):\r\n if details == -1:\r\n row = matrix[0, ]\r\n else:\r\n row = matrix[matrix.shape[0] - 1, ]\r\n\r\n min_left = np.argmin(row)\r\n min_right = np.argmin(row[::-1])\r\n\r\n if min_left > min_gap:\r\n matrix = matrix[:, min_left - min_gap:]\r\n\r\n if min_right > min_gap:\r\n matrix = matrix[:, 0:len(row) - (min_right - min_gap)]\r\n\r\n return matrix","def _elimination_matrix(dim):\n n = dimension_to_number_of_triangular_elements(dim)\n\n counter = np.zeros((dim, dim), int) - 1\n counter[np.tril_indices(dim)] = np.arange(n, dtype=int)\n\n columns = [_unit_vector_or_zeros(i, n) for i in counter.ravel(\"F\")]\n\n eliminator = np.column_stack(columns)\n return eliminator","def _trim_margins(self, img):\n oldsize = (0, 0)\n while oldsize != img.shape: # while the size is changing\n oldsize = img.shape\n for i in range(4): # 4 times\n img = num.rot90(img) # rotate 90\n if num.std(img[0, :]) < self.trim_std: # if low std\n img = img[1:, :] # trim edge\n\n return img","def trim(x):\n # make sure we get a 3D stack not 2D slice\n assert (x.shape) != 3\n if x.shape[-1] > 576:\n newx = x[:,32:-32, 32:-32]\n else:\n newx = x\n return newx[np.newaxis,...]","def trim_dataset(mat, batch_size):\n no_of_rows_drop = mat.shape[0]%batch_size\n print(\"number of rows dropped\", no_of_rows_drop)\n if(no_of_rows_drop > 0):\n return mat[:-no_of_rows_drop]\n else:\n return mat","def remove_blanks(mat):\n ids = []\n for idx, row in enumerate(mat):\n if not 1 in row:\n ids.append(idx)\n mat = np.delete(mat, ids, 0)\n mat = np.delete(mat, ids, 1)\n return mat","def _drop_empty_rows_and_cols(confmat: Tensor) ->Tensor:\n confmat = confmat[confmat.sum(1) != 0]\n confmat = confmat[:, confmat.sum(0) != 0]\n return confmat","def remove_border(src): #---- remove blank border\r\n rows = src.shape[0]; VMIN= 0; VMAX= rows; \r\n cols = src.shape[0]; UMIN= 0; UMAX= cols;\r\n for ky in range(1,rows):\r\n sum0 = np.sum(src[ky,:,:]);\r\n sum1 = np.sum(src[rows-ky-1,:,:]);\r\n if sum0== 0 and VMIN== ky-1: VMIN= ky;\r\n if sum1== 0 and VMAX== rows-ky+1: VMAX= rows-ky;\r\n for kx in range(1,cols):\r\n sum0 = np.sum(src[:,kx,:]);\r\n sum1 = np.sum(src[:,cols-kx-1,:]);\r\n if sum0== 0 and UMIN== kx-1: UMIN= kx;\r\n if sum1== 0 and UMAX== cols-kx+1: UMAX= cols-kx;\r\n #--- --- \r\n DV = np.minimum(VMIN, rows-VMAX);\r\n DU = np.minimum(UMIN, cols-UMAX);\r\n return src[DV:(rows-DV), DU:(cols-DU), :];","def unscrew( S ):\n S = asarray(S)\n assert allclose(S[...,:3,:3].transpose(0,1),-S[...,:3,:3]),\"S[...,:3,:3] is skew\"\n assert allclose(S[...,3,:],0),\"Bottom row is 0\"\n return unscrew_UNSAFE(S)","def trim_axs(axs, N):\n axs = axs.flat\n for ax in axs[N:]:\n ax.remove()\n return axs[:N]","def trim_axs(axs, N):\n axs = axs.flat\n for ax in axs[N:]:\n ax.remove()\n return axs[:N]","def trim_axs(axs, N):\n axs = axs.flat\n for ax in axs[N:]:\n ax.remove()\n return axs[:N]","def trim_axs(axs, n):\n axs = axs.flat\n for ax in axs[n:]:\n ax.remove()\n return axs[:n]","def trim_zeros(x):\n assert len(x.shape) == 2\n return x[~np.all(x == 0, axis=1)]","def _reduce_triangle(to_reduce):\n last_row = to_reduce[-1]\n for index in xrange(len(to_reduce) - 1):\n to_reduce[-2][index] += max(last_row[index:index + 2])\n del to_reduce[-1]","def trim_zeros(array):\n multislice = []\n for i in range(array.ndim):\n sum_axes = tuple(j for j in range(array.ndim) if j is not i)\n edges = np.where(np.sum(array, axis=sum_axes) > 0)\n if edges[0].size == 0:\n return np.array([], dtype=array.dtype)\n low = edges[0][0]\n high = edges[0][-1]\n multislice.append(slice(low, high+1, 1))\n return array[tuple(multislice)]","def eliminate(self, A):\r\n length = len(A)\r\n width = len(A[0])\r\n for i in range(length):\r\n if A[i][i] != 1:\r\n # if current entry on the diagonal is 0 (off), proceed row interchange. \r\n for k in range(i + 1, length):\r\n if A[k][i] == 1:\r\n A[i], A[k] = A[k], A[i]\r\n self.step += 1\r\n break\r\n for j in range(i + 1, length):\r\n # eliminate the entries under the diagonal\r\n if A[j][i] == 1:\r\n A[j] = [(A[i][m] + A[j][m]) % 2 for m in range(width)]\r\n self.step += width\r\n\r\n for i in range(length - 1, -1, -1):\r\n # eliminate the entries above the diagonal\r\n for j in range(i - 1, -1, -1):\r\n if A[j][i] != 0:\r\n A[j] = [(A[i][k] + A[j][k]) % 2 for k in range(width)]\r\n self.step += width\r\n # Now, only the entries on the diagonal are 1, the rest are 0.\r","def upper(mat):\n idx = np.triu_indices_from(mat, k=1)\n return mat[idx]","def trim(args) :\n from trimmer import trim_reads\n trim_reads(args)","def triu(m, k=0):\r\n m = asanyarray(m)\r\n out = multiply((1-tri(m.shape[0], m.shape[1], k-1, int)),m)\r\n return out","def cut_array_border(array): \n array[:, [0, array.shape[1]-1]]=0\n array[[0, array.shape[0]-1], :]=0\n \n \n return array","def _flatten_lower_triangle(matrix):\r\n matrix = asarray(matrix)\r\n flattened = []\r\n for col_num in range(matrix.shape[1]):\r\n for row_num in range(matrix.shape[0]):\r\n if col_num < row_num:\r\n flattened.append(matrix[row_num][col_num])\r\n return flattened","def reduce_triangle(to_reduce):\r\n last_row = to_reduce[-1]\r\n for index in xrange(len(to_reduce)-1):\r\n to_reduce[-2][index] += max(last_row[index:index+2])\r\n del to_reduce[-1]\r\n return to_reduce","def offDiagPairs(self):\n return np.transpose(np.nonzero(np.triu(self.LaplacianMatrix,k=2)))","def upper_triangular_matrix_to_full_matrix(arr, n):\n triu0 = np.triu_indices(n, 0)\n triu1 = np.triu_indices(n, 1)\n tril1 = np.tril_indices(n, -1)\n\n mat = np.zeros((n, n), dtype=float)\n mat[triu0] = arr\n mat[tril1] = mat[triu1]\n\n return mat","def Problem2(n):\n diag_entries = np.empty((3,n))\n diag_entries[0] = np.ones(n)*(-1)\n diag_entries[1] = np.ones(n)*2\n diag_entries[2] = np.ones(n)*(-1)\n A = sparse.spdiags(diag_entries, [-1,0,1],n,n,format=\"csr\")\n return A","def get_off_diagonal(matrix):\n\toff_diag = scipy.array(matrix, dtype=matrix.dtype)\n\toff_diag[scipy.diag_indices_from(matrix)] = 0\n\treturn off_diag","def reduce_size(data):\r\n data_ = pd.DataFrame(data)\r\n nonzero_idxs = np.flatnonzero(np.diag(data_))\r\n cov_reduced = data_.iloc[nonzero_idxs, nonzero_idxs]\r\n return nonzero_idxs, cov_reduced","def delete_all_gap(self):\n # pdb.set_trace()\n\n rem = set(self.get_all_gap_cols())\n subset = [x for x in range(0, self.get_length()) if x not in rem]\n self.remove_columns(set(rem))\n #_LOG.debug(\"Alignment length reduced to %d\" % len(subset))\n return subset","def EliminateRowsCols(self, *args):\n return _hypre.HypreParMatrix_EliminateRowsCols(self, *args)","def cut_transformed_array_borders(array): \n for col in range(array.shape[1]): \n col_=array[:, col]\n \n where=np.where(col_>0)\n \n if len(where[0])>0:\n \n col_[[np.min(where[0]),np.min(where[0])+1, np.max(where[0]), np.max(where[0])-1 ]]=0\n \n array[:,col]=col_\n \n for row in range(array.shape[0]): \n row_=array[row,:]\n \n where=np.where(row_>0)\n if len(where[0])>0:\n\n row_[[np.min(where[0]),np.min(where[0])+1, np.max(where[0]), np.max(where[0])-1 ]]=0\n \n array[row,:]=row_\n \n return array","def triu(m, k=0):\r\n return m * (1 - tri(m.shape[0], m.shape[1], k=k - 1, dtype=m.dtype))","def trim_vocab(n: int, vocab: Dict[str, int]) -> None:\r\n pair_counts = sorted(vocab.items(), key=lambda p: -p[1])\r\n pairs_to_trim = [pair for pair, count in pair_counts[n:]]\r\n for pair in pairs_to_trim:\r\n del vocab[pair]","def square_matrix(triangular_matrix):\n length = len(triangular_matrix)\n zero = [0.0]\n\n for item in triangular_matrix:\n item.extend(zero * (length - len(item)))\n\n return triangular_matrix","def tri(N, M=None, k=0, dtype=float):\r\n if M is None: M = N\r\n m = greater_equal(subtract.outer(arange(N), arange(M)),-k)\r\n return m.astype(dtype)","def block_diag_full(W_):\n assert(W_.ndim == 3)\n bsize = W_.shape[0]\n full = np.concatenate([\n np.concatenate([ np.diag(W_[:,i,j]) for j in range(W_.shape[2]) ], axis=1)\n for i in range(W_.shape[1]) ], axis=0)\n return full","def test_nullspace_blocked(input_matrix):\n mat = sp.Matrix(input_matrix)\n assert sorted(mat.nullspace()) == sorted(nullspace_blocked(mat))","def diagonalsNeg (matrix, cols, rows):\r\n for diagonal in ([(j, i - cols + j + 1) for j in range(cols)] for i in range(cols + rows - 1)):\r\n yield [matrix[i][j] for i, j in diagonal if i >= 0 and j >= 0 and i < cols and j < rows]","def pfd_clear(n):\n\tglobal matrix\n\tr = []\n\tfor i in xrange(size[0]):\n\t\tif matrix[i][n] == 1:\n\t\t\tmatrix[i][n] = 0\n\t\t\tr.append(i)\n\treturn r","def trim_features():\n pass","def removeFirstMatrix(file):\n file1 = open(file, \"r\") #In the future this function can just reside in readMatrix() \n \n rawData = file1.readlines() \n \n n = round (len(rawData[0])/2)\n \n newData = rawData[n+1:]\n \n file1 = open(file, \"w\")\n\n for line in newData:\n file1.write(line)\n file1.close()","def at_ftrim(seq):\n at(\"FTRIM\", seq, [])","def eliminate_kxk(sudoku, k):\n steps = range(len(sudoku) // k)\n for i in steps:\n for j in steps:\n\n rows = sudoku[i*k:(i+1)*k]\n part = [item[j*k:(j+1)*k] for item in rows]\n part = [item for sublist in part for item in sublist]\n\n cleaned_part = eliminate_rows([part])[0]\n\n count = 0\n for l in range(i*k, (i+1)*k):\n for m in range(j*k, (j+1)*k):\n sudoku[l][m] = sorted(cleaned_part[count])\n count += 1\n\n return sudoku","def write_untrim08(self,fn):\n UnTRIM08Grid(grid=self).write_untrim08(fn)","def OwnsDiag(self):\n return _hypre.HypreParMatrix_OwnsDiag(self)","def upper_tri(A):\n d = pairwise_distances(A,A)\n m = d.shape[0]\n r,c = np.triu_indices(m,1)\n return(d[r,c])","def EliminateRows(self, rows):\n return _hypre.HypreParMatrix_EliminateRows(self, rows)","def mat_from_diag_triu_tril(diag, tri_upp, tri_low):\n n = diag.shape[-1]\n (i,) = diag_indices(n, ndim=1)\n j, k = triu_indices(n, k=1)\n mat = _torch.zeros((diag.shape + (n,)))\n mat[..., i, i] = diag\n mat[..., j, k] = tri_upp\n mat[..., k, j] = tri_low\n return mat","def eliminate(board, i, j):\n value = board[i][j][0]\n # Horizontal propagation\n for k in range(n):\n if j!=k and value in board[i][k]:\n board[i][k].remove(value)\n # Vertical propagation\n for l in range(n):\n if i!=l and value in board[l][j]:\n board[l][j].remove(value)\n # Square propagation\n for k in range(i/3*3, i/3*3+3):\n for l in range(j/3*3, j/3*3+3):\n if k!=i and l!=j and (value in board[k][l]):\n board[k][l].remove(value)\n return 0","def trim(self, start, stop=None):\n if stop is None:\n stop = self.data.shape[0]\n\n start = max(start, 0)\n stop = min(stop, self.data.shape[0])\n self.data = self.data.iloc[start:stop,:]","def half(self, matrix, k=1):\r\n return [matrix[i][i:] if k == 0 else matrix[i][:i+1] for i in range(len(matrix))]","def remove_outliers(self, matrix):\n input = matrix[:, :-1]\n row_incides_to_delete = []\n for j, column in enumerate(input.transpose()):\n self.feature_means.append(np.mean(column))\n self.feature_stds.append(np.std(column))\n\n for i, row in enumerate(input):\n cell = input[i, j]\n if cell > self.feature_means[j] + 3 * self.feature_stds[j] or cell < self.feature_means[j] - 3 * \\\n self.feature_stds[j]:\n row_incides_to_delete.append(i)\n matrix = np.delete(matrix, row_incides_to_delete, 0)\n return matrix, len(list(set(row_incides_to_delete)))","def trim_occluded_throats(self):\n occluded_ts = list(self.throats()[self[\"throat.area\"]==0])\n #occluded_throats = np.asarray(occluded_throats)\n if len(occluded_ts) > 0:\n self.trim(throats=occluded_ts)\n \"Also get rid of isolated pores\"\n isolated_ps = self.check_network_health()['isolated_pores']\n if len(isolated_ps) > 0:\n self.trim(isolated_ps)","def zero_diag(mat):\n\n return replace_diag(mat, np.zeros(mat.shape[0]))","def UTrim(self, *args):\n return _Adaptor3d.Adaptor3d_Surface_UTrim(self, *args)","def flat_triang_to_full(triang):\n rows = int(1/2*(np.sqrt(8*len(triang)+1)-1))\n mask = np.tri(rows,dtype=bool).T # transpose: lower to upper\n full = np.zeros((rows,rows))\n full[mask] = triang\n full += full.T*np.tri(rows,k=-1)\n return full","def diagonal(self):\n M = self.rep\n m, n = self.shape\n return [M[i, i] for i in range(min(m, n))]","def form_triu_matrix(arr):\n n = int(np.ceil((np.sqrt(1 + 8 * len(arr)) - 1) * 0.5))\n M = np.zeros((n, n))\n c = 0\n for i in range(n):\n for j in range(n):\n if j >= i:\n if c < len(arr):\n M[i, j] = arr[c]\n c += 1\n else:\n break\n return M","def remove_top_layer(I_m, board_size=4, *args, **kwargs):\n for j in range(1, board_size+1):\n for k in range(1, board_size+1):\n if len(I_m.get((j, k))) > 0:\n I_m[(j, k)].pop()\n return I_m","def trim_empty_rows(self, table: Table):\n self._requires_table(table)\n\n empty = []\n for idx in reversed(table.index):\n row = table[idx]\n if any(value is not None for value in row):\n break\n empty.append(idx)\n\n table.delete_rows(empty)","def get_diagonal_up(self, x, y):\n\n while 0 <= x < 7 and 0 <= y < 6:\n x -= 1\n y += 1\n\n return [self._board[y-i][x+i] for i in xrange(7)\n if 0 <= (x+i) < 7 and 0 <= (y-i) < 6]","def shrinkTrackMatrix(self):\n self.tracksMatrix = self.tracksMatrix[0:(len(self.tracksMatrix)-1)]\n self.attributesMatrix = self.attributesMatrix[0:(len(self.attributesMatrix)-1)]","def construct_matrix_A(n):\n n2 = n**2\n D0 = 4*np.ones(n2) # 0th diagonal\n D1 = - np.ones(n2 - 1) # -1st, 1st diagonals\n D1[n-1::n] = 0 # Setting every k*n-1 entries = 0 for k < n\n DN = - np.ones(n2 - n) # -nth, nth diagonals\n return scipy.sparse.diags((DN, D1, D0, D1, DN), (-n, -1, 0, 1, n),\n shape=(n2, n2), format=\"csr\")","def trim2DArray(input_arr, threshold=0): \n\n print(input_arr.shape)\n ul_x, ul_y = calculateTrimOffsetForward(input_arr, threshold)\n lr_x, lr_y = calculateTrimOffsetBackward(input_arr, threshold)\n\n output_arr = input_arr[ul_y:lr_y, ul_x:lr_x]\n\n print(output_arr.shape)\n\n return (output_arr)","def get_reverse_diagonal(self):\n start = 0\n end = 9\n step = 4\n\n return self.grid[start:end:step] # array[start:end:step]","def trim(self):\n for i in range(len(self)):\n if self[i] != TRIT_ZERO:\n return self.__class__(self[i:])\n return self.__class__([])","def trim(self):\n\n # identify edges outside window\n\n min_time = self.latest_time - self.t_window\n edges_to_trim = self.graph.es.select(time_lt=min_time)\n if self.verbose: print(\"Edges to trim: \"+str(edges_to_trim))\n\n # remove edges outside of t_window\n self.graph.delete_edges(edges_to_trim)\n\n # identify vertices with 0 degree to delete\n vertices_to_trim = self.graph.vs.select(_degree=0)\n if self.verbose: print(\"Vertices to trim: \"+str(vertices_to_trim._name_index))\n self.graph.delete_vertices(vertices_to_trim)","def zero_matrix(matrix):\n rows = set()\n columns = set()\n m = len(matrix)\n n = len(matrix[0])\n for i in range(m):\n for j in range(n):\n if matrix[i][j] == 0:\n rows.add(i)\n columns.add(j)\n\n for i in range(m):\n for j in range(n):\n if i in rows or j in columns:\n matrix[i][j] = 0\n return matrix","def _trim_border(img):\n for i in range(img.shape[0]):\n if np.any(img[i, :, :] != 255):\n img = img[i:, :, :]\n break\n\n for i in range(img.shape[0] - 1, 0, -1):\n if np.any(img[i, :, :] != 255):\n img = img[: i + 1, :, :]\n break\n\n for i in range(img.shape[1]):\n if np.any(img[:, i, :] != 255):\n img = img[:, i:, :]\n break\n\n for i in range(img.shape[1] - 1, 0, -1):\n if np.any(img[:, i, :] != 255):\n img = img[:, : i + 1, :]\n break\n\n return img","def trim(elements, n):\n if len(elements) >= n: # if there are enough elements,\n elements[:n] = True # set `n` to `True` and leave\n return elements # the rest to `False`.","def trim_data(data, attributes):\n return data.drop(attributes, axis=1)","def gen_square_subsequent_mask(sz: int) -> torch.Tensor:\n return torch.triu(torch.ones(sz, sz) * float('-inf'), diagonal=1)","def test_remove_middle_axis():\n test_array = np.ones((13, 17, 19, 3, 3, 11, 21))\n out_array = utils.remove_auto_correlations(test_array, axes=(3, 4))\n assert (13, 17, 19, 6, 11, 21) == out_array.shape","def ltrim(self, record_provenance=True):\n tables = self.dump_tables()\n tables.ltrim(record_provenance)\n return tables.tree_sequence()","def trim(self, record_provenance=True):\n tables = self.dump_tables()\n tables.trim(record_provenance)\n return tables.tree_sequence()","def generate_square_subsequent_mask(sz: int) -> Tensor:\n return torch.triu(torch.ones(sz, sz) * float('-inf'), diagonal=1)","def trim(self):\n while np.any(self.vertex_valance <= 1):\n edge_to_keep = np.all(self.vertex_valance[self.edges] > 1,\n axis=1).tolist();\n self.raw_wires.filter_edges(edge_to_keep);\n vertex_to_keep = [len(self.get_vertex_neighbors(i)) > 0 for i in\n range(self.num_vertices)];\n self.raw_wires.filter_vertices(vertex_to_keep);\n\n self.__initialize_wires();\n if len(self.vertices) == 0:\n raise RuntimeError(\"Zero vertices left after trimming.\");","def triu_matrix(matrix, kind='upper'):\r\n matrix_ = pd.DataFrame(matrix)\r\n index = matrix_.index\r\n columns = matrix_.columns\r\n values = matrix_.values\r\n if kind == 'upper': \r\n index_lower = np.tril_indices(matrix_.shape[0], -1)\r\n values[index_lower] = values.T[index_lower]\r\n elif kind == 'lower':\r\n index_upper = np.triu_indices(matrix_.shape[0], 1)\r\n values[index_upper] = values.T[index_upper]\r\n return CategoryCov(pd.DataFrame(values, index=index, columns=columns))","def trim_axes(axes, N):\n if N>1:\n axes = axes.flat\n for ax in axes[N:]:\n ax.remove()\n return axes[:N]\n else:\n return [axes]","def test_triu_tril():\n A = np.array(\n [[1, 2, 3, 4], [5, 6, 7, 8], [9, 10, 11, 12], [13, 14, 16, 16]]\n )\n A_u = np.array([[1, 2, 3, 4], [0, 6, 7, 8], [0, 0, 11, 12], [0, 0, 0, 16]])\n A_l = np.array(\n [[1, 0, 0, 0], [5, 6, 0, 0], [9, 10, 11, 0], [13, 14, 16, 16]]\n )\n assert norm(A_u - triu(A)) < 1e-12\n assert norm(A_l - tril(A)) < 1e-12\n assert (\n norm(scipy.sparse.coo_matrix(A_u) - triu(scipy.sparse.coo_matrix((A))))\n < 1e-12\n )\n assert (\n norm(scipy.sparse.coo_matrix(A_l) - tril(scipy.sparse.coo_matrix((A))))\n < 1e-12\n )","def gen_diagonal_search_bottomleft_topright(n, grid):\n width, height = len(grid[0]), len(grid)\n if width < n or height < n:\n yield None\n\n for y in range(n-1, height):\n for x in range(0, width - n + 1):\n sample = [grid[y - i][x + i] for i in range(n)]\n yield sample","def trim(self, xoffset, xsize, yoffset, ysize, zoffset, zsize):\n xoffset = int(xoffset)\n yoffset = int(yoffset)\n zoffset = int(zoffset)\n self.data = self.data[:, zoffset:zoffset+zsize, yoffset:yoffset+ysize, xoffset:xoffset+xsize]","def removeCols(self) -> List['StateNode']:\n cols = self.state[1]\n states: List[StateNode] = []\n for i in range(len(cols)):\n for j in range(i + 1, len(cols) + 1):\n # for j in range(i + 1, i + 2):\n new_cols = cols[:i] + cols[j:]\n if len(new_cols) == 0:\n continue\n states.append(StateNode(self.table, \n (self.state[0], new_cols),\n ([], cols[i:j]),\n self.cost + j - i + self.count_pairs(self.state[0], cols[i:j]),\n self))\n return states","def triangle(row):\n if len(row) == 0:\n raise ValidationError(\"Row empty\")\n if len(row) == 1:\n return row\n if len(row) < 8:\n return small_triangles(row)\n make_steps()\n while len(row) > 50:\n streams = defaultdict(list)\n j = 0\n streams[j] = list(row)\n for i in range(len(row) - 4):\n for j in range(10):\n step = j * 4\n if i >= step:\n streams[j + 1].append(do_row(streams[j], i - step))\n row = streams[j]\n return small_triangles(row)","def strip_zeros(a):\n\n return np.trim_zeros(a, trim='b')","def triu_indices(n, k=0):\r\n return mask_indices(n, np.triu, k)","def _clear_matrix(self):\n\t\tself._w2i_matrix = self._i2w_matrix = None","def remove_gap_traces(st):\n # Check 1:\n gaps = st.get_gaps()\n for i in range(len(gaps)):\n for tr in st.select(network=gaps[i][0], station=gaps[i][1], location=gaps[i][2], channel=gaps[i][3]):\n st.remove(tr)\n # Check 2:\n masked = [np.ma.is_masked(tr.data) for tr in st]\n for i, tr in enumerate(st):\n if masked[i]:\n st.remove(tr)\n return st","def unique_spaces(mask):\n ncols = mask.shape[1]\n\n # Do some magic with views so ``unique`` can be used to find the unique\n # sets of dimensions.\n dtype = mask.dtype.descr * ncols\n struct = mask.view(dtype)\n\n uniq = np.unique(struct)\n uniq = uniq.view(mask.dtype).reshape(-1, ncols)\n return ~uniq","def test_take_fifth_diagonal_below_main(self):\n offset = -5\n matrix = [['.', '.', '.', '.', '.', '.', '.', '.', '.', '.', '.', '.', '.', '.', '.'],\n ['.', '.', '.', '.', '.', '.', '.', '.', '.', '.', '.', '.', '.', '.', '.'],\n ['.', '.', '.', '.', '.', 'x', '.', '.', '.', '.', '.', '.', '.', '.', '.'],\n ['.', '.', '.', '.', '.', '.', 'o', '.', '.', '.', '.', '.', '.', '.', '.'],\n ['.', '.', '.', '.', '.', '.', '.', 'o', '.', '.', '.', '.', '.', '.', '.'],\n ['.', '.', '.', '.', '.', '.', '.', '.', 'x', '.', '.', '.', '.', '.', '.'],\n ['.', 'o', '.', '.', '.', '.', '.', '.', '.', 'x', '.', '.', '.', '.', '.'],\n ['.', '.', 'o', '.', '.', '.', '.', '.', '.', '.', 'o', '.', '.', '.', '.'],\n ['.', '.', '.', 'o', '.', '.', '.', '.', '.', '.', '.', 'x', '.', '.', '.'],\n ['.', '.', '.', '.', 'o', '.', '.', '.', '.', '.', '.', '.', 'o', '.', '.'],\n ['.', '.', '.', '.', '.', 'o', '.', '.', '.', '.', '.', '.', '.', '.', '.'],\n ['.', '.', '.', '.', '.', '.', 'o', '.', '.', '.', '.', '.', '.', '.', '.'],\n ['.', '.', '.', '.', '.', '.', '.', 'o', '.', '.', '.', '.', '.', '.', '.'],\n ['.', '.', '.', '.', '.', '.', '.', '.', 'o', '.', '.', '.', '.', '.', '.'],\n ['.', '.', '.', '.', '.', '.', '.', '.', '.', 'o', '.', '.', '.', '.', '.']]\n table = self._create_table(matrix)\n\n expected = ['.', 'o', 'o', 'o', 'o', 'o', 'o', 'o', 'o', 'o']\n current_list = utils.get_diagonal(table, offset)\n\n self.assertListEqual(expected, current_list)","def remove_center(X, N=10, border=2):\n \n #Translate X into original block shape\n X2 = X.reshape((len(X),N,N,6))\n\n # Decompose matrix as follows:\n \n # N N N N N N N N\n # N N N N N N N N\n # W W E E\n # W W E E\n # W W E E\n # W W E E\n # S S S S S S S S\n # S S S S S S S S\n \n # block is then flattened as well\n North = X2[:, :border].reshape(len(X), N*border*6)\n South = X2[:, -border:].reshape(len(X), N*border*6)\n West = X2[:, border:-border, :border].reshape(len(X), (N-2*border)*border*6)\n East = X2[:, border:-border, -border:].reshape(len(X), (N-2*border)*border*6)\n \n # Stack the vectors horizontally, e.g.\n # N_,1 .., N_x, W_1, .. W_x, E_1, .., E_x, S_1, .., S_x\n return np.hstack((North, West, East, South))","def _trimRecords(self):\n self.highpassrecords.resize(self.nhighpassrecords, refcheck=False)\n self.lowpassrecords.resize(self.nlowpassrecords, refcheck=False)\n self.digitalsvalrecords.resize(self.ndigitalsvalrecords, refcheck=False)\n # cleanup by deleting any struct arrays of len 0\n for recname in ('highpassrecords', 'lowpassrecords', 'digitalsvalrecords'):\n if len(self.__getattribute__(recname)) == 0:\n self.__delattr__(recname)","def remove_row(self, index):\n _ = self.matrix.pop(index) ## Delete row\n self.matrix.append(self.empty_row.copy())","def clear_rows(self):\n ### Previous version had a bug, in that it assumed the set of ###\n ### indices of full rows had to be a contiguous sequence! ###\n full_rows = [j for j in range(ROWS) if all(\n (i, j) in self.locked_squares for i in range(COLS))]\n if not full_rows: return\n ### Calculate how for to drop each other row, and do it ###\n drop = {j: len([k for k in full_rows if k > j]) for j in range(ROWS)}\n self.locked_squares = {(i, j+drop[j]): color for (i, j), color in\n self.locked_squares.items() if j not in full_rows}\n ### Now just update score, etc. ###\n d = len(full_rows)\n self.increment_lines(d)\n self.increment_score(self.level*{1: 40, 2: 100, 3: 300, 4: 1200}[d])\n if self.level < self.lines // 10 + 1:\n self.increment_level()","def squeeze(self):\n remove_axes = []\n for axes_ix, axes_value in enumerate(self.coords):\n if len(axes_value) == 1:\n remove_axes.append(axes_ix)\n\n reverse_remove_axes = remove_axes[::-1]\n for index_ix, index_value in enumerate(reverse_remove_axes):\n self.coords.pop(index_value)\n self.dims.pop(index_value)\n self.values = np.squeeze(self.values)","def smith_nf(matrix):\n\n A=np.copy(matrix)\n if (np.around(A) != A).any():\n raise Exception('This function requires integer input.')\n\n # This looks much like an SVD algorithm that first bidiagonalizes\n # A by Givens rotations and then chases zeros, except for\n # the construction of the 2 by 2 elementary transformation.\n\n m, n = A.shape\n\n S = A\n U = np.eye(m)\n V = np.eye(n)\n\n # Bidiagonalize S with elementary Hermite transforms.\n for j in range(min(m, n)):\n # Zero column j below the diagonal.\n for i in range(j+1, m):\n if S[i, j]:\n # Construct an elementary Hermite transformation E\n # to zero S(i,j) by combining rows i and j.\n E = ehermite(S[j, j], S[i, j])\n # Apply the transform to S and U.\n S[[j, i], :] = np.dot(E, S[[j, i], :])\n # U[:, [j, i]] = U[:, [j, i]] / E\n U[:, [j, i]] = left_matrix_division(U[:, [j, i]], E) # solving the left matrix division\n\n # % Zero row j after the superdiagonal.\n for i in range(j+2, n):\n if S[j, i]:\n # Construct an elementary Hermite transformation E\n # to zero S(j,i) by combining columns j+1 and i.\n E = ehermite(S[j, j+1], S[j, i])\n # Apply the transform to S and V.\n S[:, [j+1, i]] = np.dot(S[:, [j+1, i]], E.T)\n # V[:, [j+1, i]] = V[:, [j+1, i]] / E\n V[:, [j+1, i]] = left_matrix_division(V[:, [j+1, i]], E) # solving the left matrix division\n\n # Now S is upper bidiagonal.\n # Chase the superdiagonal nonzeros away.\n\n D = np.diag(S, 1)\n while any(D):\n b = min(np.where(D))[0]\n # Start chasing bulge at first nonzero superdiagonal element.\n # To guarantee reduction in S(b,b), first make S(b,b) positive\n # and make S(b,b+1) nonnegative and less than S(b,b).\n if S[b, b] < 0:\n S[b, :] = -S[b, :]\n U[:, b] = -U[:, b]\n\n q = np.floor(S[b, b+1] / S[b, b])\n E = np.array([[1, 0], [-q, 1]])\n S[:, [b, b+1]] = np.dot(S[:, [b, b+1]], E.T)\n # V[:, [b, b+1]] = V[:, [b, b+1]] / E\n V[:, [b, b+1]] = left_matrix_division(V[:, [b, b+1]], E) # solving the left matrix division\n\n if S[b, b+1]:\n # Zero the first nonzero superdiagonal element\n # using columns b and b+1, to start the bulge at S(b+1,b).\n E = ehermite(S[b, b], S[b, b+1])\n S[:, [b, b+1]] = np.dot(S[:, [b, b+1]], E.T)\n # V[:, [b, b+1]] = V[:, [b, b+1]] / E\n V[:, [b, b+1]] = left_matrix_division(V[:, [b, b+1]], E)\n\n for j in range(min(m, n)):\n if j+1 < m:\n # Zero S(j+1,j) using rows j and j+1.\n E = ehermite(S[j, j], S[j+1, j])\n S[[j, j+1], :] = np.dot(E, S[[j, j+1], :])\n # U[:, [j, j+1]] = U[:, [j, j+1]] / E\n U[:, [j, j+1]] = left_matrix_division(U[:, [j, j+1]], E)\n if j+2 < n:\n # Zero S(j,j+2) using columns j+1 and j+2.\n E = ehermite(S[j, j+1], S[j, j+2])\n S[:, [j+1, j+2]] = np.dot(S[:, [j+1, j+2]], E.T)\n # V[:, [j+1, j+2]] = V[:, [j+1, j+2]] / E\n V[:, [j+1, j+2]] = left_matrix_division(V[:, [j+1, j+2]], E)\n D = np.diag(S, 1)\n\n # Now S is diagonal. Make it nonnegative.\n\n for j in range(min(m, n)):\n if S[j, j] < 0:\n S[j, :] = -S[j, :]\n U[:, j] = -U[:, j]\n\n # Squeeze factors to lower right to enforce divisibility condition.\n\n for i in range(min(m, n)):\n for j in range(i+1, min(m, n)):\n # Replace S(i,i), S(j,j) by their gcd and lcm respectively.\n a = S[i, i]\n b = S[j, j]\n [c, d, g] = extgcd(a, b)\n E = np.array([[1, d], [-b/g, a*c/g]])\n F = np.array([[c, 1], [-b*d/g, a/g]])\n S[np.ix_([i, j], [i, j])] = np.dot(np.dot(E, S[:, [i, j]][[i, j], :]), F.T)\n # S[i, i] = tmp_arr[0, 0]\n # S[i, j] = tmp_arr[0, 1]\n # S[j, i] = tmp_arr[1, 0]\n # S[j, j] = tmp_arr[1, 1]\n U[:, [i, j]] = left_matrix_division(U[:, [i, j]], E)\n V[:, [i, j]] = left_matrix_division(V[:, [i, j]], F)\n\n U = np.around(U)\n V = np.around(V)\n return U, S, V","def rtrim(self, record_provenance=True):\n tables = self.dump_tables()\n tables.rtrim(record_provenance)\n return tables.tree_sequence()","def finalise_matrix(matrix):\n if get_density(matrix) < DENSITY_THRESHOLD:\n matrix = matrix.tocsc()\n matrix.sort_indices()\n else:\n matrix = matrix.toarray()\n if is_integer(matrix):\n matrix = convert_to_smallest_int_type(matrix)\n return matrix","def upper_diagonal_constraint(board: Board, row: int, col: int) -> bool:\n #move to first tile in diagonal\n row -= 1\n col -= 1\n #while still in board, test and move to next position return false if fails\n while row >= 0 and col >= 0:\n if board.is_queen(row=row, col=col):\n return False\n row -= 1\n col -= 1\n return True","def getdiag(self):\n out = []\n for x in xrange(0, self.lendiag()):\n out.append(self.retrieve(x))\n return out"],"string":"[\n \"def trim_whitespace(matrix, details, min_gap):\\r\\n if details == -1:\\r\\n row = matrix[0, ]\\r\\n else:\\r\\n row = matrix[matrix.shape[0] - 1, ]\\r\\n\\r\\n min_left = np.argmin(row)\\r\\n min_right = np.argmin(row[::-1])\\r\\n\\r\\n if min_left > min_gap:\\r\\n matrix = matrix[:, min_left - min_gap:]\\r\\n\\r\\n if min_right > min_gap:\\r\\n matrix = matrix[:, 0:len(row) - (min_right - min_gap)]\\r\\n\\r\\n return matrix\",\n \"def _elimination_matrix(dim):\\n n = dimension_to_number_of_triangular_elements(dim)\\n\\n counter = np.zeros((dim, dim), int) - 1\\n counter[np.tril_indices(dim)] = np.arange(n, dtype=int)\\n\\n columns = [_unit_vector_or_zeros(i, n) for i in counter.ravel(\\\"F\\\")]\\n\\n eliminator = np.column_stack(columns)\\n return eliminator\",\n \"def _trim_margins(self, img):\\n oldsize = (0, 0)\\n while oldsize != img.shape: # while the size is changing\\n oldsize = img.shape\\n for i in range(4): # 4 times\\n img = num.rot90(img) # rotate 90\\n if num.std(img[0, :]) < self.trim_std: # if low std\\n img = img[1:, :] # trim edge\\n\\n return img\",\n \"def trim(x):\\n # make sure we get a 3D stack not 2D slice\\n assert (x.shape) != 3\\n if x.shape[-1] > 576:\\n newx = x[:,32:-32, 32:-32]\\n else:\\n newx = x\\n return newx[np.newaxis,...]\",\n \"def trim_dataset(mat, batch_size):\\n no_of_rows_drop = mat.shape[0]%batch_size\\n print(\\\"number of rows dropped\\\", no_of_rows_drop)\\n if(no_of_rows_drop > 0):\\n return mat[:-no_of_rows_drop]\\n else:\\n return mat\",\n \"def remove_blanks(mat):\\n ids = []\\n for idx, row in enumerate(mat):\\n if not 1 in row:\\n ids.append(idx)\\n mat = np.delete(mat, ids, 0)\\n mat = np.delete(mat, ids, 1)\\n return mat\",\n \"def _drop_empty_rows_and_cols(confmat: Tensor) ->Tensor:\\n confmat = confmat[confmat.sum(1) != 0]\\n confmat = confmat[:, confmat.sum(0) != 0]\\n return confmat\",\n \"def remove_border(src): #---- remove blank border\\r\\n rows = src.shape[0]; VMIN= 0; VMAX= rows; \\r\\n cols = src.shape[0]; UMIN= 0; UMAX= cols;\\r\\n for ky in range(1,rows):\\r\\n sum0 = np.sum(src[ky,:,:]);\\r\\n sum1 = np.sum(src[rows-ky-1,:,:]);\\r\\n if sum0== 0 and VMIN== ky-1: VMIN= ky;\\r\\n if sum1== 0 and VMAX== rows-ky+1: VMAX= rows-ky;\\r\\n for kx in range(1,cols):\\r\\n sum0 = np.sum(src[:,kx,:]);\\r\\n sum1 = np.sum(src[:,cols-kx-1,:]);\\r\\n if sum0== 0 and UMIN== kx-1: UMIN= kx;\\r\\n if sum1== 0 and UMAX== cols-kx+1: UMAX= cols-kx;\\r\\n #--- --- \\r\\n DV = np.minimum(VMIN, rows-VMAX);\\r\\n DU = np.minimum(UMIN, cols-UMAX);\\r\\n return src[DV:(rows-DV), DU:(cols-DU), :];\",\n \"def unscrew( S ):\\n S = asarray(S)\\n assert allclose(S[...,:3,:3].transpose(0,1),-S[...,:3,:3]),\\\"S[...,:3,:3] is skew\\\"\\n assert allclose(S[...,3,:],0),\\\"Bottom row is 0\\\"\\n return unscrew_UNSAFE(S)\",\n \"def trim_axs(axs, N):\\n axs = axs.flat\\n for ax in axs[N:]:\\n ax.remove()\\n return axs[:N]\",\n \"def trim_axs(axs, N):\\n axs = axs.flat\\n for ax in axs[N:]:\\n ax.remove()\\n return axs[:N]\",\n \"def trim_axs(axs, N):\\n axs = axs.flat\\n for ax in axs[N:]:\\n ax.remove()\\n return axs[:N]\",\n \"def trim_axs(axs, n):\\n axs = axs.flat\\n for ax in axs[n:]:\\n ax.remove()\\n return axs[:n]\",\n \"def trim_zeros(x):\\n assert len(x.shape) == 2\\n return x[~np.all(x == 0, axis=1)]\",\n \"def _reduce_triangle(to_reduce):\\n last_row = to_reduce[-1]\\n for index in xrange(len(to_reduce) - 1):\\n to_reduce[-2][index] += max(last_row[index:index + 2])\\n del to_reduce[-1]\",\n \"def trim_zeros(array):\\n multislice = []\\n for i in range(array.ndim):\\n sum_axes = tuple(j for j in range(array.ndim) if j is not i)\\n edges = np.where(np.sum(array, axis=sum_axes) > 0)\\n if edges[0].size == 0:\\n return np.array([], dtype=array.dtype)\\n low = edges[0][0]\\n high = edges[0][-1]\\n multislice.append(slice(low, high+1, 1))\\n return array[tuple(multislice)]\",\n \"def eliminate(self, A):\\r\\n length = len(A)\\r\\n width = len(A[0])\\r\\n for i in range(length):\\r\\n if A[i][i] != 1:\\r\\n # if current entry on the diagonal is 0 (off), proceed row interchange. \\r\\n for k in range(i + 1, length):\\r\\n if A[k][i] == 1:\\r\\n A[i], A[k] = A[k], A[i]\\r\\n self.step += 1\\r\\n break\\r\\n for j in range(i + 1, length):\\r\\n # eliminate the entries under the diagonal\\r\\n if A[j][i] == 1:\\r\\n A[j] = [(A[i][m] + A[j][m]) % 2 for m in range(width)]\\r\\n self.step += width\\r\\n\\r\\n for i in range(length - 1, -1, -1):\\r\\n # eliminate the entries above the diagonal\\r\\n for j in range(i - 1, -1, -1):\\r\\n if A[j][i] != 0:\\r\\n A[j] = [(A[i][k] + A[j][k]) % 2 for k in range(width)]\\r\\n self.step += width\\r\\n # Now, only the entries on the diagonal are 1, the rest are 0.\\r\",\n \"def upper(mat):\\n idx = np.triu_indices_from(mat, k=1)\\n return mat[idx]\",\n \"def trim(args) :\\n from trimmer import trim_reads\\n trim_reads(args)\",\n \"def triu(m, k=0):\\r\\n m = asanyarray(m)\\r\\n out = multiply((1-tri(m.shape[0], m.shape[1], k-1, int)),m)\\r\\n return out\",\n \"def cut_array_border(array): \\n array[:, [0, array.shape[1]-1]]=0\\n array[[0, array.shape[0]-1], :]=0\\n \\n \\n return array\",\n \"def _flatten_lower_triangle(matrix):\\r\\n matrix = asarray(matrix)\\r\\n flattened = []\\r\\n for col_num in range(matrix.shape[1]):\\r\\n for row_num in range(matrix.shape[0]):\\r\\n if col_num < row_num:\\r\\n flattened.append(matrix[row_num][col_num])\\r\\n return flattened\",\n \"def reduce_triangle(to_reduce):\\r\\n last_row = to_reduce[-1]\\r\\n for index in xrange(len(to_reduce)-1):\\r\\n to_reduce[-2][index] += max(last_row[index:index+2])\\r\\n del to_reduce[-1]\\r\\n return to_reduce\",\n \"def offDiagPairs(self):\\n return np.transpose(np.nonzero(np.triu(self.LaplacianMatrix,k=2)))\",\n \"def upper_triangular_matrix_to_full_matrix(arr, n):\\n triu0 = np.triu_indices(n, 0)\\n triu1 = np.triu_indices(n, 1)\\n tril1 = np.tril_indices(n, -1)\\n\\n mat = np.zeros((n, n), dtype=float)\\n mat[triu0] = arr\\n mat[tril1] = mat[triu1]\\n\\n return mat\",\n \"def Problem2(n):\\n diag_entries = np.empty((3,n))\\n diag_entries[0] = np.ones(n)*(-1)\\n diag_entries[1] = np.ones(n)*2\\n diag_entries[2] = np.ones(n)*(-1)\\n A = sparse.spdiags(diag_entries, [-1,0,1],n,n,format=\\\"csr\\\")\\n return A\",\n \"def get_off_diagonal(matrix):\\n\\toff_diag = scipy.array(matrix, dtype=matrix.dtype)\\n\\toff_diag[scipy.diag_indices_from(matrix)] = 0\\n\\treturn off_diag\",\n \"def reduce_size(data):\\r\\n data_ = pd.DataFrame(data)\\r\\n nonzero_idxs = np.flatnonzero(np.diag(data_))\\r\\n cov_reduced = data_.iloc[nonzero_idxs, nonzero_idxs]\\r\\n return nonzero_idxs, cov_reduced\",\n \"def delete_all_gap(self):\\n # pdb.set_trace()\\n\\n rem = set(self.get_all_gap_cols())\\n subset = [x for x in range(0, self.get_length()) if x not in rem]\\n self.remove_columns(set(rem))\\n #_LOG.debug(\\\"Alignment length reduced to %d\\\" % len(subset))\\n return subset\",\n \"def EliminateRowsCols(self, *args):\\n return _hypre.HypreParMatrix_EliminateRowsCols(self, *args)\",\n \"def cut_transformed_array_borders(array): \\n for col in range(array.shape[1]): \\n col_=array[:, col]\\n \\n where=np.where(col_>0)\\n \\n if len(where[0])>0:\\n \\n col_[[np.min(where[0]),np.min(where[0])+1, np.max(where[0]), np.max(where[0])-1 ]]=0\\n \\n array[:,col]=col_\\n \\n for row in range(array.shape[0]): \\n row_=array[row,:]\\n \\n where=np.where(row_>0)\\n if len(where[0])>0:\\n\\n row_[[np.min(where[0]),np.min(where[0])+1, np.max(where[0]), np.max(where[0])-1 ]]=0\\n \\n array[row,:]=row_\\n \\n return array\",\n \"def triu(m, k=0):\\r\\n return m * (1 - tri(m.shape[0], m.shape[1], k=k - 1, dtype=m.dtype))\",\n \"def trim_vocab(n: int, vocab: Dict[str, int]) -> None:\\r\\n pair_counts = sorted(vocab.items(), key=lambda p: -p[1])\\r\\n pairs_to_trim = [pair for pair, count in pair_counts[n:]]\\r\\n for pair in pairs_to_trim:\\r\\n del vocab[pair]\",\n \"def square_matrix(triangular_matrix):\\n length = len(triangular_matrix)\\n zero = [0.0]\\n\\n for item in triangular_matrix:\\n item.extend(zero * (length - len(item)))\\n\\n return triangular_matrix\",\n \"def tri(N, M=None, k=0, dtype=float):\\r\\n if M is None: M = N\\r\\n m = greater_equal(subtract.outer(arange(N), arange(M)),-k)\\r\\n return m.astype(dtype)\",\n \"def block_diag_full(W_):\\n assert(W_.ndim == 3)\\n bsize = W_.shape[0]\\n full = np.concatenate([\\n np.concatenate([ np.diag(W_[:,i,j]) for j in range(W_.shape[2]) ], axis=1)\\n for i in range(W_.shape[1]) ], axis=0)\\n return full\",\n \"def test_nullspace_blocked(input_matrix):\\n mat = sp.Matrix(input_matrix)\\n assert sorted(mat.nullspace()) == sorted(nullspace_blocked(mat))\",\n \"def diagonalsNeg (matrix, cols, rows):\\r\\n for diagonal in ([(j, i - cols + j + 1) for j in range(cols)] for i in range(cols + rows - 1)):\\r\\n yield [matrix[i][j] for i, j in diagonal if i >= 0 and j >= 0 and i < cols and j < rows]\",\n \"def pfd_clear(n):\\n\\tglobal matrix\\n\\tr = []\\n\\tfor i in xrange(size[0]):\\n\\t\\tif matrix[i][n] == 1:\\n\\t\\t\\tmatrix[i][n] = 0\\n\\t\\t\\tr.append(i)\\n\\treturn r\",\n \"def trim_features():\\n pass\",\n \"def removeFirstMatrix(file):\\n file1 = open(file, \\\"r\\\") #In the future this function can just reside in readMatrix() \\n \\n rawData = file1.readlines() \\n \\n n = round (len(rawData[0])/2)\\n \\n newData = rawData[n+1:]\\n \\n file1 = open(file, \\\"w\\\")\\n\\n for line in newData:\\n file1.write(line)\\n file1.close()\",\n \"def at_ftrim(seq):\\n at(\\\"FTRIM\\\", seq, [])\",\n \"def eliminate_kxk(sudoku, k):\\n steps = range(len(sudoku) // k)\\n for i in steps:\\n for j in steps:\\n\\n rows = sudoku[i*k:(i+1)*k]\\n part = [item[j*k:(j+1)*k] for item in rows]\\n part = [item for sublist in part for item in sublist]\\n\\n cleaned_part = eliminate_rows([part])[0]\\n\\n count = 0\\n for l in range(i*k, (i+1)*k):\\n for m in range(j*k, (j+1)*k):\\n sudoku[l][m] = sorted(cleaned_part[count])\\n count += 1\\n\\n return sudoku\",\n \"def write_untrim08(self,fn):\\n UnTRIM08Grid(grid=self).write_untrim08(fn)\",\n \"def OwnsDiag(self):\\n return _hypre.HypreParMatrix_OwnsDiag(self)\",\n \"def upper_tri(A):\\n d = pairwise_distances(A,A)\\n m = d.shape[0]\\n r,c = np.triu_indices(m,1)\\n return(d[r,c])\",\n \"def EliminateRows(self, rows):\\n return _hypre.HypreParMatrix_EliminateRows(self, rows)\",\n \"def mat_from_diag_triu_tril(diag, tri_upp, tri_low):\\n n = diag.shape[-1]\\n (i,) = diag_indices(n, ndim=1)\\n j, k = triu_indices(n, k=1)\\n mat = _torch.zeros((diag.shape + (n,)))\\n mat[..., i, i] = diag\\n mat[..., j, k] = tri_upp\\n mat[..., k, j] = tri_low\\n return mat\",\n \"def eliminate(board, i, j):\\n value = board[i][j][0]\\n # Horizontal propagation\\n for k in range(n):\\n if j!=k and value in board[i][k]:\\n board[i][k].remove(value)\\n # Vertical propagation\\n for l in range(n):\\n if i!=l and value in board[l][j]:\\n board[l][j].remove(value)\\n # Square propagation\\n for k in range(i/3*3, i/3*3+3):\\n for l in range(j/3*3, j/3*3+3):\\n if k!=i and l!=j and (value in board[k][l]):\\n board[k][l].remove(value)\\n return 0\",\n \"def trim(self, start, stop=None):\\n if stop is None:\\n stop = self.data.shape[0]\\n\\n start = max(start, 0)\\n stop = min(stop, self.data.shape[0])\\n self.data = self.data.iloc[start:stop,:]\",\n \"def half(self, matrix, k=1):\\r\\n return [matrix[i][i:] if k == 0 else matrix[i][:i+1] for i in range(len(matrix))]\",\n \"def remove_outliers(self, matrix):\\n input = matrix[:, :-1]\\n row_incides_to_delete = []\\n for j, column in enumerate(input.transpose()):\\n self.feature_means.append(np.mean(column))\\n self.feature_stds.append(np.std(column))\\n\\n for i, row in enumerate(input):\\n cell = input[i, j]\\n if cell > self.feature_means[j] + 3 * self.feature_stds[j] or cell < self.feature_means[j] - 3 * \\\\\\n self.feature_stds[j]:\\n row_incides_to_delete.append(i)\\n matrix = np.delete(matrix, row_incides_to_delete, 0)\\n return matrix, len(list(set(row_incides_to_delete)))\",\n \"def trim_occluded_throats(self):\\n occluded_ts = list(self.throats()[self[\\\"throat.area\\\"]==0])\\n #occluded_throats = np.asarray(occluded_throats)\\n if len(occluded_ts) > 0:\\n self.trim(throats=occluded_ts)\\n \\\"Also get rid of isolated pores\\\"\\n isolated_ps = self.check_network_health()['isolated_pores']\\n if len(isolated_ps) > 0:\\n self.trim(isolated_ps)\",\n \"def zero_diag(mat):\\n\\n return replace_diag(mat, np.zeros(mat.shape[0]))\",\n \"def UTrim(self, *args):\\n return _Adaptor3d.Adaptor3d_Surface_UTrim(self, *args)\",\n \"def flat_triang_to_full(triang):\\n rows = int(1/2*(np.sqrt(8*len(triang)+1)-1))\\n mask = np.tri(rows,dtype=bool).T # transpose: lower to upper\\n full = np.zeros((rows,rows))\\n full[mask] = triang\\n full += full.T*np.tri(rows,k=-1)\\n return full\",\n \"def diagonal(self):\\n M = self.rep\\n m, n = self.shape\\n return [M[i, i] for i in range(min(m, n))]\",\n \"def form_triu_matrix(arr):\\n n = int(np.ceil((np.sqrt(1 + 8 * len(arr)) - 1) * 0.5))\\n M = np.zeros((n, n))\\n c = 0\\n for i in range(n):\\n for j in range(n):\\n if j >= i:\\n if c < len(arr):\\n M[i, j] = arr[c]\\n c += 1\\n else:\\n break\\n return M\",\n \"def remove_top_layer(I_m, board_size=4, *args, **kwargs):\\n for j in range(1, board_size+1):\\n for k in range(1, board_size+1):\\n if len(I_m.get((j, k))) > 0:\\n I_m[(j, k)].pop()\\n return I_m\",\n \"def trim_empty_rows(self, table: Table):\\n self._requires_table(table)\\n\\n empty = []\\n for idx in reversed(table.index):\\n row = table[idx]\\n if any(value is not None for value in row):\\n break\\n empty.append(idx)\\n\\n table.delete_rows(empty)\",\n \"def get_diagonal_up(self, x, y):\\n\\n while 0 <= x < 7 and 0 <= y < 6:\\n x -= 1\\n y += 1\\n\\n return [self._board[y-i][x+i] for i in xrange(7)\\n if 0 <= (x+i) < 7 and 0 <= (y-i) < 6]\",\n \"def shrinkTrackMatrix(self):\\n self.tracksMatrix = self.tracksMatrix[0:(len(self.tracksMatrix)-1)]\\n self.attributesMatrix = self.attributesMatrix[0:(len(self.attributesMatrix)-1)]\",\n \"def construct_matrix_A(n):\\n n2 = n**2\\n D0 = 4*np.ones(n2) # 0th diagonal\\n D1 = - np.ones(n2 - 1) # -1st, 1st diagonals\\n D1[n-1::n] = 0 # Setting every k*n-1 entries = 0 for k < n\\n DN = - np.ones(n2 - n) # -nth, nth diagonals\\n return scipy.sparse.diags((DN, D1, D0, D1, DN), (-n, -1, 0, 1, n),\\n shape=(n2, n2), format=\\\"csr\\\")\",\n \"def trim2DArray(input_arr, threshold=0): \\n\\n print(input_arr.shape)\\n ul_x, ul_y = calculateTrimOffsetForward(input_arr, threshold)\\n lr_x, lr_y = calculateTrimOffsetBackward(input_arr, threshold)\\n\\n output_arr = input_arr[ul_y:lr_y, ul_x:lr_x]\\n\\n print(output_arr.shape)\\n\\n return (output_arr)\",\n \"def get_reverse_diagonal(self):\\n start = 0\\n end = 9\\n step = 4\\n\\n return self.grid[start:end:step] # array[start:end:step]\",\n \"def trim(self):\\n for i in range(len(self)):\\n if self[i] != TRIT_ZERO:\\n return self.__class__(self[i:])\\n return self.__class__([])\",\n \"def trim(self):\\n\\n # identify edges outside window\\n\\n min_time = self.latest_time - self.t_window\\n edges_to_trim = self.graph.es.select(time_lt=min_time)\\n if self.verbose: print(\\\"Edges to trim: \\\"+str(edges_to_trim))\\n\\n # remove edges outside of t_window\\n self.graph.delete_edges(edges_to_trim)\\n\\n # identify vertices with 0 degree to delete\\n vertices_to_trim = self.graph.vs.select(_degree=0)\\n if self.verbose: print(\\\"Vertices to trim: \\\"+str(vertices_to_trim._name_index))\\n self.graph.delete_vertices(vertices_to_trim)\",\n \"def zero_matrix(matrix):\\n rows = set()\\n columns = set()\\n m = len(matrix)\\n n = len(matrix[0])\\n for i in range(m):\\n for j in range(n):\\n if matrix[i][j] == 0:\\n rows.add(i)\\n columns.add(j)\\n\\n for i in range(m):\\n for j in range(n):\\n if i in rows or j in columns:\\n matrix[i][j] = 0\\n return matrix\",\n \"def _trim_border(img):\\n for i in range(img.shape[0]):\\n if np.any(img[i, :, :] != 255):\\n img = img[i:, :, :]\\n break\\n\\n for i in range(img.shape[0] - 1, 0, -1):\\n if np.any(img[i, :, :] != 255):\\n img = img[: i + 1, :, :]\\n break\\n\\n for i in range(img.shape[1]):\\n if np.any(img[:, i, :] != 255):\\n img = img[:, i:, :]\\n break\\n\\n for i in range(img.shape[1] - 1, 0, -1):\\n if np.any(img[:, i, :] != 255):\\n img = img[:, : i + 1, :]\\n break\\n\\n return img\",\n \"def trim(elements, n):\\n if len(elements) >= n: # if there are enough elements,\\n elements[:n] = True # set `n` to `True` and leave\\n return elements # the rest to `False`.\",\n \"def trim_data(data, attributes):\\n return data.drop(attributes, axis=1)\",\n \"def gen_square_subsequent_mask(sz: int) -> torch.Tensor:\\n return torch.triu(torch.ones(sz, sz) * float('-inf'), diagonal=1)\",\n \"def test_remove_middle_axis():\\n test_array = np.ones((13, 17, 19, 3, 3, 11, 21))\\n out_array = utils.remove_auto_correlations(test_array, axes=(3, 4))\\n assert (13, 17, 19, 6, 11, 21) == out_array.shape\",\n \"def ltrim(self, record_provenance=True):\\n tables = self.dump_tables()\\n tables.ltrim(record_provenance)\\n return tables.tree_sequence()\",\n \"def trim(self, record_provenance=True):\\n tables = self.dump_tables()\\n tables.trim(record_provenance)\\n return tables.tree_sequence()\",\n \"def generate_square_subsequent_mask(sz: int) -> Tensor:\\n return torch.triu(torch.ones(sz, sz) * float('-inf'), diagonal=1)\",\n \"def trim(self):\\n while np.any(self.vertex_valance <= 1):\\n edge_to_keep = np.all(self.vertex_valance[self.edges] > 1,\\n axis=1).tolist();\\n self.raw_wires.filter_edges(edge_to_keep);\\n vertex_to_keep = [len(self.get_vertex_neighbors(i)) > 0 for i in\\n range(self.num_vertices)];\\n self.raw_wires.filter_vertices(vertex_to_keep);\\n\\n self.__initialize_wires();\\n if len(self.vertices) == 0:\\n raise RuntimeError(\\\"Zero vertices left after trimming.\\\");\",\n \"def triu_matrix(matrix, kind='upper'):\\r\\n matrix_ = pd.DataFrame(matrix)\\r\\n index = matrix_.index\\r\\n columns = matrix_.columns\\r\\n values = matrix_.values\\r\\n if kind == 'upper': \\r\\n index_lower = np.tril_indices(matrix_.shape[0], -1)\\r\\n values[index_lower] = values.T[index_lower]\\r\\n elif kind == 'lower':\\r\\n index_upper = np.triu_indices(matrix_.shape[0], 1)\\r\\n values[index_upper] = values.T[index_upper]\\r\\n return CategoryCov(pd.DataFrame(values, index=index, columns=columns))\",\n \"def trim_axes(axes, N):\\n if N>1:\\n axes = axes.flat\\n for ax in axes[N:]:\\n ax.remove()\\n return axes[:N]\\n else:\\n return [axes]\",\n \"def test_triu_tril():\\n A = np.array(\\n [[1, 2, 3, 4], [5, 6, 7, 8], [9, 10, 11, 12], [13, 14, 16, 16]]\\n )\\n A_u = np.array([[1, 2, 3, 4], [0, 6, 7, 8], [0, 0, 11, 12], [0, 0, 0, 16]])\\n A_l = np.array(\\n [[1, 0, 0, 0], [5, 6, 0, 0], [9, 10, 11, 0], [13, 14, 16, 16]]\\n )\\n assert norm(A_u - triu(A)) < 1e-12\\n assert norm(A_l - tril(A)) < 1e-12\\n assert (\\n norm(scipy.sparse.coo_matrix(A_u) - triu(scipy.sparse.coo_matrix((A))))\\n < 1e-12\\n )\\n assert (\\n norm(scipy.sparse.coo_matrix(A_l) - tril(scipy.sparse.coo_matrix((A))))\\n < 1e-12\\n )\",\n \"def gen_diagonal_search_bottomleft_topright(n, grid):\\n width, height = len(grid[0]), len(grid)\\n if width < n or height < n:\\n yield None\\n\\n for y in range(n-1, height):\\n for x in range(0, width - n + 1):\\n sample = [grid[y - i][x + i] for i in range(n)]\\n yield sample\",\n \"def trim(self, xoffset, xsize, yoffset, ysize, zoffset, zsize):\\n xoffset = int(xoffset)\\n yoffset = int(yoffset)\\n zoffset = int(zoffset)\\n self.data = self.data[:, zoffset:zoffset+zsize, yoffset:yoffset+ysize, xoffset:xoffset+xsize]\",\n \"def removeCols(self) -> List['StateNode']:\\n cols = self.state[1]\\n states: List[StateNode] = []\\n for i in range(len(cols)):\\n for j in range(i + 1, len(cols) + 1):\\n # for j in range(i + 1, i + 2):\\n new_cols = cols[:i] + cols[j:]\\n if len(new_cols) == 0:\\n continue\\n states.append(StateNode(self.table, \\n (self.state[0], new_cols),\\n ([], cols[i:j]),\\n self.cost + j - i + self.count_pairs(self.state[0], cols[i:j]),\\n self))\\n return states\",\n \"def triangle(row):\\n if len(row) == 0:\\n raise ValidationError(\\\"Row empty\\\")\\n if len(row) == 1:\\n return row\\n if len(row) < 8:\\n return small_triangles(row)\\n make_steps()\\n while len(row) > 50:\\n streams = defaultdict(list)\\n j = 0\\n streams[j] = list(row)\\n for i in range(len(row) - 4):\\n for j in range(10):\\n step = j * 4\\n if i >= step:\\n streams[j + 1].append(do_row(streams[j], i - step))\\n row = streams[j]\\n return small_triangles(row)\",\n \"def strip_zeros(a):\\n\\n return np.trim_zeros(a, trim='b')\",\n \"def triu_indices(n, k=0):\\r\\n return mask_indices(n, np.triu, k)\",\n \"def _clear_matrix(self):\\n\\t\\tself._w2i_matrix = self._i2w_matrix = None\",\n \"def remove_gap_traces(st):\\n # Check 1:\\n gaps = st.get_gaps()\\n for i in range(len(gaps)):\\n for tr in st.select(network=gaps[i][0], station=gaps[i][1], location=gaps[i][2], channel=gaps[i][3]):\\n st.remove(tr)\\n # Check 2:\\n masked = [np.ma.is_masked(tr.data) for tr in st]\\n for i, tr in enumerate(st):\\n if masked[i]:\\n st.remove(tr)\\n return st\",\n \"def unique_spaces(mask):\\n ncols = mask.shape[1]\\n\\n # Do some magic with views so ``unique`` can be used to find the unique\\n # sets of dimensions.\\n dtype = mask.dtype.descr * ncols\\n struct = mask.view(dtype)\\n\\n uniq = np.unique(struct)\\n uniq = uniq.view(mask.dtype).reshape(-1, ncols)\\n return ~uniq\",\n \"def test_take_fifth_diagonal_below_main(self):\\n offset = -5\\n matrix = [['.', '.', '.', '.', '.', '.', '.', '.', '.', '.', '.', '.', '.', '.', '.'],\\n ['.', '.', '.', '.', '.', '.', '.', '.', '.', '.', '.', '.', '.', '.', '.'],\\n ['.', '.', '.', '.', '.', 'x', '.', '.', '.', '.', '.', '.', '.', '.', '.'],\\n ['.', '.', '.', '.', '.', '.', 'o', '.', '.', '.', '.', '.', '.', '.', '.'],\\n ['.', '.', '.', '.', '.', '.', '.', 'o', '.', '.', '.', '.', '.', '.', '.'],\\n ['.', '.', '.', '.', '.', '.', '.', '.', 'x', '.', '.', '.', '.', '.', '.'],\\n ['.', 'o', '.', '.', '.', '.', '.', '.', '.', 'x', '.', '.', '.', '.', '.'],\\n ['.', '.', 'o', '.', '.', '.', '.', '.', '.', '.', 'o', '.', '.', '.', '.'],\\n ['.', '.', '.', 'o', '.', '.', '.', '.', '.', '.', '.', 'x', '.', '.', '.'],\\n ['.', '.', '.', '.', 'o', '.', '.', '.', '.', '.', '.', '.', 'o', '.', '.'],\\n ['.', '.', '.', '.', '.', 'o', '.', '.', '.', '.', '.', '.', '.', '.', '.'],\\n ['.', '.', '.', '.', '.', '.', 'o', '.', '.', '.', '.', '.', '.', '.', '.'],\\n ['.', '.', '.', '.', '.', '.', '.', 'o', '.', '.', '.', '.', '.', '.', '.'],\\n ['.', '.', '.', '.', '.', '.', '.', '.', 'o', '.', '.', '.', '.', '.', '.'],\\n ['.', '.', '.', '.', '.', '.', '.', '.', '.', 'o', '.', '.', '.', '.', '.']]\\n table = self._create_table(matrix)\\n\\n expected = ['.', 'o', 'o', 'o', 'o', 'o', 'o', 'o', 'o', 'o']\\n current_list = utils.get_diagonal(table, offset)\\n\\n self.assertListEqual(expected, current_list)\",\n \"def remove_center(X, N=10, border=2):\\n \\n #Translate X into original block shape\\n X2 = X.reshape((len(X),N,N,6))\\n\\n # Decompose matrix as follows:\\n \\n # N N N N N N N N\\n # N N N N N N N N\\n # W W E E\\n # W W E E\\n # W W E E\\n # W W E E\\n # S S S S S S S S\\n # S S S S S S S S\\n \\n # block is then flattened as well\\n North = X2[:, :border].reshape(len(X), N*border*6)\\n South = X2[:, -border:].reshape(len(X), N*border*6)\\n West = X2[:, border:-border, :border].reshape(len(X), (N-2*border)*border*6)\\n East = X2[:, border:-border, -border:].reshape(len(X), (N-2*border)*border*6)\\n \\n # Stack the vectors horizontally, e.g.\\n # N_,1 .., N_x, W_1, .. W_x, E_1, .., E_x, S_1, .., S_x\\n return np.hstack((North, West, East, South))\",\n \"def _trimRecords(self):\\n self.highpassrecords.resize(self.nhighpassrecords, refcheck=False)\\n self.lowpassrecords.resize(self.nlowpassrecords, refcheck=False)\\n self.digitalsvalrecords.resize(self.ndigitalsvalrecords, refcheck=False)\\n # cleanup by deleting any struct arrays of len 0\\n for recname in ('highpassrecords', 'lowpassrecords', 'digitalsvalrecords'):\\n if len(self.__getattribute__(recname)) == 0:\\n self.__delattr__(recname)\",\n \"def remove_row(self, index):\\n _ = self.matrix.pop(index) ## Delete row\\n self.matrix.append(self.empty_row.copy())\",\n \"def clear_rows(self):\\n ### Previous version had a bug, in that it assumed the set of ###\\n ### indices of full rows had to be a contiguous sequence! ###\\n full_rows = [j for j in range(ROWS) if all(\\n (i, j) in self.locked_squares for i in range(COLS))]\\n if not full_rows: return\\n ### Calculate how for to drop each other row, and do it ###\\n drop = {j: len([k for k in full_rows if k > j]) for j in range(ROWS)}\\n self.locked_squares = {(i, j+drop[j]): color for (i, j), color in\\n self.locked_squares.items() if j not in full_rows}\\n ### Now just update score, etc. ###\\n d = len(full_rows)\\n self.increment_lines(d)\\n self.increment_score(self.level*{1: 40, 2: 100, 3: 300, 4: 1200}[d])\\n if self.level < self.lines // 10 + 1:\\n self.increment_level()\",\n \"def squeeze(self):\\n remove_axes = []\\n for axes_ix, axes_value in enumerate(self.coords):\\n if len(axes_value) == 1:\\n remove_axes.append(axes_ix)\\n\\n reverse_remove_axes = remove_axes[::-1]\\n for index_ix, index_value in enumerate(reverse_remove_axes):\\n self.coords.pop(index_value)\\n self.dims.pop(index_value)\\n self.values = np.squeeze(self.values)\",\n \"def smith_nf(matrix):\\n\\n A=np.copy(matrix)\\n if (np.around(A) != A).any():\\n raise Exception('This function requires integer input.')\\n\\n # This looks much like an SVD algorithm that first bidiagonalizes\\n # A by Givens rotations and then chases zeros, except for\\n # the construction of the 2 by 2 elementary transformation.\\n\\n m, n = A.shape\\n\\n S = A\\n U = np.eye(m)\\n V = np.eye(n)\\n\\n # Bidiagonalize S with elementary Hermite transforms.\\n for j in range(min(m, n)):\\n # Zero column j below the diagonal.\\n for i in range(j+1, m):\\n if S[i, j]:\\n # Construct an elementary Hermite transformation E\\n # to zero S(i,j) by combining rows i and j.\\n E = ehermite(S[j, j], S[i, j])\\n # Apply the transform to S and U.\\n S[[j, i], :] = np.dot(E, S[[j, i], :])\\n # U[:, [j, i]] = U[:, [j, i]] / E\\n U[:, [j, i]] = left_matrix_division(U[:, [j, i]], E) # solving the left matrix division\\n\\n # % Zero row j after the superdiagonal.\\n for i in range(j+2, n):\\n if S[j, i]:\\n # Construct an elementary Hermite transformation E\\n # to zero S(j,i) by combining columns j+1 and i.\\n E = ehermite(S[j, j+1], S[j, i])\\n # Apply the transform to S and V.\\n S[:, [j+1, i]] = np.dot(S[:, [j+1, i]], E.T)\\n # V[:, [j+1, i]] = V[:, [j+1, i]] / E\\n V[:, [j+1, i]] = left_matrix_division(V[:, [j+1, i]], E) # solving the left matrix division\\n\\n # Now S is upper bidiagonal.\\n # Chase the superdiagonal nonzeros away.\\n\\n D = np.diag(S, 1)\\n while any(D):\\n b = min(np.where(D))[0]\\n # Start chasing bulge at first nonzero superdiagonal element.\\n # To guarantee reduction in S(b,b), first make S(b,b) positive\\n # and make S(b,b+1) nonnegative and less than S(b,b).\\n if S[b, b] < 0:\\n S[b, :] = -S[b, :]\\n U[:, b] = -U[:, b]\\n\\n q = np.floor(S[b, b+1] / S[b, b])\\n E = np.array([[1, 0], [-q, 1]])\\n S[:, [b, b+1]] = np.dot(S[:, [b, b+1]], E.T)\\n # V[:, [b, b+1]] = V[:, [b, b+1]] / E\\n V[:, [b, b+1]] = left_matrix_division(V[:, [b, b+1]], E) # solving the left matrix division\\n\\n if S[b, b+1]:\\n # Zero the first nonzero superdiagonal element\\n # using columns b and b+1, to start the bulge at S(b+1,b).\\n E = ehermite(S[b, b], S[b, b+1])\\n S[:, [b, b+1]] = np.dot(S[:, [b, b+1]], E.T)\\n # V[:, [b, b+1]] = V[:, [b, b+1]] / E\\n V[:, [b, b+1]] = left_matrix_division(V[:, [b, b+1]], E)\\n\\n for j in range(min(m, n)):\\n if j+1 < m:\\n # Zero S(j+1,j) using rows j and j+1.\\n E = ehermite(S[j, j], S[j+1, j])\\n S[[j, j+1], :] = np.dot(E, S[[j, j+1], :])\\n # U[:, [j, j+1]] = U[:, [j, j+1]] / E\\n U[:, [j, j+1]] = left_matrix_division(U[:, [j, j+1]], E)\\n if j+2 < n:\\n # Zero S(j,j+2) using columns j+1 and j+2.\\n E = ehermite(S[j, j+1], S[j, j+2])\\n S[:, [j+1, j+2]] = np.dot(S[:, [j+1, j+2]], E.T)\\n # V[:, [j+1, j+2]] = V[:, [j+1, j+2]] / E\\n V[:, [j+1, j+2]] = left_matrix_division(V[:, [j+1, j+2]], E)\\n D = np.diag(S, 1)\\n\\n # Now S is diagonal. Make it nonnegative.\\n\\n for j in range(min(m, n)):\\n if S[j, j] < 0:\\n S[j, :] = -S[j, :]\\n U[:, j] = -U[:, j]\\n\\n # Squeeze factors to lower right to enforce divisibility condition.\\n\\n for i in range(min(m, n)):\\n for j in range(i+1, min(m, n)):\\n # Replace S(i,i), S(j,j) by their gcd and lcm respectively.\\n a = S[i, i]\\n b = S[j, j]\\n [c, d, g] = extgcd(a, b)\\n E = np.array([[1, d], [-b/g, a*c/g]])\\n F = np.array([[c, 1], [-b*d/g, a/g]])\\n S[np.ix_([i, j], [i, j])] = np.dot(np.dot(E, S[:, [i, j]][[i, j], :]), F.T)\\n # S[i, i] = tmp_arr[0, 0]\\n # S[i, j] = tmp_arr[0, 1]\\n # S[j, i] = tmp_arr[1, 0]\\n # S[j, j] = tmp_arr[1, 1]\\n U[:, [i, j]] = left_matrix_division(U[:, [i, j]], E)\\n V[:, [i, j]] = left_matrix_division(V[:, [i, j]], F)\\n\\n U = np.around(U)\\n V = np.around(V)\\n return U, S, V\",\n \"def rtrim(self, record_provenance=True):\\n tables = self.dump_tables()\\n tables.rtrim(record_provenance)\\n return tables.tree_sequence()\",\n \"def finalise_matrix(matrix):\\n if get_density(matrix) < DENSITY_THRESHOLD:\\n matrix = matrix.tocsc()\\n matrix.sort_indices()\\n else:\\n matrix = matrix.toarray()\\n if is_integer(matrix):\\n matrix = convert_to_smallest_int_type(matrix)\\n return matrix\",\n \"def upper_diagonal_constraint(board: Board, row: int, col: int) -> bool:\\n #move to first tile in diagonal\\n row -= 1\\n col -= 1\\n #while still in board, test and move to next position return false if fails\\n while row >= 0 and col >= 0:\\n if board.is_queen(row=row, col=col):\\n return False\\n row -= 1\\n col -= 1\\n return True\",\n \"def getdiag(self):\\n out = []\\n for x in xrange(0, self.lendiag()):\\n out.append(self.retrieve(x))\\n return out\"\n]","isConversational":false},"negative_scores":{"kind":"list like","value":["0.64127666","0.58649224","0.57881296","0.5781573","0.5728801","0.57002896","0.5696073","0.568997","0.5671774","0.566362","0.566362","0.566362","0.56233585","0.5622041","0.5614353","0.55710584","0.55435014","0.5482823","0.5455417","0.54416484","0.54353005","0.54291195","0.5388212","0.5386462","0.53654027","0.5352046","0.53428036","0.53383636","0.5330673","0.53039026","0.53025806","0.52629703","0.524622","0.52387524","0.5216878","0.52160853","0.52054316","0.51907176","0.5154897","0.51545423","0.5128656","0.512444","0.51132137","0.51105976","0.51092994","0.5080151","0.50778395","0.50752676","0.5074114","0.506006","0.50572443","0.50561225","0.50480235","0.5038595","0.5037804","0.5026798","0.5003939","0.50032073","0.5001434","0.4997463","0.4995844","0.49881756","0.49793905","0.49693146","0.4965232","0.4963132","0.49588615","0.49517605","0.49234173","0.49150252","0.49114698","0.49083108","0.49081662","0.4906831","0.49068072","0.4899175","0.48947784","0.48920357","0.48905042","0.48843327","0.48743632","0.4873587","0.48662028","0.4859839","0.4846651","0.48218048","0.48129877","0.48102874","0.4806385","0.48032945","0.48026845","0.4798354","0.47971728","0.47918394","0.4789189","0.47890794","0.47890687","0.4788829","0.47864717","0.47859618"],"string":"[\n \"0.64127666\",\n \"0.58649224\",\n \"0.57881296\",\n \"0.5781573\",\n \"0.5728801\",\n \"0.57002896\",\n \"0.5696073\",\n \"0.568997\",\n \"0.5671774\",\n \"0.566362\",\n \"0.566362\",\n \"0.566362\",\n \"0.56233585\",\n \"0.5622041\",\n \"0.5614353\",\n \"0.55710584\",\n \"0.55435014\",\n \"0.5482823\",\n \"0.5455417\",\n \"0.54416484\",\n \"0.54353005\",\n \"0.54291195\",\n \"0.5388212\",\n \"0.5386462\",\n \"0.53654027\",\n \"0.5352046\",\n \"0.53428036\",\n \"0.53383636\",\n \"0.5330673\",\n \"0.53039026\",\n \"0.53025806\",\n \"0.52629703\",\n \"0.524622\",\n \"0.52387524\",\n \"0.5216878\",\n \"0.52160853\",\n \"0.52054316\",\n \"0.51907176\",\n \"0.5154897\",\n \"0.51545423\",\n \"0.5128656\",\n \"0.512444\",\n \"0.51132137\",\n \"0.51105976\",\n \"0.51092994\",\n \"0.5080151\",\n \"0.50778395\",\n \"0.50752676\",\n \"0.5074114\",\n \"0.506006\",\n \"0.50572443\",\n \"0.50561225\",\n \"0.50480235\",\n \"0.5038595\",\n \"0.5037804\",\n \"0.5026798\",\n \"0.5003939\",\n \"0.50032073\",\n \"0.5001434\",\n \"0.4997463\",\n \"0.4995844\",\n \"0.49881756\",\n \"0.49793905\",\n \"0.49693146\",\n \"0.4965232\",\n \"0.4963132\",\n \"0.49588615\",\n \"0.49517605\",\n \"0.49234173\",\n \"0.49150252\",\n \"0.49114698\",\n \"0.49083108\",\n \"0.49081662\",\n \"0.4906831\",\n \"0.49068072\",\n \"0.4899175\",\n \"0.48947784\",\n \"0.48920357\",\n \"0.48905042\",\n \"0.48843327\",\n \"0.48743632\",\n \"0.4873587\",\n \"0.48662028\",\n \"0.4859839\",\n \"0.4846651\",\n \"0.48218048\",\n \"0.48129877\",\n \"0.48102874\",\n \"0.4806385\",\n \"0.48032945\",\n \"0.48026845\",\n \"0.4798354\",\n \"0.47971728\",\n \"0.47918394\",\n \"0.4789189\",\n \"0.47890794\",\n \"0.47890687\",\n \"0.4788829\",\n \"0.47864717\",\n \"0.47859618\"\n]","isConversational":false},"document_score":{"kind":"string","value":"0.7814169"},"document_rank":{"kind":"string","value":"0"}}},{"rowIdx":22679,"cells":{"query":{"kind":"string","value":"Computes genomic distance law by averaging over each diagonal in the upper triangle matrix. If a list of detectable bins is provided, pixels in missing bins will be excluded from the averages. A maximum distance can be specified to define how many diagonals should be computed."},"document":{"kind":"string","value":"def distance_law(\n matrix, detectable_bins=None, max_dist=None, smooth=True, fun=np.nanmean\n):\n mat_n = matrix.shape[0]\n if max_dist is None:\n max_dist = mat_n\n n_diags = min(mat_n, max_dist + 1)\n dist = np.zeros(mat_n)\n if detectable_bins is None:\n detectable_bins = np.array(range(mat_n))\n\n for diag in range(n_diags):\n # Find detectable which fall in diagonal\n detect_mask = np.zeros(mat_n, dtype=bool)\n detect_mask[detectable_bins] = 1\n # Find bins which are detectable in the diagonal (intersect of\n # hori and verti)\n detect_mask_h = detect_mask[: (mat_n - diag)]\n detect_mask_v = detect_mask[mat_n - (mat_n - diag) :]\n detect_mask_diag = detect_mask_h & detect_mask_v\n detect_diag = matrix.diagonal(diag)[detect_mask_diag]\n dist[diag] = fun(detect_diag[detect_diag > 0])\n # Smooth the curve using isotonic regression: Find closest approximation\n # with the condition that point n+1 cannot be higher than point n.\n # (i.e. contacts can only decrease when increasing distance)\n if smooth and mat_n > 2:\n ir = IsotonicRegression(increasing=False)\n dist[~np.isfinite(dist)] = 0\n dist = ir.fit_transform(range(len(dist)), dist)\n\n return dist"},"metadata":{"kind":"string","value":"{\n \"objective\": {\n \"self\": [],\n \"paired\": [],\n \"triplet\": [\n [\n \"query\",\n \"document\",\n \"negatives\"\n ]\n ]\n }\n}"},"negatives":{"kind":"list like","value":["def sum_diag(max_lines):\r\n dsum = 1 # sum of diagonals\r\n cpt = 1 # number of lines processed\r\n val = 1 # value of the current place in the square\r\n inc = 0 # the increment between number for one line\r\n \r\n while cpt < max_lines:\r\n cpt += 2\r\n inc += 2\r\n \r\n for corner in range(4):\r\n val += inc\r\n dsum += val\r\n\r\n return dsum","def _calculate_mi(self, array, corr_range, bins, tau_max, lag_mode):\n\n # lag_mode dict\n mode = self.lag_modi[lag_mode]\n only_tri = int(self.only_tri)\n\n # Initialize\n hist2D = numpy.zeros((bins, bins), dtype=\"int32\")\n if lag_mode == 'all':\n corrmat = numpy.zeros((2*tau_max + 1, self.N, self.N),\n dtype='float32')\n elif lag_mode == 'sum':\n corrmat = numpy.zeros((2, self.N, self.N), dtype='float32')\n elif lag_mode == 'max':\n corrmat = numpy.zeros((2, self.N, self.N), dtype='float32')\n\n # Precalculation of the log\n gfunc = numpy.zeros(corr_range+1)\n for t in range(1, corr_range + 1):\n gfunc[t] = t*numpy.log(t)\n\n # loop over all node pairs, NOT symmetric due to time shifts!\n for i in range(self.N-only_tri):\n for j in range((i+1)*only_tri, self.N):\n\n if mode == 2:\n maxcross = 0.0\n argmax = 0\n\n # loop over taus from -tau_max to tau_max INCLUDING the last\n # tau value\n for t in range(2*tau_max + 1):\n tau = t - tau_max\n\n # here the joint probability distribution is calculated\n for k in range(corr_range):\n indexi = array[tau_max, i, k]\n indexj = array[t, j, k]\n hist2D[indexi, indexj] += 1\n\n # here the joint entropy is calculated by summing over all\n # pattern combinations\n jointent = 0.0\n for l in range(bins):\n for m in range(bins):\n jointent -= gfunc[hist2D[l, m]]\n hist2D[l, m] = 0\n\n jointent /= float(corr_range)\n jointent += numpy.log(float(corr_range))\n\n # Mutual Information is...\n mi = 0.0\n mi = 2. * numpy.log(bins) - jointent\n\n # norm the mi\n mi /= numpy.log(bins)\n\n # fill in values in matrix depending on lag_mode\n if mode == 0:\n corrmat[t, i, j] = mi\n\n elif mode == 1:\n if t <= tau_max:\n corrmat[1, i, j] += mi\n if t >= tau_max:\n corrmat[0, i, j] += mi\n\n elif mode == 2:\n # calculate max and argmax by comparing to previous\n # value and storing max\n if mi > maxcross:\n maxcross = mi\n argmax = tau\n\n if mode == 2:\n corrmat[0, i, j] = maxcross\n corrmat[1, i, j] = argmax\n\n if self.only_tri:\n if lag_mode == 'all':\n corrmat = corrmat + corrmat.transpose(0, 2, 1)[::-1]\n if lag_mode == 'sum':\n corrmat[0] += corrmat[1].transpose()\n corrmat[1] = corrmat[0].transpose()\n elif lag_mode == 'max':\n corrmat[0] += corrmat[0].transpose()\n corrmat[1] -= corrmat[1].transpose()\n\n return corrmat","def endtoend(d):\n dists = distance_matrix(d)\n avgdists = np.array([np.diag(dists, i).mean() for i in range(dists.shape[0])])\n return avgdists","def _CalculateMaxDistanceBetweenBins(self):\n total_ops = len(self._mix_bins) * len(self._mix_bins)\n print 'Calculating max distance between bins. Total calculations: %s' % (\n total_ops)\n self._max_distance_between_bins = 0\n \n for bin_i in self._mix_bins:\n for bin_j in self._mix_bins:\n d = _Dist(bin_i.GetFixedMean(), bin_j.GetFixedMean())\n if self._max_distance_between_bins < d:\n self._max_distance_between_bins = d\n print 'Max distance is calculated'","def _compute_hist_distances(\n self,\n all_histograms: Dict,\n n_attr: int\n ) -> np.ndarray:\n all_distances = np.empty((self.n_keep_nuclei, self.n_class_pairs, n_attr))\n for k_id , k in enumerate(self.keep_nuclei_list):\n omega = 0\n for tx in range(self.n_tumors):\n for ty in range(self.n_tumors):\n if tx < ty:\n for attr_id in range(n_attr):\n all_distances[k_id, omega, attr_id] = wasserstein_distance(\n all_histograms[k][tx][attr_id],\n all_histograms[k][ty][attr_id]\n )\n omega += 1\n return all_distances","def ratio_cut_laplacian(affinity_mat):\n D_mat = np.zeros(affinity_mat.shape)\n d_trace = np.sum(affinity_mat, axis=0)\n D_mat.flat[::D_mat.shape[0]+1] = d_trace\n return D_mat - affinity_mat","def thin_edges(magnitude, angle, low):\n # define footprints for the angle cases (1, 2, 3 and 4)\n t1 = time.time()\n f1 = np.array([[1, 0, 1]])\n f2 = np.array([[1, 0, 0], [0, 0, 0], [0, 0, 1]]) # hier had ik f4 verwacht, ma de hoeken kloppen niet\n f3 = np.array([[1], [0], [1]])\n f4 = np.array([[0, 0, 1], [0, 0, 0], [1, 0, 0]]) # hier had ik f2 verwacht\n # define conditions\n t2 = time.time()\n cond1 = magnitude >= ndimage.maximum_filter(magnitude, footprint=f1, mode='constant', cval=-np.inf) # True/False matrix\n cond2 = magnitude >= ndimage.maximum_filter(magnitude, footprint=f2, mode='constant', cval=-np.inf) # True/False matrix\n cond3 = magnitude >= ndimage.maximum_filter(magnitude, footprint=f3, mode='constant', cval=-np.inf) # True/False matrix\n cond4 = magnitude >= ndimage.maximum_filter(magnitude, footprint=f4, mode='constant', cval=-np.inf) # True/False matrixcond1 = magnitude >= ndimage.maximum_filter(magnitude, footprint=f1, mode='constant', cval=-np.inf) # True/False matrix\n t3 = time.time()\n pos_ang = np.where(angle < 0, angle + 180, angle) # make the negative angles positive, works for this application\n t4 = time.time()\n # transform the angle matrix to a matrix of 1/0, indicating wether the element is the highest along its gradient\n ang_to_bool = np.where(pos_ang <= 22.5, cond1, np.where(pos_ang <= 67.5, cond2, np.where(pos_ang <= 112.5, cond3, np.where(\n pos_ang <= 157.5, cond4, np.where(pos_ang > 157.5, cond1, pos_ang)))))\n t5 = time.time()\n filtered = np.where(magnitude > low, ang_to_bool, 0) # keep only the bools of the values higher than the low th\n t6 = time.time()\n remasked = np.where(filtered, magnitude, 0) # og waardes er weer over trekken\n t7 = time.time()\n\n global timed\n if timed:\n print(\"EDGE THINNING:\")\n print(f\"Defining footprints: {t2-t1}s\")\n print(f\"Defining conditions: {t3-t2}s\")\n print(f\"Calculating pos_ang: {t4-t3}s\")\n print(f\"Calculating ang_to_bool: {t5-t4}s\")\n print(f\"Filtering: {t6-t5}s\")\n print(f\"Remasking: {t7-t6}s\")\n print(\"-----------------------------------\")\n print(f\"TOTAL TIME: {t7-t1}s\\n\")\n\n return remasked","def compute_density(\n traj,\n area,\n surface_normal_dim=2,\n pore_center=0.0,\n max_distance = 1.0,\n bin_width = 0.01\n ):\n distances = traj.xyz[:,:,surface_normal_dim] - pore_center\n bin_centers = []\n density = []\n for bin_center in np.arange(-max_distance, max_distance, bin_width):\n mask = np.logical_and(\n distances > bin_center - 0.5 * bin_width,\n distances < bin_center + 0.5 * bin_width\n )\n bin_centers.append(bin_center)\n #changed the density from original below: added the conversion factor nm^2 to A^2 to mult by 10**2\n #density.append(mask.sum() / (area * bin_width * traj.n_frames))\n density.append(10**2 * mask.sum() / (area * bin_width * traj.n_frames))\n return bin_centers, density","def freedman_diaconis_bins(self, arr):\n # From https://stats.stackexchange.com/questions/798/\n if len(arr) < 2:\n return 1\n # Calculate the iqr ranges.\n self.iqr(arr)\n # Calculate the h\n h = 2 * (self.q3 - self.q1) / (len(arr) ** (1 / 3))\n # fall back to sqrt(a) bins if iqr is 0\n if h == 0:\n return int(np.sqrt(arr.size))\n else:\n return int(np.ceil((arr.max() - arr.min()) / h))","def metricize(dist):\n dist = np.sqrt(dist)\n olddist = dist + 1\n d_ij = dist\n different = (olddist == dist).all()\n while(not different):\n # rint 'in loop'\n olddist = dist\n for i in range(len(dist)):\n for j in range(len(dist)):\n for k in range(len(dist)):\n dijk = dist[i, k] + dist[k, j]\n d_ij[i, j] = np.amin([d_ij[i, j], dijk])\n dist[i, j] = d_ij[i, j]\n different = (olddist == dist).all()\n return dist ** 2","def avg_dists(self):\n \n d = self.descriptors\n # make an empty array to fill b/c it is a touch faster\n averages = np.empty([1, self.d_length])\n for i, u in enumerate(d):\n s = 0\n for j, v in enumerate(d):\n if i != j:\n s += self.jaccard(u, v)\n averages[0, i] = (s / (self.d_length-1))\n return averages[0]","def define_areas(\n pixel_filtered_map: np.ndarray, district_heating_zone_threshold: float\n):\n structure = np.ones((3, 3)).astype(int)\n expanded_map = binary_dilation(input=pixel_filtered_map, structure=structure)\n eroded_map = binary_erosion(input=expanded_map, structure=structure)\n labels_array, n_label = measurements.label(\n input=eroded_map,\n structure=structure,\n )\n\n # labels start from 1, therefore the array size is 'num_labels_array + 1'\n areas_potential = np.zeros((n_label + 1)).astype(float)\n if n_label > 0:\n end, start, sorted_array = get_browsing_indexes(\n labels_array=labels_array,\n pixel_filtered_map=pixel_filtered_map,\n n_label=n_label,\n )\n\n for i, (start_index, end_index) in enumerate(zip(start, end)):\n area = sorted_array[start_index:end_index, 3]\n area_potential = np.sum(area)\n if area_potential >= district_heating_zone_threshold:\n # i+1 because labeling starts from 1 and not from 0\n # factor 0.001 for conversion from MWh/ha to GWh/ha\n areas_potential[i + 1] = np.around(np.sum(area_potential) / 1000, 2)\n\n areas = areas_potential[labels_array]\n filtered_map = pixel_filtered_map * (areas > 0).astype(int)\n total_potential = np.sum(areas_potential)\n return areas, filtered_map, total_potential, areas_potential[1:]","def CL_histogram_MMD(sketch,Phi,domain,dimension,nb_cat_per_dim=None,bins_cont=10):\n ## 0) Parsing the inputs\n # Number of categorical inputs\n if nb_cat_per_dim is None:\n nb_cat_per_dim = np.zeros(Phi.d)\n \n is_integer_dimension = False\n if nb_cat_per_dim[dimension] > 0:\n # The data is integer-type\n is_integer_dimension = True\n bins = int(nb_cat_per_dim[dimension])\n else:\n bins = bins_cont\n\n m = sketch.size\n # 1) Construct the A matrix\n A = 1j*np.zeros((m,bins)) # Pre-allocation\n bin_edges = np.linspace(domain[dimension,0],domain[dimension,1],bins+1)\n box = domain.copy()\n for p in range(bins):\n # move to the next box\n if is_integer_dimension:\n box[dimension,0] = p\n box[dimension,1] = p\n else:\n box[dimension,0] = bin_edges[p]\n box[dimension,1] = bin_edges[p+1]\n A[:,p] = fourierSketchOfBox(box,Phi,nb_cat_per_dim) \n \n # 1.b) cast to real \n Ari = np.r_[A.real, A.imag]\n \n # 2) create b vector\n b = np.r_[sketch.real, sketch.imag]\n \n # 3) solve the optimization problem\n def _f_grad(x):\n r = Ari@x-b\n f = 0.5*np.linalg.norm(r)**2\n grad = Ari.T@r\n return (f,grad)\n \n # Starting point\n x0 = np.ones(bins)/bins\n # Linear constraints\n A_constr = np.zeros((bins,bins))\n l_constr = 0*np.ones(bins) # Positive constraints\n A_constr[:bins,:bins] = np.eye(bins)\n upper_bound = 5 # weird that it must be large\n u_constr = upper_bound*np.ones(bins) # Sum-to one constraints\n constr = LinearConstraint(A_constr,l_constr,u_constr)\n\n # Solve\n sol = minimize(_f_grad, x0, method='trust-constr', bounds=None, constraints=constr, jac=True, options={'verbose': 0})\n\n return project_probabilitySimplex(sol.x)","def hyperdiagonal(coords):\n \n mini = coords.min(axis=0)\n maxi = coords.max(axis=0)\n dist = (maxi - mini)**2\n dist = np.sqrt(dist.sum())\n return dist","def diagonalizing_gates(self):\n raise NotImplementedError","def adist(field, bins=(40,80), weights=1.0, \\\n meridian=0, equator= 0, rrange=[0, None]):\n pos = field['locations']\n boxsize = field.boxsize\n center = field.origin + field.boxsize / 2\n x = pos[:, 0] - center[0]\n y = pos[:, 1] - center[1]\n z = pos[:, 2] - center[2]\n \n if isscalar(rrange): \n rmin = 0\n rmax = rrange\n else :\n rmin = rrange[0]\n rmax = rrange[1]\n\n if rmax == None : rmax = boxsize.min()/2\n\n r = sqrt(x**2 + y ** 2 + z**2)\n mask = (rmin < r) & (r < rmax)\n\n x = x[mask]\n y = y[mask]\n z = z[mask]\n r = r[mask]\n\n xx,yy = sinusoid(x,y,z, meridian=meridian, equator=equator)\n\n weights = field[weights][mask]\n\n print xx.max(), xx.min(), weights,yy.max(), yy.min()\n N, xe, ye = histogram2d(xx, yy, bins=bins, weights=weights)\n print xe, ye\n field = field['default'][mask]\n\n h, xe, ye = histogram2d(xx, yy, bins=(xe,ye), weights=weights * field)\n\n h[N == 0] = NaN\n\n h /= N\n\n return h, N, xe, ye","def adaptiveCentroid(data=None,sigma=None):\n nrow,ncol=data.shape\n Isum = data.sum()\n Icol = data.sum(axis=0) # sum over all rows\n Irow = data.sum(axis=1) # sum over all cols\n colgrid = np.arange(ncol)\n rowgrid = np.arange(nrow)\n rowmean=np.sum(rowgrid*Irow)/Isum\n colmean=np.sum(colgrid*Icol)/Isum\n ROW,COL=np.indices((nrow,ncol))\n maxItr = 50\n EP = 0.0001\n for i in range(maxItr):\n wrmat = wr(ROW,COL,rowmean,colmean,sigma)\n IWmat = data*wrmat\n IWcol = IWmat.sum(axis=0)\n IWrow = IWmat.sum(axis=1)\n drowmean = np.sum((rowgrid-rowmean)*IWrow)/np.sum(IWrow)\n dcolmean = np.sum((colgrid-colmean)*IWcol)/np.sum(IWcol)\n rowmean = rowmean+2.*drowmean\n colmean = colmean+2.*dcolmean\n if drowmean**2+dcolmean**2 <= EP:\n break\n\n return rowmean,colmean","def distance_metric(seg_A, seg_B, dx):\n table_md = []\n table_hd = []\n X, Y, Z = seg_A.shape\n for z in range(Z):\n # Binary mask at this slice\n slice_A = seg_A[:, :, z].astype(np.uint8)\n slice_B = seg_B[:, :, z].astype(np.uint8)\n\n # The distance is defined only when both contours exist on this slice\n if np.sum(slice_A) > 0 and np.sum(slice_B) > 0:\n # Find contours and retrieve all the points\n _, contours, _ = cv2.findContours(cv2.inRange(slice_A, 1, 1),\n cv2.RETR_EXTERNAL,\n cv2.CHAIN_APPROX_NONE)\n pts_A = contours[0]\n for i in range(1, len(contours)):\n pts_A = np.vstack((pts_A, contours[i]))\n\n _, contours, _ = cv2.findContours(cv2.inRange(slice_B, 1, 1),\n cv2.RETR_EXTERNAL,\n cv2.CHAIN_APPROX_NONE)\n pts_B = contours[0]\n for i in range(1, len(contours)):\n pts_B = np.vstack((pts_B, contours[i]))\n\n # Distance matrix between point sets\n M = np.zeros((len(pts_A), len(pts_B)))\n for i in range(len(pts_A)):\n for j in range(len(pts_B)):\n M[i, j] = np.linalg.norm(pts_A[i, 0] - pts_B[j, 0])\n\n # Mean distance and hausdorff distance\n md = 0.5 * (np.mean(np.min(M, axis=0)) + np.mean(np.min(M, axis=1))) * dx\n hd = np.max([np.max(np.min(M, axis=0)), np.max(np.min(M, axis=1))]) * dx\n table_md += [md]\n table_hd += [hd]\n\n # Return the mean distance and Hausdorff distance across 2D slices\n mean_md = np.mean(table_md) if table_md else None\n mean_hd = np.mean(table_hd) if table_hd else None\n return mean_md, mean_hd","def compute_gradient_mean(chol, y_unbiased, n):\n solve = cho_solve(chol, -1.0 * np.ones(n))\n return -1.0 * np.dot(y_unbiased, solve)","def demo_diag21():\n n_dim = 2\n A = np.diag([2, 1])\n covar = np.eye(n_dim)\n mean = np.zeros(n_dim)\n approx = approx_quad_form(mean, covar, A)\n\n # Sample from true dist\n n_sample = 10000\n x = np.random.multivariate_normal(mean, covar, n_sample)\n q_samples = np.zeros(n_sample)\n for i in range(n_sample):\n q_samples[i] = x[i] @ A @ x[i]\n\n q = np.linspace(0, 10)\n\n plt.plot(\n q, approx(q), label='Approx.',\n color='tab:blue', linestyle='--')\n bins = np.linspace(0, 10, 51)\n bins[-1] = np.inf\n plt.hist(\n q_samples, density=True, histtype='stepfilled',\n bins=bins,\n alpha=0.5, color='black', label='Samples')\n plt.xlabel('q')\n plt.ylabel('pdf(q) [-]')\n plt.legend()","def diag_multidim_gaussian_log_likelihood(z_u, mean_u, logvar_u, varmin):\n return np.sum(diag_gaussian_log_likelihood(z_u, mean_u, logvar_u, varmin), axis=0)","def get_detectable_bins(mat, n_mads=3, inter=False):\n matrix = mat.copy()\n matrix.eliminate_zeros()\n\n def mad(x): return ss.median_abs_deviation(x, nan_policy=\"omit\")\n\n if not inter:\n if matrix.shape[0] != matrix.shape[1]:\n raise ValueError(\"Intrachromosomal matrices must be symmetric.\")\n # Replace nonzero pixels by ones to work on prop. of nonzero pixels\n matrix.data = np.ones(matrix.data.shape)\n # Compute number of nonzero values in each bin\n sum_bins = sum_mat_bins(matrix)\n # Compute variation in the number of nonzero pixels\n sum_mad = mad(sum_bins)\n # Find poor interacting rows and columns\n sum_med = np.median(sum_bins)\n detect_threshold = max(1, sum_med - sum_mad * n_mads)\n\n # Removal of poor interacting rows and columns\n good_bins = np.flatnonzero(sum_bins >= detect_threshold)\n good_bins = (good_bins, good_bins)\n else:\n # Adapted for asymetric matrices (need to compute rows and columns)\n sum_rows, sum_cols = matrix.sum(axis=1).A1, matrix.sum(axis=0).A1\n mad_rows, mad_cols = mad(sum_rows), mad(sum_cols)\n med_rows, med_cols = np.median(sum_rows), np.median(sum_cols)\n detect_threshold_rows = max(1, med_rows - mad_rows * n_mads)\n detect_threshold_cols = max(1, med_cols - mad_cols * n_mads)\n good_rows = np.flatnonzero(sum_rows > detect_threshold_rows)\n good_cols = np.flatnonzero(sum_cols > detect_threshold_cols)\n good_bins = (good_rows, good_cols)\n return good_bins","def bin_discretize(self, variables=[], bins=3,\n min_const_samples_bin_size=1.0/3):\n self.edges=np.zeros((self.arity.size,bins+1))\n for i in variables:\n un_cnt=np.unique(self.data[:,i],return_counts=True)\n constvals=un_cnt[0][un_cnt[1]>self.data.shape[0]*min_const_samples_bin_size]\n mask=np.ones(self.data.shape[0],dtype=bool)\n if constvals.size>0:\n for j,cv in enumerate(constvals):\n mask*=(self.data[:,i]!=cv)\n self.data[self.data[:,i]==cv,i]=j\n\n size=np.sum(mask)/bins\n sorted_i=np.argsort(self.data[mask,i])\n edges=[self.data[mask,i][sorted_i[int(size*num)-1]] for num in range(1,bins)]\n self.edges[i]=[self.data[mask,i][sorted_i[0]]]+edges+[self.data[mask,i][sorted_i[-1]]]\n self.data[mask,i]=np.searchsorted(edges,self.data[mask,i])+constvals.size\n self.arity[i]=len(edges)+1+constvals.size","def correlation_d(mat):\n\n print(\"DO NOT USE. BROKEN?\")\n\n if mat.ndim != 2:\n raise ValueError(\"mat must be a 2d matrix\")\n if np.any(mat > 1) or np.any(mat < 0):\n raise ValueError(\"mat must be binary\")\n\n N = mat.size\n g = np.diagonal(mat)\n # g = np.tril(mat, -1) # g is the sum over the heavside used in Grassberger\n # g = g[g.nonzero()]\n g = g.sum()\n\n return (2.0 / N * (N - 1)) * g","def bin_width_doane(a):\n bad = np.isnan(a) | np.isinf(a)\n data = a[~bad]\n n = data.size\n g1 = skew(data)\n sigma_g1 = np.sqrt(6 * (n - 2) / ((n + 1) * (n + 3)))\n k = 1 + np.log2(n) + np.log2(1 + np.abs(g1) / sigma_g1)\n acc = (data.max() - data.min()) / k\n return acc","def create_diags(self):\n\n num_diags = self.rows + self.cols - 2\n diag_counts = [0 for i in range(num_diags)]\n for diag_index in range(num_diags):\n first = (0,0)\n second = (0,0)\n if diag_index < self.rows - 1:\n first = (diag_index+1,0)\n elif diag_index == self.rows - 1:\n first = (diag_index,0)\n else:\n first = (self.rows-1,diag_index-self.rows+1)\n if diag_index < self.cols - 1:\n second = (0,diag_index+1)\n elif diag_index == self.cols - 1:\n second = (0,diag_index)\n else:\n second = (diag_index-self.cols+1,self.cols-1)\n #print str(first) + \" \" + str(second)\n diag_counts[diag_index] = dist_points(first,second) \n \n \"\"\"holds the sum of edges in diagonals previous to a given edge\"\"\"\n diag_full = [0 for i in range(num_diags + 1)]\n for i in range(1,num_diags+1):\n diag_full[i] = diag_full[i-1] + diag_counts[i-1]\n\n #print diag_counts\n #print diag_full\n return diag_full","def calculate_sdm_min_diagonal(sdm, window_size = 48, is_partial = True):\n length = len(sdm)\n dig_mean = np.zeros(length)\n for i in range(length):\n diag = np.diag(sdm, -i)\n if is_partial and len(diag) > window_size:\n window = np.ones(window_size) / window_size\n dig_mean[i] = np.min(np.convolve(diag, window, mode = 'valid'))\n else:\n dig_mean[i] = np.sum(np.diag(sdm, -i)) / (length - i)\n\n return dig_mean","def image_quad_norm(inarray):\n # If there is an hermitian symmetry\n if inarray.shape[-1] != inarray.shape[-2]:\n return 2 * np.sum(np.sum(np.abs(inarray)**2, axis=-1), axis=-1) - \\\n np.sum(np.abs(inarray[..., 0])**2, axis=-1)\n else:\n return np.sum(np.sum(np.abs(inarray)**2, axis=-1), axis=-1)","def calculate_sdm(feature_matrix, is_normalization = False):\n length = len(feature_matrix)\n self_distance_matrix = np.zeros((length, length))\n for i in range(length):\n row1 = feature_matrix[i, :]\n for j in range(length):\n row2 = feature_matrix[j, :]\n self_distance_matrix[i, j] = np.sqrt(np.sum(np.square(row1 - row2)))\n\n if is_normalization:\n minima = np.min(self_distance_matrix)\n maxima = np.max(self_distance_matrix)\n self_distance_matrix = (self_distance_matrix - minima) / (maxima - minima)\n return self_distance_matrix","def guess_2D_gauss(data):\n total = data.sum()\n Y, X = np.indices(data.shape)\n yCenter = (Y*data).sum()/total\n xCenter = (X*data).sum()/total\n col = data[int(yCenter),:]\n xWidth = np.sqrt(((X[0]-xCenter)**2*col).sum()/col.sum())\n row = data[:,int(xCenter)]\n yWidth = np.sqrt(((Y[:,0]-yCenter)**2*row).sum()/row.sum())\n\n offset = np.min(data)\n amplitude = np.max(data)-offset\n\n return [amplitude, xCenter, yCenter, xWidth, yWidth, offset, 0.]","def calculate_genomic_area(self, counts, intervals):\n # compute area of each cell in the interval grid\n intervals = np.array([0] + intervals, dtype=np.float32) / 1.0e6\n areas = np.zeros([len(intervals) - 1, len(intervals) - 1], dtype=np.float32)\n for row in xrange(1, len(intervals)):\n for col in xrange(row, len(intervals)):\n areas[row - 1, col - 1] = (intervals[row] - intervals[row - 1]) * (intervals[col] - intervals[col - 1])\n if col > row:\n areas[col - 1, row - 1] = areas[row - 1, col - 1]\n\n areas_masked = OrderedDict()\n denom = np.sum(np.array(self.sizes) / 1.0e6) ** 2\n for combo, vals in enumerate(counts):\n factor = 1\n areas_masked.update({str(subspecies.to_string(combo, True)): np.sum((vals > 0) * areas * factor) / denom})\n return areas_masked","def _calculate_distances(self):\n all_dists = []\n for ref in range(len(self.atoms)):\n if self.atoms[ref].symbol in self.exclude:\n continue\n indices = list(range(ref+1, len(self.atoms)))\n indices = self._filter_excluded(indices)\n if len(indices) == 0:\n continue\n dists = self.atoms.get_distances(ref, indices, mic=True)\n all_dists += list(dists)\n \n # Normalize by the mean distance\n return np.array(all_dists)/np.mean(all_dists)","def NBIAS(self):\n return len(self.STARS[\"dist\"])","def calculateDistanceArray(x,data,maxInst):\r\n #make empty distance array container (we will only fill up the non redundant half of the array\r\n distArray = []\r\n aveDist = 0\r\n count = 0\r\n for i in range(maxInst):\r\n distArray.append([])\r\n for j in range(maxInst):\r\n distArray[i].append(None)\r\n \r\n for i in range(1, maxInst):\r\n for j in range(0,i):\r\n distArray[i][j] = calculate_distance(x[i],x[j],data)\r\n count += 1\r\n aveDist += distArray[i][j]\r\n \r\n aveDist = aveDist/float(count)\r\n returnObject = [distArray,aveDist]\r\n return returnObject","def EntropyD(dist): \n if np.any(dist>0):\n dist2 = dist/np.sum(dist)\n return -np.sum(dist2[dist2>0]*np.log2(dist2[dist2>0]))\n else:\n return 0","def plot_distance_histogram(max_distance, mean_distance):\n\n fig = px.histogram(max_distance[max_distance > 0.01], nbins=50,\n width=500, height=300,\n title='Maximum distance')\n st.plotly_chart(fig)\n\n fig = px.histogram(mean_distance, nbins=50,\n width=500, height=300,\n title='Mean distance')\n st.plotly_chart(fig)","def calculate_bin_edges(n_bins, geo):\n #Gefittete offsets: x,y,factor: factor*(x+x_off)\n #[6.19, 0.064, 1.0128]\n \n #print \"Reading detector geometry in order to calculate the detector dimensions from file \" + fname_geo_limits\n #geo = np.loadtxt(fname_geo_limits)\n\n # derive maximum and minimum x,y,z coordinates of the geometry input [[first_OM_id, xmin, ymin, zmin], [last_OM_id, xmax, ymax, zmax]]\n geo_limits = np.nanmin(geo, axis = 0), np.nanmax(geo, axis = 0)\n #print ('Detector dimensions [[first_OM_id, xmin, ymin, zmin], [last_OM_id, xmax, ymax, zmax]]: ' + str(geo_limits))\n\n x_bin_edges = np.linspace(geo_limits[0][1] - 9.95, geo_limits[1][1] + 9.95, num=n_bins[0] + 1) #try to get the lines in the bin center 9.95*2 = average x-separation of two lines\n y_bin_edges = np.linspace(geo_limits[0][2] - 9.75, geo_limits[1][2] + 9.75, num=n_bins[1] + 1) # Delta y = 19.483\n z_bin_edges = np.linspace(geo_limits[0][3] - 4.665, geo_limits[1][3] + 4.665, num=n_bins[2] + 1) # Delta z = 9.329\n\n #offset_x, offset_y, scale = [6.19, 0.064, 1.0128]\n #x_bin_edges = (x_bin_edges + offset_x )*scale\n #y_bin_edges = (y_bin_edges + offset_y )*scale\n\n #calculate_bin_edges_test(geo, y_bin_edges, z_bin_edges) # test disabled by default. Activate it, if you change the offsets in x/y/z-bin-edges\n\n return x_bin_edges, y_bin_edges, z_bin_edges","def mntd(distmat):\r\n return masked_array(distmat, eye(distmat.shape[0])).min(0).mean()","def estimate_density(X, h, complete_random = 2):\n\n n, d = X.shape#number of points and dimensions\n dist_d = np.zeros([d,h])\n\n if complete_random == 1:\n dist_d = dist_d + 1/h\n return dist_d\n\n bin_width = 1/h\n X_bin = np.floor(X / bin_width).astype(int)\n #make max value to h-1 bin (or use a more efficient fix by adding a very small value to max for each dimension)\n X_bin[X_bin == h] = h - 1\n\n for i in range(d):\n unique, counts = np.unique(X_bin[:,i], return_counts=True)\n for j in range(unique.shape[0]):\n bin_id = unique[j]\n dist_d[i, bin_id] = counts[j] / n\n\n if complete_random == 2:\n dist_d_random = 1/h\n data_weight = 0.75\n random_weight = 1 - data_weight\n dist_d = data_weight * dist_d + random_weight * dist_d_random\n\n return dist_d","def mask_density(mask):\n return get_number_of_unpruned_weights(mask).float() / get_number_of_weights(mask).float()","def island_perimeter(grid):\n total = 0\n for b in range(len(grid)):\n for a in range(len(grid[b])):\n # left corner\n if (a == 0) and (b == 0):\n if grid[b][a] == 1:\n total = total + 2\n if grid[b][a + 1] == 0:\n total = total + 1\n if grid[b + 1][a] == 0:\n total = total + 1\n # right corner\n elif (a == len(grid[b]) - 1) and b == 0:\n if grid[b][a] == 1:\n total = total + 2\n if grid[b + 1][a] == 0:\n total = total + 1\n if grid[b][a - 1] == 0:\n total = total + 1\n # lower-left corner\n elif a == 0 and b == (len(grid) - 1):\n if grid[b][a] == 1:\n total = total + 2\n if grid[b][a + 1] == 0:\n total = total + 1\n if grid[b - 1][a] == 0:\n total = total + 1\n # lower-right corner\n elif b == (len(grid) - 1) and a == (len(grid[b]) - 1):\n if grid[b][a] == 1:\n total = total + 2\n if grid[b - 1][a] == 0:\n total = total + 1\n if grid[b][a - 1] == 0:\n total = total + 1\n # top edge\n elif (b == 0 and a > 0) and a < (len(grid[b]) - 1):\n if grid[b][a] == 1:\n total = total + 1\n if grid[b][a - 1] == 0:\n total = total + 1\n if grid[b + 1][a] == 0:\n total = total + 1\n if grid[b][a + 1] == 0:\n total = total + 1\n # left edge\n elif (b > 0 and b < (len(grid) - 1)) and ((a == 0) and a <\n len(grid[b]) - 1):\n if grid[b][a] == 1:\n total = total + 1\n if grid[b - 1][a] == 0:\n total = total + 1\n if grid[b][a + 1] == 0:\n total = total + 1\n if grid[b + 1][a] == 0:\n total = total + 1\n # right edge\n elif (b > 0 and (b < len(grid) - 1)) and (a == len(grid[b]) - 1):\n if grid[b][a] == 1:\n total = total + 1\n if grid[b - 1][a] == 0:\n total = total + 1\n if grid[b][a - 1] == 0:\n total = total + 1\n if grid[b + 1][a] == 0:\n total = total + 1\n # bottom edge\n elif (b == len(grid) - 1) and a > 0 and a < len(grid[b]) - 1:\n if grid[b][a] == 1:\n total = total + 1\n if grid[b][a - 1] == 0:\n total = total + 1\n if grid[b - 1][a] == 0:\n total = total + 1\n if grid[b][a + 1] == 0:\n total = total + 1\n # cases that are neither edges nor corners\n elif (b > 0 and b < len(grid) - 1) and (a > 0 and a <\n len(grid[b]) - 1):\n if grid[b][a] == 1:\n if grid[b][a - 1] == 0:\n total = total + 1\n if grid[b][a + 1] == 0:\n total = total + 1\n if grid[b - 1][a] == 0:\n total = total + 1\n if grid[b + 1][a] == 0:\n total = total + 1\n return total","def compute_distmat(data, distfn):\n out = np.zeros((data.shape[0], data.shape[0]))\n for i in xrange(data.shape[0]):\n for j in xrange(data.shape[0]):\n if i == j: continue\n out[i,j] = distfn(data[i,:,:], data[j,:,:])\n return out","def calculate_density(self, dc, cut_off=False):\n data_size, distance = self.data_size, self.distance\n logger.info('calculate density begin')\n func = lambda dij, dc: math.exp(- (dij / dc) ** 2)\n if cut_off:\n func = lambda dij, dc: 1 if dij < dc else 0\n max_density = -1\n for index in range(data_size):\n density = 0\n for front in range(index):\n density += func(distance[(front, index)], dc)\n for later in range(index + 1, data_size):\n density += func(distance[(index, later)], dc)\n self.result.append([density, float(\"inf\")])\n max_density = max(max_density, density)\n if max_density == density:\n self.max_pos = index\n self.max_density = max_density\n self.result = np.array(self.result)\n self.rho_des_index = np.argsort(-self.result[:, 0])\n logger.info('calculate density end')","def make_weight_map(masks, w0=10, sigma=5, longest_max_size=-1):\n nrows, ncols = masks.shape[1:]\n\n if longest_max_size > 0:\n old_rows, old_cols = nrows, ncols\n max_size = max(nrows, ncols)\n new_rows, new_cols = longest_max_size * nrows // max_size, longest_max_size * ncols // max_size\n\n resized_masks = []\n for mask in masks:\n resized_masks.append(cv2.resize(mask, (new_cols, new_rows), interpolation=0))\n masks = np.stack(resized_masks)\n nrows, ncols = new_rows, new_cols\n\n masks = (masks > 0).astype(int)\n distMap = np.zeros((nrows * ncols, masks.shape[0]))\n X1, Y1 = np.meshgrid(np.arange(nrows), np.arange(ncols))\n X1, Y1 = np.c_[X1.ravel(), Y1.ravel()].T\n for i, mask in enumerate(masks):\n # find the boundary of each mask,\n # compute the distance of each pixel from this boundary\n bounds = find_boundaries(mask, mode='inner')\n X2, Y2 = np.nonzero(bounds)\n xSum = (X2.reshape(-1, 1) - X1.reshape(1, -1)) ** 2\n ySum = (Y2.reshape(-1, 1) - Y1.reshape(1, -1)) ** 2\n distMap[:, i] = np.sqrt(xSum + ySum).min(axis=0)\n ix = np.arange(distMap.shape[0])\n if distMap.shape[1] == 1:\n d1 = distMap.ravel()\n border_loss_map = w0 * np.exp((-1 * (d1) ** 2) / (2 * (sigma ** 2)))\n else:\n if distMap.shape[1] == 2:\n d1_ix, d2_ix = np.argpartition(distMap, 1, axis=1)[:, :2].T\n else:\n d1_ix, d2_ix = np.argpartition(distMap, 2, axis=1)[:, :2].T\n d1 = distMap[ix, d1_ix]\n d2 = distMap[ix, d2_ix]\n border_loss_map = w0 * np.exp((-1 * (d1 + d2) ** 2) / (2 * (sigma ** 2)))\n xBLoss = np.zeros((nrows, ncols))\n xBLoss[X1, Y1] = border_loss_map\n # class weight map\n loss = np.zeros((nrows, ncols))\n w_1 = 1 - masks.sum() / loss.size\n w_0 = 1 - w_1\n loss[masks.sum(0) == 1] = w_1\n loss[masks.sum(0) == 0] = w_0\n ZZ = xBLoss + loss\n\n if longest_max_size > 0:\n ZZ = cv2.resize(ZZ, (old_cols, old_rows))\n return ZZ","def CLF(mag,L_bins,dL,Mhost_min,Mhost_max,Mhost):\n\n mag_bin = np.asarray([mag[i] for i in range(len(mag)) if Mhost_min<log(Mhost[i],10)<=Mhost_max]) \n Nhosts = len(mag_bin)\n print \"len(\"+str((Mhost_max+Mhost_min)/2)+\")\"\n print Nhosts\n Msun = 4.76 # The Sun's absolute magnitude\n L = ((Msun-mag_bin)/2.5) # Calculated the luminosity \n counts = np.histogram(L,L_bins)[0]\n CLF = counts/(dL*Nhosts)\n return CLF,L_bins","def avghist(data, weights, bins=None):\n if bins is not None:\n sums = np.histogram(data, weights=weights, bins=bins)[0]\n (counts, buckets) = np.histogram(data, bins=bins)\n buckets=bins\n else:\n sums = np.histogram(data, weights=weights)[0]\n (counts, buckets) = np.histogram(data)\n\n #elements with zero count will by definition have zero sum, ignore div by zero\n counts[counts == 0] = 1\n\n bin_means = np.divide(sums, counts)\n return (bin_means, buckets)","def norm_bound(self, input_mags):\n return np.sum(input_mags)","def generate_map(nrows, ncols, nrooms, max_col_size, max_row_size):\n arr = np.zeros((nrows, ncols), dtype=np.int8)\n\n for i in range(nrooms):\n rand_row_start = np.random.randint(nrows)\n rand_col_start = np.random.randint(ncols)\n\n rand_row_size = np.random.randint(max_row_size / 2, max_row_size)\n rand_col_size = np.random.randint(max_col_size / 2, max_col_size)\n\n arr[rand_row_start:rand_row_start + rand_row_size, rand_col_start:rand_col_start + rand_col_size] = 1\n\n labels = measure.label(arr)\n regions = measure.regionprops(labels)\n\n centroids = list()\n for region in regions:\n centroids.append(region.centroid)\n\n num_centroids = len(centroids)\n\n # get distances between every pair of centroids\n dists = scipy.spatial.distance.cdist(centroids, centroids)\n\n # get a distance that is greater than all current distances\n max_dist = np.max(dists) + 1\n\n # make sure upper triangle is at least max_dist so that when picking closest\n # pairs, we won't choose a diagonal element or a duplicate connection\n dists = dists + np.triu(np.ones((num_centroids, num_centroids))) * max_dist\n\n for i in range(num_centroids - 1):\n min_dist_idx = np.argmin(dists)\n min_dist_idx = np.unravel_index(min_dist_idx, dists.shape)\n\n # create a hallway between regionprops\n centroid1 = np.array(centroids[min_dist_idx[0]], dtype=np.int)\n centroid2 = np.array(centroids[min_dist_idx[1]], dtype=np.int)\n\n [row_centroid_1, row_centroid_2] = sorted([centroid1, centroid2], key=lambda x: x[0])\n [col_centroid_1, col_centroid_2] = sorted([centroid1, centroid2], key=lambda x: x[1])\n\n arr[row_centroid_1[0]:row_centroid_2[0] + 1, row_centroid_1[1]] = 1\n arr[row_centroid_2[0], col_centroid_1[1]:col_centroid_2[1] + 1] = 1\n\n dists[:, min_dist_idx[1]] += max_dist\n\n return arr","def obtain_pairwise_distances(normal, abnormal, num_comparisons):\n avg_dist = np.zeros(len(DIST_METRICS), dtype=float)\n for i, metric in enumerate(sorted(DIST_METRICS)):\n\n # Iterate through all combinations of pairs of vectors\n for idx_g1 in range(len(normal)):\n for idx_g2 in range(len(abnormal)):\n t1 = normal[idx_g1]\n t2 = abnormal[idx_g2]\n\n # Determine which metric to compute by indexing dictionary\n dist = DIST_METRICS[metric](t1, t2)\n avg_dist[i] = avg_dist[i] + dist\n\n # Compute the average\n avg_dist = avg_dist / num_comparisons\n\n return avg_dist","def norm_leg_coefs(self, cl):\n new_cl = np.zeros_like( cl )\n for i in range(cl.shape[1]):\n for j in range(cl.shape[2]):\n if (cl[:,i,j]).max() >0:\n new_cl[:,i,j] = cl[:,i,j]/(cl[:,i,j]).max()\n\n return new_cl","def distance_metric(seg_A, seg_B, dx, k):\n\n # Extract the label k from the segmentation maps to generate binary maps\n seg_A = (seg_A == k)\n seg_B = (seg_B == k)\n\n table_md = []\n table_hd = []\n X, Y, Z = seg_A.shape\n for z in range(Z):\n # Binary mask at this slice\n slice_A = seg_A[:, :, z].astype(np.uint8)\n slice_B = seg_B[:, :, z].astype(np.uint8)\n\n # The distance is defined only when both contours exist on this slice\n if np.sum(slice_A) > 0 and np.sum(slice_B) > 0:\n # Find contours and retrieve all the points\n contours, hierarchy = cv2.findContours(cv2.inRange(slice_A, 1, 1),\n cv2.RETR_EXTERNAL,\n cv2.CHAIN_APPROX_NONE)\n pts_A = contours[0]\n for i in range(1, len(contours)):\n pts_A = np.vstack((pts_A, contours[i]))\n\n contours, hierarchy = cv2.findContours(cv2.inRange(slice_B, 1, 1),\n cv2.RETR_EXTERNAL,\n cv2.CHAIN_APPROX_NONE)\n pts_B = contours[0]\n for i in range(1, len(contours)):\n pts_B = np.vstack((pts_B, contours[i]))\n\n # Distance matrix between point sets\n M = np.zeros((len(pts_A), len(pts_B)))\n for i in range(len(pts_A)):\n for j in range(len(pts_B)):\n M[i, j] = np.linalg.norm(pts_A[i, 0] - pts_B[j, 0])\n\n # Mean distance and hausdorff distance\n md = 0.5 * (np.mean(np.min(M, axis=0)) + np.mean(np.min(M, axis=1))) * dx\n hd = np.max([np.max(np.min(M, axis=0)), np.max(np.min(M, axis=1))]) * dx\n table_md += [md]\n table_hd += [hd]\n\n # Return the mean distance and Hausdorff distance across 2D slices\n mean_md = np.mean(table_md) if table_md else None\n mean_hd = np.mean(table_hd) if table_hd else None\n return mean_md, mean_hd","def _reduce_distances(self, threshold):\n reduced = self.orig_dists.copy()\n reduced[reduced <= threshold] = 0\n # Remove ignored from all consideration\n ignrd_indices = [self.index[name] for name in self.ignored]\n if ignrd_indices:\n reduced[:,ignrd_indices] = np.inf\n reduced[ignrd_indices,:] = np.inf\n # Check if the given parameters are feasible\n chsn_indices = set(self.index[name] for name in self.chosen)\n avail_indices = set(self.index[name] for name in self.available)\n ca_indices = chsn_indices | avail_indices\n unassigned_indices = np.array(list(self._not_ignored_inds - ca_indices))\n if len(unassigned_indices) == 0:\n unassigned_orphans = unassigned_indices\n else:\n ca_indices = list(ca_indices)\n avail_in_range = np.count_nonzero(reduced[np.ix_(unassigned_indices,ca_indices)] == 0, axis=1)\n unassigned_orphans = unassigned_indices[avail_in_range == 0]\n return reduced, unassigned_orphans","def average_distance(self):\r\n total = 0\r\n edges = 0\r\n for code, _list in self.edges.items():\r\n for edge in _list:\r\n total += edge.distance\r\n edges += 1\r\n return total / edges","def inter_autodownsample(matrix, max_pixels):\n size = np.shape(matrix)[0] * np.shape(matrix)[1]\n if size <= max_pixels:\n return int(0)\n \n n = float(size) / max_pixels;\n return np.sqrt(n);","def compute_neighbors_if_necessary(self, thresh, max_words):\n thresh = float(thresh) # dang python 2.7!\n if (\n self._neighbors is not None\n and self.thresh == thresh\n and self.max_words == max_words\n ):\n return\n print(\"Computing neighbors...\")\n self.thresh = thresh\n self.max_words = max_words\n vecs = self._vecs[:max_words]\n dots = vecs.dot(vecs.T)\n dots = scipy.sparse.csr_matrix(dots * (dots >= 1 - thresh / 2))\n from collections import Counter\n\n rows, cols = dots.nonzero()\n nums = list(Counter(rows).values())\n print(\"Mean number of neighbors per word:\", np.mean(nums) - 1)\n print(\"Median of number of neighbors per word:\", np.median(nums) - 1)\n rows, cols, vecs = zip(\n *[\n (i, j, vecs[i] - vecs[j])\n for i, j, x in zip(rows, cols, dots.data)\n if i < j\n ]\n )\n self._neighbors = rows, cols, np.array([v / np.linalg.norm(v) for v in vecs])","def _calc_dim_average(vectors_array):\n array = np.array(vectors_array)\n threshold = 0.001\n res = array.sum(axis=0, dtype='float32')\n valid_count = (array > threshold).sum(axis=0)\n valid_count[valid_count == 0] = 1\n res /= valid_count\n return res","def _compute_rho0(self, distance_matrix):\n nr_samples = distance_matrix.shape[1]\n\n # both modes are equivalent, MODE=1 allows to easier compare with ref3.\n MODE = 1\n if MODE == 1: # according to Berry code\n # keep only nearest neighbors\n distance_matrix = np.sort(np.sqrt(distance_matrix), axis=1)[\n :, 1 : self.k + 1\n ]\n rho0 = np.sqrt(np.mean(distance_matrix ** 2, axis=1))\n else: # MODE == 2: , more performant if required\n if self.k < nr_samples:\n # TODO: have to revert setting the inf\n # -> check that the diagonal is all zeros\n # -> set to inf\n # -> after computation set all infs back to zero\n # -> this is also very similar to continous-nn in PCManifold\n\n # this allows to ignore the trivial distance=0 to itself\n np.fill_diagonal(distance_matrix, np.inf)\n\n # more efficient than sorting, everything\n distance_matrix.partition(self.k, axis=1)\n distance_matrix = np.sort(distance_matrix[:, : self.k], axis=1)\n distance_matrix = distance_matrix * distance_matrix\n else: # self.k == self.N\n np.fill_diagonal(distance_matrix, np.nan)\n bool_mask = ~np.diag(np.ones(nr_samples)).astype(bool)\n distance_matrix = distance_matrix[bool_mask].reshape(\n distance_matrix.shape[0], distance_matrix.shape[1] - 1\n )\n\n # experimental: --------------------------------------------------------------\n # paper: in var-bw paper (ref2) pdfp. 7\n # it is mentioned to IGNORE non-zero entries -- this is not detailed more.\n # a consequence is that the NN and kernel looses symmetry, so does (K+K^T) / 2\n # This is with a cut-off rate:\n # val = 1E-2\n # distance_matrix[distance_matrix < val] = np.nan\n # experimental END -----------------------------------------------------------\n\n # nanmean only for the experimental part, if leaving this out, np.mean\n # suffices\n rho0 = np.sqrt(np.nanmean(distance_matrix, axis=1))\n\n return rho0","def distance_map(self, scaling='sum'):\n\n if scaling not in ['sum', 'mean']:\n raise ValueError(f'scaling should be either \"sum\" or \"mean\" ('\n f'\"{scaling}\" not valid)')\n\n um = nan * zeros((self._weights.shape[0],\n self._weights.shape[1],\n 8)) # 2 spots more for hexagonal topology\n\n ii = [[0, -1, -1, -1, 0, 1, 1, 1]]*2\n jj = [[-1, -1, 0, 1, 1, 1, 0, -1]]*2\n\n if self.topology == 'hexagonal':\n ii = [[1, 1, 1, 0, -1, 0], [0, 1, 0, -1, -1, -1]]\n jj = [[1, 0, -1, -1, 0, 1], [1, 0, -1, -1, 0, 1]]\n\n for x in range(self._weights.shape[0]):\n for y in range(self._weights.shape[1]):\n w_2 = self._weights[x, y]\n e = y % 2 == 0 # only used on hexagonal topology\n for k, (i, j) in enumerate(zip(ii[e], jj[e])):\n if (x+i >= 0 and x+i < self._weights.shape[0] and\n y+j >= 0 and y+j < self._weights.shape[1]):\n w_1 = self._weights[x+i, y+j]\n um[x, y, k] = fast_norm(w_2-w_1)\n\n if scaling == 'mean':\n um = nanmean(um, axis=2)\n if scaling == 'sum':\n um = nansum(um, axis=2)\n\n return um/um.max()","def detrend(\n matrix,\n detectable_bins=None,\n max_dist=None,\n smooth=False,\n fun=np.nanmean,\n max_val=10,\n):\n matrix = matrix.tocsr()\n y = distance_law(\n matrix,\n detectable_bins=detectable_bins,\n max_dist=max_dist,\n smooth=smooth,\n fun=fun,\n )\n y[np.isnan(y)] = 0.0\n # Detrending by the distance law\n clean_mat = matrix.tocoo()\n # clean_mat.data /= y_savgol[abs(clean_mat.row - clean_mat.col)]\n try:\n clean_mat.data = clean_mat.data / y[abs(clean_mat.row - clean_mat.col)]\n # If no nonzero value in matrix, do nothing\n except TypeError:\n pass\n clean_mat = clean_mat.tocsr()\n if max_val is not None:\n clean_mat[clean_mat >= max_val] = 1\n return clean_mat","def correlation_bins(shred):\n return 0","def mean_crosses(magnitudes):\n mean = np.mean(magnitudes)\n std = np.std(magnitudes)\n dev_above = mean + std * 0.1\n dev_below = mean - std * 0.1\n\n above = magnitudes[0] > mean\n\n mags = magnitudes[:,0]\n\n relevant_mags = mags[np.where( (mags > dev_above) | (mags < dev_below) )]\n\n relevant_norm_mags = relevant_mags - mean\n\n rel_mags_a = relevant_norm_mags[0:-1]\n rel_mags_b = relevant_norm_mags[1:]\n\n are_crosses = np.multiply(rel_mags_a, rel_mags_b)\n\n crosses = are_crosses[are_crosses < 0.0].size\n\n return crosses","def gen_dist(genes):\n\n # First generate an NxNxB matrix that has False where\n # i and j individuals have the same kth gene and True\n # otherwise (XOR operation). Then sum along\n # the genome axis to get distance\n return np.sum(genes[:,None,:] ^ genes, axis=-1)","def _create_distance_matrix(mesh):\n l = len(mesh.faces)\n\n faces = polygons(mesh.faces, mesh.vertices, mesh.face_normals, mesh.area_faces)\n # map from edge-key to adjacent faces\n adj_faces_map = {}\n # find adjacent faces by iterating edges\n for index, face in enumerate(faces):\n for edge in face.edge_keys:\n if (edge[0] > edge[1]):\n new_edge = (edge[1], edge[0])\n else:\n new_edge = (edge[0], edge[1])\n if new_edge in adj_faces_map:\n adj_faces_map[new_edge].append(index) # 一对多\n else:\n adj_faces_map[new_edge] = [index]\n\n # helping vectors to create sparse matrix later on\n row_indices = []\n col_indices = []\n Gval = [] # values for matrix of angular distances\n Aval = [] # values for matrix of geodesic distances\n # iterate adjacent faces and calculate distances\n for edge, adj_faces in adj_faces_map.items():\n if len(adj_faces) == 2:\n i = adj_faces[0]\n j = adj_faces[1]\n # 一条边连接的两个面\n Gtemp = _geodesic_distance(mesh, faces[i], faces[j], edge) # 测地距离\n Atemp = _angular_distance(mesh, faces[i], faces[j]) # 角距离 # 其实是余弦距离\n Gval.append(Gtemp)\n Aval.append(Atemp)\n row_indices.append(i)\n col_indices.append(j)\n # add symmetric entry\n Gval.append(Gtemp)\n Aval.append(Atemp)\n row_indices.append(j)\n col_indices.append(i)\n\n elif len(adj_faces) > 2:\n print(\"Edge with more than 2 adjacent faces: \" + str(adj_faces) + \"!\")\n\n Gval = numpy.array(Gval)\n Aval = numpy.array(Aval)\n # delta是去全局变量，外部传入的\n values = delta * Gval / numpy.mean(Gval) + \\\n (1.0 - delta) * Aval / numpy.mean(Aval)\n\n # create sparse matrix\n distance_matrix = scipy.sparse.csr_matrix(\n (values, (row_indices, col_indices)), shape=(l, l))\n return distance_matrix","def gradient_dist(var, bins=40, range=None, diff_max=None, scale=1,\n percentile=99.9, interpolate=False, period=None):\n diffs = np.diff(var, axis=1).flatten()\n if period is not None:\n diffs = (diffs + period / 2) % period - period / 2\n diffs /= scale\n diff_max = diff_max or np.percentile(np.abs(diffs), percentile)\n diffs = diffs[(np.abs(diffs) < diff_max)]\n norm = np.sqrt(np.mean(diffs**2))\n counts, centers = continuous_dist(diffs, bins=bins, range=range,\n interpolate=interpolate)\n return centers, counts, norm","def rhombus_area(diagonal_1, diagonal_2):\n return (diagonal_1 * diagonal_2) / 2","def manhattan(self):\n distance = 0\n for i in range(3):\n for j in range(3):\n if self.plateau[i][j] != 0:\n x, y = divmod(self.plateau[i][j]-1, 3)\n distance += abs(x - i) + abs(y - j)\n return distance","def avg_sil_width(cluster_vec, centroid):\n tot_dis = 0\n for i in xrange(len(cluster_vec)):\n cos_dis = cos_sim(cluster_vec[i], centroid)\n tot_dis = tot_dis + cos_dis\n return tot_dis/len(cluster_vec)","def _calc_u_matrix_distances(self) -> None:\n for u_node in itertools.product(\n range(self.n_rows * 2 - 1), range(self.n_columns * 2 - 1)\n ):\n # neighbor vector\n nb = (0, 0)\n\n if not (u_node[0] % 2) and (u_node[1] % 2):\n # mean horizontally\n nb = (0, 1)\n\n elif (u_node[0] % 2) and not (u_node[1] % 2):\n # mean vertically\n nb = (1, 0)\n\n self.u_matrix[u_node] = np.linalg.norm(\n self.unsuper_som_[u_node[0] // 2][u_node[1] // 2]\n - self.unsuper_som_[u_node[0] // 2 + nb[0]][\n u_node[1] // 2 + nb[1]\n ],\n axis=0,\n )","def compute(self): \r\n hist = self.confusion_matrix\r\n iu = np.diag(hist) / (hist.sum(axis=1) + hist.sum(axis=0) - np.diag(hist))\r\n mean_iu = np.nanmean(iu)\r\n return mean_iu","def smooth(F):\n import numpy as np\n m, n, k = F.shape\n for i in range(k):\n\n# \taverage for cells surrounded by 8 others\n\n C = F[1:m-1,1:n-1,i]\n for ind, value in np.ndenumerate(C):\n C[ind[0],ind[1]]=(1/9)*(int(value)+int(F[ind[0]+1,ind[1],i])+int(F[ind[0]-1,ind[1],i])+int(F[ind[0],ind[1]+1,i])+int(F[ind[0],ind[1]-1,i])+int(F[ind[0]+1,ind[1]+1,i])+int(F[ind[0]-1,ind[1]+1,i])+int(F[ind[0]-1,ind[1]-1,i])+int(F[ind[0]+1,ind[1]-1,i]))\n \t\n# \taverage for corners clockwise from (0,0)\n\n X = 0.25*(int(F[0,0,i]) + int(F[0,1,i]) + int(F[1,0,i]) + int(F[1,1,i]) )\n Y = 0.25*(int(F[m-1,0,i]) + int(F[m-1,1,i]) + int(F[m-2,0,i]) + int(F[m-2,1,i]) )\n Z = 0.25*(int(F[m-1,n-1,i]) + int(F[m-2,n-1,i]) + int(F[m-1,n-2,i]) + int(F[m-2,n-2,i]) )\n T = 0.25*(int(F[0,n-1,i]) + int(F[1,n-1,i]) + int(F[0,n-2,i]) + int(F[1,n-2,i]) )\n \t\n#\taverage for edges excluding corners clockwise from (0,0)\n\n N = F[0,1:n-1,i]\n for ind, value in np.ndenumerate(N):\n N[ind[0]] = (1/6)*(int(value)+int(F[0+1,ind[0],i])+int(F[0,ind[0]+1,i])+int(F[0,ind[0]-1,i])+int(F[0+1,ind[0]+1,i])+int(F[0+1,ind[0]-1,i]))\n E = F[1:m-1,0,i]\n for ind, value in np.ndenumerate(E):\n E[ind[0]] = (1/6)*(int(value)+int(F[ind[0]+1,0,i])+int(F[ind[0]-1,0,i])+int(F[ind[0],0+1,i])+int(F[ind[0]+1,0+1,i])+int(F[ind[0]-1,0+1,i]))\n S = F[m-1,1:n-1,i]\n for ind, value in np.ndenumerate(S):\n S[ind[0]] = (1/6)*(int(value)+int(F[m-1-1,ind[0],i])+int(F[m-1,ind[0]+1,i])+int(F[m-1,ind[0]-1,i])+int(F[m-1-1,ind[0]+1,i])+int(F[m-1-1,ind[0]-1,i]))\n W = F[1:m-1,n-1,i]\n for ind, value in np.ndenumerate(W):\n W[ind[0]] = (1/6)*(int(value)+int(F[ind[0]+1,n-1,i])+int(F[ind[0]-1,n-1,i])+int(F[ind[0],n-1-1,i])+int(F[ind[0]+1,n-1-1,i])+int(F[ind[0]-1,n-1-1,i]))\n \t\n# \tReassign F \n\n F[1:m-1,1:n-1,i] = C\n F[0,1:n-1,i] = N\n F[1:m-1,0,i] = E\n F[m-1,1:n-1,i] = S\n F[1:m-1,n-1,i] = W\n F[0,0,i] = X\n F[m-1,0,i] = Y\n F[m-1,n-1,i] = Z\n F[0,n-1,i] = T\n \n# \tNormalise F\n\n big = F.max()\n F = (1/big)*F\n return F","def _getnorm(q, mask):\n maxq = np.max(q.reshape(-1, 3), axis=0)\n hist = np.histogramdd(q.reshape(-1, 3), bins=maxq + 1, range=[[-0.5, mq + 0.5] for mq in maxq], weights=~mask.ravel())[0]\n ret = hist[q.reshape(-1, 3)[:, 0], q.reshape(-1, 3)[:, 1], q.reshape(-1, 3)[:, 2]].reshape(q.shape[:2])\n ret[mask] = 1\n return ret","def hog_feature(im):\n # convert rgb to grayscale if needed\n if im.ndim == 3:\n image = rgb2gray(im)\n else:\n image = np.at_least_2d(im)\n\n sx, sy = image.shape # image size\n orientations = 9 # number of gradient bins\n cx, cy = (8, 8) # pixels per cell\n\n gx = np.zeros(image.shape)\n gy = np.zeros(image.shape)\n gx[:, :-1] = np.diff(image, n=1, axis=1) # compute gradient on x-direction\n gy[:-1, :] = np.diff(image, n=1, axis=0) # compute gradient on y-direction\n grad_mag = np.sqrt(gx**2 + gy**2) # gradient magnitude\n grad_ori = np.arctan2(\n gx, (gy + 1e-15)) * (180 / np.pi) + 90 # gradient orientation\n\n n_cellsx = int(np.floor(sx / cx)) # number of cells in x\n n_cellsy = int(np.floor(sy / cy)) # number of cells in y\n orientation_histogram = np.zeros((n_cellsx, n_cellsy, orientations))\n for i in range(orientations):\n temp_ori = np.where(grad_ori < (180 / orientations) * (i + 1),\n grad_ori, 0)\n temp_ori = np.where(temp_ori >= (180 / orientations) * i, temp_ori, 0)\n temp_mag = np.where(temp_ori > 0, grad_mag, 0)\n orientation_histogram[:, :, i] = uniform_filter(\n temp_mag, size=(cx, cy))[round(cx / 2)::cx,\n round(cy / 2)::cy].T\n\n return orientation_histogram.ravel()","def get_grad(grad_matrix, feature_matrix, average_occurrences=False):\n assert grad_matrix.shape == feature_matrix.shape\n # Prune out the all-zero columns that pop up sometimes\n valid_cols = []\n for col_idx in range(feature_matrix.size(1)):\n if not torch.all(feature_matrix[:, col_idx] == 0):\n valid_cols.append(col_idx)\n feature_matrix = feature_matrix[:, valid_cols]\n grad_matrix = grad_matrix[:, valid_cols]\n\n _, D = feature_matrix.shape\n if average_occurrences:\n avg_grad = torch.zeros(D)\n avg_grad_var = torch.zeros(D)\n for d in range(D):\n current_feature = feature_matrix[:, d].clone().detach()\n instances, indices, counts = torch.unique(\n current_feature, return_inverse=True, return_counts=True\n )\n weight_vector = torch.tensor([counts[i] for i in indices]).type(torch.float)\n weight_vector /= weight_vector.sum()\n mean = torch.sum(weight_vector * grad_matrix[:, d])\n # Compute the empirical variance of gradients\n variance = torch.sum(weight_vector * grad_matrix[:, d] ** 2) - mean**2\n avg_grad[d] = mean\n avg_grad_var[d] = variance\n return avg_grad, avg_grad_var, feature_matrix.sum(dim=0)\n else:\n # The maximum number possible occurrences -- 7 is an example, if problem occurs, maybe we can increase this\n # number. But for now, for both NAS-Bench datasets, this should be more than enough!\n max_occur = 7\n avg_grad = torch.zeros(D, max_occur)\n avg_grad_var = torch.zeros(D, max_occur)\n incidences = torch.zeros(D, max_occur)\n for d in range(D):\n current_feature = feature_matrix[:, d].clone().detach()\n instances, indices, counts = torch.unique(\n current_feature, return_inverse=True, return_counts=True\n )\n for i, val in enumerate(instances):\n # Find index of all feature counts that are equal to the current val\n feature_at_val = grad_matrix[current_feature == val]\n avg_grad[d, int(val)] = torch.mean(feature_at_val)\n avg_grad_var[d, int(val)] = torch.var(feature_at_val)\n incidences[d, int(val)] = counts[i]\n return avg_grad, avg_grad_var, incidences","def compute_histogram(self):\n # compute distance between points \n distmatrix = np.sqrt(pdist(self.points))\n if not self.mean_dist:\n self.mean_dist = np.mean(distmatrix)\n distmatrix = distmatrix/self.mean_dist\n distmatrix = squareform(distmatrix)\n #compute angles between points\n angles = compute_angles(self.points)\n #quantize angles to a bin\n tbins = np.floor(angles / (2 * pi / self.nbins_theta))\n lg = np.logspace(self.r1, self.r2, num=5)\n #quantize radious to bins\n rbins = np.ones(angles.shape) * -1\n for r in lg:\n counts = (distmatrix < r) \n rbins = rbins + counts.astype(int) \n return rbins, tbins","def densityCorrelation(self, int_max=None, nBoxes=None):\n\n particleDensity = self.nParticleDensity(int_max=int_max, nBoxes=nBoxes)\n\n _G2D = np.array(list(map(\n lambda _rho:\n (lambda FFT: np.real(np.fft.ifft2(np.conj(FFT)*FFT)))\n (np.fft.fft2(_rho - self.rho)),\n particleDensity)))/(nBoxes**2)\n\n return g2Dto1D(_G2D.mean(axis=0), self.L)","def gen_uniform_conf(M, avg_size=0.5, std_size=0.1, avg_dist = 1., std_dist=0.1, dist_toleft=0.1, dist_toright=0.1) :\n lengths = avg_size*np.ones(shape=M)\n distances = avg_size*np.ones(shape=M-1)\n positions = np.zeros(M)\n positions[0] = lengths[0] + dist_toleft\n for n in range(1, M) :\n positions[n] = positions[n-1] + lengths[n-1] + lengths[n] + distances[n-1]\n \n Ltot = np.sum(distances) + 2*np.sum(lengths) + dist_toleft + dist_toright\n \n lumens, bridges = make_network(lengths, positions, Ltot)\n \n return lumens, bridges, Ltot","def compute_D_hmin_hmax_(mesh0: trimesh.Trimesh):\n mesh = copy.deepcopy(mesh0)\n mesh = center_align_mesh(mesh)\n\n x = mesh.vertices[:,0]\n y = mesh.vertices[:,1]\n z = mesh.vertices[:,2]\n\n D = np.mean(np.ptp(mesh.vertices[:,:2], axis=0))\n\n # assume the cell is radially symmetric; we measure the mean height along r in bins\n # and then take the minimum and maximum\n r = np.sqrt(x**2 + y**2)\n\n nv = len(x)\n bin_edges = np.linspace(0, D/2, nv//100, endpoint=True)\n bin_r = (bin_edges[:-1] + bin_edges[1:]) / 2\n bin_h = []\n\n for r0, r1 in zip(bin_edges[:-1], bin_edges[1:]):\n idx = np.argwhere(np.logical_and(r >= r0, r < r1))\n if len(idx):\n h = np.ptp(z[idx])\n else:\n h = 0\n bin_h.append(h)\n\n bin_h = np.array(bin_h)\n\n hmax = np.max(bin_h)\n # hmin is the minimum height of the cell in the inside region,\n # i.e. we must filter out the particles that have a larger radial coordinate\n # than those at hmax.\n idmax = np.argmax(np.abs(bin_h))\n rhmax = bin_r[idmax]\n idx = np.argwhere(bin_r <= rhmax)\n hmin = np.min(bin_h[idx])\n\n return D, hmin, hmax","def seg_to_affgraph(seg_gt, nhood):\n nhood = np.ascontiguousarray(nhood, np.int32)\n shape = seg_gt.shape\n n_edge = nhood.shape[0]\n aff = np.zeros((n_edge,)+shape,dtype=np.int16)\n\n for e in range(n_edge):\n aff[e, \\\n max(0,-nhood[e,0]):min(shape[0],shape[0]-nhood[e,0]), \\\n max(0,-nhood[e,1]):min(shape[1],shape[1]-nhood[e,1]), \\\n max(0,-nhood[e,2]):min(shape[2],shape[2]-nhood[e,2])] = \\\n (seg_gt[max(0,-nhood[e,0]):min(shape[0],shape[0]-nhood[e,0]), \\\n max(0,-nhood[e,1]):min(shape[1],shape[1]-nhood[e,1]), \\\n max(0,-nhood[e,2]):min(shape[2],shape[2]-nhood[e,2])] == \\\n seg_gt[max(0,nhood[e,0]):min(shape[0],shape[0]+nhood[e,0]), \\\n max(0,nhood[e,1]):min(shape[1],shape[1]+nhood[e,1]), \\\n max(0,nhood[e,2]):min(shape[2],shape[2]+nhood[e,2])] ) \\\n * ( seg_gt[max(0,-nhood[e,0]):min(shape[0],shape[0]-nhood[e,0]), \\\n max(0,-nhood[e,1]):min(shape[1],shape[1]-nhood[e,1]), \\\n max(0,-nhood[e,2]):min(shape[2],shape[2]-nhood[e,2])] > 0 ) \\\n * ( seg_gt[max(0,nhood[e,0]):min(shape[0],shape[0]+nhood[e,0]), \\\n max(0,nhood[e,1]):min(shape[1],shape[1]+nhood[e,1]), \\\n max(0,nhood[e,2]):min(shape[2],shape[2]+nhood[e,2])] > 0 )\n\n return aff","def calcAvgDistances(centroids, clusters, class_header=\"Class\"):\n avg_distances = [0] * len(centroids)\n multiprocess_count = multiprocessing.cpu_count() # Find processor count\n for centroid_row_index, centroid_tuple in enumerate(centroids.iterrows()): # For each cluster\n work_list = [] # initialize multiprocessing structures\n set_list = []\n for _, datum in clusters[centroid_row_index].iterrows(): # For each point in the medoid cluster\n work_list.append((centroid_tuple[1], datum, class_header)) # add calculation to work list\n\n partition_size = math.ceil(len(work_list) / multiprocess_count) # find size of each work subeset\n for i in range(multiprocess_count - 1): # repeat for every subset\n sample = work_list[i * partition_size: (i + 1) * partition_size] # break work list into fair subsets\n set_list.append(sample)\n set_list.append((work_list[(multiprocess_count - 1) * partition_size:]))\n pool = multiprocessing.Pool(processes=multiprocess_count) # create multiprocessing pool\n # calculate sum of list of all distances from work list tasks\n avg_distances[centroid_row_index] = sum(sum(pool.map(Cluster.calcDistanceList, set_list), []))\n pool.close()\n pool.join()\n\n if avg_distances[centroid_row_index] is not 0: # make sure we do not divide by 0\n # calculate average of distance list\n avg_distances[centroid_row_index] = avg_distances[centroid_row_index] / len(clusters[centroid_row_index])\n return avg_distances","def per_class_iu(hist):\n np.seterr(divide=\"ignore\", invalid=\"ignore\")\n res = np.diag(hist) / (hist.sum(1) + hist.sum(0) - np.diag(hist))\n np.seterr(divide=\"warn\", invalid=\"warn\")\n res[np.isnan(res)] = 0.\n return res","def calculate_square_form(diagonal_matrix, total_sorts):\n n = len(diagonal_matrix)\n\n matrix = np.ndarray(shape=(n,n))\n\n for i in range(n):\n for j in range(len(diagonal_matrix[i])):\n # Also calculate the dissimilarity matrix\n matrix[i][j] = 100 - 100 * diagonal_matrix[i][j] / total_sorts\n matrix[j][i] = 100 - 100 * diagonal_matrix[i][j] / total_sorts\n if i == j:\n matrix[i][j] = 0\n\n return matrix\n\n # matrix = np.tril(diagonal_matrix, k=-1)\n # matrix = matrix + matrix.T\n # matrix = matrix * (-100 / total_sorts) + 100\n # np.fill_diagonal(matrix, 0)\n # return matrix","def _determine_histogram_bins(self, ma_maps):\n if isinstance(ma_maps, list):\n ma_values = self.masker.transform(ma_maps)\n elif isinstance(ma_maps, np.ndarray):\n ma_values = ma_maps.copy()\n else:\n raise ValueError(f\"Unsupported data type '{type(ma_maps)}'\")\n\n # Determine bins for null distribution histogram\n # Remember that numpy histogram bins are bin edges, not centers\n # Assuming values of 0, .001, .002, etc., bins are -.0005-.0005, .0005-.0015, etc.\n INV_STEP_SIZE = 100000\n step_size = 1 / INV_STEP_SIZE\n max_ma_values = np.max(ma_values, axis=1)\n # round up based on resolution\n max_ma_values = np.ceil(max_ma_values * INV_STEP_SIZE) / INV_STEP_SIZE\n max_poss_ale = self.compute_summarystat(max_ma_values)\n # create bin centers\n hist_bins = np.round(np.arange(0, max_poss_ale + (1.5 * step_size), step_size), 5)\n self.null_distributions_[\"histogram_bins\"] = hist_bins","def getSigmaArea(self, detMap):\r\n sigmaArea = np.sum((self.array * detMap)[:])\r\n return sigmaArea","def computeAC(bins):\n halfn = len(bins)/2\n Npos = sum(bins[halfn:])\n Nneg = sum(bins[:halfn])\n return float(Npos-Nneg)/(Npos+Nneg)","def make_missing_mask(\n shape, valid_rows, valid_cols, max_dist=None, sym_upper=False\n):\n # Error if the matrix upper symmetric but shape is rectangle or missing\n # rows and cols are different\n sm, sn = shape\n if sym_upper and (sm != sn or len(valid_rows) != len(valid_cols)):\n raise ValueError(\"Rectangular matrices cannot be upper symmetric\")\n\n # Get a boolean array of missing (1) and valid (0) rows\n missing_rows = valid_to_missing(valid_rows, sm)\n # When matrix is sym., rows and cols are synonym, no need to compute 2x\n if sym_upper:\n missing_cols = missing_rows\n else:\n missing_cols = valid_to_missing(valid_cols, sn)\n # If upper sym., fill only upper diag up to max_dist.\n # E. g. with bins 1 and 3 missing\n # and a max_dist of 1:\n # 0 1 0 0 0\n # 0 1 1 0 0\n # 0 0 0 1 0\n # 0 0 0 1 1\n # 0 0 0 0 0\n # For each missing bin, mask is apply 1 pixel upwards and 1 to the right\n # to fill only the upper triangle up to max_dist\n if sym_upper:\n # If no max dist has been specified, fill the whole upper triangle\n if max_dist is None:\n max_dist = min(shape)\n # Generate matrix of distance shifts by row.\n # Shape is len(missing_rows) x (max_dist + 1)\n # e.g.: 2 missing rows and max dist of 1\n # 0 0\n # 1 1\n row_shifts = np.tile(\n np.array(range(max_dist + 1)), (len(missing_rows), 1)\n ).T\n # Compute row positions upwards to diagonal by subtracting missing rows\n # to the shifts. Following the previous example, if missing rows are\n # bins 1 and 3:\n # 1 3\n # 0 2\n rows_before = (missing_rows - row_shifts).flatten(\"F\")\n # looking at pixels up from the bins, cols remain the same:\n # 1 3\n # 1 3\n cols_before = np.repeat(missing_rows, max_dist+1)\n # Compute col position to the right until diagonal by adding the shift\n # Note: upper symmetric, so row_shifts = col_shift_\n # 1 3\n # 2 4\n cols_after = (missing_cols + row_shifts).flatten(\"F\")\n # This time, rows remain constant since we are computing positions to\n # the right\n rows_after = np.repeat(missing_cols, max_dist+1)\n # Combine positions to the right and upwards\n rows = np.concatenate([rows_before, rows_after])\n cols = np.concatenate([cols_before, cols_after])\n data = np.ones(rows.shape, dtype=bool)\n # Remove entries where rows or cols are negative or larger than shape\n valid = (cols < sm) & (cols >= 0) & (rows < sm) & (rows >= 0)\n # Build mask mat with miss bins up to max scan dist in upper triangle\n mask = sp.coo_matrix(\n (data[valid], (rows[valid], cols[valid])), shape=shape, dtype=bool\n ).tocsr()\n else:\n mask = sp.csr_matrix(shape, dtype=bool)\n mask[missing_rows, :] = 1\n mask[:, missing_cols] = 1\n\n return mask","def __calculate_average_distance(self):\n game = self.__game # type: Game\n all_icebergs = game.get_all_icebergs()\n all_icebergs_length = len(all_icebergs)\n sum_distances = 0\n for i in range(all_icebergs_length):\n for j in range(i + 1, all_icebergs_length):\n iceberg1 = all_icebergs[i]\n iceberg2 = all_icebergs[j]\n sum_distances += iceberg1.get_turns_till_arrival(iceberg2)\n\n return sum_distances / (all_icebergs_length * (all_icebergs_length - 1) / 2)","def mpd(distmat):\r\n return distmat.sum() / (distmat.size - distmat.shape[0])","def _calc_distances(preds, targets, mask, normalize):\n N, K, _ = preds.shape\n _mask = mask.copy()\n _mask[np.where((normalize == 0).sum(1))[0], :] = False\n distances = np.full((N, K), -1, dtype=np.float32)\n normalize[np.where(normalize <= 0)] = 1000000.0\n distances[_mask] = np.linalg.norm(((preds - targets) / normalize[:, None, :])[_mask], axis=-1)\n return distances.T","def compute_gradmag(image_arr):\n assert image_arr.ndim == 2\n dy = sobel(image_arr, axis=0)\n dx = sobel(image_arr, axis=1)\n return np.hypot(dx, dy)","def _calculate_cluster_measures(\n arr4d,\n threshold,\n bin_struct,\n two_sided_test=False,\n):\n n_regressors = arr4d.shape[3]\n\n max_sizes = np.zeros(n_regressors, int)\n max_masses = np.zeros(n_regressors, float)\n\n for i_regressor in range(n_regressors):\n arr3d = arr4d[..., i_regressor].copy()\n\n if two_sided_test:\n arr3d[np.abs(arr3d) <= threshold] = 0\n else:\n arr3d[arr3d <= threshold] = 0\n\n labeled_arr3d, _ = label(arr3d > 0, bin_struct)\n\n if two_sided_test:\n # Label positive and negative clusters separately\n n_positive_clusters = np.max(labeled_arr3d)\n temp_labeled_arr3d, _ = label(\n arr3d < 0,\n bin_struct,\n )\n temp_labeled_arr3d[temp_labeled_arr3d > 0] += n_positive_clusters\n labeled_arr3d = labeled_arr3d + temp_labeled_arr3d\n del temp_labeled_arr3d\n\n clust_vals, clust_sizes = np.unique(labeled_arr3d, return_counts=True)\n assert clust_vals[0] == 0\n\n clust_vals = clust_vals[1:] # First cluster is zeros in matrix\n clust_sizes = clust_sizes[1:]\n\n # Cluster mass-based inference\n max_mass = 0\n for unique_val in clust_vals:\n ss_vals = np.abs(arr3d[labeled_arr3d == unique_val]) - threshold\n max_mass = np.maximum(max_mass, np.sum(ss_vals))\n\n # Cluster size-based inference\n max_size = 0\n if clust_sizes.size:\n max_size = np.max(clust_sizes)\n\n max_sizes[i_regressor], max_masses[i_regressor] = max_size, max_mass\n\n return max_sizes, max_masses","def boxcar_average(D,Y,N):\n boxY = np.convolve(Y, np.ones((N,))/N, mode='valid')\n edge = (len(Y)-len(boxY))/2 \n boxD = D[math.floor(edge):-math.ceil(edge)]\n \n return (boxD, boxY)","def cchalf(dataframe, function, bins):\n dist = dataframe.set_index(['H', 'K', 'L'])['D'].drop_duplicates()\n dmin = dist.min()\n dmax = dist.max()\n binedges = np.linspace(dmin**-2, dmax**-2, bins+1)**-0.5\n binedges = list(zip(binedges[:-1], binedges[1:]))\n a,b = split(dataframe)\n xval_a, xval_b = function(a), function(b)\n#TODO: Fix this awful hack\n key = [i for i in xval_a if i!='D'][0]\n xval_a, xval_b = xval_a.join(dist),xval_b.join(dist)\n idx = xval_a.index.intersection(xval_b.index)\n xval_a,xval_b = xval_a.loc[idx],xval_b.loc[idx]\n cchalf = []\n for dmin,dmax in binedges:\n idx = (xval_a['D'] > dmin) & (xval_a['D'] < dmax)\n a = np.array(xval_a[idx][key]).flatten()\n b = np.array(xval_b[idx][key]).flatten()\n cchalf.append(np.corrcoef(a,b)[0, 1])\n return cchalf, binedges","def geneffcut(energy, array, cutvals=hads, bins=BINS):\n binning = np.digitize(energy, bins) - 1\n binning[binning < 0] = 0.\n binning[binning >= len(bins)-1] = 0.\n hadeffcut = np.zeros(len(energy), dtype=bool)\n for i, cutval in enumerate(cutvals):\n binmask = binning == i\n hadeffcut[binmask] = array[binmask] < cutval\n binning = np.digitize(energy, bins) - 1\n binning[binning < 0] = -1\n binning[binning >= len(bins)-1] = -1\n hadeffcut[binning == -1] = 0\n\n return hadeffcut","def G_bin2d(self, mgs, mnus, g_corr, bins, resample=1, ig_nums=0):\n # half of the bin number\n ny, nx = int((len(bins[0]) - 1)/2), int((len(bins[1]) - 1)/2)\n chi_sq = 0\n mu = [0, 0]\n cov = [[abs(2*g_corr), g_corr],\n [g_corr, abs(2*g_corr)]]\n data_len = len(mgs[0])\n for i in range(resample):\n\n g_distri = numpy.random.multivariate_normal(mu,cov,data_len)\n\n mg1 = mgs[0] - mnus[0]*g_distri[:,0]\n mg2 = mgs[1] - mnus[1]*g_distri[:,1]\n num_arr = numpy.histogram2d(mg1, mg2, bins)[0]\n # | arr_1 | arr_2 |\n # | arr_3 | arr_4 |\n # chi square = 0.2*SUM[(arr_2 + arr_3 - arr_1 - arr_4)**2/(arr_1 + arr_2 + arr_3 + arr_4)]\n arr_1 = num_arr[0:ny, 0:nx][:,range(ny-1,-1,-1)]\n arr_2 = num_arr[0:ny, nx:2*nx]\n arr_3 = num_arr[ny:2*ny, 0:nx][range(ny-1,-1,-1)][:,range(nx-1,-1,-1)]\n arr_4 = num_arr[ny:2*ny, nx:2*nx][range(ny-1,-1,-1)]\n chi_sq += 0.5 * numpy.sum(((arr_2 + arr_3 - arr_1 - arr_4) ** 2) / (arr_1 + arr_2 + arr_3 + arr_4))\n\n return chi_sq/resample","def infer_depths_cells_from_edges(self):\n sel_cells = np.nonzero(np.isnan(self.cells['depth_mean']))[0]\n # iterate, since number of sides varies\n edges = self.cells['edges'][sel_cells]\n edge_depths = self.edges['depth_mean'][edges]\n edge_depths[edges<0] = -np.inf # to avoid missing edges for triangles\n \n self.cells['depth_mean'][sel_cells] = edge_depths.max(axis=1)\n self.cells['depth_max'][sel_cells] = self.cells['depth_mean'][sel_cells]","def find_avg(centroids, short_cut=False, sim_scores=None):\n \n total_sim = 0.0\n total_comparisons = 0\n \n if short_cut:\n total_comparisons = len(sim_scores)\n \n for score in sim_scores:\n total_sim += score\n \n return (total_sim / total_comparisons)\n\n length = len(centroids)\n\n for i in xrange(0, length):\n for j in xrange(i + 1, length):\n total_sim += similarity(centroids[i], centroids[j])\n total_comparisons += 1\n\n return (total_sim / total_comparisons)","def pcf2_iso_histo(data_location='../../fake_DATA/DATOS/data_500.dat',rand_location='../../fake_DATA/DATOS/rand0_500.dat', d_max=180.0, bins_number=30):\n \n data = np.loadtxt(fname=data_location, delimiter=\" \", usecols=(0,1,2))\n rand0 = np.loadtxt(fname=rand_location, delimiter=\" \", usecols=(0,1,2))\n \n if not data.shape == rand0.shape:\n raise Exception(\"The data file and rand file do not have the same size\")\n #351 s\n\n #Pure histograms\n start = time.perf_counter()\n print('start DDD distances')\n triangle_points = np.array(list(combinations(data,3)))\n r_12 = triangle_points[:,0,:]-triangle_points[:,1,:]\n r_12=r_12**2\n r_12 = r_12[:,0]+r_12[:,1]+r_12[:,2]\n r_12 = np.sqrt(r_12)\n\n r_23 = triangle_points[:,1,:]-triangle_points[:,2,:]\n r_23 = r_23**2\n r_23 = r_23[:,0]+r_23[:,1]+r_23[:,2]\n r_23 = np.sqrt(r_23)\n\n r_31 = triangle_points[:,2,:]-triangle_points[:,0,:]\n r_31 = r_31**2\n r_31 = r_31[:,0]+r_31[:,1]+r_31[:,2]\n r_31 = np.sqrt(r_31)\n\n DDD, edges = np.histogramdd(np.column_stack((r_12, r_23, r_31)), bins=(bins_number,bins_number,bins_number), range=[[0,d_max],[0,d_max],[0,d_max]])\n \n end = time.perf_counter()\n print(f'Finished creating the DDD histo in {end-start} s')\n\n start = time.perf_counter()\n print('start RRR distances')\n triangle_points = np.array(list(combinations(rand0,3)))\n r_12 = triangle_points[:,0,:]-triangle_points[:,1,:]\n r_12=r_12**2\n r_12 = r_12[:,0]+r_12[:,1]+r_12[:,2]\n r_12 = np.sqrt(r_12)\n\n r_23 = triangle_points[:,1,:]-triangle_points[:,2,:]\n r_23 = r_23**2\n r_23 = r_23[:,0]+r_23[:,1]+r_23[:,2]\n r_23 = np.sqrt(r_23)\n\n r_31 = triangle_points[:,2,:]-triangle_points[:,0,:]\n r_31 = r_31**2\n r_31 = r_31[:,0]+r_31[:,1]+r_31[:,2]\n r_31 = np.sqrt(r_31)\n\n RRR, edges = np.histogramdd(np.column_stack((r_12, r_23, r_31)), bins=(bins_number,bins_number,bins_number), range=[[0,d_max],[0,d_max],[0,d_max]])\n \n end = time.perf_counter()\n\n\n print(f'Finished creating the RRR histo in {end-start} s')\n\n #Mixed histogram\n start = time.perf_counter()\n print(\"Started gathering the data points pairs\")\n DD_side_points = np.array(list(combinations(data,2)))\n print(\"Finished data points pairs\")\n\n print(\"Started gathering the rand0 points pairs\")\n RR_side_points = np.array(list(combinations(rand0,2)))\n print(\"Finished rand0 points pairs\")\n\n print(\"Started loop for DDR and RRD histograms\")\n \n DDR = np.zeros((bins_number,bins_number,bins_number))\n RRD = np.zeros((bins_number,bins_number,bins_number))\n\n for data_point, rand_point in zip(data, rand0):\n ##DDR\n r_12 = rand_point-DD_side_points[:,0,:]\n r_12=r_12**2\n r_12 = r_12[:,0]+r_12[:,1]+r_12[:,2]\n r_12 = np.sqrt(r_12)\n\n r_23 = DD_side_points[:,0,:]-DD_side_points[:,1,:]\n r_23 = r_23**2\n r_23 = r_23[:,0]+r_23[:,1]+r_23[:,2]\n r_23 = np.sqrt(r_23)\n\n r_31 = DD_side_points[:,1,:]-rand_point\n r_31 = r_31**2\n r_31 = r_31[:,0]+r_31[:,1]+r_31[:,2]\n r_31 = np.sqrt(r_31)\n H_DDR, edges = np.histogramdd(np.column_stack((r_12, r_23, r_31)), bins=(bins_number,bins_number,bins_number), range=[[0,d_max],[0,d_max],[0,d_max]])\n DDR += H_DDR\n\n #RRD\n r_12 = data_point-RR_side_points[:,0,:]\n r_12=r_12**2\n r_12 = r_12[:,0]+r_12[:,1]+r_12[:,2]\n r_12 = np.sqrt(r_12)\n\n r_23 = RR_side_points[:,0,:]-RR_side_points[:,1,:]\n r_23 = r_23**2\n r_23 = r_23[:,0]+r_23[:,1]+r_23[:,2]\n r_23 = np.sqrt(r_23)\n\n r_31 = RR_side_points[:,1,:]-data_point\n r_31 = r_31**2\n r_31 = r_31[:,0]+r_31[:,1]+r_31[:,2]\n r_31 = np.sqrt(r_31)\n H_RRD, edges = np.histogramdd(np.column_stack((r_12, r_23, r_31)), bins=(bins_number,bins_number,bins_number), range=[[0,d_max],[0,d_max],[0,d_max]])\n RRD += H_RRD\n \n DDR = DDR/3\n RRD = RRD/3\n end = time.perf_counter()\n print(f'Finished the mixed histograms DDR an RRD in {end-start} s')\n\n return RRR, DDD, DDR, RRD, edges","def peakDistances(self,thresholds,norm=False):\n\n\t\t#Locate peaks first\n\t\theight,loc = self.locatePeaks(thresholds,norm)\n\n\t\t#Measure pairwise distances\n\t\tdistances = np.sqrt(((loc[None]-loc[:,None])**2).sum(-1))\n\t\ti,j = np.indices(distances.shape)\n\n\t\t#Return\n\t\treturn distances[i>j]","def allele_freqs(self):\n diploid = self.geno.sum(2) * 0.5\n return np.nanmean(diploid, axis = 0)","def w_hungarian(prev_centroids,next_centroids,max_distance=50):\n xs0,ys0 = prev_centroids\n xs1,ys1 = next_centroids\n\n cost_matrix = fillCostMatrix(xs0,ys0,xs1,ys1)\n #make it disatvantageous to select any distance >max_distance\n cost_matrix[cost_matrix>(max_distance**2)]=np.max(cost_matrix)\n cost_matrix[cost_matrix==-1]=np.max(cost_matrix)\n xs,ys = linear_sum_assignment(cost_matrix)\n \n correspondance_list=[]\n for i in range(xs.size):\n correspondance_list.append( (xs[i],ys[i]) )\n apparition_list = [] \n elements_to_remove = [] \n for i, coords in enumerate(correspondance_list):\n if cost_matrix[coords]>max_distance**2:\n if coords[0]<len(xs0): #the left element exists\n correspondance_list[i] = (coords[0],-1)\n else: #the left element does not exists\n elements_to_remove.append(i)\n if coords[1]<len(xs1): #Add the right element only if it exists.\n apparition_list.append((-1,coords[1]))\n for j in range(len(elements_to_remove)):\n correspondance_list.pop(elements_to_remove[-(j+1)])\n correspondance_list.extend(apparition_list)\n return correspondance_list"],"string":"[\n \"def sum_diag(max_lines):\\r\\n dsum = 1 # sum of diagonals\\r\\n cpt = 1 # number of lines processed\\r\\n val = 1 # value of the current place in the square\\r\\n inc = 0 # the increment between number for one line\\r\\n \\r\\n while cpt < max_lines:\\r\\n cpt += 2\\r\\n inc += 2\\r\\n \\r\\n for corner in range(4):\\r\\n val += inc\\r\\n dsum += val\\r\\n\\r\\n return dsum\",\n \"def _calculate_mi(self, array, corr_range, bins, tau_max, lag_mode):\\n\\n # lag_mode dict\\n mode = self.lag_modi[lag_mode]\\n only_tri = int(self.only_tri)\\n\\n # Initialize\\n hist2D = numpy.zeros((bins, bins), dtype=\\\"int32\\\")\\n if lag_mode == 'all':\\n corrmat = numpy.zeros((2*tau_max + 1, self.N, self.N),\\n dtype='float32')\\n elif lag_mode == 'sum':\\n corrmat = numpy.zeros((2, self.N, self.N), dtype='float32')\\n elif lag_mode == 'max':\\n corrmat = numpy.zeros((2, self.N, self.N), dtype='float32')\\n\\n # Precalculation of the log\\n gfunc = numpy.zeros(corr_range+1)\\n for t in range(1, corr_range + 1):\\n gfunc[t] = t*numpy.log(t)\\n\\n # loop over all node pairs, NOT symmetric due to time shifts!\\n for i in range(self.N-only_tri):\\n for j in range((i+1)*only_tri, self.N):\\n\\n if mode == 2:\\n maxcross = 0.0\\n argmax = 0\\n\\n # loop over taus from -tau_max to tau_max INCLUDING the last\\n # tau value\\n for t in range(2*tau_max + 1):\\n tau = t - tau_max\\n\\n # here the joint probability distribution is calculated\\n for k in range(corr_range):\\n indexi = array[tau_max, i, k]\\n indexj = array[t, j, k]\\n hist2D[indexi, indexj] += 1\\n\\n # here the joint entropy is calculated by summing over all\\n # pattern combinations\\n jointent = 0.0\\n for l in range(bins):\\n for m in range(bins):\\n jointent -= gfunc[hist2D[l, m]]\\n hist2D[l, m] = 0\\n\\n jointent /= float(corr_range)\\n jointent += numpy.log(float(corr_range))\\n\\n # Mutual Information is...\\n mi = 0.0\\n mi = 2. * numpy.log(bins) - jointent\\n\\n # norm the mi\\n mi /= numpy.log(bins)\\n\\n # fill in values in matrix depending on lag_mode\\n if mode == 0:\\n corrmat[t, i, j] = mi\\n\\n elif mode == 1:\\n if t <= tau_max:\\n corrmat[1, i, j] += mi\\n if t >= tau_max:\\n corrmat[0, i, j] += mi\\n\\n elif mode == 2:\\n # calculate max and argmax by comparing to previous\\n # value and storing max\\n if mi > maxcross:\\n maxcross = mi\\n argmax = tau\\n\\n if mode == 2:\\n corrmat[0, i, j] = maxcross\\n corrmat[1, i, j] = argmax\\n\\n if self.only_tri:\\n if lag_mode == 'all':\\n corrmat = corrmat + corrmat.transpose(0, 2, 1)[::-1]\\n if lag_mode == 'sum':\\n corrmat[0] += corrmat[1].transpose()\\n corrmat[1] = corrmat[0].transpose()\\n elif lag_mode == 'max':\\n corrmat[0] += corrmat[0].transpose()\\n corrmat[1] -= corrmat[1].transpose()\\n\\n return corrmat\",\n \"def endtoend(d):\\n dists = distance_matrix(d)\\n avgdists = np.array([np.diag(dists, i).mean() for i in range(dists.shape[0])])\\n return avgdists\",\n \"def _CalculateMaxDistanceBetweenBins(self):\\n total_ops = len(self._mix_bins) * len(self._mix_bins)\\n print 'Calculating max distance between bins. Total calculations: %s' % (\\n total_ops)\\n self._max_distance_between_bins = 0\\n \\n for bin_i in self._mix_bins:\\n for bin_j in self._mix_bins:\\n d = _Dist(bin_i.GetFixedMean(), bin_j.GetFixedMean())\\n if self._max_distance_between_bins < d:\\n self._max_distance_between_bins = d\\n print 'Max distance is calculated'\",\n \"def _compute_hist_distances(\\n self,\\n all_histograms: Dict,\\n n_attr: int\\n ) -> np.ndarray:\\n all_distances = np.empty((self.n_keep_nuclei, self.n_class_pairs, n_attr))\\n for k_id , k in enumerate(self.keep_nuclei_list):\\n omega = 0\\n for tx in range(self.n_tumors):\\n for ty in range(self.n_tumors):\\n if tx < ty:\\n for attr_id in range(n_attr):\\n all_distances[k_id, omega, attr_id] = wasserstein_distance(\\n all_histograms[k][tx][attr_id],\\n all_histograms[k][ty][attr_id]\\n )\\n omega += 1\\n return all_distances\",\n \"def ratio_cut_laplacian(affinity_mat):\\n D_mat = np.zeros(affinity_mat.shape)\\n d_trace = np.sum(affinity_mat, axis=0)\\n D_mat.flat[::D_mat.shape[0]+1] = d_trace\\n return D_mat - affinity_mat\",\n \"def thin_edges(magnitude, angle, low):\\n # define footprints for the angle cases (1, 2, 3 and 4)\\n t1 = time.time()\\n f1 = np.array([[1, 0, 1]])\\n f2 = np.array([[1, 0, 0], [0, 0, 0], [0, 0, 1]]) # hier had ik f4 verwacht, ma de hoeken kloppen niet\\n f3 = np.array([[1], [0], [1]])\\n f4 = np.array([[0, 0, 1], [0, 0, 0], [1, 0, 0]]) # hier had ik f2 verwacht\\n # define conditions\\n t2 = time.time()\\n cond1 = magnitude >= ndimage.maximum_filter(magnitude, footprint=f1, mode='constant', cval=-np.inf) # True/False matrix\\n cond2 = magnitude >= ndimage.maximum_filter(magnitude, footprint=f2, mode='constant', cval=-np.inf) # True/False matrix\\n cond3 = magnitude >= ndimage.maximum_filter(magnitude, footprint=f3, mode='constant', cval=-np.inf) # True/False matrix\\n cond4 = magnitude >= ndimage.maximum_filter(magnitude, footprint=f4, mode='constant', cval=-np.inf) # True/False matrixcond1 = magnitude >= ndimage.maximum_filter(magnitude, footprint=f1, mode='constant', cval=-np.inf) # True/False matrix\\n t3 = time.time()\\n pos_ang = np.where(angle < 0, angle + 180, angle) # make the negative angles positive, works for this application\\n t4 = time.time()\\n # transform the angle matrix to a matrix of 1/0, indicating wether the element is the highest along its gradient\\n ang_to_bool = np.where(pos_ang <= 22.5, cond1, np.where(pos_ang <= 67.5, cond2, np.where(pos_ang <= 112.5, cond3, np.where(\\n pos_ang <= 157.5, cond4, np.where(pos_ang > 157.5, cond1, pos_ang)))))\\n t5 = time.time()\\n filtered = np.where(magnitude > low, ang_to_bool, 0) # keep only the bools of the values higher than the low th\\n t6 = time.time()\\n remasked = np.where(filtered, magnitude, 0) # og waardes er weer over trekken\\n t7 = time.time()\\n\\n global timed\\n if timed:\\n print(\\\"EDGE THINNING:\\\")\\n print(f\\\"Defining footprints: {t2-t1}s\\\")\\n print(f\\\"Defining conditions: {t3-t2}s\\\")\\n print(f\\\"Calculating pos_ang: {t4-t3}s\\\")\\n print(f\\\"Calculating ang_to_bool: {t5-t4}s\\\")\\n print(f\\\"Filtering: {t6-t5}s\\\")\\n print(f\\\"Remasking: {t7-t6}s\\\")\\n print(\\\"-----------------------------------\\\")\\n print(f\\\"TOTAL TIME: {t7-t1}s\\\\n\\\")\\n\\n return remasked\",\n \"def compute_density(\\n traj,\\n area,\\n surface_normal_dim=2,\\n pore_center=0.0,\\n max_distance = 1.0,\\n bin_width = 0.01\\n ):\\n distances = traj.xyz[:,:,surface_normal_dim] - pore_center\\n bin_centers = []\\n density = []\\n for bin_center in np.arange(-max_distance, max_distance, bin_width):\\n mask = np.logical_and(\\n distances > bin_center - 0.5 * bin_width,\\n distances < bin_center + 0.5 * bin_width\\n )\\n bin_centers.append(bin_center)\\n #changed the density from original below: added the conversion factor nm^2 to A^2 to mult by 10**2\\n #density.append(mask.sum() / (area * bin_width * traj.n_frames))\\n density.append(10**2 * mask.sum() / (area * bin_width * traj.n_frames))\\n return bin_centers, density\",\n \"def freedman_diaconis_bins(self, arr):\\n # From https://stats.stackexchange.com/questions/798/\\n if len(arr) < 2:\\n return 1\\n # Calculate the iqr ranges.\\n self.iqr(arr)\\n # Calculate the h\\n h = 2 * (self.q3 - self.q1) / (len(arr) ** (1 / 3))\\n # fall back to sqrt(a) bins if iqr is 0\\n if h == 0:\\n return int(np.sqrt(arr.size))\\n else:\\n return int(np.ceil((arr.max() - arr.min()) / h))\",\n \"def metricize(dist):\\n dist = np.sqrt(dist)\\n olddist = dist + 1\\n d_ij = dist\\n different = (olddist == dist).all()\\n while(not different):\\n # rint 'in loop'\\n olddist = dist\\n for i in range(len(dist)):\\n for j in range(len(dist)):\\n for k in range(len(dist)):\\n dijk = dist[i, k] + dist[k, j]\\n d_ij[i, j] = np.amin([d_ij[i, j], dijk])\\n dist[i, j] = d_ij[i, j]\\n different = (olddist == dist).all()\\n return dist ** 2\",\n \"def avg_dists(self):\\n \\n d = self.descriptors\\n # make an empty array to fill b/c it is a touch faster\\n averages = np.empty([1, self.d_length])\\n for i, u in enumerate(d):\\n s = 0\\n for j, v in enumerate(d):\\n if i != j:\\n s += self.jaccard(u, v)\\n averages[0, i] = (s / (self.d_length-1))\\n return averages[0]\",\n \"def define_areas(\\n pixel_filtered_map: np.ndarray, district_heating_zone_threshold: float\\n):\\n structure = np.ones((3, 3)).astype(int)\\n expanded_map = binary_dilation(input=pixel_filtered_map, structure=structure)\\n eroded_map = binary_erosion(input=expanded_map, structure=structure)\\n labels_array, n_label = measurements.label(\\n input=eroded_map,\\n structure=structure,\\n )\\n\\n # labels start from 1, therefore the array size is 'num_labels_array + 1'\\n areas_potential = np.zeros((n_label + 1)).astype(float)\\n if n_label > 0:\\n end, start, sorted_array = get_browsing_indexes(\\n labels_array=labels_array,\\n pixel_filtered_map=pixel_filtered_map,\\n n_label=n_label,\\n )\\n\\n for i, (start_index, end_index) in enumerate(zip(start, end)):\\n area = sorted_array[start_index:end_index, 3]\\n area_potential = np.sum(area)\\n if area_potential >= district_heating_zone_threshold:\\n # i+1 because labeling starts from 1 and not from 0\\n # factor 0.001 for conversion from MWh/ha to GWh/ha\\n areas_potential[i + 1] = np.around(np.sum(area_potential) / 1000, 2)\\n\\n areas = areas_potential[labels_array]\\n filtered_map = pixel_filtered_map * (areas > 0).astype(int)\\n total_potential = np.sum(areas_potential)\\n return areas, filtered_map, total_potential, areas_potential[1:]\",\n \"def CL_histogram_MMD(sketch,Phi,domain,dimension,nb_cat_per_dim=None,bins_cont=10):\\n ## 0) Parsing the inputs\\n # Number of categorical inputs\\n if nb_cat_per_dim is None:\\n nb_cat_per_dim = np.zeros(Phi.d)\\n \\n is_integer_dimension = False\\n if nb_cat_per_dim[dimension] > 0:\\n # The data is integer-type\\n is_integer_dimension = True\\n bins = int(nb_cat_per_dim[dimension])\\n else:\\n bins = bins_cont\\n\\n m = sketch.size\\n # 1) Construct the A matrix\\n A = 1j*np.zeros((m,bins)) # Pre-allocation\\n bin_edges = np.linspace(domain[dimension,0],domain[dimension,1],bins+1)\\n box = domain.copy()\\n for p in range(bins):\\n # move to the next box\\n if is_integer_dimension:\\n box[dimension,0] = p\\n box[dimension,1] = p\\n else:\\n box[dimension,0] = bin_edges[p]\\n box[dimension,1] = bin_edges[p+1]\\n A[:,p] = fourierSketchOfBox(box,Phi,nb_cat_per_dim) \\n \\n # 1.b) cast to real \\n Ari = np.r_[A.real, A.imag]\\n \\n # 2) create b vector\\n b = np.r_[sketch.real, sketch.imag]\\n \\n # 3) solve the optimization problem\\n def _f_grad(x):\\n r = Ari@x-b\\n f = 0.5*np.linalg.norm(r)**2\\n grad = Ari.T@r\\n return (f,grad)\\n \\n # Starting point\\n x0 = np.ones(bins)/bins\\n # Linear constraints\\n A_constr = np.zeros((bins,bins))\\n l_constr = 0*np.ones(bins) # Positive constraints\\n A_constr[:bins,:bins] = np.eye(bins)\\n upper_bound = 5 # weird that it must be large\\n u_constr = upper_bound*np.ones(bins) # Sum-to one constraints\\n constr = LinearConstraint(A_constr,l_constr,u_constr)\\n\\n # Solve\\n sol = minimize(_f_grad, x0, method='trust-constr', bounds=None, constraints=constr, jac=True, options={'verbose': 0})\\n\\n return project_probabilitySimplex(sol.x)\",\n \"def hyperdiagonal(coords):\\n \\n mini = coords.min(axis=0)\\n maxi = coords.max(axis=0)\\n dist = (maxi - mini)**2\\n dist = np.sqrt(dist.sum())\\n return dist\",\n \"def diagonalizing_gates(self):\\n raise NotImplementedError\",\n \"def adist(field, bins=(40,80), weights=1.0, \\\\\\n meridian=0, equator= 0, rrange=[0, None]):\\n pos = field['locations']\\n boxsize = field.boxsize\\n center = field.origin + field.boxsize / 2\\n x = pos[:, 0] - center[0]\\n y = pos[:, 1] - center[1]\\n z = pos[:, 2] - center[2]\\n \\n if isscalar(rrange): \\n rmin = 0\\n rmax = rrange\\n else :\\n rmin = rrange[0]\\n rmax = rrange[1]\\n\\n if rmax == None : rmax = boxsize.min()/2\\n\\n r = sqrt(x**2 + y ** 2 + z**2)\\n mask = (rmin < r) & (r < rmax)\\n\\n x = x[mask]\\n y = y[mask]\\n z = z[mask]\\n r = r[mask]\\n\\n xx,yy = sinusoid(x,y,z, meridian=meridian, equator=equator)\\n\\n weights = field[weights][mask]\\n\\n print xx.max(), xx.min(), weights,yy.max(), yy.min()\\n N, xe, ye = histogram2d(xx, yy, bins=bins, weights=weights)\\n print xe, ye\\n field = field['default'][mask]\\n\\n h, xe, ye = histogram2d(xx, yy, bins=(xe,ye), weights=weights * field)\\n\\n h[N == 0] = NaN\\n\\n h /= N\\n\\n return h, N, xe, ye\",\n \"def adaptiveCentroid(data=None,sigma=None):\\n nrow,ncol=data.shape\\n Isum = data.sum()\\n Icol = data.sum(axis=0) # sum over all rows\\n Irow = data.sum(axis=1) # sum over all cols\\n colgrid = np.arange(ncol)\\n rowgrid = np.arange(nrow)\\n rowmean=np.sum(rowgrid*Irow)/Isum\\n colmean=np.sum(colgrid*Icol)/Isum\\n ROW,COL=np.indices((nrow,ncol))\\n maxItr = 50\\n EP = 0.0001\\n for i in range(maxItr):\\n wrmat = wr(ROW,COL,rowmean,colmean,sigma)\\n IWmat = data*wrmat\\n IWcol = IWmat.sum(axis=0)\\n IWrow = IWmat.sum(axis=1)\\n drowmean = np.sum((rowgrid-rowmean)*IWrow)/np.sum(IWrow)\\n dcolmean = np.sum((colgrid-colmean)*IWcol)/np.sum(IWcol)\\n rowmean = rowmean+2.*drowmean\\n colmean = colmean+2.*dcolmean\\n if drowmean**2+dcolmean**2 <= EP:\\n break\\n\\n return rowmean,colmean\",\n \"def distance_metric(seg_A, seg_B, dx):\\n table_md = []\\n table_hd = []\\n X, Y, Z = seg_A.shape\\n for z in range(Z):\\n # Binary mask at this slice\\n slice_A = seg_A[:, :, z].astype(np.uint8)\\n slice_B = seg_B[:, :, z].astype(np.uint8)\\n\\n # The distance is defined only when both contours exist on this slice\\n if np.sum(slice_A) > 0 and np.sum(slice_B) > 0:\\n # Find contours and retrieve all the points\\n _, contours, _ = cv2.findContours(cv2.inRange(slice_A, 1, 1),\\n cv2.RETR_EXTERNAL,\\n cv2.CHAIN_APPROX_NONE)\\n pts_A = contours[0]\\n for i in range(1, len(contours)):\\n pts_A = np.vstack((pts_A, contours[i]))\\n\\n _, contours, _ = cv2.findContours(cv2.inRange(slice_B, 1, 1),\\n cv2.RETR_EXTERNAL,\\n cv2.CHAIN_APPROX_NONE)\\n pts_B = contours[0]\\n for i in range(1, len(contours)):\\n pts_B = np.vstack((pts_B, contours[i]))\\n\\n # Distance matrix between point sets\\n M = np.zeros((len(pts_A), len(pts_B)))\\n for i in range(len(pts_A)):\\n for j in range(len(pts_B)):\\n M[i, j] = np.linalg.norm(pts_A[i, 0] - pts_B[j, 0])\\n\\n # Mean distance and hausdorff distance\\n md = 0.5 * (np.mean(np.min(M, axis=0)) + np.mean(np.min(M, axis=1))) * dx\\n hd = np.max([np.max(np.min(M, axis=0)), np.max(np.min(M, axis=1))]) * dx\\n table_md += [md]\\n table_hd += [hd]\\n\\n # Return the mean distance and Hausdorff distance across 2D slices\\n mean_md = np.mean(table_md) if table_md else None\\n mean_hd = np.mean(table_hd) if table_hd else None\\n return mean_md, mean_hd\",\n \"def compute_gradient_mean(chol, y_unbiased, n):\\n solve = cho_solve(chol, -1.0 * np.ones(n))\\n return -1.0 * np.dot(y_unbiased, solve)\",\n \"def demo_diag21():\\n n_dim = 2\\n A = np.diag([2, 1])\\n covar = np.eye(n_dim)\\n mean = np.zeros(n_dim)\\n approx = approx_quad_form(mean, covar, A)\\n\\n # Sample from true dist\\n n_sample = 10000\\n x = np.random.multivariate_normal(mean, covar, n_sample)\\n q_samples = np.zeros(n_sample)\\n for i in range(n_sample):\\n q_samples[i] = x[i] @ A @ x[i]\\n\\n q = np.linspace(0, 10)\\n\\n plt.plot(\\n q, approx(q), label='Approx.',\\n color='tab:blue', linestyle='--')\\n bins = np.linspace(0, 10, 51)\\n bins[-1] = np.inf\\n plt.hist(\\n q_samples, density=True, histtype='stepfilled',\\n bins=bins,\\n alpha=0.5, color='black', label='Samples')\\n plt.xlabel('q')\\n plt.ylabel('pdf(q) [-]')\\n plt.legend()\",\n \"def diag_multidim_gaussian_log_likelihood(z_u, mean_u, logvar_u, varmin):\\n return np.sum(diag_gaussian_log_likelihood(z_u, mean_u, logvar_u, varmin), axis=0)\",\n \"def get_detectable_bins(mat, n_mads=3, inter=False):\\n matrix = mat.copy()\\n matrix.eliminate_zeros()\\n\\n def mad(x): return ss.median_abs_deviation(x, nan_policy=\\\"omit\\\")\\n\\n if not inter:\\n if matrix.shape[0] != matrix.shape[1]:\\n raise ValueError(\\\"Intrachromosomal matrices must be symmetric.\\\")\\n # Replace nonzero pixels by ones to work on prop. of nonzero pixels\\n matrix.data = np.ones(matrix.data.shape)\\n # Compute number of nonzero values in each bin\\n sum_bins = sum_mat_bins(matrix)\\n # Compute variation in the number of nonzero pixels\\n sum_mad = mad(sum_bins)\\n # Find poor interacting rows and columns\\n sum_med = np.median(sum_bins)\\n detect_threshold = max(1, sum_med - sum_mad * n_mads)\\n\\n # Removal of poor interacting rows and columns\\n good_bins = np.flatnonzero(sum_bins >= detect_threshold)\\n good_bins = (good_bins, good_bins)\\n else:\\n # Adapted for asymetric matrices (need to compute rows and columns)\\n sum_rows, sum_cols = matrix.sum(axis=1).A1, matrix.sum(axis=0).A1\\n mad_rows, mad_cols = mad(sum_rows), mad(sum_cols)\\n med_rows, med_cols = np.median(sum_rows), np.median(sum_cols)\\n detect_threshold_rows = max(1, med_rows - mad_rows * n_mads)\\n detect_threshold_cols = max(1, med_cols - mad_cols * n_mads)\\n good_rows = np.flatnonzero(sum_rows > detect_threshold_rows)\\n good_cols = np.flatnonzero(sum_cols > detect_threshold_cols)\\n good_bins = (good_rows, good_cols)\\n return good_bins\",\n \"def bin_discretize(self, variables=[], bins=3,\\n min_const_samples_bin_size=1.0/3):\\n self.edges=np.zeros((self.arity.size,bins+1))\\n for i in variables:\\n un_cnt=np.unique(self.data[:,i],return_counts=True)\\n constvals=un_cnt[0][un_cnt[1]>self.data.shape[0]*min_const_samples_bin_size]\\n mask=np.ones(self.data.shape[0],dtype=bool)\\n if constvals.size>0:\\n for j,cv in enumerate(constvals):\\n mask*=(self.data[:,i]!=cv)\\n self.data[self.data[:,i]==cv,i]=j\\n\\n size=np.sum(mask)/bins\\n sorted_i=np.argsort(self.data[mask,i])\\n edges=[self.data[mask,i][sorted_i[int(size*num)-1]] for num in range(1,bins)]\\n self.edges[i]=[self.data[mask,i][sorted_i[0]]]+edges+[self.data[mask,i][sorted_i[-1]]]\\n self.data[mask,i]=np.searchsorted(edges,self.data[mask,i])+constvals.size\\n self.arity[i]=len(edges)+1+constvals.size\",\n \"def correlation_d(mat):\\n\\n print(\\\"DO NOT USE. BROKEN?\\\")\\n\\n if mat.ndim != 2:\\n raise ValueError(\\\"mat must be a 2d matrix\\\")\\n if np.any(mat > 1) or np.any(mat < 0):\\n raise ValueError(\\\"mat must be binary\\\")\\n\\n N = mat.size\\n g = np.diagonal(mat)\\n # g = np.tril(mat, -1) # g is the sum over the heavside used in Grassberger\\n # g = g[g.nonzero()]\\n g = g.sum()\\n\\n return (2.0 / N * (N - 1)) * g\",\n \"def bin_width_doane(a):\\n bad = np.isnan(a) | np.isinf(a)\\n data = a[~bad]\\n n = data.size\\n g1 = skew(data)\\n sigma_g1 = np.sqrt(6 * (n - 2) / ((n + 1) * (n + 3)))\\n k = 1 + np.log2(n) + np.log2(1 + np.abs(g1) / sigma_g1)\\n acc = (data.max() - data.min()) / k\\n return acc\",\n \"def create_diags(self):\\n\\n num_diags = self.rows + self.cols - 2\\n diag_counts = [0 for i in range(num_diags)]\\n for diag_index in range(num_diags):\\n first = (0,0)\\n second = (0,0)\\n if diag_index < self.rows - 1:\\n first = (diag_index+1,0)\\n elif diag_index == self.rows - 1:\\n first = (diag_index,0)\\n else:\\n first = (self.rows-1,diag_index-self.rows+1)\\n if diag_index < self.cols - 1:\\n second = (0,diag_index+1)\\n elif diag_index == self.cols - 1:\\n second = (0,diag_index)\\n else:\\n second = (diag_index-self.cols+1,self.cols-1)\\n #print str(first) + \\\" \\\" + str(second)\\n diag_counts[diag_index] = dist_points(first,second) \\n \\n \\\"\\\"\\\"holds the sum of edges in diagonals previous to a given edge\\\"\\\"\\\"\\n diag_full = [0 for i in range(num_diags + 1)]\\n for i in range(1,num_diags+1):\\n diag_full[i] = diag_full[i-1] + diag_counts[i-1]\\n\\n #print diag_counts\\n #print diag_full\\n return diag_full\",\n \"def calculate_sdm_min_diagonal(sdm, window_size = 48, is_partial = True):\\n length = len(sdm)\\n dig_mean = np.zeros(length)\\n for i in range(length):\\n diag = np.diag(sdm, -i)\\n if is_partial and len(diag) > window_size:\\n window = np.ones(window_size) / window_size\\n dig_mean[i] = np.min(np.convolve(diag, window, mode = 'valid'))\\n else:\\n dig_mean[i] = np.sum(np.diag(sdm, -i)) / (length - i)\\n\\n return dig_mean\",\n \"def image_quad_norm(inarray):\\n # If there is an hermitian symmetry\\n if inarray.shape[-1] != inarray.shape[-2]:\\n return 2 * np.sum(np.sum(np.abs(inarray)**2, axis=-1), axis=-1) - \\\\\\n np.sum(np.abs(inarray[..., 0])**2, axis=-1)\\n else:\\n return np.sum(np.sum(np.abs(inarray)**2, axis=-1), axis=-1)\",\n \"def calculate_sdm(feature_matrix, is_normalization = False):\\n length = len(feature_matrix)\\n self_distance_matrix = np.zeros((length, length))\\n for i in range(length):\\n row1 = feature_matrix[i, :]\\n for j in range(length):\\n row2 = feature_matrix[j, :]\\n self_distance_matrix[i, j] = np.sqrt(np.sum(np.square(row1 - row2)))\\n\\n if is_normalization:\\n minima = np.min(self_distance_matrix)\\n maxima = np.max(self_distance_matrix)\\n self_distance_matrix = (self_distance_matrix - minima) / (maxima - minima)\\n return self_distance_matrix\",\n \"def guess_2D_gauss(data):\\n total = data.sum()\\n Y, X = np.indices(data.shape)\\n yCenter = (Y*data).sum()/total\\n xCenter = (X*data).sum()/total\\n col = data[int(yCenter),:]\\n xWidth = np.sqrt(((X[0]-xCenter)**2*col).sum()/col.sum())\\n row = data[:,int(xCenter)]\\n yWidth = np.sqrt(((Y[:,0]-yCenter)**2*row).sum()/row.sum())\\n\\n offset = np.min(data)\\n amplitude = np.max(data)-offset\\n\\n return [amplitude, xCenter, yCenter, xWidth, yWidth, offset, 0.]\",\n \"def calculate_genomic_area(self, counts, intervals):\\n # compute area of each cell in the interval grid\\n intervals = np.array([0] + intervals, dtype=np.float32) / 1.0e6\\n areas = np.zeros([len(intervals) - 1, len(intervals) - 1], dtype=np.float32)\\n for row in xrange(1, len(intervals)):\\n for col in xrange(row, len(intervals)):\\n areas[row - 1, col - 1] = (intervals[row] - intervals[row - 1]) * (intervals[col] - intervals[col - 1])\\n if col > row:\\n areas[col - 1, row - 1] = areas[row - 1, col - 1]\\n\\n areas_masked = OrderedDict()\\n denom = np.sum(np.array(self.sizes) / 1.0e6) ** 2\\n for combo, vals in enumerate(counts):\\n factor = 1\\n areas_masked.update({str(subspecies.to_string(combo, True)): np.sum((vals > 0) * areas * factor) / denom})\\n return areas_masked\",\n \"def _calculate_distances(self):\\n all_dists = []\\n for ref in range(len(self.atoms)):\\n if self.atoms[ref].symbol in self.exclude:\\n continue\\n indices = list(range(ref+1, len(self.atoms)))\\n indices = self._filter_excluded(indices)\\n if len(indices) == 0:\\n continue\\n dists = self.atoms.get_distances(ref, indices, mic=True)\\n all_dists += list(dists)\\n \\n # Normalize by the mean distance\\n return np.array(all_dists)/np.mean(all_dists)\",\n \"def NBIAS(self):\\n return len(self.STARS[\\\"dist\\\"])\",\n \"def calculateDistanceArray(x,data,maxInst):\\r\\n #make empty distance array container (we will only fill up the non redundant half of the array\\r\\n distArray = []\\r\\n aveDist = 0\\r\\n count = 0\\r\\n for i in range(maxInst):\\r\\n distArray.append([])\\r\\n for j in range(maxInst):\\r\\n distArray[i].append(None)\\r\\n \\r\\n for i in range(1, maxInst):\\r\\n for j in range(0,i):\\r\\n distArray[i][j] = calculate_distance(x[i],x[j],data)\\r\\n count += 1\\r\\n aveDist += distArray[i][j]\\r\\n \\r\\n aveDist = aveDist/float(count)\\r\\n returnObject = [distArray,aveDist]\\r\\n return returnObject\",\n \"def EntropyD(dist): \\n if np.any(dist>0):\\n dist2 = dist/np.sum(dist)\\n return -np.sum(dist2[dist2>0]*np.log2(dist2[dist2>0]))\\n else:\\n return 0\",\n \"def plot_distance_histogram(max_distance, mean_distance):\\n\\n fig = px.histogram(max_distance[max_distance > 0.01], nbins=50,\\n width=500, height=300,\\n title='Maximum distance')\\n st.plotly_chart(fig)\\n\\n fig = px.histogram(mean_distance, nbins=50,\\n width=500, height=300,\\n title='Mean distance')\\n st.plotly_chart(fig)\",\n \"def calculate_bin_edges(n_bins, geo):\\n #Gefittete offsets: x,y,factor: factor*(x+x_off)\\n #[6.19, 0.064, 1.0128]\\n \\n #print \\\"Reading detector geometry in order to calculate the detector dimensions from file \\\" + fname_geo_limits\\n #geo = np.loadtxt(fname_geo_limits)\\n\\n # derive maximum and minimum x,y,z coordinates of the geometry input [[first_OM_id, xmin, ymin, zmin], [last_OM_id, xmax, ymax, zmax]]\\n geo_limits = np.nanmin(geo, axis = 0), np.nanmax(geo, axis = 0)\\n #print ('Detector dimensions [[first_OM_id, xmin, ymin, zmin], [last_OM_id, xmax, ymax, zmax]]: ' + str(geo_limits))\\n\\n x_bin_edges = np.linspace(geo_limits[0][1] - 9.95, geo_limits[1][1] + 9.95, num=n_bins[0] + 1) #try to get the lines in the bin center 9.95*2 = average x-separation of two lines\\n y_bin_edges = np.linspace(geo_limits[0][2] - 9.75, geo_limits[1][2] + 9.75, num=n_bins[1] + 1) # Delta y = 19.483\\n z_bin_edges = np.linspace(geo_limits[0][3] - 4.665, geo_limits[1][3] + 4.665, num=n_bins[2] + 1) # Delta z = 9.329\\n\\n #offset_x, offset_y, scale = [6.19, 0.064, 1.0128]\\n #x_bin_edges = (x_bin_edges + offset_x )*scale\\n #y_bin_edges = (y_bin_edges + offset_y )*scale\\n\\n #calculate_bin_edges_test(geo, y_bin_edges, z_bin_edges) # test disabled by default. Activate it, if you change the offsets in x/y/z-bin-edges\\n\\n return x_bin_edges, y_bin_edges, z_bin_edges\",\n \"def mntd(distmat):\\r\\n return masked_array(distmat, eye(distmat.shape[0])).min(0).mean()\",\n \"def estimate_density(X, h, complete_random = 2):\\n\\n n, d = X.shape#number of points and dimensions\\n dist_d = np.zeros([d,h])\\n\\n if complete_random == 1:\\n dist_d = dist_d + 1/h\\n return dist_d\\n\\n bin_width = 1/h\\n X_bin = np.floor(X / bin_width).astype(int)\\n #make max value to h-1 bin (or use a more efficient fix by adding a very small value to max for each dimension)\\n X_bin[X_bin == h] = h - 1\\n\\n for i in range(d):\\n unique, counts = np.unique(X_bin[:,i], return_counts=True)\\n for j in range(unique.shape[0]):\\n bin_id = unique[j]\\n dist_d[i, bin_id] = counts[j] / n\\n\\n if complete_random == 2:\\n dist_d_random = 1/h\\n data_weight = 0.75\\n random_weight = 1 - data_weight\\n dist_d = data_weight * dist_d + random_weight * dist_d_random\\n\\n return dist_d\",\n \"def mask_density(mask):\\n return get_number_of_unpruned_weights(mask).float() / get_number_of_weights(mask).float()\",\n \"def island_perimeter(grid):\\n total = 0\\n for b in range(len(grid)):\\n for a in range(len(grid[b])):\\n # left corner\\n if (a == 0) and (b == 0):\\n if grid[b][a] == 1:\\n total = total + 2\\n if grid[b][a + 1] == 0:\\n total = total + 1\\n if grid[b + 1][a] == 0:\\n total = total + 1\\n # right corner\\n elif (a == len(grid[b]) - 1) and b == 0:\\n if grid[b][a] == 1:\\n total = total + 2\\n if grid[b + 1][a] == 0:\\n total = total + 1\\n if grid[b][a - 1] == 0:\\n total = total + 1\\n # lower-left corner\\n elif a == 0 and b == (len(grid) - 1):\\n if grid[b][a] == 1:\\n total = total + 2\\n if grid[b][a + 1] == 0:\\n total = total + 1\\n if grid[b - 1][a] == 0:\\n total = total + 1\\n # lower-right corner\\n elif b == (len(grid) - 1) and a == (len(grid[b]) - 1):\\n if grid[b][a] == 1:\\n total = total + 2\\n if grid[b - 1][a] == 0:\\n total = total + 1\\n if grid[b][a - 1] == 0:\\n total = total + 1\\n # top edge\\n elif (b == 0 and a > 0) and a < (len(grid[b]) - 1):\\n if grid[b][a] == 1:\\n total = total + 1\\n if grid[b][a - 1] == 0:\\n total = total + 1\\n if grid[b + 1][a] == 0:\\n total = total + 1\\n if grid[b][a + 1] == 0:\\n total = total + 1\\n # left edge\\n elif (b > 0 and b < (len(grid) - 1)) and ((a == 0) and a <\\n len(grid[b]) - 1):\\n if grid[b][a] == 1:\\n total = total + 1\\n if grid[b - 1][a] == 0:\\n total = total + 1\\n if grid[b][a + 1] == 0:\\n total = total + 1\\n if grid[b + 1][a] == 0:\\n total = total + 1\\n # right edge\\n elif (b > 0 and (b < len(grid) - 1)) and (a == len(grid[b]) - 1):\\n if grid[b][a] == 1:\\n total = total + 1\\n if grid[b - 1][a] == 0:\\n total = total + 1\\n if grid[b][a - 1] == 0:\\n total = total + 1\\n if grid[b + 1][a] == 0:\\n total = total + 1\\n # bottom edge\\n elif (b == len(grid) - 1) and a > 0 and a < len(grid[b]) - 1:\\n if grid[b][a] == 1:\\n total = total + 1\\n if grid[b][a - 1] == 0:\\n total = total + 1\\n if grid[b - 1][a] == 0:\\n total = total + 1\\n if grid[b][a + 1] == 0:\\n total = total + 1\\n # cases that are neither edges nor corners\\n elif (b > 0 and b < len(grid) - 1) and (a > 0 and a <\\n len(grid[b]) - 1):\\n if grid[b][a] == 1:\\n if grid[b][a - 1] == 0:\\n total = total + 1\\n if grid[b][a + 1] == 0:\\n total = total + 1\\n if grid[b - 1][a] == 0:\\n total = total + 1\\n if grid[b + 1][a] == 0:\\n total = total + 1\\n return total\",\n \"def compute_distmat(data, distfn):\\n out = np.zeros((data.shape[0], data.shape[0]))\\n for i in xrange(data.shape[0]):\\n for j in xrange(data.shape[0]):\\n if i == j: continue\\n out[i,j] = distfn(data[i,:,:], data[j,:,:])\\n return out\",\n \"def calculate_density(self, dc, cut_off=False):\\n data_size, distance = self.data_size, self.distance\\n logger.info('calculate density begin')\\n func = lambda dij, dc: math.exp(- (dij / dc) ** 2)\\n if cut_off:\\n func = lambda dij, dc: 1 if dij < dc else 0\\n max_density = -1\\n for index in range(data_size):\\n density = 0\\n for front in range(index):\\n density += func(distance[(front, index)], dc)\\n for later in range(index + 1, data_size):\\n density += func(distance[(index, later)], dc)\\n self.result.append([density, float(\\\"inf\\\")])\\n max_density = max(max_density, density)\\n if max_density == density:\\n self.max_pos = index\\n self.max_density = max_density\\n self.result = np.array(self.result)\\n self.rho_des_index = np.argsort(-self.result[:, 0])\\n logger.info('calculate density end')\",\n \"def make_weight_map(masks, w0=10, sigma=5, longest_max_size=-1):\\n nrows, ncols = masks.shape[1:]\\n\\n if longest_max_size > 0:\\n old_rows, old_cols = nrows, ncols\\n max_size = max(nrows, ncols)\\n new_rows, new_cols = longest_max_size * nrows // max_size, longest_max_size * ncols // max_size\\n\\n resized_masks = []\\n for mask in masks:\\n resized_masks.append(cv2.resize(mask, (new_cols, new_rows), interpolation=0))\\n masks = np.stack(resized_masks)\\n nrows, ncols = new_rows, new_cols\\n\\n masks = (masks > 0).astype(int)\\n distMap = np.zeros((nrows * ncols, masks.shape[0]))\\n X1, Y1 = np.meshgrid(np.arange(nrows), np.arange(ncols))\\n X1, Y1 = np.c_[X1.ravel(), Y1.ravel()].T\\n for i, mask in enumerate(masks):\\n # find the boundary of each mask,\\n # compute the distance of each pixel from this boundary\\n bounds = find_boundaries(mask, mode='inner')\\n X2, Y2 = np.nonzero(bounds)\\n xSum = (X2.reshape(-1, 1) - X1.reshape(1, -1)) ** 2\\n ySum = (Y2.reshape(-1, 1) - Y1.reshape(1, -1)) ** 2\\n distMap[:, i] = np.sqrt(xSum + ySum).min(axis=0)\\n ix = np.arange(distMap.shape[0])\\n if distMap.shape[1] == 1:\\n d1 = distMap.ravel()\\n border_loss_map = w0 * np.exp((-1 * (d1) ** 2) / (2 * (sigma ** 2)))\\n else:\\n if distMap.shape[1] == 2:\\n d1_ix, d2_ix = np.argpartition(distMap, 1, axis=1)[:, :2].T\\n else:\\n d1_ix, d2_ix = np.argpartition(distMap, 2, axis=1)[:, :2].T\\n d1 = distMap[ix, d1_ix]\\n d2 = distMap[ix, d2_ix]\\n border_loss_map = w0 * np.exp((-1 * (d1 + d2) ** 2) / (2 * (sigma ** 2)))\\n xBLoss = np.zeros((nrows, ncols))\\n xBLoss[X1, Y1] = border_loss_map\\n # class weight map\\n loss = np.zeros((nrows, ncols))\\n w_1 = 1 - masks.sum() / loss.size\\n w_0 = 1 - w_1\\n loss[masks.sum(0) == 1] = w_1\\n loss[masks.sum(0) == 0] = w_0\\n ZZ = xBLoss + loss\\n\\n if longest_max_size > 0:\\n ZZ = cv2.resize(ZZ, (old_cols, old_rows))\\n return ZZ\",\n \"def CLF(mag,L_bins,dL,Mhost_min,Mhost_max,Mhost):\\n\\n mag_bin = np.asarray([mag[i] for i in range(len(mag)) if Mhost_min<log(Mhost[i],10)<=Mhost_max]) \\n Nhosts = len(mag_bin)\\n print \\\"len(\\\"+str((Mhost_max+Mhost_min)/2)+\\\")\\\"\\n print Nhosts\\n Msun = 4.76 # The Sun's absolute magnitude\\n L = ((Msun-mag_bin)/2.5) # Calculated the luminosity \\n counts = np.histogram(L,L_bins)[0]\\n CLF = counts/(dL*Nhosts)\\n return CLF,L_bins\",\n \"def avghist(data, weights, bins=None):\\n if bins is not None:\\n sums = np.histogram(data, weights=weights, bins=bins)[0]\\n (counts, buckets) = np.histogram(data, bins=bins)\\n buckets=bins\\n else:\\n sums = np.histogram(data, weights=weights)[0]\\n (counts, buckets) = np.histogram(data)\\n\\n #elements with zero count will by definition have zero sum, ignore div by zero\\n counts[counts == 0] = 1\\n\\n bin_means = np.divide(sums, counts)\\n return (bin_means, buckets)\",\n \"def norm_bound(self, input_mags):\\n return np.sum(input_mags)\",\n \"def generate_map(nrows, ncols, nrooms, max_col_size, max_row_size):\\n arr = np.zeros((nrows, ncols), dtype=np.int8)\\n\\n for i in range(nrooms):\\n rand_row_start = np.random.randint(nrows)\\n rand_col_start = np.random.randint(ncols)\\n\\n rand_row_size = np.random.randint(max_row_size / 2, max_row_size)\\n rand_col_size = np.random.randint(max_col_size / 2, max_col_size)\\n\\n arr[rand_row_start:rand_row_start + rand_row_size, rand_col_start:rand_col_start + rand_col_size] = 1\\n\\n labels = measure.label(arr)\\n regions = measure.regionprops(labels)\\n\\n centroids = list()\\n for region in regions:\\n centroids.append(region.centroid)\\n\\n num_centroids = len(centroids)\\n\\n # get distances between every pair of centroids\\n dists = scipy.spatial.distance.cdist(centroids, centroids)\\n\\n # get a distance that is greater than all current distances\\n max_dist = np.max(dists) + 1\\n\\n # make sure upper triangle is at least max_dist so that when picking closest\\n # pairs, we won't choose a diagonal element or a duplicate connection\\n dists = dists + np.triu(np.ones((num_centroids, num_centroids))) * max_dist\\n\\n for i in range(num_centroids - 1):\\n min_dist_idx = np.argmin(dists)\\n min_dist_idx = np.unravel_index(min_dist_idx, dists.shape)\\n\\n # create a hallway between regionprops\\n centroid1 = np.array(centroids[min_dist_idx[0]], dtype=np.int)\\n centroid2 = np.array(centroids[min_dist_idx[1]], dtype=np.int)\\n\\n [row_centroid_1, row_centroid_2] = sorted([centroid1, centroid2], key=lambda x: x[0])\\n [col_centroid_1, col_centroid_2] = sorted([centroid1, centroid2], key=lambda x: x[1])\\n\\n arr[row_centroid_1[0]:row_centroid_2[0] + 1, row_centroid_1[1]] = 1\\n arr[row_centroid_2[0], col_centroid_1[1]:col_centroid_2[1] + 1] = 1\\n\\n dists[:, min_dist_idx[1]] += max_dist\\n\\n return arr\",\n \"def obtain_pairwise_distances(normal, abnormal, num_comparisons):\\n avg_dist = np.zeros(len(DIST_METRICS), dtype=float)\\n for i, metric in enumerate(sorted(DIST_METRICS)):\\n\\n # Iterate through all combinations of pairs of vectors\\n for idx_g1 in range(len(normal)):\\n for idx_g2 in range(len(abnormal)):\\n t1 = normal[idx_g1]\\n t2 = abnormal[idx_g2]\\n\\n # Determine which metric to compute by indexing dictionary\\n dist = DIST_METRICS[metric](t1, t2)\\n avg_dist[i] = avg_dist[i] + dist\\n\\n # Compute the average\\n avg_dist = avg_dist / num_comparisons\\n\\n return avg_dist\",\n \"def norm_leg_coefs(self, cl):\\n new_cl = np.zeros_like( cl )\\n for i in range(cl.shape[1]):\\n for j in range(cl.shape[2]):\\n if (cl[:,i,j]).max() >0:\\n new_cl[:,i,j] = cl[:,i,j]/(cl[:,i,j]).max()\\n\\n return new_cl\",\n \"def distance_metric(seg_A, seg_B, dx, k):\\n\\n # Extract the label k from the segmentation maps to generate binary maps\\n seg_A = (seg_A == k)\\n seg_B = (seg_B == k)\\n\\n table_md = []\\n table_hd = []\\n X, Y, Z = seg_A.shape\\n for z in range(Z):\\n # Binary mask at this slice\\n slice_A = seg_A[:, :, z].astype(np.uint8)\\n slice_B = seg_B[:, :, z].astype(np.uint8)\\n\\n # The distance is defined only when both contours exist on this slice\\n if np.sum(slice_A) > 0 and np.sum(slice_B) > 0:\\n # Find contours and retrieve all the points\\n contours, hierarchy = cv2.findContours(cv2.inRange(slice_A, 1, 1),\\n cv2.RETR_EXTERNAL,\\n cv2.CHAIN_APPROX_NONE)\\n pts_A = contours[0]\\n for i in range(1, len(contours)):\\n pts_A = np.vstack((pts_A, contours[i]))\\n\\n contours, hierarchy = cv2.findContours(cv2.inRange(slice_B, 1, 1),\\n cv2.RETR_EXTERNAL,\\n cv2.CHAIN_APPROX_NONE)\\n pts_B = contours[0]\\n for i in range(1, len(contours)):\\n pts_B = np.vstack((pts_B, contours[i]))\\n\\n # Distance matrix between point sets\\n M = np.zeros((len(pts_A), len(pts_B)))\\n for i in range(len(pts_A)):\\n for j in range(len(pts_B)):\\n M[i, j] = np.linalg.norm(pts_A[i, 0] - pts_B[j, 0])\\n\\n # Mean distance and hausdorff distance\\n md = 0.5 * (np.mean(np.min(M, axis=0)) + np.mean(np.min(M, axis=1))) * dx\\n hd = np.max([np.max(np.min(M, axis=0)), np.max(np.min(M, axis=1))]) * dx\\n table_md += [md]\\n table_hd += [hd]\\n\\n # Return the mean distance and Hausdorff distance across 2D slices\\n mean_md = np.mean(table_md) if table_md else None\\n mean_hd = np.mean(table_hd) if table_hd else None\\n return mean_md, mean_hd\",\n \"def _reduce_distances(self, threshold):\\n reduced = self.orig_dists.copy()\\n reduced[reduced <= threshold] = 0\\n # Remove ignored from all consideration\\n ignrd_indices = [self.index[name] for name in self.ignored]\\n if ignrd_indices:\\n reduced[:,ignrd_indices] = np.inf\\n reduced[ignrd_indices,:] = np.inf\\n # Check if the given parameters are feasible\\n chsn_indices = set(self.index[name] for name in self.chosen)\\n avail_indices = set(self.index[name] for name in self.available)\\n ca_indices = chsn_indices | avail_indices\\n unassigned_indices = np.array(list(self._not_ignored_inds - ca_indices))\\n if len(unassigned_indices) == 0:\\n unassigned_orphans = unassigned_indices\\n else:\\n ca_indices = list(ca_indices)\\n avail_in_range = np.count_nonzero(reduced[np.ix_(unassigned_indices,ca_indices)] == 0, axis=1)\\n unassigned_orphans = unassigned_indices[avail_in_range == 0]\\n return reduced, unassigned_orphans\",\n \"def average_distance(self):\\r\\n total = 0\\r\\n edges = 0\\r\\n for code, _list in self.edges.items():\\r\\n for edge in _list:\\r\\n total += edge.distance\\r\\n edges += 1\\r\\n return total / edges\",\n \"def inter_autodownsample(matrix, max_pixels):\\n size = np.shape(matrix)[0] * np.shape(matrix)[1]\\n if size <= max_pixels:\\n return int(0)\\n \\n n = float(size) / max_pixels;\\n return np.sqrt(n);\",\n \"def compute_neighbors_if_necessary(self, thresh, max_words):\\n thresh = float(thresh) # dang python 2.7!\\n if (\\n self._neighbors is not None\\n and self.thresh == thresh\\n and self.max_words == max_words\\n ):\\n return\\n print(\\\"Computing neighbors...\\\")\\n self.thresh = thresh\\n self.max_words = max_words\\n vecs = self._vecs[:max_words]\\n dots = vecs.dot(vecs.T)\\n dots = scipy.sparse.csr_matrix(dots * (dots >= 1 - thresh / 2))\\n from collections import Counter\\n\\n rows, cols = dots.nonzero()\\n nums = list(Counter(rows).values())\\n print(\\\"Mean number of neighbors per word:\\\", np.mean(nums) - 1)\\n print(\\\"Median of number of neighbors per word:\\\", np.median(nums) - 1)\\n rows, cols, vecs = zip(\\n *[\\n (i, j, vecs[i] - vecs[j])\\n for i, j, x in zip(rows, cols, dots.data)\\n if i < j\\n ]\\n )\\n self._neighbors = rows, cols, np.array([v / np.linalg.norm(v) for v in vecs])\",\n \"def _calc_dim_average(vectors_array):\\n array = np.array(vectors_array)\\n threshold = 0.001\\n res = array.sum(axis=0, dtype='float32')\\n valid_count = (array > threshold).sum(axis=0)\\n valid_count[valid_count == 0] = 1\\n res /= valid_count\\n return res\",\n \"def _compute_rho0(self, distance_matrix):\\n nr_samples = distance_matrix.shape[1]\\n\\n # both modes are equivalent, MODE=1 allows to easier compare with ref3.\\n MODE = 1\\n if MODE == 1: # according to Berry code\\n # keep only nearest neighbors\\n distance_matrix = np.sort(np.sqrt(distance_matrix), axis=1)[\\n :, 1 : self.k + 1\\n ]\\n rho0 = np.sqrt(np.mean(distance_matrix ** 2, axis=1))\\n else: # MODE == 2: , more performant if required\\n if self.k < nr_samples:\\n # TODO: have to revert setting the inf\\n # -> check that the diagonal is all zeros\\n # -> set to inf\\n # -> after computation set all infs back to zero\\n # -> this is also very similar to continous-nn in PCManifold\\n\\n # this allows to ignore the trivial distance=0 to itself\\n np.fill_diagonal(distance_matrix, np.inf)\\n\\n # more efficient than sorting, everything\\n distance_matrix.partition(self.k, axis=1)\\n distance_matrix = np.sort(distance_matrix[:, : self.k], axis=1)\\n distance_matrix = distance_matrix * distance_matrix\\n else: # self.k == self.N\\n np.fill_diagonal(distance_matrix, np.nan)\\n bool_mask = ~np.diag(np.ones(nr_samples)).astype(bool)\\n distance_matrix = distance_matrix[bool_mask].reshape(\\n distance_matrix.shape[0], distance_matrix.shape[1] - 1\\n )\\n\\n # experimental: --------------------------------------------------------------\\n # paper: in var-bw paper (ref2) pdfp. 7\\n # it is mentioned to IGNORE non-zero entries -- this is not detailed more.\\n # a consequence is that the NN and kernel looses symmetry, so does (K+K^T) / 2\\n # This is with a cut-off rate:\\n # val = 1E-2\\n # distance_matrix[distance_matrix < val] = np.nan\\n # experimental END -----------------------------------------------------------\\n\\n # nanmean only for the experimental part, if leaving this out, np.mean\\n # suffices\\n rho0 = np.sqrt(np.nanmean(distance_matrix, axis=1))\\n\\n return rho0\",\n \"def distance_map(self, scaling='sum'):\\n\\n if scaling not in ['sum', 'mean']:\\n raise ValueError(f'scaling should be either \\\"sum\\\" or \\\"mean\\\" ('\\n f'\\\"{scaling}\\\" not valid)')\\n\\n um = nan * zeros((self._weights.shape[0],\\n self._weights.shape[1],\\n 8)) # 2 spots more for hexagonal topology\\n\\n ii = [[0, -1, -1, -1, 0, 1, 1, 1]]*2\\n jj = [[-1, -1, 0, 1, 1, 1, 0, -1]]*2\\n\\n if self.topology == 'hexagonal':\\n ii = [[1, 1, 1, 0, -1, 0], [0, 1, 0, -1, -1, -1]]\\n jj = [[1, 0, -1, -1, 0, 1], [1, 0, -1, -1, 0, 1]]\\n\\n for x in range(self._weights.shape[0]):\\n for y in range(self._weights.shape[1]):\\n w_2 = self._weights[x, y]\\n e = y % 2 == 0 # only used on hexagonal topology\\n for k, (i, j) in enumerate(zip(ii[e], jj[e])):\\n if (x+i >= 0 and x+i < self._weights.shape[0] and\\n y+j >= 0 and y+j < self._weights.shape[1]):\\n w_1 = self._weights[x+i, y+j]\\n um[x, y, k] = fast_norm(w_2-w_1)\\n\\n if scaling == 'mean':\\n um = nanmean(um, axis=2)\\n if scaling == 'sum':\\n um = nansum(um, axis=2)\\n\\n return um/um.max()\",\n \"def detrend(\\n matrix,\\n detectable_bins=None,\\n max_dist=None,\\n smooth=False,\\n fun=np.nanmean,\\n max_val=10,\\n):\\n matrix = matrix.tocsr()\\n y = distance_law(\\n matrix,\\n detectable_bins=detectable_bins,\\n max_dist=max_dist,\\n smooth=smooth,\\n fun=fun,\\n )\\n y[np.isnan(y)] = 0.0\\n # Detrending by the distance law\\n clean_mat = matrix.tocoo()\\n # clean_mat.data /= y_savgol[abs(clean_mat.row - clean_mat.col)]\\n try:\\n clean_mat.data = clean_mat.data / y[abs(clean_mat.row - clean_mat.col)]\\n # If no nonzero value in matrix, do nothing\\n except TypeError:\\n pass\\n clean_mat = clean_mat.tocsr()\\n if max_val is not None:\\n clean_mat[clean_mat >= max_val] = 1\\n return clean_mat\",\n \"def correlation_bins(shred):\\n return 0\",\n \"def mean_crosses(magnitudes):\\n mean = np.mean(magnitudes)\\n std = np.std(magnitudes)\\n dev_above = mean + std * 0.1\\n dev_below = mean - std * 0.1\\n\\n above = magnitudes[0] > mean\\n\\n mags = magnitudes[:,0]\\n\\n relevant_mags = mags[np.where( (mags > dev_above) | (mags < dev_below) )]\\n\\n relevant_norm_mags = relevant_mags - mean\\n\\n rel_mags_a = relevant_norm_mags[0:-1]\\n rel_mags_b = relevant_norm_mags[1:]\\n\\n are_crosses = np.multiply(rel_mags_a, rel_mags_b)\\n\\n crosses = are_crosses[are_crosses < 0.0].size\\n\\n return crosses\",\n \"def gen_dist(genes):\\n\\n # First generate an NxNxB matrix that has False where\\n # i and j individuals have the same kth gene and True\\n # otherwise (XOR operation). Then sum along\\n # the genome axis to get distance\\n return np.sum(genes[:,None,:] ^ genes, axis=-1)\",\n \"def _create_distance_matrix(mesh):\\n l = len(mesh.faces)\\n\\n faces = polygons(mesh.faces, mesh.vertices, mesh.face_normals, mesh.area_faces)\\n # map from edge-key to adjacent faces\\n adj_faces_map = {}\\n # find adjacent faces by iterating edges\\n for index, face in enumerate(faces):\\n for edge in face.edge_keys:\\n if (edge[0] > edge[1]):\\n new_edge = (edge[1], edge[0])\\n else:\\n new_edge = (edge[0], edge[1])\\n if new_edge in adj_faces_map:\\n adj_faces_map[new_edge].append(index) # 一对多\\n else:\\n adj_faces_map[new_edge] = [index]\\n\\n # helping vectors to create sparse matrix later on\\n row_indices = []\\n col_indices = []\\n Gval = [] # values for matrix of angular distances\\n Aval = [] # values for matrix of geodesic distances\\n # iterate adjacent faces and calculate distances\\n for edge, adj_faces in adj_faces_map.items():\\n if len(adj_faces) == 2:\\n i = adj_faces[0]\\n j = adj_faces[1]\\n # 一条边连接的两个面\\n Gtemp = _geodesic_distance(mesh, faces[i], faces[j], edge) # 测地距离\\n Atemp = _angular_distance(mesh, faces[i], faces[j]) # 角距离 # 其实是余弦距离\\n Gval.append(Gtemp)\\n Aval.append(Atemp)\\n row_indices.append(i)\\n col_indices.append(j)\\n # add symmetric entry\\n Gval.append(Gtemp)\\n Aval.append(Atemp)\\n row_indices.append(j)\\n col_indices.append(i)\\n\\n elif len(adj_faces) > 2:\\n print(\\\"Edge with more than 2 adjacent faces: \\\" + str(adj_faces) + \\\"!\\\")\\n\\n Gval = numpy.array(Gval)\\n Aval = numpy.array(Aval)\\n # delta是去全局变量，外部传入的\\n values = delta * Gval / numpy.mean(Gval) + \\\\\\n (1.0 - delta) * Aval / numpy.mean(Aval)\\n\\n # create sparse matrix\\n distance_matrix = scipy.sparse.csr_matrix(\\n (values, (row_indices, col_indices)), shape=(l, l))\\n return distance_matrix\",\n \"def gradient_dist(var, bins=40, range=None, diff_max=None, scale=1,\\n percentile=99.9, interpolate=False, period=None):\\n diffs = np.diff(var, axis=1).flatten()\\n if period is not None:\\n diffs = (diffs + period / 2) % period - period / 2\\n diffs /= scale\\n diff_max = diff_max or np.percentile(np.abs(diffs), percentile)\\n diffs = diffs[(np.abs(diffs) < diff_max)]\\n norm = np.sqrt(np.mean(diffs**2))\\n counts, centers = continuous_dist(diffs, bins=bins, range=range,\\n interpolate=interpolate)\\n return centers, counts, norm\",\n \"def rhombus_area(diagonal_1, diagonal_2):\\n return (diagonal_1 * diagonal_2) / 2\",\n \"def manhattan(self):\\n distance = 0\\n for i in range(3):\\n for j in range(3):\\n if self.plateau[i][j] != 0:\\n x, y = divmod(self.plateau[i][j]-1, 3)\\n distance += abs(x - i) + abs(y - j)\\n return distance\",\n \"def avg_sil_width(cluster_vec, centroid):\\n tot_dis = 0\\n for i in xrange(len(cluster_vec)):\\n cos_dis = cos_sim(cluster_vec[i], centroid)\\n tot_dis = tot_dis + cos_dis\\n return tot_dis/len(cluster_vec)\",\n \"def _calc_u_matrix_distances(self) -> None:\\n for u_node in itertools.product(\\n range(self.n_rows * 2 - 1), range(self.n_columns * 2 - 1)\\n ):\\n # neighbor vector\\n nb = (0, 0)\\n\\n if not (u_node[0] % 2) and (u_node[1] % 2):\\n # mean horizontally\\n nb = (0, 1)\\n\\n elif (u_node[0] % 2) and not (u_node[1] % 2):\\n # mean vertically\\n nb = (1, 0)\\n\\n self.u_matrix[u_node] = np.linalg.norm(\\n self.unsuper_som_[u_node[0] // 2][u_node[1] // 2]\\n - self.unsuper_som_[u_node[0] // 2 + nb[0]][\\n u_node[1] // 2 + nb[1]\\n ],\\n axis=0,\\n )\",\n \"def compute(self): \\r\\n hist = self.confusion_matrix\\r\\n iu = np.diag(hist) / (hist.sum(axis=1) + hist.sum(axis=0) - np.diag(hist))\\r\\n mean_iu = np.nanmean(iu)\\r\\n return mean_iu\",\n \"def smooth(F):\\n import numpy as np\\n m, n, k = F.shape\\n for i in range(k):\\n\\n# \\taverage for cells surrounded by 8 others\\n\\n C = F[1:m-1,1:n-1,i]\\n for ind, value in np.ndenumerate(C):\\n C[ind[0],ind[1]]=(1/9)*(int(value)+int(F[ind[0]+1,ind[1],i])+int(F[ind[0]-1,ind[1],i])+int(F[ind[0],ind[1]+1,i])+int(F[ind[0],ind[1]-1,i])+int(F[ind[0]+1,ind[1]+1,i])+int(F[ind[0]-1,ind[1]+1,i])+int(F[ind[0]-1,ind[1]-1,i])+int(F[ind[0]+1,ind[1]-1,i]))\\n \\t\\n# \\taverage for corners clockwise from (0,0)\\n\\n X = 0.25*(int(F[0,0,i]) + int(F[0,1,i]) + int(F[1,0,i]) + int(F[1,1,i]) )\\n Y = 0.25*(int(F[m-1,0,i]) + int(F[m-1,1,i]) + int(F[m-2,0,i]) + int(F[m-2,1,i]) )\\n Z = 0.25*(int(F[m-1,n-1,i]) + int(F[m-2,n-1,i]) + int(F[m-1,n-2,i]) + int(F[m-2,n-2,i]) )\\n T = 0.25*(int(F[0,n-1,i]) + int(F[1,n-1,i]) + int(F[0,n-2,i]) + int(F[1,n-2,i]) )\\n \\t\\n#\\taverage for edges excluding corners clockwise from (0,0)\\n\\n N = F[0,1:n-1,i]\\n for ind, value in np.ndenumerate(N):\\n N[ind[0]] = (1/6)*(int(value)+int(F[0+1,ind[0],i])+int(F[0,ind[0]+1,i])+int(F[0,ind[0]-1,i])+int(F[0+1,ind[0]+1,i])+int(F[0+1,ind[0]-1,i]))\\n E = F[1:m-1,0,i]\\n for ind, value in np.ndenumerate(E):\\n E[ind[0]] = (1/6)*(int(value)+int(F[ind[0]+1,0,i])+int(F[ind[0]-1,0,i])+int(F[ind[0],0+1,i])+int(F[ind[0]+1,0+1,i])+int(F[ind[0]-1,0+1,i]))\\n S = F[m-1,1:n-1,i]\\n for ind, value in np.ndenumerate(S):\\n S[ind[0]] = (1/6)*(int(value)+int(F[m-1-1,ind[0],i])+int(F[m-1,ind[0]+1,i])+int(F[m-1,ind[0]-1,i])+int(F[m-1-1,ind[0]+1,i])+int(F[m-1-1,ind[0]-1,i]))\\n W = F[1:m-1,n-1,i]\\n for ind, value in np.ndenumerate(W):\\n W[ind[0]] = (1/6)*(int(value)+int(F[ind[0]+1,n-1,i])+int(F[ind[0]-1,n-1,i])+int(F[ind[0],n-1-1,i])+int(F[ind[0]+1,n-1-1,i])+int(F[ind[0]-1,n-1-1,i]))\\n \\t\\n# \\tReassign F \\n\\n F[1:m-1,1:n-1,i] = C\\n F[0,1:n-1,i] = N\\n F[1:m-1,0,i] = E\\n F[m-1,1:n-1,i] = S\\n F[1:m-1,n-1,i] = W\\n F[0,0,i] = X\\n F[m-1,0,i] = Y\\n F[m-1,n-1,i] = Z\\n F[0,n-1,i] = T\\n \\n# \\tNormalise F\\n\\n big = F.max()\\n F = (1/big)*F\\n return F\",\n \"def _getnorm(q, mask):\\n maxq = np.max(q.reshape(-1, 3), axis=0)\\n hist = np.histogramdd(q.reshape(-1, 3), bins=maxq + 1, range=[[-0.5, mq + 0.5] for mq in maxq], weights=~mask.ravel())[0]\\n ret = hist[q.reshape(-1, 3)[:, 0], q.reshape(-1, 3)[:, 1], q.reshape(-1, 3)[:, 2]].reshape(q.shape[:2])\\n ret[mask] = 1\\n return ret\",\n \"def hog_feature(im):\\n # convert rgb to grayscale if needed\\n if im.ndim == 3:\\n image = rgb2gray(im)\\n else:\\n image = np.at_least_2d(im)\\n\\n sx, sy = image.shape # image size\\n orientations = 9 # number of gradient bins\\n cx, cy = (8, 8) # pixels per cell\\n\\n gx = np.zeros(image.shape)\\n gy = np.zeros(image.shape)\\n gx[:, :-1] = np.diff(image, n=1, axis=1) # compute gradient on x-direction\\n gy[:-1, :] = np.diff(image, n=1, axis=0) # compute gradient on y-direction\\n grad_mag = np.sqrt(gx**2 + gy**2) # gradient magnitude\\n grad_ori = np.arctan2(\\n gx, (gy + 1e-15)) * (180 / np.pi) + 90 # gradient orientation\\n\\n n_cellsx = int(np.floor(sx / cx)) # number of cells in x\\n n_cellsy = int(np.floor(sy / cy)) # number of cells in y\\n orientation_histogram = np.zeros((n_cellsx, n_cellsy, orientations))\\n for i in range(orientations):\\n temp_ori = np.where(grad_ori < (180 / orientations) * (i + 1),\\n grad_ori, 0)\\n temp_ori = np.where(temp_ori >= (180 / orientations) * i, temp_ori, 0)\\n temp_mag = np.where(temp_ori > 0, grad_mag, 0)\\n orientation_histogram[:, :, i] = uniform_filter(\\n temp_mag, size=(cx, cy))[round(cx / 2)::cx,\\n round(cy / 2)::cy].T\\n\\n return orientation_histogram.ravel()\",\n \"def get_grad(grad_matrix, feature_matrix, average_occurrences=False):\\n assert grad_matrix.shape == feature_matrix.shape\\n # Prune out the all-zero columns that pop up sometimes\\n valid_cols = []\\n for col_idx in range(feature_matrix.size(1)):\\n if not torch.all(feature_matrix[:, col_idx] == 0):\\n valid_cols.append(col_idx)\\n feature_matrix = feature_matrix[:, valid_cols]\\n grad_matrix = grad_matrix[:, valid_cols]\\n\\n _, D = feature_matrix.shape\\n if average_occurrences:\\n avg_grad = torch.zeros(D)\\n avg_grad_var = torch.zeros(D)\\n for d in range(D):\\n current_feature = feature_matrix[:, d].clone().detach()\\n instances, indices, counts = torch.unique(\\n current_feature, return_inverse=True, return_counts=True\\n )\\n weight_vector = torch.tensor([counts[i] for i in indices]).type(torch.float)\\n weight_vector /= weight_vector.sum()\\n mean = torch.sum(weight_vector * grad_matrix[:, d])\\n # Compute the empirical variance of gradients\\n variance = torch.sum(weight_vector * grad_matrix[:, d] ** 2) - mean**2\\n avg_grad[d] = mean\\n avg_grad_var[d] = variance\\n return avg_grad, avg_grad_var, feature_matrix.sum(dim=0)\\n else:\\n # The maximum number possible occurrences -- 7 is an example, if problem occurs, maybe we can increase this\\n # number. But for now, for both NAS-Bench datasets, this should be more than enough!\\n max_occur = 7\\n avg_grad = torch.zeros(D, max_occur)\\n avg_grad_var = torch.zeros(D, max_occur)\\n incidences = torch.zeros(D, max_occur)\\n for d in range(D):\\n current_feature = feature_matrix[:, d].clone().detach()\\n instances, indices, counts = torch.unique(\\n current_feature, return_inverse=True, return_counts=True\\n )\\n for i, val in enumerate(instances):\\n # Find index of all feature counts that are equal to the current val\\n feature_at_val = grad_matrix[current_feature == val]\\n avg_grad[d, int(val)] = torch.mean(feature_at_val)\\n avg_grad_var[d, int(val)] = torch.var(feature_at_val)\\n incidences[d, int(val)] = counts[i]\\n return avg_grad, avg_grad_var, incidences\",\n \"def compute_histogram(self):\\n # compute distance between points \\n distmatrix = np.sqrt(pdist(self.points))\\n if not self.mean_dist:\\n self.mean_dist = np.mean(distmatrix)\\n distmatrix = distmatrix/self.mean_dist\\n distmatrix = squareform(distmatrix)\\n #compute angles between points\\n angles = compute_angles(self.points)\\n #quantize angles to a bin\\n tbins = np.floor(angles / (2 * pi / self.nbins_theta))\\n lg = np.logspace(self.r1, self.r2, num=5)\\n #quantize radious to bins\\n rbins = np.ones(angles.shape) * -1\\n for r in lg:\\n counts = (distmatrix < r) \\n rbins = rbins + counts.astype(int) \\n return rbins, tbins\",\n \"def densityCorrelation(self, int_max=None, nBoxes=None):\\n\\n particleDensity = self.nParticleDensity(int_max=int_max, nBoxes=nBoxes)\\n\\n _G2D = np.array(list(map(\\n lambda _rho:\\n (lambda FFT: np.real(np.fft.ifft2(np.conj(FFT)*FFT)))\\n (np.fft.fft2(_rho - self.rho)),\\n particleDensity)))/(nBoxes**2)\\n\\n return g2Dto1D(_G2D.mean(axis=0), self.L)\",\n \"def gen_uniform_conf(M, avg_size=0.5, std_size=0.1, avg_dist = 1., std_dist=0.1, dist_toleft=0.1, dist_toright=0.1) :\\n lengths = avg_size*np.ones(shape=M)\\n distances = avg_size*np.ones(shape=M-1)\\n positions = np.zeros(M)\\n positions[0] = lengths[0] + dist_toleft\\n for n in range(1, M) :\\n positions[n] = positions[n-1] + lengths[n-1] + lengths[n] + distances[n-1]\\n \\n Ltot = np.sum(distances) + 2*np.sum(lengths) + dist_toleft + dist_toright\\n \\n lumens, bridges = make_network(lengths, positions, Ltot)\\n \\n return lumens, bridges, Ltot\",\n \"def compute_D_hmin_hmax_(mesh0: trimesh.Trimesh):\\n mesh = copy.deepcopy(mesh0)\\n mesh = center_align_mesh(mesh)\\n\\n x = mesh.vertices[:,0]\\n y = mesh.vertices[:,1]\\n z = mesh.vertices[:,2]\\n\\n D = np.mean(np.ptp(mesh.vertices[:,:2], axis=0))\\n\\n # assume the cell is radially symmetric; we measure the mean height along r in bins\\n # and then take the minimum and maximum\\n r = np.sqrt(x**2 + y**2)\\n\\n nv = len(x)\\n bin_edges = np.linspace(0, D/2, nv//100, endpoint=True)\\n bin_r = (bin_edges[:-1] + bin_edges[1:]) / 2\\n bin_h = []\\n\\n for r0, r1 in zip(bin_edges[:-1], bin_edges[1:]):\\n idx = np.argwhere(np.logical_and(r >= r0, r < r1))\\n if len(idx):\\n h = np.ptp(z[idx])\\n else:\\n h = 0\\n bin_h.append(h)\\n\\n bin_h = np.array(bin_h)\\n\\n hmax = np.max(bin_h)\\n # hmin is the minimum height of the cell in the inside region,\\n # i.e. we must filter out the particles that have a larger radial coordinate\\n # than those at hmax.\\n idmax = np.argmax(np.abs(bin_h))\\n rhmax = bin_r[idmax]\\n idx = np.argwhere(bin_r <= rhmax)\\n hmin = np.min(bin_h[idx])\\n\\n return D, hmin, hmax\",\n \"def seg_to_affgraph(seg_gt, nhood):\\n nhood = np.ascontiguousarray(nhood, np.int32)\\n shape = seg_gt.shape\\n n_edge = nhood.shape[0]\\n aff = np.zeros((n_edge,)+shape,dtype=np.int16)\\n\\n for e in range(n_edge):\\n aff[e, \\\\\\n max(0,-nhood[e,0]):min(shape[0],shape[0]-nhood[e,0]), \\\\\\n max(0,-nhood[e,1]):min(shape[1],shape[1]-nhood[e,1]), \\\\\\n max(0,-nhood[e,2]):min(shape[2],shape[2]-nhood[e,2])] = \\\\\\n (seg_gt[max(0,-nhood[e,0]):min(shape[0],shape[0]-nhood[e,0]), \\\\\\n max(0,-nhood[e,1]):min(shape[1],shape[1]-nhood[e,1]), \\\\\\n max(0,-nhood[e,2]):min(shape[2],shape[2]-nhood[e,2])] == \\\\\\n seg_gt[max(0,nhood[e,0]):min(shape[0],shape[0]+nhood[e,0]), \\\\\\n max(0,nhood[e,1]):min(shape[1],shape[1]+nhood[e,1]), \\\\\\n max(0,nhood[e,2]):min(shape[2],shape[2]+nhood[e,2])] ) \\\\\\n * ( seg_gt[max(0,-nhood[e,0]):min(shape[0],shape[0]-nhood[e,0]), \\\\\\n max(0,-nhood[e,1]):min(shape[1],shape[1]-nhood[e,1]), \\\\\\n max(0,-nhood[e,2]):min(shape[2],shape[2]-nhood[e,2])] > 0 ) \\\\\\n * ( seg_gt[max(0,nhood[e,0]):min(shape[0],shape[0]+nhood[e,0]), \\\\\\n max(0,nhood[e,1]):min(shape[1],shape[1]+nhood[e,1]), \\\\\\n max(0,nhood[e,2]):min(shape[2],shape[2]+nhood[e,2])] > 0 )\\n\\n return aff\",\n \"def calcAvgDistances(centroids, clusters, class_header=\\\"Class\\\"):\\n avg_distances = [0] * len(centroids)\\n multiprocess_count = multiprocessing.cpu_count() # Find processor count\\n for centroid_row_index, centroid_tuple in enumerate(centroids.iterrows()): # For each cluster\\n work_list = [] # initialize multiprocessing structures\\n set_list = []\\n for _, datum in clusters[centroid_row_index].iterrows(): # For each point in the medoid cluster\\n work_list.append((centroid_tuple[1], datum, class_header)) # add calculation to work list\\n\\n partition_size = math.ceil(len(work_list) / multiprocess_count) # find size of each work subeset\\n for i in range(multiprocess_count - 1): # repeat for every subset\\n sample = work_list[i * partition_size: (i + 1) * partition_size] # break work list into fair subsets\\n set_list.append(sample)\\n set_list.append((work_list[(multiprocess_count - 1) * partition_size:]))\\n pool = multiprocessing.Pool(processes=multiprocess_count) # create multiprocessing pool\\n # calculate sum of list of all distances from work list tasks\\n avg_distances[centroid_row_index] = sum(sum(pool.map(Cluster.calcDistanceList, set_list), []))\\n pool.close()\\n pool.join()\\n\\n if avg_distances[centroid_row_index] is not 0: # make sure we do not divide by 0\\n # calculate average of distance list\\n avg_distances[centroid_row_index] = avg_distances[centroid_row_index] / len(clusters[centroid_row_index])\\n return avg_distances\",\n \"def per_class_iu(hist):\\n np.seterr(divide=\\\"ignore\\\", invalid=\\\"ignore\\\")\\n res = np.diag(hist) / (hist.sum(1) + hist.sum(0) - np.diag(hist))\\n np.seterr(divide=\\\"warn\\\", invalid=\\\"warn\\\")\\n res[np.isnan(res)] = 0.\\n return res\",\n \"def calculate_square_form(diagonal_matrix, total_sorts):\\n n = len(diagonal_matrix)\\n\\n matrix = np.ndarray(shape=(n,n))\\n\\n for i in range(n):\\n for j in range(len(diagonal_matrix[i])):\\n # Also calculate the dissimilarity matrix\\n matrix[i][j] = 100 - 100 * diagonal_matrix[i][j] / total_sorts\\n matrix[j][i] = 100 - 100 * diagonal_matrix[i][j] / total_sorts\\n if i == j:\\n matrix[i][j] = 0\\n\\n return matrix\\n\\n # matrix = np.tril(diagonal_matrix, k=-1)\\n # matrix = matrix + matrix.T\\n # matrix = matrix * (-100 / total_sorts) + 100\\n # np.fill_diagonal(matrix, 0)\\n # return matrix\",\n \"def _determine_histogram_bins(self, ma_maps):\\n if isinstance(ma_maps, list):\\n ma_values = self.masker.transform(ma_maps)\\n elif isinstance(ma_maps, np.ndarray):\\n ma_values = ma_maps.copy()\\n else:\\n raise ValueError(f\\\"Unsupported data type '{type(ma_maps)}'\\\")\\n\\n # Determine bins for null distribution histogram\\n # Remember that numpy histogram bins are bin edges, not centers\\n # Assuming values of 0, .001, .002, etc., bins are -.0005-.0005, .0005-.0015, etc.\\n INV_STEP_SIZE = 100000\\n step_size = 1 / INV_STEP_SIZE\\n max_ma_values = np.max(ma_values, axis=1)\\n # round up based on resolution\\n max_ma_values = np.ceil(max_ma_values * INV_STEP_SIZE) / INV_STEP_SIZE\\n max_poss_ale = self.compute_summarystat(max_ma_values)\\n # create bin centers\\n hist_bins = np.round(np.arange(0, max_poss_ale + (1.5 * step_size), step_size), 5)\\n self.null_distributions_[\\\"histogram_bins\\\"] = hist_bins\",\n \"def getSigmaArea(self, detMap):\\r\\n sigmaArea = np.sum((self.array * detMap)[:])\\r\\n return sigmaArea\",\n \"def computeAC(bins):\\n halfn = len(bins)/2\\n Npos = sum(bins[halfn:])\\n Nneg = sum(bins[:halfn])\\n return float(Npos-Nneg)/(Npos+Nneg)\",\n \"def make_missing_mask(\\n shape, valid_rows, valid_cols, max_dist=None, sym_upper=False\\n):\\n # Error if the matrix upper symmetric but shape is rectangle or missing\\n # rows and cols are different\\n sm, sn = shape\\n if sym_upper and (sm != sn or len(valid_rows) != len(valid_cols)):\\n raise ValueError(\\\"Rectangular matrices cannot be upper symmetric\\\")\\n\\n # Get a boolean array of missing (1) and valid (0) rows\\n missing_rows = valid_to_missing(valid_rows, sm)\\n # When matrix is sym., rows and cols are synonym, no need to compute 2x\\n if sym_upper:\\n missing_cols = missing_rows\\n else:\\n missing_cols = valid_to_missing(valid_cols, sn)\\n # If upper sym., fill only upper diag up to max_dist.\\n # E. g. with bins 1 and 3 missing\\n # and a max_dist of 1:\\n # 0 1 0 0 0\\n # 0 1 1 0 0\\n # 0 0 0 1 0\\n # 0 0 0 1 1\\n # 0 0 0 0 0\\n # For each missing bin, mask is apply 1 pixel upwards and 1 to the right\\n # to fill only the upper triangle up to max_dist\\n if sym_upper:\\n # If no max dist has been specified, fill the whole upper triangle\\n if max_dist is None:\\n max_dist = min(shape)\\n # Generate matrix of distance shifts by row.\\n # Shape is len(missing_rows) x (max_dist + 1)\\n # e.g.: 2 missing rows and max dist of 1\\n # 0 0\\n # 1 1\\n row_shifts = np.tile(\\n np.array(range(max_dist + 1)), (len(missing_rows), 1)\\n ).T\\n # Compute row positions upwards to diagonal by subtracting missing rows\\n # to the shifts. Following the previous example, if missing rows are\\n # bins 1 and 3:\\n # 1 3\\n # 0 2\\n rows_before = (missing_rows - row_shifts).flatten(\\\"F\\\")\\n # looking at pixels up from the bins, cols remain the same:\\n # 1 3\\n # 1 3\\n cols_before = np.repeat(missing_rows, max_dist+1)\\n # Compute col position to the right until diagonal by adding the shift\\n # Note: upper symmetric, so row_shifts = col_shift_\\n # 1 3\\n # 2 4\\n cols_after = (missing_cols + row_shifts).flatten(\\\"F\\\")\\n # This time, rows remain constant since we are computing positions to\\n # the right\\n rows_after = np.repeat(missing_cols, max_dist+1)\\n # Combine positions to the right and upwards\\n rows = np.concatenate([rows_before, rows_after])\\n cols = np.concatenate([cols_before, cols_after])\\n data = np.ones(rows.shape, dtype=bool)\\n # Remove entries where rows or cols are negative or larger than shape\\n valid = (cols < sm) & (cols >= 0) & (rows < sm) & (rows >= 0)\\n # Build mask mat with miss bins up to max scan dist in upper triangle\\n mask = sp.coo_matrix(\\n (data[valid], (rows[valid], cols[valid])), shape=shape, dtype=bool\\n ).tocsr()\\n else:\\n mask = sp.csr_matrix(shape, dtype=bool)\\n mask[missing_rows, :] = 1\\n mask[:, missing_cols] = 1\\n\\n return mask\",\n \"def __calculate_average_distance(self):\\n game = self.__game # type: Game\\n all_icebergs = game.get_all_icebergs()\\n all_icebergs_length = len(all_icebergs)\\n sum_distances = 0\\n for i in range(all_icebergs_length):\\n for j in range(i + 1, all_icebergs_length):\\n iceberg1 = all_icebergs[i]\\n iceberg2 = all_icebergs[j]\\n sum_distances += iceberg1.get_turns_till_arrival(iceberg2)\\n\\n return sum_distances / (all_icebergs_length * (all_icebergs_length - 1) / 2)\",\n \"def mpd(distmat):\\r\\n return distmat.sum() / (distmat.size - distmat.shape[0])\",\n \"def _calc_distances(preds, targets, mask, normalize):\\n N, K, _ = preds.shape\\n _mask = mask.copy()\\n _mask[np.where((normalize == 0).sum(1))[0], :] = False\\n distances = np.full((N, K), -1, dtype=np.float32)\\n normalize[np.where(normalize <= 0)] = 1000000.0\\n distances[_mask] = np.linalg.norm(((preds - targets) / normalize[:, None, :])[_mask], axis=-1)\\n return distances.T\",\n \"def compute_gradmag(image_arr):\\n assert image_arr.ndim == 2\\n dy = sobel(image_arr, axis=0)\\n dx = sobel(image_arr, axis=1)\\n return np.hypot(dx, dy)\",\n \"def _calculate_cluster_measures(\\n arr4d,\\n threshold,\\n bin_struct,\\n two_sided_test=False,\\n):\\n n_regressors = arr4d.shape[3]\\n\\n max_sizes = np.zeros(n_regressors, int)\\n max_masses = np.zeros(n_regressors, float)\\n\\n for i_regressor in range(n_regressors):\\n arr3d = arr4d[..., i_regressor].copy()\\n\\n if two_sided_test:\\n arr3d[np.abs(arr3d) <= threshold] = 0\\n else:\\n arr3d[arr3d <= threshold] = 0\\n\\n labeled_arr3d, _ = label(arr3d > 0, bin_struct)\\n\\n if two_sided_test:\\n # Label positive and negative clusters separately\\n n_positive_clusters = np.max(labeled_arr3d)\\n temp_labeled_arr3d, _ = label(\\n arr3d < 0,\\n bin_struct,\\n )\\n temp_labeled_arr3d[temp_labeled_arr3d > 0] += n_positive_clusters\\n labeled_arr3d = labeled_arr3d + temp_labeled_arr3d\\n del temp_labeled_arr3d\\n\\n clust_vals, clust_sizes = np.unique(labeled_arr3d, return_counts=True)\\n assert clust_vals[0] == 0\\n\\n clust_vals = clust_vals[1:] # First cluster is zeros in matrix\\n clust_sizes = clust_sizes[1:]\\n\\n # Cluster mass-based inference\\n max_mass = 0\\n for unique_val in clust_vals:\\n ss_vals = np.abs(arr3d[labeled_arr3d == unique_val]) - threshold\\n max_mass = np.maximum(max_mass, np.sum(ss_vals))\\n\\n # Cluster size-based inference\\n max_size = 0\\n if clust_sizes.size:\\n max_size = np.max(clust_sizes)\\n\\n max_sizes[i_regressor], max_masses[i_regressor] = max_size, max_mass\\n\\n return max_sizes, max_masses\",\n \"def boxcar_average(D,Y,N):\\n boxY = np.convolve(Y, np.ones((N,))/N, mode='valid')\\n edge = (len(Y)-len(boxY))/2 \\n boxD = D[math.floor(edge):-math.ceil(edge)]\\n \\n return (boxD, boxY)\",\n \"def cchalf(dataframe, function, bins):\\n dist = dataframe.set_index(['H', 'K', 'L'])['D'].drop_duplicates()\\n dmin = dist.min()\\n dmax = dist.max()\\n binedges = np.linspace(dmin**-2, dmax**-2, bins+1)**-0.5\\n binedges = list(zip(binedges[:-1], binedges[1:]))\\n a,b = split(dataframe)\\n xval_a, xval_b = function(a), function(b)\\n#TODO: Fix this awful hack\\n key = [i for i in xval_a if i!='D'][0]\\n xval_a, xval_b = xval_a.join(dist),xval_b.join(dist)\\n idx = xval_a.index.intersection(xval_b.index)\\n xval_a,xval_b = xval_a.loc[idx],xval_b.loc[idx]\\n cchalf = []\\n for dmin,dmax in binedges:\\n idx = (xval_a['D'] > dmin) & (xval_a['D'] < dmax)\\n a = np.array(xval_a[idx][key]).flatten()\\n b = np.array(xval_b[idx][key]).flatten()\\n cchalf.append(np.corrcoef(a,b)[0, 1])\\n return cchalf, binedges\",\n \"def geneffcut(energy, array, cutvals=hads, bins=BINS):\\n binning = np.digitize(energy, bins) - 1\\n binning[binning < 0] = 0.\\n binning[binning >= len(bins)-1] = 0.\\n hadeffcut = np.zeros(len(energy), dtype=bool)\\n for i, cutval in enumerate(cutvals):\\n binmask = binning == i\\n hadeffcut[binmask] = array[binmask] < cutval\\n binning = np.digitize(energy, bins) - 1\\n binning[binning < 0] = -1\\n binning[binning >= len(bins)-1] = -1\\n hadeffcut[binning == -1] = 0\\n\\n return hadeffcut\",\n \"def G_bin2d(self, mgs, mnus, g_corr, bins, resample=1, ig_nums=0):\\n # half of the bin number\\n ny, nx = int((len(bins[0]) - 1)/2), int((len(bins[1]) - 1)/2)\\n chi_sq = 0\\n mu = [0, 0]\\n cov = [[abs(2*g_corr), g_corr],\\n [g_corr, abs(2*g_corr)]]\\n data_len = len(mgs[0])\\n for i in range(resample):\\n\\n g_distri = numpy.random.multivariate_normal(mu,cov,data_len)\\n\\n mg1 = mgs[0] - mnus[0]*g_distri[:,0]\\n mg2 = mgs[1] - mnus[1]*g_distri[:,1]\\n num_arr = numpy.histogram2d(mg1, mg2, bins)[0]\\n # | arr_1 | arr_2 |\\n # | arr_3 | arr_4 |\\n # chi square = 0.2*SUM[(arr_2 + arr_3 - arr_1 - arr_4)**2/(arr_1 + arr_2 + arr_3 + arr_4)]\\n arr_1 = num_arr[0:ny, 0:nx][:,range(ny-1,-1,-1)]\\n arr_2 = num_arr[0:ny, nx:2*nx]\\n arr_3 = num_arr[ny:2*ny, 0:nx][range(ny-1,-1,-1)][:,range(nx-1,-1,-1)]\\n arr_4 = num_arr[ny:2*ny, nx:2*nx][range(ny-1,-1,-1)]\\n chi_sq += 0.5 * numpy.sum(((arr_2 + arr_3 - arr_1 - arr_4) ** 2) / (arr_1 + arr_2 + arr_3 + arr_4))\\n\\n return chi_sq/resample\",\n \"def infer_depths_cells_from_edges(self):\\n sel_cells = np.nonzero(np.isnan(self.cells['depth_mean']))[0]\\n # iterate, since number of sides varies\\n edges = self.cells['edges'][sel_cells]\\n edge_depths = self.edges['depth_mean'][edges]\\n edge_depths[edges<0] = -np.inf # to avoid missing edges for triangles\\n \\n self.cells['depth_mean'][sel_cells] = edge_depths.max(axis=1)\\n self.cells['depth_max'][sel_cells] = self.cells['depth_mean'][sel_cells]\",\n \"def find_avg(centroids, short_cut=False, sim_scores=None):\\n \\n total_sim = 0.0\\n total_comparisons = 0\\n \\n if short_cut:\\n total_comparisons = len(sim_scores)\\n \\n for score in sim_scores:\\n total_sim += score\\n \\n return (total_sim / total_comparisons)\\n\\n length = len(centroids)\\n\\n for i in xrange(0, length):\\n for j in xrange(i + 1, length):\\n total_sim += similarity(centroids[i], centroids[j])\\n total_comparisons += 1\\n\\n return (total_sim / total_comparisons)\",\n \"def pcf2_iso_histo(data_location='../../fake_DATA/DATOS/data_500.dat',rand_location='../../fake_DATA/DATOS/rand0_500.dat', d_max=180.0, bins_number=30):\\n \\n data = np.loadtxt(fname=data_location, delimiter=\\\" \\\", usecols=(0,1,2))\\n rand0 = np.loadtxt(fname=rand_location, delimiter=\\\" \\\", usecols=(0,1,2))\\n \\n if not data.shape == rand0.shape:\\n raise Exception(\\\"The data file and rand file do not have the same size\\\")\\n #351 s\\n\\n #Pure histograms\\n start = time.perf_counter()\\n print('start DDD distances')\\n triangle_points = np.array(list(combinations(data,3)))\\n r_12 = triangle_points[:,0,:]-triangle_points[:,1,:]\\n r_12=r_12**2\\n r_12 = r_12[:,0]+r_12[:,1]+r_12[:,2]\\n r_12 = np.sqrt(r_12)\\n\\n r_23 = triangle_points[:,1,:]-triangle_points[:,2,:]\\n r_23 = r_23**2\\n r_23 = r_23[:,0]+r_23[:,1]+r_23[:,2]\\n r_23 = np.sqrt(r_23)\\n\\n r_31 = triangle_points[:,2,:]-triangle_points[:,0,:]\\n r_31 = r_31**2\\n r_31 = r_31[:,0]+r_31[:,1]+r_31[:,2]\\n r_31 = np.sqrt(r_31)\\n\\n DDD, edges = np.histogramdd(np.column_stack((r_12, r_23, r_31)), bins=(bins_number,bins_number,bins_number), range=[[0,d_max],[0,d_max],[0,d_max]])\\n \\n end = time.perf_counter()\\n print(f'Finished creating the DDD histo in {end-start} s')\\n\\n start = time.perf_counter()\\n print('start RRR distances')\\n triangle_points = np.array(list(combinations(rand0,3)))\\n r_12 = triangle_points[:,0,:]-triangle_points[:,1,:]\\n r_12=r_12**2\\n r_12 = r_12[:,0]+r_12[:,1]+r_12[:,2]\\n r_12 = np.sqrt(r_12)\\n\\n r_23 = triangle_points[:,1,:]-triangle_points[:,2,:]\\n r_23 = r_23**2\\n r_23 = r_23[:,0]+r_23[:,1]+r_23[:,2]\\n r_23 = np.sqrt(r_23)\\n\\n r_31 = triangle_points[:,2,:]-triangle_points[:,0,:]\\n r_31 = r_31**2\\n r_31 = r_31[:,0]+r_31[:,1]+r_31[:,2]\\n r_31 = np.sqrt(r_31)\\n\\n RRR, edges = np.histogramdd(np.column_stack((r_12, r_23, r_31)), bins=(bins_number,bins_number,bins_number), range=[[0,d_max],[0,d_max],[0,d_max]])\\n \\n end = time.perf_counter()\\n\\n\\n print(f'Finished creating the RRR histo in {end-start} s')\\n\\n #Mixed histogram\\n start = time.perf_counter()\\n print(\\\"Started gathering the data points pairs\\\")\\n DD_side_points = np.array(list(combinations(data,2)))\\n print(\\\"Finished data points pairs\\\")\\n\\n print(\\\"Started gathering the rand0 points pairs\\\")\\n RR_side_points = np.array(list(combinations(rand0,2)))\\n print(\\\"Finished rand0 points pairs\\\")\\n\\n print(\\\"Started loop for DDR and RRD histograms\\\")\\n \\n DDR = np.zeros((bins_number,bins_number,bins_number))\\n RRD = np.zeros((bins_number,bins_number,bins_number))\\n\\n for data_point, rand_point in zip(data, rand0):\\n ##DDR\\n r_12 = rand_point-DD_side_points[:,0,:]\\n r_12=r_12**2\\n r_12 = r_12[:,0]+r_12[:,1]+r_12[:,2]\\n r_12 = np.sqrt(r_12)\\n\\n r_23 = DD_side_points[:,0,:]-DD_side_points[:,1,:]\\n r_23 = r_23**2\\n r_23 = r_23[:,0]+r_23[:,1]+r_23[:,2]\\n r_23 = np.sqrt(r_23)\\n\\n r_31 = DD_side_points[:,1,:]-rand_point\\n r_31 = r_31**2\\n r_31 = r_31[:,0]+r_31[:,1]+r_31[:,2]\\n r_31 = np.sqrt(r_31)\\n H_DDR, edges = np.histogramdd(np.column_stack((r_12, r_23, r_31)), bins=(bins_number,bins_number,bins_number), range=[[0,d_max],[0,d_max],[0,d_max]])\\n DDR += H_DDR\\n\\n #RRD\\n r_12 = data_point-RR_side_points[:,0,:]\\n r_12=r_12**2\\n r_12 = r_12[:,0]+r_12[:,1]+r_12[:,2]\\n r_12 = np.sqrt(r_12)\\n\\n r_23 = RR_side_points[:,0,:]-RR_side_points[:,1,:]\\n r_23 = r_23**2\\n r_23 = r_23[:,0]+r_23[:,1]+r_23[:,2]\\n r_23 = np.sqrt(r_23)\\n\\n r_31 = RR_side_points[:,1,:]-data_point\\n r_31 = r_31**2\\n r_31 = r_31[:,0]+r_31[:,1]+r_31[:,2]\\n r_31 = np.sqrt(r_31)\\n H_RRD, edges = np.histogramdd(np.column_stack((r_12, r_23, r_31)), bins=(bins_number,bins_number,bins_number), range=[[0,d_max],[0,d_max],[0,d_max]])\\n RRD += H_RRD\\n \\n DDR = DDR/3\\n RRD = RRD/3\\n end = time.perf_counter()\\n print(f'Finished the mixed histograms DDR an RRD in {end-start} s')\\n\\n return RRR, DDD, DDR, RRD, edges\",\n \"def peakDistances(self,thresholds,norm=False):\\n\\n\\t\\t#Locate peaks first\\n\\t\\theight,loc = self.locatePeaks(thresholds,norm)\\n\\n\\t\\t#Measure pairwise distances\\n\\t\\tdistances = np.sqrt(((loc[None]-loc[:,None])**2).sum(-1))\\n\\t\\ti,j = np.indices(distances.shape)\\n\\n\\t\\t#Return\\n\\t\\treturn distances[i>j]\",\n \"def allele_freqs(self):\\n diploid = self.geno.sum(2) * 0.5\\n return np.nanmean(diploid, axis = 0)\",\n \"def w_hungarian(prev_centroids,next_centroids,max_distance=50):\\n xs0,ys0 = prev_centroids\\n xs1,ys1 = next_centroids\\n\\n cost_matrix = fillCostMatrix(xs0,ys0,xs1,ys1)\\n #make it disatvantageous to select any distance >max_distance\\n cost_matrix[cost_matrix>(max_distance**2)]=np.max(cost_matrix)\\n cost_matrix[cost_matrix==-1]=np.max(cost_matrix)\\n xs,ys = linear_sum_assignment(cost_matrix)\\n \\n correspondance_list=[]\\n for i in range(xs.size):\\n correspondance_list.append( (xs[i],ys[i]) )\\n apparition_list = [] \\n elements_to_remove = [] \\n for i, coords in enumerate(correspondance_list):\\n if cost_matrix[coords]>max_distance**2:\\n if coords[0]<len(xs0): #the left element exists\\n correspondance_list[i] = (coords[0],-1)\\n else: #the left element does not exists\\n elements_to_remove.append(i)\\n if coords[1]<len(xs1): #Add the right element only if it exists.\\n apparition_list.append((-1,coords[1]))\\n for j in range(len(elements_to_remove)):\\n correspondance_list.pop(elements_to_remove[-(j+1)])\\n correspondance_list.extend(apparition_list)\\n return correspondance_list\"\n]","isConversational":false},"negative_scores":{"kind":"list like","value":["0.5350398","0.52518773","0.5155675","0.5135908","0.50542927","0.502655","0.5009027","0.5004774","0.4977624","0.49264264","0.48782477","0.48724344","0.4847657","0.4825151","0.48010293","0.4734856","0.47299528","0.47192916","0.47074535","0.46959183","0.46754423","0.46545872","0.46416897","0.4636898","0.46058112","0.45979014","0.45874825","0.456804","0.45655823","0.45610264","0.45517734","0.45478332","0.45352226","0.45322764","0.45240256","0.45191884","0.45190036","0.4518538","0.45097876","0.44687754","0.44652903","0.444843","0.44432825","0.44400352","0.44324452","0.44225916","0.4420125","0.4418774","0.44157007","0.4414801","0.44117016","0.4403292","0.43985155","0.43962395","0.43956894","0.439221","0.43883038","0.43878278","0.43852934","0.43819237","0.43712634","0.43703896","0.43674788","0.43669394","0.43623555","0.43603134","0.4344003","0.4340672","0.4338959","0.43381143","0.43317723","0.4329995","0.4300097","0.42973238","0.42933863","0.42793792","0.427205","0.42674923","0.4265479","0.4263881","0.4257508","0.42560473","0.42492452","0.42464107","0.4246083","0.42439243","0.42431176","0.42411935","0.42411208","0.4238003","0.42324418","0.42322516","0.42267075","0.42255858","0.42247528","0.422451","0.4222898","0.42220238","0.4215527","0.42136177"],"string":"[\n \"0.5350398\",\n \"0.52518773\",\n \"0.5155675\",\n \"0.5135908\",\n \"0.50542927\",\n \"0.502655\",\n \"0.5009027\",\n \"0.5004774\",\n \"0.4977624\",\n \"0.49264264\",\n \"0.48782477\",\n \"0.48724344\",\n \"0.4847657\",\n \"0.4825151\",\n \"0.48010293\",\n \"0.4734856\",\n \"0.47299528\",\n \"0.47192916\",\n \"0.47074535\",\n \"0.46959183\",\n \"0.46754423\",\n \"0.46545872\",\n \"0.46416897\",\n \"0.4636898\",\n \"0.46058112\",\n \"0.45979014\",\n \"0.45874825\",\n \"0.456804\",\n \"0.45655823\",\n \"0.45610264\",\n \"0.45517734\",\n \"0.45478332\",\n \"0.45352226\",\n \"0.45322764\",\n \"0.45240256\",\n \"0.45191884\",\n \"0.45190036\",\n \"0.4518538\",\n \"0.45097876\",\n \"0.44687754\",\n \"0.44652903\",\n \"0.444843\",\n \"0.44432825\",\n \"0.44400352\",\n \"0.44324452\",\n \"0.44225916\",\n \"0.4420125\",\n \"0.4418774\",\n \"0.44157007\",\n \"0.4414801\",\n \"0.44117016\",\n \"0.4403292\",\n \"0.43985155\",\n \"0.43962395\",\n \"0.43956894\",\n \"0.439221\",\n \"0.43883038\",\n \"0.43878278\",\n \"0.43852934\",\n \"0.43819237\",\n \"0.43712634\",\n \"0.43703896\",\n \"0.43674788\",\n \"0.43669394\",\n \"0.43623555\",\n \"0.43603134\",\n \"0.4344003\",\n \"0.4340672\",\n \"0.4338959\",\n \"0.43381143\",\n \"0.43317723\",\n \"0.4329995\",\n \"0.4300097\",\n \"0.42973238\",\n \"0.42933863\",\n \"0.42793792\",\n \"0.427205\",\n \"0.42674923\",\n \"0.4265479\",\n \"0.4263881\",\n \"0.4257508\",\n \"0.42560473\",\n \"0.42492452\",\n \"0.42464107\",\n \"0.4246083\",\n \"0.42439243\",\n \"0.42431176\",\n \"0.42411935\",\n \"0.42411208\",\n \"0.4238003\",\n \"0.42324418\",\n \"0.42322516\",\n \"0.42267075\",\n \"0.42255858\",\n \"0.42247528\",\n \"0.422451\",\n \"0.4222898\",\n \"0.42220238\",\n \"0.4215527\",\n \"0.42136177\"\n]","isConversational":false},"document_score":{"kind":"string","value":"0.6471979"},"document_rank":{"kind":"string","value":"0"}}},{"rowIdx":22680,"cells":{"query":{"kind":"string","value":"Returns lists of detectable indices after excluding low interacting bins based on the proportion of zero pixel values in the matrix bins."},"document":{"kind":"string","value":"def get_detectable_bins(mat, n_mads=3, inter=False):\n matrix = mat.copy()\n matrix.eliminate_zeros()\n\n def mad(x): return ss.median_abs_deviation(x, nan_policy=\"omit\")\n\n if not inter:\n if matrix.shape[0] != matrix.shape[1]:\n raise ValueError(\"Intrachromosomal matrices must be symmetric.\")\n # Replace nonzero pixels by ones to work on prop. of nonzero pixels\n matrix.data = np.ones(matrix.data.shape)\n # Compute number of nonzero values in each bin\n sum_bins = sum_mat_bins(matrix)\n # Compute variation in the number of nonzero pixels\n sum_mad = mad(sum_bins)\n # Find poor interacting rows and columns\n sum_med = np.median(sum_bins)\n detect_threshold = max(1, sum_med - sum_mad * n_mads)\n\n # Removal of poor interacting rows and columns\n good_bins = np.flatnonzero(sum_bins >= detect_threshold)\n good_bins = (good_bins, good_bins)\n else:\n # Adapted for asymetric matrices (need to compute rows and columns)\n sum_rows, sum_cols = matrix.sum(axis=1).A1, matrix.sum(axis=0).A1\n mad_rows, mad_cols = mad(sum_rows), mad(sum_cols)\n med_rows, med_cols = np.median(sum_rows), np.median(sum_cols)\n detect_threshold_rows = max(1, med_rows - mad_rows * n_mads)\n detect_threshold_cols = max(1, med_cols - mad_cols * n_mads)\n good_rows = np.flatnonzero(sum_rows > detect_threshold_rows)\n good_cols = np.flatnonzero(sum_cols > detect_threshold_cols)\n good_bins = (good_rows, good_cols)\n return good_bins"},"metadata":{"kind":"string","value":"{\n \"objective\": {\n \"self\": [],\n \"paired\": [],\n \"triplet\": [\n [\n \"query\",\n \"document\",\n \"negatives\"\n ]\n ]\n }\n}"},"negatives":{"kind":"list like","value":["def vir_indices(self):\n indices = []\n for index,item in enumerate(self):\n if item==0:\n indices.append(index)\n return indices","def get_agent_indices(array):\t\n\tagent_indices = np.argwhere(array != 0)\n\treturn agent_indices","def pick_nonzero_bin(binning, r_idx, phi_idx, dom_radius, speed_of_light):\n r0 = binning.r.midpoints[r_idx].m_as('m')\n dr = r0 - dom_radius # m\n t0 = dr / speed_of_light * 1e9 # ns\n phi0 = binning.phi.midpoints[phi_idx].m_as('rad')\n phidir0 = (phi0 + np.pi) % (2*np.pi)\n\n point = np.array([[r0, phi0, t0, phidir0]])\n\n hist, _ = np.histogramdd(\n point,\n bins=binning.bin_edges,\n normed=False\n )\n indices = tuple(c[0] for c in np.where(hist == 1))\n return binning[indices], indices","def conceptcover(bin_arr, limit=1, uncovered=0.1):\n arr = np.copy(bin_arr)\n arr_sum = np.sum(arr)\n result = []\n while True:\n k = kernel(arr)\n i = intent(bin_arr, k)\n e = extent(bin_arr, i)\n if len(e)*len(i) < limit or (e, i) in result: break\n result.append((e, i))\n arr = removed(arr, e, i)\n if np.sum(arr)/arr_sum < uncovered: break\n return result","def get_indices(waves):\n prob_ = np.abs(waves)**2\n # batch\n prob = [np.sum(prob_[i:i+4,:], axis=0) for i in range(0, len(waves[:,0]), 4)]\n prob = np.asarray(prob)\n prob_tot = np.sum(prob, axis=0)\n \n # cutoff\n length = np.size(prob[:,0])\n len10 = int(length/10)\n flags = np.zeros((prob.shape[1]), dtype=int)\n # hinges\n # 50% within 10% of corners\n\n # surface\n # 50% within 10% of surfaces\n # not already labelled hinges\n prob_left = np.sum(prob[0:len10,:], axis=0)\n frac_left = prob_left/prob_tot\n\n prob_right = np.sum(prob[length-len10:length,:], axis=0)\n frac_right = np.divide(prob_right, prob_tot)\n\n for i in range(len(flags)):\n if frac_left[i]>0.5 or frac_right[i]>0.5:\n flags[i] = 1\n \n indices = [i for i, x in enumerate(flags) if x == 1]\n indices0 = [i for i, x in enumerate(flags) if x == 0]\n \n return indices, indices0","def get_5index_list(self):\n msk = self.load_mask()\n return [i for (i,v) in enumerate(msk) if v==0]","def get_vacancy_indices(array):\t\n\tvacancy_indices = np.argwhere(array == 0)\n\treturn vacancy_indices","def non_masked_indices(mask):\n\treturn np.nonzero(np.ravel(mask-1,order='C'))[0]","def masked_indices(mask):\n\treturn np.nonzero(np.ravel(mask,order='C'))[0]","def get_active_register_indices(self):\n assert self.sketch.ndim == 1, 'Currently only support 1-dimensional sketch.'\n return np.flatnonzero(self.sketch)","def cell_detects(self, masked=False):\r\n grd = self.grd\r\n ncells = len(grd.cells['depth'])\r\n detects_i_tr = np.zeros(ncells, np.int32)\r\n if masked:\r\n not_flagged = np.where(self.rec_track.flagged==0)[0]\r\n rec_track = self.rec_track[not_flagged]\r\n else:\r\n rec_track = self.rec_track\r\n ndetects = len(rec_track)\r\n for nd in range(ndetects):\r\n tr = rec_track[nd]\r\n i = tr.i\r\n if i >= 0:\r\n detects_i_tr[i] += 1\r\n \r\n return detects_i_tr","def _filter_imgs(self, min_size=32):\n\n valid_inds = []\n for i, img_info in enumerate(self.data_infos):\n if min(img_info[\"width\"], img_info[\"height\"]) < min_size:\n continue\n if self.filter_empty_gt and len(img_info[\"ann\"][\"bboxes\"]) > 0:\n valid_inds.append(i)\n else:\n valid_inds.append(i)\n\n return valid_inds","def noisy_cells(self, hists, thresholds):\n return [[[x + 1, z + 1, i + 1] for x in range(h.GetNbinsX()) for z in range(h.GetNbinsY()) if h.GetBinContent(x + 1, z + 1) > threshold] for i, (h, threshold) in enumerate(zip(hists, thresholds))]","def get_34index_list(self):\n msk = self.load_mask()\n return [i for (i,v) in enumerate(msk) if v==1]","def occ_indices(self):\n indices = []\n for index,item in enumerate(self):\n if item==1:\n indices.append(index)\n return indices","def _filter_imgs(self, min_size=32):\n valid_inds = []\n for i, img_info in enumerate(self.img_infos):\n # Filter out empty images\n if img_info['ann']['bboxes'].shape[0] > 0:\n if min(img_info['width'], img_info['height']) >= min_size:\n valid_inds.append(i)\n return valid_inds","def cell_occupancy(self, masked=False):\r\n grd = self.grd\r\n occupancy_i = []\r\n if masked:\r\n not_flagged = np.where(self.rec_track.flagged==0)[0]\r\n rec_track = self.rec_track[not_flagged]\r\n else:\r\n rec_track = self.rec_track\r\n ndetects = len(rec_track)\r\n for nd in range(ndetects):\r\n tr = rec_track[nd]\r\n i = tr.i\r\n if i >= 0:\r\n occupancy_i.append(i)\r\n\r\n occupancy_i = np.unique(occupancy_i)\r\n\r\n return occupancy_i","def get_biomass(binary_mask):\n\n white_pixels = cv2.countNonZero(binary_mask)\n return white_pixels","def kernel(bin_arr):\n mf = minusframe(bin_arr)\n mf.sort('i', inplace=True, ascending=False) # prefer smaller extents\n max_wi = mf['w'].idxmax()\n max_i = mf.loc[max_wi, 'i']\n return mf[mf['i'] >= max_i].index","def compute_bin_indices(X_part, bin_limits=None, n_bins=20):\n if bin_limits is None:\n bin_limits = []\n for variable_data in range(X_part.shape[1]):\n bin_limits.append(numpy.linspace(numpy.min(variable_data), numpy.max(variable_data), n_bins + 1)[1: -1])\n\n bin_indices = numpy.zeros(len(X_part), dtype=numpy.int)\n for axis, bin_limits_axis in enumerate(bin_limits):\n bin_indices *= (len(bin_limits_axis) + 1)\n bin_indices += numpy.searchsorted(bin_limits_axis, X_part[:, axis])\n\n return bin_indices","def get_nucleus_ids(img):\n\n values = np.unique(img)\n values = values[values > 0]\n\n return values","def _filter_imgs(self, min_size=32):\r\n valid_inds = []\r\n ids_with_ann = set(_['image_id'] for _ in self.coco.anns.values())\r\n for i, img_info in enumerate(self.img_infos):\r\n if self.filter_empty_gt and self.img_ids[i] not in ids_with_ann:\r\n continue\r\n if min(img_info['width'], img_info['height']) >= min_size:\r\n valid_inds.append(i)\r\n return valid_inds","def _get_index_lists(self, mat):\n n_row, n_col = mat.shape\n \n col_ind_at_row, row_ind_at_col = [],[]\n for i in range(n_row):\n aux_ind = _np.where(mat[i]>0)[0]\n if len(aux_ind) == 0:\n raise Exception('Row {} is composed of zeros'.format(i))\n col_ind_at_row.append(aux_ind)\n \n for j in range(n_col):\n aux_ind = _np.where(mat[:,j]>0)[0]\n if len(aux_ind) == 0:\n raise Exception('Column {} is composed of zeros'.format(j))\n \n row_ind_at_col.append(aux_ind)\n \n return col_ind_at_row, row_ind_at_col","def get_unprescribed_indexes(self):\n total_dof = DOF_PER_NODE_STRUCTURAL * len(self.preprocessor.nodes)\n all_indexes = np.arange(total_dof)\n return np.delete(all_indexes, self.prescribed_indexes)","def immed_nbrs(tile):\n return [nbr for nbr in [TILES_BY_IDX.get(xy) for xy in xy_nbrs(tile['xyidx'])]\n if nbr is not None]","def get_cont_indices(self, X, max_ord):\n indices = np.zeros(X.shape[1]).astype(bool)\n for i, col in enumerate(X.T):\n col_nonan = col[~np.isnan(col)]\n col_unique = np.unique(col_nonan)\n if len(col_unique) > max_ord:\n indices[i] = True\n return indices","def get_indications(self):\n indications = np.zeros_like(self.predictions)\n for i in range(self.predictions.shape[0]):\n ind = np.where(self.predictions[i, :] - self.labels != 0.0)[0]\n indications[i, ind] = 1.0\n\n return indications","def _filter_imgs(self, min_size=32):\n valid_inds = []\n ids_with_ann = set(_['image_id'] for _ in self.coco.anns.values())\n for i, img_info in enumerate(self.img_infos):\n if self.img_ids[i] not in ids_with_ann:\n continue\n if min(img_info['width'], img_info['height']) >= min_size:\n valid_inds.append(i)\n return valid_inds","def _filter_imgs(self, min_size=32):\n valid_inds = []\n ids_with_ann = set(_['image_id'] for _ in self.coco.anns.values())\n for i, img_info in enumerate(self.img_infos):\n if self.img_ids[i] not in ids_with_ann:\n continue\n if min(img_info['width'], img_info['height']) >= min_size:\n valid_inds.append(i)\n return valid_inds","def _filter_imgs(self, min_size=32):\n valid_inds = []\n ids_with_ann = set(_['image_id'] for _ in self.coco.anns.values())\n for i, img_info in enumerate(self.img_infos):\n if self.img_ids[i] not in ids_with_ann:\n continue\n if min(img_info['width'], img_info['height']) >= min_size:\n valid_inds.append(i)\n return valid_inds","def index_col(self, i0, i1, j0, j1):\n edges = self.h5['indexes']['bin1_offset'][i0:i1 + 1]\n index = []\n for lo1, hi1 in zip(edges[:-1], edges[1:]):\n if hi1 - lo1 > 0:\n bin2 = self.h5['pixels']['bin2_id'][lo1:hi1]\n mask = (bin2 >= j0) & (bin2 < j1)\n index.append(lo1 + np.flatnonzero(mask))\n if not index:\n return np.array([], dtype=int)\n else:\n return np.concatenate(index, axis=0)","def nondetects(self, masked=False):\r\n grd = self.grd\r\n xnd = []\r\n ynd = []\r\n ncells = len(grd.cells['depth'])\r\n non_detects_i_tr = np.zeros(ncells, np.int32)\r\n if masked:\r\n not_flagged = np.where(self.rec_track.flagged==0)[0]\r\n rec_track = self.rec_track[not_flagged]\r\n rec_seg = self.make_segments(set_depth=True, \r\n input_rec_track=rec_track)\r\n else:\r\n rec_seg = self.rec_seg\r\n for nr, rseg in enumerate(rec_seg):\r\n seg = rec_seg[nr]\r\n dt = seg.dt\r\n if dt > dt_signal+1:\r\n t1 = seg.t1\r\n t2 = seg.t2\r\n nint = int(np.rint((t2-t1)/dt_signal)) - 1\r\n x1 = seg.x1\r\n x2 = seg.x2\r\n y1 = seg.y1\r\n y2 = seg.y2\r\n dx_nd = (x2 - x1)/float(nint+1)\r\n dy_nd = (y2 - y1)/float(nint+1)\r\n if nint < 120: # 10 minute cutoff for nondetect filling\r\n xint = [x1 + n*dx_nd for n in range(1,nint)]\r\n yint = [y1 + n*dy_nd for n in range(1,nint)]\r\n xnd = xnd + xint\r\n ynd = ynd + yint\r\n\r\n for nd in range(len(xnd)):\r\n xy = [xnd[nd], ynd[nd]]\r\n i = grd.select_cells_nearest(xy)\r\n if (i is not None) and (i >= 0):\r\n non_detects_i_tr[i] += 1\r\n\r\n return non_detects_i_tr","def get_neighbours(self):\n shape=self.cubeshape[1:]\n neighboursx=np.arange(self.xpos-(self.blocksize-1)/2,(self.xpos+(self.blocksize-1)/2)+1,dtype='int' )\n neighboursx=[x if (x>=0) & (x<=shape[1]-1) else np.nan for x in neighboursx ]\n neighboursy=np.arange(self.ypos-(self.blocksize-1)/2,(self.ypos+(self.blocksize-1)/2)+1,dtype='int' )\n neighboursy=[y if (y>=0) & (y<=shape[0]-1) else np.nan for y in neighboursy ]\n keys=[np.ravel_multi_index([y,x], shape) if np.all(np.isfinite(np.asarray([y,x]))) else np.nan for y in neighboursy for x in neighboursx]\n\n return keys","def filter_halo_pnum(data, Ncut=1000):\n npart = np.array(data['np'][0])\n ind =np.where(npart > Ncut)[0]\n print(\"# of halos:\",len(ind))\n return ind","def getNeighbours(seg,meta,inversedIndex):\n return np.unique(np.fromiter( (inversedIndex[x] for x in np.concatenate([meta.loc[seg]['ins'],meta.loc[seg]['outs']])),dtype=np.int))","def __getStableConcepts(weight_matrix: Union[np.ndarray, pd.DataFrame]) -> np.ndarray:\n stables = []\n for i in range(len(weight_matrix)):\n if np.all(weight_matrix[i] == 0):\n stables.append(i)\n\n return stables","def get_idxes_from_mask(mask):\n if len(mask) > 1:\n return torch.nonzero(mask.squeeze(), as_tuple=False).reshape(1, -1)[0]\n elif len(mask) == 1:\n return torch.tensor([0], dtype=torch.int64) if mask.sum() == 1 else torch.tensor([], dtype=torch.int64)\n return torch.tensor([], dtype=torch.int64)","def bin_indices(self, coordinates, fractional=True):\n coords = numpy.asarray(coordinates).transpose()\n indices = [numpy.interp(coo, cen, range(n))\n for (coo, cen, n) in zip(coords, self.centers, self.shape)]\n index_arr = numpy.atleast_2d(numpy.array(indices).transpose())\n if fractional:\n return index_arr\n return numpy.floor(index_arr + 0.5).astype(numpy.int_)","def get_detector_faces_from_pixel_mapping(\n mapping: PixelMapping,\n) -> List[Tuple[int, int]]:\n detector_faces: List[Tuple[int, int]] = []\n idx: Tuple[int, int]\n for idx in enumerate(mapping.pixel_ids):\n if idx[1] is not None:\n detector_faces.append((idx[0], idx[1]))\n\n return detector_faces","def filter_positive_detections(detections):\n class_idx = 0\n assert(isinstance(detections, mx.nd.NDArray) or isinstance(detections, np.ndarray))\n detections_per_image = []\n # for each image\n for i in range(detections.shape[0]):\n result = []\n det = detections[i, :, :]\n for obj in det:\n if obj[class_idx] >= 0:\n result.append(obj)\n detections_per_image.append(result)\n logging.info(\"%d positive detections\", len(result))\n return detections_per_image","def get_possible_indexes(self, factors_lock, random_state):\n factor_list = [[]]\n for i, lock_val in enumerate(factors_lock):\n ## if unlocked, number of possible factor-samples are multiplied by the number of possible unlocked factor values\n if lock_val == -1 : \n base = factor_list\n factor_list = []\n for b in base:\n bc = b.copy()\n for f in range(self.factor_sizes[i]):\n bc.append(f)\n factor_list.append(bc)\n bc = b.copy()\n \n else: ## if factor is locked, just append the locked factor value\n for i in range(len(factor_list)):\n factor_list[i].append(lock_val)\n #transform to index\n possible_indexes = self._features_to_state_space_index(np.asarray(factor_list)) \n return possible_indexes","def intermodes(I):\n n = 255\n y = np.histogram(I.ravel(), bins=n+1, range=(0, n))[0].astype(float)\n iter = 0\n while not bimodtest(y):\n h = np.ones(3) / 3\n y = np.convolve(y, h, mode='same')\n iter += 1\n if iter > 10000:\n return 0\n\n TT = []\n for k in range(1, n - 1):\n if y[k - 1] < y[k] > y[k + 1]:\n TT.append(k)\n return int(np.floor(np.mean(TT)))","def indices(self):\n\n # We used lookup tables here. Read more about other methods here:\n # https://chessprogramming.wikispaces.com/Bitboard+Serialization\n\n if self.num == 0:\n return []\n\n bits = []\n\n for i in [0, 1, 2, 3, 4, 5, 6, 7]:\n row = (self.num >> UINT64_PADDING[i]) & EIGHT_ONES\n indices = row_to_indices[row]\n for index in indices:\n bits.append(index + i*8)\n\n return bits","def find_excited_locations(self):\n return np.asarray(np.where(self._grid == 8)).T","def get_detector_number_from_pixel_mapping(\n mapping: PixelMapping,\n) -> List[int]:\n detector_numbers = [id for id in mapping.pixel_ids if id is not None]\n return detector_numbers","def extract_pixels(img_):\n non_zero_pixels = np.argwhere(0 < img_)\n x = non_zero_pixels.T[0].astype(np.float32)\n y = non_zero_pixels.T[1].astype(np.float32)\n return x, y","def _find_neighbours(self):\n\n neighbours = []\n for i, p in enumerate(self.frame_0):\n nearests = np.where(np.linalg.norm(self.frame_0 - p, axis=1) <= self.R_n)[0]\n # delete self index\n index = np.argwhere(nearests==i)\n nearests = np.delete(nearests, index)\n neighbours.append(nearests)\n\n return neighbours","def hist_and_thresh(self):\n bins, occ, _ = self.histogram()\n self.thresh = np.mean(bins) # initial guess\n self.peaks_and_thresh() # in case peak calculation fails\n # if np.size(self.peak_indexes) == 2: # est_param will only find one peak if the number of bins is small\n # # set the threshold where the fidelity is max\n # self.search_fidelity(self.peak_centre[0], self.peak_widths[0] ,self.peak_centre[1])\n try: \n thresh = threshold_minimum(np.array(self.stats['Counts']), len(bins))\n int(np.log(thresh)) # if thresh <= 0 this gives ValueError\n self.thresh = thresh\n except (ValueError, RuntimeError, OverflowError): pass\n try:\n # atom is present if the counts are above threshold\n self.stats['Atom detected'] = [x // self.thresh for x in self.stats['Counts']]\n # self.fidelity, self. err_fidelity = np.around(self.get_fidelity(), 4) # this is a relatively slow operation\n except (ValueError, OverflowError): pass\n return bins, occ, self.thresh","def detect(self, mask):\n # 1) Return Non zero indices\n det_idx = np.where(mask > 0.0)\n idx_x, idx_y = det_idx[0], det_idx[1]\n # 2) Create 1x1 box for each pixel detected.\n detections = []\n for i in range(0, len(idx_x)):\n x, y = idx_x[i], idx_y[i]\n detections.append((x, y, x+1, y+1, 1)) # x1, y1, x2, y2, area\n # 3) merge boxes\n bounding_boxes = self.bounding_boxes(detections)\n return bounding_boxes","def get_flagged_neighbours(self, row, col):\n return [cell for cell in self.get_neighbours(row, col) if cell.state == 'X']","def get_energy_bins(image, num_bins=4):\r\n h, _ = image.shape\r\n C = np.linspace(0, h//2, num=num_bins+1, dtype=\"int\")\r\n # Create a list of dense square mask\r\n mask_list = [square_mask(image, c) for c in C[1:]]\r\n # Extract a list of hollow mask\r\n square_zones = [~mask_list[k]*mask_list[k+1] for k in range(len(mask_list)-1)]\r\n # add center square and whole image square to the list\r\n square_zones = [mask_list[0]] + square_zones + [mask_list[-1]]\r\n # Apply these masks to the image\r\n image_bins = []\r\n for zone in square_zones:\r\n img = image.copy()\r\n img[~zone] = np.nan\r\n image_bins.append(img)\r\n # Compute the energy of each masked image\r\n energy_bins = [np.nansum(_bin) for _bin in image_bins]\r\n total_energy = energy_bins[-1]\r\n # normalize the energy repartition\r\n energy_bins /= total_energy\r\n return energy_bins[:-1], image_bins[:-1]","def get_marginal_indexes(actions, mode):\n vi = []\n for v in range(actions[mode].max()+1):\n vals = actions[actions[mode] == v].index.values\n if len(vals) > 0:\n vi.append(vals)\n else:\n vi.append(np.array([0]))\n return vi","def filter_binaries_beamprofile(bin_arr, beamprofile, cutoff=0.75, dilate=0):\r\n bp_bool = beamprofile < cutoff * beamprofile.max()\r\n out_binary = np.empty_like(bin_arr, dtype=int)\r\n total_cells = 0\r\n removed_cells = 0\r\n\r\n for i, img in enumerate(bin_arr):\r\n labeled, n = mh.labeled.label(img)\r\n total_cells += n\r\n for l in np.unique(labeled)[1:]:\r\n selected_binary = multi_dilate(labeled == l, dilate)\r\n if np.any(np.logical_and(selected_binary, bp_bool)): # Cell lies outside of\r\n labeled[labeled == l] = 0\r\n removed_cells += 1\r\n out_binary[i] = labeled\r\n print('Removed {} cells out of a total of {} cells.'.format(removed_cells, total_cells))\r\n return out_binary","def contiguified_channels(chs):\n orig=chs.copy()-np.min(chs)\n counts=np.bincount(orig)\n #print(np.argwhere(counts==0))\n cumul=np.cumsum(np.where(counts==0, 0, 1))\n return np.array([cumul[i] for i in orig])","def bins_from_image(path):\n _, Y = util.imread(path)\n closest_bins = util.closest_bins(Y).flatten()\n count = np.zeros(len(util.bins))\n for closest_bin in closest_bins:\n count[closest_bin] += 1\n return count","def _free_indicies(self):\n return np.logical_not(self._fixed_indicies)","def _get_optimal_threshold(arr, num_bins=1001, num_quantized_bins=255):\n if not isinstance(arr, np.ndarray):\n raise TypeError('get_optimal_threshold only supports input type of np.ndarray,'\n ' while received type=%s' % (str(type(arr))))\n min_val = np.min(arr)\n max_val = np.max(arr)\n th = max(abs(min_val), abs(max_val))\n\n hist, hist_edges = np.histogram(arr, bins=num_bins, range=(-th, th))\n zero_bin_idx = num_bins // 2\n num_half_quantized_bins = num_quantized_bins // 2\n assert np.allclose(hist_edges[zero_bin_idx] + hist_edges[zero_bin_idx + 1],\n 0, rtol=1e-5, atol=1e-7)\n\n thresholds = np.zeros(num_bins // 2 + 1 - num_quantized_bins // 2)\n divergence = np.zeros_like(thresholds)\n quantized_bins = np.zeros(num_quantized_bins, dtype=np.int32)\n # i means the number of bins on half axis excluding the zero bin.\n for i in range(num_quantized_bins // 2,\n num_bins // 2 + 1):\n p_bin_idx_start = zero_bin_idx - i\n p_bin_idx_stop = zero_bin_idx + i + 1\n thresholds[i - num_half_quantized_bins] = hist_edges[p_bin_idx_stop]\n sliced_nd_hist = hist[p_bin_idx_start:p_bin_idx_stop]\n\n # generate reference distribution p\n p = sliced_nd_hist.copy()\n assert p.size % 2 == 1\n assert p.size >= num_quantized_bins\n # put left outlier count in p[0]\n left_outlier_count = np.sum(hist[0:p_bin_idx_start])\n p[0] += left_outlier_count\n # put right outlier count in p[-1]\n right_outlier_count = np.sum(hist[p_bin_idx_stop:])\n p[-1] += right_outlier_count\n # is_nonzeros[k] indicates whether hist[k] is nonzero\n is_nonzeros = (sliced_nd_hist != 0).astype(np.int32)\n\n # calculate how many bins should be merged to generate quantized distribution q\n num_merged_bins = p.size // num_quantized_bins\n # merge hist into num_quantized_bins bins\n for j in range(num_quantized_bins):\n start = j * num_merged_bins\n stop = start + num_merged_bins\n quantized_bins[j] = sliced_nd_hist[start:stop].sum()\n quantized_bins[-1] += sliced_nd_hist[num_quantized_bins * num_merged_bins:].sum()\n # expand quantized_bins into p.size bins\n q = np.zeros(p.size, dtype=np.float32)\n for j in range(num_quantized_bins):\n start = j * num_merged_bins\n if j == num_quantized_bins - 1:\n stop = -1\n else:\n stop = start + num_merged_bins\n norm = is_nonzeros[start:stop].sum()\n if norm != 0:\n q[start:stop] = float(quantized_bins[j]) / float(norm)\n q[sliced_nd_hist == 0] = 0\n p = _smooth_distribution(p)\n # There is a chance that q is an invalid probability distribution.\n try:\n q = _smooth_distribution(q)\n except ValueError:\n divergence[i - num_half_quantized_bins] = float(\"inf\")\n else:\n divergence[i - num_half_quantized_bins] = stats.entropy(p, q)\n quantized_bins[:] = 0\n\n min_divergence_idx = np.argmin(divergence)\n min_divergence = divergence[min_divergence_idx]\n opt_th = thresholds[min_divergence_idx]\n return min_val, max_val, min_divergence, opt_th","def find_halos(pos, ngrid, log, level=3000):\n print('Binning particles', file=log)\n cells = get_cells(pos, ngrid, log)\n count = bincount(cells, minlength=ngrid**3)\n count.shape = (ngrid,ngrid,ngrid)\n print('Count in', count.min(), count.max(), file=log)\n idx = flatnonzero(count>level)\n print('Number of cells above', level, 'is', len(idx), file=log)\n \n \n labels, num_features = ndimage.label(count>level)\n print('Number fo features', num_features, file=log)\n print('Labels in', labels.min(), labels.max(), file=log)\n locations = ndimage.find_objects(labels)\n\n dense_regions = []\n\n for i in range(num_features):\n loc = locations[i]\n hw = max(l.stop - l.start for l in loc) * 0.5 /ngrid\n hw_padded = hw + 0.0/ngrid\n\n ctr =[(0.5/ngrid)*(l.stop + l.start) for l in loc]\n count_i = count[loc][labels[loc]==(i+1)].sum()\n print('Count', count_i, file=log)\n dense_regions.append((count_i, ctr, hw_padded))\n\n # sort by number of particles in the region\n dense_regions = sorted(dense_regions, key = lambda num_ctr_hw :num_ctr_hw[0], reverse=True)\n\n return dense_regions","def computeTopSurfaceIndices(top):\n itop = np.array([(top[i,j], j, i) \\\n for i in range(top.shape[0]) \\\n for j in range(top.shape[1]) \\\n if top[i,j] >= 0])\n return itop","def get_bin_color_features(img, p):\n return cv2.resize(img, p.spatial_size).ravel()","def clean_detections(npts, on_off):\n on = on_off[:,0]\n off = on_off[:,1]\n idx_on = [on[0]]\n idx_off = [off[0]]\n lowest_idx = on[0]\n\n for ion, ioff in zip(on, off):\n if ion > lowest_idx + npts:\n idx_on.append(ion)\n idx_off.append(ioff)\n lowest_idx = ion\n\n return np.asarray((idx_on, idx_off)).T","def bin_thresh(img: np.ndarray, thresh: Number) -> np.ndarray:\n res = img >= thresh\n return res","def findNeighbours(self):\n neighbours = []\n\n for i in range(self.xCoordinate - 1, self.xCoordinate + 2):\n for j in range(self.yCoordinate - 1, self.yCoordinate + 2):\n if (not (i == self.xCoordinate and j == self.yCoordinate)) and (0 <= i <= 394 and 0 <= j <= 499):\n neighbours.append(PixelPosition(i, j))\n\n return neighbours","def get_none_zero_region(im, margin):\n input_shape = im.shape\n if(type(margin) is int ):\n margin = [margin]*len(input_shape)\n assert(len(input_shape) == len(margin))\n indxes = np.nonzero(im)\n idx_min = []\n idx_max = []\n for i in range(len(input_shape)):\n idx_min.append(indxes[i].min())\n idx_max.append(indxes[i].max())\n\n for i in range(len(input_shape)):\n idx_min[i] = max(idx_min[i] - margin[i], 0)\n idx_max[i] = min(idx_max[i] + margin[i], input_shape[i] - 1)\n return idx_min, idx_max","def count_oob(cube):\n out_range = (cube > 4).any(1) | (cube < -4).any(1)\n out_range = out_range.sum() / cube.shape[0]\n return out_range","def filter_by_size(img_segm,mini_nb_pix):\n numbers = np.zeros(int(np.max(img_segm)))\n for i in range(1,int(np.max(img_segm))+1):\n numbers[i-1] = np.count_nonzero(img_segm==i)\n \n indexes = np.arange(1,np.max(img_segm)+1)\n #indexes = indexes[numbers>np.mean(numbers)] #Deletes the 1-pixel elements\n indexes = indexes[numbers>mini_nb_pix] #Deletes the 1-pixel elements\n \n segm_filtered = np.zeros(img_segm.shape)\n j=1\n for i in (indexes):\n segm_filtered[img_segm==i] = j\n j+=1\n return segm_filtered","def filter_by_size(img_segm):\n \n numbers = np.zeros(np.max(img_segm-1))\n for i in range(1,np.max(img_segm)):\n numbers[i-1] = np.sum(img_segm==i)\n \n indexes = np.arange(1,np.max(img_segm))\n #indexes = indexes[numbers>np.mean(numbers)] #Deletes the 1-pixel elements\n indexes = indexes[numbers>500] #Deletes the 1-pixel elements\n \n segm_filtered = np.zeros(img_segm.shape)\n j=1\n for i in (indexes):\n segm_filtered[img_segm==i] = j\n j+=1\n return segm_filtered","def calculate_binaries(dict_data):\n list_all_preprocessed_binaries = []\n for index_patient, patient in enumerate(dict_data):\n # pick and convert image\n image = dict_data[patient][1]\n image = image.astype(\"uint8\")\n # blur image\n image_blurred = cv2.medianBlur(image, 29)\n # segment image using k-means segmentation\n image_segmented = run_kmean_on_single_image(image_blurred, k=10,\n precision=10000, max_iterations=1000)\n # find lower threshold for binarizing images\n \"\"\" the idea i had here was that all the electrodes always occupy the same area on each picture.\n this function basically returns the pixel value, at which we need to threshold in our binary\n function, so that all pixels that have a higher intensity will collectively make up at least \n \"fraction_of_image_threshold\" percent of the picture - electrodes seem to take up about 5-10% of each\n image\"\"\"\n lower_threshold = intelligent_get_threshold(image_segmented,\n fraction_of_image_threshold=0.08)\n # binarize image\n image_binary = binarize_image(image_segmented, \n lower_threshold=lower_threshold, upper_threshold=255)\n list_all_preprocessed_binaries.append(image_binary)\n return list_all_preprocessed_binaries","def get_max_indices(self, input):\n \n min_element = torch.min(torch.abs(input.contiguous().view(-1)))\n input_temp = input + min_element + self.epsilon\n masked_input_temp = input_temp * self.mask\n values, indices = torch.sort(masked_input_temp, dim=1, descending=True)\n\n return indices[:, :self.num_active_nodes,:]","def get_prescribed_indexes(self):\n global_prescribed = []\n for node in self.preprocessor.nodes.values():\n if node.there_are_prescribed_dofs:\n starting_position = node.global_index * DOF_PER_NODE_STRUCTURAL\n dofs = np.array(node.get_prescribed_dofs_bc_indexes()) + starting_position\n global_prescribed.extend(dofs)\n return global_prescribed","def dummy_nms(list_maps, prob, parent_idx, scores):# slice_idx=0):\n rval = []\n for slice_idx, maps in zip(parent_idx, list_maps):\n for s in maps:\n # Apply the filter based on proba\n maps[s] = maps[s] * (maps[s] > prob - scores[slice_idx])\n n_z = np.transpose(np.nonzero(maps[s]))\n rval.extend([[s,\n n_z[e, 0], n_z[e, 1],\n maps[s][n_z[e, 0], n_z[e, 1]],\n slice_idx]\n for e in range(len(n_z))])\n #print 'nb of nonzero patches :', len(rval)\n if rval != []:\n rval.sort(key=lambda x: x[3], reverse=True)\n #print 'min :', min(rval, key=lambda x: x[3])\n #print'max :', max(rval, key=lambda x: x[3])\n return rval","def filterBankEdges(img):\n imgE = Views.extendBorder(img)\n opTop = as2DKernel(imgE, [-1]*3 + [0]*3 + [1]*3)\n opBottom = as2DKernel(imgE, [1]*3 + [0]*3 + [-1]*3)\n opLeft = as2DKernel(imgE, [-1, 0, 1] * 3)\n opRight = as2DKernel(imgE, [1, 0, -1] * 3)\n return [opTop, opBottom, opLeft, opRight]","def find_critical_cells(segment_candidates):\n\n L = []\n for idx, candidates in enumerate(segment_candidates):\n critical_blacks = _find_common_cells(candidates, cell_value=1)\n if len(critical_blacks):\n L.append((critical_blacks, idx, 1))\n\n critical_whites = _find_common_cells(candidates, cell_value=0)\n if len(critical_whites):\n L.append((critical_whites, idx, 0))\n\n return L","def get_bucket_boundaries(feature):\n return np.unique(np.percentile(feature, range(0, 100))).tolist()","def cardinal_indices(self, index):\n cardinals = [\n self.north_index(index),\n self.east_index(index),\n self.south_index(index),\n self.west_index(index)\n ]\n return [i for i in cardinals if 0 < i < (self.size * self.size)]","def get_contour_features(mask,selectcell=\"centered\"):\r\n \r\n #binarize image (everything above 0 becomes 1)\r\n mask = np.clip(mask,a_min=0,a_max=1)\r\n\r\n #for contours, dont use RETR_TREE, but RETR_EXTERNAL as we are not interested in internal objects\r\n contours, _ = cv2.findContours(mask, cv2.RETR_EXTERNAL, cv2.CHAIN_APPROX_NONE)\r\n contours = list(contours)\r\n \r\n #in case there is no contour found, add a dummy contour\r\n if len(contours)==0:\r\n contours = [np.array([[[0, 0]],[[0, 1]],[[1, 1]],[[1, 0]]])] #generate a dummy contour\r\n\r\n #Sort contours, longest first\r\n contours.sort(key=len,reverse=True)\r\n contours = [c for c in contours if len(c)>4] #proper contour should have at least 5 points\r\n hulls = [cv2.convexHull(contour,returnPoints=True) for contour in contours]\r\n\r\n mu_origs = [cv2.moments(contour) for contour in contours]\r\n mu_hulls = [cv2.moments(hull) for hull in hulls]\r\n\r\n area_origs = [mu_orig[\"m00\"] for mu_orig in mu_origs]\r\n area_hulls = [mu_hull[\"m00\"] for mu_hull in mu_hulls]\r\n\r\n #drop events where area is zero\r\n hulls = [hulls[i] for i in range(len(hulls)) if area_origs[i]>0] \r\n contours = [contours[i] for i in range(len(contours)) if area_origs[i]>0]\r\n mu_origs = [mu_origs[i] for i in range(len(mu_origs)) if area_origs[i]>0]\r\n mu_hulls = [mu_hulls[i] for i in range(len(mu_hulls)) if area_origs[i]>0]\r\n area_hulls = [area_hulls[i] for i in range(len(area_hulls)) if area_origs[i]>0]\r\n area_origs = [area_origs[i] for i in range(len(area_origs)) if area_origs[i]>0]\r\n \r\n \r\n pos_x = [int(mu_orig['m10']/mu_orig['m00']) for mu_orig in mu_origs]\r\n pos_y = [int(mu_orig['m01']/mu_orig['m00']) for mu_orig in mu_origs]\r\n\r\n \r\n if selectcell == \"smooth\":\r\n #compute the area ratio (roughness of contour)\r\n area_ratio = np.array(area_hulls)/np.array(area_origs)\r\n #get the contour with minimum roughness (smooth contour)\r\n sorter = np.argsort(area_ratio) #smallest first\r\n\r\n if selectcell == \"centered\":\r\n #select contour that is closest to the center of the image. \r\n #In iPAC, cells are usually in the center.\r\n mid_x,mid_y = mask.shape[0]/2,mask.shape[1]/2 #middle of the image\r\n BB = [cv2.boundingRect(c) for c in contours] #get a bounding box around the object\r\n distances = [np.sqrt((mid_x-bb[0])**2 + (mid_y-bb[1])**2) for bb in BB]\r\n sorter = np.argsort(distances) #smallest first\r\n \r\n #sort values with respect to chosen metric (area_ratio or distance)\r\n contours = [contours[s] for s in sorter]\r\n hulls = [hulls[s] for s in sorter]\r\n pos_x = [pos_x[s] for s in sorter]\r\n pos_y = [pos_y[s] for s in sorter]\r\n mu_origs = [mu_origs[s] for s in sorter]\r\n area_origs = [area_origs[s] for s in sorter]\r\n area_hulls = [area_hulls[s] for s in sorter]\r\n \r\n # draw mask of the chosen contour\r\n mask = np.zeros_like(mask)\r\n cv2.drawContours(mask,contours,0,1,cv2.FILLED)# produce a contour that is filled inside\r\n\r\n hull = hulls[0]#[0:n_contours]\r\n pos_x = pos_x[0]\r\n pos_y = pos_y[0] \r\n mu_orig = mu_origs[0]#[0:n_contours]\r\n area_orig = area_origs[0]#[0:n_contours]\r\n area_hull = area_hulls[0]#[0:n_contours]\r\n \r\n if area_orig>0:\r\n area_ratio = area_hull/area_orig\r\n else:\r\n area_ratio = np.nan\r\n\r\n arc = cv2.arcLength(hull, True) \r\n circularity = 2.0 * np.sqrt(np.pi * mu_orig[\"m00\"]) / arc\r\n\r\n\r\n dic = {\"mask\":mask,\"pos_x\":pos_x,\"pos_y\":pos_y,\"area_orig\":area_orig,\"area_hull\":area_hull,\\\r\n \"area_ratio\":area_ratio,\"circularity\":circularity}\r\n return dic","def cell_based_hist_segmentation(img, cells=[32, 32]):\n descriptor = []\n w,h = img.shape[:2]\n w_ranges = [(i*w)//cells[0] for i in range(cells[0])]+[-1]\n h_ranges = [(i*h)//cells[1] for i in range(cells[1])]+[-1]\n\n celled_hist = obtain_celled_histograms(img, cells, w_ranges, h_ranges)\n\n rectangular_segm = obtain_rectangular_segmentation(celled_hist, cells)\n mask = create_mask(rectangular_segm, img, cells)\n return mask","def _filter_imgs(self, min_size=32):\n valid_inds = []\n for i, img_info in enumerate(self.img_infos):\n if min(img_info['width'], img_info['height']) >= min_size:\n valid_inds.append(i)\n return valid_inds","def _filter_imgs(self, min_size=32):\n valid_inds = []\n for i, img_info in enumerate(self.img_infos):\n if min(img_info['width'], img_info['height']) >= min_size:\n valid_inds.append(i)\n return valid_inds","def getValidIndicies(self, points):\n try:\n inds = [ i for i in range(points.size) if points.flat[i] in self.maskSet ]\n return inds\n except Exception as error:\n print(\"failed in getValidIndicies\", error)\n return -1","def arraybin(array, bins):\n bin_it = lambda value: (i for i in range(len(array)) if array[i] >= value)\n splits = [next(bin_it(value), len(array)) for value in bins]\n return [list(range(start_idx, stop_idx)) for (start_idx, stop_idx)\n in zip([0] + splits, splits + [len(array)])]","def extract_histogram(self, bins=10):\n assert len(self.images) > 0, 'No images loaded! Did you call ' \\\n 'load_images() ?'\n histograms = []\n for image in self.images:\n grey = skicol.rgb2gray(image)\n hist_values, bins = np.histogram(grey, range=(0, 1), bins=bins)\n histograms.append(hist_values)\n histograms = np.array(histograms)\n histograms = histograms.astype('float')\n return histograms","def filter_zeros(X):\n\tnoNonzeros = np.count_nonzero(X, axis=1)\n\tmask = np.where(noNonzeros > 0)\n\treturn X[mask[0], :]","def possibilities(board):\n return board[np.where(board == 0)]","def getLandmarkindices(self):\n return self.subsetindices","def indices(self):\n _indices = []\n for h in self.miller.indices():\n _indices.append(self.indices_hkl(*h)[0])\n return _indices","def getLandmarkindices(self):\n return self.subsetnodes_indices","def getShadeMap(img):\n return np.bincount(img.astype(int).flat, minlength=256)","def getHists(img,bins=50):\n hists = np.array([])\n for i in range(3):#Images are loaded as three-dimensional matrices with three channels\n hists = np.append(hists,np.histogram(img[:,:,i], bins, density = True)[0])\n return hists","def nonzero_indices(a):\n return (np.nonzero(a)[0])","def _getNonPrototypeIndices(self, clusters: ndarray) -> ndarray:\n return np.delete(np.arange(self.dataSize), clusters.flatten())","def get_near(self,map):\n near_cells = []\n for i in range(self.x-1, self.x+2):\n for j in range(self.y-1, self.y+2):\n if(i>=0 and i<map.size and j>=0 and j<map.size): near_cells.append(map.search(i,j))\n return near_cells","def get_centers_from_bins(bins):\n return 0.5 * (bins[:-1] + bins[1:])","def find_cell_intersections(num_cells, cells_list):\n cells_intersect_ids = []\n\n for i in range(num_cells):\n inters_list = []\n for j in range(num_cells):\n if i != j:\n # count number of intersections\n inters = np.intersect1d(cells_list[i], cells_list[j])\n if (inters.size != 0):\n inters_list.append(np.asscalar(inters))\n\n cells_intersect_ids.append(np.asarray(inters_list))\n\n\n\n return cells_intersect_ids","def imthresh(img):\n img_vecs = img.flatten()\n\n # pre-calculate the histogram and cumulative histogram.\n vbins = np.arange(0, 257, 1)\n img_hist, hist_edges = np.histogram(img_vecs, vbins)\n vbins = (hist_edges[:-1] + hist_edges[1:])/2\n \n hist_times_gray = np.cumsum(img_hist * np.arange(0, 256, 1))\n cum_hist = np.cumsum(img_hist)\n\n # A first approximation of the background mean mean_1 is the mean of the corner pixels.\n # The third corner's index seems to be wrong!\n m, n = img.shape\n sum_bg = np.sum(img_vecs[[0, n - 1, n * (m - 1), m * n - 1]])\n num_pix_bg = 4\n mean1 = sum_bg/4\n mean2 = (np.sum(img_vecs) - sum_bg)/(m *n - num_pix_bg)\n threshold_val = np.uint8(np.ceil((mean1 + mean2)/2))\n\n\n if (threshold_val != 0) and (cum_hist[threshold_val - 1] == 0):\n threshold_val_old = threshold_val\n\n threshold_val_old = 0 # weird\n while threshold_val != threshold_val_old:\n threshold_val_old = threshold_val\n mean1 = hist_times_gray[threshold_val - 1]/cum_hist[threshold_val - 1]\n mean2 = (hist_times_gray[-1] - hist_times_gray[threshold_val - 1])/(cum_hist[-1] - cum_hist[threshold_val - 1])\n\n threshold_val = np.uint8(np.ceil((mean1 + mean2)/2))\n\n\n img_out = img >= threshold_val\n return img_out, threshold_val","def neighbors(i , j) :\n ns = []\n # vector de direction\n dx = [+1, +1, 0, 1]\n dy = [0, +1, 1, -1]\n for d in range(4) :\n ns.append((i + dx[d], j + dy[d]))\n #remove neagative element\n ns = [i for i in ns if i[0] >= 0 and i[1] >= 0]\n return ns","def _fast_hist_2d(data, bin_edges):\n # Yes, I've tested this against histogramdd().\n xassign = np.digitize(data[:,0], bin_edges[1:-1]) \n yassign = np.digitize(data[:,1], bin_edges[1:-1])\n nbins = len(bin_edges) - 1\n flatcount = np.bincount(xassign + yassign * nbins, minlength=nbins*nbins)\n return flatcount.reshape((nbins, nbins))","def get_empty_cells(state):\n cells = []\n for row_index, row in enumerate(state.board):\n for col_index, cell in enumerate(row):\n if cell == 0:\n cells.append([row_index, col_index])\n return cells","def get_bit_positions(bit_mask):\n\tbit_positions = []\n\t# find bit positions of enabled bits in mask\n\tfor i in range(16):\n\t\tif (bit_mask & (1 << i)) != 0:\n\t\t\tbit_positions.append(i)\n\treturn bit_positions","def findAllColors(img):\n hist = cv2.calcHist([img], [0, 1, 2], None, [256] * 3, [0, 256] * 3)\n allColors = np.argwhere(hist != 0)\n return allColors"],"string":"[\n \"def vir_indices(self):\\n indices = []\\n for index,item in enumerate(self):\\n if item==0:\\n indices.append(index)\\n return indices\",\n \"def get_agent_indices(array):\\t\\n\\tagent_indices = np.argwhere(array != 0)\\n\\treturn agent_indices\",\n \"def pick_nonzero_bin(binning, r_idx, phi_idx, dom_radius, speed_of_light):\\n r0 = binning.r.midpoints[r_idx].m_as('m')\\n dr = r0 - dom_radius # m\\n t0 = dr / speed_of_light * 1e9 # ns\\n phi0 = binning.phi.midpoints[phi_idx].m_as('rad')\\n phidir0 = (phi0 + np.pi) % (2*np.pi)\\n\\n point = np.array([[r0, phi0, t0, phidir0]])\\n\\n hist, _ = np.histogramdd(\\n point,\\n bins=binning.bin_edges,\\n normed=False\\n )\\n indices = tuple(c[0] for c in np.where(hist == 1))\\n return binning[indices], indices\",\n \"def conceptcover(bin_arr, limit=1, uncovered=0.1):\\n arr = np.copy(bin_arr)\\n arr_sum = np.sum(arr)\\n result = []\\n while True:\\n k = kernel(arr)\\n i = intent(bin_arr, k)\\n e = extent(bin_arr, i)\\n if len(e)*len(i) < limit or (e, i) in result: break\\n result.append((e, i))\\n arr = removed(arr, e, i)\\n if np.sum(arr)/arr_sum < uncovered: break\\n return result\",\n \"def get_indices(waves):\\n prob_ = np.abs(waves)**2\\n # batch\\n prob = [np.sum(prob_[i:i+4,:], axis=0) for i in range(0, len(waves[:,0]), 4)]\\n prob = np.asarray(prob)\\n prob_tot = np.sum(prob, axis=0)\\n \\n # cutoff\\n length = np.size(prob[:,0])\\n len10 = int(length/10)\\n flags = np.zeros((prob.shape[1]), dtype=int)\\n # hinges\\n # 50% within 10% of corners\\n\\n # surface\\n # 50% within 10% of surfaces\\n # not already labelled hinges\\n prob_left = np.sum(prob[0:len10,:], axis=0)\\n frac_left = prob_left/prob_tot\\n\\n prob_right = np.sum(prob[length-len10:length,:], axis=0)\\n frac_right = np.divide(prob_right, prob_tot)\\n\\n for i in range(len(flags)):\\n if frac_left[i]>0.5 or frac_right[i]>0.5:\\n flags[i] = 1\\n \\n indices = [i for i, x in enumerate(flags) if x == 1]\\n indices0 = [i for i, x in enumerate(flags) if x == 0]\\n \\n return indices, indices0\",\n \"def get_5index_list(self):\\n msk = self.load_mask()\\n return [i for (i,v) in enumerate(msk) if v==0]\",\n \"def get_vacancy_indices(array):\\t\\n\\tvacancy_indices = np.argwhere(array == 0)\\n\\treturn vacancy_indices\",\n \"def non_masked_indices(mask):\\n\\treturn np.nonzero(np.ravel(mask-1,order='C'))[0]\",\n \"def masked_indices(mask):\\n\\treturn np.nonzero(np.ravel(mask,order='C'))[0]\",\n \"def get_active_register_indices(self):\\n assert self.sketch.ndim == 1, 'Currently only support 1-dimensional sketch.'\\n return np.flatnonzero(self.sketch)\",\n \"def cell_detects(self, masked=False):\\r\\n grd = self.grd\\r\\n ncells = len(grd.cells['depth'])\\r\\n detects_i_tr = np.zeros(ncells, np.int32)\\r\\n if masked:\\r\\n not_flagged = np.where(self.rec_track.flagged==0)[0]\\r\\n rec_track = self.rec_track[not_flagged]\\r\\n else:\\r\\n rec_track = self.rec_track\\r\\n ndetects = len(rec_track)\\r\\n for nd in range(ndetects):\\r\\n tr = rec_track[nd]\\r\\n i = tr.i\\r\\n if i >= 0:\\r\\n detects_i_tr[i] += 1\\r\\n \\r\\n return detects_i_tr\",\n \"def _filter_imgs(self, min_size=32):\\n\\n valid_inds = []\\n for i, img_info in enumerate(self.data_infos):\\n if min(img_info[\\\"width\\\"], img_info[\\\"height\\\"]) < min_size:\\n continue\\n if self.filter_empty_gt and len(img_info[\\\"ann\\\"][\\\"bboxes\\\"]) > 0:\\n valid_inds.append(i)\\n else:\\n valid_inds.append(i)\\n\\n return valid_inds\",\n \"def noisy_cells(self, hists, thresholds):\\n return [[[x + 1, z + 1, i + 1] for x in range(h.GetNbinsX()) for z in range(h.GetNbinsY()) if h.GetBinContent(x + 1, z + 1) > threshold] for i, (h, threshold) in enumerate(zip(hists, thresholds))]\",\n \"def get_34index_list(self):\\n msk = self.load_mask()\\n return [i for (i,v) in enumerate(msk) if v==1]\",\n \"def occ_indices(self):\\n indices = []\\n for index,item in enumerate(self):\\n if item==1:\\n indices.append(index)\\n return indices\",\n \"def _filter_imgs(self, min_size=32):\\n valid_inds = []\\n for i, img_info in enumerate(self.img_infos):\\n # Filter out empty images\\n if img_info['ann']['bboxes'].shape[0] > 0:\\n if min(img_info['width'], img_info['height']) >= min_size:\\n valid_inds.append(i)\\n return valid_inds\",\n \"def cell_occupancy(self, masked=False):\\r\\n grd = self.grd\\r\\n occupancy_i = []\\r\\n if masked:\\r\\n not_flagged = np.where(self.rec_track.flagged==0)[0]\\r\\n rec_track = self.rec_track[not_flagged]\\r\\n else:\\r\\n rec_track = self.rec_track\\r\\n ndetects = len(rec_track)\\r\\n for nd in range(ndetects):\\r\\n tr = rec_track[nd]\\r\\n i = tr.i\\r\\n if i >= 0:\\r\\n occupancy_i.append(i)\\r\\n\\r\\n occupancy_i = np.unique(occupancy_i)\\r\\n\\r\\n return occupancy_i\",\n \"def get_biomass(binary_mask):\\n\\n white_pixels = cv2.countNonZero(binary_mask)\\n return white_pixels\",\n \"def kernel(bin_arr):\\n mf = minusframe(bin_arr)\\n mf.sort('i', inplace=True, ascending=False) # prefer smaller extents\\n max_wi = mf['w'].idxmax()\\n max_i = mf.loc[max_wi, 'i']\\n return mf[mf['i'] >= max_i].index\",\n \"def compute_bin_indices(X_part, bin_limits=None, n_bins=20):\\n if bin_limits is None:\\n bin_limits = []\\n for variable_data in range(X_part.shape[1]):\\n bin_limits.append(numpy.linspace(numpy.min(variable_data), numpy.max(variable_data), n_bins + 1)[1: -1])\\n\\n bin_indices = numpy.zeros(len(X_part), dtype=numpy.int)\\n for axis, bin_limits_axis in enumerate(bin_limits):\\n bin_indices *= (len(bin_limits_axis) + 1)\\n bin_indices += numpy.searchsorted(bin_limits_axis, X_part[:, axis])\\n\\n return bin_indices\",\n \"def get_nucleus_ids(img):\\n\\n values = np.unique(img)\\n values = values[values > 0]\\n\\n return values\",\n \"def _filter_imgs(self, min_size=32):\\r\\n valid_inds = []\\r\\n ids_with_ann = set(_['image_id'] for _ in self.coco.anns.values())\\r\\n for i, img_info in enumerate(self.img_infos):\\r\\n if self.filter_empty_gt and self.img_ids[i] not in ids_with_ann:\\r\\n continue\\r\\n if min(img_info['width'], img_info['height']) >= min_size:\\r\\n valid_inds.append(i)\\r\\n return valid_inds\",\n \"def _get_index_lists(self, mat):\\n n_row, n_col = mat.shape\\n \\n col_ind_at_row, row_ind_at_col = [],[]\\n for i in range(n_row):\\n aux_ind = _np.where(mat[i]>0)[0]\\n if len(aux_ind) == 0:\\n raise Exception('Row {} is composed of zeros'.format(i))\\n col_ind_at_row.append(aux_ind)\\n \\n for j in range(n_col):\\n aux_ind = _np.where(mat[:,j]>0)[0]\\n if len(aux_ind) == 0:\\n raise Exception('Column {} is composed of zeros'.format(j))\\n \\n row_ind_at_col.append(aux_ind)\\n \\n return col_ind_at_row, row_ind_at_col\",\n \"def get_unprescribed_indexes(self):\\n total_dof = DOF_PER_NODE_STRUCTURAL * len(self.preprocessor.nodes)\\n all_indexes = np.arange(total_dof)\\n return np.delete(all_indexes, self.prescribed_indexes)\",\n \"def immed_nbrs(tile):\\n return [nbr for nbr in [TILES_BY_IDX.get(xy) for xy in xy_nbrs(tile['xyidx'])]\\n if nbr is not None]\",\n \"def get_cont_indices(self, X, max_ord):\\n indices = np.zeros(X.shape[1]).astype(bool)\\n for i, col in enumerate(X.T):\\n col_nonan = col[~np.isnan(col)]\\n col_unique = np.unique(col_nonan)\\n if len(col_unique) > max_ord:\\n indices[i] = True\\n return indices\",\n \"def get_indications(self):\\n indications = np.zeros_like(self.predictions)\\n for i in range(self.predictions.shape[0]):\\n ind = np.where(self.predictions[i, :] - self.labels != 0.0)[0]\\n indications[i, ind] = 1.0\\n\\n return indications\",\n \"def _filter_imgs(self, min_size=32):\\n valid_inds = []\\n ids_with_ann = set(_['image_id'] for _ in self.coco.anns.values())\\n for i, img_info in enumerate(self.img_infos):\\n if self.img_ids[i] not in ids_with_ann:\\n continue\\n if min(img_info['width'], img_info['height']) >= min_size:\\n valid_inds.append(i)\\n return valid_inds\",\n \"def _filter_imgs(self, min_size=32):\\n valid_inds = []\\n ids_with_ann = set(_['image_id'] for _ in self.coco.anns.values())\\n for i, img_info in enumerate(self.img_infos):\\n if self.img_ids[i] not in ids_with_ann:\\n continue\\n if min(img_info['width'], img_info['height']) >= min_size:\\n valid_inds.append(i)\\n return valid_inds\",\n \"def _filter_imgs(self, min_size=32):\\n valid_inds = []\\n ids_with_ann = set(_['image_id'] for _ in self.coco.anns.values())\\n for i, img_info in enumerate(self.img_infos):\\n if self.img_ids[i] not in ids_with_ann:\\n continue\\n if min(img_info['width'], img_info['height']) >= min_size:\\n valid_inds.append(i)\\n return valid_inds\",\n \"def index_col(self, i0, i1, j0, j1):\\n edges = self.h5['indexes']['bin1_offset'][i0:i1 + 1]\\n index = []\\n for lo1, hi1 in zip(edges[:-1], edges[1:]):\\n if hi1 - lo1 > 0:\\n bin2 = self.h5['pixels']['bin2_id'][lo1:hi1]\\n mask = (bin2 >= j0) & (bin2 < j1)\\n index.append(lo1 + np.flatnonzero(mask))\\n if not index:\\n return np.array([], dtype=int)\\n else:\\n return np.concatenate(index, axis=0)\",\n \"def nondetects(self, masked=False):\\r\\n grd = self.grd\\r\\n xnd = []\\r\\n ynd = []\\r\\n ncells = len(grd.cells['depth'])\\r\\n non_detects_i_tr = np.zeros(ncells, np.int32)\\r\\n if masked:\\r\\n not_flagged = np.where(self.rec_track.flagged==0)[0]\\r\\n rec_track = self.rec_track[not_flagged]\\r\\n rec_seg = self.make_segments(set_depth=True, \\r\\n input_rec_track=rec_track)\\r\\n else:\\r\\n rec_seg = self.rec_seg\\r\\n for nr, rseg in enumerate(rec_seg):\\r\\n seg = rec_seg[nr]\\r\\n dt = seg.dt\\r\\n if dt > dt_signal+1:\\r\\n t1 = seg.t1\\r\\n t2 = seg.t2\\r\\n nint = int(np.rint((t2-t1)/dt_signal)) - 1\\r\\n x1 = seg.x1\\r\\n x2 = seg.x2\\r\\n y1 = seg.y1\\r\\n y2 = seg.y2\\r\\n dx_nd = (x2 - x1)/float(nint+1)\\r\\n dy_nd = (y2 - y1)/float(nint+1)\\r\\n if nint < 120: # 10 minute cutoff for nondetect filling\\r\\n xint = [x1 + n*dx_nd for n in range(1,nint)]\\r\\n yint = [y1 + n*dy_nd for n in range(1,nint)]\\r\\n xnd = xnd + xint\\r\\n ynd = ynd + yint\\r\\n\\r\\n for nd in range(len(xnd)):\\r\\n xy = [xnd[nd], ynd[nd]]\\r\\n i = grd.select_cells_nearest(xy)\\r\\n if (i is not None) and (i >= 0):\\r\\n non_detects_i_tr[i] += 1\\r\\n\\r\\n return non_detects_i_tr\",\n \"def get_neighbours(self):\\n shape=self.cubeshape[1:]\\n neighboursx=np.arange(self.xpos-(self.blocksize-1)/2,(self.xpos+(self.blocksize-1)/2)+1,dtype='int' )\\n neighboursx=[x if (x>=0) & (x<=shape[1]-1) else np.nan for x in neighboursx ]\\n neighboursy=np.arange(self.ypos-(self.blocksize-1)/2,(self.ypos+(self.blocksize-1)/2)+1,dtype='int' )\\n neighboursy=[y if (y>=0) & (y<=shape[0]-1) else np.nan for y in neighboursy ]\\n keys=[np.ravel_multi_index([y,x], shape) if np.all(np.isfinite(np.asarray([y,x]))) else np.nan for y in neighboursy for x in neighboursx]\\n\\n return keys\",\n \"def filter_halo_pnum(data, Ncut=1000):\\n npart = np.array(data['np'][0])\\n ind =np.where(npart > Ncut)[0]\\n print(\\\"# of halos:\\\",len(ind))\\n return ind\",\n \"def getNeighbours(seg,meta,inversedIndex):\\n return np.unique(np.fromiter( (inversedIndex[x] for x in np.concatenate([meta.loc[seg]['ins'],meta.loc[seg]['outs']])),dtype=np.int))\",\n \"def __getStableConcepts(weight_matrix: Union[np.ndarray, pd.DataFrame]) -> np.ndarray:\\n stables = []\\n for i in range(len(weight_matrix)):\\n if np.all(weight_matrix[i] == 0):\\n stables.append(i)\\n\\n return stables\",\n \"def get_idxes_from_mask(mask):\\n if len(mask) > 1:\\n return torch.nonzero(mask.squeeze(), as_tuple=False).reshape(1, -1)[0]\\n elif len(mask) == 1:\\n return torch.tensor([0], dtype=torch.int64) if mask.sum() == 1 else torch.tensor([], dtype=torch.int64)\\n return torch.tensor([], dtype=torch.int64)\",\n \"def bin_indices(self, coordinates, fractional=True):\\n coords = numpy.asarray(coordinates).transpose()\\n indices = [numpy.interp(coo, cen, range(n))\\n for (coo, cen, n) in zip(coords, self.centers, self.shape)]\\n index_arr = numpy.atleast_2d(numpy.array(indices).transpose())\\n if fractional:\\n return index_arr\\n return numpy.floor(index_arr + 0.5).astype(numpy.int_)\",\n \"def get_detector_faces_from_pixel_mapping(\\n mapping: PixelMapping,\\n) -> List[Tuple[int, int]]:\\n detector_faces: List[Tuple[int, int]] = []\\n idx: Tuple[int, int]\\n for idx in enumerate(mapping.pixel_ids):\\n if idx[1] is not None:\\n detector_faces.append((idx[0], idx[1]))\\n\\n return detector_faces\",\n \"def filter_positive_detections(detections):\\n class_idx = 0\\n assert(isinstance(detections, mx.nd.NDArray) or isinstance(detections, np.ndarray))\\n detections_per_image = []\\n # for each image\\n for i in range(detections.shape[0]):\\n result = []\\n det = detections[i, :, :]\\n for obj in det:\\n if obj[class_idx] >= 0:\\n result.append(obj)\\n detections_per_image.append(result)\\n logging.info(\\\"%d positive detections\\\", len(result))\\n return detections_per_image\",\n \"def get_possible_indexes(self, factors_lock, random_state):\\n factor_list = [[]]\\n for i, lock_val in enumerate(factors_lock):\\n ## if unlocked, number of possible factor-samples are multiplied by the number of possible unlocked factor values\\n if lock_val == -1 : \\n base = factor_list\\n factor_list = []\\n for b in base:\\n bc = b.copy()\\n for f in range(self.factor_sizes[i]):\\n bc.append(f)\\n factor_list.append(bc)\\n bc = b.copy()\\n \\n else: ## if factor is locked, just append the locked factor value\\n for i in range(len(factor_list)):\\n factor_list[i].append(lock_val)\\n #transform to index\\n possible_indexes = self._features_to_state_space_index(np.asarray(factor_list)) \\n return possible_indexes\",\n \"def intermodes(I):\\n n = 255\\n y = np.histogram(I.ravel(), bins=n+1, range=(0, n))[0].astype(float)\\n iter = 0\\n while not bimodtest(y):\\n h = np.ones(3) / 3\\n y = np.convolve(y, h, mode='same')\\n iter += 1\\n if iter > 10000:\\n return 0\\n\\n TT = []\\n for k in range(1, n - 1):\\n if y[k - 1] < y[k] > y[k + 1]:\\n TT.append(k)\\n return int(np.floor(np.mean(TT)))\",\n \"def indices(self):\\n\\n # We used lookup tables here. Read more about other methods here:\\n # https://chessprogramming.wikispaces.com/Bitboard+Serialization\\n\\n if self.num == 0:\\n return []\\n\\n bits = []\\n\\n for i in [0, 1, 2, 3, 4, 5, 6, 7]:\\n row = (self.num >> UINT64_PADDING[i]) & EIGHT_ONES\\n indices = row_to_indices[row]\\n for index in indices:\\n bits.append(index + i*8)\\n\\n return bits\",\n \"def find_excited_locations(self):\\n return np.asarray(np.where(self._grid == 8)).T\",\n \"def get_detector_number_from_pixel_mapping(\\n mapping: PixelMapping,\\n) -> List[int]:\\n detector_numbers = [id for id in mapping.pixel_ids if id is not None]\\n return detector_numbers\",\n \"def extract_pixels(img_):\\n non_zero_pixels = np.argwhere(0 < img_)\\n x = non_zero_pixels.T[0].astype(np.float32)\\n y = non_zero_pixels.T[1].astype(np.float32)\\n return x, y\",\n \"def _find_neighbours(self):\\n\\n neighbours = []\\n for i, p in enumerate(self.frame_0):\\n nearests = np.where(np.linalg.norm(self.frame_0 - p, axis=1) <= self.R_n)[0]\\n # delete self index\\n index = np.argwhere(nearests==i)\\n nearests = np.delete(nearests, index)\\n neighbours.append(nearests)\\n\\n return neighbours\",\n \"def hist_and_thresh(self):\\n bins, occ, _ = self.histogram()\\n self.thresh = np.mean(bins) # initial guess\\n self.peaks_and_thresh() # in case peak calculation fails\\n # if np.size(self.peak_indexes) == 2: # est_param will only find one peak if the number of bins is small\\n # # set the threshold where the fidelity is max\\n # self.search_fidelity(self.peak_centre[0], self.peak_widths[0] ,self.peak_centre[1])\\n try: \\n thresh = threshold_minimum(np.array(self.stats['Counts']), len(bins))\\n int(np.log(thresh)) # if thresh <= 0 this gives ValueError\\n self.thresh = thresh\\n except (ValueError, RuntimeError, OverflowError): pass\\n try:\\n # atom is present if the counts are above threshold\\n self.stats['Atom detected'] = [x // self.thresh for x in self.stats['Counts']]\\n # self.fidelity, self. err_fidelity = np.around(self.get_fidelity(), 4) # this is a relatively slow operation\\n except (ValueError, OverflowError): pass\\n return bins, occ, self.thresh\",\n \"def detect(self, mask):\\n # 1) Return Non zero indices\\n det_idx = np.where(mask > 0.0)\\n idx_x, idx_y = det_idx[0], det_idx[1]\\n # 2) Create 1x1 box for each pixel detected.\\n detections = []\\n for i in range(0, len(idx_x)):\\n x, y = idx_x[i], idx_y[i]\\n detections.append((x, y, x+1, y+1, 1)) # x1, y1, x2, y2, area\\n # 3) merge boxes\\n bounding_boxes = self.bounding_boxes(detections)\\n return bounding_boxes\",\n \"def get_flagged_neighbours(self, row, col):\\n return [cell for cell in self.get_neighbours(row, col) if cell.state == 'X']\",\n \"def get_energy_bins(image, num_bins=4):\\r\\n h, _ = image.shape\\r\\n C = np.linspace(0, h//2, num=num_bins+1, dtype=\\\"int\\\")\\r\\n # Create a list of dense square mask\\r\\n mask_list = [square_mask(image, c) for c in C[1:]]\\r\\n # Extract a list of hollow mask\\r\\n square_zones = [~mask_list[k]*mask_list[k+1] for k in range(len(mask_list)-1)]\\r\\n # add center square and whole image square to the list\\r\\n square_zones = [mask_list[0]] + square_zones + [mask_list[-1]]\\r\\n # Apply these masks to the image\\r\\n image_bins = []\\r\\n for zone in square_zones:\\r\\n img = image.copy()\\r\\n img[~zone] = np.nan\\r\\n image_bins.append(img)\\r\\n # Compute the energy of each masked image\\r\\n energy_bins = [np.nansum(_bin) for _bin in image_bins]\\r\\n total_energy = energy_bins[-1]\\r\\n # normalize the energy repartition\\r\\n energy_bins /= total_energy\\r\\n return energy_bins[:-1], image_bins[:-1]\",\n \"def get_marginal_indexes(actions, mode):\\n vi = []\\n for v in range(actions[mode].max()+1):\\n vals = actions[actions[mode] == v].index.values\\n if len(vals) > 0:\\n vi.append(vals)\\n else:\\n vi.append(np.array([0]))\\n return vi\",\n \"def filter_binaries_beamprofile(bin_arr, beamprofile, cutoff=0.75, dilate=0):\\r\\n bp_bool = beamprofile < cutoff * beamprofile.max()\\r\\n out_binary = np.empty_like(bin_arr, dtype=int)\\r\\n total_cells = 0\\r\\n removed_cells = 0\\r\\n\\r\\n for i, img in enumerate(bin_arr):\\r\\n labeled, n = mh.labeled.label(img)\\r\\n total_cells += n\\r\\n for l in np.unique(labeled)[1:]:\\r\\n selected_binary = multi_dilate(labeled == l, dilate)\\r\\n if np.any(np.logical_and(selected_binary, bp_bool)): # Cell lies outside of\\r\\n labeled[labeled == l] = 0\\r\\n removed_cells += 1\\r\\n out_binary[i] = labeled\\r\\n print('Removed {} cells out of a total of {} cells.'.format(removed_cells, total_cells))\\r\\n return out_binary\",\n \"def contiguified_channels(chs):\\n orig=chs.copy()-np.min(chs)\\n counts=np.bincount(orig)\\n #print(np.argwhere(counts==0))\\n cumul=np.cumsum(np.where(counts==0, 0, 1))\\n return np.array([cumul[i] for i in orig])\",\n \"def bins_from_image(path):\\n _, Y = util.imread(path)\\n closest_bins = util.closest_bins(Y).flatten()\\n count = np.zeros(len(util.bins))\\n for closest_bin in closest_bins:\\n count[closest_bin] += 1\\n return count\",\n \"def _free_indicies(self):\\n return np.logical_not(self._fixed_indicies)\",\n \"def _get_optimal_threshold(arr, num_bins=1001, num_quantized_bins=255):\\n if not isinstance(arr, np.ndarray):\\n raise TypeError('get_optimal_threshold only supports input type of np.ndarray,'\\n ' while received type=%s' % (str(type(arr))))\\n min_val = np.min(arr)\\n max_val = np.max(arr)\\n th = max(abs(min_val), abs(max_val))\\n\\n hist, hist_edges = np.histogram(arr, bins=num_bins, range=(-th, th))\\n zero_bin_idx = num_bins // 2\\n num_half_quantized_bins = num_quantized_bins // 2\\n assert np.allclose(hist_edges[zero_bin_idx] + hist_edges[zero_bin_idx + 1],\\n 0, rtol=1e-5, atol=1e-7)\\n\\n thresholds = np.zeros(num_bins // 2 + 1 - num_quantized_bins // 2)\\n divergence = np.zeros_like(thresholds)\\n quantized_bins = np.zeros(num_quantized_bins, dtype=np.int32)\\n # i means the number of bins on half axis excluding the zero bin.\\n for i in range(num_quantized_bins // 2,\\n num_bins // 2 + 1):\\n p_bin_idx_start = zero_bin_idx - i\\n p_bin_idx_stop = zero_bin_idx + i + 1\\n thresholds[i - num_half_quantized_bins] = hist_edges[p_bin_idx_stop]\\n sliced_nd_hist = hist[p_bin_idx_start:p_bin_idx_stop]\\n\\n # generate reference distribution p\\n p = sliced_nd_hist.copy()\\n assert p.size % 2 == 1\\n assert p.size >= num_quantized_bins\\n # put left outlier count in p[0]\\n left_outlier_count = np.sum(hist[0:p_bin_idx_start])\\n p[0] += left_outlier_count\\n # put right outlier count in p[-1]\\n right_outlier_count = np.sum(hist[p_bin_idx_stop:])\\n p[-1] += right_outlier_count\\n # is_nonzeros[k] indicates whether hist[k] is nonzero\\n is_nonzeros = (sliced_nd_hist != 0).astype(np.int32)\\n\\n # calculate how many bins should be merged to generate quantized distribution q\\n num_merged_bins = p.size // num_quantized_bins\\n # merge hist into num_quantized_bins bins\\n for j in range(num_quantized_bins):\\n start = j * num_merged_bins\\n stop = start + num_merged_bins\\n quantized_bins[j] = sliced_nd_hist[start:stop].sum()\\n quantized_bins[-1] += sliced_nd_hist[num_quantized_bins * num_merged_bins:].sum()\\n # expand quantized_bins into p.size bins\\n q = np.zeros(p.size, dtype=np.float32)\\n for j in range(num_quantized_bins):\\n start = j * num_merged_bins\\n if j == num_quantized_bins - 1:\\n stop = -1\\n else:\\n stop = start + num_merged_bins\\n norm = is_nonzeros[start:stop].sum()\\n if norm != 0:\\n q[start:stop] = float(quantized_bins[j]) / float(norm)\\n q[sliced_nd_hist == 0] = 0\\n p = _smooth_distribution(p)\\n # There is a chance that q is an invalid probability distribution.\\n try:\\n q = _smooth_distribution(q)\\n except ValueError:\\n divergence[i - num_half_quantized_bins] = float(\\\"inf\\\")\\n else:\\n divergence[i - num_half_quantized_bins] = stats.entropy(p, q)\\n quantized_bins[:] = 0\\n\\n min_divergence_idx = np.argmin(divergence)\\n min_divergence = divergence[min_divergence_idx]\\n opt_th = thresholds[min_divergence_idx]\\n return min_val, max_val, min_divergence, opt_th\",\n \"def find_halos(pos, ngrid, log, level=3000):\\n print('Binning particles', file=log)\\n cells = get_cells(pos, ngrid, log)\\n count = bincount(cells, minlength=ngrid**3)\\n count.shape = (ngrid,ngrid,ngrid)\\n print('Count in', count.min(), count.max(), file=log)\\n idx = flatnonzero(count>level)\\n print('Number of cells above', level, 'is', len(idx), file=log)\\n \\n \\n labels, num_features = ndimage.label(count>level)\\n print('Number fo features', num_features, file=log)\\n print('Labels in', labels.min(), labels.max(), file=log)\\n locations = ndimage.find_objects(labels)\\n\\n dense_regions = []\\n\\n for i in range(num_features):\\n loc = locations[i]\\n hw = max(l.stop - l.start for l in loc) * 0.5 /ngrid\\n hw_padded = hw + 0.0/ngrid\\n\\n ctr =[(0.5/ngrid)*(l.stop + l.start) for l in loc]\\n count_i = count[loc][labels[loc]==(i+1)].sum()\\n print('Count', count_i, file=log)\\n dense_regions.append((count_i, ctr, hw_padded))\\n\\n # sort by number of particles in the region\\n dense_regions = sorted(dense_regions, key = lambda num_ctr_hw :num_ctr_hw[0], reverse=True)\\n\\n return dense_regions\",\n \"def computeTopSurfaceIndices(top):\\n itop = np.array([(top[i,j], j, i) \\\\\\n for i in range(top.shape[0]) \\\\\\n for j in range(top.shape[1]) \\\\\\n if top[i,j] >= 0])\\n return itop\",\n \"def get_bin_color_features(img, p):\\n return cv2.resize(img, p.spatial_size).ravel()\",\n \"def clean_detections(npts, on_off):\\n on = on_off[:,0]\\n off = on_off[:,1]\\n idx_on = [on[0]]\\n idx_off = [off[0]]\\n lowest_idx = on[0]\\n\\n for ion, ioff in zip(on, off):\\n if ion > lowest_idx + npts:\\n idx_on.append(ion)\\n idx_off.append(ioff)\\n lowest_idx = ion\\n\\n return np.asarray((idx_on, idx_off)).T\",\n \"def bin_thresh(img: np.ndarray, thresh: Number) -> np.ndarray:\\n res = img >= thresh\\n return res\",\n \"def findNeighbours(self):\\n neighbours = []\\n\\n for i in range(self.xCoordinate - 1, self.xCoordinate + 2):\\n for j in range(self.yCoordinate - 1, self.yCoordinate + 2):\\n if (not (i == self.xCoordinate and j == self.yCoordinate)) and (0 <= i <= 394 and 0 <= j <= 499):\\n neighbours.append(PixelPosition(i, j))\\n\\n return neighbours\",\n \"def get_none_zero_region(im, margin):\\n input_shape = im.shape\\n if(type(margin) is int ):\\n margin = [margin]*len(input_shape)\\n assert(len(input_shape) == len(margin))\\n indxes = np.nonzero(im)\\n idx_min = []\\n idx_max = []\\n for i in range(len(input_shape)):\\n idx_min.append(indxes[i].min())\\n idx_max.append(indxes[i].max())\\n\\n for i in range(len(input_shape)):\\n idx_min[i] = max(idx_min[i] - margin[i], 0)\\n idx_max[i] = min(idx_max[i] + margin[i], input_shape[i] - 1)\\n return idx_min, idx_max\",\n \"def count_oob(cube):\\n out_range = (cube > 4).any(1) | (cube < -4).any(1)\\n out_range = out_range.sum() / cube.shape[0]\\n return out_range\",\n \"def filter_by_size(img_segm,mini_nb_pix):\\n numbers = np.zeros(int(np.max(img_segm)))\\n for i in range(1,int(np.max(img_segm))+1):\\n numbers[i-1] = np.count_nonzero(img_segm==i)\\n \\n indexes = np.arange(1,np.max(img_segm)+1)\\n #indexes = indexes[numbers>np.mean(numbers)] #Deletes the 1-pixel elements\\n indexes = indexes[numbers>mini_nb_pix] #Deletes the 1-pixel elements\\n \\n segm_filtered = np.zeros(img_segm.shape)\\n j=1\\n for i in (indexes):\\n segm_filtered[img_segm==i] = j\\n j+=1\\n return segm_filtered\",\n \"def filter_by_size(img_segm):\\n \\n numbers = np.zeros(np.max(img_segm-1))\\n for i in range(1,np.max(img_segm)):\\n numbers[i-1] = np.sum(img_segm==i)\\n \\n indexes = np.arange(1,np.max(img_segm))\\n #indexes = indexes[numbers>np.mean(numbers)] #Deletes the 1-pixel elements\\n indexes = indexes[numbers>500] #Deletes the 1-pixel elements\\n \\n segm_filtered = np.zeros(img_segm.shape)\\n j=1\\n for i in (indexes):\\n segm_filtered[img_segm==i] = j\\n j+=1\\n return segm_filtered\",\n \"def calculate_binaries(dict_data):\\n list_all_preprocessed_binaries = []\\n for index_patient, patient in enumerate(dict_data):\\n # pick and convert image\\n image = dict_data[patient][1]\\n image = image.astype(\\\"uint8\\\")\\n # blur image\\n image_blurred = cv2.medianBlur(image, 29)\\n # segment image using k-means segmentation\\n image_segmented = run_kmean_on_single_image(image_blurred, k=10,\\n precision=10000, max_iterations=1000)\\n # find lower threshold for binarizing images\\n \\\"\\\"\\\" the idea i had here was that all the electrodes always occupy the same area on each picture.\\n this function basically returns the pixel value, at which we need to threshold in our binary\\n function, so that all pixels that have a higher intensity will collectively make up at least \\n \\\"fraction_of_image_threshold\\\" percent of the picture - electrodes seem to take up about 5-10% of each\\n image\\\"\\\"\\\"\\n lower_threshold = intelligent_get_threshold(image_segmented,\\n fraction_of_image_threshold=0.08)\\n # binarize image\\n image_binary = binarize_image(image_segmented, \\n lower_threshold=lower_threshold, upper_threshold=255)\\n list_all_preprocessed_binaries.append(image_binary)\\n return list_all_preprocessed_binaries\",\n \"def get_max_indices(self, input):\\n \\n min_element = torch.min(torch.abs(input.contiguous().view(-1)))\\n input_temp = input + min_element + self.epsilon\\n masked_input_temp = input_temp * self.mask\\n values, indices = torch.sort(masked_input_temp, dim=1, descending=True)\\n\\n return indices[:, :self.num_active_nodes,:]\",\n \"def get_prescribed_indexes(self):\\n global_prescribed = []\\n for node in self.preprocessor.nodes.values():\\n if node.there_are_prescribed_dofs:\\n starting_position = node.global_index * DOF_PER_NODE_STRUCTURAL\\n dofs = np.array(node.get_prescribed_dofs_bc_indexes()) + starting_position\\n global_prescribed.extend(dofs)\\n return global_prescribed\",\n \"def dummy_nms(list_maps, prob, parent_idx, scores):# slice_idx=0):\\n rval = []\\n for slice_idx, maps in zip(parent_idx, list_maps):\\n for s in maps:\\n # Apply the filter based on proba\\n maps[s] = maps[s] * (maps[s] > prob - scores[slice_idx])\\n n_z = np.transpose(np.nonzero(maps[s]))\\n rval.extend([[s,\\n n_z[e, 0], n_z[e, 1],\\n maps[s][n_z[e, 0], n_z[e, 1]],\\n slice_idx]\\n for e in range(len(n_z))])\\n #print 'nb of nonzero patches :', len(rval)\\n if rval != []:\\n rval.sort(key=lambda x: x[3], reverse=True)\\n #print 'min :', min(rval, key=lambda x: x[3])\\n #print'max :', max(rval, key=lambda x: x[3])\\n return rval\",\n \"def filterBankEdges(img):\\n imgE = Views.extendBorder(img)\\n opTop = as2DKernel(imgE, [-1]*3 + [0]*3 + [1]*3)\\n opBottom = as2DKernel(imgE, [1]*3 + [0]*3 + [-1]*3)\\n opLeft = as2DKernel(imgE, [-1, 0, 1] * 3)\\n opRight = as2DKernel(imgE, [1, 0, -1] * 3)\\n return [opTop, opBottom, opLeft, opRight]\",\n \"def find_critical_cells(segment_candidates):\\n\\n L = []\\n for idx, candidates in enumerate(segment_candidates):\\n critical_blacks = _find_common_cells(candidates, cell_value=1)\\n if len(critical_blacks):\\n L.append((critical_blacks, idx, 1))\\n\\n critical_whites = _find_common_cells(candidates, cell_value=0)\\n if len(critical_whites):\\n L.append((critical_whites, idx, 0))\\n\\n return L\",\n \"def get_bucket_boundaries(feature):\\n return np.unique(np.percentile(feature, range(0, 100))).tolist()\",\n \"def cardinal_indices(self, index):\\n cardinals = [\\n self.north_index(index),\\n self.east_index(index),\\n self.south_index(index),\\n self.west_index(index)\\n ]\\n return [i for i in cardinals if 0 < i < (self.size * self.size)]\",\n \"def get_contour_features(mask,selectcell=\\\"centered\\\"):\\r\\n \\r\\n #binarize image (everything above 0 becomes 1)\\r\\n mask = np.clip(mask,a_min=0,a_max=1)\\r\\n\\r\\n #for contours, dont use RETR_TREE, but RETR_EXTERNAL as we are not interested in internal objects\\r\\n contours, _ = cv2.findContours(mask, cv2.RETR_EXTERNAL, cv2.CHAIN_APPROX_NONE)\\r\\n contours = list(contours)\\r\\n \\r\\n #in case there is no contour found, add a dummy contour\\r\\n if len(contours)==0:\\r\\n contours = [np.array([[[0, 0]],[[0, 1]],[[1, 1]],[[1, 0]]])] #generate a dummy contour\\r\\n\\r\\n #Sort contours, longest first\\r\\n contours.sort(key=len,reverse=True)\\r\\n contours = [c for c in contours if len(c)>4] #proper contour should have at least 5 points\\r\\n hulls = [cv2.convexHull(contour,returnPoints=True) for contour in contours]\\r\\n\\r\\n mu_origs = [cv2.moments(contour) for contour in contours]\\r\\n mu_hulls = [cv2.moments(hull) for hull in hulls]\\r\\n\\r\\n area_origs = [mu_orig[\\\"m00\\\"] for mu_orig in mu_origs]\\r\\n area_hulls = [mu_hull[\\\"m00\\\"] for mu_hull in mu_hulls]\\r\\n\\r\\n #drop events where area is zero\\r\\n hulls = [hulls[i] for i in range(len(hulls)) if area_origs[i]>0] \\r\\n contours = [contours[i] for i in range(len(contours)) if area_origs[i]>0]\\r\\n mu_origs = [mu_origs[i] for i in range(len(mu_origs)) if area_origs[i]>0]\\r\\n mu_hulls = [mu_hulls[i] for i in range(len(mu_hulls)) if area_origs[i]>0]\\r\\n area_hulls = [area_hulls[i] for i in range(len(area_hulls)) if area_origs[i]>0]\\r\\n area_origs = [area_origs[i] for i in range(len(area_origs)) if area_origs[i]>0]\\r\\n \\r\\n \\r\\n pos_x = [int(mu_orig['m10']/mu_orig['m00']) for mu_orig in mu_origs]\\r\\n pos_y = [int(mu_orig['m01']/mu_orig['m00']) for mu_orig in mu_origs]\\r\\n\\r\\n \\r\\n if selectcell == \\\"smooth\\\":\\r\\n #compute the area ratio (roughness of contour)\\r\\n area_ratio = np.array(area_hulls)/np.array(area_origs)\\r\\n #get the contour with minimum roughness (smooth contour)\\r\\n sorter = np.argsort(area_ratio) #smallest first\\r\\n\\r\\n if selectcell == \\\"centered\\\":\\r\\n #select contour that is closest to the center of the image. \\r\\n #In iPAC, cells are usually in the center.\\r\\n mid_x,mid_y = mask.shape[0]/2,mask.shape[1]/2 #middle of the image\\r\\n BB = [cv2.boundingRect(c) for c in contours] #get a bounding box around the object\\r\\n distances = [np.sqrt((mid_x-bb[0])**2 + (mid_y-bb[1])**2) for bb in BB]\\r\\n sorter = np.argsort(distances) #smallest first\\r\\n \\r\\n #sort values with respect to chosen metric (area_ratio or distance)\\r\\n contours = [contours[s] for s in sorter]\\r\\n hulls = [hulls[s] for s in sorter]\\r\\n pos_x = [pos_x[s] for s in sorter]\\r\\n pos_y = [pos_y[s] for s in sorter]\\r\\n mu_origs = [mu_origs[s] for s in sorter]\\r\\n area_origs = [area_origs[s] for s in sorter]\\r\\n area_hulls = [area_hulls[s] for s in sorter]\\r\\n \\r\\n # draw mask of the chosen contour\\r\\n mask = np.zeros_like(mask)\\r\\n cv2.drawContours(mask,contours,0,1,cv2.FILLED)# produce a contour that is filled inside\\r\\n\\r\\n hull = hulls[0]#[0:n_contours]\\r\\n pos_x = pos_x[0]\\r\\n pos_y = pos_y[0] \\r\\n mu_orig = mu_origs[0]#[0:n_contours]\\r\\n area_orig = area_origs[0]#[0:n_contours]\\r\\n area_hull = area_hulls[0]#[0:n_contours]\\r\\n \\r\\n if area_orig>0:\\r\\n area_ratio = area_hull/area_orig\\r\\n else:\\r\\n area_ratio = np.nan\\r\\n\\r\\n arc = cv2.arcLength(hull, True) \\r\\n circularity = 2.0 * np.sqrt(np.pi * mu_orig[\\\"m00\\\"]) / arc\\r\\n\\r\\n\\r\\n dic = {\\\"mask\\\":mask,\\\"pos_x\\\":pos_x,\\\"pos_y\\\":pos_y,\\\"area_orig\\\":area_orig,\\\"area_hull\\\":area_hull,\\\\\\r\\n \\\"area_ratio\\\":area_ratio,\\\"circularity\\\":circularity}\\r\\n return dic\",\n \"def cell_based_hist_segmentation(img, cells=[32, 32]):\\n descriptor = []\\n w,h = img.shape[:2]\\n w_ranges = [(i*w)//cells[0] for i in range(cells[0])]+[-1]\\n h_ranges = [(i*h)//cells[1] for i in range(cells[1])]+[-1]\\n\\n celled_hist = obtain_celled_histograms(img, cells, w_ranges, h_ranges)\\n\\n rectangular_segm = obtain_rectangular_segmentation(celled_hist, cells)\\n mask = create_mask(rectangular_segm, img, cells)\\n return mask\",\n \"def _filter_imgs(self, min_size=32):\\n valid_inds = []\\n for i, img_info in enumerate(self.img_infos):\\n if min(img_info['width'], img_info['height']) >= min_size:\\n valid_inds.append(i)\\n return valid_inds\",\n \"def _filter_imgs(self, min_size=32):\\n valid_inds = []\\n for i, img_info in enumerate(self.img_infos):\\n if min(img_info['width'], img_info['height']) >= min_size:\\n valid_inds.append(i)\\n return valid_inds\",\n \"def getValidIndicies(self, points):\\n try:\\n inds = [ i for i in range(points.size) if points.flat[i] in self.maskSet ]\\n return inds\\n except Exception as error:\\n print(\\\"failed in getValidIndicies\\\", error)\\n return -1\",\n \"def arraybin(array, bins):\\n bin_it = lambda value: (i for i in range(len(array)) if array[i] >= value)\\n splits = [next(bin_it(value), len(array)) for value in bins]\\n return [list(range(start_idx, stop_idx)) for (start_idx, stop_idx)\\n in zip([0] + splits, splits + [len(array)])]\",\n \"def extract_histogram(self, bins=10):\\n assert len(self.images) > 0, 'No images loaded! Did you call ' \\\\\\n 'load_images() ?'\\n histograms = []\\n for image in self.images:\\n grey = skicol.rgb2gray(image)\\n hist_values, bins = np.histogram(grey, range=(0, 1), bins=bins)\\n histograms.append(hist_values)\\n histograms = np.array(histograms)\\n histograms = histograms.astype('float')\\n return histograms\",\n \"def filter_zeros(X):\\n\\tnoNonzeros = np.count_nonzero(X, axis=1)\\n\\tmask = np.where(noNonzeros > 0)\\n\\treturn X[mask[0], :]\",\n \"def possibilities(board):\\n return board[np.where(board == 0)]\",\n \"def getLandmarkindices(self):\\n return self.subsetindices\",\n \"def indices(self):\\n _indices = []\\n for h in self.miller.indices():\\n _indices.append(self.indices_hkl(*h)[0])\\n return _indices\",\n \"def getLandmarkindices(self):\\n return self.subsetnodes_indices\",\n \"def getShadeMap(img):\\n return np.bincount(img.astype(int).flat, minlength=256)\",\n \"def getHists(img,bins=50):\\n hists = np.array([])\\n for i in range(3):#Images are loaded as three-dimensional matrices with three channels\\n hists = np.append(hists,np.histogram(img[:,:,i], bins, density = True)[0])\\n return hists\",\n \"def nonzero_indices(a):\\n return (np.nonzero(a)[0])\",\n \"def _getNonPrototypeIndices(self, clusters: ndarray) -> ndarray:\\n return np.delete(np.arange(self.dataSize), clusters.flatten())\",\n \"def get_near(self,map):\\n near_cells = []\\n for i in range(self.x-1, self.x+2):\\n for j in range(self.y-1, self.y+2):\\n if(i>=0 and i<map.size and j>=0 and j<map.size): near_cells.append(map.search(i,j))\\n return near_cells\",\n \"def get_centers_from_bins(bins):\\n return 0.5 * (bins[:-1] + bins[1:])\",\n \"def find_cell_intersections(num_cells, cells_list):\\n cells_intersect_ids = []\\n\\n for i in range(num_cells):\\n inters_list = []\\n for j in range(num_cells):\\n if i != j:\\n # count number of intersections\\n inters = np.intersect1d(cells_list[i], cells_list[j])\\n if (inters.size != 0):\\n inters_list.append(np.asscalar(inters))\\n\\n cells_intersect_ids.append(np.asarray(inters_list))\\n\\n\\n\\n return cells_intersect_ids\",\n \"def imthresh(img):\\n img_vecs = img.flatten()\\n\\n # pre-calculate the histogram and cumulative histogram.\\n vbins = np.arange(0, 257, 1)\\n img_hist, hist_edges = np.histogram(img_vecs, vbins)\\n vbins = (hist_edges[:-1] + hist_edges[1:])/2\\n \\n hist_times_gray = np.cumsum(img_hist * np.arange(0, 256, 1))\\n cum_hist = np.cumsum(img_hist)\\n\\n # A first approximation of the background mean mean_1 is the mean of the corner pixels.\\n # The third corner's index seems to be wrong!\\n m, n = img.shape\\n sum_bg = np.sum(img_vecs[[0, n - 1, n * (m - 1), m * n - 1]])\\n num_pix_bg = 4\\n mean1 = sum_bg/4\\n mean2 = (np.sum(img_vecs) - sum_bg)/(m *n - num_pix_bg)\\n threshold_val = np.uint8(np.ceil((mean1 + mean2)/2))\\n\\n\\n if (threshold_val != 0) and (cum_hist[threshold_val - 1] == 0):\\n threshold_val_old = threshold_val\\n\\n threshold_val_old = 0 # weird\\n while threshold_val != threshold_val_old:\\n threshold_val_old = threshold_val\\n mean1 = hist_times_gray[threshold_val - 1]/cum_hist[threshold_val - 1]\\n mean2 = (hist_times_gray[-1] - hist_times_gray[threshold_val - 1])/(cum_hist[-1] - cum_hist[threshold_val - 1])\\n\\n threshold_val = np.uint8(np.ceil((mean1 + mean2)/2))\\n\\n\\n img_out = img >= threshold_val\\n return img_out, threshold_val\",\n \"def neighbors(i , j) :\\n ns = []\\n # vector de direction\\n dx = [+1, +1, 0, 1]\\n dy = [0, +1, 1, -1]\\n for d in range(4) :\\n ns.append((i + dx[d], j + dy[d]))\\n #remove neagative element\\n ns = [i for i in ns if i[0] >= 0 and i[1] >= 0]\\n return ns\",\n \"def _fast_hist_2d(data, bin_edges):\\n # Yes, I've tested this against histogramdd().\\n xassign = np.digitize(data[:,0], bin_edges[1:-1]) \\n yassign = np.digitize(data[:,1], bin_edges[1:-1])\\n nbins = len(bin_edges) - 1\\n flatcount = np.bincount(xassign + yassign * nbins, minlength=nbins*nbins)\\n return flatcount.reshape((nbins, nbins))\",\n \"def get_empty_cells(state):\\n cells = []\\n for row_index, row in enumerate(state.board):\\n for col_index, cell in enumerate(row):\\n if cell == 0:\\n cells.append([row_index, col_index])\\n return cells\",\n \"def get_bit_positions(bit_mask):\\n\\tbit_positions = []\\n\\t# find bit positions of enabled bits in mask\\n\\tfor i in range(16):\\n\\t\\tif (bit_mask & (1 << i)) != 0:\\n\\t\\t\\tbit_positions.append(i)\\n\\treturn bit_positions\",\n \"def findAllColors(img):\\n hist = cv2.calcHist([img], [0, 1, 2], None, [256] * 3, [0, 256] * 3)\\n allColors = np.argwhere(hist != 0)\\n return allColors\"\n]","isConversational":false},"negative_scores":{"kind":"list like","value":["0.6325455","0.63100487","0.6245059","0.6240828","0.62382156","0.622966","0.6220698","0.62201536","0.6188076","0.6029027","0.59354","0.5917809","0.59095675","0.5891025","0.5851099","0.58345515","0.5804097","0.5793994","0.5785157","0.5784481","0.5775276","0.5772233","0.575881","0.57444346","0.5743837","0.57414955","0.5714774","0.5709631","0.5709631","0.5709631","0.56963235","0.5644732","0.5641252","0.56352025","0.5633108","0.5625824","0.5601571","0.56012017","0.5597387","0.55833745","0.55804235","0.55763304","0.55743015","0.55630136","0.55613947","0.55440056","0.55398476","0.5537104","0.55354404","0.55343354","0.55286944","0.55271804","0.5518714","0.55157685","0.5506453","0.54935646","0.549345","0.54890764","0.54859793","0.5477729","0.5457373","0.5456552","0.5450731","0.5436343","0.5436232","0.5435693","0.5424106","0.54210126","0.5412422","0.5405309","0.54002225","0.5399409","0.53957784","0.538982","0.5387654","0.53863376","0.53841466","0.5367121","0.5367121","0.53640836","0.53590286","0.53550494","0.53494686","0.534725","0.5341234","0.5340065","0.5339024","0.5335855","0.53334534","0.5332681","0.53316414","0.5331217","0.53303975","0.53288954","0.53273326","0.53256595","0.53209484","0.53198594","0.53172773","0.53159684"],"string":"[\n \"0.6325455\",\n \"0.63100487\",\n \"0.6245059\",\n \"0.6240828\",\n \"0.62382156\",\n \"0.622966\",\n \"0.6220698\",\n \"0.62201536\",\n \"0.6188076\",\n \"0.6029027\",\n \"0.59354\",\n \"0.5917809\",\n \"0.59095675\",\n \"0.5891025\",\n \"0.5851099\",\n \"0.58345515\",\n \"0.5804097\",\n \"0.5793994\",\n \"0.5785157\",\n \"0.5784481\",\n \"0.5775276\",\n \"0.5772233\",\n \"0.575881\",\n \"0.57444346\",\n \"0.5743837\",\n \"0.57414955\",\n \"0.5714774\",\n \"0.5709631\",\n \"0.5709631\",\n \"0.5709631\",\n \"0.56963235\",\n \"0.5644732\",\n \"0.5641252\",\n \"0.56352025\",\n \"0.5633108\",\n \"0.5625824\",\n \"0.5601571\",\n \"0.56012017\",\n \"0.5597387\",\n \"0.55833745\",\n \"0.55804235\",\n \"0.55763304\",\n \"0.55743015\",\n \"0.55630136\",\n \"0.55613947\",\n \"0.55440056\",\n \"0.55398476\",\n \"0.5537104\",\n \"0.55354404\",\n \"0.55343354\",\n \"0.55286944\",\n \"0.55271804\",\n \"0.5518714\",\n \"0.55157685\",\n \"0.5506453\",\n \"0.54935646\",\n \"0.549345\",\n \"0.54890764\",\n \"0.54859793\",\n \"0.5477729\",\n \"0.5457373\",\n \"0.5456552\",\n \"0.5450731\",\n \"0.5436343\",\n \"0.5436232\",\n \"0.5435693\",\n \"0.5424106\",\n \"0.54210126\",\n \"0.5412422\",\n \"0.5405309\",\n \"0.54002225\",\n \"0.5399409\",\n \"0.53957784\",\n \"0.538982\",\n \"0.5387654\",\n \"0.53863376\",\n \"0.53841466\",\n \"0.5367121\",\n \"0.5367121\",\n \"0.53640836\",\n \"0.53590286\",\n \"0.53550494\",\n \"0.53494686\",\n \"0.534725\",\n \"0.5341234\",\n \"0.5340065\",\n \"0.5339024\",\n \"0.5335855\",\n \"0.53334534\",\n \"0.5332681\",\n \"0.53316414\",\n \"0.5331217\",\n \"0.53303975\",\n \"0.53288954\",\n \"0.53273326\",\n \"0.53256595\",\n \"0.53209484\",\n \"0.53198594\",\n \"0.53172773\",\n \"0.53159684\"\n]","isConversational":false},"document_score":{"kind":"string","value":"0.57897145"},"document_rank":{"kind":"string","value":"18"}}},{"rowIdx":22681,"cells":{"query":{"kind":"string","value":"Detrends a HiC matrix by the distance law. The input matrix should have been normalised beforehandand."},"document":{"kind":"string","value":"def detrend(\n matrix,\n detectable_bins=None,\n max_dist=None,\n smooth=False,\n fun=np.nanmean,\n max_val=10,\n):\n matrix = matrix.tocsr()\n y = distance_law(\n matrix,\n detectable_bins=detectable_bins,\n max_dist=max_dist,\n smooth=smooth,\n fun=fun,\n )\n y[np.isnan(y)] = 0.0\n # Detrending by the distance law\n clean_mat = matrix.tocoo()\n # clean_mat.data /= y_savgol[abs(clean_mat.row - clean_mat.col)]\n try:\n clean_mat.data = clean_mat.data / y[abs(clean_mat.row - clean_mat.col)]\n # If no nonzero value in matrix, do nothing\n except TypeError:\n pass\n clean_mat = clean_mat.tocsr()\n if max_val is not None:\n clean_mat[clean_mat >= max_val] = 1\n return clean_mat"},"metadata":{"kind":"string","value":"{\n \"objective\": {\n \"self\": [],\n \"paired\": [],\n \"triplet\": [\n [\n \"query\",\n \"document\",\n \"negatives\"\n ]\n ]\n }\n}"},"negatives":{"kind":"list like","value":["def det(A):\n # Section 1: Establish n parameter and copy A\n n = len(A)\n AM = A[:]\n\n # Section 2: Row manipulate A into an upper triangle matrix\n for fd in range(n): # fd stands for focus diagonal\n if AM[fd][fd] == 0:\n AM[fd][fd] = 1.0e-18 # Cheating by adding zero + ~zero\n for i in range(fd+1, n): # skip row with fd in it.\n crScaler = AM[i][fd] / AM[fd][fd] # cr stands for \"current row\".\n for j in range(n): # cr - crScaler * fdRow, one element at a time.\n AM[i][j] = AM[i][j] - crScaler * AM[fd][j]\n\n # Section 3: Once AM is in upper triangle form ...\n product = 1.0\n for i in range(n):\n product *= AM[i][i] # ... product of diagonals is determinant\n\n return product","def post_heatdiag(self,ds):\n #\n self.drmid=self.rmid*0 # mem allocation\n self.drmid[1:-1]=(self.rmid[2:]-self.rmid[0:-2])*0.5\n self.drmid[0]=self.drmid[1]\n self.drmid[-1]=self.drmid[-2]\n\n dt = np.zeros_like(self.time)\n dt[1:] = self.time[1:] - self.time[0:-1]\n dt[0] = dt[1]\n rst=np.nonzero(dt<0) #index when restat happen\n dt[rst]=dt[rst[0]+1]\n self.dt = dt\n\n #get separatrix r\n self.rs=np.interp([1],self.psin,self.rmid)\n \n self.rmidsepmm=(self.rmid-self.rs)*1E3 # dist from sep in mm\n\n #get heat\n self.qe=np.transpose(self.e_perp_energy_psi + self.e_para_energy_psi)/dt/ds\n self.qi=np.transpose(self.i_perp_energy_psi + self.i_para_energy_psi)/dt/ds\n self.ge=np.transpose(self.e_number_psi)/dt/ds\n self.gi=np.transpose(self.i_number_psi)/dt/ds\n\n self.qe = np.transpose(self.qe)\n self.qi = np.transpose(self.qi)\n self.ge = np.transpose(self.ge)\n self.gi = np.transpose(self.gi)\n\n self.qt=self.qe+self.qi\n #imx=self.qt.argmax(axis=1)\n mx=np.amax(self.qt,axis=1)\n self.lq_int=mx*0 #mem allocation\n\n for i in range(mx.shape[0]):\n self.lq_int[i]=np.sum(self.qt[i,:]*self.drmid)/mx[i]","def det2(m):\n\t(a,b), (c,d) = m\n\treturn a*d - b*c","def det_matrix(self):\n return np.linalg.det(self.take_matrix())","def _calc_del(dis):\n ndist = len(dis)\n deldis = np.zeros((ndist))\n deldis[0] = (dis[1] - dis[0]) * 0.5\n deldis[1:-1] = (dis[2:] - dis[0:-2]) * 0.5\n deldis[-1] = (dis[-1] - dis[-2]) * 0.5\n return deldis","def det(self,mat):\n if(len(mat[0])==len(mat)):\n result = np.linalg.det(mat)\n self.determinant = result\n return self.determinant\n else:\n print(\"Not a square Matrix\")","def _compute_diag_H(self, t, index, update_derivatives=False, stationary=False):\r\n\r\n \"\"\"if delta_i~=delta_j:\r\n [h, dh_dD_i, dh_dD_j, dh_dsigma] = np.diag(simComputeH(t, index, t, index, update_derivatives=True, stationary=self.is_stationary))\r\n else:\r\n Decay = self.decay[index]\r\n if self.delay is not None:\r\n t = t - self.delay[index]\r\n \r\n t_squared = t*t\r\n half_sigma_decay = 0.5*self.sigma*Decay\r\n [ln_part_1, sign1] = ln_diff_erfs(half_sigma_decay + t/self.sigma,\r\n half_sigma_decay)\r\n \r\n [ln_part_2, sign2] = ln_diff_erfs(half_sigma_decay,\r\n half_sigma_decay - t/self.sigma)\r\n \r\n h = (sign1*np.exp(half_sigma_decay*half_sigma_decay\r\n + ln_part_1\r\n - log(Decay + D_j)) \r\n - sign2*np.exp(half_sigma_decay*half_sigma_decay\r\n - (Decay + D_j)*t\r\n + ln_part_2 \r\n - log(Decay + D_j)))\r\n \r\n sigma2 = self.sigma*self.sigma\r\n\r\n if update_derivatives:\r\n \r\n dh_dD_i = ((0.5*Decay*sigma2*(Decay + D_j)-1)*h \r\n + t*sign2*np.exp(\r\n half_sigma_decay*half_sigma_decay-(Decay+D_j)*t + ln_part_2\r\n )\r\n + self.sigma/np.sqrt(np.pi)*\r\n (-1 + np.exp(-t_squared/sigma2-Decay*t)\r\n + np.exp(-t_squared/sigma2-D_j*t)\r\n - np.exp(-(Decay + D_j)*t)))\r\n \r\n dh_dD_i = (dh_dD_i/(Decay+D_j)).real\r\n \r\n \r\n \r\n dh_dD_j = (t*sign2*np.exp(\r\n half_sigma_decay*half_sigma_decay-(Decay + D_j)*t+ln_part_2\r\n )\r\n -h)\r\n dh_dD_j = (dh_dD_j/(Decay + D_j)).real\r\n\r\n dh_dsigma = 0.5*Decay*Decay*self.sigma*h \\\r\n + 2/(np.sqrt(np.pi)*(Decay+D_j))\\\r\n *((-Decay/2) \\\r\n + (-t/sigma2+Decay/2)*np.exp(-t_squared/sigma2 - Decay*t) \\\r\n - (-t/sigma2-Decay/2)*np.exp(-t_squared/sigma2 - D_j*t) \\\r\n - Decay/2*np.exp(-(Decay+D_j)*t))\"\"\"\r\n pass","def fast_logdet(matrix):\n sign, ld = np.linalg.slogdet(matrix)\n if not sign > 0:\n return -np.inf\n return ld","def _det(mat):\n\n return (mat[0][0] * (mat[1][1] * mat[2][2] - mat[1][2] * mat[2][1])\n + mat[0][1] * (mat[1][2] * mat[2][0] - mat[1][0] *\n mat[2][2]) + mat[0][2] * (mat[1][0] * mat[2][1] -\n mat[1][1] * mat[2][0]))","def Distmatrix(self):\n self.Dismatrix = np.zeros((self.nodenum, self.nodenum))\n for i in range(len(self.Dismatrix)):\n for j in range(len(self.Dismatrix)):\n self.Dismatrix[i, j] = sf.dist(self.y[i], self.x[i], self.y[j], self.x[j])\n self.Dismatrix[j, i] = self.Dismatrix[i, j]","def det_matrix_2x2(m: list):\n return m[0][0]*m[1][1] - m[0][1]*m[1][0]","def det(a):\n a = copy.deepcopy(a)\n n = len(a)\n det = 1\n com_k = 1\n for k in range(n-1):\n step = 1\n\n while a[k][k] == 0:\n a[k+step], a[k] = a[k], a[k+step]\n det = -det\n step += 1\n mul = a[k][k]\n\n for i in range(k+1, n):\n for j in range(k+1, n):\n a[i][j] *= mul\n a[i][j] -= a[i][k] * a[k][j]\n a[i][j] /= com_k\n\n com_k = mul\n\n det = det * a[-1][-1]\n\n return det","def build_cumulative_downhill_matrix(self):\n\n import time\n from scipy import sparse as sparse\n\n\n walltime = time.clock()\n\n downHillaccuMat = self.downhillMat.copy() \n accuM = self.downhillMat.copy() # work matrix\n\n DX = np.ones(self.tri.npoints) # measure when all the info has been propagated out.\n previous_nonzero = 0\n it = 0\n\n while np.count_nonzero(DX) != previous_nonzero:\n accuM = accuM.dot(self.downhillMat)\n downHillaccuMat = downHillaccuMat + accuM \n previous_nonzero = np.count_nonzero(DX)\n\n DX = self.downhillMat.dot(DX) \n\n it += 1\n \n\n print \" - Dense downhill matrix storage time \", time.clock() - walltime\n print \" - Maximum path length \",it\n\n walltime = time.clock()\n\n\n # Turn this into a loop !\n\n A1 = self.downhillMat.tocsr()\n A2 = A1.dot(A1)\n A2a = A1 + A2\n A4 = A2.dot(A2)\n A4a = A2a + A2.dot(A2a)\n A8 = A4.dot(A4)\n A8a = A4a + A4.dot(A4a)\n A16 = A8.dot(A8)\n A16a = A8a + A8.dot(A8a)\n A32 = A16.dot(A16)\n A32a = A16a + A16.dot(A16a)\n A64 = A32.dot(A32)\n A64a = A32a + A32.dot(A32a)\n A128 = A64.dot(A64)\n A128a = A64a + A64.dot(A64a)\n\n print \"A32.nnz = \", A32.nnz\n print \"A64.nnz = \", A64.nnz\n print \"A128.nnz = \", A128.nnz\n\n\n print \" - Dense downhill matrix storage time v2\", time.clock() - walltime\n print \" - Maximum path length \", 128\n\n\n downHillaccuMat = downHillaccuMat + sparse.identity(self.tri.npoints, format='csr')\n\n downHillaccuMat2 = A128a + sparse.identity(self.tri.npoints, format='csr')\n\n\n return downHillaccuMat, downHillaccuMat2","def det(self):\n # XXX: At least the first three algorithms described above should also\n # be implemented in the pure Python DDM and SDM classes which at the\n # time of writng just use Bareiss for all matrices and domains.\n # Probably in Python the thresholds would be different though.\n return self.rep.det()","def _compute_rho0(self, distance_matrix):\n nr_samples = distance_matrix.shape[1]\n\n # both modes are equivalent, MODE=1 allows to easier compare with ref3.\n MODE = 1\n if MODE == 1: # according to Berry code\n # keep only nearest neighbors\n distance_matrix = np.sort(np.sqrt(distance_matrix), axis=1)[\n :, 1 : self.k + 1\n ]\n rho0 = np.sqrt(np.mean(distance_matrix ** 2, axis=1))\n else: # MODE == 2: , more performant if required\n if self.k < nr_samples:\n # TODO: have to revert setting the inf\n # -> check that the diagonal is all zeros\n # -> set to inf\n # -> after computation set all infs back to zero\n # -> this is also very similar to continous-nn in PCManifold\n\n # this allows to ignore the trivial distance=0 to itself\n np.fill_diagonal(distance_matrix, np.inf)\n\n # more efficient than sorting, everything\n distance_matrix.partition(self.k, axis=1)\n distance_matrix = np.sort(distance_matrix[:, : self.k], axis=1)\n distance_matrix = distance_matrix * distance_matrix\n else: # self.k == self.N\n np.fill_diagonal(distance_matrix, np.nan)\n bool_mask = ~np.diag(np.ones(nr_samples)).astype(bool)\n distance_matrix = distance_matrix[bool_mask].reshape(\n distance_matrix.shape[0], distance_matrix.shape[1] - 1\n )\n\n # experimental: --------------------------------------------------------------\n # paper: in var-bw paper (ref2) pdfp. 7\n # it is mentioned to IGNORE non-zero entries -- this is not detailed more.\n # a consequence is that the NN and kernel looses symmetry, so does (K+K^T) / 2\n # This is with a cut-off rate:\n # val = 1E-2\n # distance_matrix[distance_matrix < val] = np.nan\n # experimental END -----------------------------------------------------------\n\n # nanmean only for the experimental part, if leaving this out, np.mean\n # suffices\n rho0 = np.sqrt(np.nanmean(distance_matrix, axis=1))\n\n return rho0","def det(self):\n\n if self.rows != self.columns:\n raise ValueError(\"Matrix must be square\")\n\n if self.rows == 1:\n return self.row(1)[0]\n\n if self.rows == 2:\n return self.entry(1,1) * self.entry(2,2) - self.entry(1,2) * self.entry(2,1)\n\n det = 0\n row_to_expand = 1\n\n for i in range(1, self.columns + 1):\n det += self.entry(row_to_expand, i) * self._cofactor(row_to_expand, i)\n\n return det","def determinant(self):\n if self.cols != self.rows:\n raise Exception ('Matrix is not square!')\n for i in range(self.rows):\n if self.values[i][i] == 0:\n raise Exception ('There is zero on the main diagonal')\n #TODO: Rearrange the lines, that the main diagonal don't have a zero values \n\n arr = self.values[:]\n for i in range(self.rows):\n for j in range(self.cols):\n diag = [arr[l][p] for p in range(self.cols) for l in range(self.rows) if l == p ]\n if i > j :\n arr2 = arr[i][j]/diag[j]\n arr1 = [round(x * arr2, 4) for x in arr[i-i+j]]\n arr[i] = map(lambda x,y: round(x - y, 4) , arr[i], arr1 )\n\n diag = [arr[l][p] for p in range(self.cols) for l in range(self.rows) if l == p ]\n det = 1\n for i in range(len(diag)):\n det *= diag[i]\n if det != 0 :\n return True\n else:\n return False","def _derive_euclidean_dm(self, cat_mat, dim):\r\n res_mat = []\r\n\r\n for i in range(dim):\r\n res_mat.append([0 for k in range(dim)])\r\n for j in range(i):\r\n res_mat[i][j] = self._vector_dist(cat_mat[i], cat_mat[j])\r\n res_mat[j][i] = res_mat[i][j]\r\n\r\n return DistanceMatrix(res_mat, self.DistanceMatrices[0].ids)","def calc_det_dzh(theta):\n return 919.49 - 27.018 * theta + 0.26209 * theta ** 2 - 0.00083803 * theta ** 3","def test_frechet_distance_diagonal_covariance(self):\n N = 15\n mu_x = torch.rand((N,), device=self.device)\n sigma_x = torch.diag(torch.rand((N,), device=self.device))\n\n mu_y = torch.rand((N,), device=self.device)\n sigma_y = torch.diag(torch.rand((N,), device=self.device))\n\n expected = (\n torch.sum((mu_x - mu_y) ** 2) + torch.sum(sigma_x + sigma_y) - 2 * torch.sum(torch.sqrt(sigma_x * sigma_y))\n )\n expected = expected.item()\n actual = F.frechet_distance(mu_x, sigma_x, mu_y, sigma_y)\n\n self.assertEqual(expected, actual)","def construct_H_with_KNN_from_distance(dis_mat, k_neig, is_probH=True, m_prob=1, sig=0.002):\n n_obj = dis_mat.shape[0]\n # construct hyperedge from the central feature space of each node\n n_edge = n_obj\n H = np.zeros((n_obj, n_edge))\n for center_idx in range(n_obj):\n dis_mat[center_idx, center_idx] = 0\n dis_vec = dis_mat[center_idx]\n nearest_idx = np.array(np.argsort(dis_vec)).squeeze()\n #avg_dis = np.average(dis_vec)\n if not np.any(nearest_idx[:k_neig] == center_idx):\n nearest_idx[k_neig - 1] = center_idx\n\n for node_idx in nearest_idx[:k_neig]:\n if is_probH:\n H[node_idx, center_idx] = np.exp(- sig * dis_vec[0, node_idx] )\n #print(H[node_idx, center_idx])\n #H[node_idx, center_idx] = dis_vec[0, node_idx]\n else:\n H[node_idx, center_idx] = 1.0\n return H","def determinant(self):\n if not self.is_square():\n raise(ValueError, \"Cannot calculate determinant of non-square matrix.\")\n if self.h > 2:\n raise(NotImplementedError, \"Calculating determinant not implemented for matrices largerer than 2x2.\")\n\n # TODO - your code here\n if self.h == 1:\n return self.g[0][0] # a 1x1 matrix\n else:\n return ((self.g[0][0] * self.g[1][1]) - (self.g[0][1] * self.g[1][0])) # a 2x2 matrix\n # TODO - your code here","def normalize_distancematrix(self):\n INF = self.distmat.max().max()\n df = self.distmat.fillna(INF)\n self.distmat = (df - df.min()) / (df.max() - df.min())","def determinant_fast(A):\n # Section 1: Establish n parameter and copy A\n n = len(A)\n AM = copy_matrix(A)\n\n # Section 2: Row manipulate A into an upper triangle matrix\n for fd in range(n): # fd stands for focus diagonal\n if AM[fd][fd] == 0: \n AM[fd][fd] = 1.0e-18 # Cheating by adding zero + ~zero\n for i in range(fd+1,n): # skip row with fd in it.\n crScaler = AM[i][fd] / AM[fd][fd] # cr stands for \"current row\".\n for j in range(n): # cr - crScaler * fdRow, but one element at a time.\n AM[i][j] = AM[i][j] - crScaler * AM[fd][j]\n \n # Section 3: Once AM is in upper triangle form ...\n product = 1.0\n for i in range(n):\n product *= AM[i][i] # ... product of diagonals is determinant\n\n return product","def test_sd_nonlinear(self):\n z_matrix = np.array(\n [[0.0, 0.0, 0.0],\n [0.0, 0.0, 1.0],\n [0.2, 0.5, 0.0],\n [0.2, 0.5, 1.0],\n [0.4, 1.0, 0.0],\n [0.4, 1.0, 1.0],\n [0.6, 1.0, 0.0],\n [0.6, 1.0, 1.0],\n [0.8, 0.5, 0.0],\n [0.8, 0.5, 1.0],\n [1.0, 0.0, 0.0],\n [1.0, 0.0, 1.0]],\n dtype=np.float64)\n obtained_w_vector = mcdm.weigh(z_matrix, \"SD\")\n expected_w_vector = np.array(\n [0.27329284, 0.32664742, 0.40005975],\n dtype=np.float64)\n np.testing.assert_allclose(obtained_w_vector, expected_w_vector)\n self.assertEqual(obtained_w_vector.dtype, expected_w_vector.dtype)","def _build_downhill_matrices(self, weight=0.6667):\n\n from scipy import sparse as sparse\n \n\n down_neighbour = np.empty(self.tri.npoints, dtype=np.int)\n\n for node in range (0,self.tri.npoints):\n down_neighbour[node] = self.neighbour_array_lo_hi[node][0]\n\n # Build a matrix of downhill-ness - one entry per node ! \n \n size = self.tri.npoints\n row_array = np.empty(size, dtype = int)\n col_array = np.empty(size, dtype = int)\n down_array = np.ones(size)\n accu_array = np.ones(size)\n\n\n for row in range(0, self.tri.npoints): \n row_array[row] = row\n col_array[row] = down_neighbour[row]\n \n accuMCOO = sparse.coo_matrix( (accu_array, (row_array, col_array)), shape=(size,size) ).T \n\n self.accumulatorMat = accuMCOO.tocsr() \n\n self._build_adjacency_matrix_1()\n self._build_adjacency_matrix_2()\n \n self.downhillMat = weight * self.adjacency1 + (1.0-weight) * self.adjacency2\n\n # A1 = self.downhillMat\n # A2 = self.downhillMat.dot(self.downhillMat)\n # A2a = A1 + A2\n # A4 = A2.dot(A2)\n # A4a = A2a + A2.dot(A2a)\n # A8 = A4.dot(A4)\n # A8a = A4a + A4.dot(A4a)\n # A16 = A8.dot(A8)\n # A16a = A8a + A8.dot(A8a)\n\n # self.downhillMat16 = A16\n # self.downhillMat8 = A8\n # self.downhillMat16a = A16a\n # self.downhillMat8a = A8a\n\n # We make it optional to build these as they are not sparse \n # This cleans up previously stored matrices\n\n self.downhillCumulativeMat = None\n self.sweepDownToOutflowMat = None\n \n return","def Dmat(numpts, delta=1):\n a = 0.5 / delta * ones(numpts)\n a[0] = 0\n a[-2] = 0\n #b=-2./delta**2*ones(numpts); b[0]=0;b[-1]=0\n c = -0.5 / delta * ones(numpts)\n c[1] = 0\n c[-1] = 0\n return sparse.spdiags([a, c], [-1, 1], numpts, numpts)","def det(self):\n if self.x == 0 or self.y == 0:\n return None\n elif self.x == 1 or self.y == 1:\n return self.retrieve(0,0)\n else:\n out = 0.0\n for x in xrange(0, self.x):\n out += self.retrieve(0,x)*self.C(0,x)\n return out","def normalize_adj(adj): # 图归一化操作\n adj = sp.coo_matrix(adj) # A_hat\n rowsum = np.array(adj.sum(1))\n d_inv_sqrt = np.power(rowsum, -0.5).flatten()\n d_inv_sqrt[np.isinf(d_inv_sqrt)] = 0.\n d_mat_inv_sqrt = sp.diags(d_inv_sqrt) # D_hat\n return adj.dot(d_mat_inv_sqrt).transpose().dot(d_mat_inv_sqrt).tocoo()","def compute_determinant(matrix):\n det = np.linalg.det(matrix)\n #if det == 0.:\n # The det = 0 could be related to the third feature\n # det = np.linalg.det(matrix[:2, :2])\n if det == 0.:\n # Singular covariance matrix, should not be taken into account\n det = np.nan\n if np.isclose(det, 0):\n det = np.abs(det)\n return det","def correlation_d(mat):\n\n print(\"DO NOT USE. BROKEN?\")\n\n if mat.ndim != 2:\n raise ValueError(\"mat must be a 2d matrix\")\n if np.any(mat > 1) or np.any(mat < 0):\n raise ValueError(\"mat must be binary\")\n\n N = mat.size\n g = np.diagonal(mat)\n # g = np.tril(mat, -1) # g is the sum over the heavside used in Grassberger\n # g = g[g.nonzero()]\n g = g.sum()\n\n return (2.0 / N * (N - 1)) * g","def _matrix_store_smooth_downhill(self):\n \n import time\n from scipy import sparse as sparse\n from scipy.sparse import linalg as linalgs \n \n\n t = time.clock()\n\n\n size = 0\n for nl in self.neighbour_array_lo_hi:\n size += 3 # len(nl)\n\n row_array = np.empty(size, dtype = int)\n col_array = np.empty(size, dtype = int)\n slope_array = np.zeros(size)\n local_slope_array = np.zeros(64)\n\n\n idx=0 \n for row in range(0, len(self.neighbour_array_lo_hi)): \n neighbours = self.neighbour_array_lo_hi[row] \n npoints = self.tri.points[neighbours]\n\n ## work out (downhill) gradient to (max of three) nearby neighbours\n \n\n for col, column in enumerate(neighbours[0:3]): \n \n delta_h = self.height[column] - self.height[row] \n\n\n if delta_h < 0.0:\n delta_s2 = (self.x[column] - self.x[row])**2 + (self.y[column] - self.y[row])**2\n local_slope_array[col] = ( delta_h**2 / delta_s2 )**5\n\n elif delta_h == 0.0 and self.bmask[row] == False:\n local_slope_array[col] = 1.0e-20\n\n else:\n local_slope_array[col] = 1.0e-20 \n \n # Normalise this so that it conserves mass (note - low points will have no contributions here !) \n \n norm = local_slope_array[0:len(neighbours)].sum()\n if norm != 0.0:\n norm = 1.0 / norm\n\n for col, column in enumerate(neighbours[0:3]): \n row_array[idx] = row\n col_array[idx] = column \n slope_array[idx] = local_slope_array[col] * norm\n\n idx += 1\n\n # We can re-pack this array into a sparse matrix for v. fast computation of downhill operator \n\n slopeCOO = sparse.coo_matrix( (slope_array, (row_array, col_array)) ).T\n slopeMat = slopeCOO.tocsr() \n \n print \"SlopeMat.shape \", slopeMat.shape, size\n\n # slopeNormVec = np.array(slopeMat.sum(axis=1)).T[0]\n # slopeNormVec[slopeNormVec != 0.0] = 1.0 / slopeNormVec[slopeNormVec != 0.0]\n # slopeNormMat = sparse.eye(self.tri.npoints)\n # slopeNormMat.setdiag(slopeNormVec)\n # slopeMat = slopeNormMat.dot(slopeMat)\n\n slopeMat.eliminate_zeros()\n self.smoothDownhillMat = slopeMat\n\n return","def matrix_det(A):\n\tx = A[0,0]*A[1,1]*A[2,2] + A[0,1]*A[1,2]*A[2,0] + A[0,2]*A[1,0]*A[2,1]\n\ty = A[0,0]*A[1,2]*A[2,1] + A[0,1]*A[1,0]*A[2,2] + A[0,2]*A[1,1]*A[2,0]\n\treturn x - y","def construct_H_with_KNN_from_distance(dis_mat, k_neig, is_probH=False, m_prob=1):\n n_obj = dis_mat.shape[0]\n # construct hyperedge from the central feature space of each node\n n_edge = n_obj\n H = np.zeros((n_obj, n_edge))\n for center_idx in range(n_obj):\n dis_mat[center_idx, center_idx] = 0\n dis_vec = dis_mat[center_idx]\n nearest_idx = np.array(np.argsort(dis_vec)).squeeze()\n avg_dis = np.average(dis_vec)\n if not np.any(nearest_idx[:k_neig] == center_idx):\n nearest_idx[k_neig - 1] = center_idx\n\n for node_idx in nearest_idx[:k_neig]:\n if is_probH:\n H[node_idx, center_idx] = np.exp(-dis_vec[0, node_idx] ** 2 / (m_prob * avg_dis) ** 2)\n else:\n H[node_idx, center_idx] = 1.0\n return H","def _make_edisp_hdu(cls, table_energy, table_migra, table_theta, matrix):\n\n table = Table(\n {\n \"ENERG_LO\": table_energy[\"ETRUE_LO\"][np.newaxis, :].data\n * table_energy[\"ETRUE_LO\"].unit,\n \"ENERG_HI\": table_energy[\"ETRUE_HI\"][np.newaxis, :].data\n * table_energy[\"ETRUE_HI\"].unit,\n \"MIGRA_LO\": table_migra[\"MIGRA_LO\"][np.newaxis, :].data\n * table_migra[\"MIGRA_LO\"].unit,\n \"MIGRA_HI\": table_migra[\"MIGRA_HI\"][np.newaxis, :].data\n * table_migra[\"MIGRA_HI\"].unit,\n \"THETA_LO\": table_theta[\"THETA_LO\"][np.newaxis, :].data\n * table_theta[\"THETA_LO\"].unit,\n \"THETA_HI\": table_theta[\"THETA_HI\"][np.newaxis, :].data\n * table_theta[\"THETA_HI\"].unit,\n \"MATRIX\": matrix[\"MATRIX\"][np.newaxis, :, :] * matrix[\"MATRIX\"].unit,\n }\n )\n\n header = fits.Header()\n header[\"HDUDOC\"] = (\n \"https://gamma-astro-data-formats.readthedocs.io/en/latest/irfs/index.html\",\n \"\",\n )\n header[\"HDUCLASS\"] = \"GADF\", \"\"\n header[\"HDUCLAS1\"] = \"RESPONSE\", \"\"\n header[\"HDUCLAS2\"] = \"EDISP\", \"\"\n header[\"HDUCLAS3\"] = \"POINT-LIKE\", \"\"\n header[\"HDUCLAS4\"] = \"EDISP_2D\", \"\"\n\n edisp_hdu = fits.BinTableHDU(table, header, name=\"ENERGY DISPERSION\")\n\n # primary_hdu = fits.PrimaryHDU()\n # hdulist = fits.HDUList([primary_hdu, edisp_hdu])\n #\n # return hdulist\n return edisp_hdu","def distance_matrix(X, Y, metric):\n distance = np.zeros((len(X), len(Y)))\n for i in range(len(X)):\n for j in range(len(Y)):\n m = metric(X[i], Y[j])\n if np.isnan(m):\n pdb.set_trace()\n distance[i, j] = m\n return distance","def _dmatrix(kn_u, kn_d):\n d = np.zeros((kn_u.size, 4, 4), np.complex128)\n d_inv = np.zeros_like(d)\n\n d[:, 0, 0] = 1\n d[:, 0, 1] = 1\n d[:, 1, 0] = kn_u\n d[:, 1, 1] = -kn_u\n\n d[:, 2, 2] = 1\n d[:, 2, 3] = 1\n d[:, 3, 2] = kn_d\n d[:, 3, 3] = -kn_d\n\n # an analytic matrix inverse saves time\n inv_kn_u = 0.5 / kn_u\n inv_kn_d = 0.5 / kn_d\n\n d_inv[:, 0, 0] = 0.5\n d_inv[:, 0, 1] = inv_kn_u\n d_inv[:, 1, 0] = 0.5\n d_inv[:, 1, 1] = -inv_kn_u\n\n d_inv[:, 2, 2] = 0.5\n d_inv[:, 2, 3] = inv_kn_d\n d_inv[:, 3, 2] = 0.5\n d_inv[:, 3, 3] = -inv_kn_d\n\n return d, d_inv","def test_sd_linear(self):\n z_matrix = np.array(\n [[0.0, 0.0, 1.0],\n [0.1, 0.2, 0.8],\n [0.2, 0.4, 0.6],\n [0.3, 0.7, 0.3],\n [0.6, 0.8, 0.2],\n [0.8, 0.9, 0.1],\n [1.0, 1.0, 0.0]],\n dtype=np.float64)\n obtained_w_vector = mcdm.weigh(z_matrix, \"SD\")\n expected_w_vector = np.array(\n [0.33333333, 0.33333333, 0.33333333],\n dtype=np.float64)\n np.testing.assert_allclose(obtained_w_vector, expected_w_vector)\n self.assertEqual(obtained_w_vector.dtype, expected_w_vector.dtype)","def local_det_chol(node):\r\n if node.op == det:\r\n x, = node.inputs\r\n for (cl, xpos) in x.clients:\r\n if isinstance(cl.op, Cholesky):\r\n L = cl.outputs[0]\r\n return [tensor.prod(extract_diag(L) ** 2)]","def build_dispersion_diags(self):\n N = self.N\n j = self._j # Index of the mid-point\n diags = np.zeros((2*self._j+1, self.N))\n\n dx3 = np.power(self.dx, 3.)\n\n ## This tells us how the diagonal matrix construction works\n #diags[j-2,:-4] = np.arange(1,N+1)[4:]\n #diags[j-1,:-2] = np.arange(1,N+1)[2:]\n #diags[j+1,:] = np.arange(1,N+1)\n #diags[j+2,:] = np.arange(1,N+1)\n\n #diags[j,0:2] = 11\n #diags[j+1,1:3] = 12\n #diags[j+2,2:4] = 13\n #diags[j+3,3:5]= 14\n\n\n # Original method had assymmetric diagonals\n #cff = -self.beta/(2*dx3)\n #diags[j-2,:] += -1*cff\n #diags[j-1,:] += 2*cff\n #diags[j+1,:] += -2*cff\n #diags[j+2,:] += 1*cff\n\n # Need to stagger these diagonals so lower and upper bands are symmetric\n cff = -1/(2*dx3)\n beta = self.beta\n diags[j-2,:-4] += -1*cff*beta[4:]\n diags[j-1,:-2] += 2*cff*beta[2:]\n diags[j+1,:] += -2*cff*beta\n diags[j+2,:] += 1*cff*beta\n\n ## Left boundary - use forward differencing\n diags[j-1,0] = 0\n diags[j,0:2] = -2*cff*beta[0]\n diags[j+1,1:3] = 6*cff*beta[0]\n diags[j+2,2:4] = -6*cff*beta[0]\n diags[j+3,3:5] = 2*cff*beta[0]\n\n # Zero first two points\n #diags[j-1,0] = 0\n #diags[j,0:2] = 0 \n #diags[j+1,1:3] = 0 \n #diags[j+2,2:4] = 0 \n #if self._j>2:\n # diags[j+3,3:5] = 0 \n\n return diags","def dKdiag_dtheta(self,dL_dKdiag,X,target):\r\n FX = self._cos(self.basis_alpha[None,:],self.basis_omega[None,:],self.basis_phi[None,:])(X)\r\n\r\n La = np.column_stack((self.a[0]*np.ones((self.n_basis,1)), self.a[1]*self.basis_omega, self.a[2]*self.basis_omega**2, self.a[3]*self.basis_omega**3))\r\n Lo = np.column_stack((self.basis_omega, self.basis_omega, self.basis_omega, self.basis_omega))\r\n Lp = np.column_stack((self.basis_phi, self.basis_phi+np.pi/2, self.basis_phi+np.pi, self.basis_phi+np.pi*3/2))\r\n r,omega,phi = self._cos_factorization(La,Lo,Lp)\r\n Gint = self._int_computation( r,omega,phi, r,omega,phi)\r\n\r\n Flower = np.array(self._cos(self.basis_alpha,self.basis_omega,self.basis_phi)(self.lower))[:,None]\r\n F1lower = np.array(self._cos(self.basis_alpha*self.basis_omega,self.basis_omega,self.basis_phi+np.pi/2)(self.lower))[:,None]\r\n F2lower = np.array(self._cos(self.basis_alpha*self.basis_omega**2,self.basis_omega,self.basis_phi+np.pi)(self.lower))[:,None]\r\n\r\n #dK_dvar\r\n dK_dvar = 1. / self.variance * mdot(FX, self.Gi, FX.T)\r\n\r\n #dK_dlen\r\n da_dlen = [-3*self.a[0]/self.lengthscale, -2*self.a[1]/self.lengthscale, -self.a[2]/self.lengthscale, 0.]\r\n db_dlen = [0., 4*self.b[1]/self.lengthscale, 2*self.b[2]/self.lengthscale, 2*self.b[3]/self.lengthscale, 2*self.b[4]/self.lengthscale]\r\n dLa_dlen = np.column_stack((da_dlen[0]*np.ones((self.n_basis,1)), da_dlen[1]*self.basis_omega, da_dlen[2]*self.basis_omega**2, da_dlen[3]*self.basis_omega**3))\r\n r1,omega1,phi1 = self._cos_factorization(dLa_dlen,Lo,Lp)\r\n dGint_dlen = self._int_computation(r1,omega1,phi1, r,omega,phi)\r\n dGint_dlen = dGint_dlen + dGint_dlen.T\r\n dlower_terms_dlen = db_dlen[0]*np.dot(Flower,Flower.T) + db_dlen[1]*np.dot(F2lower,F2lower.T) + db_dlen[2]*np.dot(F1lower,F1lower.T) + db_dlen[3]*np.dot(F2lower,Flower.T) + db_dlen[4]*np.dot(Flower,F2lower.T)\r\n dG_dlen = 15*self.lengthscale**4/(400*np.sqrt(5))*Gint + 3*self.lengthscale**5/(400*np.sqrt(5))*dGint_dlen + dlower_terms_dlen\r\n dK_dlen = -mdot(FX,self.Gi,dG_dlen/self.variance,self.Gi,FX.T)\r\n\r\n #dK_dper\r\n dFX_dper = self._cos(-self.basis_alpha[None,:]*self.basis_omega[None,:]/self.period*X ,self.basis_omega[None,:],self.basis_phi[None,:]+np.pi/2)(X)\r\n\r\n dLa_dper = np.column_stack((-self.a[0]*self.basis_omega/self.period, -self.a[1]*self.basis_omega**2/self.period, -self.a[2]*self.basis_omega**3/self.period, -self.a[3]*self.basis_omega**4/self.period))\r\n dLp_dper = np.column_stack((self.basis_phi+np.pi/2,self.basis_phi+np.pi,self.basis_phi+np.pi*3/2,self.basis_phi))\r\n r1,omega1,phi1 = self._cos_factorization(dLa_dper,Lo,dLp_dper)\r\n\r\n IPPprim1 = self.upper*(1./(omega+omega1.T)*np.cos((omega+omega1.T)*self.upper+phi+phi1.T-np.pi/2) + 1./(omega-omega1.T)*np.cos((omega-omega1.T)*self.upper+phi-phi1.T-np.pi/2))\r\n IPPprim1 -= self.lower*(1./(omega+omega1.T)*np.cos((omega+omega1.T)*self.lower+phi+phi1.T-np.pi/2) + 1./(omega-omega1.T)*np.cos((omega-omega1.T)*self.lower+phi-phi1.T-np.pi/2))\r\n IPPprim2 = self.upper*(1./(omega+omega1.T)*np.cos((omega+omega1.T)*self.upper+phi+phi1.T-np.pi/2) + self.upper*np.cos(phi-phi1.T))\r\n IPPprim2 -= self.lower*(1./(omega+omega1.T)*np.cos((omega+omega1.T)*self.lower+phi+phi1.T-np.pi/2) + self.lower*np.cos(phi-phi1.T))\r\n IPPprim = np.where(np.isnan(IPPprim1),IPPprim2,IPPprim1)\r\n\r\n IPPint1 = 1./(omega+omega1.T)**2*np.cos((omega+omega1.T)*self.upper+phi+phi1.T-np.pi) + 1./(omega-omega1.T)**2*np.cos((omega-omega1.T)*self.upper+phi-phi1.T-np.pi)\r\n IPPint1 -= 1./(omega+omega1.T)**2*np.cos((omega+omega1.T)*self.lower+phi+phi1.T-np.pi) + 1./(omega-omega1.T)**2*np.cos((omega-omega1.T)*self.lower+phi-phi1.T-np.pi)\r\n IPPint2 = 1./(omega+omega1.T)**2*np.cos((omega+omega1.T)*self.upper+phi+phi1.T-np.pi) + .5*self.upper**2*np.cos(phi-phi1.T)\r\n IPPint2 -= 1./(omega+omega1.T)**2*np.cos((omega+omega1.T)*self.lower+phi+phi1.T-np.pi) + .5*self.lower**2*np.cos(phi-phi1.T)\r\n IPPint = np.where(np.isnan(IPPint1),IPPint2,IPPint1)\r\n\r\n dLa_dper2 = np.column_stack((-self.a[1]*self.basis_omega/self.period, -2*self.a[2]*self.basis_omega**2/self.period, -3*self.a[3]*self.basis_omega**3/self.period))\r\n dLp_dper2 = np.column_stack((self.basis_phi+np.pi/2, self.basis_phi+np.pi, self.basis_phi+np.pi*3/2))\r\n r2,omega2,phi2 = self._cos_factorization(dLa_dper2,Lo[:,0:2],dLp_dper2)\r\n\r\n dGint_dper = np.dot(r,r1.T)/2 * (IPPprim - IPPint) + self._int_computation(r2,omega2,phi2, r,omega,phi)\r\n dGint_dper = dGint_dper + dGint_dper.T\r\n\r\n dFlower_dper = np.array(self._cos(-self.lower*self.basis_alpha*self.basis_omega/self.period,self.basis_omega,self.basis_phi+np.pi/2)(self.lower))[:,None]\r\n dF1lower_dper = np.array(self._cos(-self.lower*self.basis_alpha*self.basis_omega**2/self.period,self.basis_omega,self.basis_phi+np.pi)(self.lower)+self._cos(-self.basis_alpha*self.basis_omega/self.period,self.basis_omega,self.basis_phi+np.pi/2)(self.lower))[:,None]\r\n dF2lower_dper = np.array(self._cos(-self.lower*self.basis_alpha*self.basis_omega**3/self.period,self.basis_omega,self.basis_phi+np.pi*3/2)(self.lower) + self._cos(-2*self.basis_alpha*self.basis_omega**2/self.period,self.basis_omega,self.basis_phi+np.pi)(self.lower))[:,None]\r\n\r\n dlower_terms_dper = self.b[0] * (np.dot(dFlower_dper,Flower.T) + np.dot(Flower.T,dFlower_dper))\r\n dlower_terms_dper += self.b[1] * (np.dot(dF2lower_dper,F2lower.T) + np.dot(F2lower,dF2lower_dper.T)) - 4*self.b[1]/self.period*np.dot(F2lower,F2lower.T)\r\n dlower_terms_dper += self.b[2] * (np.dot(dF1lower_dper,F1lower.T) + np.dot(F1lower,dF1lower_dper.T)) - 2*self.b[2]/self.period*np.dot(F1lower,F1lower.T)\r\n dlower_terms_dper += self.b[3] * (np.dot(dF2lower_dper,Flower.T) + np.dot(F2lower,dFlower_dper.T)) - 2*self.b[3]/self.period*np.dot(F2lower,Flower.T)\r\n dlower_terms_dper += self.b[4] * (np.dot(dFlower_dper,F2lower.T) + np.dot(Flower,dF2lower_dper.T)) - 2*self.b[4]/self.period*np.dot(Flower,F2lower.T)\r\n\r\n dG_dper = 1./self.variance*(3*self.lengthscale**5/(400*np.sqrt(5))*dGint_dper + 0.5*dlower_terms_dper)\r\n dK_dper = 2*mdot(dFX_dper,self.Gi,FX.T) - mdot(FX,self.Gi,dG_dper,self.Gi,FX.T)\r\n\r\n target[0] += np.sum(np.diag(dK_dvar)*dL_dKdiag)\r\n target[1] += np.sum(np.diag(dK_dlen)*dL_dKdiag)\r\n target[2] += np.sum(np.diag(dK_dper)*dL_dKdiag)","def mahalanobis_distance(x, y):\r\n diff = x - np.mean(y, axis=0)\r\n cov = np.cov(y.T) # Covariance matrix\r\n inv_cov = np.linalg.inv(cov)\r\n return np.dot(np.dot(diff, inv_cov), diff.T) + np.log(np.linalg.det(cov))","def det(mtx):\n if not is_square(mtx):\n raise ValueError(\"Matrix should be square\")\n if len(mtx) == 2:\n return mtx[0][0] * mtx[1][1] - mtx[0][1] * mtx[1][0]\n\n result = 0\n sign = 1\n for inx in range(len(mtx)):\n next_mtx = get_minor_mtx(mtx, 0, inx)\n result += sign * (mtx[0][inx] * det(next_mtx))\n sign *= -1\n return result","def det(input):\n is_input_dparray = isinstance(input, dparray)\n\n if not use_origin_backend(input) and is_input_dparray:\n if input.shape[-1] == input.shape[-2]:\n result = dpnp_det(input)\n\n # scalar returned\n if result.shape == (1,):\n return result.dtype.type(result[0])\n\n return result\n\n return call_origin(numpy.linalg.det, input)","def calc_det(m):\n det = m[0][0] * m [1][1] - (m[0][1] * m[1][0])\n return det","def calculate_sdm(feature_matrix, is_normalization = False):\n length = len(feature_matrix)\n self_distance_matrix = np.zeros((length, length))\n for i in range(length):\n row1 = feature_matrix[i, :]\n for j in range(length):\n row2 = feature_matrix[j, :]\n self_distance_matrix[i, j] = np.sqrt(np.sum(np.square(row1 - row2)))\n\n if is_normalization:\n minima = np.min(self_distance_matrix)\n maxima = np.max(self_distance_matrix)\n self_distance_matrix = (self_distance_matrix - minima) / (maxima - minima)\n return self_distance_matrix","def diffuse_CN(self, dt):\n\n gr = self.grid\n phi = gr.phi\n\n phinew = gr.scratch_array()\n\n alpha = self.k*dt/gr.dx**2\n\n # create the RHS of the matrix\n gr.fill_BCs()\n R = 0.5*self.k*dt*self.lap()\n R = R[gr.ilo:gr.ihi+1]\n R += phi[gr.ilo:gr.ihi+1]\n\n # create the diagonal, d+1 and d-1 parts of the matrix\n d = (1.0 + alpha)*np.ones(gr.nx)\n u = -0.5*alpha*np.ones(gr.nx)\n u[0] = 0.0\n\n l = -0.5*alpha*np.ones(gr.nx)\n l[gr.nx-1] = 0.0\n\n # set the boundary conditions by changing the matrix elements\n\n # homogeneous neumann\n d[0] = 1.0 + 0.5*alpha\n d[gr.nx-1] = 1.0 + 0.5*alpha\n\n # Dirichlet\n #d[0] = 1.0 + 1.5*alpha\n #d[gr.nx-1] = 1.0 + 1.5*alpha\n\n #R[0] += alpha*phi1\n #R[gr.nx-1] += alpha*phi1\n\n # solve\n A = np.matrix([u,d,l])\n phinew[gr.ilo:gr.ihi+1] = linalg.solve_banded((1,1), A, R)\n\n return phinew","def distance_matrix(data):\n D = numpy.zeros( (data.shape[0], data.shape[0]) )\n for i in xrange(data.shape[0]):\n for j in xrange(i):\n D[i,j] = numpy.linalg.norm(data[i,:]-data[j,:])\n D[j,i] = D[i,j]\n\n return D","def dist_morisita_horn(datamtx, strict=True):\n if strict:\n if not all(isfinite(datamtx)):\n raise ValueError(\"non finite number in input matrix\")\n if any(datamtx<0.0):\n raise ValueError(\"negative value in input matrix\")\n if rank(datamtx) != 2:\n raise ValueError(\"input matrix not 2D\")\n numrows, numcols = shape(datamtx)\n else:\n try:\n numrows, numcols = shape(datamtx)\n except ValueError:\n return zeros((0,0),'d')\n\n if numrows == 0 or numcols == 0:\n return zeros((0,0),'d')\n dists = zeros((numrows,numrows),'d')\n \n rowsums = datamtx.sum(axis=1, dtype='float')\n row_ds = (datamtx**2).sum(axis=1, dtype='float') # these are d_a, etc\n \n for i in range(numrows):\n if row_ds[i] !=0.:\n row_ds[i] = row_ds[i] / rowsums[i]**2\n # this leaves row_ds zero if actually 0/0\n for i in range(numrows):\n row1 = datamtx[i]\n N1 = rowsums[i]\n d1 = row_ds[i]\n for j in range(i):\n row2 = datamtx[j]\n N2 = rowsums[j]\n d2 = row_ds[j]\n if N2 == 0.0 and N1==0.0:\n dist = 0.0\n elif N2 == 0.0 or N1==0.0:\n dist = 1.0\n else:\n # d's zero only if N's zero, and we already checked for that\n similarity = 2*sum(row1*row2)\n similarity = similarity / ( (d1 + d2) * N1 * N2 )\n dist = 1 - similarity\n dists[i][j] = dists[j][i] = dist\n return dists","def sim_euclidean(table_CF):\n aCopy = table_CF.copy()\n #aCopy = my_preproc.mean_imputation(table_CF)\n aCopy = aCopy.fillna(0)\n euc_dist = ml_metrics.pairwise.euclidean_distances(aCopy)\n euc_dist = pd.DataFrame(euc_dist, columns = aCopy.index)\n euc_dist.index = euc_dist.columns\n return euc_dist","def matrix_neumann2D(Omega,Nx,Ny):\r\n \r\n hx = (Omega[1]-Omega[0])/Nx\r\n hy = (Omega[3]-Omega[2])/Ny\r\n hx2 = hx*hx\r\n hy2 = hy*hy\r\n\r\n # Les inconnues sont numérotés de 0 à Nx suivant x et 0 à Ny\r\n # suivant y. La taille du problème est donc (Nx+1)*(Ny+1).\r\n\r\n # Pour -Laplacien(u), la matrice est constituée de (Ny+1)x(Ny+1)\r\n # blocs de taille (Nx+1)x(Nx+1), de la forme\r\n #\r\n # A = [ A0 B ]\r\n # [ B A1 B ]\r\n # [ B A1 B ]\r\n # [ . . . ]\r\n # [ B A1 B ]\r\n # [ B A0 ]\r\n #\r\n # Au final, on peut commencer à remplir avec des diagonales\r\n N = (1+Nx)*(1+Ny)\r\n diags = np.zeros((5,N))\r\n # La diagonale est constante\r\n diags[2,:] = 2./hx2+2./hy2\r\n # Diagonale -1\r\n diags[1,:] = -1./hx2 # en général\r\n diags[1,np.arange(Nx,N,Nx+1)] = 0. # bord gauche\r\n diags[1,np.arange(Nx-1,N,Nx+1)] = -2./hx2 # bord droit\r\n # Diagonale +1\r\n diags[3,:] = -1./hx2 # en général\r\n diags[3,np.arange(0,N,Nx+1)] = 0. # bord droit\r\n diags[3,np.arange(1,N,Nx+1)] = -2./hx2 # bord gauche\r\n # Diagonale -(Nx+1)\r\n diags[0,:] = -1./hy2 # en général\r\n diags[0,(Nx+1)*(Ny-1):(Nx+1)*Ny] = -2./hy2 # bord bas\r\n # Diagonale +(Nx+1)\r\n diags[4,:] = -1./hy2 # en général\r\n diags[4,Nx+1:2*(Nx+1)] = -2./hy2 # bord haut\r\n\r\n # Construction de la matrice creuse de u --> -Laplacien(u)\r\n A = sp.spdiags(diags,[-(Nx+1),-1,0,1,(Nx+1)], (Nx+1)*(Ny+1),\r\n (Nx+1)*(Ny+1), format=\"csc\")\r\n\r\n return A","def test_derive_euclidean_dm(self):\r\n cat_mat = [('6.66', '46.8'),\r\n ('7.27', '36.05'),\r\n ('4.6', '44.86666667'),\r\n ('5.68', '40.58333333'),\r\n ('4.23', '68.63333333'),\r\n ('5.74', '36.45')]\r\n\r\n dm_lbls = ['MT2.141698', 'CA1.141704', 'BB2.141659',\r\n 'CO2.141657', 'TL3.141709', 'SN3.141650']\r\n\r\n mtx = [\r\n [0.0, 10.7672930674, 2.82513322958, 6.29343661968,\r\n 21.9681438519, 10.3908084382],\r\n [10.7672930674, 0.0, 9.21208506093, 4.80408275125,\r\n 32.7248408842, 1.58142340946],\r\n [2.82513322958, 9.21208506093, 0.0, 4.41739114202,\r\n 23.7695465697, 8.49351975531],\r\n [6.29343661968, 4.80408275125, 4.41739114202, 0.0,\r\n 28.0874527147, 4.13376879093],\r\n [21.9681438519, 32.7248408842, 23.7695465697,\r\n 28.0874527147, 0.0, 32.2187374711],\r\n [10.3908084382, 1.58142340946, 8.49351975531,\r\n 4.13376879093, 32.2187374711, 0.0]]\r\n\r\n exp = DistanceMatrix(asarray(mtx), dm_lbls)\r\n obs = self.best._derive_euclidean_dm(cat_mat,\r\n self.bv_dm_88soils.shape[0])\r\n self.assertEqual(obs.ids, exp.ids)\r\n assert_almost_equal(obs.data, exp.data)","def get_D(activity_profiles, csi_hist):\n\tL, J = activity_profiles.shape[0], activity_profiles.shape[1]-1\n\tD = np.array(L,J)\n\tfor i in range(L):\n\t\tfor j in range(1,J+1):\n\t\t\thist1 = activity_profiles[i][j].astype(float)\n\t\t\thist2 = csi_hist.astype(float)\n\t\t\tgd = pairwise.pairwise_distances(hist1.reshape(-1,1),hist2.reshape(-1,1))\n\t\t\tD[i][j] = emd(hist1, hist2, gd)\n\treturn D","def dKdiag_dtheta(self,dL_dKdiag,X,target):\r\n FX = self._cos(self.basis_alpha[None,:],self.basis_omega[None,:],self.basis_phi[None,:])(X)\r\n\r\n La = np.column_stack((self.a[0]*np.ones((self.n_basis,1)),self.a[1]*self.basis_omega))\r\n Lo = np.column_stack((self.basis_omega,self.basis_omega))\r\n Lp = np.column_stack((self.basis_phi,self.basis_phi+np.pi/2))\r\n r,omega,phi = self._cos_factorization(La,Lo,Lp)\r\n Gint = self._int_computation( r,omega,phi, r,omega,phi)\r\n\r\n Flower = np.array(self._cos(self.basis_alpha,self.basis_omega,self.basis_phi)(self.lower))[:,None]\r\n\r\n #dK_dvar\r\n dK_dvar = 1./self.variance*mdot(FX,self.Gi,FX.T)\r\n\r\n #dK_dlen\r\n da_dlen = [-1./self.lengthscale**2,0.]\r\n dLa_dlen = np.column_stack((da_dlen[0]*np.ones((self.n_basis,1)),da_dlen[1]*self.basis_omega))\r\n r1,omega1,phi1 = self._cos_factorization(dLa_dlen,Lo,Lp)\r\n dGint_dlen = self._int_computation(r1,omega1,phi1, r,omega,phi)\r\n dGint_dlen = dGint_dlen + dGint_dlen.T\r\n dG_dlen = 1./2*Gint + self.lengthscale/2*dGint_dlen\r\n dK_dlen = -mdot(FX,self.Gi,dG_dlen/self.variance,self.Gi,FX.T)\r\n\r\n #dK_dper\r\n dFX_dper = self._cos(-self.basis_alpha[None,:]*self.basis_omega[None,:]/self.period*X ,self.basis_omega[None,:],self.basis_phi[None,:]+np.pi/2)(X)\r\n\r\n dLa_dper = np.column_stack((-self.a[0]*self.basis_omega/self.period, -self.a[1]*self.basis_omega**2/self.period))\r\n dLp_dper = np.column_stack((self.basis_phi+np.pi/2,self.basis_phi+np.pi))\r\n r1,omega1,phi1 = self._cos_factorization(dLa_dper,Lo,dLp_dper)\r\n\r\n IPPprim1 = self.upper*(1./(omega+omega1.T)*np.cos((omega+omega1.T)*self.upper+phi+phi1.T-np.pi/2) + 1./(omega-omega1.T)*np.cos((omega-omega1.T)*self.upper+phi-phi1.T-np.pi/2))\r\n IPPprim1 -= self.lower*(1./(omega+omega1.T)*np.cos((omega+omega1.T)*self.lower+phi+phi1.T-np.pi/2) + 1./(omega-omega1.T)*np.cos((omega-omega1.T)*self.lower+phi-phi1.T-np.pi/2))\r\n IPPprim2 = self.upper*(1./(omega+omega1.T)*np.cos((omega+omega1.T)*self.upper+phi+phi1.T-np.pi/2) + self.upper*np.cos(phi-phi1.T))\r\n IPPprim2 -= self.lower*(1./(omega+omega1.T)*np.cos((omega+omega1.T)*self.lower+phi+phi1.T-np.pi/2) + self.lower*np.cos(phi-phi1.T))\r\n IPPprim = np.where(np.isnan(IPPprim1),IPPprim2,IPPprim1)\r\n\r\n IPPint1 = 1./(omega+omega1.T)**2*np.cos((omega+omega1.T)*self.upper+phi+phi1.T-np.pi) + 1./(omega-omega1.T)**2*np.cos((omega-omega1.T)*self.upper+phi-phi1.T-np.pi)\r\n IPPint1 -= 1./(omega+omega1.T)**2*np.cos((omega+omega1.T)*self.lower+phi+phi1.T-np.pi) + 1./(omega-omega1.T)**2*np.cos((omega-omega1.T)*self.lower+phi-phi1.T-np.pi)\r\n IPPint2 = 1./(omega+omega1.T)**2*np.cos((omega+omega1.T)*self.upper+phi+phi1.T-np.pi) + 1./2*self.upper**2*np.cos(phi-phi1.T)\r\n IPPint2 -= 1./(omega+omega1.T)**2*np.cos((omega+omega1.T)*self.lower+phi+phi1.T-np.pi) + 1./2*self.lower**2*np.cos(phi-phi1.T)\r\n IPPint = np.where(np.isnan(IPPint1),IPPint2,IPPint1)\r\n\r\n dLa_dper2 = np.column_stack((-self.a[1]*self.basis_omega/self.period))\r\n dLp_dper2 = np.column_stack((self.basis_phi+np.pi/2))\r\n r2,omega2,phi2 = dLa_dper2.T,Lo[:,0:1],dLp_dper2.T\r\n\r\n dGint_dper = np.dot(r,r1.T)/2 * (IPPprim - IPPint) + self._int_computation(r2,omega2,phi2, r,omega,phi)\r\n dGint_dper = dGint_dper + dGint_dper.T\r\n\r\n dFlower_dper = np.array(self._cos(-self.lower*self.basis_alpha*self.basis_omega/self.period,self.basis_omega,self.basis_phi+np.pi/2)(self.lower))[:,None]\r\n\r\n dG_dper = 1./self.variance*(self.lengthscale/2*dGint_dper + self.b[0]*(np.dot(dFlower_dper,Flower.T)+np.dot(Flower,dFlower_dper.T)))\r\n\r\n dK_dper = 2*mdot(dFX_dper,self.Gi,FX.T) - mdot(FX,self.Gi,dG_dper,self.Gi,FX.T)\r\n\r\n target[0] += np.sum(np.diag(dK_dvar)*dL_dKdiag)\r\n target[1] += np.sum(np.diag(dK_dlen)*dL_dKdiag)\r\n target[2] += np.sum(np.diag(dK_dper)*dL_dKdiag)","def dKdiag_dtheta(self,dL_dKdiag,X,target):\r\n FX = self._cos(self.basis_alpha[None,:],self.basis_omega[None,:],self.basis_phi[None,:])(X)\r\n\r\n La = np.column_stack((self.a[0]*np.ones((self.n_basis,1)),self.a[1]*self.basis_omega, self.a[2]*self.basis_omega**2))\r\n Lo = np.column_stack((self.basis_omega,self.basis_omega,self.basis_omega))\r\n Lp = np.column_stack((self.basis_phi,self.basis_phi+np.pi/2,self.basis_phi+np.pi))\r\n r,omega,phi = self._cos_factorization(La,Lo,Lp)\r\n Gint = self._int_computation( r,omega,phi, r,omega,phi)\r\n\r\n Flower = np.array(self._cos(self.basis_alpha,self.basis_omega,self.basis_phi)(self.lower))[:,None]\r\n F1lower = np.array(self._cos(self.basis_alpha*self.basis_omega,self.basis_omega,self.basis_phi+np.pi/2)(self.lower))[:,None]\r\n\r\n #dK_dvar\r\n dK_dvar = 1./self.variance*mdot(FX,self.Gi,FX.T)\r\n\r\n #dK_dlen\r\n da_dlen = [-6/self.lengthscale**3,-2*np.sqrt(3)/self.lengthscale**2,0.]\r\n db_dlen = [0.,2*self.lengthscale/3.]\r\n dLa_dlen = np.column_stack((da_dlen[0]*np.ones((self.n_basis,1)),da_dlen[1]*self.basis_omega,da_dlen[2]*self.basis_omega**2))\r\n r1,omega1,phi1 = self._cos_factorization(dLa_dlen,Lo,Lp)\r\n dGint_dlen = self._int_computation(r1,omega1,phi1, r,omega,phi)\r\n dGint_dlen = dGint_dlen + dGint_dlen.T\r\n dG_dlen = self.lengthscale**2/(4*np.sqrt(3))*Gint + self.lengthscale**3/(12*np.sqrt(3))*dGint_dlen + db_dlen[0]*np.dot(Flower,Flower.T) + db_dlen[1]*np.dot(F1lower,F1lower.T)\r\n dK_dlen = -mdot(FX,self.Gi,dG_dlen/self.variance,self.Gi,FX.T)\r\n\r\n #dK_dper\r\n dFX_dper = self._cos(-self.basis_alpha[None,:]*self.basis_omega[None,:]/self.period*X ,self.basis_omega[None,:],self.basis_phi[None,:]+np.pi/2)(X)\r\n\r\n dLa_dper = np.column_stack((-self.a[0]*self.basis_omega/self.period, -self.a[1]*self.basis_omega**2/self.period, -self.a[2]*self.basis_omega**3/self.period))\r\n dLp_dper = np.column_stack((self.basis_phi+np.pi/2,self.basis_phi+np.pi,self.basis_phi+np.pi*3/2))\r\n r1,omega1,phi1 = self._cos_factorization(dLa_dper,Lo,dLp_dper)\r\n\r\n IPPprim1 = self.upper*(1./(omega+omega1.T)*np.cos((omega+omega1.T)*self.upper+phi+phi1.T-np.pi/2) + 1./(omega-omega1.T)*np.cos((omega-omega1.T)*self.upper+phi-phi1.T-np.pi/2))\r\n IPPprim1 -= self.lower*(1./(omega+omega1.T)*np.cos((omega+omega1.T)*self.lower+phi+phi1.T-np.pi/2) + 1./(omega-omega1.T)*np.cos((omega-omega1.T)*self.lower+phi-phi1.T-np.pi/2))\r\n IPPprim2 = self.upper*(1./(omega+omega1.T)*np.cos((omega+omega1.T)*self.upper+phi+phi1.T-np.pi/2) + self.upper*np.cos(phi-phi1.T))\r\n IPPprim2 -= self.lower*(1./(omega+omega1.T)*np.cos((omega+omega1.T)*self.lower+phi+phi1.T-np.pi/2) + self.lower*np.cos(phi-phi1.T))\r\n IPPprim = np.where(np.isnan(IPPprim1),IPPprim2,IPPprim1)\r\n\r\n IPPint1 = 1./(omega+omega1.T)**2*np.cos((omega+omega1.T)*self.upper+phi+phi1.T-np.pi) + 1./(omega-omega1.T)**2*np.cos((omega-omega1.T)*self.upper+phi-phi1.T-np.pi)\r\n IPPint1 -= 1./(omega+omega1.T)**2*np.cos((omega+omega1.T)*self.lower+phi+phi1.T-np.pi) + 1./(omega-omega1.T)**2*np.cos((omega-omega1.T)*self.lower+phi-phi1.T-np.pi)\r\n IPPint2 = 1./(omega+omega1.T)**2*np.cos((omega+omega1.T)*self.upper+phi+phi1.T-np.pi) + 1./2*self.upper**2*np.cos(phi-phi1.T)\r\n IPPint2 -= 1./(omega+omega1.T)**2*np.cos((omega+omega1.T)*self.lower+phi+phi1.T-np.pi) + 1./2*self.lower**2*np.cos(phi-phi1.T)\r\n IPPint = np.where(np.isnan(IPPint1),IPPint2,IPPint1)\r\n\r\n dLa_dper2 = np.column_stack((-self.a[1]*self.basis_omega/self.period, -2*self.a[2]*self.basis_omega**2/self.period))\r\n dLp_dper2 = np.column_stack((self.basis_phi+np.pi/2,self.basis_phi+np.pi))\r\n r2,omega2,phi2 = self._cos_factorization(dLa_dper2,Lo[:,0:2],dLp_dper2)\r\n\r\n dGint_dper = np.dot(r,r1.T)/2 * (IPPprim - IPPint) + self._int_computation(r2,omega2,phi2, r,omega,phi)\r\n dGint_dper = dGint_dper + dGint_dper.T\r\n\r\n dFlower_dper = np.array(self._cos(-self.lower*self.basis_alpha*self.basis_omega/self.period,self.basis_omega,self.basis_phi+np.pi/2)(self.lower))[:,None]\r\n dF1lower_dper = np.array(self._cos(-self.lower*self.basis_alpha*self.basis_omega**2/self.period,self.basis_omega,self.basis_phi+np.pi)(self.lower)+self._cos(-self.basis_alpha*self.basis_omega/self.period,self.basis_omega,self.basis_phi+np.pi/2)(self.lower))[:,None]\r\n\r\n dG_dper = 1./self.variance*(self.lengthscale**3/(12*np.sqrt(3))*dGint_dper + self.b[0]*(np.dot(dFlower_dper,Flower.T)+np.dot(Flower,dFlower_dper.T)) + self.b[1]*(np.dot(dF1lower_dper,F1lower.T)+np.dot(F1lower,dF1lower_dper.T)))\r\n\r\n dK_dper = 2* mdot(dFX_dper,self.Gi,FX.T) - mdot(FX,self.Gi,dG_dper,self.Gi,FX.T)\r\n\r\n target[0] += np.sum(np.diag(dK_dvar)*dL_dKdiag)\r\n target[1] += np.sum(np.diag(dK_dlen)*dL_dKdiag)\r\n target[2] += np.sum(np.diag(dK_dper)*dL_dKdiag)","def Mat_dis(x):\n x = np.mat(x) #构建矩阵\n aa = np.sum(np.multiply(x, x), 1) #哈达玛乘积\n ab = x * x.T\n dist_mat = aa + aa.T - 2 * ab\n dist_mat[dist_mat < 0] = 0\n #dist_mat = np.sqrt(dist_mat)\n dist_mat = np.maximum(dist_mat, dist_mat.T)\n\n return dist_mat","def dist_manhattan(datamtx, strict=True):\n if strict:\n if not all(isfinite(datamtx)):\n raise ValueError(\"non finite number in input matrix\")\n if rank(datamtx) != 2:\n raise ValueError(\"input matrix not 2D\")\n numrows, numcols = shape(datamtx)\n else:\n try:\n numrows, numcols = shape(datamtx)\n except ValueError:\n return zeros((0,0),'d')\n\n if numrows == 0 or numcols == 0:\n return zeros((0,0),'d')\n dists = zeros((numrows,numrows),'d')\n for i in range(numrows):\n r1 = datamtx[i] # cache here\n for j in range(i):\n dists[i,j] = dists[j,i] = sum(abs(r1 - datamtx[j]))\n \n return dists","def mi_from_dm(distance_matrix, ns, nh, spike_train_list=None):\n \n nr = len(distance_matrix)\n nt = nr/ns\n nearest_neighbours = np.array([r.argsort()[:nh] for r in distance_matrix])\n \n if spike_train_list is not None:\n\n members_of_glob = trains_in_glob(spike_train_list)\n glob_comp = glob_composition(spike_train_list, ns, nt, nh)\n\n counts = []\n for i in range(len(nearest_neighbours)):\n c_i = 0\n \n if i not in members_of_glob:\n for j in nearest_neighbours[i]:\n if j not in members_of_glob:\n if spike_train_list[i].start_time == spike_train_list[j].start_time:\n c_i += 1 # count neigbours out of glob\n else:\n f_i = glob_comp[i]/float(sum(glob_comp.values()))\n c_i += (nh - c_i)*f_i # if one neighbour is in glob, all following neighb are as well\n break\n counts.append(c_i)\n else:\n f_i = glob_comp[i]/float(sum(glob_comp.values()))\n c_i += 1 + (nh - 1)*f_i #If in glob, take fraction of remaining neighbours except you\n counts.append(c_i)\n \n counts = np.array(counts)\n \n else:\n \n counts = []\n for i in range(len(nearest_neighbours)):\n c_i = 1\n for j in nearest_neighbours[i]:\n if (i != j and abs(i - j)%ns==0 ):\n c_i += 1 \n counts.append(c_i)\n counts = np.array(counts) \n \n I = sum(np.log2(counts*ns/float(nh))) / float(nr)\n\n return I","def _build_cumulative_downhill_matrices(self):\n\n import time\n from scipy import sparse as sparse\n\n downSweepMat = self.accumulatorMat.copy() \n downHillaccuMat = self.downhillMat.copy() \n accuM = self.downhillMat.copy() # work matrix\n\n DX = np.ones(self.tri.npoints) # measure when all the info has been propagated out.\n\n walltime = time.clock()\n\n while np.any(DX):\n downSweepMat = downSweepMat.dot(self.accumulatorMat) # N applications of the accumulator\n accuM = accuM.dot(self.downhillMat)\n downHillaccuMat = downHillaccuMat + accuM \n \n DX = self.downhillMat.dot(DX) \n \n \n print \" - Dense downhill matrix storage time \", time.clock() - walltime\n\n downHillaccuMat = downHillaccuMat + sparse.identity(self.tri.npoints, format='csr')\n\n self.downhillCumulativeMat = downHillaccuMat\n self.sweepDownToOutflowMat = downSweepMat\n\n # print \"Terminated in \",it,\" iterations\"\n\n return","def get_density(matrix):\n return matrix.getnnz() / (matrix.shape[0] * matrix.shape[1])","def GetDiag(self, *args):\n return _hypre.HypreParMatrix_GetDiag(self, *args)","def main():\n diagonals_in_hd()","def pddet(A):\r\n L = jitchol(A)\r\n logdetA = 2*sum(np.log(np.diag(L)))\r\n return logdetA","def determinant(self):\n if self.n_rows != self.n_cols:\n raise Exception('Matrix is not square')\n if self.n_rows == 2:\n return (self.data[0][0] * self.data[1][1]) - (self.data[1][0] * self.data[0][1])\n else:\n echelon, ops = reduce_to_echelon(self.data.copy(), True)\n swaps = sum([1 if row[0] == 'swap' else 0 for row in ops])\n return math.prod([echelon[i][i] for i in range(len(echelon))]) * (-1) ** swaps","def dolomite():\n\n rho = 2840.\n\n C = np.zeros((6,6), dtype=float)\n C[0,0] = 205.; C[0,1] = 71.; C[0,2] = 57.4; C[0,3] = -19.5; C[0,4] = 13.7; C[0,5] = 0.\n C[1,0] = C[0,1]; C[1,1] = 205.; C[1,2] = 57.4; C[1,3] = 19.5; C[1,4] = -13.7; C[1,5] = 0.\n C[2,0] = C[0,2]; C[2,1] = C[1,2]; C[2,2] = 113.; C[2,3] = 0.; C[2,4] = 0.; C[2,5] = 0.\n C[3,0] = C[0,3]; C[3,1] = C[1,3]; C[3,2] = C[2,3]; C[3,3] = 39.8; C[3,4] = 0.; C[3,5] = -13.7\n C[4,0] = C[0,4]; C[4,1] = C[1,4]; C[4,2] = C[2,4]; C[4,3] = C[3,4]; C[4,4] = 39.8; C[4,5] = -19.5\n C[5,0] = C[0,5]; C[5,1] = C[1,5]; C[5,2] = C[2,5]; C[5,3] = C[3,5]; C[5,4] = C[4,5]; C[5,5] = 67.\n\n return C, rho","def calc_dist_matrix(self,verbose=False):\n\n print(\"Calculating distance matrix.\"); sys.stdout.flush()\n\n nrow = self.data_vector.shape[0]\n self.dist_matrix = np.zeros((nrow, nrow),dtype=float)\n for i in range(nrow):\n if verbose:\n if i % 1000 == 0:\n print(\"Row\",i,\"of\",nrow)\n sys.stdout.flush()\n\n for j in range(i + 1, nrow):\n self.dist_matrix[i,j] = self._pairwise_dist(self.data_vector[i],self.data_vector[j])\n self.dist_matrix[j,i] = self.dist_matrix[i,j]\n \n self.dist_frame = pd.DataFrame(self.dist_matrix,\n index = self.seq_strings,\n columns = self.seq_strings)","def dist_hellinger(datamtx, strict=True):\n if strict:\n if not all(isfinite(datamtx)):\n raise ValueError(\"non finite number in input matrix\")\n if any(datamtx<0.0):\n raise ValueError(\"negative value in input matrix\")\n if rank(datamtx) != 2:\n raise ValueError(\"input matrix not 2D\")\n numrows, numcols = shape(datamtx)\n else:\n try:\n numrows, numcols = shape(datamtx)\n except ValueError:\n return zeros((0,0),'d')\n\n if numrows == 0 or numcols == 0:\n return zeros((0,0),'d')\n dists = zeros((numrows,numrows),'d')\n for i in range(numrows):\n r1 = datamtx[i]\n r1sum = sum(r1)\n for j in range(i):\n r2 = datamtx[j]\n r2sum = sum(r2)\n if r1sum == 0.0 or r2sum == 0.0:\n if r1sum == 0.0 and r2sum == 0.0:\n dist = 0.0\n else: dist = 1.0\n else:\n dist = norm(sqrt(r1/r1sum) - sqrt(r2/r2sum))\n dists[i,j] = dists[j,i] = dist\n\n return dists","def determinant(self) -> float:\n num_R, num_C = self.shape()\n assert num_R == num_C, f\"Determinant must be for a square matrix; this one is {self.shape()}.\"\n # -------------------------------------------------------\n # TODO: You write this one.\n # Note: this one should be recursive....\n if num_R == 1:\n return self.mat[0][0]\n det =0\n for i in range(num_R):\n det += self.mat[0][i] * self.get_minor(0,i).determinant() * (-1)**i\n return det\n pass # remove this when you add your code.\n # -------------------------------------------------------","def calc_metric3(K_tilda):\n trace = np.trace(K_tilda)\n # determinant = np.linalg.det(K_tilda)\n _, log_determinant = np.linalg.slogdet(K_tilda)\n diff = trace - log_determinant\n print(trace, log_determinant, diff)\n return diff","def compute_dissimilarity_matrix_wrapper(start_ind, end_ind,\n n_instances, n_features,\n tmp_dir, output_dir, dist_func,\n process_id, process_batch_size):\n X = np.memmap(tmp_dir + '/X.dat', dtype='float16', mode='r',\n shape=(n_instances, n_features))\n dissimilarity_matrix = np.memmap(\n output_dir + '/dissimilarity_matrix_{}_{}_{}.dat'.format(\n n_instances, n_instances, aid),\n dtype='float16', mode='r+', shape=(n_instances, n_instances)\n )\n dissimilarity_process_matrix = np.load(\n tmp_dir + '/dissimilarity_process_matrix.npy'\n )[start_ind:end_ind]\n\n for i in range(end_ind - start_ind):\n start_time = time.time()\n (row_start, row_end, col_start,\n col_end) = dissimilarity_process_matrix[i, :]\n X_cols = X[col_start:col_end]\n X_rows = X[row_start:row_end]\n dist_col_row = dist_func(X_cols, X_rows,\n X_batch_size=process_batch_size // 2,\n Y_batch_size=process_batch_size // 2)\n dist_col_row = dist_col_row.reshape(X_cols.shape[0], X_rows.shape[0])\n\n dissimilarity_matrix[row_start:row_end, col_start:col_end] = dist_col_row.T\n dissimilarity_matrix[col_start:col_end, row_start:row_end] = dist_col_row\n end_time = time.time()\n print('pid: {}, at {} of {}. time {} seconds.'.format(\n process_id,\n i,\n (end_ind - start_ind),\n (end_time - start_time))\n )\n del dissimilarity_matrix","def demo_diag21():\n n_dim = 2\n A = np.diag([2, 1])\n covar = np.eye(n_dim)\n mean = np.zeros(n_dim)\n approx = approx_quad_form(mean, covar, A)\n\n # Sample from true dist\n n_sample = 10000\n x = np.random.multivariate_normal(mean, covar, n_sample)\n q_samples = np.zeros(n_sample)\n for i in range(n_sample):\n q_samples[i] = x[i] @ A @ x[i]\n\n q = np.linspace(0, 10)\n\n plt.plot(\n q, approx(q), label='Approx.',\n color='tab:blue', linestyle='--')\n bins = np.linspace(0, 10, 51)\n bins[-1] = np.inf\n plt.hist(\n q_samples, density=True, histtype='stepfilled',\n bins=bins,\n alpha=0.5, color='black', label='Samples')\n plt.xlabel('q')\n plt.ylabel('pdf(q) [-]')\n plt.legend()","def dKdiag_dtheta(self,dL_dKdiag,X,target):\r\n raise NotImplementedError","def det(self):\n\t\t\n\t\trows = self._rows\n\t\tsign = +1\n\t\tsumm = 0\n\n\t\tfor perm in permutations(range(rows), rows):\n\t\t\tmul = 1\n\t\t\tsign = SquareMatrix.__parity_of_permutation(perm)\n\n\t\t\tfor i in range(rows):\n\t\t\t\tmul *= self[i][perm[i]]\n\n\t\t\tsumm += sign * mul\n\t\treturn summ","def determinant(self):\n if not self.is_square():\n raise(ValueError, \"Cannot calculate determinant of non-square matrix.\")\n if self.h > 2:\n raise(NotImplementedError, \"Calculating determinant not implemented for matrices largerer than 2x2.\")\n \n # TODO - your code here\n if self.h == 1:\n return self.g[0][0];\n else:\n return self.g[0][0]*self.g[1][1]-self.g[0][1]*self.g[1][0];","def zernike_diagnosis(Nstar=1,seeing=[0.9,0.,0.],npix=npix,zenith=0,filter='r', theta=0., phi=0,corrector='corrector',x=None,y=None,z=None,zernike_max_order=20,regular=False,noise=False,exptime=100,mag=16.,sigma=4.):\n hdu = genImgVallCCD(Nstar=Nstar,seeing=seeing,npix=npix,zenith=zenith,filter=filter, theta=theta,phi=phi, corrector=corrector,x=x,y=y,z=z,regular=regular)\n nn = len(hdu)\n data = []\n colnames = ['x','y','M20','M22','M31','M33']\n\n for hdui in hdu[1:]:\n Nobj = hdui.data.shape[0]\n M20=np.zeros(Nobj)\n M22=np.zeros(Nobj).astype(complex)\n M31=np.zeros(Nobj).astype(complex)\n M33=np.zeros(Nobj).astype(complex)\n for i in range(Nobj):\n psf = rebin(hdui.data[i][4:].reshape(npix,npix),(40,40))\n if noise == True:\n gain = 0.21 # convert electrons to ADU\n zeropoint = 26.794176 # r band, from Nikolay\n objectphoton = exptime*10**(0.4*(zeropoint - mag))\n skyphoton = 8.460140*exptime\n bkg = skyphoton*gain\n img = (psf * objectphoton + skyphoton)*gain\n img = img + add_imageNoise(img) - bkg\n else:\n img = psf\n M20[i],M22[i],M31[i],M33[i]=complexMoments(data=img,sigma=sigma)\n x=hdui.header['ccdXcen']\n y=hdui.header['ccdYcen']\n data.append([x,y,np.median(M20), np.median(M22), np.median(M31), np.median(M33)])\n data=np.array(data)\n pl.figure(figsize=(15,15))\n betaAll=[]\n betaErrAll=[]\n R2adjAll=[]\n pl.subplot(3,3,1)\n beta,betaErr,R2_adj,fitted = zernikeFit(data[:,0].real,data[:,1].real,data[:,2].real,max_order=zernike_max_order)\n betaAll.append(beta)\n betaErrAll.append(betaErr)\n R2adjAll.append(R2_adj)\n znk=showZernike(beta=beta)\n pl.colorbar()\n pl.title(colnames[2])\n for i in range(3,6):\n pl.subplot(3,3,2*i-4)\n beta,betaErr,R2_adj,fitted = zernikeFit(data[:,0].real,data[:,1].real,data[:,i].real,max_order=zernike_max_order)\n betaAll.append(beta)\n betaErrAll.append(betaErr)\n R2adjAll.append(R2_adj)\n znk=showZernike(beta=beta)\n pl.colorbar()\n pl.title(colnames[i]+'_real')\n print '--- R2_adj of the fit is: '+str(R2_adj) +'---'\n pl.subplot(3,3,2*i-3)\n beta,betaErr,R2_adj,fitted = zernikeFit(data[:,0].real,data[:,1].real,data[:,i].imag,max_order=zernike_max_order)\n betaAll.append(beta)\n betaErrAll.append(betaErr)\n R2adjAll.append(R2_adj)\n znk=showZernike(beta=beta)\n pl.colorbar()\n pl.title(colnames[i]+'_imag')\n print '--- R2_adj of the fit is: '+str(R2_adj) +'---'\n betaAll = np.array(betaAll)\n betaErrAll = np.array(betaErrAll)\n R2adjAll = np.array(R2adjAll)\n return betaAll,betaErrAll, R2adjAll","def get_correct_distance_matrix(L):\n n = len(L)\n D = np.zeros((n,n))\n for i in range(n):\n for j in range(n):\n if i != j:\n D[i][j] = get_minor(L, [i, j], [i, j]) / get_minor(L, [i], [i])\n return D","def fisher_diag(\n negative_log_likelihood: LossFun,\n params: Any,\n inputs: jnp.ndarray,\n targets: jnp.ndarray,\n) -> jnp.DeviceArray:\n return jnp.square(\n ravel(jax.grad(negative_log_likelihood)(params, inputs, targets)))","def normD(dictin):\n \n tmp1 = np.sum(np.multiply(dictin, dictin), axis=0)\n for k in range(len(tmp1)):\n if tmp1[k] == 0:\n tmp1[k] = 1e-6\n \n #tmp = 1 / np.sqrt(np.sum(np.multiply(dictin, dictin), axis=0))\n tmp = 1 / np.sqrt(tmp1)\n print(np.array(tmp1))\n return np.dot(dictin, np.diag(tmp))","def hdmatrix(self):\n hdmat = np.zeros((len(self.pulsars), len(self.pulsars)))\n\n for i,pulsar1 in enumerate(self.pulsars):\n for j,pulsar2 in enumerate(self.pulsars):\n hdmat[i,j] = hellingsdowns_factor(pulsar1, pulsar2)\n self.hdm = hdmat\n return hdmat","def cdistance(self, distances):\n self.distanceMatrix = distances\n self.dataChange()","def diagonal(step='Metropolis', iters=5000):\n X = mc.Uniform('X', lower=-1., upper=1., value=[0., 0.])\n\n @mc.potential\n def near_diag(X=X):\n if abs(X[0] - X[1]) < .1:\n return 0\n else:\n return -inf\n\n mod = setup_and_sample(vars(), step, iters)\n mod.shape = pl.array([[-1,-1], [-1,-.9], [.9,1], [1,1], [1,.9], [-.9,-1], [-1,-1]])\n mod.true_mean = [0,0]\n mod.true_iqr = ['(-.5,.5)', '(-.5,5)']\n return mod","def det_2x2(matrix: FieldMatrix) -> FlowFieldVal:\n _validate_matrix_shape(matrix, (2, 2))\n\n det = lambda a, b, c, d: a * d - b * c\n\n a, b = matrix[0]\n c, d = matrix[1]\n\n return tf.nest.map_structure(det, a, b, c, d)","def det(self):\n if not self.is_endomorphism():\n raise ArithmeticError(\"Matrix morphism must be an endomorphism.\")\n return self.matrix().determinant()","def davidson(mult_by_A, N, neig, x0=None, Adiag=None, verbose=logger.INFO):\n\n if isinstance(verbose, logger.Logger):\n log = verbose\n else:\n import sys\n log = logger.Logger(sys.stdout, verbose)\n\n cput1 = (logger.process_clock(), logger.perf_counter())\n\n Mmin = min(neig,N)\n Mmax = min(N,2000)\n tol = 1e-6\n\n #Adiagcheck = np.zeros(N,np.complex128)\n #for i in range(N):\n # test = np.zeros(N,np.complex128)\n # test[i] = 1.0\n # Adiagcheck[i] = mult_by_A(test)[i]\n #print \"Analytical Adiag == numerical Adiag?\", np.allclose(Adiag,Adiagcheck)\n\n if Adiag is None:\n Adiag = np.zeros(N,np.complex128)\n for i in range(N):\n test = np.zeros(N,np.complex128)\n test[i] = 1.0\n Adiag[i] = mult_by_A(test)[i]\n\n xi = np.zeros(N,np.complex128)\n\n lamda_k_old = 0\n lamda_k = 0\n target = 0\n conv = False\n if x0 is not None:\n assert x0.shape == (N, Mmin)\n b = x0.copy()\n\n Ab = np.zeros((N,Mmin),np.complex128)\n for m in range(Mmin):\n Ab[:,m] = mult_by_A(b[:,m])\n\n for istep,M in enumerate(range(Mmin,Mmax+1)):\n if M == Mmin:\n # Set of M unit vectors from lowest Adiag (NxM)\n b = np.zeros((N,M))\n idx = Adiag.argsort()\n for m,i in zip(range(M),idx):\n b[i,m] = 1.0\n ## Add random noise and orthogonalize\n #for m in range(M):\n # b[:,m] += 0.01*np.random.random(N)\n # b[:,m] /= np.linalg.norm(b[:,m])\n # b,R = np.linalg.qr(b)\n\n Ab = np.zeros((N,M),np.complex128)\n for m in range(M):\n Ab[:,m] = mult_by_A(b[:,m])\n else:\n Ab = np.column_stack( (Ab,mult_by_A(b[:,M-1])) )\n\n Atilde = np.dot(b.conj().transpose(),Ab)\n lamda, alpha = diagonalize_asymm(Atilde)\n lamda_k_old, lamda_k = lamda_k, lamda[target]\n alpha_k = alpha[:,target]\n\n if M == Mmax:\n break\n\n q = np.dot( Ab-lamda_k*b, alpha_k )\n log.info('davidson istep = %d root = %d E = %.15g dE = %.9g residual = %.6g',\n istep, target, lamda_k.real, (lamda_k - lamda_k_old).real, np.linalg.norm(q))\n cput1 = log.timer('davidson iter', *cput1)\n if np.linalg.norm(q) < tol:\n if target == neig-1:\n conv = True\n break\n else:\n target += 1\n #for i in range(N):\n #eps = 0.\n #if np.allclose(lamda_k,Adiag[i]):\n # eps = 1e-10\n #xi[i] = q[i]/(lamda_k-Adiag[i]+eps)\n eps = 1e-10\n xi = q/(lamda_k-Adiag+eps)\n\n # orthonormalize xi wrt b\n bxi,R = np.linalg.qr(np.column_stack((b,xi)))\n if FOUND_MPI4PY: # Ensure all processes search in same direction\n bxi = MPI_COMM.bcast(bxi)\n\n # append orthonormalized xi to b\n b = np.column_stack((b,bxi[:,-1]))\n\n #if M > Mmin and M == Mmax:\n # print(\"WARNING: Davidson algorithm reached max basis size \"\n # \"M = %d without converging.\"%(M))\n\n # Express alpha in original basis\n evecs = np.dot(b,alpha) # b is N x M, alpha is M x M\n return conv, lamda[:neig], evecs[:,:neig], istep","def get_euclidean_matrix(df):\n df.reset_index(drop=True, inplace=True)\n\n # foods = df['food_names']\n # food_examples = []\n # indices = list(range(0, len(foods)))\n # for i in indices:\n # food_examples.append(str(foods[i]) + str(i))\n # food_examples = pd.Series(food_examples)\n food_examples = df['food_names']\n\n df = df.drop(['food_names', 'height', 'weight', 'above_range', 'BMI', 'age', 'gender',\n 'glucose_tolerance_category','90-percentile_of_2h-iAUC', 'average_carbs_ratio',\n 'average_daily_carbs','average_meals_per_day', 'average_sleep_hours',\n 'average_glucose', 'baseline', 'coefficient_of_variation', 'max_2-hours_iAUC',\n 'median_fasting_glucose_level','median_of_2h-iAUC', 'night_baseline'], axis='columns')\n\n df = df.replace([-np.inf], 0).dropna(axis=1)\n\n num_examples = df.shape[0]\n\n distances = pdist(df.values, metric='euclidean')\n print(distance)\n dis_array = squareform(distances)\n print(dis_array)\n dis_df = pd.DataFrame(data = dis_array, index=food_examples, columns=food_examples)\n print(dis_df)\n writer = pd.ExcelWriter('Euclidean_distance_icarbonx.xlsx', engine='xlsxwriter')\n dis_df.to_excel(writer, sheet_name='Sheet1')\n writer.save()","def diag(B,s,H,ia,ib,ic,chia,chic):\n # Get a guess for the ground state based on the old MPS\n d = B[0].shape[0]\n theta0 = np.tensordot(np.diag(s[ia]),np.tensordot(B[ib],B[ic],axes=(2,1)),axes=(1,1))\n theta0 = np.reshape(np.transpose(theta0,(1,0,2,3)),((chia*chic)*(d**2)))\n\n # Diagonalize Hamiltonian\n e0,v0 = arp.eigsh(H,k=1,which='SA',return_eigenvectors=True,v0=theta0,ncv=20)\n \n return np.reshape(v0.squeeze(),(d*chia,d*chic)),e0","def det(self):\n self.matrix() # forces the update of the matrix in the module's default\n # basis, to make sure that the dictionary self._matrices\n # is not empty\n return self._matrices.values()[0].det() # pick a random value in the\n # dictionary self._matrices\n # and compute the determinant","def determinant(matrix):\n if matrix == [[]]:\n return 1\n if type(matrix) is not list or len(matrix) < 1 or\\\n not all(isinstance(x, list) for x in matrix):\n raise TypeError(\"matrix must be a list of lists\")\n if not all(len(matrix) == len(x) for x in matrix):\n raise ValueError(\"matrix must be a square matrix\")\n copy = list(map(list, matrix))\n dim = len(matrix)\n if dim == 1:\n return matrix[0][0]\n elif dim == 2:\n return matrix[0][0] * matrix[1][1] - matrix[1][0] * matrix[0][1]\n else:\n for cur in range(dim):\n for i in range(cur + 1, dim):\n if copy[cur][cur] == 0:\n copy[cur][cur] = 1.0e-10\n curScaler = copy[i][cur] / copy[cur][cur]\n for j in range(dim):\n copy[i][j] = copy[i][j] - curScaler * copy[cur][j]\n det = 1\n for i in range(dim):\n det *= copy[i][i]\n return round(det)","def del2(im_input):\n\n # initialize output\n im_lap = np.zeros(im_input.shape)\n\n # process rows first\n D = np.diff(im_input, axis=0)\n S = np.zeros(im_input.shape)\n S[1:-1, :] = (D[1:, :] - D[0:-1, :]) / 2\n if im_input.shape[0] > 3:\n S[0, :] = 2 * S[1, :] - S[2, :]\n S[-1, :] = 2 * S[-2, :] - S[-3, :]\n elif im_input.shape[0] == 3:\n S[0, :] = S[1, :]\n S[-1, :] = S[1, :]\n else:\n S[0, :] = 0\n S[-1, :] = 0\n im_lap += S\n\n # process columns\n D = np.diff(im_input, axis=1)\n S = np.zeros(im_input.shape)\n S[:, 1:-1] = (D[:, 1:] - D[:, 0:-1]) / 2\n if im_input.shape[1] > 3:\n S[:, 0] = 2 * S[:, 1] - S[:, 2]\n S[:, -1] = 2 * S[:, -2] - S[:, -3]\n elif im_input.shape[1] == 3:\n S[0, :] = S[:, 1]\n S[:, -1] = S[:, 1]\n else:\n S[:, 0] = 0\n S[:, -1] = 0\n im_lap += S\n\n return im_lap / 2","def dKdiag_dtheta(self, dL_dKdiag, X, target):\r\n target += 2.*self.mapping.df_dtheta(dL_dKdiag[:, None]*self.mapping.f(X), X)","def dist_chisq(datamtx, strict=True):\n if strict:\n if not all(isfinite(datamtx)):\n raise ValueError(\"non finite number in input matrix\")\n if any(datamtx<0.0):\n raise ValueError(\"negative value in input matrix\")\n if rank(datamtx) != 2:\n raise ValueError(\"input matrix not 2D\")\n numrows, numcols = shape(datamtx)\n else:\n try:\n numrows, numcols = shape(datamtx)\n except ValueError:\n return zeros((0,0),'d')\n\n if numrows == 0 or numcols == 0:\n return zeros((0,0),'d')\n dists = zeros((numrows,numrows),'d')\n sqrt_grand_sum = sqrt(sum(datamtx))\n rowsums, colsums = sum(datamtx, axis=1), sum(datamtx, axis=0)\n if not colsums.all():\n for i in range(len(colsums)):\n if colsums[i] == 0.0:\n colsums[i] = 1.0\n for i in range(numrows):\n r1 = datamtx[i]\n r1sum = rowsums[i]\n for j in range(i):\n r2 = datamtx[j]\n r2sum = rowsums[j]\n if r1sum == 0.0 or r2sum == 0.0:\n if r1sum == 0.0 and r2sum == 0.0:\n dist = 0.0\n else: dist = 1.0\n else:\n dist = sqrt_grand_sum *\\\n sqrt(sum( multiply((1./colsums) ,\n square(r1/r1sum - r2/r2sum)) ))\n dists[i,j] = dists[j,i] = dist\n return dists","def determinant (self):\n if self.is_square:\n det = 1\n for idx, row in enumerate(echelon_form(self).rows()):\n det *= row[idx]\n return det\n else:\n raise NotImplementedError(\n \"Determinant only defined for square matrices.\")","def _reclassify_disturbance(\n landuse_transition_from_matrix, landuse_transition_to_matrix):\n output_matrix = numpy.empty(landuse_transition_from_matrix.shape,\n dtype=numpy.float32)\n output_matrix[:] = NODATA_FLOAT32_MIN\n\n valid_pixels = numpy.ones(landuse_transition_from_matrix.shape,\n dtype=bool)\n if from_nodata is not None:\n valid_pixels &= ~utils.array_equals_nodata(\n landuse_transition_from_matrix, from_nodata)\n\n if to_nodata is not None:\n valid_pixels &= ~utils.array_equals_nodata(\n landuse_transition_to_matrix, to_nodata)\n\n disturbance_magnitude = disturbance_magnitude_matrix[\n landuse_transition_from_matrix[valid_pixels],\n landuse_transition_to_matrix[valid_pixels]].toarray().flatten()\n\n output_matrix[valid_pixels] = disturbance_magnitude\n return output_matrix","def compute_differential_operator(self):\n\n v_in, v_out, weights = self.get_edge_list()\n\n n = len(v_in)\n Dr = np.concatenate((np.arange(n), np.arange(n)))\n Dc = np.empty(2*n)\n Dc[:n] = v_in\n Dc[n:] = v_out\n Dv = np.empty(2*n)\n\n if self.lap_type == 'combinatorial':\n Dv[:n] = np.sqrt(weights)\n Dv[n:] = -Dv[:n]\n elif self.lap_type == 'normalized':\n Dv[:n] = np.sqrt(weights / self.dw[v_in])\n Dv[n:] = -np.sqrt(weights / self.dw[v_out])\n else:\n raise ValueError('Unknown lap_type {}'.format(self.lap_type))\n\n self._D = sparse.csc_matrix((Dv, (Dr, Dc)), shape=(n, self.N))","def inverse_3by3_double(M):\n if len(M.shape) > 1:\n M = M.flatten()\n\n M = np.array(M, 'float')\n\n determinant = 0.\n adj_M = np.zeros((9,), 'float')\n\n # First row of adjugate matrix\n adj_M[0] = (M[4] * M[8] - M[7] * M[5]) # Det #0\n adj_M[1] = -(M[1] * M[8] - M[7] * M[2])\n adj_M[2] = (M[1] * M[5] - M[4] * M[2])\n\n # Second row of adjugate matrix\n adj_M[3] = -(M[3] * M[8] - M[6] * M[5]) # Det #1\n adj_M[4] = (M[0] * M[8] - M[6] * M[2])\n adj_M[5] = -(M[0] * M[5] - M[3] * M[2])\n\n # Third row of adjugate matrix\n adj_M[6] = (M[3] * M[7] - M[6] * M[4]) # Det #2\n adj_M[7] = -(M[0] * M[7] - M[6] * M[1])\n adj_M[8] = (M[0] * M[4] - M[3] * M[1])\n\n determinant += M[0] * adj_M[0]\n determinant += M[1] * adj_M[3] # Using addition since minus is integrated in adjugate matrix.\n determinant += M[2] * adj_M[6]\n\n return (adj_M / determinant)","def _build_adjacency_matrix_1(self):\n\n from scipy import sparse as sparse\n \n down_neighbour = np.empty(self.tri.npoints)\n\n for node in range (0,self.tri.npoints):\n down_neighbour[node] = self.neighbour_array_lo_hi[node][0]\n\n # Build a matrix of downhill-ness - one entry per node ! \n \n size = self.tri.npoints\n row_array = np.empty(size)\n col_array = np.empty(size)\n down_array = np.ones(size)\n\n # Catch cases where node is local low point (i.e. it is its own low neighbour)\n\n for row in range(0, self.tri.npoints): \n row_array[row] = row\n col_array[row] = down_neighbour[row]\n if row == down_neighbour[row]:\n down_array[row] = 0.0\n \n\n downMCOO = sparse.coo_matrix( (down_array, (row_array, col_array)), shape=(size,size) ).T \n\n self.adjacency1 = downMCOO.tocsr() \n\n # Catch pathological cases - sometimes if there is a flat spot on the boundary, then \n # the filling method above will produce a non-square matrix. This is caused by\n # repetition of values in the COO list which are summed on conversion.\n\n if downMCOO.shape[0] != downMCOO.shape[1]:\n # This approach works but is a lot slower\n\n print \"\"\"\n Warning: the downhill matrices require a slow build method. This is probably\n Because there are degeneracies in the slope - particularly at the boundaries\n A small random perturbation is usually enough to fix this problem\n \"\"\"\n downMat = sparse.lil_matrix((size, size))\n\n for row in range(0, self.tri.npoints): \n downMat[down_neighbour[row],row] = 1.0\n\n for row in range(0, self.tri.npoints): \n if down_neighbour[row] == row:\n downMat[row,row] = 0.0\n \n self.adjacency1 = downMat.T.tocsr() \n \n return","def compute_det(self, log_progress=False):\n if not self.is_square():\n raise Exception(u\"Not a square matrix\")\n\n mat = clone_matrix(self.coefficients)\n size = self.get_size()[0]\n\n for i in range(size - 1):\n for j in range(i + 1, size):\n for k in range(i + 1, size):\n mat[j][k] = (mat[j][k] * mat[i][i]) - (mat[j][i] * mat[i][k])\n if i > 0:\n mat[j][k] //= mat[i - 1][i - 1]\n if log_progress:\n print(i)\n if i > 0:\n for j in range(size):\n mat[j][i - 1] = 0\n mat[i - 1][j] = 0\n\n return mat[size - 1][size - 1]","def sim_mat(fc7_feats):\n print(\"Something\")\n t = time.time()\n pdist_ = spatial.distance.pdist(fc7_feats)\n print('Created distance matrix' + ' ' + str(time.time() - t) + ' sec')\n\n t = time.time()\n dist_mat = spatial.distance.squareform(pdist_)\n print('Created square distance matrix' + ' ' + str(time.time() - t) + ' sec')\n del pdist_\n\n t = time.time()\n sigmas = np.sort(dist_mat, axis=1)[:, 7] + 1e-16\n matrice_prodotti_sigma = np.dot(sigmas[:, np.newaxis], sigmas[np.newaxis, :])\n print('Generated Sigmas' + ' ' + str(time.time() - t) + ' sec')\n\n t = time.time()\n dist_mat /= -matrice_prodotti_sigma\n print('Computed dists/-sigmas' + ' ' + str(time.time() - t) + ' sec')\n\n del matrice_prodotti_sigma\n\n t = time.time()\n W = np.exp(dist_mat, dist_mat)\n # W = np.exp(-(dist_mat / matrice_prodotti_sigma))\n np.fill_diagonal(W, 0.)\n\n # sparsify the matrix\n k = int(np.floor(np.log2(fc7_feats.shape[0])) + 1)\n n = W.shape[0]\n print('Created inplace similarity matrix' + ' ' + str(time.time() - t) + ' sec')\n\n t = time.time()\n for x in W:\n x[np.argpartition(x, n - k)[:(n - k)]] = 0.0\n\n print('Sparsify the matrix' + ' ' + str(time.time() - t) + ' sec')\n\n t = time.time()\n # matrix_S = np.zeros((n, n))\n m1 = W[np.triu_indices(n, k=1)]\n m2 = W.T[np.triu_indices(n, k=1)]\n\n W = spatial.distance.squareform(np.maximum(m1, m2))\n print('Symmetrized the similarity matrix' + ' ' + str(time.time() - t) + ' sec')\n\n return W","def _core_calc_degrad(self,bd,Ld) :\n\t\tdegrad = np.dot(Ld,bd) # Do matrix multiplication \n\t\tdegrad = np.exp(degrad) # Exponentiate to convert log to real\n\t\treturn degrad","def build_difs_matrix(xs):\n n = len(xs)\n difs = np.zeros((n, n, 2))\n for row_id in range(n):\n ident = -np.eye(n)\n ident[:, row_id] += 1\n difs[row_id, :, 0] = ident @ xs[:, 0]\n difs[row_id, :, 1] = ident @ xs[:, 1]\n return difs"],"string":"[\n \"def det(A):\\n # Section 1: Establish n parameter and copy A\\n n = len(A)\\n AM = A[:]\\n\\n # Section 2: Row manipulate A into an upper triangle matrix\\n for fd in range(n): # fd stands for focus diagonal\\n if AM[fd][fd] == 0:\\n AM[fd][fd] = 1.0e-18 # Cheating by adding zero + ~zero\\n for i in range(fd+1, n): # skip row with fd in it.\\n crScaler = AM[i][fd] / AM[fd][fd] # cr stands for \\\"current row\\\".\\n for j in range(n): # cr - crScaler * fdRow, one element at a time.\\n AM[i][j] = AM[i][j] - crScaler * AM[fd][j]\\n\\n # Section 3: Once AM is in upper triangle form ...\\n product = 1.0\\n for i in range(n):\\n product *= AM[i][i] # ... product of diagonals is determinant\\n\\n return product\",\n \"def post_heatdiag(self,ds):\\n #\\n self.drmid=self.rmid*0 # mem allocation\\n self.drmid[1:-1]=(self.rmid[2:]-self.rmid[0:-2])*0.5\\n self.drmid[0]=self.drmid[1]\\n self.drmid[-1]=self.drmid[-2]\\n\\n dt = np.zeros_like(self.time)\\n dt[1:] = self.time[1:] - self.time[0:-1]\\n dt[0] = dt[1]\\n rst=np.nonzero(dt<0) #index when restat happen\\n dt[rst]=dt[rst[0]+1]\\n self.dt = dt\\n\\n #get separatrix r\\n self.rs=np.interp([1],self.psin,self.rmid)\\n \\n self.rmidsepmm=(self.rmid-self.rs)*1E3 # dist from sep in mm\\n\\n #get heat\\n self.qe=np.transpose(self.e_perp_energy_psi + self.e_para_energy_psi)/dt/ds\\n self.qi=np.transpose(self.i_perp_energy_psi + self.i_para_energy_psi)/dt/ds\\n self.ge=np.transpose(self.e_number_psi)/dt/ds\\n self.gi=np.transpose(self.i_number_psi)/dt/ds\\n\\n self.qe = np.transpose(self.qe)\\n self.qi = np.transpose(self.qi)\\n self.ge = np.transpose(self.ge)\\n self.gi = np.transpose(self.gi)\\n\\n self.qt=self.qe+self.qi\\n #imx=self.qt.argmax(axis=1)\\n mx=np.amax(self.qt,axis=1)\\n self.lq_int=mx*0 #mem allocation\\n\\n for i in range(mx.shape[0]):\\n self.lq_int[i]=np.sum(self.qt[i,:]*self.drmid)/mx[i]\",\n \"def det2(m):\\n\\t(a,b), (c,d) = m\\n\\treturn a*d - b*c\",\n \"def det_matrix(self):\\n return np.linalg.det(self.take_matrix())\",\n \"def _calc_del(dis):\\n ndist = len(dis)\\n deldis = np.zeros((ndist))\\n deldis[0] = (dis[1] - dis[0]) * 0.5\\n deldis[1:-1] = (dis[2:] - dis[0:-2]) * 0.5\\n deldis[-1] = (dis[-1] - dis[-2]) * 0.5\\n return deldis\",\n \"def det(self,mat):\\n if(len(mat[0])==len(mat)):\\n result = np.linalg.det(mat)\\n self.determinant = result\\n return self.determinant\\n else:\\n print(\\\"Not a square Matrix\\\")\",\n \"def _compute_diag_H(self, t, index, update_derivatives=False, stationary=False):\\r\\n\\r\\n \\\"\\\"\\\"if delta_i~=delta_j:\\r\\n [h, dh_dD_i, dh_dD_j, dh_dsigma] = np.diag(simComputeH(t, index, t, index, update_derivatives=True, stationary=self.is_stationary))\\r\\n else:\\r\\n Decay = self.decay[index]\\r\\n if self.delay is not None:\\r\\n t = t - self.delay[index]\\r\\n \\r\\n t_squared = t*t\\r\\n half_sigma_decay = 0.5*self.sigma*Decay\\r\\n [ln_part_1, sign1] = ln_diff_erfs(half_sigma_decay + t/self.sigma,\\r\\n half_sigma_decay)\\r\\n \\r\\n [ln_part_2, sign2] = ln_diff_erfs(half_sigma_decay,\\r\\n half_sigma_decay - t/self.sigma)\\r\\n \\r\\n h = (sign1*np.exp(half_sigma_decay*half_sigma_decay\\r\\n + ln_part_1\\r\\n - log(Decay + D_j)) \\r\\n - sign2*np.exp(half_sigma_decay*half_sigma_decay\\r\\n - (Decay + D_j)*t\\r\\n + ln_part_2 \\r\\n - log(Decay + D_j)))\\r\\n \\r\\n sigma2 = self.sigma*self.sigma\\r\\n\\r\\n if update_derivatives:\\r\\n \\r\\n dh_dD_i = ((0.5*Decay*sigma2*(Decay + D_j)-1)*h \\r\\n + t*sign2*np.exp(\\r\\n half_sigma_decay*half_sigma_decay-(Decay+D_j)*t + ln_part_2\\r\\n )\\r\\n + self.sigma/np.sqrt(np.pi)*\\r\\n (-1 + np.exp(-t_squared/sigma2-Decay*t)\\r\\n + np.exp(-t_squared/sigma2-D_j*t)\\r\\n - np.exp(-(Decay + D_j)*t)))\\r\\n \\r\\n dh_dD_i = (dh_dD_i/(Decay+D_j)).real\\r\\n \\r\\n \\r\\n \\r\\n dh_dD_j = (t*sign2*np.exp(\\r\\n half_sigma_decay*half_sigma_decay-(Decay + D_j)*t+ln_part_2\\r\\n )\\r\\n -h)\\r\\n dh_dD_j = (dh_dD_j/(Decay + D_j)).real\\r\\n\\r\\n dh_dsigma = 0.5*Decay*Decay*self.sigma*h \\\\\\r\\n + 2/(np.sqrt(np.pi)*(Decay+D_j))\\\\\\r\\n *((-Decay/2) \\\\\\r\\n + (-t/sigma2+Decay/2)*np.exp(-t_squared/sigma2 - Decay*t) \\\\\\r\\n - (-t/sigma2-Decay/2)*np.exp(-t_squared/sigma2 - D_j*t) \\\\\\r\\n - Decay/2*np.exp(-(Decay+D_j)*t))\\\"\\\"\\\"\\r\\n pass\",\n \"def fast_logdet(matrix):\\n sign, ld = np.linalg.slogdet(matrix)\\n if not sign > 0:\\n return -np.inf\\n return ld\",\n \"def _det(mat):\\n\\n return (mat[0][0] * (mat[1][1] * mat[2][2] - mat[1][2] * mat[2][1])\\n + mat[0][1] * (mat[1][2] * mat[2][0] - mat[1][0] *\\n mat[2][2]) + mat[0][2] * (mat[1][0] * mat[2][1] -\\n mat[1][1] * mat[2][0]))\",\n \"def Distmatrix(self):\\n self.Dismatrix = np.zeros((self.nodenum, self.nodenum))\\n for i in range(len(self.Dismatrix)):\\n for j in range(len(self.Dismatrix)):\\n self.Dismatrix[i, j] = sf.dist(self.y[i], self.x[i], self.y[j], self.x[j])\\n self.Dismatrix[j, i] = self.Dismatrix[i, j]\",\n \"def det_matrix_2x2(m: list):\\n return m[0][0]*m[1][1] - m[0][1]*m[1][0]\",\n \"def det(a):\\n a = copy.deepcopy(a)\\n n = len(a)\\n det = 1\\n com_k = 1\\n for k in range(n-1):\\n step = 1\\n\\n while a[k][k] == 0:\\n a[k+step], a[k] = a[k], a[k+step]\\n det = -det\\n step += 1\\n mul = a[k][k]\\n\\n for i in range(k+1, n):\\n for j in range(k+1, n):\\n a[i][j] *= mul\\n a[i][j] -= a[i][k] * a[k][j]\\n a[i][j] /= com_k\\n\\n com_k = mul\\n\\n det = det * a[-1][-1]\\n\\n return det\",\n \"def build_cumulative_downhill_matrix(self):\\n\\n import time\\n from scipy import sparse as sparse\\n\\n\\n walltime = time.clock()\\n\\n downHillaccuMat = self.downhillMat.copy() \\n accuM = self.downhillMat.copy() # work matrix\\n\\n DX = np.ones(self.tri.npoints) # measure when all the info has been propagated out.\\n previous_nonzero = 0\\n it = 0\\n\\n while np.count_nonzero(DX) != previous_nonzero:\\n accuM = accuM.dot(self.downhillMat)\\n downHillaccuMat = downHillaccuMat + accuM \\n previous_nonzero = np.count_nonzero(DX)\\n\\n DX = self.downhillMat.dot(DX) \\n\\n it += 1\\n \\n\\n print \\\" - Dense downhill matrix storage time \\\", time.clock() - walltime\\n print \\\" - Maximum path length \\\",it\\n\\n walltime = time.clock()\\n\\n\\n # Turn this into a loop !\\n\\n A1 = self.downhillMat.tocsr()\\n A2 = A1.dot(A1)\\n A2a = A1 + A2\\n A4 = A2.dot(A2)\\n A4a = A2a + A2.dot(A2a)\\n A8 = A4.dot(A4)\\n A8a = A4a + A4.dot(A4a)\\n A16 = A8.dot(A8)\\n A16a = A8a + A8.dot(A8a)\\n A32 = A16.dot(A16)\\n A32a = A16a + A16.dot(A16a)\\n A64 = A32.dot(A32)\\n A64a = A32a + A32.dot(A32a)\\n A128 = A64.dot(A64)\\n A128a = A64a + A64.dot(A64a)\\n\\n print \\\"A32.nnz = \\\", A32.nnz\\n print \\\"A64.nnz = \\\", A64.nnz\\n print \\\"A128.nnz = \\\", A128.nnz\\n\\n\\n print \\\" - Dense downhill matrix storage time v2\\\", time.clock() - walltime\\n print \\\" - Maximum path length \\\", 128\\n\\n\\n downHillaccuMat = downHillaccuMat + sparse.identity(self.tri.npoints, format='csr')\\n\\n downHillaccuMat2 = A128a + sparse.identity(self.tri.npoints, format='csr')\\n\\n\\n return downHillaccuMat, downHillaccuMat2\",\n \"def det(self):\\n # XXX: At least the first three algorithms described above should also\\n # be implemented in the pure Python DDM and SDM classes which at the\\n # time of writng just use Bareiss for all matrices and domains.\\n # Probably in Python the thresholds would be different though.\\n return self.rep.det()\",\n \"def _compute_rho0(self, distance_matrix):\\n nr_samples = distance_matrix.shape[1]\\n\\n # both modes are equivalent, MODE=1 allows to easier compare with ref3.\\n MODE = 1\\n if MODE == 1: # according to Berry code\\n # keep only nearest neighbors\\n distance_matrix = np.sort(np.sqrt(distance_matrix), axis=1)[\\n :, 1 : self.k + 1\\n ]\\n rho0 = np.sqrt(np.mean(distance_matrix ** 2, axis=1))\\n else: # MODE == 2: , more performant if required\\n if self.k < nr_samples:\\n # TODO: have to revert setting the inf\\n # -> check that the diagonal is all zeros\\n # -> set to inf\\n # -> after computation set all infs back to zero\\n # -> this is also very similar to continous-nn in PCManifold\\n\\n # this allows to ignore the trivial distance=0 to itself\\n np.fill_diagonal(distance_matrix, np.inf)\\n\\n # more efficient than sorting, everything\\n distance_matrix.partition(self.k, axis=1)\\n distance_matrix = np.sort(distance_matrix[:, : self.k], axis=1)\\n distance_matrix = distance_matrix * distance_matrix\\n else: # self.k == self.N\\n np.fill_diagonal(distance_matrix, np.nan)\\n bool_mask = ~np.diag(np.ones(nr_samples)).astype(bool)\\n distance_matrix = distance_matrix[bool_mask].reshape(\\n distance_matrix.shape[0], distance_matrix.shape[1] - 1\\n )\\n\\n # experimental: --------------------------------------------------------------\\n # paper: in var-bw paper (ref2) pdfp. 7\\n # it is mentioned to IGNORE non-zero entries -- this is not detailed more.\\n # a consequence is that the NN and kernel looses symmetry, so does (K+K^T) / 2\\n # This is with a cut-off rate:\\n # val = 1E-2\\n # distance_matrix[distance_matrix < val] = np.nan\\n # experimental END -----------------------------------------------------------\\n\\n # nanmean only for the experimental part, if leaving this out, np.mean\\n # suffices\\n rho0 = np.sqrt(np.nanmean(distance_matrix, axis=1))\\n\\n return rho0\",\n \"def det(self):\\n\\n if self.rows != self.columns:\\n raise ValueError(\\\"Matrix must be square\\\")\\n\\n if self.rows == 1:\\n return self.row(1)[0]\\n\\n if self.rows == 2:\\n return self.entry(1,1) * self.entry(2,2) - self.entry(1,2) * self.entry(2,1)\\n\\n det = 0\\n row_to_expand = 1\\n\\n for i in range(1, self.columns + 1):\\n det += self.entry(row_to_expand, i) * self._cofactor(row_to_expand, i)\\n\\n return det\",\n \"def determinant(self):\\n if self.cols != self.rows:\\n raise Exception ('Matrix is not square!')\\n for i in range(self.rows):\\n if self.values[i][i] == 0:\\n raise Exception ('There is zero on the main diagonal')\\n #TODO: Rearrange the lines, that the main diagonal don't have a zero values \\n\\n arr = self.values[:]\\n for i in range(self.rows):\\n for j in range(self.cols):\\n diag = [arr[l][p] for p in range(self.cols) for l in range(self.rows) if l == p ]\\n if i > j :\\n arr2 = arr[i][j]/diag[j]\\n arr1 = [round(x * arr2, 4) for x in arr[i-i+j]]\\n arr[i] = map(lambda x,y: round(x - y, 4) , arr[i], arr1 )\\n\\n diag = [arr[l][p] for p in range(self.cols) for l in range(self.rows) if l == p ]\\n det = 1\\n for i in range(len(diag)):\\n det *= diag[i]\\n if det != 0 :\\n return True\\n else:\\n return False\",\n \"def _derive_euclidean_dm(self, cat_mat, dim):\\r\\n res_mat = []\\r\\n\\r\\n for i in range(dim):\\r\\n res_mat.append([0 for k in range(dim)])\\r\\n for j in range(i):\\r\\n res_mat[i][j] = self._vector_dist(cat_mat[i], cat_mat[j])\\r\\n res_mat[j][i] = res_mat[i][j]\\r\\n\\r\\n return DistanceMatrix(res_mat, self.DistanceMatrices[0].ids)\",\n \"def calc_det_dzh(theta):\\n return 919.49 - 27.018 * theta + 0.26209 * theta ** 2 - 0.00083803 * theta ** 3\",\n \"def test_frechet_distance_diagonal_covariance(self):\\n N = 15\\n mu_x = torch.rand((N,), device=self.device)\\n sigma_x = torch.diag(torch.rand((N,), device=self.device))\\n\\n mu_y = torch.rand((N,), device=self.device)\\n sigma_y = torch.diag(torch.rand((N,), device=self.device))\\n\\n expected = (\\n torch.sum((mu_x - mu_y) ** 2) + torch.sum(sigma_x + sigma_y) - 2 * torch.sum(torch.sqrt(sigma_x * sigma_y))\\n )\\n expected = expected.item()\\n actual = F.frechet_distance(mu_x, sigma_x, mu_y, sigma_y)\\n\\n self.assertEqual(expected, actual)\",\n \"def construct_H_with_KNN_from_distance(dis_mat, k_neig, is_probH=True, m_prob=1, sig=0.002):\\n n_obj = dis_mat.shape[0]\\n # construct hyperedge from the central feature space of each node\\n n_edge = n_obj\\n H = np.zeros((n_obj, n_edge))\\n for center_idx in range(n_obj):\\n dis_mat[center_idx, center_idx] = 0\\n dis_vec = dis_mat[center_idx]\\n nearest_idx = np.array(np.argsort(dis_vec)).squeeze()\\n #avg_dis = np.average(dis_vec)\\n if not np.any(nearest_idx[:k_neig] == center_idx):\\n nearest_idx[k_neig - 1] = center_idx\\n\\n for node_idx in nearest_idx[:k_neig]:\\n if is_probH:\\n H[node_idx, center_idx] = np.exp(- sig * dis_vec[0, node_idx] )\\n #print(H[node_idx, center_idx])\\n #H[node_idx, center_idx] = dis_vec[0, node_idx]\\n else:\\n H[node_idx, center_idx] = 1.0\\n return H\",\n \"def determinant(self):\\n if not self.is_square():\\n raise(ValueError, \\\"Cannot calculate determinant of non-square matrix.\\\")\\n if self.h > 2:\\n raise(NotImplementedError, \\\"Calculating determinant not implemented for matrices largerer than 2x2.\\\")\\n\\n # TODO - your code here\\n if self.h == 1:\\n return self.g[0][0] # a 1x1 matrix\\n else:\\n return ((self.g[0][0] * self.g[1][1]) - (self.g[0][1] * self.g[1][0])) # a 2x2 matrix\\n # TODO - your code here\",\n \"def normalize_distancematrix(self):\\n INF = self.distmat.max().max()\\n df = self.distmat.fillna(INF)\\n self.distmat = (df - df.min()) / (df.max() - df.min())\",\n \"def determinant_fast(A):\\n # Section 1: Establish n parameter and copy A\\n n = len(A)\\n AM = copy_matrix(A)\\n\\n # Section 2: Row manipulate A into an upper triangle matrix\\n for fd in range(n): # fd stands for focus diagonal\\n if AM[fd][fd] == 0: \\n AM[fd][fd] = 1.0e-18 # Cheating by adding zero + ~zero\\n for i in range(fd+1,n): # skip row with fd in it.\\n crScaler = AM[i][fd] / AM[fd][fd] # cr stands for \\\"current row\\\".\\n for j in range(n): # cr - crScaler * fdRow, but one element at a time.\\n AM[i][j] = AM[i][j] - crScaler * AM[fd][j]\\n \\n # Section 3: Once AM is in upper triangle form ...\\n product = 1.0\\n for i in range(n):\\n product *= AM[i][i] # ... product of diagonals is determinant\\n\\n return product\",\n \"def test_sd_nonlinear(self):\\n z_matrix = np.array(\\n [[0.0, 0.0, 0.0],\\n [0.0, 0.0, 1.0],\\n [0.2, 0.5, 0.0],\\n [0.2, 0.5, 1.0],\\n [0.4, 1.0, 0.0],\\n [0.4, 1.0, 1.0],\\n [0.6, 1.0, 0.0],\\n [0.6, 1.0, 1.0],\\n [0.8, 0.5, 0.0],\\n [0.8, 0.5, 1.0],\\n [1.0, 0.0, 0.0],\\n [1.0, 0.0, 1.0]],\\n dtype=np.float64)\\n obtained_w_vector = mcdm.weigh(z_matrix, \\\"SD\\\")\\n expected_w_vector = np.array(\\n [0.27329284, 0.32664742, 0.40005975],\\n dtype=np.float64)\\n np.testing.assert_allclose(obtained_w_vector, expected_w_vector)\\n self.assertEqual(obtained_w_vector.dtype, expected_w_vector.dtype)\",\n \"def _build_downhill_matrices(self, weight=0.6667):\\n\\n from scipy import sparse as sparse\\n \\n\\n down_neighbour = np.empty(self.tri.npoints, dtype=np.int)\\n\\n for node in range (0,self.tri.npoints):\\n down_neighbour[node] = self.neighbour_array_lo_hi[node][0]\\n\\n # Build a matrix of downhill-ness - one entry per node ! \\n \\n size = self.tri.npoints\\n row_array = np.empty(size, dtype = int)\\n col_array = np.empty(size, dtype = int)\\n down_array = np.ones(size)\\n accu_array = np.ones(size)\\n\\n\\n for row in range(0, self.tri.npoints): \\n row_array[row] = row\\n col_array[row] = down_neighbour[row]\\n \\n accuMCOO = sparse.coo_matrix( (accu_array, (row_array, col_array)), shape=(size,size) ).T \\n\\n self.accumulatorMat = accuMCOO.tocsr() \\n\\n self._build_adjacency_matrix_1()\\n self._build_adjacency_matrix_2()\\n \\n self.downhillMat = weight * self.adjacency1 + (1.0-weight) * self.adjacency2\\n\\n # A1 = self.downhillMat\\n # A2 = self.downhillMat.dot(self.downhillMat)\\n # A2a = A1 + A2\\n # A4 = A2.dot(A2)\\n # A4a = A2a + A2.dot(A2a)\\n # A8 = A4.dot(A4)\\n # A8a = A4a + A4.dot(A4a)\\n # A16 = A8.dot(A8)\\n # A16a = A8a + A8.dot(A8a)\\n\\n # self.downhillMat16 = A16\\n # self.downhillMat8 = A8\\n # self.downhillMat16a = A16a\\n # self.downhillMat8a = A8a\\n\\n # We make it optional to build these as they are not sparse \\n # This cleans up previously stored matrices\\n\\n self.downhillCumulativeMat = None\\n self.sweepDownToOutflowMat = None\\n \\n return\",\n \"def Dmat(numpts, delta=1):\\n a = 0.5 / delta * ones(numpts)\\n a[0] = 0\\n a[-2] = 0\\n #b=-2./delta**2*ones(numpts); b[0]=0;b[-1]=0\\n c = -0.5 / delta * ones(numpts)\\n c[1] = 0\\n c[-1] = 0\\n return sparse.spdiags([a, c], [-1, 1], numpts, numpts)\",\n \"def det(self):\\n if self.x == 0 or self.y == 0:\\n return None\\n elif self.x == 1 or self.y == 1:\\n return self.retrieve(0,0)\\n else:\\n out = 0.0\\n for x in xrange(0, self.x):\\n out += self.retrieve(0,x)*self.C(0,x)\\n return out\",\n \"def normalize_adj(adj): # 图归一化操作\\n adj = sp.coo_matrix(adj) # A_hat\\n rowsum = np.array(adj.sum(1))\\n d_inv_sqrt = np.power(rowsum, -0.5).flatten()\\n d_inv_sqrt[np.isinf(d_inv_sqrt)] = 0.\\n d_mat_inv_sqrt = sp.diags(d_inv_sqrt) # D_hat\\n return adj.dot(d_mat_inv_sqrt).transpose().dot(d_mat_inv_sqrt).tocoo()\",\n \"def compute_determinant(matrix):\\n det = np.linalg.det(matrix)\\n #if det == 0.:\\n # The det = 0 could be related to the third feature\\n # det = np.linalg.det(matrix[:2, :2])\\n if det == 0.:\\n # Singular covariance matrix, should not be taken into account\\n det = np.nan\\n if np.isclose(det, 0):\\n det = np.abs(det)\\n return det\",\n \"def correlation_d(mat):\\n\\n print(\\\"DO NOT USE. BROKEN?\\\")\\n\\n if mat.ndim != 2:\\n raise ValueError(\\\"mat must be a 2d matrix\\\")\\n if np.any(mat > 1) or np.any(mat < 0):\\n raise ValueError(\\\"mat must be binary\\\")\\n\\n N = mat.size\\n g = np.diagonal(mat)\\n # g = np.tril(mat, -1) # g is the sum over the heavside used in Grassberger\\n # g = g[g.nonzero()]\\n g = g.sum()\\n\\n return (2.0 / N * (N - 1)) * g\",\n \"def _matrix_store_smooth_downhill(self):\\n \\n import time\\n from scipy import sparse as sparse\\n from scipy.sparse import linalg as linalgs \\n \\n\\n t = time.clock()\\n\\n\\n size = 0\\n for nl in self.neighbour_array_lo_hi:\\n size += 3 # len(nl)\\n\\n row_array = np.empty(size, dtype = int)\\n col_array = np.empty(size, dtype = int)\\n slope_array = np.zeros(size)\\n local_slope_array = np.zeros(64)\\n\\n\\n idx=0 \\n for row in range(0, len(self.neighbour_array_lo_hi)): \\n neighbours = self.neighbour_array_lo_hi[row] \\n npoints = self.tri.points[neighbours]\\n\\n ## work out (downhill) gradient to (max of three) nearby neighbours\\n \\n\\n for col, column in enumerate(neighbours[0:3]): \\n \\n delta_h = self.height[column] - self.height[row] \\n\\n\\n if delta_h < 0.0:\\n delta_s2 = (self.x[column] - self.x[row])**2 + (self.y[column] - self.y[row])**2\\n local_slope_array[col] = ( delta_h**2 / delta_s2 )**5\\n\\n elif delta_h == 0.0 and self.bmask[row] == False:\\n local_slope_array[col] = 1.0e-20\\n\\n else:\\n local_slope_array[col] = 1.0e-20 \\n \\n # Normalise this so that it conserves mass (note - low points will have no contributions here !) \\n \\n norm = local_slope_array[0:len(neighbours)].sum()\\n if norm != 0.0:\\n norm = 1.0 / norm\\n\\n for col, column in enumerate(neighbours[0:3]): \\n row_array[idx] = row\\n col_array[idx] = column \\n slope_array[idx] = local_slope_array[col] * norm\\n\\n idx += 1\\n\\n # We can re-pack this array into a sparse matrix for v. fast computation of downhill operator \\n\\n slopeCOO = sparse.coo_matrix( (slope_array, (row_array, col_array)) ).T\\n slopeMat = slopeCOO.tocsr() \\n \\n print \\\"SlopeMat.shape \\\", slopeMat.shape, size\\n\\n # slopeNormVec = np.array(slopeMat.sum(axis=1)).T[0]\\n # slopeNormVec[slopeNormVec != 0.0] = 1.0 / slopeNormVec[slopeNormVec != 0.0]\\n # slopeNormMat = sparse.eye(self.tri.npoints)\\n # slopeNormMat.setdiag(slopeNormVec)\\n # slopeMat = slopeNormMat.dot(slopeMat)\\n\\n slopeMat.eliminate_zeros()\\n self.smoothDownhillMat = slopeMat\\n\\n return\",\n \"def matrix_det(A):\\n\\tx = A[0,0]*A[1,1]*A[2,2] + A[0,1]*A[1,2]*A[2,0] + A[0,2]*A[1,0]*A[2,1]\\n\\ty = A[0,0]*A[1,2]*A[2,1] + A[0,1]*A[1,0]*A[2,2] + A[0,2]*A[1,1]*A[2,0]\\n\\treturn x - y\",\n \"def construct_H_with_KNN_from_distance(dis_mat, k_neig, is_probH=False, m_prob=1):\\n n_obj = dis_mat.shape[0]\\n # construct hyperedge from the central feature space of each node\\n n_edge = n_obj\\n H = np.zeros((n_obj, n_edge))\\n for center_idx in range(n_obj):\\n dis_mat[center_idx, center_idx] = 0\\n dis_vec = dis_mat[center_idx]\\n nearest_idx = np.array(np.argsort(dis_vec)).squeeze()\\n avg_dis = np.average(dis_vec)\\n if not np.any(nearest_idx[:k_neig] == center_idx):\\n nearest_idx[k_neig - 1] = center_idx\\n\\n for node_idx in nearest_idx[:k_neig]:\\n if is_probH:\\n H[node_idx, center_idx] = np.exp(-dis_vec[0, node_idx] ** 2 / (m_prob * avg_dis) ** 2)\\n else:\\n H[node_idx, center_idx] = 1.0\\n return H\",\n \"def _make_edisp_hdu(cls, table_energy, table_migra, table_theta, matrix):\\n\\n table = Table(\\n {\\n \\\"ENERG_LO\\\": table_energy[\\\"ETRUE_LO\\\"][np.newaxis, :].data\\n * table_energy[\\\"ETRUE_LO\\\"].unit,\\n \\\"ENERG_HI\\\": table_energy[\\\"ETRUE_HI\\\"][np.newaxis, :].data\\n * table_energy[\\\"ETRUE_HI\\\"].unit,\\n \\\"MIGRA_LO\\\": table_migra[\\\"MIGRA_LO\\\"][np.newaxis, :].data\\n * table_migra[\\\"MIGRA_LO\\\"].unit,\\n \\\"MIGRA_HI\\\": table_migra[\\\"MIGRA_HI\\\"][np.newaxis, :].data\\n * table_migra[\\\"MIGRA_HI\\\"].unit,\\n \\\"THETA_LO\\\": table_theta[\\\"THETA_LO\\\"][np.newaxis, :].data\\n * table_theta[\\\"THETA_LO\\\"].unit,\\n \\\"THETA_HI\\\": table_theta[\\\"THETA_HI\\\"][np.newaxis, :].data\\n * table_theta[\\\"THETA_HI\\\"].unit,\\n \\\"MATRIX\\\": matrix[\\\"MATRIX\\\"][np.newaxis, :, :] * matrix[\\\"MATRIX\\\"].unit,\\n }\\n )\\n\\n header = fits.Header()\\n header[\\\"HDUDOC\\\"] = (\\n \\\"https://gamma-astro-data-formats.readthedocs.io/en/latest/irfs/index.html\\\",\\n \\\"\\\",\\n )\\n header[\\\"HDUCLASS\\\"] = \\\"GADF\\\", \\\"\\\"\\n header[\\\"HDUCLAS1\\\"] = \\\"RESPONSE\\\", \\\"\\\"\\n header[\\\"HDUCLAS2\\\"] = \\\"EDISP\\\", \\\"\\\"\\n header[\\\"HDUCLAS3\\\"] = \\\"POINT-LIKE\\\", \\\"\\\"\\n header[\\\"HDUCLAS4\\\"] = \\\"EDISP_2D\\\", \\\"\\\"\\n\\n edisp_hdu = fits.BinTableHDU(table, header, name=\\\"ENERGY DISPERSION\\\")\\n\\n # primary_hdu = fits.PrimaryHDU()\\n # hdulist = fits.HDUList([primary_hdu, edisp_hdu])\\n #\\n # return hdulist\\n return edisp_hdu\",\n \"def distance_matrix(X, Y, metric):\\n distance = np.zeros((len(X), len(Y)))\\n for i in range(len(X)):\\n for j in range(len(Y)):\\n m = metric(X[i], Y[j])\\n if np.isnan(m):\\n pdb.set_trace()\\n distance[i, j] = m\\n return distance\",\n \"def _dmatrix(kn_u, kn_d):\\n d = np.zeros((kn_u.size, 4, 4), np.complex128)\\n d_inv = np.zeros_like(d)\\n\\n d[:, 0, 0] = 1\\n d[:, 0, 1] = 1\\n d[:, 1, 0] = kn_u\\n d[:, 1, 1] = -kn_u\\n\\n d[:, 2, 2] = 1\\n d[:, 2, 3] = 1\\n d[:, 3, 2] = kn_d\\n d[:, 3, 3] = -kn_d\\n\\n # an analytic matrix inverse saves time\\n inv_kn_u = 0.5 / kn_u\\n inv_kn_d = 0.5 / kn_d\\n\\n d_inv[:, 0, 0] = 0.5\\n d_inv[:, 0, 1] = inv_kn_u\\n d_inv[:, 1, 0] = 0.5\\n d_inv[:, 1, 1] = -inv_kn_u\\n\\n d_inv[:, 2, 2] = 0.5\\n d_inv[:, 2, 3] = inv_kn_d\\n d_inv[:, 3, 2] = 0.5\\n d_inv[:, 3, 3] = -inv_kn_d\\n\\n return d, d_inv\",\n \"def test_sd_linear(self):\\n z_matrix = np.array(\\n [[0.0, 0.0, 1.0],\\n [0.1, 0.2, 0.8],\\n [0.2, 0.4, 0.6],\\n [0.3, 0.7, 0.3],\\n [0.6, 0.8, 0.2],\\n [0.8, 0.9, 0.1],\\n [1.0, 1.0, 0.0]],\\n dtype=np.float64)\\n obtained_w_vector = mcdm.weigh(z_matrix, \\\"SD\\\")\\n expected_w_vector = np.array(\\n [0.33333333, 0.33333333, 0.33333333],\\n dtype=np.float64)\\n np.testing.assert_allclose(obtained_w_vector, expected_w_vector)\\n self.assertEqual(obtained_w_vector.dtype, expected_w_vector.dtype)\",\n \"def local_det_chol(node):\\r\\n if node.op == det:\\r\\n x, = node.inputs\\r\\n for (cl, xpos) in x.clients:\\r\\n if isinstance(cl.op, Cholesky):\\r\\n L = cl.outputs[0]\\r\\n return [tensor.prod(extract_diag(L) ** 2)]\",\n \"def build_dispersion_diags(self):\\n N = self.N\\n j = self._j # Index of the mid-point\\n diags = np.zeros((2*self._j+1, self.N))\\n\\n dx3 = np.power(self.dx, 3.)\\n\\n ## This tells us how the diagonal matrix construction works\\n #diags[j-2,:-4] = np.arange(1,N+1)[4:]\\n #diags[j-1,:-2] = np.arange(1,N+1)[2:]\\n #diags[j+1,:] = np.arange(1,N+1)\\n #diags[j+2,:] = np.arange(1,N+1)\\n\\n #diags[j,0:2] = 11\\n #diags[j+1,1:3] = 12\\n #diags[j+2,2:4] = 13\\n #diags[j+3,3:5]= 14\\n\\n\\n # Original method had assymmetric diagonals\\n #cff = -self.beta/(2*dx3)\\n #diags[j-2,:] += -1*cff\\n #diags[j-1,:] += 2*cff\\n #diags[j+1,:] += -2*cff\\n #diags[j+2,:] += 1*cff\\n\\n # Need to stagger these diagonals so lower and upper bands are symmetric\\n cff = -1/(2*dx3)\\n beta = self.beta\\n diags[j-2,:-4] += -1*cff*beta[4:]\\n diags[j-1,:-2] += 2*cff*beta[2:]\\n diags[j+1,:] += -2*cff*beta\\n diags[j+2,:] += 1*cff*beta\\n\\n ## Left boundary - use forward differencing\\n diags[j-1,0] = 0\\n diags[j,0:2] = -2*cff*beta[0]\\n diags[j+1,1:3] = 6*cff*beta[0]\\n diags[j+2,2:4] = -6*cff*beta[0]\\n diags[j+3,3:5] = 2*cff*beta[0]\\n\\n # Zero first two points\\n #diags[j-1,0] = 0\\n #diags[j,0:2] = 0 \\n #diags[j+1,1:3] = 0 \\n #diags[j+2,2:4] = 0 \\n #if self._j>2:\\n # diags[j+3,3:5] = 0 \\n\\n return diags\",\n \"def dKdiag_dtheta(self,dL_dKdiag,X,target):\\r\\n FX = self._cos(self.basis_alpha[None,:],self.basis_omega[None,:],self.basis_phi[None,:])(X)\\r\\n\\r\\n La = np.column_stack((self.a[0]*np.ones((self.n_basis,1)), self.a[1]*self.basis_omega, self.a[2]*self.basis_omega**2, self.a[3]*self.basis_omega**3))\\r\\n Lo = np.column_stack((self.basis_omega, self.basis_omega, self.basis_omega, self.basis_omega))\\r\\n Lp = np.column_stack((self.basis_phi, self.basis_phi+np.pi/2, self.basis_phi+np.pi, self.basis_phi+np.pi*3/2))\\r\\n r,omega,phi = self._cos_factorization(La,Lo,Lp)\\r\\n Gint = self._int_computation( r,omega,phi, r,omega,phi)\\r\\n\\r\\n Flower = np.array(self._cos(self.basis_alpha,self.basis_omega,self.basis_phi)(self.lower))[:,None]\\r\\n F1lower = np.array(self._cos(self.basis_alpha*self.basis_omega,self.basis_omega,self.basis_phi+np.pi/2)(self.lower))[:,None]\\r\\n F2lower = np.array(self._cos(self.basis_alpha*self.basis_omega**2,self.basis_omega,self.basis_phi+np.pi)(self.lower))[:,None]\\r\\n\\r\\n #dK_dvar\\r\\n dK_dvar = 1. / self.variance * mdot(FX, self.Gi, FX.T)\\r\\n\\r\\n #dK_dlen\\r\\n da_dlen = [-3*self.a[0]/self.lengthscale, -2*self.a[1]/self.lengthscale, -self.a[2]/self.lengthscale, 0.]\\r\\n db_dlen = [0., 4*self.b[1]/self.lengthscale, 2*self.b[2]/self.lengthscale, 2*self.b[3]/self.lengthscale, 2*self.b[4]/self.lengthscale]\\r\\n dLa_dlen = np.column_stack((da_dlen[0]*np.ones((self.n_basis,1)), da_dlen[1]*self.basis_omega, da_dlen[2]*self.basis_omega**2, da_dlen[3]*self.basis_omega**3))\\r\\n r1,omega1,phi1 = self._cos_factorization(dLa_dlen,Lo,Lp)\\r\\n dGint_dlen = self._int_computation(r1,omega1,phi1, r,omega,phi)\\r\\n dGint_dlen = dGint_dlen + dGint_dlen.T\\r\\n dlower_terms_dlen = db_dlen[0]*np.dot(Flower,Flower.T) + db_dlen[1]*np.dot(F2lower,F2lower.T) + db_dlen[2]*np.dot(F1lower,F1lower.T) + db_dlen[3]*np.dot(F2lower,Flower.T) + db_dlen[4]*np.dot(Flower,F2lower.T)\\r\\n dG_dlen = 15*self.lengthscale**4/(400*np.sqrt(5))*Gint + 3*self.lengthscale**5/(400*np.sqrt(5))*dGint_dlen + dlower_terms_dlen\\r\\n dK_dlen = -mdot(FX,self.Gi,dG_dlen/self.variance,self.Gi,FX.T)\\r\\n\\r\\n #dK_dper\\r\\n dFX_dper = self._cos(-self.basis_alpha[None,:]*self.basis_omega[None,:]/self.period*X ,self.basis_omega[None,:],self.basis_phi[None,:]+np.pi/2)(X)\\r\\n\\r\\n dLa_dper = np.column_stack((-self.a[0]*self.basis_omega/self.period, -self.a[1]*self.basis_omega**2/self.period, -self.a[2]*self.basis_omega**3/self.period, -self.a[3]*self.basis_omega**4/self.period))\\r\\n dLp_dper = np.column_stack((self.basis_phi+np.pi/2,self.basis_phi+np.pi,self.basis_phi+np.pi*3/2,self.basis_phi))\\r\\n r1,omega1,phi1 = self._cos_factorization(dLa_dper,Lo,dLp_dper)\\r\\n\\r\\n IPPprim1 = self.upper*(1./(omega+omega1.T)*np.cos((omega+omega1.T)*self.upper+phi+phi1.T-np.pi/2) + 1./(omega-omega1.T)*np.cos((omega-omega1.T)*self.upper+phi-phi1.T-np.pi/2))\\r\\n IPPprim1 -= self.lower*(1./(omega+omega1.T)*np.cos((omega+omega1.T)*self.lower+phi+phi1.T-np.pi/2) + 1./(omega-omega1.T)*np.cos((omega-omega1.T)*self.lower+phi-phi1.T-np.pi/2))\\r\\n IPPprim2 = self.upper*(1./(omega+omega1.T)*np.cos((omega+omega1.T)*self.upper+phi+phi1.T-np.pi/2) + self.upper*np.cos(phi-phi1.T))\\r\\n IPPprim2 -= self.lower*(1./(omega+omega1.T)*np.cos((omega+omega1.T)*self.lower+phi+phi1.T-np.pi/2) + self.lower*np.cos(phi-phi1.T))\\r\\n IPPprim = np.where(np.isnan(IPPprim1),IPPprim2,IPPprim1)\\r\\n\\r\\n IPPint1 = 1./(omega+omega1.T)**2*np.cos((omega+omega1.T)*self.upper+phi+phi1.T-np.pi) + 1./(omega-omega1.T)**2*np.cos((omega-omega1.T)*self.upper+phi-phi1.T-np.pi)\\r\\n IPPint1 -= 1./(omega+omega1.T)**2*np.cos((omega+omega1.T)*self.lower+phi+phi1.T-np.pi) + 1./(omega-omega1.T)**2*np.cos((omega-omega1.T)*self.lower+phi-phi1.T-np.pi)\\r\\n IPPint2 = 1./(omega+omega1.T)**2*np.cos((omega+omega1.T)*self.upper+phi+phi1.T-np.pi) + .5*self.upper**2*np.cos(phi-phi1.T)\\r\\n IPPint2 -= 1./(omega+omega1.T)**2*np.cos((omega+omega1.T)*self.lower+phi+phi1.T-np.pi) + .5*self.lower**2*np.cos(phi-phi1.T)\\r\\n IPPint = np.where(np.isnan(IPPint1),IPPint2,IPPint1)\\r\\n\\r\\n dLa_dper2 = np.column_stack((-self.a[1]*self.basis_omega/self.period, -2*self.a[2]*self.basis_omega**2/self.period, -3*self.a[3]*self.basis_omega**3/self.period))\\r\\n dLp_dper2 = np.column_stack((self.basis_phi+np.pi/2, self.basis_phi+np.pi, self.basis_phi+np.pi*3/2))\\r\\n r2,omega2,phi2 = self._cos_factorization(dLa_dper2,Lo[:,0:2],dLp_dper2)\\r\\n\\r\\n dGint_dper = np.dot(r,r1.T)/2 * (IPPprim - IPPint) + self._int_computation(r2,omega2,phi2, r,omega,phi)\\r\\n dGint_dper = dGint_dper + dGint_dper.T\\r\\n\\r\\n dFlower_dper = np.array(self._cos(-self.lower*self.basis_alpha*self.basis_omega/self.period,self.basis_omega,self.basis_phi+np.pi/2)(self.lower))[:,None]\\r\\n dF1lower_dper = np.array(self._cos(-self.lower*self.basis_alpha*self.basis_omega**2/self.period,self.basis_omega,self.basis_phi+np.pi)(self.lower)+self._cos(-self.basis_alpha*self.basis_omega/self.period,self.basis_omega,self.basis_phi+np.pi/2)(self.lower))[:,None]\\r\\n dF2lower_dper = np.array(self._cos(-self.lower*self.basis_alpha*self.basis_omega**3/self.period,self.basis_omega,self.basis_phi+np.pi*3/2)(self.lower) + self._cos(-2*self.basis_alpha*self.basis_omega**2/self.period,self.basis_omega,self.basis_phi+np.pi)(self.lower))[:,None]\\r\\n\\r\\n dlower_terms_dper = self.b[0] * (np.dot(dFlower_dper,Flower.T) + np.dot(Flower.T,dFlower_dper))\\r\\n dlower_terms_dper += self.b[1] * (np.dot(dF2lower_dper,F2lower.T) + np.dot(F2lower,dF2lower_dper.T)) - 4*self.b[1]/self.period*np.dot(F2lower,F2lower.T)\\r\\n dlower_terms_dper += self.b[2] * (np.dot(dF1lower_dper,F1lower.T) + np.dot(F1lower,dF1lower_dper.T)) - 2*self.b[2]/self.period*np.dot(F1lower,F1lower.T)\\r\\n dlower_terms_dper += self.b[3] * (np.dot(dF2lower_dper,Flower.T) + np.dot(F2lower,dFlower_dper.T)) - 2*self.b[3]/self.period*np.dot(F2lower,Flower.T)\\r\\n dlower_terms_dper += self.b[4] * (np.dot(dFlower_dper,F2lower.T) + np.dot(Flower,dF2lower_dper.T)) - 2*self.b[4]/self.period*np.dot(Flower,F2lower.T)\\r\\n\\r\\n dG_dper = 1./self.variance*(3*self.lengthscale**5/(400*np.sqrt(5))*dGint_dper + 0.5*dlower_terms_dper)\\r\\n dK_dper = 2*mdot(dFX_dper,self.Gi,FX.T) - mdot(FX,self.Gi,dG_dper,self.Gi,FX.T)\\r\\n\\r\\n target[0] += np.sum(np.diag(dK_dvar)*dL_dKdiag)\\r\\n target[1] += np.sum(np.diag(dK_dlen)*dL_dKdiag)\\r\\n target[2] += np.sum(np.diag(dK_dper)*dL_dKdiag)\",\n \"def mahalanobis_distance(x, y):\\r\\n diff = x - np.mean(y, axis=0)\\r\\n cov = np.cov(y.T) # Covariance matrix\\r\\n inv_cov = np.linalg.inv(cov)\\r\\n return np.dot(np.dot(diff, inv_cov), diff.T) + np.log(np.linalg.det(cov))\",\n \"def det(mtx):\\n if not is_square(mtx):\\n raise ValueError(\\\"Matrix should be square\\\")\\n if len(mtx) == 2:\\n return mtx[0][0] * mtx[1][1] - mtx[0][1] * mtx[1][0]\\n\\n result = 0\\n sign = 1\\n for inx in range(len(mtx)):\\n next_mtx = get_minor_mtx(mtx, 0, inx)\\n result += sign * (mtx[0][inx] * det(next_mtx))\\n sign *= -1\\n return result\",\n \"def det(input):\\n is_input_dparray = isinstance(input, dparray)\\n\\n if not use_origin_backend(input) and is_input_dparray:\\n if input.shape[-1] == input.shape[-2]:\\n result = dpnp_det(input)\\n\\n # scalar returned\\n if result.shape == (1,):\\n return result.dtype.type(result[0])\\n\\n return result\\n\\n return call_origin(numpy.linalg.det, input)\",\n \"def calc_det(m):\\n det = m[0][0] * m [1][1] - (m[0][1] * m[1][0])\\n return det\",\n \"def calculate_sdm(feature_matrix, is_normalization = False):\\n length = len(feature_matrix)\\n self_distance_matrix = np.zeros((length, length))\\n for i in range(length):\\n row1 = feature_matrix[i, :]\\n for j in range(length):\\n row2 = feature_matrix[j, :]\\n self_distance_matrix[i, j] = np.sqrt(np.sum(np.square(row1 - row2)))\\n\\n if is_normalization:\\n minima = np.min(self_distance_matrix)\\n maxima = np.max(self_distance_matrix)\\n self_distance_matrix = (self_distance_matrix - minima) / (maxima - minima)\\n return self_distance_matrix\",\n \"def diffuse_CN(self, dt):\\n\\n gr = self.grid\\n phi = gr.phi\\n\\n phinew = gr.scratch_array()\\n\\n alpha = self.k*dt/gr.dx**2\\n\\n # create the RHS of the matrix\\n gr.fill_BCs()\\n R = 0.5*self.k*dt*self.lap()\\n R = R[gr.ilo:gr.ihi+1]\\n R += phi[gr.ilo:gr.ihi+1]\\n\\n # create the diagonal, d+1 and d-1 parts of the matrix\\n d = (1.0 + alpha)*np.ones(gr.nx)\\n u = -0.5*alpha*np.ones(gr.nx)\\n u[0] = 0.0\\n\\n l = -0.5*alpha*np.ones(gr.nx)\\n l[gr.nx-1] = 0.0\\n\\n # set the boundary conditions by changing the matrix elements\\n\\n # homogeneous neumann\\n d[0] = 1.0 + 0.5*alpha\\n d[gr.nx-1] = 1.0 + 0.5*alpha\\n\\n # Dirichlet\\n #d[0] = 1.0 + 1.5*alpha\\n #d[gr.nx-1] = 1.0 + 1.5*alpha\\n\\n #R[0] += alpha*phi1\\n #R[gr.nx-1] += alpha*phi1\\n\\n # solve\\n A = np.matrix([u,d,l])\\n phinew[gr.ilo:gr.ihi+1] = linalg.solve_banded((1,1), A, R)\\n\\n return phinew\",\n \"def distance_matrix(data):\\n D = numpy.zeros( (data.shape[0], data.shape[0]) )\\n for i in xrange(data.shape[0]):\\n for j in xrange(i):\\n D[i,j] = numpy.linalg.norm(data[i,:]-data[j,:])\\n D[j,i] = D[i,j]\\n\\n return D\",\n \"def dist_morisita_horn(datamtx, strict=True):\\n if strict:\\n if not all(isfinite(datamtx)):\\n raise ValueError(\\\"non finite number in input matrix\\\")\\n if any(datamtx<0.0):\\n raise ValueError(\\\"negative value in input matrix\\\")\\n if rank(datamtx) != 2:\\n raise ValueError(\\\"input matrix not 2D\\\")\\n numrows, numcols = shape(datamtx)\\n else:\\n try:\\n numrows, numcols = shape(datamtx)\\n except ValueError:\\n return zeros((0,0),'d')\\n\\n if numrows == 0 or numcols == 0:\\n return zeros((0,0),'d')\\n dists = zeros((numrows,numrows),'d')\\n \\n rowsums = datamtx.sum(axis=1, dtype='float')\\n row_ds = (datamtx**2).sum(axis=1, dtype='float') # these are d_a, etc\\n \\n for i in range(numrows):\\n if row_ds[i] !=0.:\\n row_ds[i] = row_ds[i] / rowsums[i]**2\\n # this leaves row_ds zero if actually 0/0\\n for i in range(numrows):\\n row1 = datamtx[i]\\n N1 = rowsums[i]\\n d1 = row_ds[i]\\n for j in range(i):\\n row2 = datamtx[j]\\n N2 = rowsums[j]\\n d2 = row_ds[j]\\n if N2 == 0.0 and N1==0.0:\\n dist = 0.0\\n elif N2 == 0.0 or N1==0.0:\\n dist = 1.0\\n else:\\n # d's zero only if N's zero, and we already checked for that\\n similarity = 2*sum(row1*row2)\\n similarity = similarity / ( (d1 + d2) * N1 * N2 )\\n dist = 1 - similarity\\n dists[i][j] = dists[j][i] = dist\\n return dists\",\n \"def sim_euclidean(table_CF):\\n aCopy = table_CF.copy()\\n #aCopy = my_preproc.mean_imputation(table_CF)\\n aCopy = aCopy.fillna(0)\\n euc_dist = ml_metrics.pairwise.euclidean_distances(aCopy)\\n euc_dist = pd.DataFrame(euc_dist, columns = aCopy.index)\\n euc_dist.index = euc_dist.columns\\n return euc_dist\",\n \"def matrix_neumann2D(Omega,Nx,Ny):\\r\\n \\r\\n hx = (Omega[1]-Omega[0])/Nx\\r\\n hy = (Omega[3]-Omega[2])/Ny\\r\\n hx2 = hx*hx\\r\\n hy2 = hy*hy\\r\\n\\r\\n # Les inconnues sont numérotés de 0 à Nx suivant x et 0 à Ny\\r\\n # suivant y. La taille du problème est donc (Nx+1)*(Ny+1).\\r\\n\\r\\n # Pour -Laplacien(u), la matrice est constituée de (Ny+1)x(Ny+1)\\r\\n # blocs de taille (Nx+1)x(Nx+1), de la forme\\r\\n #\\r\\n # A = [ A0 B ]\\r\\n # [ B A1 B ]\\r\\n # [ B A1 B ]\\r\\n # [ . . . ]\\r\\n # [ B A1 B ]\\r\\n # [ B A0 ]\\r\\n #\\r\\n # Au final, on peut commencer à remplir avec des diagonales\\r\\n N = (1+Nx)*(1+Ny)\\r\\n diags = np.zeros((5,N))\\r\\n # La diagonale est constante\\r\\n diags[2,:] = 2./hx2+2./hy2\\r\\n # Diagonale -1\\r\\n diags[1,:] = -1./hx2 # en général\\r\\n diags[1,np.arange(Nx,N,Nx+1)] = 0. # bord gauche\\r\\n diags[1,np.arange(Nx-1,N,Nx+1)] = -2./hx2 # bord droit\\r\\n # Diagonale +1\\r\\n diags[3,:] = -1./hx2 # en général\\r\\n diags[3,np.arange(0,N,Nx+1)] = 0. # bord droit\\r\\n diags[3,np.arange(1,N,Nx+1)] = -2./hx2 # bord gauche\\r\\n # Diagonale -(Nx+1)\\r\\n diags[0,:] = -1./hy2 # en général\\r\\n diags[0,(Nx+1)*(Ny-1):(Nx+1)*Ny] = -2./hy2 # bord bas\\r\\n # Diagonale +(Nx+1)\\r\\n diags[4,:] = -1./hy2 # en général\\r\\n diags[4,Nx+1:2*(Nx+1)] = -2./hy2 # bord haut\\r\\n\\r\\n # Construction de la matrice creuse de u --> -Laplacien(u)\\r\\n A = sp.spdiags(diags,[-(Nx+1),-1,0,1,(Nx+1)], (Nx+1)*(Ny+1),\\r\\n (Nx+1)*(Ny+1), format=\\\"csc\\\")\\r\\n\\r\\n return A\",\n \"def test_derive_euclidean_dm(self):\\r\\n cat_mat = [('6.66', '46.8'),\\r\\n ('7.27', '36.05'),\\r\\n ('4.6', '44.86666667'),\\r\\n ('5.68', '40.58333333'),\\r\\n ('4.23', '68.63333333'),\\r\\n ('5.74', '36.45')]\\r\\n\\r\\n dm_lbls = ['MT2.141698', 'CA1.141704', 'BB2.141659',\\r\\n 'CO2.141657', 'TL3.141709', 'SN3.141650']\\r\\n\\r\\n mtx = [\\r\\n [0.0, 10.7672930674, 2.82513322958, 6.29343661968,\\r\\n 21.9681438519, 10.3908084382],\\r\\n [10.7672930674, 0.0, 9.21208506093, 4.80408275125,\\r\\n 32.7248408842, 1.58142340946],\\r\\n [2.82513322958, 9.21208506093, 0.0, 4.41739114202,\\r\\n 23.7695465697, 8.49351975531],\\r\\n [6.29343661968, 4.80408275125, 4.41739114202, 0.0,\\r\\n 28.0874527147, 4.13376879093],\\r\\n [21.9681438519, 32.7248408842, 23.7695465697,\\r\\n 28.0874527147, 0.0, 32.2187374711],\\r\\n [10.3908084382, 1.58142340946, 8.49351975531,\\r\\n 4.13376879093, 32.2187374711, 0.0]]\\r\\n\\r\\n exp = DistanceMatrix(asarray(mtx), dm_lbls)\\r\\n obs = self.best._derive_euclidean_dm(cat_mat,\\r\\n self.bv_dm_88soils.shape[0])\\r\\n self.assertEqual(obs.ids, exp.ids)\\r\\n assert_almost_equal(obs.data, exp.data)\",\n \"def get_D(activity_profiles, csi_hist):\\n\\tL, J = activity_profiles.shape[0], activity_profiles.shape[1]-1\\n\\tD = np.array(L,J)\\n\\tfor i in range(L):\\n\\t\\tfor j in range(1,J+1):\\n\\t\\t\\thist1 = activity_profiles[i][j].astype(float)\\n\\t\\t\\thist2 = csi_hist.astype(float)\\n\\t\\t\\tgd = pairwise.pairwise_distances(hist1.reshape(-1,1),hist2.reshape(-1,1))\\n\\t\\t\\tD[i][j] = emd(hist1, hist2, gd)\\n\\treturn D\",\n \"def dKdiag_dtheta(self,dL_dKdiag,X,target):\\r\\n FX = self._cos(self.basis_alpha[None,:],self.basis_omega[None,:],self.basis_phi[None,:])(X)\\r\\n\\r\\n La = np.column_stack((self.a[0]*np.ones((self.n_basis,1)),self.a[1]*self.basis_omega))\\r\\n Lo = np.column_stack((self.basis_omega,self.basis_omega))\\r\\n Lp = np.column_stack((self.basis_phi,self.basis_phi+np.pi/2))\\r\\n r,omega,phi = self._cos_factorization(La,Lo,Lp)\\r\\n Gint = self._int_computation( r,omega,phi, r,omega,phi)\\r\\n\\r\\n Flower = np.array(self._cos(self.basis_alpha,self.basis_omega,self.basis_phi)(self.lower))[:,None]\\r\\n\\r\\n #dK_dvar\\r\\n dK_dvar = 1./self.variance*mdot(FX,self.Gi,FX.T)\\r\\n\\r\\n #dK_dlen\\r\\n da_dlen = [-1./self.lengthscale**2,0.]\\r\\n dLa_dlen = np.column_stack((da_dlen[0]*np.ones((self.n_basis,1)),da_dlen[1]*self.basis_omega))\\r\\n r1,omega1,phi1 = self._cos_factorization(dLa_dlen,Lo,Lp)\\r\\n dGint_dlen = self._int_computation(r1,omega1,phi1, r,omega,phi)\\r\\n dGint_dlen = dGint_dlen + dGint_dlen.T\\r\\n dG_dlen = 1./2*Gint + self.lengthscale/2*dGint_dlen\\r\\n dK_dlen = -mdot(FX,self.Gi,dG_dlen/self.variance,self.Gi,FX.T)\\r\\n\\r\\n #dK_dper\\r\\n dFX_dper = self._cos(-self.basis_alpha[None,:]*self.basis_omega[None,:]/self.period*X ,self.basis_omega[None,:],self.basis_phi[None,:]+np.pi/2)(X)\\r\\n\\r\\n dLa_dper = np.column_stack((-self.a[0]*self.basis_omega/self.period, -self.a[1]*self.basis_omega**2/self.period))\\r\\n dLp_dper = np.column_stack((self.basis_phi+np.pi/2,self.basis_phi+np.pi))\\r\\n r1,omega1,phi1 = self._cos_factorization(dLa_dper,Lo,dLp_dper)\\r\\n\\r\\n IPPprim1 = self.upper*(1./(omega+omega1.T)*np.cos((omega+omega1.T)*self.upper+phi+phi1.T-np.pi/2) + 1./(omega-omega1.T)*np.cos((omega-omega1.T)*self.upper+phi-phi1.T-np.pi/2))\\r\\n IPPprim1 -= self.lower*(1./(omega+omega1.T)*np.cos((omega+omega1.T)*self.lower+phi+phi1.T-np.pi/2) + 1./(omega-omega1.T)*np.cos((omega-omega1.T)*self.lower+phi-phi1.T-np.pi/2))\\r\\n IPPprim2 = self.upper*(1./(omega+omega1.T)*np.cos((omega+omega1.T)*self.upper+phi+phi1.T-np.pi/2) + self.upper*np.cos(phi-phi1.T))\\r\\n IPPprim2 -= self.lower*(1./(omega+omega1.T)*np.cos((omega+omega1.T)*self.lower+phi+phi1.T-np.pi/2) + self.lower*np.cos(phi-phi1.T))\\r\\n IPPprim = np.where(np.isnan(IPPprim1),IPPprim2,IPPprim1)\\r\\n\\r\\n IPPint1 = 1./(omega+omega1.T)**2*np.cos((omega+omega1.T)*self.upper+phi+phi1.T-np.pi) + 1./(omega-omega1.T)**2*np.cos((omega-omega1.T)*self.upper+phi-phi1.T-np.pi)\\r\\n IPPint1 -= 1./(omega+omega1.T)**2*np.cos((omega+omega1.T)*self.lower+phi+phi1.T-np.pi) + 1./(omega-omega1.T)**2*np.cos((omega-omega1.T)*self.lower+phi-phi1.T-np.pi)\\r\\n IPPint2 = 1./(omega+omega1.T)**2*np.cos((omega+omega1.T)*self.upper+phi+phi1.T-np.pi) + 1./2*self.upper**2*np.cos(phi-phi1.T)\\r\\n IPPint2 -= 1./(omega+omega1.T)**2*np.cos((omega+omega1.T)*self.lower+phi+phi1.T-np.pi) + 1./2*self.lower**2*np.cos(phi-phi1.T)\\r\\n IPPint = np.where(np.isnan(IPPint1),IPPint2,IPPint1)\\r\\n\\r\\n dLa_dper2 = np.column_stack((-self.a[1]*self.basis_omega/self.period))\\r\\n dLp_dper2 = np.column_stack((self.basis_phi+np.pi/2))\\r\\n r2,omega2,phi2 = dLa_dper2.T,Lo[:,0:1],dLp_dper2.T\\r\\n\\r\\n dGint_dper = np.dot(r,r1.T)/2 * (IPPprim - IPPint) + self._int_computation(r2,omega2,phi2, r,omega,phi)\\r\\n dGint_dper = dGint_dper + dGint_dper.T\\r\\n\\r\\n dFlower_dper = np.array(self._cos(-self.lower*self.basis_alpha*self.basis_omega/self.period,self.basis_omega,self.basis_phi+np.pi/2)(self.lower))[:,None]\\r\\n\\r\\n dG_dper = 1./self.variance*(self.lengthscale/2*dGint_dper + self.b[0]*(np.dot(dFlower_dper,Flower.T)+np.dot(Flower,dFlower_dper.T)))\\r\\n\\r\\n dK_dper = 2*mdot(dFX_dper,self.Gi,FX.T) - mdot(FX,self.Gi,dG_dper,self.Gi,FX.T)\\r\\n\\r\\n target[0] += np.sum(np.diag(dK_dvar)*dL_dKdiag)\\r\\n target[1] += np.sum(np.diag(dK_dlen)*dL_dKdiag)\\r\\n target[2] += np.sum(np.diag(dK_dper)*dL_dKdiag)\",\n \"def dKdiag_dtheta(self,dL_dKdiag,X,target):\\r\\n FX = self._cos(self.basis_alpha[None,:],self.basis_omega[None,:],self.basis_phi[None,:])(X)\\r\\n\\r\\n La = np.column_stack((self.a[0]*np.ones((self.n_basis,1)),self.a[1]*self.basis_omega, self.a[2]*self.basis_omega**2))\\r\\n Lo = np.column_stack((self.basis_omega,self.basis_omega,self.basis_omega))\\r\\n Lp = np.column_stack((self.basis_phi,self.basis_phi+np.pi/2,self.basis_phi+np.pi))\\r\\n r,omega,phi = self._cos_factorization(La,Lo,Lp)\\r\\n Gint = self._int_computation( r,omega,phi, r,omega,phi)\\r\\n\\r\\n Flower = np.array(self._cos(self.basis_alpha,self.basis_omega,self.basis_phi)(self.lower))[:,None]\\r\\n F1lower = np.array(self._cos(self.basis_alpha*self.basis_omega,self.basis_omega,self.basis_phi+np.pi/2)(self.lower))[:,None]\\r\\n\\r\\n #dK_dvar\\r\\n dK_dvar = 1./self.variance*mdot(FX,self.Gi,FX.T)\\r\\n\\r\\n #dK_dlen\\r\\n da_dlen = [-6/self.lengthscale**3,-2*np.sqrt(3)/self.lengthscale**2,0.]\\r\\n db_dlen = [0.,2*self.lengthscale/3.]\\r\\n dLa_dlen = np.column_stack((da_dlen[0]*np.ones((self.n_basis,1)),da_dlen[1]*self.basis_omega,da_dlen[2]*self.basis_omega**2))\\r\\n r1,omega1,phi1 = self._cos_factorization(dLa_dlen,Lo,Lp)\\r\\n dGint_dlen = self._int_computation(r1,omega1,phi1, r,omega,phi)\\r\\n dGint_dlen = dGint_dlen + dGint_dlen.T\\r\\n dG_dlen = self.lengthscale**2/(4*np.sqrt(3))*Gint + self.lengthscale**3/(12*np.sqrt(3))*dGint_dlen + db_dlen[0]*np.dot(Flower,Flower.T) + db_dlen[1]*np.dot(F1lower,F1lower.T)\\r\\n dK_dlen = -mdot(FX,self.Gi,dG_dlen/self.variance,self.Gi,FX.T)\\r\\n\\r\\n #dK_dper\\r\\n dFX_dper = self._cos(-self.basis_alpha[None,:]*self.basis_omega[None,:]/self.period*X ,self.basis_omega[None,:],self.basis_phi[None,:]+np.pi/2)(X)\\r\\n\\r\\n dLa_dper = np.column_stack((-self.a[0]*self.basis_omega/self.period, -self.a[1]*self.basis_omega**2/self.period, -self.a[2]*self.basis_omega**3/self.period))\\r\\n dLp_dper = np.column_stack((self.basis_phi+np.pi/2,self.basis_phi+np.pi,self.basis_phi+np.pi*3/2))\\r\\n r1,omega1,phi1 = self._cos_factorization(dLa_dper,Lo,dLp_dper)\\r\\n\\r\\n IPPprim1 = self.upper*(1./(omega+omega1.T)*np.cos((omega+omega1.T)*self.upper+phi+phi1.T-np.pi/2) + 1./(omega-omega1.T)*np.cos((omega-omega1.T)*self.upper+phi-phi1.T-np.pi/2))\\r\\n IPPprim1 -= self.lower*(1./(omega+omega1.T)*np.cos((omega+omega1.T)*self.lower+phi+phi1.T-np.pi/2) + 1./(omega-omega1.T)*np.cos((omega-omega1.T)*self.lower+phi-phi1.T-np.pi/2))\\r\\n IPPprim2 = self.upper*(1./(omega+omega1.T)*np.cos((omega+omega1.T)*self.upper+phi+phi1.T-np.pi/2) + self.upper*np.cos(phi-phi1.T))\\r\\n IPPprim2 -= self.lower*(1./(omega+omega1.T)*np.cos((omega+omega1.T)*self.lower+phi+phi1.T-np.pi/2) + self.lower*np.cos(phi-phi1.T))\\r\\n IPPprim = np.where(np.isnan(IPPprim1),IPPprim2,IPPprim1)\\r\\n\\r\\n IPPint1 = 1./(omega+omega1.T)**2*np.cos((omega+omega1.T)*self.upper+phi+phi1.T-np.pi) + 1./(omega-omega1.T)**2*np.cos((omega-omega1.T)*self.upper+phi-phi1.T-np.pi)\\r\\n IPPint1 -= 1./(omega+omega1.T)**2*np.cos((omega+omega1.T)*self.lower+phi+phi1.T-np.pi) + 1./(omega-omega1.T)**2*np.cos((omega-omega1.T)*self.lower+phi-phi1.T-np.pi)\\r\\n IPPint2 = 1./(omega+omega1.T)**2*np.cos((omega+omega1.T)*self.upper+phi+phi1.T-np.pi) + 1./2*self.upper**2*np.cos(phi-phi1.T)\\r\\n IPPint2 -= 1./(omega+omega1.T)**2*np.cos((omega+omega1.T)*self.lower+phi+phi1.T-np.pi) + 1./2*self.lower**2*np.cos(phi-phi1.T)\\r\\n IPPint = np.where(np.isnan(IPPint1),IPPint2,IPPint1)\\r\\n\\r\\n dLa_dper2 = np.column_stack((-self.a[1]*self.basis_omega/self.period, -2*self.a[2]*self.basis_omega**2/self.period))\\r\\n dLp_dper2 = np.column_stack((self.basis_phi+np.pi/2,self.basis_phi+np.pi))\\r\\n r2,omega2,phi2 = self._cos_factorization(dLa_dper2,Lo[:,0:2],dLp_dper2)\\r\\n\\r\\n dGint_dper = np.dot(r,r1.T)/2 * (IPPprim - IPPint) + self._int_computation(r2,omega2,phi2, r,omega,phi)\\r\\n dGint_dper = dGint_dper + dGint_dper.T\\r\\n\\r\\n dFlower_dper = np.array(self._cos(-self.lower*self.basis_alpha*self.basis_omega/self.period,self.basis_omega,self.basis_phi+np.pi/2)(self.lower))[:,None]\\r\\n dF1lower_dper = np.array(self._cos(-self.lower*self.basis_alpha*self.basis_omega**2/self.period,self.basis_omega,self.basis_phi+np.pi)(self.lower)+self._cos(-self.basis_alpha*self.basis_omega/self.period,self.basis_omega,self.basis_phi+np.pi/2)(self.lower))[:,None]\\r\\n\\r\\n dG_dper = 1./self.variance*(self.lengthscale**3/(12*np.sqrt(3))*dGint_dper + self.b[0]*(np.dot(dFlower_dper,Flower.T)+np.dot(Flower,dFlower_dper.T)) + self.b[1]*(np.dot(dF1lower_dper,F1lower.T)+np.dot(F1lower,dF1lower_dper.T)))\\r\\n\\r\\n dK_dper = 2* mdot(dFX_dper,self.Gi,FX.T) - mdot(FX,self.Gi,dG_dper,self.Gi,FX.T)\\r\\n\\r\\n target[0] += np.sum(np.diag(dK_dvar)*dL_dKdiag)\\r\\n target[1] += np.sum(np.diag(dK_dlen)*dL_dKdiag)\\r\\n target[2] += np.sum(np.diag(dK_dper)*dL_dKdiag)\",\n \"def Mat_dis(x):\\n x = np.mat(x) #构建矩阵\\n aa = np.sum(np.multiply(x, x), 1) #哈达玛乘积\\n ab = x * x.T\\n dist_mat = aa + aa.T - 2 * ab\\n dist_mat[dist_mat < 0] = 0\\n #dist_mat = np.sqrt(dist_mat)\\n dist_mat = np.maximum(dist_mat, dist_mat.T)\\n\\n return dist_mat\",\n \"def dist_manhattan(datamtx, strict=True):\\n if strict:\\n if not all(isfinite(datamtx)):\\n raise ValueError(\\\"non finite number in input matrix\\\")\\n if rank(datamtx) != 2:\\n raise ValueError(\\\"input matrix not 2D\\\")\\n numrows, numcols = shape(datamtx)\\n else:\\n try:\\n numrows, numcols = shape(datamtx)\\n except ValueError:\\n return zeros((0,0),'d')\\n\\n if numrows == 0 or numcols == 0:\\n return zeros((0,0),'d')\\n dists = zeros((numrows,numrows),'d')\\n for i in range(numrows):\\n r1 = datamtx[i] # cache here\\n for j in range(i):\\n dists[i,j] = dists[j,i] = sum(abs(r1 - datamtx[j]))\\n \\n return dists\",\n \"def mi_from_dm(distance_matrix, ns, nh, spike_train_list=None):\\n \\n nr = len(distance_matrix)\\n nt = nr/ns\\n nearest_neighbours = np.array([r.argsort()[:nh] for r in distance_matrix])\\n \\n if spike_train_list is not None:\\n\\n members_of_glob = trains_in_glob(spike_train_list)\\n glob_comp = glob_composition(spike_train_list, ns, nt, nh)\\n\\n counts = []\\n for i in range(len(nearest_neighbours)):\\n c_i = 0\\n \\n if i not in members_of_glob:\\n for j in nearest_neighbours[i]:\\n if j not in members_of_glob:\\n if spike_train_list[i].start_time == spike_train_list[j].start_time:\\n c_i += 1 # count neigbours out of glob\\n else:\\n f_i = glob_comp[i]/float(sum(glob_comp.values()))\\n c_i += (nh - c_i)*f_i # if one neighbour is in glob, all following neighb are as well\\n break\\n counts.append(c_i)\\n else:\\n f_i = glob_comp[i]/float(sum(glob_comp.values()))\\n c_i += 1 + (nh - 1)*f_i #If in glob, take fraction of remaining neighbours except you\\n counts.append(c_i)\\n \\n counts = np.array(counts)\\n \\n else:\\n \\n counts = []\\n for i in range(len(nearest_neighbours)):\\n c_i = 1\\n for j in nearest_neighbours[i]:\\n if (i != j and abs(i - j)%ns==0 ):\\n c_i += 1 \\n counts.append(c_i)\\n counts = np.array(counts) \\n \\n I = sum(np.log2(counts*ns/float(nh))) / float(nr)\\n\\n return I\",\n \"def _build_cumulative_downhill_matrices(self):\\n\\n import time\\n from scipy import sparse as sparse\\n\\n downSweepMat = self.accumulatorMat.copy() \\n downHillaccuMat = self.downhillMat.copy() \\n accuM = self.downhillMat.copy() # work matrix\\n\\n DX = np.ones(self.tri.npoints) # measure when all the info has been propagated out.\\n\\n walltime = time.clock()\\n\\n while np.any(DX):\\n downSweepMat = downSweepMat.dot(self.accumulatorMat) # N applications of the accumulator\\n accuM = accuM.dot(self.downhillMat)\\n downHillaccuMat = downHillaccuMat + accuM \\n \\n DX = self.downhillMat.dot(DX) \\n \\n \\n print \\\" - Dense downhill matrix storage time \\\", time.clock() - walltime\\n\\n downHillaccuMat = downHillaccuMat + sparse.identity(self.tri.npoints, format='csr')\\n\\n self.downhillCumulativeMat = downHillaccuMat\\n self.sweepDownToOutflowMat = downSweepMat\\n\\n # print \\\"Terminated in \\\",it,\\\" iterations\\\"\\n\\n return\",\n \"def get_density(matrix):\\n return matrix.getnnz() / (matrix.shape[0] * matrix.shape[1])\",\n \"def GetDiag(self, *args):\\n return _hypre.HypreParMatrix_GetDiag(self, *args)\",\n \"def main():\\n diagonals_in_hd()\",\n \"def pddet(A):\\r\\n L = jitchol(A)\\r\\n logdetA = 2*sum(np.log(np.diag(L)))\\r\\n return logdetA\",\n \"def determinant(self):\\n if self.n_rows != self.n_cols:\\n raise Exception('Matrix is not square')\\n if self.n_rows == 2:\\n return (self.data[0][0] * self.data[1][1]) - (self.data[1][0] * self.data[0][1])\\n else:\\n echelon, ops = reduce_to_echelon(self.data.copy(), True)\\n swaps = sum([1 if row[0] == 'swap' else 0 for row in ops])\\n return math.prod([echelon[i][i] for i in range(len(echelon))]) * (-1) ** swaps\",\n \"def dolomite():\\n\\n rho = 2840.\\n\\n C = np.zeros((6,6), dtype=float)\\n C[0,0] = 205.; C[0,1] = 71.; C[0,2] = 57.4; C[0,3] = -19.5; C[0,4] = 13.7; C[0,5] = 0.\\n C[1,0] = C[0,1]; C[1,1] = 205.; C[1,2] = 57.4; C[1,3] = 19.5; C[1,4] = -13.7; C[1,5] = 0.\\n C[2,0] = C[0,2]; C[2,1] = C[1,2]; C[2,2] = 113.; C[2,3] = 0.; C[2,4] = 0.; C[2,5] = 0.\\n C[3,0] = C[0,3]; C[3,1] = C[1,3]; C[3,2] = C[2,3]; C[3,3] = 39.8; C[3,4] = 0.; C[3,5] = -13.7\\n C[4,0] = C[0,4]; C[4,1] = C[1,4]; C[4,2] = C[2,4]; C[4,3] = C[3,4]; C[4,4] = 39.8; C[4,5] = -19.5\\n C[5,0] = C[0,5]; C[5,1] = C[1,5]; C[5,2] = C[2,5]; C[5,3] = C[3,5]; C[5,4] = C[4,5]; C[5,5] = 67.\\n\\n return C, rho\",\n \"def calc_dist_matrix(self,verbose=False):\\n\\n print(\\\"Calculating distance matrix.\\\"); sys.stdout.flush()\\n\\n nrow = self.data_vector.shape[0]\\n self.dist_matrix = np.zeros((nrow, nrow),dtype=float)\\n for i in range(nrow):\\n if verbose:\\n if i % 1000 == 0:\\n print(\\\"Row\\\",i,\\\"of\\\",nrow)\\n sys.stdout.flush()\\n\\n for j in range(i + 1, nrow):\\n self.dist_matrix[i,j] = self._pairwise_dist(self.data_vector[i],self.data_vector[j])\\n self.dist_matrix[j,i] = self.dist_matrix[i,j]\\n \\n self.dist_frame = pd.DataFrame(self.dist_matrix,\\n index = self.seq_strings,\\n columns = self.seq_strings)\",\n \"def dist_hellinger(datamtx, strict=True):\\n if strict:\\n if not all(isfinite(datamtx)):\\n raise ValueError(\\\"non finite number in input matrix\\\")\\n if any(datamtx<0.0):\\n raise ValueError(\\\"negative value in input matrix\\\")\\n if rank(datamtx) != 2:\\n raise ValueError(\\\"input matrix not 2D\\\")\\n numrows, numcols = shape(datamtx)\\n else:\\n try:\\n numrows, numcols = shape(datamtx)\\n except ValueError:\\n return zeros((0,0),'d')\\n\\n if numrows == 0 or numcols == 0:\\n return zeros((0,0),'d')\\n dists = zeros((numrows,numrows),'d')\\n for i in range(numrows):\\n r1 = datamtx[i]\\n r1sum = sum(r1)\\n for j in range(i):\\n r2 = datamtx[j]\\n r2sum = sum(r2)\\n if r1sum == 0.0 or r2sum == 0.0:\\n if r1sum == 0.0 and r2sum == 0.0:\\n dist = 0.0\\n else: dist = 1.0\\n else:\\n dist = norm(sqrt(r1/r1sum) - sqrt(r2/r2sum))\\n dists[i,j] = dists[j,i] = dist\\n\\n return dists\",\n \"def determinant(self) -> float:\\n num_R, num_C = self.shape()\\n assert num_R == num_C, f\\\"Determinant must be for a square matrix; this one is {self.shape()}.\\\"\\n # -------------------------------------------------------\\n # TODO: You write this one.\\n # Note: this one should be recursive....\\n if num_R == 1:\\n return self.mat[0][0]\\n det =0\\n for i in range(num_R):\\n det += self.mat[0][i] * self.get_minor(0,i).determinant() * (-1)**i\\n return det\\n pass # remove this when you add your code.\\n # -------------------------------------------------------\",\n \"def calc_metric3(K_tilda):\\n trace = np.trace(K_tilda)\\n # determinant = np.linalg.det(K_tilda)\\n _, log_determinant = np.linalg.slogdet(K_tilda)\\n diff = trace - log_determinant\\n print(trace, log_determinant, diff)\\n return diff\",\n \"def compute_dissimilarity_matrix_wrapper(start_ind, end_ind,\\n n_instances, n_features,\\n tmp_dir, output_dir, dist_func,\\n process_id, process_batch_size):\\n X = np.memmap(tmp_dir + '/X.dat', dtype='float16', mode='r',\\n shape=(n_instances, n_features))\\n dissimilarity_matrix = np.memmap(\\n output_dir + '/dissimilarity_matrix_{}_{}_{}.dat'.format(\\n n_instances, n_instances, aid),\\n dtype='float16', mode='r+', shape=(n_instances, n_instances)\\n )\\n dissimilarity_process_matrix = np.load(\\n tmp_dir + '/dissimilarity_process_matrix.npy'\\n )[start_ind:end_ind]\\n\\n for i in range(end_ind - start_ind):\\n start_time = time.time()\\n (row_start, row_end, col_start,\\n col_end) = dissimilarity_process_matrix[i, :]\\n X_cols = X[col_start:col_end]\\n X_rows = X[row_start:row_end]\\n dist_col_row = dist_func(X_cols, X_rows,\\n X_batch_size=process_batch_size // 2,\\n Y_batch_size=process_batch_size // 2)\\n dist_col_row = dist_col_row.reshape(X_cols.shape[0], X_rows.shape[0])\\n\\n dissimilarity_matrix[row_start:row_end, col_start:col_end] = dist_col_row.T\\n dissimilarity_matrix[col_start:col_end, row_start:row_end] = dist_col_row\\n end_time = time.time()\\n print('pid: {}, at {} of {}. time {} seconds.'.format(\\n process_id,\\n i,\\n (end_ind - start_ind),\\n (end_time - start_time))\\n )\\n del dissimilarity_matrix\",\n \"def demo_diag21():\\n n_dim = 2\\n A = np.diag([2, 1])\\n covar = np.eye(n_dim)\\n mean = np.zeros(n_dim)\\n approx = approx_quad_form(mean, covar, A)\\n\\n # Sample from true dist\\n n_sample = 10000\\n x = np.random.multivariate_normal(mean, covar, n_sample)\\n q_samples = np.zeros(n_sample)\\n for i in range(n_sample):\\n q_samples[i] = x[i] @ A @ x[i]\\n\\n q = np.linspace(0, 10)\\n\\n plt.plot(\\n q, approx(q), label='Approx.',\\n color='tab:blue', linestyle='--')\\n bins = np.linspace(0, 10, 51)\\n bins[-1] = np.inf\\n plt.hist(\\n q_samples, density=True, histtype='stepfilled',\\n bins=bins,\\n alpha=0.5, color='black', label='Samples')\\n plt.xlabel('q')\\n plt.ylabel('pdf(q) [-]')\\n plt.legend()\",\n \"def dKdiag_dtheta(self,dL_dKdiag,X,target):\\r\\n raise NotImplementedError\",\n \"def det(self):\\n\\t\\t\\n\\t\\trows = self._rows\\n\\t\\tsign = +1\\n\\t\\tsumm = 0\\n\\n\\t\\tfor perm in permutations(range(rows), rows):\\n\\t\\t\\tmul = 1\\n\\t\\t\\tsign = SquareMatrix.__parity_of_permutation(perm)\\n\\n\\t\\t\\tfor i in range(rows):\\n\\t\\t\\t\\tmul *= self[i][perm[i]]\\n\\n\\t\\t\\tsumm += sign * mul\\n\\t\\treturn summ\",\n \"def determinant(self):\\n if not self.is_square():\\n raise(ValueError, \\\"Cannot calculate determinant of non-square matrix.\\\")\\n if self.h > 2:\\n raise(NotImplementedError, \\\"Calculating determinant not implemented for matrices largerer than 2x2.\\\")\\n \\n # TODO - your code here\\n if self.h == 1:\\n return self.g[0][0];\\n else:\\n return self.g[0][0]*self.g[1][1]-self.g[0][1]*self.g[1][0];\",\n \"def zernike_diagnosis(Nstar=1,seeing=[0.9,0.,0.],npix=npix,zenith=0,filter='r', theta=0., phi=0,corrector='corrector',x=None,y=None,z=None,zernike_max_order=20,regular=False,noise=False,exptime=100,mag=16.,sigma=4.):\\n hdu = genImgVallCCD(Nstar=Nstar,seeing=seeing,npix=npix,zenith=zenith,filter=filter, theta=theta,phi=phi, corrector=corrector,x=x,y=y,z=z,regular=regular)\\n nn = len(hdu)\\n data = []\\n colnames = ['x','y','M20','M22','M31','M33']\\n\\n for hdui in hdu[1:]:\\n Nobj = hdui.data.shape[0]\\n M20=np.zeros(Nobj)\\n M22=np.zeros(Nobj).astype(complex)\\n M31=np.zeros(Nobj).astype(complex)\\n M33=np.zeros(Nobj).astype(complex)\\n for i in range(Nobj):\\n psf = rebin(hdui.data[i][4:].reshape(npix,npix),(40,40))\\n if noise == True:\\n gain = 0.21 # convert electrons to ADU\\n zeropoint = 26.794176 # r band, from Nikolay\\n objectphoton = exptime*10**(0.4*(zeropoint - mag))\\n skyphoton = 8.460140*exptime\\n bkg = skyphoton*gain\\n img = (psf * objectphoton + skyphoton)*gain\\n img = img + add_imageNoise(img) - bkg\\n else:\\n img = psf\\n M20[i],M22[i],M31[i],M33[i]=complexMoments(data=img,sigma=sigma)\\n x=hdui.header['ccdXcen']\\n y=hdui.header['ccdYcen']\\n data.append([x,y,np.median(M20), np.median(M22), np.median(M31), np.median(M33)])\\n data=np.array(data)\\n pl.figure(figsize=(15,15))\\n betaAll=[]\\n betaErrAll=[]\\n R2adjAll=[]\\n pl.subplot(3,3,1)\\n beta,betaErr,R2_adj,fitted = zernikeFit(data[:,0].real,data[:,1].real,data[:,2].real,max_order=zernike_max_order)\\n betaAll.append(beta)\\n betaErrAll.append(betaErr)\\n R2adjAll.append(R2_adj)\\n znk=showZernike(beta=beta)\\n pl.colorbar()\\n pl.title(colnames[2])\\n for i in range(3,6):\\n pl.subplot(3,3,2*i-4)\\n beta,betaErr,R2_adj,fitted = zernikeFit(data[:,0].real,data[:,1].real,data[:,i].real,max_order=zernike_max_order)\\n betaAll.append(beta)\\n betaErrAll.append(betaErr)\\n R2adjAll.append(R2_adj)\\n znk=showZernike(beta=beta)\\n pl.colorbar()\\n pl.title(colnames[i]+'_real')\\n print '--- R2_adj of the fit is: '+str(R2_adj) +'---'\\n pl.subplot(3,3,2*i-3)\\n beta,betaErr,R2_adj,fitted = zernikeFit(data[:,0].real,data[:,1].real,data[:,i].imag,max_order=zernike_max_order)\\n betaAll.append(beta)\\n betaErrAll.append(betaErr)\\n R2adjAll.append(R2_adj)\\n znk=showZernike(beta=beta)\\n pl.colorbar()\\n pl.title(colnames[i]+'_imag')\\n print '--- R2_adj of the fit is: '+str(R2_adj) +'---'\\n betaAll = np.array(betaAll)\\n betaErrAll = np.array(betaErrAll)\\n R2adjAll = np.array(R2adjAll)\\n return betaAll,betaErrAll, R2adjAll\",\n \"def get_correct_distance_matrix(L):\\n n = len(L)\\n D = np.zeros((n,n))\\n for i in range(n):\\n for j in range(n):\\n if i != j:\\n D[i][j] = get_minor(L, [i, j], [i, j]) / get_minor(L, [i], [i])\\n return D\",\n \"def fisher_diag(\\n negative_log_likelihood: LossFun,\\n params: Any,\\n inputs: jnp.ndarray,\\n targets: jnp.ndarray,\\n) -> jnp.DeviceArray:\\n return jnp.square(\\n ravel(jax.grad(negative_log_likelihood)(params, inputs, targets)))\",\n \"def normD(dictin):\\n \\n tmp1 = np.sum(np.multiply(dictin, dictin), axis=0)\\n for k in range(len(tmp1)):\\n if tmp1[k] == 0:\\n tmp1[k] = 1e-6\\n \\n #tmp = 1 / np.sqrt(np.sum(np.multiply(dictin, dictin), axis=0))\\n tmp = 1 / np.sqrt(tmp1)\\n print(np.array(tmp1))\\n return np.dot(dictin, np.diag(tmp))\",\n \"def hdmatrix(self):\\n hdmat = np.zeros((len(self.pulsars), len(self.pulsars)))\\n\\n for i,pulsar1 in enumerate(self.pulsars):\\n for j,pulsar2 in enumerate(self.pulsars):\\n hdmat[i,j] = hellingsdowns_factor(pulsar1, pulsar2)\\n self.hdm = hdmat\\n return hdmat\",\n \"def cdistance(self, distances):\\n self.distanceMatrix = distances\\n self.dataChange()\",\n \"def diagonal(step='Metropolis', iters=5000):\\n X = mc.Uniform('X', lower=-1., upper=1., value=[0., 0.])\\n\\n @mc.potential\\n def near_diag(X=X):\\n if abs(X[0] - X[1]) < .1:\\n return 0\\n else:\\n return -inf\\n\\n mod = setup_and_sample(vars(), step, iters)\\n mod.shape = pl.array([[-1,-1], [-1,-.9], [.9,1], [1,1], [1,.9], [-.9,-1], [-1,-1]])\\n mod.true_mean = [0,0]\\n mod.true_iqr = ['(-.5,.5)', '(-.5,5)']\\n return mod\",\n \"def det_2x2(matrix: FieldMatrix) -> FlowFieldVal:\\n _validate_matrix_shape(matrix, (2, 2))\\n\\n det = lambda a, b, c, d: a * d - b * c\\n\\n a, b = matrix[0]\\n c, d = matrix[1]\\n\\n return tf.nest.map_structure(det, a, b, c, d)\",\n \"def det(self):\\n if not self.is_endomorphism():\\n raise ArithmeticError(\\\"Matrix morphism must be an endomorphism.\\\")\\n return self.matrix().determinant()\",\n \"def davidson(mult_by_A, N, neig, x0=None, Adiag=None, verbose=logger.INFO):\\n\\n if isinstance(verbose, logger.Logger):\\n log = verbose\\n else:\\n import sys\\n log = logger.Logger(sys.stdout, verbose)\\n\\n cput1 = (logger.process_clock(), logger.perf_counter())\\n\\n Mmin = min(neig,N)\\n Mmax = min(N,2000)\\n tol = 1e-6\\n\\n #Adiagcheck = np.zeros(N,np.complex128)\\n #for i in range(N):\\n # test = np.zeros(N,np.complex128)\\n # test[i] = 1.0\\n # Adiagcheck[i] = mult_by_A(test)[i]\\n #print \\\"Analytical Adiag == numerical Adiag?\\\", np.allclose(Adiag,Adiagcheck)\\n\\n if Adiag is None:\\n Adiag = np.zeros(N,np.complex128)\\n for i in range(N):\\n test = np.zeros(N,np.complex128)\\n test[i] = 1.0\\n Adiag[i] = mult_by_A(test)[i]\\n\\n xi = np.zeros(N,np.complex128)\\n\\n lamda_k_old = 0\\n lamda_k = 0\\n target = 0\\n conv = False\\n if x0 is not None:\\n assert x0.shape == (N, Mmin)\\n b = x0.copy()\\n\\n Ab = np.zeros((N,Mmin),np.complex128)\\n for m in range(Mmin):\\n Ab[:,m] = mult_by_A(b[:,m])\\n\\n for istep,M in enumerate(range(Mmin,Mmax+1)):\\n if M == Mmin:\\n # Set of M unit vectors from lowest Adiag (NxM)\\n b = np.zeros((N,M))\\n idx = Adiag.argsort()\\n for m,i in zip(range(M),idx):\\n b[i,m] = 1.0\\n ## Add random noise and orthogonalize\\n #for m in range(M):\\n # b[:,m] += 0.01*np.random.random(N)\\n # b[:,m] /= np.linalg.norm(b[:,m])\\n # b,R = np.linalg.qr(b)\\n\\n Ab = np.zeros((N,M),np.complex128)\\n for m in range(M):\\n Ab[:,m] = mult_by_A(b[:,m])\\n else:\\n Ab = np.column_stack( (Ab,mult_by_A(b[:,M-1])) )\\n\\n Atilde = np.dot(b.conj().transpose(),Ab)\\n lamda, alpha = diagonalize_asymm(Atilde)\\n lamda_k_old, lamda_k = lamda_k, lamda[target]\\n alpha_k = alpha[:,target]\\n\\n if M == Mmax:\\n break\\n\\n q = np.dot( Ab-lamda_k*b, alpha_k )\\n log.info('davidson istep = %d root = %d E = %.15g dE = %.9g residual = %.6g',\\n istep, target, lamda_k.real, (lamda_k - lamda_k_old).real, np.linalg.norm(q))\\n cput1 = log.timer('davidson iter', *cput1)\\n if np.linalg.norm(q) < tol:\\n if target == neig-1:\\n conv = True\\n break\\n else:\\n target += 1\\n #for i in range(N):\\n #eps = 0.\\n #if np.allclose(lamda_k,Adiag[i]):\\n # eps = 1e-10\\n #xi[i] = q[i]/(lamda_k-Adiag[i]+eps)\\n eps = 1e-10\\n xi = q/(lamda_k-Adiag+eps)\\n\\n # orthonormalize xi wrt b\\n bxi,R = np.linalg.qr(np.column_stack((b,xi)))\\n if FOUND_MPI4PY: # Ensure all processes search in same direction\\n bxi = MPI_COMM.bcast(bxi)\\n\\n # append orthonormalized xi to b\\n b = np.column_stack((b,bxi[:,-1]))\\n\\n #if M > Mmin and M == Mmax:\\n # print(\\\"WARNING: Davidson algorithm reached max basis size \\\"\\n # \\\"M = %d without converging.\\\"%(M))\\n\\n # Express alpha in original basis\\n evecs = np.dot(b,alpha) # b is N x M, alpha is M x M\\n return conv, lamda[:neig], evecs[:,:neig], istep\",\n \"def get_euclidean_matrix(df):\\n df.reset_index(drop=True, inplace=True)\\n\\n # foods = df['food_names']\\n # food_examples = []\\n # indices = list(range(0, len(foods)))\\n # for i in indices:\\n # food_examples.append(str(foods[i]) + str(i))\\n # food_examples = pd.Series(food_examples)\\n food_examples = df['food_names']\\n\\n df = df.drop(['food_names', 'height', 'weight', 'above_range', 'BMI', 'age', 'gender',\\n 'glucose_tolerance_category','90-percentile_of_2h-iAUC', 'average_carbs_ratio',\\n 'average_daily_carbs','average_meals_per_day', 'average_sleep_hours',\\n 'average_glucose', 'baseline', 'coefficient_of_variation', 'max_2-hours_iAUC',\\n 'median_fasting_glucose_level','median_of_2h-iAUC', 'night_baseline'], axis='columns')\\n\\n df = df.replace([-np.inf], 0).dropna(axis=1)\\n\\n num_examples = df.shape[0]\\n\\n distances = pdist(df.values, metric='euclidean')\\n print(distance)\\n dis_array = squareform(distances)\\n print(dis_array)\\n dis_df = pd.DataFrame(data = dis_array, index=food_examples, columns=food_examples)\\n print(dis_df)\\n writer = pd.ExcelWriter('Euclidean_distance_icarbonx.xlsx', engine='xlsxwriter')\\n dis_df.to_excel(writer, sheet_name='Sheet1')\\n writer.save()\",\n \"def diag(B,s,H,ia,ib,ic,chia,chic):\\n # Get a guess for the ground state based on the old MPS\\n d = B[0].shape[0]\\n theta0 = np.tensordot(np.diag(s[ia]),np.tensordot(B[ib],B[ic],axes=(2,1)),axes=(1,1))\\n theta0 = np.reshape(np.transpose(theta0,(1,0,2,3)),((chia*chic)*(d**2)))\\n\\n # Diagonalize Hamiltonian\\n e0,v0 = arp.eigsh(H,k=1,which='SA',return_eigenvectors=True,v0=theta0,ncv=20)\\n \\n return np.reshape(v0.squeeze(),(d*chia,d*chic)),e0\",\n \"def det(self):\\n self.matrix() # forces the update of the matrix in the module's default\\n # basis, to make sure that the dictionary self._matrices\\n # is not empty\\n return self._matrices.values()[0].det() # pick a random value in the\\n # dictionary self._matrices\\n # and compute the determinant\",\n \"def determinant(matrix):\\n if matrix == [[]]:\\n return 1\\n if type(matrix) is not list or len(matrix) < 1 or\\\\\\n not all(isinstance(x, list) for x in matrix):\\n raise TypeError(\\\"matrix must be a list of lists\\\")\\n if not all(len(matrix) == len(x) for x in matrix):\\n raise ValueError(\\\"matrix must be a square matrix\\\")\\n copy = list(map(list, matrix))\\n dim = len(matrix)\\n if dim == 1:\\n return matrix[0][0]\\n elif dim == 2:\\n return matrix[0][0] * matrix[1][1] - matrix[1][0] * matrix[0][1]\\n else:\\n for cur in range(dim):\\n for i in range(cur + 1, dim):\\n if copy[cur][cur] == 0:\\n copy[cur][cur] = 1.0e-10\\n curScaler = copy[i][cur] / copy[cur][cur]\\n for j in range(dim):\\n copy[i][j] = copy[i][j] - curScaler * copy[cur][j]\\n det = 1\\n for i in range(dim):\\n det *= copy[i][i]\\n return round(det)\",\n \"def del2(im_input):\\n\\n # initialize output\\n im_lap = np.zeros(im_input.shape)\\n\\n # process rows first\\n D = np.diff(im_input, axis=0)\\n S = np.zeros(im_input.shape)\\n S[1:-1, :] = (D[1:, :] - D[0:-1, :]) / 2\\n if im_input.shape[0] > 3:\\n S[0, :] = 2 * S[1, :] - S[2, :]\\n S[-1, :] = 2 * S[-2, :] - S[-3, :]\\n elif im_input.shape[0] == 3:\\n S[0, :] = S[1, :]\\n S[-1, :] = S[1, :]\\n else:\\n S[0, :] = 0\\n S[-1, :] = 0\\n im_lap += S\\n\\n # process columns\\n D = np.diff(im_input, axis=1)\\n S = np.zeros(im_input.shape)\\n S[:, 1:-1] = (D[:, 1:] - D[:, 0:-1]) / 2\\n if im_input.shape[1] > 3:\\n S[:, 0] = 2 * S[:, 1] - S[:, 2]\\n S[:, -1] = 2 * S[:, -2] - S[:, -3]\\n elif im_input.shape[1] == 3:\\n S[0, :] = S[:, 1]\\n S[:, -1] = S[:, 1]\\n else:\\n S[:, 0] = 0\\n S[:, -1] = 0\\n im_lap += S\\n\\n return im_lap / 2\",\n \"def dKdiag_dtheta(self, dL_dKdiag, X, target):\\r\\n target += 2.*self.mapping.df_dtheta(dL_dKdiag[:, None]*self.mapping.f(X), X)\",\n \"def dist_chisq(datamtx, strict=True):\\n if strict:\\n if not all(isfinite(datamtx)):\\n raise ValueError(\\\"non finite number in input matrix\\\")\\n if any(datamtx<0.0):\\n raise ValueError(\\\"negative value in input matrix\\\")\\n if rank(datamtx) != 2:\\n raise ValueError(\\\"input matrix not 2D\\\")\\n numrows, numcols = shape(datamtx)\\n else:\\n try:\\n numrows, numcols = shape(datamtx)\\n except ValueError:\\n return zeros((0,0),'d')\\n\\n if numrows == 0 or numcols == 0:\\n return zeros((0,0),'d')\\n dists = zeros((numrows,numrows),'d')\\n sqrt_grand_sum = sqrt(sum(datamtx))\\n rowsums, colsums = sum(datamtx, axis=1), sum(datamtx, axis=0)\\n if not colsums.all():\\n for i in range(len(colsums)):\\n if colsums[i] == 0.0:\\n colsums[i] = 1.0\\n for i in range(numrows):\\n r1 = datamtx[i]\\n r1sum = rowsums[i]\\n for j in range(i):\\n r2 = datamtx[j]\\n r2sum = rowsums[j]\\n if r1sum == 0.0 or r2sum == 0.0:\\n if r1sum == 0.0 and r2sum == 0.0:\\n dist = 0.0\\n else: dist = 1.0\\n else:\\n dist = sqrt_grand_sum *\\\\\\n sqrt(sum( multiply((1./colsums) ,\\n square(r1/r1sum - r2/r2sum)) ))\\n dists[i,j] = dists[j,i] = dist\\n return dists\",\n \"def determinant (self):\\n if self.is_square:\\n det = 1\\n for idx, row in enumerate(echelon_form(self).rows()):\\n det *= row[idx]\\n return det\\n else:\\n raise NotImplementedError(\\n \\\"Determinant only defined for square matrices.\\\")\",\n \"def _reclassify_disturbance(\\n landuse_transition_from_matrix, landuse_transition_to_matrix):\\n output_matrix = numpy.empty(landuse_transition_from_matrix.shape,\\n dtype=numpy.float32)\\n output_matrix[:] = NODATA_FLOAT32_MIN\\n\\n valid_pixels = numpy.ones(landuse_transition_from_matrix.shape,\\n dtype=bool)\\n if from_nodata is not None:\\n valid_pixels &= ~utils.array_equals_nodata(\\n landuse_transition_from_matrix, from_nodata)\\n\\n if to_nodata is not None:\\n valid_pixels &= ~utils.array_equals_nodata(\\n landuse_transition_to_matrix, to_nodata)\\n\\n disturbance_magnitude = disturbance_magnitude_matrix[\\n landuse_transition_from_matrix[valid_pixels],\\n landuse_transition_to_matrix[valid_pixels]].toarray().flatten()\\n\\n output_matrix[valid_pixels] = disturbance_magnitude\\n return output_matrix\",\n \"def compute_differential_operator(self):\\n\\n v_in, v_out, weights = self.get_edge_list()\\n\\n n = len(v_in)\\n Dr = np.concatenate((np.arange(n), np.arange(n)))\\n Dc = np.empty(2*n)\\n Dc[:n] = v_in\\n Dc[n:] = v_out\\n Dv = np.empty(2*n)\\n\\n if self.lap_type == 'combinatorial':\\n Dv[:n] = np.sqrt(weights)\\n Dv[n:] = -Dv[:n]\\n elif self.lap_type == 'normalized':\\n Dv[:n] = np.sqrt(weights / self.dw[v_in])\\n Dv[n:] = -np.sqrt(weights / self.dw[v_out])\\n else:\\n raise ValueError('Unknown lap_type {}'.format(self.lap_type))\\n\\n self._D = sparse.csc_matrix((Dv, (Dr, Dc)), shape=(n, self.N))\",\n \"def inverse_3by3_double(M):\\n if len(M.shape) > 1:\\n M = M.flatten()\\n\\n M = np.array(M, 'float')\\n\\n determinant = 0.\\n adj_M = np.zeros((9,), 'float')\\n\\n # First row of adjugate matrix\\n adj_M[0] = (M[4] * M[8] - M[7] * M[5]) # Det #0\\n adj_M[1] = -(M[1] * M[8] - M[7] * M[2])\\n adj_M[2] = (M[1] * M[5] - M[4] * M[2])\\n\\n # Second row of adjugate matrix\\n adj_M[3] = -(M[3] * M[8] - M[6] * M[5]) # Det #1\\n adj_M[4] = (M[0] * M[8] - M[6] * M[2])\\n adj_M[5] = -(M[0] * M[5] - M[3] * M[2])\\n\\n # Third row of adjugate matrix\\n adj_M[6] = (M[3] * M[7] - M[6] * M[4]) # Det #2\\n adj_M[7] = -(M[0] * M[7] - M[6] * M[1])\\n adj_M[8] = (M[0] * M[4] - M[3] * M[1])\\n\\n determinant += M[0] * adj_M[0]\\n determinant += M[1] * adj_M[3] # Using addition since minus is integrated in adjugate matrix.\\n determinant += M[2] * adj_M[6]\\n\\n return (adj_M / determinant)\",\n \"def _build_adjacency_matrix_1(self):\\n\\n from scipy import sparse as sparse\\n \\n down_neighbour = np.empty(self.tri.npoints)\\n\\n for node in range (0,self.tri.npoints):\\n down_neighbour[node] = self.neighbour_array_lo_hi[node][0]\\n\\n # Build a matrix of downhill-ness - one entry per node ! \\n \\n size = self.tri.npoints\\n row_array = np.empty(size)\\n col_array = np.empty(size)\\n down_array = np.ones(size)\\n\\n # Catch cases where node is local low point (i.e. it is its own low neighbour)\\n\\n for row in range(0, self.tri.npoints): \\n row_array[row] = row\\n col_array[row] = down_neighbour[row]\\n if row == down_neighbour[row]:\\n down_array[row] = 0.0\\n \\n\\n downMCOO = sparse.coo_matrix( (down_array, (row_array, col_array)), shape=(size,size) ).T \\n\\n self.adjacency1 = downMCOO.tocsr() \\n\\n # Catch pathological cases - sometimes if there is a flat spot on the boundary, then \\n # the filling method above will produce a non-square matrix. This is caused by\\n # repetition of values in the COO list which are summed on conversion.\\n\\n if downMCOO.shape[0] != downMCOO.shape[1]:\\n # This approach works but is a lot slower\\n\\n print \\\"\\\"\\\"\\n Warning: the downhill matrices require a slow build method. This is probably\\n Because there are degeneracies in the slope - particularly at the boundaries\\n A small random perturbation is usually enough to fix this problem\\n \\\"\\\"\\\"\\n downMat = sparse.lil_matrix((size, size))\\n\\n for row in range(0, self.tri.npoints): \\n downMat[down_neighbour[row],row] = 1.0\\n\\n for row in range(0, self.tri.npoints): \\n if down_neighbour[row] == row:\\n downMat[row,row] = 0.0\\n \\n self.adjacency1 = downMat.T.tocsr() \\n \\n return\",\n \"def compute_det(self, log_progress=False):\\n if not self.is_square():\\n raise Exception(u\\\"Not a square matrix\\\")\\n\\n mat = clone_matrix(self.coefficients)\\n size = self.get_size()[0]\\n\\n for i in range(size - 1):\\n for j in range(i + 1, size):\\n for k in range(i + 1, size):\\n mat[j][k] = (mat[j][k] * mat[i][i]) - (mat[j][i] * mat[i][k])\\n if i > 0:\\n mat[j][k] //= mat[i - 1][i - 1]\\n if log_progress:\\n print(i)\\n if i > 0:\\n for j in range(size):\\n mat[j][i - 1] = 0\\n mat[i - 1][j] = 0\\n\\n return mat[size - 1][size - 1]\",\n \"def sim_mat(fc7_feats):\\n print(\\\"Something\\\")\\n t = time.time()\\n pdist_ = spatial.distance.pdist(fc7_feats)\\n print('Created distance matrix' + ' ' + str(time.time() - t) + ' sec')\\n\\n t = time.time()\\n dist_mat = spatial.distance.squareform(pdist_)\\n print('Created square distance matrix' + ' ' + str(time.time() - t) + ' sec')\\n del pdist_\\n\\n t = time.time()\\n sigmas = np.sort(dist_mat, axis=1)[:, 7] + 1e-16\\n matrice_prodotti_sigma = np.dot(sigmas[:, np.newaxis], sigmas[np.newaxis, :])\\n print('Generated Sigmas' + ' ' + str(time.time() - t) + ' sec')\\n\\n t = time.time()\\n dist_mat /= -matrice_prodotti_sigma\\n print('Computed dists/-sigmas' + ' ' + str(time.time() - t) + ' sec')\\n\\n del matrice_prodotti_sigma\\n\\n t = time.time()\\n W = np.exp(dist_mat, dist_mat)\\n # W = np.exp(-(dist_mat / matrice_prodotti_sigma))\\n np.fill_diagonal(W, 0.)\\n\\n # sparsify the matrix\\n k = int(np.floor(np.log2(fc7_feats.shape[0])) + 1)\\n n = W.shape[0]\\n print('Created inplace similarity matrix' + ' ' + str(time.time() - t) + ' sec')\\n\\n t = time.time()\\n for x in W:\\n x[np.argpartition(x, n - k)[:(n - k)]] = 0.0\\n\\n print('Sparsify the matrix' + ' ' + str(time.time() - t) + ' sec')\\n\\n t = time.time()\\n # matrix_S = np.zeros((n, n))\\n m1 = W[np.triu_indices(n, k=1)]\\n m2 = W.T[np.triu_indices(n, k=1)]\\n\\n W = spatial.distance.squareform(np.maximum(m1, m2))\\n print('Symmetrized the similarity matrix' + ' ' + str(time.time() - t) + ' sec')\\n\\n return W\",\n \"def _core_calc_degrad(self,bd,Ld) :\\n\\t\\tdegrad = np.dot(Ld,bd) # Do matrix multiplication \\n\\t\\tdegrad = np.exp(degrad) # Exponentiate to convert log to real\\n\\t\\treturn degrad\",\n \"def build_difs_matrix(xs):\\n n = len(xs)\\n difs = np.zeros((n, n, 2))\\n for row_id in range(n):\\n ident = -np.eye(n)\\n ident[:, row_id] += 1\\n difs[row_id, :, 0] = ident @ xs[:, 0]\\n difs[row_id, :, 1] = ident @ xs[:, 1]\\n return difs\"\n]","isConversational":false},"negative_scores":{"kind":"list like","value":["0.58057857","0.5708356","0.5658325","0.56237066","0.5622826","0.5600156","0.5581219","0.5456089","0.54544467","0.54441947","0.54128253","0.53995603","0.5385993","0.5369172","0.53661954","0.5349746","0.5349005","0.5301684","0.52880996","0.5278491","0.5276256","0.5270977","0.5267933","0.5245534","0.52192795","0.521754","0.5207545","0.5182983","0.51743263","0.51708144","0.5170241","0.51610345","0.5151333","0.5151187","0.51455796","0.51455504","0.5145071","0.51441294","0.5141663","0.5127176","0.5120079","0.51092035","0.51032925","0.50993955","0.50859773","0.5085273","0.50702184","0.506658","0.50530523","0.5047506","0.5046302","0.50439745","0.50291294","0.5024497","0.5021934","0.50205797","0.50197864","0.50077224","0.5006065","0.5002609","0.50024086","0.49996698","0.4995699","0.4993371","0.49927622","0.49802214","0.4976774","0.49720755","0.49673995","0.49622655","0.49584013","0.49510884","0.49507716","0.4939165","0.49380484","0.49294478","0.49261314","0.49251676","0.49169713","0.49141946","0.49106613","0.49097353","0.49073708","0.49039245","0.49034923","0.4894417","0.48940212","0.48903602","0.48885623","0.48864424","0.48863104","0.48850578","0.48847482","0.48840243","0.4881999","0.48768744","0.48763716","0.48760897","0.4875018","0.48749855"],"string":"[\n \"0.58057857\",\n \"0.5708356\",\n \"0.5658325\",\n \"0.56237066\",\n \"0.5622826\",\n \"0.5600156\",\n \"0.5581219\",\n \"0.5456089\",\n \"0.54544467\",\n \"0.54441947\",\n \"0.54128253\",\n \"0.53995603\",\n \"0.5385993\",\n \"0.5369172\",\n \"0.53661954\",\n \"0.5349746\",\n \"0.5349005\",\n \"0.5301684\",\n \"0.52880996\",\n \"0.5278491\",\n \"0.5276256\",\n \"0.5270977\",\n \"0.5267933\",\n \"0.5245534\",\n \"0.52192795\",\n \"0.521754\",\n \"0.5207545\",\n \"0.5182983\",\n \"0.51743263\",\n \"0.51708144\",\n \"0.5170241\",\n \"0.51610345\",\n \"0.5151333\",\n \"0.5151187\",\n \"0.51455796\",\n \"0.51455504\",\n \"0.5145071\",\n \"0.51441294\",\n \"0.5141663\",\n \"0.5127176\",\n \"0.5120079\",\n \"0.51092035\",\n \"0.51032925\",\n \"0.50993955\",\n \"0.50859773\",\n \"0.5085273\",\n \"0.50702184\",\n \"0.506658\",\n \"0.50530523\",\n \"0.5047506\",\n \"0.5046302\",\n \"0.50439745\",\n \"0.50291294\",\n \"0.5024497\",\n \"0.5021934\",\n \"0.50205797\",\n \"0.50197864\",\n \"0.50077224\",\n \"0.5006065\",\n \"0.5002609\",\n \"0.50024086\",\n \"0.49996698\",\n \"0.4995699\",\n \"0.4993371\",\n \"0.49927622\",\n \"0.49802214\",\n \"0.4976774\",\n \"0.49720755\",\n \"0.49673995\",\n \"0.49622655\",\n \"0.49584013\",\n \"0.49510884\",\n \"0.49507716\",\n \"0.4939165\",\n \"0.49380484\",\n \"0.49294478\",\n \"0.49261314\",\n \"0.49251676\",\n \"0.49169713\",\n \"0.49141946\",\n \"0.49106613\",\n \"0.49097353\",\n \"0.49073708\",\n \"0.49039245\",\n \"0.49034923\",\n \"0.4894417\",\n \"0.48940212\",\n \"0.48903602\",\n \"0.48885623\",\n \"0.48864424\",\n \"0.48863104\",\n \"0.48850578\",\n \"0.48847482\",\n \"0.48840243\",\n \"0.4881999\",\n \"0.48768744\",\n \"0.48763716\",\n \"0.48760897\",\n \"0.4875018\",\n \"0.48749855\"\n]","isConversational":false},"document_score":{"kind":"string","value":"0.515411"},"document_rank":{"kind":"string","value":"32"}}},{"rowIdx":22682,"cells":{"query":{"kind":"string","value":"Z transformation for HiC matrices."},"document":{"kind":"string","value":"def ztransform(matrix):\n\n mat = matrix.copy()\n mu = np.mean(mat.data)\n sd = np.std(mat.data)\n mat.data -= mu\n mat.data /= sd\n\n return mat"},"metadata":{"kind":"string","value":"{\n \"objective\": {\n \"self\": [],\n \"paired\": [],\n \"triplet\": [\n [\n \"query\",\n \"document\",\n \"negatives\"\n ]\n ]\n }\n}"},"negatives":{"kind":"list like","value":["def __call__(self,Zin):\r\n self.Z = np.zeros((self.Nc,))\r\n for ii in range(0,self.Nc):\r\n self.Z[ii] = np.dot(self.W[:,ii],Zin[self.ind[ii,:]])\r\n \r\n return self.Z","def zoisite():\n\n rho = 3343.\n\n C = np.zeros((6,6), dtype=float)\n C[0,0] = 279.8; C[0,1] = 94.7; C[0,2] = 88.7; C[0,3] = 0.; C[0,4] = 0.; C[0,5] = 0.\n C[1,0] = C[0,1]; C[1,1] = 249.2; C[1,2] = 27.5; C[1,3] = 0.; C[1,4] = 0.; C[1,5] = 0.\n C[2,0] = C[0,2]; C[2,1] = C[1,2]; C[2,2] = 209.4; C[2,3] = 0.; C[2,4] = 0.; C[2,5] = 0.\n C[3,0] = C[0,3]; C[3,1] = C[1,3]; C[3,2] = C[2,3]; C[3,3] = 51.8; C[3,4] = 0.; C[3,5] = 0.\n C[4,0] = C[0,4]; C[4,1] = C[1,4]; C[4,2] = C[2,4]; C[4,3] = C[3,4]; C[4,4] = 81.4; C[4,5] = 0.\n C[5,0] = C[0,5]; C[5,1] = C[1,5]; C[5,2] = C[2,5]; C[5,3] = C[3,5]; C[5,4] = C[4,5]; C[5,5] = 66.3\n\n return C, rho","def EulerZXZ2Mat(e):\n x=e[0]\n y=e[1]\n z=e[2]\n s1=np.sin(x)\n s2=np.sin(y)\n s3=np.sin(z)\n c1=np.cos(x)\n c2=np.cos(y)\n c3=np.cos(z)\n m=np.array([[c1*c3-c2*s1*s3,-c1*s3-c3*c2*s1,s1*s2],\n [s1*c3+c2*c1*s3,c1*c2*c3-s1*s3,-c1*s2],\n [s3*s2,s2*c3,c2]])\n return m","def rotateZ(self, *args, **kwargs):\n ...","def get_shower_trans_matrix (azimuth,altitude):\n\n cos_z = sin(altitude)\n sin_z = cos(altitude)\n cos_az = cos(azimuth)\n sin_az = sin(azimuth)\n\n trans = np.zeros([3,3])\n trans[0][0] = cos_z*cos_az\n trans[1][0] = sin_az\n trans[2][0] = sin_z*cos_az\n\n trans[0][1] = -cos_z*sin_az\n trans[1][1] = cos_az\n trans[2][1] = -sin_z*sin_az\n\n trans[0][2] = -sin_z\n trans[1][2] = 0.\n trans[2][2] = cos_z\n\n return trans","def homog_trans_mtx(x: float, y: float, z: float) -> numpy.array:\n return numpy.array(\n ((1, 0, 0, x), (0, 1, 0, y), (0, 0, 1, z), (0, 0, 0, 1)), dtype=numpy.float64\n )","def _derZ(self, w, x, y, z):\n raise NotImplementedError()","def z_transform(h, z, n=None):\n # Checking input params\n n = discrete.check_n(h, n, 'z_transform')\n # Construct array of z^-n where each row corresponds to a z value and each column corresponds to a n value\n A = np.fromfunction(lambda i, j: z[i.astype(int)]**(-n[j.astype(int)]), shape=(len(z), len(n)))\n # Calculate sum over all n for each z\n return np.dot(A, h)","def rotateZUp(self):\n MV = self.MV\n MV[:3, 1] = 0, 0, 1 # 2nd col is up vector, make it point along z axis\n # set bottom left and bottom right z values to zero:\n MV[2, 0] = 0\n MV[2, 2] = 0\n a = MV[0, 0] # grab top left value\n b = np.sqrt(1 - a**2) # calc new complementary value to get normalized vectors\n if MV[1, 0] < 0:\n b = -b # keep b -ve, reduce jumping around of axes\n MV[1, 0] = b\n MV[0, 2] = b\n MV[1, 2] = -a # needs to be -ve of MV[0, 0]\n self.MV = MV","def rotateZUp(self):\n MV = self.MV\n MV[:3, 1] = 0, 0, 1 # 2nd col is up vector, make it point along z axis\n # set bottom left and bottom right z values to zero:\n MV[2, 0] = 0\n MV[2, 2] = 0\n a = MV[0, 0] # grab top left value\n b = np.sqrt(1 - a**2) # calc new complementary value to get normalized vectors\n if MV[1, 0] < 0:\n b = -b # keep b -ve, reduce jumping around of axes\n MV[1, 0] = b\n MV[0, 2] = b\n MV[1, 2] = -a # needs to be -ve of MV[0, 0]\n self.MV = MV","def H(self, z):\n return self.H0 * self.E(z)","def homogeneous_Z( zTheta , pos ):\r\n return np.vstack( ( np.hstack( ( z_rot( zTheta ) , [ [ pos[0] ] , [ pos[1] ] , [ pos[2] ] ] ) ) ,\r\n np.hstack( ( [ 0 , 0 , 0 ] , [ 1 ] ) ) ) )","def Z_hipass(C, L, R_L, f):\n return 1/(1/Xind(L,f) + 1/Z_high(C, R_L, f))","def world_zaxis(self):\n return observable.MJCFFeature('xmat', self._entity.root_body)[6:]","def inverse_cayley_transform(z: torch.Tensor) -> torch.Tensor:\n identity = identity_like(z)\n i_identity = multiply_by_i(identity)\n\n z_minus_id = z - i_identity\n inv_z_plus_id = inverse(z + i_identity)\n return z_minus_id @ inv_z_plus_id","def Z(phi = None):\n if phi == None:\n return sz\n else:\n return scipy.linalg.expm(-1j * phi / 2 * sz)","def cPofZ(self,arr,zx):\n sig1=(zx-arr['zmg'])/arr['sigma_pz']\n sig2=(zx-self.zcat)/self.sigmacat\n return (1.-self.Acat)*0.5*(1 + erf(sig1/np.sqrt(2)))+self.Acat*0.5*(1 + erf(sig2/np.sqrt(2)))","def get_lz(self):\r\n return self.dz * self.nz - self.oz","def mXZ(nxz,P_dot_Dj,P_dot_ej):\n return np.divide(np.multiply(P_dot_ej, np.sum(nxz, axis=0)), P_dot_Dj)","def _derZ(self, x, y, z):\n raise NotImplementedError()","def ssc.inverse (x_ij):\n\n Hij = xyzrph2matrix (x_ij)\n Rji = Hij[0:3, 0:3]\n tij = Hij[0:3,3]\n Rij = Rji.transpose ()\n tji = -Rij.dot (tij)\n Hji = zeros ((4,4))\n Hji[0:3,0:3] = Rij\n Hji[0:3,3] = tji\n Hji[3,3] = 1\n return matrix2xyzrph (Hji)","def u2z(self, u):\n N = norm.ppf(u)\n Z = solve(self.Sigma_h, N)\n\n return Z","def NNZ(self):\n return _hypre.HypreParMatrix_NNZ(self)","def extrapToZ(zc,(x0,y0,z0),(px,py,pz)):\n x = x0+ (px/pz)*(zc-z0)\n y = y0+ (py/pz)*(zc-z0)\n\n return (x,y)","def z_rotation(self):\n before = ('R', 'r', 'U', 'u', 'L', 'l', 'D', 'd', 'M', 'E', 'x', 'y')\n after = ('U', 'u', 'L', 'l', 'D', 'd', 'R', 'r', 'E', 'M\\'', \"y\", \"x'\")\n solve = self.solve_helper.maketrans(dict(zip(before, after)))\n solve_trans = self.solve_helper.translate(solve)\n solve_trans = solve_trans.replace(\"\\'\\'\", \"\")\n self.solve_helper = solve_trans","def dct(self, z, out=None):\n z = np.asfarray(z)\n out = np.multiply(z, z, out)\n out *= -0.5\n np.exp(out, out)\n return out","def M_z(self) -> int:\n return self.params.M_z","def inverse_fisher_z_transform(z):\r\n return ((e ** (2 * z)) - 1.) / ((e ** (2 * z)) + 1.)","def zconv2(x, h, S):\n M, N = int(x.shape[0]), int(x.shape[1])\n P, Q = int(h.shape[0]), int(h.shape[1])\n\n M0 = int(S[0, 0])\n M1 = int(S[0, 1])\n M2 = int(S[1, 0])\n M3 = int(S[1, 1])\n\n NewP = ((M0-1)*(P-1))+M2*(Q-1) + P-1\n NewQ = ((M3-1)*(Q-1))+M1*(P-1) + Q-1\n\n y = np.zeros((M, N))\n\n ssum = 0\n Start1 = NewP // 2\n Start2 = NewQ // 2\n mn1 = Start1 % M\n mn2 = Start2 % N\n mn2_save = mn2\n\n for n1 in range(M):\n for n2 in range(N):\n outindexx = mn1\n outindexy = mn2\n\n for l1 in range(P):\n indexx = outindexx\n indexy = outindexy\n for l2 in range(Q):\n ssum += x[indexx, indexy] * h[l1, l2]\n indexx = indexx - M2\n\n if indexx < 0:\n indexx = indexx + M\n if indexx > M-1:\n indexx = indexx - M\n indexy = indexy - M3\n if indexy < 0:\n indexy = indexy + N\n\n outindexx = outindexx - M0\n if outindexx < 0:\n outindexx = outindexx + M\n\n outindexy = outindexy - M1\n if outindexy < 0:\n outindexy = outindexy + N\n if outindexy > N-1:\n outindexy = outindexy - N\n\n y[n1, n2] = ssum\n ssum = 0\n mn2 = mn2 + 1\n\n if mn2 > N-1:\n mn2 = mn2 - N\n\n mn2 = mn2_save\n mn1 = mn1 + 1\n if mn1 > M-1:\n mn1 = mn1 - M\n\n return y","def harzburgite():\n\n rho = 3200.\n\n C = np.zeros((6,6), dtype=float)\n C[0,0] = 226.5; C[0,1] = 75.34; C[0,2] = 74.73; C[0,3] = -0.27; C[0,4] = -2.00; C[0,5] = 1.85\n C[1,0] = C[0,1]; C[1,1] = 242.8; C[1,2] = 73.68; C[1,3] = -3.6; C[1,4] = -1.91; C[1,5] = 4.14\n C[2,0] = C[0,2]; C[2,1] = C[1,2]; C[2,2] = 230.; C[2,3] = -4.36; C[2,4] = -4.27; C[2,5] = -0.27\n C[3,0] = C[0,3]; C[3,1] = C[1,3]; C[3,2] = C[2,3]; C[3,3] = 80.75; C[3,4] = 1.81; C[3,5] = -2.19\n C[4,0] = C[0,4]; C[4,1] = C[1,4]; C[4,2] = C[2,4]; C[4,3] = C[3,4]; C[4,4] = 76.94; C[4,5] = -1.88\n C[5,0] = C[0,5]; C[5,1] = C[1,5]; C[5,2] = C[2,5]; C[5,3] = C[3,5]; C[5,4] = C[4,5]; C[5,5] = 79.15\n\n return C, rho","def set_lz(self):\r\n self._lz = self.dz * self.nz - self.oz","def matrix_translate_3d(tx: float, ty: float, tz: float) -> np.matrix:\n return np.matrix([[1, 0, 0, tx], [0, 1, 0, ty], [0, 0, 1, tz], [0, 0, 0, 1]])","def zenazi(scx_l, scx_b, scy_l, scy_b, scz_l, scz_b, src_l, src_b):\n # Zenith is the distance from the optical axis (here z)\n costheta = GreatCircle(scz_l,scz_b,src_l,src_b) \n # Azimuth is the combination of the remaining two\n cosx = GreatCircle(scx_l,scx_b,src_l,src_b)\n cosy = GreatCircle(scy_l,scy_b,src_l,src_b)\n \n # check exceptions\n # maybe not for vectorisation\n \"\"\"\n if costheta.size == 1:\n if (costheta > 1.0):\n costheta = 1.0\n if (costheta < -1.0):\n costheta = -1.0\n else:\n costheta[costheta > 1.0] = 1.0\n costheta[costheta < -1.0] = -1.0\n \"\"\"\n # theta = zenith\n theta = np.rad2deg(np.arccos(costheta))\n # phi = azimuth\n phi = np.rad2deg(np.arctan2(cosy,cosx)) # TS January 14: you sure about that? changed y and x\n \n # make azimuth going from 0 to 360 deg\n if phi.size == 1:\n if (phi < 0):\n phi += 360\n else:\n phi[phi < 0] += 360\n \n return theta,phi","def u2z(self, u):\n raise NotImplementedError","def _apply_cz(self, state, axes, **kwargs):\n ndim = self._ndim(state)\n sl_0 = _get_slice(0, axes[0], ndim)\n sl_1 = _get_slice(1, axes[0], ndim)\n\n if axes[1] > axes[0]:\n target_axes = [axes[1] - 1]\n else:\n target_axes = [axes[1]]\n\n state_z = self._apply_z(state[sl_1], axes=target_axes)\n return self._stack([state[sl_0], state_z], axis=axes[0])","def zca_whitening_matrix(X):\n # Covariance matrix [column-wise variables]: Sigma = (X-mu)' * (X-mu) / N\n sigma = np.cov(X, rowvar=True) # [M x M]\n # Singular Value Decomposition. X = U * np.diag(S) * V\n U,S,V = np.linalg.svd(sigma)\n # U: [M x M] eigenvectors of sigma.\n # S: [M x 1] eigenvalues of sigma.\n # V: [M x M] transpose of U\n # Whitening constant: prevents division by zero\n epsilon = 1e-5\n # ZCA Whitening matrix: U * Lambda * U'\n ZCAMatrix = np.dot(U, np.dot(np.diag(1.0/np.sqrt(S + epsilon)), U.T)) # [M x M]\n return ZCAMatrix","def cayley_transform(z: torch.Tensor) -> torch.Tensor:\n identity = identity_like(z)\n\n i_z_plus_id = multiply_by_i(identity + z)\n inv_z_minus_id = inverse(identity - z)\n\n return i_z_plus_id @ inv_z_minus_id","def rotate_z(self, angle):\n angle *= np.pi / 180\n return self.transform(np.matrix([[np.cos(angle), -np.sin(angle), 0],\n [np.sin(angle), np.cos(angle), 0],\n [0, 0, 1]]))","def rotateXOut(self):\n MV = self.MV\n MV[:3, 2] = 1, 0, 0 # 3rd col is normal vector, make it point along x axis\n # set top left and top middle values to zero:\n MV[0, 0] = 0\n MV[0, 1] = 0\n b = MV[2, 0] # grab bottom left value\n a = np.sqrt(1 - b**2) # calc new complementary value to get normalized vectors\n #if MV[1, 0] < 0:\n # a = -a # keep a -ve, reduce jumping around of axes\n MV[1, 0] = a\n MV[2, 1] = a\n MV[1, 1] = -b # needs to be -ve of MV[2, 0]\n self.MV = MV","def rotateXOut(self):\n MV = self.MV\n MV[:3, 2] = 1, 0, 0 # 3rd col is normal vector, make it point along x axis\n # set top left and top middle values to zero:\n MV[0, 0] = 0\n MV[0, 1] = 0\n b = MV[2, 0] # grab bottom left value\n a = np.sqrt(1 - b**2) # calc new complementary value to get normalized vectors\n #if MV[1, 0] < 0:\n # a = -a # keep a -ve, reduce jumping around of axes\n MV[1, 0] = a\n MV[2, 1] = a\n MV[1, 1] = -b # needs to be -ve of MV[2, 0]\n self.MV = MV","def H(self) -> BaseMatrix:","def H(self) -> BaseMatrix:","def _apply_z(self, state, axes, **kwargs):\n return self._apply_phase(state, axes, -1)","def Mat2EulerZXZ(m):\n threshold=0.9999999\n if m[2,2]>threshold:\n x=0\n y=0\n z=atan2(m[1,0],m[0,0])\n elif m[2,2]< -threshold:\n x=0\n y=np.pi\n z=atan2(m[0,1],m[0,0])\n else:\n x=atan2(m[0,2],-m[1,2])\n y=atan2(np.sqrt(m[2,0]**2+m[2,1]**2),m[2,2])\n z=atan2(m[2,0],m[2,1])\n if x<0: x=x+2*np.pi\n if y<0: y=y+2*np.pi\n if z<0: z=z+2*np.pi\n return x,y,z","def get_hkl(self, x, y, z):\n if self.Umat is not None:\n v5 = self.Gvec(x, y, z)\n# v6 = inv(self.Umat) * v5\n# v7 = inv(self.Bmat) * v6\n v7 = inv(self.UBmat) * v5\n return list(np.array(v7.T)[0])\n else:\n return [0.0, 0.0, 0.0]","def pz_fn(self, z):\n pass","def mxz(mXZ,P_dot_Dj,P_times_Dj):\n return np.divide(np.multiply(P_times_Dj, mXZ), P_dot_Dj)","def h(self, z):\n # See definition at end of Section 1, p2 of Arnaud et al.\n return np.sqrt(self.Om*(1.+z)**3. + self.Ol)","def _Zmodel(self,vBz,sfr,ZISM):\n yZcool = 1.0\n expo = 1.7\n\n vmax = self.params['vej'].to('km/s').value\n ZSN = self.params['ZSN']\n Mej = self.params['Mej']\n\n Zmodel = np.clip((vBz/vmax)**expo*(ZSN-ZISM)+ZISM*yZcool,None,ZSN)\n return Zmodel","def z2u(self, z):\n raise NotImplementedError","def inverse (x_ij):\n\n Hij = xyzrph2matrix (x_ij)\n Rji = Hij[0:3, 0:3]\n tij = Hij[0:3,3]\n Rij = Rji.transpose ()\n tji = -Rij.dot (tij)\n Hji = numpy.zeros ((4,4))\n Hji[0:3,0:3] = Rij\n Hji[0:3,3] = tji\n Hji[3,3] = 1\n return matrix2xyzrph (Hji)","def set_homog_trans_mtx(x: float, y: float, z: float, mtx: numpy.ndarray):\n mtx[0][3] = x\n mtx[1][3] = y\n mtx[2][3] = z","def matrix_rotate_3d_z(deg: float) -> np.matrix:\n from numpy import cos, sin, pi\n rad_z = -deg * pi/180\n c_z = cos(rad_z)\n s_z = sin(rad_z)\n return np.matrix([[c_z, -s_z, 0], [s_z, c_z, 0], [0, 0, 1]])","def get_bar_yz_transform(v, ihat, eid, xyz1, xyz2, nid1, nid2, i, Li):\n vhat = v / norm(v) # j\n try:\n z = np.cross(ihat, vhat) # k\n except ValueError:\n msg = 'Invalid vector length\\n'\n msg += 'xyz1=%s\\n' % str(xyz1)\n msg += 'xyz2=%s\\n' % str(xyz2)\n msg += 'nid1=%s\\n' % str(nid1)\n msg += 'nid2=%s\\n' % str(nid2)\n msg += 'i =%s\\n' % str(i)\n msg += 'Li =%s\\n' % str(Li)\n msg += 'ihat=%s\\n' % str(ihat)\n msg += 'v =%s\\n' % str(v)\n msg += 'vhat=%s\\n' % str(vhat)\n msg += 'z=cross(ihat, vhat)'\n print(msg)\n raise ValueError(msg)\n\n zhat = z / norm(z)\n yhat = np.cross(zhat, ihat) # j\n\n if norm(ihat) == 0.0 or norm(yhat) == 0.0 or norm(z) == 0.0:\n print(' invalid_orientation - eid=%s yhat=%s zhat=%s v=%s i=%s n%s=%s n%s=%s' % (\n eid, yhat, zhat, v, i, nid1, xyz1, nid2, xyz2))\n elif not np.allclose(norm(yhat), 1.0) or not np.allclose(norm(zhat), 1.0) or Li == 0.0:\n print(' length_error - eid=%s Li=%s Lyhat=%s Lzhat=%s'\n ' v=%s i=%s n%s=%s n%s=%s' % (\n eid, Li, norm(yhat), norm(zhat), v, i, nid1, xyz1, nid2, xyz2))\n return yhat, zhat","def H_XXZ(delta: float = 1, ud: float = 2., scale: float = 1.,\n backend: Optional[BackendType] = None,\n dtype: Optional[DtypeType] = None) -> tn.Tensor:\n U = sigU(backend=backend, dtype=dtype)\n D = sigD(backend=backend, dtype=dtype)\n Z = sigZ(backend=backend, dtype=dtype)\n UD = ud * (tn.kron(U, D) + tn.kron(D, U))\n return -(UD + delta * tn.kron(Z, Z))/(8 * scale)","def lherzolite():\n\n rho = 3270.\n\n C = np.zeros((6,6), dtype=float)\n C[0,0] = 187.4; C[0,1] = 63.71; C[0,2] = 63.87; C[0,3] = 0.78; C[0,4] = 2.02; C[0,5] = -3.2\n C[1,0] = C[0,1]; C[1,1] = 211.25; C[1,2] = 64.5; C[1,3] = -3.07; C[1,4] = 0.87; C[1,5] = -5.78\n C[2,0] = C[0,2]; C[2,1] = C[1,2]; C[2,2] = 190.; C[2,3] = 0.38; C[2,4] = 2.38; C[2,5] = -0.12\n C[3,0] = C[0,3]; C[3,1] = C[1,3]; C[3,2] = C[2,3]; C[3,3] = 67.9; C[3,4] = -2.12; C[3,5] = 1.6\n C[4,0] = C[0,4]; C[4,1] = C[1,4]; C[4,2] = C[2,4]; C[4,3] = C[3,4]; C[4,4] = 63.12; C[4,5] = -0.55\n C[5,0] = C[0,5]; C[5,1] = C[1,5]; C[5,2] = C[2,5]; C[5,3] = C[3,5]; C[5,4] = C[4,5]; C[5,5] = 66.83\n\n return C, rho","def z2u(self, z):\n return norm.cdf(dot(self.Sigma_h, z))","def m_0(Z0):\n return 35./( 1224. * cos(Z0)**2 + 1 )**0.5","def flip_z(self):\n if self.z is None:\n raise AttributeError('flip_z: no Z component')\n self.z *= -1.","def get_UBmat(self, i, j, hi, hj):\n h1c = (self.Bmat * vec(*hi)).T\n h2c = (self.Bmat * vec(*hj)).T\n\n t1c = norm_vec(h1c)\n t3c = norm_vec(np.cross(h1c, h2c))\n t2c = norm_vec(np.cross(h1c, t3c))\n Tc = np.concatenate((t1c, t2c, t3c)).T\n\n g1 = self.Gvec(self.xp[i], self.yp[i], self.zp[i]).T\n g2 = self.Gvec(self.xp[j], self.yp[j], self.zp[j]).T\n\n t1g = norm_vec(g1)\n t3g = norm_vec(np.cross(g1, g2))\n t2g = norm_vec(np.cross(g1, t3g))\n Tg = np.concatenate((t1g, t2g, t3g)).T\n\n return Tg * np.linalg.inv(Tc)","def Get_RawOutZ_Value(self):\r\n l = self.__readFromRegister(self.__REG_R_OUT_Z_L, 0xff)\r\n h_u2 = self.__readFromRegister(self.__REG_R_OUT_Z_H, 0xff)\r\n h = bitOps.TwosComplementToByte(h_u2)\r\n if (h < 0):\r\n return (h*256 - l) * self.gain\r\n elif (h >= 0):\r\n return (h*256 + l) * self.gain","def F_trans(self):\n rho_H1 = self.edp_par['rho_H1'].value\n Z_H1 = self.edp_par['Z_H1'].value\n sigma_H1 = self.edp_par['sigma_H1'].value\n rho_M = self.edp_par['rho_M'].value\n sigma_M = self.edp_par['sigma_M'].value\n psi = self.edp_par['psi'].value \n common_scale = self.edp_par['common_scale'].value\n \n # Calculate the intermediate variables\n alpha = self.qz*cos(psi) - self.qx*sin(psi)\n Z_CH2 = Z_H1 - sigma_H1\n Z_W = Z_H1 + sigma_H1\n DeltaZ_H = Z_W - Z_CH2\n \n # Calculate the Gaussian part \n FG = -rho_M*sigma_M * exp(-0.5*(alpha*sigma_M)**2)\n FG += 2*rho_H1*sigma_H1 * cos(alpha*Z_H1) * exp(-0.5*(alpha*sigma_H1)**2)\n FG *= np.sqrt(2*pi)\n \n # Calculate the strip part\n FS = -2 * sin(alpha*Z_CH2) / alpha\n \n # Calculate the bridging part\n FB = 1 / (alpha + pi/DeltaZ_H)\n FB += 1 / (alpha - pi/DeltaZ_H)\n FB *= sin(alpha*Z_W) + sin(alpha*Z_CH2)\n FB *= 0.5\n FB -= (sin(alpha*Z_W)-sin(alpha*Z_CH2)) / alpha\n \n return common_scale * (FG + FS + FB)","def userToPlotZ(z): \n return dislin.nzposn(z)","def rotation3D_z(angle: float) -> np.array:\n c = np.cos(angle)\n s = np.sin(angle)\n return np.array([[c, -s, 0.0], [s, c, 0.0], [0.0, 0.0, 1.0]])","def alphahighz(self, z):\n return self.alphaMe(3.8,self.r_vect[0],self.alpha0_vect[0]) - 0.018*(z-3.8)","def z_eq(self):\n theta = self.T_cmb/2.7\n return 25000.*self.Omega_m*self.h**2.*theta**-4.","def z(self, z_position):\n print(f\"z: {z_position}\")\n self.device_control.z = z_position \n yield","def z_rotmat(theta):\n cos_t = np.cos(theta)\n sin_t = np.sin(theta)\n return np.array([[cos_t, -sin_t, 0],\n [sin_t, cos_t, 0],\n [0, 0, 1]])","def EncodeMorton3D(x, y, z):\r\n return Expand3D(x) + (Expand3D(y) << 1) + (Expand3D(z) << 2)","def __get_z__(self):\n return self.Direction['z']","def _represent_ZGate(self, basis, **options):\n _format = options.get('format', 'sympy')\n n = 1\n definite_state = 0\n for it in reversed(self.qubit_values):\n definite_state += n*it\n n = n*2\n result = [0]*(2**self.dimension)\n result[int(definite_state)] = 1\n if _format == 'sympy':\n return Matrix(result)\n elif _format == 'numpy':\n import numpy as np\n return np.array(result, dtype='complex').transpose()\n elif _format == 'scipy.sparse':\n from scipy import sparse\n return sparse.csr_matrix(result, dtype='complex').transpose()","def Rz(q):\n sin_q, cos_q = sin(q), cos(q)\n return numpy.matrix([\n [cos_q, sin_q, 0, 0],\n [-sin_q, cos_q, 0, 0],\n [0, 0, 1, 0],\n [0, 0, 0, 1],\n ])","def z(self):\n return self._translation[2, 0]","def get_shear_matrix3d(\n center: torch.Tensor,\n sxy: Optional[torch.Tensor] = None,\n sxz: Optional[torch.Tensor] = None,\n syx: Optional[torch.Tensor] = None,\n syz: Optional[torch.Tensor] = None,\n szx: Optional[torch.Tensor] = None,\n szy: Optional[torch.Tensor] = None,\n):\n sxy = torch.tensor([0.0]).repeat(center.size(0)) if sxy is None else sxy\n sxz = torch.tensor([0.0]).repeat(center.size(0)) if sxz is None else sxz\n syx = torch.tensor([0.0]).repeat(center.size(0)) if syx is None else syx\n syz = torch.tensor([0.0]).repeat(center.size(0)) if syz is None else syz\n szx = torch.tensor([0.0]).repeat(center.size(0)) if szx is None else szx\n szy = torch.tensor([0.0]).repeat(center.size(0)) if szy is None else szy\n\n x, y, z = torch.split(center, 1, dim=-1)\n x, y, z = x.view(-1), y.view(-1), z.view(-1)\n # Prepare parameters\n sxy_tan = torch.tan(sxy) # type: ignore\n sxz_tan = torch.tan(sxz) # type: ignore\n syx_tan = torch.tan(syx) # type: ignore\n syz_tan = torch.tan(syz) # type: ignore\n szx_tan = torch.tan(szx) # type: ignore\n szy_tan = torch.tan(szy) # type: ignore\n\n # compute translation matrix\n m00, m10, m20, m01, m11, m21, m02, m12, m22 = _compute_shear_matrix_3d(\n sxy_tan, sxz_tan, syx_tan, syz_tan, szx_tan, szy_tan\n )\n\n m03 = m01 * y + m02 * z\n m13 = m10 * x + m11 * y + m12 * z - y\n m23 = m20 * x + m21 * y + m22 * z - z\n\n # shear matrix is implemented with negative values\n sxy_tan, sxz_tan, syx_tan, syz_tan, szx_tan, szy_tan = -sxy_tan, -sxz_tan, -syx_tan, -syz_tan, -szx_tan, -szy_tan\n m00, m10, m20, m01, m11, m21, m02, m12, m22 = _compute_shear_matrix_3d(\n sxy_tan, sxz_tan, syx_tan, syz_tan, szx_tan, szy_tan\n )\n\n shear_mat = torch.stack([m00, m01, m02, m03, m10, m11, m12, m13, m20, m21, m22, m23], dim=-1).view(-1, 3, 4)\n shear_mat = convert_affinematrix_to_homography3d(shear_mat)\n\n return shear_mat","def get_shear_matrix3d(\n center: Tensor,\n sxy: Tensor | None = None,\n sxz: Tensor | None = None,\n syx: Tensor | None = None,\n syz: Tensor | None = None,\n szx: Tensor | None = None,\n szy: Tensor | None = None,\n) -> Tensor:\n sxy = tensor([0.0]).repeat(center.size(0)) if sxy is None else sxy\n sxz = tensor([0.0]).repeat(center.size(0)) if sxz is None else sxz\n syx = tensor([0.0]).repeat(center.size(0)) if syx is None else syx\n syz = tensor([0.0]).repeat(center.size(0)) if syz is None else syz\n szx = tensor([0.0]).repeat(center.size(0)) if szx is None else szx\n szy = tensor([0.0]).repeat(center.size(0)) if szy is None else szy\n\n x, y, z = torch.split(center, 1, dim=-1)\n x, y, z = x.view(-1), y.view(-1), z.view(-1)\n # Prepare parameters\n sxy_tan = torch.tan(sxy)\n sxz_tan = torch.tan(sxz)\n syx_tan = torch.tan(syx)\n syz_tan = torch.tan(syz)\n szx_tan = torch.tan(szx)\n szy_tan = torch.tan(szy)\n\n # compute translation matrix\n m00, m10, m20, m01, m11, m21, m02, m12, m22 = _compute_shear_matrix_3d(\n sxy_tan, sxz_tan, syx_tan, syz_tan, szx_tan, szy_tan\n )\n\n m03 = m01 * y + m02 * z\n m13 = m10 * x + m11 * y + m12 * z - y\n m23 = m20 * x + m21 * y + m22 * z - z\n\n # shear matrix is implemented with negative values\n sxy_tan, sxz_tan, syx_tan, syz_tan, szx_tan, szy_tan = -sxy_tan, -sxz_tan, -syx_tan, -syz_tan, -szx_tan, -szy_tan\n m00, m10, m20, m01, m11, m21, m02, m12, m22 = _compute_shear_matrix_3d(\n sxy_tan, sxz_tan, syx_tan, syz_tan, szx_tan, szy_tan\n )\n\n shear_mat = stack([m00, m01, m02, m03, m10, m11, m12, m13, m20, m21, m22, m23], -1).view(-1, 3, 4)\n shear_mat = convert_affinematrix_to_homography3d(shear_mat)\n\n return shear_mat","def hypercubeToSimplex(z, r):\n z_sorted = np.concatenate(([0], np.sort(z), [1]))\n bary_coords = np.diff(z_sorted)\n return np.dot(r, bary_coords)","def TranslationMatrix(x, y, z):\n\treturn np.matrix([ [1., 0., 0., x],\n\t\t\t\t\t\t[0., 1., 0., y],\n\t\t\t\t\t\t[0., 0., 1., z],\n\t\t\t\t\t\t[0., 0., 0., 1.]])","def transl(x, y, z):\n displace_vector = [[x],\n [y],\n [z]]\n return np.matrix(displace_vector)","def F_trans(self):\n rho_H1 = self.edp_par['rho_H1'].value\n Z_H1 = self.edp_par['Z_H1'].value\n sigma_H1 = self.edp_par['sigma_H1'].value\n rho_H2 = self.edp_par['rho_H2'].value\n Z_H2 = self.edp_par['Z_H2'].value\n sigma_H2 = self.edp_par['sigma_H2'].value\n rho_M = self.edp_par['rho_M'].value\n sigma_M = self.edp_par['sigma_M'].value\n psi = self.edp_par['psi'].value \n common_scale = self.edp_par['common_scale'].value\n \n \n # Make sure Z_H2 > Z_H1. If Z_H2 < Z_H1, swap them\n if Z_H1 > Z_H2:\n Z_H1, Z_H2 = Z_H2, Z_H1\n sigma_H1, sigma_H2 = sigma_H2, sigma_H1\n rho_H1, rho_H2 = rho_H2, rho_H1\n \n # Calculate the intermediate variables\n alpha = self.qz*cos(psi) - self.qx*sin(psi)\n Z_CH2 = Z_H1 - sigma_H1\n Z_W = Z_H2 + sigma_H2\n DeltaZ_H = Z_W - Z_CH2\n \n # Calculate the Gaussian part \n FG = -rho_M*sigma_M * exp(-0.5*(alpha*sigma_M)**2)\n FG += 2*rho_H1*sigma_H1 * cos(alpha*Z_H1) * exp(-0.5*(alpha*sigma_H1)**2)\n FG += 2*rho_H2*sigma_H2 * cos(alpha*Z_H2) * exp(-0.5*(alpha*sigma_H2)**2)\n FG *= np.sqrt(2*pi)\n \n # Calculate the strip part\n FS = -2 * sin(alpha*Z_CH2) / alpha\n \n # Calculate the bridging part\n FB = 1 / (alpha + pi/DeltaZ_H)\n FB += 1 / (alpha - pi/DeltaZ_H)\n FB *= sin(alpha*Z_W) + sin(alpha*Z_CH2)\n FB *= 0.5\n FB -= (sin(alpha*Z_W)-sin(alpha*Z_CH2)) / alpha\n \n return common_scale * (FG + FS + FB)","def vec_z(self):\t\t\t\r\n if self.oz != 0:\r\n ov = self.oz\r\n lv = self.self.lz + self.oz\r\n else:\r\n ov = self.dz / 2\r\n lv = self.lz\r\n\r\n zv = \"\"\r\n for num in np.arange(ov, lv, self.dz):\r\n zv += str(num) + \" \"\r\n\r\n return zv","def Z_high(C, R_L, f):\n return Xcap(C,f) + R_L","def invertMatrixZN(M, N):\n n = M.shape[0] # shape = (nzeilen, nspalten), also shape[0] = nzeilen\n M = M.copy() # nicht an der Originalmatrix rumspielen\n I = np.identity(n, int) # Einheitsmatrix -> wird später das Ergebnis\n for row in range(n):\n if not invertierbar(M[row, row], N):\n # müssen Zeilen tauschen\n for j in range(row+1, n):\n if invertierbar(M[j, row], N):\n tmp = M[row, :].copy()\n M[row, :] = M[j, :]\n M[j, :] = tmp\n tmp = I[row, :].copy()\n I[row, :] = I[j, :]\n I[j, :] = tmp\n break\n else:\n # hier kommen wir hin wenn die for-Schleife nicht durch ein\n # break beendet wurde, also keine geeignete Zeile zum Tauschen\n # existiert\n raise ValueError(\"Matrix nicht invertierbar\")\n # Zeile mit dem Inversen des Pivot-Elements multiplizieren, um eine 1\n # auf der Diagonalen zu erreichen\n faktor = invertZN(M[row, row], N)\n M[row, :] = (M[row, :] * faktor) % N\n I[row, :] = (I[row, :] * faktor) % N\n \n # Nullen unterhalb des aktuellen Pivots erzeugen\n for j in range(row + 1, n):\n if invertierbar(M[j, row], N):\n faktor = invertZN(M[j, row], N)\n M[j, :] = (M[j, :] * faktor - M[row, :]) % N\n I[j, :] = (I[j, :] * faktor - I[row, :]) % N\n elif M[j, row] != 0:\n # In Z_N können Nullteiler auftreten, z.B. die 8 in Z_{12}.\n # Um dort eine 0 zu erzeugen, müssen wir mit dem kgV der beiden\n # Zahlen multiplizieren. Da ggt*kgv = mn gilt, können wir dazu\n # den bereits implementierten ggt-Algorithmus nehmen.\n faktor = N * M[j, row] // krypto1.ggT(N, M[j, row])\n M[j, :] = (M[j, :] * faktor) % N\n I[j, :] = (I[j, :] * faktor) % N\n # jetzt haben wir eine obere Dreiecksmatrix. Um daraus eine Diagonalmatrix\n # zu machen, müssen wir nun noch einmal von unten nach oben durchgehen\n # um die Einträge oberhalb der Diagonalen zu Nullen zu machen.\n for row in range(n-1, -1, -1):\n for j in range(row + 1, n):\n faktor = M[row, j]\n M[row, :] = (M[row, :] - faktor*M[j, :]) % N\n I[row, :] = (I[row, :] - faktor*I[j, :]) % N\n return I","def f_o(self, z):\n\t return exp(-(z/self.MU)**self.CIRC_3)","def f_o(self, z):\n\t return exp(-(z/self.MU)**self.CIRC_3)","def PCA_ZCA_whiten_transform(X, symmetric = True):\n X = np.asarray(X, dtype='float32')\n #center\n X = X - np.mean(X, axis=1).reshape((-1,1))\n C = np.dot(X.T, X) / X.shape[0]\n EigVal, EigVec = np.linalg.eigh(C)\n mval = np.max(np.real(EigVal))\n max_ratio = 1e4\n tol = mval / max_ratio\n #ngd = np.nonzero(np.real(EigVal) > mval/max_ratio)\n if symmetric:\n ngd = np.nonzero(np.real(EigVal) > tol)[0]\n P = (np.real(EigVal[ngd])**(-0.5)).reshape((-1,1)) * EigVec[:, ngd].T # [reduced dim]*[dim]\n P = np.dot(EigVec[:, ngd], P)\n else:\n EigVal[EigVal <= tol] = 1.\n P = EigVal.reshape((-1,1)) * (EigVec.T)\n X = np.dot(X, P.T)\n return X, P.T","def _vzlerchphi(self, z: np.ndarray, a: int) -> np.ndarray:\n return np.array([self._zlerchphi(z_, a) for z_ in z])","def z(self):\n return self._z","def z(self):\n return self._z","def z(self):\n return self._z","def __Us__(self, z):\r\n return pos(z - self.psi)","def complex_inverse(c1,cr):","def calc_jacobi_matrix(mesh, Z):\r\n A = lil_matrix((mesh.ntotal, mesh.ntotal))\r\n i, j, k = 0, 0, 0\r\n for i in range(1, mesh.nz-1):\r\n for j in range(1, mesh.ny-1):\r\n for k in range(1, mesh.nx-1):\r\n p = coordinate(i, j, k, mesh)\r\n\r\n if Z[i, j, k] != 0: #0の時はe-10などで近似して、なんとかする\r\n A[p.p, p.p] = 1.0\r\n\r\n else:\r\n A[p.p, p.ip1] = 1/6\r\n A[p.p, p.im1] = 1/6\r\n A[p.p, p.jp1] = 1/6\r\n A[p.p, p.jm1] = 1/6\r\n A[p.p, p.kp1] = 1/6\r\n A[p.p, p.km1] = 1/6\r\n\r\n return A.tocsc()","def spher_harm_bas(Xc,Yc,Zc,X,Y,Z,Order):\n import math as mt\n from scipy.special import lpmv\n # Construct variables from axes; no meshgrid as of 6/4/14; no potential as of 6/12/14\n nx,ny,nz=X.shape[0],Y.shape[0],Z.shape[0]\n x,y,z = np.zeros((nx,ny,nz)),np.zeros((nx,ny,nz)),np.zeros((nx,ny,nz))\n for i in range(nx):\n for j in range(ny):\n for k in range(nz):\n x[i,j,k] = X[i]-Xc\n y[i,j,k] = Y[j]-Yc\n z[i,j,k] = Z[k]-Zc\n x,y,z=np.ravel(x,order='F'),np.ravel(y,order='F'),np.ravel(z,order='F') \n r,rt=np.sqrt(x*x+y*y+z*z),np.sqrt(x*x+y*y)\n # Normalize with geometric mean, 3/15/14 (most recently); makes error go down about order of magnitude\n rsort=np.sort(r)\n rmin=rsort[1] # first element is 0 \n rmax=rsort[len(r)-1] \n rnorm=np.sqrt(rmax*rmin)\n r=r/rnorm\n # Construct theta and phi\n theta,phi=np.zeros(len(r)),np.zeros(len(r))\n for i in range(len(z)): #Set theta and phi to be correct. 10/19/13\n theta[i] = mt.atan2(rt[i],z[i])\n phi[i] = mt.atan2(y[i],x[i])\n # Make the spherical harmonic matrix in sequence of [Y00,Y-11,Y01,Y11,Y-22,Y-12,Y02,Y12,Y22...]\n # In other words: Calculate the basis vectors of the sph. harm. expansion: \n Yj = np.zeros((nx*ny*nz,(Order+1)**2))\n fp = np.sqrt(1/(4*np.pi))\n Yj[:,0] = fp*np.sqrt(2)\n mc = 1\n for n in range(1,Order+1):\n for m in range(n+1):\n #ymn = np.sqrt((2*n+1)/(4*np.pi))*r**n*lpmv(m,n,np.cos(theta))\n ymn = r**n*lpmv(m,n,np.cos(theta))\n ymn = fp*ymn*np.sqrt((2*n+1))#/(4*np.pi))\n if m==0:\n #Yj[:,mc] = ymn\n Yj[:,mc+n] = ymn\n # better ordering means we need to change external mapping, as well as trap.configuration at start of expand_field\n else: # Nm is conversion factor to spherical harmonics, exclusing the sqrt(2*n+1/4pi) portion so that there is no coefficient to the m=0\n N1 = float(mt.factorial(n-m))\n N2 = float(mt.factorial(n+m))\n Nm = (-1)**m*np.sqrt(2*N1/N2) \n psin = Nm*ymn*np.sin(m*phi)\n pcos = Nm*ymn*np.cos(m*phi)\n #Yj[:,mc+1+2*(m-1)] = pcos\n #Yj[:,mc+2+2*(m-1)] = psin\n Yj[:,mc+n+m] = pcos\n Yj[:,mc+n-m] = psin\n mc += 2*n+1\n\n return Yj,rnorm","def ztrajs(self):\n Z = np.array(self.ztraj_).astype(np.float32)\n return [Z[:, i, :] for i in range(Z.shape[1])]","def hubble_z(self, z):\n H_0 = self.h * cc.H100_s\n _hubble_z = H_0 * self.e_z(z)\n return(_hubble_z)","def _etaZ(self,x):\n return self._eta_sfr_scaling(x,'Z_cool')+self._eta_sfr_scaling(x,'Z_hot')","def getInverseMatrix(self) -> CMatrix4:\n ...","def haurwitz(zenith):\n\n # GHI = 1098 * cos(z) * exp(-0.057 / cos(z))\n clearsky_ghi = 1098.0 * np.cos(np.radians(zenith)) * np.exp(-0.057 / np.cos(np.radians(zenith)))\n\n # remove negative values\n clearsky_ghi[clearsky_ghi < 0] = 0\n\n return clearsky_ghi","def rot_z(theta):\n theta_rad = np.radians(theta)\n rotation_matrix = [[np.cos(theta_rad), -np.sin(theta_rad), 0],\n [np.sin(theta_rad), np.cos(theta_rad), 0],\n [0, 0, 1]]\n return np.matrix(rotation_matrix)","def initZ(self):\n Z = s.empty((self.N,self.K))\n for n in range(self.N):\n for k in range(self.K):\n Z[n,k] = norm.rvs(loc=0, scale=1, size=1)\n return Z"],"string":"[\n \"def __call__(self,Zin):\\r\\n self.Z = np.zeros((self.Nc,))\\r\\n for ii in range(0,self.Nc):\\r\\n self.Z[ii] = np.dot(self.W[:,ii],Zin[self.ind[ii,:]])\\r\\n \\r\\n return self.Z\",\n \"def zoisite():\\n\\n rho = 3343.\\n\\n C = np.zeros((6,6), dtype=float)\\n C[0,0] = 279.8; C[0,1] = 94.7; C[0,2] = 88.7; C[0,3] = 0.; C[0,4] = 0.; C[0,5] = 0.\\n C[1,0] = C[0,1]; C[1,1] = 249.2; C[1,2] = 27.5; C[1,3] = 0.; C[1,4] = 0.; C[1,5] = 0.\\n C[2,0] = C[0,2]; C[2,1] = C[1,2]; C[2,2] = 209.4; C[2,3] = 0.; C[2,4] = 0.; C[2,5] = 0.\\n C[3,0] = C[0,3]; C[3,1] = C[1,3]; C[3,2] = C[2,3]; C[3,3] = 51.8; C[3,4] = 0.; C[3,5] = 0.\\n C[4,0] = C[0,4]; C[4,1] = C[1,4]; C[4,2] = C[2,4]; C[4,3] = C[3,4]; C[4,4] = 81.4; C[4,5] = 0.\\n C[5,0] = C[0,5]; C[5,1] = C[1,5]; C[5,2] = C[2,5]; C[5,3] = C[3,5]; C[5,4] = C[4,5]; C[5,5] = 66.3\\n\\n return C, rho\",\n \"def EulerZXZ2Mat(e):\\n x=e[0]\\n y=e[1]\\n z=e[2]\\n s1=np.sin(x)\\n s2=np.sin(y)\\n s3=np.sin(z)\\n c1=np.cos(x)\\n c2=np.cos(y)\\n c3=np.cos(z)\\n m=np.array([[c1*c3-c2*s1*s3,-c1*s3-c3*c2*s1,s1*s2],\\n [s1*c3+c2*c1*s3,c1*c2*c3-s1*s3,-c1*s2],\\n [s3*s2,s2*c3,c2]])\\n return m\",\n \"def rotateZ(self, *args, **kwargs):\\n ...\",\n \"def get_shower_trans_matrix (azimuth,altitude):\\n\\n cos_z = sin(altitude)\\n sin_z = cos(altitude)\\n cos_az = cos(azimuth)\\n sin_az = sin(azimuth)\\n\\n trans = np.zeros([3,3])\\n trans[0][0] = cos_z*cos_az\\n trans[1][0] = sin_az\\n trans[2][0] = sin_z*cos_az\\n\\n trans[0][1] = -cos_z*sin_az\\n trans[1][1] = cos_az\\n trans[2][1] = -sin_z*sin_az\\n\\n trans[0][2] = -sin_z\\n trans[1][2] = 0.\\n trans[2][2] = cos_z\\n\\n return trans\",\n \"def homog_trans_mtx(x: float, y: float, z: float) -> numpy.array:\\n return numpy.array(\\n ((1, 0, 0, x), (0, 1, 0, y), (0, 0, 1, z), (0, 0, 0, 1)), dtype=numpy.float64\\n )\",\n \"def _derZ(self, w, x, y, z):\\n raise NotImplementedError()\",\n \"def z_transform(h, z, n=None):\\n # Checking input params\\n n = discrete.check_n(h, n, 'z_transform')\\n # Construct array of z^-n where each row corresponds to a z value and each column corresponds to a n value\\n A = np.fromfunction(lambda i, j: z[i.astype(int)]**(-n[j.astype(int)]), shape=(len(z), len(n)))\\n # Calculate sum over all n for each z\\n return np.dot(A, h)\",\n \"def rotateZUp(self):\\n MV = self.MV\\n MV[:3, 1] = 0, 0, 1 # 2nd col is up vector, make it point along z axis\\n # set bottom left and bottom right z values to zero:\\n MV[2, 0] = 0\\n MV[2, 2] = 0\\n a = MV[0, 0] # grab top left value\\n b = np.sqrt(1 - a**2) # calc new complementary value to get normalized vectors\\n if MV[1, 0] < 0:\\n b = -b # keep b -ve, reduce jumping around of axes\\n MV[1, 0] = b\\n MV[0, 2] = b\\n MV[1, 2] = -a # needs to be -ve of MV[0, 0]\\n self.MV = MV\",\n \"def rotateZUp(self):\\n MV = self.MV\\n MV[:3, 1] = 0, 0, 1 # 2nd col is up vector, make it point along z axis\\n # set bottom left and bottom right z values to zero:\\n MV[2, 0] = 0\\n MV[2, 2] = 0\\n a = MV[0, 0] # grab top left value\\n b = np.sqrt(1 - a**2) # calc new complementary value to get normalized vectors\\n if MV[1, 0] < 0:\\n b = -b # keep b -ve, reduce jumping around of axes\\n MV[1, 0] = b\\n MV[0, 2] = b\\n MV[1, 2] = -a # needs to be -ve of MV[0, 0]\\n self.MV = MV\",\n \"def H(self, z):\\n return self.H0 * self.E(z)\",\n \"def homogeneous_Z( zTheta , pos ):\\r\\n return np.vstack( ( np.hstack( ( z_rot( zTheta ) , [ [ pos[0] ] , [ pos[1] ] , [ pos[2] ] ] ) ) ,\\r\\n np.hstack( ( [ 0 , 0 , 0 ] , [ 1 ] ) ) ) )\",\n \"def Z_hipass(C, L, R_L, f):\\n return 1/(1/Xind(L,f) + 1/Z_high(C, R_L, f))\",\n \"def world_zaxis(self):\\n return observable.MJCFFeature('xmat', self._entity.root_body)[6:]\",\n \"def inverse_cayley_transform(z: torch.Tensor) -> torch.Tensor:\\n identity = identity_like(z)\\n i_identity = multiply_by_i(identity)\\n\\n z_minus_id = z - i_identity\\n inv_z_plus_id = inverse(z + i_identity)\\n return z_minus_id @ inv_z_plus_id\",\n \"def Z(phi = None):\\n if phi == None:\\n return sz\\n else:\\n return scipy.linalg.expm(-1j * phi / 2 * sz)\",\n \"def cPofZ(self,arr,zx):\\n sig1=(zx-arr['zmg'])/arr['sigma_pz']\\n sig2=(zx-self.zcat)/self.sigmacat\\n return (1.-self.Acat)*0.5*(1 + erf(sig1/np.sqrt(2)))+self.Acat*0.5*(1 + erf(sig2/np.sqrt(2)))\",\n \"def get_lz(self):\\r\\n return self.dz * self.nz - self.oz\",\n \"def mXZ(nxz,P_dot_Dj,P_dot_ej):\\n return np.divide(np.multiply(P_dot_ej, np.sum(nxz, axis=0)), P_dot_Dj)\",\n \"def _derZ(self, x, y, z):\\n raise NotImplementedError()\",\n \"def ssc.inverse (x_ij):\\n\\n Hij = xyzrph2matrix (x_ij)\\n Rji = Hij[0:3, 0:3]\\n tij = Hij[0:3,3]\\n Rij = Rji.transpose ()\\n tji = -Rij.dot (tij)\\n Hji = zeros ((4,4))\\n Hji[0:3,0:3] = Rij\\n Hji[0:3,3] = tji\\n Hji[3,3] = 1\\n return matrix2xyzrph (Hji)\",\n \"def u2z(self, u):\\n N = norm.ppf(u)\\n Z = solve(self.Sigma_h, N)\\n\\n return Z\",\n \"def NNZ(self):\\n return _hypre.HypreParMatrix_NNZ(self)\",\n \"def extrapToZ(zc,(x0,y0,z0),(px,py,pz)):\\n x = x0+ (px/pz)*(zc-z0)\\n y = y0+ (py/pz)*(zc-z0)\\n\\n return (x,y)\",\n \"def z_rotation(self):\\n before = ('R', 'r', 'U', 'u', 'L', 'l', 'D', 'd', 'M', 'E', 'x', 'y')\\n after = ('U', 'u', 'L', 'l', 'D', 'd', 'R', 'r', 'E', 'M\\\\'', \\\"y\\\", \\\"x'\\\")\\n solve = self.solve_helper.maketrans(dict(zip(before, after)))\\n solve_trans = self.solve_helper.translate(solve)\\n solve_trans = solve_trans.replace(\\\"\\\\'\\\\'\\\", \\\"\\\")\\n self.solve_helper = solve_trans\",\n \"def dct(self, z, out=None):\\n z = np.asfarray(z)\\n out = np.multiply(z, z, out)\\n out *= -0.5\\n np.exp(out, out)\\n return out\",\n \"def M_z(self) -> int:\\n return self.params.M_z\",\n \"def inverse_fisher_z_transform(z):\\r\\n return ((e ** (2 * z)) - 1.) / ((e ** (2 * z)) + 1.)\",\n \"def zconv2(x, h, S):\\n M, N = int(x.shape[0]), int(x.shape[1])\\n P, Q = int(h.shape[0]), int(h.shape[1])\\n\\n M0 = int(S[0, 0])\\n M1 = int(S[0, 1])\\n M2 = int(S[1, 0])\\n M3 = int(S[1, 1])\\n\\n NewP = ((M0-1)*(P-1))+M2*(Q-1) + P-1\\n NewQ = ((M3-1)*(Q-1))+M1*(P-1) + Q-1\\n\\n y = np.zeros((M, N))\\n\\n ssum = 0\\n Start1 = NewP // 2\\n Start2 = NewQ // 2\\n mn1 = Start1 % M\\n mn2 = Start2 % N\\n mn2_save = mn2\\n\\n for n1 in range(M):\\n for n2 in range(N):\\n outindexx = mn1\\n outindexy = mn2\\n\\n for l1 in range(P):\\n indexx = outindexx\\n indexy = outindexy\\n for l2 in range(Q):\\n ssum += x[indexx, indexy] * h[l1, l2]\\n indexx = indexx - M2\\n\\n if indexx < 0:\\n indexx = indexx + M\\n if indexx > M-1:\\n indexx = indexx - M\\n indexy = indexy - M3\\n if indexy < 0:\\n indexy = indexy + N\\n\\n outindexx = outindexx - M0\\n if outindexx < 0:\\n outindexx = outindexx + M\\n\\n outindexy = outindexy - M1\\n if outindexy < 0:\\n outindexy = outindexy + N\\n if outindexy > N-1:\\n outindexy = outindexy - N\\n\\n y[n1, n2] = ssum\\n ssum = 0\\n mn2 = mn2 + 1\\n\\n if mn2 > N-1:\\n mn2 = mn2 - N\\n\\n mn2 = mn2_save\\n mn1 = mn1 + 1\\n if mn1 > M-1:\\n mn1 = mn1 - M\\n\\n return y\",\n \"def harzburgite():\\n\\n rho = 3200.\\n\\n C = np.zeros((6,6), dtype=float)\\n C[0,0] = 226.5; C[0,1] = 75.34; C[0,2] = 74.73; C[0,3] = -0.27; C[0,4] = -2.00; C[0,5] = 1.85\\n C[1,0] = C[0,1]; C[1,1] = 242.8; C[1,2] = 73.68; C[1,3] = -3.6; C[1,4] = -1.91; C[1,5] = 4.14\\n C[2,0] = C[0,2]; C[2,1] = C[1,2]; C[2,2] = 230.; C[2,3] = -4.36; C[2,4] = -4.27; C[2,5] = -0.27\\n C[3,0] = C[0,3]; C[3,1] = C[1,3]; C[3,2] = C[2,3]; C[3,3] = 80.75; C[3,4] = 1.81; C[3,5] = -2.19\\n C[4,0] = C[0,4]; C[4,1] = C[1,4]; C[4,2] = C[2,4]; C[4,3] = C[3,4]; C[4,4] = 76.94; C[4,5] = -1.88\\n C[5,0] = C[0,5]; C[5,1] = C[1,5]; C[5,2] = C[2,5]; C[5,3] = C[3,5]; C[5,4] = C[4,5]; C[5,5] = 79.15\\n\\n return C, rho\",\n \"def set_lz(self):\\r\\n self._lz = self.dz * self.nz - self.oz\",\n \"def matrix_translate_3d(tx: float, ty: float, tz: float) -> np.matrix:\\n return np.matrix([[1, 0, 0, tx], [0, 1, 0, ty], [0, 0, 1, tz], [0, 0, 0, 1]])\",\n \"def zenazi(scx_l, scx_b, scy_l, scy_b, scz_l, scz_b, src_l, src_b):\\n # Zenith is the distance from the optical axis (here z)\\n costheta = GreatCircle(scz_l,scz_b,src_l,src_b) \\n # Azimuth is the combination of the remaining two\\n cosx = GreatCircle(scx_l,scx_b,src_l,src_b)\\n cosy = GreatCircle(scy_l,scy_b,src_l,src_b)\\n \\n # check exceptions\\n # maybe not for vectorisation\\n \\\"\\\"\\\"\\n if costheta.size == 1:\\n if (costheta > 1.0):\\n costheta = 1.0\\n if (costheta < -1.0):\\n costheta = -1.0\\n else:\\n costheta[costheta > 1.0] = 1.0\\n costheta[costheta < -1.0] = -1.0\\n \\\"\\\"\\\"\\n # theta = zenith\\n theta = np.rad2deg(np.arccos(costheta))\\n # phi = azimuth\\n phi = np.rad2deg(np.arctan2(cosy,cosx)) # TS January 14: you sure about that? changed y and x\\n \\n # make azimuth going from 0 to 360 deg\\n if phi.size == 1:\\n if (phi < 0):\\n phi += 360\\n else:\\n phi[phi < 0] += 360\\n \\n return theta,phi\",\n \"def u2z(self, u):\\n raise NotImplementedError\",\n \"def _apply_cz(self, state, axes, **kwargs):\\n ndim = self._ndim(state)\\n sl_0 = _get_slice(0, axes[0], ndim)\\n sl_1 = _get_slice(1, axes[0], ndim)\\n\\n if axes[1] > axes[0]:\\n target_axes = [axes[1] - 1]\\n else:\\n target_axes = [axes[1]]\\n\\n state_z = self._apply_z(state[sl_1], axes=target_axes)\\n return self._stack([state[sl_0], state_z], axis=axes[0])\",\n \"def zca_whitening_matrix(X):\\n # Covariance matrix [column-wise variables]: Sigma = (X-mu)' * (X-mu) / N\\n sigma = np.cov(X, rowvar=True) # [M x M]\\n # Singular Value Decomposition. X = U * np.diag(S) * V\\n U,S,V = np.linalg.svd(sigma)\\n # U: [M x M] eigenvectors of sigma.\\n # S: [M x 1] eigenvalues of sigma.\\n # V: [M x M] transpose of U\\n # Whitening constant: prevents division by zero\\n epsilon = 1e-5\\n # ZCA Whitening matrix: U * Lambda * U'\\n ZCAMatrix = np.dot(U, np.dot(np.diag(1.0/np.sqrt(S + epsilon)), U.T)) # [M x M]\\n return ZCAMatrix\",\n \"def cayley_transform(z: torch.Tensor) -> torch.Tensor:\\n identity = identity_like(z)\\n\\n i_z_plus_id = multiply_by_i(identity + z)\\n inv_z_minus_id = inverse(identity - z)\\n\\n return i_z_plus_id @ inv_z_minus_id\",\n \"def rotate_z(self, angle):\\n angle *= np.pi / 180\\n return self.transform(np.matrix([[np.cos(angle), -np.sin(angle), 0],\\n [np.sin(angle), np.cos(angle), 0],\\n [0, 0, 1]]))\",\n \"def rotateXOut(self):\\n MV = self.MV\\n MV[:3, 2] = 1, 0, 0 # 3rd col is normal vector, make it point along x axis\\n # set top left and top middle values to zero:\\n MV[0, 0] = 0\\n MV[0, 1] = 0\\n b = MV[2, 0] # grab bottom left value\\n a = np.sqrt(1 - b**2) # calc new complementary value to get normalized vectors\\n #if MV[1, 0] < 0:\\n # a = -a # keep a -ve, reduce jumping around of axes\\n MV[1, 0] = a\\n MV[2, 1] = a\\n MV[1, 1] = -b # needs to be -ve of MV[2, 0]\\n self.MV = MV\",\n \"def rotateXOut(self):\\n MV = self.MV\\n MV[:3, 2] = 1, 0, 0 # 3rd col is normal vector, make it point along x axis\\n # set top left and top middle values to zero:\\n MV[0, 0] = 0\\n MV[0, 1] = 0\\n b = MV[2, 0] # grab bottom left value\\n a = np.sqrt(1 - b**2) # calc new complementary value to get normalized vectors\\n #if MV[1, 0] < 0:\\n # a = -a # keep a -ve, reduce jumping around of axes\\n MV[1, 0] = a\\n MV[2, 1] = a\\n MV[1, 1] = -b # needs to be -ve of MV[2, 0]\\n self.MV = MV\",\n \"def H(self) -> BaseMatrix:\",\n \"def H(self) -> BaseMatrix:\",\n \"def _apply_z(self, state, axes, **kwargs):\\n return self._apply_phase(state, axes, -1)\",\n \"def Mat2EulerZXZ(m):\\n threshold=0.9999999\\n if m[2,2]>threshold:\\n x=0\\n y=0\\n z=atan2(m[1,0],m[0,0])\\n elif m[2,2]< -threshold:\\n x=0\\n y=np.pi\\n z=atan2(m[0,1],m[0,0])\\n else:\\n x=atan2(m[0,2],-m[1,2])\\n y=atan2(np.sqrt(m[2,0]**2+m[2,1]**2),m[2,2])\\n z=atan2(m[2,0],m[2,1])\\n if x<0: x=x+2*np.pi\\n if y<0: y=y+2*np.pi\\n if z<0: z=z+2*np.pi\\n return x,y,z\",\n \"def get_hkl(self, x, y, z):\\n if self.Umat is not None:\\n v5 = self.Gvec(x, y, z)\\n# v6 = inv(self.Umat) * v5\\n# v7 = inv(self.Bmat) * v6\\n v7 = inv(self.UBmat) * v5\\n return list(np.array(v7.T)[0])\\n else:\\n return [0.0, 0.0, 0.0]\",\n \"def pz_fn(self, z):\\n pass\",\n \"def mxz(mXZ,P_dot_Dj,P_times_Dj):\\n return np.divide(np.multiply(P_times_Dj, mXZ), P_dot_Dj)\",\n \"def h(self, z):\\n # See definition at end of Section 1, p2 of Arnaud et al.\\n return np.sqrt(self.Om*(1.+z)**3. + self.Ol)\",\n \"def _Zmodel(self,vBz,sfr,ZISM):\\n yZcool = 1.0\\n expo = 1.7\\n\\n vmax = self.params['vej'].to('km/s').value\\n ZSN = self.params['ZSN']\\n Mej = self.params['Mej']\\n\\n Zmodel = np.clip((vBz/vmax)**expo*(ZSN-ZISM)+ZISM*yZcool,None,ZSN)\\n return Zmodel\",\n \"def z2u(self, z):\\n raise NotImplementedError\",\n \"def inverse (x_ij):\\n\\n Hij = xyzrph2matrix (x_ij)\\n Rji = Hij[0:3, 0:3]\\n tij = Hij[0:3,3]\\n Rij = Rji.transpose ()\\n tji = -Rij.dot (tij)\\n Hji = numpy.zeros ((4,4))\\n Hji[0:3,0:3] = Rij\\n Hji[0:3,3] = tji\\n Hji[3,3] = 1\\n return matrix2xyzrph (Hji)\",\n \"def set_homog_trans_mtx(x: float, y: float, z: float, mtx: numpy.ndarray):\\n mtx[0][3] = x\\n mtx[1][3] = y\\n mtx[2][3] = z\",\n \"def matrix_rotate_3d_z(deg: float) -> np.matrix:\\n from numpy import cos, sin, pi\\n rad_z = -deg * pi/180\\n c_z = cos(rad_z)\\n s_z = sin(rad_z)\\n return np.matrix([[c_z, -s_z, 0], [s_z, c_z, 0], [0, 0, 1]])\",\n \"def get_bar_yz_transform(v, ihat, eid, xyz1, xyz2, nid1, nid2, i, Li):\\n vhat = v / norm(v) # j\\n try:\\n z = np.cross(ihat, vhat) # k\\n except ValueError:\\n msg = 'Invalid vector length\\\\n'\\n msg += 'xyz1=%s\\\\n' % str(xyz1)\\n msg += 'xyz2=%s\\\\n' % str(xyz2)\\n msg += 'nid1=%s\\\\n' % str(nid1)\\n msg += 'nid2=%s\\\\n' % str(nid2)\\n msg += 'i =%s\\\\n' % str(i)\\n msg += 'Li =%s\\\\n' % str(Li)\\n msg += 'ihat=%s\\\\n' % str(ihat)\\n msg += 'v =%s\\\\n' % str(v)\\n msg += 'vhat=%s\\\\n' % str(vhat)\\n msg += 'z=cross(ihat, vhat)'\\n print(msg)\\n raise ValueError(msg)\\n\\n zhat = z / norm(z)\\n yhat = np.cross(zhat, ihat) # j\\n\\n if norm(ihat) == 0.0 or norm(yhat) == 0.0 or norm(z) == 0.0:\\n print(' invalid_orientation - eid=%s yhat=%s zhat=%s v=%s i=%s n%s=%s n%s=%s' % (\\n eid, yhat, zhat, v, i, nid1, xyz1, nid2, xyz2))\\n elif not np.allclose(norm(yhat), 1.0) or not np.allclose(norm(zhat), 1.0) or Li == 0.0:\\n print(' length_error - eid=%s Li=%s Lyhat=%s Lzhat=%s'\\n ' v=%s i=%s n%s=%s n%s=%s' % (\\n eid, Li, norm(yhat), norm(zhat), v, i, nid1, xyz1, nid2, xyz2))\\n return yhat, zhat\",\n \"def H_XXZ(delta: float = 1, ud: float = 2., scale: float = 1.,\\n backend: Optional[BackendType] = None,\\n dtype: Optional[DtypeType] = None) -> tn.Tensor:\\n U = sigU(backend=backend, dtype=dtype)\\n D = sigD(backend=backend, dtype=dtype)\\n Z = sigZ(backend=backend, dtype=dtype)\\n UD = ud * (tn.kron(U, D) + tn.kron(D, U))\\n return -(UD + delta * tn.kron(Z, Z))/(8 * scale)\",\n \"def lherzolite():\\n\\n rho = 3270.\\n\\n C = np.zeros((6,6), dtype=float)\\n C[0,0] = 187.4; C[0,1] = 63.71; C[0,2] = 63.87; C[0,3] = 0.78; C[0,4] = 2.02; C[0,5] = -3.2\\n C[1,0] = C[0,1]; C[1,1] = 211.25; C[1,2] = 64.5; C[1,3] = -3.07; C[1,4] = 0.87; C[1,5] = -5.78\\n C[2,0] = C[0,2]; C[2,1] = C[1,2]; C[2,2] = 190.; C[2,3] = 0.38; C[2,4] = 2.38; C[2,5] = -0.12\\n C[3,0] = C[0,3]; C[3,1] = C[1,3]; C[3,2] = C[2,3]; C[3,3] = 67.9; C[3,4] = -2.12; C[3,5] = 1.6\\n C[4,0] = C[0,4]; C[4,1] = C[1,4]; C[4,2] = C[2,4]; C[4,3] = C[3,4]; C[4,4] = 63.12; C[4,5] = -0.55\\n C[5,0] = C[0,5]; C[5,1] = C[1,5]; C[5,2] = C[2,5]; C[5,3] = C[3,5]; C[5,4] = C[4,5]; C[5,5] = 66.83\\n\\n return C, rho\",\n \"def z2u(self, z):\\n return norm.cdf(dot(self.Sigma_h, z))\",\n \"def m_0(Z0):\\n return 35./( 1224. * cos(Z0)**2 + 1 )**0.5\",\n \"def flip_z(self):\\n if self.z is None:\\n raise AttributeError('flip_z: no Z component')\\n self.z *= -1.\",\n \"def get_UBmat(self, i, j, hi, hj):\\n h1c = (self.Bmat * vec(*hi)).T\\n h2c = (self.Bmat * vec(*hj)).T\\n\\n t1c = norm_vec(h1c)\\n t3c = norm_vec(np.cross(h1c, h2c))\\n t2c = norm_vec(np.cross(h1c, t3c))\\n Tc = np.concatenate((t1c, t2c, t3c)).T\\n\\n g1 = self.Gvec(self.xp[i], self.yp[i], self.zp[i]).T\\n g2 = self.Gvec(self.xp[j], self.yp[j], self.zp[j]).T\\n\\n t1g = norm_vec(g1)\\n t3g = norm_vec(np.cross(g1, g2))\\n t2g = norm_vec(np.cross(g1, t3g))\\n Tg = np.concatenate((t1g, t2g, t3g)).T\\n\\n return Tg * np.linalg.inv(Tc)\",\n \"def Get_RawOutZ_Value(self):\\r\\n l = self.__readFromRegister(self.__REG_R_OUT_Z_L, 0xff)\\r\\n h_u2 = self.__readFromRegister(self.__REG_R_OUT_Z_H, 0xff)\\r\\n h = bitOps.TwosComplementToByte(h_u2)\\r\\n if (h < 0):\\r\\n return (h*256 - l) * self.gain\\r\\n elif (h >= 0):\\r\\n return (h*256 + l) * self.gain\",\n \"def F_trans(self):\\n rho_H1 = self.edp_par['rho_H1'].value\\n Z_H1 = self.edp_par['Z_H1'].value\\n sigma_H1 = self.edp_par['sigma_H1'].value\\n rho_M = self.edp_par['rho_M'].value\\n sigma_M = self.edp_par['sigma_M'].value\\n psi = self.edp_par['psi'].value \\n common_scale = self.edp_par['common_scale'].value\\n \\n # Calculate the intermediate variables\\n alpha = self.qz*cos(psi) - self.qx*sin(psi)\\n Z_CH2 = Z_H1 - sigma_H1\\n Z_W = Z_H1 + sigma_H1\\n DeltaZ_H = Z_W - Z_CH2\\n \\n # Calculate the Gaussian part \\n FG = -rho_M*sigma_M * exp(-0.5*(alpha*sigma_M)**2)\\n FG += 2*rho_H1*sigma_H1 * cos(alpha*Z_H1) * exp(-0.5*(alpha*sigma_H1)**2)\\n FG *= np.sqrt(2*pi)\\n \\n # Calculate the strip part\\n FS = -2 * sin(alpha*Z_CH2) / alpha\\n \\n # Calculate the bridging part\\n FB = 1 / (alpha + pi/DeltaZ_H)\\n FB += 1 / (alpha - pi/DeltaZ_H)\\n FB *= sin(alpha*Z_W) + sin(alpha*Z_CH2)\\n FB *= 0.5\\n FB -= (sin(alpha*Z_W)-sin(alpha*Z_CH2)) / alpha\\n \\n return common_scale * (FG + FS + FB)\",\n \"def userToPlotZ(z): \\n return dislin.nzposn(z)\",\n \"def rotation3D_z(angle: float) -> np.array:\\n c = np.cos(angle)\\n s = np.sin(angle)\\n return np.array([[c, -s, 0.0], [s, c, 0.0], [0.0, 0.0, 1.0]])\",\n \"def alphahighz(self, z):\\n return self.alphaMe(3.8,self.r_vect[0],self.alpha0_vect[0]) - 0.018*(z-3.8)\",\n \"def z_eq(self):\\n theta = self.T_cmb/2.7\\n return 25000.*self.Omega_m*self.h**2.*theta**-4.\",\n \"def z(self, z_position):\\n print(f\\\"z: {z_position}\\\")\\n self.device_control.z = z_position \\n yield\",\n \"def z_rotmat(theta):\\n cos_t = np.cos(theta)\\n sin_t = np.sin(theta)\\n return np.array([[cos_t, -sin_t, 0],\\n [sin_t, cos_t, 0],\\n [0, 0, 1]])\",\n \"def EncodeMorton3D(x, y, z):\\r\\n return Expand3D(x) + (Expand3D(y) << 1) + (Expand3D(z) << 2)\",\n \"def __get_z__(self):\\n return self.Direction['z']\",\n \"def _represent_ZGate(self, basis, **options):\\n _format = options.get('format', 'sympy')\\n n = 1\\n definite_state = 0\\n for it in reversed(self.qubit_values):\\n definite_state += n*it\\n n = n*2\\n result = [0]*(2**self.dimension)\\n result[int(definite_state)] = 1\\n if _format == 'sympy':\\n return Matrix(result)\\n elif _format == 'numpy':\\n import numpy as np\\n return np.array(result, dtype='complex').transpose()\\n elif _format == 'scipy.sparse':\\n from scipy import sparse\\n return sparse.csr_matrix(result, dtype='complex').transpose()\",\n \"def Rz(q):\\n sin_q, cos_q = sin(q), cos(q)\\n return numpy.matrix([\\n [cos_q, sin_q, 0, 0],\\n [-sin_q, cos_q, 0, 0],\\n [0, 0, 1, 0],\\n [0, 0, 0, 1],\\n ])\",\n \"def z(self):\\n return self._translation[2, 0]\",\n \"def get_shear_matrix3d(\\n center: torch.Tensor,\\n sxy: Optional[torch.Tensor] = None,\\n sxz: Optional[torch.Tensor] = None,\\n syx: Optional[torch.Tensor] = None,\\n syz: Optional[torch.Tensor] = None,\\n szx: Optional[torch.Tensor] = None,\\n szy: Optional[torch.Tensor] = None,\\n):\\n sxy = torch.tensor([0.0]).repeat(center.size(0)) if sxy is None else sxy\\n sxz = torch.tensor([0.0]).repeat(center.size(0)) if sxz is None else sxz\\n syx = torch.tensor([0.0]).repeat(center.size(0)) if syx is None else syx\\n syz = torch.tensor([0.0]).repeat(center.size(0)) if syz is None else syz\\n szx = torch.tensor([0.0]).repeat(center.size(0)) if szx is None else szx\\n szy = torch.tensor([0.0]).repeat(center.size(0)) if szy is None else szy\\n\\n x, y, z = torch.split(center, 1, dim=-1)\\n x, y, z = x.view(-1), y.view(-1), z.view(-1)\\n # Prepare parameters\\n sxy_tan = torch.tan(sxy) # type: ignore\\n sxz_tan = torch.tan(sxz) # type: ignore\\n syx_tan = torch.tan(syx) # type: ignore\\n syz_tan = torch.tan(syz) # type: ignore\\n szx_tan = torch.tan(szx) # type: ignore\\n szy_tan = torch.tan(szy) # type: ignore\\n\\n # compute translation matrix\\n m00, m10, m20, m01, m11, m21, m02, m12, m22 = _compute_shear_matrix_3d(\\n sxy_tan, sxz_tan, syx_tan, syz_tan, szx_tan, szy_tan\\n )\\n\\n m03 = m01 * y + m02 * z\\n m13 = m10 * x + m11 * y + m12 * z - y\\n m23 = m20 * x + m21 * y + m22 * z - z\\n\\n # shear matrix is implemented with negative values\\n sxy_tan, sxz_tan, syx_tan, syz_tan, szx_tan, szy_tan = -sxy_tan, -sxz_tan, -syx_tan, -syz_tan, -szx_tan, -szy_tan\\n m00, m10, m20, m01, m11, m21, m02, m12, m22 = _compute_shear_matrix_3d(\\n sxy_tan, sxz_tan, syx_tan, syz_tan, szx_tan, szy_tan\\n )\\n\\n shear_mat = torch.stack([m00, m01, m02, m03, m10, m11, m12, m13, m20, m21, m22, m23], dim=-1).view(-1, 3, 4)\\n shear_mat = convert_affinematrix_to_homography3d(shear_mat)\\n\\n return shear_mat\",\n \"def get_shear_matrix3d(\\n center: Tensor,\\n sxy: Tensor | None = None,\\n sxz: Tensor | None = None,\\n syx: Tensor | None = None,\\n syz: Tensor | None = None,\\n szx: Tensor | None = None,\\n szy: Tensor | None = None,\\n) -> Tensor:\\n sxy = tensor([0.0]).repeat(center.size(0)) if sxy is None else sxy\\n sxz = tensor([0.0]).repeat(center.size(0)) if sxz is None else sxz\\n syx = tensor([0.0]).repeat(center.size(0)) if syx is None else syx\\n syz = tensor([0.0]).repeat(center.size(0)) if syz is None else syz\\n szx = tensor([0.0]).repeat(center.size(0)) if szx is None else szx\\n szy = tensor([0.0]).repeat(center.size(0)) if szy is None else szy\\n\\n x, y, z = torch.split(center, 1, dim=-1)\\n x, y, z = x.view(-1), y.view(-1), z.view(-1)\\n # Prepare parameters\\n sxy_tan = torch.tan(sxy)\\n sxz_tan = torch.tan(sxz)\\n syx_tan = torch.tan(syx)\\n syz_tan = torch.tan(syz)\\n szx_tan = torch.tan(szx)\\n szy_tan = torch.tan(szy)\\n\\n # compute translation matrix\\n m00, m10, m20, m01, m11, m21, m02, m12, m22 = _compute_shear_matrix_3d(\\n sxy_tan, sxz_tan, syx_tan, syz_tan, szx_tan, szy_tan\\n )\\n\\n m03 = m01 * y + m02 * z\\n m13 = m10 * x + m11 * y + m12 * z - y\\n m23 = m20 * x + m21 * y + m22 * z - z\\n\\n # shear matrix is implemented with negative values\\n sxy_tan, sxz_tan, syx_tan, syz_tan, szx_tan, szy_tan = -sxy_tan, -sxz_tan, -syx_tan, -syz_tan, -szx_tan, -szy_tan\\n m00, m10, m20, m01, m11, m21, m02, m12, m22 = _compute_shear_matrix_3d(\\n sxy_tan, sxz_tan, syx_tan, syz_tan, szx_tan, szy_tan\\n )\\n\\n shear_mat = stack([m00, m01, m02, m03, m10, m11, m12, m13, m20, m21, m22, m23], -1).view(-1, 3, 4)\\n shear_mat = convert_affinematrix_to_homography3d(shear_mat)\\n\\n return shear_mat\",\n \"def hypercubeToSimplex(z, r):\\n z_sorted = np.concatenate(([0], np.sort(z), [1]))\\n bary_coords = np.diff(z_sorted)\\n return np.dot(r, bary_coords)\",\n \"def TranslationMatrix(x, y, z):\\n\\treturn np.matrix([ [1., 0., 0., x],\\n\\t\\t\\t\\t\\t\\t[0., 1., 0., y],\\n\\t\\t\\t\\t\\t\\t[0., 0., 1., z],\\n\\t\\t\\t\\t\\t\\t[0., 0., 0., 1.]])\",\n \"def transl(x, y, z):\\n displace_vector = [[x],\\n [y],\\n [z]]\\n return np.matrix(displace_vector)\",\n \"def F_trans(self):\\n rho_H1 = self.edp_par['rho_H1'].value\\n Z_H1 = self.edp_par['Z_H1'].value\\n sigma_H1 = self.edp_par['sigma_H1'].value\\n rho_H2 = self.edp_par['rho_H2'].value\\n Z_H2 = self.edp_par['Z_H2'].value\\n sigma_H2 = self.edp_par['sigma_H2'].value\\n rho_M = self.edp_par['rho_M'].value\\n sigma_M = self.edp_par['sigma_M'].value\\n psi = self.edp_par['psi'].value \\n common_scale = self.edp_par['common_scale'].value\\n \\n \\n # Make sure Z_H2 > Z_H1. If Z_H2 < Z_H1, swap them\\n if Z_H1 > Z_H2:\\n Z_H1, Z_H2 = Z_H2, Z_H1\\n sigma_H1, sigma_H2 = sigma_H2, sigma_H1\\n rho_H1, rho_H2 = rho_H2, rho_H1\\n \\n # Calculate the intermediate variables\\n alpha = self.qz*cos(psi) - self.qx*sin(psi)\\n Z_CH2 = Z_H1 - sigma_H1\\n Z_W = Z_H2 + sigma_H2\\n DeltaZ_H = Z_W - Z_CH2\\n \\n # Calculate the Gaussian part \\n FG = -rho_M*sigma_M * exp(-0.5*(alpha*sigma_M)**2)\\n FG += 2*rho_H1*sigma_H1 * cos(alpha*Z_H1) * exp(-0.5*(alpha*sigma_H1)**2)\\n FG += 2*rho_H2*sigma_H2 * cos(alpha*Z_H2) * exp(-0.5*(alpha*sigma_H2)**2)\\n FG *= np.sqrt(2*pi)\\n \\n # Calculate the strip part\\n FS = -2 * sin(alpha*Z_CH2) / alpha\\n \\n # Calculate the bridging part\\n FB = 1 / (alpha + pi/DeltaZ_H)\\n FB += 1 / (alpha - pi/DeltaZ_H)\\n FB *= sin(alpha*Z_W) + sin(alpha*Z_CH2)\\n FB *= 0.5\\n FB -= (sin(alpha*Z_W)-sin(alpha*Z_CH2)) / alpha\\n \\n return common_scale * (FG + FS + FB)\",\n \"def vec_z(self):\\t\\t\\t\\r\\n if self.oz != 0:\\r\\n ov = self.oz\\r\\n lv = self.self.lz + self.oz\\r\\n else:\\r\\n ov = self.dz / 2\\r\\n lv = self.lz\\r\\n\\r\\n zv = \\\"\\\"\\r\\n for num in np.arange(ov, lv, self.dz):\\r\\n zv += str(num) + \\\" \\\"\\r\\n\\r\\n return zv\",\n \"def Z_high(C, R_L, f):\\n return Xcap(C,f) + R_L\",\n \"def invertMatrixZN(M, N):\\n n = M.shape[0] # shape = (nzeilen, nspalten), also shape[0] = nzeilen\\n M = M.copy() # nicht an der Originalmatrix rumspielen\\n I = np.identity(n, int) # Einheitsmatrix -> wird später das Ergebnis\\n for row in range(n):\\n if not invertierbar(M[row, row], N):\\n # müssen Zeilen tauschen\\n for j in range(row+1, n):\\n if invertierbar(M[j, row], N):\\n tmp = M[row, :].copy()\\n M[row, :] = M[j, :]\\n M[j, :] = tmp\\n tmp = I[row, :].copy()\\n I[row, :] = I[j, :]\\n I[j, :] = tmp\\n break\\n else:\\n # hier kommen wir hin wenn die for-Schleife nicht durch ein\\n # break beendet wurde, also keine geeignete Zeile zum Tauschen\\n # existiert\\n raise ValueError(\\\"Matrix nicht invertierbar\\\")\\n # Zeile mit dem Inversen des Pivot-Elements multiplizieren, um eine 1\\n # auf der Diagonalen zu erreichen\\n faktor = invertZN(M[row, row], N)\\n M[row, :] = (M[row, :] * faktor) % N\\n I[row, :] = (I[row, :] * faktor) % N\\n \\n # Nullen unterhalb des aktuellen Pivots erzeugen\\n for j in range(row + 1, n):\\n if invertierbar(M[j, row], N):\\n faktor = invertZN(M[j, row], N)\\n M[j, :] = (M[j, :] * faktor - M[row, :]) % N\\n I[j, :] = (I[j, :] * faktor - I[row, :]) % N\\n elif M[j, row] != 0:\\n # In Z_N können Nullteiler auftreten, z.B. die 8 in Z_{12}.\\n # Um dort eine 0 zu erzeugen, müssen wir mit dem kgV der beiden\\n # Zahlen multiplizieren. Da ggt*kgv = mn gilt, können wir dazu\\n # den bereits implementierten ggt-Algorithmus nehmen.\\n faktor = N * M[j, row] // krypto1.ggT(N, M[j, row])\\n M[j, :] = (M[j, :] * faktor) % N\\n I[j, :] = (I[j, :] * faktor) % N\\n # jetzt haben wir eine obere Dreiecksmatrix. Um daraus eine Diagonalmatrix\\n # zu machen, müssen wir nun noch einmal von unten nach oben durchgehen\\n # um die Einträge oberhalb der Diagonalen zu Nullen zu machen.\\n for row in range(n-1, -1, -1):\\n for j in range(row + 1, n):\\n faktor = M[row, j]\\n M[row, :] = (M[row, :] - faktor*M[j, :]) % N\\n I[row, :] = (I[row, :] - faktor*I[j, :]) % N\\n return I\",\n \"def f_o(self, z):\\n\\t return exp(-(z/self.MU)**self.CIRC_3)\",\n \"def f_o(self, z):\\n\\t return exp(-(z/self.MU)**self.CIRC_3)\",\n \"def PCA_ZCA_whiten_transform(X, symmetric = True):\\n X = np.asarray(X, dtype='float32')\\n #center\\n X = X - np.mean(X, axis=1).reshape((-1,1))\\n C = np.dot(X.T, X) / X.shape[0]\\n EigVal, EigVec = np.linalg.eigh(C)\\n mval = np.max(np.real(EigVal))\\n max_ratio = 1e4\\n tol = mval / max_ratio\\n #ngd = np.nonzero(np.real(EigVal) > mval/max_ratio)\\n if symmetric:\\n ngd = np.nonzero(np.real(EigVal) > tol)[0]\\n P = (np.real(EigVal[ngd])**(-0.5)).reshape((-1,1)) * EigVec[:, ngd].T # [reduced dim]*[dim]\\n P = np.dot(EigVec[:, ngd], P)\\n else:\\n EigVal[EigVal <= tol] = 1.\\n P = EigVal.reshape((-1,1)) * (EigVec.T)\\n X = np.dot(X, P.T)\\n return X, P.T\",\n \"def _vzlerchphi(self, z: np.ndarray, a: int) -> np.ndarray:\\n return np.array([self._zlerchphi(z_, a) for z_ in z])\",\n \"def z(self):\\n return self._z\",\n \"def z(self):\\n return self._z\",\n \"def z(self):\\n return self._z\",\n \"def __Us__(self, z):\\r\\n return pos(z - self.psi)\",\n \"def complex_inverse(c1,cr):\",\n \"def calc_jacobi_matrix(mesh, Z):\\r\\n A = lil_matrix((mesh.ntotal, mesh.ntotal))\\r\\n i, j, k = 0, 0, 0\\r\\n for i in range(1, mesh.nz-1):\\r\\n for j in range(1, mesh.ny-1):\\r\\n for k in range(1, mesh.nx-1):\\r\\n p = coordinate(i, j, k, mesh)\\r\\n\\r\\n if Z[i, j, k] != 0: #0の時はe-10などで近似して、なんとかする\\r\\n A[p.p, p.p] = 1.0\\r\\n\\r\\n else:\\r\\n A[p.p, p.ip1] = 1/6\\r\\n A[p.p, p.im1] = 1/6\\r\\n A[p.p, p.jp1] = 1/6\\r\\n A[p.p, p.jm1] = 1/6\\r\\n A[p.p, p.kp1] = 1/6\\r\\n A[p.p, p.km1] = 1/6\\r\\n\\r\\n return A.tocsc()\",\n \"def spher_harm_bas(Xc,Yc,Zc,X,Y,Z,Order):\\n import math as mt\\n from scipy.special import lpmv\\n # Construct variables from axes; no meshgrid as of 6/4/14; no potential as of 6/12/14\\n nx,ny,nz=X.shape[0],Y.shape[0],Z.shape[0]\\n x,y,z = np.zeros((nx,ny,nz)),np.zeros((nx,ny,nz)),np.zeros((nx,ny,nz))\\n for i in range(nx):\\n for j in range(ny):\\n for k in range(nz):\\n x[i,j,k] = X[i]-Xc\\n y[i,j,k] = Y[j]-Yc\\n z[i,j,k] = Z[k]-Zc\\n x,y,z=np.ravel(x,order='F'),np.ravel(y,order='F'),np.ravel(z,order='F') \\n r,rt=np.sqrt(x*x+y*y+z*z),np.sqrt(x*x+y*y)\\n # Normalize with geometric mean, 3/15/14 (most recently); makes error go down about order of magnitude\\n rsort=np.sort(r)\\n rmin=rsort[1] # first element is 0 \\n rmax=rsort[len(r)-1] \\n rnorm=np.sqrt(rmax*rmin)\\n r=r/rnorm\\n # Construct theta and phi\\n theta,phi=np.zeros(len(r)),np.zeros(len(r))\\n for i in range(len(z)): #Set theta and phi to be correct. 10/19/13\\n theta[i] = mt.atan2(rt[i],z[i])\\n phi[i] = mt.atan2(y[i],x[i])\\n # Make the spherical harmonic matrix in sequence of [Y00,Y-11,Y01,Y11,Y-22,Y-12,Y02,Y12,Y22...]\\n # In other words: Calculate the basis vectors of the sph. harm. expansion: \\n Yj = np.zeros((nx*ny*nz,(Order+1)**2))\\n fp = np.sqrt(1/(4*np.pi))\\n Yj[:,0] = fp*np.sqrt(2)\\n mc = 1\\n for n in range(1,Order+1):\\n for m in range(n+1):\\n #ymn = np.sqrt((2*n+1)/(4*np.pi))*r**n*lpmv(m,n,np.cos(theta))\\n ymn = r**n*lpmv(m,n,np.cos(theta))\\n ymn = fp*ymn*np.sqrt((2*n+1))#/(4*np.pi))\\n if m==0:\\n #Yj[:,mc] = ymn\\n Yj[:,mc+n] = ymn\\n # better ordering means we need to change external mapping, as well as trap.configuration at start of expand_field\\n else: # Nm is conversion factor to spherical harmonics, exclusing the sqrt(2*n+1/4pi) portion so that there is no coefficient to the m=0\\n N1 = float(mt.factorial(n-m))\\n N2 = float(mt.factorial(n+m))\\n Nm = (-1)**m*np.sqrt(2*N1/N2) \\n psin = Nm*ymn*np.sin(m*phi)\\n pcos = Nm*ymn*np.cos(m*phi)\\n #Yj[:,mc+1+2*(m-1)] = pcos\\n #Yj[:,mc+2+2*(m-1)] = psin\\n Yj[:,mc+n+m] = pcos\\n Yj[:,mc+n-m] = psin\\n mc += 2*n+1\\n\\n return Yj,rnorm\",\n \"def ztrajs(self):\\n Z = np.array(self.ztraj_).astype(np.float32)\\n return [Z[:, i, :] for i in range(Z.shape[1])]\",\n \"def hubble_z(self, z):\\n H_0 = self.h * cc.H100_s\\n _hubble_z = H_0 * self.e_z(z)\\n return(_hubble_z)\",\n \"def _etaZ(self,x):\\n return self._eta_sfr_scaling(x,'Z_cool')+self._eta_sfr_scaling(x,'Z_hot')\",\n \"def getInverseMatrix(self) -> CMatrix4:\\n ...\",\n \"def haurwitz(zenith):\\n\\n # GHI = 1098 * cos(z) * exp(-0.057 / cos(z))\\n clearsky_ghi = 1098.0 * np.cos(np.radians(zenith)) * np.exp(-0.057 / np.cos(np.radians(zenith)))\\n\\n # remove negative values\\n clearsky_ghi[clearsky_ghi < 0] = 0\\n\\n return clearsky_ghi\",\n \"def rot_z(theta):\\n theta_rad = np.radians(theta)\\n rotation_matrix = [[np.cos(theta_rad), -np.sin(theta_rad), 0],\\n [np.sin(theta_rad), np.cos(theta_rad), 0],\\n [0, 0, 1]]\\n return np.matrix(rotation_matrix)\",\n \"def initZ(self):\\n Z = s.empty((self.N,self.K))\\n for n in range(self.N):\\n for k in range(self.K):\\n Z[n,k] = norm.rvs(loc=0, scale=1, size=1)\\n return Z\"\n]","isConversational":false},"negative_scores":{"kind":"list like","value":["0.657526","0.65030193","0.6374585","0.6241165","0.6134121","0.6029814","0.6002619","0.5971881","0.59294695","0.59294695","0.5912561","0.59013504","0.5897728","0.5878301","0.5842042","0.57775015","0.5770409","0.5757381","0.57313776","0.5728551","0.5725833","0.5725815","0.5701485","0.56981385","0.56946766","0.5672743","0.565798","0.5653207","0.56437486","0.5643409","0.56173366","0.560409","0.5592071","0.5578903","0.5568939","0.5565409","0.555696","0.5543525","0.55184865","0.55184865","0.5510302","0.5510302","0.550975","0.54968864","0.54949886","0.54911506","0.5475862","0.54620904","0.5460703","0.54592776","0.54509085","0.5445321","0.5430134","0.54241973","0.54230917","0.54174924","0.54152864","0.54134715","0.5409087","0.54067564","0.54045063","0.5404104","0.5400289","0.539466","0.5393448","0.5393026","0.53855574","0.5384268","0.53826714","0.5381043","0.53663486","0.5364389","0.5362435","0.5353692","0.53478056","0.5345355","0.53451985","0.53354007","0.53255624","0.5320534","0.53153485","0.53115046","0.530276","0.530276","0.5299676","0.52970034","0.5295125","0.5295125","0.5295125","0.52943134","0.5293318","0.528991","0.5289557","0.5286094","0.5283753","0.527776","0.5268273","0.5258977","0.5258293","0.52565163"],"string":"[\n \"0.657526\",\n \"0.65030193\",\n \"0.6374585\",\n \"0.6241165\",\n \"0.6134121\",\n \"0.6029814\",\n \"0.6002619\",\n \"0.5971881\",\n \"0.59294695\",\n \"0.59294695\",\n \"0.5912561\",\n \"0.59013504\",\n \"0.5897728\",\n \"0.5878301\",\n \"0.5842042\",\n \"0.57775015\",\n \"0.5770409\",\n \"0.5757381\",\n \"0.57313776\",\n \"0.5728551\",\n \"0.5725833\",\n \"0.5725815\",\n \"0.5701485\",\n \"0.56981385\",\n \"0.56946766\",\n \"0.5672743\",\n \"0.565798\",\n \"0.5653207\",\n \"0.56437486\",\n \"0.5643409\",\n \"0.56173366\",\n \"0.560409\",\n \"0.5592071\",\n \"0.5578903\",\n \"0.5568939\",\n \"0.5565409\",\n \"0.555696\",\n \"0.5543525\",\n \"0.55184865\",\n \"0.55184865\",\n \"0.5510302\",\n \"0.5510302\",\n \"0.550975\",\n \"0.54968864\",\n \"0.54949886\",\n \"0.54911506\",\n \"0.5475862\",\n \"0.54620904\",\n \"0.5460703\",\n \"0.54592776\",\n \"0.54509085\",\n \"0.5445321\",\n \"0.5430134\",\n \"0.54241973\",\n \"0.54230917\",\n \"0.54174924\",\n \"0.54152864\",\n \"0.54134715\",\n \"0.5409087\",\n \"0.54067564\",\n \"0.54045063\",\n \"0.5404104\",\n \"0.5400289\",\n \"0.539466\",\n \"0.5393448\",\n \"0.5393026\",\n \"0.53855574\",\n \"0.5384268\",\n \"0.53826714\",\n \"0.5381043\",\n \"0.53663486\",\n \"0.5364389\",\n \"0.5362435\",\n \"0.5353692\",\n \"0.53478056\",\n \"0.5345355\",\n \"0.53451985\",\n \"0.53354007\",\n \"0.53255624\",\n \"0.5320534\",\n \"0.53153485\",\n \"0.53115046\",\n \"0.530276\",\n \"0.530276\",\n \"0.5299676\",\n \"0.52970034\",\n \"0.5295125\",\n \"0.5295125\",\n \"0.5295125\",\n \"0.52943134\",\n \"0.5293318\",\n \"0.528991\",\n \"0.5289557\",\n \"0.5286094\",\n \"0.5283753\",\n \"0.527776\",\n \"0.5268273\",\n \"0.5258977\",\n \"0.5258293\",\n \"0.52565163\"\n]","isConversational":false},"document_score":{"kind":"string","value":"0.55584866"},"document_rank":{"kind":"string","value":"36"}}},{"rowIdx":22683,"cells":{"query":{"kind":"string","value":"Compute the sum of matrices bins (i.e. rows or columns) using only the upper triangle, assuming symmetrical matrices."},"document":{"kind":"string","value":"def sum_mat_bins(mat):\n # Equivalaent to row or col sum on a full matrix\n # Note: mat.sum returns a 'matrix' object. A1 extracts the 1D flat array\n # from the matrix\n return mat.sum(axis=0).A1 + mat.sum(axis=1).A1 - mat.diagonal(0)"},"metadata":{"kind":"string","value":"{\n \"objective\": {\n \"self\": [],\n \"paired\": [],\n \"triplet\": [\n [\n \"query\",\n \"document\",\n \"negatives\"\n ]\n ]\n }\n}"},"negatives":{"kind":"list like","value":["def row_col_sums(i, b_j, bins, C, n_u):\n s= C[i][i]*n_u[i]*n_u[i]\n for j in range(bins[b_j], bins[b_j+1]):\n if i != j:\n s+= (C[i][j] + C[j][i])*n_u[i]*n_u[j]\n return s","def block_sum(i, bins, C, n_u):\n s= 0.0\n for j in range(bins[i], bins[i+1]):\n for k in range(bins[i], bins[i+1]):\n s+= C[j][k]*n_u[j]*n_u[k]\n return s","def _get_integrals_fast(bins):\n\n if min(bins.shape) == 1: \n flatsum = np.cumsum(bins.flat[::-1])[::-1]\n sums = np.subtract(*np.meshgrid(flatsum,flatsum))\n assert sums.shape[0] == sums.shape[1]\n return sums\n\n else: \n y_sums = np.cumsum(bins[:,::-1], axis = 1)[:,::-1]\n sums = np.cumsum(y_sums[::-1,:], axis = 0)[::-1,:]\n return sums","def _sum_on_axis(self, M, undirected=True):\n\n if undirected:\n M = (M + M.T).astype('bool')\n colsum = M.sum(axis=0) # , dtype=np.int64)\n rowsum = M.sum(axis=1) # , dtype=np.int64) # already np.int64\n return rowsum.T, colsum","def column_sums(square):\n total = 0","def cartesian_sum(matrix, rows, cols):\n\n res = 0 \n for row in rows:\n for col in cols:\n res += matrix[row, col]\n return res","def Sum2d(a):\n return(np.sum(np.sum(a,-1),-1))","def bin_input(neu, bin_size, overlap = 0):\n win_d = bin_size - overlap\n n_bins = int(((neu.shape[0]-bin_size)/win_d)+1)\n FRmat = np.empty([n_bins, neu.shape[1]])\n for i in range(n_bins):\n FRmat[i,:] = np.sum(neu[i*win_d:i*win_d+bin_size,:], axis = 0)\n \n return FRmat","def correlation_bins(shred):\n return 0","def sum_diag(max_lines):\r\n dsum = 1 # sum of diagonals\r\n cpt = 1 # number of lines processed\r\n val = 1 # value of the current place in the square\r\n inc = 0 # the increment between number for one line\r\n \r\n while cpt < max_lines:\r\n cpt += 2\r\n inc += 2\r\n \r\n for corner in range(4):\r\n val += inc\r\n dsum += val\r\n\r\n return dsum","def sum(matrix):\n\n return float(sum([sum(row) for row in matrix]))","def create(matrix):\n limit_y = len(matrix)\n limit_x = len(matrix[0])\n\n for y in range(1, limit_y):\n bit.create(matrix[y])\n\n for x in range(1, limit_x):\n for y in range(1, limit_y):\n k = y + (y & -y)\n if k < limit_y:\n matrix[k][x] += matrix[y][x]","def rowsums (self):\n return self.values.sum (axis=0)","def binned_matrix(in_tensor, method='max'):\n print(in_tensor[0])\n in_tensor = nn.Softmax(dim=2)(torch.einsum('cij -> ijc', in_tensor))\n if method == 'max':\n # Predict the bins with the highest probability\n return in_tensor.max(2)[1]\n elif method == 'avg':\n # Predict the bin that is closest to the average of the probability dist\n # predicted_bins[i][j] = round(sum(bin_index * P(bin_index at i,j)))\n bin_indices = torch.arange(in_tensor.shape[-1]).float()\n predicted_bins = torch.round(torch.sum(in_tensor.mul(bin_indices),\n dim=len(in_tensor.shape)-1))\n return predicted_bins\n else:\n raise ValueError('method must be in {\\'avg\\',\\'max\\'}')","def sum_numba(A):\n N = A.shape\n B = np.zeros((N[0], N[2]))\n for i in range(N[0]):\n for j in range(N[2]):\n for k in range(N[1]):\n B[i, j] += A[i, k, j]\n return B","def bin_binarise(self):\n pass","def area(self):\n return numpy.prod(\n numpy.meshgrid(*self.binwidths, indexing='ij'), axis=0)","def amount_of_stairs(n):\n\n matrix = [[0] * n for i in range(n)]\n\n for i in range(0, n):\n for j in range(1, i):\n matrix[i][j] = sum(matrix[i - j - 1][:j])\n matrix[i][i] = 1\n\n # print_matrix(matrix)\n return sum(matrix[n-1])","def total2d(arr: List[List[int]]) -> int: # _8 [✅]\n # ** try to solve this in one line using a list comprehension\n return sum( [sum(sub_arr) for sub_arr in arr ] )","def island_perimeter(grid):\n total = 0\n for b in range(len(grid)):\n for a in range(len(grid[b])):\n # left corner\n if (a == 0) and (b == 0):\n if grid[b][a] == 1:\n total = total + 2\n if grid[b][a + 1] == 0:\n total = total + 1\n if grid[b + 1][a] == 0:\n total = total + 1\n # right corner\n elif (a == len(grid[b]) - 1) and b == 0:\n if grid[b][a] == 1:\n total = total + 2\n if grid[b + 1][a] == 0:\n total = total + 1\n if grid[b][a - 1] == 0:\n total = total + 1\n # lower-left corner\n elif a == 0 and b == (len(grid) - 1):\n if grid[b][a] == 1:\n total = total + 2\n if grid[b][a + 1] == 0:\n total = total + 1\n if grid[b - 1][a] == 0:\n total = total + 1\n # lower-right corner\n elif b == (len(grid) - 1) and a == (len(grid[b]) - 1):\n if grid[b][a] == 1:\n total = total + 2\n if grid[b - 1][a] == 0:\n total = total + 1\n if grid[b][a - 1] == 0:\n total = total + 1\n # top edge\n elif (b == 0 and a > 0) and a < (len(grid[b]) - 1):\n if grid[b][a] == 1:\n total = total + 1\n if grid[b][a - 1] == 0:\n total = total + 1\n if grid[b + 1][a] == 0:\n total = total + 1\n if grid[b][a + 1] == 0:\n total = total + 1\n # left edge\n elif (b > 0 and b < (len(grid) - 1)) and ((a == 0) and a <\n len(grid[b]) - 1):\n if grid[b][a] == 1:\n total = total + 1\n if grid[b - 1][a] == 0:\n total = total + 1\n if grid[b][a + 1] == 0:\n total = total + 1\n if grid[b + 1][a] == 0:\n total = total + 1\n # right edge\n elif (b > 0 and (b < len(grid) - 1)) and (a == len(grid[b]) - 1):\n if grid[b][a] == 1:\n total = total + 1\n if grid[b - 1][a] == 0:\n total = total + 1\n if grid[b][a - 1] == 0:\n total = total + 1\n if grid[b + 1][a] == 0:\n total = total + 1\n # bottom edge\n elif (b == len(grid) - 1) and a > 0 and a < len(grid[b]) - 1:\n if grid[b][a] == 1:\n total = total + 1\n if grid[b][a - 1] == 0:\n total = total + 1\n if grid[b - 1][a] == 0:\n total = total + 1\n if grid[b][a + 1] == 0:\n total = total + 1\n # cases that are neither edges nor corners\n elif (b > 0 and b < len(grid) - 1) and (a > 0 and a <\n len(grid[b]) - 1):\n if grid[b][a] == 1:\n if grid[b][a - 1] == 0:\n total = total + 1\n if grid[b][a + 1] == 0:\n total = total + 1\n if grid[b - 1][a] == 0:\n total = total + 1\n if grid[b + 1][a] == 0:\n total = total + 1\n return total","def _gu_sum(a, **kwds):\n return np.sum(np.ascontiguousarray(a), axis=-1, **kwds)","def minesweeper(matrix):\n \n num_rows = len(matrix)\n num_cols = len(matrix[0])\n \n adj_mines = []\n \n adj_row = [0]*num_cols\n \n for i in range(num_rows):\n adj_mines.append(adj_row[:])\n \n for r in range(num_rows):\n for c in range(num_cols):\n if matrix[r][c] == True:\n if (r-1) in range(num_rows) and (c-1) in range(num_cols):\n adj_mines[r-1][c-1] += 1\n if (r-1) in range(num_rows) and (c) in range(num_cols):\n adj_mines[r-1][c] += 1\n if (r-1) in range(num_rows) and (c+1) in range(num_cols): \n adj_mines[r-1][c+1] += 1\n if (r) in range(num_rows) and (c-1) in range(num_cols):\n adj_mines[r][c-1] += 1\n if (r) in range(num_rows) and (c+1) in range(num_cols): \n adj_mines[r][c+1] += 1\n if (r+1) in range(num_rows) and (c-1) in range(num_cols):\n adj_mines[r+1][c-1] += 1\n if (r+1) in range(num_rows) and (c) in range(num_cols):\n adj_mines[r+1][c] += 1\n if (r+1) in range(num_rows) and (c+1) in range(num_cols): \n adj_mines[r+1][c+1] += 1\n\n \n return adj_mines","def matrixScore(self, A):\n\n def firstCol(matrix):\n \tfor row in matrix:\n \t\tif row[0] == 0:\n \t\t\tfor ind in range(len(row)):\n \t\t\t\trow[ind] ^= 1\n \treturn\n\n def nextCol(matrix):\n \tcolNum = len(matrix[0])\n \tfor col in range(1, colNum):\n \t\tcolSum = 0\n \t\tfor row in matrix:\n \t\t\tcolSum += row[col]\n \t\tif colSum < len(matrix)/2:\n \t\t\tfor row in matrix:\n \t\t\t\trow[col] ^= 1\n \treturn\n\n def binSum(matrix):\n \ttotalSum = 0\n \tfor row in matrix:\n \t\tbinStr = \"\"\n \t\tfor elem in row:\n \t\t\tbinStr += str(elem)\n \t\ttotalSum += int(binStr, 2)\n \treturn totalSum\n\n\n firstCol(A)\n nextCol(A)\n return binSum(A)","def rowSum(mtx):\n try:\n for i in range(0, len(mtx)):\n assert len(mtx[i]) == len(mtx[i-1]) # check whether each list has the same length.\n \n res = list()\n for j in range(0, len(mtx[0])): \n tmp = 0\n for i in range(0, len(mtx)): \n tmp = tmp + mtx[i][j]\n res.append(tmp)\n return(res)\n \n except AssertionError as detail:\n return ('Length of lists is irregular or input format is wrong.')\n except TypeError as detail:\n return ('Undefined operand type')","def compute_barycentrics_native(coords, mesh):\n triangle_indices = mesh.find_simplex(coords)\n X = mesh.transform[triangle_indices, :2]\n Y = coords - mesh.transform[triangle_indices, 2]\n b = np.einsum('ijk,ik->ij', X, Y)\n bcoords = np.c_[b, 1 - b.sum(axis=1)]\n return bcoords, triangle_indices","def colintegrals (self):\n dy = self.ybins[1] - self.ybins[0]\n return self.colsums * dy","def rowintegrals (self):\n dx = self.xbins[1] - self.xbins[0]\n return self.rowsums * dx","def bin_centers(radial_bins):\n\n outer = radial_bins[1:]\n inner = radial_bins[:-1]\n return 0.5 * (outer + inner)","def bin_reduce(data, axes_s):\n d1 = data\n \n # sort axes by stride distance to optimize for locality\n # doesn't seem to make much difference on modern systems...\n axes = [ (axis, d1.strides[axis]) for axis in range(d1.ndim) ]\n axes.sort(key=lambda p: p[1])\n assert len(axes_s) == data.ndim\n\n # reduce one axis at a time to shrink work for subsequent axes\n for axis in [p[0] for p in axes]:\n s = axes_s[axis]\n\n if s == 1:\n # skip useless copying for non-reducing axis\n continue\n\n # accumulate s-strided subsets that belong to each bin\n a = d1[ \n tuple(\n [ slice(None) for i in range(axis) ]\n + [ slice(0, 1-s, s) ]\n + [ slice(None) for i in range(d1.ndim - axis - 1) ]\n )\n ].astype(np.float32, copy=True)\n \n for step in range(1, s):\n a += d1[ \n tuple(\n [ slice(None) for i in range(axis) ]\n + [ slice(step, step < s and 1-s+step or None, s) ]\n + [ slice(None) for i in range(d1.ndim - axis - 1) ]\n )\n ]\n\n # compute single-axis bin averages from accumulation\n d1 = a * (1./s)\n\n return d1","def colsums (self):\n return self.values.sum (axis=1)","def constraint_sum(w):\n return sum(w) - 1","def find_bin_edges(bin_centres):\n\n if not isinstance(bin_centres, np.ndarray):\n bin_centres = np.asarray(bin_centres)\n\n edges = bin_centres[:-1] + 0.5 * (bin_centres[1:] - bin_centres[:-1])\n bins = np.concatenate(([2 * bin_centres[0] - edges[0]], edges,\n [2 * bin_centres[-1] - edges[-1]]))\n\n return bins","def bin_matrix(x, binning):\n\n N = len(x)\n B = []\n for i in range(len(binning) - 1):\n\n line = np.zeros(N)\n for j in range(N):\n\n if x[j] >= binning[i] and x[j] < binning[i+1]:\n\n size = binning[i + 1] - binning[i]\n line[j] = 1 / size\n\n B.append(line)\n\n B = np.array(B)\n\n return(B)","def trans_hellinger(m):\n m = asmatrix(m)\n row_sums = sum(m, axis=1)\n result = sqrt(m / row_sums)\n return result","def quadsum(data):\n return np.nansum(data, 0)","def test_canonicalization_connected_summations(free_alg):\n dr = free_alg\n p = dr.names\n i, j, k, l = p.R_dumms[:4]\n a, b = symbols('a b')\n t = IndexedBase('t')\n\n tensor = dr.sum(\n (i, p.R), (j, p.R[0, i]), (k, p.R[0, a]), (l, p.R[0, b]),\n t[i] * t[j] * t[k] * t[l]\n )\n res = tensor.simplify()\n sums = res.local_terms[0].sums\n\n # The desired order for the summations.\n assert not sums[0][1].bounded\n assert sums[1][1].upper == a\n assert sums[2][1].upper == b\n assert sums[3][1].upper == sums[0][0]","def build_integer_basis_of_orthogonal_complement_of_binomial_matrix(messi_network):\n binomial_matrix = build_binomial_matrix(messi_network)\n\n\n #print(\"G1\", messi_network.G1.edges())\n #print(\"G2\", messi_network.G2.edges())\n #print(\"G2 circle\", messi_network.G2_circle.edges())\n #print(\"B\", binomial_matrix)\n\n\n #print(\"binomial matrix\")\n #print(binomial_matrix)\n #print(\"build_integer_basis_of_orthogonal_complement_of_binomial_matrix\")\n ret = build_integer_basis_matrix_of_orthogonal_complement_of_matrix(binomial_matrix.transpose(), positive=False).transpose()\n #M = binomial_matrix.transpose()\n #M_ort = ret.transpose()\n #print(M @ M_ort)\n return ret","def binary_matmul_1d(row_p, col_i, other, m):\n out = np.zeros(m, dtype=FLOAT_STORAGE_np)\n for i in range(m):\n vector_sum = 0\n for r in col_i[row_p[i]:row_p[i + 1]]:\n vector_sum += other[r]\n out[i] = vector_sum\n return out","def simetrize_3dhistogram(histogram):\n N =len(histogram)\n n_histogram = np.zeros((N,N,N))\n for i in range(N):\n for j in range(i,N):\n for k in range(j, N):\n S = histogram[i][j][k] + histogram[k][i][j] + histogram[j][k][i] + histogram[i][k][j] + histogram[j][i][k] + histogram[k][j][i]\n n_histogram[i][j][k] = S\n n_histogram[k][i][j] = S\n n_histogram[j][k][i] = S\n n_histogram[i][k][j] = S\n n_histogram[j][i][k] = S\n n_histogram[k][j][i] = S\n #a[i][j][k], a[k][i][j], a[j][k][i], a[i][k][j], a[j][i][k], a[k][j][i]\n return n_histogram","def TRI2centroidBL(TRI):\n # For each row of TRI, find rows that share a bond (up to 3 of these)\n dic = {}\n # nbrs = np.zeros((len(BL),3), dtype=np.int)\n BL = np.zeros((len(TRI) * 3, 2), dtype=np.int)\n dmyi = 0\n # make sure each row has indices increasing in each row\n TRI.sort()\n\n # Compose dictionary with pairs of neighboring triangles\n for row in TRI:\n if (row[0], row[1]) in dic:\n dic[(row[0], row[1])].append(dmyi)\n else:\n dic[(row[0], row[1])] = [dmyi]\n if (row[0], row[2]) in dic:\n dic[(row[0], row[2])].append(dmyi)\n else:\n dic[(row[0], row[2])] = [dmyi]\n if (row[1], row[2]) in dic:\n dic[(row[1], row[2])].append(dmyi)\n else:\n dic[(row[1], row[2])] = [dmyi]\n dmyi += 1\n\n # print 'dic = ', dic\n # Compose BL from the pairs of indices in each dict entry\n dmyi = 0\n for key in dic:\n # if the bond is not an edge of the lattice or\n # a dangling bond, add to BL.\n # print 'key = ', key, ' -> d[key] = ', dic[key]\n if len(dic[key]) == 2:\n BL[dmyi] = dic[key]\n dmyi += 1\n\n return BL[0:dmyi]","def sum_across_rows(grid):\n pass","def sum_reduce_nb(col, a, *args):\n return np.nansum(a)","def freedman_diaconis_bins(self, arr):\n # From https://stats.stackexchange.com/questions/798/\n if len(arr) < 2:\n return 1\n # Calculate the iqr ranges.\n self.iqr(arr)\n # Calculate the h\n h = 2 * (self.q3 - self.q1) / (len(arr) ** (1 / 3))\n # fall back to sqrt(a) bins if iqr is 0\n if h == 0:\n return int(np.sqrt(arr.size))\n else:\n return int(np.ceil((arr.max() - arr.min()) / h))","def calculate_square_form(diagonal_matrix, total_sorts):\n n = len(diagonal_matrix)\n\n matrix = np.ndarray(shape=(n,n))\n\n for i in range(n):\n for j in range(len(diagonal_matrix[i])):\n # Also calculate the dissimilarity matrix\n matrix[i][j] = 100 - 100 * diagonal_matrix[i][j] / total_sorts\n matrix[j][i] = 100 - 100 * diagonal_matrix[i][j] / total_sorts\n if i == j:\n matrix[i][j] = 0\n\n return matrix\n\n # matrix = np.tril(diagonal_matrix, k=-1)\n # matrix = matrix + matrix.T\n # matrix = matrix * (-100 / total_sorts) + 100\n # np.fill_diagonal(matrix, 0)\n # return matrix","def area(boxes):\n return (boxes[:, 2] - boxes[:, 0]) * (boxes[:, 3] - boxes[:, 1])","def segment_sum(self, left, right):\n if self.empty():\n return 0\n less, _ = self.root.split_sums(left)\n _, greater = self.root.split_sums(right)\n return self.sum - less - greater","def bin_discretize(self, variables=[], bins=3,\n min_const_samples_bin_size=1.0/3):\n self.edges=np.zeros((self.arity.size,bins+1))\n for i in variables:\n un_cnt=np.unique(self.data[:,i],return_counts=True)\n constvals=un_cnt[0][un_cnt[1]>self.data.shape[0]*min_const_samples_bin_size]\n mask=np.ones(self.data.shape[0],dtype=bool)\n if constvals.size>0:\n for j,cv in enumerate(constvals):\n mask*=(self.data[:,i]!=cv)\n self.data[self.data[:,i]==cv,i]=j\n\n size=np.sum(mask)/bins\n sorted_i=np.argsort(self.data[mask,i])\n edges=[self.data[mask,i][sorted_i[int(size*num)-1]] for num in range(1,bins)]\n self.edges[i]=[self.data[mask,i][sorted_i[0]]]+edges+[self.data[mask,i][sorted_i[-1]]]\n self.data[mask,i]=np.searchsorted(edges,self.data[mask,i])+constvals.size\n self.arity[i]=len(edges)+1+constvals.size","def bin_edges_to_centres(edges):\r\n if edges.ndim == 1:\r\n steps = (edges[1:] - edges[:-1]) / 2\r\n return edges[:-1] + steps\r\n else:\r\n steps = (edges[1:, 1:] - edges[:-1, :-1]) / 2\r\n centres = edges[:-1, :-1] + steps\r\n return centres","def area(boxes):\n return (boxes[:, 2] - boxes[:, 0]) * (boxes[:, 3] - boxes[:, 1])","def upper(mat):\n idx = np.triu_indices_from(mat, k=1)\n return mat[idx]","def get_centers_from_bins(bins):\n return 0.5 * (bins[:-1] + bins[1:])","def batch_outer_sum(a, b):\n a, b = normalize_and_check_ndim([a, b], 2)\n # Due to broadcasting, this sum works analogously to batch matrix\n # multiplication. See also comments in batch_outer_product().\n return a[:, :, np.newaxis] + b[:, np.newaxis, :]","def sumRegion(self, row1, col1, row2, col2):\n \n if row1 == col1 == 0:\n return self.matrix_sum[row2][col2]\n elif row1 == 0:\n return self.matrix_sum[row2][col2] - self.matrix_sum[row2][col1-1]\n elif col1 == 0:\n return self.matrix_sum[row2][col2] - self.matrix_sum[row1-1][col2]\n else:\n return (self.matrix_sum[max(row1, row2)][max(col1, col2)] - \n self.matrix_sum[max(0, min(row1, row2)-1)][max(col1, col2)] - \n self.matrix_sum[max(row1, row2)][max(0, min(col1, col2)-1)] + \n self.matrix_sum[max(0, min(row1, row2)-1)][max(0, min(col1, col2)-1)])","def solution(n, m, r, c, k) -> int:\n xs = []\n # Add all the non-zero room widths to xs\n last_column_wall = None\n for col in c:\n if last_column_wall is not None and col - last_column_wall - 1 > 0:\n xs.append(col - last_column_wall - 1)\n last_column_wall = col\n ys = []\n # Add all the non-zero room heights to ys\n last_row_wall = None\n for row in r:\n if last_row_wall is not None and row - last_row_wall - 1 > 0:\n ys.append(row - last_row_wall - 1)\n last_row_wall = row\n return aux(xs, ys, k)","def total_power_square(x, y, serial, size):\n result = 0\n for i in range(x, x + size):\n for j in range(y, y + size):\n result += cell_power(i, j, serial)\n return result","def absolute_sum_of_elements(self) -> int:\n absolute_sum = abs(self._A[0][0])\n for y in range(0, self.num_of_rows):\n for x in range(0, self.num_of_cols):\n absolute_sum += abs(self._A[y][x])\n return absolute_sum","def _dimensionalize(x: np.ndarray, lb: np.ndarray, ub: np.ndarray) -> np.ndarray:\n return lb + x * (ub - lb)","def image_quad_norm(inarray):\n # If there is an hermitian symmetry\n if inarray.shape[-1] != inarray.shape[-2]:\n return 2 * np.sum(np.sum(np.abs(inarray)**2, axis=-1), axis=-1) - \\\n np.sum(np.abs(inarray[..., 0])**2, axis=-1)\n else:\n return np.sum(np.sum(np.abs(inarray)**2, axis=-1), axis=-1)","def norm_by_rows(matrix):\n row_sums = matrix.sum(axis=1)\n return matrix / row_sums.reshape((-1, 1))","def sqrSum(a, b, i, j):\n return (a - i)**2 + (b - j)**2","def to_bins(arr):\n result = np.zeros(len(arr)+1)\n result[1:-1] = 0.5 * (arr[1:] + arr[:-1])\n result[0] = arr[0] - 0.5*(arr[1] - arr[0])\n result[-1] = arr[-1] + 0.5*(arr[-1] - arr[-2])\n return result","def test00(self):\n N = self.N\n a = bcolz.fromiter(xrange(N), dtype=np.float64, count=N,\n rootdir=self.rootdir)\n l, s = 0, 0\n for block in bcolz.iterblocks(a):\n l += len(block)\n s += block.sum()\n self.assertEqual(l, N)\n # as per Gauss summation formula\n self.assertEqual(s, (N - 1) * (N / 2))","def sum_squares(X):\n pref_sum = np.cumsum(X)\n pref_sum_squares = np.cumsum(X**2)\n n = len(X)\n sum_squares_left = np.zeros(n+1)\n sum_squares_right = np.zeros(n+1)\n for i in range(1, n+1):\n mean_left = pref_sum[i-1]/i \n mean_right = (pref_sum[-1] - pref_sum[i-1])/(n-i) # bad for i = n overwritten later\n sum_squares_left[i] = pref_sum_squares[i-1] + i * mean_left**2 - 2 * mean_left * pref_sum[i-1] \n sum_squares_right[i] = (pref_sum_squares[-1] - pref_sum_squares [i-1]) + mean_right**2 * (n-i) - 2 * mean_right *(pref_sum[-1] - pref_sum[i-1])\n sum_squares_left[0] = 0\n sum_squares_right[0] = sum_squares_left[-1]\n sum_squares_right[-1] = 0\n return sum_squares_left, sum_squares_right","def trace(self):\n if not self.is_square():\n raise(ValueError, \"Cannot calculate the trace of a non-square matrix.\")\n\n # TODO - your code here\n sum = 0.0\n for i in range(self.h):\n for j in range(self.w):\n if i == j:\n sum = sum + self.g[i][j]\n return sum\n # TODO - your code here","def housetriang(A, B):\n m, n = shape(A); r = shape(B)[1] ; A=hstack([A,B]); \n minval = array([n, m - 1]).min()\n for k in range(minval):\n v, A[k, k] = housegen(A[k:m, k])\n v = matrix(reshape(v, (m - k, 1)))\n C = A[k:m, (k+1):(n+r)] ; A[k:m, (k + 1):(n + r)] = C - v*(v.T*C)\n R = triu(A[:, :n]); C = A[:, n:(n + r)]\n return R, C","def binary_dist_sorensen_dice(datamtx, strict=True):\n datamtx = datamtx.astype(bool)\n datamtx = datamtx.astype(float)\n if strict:\n if not all(isfinite(datamtx)):\n raise ValueError(\"non finite number in input matrix\")\n if any(datamtx<0.0):\n raise ValueError(\"negative value in input matrix\")\n if rank(datamtx) != 2:\n raise ValueError(\"input matrix not 2D\")\n numrows, numcols = shape(datamtx)\n else:\n try:\n numrows, numcols = shape(datamtx)\n except ValueError:\n return zeros((0,0),'d')\n\n if numrows == 0 or numcols == 0:\n return zeros((0,0),'d')\n dists = zeros((numrows,numrows),'d')\n rowsums = datamtx.sum(axis=1)\n \n for i in range(numrows):\n row1 = datamtx[i]\n for j in range(i):\n row2 = datamtx[j]\n bottom = float(rowsums[i] + rowsums[j])\n cur_d = 0.0\n if bottom:\n cur_d = 1-(2*logical_and(row1,row2).sum()/bottom)\n dists[i][j] = dists[j][i] = cur_d\n return dists","def effective_pairs(self):\n out = 0\n hdmat = self.hdmatrix()\n for i in xrange(len(hdmat[0])):\n for j in xrange(i+1, len(hdmat[0])): \n out += hdmat[i,j]**2\n return out","def area(boxes: Union[np.array, torch.Tensor]) -> Union[np.array, torch.Tensor]:\n return (boxes[:, 2] - boxes[:, 0]) * (boxes[:, 3] - boxes[:, 1])","def neighbors_magnitude(self):\n return sum((n.weight for n in self.neighbors))","def CL_histogram_MMD(sketch,Phi,domain,dimension,nb_cat_per_dim=None,bins_cont=10):\n ## 0) Parsing the inputs\n # Number of categorical inputs\n if nb_cat_per_dim is None:\n nb_cat_per_dim = np.zeros(Phi.d)\n \n is_integer_dimension = False\n if nb_cat_per_dim[dimension] > 0:\n # The data is integer-type\n is_integer_dimension = True\n bins = int(nb_cat_per_dim[dimension])\n else:\n bins = bins_cont\n\n m = sketch.size\n # 1) Construct the A matrix\n A = 1j*np.zeros((m,bins)) # Pre-allocation\n bin_edges = np.linspace(domain[dimension,0],domain[dimension,1],bins+1)\n box = domain.copy()\n for p in range(bins):\n # move to the next box\n if is_integer_dimension:\n box[dimension,0] = p\n box[dimension,1] = p\n else:\n box[dimension,0] = bin_edges[p]\n box[dimension,1] = bin_edges[p+1]\n A[:,p] = fourierSketchOfBox(box,Phi,nb_cat_per_dim) \n \n # 1.b) cast to real \n Ari = np.r_[A.real, A.imag]\n \n # 2) create b vector\n b = np.r_[sketch.real, sketch.imag]\n \n # 3) solve the optimization problem\n def _f_grad(x):\n r = Ari@x-b\n f = 0.5*np.linalg.norm(r)**2\n grad = Ari.T@r\n return (f,grad)\n \n # Starting point\n x0 = np.ones(bins)/bins\n # Linear constraints\n A_constr = np.zeros((bins,bins))\n l_constr = 0*np.ones(bins) # Positive constraints\n A_constr[:bins,:bins] = np.eye(bins)\n upper_bound = 5 # weird that it must be large\n u_constr = upper_bound*np.ones(bins) # Sum-to one constraints\n constr = LinearConstraint(A_constr,l_constr,u_constr)\n\n # Solve\n sol = minimize(_f_grad, x0, method='trust-constr', bounds=None, constraints=constr, jac=True, options={'verbose': 0})\n\n return project_probabilitySimplex(sol.x)","def compute_histogram(self):\n # compute distance between points \n distmatrix = np.sqrt(pdist(self.points))\n if not self.mean_dist:\n self.mean_dist = np.mean(distmatrix)\n distmatrix = distmatrix/self.mean_dist\n distmatrix = squareform(distmatrix)\n #compute angles between points\n angles = compute_angles(self.points)\n #quantize angles to a bin\n tbins = np.floor(angles / (2 * pi / self.nbins_theta))\n lg = np.logspace(self.r1, self.r2, num=5)\n #quantize radious to bins\n rbins = np.ones(angles.shape) * -1\n for r in lg:\n counts = (distmatrix < r) \n rbins = rbins + counts.astype(int) \n return rbins, tbins","def trace(self):\n # TODO 异常 非常规输入处理\n if not self.is_square():\n raise(\n ValueError, \"Cannot calculate the trace of a non-square matrix.\")\n # TODO Calculates the main diagonal num's sum\n sum = 0\n for i in range(self.h):\n sum += self[i][i]\n\n return sum","def _cartesian_add(xs):\n return sum(prefer_static.reshape(x, shape=[-1] + [1]*(len(xs) - 1 - i))\n for i, x in enumerate(xs))","def housetriang(A, B):\n\n m, n = A.shape\n try:\n _, r = B.shape\n except:\n r = 1\n\n A = np.hstack((A.copy(), B.copy()))\n\n for k in range(min(n, m-1)):\n v, A[k, k] = housegen(A[k:m, k])\n v = np.reshape(v, (m-k, 1))\n C = A[k:m, k+1:n+r]\n A[k:m, k+1:n+r] = C - v * np.dot(np.conjugate(v).T, C)\n\n R = np.triu(A[:, 0:n])\n C = A[:, n:n+r]\n\n return R, C","def getBinSize(axes, bin_numbers):\n\n size = 1\n for axis, bin_number in zip(axes, bin_numbers):\n if bin_number in (\"underflow\", \"overflow\"):\n continue\n x0, x1 = axis.getBinRange(bin_number)\n size *= x1 - x0\n return size","def from_simple_root_basis(coefficients, A):\n return np.sum([coefficient * row for coefficient, row in zip(coefficients, A)], axis=0)","def compute_cost(indexes, matrix):\n total = 0\n for row, columns in indexes:\n total += matrix[row][columns]\n return total","def area(bboxes: np.array) -> np.ndarray:\n if bboxes.ndim == 1:\n bboxes = np.expand_dims(bboxes, axis=0)\n w = (bboxes[:, 2] - bboxes[:, 0]).clip(min=0)\n h = (bboxes[:, 3] - bboxes[:, 1]).clip(min=0)\n return w * h","def sumouter(us,vs,lo=-1.0,hi=1.0,out=None):\n result = zeros((len(us[0]),len(vs[0])))\n for u,v in zip(us,vs):\n result += outer(clip(u,lo,hi),v)\n return result","def sumRegion(self, row1, col1, row2, col2):\n\n if not self.sums:\n return 0\n\n r1, c1 = row1+1, col1+1\n r2, c2 = row2+1, col2+1\n return self.sums[r2][c2] + self.sums[r1-1][c1-1] - self.sums[r2][c1-1] - self.sums[r1-1][c2]","def binning_axis(self) -> int:\r\n return 0","def accumulate_triangles(triangle_strip, shape):\n triangle_strip = numpy.asarray(triangle_strip)\n assert triangle_strip.ndim == 2 and triangle_strip.shape[1] == 2 and len(triangle_strip) > 2\n grid = numpy.indices(shape) + 0.5 # pixel centers are at (0.5, 0.5 geometrically)\n output = numpy.zeros(shape, dtype=int)\n for i in range(len(triangle_strip) - 2):\n vertices = triangle_strip[i:i+3]\n xmn, ymn = numpy.floor(vertices.min(axis=0)).astype(int)\n xmx, ymx = numpy.ceil(vertices.max(axis=0)).astype(int) + 1\n xs, ys = slice(xmn, xmx), slice(ymn, ymx)\n b_coords = barycentric_coords(vertices, grid[:, xs, ys])\n m = (b_coords >= 0).all(axis=0)\n output[xs, ys] += (b_coords >= 0).all(axis=0)\n return output","def calculateEdges(i, j, matrix):\n num = 0\n if i > 0:\n if matrix[i-1][j] == 0:\n num += 1\n if j > 0:\n if matrix[i][j-1] == 0:\n num += 1\n if i < len(matrix) - 1:\n if matrix[i+1][j] == 0:\n num += 1\n if j < len(matrix[0]) - 1:\n if matrix[i][j+1] == 0:\n num += 1\n \n return num","def sum_matrixes(A, B):\n\n\tD = np.zeros((3,3))\n\tD[0][0] = A[0][0]+ B[0][0]\n\tD[0][1] = A[0][1]+ B[0][1]\n\tD[0][2] = A[0][2]+ B[0][2]\n\tD[1][0] = A[1][0]+ B[1][0]\n\tD[1][1] = A[1][1]+ B[1][1]\n\tD[1][2] = A[1][2]+ B[1][2]\n\tD[2][0] = A[2][0]+ B[2][0]\n\tD[2][1] = A[2][1]+ B[2][1]\n\tD[2][2] = A[2][2]+ B[2][2]\n\treturn D","def computeAC(bins):\n halfn = len(bins)/2\n Npos = sum(bins[halfn:])\n Nneg = sum(bins[:halfn])\n return float(Npos-Nneg)/(Npos+Nneg)","def python_binarize_rows(l: np.ndarray) -> np.ndarray:\n\n def int32(x):\n if x > 0xFFFFFFFF:\n raise OverflowError\n if x > 0x7FFFFFFF:\n x = int(0x100000000 - x)\n if x < 2147483648:\n return -x\n else:\n return -2147483648\n return x\n\n def binarize(n: np.ndarray) -> int:\n result = 0\n for i in range(32):\n sign = n[i] >= 0\n result |= sign << (31 - i)\n return int32(result)\n\n padded = []\n for row in l:\n new_row = row.tolist()\n if len(row) % 32 != 0:\n new_row += [-1 for _ in range(32 - len(row) % 32)]\n padded.append(np.array(new_row))\n\n result = []\n for row in padded:\n new_row = []\n for sub_row in [row[i:i + 32] for i in range(0, len(row), 32)]:\n new_row.append(binarize(sub_row))\n result.append(np.array(new_row))\n\n return np.array(result)","def normalizeMatrix( self, matrix, sums ):\n matrix = matrix.astype( float )\n\n numpy.multiply( matrix, matrix, matrix )\n numpy.divide( matrix, sums, matrix ) \n matrix = numpy.transpose( matrix )\n numpy.divide( matrix, sums, matrix )\n matrix *= 100\n\n return matrix.astype( numpy.int )","def summationLoop(lower, upper):\r\n sum = 0\r\n for i in range(lower, upper + 1):\r\n sum += i\r\n return sum","def root_sum_of_squares_complex(data, dim=0):\n return torch.sqrt(complex_abs_sq(data).sum(dim))","def _reduce_triangle(to_reduce):\n last_row = to_reduce[-1]\n for index in xrange(len(to_reduce) - 1):\n to_reduce[-2][index] += max(last_row[index:index + 2])\n del to_reduce[-1]","def histogram(*args, bins=None, dim=None, weights=None, density=False,\n block_size='auto', bin_dim_suffix='_bin',\n bin_edge_suffix='_bin_edge'):\n\n N_args = len(args)\n\n # TODO: allow list of weights as well\n N_weights = 1 if weights is not None else 0\n\n # some sanity checks\n # TODO: replace this with a more robust function\n assert len(bins)==N_args\n for bin in bins:\n assert isinstance(bin, np.ndarray), 'all bins must be numpy arrays'\n\n for a in args:\n # TODO: make this a more robust check\n assert a.name is not None, 'all arrays must have a name'\n\n # we drop coords to simplify alignment\n args = [da.reset_coords(drop=True) for da in args]\n if N_weights:\n args += [weights.reset_coords(drop=True)]\n # explicitly broadcast so we understand what is going into apply_ufunc\n # (apply_ufunc might be doing this by itself again)\n args = list(xr.align(*args, join='exact'))\n\n\n\n # what happens if we skip this?\n #args = list(xr.broadcast(*args))\n a0 = args[0]\n a_dims = a0.dims\n\n # roll our own broadcasting\n # now manually expand the arrays\n all_dims = [d for a in args for d in a.dims]\n all_dims_ordered = list(OrderedDict.fromkeys(all_dims))\n args_expanded = []\n for a in args:\n expand_keys = [d for d in all_dims_ordered if d not in a.dims]\n a_expanded = a.expand_dims({k: 1 for k in expand_keys})\n args_expanded.append(a_expanded)\n\n # only transpose if necessary, to avoid creating unnecessary dask tasks\n args_transposed = []\n for a in args_expanded:\n if a.dims != all_dims_ordered:\n args_transposed.append(a.transpose(*all_dims_ordered))\n else:\n args.transposed.append(a)\n args_data = [a.data for a in args_transposed]\n\n if N_weights:\n weights_data = args_data.pop()\n else:\n weights_data = None\n\n if dim is not None:\n dims_to_keep = [d for d in all_dims_ordered if d not in dim]\n axis = [args_transposed[0].get_axis_num(d) for d in dim]\n else:\n dims_to_keep = []\n axis = None\n\n h_data = _histogram(*args_data, weights=weights_data, bins=bins, axis=axis,\n block_size=block_size)\n\n # create output dims\n new_dims = [a.name + bin_dim_suffix for a in args[:N_args]]\n output_dims = dims_to_keep + new_dims\n\n # create new coords\n bin_centers = [0.5*(bin[:-1] + bin[1:]) for bin in bins]\n new_coords = {name: ((name,), bin_center, a.attrs)\n for name, bin_center, a in zip(new_dims, bin_centers, args)}\n\n old_coords = {name: a0[name]\n for name in dims_to_keep if name in a0.coords}\n all_coords = {}\n all_coords.update(old_coords)\n all_coords.update(new_coords)\n\n # CF conventions tell us how to specify cell boundaries\n # http://cfconventions.org/Data/cf-conventions/cf-conventions-1.7/cf-conventions.html#cell-boundaries\n # However, they require introduction of an additional dimension.\n # I don't like that.\n edge_dims = [a.name + bin_edge_suffix for a in args[:N_args]]\n edge_coords = {name: ((name,), bin_edge, a.attrs)\n for name, bin_edge, a in zip(edge_dims, bins, args)}\n\n output_name = '_'.join(['histogram'] + [a.name for a in args[:N_args]])\n\n da_out = xr.DataArray(h_data, dims=output_dims, coords=all_coords,\n name=output_name)\n return da_out\n\n # we need weights to be passed through apply_func's alignment algorithm,\n # so we include it as an arg, so we create a wrapper function to do so\n # this feels like a hack\n # def _histogram_wrapped(*args, **kwargs):\n # alist = list(args)\n # weights = [alist.pop() for n in range(N_weights)]\n # if N_weights == 0:\n # weights = None\n # elif N_weights == 1:\n # weights = weights[0] # squeeze\n # return _histogram(*alist, weights=weights, **kwargs)","def compute_barycenters(self):\n barycenters = []\n for i,tri in enumerate(self.tri_pnts):\n #import pdb; pdb.set_trace()\n barycenters.append(1./3.*self.points[tri].sum(axis=0))\n tri0, tri1, tri2 = tri\n a = np.linalg.norm(self.points[tri1]-self.points[tri0])\n b = np.linalg.norm(self.points[tri2]-self.points[tri1])\n c = np.linalg.norm(self.points[tri0]-self.points[tri2])\n #import pdb; pdb.set_trace()\n self.side_length[i,:] = a,b,c\n s = 0.5 * (a+b+c)\n self.V[i] = math.sqrt(s*(s-a)*(s-b)*(s-c));\n self.lmin[i] = 2*self.V[i]/s\n return np.array(barycenters)","def rootsumsquares(a):\n\treturn np.sqrt(np.sum(np.power(a,2)))","def diagonalSum(M):\n diogonalSum = []\n print(M)\n for i in xrange(0, M.shape[0]):\n diogonalSum += M[i][i]\n print(i)\n \n #print(diogonalSum)\n return diogonalSum","def binning(S, bands):\n B = np.zeros((S.shape[0], len(bands)), dtype=S.dtype)\n for i, b in enumerate(bands):\n B[:, i] = np.mean(S[:, b[0] : b[1]], axis=1)\n\n return B","def _fast_hist_2d(data, bin_edges):\n # Yes, I've tested this against histogramdd().\n xassign = np.digitize(data[:,0], bin_edges[1:-1]) \n yassign = np.digitize(data[:,1], bin_edges[1:-1])\n nbins = len(bin_edges) - 1\n flatcount = np.bincount(xassign + yassign * nbins, minlength=nbins*nbins)\n return flatcount.reshape((nbins, nbins))","def summation_of_primes(upper):\n\tprimes = [True if x>1 else False for x in xrange(upper+1)]\n\tfor i in xrange(2, int(math.sqrt(upper+1))):\n\t\tif primes[i]:\n\t\t\tfor j in xrange(i*i, upper+1, i):\n\t\t\t\tprimes[j] = False\n\treturn sum(i for i, is_prime in enumerate(primes) if is_prime)","def triu(m, k=0):\r\n m = asanyarray(m)\r\n out = multiply((1-tri(m.shape[0], m.shape[1], k-1, int)),m)\r\n return out","def sum1d_pos(summand):\n total = 0\n for i in range(summand.size):\n total += summand[i]\n return total","def add_bias(matrix):\n L = len(matrix)\n Ones = np.ones((L,1),dtype=np.float32)\n return np.c_[Ones,matrix] #vertical concatenetion of 2 matrice"],"string":"[\n \"def row_col_sums(i, b_j, bins, C, n_u):\\n s= C[i][i]*n_u[i]*n_u[i]\\n for j in range(bins[b_j], bins[b_j+1]):\\n if i != j:\\n s+= (C[i][j] + C[j][i])*n_u[i]*n_u[j]\\n return s\",\n \"def block_sum(i, bins, C, n_u):\\n s= 0.0\\n for j in range(bins[i], bins[i+1]):\\n for k in range(bins[i], bins[i+1]):\\n s+= C[j][k]*n_u[j]*n_u[k]\\n return s\",\n \"def _get_integrals_fast(bins):\\n\\n if min(bins.shape) == 1: \\n flatsum = np.cumsum(bins.flat[::-1])[::-1]\\n sums = np.subtract(*np.meshgrid(flatsum,flatsum))\\n assert sums.shape[0] == sums.shape[1]\\n return sums\\n\\n else: \\n y_sums = np.cumsum(bins[:,::-1], axis = 1)[:,::-1]\\n sums = np.cumsum(y_sums[::-1,:], axis = 0)[::-1,:]\\n return sums\",\n \"def _sum_on_axis(self, M, undirected=True):\\n\\n if undirected:\\n M = (M + M.T).astype('bool')\\n colsum = M.sum(axis=0) # , dtype=np.int64)\\n rowsum = M.sum(axis=1) # , dtype=np.int64) # already np.int64\\n return rowsum.T, colsum\",\n \"def column_sums(square):\\n total = 0\",\n \"def cartesian_sum(matrix, rows, cols):\\n\\n res = 0 \\n for row in rows:\\n for col in cols:\\n res += matrix[row, col]\\n return res\",\n \"def Sum2d(a):\\n return(np.sum(np.sum(a,-1),-1))\",\n \"def bin_input(neu, bin_size, overlap = 0):\\n win_d = bin_size - overlap\\n n_bins = int(((neu.shape[0]-bin_size)/win_d)+1)\\n FRmat = np.empty([n_bins, neu.shape[1]])\\n for i in range(n_bins):\\n FRmat[i,:] = np.sum(neu[i*win_d:i*win_d+bin_size,:], axis = 0)\\n \\n return FRmat\",\n \"def correlation_bins(shred):\\n return 0\",\n \"def sum_diag(max_lines):\\r\\n dsum = 1 # sum of diagonals\\r\\n cpt = 1 # number of lines processed\\r\\n val = 1 # value of the current place in the square\\r\\n inc = 0 # the increment between number for one line\\r\\n \\r\\n while cpt < max_lines:\\r\\n cpt += 2\\r\\n inc += 2\\r\\n \\r\\n for corner in range(4):\\r\\n val += inc\\r\\n dsum += val\\r\\n\\r\\n return dsum\",\n \"def sum(matrix):\\n\\n return float(sum([sum(row) for row in matrix]))\",\n \"def create(matrix):\\n limit_y = len(matrix)\\n limit_x = len(matrix[0])\\n\\n for y in range(1, limit_y):\\n bit.create(matrix[y])\\n\\n for x in range(1, limit_x):\\n for y in range(1, limit_y):\\n k = y + (y & -y)\\n if k < limit_y:\\n matrix[k][x] += matrix[y][x]\",\n \"def rowsums (self):\\n return self.values.sum (axis=0)\",\n \"def binned_matrix(in_tensor, method='max'):\\n print(in_tensor[0])\\n in_tensor = nn.Softmax(dim=2)(torch.einsum('cij -> ijc', in_tensor))\\n if method == 'max':\\n # Predict the bins with the highest probability\\n return in_tensor.max(2)[1]\\n elif method == 'avg':\\n # Predict the bin that is closest to the average of the probability dist\\n # predicted_bins[i][j] = round(sum(bin_index * P(bin_index at i,j)))\\n bin_indices = torch.arange(in_tensor.shape[-1]).float()\\n predicted_bins = torch.round(torch.sum(in_tensor.mul(bin_indices),\\n dim=len(in_tensor.shape)-1))\\n return predicted_bins\\n else:\\n raise ValueError('method must be in {\\\\'avg\\\\',\\\\'max\\\\'}')\",\n \"def sum_numba(A):\\n N = A.shape\\n B = np.zeros((N[0], N[2]))\\n for i in range(N[0]):\\n for j in range(N[2]):\\n for k in range(N[1]):\\n B[i, j] += A[i, k, j]\\n return B\",\n \"def bin_binarise(self):\\n pass\",\n \"def area(self):\\n return numpy.prod(\\n numpy.meshgrid(*self.binwidths, indexing='ij'), axis=0)\",\n \"def amount_of_stairs(n):\\n\\n matrix = [[0] * n for i in range(n)]\\n\\n for i in range(0, n):\\n for j in range(1, i):\\n matrix[i][j] = sum(matrix[i - j - 1][:j])\\n matrix[i][i] = 1\\n\\n # print_matrix(matrix)\\n return sum(matrix[n-1])\",\n \"def total2d(arr: List[List[int]]) -> int: # _8 [✅]\\n # ** try to solve this in one line using a list comprehension\\n return sum( [sum(sub_arr) for sub_arr in arr ] )\",\n \"def island_perimeter(grid):\\n total = 0\\n for b in range(len(grid)):\\n for a in range(len(grid[b])):\\n # left corner\\n if (a == 0) and (b == 0):\\n if grid[b][a] == 1:\\n total = total + 2\\n if grid[b][a + 1] == 0:\\n total = total + 1\\n if grid[b + 1][a] == 0:\\n total = total + 1\\n # right corner\\n elif (a == len(grid[b]) - 1) and b == 0:\\n if grid[b][a] == 1:\\n total = total + 2\\n if grid[b + 1][a] == 0:\\n total = total + 1\\n if grid[b][a - 1] == 0:\\n total = total + 1\\n # lower-left corner\\n elif a == 0 and b == (len(grid) - 1):\\n if grid[b][a] == 1:\\n total = total + 2\\n if grid[b][a + 1] == 0:\\n total = total + 1\\n if grid[b - 1][a] == 0:\\n total = total + 1\\n # lower-right corner\\n elif b == (len(grid) - 1) and a == (len(grid[b]) - 1):\\n if grid[b][a] == 1:\\n total = total + 2\\n if grid[b - 1][a] == 0:\\n total = total + 1\\n if grid[b][a - 1] == 0:\\n total = total + 1\\n # top edge\\n elif (b == 0 and a > 0) and a < (len(grid[b]) - 1):\\n if grid[b][a] == 1:\\n total = total + 1\\n if grid[b][a - 1] == 0:\\n total = total + 1\\n if grid[b + 1][a] == 0:\\n total = total + 1\\n if grid[b][a + 1] == 0:\\n total = total + 1\\n # left edge\\n elif (b > 0 and b < (len(grid) - 1)) and ((a == 0) and a <\\n len(grid[b]) - 1):\\n if grid[b][a] == 1:\\n total = total + 1\\n if grid[b - 1][a] == 0:\\n total = total + 1\\n if grid[b][a + 1] == 0:\\n total = total + 1\\n if grid[b + 1][a] == 0:\\n total = total + 1\\n # right edge\\n elif (b > 0 and (b < len(grid) - 1)) and (a == len(grid[b]) - 1):\\n if grid[b][a] == 1:\\n total = total + 1\\n if grid[b - 1][a] == 0:\\n total = total + 1\\n if grid[b][a - 1] == 0:\\n total = total + 1\\n if grid[b + 1][a] == 0:\\n total = total + 1\\n # bottom edge\\n elif (b == len(grid) - 1) and a > 0 and a < len(grid[b]) - 1:\\n if grid[b][a] == 1:\\n total = total + 1\\n if grid[b][a - 1] == 0:\\n total = total + 1\\n if grid[b - 1][a] == 0:\\n total = total + 1\\n if grid[b][a + 1] == 0:\\n total = total + 1\\n # cases that are neither edges nor corners\\n elif (b > 0 and b < len(grid) - 1) and (a > 0 and a <\\n len(grid[b]) - 1):\\n if grid[b][a] == 1:\\n if grid[b][a - 1] == 0:\\n total = total + 1\\n if grid[b][a + 1] == 0:\\n total = total + 1\\n if grid[b - 1][a] == 0:\\n total = total + 1\\n if grid[b + 1][a] == 0:\\n total = total + 1\\n return total\",\n \"def _gu_sum(a, **kwds):\\n return np.sum(np.ascontiguousarray(a), axis=-1, **kwds)\",\n \"def minesweeper(matrix):\\n \\n num_rows = len(matrix)\\n num_cols = len(matrix[0])\\n \\n adj_mines = []\\n \\n adj_row = [0]*num_cols\\n \\n for i in range(num_rows):\\n adj_mines.append(adj_row[:])\\n \\n for r in range(num_rows):\\n for c in range(num_cols):\\n if matrix[r][c] == True:\\n if (r-1) in range(num_rows) and (c-1) in range(num_cols):\\n adj_mines[r-1][c-1] += 1\\n if (r-1) in range(num_rows) and (c) in range(num_cols):\\n adj_mines[r-1][c] += 1\\n if (r-1) in range(num_rows) and (c+1) in range(num_cols): \\n adj_mines[r-1][c+1] += 1\\n if (r) in range(num_rows) and (c-1) in range(num_cols):\\n adj_mines[r][c-1] += 1\\n if (r) in range(num_rows) and (c+1) in range(num_cols): \\n adj_mines[r][c+1] += 1\\n if (r+1) in range(num_rows) and (c-1) in range(num_cols):\\n adj_mines[r+1][c-1] += 1\\n if (r+1) in range(num_rows) and (c) in range(num_cols):\\n adj_mines[r+1][c] += 1\\n if (r+1) in range(num_rows) and (c+1) in range(num_cols): \\n adj_mines[r+1][c+1] += 1\\n\\n \\n return adj_mines\",\n \"def matrixScore(self, A):\\n\\n def firstCol(matrix):\\n \\tfor row in matrix:\\n \\t\\tif row[0] == 0:\\n \\t\\t\\tfor ind in range(len(row)):\\n \\t\\t\\t\\trow[ind] ^= 1\\n \\treturn\\n\\n def nextCol(matrix):\\n \\tcolNum = len(matrix[0])\\n \\tfor col in range(1, colNum):\\n \\t\\tcolSum = 0\\n \\t\\tfor row in matrix:\\n \\t\\t\\tcolSum += row[col]\\n \\t\\tif colSum < len(matrix)/2:\\n \\t\\t\\tfor row in matrix:\\n \\t\\t\\t\\trow[col] ^= 1\\n \\treturn\\n\\n def binSum(matrix):\\n \\ttotalSum = 0\\n \\tfor row in matrix:\\n \\t\\tbinStr = \\\"\\\"\\n \\t\\tfor elem in row:\\n \\t\\t\\tbinStr += str(elem)\\n \\t\\ttotalSum += int(binStr, 2)\\n \\treturn totalSum\\n\\n\\n firstCol(A)\\n nextCol(A)\\n return binSum(A)\",\n \"def rowSum(mtx):\\n try:\\n for i in range(0, len(mtx)):\\n assert len(mtx[i]) == len(mtx[i-1]) # check whether each list has the same length.\\n \\n res = list()\\n for j in range(0, len(mtx[0])): \\n tmp = 0\\n for i in range(0, len(mtx)): \\n tmp = tmp + mtx[i][j]\\n res.append(tmp)\\n return(res)\\n \\n except AssertionError as detail:\\n return ('Length of lists is irregular or input format is wrong.')\\n except TypeError as detail:\\n return ('Undefined operand type')\",\n \"def compute_barycentrics_native(coords, mesh):\\n triangle_indices = mesh.find_simplex(coords)\\n X = mesh.transform[triangle_indices, :2]\\n Y = coords - mesh.transform[triangle_indices, 2]\\n b = np.einsum('ijk,ik->ij', X, Y)\\n bcoords = np.c_[b, 1 - b.sum(axis=1)]\\n return bcoords, triangle_indices\",\n \"def colintegrals (self):\\n dy = self.ybins[1] - self.ybins[0]\\n return self.colsums * dy\",\n \"def rowintegrals (self):\\n dx = self.xbins[1] - self.xbins[0]\\n return self.rowsums * dx\",\n \"def bin_centers(radial_bins):\\n\\n outer = radial_bins[1:]\\n inner = radial_bins[:-1]\\n return 0.5 * (outer + inner)\",\n \"def bin_reduce(data, axes_s):\\n d1 = data\\n \\n # sort axes by stride distance to optimize for locality\\n # doesn't seem to make much difference on modern systems...\\n axes = [ (axis, d1.strides[axis]) for axis in range(d1.ndim) ]\\n axes.sort(key=lambda p: p[1])\\n assert len(axes_s) == data.ndim\\n\\n # reduce one axis at a time to shrink work for subsequent axes\\n for axis in [p[0] for p in axes]:\\n s = axes_s[axis]\\n\\n if s == 1:\\n # skip useless copying for non-reducing axis\\n continue\\n\\n # accumulate s-strided subsets that belong to each bin\\n a = d1[ \\n tuple(\\n [ slice(None) for i in range(axis) ]\\n + [ slice(0, 1-s, s) ]\\n + [ slice(None) for i in range(d1.ndim - axis - 1) ]\\n )\\n ].astype(np.float32, copy=True)\\n \\n for step in range(1, s):\\n a += d1[ \\n tuple(\\n [ slice(None) for i in range(axis) ]\\n + [ slice(step, step < s and 1-s+step or None, s) ]\\n + [ slice(None) for i in range(d1.ndim - axis - 1) ]\\n )\\n ]\\n\\n # compute single-axis bin averages from accumulation\\n d1 = a * (1./s)\\n\\n return d1\",\n \"def colsums (self):\\n return self.values.sum (axis=1)\",\n \"def constraint_sum(w):\\n return sum(w) - 1\",\n \"def find_bin_edges(bin_centres):\\n\\n if not isinstance(bin_centres, np.ndarray):\\n bin_centres = np.asarray(bin_centres)\\n\\n edges = bin_centres[:-1] + 0.5 * (bin_centres[1:] - bin_centres[:-1])\\n bins = np.concatenate(([2 * bin_centres[0] - edges[0]], edges,\\n [2 * bin_centres[-1] - edges[-1]]))\\n\\n return bins\",\n \"def bin_matrix(x, binning):\\n\\n N = len(x)\\n B = []\\n for i in range(len(binning) - 1):\\n\\n line = np.zeros(N)\\n for j in range(N):\\n\\n if x[j] >= binning[i] and x[j] < binning[i+1]:\\n\\n size = binning[i + 1] - binning[i]\\n line[j] = 1 / size\\n\\n B.append(line)\\n\\n B = np.array(B)\\n\\n return(B)\",\n \"def trans_hellinger(m):\\n m = asmatrix(m)\\n row_sums = sum(m, axis=1)\\n result = sqrt(m / row_sums)\\n return result\",\n \"def quadsum(data):\\n return np.nansum(data, 0)\",\n \"def test_canonicalization_connected_summations(free_alg):\\n dr = free_alg\\n p = dr.names\\n i, j, k, l = p.R_dumms[:4]\\n a, b = symbols('a b')\\n t = IndexedBase('t')\\n\\n tensor = dr.sum(\\n (i, p.R), (j, p.R[0, i]), (k, p.R[0, a]), (l, p.R[0, b]),\\n t[i] * t[j] * t[k] * t[l]\\n )\\n res = tensor.simplify()\\n sums = res.local_terms[0].sums\\n\\n # The desired order for the summations.\\n assert not sums[0][1].bounded\\n assert sums[1][1].upper == a\\n assert sums[2][1].upper == b\\n assert sums[3][1].upper == sums[0][0]\",\n \"def build_integer_basis_of_orthogonal_complement_of_binomial_matrix(messi_network):\\n binomial_matrix = build_binomial_matrix(messi_network)\\n\\n\\n #print(\\\"G1\\\", messi_network.G1.edges())\\n #print(\\\"G2\\\", messi_network.G2.edges())\\n #print(\\\"G2 circle\\\", messi_network.G2_circle.edges())\\n #print(\\\"B\\\", binomial_matrix)\\n\\n\\n #print(\\\"binomial matrix\\\")\\n #print(binomial_matrix)\\n #print(\\\"build_integer_basis_of_orthogonal_complement_of_binomial_matrix\\\")\\n ret = build_integer_basis_matrix_of_orthogonal_complement_of_matrix(binomial_matrix.transpose(), positive=False).transpose()\\n #M = binomial_matrix.transpose()\\n #M_ort = ret.transpose()\\n #print(M @ M_ort)\\n return ret\",\n \"def binary_matmul_1d(row_p, col_i, other, m):\\n out = np.zeros(m, dtype=FLOAT_STORAGE_np)\\n for i in range(m):\\n vector_sum = 0\\n for r in col_i[row_p[i]:row_p[i + 1]]:\\n vector_sum += other[r]\\n out[i] = vector_sum\\n return out\",\n \"def simetrize_3dhistogram(histogram):\\n N =len(histogram)\\n n_histogram = np.zeros((N,N,N))\\n for i in range(N):\\n for j in range(i,N):\\n for k in range(j, N):\\n S = histogram[i][j][k] + histogram[k][i][j] + histogram[j][k][i] + histogram[i][k][j] + histogram[j][i][k] + histogram[k][j][i]\\n n_histogram[i][j][k] = S\\n n_histogram[k][i][j] = S\\n n_histogram[j][k][i] = S\\n n_histogram[i][k][j] = S\\n n_histogram[j][i][k] = S\\n n_histogram[k][j][i] = S\\n #a[i][j][k], a[k][i][j], a[j][k][i], a[i][k][j], a[j][i][k], a[k][j][i]\\n return n_histogram\",\n \"def TRI2centroidBL(TRI):\\n # For each row of TRI, find rows that share a bond (up to 3 of these)\\n dic = {}\\n # nbrs = np.zeros((len(BL),3), dtype=np.int)\\n BL = np.zeros((len(TRI) * 3, 2), dtype=np.int)\\n dmyi = 0\\n # make sure each row has indices increasing in each row\\n TRI.sort()\\n\\n # Compose dictionary with pairs of neighboring triangles\\n for row in TRI:\\n if (row[0], row[1]) in dic:\\n dic[(row[0], row[1])].append(dmyi)\\n else:\\n dic[(row[0], row[1])] = [dmyi]\\n if (row[0], row[2]) in dic:\\n dic[(row[0], row[2])].append(dmyi)\\n else:\\n dic[(row[0], row[2])] = [dmyi]\\n if (row[1], row[2]) in dic:\\n dic[(row[1], row[2])].append(dmyi)\\n else:\\n dic[(row[1], row[2])] = [dmyi]\\n dmyi += 1\\n\\n # print 'dic = ', dic\\n # Compose BL from the pairs of indices in each dict entry\\n dmyi = 0\\n for key in dic:\\n # if the bond is not an edge of the lattice or\\n # a dangling bond, add to BL.\\n # print 'key = ', key, ' -> d[key] = ', dic[key]\\n if len(dic[key]) == 2:\\n BL[dmyi] = dic[key]\\n dmyi += 1\\n\\n return BL[0:dmyi]\",\n \"def sum_across_rows(grid):\\n pass\",\n \"def sum_reduce_nb(col, a, *args):\\n return np.nansum(a)\",\n \"def freedman_diaconis_bins(self, arr):\\n # From https://stats.stackexchange.com/questions/798/\\n if len(arr) < 2:\\n return 1\\n # Calculate the iqr ranges.\\n self.iqr(arr)\\n # Calculate the h\\n h = 2 * (self.q3 - self.q1) / (len(arr) ** (1 / 3))\\n # fall back to sqrt(a) bins if iqr is 0\\n if h == 0:\\n return int(np.sqrt(arr.size))\\n else:\\n return int(np.ceil((arr.max() - arr.min()) / h))\",\n \"def calculate_square_form(diagonal_matrix, total_sorts):\\n n = len(diagonal_matrix)\\n\\n matrix = np.ndarray(shape=(n,n))\\n\\n for i in range(n):\\n for j in range(len(diagonal_matrix[i])):\\n # Also calculate the dissimilarity matrix\\n matrix[i][j] = 100 - 100 * diagonal_matrix[i][j] / total_sorts\\n matrix[j][i] = 100 - 100 * diagonal_matrix[i][j] / total_sorts\\n if i == j:\\n matrix[i][j] = 0\\n\\n return matrix\\n\\n # matrix = np.tril(diagonal_matrix, k=-1)\\n # matrix = matrix + matrix.T\\n # matrix = matrix * (-100 / total_sorts) + 100\\n # np.fill_diagonal(matrix, 0)\\n # return matrix\",\n \"def area(boxes):\\n return (boxes[:, 2] - boxes[:, 0]) * (boxes[:, 3] - boxes[:, 1])\",\n \"def segment_sum(self, left, right):\\n if self.empty():\\n return 0\\n less, _ = self.root.split_sums(left)\\n _, greater = self.root.split_sums(right)\\n return self.sum - less - greater\",\n \"def bin_discretize(self, variables=[], bins=3,\\n min_const_samples_bin_size=1.0/3):\\n self.edges=np.zeros((self.arity.size,bins+1))\\n for i in variables:\\n un_cnt=np.unique(self.data[:,i],return_counts=True)\\n constvals=un_cnt[0][un_cnt[1]>self.data.shape[0]*min_const_samples_bin_size]\\n mask=np.ones(self.data.shape[0],dtype=bool)\\n if constvals.size>0:\\n for j,cv in enumerate(constvals):\\n mask*=(self.data[:,i]!=cv)\\n self.data[self.data[:,i]==cv,i]=j\\n\\n size=np.sum(mask)/bins\\n sorted_i=np.argsort(self.data[mask,i])\\n edges=[self.data[mask,i][sorted_i[int(size*num)-1]] for num in range(1,bins)]\\n self.edges[i]=[self.data[mask,i][sorted_i[0]]]+edges+[self.data[mask,i][sorted_i[-1]]]\\n self.data[mask,i]=np.searchsorted(edges,self.data[mask,i])+constvals.size\\n self.arity[i]=len(edges)+1+constvals.size\",\n \"def bin_edges_to_centres(edges):\\r\\n if edges.ndim == 1:\\r\\n steps = (edges[1:] - edges[:-1]) / 2\\r\\n return edges[:-1] + steps\\r\\n else:\\r\\n steps = (edges[1:, 1:] - edges[:-1, :-1]) / 2\\r\\n centres = edges[:-1, :-1] + steps\\r\\n return centres\",\n \"def area(boxes):\\n return (boxes[:, 2] - boxes[:, 0]) * (boxes[:, 3] - boxes[:, 1])\",\n \"def upper(mat):\\n idx = np.triu_indices_from(mat, k=1)\\n return mat[idx]\",\n \"def get_centers_from_bins(bins):\\n return 0.5 * (bins[:-1] + bins[1:])\",\n \"def batch_outer_sum(a, b):\\n a, b = normalize_and_check_ndim([a, b], 2)\\n # Due to broadcasting, this sum works analogously to batch matrix\\n # multiplication. See also comments in batch_outer_product().\\n return a[:, :, np.newaxis] + b[:, np.newaxis, :]\",\n \"def sumRegion(self, row1, col1, row2, col2):\\n \\n if row1 == col1 == 0:\\n return self.matrix_sum[row2][col2]\\n elif row1 == 0:\\n return self.matrix_sum[row2][col2] - self.matrix_sum[row2][col1-1]\\n elif col1 == 0:\\n return self.matrix_sum[row2][col2] - self.matrix_sum[row1-1][col2]\\n else:\\n return (self.matrix_sum[max(row1, row2)][max(col1, col2)] - \\n self.matrix_sum[max(0, min(row1, row2)-1)][max(col1, col2)] - \\n self.matrix_sum[max(row1, row2)][max(0, min(col1, col2)-1)] + \\n self.matrix_sum[max(0, min(row1, row2)-1)][max(0, min(col1, col2)-1)])\",\n \"def solution(n, m, r, c, k) -> int:\\n xs = []\\n # Add all the non-zero room widths to xs\\n last_column_wall = None\\n for col in c:\\n if last_column_wall is not None and col - last_column_wall - 1 > 0:\\n xs.append(col - last_column_wall - 1)\\n last_column_wall = col\\n ys = []\\n # Add all the non-zero room heights to ys\\n last_row_wall = None\\n for row in r:\\n if last_row_wall is not None and row - last_row_wall - 1 > 0:\\n ys.append(row - last_row_wall - 1)\\n last_row_wall = row\\n return aux(xs, ys, k)\",\n \"def total_power_square(x, y, serial, size):\\n result = 0\\n for i in range(x, x + size):\\n for j in range(y, y + size):\\n result += cell_power(i, j, serial)\\n return result\",\n \"def absolute_sum_of_elements(self) -> int:\\n absolute_sum = abs(self._A[0][0])\\n for y in range(0, self.num_of_rows):\\n for x in range(0, self.num_of_cols):\\n absolute_sum += abs(self._A[y][x])\\n return absolute_sum\",\n \"def _dimensionalize(x: np.ndarray, lb: np.ndarray, ub: np.ndarray) -> np.ndarray:\\n return lb + x * (ub - lb)\",\n \"def image_quad_norm(inarray):\\n # If there is an hermitian symmetry\\n if inarray.shape[-1] != inarray.shape[-2]:\\n return 2 * np.sum(np.sum(np.abs(inarray)**2, axis=-1), axis=-1) - \\\\\\n np.sum(np.abs(inarray[..., 0])**2, axis=-1)\\n else:\\n return np.sum(np.sum(np.abs(inarray)**2, axis=-1), axis=-1)\",\n \"def norm_by_rows(matrix):\\n row_sums = matrix.sum(axis=1)\\n return matrix / row_sums.reshape((-1, 1))\",\n \"def sqrSum(a, b, i, j):\\n return (a - i)**2 + (b - j)**2\",\n \"def to_bins(arr):\\n result = np.zeros(len(arr)+1)\\n result[1:-1] = 0.5 * (arr[1:] + arr[:-1])\\n result[0] = arr[0] - 0.5*(arr[1] - arr[0])\\n result[-1] = arr[-1] + 0.5*(arr[-1] - arr[-2])\\n return result\",\n \"def test00(self):\\n N = self.N\\n a = bcolz.fromiter(xrange(N), dtype=np.float64, count=N,\\n rootdir=self.rootdir)\\n l, s = 0, 0\\n for block in bcolz.iterblocks(a):\\n l += len(block)\\n s += block.sum()\\n self.assertEqual(l, N)\\n # as per Gauss summation formula\\n self.assertEqual(s, (N - 1) * (N / 2))\",\n \"def sum_squares(X):\\n pref_sum = np.cumsum(X)\\n pref_sum_squares = np.cumsum(X**2)\\n n = len(X)\\n sum_squares_left = np.zeros(n+1)\\n sum_squares_right = np.zeros(n+1)\\n for i in range(1, n+1):\\n mean_left = pref_sum[i-1]/i \\n mean_right = (pref_sum[-1] - pref_sum[i-1])/(n-i) # bad for i = n overwritten later\\n sum_squares_left[i] = pref_sum_squares[i-1] + i * mean_left**2 - 2 * mean_left * pref_sum[i-1] \\n sum_squares_right[i] = (pref_sum_squares[-1] - pref_sum_squares [i-1]) + mean_right**2 * (n-i) - 2 * mean_right *(pref_sum[-1] - pref_sum[i-1])\\n sum_squares_left[0] = 0\\n sum_squares_right[0] = sum_squares_left[-1]\\n sum_squares_right[-1] = 0\\n return sum_squares_left, sum_squares_right\",\n \"def trace(self):\\n if not self.is_square():\\n raise(ValueError, \\\"Cannot calculate the trace of a non-square matrix.\\\")\\n\\n # TODO - your code here\\n sum = 0.0\\n for i in range(self.h):\\n for j in range(self.w):\\n if i == j:\\n sum = sum + self.g[i][j]\\n return sum\\n # TODO - your code here\",\n \"def housetriang(A, B):\\n m, n = shape(A); r = shape(B)[1] ; A=hstack([A,B]); \\n minval = array([n, m - 1]).min()\\n for k in range(minval):\\n v, A[k, k] = housegen(A[k:m, k])\\n v = matrix(reshape(v, (m - k, 1)))\\n C = A[k:m, (k+1):(n+r)] ; A[k:m, (k + 1):(n + r)] = C - v*(v.T*C)\\n R = triu(A[:, :n]); C = A[:, n:(n + r)]\\n return R, C\",\n \"def binary_dist_sorensen_dice(datamtx, strict=True):\\n datamtx = datamtx.astype(bool)\\n datamtx = datamtx.astype(float)\\n if strict:\\n if not all(isfinite(datamtx)):\\n raise ValueError(\\\"non finite number in input matrix\\\")\\n if any(datamtx<0.0):\\n raise ValueError(\\\"negative value in input matrix\\\")\\n if rank(datamtx) != 2:\\n raise ValueError(\\\"input matrix not 2D\\\")\\n numrows, numcols = shape(datamtx)\\n else:\\n try:\\n numrows, numcols = shape(datamtx)\\n except ValueError:\\n return zeros((0,0),'d')\\n\\n if numrows == 0 or numcols == 0:\\n return zeros((0,0),'d')\\n dists = zeros((numrows,numrows),'d')\\n rowsums = datamtx.sum(axis=1)\\n \\n for i in range(numrows):\\n row1 = datamtx[i]\\n for j in range(i):\\n row2 = datamtx[j]\\n bottom = float(rowsums[i] + rowsums[j])\\n cur_d = 0.0\\n if bottom:\\n cur_d = 1-(2*logical_and(row1,row2).sum()/bottom)\\n dists[i][j] = dists[j][i] = cur_d\\n return dists\",\n \"def effective_pairs(self):\\n out = 0\\n hdmat = self.hdmatrix()\\n for i in xrange(len(hdmat[0])):\\n for j in xrange(i+1, len(hdmat[0])): \\n out += hdmat[i,j]**2\\n return out\",\n \"def area(boxes: Union[np.array, torch.Tensor]) -> Union[np.array, torch.Tensor]:\\n return (boxes[:, 2] - boxes[:, 0]) * (boxes[:, 3] - boxes[:, 1])\",\n \"def neighbors_magnitude(self):\\n return sum((n.weight for n in self.neighbors))\",\n \"def CL_histogram_MMD(sketch,Phi,domain,dimension,nb_cat_per_dim=None,bins_cont=10):\\n ## 0) Parsing the inputs\\n # Number of categorical inputs\\n if nb_cat_per_dim is None:\\n nb_cat_per_dim = np.zeros(Phi.d)\\n \\n is_integer_dimension = False\\n if nb_cat_per_dim[dimension] > 0:\\n # The data is integer-type\\n is_integer_dimension = True\\n bins = int(nb_cat_per_dim[dimension])\\n else:\\n bins = bins_cont\\n\\n m = sketch.size\\n # 1) Construct the A matrix\\n A = 1j*np.zeros((m,bins)) # Pre-allocation\\n bin_edges = np.linspace(domain[dimension,0],domain[dimension,1],bins+1)\\n box = domain.copy()\\n for p in range(bins):\\n # move to the next box\\n if is_integer_dimension:\\n box[dimension,0] = p\\n box[dimension,1] = p\\n else:\\n box[dimension,0] = bin_edges[p]\\n box[dimension,1] = bin_edges[p+1]\\n A[:,p] = fourierSketchOfBox(box,Phi,nb_cat_per_dim) \\n \\n # 1.b) cast to real \\n Ari = np.r_[A.real, A.imag]\\n \\n # 2) create b vector\\n b = np.r_[sketch.real, sketch.imag]\\n \\n # 3) solve the optimization problem\\n def _f_grad(x):\\n r = Ari@x-b\\n f = 0.5*np.linalg.norm(r)**2\\n grad = Ari.T@r\\n return (f,grad)\\n \\n # Starting point\\n x0 = np.ones(bins)/bins\\n # Linear constraints\\n A_constr = np.zeros((bins,bins))\\n l_constr = 0*np.ones(bins) # Positive constraints\\n A_constr[:bins,:bins] = np.eye(bins)\\n upper_bound = 5 # weird that it must be large\\n u_constr = upper_bound*np.ones(bins) # Sum-to one constraints\\n constr = LinearConstraint(A_constr,l_constr,u_constr)\\n\\n # Solve\\n sol = minimize(_f_grad, x0, method='trust-constr', bounds=None, constraints=constr, jac=True, options={'verbose': 0})\\n\\n return project_probabilitySimplex(sol.x)\",\n \"def compute_histogram(self):\\n # compute distance between points \\n distmatrix = np.sqrt(pdist(self.points))\\n if not self.mean_dist:\\n self.mean_dist = np.mean(distmatrix)\\n distmatrix = distmatrix/self.mean_dist\\n distmatrix = squareform(distmatrix)\\n #compute angles between points\\n angles = compute_angles(self.points)\\n #quantize angles to a bin\\n tbins = np.floor(angles / (2 * pi / self.nbins_theta))\\n lg = np.logspace(self.r1, self.r2, num=5)\\n #quantize radious to bins\\n rbins = np.ones(angles.shape) * -1\\n for r in lg:\\n counts = (distmatrix < r) \\n rbins = rbins + counts.astype(int) \\n return rbins, tbins\",\n \"def trace(self):\\n # TODO 异常 非常规输入处理\\n if not self.is_square():\\n raise(\\n ValueError, \\\"Cannot calculate the trace of a non-square matrix.\\\")\\n # TODO Calculates the main diagonal num's sum\\n sum = 0\\n for i in range(self.h):\\n sum += self[i][i]\\n\\n return sum\",\n \"def _cartesian_add(xs):\\n return sum(prefer_static.reshape(x, shape=[-1] + [1]*(len(xs) - 1 - i))\\n for i, x in enumerate(xs))\",\n \"def housetriang(A, B):\\n\\n m, n = A.shape\\n try:\\n _, r = B.shape\\n except:\\n r = 1\\n\\n A = np.hstack((A.copy(), B.copy()))\\n\\n for k in range(min(n, m-1)):\\n v, A[k, k] = housegen(A[k:m, k])\\n v = np.reshape(v, (m-k, 1))\\n C = A[k:m, k+1:n+r]\\n A[k:m, k+1:n+r] = C - v * np.dot(np.conjugate(v).T, C)\\n\\n R = np.triu(A[:, 0:n])\\n C = A[:, n:n+r]\\n\\n return R, C\",\n \"def getBinSize(axes, bin_numbers):\\n\\n size = 1\\n for axis, bin_number in zip(axes, bin_numbers):\\n if bin_number in (\\\"underflow\\\", \\\"overflow\\\"):\\n continue\\n x0, x1 = axis.getBinRange(bin_number)\\n size *= x1 - x0\\n return size\",\n \"def from_simple_root_basis(coefficients, A):\\n return np.sum([coefficient * row for coefficient, row in zip(coefficients, A)], axis=0)\",\n \"def compute_cost(indexes, matrix):\\n total = 0\\n for row, columns in indexes:\\n total += matrix[row][columns]\\n return total\",\n \"def area(bboxes: np.array) -> np.ndarray:\\n if bboxes.ndim == 1:\\n bboxes = np.expand_dims(bboxes, axis=0)\\n w = (bboxes[:, 2] - bboxes[:, 0]).clip(min=0)\\n h = (bboxes[:, 3] - bboxes[:, 1]).clip(min=0)\\n return w * h\",\n \"def sumouter(us,vs,lo=-1.0,hi=1.0,out=None):\\n result = zeros((len(us[0]),len(vs[0])))\\n for u,v in zip(us,vs):\\n result += outer(clip(u,lo,hi),v)\\n return result\",\n \"def sumRegion(self, row1, col1, row2, col2):\\n\\n if not self.sums:\\n return 0\\n\\n r1, c1 = row1+1, col1+1\\n r2, c2 = row2+1, col2+1\\n return self.sums[r2][c2] + self.sums[r1-1][c1-1] - self.sums[r2][c1-1] - self.sums[r1-1][c2]\",\n \"def binning_axis(self) -> int:\\r\\n return 0\",\n \"def accumulate_triangles(triangle_strip, shape):\\n triangle_strip = numpy.asarray(triangle_strip)\\n assert triangle_strip.ndim == 2 and triangle_strip.shape[1] == 2 and len(triangle_strip) > 2\\n grid = numpy.indices(shape) + 0.5 # pixel centers are at (0.5, 0.5 geometrically)\\n output = numpy.zeros(shape, dtype=int)\\n for i in range(len(triangle_strip) - 2):\\n vertices = triangle_strip[i:i+3]\\n xmn, ymn = numpy.floor(vertices.min(axis=0)).astype(int)\\n xmx, ymx = numpy.ceil(vertices.max(axis=0)).astype(int) + 1\\n xs, ys = slice(xmn, xmx), slice(ymn, ymx)\\n b_coords = barycentric_coords(vertices, grid[:, xs, ys])\\n m = (b_coords >= 0).all(axis=0)\\n output[xs, ys] += (b_coords >= 0).all(axis=0)\\n return output\",\n \"def calculateEdges(i, j, matrix):\\n num = 0\\n if i > 0:\\n if matrix[i-1][j] == 0:\\n num += 1\\n if j > 0:\\n if matrix[i][j-1] == 0:\\n num += 1\\n if i < len(matrix) - 1:\\n if matrix[i+1][j] == 0:\\n num += 1\\n if j < len(matrix[0]) - 1:\\n if matrix[i][j+1] == 0:\\n num += 1\\n \\n return num\",\n \"def sum_matrixes(A, B):\\n\\n\\tD = np.zeros((3,3))\\n\\tD[0][0] = A[0][0]+ B[0][0]\\n\\tD[0][1] = A[0][1]+ B[0][1]\\n\\tD[0][2] = A[0][2]+ B[0][2]\\n\\tD[1][0] = A[1][0]+ B[1][0]\\n\\tD[1][1] = A[1][1]+ B[1][1]\\n\\tD[1][2] = A[1][2]+ B[1][2]\\n\\tD[2][0] = A[2][0]+ B[2][0]\\n\\tD[2][1] = A[2][1]+ B[2][1]\\n\\tD[2][2] = A[2][2]+ B[2][2]\\n\\treturn D\",\n \"def computeAC(bins):\\n halfn = len(bins)/2\\n Npos = sum(bins[halfn:])\\n Nneg = sum(bins[:halfn])\\n return float(Npos-Nneg)/(Npos+Nneg)\",\n \"def python_binarize_rows(l: np.ndarray) -> np.ndarray:\\n\\n def int32(x):\\n if x > 0xFFFFFFFF:\\n raise OverflowError\\n if x > 0x7FFFFFFF:\\n x = int(0x100000000 - x)\\n if x < 2147483648:\\n return -x\\n else:\\n return -2147483648\\n return x\\n\\n def binarize(n: np.ndarray) -> int:\\n result = 0\\n for i in range(32):\\n sign = n[i] >= 0\\n result |= sign << (31 - i)\\n return int32(result)\\n\\n padded = []\\n for row in l:\\n new_row = row.tolist()\\n if len(row) % 32 != 0:\\n new_row += [-1 for _ in range(32 - len(row) % 32)]\\n padded.append(np.array(new_row))\\n\\n result = []\\n for row in padded:\\n new_row = []\\n for sub_row in [row[i:i + 32] for i in range(0, len(row), 32)]:\\n new_row.append(binarize(sub_row))\\n result.append(np.array(new_row))\\n\\n return np.array(result)\",\n \"def normalizeMatrix( self, matrix, sums ):\\n matrix = matrix.astype( float )\\n\\n numpy.multiply( matrix, matrix, matrix )\\n numpy.divide( matrix, sums, matrix ) \\n matrix = numpy.transpose( matrix )\\n numpy.divide( matrix, sums, matrix )\\n matrix *= 100\\n\\n return matrix.astype( numpy.int )\",\n \"def summationLoop(lower, upper):\\r\\n sum = 0\\r\\n for i in range(lower, upper + 1):\\r\\n sum += i\\r\\n return sum\",\n \"def root_sum_of_squares_complex(data, dim=0):\\n return torch.sqrt(complex_abs_sq(data).sum(dim))\",\n \"def _reduce_triangle(to_reduce):\\n last_row = to_reduce[-1]\\n for index in xrange(len(to_reduce) - 1):\\n to_reduce[-2][index] += max(last_row[index:index + 2])\\n del to_reduce[-1]\",\n \"def histogram(*args, bins=None, dim=None, weights=None, density=False,\\n block_size='auto', bin_dim_suffix='_bin',\\n bin_edge_suffix='_bin_edge'):\\n\\n N_args = len(args)\\n\\n # TODO: allow list of weights as well\\n N_weights = 1 if weights is not None else 0\\n\\n # some sanity checks\\n # TODO: replace this with a more robust function\\n assert len(bins)==N_args\\n for bin in bins:\\n assert isinstance(bin, np.ndarray), 'all bins must be numpy arrays'\\n\\n for a in args:\\n # TODO: make this a more robust check\\n assert a.name is not None, 'all arrays must have a name'\\n\\n # we drop coords to simplify alignment\\n args = [da.reset_coords(drop=True) for da in args]\\n if N_weights:\\n args += [weights.reset_coords(drop=True)]\\n # explicitly broadcast so we understand what is going into apply_ufunc\\n # (apply_ufunc might be doing this by itself again)\\n args = list(xr.align(*args, join='exact'))\\n\\n\\n\\n # what happens if we skip this?\\n #args = list(xr.broadcast(*args))\\n a0 = args[0]\\n a_dims = a0.dims\\n\\n # roll our own broadcasting\\n # now manually expand the arrays\\n all_dims = [d for a in args for d in a.dims]\\n all_dims_ordered = list(OrderedDict.fromkeys(all_dims))\\n args_expanded = []\\n for a in args:\\n expand_keys = [d for d in all_dims_ordered if d not in a.dims]\\n a_expanded = a.expand_dims({k: 1 for k in expand_keys})\\n args_expanded.append(a_expanded)\\n\\n # only transpose if necessary, to avoid creating unnecessary dask tasks\\n args_transposed = []\\n for a in args_expanded:\\n if a.dims != all_dims_ordered:\\n args_transposed.append(a.transpose(*all_dims_ordered))\\n else:\\n args.transposed.append(a)\\n args_data = [a.data for a in args_transposed]\\n\\n if N_weights:\\n weights_data = args_data.pop()\\n else:\\n weights_data = None\\n\\n if dim is not None:\\n dims_to_keep = [d for d in all_dims_ordered if d not in dim]\\n axis = [args_transposed[0].get_axis_num(d) for d in dim]\\n else:\\n dims_to_keep = []\\n axis = None\\n\\n h_data = _histogram(*args_data, weights=weights_data, bins=bins, axis=axis,\\n block_size=block_size)\\n\\n # create output dims\\n new_dims = [a.name + bin_dim_suffix for a in args[:N_args]]\\n output_dims = dims_to_keep + new_dims\\n\\n # create new coords\\n bin_centers = [0.5*(bin[:-1] + bin[1:]) for bin in bins]\\n new_coords = {name: ((name,), bin_center, a.attrs)\\n for name, bin_center, a in zip(new_dims, bin_centers, args)}\\n\\n old_coords = {name: a0[name]\\n for name in dims_to_keep if name in a0.coords}\\n all_coords = {}\\n all_coords.update(old_coords)\\n all_coords.update(new_coords)\\n\\n # CF conventions tell us how to specify cell boundaries\\n # http://cfconventions.org/Data/cf-conventions/cf-conventions-1.7/cf-conventions.html#cell-boundaries\\n # However, they require introduction of an additional dimension.\\n # I don't like that.\\n edge_dims = [a.name + bin_edge_suffix for a in args[:N_args]]\\n edge_coords = {name: ((name,), bin_edge, a.attrs)\\n for name, bin_edge, a in zip(edge_dims, bins, args)}\\n\\n output_name = '_'.join(['histogram'] + [a.name for a in args[:N_args]])\\n\\n da_out = xr.DataArray(h_data, dims=output_dims, coords=all_coords,\\n name=output_name)\\n return da_out\\n\\n # we need weights to be passed through apply_func's alignment algorithm,\\n # so we include it as an arg, so we create a wrapper function to do so\\n # this feels like a hack\\n # def _histogram_wrapped(*args, **kwargs):\\n # alist = list(args)\\n # weights = [alist.pop() for n in range(N_weights)]\\n # if N_weights == 0:\\n # weights = None\\n # elif N_weights == 1:\\n # weights = weights[0] # squeeze\\n # return _histogram(*alist, weights=weights, **kwargs)\",\n \"def compute_barycenters(self):\\n barycenters = []\\n for i,tri in enumerate(self.tri_pnts):\\n #import pdb; pdb.set_trace()\\n barycenters.append(1./3.*self.points[tri].sum(axis=0))\\n tri0, tri1, tri2 = tri\\n a = np.linalg.norm(self.points[tri1]-self.points[tri0])\\n b = np.linalg.norm(self.points[tri2]-self.points[tri1])\\n c = np.linalg.norm(self.points[tri0]-self.points[tri2])\\n #import pdb; pdb.set_trace()\\n self.side_length[i,:] = a,b,c\\n s = 0.5 * (a+b+c)\\n self.V[i] = math.sqrt(s*(s-a)*(s-b)*(s-c));\\n self.lmin[i] = 2*self.V[i]/s\\n return np.array(barycenters)\",\n \"def rootsumsquares(a):\\n\\treturn np.sqrt(np.sum(np.power(a,2)))\",\n \"def diagonalSum(M):\\n diogonalSum = []\\n print(M)\\n for i in xrange(0, M.shape[0]):\\n diogonalSum += M[i][i]\\n print(i)\\n \\n #print(diogonalSum)\\n return diogonalSum\",\n \"def binning(S, bands):\\n B = np.zeros((S.shape[0], len(bands)), dtype=S.dtype)\\n for i, b in enumerate(bands):\\n B[:, i] = np.mean(S[:, b[0] : b[1]], axis=1)\\n\\n return B\",\n \"def _fast_hist_2d(data, bin_edges):\\n # Yes, I've tested this against histogramdd().\\n xassign = np.digitize(data[:,0], bin_edges[1:-1]) \\n yassign = np.digitize(data[:,1], bin_edges[1:-1])\\n nbins = len(bin_edges) - 1\\n flatcount = np.bincount(xassign + yassign * nbins, minlength=nbins*nbins)\\n return flatcount.reshape((nbins, nbins))\",\n \"def summation_of_primes(upper):\\n\\tprimes = [True if x>1 else False for x in xrange(upper+1)]\\n\\tfor i in xrange(2, int(math.sqrt(upper+1))):\\n\\t\\tif primes[i]:\\n\\t\\t\\tfor j in xrange(i*i, upper+1, i):\\n\\t\\t\\t\\tprimes[j] = False\\n\\treturn sum(i for i, is_prime in enumerate(primes) if is_prime)\",\n \"def triu(m, k=0):\\r\\n m = asanyarray(m)\\r\\n out = multiply((1-tri(m.shape[0], m.shape[1], k-1, int)),m)\\r\\n return out\",\n \"def sum1d_pos(summand):\\n total = 0\\n for i in range(summand.size):\\n total += summand[i]\\n return total\",\n \"def add_bias(matrix):\\n L = len(matrix)\\n Ones = np.ones((L,1),dtype=np.float32)\\n return np.c_[Ones,matrix] #vertical concatenetion of 2 matrice\"\n]","isConversational":false},"negative_scores":{"kind":"list like","value":["0.7041019","0.6156732","0.6129798","0.5976102","0.59643245","0.5771837","0.57445884","0.5654173","0.5501301","0.5317589","0.5288595","0.52820784","0.5261982","0.5255658","0.52305037","0.5229471","0.52255195","0.5207664","0.5206341","0.5196019","0.5180939","0.5179308","0.51581776","0.5140066","0.5124696","0.5121876","0.5114722","0.511348","0.50980747","0.50930995","0.50731254","0.5069529","0.5067577","0.5053223","0.5044121","0.5029349","0.5017417","0.5016987","0.50145537","0.50107807","0.5006783","0.50032866","0.4992053","0.49914405","0.49891907","0.49878642","0.4987607","0.49874517","0.49862236","0.49841985","0.4964222","0.4957451","0.4957022","0.49528348","0.4941109","0.49399367","0.49331537","0.493019","0.49294487","0.49274954","0.49159175","0.49067995","0.49024644","0.4898487","0.48931086","0.48914477","0.4884763","0.4883238","0.4882211","0.48805237","0.48799765","0.48697865","0.48674086","0.4864649","0.48635986","0.48632675","0.48608115","0.48605955","0.48596963","0.485966","0.4858821","0.48581848","0.48489502","0.48478687","0.48466748","0.484066","0.48385587","0.4832486","0.48273817","0.4826263","0.4824051","0.4817379","0.48162547","0.48114496","0.4803039","0.48021033","0.47998586","0.47986576","0.4796794","0.4791704"],"string":"[\n \"0.7041019\",\n \"0.6156732\",\n \"0.6129798\",\n \"0.5976102\",\n \"0.59643245\",\n \"0.5771837\",\n \"0.57445884\",\n \"0.5654173\",\n \"0.5501301\",\n \"0.5317589\",\n \"0.5288595\",\n \"0.52820784\",\n \"0.5261982\",\n \"0.5255658\",\n \"0.52305037\",\n \"0.5229471\",\n \"0.52255195\",\n \"0.5207664\",\n \"0.5206341\",\n \"0.5196019\",\n \"0.5180939\",\n \"0.5179308\",\n \"0.51581776\",\n \"0.5140066\",\n \"0.5124696\",\n \"0.5121876\",\n \"0.5114722\",\n \"0.511348\",\n \"0.50980747\",\n \"0.50930995\",\n \"0.50731254\",\n \"0.5069529\",\n \"0.5067577\",\n \"0.5053223\",\n \"0.5044121\",\n \"0.5029349\",\n \"0.5017417\",\n \"0.5016987\",\n \"0.50145537\",\n \"0.50107807\",\n \"0.5006783\",\n \"0.50032866\",\n \"0.4992053\",\n \"0.49914405\",\n \"0.49891907\",\n \"0.49878642\",\n \"0.4987607\",\n \"0.49874517\",\n \"0.49862236\",\n \"0.49841985\",\n \"0.4964222\",\n \"0.4957451\",\n \"0.4957022\",\n \"0.49528348\",\n \"0.4941109\",\n \"0.49399367\",\n \"0.49331537\",\n \"0.493019\",\n \"0.49294487\",\n \"0.49274954\",\n \"0.49159175\",\n \"0.49067995\",\n \"0.49024644\",\n \"0.4898487\",\n \"0.48931086\",\n \"0.48914477\",\n \"0.4884763\",\n \"0.4883238\",\n \"0.4882211\",\n \"0.48805237\",\n \"0.48799765\",\n \"0.48697865\",\n \"0.48674086\",\n \"0.4864649\",\n \"0.48635986\",\n \"0.48632675\",\n \"0.48608115\",\n \"0.48605955\",\n \"0.48596963\",\n \"0.485966\",\n \"0.4858821\",\n \"0.48581848\",\n \"0.48489502\",\n \"0.48478687\",\n \"0.48466748\",\n \"0.484066\",\n \"0.48385587\",\n \"0.4832486\",\n \"0.48273817\",\n \"0.4826263\",\n \"0.4824051\",\n \"0.4817379\",\n \"0.48162547\",\n \"0.48114496\",\n \"0.4803039\",\n \"0.48021033\",\n \"0.47998586\",\n \"0.47986576\",\n \"0.4796794\",\n \"0.4791704\"\n]","isConversational":false},"document_score":{"kind":"string","value":"0.7172675"},"document_rank":{"kind":"string","value":"0"}}},{"rowIdx":22684,"cells":{"query":{"kind":"string","value":"Bootstrap sampling of contacts in a sparse HiC map."},"document":{"kind":"string","value":"def subsample_contacts(M, n_contacts):\n\n S = M.data.copy()\n # Match cell idx to cumulative number of contacts\n cum_counts = np.cumsum(S)\n # Total number of contacts to sample\n tot_contacts = int(cum_counts[-1])\n\n # Sample desired number of contacts from the range(0, n_contacts) array\n sampled_contacts = np.random.choice(\n int(tot_contacts), size=(n_contacts), replace=False\n )\n\n # Get indices of sampled contacts in the cum_counts array\n idx = np.searchsorted(cum_counts, sampled_contacts, side=\"right\")\n\n # Bin those indices to the same dimensions as matrix data to get counts\n sampled_counts = np.bincount(idx, minlength=S.shape[0])\n\n # Get nonzero values to build new sparse matrix\n nnz_mask = sampled_counts > 0\n sampled_counts = sampled_counts[nnz_mask].astype(np.float64)\n sampled_rows = M.row[nnz_mask]\n sampled_cols = M.col[nnz_mask]\n\n return sp.coo_matrix(\n (sampled_counts, (sampled_rows, sampled_cols)),\n shape=(M.shape[0], M.shape[1]),\n )"},"metadata":{"kind":"string","value":"{\n \"objective\": {\n \"self\": [],\n \"paired\": [],\n \"triplet\": [\n [\n \"query\",\n \"document\",\n \"negatives\"\n ]\n ]\n }\n}"},"negatives":{"kind":"list like","value":["def bootstrap(X):\n return X[np.random.choice(list(range(X.shape[0])), size=X.shape[0]), :]","def bootstrapping(datasample):\r\n \r\n datasample=df_to_array(datasample)\r\n \r\n boots_indexs=np.random.randint(len(datasample),size=(1,len(datasample)))\r\n \r\n whole_indexs=list(range(len(datasample)))\r\n \r\n missing_indexs=np.array(list(set(whole_indexs).difference(set(list(boots_indexs[0])))))\r\n \r\n \r\n return boots_indexs,missing_indexs","def bootstrap_resample(labels):\n idxs = np.arange(len(labels))\n num_labels = max(labels) + 1\n bootstrap_idxs = np.zeros_like(idxs)\n ptr = 0\n for i in range(num_labels):\n strat = idxs[labels == i]\n bootstrap_idxs[ptr:ptr + len(strat)] = np.random.choice(strat, len(strat), replace=True)\n ptr += len(strat)\n return bootstrap_idxs","def standard_bootstrap(dataset):\n randseed=np.random.randint(0,10000)\n np.random.seed(randseed)\n \n n = dataset.shape[0]\n b = np.random.randint(0, high=n-1, size=n)\n return dataset[b]","def bootstrap_sample(data):\n return [random.choice(data) for _ in data]","def bootstrap(data):\r\n size = int(len(data))\r\n train = resample(data, n_samples=size, replace=True)\r\n test = data.drop(train.index) \r\n return train[encoded_features], train[target], test[encoded_features], test[target]","def bootstrap_data(self):\n for i in range(self.bootstraps):\n df_i = self.training_df.groupby(\n self.random_effect, group_keys=False\n ).apply(\n lambda x: x.sample(len(x), replace=True)\n )\n self.models.append(self.convert(df=df_i))","def bootstrap_sample_generator_1D(samples: Union[NumpyFloatArray, NumpyIntArray]):\n n_samples = samples.shape[0]\n\n while True:\n _indices = np.random.randint(0, high=n_samples, size=n_samples)\n\n yield samples[_indices]","def sample_from_ibp(K, alpha, sigma, c):\n pp = poissonparams(K, alpha, sigma, c)\n new_nodes = np.random.poisson(pp)\n Ncols = new_nodes.sum()\n node_count = np.zeros(Ncols)\n\n # used to build sparse matrix, entries of each Zij=1\n colidx = [] \n rowidx = []\n rightmost_node = 0\n\n # for each clique\n for n in range(K):\n # revisit each previously seen node\n for k in range(rightmost_node):\n prob_repeat = (node_count[k] - sigma) / (n + c)\n r = np.random.rand()\n if r < prob_repeat:\n rowidx.append(n)\n colidx.append(k)\n node_count[k] += 1\n\n for k in range(rightmost_node, rightmost_node + new_nodes[n]):\n rowidx.append(n)\n colidx.append(k)\n node_count[k] += 1\n \n rightmost_node += new_nodes[n]\n\n # build sparse matrix\n data = np.ones(len(rowidx), int)\n shape = (K, Ncols)\n Z = csr_matrix((data, (rowidx, colidx)), shape)\n\n return Z","def sample_indices_from_coupling(c, num_samples=None, return_all = False, thr = 10**(-6)):\n\n if return_all:\n return [[i, j] for i in range(c.shape[0]) for j in range(c.shape[1]) if c[i,j] > thr]\n else:\n linear_samples = np.random.choice(range(c.size), size = num_samples, p = c.flat)\n \n if num_samples in [1, None]:\n linear_samples = [linear_samples]\n \n n_cols = c.shape[1]\n return np.array([[s//n_cols , s%n_cols ] for s in linear_samples])","def bootstrap_sample_generator_2D(samples: Union[NumpyFloatArray, NumpyIntArray]):\n n_samples = samples.shape[0]\n\n num_cols = samples.shape[1]\n cols = np.arange(num_cols)\n\n while True:\n # generate indices to pick N values from f_A, f_B and f_C_i\n _indices = np.random.randint(0, high=n_samples, size=samples.shape)\n\n yield samples[_indices, cols]","def bootstrap_sample_generator_3D(samples: Union[NumpyFloatArray, NumpyIntArray]):\n n_samples = samples.shape[1]\n array_shape = samples.shape[1:]\n num_cols = samples.shape[2]\n cols = np.arange(num_cols)\n\n while True:\n _indices = np.random.randint(0, high=n_samples, size=array_shape)\n\n yield samples[:, _indices, cols]","def sparse(n, k):\n z = np.zeros(n)\n for i in np.random.choice( np.arange(n), k, replace=None ): # supports of nonzero entries\n z[i] = np.random.randn()\n return z","def initialize_clusters(points, k):\r\n return points[np.random.randint(points.shape[0], size=k)]","def _sample_from_zeros(n: int, sparse: sp.csr_matrix) -> List[List[int]]:\n zeros = np.argwhere(np.logical_not(sparse.todense()))\n ids = np.random.choice(range(len(zeros)), size=(n,))\n return zeros[ids].tolist()","def cbindMatrices(hm, args):\n hm2 = heatmapper.heatmapper()\n\n # Make a dict of region name:row associations\n hm.read_matrix_file(args.matrixFile[0])\n d = dict({x: dict() for x in hm.parameters[\"group_labels\"]})\n for idx, group in enumerate(hm.parameters[\"group_labels\"]):\n s = hm.parameters[\"group_boundaries\"][idx]\n e = hm.parameters[\"group_boundaries\"][idx + 1]\n for idx2, reg in enumerate(hm.matrix.regions[s:e]):\n d[group][reg[2]] = idx2 + s\n\n # Iterate through the other matrices\n for idx in range(1, len(args.matrixFile)):\n hm2.read_matrix_file(args.matrixFile[idx])\n # Add the sample labels\n hm.parameters['sample_labels'].extend(hm2.parameters['sample_labels'])\n # Add the sample boundaries\n lens = [x + hm.parameters['sample_boundaries'][-1] for x in hm2.parameters['sample_boundaries']][1:]\n hm.parameters['sample_boundaries'].extend(lens)\n\n # Add on additional NA initialized columns\n ncol = hm.matrix.matrix.shape[1]\n hm.matrix.matrix = np.hstack((hm.matrix.matrix, np.empty(hm2.matrix.matrix.shape)))\n hm.matrix.matrix[:, ncol:] = np.NAN\n\n # Update the values\n for idx2, group in enumerate(hm2.parameters[\"group_labels\"]):\n if group not in d:\n continue\n s = hm2.parameters[\"group_boundaries\"][idx2]\n e = hm2.parameters[\"group_boundaries\"][idx2 + 1]\n for idx3, reg in enumerate(hm2.matrix.regions[s:e]):\n if reg[2] not in d[group]:\n continue\n hm.matrix.matrix[d[group][reg[2]], ncol:] = hm2.matrix.matrix[s + idx3, :]\n\n # Append the special params\n for s in hm.special_params:\n hm.parameters[s].extend(hm2.parameters[s])\n\n # Update the sample parameters\n hm.matrix.sample_labels = hm.parameters['sample_labels']\n hm.matrix.sample_boundaries = hm.parameters['sample_boundaries']","def boot_matrix(z, B):\n\n n = len(z) # sample size\n idz = np.random.randint(0, n, size=(B, n)) # indices to pick for all boostrap samples\n return z[idz]","def fetchCooler(c, regions, coolerFetch = lambda coo, ext:coo.matrix(balance=True, sparse=True).fetch(ext),\n mask=True, force=False, ):\n regions = [list(i) for i in regions]\n resolution = c.binsize\n\n for i in regions:\n if i[1] == None:\n i[1] = 0 \n if i[2] == None:\n i[2] = c.chromsizes[i[0]]\n\n \n for a in regions: \n if str(a[0]) not in c.chromnames:\n raise ValueError(\"Chromosome {0} from regions not found in cooler\".format(a))\n if (a[1] % resolution) != 0:\n raise ValueError(\"Start of an region should be a multiple fo resolution\")\n \n# bins = c.bins()[:]\n \n# # managing masks \n# if mask is False: \n# bins[\"mask\"] = 1 \n# elif mask is None:\n# assert \"mask\" in bins.columns\n# elif mask is True: \n# pass \n# elif callable(mask):\n# pass \n# else:\n# bins[\"mask\"] = mask \n \n \n for region in regions:\n matrix = coolerFetch(c, region)\n try: # setting matrix nans to zeros.\n matrix.data = np.nan_to_num(matrix.data, copy=False)\n except TypeError: #workaround for old numpy versions\n matrix.data = np.nan_to_num(matrix.data)\n# st,end = c.extent(region)\n# subbins = bins[st:end].copy()\n if mask is True: \n newmask = np.array((matrix.sum(axis=0) > 0 ))[0]\n# if callable(mask):\n# new_mask = mask(matrix)\n# subbins[\"mask\"] = newmask \n\n assert len(newmask) == matrix.shape[0]\n\n yield matrix, newmask","def boot_matrix(z, B):\n z = np.array(z).flatten()\n n = len(z) # sample size\n idz = np.random.randint(0, n, size=(B, n)) # indices to pick for all boostrap samples\n return z[idz]","def bootstrap_idxs(n, rng: np.random.Generator = None):\n if rng is None or type(rng) is not np.random.Generator:\n rng = np.random.default_rng(rng)\n in_bag = rng.integers(low=0, high=n, size=n)\n out_bag = np.array(list(set(range(n)) - set(in_bag)))\n return in_bag, out_bag","def init_centroids(X,K):\n c = random.sample(list(X),K)\n return c","def _SampleInputMatrix(nrows, bl, bu, distname='randomUniform'):\n npars = len(bl)\n x = np.zeros((nrows,npars))\n bound = bu-bl\n for i in range(nrows):\n # x[i,:]= bl + DistSelector([0.0,1.0,npars],distname='randomUniform')*bound # only used in full Vhoeys-framework\n x[i,:]= bl + np.random.rand(1,npars)*bound\n return x","def bootstrap_sample_from_data(data, weights=None, seed=0):\n # Set up the random number generator\n RNG = np.random.default_rng(seed)\n N = data.shape[0]\n\n # Set up weights\n if weights is not None:\n cutoffs = np.cumsum(weights)\n else:\n cutoffs = np.linspace(0, 1, N)\n\n # Draw random indices\n indices = np.searchsorted(cutoffs, RNG.uniform(size=N))\n\n # Create a bootstrapped sample\n new_data = deepcopy(data[indices,])\n return new_data","def sample_mapped_keys(mapping, min_coverage=50):\r\n if min_coverage == 0:\r\n return {}\r\n sample_keys = {}\r\n for key in mapping.keys():\r\n if (min_coverage > 1):\r\n sample_keys[key] = sample(mapping[key],\r\n min(min_coverage - 1, len(mapping[key])))\r\n else:\r\n sample_keys[key] = []\r\n sample_keys[key].append(key) # always include the centroid\r\n return sample_keys","def test_sparse():\n\n rng = np.random.RandomState(0)\n\n X = rng.rand(20, 2)\n X[X < 0.8] = 0\n X_csr = sp.csr_matrix(X)\n\n bisect_means = BisectingKMeans(n_clusters=3, random_state=0)\n\n bisect_means.fit(X_csr)\n sparse_centers = bisect_means.cluster_centers_\n\n bisect_means.fit(X)\n normal_centers = bisect_means.cluster_centers_\n\n # Check if results is the same for dense and sparse data\n assert_allclose(normal_centers, sparse_centers, atol=1e-8)","def simulate_sparse(n_rows, n_columns, row_density=0.001):\n # X = sparse.lil_matrix((n_rows, n_columns))\n X = []\n counter = 0\n for row in range(n_rows):\n counter += 1\n if (counter % 1000) == 0:\n print(counter)\n\n # X[row] = sparse.random(1, n_columns, density=row_density)\n X.append(sparse.random(1, n_columns, density=row_density, format='lil'))\n\n return sparse.vstack(X).tocsr()","def subsampling(dataset, class_column_index, class_max_count, class_dict):\n out = []\n for row in dataset:\n cls = row[class_column_index]\n rInt = np.random.randint(0, class_dict[cls])\n if rInt <= class_max_count:\n out.append(row)\n ss_data = np.array(out)\n\n return ss_data","def initialize_chromosomes(self):\n # Generate random chromosomes.\n for i in range(self.n_chromosomes):\n random_chromosome = self.generate_random_chromosome()\n self.chromosomes[0][i, :] = random_chromosome[0]\n self.chromosomes[1][i, :] = random_chromosome[1]\n\n self.repair_chromosomes()","def ensure_sparse_cols(self,max_density,remove_lowest=True):\n if max_density >= 1:\n max_nnz = int(max_density)\n else:\n max_nnz = int(max_density*self.shape[0])\n for j in range(self.shape[1]):\n col = self.fast_get_col(j)\n excess = col.nnz - max_nnz\n if excess > 0:\n if remove_lowest:\n zero_entries = np.argsort(col.data)[:excess]\n else:\n zero_entries = random.sample(range(col.nnz),excess)\n col.data[zero_entries] = 0\n self.fast_update_col(j,col.data)","def initialize(img):\n w, h, _ = img.shape\n for c in current_cluster_centers:\n x = np.random.randint(w)\n y = np.random.randint(h)\n c[:] = img[x, y]","def _get_bootstrap_sample(x, y, num_reps):\r\n combined = array(list(x) + list(y))\r\n total_obs = len(combined)\r\n num_x = len(x)\r\n for i in range(num_reps):\r\n # sampling with replacement\r\n indices = randint(0, total_obs, total_obs)\r\n sampled = combined.take(indices)\r\n # split into the two populations\r\n sampled_x = sampled[:num_x]\r\n sampled_y = sampled[num_x:]\r\n yield sampled_x, sampled_y","def generate_map(seed):\n def set_biomes(field, points):\n for row in range(len(field)):\n # For every cell, we find the closest point\n for cell in range(len(field[row])):\n # Store the currently closest point:\n shortest_dist = -1\n # Stores the biome of the current point:\n current_biome = '_'\n\n # Iterate over the points to find the closest one\n for point in points:\n # Calculate the euclidean distance\n xdiff = point[0] - row\n ydiff = point[1] - cell\n distance = xdiff * xdiff + ydiff * ydiff # Square root not needed since we're only comparing\n\n # If this is currently the shortest distance, set it\n if distance < shortest_dist or shortest_dist == -1:\n shortest_dist = distance\n # Set the biome that will be chosen if a shorter distance isn't found\n current_biome = point[2]\n\n # Select a random field in the biome, taking rarity into account\n\n # Get names/data of all fields in the chosen biome\n biome_fields = biomes[current_biome]['fields'].items()\n # Extract field names and their rarities (weights)\n field_data = [(name, data['rarity']) for name, data in biome_fields]\n # Choose a random field using the weights\n field_index = weighted_choice([field_weight[1] for field_weight in field_data])\n # Set the cell's field\n field[row][cell] = field_data[field_index][0]\n\n return field\n\n def poisson_disc_samples(width, height, r, k=5):\n \"\"\"\n \"Two-dimensional Poisson Disc Sampling using Robert Bridson's algorithm.\"\n Modified version of https://github.com/emulbreh/bridson.\n \"\"\"\n tau = 2 * pi\n cellsize = r / sqrt(2)\n\n grid_width = int(ceil(width / cellsize))\n grid_height = int(ceil(height / cellsize))\n grid = [None] * (grid_width * grid_height)\n\n def distance(a, b):\n dx = a[0] - b[0]\n dy = a[1] - b[1]\n return sqrt(dx * dx + dy * dy)\n\n def grid_coords(p2):\n return [int(floor(p2[0] / cellsize)), int(floor(p2[1] / cellsize))]\n\n def fits(p2, gx, gy):\n yrange = list(range(max(gy - 2, 0), min(gy + 3, grid_height)))\n\n for x in range(max(gx - 2, 0), min(gx + 3, grid_width)):\n for y in yrange:\n g = grid[x + y * grid_width]\n if g is None:\n continue\n if distance(p2, g) <= r:\n return False\n return True\n\n p = [width * rnd.random(), height * rnd.random()]\n queue = [p]\n grid_x, grid_y = grid_coords(p)\n grid[grid_x + grid_y * grid_width] = p\n\n while queue:\n qi = int(rnd.random() * len(queue))\n qx, qy = queue[qi]\n queue[qi] = queue[-1]\n queue.pop()\n\n for _ in range(k):\n alpha = tau * rnd.random()\n d = r * sqrt(3 * rnd.random() + 1)\n px = qx + d * cos(alpha)\n py = qy + d * sin(alpha)\n\n if not (0 <= px < width and 0 <= py < height):\n continue\n p = [px, py]\n grid_x, grid_y = grid_coords(p)\n\n if not fits(p, grid_x, grid_y):\n continue\n queue.append(p)\n grid[grid_x + grid_y * grid_width] = p\n return [p for p in grid if p is not None]\n\n # Define map dimensions and settings\n # Size should be at least 35x35\n\n height = 50\n width = 50\n\n # Create a new instance of Random() using a given seed\n\n rnd = random.Random(seed)\n\n # Generate a random starting location somewhere in the middle of the map\n\n x = rnd.randint(width - 10, width + 10)\n y = rnd.randint(height - 10, height + 10)\n\n # Create a 2-dimensional list for the game map\n\n field = [['_'] * width for _ in range(height)]\n\n # Create random points that will be the starting positions of biomes\n\n points = poisson_disc_samples(width, height, 3, 5)\n rnd.shuffle(points)\n\n for i in range(len(points)):\n biome = rnd.choice(list(biomes.keys())) # Set a random biome\n\n points[i][0] = int(round(points[i][0])) - 1 # x\n points[i][1] = int(round(points[i][1])) - 1 # y\n points[i].append(biome)\n\n field[points[i][1]][points[i][0]] = 'X' # not needed?\n\n # Set the biomes\n\n field = set_biomes(field, points)\n\n return ({\n 'field': field,\n 'x': x,\n 'y': y\n })","def downsample_sam(self, factor):","def sample_array(self, X, random_state=None, sample_weight=None): \n if random_state is None:\n random_state = check_random_state(random_state)\n\n X = check_array(X)\n if sample_weight is None:\n sample_weight = np.ones((X.shape[0], ))\n cdf = sample_weight.cumsum()\n cdf /= cdf[-1]\n uniform_samples = random_state.random_sample(X.shape[0])\n bootstrap_idx = cdf.searchsorted(uniform_samples, side='right')\n # searchsorted returns a scalar\n bootstrap_idx = np.array(bootstrap_idx, copy=False)\n return bootstrap_idx","def init_centroids(self, data_points):\n # print(\"Init centroid\")\n # return list(map(lambda x: x[1], random.sample(labelled_data, self.k)))\n\n # Project the data: this step will take several seconds\n\n centroids_scaled = self.naive_sharding(data_points, self.k)\n return list(centroids_scaled)\n\n #sample = np.random.permutation(len(labelled_data))[:self.k]\n\n #return list(map(lambda x: labelled_data[x][1], sample))","def initialize(X, k):\n\n if not isinstance(X, np.ndarray) or X.ndim != 2:\n return None\n\n # n: number of dada points\n # d: dimension of each data point\n n, d = X.shape\n # print(X.shape)\n # print(X)\n\n if not isinstance(k, int) or k <= 0 or k > n:\n return None\n\n # Sample k centroids from a random.uniform distribution;\n # output is an array of coordinates\n C = np.random.uniform(low=np.min(X, axis=0),\n high=np.max(X, axis=0),\n size=(k, d))\n return C","def set_sparse_signals(self):\n\t\n\t\tparams_dSs = [self.mu_dSs, self.sigma_dSs]\n\t\tparams_Ss0 = [self.mu_Ss0, self.sigma_Ss0]\n\t\tself.dSs, self.idxs = sparse_vector([self.Nn, self.Kk], \n\t\t\t\t\t\t\t\t\t\t\t\tparams_dSs,\tseed=self.seed_dSs)\n\t\t\n\t\t# Replace components with conflicting background odor \n\t\tif self.Kk_split is not None and self.Kk_split != 0:\n\t\t\tassert 0 <= self.Kk_split <= self.Kk, \\\n\t\t\t\t\"Splitting sparse signal into two levels requires Kk_split\" \\\n\t\t\t\t\" to be non-negative and less than or equal to Kk.\"\n\t\t\tassert self.mu_dSs_2 is not None \\\n\t\t\t\tand self.sigma_dSs_2 is not None, \\\n\t\t\t\t\"Splitting sparse signal into two levels requires that\" \\\n\t\t\t\t\" mu_dSs_2 and sigma_dSs_2 are set.\"\n\n\t\t\tsp.random.seed(self.seed_dSs)\n\t\t\tself.idxs_2 = sp.random.choice(self.idxs[0], self.Kk_split, \n\t\t\t\t\t\t\t\t\t\t\treplace=False)\n\t\t\tfor idx_2 in self.idxs_2:\n\t\t\t\tself.dSs[idx_2] = sp.random.normal(self.mu_dSs_2, \n\t\t\t\t\t\t\t\t\t\t\t\t\tself.sigma_dSs_2)\n\t\telse:\n\t\t\tself.idxs_2 = []\n\t\t\tself.Kk_split = 0\n\t\t\t\n\t\t# Ss0 is the ideal (learned) background stimulus without noise\n\t\tself.Ss0, self.Ss0_noisy = sparse_vector_bkgrnd([self.Nn, self.Kk], \n\t\t\t\t\t\t\t\t\t\t\t\t\t\tself.idxs, params_Ss0,\n\t\t\t\t\t\t\t\t\t\t\t\t\t\tseed=self.seed_Ss0)\n\t\t\n\t\tself.Ss = self.dSs + self.Ss0_noisy","def subsample_maps(net):\n\n # start with uniform probability that each map is selected for inclusion in a connection;\n # reduce probability of selecting a given map again whenever it is selected\n probability_reduction = .01\n interarea_probabilities = []\n interlaminar_probabilities = []\n for layer in net.layers:\n interarea_probabilities.append(np.ones(layer.m))\n interlaminar_probabilities.append(np.ones(layer.m))\n\n subsample_indices = []\n for connection in net.connections:\n n_maps = int(round(connection.pre.m * connection.c))\n pre_index = net.find_layer_index(connection.pre.name)\n\n pre_cortical_area = connection.pre.name.split('_')[0]\n post_cortical_area = connection.post.name.split('_')[0]\n if pre_cortical_area == post_cortical_area: # interlaminar\n p = interlaminar_probabilities[pre_index]\n else:\n p = interarea_probabilities[pre_index]\n\n indices = np.random.choice(range(int(connection.pre.m)), n_maps, replace=False, p=p/np.sum(p))\n for i in indices:\n p[i] = p[i]*probability_reduction\n subsample_indices.append(indices)\n\n return subsample_indices","def compute_bootstrapped_sample(X_table, y_table):\n n = len(X_table)\n X_sample = []\n y_sample = []\n for _ in range(n):\n rand_index = random.randrange(0, n)\n X_sample.append(X_table[rand_index])\n y_sample.append(y_table[rand_index])\n return X_sample, y_sample","def bootstrap(df, nclass, if_new=False):\n ori_size = Counter(df.label)\n logger.info(f'class info before resampling: {ori_size.values()}')\n ori_size_list = list(ori_size.values())\n\n if if_new:\n df_new = pd.DataFrame(data=None, columns=df.columns)\n target_size = min(ori_size_list)\n else:\n target_size = max(ori_size_list)\n df_new = df.copy()\n\n for i in range(nclass):\n name = list(ori_size.keys())[i]\n name_index = np.array(df[df.label == name].index)\n if target_size < ori_size_list[i]:\n sample_size = target_size\n elif target_size > ori_size_list[i]:\n sample_size = target_size - ori_size_list[i]\n else:\n if if_new:\n sample_size = target_size\n else:\n sample_size = 0\n\n np.random.seed(i)\n boostrap_sample = np.random.randint(0, ori_size_list[i], sample_size)\n df_new = df_new.append(df.iloc[name_index[boostrap_sample]], ignore_index=True)\n logger.info(f'class info after resampling: {Counter(df_new.label).values()}')\n return df_new","def test_optimalk_random_sample_data():\n from sklearn.datasets.samples_generator import make_blobs\n from gap_statistic import random_sample_data\n\n # Create data\n X, y = make_blobs(n_samples=int(1e3), n_features=2, centers=3)\n\n random_data = random_sample_data(X, random_sampling='uniform')\n assert random_data.shape == X.shape, \"check sampled data shape {} vs original data shape {}\".format(X.shape, random_data.shape) \n\n random_data = random_sample_data(X, random_sampling='gaussian')\n assert random_data.shape == X.shape, \"check sampled data shape {} vs original data shape {}\".format(X.shape, random_data.shape)","def _generate_indices(random_state, bootstrap, n_population, n_samples):\n # Draw sample indices\n if bootstrap:\n indices = random_state.randint(0, n_population, n_samples)\n else:\n indices = sample_without_replacement(n_population, n_samples,\n random_state=random_state)\n\n return indices","def generate_samples(self, n_samples):","def generate_samples(self, n_samples):","def generate_random_sparse_array(nrows, ncols, numdense):\n i = np.random.randint(0, nrows, numdense)\n j = np.random.randint(0, ncols, numdense)\n data = np.random.randint(1,6, numdense)\n ij = np.vstack((i,j))\n return coo_matrix((data, ij), shape=(nrows, ncols))","def sample(self, rng_key: jnp.ndarray) -> jnp.ndarray:\n pass","def _sample_mini_dataset(dataset, num_classes, num_shots):\n shuffled = list(dataset)\n random.shuffle(shuffled)\n for class_idx, class_obj in enumerate(shuffled[:num_classes]):\n for sample in class_obj.sample(num_shots):\n yield (sample, class_idx)","def sample_contact_points(contact_points, num_samples, get_idx=False):\n num_samples = min(len(contact_points), num_samples)\n idx = random.sample(range(0, len(contact_points)), num_samples)\n if get_idx:\n return [contact_points[i] for i in idx], idx\n else:\n return [contact_points[i] for i in idx]","def sample(src, coords):\n b, h, w, c = src.size()\n h_sample, w_sample = coords.shape[:2]\n max_coord = [h - 1, w - 1]\n coords = np.clip(coords, 0, max_coord)\n indices = mapping_to_indices(coords, b)\n\n sampled = src[indices].reshape((h_sample, w_sample))\n\n return sampled","def bootstrap_group(nsubj, ngroups):\n groupsize = nsubj\n samples = [(groupsize * np.random.rand(groupsize)).astype(np.int_)\n for i in range(ngroups)]\n return samples","def test_getitem_slice(self):\n random.seed(12345)\n\n nside_coverage = 32\n nside_map = 128\n\n full_map = np.zeros(hpg.nside_to_npixel(nside_map)) + hpg.UNSEEN\n full_map[0: 5000] = random.random(size=5000)\n\n sparse_map = healsparse.HealSparseMap(healpix_map=full_map, nside_coverage=nside_coverage)\n\n # Test in-range, overlap, out-of-range\n testing.assert_array_almost_equal(sparse_map[100: 500], full_map[100: 500])\n testing.assert_array_almost_equal(sparse_map[4500: 5500], full_map[4500: 5500])\n testing.assert_array_almost_equal(sparse_map[5500: 5600], full_map[5500: 5600])\n\n # Test stepped\n testing.assert_array_almost_equal(sparse_map[100: 500: 2], full_map[100: 500: 2])\n testing.assert_array_almost_equal(sparse_map[4500: 5500: 2], full_map[4500: 5500: 2])\n testing.assert_array_almost_equal(sparse_map[5500: 5600: 2], full_map[5500: 5600: 2])\n\n # Test all\n testing.assert_array_almost_equal(sparse_map[:], full_map[:])","def generate_bootstrap_samples(num_samples, test_universe, test_set_sizes):\n for sample_idx, sample_size in zip(range(num_samples), cycle(test_set_sizes)):\n yield random.sample(test_universe, sample_size)","def split_simcc_xy(self, heatmap: Union[np.ndarray, torch.Tensor]):\n size = heatmap.size()\n k = size[0] if size[0] <= 20 else 20\n maps = []\n for _ in range(k):\n xy_dict = {}\n single_heatmap = heatmap[_]\n xy_dict['x'], xy_dict['y'] = self.merge_maps(single_heatmap)\n maps.append(xy_dict)\n return maps, k","def kmeanspp_initialisation( self, X ):\n N, _ = X.shape\n k, d = self.k, self.d\n M = []\n\n # Choose one center amongst the X at random\n m = sc.random.randint( N )\n M.append( X[m] )\n\n # Choose k centers\n while( len( M ) < self.k ):\n # Create a probability distribution D^2 from the previous mean\n D = cdist( X, M ).min( 1 )**2\n assert( D.shape == (N,) )\n\n # Normalise and sample a new point\n D /= D.sum()\n\n m = sc.random.multinomial( 1, D ).argmax()\n M.append( X[m] )\n\n M = sc.column_stack( M )\n sigma = sc.sqrt(cdist( X, M.T, 'sqeuclidean').sum(0)/(N))\n w = ones( k )/float(k)\n\n return M, sigma, w","def cell_sample(mask, samplingPoints):\n maskedArea = np.array(np.where(mask)).T\n maskedAreaLength = len(maskedArea)\n randomIndex = sp.random.randint(0, maskedAreaLength, samplingPoints)\n coordsRandom = maskedArea[randomIndex] + sp.rand(samplingPoints, 2)\n return(coordsRandom)","def boot_strap_selection(X, y, weights):\n # Take random sample of indicies, with replacement\n bss_indicies = np.random.choice(range(len(y)), size = len(y), p = weights)\n \n # Subset arrays with indicies\n return X[bss_indicies,:], y[bss_indicies]","def create_cands(data):\n\n best = np.zeros(data.dim+1)\n best[0:data.dim] = data.xbest\n best[data.dim] = 1-np.sum(data.xbest)\n\n # Ncand times the best value\n cp_e = np.kron(np.ones((data.Ncand, 1)), np.asmatrix(best))\n # This generates random perturbations\n # need dim+1 to account for the \"missing\" value\n r = np.random.rand(data.Ncand, data.dim+1)\n a = r < data.pertP\n idx = np.where(np.sum(a, axis=1) == 0)\n for ii in range(len(idx[0])):\n f = np.random.permutation(data.dim+1)\n a[idx[0][ii], f[0]] = True\n randnums = np.random.randn(data.Ncand, data.dim+1)\n randnums[a == False] = 0\n pv = randnums*data.sigma_stdev\n # Create new points by adding random fluctucations to best point\n new_pts = cp_e+pv\n\n # Iterative, column wise procedure to force the randomly\n # sampled point to be in [0,1]\n for ii in range(data.dim+1):\n vec_ii = new_pts[:, ii]\n adj_l = np.where(vec_ii < data.xlow)\n vec_ii[adj_l[0]] = data.xlow + (data.xlow - vec_ii[adj_l[0]])\n adj_u = np.where(vec_ii > data.xup)\n vec_ii[adj_u[0]] = data.xup - (vec_ii[adj_u[0]]-data.xup)\n stillout_u = np.where(vec_ii > data.xup)\n vec_ii[stillout_u[0]] = data.xlow\n stillout_l = np.where(vec_ii < data.xlow)\n vec_ii[stillout_l[0]] = data.xup\n new_pts[:, ii] = copy.copy(vec_ii)\n\n new_pts = new_pts / np.sum(new_pts, axis=1)\n\n cp_e = copy.copy(new_pts)\n rand_pts = np.asmatrix(np.random.uniform(0, 1, [data.Ncand, data.dim + 1]))\n cp_r = rand_pts/np.sum(rand_pts, axis=1)\n\n CandPoint = np.concatenate((cp_e, cp_r), axis=0)\n # return only data.dim candidate points\n CandPoint_out = CandPoint[:, 0:data.dim]\n\n return CandPoint_out","def generate_map(nrows, ncols, nrooms, max_col_size, max_row_size):\n arr = np.zeros((nrows, ncols), dtype=np.int8)\n\n for i in range(nrooms):\n rand_row_start = np.random.randint(nrows)\n rand_col_start = np.random.randint(ncols)\n\n rand_row_size = np.random.randint(max_row_size / 2, max_row_size)\n rand_col_size = np.random.randint(max_col_size / 2, max_col_size)\n\n arr[rand_row_start:rand_row_start + rand_row_size, rand_col_start:rand_col_start + rand_col_size] = 1\n\n labels = measure.label(arr)\n regions = measure.regionprops(labels)\n\n centroids = list()\n for region in regions:\n centroids.append(region.centroid)\n\n num_centroids = len(centroids)\n\n # get distances between every pair of centroids\n dists = scipy.spatial.distance.cdist(centroids, centroids)\n\n # get a distance that is greater than all current distances\n max_dist = np.max(dists) + 1\n\n # make sure upper triangle is at least max_dist so that when picking closest\n # pairs, we won't choose a diagonal element or a duplicate connection\n dists = dists + np.triu(np.ones((num_centroids, num_centroids))) * max_dist\n\n for i in range(num_centroids - 1):\n min_dist_idx = np.argmin(dists)\n min_dist_idx = np.unravel_index(min_dist_idx, dists.shape)\n\n # create a hallway between regionprops\n centroid1 = np.array(centroids[min_dist_idx[0]], dtype=np.int)\n centroid2 = np.array(centroids[min_dist_idx[1]], dtype=np.int)\n\n [row_centroid_1, row_centroid_2] = sorted([centroid1, centroid2], key=lambda x: x[0])\n [col_centroid_1, col_centroid_2] = sorted([centroid1, centroid2], key=lambda x: x[1])\n\n arr[row_centroid_1[0]:row_centroid_2[0] + 1, row_centroid_1[1]] = 1\n arr[row_centroid_2[0], col_centroid_1[1]:col_centroid_2[1] + 1] = 1\n\n dists[:, min_dist_idx[1]] += max_dist\n\n return arr","def bootstrap_replicate_1d(data, func):\r\n bs_sample = np.random.choice(data, len(data))\r\n return func(bs_sample)","def sample(self, size):\n if len(self.observations) == 0:\n raise ValueError(\"need to fit KDE with observations\")\n\n samples = np.zeros(size)\n for i in range(size):\n \n # randomly select an anchor point for a gaussian\n anchor_point = np.random.choice(self.observations)\n\n # then sample from that gaussian\n sample = np.random.normal(loc=anchor_point, scale=self.bandwidth)\n samples[i] = sample\n return samples","def creaCent(data,k):\n\n Cen = random.sample(data[:,:].tolist(),k=k)\n return np.asarray(Cen)","def __init__(self):\n super().__init__()\n self.type = 'SparseGridCollocationSampler'\n self.printTag = 'SAMPLER '+self.type.upper()\n self.maxPolyOrder = None #L, the relative maximum polynomial order to use in any dimension\n self.indexSetType = None #TP, TD, or HC; the type of index set to use\n self.polyDict = {} #varName-indexed dict of polynomial types\n self.quadDict = {} #varName-indexed dict of quadrature types\n self.importanceDict = {} #varName-indexed dict of importance weights\n self.maxPolyOrder = None #integer, relative maximum polynomial order to be used in any one dimension\n self.lastOutput = None #pointer to output dataObjects object\n self.ROM = None #pointer to ROM\n self.jobHandler = None #pointer to job handler for parallel runs\n self.doInParallel = True #compute sparse grid in parallel flag, recommended True\n self.dists = {} #Contains the instance of the distribution to be used. keys are the variable names\n self.writeOut = None\n self.indexSet = None\n self.sparseGrid = None\n self.features = None\n self.sparseGridType = None\n self.addAssemblerObject('ROM', InputData.Quantity.one)","def bootstrap(x, iter=int(1E6), return_samples=False):\n\n \n means = np.empty(iter) \n dfs = []\n for i in tqdm(range(iter), desc='Performing bootstrap sampling'):\n resamp = np.random.choice(x, size=len(x), replace=True)\n means[i] = resamp.mean()\n\n if return_samples:\n _df = pd.DataFrame([])\n _df['value'] = resamp\n _df['iter'] = i + 1\n dfs.append(_df)\n\n # Compute confidence intervals of the means.\n mean_val = means.mean()\n bounds_ci = {'99%': (0.5, 99.5), '95%': (2.5, 97.5), '90%': (5, 95),\n '75%': (12.5, 87.5), '50%': (25, 75), '25%': (37.5, 62.5),\n '10%': (45, 55), '5%': (47.5, 52.5), '1%': (49.5, 50.5)} \n cis = {} \n for k, v in bounds_ci.items():\n bounds = np.percentile(means, v)\n cis[k] = bounds\n\n statistics['original_data'] = x\n statistics['resampled_means'] = means\n statistics['mean_value'] = mean_val\n statistics['confidence_intervals'] = cis\n\n if return_samples:\n _df = pd.concat(dfs, sort=False)\n return [statistics, _df]\n else:\n return statistics","def sample_inds(self, inds: List[float]):\n data_subset = self.data.create_subset(inds)\n\n if self.preload:\n data_ram_subset = [self.data_ram[i] for i in inds]\n return AtomisticDataset(data_subset, data_ram=data_ram_subset)\n else:\n return AtomisticDataset(data_subset)","def dataset_augmentation(data_start, bootstrapping = 1, epurate = 1, shuffle = True):\n data = data_start\n for ii in range(bootstrapping):\n data = data.append(data_start.apply(bootstrap_sample, axis=1), ignore_index=True)\n\n#Bugged version that weirdly works well....\n# for ii in range(bootstrapping):\n # data = data.append(bootstrap_sample(data_start), ignore_index=True)\n\n for ii in range(epurate):\n data = data.append(data_start.apply(epurate_sample, axis=1), ignore_index=True)\n\n # Shuffling (Important)\n if shuffle == True:\n data = data.sample(frac=1)\n return data","def bootstrap( M ):\n tot_ct_per_ob = M.sum(axis = 0)\n zero_list = []\n for i in range(len(tot_ct_per_ob)):\n if tot_ct_per_ob[i] <= 0:\n zero_list.append(i)\n M = np.delete(M, zero_list, 1)\n new_M = np.zeros(M.shape)\n tot_ct_per_ob = M.sum(axis = 0)\n for i in range(len(M)):\n for j in range(len(M[0])):\n new_M[i][j] = M[i][j]/tot_ct_per_ob[j]\n new_M = np.transpose(new_M)\n bootstrap = []\n for i in range(len(tot_ct_per_ob)):\n rnd_vec = np.random.multinomial(tot_ct_per_ob[i], new_M[i])\n bootstrap.append(rnd_vec)\n \n bootstrap = np.transpose(np.asarray(bootstrap)) \n return bootstrap","def pureMap(data, cutoff=1.7, contactMap=None):\n data = np.asarray(data)\n if len(data.shape) != 2:\n raise ValueError(\"Wrong dimensions of data\")\n if 3 not in data.shape:\n raise ValueError(\"Wrong size of data: %s,%s\" % data.shape)\n if data.shape[0] == 3:\n data = data.T\n data = np.asarray(data, float, order=\"C\")\n\n t = giveContacts(data, cutoff)\n N = data.shape[0]\n if contactMap is None:\n contactMap = np.zeros((N, N), \"int32\")\n contactMap[t[:, 0], t[:, 1]] += 1\n contactMap[t[:, 1], t[:, 0]] += 1\n return contactMap","def bootstrap(h0, h1, f, B=10000):\n t_star = np.zeros(B)\n m = len(h0)\n n = len(h1)\n all_h = np.concatenate([h0, h1])\n for b_id in range(B):\n b_data = np.random.choice(all_h, size=m + n, replace=True)\n t_star[b_id] = f(b_data[:m], b_data[m:])\n return t_star","def bootstrap_replicate_1d(data, func):\n bs_sample = np.random.choice(data, len(data))\n return func(bs_sample)","def _preallocate_samples(self):\n self.prealloc_samples_ = []\n for _ in range(self.num_prealloc_samples_):\n self.prealloc_samples_.append(self.sample())","def bootstrap_replicate_1d(data, func):\n bs_sample = np.random.choice(data, len(data))\n\n return func(bs_sample)","def init_cluster_centroids(x, number_of_clusters):\n return x[np.random.choice(x.shape[0], number_of_clusters, replace=False), :]","def sample_cc(self, nsamples=1, weighted=True):\n weights = self.areas / np.sum(self.areas) if weighted else None\n for index in np.random.choice(a=len(self.geometries), size=nsamples, p=weights):\n yield self.geometries[index]","def fully_pooled(at_bats, hits):\n phi_prior = Uniform(scalar_like(at_bats, 0), scalar_like(at_bats, 1))\n phi = pyro.sample(\"phi\", phi_prior)\n num_players = at_bats.shape[0]\n with pyro.plate(\"num_players\", num_players):\n return pyro.sample(\"obs\", Binomial(at_bats, phi), obs=hits)","def bootstrap(data,func,nboot):\n\n n = len(data)\n resamples = np.array([[random.choice(data) for i in range(n)]\n for j in range(nboot)])\n return np.apply_along_axis(func, 1, resamples)","def sparse_initialization_weights(self):\n W = []\n mu, sigma = 0, 1/self.non_zero_units\n\n for i in xrange(self.n_in):\n row = np.zeros(self.n_out)\n non_zeros = self.rng.normal(mu, sigma, self.non_zero_units)\n # non_zeros /= non_zeros.sum()\n non_zero_idxs = self.rng.permutation(\n self.n_out)[0:self.non_zero_units]\n for j in xrange(self.non_zero_units):\n row[non_zero_idxs[j]] = non_zeros[j]\n W.append(row)\n W = np.asarray(W, dtype=theano.config.floatX)\n return W","def sample(df: pd.DataFrame, cat: str, n_samples: int) -> np.array:\n # get optimal block size\n b_star = optimal_block_length(df[cat].values)\n b_star = math.ceil(b_star[0].b_star_cb)\n\n # (n_samples, n_observations)\n samples = tapered_block_bootstrap(df[cat].values,\n block_length=b_star,\n replications=n_samples)\n\n return samples","def rescaledMap(data, bins, cutoff=1.7, contactMap=None):\n # print data.sum(), bins.sum(), cutoff\n\n t = giveContacts(data, cutoff)\n x = np.searchsorted(bins, t[:, 0]) - 1\n y = np.searchsorted(bins, t[:, 1]) - 1\n assert x.min() >= 0\n assert y.min() >= 0\n assert x.max() < len(bins) - 1\n assert y.max() < len(bins) - 1\n matrixSize = len(bins) - 1\n index = matrixSize * x + y\n\n unique, inds = np.unique(index, return_counts=True)\n uniquex = unique // matrixSize\n uniquey = unique % matrixSize\n\n if contactMap is None:\n contactMap = np.zeros((matrixSize, matrixSize), dtype=int)\n contactMap[uniquex, uniquey] += inds\n\n return contactMap","def sample_beam_search(self, features, states=None):\n sampled_ids = []\n inputs = features.unsqueeze(0)\n beam_size = 5\n candidates = []\n all_candidates = []\n for i in range(30): # maximum sampling length\n if i==0:\n hiddens, states = self.lstm(inputs, states) # (batch_size, 1, hidden_size), \n outputs = self.linear(hiddens.squeeze(1)) # (batch_size, vocab_size) \n predictions = torch.topk(outputs,beam_size)\n for k in range(beam_size):\n candidates.append([predictions[1][0][k], predictions[0][0][k].cpu().data.numpy()[0] , hiddens , states]) \n else: \n all_candidates = []\n for k in range(beam_size):\n candidate = candidates[k]\n inputs = self.embed(candidate[0][len(candidate[0])-1])\n inputs = inputs.unsqueeze(0)\n # print(inputs)\n hiddens, states = self.lstm(inputs, candidate[3]) # (batch_size, 1, hidden_size), \n outputs = self.linear(hiddens.squeeze(1)) # (batch_size, vocab_size) \n predictions = torch.topk(outputs,beam_size)\n for k in range(beam_size):\n new_candidate = [torch.cat((candidate[0],predictions[1][0][k]),0),candidate[1] + predictions[0][0][k].cpu().data.numpy()[0], hiddens, states]\n all_candidates.append(new_candidate)\n ordered = sorted(all_candidates, key=lambda tup:tup[1], reverse = True)\n candidates = ordered[:beam_size]\n sampled_ids = candidates[0][0]\n return sampled_ids.squeeze()","def init_centroids(self, points , k):\n centroids = points.copy()\n numpy.random.shuffle(centroids)\n return centroids[0:k,:]","def bootstrap_array(f_values, values, bootstraps=1000):\n x = np.array(values)\n x_bs = x[np.random.randint(x.size, size=(bootstraps, x.size))]\n return np.array(map(f_values, x_bs))","def _initialize_mapbias(self):\n self.mapbias = sharedX(\n numpy.zeros(self.nmap),\n name='mb',\n borrow=True\n )","def seed_samples(self, sample_seed):\n np.random.seed(sample_seed)\n random.seed(sample_seed)\n torch.manual_seed(sample_seed)\n torch.cuda.manual_seed(sample_seed)\n torch.backends.cudnn.deterministic = True\n torch.backends.cudnn.benchmark = False","def seed_samples(self, sample_seed):\n np.random.seed(sample_seed)\n random.seed(sample_seed)\n torch.manual_seed(sample_seed)\n torch.cuda.manual_seed(sample_seed)\n torch.backends.cudnn.deterministic = True\n torch.backends.cudnn.benchmark = False","def fill_features(input_point, sigma=0.1):\n # to fill in f, array of features w shape (863, 1)\n f = np.zeros(X.shape[0])\n # iterate over every example to compare\n for i in range(m):\n f[i] = gaussian_kernel(input_point, X[i], sigma=sigma)\n return f","def diverse_sampler(self):\n\n # Sample number of nonzero idxs\n num_idxs = np.random.randint(low=1, high=self.K-1)\n\n # Sample actual idxs in state that are nonzero\n idxs = []\n # can have nonzero terms up to state[K-2]\n all_states = [i for i in range(self.K - 1)]\n for i in range(num_idxs):\n rand_id = np.random.randint(low=0, high=len(all_states))\n idxs.append(all_states.pop(rand_id))\n\n # sort idxs from largest to smallest to allocate\n # potential correctly\n idxs.sort()\n idxs.reverse()\n\n # allocate potential\n xs = self.simplex_sampler(num_idxs)\n\n # fill with appropriate number of pieces adding on any remaindr\n remainder = 0\n state = np.zeros(self.K+1, dtype=int)\n for i in range(num_idxs):\n idx = idxs[i]\n pot_idx = xs[i] + remainder\n num_pieces = int(pot_idx/self.weights[idx])\n state[idx] += num_pieces\n # update remainder\n remainder = pot_idx - num_pieces*self.weights[idx]\n\n return state","def mask_sparse(self, threshold=10):\n self.MaskPrefix = 's' + self.MaskPrefix\n print('Masking pixels that do not have at least {0} coherent values'.format(threshold))\n # each pixel assigned an integer corresponding to # of igrams where coherent\n # NOTE: save coverage map if it doesn't exist already\n coverage = self.get_coverage()\n sparse = ma.masked_less(coverage, threshold)\n for ig in self.Set:\n igram = self.load_ma(ig)\n igram[sparse.mask] = ma.masked\n self.save_ma(ig, igram)\n print('Done')","def _init_centroid(self, seed: int):\n random.seed(seed)\n self.centroid_info = dict()\n self.cluster_result = dict()\n self.centroid_stable_flag = dict()\n for key_index, chosen_value in enumerate(\n random.sample(self.list_data, self.n_cluster)):\n self.centroid_info.setdefault(\"c\" + str(key_index), float(chosen_value))\n self.cluster_result.setdefault(\"c\" + str(key_index), list())\n self.centroid_stable_flag.setdefault(\"c\" + str(key_index), False)","def sample_rows(df, nrows):","def _bootstrap_sampler(size: int, sampling_strategy: str='poisson') ->Tensor:\n if sampling_strategy == 'poisson':\n p = torch.distributions.Poisson(1)\n n = p.sample((size,))\n return torch.arange(size).repeat_interleave(n.long(), dim=0)\n if sampling_strategy == 'multinomial':\n idx = torch.multinomial(torch.ones(size), num_samples=size, replacement=True)\n return idx\n raise ValueError('Unknown sampling strategy')","def generate_sparse_randn(n, s):\n x = np.zeros(n)\n I = np.random.randint(0, n, s)\n x[I] = np.random.randn(s)\n return x","def test_filter_samples_from_otu_table_sample_ids_sparse(self):\r\n otu_table = parse_biom_table_str(sparse_otu_table1)\r\n\r\n # keep two samples\r\n expected_sample_ids = set(['PC.593', 'PC.607'])\r\n filtered_otu_table = filter_samples_from_otu_table(\r\n otu_table,\r\n expected_sample_ids,\r\n 0,\r\n inf)\r\n self.assertEqual(\r\n set(filtered_otu_table.SampleIds),\r\n expected_sample_ids)\r\n\r\n # keep some other samples\r\n expected_sample_ids = set(['PC.354', 'PC.635', 'PC.593'])\r\n filtered_otu_table = filter_samples_from_otu_table(\r\n otu_table,\r\n expected_sample_ids,\r\n 0,\r\n inf)\r\n self.assertEqual(\r\n set(filtered_otu_table.SampleIds),\r\n expected_sample_ids)","def imposeDSBconnections(self,Nc,breakLoci):\n #TODO\n #Adapt to any Nb\n if Nc == 0:\n return\n else:\n breakLoci = np.array(breakLoci)\n breakLoci = np.vstack((breakLoci,breakLoci+1)).T.flatten() # [A1, B1] -> [A1, A2, B1, B2]\n possible_pairs = [[a,b] for a,b in product(breakLoci, np.arange(0,self.numMonomers))]\n\n k0 = 0\n k1 = 4\n \n if breakLoci[0] == 0:\n for _ in range(2):\n possible_pairs.pop(0)\n k0 += 1\n \n if breakLoci[-1] == 99:\n for _ in range(2):\n possible_pairs.pop(-1)\n k1 -= 1\n \n for i in range(k0,k1):\n for _ in range(3):\n possible_pairs.pop(self.numMonomers*i + breakLoci[i] - 1 - 3*i + k0)\n \n for _ in range(2):\n possible_pairs.pop(breakLoci[2]-3 + k0)\n for _ in range(2):\n possible_pairs.pop(self.numMonomers + breakLoci[2] - 2*3 + k0 - 2)\n selected = np.array(possible_pairs)[np.random.choice(len(possible_pairs),size=Nc,replace=False)].T\n self.connect(selected) \n self.Nc += Nc","def randCent(data,k):\n index = set()\n while len(index) != k:\n index.add(random.randint(0, data.shape[0]))\n index = list(index)\n centroids = data[index]\n return centroids","def sampling(self,arr):\n H=0\n W=0\n if arr.shape[0]%2 == 0:\n H = arr.shape[0]/2\n else:\n H = 1+arr.shape[0]/2\n\n if arr.shape[1]%2 == 0:\n W = arr.shape[1]/2\n else:\n W = 1+arr.shape[1]/2\n \n new_arr = numpy.zeros((H,W),dtype = numpy.int)\n for i in range(H):\n for j in range(W):\n new_arr[i][j] = arr[2*i][2*j]\n return new_arr","def init_batch(self, src):\n batch, bos = src.size(1), self.src_dict.get_bos()\n return src.data.new(1, batch).fill_(bos)","def get_random_centroids(data, k) :\r\n centroids = []\r\n columns = np.size(data, axis=1)\r\n ranges = []\r\n for i in range(columns) :\r\n ranges.append([np.min(data[:,i]), np.max(data[:,i])])\r\n \r\n for i in range(k) :\r\n centroid = []\r\n for span in ranges :\r\n centroid.append(np.random.uniform(span[0], span[1]))\r\n centroids.append(centroid)\r\n \r\n return np.matrix(centroids)","def bootstrap(self, X_list, n_sampling):\n if len(X_list) < 2:\n raise ValueError('X_list must be a list containing at least two items')\n\n n_features = check_array(X_list[0]).shape[1]\n X_list_ = []\n for X in X_list:\n X_ = check_array(X)\n if X_.shape[1] != n_features:\n raise ValueError('X_list must be a list with the same number of features')\n X_list_.append(X_)\n X_list = np.array(X_list_)\n\n if isinstance(n_sampling, (numbers.Integral, np.integer)):\n if not 0 < n_sampling:\n raise ValueError('n_sampling must be an integer greater than 0.')\n else:\n raise ValueError('n_sampling must be an integer greater than 0.')\n\n # Bootstrapping\n adjacency_matrices_list = [[] for _ in range(X_list.shape[0])]\n for _ in range(n_sampling):\n resampled_X_list = [resample(X) for X in X_list]\n model = self.fit(resampled_X_list)\n for i, am in enumerate(model.adjacency_matrices_):\n adjacency_matrices_list[i].append(am)\n\n result_list = []\n for adjacency_matrices in adjacency_matrices_list:\n result_list.append(BootstrapResult(adjacency_matrices))\n\n return result_list","def initialize_k_mediods(data, k):\n return random.sample(range(len(data)), k)","def generate_initial_centroids(k, data):\n centroids = []\n used_indexes = []\n while len(centroids) < k:\n random_index = random.randint(0, len(data) - 1)\n if random_index not in used_indexes:\n centroids.append(data[random_index])\n used_indexes.append(random_index)\n return centroids"],"string":"[\n \"def bootstrap(X):\\n return X[np.random.choice(list(range(X.shape[0])), size=X.shape[0]), :]\",\n \"def bootstrapping(datasample):\\r\\n \\r\\n datasample=df_to_array(datasample)\\r\\n \\r\\n boots_indexs=np.random.randint(len(datasample),size=(1,len(datasample)))\\r\\n \\r\\n whole_indexs=list(range(len(datasample)))\\r\\n \\r\\n missing_indexs=np.array(list(set(whole_indexs).difference(set(list(boots_indexs[0])))))\\r\\n \\r\\n \\r\\n return boots_indexs,missing_indexs\",\n \"def bootstrap_resample(labels):\\n idxs = np.arange(len(labels))\\n num_labels = max(labels) + 1\\n bootstrap_idxs = np.zeros_like(idxs)\\n ptr = 0\\n for i in range(num_labels):\\n strat = idxs[labels == i]\\n bootstrap_idxs[ptr:ptr + len(strat)] = np.random.choice(strat, len(strat), replace=True)\\n ptr += len(strat)\\n return bootstrap_idxs\",\n \"def standard_bootstrap(dataset):\\n randseed=np.random.randint(0,10000)\\n np.random.seed(randseed)\\n \\n n = dataset.shape[0]\\n b = np.random.randint(0, high=n-1, size=n)\\n return dataset[b]\",\n \"def bootstrap_sample(data):\\n return [random.choice(data) for _ in data]\",\n \"def bootstrap(data):\\r\\n size = int(len(data))\\r\\n train = resample(data, n_samples=size, replace=True)\\r\\n test = data.drop(train.index) \\r\\n return train[encoded_features], train[target], test[encoded_features], test[target]\",\n \"def bootstrap_data(self):\\n for i in range(self.bootstraps):\\n df_i = self.training_df.groupby(\\n self.random_effect, group_keys=False\\n ).apply(\\n lambda x: x.sample(len(x), replace=True)\\n )\\n self.models.append(self.convert(df=df_i))\",\n \"def bootstrap_sample_generator_1D(samples: Union[NumpyFloatArray, NumpyIntArray]):\\n n_samples = samples.shape[0]\\n\\n while True:\\n _indices = np.random.randint(0, high=n_samples, size=n_samples)\\n\\n yield samples[_indices]\",\n \"def sample_from_ibp(K, alpha, sigma, c):\\n pp = poissonparams(K, alpha, sigma, c)\\n new_nodes = np.random.poisson(pp)\\n Ncols = new_nodes.sum()\\n node_count = np.zeros(Ncols)\\n\\n # used to build sparse matrix, entries of each Zij=1\\n colidx = [] \\n rowidx = []\\n rightmost_node = 0\\n\\n # for each clique\\n for n in range(K):\\n # revisit each previously seen node\\n for k in range(rightmost_node):\\n prob_repeat = (node_count[k] - sigma) / (n + c)\\n r = np.random.rand()\\n if r < prob_repeat:\\n rowidx.append(n)\\n colidx.append(k)\\n node_count[k] += 1\\n\\n for k in range(rightmost_node, rightmost_node + new_nodes[n]):\\n rowidx.append(n)\\n colidx.append(k)\\n node_count[k] += 1\\n \\n rightmost_node += new_nodes[n]\\n\\n # build sparse matrix\\n data = np.ones(len(rowidx), int)\\n shape = (K, Ncols)\\n Z = csr_matrix((data, (rowidx, colidx)), shape)\\n\\n return Z\",\n \"def sample_indices_from_coupling(c, num_samples=None, return_all = False, thr = 10**(-6)):\\n\\n if return_all:\\n return [[i, j] for i in range(c.shape[0]) for j in range(c.shape[1]) if c[i,j] > thr]\\n else:\\n linear_samples = np.random.choice(range(c.size), size = num_samples, p = c.flat)\\n \\n if num_samples in [1, None]:\\n linear_samples = [linear_samples]\\n \\n n_cols = c.shape[1]\\n return np.array([[s//n_cols , s%n_cols ] for s in linear_samples])\",\n \"def bootstrap_sample_generator_2D(samples: Union[NumpyFloatArray, NumpyIntArray]):\\n n_samples = samples.shape[0]\\n\\n num_cols = samples.shape[1]\\n cols = np.arange(num_cols)\\n\\n while True:\\n # generate indices to pick N values from f_A, f_B and f_C_i\\n _indices = np.random.randint(0, high=n_samples, size=samples.shape)\\n\\n yield samples[_indices, cols]\",\n \"def bootstrap_sample_generator_3D(samples: Union[NumpyFloatArray, NumpyIntArray]):\\n n_samples = samples.shape[1]\\n array_shape = samples.shape[1:]\\n num_cols = samples.shape[2]\\n cols = np.arange(num_cols)\\n\\n while True:\\n _indices = np.random.randint(0, high=n_samples, size=array_shape)\\n\\n yield samples[:, _indices, cols]\",\n \"def sparse(n, k):\\n z = np.zeros(n)\\n for i in np.random.choice( np.arange(n), k, replace=None ): # supports of nonzero entries\\n z[i] = np.random.randn()\\n return z\",\n \"def initialize_clusters(points, k):\\r\\n return points[np.random.randint(points.shape[0], size=k)]\",\n \"def _sample_from_zeros(n: int, sparse: sp.csr_matrix) -> List[List[int]]:\\n zeros = np.argwhere(np.logical_not(sparse.todense()))\\n ids = np.random.choice(range(len(zeros)), size=(n,))\\n return zeros[ids].tolist()\",\n \"def cbindMatrices(hm, args):\\n hm2 = heatmapper.heatmapper()\\n\\n # Make a dict of region name:row associations\\n hm.read_matrix_file(args.matrixFile[0])\\n d = dict({x: dict() for x in hm.parameters[\\\"group_labels\\\"]})\\n for idx, group in enumerate(hm.parameters[\\\"group_labels\\\"]):\\n s = hm.parameters[\\\"group_boundaries\\\"][idx]\\n e = hm.parameters[\\\"group_boundaries\\\"][idx + 1]\\n for idx2, reg in enumerate(hm.matrix.regions[s:e]):\\n d[group][reg[2]] = idx2 + s\\n\\n # Iterate through the other matrices\\n for idx in range(1, len(args.matrixFile)):\\n hm2.read_matrix_file(args.matrixFile[idx])\\n # Add the sample labels\\n hm.parameters['sample_labels'].extend(hm2.parameters['sample_labels'])\\n # Add the sample boundaries\\n lens = [x + hm.parameters['sample_boundaries'][-1] for x in hm2.parameters['sample_boundaries']][1:]\\n hm.parameters['sample_boundaries'].extend(lens)\\n\\n # Add on additional NA initialized columns\\n ncol = hm.matrix.matrix.shape[1]\\n hm.matrix.matrix = np.hstack((hm.matrix.matrix, np.empty(hm2.matrix.matrix.shape)))\\n hm.matrix.matrix[:, ncol:] = np.NAN\\n\\n # Update the values\\n for idx2, group in enumerate(hm2.parameters[\\\"group_labels\\\"]):\\n if group not in d:\\n continue\\n s = hm2.parameters[\\\"group_boundaries\\\"][idx2]\\n e = hm2.parameters[\\\"group_boundaries\\\"][idx2 + 1]\\n for idx3, reg in enumerate(hm2.matrix.regions[s:e]):\\n if reg[2] not in d[group]:\\n continue\\n hm.matrix.matrix[d[group][reg[2]], ncol:] = hm2.matrix.matrix[s + idx3, :]\\n\\n # Append the special params\\n for s in hm.special_params:\\n hm.parameters[s].extend(hm2.parameters[s])\\n\\n # Update the sample parameters\\n hm.matrix.sample_labels = hm.parameters['sample_labels']\\n hm.matrix.sample_boundaries = hm.parameters['sample_boundaries']\",\n \"def boot_matrix(z, B):\\n\\n n = len(z) # sample size\\n idz = np.random.randint(0, n, size=(B, n)) # indices to pick for all boostrap samples\\n return z[idz]\",\n \"def fetchCooler(c, regions, coolerFetch = lambda coo, ext:coo.matrix(balance=True, sparse=True).fetch(ext),\\n mask=True, force=False, ):\\n regions = [list(i) for i in regions]\\n resolution = c.binsize\\n\\n for i in regions:\\n if i[1] == None:\\n i[1] = 0 \\n if i[2] == None:\\n i[2] = c.chromsizes[i[0]]\\n\\n \\n for a in regions: \\n if str(a[0]) not in c.chromnames:\\n raise ValueError(\\\"Chromosome {0} from regions not found in cooler\\\".format(a))\\n if (a[1] % resolution) != 0:\\n raise ValueError(\\\"Start of an region should be a multiple fo resolution\\\")\\n \\n# bins = c.bins()[:]\\n \\n# # managing masks \\n# if mask is False: \\n# bins[\\\"mask\\\"] = 1 \\n# elif mask is None:\\n# assert \\\"mask\\\" in bins.columns\\n# elif mask is True: \\n# pass \\n# elif callable(mask):\\n# pass \\n# else:\\n# bins[\\\"mask\\\"] = mask \\n \\n \\n for region in regions:\\n matrix = coolerFetch(c, region)\\n try: # setting matrix nans to zeros.\\n matrix.data = np.nan_to_num(matrix.data, copy=False)\\n except TypeError: #workaround for old numpy versions\\n matrix.data = np.nan_to_num(matrix.data)\\n# st,end = c.extent(region)\\n# subbins = bins[st:end].copy()\\n if mask is True: \\n newmask = np.array((matrix.sum(axis=0) > 0 ))[0]\\n# if callable(mask):\\n# new_mask = mask(matrix)\\n# subbins[\\\"mask\\\"] = newmask \\n\\n assert len(newmask) == matrix.shape[0]\\n\\n yield matrix, newmask\",\n \"def boot_matrix(z, B):\\n z = np.array(z).flatten()\\n n = len(z) # sample size\\n idz = np.random.randint(0, n, size=(B, n)) # indices to pick for all boostrap samples\\n return z[idz]\",\n \"def bootstrap_idxs(n, rng: np.random.Generator = None):\\n if rng is None or type(rng) is not np.random.Generator:\\n rng = np.random.default_rng(rng)\\n in_bag = rng.integers(low=0, high=n, size=n)\\n out_bag = np.array(list(set(range(n)) - set(in_bag)))\\n return in_bag, out_bag\",\n \"def init_centroids(X,K):\\n c = random.sample(list(X),K)\\n return c\",\n \"def _SampleInputMatrix(nrows, bl, bu, distname='randomUniform'):\\n npars = len(bl)\\n x = np.zeros((nrows,npars))\\n bound = bu-bl\\n for i in range(nrows):\\n # x[i,:]= bl + DistSelector([0.0,1.0,npars],distname='randomUniform')*bound # only used in full Vhoeys-framework\\n x[i,:]= bl + np.random.rand(1,npars)*bound\\n return x\",\n \"def bootstrap_sample_from_data(data, weights=None, seed=0):\\n # Set up the random number generator\\n RNG = np.random.default_rng(seed)\\n N = data.shape[0]\\n\\n # Set up weights\\n if weights is not None:\\n cutoffs = np.cumsum(weights)\\n else:\\n cutoffs = np.linspace(0, 1, N)\\n\\n # Draw random indices\\n indices = np.searchsorted(cutoffs, RNG.uniform(size=N))\\n\\n # Create a bootstrapped sample\\n new_data = deepcopy(data[indices,])\\n return new_data\",\n \"def sample_mapped_keys(mapping, min_coverage=50):\\r\\n if min_coverage == 0:\\r\\n return {}\\r\\n sample_keys = {}\\r\\n for key in mapping.keys():\\r\\n if (min_coverage > 1):\\r\\n sample_keys[key] = sample(mapping[key],\\r\\n min(min_coverage - 1, len(mapping[key])))\\r\\n else:\\r\\n sample_keys[key] = []\\r\\n sample_keys[key].append(key) # always include the centroid\\r\\n return sample_keys\",\n \"def test_sparse():\\n\\n rng = np.random.RandomState(0)\\n\\n X = rng.rand(20, 2)\\n X[X < 0.8] = 0\\n X_csr = sp.csr_matrix(X)\\n\\n bisect_means = BisectingKMeans(n_clusters=3, random_state=0)\\n\\n bisect_means.fit(X_csr)\\n sparse_centers = bisect_means.cluster_centers_\\n\\n bisect_means.fit(X)\\n normal_centers = bisect_means.cluster_centers_\\n\\n # Check if results is the same for dense and sparse data\\n assert_allclose(normal_centers, sparse_centers, atol=1e-8)\",\n \"def simulate_sparse(n_rows, n_columns, row_density=0.001):\\n # X = sparse.lil_matrix((n_rows, n_columns))\\n X = []\\n counter = 0\\n for row in range(n_rows):\\n counter += 1\\n if (counter % 1000) == 0:\\n print(counter)\\n\\n # X[row] = sparse.random(1, n_columns, density=row_density)\\n X.append(sparse.random(1, n_columns, density=row_density, format='lil'))\\n\\n return sparse.vstack(X).tocsr()\",\n \"def subsampling(dataset, class_column_index, class_max_count, class_dict):\\n out = []\\n for row in dataset:\\n cls = row[class_column_index]\\n rInt = np.random.randint(0, class_dict[cls])\\n if rInt <= class_max_count:\\n out.append(row)\\n ss_data = np.array(out)\\n\\n return ss_data\",\n \"def initialize_chromosomes(self):\\n # Generate random chromosomes.\\n for i in range(self.n_chromosomes):\\n random_chromosome = self.generate_random_chromosome()\\n self.chromosomes[0][i, :] = random_chromosome[0]\\n self.chromosomes[1][i, :] = random_chromosome[1]\\n\\n self.repair_chromosomes()\",\n \"def ensure_sparse_cols(self,max_density,remove_lowest=True):\\n if max_density >= 1:\\n max_nnz = int(max_density)\\n else:\\n max_nnz = int(max_density*self.shape[0])\\n for j in range(self.shape[1]):\\n col = self.fast_get_col(j)\\n excess = col.nnz - max_nnz\\n if excess > 0:\\n if remove_lowest:\\n zero_entries = np.argsort(col.data)[:excess]\\n else:\\n zero_entries = random.sample(range(col.nnz),excess)\\n col.data[zero_entries] = 0\\n self.fast_update_col(j,col.data)\",\n \"def initialize(img):\\n w, h, _ = img.shape\\n for c in current_cluster_centers:\\n x = np.random.randint(w)\\n y = np.random.randint(h)\\n c[:] = img[x, y]\",\n \"def _get_bootstrap_sample(x, y, num_reps):\\r\\n combined = array(list(x) + list(y))\\r\\n total_obs = len(combined)\\r\\n num_x = len(x)\\r\\n for i in range(num_reps):\\r\\n # sampling with replacement\\r\\n indices = randint(0, total_obs, total_obs)\\r\\n sampled = combined.take(indices)\\r\\n # split into the two populations\\r\\n sampled_x = sampled[:num_x]\\r\\n sampled_y = sampled[num_x:]\\r\\n yield sampled_x, sampled_y\",\n \"def generate_map(seed):\\n def set_biomes(field, points):\\n for row in range(len(field)):\\n # For every cell, we find the closest point\\n for cell in range(len(field[row])):\\n # Store the currently closest point:\\n shortest_dist = -1\\n # Stores the biome of the current point:\\n current_biome = '_'\\n\\n # Iterate over the points to find the closest one\\n for point in points:\\n # Calculate the euclidean distance\\n xdiff = point[0] - row\\n ydiff = point[1] - cell\\n distance = xdiff * xdiff + ydiff * ydiff # Square root not needed since we're only comparing\\n\\n # If this is currently the shortest distance, set it\\n if distance < shortest_dist or shortest_dist == -1:\\n shortest_dist = distance\\n # Set the biome that will be chosen if a shorter distance isn't found\\n current_biome = point[2]\\n\\n # Select a random field in the biome, taking rarity into account\\n\\n # Get names/data of all fields in the chosen biome\\n biome_fields = biomes[current_biome]['fields'].items()\\n # Extract field names and their rarities (weights)\\n field_data = [(name, data['rarity']) for name, data in biome_fields]\\n # Choose a random field using the weights\\n field_index = weighted_choice([field_weight[1] for field_weight in field_data])\\n # Set the cell's field\\n field[row][cell] = field_data[field_index][0]\\n\\n return field\\n\\n def poisson_disc_samples(width, height, r, k=5):\\n \\\"\\\"\\\"\\n \\\"Two-dimensional Poisson Disc Sampling using Robert Bridson's algorithm.\\\"\\n Modified version of https://github.com/emulbreh/bridson.\\n \\\"\\\"\\\"\\n tau = 2 * pi\\n cellsize = r / sqrt(2)\\n\\n grid_width = int(ceil(width / cellsize))\\n grid_height = int(ceil(height / cellsize))\\n grid = [None] * (grid_width * grid_height)\\n\\n def distance(a, b):\\n dx = a[0] - b[0]\\n dy = a[1] - b[1]\\n return sqrt(dx * dx + dy * dy)\\n\\n def grid_coords(p2):\\n return [int(floor(p2[0] / cellsize)), int(floor(p2[1] / cellsize))]\\n\\n def fits(p2, gx, gy):\\n yrange = list(range(max(gy - 2, 0), min(gy + 3, grid_height)))\\n\\n for x in range(max(gx - 2, 0), min(gx + 3, grid_width)):\\n for y in yrange:\\n g = grid[x + y * grid_width]\\n if g is None:\\n continue\\n if distance(p2, g) <= r:\\n return False\\n return True\\n\\n p = [width * rnd.random(), height * rnd.random()]\\n queue = [p]\\n grid_x, grid_y = grid_coords(p)\\n grid[grid_x + grid_y * grid_width] = p\\n\\n while queue:\\n qi = int(rnd.random() * len(queue))\\n qx, qy = queue[qi]\\n queue[qi] = queue[-1]\\n queue.pop()\\n\\n for _ in range(k):\\n alpha = tau * rnd.random()\\n d = r * sqrt(3 * rnd.random() + 1)\\n px = qx + d * cos(alpha)\\n py = qy + d * sin(alpha)\\n\\n if not (0 <= px < width and 0 <= py < height):\\n continue\\n p = [px, py]\\n grid_x, grid_y = grid_coords(p)\\n\\n if not fits(p, grid_x, grid_y):\\n continue\\n queue.append(p)\\n grid[grid_x + grid_y * grid_width] = p\\n return [p for p in grid if p is not None]\\n\\n # Define map dimensions and settings\\n # Size should be at least 35x35\\n\\n height = 50\\n width = 50\\n\\n # Create a new instance of Random() using a given seed\\n\\n rnd = random.Random(seed)\\n\\n # Generate a random starting location somewhere in the middle of the map\\n\\n x = rnd.randint(width - 10, width + 10)\\n y = rnd.randint(height - 10, height + 10)\\n\\n # Create a 2-dimensional list for the game map\\n\\n field = [['_'] * width for _ in range(height)]\\n\\n # Create random points that will be the starting positions of biomes\\n\\n points = poisson_disc_samples(width, height, 3, 5)\\n rnd.shuffle(points)\\n\\n for i in range(len(points)):\\n biome = rnd.choice(list(biomes.keys())) # Set a random biome\\n\\n points[i][0] = int(round(points[i][0])) - 1 # x\\n points[i][1] = int(round(points[i][1])) - 1 # y\\n points[i].append(biome)\\n\\n field[points[i][1]][points[i][0]] = 'X' # not needed?\\n\\n # Set the biomes\\n\\n field = set_biomes(field, points)\\n\\n return ({\\n 'field': field,\\n 'x': x,\\n 'y': y\\n })\",\n \"def downsample_sam(self, factor):\",\n \"def sample_array(self, X, random_state=None, sample_weight=None): \\n if random_state is None:\\n random_state = check_random_state(random_state)\\n\\n X = check_array(X)\\n if sample_weight is None:\\n sample_weight = np.ones((X.shape[0], ))\\n cdf = sample_weight.cumsum()\\n cdf /= cdf[-1]\\n uniform_samples = random_state.random_sample(X.shape[0])\\n bootstrap_idx = cdf.searchsorted(uniform_samples, side='right')\\n # searchsorted returns a scalar\\n bootstrap_idx = np.array(bootstrap_idx, copy=False)\\n return bootstrap_idx\",\n \"def init_centroids(self, data_points):\\n # print(\\\"Init centroid\\\")\\n # return list(map(lambda x: x[1], random.sample(labelled_data, self.k)))\\n\\n # Project the data: this step will take several seconds\\n\\n centroids_scaled = self.naive_sharding(data_points, self.k)\\n return list(centroids_scaled)\\n\\n #sample = np.random.permutation(len(labelled_data))[:self.k]\\n\\n #return list(map(lambda x: labelled_data[x][1], sample))\",\n \"def initialize(X, k):\\n\\n if not isinstance(X, np.ndarray) or X.ndim != 2:\\n return None\\n\\n # n: number of dada points\\n # d: dimension of each data point\\n n, d = X.shape\\n # print(X.shape)\\n # print(X)\\n\\n if not isinstance(k, int) or k <= 0 or k > n:\\n return None\\n\\n # Sample k centroids from a random.uniform distribution;\\n # output is an array of coordinates\\n C = np.random.uniform(low=np.min(X, axis=0),\\n high=np.max(X, axis=0),\\n size=(k, d))\\n return C\",\n \"def set_sparse_signals(self):\\n\\t\\n\\t\\tparams_dSs = [self.mu_dSs, self.sigma_dSs]\\n\\t\\tparams_Ss0 = [self.mu_Ss0, self.sigma_Ss0]\\n\\t\\tself.dSs, self.idxs = sparse_vector([self.Nn, self.Kk], \\n\\t\\t\\t\\t\\t\\t\\t\\t\\t\\t\\t\\tparams_dSs,\\tseed=self.seed_dSs)\\n\\t\\t\\n\\t\\t# Replace components with conflicting background odor \\n\\t\\tif self.Kk_split is not None and self.Kk_split != 0:\\n\\t\\t\\tassert 0 <= self.Kk_split <= self.Kk, \\\\\\n\\t\\t\\t\\t\\\"Splitting sparse signal into two levels requires Kk_split\\\" \\\\\\n\\t\\t\\t\\t\\\" to be non-negative and less than or equal to Kk.\\\"\\n\\t\\t\\tassert self.mu_dSs_2 is not None \\\\\\n\\t\\t\\t\\tand self.sigma_dSs_2 is not None, \\\\\\n\\t\\t\\t\\t\\\"Splitting sparse signal into two levels requires that\\\" \\\\\\n\\t\\t\\t\\t\\\" mu_dSs_2 and sigma_dSs_2 are set.\\\"\\n\\n\\t\\t\\tsp.random.seed(self.seed_dSs)\\n\\t\\t\\tself.idxs_2 = sp.random.choice(self.idxs[0], self.Kk_split, \\n\\t\\t\\t\\t\\t\\t\\t\\t\\t\\t\\treplace=False)\\n\\t\\t\\tfor idx_2 in self.idxs_2:\\n\\t\\t\\t\\tself.dSs[idx_2] = sp.random.normal(self.mu_dSs_2, \\n\\t\\t\\t\\t\\t\\t\\t\\t\\t\\t\\t\\t\\tself.sigma_dSs_2)\\n\\t\\telse:\\n\\t\\t\\tself.idxs_2 = []\\n\\t\\t\\tself.Kk_split = 0\\n\\t\\t\\t\\n\\t\\t# Ss0 is the ideal (learned) background stimulus without noise\\n\\t\\tself.Ss0, self.Ss0_noisy = sparse_vector_bkgrnd([self.Nn, self.Kk], \\n\\t\\t\\t\\t\\t\\t\\t\\t\\t\\t\\t\\t\\t\\tself.idxs, params_Ss0,\\n\\t\\t\\t\\t\\t\\t\\t\\t\\t\\t\\t\\t\\t\\tseed=self.seed_Ss0)\\n\\t\\t\\n\\t\\tself.Ss = self.dSs + self.Ss0_noisy\",\n \"def subsample_maps(net):\\n\\n # start with uniform probability that each map is selected for inclusion in a connection;\\n # reduce probability of selecting a given map again whenever it is selected\\n probability_reduction = .01\\n interarea_probabilities = []\\n interlaminar_probabilities = []\\n for layer in net.layers:\\n interarea_probabilities.append(np.ones(layer.m))\\n interlaminar_probabilities.append(np.ones(layer.m))\\n\\n subsample_indices = []\\n for connection in net.connections:\\n n_maps = int(round(connection.pre.m * connection.c))\\n pre_index = net.find_layer_index(connection.pre.name)\\n\\n pre_cortical_area = connection.pre.name.split('_')[0]\\n post_cortical_area = connection.post.name.split('_')[0]\\n if pre_cortical_area == post_cortical_area: # interlaminar\\n p = interlaminar_probabilities[pre_index]\\n else:\\n p = interarea_probabilities[pre_index]\\n\\n indices = np.random.choice(range(int(connection.pre.m)), n_maps, replace=False, p=p/np.sum(p))\\n for i in indices:\\n p[i] = p[i]*probability_reduction\\n subsample_indices.append(indices)\\n\\n return subsample_indices\",\n \"def compute_bootstrapped_sample(X_table, y_table):\\n n = len(X_table)\\n X_sample = []\\n y_sample = []\\n for _ in range(n):\\n rand_index = random.randrange(0, n)\\n X_sample.append(X_table[rand_index])\\n y_sample.append(y_table[rand_index])\\n return X_sample, y_sample\",\n \"def bootstrap(df, nclass, if_new=False):\\n ori_size = Counter(df.label)\\n logger.info(f'class info before resampling: {ori_size.values()}')\\n ori_size_list = list(ori_size.values())\\n\\n if if_new:\\n df_new = pd.DataFrame(data=None, columns=df.columns)\\n target_size = min(ori_size_list)\\n else:\\n target_size = max(ori_size_list)\\n df_new = df.copy()\\n\\n for i in range(nclass):\\n name = list(ori_size.keys())[i]\\n name_index = np.array(df[df.label == name].index)\\n if target_size < ori_size_list[i]:\\n sample_size = target_size\\n elif target_size > ori_size_list[i]:\\n sample_size = target_size - ori_size_list[i]\\n else:\\n if if_new:\\n sample_size = target_size\\n else:\\n sample_size = 0\\n\\n np.random.seed(i)\\n boostrap_sample = np.random.randint(0, ori_size_list[i], sample_size)\\n df_new = df_new.append(df.iloc[name_index[boostrap_sample]], ignore_index=True)\\n logger.info(f'class info after resampling: {Counter(df_new.label).values()}')\\n return df_new\",\n \"def test_optimalk_random_sample_data():\\n from sklearn.datasets.samples_generator import make_blobs\\n from gap_statistic import random_sample_data\\n\\n # Create data\\n X, y = make_blobs(n_samples=int(1e3), n_features=2, centers=3)\\n\\n random_data = random_sample_data(X, random_sampling='uniform')\\n assert random_data.shape == X.shape, \\\"check sampled data shape {} vs original data shape {}\\\".format(X.shape, random_data.shape) \\n\\n random_data = random_sample_data(X, random_sampling='gaussian')\\n assert random_data.shape == X.shape, \\\"check sampled data shape {} vs original data shape {}\\\".format(X.shape, random_data.shape)\",\n \"def _generate_indices(random_state, bootstrap, n_population, n_samples):\\n # Draw sample indices\\n if bootstrap:\\n indices = random_state.randint(0, n_population, n_samples)\\n else:\\n indices = sample_without_replacement(n_population, n_samples,\\n random_state=random_state)\\n\\n return indices\",\n \"def generate_samples(self, n_samples):\",\n \"def generate_samples(self, n_samples):\",\n \"def generate_random_sparse_array(nrows, ncols, numdense):\\n i = np.random.randint(0, nrows, numdense)\\n j = np.random.randint(0, ncols, numdense)\\n data = np.random.randint(1,6, numdense)\\n ij = np.vstack((i,j))\\n return coo_matrix((data, ij), shape=(nrows, ncols))\",\n \"def sample(self, rng_key: jnp.ndarray) -> jnp.ndarray:\\n pass\",\n \"def _sample_mini_dataset(dataset, num_classes, num_shots):\\n shuffled = list(dataset)\\n random.shuffle(shuffled)\\n for class_idx, class_obj in enumerate(shuffled[:num_classes]):\\n for sample in class_obj.sample(num_shots):\\n yield (sample, class_idx)\",\n \"def sample_contact_points(contact_points, num_samples, get_idx=False):\\n num_samples = min(len(contact_points), num_samples)\\n idx = random.sample(range(0, len(contact_points)), num_samples)\\n if get_idx:\\n return [contact_points[i] for i in idx], idx\\n else:\\n return [contact_points[i] for i in idx]\",\n \"def sample(src, coords):\\n b, h, w, c = src.size()\\n h_sample, w_sample = coords.shape[:2]\\n max_coord = [h - 1, w - 1]\\n coords = np.clip(coords, 0, max_coord)\\n indices = mapping_to_indices(coords, b)\\n\\n sampled = src[indices].reshape((h_sample, w_sample))\\n\\n return sampled\",\n \"def bootstrap_group(nsubj, ngroups):\\n groupsize = nsubj\\n samples = [(groupsize * np.random.rand(groupsize)).astype(np.int_)\\n for i in range(ngroups)]\\n return samples\",\n \"def test_getitem_slice(self):\\n random.seed(12345)\\n\\n nside_coverage = 32\\n nside_map = 128\\n\\n full_map = np.zeros(hpg.nside_to_npixel(nside_map)) + hpg.UNSEEN\\n full_map[0: 5000] = random.random(size=5000)\\n\\n sparse_map = healsparse.HealSparseMap(healpix_map=full_map, nside_coverage=nside_coverage)\\n\\n # Test in-range, overlap, out-of-range\\n testing.assert_array_almost_equal(sparse_map[100: 500], full_map[100: 500])\\n testing.assert_array_almost_equal(sparse_map[4500: 5500], full_map[4500: 5500])\\n testing.assert_array_almost_equal(sparse_map[5500: 5600], full_map[5500: 5600])\\n\\n # Test stepped\\n testing.assert_array_almost_equal(sparse_map[100: 500: 2], full_map[100: 500: 2])\\n testing.assert_array_almost_equal(sparse_map[4500: 5500: 2], full_map[4500: 5500: 2])\\n testing.assert_array_almost_equal(sparse_map[5500: 5600: 2], full_map[5500: 5600: 2])\\n\\n # Test all\\n testing.assert_array_almost_equal(sparse_map[:], full_map[:])\",\n \"def generate_bootstrap_samples(num_samples, test_universe, test_set_sizes):\\n for sample_idx, sample_size in zip(range(num_samples), cycle(test_set_sizes)):\\n yield random.sample(test_universe, sample_size)\",\n \"def split_simcc_xy(self, heatmap: Union[np.ndarray, torch.Tensor]):\\n size = heatmap.size()\\n k = size[0] if size[0] <= 20 else 20\\n maps = []\\n for _ in range(k):\\n xy_dict = {}\\n single_heatmap = heatmap[_]\\n xy_dict['x'], xy_dict['y'] = self.merge_maps(single_heatmap)\\n maps.append(xy_dict)\\n return maps, k\",\n \"def kmeanspp_initialisation( self, X ):\\n N, _ = X.shape\\n k, d = self.k, self.d\\n M = []\\n\\n # Choose one center amongst the X at random\\n m = sc.random.randint( N )\\n M.append( X[m] )\\n\\n # Choose k centers\\n while( len( M ) < self.k ):\\n # Create a probability distribution D^2 from the previous mean\\n D = cdist( X, M ).min( 1 )**2\\n assert( D.shape == (N,) )\\n\\n # Normalise and sample a new point\\n D /= D.sum()\\n\\n m = sc.random.multinomial( 1, D ).argmax()\\n M.append( X[m] )\\n\\n M = sc.column_stack( M )\\n sigma = sc.sqrt(cdist( X, M.T, 'sqeuclidean').sum(0)/(N))\\n w = ones( k )/float(k)\\n\\n return M, sigma, w\",\n \"def cell_sample(mask, samplingPoints):\\n maskedArea = np.array(np.where(mask)).T\\n maskedAreaLength = len(maskedArea)\\n randomIndex = sp.random.randint(0, maskedAreaLength, samplingPoints)\\n coordsRandom = maskedArea[randomIndex] + sp.rand(samplingPoints, 2)\\n return(coordsRandom)\",\n \"def boot_strap_selection(X, y, weights):\\n # Take random sample of indicies, with replacement\\n bss_indicies = np.random.choice(range(len(y)), size = len(y), p = weights)\\n \\n # Subset arrays with indicies\\n return X[bss_indicies,:], y[bss_indicies]\",\n \"def create_cands(data):\\n\\n best = np.zeros(data.dim+1)\\n best[0:data.dim] = data.xbest\\n best[data.dim] = 1-np.sum(data.xbest)\\n\\n # Ncand times the best value\\n cp_e = np.kron(np.ones((data.Ncand, 1)), np.asmatrix(best))\\n # This generates random perturbations\\n # need dim+1 to account for the \\\"missing\\\" value\\n r = np.random.rand(data.Ncand, data.dim+1)\\n a = r < data.pertP\\n idx = np.where(np.sum(a, axis=1) == 0)\\n for ii in range(len(idx[0])):\\n f = np.random.permutation(data.dim+1)\\n a[idx[0][ii], f[0]] = True\\n randnums = np.random.randn(data.Ncand, data.dim+1)\\n randnums[a == False] = 0\\n pv = randnums*data.sigma_stdev\\n # Create new points by adding random fluctucations to best point\\n new_pts = cp_e+pv\\n\\n # Iterative, column wise procedure to force the randomly\\n # sampled point to be in [0,1]\\n for ii in range(data.dim+1):\\n vec_ii = new_pts[:, ii]\\n adj_l = np.where(vec_ii < data.xlow)\\n vec_ii[adj_l[0]] = data.xlow + (data.xlow - vec_ii[adj_l[0]])\\n adj_u = np.where(vec_ii > data.xup)\\n vec_ii[adj_u[0]] = data.xup - (vec_ii[adj_u[0]]-data.xup)\\n stillout_u = np.where(vec_ii > data.xup)\\n vec_ii[stillout_u[0]] = data.xlow\\n stillout_l = np.where(vec_ii < data.xlow)\\n vec_ii[stillout_l[0]] = data.xup\\n new_pts[:, ii] = copy.copy(vec_ii)\\n\\n new_pts = new_pts / np.sum(new_pts, axis=1)\\n\\n cp_e = copy.copy(new_pts)\\n rand_pts = np.asmatrix(np.random.uniform(0, 1, [data.Ncand, data.dim + 1]))\\n cp_r = rand_pts/np.sum(rand_pts, axis=1)\\n\\n CandPoint = np.concatenate((cp_e, cp_r), axis=0)\\n # return only data.dim candidate points\\n CandPoint_out = CandPoint[:, 0:data.dim]\\n\\n return CandPoint_out\",\n \"def generate_map(nrows, ncols, nrooms, max_col_size, max_row_size):\\n arr = np.zeros((nrows, ncols), dtype=np.int8)\\n\\n for i in range(nrooms):\\n rand_row_start = np.random.randint(nrows)\\n rand_col_start = np.random.randint(ncols)\\n\\n rand_row_size = np.random.randint(max_row_size / 2, max_row_size)\\n rand_col_size = np.random.randint(max_col_size / 2, max_col_size)\\n\\n arr[rand_row_start:rand_row_start + rand_row_size, rand_col_start:rand_col_start + rand_col_size] = 1\\n\\n labels = measure.label(arr)\\n regions = measure.regionprops(labels)\\n\\n centroids = list()\\n for region in regions:\\n centroids.append(region.centroid)\\n\\n num_centroids = len(centroids)\\n\\n # get distances between every pair of centroids\\n dists = scipy.spatial.distance.cdist(centroids, centroids)\\n\\n # get a distance that is greater than all current distances\\n max_dist = np.max(dists) + 1\\n\\n # make sure upper triangle is at least max_dist so that when picking closest\\n # pairs, we won't choose a diagonal element or a duplicate connection\\n dists = dists + np.triu(np.ones((num_centroids, num_centroids))) * max_dist\\n\\n for i in range(num_centroids - 1):\\n min_dist_idx = np.argmin(dists)\\n min_dist_idx = np.unravel_index(min_dist_idx, dists.shape)\\n\\n # create a hallway between regionprops\\n centroid1 = np.array(centroids[min_dist_idx[0]], dtype=np.int)\\n centroid2 = np.array(centroids[min_dist_idx[1]], dtype=np.int)\\n\\n [row_centroid_1, row_centroid_2] = sorted([centroid1, centroid2], key=lambda x: x[0])\\n [col_centroid_1, col_centroid_2] = sorted([centroid1, centroid2], key=lambda x: x[1])\\n\\n arr[row_centroid_1[0]:row_centroid_2[0] + 1, row_centroid_1[1]] = 1\\n arr[row_centroid_2[0], col_centroid_1[1]:col_centroid_2[1] + 1] = 1\\n\\n dists[:, min_dist_idx[1]] += max_dist\\n\\n return arr\",\n \"def bootstrap_replicate_1d(data, func):\\r\\n bs_sample = np.random.choice(data, len(data))\\r\\n return func(bs_sample)\",\n \"def sample(self, size):\\n if len(self.observations) == 0:\\n raise ValueError(\\\"need to fit KDE with observations\\\")\\n\\n samples = np.zeros(size)\\n for i in range(size):\\n \\n # randomly select an anchor point for a gaussian\\n anchor_point = np.random.choice(self.observations)\\n\\n # then sample from that gaussian\\n sample = np.random.normal(loc=anchor_point, scale=self.bandwidth)\\n samples[i] = sample\\n return samples\",\n \"def creaCent(data,k):\\n\\n Cen = random.sample(data[:,:].tolist(),k=k)\\n return np.asarray(Cen)\",\n \"def __init__(self):\\n super().__init__()\\n self.type = 'SparseGridCollocationSampler'\\n self.printTag = 'SAMPLER '+self.type.upper()\\n self.maxPolyOrder = None #L, the relative maximum polynomial order to use in any dimension\\n self.indexSetType = None #TP, TD, or HC; the type of index set to use\\n self.polyDict = {} #varName-indexed dict of polynomial types\\n self.quadDict = {} #varName-indexed dict of quadrature types\\n self.importanceDict = {} #varName-indexed dict of importance weights\\n self.maxPolyOrder = None #integer, relative maximum polynomial order to be used in any one dimension\\n self.lastOutput = None #pointer to output dataObjects object\\n self.ROM = None #pointer to ROM\\n self.jobHandler = None #pointer to job handler for parallel runs\\n self.doInParallel = True #compute sparse grid in parallel flag, recommended True\\n self.dists = {} #Contains the instance of the distribution to be used. keys are the variable names\\n self.writeOut = None\\n self.indexSet = None\\n self.sparseGrid = None\\n self.features = None\\n self.sparseGridType = None\\n self.addAssemblerObject('ROM', InputData.Quantity.one)\",\n \"def bootstrap(x, iter=int(1E6), return_samples=False):\\n\\n \\n means = np.empty(iter) \\n dfs = []\\n for i in tqdm(range(iter), desc='Performing bootstrap sampling'):\\n resamp = np.random.choice(x, size=len(x), replace=True)\\n means[i] = resamp.mean()\\n\\n if return_samples:\\n _df = pd.DataFrame([])\\n _df['value'] = resamp\\n _df['iter'] = i + 1\\n dfs.append(_df)\\n\\n # Compute confidence intervals of the means.\\n mean_val = means.mean()\\n bounds_ci = {'99%': (0.5, 99.5), '95%': (2.5, 97.5), '90%': (5, 95),\\n '75%': (12.5, 87.5), '50%': (25, 75), '25%': (37.5, 62.5),\\n '10%': (45, 55), '5%': (47.5, 52.5), '1%': (49.5, 50.5)} \\n cis = {} \\n for k, v in bounds_ci.items():\\n bounds = np.percentile(means, v)\\n cis[k] = bounds\\n\\n statistics['original_data'] = x\\n statistics['resampled_means'] = means\\n statistics['mean_value'] = mean_val\\n statistics['confidence_intervals'] = cis\\n\\n if return_samples:\\n _df = pd.concat(dfs, sort=False)\\n return [statistics, _df]\\n else:\\n return statistics\",\n \"def sample_inds(self, inds: List[float]):\\n data_subset = self.data.create_subset(inds)\\n\\n if self.preload:\\n data_ram_subset = [self.data_ram[i] for i in inds]\\n return AtomisticDataset(data_subset, data_ram=data_ram_subset)\\n else:\\n return AtomisticDataset(data_subset)\",\n \"def dataset_augmentation(data_start, bootstrapping = 1, epurate = 1, shuffle = True):\\n data = data_start\\n for ii in range(bootstrapping):\\n data = data.append(data_start.apply(bootstrap_sample, axis=1), ignore_index=True)\\n\\n#Bugged version that weirdly works well....\\n# for ii in range(bootstrapping):\\n # data = data.append(bootstrap_sample(data_start), ignore_index=True)\\n\\n for ii in range(epurate):\\n data = data.append(data_start.apply(epurate_sample, axis=1), ignore_index=True)\\n\\n # Shuffling (Important)\\n if shuffle == True:\\n data = data.sample(frac=1)\\n return data\",\n \"def bootstrap( M ):\\n tot_ct_per_ob = M.sum(axis = 0)\\n zero_list = []\\n for i in range(len(tot_ct_per_ob)):\\n if tot_ct_per_ob[i] <= 0:\\n zero_list.append(i)\\n M = np.delete(M, zero_list, 1)\\n new_M = np.zeros(M.shape)\\n tot_ct_per_ob = M.sum(axis = 0)\\n for i in range(len(M)):\\n for j in range(len(M[0])):\\n new_M[i][j] = M[i][j]/tot_ct_per_ob[j]\\n new_M = np.transpose(new_M)\\n bootstrap = []\\n for i in range(len(tot_ct_per_ob)):\\n rnd_vec = np.random.multinomial(tot_ct_per_ob[i], new_M[i])\\n bootstrap.append(rnd_vec)\\n \\n bootstrap = np.transpose(np.asarray(bootstrap)) \\n return bootstrap\",\n \"def pureMap(data, cutoff=1.7, contactMap=None):\\n data = np.asarray(data)\\n if len(data.shape) != 2:\\n raise ValueError(\\\"Wrong dimensions of data\\\")\\n if 3 not in data.shape:\\n raise ValueError(\\\"Wrong size of data: %s,%s\\\" % data.shape)\\n if data.shape[0] == 3:\\n data = data.T\\n data = np.asarray(data, float, order=\\\"C\\\")\\n\\n t = giveContacts(data, cutoff)\\n N = data.shape[0]\\n if contactMap is None:\\n contactMap = np.zeros((N, N), \\\"int32\\\")\\n contactMap[t[:, 0], t[:, 1]] += 1\\n contactMap[t[:, 1], t[:, 0]] += 1\\n return contactMap\",\n \"def bootstrap(h0, h1, f, B=10000):\\n t_star = np.zeros(B)\\n m = len(h0)\\n n = len(h1)\\n all_h = np.concatenate([h0, h1])\\n for b_id in range(B):\\n b_data = np.random.choice(all_h, size=m + n, replace=True)\\n t_star[b_id] = f(b_data[:m], b_data[m:])\\n return t_star\",\n \"def bootstrap_replicate_1d(data, func):\\n bs_sample = np.random.choice(data, len(data))\\n return func(bs_sample)\",\n \"def _preallocate_samples(self):\\n self.prealloc_samples_ = []\\n for _ in range(self.num_prealloc_samples_):\\n self.prealloc_samples_.append(self.sample())\",\n \"def bootstrap_replicate_1d(data, func):\\n bs_sample = np.random.choice(data, len(data))\\n\\n return func(bs_sample)\",\n \"def init_cluster_centroids(x, number_of_clusters):\\n return x[np.random.choice(x.shape[0], number_of_clusters, replace=False), :]\",\n \"def sample_cc(self, nsamples=1, weighted=True):\\n weights = self.areas / np.sum(self.areas) if weighted else None\\n for index in np.random.choice(a=len(self.geometries), size=nsamples, p=weights):\\n yield self.geometries[index]\",\n \"def fully_pooled(at_bats, hits):\\n phi_prior = Uniform(scalar_like(at_bats, 0), scalar_like(at_bats, 1))\\n phi = pyro.sample(\\\"phi\\\", phi_prior)\\n num_players = at_bats.shape[0]\\n with pyro.plate(\\\"num_players\\\", num_players):\\n return pyro.sample(\\\"obs\\\", Binomial(at_bats, phi), obs=hits)\",\n \"def bootstrap(data,func,nboot):\\n\\n n = len(data)\\n resamples = np.array([[random.choice(data) for i in range(n)]\\n for j in range(nboot)])\\n return np.apply_along_axis(func, 1, resamples)\",\n \"def sparse_initialization_weights(self):\\n W = []\\n mu, sigma = 0, 1/self.non_zero_units\\n\\n for i in xrange(self.n_in):\\n row = np.zeros(self.n_out)\\n non_zeros = self.rng.normal(mu, sigma, self.non_zero_units)\\n # non_zeros /= non_zeros.sum()\\n non_zero_idxs = self.rng.permutation(\\n self.n_out)[0:self.non_zero_units]\\n for j in xrange(self.non_zero_units):\\n row[non_zero_idxs[j]] = non_zeros[j]\\n W.append(row)\\n W = np.asarray(W, dtype=theano.config.floatX)\\n return W\",\n \"def sample(df: pd.DataFrame, cat: str, n_samples: int) -> np.array:\\n # get optimal block size\\n b_star = optimal_block_length(df[cat].values)\\n b_star = math.ceil(b_star[0].b_star_cb)\\n\\n # (n_samples, n_observations)\\n samples = tapered_block_bootstrap(df[cat].values,\\n block_length=b_star,\\n replications=n_samples)\\n\\n return samples\",\n \"def rescaledMap(data, bins, cutoff=1.7, contactMap=None):\\n # print data.sum(), bins.sum(), cutoff\\n\\n t = giveContacts(data, cutoff)\\n x = np.searchsorted(bins, t[:, 0]) - 1\\n y = np.searchsorted(bins, t[:, 1]) - 1\\n assert x.min() >= 0\\n assert y.min() >= 0\\n assert x.max() < len(bins) - 1\\n assert y.max() < len(bins) - 1\\n matrixSize = len(bins) - 1\\n index = matrixSize * x + y\\n\\n unique, inds = np.unique(index, return_counts=True)\\n uniquex = unique // matrixSize\\n uniquey = unique % matrixSize\\n\\n if contactMap is None:\\n contactMap = np.zeros((matrixSize, matrixSize), dtype=int)\\n contactMap[uniquex, uniquey] += inds\\n\\n return contactMap\",\n \"def sample_beam_search(self, features, states=None):\\n sampled_ids = []\\n inputs = features.unsqueeze(0)\\n beam_size = 5\\n candidates = []\\n all_candidates = []\\n for i in range(30): # maximum sampling length\\n if i==0:\\n hiddens, states = self.lstm(inputs, states) # (batch_size, 1, hidden_size), \\n outputs = self.linear(hiddens.squeeze(1)) # (batch_size, vocab_size) \\n predictions = torch.topk(outputs,beam_size)\\n for k in range(beam_size):\\n candidates.append([predictions[1][0][k], predictions[0][0][k].cpu().data.numpy()[0] , hiddens , states]) \\n else: \\n all_candidates = []\\n for k in range(beam_size):\\n candidate = candidates[k]\\n inputs = self.embed(candidate[0][len(candidate[0])-1])\\n inputs = inputs.unsqueeze(0)\\n # print(inputs)\\n hiddens, states = self.lstm(inputs, candidate[3]) # (batch_size, 1, hidden_size), \\n outputs = self.linear(hiddens.squeeze(1)) # (batch_size, vocab_size) \\n predictions = torch.topk(outputs,beam_size)\\n for k in range(beam_size):\\n new_candidate = [torch.cat((candidate[0],predictions[1][0][k]),0),candidate[1] + predictions[0][0][k].cpu().data.numpy()[0], hiddens, states]\\n all_candidates.append(new_candidate)\\n ordered = sorted(all_candidates, key=lambda tup:tup[1], reverse = True)\\n candidates = ordered[:beam_size]\\n sampled_ids = candidates[0][0]\\n return sampled_ids.squeeze()\",\n \"def init_centroids(self, points , k):\\n centroids = points.copy()\\n numpy.random.shuffle(centroids)\\n return centroids[0:k,:]\",\n \"def bootstrap_array(f_values, values, bootstraps=1000):\\n x = np.array(values)\\n x_bs = x[np.random.randint(x.size, size=(bootstraps, x.size))]\\n return np.array(map(f_values, x_bs))\",\n \"def _initialize_mapbias(self):\\n self.mapbias = sharedX(\\n numpy.zeros(self.nmap),\\n name='mb',\\n borrow=True\\n )\",\n \"def seed_samples(self, sample_seed):\\n np.random.seed(sample_seed)\\n random.seed(sample_seed)\\n torch.manual_seed(sample_seed)\\n torch.cuda.manual_seed(sample_seed)\\n torch.backends.cudnn.deterministic = True\\n torch.backends.cudnn.benchmark = False\",\n \"def seed_samples(self, sample_seed):\\n np.random.seed(sample_seed)\\n random.seed(sample_seed)\\n torch.manual_seed(sample_seed)\\n torch.cuda.manual_seed(sample_seed)\\n torch.backends.cudnn.deterministic = True\\n torch.backends.cudnn.benchmark = False\",\n \"def fill_features(input_point, sigma=0.1):\\n # to fill in f, array of features w shape (863, 1)\\n f = np.zeros(X.shape[0])\\n # iterate over every example to compare\\n for i in range(m):\\n f[i] = gaussian_kernel(input_point, X[i], sigma=sigma)\\n return f\",\n \"def diverse_sampler(self):\\n\\n # Sample number of nonzero idxs\\n num_idxs = np.random.randint(low=1, high=self.K-1)\\n\\n # Sample actual idxs in state that are nonzero\\n idxs = []\\n # can have nonzero terms up to state[K-2]\\n all_states = [i for i in range(self.K - 1)]\\n for i in range(num_idxs):\\n rand_id = np.random.randint(low=0, high=len(all_states))\\n idxs.append(all_states.pop(rand_id))\\n\\n # sort idxs from largest to smallest to allocate\\n # potential correctly\\n idxs.sort()\\n idxs.reverse()\\n\\n # allocate potential\\n xs = self.simplex_sampler(num_idxs)\\n\\n # fill with appropriate number of pieces adding on any remaindr\\n remainder = 0\\n state = np.zeros(self.K+1, dtype=int)\\n for i in range(num_idxs):\\n idx = idxs[i]\\n pot_idx = xs[i] + remainder\\n num_pieces = int(pot_idx/self.weights[idx])\\n state[idx] += num_pieces\\n # update remainder\\n remainder = pot_idx - num_pieces*self.weights[idx]\\n\\n return state\",\n \"def mask_sparse(self, threshold=10):\\n self.MaskPrefix = 's' + self.MaskPrefix\\n print('Masking pixels that do not have at least {0} coherent values'.format(threshold))\\n # each pixel assigned an integer corresponding to # of igrams where coherent\\n # NOTE: save coverage map if it doesn't exist already\\n coverage = self.get_coverage()\\n sparse = ma.masked_less(coverage, threshold)\\n for ig in self.Set:\\n igram = self.load_ma(ig)\\n igram[sparse.mask] = ma.masked\\n self.save_ma(ig, igram)\\n print('Done')\",\n \"def _init_centroid(self, seed: int):\\n random.seed(seed)\\n self.centroid_info = dict()\\n self.cluster_result = dict()\\n self.centroid_stable_flag = dict()\\n for key_index, chosen_value in enumerate(\\n random.sample(self.list_data, self.n_cluster)):\\n self.centroid_info.setdefault(\\\"c\\\" + str(key_index), float(chosen_value))\\n self.cluster_result.setdefault(\\\"c\\\" + str(key_index), list())\\n self.centroid_stable_flag.setdefault(\\\"c\\\" + str(key_index), False)\",\n \"def sample_rows(df, nrows):\",\n \"def _bootstrap_sampler(size: int, sampling_strategy: str='poisson') ->Tensor:\\n if sampling_strategy == 'poisson':\\n p = torch.distributions.Poisson(1)\\n n = p.sample((size,))\\n return torch.arange(size).repeat_interleave(n.long(), dim=0)\\n if sampling_strategy == 'multinomial':\\n idx = torch.multinomial(torch.ones(size), num_samples=size, replacement=True)\\n return idx\\n raise ValueError('Unknown sampling strategy')\",\n \"def generate_sparse_randn(n, s):\\n x = np.zeros(n)\\n I = np.random.randint(0, n, s)\\n x[I] = np.random.randn(s)\\n return x\",\n \"def test_filter_samples_from_otu_table_sample_ids_sparse(self):\\r\\n otu_table = parse_biom_table_str(sparse_otu_table1)\\r\\n\\r\\n # keep two samples\\r\\n expected_sample_ids = set(['PC.593', 'PC.607'])\\r\\n filtered_otu_table = filter_samples_from_otu_table(\\r\\n otu_table,\\r\\n expected_sample_ids,\\r\\n 0,\\r\\n inf)\\r\\n self.assertEqual(\\r\\n set(filtered_otu_table.SampleIds),\\r\\n expected_sample_ids)\\r\\n\\r\\n # keep some other samples\\r\\n expected_sample_ids = set(['PC.354', 'PC.635', 'PC.593'])\\r\\n filtered_otu_table = filter_samples_from_otu_table(\\r\\n otu_table,\\r\\n expected_sample_ids,\\r\\n 0,\\r\\n inf)\\r\\n self.assertEqual(\\r\\n set(filtered_otu_table.SampleIds),\\r\\n expected_sample_ids)\",\n \"def imposeDSBconnections(self,Nc,breakLoci):\\n #TODO\\n #Adapt to any Nb\\n if Nc == 0:\\n return\\n else:\\n breakLoci = np.array(breakLoci)\\n breakLoci = np.vstack((breakLoci,breakLoci+1)).T.flatten() # [A1, B1] -> [A1, A2, B1, B2]\\n possible_pairs = [[a,b] for a,b in product(breakLoci, np.arange(0,self.numMonomers))]\\n\\n k0 = 0\\n k1 = 4\\n \\n if breakLoci[0] == 0:\\n for _ in range(2):\\n possible_pairs.pop(0)\\n k0 += 1\\n \\n if breakLoci[-1] == 99:\\n for _ in range(2):\\n possible_pairs.pop(-1)\\n k1 -= 1\\n \\n for i in range(k0,k1):\\n for _ in range(3):\\n possible_pairs.pop(self.numMonomers*i + breakLoci[i] - 1 - 3*i + k0)\\n \\n for _ in range(2):\\n possible_pairs.pop(breakLoci[2]-3 + k0)\\n for _ in range(2):\\n possible_pairs.pop(self.numMonomers + breakLoci[2] - 2*3 + k0 - 2)\\n selected = np.array(possible_pairs)[np.random.choice(len(possible_pairs),size=Nc,replace=False)].T\\n self.connect(selected) \\n self.Nc += Nc\",\n \"def randCent(data,k):\\n index = set()\\n while len(index) != k:\\n index.add(random.randint(0, data.shape[0]))\\n index = list(index)\\n centroids = data[index]\\n return centroids\",\n \"def sampling(self,arr):\\n H=0\\n W=0\\n if arr.shape[0]%2 == 0:\\n H = arr.shape[0]/2\\n else:\\n H = 1+arr.shape[0]/2\\n\\n if arr.shape[1]%2 == 0:\\n W = arr.shape[1]/2\\n else:\\n W = 1+arr.shape[1]/2\\n \\n new_arr = numpy.zeros((H,W),dtype = numpy.int)\\n for i in range(H):\\n for j in range(W):\\n new_arr[i][j] = arr[2*i][2*j]\\n return new_arr\",\n \"def init_batch(self, src):\\n batch, bos = src.size(1), self.src_dict.get_bos()\\n return src.data.new(1, batch).fill_(bos)\",\n \"def get_random_centroids(data, k) :\\r\\n centroids = []\\r\\n columns = np.size(data, axis=1)\\r\\n ranges = []\\r\\n for i in range(columns) :\\r\\n ranges.append([np.min(data[:,i]), np.max(data[:,i])])\\r\\n \\r\\n for i in range(k) :\\r\\n centroid = []\\r\\n for span in ranges :\\r\\n centroid.append(np.random.uniform(span[0], span[1]))\\r\\n centroids.append(centroid)\\r\\n \\r\\n return np.matrix(centroids)\",\n \"def bootstrap(self, X_list, n_sampling):\\n if len(X_list) < 2:\\n raise ValueError('X_list must be a list containing at least two items')\\n\\n n_features = check_array(X_list[0]).shape[1]\\n X_list_ = []\\n for X in X_list:\\n X_ = check_array(X)\\n if X_.shape[1] != n_features:\\n raise ValueError('X_list must be a list with the same number of features')\\n X_list_.append(X_)\\n X_list = np.array(X_list_)\\n\\n if isinstance(n_sampling, (numbers.Integral, np.integer)):\\n if not 0 < n_sampling:\\n raise ValueError('n_sampling must be an integer greater than 0.')\\n else:\\n raise ValueError('n_sampling must be an integer greater than 0.')\\n\\n # Bootstrapping\\n adjacency_matrices_list = [[] for _ in range(X_list.shape[0])]\\n for _ in range(n_sampling):\\n resampled_X_list = [resample(X) for X in X_list]\\n model = self.fit(resampled_X_list)\\n for i, am in enumerate(model.adjacency_matrices_):\\n adjacency_matrices_list[i].append(am)\\n\\n result_list = []\\n for adjacency_matrices in adjacency_matrices_list:\\n result_list.append(BootstrapResult(adjacency_matrices))\\n\\n return result_list\",\n \"def initialize_k_mediods(data, k):\\n return random.sample(range(len(data)), k)\",\n \"def generate_initial_centroids(k, data):\\n centroids = []\\n used_indexes = []\\n while len(centroids) < k:\\n random_index = random.randint(0, len(data) - 1)\\n if random_index not in used_indexes:\\n centroids.append(data[random_index])\\n used_indexes.append(random_index)\\n return centroids\"\n]","isConversational":false},"negative_scores":{"kind":"list like","value":["0.60307676","0.5836601","0.57376236","0.56982666","0.5689801","0.5620543","0.5541793","0.5484117","0.54839253","0.5435804","0.54210573","0.53805405","0.5368052","0.5355705","0.5346038","0.5340237","0.5306805","0.53025925","0.5296711","0.5295456","0.5281978","0.5273258","0.526737","0.52478176","0.52470666","0.5246784","0.52421373","0.5195706","0.519006","0.51863027","0.5185907","0.5179668","0.5171682","0.5170508","0.516804","0.5165697","0.51584935","0.5148621","0.5116375","0.50996006","0.5098497","0.50945467","0.50904626","0.50904626","0.5087923","0.5086497","0.5086355","0.508599","0.5069937","0.5066644","0.50638014","0.5055109","0.505205","0.5046515","0.50417227","0.50417227","0.50380737","0.50352085","0.5010728","0.500629","0.5003678","0.49990618","0.4996591","0.499011","0.49835196","0.49832413","0.49827522","0.4976602","0.4976406","0.49745733","0.49686095","0.49558315","0.49513203","0.49487084","0.49397898","0.49386036","0.4934496","0.4934202","0.49289834","0.4925178","0.49251708","0.49249062","0.49227616","0.49227616","0.49210522","0.4915336","0.4911301","0.49100274","0.49081597","0.49046862","0.4904392","0.4902469","0.49009144","0.48991403","0.48966452","0.48949075","0.4890581","0.48882836","0.4879237","0.48788518"],"string":"[\n \"0.60307676\",\n \"0.5836601\",\n \"0.57376236\",\n \"0.56982666\",\n \"0.5689801\",\n \"0.5620543\",\n \"0.5541793\",\n \"0.5484117\",\n \"0.54839253\",\n \"0.5435804\",\n \"0.54210573\",\n \"0.53805405\",\n \"0.5368052\",\n \"0.5355705\",\n \"0.5346038\",\n \"0.5340237\",\n \"0.5306805\",\n \"0.53025925\",\n \"0.5296711\",\n \"0.5295456\",\n \"0.5281978\",\n \"0.5273258\",\n \"0.526737\",\n \"0.52478176\",\n \"0.52470666\",\n \"0.5246784\",\n \"0.52421373\",\n \"0.5195706\",\n \"0.519006\",\n \"0.51863027\",\n \"0.5185907\",\n \"0.5179668\",\n \"0.5171682\",\n \"0.5170508\",\n \"0.516804\",\n \"0.5165697\",\n \"0.51584935\",\n \"0.5148621\",\n \"0.5116375\",\n \"0.50996006\",\n \"0.5098497\",\n \"0.50945467\",\n \"0.50904626\",\n \"0.50904626\",\n \"0.5087923\",\n \"0.5086497\",\n \"0.5086355\",\n \"0.508599\",\n \"0.5069937\",\n \"0.5066644\",\n \"0.50638014\",\n \"0.5055109\",\n \"0.505205\",\n \"0.5046515\",\n \"0.50417227\",\n \"0.50417227\",\n \"0.50380737\",\n \"0.50352085\",\n \"0.5010728\",\n \"0.500629\",\n \"0.5003678\",\n \"0.49990618\",\n \"0.4996591\",\n \"0.499011\",\n \"0.49835196\",\n \"0.49832413\",\n \"0.49827522\",\n \"0.4976602\",\n \"0.4976406\",\n \"0.49745733\",\n \"0.49686095\",\n \"0.49558315\",\n \"0.49513203\",\n \"0.49487084\",\n \"0.49397898\",\n \"0.49386036\",\n \"0.4934496\",\n \"0.4934202\",\n \"0.49289834\",\n \"0.4925178\",\n \"0.49251708\",\n \"0.49249062\",\n \"0.49227616\",\n \"0.49227616\",\n \"0.49210522\",\n \"0.4915336\",\n \"0.4911301\",\n \"0.49100274\",\n \"0.49081597\",\n \"0.49046862\",\n \"0.4904392\",\n \"0.4902469\",\n \"0.49009144\",\n \"0.48991403\",\n \"0.48966452\",\n \"0.48949075\",\n \"0.4890581\",\n \"0.48882836\",\n \"0.4879237\",\n \"0.48788518\"\n]","isConversational":false},"document_score":{"kind":"string","value":"0.59620684"},"document_rank":{"kind":"string","value":"1"}}},{"rowIdx":22685,"cells":{"query":{"kind":"string","value":"Adds a frame around input mask, given a kernel. The goal of this frame is define margins around the matrix where the kernel will not perform convolution (denoted by 1). If the matrix is upper symmetric, a margin of half the kernel's width is added below the diagonal and a maximum distance from the diagonal above which margins need not be drawn can be considered. Otherwise Margins are simply added on all 4 sides of the matrix."},"document":{"kind":"string","value":"def frame_missing_mask(mask, kernel_shape, sym_upper=False, max_dist=None):\n if mask.dtype != bool:\n raise ValueError(\"Mask must contain boolean values\")\n if not sp.issparse(mask):\n raise ValueError(\"Mask must be a sparse matrix\")\n\n framed_mask = mask.copy()\n ms, ns = mask.shape\n mk, nk = kernel_shape\n if sym_upper and (max_dist is not None):\n # Remove diagonals further than scan distance in the input mask\n framed_mask = diag_trim(framed_mask, max_dist + max(nk, mk)).tocsr()\n max_m = max_dist + mk\n max_n = max_dist + nk\n else:\n max_m, max_n = ms, ns\n # Up and down margins initialized with zeros and filled as needed\n margin_1 = sp.csr_matrix((mk - 1, ns), dtype=bool)\n margin_2 = sp.csr_matrix((mk - 1, ns), dtype=bool)\n if sym_upper and (max_dist is not None):\n # Margin 1 (top) is in upper triangle -> fill missing up to scan dist\n margin_1[:, :max_n] = 1\n else:\n margin_1[:, :] = 1\n margin_2[:, :] = 1\n framed_mask = sp.vstack([margin_1, framed_mask, margin_2], format=\"csr\")\n\n # Left and right\n margin_1 = sp.csr_matrix((ms + 2 * (mk - 1), nk - 1), dtype=bool)\n margin_2 = sp.csr_matrix((ms + 2 * (mk - 1), nk - 1), dtype=bool)\n\n if sym_upper and (max_dist is not None):\n # Margin 2 (right) is in upper triangle-> fill missing up to scan dist\n margin_2[-(max_m + 1) :, :] = 1\n # Fill only the start of left margin for the top-left corner\n margin_1[: mk - 1, :] = 1\n else:\n margin_1[:, :] = 1\n margin_2[:, :] = 1\n framed_mask = sp.hstack([margin_1, framed_mask, margin_2], format=\"csr\")\n\n if sym_upper:\n # LIL format is much faster when changing sparsity\n framed_mask = framed_mask.tolil()\n # Add margin below diagonal\n big_k = max(nk, mk)\n dia_margins = np.ones(big_k)\n dia_offsets = np.arange(-1, -big_k-1, -1)\n framed_mask += sp.diags(\n dia_margins,\n dia_offsets,\n shape=framed_mask.shape,\n format=\"lil\",\n dtype=bool,\n )\n framed_mask = framed_mask.tocsr()\n return framed_mask"},"metadata":{"kind":"string","value":"{\n \"objective\": {\n \"self\": [],\n \"paired\": [],\n \"triplet\": [\n [\n \"query\",\n \"document\",\n \"negatives\"\n ]\n ]\n }\n}"},"negatives":{"kind":"list like","value":["def slidekernelthroughdiagonal(kernel, matrix):\n size_kernel = kernel.shape[0]\n size_matrix = matrix.shape[0]\n result = np.zeros([size_matrix])\n for i in range(size_matrix):\n # Calculate zero padding needed\n padding_b = -min(i - int(size_kernel/2), 0)\n padding_a = -min(size_matrix - int(i + size_kernel/2), 0)\n matrix_selection = matrix[max(0, i-int(size_kernel/2)):min(size_matrix, i+int(size_kernel/2)),max(0, i-int(size_kernel/2)):min(size_matrix, i+int(size_kernel/2))]\n matrix_padded = np.pad(matrix_selection, [(padding_b, padding_a), (padding_b, padding_a)])\n result[i] = np.sum(matrix_padded*kernel)\n return result","def add_context_margin(image, margin_size, **pad_kwargs):\n return np.pad(image,\n ((margin_size, margin_size),\n (margin_size, margin_size),\n (0, 0)), **pad_kwargs)","def add_kernel(org: np.ndarray, kernel: np.ndarray, center):\n # place filter at the array, and exclude any locations that are out of bounds\n c_r, c_c = center.row, center.column\n k_r, k_c = kernel.shape\n row_start = max(0, k_r/2 - c_r)\n pass","def applyKernelToPoints(image,pts,kernel,border_type='BLACK'):\n \n \n pts=np.asarray(pts)\n image=np.asarray(image)\n image.shape\n if len(image.shape)>2:\n grayscale=False\n shaperesult=(len(pts),image.shape[2])\n elif len(image.shape)==1:\n image=image.reshape(1,image.shape[0])\n shaperesult=len(pts)\n grayscale=True\n\n else:\n grayscale=True\n\n # Kernel dimensions - they are integers\n krows=kernel.shape[0] \n kcols=kernel.shape[1]\n\n if krows%2==0:\n # Is even\n ldrows=(krows/2)-1\n udrows=krows/2\n \n else:\n # Is odd\n ldrows=krows/2\n udrows=krows/2\n\n if kcols%2==0:\n # Is even\n ldcols=(kcols/2)-1\n udcols=kcols/2\n else:\n # Is odd\n ldcols=kcols/2\n udcols=kcols/2\n\n #------------------------------------\n # ADD FRAME TO THE ORIGINAL IMAGE\n #------------------------------------\n\n dummyM=image.shape[0]+krows-1\n dummyN=image.shape[1]+kcols-1\n \n if grayscale==True:\n dummyimage=np.asarray(np.zeros((dummyM,dummyN)))\n \n else:\n dummyimage=np.asarray(np.zeros((dummyM,dummyN,image.shape[2])))\n\n if border_type==\"WHITE\":\n dummyimage=dummyimage+255\n\n elif border_type==\"ANTIALIAS\":\n # Fills top border\n dummyimage[0:ldrows,ldcols:ldcols+image.shape[1]]=image[image.shape[0]-ldrows:image.shape[0],:]\n\n # Fills bottom border\n dummyimage[(ldrows+image.shape[0]):,ldcols:(ldcols+image.shape[1])]=image[0:udrows,:]\n \n # Fills left border\n dummyimage[ldrows:ldrows+image.shape[0],0:ldcols]=image[:,image.shape[1]-ldcols:]\n\n # Fills right border\n dummyimage[ldrows:ldrows+image.shape[0],(ldcols+image.shape[1]):]=image[:,0:udcols]\n \n # Fills top, left corner\n dummyimage[0:ldrows,0:ldcols]=image[image.shape[0]-ldrows,image.shape[1]-ldcols]\n\n # Fills bottom, left corner\n dummyimage[(ldrows+image.shape[0]):,0:ldcols]=image[0:udrows,(image.shape[1]-ldcols):]\n \n # Fills top, right corner\n dummyimage[0:ldrows,(ldcols+image.shape[1]):]=image[(image.shape[0]-ldrows):,0:udcols]\n \n # Fills bottom, right corner\n dummyimage[(ldrows+image.shape[0]):,(ldcols+image.shape[1]):]=image[0:udrows,0:udcols]\n \n dummyimage[ldrows:ldrows+image.shape[0],ldcols:ldcols+image.shape[1]]=image \n \n result=np.asarray(np.zeros(shaperesult))\n \n pts[:,0]=pts[:,0]+ldrows\n pts[:,1]=pts[:,1]+ldcols\n \n for k in range(len(pts)):\n total=0\n \n for i in range(-ldrows,udrows+1):\n for j in range(-ldcols,udcols+1):\n total=total+dummyimage[i+pts[k,0],j+pts[k,1]]*kernel[i+ldrows,j+ldcols]\n \n \n result[k]=total\n \n \n return result","def adjust_border(input_masks, device, margin=3):\n # Convert the mask to numpy array\n dtype = input_masks.dtype\n input_masks = np.squeeze(input_masks.cpu().numpy(), axis=1)\n\n erosion_kernel = cv2.getStructuringElement(cv2.MORPH_ELLIPSE,\n (margin*2, margin*2))\n batch_size = input_masks.shape[0]\n \n output_mask_lst = []\n # Erode all the masks\n for i in range(batch_size):\n output_mask = cv2.erode(input_masks[i, ...], erosion_kernel)\n\n output_mask_lst.append(\n torch.tensor(output_mask, dtype=dtype, device=device)[None])\n \n # Concat back along the batch dimension.\n output_masks = torch.cat(output_mask_lst, dim=0)\n return output_masks.unsqueeze(dim=1)","def cs4243_filter(image, kernel):\n Hi, Wi = image.shape\n Hk, Wk = kernel.shape\n filtered_image = np.zeros((Hi, Wi))\n\n ###Your code here####\n # pad image to handle border pixels\n pad_height = (int)((Hk - 1)/2)\n pad_width = (int)((Wk - 1)/2)\n image_pad = pad_zeros(image, pad_height, pad_width)\n\n # Flip the kernel horizontal and vertical\n kernel = cs4243_rotate180(kernel)\n \n # compute effective output size, assume stride=1\n out_height = 1 + Hi - Hk + 2*pad_height\n out_width = 1 + Wi - Wk + 2*pad_width\n \n # get initial nodes of receptive fields\n recep_fields_h = [i for i in range(out_height)]\n recep_fields_w = [i for i in range(out_width)]\n \n for i in recep_fields_h:\n for j in recep_fields_w: \n # get receptive area\n recep_area = image_pad[i:i+Hk, j:j+Wk] \n\n # multiply recep_area with kernel\n conv_sum = 0.0\n for y in range(Hk):\n for x in range(Wk): \n conv_sum += kernel[y][x] * recep_area[y][x]\n filtered_image[i, j] = conv_sum\n ###\n\n return filtered_image","def shift_kernel(kernel, shape, centre):\n h, w = kernel.shape\n assert(h % 2 == 1)\n assert(w % 2 == 1)\n half_h = np.floor(h/2)\n half_w = np.floor(w/2)\n \n result = np.zeros((shape[0]+2*half_h, shape[1]+2*half_w)) #zero pad to simplify edge handling \n\n ind_h = centre[0] + np.arange(0, 2*half_h+1, dtype='int') \n ind_w = centre[1] + np.arange(0, 2*half_w+1, dtype='int')\n result[ind_h[:,np.newaxis], ind_w] = kernel\n result = result[half_h:-half_h,half_w:-half_w]\n return result","def convolution(matrix, kernel):\n assert assert_odd(kernel.shape[0])\n \n # Padded matrix (0s on the outsides)\n N = kernel.shape[0] # Get the dim for the kernel\n I = np.pad(matrix, int(N/2), \"constant\")\n \n # Now do the convolution\n C = np.zeros(matrix.shape) # This is the convolved image\n h, w = C.shape # Get width and height\n s = int(N/2) # Spacing of the matrix\n positions = [(i,j) for i in range(h) for j in range(w)]\n for (i,j) in positions:\n y, x = i+s,j+s # Shift the center to the right position\n \n # Calc the convolution at each pixel\n C[i,j] = np.sum(np.multiply(kernel, I[y-s:y+s+1,x-s:x+s+1]))\n \n # Return the clipped array as uint8\n return C","def _pad_with_zeros(self, X, margin):\n newX = np.zeros((X.shape[0] + 2 * margin, X.shape[1] + 2 * margin, X.shape[2]))\n x_offset = margin\n y_offset = margin\n newX[x_offset:X.shape[0] + x_offset, y_offset:X.shape[1] + y_offset, :] = X\n return newX","def gaussian_filter(img,f=5,K=1,var=1):\n i_x, i_y = np.shape(img) # image size\n radi = f//2 # window radius\n\n # create gaussian kernel\n def gaussian_kernel(f,K,var):\n \n # create coordinate information \n if f//2 == 0:\n x = np.linspace(-radi,radi,f+1)\n y = np.linspace(-radi,radi,f+1)\n x = np.delete(x, radi)\n y = np.delete(y, radi)\n else:\n x = np.linspace(-radi,radi,f)\n y = np.linspace(-radi,radi,f)\n\n m_x, m_y = np.meshgrid(x,y) # create coordinate\n r_gauss = m_x**2 + m_y**2 # distance to origin\n gauss = K*(np.exp(-r_gauss/(2*(var**2)))) # create kernel\n return gauss/gauss.sum()\n \n #mirror padding\n def mir_padding(img,f):\n img_p = np.zeros((i_x+2*radi,i_y+2*radi)) #create padding image\n img_p[radi:i_x+radi,radi:i_y+radi] = img #throw original image to padding image\n img_p[0:radi,radi:i_y+radi] = img[radi-1::-1,:] # padding top rows\n img_p[-radi::1,radi:i_y+radi] = img[-1:-radi-1:-1,:] # padding bottom rows\n img_p[radi:i_x+radi,0:radi] = img[:,radi-1::-1] # padding left column\n img_p[radi:i_x+radi,-radi::1] = img[:,-1:-radi-1:-1] # padding right column\n for i in range(f):\n img_p[0:radi,i] = img[radi-1-i,radi-1::-1] # padding upper-left corner\n img_p[0:radi,-i] = img[radi-1-i,-radi::1] # padding upper-righ corner\n img_p[-1:-radi-1:-1,i] = img[-radi+i,radi-1::-1] # padding lower-left corner\n img_p[-1:-radi-1:-1,-i] = img[-radi+i,-radi::1] # padding lower-right corner\n return img_p\n\n img_p = mir_padding(img,f) # create padding image\n g_kernel = gaussian_kernel(f,K,var) # create gaussian kernel\n\n #seperate kernel\n E = g_kernel[0,0]\n c = g_kernel[:,0]\n wT = np.reshape(g_kernel[0,:]/E,(f,1))\n\n gauss_image = np.zeros([i_x,i_y]) # create gauss image\n temp_image = np.zeros([i_x,i_y]) # create temp image for two 1D convolution\n old_c_sum = c.sum() # calculate sum of c before modification\n\n # if elements of kernel are located within area of padding, substitute value with 0\n # calculate new value base on ratio between sum before and after modification\n for j in range(i_y):\n y_bound = i_y - j\n mod_c = c.copy()\n if j < radi:\n mod_c[0:radi-j] = 0 \n new_c_sum = mod_c.sum()\n mod_c = mod_c*old_c_sum/new_c_sum \n if j > i_y - radi - 1:\n mod_c[-1:-radi+y_bound-1:-1] = 0 \n new_c_sum = mod_c.sum()\n mod_c = mod_c*old_c_sum/new_c_sum \n for i in range(i_x):\n temp_image[i,j] = np.sum(img_p[i+radi,j:j+f]*mod_c)\n\n temp_image = mir_padding(temp_image,f) # create padding temp image for next 1D convolution\n old_wT_sum = wT.sum() # calculate sum of wT before modification\n\n # if elements of kernel are located within area of padding, substitute value with 0\n # calculate new value base on ratio between sum before and after modification\n for i in range(i_x):\n x_bound = i_x - i\n mod_wT = wT.copy()\n if i < radi:\n mod_wT[0:radi-i] = 0 \n new_wT_sum = mod_wT.sum()\n mod_wT = mod_wT*old_wT_sum/new_wT_sum \n if i > i_x - radi - 1:\n mod_wT[-1:-radi+x_bound-1:-1] = 0 \n new_wT_sum = mod_wT.sum()\n mod_wT = mod_wT*old_wT_sum/new_wT_sum \n for j in range(i_y):\n gauss_image[i,j] = np.sum(temp_image[i:i+f,j+radi]*mod_wT.T)\n\n return gauss_image","def _zero_pad(self, kernel, size):\n if len(size) != kernel.ndim:\n size = kernel.shape[:1] + tuple(size) + kernel.shape[-1:]\n padsize = np.array(size) - np.array(kernel.shape)\n paddown = padsize // 2\n padup = padsize - paddown\n padarray = np.concatenate((padup[..., None],\n paddown[..., None]), axis=1)\n pads = tuple([tuple(p) for p in padarray])\n kernel_pad = np.pad(kernel, pads, 'constant', constant_values=0)\n return kernel_pad","def conv(image, kernel):\n Hi, Wi = image.shape\n Hk, Wk = kernel.shape\n out = np.zeros((Hi, Wi))\n\n # For this assignment, we will use edge values to pad the images.\n # Zero padding will make derivatives at the image boundary very big,\n # whereas we want to ignore the edges at the boundary.\n pad_width0 = Hk // 2\n pad_width1 = Wk // 2\n pad_width = ((pad_width0,pad_width0),(pad_width1,pad_width1))\n padded = np.pad(image, pad_width, mode='edge')\n\n ### YOUR CODE HERE\n for i in range(Hi):\n for j in range(Wi):\n out[i,j] = np.sum(padded[i : i + Hk, j : j + Wk] * np.flip(kernel))\n ### END YOUR CODE\n\n return out","def conv(image, kernel):\n Hi, Wi = image.shape\n Hk, Wk = kernel.shape\n out = np.zeros((Hi, Wi))\n\n # For this assignment, we will use edge values to pad the images.\n # Zero padding as used in the previous assignment can make\n # derivatives at the image boundary very big.\n \n pad_width0 = Hk // 2\n pad_width1 = Wk // 2\n pad_width = ((pad_width0,pad_width0),(pad_width1,pad_width1))\n padded = np.pad(image, pad_width, mode='edge') \n\n #####################################\n # START YOUR CODE HERE #\n #####################################\n kernel = np.flipud(np.fliplr(kernel)) # flip h/v\n for h in range(Hi):\n for w in range(Wi):\n out[h, w] = np.sum(np.multiply(kernel, padded[h : h + Hk, w : w + Wk]))\n ######################################\n # END OF YOUR CODE #\n ######################################\n\n return out","def frame_fix_badpix_isolated(array, bpm_mask=None, sigma_clip=3, num_neig=5,\n size=5, protect_mask=0, cxy=None, mad=False, \n ignore_nan=True, verbose=True, full_output=False):\n if array.ndim != 2:\n raise TypeError('Array is not a 2d array or single frame')\n if size % 2 == 0:\n raise TypeError('Size of the median blur kernel must be an odd integer')\n\n if bpm_mask is not None:\n bpm_mask = bpm_mask.astype('bool')\n\n if verbose: start = time_ini()\n\n if num_neig > 0:\n neigh = True\n else:\n neigh = False\n\n frame = array.copy()\n if cxy is None:\n cy, cx = frame_center(frame)\n else:\n cx, cy = cxy\n \n if bpm_mask is None:\n ori_nan_mask = np.where(np.isnan(frame))\n ind = clip_array(frame, sigma_clip, sigma_clip, neighbor=neigh,\n num_neighbor=num_neig, mad=mad)\n bpm_mask = np.zeros_like(frame)\n bpm_mask[ind] = 1\n if ignore_nan:\n bpm_mask[ori_nan_mask] = 0\n if protect_mask:\n cir = disk((cy, cx), protect_mask, shape=bpm_mask.shape)\n bpm_mask[cir] = 0\n bpm_mask = bpm_mask.astype('bool')\n\n smoothed = median_filter(frame, size, mode='mirror')\n frame[np.where(bpm_mask)] = smoothed[np.where(bpm_mask)]\n array_out = frame\n count_bp = np.sum(bpm_mask)\n \n if verbose:\n msg = \"/nDone replacing {} bad pixels using the median of neighbors\"\n print(msg.format(count_bp))\n timing(start)\n \n if full_output:\n return array_out, bpm_mask\n else:\n return array_out","def mask_frame(image, mask):\n # For segmentation mask display\n mask[mask == 1] = 2\n mask[mask == 0] = 1\n\n # segmentation will display final output\n segmentation = image\n segmentation = cv2.cvtColor(segmentation, cv2.COLOR_BGR2RGB)\n\n segmentation[:, :, 0] = segmentation[:, :, 0] * mask\n segmentation[:, :, 1] = segmentation[:, :, 1] * mask\n return segmentation","def insert_padding(img, pad_h, pad_w):\n global frame_height, frame_width\n padding_3_dims = ((pad_h, pad_h), (pad_w, pad_w), (0, 0))\n # apply padding in the above dimensions with values 0\n padded_img = numpy.pad(img, padding_3_dims, 'constant', constant_values=0)\n return padded_img","def dilate_kernel(self, kernel, dilation):\n if dilation == 0:\n return kernel \n # inside padding based on the scaling law\n dilation = torch.tensor(dilation).float()\n delta = dilation%1\n\n d_in = torch.ceil(dilation**2).int()\n new_in = kernel.shape[2] + (kernel.shape[2]-1)*d_in\n\n d_h = torch.ceil(dilation).int()\n new_h = kernel.shape[3] + (kernel.shape[3]-1)*d_h\n\n d_w = torch.ceil(dilation).int()\n new_w = kernel.shape[4] + (kernel.shape[4]-1)*d_h\n\n new_kernel = torch.zeros(kernel.shape[0], kernel.shape[1], new_in, new_h, new_w)\n new_kernel[:,:,::(d_in+1),::(d_h+1), ::(d_w+1)] = kernel\n dilate_factor = 1\n \n new_kernel = F.pad(new_kernel, ((kernel.shape[4]-1)//2, (kernel.shape[4]-1)//2)*3)\n\n dilate_factor = (new_kernel.shape[-1] - 1 - (kernel.shape[4]-1)*(delta))/(new_kernel.shape[-1] - 1) \n\n grid = torch.meshgrid(torch.linspace(-1, 1, new_in)*(dilate_factor**2), \n torch.linspace(-1, 1, new_h)*dilate_factor, \n torch.linspace(-1, 1, new_w)*dilate_factor)\n\n grid = torch.cat([grid[2].unsqueeze(0).unsqueeze(-1), \n grid[1].unsqueeze(0).unsqueeze(-1), \n grid[0].unsqueeze(0).unsqueeze(-1)], dim = -1).repeat(kernel.shape[0],1,1,1,1)\n\n new_kernel = F.grid_sample(new_kernel, grid) \n \n return new_kernel[:,:,-kernel.shape[2]:]","def convolution(img, kernel, padding=True):\n result = np.zeros_like(img)\n p_size_i = kernel.shape[0] // 2\n p_size_j = kernel.shape[1] // 2\n\n if padding:\n padded_img = np.zeros((img.shape[0] + 2 * p_size_i, img.shape[1] + 2 * p_size_j))\n i_first = p_size_i\n i_last = padded_img.shape[0] - p_size_i - 1\n j_first = p_size_j\n j_last = padded_img.shape[1] - p_size_j - 1\n padded_img[i_first: i_last + 1, j_first: j_last + 1] = img\n else:\n padded_img = img.copy()\n i_first = p_size_i\n i_last = padded_img.shape[0] - p_size_i - 1\n j_first = p_size_j\n j_last = padded_img.shape[1] - p_size_j - 1\n \n for i in range(i_first, i_last):\n for j in range(j_first, j_last):\n window = padded_img[i - p_size_i: i + p_size_i + 1, j - p_size_j: j + p_size_j + 1]\n res_pix = np.sum(window * kernel)\n result[i - p_size_i, j - p_size_j] = res_pix\n return result","def Padding_Signal(signal,M = 10):\t\t\t\t\t\t\t\t\t\t# Function to pad a signal\n\ts = signal.shape[0]\n\tsignal_change = np.zeros(s+2*M)\n\tsignal_change[M:s+M] = signal\n\t\n\treturn signal_change","def zero_pad_sparse(mat, margin_h, margin_v, fmt=\"coo\"):\n\n sm, sn = mat.shape\n padded_mat = mat.copy()\n # Up and down margins initialized with zeros and filled as needed\n margin_h_0 = sp.csr_matrix((sm, margin_h), dtype=mat.dtype)\n margin_v_0 = sp.csr_matrix((margin_v, sn + 2 * margin_h), dtype=mat.dtype)\n padded_mat = sp.hstack([margin_h_0, padded_mat, margin_h_0], format=\"csr\")\n padded_mat = sp.vstack([margin_v_0, padded_mat, margin_v_0], format=\"csr\")\n\n return padded_mat","def hook(module, input):\n image_dimensions = input[0].size()[-2:]\n module.padding = _determine_padding_from_tf_same(image_dimensions, kernel_size, stride)","def convolution(image: np.array, kernel: np.array) -> np.array:\n\n # default condition: apply SAME padding, and keep stride at 1\n stride_x = 1\n stride_y = 1\n padding_y = int(len(kernel - 1) / 2)\n padding_x = int(len((kernel[0]) - 1) / 2)\n # create the return array with with the same dimensions as <image>,\n # and then create a padded image\n convolved_image = np.zeros((len(image), len(image[0])))\n padded_image = np.zeros((len(image) + 2 * padding_y,\n len(image[0]) + 2 * padding_x))\n padded_image[padding_x: -padding_x, padding_y: -padding_y] = image\n\n for py in range(0, len(padded_image) - len(kernel), stride_y):\n for px in range(0, len(padded_image[0]) - len(kernel[0]), stride_x):\n # scan the matrix over columns in image array, then shift the matrix\n # down, and repeat\n padded_image_section = padded_image[py: py + len(kernel[0]),\n px: px + len(kernel)]\n # print(padded_image_section)\n convolved_image[py, px] = int(np.tensordot(padded_image_section,\n kernel))\n\n return convolved_image","def fixed_padding(inputs, kernel_size, data_format='channels_last'):\n pad_total = kernel_size - 1\n pad_beg = pad_total // 2\n pad_end = pad_total - pad_beg\n return _padding(inputs, (pad_beg, pad_end), data_format)","def add_padding(im, pad):\n\n return np.pad(im, pad_width=((pad, pad), (pad, pad), (0, 0)), mode='symmetric')","def cs4243_filter_faster(image, kernel):\n Hi, Wi = image.shape\n Hk, Wk = kernel.shape\n filtered_image = np.zeros((Hi, Wi))\n\n ###Your code here####\n \n # pad image to handle border pixels\n pad_height = (int)((Hk - 1)/2)\n pad_width = (int)((Wk - 1)/2)\n image_pad = pad_zeros(image, pad_height, pad_width)\n \n # compute effective output size, assume stride=1\n out_height = 1 + Hi - Hk + 2*pad_height\n out_width = 1 + Wi - Wk + 2*pad_width\n \n # get initial nodes of receptive fields\n recep_fields_h = [i for i in range(out_height)]\n recep_fields_w = [i for i in range(out_width)]\n \n # extract receptive area into matrix of shape (Hi*Wi, Hk*Wk)\n recep_areas = []\n for i in recep_fields_h:\n for j in recep_fields_w:\n recep_areas.append(image_pad[i: i+Hk, j: j+Wk].reshape(-1))\n out = np.stack(recep_areas)\n \n # Flip the kernel horizontal and vertical\n kernel = cs4243_rotate180(kernel).reshape(Hk*Wk, 1)\n \n # dot product kernel and receptive areas\n filtered_image = np.dot(out, kernel).reshape(Hi, Wi)\n \n ###\n\n return filtered_image","def pad(self):\n if self._mg_problem.boundaries[0] == 'periodic':\n # left side\n self.left[:] = self.mid[-self.borders[0]:]\n # right side\n self.right[:] = self.mid[:self.borders[1]]\n elif self._mg_problem.boundaries[0] == 'dirichlet':\n\n # left from border\n l_f_b = self.space_tensor[0:self.borders[0]]\n # right_from_border\n r_f_b = self.space_tensor[-self.borders[1]:]\n # left side\n self.left[:] = self.fl(l_f_b)\n # right side\n self.right[:] = self.fr(r_f_b)","def cs4243_filter_fast(image, kernel):\n Hi, Wi = image.shape\n Hk, Wk = kernel.shape\n filtered_image = np.zeros((Hi, Wi))\n\n ###Your code here####\n \n # pad image to handle border pixels\n pad_height = (int)((Hk - 1)/2)\n pad_width = (int)((Wk - 1)/2)\n image_pad = pad_zeros(image, pad_height, pad_width)\n\n # Flip the kernel horizontal and vertical\n kernel = cs4243_rotate180(kernel)\n \n # compute effective output size, assume stride=1\n out_height = 1 + Hi - Hk + 2*pad_height\n out_width = 1 + Wi - Wk + 2*pad_width\n \n # get initial nodes of receptive fields\n recep_fields_h = [i for i in range(out_height)]\n recep_fields_w = [i for i in range(out_width)]\n \n for i in recep_fields_h:\n for j in recep_fields_w: \n # get receptive area\n recep_area = image_pad[i:i+Hk, j:j+Wk] \n filtered_image[i, j] = np.multiply(kernel, recep_area).sum()\n ###\n\n return filtered_image","def conv_matrix(matrix, kernel):","def make_padding(kernel_size, stride, dilation):\n return -((-kernel_size - (kernel_size - 1) * (dilation - 1)) // stride + 1) // 2","def kernel(self):\n\n # Create a blank kernel the appropriate size\n kernel = np.zeros((self.n_rows, self.n_cols), dtype=np.int)\n\n # Iterate through the offsets, turning on the correct pixels\n for offset in self.offsets:\n row, col = offset\n if np.all(offset == self.index):\n kernel[row, col] = 2\n else:\n kernel[row, col] = 1\n\n # Ensure that the index pixel is not zero for footprints where the\n # index pixel is not part of the footprint\n if kernel[self.index[0], self.index[1]] == 0:\n kernel[self.index[0], self.index[1]] = 3\n return kernel","def convolve_im(im: np.array,\n kernel: np.array,\n verbose=True):\n ### START YOUR CODE HERE ### (You can change anything inside this block)\n \"\"\"\n\tcompared to the 4a solution this just adds padding to the filter if its smaller than the image\n\tthis is done by using the second parameter in fft.fft2 \n\t\n\tfirst it applies fourier transforms on the kernel and the image\n\tthen it sets the image to be the pointwise multiplication of the transforms\n\n the image is inverse fourier transformed and filtered for real values\n the domain image is shifted and taken the absolute value of\n the fourier transform of the image and kernel are also shifted and set to be the absolute value\n\tlastly everything is displayed in the subplots\n \"\"\"\n conv_result = im \n \n if verbose:\n fftKernel=np.fft.fft2(kernel,im.shape)\n fftImage=np.fft.fft2(conv_result)\n\t\t\n\t\t\n\t\t\n conv_result=np.multiply(fftImage,fftKernel)\n fftImageTransformed=conv_result\n\t\t\n \n conv_result=np.fft.ifft2(conv_result)\n \n conv_result=np.real(conv_result)\n\n fftImageTransformed=np.fft.fftshift(fftImageTransformed)\n fftImage=np.fft.fftshift(fftImage)\n fftKernel=np.fft.fftshift(fftKernel)\n\n fftImageTransformed=np.absolute(fftImageTransformed)\n fftImage=np.absolute(fftImage)\n fftKernel=np.absolute(fftKernel)\n\t\t\n\t\t\n # Use plt.subplot to place two or more images beside eachother\n plt.figure(figsize=(20, 4))\n # plt.subplot(num_rows, num_cols, position (1-indexed))\n plt.subplot(1, 5, 1)\n plt.imshow(im, cmap=\"gray\")\n plt.subplot(1, 5, 2)\n plt.imshow(fftImage, cmap=\"gray\")\n plt.subplot(1, 5, 3)\n plt.imshow(fftKernel, cmap=\"gray\")\n plt.subplot(1, 5, 4)\n plt.imshow(fftImageTransformed, cmap=\"gray\")\n plt.subplot(1, 5, 5)\n plt.imshow(conv_result, cmap=\"gray\")\n ### END YOUR CODE HERE ###\n return conv_result","def convolution(img, kernel, padding='fill'):\n kernel = np.rot90(kernel, 2)\n h,w = kernel.shape[:2]\n t,b,l,r = (h-1)//2, h//2, (w-1)//2, w//2 # Use numpy padding because it works for >2d\n padshape = [(t,b),(l,r)]+[(0,0)]*(len(img.shape[2:]))\n padded_img = np.pad(img, padshape, mode={'fill':'constant','replicate':'edge'}[padding])\n conved_img = np.zeros_like(img)\n for i in 1+np.arange(-h//2,h//2):\n for j in 1+np.arange(-w//2,w//2):\n if kernel[t+i,l+j]==0: continue\n conved_img += kernel[t+i,l+j]*padded_img[t+i:-b+i or None,l+j:-r+j or None]\n return conved_img","def fixed_padding_2d3d(self, inputs, kernel_size):\n pad_total = kernel_size - 1\n pad_beg = pad_total // 2\n pad_end = pad_total - pad_beg\n if self.data_format == 'channels_first':\n if len(inputs.shape)==4:\n padded_inputs = tf.pad(inputs, [[0, 0], [0, 0],\n [pad_beg, pad_end], [pad_beg, pad_end]])\n elif len(inputs.shape)==5:\n padded_inputs = tf.pad(inputs, [[0, 0], [0, 0],\n [pad_beg, pad_end], [pad_beg, pad_end], [pad_beg, pad_end]])\n else:\n if len(inputs.shape)==4:\n padded_inputs = tf.pad(inputs, [[0, 0], [pad_beg, pad_end],\n [pad_beg, pad_end], [0, 0]])\n elif len(inputs.shape)==5:\n padded_inputs = tf.pad(inputs, [[0, 0], [pad_beg, pad_end],\n [pad_beg, pad_end], [pad_beg, pad_end], [0, 0]])\n return padded_inputs","def convolution(image, kernel):\n\n #Se encuentra la dimencion de la imagen\n if len(image.shape) == 3: #De 3 dimenciones\n print(\"Dimenciones de imagen: {}\".format(image.shape))\n image = cv2.cvtColor(image, cv2.COLOR_BGR2GRAY) #Se cambia a dos dimenciones\n print(\"Nuevas dimenciones: {}\".format(image.shape))\n else:\n print(\"Dimenciones de imagen: {}\".format(image.shape))\n\n image_row, image_col = image.shape #asigna alto y ancho de la imagen \n kernel_row, kernel_col = kernel.shape #asigna alto y ancho del filtro\n\n output_x = (image_col - (kernel_col / 2) * 2) + 1 #asigna el ancho del output\n output_y = (image_row - (kernel_row / 2) * 2) + 1 #asigna el alto del output\n \n output = np.zeros([int(output_y), int(output_x)]) #matriz donde se guarda el resultado\n\n padded_size = int((kernel_row - 1) / 2) #Tamaño de padding\n\n #Obtenemos la imagen con padding\n padded_image = padding(image,padded_size)\n \n for row in range(int(output_y)):\n for col in range(int(output_x)):\n output[row, col] = conv_helper(\n padded_image[row:row + kernel_row, \n col:col + kernel_col], kernel)\n \n # Se muestra la imagen en pantalla\n plt.imshow(output, cmap='gray')\n plt.title(\"Edge detection\")\n plt.show()\n\n return output","def _fixed_padding(inputs, kernel_size, *args, mode='CONSTANT', **kwargs):\n pad_total = kernel_size - 1\n pad_beg = pad_total // 2\n pad_end = pad_total - pad_beg\n\n if kwargs['data_format'] == 'NCHW':\n padded_inputs = tf.pad(inputs, [[0, 0], [0, 0],\n [pad_beg, pad_end],\n [pad_beg, pad_end]],\n mode=mode)\n else:\n padded_inputs = tf.pad(inputs, [[0, 0], [pad_beg, pad_end],\n [pad_beg, pad_end], [0, 0]], mode=mode)\n return padded_inputs","def fixed_padding(inputs, kernel_size, data_format):\n pad_total = kernel_size - 1\n pad_beg = pad_total // 2\n pad_end = pad_total - pad_beg\n\n if data_format == 'channels_first':\n padded_inputs = tf.pad(inputs, [[0, 0], [0, 0], [pad_beg, pad_end], [pad_beg, pad_end]])\n else:\n padded_inputs = tf.pad(inputs, [[0, 0], [pad_beg, pad_end], [pad_beg, pad_end], [0, 0]])\n\n return padded_inputs","def thinning_morph(image, kernel):\n thining_image = np.zeros_like(image)\n img = image.copy()\n\n while 1:\n erosion = cv.erode(img, kernel, iterations = 1)\n dilatate = cv.dilate(erosion, kernel, iterations = 1)\n\n subs_img = np.subtract(img, dilatate)\n cv.bitwise_or(thining_image, subs_img, thining_image)\n img = erosion.copy()\n\n done = (np.sum(img) == 0)\n\n if done:\n break\n\n # shift down and compare one pixel offset\n down = np.zeros_like(thining_image)\n down[1:-1, :] = thining_image[0:-2, ]\n down_mask = np.subtract(down, thining_image)\n down_mask[0:-2, :] = down_mask[1:-1, ]\n cv.imshow('down', down_mask)\n\n # shift right and compare one pixel offset\n left = np.zeros_like(thining_image)\n left[:, 1:-1] = thining_image[:, 0:-2]\n left_mask = np.subtract(left, thining_image)\n left_mask[:, 0:-2] = left_mask[:, 1:-1]\n cv.imshow('left', left_mask)\n\n # combine left and down mask\n cv.bitwise_or(down_mask, down_mask, thining_image)\n output = np.zeros_like(thining_image)\n output[thining_image < 250] = 255\n\n return output","def hook(module, input):\n image_dimensions = input[0].size()[-2:]\n module.padding = _determine_inverse_padding_from_tf_same(image_dimensions, kernel_size, stride)","def pad_out(self, out, index):\n if self.kernel_sizes[index] % 2 == 0:\n # If kernel size is even, add one more padding value on the right.\n out = F.pad(out, (0, 1), \"constant\", 0)\n return out","def _fixed_padding(inputs, kernel_size, *args, mode='CONSTANT', **kwargs):\n pad_total = kernel_size - 1\n pad_beg = pad_total // 2\n pad_end = pad_total - pad_beg\n\n padded_inputs = tf.pad(inputs, [[0, 0], [pad_beg, pad_end],\n [pad_beg, pad_end], [0, 0]], mode=mode)\n return padded_inputs","def fixed_padding(inputs, kernel_size, data_format):\n pad_total = kernel_size - 1\n pad_beg = pad_total // 2\n pad_end = pad_total - pad_beg\n\n if data_format == 'channels_first':\n padded_inputs = tf.pad(tensor=inputs,\n paddings=[[0, 0], [0, 0], [pad_beg, pad_end],\n [pad_beg, pad_end]])\n else:\n padded_inputs = tf.pad(tensor=inputs,\n paddings=[[0, 0], [pad_beg, pad_end],\n [pad_beg, pad_end], [0, 0]])\n return padded_inputs","def pad_same(width, kernel, slide):\n res = (width - kernel) / slide + 1\n pad = (width - res) / 2\n return pad","def fixed_padding(inputs, kernel_size, data_format='channels_first'):\n pad_total = kernel_size - 1\n pad_beg = pad_total // 2\n pad_end = pad_total - pad_beg\n if data_format == 'channels_first':\n padded_inputs = tf.pad(inputs, [[0, 0], [0, 0],\n [pad_beg, pad_end], [pad_beg, pad_end]])\n else:\n padded_inputs = tf.pad(inputs, [[0, 0], [pad_beg, pad_end],\n [pad_beg, pad_end], [0, 0]])\n\n return padded_inputs","def _create_margin_shape(self):\n\n a, b = gc((self.size/2 - self.margin),\n self._ZERO_DEGREES - self.angle,\n self._180_DEGREES + self.angle)\n self.margin_points = zip(a, b)\n self.margin_polygon = gdspy.Polygon(self.margin_points, self.MARGIN_LAYER)\n self.cell.add(self.margin_polygon)","def extend_mask_nonlocal(mask,kernel=np.ones((3,3))):\n\tassert (mask.dtype is np.dtype(np.bool)), \"input mask must be of bool type\"\n\n\text_mask = mask.copy()\n\tinp_ind = masked_indices(mask)\n\tim_h, im_w = mask.shape\n\n\tker_y, ker_x = kernel.shape\n\tassert(ker_x%2>0), \"kernel must have odd dimensions\"\n\tassert(ker_y%2>0), \"kernel must have odd dimensions\"\n\n\t# indices of the nonzero kernel elements\n\tker_ind_y, ker_ind_x = np.nonzero(kernel)\n\tker_ind_x -= ker_x//2\n\tker_ind_y -= ker_y//2\n\n\tfor ind_x, ind_y in zip(inp_ind%im_w, inp_ind//im_w):\n\t\tfor i, j in zip(ker_ind_x, ker_ind_y):\n\t\t\text_mask[min(max(0,ind_y+j),im_h-1),min(max(0,ind_x+i),im_w-1)] = True\n\n\treturn ext_mask","def fixed_padding(inputs, kernel_size, rate=1):\n kernel_size_effective = kernel_size + (kernel_size - 1) * (rate - 1)\n pad_total = kernel_size_effective - 1\n pad_beg = pad_total // 2\n pad_end = pad_total - pad_beg\n padded_inputs = tf.pad(inputs, [[0, 0], [pad_beg, pad_end],\n [pad_beg, pad_end], [0, 0]])\n return padded_inputs","def padding(image, padded_size):\n image_row, image_col = image.shape #asigna alto y ancho de la imagen \n\n padded_image = np.zeros((image_row + padded_size*2, image_col + padded_size*2)) #matriz de imagen con padding en zeros\n print(\"Padded image zeros:\")\n print(padded_image)\n\n padded_image[padded_size:padded_size + image_row, padded_size:padded_size + image_col] = image #matriz de imagen con padding\n print(\"Padded image:\")\n print(padded_image)\n\n \n return padded_image","def convolution(image, kernel, scale=None, offset=0):\n kernel = np.array(kernel).flatten().tolist()\n if len(kernel)==9:\n size = (3,3)\n elif len(kernel)==25:\n size = (5,5)\n else:\n raise ValueError('Kernel size must be (3,3) or (5,5).')\n return image.filter(ImageFilter.Kernel(size, kernel, scale, offset))","def fixed_padding(inputs, kernel_size, data_format):\n pad_total = kernel_size - 1\n pad_beg = pad_total // 2\n pad_end = pad_total - pad_beg\n\n if data_format == 'channels_first':\n padded_inputs = tf.pad(inputs, [[0, 0], [0, 0],\n [pad_beg, pad_end], [pad_beg, pad_end]])\n else:\n padded_inputs = tf.pad(inputs, [[0, 0], [pad_beg, pad_end],\n [pad_beg, pad_end], [0, 0]])\n return padded_inputs","def fixed_padding(inputs, kernel_size, data_format):\n pad_total = kernel_size - 1\n pad_beg = pad_total // 2\n pad_end = pad_total - pad_beg\n\n if data_format == 'channels_first':\n padded_inputs = tf.pad(inputs, [[0, 0], [0, 0],\n [pad_beg, pad_end], [pad_beg, pad_end]])\n else:\n padded_inputs = tf.pad(inputs, [[0, 0], [pad_beg, pad_end],\n [pad_beg, pad_end], [0, 0]])\n return padded_inputs","def erode(img, kernel_h, kernel_w):\n # kernel_w and kernel_h chosen to get 4 clear markers in first frame to set initial limb coordinates.\n \"\"\" if majority of kernel is blue, else erase\"\"\"\n y = 0\n print(frame_height)\n while y < frame_height - kernel_h:\n x = 0\n while x < frame_width - kernel_w:\n # where Red exist\n kernel = img[y:y+kernel_h, x:x+kernel_w, 2] > 0 + numpy.zeros((kernel_h, kernel_w))\n\n if numpy.all(kernel == 0):\n pass\n elif numpy.sum(kernel) < (kernel_w*kernel_h)*0.85:\n img[y:y+kernel_h, x:x+kernel_w, 1:3] = 0\n else:\n img[y:y+kernel_h, x:x+kernel_w, 2] = 255\n\n x += kernel_w\n y += kernel_h","def make_inference_attention_mask_3d(source_block, target_block, pad_id):\n # mask = (target_block[:, None, :] != pad_id) * (source_block[:, :, None] != pad_id)\n return make_attention_mask_3d(source_block != pad_id, target_block != pad_id)","def conv2D(inImage: np.ndarray, kernel2: np.ndarray) -> np.ndarray:\r\n flip_kernel = np.flipud(np.fliplr(kernel2))\r\n kernel_row = flip_kernel.shape[0]\r\n kernel_col = flip_kernel.shape[1]\r\n\r\n new_img = np.zeros_like(inImage)\r\n padded_img = padded_replicate(inImage, kernel_row, kernel_col)\r\n\r\n for x in range(inImage.shape[0]):\r\n for y in range(inImage.shape[1]):\r\n new_img[x, y] = (padded_img[x: x + kernel_col, y: y + kernel_row] * flip_kernel).sum()\r\n if flip_kernel.sum() != 0:\r\n new_img[x, y] /= flip_kernel.sum()\r\n\r\n return new_img","def __padding(self, image, boxes, height, width):\n temp = boxes[:, :4].astype(np.int)\n y1 = np.where(temp[:, 0] < 0)[0]\n if len(y1) > 0:\n temp[y1, 0] = 0\n x1 = np.where(temp[:, 1] < 0)[0]\n if len(x1) > 0:\n temp[x1, 0] = 0\n y2 = np.where(temp[:, 2] > image.shape[0] - 1)[0]\n if len(y2) > 0:\n temp[y2, 0] = image.shape[0] - 1\n x2 = np.where(temp[:, 3] > image.shape[1] - 1)[0]\n if len(x2) > 0:\n temp[x2, 0] = image.shape[1] - 1\n pad_top = np.abs(temp[:, 0] - boxes[:, 0]).astype(np.int)\n pad_left = np.abs(temp[:, 1] - boxes[:, 1]).astype(np.int)\n pad_bottom = np.abs(temp[:, 2] - boxes[:, 2]).astype(np.int)\n pad_right = np.abs(temp[:, 3] - boxes[:, 3]).astype(np.int)\n input_data = np.empty([boxes.shape[0], 3, height, width], dtype=np.float32)\n for i in range(boxes.shape[0]):\n crop_img = image[temp[i, 0]:temp[i, 2] + 1, temp[i, 1]:temp[i, 3] + 1, :]\n crop_img = cv2.copyMakeBorder(crop_img, pad_top[i], pad_bottom[i], \\\n pad_left[i], pad_right[i], cv2.BORDER_CONSTANT, value=0)\n if crop_img is None:\n continue\n crop_img = cv2.resize(crop_img, (width, height)).astype(np.float32)\n crop_img[:, :, 0] -= self.mean[0]\n crop_img[:, :, 1] -= self.mean[1]\n crop_img[:, :, 2] -= self.mean[2]\n crop_img *= self.scale_factor\n crop_img = np.transpose(crop_img, (2, 0, 1))\n input_data[i] = crop_img.copy()\n return input_data","def _pad(x, depth=4):\n divisor = np.power(2, depth)\n remainder = x.shape[0] % divisor\n\n # no padding because already of even shape\n if remainder == 0:\n return x\n # add zero rows after 1D feature\n elif len(x.shape) == 2:\n return np.pad(x, [(0, divisor - remainder), (0, 0)], \"constant\")\n # add zero columns and rows after 2D feature\n elif len(x.shape) == 3:\n return np.pad(x, [(0, divisor - remainder), (0, divisor - remainder),\n (0, 0)], \"constant\")","def fixed_padding(inputs, kernel_size, data_format):\n pad_total = kernel_size - 1\n pad_beg = pad_total // 2\n pad_end = pad_total - pad_beg\n\n if is_NHWC(data_format):\n padded_inputs = tf.pad(inputs, [[0, 0], [pad_beg, pad_end],\n [pad_beg, pad_end], [0, 0]])\n else:\n padded_inputs = tf.pad(inputs, [[0, 0], [0, 0],\n [pad_beg, pad_end], [pad_beg, pad_end]])\n return padded_inputs","def space_to_depth_fixed_padding(inputs, kernel_size,\n data_format='channels_last', block_size=2):\n pad_total = kernel_size - 1\n pad_beg = (pad_total // 2 + 1) // block_size\n pad_end = (pad_total // 2) // block_size\n return _padding(inputs, (pad_beg, pad_end), data_format)","def create_padding_mask(seq):\r\n seq = tf.cast(tf.math.equal(seq, 0), tf.float32)\r\n return seq[:, tf.newaxis, tf.newaxis, :] # (batch_size, 1, 1, seq_len)\r","def img_conv_2D(img,kernel,stride=1,pad_type='None'):\n\n m,n = img.shape\n r,c = kernel.shape\n\n pad_m = m * (stride-1) + r -1\n pad_n = n * (stride-1) + r -1\n\n img_pad = np.zeros((m+pad_m,n+pad_n),dtype=float)\n img_pad[:m,:n] = np.copy(img)\n\n if pad_type == 'zero_pad':\n return conv_2D(img_pad,kernel,stride) # define\n\n elif pad_type == 'replicate_pad':\n\n for i in range(m,m+pad_m):\n img_pad[i,:] = np.copy(img_pad[m-1,:])\n\n for j in range(n,n+pad_n):\n img_pad[:,j] = np.copy(img_pad[:,n-1])\n\n return conv_2D(img_pad,kernel,stride) # define\n\n elif pad_type == 'wrap_pad':\n\n for i in range(m,m+pad_m):\n img_pad[i,:] = np.copy(img_pad[i-m,:])\n\n for j in range(n,n+pad_n):\n img_pad[:,j] = np.copy(img_pad[:,j-n])\n\n return conv_2D(img_pad,kernel,stride) # define\n\n else:\n\n return conv_2D(np.copy(img),kernel,stride) # define","def conv2mat(im_shape,kernel,format=\"channels_first\",dtype=_dtype):\n\tif len(im_shape)==2:\n\t\tim_size_y, im_size_x = im_shape\n\t\tnum_channels = 1\n\telse:\n\t\tif format==\"channels_last\":\n\t\t\tim_size_y, im_size_x, num_channels = im_shape\n\t\telse:\n\t\t\tnum_channels, im_size_y, im_size_x = im_shape\n\n\tker_size_y, ker_size_x = kernel.shape\n\tker_size = kernel.size\n\tkernel = kernel.ravel()\n\n\tmat_size_x = im_size_x * im_size_y\n\tmat = sp.dia_matrix( (mat_size_x,mat_size_x), dtype=dtype )\n\n\t# diagonal blocks corresponding to the rows of the kernel\n\tfor j in range(-(ker_size_y//2),ker_size_y//2+1):\n\t\t# diagonal of the block corresponding to the given row of the kernel\n\t\t# correlation\n\t\tdiag = sp.eye(im_size_y,im_size_y,-j,dtype=dtype)\n\t\t# # convolution\n\t\t# diag = sp.eye(im_size_y,im_size_y,-j,dtype=dtype)\n\n\t\t# contribution from convolution matrix corresponding to the given row of the kernel\n\t\tmat += sp.kron( diag, convmat(im_size_x,kernel[ker_size//2-ker_size_x//2-j*ker_size_x:ker_size//2-ker_size_x//2-(j-1)*ker_size_x],dtype=dtype) ) #.astype(dtype=dtype)\n\n\tif num_channels>1:\n\t\tif format==\"channels_last\":\n\t\t\tdiag_ch = sp.eye(num_channels,num_channels,dtype=dtype)\n\t\t\treturn sp.kron(mat,diag_ch) #.tocsr()\n\t\telse:\n\t\t\tdiag_ch = sp.eye(num_channels,num_channels,dtype=dtype)\n\t\t\treturn sp.kron(diag_ch,mat) #.tocsr()\n\n\treturn mat #.tocsr()","def pad_with_filter(cls, position, filter_size, filter_number, pad_size, rect_size, funnel_width, funnel_end):\n \n position = np.array(position)\n filter_size = np.array(filter_size)\n filter_number = np.array(filter_number)\n \n #with of rectangle between half-circle and filters\n init_square = rect_size\n\n #lower-left position of the first \"filter-channel\n init_pos = np.array([position[0]-np.sum(filter_size*filter_number*2)-funnel_width,position[1]-pad_size/2])\n\n #pattern contains all coordinates composing the filter\n #first define the funnel part connecting to a channel\n pattern = [np.array([[position[0]-funnel_width,position[1]-pad_size/2],[position[0]-funnel_width,position[1]+pad_size/2],\n [position[0],position[1]+funnel_end/2],[position[0],position[1]-funnel_end/2]])]\n\n #define rectangular part between half-circle and filter\n pattern.append(np.array([[init_pos[0]-init_square,init_pos[1]],[init_pos[0],init_pos[1]],\n [init_pos[0],init_pos[1]+pad_size],[init_pos[0]-init_square,init_pos[1]+pad_size]]))\n\n #define the half-circle punching pad\n half_circle = np.array([[init_pos[0]-init_square+pad_size/2*np.cos(x),position[1]+pad_size/2*np.sin(x)] \n for x in np.linspace(np.pi/2,np.pi*3/2)])\n pattern.append(half_circle)\n\n #define filter region composed of vertical channels and horizontal squares\n for i in range(len(filter_size)):\n for f in range(filter_number[i]):\n\n shift = np.mod(f,2)*filter_size[i]\n sq_coord = [[init_pos[0]+filter_size[i],init_pos[1]],[init_pos[0]+2*filter_size[i],init_pos[1]],\n [init_pos[0]+2*filter_size[i],init_pos[1]+pad_size],[init_pos[0]+filter_size[i],init_pos[1]+pad_size]]\n pattern.append(np.array(sq_coord))\n\n for j in range(int(np.round(pad_size/filter_size[i]/2))):\n\n sq_coord = [[init_pos[0],init_pos[1]+shift],[init_pos[0]+filter_size[i],init_pos[1]+shift],\n [init_pos[0]+filter_size[i],init_pos[1]+filter_size[i]+shift],[init_pos[0],init_pos[1]+filter_size[i]+shift]]\n pattern.append(np.array(sq_coord))\n init_pos = init_pos+np.array([0,2*filter_size[i]])\n init_pos[0]=init_pos[0]+2*filter_size[i]\n init_pos[1]=position[1]-pad_size/2\n \n filter_pad_obj = cls()\n filter_pad_obj.coord = pattern\n \n return filter_pad_obj","def _fixed_padding(inputs, kernel_size, rate=1):\n kernel_size_effective = [kernel_size[0] + (kernel_size[0] - 1) * (rate - 1),\n kernel_size[0] + (kernel_size[0] - 1) * (rate - 1)]\n pad_total = [kernel_size_effective[0] - 1, kernel_size_effective[1] - 1]\n pad_beg = [pad_total[0] // 2, pad_total[1] // 2]\n pad_end = [pad_total[0] - pad_beg[0], pad_total[1] - pad_beg[1]]\n padded_inputs = tf.pad(inputs, [[0, 0], [pad_beg[0], pad_end[0]],\n [pad_beg[1], pad_end[1]], [0, 0]])\n return padded_inputs","def pad_img(image, label):\n paddings = [[2,2],[2,2],[0,0]]\n return tf.pad(image, paddings, mode=\"CONSTANT\", constant_values=0.0), label","def fold(input_f, input_shape, kernel_size=(3,3), stride=(1,1), padding=(0,0)):\n batch_size, in_chan, in_height, in_width = input_shape\n \n height_padded, width_padded = in_height + 2*padding[0], in_width + 2*padding[1]\n input_padded = np.zeros((batch_size, in_chan, height_padded, width_padded), dtype=input_f.dtype)\n \n k, i, j = get_indices(input_shape, kernel_size, stride, padding)\n \n input = input_f.reshape(in_chan*kernel_size[0]*kernel_size[1], -1, batch_size)\n input = input.transpose((2, 0, 1))\n np.add.at(input_padded, (slice(None), k, i, j), input)\n if padding == (0,0):\n return input_padded\n return input_padded[:, :, padding[0]: -padding[0], padding[1]: -padding[1]]","def blur_mask(mask, blur_kernel, threshold=0.1):\n k = pyfits.getdata(blur_kernel)\n k = k / k.sum()\n mask = hconvolve.hconvolve(mask, k)\n mask = np.where(mask >= threshold, 1, 0).astype('int')\n return mask","def filter2D(img, kernel = (5,5)):\n\ttmp = img.copy()\n\tk = np.ones((kernel[0], kernel[1]), np.float32) / (kernel[0]*kernel[1])\n\tdst = cv2.filter2D(tmp, -1, k)\n\treturn dst","def pad_edges(self, pad):\n weights=[]\n for dim, xy in zip([0, 1], [self.x, self.y]):\n xy0 = np.mean(xy)\n W = xy[-1]-xy[0]\n dist = np.abs(xy-xy0)\n wt=np.ones_like(dist)\n wt[ dist >= W/2 - pad] = 0\n weights += [wt]\n self.weight *= weights[0][:,None].dot(weights[1][None,:])","def with_kernel(self, kernel, kernel_format):\n\t\tself.variables['kernel_format'] = kernel_format\n\t\tself.with_weights(kernel)\n\t\treturn self","def createkSpaceMask (im_size, num_half_lines):\n \n if im_size[0] < 32 or im_size[1] < 32:\n sys.exit(\"side < 32\")\n\n if num_half_lines < 20:\n sys.exit(\"numOfLines < 10\")\n \n center = np.floor((im_size + 2) / 2)\n half_diagonal = np.linalg.norm(im_size) / 2\n step_length = 0.5\n num_steps = int(np.round(half_diagonal / step_length + 1))\n sampling_mask = np.zeros(im_size, float)\n\n for lineNum in range(num_half_lines):\n theta = 2 * np.pi * lineNum / num_half_lines\n direction = np.array([np.cos(theta), np.sin(theta)])\n for stepNum in range(num_steps):\n location = np.round(center + direction * stepNum * step_length).astype(int)\n if (location[0] >= 0) and (location[0] < im_size[0]) and (location[1] >= 0) and (location[1] < im_size[1]):\n sampling_mask[location[0], location[1]] = 1\n\n # take the center of kspace to the corners\n sampling_mask = np.fft.fftshift(sampling_mask)\n\n return sampling_mask","def change_margin(self, margin):\n self.margin = margin * self._MM_IN_MICRONS\n\n self._create_drawing_area()\n self.partition(self.rows, self.cols)","def fixBorder(frame):\n s = frame.shape\n # Scale the image 2% without moving the center\n T = cv2.getRotationMatrix2D((s[1]/2, s[0]/2), 0, 1.02)\n frame = cv2.warpAffine(frame, T, (s[1], s[0]))\n return frame","def correctFrameShift(stack):\n\n maxHeight=0\n frameList=[]\n midStripsFrame = np.empty((stack.shape[1],stack.shape[0])) #needs to have the number of columns as the depth of the stack!\n\n #I think this should be powerfule enough that I don't need to worry about onh for the frame adjustments\n midStripsFrame = np.mean(stack, axis=2).T\n\n shifts = shiftDetector(midStripsFrame)\n minShift = min(shifts)\n allPositiveShifts = [shift-minShift for shift in shifts]\n \n shiftedFrames = []\n i=0\n # writeText('\\n')\n for shift, frame in zip(allPositiveShifts, stack):\n newFrame = np.lib.pad(frame, ((0,shift),(0,0)),'constant',constant_values=0)\n shiftedFrames.append(newFrame)\n if newFrame.shape[0] > maxHeight:\n maxHeight = newFrame.shape[0]\n print('\\rCorrecting Frame Shifts/Vertical Curvature: {:.2f} % done'.format((100.0*((i+1)/len(stack)))), end='', flush=True)\n i+=1\n print('\\n')\n \n shiftedStack = padFrames(shiftedFrames, maxHeight)\n \n return shiftedStack","def run_padding(self):\n\n image_padded, mask, self.pad_to_right, self.pad_to_bottom = gen_padded_image_and_mask (os.path.join('utils_dfn/temp', self.file_name_with_ext),\n self.new_height, self.new_width)\n cv2.imwrite(os.path.join('utils_dfn/img', self.file_name + '_padded_resized.png'), image_padded)\n cv2.imwrite(os.path.join('utils_dfn/mask', self.file_name + '_mask.png'), mask)","def convolve_im(im: np.array,\n kernel: np.array,\n verbose=True):\n\t\n ### START YOUR CODE HERE ### (You can change anything inside this block) \n\t\n H,W = np.shape(im)\n h,w = np.shape(kernel)\n t_b = (H-h)//2\n l_r = (W-w)//2\n kernel_padded = np.pad(kernel, ((t_b, t_b+1),(l_r, l_r+1)), 'constant')\n kernel_padded = np.pad(kernel, ((0, 2*t_b),(0, 2*l_r)), 'constant')\n fft_kernel = np.fft.fft2(kernel_padded, s=None, axes=(-2, -1), norm=None)\n \n \n im_fft = np.fft.fft2(im, s=None, axes=(-2, -1), norm=None) \n im_filt = im_fft*fft_kernel \n conv_result = np.fft.ifft2(im_filt, s=None, axes=(-2, -1), norm=None).real \n\n if verbose:\n # Use plt.subplot to place two or more images beside eachother\n plt.figure(figsize=(12, 4))\n # plt.subplot(num_rows, num_cols, position (1-indexed))\n plt.subplot(1, 2, 1)\n plt.imshow(im, cmap=\"gray\")\n plt.subplot(1, 2, 2) \n plt.imshow(conv_result, cmap=\"gray\")\n\n ### END YOUR CODE HERE ###\n return conv_result","def _margin_loss(self,\n logits,\n labels,\n m_plus=0.9,\n m_minus=0.1,\n lambda_=0.5):\n logits_shape = logits.get_shape()\n num_caps = logits_shape[1]\n\n max_square_plus = tf.square(tf.maximum(\n 0., m_plus - utils.get_vec_length(\n logits, self.batch_size, self.cfg.EPSILON)))\n max_square_minus = tf.square(tf.maximum(\n 0., utils.get_vec_length(\n logits, self.batch_size, self.cfg.EPSILON) - m_minus))\n # max_square_plus & max_plus shape: (batch_size, num_caps)\n assert max_square_plus.get_shape() == (self.batch_size, num_caps)\n\n loss_c = tf.multiply(labels, max_square_plus) + \\\n lambda_ * tf.multiply((1 - labels), max_square_minus)\n\n # Total margin loss\n margin_loss = tf.reduce_mean(tf.reduce_sum(loss_c, axis=1))\n\n return margin_loss","def add_padding(img, x_padding):\n w = img.shape[1] + x_padding * 2\n img_with_padding = np.zeros((img.shape[0], w, 3), dtype=img.dtype)\n img_with_padding[:, x_padding:img.shape[1] + x_padding] = img\n return img_with_padding","def _do_adaptive_padding(self, im):\n im_sz = list(im.shape)\n dim = len(im_sz)\n dim_to_pad = [dim_sz%self.adaptive_padding!=0 and dim_sz>3 for dim_sz in im_sz]\n dim_rem = [dim_sz//self.adaptive_padding for dim_sz in im_sz]\n new_dim_sz = [(dim_rem[i]+1)*self.adaptive_padding if dim_to_pad[i] else im_sz[i] for i in range(dim)]\n before_id = [(new_dim_sz[i] -im_sz[i]+1)//2 for i in range(dim)]\n after_id = [new_dim_sz[i] - im_sz[i] - before_id[i] for i in range(dim)]\n padding_loc = tuple([(before_id[i],after_id[i]) for i in range(dim)])\n new_img = np.lib.pad(im, padding_loc, 'edge')\n return new_img","def conv_nested(image, kernel):\n Hi, Wi = image.shape\n Hk, Wk = kernel.shape\n out = np.zeros((Hi, Wi))\n\n H = Hk // 2\n W = Wk // 2\n for i1 in range(Hi):\n for j1 in range(Wi):\n for i2 in range(Hk):\n for j2 in range(Wk):\n i = i2 - H\n j = j2 - W\n if i1-i<0 or j1-j<0 or i1-i>=Hi or j1-j>=Wi:\n continue\n out[i1, j1] += kernel[i2, j2]*image[i1-i, j1-j]\n return out","def pad_to_width(kernel, width):\n current_width = len(kernel)\n delta = (width - current_width) / 2\n\n pad0 = tf.cast(delta, tf.int32)\n pad1 = tf.cast(tf.math.ceil(delta), tf.int32)\n\n paddings = [[pad0, pad1], [pad0, pad1], [0, 0], [0, 0]]\n\n kernel = tf.pad(kernel, paddings)\n return kernel","def _pad_traces(self, X):\n # Padding is done using using keras.preprocessing.sequence.pad_sequences(sequences, ...)\n # maxlen: Int, maximum length of all sequences.\n # truncating='pre' remove values at the beginning from sequences larger than maxlen\n # padding='pre' pads each trace at the beginning with a special integer (e.g., 0)\n X = pad_sequences(X, maxlen=self.trace_size, dtype=np.int16,\n truncating='pre', padding='pre', value=DEFAULTS['padding_symbol'])\n\n print(X)\n # Shape X is (n_traces, self.trace_size)\n # e.g., (25002, 20)\n\n return X","def crop_kernel(kernel, target_size):\n # Use list for mutability\n target = [d for d in target_size]\n adjusted = False\n for dim in range(len(target)):\n if not target[dim] % 2:\n target[dim] += 1\n adjusted = True\n if adjusted:\n sys.stderr.write(\n \"WARNING: Cropped kernel size adjusted to \"\n f\"{target[0]}x{target[1]} to keep odd dimensions.\\n\"\n )\n\n source_m, source_n = kernel.shape\n target_m, target_n = target\n # Define horizontal and vertical margins to trim\n if source_m > target_m:\n margin_rows = (source_m - target_m) // 2\n else:\n margin_rows = 0\n if source_n > target_n:\n margin_cols = (source_n - target_n) // 2\n else:\n margin_cols = 0\n\n cropped = kernel[\n margin_rows : (source_m - margin_rows),\n margin_cols : (source_n - margin_cols),\n ]\n\n return cropped","def segment_cells(frame, mask=None):\n \n blurred = filters.gaussian(frame, 2)\n ridges = enhance_ridges(frame)\n \n # threshold ridge image\n thresh = filters.threshold_otsu(ridges)\n thresh_factor = 0.5\n prominent_ridges = ridges > thresh_factor*thresh\n prominent_ridges = morphology.remove_small_objects(prominent_ridges, min_size=256)\n prominent_ridges = morphology.binary_closing(prominent_ridges)\n prominent_ridges = morphology.binary_dilation(prominent_ridges)\n \n # skeletonize\n ridge_skeleton = morphology.medial_axis(prominent_ridges)\n ridge_skeleton = morphology.binary_dilation(ridge_skeleton)\n ridge_skeleton *= mask\n ridge_skeleton = np.bitwise_xor(ridge_skeleton, mask)\n \n # label\n cell_label_im = measure.label(ridge_skeleton)\n \n # morphological closing to fill in the cracks\n for cell_num in range(1, cell_label_im.max()+1):\n cell_mask = cell_label_im==cell_num\n cell_mask = morphology.binary_closing(cell_mask, disk(3))\n cell_label_im[cell_mask] = cell_num\n \n return cell_label_im","def clip_filters(W, threshold=0.5, pad=3):\n W_clipped = []\n for w in W:\n L, A = w.shape\n entropy = np.log2(4) + np.sum(w * np.log2(w + 1e-7), axis=1)\n index = np.where(entropy > threshold)[0]\n if index.any():\n start = np.maximum(np.min(index) - pad, 0)\n end = np.minimum(np.max(index) + pad + 1, L)\n W_clipped.append(w[start:end, :])\n else:\n W_clipped.append(w)\n\n return W_clipped","def wrap_pad(input, size):\n M1 = tf.concat([input[:, :, -size[1]:, :], input, input[:, :, 0:size[1], :]], 2)\n M1 = tf.concat([M1[:, -size[0]:, :, :], M1, M1[:, 0:size[0], :, :]], 1)\n return M1","def test_2D_padding(self):\n field_names = [\"text\"]\n mask_field_names = ['text_mask']\n data_path = \"mldp/tests/data/news.csv\"\n pad_symbol = \"<PAD>\"\n mask_field_name_suffix = \"mask\"\n padding_modes = ['left', 'right', 'both']\n symbols_to_mask = [\"The\", \"a\", \"to\", \"as\"]\n axis = 1\n\n data_chunk = read_data_from_csv_file(data_path, sep=\"\\t\")\n\n # tokenize field values\n for fn in field_names:\n data_chunk[fn] = np.array([seq.split() for seq in data_chunk[fn]])\n\n for padding_mode, symbol_to_mask in product(padding_modes,\n symbols_to_mask):\n padder = Padder(field_names, pad_symbol=pad_symbol,\n new_mask_fname=mask_field_names,\n padding_mode=padding_mode, axis=axis,\n symbol_to_mask=symbol_to_mask)\n padded_data_chunk = padder(copy.deepcopy(data_chunk))\n\n for fn, mask_fn in zip(field_names, mask_field_names):\n padded_fv = padded_data_chunk[fn]\n mask = padded_data_chunk[mask_fn]\n original_fv = data_chunk[fn]\n\n self.assertTrue(len(padded_fv.shape) == 2)\n self._test_padded_values(original_field_values=original_fv,\n padded_field_values=padded_fv,\n mask=mask, pad_symbol=pad_symbol,\n symbol_to_mask=symbol_to_mask)","def obj_margin(x0, X, y, alpha, n_class, weights, L, kernel_type, loss_function, sample_weight):\n\n w = x0[:X.shape[1]]\n c = x0[X.shape[1]:]\n theta = L.dot(c)\n loss_fd = weights[y]\n\n Xw = X.dot(w)\n Alpha = theta[:, None] - Xw # (n_class - 1, n_samples)\n S = np.sign(np.arange(n_class - 1)[:, None] - y + 0.5)\n\n if(loss_function == 'logistic'):\n err = loss_fd.T * log_loss(S * Alpha)\n elif(loss_function == 'hinge'):\n err = loss_fd.T * hinge_loss(S * Alpha)\n \n if sample_weight is not None:\n err *= sample_weight\n obj = np.sum(err)\n \n ## Regularization based on kernel \n if(kernel_type == 'linear'):\n obj += 0.5 * alpha * (np.dot(w, w))\n else:\n obj += 0.5 * alpha * (np.dot(w, w)) #np.dot(np.dot(w.T, X).T, w)\n \n return obj","def cube_fix_badpix_isolated(array, bpm_mask=None, sigma_clip=3, num_neig=5, \n size=5, frame_by_frame=False, protect_mask=0, \n cxy=None, mad=False, ignore_nan=True, verbose=True, \n full_output=False):\n if array.ndim != 3:\n raise TypeError('Array is not a 3d array or cube')\n if size % 2 == 0:\n raise TypeError('Size of the median blur kernel must be an odd integer')\n \n if bpm_mask is not None:\n bpm_mask = bpm_mask.astype('bool')\n \n if verbose: start = time_ini()\n \n if num_neig > 0:\n neigh = True\n else:\n neigh = False\n \n nz = array.shape[0]\n \n if cxy is None:\n cy, cx = frame_center(array[0])\n elif isinstance(cxy, tuple):\n cx, cy = cxy\n elif isinstance(cxy, np.ndarray):\n if cxy.shape[0] != nz or cxy.shape[1] != 2 or cxy.ndim != 2:\n raise ValueError(\"cxy does not have right shape\")\n elif not frame_by_frame:\n msg = \"cxy must be a tuple or None if not in frame_by_frame mode\"\n raise ValueError(msg)\n else:\n cx = cxy[:,0]\n cy = cxy[:,1]\n\n \n array_out = array.copy()\n final_bpm = np.zeros_like(array_out, dtype=bool)\n n_frames = array.shape[0]\n count_bp = 0\n if frame_by_frame:\n if np.isscalar(cx):\n cx = [cx]*nz\n cy = [cy]*nz\n if bpm_mask is not None:\n if bpm_mask.ndim == 2:\n bpm_mask = [bpm_mask]*n_frames\n bpm_mask = np.array(bpm_mask)\n for i in Progressbar(range(n_frames), desc=\"processing frames\"):\n if bpm_mask is not None:\n bpm_mask_tmp = bpm_mask[i]\n else:\n bpm_mask_tmp = None\n res = frame_fix_badpix_isolated(array[i], bpm_mask=bpm_mask_tmp, \n sigma_clip=sigma_clip,\n num_neig=num_neig, size=size, \n protect_mask=protect_mask, \n verbose=False, cxy=(cx[i],cy[i]), \n ignore_nan=ignore_nan,\n full_output=True)\n array_out[i] = res[0]\n final_bpm[i] = res[1] \n count_bp = np.sum(final_bpm) \n else: \n if bpm_mask is None:\n ori_nan_mask = np.where(np.isnan(np.nanmean(array, axis=0)))\n ind = clip_array(np.nanmean(array, axis=0), sigma_clip, sigma_clip,\n neighbor=neigh, num_neighbor=num_neig, mad=mad)\n final_bpm = np.zeros_like(array[0], dtype=bool)\n final_bpm[ind] = 1\n if ignore_nan:\n final_bpm[ori_nan_mask] = 0\n if protect_mask:\n cir = disk((cy, cx), protect_mask, shape=final_bpm.shape)\n final_bpm[cir] = 0\n final_bpm = final_bpm.astype('bool')\n else:\n if bpm_mask.ndim == 3:\n final_bpm = np.median(bpm_mask, axis=0)\n else:\n final_bpm = bpm_mask.copy()\n \n for i in Progressbar(range(n_frames), desc=\"processing frames\"):\n frame = array_out[i]\n smoothed = median_filter(frame, size, mode='mirror')\n frame[np.where(final_bpm)] = smoothed[np.where(final_bpm)]\n if verbose: \n count_bp += np.sum(final_bpm)\n \n if verbose: \n msg = \"Done replacing {:.0f} bad pixels using the median of neighbors\"\n print(msg.format(count_bp))\n if not frame_by_frame:\n msg = \"(i.e. {:.0f} static bad pixels per channel))\"\n print(msg.format(count_bp/n_frames)) \n timing(start)\n \n if full_output:\n return array_out, final_bpm\n else:\n return array_out","def _pad(input_data):\n h = input_data['img'].shape[0]\n w = input_data['img'].shape[1]\n max_size = max([int(np.sqrt(np.power(h, 2) + np.power(w, 2))),\n int(w + h * np.cos(11 / 36)),\n int(max(h, w) * 2)\n ]) + 5\n\n up = (max_size - h) // 2\n down = max_size - up - h\n left = (max_size - w) // 2\n right = max_size - left - w\n\n input_data['img'] = np.pad(input_data['img'], ((up, down), (left, right), (0, 0)), mode='constant')\n\n if DataAugmentor._is_synthtext(input_data):\n input_data['contour'][0] = list(\n map(lambda x: np.stack([x[:, :, 0] + up, x[:, :, 1] + left], axis=-1), # x: np.array(n,1,2)\n input_data['contour'][0]))\n input_data['contour'][1] = list(\n map(lambda x: np.stack([x[:, :, 0] + up, x[:, :, 1] + left], axis=-1), # x: np.array(n,1,2)\n input_data['contour'][1]))\n else:\n input_data['contour'] = list(\n map(lambda x: np.stack([x[:, :, 0] + up, x[:, :, 1] + left], axis=-1), # x: np.array(n,1,2)\n input_data['contour']))\n\n input_data['center_point'] = list(\n map(lambda x: (x[0] + up, x[1] + left),\n input_data['center_point']))\n return input_data","def filter_on_border(self, image, labeled_image):\n return labeled_image\n \"\"\"\n if self.exclude_border_objects.value:\n border_labels = list(labeled_image[0,:])\n border_labels.extend(labeled_image[:,0])\n border_labels.extend(labeled_image[labeled_image.shape[0]-1,:])\n border_labels.extend(labeled_image[:,labeled_image.shape[1]-1])\n border_labels = np.array(border_labels)\n #\n # the following histogram has a value > 0 for any object\n # with a border pixel\n #\n histogram = scipy.sparse.coo_matrix((np.ones(border_labels.shape),\n (border_labels,\n np.zeros(border_labels.shape))),\n shape=(np.max(labeled_image)+1,1)).todense()\n histogram = np.array(histogram).flatten()\n if any(histogram[1:] > 0):\n histogram_image = histogram[labeled_image]\n labeled_image[histogram_image > 0] = 0\n elif image.has_mask:\n # The assumption here is that, if nothing touches the border,\n # the mask is a large, elliptical mask that tells you where the\n # well is. That's the way the old Matlab code works and it's duplicated here\n #\n # The operation below gets the mask pixels that are on the border of the mask\n # The erosion turns all pixels touching an edge to zero. The not of this\n # is the border + formerly masked-out pixels.\n mask_border = np.logical_not(scipy.ndimage.binary_erosion(image.mask))\n mask_border = np.logical_and(mask_border,image.mask)\n border_labels = labeled_image[mask_border]\n border_labels = border_labels.flatten()\n histogram = scipy.sparse.coo_matrix((np.ones(border_labels.shape),\n (border_labels,\n np.zeros(border_labels.shape))),\n shape=(np.max(labeled_image)+1,1)).todense()\n histogram = np.array(histogram).flatten()\n if any(histogram[1:] > 0):\n histogram_image = histogram[labeled_image]\n labeled_image[histogram_image > 0] = 0\n return labeled_image\n \"\"\"","def plot_filtered_spots(\n adata, \n kernel_matrix, \n contrib_thresh,\n row_key='row',\n col_key='col',\n ax=None,\n figure=None,\n dsize=37,\n ticks=True,\n fig_path=None,\n fig_format='pdf',\n fig_dpi=150\n ):\n if ax is None:\n width = 5\n figure, ax = plt.subplots(\n 1,\n 1,\n figsize=(width,5)\n )\n\n # Filter spots with too little contribution\n # from neighbors\n contrib = np.sum(kernel_matrix, axis=1)\n keep_inds = [\n i\n for i, c in enumerate(contrib)\n if c >= contrib_thresh\n ]\n print('Kept {}/{} spots.'.format(len(keep_inds), len(adata.obs)))\n\n cat = []\n keep_inds = set(keep_inds)\n for ind in range(adata.obs.shape[0]):\n if ind in keep_inds:\n cat.append('Kept')\n else:\n cat.append('Filtered')\n cat_palette = ['#595959', '#d9d9d9']\n plot_slide(\n adata.obs,\n cat,\n cmap='categorical',\n colorbar=False,\n vmin=None,\n vmax=None,\n title='Filtered Spots',\n ax=ax,\n figure=figure,\n ticks=ticks,\n dsize=dsize,\n row_key=row_key,\n col_key=col_key,\n cat_palette=cat_palette\n )\n\n if fig_path:\n plt.tight_layout()\n figure.savefig(\n fig_path,\n format=fig_format,\n dpi=fig_dpi\n )\n plt.show()","def mask(self, byclass, height, width, margin=0, figsize=(10, 10), dpi=180):\n # make ~binary mask using available classes\n style = {cls: ('k', '-') for cls in byclass}\n fig = Figure(figsize=figsize)\n fig.tight_layout(pad=0)\n fig.subplots_adjust(hspace=0, wspace=0, left=0, right=1, bottom=0, top=1)\n canvas = FigureCanvas(fig)\n ax = fig.subplots(1, 1)\n self.show_style(ax, style, byclass)\n ax.set_xlim(0 - margin, height + margin)\n ax.set_ylim(0 - margin, width + margin)\n canvas.draw()\n mask = self.figure_buffer(fig, dpi=dpi)\n mask = cv2.cvtColor(mask, cv2.COLOR_BGR2GRAY)\n # fill in the gaps via:\n # https://www.learnopencv.com/filling-holes-in-an-image-using-opencv-python-c/\n _, thresholded = cv2.threshold(mask, 220, 255, cv2.THRESH_BINARY_INV);\n floodfilled = thresholded.copy()\n h, w = thresholded.shape[:2]\n mask = np.zeros((h + 2, w + 2), np.uint8)\n cv2.floodFill(floodfilled, mask, (0, 0), 255);\n mask = cv2.bitwise_not(thresholded | cv2.bitwise_not(floodfilled))\n return mask","def draw(self, frame, offset=OFS):\n frame[\n OFS : OFS + self.image.shape[0], OFS : OFS + self.image.shape[1]\n ] = self.image","def padFrames(frameList, maxHeight):\n \n print('\\n')\n for i, frame in enumerate(frameList):\n extraSpace = maxHeight - frame.shape[0]\n #frameList[i] = np.lib.pad(frame,((int(np.floor(extraSpace/2)),int(np.ceil(extraSpace/2))),(0,0)),'constant', constant_values=(4000,8000))\n frameList[i] = np.lib.pad(frame,((extraSpace,0),(0,0)),'constant', constant_values=0)\n print('\\rPadding Frames {:.2f}% done'.format(100.0*((i+1)/len(frameList))),end='', flush=True)\n stack = np.stack(frameList, axis=0)\n \n return stack","def reflection_pad(images, filter_size):\n num = filter_size // 2\n return tf.pad(images, [[0, 0], [num, num], [num, num], [0, 0]], mode='REFLECT')","def conv_fast(image, kernel):\n Hi, Wi = image.shape\n Hk, Wk = kernel.shape\n out = np.zeros((Hi, Wi))\n\n k = np.flip(np.flip(kernel, 1), 0)\n padding_image = zero_pad(image, Hk//2, Wk//2)\n for i in range(Hi):\n for j in range(Wi):\n out[i, j] = np.sum(np.multiply(padding_image[i:i+Hk, j:j+Wk], k))\n\n return out","def calc_pad_dims_2D(X_shape, out_dim, kernel_shape, stride, dilation=0):\n if not isinstance(X_shape, tuple):\n raise ValueError(\"`X_shape` must be of type tuple\")\n\n if not isinstance(out_dim, tuple):\n raise ValueError(\"`out_dim` must be of type tuple\")\n\n if not isinstance(kernel_shape, tuple):\n raise ValueError(\"`kernel_shape` must be of type tuple\")\n\n if not isinstance(stride, int):\n raise ValueError(\"`stride` must be of type int\")\n\n d = dilation\n fr, fc = kernel_shape\n out_rows, out_cols = out_dim\n n_ex, in_rows, in_cols, in_ch = X_shape\n\n # update effective filter shape based on dilation factor\n _fr, _fc = fr * (d + 1) - d, fc * (d + 1) - d\n\n pr = int((stride * (out_rows - 1) + _fr - in_rows) / 2)\n pc = int((stride * (out_cols - 1) + _fc - in_cols) / 2)\n\n out_rows1 = int(1 + (in_rows + 2 * pr - _fr) / stride)\n out_cols1 = int(1 + (in_cols + 2 * pc - _fc) / stride)\n\n # add asymmetric padding pixels to right / bottom\n pr1, pr2 = pr, pr\n if out_rows1 == out_rows - 1:\n pr1, pr2 = pr, pr + 1\n elif out_rows1 != out_rows:\n raise AssertionError\n\n pc1, pc2 = pc, pc\n if out_cols1 == out_cols - 1:\n pc1, pc2 = pc, pc + 1\n elif out_cols1 != out_cols:\n raise AssertionError\n\n if any(np.array([pr1, pr2, pc1, pc2]) < 0):\n raise ValueError(\n \"Padding cannot be less than 0. Got: {}\".format((pr1, pr2, pc1, pc2))\n )\n return (pr1, pr2, pc1, pc2)","def opening2d(value, kernel, stride=1, padding=\"SAME\"):\n strides = [1, stride, stride, 1]\n rates = [1, 1, 1, 1]\n out = tf.nn.erosion2d(value, kernel, strides, rates, padding)\n out = tf.nn.dilation2d(out, kernel, strides, rates, padding)\n return out","def _pad(phi, det):\r\n npadx = (det.x - phi.shape[1]) // 2\r\n npady = (det.y - phi.shape[1]) // 2\r\n return np.pad(phi, ((0, 0), (npadx, npadx), (npady, npady)), mode='constant')","def maskLowerLeftBorder(\n depth_image,\n background_mask,\n sigma=1, l_thresh=0, h_thresh=1000,\n axis_tol=0.1, hough_thresh_ratio=0.4,\n x_max_thresh=0.1, y_min_thresh=0.75, margin=10):\n\n # Find rudimentary edges in the image\n masked_image = depth_image.copy()\n masked_image[background_mask] = 0\n edge_image = feature.canny(\n masked_image,\n sigma=sigma,\n low_threshold=l_thresh,\n high_threshold=h_thresh\n )\n\n num_rows, num_cols = edge_image.shape\n\n x_mid = num_cols / 2\n y_mid = num_rows / 2\n x_max = x_max_thresh * num_cols\n y_min = y_min_thresh * num_rows\n\n hough_mask = np.zeros_like(edge_image, dtype=bool)\n\n # Detect lines using the Hough transform\n h, theta, d = transform.hough_line(edge_image)\n __, angles, dists = transform.hough_line_peaks(\n h, theta, d,\n threshold=hough_thresh_ratio * h.max()\n )\n\n # Filter Hough lines and mask the border if appropriate\n for angle, dist in zip(angles, dists):\n if geometry.axisAligned(angle, tol=axis_tol, axis='horizontal'):\n y = geometry.solveLine(angle, dist, x=x_mid)\n if y > y_min:\n hough_mask[int(y) - margin:, :] = True\n elif geometry.axisAligned(angle, tol=axis_tol, axis='vertical'):\n x = geometry.solveLine(angle, dist, y=y_mid)\n if x < x_max:\n hough_mask[:, :int(x) + margin] = True\n else:\n continue\n\n return hough_mask","def build_attention_mask_3d_padding(source_mask, target_mask):\n mask = make_attention_mask_3d(source_mask, target_mask)\n # invert mask for Megatron\n return mask < 0.5"],"string":"[\n \"def slidekernelthroughdiagonal(kernel, matrix):\\n size_kernel = kernel.shape[0]\\n size_matrix = matrix.shape[0]\\n result = np.zeros([size_matrix])\\n for i in range(size_matrix):\\n # Calculate zero padding needed\\n padding_b = -min(i - int(size_kernel/2), 0)\\n padding_a = -min(size_matrix - int(i + size_kernel/2), 0)\\n matrix_selection = matrix[max(0, i-int(size_kernel/2)):min(size_matrix, i+int(size_kernel/2)),max(0, i-int(size_kernel/2)):min(size_matrix, i+int(size_kernel/2))]\\n matrix_padded = np.pad(matrix_selection, [(padding_b, padding_a), (padding_b, padding_a)])\\n result[i] = np.sum(matrix_padded*kernel)\\n return result\",\n \"def add_context_margin(image, margin_size, **pad_kwargs):\\n return np.pad(image,\\n ((margin_size, margin_size),\\n (margin_size, margin_size),\\n (0, 0)), **pad_kwargs)\",\n \"def add_kernel(org: np.ndarray, kernel: np.ndarray, center):\\n # place filter at the array, and exclude any locations that are out of bounds\\n c_r, c_c = center.row, center.column\\n k_r, k_c = kernel.shape\\n row_start = max(0, k_r/2 - c_r)\\n pass\",\n \"def applyKernelToPoints(image,pts,kernel,border_type='BLACK'):\\n \\n \\n pts=np.asarray(pts)\\n image=np.asarray(image)\\n image.shape\\n if len(image.shape)>2:\\n grayscale=False\\n shaperesult=(len(pts),image.shape[2])\\n elif len(image.shape)==1:\\n image=image.reshape(1,image.shape[0])\\n shaperesult=len(pts)\\n grayscale=True\\n\\n else:\\n grayscale=True\\n\\n # Kernel dimensions - they are integers\\n krows=kernel.shape[0] \\n kcols=kernel.shape[1]\\n\\n if krows%2==0:\\n # Is even\\n ldrows=(krows/2)-1\\n udrows=krows/2\\n \\n else:\\n # Is odd\\n ldrows=krows/2\\n udrows=krows/2\\n\\n if kcols%2==0:\\n # Is even\\n ldcols=(kcols/2)-1\\n udcols=kcols/2\\n else:\\n # Is odd\\n ldcols=kcols/2\\n udcols=kcols/2\\n\\n #------------------------------------\\n # ADD FRAME TO THE ORIGINAL IMAGE\\n #------------------------------------\\n\\n dummyM=image.shape[0]+krows-1\\n dummyN=image.shape[1]+kcols-1\\n \\n if grayscale==True:\\n dummyimage=np.asarray(np.zeros((dummyM,dummyN)))\\n \\n else:\\n dummyimage=np.asarray(np.zeros((dummyM,dummyN,image.shape[2])))\\n\\n if border_type==\\\"WHITE\\\":\\n dummyimage=dummyimage+255\\n\\n elif border_type==\\\"ANTIALIAS\\\":\\n # Fills top border\\n dummyimage[0:ldrows,ldcols:ldcols+image.shape[1]]=image[image.shape[0]-ldrows:image.shape[0],:]\\n\\n # Fills bottom border\\n dummyimage[(ldrows+image.shape[0]):,ldcols:(ldcols+image.shape[1])]=image[0:udrows,:]\\n \\n # Fills left border\\n dummyimage[ldrows:ldrows+image.shape[0],0:ldcols]=image[:,image.shape[1]-ldcols:]\\n\\n # Fills right border\\n dummyimage[ldrows:ldrows+image.shape[0],(ldcols+image.shape[1]):]=image[:,0:udcols]\\n \\n # Fills top, left corner\\n dummyimage[0:ldrows,0:ldcols]=image[image.shape[0]-ldrows,image.shape[1]-ldcols]\\n\\n # Fills bottom, left corner\\n dummyimage[(ldrows+image.shape[0]):,0:ldcols]=image[0:udrows,(image.shape[1]-ldcols):]\\n \\n # Fills top, right corner\\n dummyimage[0:ldrows,(ldcols+image.shape[1]):]=image[(image.shape[0]-ldrows):,0:udcols]\\n \\n # Fills bottom, right corner\\n dummyimage[(ldrows+image.shape[0]):,(ldcols+image.shape[1]):]=image[0:udrows,0:udcols]\\n \\n dummyimage[ldrows:ldrows+image.shape[0],ldcols:ldcols+image.shape[1]]=image \\n \\n result=np.asarray(np.zeros(shaperesult))\\n \\n pts[:,0]=pts[:,0]+ldrows\\n pts[:,1]=pts[:,1]+ldcols\\n \\n for k in range(len(pts)):\\n total=0\\n \\n for i in range(-ldrows,udrows+1):\\n for j in range(-ldcols,udcols+1):\\n total=total+dummyimage[i+pts[k,0],j+pts[k,1]]*kernel[i+ldrows,j+ldcols]\\n \\n \\n result[k]=total\\n \\n \\n return result\",\n \"def adjust_border(input_masks, device, margin=3):\\n # Convert the mask to numpy array\\n dtype = input_masks.dtype\\n input_masks = np.squeeze(input_masks.cpu().numpy(), axis=1)\\n\\n erosion_kernel = cv2.getStructuringElement(cv2.MORPH_ELLIPSE,\\n (margin*2, margin*2))\\n batch_size = input_masks.shape[0]\\n \\n output_mask_lst = []\\n # Erode all the masks\\n for i in range(batch_size):\\n output_mask = cv2.erode(input_masks[i, ...], erosion_kernel)\\n\\n output_mask_lst.append(\\n torch.tensor(output_mask, dtype=dtype, device=device)[None])\\n \\n # Concat back along the batch dimension.\\n output_masks = torch.cat(output_mask_lst, dim=0)\\n return output_masks.unsqueeze(dim=1)\",\n \"def cs4243_filter(image, kernel):\\n Hi, Wi = image.shape\\n Hk, Wk = kernel.shape\\n filtered_image = np.zeros((Hi, Wi))\\n\\n ###Your code here####\\n # pad image to handle border pixels\\n pad_height = (int)((Hk - 1)/2)\\n pad_width = (int)((Wk - 1)/2)\\n image_pad = pad_zeros(image, pad_height, pad_width)\\n\\n # Flip the kernel horizontal and vertical\\n kernel = cs4243_rotate180(kernel)\\n \\n # compute effective output size, assume stride=1\\n out_height = 1 + Hi - Hk + 2*pad_height\\n out_width = 1 + Wi - Wk + 2*pad_width\\n \\n # get initial nodes of receptive fields\\n recep_fields_h = [i for i in range(out_height)]\\n recep_fields_w = [i for i in range(out_width)]\\n \\n for i in recep_fields_h:\\n for j in recep_fields_w: \\n # get receptive area\\n recep_area = image_pad[i:i+Hk, j:j+Wk] \\n\\n # multiply recep_area with kernel\\n conv_sum = 0.0\\n for y in range(Hk):\\n for x in range(Wk): \\n conv_sum += kernel[y][x] * recep_area[y][x]\\n filtered_image[i, j] = conv_sum\\n ###\\n\\n return filtered_image\",\n \"def shift_kernel(kernel, shape, centre):\\n h, w = kernel.shape\\n assert(h % 2 == 1)\\n assert(w % 2 == 1)\\n half_h = np.floor(h/2)\\n half_w = np.floor(w/2)\\n \\n result = np.zeros((shape[0]+2*half_h, shape[1]+2*half_w)) #zero pad to simplify edge handling \\n\\n ind_h = centre[0] + np.arange(0, 2*half_h+1, dtype='int') \\n ind_w = centre[1] + np.arange(0, 2*half_w+1, dtype='int')\\n result[ind_h[:,np.newaxis], ind_w] = kernel\\n result = result[half_h:-half_h,half_w:-half_w]\\n return result\",\n \"def convolution(matrix, kernel):\\n assert assert_odd(kernel.shape[0])\\n \\n # Padded matrix (0s on the outsides)\\n N = kernel.shape[0] # Get the dim for the kernel\\n I = np.pad(matrix, int(N/2), \\\"constant\\\")\\n \\n # Now do the convolution\\n C = np.zeros(matrix.shape) # This is the convolved image\\n h, w = C.shape # Get width and height\\n s = int(N/2) # Spacing of the matrix\\n positions = [(i,j) for i in range(h) for j in range(w)]\\n for (i,j) in positions:\\n y, x = i+s,j+s # Shift the center to the right position\\n \\n # Calc the convolution at each pixel\\n C[i,j] = np.sum(np.multiply(kernel, I[y-s:y+s+1,x-s:x+s+1]))\\n \\n # Return the clipped array as uint8\\n return C\",\n \"def _pad_with_zeros(self, X, margin):\\n newX = np.zeros((X.shape[0] + 2 * margin, X.shape[1] + 2 * margin, X.shape[2]))\\n x_offset = margin\\n y_offset = margin\\n newX[x_offset:X.shape[0] + x_offset, y_offset:X.shape[1] + y_offset, :] = X\\n return newX\",\n \"def gaussian_filter(img,f=5,K=1,var=1):\\n i_x, i_y = np.shape(img) # image size\\n radi = f//2 # window radius\\n\\n # create gaussian kernel\\n def gaussian_kernel(f,K,var):\\n \\n # create coordinate information \\n if f//2 == 0:\\n x = np.linspace(-radi,radi,f+1)\\n y = np.linspace(-radi,radi,f+1)\\n x = np.delete(x, radi)\\n y = np.delete(y, radi)\\n else:\\n x = np.linspace(-radi,radi,f)\\n y = np.linspace(-radi,radi,f)\\n\\n m_x, m_y = np.meshgrid(x,y) # create coordinate\\n r_gauss = m_x**2 + m_y**2 # distance to origin\\n gauss = K*(np.exp(-r_gauss/(2*(var**2)))) # create kernel\\n return gauss/gauss.sum()\\n \\n #mirror padding\\n def mir_padding(img,f):\\n img_p = np.zeros((i_x+2*radi,i_y+2*radi)) #create padding image\\n img_p[radi:i_x+radi,radi:i_y+radi] = img #throw original image to padding image\\n img_p[0:radi,radi:i_y+radi] = img[radi-1::-1,:] # padding top rows\\n img_p[-radi::1,radi:i_y+radi] = img[-1:-radi-1:-1,:] # padding bottom rows\\n img_p[radi:i_x+radi,0:radi] = img[:,radi-1::-1] # padding left column\\n img_p[radi:i_x+radi,-radi::1] = img[:,-1:-radi-1:-1] # padding right column\\n for i in range(f):\\n img_p[0:radi,i] = img[radi-1-i,radi-1::-1] # padding upper-left corner\\n img_p[0:radi,-i] = img[radi-1-i,-radi::1] # padding upper-righ corner\\n img_p[-1:-radi-1:-1,i] = img[-radi+i,radi-1::-1] # padding lower-left corner\\n img_p[-1:-radi-1:-1,-i] = img[-radi+i,-radi::1] # padding lower-right corner\\n return img_p\\n\\n img_p = mir_padding(img,f) # create padding image\\n g_kernel = gaussian_kernel(f,K,var) # create gaussian kernel\\n\\n #seperate kernel\\n E = g_kernel[0,0]\\n c = g_kernel[:,0]\\n wT = np.reshape(g_kernel[0,:]/E,(f,1))\\n\\n gauss_image = np.zeros([i_x,i_y]) # create gauss image\\n temp_image = np.zeros([i_x,i_y]) # create temp image for two 1D convolution\\n old_c_sum = c.sum() # calculate sum of c before modification\\n\\n # if elements of kernel are located within area of padding, substitute value with 0\\n # calculate new value base on ratio between sum before and after modification\\n for j in range(i_y):\\n y_bound = i_y - j\\n mod_c = c.copy()\\n if j < radi:\\n mod_c[0:radi-j] = 0 \\n new_c_sum = mod_c.sum()\\n mod_c = mod_c*old_c_sum/new_c_sum \\n if j > i_y - radi - 1:\\n mod_c[-1:-radi+y_bound-1:-1] = 0 \\n new_c_sum = mod_c.sum()\\n mod_c = mod_c*old_c_sum/new_c_sum \\n for i in range(i_x):\\n temp_image[i,j] = np.sum(img_p[i+radi,j:j+f]*mod_c)\\n\\n temp_image = mir_padding(temp_image,f) # create padding temp image for next 1D convolution\\n old_wT_sum = wT.sum() # calculate sum of wT before modification\\n\\n # if elements of kernel are located within area of padding, substitute value with 0\\n # calculate new value base on ratio between sum before and after modification\\n for i in range(i_x):\\n x_bound = i_x - i\\n mod_wT = wT.copy()\\n if i < radi:\\n mod_wT[0:radi-i] = 0 \\n new_wT_sum = mod_wT.sum()\\n mod_wT = mod_wT*old_wT_sum/new_wT_sum \\n if i > i_x - radi - 1:\\n mod_wT[-1:-radi+x_bound-1:-1] = 0 \\n new_wT_sum = mod_wT.sum()\\n mod_wT = mod_wT*old_wT_sum/new_wT_sum \\n for j in range(i_y):\\n gauss_image[i,j] = np.sum(temp_image[i:i+f,j+radi]*mod_wT.T)\\n\\n return gauss_image\",\n \"def _zero_pad(self, kernel, size):\\n if len(size) != kernel.ndim:\\n size = kernel.shape[:1] + tuple(size) + kernel.shape[-1:]\\n padsize = np.array(size) - np.array(kernel.shape)\\n paddown = padsize // 2\\n padup = padsize - paddown\\n padarray = np.concatenate((padup[..., None],\\n paddown[..., None]), axis=1)\\n pads = tuple([tuple(p) for p in padarray])\\n kernel_pad = np.pad(kernel, pads, 'constant', constant_values=0)\\n return kernel_pad\",\n \"def conv(image, kernel):\\n Hi, Wi = image.shape\\n Hk, Wk = kernel.shape\\n out = np.zeros((Hi, Wi))\\n\\n # For this assignment, we will use edge values to pad the images.\\n # Zero padding will make derivatives at the image boundary very big,\\n # whereas we want to ignore the edges at the boundary.\\n pad_width0 = Hk // 2\\n pad_width1 = Wk // 2\\n pad_width = ((pad_width0,pad_width0),(pad_width1,pad_width1))\\n padded = np.pad(image, pad_width, mode='edge')\\n\\n ### YOUR CODE HERE\\n for i in range(Hi):\\n for j in range(Wi):\\n out[i,j] = np.sum(padded[i : i + Hk, j : j + Wk] * np.flip(kernel))\\n ### END YOUR CODE\\n\\n return out\",\n \"def conv(image, kernel):\\n Hi, Wi = image.shape\\n Hk, Wk = kernel.shape\\n out = np.zeros((Hi, Wi))\\n\\n # For this assignment, we will use edge values to pad the images.\\n # Zero padding as used in the previous assignment can make\\n # derivatives at the image boundary very big.\\n \\n pad_width0 = Hk // 2\\n pad_width1 = Wk // 2\\n pad_width = ((pad_width0,pad_width0),(pad_width1,pad_width1))\\n padded = np.pad(image, pad_width, mode='edge') \\n\\n #####################################\\n # START YOUR CODE HERE #\\n #####################################\\n kernel = np.flipud(np.fliplr(kernel)) # flip h/v\\n for h in range(Hi):\\n for w in range(Wi):\\n out[h, w] = np.sum(np.multiply(kernel, padded[h : h + Hk, w : w + Wk]))\\n ######################################\\n # END OF YOUR CODE #\\n ######################################\\n\\n return out\",\n \"def frame_fix_badpix_isolated(array, bpm_mask=None, sigma_clip=3, num_neig=5,\\n size=5, protect_mask=0, cxy=None, mad=False, \\n ignore_nan=True, verbose=True, full_output=False):\\n if array.ndim != 2:\\n raise TypeError('Array is not a 2d array or single frame')\\n if size % 2 == 0:\\n raise TypeError('Size of the median blur kernel must be an odd integer')\\n\\n if bpm_mask is not None:\\n bpm_mask = bpm_mask.astype('bool')\\n\\n if verbose: start = time_ini()\\n\\n if num_neig > 0:\\n neigh = True\\n else:\\n neigh = False\\n\\n frame = array.copy()\\n if cxy is None:\\n cy, cx = frame_center(frame)\\n else:\\n cx, cy = cxy\\n \\n if bpm_mask is None:\\n ori_nan_mask = np.where(np.isnan(frame))\\n ind = clip_array(frame, sigma_clip, sigma_clip, neighbor=neigh,\\n num_neighbor=num_neig, mad=mad)\\n bpm_mask = np.zeros_like(frame)\\n bpm_mask[ind] = 1\\n if ignore_nan:\\n bpm_mask[ori_nan_mask] = 0\\n if protect_mask:\\n cir = disk((cy, cx), protect_mask, shape=bpm_mask.shape)\\n bpm_mask[cir] = 0\\n bpm_mask = bpm_mask.astype('bool')\\n\\n smoothed = median_filter(frame, size, mode='mirror')\\n frame[np.where(bpm_mask)] = smoothed[np.where(bpm_mask)]\\n array_out = frame\\n count_bp = np.sum(bpm_mask)\\n \\n if verbose:\\n msg = \\\"/nDone replacing {} bad pixels using the median of neighbors\\\"\\n print(msg.format(count_bp))\\n timing(start)\\n \\n if full_output:\\n return array_out, bpm_mask\\n else:\\n return array_out\",\n \"def mask_frame(image, mask):\\n # For segmentation mask display\\n mask[mask == 1] = 2\\n mask[mask == 0] = 1\\n\\n # segmentation will display final output\\n segmentation = image\\n segmentation = cv2.cvtColor(segmentation, cv2.COLOR_BGR2RGB)\\n\\n segmentation[:, :, 0] = segmentation[:, :, 0] * mask\\n segmentation[:, :, 1] = segmentation[:, :, 1] * mask\\n return segmentation\",\n \"def insert_padding(img, pad_h, pad_w):\\n global frame_height, frame_width\\n padding_3_dims = ((pad_h, pad_h), (pad_w, pad_w), (0, 0))\\n # apply padding in the above dimensions with values 0\\n padded_img = numpy.pad(img, padding_3_dims, 'constant', constant_values=0)\\n return padded_img\",\n \"def dilate_kernel(self, kernel, dilation):\\n if dilation == 0:\\n return kernel \\n # inside padding based on the scaling law\\n dilation = torch.tensor(dilation).float()\\n delta = dilation%1\\n\\n d_in = torch.ceil(dilation**2).int()\\n new_in = kernel.shape[2] + (kernel.shape[2]-1)*d_in\\n\\n d_h = torch.ceil(dilation).int()\\n new_h = kernel.shape[3] + (kernel.shape[3]-1)*d_h\\n\\n d_w = torch.ceil(dilation).int()\\n new_w = kernel.shape[4] + (kernel.shape[4]-1)*d_h\\n\\n new_kernel = torch.zeros(kernel.shape[0], kernel.shape[1], new_in, new_h, new_w)\\n new_kernel[:,:,::(d_in+1),::(d_h+1), ::(d_w+1)] = kernel\\n dilate_factor = 1\\n \\n new_kernel = F.pad(new_kernel, ((kernel.shape[4]-1)//2, (kernel.shape[4]-1)//2)*3)\\n\\n dilate_factor = (new_kernel.shape[-1] - 1 - (kernel.shape[4]-1)*(delta))/(new_kernel.shape[-1] - 1) \\n\\n grid = torch.meshgrid(torch.linspace(-1, 1, new_in)*(dilate_factor**2), \\n torch.linspace(-1, 1, new_h)*dilate_factor, \\n torch.linspace(-1, 1, new_w)*dilate_factor)\\n\\n grid = torch.cat([grid[2].unsqueeze(0).unsqueeze(-1), \\n grid[1].unsqueeze(0).unsqueeze(-1), \\n grid[0].unsqueeze(0).unsqueeze(-1)], dim = -1).repeat(kernel.shape[0],1,1,1,1)\\n\\n new_kernel = F.grid_sample(new_kernel, grid) \\n \\n return new_kernel[:,:,-kernel.shape[2]:]\",\n \"def convolution(img, kernel, padding=True):\\n result = np.zeros_like(img)\\n p_size_i = kernel.shape[0] // 2\\n p_size_j = kernel.shape[1] // 2\\n\\n if padding:\\n padded_img = np.zeros((img.shape[0] + 2 * p_size_i, img.shape[1] + 2 * p_size_j))\\n i_first = p_size_i\\n i_last = padded_img.shape[0] - p_size_i - 1\\n j_first = p_size_j\\n j_last = padded_img.shape[1] - p_size_j - 1\\n padded_img[i_first: i_last + 1, j_first: j_last + 1] = img\\n else:\\n padded_img = img.copy()\\n i_first = p_size_i\\n i_last = padded_img.shape[0] - p_size_i - 1\\n j_first = p_size_j\\n j_last = padded_img.shape[1] - p_size_j - 1\\n \\n for i in range(i_first, i_last):\\n for j in range(j_first, j_last):\\n window = padded_img[i - p_size_i: i + p_size_i + 1, j - p_size_j: j + p_size_j + 1]\\n res_pix = np.sum(window * kernel)\\n result[i - p_size_i, j - p_size_j] = res_pix\\n return result\",\n \"def Padding_Signal(signal,M = 10):\\t\\t\\t\\t\\t\\t\\t\\t\\t\\t# Function to pad a signal\\n\\ts = signal.shape[0]\\n\\tsignal_change = np.zeros(s+2*M)\\n\\tsignal_change[M:s+M] = signal\\n\\t\\n\\treturn signal_change\",\n \"def zero_pad_sparse(mat, margin_h, margin_v, fmt=\\\"coo\\\"):\\n\\n sm, sn = mat.shape\\n padded_mat = mat.copy()\\n # Up and down margins initialized with zeros and filled as needed\\n margin_h_0 = sp.csr_matrix((sm, margin_h), dtype=mat.dtype)\\n margin_v_0 = sp.csr_matrix((margin_v, sn + 2 * margin_h), dtype=mat.dtype)\\n padded_mat = sp.hstack([margin_h_0, padded_mat, margin_h_0], format=\\\"csr\\\")\\n padded_mat = sp.vstack([margin_v_0, padded_mat, margin_v_0], format=\\\"csr\\\")\\n\\n return padded_mat\",\n \"def hook(module, input):\\n image_dimensions = input[0].size()[-2:]\\n module.padding = _determine_padding_from_tf_same(image_dimensions, kernel_size, stride)\",\n \"def convolution(image: np.array, kernel: np.array) -> np.array:\\n\\n # default condition: apply SAME padding, and keep stride at 1\\n stride_x = 1\\n stride_y = 1\\n padding_y = int(len(kernel - 1) / 2)\\n padding_x = int(len((kernel[0]) - 1) / 2)\\n # create the return array with with the same dimensions as <image>,\\n # and then create a padded image\\n convolved_image = np.zeros((len(image), len(image[0])))\\n padded_image = np.zeros((len(image) + 2 * padding_y,\\n len(image[0]) + 2 * padding_x))\\n padded_image[padding_x: -padding_x, padding_y: -padding_y] = image\\n\\n for py in range(0, len(padded_image) - len(kernel), stride_y):\\n for px in range(0, len(padded_image[0]) - len(kernel[0]), stride_x):\\n # scan the matrix over columns in image array, then shift the matrix\\n # down, and repeat\\n padded_image_section = padded_image[py: py + len(kernel[0]),\\n px: px + len(kernel)]\\n # print(padded_image_section)\\n convolved_image[py, px] = int(np.tensordot(padded_image_section,\\n kernel))\\n\\n return convolved_image\",\n \"def fixed_padding(inputs, kernel_size, data_format='channels_last'):\\n pad_total = kernel_size - 1\\n pad_beg = pad_total // 2\\n pad_end = pad_total - pad_beg\\n return _padding(inputs, (pad_beg, pad_end), data_format)\",\n \"def add_padding(im, pad):\\n\\n return np.pad(im, pad_width=((pad, pad), (pad, pad), (0, 0)), mode='symmetric')\",\n \"def cs4243_filter_faster(image, kernel):\\n Hi, Wi = image.shape\\n Hk, Wk = kernel.shape\\n filtered_image = np.zeros((Hi, Wi))\\n\\n ###Your code here####\\n \\n # pad image to handle border pixels\\n pad_height = (int)((Hk - 1)/2)\\n pad_width = (int)((Wk - 1)/2)\\n image_pad = pad_zeros(image, pad_height, pad_width)\\n \\n # compute effective output size, assume stride=1\\n out_height = 1 + Hi - Hk + 2*pad_height\\n out_width = 1 + Wi - Wk + 2*pad_width\\n \\n # get initial nodes of receptive fields\\n recep_fields_h = [i for i in range(out_height)]\\n recep_fields_w = [i for i in range(out_width)]\\n \\n # extract receptive area into matrix of shape (Hi*Wi, Hk*Wk)\\n recep_areas = []\\n for i in recep_fields_h:\\n for j in recep_fields_w:\\n recep_areas.append(image_pad[i: i+Hk, j: j+Wk].reshape(-1))\\n out = np.stack(recep_areas)\\n \\n # Flip the kernel horizontal and vertical\\n kernel = cs4243_rotate180(kernel).reshape(Hk*Wk, 1)\\n \\n # dot product kernel and receptive areas\\n filtered_image = np.dot(out, kernel).reshape(Hi, Wi)\\n \\n ###\\n\\n return filtered_image\",\n \"def pad(self):\\n if self._mg_problem.boundaries[0] == 'periodic':\\n # left side\\n self.left[:] = self.mid[-self.borders[0]:]\\n # right side\\n self.right[:] = self.mid[:self.borders[1]]\\n elif self._mg_problem.boundaries[0] == 'dirichlet':\\n\\n # left from border\\n l_f_b = self.space_tensor[0:self.borders[0]]\\n # right_from_border\\n r_f_b = self.space_tensor[-self.borders[1]:]\\n # left side\\n self.left[:] = self.fl(l_f_b)\\n # right side\\n self.right[:] = self.fr(r_f_b)\",\n \"def cs4243_filter_fast(image, kernel):\\n Hi, Wi = image.shape\\n Hk, Wk = kernel.shape\\n filtered_image = np.zeros((Hi, Wi))\\n\\n ###Your code here####\\n \\n # pad image to handle border pixels\\n pad_height = (int)((Hk - 1)/2)\\n pad_width = (int)((Wk - 1)/2)\\n image_pad = pad_zeros(image, pad_height, pad_width)\\n\\n # Flip the kernel horizontal and vertical\\n kernel = cs4243_rotate180(kernel)\\n \\n # compute effective output size, assume stride=1\\n out_height = 1 + Hi - Hk + 2*pad_height\\n out_width = 1 + Wi - Wk + 2*pad_width\\n \\n # get initial nodes of receptive fields\\n recep_fields_h = [i for i in range(out_height)]\\n recep_fields_w = [i for i in range(out_width)]\\n \\n for i in recep_fields_h:\\n for j in recep_fields_w: \\n # get receptive area\\n recep_area = image_pad[i:i+Hk, j:j+Wk] \\n filtered_image[i, j] = np.multiply(kernel, recep_area).sum()\\n ###\\n\\n return filtered_image\",\n \"def conv_matrix(matrix, kernel):\",\n \"def make_padding(kernel_size, stride, dilation):\\n return -((-kernel_size - (kernel_size - 1) * (dilation - 1)) // stride + 1) // 2\",\n \"def kernel(self):\\n\\n # Create a blank kernel the appropriate size\\n kernel = np.zeros((self.n_rows, self.n_cols), dtype=np.int)\\n\\n # Iterate through the offsets, turning on the correct pixels\\n for offset in self.offsets:\\n row, col = offset\\n if np.all(offset == self.index):\\n kernel[row, col] = 2\\n else:\\n kernel[row, col] = 1\\n\\n # Ensure that the index pixel is not zero for footprints where the\\n # index pixel is not part of the footprint\\n if kernel[self.index[0], self.index[1]] == 0:\\n kernel[self.index[0], self.index[1]] = 3\\n return kernel\",\n \"def convolve_im(im: np.array,\\n kernel: np.array,\\n verbose=True):\\n ### START YOUR CODE HERE ### (You can change anything inside this block)\\n \\\"\\\"\\\"\\n\\tcompared to the 4a solution this just adds padding to the filter if its smaller than the image\\n\\tthis is done by using the second parameter in fft.fft2 \\n\\t\\n\\tfirst it applies fourier transforms on the kernel and the image\\n\\tthen it sets the image to be the pointwise multiplication of the transforms\\n\\n the image is inverse fourier transformed and filtered for real values\\n the domain image is shifted and taken the absolute value of\\n the fourier transform of the image and kernel are also shifted and set to be the absolute value\\n\\tlastly everything is displayed in the subplots\\n \\\"\\\"\\\"\\n conv_result = im \\n \\n if verbose:\\n fftKernel=np.fft.fft2(kernel,im.shape)\\n fftImage=np.fft.fft2(conv_result)\\n\\t\\t\\n\\t\\t\\n\\t\\t\\n conv_result=np.multiply(fftImage,fftKernel)\\n fftImageTransformed=conv_result\\n\\t\\t\\n \\n conv_result=np.fft.ifft2(conv_result)\\n \\n conv_result=np.real(conv_result)\\n\\n fftImageTransformed=np.fft.fftshift(fftImageTransformed)\\n fftImage=np.fft.fftshift(fftImage)\\n fftKernel=np.fft.fftshift(fftKernel)\\n\\n fftImageTransformed=np.absolute(fftImageTransformed)\\n fftImage=np.absolute(fftImage)\\n fftKernel=np.absolute(fftKernel)\\n\\t\\t\\n\\t\\t\\n # Use plt.subplot to place two or more images beside eachother\\n plt.figure(figsize=(20, 4))\\n # plt.subplot(num_rows, num_cols, position (1-indexed))\\n plt.subplot(1, 5, 1)\\n plt.imshow(im, cmap=\\\"gray\\\")\\n plt.subplot(1, 5, 2)\\n plt.imshow(fftImage, cmap=\\\"gray\\\")\\n plt.subplot(1, 5, 3)\\n plt.imshow(fftKernel, cmap=\\\"gray\\\")\\n plt.subplot(1, 5, 4)\\n plt.imshow(fftImageTransformed, cmap=\\\"gray\\\")\\n plt.subplot(1, 5, 5)\\n plt.imshow(conv_result, cmap=\\\"gray\\\")\\n ### END YOUR CODE HERE ###\\n return conv_result\",\n \"def convolution(img, kernel, padding='fill'):\\n kernel = np.rot90(kernel, 2)\\n h,w = kernel.shape[:2]\\n t,b,l,r = (h-1)//2, h//2, (w-1)//2, w//2 # Use numpy padding because it works for >2d\\n padshape = [(t,b),(l,r)]+[(0,0)]*(len(img.shape[2:]))\\n padded_img = np.pad(img, padshape, mode={'fill':'constant','replicate':'edge'}[padding])\\n conved_img = np.zeros_like(img)\\n for i in 1+np.arange(-h//2,h//2):\\n for j in 1+np.arange(-w//2,w//2):\\n if kernel[t+i,l+j]==0: continue\\n conved_img += kernel[t+i,l+j]*padded_img[t+i:-b+i or None,l+j:-r+j or None]\\n return conved_img\",\n \"def fixed_padding_2d3d(self, inputs, kernel_size):\\n pad_total = kernel_size - 1\\n pad_beg = pad_total // 2\\n pad_end = pad_total - pad_beg\\n if self.data_format == 'channels_first':\\n if len(inputs.shape)==4:\\n padded_inputs = tf.pad(inputs, [[0, 0], [0, 0],\\n [pad_beg, pad_end], [pad_beg, pad_end]])\\n elif len(inputs.shape)==5:\\n padded_inputs = tf.pad(inputs, [[0, 0], [0, 0],\\n [pad_beg, pad_end], [pad_beg, pad_end], [pad_beg, pad_end]])\\n else:\\n if len(inputs.shape)==4:\\n padded_inputs = tf.pad(inputs, [[0, 0], [pad_beg, pad_end],\\n [pad_beg, pad_end], [0, 0]])\\n elif len(inputs.shape)==5:\\n padded_inputs = tf.pad(inputs, [[0, 0], [pad_beg, pad_end],\\n [pad_beg, pad_end], [pad_beg, pad_end], [0, 0]])\\n return padded_inputs\",\n \"def convolution(image, kernel):\\n\\n #Se encuentra la dimencion de la imagen\\n if len(image.shape) == 3: #De 3 dimenciones\\n print(\\\"Dimenciones de imagen: {}\\\".format(image.shape))\\n image = cv2.cvtColor(image, cv2.COLOR_BGR2GRAY) #Se cambia a dos dimenciones\\n print(\\\"Nuevas dimenciones: {}\\\".format(image.shape))\\n else:\\n print(\\\"Dimenciones de imagen: {}\\\".format(image.shape))\\n\\n image_row, image_col = image.shape #asigna alto y ancho de la imagen \\n kernel_row, kernel_col = kernel.shape #asigna alto y ancho del filtro\\n\\n output_x = (image_col - (kernel_col / 2) * 2) + 1 #asigna el ancho del output\\n output_y = (image_row - (kernel_row / 2) * 2) + 1 #asigna el alto del output\\n \\n output = np.zeros([int(output_y), int(output_x)]) #matriz donde se guarda el resultado\\n\\n padded_size = int((kernel_row - 1) / 2) #Tamaño de padding\\n\\n #Obtenemos la imagen con padding\\n padded_image = padding(image,padded_size)\\n \\n for row in range(int(output_y)):\\n for col in range(int(output_x)):\\n output[row, col] = conv_helper(\\n padded_image[row:row + kernel_row, \\n col:col + kernel_col], kernel)\\n \\n # Se muestra la imagen en pantalla\\n plt.imshow(output, cmap='gray')\\n plt.title(\\\"Edge detection\\\")\\n plt.show()\\n\\n return output\",\n \"def _fixed_padding(inputs, kernel_size, *args, mode='CONSTANT', **kwargs):\\n pad_total = kernel_size - 1\\n pad_beg = pad_total // 2\\n pad_end = pad_total - pad_beg\\n\\n if kwargs['data_format'] == 'NCHW':\\n padded_inputs = tf.pad(inputs, [[0, 0], [0, 0],\\n [pad_beg, pad_end],\\n [pad_beg, pad_end]],\\n mode=mode)\\n else:\\n padded_inputs = tf.pad(inputs, [[0, 0], [pad_beg, pad_end],\\n [pad_beg, pad_end], [0, 0]], mode=mode)\\n return padded_inputs\",\n \"def fixed_padding(inputs, kernel_size, data_format):\\n pad_total = kernel_size - 1\\n pad_beg = pad_total // 2\\n pad_end = pad_total - pad_beg\\n\\n if data_format == 'channels_first':\\n padded_inputs = tf.pad(inputs, [[0, 0], [0, 0], [pad_beg, pad_end], [pad_beg, pad_end]])\\n else:\\n padded_inputs = tf.pad(inputs, [[0, 0], [pad_beg, pad_end], [pad_beg, pad_end], [0, 0]])\\n\\n return padded_inputs\",\n \"def thinning_morph(image, kernel):\\n thining_image = np.zeros_like(image)\\n img = image.copy()\\n\\n while 1:\\n erosion = cv.erode(img, kernel, iterations = 1)\\n dilatate = cv.dilate(erosion, kernel, iterations = 1)\\n\\n subs_img = np.subtract(img, dilatate)\\n cv.bitwise_or(thining_image, subs_img, thining_image)\\n img = erosion.copy()\\n\\n done = (np.sum(img) == 0)\\n\\n if done:\\n break\\n\\n # shift down and compare one pixel offset\\n down = np.zeros_like(thining_image)\\n down[1:-1, :] = thining_image[0:-2, ]\\n down_mask = np.subtract(down, thining_image)\\n down_mask[0:-2, :] = down_mask[1:-1, ]\\n cv.imshow('down', down_mask)\\n\\n # shift right and compare one pixel offset\\n left = np.zeros_like(thining_image)\\n left[:, 1:-1] = thining_image[:, 0:-2]\\n left_mask = np.subtract(left, thining_image)\\n left_mask[:, 0:-2] = left_mask[:, 1:-1]\\n cv.imshow('left', left_mask)\\n\\n # combine left and down mask\\n cv.bitwise_or(down_mask, down_mask, thining_image)\\n output = np.zeros_like(thining_image)\\n output[thining_image < 250] = 255\\n\\n return output\",\n \"def hook(module, input):\\n image_dimensions = input[0].size()[-2:]\\n module.padding = _determine_inverse_padding_from_tf_same(image_dimensions, kernel_size, stride)\",\n \"def pad_out(self, out, index):\\n if self.kernel_sizes[index] % 2 == 0:\\n # If kernel size is even, add one more padding value on the right.\\n out = F.pad(out, (0, 1), \\\"constant\\\", 0)\\n return out\",\n \"def _fixed_padding(inputs, kernel_size, *args, mode='CONSTANT', **kwargs):\\n pad_total = kernel_size - 1\\n pad_beg = pad_total // 2\\n pad_end = pad_total - pad_beg\\n\\n padded_inputs = tf.pad(inputs, [[0, 0], [pad_beg, pad_end],\\n [pad_beg, pad_end], [0, 0]], mode=mode)\\n return padded_inputs\",\n \"def fixed_padding(inputs, kernel_size, data_format):\\n pad_total = kernel_size - 1\\n pad_beg = pad_total // 2\\n pad_end = pad_total - pad_beg\\n\\n if data_format == 'channels_first':\\n padded_inputs = tf.pad(tensor=inputs,\\n paddings=[[0, 0], [0, 0], [pad_beg, pad_end],\\n [pad_beg, pad_end]])\\n else:\\n padded_inputs = tf.pad(tensor=inputs,\\n paddings=[[0, 0], [pad_beg, pad_end],\\n [pad_beg, pad_end], [0, 0]])\\n return padded_inputs\",\n \"def pad_same(width, kernel, slide):\\n res = (width - kernel) / slide + 1\\n pad = (width - res) / 2\\n return pad\",\n \"def fixed_padding(inputs, kernel_size, data_format='channels_first'):\\n pad_total = kernel_size - 1\\n pad_beg = pad_total // 2\\n pad_end = pad_total - pad_beg\\n if data_format == 'channels_first':\\n padded_inputs = tf.pad(inputs, [[0, 0], [0, 0],\\n [pad_beg, pad_end], [pad_beg, pad_end]])\\n else:\\n padded_inputs = tf.pad(inputs, [[0, 0], [pad_beg, pad_end],\\n [pad_beg, pad_end], [0, 0]])\\n\\n return padded_inputs\",\n \"def _create_margin_shape(self):\\n\\n a, b = gc((self.size/2 - self.margin),\\n self._ZERO_DEGREES - self.angle,\\n self._180_DEGREES + self.angle)\\n self.margin_points = zip(a, b)\\n self.margin_polygon = gdspy.Polygon(self.margin_points, self.MARGIN_LAYER)\\n self.cell.add(self.margin_polygon)\",\n \"def extend_mask_nonlocal(mask,kernel=np.ones((3,3))):\\n\\tassert (mask.dtype is np.dtype(np.bool)), \\\"input mask must be of bool type\\\"\\n\\n\\text_mask = mask.copy()\\n\\tinp_ind = masked_indices(mask)\\n\\tim_h, im_w = mask.shape\\n\\n\\tker_y, ker_x = kernel.shape\\n\\tassert(ker_x%2>0), \\\"kernel must have odd dimensions\\\"\\n\\tassert(ker_y%2>0), \\\"kernel must have odd dimensions\\\"\\n\\n\\t# indices of the nonzero kernel elements\\n\\tker_ind_y, ker_ind_x = np.nonzero(kernel)\\n\\tker_ind_x -= ker_x//2\\n\\tker_ind_y -= ker_y//2\\n\\n\\tfor ind_x, ind_y in zip(inp_ind%im_w, inp_ind//im_w):\\n\\t\\tfor i, j in zip(ker_ind_x, ker_ind_y):\\n\\t\\t\\text_mask[min(max(0,ind_y+j),im_h-1),min(max(0,ind_x+i),im_w-1)] = True\\n\\n\\treturn ext_mask\",\n \"def fixed_padding(inputs, kernel_size, rate=1):\\n kernel_size_effective = kernel_size + (kernel_size - 1) * (rate - 1)\\n pad_total = kernel_size_effective - 1\\n pad_beg = pad_total // 2\\n pad_end = pad_total - pad_beg\\n padded_inputs = tf.pad(inputs, [[0, 0], [pad_beg, pad_end],\\n [pad_beg, pad_end], [0, 0]])\\n return padded_inputs\",\n \"def padding(image, padded_size):\\n image_row, image_col = image.shape #asigna alto y ancho de la imagen \\n\\n padded_image = np.zeros((image_row + padded_size*2, image_col + padded_size*2)) #matriz de imagen con padding en zeros\\n print(\\\"Padded image zeros:\\\")\\n print(padded_image)\\n\\n padded_image[padded_size:padded_size + image_row, padded_size:padded_size + image_col] = image #matriz de imagen con padding\\n print(\\\"Padded image:\\\")\\n print(padded_image)\\n\\n \\n return padded_image\",\n \"def convolution(image, kernel, scale=None, offset=0):\\n kernel = np.array(kernel).flatten().tolist()\\n if len(kernel)==9:\\n size = (3,3)\\n elif len(kernel)==25:\\n size = (5,5)\\n else:\\n raise ValueError('Kernel size must be (3,3) or (5,5).')\\n return image.filter(ImageFilter.Kernel(size, kernel, scale, offset))\",\n \"def fixed_padding(inputs, kernel_size, data_format):\\n pad_total = kernel_size - 1\\n pad_beg = pad_total // 2\\n pad_end = pad_total - pad_beg\\n\\n if data_format == 'channels_first':\\n padded_inputs = tf.pad(inputs, [[0, 0], [0, 0],\\n [pad_beg, pad_end], [pad_beg, pad_end]])\\n else:\\n padded_inputs = tf.pad(inputs, [[0, 0], [pad_beg, pad_end],\\n [pad_beg, pad_end], [0, 0]])\\n return padded_inputs\",\n \"def fixed_padding(inputs, kernel_size, data_format):\\n pad_total = kernel_size - 1\\n pad_beg = pad_total // 2\\n pad_end = pad_total - pad_beg\\n\\n if data_format == 'channels_first':\\n padded_inputs = tf.pad(inputs, [[0, 0], [0, 0],\\n [pad_beg, pad_end], [pad_beg, pad_end]])\\n else:\\n padded_inputs = tf.pad(inputs, [[0, 0], [pad_beg, pad_end],\\n [pad_beg, pad_end], [0, 0]])\\n return padded_inputs\",\n \"def erode(img, kernel_h, kernel_w):\\n # kernel_w and kernel_h chosen to get 4 clear markers in first frame to set initial limb coordinates.\\n \\\"\\\"\\\" if majority of kernel is blue, else erase\\\"\\\"\\\"\\n y = 0\\n print(frame_height)\\n while y < frame_height - kernel_h:\\n x = 0\\n while x < frame_width - kernel_w:\\n # where Red exist\\n kernel = img[y:y+kernel_h, x:x+kernel_w, 2] > 0 + numpy.zeros((kernel_h, kernel_w))\\n\\n if numpy.all(kernel == 0):\\n pass\\n elif numpy.sum(kernel) < (kernel_w*kernel_h)*0.85:\\n img[y:y+kernel_h, x:x+kernel_w, 1:3] = 0\\n else:\\n img[y:y+kernel_h, x:x+kernel_w, 2] = 255\\n\\n x += kernel_w\\n y += kernel_h\",\n \"def make_inference_attention_mask_3d(source_block, target_block, pad_id):\\n # mask = (target_block[:, None, :] != pad_id) * (source_block[:, :, None] != pad_id)\\n return make_attention_mask_3d(source_block != pad_id, target_block != pad_id)\",\n \"def conv2D(inImage: np.ndarray, kernel2: np.ndarray) -> np.ndarray:\\r\\n flip_kernel = np.flipud(np.fliplr(kernel2))\\r\\n kernel_row = flip_kernel.shape[0]\\r\\n kernel_col = flip_kernel.shape[1]\\r\\n\\r\\n new_img = np.zeros_like(inImage)\\r\\n padded_img = padded_replicate(inImage, kernel_row, kernel_col)\\r\\n\\r\\n for x in range(inImage.shape[0]):\\r\\n for y in range(inImage.shape[1]):\\r\\n new_img[x, y] = (padded_img[x: x + kernel_col, y: y + kernel_row] * flip_kernel).sum()\\r\\n if flip_kernel.sum() != 0:\\r\\n new_img[x, y] /= flip_kernel.sum()\\r\\n\\r\\n return new_img\",\n \"def __padding(self, image, boxes, height, width):\\n temp = boxes[:, :4].astype(np.int)\\n y1 = np.where(temp[:, 0] < 0)[0]\\n if len(y1) > 0:\\n temp[y1, 0] = 0\\n x1 = np.where(temp[:, 1] < 0)[0]\\n if len(x1) > 0:\\n temp[x1, 0] = 0\\n y2 = np.where(temp[:, 2] > image.shape[0] - 1)[0]\\n if len(y2) > 0:\\n temp[y2, 0] = image.shape[0] - 1\\n x2 = np.where(temp[:, 3] > image.shape[1] - 1)[0]\\n if len(x2) > 0:\\n temp[x2, 0] = image.shape[1] - 1\\n pad_top = np.abs(temp[:, 0] - boxes[:, 0]).astype(np.int)\\n pad_left = np.abs(temp[:, 1] - boxes[:, 1]).astype(np.int)\\n pad_bottom = np.abs(temp[:, 2] - boxes[:, 2]).astype(np.int)\\n pad_right = np.abs(temp[:, 3] - boxes[:, 3]).astype(np.int)\\n input_data = np.empty([boxes.shape[0], 3, height, width], dtype=np.float32)\\n for i in range(boxes.shape[0]):\\n crop_img = image[temp[i, 0]:temp[i, 2] + 1, temp[i, 1]:temp[i, 3] + 1, :]\\n crop_img = cv2.copyMakeBorder(crop_img, pad_top[i], pad_bottom[i], \\\\\\n pad_left[i], pad_right[i], cv2.BORDER_CONSTANT, value=0)\\n if crop_img is None:\\n continue\\n crop_img = cv2.resize(crop_img, (width, height)).astype(np.float32)\\n crop_img[:, :, 0] -= self.mean[0]\\n crop_img[:, :, 1] -= self.mean[1]\\n crop_img[:, :, 2] -= self.mean[2]\\n crop_img *= self.scale_factor\\n crop_img = np.transpose(crop_img, (2, 0, 1))\\n input_data[i] = crop_img.copy()\\n return input_data\",\n \"def _pad(x, depth=4):\\n divisor = np.power(2, depth)\\n remainder = x.shape[0] % divisor\\n\\n # no padding because already of even shape\\n if remainder == 0:\\n return x\\n # add zero rows after 1D feature\\n elif len(x.shape) == 2:\\n return np.pad(x, [(0, divisor - remainder), (0, 0)], \\\"constant\\\")\\n # add zero columns and rows after 2D feature\\n elif len(x.shape) == 3:\\n return np.pad(x, [(0, divisor - remainder), (0, divisor - remainder),\\n (0, 0)], \\\"constant\\\")\",\n \"def fixed_padding(inputs, kernel_size, data_format):\\n pad_total = kernel_size - 1\\n pad_beg = pad_total // 2\\n pad_end = pad_total - pad_beg\\n\\n if is_NHWC(data_format):\\n padded_inputs = tf.pad(inputs, [[0, 0], [pad_beg, pad_end],\\n [pad_beg, pad_end], [0, 0]])\\n else:\\n padded_inputs = tf.pad(inputs, [[0, 0], [0, 0],\\n [pad_beg, pad_end], [pad_beg, pad_end]])\\n return padded_inputs\",\n \"def space_to_depth_fixed_padding(inputs, kernel_size,\\n data_format='channels_last', block_size=2):\\n pad_total = kernel_size - 1\\n pad_beg = (pad_total // 2 + 1) // block_size\\n pad_end = (pad_total // 2) // block_size\\n return _padding(inputs, (pad_beg, pad_end), data_format)\",\n \"def create_padding_mask(seq):\\r\\n seq = tf.cast(tf.math.equal(seq, 0), tf.float32)\\r\\n return seq[:, tf.newaxis, tf.newaxis, :] # (batch_size, 1, 1, seq_len)\\r\",\n \"def img_conv_2D(img,kernel,stride=1,pad_type='None'):\\n\\n m,n = img.shape\\n r,c = kernel.shape\\n\\n pad_m = m * (stride-1) + r -1\\n pad_n = n * (stride-1) + r -1\\n\\n img_pad = np.zeros((m+pad_m,n+pad_n),dtype=float)\\n img_pad[:m,:n] = np.copy(img)\\n\\n if pad_type == 'zero_pad':\\n return conv_2D(img_pad,kernel,stride) # define\\n\\n elif pad_type == 'replicate_pad':\\n\\n for i in range(m,m+pad_m):\\n img_pad[i,:] = np.copy(img_pad[m-1,:])\\n\\n for j in range(n,n+pad_n):\\n img_pad[:,j] = np.copy(img_pad[:,n-1])\\n\\n return conv_2D(img_pad,kernel,stride) # define\\n\\n elif pad_type == 'wrap_pad':\\n\\n for i in range(m,m+pad_m):\\n img_pad[i,:] = np.copy(img_pad[i-m,:])\\n\\n for j in range(n,n+pad_n):\\n img_pad[:,j] = np.copy(img_pad[:,j-n])\\n\\n return conv_2D(img_pad,kernel,stride) # define\\n\\n else:\\n\\n return conv_2D(np.copy(img),kernel,stride) # define\",\n \"def conv2mat(im_shape,kernel,format=\\\"channels_first\\\",dtype=_dtype):\\n\\tif len(im_shape)==2:\\n\\t\\tim_size_y, im_size_x = im_shape\\n\\t\\tnum_channels = 1\\n\\telse:\\n\\t\\tif format==\\\"channels_last\\\":\\n\\t\\t\\tim_size_y, im_size_x, num_channels = im_shape\\n\\t\\telse:\\n\\t\\t\\tnum_channels, im_size_y, im_size_x = im_shape\\n\\n\\tker_size_y, ker_size_x = kernel.shape\\n\\tker_size = kernel.size\\n\\tkernel = kernel.ravel()\\n\\n\\tmat_size_x = im_size_x * im_size_y\\n\\tmat = sp.dia_matrix( (mat_size_x,mat_size_x), dtype=dtype )\\n\\n\\t# diagonal blocks corresponding to the rows of the kernel\\n\\tfor j in range(-(ker_size_y//2),ker_size_y//2+1):\\n\\t\\t# diagonal of the block corresponding to the given row of the kernel\\n\\t\\t# correlation\\n\\t\\tdiag = sp.eye(im_size_y,im_size_y,-j,dtype=dtype)\\n\\t\\t# # convolution\\n\\t\\t# diag = sp.eye(im_size_y,im_size_y,-j,dtype=dtype)\\n\\n\\t\\t# contribution from convolution matrix corresponding to the given row of the kernel\\n\\t\\tmat += sp.kron( diag, convmat(im_size_x,kernel[ker_size//2-ker_size_x//2-j*ker_size_x:ker_size//2-ker_size_x//2-(j-1)*ker_size_x],dtype=dtype) ) #.astype(dtype=dtype)\\n\\n\\tif num_channels>1:\\n\\t\\tif format==\\\"channels_last\\\":\\n\\t\\t\\tdiag_ch = sp.eye(num_channels,num_channels,dtype=dtype)\\n\\t\\t\\treturn sp.kron(mat,diag_ch) #.tocsr()\\n\\t\\telse:\\n\\t\\t\\tdiag_ch = sp.eye(num_channels,num_channels,dtype=dtype)\\n\\t\\t\\treturn sp.kron(diag_ch,mat) #.tocsr()\\n\\n\\treturn mat #.tocsr()\",\n \"def pad_with_filter(cls, position, filter_size, filter_number, pad_size, rect_size, funnel_width, funnel_end):\\n \\n position = np.array(position)\\n filter_size = np.array(filter_size)\\n filter_number = np.array(filter_number)\\n \\n #with of rectangle between half-circle and filters\\n init_square = rect_size\\n\\n #lower-left position of the first \\\"filter-channel\\n init_pos = np.array([position[0]-np.sum(filter_size*filter_number*2)-funnel_width,position[1]-pad_size/2])\\n\\n #pattern contains all coordinates composing the filter\\n #first define the funnel part connecting to a channel\\n pattern = [np.array([[position[0]-funnel_width,position[1]-pad_size/2],[position[0]-funnel_width,position[1]+pad_size/2],\\n [position[0],position[1]+funnel_end/2],[position[0],position[1]-funnel_end/2]])]\\n\\n #define rectangular part between half-circle and filter\\n pattern.append(np.array([[init_pos[0]-init_square,init_pos[1]],[init_pos[0],init_pos[1]],\\n [init_pos[0],init_pos[1]+pad_size],[init_pos[0]-init_square,init_pos[1]+pad_size]]))\\n\\n #define the half-circle punching pad\\n half_circle = np.array([[init_pos[0]-init_square+pad_size/2*np.cos(x),position[1]+pad_size/2*np.sin(x)] \\n for x in np.linspace(np.pi/2,np.pi*3/2)])\\n pattern.append(half_circle)\\n\\n #define filter region composed of vertical channels and horizontal squares\\n for i in range(len(filter_size)):\\n for f in range(filter_number[i]):\\n\\n shift = np.mod(f,2)*filter_size[i]\\n sq_coord = [[init_pos[0]+filter_size[i],init_pos[1]],[init_pos[0]+2*filter_size[i],init_pos[1]],\\n [init_pos[0]+2*filter_size[i],init_pos[1]+pad_size],[init_pos[0]+filter_size[i],init_pos[1]+pad_size]]\\n pattern.append(np.array(sq_coord))\\n\\n for j in range(int(np.round(pad_size/filter_size[i]/2))):\\n\\n sq_coord = [[init_pos[0],init_pos[1]+shift],[init_pos[0]+filter_size[i],init_pos[1]+shift],\\n [init_pos[0]+filter_size[i],init_pos[1]+filter_size[i]+shift],[init_pos[0],init_pos[1]+filter_size[i]+shift]]\\n pattern.append(np.array(sq_coord))\\n init_pos = init_pos+np.array([0,2*filter_size[i]])\\n init_pos[0]=init_pos[0]+2*filter_size[i]\\n init_pos[1]=position[1]-pad_size/2\\n \\n filter_pad_obj = cls()\\n filter_pad_obj.coord = pattern\\n \\n return filter_pad_obj\",\n \"def _fixed_padding(inputs, kernel_size, rate=1):\\n kernel_size_effective = [kernel_size[0] + (kernel_size[0] - 1) * (rate - 1),\\n kernel_size[0] + (kernel_size[0] - 1) * (rate - 1)]\\n pad_total = [kernel_size_effective[0] - 1, kernel_size_effective[1] - 1]\\n pad_beg = [pad_total[0] // 2, pad_total[1] // 2]\\n pad_end = [pad_total[0] - pad_beg[0], pad_total[1] - pad_beg[1]]\\n padded_inputs = tf.pad(inputs, [[0, 0], [pad_beg[0], pad_end[0]],\\n [pad_beg[1], pad_end[1]], [0, 0]])\\n return padded_inputs\",\n \"def pad_img(image, label):\\n paddings = [[2,2],[2,2],[0,0]]\\n return tf.pad(image, paddings, mode=\\\"CONSTANT\\\", constant_values=0.0), label\",\n \"def fold(input_f, input_shape, kernel_size=(3,3), stride=(1,1), padding=(0,0)):\\n batch_size, in_chan, in_height, in_width = input_shape\\n \\n height_padded, width_padded = in_height + 2*padding[0], in_width + 2*padding[1]\\n input_padded = np.zeros((batch_size, in_chan, height_padded, width_padded), dtype=input_f.dtype)\\n \\n k, i, j = get_indices(input_shape, kernel_size, stride, padding)\\n \\n input = input_f.reshape(in_chan*kernel_size[0]*kernel_size[1], -1, batch_size)\\n input = input.transpose((2, 0, 1))\\n np.add.at(input_padded, (slice(None), k, i, j), input)\\n if padding == (0,0):\\n return input_padded\\n return input_padded[:, :, padding[0]: -padding[0], padding[1]: -padding[1]]\",\n \"def blur_mask(mask, blur_kernel, threshold=0.1):\\n k = pyfits.getdata(blur_kernel)\\n k = k / k.sum()\\n mask = hconvolve.hconvolve(mask, k)\\n mask = np.where(mask >= threshold, 1, 0).astype('int')\\n return mask\",\n \"def filter2D(img, kernel = (5,5)):\\n\\ttmp = img.copy()\\n\\tk = np.ones((kernel[0], kernel[1]), np.float32) / (kernel[0]*kernel[1])\\n\\tdst = cv2.filter2D(tmp, -1, k)\\n\\treturn dst\",\n \"def pad_edges(self, pad):\\n weights=[]\\n for dim, xy in zip([0, 1], [self.x, self.y]):\\n xy0 = np.mean(xy)\\n W = xy[-1]-xy[0]\\n dist = np.abs(xy-xy0)\\n wt=np.ones_like(dist)\\n wt[ dist >= W/2 - pad] = 0\\n weights += [wt]\\n self.weight *= weights[0][:,None].dot(weights[1][None,:])\",\n \"def with_kernel(self, kernel, kernel_format):\\n\\t\\tself.variables['kernel_format'] = kernel_format\\n\\t\\tself.with_weights(kernel)\\n\\t\\treturn self\",\n \"def createkSpaceMask (im_size, num_half_lines):\\n \\n if im_size[0] < 32 or im_size[1] < 32:\\n sys.exit(\\\"side < 32\\\")\\n\\n if num_half_lines < 20:\\n sys.exit(\\\"numOfLines < 10\\\")\\n \\n center = np.floor((im_size + 2) / 2)\\n half_diagonal = np.linalg.norm(im_size) / 2\\n step_length = 0.5\\n num_steps = int(np.round(half_diagonal / step_length + 1))\\n sampling_mask = np.zeros(im_size, float)\\n\\n for lineNum in range(num_half_lines):\\n theta = 2 * np.pi * lineNum / num_half_lines\\n direction = np.array([np.cos(theta), np.sin(theta)])\\n for stepNum in range(num_steps):\\n location = np.round(center + direction * stepNum * step_length).astype(int)\\n if (location[0] >= 0) and (location[0] < im_size[0]) and (location[1] >= 0) and (location[1] < im_size[1]):\\n sampling_mask[location[0], location[1]] = 1\\n\\n # take the center of kspace to the corners\\n sampling_mask = np.fft.fftshift(sampling_mask)\\n\\n return sampling_mask\",\n \"def change_margin(self, margin):\\n self.margin = margin * self._MM_IN_MICRONS\\n\\n self._create_drawing_area()\\n self.partition(self.rows, self.cols)\",\n \"def fixBorder(frame):\\n s = frame.shape\\n # Scale the image 2% without moving the center\\n T = cv2.getRotationMatrix2D((s[1]/2, s[0]/2), 0, 1.02)\\n frame = cv2.warpAffine(frame, T, (s[1], s[0]))\\n return frame\",\n \"def correctFrameShift(stack):\\n\\n maxHeight=0\\n frameList=[]\\n midStripsFrame = np.empty((stack.shape[1],stack.shape[0])) #needs to have the number of columns as the depth of the stack!\\n\\n #I think this should be powerfule enough that I don't need to worry about onh for the frame adjustments\\n midStripsFrame = np.mean(stack, axis=2).T\\n\\n shifts = shiftDetector(midStripsFrame)\\n minShift = min(shifts)\\n allPositiveShifts = [shift-minShift for shift in shifts]\\n \\n shiftedFrames = []\\n i=0\\n # writeText('\\\\n')\\n for shift, frame in zip(allPositiveShifts, stack):\\n newFrame = np.lib.pad(frame, ((0,shift),(0,0)),'constant',constant_values=0)\\n shiftedFrames.append(newFrame)\\n if newFrame.shape[0] > maxHeight:\\n maxHeight = newFrame.shape[0]\\n print('\\\\rCorrecting Frame Shifts/Vertical Curvature: {:.2f} % done'.format((100.0*((i+1)/len(stack)))), end='', flush=True)\\n i+=1\\n print('\\\\n')\\n \\n shiftedStack = padFrames(shiftedFrames, maxHeight)\\n \\n return shiftedStack\",\n \"def run_padding(self):\\n\\n image_padded, mask, self.pad_to_right, self.pad_to_bottom = gen_padded_image_and_mask (os.path.join('utils_dfn/temp', self.file_name_with_ext),\\n self.new_height, self.new_width)\\n cv2.imwrite(os.path.join('utils_dfn/img', self.file_name + '_padded_resized.png'), image_padded)\\n cv2.imwrite(os.path.join('utils_dfn/mask', self.file_name + '_mask.png'), mask)\",\n \"def convolve_im(im: np.array,\\n kernel: np.array,\\n verbose=True):\\n\\t\\n ### START YOUR CODE HERE ### (You can change anything inside this block) \\n\\t\\n H,W = np.shape(im)\\n h,w = np.shape(kernel)\\n t_b = (H-h)//2\\n l_r = (W-w)//2\\n kernel_padded = np.pad(kernel, ((t_b, t_b+1),(l_r, l_r+1)), 'constant')\\n kernel_padded = np.pad(kernel, ((0, 2*t_b),(0, 2*l_r)), 'constant')\\n fft_kernel = np.fft.fft2(kernel_padded, s=None, axes=(-2, -1), norm=None)\\n \\n \\n im_fft = np.fft.fft2(im, s=None, axes=(-2, -1), norm=None) \\n im_filt = im_fft*fft_kernel \\n conv_result = np.fft.ifft2(im_filt, s=None, axes=(-2, -1), norm=None).real \\n\\n if verbose:\\n # Use plt.subplot to place two or more images beside eachother\\n plt.figure(figsize=(12, 4))\\n # plt.subplot(num_rows, num_cols, position (1-indexed))\\n plt.subplot(1, 2, 1)\\n plt.imshow(im, cmap=\\\"gray\\\")\\n plt.subplot(1, 2, 2) \\n plt.imshow(conv_result, cmap=\\\"gray\\\")\\n\\n ### END YOUR CODE HERE ###\\n return conv_result\",\n \"def _margin_loss(self,\\n logits,\\n labels,\\n m_plus=0.9,\\n m_minus=0.1,\\n lambda_=0.5):\\n logits_shape = logits.get_shape()\\n num_caps = logits_shape[1]\\n\\n max_square_plus = tf.square(tf.maximum(\\n 0., m_plus - utils.get_vec_length(\\n logits, self.batch_size, self.cfg.EPSILON)))\\n max_square_minus = tf.square(tf.maximum(\\n 0., utils.get_vec_length(\\n logits, self.batch_size, self.cfg.EPSILON) - m_minus))\\n # max_square_plus & max_plus shape: (batch_size, num_caps)\\n assert max_square_plus.get_shape() == (self.batch_size, num_caps)\\n\\n loss_c = tf.multiply(labels, max_square_plus) + \\\\\\n lambda_ * tf.multiply((1 - labels), max_square_minus)\\n\\n # Total margin loss\\n margin_loss = tf.reduce_mean(tf.reduce_sum(loss_c, axis=1))\\n\\n return margin_loss\",\n \"def add_padding(img, x_padding):\\n w = img.shape[1] + x_padding * 2\\n img_with_padding = np.zeros((img.shape[0], w, 3), dtype=img.dtype)\\n img_with_padding[:, x_padding:img.shape[1] + x_padding] = img\\n return img_with_padding\",\n \"def _do_adaptive_padding(self, im):\\n im_sz = list(im.shape)\\n dim = len(im_sz)\\n dim_to_pad = [dim_sz%self.adaptive_padding!=0 and dim_sz>3 for dim_sz in im_sz]\\n dim_rem = [dim_sz//self.adaptive_padding for dim_sz in im_sz]\\n new_dim_sz = [(dim_rem[i]+1)*self.adaptive_padding if dim_to_pad[i] else im_sz[i] for i in range(dim)]\\n before_id = [(new_dim_sz[i] -im_sz[i]+1)//2 for i in range(dim)]\\n after_id = [new_dim_sz[i] - im_sz[i] - before_id[i] for i in range(dim)]\\n padding_loc = tuple([(before_id[i],after_id[i]) for i in range(dim)])\\n new_img = np.lib.pad(im, padding_loc, 'edge')\\n return new_img\",\n \"def conv_nested(image, kernel):\\n Hi, Wi = image.shape\\n Hk, Wk = kernel.shape\\n out = np.zeros((Hi, Wi))\\n\\n H = Hk // 2\\n W = Wk // 2\\n for i1 in range(Hi):\\n for j1 in range(Wi):\\n for i2 in range(Hk):\\n for j2 in range(Wk):\\n i = i2 - H\\n j = j2 - W\\n if i1-i<0 or j1-j<0 or i1-i>=Hi or j1-j>=Wi:\\n continue\\n out[i1, j1] += kernel[i2, j2]*image[i1-i, j1-j]\\n return out\",\n \"def pad_to_width(kernel, width):\\n current_width = len(kernel)\\n delta = (width - current_width) / 2\\n\\n pad0 = tf.cast(delta, tf.int32)\\n pad1 = tf.cast(tf.math.ceil(delta), tf.int32)\\n\\n paddings = [[pad0, pad1], [pad0, pad1], [0, 0], [0, 0]]\\n\\n kernel = tf.pad(kernel, paddings)\\n return kernel\",\n \"def _pad_traces(self, X):\\n # Padding is done using using keras.preprocessing.sequence.pad_sequences(sequences, ...)\\n # maxlen: Int, maximum length of all sequences.\\n # truncating='pre' remove values at the beginning from sequences larger than maxlen\\n # padding='pre' pads each trace at the beginning with a special integer (e.g., 0)\\n X = pad_sequences(X, maxlen=self.trace_size, dtype=np.int16,\\n truncating='pre', padding='pre', value=DEFAULTS['padding_symbol'])\\n\\n print(X)\\n # Shape X is (n_traces, self.trace_size)\\n # e.g., (25002, 20)\\n\\n return X\",\n \"def crop_kernel(kernel, target_size):\\n # Use list for mutability\\n target = [d for d in target_size]\\n adjusted = False\\n for dim in range(len(target)):\\n if not target[dim] % 2:\\n target[dim] += 1\\n adjusted = True\\n if adjusted:\\n sys.stderr.write(\\n \\\"WARNING: Cropped kernel size adjusted to \\\"\\n f\\\"{target[0]}x{target[1]} to keep odd dimensions.\\\\n\\\"\\n )\\n\\n source_m, source_n = kernel.shape\\n target_m, target_n = target\\n # Define horizontal and vertical margins to trim\\n if source_m > target_m:\\n margin_rows = (source_m - target_m) // 2\\n else:\\n margin_rows = 0\\n if source_n > target_n:\\n margin_cols = (source_n - target_n) // 2\\n else:\\n margin_cols = 0\\n\\n cropped = kernel[\\n margin_rows : (source_m - margin_rows),\\n margin_cols : (source_n - margin_cols),\\n ]\\n\\n return cropped\",\n \"def segment_cells(frame, mask=None):\\n \\n blurred = filters.gaussian(frame, 2)\\n ridges = enhance_ridges(frame)\\n \\n # threshold ridge image\\n thresh = filters.threshold_otsu(ridges)\\n thresh_factor = 0.5\\n prominent_ridges = ridges > thresh_factor*thresh\\n prominent_ridges = morphology.remove_small_objects(prominent_ridges, min_size=256)\\n prominent_ridges = morphology.binary_closing(prominent_ridges)\\n prominent_ridges = morphology.binary_dilation(prominent_ridges)\\n \\n # skeletonize\\n ridge_skeleton = morphology.medial_axis(prominent_ridges)\\n ridge_skeleton = morphology.binary_dilation(ridge_skeleton)\\n ridge_skeleton *= mask\\n ridge_skeleton = np.bitwise_xor(ridge_skeleton, mask)\\n \\n # label\\n cell_label_im = measure.label(ridge_skeleton)\\n \\n # morphological closing to fill in the cracks\\n for cell_num in range(1, cell_label_im.max()+1):\\n cell_mask = cell_label_im==cell_num\\n cell_mask = morphology.binary_closing(cell_mask, disk(3))\\n cell_label_im[cell_mask] = cell_num\\n \\n return cell_label_im\",\n \"def clip_filters(W, threshold=0.5, pad=3):\\n W_clipped = []\\n for w in W:\\n L, A = w.shape\\n entropy = np.log2(4) + np.sum(w * np.log2(w + 1e-7), axis=1)\\n index = np.where(entropy > threshold)[0]\\n if index.any():\\n start = np.maximum(np.min(index) - pad, 0)\\n end = np.minimum(np.max(index) + pad + 1, L)\\n W_clipped.append(w[start:end, :])\\n else:\\n W_clipped.append(w)\\n\\n return W_clipped\",\n \"def wrap_pad(input, size):\\n M1 = tf.concat([input[:, :, -size[1]:, :], input, input[:, :, 0:size[1], :]], 2)\\n M1 = tf.concat([M1[:, -size[0]:, :, :], M1, M1[:, 0:size[0], :, :]], 1)\\n return M1\",\n \"def test_2D_padding(self):\\n field_names = [\\\"text\\\"]\\n mask_field_names = ['text_mask']\\n data_path = \\\"mldp/tests/data/news.csv\\\"\\n pad_symbol = \\\"<PAD>\\\"\\n mask_field_name_suffix = \\\"mask\\\"\\n padding_modes = ['left', 'right', 'both']\\n symbols_to_mask = [\\\"The\\\", \\\"a\\\", \\\"to\\\", \\\"as\\\"]\\n axis = 1\\n\\n data_chunk = read_data_from_csv_file(data_path, sep=\\\"\\\\t\\\")\\n\\n # tokenize field values\\n for fn in field_names:\\n data_chunk[fn] = np.array([seq.split() for seq in data_chunk[fn]])\\n\\n for padding_mode, symbol_to_mask in product(padding_modes,\\n symbols_to_mask):\\n padder = Padder(field_names, pad_symbol=pad_symbol,\\n new_mask_fname=mask_field_names,\\n padding_mode=padding_mode, axis=axis,\\n symbol_to_mask=symbol_to_mask)\\n padded_data_chunk = padder(copy.deepcopy(data_chunk))\\n\\n for fn, mask_fn in zip(field_names, mask_field_names):\\n padded_fv = padded_data_chunk[fn]\\n mask = padded_data_chunk[mask_fn]\\n original_fv = data_chunk[fn]\\n\\n self.assertTrue(len(padded_fv.shape) == 2)\\n self._test_padded_values(original_field_values=original_fv,\\n padded_field_values=padded_fv,\\n mask=mask, pad_symbol=pad_symbol,\\n symbol_to_mask=symbol_to_mask)\",\n \"def obj_margin(x0, X, y, alpha, n_class, weights, L, kernel_type, loss_function, sample_weight):\\n\\n w = x0[:X.shape[1]]\\n c = x0[X.shape[1]:]\\n theta = L.dot(c)\\n loss_fd = weights[y]\\n\\n Xw = X.dot(w)\\n Alpha = theta[:, None] - Xw # (n_class - 1, n_samples)\\n S = np.sign(np.arange(n_class - 1)[:, None] - y + 0.5)\\n\\n if(loss_function == 'logistic'):\\n err = loss_fd.T * log_loss(S * Alpha)\\n elif(loss_function == 'hinge'):\\n err = loss_fd.T * hinge_loss(S * Alpha)\\n \\n if sample_weight is not None:\\n err *= sample_weight\\n obj = np.sum(err)\\n \\n ## Regularization based on kernel \\n if(kernel_type == 'linear'):\\n obj += 0.5 * alpha * (np.dot(w, w))\\n else:\\n obj += 0.5 * alpha * (np.dot(w, w)) #np.dot(np.dot(w.T, X).T, w)\\n \\n return obj\",\n \"def cube_fix_badpix_isolated(array, bpm_mask=None, sigma_clip=3, num_neig=5, \\n size=5, frame_by_frame=False, protect_mask=0, \\n cxy=None, mad=False, ignore_nan=True, verbose=True, \\n full_output=False):\\n if array.ndim != 3:\\n raise TypeError('Array is not a 3d array or cube')\\n if size % 2 == 0:\\n raise TypeError('Size of the median blur kernel must be an odd integer')\\n \\n if bpm_mask is not None:\\n bpm_mask = bpm_mask.astype('bool')\\n \\n if verbose: start = time_ini()\\n \\n if num_neig > 0:\\n neigh = True\\n else:\\n neigh = False\\n \\n nz = array.shape[0]\\n \\n if cxy is None:\\n cy, cx = frame_center(array[0])\\n elif isinstance(cxy, tuple):\\n cx, cy = cxy\\n elif isinstance(cxy, np.ndarray):\\n if cxy.shape[0] != nz or cxy.shape[1] != 2 or cxy.ndim != 2:\\n raise ValueError(\\\"cxy does not have right shape\\\")\\n elif not frame_by_frame:\\n msg = \\\"cxy must be a tuple or None if not in frame_by_frame mode\\\"\\n raise ValueError(msg)\\n else:\\n cx = cxy[:,0]\\n cy = cxy[:,1]\\n\\n \\n array_out = array.copy()\\n final_bpm = np.zeros_like(array_out, dtype=bool)\\n n_frames = array.shape[0]\\n count_bp = 0\\n if frame_by_frame:\\n if np.isscalar(cx):\\n cx = [cx]*nz\\n cy = [cy]*nz\\n if bpm_mask is not None:\\n if bpm_mask.ndim == 2:\\n bpm_mask = [bpm_mask]*n_frames\\n bpm_mask = np.array(bpm_mask)\\n for i in Progressbar(range(n_frames), desc=\\\"processing frames\\\"):\\n if bpm_mask is not None:\\n bpm_mask_tmp = bpm_mask[i]\\n else:\\n bpm_mask_tmp = None\\n res = frame_fix_badpix_isolated(array[i], bpm_mask=bpm_mask_tmp, \\n sigma_clip=sigma_clip,\\n num_neig=num_neig, size=size, \\n protect_mask=protect_mask, \\n verbose=False, cxy=(cx[i],cy[i]), \\n ignore_nan=ignore_nan,\\n full_output=True)\\n array_out[i] = res[0]\\n final_bpm[i] = res[1] \\n count_bp = np.sum(final_bpm) \\n else: \\n if bpm_mask is None:\\n ori_nan_mask = np.where(np.isnan(np.nanmean(array, axis=0)))\\n ind = clip_array(np.nanmean(array, axis=0), sigma_clip, sigma_clip,\\n neighbor=neigh, num_neighbor=num_neig, mad=mad)\\n final_bpm = np.zeros_like(array[0], dtype=bool)\\n final_bpm[ind] = 1\\n if ignore_nan:\\n final_bpm[ori_nan_mask] = 0\\n if protect_mask:\\n cir = disk((cy, cx), protect_mask, shape=final_bpm.shape)\\n final_bpm[cir] = 0\\n final_bpm = final_bpm.astype('bool')\\n else:\\n if bpm_mask.ndim == 3:\\n final_bpm = np.median(bpm_mask, axis=0)\\n else:\\n final_bpm = bpm_mask.copy()\\n \\n for i in Progressbar(range(n_frames), desc=\\\"processing frames\\\"):\\n frame = array_out[i]\\n smoothed = median_filter(frame, size, mode='mirror')\\n frame[np.where(final_bpm)] = smoothed[np.where(final_bpm)]\\n if verbose: \\n count_bp += np.sum(final_bpm)\\n \\n if verbose: \\n msg = \\\"Done replacing {:.0f} bad pixels using the median of neighbors\\\"\\n print(msg.format(count_bp))\\n if not frame_by_frame:\\n msg = \\\"(i.e. {:.0f} static bad pixels per channel))\\\"\\n print(msg.format(count_bp/n_frames)) \\n timing(start)\\n \\n if full_output:\\n return array_out, final_bpm\\n else:\\n return array_out\",\n \"def _pad(input_data):\\n h = input_data['img'].shape[0]\\n w = input_data['img'].shape[1]\\n max_size = max([int(np.sqrt(np.power(h, 2) + np.power(w, 2))),\\n int(w + h * np.cos(11 / 36)),\\n int(max(h, w) * 2)\\n ]) + 5\\n\\n up = (max_size - h) // 2\\n down = max_size - up - h\\n left = (max_size - w) // 2\\n right = max_size - left - w\\n\\n input_data['img'] = np.pad(input_data['img'], ((up, down), (left, right), (0, 0)), mode='constant')\\n\\n if DataAugmentor._is_synthtext(input_data):\\n input_data['contour'][0] = list(\\n map(lambda x: np.stack([x[:, :, 0] + up, x[:, :, 1] + left], axis=-1), # x: np.array(n,1,2)\\n input_data['contour'][0]))\\n input_data['contour'][1] = list(\\n map(lambda x: np.stack([x[:, :, 0] + up, x[:, :, 1] + left], axis=-1), # x: np.array(n,1,2)\\n input_data['contour'][1]))\\n else:\\n input_data['contour'] = list(\\n map(lambda x: np.stack([x[:, :, 0] + up, x[:, :, 1] + left], axis=-1), # x: np.array(n,1,2)\\n input_data['contour']))\\n\\n input_data['center_point'] = list(\\n map(lambda x: (x[0] + up, x[1] + left),\\n input_data['center_point']))\\n return input_data\",\n \"def filter_on_border(self, image, labeled_image):\\n return labeled_image\\n \\\"\\\"\\\"\\n if self.exclude_border_objects.value:\\n border_labels = list(labeled_image[0,:])\\n border_labels.extend(labeled_image[:,0])\\n border_labels.extend(labeled_image[labeled_image.shape[0]-1,:])\\n border_labels.extend(labeled_image[:,labeled_image.shape[1]-1])\\n border_labels = np.array(border_labels)\\n #\\n # the following histogram has a value > 0 for any object\\n # with a border pixel\\n #\\n histogram = scipy.sparse.coo_matrix((np.ones(border_labels.shape),\\n (border_labels,\\n np.zeros(border_labels.shape))),\\n shape=(np.max(labeled_image)+1,1)).todense()\\n histogram = np.array(histogram).flatten()\\n if any(histogram[1:] > 0):\\n histogram_image = histogram[labeled_image]\\n labeled_image[histogram_image > 0] = 0\\n elif image.has_mask:\\n # The assumption here is that, if nothing touches the border,\\n # the mask is a large, elliptical mask that tells you where the\\n # well is. That's the way the old Matlab code works and it's duplicated here\\n #\\n # The operation below gets the mask pixels that are on the border of the mask\\n # The erosion turns all pixels touching an edge to zero. The not of this\\n # is the border + formerly masked-out pixels.\\n mask_border = np.logical_not(scipy.ndimage.binary_erosion(image.mask))\\n mask_border = np.logical_and(mask_border,image.mask)\\n border_labels = labeled_image[mask_border]\\n border_labels = border_labels.flatten()\\n histogram = scipy.sparse.coo_matrix((np.ones(border_labels.shape),\\n (border_labels,\\n np.zeros(border_labels.shape))),\\n shape=(np.max(labeled_image)+1,1)).todense()\\n histogram = np.array(histogram).flatten()\\n if any(histogram[1:] > 0):\\n histogram_image = histogram[labeled_image]\\n labeled_image[histogram_image > 0] = 0\\n return labeled_image\\n \\\"\\\"\\\"\",\n \"def plot_filtered_spots(\\n adata, \\n kernel_matrix, \\n contrib_thresh,\\n row_key='row',\\n col_key='col',\\n ax=None,\\n figure=None,\\n dsize=37,\\n ticks=True,\\n fig_path=None,\\n fig_format='pdf',\\n fig_dpi=150\\n ):\\n if ax is None:\\n width = 5\\n figure, ax = plt.subplots(\\n 1,\\n 1,\\n figsize=(width,5)\\n )\\n\\n # Filter spots with too little contribution\\n # from neighbors\\n contrib = np.sum(kernel_matrix, axis=1)\\n keep_inds = [\\n i\\n for i, c in enumerate(contrib)\\n if c >= contrib_thresh\\n ]\\n print('Kept {}/{} spots.'.format(len(keep_inds), len(adata.obs)))\\n\\n cat = []\\n keep_inds = set(keep_inds)\\n for ind in range(adata.obs.shape[0]):\\n if ind in keep_inds:\\n cat.append('Kept')\\n else:\\n cat.append('Filtered')\\n cat_palette = ['#595959', '#d9d9d9']\\n plot_slide(\\n adata.obs,\\n cat,\\n cmap='categorical',\\n colorbar=False,\\n vmin=None,\\n vmax=None,\\n title='Filtered Spots',\\n ax=ax,\\n figure=figure,\\n ticks=ticks,\\n dsize=dsize,\\n row_key=row_key,\\n col_key=col_key,\\n cat_palette=cat_palette\\n )\\n\\n if fig_path:\\n plt.tight_layout()\\n figure.savefig(\\n fig_path,\\n format=fig_format,\\n dpi=fig_dpi\\n )\\n plt.show()\",\n \"def mask(self, byclass, height, width, margin=0, figsize=(10, 10), dpi=180):\\n # make ~binary mask using available classes\\n style = {cls: ('k', '-') for cls in byclass}\\n fig = Figure(figsize=figsize)\\n fig.tight_layout(pad=0)\\n fig.subplots_adjust(hspace=0, wspace=0, left=0, right=1, bottom=0, top=1)\\n canvas = FigureCanvas(fig)\\n ax = fig.subplots(1, 1)\\n self.show_style(ax, style, byclass)\\n ax.set_xlim(0 - margin, height + margin)\\n ax.set_ylim(0 - margin, width + margin)\\n canvas.draw()\\n mask = self.figure_buffer(fig, dpi=dpi)\\n mask = cv2.cvtColor(mask, cv2.COLOR_BGR2GRAY)\\n # fill in the gaps via:\\n # https://www.learnopencv.com/filling-holes-in-an-image-using-opencv-python-c/\\n _, thresholded = cv2.threshold(mask, 220, 255, cv2.THRESH_BINARY_INV);\\n floodfilled = thresholded.copy()\\n h, w = thresholded.shape[:2]\\n mask = np.zeros((h + 2, w + 2), np.uint8)\\n cv2.floodFill(floodfilled, mask, (0, 0), 255);\\n mask = cv2.bitwise_not(thresholded | cv2.bitwise_not(floodfilled))\\n return mask\",\n \"def draw(self, frame, offset=OFS):\\n frame[\\n OFS : OFS + self.image.shape[0], OFS : OFS + self.image.shape[1]\\n ] = self.image\",\n \"def padFrames(frameList, maxHeight):\\n \\n print('\\\\n')\\n for i, frame in enumerate(frameList):\\n extraSpace = maxHeight - frame.shape[0]\\n #frameList[i] = np.lib.pad(frame,((int(np.floor(extraSpace/2)),int(np.ceil(extraSpace/2))),(0,0)),'constant', constant_values=(4000,8000))\\n frameList[i] = np.lib.pad(frame,((extraSpace,0),(0,0)),'constant', constant_values=0)\\n print('\\\\rPadding Frames {:.2f}% done'.format(100.0*((i+1)/len(frameList))),end='', flush=True)\\n stack = np.stack(frameList, axis=0)\\n \\n return stack\",\n \"def reflection_pad(images, filter_size):\\n num = filter_size // 2\\n return tf.pad(images, [[0, 0], [num, num], [num, num], [0, 0]], mode='REFLECT')\",\n \"def conv_fast(image, kernel):\\n Hi, Wi = image.shape\\n Hk, Wk = kernel.shape\\n out = np.zeros((Hi, Wi))\\n\\n k = np.flip(np.flip(kernel, 1), 0)\\n padding_image = zero_pad(image, Hk//2, Wk//2)\\n for i in range(Hi):\\n for j in range(Wi):\\n out[i, j] = np.sum(np.multiply(padding_image[i:i+Hk, j:j+Wk], k))\\n\\n return out\",\n \"def calc_pad_dims_2D(X_shape, out_dim, kernel_shape, stride, dilation=0):\\n if not isinstance(X_shape, tuple):\\n raise ValueError(\\\"`X_shape` must be of type tuple\\\")\\n\\n if not isinstance(out_dim, tuple):\\n raise ValueError(\\\"`out_dim` must be of type tuple\\\")\\n\\n if not isinstance(kernel_shape, tuple):\\n raise ValueError(\\\"`kernel_shape` must be of type tuple\\\")\\n\\n if not isinstance(stride, int):\\n raise ValueError(\\\"`stride` must be of type int\\\")\\n\\n d = dilation\\n fr, fc = kernel_shape\\n out_rows, out_cols = out_dim\\n n_ex, in_rows, in_cols, in_ch = X_shape\\n\\n # update effective filter shape based on dilation factor\\n _fr, _fc = fr * (d + 1) - d, fc * (d + 1) - d\\n\\n pr = int((stride * (out_rows - 1) + _fr - in_rows) / 2)\\n pc = int((stride * (out_cols - 1) + _fc - in_cols) / 2)\\n\\n out_rows1 = int(1 + (in_rows + 2 * pr - _fr) / stride)\\n out_cols1 = int(1 + (in_cols + 2 * pc - _fc) / stride)\\n\\n # add asymmetric padding pixels to right / bottom\\n pr1, pr2 = pr, pr\\n if out_rows1 == out_rows - 1:\\n pr1, pr2 = pr, pr + 1\\n elif out_rows1 != out_rows:\\n raise AssertionError\\n\\n pc1, pc2 = pc, pc\\n if out_cols1 == out_cols - 1:\\n pc1, pc2 = pc, pc + 1\\n elif out_cols1 != out_cols:\\n raise AssertionError\\n\\n if any(np.array([pr1, pr2, pc1, pc2]) < 0):\\n raise ValueError(\\n \\\"Padding cannot be less than 0. Got: {}\\\".format((pr1, pr2, pc1, pc2))\\n )\\n return (pr1, pr2, pc1, pc2)\",\n \"def opening2d(value, kernel, stride=1, padding=\\\"SAME\\\"):\\n strides = [1, stride, stride, 1]\\n rates = [1, 1, 1, 1]\\n out = tf.nn.erosion2d(value, kernel, strides, rates, padding)\\n out = tf.nn.dilation2d(out, kernel, strides, rates, padding)\\n return out\",\n \"def _pad(phi, det):\\r\\n npadx = (det.x - phi.shape[1]) // 2\\r\\n npady = (det.y - phi.shape[1]) // 2\\r\\n return np.pad(phi, ((0, 0), (npadx, npadx), (npady, npady)), mode='constant')\",\n \"def maskLowerLeftBorder(\\n depth_image,\\n background_mask,\\n sigma=1, l_thresh=0, h_thresh=1000,\\n axis_tol=0.1, hough_thresh_ratio=0.4,\\n x_max_thresh=0.1, y_min_thresh=0.75, margin=10):\\n\\n # Find rudimentary edges in the image\\n masked_image = depth_image.copy()\\n masked_image[background_mask] = 0\\n edge_image = feature.canny(\\n masked_image,\\n sigma=sigma,\\n low_threshold=l_thresh,\\n high_threshold=h_thresh\\n )\\n\\n num_rows, num_cols = edge_image.shape\\n\\n x_mid = num_cols / 2\\n y_mid = num_rows / 2\\n x_max = x_max_thresh * num_cols\\n y_min = y_min_thresh * num_rows\\n\\n hough_mask = np.zeros_like(edge_image, dtype=bool)\\n\\n # Detect lines using the Hough transform\\n h, theta, d = transform.hough_line(edge_image)\\n __, angles, dists = transform.hough_line_peaks(\\n h, theta, d,\\n threshold=hough_thresh_ratio * h.max()\\n )\\n\\n # Filter Hough lines and mask the border if appropriate\\n for angle, dist in zip(angles, dists):\\n if geometry.axisAligned(angle, tol=axis_tol, axis='horizontal'):\\n y = geometry.solveLine(angle, dist, x=x_mid)\\n if y > y_min:\\n hough_mask[int(y) - margin:, :] = True\\n elif geometry.axisAligned(angle, tol=axis_tol, axis='vertical'):\\n x = geometry.solveLine(angle, dist, y=y_mid)\\n if x < x_max:\\n hough_mask[:, :int(x) + margin] = True\\n else:\\n continue\\n\\n return hough_mask\",\n \"def build_attention_mask_3d_padding(source_mask, target_mask):\\n mask = make_attention_mask_3d(source_mask, target_mask)\\n # invert mask for Megatron\\n return mask < 0.5\"\n]","isConversational":false},"negative_scores":{"kind":"list like","value":["0.56605875","0.5587884","0.5477105","0.5454366","0.5391526","0.5310862","0.52129424","0.509257","0.50285244","0.5000354","0.49889517","0.49485168","0.49176568","0.48939496","0.48860693","0.4881778","0.4866566","0.48574573","0.4845596","0.48219603","0.47991547","0.4791694","0.4751322","0.47490332","0.46867856","0.46789053","0.46648562","0.46599883","0.46580628","0.46489722","0.46476352","0.4646831","0.4646161","0.4632356","0.4613405","0.4599905","0.45980456","0.459602","0.4590883","0.45799008","0.45688388","0.45664224","0.45625493","0.45617163","0.45594618","0.45538303","0.4545012","0.45436484","0.453739","0.45333752","0.45313996","0.45296314","0.4521521","0.44900054","0.44798493","0.44760737","0.44670543","0.4466911","0.44375837","0.4437284","0.44234306","0.44143528","0.44138062","0.4408258","0.44044694","0.43976733","0.43967488","0.439489","0.43932444","0.43912044","0.43863648","0.43818617","0.43813232","0.43773022","0.43716016","0.43707642","0.43690675","0.43679032","0.4366162","0.4360841","0.43556377","0.43483487","0.43396962","0.43324187","0.43187594","0.43128347","0.43058658","0.4285949","0.42830616","0.4280097","0.42783365","0.4270114","0.42698446","0.42687517","0.42609808","0.4244555","0.42281368","0.42209506","0.42048365","0.42016703"],"string":"[\n \"0.56605875\",\n \"0.5587884\",\n \"0.5477105\",\n \"0.5454366\",\n \"0.5391526\",\n \"0.5310862\",\n \"0.52129424\",\n \"0.509257\",\n \"0.50285244\",\n \"0.5000354\",\n \"0.49889517\",\n \"0.49485168\",\n \"0.49176568\",\n \"0.48939496\",\n \"0.48860693\",\n \"0.4881778\",\n \"0.4866566\",\n \"0.48574573\",\n \"0.4845596\",\n \"0.48219603\",\n \"0.47991547\",\n \"0.4791694\",\n \"0.4751322\",\n \"0.47490332\",\n \"0.46867856\",\n \"0.46789053\",\n \"0.46648562\",\n \"0.46599883\",\n \"0.46580628\",\n \"0.46489722\",\n \"0.46476352\",\n \"0.4646831\",\n \"0.4646161\",\n \"0.4632356\",\n \"0.4613405\",\n \"0.4599905\",\n \"0.45980456\",\n \"0.459602\",\n \"0.4590883\",\n \"0.45799008\",\n \"0.45688388\",\n \"0.45664224\",\n \"0.45625493\",\n \"0.45617163\",\n \"0.45594618\",\n \"0.45538303\",\n \"0.4545012\",\n \"0.45436484\",\n \"0.453739\",\n \"0.45333752\",\n \"0.45313996\",\n \"0.45296314\",\n \"0.4521521\",\n \"0.44900054\",\n \"0.44798493\",\n \"0.44760737\",\n \"0.44670543\",\n \"0.4466911\",\n \"0.44375837\",\n \"0.4437284\",\n \"0.44234306\",\n \"0.44143528\",\n \"0.44138062\",\n \"0.4408258\",\n \"0.44044694\",\n \"0.43976733\",\n \"0.43967488\",\n \"0.439489\",\n \"0.43932444\",\n \"0.43912044\",\n \"0.43863648\",\n \"0.43818617\",\n \"0.43813232\",\n \"0.43773022\",\n \"0.43716016\",\n \"0.43707642\",\n \"0.43690675\",\n \"0.43679032\",\n \"0.4366162\",\n \"0.4360841\",\n \"0.43556377\",\n \"0.43483487\",\n \"0.43396962\",\n \"0.43324187\",\n \"0.43187594\",\n \"0.43128347\",\n \"0.43058658\",\n \"0.4285949\",\n \"0.42830616\",\n \"0.4280097\",\n \"0.42783365\",\n \"0.4270114\",\n \"0.42698446\",\n \"0.42687517\",\n \"0.42609808\",\n \"0.4244555\",\n \"0.42281368\",\n \"0.42209506\",\n \"0.42048365\",\n \"0.42016703\"\n]","isConversational":false},"document_score":{"kind":"string","value":"0.5740741"},"document_rank":{"kind":"string","value":"0"}}},{"rowIdx":22686,"cells":{"query":{"kind":"string","value":"Ensure all elements defined as missing by the mask are set to zero in the signal. If this is not the case, raises an error."},"document":{"kind":"string","value":"def check_missing_mask(signal, mask):\n\n if sp.issparse(mask):\n # Check if there are nonzero values in the signal reported as missing\n # by the mask\n missing_with_signal = np.nonzero(\n abs(signal[mask.nonzero()[0], mask.nonzero()[1]]) > 0\n )[0]\n if len(missing_with_signal) > 0:\n raise ValueError(\n \"There are\",\n len(missing_with_signal),\n \"non-zero elements reported as missing.\",\n )\n else:\n if np.sum(abs(signal[mask > 0])) > 1e-10:\n raise ValueError(\n \"There are\",\n str(np.sum(abs(signal[mask > 0]))),\n \"non-zero elements reported as missing.\",\n )"},"metadata":{"kind":"string","value":"{\n \"objective\": {\n \"self\": [],\n \"paired\": [],\n \"triplet\": [\n [\n \"query\",\n \"document\",\n \"negatives\"\n ]\n ]\n }\n}"},"negatives":{"kind":"list like","value":["def applymask(self,mask):\n self.spec[mask==0]=np.nan","def _check_missing_value_mask(self, missing_mask):\n if not missing_mask.any():\n raise ValueError(\"Input matrix is not missing any values\")\n if missing_mask.all():\n raise ValueError(\"Input matrix must have some non-missing values\")","def zeros_like(self):\n raise NotImplementedError","def reset_mask(self):\n\n self.mask = np.ones(self.dispersion.shape, dtype=bool)","def aerosols(self, mask: Mask):\n raise NotImplementedError(\"Subclass must implement\")","def getMask(self):\r\n mask = np.array(self.array, dtype=np.float32)\r\n mask[mask == 0] = np.nan\r\n return mask","def _nodata_mask(self):\n if self.nodata_value is None:\n return np.ones_like(self.array, dtype=np.bool)\n return self.array != self.nodata_value","def erase_missing(signal, valid_rows, valid_cols, sym_upper=True):\n if sym_upper and sp.issparse(signal):\n if np.any(valid_rows != valid_cols):\n raise ValueError(\n \"Valid rows and columns must be identical with sym_upper=True\"\n )\n if signal.shape[0] != signal.shape[1]:\n raise ValueError(\n \"Input matrix must be square when using sym_upper=True\"\n )\n # Make a boolean mask from good bins\n good_mask = np.isin(range(signal.shape[0]), valid_rows)\n # Set all pixels in a nondetectable bin to 0\n # For faster masking of bins, mask bins using dot product with an\n # identify matrix where bad bins have been masked on the diagonal\n # E.g. if removing the second bin (row and column):\n # 1 0 0 9 6 5 1 0 0 9 0 5\n # 0 0 0 X 6 8 7 X 0 0 0 = 0 0 0\n # 0 0 1 6 7 8 0 0 1 6 0 8\n mask_mat = sp.eye(signal.shape[0])\n mask_mat.data[0][~good_mask] = 0\n erased = mask_mat.dot(signal).dot(mask_mat)\n else:\n # Get a boolean array of missing (1) and valid (0) rows\n missing_rows = valid_to_missing(valid_rows, signal.shape[0])\n missing_cols = valid_to_missing(valid_cols, signal.shape[1])\n erased = signal.copy()\n erased[missing_rows, :] = 0\n erased[:, missing_cols] = 0\n\n return erased","def unmasked_data(self):\n return numpy.ma.filled(self.data.astype(numpy.float_),\n fill_value=numpy.nan)","def zero_mask(self):\n accum = 0\n for i in range(self.data.itemsize):\n accum += (0x55 << (i << 3))\n return accum","def valid_to_missing(valid, size):\n missing = np.ones(size, dtype=bool)\n try:\n missing[valid] = False\n # In case there is no valid index\n except IndexError:\n pass\n missing = np.flatnonzero(missing)\n return missing","def mask(self, mask, logger=logger):\n if self.nodata is not None:\n da_masked = self._obj.where(mask != 0, self.nodata)\n else:\n logger.warning(\"Nodata value missing, skipping mask\")\n da_masked = self._obj\n return da_masked","def test_csum_ignore_nans():\n source = [np.zeros((16,), dtype=float) for _ in range(10)]\n source.append(np.full((16,), fill_value=np.nan))\n summed = csum(source, ignore_nan=True)\n assert np.allclose(summed, np.zeros_like(summed))","def test_check_data_masked_input_data_non_nans(self):\n cube = self.cube.copy()\n cube.data[:, 0, 0] = 1000\n cube.data = np.ma.masked_equal(cube.data, 1000)\n expected_data = np.array(\n [self.percentile_25, self.percentile_50, self.percentile_75]\n )\n expected_data[:, 0, 0] = np.nan\n expected_data = np.ma.masked_invalid(expected_data)\n result = Plugin().process(cube)\n self.assertArrayAlmostEqual(result.data.data, expected_data.data, decimal=5)\n self.assertArrayEqual(result.data.mask, expected_data.mask)","def setMask(self, mask):\n try:\n self.mask = mask\n self.inds = na.nonzero(self.mask.flat)[0]\n #print \"length of self.inds\",len(self.inds)\n #print self.inds\n self.dim = self.mask.shape[::-1]\n #print self.mask.shape\n return True\n except Exception as error:\n print(\"failed in setMask\", error)","def updateEmptiesSet(self):\n self.emptiesSet = []\n for i in self.Range:\n if self.get_cell(i) == 0:\n self.emptiesSet.append(i)","def _zero(array):\n a = array.ravel()\n for i in numba.prange(len(a)):\n a[i] = 0\n return True","def zero_by_mask(mask, vals, replace_with=0.0):\n assert mask.dtype == tf.as_dtype(np.bool)\n ms = mask.get_shape().as_list()\n vs = vals.get_shape().as_list()\n mask = tf.ensure_shape(mask, vs[:-1] + [1])\n vals = tf.ensure_shape(vals, ms[:-1] + [vs[-1]])\n vals = tf.where_v2(mask, vals, replace_with)\n return vals","def test_offcenter(self):\n actual = cm.ring_mask((5, 5), 1, 2, center=(2, 3))\n expected = np.array([[False, False, False, True, False],\n [False, False, True, False, True],\n [False, True, False, False, False],\n [False, False, True, False, True],\n [False, False, False, True, False]])\n self.assertIsNone(np.testing.assert_array_equal(actual, expected))","def test_get_mean_mask():\n arr = np.array([-5, 4, 0, 3, -2, 7, 10, -10, 5, 6])\n m_arr = np.ma.masked_where(arr < 0, arr)\n assert_allclose(iqcalc.get_mean(m_arr), 5.0)","def shrink_mask(self):\n m = self._mask\n if m.ndim and not m.any():\n self._mask = nomask\n return self","def _initalize_mask(dataSubStack):\n # Initalize an array to store the output mask values\n outMask = np.zeros(dataSubStack.shape, dtype=bool)\n\n # Start by masking out NaNs or Infs\n NaNsOrInfs = np.logical_not(np.isfinite(dataSubStack.data))\n dataSubStack.mask = NaNsOrInfs\n\n return outMask, dataSubStack","def clean_mask(image, sig=5, iters=3):\n\n mean, median, stddev = sigma_clipped_stats(image[image>0], sigma=sig, iters=iters)\n\n mask_bad = (np.abs(image - median) > sig * stddev) | (image == 0)\n image_ret = np.copy(image)\n image_ret[mask_bad] = 0\n \n return image_ret, mask_bad","def _mask(self):\n if self.__mask is None:\n # need this to be *exactly* the numpy boolean False\n return nomask\n return self.__mask","def getSignedMask(self):\r\n signedMask = np.array(self.array * self.sign, dtype=np.float32)\r\n signedMask[signedMask == 0] = np.nan\r\n return signedMask","def mask_check(mask):\n if isinstance(mask, u.Quantity):\n if not (mask.unit == u.dimensionless_unscaled):\n raise TypeError(\n \"Mask should not be a non-dimensionless and unscaled Quantity!\"\n )\n mask = mask.value\n\n # Check for values of mask\n if np.any(mask > 1) or np.any(mask < 0):\n raise ValueError(\"Mask should be within range from 0 to 1 only.\")","def test_set_vec_to_zero(self):\n self.init()\n set_to_zero_by_ptr(self.f64_1)\n set_to_zero_by_ref(self.ff64_1)\n set_to_zero_by_ref(self.i32_1)\n set_to_zero_by_ref(self.fi32_1)\n set_to_zero_by_ref(self.i64_1)\n set_to_zero_by_ref(self.fi64_1)\n set_to_zero_by_ref(self.f32_1)\n assert np.all(self.f64_1 == 0.)\n assert np.all(self.ff64_1 == 0.)\n assert np.all(self.i32_1 == 0)\n assert np.all(self.fi32_1 == 0)\n assert np.all(self.i64_1 == 0)\n assert np.all(self.fi64_1 == 0)\n assert np.all(self.f32_1 == 0.)\n assert np.all(self.ff32_1 == 0.)","def mask(self):\n return np.ones((self.size, self.size))","def test_nan_inf_mask(value):\n\n data = np.ones((9, 9))\n mask = np.zeros_like(data, dtype=bool)\n data[4, 4] = value\n mask[4, 4] = True\n radius = 2.0\n aper = CircularAperture((4, 4), radius)\n tbl = aperture_photometry(data, aper, mask=mask)\n desired = (np.pi * radius**2) - 1\n assert_allclose(tbl['aperture_sum'], desired)","def nozero(arr):\n vals=sorted(list(set(np.array(arr).flatten())))\n if vals[0]<0:\n print(\"correcting for div/zero by replacing 0 with\",vals[1])\n arr[arr==0]=vals[1]\n return arr","def __nonzero__(self):\n for e in self:\n if e != 0:\n return True\n return False","def _clean(self, dataset):\n # Replace missing values with numpy's NaN. The missing value is\n # usually 1e+20, but values can be like 1.0000002e+20, which is\n # different. Ergo the inequality.\n for var in dataset.data_vars.itervalues():\n if 'missing_value' in var.attrs:\n missing_data_value = var.missing_value\n try:\n var.values[var.values >= missing_data_value] = np.NaN\n except ValueError:\n print \"Encountered ValueError in {0}. Ignoring\".format(var.name)","def mask(self):","def isEmpty(self):\n return len(self.mask) == 0","def _mask_check(self, mask, method):\n if sum(mask) == 0 or sum(mask) == len(mask):\n raise ValueError(\"No coordinates assigned to functional group; try another \"\n \"splitting method other than '%s.'\" % method)","def mask(self):\n\n mask = self.freqs >= self.minimum_threshold\n mask = mask.astype(int)\n self.freqs = self.freqs * mask\n self.sums = self.sums * mask","def _pure_data(data, missing_mask):\n missing_rows = np.where(missing_mask)[0]\n pure_data = np.delete(data, missing_rows, axis=0)\n\n return pure_data","def trim_zeros(x):\n assert len(x.shape) == 2\n return x[~np.all(x == 0, axis=1)]","def clear(self):\n self.np.fill(OFF)\n self.np.show()\n return True","def missing_values(dataset, inverse=False):\n zero = numpy.int8(0)\n one = numpy.int8(1)\n if inverse:\n converter = numpy.vectorize(lambda x:\n zero if (x == 0.0 and math.copysign(1, x) < 0.0) else one)\n else:\n converter = numpy.vectorize(lambda x:\n one if (x == 0.0 and math.copysign(1, x) < 0.0) else zero)\n return converter(dataset)","def set_nan(x):\n x[x == -999] = np.nan\n return x","def assert_no_nans(x):\n assert not torch.isnan(x).any()","def crop_missing(self):\n new_data = numpy.ma.copy(self.data)\n new_edges = list(self.bset.edges) # Mutable copy\n\n # Remove all-masked edge slices along all dimensions\n for axis in range(new_data.ndim):\n # Bring axis to front\n new_data = numpy.ma.swapaxes(new_data, 0, axis)\n\n # Find first slice to keep\n try:\n first = next(i for (i, mask) in\n enumerate(numpy.ma.getmaskarray(new_data))\n if not mask.all())\n new_data = new_data[first:]\n new_edges[axis] = new_edges[axis][first:]\n except StopIteration:\n pass\n\n # Find last slice to keep\n try:\n last = next(i for (i, mask) in\n enumerate(numpy.ma.getmaskarray(new_data)[::-1])\n if not mask.all())\n if last != 0:\n new_data = new_data[:-last]\n new_edges[axis] = new_edges[axis][:-last]\n except StopIteration:\n pass\n\n # Swap back axis\n new_data = numpy.ma.swapaxes(new_data, 0, axis)\n\n return type(self)(new_data, new_edges)","def test_unfixable_mask(self):\n unfixable_mask = mapreader.Map(os.path.join(tests.TEST_DATA_PATH, 'segmentations', 'test_unfixable_mask.map'))\n self.assertFalse(unfixable_mask.is_mask)\n with self.assertRaises(ValueError):\n unfixable_mask.fix_mask()\n self.assertFalse(unfixable_mask.is_mask)","def mod_mask(self):\n # Check the *_masq values\n self.__log.debug(\"Checking the *_masq arrays\")\n # Retrieve the kid boxes\n masq_names = np.unique([\"{}_masq\".format(item[1]) for item in self.list_detector])\n self.__check_attributes(masq_names, read_missing=False)\n # Check that they are all the same\n warnings.warn(\"Temporary fix to int8\")\n masqs = [getattr(self, masq).astype(np.int8) for masq in masq_names]\n\n if np.any(np.std(masqs, axis=0) != 0):\n self.__log.error(\"*_masq is varying -- Please check : {}\".format(pprint_list(masq_names, \"_masq\")))\n\n # AB private comm) main_flag should be the bitwise_or of all boxes\n # Well not exactly....\n # cast into 8 bit, is more than enough, only 3 bits used anyway...\n masq = np.bitwise_or.reduce(masqs, axis=0).astype(np.int8)\n\n # AB (#CONCERTO_DAQ January 11 13:02)\n # _flag_balayage_en_cours & _flag_blanking_synthe\n # Ainsi on aura la modulation en bit0 et 1 et le flag blanking en bit\n # AB (#CONCERTO_DAQ February 11 11:07)\n # bit 1 & 2 code the modulation as a signed integer -1 0 1 : 11 00 01 ie 3 0 1\n # bit 3 is a blanking bit, which does not exist for KISS, but should not be taken into account for CONCERTO\n\n # Thus as a temporary fix, let's clear the 3rd bit, actually a bad idea...\n # self.__log.warning(\"Temporary fix : clearing the 3rd bit of masq\")\n # masq = masq & ~(1 << 2)\n\n return masq","def clear(self):\n\t\tself.n = 0\n\t\tself.bv = BitVector.BitVector(size = self.m)","def test_op_zero_int(self):\n\n device = pymic.devices[0]\n stream = device.get_default_stream()\n a = numpy.arange(1, 4711 * 1024, dtype=int)\n offl_a = stream.bind(a)\n offl_a.zero()\n offl_a.update_host()\n stream.sync()\n self.assertEqual(sum(a), 0,\n \"Array should be all zeros.\")","def compute_mask(self, inputs, mask=None):\n return None","def is_zero(self, a):\n return not a","def _free_indicies(self):\n return np.logical_not(self._fixed_indicies)","def correct_miss_fill(ds):\n for d in ds.data_vars:\n try:\n ds[d].attrs.update({'missing_value': ds[d]._FillValue})\n except:\n pass\n return xr.decode_cf(ds)","def get_mask(self):\n # use the feature array a to calculate which channels to include etc\n sums = np.sum(self.feature_array, 0)\n feature_mask = np.repeat(np.ones(4, dtype=int), self.n_features)\n # if there are \"missing\" channels use the older version of KK\n zero_sums = sums == 0\n if np.any(zero_sums):\n self.distribution = 1\n feature_mask[zero_sums] = 0\n self.feature_mask = feature_mask\n return feature_mask","def __nonzero__(self):\n\n return not ipset.ipset_is_empty(self.set)","def missing(self) -> float:\n return self._coreEstimation.noMaskScore","def zeros_like(tensor):\n raise NotImplementedError","def test_check_data_masked_input_data(self):\n cube = self.cube.copy()\n cube.data[:, 0, 0] = np.nan\n cube.data = np.ma.masked_invalid(cube.data)\n expected_data = np.array(\n [self.percentile_25, self.percentile_50, self.percentile_75]\n )\n expected_data[:, 0, 0] = np.nan\n expected_data = np.ma.masked_invalid(expected_data)\n result = Plugin().process(cube)\n self.assertArrayAlmostEqual(result.data.data, expected_data.data, decimal=5)\n self.assertArrayEqual(result.data.mask, expected_data.mask)","def _check_zero(self, h, i, j, u, v, w):\n return self._.p[u, h, i] != 0 and self._.p[v, h, j] != 0 and \\\n self._.p[w, i, j] != 0","def test_shift_ruptures_no_shift(midday):\n shift_mask, shift_amounts = time.shifts_ruptures(\n midday, midday\n )\n assert not shift_mask.any()\n assert_series_equal(\n shift_amounts,\n pd.Series(0, index=midday.index, dtype='int64'),\n check_names=False\n )","def filter_zeros(X):\n\tnoNonzeros = np.count_nonzero(X, axis=1)\n\tmask = np.where(noNonzeros > 0)\n\treturn X[mask[0], :]","def nonans(array):\n return array[~np.isnan(array)]","def test_zero_clumper():\n mask0 = np.array([0, 1, 1, 0, 0, 0, 1, 1, 0, 0, 1, 0, 0, 0, 1, 1, 0], dtype=bool)\n expected_clump0 = [\n np.array([0], dtype=bool),\n np.array([0, 0, 0], dtype=bool),\n np.array([0, 0], dtype=bool),\n np.array([0, 0, 0], dtype=bool),\n np.array([0], dtype=bool),\n ]\n\n clumps0 = org_zeros_clumper(mask0)\n clumps0_corr = correct_zeros_clumper(mask0)\n # For mask0 both methods work.\n for i, __ in enumerate(clumps0):\n assert np.all(clumps0[i] == expected_clump0[i])\n assert np.all(clumps0_corr[i] == expected_clump0[i])\n\n clumps0_inverted = org_zeros_clumper(~mask0)\n clumps0_corr_inverted = correct_zeros_clumper(~mask0)\n # Check number of clumps if inverted\n # Should change since mask0 has uneven group numbers. 5 False, 4 True.\n assert not (len(clumps0_inverted) != len(clumps0)) # This should not be the case\n assert len(clumps0_corr_inverted) != len(clumps0_corr)\n\n # This mask starts with group of 1s so fails for original code.\n mask1 = np.array([1, 1, 1, 0, 0, 0, 1, 1, 0, 0, 1, 0, 0, 0, 1, 1, 0], dtype=bool)\n expected_clump1 = [\n np.array([0, 0, 0], dtype=bool),\n np.array([0, 0], dtype=bool),\n np.array([0, 0, 0], dtype=bool),\n np.array([0], dtype=bool),\n ]\n clumps1 = org_zeros_clumper(mask1)\n clumps1_corr = correct_zeros_clumper(mask1)\n\n for i, __ in enumerate(clumps1):\n assert not (np.all(clumps1[i] == expected_clump1[i])) # Failed original case\n assert np.all(clumps1_corr[i] == expected_clump1[i])\n\n # Testing corner cases\n masked_zeros = np.array([0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0], dtype=bool)\n expected_zeros = [masked_zeros]\n all_zeros_clumped = correct_zeros_clumper(masked_zeros)\n assert len(expected_zeros) == len(all_zeros_clumped)\n for i, __ in enumerate(all_zeros_clumped):\n assert np.all(all_zeros_clumped[i] == expected_zeros[i])\n\n masked_ones = np.array([1, 1, 1, 1, 1, 1], dtype=bool)\n expected_ones = []\n all_ones_clumped = correct_zeros_clumper(masked_ones)\n assert len(expected_ones) == len(all_ones_clumped)\n assert all_ones_clumped == []","def interpolate_none(self):\n\n # Reset processed data\n self.u_processed_mps = np.copy(self.u_mps)\n self.v_processed_mps = np.copy(self.v_mps)\n self.u_processed_mps[self.valid_data[0, :] == False] = np.nan\n self.v_processed_mps[self.valid_data[0, :] == False] = np.nan","def zeros(cls, shape, domain, *, fmt='sparse'):\n return cls.from_rep(SDM.zeros(shape, domain))","def _initial_set_mask(self, mask):\n if mask is None:\n mask = BooleanArrayMask(np.ones_like(self.value, dtype=bool),\n self._wcs, shape=self.value.shape)\n elif isinstance(mask, np.ndarray):\n if mask.shape != self.value.shape:\n raise ValueError(\"Mask shape must match the {0} shape.\"\n .format(self.__class__.__name__)\n )\n mask = BooleanArrayMask(mask, self._wcs, shape=self.value.shape)\n elif isinstance(mask, MaskBase):\n pass\n else:\n raise TypeError(\"mask of type {} is not a supported mask \"\n \"type.\".format(type(mask)))\n\n # Validate the mask before setting\n mask._validate_wcs(new_data=self.value, new_wcs=self._wcs,\n wcs_tolerance=self._wcs_tolerance)\n\n self._mask = mask","def zeros(cls, shape, domain):\n func = cls._get_flint_func(domain)\n return cls._new(func(*shape), shape, domain)","def test_op_zero_float(self):\n\n device = pymic.devices[0]\n stream = device.get_default_stream()\n a = numpy.arange(1.0, 4711.0 * 1024, dtype=float)\n offl_a = stream.bind(a)\n offl_a.zero()\n offl_a.update_host()\n stream.sync()\n self.assertEqual(sum(a), 0.0,\n \"Array should be all zeros.\")","def is_masked(self):\n return False","def get_flat_miss_mask(self, df: np.ndarray,\n p_missing: float = None) -> np.array:\n df_size = df.size\n miss_size = int(df_size * p_missing)\n indx = np.arange(df_size)\n flat_miss_mask = np.random.choice(indx, miss_size, replace=False)\n return flat_miss_mask","def set_mask_bad(self, _=None):\n self.set_mask_type(\"bad\")","def check_mask(f):\n def wrapper(*args, **kwargs):\n data = args[0]\n try:\n mask = data.mask\n except AttributeError:\n data = np.ma.array(data, mask=np.zeros(data.shape, dtype=np.bool))\n mask = data.mask\n args = list(args)\n args[0] = data\n args = tuple(args)\n return f(*args, **kwargs)\n return wrapper","def nan(self, check_inf = True):\n return self.foreach(\n lambda k,v: (k, numpy.isnan(v) + (check_inf == True) * numpy.isinf(v)),\n dimensions = self.dims,\n shape = self.shape,\n )","def test_allnegative(self):\n\n slot = [[-1,-1,-1,0,0,0], [0,-1,0,0,0,0], [-1,-1,-1,0,0,0], [0,-9,2,-4,-4,0], [-7,0,0,-2,0,0],[0,0,-1,-2,-4,0]]\n self.assertEqual(array_func(slot), 0)","def zero_blind_range(data):\n try:\n start_i = data['first_data_bin']\n except:\n start_i = 0\n data['data'][...,:start_i] = 0.0","def convert_fill_zeros_like(g, op, block):\n\n x = g.get_node(op.input(\"X\")[0])\n dtype = op.attr(\"dtype\")\n dtype = _convert_dtype_value(dtype)\n value = _expr.const(0, dtype=dtype)\n out = _op.transform.full_like(x, value).astype(dtype)\n g.add_node(op.output(\"Out\")[0], out)","def _clear(self):\n self.xi.ravel()[:] = 0\n self.xi_im.ravel()[:] = 0\n self.meanr.ravel()[:] = 0\n self.meanlogr.ravel()[:] = 0\n self.weight.ravel()[:] = 0\n self.npairs.ravel()[:] = 0\n self._varxi = None\n self._cov = None","def zero(*_, **__) -> None:\n return","def test_spike_masked(self):\n\n arr = [10, 12, 999.99, 13, 15, 40, 9, 9, None, 10, 10, 999.99, 10, None]\n\n # First and last elements should always be good data, unless someone\n # has set a threshold to zero.\n expected = [1, 4, 4, 4, 1, 3, 1, 1, 9, 9, 4, 4, 4, 9]\n\n inputs = [\n arr,\n np.asarray(arr, dtype=np.floating),\n dask_arr(np.asarray(arr, dtype=np.floating))\n ]\n for i in inputs:\n npt.assert_array_equal(\n qartod.spike_test(\n inp=i,\n suspect_threshold=self.suspect_threshold,\n fail_threshold=self.fail_threshold\n ),\n expected\n )","def test_centroid_com_mask_shape():\n with pytest.raises(ValueError):\n mask = np.zeros((2, 2), dtype=bool)\n centroid_com(np.zeros((4, 4)), mask=mask)","def set_mask_good(self, _=None):\n self.set_mask_type(\"good\")","def zero(self):\n raise NotImplementedError(\"Not implemented yet.\")","def test_nonans_prisms_mask(dummy_layer): # pylint: disable=redefined-outer-name\n (easting, northing), surface, reference, _ = dummy_layer\n shape = (northing.size, easting.size)\n\n # No nan in top nor bottom\n # ------------------------\n layer = prism_layer((easting, northing), surface, reference)\n expected_mask = np.ones(shape, dtype=bool)\n mask = layer.prism_layer._get_nonans_mask()\n npt.assert_allclose(mask, expected_mask)\n\n # Nans in top only\n # ----------------\n layer = prism_layer((easting, northing), surface, reference)\n expected_mask = np.ones(shape, dtype=bool)\n for index in ((1, 2), (2, 3)):\n layer.top[index] = np.nan\n expected_mask[index] = False\n mask = layer.prism_layer._get_nonans_mask()\n npt.assert_allclose(mask, expected_mask)\n\n # Nans in bottom only\n # -------------------\n layer = prism_layer((easting, northing), surface, reference)\n expected_mask = np.ones(shape, dtype=bool)\n for index in ((2, 1), (3, 2)):\n layer.bottom[index] = np.nan\n expected_mask[index] = False\n mask = layer.prism_layer._get_nonans_mask()\n npt.assert_allclose(mask, expected_mask)\n\n # Nans in top and bottom\n # ----------------------\n layer = prism_layer((easting, northing), surface, reference)\n expected_mask = np.ones(shape, dtype=bool)\n for index in ((1, 2), (2, 3)):\n layer.top[index] = np.nan\n expected_mask[index] = False\n for index in ((1, 2), (2, 1), (3, 2)):\n layer.bottom[index] = np.nan\n expected_mask[index] = False\n mask = layer.prism_layer._get_nonans_mask()\n npt.assert_allclose(mask, expected_mask)","def nanmasked(x):\n mask = ~np.isnan(x)\n maskA = x[mask]\n return (maskA,mask)","def faca_zero(self):\n n = self.n\n\n for i in range(n):\n if comozero(self[i]):\n self[i] = 0\n\n return self","def get_missing(self, df: np.ndarray,\n flat_miss_mask: np.array) -> Tuple[pd.DataFrame, np.ndarray]:\n miss_flat = np.copy(df)\n miss_flat = miss_flat.reshape(-1, )\n miss_flat[flat_miss_mask] = np.nan\n cols = [\"_\".join(['x', str(i)]) for i in range(df.shape[1])]\n df_miss = pd.DataFrame(data=miss_flat.reshape(df.shape), columns=cols)\n del df, miss_flat\n gc.collect()\n return df_miss, flat_miss_mask","def test_empty_input(self):\n discs = calc_disc(np.column_stack((np.ones(0), np.ones(0), np.ones(0))))\n np.testing.assert_almost_equal(discs, np.array([]))","def ignore_biasbn(directions):\n for d in directions:\n if d.dim() <= 1:\n d.fill_(0)","def mask_full(self):\n size = self.size\n\n if self.name == \"KEEP\":\n m = np.zeros((2*size+1,size+5), dtype = np.int8)\n m[:size,:size] = np.ones((size,size)) # keep\n m[size:size+1,2:5] = np.ones((1,3)) # keepdoor\n m[size+1:2*size+1,:size] = np.ones((size,size)) # campfire\n m[2:7,size:size+5] = np.ones((5,5)) # stockpile\n elif self.name in [\"OIL_SMELTER\", \"ENGINEERS_GUILD\", \"TUNNELORS_GUILD\"]:\n m = np.ones((2*size, size), dtype=np.int8)\n elif self.name in [\"MERCENARY_POST\", \"BARRACKS\"]:\n m = np.ones((2*size, 2*size), dtype=np.int8)\n else:\n m = self.mask()\n return m","def _get_zero_like(cls, sflux, axis=-1):\n # _sflux = _drop_units(sflux)\n # shape = _sflux.shape\n # if axis < 0:\n # axis = len(shape) + axis\n # newshape = shape[:axis] + shape[axis + 1:]\n # return np.zeros(newshape, _sflux.dtype)\n return np.zeros_like(sflux).sum(axis=axis)","def zero(self, value):\n raise NotImplementedError","def remove_invalid_values(self, lower_bound=float('-inf'), upper_bound=float('inf')) :\n valid_indices = (lower_bound < self.signal) * (self.signal < upper_bound)\n self.time_scale = self.time_scale[valid_indices]\n self.signal = self.signal[valid_indices]","def frame_missing_mask(mask, kernel_shape, sym_upper=False, max_dist=None):\n if mask.dtype != bool:\n raise ValueError(\"Mask must contain boolean values\")\n if not sp.issparse(mask):\n raise ValueError(\"Mask must be a sparse matrix\")\n\n framed_mask = mask.copy()\n ms, ns = mask.shape\n mk, nk = kernel_shape\n if sym_upper and (max_dist is not None):\n # Remove diagonals further than scan distance in the input mask\n framed_mask = diag_trim(framed_mask, max_dist + max(nk, mk)).tocsr()\n max_m = max_dist + mk\n max_n = max_dist + nk\n else:\n max_m, max_n = ms, ns\n # Up and down margins initialized with zeros and filled as needed\n margin_1 = sp.csr_matrix((mk - 1, ns), dtype=bool)\n margin_2 = sp.csr_matrix((mk - 1, ns), dtype=bool)\n if sym_upper and (max_dist is not None):\n # Margin 1 (top) is in upper triangle -> fill missing up to scan dist\n margin_1[:, :max_n] = 1\n else:\n margin_1[:, :] = 1\n margin_2[:, :] = 1\n framed_mask = sp.vstack([margin_1, framed_mask, margin_2], format=\"csr\")\n\n # Left and right\n margin_1 = sp.csr_matrix((ms + 2 * (mk - 1), nk - 1), dtype=bool)\n margin_2 = sp.csr_matrix((ms + 2 * (mk - 1), nk - 1), dtype=bool)\n\n if sym_upper and (max_dist is not None):\n # Margin 2 (right) is in upper triangle-> fill missing up to scan dist\n margin_2[-(max_m + 1) :, :] = 1\n # Fill only the start of left margin for the top-left corner\n margin_1[: mk - 1, :] = 1\n else:\n margin_1[:, :] = 1\n margin_2[:, :] = 1\n framed_mask = sp.hstack([margin_1, framed_mask, margin_2], format=\"csr\")\n\n if sym_upper:\n # LIL format is much faster when changing sparsity\n framed_mask = framed_mask.tolil()\n # Add margin below diagonal\n big_k = max(nk, mk)\n dia_margins = np.ones(big_k)\n dia_offsets = np.arange(-1, -big_k-1, -1)\n framed_mask += sp.diags(\n dia_margins,\n dia_offsets,\n shape=framed_mask.shape,\n format=\"lil\",\n dtype=bool,\n )\n framed_mask = framed_mask.tocsr()\n return framed_mask","def zero_val(self):\r\n self.piDD = {\"[0]\": None}\r\n self.top_node = \"[0]\"\r\n self.dim = 0","def zeros(shape, dtype=None):\n raise NotImplementedError","def __nonzero__(self):\n return not self.as_point == (0, 0)","def has_mask(self) -> bool:\n return self._mask_array is not None","def _check_nan_array(array):\n # count nan\n mask = np.isnan(array)\n x = mask.sum()\n\n # check the NaN values of the array\n if x > 0:\n raise ValueError(\"Array has {0} NaN values.\".format(x))","def _check_data_valid(self):\n\n is_valid = (sum(~np.isnan(self.data).flatten()) > 0 and self.data.flatten().sum() != 0)\n if not is_valid:\n raise FITSException(f\"No data in {self.survey}\")","def is_zero(self):\n for t in self:\n if t != TRIT_ZERO:\n return False\n return True","def nans(shape, dtype=float):\n a = np.empty(shape, dtype)\n a.fill(np.nan)\n return a","def __isZeroEverywhere(self, array):\n epsilon = numpy.finfo( type(array[0]) ).eps\n boolList = numpy.less_equal(numpy.abs(array), epsilon)\n\n for b in boolList:\n if not b:\n return False\n return True"],"string":"[\n \"def applymask(self,mask):\\n self.spec[mask==0]=np.nan\",\n \"def _check_missing_value_mask(self, missing_mask):\\n if not missing_mask.any():\\n raise ValueError(\\\"Input matrix is not missing any values\\\")\\n if missing_mask.all():\\n raise ValueError(\\\"Input matrix must have some non-missing values\\\")\",\n \"def zeros_like(self):\\n raise NotImplementedError\",\n \"def reset_mask(self):\\n\\n self.mask = np.ones(self.dispersion.shape, dtype=bool)\",\n \"def aerosols(self, mask: Mask):\\n raise NotImplementedError(\\\"Subclass must implement\\\")\",\n \"def getMask(self):\\r\\n mask = np.array(self.array, dtype=np.float32)\\r\\n mask[mask == 0] = np.nan\\r\\n return mask\",\n \"def _nodata_mask(self):\\n if self.nodata_value is None:\\n return np.ones_like(self.array, dtype=np.bool)\\n return self.array != self.nodata_value\",\n \"def erase_missing(signal, valid_rows, valid_cols, sym_upper=True):\\n if sym_upper and sp.issparse(signal):\\n if np.any(valid_rows != valid_cols):\\n raise ValueError(\\n \\\"Valid rows and columns must be identical with sym_upper=True\\\"\\n )\\n if signal.shape[0] != signal.shape[1]:\\n raise ValueError(\\n \\\"Input matrix must be square when using sym_upper=True\\\"\\n )\\n # Make a boolean mask from good bins\\n good_mask = np.isin(range(signal.shape[0]), valid_rows)\\n # Set all pixels in a nondetectable bin to 0\\n # For faster masking of bins, mask bins using dot product with an\\n # identify matrix where bad bins have been masked on the diagonal\\n # E.g. if removing the second bin (row and column):\\n # 1 0 0 9 6 5 1 0 0 9 0 5\\n # 0 0 0 X 6 8 7 X 0 0 0 = 0 0 0\\n # 0 0 1 6 7 8 0 0 1 6 0 8\\n mask_mat = sp.eye(signal.shape[0])\\n mask_mat.data[0][~good_mask] = 0\\n erased = mask_mat.dot(signal).dot(mask_mat)\\n else:\\n # Get a boolean array of missing (1) and valid (0) rows\\n missing_rows = valid_to_missing(valid_rows, signal.shape[0])\\n missing_cols = valid_to_missing(valid_cols, signal.shape[1])\\n erased = signal.copy()\\n erased[missing_rows, :] = 0\\n erased[:, missing_cols] = 0\\n\\n return erased\",\n \"def unmasked_data(self):\\n return numpy.ma.filled(self.data.astype(numpy.float_),\\n fill_value=numpy.nan)\",\n \"def zero_mask(self):\\n accum = 0\\n for i in range(self.data.itemsize):\\n accum += (0x55 << (i << 3))\\n return accum\",\n \"def valid_to_missing(valid, size):\\n missing = np.ones(size, dtype=bool)\\n try:\\n missing[valid] = False\\n # In case there is no valid index\\n except IndexError:\\n pass\\n missing = np.flatnonzero(missing)\\n return missing\",\n \"def mask(self, mask, logger=logger):\\n if self.nodata is not None:\\n da_masked = self._obj.where(mask != 0, self.nodata)\\n else:\\n logger.warning(\\\"Nodata value missing, skipping mask\\\")\\n da_masked = self._obj\\n return da_masked\",\n \"def test_csum_ignore_nans():\\n source = [np.zeros((16,), dtype=float) for _ in range(10)]\\n source.append(np.full((16,), fill_value=np.nan))\\n summed = csum(source, ignore_nan=True)\\n assert np.allclose(summed, np.zeros_like(summed))\",\n \"def test_check_data_masked_input_data_non_nans(self):\\n cube = self.cube.copy()\\n cube.data[:, 0, 0] = 1000\\n cube.data = np.ma.masked_equal(cube.data, 1000)\\n expected_data = np.array(\\n [self.percentile_25, self.percentile_50, self.percentile_75]\\n )\\n expected_data[:, 0, 0] = np.nan\\n expected_data = np.ma.masked_invalid(expected_data)\\n result = Plugin().process(cube)\\n self.assertArrayAlmostEqual(result.data.data, expected_data.data, decimal=5)\\n self.assertArrayEqual(result.data.mask, expected_data.mask)\",\n \"def setMask(self, mask):\\n try:\\n self.mask = mask\\n self.inds = na.nonzero(self.mask.flat)[0]\\n #print \\\"length of self.inds\\\",len(self.inds)\\n #print self.inds\\n self.dim = self.mask.shape[::-1]\\n #print self.mask.shape\\n return True\\n except Exception as error:\\n print(\\\"failed in setMask\\\", error)\",\n \"def updateEmptiesSet(self):\\n self.emptiesSet = []\\n for i in self.Range:\\n if self.get_cell(i) == 0:\\n self.emptiesSet.append(i)\",\n \"def _zero(array):\\n a = array.ravel()\\n for i in numba.prange(len(a)):\\n a[i] = 0\\n return True\",\n \"def zero_by_mask(mask, vals, replace_with=0.0):\\n assert mask.dtype == tf.as_dtype(np.bool)\\n ms = mask.get_shape().as_list()\\n vs = vals.get_shape().as_list()\\n mask = tf.ensure_shape(mask, vs[:-1] + [1])\\n vals = tf.ensure_shape(vals, ms[:-1] + [vs[-1]])\\n vals = tf.where_v2(mask, vals, replace_with)\\n return vals\",\n \"def test_offcenter(self):\\n actual = cm.ring_mask((5, 5), 1, 2, center=(2, 3))\\n expected = np.array([[False, False, False, True, False],\\n [False, False, True, False, True],\\n [False, True, False, False, False],\\n [False, False, True, False, True],\\n [False, False, False, True, False]])\\n self.assertIsNone(np.testing.assert_array_equal(actual, expected))\",\n \"def test_get_mean_mask():\\n arr = np.array([-5, 4, 0, 3, -2, 7, 10, -10, 5, 6])\\n m_arr = np.ma.masked_where(arr < 0, arr)\\n assert_allclose(iqcalc.get_mean(m_arr), 5.0)\",\n \"def shrink_mask(self):\\n m = self._mask\\n if m.ndim and not m.any():\\n self._mask = nomask\\n return self\",\n \"def _initalize_mask(dataSubStack):\\n # Initalize an array to store the output mask values\\n outMask = np.zeros(dataSubStack.shape, dtype=bool)\\n\\n # Start by masking out NaNs or Infs\\n NaNsOrInfs = np.logical_not(np.isfinite(dataSubStack.data))\\n dataSubStack.mask = NaNsOrInfs\\n\\n return outMask, dataSubStack\",\n \"def clean_mask(image, sig=5, iters=3):\\n\\n mean, median, stddev = sigma_clipped_stats(image[image>0], sigma=sig, iters=iters)\\n\\n mask_bad = (np.abs(image - median) > sig * stddev) | (image == 0)\\n image_ret = np.copy(image)\\n image_ret[mask_bad] = 0\\n \\n return image_ret, mask_bad\",\n \"def _mask(self):\\n if self.__mask is None:\\n # need this to be *exactly* the numpy boolean False\\n return nomask\\n return self.__mask\",\n \"def getSignedMask(self):\\r\\n signedMask = np.array(self.array * self.sign, dtype=np.float32)\\r\\n signedMask[signedMask == 0] = np.nan\\r\\n return signedMask\",\n \"def mask_check(mask):\\n if isinstance(mask, u.Quantity):\\n if not (mask.unit == u.dimensionless_unscaled):\\n raise TypeError(\\n \\\"Mask should not be a non-dimensionless and unscaled Quantity!\\\"\\n )\\n mask = mask.value\\n\\n # Check for values of mask\\n if np.any(mask > 1) or np.any(mask < 0):\\n raise ValueError(\\\"Mask should be within range from 0 to 1 only.\\\")\",\n \"def test_set_vec_to_zero(self):\\n self.init()\\n set_to_zero_by_ptr(self.f64_1)\\n set_to_zero_by_ref(self.ff64_1)\\n set_to_zero_by_ref(self.i32_1)\\n set_to_zero_by_ref(self.fi32_1)\\n set_to_zero_by_ref(self.i64_1)\\n set_to_zero_by_ref(self.fi64_1)\\n set_to_zero_by_ref(self.f32_1)\\n assert np.all(self.f64_1 == 0.)\\n assert np.all(self.ff64_1 == 0.)\\n assert np.all(self.i32_1 == 0)\\n assert np.all(self.fi32_1 == 0)\\n assert np.all(self.i64_1 == 0)\\n assert np.all(self.fi64_1 == 0)\\n assert np.all(self.f32_1 == 0.)\\n assert np.all(self.ff32_1 == 0.)\",\n \"def mask(self):\\n return np.ones((self.size, self.size))\",\n \"def test_nan_inf_mask(value):\\n\\n data = np.ones((9, 9))\\n mask = np.zeros_like(data, dtype=bool)\\n data[4, 4] = value\\n mask[4, 4] = True\\n radius = 2.0\\n aper = CircularAperture((4, 4), radius)\\n tbl = aperture_photometry(data, aper, mask=mask)\\n desired = (np.pi * radius**2) - 1\\n assert_allclose(tbl['aperture_sum'], desired)\",\n \"def nozero(arr):\\n vals=sorted(list(set(np.array(arr).flatten())))\\n if vals[0]<0:\\n print(\\\"correcting for div/zero by replacing 0 with\\\",vals[1])\\n arr[arr==0]=vals[1]\\n return arr\",\n \"def __nonzero__(self):\\n for e in self:\\n if e != 0:\\n return True\\n return False\",\n \"def _clean(self, dataset):\\n # Replace missing values with numpy's NaN. The missing value is\\n # usually 1e+20, but values can be like 1.0000002e+20, which is\\n # different. Ergo the inequality.\\n for var in dataset.data_vars.itervalues():\\n if 'missing_value' in var.attrs:\\n missing_data_value = var.missing_value\\n try:\\n var.values[var.values >= missing_data_value] = np.NaN\\n except ValueError:\\n print \\\"Encountered ValueError in {0}. Ignoring\\\".format(var.name)\",\n \"def mask(self):\",\n \"def isEmpty(self):\\n return len(self.mask) == 0\",\n \"def _mask_check(self, mask, method):\\n if sum(mask) == 0 or sum(mask) == len(mask):\\n raise ValueError(\\\"No coordinates assigned to functional group; try another \\\"\\n \\\"splitting method other than '%s.'\\\" % method)\",\n \"def mask(self):\\n\\n mask = self.freqs >= self.minimum_threshold\\n mask = mask.astype(int)\\n self.freqs = self.freqs * mask\\n self.sums = self.sums * mask\",\n \"def _pure_data(data, missing_mask):\\n missing_rows = np.where(missing_mask)[0]\\n pure_data = np.delete(data, missing_rows, axis=0)\\n\\n return pure_data\",\n \"def trim_zeros(x):\\n assert len(x.shape) == 2\\n return x[~np.all(x == 0, axis=1)]\",\n \"def clear(self):\\n self.np.fill(OFF)\\n self.np.show()\\n return True\",\n \"def missing_values(dataset, inverse=False):\\n zero = numpy.int8(0)\\n one = numpy.int8(1)\\n if inverse:\\n converter = numpy.vectorize(lambda x:\\n zero if (x == 0.0 and math.copysign(1, x) < 0.0) else one)\\n else:\\n converter = numpy.vectorize(lambda x:\\n one if (x == 0.0 and math.copysign(1, x) < 0.0) else zero)\\n return converter(dataset)\",\n \"def set_nan(x):\\n x[x == -999] = np.nan\\n return x\",\n \"def assert_no_nans(x):\\n assert not torch.isnan(x).any()\",\n \"def crop_missing(self):\\n new_data = numpy.ma.copy(self.data)\\n new_edges = list(self.bset.edges) # Mutable copy\\n\\n # Remove all-masked edge slices along all dimensions\\n for axis in range(new_data.ndim):\\n # Bring axis to front\\n new_data = numpy.ma.swapaxes(new_data, 0, axis)\\n\\n # Find first slice to keep\\n try:\\n first = next(i for (i, mask) in\\n enumerate(numpy.ma.getmaskarray(new_data))\\n if not mask.all())\\n new_data = new_data[first:]\\n new_edges[axis] = new_edges[axis][first:]\\n except StopIteration:\\n pass\\n\\n # Find last slice to keep\\n try:\\n last = next(i for (i, mask) in\\n enumerate(numpy.ma.getmaskarray(new_data)[::-1])\\n if not mask.all())\\n if last != 0:\\n new_data = new_data[:-last]\\n new_edges[axis] = new_edges[axis][:-last]\\n except StopIteration:\\n pass\\n\\n # Swap back axis\\n new_data = numpy.ma.swapaxes(new_data, 0, axis)\\n\\n return type(self)(new_data, new_edges)\",\n \"def test_unfixable_mask(self):\\n unfixable_mask = mapreader.Map(os.path.join(tests.TEST_DATA_PATH, 'segmentations', 'test_unfixable_mask.map'))\\n self.assertFalse(unfixable_mask.is_mask)\\n with self.assertRaises(ValueError):\\n unfixable_mask.fix_mask()\\n self.assertFalse(unfixable_mask.is_mask)\",\n \"def mod_mask(self):\\n # Check the *_masq values\\n self.__log.debug(\\\"Checking the *_masq arrays\\\")\\n # Retrieve the kid boxes\\n masq_names = np.unique([\\\"{}_masq\\\".format(item[1]) for item in self.list_detector])\\n self.__check_attributes(masq_names, read_missing=False)\\n # Check that they are all the same\\n warnings.warn(\\\"Temporary fix to int8\\\")\\n masqs = [getattr(self, masq).astype(np.int8) for masq in masq_names]\\n\\n if np.any(np.std(masqs, axis=0) != 0):\\n self.__log.error(\\\"*_masq is varying -- Please check : {}\\\".format(pprint_list(masq_names, \\\"_masq\\\")))\\n\\n # AB private comm) main_flag should be the bitwise_or of all boxes\\n # Well not exactly....\\n # cast into 8 bit, is more than enough, only 3 bits used anyway...\\n masq = np.bitwise_or.reduce(masqs, axis=0).astype(np.int8)\\n\\n # AB (#CONCERTO_DAQ January 11 13:02)\\n # _flag_balayage_en_cours & _flag_blanking_synthe\\n # Ainsi on aura la modulation en bit0 et 1 et le flag blanking en bit\\n # AB (#CONCERTO_DAQ February 11 11:07)\\n # bit 1 & 2 code the modulation as a signed integer -1 0 1 : 11 00 01 ie 3 0 1\\n # bit 3 is a blanking bit, which does not exist for KISS, but should not be taken into account for CONCERTO\\n\\n # Thus as a temporary fix, let's clear the 3rd bit, actually a bad idea...\\n # self.__log.warning(\\\"Temporary fix : clearing the 3rd bit of masq\\\")\\n # masq = masq & ~(1 << 2)\\n\\n return masq\",\n \"def clear(self):\\n\\t\\tself.n = 0\\n\\t\\tself.bv = BitVector.BitVector(size = self.m)\",\n \"def test_op_zero_int(self):\\n\\n device = pymic.devices[0]\\n stream = device.get_default_stream()\\n a = numpy.arange(1, 4711 * 1024, dtype=int)\\n offl_a = stream.bind(a)\\n offl_a.zero()\\n offl_a.update_host()\\n stream.sync()\\n self.assertEqual(sum(a), 0,\\n \\\"Array should be all zeros.\\\")\",\n \"def compute_mask(self, inputs, mask=None):\\n return None\",\n \"def is_zero(self, a):\\n return not a\",\n \"def _free_indicies(self):\\n return np.logical_not(self._fixed_indicies)\",\n \"def correct_miss_fill(ds):\\n for d in ds.data_vars:\\n try:\\n ds[d].attrs.update({'missing_value': ds[d]._FillValue})\\n except:\\n pass\\n return xr.decode_cf(ds)\",\n \"def get_mask(self):\\n # use the feature array a to calculate which channels to include etc\\n sums = np.sum(self.feature_array, 0)\\n feature_mask = np.repeat(np.ones(4, dtype=int), self.n_features)\\n # if there are \\\"missing\\\" channels use the older version of KK\\n zero_sums = sums == 0\\n if np.any(zero_sums):\\n self.distribution = 1\\n feature_mask[zero_sums] = 0\\n self.feature_mask = feature_mask\\n return feature_mask\",\n \"def __nonzero__(self):\\n\\n return not ipset.ipset_is_empty(self.set)\",\n \"def missing(self) -> float:\\n return self._coreEstimation.noMaskScore\",\n \"def zeros_like(tensor):\\n raise NotImplementedError\",\n \"def test_check_data_masked_input_data(self):\\n cube = self.cube.copy()\\n cube.data[:, 0, 0] = np.nan\\n cube.data = np.ma.masked_invalid(cube.data)\\n expected_data = np.array(\\n [self.percentile_25, self.percentile_50, self.percentile_75]\\n )\\n expected_data[:, 0, 0] = np.nan\\n expected_data = np.ma.masked_invalid(expected_data)\\n result = Plugin().process(cube)\\n self.assertArrayAlmostEqual(result.data.data, expected_data.data, decimal=5)\\n self.assertArrayEqual(result.data.mask, expected_data.mask)\",\n \"def _check_zero(self, h, i, j, u, v, w):\\n return self._.p[u, h, i] != 0 and self._.p[v, h, j] != 0 and \\\\\\n self._.p[w, i, j] != 0\",\n \"def test_shift_ruptures_no_shift(midday):\\n shift_mask, shift_amounts = time.shifts_ruptures(\\n midday, midday\\n )\\n assert not shift_mask.any()\\n assert_series_equal(\\n shift_amounts,\\n pd.Series(0, index=midday.index, dtype='int64'),\\n check_names=False\\n )\",\n \"def filter_zeros(X):\\n\\tnoNonzeros = np.count_nonzero(X, axis=1)\\n\\tmask = np.where(noNonzeros > 0)\\n\\treturn X[mask[0], :]\",\n \"def nonans(array):\\n return array[~np.isnan(array)]\",\n \"def test_zero_clumper():\\n mask0 = np.array([0, 1, 1, 0, 0, 0, 1, 1, 0, 0, 1, 0, 0, 0, 1, 1, 0], dtype=bool)\\n expected_clump0 = [\\n np.array([0], dtype=bool),\\n np.array([0, 0, 0], dtype=bool),\\n np.array([0, 0], dtype=bool),\\n np.array([0, 0, 0], dtype=bool),\\n np.array([0], dtype=bool),\\n ]\\n\\n clumps0 = org_zeros_clumper(mask0)\\n clumps0_corr = correct_zeros_clumper(mask0)\\n # For mask0 both methods work.\\n for i, __ in enumerate(clumps0):\\n assert np.all(clumps0[i] == expected_clump0[i])\\n assert np.all(clumps0_corr[i] == expected_clump0[i])\\n\\n clumps0_inverted = org_zeros_clumper(~mask0)\\n clumps0_corr_inverted = correct_zeros_clumper(~mask0)\\n # Check number of clumps if inverted\\n # Should change since mask0 has uneven group numbers. 5 False, 4 True.\\n assert not (len(clumps0_inverted) != len(clumps0)) # This should not be the case\\n assert len(clumps0_corr_inverted) != len(clumps0_corr)\\n\\n # This mask starts with group of 1s so fails for original code.\\n mask1 = np.array([1, 1, 1, 0, 0, 0, 1, 1, 0, 0, 1, 0, 0, 0, 1, 1, 0], dtype=bool)\\n expected_clump1 = [\\n np.array([0, 0, 0], dtype=bool),\\n np.array([0, 0], dtype=bool),\\n np.array([0, 0, 0], dtype=bool),\\n np.array([0], dtype=bool),\\n ]\\n clumps1 = org_zeros_clumper(mask1)\\n clumps1_corr = correct_zeros_clumper(mask1)\\n\\n for i, __ in enumerate(clumps1):\\n assert not (np.all(clumps1[i] == expected_clump1[i])) # Failed original case\\n assert np.all(clumps1_corr[i] == expected_clump1[i])\\n\\n # Testing corner cases\\n masked_zeros = np.array([0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0], dtype=bool)\\n expected_zeros = [masked_zeros]\\n all_zeros_clumped = correct_zeros_clumper(masked_zeros)\\n assert len(expected_zeros) == len(all_zeros_clumped)\\n for i, __ in enumerate(all_zeros_clumped):\\n assert np.all(all_zeros_clumped[i] == expected_zeros[i])\\n\\n masked_ones = np.array([1, 1, 1, 1, 1, 1], dtype=bool)\\n expected_ones = []\\n all_ones_clumped = correct_zeros_clumper(masked_ones)\\n assert len(expected_ones) == len(all_ones_clumped)\\n assert all_ones_clumped == []\",\n \"def interpolate_none(self):\\n\\n # Reset processed data\\n self.u_processed_mps = np.copy(self.u_mps)\\n self.v_processed_mps = np.copy(self.v_mps)\\n self.u_processed_mps[self.valid_data[0, :] == False] = np.nan\\n self.v_processed_mps[self.valid_data[0, :] == False] = np.nan\",\n \"def zeros(cls, shape, domain, *, fmt='sparse'):\\n return cls.from_rep(SDM.zeros(shape, domain))\",\n \"def _initial_set_mask(self, mask):\\n if mask is None:\\n mask = BooleanArrayMask(np.ones_like(self.value, dtype=bool),\\n self._wcs, shape=self.value.shape)\\n elif isinstance(mask, np.ndarray):\\n if mask.shape != self.value.shape:\\n raise ValueError(\\\"Mask shape must match the {0} shape.\\\"\\n .format(self.__class__.__name__)\\n )\\n mask = BooleanArrayMask(mask, self._wcs, shape=self.value.shape)\\n elif isinstance(mask, MaskBase):\\n pass\\n else:\\n raise TypeError(\\\"mask of type {} is not a supported mask \\\"\\n \\\"type.\\\".format(type(mask)))\\n\\n # Validate the mask before setting\\n mask._validate_wcs(new_data=self.value, new_wcs=self._wcs,\\n wcs_tolerance=self._wcs_tolerance)\\n\\n self._mask = mask\",\n \"def zeros(cls, shape, domain):\\n func = cls._get_flint_func(domain)\\n return cls._new(func(*shape), shape, domain)\",\n \"def test_op_zero_float(self):\\n\\n device = pymic.devices[0]\\n stream = device.get_default_stream()\\n a = numpy.arange(1.0, 4711.0 * 1024, dtype=float)\\n offl_a = stream.bind(a)\\n offl_a.zero()\\n offl_a.update_host()\\n stream.sync()\\n self.assertEqual(sum(a), 0.0,\\n \\\"Array should be all zeros.\\\")\",\n \"def is_masked(self):\\n return False\",\n \"def get_flat_miss_mask(self, df: np.ndarray,\\n p_missing: float = None) -> np.array:\\n df_size = df.size\\n miss_size = int(df_size * p_missing)\\n indx = np.arange(df_size)\\n flat_miss_mask = np.random.choice(indx, miss_size, replace=False)\\n return flat_miss_mask\",\n \"def set_mask_bad(self, _=None):\\n self.set_mask_type(\\\"bad\\\")\",\n \"def check_mask(f):\\n def wrapper(*args, **kwargs):\\n data = args[0]\\n try:\\n mask = data.mask\\n except AttributeError:\\n data = np.ma.array(data, mask=np.zeros(data.shape, dtype=np.bool))\\n mask = data.mask\\n args = list(args)\\n args[0] = data\\n args = tuple(args)\\n return f(*args, **kwargs)\\n return wrapper\",\n \"def nan(self, check_inf = True):\\n return self.foreach(\\n lambda k,v: (k, numpy.isnan(v) + (check_inf == True) * numpy.isinf(v)),\\n dimensions = self.dims,\\n shape = self.shape,\\n )\",\n \"def test_allnegative(self):\\n\\n slot = [[-1,-1,-1,0,0,0], [0,-1,0,0,0,0], [-1,-1,-1,0,0,0], [0,-9,2,-4,-4,0], [-7,0,0,-2,0,0],[0,0,-1,-2,-4,0]]\\n self.assertEqual(array_func(slot), 0)\",\n \"def zero_blind_range(data):\\n try:\\n start_i = data['first_data_bin']\\n except:\\n start_i = 0\\n data['data'][...,:start_i] = 0.0\",\n \"def convert_fill_zeros_like(g, op, block):\\n\\n x = g.get_node(op.input(\\\"X\\\")[0])\\n dtype = op.attr(\\\"dtype\\\")\\n dtype = _convert_dtype_value(dtype)\\n value = _expr.const(0, dtype=dtype)\\n out = _op.transform.full_like(x, value).astype(dtype)\\n g.add_node(op.output(\\\"Out\\\")[0], out)\",\n \"def _clear(self):\\n self.xi.ravel()[:] = 0\\n self.xi_im.ravel()[:] = 0\\n self.meanr.ravel()[:] = 0\\n self.meanlogr.ravel()[:] = 0\\n self.weight.ravel()[:] = 0\\n self.npairs.ravel()[:] = 0\\n self._varxi = None\\n self._cov = None\",\n \"def zero(*_, **__) -> None:\\n return\",\n \"def test_spike_masked(self):\\n\\n arr = [10, 12, 999.99, 13, 15, 40, 9, 9, None, 10, 10, 999.99, 10, None]\\n\\n # First and last elements should always be good data, unless someone\\n # has set a threshold to zero.\\n expected = [1, 4, 4, 4, 1, 3, 1, 1, 9, 9, 4, 4, 4, 9]\\n\\n inputs = [\\n arr,\\n np.asarray(arr, dtype=np.floating),\\n dask_arr(np.asarray(arr, dtype=np.floating))\\n ]\\n for i in inputs:\\n npt.assert_array_equal(\\n qartod.spike_test(\\n inp=i,\\n suspect_threshold=self.suspect_threshold,\\n fail_threshold=self.fail_threshold\\n ),\\n expected\\n )\",\n \"def test_centroid_com_mask_shape():\\n with pytest.raises(ValueError):\\n mask = np.zeros((2, 2), dtype=bool)\\n centroid_com(np.zeros((4, 4)), mask=mask)\",\n \"def set_mask_good(self, _=None):\\n self.set_mask_type(\\\"good\\\")\",\n \"def zero(self):\\n raise NotImplementedError(\\\"Not implemented yet.\\\")\",\n \"def test_nonans_prisms_mask(dummy_layer): # pylint: disable=redefined-outer-name\\n (easting, northing), surface, reference, _ = dummy_layer\\n shape = (northing.size, easting.size)\\n\\n # No nan in top nor bottom\\n # ------------------------\\n layer = prism_layer((easting, northing), surface, reference)\\n expected_mask = np.ones(shape, dtype=bool)\\n mask = layer.prism_layer._get_nonans_mask()\\n npt.assert_allclose(mask, expected_mask)\\n\\n # Nans in top only\\n # ----------------\\n layer = prism_layer((easting, northing), surface, reference)\\n expected_mask = np.ones(shape, dtype=bool)\\n for index in ((1, 2), (2, 3)):\\n layer.top[index] = np.nan\\n expected_mask[index] = False\\n mask = layer.prism_layer._get_nonans_mask()\\n npt.assert_allclose(mask, expected_mask)\\n\\n # Nans in bottom only\\n # -------------------\\n layer = prism_layer((easting, northing), surface, reference)\\n expected_mask = np.ones(shape, dtype=bool)\\n for index in ((2, 1), (3, 2)):\\n layer.bottom[index] = np.nan\\n expected_mask[index] = False\\n mask = layer.prism_layer._get_nonans_mask()\\n npt.assert_allclose(mask, expected_mask)\\n\\n # Nans in top and bottom\\n # ----------------------\\n layer = prism_layer((easting, northing), surface, reference)\\n expected_mask = np.ones(shape, dtype=bool)\\n for index in ((1, 2), (2, 3)):\\n layer.top[index] = np.nan\\n expected_mask[index] = False\\n for index in ((1, 2), (2, 1), (3, 2)):\\n layer.bottom[index] = np.nan\\n expected_mask[index] = False\\n mask = layer.prism_layer._get_nonans_mask()\\n npt.assert_allclose(mask, expected_mask)\",\n \"def nanmasked(x):\\n mask = ~np.isnan(x)\\n maskA = x[mask]\\n return (maskA,mask)\",\n \"def faca_zero(self):\\n n = self.n\\n\\n for i in range(n):\\n if comozero(self[i]):\\n self[i] = 0\\n\\n return self\",\n \"def get_missing(self, df: np.ndarray,\\n flat_miss_mask: np.array) -> Tuple[pd.DataFrame, np.ndarray]:\\n miss_flat = np.copy(df)\\n miss_flat = miss_flat.reshape(-1, )\\n miss_flat[flat_miss_mask] = np.nan\\n cols = [\\\"_\\\".join(['x', str(i)]) for i in range(df.shape[1])]\\n df_miss = pd.DataFrame(data=miss_flat.reshape(df.shape), columns=cols)\\n del df, miss_flat\\n gc.collect()\\n return df_miss, flat_miss_mask\",\n \"def test_empty_input(self):\\n discs = calc_disc(np.column_stack((np.ones(0), np.ones(0), np.ones(0))))\\n np.testing.assert_almost_equal(discs, np.array([]))\",\n \"def ignore_biasbn(directions):\\n for d in directions:\\n if d.dim() <= 1:\\n d.fill_(0)\",\n \"def mask_full(self):\\n size = self.size\\n\\n if self.name == \\\"KEEP\\\":\\n m = np.zeros((2*size+1,size+5), dtype = np.int8)\\n m[:size,:size] = np.ones((size,size)) # keep\\n m[size:size+1,2:5] = np.ones((1,3)) # keepdoor\\n m[size+1:2*size+1,:size] = np.ones((size,size)) # campfire\\n m[2:7,size:size+5] = np.ones((5,5)) # stockpile\\n elif self.name in [\\\"OIL_SMELTER\\\", \\\"ENGINEERS_GUILD\\\", \\\"TUNNELORS_GUILD\\\"]:\\n m = np.ones((2*size, size), dtype=np.int8)\\n elif self.name in [\\\"MERCENARY_POST\\\", \\\"BARRACKS\\\"]:\\n m = np.ones((2*size, 2*size), dtype=np.int8)\\n else:\\n m = self.mask()\\n return m\",\n \"def _get_zero_like(cls, sflux, axis=-1):\\n # _sflux = _drop_units(sflux)\\n # shape = _sflux.shape\\n # if axis < 0:\\n # axis = len(shape) + axis\\n # newshape = shape[:axis] + shape[axis + 1:]\\n # return np.zeros(newshape, _sflux.dtype)\\n return np.zeros_like(sflux).sum(axis=axis)\",\n \"def zero(self, value):\\n raise NotImplementedError\",\n \"def remove_invalid_values(self, lower_bound=float('-inf'), upper_bound=float('inf')) :\\n valid_indices = (lower_bound < self.signal) * (self.signal < upper_bound)\\n self.time_scale = self.time_scale[valid_indices]\\n self.signal = self.signal[valid_indices]\",\n \"def frame_missing_mask(mask, kernel_shape, sym_upper=False, max_dist=None):\\n if mask.dtype != bool:\\n raise ValueError(\\\"Mask must contain boolean values\\\")\\n if not sp.issparse(mask):\\n raise ValueError(\\\"Mask must be a sparse matrix\\\")\\n\\n framed_mask = mask.copy()\\n ms, ns = mask.shape\\n mk, nk = kernel_shape\\n if sym_upper and (max_dist is not None):\\n # Remove diagonals further than scan distance in the input mask\\n framed_mask = diag_trim(framed_mask, max_dist + max(nk, mk)).tocsr()\\n max_m = max_dist + mk\\n max_n = max_dist + nk\\n else:\\n max_m, max_n = ms, ns\\n # Up and down margins initialized with zeros and filled as needed\\n margin_1 = sp.csr_matrix((mk - 1, ns), dtype=bool)\\n margin_2 = sp.csr_matrix((mk - 1, ns), dtype=bool)\\n if sym_upper and (max_dist is not None):\\n # Margin 1 (top) is in upper triangle -> fill missing up to scan dist\\n margin_1[:, :max_n] = 1\\n else:\\n margin_1[:, :] = 1\\n margin_2[:, :] = 1\\n framed_mask = sp.vstack([margin_1, framed_mask, margin_2], format=\\\"csr\\\")\\n\\n # Left and right\\n margin_1 = sp.csr_matrix((ms + 2 * (mk - 1), nk - 1), dtype=bool)\\n margin_2 = sp.csr_matrix((ms + 2 * (mk - 1), nk - 1), dtype=bool)\\n\\n if sym_upper and (max_dist is not None):\\n # Margin 2 (right) is in upper triangle-> fill missing up to scan dist\\n margin_2[-(max_m + 1) :, :] = 1\\n # Fill only the start of left margin for the top-left corner\\n margin_1[: mk - 1, :] = 1\\n else:\\n margin_1[:, :] = 1\\n margin_2[:, :] = 1\\n framed_mask = sp.hstack([margin_1, framed_mask, margin_2], format=\\\"csr\\\")\\n\\n if sym_upper:\\n # LIL format is much faster when changing sparsity\\n framed_mask = framed_mask.tolil()\\n # Add margin below diagonal\\n big_k = max(nk, mk)\\n dia_margins = np.ones(big_k)\\n dia_offsets = np.arange(-1, -big_k-1, -1)\\n framed_mask += sp.diags(\\n dia_margins,\\n dia_offsets,\\n shape=framed_mask.shape,\\n format=\\\"lil\\\",\\n dtype=bool,\\n )\\n framed_mask = framed_mask.tocsr()\\n return framed_mask\",\n \"def zero_val(self):\\r\\n self.piDD = {\\\"[0]\\\": None}\\r\\n self.top_node = \\\"[0]\\\"\\r\\n self.dim = 0\",\n \"def zeros(shape, dtype=None):\\n raise NotImplementedError\",\n \"def __nonzero__(self):\\n return not self.as_point == (0, 0)\",\n \"def has_mask(self) -> bool:\\n return self._mask_array is not None\",\n \"def _check_nan_array(array):\\n # count nan\\n mask = np.isnan(array)\\n x = mask.sum()\\n\\n # check the NaN values of the array\\n if x > 0:\\n raise ValueError(\\\"Array has {0} NaN values.\\\".format(x))\",\n \"def _check_data_valid(self):\\n\\n is_valid = (sum(~np.isnan(self.data).flatten()) > 0 and self.data.flatten().sum() != 0)\\n if not is_valid:\\n raise FITSException(f\\\"No data in {self.survey}\\\")\",\n \"def is_zero(self):\\n for t in self:\\n if t != TRIT_ZERO:\\n return False\\n return True\",\n \"def nans(shape, dtype=float):\\n a = np.empty(shape, dtype)\\n a.fill(np.nan)\\n return a\",\n \"def __isZeroEverywhere(self, array):\\n epsilon = numpy.finfo( type(array[0]) ).eps\\n boolList = numpy.less_equal(numpy.abs(array), epsilon)\\n\\n for b in boolList:\\n if not b:\\n return False\\n return True\"\n]","isConversational":false},"negative_scores":{"kind":"list like","value":["0.7038222","0.6567302","0.63071376","0.6267162","0.6249988","0.6000625","0.5920129","0.5898875","0.5888325","0.5880828","0.5875885","0.58599186","0.5855308","0.5855247","0.5853476","0.5831566","0.5795374","0.5774761","0.5770358","0.57691586","0.5761126","0.57492924","0.5739421","0.5736292","0.5720537","0.5681831","0.5681092","0.56380415","0.5630747","0.5626749","0.5603546","0.5597096","0.5595108","0.5585593","0.5528496","0.55269307","0.5514129","0.5498923","0.54913706","0.5482379","0.5473074","0.54710954","0.5462285","0.54590774","0.54585177","0.54563004","0.5449673","0.5433273","0.54312855","0.5426266","0.54250324","0.542044","0.5420337","0.5401231","0.54002386","0.5397853","0.5396041","0.539095","0.53897685","0.5389414","0.5385449","0.5364367","0.5361424","0.5360036","0.5359528","0.53561825","0.5351823","0.5349992","0.53480315","0.53473103","0.53345513","0.53342354","0.53206193","0.5319465","0.5313372","0.53076994","0.53047204","0.5304286","0.5303629","0.5303207","0.5301299","0.5296986","0.52914965","0.5288457","0.5270548","0.52608734","0.5258581","0.5257278","0.5249315","0.52469504","0.52462125","0.52457315","0.5245063","0.5243656","0.5237008","0.52336365","0.5232882","0.5232803","0.52240723","0.5217285"],"string":"[\n \"0.7038222\",\n \"0.6567302\",\n \"0.63071376\",\n \"0.6267162\",\n \"0.6249988\",\n \"0.6000625\",\n \"0.5920129\",\n \"0.5898875\",\n \"0.5888325\",\n \"0.5880828\",\n \"0.5875885\",\n \"0.58599186\",\n \"0.5855308\",\n \"0.5855247\",\n \"0.5853476\",\n \"0.5831566\",\n \"0.5795374\",\n \"0.5774761\",\n \"0.5770358\",\n \"0.57691586\",\n \"0.5761126\",\n \"0.57492924\",\n \"0.5739421\",\n \"0.5736292\",\n \"0.5720537\",\n \"0.5681831\",\n \"0.5681092\",\n \"0.56380415\",\n \"0.5630747\",\n \"0.5626749\",\n \"0.5603546\",\n \"0.5597096\",\n \"0.5595108\",\n \"0.5585593\",\n \"0.5528496\",\n \"0.55269307\",\n \"0.5514129\",\n \"0.5498923\",\n \"0.54913706\",\n \"0.5482379\",\n \"0.5473074\",\n \"0.54710954\",\n \"0.5462285\",\n \"0.54590774\",\n \"0.54585177\",\n \"0.54563004\",\n \"0.5449673\",\n \"0.5433273\",\n \"0.54312855\",\n \"0.5426266\",\n \"0.54250324\",\n \"0.542044\",\n \"0.5420337\",\n \"0.5401231\",\n \"0.54002386\",\n \"0.5397853\",\n \"0.5396041\",\n \"0.539095\",\n \"0.53897685\",\n \"0.5389414\",\n \"0.5385449\",\n \"0.5364367\",\n \"0.5361424\",\n \"0.5360036\",\n \"0.5359528\",\n \"0.53561825\",\n \"0.5351823\",\n \"0.5349992\",\n \"0.53480315\",\n \"0.53473103\",\n \"0.53345513\",\n \"0.53342354\",\n \"0.53206193\",\n \"0.5319465\",\n \"0.5313372\",\n \"0.53076994\",\n \"0.53047204\",\n \"0.5304286\",\n \"0.5303629\",\n \"0.5303207\",\n \"0.5301299\",\n \"0.5296986\",\n \"0.52914965\",\n \"0.5288457\",\n \"0.5270548\",\n \"0.52608734\",\n \"0.5258581\",\n \"0.5257278\",\n \"0.5249315\",\n \"0.52469504\",\n \"0.52462125\",\n \"0.52457315\",\n \"0.5245063\",\n \"0.5243656\",\n \"0.5237008\",\n \"0.52336365\",\n \"0.5232882\",\n \"0.5232803\",\n \"0.52240723\",\n \"0.5217285\"\n]","isConversational":false},"document_score":{"kind":"string","value":"0.6792724"},"document_rank":{"kind":"string","value":"1"}}},{"rowIdx":22687,"cells":{"query":{"kind":"string","value":"Given lists of valid rows and columns, generate a sparse matrix mask with missing pixels denoted as 1 and valid pixels as 0. If a max_dist is provided, upper symmetric matrices will only be flagged up to max_dist pixels from the diagonal."},"document":{"kind":"string","value":"def make_missing_mask(\n shape, valid_rows, valid_cols, max_dist=None, sym_upper=False\n):\n # Error if the matrix upper symmetric but shape is rectangle or missing\n # rows and cols are different\n sm, sn = shape\n if sym_upper and (sm != sn or len(valid_rows) != len(valid_cols)):\n raise ValueError(\"Rectangular matrices cannot be upper symmetric\")\n\n # Get a boolean array of missing (1) and valid (0) rows\n missing_rows = valid_to_missing(valid_rows, sm)\n # When matrix is sym., rows and cols are synonym, no need to compute 2x\n if sym_upper:\n missing_cols = missing_rows\n else:\n missing_cols = valid_to_missing(valid_cols, sn)\n # If upper sym., fill only upper diag up to max_dist.\n # E. g. with bins 1 and 3 missing\n # and a max_dist of 1:\n # 0 1 0 0 0\n # 0 1 1 0 0\n # 0 0 0 1 0\n # 0 0 0 1 1\n # 0 0 0 0 0\n # For each missing bin, mask is apply 1 pixel upwards and 1 to the right\n # to fill only the upper triangle up to max_dist\n if sym_upper:\n # If no max dist has been specified, fill the whole upper triangle\n if max_dist is None:\n max_dist = min(shape)\n # Generate matrix of distance shifts by row.\n # Shape is len(missing_rows) x (max_dist + 1)\n # e.g.: 2 missing rows and max dist of 1\n # 0 0\n # 1 1\n row_shifts = np.tile(\n np.array(range(max_dist + 1)), (len(missing_rows), 1)\n ).T\n # Compute row positions upwards to diagonal by subtracting missing rows\n # to the shifts. Following the previous example, if missing rows are\n # bins 1 and 3:\n # 1 3\n # 0 2\n rows_before = (missing_rows - row_shifts).flatten(\"F\")\n # looking at pixels up from the bins, cols remain the same:\n # 1 3\n # 1 3\n cols_before = np.repeat(missing_rows, max_dist+1)\n # Compute col position to the right until diagonal by adding the shift\n # Note: upper symmetric, so row_shifts = col_shift_\n # 1 3\n # 2 4\n cols_after = (missing_cols + row_shifts).flatten(\"F\")\n # This time, rows remain constant since we are computing positions to\n # the right\n rows_after = np.repeat(missing_cols, max_dist+1)\n # Combine positions to the right and upwards\n rows = np.concatenate([rows_before, rows_after])\n cols = np.concatenate([cols_before, cols_after])\n data = np.ones(rows.shape, dtype=bool)\n # Remove entries where rows or cols are negative or larger than shape\n valid = (cols < sm) & (cols >= 0) & (rows < sm) & (rows >= 0)\n # Build mask mat with miss bins up to max scan dist in upper triangle\n mask = sp.coo_matrix(\n (data[valid], (rows[valid], cols[valid])), shape=shape, dtype=bool\n ).tocsr()\n else:\n mask = sp.csr_matrix(shape, dtype=bool)\n mask[missing_rows, :] = 1\n mask[:, missing_cols] = 1\n\n return mask"},"metadata":{"kind":"string","value":"{\n \"objective\": {\n \"self\": [],\n \"paired\": [],\n \"triplet\": [\n [\n \"query\",\n \"document\",\n \"negatives\"\n ]\n ]\n }\n}"},"negatives":{"kind":"list like","value":["def frame_missing_mask(mask, kernel_shape, sym_upper=False, max_dist=None):\n if mask.dtype != bool:\n raise ValueError(\"Mask must contain boolean values\")\n if not sp.issparse(mask):\n raise ValueError(\"Mask must be a sparse matrix\")\n\n framed_mask = mask.copy()\n ms, ns = mask.shape\n mk, nk = kernel_shape\n if sym_upper and (max_dist is not None):\n # Remove diagonals further than scan distance in the input mask\n framed_mask = diag_trim(framed_mask, max_dist + max(nk, mk)).tocsr()\n max_m = max_dist + mk\n max_n = max_dist + nk\n else:\n max_m, max_n = ms, ns\n # Up and down margins initialized with zeros and filled as needed\n margin_1 = sp.csr_matrix((mk - 1, ns), dtype=bool)\n margin_2 = sp.csr_matrix((mk - 1, ns), dtype=bool)\n if sym_upper and (max_dist is not None):\n # Margin 1 (top) is in upper triangle -> fill missing up to scan dist\n margin_1[:, :max_n] = 1\n else:\n margin_1[:, :] = 1\n margin_2[:, :] = 1\n framed_mask = sp.vstack([margin_1, framed_mask, margin_2], format=\"csr\")\n\n # Left and right\n margin_1 = sp.csr_matrix((ms + 2 * (mk - 1), nk - 1), dtype=bool)\n margin_2 = sp.csr_matrix((ms + 2 * (mk - 1), nk - 1), dtype=bool)\n\n if sym_upper and (max_dist is not None):\n # Margin 2 (right) is in upper triangle-> fill missing up to scan dist\n margin_2[-(max_m + 1) :, :] = 1\n # Fill only the start of left margin for the top-left corner\n margin_1[: mk - 1, :] = 1\n else:\n margin_1[:, :] = 1\n margin_2[:, :] = 1\n framed_mask = sp.hstack([margin_1, framed_mask, margin_2], format=\"csr\")\n\n if sym_upper:\n # LIL format is much faster when changing sparsity\n framed_mask = framed_mask.tolil()\n # Add margin below diagonal\n big_k = max(nk, mk)\n dia_margins = np.ones(big_k)\n dia_offsets = np.arange(-1, -big_k-1, -1)\n framed_mask += sp.diags(\n dia_margins,\n dia_offsets,\n shape=framed_mask.shape,\n format=\"lil\",\n dtype=bool,\n )\n framed_mask = framed_mask.tocsr()\n return framed_mask","def ensure_sparse_cols(self,max_density,remove_lowest=True):\n if max_density >= 1:\n max_nnz = int(max_density)\n else:\n max_nnz = int(max_density*self.shape[0])\n for j in range(self.shape[1]):\n col = self.fast_get_col(j)\n excess = col.nnz - max_nnz\n if excess > 0:\n if remove_lowest:\n zero_entries = np.argsort(col.data)[:excess]\n else:\n zero_entries = random.sample(range(col.nnz),excess)\n col.data[zero_entries] = 0\n self.fast_update_col(j,col.data)","def makesparse(matrix):\n n = matrix[0].size\n elements = []\n for i in range(n):\n for j in range(n):\n if matrix[i][j] != 0 :\n temp = MatrixElement(i, j, matrix[i][j])\n elements.append(temp)\n return SparseMatrix(n, elements)","def mask_sparse(self, threshold=10):\n self.MaskPrefix = 's' + self.MaskPrefix\n print('Masking pixels that do not have at least {0} coherent values'.format(threshold))\n # each pixel assigned an integer corresponding to # of igrams where coherent\n # NOTE: save coverage map if it doesn't exist already\n coverage = self.get_coverage()\n sparse = ma.masked_less(coverage, threshold)\n for ig in self.Set:\n igram = self.load_ma(ig)\n igram[sparse.mask] = ma.masked\n self.save_ma(ig, igram)\n print('Done')","def list_to_sparse(inputs):\n\n data = list(itertools.chain(*inputs))\n row = list(itertools.chain(\n *[itertools.repeat(i, len(x)) for i, x in enumerate(inputs)]))\n col = list(itertools.chain(*[range(len(x)) for x in inputs]))\n\n s = coo_matrix((data, (row, col)), shape=(\n len(inputs), np.max([len(x) for x in inputs])))\n\n return s","def _set_builder_fast(m, distances):\n\n if not is_matrix(m):\n raise ValueError(\"Expected numpy matrix\")\n\n if not is_matrix_binary(m):\n raise ValueError(\"Expected binary matrix\")\n\n if not is_matrix_correct(m):\n raise ValueError(\"Expected matrix that has valid structure (not more than one '1' in row)\")\n\n n, k = np.shape(m)\n ones_count, one_pos, has_one, where_one, zero_rows = get_matrix_info(m, n)\n\n # run through all distances in D\n for distance in distances:\n\n # check that distance is correct\n if distance < 0 or distance > ones_count + n:\n continue\n\n if distance == 0:\n yield m.copy()\n continue\n\n for num_ones_removed in range(np.min([distance, ones_count]) + 1):\n for clear_rows in combinations(one_pos[0], num_ones_removed):\n\n cleared_matrix = m.copy()\n cleared_matrix[clear_rows, :] = 0\n remained_rows = np.array(list((set(range(n)) - set(one_pos[0])).union(set(clear_rows))))\n\n for rows in combinations(remained_rows, distance - num_ones_removed):\n\n if len(rows) == 0:\n yield cleared_matrix.copy()\n continue\n\n avaliable_column_indices = [get_zero_indices(where_one[row], k) for row in rows]\n\n rows = np.array(rows, dtype=np.int32)\n rows_count = rows.size\n\n if (k ** rows_count)*n*k*4//1024//1024 > 1024:\n raise ValueError(\"Looks that you don't have enough RAM\")\n\n indices = np.array(np.meshgrid(*avaliable_column_indices)).T.reshape(-1, rows_count)\n\n cache_size = np.shape(indices)[0]\n\n indices += rows*k\n indices = (indices.T + np.arange(cache_size)*(n*k)).T\n indices = indices.reshape(indices.size)\n\n matrix_cache = np.tile(cleared_matrix, (cache_size, 1, 1))\n matrix_cache.put(indices, 1)\n\n # print(\"Mb for cache: \" + str((cache_size*rows_count + cache_size*n*k)*4//1024//1024))\n\n for mat in matrix_cache:\n yield mat","def generate_inpaint_mask(n_samples, n_colors, spatial_width):\n mask = np.zeros((n_samples, n_colors, spatial_width, spatial_width), dtype=bool)\n # simple mask -- just mask out half the image\n mask[:,:,:,spatial_width/2:] = True\n return mask.ravel()","def sparse_initialization_weights(self):\n W = []\n mu, sigma = 0, 1/self.non_zero_units\n\n for i in xrange(self.n_in):\n row = np.zeros(self.n_out)\n non_zeros = self.rng.normal(mu, sigma, self.non_zero_units)\n # non_zeros /= non_zeros.sum()\n non_zero_idxs = self.rng.permutation(\n self.n_out)[0:self.non_zero_units]\n for j in xrange(self.non_zero_units):\n row[non_zero_idxs[j]] = non_zeros[j]\n W.append(row)\n W = np.asarray(W, dtype=theano.config.floatX)\n return W","def idseqs_to_mask(idseqs: List[List[int]],\n n_seqlen: Optional[int] = None,\n n_vocab_sz: Optional[int] = None,\n ignore: Optional[List[int]] = [],\n dtype: Optional[torch.dtype] = torch.bool,\n dense: Optional[bool] = False\n ) -> torch.sparse.FloatTensor:\n if n_seqlen is None:\n n_seqlen = max([len(seq) for seq in idseqs])\n\n # create a list of IDs\n if n_vocab_sz is None:\n ids = set(itertools.chain(*idseqs))\n else:\n ids = set(range(0, n_vocab_sz))\n\n # remove IDs that we ignore\n ids = ids.difference(set(ignore))\n n_features = len(ids)\n\n # convert to list to lookup with .index() method\n ids = list(ids)\n\n # loop over each ID sequence\n masks = []\n for seq in idseqs:\n # extract index pairs of the sparse matrix\n featidx = []\n seqidx = []\n for step, elem in enumerate(seq[:n_seqlen]):\n try:\n featidx.append(ids.index(elem))\n seqidx.append(step)\n except Exception:\n pass\n # convert to COO matrix\n tmp = torch.sparse.FloatTensor(\n indices=torch.LongTensor([seqidx, featidx]),\n values=torch.FloatTensor([1.0 for _ in range(len(seqidx))]),\n size=torch.Size([n_seqlen, n_features])\n ).coalesce()\n # save it\n masks.append(tmp)\n\n # stack into one 3D tensor <batch_sz, n_seqlen, vocab_sz>\n masks = torch.stack(masks).coalesce()\n\n # convert to dense matrix if requested\n if dense:\n masks = masks.to_dense().type(dtype)\n\n # done\n return masks","def generate_random_sparse_array(nrows, ncols, numdense):\n i = np.random.randint(0, nrows, numdense)\n j = np.random.randint(0, ncols, numdense)\n data = np.random.randint(1,6, numdense)\n ij = np.vstack((i,j))\n return coo_matrix((data, ij), shape=(nrows, ncols))","def _generate_sketch_matrix(rand_h, rand_s, output_dim):\n\n # Generate a sparse matrix for tensor count sketch\n assert (rand_h.ndim == 1 and rand_s.ndim == 1 and len(rand_h) == len(rand_s))\n assert (np.all(rand_h >= 0) and np.all(rand_h < output_dim))\n\n input_dim = len(rand_h)\n indices = np.concatenate((np.arange(input_dim)[..., np.newaxis],\n rand_h[..., np.newaxis]), axis=1)\n i = torch.LongTensor(indices).t()\n v = torch.IntTensor(rand_s)\n sparse_sketch_matrix = torch.sparse.IntTensor(i, v, torch.Size([input_dim, output_dim] )).to_dense().float().cuda()\n # I used to want to used sparse matrix, but the autograd is not suported.\n return Variable(sparse_sketch_matrix)","def generate_square_subsequent_mask(sz: int, device: torch.device) -> torch.Tensor:\n mask = (torch.triu(torch.ones(sz, sz, device=device)) == 1).transpose(0, 1)\n mask = (\n mask.float()\n .masked_fill(mask == 0, float(\"-inf\"))\n .masked_fill(mask == 1, float(0.0))\n )\n return mask","def test_sparse_distance_matrix(coordinates, max_dist, p):\n original_tree = scipyKDTree(coordinates)\n redis_tree = redisKDTree(coordinates)\n\n original_output = original_tree.sparse_distance_matrix(original_tree, max_dist, p)\n redis_output = redis_tree.sparse_distance_matrix(redis_tree, max_dist, p)\n\n assert dict_close(redis_output, original_output)","def symmetric_submatrix(self, *clip_ids):\n with self._rw_lock.read_lock():\n with SimpleTimer(\"Checking inputs\", self._log):\n if not self.is_symmetric():\n raise RuntimeError(\"Cannot get a symmetric sub-matrix if \"\n \"the kernel is not square!\")\n # DEPRECATED: Allowing the use of this method without explicitly\n # providing background cIDs. This object will\n # probably not ever be used this way, but there's no\n # reason to explicitly disallow it.\n # if self._bg_cid_vec is None:\n # raise RuntimeError(\"Cannot create the square submatrix \"\n # \"without the background flag vector!\")\n\n try:\n clip_ids = [int(e) for e in clip_ids]\n except:\n raise ValueError(\"Not all clip IDs could be used as ints! \"\n \"Given: %s\" % clip_ids)\n\n id_diff = set(clip_ids).difference(self._row_id_index_map)\n assert not id_diff, \\\n \"Not all clip IDs provided are represented in this \" \\\n \"distance kernel matrix! (difference: %s)\" \\\n % id_diff\n del id_diff\n\n with SimpleTimer(\"Computing union of BG clips and provided IDs\",\n self._log):\n if self._bg_cid_set is not None:\n all_cids = self._bg_cid_set.union(clip_ids)\n else:\n all_cids = set(clip_ids)\n\n # Reorder the given clip IDs so that they are in the same relative\n # order as the kernel matrix edges.\n focus_indices = []\n focus_clipids = []\n for idx, cid in enumerate(self._row_id_index_map):\n if (cid in all_cids) and (cid not in focus_clipids):\n focus_indices.append(idx)\n focus_clipids.append(cid)\n\n # index-to-isBG map for return\n # -> IDs provided as arguments are to be considered non-background,\n # even if a the ID is in the background set. All other IDs in the\n # union then must be from the background set.\n focus_id2isbg = []\n for idx in focus_indices:\n cid = self._row_id_index_map[idx]\n focus_id2isbg.append(False if cid in clip_ids else True)\n\n # Create new matrix from row-column intersections\n ret_mat = self._kernel[focus_indices, :][:, focus_indices]\n\n return focus_clipids, focus_id2isbg, ret_mat","def set_zero(mn_matrix): \r\n \r\n row_flag = [0]*len(mn_matrix)\r\n column_flag = [0]*len(mn_matrix[0])\r\n for i in range(0, len(row_flag)):\r\n for j in range(0, len(column_flag)):\r\n if mn_matrix[i][j] == 0:\r\n row_flag[i] = 1\r\n column_flag[j] = 1\r\n \r\n for i in range(0, len(row_flag)):\r\n for j in range(0, len(column_flag)):\r\n if row_flag[i] == 1 or column_flag[j] == 1: \r\n mn_matrix[i][j] = 0","def remove_bad_cells(self, *dims):\n ranges = [DimRange(d, 0, np.inf) for d in dims]\n return self.gate(*ranges)","def create_hop_matrix(G, max_hops, node_list):\n distances = dict(nx.all_pairs_dijkstra_path_length(G))\n hop_matrix = torch.zeros(max_hops, G.number_of_nodes(), G.number_of_nodes(), dtype=torch.int)\n for hop in range(max_hops):\n for i, node_from in enumerate(node_list):\n for j, node_to in enumerate(node_list):\n if node_to in distances[node_from].keys() and distances[node_from][node_to] == hop:\n hop_matrix[hop,i,j] = 1\n return hop_matrix","def validity_mask(tetrimino, columns):\n validity = np.zeros((4, columns), dtype=bool)\n for i, t in enumerate(tetrimino_dict[tetrimino]):\n l = columns - t.shape[1] + 1\n validity[i, :l] = 1\n return validity.flatten()","def to_sparse_matrix(self, grid, format=None):\n S = self.centered_stencil()\n # print(\"grid :\")\n\n grid = tuple(grid)\n # print(grid)\n if not (np.asarray(S.shape) % 2 == 1).all():\n raise ValueError('all stencil dimensions must be odd')\n\n assert_condition(len(grid) == np.rank(S), ValueError,\n 'stencil rank must equal number of grid dimensions')\n assert_condition(min(grid) >= 1, ValueError,\n 'grid dimensions must be positive')\n\n N_v = np.prod(grid) # number of vertices in the mesh\n N_s = (S != 0).sum() # number of nonzero stencil entries\n\n # diagonal offsets\n diags = np.zeros(N_s, dtype=int)\n\n # compute index offset of each dof within the stencil\n strides = np.cumprod([1] + list(reversed(grid)))[:-1]\n indices = tuple(i.copy() for i in S.nonzero())\n for i,s in zip(indices,S.shape):\n i -= s // 2\n for stride,coords in zip(strides, reversed(indices)):\n diags += stride * coords\n\n #\n data = S[S != 0].repeat(N_v).reshape(N_s, N_v)\n indices = np.vstack(indices).T\n\n # zero boundary connections\n for index,diag in zip(indices,data):\n diag = diag.reshape(grid)\n for n,i in enumerate(index):\n if i > 0:\n s = [ slice(None) ]*len(grid)\n s[n] = slice(0,i)\n diag[s] = 0\n elif i < 0:\n s = [ slice(None) ]*len(grid)\n s[n] = slice(i,None)\n diag[s] = 0\n\n # remove diagonals that lie outside matrix\n mask = abs(diags) < N_v\n if not mask.all():\n diags = diags[mask]\n data = data[mask]\n\n # sum duplicate diagonals\n if len(np.unique(diags)) != len(diags):\n new_diags = np.unique(diags)\n new_data = np.zeros( (len(new_diags),data.shape[1]), dtype=data.dtype)\n for dia,dat in zip(diags,data):\n n = np.searchsorted(new_diags,dia)\n new_data[n,:] += dat\n\n diags = new_diags\n data = new_data\n\n return sprs.dia_matrix((data,diags), shape=(N_v, N_v)).asformat(format)","def zero_matrix(matrix):\n rows = set()\n columns = set()\n m = len(matrix)\n n = len(matrix[0])\n for i in range(m):\n for j in range(n):\n if matrix[i][j] == 0:\n rows.add(i)\n columns.add(j)\n\n for i in range(m):\n for j in range(n):\n if i in rows or j in columns:\n matrix[i][j] = 0\n return matrix","def get_diagonal_mask(data):\n mask = np.zeros_like(data, dtype=np.bool)\n mask[np.triu_indices_from(mask)] = True\n return mask","def _fix_uniq_col(self):\n # subgradient; for two boolean arrays, multiplication seems to be the best way \n # (equivalent to logical_and)\n n_covered_col = self.a_csr.dot(np.ones(self.ncols)) \n ifix = np.zeros(self.ncols, dtype=bool)\n if (np.count_nonzero(n_covered_col) != self.mrows):\n raise ValueError(\"There are uncovered rows! Please check your input!\")\n if (np.any(n_covered_col==1)):\n inonzero = self.a_csr[n_covered_col==1,:].nonzero()\n ifix[inonzero[1]] = True\n\n return ifix","def compress_csr(self):\n _, unique, indices = np.unique(\n self.m*self.rows + self.cols,\n return_index=True, return_inverse=True)\n self.rows = self.rows[unique]\n self.cols = self.cols[unique]\n self.vals = np.bincount(indices, weights=self.vals)","def simulate_sparse(n_rows, n_columns, row_density=0.001):\n # X = sparse.lil_matrix((n_rows, n_columns))\n X = []\n counter = 0\n for row in range(n_rows):\n counter += 1\n if (counter % 1000) == 0:\n print(counter)\n\n # X[row] = sparse.random(1, n_columns, density=row_density)\n X.append(sparse.random(1, n_columns, density=row_density, format='lil'))\n\n return sparse.vstack(X).tocsr()","def generate_sparse(n, s):\n x = np.zeros(n)\n I = np.random.randint(0, n, s)\n x[I] = 1\n return x","def Problem2(n):\n diag_entries = np.empty((3,n))\n diag_entries[0] = np.ones(n)*(-1)\n diag_entries[1] = np.ones(n)*2\n diag_entries[2] = np.ones(n)*(-1)\n A = sparse.spdiags(diag_entries, [-1,0,1],n,n,format=\"csr\")\n return A","def makeZero(m, n):\n Matrix.validate_dimensions(m, n)\n data = [[False for j in range(n)] for i in range(m)]\n return BooleanMatrix(m, n, data)","def generate_sketch_matrix(rand_h, rand_s, output_dim):\n\n # Generate a sparse matrix for tensor count sketch\n rand_h = rand_h.astype(np.int64)\n rand_s = rand_s.astype(np.float32)\n assert(rand_h.ndim == 1 and rand_s.ndim ==\n 1 and len(rand_h) == len(rand_s))\n assert(np.all(rand_h >= 0) and np.all(rand_h < output_dim))\n\n input_dim = len(rand_h)\n indices = np.concatenate((np.arange(input_dim)[..., np.newaxis],\n rand_h[..., np.newaxis]), axis=1)\n indices = torch.from_numpy(indices)\n rand_s = torch.from_numpy(rand_s)\n sparse_sketch_matrix = torch.sparse.FloatTensor(\n indices.t(), rand_s, torch.Size([input_dim, output_dim]))\n return sparse_sketch_matrix.to_dense().cuda()","def generate_sketch_matrix(rand_h, rand_s, output_dim):\n\n # Generate a sparse matrix for tensor count sketch\n rand_h = rand_h.astype(np.int64)\n rand_s = rand_s.astype(np.float32)\n assert(rand_h.ndim == 1 and rand_s.ndim ==\n 1 and len(rand_h) == len(rand_s))\n assert(np.all(rand_h >= 0) and np.all(rand_h < output_dim))\n\n input_dim = len(rand_h)\n indices = np.concatenate((np.arange(input_dim)[..., np.newaxis],\n rand_h[..., np.newaxis]), axis=1)\n indices = torch.from_numpy(indices)\n rand_s = torch.from_numpy(rand_s)\n sparse_sketch_matrix = torch.sparse.FloatTensor(\n indices.t(), rand_s, torch.Size([input_dim, output_dim]))\n return sparse_sketch_matrix.to_dense().cuda()","def _freespace_matrix(distance):\n\n return np.array([[1., distance], [0., 1.]])","def mask(n, start, end):\n columns = []\n value = 1\n for i in range(n):\n if start <= end:\n columns.append(value if (start <= i < end) else 0)\n else:\n columns.append(value if (start <= i or i < end) else 0)\n value <<= 1\n return BitColumnMatrix(columns)","def create_sparse_coo_matrix(df, n_users, n_items, movie_dict):\n\n\t# Map the movie_ids in the data to the new movie_ids given by the dictionary movie_dict\n\tmovie_id_list = list(map(lambda x: movie_dict[x], df['movieId'].tolist()))\n\t# Map the user_id in the dataframe to userid - 1 [to account for zero based indexing]\n\tuser_id_list = list(map(lambda x: x - 1, df['userId'].tolist()))\n\tsparse_matrix_coo = sparse.coo_matrix((df['rating'].tolist(),(user_id_list, movie_id_list)),shape=(n_users,n_items))\n\tlogger.debug(\"Shape of created sparse matrix: \" + str(sparse_matrix_coo.shape))\n\tlogger.debug(\"Number of non_zero elements in the sparse matrix: \" + str(sparse_matrix_coo.nnz))\n\tlogger.debug(\"Number of entries in the input dataframe:[should match the number of non zero entries in sparse matrix] \" + str(df.shape[0]))\n\treturn sparse_matrix_coo","def compute_sparse_masks(cls):\n with torch.no_grad():\n for module_name, module, p_name, p, mask, pruned in cls.__sparse_parameters:\n if mask.sum() < mask.numel(): # when recalculating masks\n # restore dense parameter if allow_recompute_mask is enabled\n assert (pruned is not None), \"Unable to restore dense parameter because allow_recompute_mask == False\"\n p.add_(pruned.cuda())\n\n mask.set_(cls.__calculate_mask(p))\n\n if pruned is not None: # stow away pruned weights to cpu\n pruned.set_((p * (~mask)).cpu())\n\n p.mul_(mask) # in-place multiplication, so pruned weights are 0-values, hence checkpoint will have 0s for pruned weights\n if cls.__verbosity >= 2:\n print(\"[ASP] Enabled %.2f%% sparsity for %s::%s of size=%s and type=%s\" % (100.0*mask.sum()/mask.numel(), module_name, p_name, str(p.size()), str(p.dtype)))","def zero_matrix(matrix):\n n = len(matrix)\n if n == 0:\n return True\n\n m = len(matrix[0])\n if m == 0:\n return True\n\n zero_rows = set()\n zero_columns = set()\n for i in range(n):\n for j in range(m):\n if matrix[i][j] == 0:\n zero_rows.add(i)\n zero_columns.add(j)\n \n for i in zero_rows:\n for j in range(m):\n matrix[i][j] = 0\n\n for i in range(n):\n for j in zero_columns:\n matrix[i][j] = 0\n\n return True","def create_diagonal_mask(low_to_high_map, target_value=1):\n low_to_high_map.drop_duplicates()\n grouped = low_to_high_map.groupby(low_to_high_map.columns[1])\n ordered_low_level_names = list()\n group_matrices = []\n for name, group in grouped:\n group_size = group.shape[0]\n # build up row/col names, order doesn't matter within a group = they are all equal\n ordered_low_level_names = ordered_low_level_names + group.iloc[:, 0].tolist()\n # set the diagonal matrix to be the target value\n single_group_matrix = np.full(shape=(group_size, group_size), fill_value=target_value)\n group_matrices.append(single_group_matrix)\n # add the individual matrices along the diagonal\n relationship_matrix = scipy.linalg.block_diag(*group_matrices)\n # convert to pandas dataframe and set names\n relationship_df = pd.DataFrame(relationship_matrix, columns=ordered_low_level_names, index=ordered_low_level_names)\n\n return relationship_df","def make_weight_map(masks, w0=10, sigma=5, longest_max_size=-1):\n nrows, ncols = masks.shape[1:]\n\n if longest_max_size > 0:\n old_rows, old_cols = nrows, ncols\n max_size = max(nrows, ncols)\n new_rows, new_cols = longest_max_size * nrows // max_size, longest_max_size * ncols // max_size\n\n resized_masks = []\n for mask in masks:\n resized_masks.append(cv2.resize(mask, (new_cols, new_rows), interpolation=0))\n masks = np.stack(resized_masks)\n nrows, ncols = new_rows, new_cols\n\n masks = (masks > 0).astype(int)\n distMap = np.zeros((nrows * ncols, masks.shape[0]))\n X1, Y1 = np.meshgrid(np.arange(nrows), np.arange(ncols))\n X1, Y1 = np.c_[X1.ravel(), Y1.ravel()].T\n for i, mask in enumerate(masks):\n # find the boundary of each mask,\n # compute the distance of each pixel from this boundary\n bounds = find_boundaries(mask, mode='inner')\n X2, Y2 = np.nonzero(bounds)\n xSum = (X2.reshape(-1, 1) - X1.reshape(1, -1)) ** 2\n ySum = (Y2.reshape(-1, 1) - Y1.reshape(1, -1)) ** 2\n distMap[:, i] = np.sqrt(xSum + ySum).min(axis=0)\n ix = np.arange(distMap.shape[0])\n if distMap.shape[1] == 1:\n d1 = distMap.ravel()\n border_loss_map = w0 * np.exp((-1 * (d1) ** 2) / (2 * (sigma ** 2)))\n else:\n if distMap.shape[1] == 2:\n d1_ix, d2_ix = np.argpartition(distMap, 1, axis=1)[:, :2].T\n else:\n d1_ix, d2_ix = np.argpartition(distMap, 2, axis=1)[:, :2].T\n d1 = distMap[ix, d1_ix]\n d2 = distMap[ix, d2_ix]\n border_loss_map = w0 * np.exp((-1 * (d1 + d2) ** 2) / (2 * (sigma ** 2)))\n xBLoss = np.zeros((nrows, ncols))\n xBLoss[X1, Y1] = border_loss_map\n # class weight map\n loss = np.zeros((nrows, ncols))\n w_1 = 1 - masks.sum() / loss.size\n w_0 = 1 - w_1\n loss[masks.sum(0) == 1] = w_1\n loss[masks.sum(0) == 0] = w_0\n ZZ = xBLoss + loss\n\n if longest_max_size > 0:\n ZZ = cv2.resize(ZZ, (old_cols, old_rows))\n return ZZ","def mask_infeasible(self):\n ns = len(self)-1\n # mask entries with i+j+k > ns\n for ii in range(len(self)):\n for jj in range(len(self)):\n for kk in range(len(self)):\n if ii+jj+kk > ns:\n self.mask[ii,jj,kk] = True\n \n return self","def _sample_from_zeros(n: int, sparse: sp.csr_matrix) -> List[List[int]]:\n zeros = np.argwhere(np.logical_not(sparse.todense()))\n ids = np.random.choice(range(len(zeros)), size=(n,))\n return zeros[ids].tolist()","def _make_causal_mask(\n input_ids_shape: torch.Size, dtype: torch.dtype, device: torch.device, past_key_values_length: int = 0\n):\n bsz, tgt_len = input_ids_shape\n mask = torch.full((tgt_len, tgt_len), torch.finfo(dtype).min, device=device)\n mask_cond = torch.arange(mask.size(-1), device=device)\n mask.masked_fill_(mask_cond < (mask_cond + 1).view(mask.size(-1), 1), 0)\n mask = mask.to(dtype)\n\n if past_key_values_length > 0:\n mask = torch.cat([torch.zeros(tgt_len, past_key_values_length, dtype=dtype, device=device), mask], dim=-1)\n return mask[None, None, :, :].expand(bsz, 1, tgt_len, tgt_len + past_key_values_length)","def update_distmatrix(min_idx, dist_matrix):\n i, j = min_idx\n new_cluster_dist = np.minimum(dist_matrix[i, :], dist_matrix[j, :])\n new_cluster_dist[i] = np.inf\n \n dist_matrix[i, :] = new_cluster_dist\n dist_matrix[:, i] = new_cluster_dist\n \n dist_matrix[j, :] = np.inf\n dist_matrix[:, j] = np.inf\n \n return dist_matrix","def _generate_square_subsequent_mask(self, sz: int):\n mask = (torch.triu(torch.ones(sz, sz)) == 1).transpose(0, 1)\n mask = mask.float().masked_fill(mask == 0, float(\n '-inf')).masked_fill(mask == 1, float(0.0))\n return mask","def init_zero_matrix(self,rows,cols):\n\t\ttmpMatrix = []\n\t\tfor i in range(rows):\n\t\t\ttmp = [0 for j in range(cols)]\n\t\t\ttmpMatrix.append(tmp)\n\t\treturn tmpMatrix","def _get_support_mask(self):\n mask = np.zeros(self.scores_.shape, dtype=bool)\n mask[self.scores_ >= self.min_count] = True\n return mask","def get_mask(m, n, i):\n\n row = i // n\n col = i % n\n \n ind = np.arange(m*n)\n \n mask = np.bitwise_and(np.abs(ind % n - col) <= 1, # indicies are 1 (or less) columns away\n np.abs(ind // n - row) <= 1) # and indicies are 1 (or less) rows away\n mask = mask.astype(int)\n return mask","def test_build_classification_matrix_sparse():\n import scnym\n\n # generate a sparse matrix with ~10% of elements filled\n B = np.zeros((100, 10))\n ridx = np.random.choice(B.size, size=100, replace=True)\n B.flat[ridx] = 1\n B = sparse.csr_matrix(B)\n\n # create dummy gene names where the order of\n # genes in `B` is permuted\n A_genes = np.arange(10)\n B_genes = np.random.permutation(A_genes)\n\n # build the classification matrix\n X = scnym.utils.build_classification_matrix(\n X=B,\n model_genes=A_genes,\n sample_genes=B_genes,\n )\n assert sparse.issparse(X)\n\n # X should have the genes of B in the order of A\n for i, g in enumerate(A_genes):\n j = int(np.where(B_genes == g)[0])\n assert np.all(X[:, i].toarray() == B[:, j].toarray())\n return","def miserables(metadata: bool = False) -> Union[sparse.csr_matrix, Bunch]:\n row = np.array(\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 2, 2, 3, 10, 11, 11, 11, 11, 11, 11, 11, 11, 11, 11,\n 11, 11, 11, 11, 11, 11, 11, 11, 11, 11, 11, 11, 11, 11, 11, 11, 11, 11, 11, 11, 11, 11, 12,\n 16, 16, 16, 16, 16, 16, 16, 16, 16, 17, 17, 17, 17, 17, 17, 18, 18, 18, 18, 18, 19, 19, 19, 19,\n 20, 20, 20, 21, 21, 22, 23, 23, 23, 23, 23, 23, 24, 24, 24, 24, 24, 24, 24, 24, 24, 25, 25, 25,\n 25, 25, 25, 25, 25, 25, 25, 25, 25, 25, 26, 26, 26, 26, 26, 26, 26, 27, 27, 27, 27, 27, 27, 27,\n 27, 27, 27, 27, 27, 28, 28, 29, 29, 29, 29, 29, 30, 34, 34, 34, 34, 35, 35, 35, 36, 36, 37, 39,\n 39, 41, 41, 41, 41, 41, 41, 41, 41, 41, 46, 47, 48, 48, 48, 48, 48, 48, 48, 48, 48, 48, 48, 48,\n 48, 48, 48, 48, 48, 48, 49, 49, 49, 49, 49, 51, 51, 51, 51, 54, 55, 55, 55, 55, 55, 55, 55, 55,\n 55, 57, 57, 57, 57, 57, 57, 57, 57, 58, 58, 58, 58, 58, 58, 58, 58, 58, 58, 59, 59, 59, 59, 59,\n 59, 59, 60, 60, 60, 60, 60, 60, 61, 61, 61, 61, 61, 62, 62, 62, 62, 62, 63, 63, 63, 63, 64, 64,\n 64, 65, 65, 66, 68, 68, 68, 68, 69, 69, 69, 70, 70, 71, 73])\n col = np.array(\n [1, 2, 3, 4, 5, 6, 7, 8, 9, 11, 3, 11, 11, 11, 12, 13, 14,\n 15, 23, 24, 25, 26, 27, 28, 29, 31, 32, 33, 34, 35, 36, 37, 38, 43,\n 44, 48, 49, 51, 55, 58, 64, 68, 69, 70, 71, 72, 23, 17, 18, 19, 20,\n 21, 22, 23, 26, 55, 18, 19, 20, 21, 22, 23, 19, 20, 21, 22, 23, 20,\n 21, 22, 23, 21, 22, 23, 22, 23, 23, 24, 25, 27, 29, 30, 31, 25, 26,\n 27, 41, 42, 50, 68, 69, 70, 26, 27, 39, 40, 41, 42, 48, 55, 68, 69,\n 70, 71, 75, 27, 43, 49, 51, 54, 55, 72, 28, 29, 31, 33, 43, 48, 58,\n 68, 69, 70, 71, 72, 44, 45, 34, 35, 36, 37, 38, 31, 35, 36, 37, 38,\n 36, 37, 38, 37, 38, 38, 52, 55, 42, 55, 57, 62, 68, 69, 70, 71, 75,\n 47, 48, 55, 57, 58, 59, 60, 61, 62, 63, 64, 65, 66, 68, 69, 71, 73,\n 74, 75, 76, 50, 51, 54, 55, 56, 52, 53, 54, 55, 55, 56, 57, 58, 59,\n 61, 62, 63, 64, 65, 58, 59, 61, 62, 63, 64, 65, 67, 59, 60, 61, 62,\n 63, 64, 65, 66, 70, 76, 60, 61, 62, 63, 64, 65, 66, 61, 62, 63, 64,\n 65, 66, 62, 63, 64, 65, 66, 63, 64, 65, 66, 76, 64, 65, 66, 76, 65,\n 66, 76, 66, 76, 76, 69, 70, 71, 75, 70, 71, 75, 71, 75, 75, 74])\n data = np.array(\n [1, 8, 10, 1, 1, 1, 1, 2, 1, 5, 6, 3, 3, 1, 1, 1, 1,\n 1, 9, 7, 12, 31, 17, 8, 2, 3, 1, 2, 3, 3, 2, 2, 2, 3,\n 1, 1, 2, 2, 19, 4, 1, 1, 1, 1, 1, 1, 2, 4, 4, 4, 3,\n 3, 3, 3, 1, 1, 4, 4, 3, 3, 3, 3, 4, 3, 3, 3, 3, 4,\n 3, 3, 3, 5, 4, 4, 4, 4, 4, 2, 1, 5, 1, 1, 2, 13, 4,\n 1, 2, 1, 1, 1, 1, 1, 1, 5, 1, 1, 3, 2, 1, 2, 5, 6,\n 4, 1, 3, 1, 1, 3, 2, 1, 21, 2, 1, 1, 1, 1, 1, 1, 6,\n 1, 2, 1, 1, 1, 3, 2, 2, 2, 1, 1, 1, 2, 3, 2, 2, 2,\n 2, 2, 2, 2, 2, 2, 1, 1, 2, 5, 1, 1, 1, 1, 1, 1, 1,\n 1, 2, 4, 1, 7, 6, 1, 2, 7, 5, 5, 3, 1, 1, 1, 1, 2,\n 2, 1, 1, 1, 9, 1, 12, 1, 1, 1, 2, 6, 1, 1, 1, 7, 5,\n 1, 9, 1, 5, 2, 1, 2, 1, 2, 2, 1, 1, 3, 15, 4, 6, 17,\n 4, 10, 5, 3, 1, 1, 2, 5, 13, 5, 9, 5, 1, 2, 3, 2, 2,\n 2, 1, 6, 3, 6, 5, 1, 6, 12, 5, 2, 1, 4, 5, 1, 1, 7,\n 3, 1, 2, 1, 1, 6, 4, 2, 3, 4, 2, 3, 2, 1, 1, 3])\n adjacency = sparse.csr_matrix((data, (row, col)), shape=(77, 77))\n adjacency = adjacency + adjacency.T\n\n if metadata:\n names = ['Myriel', 'Napoleon', 'Mlle Baptistine', 'Mme Magloire', 'Countess de Lo', 'Geborand',\n 'Champtercier', 'Cravatte', 'Count', 'Old man', 'Labarre', 'Valjean', 'Marguerite', 'Mme Der',\n 'Isabeau', 'Gervais', 'Tholomyes', 'Listolier', 'Fameuil', 'Blacheville', 'Favourite', 'Dahlia',\n 'Zephine', 'Fantine', 'Mme Thenardier', 'Thenardier', 'Cosette', 'Javert', 'Fauchelevent',\n 'Bamatabois', 'Perpetue', 'Simplice', 'Scaufflaire', 'Woman1', 'Judge', 'Champmathieu', 'Brevet',\n 'Chenildieu', 'Cochepaille', 'Pontmercy', 'Boulatruelle', 'Eponine', 'Anzelma', 'Woman2',\n 'Mother Innocent', 'Gribier', 'Jondrette', 'Mme Burgon', 'Gavroche', 'Gillenormand', 'Magnon',\n 'Mlle Gillenormand', 'Mme Pontmercy', 'Mlle Vaubois', 'Lt Gillenormand', 'Marius', 'Baroness',\n 'Mabeuf', 'Enjolras', 'Combeferre', 'Prouvaire', 'Feuilly', 'Courfeyrac', 'Bahorel', 'Bossuet',\n 'Joly', 'Grantaire', 'MotherPlutarch', 'Gueulemer', 'Babet', 'Claquesous', 'Montparnasse',\n 'Toussaint', 'Child1', 'Child2', 'Brujon', 'Mme Hucheloup']\n x = np.array(\n [0.53, 0.98, 0.41, 0.4, 1., 0.92, 0.84, 0.74, 0.78, 1., 0.51, 0.09, -0., 0.29, 0.37,\n 0.41, -0.35, -0.46, -0.42, -0.46, -0.41, -0.37, -0.36, -0.2, -0.06, -0.04, -0.01, -0.02, 0.33,\n 0.17, -0.29, -0.1, 0.58, 0.29, 0.29, 0.26, 0.29, 0.37, 0.35, 0.04, -0.01, -0.18, -0.09,\n 0.2, 0.51, 0.7, -0.95, -0.7, -0.37, -0.08, -0.18, -0.05, 0.04, -0.12, -0.06, -0.13, -0.24, -0.48,\n -0.25, -0.33, -0.43, -0.39, -0.33, -0.42, -0.31, -0.38, -0.48, -0.74, -0.08, -0.1, -0.02, -0.1,\n 0.14, -0.76, -0.75, -0.18, -0.58])\n y = np.array(\n [-0.23, -0.42, -0.14, -0.18, -0.31, -0.52, -0.6, -0.65, -0.38, -0.19, 0.39, 0.03, 0.44, -0.44,\n 0.51, -0.36, 0.27, 0.37, 0.4, 0.32, 0.32, 0.36, 0.4, 0.2, 0.07, 0.14, -0.05, 0.06, 0.06,\n 0.24, -0.26, -0.1, 0.24, -0.04, 0.17, 0.23, 0.31, 0.21, 0.27, -0.36, 0.69, 0.11, 0.38, -0.09,\n 0.05, 0.12, 0.82, 0.44, 0.06, -0.2, -0.4, -0.28, -0.68, -0.79, -0.4, -0.07, -0.51, -0.17, -0.03,\n -0.09, -0.14, -0.04, -0.04, -0.07, -0.06, -0.11, -0.06, -0.35, 0.24, 0.19, 0.22, 0.29, -0.2,\n 0.06, 0.14, 0.3, -0.1])\n graph = Bunch()\n graph.adjacency = adjacency\n graph.names = np.array(names)\n graph.position = np.vstack((x, y)).T\n graph.name = 'miserables'\n return graph\n else:\n return adjacency","def zero_diag(mat):\n\n return replace_diag(mat, np.zeros(mat.shape[0]))","def mask_(matrices, maskval=0.0, mask_diagonal=True):\n\n b, h, w = matrices.size()\n\n indices = torch.triu_indices(h, w, offset=0 if mask_diagonal else 1)\n matrices[:, indices[0], indices[1]] = maskval","def _test_sparsemax_loss_of_inf(self, dtype, random, use_gpu):\n q = np.asarray([[0, 0, 1], [0, 0, 1], [0, 0, 1], [0, 0, 1]])\n z_neg = np.asarray([\n [0, -np.inf, 0],\n [0, -np.inf, -np.inf],\n [-np.inf, -np.inf, 0],\n [-np.inf, -np.inf, -np.inf],\n ]).astype(dtype)\n z_pos = np.asarray([[0, np.inf, 0], [0, np.inf,\n np.inf], [np.inf, np.inf, 0],\n [np.inf, np.inf, np.inf]]).astype(dtype)\n z_mix = np.asarray([[0, np.inf, 0], [0, np.inf, -np.inf],\n [-np.inf, np.inf, 0], [-np.inf, np.inf,\n -np.inf]]).astype(dtype)\n\n _, tf_loss_neg = self._tf_sparsemax_loss(z_neg, q, dtype, use_gpu)\n self.assertAllCloseAccordingToType([0.25, np.inf, 0, np.nan], tf_loss_neg)\n\n _, tf_loss_pos = self._tf_sparsemax_loss(z_pos, q, dtype, use_gpu)\n self.assertAllCloseAccordingToType([np.nan, np.nan, np.nan, np.nan],\n tf_loss_pos)\n\n _, tf_loss_mix = self._tf_sparsemax_loss(z_mix, q, dtype, use_gpu)\n self.assertAllCloseAccordingToType([np.nan, np.nan, np.nan, np.nan],\n tf_loss_mix)","def matrix_non_negative(data, allow_equal=True):\n return matrix_min(data) >= 0 if allow_equal else matrix_min(data) > 0","def gen_masks(num_masks, features, hidden_layers, hidden_units):\n\n # This array should contain numbers 1-784\n features_indices = []\n for i in range(features):\n features_indices.append(i + 1)\n masks = []\n indices = []\n for i in range(num_masks):\n set_masks = [] # Will contain all masks for the set\n # Randomize the input (and output, since they have to be the same)\n # ordering\n set_features = [] # Input and output node indices for the set\n for index in features_indices:\n set_features.append(index)\n np.random.RandomState(np.random.randint(0, 2**32)).shuffle(\n set_features)\n indices.append(set_features)\n prev_indices = set_features\n for j in range(hidden_layers):\n layer_indices = []\n for k in range(hidden_units):\n # The hidden nodes' indices need to be between the minimum\n # index from the previous layer and one less than the number\n # of features, inclusive.\n layer_indices.append(np.random.randint(low=min(prev_indices),\n high=features))\n mask = np.zeros((len(prev_indices), len(layer_indices)),\n dtype=np.float32)\n for k in range(len(prev_indices)):\n for l in range(len(layer_indices)):\n # The mask value will be one when the autoregressive\n # condition is met.\n mask[k][l] = float(int(prev_indices[k] <= layer_indices[l]))\n mask = tf.convert_to_tensor(mask, dtype=tf.float32)\n set_masks.append(mask)\n prev_indices = layer_indices\n output_mask = np.zeros((len(prev_indices), features), dtype=np.float32)\n for j in range(len(prev_indices)):\n for k in range(len(set_features)):\n output_mask[j][k] = float(int(prev_indices[j] < set_features[k]))\n output_mask = tf.convert_to_tensor(output_mask, dtype=tf.float32)\n set_masks.append(output_mask)\n direct_mask = np.zeros((features, features), dtype=np.float32)\n for j in range(features):\n for k in range(features):\n direct_mask[j][k] = float(int(set_features[j] < set_features[k]))\n direct_mask = tf.convert_to_tensor(direct_mask, dtype=tf.float32)\n set_masks.append(direct_mask)\n masks.append(set_masks)\n return{'masks': masks, 'indices': indices}","def _build_sparse_matrix(L):\n shape = L.shape\n i = torch.LongTensor(np.vstack((L.row, L.col)).astype(int))\n v = torch.FloatTensor(L.data)\n return torch.sparse.FloatTensor(i, v, torch.Size(shape))","def delete_from_csr(mat, row_indices=[], col_indices=[]):\n\n rows = []\n cols = []\n if row_indices:\n rows = list(row_indices)\n if col_indices:\n cols = list(col_indices)\n\n if len(rows) > 0 and len(cols) > 0:\n row_mask = np.ones(mat.shape[0], dtype=bool)\n row_mask[rows] = False\n col_mask = np.ones(mat.shape[1], dtype=bool)\n col_mask[cols] = False\n return mat[row_mask][:,col_mask]\n elif len(rows) > 0:\n mask = np.ones(mat.shape[0], dtype=bool)\n mask[rows] = False\n return mat[mask]\n elif len(cols) > 0:\n mask = np.ones(mat.shape[1], dtype=bool)\n mask[cols] = False\n return mat[:,mask]\n else:\n return mat","def setZeroes(self, matrix: List[List[int]]) -> None:\n if not matrix:\n return\n row = set()\n col = set()\n rowl = len(matrix[0])\n for i, r in enumerate(matrix):\n for j, c in enumerate(r):\n if c == 0:\n row.add(i)\n col.add(j)\n \n for r in row:\n matrix[r] = [0]*rowl\n for j in col:\n for i in range(len(matrix)):\n matrix[i][j] = 0","def ident_zeros(A):\n assert A.size[0] == A.size[1]\n A.assemble()\n o_range = A.getOwnershipRange()\n rows = []\n for i in range(o_range[1]-o_range[0]):\n indices, values = A.getRow(o_range[0]+i)\n absrow = sum(abs(values))\n if absrow < 1e-6:\n rows.append(o_range[0] + i)\n add_diagonal(A.handle, numpy.array(rows))","def erase_missing(signal, valid_rows, valid_cols, sym_upper=True):\n if sym_upper and sp.issparse(signal):\n if np.any(valid_rows != valid_cols):\n raise ValueError(\n \"Valid rows and columns must be identical with sym_upper=True\"\n )\n if signal.shape[0] != signal.shape[1]:\n raise ValueError(\n \"Input matrix must be square when using sym_upper=True\"\n )\n # Make a boolean mask from good bins\n good_mask = np.isin(range(signal.shape[0]), valid_rows)\n # Set all pixels in a nondetectable bin to 0\n # For faster masking of bins, mask bins using dot product with an\n # identify matrix where bad bins have been masked on the diagonal\n # E.g. if removing the second bin (row and column):\n # 1 0 0 9 6 5 1 0 0 9 0 5\n # 0 0 0 X 6 8 7 X 0 0 0 = 0 0 0\n # 0 0 1 6 7 8 0 0 1 6 0 8\n mask_mat = sp.eye(signal.shape[0])\n mask_mat.data[0][~good_mask] = 0\n erased = mask_mat.dot(signal).dot(mask_mat)\n else:\n # Get a boolean array of missing (1) and valid (0) rows\n missing_rows = valid_to_missing(valid_rows, signal.shape[0])\n missing_cols = valid_to_missing(valid_cols, signal.shape[1])\n erased = signal.copy()\n erased[missing_rows, :] = 0\n erased[:, missing_cols] = 0\n\n return erased","def zeros_like(self):\n return MultiterminalDevice.zeros(\n self.dims,\n self.center.shape,\n tuple(i.shape for i in self.leads),\n self.connections,\n )","def compute_pw_sparse_out_of_memory(tr,\n row_size = 500,\n pm_processes = 2,\n pm_pbar = True,\n max_distance = 50,\n matrix_name = 'rw_beta',\n reassemble = True,\n cleanup = True): \n dest = secrets.token_hex(6)\n os.mkdir(dest)\n print(f\"CREATED /{dest}/ FOR HOLDING DISTANCE OUT OF MEMORY\")\n row_chunks = memory._partition(range(tr.clone_df.shape[0]), row_size)\n\n smatrix_chunks = [(tr, ind, f\"{dest}/{i}.{matrix_name}.npz\") for i,ind in enumerate(row_chunks)]\n csrfragments = parmap.starmap(memory.gen_sparse_rw_on_fragment, \n smatrix_chunks, \n matrix_name = matrix_name, \n max_distance=max_distance, \n pm_pbar=pm_pbar, \n pm_processes = pm_processes)\n if reassemble:\n csr_full = memory.collapse_csrs([x[2] for x in smatrix_chunks])\n print(f\"RETURNING scipy.sparse csr_matrix w/dims {csr_full.shape}\")\n else: \n csr_full = None\n \n if cleanup: \n assert os.path.isdir(dest)\n print(f\"CLEANING UP {dest}\")\n shutil.rmtree(dest)\n \n \n return csr_full, smatrix_chunks","def construct_matrix_A(n):\n n2 = n**2\n D0 = 4*np.ones(n2) # 0th diagonal\n D1 = - np.ones(n2 - 1) # -1st, 1st diagonals\n D1[n-1::n] = 0 # Setting every k*n-1 entries = 0 for k < n\n DN = - np.ones(n2 - n) # -nth, nth diagonals\n return scipy.sparse.diags((DN, D1, D0, D1, DN), (-n, -1, 0, 1, n),\n shape=(n2, n2), format=\"csr\")","def create_sparse_matrix(self, matrix_df):\n\n print('creating sparse matrix...')\n sparse_seg_tmp_df = matrix_df.groupby(['segment_id','day_of_week','time_idx'])[self.args['cluster_variable']].mean().reset_index()\n sparse_rt_tmp_df = matrix_df.groupby(['road_type','day_of_week','time_idx'])[self.args['cluster_variable']].mean().reset_index()\n time_seg_df = sparse_seg_tmp_df.groupby(['day_of_week','time_idx'])[self.args['cluster_variable']].mean().reset_index()\n time_rt_df = sparse_rt_tmp_df.groupby(['day_of_week','time_idx'])[self.args['cluster_variable']].mean().reset_index()\n #time_seg_df['time_id'] = time_seg_df.index\n #time_rt_df['time_id'] = time_rt_df.index\n times = list(range(24*60/self.args['time_resolution']))\n full_time_idx = pd.DataFrame([i * 30 for i in times],columns = ['time_idx'])\n full_time_idx['key'] = 1\n full_day_of_week = pd.DataFrame(list(range(7)), columns = ['day_of_week'])\n full_day_of_week['key'] = 1\n full_times = pd.merge(full_time_idx, full_day_of_week, on='key')\n full_times['time_id'] = full_times.index\n time_seg_df = pd.merge(time_seg_df, full_times[['time_idx','day_of_week','time_id']], on=['time_idx','day_of_week'])\n time_rt_df = pd.merge(time_rt_df, full_times[['time_idx','day_of_week','time_id']], on=['time_idx','day_of_week'])\n \n matrix_seg_keys_df = pd.merge(sparse_seg_tmp_df, time_seg_df[['time_id','day_of_week','time_idx']], how='left', on=['day_of_week','time_idx'])\n matrix_rt_keys_df = pd.merge(sparse_rt_tmp_df, time_rt_df[['time_id','day_of_week','time_idx']], how='left', on=['day_of_week','time_idx'])\n\n time_seg_array = np.array(matrix_seg_keys_df['time_id'])\n time_rt_array = np.array(matrix_rt_keys_df['time_id'])\n segment_array = np.array(matrix_seg_keys_df['segment_id'])\n rt_array = np.array(matrix_rt_keys_df['road_type'])\n\n uniquesegments = np.array(list(set(segment_array)))\n uniquerts = np.array(list(set(rt_array)))\n keyuniquesegments = np.array(range(len(uniquesegments)))\n keyuniquerts = np.array(range(len(uniquerts)))\n uniquesegments_df = pd.DataFrame({'segmentskey':keyuniquesegments, 'segment_id':uniquesegments})\n uniquerts_df = pd.DataFrame({'roadtypekey':keyuniquerts, 'road_type':uniquerts})\n\n segments_df = pd.DataFrame(segment_array, columns = ['segment_id'])\n rt_df = pd.DataFrame(rt_array, columns = ['road_type'])\n segments_keys_df = pd.merge(segments_df, uniquesegments_df, how='left', on=['segment_id'])\n rt_keys_df = pd.merge(rt_df, uniquerts_df, how='left', on=['road_type'])\n segmentkeys = np.array(segments_keys_df['segmentskey'])\n rtkeys = np.array(rt_keys_df['road_type'])\n\n level_array_seg = np.array(matrix_seg_keys_df['level_max'])\n sparse_matrix_s = csr_matrix((level_array_seg, (segmentkeys,time_seg_array))).toarray()\n sparse_matrix_seg = preprocessing.scale(sparse_matrix_s)\n level_array_rt = np.array(matrix_rt_keys_df['level_max'])\n sparse_matrix_r = csr_matrix((level_array_rt, (rtkeys,time_rt_array))).toarray()\n sparse_matrix_rt = preprocessing.scale(sparse_matrix_r)\n \n if self.args['perform_pca']:\n sparse_matrix_seg, self.pca_model = self.run_PCA(sparse_matrix_seg)\n sparse_matrix_rt, self.pca_model = self.run_PCA(sparse_matrix_rt)\n else:\n sparse_matrix_seg = sparse_matrix_seg\n sparse_matrix_rt = sparse_matrix_rt\n \n sparse_matrix_withsegkey = pd.DataFrame(sparse_matrix_seg)\n sparse_matrix_withrtkey = pd.DataFrame(sparse_matrix_rt)\n sparse_matrix_withsegkey['segmentskey'] = sparse_matrix_withsegkey.index\n sparse_matrix_withseg = pd.merge(uniquesegments_df, sparse_matrix_withsegkey, on=['segmentskey'])\n sparse_matrix_withrtkey['roadtypekey'] = sparse_matrix_withrtkey.index\n sparse_matrix_withrt = pd.merge(uniquerts_df, sparse_matrix_withrtkey, on=['roadtypekey'])\n \n # write sparse_matrix to database as 'clustering' table\n print('writing sparse matrix to db...')\n sqlalchemy_conn_str = open('../conf/sqlalchemy_conn_str.txt', 'r').read()\n engine = create_engine(sqlalchemy_conn_str)\n if self.split_type == 'random':\n sparse_matrix_withseg.to_sql(name='clust_sparse_avebysegment_random', con=engine, if_exists='replace')\n sparse_matrix_withrt.to_sql(name='clust_sparse_avebyrt_random', con=engine, if_exists='replace')\n elif self.split_type == 'date':\n sparse_matrix_withseg.to_sql(name='clust_sparse_avebysegment_date', con=engine, if_exists='replace')\n sparse_matrix_withrt.to_sql(name='clust_sparse_avebyrt_date', con=engine, if_exists='replace')\n \n print('returning train sparse matrix...')\n return (uniquesegments_df, sparse_matrix_seg)","def create_mask(neurons: list):\n\n return np.zeros_like(neurons[0])","def sparse_random_inputs(format, shape, n=1, out_dtype=None, p=0.5, gap=None,\r\n explicit_zero=False, unsorted_indices=False):\r\n\r\n if out_dtype is None:\r\n out_dtype = theano.config.floatX\r\n\r\n assert 0 <= p and p <= 1\r\n assert len(shape) == 2\r\n assert out_dtype in sparse.all_dtypes\r\n assert gap is None or isinstance(gap, (tuple, list))\r\n if gap is not None and out_dtype.startswith('u'):\r\n assert gap[0] >= 0\r\n\r\n def _rand():\r\n where = numpy.random.binomial(1, p, size=shape).astype('int8')\r\n\r\n if out_dtype in sparse.discrete_dtypes:\r\n if not gap:\r\n value = numpy.random.randint(50, size=shape)\r\n elif len(gap) == 2:\r\n value = numpy.random.randint(gap[0], gap[1], size=shape)\r\n else:\r\n value = numpy.random.randint(gap[0], size=shape)\r\n else:\r\n if not gap:\r\n value = numpy.random.random(shape)\r\n elif len(gap) == 2:\r\n a, b = gap\r\n value = a + numpy.random.random(shape) * (b - a)\r\n else:\r\n value = numpy.random.random(shape) * gap[0]\r\n return (where * value).astype(out_dtype)\r\n\r\n variable = [getattr(theano.sparse, format + '_matrix')(dtype=out_dtype)\r\n for k in range(n)]\r\n data = [getattr(scipy.sparse, format + '_matrix')(_rand(), dtype=out_dtype)\r\n for k in range(n)]\r\n if unsorted_indices:\r\n for idx in range(n):\r\n d = data[idx]\r\n d = d[range(d.shape[0])]\r\n assert not d.has_sorted_indices\r\n data[idx] = d\r\n if explicit_zero:\r\n for idx in range(n):\r\n assert data[idx].nnz > 1, (\r\n \"can't make a sparse matrix with explicit 0\")\r\n d_idx = numpy.random.randint(data[idx].nnz)\r\n data[idx].data[d_idx] = 0\r\n\r\n #numpy 1.5.0 with scipy 0.9.0 have scipy.sparse.XXX_matrix return\r\n #typenum 10(ulonglong) instead of 8(uint64) event if they are the same!\r\n #Theano don't like ulonglong type_num\r\n dtype = numpy.dtype(out_dtype) # Convert into dtype object.\r\n if data[0].dtype.num != dtype.num and dtype.str == data[0].dtype.str:\r\n data[0].data = theano._asarray(data[0].data, out_dtype)\r\n assert data[0].dtype.num == dtype.num\r\n return (variable, data)","def sparse(n, k):\n z = np.zeros(n)\n for i in np.random.choice( np.arange(n), k, replace=None ): # supports of nonzero entries\n z[i] = np.random.randn()\n return z","def mask(self):\n mask = np.full(self._grid.shape, False)\n mask[np.ix_(self._lat_indices, self._lon_indices)] = True\n return mask","def create_mask(shape):\n return np.zeros(shape).astype(bool)","def dict2sparseMatrix(wDict,std=0,diag=0):\n data = lil_matrix((len(list(wDict.keys())),len(list(wDict.keys()))))\n nAreas = len(list(wDict.keys()))\n for i in wDict:\n data[i,i] = diag\n ne = len(wDict[i])+ diag\n for j in wDict[i]:\n if std:\n data[i,j] = 1 / float(ne)\n else:\n data[i,j] = 1\n return data","def _buildMatrix(self, SparseMatrix, Ncells, MaxFaces, coeff):\n return (0, 0)","def _create_sparse_train_and_test(ratings, n_users, n_items):\n \n # pick a random set of data as testing data, sorted ascending\n test_set_size = len(ratings) / TEST_SET_RATIO\n test_set_idx = np.random.choice(xrange(len(ratings)), size=test_set_size, replace=False)\n test_set_idx = sorted(test_set_idx)\n \n # use the remaining data to create a training set\n ts_ratings = ratings[test_set_idx]\n tr_ratings = np.delete(ratings, test_set_idx, axis=0)\n \n # create training and test matrices as coo_matrix\n u_tr, i_tr, r_tr = zip(*tr_ratings)\n tr_sparse = coo_matrix((r_tr, (u_tr, i_tr)), shape=(n_users, n_items))\n u_ts, i_ts, r_ts = zip(*ts_ratings)\n test_sparse = coo_matrix((r_ts, (u_ts, i_ts)), shape=(n_users, n_items))\n \n return tr_sparse, test_sparse","def generateRandomStandardizedLinkMatrix(size, empty, autoRefLink):\n\n #We start by generating our matrix\n res = np.zeros((size,size),dtype=float);\n\n #If we want to work with a sparse matrix\n #We Generate the index vector (witch vector to populate?)\n emptyVecIndexes = np.random.choice(2,size, p=[EMPTINESS_RATIO,1-EMPTINESS_RATIO])\n\n\n for i in range(size):\n\n ## SPARSE MATRIX ##\n if(empty):\n\n #We generate random vectors for only few columns\n if(emptyVecIndexes[i]==1):\n res[i] = generateProbabilisticVector(size,True)\n\n #We postprocess the non empty columns to ensure certain properties (diag = 0 | sum = (strict) 1 )\n if(res[i].sum()!=0):\n index = np.random.choice(size,1)\n\n while(index==i):\n index = np.random.choice(size,1)\n\n\n if(autoRefLink==False):\n res[i][index]+=res[i][i]\n res[i][i]=0\n\n #float precision sum problem => we ensure normalization of columns\n if(isProbabilisticVector(res[i]) == False):\n diff = 1-res[i].sum()\n res[i][index]+=diff\n\n #for vectors with no link => Same chances to go anywhere\n else:\n #fullfill empty vectors with the same prob\n res[i]= np.full(size,1/size)\n\n ## NORMAL MATRIX ##\n else:\n res[i] = generateProbabilisticVector(size,False)\n\n #Very unlikely but we do it just to be sure\n if res[i].sum()==0:\n\n #fullfill empty vectors with the same prob\n res[i]= np.full(size,1/size)\n\n\n #We postprocess the non empty columns to ensure certain properties (diag = 0 | sum = (strict) 1 )\n else:\n index = np.random.choice(size,1)\n\n while(index==i):\n index = np.random.choice(size,1)\n\n if(autoRefLink==False):\n res[i][index]+=res[i][i]\n res[i][i]=0\n\n #float precision sum problem => we ensure normalization of columns\n if(isProbabilisticVector(res[i]) == False):\n diff = 1-res[i].sum()\n res[i][index]+=diff\n\n #to remove\n #print(np.transpose(res));\n return np.transpose(res)","def diagonalsNeg (matrix, cols, rows):\r\n for diagonal in ([(j, i - cols + j + 1) for j in range(cols)] for i in range(cols + rows - 1)):\r\n yield [matrix[i][j] for i, j in diagonal if i >= 0 and j >= 0 and i < cols and j < rows]","def _source_mask(self, ilens):\n x_masks = make_non_pad_mask(ilens)\n return x_masks.unsqueeze(-2)","def generate_square_subsequent_mask(self, sz: int) -> Tensor:\n\t\tmask = (torch.triu(torch.ones(sz, sz)) == 1).transpose(0, 1)\n\t\tmask = mask.float().masked_fill(mask == 0, float('-inf')).masked_fill(mask == 1, float(0.0))\n\t\treturn mask","def zeros_matrix(rows, cols):\n M = []\n while len(M) < rows:\n M.append([])\n while len(M[-1]) < cols:\n M[-1].append(0.0)\n\n return M","def zeros_matrix(rows, cols):\n M = []\n while len(M) < rows:\n M.append([])\n while len(M[-1]) < cols:\n M[-1].append(0.0)\n\n return M","def zeros_matrix(rows, cols):\n M = []\n while len(M) < rows:\n M.append([])\n while len(M[-1]) < cols:\n M[-1].append(0.0)\n \n return M","def zeros_matrix(self, rows, cols):\r\n M = []\r\n while len(M) < rows:\r\n M.append([])\r\n while len(M[-1]) < cols:\r\n M[-1].append(0.0)\r\n\r\n return M","def setZeroes(self, matrix: List[List[int]]) -> None:\n m = len(matrix)\n n = len(matrix[0])\n rows = set()\n columns = set()\n for i in range(m):\n for j in range(n):\n if not matrix[i][j]:\n rows.add(i)\n columns.add(j)\n\n for i in rows:\n for j in range(n):\n matrix[i][j] = 0\n for j in columns:\n for i in range(m):\n matrix[i][j] = 0","def subgraph_mask(self, size):\n init_matrix = np.random.randn(size,size)\n Tcs = csgraph.minimum_spanning_tree(init_matrix)\n mask_matrix = Tcs.toarray()\n return mask_matrix","def generate_sparse_randn(n, s):\n x = np.zeros(n)\n I = np.random.randint(0, n, s)\n x[I] = np.random.randn(s)\n return x","def cmask(self):\n mask = np.zeros(18)\n if 'full' in self.CONS: mask[:] = 1\n if 'f0' in self.CONS: mask[0] = 1\n if 'f1' in self.CONS: mask[1:4] = 1\n if 'f2' in self.CONS: mask[4:10] = 1\n if 'vx' in self.CONS: mask[10] = 1\n if 'vy' in self.CONS: mask[11] = 1\n if 'vz' in self.CONS: mask[12] = 1\n if 'TG' in self.CONS: mask[13:18] = 1\n return mask>0","def generatePattern(numCols=100, minOnes=21, maxOnes=25):\n assert minOnes < maxOnes\n assert maxOnes < numCols\n\n nOnes = random.randint(minOnes, maxOnes)\n ind = random.sample(xrange(numCols), nOnes)\n x = numpy.zeros(numCols, dtype='float32')\n x[ind] = 1\n\n return x","def heatmap_tiles_generator_diag(clr, chroms, pad_size, tile_size, band_to_cover):\n\n for chrom in chroms:\n chr_start, chr_stop = clr.extent(chrom)\n for tilei, tilej in square_matrix_tiling(chr_start,\n chr_stop,\n tile_size,\n pad_size):\n # check if a given tile intersects with \n # with the diagonal band of interest ...\n diag_from = tilej[0] - tilei[1]\n diag_to = tilej[1] - tilei[0]\n #\n band_from = 0\n band_to = band_to_cover\n # we are using this >2*padding trick to exclude\n # tiles from the lower triangle from calculations ...\n if (min(band_to,diag_to) - max(band_from,diag_from)) > 2*pad_size:\n yield chrom, tilei, tilej","def bad_central_mask(weights, badmask, index, wmin = 0.1):\n\n \n # Weights of fibers at the source index\n sel = weights[:, index] > wmin\n\n #print(weights[:, index])\n #print(badmask[sel, index - 1 : index + 2])\n\n bad_test = any(badmask[sel, index - 1 : index + 2].ravel()) \n bad_test = bad_test| all(badmask[:, index - 1 : index + 2].ravel())\n\n if bad_test:\n return False\n else:\n return True","def init_safeboard() ->None:\r\n for x in range(shape):\r\n for y in range(shape):\r\n if conflict_space[x, y] != 0:\r\n safeboard[x, y] = 1","def setZeroes(self, matrix: List[List[int]]) -> None:\n m = len(matrix)\n if m == 0:\n return\n n = len(matrix[0])\n\n row_zero = False\n for i in range(m):\n if matrix[i][0] == 0:\n row_zero = True\n col_zero = False\n for j in range(n):\n if matrix[0][j] == 0:\n col_zero = True\n\n for i in range(1, m):\n for j in range(1, n):\n if matrix[i][j] == 0:\n matrix[i][0] = 0\n matrix[0][j] = 0\n\n for i in range(1, m):\n if matrix[i][0] == 0:\n for j in range(1, n):\n matrix[i][j] = 0\n\n for j in range(1, n):\n if matrix[0][j] == 0:\n for i in range(1, m):\n matrix[i][j] = 0\n\n if col_zero:\n for j in range(n):\n matrix[0][j] = 0\n if row_zero:\n for i in range(m):\n matrix[i][0] = 0","def createsolution(self,rows):\r\n l = [0,0]\r\n if not self.findzero(l):\r\n return True\r\n x = l[0]\r\n y = l[1]\r\n for i in range(1,10):\r\n if self.check(i, x, y):\r\n rows[x][y] = i\r\n if self.createsolution(rows):\r\n return True\r\n rows[x][y] = 0\r\n return False","def setZeroes(self, matrix: List[List[int]]) -> None:\n m, n = len(matrix), len(matrix[0])\n zero_row, zero_col = set(), set()\n for row in range(m):\n for col in range(n):\n if matrix[row][col] == 0:\n if row not in zero_row:\n zero_row.add(row)\n if col not in zero_col:\n zero_col.add(col)\n for row in zero_row:\n for col in range(n):\n matrix[row][col] = 0\n for col in zero_col:\n for row in range(m):\n matrix[row][col] = 0","def construct_M_N(n):\n n2 = n**2\n D0 = 2*np.ones(n2) # 0th diagonal\n D1 = - np.ones(n2 - 1) # -1st, 1st diagonals\n D1[n-1::n] = 0 # Setting every k*n-1 entries = 0 for k < n\n DN = - np.ones(n2 - n) # -nth, nth diagonals\n return (scipy.sparse.diags((D1, D0, D1), (-1, 0, 1), shape=(n2, n2), format=\"csr\"),\n scipy.sparse.diags((DN, D0, DN), (-n, 0, n), shape=(n2, n2), format=\"csr\"))","def island():\n\n grid = [\n [\"1\", \"1\", \"0\", \"0\", \"0\"],\n [\"1\", \"1\", \"0\", \"0\", \"0\"],\n [\"0\", \"0\", \"1\", \"0\", \"0\"],\n [\"0\", \"0\", \"0\", \"1\", \"1\"]\n ]\n\n def dfs():\n rows = len(grid)\n cols = len(grid[0])\n count = 0\n for i in range(0, rows):\n for j in range(0, cols):\n if grid[i][j] == '1':\n check_valid(i, j, grid)\n count = count + 1\n return count\n\n def check_valid(i, j, grid=None):\n rows = len(grid)\n cols = len(grid[0])\n\n if not 0 <= i < rows or not 0 <= j < cols or grid[i][j] != '1':\n return\n\n grid[i][j] = '0'\n\n check_valid(i + 1, j, grid)\n check_valid(i - 1, j, grid)\n check_valid(i, j + 1, grid)\n check_valid(i, j - 1, grid)\n\n return dfs()","def fim_mask(model, dataset, samples, threshold):\n fisher_diagonal = fisher_matrix(model, dataset, samples)\n mask = [tensor < threshold for tensor in fisher_diagonal]\n return mask","def square_mask(ys_in_pad, ignore_id):\n ys_mask = (ys_in_pad != ignore_id).unsqueeze(-2)\n ymax = ys_mask.size(-1)\n ys_mask_tmp = ys_mask.transpose(1, 2).repeat(1, 1, ymax)\n ys_mask = ys_mask.repeat(1, ymax, 1) & ys_mask_tmp\n\n return ys_mask","def make_sparse_batch(instance_indices, instance_values, max_index):\n batch_indices = make_batch_indices(instance_indices)\n batch_values = list(itertools.chain.from_iterable(instance_values))\n batch_shape = (len(instance_indices), max_index)\n return tf.SparseTensorValue(batch_indices, batch_values, batch_shape)","def setZeroes(matrix):\r\n \r\n #An average O(n^2) time traversal solution with memoization\r\n #for each 0 we encounter, we update the entire row and column to 0s, but on the condition that the row/column has not been updated yet\r\n \r\n row_cache = {}\r\n column_cache = {}\r\n \r\n for r in range(0,rows := len(matrix)):\r\n for c in range(0,cols := len(matrix[0])):\r\n \r\n if matrix[r][c] == 0:\r\n \r\n if not row_cache.get(r):\r\n for i in range(0,cols):\r\n if matrix[r][i] != 0:\r\n matrix[r][i] = '0' #we use strings so we only consider the initial 0s\r\n row_cache[r] = True\r\n \r\n if not column_cache.get(c):\r\n for i in range(0,rows):\r\n if matrix[i][c] != 0:\r\n matrix[i][c] = '0'\r\n column_cache[c] = True\r\n return","def gen_square_subsequent_mask(sz: int) -> torch.Tensor:\n return torch.triu(torch.ones(sz, sz) * float('-inf'), diagonal=1)","def from_mask(self, lmax, nwins, dh_mask, sampling=1):\n tapers, eigenvalues = SHReturnTapersMap(dh_mask, lmax, sampling=sampling, Ntapers=nwins)\n return SHAsymmetricWindow(tapers,eigenvalues)","def set_sparse_signals(self):\n\t\n\t\tparams_dSs = [self.mu_dSs, self.sigma_dSs]\n\t\tparams_Ss0 = [self.mu_Ss0, self.sigma_Ss0]\n\t\tself.dSs, self.idxs = sparse_vector([self.Nn, self.Kk], \n\t\t\t\t\t\t\t\t\t\t\t\tparams_dSs,\tseed=self.seed_dSs)\n\t\t\n\t\t# Replace components with conflicting background odor \n\t\tif self.Kk_split is not None and self.Kk_split != 0:\n\t\t\tassert 0 <= self.Kk_split <= self.Kk, \\\n\t\t\t\t\"Splitting sparse signal into two levels requires Kk_split\" \\\n\t\t\t\t\" to be non-negative and less than or equal to Kk.\"\n\t\t\tassert self.mu_dSs_2 is not None \\\n\t\t\t\tand self.sigma_dSs_2 is not None, \\\n\t\t\t\t\"Splitting sparse signal into two levels requires that\" \\\n\t\t\t\t\" mu_dSs_2 and sigma_dSs_2 are set.\"\n\n\t\t\tsp.random.seed(self.seed_dSs)\n\t\t\tself.idxs_2 = sp.random.choice(self.idxs[0], self.Kk_split, \n\t\t\t\t\t\t\t\t\t\t\treplace=False)\n\t\t\tfor idx_2 in self.idxs_2:\n\t\t\t\tself.dSs[idx_2] = sp.random.normal(self.mu_dSs_2, \n\t\t\t\t\t\t\t\t\t\t\t\t\tself.sigma_dSs_2)\n\t\telse:\n\t\t\tself.idxs_2 = []\n\t\t\tself.Kk_split = 0\n\t\t\t\n\t\t# Ss0 is the ideal (learned) background stimulus without noise\n\t\tself.Ss0, self.Ss0_noisy = sparse_vector_bkgrnd([self.Nn, self.Kk], \n\t\t\t\t\t\t\t\t\t\t\t\t\t\tself.idxs, params_Ss0,\n\t\t\t\t\t\t\t\t\t\t\t\t\t\tseed=self.seed_Ss0)\n\t\t\n\t\tself.Ss = self.dSs + self.Ss0_noisy","def setZeroes(self, matrix: List[List[int]]) -> None:\n m = len(matrix)\n n = len(matrix[0])\n set_rows = set()\n set_cols = set()\n for i in range(m):\n for j in range(n):\n if matrix[i][j] == 0:\n set_rows.add(i)\n set_cols.add(j)\n \n while set_rows:\n temp = set_rows.pop()\n for j in range(n):\n matrix[temp][j] = 0\n \n while set_cols:\n temp = set_cols.pop()\n for i in range(m):\n matrix[i][temp] = 0","def one_dim_sparse_laplacian(m: int):\n return sp.diags([1.0, -2.0, 1.0], [-1, 0, 1], dtype='float64', shape=(m, m), format='lil')","def __generate_mask(self):\n mask = np.concatenate([np.ones(len(self.fixed[0])),\n np.zeros(self.num_points),\n np.ones(len(self.fixed[1]))])\n return mask","def build_matrix(docs):\n nrows = len(docs)\n idx = {}\n tid = 0\n nnz = 0\n # Remove all ratings\n for d in docs:\n #d = d[1:]\n nnz += len(set(d))\n for w in d:\n if w not in idx:\n idx[w] = tid\n tid += 1\n ncols = len(idx)\n print nrows\n print ncols\n # set up memory\n ind = np.zeros(nnz, dtype=np.int)\n val = np.zeros(nnz, dtype=np.double)\n ptr = np.zeros(nrows+1, dtype=np.int)\n i = 0 # document ID / row counter\n n = 0 # non-zero counter\n # transfer values\n for d in docs:\n #d = d[1:]\n cnt = Counter(d)\n keys = list(k for k,_ in cnt.most_common())\n l = len(keys)\n for j,k in enumerate(keys):\n ind[j+n] = idx[k]\n val[j+n] = cnt[k]\n ptr[i+1] = ptr[i] + l\n n += l\n i += 1\n\n mat = csr_matrix((val, ind, ptr), shape=(nrows, ncols), dtype=np.double)\n mat.sort_indices()\n\n return mat"],"string":"[\n \"def frame_missing_mask(mask, kernel_shape, sym_upper=False, max_dist=None):\\n if mask.dtype != bool:\\n raise ValueError(\\\"Mask must contain boolean values\\\")\\n if not sp.issparse(mask):\\n raise ValueError(\\\"Mask must be a sparse matrix\\\")\\n\\n framed_mask = mask.copy()\\n ms, ns = mask.shape\\n mk, nk = kernel_shape\\n if sym_upper and (max_dist is not None):\\n # Remove diagonals further than scan distance in the input mask\\n framed_mask = diag_trim(framed_mask, max_dist + max(nk, mk)).tocsr()\\n max_m = max_dist + mk\\n max_n = max_dist + nk\\n else:\\n max_m, max_n = ms, ns\\n # Up and down margins initialized with zeros and filled as needed\\n margin_1 = sp.csr_matrix((mk - 1, ns), dtype=bool)\\n margin_2 = sp.csr_matrix((mk - 1, ns), dtype=bool)\\n if sym_upper and (max_dist is not None):\\n # Margin 1 (top) is in upper triangle -> fill missing up to scan dist\\n margin_1[:, :max_n] = 1\\n else:\\n margin_1[:, :] = 1\\n margin_2[:, :] = 1\\n framed_mask = sp.vstack([margin_1, framed_mask, margin_2], format=\\\"csr\\\")\\n\\n # Left and right\\n margin_1 = sp.csr_matrix((ms + 2 * (mk - 1), nk - 1), dtype=bool)\\n margin_2 = sp.csr_matrix((ms + 2 * (mk - 1), nk - 1), dtype=bool)\\n\\n if sym_upper and (max_dist is not None):\\n # Margin 2 (right) is in upper triangle-> fill missing up to scan dist\\n margin_2[-(max_m + 1) :, :] = 1\\n # Fill only the start of left margin for the top-left corner\\n margin_1[: mk - 1, :] = 1\\n else:\\n margin_1[:, :] = 1\\n margin_2[:, :] = 1\\n framed_mask = sp.hstack([margin_1, framed_mask, margin_2], format=\\\"csr\\\")\\n\\n if sym_upper:\\n # LIL format is much faster when changing sparsity\\n framed_mask = framed_mask.tolil()\\n # Add margin below diagonal\\n big_k = max(nk, mk)\\n dia_margins = np.ones(big_k)\\n dia_offsets = np.arange(-1, -big_k-1, -1)\\n framed_mask += sp.diags(\\n dia_margins,\\n dia_offsets,\\n shape=framed_mask.shape,\\n format=\\\"lil\\\",\\n dtype=bool,\\n )\\n framed_mask = framed_mask.tocsr()\\n return framed_mask\",\n \"def ensure_sparse_cols(self,max_density,remove_lowest=True):\\n if max_density >= 1:\\n max_nnz = int(max_density)\\n else:\\n max_nnz = int(max_density*self.shape[0])\\n for j in range(self.shape[1]):\\n col = self.fast_get_col(j)\\n excess = col.nnz - max_nnz\\n if excess > 0:\\n if remove_lowest:\\n zero_entries = np.argsort(col.data)[:excess]\\n else:\\n zero_entries = random.sample(range(col.nnz),excess)\\n col.data[zero_entries] = 0\\n self.fast_update_col(j,col.data)\",\n \"def makesparse(matrix):\\n n = matrix[0].size\\n elements = []\\n for i in range(n):\\n for j in range(n):\\n if matrix[i][j] != 0 :\\n temp = MatrixElement(i, j, matrix[i][j])\\n elements.append(temp)\\n return SparseMatrix(n, elements)\",\n \"def mask_sparse(self, threshold=10):\\n self.MaskPrefix = 's' + self.MaskPrefix\\n print('Masking pixels that do not have at least {0} coherent values'.format(threshold))\\n # each pixel assigned an integer corresponding to # of igrams where coherent\\n # NOTE: save coverage map if it doesn't exist already\\n coverage = self.get_coverage()\\n sparse = ma.masked_less(coverage, threshold)\\n for ig in self.Set:\\n igram = self.load_ma(ig)\\n igram[sparse.mask] = ma.masked\\n self.save_ma(ig, igram)\\n print('Done')\",\n \"def list_to_sparse(inputs):\\n\\n data = list(itertools.chain(*inputs))\\n row = list(itertools.chain(\\n *[itertools.repeat(i, len(x)) for i, x in enumerate(inputs)]))\\n col = list(itertools.chain(*[range(len(x)) for x in inputs]))\\n\\n s = coo_matrix((data, (row, col)), shape=(\\n len(inputs), np.max([len(x) for x in inputs])))\\n\\n return s\",\n \"def _set_builder_fast(m, distances):\\n\\n if not is_matrix(m):\\n raise ValueError(\\\"Expected numpy matrix\\\")\\n\\n if not is_matrix_binary(m):\\n raise ValueError(\\\"Expected binary matrix\\\")\\n\\n if not is_matrix_correct(m):\\n raise ValueError(\\\"Expected matrix that has valid structure (not more than one '1' in row)\\\")\\n\\n n, k = np.shape(m)\\n ones_count, one_pos, has_one, where_one, zero_rows = get_matrix_info(m, n)\\n\\n # run through all distances in D\\n for distance in distances:\\n\\n # check that distance is correct\\n if distance < 0 or distance > ones_count + n:\\n continue\\n\\n if distance == 0:\\n yield m.copy()\\n continue\\n\\n for num_ones_removed in range(np.min([distance, ones_count]) + 1):\\n for clear_rows in combinations(one_pos[0], num_ones_removed):\\n\\n cleared_matrix = m.copy()\\n cleared_matrix[clear_rows, :] = 0\\n remained_rows = np.array(list((set(range(n)) - set(one_pos[0])).union(set(clear_rows))))\\n\\n for rows in combinations(remained_rows, distance - num_ones_removed):\\n\\n if len(rows) == 0:\\n yield cleared_matrix.copy()\\n continue\\n\\n avaliable_column_indices = [get_zero_indices(where_one[row], k) for row in rows]\\n\\n rows = np.array(rows, dtype=np.int32)\\n rows_count = rows.size\\n\\n if (k ** rows_count)*n*k*4//1024//1024 > 1024:\\n raise ValueError(\\\"Looks that you don't have enough RAM\\\")\\n\\n indices = np.array(np.meshgrid(*avaliable_column_indices)).T.reshape(-1, rows_count)\\n\\n cache_size = np.shape(indices)[0]\\n\\n indices += rows*k\\n indices = (indices.T + np.arange(cache_size)*(n*k)).T\\n indices = indices.reshape(indices.size)\\n\\n matrix_cache = np.tile(cleared_matrix, (cache_size, 1, 1))\\n matrix_cache.put(indices, 1)\\n\\n # print(\\\"Mb for cache: \\\" + str((cache_size*rows_count + cache_size*n*k)*4//1024//1024))\\n\\n for mat in matrix_cache:\\n yield mat\",\n \"def generate_inpaint_mask(n_samples, n_colors, spatial_width):\\n mask = np.zeros((n_samples, n_colors, spatial_width, spatial_width), dtype=bool)\\n # simple mask -- just mask out half the image\\n mask[:,:,:,spatial_width/2:] = True\\n return mask.ravel()\",\n \"def sparse_initialization_weights(self):\\n W = []\\n mu, sigma = 0, 1/self.non_zero_units\\n\\n for i in xrange(self.n_in):\\n row = np.zeros(self.n_out)\\n non_zeros = self.rng.normal(mu, sigma, self.non_zero_units)\\n # non_zeros /= non_zeros.sum()\\n non_zero_idxs = self.rng.permutation(\\n self.n_out)[0:self.non_zero_units]\\n for j in xrange(self.non_zero_units):\\n row[non_zero_idxs[j]] = non_zeros[j]\\n W.append(row)\\n W = np.asarray(W, dtype=theano.config.floatX)\\n return W\",\n \"def idseqs_to_mask(idseqs: List[List[int]],\\n n_seqlen: Optional[int] = None,\\n n_vocab_sz: Optional[int] = None,\\n ignore: Optional[List[int]] = [],\\n dtype: Optional[torch.dtype] = torch.bool,\\n dense: Optional[bool] = False\\n ) -> torch.sparse.FloatTensor:\\n if n_seqlen is None:\\n n_seqlen = max([len(seq) for seq in idseqs])\\n\\n # create a list of IDs\\n if n_vocab_sz is None:\\n ids = set(itertools.chain(*idseqs))\\n else:\\n ids = set(range(0, n_vocab_sz))\\n\\n # remove IDs that we ignore\\n ids = ids.difference(set(ignore))\\n n_features = len(ids)\\n\\n # convert to list to lookup with .index() method\\n ids = list(ids)\\n\\n # loop over each ID sequence\\n masks = []\\n for seq in idseqs:\\n # extract index pairs of the sparse matrix\\n featidx = []\\n seqidx = []\\n for step, elem in enumerate(seq[:n_seqlen]):\\n try:\\n featidx.append(ids.index(elem))\\n seqidx.append(step)\\n except Exception:\\n pass\\n # convert to COO matrix\\n tmp = torch.sparse.FloatTensor(\\n indices=torch.LongTensor([seqidx, featidx]),\\n values=torch.FloatTensor([1.0 for _ in range(len(seqidx))]),\\n size=torch.Size([n_seqlen, n_features])\\n ).coalesce()\\n # save it\\n masks.append(tmp)\\n\\n # stack into one 3D tensor <batch_sz, n_seqlen, vocab_sz>\\n masks = torch.stack(masks).coalesce()\\n\\n # convert to dense matrix if requested\\n if dense:\\n masks = masks.to_dense().type(dtype)\\n\\n # done\\n return masks\",\n \"def generate_random_sparse_array(nrows, ncols, numdense):\\n i = np.random.randint(0, nrows, numdense)\\n j = np.random.randint(0, ncols, numdense)\\n data = np.random.randint(1,6, numdense)\\n ij = np.vstack((i,j))\\n return coo_matrix((data, ij), shape=(nrows, ncols))\",\n \"def _generate_sketch_matrix(rand_h, rand_s, output_dim):\\n\\n # Generate a sparse matrix for tensor count sketch\\n assert (rand_h.ndim == 1 and rand_s.ndim == 1 and len(rand_h) == len(rand_s))\\n assert (np.all(rand_h >= 0) and np.all(rand_h < output_dim))\\n\\n input_dim = len(rand_h)\\n indices = np.concatenate((np.arange(input_dim)[..., np.newaxis],\\n rand_h[..., np.newaxis]), axis=1)\\n i = torch.LongTensor(indices).t()\\n v = torch.IntTensor(rand_s)\\n sparse_sketch_matrix = torch.sparse.IntTensor(i, v, torch.Size([input_dim, output_dim] )).to_dense().float().cuda()\\n # I used to want to used sparse matrix, but the autograd is not suported.\\n return Variable(sparse_sketch_matrix)\",\n \"def generate_square_subsequent_mask(sz: int, device: torch.device) -> torch.Tensor:\\n mask = (torch.triu(torch.ones(sz, sz, device=device)) == 1).transpose(0, 1)\\n mask = (\\n mask.float()\\n .masked_fill(mask == 0, float(\\\"-inf\\\"))\\n .masked_fill(mask == 1, float(0.0))\\n )\\n return mask\",\n \"def test_sparse_distance_matrix(coordinates, max_dist, p):\\n original_tree = scipyKDTree(coordinates)\\n redis_tree = redisKDTree(coordinates)\\n\\n original_output = original_tree.sparse_distance_matrix(original_tree, max_dist, p)\\n redis_output = redis_tree.sparse_distance_matrix(redis_tree, max_dist, p)\\n\\n assert dict_close(redis_output, original_output)\",\n \"def symmetric_submatrix(self, *clip_ids):\\n with self._rw_lock.read_lock():\\n with SimpleTimer(\\\"Checking inputs\\\", self._log):\\n if not self.is_symmetric():\\n raise RuntimeError(\\\"Cannot get a symmetric sub-matrix if \\\"\\n \\\"the kernel is not square!\\\")\\n # DEPRECATED: Allowing the use of this method without explicitly\\n # providing background cIDs. This object will\\n # probably not ever be used this way, but there's no\\n # reason to explicitly disallow it.\\n # if self._bg_cid_vec is None:\\n # raise RuntimeError(\\\"Cannot create the square submatrix \\\"\\n # \\\"without the background flag vector!\\\")\\n\\n try:\\n clip_ids = [int(e) for e in clip_ids]\\n except:\\n raise ValueError(\\\"Not all clip IDs could be used as ints! \\\"\\n \\\"Given: %s\\\" % clip_ids)\\n\\n id_diff = set(clip_ids).difference(self._row_id_index_map)\\n assert not id_diff, \\\\\\n \\\"Not all clip IDs provided are represented in this \\\" \\\\\\n \\\"distance kernel matrix! (difference: %s)\\\" \\\\\\n % id_diff\\n del id_diff\\n\\n with SimpleTimer(\\\"Computing union of BG clips and provided IDs\\\",\\n self._log):\\n if self._bg_cid_set is not None:\\n all_cids = self._bg_cid_set.union(clip_ids)\\n else:\\n all_cids = set(clip_ids)\\n\\n # Reorder the given clip IDs so that they are in the same relative\\n # order as the kernel matrix edges.\\n focus_indices = []\\n focus_clipids = []\\n for idx, cid in enumerate(self._row_id_index_map):\\n if (cid in all_cids) and (cid not in focus_clipids):\\n focus_indices.append(idx)\\n focus_clipids.append(cid)\\n\\n # index-to-isBG map for return\\n # -> IDs provided as arguments are to be considered non-background,\\n # even if a the ID is in the background set. All other IDs in the\\n # union then must be from the background set.\\n focus_id2isbg = []\\n for idx in focus_indices:\\n cid = self._row_id_index_map[idx]\\n focus_id2isbg.append(False if cid in clip_ids else True)\\n\\n # Create new matrix from row-column intersections\\n ret_mat = self._kernel[focus_indices, :][:, focus_indices]\\n\\n return focus_clipids, focus_id2isbg, ret_mat\",\n \"def set_zero(mn_matrix): \\r\\n \\r\\n row_flag = [0]*len(mn_matrix)\\r\\n column_flag = [0]*len(mn_matrix[0])\\r\\n for i in range(0, len(row_flag)):\\r\\n for j in range(0, len(column_flag)):\\r\\n if mn_matrix[i][j] == 0:\\r\\n row_flag[i] = 1\\r\\n column_flag[j] = 1\\r\\n \\r\\n for i in range(0, len(row_flag)):\\r\\n for j in range(0, len(column_flag)):\\r\\n if row_flag[i] == 1 or column_flag[j] == 1: \\r\\n mn_matrix[i][j] = 0\",\n \"def remove_bad_cells(self, *dims):\\n ranges = [DimRange(d, 0, np.inf) for d in dims]\\n return self.gate(*ranges)\",\n \"def create_hop_matrix(G, max_hops, node_list):\\n distances = dict(nx.all_pairs_dijkstra_path_length(G))\\n hop_matrix = torch.zeros(max_hops, G.number_of_nodes(), G.number_of_nodes(), dtype=torch.int)\\n for hop in range(max_hops):\\n for i, node_from in enumerate(node_list):\\n for j, node_to in enumerate(node_list):\\n if node_to in distances[node_from].keys() and distances[node_from][node_to] == hop:\\n hop_matrix[hop,i,j] = 1\\n return hop_matrix\",\n \"def validity_mask(tetrimino, columns):\\n validity = np.zeros((4, columns), dtype=bool)\\n for i, t in enumerate(tetrimino_dict[tetrimino]):\\n l = columns - t.shape[1] + 1\\n validity[i, :l] = 1\\n return validity.flatten()\",\n \"def to_sparse_matrix(self, grid, format=None):\\n S = self.centered_stencil()\\n # print(\\\"grid :\\\")\\n\\n grid = tuple(grid)\\n # print(grid)\\n if not (np.asarray(S.shape) % 2 == 1).all():\\n raise ValueError('all stencil dimensions must be odd')\\n\\n assert_condition(len(grid) == np.rank(S), ValueError,\\n 'stencil rank must equal number of grid dimensions')\\n assert_condition(min(grid) >= 1, ValueError,\\n 'grid dimensions must be positive')\\n\\n N_v = np.prod(grid) # number of vertices in the mesh\\n N_s = (S != 0).sum() # number of nonzero stencil entries\\n\\n # diagonal offsets\\n diags = np.zeros(N_s, dtype=int)\\n\\n # compute index offset of each dof within the stencil\\n strides = np.cumprod([1] + list(reversed(grid)))[:-1]\\n indices = tuple(i.copy() for i in S.nonzero())\\n for i,s in zip(indices,S.shape):\\n i -= s // 2\\n for stride,coords in zip(strides, reversed(indices)):\\n diags += stride * coords\\n\\n #\\n data = S[S != 0].repeat(N_v).reshape(N_s, N_v)\\n indices = np.vstack(indices).T\\n\\n # zero boundary connections\\n for index,diag in zip(indices,data):\\n diag = diag.reshape(grid)\\n for n,i in enumerate(index):\\n if i > 0:\\n s = [ slice(None) ]*len(grid)\\n s[n] = slice(0,i)\\n diag[s] = 0\\n elif i < 0:\\n s = [ slice(None) ]*len(grid)\\n s[n] = slice(i,None)\\n diag[s] = 0\\n\\n # remove diagonals that lie outside matrix\\n mask = abs(diags) < N_v\\n if not mask.all():\\n diags = diags[mask]\\n data = data[mask]\\n\\n # sum duplicate diagonals\\n if len(np.unique(diags)) != len(diags):\\n new_diags = np.unique(diags)\\n new_data = np.zeros( (len(new_diags),data.shape[1]), dtype=data.dtype)\\n for dia,dat in zip(diags,data):\\n n = np.searchsorted(new_diags,dia)\\n new_data[n,:] += dat\\n\\n diags = new_diags\\n data = new_data\\n\\n return sprs.dia_matrix((data,diags), shape=(N_v, N_v)).asformat(format)\",\n \"def zero_matrix(matrix):\\n rows = set()\\n columns = set()\\n m = len(matrix)\\n n = len(matrix[0])\\n for i in range(m):\\n for j in range(n):\\n if matrix[i][j] == 0:\\n rows.add(i)\\n columns.add(j)\\n\\n for i in range(m):\\n for j in range(n):\\n if i in rows or j in columns:\\n matrix[i][j] = 0\\n return matrix\",\n \"def get_diagonal_mask(data):\\n mask = np.zeros_like(data, dtype=np.bool)\\n mask[np.triu_indices_from(mask)] = True\\n return mask\",\n \"def _fix_uniq_col(self):\\n # subgradient; for two boolean arrays, multiplication seems to be the best way \\n # (equivalent to logical_and)\\n n_covered_col = self.a_csr.dot(np.ones(self.ncols)) \\n ifix = np.zeros(self.ncols, dtype=bool)\\n if (np.count_nonzero(n_covered_col) != self.mrows):\\n raise ValueError(\\\"There are uncovered rows! Please check your input!\\\")\\n if (np.any(n_covered_col==1)):\\n inonzero = self.a_csr[n_covered_col==1,:].nonzero()\\n ifix[inonzero[1]] = True\\n\\n return ifix\",\n \"def compress_csr(self):\\n _, unique, indices = np.unique(\\n self.m*self.rows + self.cols,\\n return_index=True, return_inverse=True)\\n self.rows = self.rows[unique]\\n self.cols = self.cols[unique]\\n self.vals = np.bincount(indices, weights=self.vals)\",\n \"def simulate_sparse(n_rows, n_columns, row_density=0.001):\\n # X = sparse.lil_matrix((n_rows, n_columns))\\n X = []\\n counter = 0\\n for row in range(n_rows):\\n counter += 1\\n if (counter % 1000) == 0:\\n print(counter)\\n\\n # X[row] = sparse.random(1, n_columns, density=row_density)\\n X.append(sparse.random(1, n_columns, density=row_density, format='lil'))\\n\\n return sparse.vstack(X).tocsr()\",\n \"def generate_sparse(n, s):\\n x = np.zeros(n)\\n I = np.random.randint(0, n, s)\\n x[I] = 1\\n return x\",\n \"def Problem2(n):\\n diag_entries = np.empty((3,n))\\n diag_entries[0] = np.ones(n)*(-1)\\n diag_entries[1] = np.ones(n)*2\\n diag_entries[2] = np.ones(n)*(-1)\\n A = sparse.spdiags(diag_entries, [-1,0,1],n,n,format=\\\"csr\\\")\\n return A\",\n \"def makeZero(m, n):\\n Matrix.validate_dimensions(m, n)\\n data = [[False for j in range(n)] for i in range(m)]\\n return BooleanMatrix(m, n, data)\",\n \"def generate_sketch_matrix(rand_h, rand_s, output_dim):\\n\\n # Generate a sparse matrix for tensor count sketch\\n rand_h = rand_h.astype(np.int64)\\n rand_s = rand_s.astype(np.float32)\\n assert(rand_h.ndim == 1 and rand_s.ndim ==\\n 1 and len(rand_h) == len(rand_s))\\n assert(np.all(rand_h >= 0) and np.all(rand_h < output_dim))\\n\\n input_dim = len(rand_h)\\n indices = np.concatenate((np.arange(input_dim)[..., np.newaxis],\\n rand_h[..., np.newaxis]), axis=1)\\n indices = torch.from_numpy(indices)\\n rand_s = torch.from_numpy(rand_s)\\n sparse_sketch_matrix = torch.sparse.FloatTensor(\\n indices.t(), rand_s, torch.Size([input_dim, output_dim]))\\n return sparse_sketch_matrix.to_dense().cuda()\",\n \"def generate_sketch_matrix(rand_h, rand_s, output_dim):\\n\\n # Generate a sparse matrix for tensor count sketch\\n rand_h = rand_h.astype(np.int64)\\n rand_s = rand_s.astype(np.float32)\\n assert(rand_h.ndim == 1 and rand_s.ndim ==\\n 1 and len(rand_h) == len(rand_s))\\n assert(np.all(rand_h >= 0) and np.all(rand_h < output_dim))\\n\\n input_dim = len(rand_h)\\n indices = np.concatenate((np.arange(input_dim)[..., np.newaxis],\\n rand_h[..., np.newaxis]), axis=1)\\n indices = torch.from_numpy(indices)\\n rand_s = torch.from_numpy(rand_s)\\n sparse_sketch_matrix = torch.sparse.FloatTensor(\\n indices.t(), rand_s, torch.Size([input_dim, output_dim]))\\n return sparse_sketch_matrix.to_dense().cuda()\",\n \"def _freespace_matrix(distance):\\n\\n return np.array([[1., distance], [0., 1.]])\",\n \"def mask(n, start, end):\\n columns = []\\n value = 1\\n for i in range(n):\\n if start <= end:\\n columns.append(value if (start <= i < end) else 0)\\n else:\\n columns.append(value if (start <= i or i < end) else 0)\\n value <<= 1\\n return BitColumnMatrix(columns)\",\n \"def create_sparse_coo_matrix(df, n_users, n_items, movie_dict):\\n\\n\\t# Map the movie_ids in the data to the new movie_ids given by the dictionary movie_dict\\n\\tmovie_id_list = list(map(lambda x: movie_dict[x], df['movieId'].tolist()))\\n\\t# Map the user_id in the dataframe to userid - 1 [to account for zero based indexing]\\n\\tuser_id_list = list(map(lambda x: x - 1, df['userId'].tolist()))\\n\\tsparse_matrix_coo = sparse.coo_matrix((df['rating'].tolist(),(user_id_list, movie_id_list)),shape=(n_users,n_items))\\n\\tlogger.debug(\\\"Shape of created sparse matrix: \\\" + str(sparse_matrix_coo.shape))\\n\\tlogger.debug(\\\"Number of non_zero elements in the sparse matrix: \\\" + str(sparse_matrix_coo.nnz))\\n\\tlogger.debug(\\\"Number of entries in the input dataframe:[should match the number of non zero entries in sparse matrix] \\\" + str(df.shape[0]))\\n\\treturn sparse_matrix_coo\",\n \"def compute_sparse_masks(cls):\\n with torch.no_grad():\\n for module_name, module, p_name, p, mask, pruned in cls.__sparse_parameters:\\n if mask.sum() < mask.numel(): # when recalculating masks\\n # restore dense parameter if allow_recompute_mask is enabled\\n assert (pruned is not None), \\\"Unable to restore dense parameter because allow_recompute_mask == False\\\"\\n p.add_(pruned.cuda())\\n\\n mask.set_(cls.__calculate_mask(p))\\n\\n if pruned is not None: # stow away pruned weights to cpu\\n pruned.set_((p * (~mask)).cpu())\\n\\n p.mul_(mask) # in-place multiplication, so pruned weights are 0-values, hence checkpoint will have 0s for pruned weights\\n if cls.__verbosity >= 2:\\n print(\\\"[ASP] Enabled %.2f%% sparsity for %s::%s of size=%s and type=%s\\\" % (100.0*mask.sum()/mask.numel(), module_name, p_name, str(p.size()), str(p.dtype)))\",\n \"def zero_matrix(matrix):\\n n = len(matrix)\\n if n == 0:\\n return True\\n\\n m = len(matrix[0])\\n if m == 0:\\n return True\\n\\n zero_rows = set()\\n zero_columns = set()\\n for i in range(n):\\n for j in range(m):\\n if matrix[i][j] == 0:\\n zero_rows.add(i)\\n zero_columns.add(j)\\n \\n for i in zero_rows:\\n for j in range(m):\\n matrix[i][j] = 0\\n\\n for i in range(n):\\n for j in zero_columns:\\n matrix[i][j] = 0\\n\\n return True\",\n \"def create_diagonal_mask(low_to_high_map, target_value=1):\\n low_to_high_map.drop_duplicates()\\n grouped = low_to_high_map.groupby(low_to_high_map.columns[1])\\n ordered_low_level_names = list()\\n group_matrices = []\\n for name, group in grouped:\\n group_size = group.shape[0]\\n # build up row/col names, order doesn't matter within a group = they are all equal\\n ordered_low_level_names = ordered_low_level_names + group.iloc[:, 0].tolist()\\n # set the diagonal matrix to be the target value\\n single_group_matrix = np.full(shape=(group_size, group_size), fill_value=target_value)\\n group_matrices.append(single_group_matrix)\\n # add the individual matrices along the diagonal\\n relationship_matrix = scipy.linalg.block_diag(*group_matrices)\\n # convert to pandas dataframe and set names\\n relationship_df = pd.DataFrame(relationship_matrix, columns=ordered_low_level_names, index=ordered_low_level_names)\\n\\n return relationship_df\",\n \"def make_weight_map(masks, w0=10, sigma=5, longest_max_size=-1):\\n nrows, ncols = masks.shape[1:]\\n\\n if longest_max_size > 0:\\n old_rows, old_cols = nrows, ncols\\n max_size = max(nrows, ncols)\\n new_rows, new_cols = longest_max_size * nrows // max_size, longest_max_size * ncols // max_size\\n\\n resized_masks = []\\n for mask in masks:\\n resized_masks.append(cv2.resize(mask, (new_cols, new_rows), interpolation=0))\\n masks = np.stack(resized_masks)\\n nrows, ncols = new_rows, new_cols\\n\\n masks = (masks > 0).astype(int)\\n distMap = np.zeros((nrows * ncols, masks.shape[0]))\\n X1, Y1 = np.meshgrid(np.arange(nrows), np.arange(ncols))\\n X1, Y1 = np.c_[X1.ravel(), Y1.ravel()].T\\n for i, mask in enumerate(masks):\\n # find the boundary of each mask,\\n # compute the distance of each pixel from this boundary\\n bounds = find_boundaries(mask, mode='inner')\\n X2, Y2 = np.nonzero(bounds)\\n xSum = (X2.reshape(-1, 1) - X1.reshape(1, -1)) ** 2\\n ySum = (Y2.reshape(-1, 1) - Y1.reshape(1, -1)) ** 2\\n distMap[:, i] = np.sqrt(xSum + ySum).min(axis=0)\\n ix = np.arange(distMap.shape[0])\\n if distMap.shape[1] == 1:\\n d1 = distMap.ravel()\\n border_loss_map = w0 * np.exp((-1 * (d1) ** 2) / (2 * (sigma ** 2)))\\n else:\\n if distMap.shape[1] == 2:\\n d1_ix, d2_ix = np.argpartition(distMap, 1, axis=1)[:, :2].T\\n else:\\n d1_ix, d2_ix = np.argpartition(distMap, 2, axis=1)[:, :2].T\\n d1 = distMap[ix, d1_ix]\\n d2 = distMap[ix, d2_ix]\\n border_loss_map = w0 * np.exp((-1 * (d1 + d2) ** 2) / (2 * (sigma ** 2)))\\n xBLoss = np.zeros((nrows, ncols))\\n xBLoss[X1, Y1] = border_loss_map\\n # class weight map\\n loss = np.zeros((nrows, ncols))\\n w_1 = 1 - masks.sum() / loss.size\\n w_0 = 1 - w_1\\n loss[masks.sum(0) == 1] = w_1\\n loss[masks.sum(0) == 0] = w_0\\n ZZ = xBLoss + loss\\n\\n if longest_max_size > 0:\\n ZZ = cv2.resize(ZZ, (old_cols, old_rows))\\n return ZZ\",\n \"def mask_infeasible(self):\\n ns = len(self)-1\\n # mask entries with i+j+k > ns\\n for ii in range(len(self)):\\n for jj in range(len(self)):\\n for kk in range(len(self)):\\n if ii+jj+kk > ns:\\n self.mask[ii,jj,kk] = True\\n \\n return self\",\n \"def _sample_from_zeros(n: int, sparse: sp.csr_matrix) -> List[List[int]]:\\n zeros = np.argwhere(np.logical_not(sparse.todense()))\\n ids = np.random.choice(range(len(zeros)), size=(n,))\\n return zeros[ids].tolist()\",\n \"def _make_causal_mask(\\n input_ids_shape: torch.Size, dtype: torch.dtype, device: torch.device, past_key_values_length: int = 0\\n):\\n bsz, tgt_len = input_ids_shape\\n mask = torch.full((tgt_len, tgt_len), torch.finfo(dtype).min, device=device)\\n mask_cond = torch.arange(mask.size(-1), device=device)\\n mask.masked_fill_(mask_cond < (mask_cond + 1).view(mask.size(-1), 1), 0)\\n mask = mask.to(dtype)\\n\\n if past_key_values_length > 0:\\n mask = torch.cat([torch.zeros(tgt_len, past_key_values_length, dtype=dtype, device=device), mask], dim=-1)\\n return mask[None, None, :, :].expand(bsz, 1, tgt_len, tgt_len + past_key_values_length)\",\n \"def update_distmatrix(min_idx, dist_matrix):\\n i, j = min_idx\\n new_cluster_dist = np.minimum(dist_matrix[i, :], dist_matrix[j, :])\\n new_cluster_dist[i] = np.inf\\n \\n dist_matrix[i, :] = new_cluster_dist\\n dist_matrix[:, i] = new_cluster_dist\\n \\n dist_matrix[j, :] = np.inf\\n dist_matrix[:, j] = np.inf\\n \\n return dist_matrix\",\n \"def _generate_square_subsequent_mask(self, sz: int):\\n mask = (torch.triu(torch.ones(sz, sz)) == 1).transpose(0, 1)\\n mask = mask.float().masked_fill(mask == 0, float(\\n '-inf')).masked_fill(mask == 1, float(0.0))\\n return mask\",\n \"def init_zero_matrix(self,rows,cols):\\n\\t\\ttmpMatrix = []\\n\\t\\tfor i in range(rows):\\n\\t\\t\\ttmp = [0 for j in range(cols)]\\n\\t\\t\\ttmpMatrix.append(tmp)\\n\\t\\treturn tmpMatrix\",\n \"def _get_support_mask(self):\\n mask = np.zeros(self.scores_.shape, dtype=bool)\\n mask[self.scores_ >= self.min_count] = True\\n return mask\",\n \"def get_mask(m, n, i):\\n\\n row = i // n\\n col = i % n\\n \\n ind = np.arange(m*n)\\n \\n mask = np.bitwise_and(np.abs(ind % n - col) <= 1, # indicies are 1 (or less) columns away\\n np.abs(ind // n - row) <= 1) # and indicies are 1 (or less) rows away\\n mask = mask.astype(int)\\n return mask\",\n \"def test_build_classification_matrix_sparse():\\n import scnym\\n\\n # generate a sparse matrix with ~10% of elements filled\\n B = np.zeros((100, 10))\\n ridx = np.random.choice(B.size, size=100, replace=True)\\n B.flat[ridx] = 1\\n B = sparse.csr_matrix(B)\\n\\n # create dummy gene names where the order of\\n # genes in `B` is permuted\\n A_genes = np.arange(10)\\n B_genes = np.random.permutation(A_genes)\\n\\n # build the classification matrix\\n X = scnym.utils.build_classification_matrix(\\n X=B,\\n model_genes=A_genes,\\n sample_genes=B_genes,\\n )\\n assert sparse.issparse(X)\\n\\n # X should have the genes of B in the order of A\\n for i, g in enumerate(A_genes):\\n j = int(np.where(B_genes == g)[0])\\n assert np.all(X[:, i].toarray() == B[:, j].toarray())\\n return\",\n \"def miserables(metadata: bool = False) -> Union[sparse.csr_matrix, Bunch]:\\n row = np.array(\\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 2, 2, 3, 10, 11, 11, 11, 11, 11, 11, 11, 11, 11, 11,\\n 11, 11, 11, 11, 11, 11, 11, 11, 11, 11, 11, 11, 11, 11, 11, 11, 11, 11, 11, 11, 11, 11, 12,\\n 16, 16, 16, 16, 16, 16, 16, 16, 16, 17, 17, 17, 17, 17, 17, 18, 18, 18, 18, 18, 19, 19, 19, 19,\\n 20, 20, 20, 21, 21, 22, 23, 23, 23, 23, 23, 23, 24, 24, 24, 24, 24, 24, 24, 24, 24, 25, 25, 25,\\n 25, 25, 25, 25, 25, 25, 25, 25, 25, 25, 26, 26, 26, 26, 26, 26, 26, 27, 27, 27, 27, 27, 27, 27,\\n 27, 27, 27, 27, 27, 28, 28, 29, 29, 29, 29, 29, 30, 34, 34, 34, 34, 35, 35, 35, 36, 36, 37, 39,\\n 39, 41, 41, 41, 41, 41, 41, 41, 41, 41, 46, 47, 48, 48, 48, 48, 48, 48, 48, 48, 48, 48, 48, 48,\\n 48, 48, 48, 48, 48, 48, 49, 49, 49, 49, 49, 51, 51, 51, 51, 54, 55, 55, 55, 55, 55, 55, 55, 55,\\n 55, 57, 57, 57, 57, 57, 57, 57, 57, 58, 58, 58, 58, 58, 58, 58, 58, 58, 58, 59, 59, 59, 59, 59,\\n 59, 59, 60, 60, 60, 60, 60, 60, 61, 61, 61, 61, 61, 62, 62, 62, 62, 62, 63, 63, 63, 63, 64, 64,\\n 64, 65, 65, 66, 68, 68, 68, 68, 69, 69, 69, 70, 70, 71, 73])\\n col = np.array(\\n [1, 2, 3, 4, 5, 6, 7, 8, 9, 11, 3, 11, 11, 11, 12, 13, 14,\\n 15, 23, 24, 25, 26, 27, 28, 29, 31, 32, 33, 34, 35, 36, 37, 38, 43,\\n 44, 48, 49, 51, 55, 58, 64, 68, 69, 70, 71, 72, 23, 17, 18, 19, 20,\\n 21, 22, 23, 26, 55, 18, 19, 20, 21, 22, 23, 19, 20, 21, 22, 23, 20,\\n 21, 22, 23, 21, 22, 23, 22, 23, 23, 24, 25, 27, 29, 30, 31, 25, 26,\\n 27, 41, 42, 50, 68, 69, 70, 26, 27, 39, 40, 41, 42, 48, 55, 68, 69,\\n 70, 71, 75, 27, 43, 49, 51, 54, 55, 72, 28, 29, 31, 33, 43, 48, 58,\\n 68, 69, 70, 71, 72, 44, 45, 34, 35, 36, 37, 38, 31, 35, 36, 37, 38,\\n 36, 37, 38, 37, 38, 38, 52, 55, 42, 55, 57, 62, 68, 69, 70, 71, 75,\\n 47, 48, 55, 57, 58, 59, 60, 61, 62, 63, 64, 65, 66, 68, 69, 71, 73,\\n 74, 75, 76, 50, 51, 54, 55, 56, 52, 53, 54, 55, 55, 56, 57, 58, 59,\\n 61, 62, 63, 64, 65, 58, 59, 61, 62, 63, 64, 65, 67, 59, 60, 61, 62,\\n 63, 64, 65, 66, 70, 76, 60, 61, 62, 63, 64, 65, 66, 61, 62, 63, 64,\\n 65, 66, 62, 63, 64, 65, 66, 63, 64, 65, 66, 76, 64, 65, 66, 76, 65,\\n 66, 76, 66, 76, 76, 69, 70, 71, 75, 70, 71, 75, 71, 75, 75, 74])\\n data = np.array(\\n [1, 8, 10, 1, 1, 1, 1, 2, 1, 5, 6, 3, 3, 1, 1, 1, 1,\\n 1, 9, 7, 12, 31, 17, 8, 2, 3, 1, 2, 3, 3, 2, 2, 2, 3,\\n 1, 1, 2, 2, 19, 4, 1, 1, 1, 1, 1, 1, 2, 4, 4, 4, 3,\\n 3, 3, 3, 1, 1, 4, 4, 3, 3, 3, 3, 4, 3, 3, 3, 3, 4,\\n 3, 3, 3, 5, 4, 4, 4, 4, 4, 2, 1, 5, 1, 1, 2, 13, 4,\\n 1, 2, 1, 1, 1, 1, 1, 1, 5, 1, 1, 3, 2, 1, 2, 5, 6,\\n 4, 1, 3, 1, 1, 3, 2, 1, 21, 2, 1, 1, 1, 1, 1, 1, 6,\\n 1, 2, 1, 1, 1, 3, 2, 2, 2, 1, 1, 1, 2, 3, 2, 2, 2,\\n 2, 2, 2, 2, 2, 2, 1, 1, 2, 5, 1, 1, 1, 1, 1, 1, 1,\\n 1, 2, 4, 1, 7, 6, 1, 2, 7, 5, 5, 3, 1, 1, 1, 1, 2,\\n 2, 1, 1, 1, 9, 1, 12, 1, 1, 1, 2, 6, 1, 1, 1, 7, 5,\\n 1, 9, 1, 5, 2, 1, 2, 1, 2, 2, 1, 1, 3, 15, 4, 6, 17,\\n 4, 10, 5, 3, 1, 1, 2, 5, 13, 5, 9, 5, 1, 2, 3, 2, 2,\\n 2, 1, 6, 3, 6, 5, 1, 6, 12, 5, 2, 1, 4, 5, 1, 1, 7,\\n 3, 1, 2, 1, 1, 6, 4, 2, 3, 4, 2, 3, 2, 1, 1, 3])\\n adjacency = sparse.csr_matrix((data, (row, col)), shape=(77, 77))\\n adjacency = adjacency + adjacency.T\\n\\n if metadata:\\n names = ['Myriel', 'Napoleon', 'Mlle Baptistine', 'Mme Magloire', 'Countess de Lo', 'Geborand',\\n 'Champtercier', 'Cravatte', 'Count', 'Old man', 'Labarre', 'Valjean', 'Marguerite', 'Mme Der',\\n 'Isabeau', 'Gervais', 'Tholomyes', 'Listolier', 'Fameuil', 'Blacheville', 'Favourite', 'Dahlia',\\n 'Zephine', 'Fantine', 'Mme Thenardier', 'Thenardier', 'Cosette', 'Javert', 'Fauchelevent',\\n 'Bamatabois', 'Perpetue', 'Simplice', 'Scaufflaire', 'Woman1', 'Judge', 'Champmathieu', 'Brevet',\\n 'Chenildieu', 'Cochepaille', 'Pontmercy', 'Boulatruelle', 'Eponine', 'Anzelma', 'Woman2',\\n 'Mother Innocent', 'Gribier', 'Jondrette', 'Mme Burgon', 'Gavroche', 'Gillenormand', 'Magnon',\\n 'Mlle Gillenormand', 'Mme Pontmercy', 'Mlle Vaubois', 'Lt Gillenormand', 'Marius', 'Baroness',\\n 'Mabeuf', 'Enjolras', 'Combeferre', 'Prouvaire', 'Feuilly', 'Courfeyrac', 'Bahorel', 'Bossuet',\\n 'Joly', 'Grantaire', 'MotherPlutarch', 'Gueulemer', 'Babet', 'Claquesous', 'Montparnasse',\\n 'Toussaint', 'Child1', 'Child2', 'Brujon', 'Mme Hucheloup']\\n x = np.array(\\n [0.53, 0.98, 0.41, 0.4, 1., 0.92, 0.84, 0.74, 0.78, 1., 0.51, 0.09, -0., 0.29, 0.37,\\n 0.41, -0.35, -0.46, -0.42, -0.46, -0.41, -0.37, -0.36, -0.2, -0.06, -0.04, -0.01, -0.02, 0.33,\\n 0.17, -0.29, -0.1, 0.58, 0.29, 0.29, 0.26, 0.29, 0.37, 0.35, 0.04, -0.01, -0.18, -0.09,\\n 0.2, 0.51, 0.7, -0.95, -0.7, -0.37, -0.08, -0.18, -0.05, 0.04, -0.12, -0.06, -0.13, -0.24, -0.48,\\n -0.25, -0.33, -0.43, -0.39, -0.33, -0.42, -0.31, -0.38, -0.48, -0.74, -0.08, -0.1, -0.02, -0.1,\\n 0.14, -0.76, -0.75, -0.18, -0.58])\\n y = np.array(\\n [-0.23, -0.42, -0.14, -0.18, -0.31, -0.52, -0.6, -0.65, -0.38, -0.19, 0.39, 0.03, 0.44, -0.44,\\n 0.51, -0.36, 0.27, 0.37, 0.4, 0.32, 0.32, 0.36, 0.4, 0.2, 0.07, 0.14, -0.05, 0.06, 0.06,\\n 0.24, -0.26, -0.1, 0.24, -0.04, 0.17, 0.23, 0.31, 0.21, 0.27, -0.36, 0.69, 0.11, 0.38, -0.09,\\n 0.05, 0.12, 0.82, 0.44, 0.06, -0.2, -0.4, -0.28, -0.68, -0.79, -0.4, -0.07, -0.51, -0.17, -0.03,\\n -0.09, -0.14, -0.04, -0.04, -0.07, -0.06, -0.11, -0.06, -0.35, 0.24, 0.19, 0.22, 0.29, -0.2,\\n 0.06, 0.14, 0.3, -0.1])\\n graph = Bunch()\\n graph.adjacency = adjacency\\n graph.names = np.array(names)\\n graph.position = np.vstack((x, y)).T\\n graph.name = 'miserables'\\n return graph\\n else:\\n return adjacency\",\n \"def zero_diag(mat):\\n\\n return replace_diag(mat, np.zeros(mat.shape[0]))\",\n \"def mask_(matrices, maskval=0.0, mask_diagonal=True):\\n\\n b, h, w = matrices.size()\\n\\n indices = torch.triu_indices(h, w, offset=0 if mask_diagonal else 1)\\n matrices[:, indices[0], indices[1]] = maskval\",\n \"def _test_sparsemax_loss_of_inf(self, dtype, random, use_gpu):\\n q = np.asarray([[0, 0, 1], [0, 0, 1], [0, 0, 1], [0, 0, 1]])\\n z_neg = np.asarray([\\n [0, -np.inf, 0],\\n [0, -np.inf, -np.inf],\\n [-np.inf, -np.inf, 0],\\n [-np.inf, -np.inf, -np.inf],\\n ]).astype(dtype)\\n z_pos = np.asarray([[0, np.inf, 0], [0, np.inf,\\n np.inf], [np.inf, np.inf, 0],\\n [np.inf, np.inf, np.inf]]).astype(dtype)\\n z_mix = np.asarray([[0, np.inf, 0], [0, np.inf, -np.inf],\\n [-np.inf, np.inf, 0], [-np.inf, np.inf,\\n -np.inf]]).astype(dtype)\\n\\n _, tf_loss_neg = self._tf_sparsemax_loss(z_neg, q, dtype, use_gpu)\\n self.assertAllCloseAccordingToType([0.25, np.inf, 0, np.nan], tf_loss_neg)\\n\\n _, tf_loss_pos = self._tf_sparsemax_loss(z_pos, q, dtype, use_gpu)\\n self.assertAllCloseAccordingToType([np.nan, np.nan, np.nan, np.nan],\\n tf_loss_pos)\\n\\n _, tf_loss_mix = self._tf_sparsemax_loss(z_mix, q, dtype, use_gpu)\\n self.assertAllCloseAccordingToType([np.nan, np.nan, np.nan, np.nan],\\n tf_loss_mix)\",\n \"def matrix_non_negative(data, allow_equal=True):\\n return matrix_min(data) >= 0 if allow_equal else matrix_min(data) > 0\",\n \"def gen_masks(num_masks, features, hidden_layers, hidden_units):\\n\\n # This array should contain numbers 1-784\\n features_indices = []\\n for i in range(features):\\n features_indices.append(i + 1)\\n masks = []\\n indices = []\\n for i in range(num_masks):\\n set_masks = [] # Will contain all masks for the set\\n # Randomize the input (and output, since they have to be the same)\\n # ordering\\n set_features = [] # Input and output node indices for the set\\n for index in features_indices:\\n set_features.append(index)\\n np.random.RandomState(np.random.randint(0, 2**32)).shuffle(\\n set_features)\\n indices.append(set_features)\\n prev_indices = set_features\\n for j in range(hidden_layers):\\n layer_indices = []\\n for k in range(hidden_units):\\n # The hidden nodes' indices need to be between the minimum\\n # index from the previous layer and one less than the number\\n # of features, inclusive.\\n layer_indices.append(np.random.randint(low=min(prev_indices),\\n high=features))\\n mask = np.zeros((len(prev_indices), len(layer_indices)),\\n dtype=np.float32)\\n for k in range(len(prev_indices)):\\n for l in range(len(layer_indices)):\\n # The mask value will be one when the autoregressive\\n # condition is met.\\n mask[k][l] = float(int(prev_indices[k] <= layer_indices[l]))\\n mask = tf.convert_to_tensor(mask, dtype=tf.float32)\\n set_masks.append(mask)\\n prev_indices = layer_indices\\n output_mask = np.zeros((len(prev_indices), features), dtype=np.float32)\\n for j in range(len(prev_indices)):\\n for k in range(len(set_features)):\\n output_mask[j][k] = float(int(prev_indices[j] < set_features[k]))\\n output_mask = tf.convert_to_tensor(output_mask, dtype=tf.float32)\\n set_masks.append(output_mask)\\n direct_mask = np.zeros((features, features), dtype=np.float32)\\n for j in range(features):\\n for k in range(features):\\n direct_mask[j][k] = float(int(set_features[j] < set_features[k]))\\n direct_mask = tf.convert_to_tensor(direct_mask, dtype=tf.float32)\\n set_masks.append(direct_mask)\\n masks.append(set_masks)\\n return{'masks': masks, 'indices': indices}\",\n \"def _build_sparse_matrix(L):\\n shape = L.shape\\n i = torch.LongTensor(np.vstack((L.row, L.col)).astype(int))\\n v = torch.FloatTensor(L.data)\\n return torch.sparse.FloatTensor(i, v, torch.Size(shape))\",\n \"def delete_from_csr(mat, row_indices=[], col_indices=[]):\\n\\n rows = []\\n cols = []\\n if row_indices:\\n rows = list(row_indices)\\n if col_indices:\\n cols = list(col_indices)\\n\\n if len(rows) > 0 and len(cols) > 0:\\n row_mask = np.ones(mat.shape[0], dtype=bool)\\n row_mask[rows] = False\\n col_mask = np.ones(mat.shape[1], dtype=bool)\\n col_mask[cols] = False\\n return mat[row_mask][:,col_mask]\\n elif len(rows) > 0:\\n mask = np.ones(mat.shape[0], dtype=bool)\\n mask[rows] = False\\n return mat[mask]\\n elif len(cols) > 0:\\n mask = np.ones(mat.shape[1], dtype=bool)\\n mask[cols] = False\\n return mat[:,mask]\\n else:\\n return mat\",\n \"def setZeroes(self, matrix: List[List[int]]) -> None:\\n if not matrix:\\n return\\n row = set()\\n col = set()\\n rowl = len(matrix[0])\\n for i, r in enumerate(matrix):\\n for j, c in enumerate(r):\\n if c == 0:\\n row.add(i)\\n col.add(j)\\n \\n for r in row:\\n matrix[r] = [0]*rowl\\n for j in col:\\n for i in range(len(matrix)):\\n matrix[i][j] = 0\",\n \"def ident_zeros(A):\\n assert A.size[0] == A.size[1]\\n A.assemble()\\n o_range = A.getOwnershipRange()\\n rows = []\\n for i in range(o_range[1]-o_range[0]):\\n indices, values = A.getRow(o_range[0]+i)\\n absrow = sum(abs(values))\\n if absrow < 1e-6:\\n rows.append(o_range[0] + i)\\n add_diagonal(A.handle, numpy.array(rows))\",\n \"def erase_missing(signal, valid_rows, valid_cols, sym_upper=True):\\n if sym_upper and sp.issparse(signal):\\n if np.any(valid_rows != valid_cols):\\n raise ValueError(\\n \\\"Valid rows and columns must be identical with sym_upper=True\\\"\\n )\\n if signal.shape[0] != signal.shape[1]:\\n raise ValueError(\\n \\\"Input matrix must be square when using sym_upper=True\\\"\\n )\\n # Make a boolean mask from good bins\\n good_mask = np.isin(range(signal.shape[0]), valid_rows)\\n # Set all pixels in a nondetectable bin to 0\\n # For faster masking of bins, mask bins using dot product with an\\n # identify matrix where bad bins have been masked on the diagonal\\n # E.g. if removing the second bin (row and column):\\n # 1 0 0 9 6 5 1 0 0 9 0 5\\n # 0 0 0 X 6 8 7 X 0 0 0 = 0 0 0\\n # 0 0 1 6 7 8 0 0 1 6 0 8\\n mask_mat = sp.eye(signal.shape[0])\\n mask_mat.data[0][~good_mask] = 0\\n erased = mask_mat.dot(signal).dot(mask_mat)\\n else:\\n # Get a boolean array of missing (1) and valid (0) rows\\n missing_rows = valid_to_missing(valid_rows, signal.shape[0])\\n missing_cols = valid_to_missing(valid_cols, signal.shape[1])\\n erased = signal.copy()\\n erased[missing_rows, :] = 0\\n erased[:, missing_cols] = 0\\n\\n return erased\",\n \"def zeros_like(self):\\n return MultiterminalDevice.zeros(\\n self.dims,\\n self.center.shape,\\n tuple(i.shape for i in self.leads),\\n self.connections,\\n )\",\n \"def compute_pw_sparse_out_of_memory(tr,\\n row_size = 500,\\n pm_processes = 2,\\n pm_pbar = True,\\n max_distance = 50,\\n matrix_name = 'rw_beta',\\n reassemble = True,\\n cleanup = True): \\n dest = secrets.token_hex(6)\\n os.mkdir(dest)\\n print(f\\\"CREATED /{dest}/ FOR HOLDING DISTANCE OUT OF MEMORY\\\")\\n row_chunks = memory._partition(range(tr.clone_df.shape[0]), row_size)\\n\\n smatrix_chunks = [(tr, ind, f\\\"{dest}/{i}.{matrix_name}.npz\\\") for i,ind in enumerate(row_chunks)]\\n csrfragments = parmap.starmap(memory.gen_sparse_rw_on_fragment, \\n smatrix_chunks, \\n matrix_name = matrix_name, \\n max_distance=max_distance, \\n pm_pbar=pm_pbar, \\n pm_processes = pm_processes)\\n if reassemble:\\n csr_full = memory.collapse_csrs([x[2] for x in smatrix_chunks])\\n print(f\\\"RETURNING scipy.sparse csr_matrix w/dims {csr_full.shape}\\\")\\n else: \\n csr_full = None\\n \\n if cleanup: \\n assert os.path.isdir(dest)\\n print(f\\\"CLEANING UP {dest}\\\")\\n shutil.rmtree(dest)\\n \\n \\n return csr_full, smatrix_chunks\",\n \"def construct_matrix_A(n):\\n n2 = n**2\\n D0 = 4*np.ones(n2) # 0th diagonal\\n D1 = - np.ones(n2 - 1) # -1st, 1st diagonals\\n D1[n-1::n] = 0 # Setting every k*n-1 entries = 0 for k < n\\n DN = - np.ones(n2 - n) # -nth, nth diagonals\\n return scipy.sparse.diags((DN, D1, D0, D1, DN), (-n, -1, 0, 1, n),\\n shape=(n2, n2), format=\\\"csr\\\")\",\n \"def create_sparse_matrix(self, matrix_df):\\n\\n print('creating sparse matrix...')\\n sparse_seg_tmp_df = matrix_df.groupby(['segment_id','day_of_week','time_idx'])[self.args['cluster_variable']].mean().reset_index()\\n sparse_rt_tmp_df = matrix_df.groupby(['road_type','day_of_week','time_idx'])[self.args['cluster_variable']].mean().reset_index()\\n time_seg_df = sparse_seg_tmp_df.groupby(['day_of_week','time_idx'])[self.args['cluster_variable']].mean().reset_index()\\n time_rt_df = sparse_rt_tmp_df.groupby(['day_of_week','time_idx'])[self.args['cluster_variable']].mean().reset_index()\\n #time_seg_df['time_id'] = time_seg_df.index\\n #time_rt_df['time_id'] = time_rt_df.index\\n times = list(range(24*60/self.args['time_resolution']))\\n full_time_idx = pd.DataFrame([i * 30 for i in times],columns = ['time_idx'])\\n full_time_idx['key'] = 1\\n full_day_of_week = pd.DataFrame(list(range(7)), columns = ['day_of_week'])\\n full_day_of_week['key'] = 1\\n full_times = pd.merge(full_time_idx, full_day_of_week, on='key')\\n full_times['time_id'] = full_times.index\\n time_seg_df = pd.merge(time_seg_df, full_times[['time_idx','day_of_week','time_id']], on=['time_idx','day_of_week'])\\n time_rt_df = pd.merge(time_rt_df, full_times[['time_idx','day_of_week','time_id']], on=['time_idx','day_of_week'])\\n \\n matrix_seg_keys_df = pd.merge(sparse_seg_tmp_df, time_seg_df[['time_id','day_of_week','time_idx']], how='left', on=['day_of_week','time_idx'])\\n matrix_rt_keys_df = pd.merge(sparse_rt_tmp_df, time_rt_df[['time_id','day_of_week','time_idx']], how='left', on=['day_of_week','time_idx'])\\n\\n time_seg_array = np.array(matrix_seg_keys_df['time_id'])\\n time_rt_array = np.array(matrix_rt_keys_df['time_id'])\\n segment_array = np.array(matrix_seg_keys_df['segment_id'])\\n rt_array = np.array(matrix_rt_keys_df['road_type'])\\n\\n uniquesegments = np.array(list(set(segment_array)))\\n uniquerts = np.array(list(set(rt_array)))\\n keyuniquesegments = np.array(range(len(uniquesegments)))\\n keyuniquerts = np.array(range(len(uniquerts)))\\n uniquesegments_df = pd.DataFrame({'segmentskey':keyuniquesegments, 'segment_id':uniquesegments})\\n uniquerts_df = pd.DataFrame({'roadtypekey':keyuniquerts, 'road_type':uniquerts})\\n\\n segments_df = pd.DataFrame(segment_array, columns = ['segment_id'])\\n rt_df = pd.DataFrame(rt_array, columns = ['road_type'])\\n segments_keys_df = pd.merge(segments_df, uniquesegments_df, how='left', on=['segment_id'])\\n rt_keys_df = pd.merge(rt_df, uniquerts_df, how='left', on=['road_type'])\\n segmentkeys = np.array(segments_keys_df['segmentskey'])\\n rtkeys = np.array(rt_keys_df['road_type'])\\n\\n level_array_seg = np.array(matrix_seg_keys_df['level_max'])\\n sparse_matrix_s = csr_matrix((level_array_seg, (segmentkeys,time_seg_array))).toarray()\\n sparse_matrix_seg = preprocessing.scale(sparse_matrix_s)\\n level_array_rt = np.array(matrix_rt_keys_df['level_max'])\\n sparse_matrix_r = csr_matrix((level_array_rt, (rtkeys,time_rt_array))).toarray()\\n sparse_matrix_rt = preprocessing.scale(sparse_matrix_r)\\n \\n if self.args['perform_pca']:\\n sparse_matrix_seg, self.pca_model = self.run_PCA(sparse_matrix_seg)\\n sparse_matrix_rt, self.pca_model = self.run_PCA(sparse_matrix_rt)\\n else:\\n sparse_matrix_seg = sparse_matrix_seg\\n sparse_matrix_rt = sparse_matrix_rt\\n \\n sparse_matrix_withsegkey = pd.DataFrame(sparse_matrix_seg)\\n sparse_matrix_withrtkey = pd.DataFrame(sparse_matrix_rt)\\n sparse_matrix_withsegkey['segmentskey'] = sparse_matrix_withsegkey.index\\n sparse_matrix_withseg = pd.merge(uniquesegments_df, sparse_matrix_withsegkey, on=['segmentskey'])\\n sparse_matrix_withrtkey['roadtypekey'] = sparse_matrix_withrtkey.index\\n sparse_matrix_withrt = pd.merge(uniquerts_df, sparse_matrix_withrtkey, on=['roadtypekey'])\\n \\n # write sparse_matrix to database as 'clustering' table\\n print('writing sparse matrix to db...')\\n sqlalchemy_conn_str = open('../conf/sqlalchemy_conn_str.txt', 'r').read()\\n engine = create_engine(sqlalchemy_conn_str)\\n if self.split_type == 'random':\\n sparse_matrix_withseg.to_sql(name='clust_sparse_avebysegment_random', con=engine, if_exists='replace')\\n sparse_matrix_withrt.to_sql(name='clust_sparse_avebyrt_random', con=engine, if_exists='replace')\\n elif self.split_type == 'date':\\n sparse_matrix_withseg.to_sql(name='clust_sparse_avebysegment_date', con=engine, if_exists='replace')\\n sparse_matrix_withrt.to_sql(name='clust_sparse_avebyrt_date', con=engine, if_exists='replace')\\n \\n print('returning train sparse matrix...')\\n return (uniquesegments_df, sparse_matrix_seg)\",\n \"def create_mask(neurons: list):\\n\\n return np.zeros_like(neurons[0])\",\n \"def sparse_random_inputs(format, shape, n=1, out_dtype=None, p=0.5, gap=None,\\r\\n explicit_zero=False, unsorted_indices=False):\\r\\n\\r\\n if out_dtype is None:\\r\\n out_dtype = theano.config.floatX\\r\\n\\r\\n assert 0 <= p and p <= 1\\r\\n assert len(shape) == 2\\r\\n assert out_dtype in sparse.all_dtypes\\r\\n assert gap is None or isinstance(gap, (tuple, list))\\r\\n if gap is not None and out_dtype.startswith('u'):\\r\\n assert gap[0] >= 0\\r\\n\\r\\n def _rand():\\r\\n where = numpy.random.binomial(1, p, size=shape).astype('int8')\\r\\n\\r\\n if out_dtype in sparse.discrete_dtypes:\\r\\n if not gap:\\r\\n value = numpy.random.randint(50, size=shape)\\r\\n elif len(gap) == 2:\\r\\n value = numpy.random.randint(gap[0], gap[1], size=shape)\\r\\n else:\\r\\n value = numpy.random.randint(gap[0], size=shape)\\r\\n else:\\r\\n if not gap:\\r\\n value = numpy.random.random(shape)\\r\\n elif len(gap) == 2:\\r\\n a, b = gap\\r\\n value = a + numpy.random.random(shape) * (b - a)\\r\\n else:\\r\\n value = numpy.random.random(shape) * gap[0]\\r\\n return (where * value).astype(out_dtype)\\r\\n\\r\\n variable = [getattr(theano.sparse, format + '_matrix')(dtype=out_dtype)\\r\\n for k in range(n)]\\r\\n data = [getattr(scipy.sparse, format + '_matrix')(_rand(), dtype=out_dtype)\\r\\n for k in range(n)]\\r\\n if unsorted_indices:\\r\\n for idx in range(n):\\r\\n d = data[idx]\\r\\n d = d[range(d.shape[0])]\\r\\n assert not d.has_sorted_indices\\r\\n data[idx] = d\\r\\n if explicit_zero:\\r\\n for idx in range(n):\\r\\n assert data[idx].nnz > 1, (\\r\\n \\\"can't make a sparse matrix with explicit 0\\\")\\r\\n d_idx = numpy.random.randint(data[idx].nnz)\\r\\n data[idx].data[d_idx] = 0\\r\\n\\r\\n #numpy 1.5.0 with scipy 0.9.0 have scipy.sparse.XXX_matrix return\\r\\n #typenum 10(ulonglong) instead of 8(uint64) event if they are the same!\\r\\n #Theano don't like ulonglong type_num\\r\\n dtype = numpy.dtype(out_dtype) # Convert into dtype object.\\r\\n if data[0].dtype.num != dtype.num and dtype.str == data[0].dtype.str:\\r\\n data[0].data = theano._asarray(data[0].data, out_dtype)\\r\\n assert data[0].dtype.num == dtype.num\\r\\n return (variable, data)\",\n \"def sparse(n, k):\\n z = np.zeros(n)\\n for i in np.random.choice( np.arange(n), k, replace=None ): # supports of nonzero entries\\n z[i] = np.random.randn()\\n return z\",\n \"def mask(self):\\n mask = np.full(self._grid.shape, False)\\n mask[np.ix_(self._lat_indices, self._lon_indices)] = True\\n return mask\",\n \"def create_mask(shape):\\n return np.zeros(shape).astype(bool)\",\n \"def dict2sparseMatrix(wDict,std=0,diag=0):\\n data = lil_matrix((len(list(wDict.keys())),len(list(wDict.keys()))))\\n nAreas = len(list(wDict.keys()))\\n for i in wDict:\\n data[i,i] = diag\\n ne = len(wDict[i])+ diag\\n for j in wDict[i]:\\n if std:\\n data[i,j] = 1 / float(ne)\\n else:\\n data[i,j] = 1\\n return data\",\n \"def _buildMatrix(self, SparseMatrix, Ncells, MaxFaces, coeff):\\n return (0, 0)\",\n \"def _create_sparse_train_and_test(ratings, n_users, n_items):\\n \\n # pick a random set of data as testing data, sorted ascending\\n test_set_size = len(ratings) / TEST_SET_RATIO\\n test_set_idx = np.random.choice(xrange(len(ratings)), size=test_set_size, replace=False)\\n test_set_idx = sorted(test_set_idx)\\n \\n # use the remaining data to create a training set\\n ts_ratings = ratings[test_set_idx]\\n tr_ratings = np.delete(ratings, test_set_idx, axis=0)\\n \\n # create training and test matrices as coo_matrix\\n u_tr, i_tr, r_tr = zip(*tr_ratings)\\n tr_sparse = coo_matrix((r_tr, (u_tr, i_tr)), shape=(n_users, n_items))\\n u_ts, i_ts, r_ts = zip(*ts_ratings)\\n test_sparse = coo_matrix((r_ts, (u_ts, i_ts)), shape=(n_users, n_items))\\n \\n return tr_sparse, test_sparse\",\n \"def generateRandomStandardizedLinkMatrix(size, empty, autoRefLink):\\n\\n #We start by generating our matrix\\n res = np.zeros((size,size),dtype=float);\\n\\n #If we want to work with a sparse matrix\\n #We Generate the index vector (witch vector to populate?)\\n emptyVecIndexes = np.random.choice(2,size, p=[EMPTINESS_RATIO,1-EMPTINESS_RATIO])\\n\\n\\n for i in range(size):\\n\\n ## SPARSE MATRIX ##\\n if(empty):\\n\\n #We generate random vectors for only few columns\\n if(emptyVecIndexes[i]==1):\\n res[i] = generateProbabilisticVector(size,True)\\n\\n #We postprocess the non empty columns to ensure certain properties (diag = 0 | sum = (strict) 1 )\\n if(res[i].sum()!=0):\\n index = np.random.choice(size,1)\\n\\n while(index==i):\\n index = np.random.choice(size,1)\\n\\n\\n if(autoRefLink==False):\\n res[i][index]+=res[i][i]\\n res[i][i]=0\\n\\n #float precision sum problem => we ensure normalization of columns\\n if(isProbabilisticVector(res[i]) == False):\\n diff = 1-res[i].sum()\\n res[i][index]+=diff\\n\\n #for vectors with no link => Same chances to go anywhere\\n else:\\n #fullfill empty vectors with the same prob\\n res[i]= np.full(size,1/size)\\n\\n ## NORMAL MATRIX ##\\n else:\\n res[i] = generateProbabilisticVector(size,False)\\n\\n #Very unlikely but we do it just to be sure\\n if res[i].sum()==0:\\n\\n #fullfill empty vectors with the same prob\\n res[i]= np.full(size,1/size)\\n\\n\\n #We postprocess the non empty columns to ensure certain properties (diag = 0 | sum = (strict) 1 )\\n else:\\n index = np.random.choice(size,1)\\n\\n while(index==i):\\n index = np.random.choice(size,1)\\n\\n if(autoRefLink==False):\\n res[i][index]+=res[i][i]\\n res[i][i]=0\\n\\n #float precision sum problem => we ensure normalization of columns\\n if(isProbabilisticVector(res[i]) == False):\\n diff = 1-res[i].sum()\\n res[i][index]+=diff\\n\\n #to remove\\n #print(np.transpose(res));\\n return np.transpose(res)\",\n \"def diagonalsNeg (matrix, cols, rows):\\r\\n for diagonal in ([(j, i - cols + j + 1) for j in range(cols)] for i in range(cols + rows - 1)):\\r\\n yield [matrix[i][j] for i, j in diagonal if i >= 0 and j >= 0 and i < cols and j < rows]\",\n \"def _source_mask(self, ilens):\\n x_masks = make_non_pad_mask(ilens)\\n return x_masks.unsqueeze(-2)\",\n \"def generate_square_subsequent_mask(self, sz: int) -> Tensor:\\n\\t\\tmask = (torch.triu(torch.ones(sz, sz)) == 1).transpose(0, 1)\\n\\t\\tmask = mask.float().masked_fill(mask == 0, float('-inf')).masked_fill(mask == 1, float(0.0))\\n\\t\\treturn mask\",\n \"def zeros_matrix(rows, cols):\\n M = []\\n while len(M) < rows:\\n M.append([])\\n while len(M[-1]) < cols:\\n M[-1].append(0.0)\\n\\n return M\",\n \"def zeros_matrix(rows, cols):\\n M = []\\n while len(M) < rows:\\n M.append([])\\n while len(M[-1]) < cols:\\n M[-1].append(0.0)\\n\\n return M\",\n \"def zeros_matrix(rows, cols):\\n M = []\\n while len(M) < rows:\\n M.append([])\\n while len(M[-1]) < cols:\\n M[-1].append(0.0)\\n \\n return M\",\n \"def zeros_matrix(self, rows, cols):\\r\\n M = []\\r\\n while len(M) < rows:\\r\\n M.append([])\\r\\n while len(M[-1]) < cols:\\r\\n M[-1].append(0.0)\\r\\n\\r\\n return M\",\n \"def setZeroes(self, matrix: List[List[int]]) -> None:\\n m = len(matrix)\\n n = len(matrix[0])\\n rows = set()\\n columns = set()\\n for i in range(m):\\n for j in range(n):\\n if not matrix[i][j]:\\n rows.add(i)\\n columns.add(j)\\n\\n for i in rows:\\n for j in range(n):\\n matrix[i][j] = 0\\n for j in columns:\\n for i in range(m):\\n matrix[i][j] = 0\",\n \"def subgraph_mask(self, size):\\n init_matrix = np.random.randn(size,size)\\n Tcs = csgraph.minimum_spanning_tree(init_matrix)\\n mask_matrix = Tcs.toarray()\\n return mask_matrix\",\n \"def generate_sparse_randn(n, s):\\n x = np.zeros(n)\\n I = np.random.randint(0, n, s)\\n x[I] = np.random.randn(s)\\n return x\",\n \"def cmask(self):\\n mask = np.zeros(18)\\n if 'full' in self.CONS: mask[:] = 1\\n if 'f0' in self.CONS: mask[0] = 1\\n if 'f1' in self.CONS: mask[1:4] = 1\\n if 'f2' in self.CONS: mask[4:10] = 1\\n if 'vx' in self.CONS: mask[10] = 1\\n if 'vy' in self.CONS: mask[11] = 1\\n if 'vz' in self.CONS: mask[12] = 1\\n if 'TG' in self.CONS: mask[13:18] = 1\\n return mask>0\",\n \"def generatePattern(numCols=100, minOnes=21, maxOnes=25):\\n assert minOnes < maxOnes\\n assert maxOnes < numCols\\n\\n nOnes = random.randint(minOnes, maxOnes)\\n ind = random.sample(xrange(numCols), nOnes)\\n x = numpy.zeros(numCols, dtype='float32')\\n x[ind] = 1\\n\\n return x\",\n \"def heatmap_tiles_generator_diag(clr, chroms, pad_size, tile_size, band_to_cover):\\n\\n for chrom in chroms:\\n chr_start, chr_stop = clr.extent(chrom)\\n for tilei, tilej in square_matrix_tiling(chr_start,\\n chr_stop,\\n tile_size,\\n pad_size):\\n # check if a given tile intersects with \\n # with the diagonal band of interest ...\\n diag_from = tilej[0] - tilei[1]\\n diag_to = tilej[1] - tilei[0]\\n #\\n band_from = 0\\n band_to = band_to_cover\\n # we are using this >2*padding trick to exclude\\n # tiles from the lower triangle from calculations ...\\n if (min(band_to,diag_to) - max(band_from,diag_from)) > 2*pad_size:\\n yield chrom, tilei, tilej\",\n \"def bad_central_mask(weights, badmask, index, wmin = 0.1):\\n\\n \\n # Weights of fibers at the source index\\n sel = weights[:, index] > wmin\\n\\n #print(weights[:, index])\\n #print(badmask[sel, index - 1 : index + 2])\\n\\n bad_test = any(badmask[sel, index - 1 : index + 2].ravel()) \\n bad_test = bad_test| all(badmask[:, index - 1 : index + 2].ravel())\\n\\n if bad_test:\\n return False\\n else:\\n return True\",\n \"def init_safeboard() ->None:\\r\\n for x in range(shape):\\r\\n for y in range(shape):\\r\\n if conflict_space[x, y] != 0:\\r\\n safeboard[x, y] = 1\",\n \"def setZeroes(self, matrix: List[List[int]]) -> None:\\n m = len(matrix)\\n if m == 0:\\n return\\n n = len(matrix[0])\\n\\n row_zero = False\\n for i in range(m):\\n if matrix[i][0] == 0:\\n row_zero = True\\n col_zero = False\\n for j in range(n):\\n if matrix[0][j] == 0:\\n col_zero = True\\n\\n for i in range(1, m):\\n for j in range(1, n):\\n if matrix[i][j] == 0:\\n matrix[i][0] = 0\\n matrix[0][j] = 0\\n\\n for i in range(1, m):\\n if matrix[i][0] == 0:\\n for j in range(1, n):\\n matrix[i][j] = 0\\n\\n for j in range(1, n):\\n if matrix[0][j] == 0:\\n for i in range(1, m):\\n matrix[i][j] = 0\\n\\n if col_zero:\\n for j in range(n):\\n matrix[0][j] = 0\\n if row_zero:\\n for i in range(m):\\n matrix[i][0] = 0\",\n \"def createsolution(self,rows):\\r\\n l = [0,0]\\r\\n if not self.findzero(l):\\r\\n return True\\r\\n x = l[0]\\r\\n y = l[1]\\r\\n for i in range(1,10):\\r\\n if self.check(i, x, y):\\r\\n rows[x][y] = i\\r\\n if self.createsolution(rows):\\r\\n return True\\r\\n rows[x][y] = 0\\r\\n return False\",\n \"def setZeroes(self, matrix: List[List[int]]) -> None:\\n m, n = len(matrix), len(matrix[0])\\n zero_row, zero_col = set(), set()\\n for row in range(m):\\n for col in range(n):\\n if matrix[row][col] == 0:\\n if row not in zero_row:\\n zero_row.add(row)\\n if col not in zero_col:\\n zero_col.add(col)\\n for row in zero_row:\\n for col in range(n):\\n matrix[row][col] = 0\\n for col in zero_col:\\n for row in range(m):\\n matrix[row][col] = 0\",\n \"def construct_M_N(n):\\n n2 = n**2\\n D0 = 2*np.ones(n2) # 0th diagonal\\n D1 = - np.ones(n2 - 1) # -1st, 1st diagonals\\n D1[n-1::n] = 0 # Setting every k*n-1 entries = 0 for k < n\\n DN = - np.ones(n2 - n) # -nth, nth diagonals\\n return (scipy.sparse.diags((D1, D0, D1), (-1, 0, 1), shape=(n2, n2), format=\\\"csr\\\"),\\n scipy.sparse.diags((DN, D0, DN), (-n, 0, n), shape=(n2, n2), format=\\\"csr\\\"))\",\n \"def island():\\n\\n grid = [\\n [\\\"1\\\", \\\"1\\\", \\\"0\\\", \\\"0\\\", \\\"0\\\"],\\n [\\\"1\\\", \\\"1\\\", \\\"0\\\", \\\"0\\\", \\\"0\\\"],\\n [\\\"0\\\", \\\"0\\\", \\\"1\\\", \\\"0\\\", \\\"0\\\"],\\n [\\\"0\\\", \\\"0\\\", \\\"0\\\", \\\"1\\\", \\\"1\\\"]\\n ]\\n\\n def dfs():\\n rows = len(grid)\\n cols = len(grid[0])\\n count = 0\\n for i in range(0, rows):\\n for j in range(0, cols):\\n if grid[i][j] == '1':\\n check_valid(i, j, grid)\\n count = count + 1\\n return count\\n\\n def check_valid(i, j, grid=None):\\n rows = len(grid)\\n cols = len(grid[0])\\n\\n if not 0 <= i < rows or not 0 <= j < cols or grid[i][j] != '1':\\n return\\n\\n grid[i][j] = '0'\\n\\n check_valid(i + 1, j, grid)\\n check_valid(i - 1, j, grid)\\n check_valid(i, j + 1, grid)\\n check_valid(i, j - 1, grid)\\n\\n return dfs()\",\n \"def fim_mask(model, dataset, samples, threshold):\\n fisher_diagonal = fisher_matrix(model, dataset, samples)\\n mask = [tensor < threshold for tensor in fisher_diagonal]\\n return mask\",\n \"def square_mask(ys_in_pad, ignore_id):\\n ys_mask = (ys_in_pad != ignore_id).unsqueeze(-2)\\n ymax = ys_mask.size(-1)\\n ys_mask_tmp = ys_mask.transpose(1, 2).repeat(1, 1, ymax)\\n ys_mask = ys_mask.repeat(1, ymax, 1) & ys_mask_tmp\\n\\n return ys_mask\",\n \"def make_sparse_batch(instance_indices, instance_values, max_index):\\n batch_indices = make_batch_indices(instance_indices)\\n batch_values = list(itertools.chain.from_iterable(instance_values))\\n batch_shape = (len(instance_indices), max_index)\\n return tf.SparseTensorValue(batch_indices, batch_values, batch_shape)\",\n \"def setZeroes(matrix):\\r\\n \\r\\n #An average O(n^2) time traversal solution with memoization\\r\\n #for each 0 we encounter, we update the entire row and column to 0s, but on the condition that the row/column has not been updated yet\\r\\n \\r\\n row_cache = {}\\r\\n column_cache = {}\\r\\n \\r\\n for r in range(0,rows := len(matrix)):\\r\\n for c in range(0,cols := len(matrix[0])):\\r\\n \\r\\n if matrix[r][c] == 0:\\r\\n \\r\\n if not row_cache.get(r):\\r\\n for i in range(0,cols):\\r\\n if matrix[r][i] != 0:\\r\\n matrix[r][i] = '0' #we use strings so we only consider the initial 0s\\r\\n row_cache[r] = True\\r\\n \\r\\n if not column_cache.get(c):\\r\\n for i in range(0,rows):\\r\\n if matrix[i][c] != 0:\\r\\n matrix[i][c] = '0'\\r\\n column_cache[c] = True\\r\\n return\",\n \"def gen_square_subsequent_mask(sz: int) -> torch.Tensor:\\n return torch.triu(torch.ones(sz, sz) * float('-inf'), diagonal=1)\",\n \"def from_mask(self, lmax, nwins, dh_mask, sampling=1):\\n tapers, eigenvalues = SHReturnTapersMap(dh_mask, lmax, sampling=sampling, Ntapers=nwins)\\n return SHAsymmetricWindow(tapers,eigenvalues)\",\n \"def set_sparse_signals(self):\\n\\t\\n\\t\\tparams_dSs = [self.mu_dSs, self.sigma_dSs]\\n\\t\\tparams_Ss0 = [self.mu_Ss0, self.sigma_Ss0]\\n\\t\\tself.dSs, self.idxs = sparse_vector([self.Nn, self.Kk], \\n\\t\\t\\t\\t\\t\\t\\t\\t\\t\\t\\t\\tparams_dSs,\\tseed=self.seed_dSs)\\n\\t\\t\\n\\t\\t# Replace components with conflicting background odor \\n\\t\\tif self.Kk_split is not None and self.Kk_split != 0:\\n\\t\\t\\tassert 0 <= self.Kk_split <= self.Kk, \\\\\\n\\t\\t\\t\\t\\\"Splitting sparse signal into two levels requires Kk_split\\\" \\\\\\n\\t\\t\\t\\t\\\" to be non-negative and less than or equal to Kk.\\\"\\n\\t\\t\\tassert self.mu_dSs_2 is not None \\\\\\n\\t\\t\\t\\tand self.sigma_dSs_2 is not None, \\\\\\n\\t\\t\\t\\t\\\"Splitting sparse signal into two levels requires that\\\" \\\\\\n\\t\\t\\t\\t\\\" mu_dSs_2 and sigma_dSs_2 are set.\\\"\\n\\n\\t\\t\\tsp.random.seed(self.seed_dSs)\\n\\t\\t\\tself.idxs_2 = sp.random.choice(self.idxs[0], self.Kk_split, \\n\\t\\t\\t\\t\\t\\t\\t\\t\\t\\t\\treplace=False)\\n\\t\\t\\tfor idx_2 in self.idxs_2:\\n\\t\\t\\t\\tself.dSs[idx_2] = sp.random.normal(self.mu_dSs_2, \\n\\t\\t\\t\\t\\t\\t\\t\\t\\t\\t\\t\\t\\tself.sigma_dSs_2)\\n\\t\\telse:\\n\\t\\t\\tself.idxs_2 = []\\n\\t\\t\\tself.Kk_split = 0\\n\\t\\t\\t\\n\\t\\t# Ss0 is the ideal (learned) background stimulus without noise\\n\\t\\tself.Ss0, self.Ss0_noisy = sparse_vector_bkgrnd([self.Nn, self.Kk], \\n\\t\\t\\t\\t\\t\\t\\t\\t\\t\\t\\t\\t\\t\\tself.idxs, params_Ss0,\\n\\t\\t\\t\\t\\t\\t\\t\\t\\t\\t\\t\\t\\t\\tseed=self.seed_Ss0)\\n\\t\\t\\n\\t\\tself.Ss = self.dSs + self.Ss0_noisy\",\n \"def setZeroes(self, matrix: List[List[int]]) -> None:\\n m = len(matrix)\\n n = len(matrix[0])\\n set_rows = set()\\n set_cols = set()\\n for i in range(m):\\n for j in range(n):\\n if matrix[i][j] == 0:\\n set_rows.add(i)\\n set_cols.add(j)\\n \\n while set_rows:\\n temp = set_rows.pop()\\n for j in range(n):\\n matrix[temp][j] = 0\\n \\n while set_cols:\\n temp = set_cols.pop()\\n for i in range(m):\\n matrix[i][temp] = 0\",\n \"def one_dim_sparse_laplacian(m: int):\\n return sp.diags([1.0, -2.0, 1.0], [-1, 0, 1], dtype='float64', shape=(m, m), format='lil')\",\n \"def __generate_mask(self):\\n mask = np.concatenate([np.ones(len(self.fixed[0])),\\n np.zeros(self.num_points),\\n np.ones(len(self.fixed[1]))])\\n return mask\",\n \"def build_matrix(docs):\\n nrows = len(docs)\\n idx = {}\\n tid = 0\\n nnz = 0\\n # Remove all ratings\\n for d in docs:\\n #d = d[1:]\\n nnz += len(set(d))\\n for w in d:\\n if w not in idx:\\n idx[w] = tid\\n tid += 1\\n ncols = len(idx)\\n print nrows\\n print ncols\\n # set up memory\\n ind = np.zeros(nnz, dtype=np.int)\\n val = np.zeros(nnz, dtype=np.double)\\n ptr = np.zeros(nrows+1, dtype=np.int)\\n i = 0 # document ID / row counter\\n n = 0 # non-zero counter\\n # transfer values\\n for d in docs:\\n #d = d[1:]\\n cnt = Counter(d)\\n keys = list(k for k,_ in cnt.most_common())\\n l = len(keys)\\n for j,k in enumerate(keys):\\n ind[j+n] = idx[k]\\n val[j+n] = cnt[k]\\n ptr[i+1] = ptr[i] + l\\n n += l\\n i += 1\\n\\n mat = csr_matrix((val, ind, ptr), shape=(nrows, ncols), dtype=np.double)\\n mat.sort_indices()\\n\\n return mat\"\n]","isConversational":false},"negative_scores":{"kind":"list like","value":["0.5894775","0.58664924","0.5579","0.55251396","0.545152","0.53834623","0.53789914","0.5377302","0.53023285","0.5288333","0.51945496","0.51902205","0.5188426","0.51522386","0.5135295","0.5130921","0.5122746","0.51174325","0.51109743","0.508941","0.5082233","0.5076851","0.5067473","0.50432616","0.5035504","0.5027917","0.50037354","0.50015986","0.50015986","0.49865156","0.4985124","0.49775416","0.4974699","0.49732327","0.4971721","0.49331063","0.49128816","0.48849115","0.48746836","0.4874509","0.48710722","0.48611695","0.4851376","0.48477265","0.4834005","0.48313034","0.48296145","0.4827374","0.48128748","0.47952715","0.4784611","0.47840852","0.47736698","0.476848","0.47593582","0.475847","0.47548854","0.47509235","0.4745663","0.47452152","0.47393113","0.4738303","0.47325647","0.4732149","0.4731023","0.47268826","0.47229648","0.47207838","0.46976694","0.46861723","0.46817812","0.46790045","0.46751186","0.46751186","0.4667695","0.46632665","0.46631315","0.4659397","0.46578246","0.46561795","0.46537167","0.465285","0.46512574","0.464936","0.46456575","0.46443272","0.46430862","0.46414828","0.4641035","0.46398714","0.46311793","0.46229056","0.46218488","0.46185446","0.46144974","0.4612665","0.4608459","0.46081334","0.46011096","0.45963666"],"string":"[\n \"0.5894775\",\n \"0.58664924\",\n \"0.5579\",\n \"0.55251396\",\n \"0.545152\",\n \"0.53834623\",\n \"0.53789914\",\n \"0.5377302\",\n \"0.53023285\",\n \"0.5288333\",\n \"0.51945496\",\n \"0.51902205\",\n \"0.5188426\",\n \"0.51522386\",\n \"0.5135295\",\n \"0.5130921\",\n \"0.5122746\",\n \"0.51174325\",\n \"0.51109743\",\n \"0.508941\",\n \"0.5082233\",\n \"0.5076851\",\n \"0.5067473\",\n \"0.50432616\",\n \"0.5035504\",\n \"0.5027917\",\n \"0.50037354\",\n \"0.50015986\",\n \"0.50015986\",\n \"0.49865156\",\n \"0.4985124\",\n \"0.49775416\",\n \"0.4974699\",\n \"0.49732327\",\n \"0.4971721\",\n \"0.49331063\",\n \"0.49128816\",\n \"0.48849115\",\n \"0.48746836\",\n \"0.4874509\",\n \"0.48710722\",\n \"0.48611695\",\n \"0.4851376\",\n \"0.48477265\",\n \"0.4834005\",\n \"0.48313034\",\n \"0.48296145\",\n \"0.4827374\",\n \"0.48128748\",\n \"0.47952715\",\n \"0.4784611\",\n \"0.47840852\",\n \"0.47736698\",\n \"0.476848\",\n \"0.47593582\",\n \"0.475847\",\n \"0.47548854\",\n \"0.47509235\",\n \"0.4745663\",\n \"0.47452152\",\n \"0.47393113\",\n \"0.4738303\",\n \"0.47325647\",\n \"0.4732149\",\n \"0.4731023\",\n \"0.47268826\",\n \"0.47229648\",\n \"0.47207838\",\n \"0.46976694\",\n \"0.46861723\",\n \"0.46817812\",\n \"0.46790045\",\n \"0.46751186\",\n \"0.46751186\",\n \"0.4667695\",\n \"0.46632665\",\n \"0.46631315\",\n \"0.4659397\",\n \"0.46578246\",\n \"0.46561795\",\n \"0.46537167\",\n \"0.465285\",\n \"0.46512574\",\n \"0.464936\",\n \"0.46456575\",\n \"0.46443272\",\n \"0.46430862\",\n \"0.46414828\",\n \"0.4641035\",\n \"0.46398714\",\n \"0.46311793\",\n \"0.46229056\",\n \"0.46218488\",\n \"0.46185446\",\n \"0.46144974\",\n \"0.4612665\",\n \"0.4608459\",\n \"0.46081334\",\n \"0.46011096\",\n \"0.45963666\"\n]","isConversational":false},"document_score":{"kind":"string","value":"0.7036244"},"document_rank":{"kind":"string","value":"0"}}},{"rowIdx":22688,"cells":{"query":{"kind":"string","value":"Adds margin of zeros around an input sparse matrix."},"document":{"kind":"string","value":"def zero_pad_sparse(mat, margin_h, margin_v, fmt=\"coo\"):\n\n sm, sn = mat.shape\n padded_mat = mat.copy()\n # Up and down margins initialized with zeros and filled as needed\n margin_h_0 = sp.csr_matrix((sm, margin_h), dtype=mat.dtype)\n margin_v_0 = sp.csr_matrix((margin_v, sn + 2 * margin_h), dtype=mat.dtype)\n padded_mat = sp.hstack([margin_h_0, padded_mat, margin_h_0], format=\"csr\")\n padded_mat = sp.vstack([margin_v_0, padded_mat, margin_v_0], format=\"csr\")\n\n return padded_mat"},"metadata":{"kind":"string","value":"{\n \"objective\": {\n \"self\": [],\n \"paired\": [],\n \"triplet\": [\n [\n \"query\",\n \"document\",\n \"negatives\"\n ]\n ]\n }\n}"},"negatives":{"kind":"list like","value":["def _pad_with_zeros(self, X, margin):\n newX = np.zeros((X.shape[0] + 2 * margin, X.shape[1] + 2 * margin, X.shape[2]))\n x_offset = margin\n y_offset = margin\n newX[x_offset:X.shape[0] + x_offset, y_offset:X.shape[1] + y_offset, :] = X\n return newX","def OffsetSparseMatrix(SparseMatrix, numberOfVariables, numberOfEquations):\n\n class OffsetSparseMatrixClass(SparseMatrix):\n equationIndex = 0\n varIndex = 0\n\n def __init__(self, mesh, bandwidth=0, sizeHint=None,\n numberOfVariables=numberOfVariables, numberOfEquations=numberOfEquations):\n SparseMatrix.__init__(self, mesh=mesh, bandwidth=bandwidth, sizeHint=sizeHint,\n numberOfVariables=numberOfVariables, numberOfEquations=numberOfEquations)\n\n def put(self, vector, id1, id2):\n SparseMatrix.put(self, vector, id1 + self.mesh.numberOfCells * self.equationIndex, id2 + self.mesh.numberOfCells * self.varIndex)\n\n def addAt(self, vector, id1, id2):\n SparseMatrix.addAt(self, vector, id1 + self.mesh.numberOfCells * self.equationIndex, id2 + self.mesh.numberOfCells * self.varIndex)\n\n def addAtDiagonal(self, vector):\n if type(vector) in [type(1), type(1.)]:\n tmp = numerix.zeros((self.mesh.numberOfCells,), 'd')\n tmp[:] = vector\n SparseMatrix.addAtDiagonal(self, tmp)\n else:\n SparseMatrix.addAtDiagonal(self, vector)\n\n return OffsetSparseMatrixClass","def OffsetSparseMatrix(SparseMatrix, numberOfVariables, numberOfEquations):\n\n class OffsetSparseMatrixClass(SparseMatrix):\n equationIndex = 0\n varIndex = 0\n\n def __init__(self, mesh, bandwidth=0, sizeHint=None,\n numberOfVariables=numberOfVariables, numberOfEquations=numberOfEquations):\n SparseMatrix.__init__(self, mesh=mesh, bandwidth=bandwidth, sizeHint=sizeHint,\n numberOfVariables=numberOfVariables, numberOfEquations=numberOfEquations)\n\n def put(self, vector, id1, id2):\n SparseMatrix.put(self, vector, id1 + self.mesh.numberOfCells * self.equationIndex, id2 + self.mesh.numberOfCells * self.varIndex)\n\n def addAt(self, vector, id1, id2):\n SparseMatrix.addAt(self, vector, id1 + self.mesh.numberOfCells * self.equationIndex, id2 + self.mesh.numberOfCells * self.varIndex)\n\n def addAtDiagonal(self, vector):\n if type(vector) in [type(1), type(1.)]:\n tmp = numerix.zeros((self.mesh.numberOfCells,), 'd')\n tmp[:] = vector\n SparseMatrix.addAtDiagonal(self, tmp)\n else:\n SparseMatrix.addAtDiagonal(self, vector)\n\n return OffsetSparseMatrixClass","def makesparse(matrix):\n n = matrix[0].size\n elements = []\n for i in range(n):\n for j in range(n):\n if matrix[i][j] != 0 :\n temp = MatrixElement(i, j, matrix[i][j])\n elements.append(temp)\n return SparseMatrix(n, elements)","def pad_csr(a, newshape):\n n, m = a.shape\n a._shape = newshape\n a.indptr = np.pad(a.indptr, (0, newshape[0] - n), 'edge')","def ident_zeros(A):\n assert A.size[0] == A.size[1]\n A.assemble()\n o_range = A.getOwnershipRange()\n rows = []\n for i in range(o_range[1]-o_range[0]):\n indices, values = A.getRow(o_range[0]+i)\n absrow = sum(abs(values))\n if absrow < 1e-6:\n rows.append(o_range[0] + i)\n add_diagonal(A.handle, numpy.array(rows))","def pad_zeroes(m, axis, thick):\n\n if axis == 1:\n rows = m.shape[0]\n cols = thick\n elif axis == 0:\n rows = thick\n cols = m.shape[1]\n\n return np.concatenate((m, np.zeros((rows,cols))), \n axis)","def sp_zeros_like(x):\r\n\r\n # TODO: don't restrict to CSM formats\r\n _, _, indptr, shape = csm_properties(x)\r\n return CSM(format=x.format)(data=numpy.array([], dtype=x.type.dtype),\r\n indices=numpy.array([], dtype='int32'),\r\n indptr=tensor.zeros_like(indptr),\r\n shape=shape)","def pad_zeros(x):\n dim = tf.shape(x)[0]\n log2_dim = tf.math.log(tf.cast(dim, tf.float32)) / tf.math.log(2.0)\n pad_dim = tf.pow(2, tf.cast(tf.math.ceil(log2_dim), tf.int32))\n with tf.control_dependencies([tf.debugging.assert_rank(x, 1)]):\n return tf.pad(x, [[0, tf.maximum(0, pad_dim - dim)]])","def _zero_pad(self, kernel, size):\n if len(size) != kernel.ndim:\n size = kernel.shape[:1] + tuple(size) + kernel.shape[-1:]\n padsize = np.array(size) - np.array(kernel.shape)\n paddown = padsize // 2\n padup = padsize - paddown\n padarray = np.concatenate((padup[..., None],\n paddown[..., None]), axis=1)\n pads = tuple([tuple(p) for p in padarray])\n kernel_pad = np.pad(kernel, pads, 'constant', constant_values=0)\n return kernel_pad","def setZeroes(self, matrix: List[List[int]]) -> None:\n zero_columns = []\n zero_rows = []\n for i in range(len(matrix)):\n for j in range(len(matrix[0])):\n if matrix[i][j] == 0:\n zero_columns.append(j)\n zero_rows.append(i)\n for zero_column in zero_columns:\n for row in matrix:\n row[zero_column] = 0\n for zero_row in zero_rows:\n matrix[zero_row] = [0] * len(matrix[0])","def add_zero(matrix):\n if len(matrix.shape) == 2:\n matrix = numpy.insert(matrix, 0, 0, axis=1)\n matrix = numpy.insert(matrix, matrix.shape[1], 0, axis=1)\n row01 = numpy.zeros((matrix.shape[1]), numpy.uint8)\n matrix = numpy.insert(matrix, 0, row01, axis=0)\n matrix = numpy.insert(matrix, matrix.shape[0], row01, axis=0)\n\n if len(matrix.shape) == 3:\n pixel = numpy.zeros((matrix.shape[2]), numpy.uint8)\n matrix = numpy.insert(matrix, 0, pixel, axis=1)\n matrix = numpy.insert(matrix, matrix.shape[1], pixel, axis=1)\n row = numpy.zeros((1, matrix.shape[1], 3), numpy.uint8)\n matrix = numpy.insert(matrix, 0, row, axis=0)\n matrix = numpy.insert(matrix, matrix.shape[0], row, axis=0)\n\n return matrix","def zero_matrix(matrix):\n cols = []\n for row in range(0, len(matrix)):\n for col in range(0, len(matrix[row])):\n if matrix[row][col] == 0:\n cols.append(col)\n matrix[row] = [0 for _ in range(0, len(matrix[row]))]\n break\n for col in cols:\n for row in range(0, len(matrix)):\n matrix[row][col] = 0\n return matrix","def zero_pad(X, padding_width, dims):\n dims = (dims) if isinstance(dims, int) else dims\n pad = [(0, 0) if idx not in dims else (padding_width, padding_width)\n for idx in range(len(X.shape))]\n X_padded = np.pad(X, pad, 'constant')\n return X_padded","def zero_pad(data):\n N = len(data)\n pow_2 = np.ceil(np.log2(N))\n return np.pad(data,(0,int((2**pow_2)-N)),'constant')","def zero_matrix(matrix):\n if not matrix:\n return\n\n n_rows = len(matrix)\n n_cols = len(matrix[0])\n\n # First row and first column of the matrix are used to store row and columns to nullify.\n # At the beginning we determine if the first row and/or first column contain a zero.\n first_row_zero = False\n first_col_zero = False\n for col in range(n_cols):\n if not matrix[0][col]:\n first_row_zero = True\n break\n for row in range(n_rows):\n if not matrix[row][0]:\n first_col_zero = True\n break\n\n # Then go through the rest of the matrix and determine which rows and columns must be zeroed.\n for row in range(1, n_rows):\n for col in range(1, n_cols):\n if not matrix[row][col]:\n matrix[0][col] = 0\n matrix[row][0] = 0\n\n # Go through the rest of the matrix again and insert zeros where needed.\n for row in range(1, n_rows):\n for col in range(1, n_cols):\n if matrix[0][col] == 0 or matrix[row][0] == 0:\n matrix[row][col] = 0\n\n # Nullify first row and column if needed.\n if first_row_zero:\n for col in range(n_cols):\n matrix[0][col] = 0\n if first_col_zero:\n for row in range(n_rows):\n matrix[row][0] = 0","def zero_diag(mat):\n\n return replace_diag(mat, np.zeros(mat.shape[0]))","def zeroes(height, width):\n g = [[0.0 for _ in range(width)] for __ in range(height)]\n return Matrix(g)","def spzeros(size, device='cuda:0'):\n\n return SparseTensor(size=size).to(device=device)","def posM(M):\r\n M[np.where(M < 0)] = 0\r\n return M","def setZeroes(self, matrix: List[List[int]]) -> None:\n first_col = False\n for i in range(len(matrix)):\n for j in range(len(matrix[0])):\n if matrix[i][j] == 0:\n matrix[i][0] = 0\n if j == 0:\n first_col = True\n else:\n matrix[0][j] = 0\n for i in range(1, len(matrix[0])):\n if matrix[0][i] == 0:\n for j in range(len(matrix)):\n matrix[j][i] = 0\n if matrix[0][0] == 0:\n matrix[0] = [0 for x in range(len(matrix[0]))]\n for i in range(1, len(matrix)):\n if matrix[i][0] == 0:\n for j in range(len(matrix[0])):\n matrix[i][j] = 0\n if first_col:\n for i in range(len(matrix)):\n matrix[i][0] = 0","def set_zero(mn_matrix): \r\n \r\n row_flag = [0]*len(mn_matrix)\r\n column_flag = [0]*len(mn_matrix[0])\r\n for i in range(0, len(row_flag)):\r\n for j in range(0, len(column_flag)):\r\n if mn_matrix[i][j] == 0:\r\n row_flag[i] = 1\r\n column_flag[j] = 1\r\n \r\n for i in range(0, len(row_flag)):\r\n for j in range(0, len(column_flag)):\r\n if row_flag[i] == 1 or column_flag[j] == 1: \r\n mn_matrix[i][j] = 0","def zeroes(height, width):\n g = [[0.0 for _ in range(width)] for __ in range(height)]\n return Matrix(g)","def setZeroes(self, matrix: List[List[int]]) -> None:\n rows, cols = [], []\n for i in range(len(matrix)):\n for j in range(len(matrix[i])):\n if matrix[i][j] == 0:\n rows.append(i)\n cols.append(j)\n for i in rows:\n for j in range(len(matrix[i])):\n matrix[i][j] = 0\n for j in cols:\n for i in range(len(matrix)):\n matrix[i][j] = 0\n\n return","def setZeroes(self, matrix: List[List[int]]) -> None:\r\n \r\n rows, cols = len(matrix), len(matrix[0])\r\n \r\n #This solution has been done in constant space\r\n #using first row and first column for deciding which rows and columns\r\n #should be zero\r\n \r\n #using one extra variable for first row\r\n #because first cell is common in first row and column\r\n first_row = 1\r\n \r\n for i in range(rows):\r\n for j in range(cols):\r\n if matrix[i][j]==0:\r\n #for column\r\n matrix[0][j] = 0\r\n \r\n #for row\r\n if i==0:\r\n first_row = 0\r\n else:\r\n matrix[i][0] = 0\r\n \r\n #checking for rows except first row\r\n for i in range(1,rows):\r\n if matrix[i][0] == 0:\r\n for j in range(cols):\r\n matrix[i][j] = 0\r\n \r\n #checking for columns except first column\r\n for j in range(1, cols):\r\n if matrix[0][j] == 0:\r\n for i in range(rows):\r\n matrix[i][j] = 0\r\n \r\n #for first column\r\n if matrix[0][0] == 0:\r\n for i in range(rows):\r\n matrix[i][0] = 0\r\n \r\n #for first row\r\n if first_row == 0:\r\n for j in range(cols):\r\n matrix[0][j] = 0\r\n \r\n #print(matrix)","def setZeroes(self, matrix: List[List[int]]) -> None:\n if not matrix:\n return\n row = set()\n col = set()\n rowl = len(matrix[0])\n for i, r in enumerate(matrix):\n for j, c in enumerate(r):\n if c == 0:\n row.add(i)\n col.add(j)\n \n for r in row:\n matrix[r] = [0]*rowl\n for j in col:\n for i in range(len(matrix)):\n matrix[i][j] = 0","def zero_matrix(matrix):\n rows = set()\n columns = set()\n m = len(matrix)\n n = len(matrix[0])\n for i in range(m):\n for j in range(n):\n if matrix[i][j] == 0:\n rows.add(i)\n columns.add(j)\n\n for i in range(m):\n for j in range(n):\n if i in rows or j in columns:\n matrix[i][j] = 0\n return matrix","def test_add_nans_to_weights():\n # create input sparse matrix with one empty row (j=2)\n row = np.array([0, 3, 1, 0])\n col = np.array([0, 3, 1, 2])\n data = np.array([4., 5., 7., 9.])\n Matin = sps.coo_matrix((data, (row, col)), shape=(4, 4))\n\n # this is what is expected to come out (Nan added at i=0, j=2)\n row = np.array([0, 3, 1, 0, 2])\n col = np.array([0, 3, 1, 2, 0])\n data = np.array([4., 5., 7., 9., np.nan])\n expected = sps.coo_matrix((data, (row, col)), shape=(4, 4))\n\n Matout = xe.smm.add_nans_to_weights(Matin)\n assert np.allclose(expected.toarray(), Matout.toarray(), equal_nan=True)\n\n # Matrix without empty rows should return the same\n row = np.array([0, 3, 1, 0, 2])\n col = np.array([0, 3, 1, 2, 1])\n data = np.array([4., 5., 7., 9., 10.])\n Matin = sps.coo_matrix((data, (row, col)), shape=(4, 4))\n\n Matout = xe.smm.add_nans_to_weights(Matin)\n assert np.allclose(Matin.toarray(), Matout.toarray())","def setZeroes(self, matrix: List[List[int]]) -> None:\n m, n = len(matrix), len(matrix[0]) if matrix else 0\n \n # Use first row to store zero-info for each col\n # Use first col to store zero-info for each row\n # matrix[0][0] will store col info\n \n is_first_row = any(x == 0 for x in matrix[0])\n \n for i in range(1, m):\n for j in range(n):\n if matrix[i][j] == 0:\n matrix[0][j] = matrix[i][0] = 0\n \n for i in range(1, m):\n for j in range(1, n):\n if matrix[i][0] * matrix[0][j] == 0:\n matrix[i][j] = 0\n \n if matrix[0][0] == 0:\n for i in range(1, m):\n matrix[i][0] = 0\n \n if is_first_row:\n for j in range(n):\n matrix[0][j] = 0","def setZeroes(self, matrix: List[List[int]]) -> None:\n colsToZero = set()\n rowsToZero = set()\n for rowIdx, row in enumerate(matrix):\n for colIdx, num in enumerate(row): \n if num == 0: \n colsToZero.add(colIdx)\n rowsToZero.add(rowIdx)\n \n for col in colsToZero:\n self.writeZeroCol(col, matrix)\n for row in rowsToZero:\n self.writeZeroRow(row, matrix)","def setZeroes(self, matrix: List[List[int]]) -> None:\n m = len(matrix)\n n = len(matrix[0])\n set_rows = set()\n set_cols = set()\n for i in range(m):\n for j in range(n):\n if matrix[i][j] == 0:\n set_rows.add(i)\n set_cols.add(j)\n \n while set_rows:\n temp = set_rows.pop()\n for j in range(n):\n matrix[temp][j] = 0\n \n while set_cols:\n temp = set_cols.pop()\n for i in range(m):\n matrix[i][temp] = 0","def setZeroes(self, matrix: List[List[int]]) -> None:\n row = []\n col = []\n for i in range(len(matrix)):\n for j in range(len(matrix[i])):\n if matrix[i][j] == 0:\n col.append(j)\n row.append(i)\n\n for i in range(len(row)):\n self.col(matrix, col[i])\n for i in range(len(col)):\n self.row(matrix, row[i])","def setZeroes(self, matrix: List[List[int]]) -> None:\n m = len(matrix)\n n = len(matrix[0])\n rows = set()\n columns = set()\n for i in range(m):\n for j in range(n):\n if not matrix[i][j]:\n rows.add(i)\n columns.add(j)\n\n for i in rows:\n for j in range(n):\n matrix[i][j] = 0\n for j in columns:\n for i in range(m):\n matrix[i][j] = 0","def setZeroes(self, matrix: List[List[int]]) -> None:\n m, n = len(matrix), len(matrix[0])\n firstRow, firstCol = False, False\n for i in range(m):\n for j in range(n):\n if i == 0 and matrix[i][j] == 0:\n firstRow = True\n if j == 0 and matrix[i][j] == 0:\n firstCol = True\n if i != 0 and j != 0 and matrix[i][j] == 0:\n matrix[i][0] = 0\n matrix[0][j] = 0\n for i in range(1, m):\n for j in range(1, n):\n if matrix[i][0] == 0 or matrix[0][j] == 0:\n matrix[i][j] = 0\n if firstRow:\n for i in range(n):\n matrix[0][i] = 0\n if firstCol:\n for i in range(m):\n matrix[i][0] = 0","def setZeroes(self, matrix) -> None:\n if not matrix: return\n h, w = len(matrix), len(matrix[0])\n col0 = False\n for i in range(h):\n if matrix[i][0] == 0:\n col0 = True # 第一列是否要全置为0\n for j in range(1, w): # 从第二列开始\n if matrix[i][j] == 0: # (i,j)为0\n matrix[0][j] = 0 # 第i行为0\n matrix[i][0] = 0 # 第j列为0\n for i in range(1, h):\n for j in range(1, w):\n if matrix[i][j] != 0 and (matrix[i][0] == 0 or matrix[0][j] == 0):\n matrix[i][j] = 0 # 需要置为0的位置\n if matrix[0][0] == 0: # 第一行是否要全置为0\n for j in range(w):\n matrix[0][j] = 0\n if col0: # 第一列是否要全置为0\n for i in range(h):\n matrix[i][0] = 0","def setZeroes(self, matrix: List[List[int]]) -> None:\n\n row = len(matrix)\n column = len(matrix[0])\n\n for i in range(row):\n for j in range(column):\n if matrix[i][j] == 0:\n for r in range(row):\n if matrix[r][j] != 0:\n matrix[r][j] = 'c'\n for c in range(column):\n if matrix[i][c] != 0:\n matrix[i][c] = 'c'\n\n for i in range(row):\n for j in range(column):\n if matrix[i][j] == 'c':\n matrix[i][j] = 0","def zeroMatrix(width, height):\n returnvalue = Matrix()\n for i in range(width):\n newrow = [0] * height\n returnvalue.addRow(*newrow)\n return returnvalue","def setZeroes(self, matrix: List[List[int]]) -> None:\n \n m = len(matrix)\n n = len(matrix[0])\n \n i_zeros = []\n j_zeros = []\n \n for i in range(m): ## find the zeros\n for j in range(n):\n \n if matrix[i][j] == 0:\n i_zeros.append(i)\n j_zeros.append(j)\n \n for i in i_zeros:\n matrix[i] = [0]*n\n \n for j in j_zeros:\n for i in range(m):\n matrix[i][j] = 0","def setZeroes(self, matrix: List[List[int]]) -> None:\n m = len(matrix)\n n = len(matrix[0])\n \n i = 0\n j = 0\n row_zero = set()\n col_zero = set()\n \n for i in range(m):\n for j in range(n):\n if matrix[i][j] == 0:\n row_zero.update({i})\n col_zero.update({j})\n \n ## update rows with zero\n for t in row_zero:\n matrix[t][0:n] = [0 for i in range(n)]\n \n ## update cols with zero\n for i in range(m):\n for c in col_zero:\n #print(i, c)\n matrix[i][c] = 0","def zeros_like(self):\n raise NotImplementedError","def setZeroes(self, matrix: List[List[int]]) -> None:\n n = len(matrix)\n m = len(matrix[0])\n zeroRow = False\n zeroColumn = False\n for i in range(0,n):\n if matrix[i][0] == 0:\n zeroColumn = True\n break\n for j in range(0,m):\n if matrix[0][j] == 0:\n zeroRow = True\n break\n for i in range(1,n):\n for j in range(1,m):\n if matrix[i][j] == 0:\n matrix[0][j] = 0\n matrix[i][0] = 0\n for i in range(1,n):\n if matrix[i][0] == 0:\n for j in range(0,m):\n matrix[i][j] = 0\n for j in range(1,m):\n if matrix[0][j] == 0:\n for i in range(0,n):\n matrix[i][j] = 0\n if zeroColumn:\n for i in range(0,n):\n matrix[i][0] = 0\n if zeroRow:\n for i in range(0,m):\n matrix[0][i] = 0","def setZeroes(self, matrix: List[List[int]]) -> None:\n \n m = len(matrix)\n n = len(matrix[0])\n \n firstrowhaszero = False\n firstcolumnhaszero = False\n for i in range(m):\n if matrix[i][0] == 0:\n firstcolumnhaszero = True\n break\n for j in range(n):\n if matrix[0][j] == 0:\n firstrowhaszero = True\n break\n \n for i in range(1, m):\n for j in range(1, n):\n if matrix[i][j] == 0:\n matrix[i][0] = 0\n matrix[0][j] = 0\n \n for i in range(1, m):\n for j in range(1, n):\n if matrix[i][0] == 0 or matrix[0][j] == 0:\n matrix[i][j] = 0\n \n if firstcolumnhaszero:\n for i in range(m):\n matrix[i][0] = 0\n \n if firstrowhaszero:\n for j in range(n):\n matrix[0][j] = 0","def PadSparseImage(self, out_file):\n raise NotImplementedError","def setZeroes(self, matrix):\n for i in range(len(matrix)):\n for j in range(len(matrix[i])):\n if matrix[i][j] == 0 and (i, j) not in self.visited:\n for neighbor in self.setter(matrix, i, j):\n matrix[neighbor[0]][neighbor[1]] = 0\n self.visited.add((neighbor[0], neighbor[1]))\n print(matrix)","def setZeroes(self, matrix: List[List[int]]) -> None:\n m = len(matrix)\n n = len(matrix[0])\n \n row_1st_0 = False\n col_1st_0 = False\n \n ## check whether 1st row and 1st col contains 0\n for i in range(n):\n if matrix[0][i] == 0:\n row_1st_0 = True\n break\n for j in range(m):\n if matrix[j][0] == 0:\n col_1st_0 = True\n break\n \n ## check row 1:m and col 1:n, store the 0 indicator to 1st row and 1st col\n for i in range(1, m):\n for j in range(1, n):\n if matrix[i][j] == 0:\n matrix[i][0] = 0\n matrix[0][j] = 0\n \n ## update cols to zero except 1st row\n for i in range(1, n):\n if matrix[0][i] == 0:\n for r in range(1, m):\n matrix[r][i] = 0\n \n ## update rows to zero except 1st col\n for j in range(1, m):\n if matrix[j][0] == 0:\n matrix[j][1:n] = [0 for i in range(n-1)]\n \n ## update 1st row and 1st col to 0\n if row_1st_0:\n matrix[0][0:n] = [0 for i in range(n)]\n if col_1st_0:\n for r in range(m):\n matrix[r][0] = 0","def test_pad_coords_matrix(self):\r\n m = array([[1, 2, 3], [4, 5, 6], [7, 8, 9], [10, 11, 12]])\r\n actual = pad_coords_matrix(m, 0)\r\n assert_almost_equal(actual, m)\r\n\r\n m = array([[1, 2, 3], [4, 5, 6], [7, 8, 9], [10, 11, 12]])\r\n expected = array([[1, 2, 3, 0., 0.], [4, 5, 6, 0., 0.],\r\n [7, 8, 9, 0., 0.], [10, 11, 12, 0., 0.]])\r\n actual = pad_coords_matrix(m, 2)\r\n assert_almost_equal(actual, expected)\r\n\r\n m = array([[1, 2, 3], [4, 5, 6], [7, 8, 9], [10, 11, 12]])\r\n self.assertRaises(ValueError, pad_coords_matrix, m, -3)","def setZeroes(self, matrix: List[List[int]]) -> None:\n zeros = []\n m, n = len(matrix), len(matrix[0])\n for i in range(m):\n for j in range(n):\n if matrix[i][j] == 0:\n zeros.append([i, j])\n for index in zeros:\n row = index[0]\n col = index[1]\n for r in range(m):\n matrix[r][col] = 0\n for c in range(n):\n matrix[row][c] = 0","def setZeroes(self, matrix: List[List[int]]) -> None:\n m, n = len(matrix), len(matrix[0])\n row, col = [0 for i in range(m)], [0 for j in range(n)]\n for i in range(m):\n for j in range(n):\n if not matrix[i][j]:\n row[i] = col[j] = 1\n for i in range(m):\n if row[i]:\n for j in range(n):\n matrix[i][j] = 0\n for j in range(n):\n if col[j]:\n for i in range(m):\n matrix[i][j] = 0","def setZeroes(self, matrix: List[List[int]]) -> None:\r\n import copy\r\n m=len(matrix)\r\n n=len(matrix[0])\r\n m_copy=copy.deepcopy(matrix)\r\n for i in range(m):\r\n for j in range(n):\r\n if m_copy[i][j]==0:\r\n matrix[i]=[0]*n\r\n for x in range(m):\r\n matrix[x][j]=0","def zeros_matrix(self, rows, cols):\r\n M = []\r\n while len(M) < rows:\r\n M.append([])\r\n while len(M[-1]) < cols:\r\n M[-1].append(0.0)\r\n\r\n return M","def setZeroes(self, matrix: List[List[int]]) -> None:\n m, n = len(matrix), len(matrix[0])\n zero_row, zero_col = set(), set()\n for row in range(m):\n for col in range(n):\n if matrix[row][col] == 0:\n if row not in zero_row:\n zero_row.add(row)\n if col not in zero_col:\n zero_col.add(col)\n for row in zero_row:\n for col in range(n):\n matrix[row][col] = 0\n for col in zero_col:\n for row in range(m):\n matrix[row][col] = 0","def test_add_nans_to_weights():\n # create input sparse matrix with one empty row (j=2)\n coords = np.array([[0, 3, 1, 0], [0, 3, 1, 2]])\n data = np.array([4.0, 5.0, 7.0, 9.0])\n Matin = sps.COO(coords, data, shape=(4, 4))\n\n # this is what is expected to come out (Nan added at i=0, j=2)\n coords = np.array([[0, 3, 1, 0, 2], [0, 3, 1, 2, 0]])\n data = np.array([4.0, 5.0, 7.0, 9.0, np.nan])\n expected = sps.COO(coords, data, shape=(4, 4))\n\n Matout = xe.smm.add_nans_to_weights(xr.DataArray(Matin, dims=('in', 'out')))\n assert np.allclose(expected.todense(), Matout.data.todense(), equal_nan=True)\n\n # Matrix without empty rows should return the same\n coords = np.array([[0, 3, 1, 0, 2], [0, 3, 1, 2, 1]])\n data = np.array([4.0, 5.0, 7.0, 9.0, 10.0])\n Matin = sps.COO(coords, data, shape=(4, 4))\n\n Matout = xe.smm.add_nans_to_weights(xr.DataArray(Matin, dims=('in', 'out')))\n assert np.allclose(Matin.todense(), Matout.data.todense())","def _insert_zero(self, x, indices_removed):\n for i in indices_removed:\n x = np.insert(x, i, 0)\n return x","def zero_like(matrix):\n dtype = numpy.int_ if is_integer(matrix) else numpy.float_\n return numpy.zeros(matrix.shape, dtype=dtype)","def setZeroes(self, matrix: List[List[int]]) -> None:\n # 把第一行，第一列当作标志位\n row_flag = False\n col_flag = False # 防止第一行第一列无效\n row = len(matrix)\n col = len(matrix[0]) # 这种表征\n for j in range(col):\n if matrix[0][j] == 0:\n row_flag = True\n break\n for i in range(row):\n if matrix[i][0] == 0:\n col_flag = True\n break\n for i in range(1,row):\n for j in range(1,col):\n if matrix[i][j] == 0:\n matrix[i][0] = matrix[0][j] = 0\n # 检查,第二遍扫描\n for i in range(1,row):\n for j in range(1,col):\n if matrix[i][0]==0 or matrix[0][j] ==0:\n matrix[i][j] = 0\n # 防止标志位本身也是\n if row_flag:\n for j in range(col):\n matrix[0][j] = 0\n if col_flag:\n for i in range(row):\n matrix[i][0] = 0","def setZeroes(self, matrix: List[List[int]]) -> None:\n m = len(matrix)\n if m == 0:\n return\n n = len(matrix[0])\n\n row_zero = False\n for i in range(m):\n if matrix[i][0] == 0:\n row_zero = True\n col_zero = False\n for j in range(n):\n if matrix[0][j] == 0:\n col_zero = True\n\n for i in range(1, m):\n for j in range(1, n):\n if matrix[i][j] == 0:\n matrix[i][0] = 0\n matrix[0][j] = 0\n\n for i in range(1, m):\n if matrix[i][0] == 0:\n for j in range(1, n):\n matrix[i][j] = 0\n\n for j in range(1, n):\n if matrix[0][j] == 0:\n for i in range(1, m):\n matrix[i][j] = 0\n\n if col_zero:\n for j in range(n):\n matrix[0][j] = 0\n if row_zero:\n for i in range(m):\n matrix[i][0] = 0","def test_pad_one(self):\n # Define some data with three features\n data = array([[1, 2],\n [3, 4]])\n\n # Get a CData object\n cdata = CData(data)\n\n # Pad a zero to each feature vector\n cdata.pad_one()\n\n # Define the correct array\n correct = array([[1, 2, 0],\n [3, 4, 0]])\n\n self.assertEqual(cdata.num_features, 3)\n self.assertTrue(array_equal(cdata.data, correct))","def _buildMatrix(self, SparseMatrix, Ncells, MaxFaces, coeff):\n return (0, 0)","def add_dummy_location_to_matrix(matrix):\n matrix = [row + [0] for row in matrix]\n last_row = [0 for _ in range(len(matrix) + 1)]\n matrix.append(last_row)\n return matrix","def _compute_squeezed_spacing(self,spacing0, dim0, sz0, dimSqueezed):\n spacing = np.zeros(dimSqueezed)\n j = 0\n for i in range(dim0):\n if sz0[i] != 1:\n spacing[j] = spacing0[i]\n j += 1\n return spacing","def zeros_like(data):\n return _make.zeros_like(data)","def setZeroes(self, matrix: List[List[int]]) -> None:\n m, n = len(matrix), len(matrix[0])\n zero_row, zero_col = False, False\n\n for i in range(m):\n for j in range(n):\n if matrix[i][j] == 0:\n matrix[i][0] = matrix[0][j] = 0\n zero_row = True if i == 0 else zero_row\n zero_col = True if j == 0 else zero_col\n\n for j in range(1, n):\n if matrix[0][j] == 0:\n for i in range(1, m):\n matrix[i][j] = 0\n for i in range(1, m):\n if matrix[i][0] == 0:\n for j in range(1, n):\n matrix[i][j] = 0\n\n if zero_row:\n for j in range(n):\n matrix[0][j] = 0\n if zero_col:\n for i in range(m):\n matrix[i][0] = 0\n return","def setZeroes(matrix):\r\n \r\n #An average O(n^2) time traversal solution with memoization\r\n #for each 0 we encounter, we update the entire row and column to 0s, but on the condition that the row/column has not been updated yet\r\n \r\n row_cache = {}\r\n column_cache = {}\r\n \r\n for r in range(0,rows := len(matrix)):\r\n for c in range(0,cols := len(matrix[0])):\r\n \r\n if matrix[r][c] == 0:\r\n \r\n if not row_cache.get(r):\r\n for i in range(0,cols):\r\n if matrix[r][i] != 0:\r\n matrix[r][i] = '0' #we use strings so we only consider the initial 0s\r\n row_cache[r] = True\r\n \r\n if not column_cache.get(c):\r\n for i in range(0,rows):\r\n if matrix[i][c] != 0:\r\n matrix[i][c] = '0'\r\n column_cache[c] = True\r\n return","def _pad(x, depth=4):\n divisor = np.power(2, depth)\n remainder = x.shape[0] % divisor\n\n # no padding because already of even shape\n if remainder == 0:\n return x\n # add zero rows after 1D feature\n elif len(x.shape) == 2:\n return np.pad(x, [(0, divisor - remainder), (0, 0)], \"constant\")\n # add zero columns and rows after 2D feature\n elif len(x.shape) == 3:\n return np.pad(x, [(0, divisor - remainder), (0, divisor - remainder),\n (0, 0)], \"constant\")","def Dmat(numpts, delta=1):\n a = 0.5 / delta * ones(numpts)\n a[0] = 0\n a[-2] = 0\n #b=-2./delta**2*ones(numpts); b[0]=0;b[-1]=0\n c = -0.5 / delta * ones(numpts)\n c[1] = 0\n c[-1] = 0\n return sparse.spdiags([a, c], [-1, 1], numpts, numpts)","def setZeroes(self, matrix: List[List[int]]) -> None:\n m, n = len(matrix), len(matrix[0])\n\n col_0_flag = any(matrix[i][0] == 0 for i in range(m))\n row_0_flag = any(matrix[0][i] == 0 for i in range(n))\n\n # 第 0 列， 第 0 行可以作为 [i,j] 的指示存储\n # 例如，如果 [i,j] 为 0， 那么 [0, j], [i, 0] 可以置 0 \n # 再次遍历矩阵，通过判断 [0, j], [i, 0] 去把该行，该列置 0\n # 这样的话就不需要一个额外的矩阵去 for i,j 遍历是否为 0 了\n\n # 注意是从 1 开始的。 \n for i in range(1, m):\n for j in range(1, n):\n if matrix[i][j] == 0:\n matrix[i][0] = matrix[0][j] = 0 \n\n for i in range(1, m):\n for j in range(1, n):\n if matrix[i][0] == 0 or matrix[0][j] == 0:\n matrix[i][j] = 0\n\n if col_0_flag:\n for i in range(m):\n matrix[i][0] = 0\n\n if row_0_flag:\n for j in range(n):\n matrix[0][j] = 0","def setZeroes(self, matrix: List[List[int]]) -> None:\n row = set()\n column = set()\n \n m = len(matrix)\n n = len(matrix[0])\n \n for i in range(m):\n for j in range(n):\n if matrix[i][j] == 0:\n row.add(i)\n column.add(j)\n \n for i in range(m):\n for j in range(n):\n if i in row or j in column:\n matrix[i][j] = 0","def test_normalize_matrix_with_only_zeros(self):\n input_matrix = [\n [0, 0],\n [0, 0]\n ]\n\n expected = [\n [0, 0],\n [0, 0]\n ]\n\n result = self.summarizer.normalize_matrix(input_matrix)\n\n self.assertEqual(expected, result)","def tocsr(self):\n\n indptr = np.asarray([len(x) for x in self.rows], dtype=np.intc)\n indptr = np.concatenate( (np.array([0], dtype=np.intc), np.cumsum(indptr)) )\n\n nnz = indptr[-1]\n\n indices = []\n for x in self.rows:\n indices.extend(x)\n indices = np.asarray(indices, dtype=np.intc)\n\n data = []\n for x in self.data:\n data.extend(x)\n data = np.asarray(data, dtype=self.dtype)\n\n from csr import csr_matrix\n return csr_matrix((data, indices, indptr), shape=self.shape)","def zeros_matrix(rows, cols):\n M = []\n while len(M) < rows:\n M.append([])\n while len(M[-1]) < cols:\n M[-1].append(0.0)\n \n return M","def zero_matrix(matrix):\n n = len(matrix)\n if n == 0:\n return True\n\n m = len(matrix[0])\n if m == 0:\n return True\n\n zero_rows = set()\n zero_columns = set()\n for i in range(n):\n for j in range(m):\n if matrix[i][j] == 0:\n zero_rows.add(i)\n zero_columns.add(j)\n \n for i in zero_rows:\n for j in range(m):\n matrix[i][j] = 0\n\n for i in range(n):\n for j in zero_columns:\n matrix[i][j] = 0\n\n return True","def test_sparse():\n\n rng = np.random.RandomState(0)\n\n X = rng.rand(20, 2)\n X[X < 0.8] = 0\n X_csr = sp.csr_matrix(X)\n\n bisect_means = BisectingKMeans(n_clusters=3, random_state=0)\n\n bisect_means.fit(X_csr)\n sparse_centers = bisect_means.cluster_centers_\n\n bisect_means.fit(X)\n normal_centers = bisect_means.cluster_centers_\n\n # Check if results is the same for dense and sparse data\n assert_allclose(normal_centers, sparse_centers, atol=1e-8)","def _identity_sparse(d, stype=\"csr\", dtype=complex):\n return sp.eye(d, dtype=dtype, format=stype)","def pad(self, matrices, pad_value):\n shapes = [m.shape for m in matrices]\n M, N = sum([s[0] for s in shapes]), sum([s[1] for s in shapes])\n zeros = torch.FloatTensor(np.zeros((M, N))).to(device)\n pad_matrices = pad_value + zeros\n i, j = 0, 0\n for k, matrix in enumerate(matrices):\n m, n = shapes[k]\n pad_matrices[i:i+m, j:j+n] = matrix\n i += m\n j += n\n return pad_matrices","def setZeroes(self, matrix: List[List[int]]) -> None:\n new_matrix = [row.copy() for row in matrix]\n for i in range(len(matrix)):\n for j in range(len(matrix[0])):\n if matrix[i][j] == 0 and new_matrix[i][j] == 0:\n self.setZero(matrix, i, j)","def setZeroes(self, matrix: List[List[int]]) -> None:\n row_num, col_num = len(matrix), len(matrix[0])\n # 创建集合set()用于存放需要置零的行和列\n row_set, col_set = set(), set()\n for row in range(row_num):\n for col in range(col_num):\n if matrix[row][col]==0:\n row_set.add(row)\n col_set.add(col)\n # 将记录的行、列中的元素赋值为0\n # 再次遍历赋值\n for row in range(row_num):\n for col in range(col_num):\n if row in row_set or col in col_set:\n matrix[row][col] = 0\n # # 或者行列单独赋值均可\n # for row in row_set:\n # for col in range(col_num):\n # matrix[row][col] = 0\n # for col in col_set:\n # for row in range(row_num):\n # matrix[row][col] = 0","def zeros_matrix(rows, cols):\n M = []\n while len(M) < rows:\n M.append([])\n while len(M[-1]) < cols:\n M[-1].append(0.0)\n\n return M","def zeros_matrix(rows, cols):\n M = []\n while len(M) < rows:\n M.append([])\n while len(M[-1]) < cols:\n M[-1].append(0.0)\n\n return M","def matZeros(shape):\n return [[0 for y in range(shape[1])] \\\n for x in range(shape[0])]","def setZeroes(self, matrix) -> None:\n m, n = len(matrix), len(matrix[0])\n rows, cols = set(), set()\n # 获取0所在的位置对应的行和列\n for i in range(m):\n for j in range(n):\n if matrix[i][j] == 0:\n rows.add(i)\n cols.add(j)\n # 将该行和列的所有元素置为0\n for row in rows:\n for j in range(n):\n matrix[row][j] = 0\n for col in cols:\n for i in range(m):\n matrix[i][col] = 0","def drawSparseMatrix(ax, mat, **kwargs):\n row = kwargs.pop('rowOffset', 0)\n col = kwargs.pop('colOffset', 0)\n color = kwargs.pop('color', None)\n\n mat = pg.utils.sparseMatrix2coo(mat)\n mat.row += row\n mat.col += col\n gci = ax.spy(mat, color=color, **kwargs)\n\n ax.autoscale(enable=True, axis='both', tight=True)\n return gci","def sparse_matlab(i, j, v, m, n):\n return csr_matrix((v, (i, j)), shape=(m, n))","def sparse_initialization_weights(self):\n W = []\n mu, sigma = 0, 1/self.non_zero_units\n\n for i in xrange(self.n_in):\n row = np.zeros(self.n_out)\n non_zeros = self.rng.normal(mu, sigma, self.non_zero_units)\n # non_zeros /= non_zeros.sum()\n non_zero_idxs = self.rng.permutation(\n self.n_out)[0:self.non_zero_units]\n for j in xrange(self.non_zero_units):\n row[non_zero_idxs[j]] = non_zeros[j]\n W.append(row)\n W = np.asarray(W, dtype=theano.config.floatX)\n return W","def zeros_like(a):\n if isinstance(a, np.ndarray):\n return np.zeros_like(a)\n else:\n return K.zeros_like(a)","def setZeroes(self, matrix: List[List[int]]) -> None:\n m = len(matrix)\n n = len(matrix[0])\n # replace zeros by None\n for i in range(m):\n for j in range(n):\n if matrix[i][j] == 0:\n matrix[i][j] = None\n # do the rows\n for i, row in enumerate(matrix):\n if None in row:\n for j, x in enumerate(row):\n if x is not None:\n matrix[i][j] = 0\n # do the columns\n for j in range(n):\n # false if the column had no zeros\n z = False\n for i in range(m):\n if matrix[i][j] is None:\n z = True\n break\n if z:\n for i in range(m):\n if matrix[i][j] is not None:\n matrix[i][j] = 0\n # undo replace zeros by None\n for i in range(m):\n for j in range(n):\n if matrix[i][j] is None:\n matrix[i][j] = 0","def make_all_zero(curr_clusters, k, num_of_cords):\r\n for i in range(k):\r\n for j in range(num_of_cords):\r\n curr_clusters[i][j] = 0","def pad_spatial(self, x):\n n, t, c, h, w = x.size()\n\n pad_h = (4 - h % 4) % 4\n pad_w = (4 - w % 4) % 4\n\n # padding\n x = x.view(-1, c, h, w)\n x = F.pad(x, [0, pad_w, 0, pad_h], mode='reflect')\n\n return x.view(n, t, c, h + pad_h, w + pad_w)","def setZeroes(self, matrix: List[List[int]]) -> None:\n m, n = len(matrix), len(matrix[0]) if matrix else 0\n \n row = [False for _ in range(m)]\n col = [False for _ in range(n)]\n \n for i in range(m):\n for j in range(n):\n if matrix[i][j] == 0:\n row[i] = col[j] = True\n \n for i in range(m):\n for j in range(n):\n if row[i] or col[j]:\n matrix[i][j] = 0","def pad_zeros(n, before, after):\n pad_1_dim = (before, after)\n pad_2_dim = ((before, after), (0, 0))\n m = before + n + after\n def times(x):\n return jnp.pad(x, pad_1_dim)\n def trans(x):\n return x[before:before+n]\n return Operator(times=times, trans=trans, shape=(m,n), matrix_safe=False)","def zeros(shape, dtype=float):\n if not mathutil.is_shape(shape, ndim=2):\n raise ValueError(\"invalid shape\")\n\n sc = SparkContext.getOrCreate()\n\n nelem = 0\n\n rdd = sc.emptyRDD()\n\n return Matrix(rdd, shape, dtype=dtype, nelem=nelem)","def RowXchageZero(matrix,vector):\r\n\r\n for i in range(len(matrix)):\r\n for j in range(i, len(matrix)):\r\n # The pivot member is not zero\r\n if matrix[i][i] == 0:\r\n temp = matrix[j]\r\n temp_b = vector[j]\r\n matrix[j] = matrix[i]\r\n vector[j] = vector[i]\r\n matrix[i] = temp\r\n vector[i] = temp_b\r\n\r\n\r\n return [matrix, vector]","def _fill_in_missing(x):\n default_value = '' if x.dtype == tf.string else 0\n return tf.squeeze(\n tf.sparse.to_dense(\n tf.SparseTensor(x.indices, x.values, [x.dense_shape[0], 1]),\n default_value),\n axis=1)","def replace_zeros(self):\n\n min_c = np.array(self.map[self.column])\n self.map.loc[self.map[self.column]==0, self.column] = np.min(min_c[np.nonzero(min_c)])","def _pad(A, N):\n n, _ = A.shape\n if n >= N:\n return A\n else:\n if issparse(A):\n # thrown if we try to np.concatenate sparse matrices\n side = sps.csr_matrix((n, N - n))\n bottom = sps.csr_matrix((N - n, N))\n A_pad = sps.hstack([A, side])\n A_pad = sps.vstack([A_pad, bottom])\n else:\n side = np.zeros((n, N - n))\n bottom = np.zeros((N - n, N))\n A_pad = np.concatenate([A, side], axis=1)\n A_pad = np.concatenate([A_pad, bottom])\n return A_pad","def create_sparseDB():\n datas = data.Kmercount_to_matrix()\n datas.run()\n print('***Sparse matrix created***')","def zeros_like(tensor):\n raise NotImplementedError","def to_positive(A_diag, eps=0.00001):\n A_diag[A_diag < 0] = eps\n inds = torch.isclose(A_diag, torch.zeros(1, device=cuda.device()))\n A_diag[inds] = eps\n return A_diag","def unwrapOutput(self,x):\n sx = SS.csr_matrix(x)\n sx.eliminate_zeros()\n return sx","def _trace_sparse(op):\n return np.sum(op.diagonal())","def zeros(cls, shape, domain, *, fmt='sparse'):\n return cls.from_rep(SDM.zeros(shape, domain))"],"string":"[\n \"def _pad_with_zeros(self, X, margin):\\n newX = np.zeros((X.shape[0] + 2 * margin, X.shape[1] + 2 * margin, X.shape[2]))\\n x_offset = margin\\n y_offset = margin\\n newX[x_offset:X.shape[0] + x_offset, y_offset:X.shape[1] + y_offset, :] = X\\n return newX\",\n \"def OffsetSparseMatrix(SparseMatrix, numberOfVariables, numberOfEquations):\\n\\n class OffsetSparseMatrixClass(SparseMatrix):\\n equationIndex = 0\\n varIndex = 0\\n\\n def __init__(self, mesh, bandwidth=0, sizeHint=None,\\n numberOfVariables=numberOfVariables, numberOfEquations=numberOfEquations):\\n SparseMatrix.__init__(self, mesh=mesh, bandwidth=bandwidth, sizeHint=sizeHint,\\n numberOfVariables=numberOfVariables, numberOfEquations=numberOfEquations)\\n\\n def put(self, vector, id1, id2):\\n SparseMatrix.put(self, vector, id1 + self.mesh.numberOfCells * self.equationIndex, id2 + self.mesh.numberOfCells * self.varIndex)\\n\\n def addAt(self, vector, id1, id2):\\n SparseMatrix.addAt(self, vector, id1 + self.mesh.numberOfCells * self.equationIndex, id2 + self.mesh.numberOfCells * self.varIndex)\\n\\n def addAtDiagonal(self, vector):\\n if type(vector) in [type(1), type(1.)]:\\n tmp = numerix.zeros((self.mesh.numberOfCells,), 'd')\\n tmp[:] = vector\\n SparseMatrix.addAtDiagonal(self, tmp)\\n else:\\n SparseMatrix.addAtDiagonal(self, vector)\\n\\n return OffsetSparseMatrixClass\",\n \"def OffsetSparseMatrix(SparseMatrix, numberOfVariables, numberOfEquations):\\n\\n class OffsetSparseMatrixClass(SparseMatrix):\\n equationIndex = 0\\n varIndex = 0\\n\\n def __init__(self, mesh, bandwidth=0, sizeHint=None,\\n numberOfVariables=numberOfVariables, numberOfEquations=numberOfEquations):\\n SparseMatrix.__init__(self, mesh=mesh, bandwidth=bandwidth, sizeHint=sizeHint,\\n numberOfVariables=numberOfVariables, numberOfEquations=numberOfEquations)\\n\\n def put(self, vector, id1, id2):\\n SparseMatrix.put(self, vector, id1 + self.mesh.numberOfCells * self.equationIndex, id2 + self.mesh.numberOfCells * self.varIndex)\\n\\n def addAt(self, vector, id1, id2):\\n SparseMatrix.addAt(self, vector, id1 + self.mesh.numberOfCells * self.equationIndex, id2 + self.mesh.numberOfCells * self.varIndex)\\n\\n def addAtDiagonal(self, vector):\\n if type(vector) in [type(1), type(1.)]:\\n tmp = numerix.zeros((self.mesh.numberOfCells,), 'd')\\n tmp[:] = vector\\n SparseMatrix.addAtDiagonal(self, tmp)\\n else:\\n SparseMatrix.addAtDiagonal(self, vector)\\n\\n return OffsetSparseMatrixClass\",\n \"def makesparse(matrix):\\n n = matrix[0].size\\n elements = []\\n for i in range(n):\\n for j in range(n):\\n if matrix[i][j] != 0 :\\n temp = MatrixElement(i, j, matrix[i][j])\\n elements.append(temp)\\n return SparseMatrix(n, elements)\",\n \"def pad_csr(a, newshape):\\n n, m = a.shape\\n a._shape = newshape\\n a.indptr = np.pad(a.indptr, (0, newshape[0] - n), 'edge')\",\n \"def ident_zeros(A):\\n assert A.size[0] == A.size[1]\\n A.assemble()\\n o_range = A.getOwnershipRange()\\n rows = []\\n for i in range(o_range[1]-o_range[0]):\\n indices, values = A.getRow(o_range[0]+i)\\n absrow = sum(abs(values))\\n if absrow < 1e-6:\\n rows.append(o_range[0] + i)\\n add_diagonal(A.handle, numpy.array(rows))\",\n \"def pad_zeroes(m, axis, thick):\\n\\n if axis == 1:\\n rows = m.shape[0]\\n cols = thick\\n elif axis == 0:\\n rows = thick\\n cols = m.shape[1]\\n\\n return np.concatenate((m, np.zeros((rows,cols))), \\n axis)\",\n \"def sp_zeros_like(x):\\r\\n\\r\\n # TODO: don't restrict to CSM formats\\r\\n _, _, indptr, shape = csm_properties(x)\\r\\n return CSM(format=x.format)(data=numpy.array([], dtype=x.type.dtype),\\r\\n indices=numpy.array([], dtype='int32'),\\r\\n indptr=tensor.zeros_like(indptr),\\r\\n shape=shape)\",\n \"def pad_zeros(x):\\n dim = tf.shape(x)[0]\\n log2_dim = tf.math.log(tf.cast(dim, tf.float32)) / tf.math.log(2.0)\\n pad_dim = tf.pow(2, tf.cast(tf.math.ceil(log2_dim), tf.int32))\\n with tf.control_dependencies([tf.debugging.assert_rank(x, 1)]):\\n return tf.pad(x, [[0, tf.maximum(0, pad_dim - dim)]])\",\n \"def _zero_pad(self, kernel, size):\\n if len(size) != kernel.ndim:\\n size = kernel.shape[:1] + tuple(size) + kernel.shape[-1:]\\n padsize = np.array(size) - np.array(kernel.shape)\\n paddown = padsize // 2\\n padup = padsize - paddown\\n padarray = np.concatenate((padup[..., None],\\n paddown[..., None]), axis=1)\\n pads = tuple([tuple(p) for p in padarray])\\n kernel_pad = np.pad(kernel, pads, 'constant', constant_values=0)\\n return kernel_pad\",\n \"def setZeroes(self, matrix: List[List[int]]) -> None:\\n zero_columns = []\\n zero_rows = []\\n for i in range(len(matrix)):\\n for j in range(len(matrix[0])):\\n if matrix[i][j] == 0:\\n zero_columns.append(j)\\n zero_rows.append(i)\\n for zero_column in zero_columns:\\n for row in matrix:\\n row[zero_column] = 0\\n for zero_row in zero_rows:\\n matrix[zero_row] = [0] * len(matrix[0])\",\n \"def add_zero(matrix):\\n if len(matrix.shape) == 2:\\n matrix = numpy.insert(matrix, 0, 0, axis=1)\\n matrix = numpy.insert(matrix, matrix.shape[1], 0, axis=1)\\n row01 = numpy.zeros((matrix.shape[1]), numpy.uint8)\\n matrix = numpy.insert(matrix, 0, row01, axis=0)\\n matrix = numpy.insert(matrix, matrix.shape[0], row01, axis=0)\\n\\n if len(matrix.shape) == 3:\\n pixel = numpy.zeros((matrix.shape[2]), numpy.uint8)\\n matrix = numpy.insert(matrix, 0, pixel, axis=1)\\n matrix = numpy.insert(matrix, matrix.shape[1], pixel, axis=1)\\n row = numpy.zeros((1, matrix.shape[1], 3), numpy.uint8)\\n matrix = numpy.insert(matrix, 0, row, axis=0)\\n matrix = numpy.insert(matrix, matrix.shape[0], row, axis=0)\\n\\n return matrix\",\n \"def zero_matrix(matrix):\\n cols = []\\n for row in range(0, len(matrix)):\\n for col in range(0, len(matrix[row])):\\n if matrix[row][col] == 0:\\n cols.append(col)\\n matrix[row] = [0 for _ in range(0, len(matrix[row]))]\\n break\\n for col in cols:\\n for row in range(0, len(matrix)):\\n matrix[row][col] = 0\\n return matrix\",\n \"def zero_pad(X, padding_width, dims):\\n dims = (dims) if isinstance(dims, int) else dims\\n pad = [(0, 0) if idx not in dims else (padding_width, padding_width)\\n for idx in range(len(X.shape))]\\n X_padded = np.pad(X, pad, 'constant')\\n return X_padded\",\n \"def zero_pad(data):\\n N = len(data)\\n pow_2 = np.ceil(np.log2(N))\\n return np.pad(data,(0,int((2**pow_2)-N)),'constant')\",\n \"def zero_matrix(matrix):\\n if not matrix:\\n return\\n\\n n_rows = len(matrix)\\n n_cols = len(matrix[0])\\n\\n # First row and first column of the matrix are used to store row and columns to nullify.\\n # At the beginning we determine if the first row and/or first column contain a zero.\\n first_row_zero = False\\n first_col_zero = False\\n for col in range(n_cols):\\n if not matrix[0][col]:\\n first_row_zero = True\\n break\\n for row in range(n_rows):\\n if not matrix[row][0]:\\n first_col_zero = True\\n break\\n\\n # Then go through the rest of the matrix and determine which rows and columns must be zeroed.\\n for row in range(1, n_rows):\\n for col in range(1, n_cols):\\n if not matrix[row][col]:\\n matrix[0][col] = 0\\n matrix[row][0] = 0\\n\\n # Go through the rest of the matrix again and insert zeros where needed.\\n for row in range(1, n_rows):\\n for col in range(1, n_cols):\\n if matrix[0][col] == 0 or matrix[row][0] == 0:\\n matrix[row][col] = 0\\n\\n # Nullify first row and column if needed.\\n if first_row_zero:\\n for col in range(n_cols):\\n matrix[0][col] = 0\\n if first_col_zero:\\n for row in range(n_rows):\\n matrix[row][0] = 0\",\n \"def zero_diag(mat):\\n\\n return replace_diag(mat, np.zeros(mat.shape[0]))\",\n \"def zeroes(height, width):\\n g = [[0.0 for _ in range(width)] for __ in range(height)]\\n return Matrix(g)\",\n \"def spzeros(size, device='cuda:0'):\\n\\n return SparseTensor(size=size).to(device=device)\",\n \"def posM(M):\\r\\n M[np.where(M < 0)] = 0\\r\\n return M\",\n \"def setZeroes(self, matrix: List[List[int]]) -> None:\\n first_col = False\\n for i in range(len(matrix)):\\n for j in range(len(matrix[0])):\\n if matrix[i][j] == 0:\\n matrix[i][0] = 0\\n if j == 0:\\n first_col = True\\n else:\\n matrix[0][j] = 0\\n for i in range(1, len(matrix[0])):\\n if matrix[0][i] == 0:\\n for j in range(len(matrix)):\\n matrix[j][i] = 0\\n if matrix[0][0] == 0:\\n matrix[0] = [0 for x in range(len(matrix[0]))]\\n for i in range(1, len(matrix)):\\n if matrix[i][0] == 0:\\n for j in range(len(matrix[0])):\\n matrix[i][j] = 0\\n if first_col:\\n for i in range(len(matrix)):\\n matrix[i][0] = 0\",\n \"def set_zero(mn_matrix): \\r\\n \\r\\n row_flag = [0]*len(mn_matrix)\\r\\n column_flag = [0]*len(mn_matrix[0])\\r\\n for i in range(0, len(row_flag)):\\r\\n for j in range(0, len(column_flag)):\\r\\n if mn_matrix[i][j] == 0:\\r\\n row_flag[i] = 1\\r\\n column_flag[j] = 1\\r\\n \\r\\n for i in range(0, len(row_flag)):\\r\\n for j in range(0, len(column_flag)):\\r\\n if row_flag[i] == 1 or column_flag[j] == 1: \\r\\n mn_matrix[i][j] = 0\",\n \"def zeroes(height, width):\\n g = [[0.0 for _ in range(width)] for __ in range(height)]\\n return Matrix(g)\",\n \"def setZeroes(self, matrix: List[List[int]]) -> None:\\n rows, cols = [], []\\n for i in range(len(matrix)):\\n for j in range(len(matrix[i])):\\n if matrix[i][j] == 0:\\n rows.append(i)\\n cols.append(j)\\n for i in rows:\\n for j in range(len(matrix[i])):\\n matrix[i][j] = 0\\n for j in cols:\\n for i in range(len(matrix)):\\n matrix[i][j] = 0\\n\\n return\",\n \"def setZeroes(self, matrix: List[List[int]]) -> None:\\r\\n \\r\\n rows, cols = len(matrix), len(matrix[0])\\r\\n \\r\\n #This solution has been done in constant space\\r\\n #using first row and first column for deciding which rows and columns\\r\\n #should be zero\\r\\n \\r\\n #using one extra variable for first row\\r\\n #because first cell is common in first row and column\\r\\n first_row = 1\\r\\n \\r\\n for i in range(rows):\\r\\n for j in range(cols):\\r\\n if matrix[i][j]==0:\\r\\n #for column\\r\\n matrix[0][j] = 0\\r\\n \\r\\n #for row\\r\\n if i==0:\\r\\n first_row = 0\\r\\n else:\\r\\n matrix[i][0] = 0\\r\\n \\r\\n #checking for rows except first row\\r\\n for i in range(1,rows):\\r\\n if matrix[i][0] == 0:\\r\\n for j in range(cols):\\r\\n matrix[i][j] = 0\\r\\n \\r\\n #checking for columns except first column\\r\\n for j in range(1, cols):\\r\\n if matrix[0][j] == 0:\\r\\n for i in range(rows):\\r\\n matrix[i][j] = 0\\r\\n \\r\\n #for first column\\r\\n if matrix[0][0] == 0:\\r\\n for i in range(rows):\\r\\n matrix[i][0] = 0\\r\\n \\r\\n #for first row\\r\\n if first_row == 0:\\r\\n for j in range(cols):\\r\\n matrix[0][j] = 0\\r\\n \\r\\n #print(matrix)\",\n \"def setZeroes(self, matrix: List[List[int]]) -> None:\\n if not matrix:\\n return\\n row = set()\\n col = set()\\n rowl = len(matrix[0])\\n for i, r in enumerate(matrix):\\n for j, c in enumerate(r):\\n if c == 0:\\n row.add(i)\\n col.add(j)\\n \\n for r in row:\\n matrix[r] = [0]*rowl\\n for j in col:\\n for i in range(len(matrix)):\\n matrix[i][j] = 0\",\n \"def zero_matrix(matrix):\\n rows = set()\\n columns = set()\\n m = len(matrix)\\n n = len(matrix[0])\\n for i in range(m):\\n for j in range(n):\\n if matrix[i][j] == 0:\\n rows.add(i)\\n columns.add(j)\\n\\n for i in range(m):\\n for j in range(n):\\n if i in rows or j in columns:\\n matrix[i][j] = 0\\n return matrix\",\n \"def test_add_nans_to_weights():\\n # create input sparse matrix with one empty row (j=2)\\n row = np.array([0, 3, 1, 0])\\n col = np.array([0, 3, 1, 2])\\n data = np.array([4., 5., 7., 9.])\\n Matin = sps.coo_matrix((data, (row, col)), shape=(4, 4))\\n\\n # this is what is expected to come out (Nan added at i=0, j=2)\\n row = np.array([0, 3, 1, 0, 2])\\n col = np.array([0, 3, 1, 2, 0])\\n data = np.array([4., 5., 7., 9., np.nan])\\n expected = sps.coo_matrix((data, (row, col)), shape=(4, 4))\\n\\n Matout = xe.smm.add_nans_to_weights(Matin)\\n assert np.allclose(expected.toarray(), Matout.toarray(), equal_nan=True)\\n\\n # Matrix without empty rows should return the same\\n row = np.array([0, 3, 1, 0, 2])\\n col = np.array([0, 3, 1, 2, 1])\\n data = np.array([4., 5., 7., 9., 10.])\\n Matin = sps.coo_matrix((data, (row, col)), shape=(4, 4))\\n\\n Matout = xe.smm.add_nans_to_weights(Matin)\\n assert np.allclose(Matin.toarray(), Matout.toarray())\",\n \"def setZeroes(self, matrix: List[List[int]]) -> None:\\n m, n = len(matrix), len(matrix[0]) if matrix else 0\\n \\n # Use first row to store zero-info for each col\\n # Use first col to store zero-info for each row\\n # matrix[0][0] will store col info\\n \\n is_first_row = any(x == 0 for x in matrix[0])\\n \\n for i in range(1, m):\\n for j in range(n):\\n if matrix[i][j] == 0:\\n matrix[0][j] = matrix[i][0] = 0\\n \\n for i in range(1, m):\\n for j in range(1, n):\\n if matrix[i][0] * matrix[0][j] == 0:\\n matrix[i][j] = 0\\n \\n if matrix[0][0] == 0:\\n for i in range(1, m):\\n matrix[i][0] = 0\\n \\n if is_first_row:\\n for j in range(n):\\n matrix[0][j] = 0\",\n \"def setZeroes(self, matrix: List[List[int]]) -> None:\\n colsToZero = set()\\n rowsToZero = set()\\n for rowIdx, row in enumerate(matrix):\\n for colIdx, num in enumerate(row): \\n if num == 0: \\n colsToZero.add(colIdx)\\n rowsToZero.add(rowIdx)\\n \\n for col in colsToZero:\\n self.writeZeroCol(col, matrix)\\n for row in rowsToZero:\\n self.writeZeroRow(row, matrix)\",\n \"def setZeroes(self, matrix: List[List[int]]) -> None:\\n m = len(matrix)\\n n = len(matrix[0])\\n set_rows = set()\\n set_cols = set()\\n for i in range(m):\\n for j in range(n):\\n if matrix[i][j] == 0:\\n set_rows.add(i)\\n set_cols.add(j)\\n \\n while set_rows:\\n temp = set_rows.pop()\\n for j in range(n):\\n matrix[temp][j] = 0\\n \\n while set_cols:\\n temp = set_cols.pop()\\n for i in range(m):\\n matrix[i][temp] = 0\",\n \"def setZeroes(self, matrix: List[List[int]]) -> None:\\n row = []\\n col = []\\n for i in range(len(matrix)):\\n for j in range(len(matrix[i])):\\n if matrix[i][j] == 0:\\n col.append(j)\\n row.append(i)\\n\\n for i in range(len(row)):\\n self.col(matrix, col[i])\\n for i in range(len(col)):\\n self.row(matrix, row[i])\",\n \"def setZeroes(self, matrix: List[List[int]]) -> None:\\n m = len(matrix)\\n n = len(matrix[0])\\n rows = set()\\n columns = set()\\n for i in range(m):\\n for j in range(n):\\n if not matrix[i][j]:\\n rows.add(i)\\n columns.add(j)\\n\\n for i in rows:\\n for j in range(n):\\n matrix[i][j] = 0\\n for j in columns:\\n for i in range(m):\\n matrix[i][j] = 0\",\n \"def setZeroes(self, matrix: List[List[int]]) -> None:\\n m, n = len(matrix), len(matrix[0])\\n firstRow, firstCol = False, False\\n for i in range(m):\\n for j in range(n):\\n if i == 0 and matrix[i][j] == 0:\\n firstRow = True\\n if j == 0 and matrix[i][j] == 0:\\n firstCol = True\\n if i != 0 and j != 0 and matrix[i][j] == 0:\\n matrix[i][0] = 0\\n matrix[0][j] = 0\\n for i in range(1, m):\\n for j in range(1, n):\\n if matrix[i][0] == 0 or matrix[0][j] == 0:\\n matrix[i][j] = 0\\n if firstRow:\\n for i in range(n):\\n matrix[0][i] = 0\\n if firstCol:\\n for i in range(m):\\n matrix[i][0] = 0\",\n \"def setZeroes(self, matrix) -> None:\\n if not matrix: return\\n h, w = len(matrix), len(matrix[0])\\n col0 = False\\n for i in range(h):\\n if matrix[i][0] == 0:\\n col0 = True # 第一列是否要全置为0\\n for j in range(1, w): # 从第二列开始\\n if matrix[i][j] == 0: # (i,j)为0\\n matrix[0][j] = 0 # 第i行为0\\n matrix[i][0] = 0 # 第j列为0\\n for i in range(1, h):\\n for j in range(1, w):\\n if matrix[i][j] != 0 and (matrix[i][0] == 0 or matrix[0][j] == 0):\\n matrix[i][j] = 0 # 需要置为0的位置\\n if matrix[0][0] == 0: # 第一行是否要全置为0\\n for j in range(w):\\n matrix[0][j] = 0\\n if col0: # 第一列是否要全置为0\\n for i in range(h):\\n matrix[i][0] = 0\",\n \"def setZeroes(self, matrix: List[List[int]]) -> None:\\n\\n row = len(matrix)\\n column = len(matrix[0])\\n\\n for i in range(row):\\n for j in range(column):\\n if matrix[i][j] == 0:\\n for r in range(row):\\n if matrix[r][j] != 0:\\n matrix[r][j] = 'c'\\n for c in range(column):\\n if matrix[i][c] != 0:\\n matrix[i][c] = 'c'\\n\\n for i in range(row):\\n for j in range(column):\\n if matrix[i][j] == 'c':\\n matrix[i][j] = 0\",\n \"def zeroMatrix(width, height):\\n returnvalue = Matrix()\\n for i in range(width):\\n newrow = [0] * height\\n returnvalue.addRow(*newrow)\\n return returnvalue\",\n \"def setZeroes(self, matrix: List[List[int]]) -> None:\\n \\n m = len(matrix)\\n n = len(matrix[0])\\n \\n i_zeros = []\\n j_zeros = []\\n \\n for i in range(m): ## find the zeros\\n for j in range(n):\\n \\n if matrix[i][j] == 0:\\n i_zeros.append(i)\\n j_zeros.append(j)\\n \\n for i in i_zeros:\\n matrix[i] = [0]*n\\n \\n for j in j_zeros:\\n for i in range(m):\\n matrix[i][j] = 0\",\n \"def setZeroes(self, matrix: List[List[int]]) -> None:\\n m = len(matrix)\\n n = len(matrix[0])\\n \\n i = 0\\n j = 0\\n row_zero = set()\\n col_zero = set()\\n \\n for i in range(m):\\n for j in range(n):\\n if matrix[i][j] == 0:\\n row_zero.update({i})\\n col_zero.update({j})\\n \\n ## update rows with zero\\n for t in row_zero:\\n matrix[t][0:n] = [0 for i in range(n)]\\n \\n ## update cols with zero\\n for i in range(m):\\n for c in col_zero:\\n #print(i, c)\\n matrix[i][c] = 0\",\n \"def zeros_like(self):\\n raise NotImplementedError\",\n \"def setZeroes(self, matrix: List[List[int]]) -> None:\\n n = len(matrix)\\n m = len(matrix[0])\\n zeroRow = False\\n zeroColumn = False\\n for i in range(0,n):\\n if matrix[i][0] == 0:\\n zeroColumn = True\\n break\\n for j in range(0,m):\\n if matrix[0][j] == 0:\\n zeroRow = True\\n break\\n for i in range(1,n):\\n for j in range(1,m):\\n if matrix[i][j] == 0:\\n matrix[0][j] = 0\\n matrix[i][0] = 0\\n for i in range(1,n):\\n if matrix[i][0] == 0:\\n for j in range(0,m):\\n matrix[i][j] = 0\\n for j in range(1,m):\\n if matrix[0][j] == 0:\\n for i in range(0,n):\\n matrix[i][j] = 0\\n if zeroColumn:\\n for i in range(0,n):\\n matrix[i][0] = 0\\n if zeroRow:\\n for i in range(0,m):\\n matrix[0][i] = 0\",\n \"def setZeroes(self, matrix: List[List[int]]) -> None:\\n \\n m = len(matrix)\\n n = len(matrix[0])\\n \\n firstrowhaszero = False\\n firstcolumnhaszero = False\\n for i in range(m):\\n if matrix[i][0] == 0:\\n firstcolumnhaszero = True\\n break\\n for j in range(n):\\n if matrix[0][j] == 0:\\n firstrowhaszero = True\\n break\\n \\n for i in range(1, m):\\n for j in range(1, n):\\n if matrix[i][j] == 0:\\n matrix[i][0] = 0\\n matrix[0][j] = 0\\n \\n for i in range(1, m):\\n for j in range(1, n):\\n if matrix[i][0] == 0 or matrix[0][j] == 0:\\n matrix[i][j] = 0\\n \\n if firstcolumnhaszero:\\n for i in range(m):\\n matrix[i][0] = 0\\n \\n if firstrowhaszero:\\n for j in range(n):\\n matrix[0][j] = 0\",\n \"def PadSparseImage(self, out_file):\\n raise NotImplementedError\",\n \"def setZeroes(self, matrix):\\n for i in range(len(matrix)):\\n for j in range(len(matrix[i])):\\n if matrix[i][j] == 0 and (i, j) not in self.visited:\\n for neighbor in self.setter(matrix, i, j):\\n matrix[neighbor[0]][neighbor[1]] = 0\\n self.visited.add((neighbor[0], neighbor[1]))\\n print(matrix)\",\n \"def setZeroes(self, matrix: List[List[int]]) -> None:\\n m = len(matrix)\\n n = len(matrix[0])\\n \\n row_1st_0 = False\\n col_1st_0 = False\\n \\n ## check whether 1st row and 1st col contains 0\\n for i in range(n):\\n if matrix[0][i] == 0:\\n row_1st_0 = True\\n break\\n for j in range(m):\\n if matrix[j][0] == 0:\\n col_1st_0 = True\\n break\\n \\n ## check row 1:m and col 1:n, store the 0 indicator to 1st row and 1st col\\n for i in range(1, m):\\n for j in range(1, n):\\n if matrix[i][j] == 0:\\n matrix[i][0] = 0\\n matrix[0][j] = 0\\n \\n ## update cols to zero except 1st row\\n for i in range(1, n):\\n if matrix[0][i] == 0:\\n for r in range(1, m):\\n matrix[r][i] = 0\\n \\n ## update rows to zero except 1st col\\n for j in range(1, m):\\n if matrix[j][0] == 0:\\n matrix[j][1:n] = [0 for i in range(n-1)]\\n \\n ## update 1st row and 1st col to 0\\n if row_1st_0:\\n matrix[0][0:n] = [0 for i in range(n)]\\n if col_1st_0:\\n for r in range(m):\\n matrix[r][0] = 0\",\n \"def test_pad_coords_matrix(self):\\r\\n m = array([[1, 2, 3], [4, 5, 6], [7, 8, 9], [10, 11, 12]])\\r\\n actual = pad_coords_matrix(m, 0)\\r\\n assert_almost_equal(actual, m)\\r\\n\\r\\n m = array([[1, 2, 3], [4, 5, 6], [7, 8, 9], [10, 11, 12]])\\r\\n expected = array([[1, 2, 3, 0., 0.], [4, 5, 6, 0., 0.],\\r\\n [7, 8, 9, 0., 0.], [10, 11, 12, 0., 0.]])\\r\\n actual = pad_coords_matrix(m, 2)\\r\\n assert_almost_equal(actual, expected)\\r\\n\\r\\n m = array([[1, 2, 3], [4, 5, 6], [7, 8, 9], [10, 11, 12]])\\r\\n self.assertRaises(ValueError, pad_coords_matrix, m, -3)\",\n \"def setZeroes(self, matrix: List[List[int]]) -> None:\\n zeros = []\\n m, n = len(matrix), len(matrix[0])\\n for i in range(m):\\n for j in range(n):\\n if matrix[i][j] == 0:\\n zeros.append([i, j])\\n for index in zeros:\\n row = index[0]\\n col = index[1]\\n for r in range(m):\\n matrix[r][col] = 0\\n for c in range(n):\\n matrix[row][c] = 0\",\n \"def setZeroes(self, matrix: List[List[int]]) -> None:\\n m, n = len(matrix), len(matrix[0])\\n row, col = [0 for i in range(m)], [0 for j in range(n)]\\n for i in range(m):\\n for j in range(n):\\n if not matrix[i][j]:\\n row[i] = col[j] = 1\\n for i in range(m):\\n if row[i]:\\n for j in range(n):\\n matrix[i][j] = 0\\n for j in range(n):\\n if col[j]:\\n for i in range(m):\\n matrix[i][j] = 0\",\n \"def setZeroes(self, matrix: List[List[int]]) -> None:\\r\\n import copy\\r\\n m=len(matrix)\\r\\n n=len(matrix[0])\\r\\n m_copy=copy.deepcopy(matrix)\\r\\n for i in range(m):\\r\\n for j in range(n):\\r\\n if m_copy[i][j]==0:\\r\\n matrix[i]=[0]*n\\r\\n for x in range(m):\\r\\n matrix[x][j]=0\",\n \"def zeros_matrix(self, rows, cols):\\r\\n M = []\\r\\n while len(M) < rows:\\r\\n M.append([])\\r\\n while len(M[-1]) < cols:\\r\\n M[-1].append(0.0)\\r\\n\\r\\n return M\",\n \"def setZeroes(self, matrix: List[List[int]]) -> None:\\n m, n = len(matrix), len(matrix[0])\\n zero_row, zero_col = set(), set()\\n for row in range(m):\\n for col in range(n):\\n if matrix[row][col] == 0:\\n if row not in zero_row:\\n zero_row.add(row)\\n if col not in zero_col:\\n zero_col.add(col)\\n for row in zero_row:\\n for col in range(n):\\n matrix[row][col] = 0\\n for col in zero_col:\\n for row in range(m):\\n matrix[row][col] = 0\",\n \"def test_add_nans_to_weights():\\n # create input sparse matrix with one empty row (j=2)\\n coords = np.array([[0, 3, 1, 0], [0, 3, 1, 2]])\\n data = np.array([4.0, 5.0, 7.0, 9.0])\\n Matin = sps.COO(coords, data, shape=(4, 4))\\n\\n # this is what is expected to come out (Nan added at i=0, j=2)\\n coords = np.array([[0, 3, 1, 0, 2], [0, 3, 1, 2, 0]])\\n data = np.array([4.0, 5.0, 7.0, 9.0, np.nan])\\n expected = sps.COO(coords, data, shape=(4, 4))\\n\\n Matout = xe.smm.add_nans_to_weights(xr.DataArray(Matin, dims=('in', 'out')))\\n assert np.allclose(expected.todense(), Matout.data.todense(), equal_nan=True)\\n\\n # Matrix without empty rows should return the same\\n coords = np.array([[0, 3, 1, 0, 2], [0, 3, 1, 2, 1]])\\n data = np.array([4.0, 5.0, 7.0, 9.0, 10.0])\\n Matin = sps.COO(coords, data, shape=(4, 4))\\n\\n Matout = xe.smm.add_nans_to_weights(xr.DataArray(Matin, dims=('in', 'out')))\\n assert np.allclose(Matin.todense(), Matout.data.todense())\",\n \"def _insert_zero(self, x, indices_removed):\\n for i in indices_removed:\\n x = np.insert(x, i, 0)\\n return x\",\n \"def zero_like(matrix):\\n dtype = numpy.int_ if is_integer(matrix) else numpy.float_\\n return numpy.zeros(matrix.shape, dtype=dtype)\",\n \"def setZeroes(self, matrix: List[List[int]]) -> None:\\n # 把第一行，第一列当作标志位\\n row_flag = False\\n col_flag = False # 防止第一行第一列无效\\n row = len(matrix)\\n col = len(matrix[0]) # 这种表征\\n for j in range(col):\\n if matrix[0][j] == 0:\\n row_flag = True\\n break\\n for i in range(row):\\n if matrix[i][0] == 0:\\n col_flag = True\\n break\\n for i in range(1,row):\\n for j in range(1,col):\\n if matrix[i][j] == 0:\\n matrix[i][0] = matrix[0][j] = 0\\n # 检查,第二遍扫描\\n for i in range(1,row):\\n for j in range(1,col):\\n if matrix[i][0]==0 or matrix[0][j] ==0:\\n matrix[i][j] = 0\\n # 防止标志位本身也是\\n if row_flag:\\n for j in range(col):\\n matrix[0][j] = 0\\n if col_flag:\\n for i in range(row):\\n matrix[i][0] = 0\",\n \"def setZeroes(self, matrix: List[List[int]]) -> None:\\n m = len(matrix)\\n if m == 0:\\n return\\n n = len(matrix[0])\\n\\n row_zero = False\\n for i in range(m):\\n if matrix[i][0] == 0:\\n row_zero = True\\n col_zero = False\\n for j in range(n):\\n if matrix[0][j] == 0:\\n col_zero = True\\n\\n for i in range(1, m):\\n for j in range(1, n):\\n if matrix[i][j] == 0:\\n matrix[i][0] = 0\\n matrix[0][j] = 0\\n\\n for i in range(1, m):\\n if matrix[i][0] == 0:\\n for j in range(1, n):\\n matrix[i][j] = 0\\n\\n for j in range(1, n):\\n if matrix[0][j] == 0:\\n for i in range(1, m):\\n matrix[i][j] = 0\\n\\n if col_zero:\\n for j in range(n):\\n matrix[0][j] = 0\\n if row_zero:\\n for i in range(m):\\n matrix[i][0] = 0\",\n \"def test_pad_one(self):\\n # Define some data with three features\\n data = array([[1, 2],\\n [3, 4]])\\n\\n # Get a CData object\\n cdata = CData(data)\\n\\n # Pad a zero to each feature vector\\n cdata.pad_one()\\n\\n # Define the correct array\\n correct = array([[1, 2, 0],\\n [3, 4, 0]])\\n\\n self.assertEqual(cdata.num_features, 3)\\n self.assertTrue(array_equal(cdata.data, correct))\",\n \"def _buildMatrix(self, SparseMatrix, Ncells, MaxFaces, coeff):\\n return (0, 0)\",\n \"def add_dummy_location_to_matrix(matrix):\\n matrix = [row + [0] for row in matrix]\\n last_row = [0 for _ in range(len(matrix) + 1)]\\n matrix.append(last_row)\\n return matrix\",\n \"def _compute_squeezed_spacing(self,spacing0, dim0, sz0, dimSqueezed):\\n spacing = np.zeros(dimSqueezed)\\n j = 0\\n for i in range(dim0):\\n if sz0[i] != 1:\\n spacing[j] = spacing0[i]\\n j += 1\\n return spacing\",\n \"def zeros_like(data):\\n return _make.zeros_like(data)\",\n \"def setZeroes(self, matrix: List[List[int]]) -> None:\\n m, n = len(matrix), len(matrix[0])\\n zero_row, zero_col = False, False\\n\\n for i in range(m):\\n for j in range(n):\\n if matrix[i][j] == 0:\\n matrix[i][0] = matrix[0][j] = 0\\n zero_row = True if i == 0 else zero_row\\n zero_col = True if j == 0 else zero_col\\n\\n for j in range(1, n):\\n if matrix[0][j] == 0:\\n for i in range(1, m):\\n matrix[i][j] = 0\\n for i in range(1, m):\\n if matrix[i][0] == 0:\\n for j in range(1, n):\\n matrix[i][j] = 0\\n\\n if zero_row:\\n for j in range(n):\\n matrix[0][j] = 0\\n if zero_col:\\n for i in range(m):\\n matrix[i][0] = 0\\n return\",\n \"def setZeroes(matrix):\\r\\n \\r\\n #An average O(n^2) time traversal solution with memoization\\r\\n #for each 0 we encounter, we update the entire row and column to 0s, but on the condition that the row/column has not been updated yet\\r\\n \\r\\n row_cache = {}\\r\\n column_cache = {}\\r\\n \\r\\n for r in range(0,rows := len(matrix)):\\r\\n for c in range(0,cols := len(matrix[0])):\\r\\n \\r\\n if matrix[r][c] == 0:\\r\\n \\r\\n if not row_cache.get(r):\\r\\n for i in range(0,cols):\\r\\n if matrix[r][i] != 0:\\r\\n matrix[r][i] = '0' #we use strings so we only consider the initial 0s\\r\\n row_cache[r] = True\\r\\n \\r\\n if not column_cache.get(c):\\r\\n for i in range(0,rows):\\r\\n if matrix[i][c] != 0:\\r\\n matrix[i][c] = '0'\\r\\n column_cache[c] = True\\r\\n return\",\n \"def _pad(x, depth=4):\\n divisor = np.power(2, depth)\\n remainder = x.shape[0] % divisor\\n\\n # no padding because already of even shape\\n if remainder == 0:\\n return x\\n # add zero rows after 1D feature\\n elif len(x.shape) == 2:\\n return np.pad(x, [(0, divisor - remainder), (0, 0)], \\\"constant\\\")\\n # add zero columns and rows after 2D feature\\n elif len(x.shape) == 3:\\n return np.pad(x, [(0, divisor - remainder), (0, divisor - remainder),\\n (0, 0)], \\\"constant\\\")\",\n \"def Dmat(numpts, delta=1):\\n a = 0.5 / delta * ones(numpts)\\n a[0] = 0\\n a[-2] = 0\\n #b=-2./delta**2*ones(numpts); b[0]=0;b[-1]=0\\n c = -0.5 / delta * ones(numpts)\\n c[1] = 0\\n c[-1] = 0\\n return sparse.spdiags([a, c], [-1, 1], numpts, numpts)\",\n \"def setZeroes(self, matrix: List[List[int]]) -> None:\\n m, n = len(matrix), len(matrix[0])\\n\\n col_0_flag = any(matrix[i][0] == 0 for i in range(m))\\n row_0_flag = any(matrix[0][i] == 0 for i in range(n))\\n\\n # 第 0 列， 第 0 行可以作为 [i,j] 的指示存储\\n # 例如，如果 [i,j] 为 0， 那么 [0, j], [i, 0] 可以置 0 \\n # 再次遍历矩阵，通过判断 [0, j], [i, 0] 去把该行，该列置 0\\n # 这样的话就不需要一个额外的矩阵去 for i,j 遍历是否为 0 了\\n\\n # 注意是从 1 开始的。 \\n for i in range(1, m):\\n for j in range(1, n):\\n if matrix[i][j] == 0:\\n matrix[i][0] = matrix[0][j] = 0 \\n\\n for i in range(1, m):\\n for j in range(1, n):\\n if matrix[i][0] == 0 or matrix[0][j] == 0:\\n matrix[i][j] = 0\\n\\n if col_0_flag:\\n for i in range(m):\\n matrix[i][0] = 0\\n\\n if row_0_flag:\\n for j in range(n):\\n matrix[0][j] = 0\",\n \"def setZeroes(self, matrix: List[List[int]]) -> None:\\n row = set()\\n column = set()\\n \\n m = len(matrix)\\n n = len(matrix[0])\\n \\n for i in range(m):\\n for j in range(n):\\n if matrix[i][j] == 0:\\n row.add(i)\\n column.add(j)\\n \\n for i in range(m):\\n for j in range(n):\\n if i in row or j in column:\\n matrix[i][j] = 0\",\n \"def test_normalize_matrix_with_only_zeros(self):\\n input_matrix = [\\n [0, 0],\\n [0, 0]\\n ]\\n\\n expected = [\\n [0, 0],\\n [0, 0]\\n ]\\n\\n result = self.summarizer.normalize_matrix(input_matrix)\\n\\n self.assertEqual(expected, result)\",\n \"def tocsr(self):\\n\\n indptr = np.asarray([len(x) for x in self.rows], dtype=np.intc)\\n indptr = np.concatenate( (np.array([0], dtype=np.intc), np.cumsum(indptr)) )\\n\\n nnz = indptr[-1]\\n\\n indices = []\\n for x in self.rows:\\n indices.extend(x)\\n indices = np.asarray(indices, dtype=np.intc)\\n\\n data = []\\n for x in self.data:\\n data.extend(x)\\n data = np.asarray(data, dtype=self.dtype)\\n\\n from csr import csr_matrix\\n return csr_matrix((data, indices, indptr), shape=self.shape)\",\n \"def zeros_matrix(rows, cols):\\n M = []\\n while len(M) < rows:\\n M.append([])\\n while len(M[-1]) < cols:\\n M[-1].append(0.0)\\n \\n return M\",\n \"def zero_matrix(matrix):\\n n = len(matrix)\\n if n == 0:\\n return True\\n\\n m = len(matrix[0])\\n if m == 0:\\n return True\\n\\n zero_rows = set()\\n zero_columns = set()\\n for i in range(n):\\n for j in range(m):\\n if matrix[i][j] == 0:\\n zero_rows.add(i)\\n zero_columns.add(j)\\n \\n for i in zero_rows:\\n for j in range(m):\\n matrix[i][j] = 0\\n\\n for i in range(n):\\n for j in zero_columns:\\n matrix[i][j] = 0\\n\\n return True\",\n \"def test_sparse():\\n\\n rng = np.random.RandomState(0)\\n\\n X = rng.rand(20, 2)\\n X[X < 0.8] = 0\\n X_csr = sp.csr_matrix(X)\\n\\n bisect_means = BisectingKMeans(n_clusters=3, random_state=0)\\n\\n bisect_means.fit(X_csr)\\n sparse_centers = bisect_means.cluster_centers_\\n\\n bisect_means.fit(X)\\n normal_centers = bisect_means.cluster_centers_\\n\\n # Check if results is the same for dense and sparse data\\n assert_allclose(normal_centers, sparse_centers, atol=1e-8)\",\n \"def _identity_sparse(d, stype=\\\"csr\\\", dtype=complex):\\n return sp.eye(d, dtype=dtype, format=stype)\",\n \"def pad(self, matrices, pad_value):\\n shapes = [m.shape for m in matrices]\\n M, N = sum([s[0] for s in shapes]), sum([s[1] for s in shapes])\\n zeros = torch.FloatTensor(np.zeros((M, N))).to(device)\\n pad_matrices = pad_value + zeros\\n i, j = 0, 0\\n for k, matrix in enumerate(matrices):\\n m, n = shapes[k]\\n pad_matrices[i:i+m, j:j+n] = matrix\\n i += m\\n j += n\\n return pad_matrices\",\n \"def setZeroes(self, matrix: List[List[int]]) -> None:\\n new_matrix = [row.copy() for row in matrix]\\n for i in range(len(matrix)):\\n for j in range(len(matrix[0])):\\n if matrix[i][j] == 0 and new_matrix[i][j] == 0:\\n self.setZero(matrix, i, j)\",\n \"def setZeroes(self, matrix: List[List[int]]) -> None:\\n row_num, col_num = len(matrix), len(matrix[0])\\n # 创建集合set()用于存放需要置零的行和列\\n row_set, col_set = set(), set()\\n for row in range(row_num):\\n for col in range(col_num):\\n if matrix[row][col]==0:\\n row_set.add(row)\\n col_set.add(col)\\n # 将记录的行、列中的元素赋值为0\\n # 再次遍历赋值\\n for row in range(row_num):\\n for col in range(col_num):\\n if row in row_set or col in col_set:\\n matrix[row][col] = 0\\n # # 或者行列单独赋值均可\\n # for row in row_set:\\n # for col in range(col_num):\\n # matrix[row][col] = 0\\n # for col in col_set:\\n # for row in range(row_num):\\n # matrix[row][col] = 0\",\n \"def zeros_matrix(rows, cols):\\n M = []\\n while len(M) < rows:\\n M.append([])\\n while len(M[-1]) < cols:\\n M[-1].append(0.0)\\n\\n return M\",\n \"def zeros_matrix(rows, cols):\\n M = []\\n while len(M) < rows:\\n M.append([])\\n while len(M[-1]) < cols:\\n M[-1].append(0.0)\\n\\n return M\",\n \"def matZeros(shape):\\n return [[0 for y in range(shape[1])] \\\\\\n for x in range(shape[0])]\",\n \"def setZeroes(self, matrix) -> None:\\n m, n = len(matrix), len(matrix[0])\\n rows, cols = set(), set()\\n # 获取0所在的位置对应的行和列\\n for i in range(m):\\n for j in range(n):\\n if matrix[i][j] == 0:\\n rows.add(i)\\n cols.add(j)\\n # 将该行和列的所有元素置为0\\n for row in rows:\\n for j in range(n):\\n matrix[row][j] = 0\\n for col in cols:\\n for i in range(m):\\n matrix[i][col] = 0\",\n \"def drawSparseMatrix(ax, mat, **kwargs):\\n row = kwargs.pop('rowOffset', 0)\\n col = kwargs.pop('colOffset', 0)\\n color = kwargs.pop('color', None)\\n\\n mat = pg.utils.sparseMatrix2coo(mat)\\n mat.row += row\\n mat.col += col\\n gci = ax.spy(mat, color=color, **kwargs)\\n\\n ax.autoscale(enable=True, axis='both', tight=True)\\n return gci\",\n \"def sparse_matlab(i, j, v, m, n):\\n return csr_matrix((v, (i, j)), shape=(m, n))\",\n \"def sparse_initialization_weights(self):\\n W = []\\n mu, sigma = 0, 1/self.non_zero_units\\n\\n for i in xrange(self.n_in):\\n row = np.zeros(self.n_out)\\n non_zeros = self.rng.normal(mu, sigma, self.non_zero_units)\\n # non_zeros /= non_zeros.sum()\\n non_zero_idxs = self.rng.permutation(\\n self.n_out)[0:self.non_zero_units]\\n for j in xrange(self.non_zero_units):\\n row[non_zero_idxs[j]] = non_zeros[j]\\n W.append(row)\\n W = np.asarray(W, dtype=theano.config.floatX)\\n return W\",\n \"def zeros_like(a):\\n if isinstance(a, np.ndarray):\\n return np.zeros_like(a)\\n else:\\n return K.zeros_like(a)\",\n \"def setZeroes(self, matrix: List[List[int]]) -> None:\\n m = len(matrix)\\n n = len(matrix[0])\\n # replace zeros by None\\n for i in range(m):\\n for j in range(n):\\n if matrix[i][j] == 0:\\n matrix[i][j] = None\\n # do the rows\\n for i, row in enumerate(matrix):\\n if None in row:\\n for j, x in enumerate(row):\\n if x is not None:\\n matrix[i][j] = 0\\n # do the columns\\n for j in range(n):\\n # false if the column had no zeros\\n z = False\\n for i in range(m):\\n if matrix[i][j] is None:\\n z = True\\n break\\n if z:\\n for i in range(m):\\n if matrix[i][j] is not None:\\n matrix[i][j] = 0\\n # undo replace zeros by None\\n for i in range(m):\\n for j in range(n):\\n if matrix[i][j] is None:\\n matrix[i][j] = 0\",\n \"def make_all_zero(curr_clusters, k, num_of_cords):\\r\\n for i in range(k):\\r\\n for j in range(num_of_cords):\\r\\n curr_clusters[i][j] = 0\",\n \"def pad_spatial(self, x):\\n n, t, c, h, w = x.size()\\n\\n pad_h = (4 - h % 4) % 4\\n pad_w = (4 - w % 4) % 4\\n\\n # padding\\n x = x.view(-1, c, h, w)\\n x = F.pad(x, [0, pad_w, 0, pad_h], mode='reflect')\\n\\n return x.view(n, t, c, h + pad_h, w + pad_w)\",\n \"def setZeroes(self, matrix: List[List[int]]) -> None:\\n m, n = len(matrix), len(matrix[0]) if matrix else 0\\n \\n row = [False for _ in range(m)]\\n col = [False for _ in range(n)]\\n \\n for i in range(m):\\n for j in range(n):\\n if matrix[i][j] == 0:\\n row[i] = col[j] = True\\n \\n for i in range(m):\\n for j in range(n):\\n if row[i] or col[j]:\\n matrix[i][j] = 0\",\n \"def pad_zeros(n, before, after):\\n pad_1_dim = (before, after)\\n pad_2_dim = ((before, after), (0, 0))\\n m = before + n + after\\n def times(x):\\n return jnp.pad(x, pad_1_dim)\\n def trans(x):\\n return x[before:before+n]\\n return Operator(times=times, trans=trans, shape=(m,n), matrix_safe=False)\",\n \"def zeros(shape, dtype=float):\\n if not mathutil.is_shape(shape, ndim=2):\\n raise ValueError(\\\"invalid shape\\\")\\n\\n sc = SparkContext.getOrCreate()\\n\\n nelem = 0\\n\\n rdd = sc.emptyRDD()\\n\\n return Matrix(rdd, shape, dtype=dtype, nelem=nelem)\",\n \"def RowXchageZero(matrix,vector):\\r\\n\\r\\n for i in range(len(matrix)):\\r\\n for j in range(i, len(matrix)):\\r\\n # The pivot member is not zero\\r\\n if matrix[i][i] == 0:\\r\\n temp = matrix[j]\\r\\n temp_b = vector[j]\\r\\n matrix[j] = matrix[i]\\r\\n vector[j] = vector[i]\\r\\n matrix[i] = temp\\r\\n vector[i] = temp_b\\r\\n\\r\\n\\r\\n return [matrix, vector]\",\n \"def _fill_in_missing(x):\\n default_value = '' if x.dtype == tf.string else 0\\n return tf.squeeze(\\n tf.sparse.to_dense(\\n tf.SparseTensor(x.indices, x.values, [x.dense_shape[0], 1]),\\n default_value),\\n axis=1)\",\n \"def replace_zeros(self):\\n\\n min_c = np.array(self.map[self.column])\\n self.map.loc[self.map[self.column]==0, self.column] = np.min(min_c[np.nonzero(min_c)])\",\n \"def _pad(A, N):\\n n, _ = A.shape\\n if n >= N:\\n return A\\n else:\\n if issparse(A):\\n # thrown if we try to np.concatenate sparse matrices\\n side = sps.csr_matrix((n, N - n))\\n bottom = sps.csr_matrix((N - n, N))\\n A_pad = sps.hstack([A, side])\\n A_pad = sps.vstack([A_pad, bottom])\\n else:\\n side = np.zeros((n, N - n))\\n bottom = np.zeros((N - n, N))\\n A_pad = np.concatenate([A, side], axis=1)\\n A_pad = np.concatenate([A_pad, bottom])\\n return A_pad\",\n \"def create_sparseDB():\\n datas = data.Kmercount_to_matrix()\\n datas.run()\\n print('***Sparse matrix created***')\",\n \"def zeros_like(tensor):\\n raise NotImplementedError\",\n \"def to_positive(A_diag, eps=0.00001):\\n A_diag[A_diag < 0] = eps\\n inds = torch.isclose(A_diag, torch.zeros(1, device=cuda.device()))\\n A_diag[inds] = eps\\n return A_diag\",\n \"def unwrapOutput(self,x):\\n sx = SS.csr_matrix(x)\\n sx.eliminate_zeros()\\n return sx\",\n \"def _trace_sparse(op):\\n return np.sum(op.diagonal())\",\n \"def zeros(cls, shape, domain, *, fmt='sparse'):\\n return cls.from_rep(SDM.zeros(shape, domain))\"\n]","isConversational":false},"negative_scores":{"kind":"list like","value":["0.6846492","0.6157045","0.6157045","0.6109819","0.59839016","0.59775317","0.59726745","0.59028137","0.5901642","0.5830433","0.57942516","0.57754564","0.5743076","0.57350683","0.57294387","0.57047725","0.5701384","0.5644425","0.5624889","0.56114304","0.5601074","0.55881506","0.55743366","0.5559454","0.5554051","0.5549057","0.55429107","0.5538183","0.553792","0.55256796","0.5510753","0.5495813","0.5494749","0.54858935","0.54845476","0.5481262","0.5480917","0.5467117","0.5467015","0.54642296","0.54542786","0.5446574","0.5443852","0.5436871","0.54330504","0.54145586","0.5412839","0.5410676","0.54075265","0.5407442","0.5404768","0.53873175","0.53758526","0.5357493","0.53549314","0.5354037","0.53422815","0.53237885","0.53171545","0.53048897","0.53004485","0.5297351","0.52825487","0.5281638","0.52805865","0.527763","0.5276398","0.5267411","0.52652323","0.5256833","0.52390826","0.52390015","0.522949","0.52259594","0.52249366","0.52144116","0.5212559","0.5212559","0.52012205","0.5185859","0.5181596","0.5180081","0.5160519","0.5148697","0.5148657","0.51478684","0.51468664","0.51446307","0.5121889","0.51217383","0.5115485","0.5115065","0.5105313","0.5100362","0.50972784","0.5094695","0.5094006","0.5089302","0.5076329","0.5069121"],"string":"[\n \"0.6846492\",\n \"0.6157045\",\n \"0.6157045\",\n \"0.6109819\",\n \"0.59839016\",\n \"0.59775317\",\n \"0.59726745\",\n \"0.59028137\",\n \"0.5901642\",\n \"0.5830433\",\n \"0.57942516\",\n \"0.57754564\",\n \"0.5743076\",\n \"0.57350683\",\n \"0.57294387\",\n \"0.57047725\",\n \"0.5701384\",\n \"0.5644425\",\n \"0.5624889\",\n \"0.56114304\",\n \"0.5601074\",\n \"0.55881506\",\n \"0.55743366\",\n \"0.5559454\",\n \"0.5554051\",\n \"0.5549057\",\n \"0.55429107\",\n \"0.5538183\",\n \"0.553792\",\n \"0.55256796\",\n \"0.5510753\",\n \"0.5495813\",\n \"0.5494749\",\n \"0.54858935\",\n \"0.54845476\",\n \"0.5481262\",\n \"0.5480917\",\n \"0.5467117\",\n \"0.5467015\",\n \"0.54642296\",\n \"0.54542786\",\n \"0.5446574\",\n \"0.5443852\",\n \"0.5436871\",\n \"0.54330504\",\n \"0.54145586\",\n \"0.5412839\",\n \"0.5410676\",\n \"0.54075265\",\n \"0.5407442\",\n \"0.5404768\",\n \"0.53873175\",\n \"0.53758526\",\n \"0.5357493\",\n \"0.53549314\",\n \"0.5354037\",\n \"0.53422815\",\n \"0.53237885\",\n \"0.53171545\",\n \"0.53048897\",\n \"0.53004485\",\n \"0.5297351\",\n \"0.52825487\",\n \"0.5281638\",\n \"0.52805865\",\n \"0.527763\",\n \"0.5276398\",\n \"0.5267411\",\n \"0.52652323\",\n \"0.5256833\",\n \"0.52390826\",\n \"0.52390015\",\n \"0.522949\",\n \"0.52259594\",\n \"0.52249366\",\n \"0.52144116\",\n \"0.5212559\",\n \"0.5212559\",\n \"0.52012205\",\n \"0.5185859\",\n \"0.5181596\",\n \"0.5180081\",\n \"0.5160519\",\n \"0.5148697\",\n \"0.5148657\",\n \"0.51478684\",\n \"0.51468664\",\n \"0.51446307\",\n \"0.5121889\",\n \"0.51217383\",\n \"0.5115485\",\n \"0.5115065\",\n \"0.5105313\",\n \"0.5100362\",\n \"0.50972784\",\n \"0.5094695\",\n \"0.5094006\",\n \"0.5089302\",\n \"0.5076329\",\n \"0.5069121\"\n]","isConversational":false},"document_score":{"kind":"string","value":"0.7736398"},"document_rank":{"kind":"string","value":"0"}}},{"rowIdx":22689,"cells":{"query":{"kind":"string","value":"Crop a kernel matrix to target size horizontally and vertically. If the target size is even, the target size is adjusted to the next integer up."},"document":{"kind":"string","value":"def crop_kernel(kernel, target_size):\n # Use list for mutability\n target = [d for d in target_size]\n adjusted = False\n for dim in range(len(target)):\n if not target[dim] % 2:\n target[dim] += 1\n adjusted = True\n if adjusted:\n sys.stderr.write(\n \"WARNING: Cropped kernel size adjusted to \"\n f\"{target[0]}x{target[1]} to keep odd dimensions.\\n\"\n )\n\n source_m, source_n = kernel.shape\n target_m, target_n = target\n # Define horizontal and vertical margins to trim\n if source_m > target_m:\n margin_rows = (source_m - target_m) // 2\n else:\n margin_rows = 0\n if source_n > target_n:\n margin_cols = (source_n - target_n) // 2\n else:\n margin_cols = 0\n\n cropped = kernel[\n margin_rows : (source_m - margin_rows),\n margin_cols : (source_n - margin_cols),\n ]\n\n return cropped"},"metadata":{"kind":"string","value":"{\n \"objective\": {\n \"self\": [],\n \"paired\": [],\n \"triplet\": [\n [\n \"query\",\n \"document\",\n \"negatives\"\n ]\n ]\n }\n}"},"negatives":{"kind":"list like","value":["def crop_to_target(x, target):\n\n if target.ndim==3:\n t_h, t_w = target.shape[1], target.shape[2]\n elif target.ndim==4:\n t_h, t_w = target.shape[2], target.shape[3]\n cr = int((x.shape[2] - t_h) / 2)\n cc = int((x.shape[3] - t_w) / 2)\n x_cropped = x[:, :, cr:cr + t_h, cc:cc + t_w]\n return x_cropped","def crop(X,size_crop=_size_crop):\n b = size_crop//2\n shape = tf.shape(X)\n cx= shape[0]//2\n cy= shape[1]//2\n return X[cx-b:cx+b,cy-b:cy+b,...]","def shrink_kernel(self, kernel, up_scale):\n up_scale = torch.tensor(up_scale).float()\n # boundary padding based on the scaling law\n pad_in = (torch.ceil(up_scale**2).int())*((kernel.shape[2]-1)//2)\n pad_h = (torch.ceil(up_scale).int())*((kernel.shape[3]-1)//2)\n pad_w = (torch.ceil(up_scale).int())*((kernel.shape[4]-1)//2)\n padded_kernel = F.pad(kernel, (pad_w, pad_w, pad_h, pad_h, pad_in, pad_in))\n delta = up_scale%1\n \n if delta == 0:\n shrink_factor = 1\n else:\n # shrink_factor for coordinates.\n shrink_factor = (((kernel.shape[4]-1))/(padded_kernel.shape[-1]-1)*(up_scale+1))\n \n # Adjustment to deal with weird filtering on the grid sample function.\n shrink_factor = 1.5*(shrink_factor-0.5)**3 + 0.57 \n\n grid = torch.meshgrid(torch.linspace(-1, 1, kernel.shape[2])*(shrink_factor**2),\n torch.linspace(-1, 1, kernel.shape[3])*shrink_factor, \n torch.linspace(-1, 1, kernel.shape[4])*shrink_factor)\n\n grid = torch.cat([grid[2].unsqueeze(0).unsqueeze(-1), \n grid[1].unsqueeze(0).unsqueeze(-1), \n grid[0].unsqueeze(0).unsqueeze(-1)], dim = -1).repeat(kernel.shape[0],1,1,1,1)\n\n new_kernel = F.grid_sample(padded_kernel, grid.to(device))\n if kernel.shape[-1] - 2*up_scale > 0:\n new_kernel = new_kernel * (kernel.shape[-1]**2/((kernel.shape[-1] - 2*up_scale)**2 + 0.01))\n return new_kernel","def shift_kernel(kernel, shape, centre):\n h, w = kernel.shape\n assert(h % 2 == 1)\n assert(w % 2 == 1)\n half_h = np.floor(h/2)\n half_w = np.floor(w/2)\n \n result = np.zeros((shape[0]+2*half_h, shape[1]+2*half_w)) #zero pad to simplify edge handling \n\n ind_h = centre[0] + np.arange(0, 2*half_h+1, dtype='int') \n ind_w = centre[1] + np.arange(0, 2*half_w+1, dtype='int')\n result[ind_h[:,np.newaxis], ind_w] = kernel\n result = result[half_h:-half_h,half_w:-half_w]\n return result","def crop(img: torch.Tensor, new_size: Union[int, Tuple[int, int]]) ->torch.Tensor:\n new_size = to_tuple(new_size)\n return F.center_crop(img, output_size=new_size)","def upsampleImage( arr, kernelSize ):\n return scipy.ndimage.zoom( arr, kernelSize )","def slidekernelthroughdiagonal(kernel, matrix):\n size_kernel = kernel.shape[0]\n size_matrix = matrix.shape[0]\n result = np.zeros([size_matrix])\n for i in range(size_matrix):\n # Calculate zero padding needed\n padding_b = -min(i - int(size_kernel/2), 0)\n padding_a = -min(size_matrix - int(i + size_kernel/2), 0)\n matrix_selection = matrix[max(0, i-int(size_kernel/2)):min(size_matrix, i+int(size_kernel/2)),max(0, i-int(size_kernel/2)):min(size_matrix, i+int(size_kernel/2))]\n matrix_padded = np.pad(matrix_selection, [(padding_b, padding_a), (padding_b, padding_a)])\n result[i] = np.sum(matrix_padded*kernel)\n return result","def crop_and_resize(image, boxes, size):\n box_ind = keras.backend.zeros_like(boxes, tensorflow.int32)\n box_ind = box_ind[..., 0]\n box_ind = keras.backend.reshape(box_ind, [-1])\n\n boxes = keras.backend.reshape(boxes, [-1, 4])\n\n return tensorflow.image.crop_and_resize(image, boxes, box_ind, size)","def conv_matrix(matrix, kernel):","def center_crop(img:np.array,output_size:List[int])->np.array:\n\n if isinstance(output_size,numbers.Number):\n output_size = (int(output_size),int(output_size))\n elif isinstance(output_size,(tuple,list)) and len(output_size)==1:\n output_size =(output_size[0],output_size[0])\n \n image_height,image_width,_=img.shape\n crop_height,crop_width=output_size\n if crop_width > image_width or crop_height > image_height:\n padding_ltrb = [\n (crop_width - image_width) // 2 if crop_width > image_width else 0,\n (crop_height - image_height) // 2 if crop_height > image_height else 0,\n (crop_width - image_width + 1) // 2 if crop_width > image_width else 0,\n (crop_height - image_height + 1) // 2 if crop_height > image_height else 0,\n ]\n img=cv.copyMakeBorder(img,padding_ltrb[1],padding_ltrb[3],padding_ltrb[0],padding_ltrb[2],cv.BORDER_CONSTANT,value=(0,0,0))\n image_height,image_width,_=img.shape\n if crop_width == image_width and crop_height == image_height:\n return img\n\n crop_top = int(round((image_height - crop_height) / 2.))\n crop_left = int(round((image_width - crop_width) / 2.))\n return img[crop_top:crop_top+crop_height,crop_left:crop_left+crop_width]","def crop_resize_img(img_path, target_size, crop_amount):\n img = image.load_img(img_path)\n x = image.img_to_array(img)\n x = x[crop_amount:-crop_amount, crop_amount:-crop_amount, :]\n ximg = Image.fromarray(np.uint8(x))\n ximg_resize = ximg.resize((target_size[0], target_size[1]))\n x = image.img_to_array(ximg_resize)\n\n return x","def get_crop_torch(im: torch.Tensor, pos: torch.Tensor, sample_sz: torch.Tensor, output_sz: torch.Tensor = None,\n mode: str = 'replicate', max_scale_change=None, is_mask=False):\n\n # if mode not in ['replicate', 'inside']:\n # raise ValueError('Unknown border mode \\'{}\\'.'.format(mode))\n\n # copy and convert\n posl = pos.long().clone()\n\n pad_mode = mode\n\n # Get new sample size if forced inside the image\n if mode == 'inside' or mode == 'inside_major':\n pad_mode = 'replicate'\n im_sz = torch.tensor([im.shape[2], im.shape[3]], device=im.device)\n shrink_factor = (sample_sz.float() / im_sz)\n if mode == 'inside':\n shrink_factor = shrink_factor.max()\n elif mode == 'inside_major':\n shrink_factor = shrink_factor.min()\n shrink_factor.clamp_(min=1, max=max_scale_change)\n sample_sz = (sample_sz.float() / shrink_factor).long()\n\n # Compute pre-downsampling factor\n if output_sz is not None:\n resize_factor = torch.min(sample_sz.float() / output_sz.float()).item()\n df = int(max(int(resize_factor - 0.1), 1))\n else:\n df = int(1)\n\n sz = sample_sz.float() / df # new size\n\n # Do downsampling\n if df > 1:\n os = posl % df # offset\n posl = (posl - os) // df # new position\n im2 = im[..., os[0].item()::df, os[1].item()::df] # downsample\n else:\n im2 = im\n\n # compute size to crop\n szl = torch.max(sz.round(), torch.tensor([2.0], dtype=sz.dtype, device=sz.device)).long()\n\n # Extract top and bottom coordinates\n tl = posl - (szl - 1) // 2\n br = posl + szl // 2 + 1\n\n # Shift the crop to inside\n if mode == 'inside' or mode == 'inside_major':\n im2_sz = torch.LongTensor([im2.shape[2], im2.shape[3]])\n shift = (-tl).clamp(0) - (br - im2_sz).clamp(0)\n tl += shift\n br += shift\n\n outside = ((-tl).clamp(0) + (br - im2_sz).clamp(0)) // 2\n shift = (-tl - outside) * (outside > 0).long()\n tl += shift\n br += shift\n\n # Get image patch\n # im_patch = im2[...,tl[0].item():br[0].item(),tl[1].item():br[1].item()]\n\n\n # Get image patch\n if not is_mask:\n im_patch = F.pad(im2, (-tl[1].item(), br[1].item() - im2.shape[3], -tl[0].item(), br[0].item() - im2.shape[2]),\n mode=pad_mode)\n else:\n im_patch = F.pad(im2, (-tl[1].item(), br[1].item() - im2.shape[3], -tl[0].item(), br[0].item() - im2.shape[2]))\n\n # Get image coordinates\n patch_coord = df * torch.cat((tl, br)).view(1, 4)\n\n scale = output_sz / (torch.tensor(im_patch.shape, device=im_patch.device)[-2:] * df)\n\n if output_sz is None or (im_patch.shape[-2] == output_sz[0] and im_patch.shape[-1] == output_sz[1]):\n return im_patch.clone(), patch_coord, scale\n\n # Resample\n if not is_mask:\n im_patch = F.interpolate(im_patch, output_sz.long().tolist(), mode='bilinear')\n else:\n im_patch = F.interpolate(im_patch, output_sz.long().tolist(), mode='nearest')\n\n return im_patch, patch_coord, scale","def center_crop(image, model_input_image_size):\n im_size = image.get_shape().as_list()\n target_height = model_input_image_size[0]\n target_width = model_input_image_size[1]\n if len(im_size) == 3:\n return tf.image.resize_image_with_crop_or_pad(\n image,\n target_height=target_height,\n target_width=target_width)\n elif len(im_size) == 4:\n time_split_image = tf.split(image, im_size[0], axis=0)\n crops = []\n for idx in range(len(time_split_image)):\n it_crop = tf.image.resize_image_with_crop_or_pad(\n tf.squeeze(time_split_image[idx], axis=0),\n target_height=target_height,\n target_width=target_width)\n crops += [tf.expand_dims(it_crop, axis=0)]\n return tf.concat(crops, axis=0)\n else:\n raise NotImplementedError","def squeeze(x,kernel=3):\n k = kernel // 2\n idx = [list(i) for i in itertools.product(range(-k,k+1),range(-k,k+1))]\n \n xPad = torch.zeros(x.shape[0]+kernel-1,x.shape[1]+kernel-1)\n xPad[k:-k,k:-k] = x\n \n xSqueezed = [torch.roll(xPad,shifts=(i[0],i[1]),dims=(0,1)) for i in idx]\n xSqueezed = torch.stack(xSqueezed)\n \n return xSqueezed[:,k:-k,k:-k]","def img_preprocess(self, img, output_size=64):\n w, h = img.size\n if w > h:\n box_param = (int(w * 0.5 - h * 0.5), 0, int(w * 0.5 + h * 0.5), h)\n cropped = img.crop(box_param)\n else: # w < h\n box_param = (0, int(h * 0.5 - w * 0.5), w, int(h * 0.5 + w * 0.5))\n cropped = img.crop(box_param)\n\n resized = cropped.resize((output_size, output_size))\n resized = np.asarray(resized)\n\n return resized","def unfold(input, kernel_size, dilation=1, padding=0, stride=1):\n nd_util = utils._ntuple(input.ndimension() - 2)\n out = FunctionLib.apply(\n 'Im2Col',\n input.device,\n [input],\n kernel_shape=nd_util(kernel_size),\n strides=nd_util(stride),\n pads=nd_util(padding),\n dilations=nd_util(dilation))\n return out.flatten_(2)","def crop(arr, target_shape):\n arr_shape = arr.shape\n ncrop = ()\n for dim in range(len(arr_shape)):\n diff = arr_shape[dim] - target_shape[dim]\n if diff > 0:\n start = int(diff / 2)\n stop = start + target_shape[dim]\n ncrop += np.index_exp[start:stop]\n else:\n ncrop += np.index_exp[:]\n cropped = arr[ncrop]\n return cropped","def crop_and_downsample(originalX, downsample_size=32):\n current_dim = 250\n target_dim = 128\n margin = int((current_dim - target_dim) / 2)\n left_margin = margin\n right_margin = current_dim - margin\n\n # newim is shape (6, 128, 128)\n newim = originalX[:, left_margin:right_margin, left_margin:right_margin]\n\n # resized are shape (feature_width, feature_height, 3)\n feature_width = feature_height = downsample_size\n resized1 = imresize(newim[0:3, :, :], (feature_width, feature_height), interp=\"bicubic\", mode=\"RGB\")\n resized2 = imresize(newim[3:6, :, :], (feature_width, feature_height), interp=\"bicubic\", mode=\"RGB\")\n\n # re-packge into a new X entry\n newX = np.concatenate([resized1, resized2], axis=2)\n\n # the next line is EXTREMELY important.\n # if you don't normalize your data, all predictions will be 0 forever.\n newX = newX / 255.0\n\n return newX","def crop_or_pad(img: torch.Tensor, new_size: Union[int, Tuple[int, int]]):\n new_size = to_tuple(new_size)\n if list(new_size) == list(img.shape[-2:]):\n return img\n img = pad(img, new_size)\n img = crop(img, new_size)\n return img","def center_crop(img, output_size, data_format='CHW'):\n _assert_image_tensor(img, data_format)\n\n if isinstance(output_size, numbers.Number):\n output_size = (int(output_size), int(output_size))\n\n image_width, image_height = _get_image_size(img, data_format)\n crop_height, crop_width = output_size\n crop_top = int(round((image_height - crop_height) / 2.0))\n crop_left = int(round((image_width - crop_width) / 2.0))\n return crop(\n img,\n crop_top,\n crop_left,\n crop_height,\n crop_width,\n data_format=data_format,\n )","def crop_to_square(self, image):\n orig_height, orig_width, orig_channels = image.shape\n if orig_height > orig_width:\n return image[:orig_width, ...]\n elif orig_height < orig_width:\n return image[:, :orig_height, ...]\n return image","def downsample2d(inputArray, kernelSize):\n average_kernel = np.ones((kernelSize,kernelSize))\n\n blurred_array = sig.convolve2d(inputArray, average_kernel, mode='same')\n downsampled_array = blurred_array[::kernelSize,::kernelSize]\n return downsampled_array","def crop_and_pad(data, offset, crop_shape, target_shape=None):\n # Spatial dimensions only. All vars in zyx.\n shape = np.array(data.shape[1:])\n crop_shape = np.array(crop_shape)\n offset = np.array(offset[::-1])\n\n start = shape // 2 - crop_shape // 2 + offset\n end = start + crop_shape\n\n assert np.all(start >= 0)\n\n selector = [slice(s, e) for s, e in zip(start, end)]\n selector = tuple([slice(None)] + selector)\n cropped = data[selector]\n\n if target_shape is not None:\n target_shape = np.array(target_shape)\n delta = target_shape - crop_shape\n pre = delta // 2\n post = delta - delta // 2\n\n paddings = [(0, 0)] # no padding for batch\n paddings.extend(zip(pre, post))\n paddings.append((0, 0)) # no padding for channels\n\n cropped = np.pad(cropped, paddings, mode='constant')\n\n return cropped","def unfold(input, kernel_size=(3,3), stride=(1,1), padding=(0,0)):\n batch_size, in_chan, in_height, in_width = input.shape\n \n vals = ((0,0), (0,0), (padding[0], padding[0]), (padding[1], padding[1]))\n input_padded = np.pad(input, vals, mode='constant')\n\n k, i, j = get_indices(input.shape, kernel_size, stride, padding)\n \n input_f = input_padded[:, k, i, j]\n\n input_f = input_f.transpose(1, 2, 0).reshape(in_chan*kernel_size[0]*kernel_size[1], -1)\n return input_f","def crop(img: 'np.ndarray', x: int, y: int, width: int, height: int) -> 'np.ndarray':\n return img[y:y+height, x:x+width]","def test_resize_outputs_crop(self, X: torch.Tensor, scale_factor: float):\n xc, yc = resize_batch(X, X, scale_factor, 'crop', resize_targets=True)\n assert torch.equal(xc, yc)","def _crop_video(numpy_video, size, desired_size):\r\n\r\n w, h = size\r\n h1, h2 = int(h/2) - int(desired_size/2), int(h/2) + int(desired_size/2)\r\n w1, w2 = int(w/2) - int(desired_size/2), int(w/2) + int(desired_size/2)\r\n return numpy_video[:, :, h1:h2, w1:w2, :]","def center_crop(image, source=(218, 178, 3), target=128):\n height, width, channel = source\n\n off_h = np.ceil((height - target) / 2).astype(int)\n off_w = np.ceil((width - target) / 2).astype(int)\n return image[off_h: off_h+target, off_w: off_w+target, :]","def get_crop_matrix_batch(bbx):\n shape = tf.shape(bbx)\n batch_size = shape[0]\n bbx = tf.reshape(bbx, [batch_size, 4, 1])\n\n tmp = tf.convert_to_tensor([[0.0, -1.0, 0.0, 1.0], \n [-1.0, 0.0, 1.0, 0.0]])\n tmp = tf.broadcast_to(tmp, shape=[batch_size, 2, 4])\n # (N, 2, 1) <- (N, 2, 4) x (N, 4, 1)\n cwh = tf.matmul(tmp, bbx)\n cwh = tf.div(1.0, cwh)\n # (N, 3, 1)\n cwh = tf.pad(cwh, paddings=((0, 0), (0, 1), (0, 0)), mode='CONSTANT', constant_values=1.0)\n # (3, 3)\n eye = tf.eye(3)\n # (N, 3, 3)\n eye = tf.broadcast_to(eye, shape=[batch_size, 3, 3])\n # (N, 3)\n cwh = tf.reshape(cwh, [batch_size, 3])\n # (N, 3, 3)\n cwh = tf.linalg.diag(cwh)\n \n # (2, 4)\n xym = tf.convert_to_tensor([[0.0, -1.0, 0.0, 0.0], \n [-1.0, 0.0, 0.0, 0.0]])\n # (N, 2, 4)\n xym = tf.broadcast_to(xym, shape=[batch_size, 2, 4])\n # (N, 2, 1) <- (N, 2, 4) x (N, 4, 1)\n xym = tf.matmul(xym, bbx)\n # (N, 3, 3)\n xym = tf.pad(xym, paddings=((0, 0), (0, 1), (2, 0)), mode='CONSTANT', constant_values=0.0)\n xym = eye + xym\n \n # (N, 3, 3) <- (N, 3, 3) x (N, 3, 3)\n trans = tf.matmul(cwh, xym)\n return trans","def center_crop_and_pad(data, coor, target_shape):\n target_shape = np.array(target_shape)\n\n start = coor - target_shape // 2\n end = start + target_shape\n\n # assert np.all(start >= 0)\n\n selector = [slice(s, e) for s, e in zip(start, end)]\n cropped = data[tuple(selector)]\n\n if target_shape is not None:\n\n if len(cropped.shape) > 3:\n target_shape = np.array(target_shape)\n delta = target_shape - cropped.shape[:-1]\n pre = delta // 2\n post = delta - delta // 2\n\n paddings = [] # no padding for batch\n paddings.extend(zip(pre, post))\n paddings.append((0, 0))\n cropped = np.pad(cropped, paddings, mode='constant')\n else:\n target_shape = np.array(target_shape)\n delta = target_shape - cropped.shape\n pre = delta // 2\n post = delta - delta // 2\n\n paddings = [] # no padding for batch\n paddings.extend(zip(pre, post))\n cropped = np.pad(cropped, paddings, mode='constant')\n\n return cropped","def crop(img):\n new_shape = min(img.shape[0], img.shape[1])\n \n return img[0:new_shape, 0:new_shape, ...]","def __crop_img(img, cx, cy, max_axis, padding=0):\n\n new_height = max_axis\n new_width = max_axis\n\n cy -= new_height // 2\n cx -= new_width // 2\n\n if (cy + new_height) > img.shape[0]:\n shift = (cy + new_height) - img.shape[0]\n cy -= shift\n\n if (cx + new_width) > img.shape[1]:\n shift = (cx + new_width) - img.shape[1]\n cx -= shift\n\n cy = max(0., cy)\n cx = max(0., cx)\n\n cx = padding if cx == 0 else cx\n cy = padding if cy == 0 else cy\n\n cropped_img = img[cy - padding:cy + new_height + padding, cx - padding:cx + new_width + padding, :]\n\n return cropped_img","def crop_to_center(tensor, border=50):\n return tensor[:, :, border:-border, border:-border]","def to_shape(arr, target_shape, constant_values=0):\n # pad\n padded = pad(arr, target_shape)\n # crop\n if padded.shape != target_shape:\n cropped = crop(padded, target_shape)\n else:\n cropped = padded\n return cropped","def get_crop_numpy(im: np.ndarray, pos: np.ndarray, sample_sz: np.ndarray, output_sz: np.ndarray = None,\n mode: str = 'constant', avg_chans=(0, 0, 0), max_scale_change=None):\n\n # if mode not in ['replicate', 'inside']:\n # raise ValueError('Unknown border mode \\'{}\\'.'.format(mode))\n\n # copy and convert\n posl = pos.astype(np.int).copy()\n\n # Get new sample size if forced inside the image\n if mode == 'inside' or mode == 'inside_major':\n pad_mode = 'replicate'\n # im_sz = torch.tensor([im.shape[2], im.shape[3]], device=im.device)\n # shrink_factor = (sample_sz.float() / im_sz)\n im_sz = np.array([im.shape[0], im.shape[1]])\n shrink_factor = (sample_sz.astype(np.float) / im_sz)\n if mode == 'inside':\n shrink_factor = shrink_factor.max()\n elif mode == 'inside_major':\n shrink_factor = shrink_factor.min()\n shrink_factor.clamp_(min=1, max=max_scale_change)\n # sample_sz = (sample_sz.float() / shrink_factor).long()\n sample_sz = (sample_sz.astype(np.float) / shrink_factor).astype(np.int)\n\n # Compute pre-downsampling factor\n if output_sz is not None:\n # resize_factor = torch.min(sample_sz.float() / output_sz.float()).item()\n resize_factor = np.min(sample_sz.astype(np.float) / output_sz.astype(np.float)).item()\n df = int(max(int(resize_factor - 0.1), 1))\n else:\n df = int(1)\n\n # sz = sample_sz.float() / df # new size\n sz = sample_sz.astype(np.float) / df\n\n # Do downsampling\n if df > 1:\n os = posl % df # offset\n posl = (posl - os) // df # new position\n im2 = im[os[0].item()::df, os[1].item()::df, :] # downsample\n else:\n im2 = im\n\n # compute size to crop\n # szl = torch.max(sz.round(), torch.tensor([2.0], dtype=sz.dtype, device=sz.device)).long()\n szl = np.maximum(np.round(sz), 2.0).astype(np.int)\n\n # Extract top and bottom coordinates\n tl = posl - (szl - 1) // 2\n br = posl + szl // 2 + 1\n\n # Shift the crop to inside\n if mode == 'inside' or mode == 'inside_major':\n # im2_sz = torch.LongTensor([im2.shape[2], im2.shape[3]])\n # shift = (-tl).clamp(0) - (br - im2_sz).clamp(0)\n im2_sz = np.array([im2.shape[0], im2.shape[1]], dtype=np.int)\n shift = np.clip(-tl, 0) - np.clip(br - im2_sz, 0)\n tl += shift\n br += shift\n\n # outside = ((-tl).clamp(0) + (br - im2_sz).clamp(0)) // 2\n # shift = (-tl - outside) * (outside > 0).long()\n outside = (np.clip(-tl, 0) - np.clip(br - im2_sz, 0)) // 2\n shift = (-tl - outside) * (outside > 0).astype(np.int)\n tl += shift\n br += shift\n\n # Get image patch\n # im_patch = im2[...,tl[0].item():br[0].item(),tl[1].item():br[1].item()]\n\n crop_xyxy = np.array([tl[1], tl[0], br[1], br[0]])\n # warpAffine transform matrix\n M_13 = crop_xyxy[0]\n M_23 = crop_xyxy[1]\n M_11 = (crop_xyxy[2] - M_13) / (output_sz[0] - 1)\n M_22 = (crop_xyxy[3] - M_23) / (output_sz[1] - 1)\n mat2x3 = np.array([\n M_11,\n 0,\n M_13,\n 0,\n M_22,\n M_23,\n ]).reshape(2, 3)\n im_patch = cv2.warpAffine(im2,\n mat2x3, (output_sz[0], output_sz[1]),\n flags=(cv2.INTER_LINEAR | cv2.WARP_INVERSE_MAP),\n borderMode=cv2.BORDER_CONSTANT,\n borderValue=tuple(map(int, avg_chans)))\n # Get image coordinates\n patch_coord = df * np.concatenate([tl, br]).reshape(1, 4)\n scale = output_sz / (np.array([br[1] - tl[1] + 1, br[0] - tl[0] + 1]) * df)\n return im_patch, patch_coord, scale","def scale_and_crop_x2(bayer):\n assert bayer.ndim == 2\n\n cropped = bayer.copy()\n width = cropped.shape[1]\n height = cropped.shape[0]\n\n cropped = cropped[height//4: height//4 + height//2, width//4: width//4 + width//2]\n #print(cropped[0:8, 0:8])\n cropped = nearest_up_x2(cropped)\n #print(cropped[0:8, 0:8])\n\n assert cropped.ndim == 2\n return cropped","def center_crop(img, output_size):\n if isinstance(output_size, numbers.Number):\n output_size = (int(output_size), int(output_size))\n image_height, image_width = img.shape[:2]\n crop_height, crop_width = output_size\n crop_top = int(round((image_height - crop_height) / 2.))\n crop_left = int(round((image_width - crop_width) / 2.))\n return crop(img, crop_top, crop_left, crop_height, crop_width)","def crop_around_center(image: np.ndarray, size: Union[tuple, list, np.ndarray]) -> np.ndarray:\n\n if type(size) is int and isinstance(size, str) and len(size) < 2:\n raise ValueError(\"size has to be a list or tuple or array with at least two int elements\")\n\n # For tensor processing\n image = _convert_tensor_to_numpy_if_possible(image)\n\n height = size[0]\n width = size[1]\n\n image_size = (image.shape[1], image.shape[0])\n image_center = (int(image_size[0] * 0.5), int(image_size[1] * 0.5))\n\n if width > image_size[0]:\n width = image_size[0]\n\n if height > image_size[1]:\n height = image_size[1]\n\n x1 = int(image_center[0] - width * 0.5)\n x2 = int(image_center[0] + width * 0.5)\n y1 = int(image_center[1] - height * 0.5)\n y2 = int(image_center[1] + height * 0.5)\n\n return image[y1:y2, x1:x2]","def crop(img, size, point=(0, 0)):\n y, x = point\n w, h = size\n hf, wf, _ = img.shape\n\n if not isinstance(x, int):\n y = min(int(wf * y), wf)\n x = min(int(hf * x), hf)\n\n if not isinstance(w, int):\n w = int(wf * w)\n h = int(hf * h)\n\n x2 = min(x + h, hf) - 1\n y2 = min(y + w, wf) - 1\n log.debug(\"w = %d, x2=%d, %s\" % (w, x2, img.shape))\n img2 = img[x:x2, y:y2, :].copy()\n return img2","def _zero_pad(self, kernel, size):\n if len(size) != kernel.ndim:\n size = kernel.shape[:1] + tuple(size) + kernel.shape[-1:]\n padsize = np.array(size) - np.array(kernel.shape)\n paddown = padsize // 2\n padup = padsize - paddown\n padarray = np.concatenate((padup[..., None],\n paddown[..., None]), axis=1)\n pads = tuple([tuple(p) for p in padarray])\n kernel_pad = np.pad(kernel, pads, 'constant', constant_values=0)\n return kernel_pad","def upsample_2d(x, size=(2, 2)):\n h, w, _ = x.get_shape().as_list()[1:]\n size_x, size_y = size\n output_h = h * size_x\n output_w = w * size_y\n return tf.image.resize_bilinear(x, (output_h, output_w), align_corners=None, name='upsampling')","def crop_image(X, patch_size=(8, 8)):\n (h, w) = np.shape(X)\n cropped_h = h - patch_size[0] + 1\n cropped_w = w - patch_size[1] + 1\n middle_linear_index = int(\n ((patch_size[0] / 2) * patch_size[1]) + (patch_size[1] / 2))\n columns = im2col(X, patch_size)\n return np.reshape(columns[middle_linear_index, :], [cropped_h, cropped_w])","def slice_image(im, target_size = [1024, 1024], padcolor = [126, 148, 137]):\n ncells = [0, 0]\n input_size = im.shape[:2]\n if len(im.shape) < 3:\n im = np.expand_dims(im, 2) # add a dummy dim for more streamlined code\n nchannels = im.shape[2]\n for dim in range(2):\n ncells[dim] = input_size[dim]//target_size[dim] + 1 #one extra\n imcanvas = np.zeros([a*b for a,b in zip(ncells,target_size)] + [nchannels], dtype = im.dtype)\n for channel in range(nchannels):\n imcanvas[:, :, channel] = padcolor[channel]\n \n imcanvas[:input_size[0], :input_size[1], :] = im\n imlist = []\n for cell_row in range(ncells[0]):\n for cell_col in range(ncells[1]):\n if nchannels > 1:\n imlist.append(imcanvas[cell_row*target_size[0]:(cell_row+1)*target_size[0], cell_col*target_size[1] : (cell_col+1)*target_size[1], :])\n else:\n imlist.append(imcanvas[cell_row*target_size[0]:(cell_row+1)*target_size[0], cell_col*target_size[1] : (cell_col+1)*target_size[1], 0])\n return (imlist, ncells)","def make_padding(kernel_size, stride, dilation):\n return -((-kernel_size - (kernel_size - 1) * (dilation - 1)) // stride + 1) // 2","def _reduced_kernel_size_for_small_input(input_tensor, kernel_size):\n shape = input_tensor.get_shape().as_list()\n if shape[1] is None or shape[2] is None:\n kernel_size_out = kernel_size\n else:\n kernel_size_out = [min(shape[1], kernel_size[0]),\n min(shape[2], kernel_size[1])]\n return kernel_size_out","def _reduced_kernel_size_for_small_input(input_tensor, kernel_size):\n shape = input_tensor.get_shape().as_list()\n if shape[1] is None or shape[2] is None:\n kernel_size_out = kernel_size\n else:\n kernel_size_out = [min(shape[1], kernel_size[0]),\n min(shape[2], kernel_size[1])]\n return kernel_size_out","def _vshifted_pool(x):\n x = ZeroPadding2D([[1,0],[0,0]])(x)\n x = Cropping2D([[0,1],[0,0]])(x)\n\n x = MaxPooling2D(pool_size=2,strides=2,padding='same')(x)\n\n return x","def crop(img, top, left, height, width, data_format='CHW'):\n _assert_image_tensor(img, data_format)\n\n if _is_channel_first(data_format):\n return img[:, top : top + height, left : left + width]\n else:\n return img[top : top + height, left : left + width, :]","def take_center(array,size):\n size /= 200\n Nc,Nx,Ny,Nz = array.shape\n nx,ny,nz = int(Nx/2), int(Ny/2),int(Nz/2)\n rx,ry,rz = math.ceil(Nx*size), math.ceil(Ny*size), math.ceil(Nz*size)\n reduced = array[:,nx-rx:nx+rx,ny-ry:ny+ry,nz-rz:nz+rz]\n #print(nx,ny,nz,rx,ry,rz)\n return reduced","def conv_output_shape(\n h_w: Tuple[int, int],\n kernel_size: int = 1,\n stride: int = 1,\n pad: int = 0,\n dilation: int = 1,\n ):\n h = floor(\n ((h_w[0] + (2 * pad) - (dilation * (kernel_size - 1)) - 1) / stride) + 1\n )\n w = floor(\n ((h_w[1] + (2 * pad) - (dilation * (kernel_size - 1)) - 1) / stride) + 1\n )\n return h, w","def _output_size_conv2d(conv, size):\n o_size = np.array(size) + 2 * np.array(conv.padding)\n o_size -= np.array(conv.dilation) * (np.array(conv.kernel_size) - 1)\n o_size -= 1\n o_size = o_size / np.array(conv.stride) + 1\n return np.floor(o_size)","def cutout(yx, half_size, shape=None):\n\n if shape is None:\n shape = (inf, inf)\n if not np.iterable(half_size):\n half_size = (half_size, half_size)\n\n s = np.s_[max(yx[0] - half_size[0], 0):\n min(yx[0] + half_size[0] + 1, shape[0]),\n max(yx[1] - half_size[1], 0):\n min(yx[1] + half_size[1] + 1, shape[1])]\n return s","def dilate_kernel(self, kernel, dilation):\n if dilation == 0:\n return kernel \n # inside padding based on the scaling law\n dilation = torch.tensor(dilation).float()\n delta = dilation%1\n\n d_in = torch.ceil(dilation**2).int()\n new_in = kernel.shape[2] + (kernel.shape[2]-1)*d_in\n\n d_h = torch.ceil(dilation).int()\n new_h = kernel.shape[3] + (kernel.shape[3]-1)*d_h\n\n d_w = torch.ceil(dilation).int()\n new_w = kernel.shape[4] + (kernel.shape[4]-1)*d_h\n\n new_kernel = torch.zeros(kernel.shape[0], kernel.shape[1], new_in, new_h, new_w)\n new_kernel[:,:,::(d_in+1),::(d_h+1), ::(d_w+1)] = kernel\n dilate_factor = 1\n \n new_kernel = F.pad(new_kernel, ((kernel.shape[4]-1)//2, (kernel.shape[4]-1)//2)*3)\n\n dilate_factor = (new_kernel.shape[-1] - 1 - (kernel.shape[4]-1)*(delta))/(new_kernel.shape[-1] - 1) \n\n grid = torch.meshgrid(torch.linspace(-1, 1, new_in)*(dilate_factor**2), \n torch.linspace(-1, 1, new_h)*dilate_factor, \n torch.linspace(-1, 1, new_w)*dilate_factor)\n\n grid = torch.cat([grid[2].unsqueeze(0).unsqueeze(-1), \n grid[1].unsqueeze(0).unsqueeze(-1), \n grid[0].unsqueeze(0).unsqueeze(-1)], dim = -1).repeat(kernel.shape[0],1,1,1,1)\n\n new_kernel = F.grid_sample(new_kernel, grid) \n \n return new_kernel[:,:,-kernel.shape[2]:]","def reshape_and_pad(image: np.ndarray, target_size: Union[List[int], Tuple[int]]):\r\n # input shapes\r\n input_shape = image.shape[:2]\r\n # calculate the width and height\r\n w_ratio = float(target_size[1]) / input_shape[1]\r\n h_ratio = float(target_size[0]) / input_shape[0]\r\n # take the smaller ratio to ensure the whole image fits in the new shape\r\n ratio = min(w_ratio, h_ratio)\r\n # calculate the new size\r\n new_size = tuple([int(x * ratio) for x in input_shape])\r\n\r\n # resize the image\r\n scaled_image = cv2.resize(image, (new_size[1], new_size[0]))\r\n\r\n # width and height differences\r\n delta_w = target_size[1] - new_size[1]\r\n delta_h = target_size[0] - new_size[0]\r\n\r\n # image position within the new image\r\n top, bottom = delta_h // 2, delta_h - (delta_h // 2)\r\n left, right = delta_w // 2, delta_w - (delta_w // 2)\r\n\r\n # padding color\r\n padding_color = [0, 0, 0]\r\n new_image = cv2.copyMakeBorder(scaled_image, top, bottom, left, right, cv2.BORDER_CONSTANT, value=padding_color)\r\n\r\n return new_image","def sobel(kernel_size: int = 3) -> Tensor:\n assert kernel_size % 2 == 1\n s = kernel_size // 2\n k = torch.linspace(-s, s, kernel_size)\n kernel = torch.stack([k] * kernel_size)\n k[k == 0] = 1e-7\n div = torch.stack([k] * kernel_size)\n return kernel / (div ** 2 + div.T ** 2)","def crop_output(self):\n s = self.kspace_data.shape\n npx_x, npx_y = s[1] / self.Q / 2, s[0] / self.Q / 2\n cx_left, cx_right = int(e.ceil(npx_x)), int(e.floor(npx_x))\n cy_top, cy_bottom = int(e.ceil(npx_y)), int(e.floor(npx_y))\n self.spatial_data = self.spatial_data[cy_top:-cy_bottom, cx_left:-cx_right]\n self.spatial_x = self.spatial_x[cx_left:-cx_right]\n self.spatial_y = self.spatial_y[cy_top:-cy_bottom]\n return self","def five_crop(img, size):\n if isinstance(size, numbers.Number):\n size = (int(size), int(size))\n else:\n assert len(size) == 2, \"Please provide only two dimensions (h, w) for size.\"\n\n # w, h = img.size\n h, w = img.shape[:2]\n crop_h, crop_w = size\n if crop_w > w or crop_h > h:\n raise ValueError(\"Requested crop size {} is bigger than input size {}\".format(size,\n (h, w)))\n # img[i:i+h, j:j+w]\n # tl = img.crop((0, 0, crop_w, crop_h))\n tl = img[0:0+crop_h, 0:0+crop_w]\n # tr = img.crop((w - crop_w, 0, w, crop_h))\n tr = img[0:0+crop_h, w-crop_w:]\n # bl = img.crop((0, h - crop_h, crop_w, h))\n bl = img[h-crop_h:, 0:0+crop_w]\n # br = img.crop((w - crop_w, h - crop_h, w, h))\n br = img[h-crop_h:,w-crop_w:]\n center = center_crop(img, (crop_h, crop_w))\n return (tl, tr, bl, br, center)","def get_laplacian_kernel2d(kernel_size: int) -> torch.Tensor:\n if not isinstance(kernel_size, int) or kernel_size % 2 == 0 or \\\n kernel_size <= 0:\n raise TypeError(\"ksize must be an odd positive integer. Got {}\"\n .format(kernel_size))\n\n kernel = torch.ones((kernel_size, kernel_size))\n mid = kernel_size // 2\n kernel[mid, mid] = 1 - kernel_size ** 2\n kernel_2d: torch.Tensor = kernel\n return kernel_2d","def _correct_pad(self, inputs, kernel_size):\n input_size = inputs.shape[1:3]\n if isinstance(kernel_size, int):\n kernel_size = (kernel_size, kernel_size)\n if input_size[0] is None:\n adjust = (1, 1)\n else:\n adjust = (1 - input_size[0] % 2, 1 - input_size[1] % 2)\n correct = (kernel_size[0] // 2, kernel_size[1] // 2)\n return (\n (correct[0] - adjust[0], correct[0]),\n (correct[1] - adjust[1], correct[1]),\n )","def row_conv2d(inputs, kernel, kernel_size, strides, output_shape, data_format=None):\n data_format = K.normalize_data_format(data_format)\n\n stride_row, stride_col = strides\n output_row, output_col = output_shape\n\n out = []\n for i in range(output_row):\n # Slice the rows with the neighbors they need\n slice_row = slice(i * stride_row, i * stride_col + kernel_size[0])\n if data_format == 'channels_first':\n x = inputs[:, :, slice_row, :] # batch, 16, 5, 144\n else:\n x = inputs[:, slice_row, :, :] # batch, 5, 144, 16\n # Convolve, resulting in an array with only one row: batch, 1, 140, 6 or batch, 6, 1, 140\n x = K.conv2d(x, kernel[i], strides=strides, padding='valid', data_format=data_format)\n out.append(x)\n\n if data_format == 'channels_first':\n output = K.concatenate(out, axis=2)\n else:\n output = K.concatenate(out, axis=1)\n del x\n del out\n return output","def _data_move_out_last_dim_be_one_block(tik_inst, dst, src, data_pos_info):\n\n sub_h_size, sub_w_size, c_size, h_size, w_size, out_offset = data_pos_info\n data_size_one_block = _get_elment_cnt_one_block(src.dtype)\n is_w_block_align = w_size % data_size_one_block == 0\n sub_w_block = sub_w_size // data_size_one_block\n sub_w_left = sub_w_size % data_size_one_block\n cw_block = c_size * w_size // data_size_one_block\n stride_len = cw_block - sub_w_block\n\n with tik_inst.if_scope(is_w_block_align):\n with tik_inst.if_scope(tik.any(stride_len > MTE_STRIDES, sub_h_size > REPEAT_LIMIT_MTE)):\n with tik_inst.for_range(0, sub_h_size) as sub_h_idx:\n tik_inst.data_move(dst[sub_h_idx * c_size * w_size + out_offset],\n src[sub_h_idx * sub_w_size],\n 0, 1, sub_w_block, 0, 0)\n with tik_inst.else_scope():\n tik_inst.data_move(dst[out_offset], src, 0, sub_h_size, sub_w_block, 0, stride_len)\n\n with tik_inst.else_scope():\n # suppose w_size > data_size_one_block\n with tik_inst.for_range(0, sub_h_size) as sub_h_idx:\n with tik_inst.if_scope(sub_w_block > 0):\n tik_inst.data_move(dst[sub_h_idx * c_size * w_size + out_offset],\n src[sub_h_idx * (sub_w_block + 1) * data_size_one_block],\n 0, 1, sub_w_block, 0, 0)\n with tik_inst.if_scope(sub_w_left > 0):\n tik_inst.data_move(dst[sub_h_idx * c_size * w_size + out_offset +\n sub_w_size - data_size_one_block],\n src[(sub_h_idx * (sub_w_block + 1) + sub_w_block) *\n data_size_one_block],\n 0, 1, 1, 0, 0)","def crop_vertical(img, scale=0.5, resize=(299,299), clip=0.5):\n img = imresize(img, scale)\n _img = np.copy(img)\n\n _img = equalize_adapthist(_img)\n _img = apply_adapted_thresh(_img)\n\n size = 7\n weights = np.ones((size,size))\n _img = convolve(_img, weights)\n _img[_img < size ** 2] = 0\n _img[_img >= size ** 2] = 1\n\n _img = canny(_img, sigma=5)\n\n theta_offset = 0.15\n n_buckets = 20\n theta = np.linspace(-theta_offset, theta_offset, n_buckets)\n lines = probabilistic_hough_line(_img, threshold=10, line_length=30, line_gap=5, theta=theta)\n\n left, right = split_segments_vertically(lines, img.shape)\n\n longest_left = find_longest_line(left, img.shape)\n longest_right = find_longest_line(right, img.shape)\n\n min_length = 0.5\n left_crop = 0\n right_crop = img.shape[1]\n\n if longest_left is not None and longest_left[0] >= img.shape[0] * min_length:\n left_crop = longest_left[-1]\n\n if longest_right is not None and longest_right[0] >= img.shape[0] * min_length:\n right_crop = longest_right[-1]\n\n\n _img = img[:int(img.shape[0]*clip), left_crop:right_crop]\n if resize:\n _img = imresize(_img, resize)\n\n return _img","def get_resized_sub_sample(self, off_x, off_y):\n sub_sample = self.original[off_y:off_y + self.eff_box_size, off_x:off_x + self.eff_box_size, :]\n new_size = (self.box_size, self.box_size)\n return cv2.resize(sub_sample, new_size)","def unfold(input, kernel_size: int, stride: int):\n shape = list(input.shape)\n length = shape.pop(-1)\n n_frames = math.ceil((max(length, kernel_size) - kernel_size) / stride) + 1\n tgt_length = (n_frames - 1) * stride + kernel_size\n padded = F.pad(input, (0, tgt_length - length)).contiguous()\n strides: tp.List[int] = []\n for dim in range(padded.dim()):\n strides.append(padded.stride(dim))\n assert strides.pop(-1) == 1, 'data should be contiguous'\n strides = strides + [stride, 1]\n return padded.as_strided(shape + [n_frames, kernel_size], strides)","def crop_bounding_box(im, x, y, w, h):\n return im[y:y+h, x:x+w]","def crop_like(src, tgt):\n src_sz = np.array(src.shape)\n tgt_sz = np.array(tgt.shape)\n\n # Assumes the spatial dimensions are the last two\n delta = (src_sz[2:4]-tgt_sz[2:4])\n crop = np.maximum(delta // 2, 0) # no negative crop\n crop2 = delta - crop\n\n if (crop > 0).any() or (crop2 > 0).any():\n # NOTE: convert to ints to enable static slicing in ONNX conversion\n src_sz = [int(x) for x in src_sz]\n crop = [int(x) for x in crop]\n crop2 = [int(x) for x in crop2]\n return src[..., crop[0]:src_sz[-2]-crop2[0],\n crop[1]:src_sz[-1]-crop2[1]]\n else:\n return src","def trim2DArray(input_arr, threshold=0): \n\n print(input_arr.shape)\n ul_x, ul_y = calculateTrimOffsetForward(input_arr, threshold)\n lr_x, lr_y = calculateTrimOffsetBackward(input_arr, threshold)\n\n output_arr = input_arr[ul_y:lr_y, ul_x:lr_x]\n\n print(output_arr.shape)\n\n return (output_arr)","def _central_crop(image, crop_size):\r\n shape = tf.shape(input=image)\r\n height, width = shape[0], shape[1]\r\n\r\n amount_to_be_cropped_h = (height - crop_size[0])\r\n crop_top = amount_to_be_cropped_h // 2\r\n amount_to_be_cropped_w = (width - crop_size[1])\r\n crop_left = amount_to_be_cropped_w // 2\r\n return tf.slice(\r\n image, [crop_top, crop_left, 0], [crop_size[0], crop_size[1], -1])","def modcrop(image, scale=3):\n if len(image.shape) == 3:\n h, w, _ = image.shape\n h = h - np.mod(h, scale)\n w = w - np.mod(w, scale)\n image = image[0:h, 0:w, :]\n else:\n h, w = image.shape\n h = h - np.mod(h, scale)\n w = w - np.mod(w, scale)\n image = image[0:h, 0:w]\n return image","def modcrop(image, scale=3):\n if len(image.shape) == 3:\n h, w, _ = image.shape\n h = h - np.mod(h, scale)\n w = w - np.mod(w, scale)\n image = image[0:h, 0:w, :]\n else:\n h, w = image.shape\n h = h - np.mod(h, scale)\n w = w - np.mod(w, scale)\n image = image[0:h, 0:w]\n return image","def cutout(\n target: str,\n shape: tuple = (5, 5),\n sector: Optional[int] = None,\n author: str = \"spoc\",\n provider: Optional[str] = None,\n images: Optional[int] = None,\n) -> TargetPixelFile:\n locresult = locate(target=target, sector=sector)\n if len(locresult) < 1:\n raise ValueError(\"Target not observed by TESS.\")\n if len(locresult) > 1:\n warnings.warn(\n f\"Target observed in {len(locresult)} sector(s): \"\n f\"{', '.join([str(r.sector) for r in locresult])}. \\n\"\n f\"Defaulting to sector {locresult[0].sector}. \"\n f\"You can change this by passing the `sector` argument.\",\n TessCloudWarning,\n )\n crd = locresult[0]\n imagelist = crd.list_images(author=author, provider=provider)\n if images:\n imagelist = imagelist[:images]\n\n return imagelist.cutout(column=crd.column, row=crd.row, shape=shape)","def blockshaped(input_suduko_2d):\n h = input_suduko_2d.shape[0]\n return (input_suduko_2d.reshape(h // 3, 3, -1, 3)\n .swapaxes(1, 2)\n .reshape(-1, 3, 3))","def remove_border(src): #---- remove blank border\r\n rows = src.shape[0]; VMIN= 0; VMAX= rows; \r\n cols = src.shape[0]; UMIN= 0; UMAX= cols;\r\n for ky in range(1,rows):\r\n sum0 = np.sum(src[ky,:,:]);\r\n sum1 = np.sum(src[rows-ky-1,:,:]);\r\n if sum0== 0 and VMIN== ky-1: VMIN= ky;\r\n if sum1== 0 and VMAX== rows-ky+1: VMAX= rows-ky;\r\n for kx in range(1,cols):\r\n sum0 = np.sum(src[:,kx,:]);\r\n sum1 = np.sum(src[:,cols-kx-1,:]);\r\n if sum0== 0 and UMIN== kx-1: UMIN= kx;\r\n if sum1== 0 and UMAX== cols-kx+1: UMAX= cols-kx;\r\n #--- --- \r\n DV = np.minimum(VMIN, rows-VMAX);\r\n DU = np.minimum(UMIN, cols-UMAX);\r\n return src[DV:(rows-DV), DU:(cols-DU), :];","def _crop(image, offset_height, offset_width, crop_height, crop_width):\n original_shape = tf.shape(image)\n\n rank_assertion = tf.Assert(\n tf.equal(tf.rank(image), 3),\n ['Rank of image must be equal to 3.'])\n with tf.control_dependencies([rank_assertion]):\n cropped_shape = tf.stack([crop_height, crop_width, original_shape[2]])\n\n size_assertion = tf.Assert(\n tf.logical_and(\n tf.greater_equal(original_shape[0], crop_height),\n tf.greater_equal(original_shape[1], crop_width)),\n ['Crop size greater than the image size.'])\n\n offsets = tf.to_int32(tf.stack([offset_height, offset_width, 0]))\n\n # Use tf.strided_slice instead of crop_to_bounding box as it accepts tensors\n # to define the crop size.\n with tf.control_dependencies([size_assertion]):\n image = tf.strided_slice(image, offsets, offsets + cropped_shape,\n strides=tf.ones_like(offsets))\n return tf.reshape(image, cropped_shape)","def conv_output_shape(h_w, kernel_size=1, stride=1, pad=0, dilation=1):\n from math import floor\n if type(kernel_size) is not tuple:\n kernel_size = (kernel_size, kernel_size)\n h = floor(((h_w[0] + (2 * pad) - (dilation *\n (kernel_size[0] - 1)) - 1) / stride) + 1)\n w = floor(((h_w[1] + (2 * pad) - (dilation *\n (kernel_size[1] - 1)) - 1) / stride) + 1)\n return h, w","def cutout_img(img, size=2):\n img_height, img_width, num_channels = (img.shape[0], img.shape[1],\n img.shape[2])\n assert len(img.shape) == 3\n mask, _, _ = create_cutout_mask(img_height, img_width, num_channels, size)\n return img * mask","def modcrop(image, scale=3):\n if len(image.shape) == 3:\n h, w, _ = image.shape\n h = h - np.mod(h, scale)\n w = w - np.mod(w, scale)\n image = image[0:h, 0:w, :]\n else:\n h, w = image.shape\n h = h - np.mod(h, scale)\n w = w - np.mod(w, scale)\n image = image[0:h, 0:w]\n return image","def crop_frame(frame):\n (h,w,c) = frame.shape\n return frame[int(h/2):h, 0:w]","def resize_and_pad(img, target_size): \n max_shape = np.max(img.shape)\n max_index = np.argmax(img.shape)\n if img.shape[1] < target_size and img.shape[2] < target_size: \n #interpolate\n factor = float(target_size)/max_shape\n scaled_img = misc.imresize(img, (max(1,int(img.shape[1]*factor)), max(1,int(img.shape[2]*factor))), interp=\"nearest\")\n else:\n #downsample maximum dimension to target size \n factor = max_shape/float(target_size)\n scaled_img = misc.imresize(img, (max(1,int(img.shape[1]/factor)), max(1,int(img.shape[2]/factor))), interp=\"nearest\") \n\n #fill up \n padded_image = np.stack([np.lib.pad(scaled_img[:,:,0], \n ((0,target_size-scaled_img.shape[0]),\n (0,target_size-scaled_img.shape[1])), \n 'constant', constant_values=(255)),\n np.lib.pad(scaled_img[:,:,1], \n ((0,target_size-scaled_img.shape[0]),\n (0,target_size-scaled_img.shape[1])), \n 'constant', constant_values=(255)),\n np.lib.pad(scaled_img[:,:,2], \n ((0,target_size-scaled_img.shape[0]),\n (0,target_size-scaled_img.shape[1])), \n 'constant', constant_values=(255))])\n \n \n return padded_image","def conv_output_shape(h_w, kernel_size=1, stride=1, pad=0, dilation=1):\n from math import floor\n if type(kernel_size) is not tuple:\n kernel_size = (kernel_size, kernel_size)\n\n if type(pad) is not tuple:\n pad = (pad, pad)\n\n h = floor(((h_w[0] + (2 * pad[0]) - (dilation * (kernel_size[0] - 1)) - 1) / stride) + 1)\n w = floor(((h_w[1] + (2 * pad[1]) - (dilation * (kernel_size[1] - 1)) - 1) / stride) + 1)\n return h, w","def crop_yeast(frame):\n frame = frame.astype(np.float)[:, 20:]\n frame = frame - frame.min()\n frame = frame / frame.max()\n\n E = convolve(frame, np.ones((21, 21)) / (21 ** 2), mode='mirror')\n var = convolve((frame - E) ** 2, np.ones((21, 21)) / (21 ** 2), mode='mirror')\n amx = np.argmax(var)\n cx, cy = amx // frame.shape[0], amx % frame.shape[1]\n start = max(0, cy + 20 - 64)\n stop = min(start + 128, frame.shape[1]) \n crop = frame[:, start:stop]\n return crop","def resize(im, target_size, max_size, stride=0, interpolation=cv2.INTER_LINEAR):\r\n im_shape = im.shape\r\n im_size_min = np.min(im_shape[0:2])\r\n im_size_max = np.max(im_shape[0:2])\r\n im_scale = float(target_size) / float(im_size_min)\r\n # prevent bigger axis from being more than max_size:\r\n if np.round(im_scale * im_size_max) > max_size:\r\n im_scale = float(max_size) / float(im_size_max)\r\n im = cv2.resize(im, None, None, fx=im_scale, fy=im_scale, interpolation=interpolation)\r\n\r\n if stride == 0:\r\n return im, im_scale\r\n else:\r\n # pad to product of stride\r\n im_height = int(np.ceil(im.shape[0] / float(stride)) * stride)\r\n im_width = int(np.ceil(im.shape[1] / float(stride)) * stride)\r\n im_channel = im.shape[2]\r\n padded_im = np.zeros((im_height, im_width, im_channel))\r\n padded_im[:im.shape[0], :im.shape[1], :] = im\r\n return padded_im, im_scale","def pad_image(img, target_size):\r\n rows_missing = target_size[0] - img.shape[2]\r\n cols_missing = target_size[1] - img.shape[3]\r\n padded_img = np.pad(img, ((0, 0), (0, 0), (0, rows_missing), (0, cols_missing)), 'constant')\r\n return padded_img","def doCrop(image, x, y, w, h):\n\tcrop_height = int((config.FACE_HEIGHT / float(config.FACE_WIDTH)) * w)\n\tmidy = y + h/2\n\ty1 = max(0, midy-crop_height/2)\n\ty2 = min(image.shape[0]-1, midy+crop_height/2)\n\treturn image[y1:y2, x:x+w]","def center_crop7(x):\n\n return x[:, :, 2:-2, 2:-2].contiguous()","def conv_output_shape(h_w, kernel_size=1, stride=1, pad=0, dilation=1):\n if type(kernel_size) is not tuple:\n kernel_size = (kernel_size, kernel_size)\n h = math.floor( ((h_w[0] + (2 * pad) - ( dilation * (kernel_size[0] - 1)\n ) - 1 )/ stride) + 1)\n w = math.floor( ((h_w[1] + (2 * pad) - ( dilation * (kernel_size[1] - 1)\n ) - 1 )/ stride) + 1)\n return h, w","def conv2D(inImage: np.ndarray, kernel2: np.ndarray) -> np.ndarray:\r\n flip_kernel = np.flipud(np.fliplr(kernel2))\r\n kernel_row = flip_kernel.shape[0]\r\n kernel_col = flip_kernel.shape[1]\r\n\r\n new_img = np.zeros_like(inImage)\r\n padded_img = padded_replicate(inImage, kernel_row, kernel_col)\r\n\r\n for x in range(inImage.shape[0]):\r\n for y in range(inImage.shape[1]):\r\n new_img[x, y] = (padded_img[x: x + kernel_col, y: y + kernel_row] * flip_kernel).sum()\r\n if flip_kernel.sum() != 0:\r\n new_img[x, y] /= flip_kernel.sum()\r\n\r\n return new_img","def upsample_filt(size):\r\n factor = (size + 1) // 2\r\n if size % 2 == 1:\r\n center = factor - 1\r\n else:\r\n center = factor - 0.5\r\n og = np.ogrid[:size, :size]\r\n return (1 - abs(og[0] - center) / factor) * \\\r\n (1 - abs(og[1] - center) / factor)","def resize(image_array, original_pixel_spacing_list, target_field_of_view_list=[140,140,80],\n target_pixel_spacing_list=[0.5,0.5,0.5], is_seg=False):\n import math\n\n num_dim = len(image_array.shape)\n compression_factor_list = [original_pixel_spacing_list[i]/target_pixel_spacing_list[i] for i in range(num_dim)]\n image_resampled = resample_by(image_array=image_array, compression_factor_list= compression_factor_list)\n current_dim_in_pixels_list = list(image_resampled.shape)\n target_dim_in_pixels_list = [int(target_field_of_view_list[i]/target_pixel_spacing_list[i]) for i in range(num_dim)]\n\n #pad or crop in xy (while ignoring z)\n if current_dim_in_pixels_list[0] >= target_dim_in_pixels_list[0]:\n print('Cropping image in xy')\n image_resampled_cropped = crop(image_array=image_resampled,\n final_dims_in_pixels=[target_dim_in_pixels_list[0],\n target_dim_in_pixels_list[1],\n current_dim_in_pixels_list[2]])\n else:\n print('Padding image in xy')\n image_resampled_cropped = pad(image_array=image_resampled,\n final_dims_in_pixels=[target_dim_in_pixels_list[0],\n target_dim_in_pixels_list[1],\n current_dim_in_pixels_list[2]])\n\n #pad or crop in z (while ignoring xy)\n current_dim_in_pixels_list = list(image_resampled_cropped.shape)\n\n if current_dim_in_pixels_list[2] >= target_dim_in_pixels_list[2]:\n print('Cropping image in z')\n image_resampled_cropped = crop(image_array=image_resampled_cropped,\n final_dims_in_pixels=[current_dim_in_pixels_list[0],\n current_dim_in_pixels_list[1],\n target_dim_in_pixels_list[2]])\n else:\n print('Padding image in z')\n image_resampled_cropped = pad(image_array=image_resampled_cropped,\n final_dims_in_pixels=[current_dim_in_pixels_list[0],\n current_dim_in_pixels_list[1],\n target_dim_in_pixels_list[2]])\n\n return(image_resampled_cropped)","def crop(self, N):\n self.data = self.data[:,:N]","def resize(im, target_size, max_size, stride=0, interpolation=cv2.INTER_LINEAR):\n im_shape = im.shape\n im_size_min = np.min(im_shape[0:2])\n im_size_max = np.max(im_shape[0:2])\n im_scale = float(target_size) / float(im_size_min)\n # prevent bigger axis from being more than max_size:\n if np.round(im_scale * im_size_max) > max_size:\n im_scale = float(max_size) / float(im_size_max)\n im = cv2.resize(im, None, None, fx=im_scale, fy=im_scale, interpolation=interpolation)\n\n if stride == 0:\n return im, im_scale\n else:\n # pad to product of stride\n im_height = int(np.ceil(im.shape[0] / float(stride)) * stride)\n im_width = int(np.ceil(im.shape[1] / float(stride)) * stride)\n im_channel = im.shape[2]\n padded_im = np.zeros((im_height, im_width, im_channel))\n padded_im[:im.shape[0], :im.shape[1], :] = im\n return padded_im, im_scale","def remove_center(X, N=10, border=2):\n \n #Translate X into original block shape\n X2 = X.reshape((len(X),N,N,6))\n\n # Decompose matrix as follows:\n \n # N N N N N N N N\n # N N N N N N N N\n # W W E E\n # W W E E\n # W W E E\n # W W E E\n # S S S S S S S S\n # S S S S S S S S\n \n # block is then flattened as well\n North = X2[:, :border].reshape(len(X), N*border*6)\n South = X2[:, -border:].reshape(len(X), N*border*6)\n West = X2[:, border:-border, :border].reshape(len(X), (N-2*border)*border*6)\n East = X2[:, border:-border, -border:].reshape(len(X), (N-2*border)*border*6)\n \n # Stack the vectors horizontally, e.g.\n # N_,1 .., N_x, W_1, .. W_x, E_1, .., E_x, S_1, .., S_x\n return np.hstack((North, West, East, South))","def resize(im, target_size, max_size, stride=0, interpolation=cv2.INTER_LINEAR):\n im_shape = im.shape\n im_size_min = np.min(im_shape[0:2])\n im_size_max = np.max(im_shape[0:2])\n im_scale = float(target_size) / float(im_size_min)\n # prevent bigger axis from being more than max_size:\n if np.round(im_scale * im_size_max) > max_size:\n im_scale = float(max_size) / float(im_size_max)\n im = cv2.resize(\n im, None, None, fx=im_scale, fy=im_scale, interpolation=interpolation\n )\n\n if stride == 0:\n return im, im_scale\n else:\n # pad to product of stride\n im_height = int(np.ceil(im.shape[0] / float(stride)) * stride)\n im_width = int(np.ceil(im.shape[1] / float(stride)) * stride)\n im_channel = im.shape[2]\n padded_im = np.zeros((im_height, im_width, im_channel))\n padded_im[: im.shape[0], : im.shape[1], :] = im\n return padded_im, im_scale","def crop_center(image: Matrix, crop_x: int, crop_y: int) -> Matrix:\n y, x = image.shape\n start_x = x // 2 - (crop_x // 2)\n start_y = y // 2 - (crop_y // 2)\n return image[start_y:start_y + crop_y, start_x:start_x + crop_x].copy()","def __resize_bilinear_with_target(self, x, target_size=None, data_format=None):\n target_height = target_size[0]\n target_width = target_size[1]\n if data_format == 'channels_first':\n new_shape = tf.constant(np.array((target_height, target_width)).astype('int32'))\n X = K.permute_dimensions(x, [0, 2, 3, 1])\n X = tf.image.resize_bilinear(X, new_shape)\n X = K.permute_dimensions(X, [0, 3, 1, 2])\n return X\n else:\n raise Exception(\"Invilid data format\", data_format)","def infer_kernel_sizes(h_sizes):\n kernel_sizes = copy(h_sizes)\n kernel_sizes.pop(-1) # pop last because it is the output of the last CNN layer\n kernel_sizes.insert(0, input_size) # the first kernel size should be (cnn_win_size, input_size)\n kernel_sizes = [(self.cnn_win_size, ks) for ks in kernel_sizes]\n return kernel_sizes","def cut_kernel(array, xpos, ypos, dist_from_point):\n\n if array.ndim != 2:\n raise IndexError('Cut kernel only allows 2D arrays.')\n\n kernel = np.zeros((dist_from_point*2+1, dist_from_point*2+1)) * np.nan\n\n if xpos - dist_from_point >= 0:\n xmin = 0\n xmindist = dist_from_point\n else:\n xmin = (xpos - dist_from_point) * -1\n xmindist = dist_from_point + (xpos - dist_from_point)\n\n if ypos - dist_from_point >= 0:\n ymin = 0\n ymindist = dist_from_point\n else:\n ymin = (ypos - dist_from_point) * -1\n ymindist = dist_from_point + (ypos - dist_from_point)\n\n if xpos + dist_from_point < array.shape[1]:\n xmax = kernel.shape[1]\n xmaxdist = dist_from_point + 1\n else:\n xmax = dist_from_point - (xpos - array.shape[1])\n xmaxdist = dist_from_point - (xpos + dist_from_point - array.shape[1])\n\n if ypos + dist_from_point < array.shape[0]:\n ymax = kernel.shape[0]\n ymaxdist = dist_from_point + 1\n else:\n ymax = dist_from_point - (ypos - array.shape[0])\n ymaxdist = dist_from_point - (ypos + dist_from_point - array.shape[0])\n\n cutk = array[ypos - ymindist: ypos + ymaxdist, xpos - xmindist: xpos + xmaxdist]\n\n\n kernel[ymin: ymax, xmin:xmax] = cutk\n\n return kernel","def crop_image(image):\r\n return image[40:-20, :]","def crop_resize_image(image: np.ndarray, size) -> np.ndarray:\n width, height = image.size\n if width > height:\n left = (width - height) / 2\n right = width - left\n top = 0\n bottom = height\n else:\n top = (height - width) / 2\n bottom = height - top\n left = 0\n right = width\n image = image.crop((left, top, right, bottom))\n image = image.resize(size, Image.ANTIALIAS)\n return image","def cropping_center(x, crop_shape, batch=False):\n orig_shape = x.shape\n if not batch:\n h0 = int((orig_shape[0] - crop_shape[0]) * 0.5)\n w0 = int((orig_shape[1] - crop_shape[1]) * 0.5)\n x = x[h0 : h0 + crop_shape[0], w0 : w0 + crop_shape[1]]\n else:\n h0 = int((orig_shape[1] - crop_shape[0]) * 0.5)\n w0 = int((orig_shape[2] - crop_shape[1]) * 0.5)\n x = x[:, h0 : h0 + crop_shape[0], w0 : w0 + crop_shape[1]]\n return x"],"string":"[\n \"def crop_to_target(x, target):\\n\\n if target.ndim==3:\\n t_h, t_w = target.shape[1], target.shape[2]\\n elif target.ndim==4:\\n t_h, t_w = target.shape[2], target.shape[3]\\n cr = int((x.shape[2] - t_h) / 2)\\n cc = int((x.shape[3] - t_w) / 2)\\n x_cropped = x[:, :, cr:cr + t_h, cc:cc + t_w]\\n return x_cropped\",\n \"def crop(X,size_crop=_size_crop):\\n b = size_crop//2\\n shape = tf.shape(X)\\n cx= shape[0]//2\\n cy= shape[1]//2\\n return X[cx-b:cx+b,cy-b:cy+b,...]\",\n \"def shrink_kernel(self, kernel, up_scale):\\n up_scale = torch.tensor(up_scale).float()\\n # boundary padding based on the scaling law\\n pad_in = (torch.ceil(up_scale**2).int())*((kernel.shape[2]-1)//2)\\n pad_h = (torch.ceil(up_scale).int())*((kernel.shape[3]-1)//2)\\n pad_w = (torch.ceil(up_scale).int())*((kernel.shape[4]-1)//2)\\n padded_kernel = F.pad(kernel, (pad_w, pad_w, pad_h, pad_h, pad_in, pad_in))\\n delta = up_scale%1\\n \\n if delta == 0:\\n shrink_factor = 1\\n else:\\n # shrink_factor for coordinates.\\n shrink_factor = (((kernel.shape[4]-1))/(padded_kernel.shape[-1]-1)*(up_scale+1))\\n \\n # Adjustment to deal with weird filtering on the grid sample function.\\n shrink_factor = 1.5*(shrink_factor-0.5)**3 + 0.57 \\n\\n grid = torch.meshgrid(torch.linspace(-1, 1, kernel.shape[2])*(shrink_factor**2),\\n torch.linspace(-1, 1, kernel.shape[3])*shrink_factor, \\n torch.linspace(-1, 1, kernel.shape[4])*shrink_factor)\\n\\n grid = torch.cat([grid[2].unsqueeze(0).unsqueeze(-1), \\n grid[1].unsqueeze(0).unsqueeze(-1), \\n grid[0].unsqueeze(0).unsqueeze(-1)], dim = -1).repeat(kernel.shape[0],1,1,1,1)\\n\\n new_kernel = F.grid_sample(padded_kernel, grid.to(device))\\n if kernel.shape[-1] - 2*up_scale > 0:\\n new_kernel = new_kernel * (kernel.shape[-1]**2/((kernel.shape[-1] - 2*up_scale)**2 + 0.01))\\n return new_kernel\",\n \"def shift_kernel(kernel, shape, centre):\\n h, w = kernel.shape\\n assert(h % 2 == 1)\\n assert(w % 2 == 1)\\n half_h = np.floor(h/2)\\n half_w = np.floor(w/2)\\n \\n result = np.zeros((shape[0]+2*half_h, shape[1]+2*half_w)) #zero pad to simplify edge handling \\n\\n ind_h = centre[0] + np.arange(0, 2*half_h+1, dtype='int') \\n ind_w = centre[1] + np.arange(0, 2*half_w+1, dtype='int')\\n result[ind_h[:,np.newaxis], ind_w] = kernel\\n result = result[half_h:-half_h,half_w:-half_w]\\n return result\",\n \"def crop(img: torch.Tensor, new_size: Union[int, Tuple[int, int]]) ->torch.Tensor:\\n new_size = to_tuple(new_size)\\n return F.center_crop(img, output_size=new_size)\",\n \"def upsampleImage( arr, kernelSize ):\\n return scipy.ndimage.zoom( arr, kernelSize )\",\n \"def slidekernelthroughdiagonal(kernel, matrix):\\n size_kernel = kernel.shape[0]\\n size_matrix = matrix.shape[0]\\n result = np.zeros([size_matrix])\\n for i in range(size_matrix):\\n # Calculate zero padding needed\\n padding_b = -min(i - int(size_kernel/2), 0)\\n padding_a = -min(size_matrix - int(i + size_kernel/2), 0)\\n matrix_selection = matrix[max(0, i-int(size_kernel/2)):min(size_matrix, i+int(size_kernel/2)),max(0, i-int(size_kernel/2)):min(size_matrix, i+int(size_kernel/2))]\\n matrix_padded = np.pad(matrix_selection, [(padding_b, padding_a), (padding_b, padding_a)])\\n result[i] = np.sum(matrix_padded*kernel)\\n return result\",\n \"def crop_and_resize(image, boxes, size):\\n box_ind = keras.backend.zeros_like(boxes, tensorflow.int32)\\n box_ind = box_ind[..., 0]\\n box_ind = keras.backend.reshape(box_ind, [-1])\\n\\n boxes = keras.backend.reshape(boxes, [-1, 4])\\n\\n return tensorflow.image.crop_and_resize(image, boxes, box_ind, size)\",\n \"def conv_matrix(matrix, kernel):\",\n \"def center_crop(img:np.array,output_size:List[int])->np.array:\\n\\n if isinstance(output_size,numbers.Number):\\n output_size = (int(output_size),int(output_size))\\n elif isinstance(output_size,(tuple,list)) and len(output_size)==1:\\n output_size =(output_size[0],output_size[0])\\n \\n image_height,image_width,_=img.shape\\n crop_height,crop_width=output_size\\n if crop_width > image_width or crop_height > image_height:\\n padding_ltrb = [\\n (crop_width - image_width) // 2 if crop_width > image_width else 0,\\n (crop_height - image_height) // 2 if crop_height > image_height else 0,\\n (crop_width - image_width + 1) // 2 if crop_width > image_width else 0,\\n (crop_height - image_height + 1) // 2 if crop_height > image_height else 0,\\n ]\\n img=cv.copyMakeBorder(img,padding_ltrb[1],padding_ltrb[3],padding_ltrb[0],padding_ltrb[2],cv.BORDER_CONSTANT,value=(0,0,0))\\n image_height,image_width,_=img.shape\\n if crop_width == image_width and crop_height == image_height:\\n return img\\n\\n crop_top = int(round((image_height - crop_height) / 2.))\\n crop_left = int(round((image_width - crop_width) / 2.))\\n return img[crop_top:crop_top+crop_height,crop_left:crop_left+crop_width]\",\n \"def crop_resize_img(img_path, target_size, crop_amount):\\n img = image.load_img(img_path)\\n x = image.img_to_array(img)\\n x = x[crop_amount:-crop_amount, crop_amount:-crop_amount, :]\\n ximg = Image.fromarray(np.uint8(x))\\n ximg_resize = ximg.resize((target_size[0], target_size[1]))\\n x = image.img_to_array(ximg_resize)\\n\\n return x\",\n \"def get_crop_torch(im: torch.Tensor, pos: torch.Tensor, sample_sz: torch.Tensor, output_sz: torch.Tensor = None,\\n mode: str = 'replicate', max_scale_change=None, is_mask=False):\\n\\n # if mode not in ['replicate', 'inside']:\\n # raise ValueError('Unknown border mode \\\\'{}\\\\'.'.format(mode))\\n\\n # copy and convert\\n posl = pos.long().clone()\\n\\n pad_mode = mode\\n\\n # Get new sample size if forced inside the image\\n if mode == 'inside' or mode == 'inside_major':\\n pad_mode = 'replicate'\\n im_sz = torch.tensor([im.shape[2], im.shape[3]], device=im.device)\\n shrink_factor = (sample_sz.float() / im_sz)\\n if mode == 'inside':\\n shrink_factor = shrink_factor.max()\\n elif mode == 'inside_major':\\n shrink_factor = shrink_factor.min()\\n shrink_factor.clamp_(min=1, max=max_scale_change)\\n sample_sz = (sample_sz.float() / shrink_factor).long()\\n\\n # Compute pre-downsampling factor\\n if output_sz is not None:\\n resize_factor = torch.min(sample_sz.float() / output_sz.float()).item()\\n df = int(max(int(resize_factor - 0.1), 1))\\n else:\\n df = int(1)\\n\\n sz = sample_sz.float() / df # new size\\n\\n # Do downsampling\\n if df > 1:\\n os = posl % df # offset\\n posl = (posl - os) // df # new position\\n im2 = im[..., os[0].item()::df, os[1].item()::df] # downsample\\n else:\\n im2 = im\\n\\n # compute size to crop\\n szl = torch.max(sz.round(), torch.tensor([2.0], dtype=sz.dtype, device=sz.device)).long()\\n\\n # Extract top and bottom coordinates\\n tl = posl - (szl - 1) // 2\\n br = posl + szl // 2 + 1\\n\\n # Shift the crop to inside\\n if mode == 'inside' or mode == 'inside_major':\\n im2_sz = torch.LongTensor([im2.shape[2], im2.shape[3]])\\n shift = (-tl).clamp(0) - (br - im2_sz).clamp(0)\\n tl += shift\\n br += shift\\n\\n outside = ((-tl).clamp(0) + (br - im2_sz).clamp(0)) // 2\\n shift = (-tl - outside) * (outside > 0).long()\\n tl += shift\\n br += shift\\n\\n # Get image patch\\n # im_patch = im2[...,tl[0].item():br[0].item(),tl[1].item():br[1].item()]\\n\\n\\n # Get image patch\\n if not is_mask:\\n im_patch = F.pad(im2, (-tl[1].item(), br[1].item() - im2.shape[3], -tl[0].item(), br[0].item() - im2.shape[2]),\\n mode=pad_mode)\\n else:\\n im_patch = F.pad(im2, (-tl[1].item(), br[1].item() - im2.shape[3], -tl[0].item(), br[0].item() - im2.shape[2]))\\n\\n # Get image coordinates\\n patch_coord = df * torch.cat((tl, br)).view(1, 4)\\n\\n scale = output_sz / (torch.tensor(im_patch.shape, device=im_patch.device)[-2:] * df)\\n\\n if output_sz is None or (im_patch.shape[-2] == output_sz[0] and im_patch.shape[-1] == output_sz[1]):\\n return im_patch.clone(), patch_coord, scale\\n\\n # Resample\\n if not is_mask:\\n im_patch = F.interpolate(im_patch, output_sz.long().tolist(), mode='bilinear')\\n else:\\n im_patch = F.interpolate(im_patch, output_sz.long().tolist(), mode='nearest')\\n\\n return im_patch, patch_coord, scale\",\n \"def center_crop(image, model_input_image_size):\\n im_size = image.get_shape().as_list()\\n target_height = model_input_image_size[0]\\n target_width = model_input_image_size[1]\\n if len(im_size) == 3:\\n return tf.image.resize_image_with_crop_or_pad(\\n image,\\n target_height=target_height,\\n target_width=target_width)\\n elif len(im_size) == 4:\\n time_split_image = tf.split(image, im_size[0], axis=0)\\n crops = []\\n for idx in range(len(time_split_image)):\\n it_crop = tf.image.resize_image_with_crop_or_pad(\\n tf.squeeze(time_split_image[idx], axis=0),\\n target_height=target_height,\\n target_width=target_width)\\n crops += [tf.expand_dims(it_crop, axis=0)]\\n return tf.concat(crops, axis=0)\\n else:\\n raise NotImplementedError\",\n \"def squeeze(x,kernel=3):\\n k = kernel // 2\\n idx = [list(i) for i in itertools.product(range(-k,k+1),range(-k,k+1))]\\n \\n xPad = torch.zeros(x.shape[0]+kernel-1,x.shape[1]+kernel-1)\\n xPad[k:-k,k:-k] = x\\n \\n xSqueezed = [torch.roll(xPad,shifts=(i[0],i[1]),dims=(0,1)) for i in idx]\\n xSqueezed = torch.stack(xSqueezed)\\n \\n return xSqueezed[:,k:-k,k:-k]\",\n \"def img_preprocess(self, img, output_size=64):\\n w, h = img.size\\n if w > h:\\n box_param = (int(w * 0.5 - h * 0.5), 0, int(w * 0.5 + h * 0.5), h)\\n cropped = img.crop(box_param)\\n else: # w < h\\n box_param = (0, int(h * 0.5 - w * 0.5), w, int(h * 0.5 + w * 0.5))\\n cropped = img.crop(box_param)\\n\\n resized = cropped.resize((output_size, output_size))\\n resized = np.asarray(resized)\\n\\n return resized\",\n \"def unfold(input, kernel_size, dilation=1, padding=0, stride=1):\\n nd_util = utils._ntuple(input.ndimension() - 2)\\n out = FunctionLib.apply(\\n 'Im2Col',\\n input.device,\\n [input],\\n kernel_shape=nd_util(kernel_size),\\n strides=nd_util(stride),\\n pads=nd_util(padding),\\n dilations=nd_util(dilation))\\n return out.flatten_(2)\",\n \"def crop(arr, target_shape):\\n arr_shape = arr.shape\\n ncrop = ()\\n for dim in range(len(arr_shape)):\\n diff = arr_shape[dim] - target_shape[dim]\\n if diff > 0:\\n start = int(diff / 2)\\n stop = start + target_shape[dim]\\n ncrop += np.index_exp[start:stop]\\n else:\\n ncrop += np.index_exp[:]\\n cropped = arr[ncrop]\\n return cropped\",\n \"def crop_and_downsample(originalX, downsample_size=32):\\n current_dim = 250\\n target_dim = 128\\n margin = int((current_dim - target_dim) / 2)\\n left_margin = margin\\n right_margin = current_dim - margin\\n\\n # newim is shape (6, 128, 128)\\n newim = originalX[:, left_margin:right_margin, left_margin:right_margin]\\n\\n # resized are shape (feature_width, feature_height, 3)\\n feature_width = feature_height = downsample_size\\n resized1 = imresize(newim[0:3, :, :], (feature_width, feature_height), interp=\\\"bicubic\\\", mode=\\\"RGB\\\")\\n resized2 = imresize(newim[3:6, :, :], (feature_width, feature_height), interp=\\\"bicubic\\\", mode=\\\"RGB\\\")\\n\\n # re-packge into a new X entry\\n newX = np.concatenate([resized1, resized2], axis=2)\\n\\n # the next line is EXTREMELY important.\\n # if you don't normalize your data, all predictions will be 0 forever.\\n newX = newX / 255.0\\n\\n return newX\",\n \"def crop_or_pad(img: torch.Tensor, new_size: Union[int, Tuple[int, int]]):\\n new_size = to_tuple(new_size)\\n if list(new_size) == list(img.shape[-2:]):\\n return img\\n img = pad(img, new_size)\\n img = crop(img, new_size)\\n return img\",\n \"def center_crop(img, output_size, data_format='CHW'):\\n _assert_image_tensor(img, data_format)\\n\\n if isinstance(output_size, numbers.Number):\\n output_size = (int(output_size), int(output_size))\\n\\n image_width, image_height = _get_image_size(img, data_format)\\n crop_height, crop_width = output_size\\n crop_top = int(round((image_height - crop_height) / 2.0))\\n crop_left = int(round((image_width - crop_width) / 2.0))\\n return crop(\\n img,\\n crop_top,\\n crop_left,\\n crop_height,\\n crop_width,\\n data_format=data_format,\\n )\",\n \"def crop_to_square(self, image):\\n orig_height, orig_width, orig_channels = image.shape\\n if orig_height > orig_width:\\n return image[:orig_width, ...]\\n elif orig_height < orig_width:\\n return image[:, :orig_height, ...]\\n return image\",\n \"def downsample2d(inputArray, kernelSize):\\n average_kernel = np.ones((kernelSize,kernelSize))\\n\\n blurred_array = sig.convolve2d(inputArray, average_kernel, mode='same')\\n downsampled_array = blurred_array[::kernelSize,::kernelSize]\\n return downsampled_array\",\n \"def crop_and_pad(data, offset, crop_shape, target_shape=None):\\n # Spatial dimensions only. All vars in zyx.\\n shape = np.array(data.shape[1:])\\n crop_shape = np.array(crop_shape)\\n offset = np.array(offset[::-1])\\n\\n start = shape // 2 - crop_shape // 2 + offset\\n end = start + crop_shape\\n\\n assert np.all(start >= 0)\\n\\n selector = [slice(s, e) for s, e in zip(start, end)]\\n selector = tuple([slice(None)] + selector)\\n cropped = data[selector]\\n\\n if target_shape is not None:\\n target_shape = np.array(target_shape)\\n delta = target_shape - crop_shape\\n pre = delta // 2\\n post = delta - delta // 2\\n\\n paddings = [(0, 0)] # no padding for batch\\n paddings.extend(zip(pre, post))\\n paddings.append((0, 0)) # no padding for channels\\n\\n cropped = np.pad(cropped, paddings, mode='constant')\\n\\n return cropped\",\n \"def unfold(input, kernel_size=(3,3), stride=(1,1), padding=(0,0)):\\n batch_size, in_chan, in_height, in_width = input.shape\\n \\n vals = ((0,0), (0,0), (padding[0], padding[0]), (padding[1], padding[1]))\\n input_padded = np.pad(input, vals, mode='constant')\\n\\n k, i, j = get_indices(input.shape, kernel_size, stride, padding)\\n \\n input_f = input_padded[:, k, i, j]\\n\\n input_f = input_f.transpose(1, 2, 0).reshape(in_chan*kernel_size[0]*kernel_size[1], -1)\\n return input_f\",\n \"def crop(img: 'np.ndarray', x: int, y: int, width: int, height: int) -> 'np.ndarray':\\n return img[y:y+height, x:x+width]\",\n \"def test_resize_outputs_crop(self, X: torch.Tensor, scale_factor: float):\\n xc, yc = resize_batch(X, X, scale_factor, 'crop', resize_targets=True)\\n assert torch.equal(xc, yc)\",\n \"def _crop_video(numpy_video, size, desired_size):\\r\\n\\r\\n w, h = size\\r\\n h1, h2 = int(h/2) - int(desired_size/2), int(h/2) + int(desired_size/2)\\r\\n w1, w2 = int(w/2) - int(desired_size/2), int(w/2) + int(desired_size/2)\\r\\n return numpy_video[:, :, h1:h2, w1:w2, :]\",\n \"def center_crop(image, source=(218, 178, 3), target=128):\\n height, width, channel = source\\n\\n off_h = np.ceil((height - target) / 2).astype(int)\\n off_w = np.ceil((width - target) / 2).astype(int)\\n return image[off_h: off_h+target, off_w: off_w+target, :]\",\n \"def get_crop_matrix_batch(bbx):\\n shape = tf.shape(bbx)\\n batch_size = shape[0]\\n bbx = tf.reshape(bbx, [batch_size, 4, 1])\\n\\n tmp = tf.convert_to_tensor([[0.0, -1.0, 0.0, 1.0], \\n [-1.0, 0.0, 1.0, 0.0]])\\n tmp = tf.broadcast_to(tmp, shape=[batch_size, 2, 4])\\n # (N, 2, 1) <- (N, 2, 4) x (N, 4, 1)\\n cwh = tf.matmul(tmp, bbx)\\n cwh = tf.div(1.0, cwh)\\n # (N, 3, 1)\\n cwh = tf.pad(cwh, paddings=((0, 0), (0, 1), (0, 0)), mode='CONSTANT', constant_values=1.0)\\n # (3, 3)\\n eye = tf.eye(3)\\n # (N, 3, 3)\\n eye = tf.broadcast_to(eye, shape=[batch_size, 3, 3])\\n # (N, 3)\\n cwh = tf.reshape(cwh, [batch_size, 3])\\n # (N, 3, 3)\\n cwh = tf.linalg.diag(cwh)\\n \\n # (2, 4)\\n xym = tf.convert_to_tensor([[0.0, -1.0, 0.0, 0.0], \\n [-1.0, 0.0, 0.0, 0.0]])\\n # (N, 2, 4)\\n xym = tf.broadcast_to(xym, shape=[batch_size, 2, 4])\\n # (N, 2, 1) <- (N, 2, 4) x (N, 4, 1)\\n xym = tf.matmul(xym, bbx)\\n # (N, 3, 3)\\n xym = tf.pad(xym, paddings=((0, 0), (0, 1), (2, 0)), mode='CONSTANT', constant_values=0.0)\\n xym = eye + xym\\n \\n # (N, 3, 3) <- (N, 3, 3) x (N, 3, 3)\\n trans = tf.matmul(cwh, xym)\\n return trans\",\n \"def center_crop_and_pad(data, coor, target_shape):\\n target_shape = np.array(target_shape)\\n\\n start = coor - target_shape // 2\\n end = start + target_shape\\n\\n # assert np.all(start >= 0)\\n\\n selector = [slice(s, e) for s, e in zip(start, end)]\\n cropped = data[tuple(selector)]\\n\\n if target_shape is not None:\\n\\n if len(cropped.shape) > 3:\\n target_shape = np.array(target_shape)\\n delta = target_shape - cropped.shape[:-1]\\n pre = delta // 2\\n post = delta - delta // 2\\n\\n paddings = [] # no padding for batch\\n paddings.extend(zip(pre, post))\\n paddings.append((0, 0))\\n cropped = np.pad(cropped, paddings, mode='constant')\\n else:\\n target_shape = np.array(target_shape)\\n delta = target_shape - cropped.shape\\n pre = delta // 2\\n post = delta - delta // 2\\n\\n paddings = [] # no padding for batch\\n paddings.extend(zip(pre, post))\\n cropped = np.pad(cropped, paddings, mode='constant')\\n\\n return cropped\",\n \"def crop(img):\\n new_shape = min(img.shape[0], img.shape[1])\\n \\n return img[0:new_shape, 0:new_shape, ...]\",\n \"def __crop_img(img, cx, cy, max_axis, padding=0):\\n\\n new_height = max_axis\\n new_width = max_axis\\n\\n cy -= new_height // 2\\n cx -= new_width // 2\\n\\n if (cy + new_height) > img.shape[0]:\\n shift = (cy + new_height) - img.shape[0]\\n cy -= shift\\n\\n if (cx + new_width) > img.shape[1]:\\n shift = (cx + new_width) - img.shape[1]\\n cx -= shift\\n\\n cy = max(0., cy)\\n cx = max(0., cx)\\n\\n cx = padding if cx == 0 else cx\\n cy = padding if cy == 0 else cy\\n\\n cropped_img = img[cy - padding:cy + new_height + padding, cx - padding:cx + new_width + padding, :]\\n\\n return cropped_img\",\n \"def crop_to_center(tensor, border=50):\\n return tensor[:, :, border:-border, border:-border]\",\n \"def to_shape(arr, target_shape, constant_values=0):\\n # pad\\n padded = pad(arr, target_shape)\\n # crop\\n if padded.shape != target_shape:\\n cropped = crop(padded, target_shape)\\n else:\\n cropped = padded\\n return cropped\",\n \"def get_crop_numpy(im: np.ndarray, pos: np.ndarray, sample_sz: np.ndarray, output_sz: np.ndarray = None,\\n mode: str = 'constant', avg_chans=(0, 0, 0), max_scale_change=None):\\n\\n # if mode not in ['replicate', 'inside']:\\n # raise ValueError('Unknown border mode \\\\'{}\\\\'.'.format(mode))\\n\\n # copy and convert\\n posl = pos.astype(np.int).copy()\\n\\n # Get new sample size if forced inside the image\\n if mode == 'inside' or mode == 'inside_major':\\n pad_mode = 'replicate'\\n # im_sz = torch.tensor([im.shape[2], im.shape[3]], device=im.device)\\n # shrink_factor = (sample_sz.float() / im_sz)\\n im_sz = np.array([im.shape[0], im.shape[1]])\\n shrink_factor = (sample_sz.astype(np.float) / im_sz)\\n if mode == 'inside':\\n shrink_factor = shrink_factor.max()\\n elif mode == 'inside_major':\\n shrink_factor = shrink_factor.min()\\n shrink_factor.clamp_(min=1, max=max_scale_change)\\n # sample_sz = (sample_sz.float() / shrink_factor).long()\\n sample_sz = (sample_sz.astype(np.float) / shrink_factor).astype(np.int)\\n\\n # Compute pre-downsampling factor\\n if output_sz is not None:\\n # resize_factor = torch.min(sample_sz.float() / output_sz.float()).item()\\n resize_factor = np.min(sample_sz.astype(np.float) / output_sz.astype(np.float)).item()\\n df = int(max(int(resize_factor - 0.1), 1))\\n else:\\n df = int(1)\\n\\n # sz = sample_sz.float() / df # new size\\n sz = sample_sz.astype(np.float) / df\\n\\n # Do downsampling\\n if df > 1:\\n os = posl % df # offset\\n posl = (posl - os) // df # new position\\n im2 = im[os[0].item()::df, os[1].item()::df, :] # downsample\\n else:\\n im2 = im\\n\\n # compute size to crop\\n # szl = torch.max(sz.round(), torch.tensor([2.0], dtype=sz.dtype, device=sz.device)).long()\\n szl = np.maximum(np.round(sz), 2.0).astype(np.int)\\n\\n # Extract top and bottom coordinates\\n tl = posl - (szl - 1) // 2\\n br = posl + szl // 2 + 1\\n\\n # Shift the crop to inside\\n if mode == 'inside' or mode == 'inside_major':\\n # im2_sz = torch.LongTensor([im2.shape[2], im2.shape[3]])\\n # shift = (-tl).clamp(0) - (br - im2_sz).clamp(0)\\n im2_sz = np.array([im2.shape[0], im2.shape[1]], dtype=np.int)\\n shift = np.clip(-tl, 0) - np.clip(br - im2_sz, 0)\\n tl += shift\\n br += shift\\n\\n # outside = ((-tl).clamp(0) + (br - im2_sz).clamp(0)) // 2\\n # shift = (-tl - outside) * (outside > 0).long()\\n outside = (np.clip(-tl, 0) - np.clip(br - im2_sz, 0)) // 2\\n shift = (-tl - outside) * (outside > 0).astype(np.int)\\n tl += shift\\n br += shift\\n\\n # Get image patch\\n # im_patch = im2[...,tl[0].item():br[0].item(),tl[1].item():br[1].item()]\\n\\n crop_xyxy = np.array([tl[1], tl[0], br[1], br[0]])\\n # warpAffine transform matrix\\n M_13 = crop_xyxy[0]\\n M_23 = crop_xyxy[1]\\n M_11 = (crop_xyxy[2] - M_13) / (output_sz[0] - 1)\\n M_22 = (crop_xyxy[3] - M_23) / (output_sz[1] - 1)\\n mat2x3 = np.array([\\n M_11,\\n 0,\\n M_13,\\n 0,\\n M_22,\\n M_23,\\n ]).reshape(2, 3)\\n im_patch = cv2.warpAffine(im2,\\n mat2x3, (output_sz[0], output_sz[1]),\\n flags=(cv2.INTER_LINEAR | cv2.WARP_INVERSE_MAP),\\n borderMode=cv2.BORDER_CONSTANT,\\n borderValue=tuple(map(int, avg_chans)))\\n # Get image coordinates\\n patch_coord = df * np.concatenate([tl, br]).reshape(1, 4)\\n scale = output_sz / (np.array([br[1] - tl[1] + 1, br[0] - tl[0] + 1]) * df)\\n return im_patch, patch_coord, scale\",\n \"def scale_and_crop_x2(bayer):\\n assert bayer.ndim == 2\\n\\n cropped = bayer.copy()\\n width = cropped.shape[1]\\n height = cropped.shape[0]\\n\\n cropped = cropped[height//4: height//4 + height//2, width//4: width//4 + width//2]\\n #print(cropped[0:8, 0:8])\\n cropped = nearest_up_x2(cropped)\\n #print(cropped[0:8, 0:8])\\n\\n assert cropped.ndim == 2\\n return cropped\",\n \"def center_crop(img, output_size):\\n if isinstance(output_size, numbers.Number):\\n output_size = (int(output_size), int(output_size))\\n image_height, image_width = img.shape[:2]\\n crop_height, crop_width = output_size\\n crop_top = int(round((image_height - crop_height) / 2.))\\n crop_left = int(round((image_width - crop_width) / 2.))\\n return crop(img, crop_top, crop_left, crop_height, crop_width)\",\n \"def crop_around_center(image: np.ndarray, size: Union[tuple, list, np.ndarray]) -> np.ndarray:\\n\\n if type(size) is int and isinstance(size, str) and len(size) < 2:\\n raise ValueError(\\\"size has to be a list or tuple or array with at least two int elements\\\")\\n\\n # For tensor processing\\n image = _convert_tensor_to_numpy_if_possible(image)\\n\\n height = size[0]\\n width = size[1]\\n\\n image_size = (image.shape[1], image.shape[0])\\n image_center = (int(image_size[0] * 0.5), int(image_size[1] * 0.5))\\n\\n if width > image_size[0]:\\n width = image_size[0]\\n\\n if height > image_size[1]:\\n height = image_size[1]\\n\\n x1 = int(image_center[0] - width * 0.5)\\n x2 = int(image_center[0] + width * 0.5)\\n y1 = int(image_center[1] - height * 0.5)\\n y2 = int(image_center[1] + height * 0.5)\\n\\n return image[y1:y2, x1:x2]\",\n \"def crop(img, size, point=(0, 0)):\\n y, x = point\\n w, h = size\\n hf, wf, _ = img.shape\\n\\n if not isinstance(x, int):\\n y = min(int(wf * y), wf)\\n x = min(int(hf * x), hf)\\n\\n if not isinstance(w, int):\\n w = int(wf * w)\\n h = int(hf * h)\\n\\n x2 = min(x + h, hf) - 1\\n y2 = min(y + w, wf) - 1\\n log.debug(\\\"w = %d, x2=%d, %s\\\" % (w, x2, img.shape))\\n img2 = img[x:x2, y:y2, :].copy()\\n return img2\",\n \"def _zero_pad(self, kernel, size):\\n if len(size) != kernel.ndim:\\n size = kernel.shape[:1] + tuple(size) + kernel.shape[-1:]\\n padsize = np.array(size) - np.array(kernel.shape)\\n paddown = padsize // 2\\n padup = padsize - paddown\\n padarray = np.concatenate((padup[..., None],\\n paddown[..., None]), axis=1)\\n pads = tuple([tuple(p) for p in padarray])\\n kernel_pad = np.pad(kernel, pads, 'constant', constant_values=0)\\n return kernel_pad\",\n \"def upsample_2d(x, size=(2, 2)):\\n h, w, _ = x.get_shape().as_list()[1:]\\n size_x, size_y = size\\n output_h = h * size_x\\n output_w = w * size_y\\n return tf.image.resize_bilinear(x, (output_h, output_w), align_corners=None, name='upsampling')\",\n \"def crop_image(X, patch_size=(8, 8)):\\n (h, w) = np.shape(X)\\n cropped_h = h - patch_size[0] + 1\\n cropped_w = w - patch_size[1] + 1\\n middle_linear_index = int(\\n ((patch_size[0] / 2) * patch_size[1]) + (patch_size[1] / 2))\\n columns = im2col(X, patch_size)\\n return np.reshape(columns[middle_linear_index, :], [cropped_h, cropped_w])\",\n \"def slice_image(im, target_size = [1024, 1024], padcolor = [126, 148, 137]):\\n ncells = [0, 0]\\n input_size = im.shape[:2]\\n if len(im.shape) < 3:\\n im = np.expand_dims(im, 2) # add a dummy dim for more streamlined code\\n nchannels = im.shape[2]\\n for dim in range(2):\\n ncells[dim] = input_size[dim]//target_size[dim] + 1 #one extra\\n imcanvas = np.zeros([a*b for a,b in zip(ncells,target_size)] + [nchannels], dtype = im.dtype)\\n for channel in range(nchannels):\\n imcanvas[:, :, channel] = padcolor[channel]\\n \\n imcanvas[:input_size[0], :input_size[1], :] = im\\n imlist = []\\n for cell_row in range(ncells[0]):\\n for cell_col in range(ncells[1]):\\n if nchannels > 1:\\n imlist.append(imcanvas[cell_row*target_size[0]:(cell_row+1)*target_size[0], cell_col*target_size[1] : (cell_col+1)*target_size[1], :])\\n else:\\n imlist.append(imcanvas[cell_row*target_size[0]:(cell_row+1)*target_size[0], cell_col*target_size[1] : (cell_col+1)*target_size[1], 0])\\n return (imlist, ncells)\",\n \"def make_padding(kernel_size, stride, dilation):\\n return -((-kernel_size - (kernel_size - 1) * (dilation - 1)) // stride + 1) // 2\",\n \"def _reduced_kernel_size_for_small_input(input_tensor, kernel_size):\\n shape = input_tensor.get_shape().as_list()\\n if shape[1] is None or shape[2] is None:\\n kernel_size_out = kernel_size\\n else:\\n kernel_size_out = [min(shape[1], kernel_size[0]),\\n min(shape[2], kernel_size[1])]\\n return kernel_size_out\",\n \"def _reduced_kernel_size_for_small_input(input_tensor, kernel_size):\\n shape = input_tensor.get_shape().as_list()\\n if shape[1] is None or shape[2] is None:\\n kernel_size_out = kernel_size\\n else:\\n kernel_size_out = [min(shape[1], kernel_size[0]),\\n min(shape[2], kernel_size[1])]\\n return kernel_size_out\",\n \"def _vshifted_pool(x):\\n x = ZeroPadding2D([[1,0],[0,0]])(x)\\n x = Cropping2D([[0,1],[0,0]])(x)\\n\\n x = MaxPooling2D(pool_size=2,strides=2,padding='same')(x)\\n\\n return x\",\n \"def crop(img, top, left, height, width, data_format='CHW'):\\n _assert_image_tensor(img, data_format)\\n\\n if _is_channel_first(data_format):\\n return img[:, top : top + height, left : left + width]\\n else:\\n return img[top : top + height, left : left + width, :]\",\n \"def take_center(array,size):\\n size /= 200\\n Nc,Nx,Ny,Nz = array.shape\\n nx,ny,nz = int(Nx/2), int(Ny/2),int(Nz/2)\\n rx,ry,rz = math.ceil(Nx*size), math.ceil(Ny*size), math.ceil(Nz*size)\\n reduced = array[:,nx-rx:nx+rx,ny-ry:ny+ry,nz-rz:nz+rz]\\n #print(nx,ny,nz,rx,ry,rz)\\n return reduced\",\n \"def conv_output_shape(\\n h_w: Tuple[int, int],\\n kernel_size: int = 1,\\n stride: int = 1,\\n pad: int = 0,\\n dilation: int = 1,\\n ):\\n h = floor(\\n ((h_w[0] + (2 * pad) - (dilation * (kernel_size - 1)) - 1) / stride) + 1\\n )\\n w = floor(\\n ((h_w[1] + (2 * pad) - (dilation * (kernel_size - 1)) - 1) / stride) + 1\\n )\\n return h, w\",\n \"def _output_size_conv2d(conv, size):\\n o_size = np.array(size) + 2 * np.array(conv.padding)\\n o_size -= np.array(conv.dilation) * (np.array(conv.kernel_size) - 1)\\n o_size -= 1\\n o_size = o_size / np.array(conv.stride) + 1\\n return np.floor(o_size)\",\n \"def cutout(yx, half_size, shape=None):\\n\\n if shape is None:\\n shape = (inf, inf)\\n if not np.iterable(half_size):\\n half_size = (half_size, half_size)\\n\\n s = np.s_[max(yx[0] - half_size[0], 0):\\n min(yx[0] + half_size[0] + 1, shape[0]),\\n max(yx[1] - half_size[1], 0):\\n min(yx[1] + half_size[1] + 1, shape[1])]\\n return s\",\n \"def dilate_kernel(self, kernel, dilation):\\n if dilation == 0:\\n return kernel \\n # inside padding based on the scaling law\\n dilation = torch.tensor(dilation).float()\\n delta = dilation%1\\n\\n d_in = torch.ceil(dilation**2).int()\\n new_in = kernel.shape[2] + (kernel.shape[2]-1)*d_in\\n\\n d_h = torch.ceil(dilation).int()\\n new_h = kernel.shape[3] + (kernel.shape[3]-1)*d_h\\n\\n d_w = torch.ceil(dilation).int()\\n new_w = kernel.shape[4] + (kernel.shape[4]-1)*d_h\\n\\n new_kernel = torch.zeros(kernel.shape[0], kernel.shape[1], new_in, new_h, new_w)\\n new_kernel[:,:,::(d_in+1),::(d_h+1), ::(d_w+1)] = kernel\\n dilate_factor = 1\\n \\n new_kernel = F.pad(new_kernel, ((kernel.shape[4]-1)//2, (kernel.shape[4]-1)//2)*3)\\n\\n dilate_factor = (new_kernel.shape[-1] - 1 - (kernel.shape[4]-1)*(delta))/(new_kernel.shape[-1] - 1) \\n\\n grid = torch.meshgrid(torch.linspace(-1, 1, new_in)*(dilate_factor**2), \\n torch.linspace(-1, 1, new_h)*dilate_factor, \\n torch.linspace(-1, 1, new_w)*dilate_factor)\\n\\n grid = torch.cat([grid[2].unsqueeze(0).unsqueeze(-1), \\n grid[1].unsqueeze(0).unsqueeze(-1), \\n grid[0].unsqueeze(0).unsqueeze(-1)], dim = -1).repeat(kernel.shape[0],1,1,1,1)\\n\\n new_kernel = F.grid_sample(new_kernel, grid) \\n \\n return new_kernel[:,:,-kernel.shape[2]:]\",\n \"def reshape_and_pad(image: np.ndarray, target_size: Union[List[int], Tuple[int]]):\\r\\n # input shapes\\r\\n input_shape = image.shape[:2]\\r\\n # calculate the width and height\\r\\n w_ratio = float(target_size[1]) / input_shape[1]\\r\\n h_ratio = float(target_size[0]) / input_shape[0]\\r\\n # take the smaller ratio to ensure the whole image fits in the new shape\\r\\n ratio = min(w_ratio, h_ratio)\\r\\n # calculate the new size\\r\\n new_size = tuple([int(x * ratio) for x in input_shape])\\r\\n\\r\\n # resize the image\\r\\n scaled_image = cv2.resize(image, (new_size[1], new_size[0]))\\r\\n\\r\\n # width and height differences\\r\\n delta_w = target_size[1] - new_size[1]\\r\\n delta_h = target_size[0] - new_size[0]\\r\\n\\r\\n # image position within the new image\\r\\n top, bottom = delta_h // 2, delta_h - (delta_h // 2)\\r\\n left, right = delta_w // 2, delta_w - (delta_w // 2)\\r\\n\\r\\n # padding color\\r\\n padding_color = [0, 0, 0]\\r\\n new_image = cv2.copyMakeBorder(scaled_image, top, bottom, left, right, cv2.BORDER_CONSTANT, value=padding_color)\\r\\n\\r\\n return new_image\",\n \"def sobel(kernel_size: int = 3) -> Tensor:\\n assert kernel_size % 2 == 1\\n s = kernel_size // 2\\n k = torch.linspace(-s, s, kernel_size)\\n kernel = torch.stack([k] * kernel_size)\\n k[k == 0] = 1e-7\\n div = torch.stack([k] * kernel_size)\\n return kernel / (div ** 2 + div.T ** 2)\",\n \"def crop_output(self):\\n s = self.kspace_data.shape\\n npx_x, npx_y = s[1] / self.Q / 2, s[0] / self.Q / 2\\n cx_left, cx_right = int(e.ceil(npx_x)), int(e.floor(npx_x))\\n cy_top, cy_bottom = int(e.ceil(npx_y)), int(e.floor(npx_y))\\n self.spatial_data = self.spatial_data[cy_top:-cy_bottom, cx_left:-cx_right]\\n self.spatial_x = self.spatial_x[cx_left:-cx_right]\\n self.spatial_y = self.spatial_y[cy_top:-cy_bottom]\\n return self\",\n \"def five_crop(img, size):\\n if isinstance(size, numbers.Number):\\n size = (int(size), int(size))\\n else:\\n assert len(size) == 2, \\\"Please provide only two dimensions (h, w) for size.\\\"\\n\\n # w, h = img.size\\n h, w = img.shape[:2]\\n crop_h, crop_w = size\\n if crop_w > w or crop_h > h:\\n raise ValueError(\\\"Requested crop size {} is bigger than input size {}\\\".format(size,\\n (h, w)))\\n # img[i:i+h, j:j+w]\\n # tl = img.crop((0, 0, crop_w, crop_h))\\n tl = img[0:0+crop_h, 0:0+crop_w]\\n # tr = img.crop((w - crop_w, 0, w, crop_h))\\n tr = img[0:0+crop_h, w-crop_w:]\\n # bl = img.crop((0, h - crop_h, crop_w, h))\\n bl = img[h-crop_h:, 0:0+crop_w]\\n # br = img.crop((w - crop_w, h - crop_h, w, h))\\n br = img[h-crop_h:,w-crop_w:]\\n center = center_crop(img, (crop_h, crop_w))\\n return (tl, tr, bl, br, center)\",\n \"def get_laplacian_kernel2d(kernel_size: int) -> torch.Tensor:\\n if not isinstance(kernel_size, int) or kernel_size % 2 == 0 or \\\\\\n kernel_size <= 0:\\n raise TypeError(\\\"ksize must be an odd positive integer. Got {}\\\"\\n .format(kernel_size))\\n\\n kernel = torch.ones((kernel_size, kernel_size))\\n mid = kernel_size // 2\\n kernel[mid, mid] = 1 - kernel_size ** 2\\n kernel_2d: torch.Tensor = kernel\\n return kernel_2d\",\n \"def _correct_pad(self, inputs, kernel_size):\\n input_size = inputs.shape[1:3]\\n if isinstance(kernel_size, int):\\n kernel_size = (kernel_size, kernel_size)\\n if input_size[0] is None:\\n adjust = (1, 1)\\n else:\\n adjust = (1 - input_size[0] % 2, 1 - input_size[1] % 2)\\n correct = (kernel_size[0] // 2, kernel_size[1] // 2)\\n return (\\n (correct[0] - adjust[0], correct[0]),\\n (correct[1] - adjust[1], correct[1]),\\n )\",\n \"def row_conv2d(inputs, kernel, kernel_size, strides, output_shape, data_format=None):\\n data_format = K.normalize_data_format(data_format)\\n\\n stride_row, stride_col = strides\\n output_row, output_col = output_shape\\n\\n out = []\\n for i in range(output_row):\\n # Slice the rows with the neighbors they need\\n slice_row = slice(i * stride_row, i * stride_col + kernel_size[0])\\n if data_format == 'channels_first':\\n x = inputs[:, :, slice_row, :] # batch, 16, 5, 144\\n else:\\n x = inputs[:, slice_row, :, :] # batch, 5, 144, 16\\n # Convolve, resulting in an array with only one row: batch, 1, 140, 6 or batch, 6, 1, 140\\n x = K.conv2d(x, kernel[i], strides=strides, padding='valid', data_format=data_format)\\n out.append(x)\\n\\n if data_format == 'channels_first':\\n output = K.concatenate(out, axis=2)\\n else:\\n output = K.concatenate(out, axis=1)\\n del x\\n del out\\n return output\",\n \"def _data_move_out_last_dim_be_one_block(tik_inst, dst, src, data_pos_info):\\n\\n sub_h_size, sub_w_size, c_size, h_size, w_size, out_offset = data_pos_info\\n data_size_one_block = _get_elment_cnt_one_block(src.dtype)\\n is_w_block_align = w_size % data_size_one_block == 0\\n sub_w_block = sub_w_size // data_size_one_block\\n sub_w_left = sub_w_size % data_size_one_block\\n cw_block = c_size * w_size // data_size_one_block\\n stride_len = cw_block - sub_w_block\\n\\n with tik_inst.if_scope(is_w_block_align):\\n with tik_inst.if_scope(tik.any(stride_len > MTE_STRIDES, sub_h_size > REPEAT_LIMIT_MTE)):\\n with tik_inst.for_range(0, sub_h_size) as sub_h_idx:\\n tik_inst.data_move(dst[sub_h_idx * c_size * w_size + out_offset],\\n src[sub_h_idx * sub_w_size],\\n 0, 1, sub_w_block, 0, 0)\\n with tik_inst.else_scope():\\n tik_inst.data_move(dst[out_offset], src, 0, sub_h_size, sub_w_block, 0, stride_len)\\n\\n with tik_inst.else_scope():\\n # suppose w_size > data_size_one_block\\n with tik_inst.for_range(0, sub_h_size) as sub_h_idx:\\n with tik_inst.if_scope(sub_w_block > 0):\\n tik_inst.data_move(dst[sub_h_idx * c_size * w_size + out_offset],\\n src[sub_h_idx * (sub_w_block + 1) * data_size_one_block],\\n 0, 1, sub_w_block, 0, 0)\\n with tik_inst.if_scope(sub_w_left > 0):\\n tik_inst.data_move(dst[sub_h_idx * c_size * w_size + out_offset +\\n sub_w_size - data_size_one_block],\\n src[(sub_h_idx * (sub_w_block + 1) + sub_w_block) *\\n data_size_one_block],\\n 0, 1, 1, 0, 0)\",\n \"def crop_vertical(img, scale=0.5, resize=(299,299), clip=0.5):\\n img = imresize(img, scale)\\n _img = np.copy(img)\\n\\n _img = equalize_adapthist(_img)\\n _img = apply_adapted_thresh(_img)\\n\\n size = 7\\n weights = np.ones((size,size))\\n _img = convolve(_img, weights)\\n _img[_img < size ** 2] = 0\\n _img[_img >= size ** 2] = 1\\n\\n _img = canny(_img, sigma=5)\\n\\n theta_offset = 0.15\\n n_buckets = 20\\n theta = np.linspace(-theta_offset, theta_offset, n_buckets)\\n lines = probabilistic_hough_line(_img, threshold=10, line_length=30, line_gap=5, theta=theta)\\n\\n left, right = split_segments_vertically(lines, img.shape)\\n\\n longest_left = find_longest_line(left, img.shape)\\n longest_right = find_longest_line(right, img.shape)\\n\\n min_length = 0.5\\n left_crop = 0\\n right_crop = img.shape[1]\\n\\n if longest_left is not None and longest_left[0] >= img.shape[0] * min_length:\\n left_crop = longest_left[-1]\\n\\n if longest_right is not None and longest_right[0] >= img.shape[0] * min_length:\\n right_crop = longest_right[-1]\\n\\n\\n _img = img[:int(img.shape[0]*clip), left_crop:right_crop]\\n if resize:\\n _img = imresize(_img, resize)\\n\\n return _img\",\n \"def get_resized_sub_sample(self, off_x, off_y):\\n sub_sample = self.original[off_y:off_y + self.eff_box_size, off_x:off_x + self.eff_box_size, :]\\n new_size = (self.box_size, self.box_size)\\n return cv2.resize(sub_sample, new_size)\",\n \"def unfold(input, kernel_size: int, stride: int):\\n shape = list(input.shape)\\n length = shape.pop(-1)\\n n_frames = math.ceil((max(length, kernel_size) - kernel_size) / stride) + 1\\n tgt_length = (n_frames - 1) * stride + kernel_size\\n padded = F.pad(input, (0, tgt_length - length)).contiguous()\\n strides: tp.List[int] = []\\n for dim in range(padded.dim()):\\n strides.append(padded.stride(dim))\\n assert strides.pop(-1) == 1, 'data should be contiguous'\\n strides = strides + [stride, 1]\\n return padded.as_strided(shape + [n_frames, kernel_size], strides)\",\n \"def crop_bounding_box(im, x, y, w, h):\\n return im[y:y+h, x:x+w]\",\n \"def crop_like(src, tgt):\\n src_sz = np.array(src.shape)\\n tgt_sz = np.array(tgt.shape)\\n\\n # Assumes the spatial dimensions are the last two\\n delta = (src_sz[2:4]-tgt_sz[2:4])\\n crop = np.maximum(delta // 2, 0) # no negative crop\\n crop2 = delta - crop\\n\\n if (crop > 0).any() or (crop2 > 0).any():\\n # NOTE: convert to ints to enable static slicing in ONNX conversion\\n src_sz = [int(x) for x in src_sz]\\n crop = [int(x) for x in crop]\\n crop2 = [int(x) for x in crop2]\\n return src[..., crop[0]:src_sz[-2]-crop2[0],\\n crop[1]:src_sz[-1]-crop2[1]]\\n else:\\n return src\",\n \"def trim2DArray(input_arr, threshold=0): \\n\\n print(input_arr.shape)\\n ul_x, ul_y = calculateTrimOffsetForward(input_arr, threshold)\\n lr_x, lr_y = calculateTrimOffsetBackward(input_arr, threshold)\\n\\n output_arr = input_arr[ul_y:lr_y, ul_x:lr_x]\\n\\n print(output_arr.shape)\\n\\n return (output_arr)\",\n \"def _central_crop(image, crop_size):\\r\\n shape = tf.shape(input=image)\\r\\n height, width = shape[0], shape[1]\\r\\n\\r\\n amount_to_be_cropped_h = (height - crop_size[0])\\r\\n crop_top = amount_to_be_cropped_h // 2\\r\\n amount_to_be_cropped_w = (width - crop_size[1])\\r\\n crop_left = amount_to_be_cropped_w // 2\\r\\n return tf.slice(\\r\\n image, [crop_top, crop_left, 0], [crop_size[0], crop_size[1], -1])\",\n \"def modcrop(image, scale=3):\\n if len(image.shape) == 3:\\n h, w, _ = image.shape\\n h = h - np.mod(h, scale)\\n w = w - np.mod(w, scale)\\n image = image[0:h, 0:w, :]\\n else:\\n h, w = image.shape\\n h = h - np.mod(h, scale)\\n w = w - np.mod(w, scale)\\n image = image[0:h, 0:w]\\n return image\",\n \"def modcrop(image, scale=3):\\n if len(image.shape) == 3:\\n h, w, _ = image.shape\\n h = h - np.mod(h, scale)\\n w = w - np.mod(w, scale)\\n image = image[0:h, 0:w, :]\\n else:\\n h, w = image.shape\\n h = h - np.mod(h, scale)\\n w = w - np.mod(w, scale)\\n image = image[0:h, 0:w]\\n return image\",\n \"def cutout(\\n target: str,\\n shape: tuple = (5, 5),\\n sector: Optional[int] = None,\\n author: str = \\\"spoc\\\",\\n provider: Optional[str] = None,\\n images: Optional[int] = None,\\n) -> TargetPixelFile:\\n locresult = locate(target=target, sector=sector)\\n if len(locresult) < 1:\\n raise ValueError(\\\"Target not observed by TESS.\\\")\\n if len(locresult) > 1:\\n warnings.warn(\\n f\\\"Target observed in {len(locresult)} sector(s): \\\"\\n f\\\"{', '.join([str(r.sector) for r in locresult])}. \\\\n\\\"\\n f\\\"Defaulting to sector {locresult[0].sector}. \\\"\\n f\\\"You can change this by passing the `sector` argument.\\\",\\n TessCloudWarning,\\n )\\n crd = locresult[0]\\n imagelist = crd.list_images(author=author, provider=provider)\\n if images:\\n imagelist = imagelist[:images]\\n\\n return imagelist.cutout(column=crd.column, row=crd.row, shape=shape)\",\n \"def blockshaped(input_suduko_2d):\\n h = input_suduko_2d.shape[0]\\n return (input_suduko_2d.reshape(h // 3, 3, -1, 3)\\n .swapaxes(1, 2)\\n .reshape(-1, 3, 3))\",\n \"def remove_border(src): #---- remove blank border\\r\\n rows = src.shape[0]; VMIN= 0; VMAX= rows; \\r\\n cols = src.shape[0]; UMIN= 0; UMAX= cols;\\r\\n for ky in range(1,rows):\\r\\n sum0 = np.sum(src[ky,:,:]);\\r\\n sum1 = np.sum(src[rows-ky-1,:,:]);\\r\\n if sum0== 0 and VMIN== ky-1: VMIN= ky;\\r\\n if sum1== 0 and VMAX== rows-ky+1: VMAX= rows-ky;\\r\\n for kx in range(1,cols):\\r\\n sum0 = np.sum(src[:,kx,:]);\\r\\n sum1 = np.sum(src[:,cols-kx-1,:]);\\r\\n if sum0== 0 and UMIN== kx-1: UMIN= kx;\\r\\n if sum1== 0 and UMAX== cols-kx+1: UMAX= cols-kx;\\r\\n #--- --- \\r\\n DV = np.minimum(VMIN, rows-VMAX);\\r\\n DU = np.minimum(UMIN, cols-UMAX);\\r\\n return src[DV:(rows-DV), DU:(cols-DU), :];\",\n \"def _crop(image, offset_height, offset_width, crop_height, crop_width):\\n original_shape = tf.shape(image)\\n\\n rank_assertion = tf.Assert(\\n tf.equal(tf.rank(image), 3),\\n ['Rank of image must be equal to 3.'])\\n with tf.control_dependencies([rank_assertion]):\\n cropped_shape = tf.stack([crop_height, crop_width, original_shape[2]])\\n\\n size_assertion = tf.Assert(\\n tf.logical_and(\\n tf.greater_equal(original_shape[0], crop_height),\\n tf.greater_equal(original_shape[1], crop_width)),\\n ['Crop size greater than the image size.'])\\n\\n offsets = tf.to_int32(tf.stack([offset_height, offset_width, 0]))\\n\\n # Use tf.strided_slice instead of crop_to_bounding box as it accepts tensors\\n # to define the crop size.\\n with tf.control_dependencies([size_assertion]):\\n image = tf.strided_slice(image, offsets, offsets + cropped_shape,\\n strides=tf.ones_like(offsets))\\n return tf.reshape(image, cropped_shape)\",\n \"def conv_output_shape(h_w, kernel_size=1, stride=1, pad=0, dilation=1):\\n from math import floor\\n if type(kernel_size) is not tuple:\\n kernel_size = (kernel_size, kernel_size)\\n h = floor(((h_w[0] + (2 * pad) - (dilation *\\n (kernel_size[0] - 1)) - 1) / stride) + 1)\\n w = floor(((h_w[1] + (2 * pad) - (dilation *\\n (kernel_size[1] - 1)) - 1) / stride) + 1)\\n return h, w\",\n \"def cutout_img(img, size=2):\\n img_height, img_width, num_channels = (img.shape[0], img.shape[1],\\n img.shape[2])\\n assert len(img.shape) == 3\\n mask, _, _ = create_cutout_mask(img_height, img_width, num_channels, size)\\n return img * mask\",\n \"def modcrop(image, scale=3):\\n if len(image.shape) == 3:\\n h, w, _ = image.shape\\n h = h - np.mod(h, scale)\\n w = w - np.mod(w, scale)\\n image = image[0:h, 0:w, :]\\n else:\\n h, w = image.shape\\n h = h - np.mod(h, scale)\\n w = w - np.mod(w, scale)\\n image = image[0:h, 0:w]\\n return image\",\n \"def crop_frame(frame):\\n (h,w,c) = frame.shape\\n return frame[int(h/2):h, 0:w]\",\n \"def resize_and_pad(img, target_size): \\n max_shape = np.max(img.shape)\\n max_index = np.argmax(img.shape)\\n if img.shape[1] < target_size and img.shape[2] < target_size: \\n #interpolate\\n factor = float(target_size)/max_shape\\n scaled_img = misc.imresize(img, (max(1,int(img.shape[1]*factor)), max(1,int(img.shape[2]*factor))), interp=\\\"nearest\\\")\\n else:\\n #downsample maximum dimension to target size \\n factor = max_shape/float(target_size)\\n scaled_img = misc.imresize(img, (max(1,int(img.shape[1]/factor)), max(1,int(img.shape[2]/factor))), interp=\\\"nearest\\\") \\n\\n #fill up \\n padded_image = np.stack([np.lib.pad(scaled_img[:,:,0], \\n ((0,target_size-scaled_img.shape[0]),\\n (0,target_size-scaled_img.shape[1])), \\n 'constant', constant_values=(255)),\\n np.lib.pad(scaled_img[:,:,1], \\n ((0,target_size-scaled_img.shape[0]),\\n (0,target_size-scaled_img.shape[1])), \\n 'constant', constant_values=(255)),\\n np.lib.pad(scaled_img[:,:,2], \\n ((0,target_size-scaled_img.shape[0]),\\n (0,target_size-scaled_img.shape[1])), \\n 'constant', constant_values=(255))])\\n \\n \\n return padded_image\",\n \"def conv_output_shape(h_w, kernel_size=1, stride=1, pad=0, dilation=1):\\n from math import floor\\n if type(kernel_size) is not tuple:\\n kernel_size = (kernel_size, kernel_size)\\n\\n if type(pad) is not tuple:\\n pad = (pad, pad)\\n\\n h = floor(((h_w[0] + (2 * pad[0]) - (dilation * (kernel_size[0] - 1)) - 1) / stride) + 1)\\n w = floor(((h_w[1] + (2 * pad[1]) - (dilation * (kernel_size[1] - 1)) - 1) / stride) + 1)\\n return h, w\",\n \"def crop_yeast(frame):\\n frame = frame.astype(np.float)[:, 20:]\\n frame = frame - frame.min()\\n frame = frame / frame.max()\\n\\n E = convolve(frame, np.ones((21, 21)) / (21 ** 2), mode='mirror')\\n var = convolve((frame - E) ** 2, np.ones((21, 21)) / (21 ** 2), mode='mirror')\\n amx = np.argmax(var)\\n cx, cy = amx // frame.shape[0], amx % frame.shape[1]\\n start = max(0, cy + 20 - 64)\\n stop = min(start + 128, frame.shape[1]) \\n crop = frame[:, start:stop]\\n return crop\",\n \"def resize(im, target_size, max_size, stride=0, interpolation=cv2.INTER_LINEAR):\\r\\n im_shape = im.shape\\r\\n im_size_min = np.min(im_shape[0:2])\\r\\n im_size_max = np.max(im_shape[0:2])\\r\\n im_scale = float(target_size) / float(im_size_min)\\r\\n # prevent bigger axis from being more than max_size:\\r\\n if np.round(im_scale * im_size_max) > max_size:\\r\\n im_scale = float(max_size) / float(im_size_max)\\r\\n im = cv2.resize(im, None, None, fx=im_scale, fy=im_scale, interpolation=interpolation)\\r\\n\\r\\n if stride == 0:\\r\\n return im, im_scale\\r\\n else:\\r\\n # pad to product of stride\\r\\n im_height = int(np.ceil(im.shape[0] / float(stride)) * stride)\\r\\n im_width = int(np.ceil(im.shape[1] / float(stride)) * stride)\\r\\n im_channel = im.shape[2]\\r\\n padded_im = np.zeros((im_height, im_width, im_channel))\\r\\n padded_im[:im.shape[0], :im.shape[1], :] = im\\r\\n return padded_im, im_scale\",\n \"def pad_image(img, target_size):\\r\\n rows_missing = target_size[0] - img.shape[2]\\r\\n cols_missing = target_size[1] - img.shape[3]\\r\\n padded_img = np.pad(img, ((0, 0), (0, 0), (0, rows_missing), (0, cols_missing)), 'constant')\\r\\n return padded_img\",\n \"def doCrop(image, x, y, w, h):\\n\\tcrop_height = int((config.FACE_HEIGHT / float(config.FACE_WIDTH)) * w)\\n\\tmidy = y + h/2\\n\\ty1 = max(0, midy-crop_height/2)\\n\\ty2 = min(image.shape[0]-1, midy+crop_height/2)\\n\\treturn image[y1:y2, x:x+w]\",\n \"def center_crop7(x):\\n\\n return x[:, :, 2:-2, 2:-2].contiguous()\",\n \"def conv_output_shape(h_w, kernel_size=1, stride=1, pad=0, dilation=1):\\n if type(kernel_size) is not tuple:\\n kernel_size = (kernel_size, kernel_size)\\n h = math.floor( ((h_w[0] + (2 * pad) - ( dilation * (kernel_size[0] - 1)\\n ) - 1 )/ stride) + 1)\\n w = math.floor( ((h_w[1] + (2 * pad) - ( dilation * (kernel_size[1] - 1)\\n ) - 1 )/ stride) + 1)\\n return h, w\",\n \"def conv2D(inImage: np.ndarray, kernel2: np.ndarray) -> np.ndarray:\\r\\n flip_kernel = np.flipud(np.fliplr(kernel2))\\r\\n kernel_row = flip_kernel.shape[0]\\r\\n kernel_col = flip_kernel.shape[1]\\r\\n\\r\\n new_img = np.zeros_like(inImage)\\r\\n padded_img = padded_replicate(inImage, kernel_row, kernel_col)\\r\\n\\r\\n for x in range(inImage.shape[0]):\\r\\n for y in range(inImage.shape[1]):\\r\\n new_img[x, y] = (padded_img[x: x + kernel_col, y: y + kernel_row] * flip_kernel).sum()\\r\\n if flip_kernel.sum() != 0:\\r\\n new_img[x, y] /= flip_kernel.sum()\\r\\n\\r\\n return new_img\",\n \"def upsample_filt(size):\\r\\n factor = (size + 1) // 2\\r\\n if size % 2 == 1:\\r\\n center = factor - 1\\r\\n else:\\r\\n center = factor - 0.5\\r\\n og = np.ogrid[:size, :size]\\r\\n return (1 - abs(og[0] - center) / factor) * \\\\\\r\\n (1 - abs(og[1] - center) / factor)\",\n \"def resize(image_array, original_pixel_spacing_list, target_field_of_view_list=[140,140,80],\\n target_pixel_spacing_list=[0.5,0.5,0.5], is_seg=False):\\n import math\\n\\n num_dim = len(image_array.shape)\\n compression_factor_list = [original_pixel_spacing_list[i]/target_pixel_spacing_list[i] for i in range(num_dim)]\\n image_resampled = resample_by(image_array=image_array, compression_factor_list= compression_factor_list)\\n current_dim_in_pixels_list = list(image_resampled.shape)\\n target_dim_in_pixels_list = [int(target_field_of_view_list[i]/target_pixel_spacing_list[i]) for i in range(num_dim)]\\n\\n #pad or crop in xy (while ignoring z)\\n if current_dim_in_pixels_list[0] >= target_dim_in_pixels_list[0]:\\n print('Cropping image in xy')\\n image_resampled_cropped = crop(image_array=image_resampled,\\n final_dims_in_pixels=[target_dim_in_pixels_list[0],\\n target_dim_in_pixels_list[1],\\n current_dim_in_pixels_list[2]])\\n else:\\n print('Padding image in xy')\\n image_resampled_cropped = pad(image_array=image_resampled,\\n final_dims_in_pixels=[target_dim_in_pixels_list[0],\\n target_dim_in_pixels_list[1],\\n current_dim_in_pixels_list[2]])\\n\\n #pad or crop in z (while ignoring xy)\\n current_dim_in_pixels_list = list(image_resampled_cropped.shape)\\n\\n if current_dim_in_pixels_list[2] >= target_dim_in_pixels_list[2]:\\n print('Cropping image in z')\\n image_resampled_cropped = crop(image_array=image_resampled_cropped,\\n final_dims_in_pixels=[current_dim_in_pixels_list[0],\\n current_dim_in_pixels_list[1],\\n target_dim_in_pixels_list[2]])\\n else:\\n print('Padding image in z')\\n image_resampled_cropped = pad(image_array=image_resampled_cropped,\\n final_dims_in_pixels=[current_dim_in_pixels_list[0],\\n current_dim_in_pixels_list[1],\\n target_dim_in_pixels_list[2]])\\n\\n return(image_resampled_cropped)\",\n \"def crop(self, N):\\n self.data = self.data[:,:N]\",\n \"def resize(im, target_size, max_size, stride=0, interpolation=cv2.INTER_LINEAR):\\n im_shape = im.shape\\n im_size_min = np.min(im_shape[0:2])\\n im_size_max = np.max(im_shape[0:2])\\n im_scale = float(target_size) / float(im_size_min)\\n # prevent bigger axis from being more than max_size:\\n if np.round(im_scale * im_size_max) > max_size:\\n im_scale = float(max_size) / float(im_size_max)\\n im = cv2.resize(im, None, None, fx=im_scale, fy=im_scale, interpolation=interpolation)\\n\\n if stride == 0:\\n return im, im_scale\\n else:\\n # pad to product of stride\\n im_height = int(np.ceil(im.shape[0] / float(stride)) * stride)\\n im_width = int(np.ceil(im.shape[1] / float(stride)) * stride)\\n im_channel = im.shape[2]\\n padded_im = np.zeros((im_height, im_width, im_channel))\\n padded_im[:im.shape[0], :im.shape[1], :] = im\\n return padded_im, im_scale\",\n \"def remove_center(X, N=10, border=2):\\n \\n #Translate X into original block shape\\n X2 = X.reshape((len(X),N,N,6))\\n\\n # Decompose matrix as follows:\\n \\n # N N N N N N N N\\n # N N N N N N N N\\n # W W E E\\n # W W E E\\n # W W E E\\n # W W E E\\n # S S S S S S S S\\n # S S S S S S S S\\n \\n # block is then flattened as well\\n North = X2[:, :border].reshape(len(X), N*border*6)\\n South = X2[:, -border:].reshape(len(X), N*border*6)\\n West = X2[:, border:-border, :border].reshape(len(X), (N-2*border)*border*6)\\n East = X2[:, border:-border, -border:].reshape(len(X), (N-2*border)*border*6)\\n \\n # Stack the vectors horizontally, e.g.\\n # N_,1 .., N_x, W_1, .. W_x, E_1, .., E_x, S_1, .., S_x\\n return np.hstack((North, West, East, South))\",\n \"def resize(im, target_size, max_size, stride=0, interpolation=cv2.INTER_LINEAR):\\n im_shape = im.shape\\n im_size_min = np.min(im_shape[0:2])\\n im_size_max = np.max(im_shape[0:2])\\n im_scale = float(target_size) / float(im_size_min)\\n # prevent bigger axis from being more than max_size:\\n if np.round(im_scale * im_size_max) > max_size:\\n im_scale = float(max_size) / float(im_size_max)\\n im = cv2.resize(\\n im, None, None, fx=im_scale, fy=im_scale, interpolation=interpolation\\n )\\n\\n if stride == 0:\\n return im, im_scale\\n else:\\n # pad to product of stride\\n im_height = int(np.ceil(im.shape[0] / float(stride)) * stride)\\n im_width = int(np.ceil(im.shape[1] / float(stride)) * stride)\\n im_channel = im.shape[2]\\n padded_im = np.zeros((im_height, im_width, im_channel))\\n padded_im[: im.shape[0], : im.shape[1], :] = im\\n return padded_im, im_scale\",\n \"def crop_center(image: Matrix, crop_x: int, crop_y: int) -> Matrix:\\n y, x = image.shape\\n start_x = x // 2 - (crop_x // 2)\\n start_y = y // 2 - (crop_y // 2)\\n return image[start_y:start_y + crop_y, start_x:start_x + crop_x].copy()\",\n \"def __resize_bilinear_with_target(self, x, target_size=None, data_format=None):\\n target_height = target_size[0]\\n target_width = target_size[1]\\n if data_format == 'channels_first':\\n new_shape = tf.constant(np.array((target_height, target_width)).astype('int32'))\\n X = K.permute_dimensions(x, [0, 2, 3, 1])\\n X = tf.image.resize_bilinear(X, new_shape)\\n X = K.permute_dimensions(X, [0, 3, 1, 2])\\n return X\\n else:\\n raise Exception(\\\"Invilid data format\\\", data_format)\",\n \"def infer_kernel_sizes(h_sizes):\\n kernel_sizes = copy(h_sizes)\\n kernel_sizes.pop(-1) # pop last because it is the output of the last CNN layer\\n kernel_sizes.insert(0, input_size) # the first kernel size should be (cnn_win_size, input_size)\\n kernel_sizes = [(self.cnn_win_size, ks) for ks in kernel_sizes]\\n return kernel_sizes\",\n \"def cut_kernel(array, xpos, ypos, dist_from_point):\\n\\n if array.ndim != 2:\\n raise IndexError('Cut kernel only allows 2D arrays.')\\n\\n kernel = np.zeros((dist_from_point*2+1, dist_from_point*2+1)) * np.nan\\n\\n if xpos - dist_from_point >= 0:\\n xmin = 0\\n xmindist = dist_from_point\\n else:\\n xmin = (xpos - dist_from_point) * -1\\n xmindist = dist_from_point + (xpos - dist_from_point)\\n\\n if ypos - dist_from_point >= 0:\\n ymin = 0\\n ymindist = dist_from_point\\n else:\\n ymin = (ypos - dist_from_point) * -1\\n ymindist = dist_from_point + (ypos - dist_from_point)\\n\\n if xpos + dist_from_point < array.shape[1]:\\n xmax = kernel.shape[1]\\n xmaxdist = dist_from_point + 1\\n else:\\n xmax = dist_from_point - (xpos - array.shape[1])\\n xmaxdist = dist_from_point - (xpos + dist_from_point - array.shape[1])\\n\\n if ypos + dist_from_point < array.shape[0]:\\n ymax = kernel.shape[0]\\n ymaxdist = dist_from_point + 1\\n else:\\n ymax = dist_from_point - (ypos - array.shape[0])\\n ymaxdist = dist_from_point - (ypos + dist_from_point - array.shape[0])\\n\\n cutk = array[ypos - ymindist: ypos + ymaxdist, xpos - xmindist: xpos + xmaxdist]\\n\\n\\n kernel[ymin: ymax, xmin:xmax] = cutk\\n\\n return kernel\",\n \"def crop_image(image):\\r\\n return image[40:-20, :]\",\n \"def crop_resize_image(image: np.ndarray, size) -> np.ndarray:\\n width, height = image.size\\n if width > height:\\n left = (width - height) / 2\\n right = width - left\\n top = 0\\n bottom = height\\n else:\\n top = (height - width) / 2\\n bottom = height - top\\n left = 0\\n right = width\\n image = image.crop((left, top, right, bottom))\\n image = image.resize(size, Image.ANTIALIAS)\\n return image\",\n \"def cropping_center(x, crop_shape, batch=False):\\n orig_shape = x.shape\\n if not batch:\\n h0 = int((orig_shape[0] - crop_shape[0]) * 0.5)\\n w0 = int((orig_shape[1] - crop_shape[1]) * 0.5)\\n x = x[h0 : h0 + crop_shape[0], w0 : w0 + crop_shape[1]]\\n else:\\n h0 = int((orig_shape[1] - crop_shape[0]) * 0.5)\\n w0 = int((orig_shape[2] - crop_shape[1]) * 0.5)\\n x = x[:, h0 : h0 + crop_shape[0], w0 : w0 + crop_shape[1]]\\n return x\"\n]","isConversational":false},"negative_scores":{"kind":"list like","value":["0.657598","0.62372273","0.6060326","0.58836097","0.56814396","0.5558945","0.5557947","0.55271006","0.54924375","0.5463341","0.54487395","0.5447843","0.5445061","0.544338","0.54312927","0.54123294","0.54112655","0.5396363","0.5391804","0.53821915","0.5377355","0.5306529","0.5298422","0.5284605","0.5283586","0.5277709","0.52731436","0.5270116","0.5266266","0.52546686","0.52501976","0.5235357","0.523403","0.52292925","0.52126396","0.52079546","0.5204998","0.51875436","0.5180559","0.5177283","0.5175038","0.51667887","0.5161788","0.5154279","0.5129569","0.5129569","0.5112358","0.5104358","0.51023126","0.50925595","0.5090708","0.50730485","0.5062507","0.50537235","0.50534254","0.504701","0.50465155","0.50452876","0.5043448","0.504092","0.50344","0.5028724","0.5018998","0.5008645","0.5002433","0.5001555","0.49904042","0.49896595","0.4989515","0.4989515","0.49656206","0.49652368","0.4961098","0.49520034","0.49475867","0.49466282","0.4941612","0.49381596","0.4933273","0.49300915","0.4929563","0.4919277","0.49167752","0.49162593","0.49061328","0.4892209","0.48920077","0.4887671","0.48873278","0.48840043","0.4883898","0.4881363","0.48711374","0.48695758","0.4869575","0.48671034","0.48664165","0.486538","0.48530918","0.48503637"],"string":"[\n \"0.657598\",\n \"0.62372273\",\n \"0.6060326\",\n \"0.58836097\",\n \"0.56814396\",\n \"0.5558945\",\n \"0.5557947\",\n \"0.55271006\",\n \"0.54924375\",\n \"0.5463341\",\n \"0.54487395\",\n \"0.5447843\",\n \"0.5445061\",\n \"0.544338\",\n \"0.54312927\",\n \"0.54123294\",\n \"0.54112655\",\n \"0.5396363\",\n \"0.5391804\",\n \"0.53821915\",\n \"0.5377355\",\n \"0.5306529\",\n \"0.5298422\",\n \"0.5284605\",\n \"0.5283586\",\n \"0.5277709\",\n \"0.52731436\",\n \"0.5270116\",\n \"0.5266266\",\n \"0.52546686\",\n \"0.52501976\",\n \"0.5235357\",\n \"0.523403\",\n \"0.52292925\",\n \"0.52126396\",\n \"0.52079546\",\n \"0.5204998\",\n \"0.51875436\",\n \"0.5180559\",\n \"0.5177283\",\n \"0.5175038\",\n \"0.51667887\",\n \"0.5161788\",\n \"0.5154279\",\n \"0.5129569\",\n \"0.5129569\",\n \"0.5112358\",\n \"0.5104358\",\n \"0.51023126\",\n \"0.50925595\",\n \"0.5090708\",\n \"0.50730485\",\n \"0.5062507\",\n \"0.50537235\",\n \"0.50534254\",\n \"0.504701\",\n \"0.50465155\",\n \"0.50452876\",\n \"0.5043448\",\n \"0.504092\",\n \"0.50344\",\n \"0.5028724\",\n \"0.5018998\",\n \"0.5008645\",\n \"0.5002433\",\n \"0.5001555\",\n \"0.49904042\",\n \"0.49896595\",\n \"0.4989515\",\n \"0.4989515\",\n \"0.49656206\",\n \"0.49652368\",\n \"0.4961098\",\n \"0.49520034\",\n \"0.49475867\",\n \"0.49466282\",\n \"0.4941612\",\n \"0.49381596\",\n \"0.4933273\",\n \"0.49300915\",\n \"0.4929563\",\n \"0.4919277\",\n \"0.49167752\",\n \"0.49162593\",\n \"0.49061328\",\n \"0.4892209\",\n \"0.48920077\",\n \"0.4887671\",\n \"0.48873278\",\n \"0.48840043\",\n \"0.4883898\",\n \"0.4881363\",\n \"0.48711374\",\n \"0.48695758\",\n \"0.4869575\",\n \"0.48671034\",\n \"0.48664165\",\n \"0.486538\",\n \"0.48530918\",\n \"0.48503637\"\n]","isConversational":false},"document_score":{"kind":"string","value":"0.78084946"},"document_rank":{"kind":"string","value":"0"}}},{"rowIdx":22690,"cells":{"query":{"kind":"string","value":"Resize a kernel matrix based on the resolution at which it was defined and the signal resolution. E.g. if a kernel matrix was generated for 10kb and the input signal is 20kb, kernel size will be divided by two. If the kernel is enlarged, pixels are interpolated with a spline of degree 1. Alternatively, a resize factor can be provided. In the example above, the factor would be 0.5."},"document":{"kind":"string","value":"def resize_kernel(\n kernel,\n kernel_res=None,\n signal_res=None,\n factor=None,\n min_size=7,\n quiet=False,\n):\n km, kn = kernel.shape\n if km != kn:\n raise ValueError(\"kernel must be square.\")\n if not (km % 2) or not (kn % 2):\n raise ValueError(\"kernel size must be odd.\")\n\n if factor is not None:\n if kernel_res is not None or signal_res is not None:\n raise ValueError(\n \"factor is mutually exclusive with resolution \"\n \"parameters (kernel_res and signal_res).\"\n )\n resize_factor = factor\n else:\n if kernel_res is None or signal_res is None:\n raise ValueError(\n \"You must provide either a resize factor or the signal and \"\n \"kernel resolutions.\"\n )\n # Define by how many times kernel must be enlarged for its pixels to\n # match the signal's pixels\n resize_factor = kernel_res / signal_res\n if km * resize_factor < min_size:\n resize_factor = min_size / km\n\n resized_kernel = ndi.zoom(kernel, resize_factor, order=1)\n\n if not resized_kernel.shape[0] % 2:\n # Compute the factor required to yield a dimension smaller by one\n adj_resize_factor = (resized_kernel.shape[0] - 1) / km\n if not quiet:\n sys.stderr.write(\n f\"Adjusting resize factor from {resize_factor} to {adj_resize_factor}.\\n\"\n )\n resized_kernel = ndi.zoom(kernel, adj_resize_factor, order=1)\n\n return resized_kernel"},"metadata":{"kind":"string","value":"{\n \"objective\": {\n \"self\": [],\n \"paired\": [],\n \"triplet\": [\n [\n \"query\",\n \"document\",\n \"negatives\"\n ]\n ]\n }\n}"},"negatives":{"kind":"list like","value":["def shrink_kernel(self, kernel, up_scale):\n up_scale = torch.tensor(up_scale).float()\n # boundary padding based on the scaling law\n pad_in = (torch.ceil(up_scale**2).int())*((kernel.shape[2]-1)//2)\n pad_h = (torch.ceil(up_scale).int())*((kernel.shape[3]-1)//2)\n pad_w = (torch.ceil(up_scale).int())*((kernel.shape[4]-1)//2)\n padded_kernel = F.pad(kernel, (pad_w, pad_w, pad_h, pad_h, pad_in, pad_in))\n delta = up_scale%1\n \n if delta == 0:\n shrink_factor = 1\n else:\n # shrink_factor for coordinates.\n shrink_factor = (((kernel.shape[4]-1))/(padded_kernel.shape[-1]-1)*(up_scale+1))\n \n # Adjustment to deal with weird filtering on the grid sample function.\n shrink_factor = 1.5*(shrink_factor-0.5)**3 + 0.57 \n\n grid = torch.meshgrid(torch.linspace(-1, 1, kernel.shape[2])*(shrink_factor**2),\n torch.linspace(-1, 1, kernel.shape[3])*shrink_factor, \n torch.linspace(-1, 1, kernel.shape[4])*shrink_factor)\n\n grid = torch.cat([grid[2].unsqueeze(0).unsqueeze(-1), \n grid[1].unsqueeze(0).unsqueeze(-1), \n grid[0].unsqueeze(0).unsqueeze(-1)], dim = -1).repeat(kernel.shape[0],1,1,1,1)\n\n new_kernel = F.grid_sample(padded_kernel, grid.to(device))\n if kernel.shape[-1] - 2*up_scale > 0:\n new_kernel = new_kernel * (kernel.shape[-1]**2/((kernel.shape[-1] - 2*up_scale)**2 + 0.01))\n return new_kernel","def resize(self, size):\n if len(size) != len(self._Fkernel.shape[1:-1]):\n raise RuntimeError(\"length of resize shape is incorrect.\")\n if not np.all(size >= self._Fkernel.shape[1:-1]):\n raise RuntimeError(\"resize shape is too small.\")\n kernel = self._frequency_2_real()\n kernel_pad = self._zero_pad(kernel, size)\n self._Fkernel = self._real_2_frequency(kernel_pad)\n self.basis._axes_shape = kernel_pad.shape[1:-1]","def get_kernel_size(factor):\r\n return 2 * factor - factor % 2","def __resize_bilinear_with_factor(self, x, factor_size=None, data_format=None):\n height_factor = factor_size[0]\n width_factor = factor_size[1]\n if data_format == 'channels_first':\n height = x.shape[2] * height_factor\n width = x.shape[3] * width_factor\n new_shape = tf.constant(np.array([height, width]).astype('int32'))\n X = K.permute_dimensions(x, [0, 2, 3, 1])\n X = tf.image.resize_bilinear(X, new_shape)\n X = K.permute_dimensions(X, [0, 3, 1, 2])\n return X\n else:\n raise Exception(\"invilid data format\", data_format)","def resize_function(input):\n\n from keras.backend import tf as ktf\n return ktf.image.resize_images(input, (64, 64))","def resize(orig, factor, method=\"nearest\"):\r\n method_dict = {'nearest': 0, 'bilinear': 1, 'cubic': 2}\r\n if method.lower() not in method_dict:\r\n raise ValueError(\"Invalid interpolation method. Options are: \" + \", \".join(method_dict.keys()))\r\n try:\r\n return zoom(orig, factor, order=method_dict[method.lower()])\r\n except RuntimeError:\r\n # raised by zoom when factor length does not match orig.shape length\r\n raise ValueError(\"Factor sequence length does not match input length\")","def resize_coeff(x, new_x):\n return new_x / x","def resize_coeff(x, new_x):\n return new_x / x","def _reduced_kernel_size_for_small_input(input_tensor, kernel_size):\n shape = input_tensor.get_shape().as_list()\n if shape[1] is None or shape[2] is None:\n kernel_size_out = kernel_size\n else:\n kernel_size_out = [min(shape[1], kernel_size[0]),\n min(shape[2], kernel_size[1])]\n return kernel_size_out","def _reduced_kernel_size_for_small_input(input_tensor, kernel_size):\n shape = input_tensor.get_shape().as_list()\n if shape[1] is None or shape[2] is None:\n kernel_size_out = kernel_size\n else:\n kernel_size_out = [min(shape[1], kernel_size[0]),\n min(shape[2], kernel_size[1])]\n return kernel_size_out","def _resize(img, max_dim=128):\n if max(img.shape[:3]) <= max_dim:\n return img\n else:\n new_size = [max_dim / s if s >= max_dim else 1.0 for s in img.shape[:3]]\n new_size.append(1.0) # for channel\n return scipy.ndimage.zoom(img, new_size, order=2)","def resize(self):\n e = self.e\n if abs(self.dnp) * ( self.np-self.np_req) > 0:\n e = self.er\n self.dsize = numpy.clip((self.np_req/self.np)**(1./e), 1/self.r, self.r)\n self.size *= self.dsize","def kernel_size(self, frequency, taper=4.):\n return 2. * taper * self._sigma(frequency)","def rescale(self, factor):\n scaled_size = (int(self.width * factor), int(self.height * factor))\n return self.resize(scaled_size)","def scale_2d(orig_matrix, height=None, width=None):\n matrix = orig_matrix.copy()\n\n if len(matrix.shape) != 2:\n raise ValueError(\"The Matrix must have 2 and only 2 dimensions\")\n\n orig_h = matrix.shape[0]\n orig_w = matrix.shape[1]\n\n dim = (0, 0)\n\n if not height and not width:\n dim = matrix.shape\n\n elif not height:\n width = width\n new_ratio = float(width) / float(orig_w)\n height = orig_h * new_ratio\n dim = (width, height)\n\n elif not width:\n height = height\n new_ratio = float(height) / float(orig_h)\n width = orig_w * new_ratio\n dim = (width, height)\n\n else:\n dim = (height, width)\n\n dim = tuple([int(x) for x in dim])\n return cv2.resize(matrix, dim, interpolation=cv2.INTER_AREA)","def setPixelsPerInchShrinkToFit(self,value):\n self.PDFreactorConfiguration.in1[\"pixelsPerInchShrinkToFit\"] = value","def resize3D(img, target_size, bspline_order=3, mode='constant'): \n # compute zoom values\n target_size = np.array(target_size, dtype=float)\n image_shape = np.array(img.shape, dtype=float)\n zoom_factors = np.divide(target_size,image_shape)\n print \"Target Size\"\n print target_size\n \n\n print \"Zoom Factors\"\n print zoom_factors\n\n \n # zoom image\n img = zoom(img, zoom_factors, order=bspline_order, mode=mode)\n\n print \"image_shape\"\n print img.shape\n\n return img","def resize_to_box(im, size):\n #mx = np.max(im.shape[:2])\n\n factors = [size[i]/im.shape[i] for i in range(2)]\n\n f = np.min(factors)\n if f < 1.0:\n return resize_with_factor_new(im, f)\n else:\n return im","def zoom(self, factor):\n adj = self.canvas.get_hadjustment()\n oldCenter = adj.value + adj.page_size // 2\n\n self.scale *= factor\n self.resizer.rescale()\n self.resize(self.timeExtent * self.scale, self.height)\n for f in self.resizeCallbacks:\n f()\n\n adj.value = oldCenter * factor - adj.page_size // 2","def resize(im, new_size, preserve_aspect_ratio=True, prefilter=True):\n factors = [new_size[i] / im.shape[i] for i in range(2)]\n\n #assert factors[0] == factors[1], \"Must have same factor for now\"\n f = factors[0] \n \n if f < 1:\n im2 = pyramid_reduce(im, downscale=1/f)\n elif f > 1:\n im2 = pyramid_expand(im, upscale=f)\n else:\n im2 = im\n\n assert im2.shape[:2] == tuple(new_size), \"{0} != {1} (original size: {2})\".format(im2.shape, new_size, im.shape)\n \n return im2","def bilinear_interpolation_kernel(in_channels, out_channels, ksize):\n\n factor = (ksize + 1) / 2\n if ksize % 2 == 1:\n center = factor - 1\n else:\n center = factor - 0.5\n og = np.ogrid[:ksize, :ksize]\n k = (1 - abs(og[0] - center) / factor) * (1 - abs(og[1] - center) / factor)\n \n W = np.zeros((in_channels, out_channels, ksize, ksize)).astype(np.float32)\n W[range(in_channels), range(out_channels), :, :] = k\n return W","def scale_image(img, factor=1):\n return cv2.resize(img, (int(img.shape[1] * factor), int(img.shape[0] * factor)))","def _aspect_preserving_resize(image, resize_min):\r\n shape = tf.shape(input=image)\r\n height, width = shape[0], shape[1]\r\n\r\n new_height, new_width = _smallest_size_at_least(height, width, resize_min)\r\n\r\n return _resize_image(image, new_height, new_width)","def resize_image(image, resizeFactor):\n\n return cv.resize(image, (0,0), fx=resizeFactor, fy=resizeFactor)","def shrink(self, factor_x:int=1, factor_y:int=1):\n # vertical shrink\n shrunk = self._pixels[::factor_y]\n # horizontal shrink\n shrunk = self._outer(_row[::factor_x] for _row in shrunk)\n return type(self)(shrunk, _0=self._0, _1=self._1)","def coral_image_resize(im, scaling_method, scaling_factor, height_cm):\n\n if scaling_method == 'scale':\n scale = float(scaling_factor) # here scaling_factor is the desired image scaling.\n elif scaling_method == 'ratio':\n scale = float(scaling_factor) * height_cm / im.shape[0] # here scaling_factor is the desited px_cm_ratio.\n im = scipy.misc.imresize(im, scale)\n return (im, scale)","def downScaleResolution(kv, factor=10):\n sub_img_name = kv[0]\n sub_image = kv[1]\n img_dimension = len(sub_image)\n big_image = sub_image\n Nbig = img_dimension\n Nsmall = Nbig//factor\n small_image = big_image.reshape([Nsmall, Nbig // Nsmall, Nsmall, Nbig // Nsmall]).mean(3).mean(1)\n return (sub_img_name,small_image)","def resize(im, target_size, max_size, stride=0, interpolation=cv2.INTER_LINEAR):\r\n im_shape = im.shape\r\n im_size_min = np.min(im_shape[0:2])\r\n im_size_max = np.max(im_shape[0:2])\r\n im_scale = float(target_size) / float(im_size_min)\r\n # prevent bigger axis from being more than max_size:\r\n if np.round(im_scale * im_size_max) > max_size:\r\n im_scale = float(max_size) / float(im_size_max)\r\n im = cv2.resize(im, None, None, fx=im_scale, fy=im_scale, interpolation=interpolation)\r\n\r\n if stride == 0:\r\n return im, im_scale\r\n else:\r\n # pad to product of stride\r\n im_height = int(np.ceil(im.shape[0] / float(stride)) * stride)\r\n im_width = int(np.ceil(im.shape[1] / float(stride)) * stride)\r\n im_channel = im.shape[2]\r\n padded_im = np.zeros((im_height, im_width, im_channel))\r\n padded_im[:im.shape[0], :im.shape[1], :] = im\r\n return padded_im, im_scale","def scale_image(img, factor=1):\n\treturn cv2.resize(img,(int(img.shape[1]*factor), int(img.shape[0]*factor)))","def resize(img, shape, mode='constant', orig_shape=(155, 240, 240)):\n assert len(shape) == 3, \"Can not have more than 3 dimensions\"\n factors = (\n shape[0] / orig_shape[0],\n shape[1] / orig_shape[1],\n shape[2] / orig_shape[2]\n )\n\n # Resize to the given shape\n return zoom(img, factors, mode=mode)","def resize(img,scale):\n sdims=img.shape\n datatype=c_double\n if img.dtype!=datatype:\n print \"Error the image must be of doubles!\"\n raise RuntimeError\n\n if scale>1.0:\n print \"Invalid scaling factor!\"\n raise RuntimeError \n \n img = asfortranarray(img,c_double) # make array continguous\n \n try:\n mresize = ctypeslib.load_library(\"libresize.so\",\".\") \n except:\n print \"Unable to load resize library\"\n raise RuntimeError\n \n #use two times the 1d resize to get a 2d resize\n fresize = mresize.resize1dtran\n fresize.restype = None\n fresize.argtypes = [ ctypeslib.ndpointer(dtype=datatype, ndim=3), c_int,ctypeslib.ndpointer(dtype=datatype, ndim=3), c_int, c_int , c_int ]\n ddims = [int(round(sdims[0]*scale)),int(round(sdims[1]*scale)),sdims[2]];\n mxdst = zeros((ddims), dtype=datatype)\n tmp = zeros((ddims[0],sdims[1],sdims[2]), dtype=datatype)\n img1=img\n t1=time()\n fresize(img1, sdims[0], tmp, ddims[0], sdims[1], sdims[2]);\n fresize(tmp, sdims[1], mxdst, ddims[1], ddims[0], sdims[2]);\n t2=time()\n return mxdst.reshape(ddims[2],ddims[1],ddims[0]).T","def resized(self,size=1.,tol=1.e-5):\n s = self.sizes()\n s[s<tol*s.max()] = size\n return self.scale(size/s)","def resize(im, target_size, max_size, stride=0, interpolation=cv2.INTER_LINEAR):\n im_shape = im.shape\n im_size_min = np.min(im_shape[0:2])\n im_size_max = np.max(im_shape[0:2])\n im_scale = float(target_size) / float(im_size_min)\n # prevent bigger axis from being more than max_size:\n if np.round(im_scale * im_size_max) > max_size:\n im_scale = float(max_size) / float(im_size_max)\n im = cv2.resize(\n im, None, None, fx=im_scale, fy=im_scale, interpolation=interpolation\n )\n\n if stride == 0:\n return im, im_scale\n else:\n # pad to product of stride\n im_height = int(np.ceil(im.shape[0] / float(stride)) * stride)\n im_width = int(np.ceil(im.shape[1] / float(stride)) * stride)\n im_channel = im.shape[2]\n padded_im = np.zeros((im_height, im_width, im_channel))\n padded_im[: im.shape[0], : im.shape[1], :] = im\n return padded_im, im_scale","def rescale_by_width(image, target_width, method=cv2.INTER_LANCZOS4):\r\n h = int(round(target_width * image.shape[0] / image.shape[1]))\r\n return cv2.resize(image, (target_width, h), interpolation=method)","def _shrink(values, last_dim, reduction_factor):\n\n shape = tf.shape(values)\n new_shape = [\n shape[0],\n shape[1] // reduction_factor,\n last_dim * reduction_factor\n ]\n values = tf.reshape(values, new_shape)\n return values","def resize_volume(img):\n desired_depth = 64\n desired_width = 128\n desired_height = 128\n\n current_depth = img.shape[-1]\n current_width = img.shape[0]\n current_height = img.shape[1]\n\n depth = current_depth / desired_depth\n width = current_width / desired_width\n height = current_height / desired_height\n\n depth_factor = 1 / depth\n width_factor = 1 / width\n height_factor = 1 / height\n\n img = ndimage.rotate(input=img, angle=90, reshape=False)\n img = ndimage.zoom(input=img, zoom=(width_factor, height_factor, depth_factor), order=1)\n\n return img","def upsampleImage( arr, kernelSize ):\n return scipy.ndimage.zoom( arr, kernelSize )","def resize(im, target_size, max_size, stride=0, interpolation=cv2.INTER_LINEAR):\n im_shape = im.shape\n im_size_min = np.min(im_shape[0:2])\n im_size_max = np.max(im_shape[0:2])\n im_scale = float(target_size) / float(im_size_min)\n # prevent bigger axis from being more than max_size:\n if np.round(im_scale * im_size_max) > max_size:\n im_scale = float(max_size) / float(im_size_max)\n im = cv2.resize(im, None, None, fx=im_scale, fy=im_scale, interpolation=interpolation)\n\n if stride == 0:\n return im, im_scale\n else:\n # pad to product of stride\n im_height = int(np.ceil(im.shape[0] / float(stride)) * stride)\n im_width = int(np.ceil(im.shape[1] / float(stride)) * stride)\n im_channel = im.shape[2]\n padded_im = np.zeros((im_height, im_width, im_channel))\n padded_im[:im.shape[0], :im.shape[1], :] = im\n return padded_im, im_scale","def resize(input, size):\n layer = tf.image.resize_images(input, size, method=tf.image.ResizeMethod.BICUBIC, align_corners=True,\n preserve_aspect_ratio=True)\n return layer","def resize(img, size):\n img = tf.expand_dims(img, 0)\n return tf.image.resize_bilinear(img, size)[0,:,:,:]","def scaling(mat, factor):\n\treturn mat / (mat + factor)","def resize_image(image_data, resize_factor = 0.5):\n \n # calculate new shape:\n width_new = int(resize_factor*image_data.shape[1]);\n height_new = int(resize_factor*image_data.shape[0]);\n \n # return result:\n return _cv2.resize(image_data,\n (width_new, height_new),\n interpolation = _cv2.INTER_AREA);","def resize_image(img):\n ratio = float(img.size[1]) / float(img.size[0])\n\n if ratio > 1.73:\n h, w = 320, 160 # 512, 256\n elif ratio < 1.0 / 1.73:\n h, w = 160, 320 # 256, 512\n elif ratio > 1.41:\n h, w = 384, 256\n elif ratio < 1. / 1.41:\n h, w = 256, 384\n elif ratio > 1.15:\n h, w = 320, 240 # 512, 384\n elif ratio < 1. / 1.15:\n h, w = 240, 320 # 384, 512\n else:\n h, w = 320, 320 # 384, 384\n\n h = int(h)\n w = int(w)\n img = TF.resize(img, size=[h, w], interpolation=Image.BILINEAR)\n return img","def set_scale_factors_to_output_size(self):\n # Compute the scale_factor using rounded scaled image size.\n height = tf.shape(self._image)[0]\n width = tf.shape(self._image)[1]\n max_image_size = tf.to_float(tf.maximum(height, width))\n image_scale = tf.to_float(self._output_size) / max_image_size\n scaled_height = tf.to_int32(tf.to_float(height) * image_scale)\n scaled_width = tf.to_int32(tf.to_float(width) * image_scale)\n self._image_scale = image_scale\n self._scaled_height = scaled_height\n self._scaled_width = scaled_width","def resize(img, height=800):\n ratio = height / img.shape[0]\n return cv2.resize(img, (int(ratio * img.shape[1]), height))","def resize_spacing(img_sz,img_sp,factor):\n img_sz_np = np.array(list(img_sz))\n img_sp_np = np.array(list(img_sp))\n new_sz_np = img_sz_np*factor\n new_sp = img_sp_np*(img_sz_np-1)/(new_sz_np-1)\n return tuple(list(new_sp))","def resize_image(image: Image) -> Image:\n expected_x: int = 1024\n expected_y: int = 768\n x, y = image.size\n if x > expected_x or y > expected_y:\n scale = min(expected_x / x, expected_y / y)\n return image.resize((int(x * scale), int(y * scale)))\n else:\n return image.resize((int(0.9*x), int(0.9*y)))","def resize(image, size):\n return np.array(Image.fromarray(image).resize(size))","def _fixed_sides_resize(image, output_height, output_width):\n output_height = tf.convert_to_tensor(output_height, dtype=tf.int32)\n output_width = tf.convert_to_tensor(output_width, dtype=tf.int32)\n\n image = tf.expand_dims(image, 0)\n resized_image = tf.image.resize_nearest_neighbor(\n image, [output_height, output_width], align_corners=False)\n resized_image = tf.squeeze(resized_image)\n resized_image.set_shape([None, None, 3])\n return resized_image","def resized_map(self, new_size):\n\n new_map = cv2.resize(self.map.copy(), new_size)\n cur_count = np.sum(new_map)\n\n # Avoid dividing by zero\n if cur_count == 0:\n return new_map\n\n scale = self.count / cur_count\n new_map *= scale\n return new_map","def kernel_size(sigma, odd=False, cutoff=4):\n size = int(ceil(2 * cutoff * sigma + 1))\n if odd and size % 2 == 0:\n size += 1\n return size","def diff_resize_area(tensor, new_height_width):\n new_h, new_w = new_height_width\n unused_b, curr_h, curr_w, unused_d = tensor.shape.as_list()\n # The least common multiplier used to determine the intermediate resize\n # operation.\n l_h = np.lcm(curr_h, new_h)\n l_w = np.lcm(curr_w, new_w)\n if l_h == curr_h and l_w == curr_w:\n im = tensor\n elif (l_h < (10 * new_h) and l_w < (10 * new_w)):\n im = tf.compat.v1.image.resize_bilinear(\n tensor, [l_h, l_w], half_pixel_centers=True)\n else:\n raise RuntimeError(\"DifferentiableResizeArea is memory inefficient\"\n \"for resizing from (%d, %d) -> (%d, %d)\" %\n (curr_h, curr_w, new_h, new_w))\n lh_factor = l_h // new_h\n lw_factor = l_w // new_w\n if lh_factor == lw_factor == 1:\n return im\n return tf.nn.avg_pool2d(\n im, [lh_factor, lw_factor], [lh_factor, lw_factor], padding=\"VALID\")","def patch_resize(c, graph, node_select):\r\n return c","def set_resize_factor(self, factor):\n\n self.RESIZE_FACTOR = factor\n f = open(self.factor_file, 'w')\n f.write(str(factor) + '\\n')\n f.close()\n width = self.ORIGINAL_WIDTH * factor\n height = self.ORIGINAL_HEIGHT * factor\n src = RESOURCE_PATH\n orig_default = src + '/not-found.png'\n orig_loading = src + '/loading.png'\n resize_png(width, height, orig_default, self.default)\n resize_png(width, height, orig_loading, self.loading)","def reduce_resolution(input_dir, output_dir, factor=2):\n # Get the images.\n downsample_ratio = 1.0 / args.factor\n tiles = glob.glob(os.path.join(args.input_dir, 'S_*/*.tif*'))\n tiles.sort()\n print('Number of Tiles: ', len(tiles))\n print('Downsample Ratio: ', downsample_ratio)\n\n # Downsample the images by the requested factor with OpenCV's resize().\n for t in tqdm(tiles):\n dirname = os.path.basename(os.path.dirname(t))\n filename = os.path.basename(t)\n os.makedirs(os.path.join(args.output_dir, dirname), exist_ok=True)\n f_out = os.path.join(args.output_dir, dirname, filename)\n im = cv2.imread(t, 0)\n im_down = cv2.resize(im, (0, 0), fx=downsample_ratio, fy=downsample_ratio)\n cv2.imwrite(f_out, im_down)","def resize(img, x_scale, y_scale, optimize=True):\n interpolation = cv.INTER_LINEAR\n\n # pick an optimized scaler if asked to\n if optimize:\n if x_scale > 1 and y_scale > 1:\n interpolation = cv.INTER_CUBIC\n else:\n interpolation = cv.INTER_AREA\n\n return cv.resize(\n img,\n None,\n fx=x_scale,\n fy=y_scale,\n interpolation=interpolation,\n )","def resize_images_interpolated(X, height_factor, width_factor, order, dim_ordering):\n resizeMethod = 0\n if order==0:\n resizeMethod = tf.image.ResizeMethod.NEAREST_NEIGHBOR\n elif order==1:\n resizeMethod = tf.image.ResizeMethod.BILINEAR\n elif order==2:\n resizeMethod = tf.image.ResizeMethod.BICUBIC\n else:\n raise Exception('Incorrect interpolation method order [%s], currently available values is 0,1,2')\n if dim_ordering == 'th':\n original_shape = K.int_shape(X)\n new_shape = tf.shape(X)[2:]\n new_shape *= tf.constant(np.array([height_factor, width_factor]).astype('int32'))\n X = K.permute_dimensions(X, [0, 2, 3, 1])\n X = tf.image.resize_images(X, new_shape, method=resizeMethod)\n # X = tf.image.resize_nearest_neighbor(X, new_shape)\n X = K.permute_dimensions(X, [0, 3, 1, 2])\n X.set_shape((None, None, original_shape[2] * height_factor if original_shape[2] is not None else None,\n original_shape[3] * width_factor if original_shape[3] is not None else None))\n return X\n elif dim_ordering == 'tf':\n original_shape = K.int_shape(X)\n new_shape = tf.shape(X)[1:3]\n new_shape *= tf.constant(np.array([height_factor, width_factor]).astype('int32'))\n # X = tf.image.resize_nearest_neighbor(X, new_shape)\n X = tf.image.resize_images(X, new_shape, method=resizeMethod)\n X.set_shape((None, original_shape[1] * height_factor if original_shape[1] is not None else None,\n original_shape[2] * width_factor if original_shape[2] is not None else None, None))\n return X\n else:\n raise ValueError('Invalid dim_ordering:', dim_ordering)","def resize(im: Image) -> Image:\n size_factor = random.uniform(*ImageOperations.config.get('resize_interval'))\n return im.resize((\n int(round(im.height * size_factor, 0)),\n int(round(im.width * size_factor, 0))\n ))","def interpolate(\n input,\n size=None,\n scale_factor=None,\n mode='nearest',\n align_corners=False,\n):\n if size is not None:\n size = nest.flatten(size)\n if scale_factor is not None:\n scale_factor = nest.flatten(scale_factor)\n mode = mode.upper()\n mode = mode.replace('BILINEAR', 'LINEAR')\n mode = mode.replace('TRILINEAR', 'LINEAR')\n return FunctionLib.apply(\n 'Resize', input.device, [input],\n mode=mode, align_corners=align_corners,\n num_sizes=len(size) if size is not None else 0,\n num_scales=len(scale_factor) if scale_factor is not None else 0,\n sizes=size, scales=scale_factor)","def kernel_width(self):\n\t\treturn self.kernel_shape_param('W')","def _calculate_shrinking_factor(initial_shrinking_factor: float, step_number: int, n_dim: int) -> float:\n assert 0 < initial_shrinking_factor < 1, (\n f\"Shrinking factor must be between 0 and 1. \" f\"(Was {initial_shrinking_factor}).\"\n )\n assert step_number >= 1 and n_dim >= 1, (\n f\"Step number and number of dimensions must be greater than 0. \"\n f\"(Where step_number={step_number}, n_dim={n_dim}).\"\n )\n\n return initial_shrinking_factor ** (step_number / n_dim)","def _gaussian_kernel(kernel_size):\n curr_kernel = _binoms(kernel_size)\n curr_kernel = curr_kernel.reshape(kernel_size, 1)\n kernel2d = convolve2d(curr_kernel.transpose(), curr_kernel)\n kernel2d = np.divide(kernel2d, np.sum(kernel2d))\n return kernel2d","def sobel(kernel_size: int = 3) -> Tensor:\n assert kernel_size % 2 == 1\n s = kernel_size // 2\n k = torch.linspace(-s, s, kernel_size)\n kernel = torch.stack([k] * kernel_size)\n k[k == 0] = 1e-7\n div = torch.stack([k] * kernel_size)\n return kernel / (div ** 2 + div.T ** 2)","def Pane_Resized( self, new_sizes ):\r\n if(new_sizes[0] > 200 ):\r\n cb.xtotal = new_sizes[0]-100\r\n self.canvas_one.config(width = new_sizes[0])\r\n self.canvas_scale.config(width = new_sizes[0])\r\n else:\r\n cb.xtotal = 200-100\r\n self.canvas_one.config(width = 200)\r\n self.canvas_scale.config(width = 200)\r\n if (len(new_sizes) > 1 ):\r\n self.canvas_two.config(width=new_sizes[1])\r\n self.system.Draw()","def gaussian_kernel(kernel_size: (int, tuple, list), width: float):\n kernel_size = np.asarray(to_list(kernel_size, 2), np.float)\n half_ksize = (kernel_size - 1) / 2.0\n x, y = np.mgrid[-half_ksize[0]:half_ksize[0] + 1, -half_ksize[1]:half_ksize[1] + 1]\n kernel = np.exp(-(x ** 2 + y ** 2) / (2 * width ** 2))\n return kernel / (kernel.sum() + 1e-08)","def resizeImage(image, maxW, maxH):\n\timageW, imageH = image.size\n\tif imageW == maxW and imageH == maxH:\n\t\treturn image\n\t# find which axis requires the biggest zoom (smallest relative max dimension)\n\tzoomW = float(imageW) / float(maxW)\n\tzoomH = float(imageH) / float(maxH)\n\tzoom = max(zoomW, zoomH)\n\tif zoomW >= zoomH:\t# size is defined by width\n\t\tmaxH = int(imageH//zoom)\t# calculate the new height\n\telse:\n\t\tmaxW = int(imageW//zoom)\n\treturn image.resize((maxW, maxH))","def resize(img, height=800):\n if img.shape[0] > height:\n ratio = height / img.shape[0]\n return cv2.resize(img, (int(ratio * img.shape[1]), height))","def resample(medical_image, image, new_spacing=[1, 1, 1]):\n image_shape = np.array(list(medical_image.header.get_data_shape()))\n spacing = np.array(list(medical_image.header.get_zooms()))\n resize_factor = spacing / new_spacing\n\n new_shape = image_shape * resize_factor\n new_shape = np.round(new_shape)\n\n real_resize_factor = new_shape / image_shape\n\n new_spacing = spacing / real_resize_factor\n resampled_image = scipy.ndimage.interpolation.zoom(image, real_resize_factor)\n\n return resampled_image, new_spacing","def conv_matrix(matrix, kernel):","def change_resolution(img):\n scale_factor = np.random.choice(list(range(0, 6, 2)))\n if scale_factor == 0:\n return img\n downsample = nn.AvgPool2d(scale_factor)\n upsample = nn.UpsamplingNearest2d(scale_factor=scale_factor)\n new_res_img = upsample(downsample(img.unsqueeze(dim=1))).squeeze()\n return new_res_img","def resize(img, new_h=None, new_w=None):\n if isinstance(img, tf.Tensor):\n input_is_tensor = True\n tensor = img\n else:\n input_is_tensor = False\n tensor = tf.convert_to_tensor(img)\n\n # Original size\n hw = tf.shape(tensor)\n h, w = hw[0], hw[1] # both 0D tensors\n\n # Figure out new size\n if new_h is not None and new_w is not None:\n if not tf.equal(tf.cast(h / w * new_w, h.dtype), new_h):\n logger.warn((\n \"Aspect ratio changed in resizing: original size is %s; \"\n \"new size is %s\"), (h, w), (new_h, new_w))\n elif new_h is None and new_w is not None:\n new_h = tf.cast(h / w * new_w, h.dtype)\n elif new_h is not None and new_w is None:\n new_w = tf.cast(w / h * new_h, h.dtype)\n else:\n raise ValueError(\"At least one of new height or width must be given\")\n new_shape = (new_h, new_w)\n\n resized = tf.image.resize(\n tensor, new_shape, method='bilinear', antialias=True)\n\n if input_is_tensor:\n return resized\n\n # Restore the original data type if input is not a tensor\n orig_dtype = img.dtype if isinstance(img, np.ndarray) else img.numpy().dtype\n return resized.numpy().astype(orig_dtype)","def gaussian_kernel(kernel_size: (int, tuple, list), width: float):\n\n kernel_size = np.asarray(to_list(kernel_size, 2), np.float)\n half_ksize = (kernel_size - 1) / 2.0\n x, y = np.mgrid[-half_ksize[0]:half_ksize[0] + 1,\n -half_ksize[1]:half_ksize[1] + 1]\n kernel = np.exp(-(x ** 2 + y ** 2) / (2 * width ** 2))\n return kernel / (kernel.sum() + 1e-8)","def imresize(img, size):\n if hasattr(size, \"__len__\"):\n num_rows, num_cols = size\n assert (num_rows > 0) or (num_cols > 0)\n if num_rows < 0:\n num_rows = num_cols * img.shape[0] / img.shape[1]\n if num_cols < 0:\n num_cols = num_rows * img.shape[1] / img.shape[0]\n else:\n num_rows = int(round(img.shape[0] * size))\n num_cols = int(round(img.shape[1] * size))\n return skimage.transform.resize(img, (num_rows, num_cols))","def upscale(x, n):\n if n == 1:\n return x\n x_shape = tf.shape(x)\n height, width = x_shape[1], x_shape[2]\n return tf.image.resize_nearest_neighbor(x, [n * height, n * width])\n # return tf.batch_to_space(tf.tile(x, [n**2, 1, 1, 1]), [[0, 0], [0, 0]], n)","def resize(img, min_size=512):\n width, height, _ = tf.unstack(tf.shape(img), num=3)\n if height < width:\n new_height = min_size\n new_width = int(width * new_height / height)\n else:\n new_width = min_size\n new_height = int(height * new_width / width)\n\n img = tf.image.resize(img, size=(new_width, new_height))\n return img","def resizeImg(img, maxXSize=720):\r\n if img.size[1] > maxXSize:\r\n downRat = maxXSize / img.size[1]\r\n outImg = img.resize((int(img.size[0] * downRat),maxXSize))\r\n else:\r\n outImg = img\r\n return outImg","def downsample_x2(x, fsize=5, sigma=1.4):\n\n\n #\n # Your code here\n #\n\n G = gaussian_kernel(fsize, sigma)\n g_img = convolve2d(x, G, mode='same', boundary='symm')\n x = g_img[0::2, 0::2]\n\n return x","def resample(img, hdr, target_spacing, bspline_order=3, mode='constant'):\n if isinstance(target_spacing, numbers.Number):\n target_spacing = [target_spacing] * img.ndim\n \n # compute zoom values\n zoom_factors = [old / float(new) for new, old in zip(target_spacing, header.get_pixel_spacing(hdr))]\n\n print \"Zoom Factors\"\n print zoom_factors\n\n oldImageShape = img.shape\n \n # zoom image\n img = zoom(img, zoom_factors, order=bspline_order, mode=mode)\n\n newImageShape = img.shape\n old_pixel_spacing = header.get_pixel_spacing(hdr)\n new_pixel_spacing = np.round(np.divide(np.multiply(oldImageShape,old_pixel_spacing),newImageShape),7)\n print \"Target Pixel Spacing\"\n print target_spacing\n\n print \"Actual Pixel Spacing\"\n print new_pixel_spacing\n # set new voxel spacing\n header.set_pixel_spacing(hdr, new_pixel_spacing)\n \n return img, hdr","def FlyResize( image, log_mess, nimages, method = Image.ANTIALIAS ):\n oldw, oldh = image.size\n resl = [8, 10, 14, 16, 20, 22, 24, 32, 40, 48, 64, 96, 128, 256]\n \n if oldw > 256 or oldh > 256:\n newsiz = min(resl, key = lambda x:abs(x - max(oldw, oldh)))\n image.thumbnail((newsiz, newsiz), method)\n neww, newh = image.size\n log_mess += ' and new size scaled = %s x %s' %(neww, newh)\n elif nimages > 1:\n log_mess += ' and size = %s x %s' %(oldw, oldh)\n \n return oldw, oldh, image, log_mess","def resize(self, **kwargs):\n\n if self.image is None:\n raise ValueError('self.image is None! The image has to be initialized!')\n\n with warnings.catch_warnings():\n warnings.simplefilter(\"ignore\")\n self.image = ndimage.interpolation.zoom(self.image * 1., **kwargs)\n\n # if size <= 3, pad with zeros\n\n if np.min(self.image.shape) < 5:\n self.image = np.pad(self.image, pad_width=3, mode='constant', constant_values=0)\n\n if self.image.max() > 0:\n self.image = rescale_intensity(self.image, out_range=(0, 255))\n\n if 'Voxel size x' in self.metadata.index and 'Voxel size y' in self.metadata.index \\\n and 'Voxel size z' in self.metadata.index:\n new_voxel_size = np.array([self.metadata['Voxel size z'], self.metadata['Voxel size y'],\n self.metadata['Voxel size x']]) / kwargs['zoom']\n self.metadata['Voxel size'] = str(new_voxel_size)\n self.metadata['Voxel size z'], self.metadata['Voxel size y'], self.metadata['Voxel size x'] = new_voxel_size\n\n return self.image","def get_kernel(kernel_size, blur=1 / 20, halo=.001):\n\n # generate x and y grids\n x, y = np.mgrid[0:kernel_size * 2 + 1, 0:kernel_size * 2 + 1]\n\n center = kernel_size + 1 # center pixel\n r = np.sqrt((x - center) ** 2 + (y - center) ** 2) # distance from center\n\n # now compute the kernel. This function is a bit arbitrary.\n # adjust this to get the effect you want.\n kernel = np.exp(-r / kernel_size / blur) + (1 - r / r[center, 0]).clip(0) * halo\n return kernel","def resize(self, inc=True, require_val=False, val=None):\n if require_val: # Set to value\n if not val:\n val = self.default_width\n try:\n val = int(val)\n except:\n message = \"Library width must be an integer\"\n self.vimiv.statusbar.err_message(message)\n return\n self.width = val\n else: # Grow/shrink by value\n if not val:\n val = 20\n try:\n val = int(val)\n except:\n message = \"Library width must be an integer\"\n self.vimiv.statusbar.err_message(message)\n return\n if inc:\n self.width += val\n else:\n self.width -= val\n # Set some reasonable limits to the library size\n if self.width > self.vimiv.winsize[0] - 200:\n self.width = self.vimiv.winsize[0] - 200\n elif self.width < 100:\n self.width = 100\n self.scrollable_treeview.set_size_request(self.width, 10)\n # Rezoom image\n if not self.vimiv.image.user_zoomed and self.vimiv.paths:\n self.vimiv.image.zoom_to(0)","def setRescaleFactor(self, value):\n return self._set(rescaleFactor=value)","def dilate_kernel(self, kernel, dilation):\n if dilation == 0:\n return kernel \n # inside padding based on the scaling law\n dilation = torch.tensor(dilation).float()\n delta = dilation%1\n\n d_in = torch.ceil(dilation**2).int()\n new_in = kernel.shape[2] + (kernel.shape[2]-1)*d_in\n\n d_h = torch.ceil(dilation).int()\n new_h = kernel.shape[3] + (kernel.shape[3]-1)*d_h\n\n d_w = torch.ceil(dilation).int()\n new_w = kernel.shape[4] + (kernel.shape[4]-1)*d_h\n\n new_kernel = torch.zeros(kernel.shape[0], kernel.shape[1], new_in, new_h, new_w)\n new_kernel[:,:,::(d_in+1),::(d_h+1), ::(d_w+1)] = kernel\n dilate_factor = 1\n \n new_kernel = F.pad(new_kernel, ((kernel.shape[4]-1)//2, (kernel.shape[4]-1)//2)*3)\n\n dilate_factor = (new_kernel.shape[-1] - 1 - (kernel.shape[4]-1)*(delta))/(new_kernel.shape[-1] - 1) \n\n grid = torch.meshgrid(torch.linspace(-1, 1, new_in)*(dilate_factor**2), \n torch.linspace(-1, 1, new_h)*dilate_factor, \n torch.linspace(-1, 1, new_w)*dilate_factor)\n\n grid = torch.cat([grid[2].unsqueeze(0).unsqueeze(-1), \n grid[1].unsqueeze(0).unsqueeze(-1), \n grid[0].unsqueeze(0).unsqueeze(-1)], dim = -1).repeat(kernel.shape[0],1,1,1,1)\n\n new_kernel = F.grid_sample(new_kernel, grid) \n \n return new_kernel[:,:,-kernel.shape[2]:]","def bdev_daos_resize(client, name, new_size):\n params = {\n 'name': name,\n 'new_size': new_size,\n }\n return client.call('bdev_daos_resize', params)","def resize_image(image, newN, newM):\n resize = np.zeros((newM, newN))\n row, col = resize.shape\n try:\n for j in range(col):\n for i in range(row):\n resize[i, j] = image[i*2, j*2]\n except:\n pass\n\n return resize","def update_resize(self, viewer, dims):\n self.recalc(viewer)","def resize(self, image, fx, fy, interpolation):\n\n if interpolation == 'bilinear':\n return self.bilinear_interpolation(image, fx, fy)\n\n elif interpolation == 'nearest_neighbor':\n return self.nearest_neighbor(image, fx, fy)","def __resize_bilinear_with_target(self, x, target_size=None, data_format=None):\n target_height = target_size[0]\n target_width = target_size[1]\n if data_format == 'channels_first':\n new_shape = tf.constant(np.array((target_height, target_width)).astype('int32'))\n X = K.permute_dimensions(x, [0, 2, 3, 1])\n X = tf.image.resize_bilinear(X, new_shape)\n X = K.permute_dimensions(X, [0, 3, 1, 2])\n return X\n else:\n raise Exception(\"Invilid data format\", data_format)","def change_scale_factor(self, new_factor):\n if 0 < float(new_factor) < self._MAX_ZOOM:\n self._zoom_factor = new_factor","def crop_kernel(kernel, target_size):\n # Use list for mutability\n target = [d for d in target_size]\n adjusted = False\n for dim in range(len(target)):\n if not target[dim] % 2:\n target[dim] += 1\n adjusted = True\n if adjusted:\n sys.stderr.write(\n \"WARNING: Cropped kernel size adjusted to \"\n f\"{target[0]}x{target[1]} to keep odd dimensions.\\n\"\n )\n\n source_m, source_n = kernel.shape\n target_m, target_n = target\n # Define horizontal and vertical margins to trim\n if source_m > target_m:\n margin_rows = (source_m - target_m) // 2\n else:\n margin_rows = 0\n if source_n > target_n:\n margin_cols = (source_n - target_n) // 2\n else:\n margin_cols = 0\n\n cropped = kernel[\n margin_rows : (source_m - margin_rows),\n margin_cols : (source_n - margin_cols),\n ]\n\n return cropped","def resize_img(img, fx=.5, fy=.5, interpolation=cv2.INTER_LINEAR):\n return cv2.resize(img, None, fx=fx, fy=fy, interpolation=interpolation)","def resize_image(image, size):\n return skimage.transform.resize(image, size, mode='reflect', preserve_range=True).astype(image.dtype)","def resize_image_skipping(image_to_resize, resize_factor=5):\n # size info\n input_height = image_to_resize.height # == input_img_array.shape[0] ???\n input_width = image_to_resize.width\n\n # get as Numpy Array\n # transform the Pillow image into a Numpy array to skip rows and columns of pixels\n input_img_array = np.array(copy_image)\n print(\"dim of input_img_arry: {}\".format(input_img_array.shape))\n\n # RESIZE\n # 1) Reduce by Rows\n rows_to_keep = [row for row in range(0, input_height, resize_factor)]\n img_reduced_by_rows = input_img_array[rows_to_keep, :, :]\n # 2) Reduce by Cols\n cols_to_keep = [col for col in range(0, input_width, resize_factor)]\n img_reduced_by_rows_and_cols = img_reduced_by_rows[:, cols_to_keep, :]\n\n print(\"dim of output_img_arry: {}\".format(img_reduced_by_rows_and_cols.shape))\n\n # new resized image\n new_image = Image.fromarray(img_reduced_by_rows_and_cols, mode=copy_image.mode)\n return new_image","def calculate_rf_size(rf_size, downsample):\n h = 61 # 24\" monitor\n d = 10 # 10cm from the right eye\n r = 1080 / downsample # Vertical resolution\n d_px = np.degrees(math.atan2(h / 2, d)) / (r / 2)\n return rf_size * d_px","def resize(x, output_shape):\n if not _available:\n raise ValueError('cv2 is not installed on your environment, '\n 'so nothing will be plotted at this time. '\n 'Please install OpenCV.\\n\\n Under Anaconda '\n ' environment, you can install it by '\n '$ conda install -c menpo opencv=2.4.11\\n')\n\n H, W = output_shape\n x = x.transpose(1, 2, 0)\n x = cv2.resize(x, dsize=(W, H))\n x = x.transpose(2, 0, 1)\n return x","def zoom(self, newsize, order=1):\n try:\n Nx2, Ny2 = newsize\n except TypeError:\n Nx2 = Ny2 = newsize\n zoom = ((Ny2+0.1)/self.Ny, (Nx2+0.1)/self.Nx)\n if abs(zoom[0] - zoom[1]) > 1e-3:\n raise RuntimeError(\"image aspect ratio cannot be changed\")\n\n pixelsize_old = self.pixelsize\n self.image = ndimage.zoom(self.image, zoom=zoom, order=order)\n self.pixelsize = pixelsize_old * (self.Nx / Nx2)\n return self.image","def bottleneck(input_tensor, filters, strides, expansion_factor):\r\n ## part of Fast-SCNN\r\n _, input_height, input_width, input_channels = input_tensor.shape\r\n tensor = Conv2D(filters=input_channels * expansion_factor,\r\n kernel_size=1,\r\n strides=1,\r\n padding=\"same\",\r\n activation=\"relu\")(input_tensor)\r\n tensor = BatchNormalization()(tensor)\r\n tensor = Activation('relu')(tensor)\r\n\r\n tensor = DepthwiseConv2D(kernel_size=3,\r\n strides=strides,\r\n padding=\"same\")(tensor)\r\n tensor = BatchNormalization()(tensor)\r\n tensor = Activation('relu')(tensor)\r\n\r\n tensor = Conv2D(filters=filters,\r\n kernel_size=1,\r\n strides=1,\r\n padding=\"same\")(tensor)\r\n tensor = BatchNormalization()(tensor)\r\n output_tensor = Activation('relu')(tensor)\r\n return output_tensor","def resize_image(img: torch.Tensor, new_size: Union[int, Tuple[int, int]], resize_method: str, crop_or_pad_constant: str=CROP_OR_PAD, interpolate_constant: str=INTERPOLATE) ->torch.Tensor:\n new_size = to_tuple(new_size)\n if list(img.shape[-2:]) != list(new_size):\n if resize_method == crop_or_pad_constant:\n return crop_or_pad(img, new_size)\n elif resize_method == interpolate_constant:\n return F.resize(img, new_size)\n raise ValueError(f'Invalid image resize method: {resize_method}')\n return img","def _filter_scaling(reduction_indices, start_cell_num):\n filter_scaling = 1.0\n for ind in reduction_indices:\n if ind < start_cell_num:\n filter_scaling *= 2.0\n return filter_scaling"],"string":"[\n \"def shrink_kernel(self, kernel, up_scale):\\n up_scale = torch.tensor(up_scale).float()\\n # boundary padding based on the scaling law\\n pad_in = (torch.ceil(up_scale**2).int())*((kernel.shape[2]-1)//2)\\n pad_h = (torch.ceil(up_scale).int())*((kernel.shape[3]-1)//2)\\n pad_w = (torch.ceil(up_scale).int())*((kernel.shape[4]-1)//2)\\n padded_kernel = F.pad(kernel, (pad_w, pad_w, pad_h, pad_h, pad_in, pad_in))\\n delta = up_scale%1\\n \\n if delta == 0:\\n shrink_factor = 1\\n else:\\n # shrink_factor for coordinates.\\n shrink_factor = (((kernel.shape[4]-1))/(padded_kernel.shape[-1]-1)*(up_scale+1))\\n \\n # Adjustment to deal with weird filtering on the grid sample function.\\n shrink_factor = 1.5*(shrink_factor-0.5)**3 + 0.57 \\n\\n grid = torch.meshgrid(torch.linspace(-1, 1, kernel.shape[2])*(shrink_factor**2),\\n torch.linspace(-1, 1, kernel.shape[3])*shrink_factor, \\n torch.linspace(-1, 1, kernel.shape[4])*shrink_factor)\\n\\n grid = torch.cat([grid[2].unsqueeze(0).unsqueeze(-1), \\n grid[1].unsqueeze(0).unsqueeze(-1), \\n grid[0].unsqueeze(0).unsqueeze(-1)], dim = -1).repeat(kernel.shape[0],1,1,1,1)\\n\\n new_kernel = F.grid_sample(padded_kernel, grid.to(device))\\n if kernel.shape[-1] - 2*up_scale > 0:\\n new_kernel = new_kernel * (kernel.shape[-1]**2/((kernel.shape[-1] - 2*up_scale)**2 + 0.01))\\n return new_kernel\",\n \"def resize(self, size):\\n if len(size) != len(self._Fkernel.shape[1:-1]):\\n raise RuntimeError(\\\"length of resize shape is incorrect.\\\")\\n if not np.all(size >= self._Fkernel.shape[1:-1]):\\n raise RuntimeError(\\\"resize shape is too small.\\\")\\n kernel = self._frequency_2_real()\\n kernel_pad = self._zero_pad(kernel, size)\\n self._Fkernel = self._real_2_frequency(kernel_pad)\\n self.basis._axes_shape = kernel_pad.shape[1:-1]\",\n \"def get_kernel_size(factor):\\r\\n return 2 * factor - factor % 2\",\n \"def __resize_bilinear_with_factor(self, x, factor_size=None, data_format=None):\\n height_factor = factor_size[0]\\n width_factor = factor_size[1]\\n if data_format == 'channels_first':\\n height = x.shape[2] * height_factor\\n width = x.shape[3] * width_factor\\n new_shape = tf.constant(np.array([height, width]).astype('int32'))\\n X = K.permute_dimensions(x, [0, 2, 3, 1])\\n X = tf.image.resize_bilinear(X, new_shape)\\n X = K.permute_dimensions(X, [0, 3, 1, 2])\\n return X\\n else:\\n raise Exception(\\\"invilid data format\\\", data_format)\",\n \"def resize_function(input):\\n\\n from keras.backend import tf as ktf\\n return ktf.image.resize_images(input, (64, 64))\",\n \"def resize(orig, factor, method=\\\"nearest\\\"):\\r\\n method_dict = {'nearest': 0, 'bilinear': 1, 'cubic': 2}\\r\\n if method.lower() not in method_dict:\\r\\n raise ValueError(\\\"Invalid interpolation method. Options are: \\\" + \\\", \\\".join(method_dict.keys()))\\r\\n try:\\r\\n return zoom(orig, factor, order=method_dict[method.lower()])\\r\\n except RuntimeError:\\r\\n # raised by zoom when factor length does not match orig.shape length\\r\\n raise ValueError(\\\"Factor sequence length does not match input length\\\")\",\n \"def resize_coeff(x, new_x):\\n return new_x / x\",\n \"def resize_coeff(x, new_x):\\n return new_x / x\",\n \"def _reduced_kernel_size_for_small_input(input_tensor, kernel_size):\\n shape = input_tensor.get_shape().as_list()\\n if shape[1] is None or shape[2] is None:\\n kernel_size_out = kernel_size\\n else:\\n kernel_size_out = [min(shape[1], kernel_size[0]),\\n min(shape[2], kernel_size[1])]\\n return kernel_size_out\",\n \"def _reduced_kernel_size_for_small_input(input_tensor, kernel_size):\\n shape = input_tensor.get_shape().as_list()\\n if shape[1] is None or shape[2] is None:\\n kernel_size_out = kernel_size\\n else:\\n kernel_size_out = [min(shape[1], kernel_size[0]),\\n min(shape[2], kernel_size[1])]\\n return kernel_size_out\",\n \"def _resize(img, max_dim=128):\\n if max(img.shape[:3]) <= max_dim:\\n return img\\n else:\\n new_size = [max_dim / s if s >= max_dim else 1.0 for s in img.shape[:3]]\\n new_size.append(1.0) # for channel\\n return scipy.ndimage.zoom(img, new_size, order=2)\",\n \"def resize(self):\\n e = self.e\\n if abs(self.dnp) * ( self.np-self.np_req) > 0:\\n e = self.er\\n self.dsize = numpy.clip((self.np_req/self.np)**(1./e), 1/self.r, self.r)\\n self.size *= self.dsize\",\n \"def kernel_size(self, frequency, taper=4.):\\n return 2. * taper * self._sigma(frequency)\",\n \"def rescale(self, factor):\\n scaled_size = (int(self.width * factor), int(self.height * factor))\\n return self.resize(scaled_size)\",\n \"def scale_2d(orig_matrix, height=None, width=None):\\n matrix = orig_matrix.copy()\\n\\n if len(matrix.shape) != 2:\\n raise ValueError(\\\"The Matrix must have 2 and only 2 dimensions\\\")\\n\\n orig_h = matrix.shape[0]\\n orig_w = matrix.shape[1]\\n\\n dim = (0, 0)\\n\\n if not height and not width:\\n dim = matrix.shape\\n\\n elif not height:\\n width = width\\n new_ratio = float(width) / float(orig_w)\\n height = orig_h * new_ratio\\n dim = (width, height)\\n\\n elif not width:\\n height = height\\n new_ratio = float(height) / float(orig_h)\\n width = orig_w * new_ratio\\n dim = (width, height)\\n\\n else:\\n dim = (height, width)\\n\\n dim = tuple([int(x) for x in dim])\\n return cv2.resize(matrix, dim, interpolation=cv2.INTER_AREA)\",\n \"def setPixelsPerInchShrinkToFit(self,value):\\n self.PDFreactorConfiguration.in1[\\\"pixelsPerInchShrinkToFit\\\"] = value\",\n \"def resize3D(img, target_size, bspline_order=3, mode='constant'): \\n # compute zoom values\\n target_size = np.array(target_size, dtype=float)\\n image_shape = np.array(img.shape, dtype=float)\\n zoom_factors = np.divide(target_size,image_shape)\\n print \\\"Target Size\\\"\\n print target_size\\n \\n\\n print \\\"Zoom Factors\\\"\\n print zoom_factors\\n\\n \\n # zoom image\\n img = zoom(img, zoom_factors, order=bspline_order, mode=mode)\\n\\n print \\\"image_shape\\\"\\n print img.shape\\n\\n return img\",\n \"def resize_to_box(im, size):\\n #mx = np.max(im.shape[:2])\\n\\n factors = [size[i]/im.shape[i] for i in range(2)]\\n\\n f = np.min(factors)\\n if f < 1.0:\\n return resize_with_factor_new(im, f)\\n else:\\n return im\",\n \"def zoom(self, factor):\\n adj = self.canvas.get_hadjustment()\\n oldCenter = adj.value + adj.page_size // 2\\n\\n self.scale *= factor\\n self.resizer.rescale()\\n self.resize(self.timeExtent * self.scale, self.height)\\n for f in self.resizeCallbacks:\\n f()\\n\\n adj.value = oldCenter * factor - adj.page_size // 2\",\n \"def resize(im, new_size, preserve_aspect_ratio=True, prefilter=True):\\n factors = [new_size[i] / im.shape[i] for i in range(2)]\\n\\n #assert factors[0] == factors[1], \\\"Must have same factor for now\\\"\\n f = factors[0] \\n \\n if f < 1:\\n im2 = pyramid_reduce(im, downscale=1/f)\\n elif f > 1:\\n im2 = pyramid_expand(im, upscale=f)\\n else:\\n im2 = im\\n\\n assert im2.shape[:2] == tuple(new_size), \\\"{0} != {1} (original size: {2})\\\".format(im2.shape, new_size, im.shape)\\n \\n return im2\",\n \"def bilinear_interpolation_kernel(in_channels, out_channels, ksize):\\n\\n factor = (ksize + 1) / 2\\n if ksize % 2 == 1:\\n center = factor - 1\\n else:\\n center = factor - 0.5\\n og = np.ogrid[:ksize, :ksize]\\n k = (1 - abs(og[0] - center) / factor) * (1 - abs(og[1] - center) / factor)\\n \\n W = np.zeros((in_channels, out_channels, ksize, ksize)).astype(np.float32)\\n W[range(in_channels), range(out_channels), :, :] = k\\n return W\",\n \"def scale_image(img, factor=1):\\n return cv2.resize(img, (int(img.shape[1] * factor), int(img.shape[0] * factor)))\",\n \"def _aspect_preserving_resize(image, resize_min):\\r\\n shape = tf.shape(input=image)\\r\\n height, width = shape[0], shape[1]\\r\\n\\r\\n new_height, new_width = _smallest_size_at_least(height, width, resize_min)\\r\\n\\r\\n return _resize_image(image, new_height, new_width)\",\n \"def resize_image(image, resizeFactor):\\n\\n return cv.resize(image, (0,0), fx=resizeFactor, fy=resizeFactor)\",\n \"def shrink(self, factor_x:int=1, factor_y:int=1):\\n # vertical shrink\\n shrunk = self._pixels[::factor_y]\\n # horizontal shrink\\n shrunk = self._outer(_row[::factor_x] for _row in shrunk)\\n return type(self)(shrunk, _0=self._0, _1=self._1)\",\n \"def coral_image_resize(im, scaling_method, scaling_factor, height_cm):\\n\\n if scaling_method == 'scale':\\n scale = float(scaling_factor) # here scaling_factor is the desired image scaling.\\n elif scaling_method == 'ratio':\\n scale = float(scaling_factor) * height_cm / im.shape[0] # here scaling_factor is the desited px_cm_ratio.\\n im = scipy.misc.imresize(im, scale)\\n return (im, scale)\",\n \"def downScaleResolution(kv, factor=10):\\n sub_img_name = kv[0]\\n sub_image = kv[1]\\n img_dimension = len(sub_image)\\n big_image = sub_image\\n Nbig = img_dimension\\n Nsmall = Nbig//factor\\n small_image = big_image.reshape([Nsmall, Nbig // Nsmall, Nsmall, Nbig // Nsmall]).mean(3).mean(1)\\n return (sub_img_name,small_image)\",\n \"def resize(im, target_size, max_size, stride=0, interpolation=cv2.INTER_LINEAR):\\r\\n im_shape = im.shape\\r\\n im_size_min = np.min(im_shape[0:2])\\r\\n im_size_max = np.max(im_shape[0:2])\\r\\n im_scale = float(target_size) / float(im_size_min)\\r\\n # prevent bigger axis from being more than max_size:\\r\\n if np.round(im_scale * im_size_max) > max_size:\\r\\n im_scale = float(max_size) / float(im_size_max)\\r\\n im = cv2.resize(im, None, None, fx=im_scale, fy=im_scale, interpolation=interpolation)\\r\\n\\r\\n if stride == 0:\\r\\n return im, im_scale\\r\\n else:\\r\\n # pad to product of stride\\r\\n im_height = int(np.ceil(im.shape[0] / float(stride)) * stride)\\r\\n im_width = int(np.ceil(im.shape[1] / float(stride)) * stride)\\r\\n im_channel = im.shape[2]\\r\\n padded_im = np.zeros((im_height, im_width, im_channel))\\r\\n padded_im[:im.shape[0], :im.shape[1], :] = im\\r\\n return padded_im, im_scale\",\n \"def scale_image(img, factor=1):\\n\\treturn cv2.resize(img,(int(img.shape[1]*factor), int(img.shape[0]*factor)))\",\n \"def resize(img, shape, mode='constant', orig_shape=(155, 240, 240)):\\n assert len(shape) == 3, \\\"Can not have more than 3 dimensions\\\"\\n factors = (\\n shape[0] / orig_shape[0],\\n shape[1] / orig_shape[1],\\n shape[2] / orig_shape[2]\\n )\\n\\n # Resize to the given shape\\n return zoom(img, factors, mode=mode)\",\n \"def resize(img,scale):\\n sdims=img.shape\\n datatype=c_double\\n if img.dtype!=datatype:\\n print \\\"Error the image must be of doubles!\\\"\\n raise RuntimeError\\n\\n if scale>1.0:\\n print \\\"Invalid scaling factor!\\\"\\n raise RuntimeError \\n \\n img = asfortranarray(img,c_double) # make array continguous\\n \\n try:\\n mresize = ctypeslib.load_library(\\\"libresize.so\\\",\\\".\\\") \\n except:\\n print \\\"Unable to load resize library\\\"\\n raise RuntimeError\\n \\n #use two times the 1d resize to get a 2d resize\\n fresize = mresize.resize1dtran\\n fresize.restype = None\\n fresize.argtypes = [ ctypeslib.ndpointer(dtype=datatype, ndim=3), c_int,ctypeslib.ndpointer(dtype=datatype, ndim=3), c_int, c_int , c_int ]\\n ddims = [int(round(sdims[0]*scale)),int(round(sdims[1]*scale)),sdims[2]];\\n mxdst = zeros((ddims), dtype=datatype)\\n tmp = zeros((ddims[0],sdims[1],sdims[2]), dtype=datatype)\\n img1=img\\n t1=time()\\n fresize(img1, sdims[0], tmp, ddims[0], sdims[1], sdims[2]);\\n fresize(tmp, sdims[1], mxdst, ddims[1], ddims[0], sdims[2]);\\n t2=time()\\n return mxdst.reshape(ddims[2],ddims[1],ddims[0]).T\",\n \"def resized(self,size=1.,tol=1.e-5):\\n s = self.sizes()\\n s[s<tol*s.max()] = size\\n return self.scale(size/s)\",\n \"def resize(im, target_size, max_size, stride=0, interpolation=cv2.INTER_LINEAR):\\n im_shape = im.shape\\n im_size_min = np.min(im_shape[0:2])\\n im_size_max = np.max(im_shape[0:2])\\n im_scale = float(target_size) / float(im_size_min)\\n # prevent bigger axis from being more than max_size:\\n if np.round(im_scale * im_size_max) > max_size:\\n im_scale = float(max_size) / float(im_size_max)\\n im = cv2.resize(\\n im, None, None, fx=im_scale, fy=im_scale, interpolation=interpolation\\n )\\n\\n if stride == 0:\\n return im, im_scale\\n else:\\n # pad to product of stride\\n im_height = int(np.ceil(im.shape[0] / float(stride)) * stride)\\n im_width = int(np.ceil(im.shape[1] / float(stride)) * stride)\\n im_channel = im.shape[2]\\n padded_im = np.zeros((im_height, im_width, im_channel))\\n padded_im[: im.shape[0], : im.shape[1], :] = im\\n return padded_im, im_scale\",\n \"def rescale_by_width(image, target_width, method=cv2.INTER_LANCZOS4):\\r\\n h = int(round(target_width * image.shape[0] / image.shape[1]))\\r\\n return cv2.resize(image, (target_width, h), interpolation=method)\",\n \"def _shrink(values, last_dim, reduction_factor):\\n\\n shape = tf.shape(values)\\n new_shape = [\\n shape[0],\\n shape[1] // reduction_factor,\\n last_dim * reduction_factor\\n ]\\n values = tf.reshape(values, new_shape)\\n return values\",\n \"def resize_volume(img):\\n desired_depth = 64\\n desired_width = 128\\n desired_height = 128\\n\\n current_depth = img.shape[-1]\\n current_width = img.shape[0]\\n current_height = img.shape[1]\\n\\n depth = current_depth / desired_depth\\n width = current_width / desired_width\\n height = current_height / desired_height\\n\\n depth_factor = 1 / depth\\n width_factor = 1 / width\\n height_factor = 1 / height\\n\\n img = ndimage.rotate(input=img, angle=90, reshape=False)\\n img = ndimage.zoom(input=img, zoom=(width_factor, height_factor, depth_factor), order=1)\\n\\n return img\",\n \"def upsampleImage( arr, kernelSize ):\\n return scipy.ndimage.zoom( arr, kernelSize )\",\n \"def resize(im, target_size, max_size, stride=0, interpolation=cv2.INTER_LINEAR):\\n im_shape = im.shape\\n im_size_min = np.min(im_shape[0:2])\\n im_size_max = np.max(im_shape[0:2])\\n im_scale = float(target_size) / float(im_size_min)\\n # prevent bigger axis from being more than max_size:\\n if np.round(im_scale * im_size_max) > max_size:\\n im_scale = float(max_size) / float(im_size_max)\\n im = cv2.resize(im, None, None, fx=im_scale, fy=im_scale, interpolation=interpolation)\\n\\n if stride == 0:\\n return im, im_scale\\n else:\\n # pad to product of stride\\n im_height = int(np.ceil(im.shape[0] / float(stride)) * stride)\\n im_width = int(np.ceil(im.shape[1] / float(stride)) * stride)\\n im_channel = im.shape[2]\\n padded_im = np.zeros((im_height, im_width, im_channel))\\n padded_im[:im.shape[0], :im.shape[1], :] = im\\n return padded_im, im_scale\",\n \"def resize(input, size):\\n layer = tf.image.resize_images(input, size, method=tf.image.ResizeMethod.BICUBIC, align_corners=True,\\n preserve_aspect_ratio=True)\\n return layer\",\n \"def resize(img, size):\\n img = tf.expand_dims(img, 0)\\n return tf.image.resize_bilinear(img, size)[0,:,:,:]\",\n \"def scaling(mat, factor):\\n\\treturn mat / (mat + factor)\",\n \"def resize_image(image_data, resize_factor = 0.5):\\n \\n # calculate new shape:\\n width_new = int(resize_factor*image_data.shape[1]);\\n height_new = int(resize_factor*image_data.shape[0]);\\n \\n # return result:\\n return _cv2.resize(image_data,\\n (width_new, height_new),\\n interpolation = _cv2.INTER_AREA);\",\n \"def resize_image(img):\\n ratio = float(img.size[1]) / float(img.size[0])\\n\\n if ratio > 1.73:\\n h, w = 320, 160 # 512, 256\\n elif ratio < 1.0 / 1.73:\\n h, w = 160, 320 # 256, 512\\n elif ratio > 1.41:\\n h, w = 384, 256\\n elif ratio < 1. / 1.41:\\n h, w = 256, 384\\n elif ratio > 1.15:\\n h, w = 320, 240 # 512, 384\\n elif ratio < 1. / 1.15:\\n h, w = 240, 320 # 384, 512\\n else:\\n h, w = 320, 320 # 384, 384\\n\\n h = int(h)\\n w = int(w)\\n img = TF.resize(img, size=[h, w], interpolation=Image.BILINEAR)\\n return img\",\n \"def set_scale_factors_to_output_size(self):\\n # Compute the scale_factor using rounded scaled image size.\\n height = tf.shape(self._image)[0]\\n width = tf.shape(self._image)[1]\\n max_image_size = tf.to_float(tf.maximum(height, width))\\n image_scale = tf.to_float(self._output_size) / max_image_size\\n scaled_height = tf.to_int32(tf.to_float(height) * image_scale)\\n scaled_width = tf.to_int32(tf.to_float(width) * image_scale)\\n self._image_scale = image_scale\\n self._scaled_height = scaled_height\\n self._scaled_width = scaled_width\",\n \"def resize(img, height=800):\\n ratio = height / img.shape[0]\\n return cv2.resize(img, (int(ratio * img.shape[1]), height))\",\n \"def resize_spacing(img_sz,img_sp,factor):\\n img_sz_np = np.array(list(img_sz))\\n img_sp_np = np.array(list(img_sp))\\n new_sz_np = img_sz_np*factor\\n new_sp = img_sp_np*(img_sz_np-1)/(new_sz_np-1)\\n return tuple(list(new_sp))\",\n \"def resize_image(image: Image) -> Image:\\n expected_x: int = 1024\\n expected_y: int = 768\\n x, y = image.size\\n if x > expected_x or y > expected_y:\\n scale = min(expected_x / x, expected_y / y)\\n return image.resize((int(x * scale), int(y * scale)))\\n else:\\n return image.resize((int(0.9*x), int(0.9*y)))\",\n \"def resize(image, size):\\n return np.array(Image.fromarray(image).resize(size))\",\n \"def _fixed_sides_resize(image, output_height, output_width):\\n output_height = tf.convert_to_tensor(output_height, dtype=tf.int32)\\n output_width = tf.convert_to_tensor(output_width, dtype=tf.int32)\\n\\n image = tf.expand_dims(image, 0)\\n resized_image = tf.image.resize_nearest_neighbor(\\n image, [output_height, output_width], align_corners=False)\\n resized_image = tf.squeeze(resized_image)\\n resized_image.set_shape([None, None, 3])\\n return resized_image\",\n \"def resized_map(self, new_size):\\n\\n new_map = cv2.resize(self.map.copy(), new_size)\\n cur_count = np.sum(new_map)\\n\\n # Avoid dividing by zero\\n if cur_count == 0:\\n return new_map\\n\\n scale = self.count / cur_count\\n new_map *= scale\\n return new_map\",\n \"def kernel_size(sigma, odd=False, cutoff=4):\\n size = int(ceil(2 * cutoff * sigma + 1))\\n if odd and size % 2 == 0:\\n size += 1\\n return size\",\n \"def diff_resize_area(tensor, new_height_width):\\n new_h, new_w = new_height_width\\n unused_b, curr_h, curr_w, unused_d = tensor.shape.as_list()\\n # The least common multiplier used to determine the intermediate resize\\n # operation.\\n l_h = np.lcm(curr_h, new_h)\\n l_w = np.lcm(curr_w, new_w)\\n if l_h == curr_h and l_w == curr_w:\\n im = tensor\\n elif (l_h < (10 * new_h) and l_w < (10 * new_w)):\\n im = tf.compat.v1.image.resize_bilinear(\\n tensor, [l_h, l_w], half_pixel_centers=True)\\n else:\\n raise RuntimeError(\\\"DifferentiableResizeArea is memory inefficient\\\"\\n \\\"for resizing from (%d, %d) -> (%d, %d)\\\" %\\n (curr_h, curr_w, new_h, new_w))\\n lh_factor = l_h // new_h\\n lw_factor = l_w // new_w\\n if lh_factor == lw_factor == 1:\\n return im\\n return tf.nn.avg_pool2d(\\n im, [lh_factor, lw_factor], [lh_factor, lw_factor], padding=\\\"VALID\\\")\",\n \"def patch_resize(c, graph, node_select):\\r\\n return c\",\n \"def set_resize_factor(self, factor):\\n\\n self.RESIZE_FACTOR = factor\\n f = open(self.factor_file, 'w')\\n f.write(str(factor) + '\\\\n')\\n f.close()\\n width = self.ORIGINAL_WIDTH * factor\\n height = self.ORIGINAL_HEIGHT * factor\\n src = RESOURCE_PATH\\n orig_default = src + '/not-found.png'\\n orig_loading = src + '/loading.png'\\n resize_png(width, height, orig_default, self.default)\\n resize_png(width, height, orig_loading, self.loading)\",\n \"def reduce_resolution(input_dir, output_dir, factor=2):\\n # Get the images.\\n downsample_ratio = 1.0 / args.factor\\n tiles = glob.glob(os.path.join(args.input_dir, 'S_*/*.tif*'))\\n tiles.sort()\\n print('Number of Tiles: ', len(tiles))\\n print('Downsample Ratio: ', downsample_ratio)\\n\\n # Downsample the images by the requested factor with OpenCV's resize().\\n for t in tqdm(tiles):\\n dirname = os.path.basename(os.path.dirname(t))\\n filename = os.path.basename(t)\\n os.makedirs(os.path.join(args.output_dir, dirname), exist_ok=True)\\n f_out = os.path.join(args.output_dir, dirname, filename)\\n im = cv2.imread(t, 0)\\n im_down = cv2.resize(im, (0, 0), fx=downsample_ratio, fy=downsample_ratio)\\n cv2.imwrite(f_out, im_down)\",\n \"def resize(img, x_scale, y_scale, optimize=True):\\n interpolation = cv.INTER_LINEAR\\n\\n # pick an optimized scaler if asked to\\n if optimize:\\n if x_scale > 1 and y_scale > 1:\\n interpolation = cv.INTER_CUBIC\\n else:\\n interpolation = cv.INTER_AREA\\n\\n return cv.resize(\\n img,\\n None,\\n fx=x_scale,\\n fy=y_scale,\\n interpolation=interpolation,\\n )\",\n \"def resize_images_interpolated(X, height_factor, width_factor, order, dim_ordering):\\n resizeMethod = 0\\n if order==0:\\n resizeMethod = tf.image.ResizeMethod.NEAREST_NEIGHBOR\\n elif order==1:\\n resizeMethod = tf.image.ResizeMethod.BILINEAR\\n elif order==2:\\n resizeMethod = tf.image.ResizeMethod.BICUBIC\\n else:\\n raise Exception('Incorrect interpolation method order [%s], currently available values is 0,1,2')\\n if dim_ordering == 'th':\\n original_shape = K.int_shape(X)\\n new_shape = tf.shape(X)[2:]\\n new_shape *= tf.constant(np.array([height_factor, width_factor]).astype('int32'))\\n X = K.permute_dimensions(X, [0, 2, 3, 1])\\n X = tf.image.resize_images(X, new_shape, method=resizeMethod)\\n # X = tf.image.resize_nearest_neighbor(X, new_shape)\\n X = K.permute_dimensions(X, [0, 3, 1, 2])\\n X.set_shape((None, None, original_shape[2] * height_factor if original_shape[2] is not None else None,\\n original_shape[3] * width_factor if original_shape[3] is not None else None))\\n return X\\n elif dim_ordering == 'tf':\\n original_shape = K.int_shape(X)\\n new_shape = tf.shape(X)[1:3]\\n new_shape *= tf.constant(np.array([height_factor, width_factor]).astype('int32'))\\n # X = tf.image.resize_nearest_neighbor(X, new_shape)\\n X = tf.image.resize_images(X, new_shape, method=resizeMethod)\\n X.set_shape((None, original_shape[1] * height_factor if original_shape[1] is not None else None,\\n original_shape[2] * width_factor if original_shape[2] is not None else None, None))\\n return X\\n else:\\n raise ValueError('Invalid dim_ordering:', dim_ordering)\",\n \"def resize(im: Image) -> Image:\\n size_factor = random.uniform(*ImageOperations.config.get('resize_interval'))\\n return im.resize((\\n int(round(im.height * size_factor, 0)),\\n int(round(im.width * size_factor, 0))\\n ))\",\n \"def interpolate(\\n input,\\n size=None,\\n scale_factor=None,\\n mode='nearest',\\n align_corners=False,\\n):\\n if size is not None:\\n size = nest.flatten(size)\\n if scale_factor is not None:\\n scale_factor = nest.flatten(scale_factor)\\n mode = mode.upper()\\n mode = mode.replace('BILINEAR', 'LINEAR')\\n mode = mode.replace('TRILINEAR', 'LINEAR')\\n return FunctionLib.apply(\\n 'Resize', input.device, [input],\\n mode=mode, align_corners=align_corners,\\n num_sizes=len(size) if size is not None else 0,\\n num_scales=len(scale_factor) if scale_factor is not None else 0,\\n sizes=size, scales=scale_factor)\",\n \"def kernel_width(self):\\n\\t\\treturn self.kernel_shape_param('W')\",\n \"def _calculate_shrinking_factor(initial_shrinking_factor: float, step_number: int, n_dim: int) -> float:\\n assert 0 < initial_shrinking_factor < 1, (\\n f\\\"Shrinking factor must be between 0 and 1. \\\" f\\\"(Was {initial_shrinking_factor}).\\\"\\n )\\n assert step_number >= 1 and n_dim >= 1, (\\n f\\\"Step number and number of dimensions must be greater than 0. \\\"\\n f\\\"(Where step_number={step_number}, n_dim={n_dim}).\\\"\\n )\\n\\n return initial_shrinking_factor ** (step_number / n_dim)\",\n \"def _gaussian_kernel(kernel_size):\\n curr_kernel = _binoms(kernel_size)\\n curr_kernel = curr_kernel.reshape(kernel_size, 1)\\n kernel2d = convolve2d(curr_kernel.transpose(), curr_kernel)\\n kernel2d = np.divide(kernel2d, np.sum(kernel2d))\\n return kernel2d\",\n \"def sobel(kernel_size: int = 3) -> Tensor:\\n assert kernel_size % 2 == 1\\n s = kernel_size // 2\\n k = torch.linspace(-s, s, kernel_size)\\n kernel = torch.stack([k] * kernel_size)\\n k[k == 0] = 1e-7\\n div = torch.stack([k] * kernel_size)\\n return kernel / (div ** 2 + div.T ** 2)\",\n \"def Pane_Resized( self, new_sizes ):\\r\\n if(new_sizes[0] > 200 ):\\r\\n cb.xtotal = new_sizes[0]-100\\r\\n self.canvas_one.config(width = new_sizes[0])\\r\\n self.canvas_scale.config(width = new_sizes[0])\\r\\n else:\\r\\n cb.xtotal = 200-100\\r\\n self.canvas_one.config(width = 200)\\r\\n self.canvas_scale.config(width = 200)\\r\\n if (len(new_sizes) > 1 ):\\r\\n self.canvas_two.config(width=new_sizes[1])\\r\\n self.system.Draw()\",\n \"def gaussian_kernel(kernel_size: (int, tuple, list), width: float):\\n kernel_size = np.asarray(to_list(kernel_size, 2), np.float)\\n half_ksize = (kernel_size - 1) / 2.0\\n x, y = np.mgrid[-half_ksize[0]:half_ksize[0] + 1, -half_ksize[1]:half_ksize[1] + 1]\\n kernel = np.exp(-(x ** 2 + y ** 2) / (2 * width ** 2))\\n return kernel / (kernel.sum() + 1e-08)\",\n \"def resizeImage(image, maxW, maxH):\\n\\timageW, imageH = image.size\\n\\tif imageW == maxW and imageH == maxH:\\n\\t\\treturn image\\n\\t# find which axis requires the biggest zoom (smallest relative max dimension)\\n\\tzoomW = float(imageW) / float(maxW)\\n\\tzoomH = float(imageH) / float(maxH)\\n\\tzoom = max(zoomW, zoomH)\\n\\tif zoomW >= zoomH:\\t# size is defined by width\\n\\t\\tmaxH = int(imageH//zoom)\\t# calculate the new height\\n\\telse:\\n\\t\\tmaxW = int(imageW//zoom)\\n\\treturn image.resize((maxW, maxH))\",\n \"def resize(img, height=800):\\n if img.shape[0] > height:\\n ratio = height / img.shape[0]\\n return cv2.resize(img, (int(ratio * img.shape[1]), height))\",\n \"def resample(medical_image, image, new_spacing=[1, 1, 1]):\\n image_shape = np.array(list(medical_image.header.get_data_shape()))\\n spacing = np.array(list(medical_image.header.get_zooms()))\\n resize_factor = spacing / new_spacing\\n\\n new_shape = image_shape * resize_factor\\n new_shape = np.round(new_shape)\\n\\n real_resize_factor = new_shape / image_shape\\n\\n new_spacing = spacing / real_resize_factor\\n resampled_image = scipy.ndimage.interpolation.zoom(image, real_resize_factor)\\n\\n return resampled_image, new_spacing\",\n \"def conv_matrix(matrix, kernel):\",\n \"def change_resolution(img):\\n scale_factor = np.random.choice(list(range(0, 6, 2)))\\n if scale_factor == 0:\\n return img\\n downsample = nn.AvgPool2d(scale_factor)\\n upsample = nn.UpsamplingNearest2d(scale_factor=scale_factor)\\n new_res_img = upsample(downsample(img.unsqueeze(dim=1))).squeeze()\\n return new_res_img\",\n \"def resize(img, new_h=None, new_w=None):\\n if isinstance(img, tf.Tensor):\\n input_is_tensor = True\\n tensor = img\\n else:\\n input_is_tensor = False\\n tensor = tf.convert_to_tensor(img)\\n\\n # Original size\\n hw = tf.shape(tensor)\\n h, w = hw[0], hw[1] # both 0D tensors\\n\\n # Figure out new size\\n if new_h is not None and new_w is not None:\\n if not tf.equal(tf.cast(h / w * new_w, h.dtype), new_h):\\n logger.warn((\\n \\\"Aspect ratio changed in resizing: original size is %s; \\\"\\n \\\"new size is %s\\\"), (h, w), (new_h, new_w))\\n elif new_h is None and new_w is not None:\\n new_h = tf.cast(h / w * new_w, h.dtype)\\n elif new_h is not None and new_w is None:\\n new_w = tf.cast(w / h * new_h, h.dtype)\\n else:\\n raise ValueError(\\\"At least one of new height or width must be given\\\")\\n new_shape = (new_h, new_w)\\n\\n resized = tf.image.resize(\\n tensor, new_shape, method='bilinear', antialias=True)\\n\\n if input_is_tensor:\\n return resized\\n\\n # Restore the original data type if input is not a tensor\\n orig_dtype = img.dtype if isinstance(img, np.ndarray) else img.numpy().dtype\\n return resized.numpy().astype(orig_dtype)\",\n \"def gaussian_kernel(kernel_size: (int, tuple, list), width: float):\\n\\n kernel_size = np.asarray(to_list(kernel_size, 2), np.float)\\n half_ksize = (kernel_size - 1) / 2.0\\n x, y = np.mgrid[-half_ksize[0]:half_ksize[0] + 1,\\n -half_ksize[1]:half_ksize[1] + 1]\\n kernel = np.exp(-(x ** 2 + y ** 2) / (2 * width ** 2))\\n return kernel / (kernel.sum() + 1e-8)\",\n \"def imresize(img, size):\\n if hasattr(size, \\\"__len__\\\"):\\n num_rows, num_cols = size\\n assert (num_rows > 0) or (num_cols > 0)\\n if num_rows < 0:\\n num_rows = num_cols * img.shape[0] / img.shape[1]\\n if num_cols < 0:\\n num_cols = num_rows * img.shape[1] / img.shape[0]\\n else:\\n num_rows = int(round(img.shape[0] * size))\\n num_cols = int(round(img.shape[1] * size))\\n return skimage.transform.resize(img, (num_rows, num_cols))\",\n \"def upscale(x, n):\\n if n == 1:\\n return x\\n x_shape = tf.shape(x)\\n height, width = x_shape[1], x_shape[2]\\n return tf.image.resize_nearest_neighbor(x, [n * height, n * width])\\n # return tf.batch_to_space(tf.tile(x, [n**2, 1, 1, 1]), [[0, 0], [0, 0]], n)\",\n \"def resize(img, min_size=512):\\n width, height, _ = tf.unstack(tf.shape(img), num=3)\\n if height < width:\\n new_height = min_size\\n new_width = int(width * new_height / height)\\n else:\\n new_width = min_size\\n new_height = int(height * new_width / width)\\n\\n img = tf.image.resize(img, size=(new_width, new_height))\\n return img\",\n \"def resizeImg(img, maxXSize=720):\\r\\n if img.size[1] > maxXSize:\\r\\n downRat = maxXSize / img.size[1]\\r\\n outImg = img.resize((int(img.size[0] * downRat),maxXSize))\\r\\n else:\\r\\n outImg = img\\r\\n return outImg\",\n \"def downsample_x2(x, fsize=5, sigma=1.4):\\n\\n\\n #\\n # Your code here\\n #\\n\\n G = gaussian_kernel(fsize, sigma)\\n g_img = convolve2d(x, G, mode='same', boundary='symm')\\n x = g_img[0::2, 0::2]\\n\\n return x\",\n \"def resample(img, hdr, target_spacing, bspline_order=3, mode='constant'):\\n if isinstance(target_spacing, numbers.Number):\\n target_spacing = [target_spacing] * img.ndim\\n \\n # compute zoom values\\n zoom_factors = [old / float(new) for new, old in zip(target_spacing, header.get_pixel_spacing(hdr))]\\n\\n print \\\"Zoom Factors\\\"\\n print zoom_factors\\n\\n oldImageShape = img.shape\\n \\n # zoom image\\n img = zoom(img, zoom_factors, order=bspline_order, mode=mode)\\n\\n newImageShape = img.shape\\n old_pixel_spacing = header.get_pixel_spacing(hdr)\\n new_pixel_spacing = np.round(np.divide(np.multiply(oldImageShape,old_pixel_spacing),newImageShape),7)\\n print \\\"Target Pixel Spacing\\\"\\n print target_spacing\\n\\n print \\\"Actual Pixel Spacing\\\"\\n print new_pixel_spacing\\n # set new voxel spacing\\n header.set_pixel_spacing(hdr, new_pixel_spacing)\\n \\n return img, hdr\",\n \"def FlyResize( image, log_mess, nimages, method = Image.ANTIALIAS ):\\n oldw, oldh = image.size\\n resl = [8, 10, 14, 16, 20, 22, 24, 32, 40, 48, 64, 96, 128, 256]\\n \\n if oldw > 256 or oldh > 256:\\n newsiz = min(resl, key = lambda x:abs(x - max(oldw, oldh)))\\n image.thumbnail((newsiz, newsiz), method)\\n neww, newh = image.size\\n log_mess += ' and new size scaled = %s x %s' %(neww, newh)\\n elif nimages > 1:\\n log_mess += ' and size = %s x %s' %(oldw, oldh)\\n \\n return oldw, oldh, image, log_mess\",\n \"def resize(self, **kwargs):\\n\\n if self.image is None:\\n raise ValueError('self.image is None! The image has to be initialized!')\\n\\n with warnings.catch_warnings():\\n warnings.simplefilter(\\\"ignore\\\")\\n self.image = ndimage.interpolation.zoom(self.image * 1., **kwargs)\\n\\n # if size <= 3, pad with zeros\\n\\n if np.min(self.image.shape) < 5:\\n self.image = np.pad(self.image, pad_width=3, mode='constant', constant_values=0)\\n\\n if self.image.max() > 0:\\n self.image = rescale_intensity(self.image, out_range=(0, 255))\\n\\n if 'Voxel size x' in self.metadata.index and 'Voxel size y' in self.metadata.index \\\\\\n and 'Voxel size z' in self.metadata.index:\\n new_voxel_size = np.array([self.metadata['Voxel size z'], self.metadata['Voxel size y'],\\n self.metadata['Voxel size x']]) / kwargs['zoom']\\n self.metadata['Voxel size'] = str(new_voxel_size)\\n self.metadata['Voxel size z'], self.metadata['Voxel size y'], self.metadata['Voxel size x'] = new_voxel_size\\n\\n return self.image\",\n \"def get_kernel(kernel_size, blur=1 / 20, halo=.001):\\n\\n # generate x and y grids\\n x, y = np.mgrid[0:kernel_size * 2 + 1, 0:kernel_size * 2 + 1]\\n\\n center = kernel_size + 1 # center pixel\\n r = np.sqrt((x - center) ** 2 + (y - center) ** 2) # distance from center\\n\\n # now compute the kernel. This function is a bit arbitrary.\\n # adjust this to get the effect you want.\\n kernel = np.exp(-r / kernel_size / blur) + (1 - r / r[center, 0]).clip(0) * halo\\n return kernel\",\n \"def resize(self, inc=True, require_val=False, val=None):\\n if require_val: # Set to value\\n if not val:\\n val = self.default_width\\n try:\\n val = int(val)\\n except:\\n message = \\\"Library width must be an integer\\\"\\n self.vimiv.statusbar.err_message(message)\\n return\\n self.width = val\\n else: # Grow/shrink by value\\n if not val:\\n val = 20\\n try:\\n val = int(val)\\n except:\\n message = \\\"Library width must be an integer\\\"\\n self.vimiv.statusbar.err_message(message)\\n return\\n if inc:\\n self.width += val\\n else:\\n self.width -= val\\n # Set some reasonable limits to the library size\\n if self.width > self.vimiv.winsize[0] - 200:\\n self.width = self.vimiv.winsize[0] - 200\\n elif self.width < 100:\\n self.width = 100\\n self.scrollable_treeview.set_size_request(self.width, 10)\\n # Rezoom image\\n if not self.vimiv.image.user_zoomed and self.vimiv.paths:\\n self.vimiv.image.zoom_to(0)\",\n \"def setRescaleFactor(self, value):\\n return self._set(rescaleFactor=value)\",\n \"def dilate_kernel(self, kernel, dilation):\\n if dilation == 0:\\n return kernel \\n # inside padding based on the scaling law\\n dilation = torch.tensor(dilation).float()\\n delta = dilation%1\\n\\n d_in = torch.ceil(dilation**2).int()\\n new_in = kernel.shape[2] + (kernel.shape[2]-1)*d_in\\n\\n d_h = torch.ceil(dilation).int()\\n new_h = kernel.shape[3] + (kernel.shape[3]-1)*d_h\\n\\n d_w = torch.ceil(dilation).int()\\n new_w = kernel.shape[4] + (kernel.shape[4]-1)*d_h\\n\\n new_kernel = torch.zeros(kernel.shape[0], kernel.shape[1], new_in, new_h, new_w)\\n new_kernel[:,:,::(d_in+1),::(d_h+1), ::(d_w+1)] = kernel\\n dilate_factor = 1\\n \\n new_kernel = F.pad(new_kernel, ((kernel.shape[4]-1)//2, (kernel.shape[4]-1)//2)*3)\\n\\n dilate_factor = (new_kernel.shape[-1] - 1 - (kernel.shape[4]-1)*(delta))/(new_kernel.shape[-1] - 1) \\n\\n grid = torch.meshgrid(torch.linspace(-1, 1, new_in)*(dilate_factor**2), \\n torch.linspace(-1, 1, new_h)*dilate_factor, \\n torch.linspace(-1, 1, new_w)*dilate_factor)\\n\\n grid = torch.cat([grid[2].unsqueeze(0).unsqueeze(-1), \\n grid[1].unsqueeze(0).unsqueeze(-1), \\n grid[0].unsqueeze(0).unsqueeze(-1)], dim = -1).repeat(kernel.shape[0],1,1,1,1)\\n\\n new_kernel = F.grid_sample(new_kernel, grid) \\n \\n return new_kernel[:,:,-kernel.shape[2]:]\",\n \"def bdev_daos_resize(client, name, new_size):\\n params = {\\n 'name': name,\\n 'new_size': new_size,\\n }\\n return client.call('bdev_daos_resize', params)\",\n \"def resize_image(image, newN, newM):\\n resize = np.zeros((newM, newN))\\n row, col = resize.shape\\n try:\\n for j in range(col):\\n for i in range(row):\\n resize[i, j] = image[i*2, j*2]\\n except:\\n pass\\n\\n return resize\",\n \"def update_resize(self, viewer, dims):\\n self.recalc(viewer)\",\n \"def resize(self, image, fx, fy, interpolation):\\n\\n if interpolation == 'bilinear':\\n return self.bilinear_interpolation(image, fx, fy)\\n\\n elif interpolation == 'nearest_neighbor':\\n return self.nearest_neighbor(image, fx, fy)\",\n \"def __resize_bilinear_with_target(self, x, target_size=None, data_format=None):\\n target_height = target_size[0]\\n target_width = target_size[1]\\n if data_format == 'channels_first':\\n new_shape = tf.constant(np.array((target_height, target_width)).astype('int32'))\\n X = K.permute_dimensions(x, [0, 2, 3, 1])\\n X = tf.image.resize_bilinear(X, new_shape)\\n X = K.permute_dimensions(X, [0, 3, 1, 2])\\n return X\\n else:\\n raise Exception(\\\"Invilid data format\\\", data_format)\",\n \"def change_scale_factor(self, new_factor):\\n if 0 < float(new_factor) < self._MAX_ZOOM:\\n self._zoom_factor = new_factor\",\n \"def crop_kernel(kernel, target_size):\\n # Use list for mutability\\n target = [d for d in target_size]\\n adjusted = False\\n for dim in range(len(target)):\\n if not target[dim] % 2:\\n target[dim] += 1\\n adjusted = True\\n if adjusted:\\n sys.stderr.write(\\n \\\"WARNING: Cropped kernel size adjusted to \\\"\\n f\\\"{target[0]}x{target[1]} to keep odd dimensions.\\\\n\\\"\\n )\\n\\n source_m, source_n = kernel.shape\\n target_m, target_n = target\\n # Define horizontal and vertical margins to trim\\n if source_m > target_m:\\n margin_rows = (source_m - target_m) // 2\\n else:\\n margin_rows = 0\\n if source_n > target_n:\\n margin_cols = (source_n - target_n) // 2\\n else:\\n margin_cols = 0\\n\\n cropped = kernel[\\n margin_rows : (source_m - margin_rows),\\n margin_cols : (source_n - margin_cols),\\n ]\\n\\n return cropped\",\n \"def resize_img(img, fx=.5, fy=.5, interpolation=cv2.INTER_LINEAR):\\n return cv2.resize(img, None, fx=fx, fy=fy, interpolation=interpolation)\",\n \"def resize_image(image, size):\\n return skimage.transform.resize(image, size, mode='reflect', preserve_range=True).astype(image.dtype)\",\n \"def resize_image_skipping(image_to_resize, resize_factor=5):\\n # size info\\n input_height = image_to_resize.height # == input_img_array.shape[0] ???\\n input_width = image_to_resize.width\\n\\n # get as Numpy Array\\n # transform the Pillow image into a Numpy array to skip rows and columns of pixels\\n input_img_array = np.array(copy_image)\\n print(\\\"dim of input_img_arry: {}\\\".format(input_img_array.shape))\\n\\n # RESIZE\\n # 1) Reduce by Rows\\n rows_to_keep = [row for row in range(0, input_height, resize_factor)]\\n img_reduced_by_rows = input_img_array[rows_to_keep, :, :]\\n # 2) Reduce by Cols\\n cols_to_keep = [col for col in range(0, input_width, resize_factor)]\\n img_reduced_by_rows_and_cols = img_reduced_by_rows[:, cols_to_keep, :]\\n\\n print(\\\"dim of output_img_arry: {}\\\".format(img_reduced_by_rows_and_cols.shape))\\n\\n # new resized image\\n new_image = Image.fromarray(img_reduced_by_rows_and_cols, mode=copy_image.mode)\\n return new_image\",\n \"def calculate_rf_size(rf_size, downsample):\\n h = 61 # 24\\\" monitor\\n d = 10 # 10cm from the right eye\\n r = 1080 / downsample # Vertical resolution\\n d_px = np.degrees(math.atan2(h / 2, d)) / (r / 2)\\n return rf_size * d_px\",\n \"def resize(x, output_shape):\\n if not _available:\\n raise ValueError('cv2 is not installed on your environment, '\\n 'so nothing will be plotted at this time. '\\n 'Please install OpenCV.\\\\n\\\\n Under Anaconda '\\n ' environment, you can install it by '\\n '$ conda install -c menpo opencv=2.4.11\\\\n')\\n\\n H, W = output_shape\\n x = x.transpose(1, 2, 0)\\n x = cv2.resize(x, dsize=(W, H))\\n x = x.transpose(2, 0, 1)\\n return x\",\n \"def zoom(self, newsize, order=1):\\n try:\\n Nx2, Ny2 = newsize\\n except TypeError:\\n Nx2 = Ny2 = newsize\\n zoom = ((Ny2+0.1)/self.Ny, (Nx2+0.1)/self.Nx)\\n if abs(zoom[0] - zoom[1]) > 1e-3:\\n raise RuntimeError(\\\"image aspect ratio cannot be changed\\\")\\n\\n pixelsize_old = self.pixelsize\\n self.image = ndimage.zoom(self.image, zoom=zoom, order=order)\\n self.pixelsize = pixelsize_old * (self.Nx / Nx2)\\n return self.image\",\n \"def bottleneck(input_tensor, filters, strides, expansion_factor):\\r\\n ## part of Fast-SCNN\\r\\n _, input_height, input_width, input_channels = input_tensor.shape\\r\\n tensor = Conv2D(filters=input_channels * expansion_factor,\\r\\n kernel_size=1,\\r\\n strides=1,\\r\\n padding=\\\"same\\\",\\r\\n activation=\\\"relu\\\")(input_tensor)\\r\\n tensor = BatchNormalization()(tensor)\\r\\n tensor = Activation('relu')(tensor)\\r\\n\\r\\n tensor = DepthwiseConv2D(kernel_size=3,\\r\\n strides=strides,\\r\\n padding=\\\"same\\\")(tensor)\\r\\n tensor = BatchNormalization()(tensor)\\r\\n tensor = Activation('relu')(tensor)\\r\\n\\r\\n tensor = Conv2D(filters=filters,\\r\\n kernel_size=1,\\r\\n strides=1,\\r\\n padding=\\\"same\\\")(tensor)\\r\\n tensor = BatchNormalization()(tensor)\\r\\n output_tensor = Activation('relu')(tensor)\\r\\n return output_tensor\",\n \"def resize_image(img: torch.Tensor, new_size: Union[int, Tuple[int, int]], resize_method: str, crop_or_pad_constant: str=CROP_OR_PAD, interpolate_constant: str=INTERPOLATE) ->torch.Tensor:\\n new_size = to_tuple(new_size)\\n if list(img.shape[-2:]) != list(new_size):\\n if resize_method == crop_or_pad_constant:\\n return crop_or_pad(img, new_size)\\n elif resize_method == interpolate_constant:\\n return F.resize(img, new_size)\\n raise ValueError(f'Invalid image resize method: {resize_method}')\\n return img\",\n \"def _filter_scaling(reduction_indices, start_cell_num):\\n filter_scaling = 1.0\\n for ind in reduction_indices:\\n if ind < start_cell_num:\\n filter_scaling *= 2.0\\n return filter_scaling\"\n]","isConversational":false},"negative_scores":{"kind":"list like","value":["0.6673881","0.64376324","0.63498217","0.6145752","0.60997856","0.6078737","0.5899416","0.5899416","0.5854729","0.5854729","0.580574","0.58015907","0.57691246","0.57011503","0.56532955","0.55739254","0.5490137","0.5478357","0.5461603","0.5452446","0.5299778","0.52933866","0.52694404","0.5243633","0.52357435","0.52345717","0.52280074","0.5209974","0.52011526","0.5189116","0.51798445","0.5171091","0.51683277","0.51623404","0.51569676","0.5148738","0.5144407","0.51406145","0.5137854","0.5120094","0.5118597","0.51088274","0.5097713","0.506142","0.50431323","0.5019004","0.50140214","0.5001364","0.49977076","0.49936634","0.49922523","0.4988551","0.49742883","0.49695408","0.4962335","0.4958226","0.4955561","0.49553144","0.49543947","0.49532452","0.49514621","0.49455348","0.49422565","0.49382222","0.4936383","0.49277553","0.49268442","0.4924117","0.49240968","0.49179494","0.49012","0.4896912","0.48824793","0.4881687","0.48780787","0.4856363","0.48478332","0.48476776","0.484295","0.48369727","0.48347756","0.48334607","0.48325473","0.4830901","0.48283556","0.4822296","0.48175126","0.4816513","0.4815428","0.48091754","0.48066026","0.48031512","0.48014376","0.48013896","0.47970346","0.4795296","0.4790367","0.4786883","0.47766438","0.47714105"],"string":"[\n \"0.6673881\",\n \"0.64376324\",\n \"0.63498217\",\n \"0.6145752\",\n \"0.60997856\",\n \"0.6078737\",\n \"0.5899416\",\n \"0.5899416\",\n \"0.5854729\",\n \"0.5854729\",\n \"0.580574\",\n \"0.58015907\",\n \"0.57691246\",\n \"0.57011503\",\n \"0.56532955\",\n \"0.55739254\",\n \"0.5490137\",\n \"0.5478357\",\n \"0.5461603\",\n \"0.5452446\",\n \"0.5299778\",\n \"0.52933866\",\n \"0.52694404\",\n \"0.5243633\",\n \"0.52357435\",\n \"0.52345717\",\n \"0.52280074\",\n \"0.5209974\",\n \"0.52011526\",\n \"0.5189116\",\n \"0.51798445\",\n \"0.5171091\",\n \"0.51683277\",\n \"0.51623404\",\n \"0.51569676\",\n \"0.5148738\",\n \"0.5144407\",\n \"0.51406145\",\n \"0.5137854\",\n \"0.5120094\",\n \"0.5118597\",\n \"0.51088274\",\n \"0.5097713\",\n \"0.506142\",\n \"0.50431323\",\n \"0.5019004\",\n \"0.50140214\",\n \"0.5001364\",\n \"0.49977076\",\n \"0.49936634\",\n \"0.49922523\",\n \"0.4988551\",\n \"0.49742883\",\n \"0.49695408\",\n \"0.4962335\",\n \"0.4958226\",\n \"0.4955561\",\n \"0.49553144\",\n \"0.49543947\",\n \"0.49532452\",\n \"0.49514621\",\n \"0.49455348\",\n \"0.49422565\",\n \"0.49382222\",\n \"0.4936383\",\n \"0.49277553\",\n \"0.49268442\",\n \"0.4924117\",\n \"0.49240968\",\n \"0.49179494\",\n \"0.49012\",\n \"0.4896912\",\n \"0.48824793\",\n \"0.4881687\",\n \"0.48780787\",\n \"0.4856363\",\n \"0.48478332\",\n \"0.48476776\",\n \"0.484295\",\n \"0.48369727\",\n \"0.48347756\",\n \"0.48334607\",\n \"0.48325473\",\n \"0.4830901\",\n \"0.48283556\",\n \"0.4822296\",\n \"0.48175126\",\n \"0.4816513\",\n \"0.4815428\",\n \"0.48091754\",\n \"0.48066026\",\n \"0.48031512\",\n \"0.48014376\",\n \"0.48013896\",\n \"0.47970346\",\n \"0.4795296\",\n \"0.4790367\",\n \"0.4786883\",\n \"0.47766438\",\n \"0.47714105\"\n]","isConversational":false},"document_score":{"kind":"string","value":"0.7894084"},"document_rank":{"kind":"string","value":"0"}}},{"rowIdx":22691,"cells":{"query":{"kind":"string","value":"Performs truncated SVD on an input kernel, returning the singular vectors necessary to retain a given proportion of information contained in the kernel."},"document":{"kind":"string","value":"def factorise_kernel(kernel, prop_info=0.999):\n u, sigma, v = la.svd(kernel)\n total_info = np.sum(sigma ** 2)\n # Compute min. number of singular vectors to retain enough info\n keep_k = np.flatnonzero(np.cumsum(sigma ** 2) > prop_info * total_info)[0] + 1\n if keep_k > np.floor(min(kernel.shape) / 2):\n sys.stderr.write(\n f\"Warning: Kernel factorisation required {keep_k} singular,\"\n \"vectors this may result in slow operations.\\n\",\n )\n # Truncate singular matrix to the keep only required vectors\n u = u[:, :keep_k]\n v = v[:keep_k, :]\n # Multiply each singular vector by the sqrt of its singular value\n for i in range(keep_k):\n u[:, i] *= np.sqrt(sigma[i])\n v[i, :] *= np.sqrt(sigma[i])\n\n return (u, v)"},"metadata":{"kind":"string","value":"{\n \"objective\": {\n \"self\": [],\n \"paired\": [],\n \"triplet\": [\n [\n \"query\",\n \"document\",\n \"negatives\"\n ]\n ]\n }\n}"},"negatives":{"kind":"list like","value":["def truncated_svd(A,k=None):","def svd_shrink(X, tau):\n U,s,V = np.linalg.svd(X, full_matrices=False)\n return np.dot(U, np.dot(np.diag(shrink(s, tau)), V))","def reduce_svd(embeddings, seed=0):\n svd = TruncatedSVD(n_components=2, n_iter=10, random_state=seed)\n return svd.fit_transform(embeddings)","def computeTruncatedSVD(docTermMatrix, dim=500):\r\n T, S, D = np.linalg.svd(np.transpose(docTermMatrix), full_matrices=False)\r\n\r\n diagS = np.diag(S)\r\n shape = np.shape(diagS)\r\n\r\n if dim <= shape[0] and dim <= shape[1]:\r\n subT = T[:,:dim]\r\n subS = diagS[:dim,:dim]\r\n subD = np.transpose(D)[:,:dim]\r\n else:\r\n subT = T\r\n subS = diagS\r\n subD = np.transpose(D)\r\n\r\n return subT, subS, subD","def truncated_svd(A,k=None):\n \n \n \n AHA=np.conj(A).T.dot(A)\n evals,evecs=la.eig(AHA)\n order=np.argsort(evals)\n\n evals=evals[order][::-1].copy()\n evecs=evecs.T[order][::-1].copy()\n m,n=AHA.shape\n \n tol=1e-12\n Vh=[]\n for i in xrange(0,m):\n\t\t if np.abs(evals[i])>=tol:\n\t \t\tVh+=[evecs[i]]\n \n Vh=np.array(Vh)\n s=np.sqrt(evals[:Vh.shape[0]])\n U=[]\n for i in xrange(0,len(s)):\n U+=[(1./s[i])*A.dot(Vh[i])]\n U=np.array(U).T\n \n return U,s,Vh","def tsvd(A, threshold=0.99999, avoid_pathological=True):\n M,N = A.shape\n full_matrices = False\n\n if is_int(threshold):\n # Assume specific number is requested\n r = threshold\n assert 1 <= r <= max(M,N)\n if r > min(M,N):\n full_matrices = True\n r = min(M,N)\n\n U,s,VT = sla.svd(A, full_matrices)\n\n if isinstance(threshold,float):\n # Assume proportion is requested\n r = truncate_rank(s,threshold,avoid_pathological)\n\n # Truncate\n U = U [:,:r]\n VT = VT[ :r]\n s = s [ :r]\n return U,s,VT","def svd(self, X): # [5pts]\n N,D = X.shape[0],X.shape[1]\n if X.ndim == 3:\n U = np.zeros((N,N,3))\n S = np.zeros((min(N,D),3))\n V = np.zeros((D,D,3))\n for i in range(3):\n U_temp,S_temp,V_temp = np.linalg.svd(X[:,:,i],compute_uv=True, full_matrices=True,hermitian=False)\n U[:,:,i] = U_temp\n S[:,i] = S_temp\n V[:,:,i] = V_temp\n else:\n U,S,V = np.linalg.svd(X,compute_uv=True,full_matrices=True, hermitian=False)\n return U,S,V","def test_truncate2():\n X = rand(5,5,5)\n T = hosvd(X)\n k = 3\n Tk = T.truncate(k)\n E = X - Tk.asarray()\n Cdk = T.X\n Cdk[:k,:k,:k] = 0\n assert np.allclose(fro_norm(E), fro_norm(Cdk))","def svdSoft(A, lmbda, k):\n if not scipy.sparse.issparse(A): \n raise ValueError(\"A must be a sparse matrix\")\n \n #U, s, V = scipy.sparse.linalg.svds(A, k)\n U, s, V = sparsesvd(A, k) \n U = U.T\n inds = numpy.flipud(numpy.argsort(s))\n U, s, V = Util.indSvd(U, s, V, inds) \n \n #Soft threshold \n s = s - lmbda\n s = numpy.clip(s, 0, numpy.max(s))\n\n return U, s, V","def update_model(X, U, S, k, n, mu,\n svdmethod='full',\n missingmethod='zero'):\n\n if len(X) == 0:\n printt(\"Error: No data in X.\")\n return None, None, None, -1, None\n #print('%d items in X' % X.shape[1])\n #print('init U:', U)\n\n # If there is no previous U, and we just got a single item in X,\n # set U to all 0's (degenerate SVD),\n # and return it with mu.\n # (PR #22 sets first value to 1; see decals implementation)\n if len(U) == 0 and X.shape[1] == 1:\n mu = X\n # Do this no matter what. Let mu get NaNs in it as needed.\n U = np.zeros_like(mu)\n U[0] = 1\n S = np.array([0])\n n = 1\n pcts = [1.0]\n return U, S, mu, n, pcts\n\n ###########################################################################\n # Do full SVD of X if this is requested, regardless of what is in U \n # Also, if n = 0 or U is empty, start from scratch\n output_k = False\n if svdmethod == 'full' or len(U) == 0 or n == 0:\n if n == 0:\n if len(U) == 0:\n printt(\"----- initial SVD -----\")\n output_k = True\n else:\n # Reshape so we don't have an empty dimension (yay python)\n U = U.reshape(-1, 1)\n elif len(U) == 0:\n printt(\"WARNING: N (number of items modeled by U) is %d, not zero, but U is empty!\" % n)\n\n # Bootstrap\n if missingmethod == 'ignore':\n printt(\"ERROR: ignore with full is not possible under ordinary circumstances.\")\n printt(\"Use --increm-brand to impute for NaNs.\")\n printt(\"For now, we are filling NaNs with 0.\")\n X = copy.deepcopy(X)\n z = np.where(np.isnan(X))\n X[z] = 0\n\n mu = np.mean(X, axis=1).reshape(-1,1)\n X = X - mu\n U, S, V = linalg.svd(X, full_matrices=False)\n printt('Just did full SVD on %d items.' % X.shape[1])\n #print('X:', X)\n #print('U:', U)\n # Reset U to all 0's if we only have one item in X (degenerate SVD)\n if X.shape[1] == 1:\n U = np.zeros_like(U)\n \n # Keep only the first k components\n S_full = S\n S = S[0:k]\n U = U[:,0:k]\n\n # Update n to number of new items in X\n n = X.shape[1]\n \n ###########################################################################\n # Incremental SVD from Ross\n elif svdmethod == 'increm-ross':\n # Incremental SVD from Ross et al. 2008\n # \"Incremental Learning for Robust Visual Tracking\"\n # based on Lim and Ross's sklm.m implementation in MATLAB.\n\n # This method DOES NOT handle missing values.\n if missingmethod == 'ignore':\n print('ERROR: increm-ross cannot handle missing values.')\n print('If they are present, try svdmethod=increm-brand')\n print(' or use missingmethod=zero to zero-fill.')\n print('If there are no missing values, specify missingmethod=none.')\n sys.exit(1)\n\n n_new = X.shape[1]\n \n # Compute mean\n # Weirdly, the later 'X-mu_new' is MUCH faster if you reshape as shown.\n # This is because of differences in the way numpy treats a 1d array versus a 2d column.\n mu_new = np.mean(X, axis=1).reshape(-1,1)\n\n # Subtract the mean, append it as a column vector, and update mu\n # X - mu_new will be zero if X has only 1 item\n mu_old = mu\n # New mu is a weighted sum of old and new mus\n mu = (n * mu_old + n_new * mu_new) / (n + n_new)\n B = np.hstack((X - mu,\n math.sqrt(n_new * n/float(n_new+n)) * \\\n (mu_old - mu_new)))\n printt(\"Now tracking mean for %d -> %d items; mu.min %f, mu.max %f \" % \\\n (n, n+n_new, np.nanmin(mu), np.nanmax(mu)))\n n = n + n_new\n\n if S.all() == 0:\n npcs = U.shape[1]\n diagS = np.zeros((npcs, npcs))\n else:\n diagS = np.diag(S)\n\n # I don't think this is right. At this point B is the augmented\n # matrix rather than the single observation.\n proj = np.dot(U.T, B)\n reproj_err = B - np.dot(U, proj)\n\n # to get orthogonal form of reproj_err\n # This should return q with dimensions [d(X) by n_new+1], square\n q, dummy = linalg.qr(reproj_err, mode='full')\n # print('q.shape should be 7x2: ', q.shape)\n Q = np.hstack((U, q))\n\n # From Ross and Lim, 2008\n # R = [ [ Sigma, U.T * X ] [ 0, orthog. component of reproj error ] ]\n k_now = diagS.shape[0]\n new_dim = k_now + n_new + 1\n R = np.zeros((new_dim, new_dim))\n R[0:k_now,0:k_now] = diagS\n R[0:k_now,k_now:] = proj\n orthog_reproj_err = np.dot(q.T, reproj_err)\n R[k_now:, k_now:] = orthog_reproj_err\n \n # Perform SVD of R. Then finally update U.\n U, S, V = linalg.svd(R, full_matrices=False)\n printt('Just did increm-ross SVD on %d items.' % n)\n\n U = np.dot(Q, U)\n \n # Keep only the first k components\n U = U[:,0:min([n,k])]\n S_full = S\n S = S[0:min([n,k])]\n\n ###########################################################################\n # Incremental SVD from Brand\n elif svdmethod == 'increm-brand':\n # Pulled out James's attempt to handle NaNs into\n # increm-brand-james.py. Starting over from scratch here.\n n_new = X.shape[1]\n\n if n_new != 1:\n print(\"WARNING: increm-brand will probably only work by adding one item at a time.\")\n input('\\nPress enter to continue or ^C/EOF to exit. ')\n\n if missingmethod == 'ignore':\n # 1. Update mu\n mu_old = mu\n mu_new = X\n\n # Be careful! For any pre-existing NaNs in mu,\n # let mu_new fill them in. Can't get any worse!\n naninds = np.where(np.isnan(mu_old))[0]\n if naninds.size > 0:\n mu_old[naninds,0] = mu_new[naninds,0]\n # And likewise for mu_new -- fill with good values from mu_old.\n naninds = np.where(np.isnan(mu_new))[0]\n if naninds.size > 0:\n mu_new[naninds,0] = mu_old[naninds,0]\n # At this point, the only NaNs that should appear are\n # values that were NaN for both mu and X to start with.\n # They will stay NaN and that's okay.\n \n # New mu is a weighted sum of old and new mus\n mu = (n * mu_old + n_new * mu_new) / (n + n_new)\n printt(\"Now tracking mean for %d -> %d items; mu.min %f, mu.max %f \" % \\\n (n, n+n_new, np.nanmin(mu), np.nanmax(mu)))\n n = n + n_new\n\n # 2. Subtract off the mean\n X = X - mu\n\n # 3. Compute L, the projection of X onto U\n # Note: this will only work for a single item in X\n goodinds = np.where(~np.isnan(X))[0]\n #print('X: %d of %d are good.' % (len(goodinds), X.shape[0]))\n\n diagS = np.diag(S)\n # This is Brand's method, which involves S:\n L = np.dot(diagS,\n np.dot(np.linalg.pinv(np.dot(U[goodinds,:],\n diagS)),\n X[goodinds,:]))\n # Simplified version that does not use S (but is probably wrong):\n #L = np.dot(U[goodinds,:].T,\n # X[goodinds,:])\n # Top row of the Q matrix (eqn 12, Brand 2002)\n Q1 = np.hstack([diagS, L])\n\n # 4. Compute J, the orthogonal basis of H, which is\n # the component of X orthog to U (i.e., unrepresentable direction)\n # 5. Compute K, the projection of X onto J (i.e., unrep. content)\n K = linalg.norm(X[goodinds,:] - np.dot(U[goodinds,:],\n np.dot(U[goodinds,:].T,\n X[goodinds,:])))\n # H = X - UL\n J = np.zeros((U.shape[0], 1))\n J[goodinds] = np.dot(K,\n np.linalg.pinv(X[goodinds,:] -\n np.dot(U[goodinds,:],\n L))).T\n \n # Bottom row of Q matrix (eqn 12, Brand 2002)\n Q2 = np.hstack([np.zeros([1, len(S)]), np.array(K).reshape(1,1)])\n Q = np.vstack([Q1, Q2])\n\n # 6. Take the SVD of Q\n Uq, Sq, Vq = linalg.svd(Q, full_matrices=False)\n\n # 7. Update U and S (eqn 4, Brand 2002)\n # Note: Since J is zero-filled for badinds, now U is too.\n # Alternatively, we give J NaNs and let them get into U as well.\n # I think that is a worse idea though.\n U = np.dot(np.hstack([U, J]), Uq)\n S = Sq\n # Updating V requires knowing old V,\n # but we don't need the new one either so it's okay to skip.\n\n printt('Just did increm-brand SVD on %d items.' % n)\n \n ############# end ###########\n \n else: # No missing values (or not 'ignore')\n # 1. Update mu\n mu_old = mu\n mu_new = X\n # New mu is a weighted sum of old and new mus\n mu = (n * mu_old + n_new * mu_new) / (n + n_new)\n n = n + n_new\n\n # 2. Subtract off the mean\n X = X - mu\n\n # 3. Compute L, the projection of X onto U\n L = np.dot(U.T, X)\n Q1 = np.hstack([np.diag(S), L])\n\n # 4. Compute J, the orthogonal basis of H, which is\n # the component of X orthog to U (i.e., unrepresentable direction)\n # 5. Compute K, the projection of X onto J (i.e., unrep. content)\n JK = X - np.dot(U, L)\n (J, K) = linalg.qr(JK)\n\n Q2 = np.hstack([np.zeros([1, len(S)]), np.array(K).reshape(1,1)])\n Q = np.vstack([Q1, Q2])\n\n # 6. Take the SVD of Q\n Uq, Sq, Vq = linalg.svd(Q, full_matrices=False)\n\n # 7. Update U and S (eqn 4, Brand 2002)\n U = np.dot(np.hstack([U, J]), Uq)\n S = Sq\n # V requires knowing old V,\n # but we don't need the new one either so it's okay.\n \n printt('Just did regular increm SVD on %d items.' % n)\n\n # Keep only the first k components\n U = U[:,0:min([n,k])]\n S = S[0:min([n,k])]\n\n Usum = U.sum(1)\n\n\n ###########################################################################\n # We have a bad svdmethod, but somehow didn't catch it earlier.\n else:\n printt(\"504: Bad Gateway in protocol <Skynet_authentication.exe>\")\n return None, None, None, None, None\n\n indivpcts = None\n\n # This only works if a full SVD was done\n if (svdmethod == 'full' and output_k and opts['k_var'] == -773038.0):\n # Calculate percent variance captured by each \n cumsum = np.cumsum(S_full)\n #print(cumsum.shape)\n if cumsum[-1] != 0:\n indivpcts = S / cumsum[-1]\n indivpcts = indivpcts[0:k] # truncate to first k\n cumpercents = cumsum / cumsum[-1]\n else:\n indivpcts = []\n\n # Calculate percent variance captured\n if k >= cumsum.shape[0]:\n printt('Cannot estimate data variance; specified k (%d) exceeds the number of SVs (%d).' % (k, cumsum.shape[0]))\n else:\n printt(\"Selected value of k=%d captures %5.2f%% of the data variance\" % \\\n (k, cumpercents[k-1] * 100))\n if opts['pause']: input(\"Press enter to continue\\n\")\n\n #print('U:', U)\n #print('mu:', mu)\n return U, S, mu, n, indivpcts","def reduce_to_k_dim(M, k=2): \n n_iters = 10 # Use this parameter in your call to `TruncatedSVD`\n M_reduced = None\n print(\"Running Truncated SVD over %i words...\" % (M.shape[0]))\n \n # YOUR CODE HERE\n \n svd = TruncatedSVD(n_components = k, n_iter = 10, random_state = 0)\n \n M_reduced = svd.fit_transform(M)\n \n #raise NotImplementedError()\n\n print(\"Done.\")\n return M_reduced","def ES_SVD(U, sigma, V, time, f_fault, f_side, PMItreshold, estimate_xi_func=get_SVDxi, estimate_xi_func_params=None):\n\n # Get the search region\n m = sigma.size\n f_fault = np.asanyarray(f_fault)\n f_side = np.asanyarray(f_side)\n dt = time[1] - time[0]\n Fs = 1.0/dt\n PMI = [] #PMI is here the envelope score\n W = []\n for i in range(0, f_fault.size):\n PMI.append(np.zeros(m))\n W.append(np.zeros(m))\n\n # Calculate PMI for each fault type\n for i in range(0, m):\n if estimate_xi_func_params is None:\n a_i = estimate_xi_func(U, sigma, V, i)\n else:\n a_i = estimate_xi_func(U, sigma, V, i, estimate_xi_func_params)\n a_i = envelope(a_i)\n Y, df = fft(a_i, Fs)\n # Calculate PMI for each fault type\n for k in range(0, f_fault.size):\n PMI[k][i] = diagnosefft(Y, df, f_fault[k], 1.0, f_side[k])\n\n # Calculate weights\n for k in range(0, f_fault.size):\n temp = 0.0\n for i in range(0, m):\n if PMI[k][i] > PMItreshold:\n temp += PMI[k][i]\n for i in range(0, m):\n if PMI[k][i] > PMItreshold:\n W[k][i] = PMI[k][i]/temp\n\n # Return data\n return PMI, W","def reduce_dimentions(users):\n\tsvd = TruncatedSVD(n_components=300, n_iter=10, random_state=42)\n\tsvd.fit(users)\n\tusers_svd = svd.transform(users)\n\tusers_svd = pd.DataFrame(users_svd, index=users.index)\n\treturn users_svd","def svd0(A):\n M,N = A.shape\n if M>N: return sla.svd(A, full_matrices=True)\n else: return sla.svd(A, full_matrices=False)","def reduce_to_k_dim(M, k=2): \n n_iters = 10 # Use this parameter in your call to `TruncatedSVD`\n M_reduced = None\n print(\"Running Truncated SVD over %i words...\" % (M.shape[0]))\n \n # ------------------\n # Write your implementation here.\n svd = TruncatedSVD(n_components=k, n_iter=n_iters)\n M_reduced = svd.fit_transform(M)\n \n # ------------------\n\n print(\"Done.\")\n return M_reduced","def HOSVD(A, k=None, tol=None):\n\n d=len(A.shape)\n\n if d==2:\n u, s, vt=svd(A, full_matrices=False)\n U=[u, vt.T]\n S=np.diag(s)\n else:\n U=[None]*d\n for j in range(0, d):\n U[j], s, vt=svd(unfold(A, j), full_matrices=False)\n\n S=A.copy()\n for i in range(0, d):\n S=nModeProduct(S, U[i].T, i)\n\n if k is not None:\n if isinstance(k, int): # if only one integer is assigned to k\n k=k*np.ones((len(A.shape),), dtype=int)\n\n S=subTensor(S, k=k)\n for j in range(0, d):\n U[j]=U[j][:, :k[j]]\n\n return S, U","def svd(T):\n try:\n U, S, V = splinalg.svd(T, full_matrices=False)\n except splinalg.LinAlgError:\n U, S, V = splinalg.svd(T, full_matrices=False, lapack_driver='gesvd')\n maxU, minU = U.max(0), U.min(0)\n maxV, minV = V.max(1), V.min(1)\n ind = (np.abs(minU) > maxU) & (np.abs(minV) > maxV)\n U[:, ind] *= -1\n V[ind] *= -1\n return U, S, V","def test_truncate():\n # rank 1 tensor\n X = outer(rand(3), rand(4), rand(5))\n T = hosvd(X)\n assert find_truncation_rank(T.X, 1e-12) == (1,1,1)\n T1 = T.truncate(1)\n assert np.allclose(X, T1.asarray())","def get_kernel_vector_unit(name, s, e, x, d_1, kernel, hyps,\n cutoffs, hyps_mask):\n\n size = (e-s)\n ds = [1, 2, 3]\n\n args = from_mask_to_args(hyps, hyps_mask, cutoffs)\n\n k_v = np.zeros(size*3, )\n\n for m_index in range(size):\n x_2 = _global_training_data[name][m_index+s]\n for d_2 in ds:\n k_v[m_index*3+d_2-1] = kernel(x, x_2, d_1, d_2, *args)\n\n return k_v","def calculate_k_SVD(smooth_spreadsheet_matrix, k):\n U_unitary_matrix, singular_value, V_unitary_matrix = linalg.svd(smooth_spreadsheet_matrix)\n S_full_squared_matrix = np.zeros((k, k))\n np.fill_diagonal(S_full_squared_matrix, np.sqrt(singular_value[:k]))\n U_unitary_matrix = U_unitary_matrix[:, :k]\n return U_unitary_matrix, S_full_squared_matrix","def svt(X, tau):\n U, S, Vt = la.svd(X,full_matrices=False)\n Xs = np.dot(U * st(S,tau), Vt)\n return Xs","def svd(self):\n U, s, Vh = la.svd(self)\n S = np.zeros(self.shape)\n np.fill_diagonal(S, s)\n return (Matrix(U), Matrix(S), Matrix(Vh))","def compute_svd(self,data,k):\n m, n =data.shape\n n = self.comm1.allreduce(n)\n print(m,n)\n if k==-1:\n k = min(m,n)\n args = parse()\n args.m,args.n,args.k,args.comm = m,n,k,self.comms\n args.eps = np.finfo(data.dtype).eps\n if args.m<args.n: args.p_r,args.p_c = 1,self.size\n dsvd = DistSVD(args, data)\n singularValues, U, V = dsvd.svd()\n rel_error = dsvd.rel_error(U, np.diag(singularValues), V)\n if self.global_rank==0: print('relative error is:', rel_error )\n return singularValues,U,V,rel_error","def svd(matrix):\n u = None\n s = None\n v = None\n ### YOUR CODE HERE\n (u,s,v)=np.linalg.svd(matrix)\n ### END YOUR CODE\n\n return u, s, v","def get_svd(self,new_df):\n\n\t\tnew_matrix = new_df.as_matrix()\n\t\t\n\t\t#get the mean score of users\n\t\tmean_score = np.mean(new_matrix,axis = 1)\n\t\tnew_matrix_dm = new_matrix - mean_score.reshape(-1,1)\n\t\t#decompose interaction matrix into U, Vt, Sigma\n\t\tU, sigma, Vt = svds(new_matrix_dm, k = 5)\n\t\tsigma = np.diag(sigma)\n\t\tall_user_predicted_ratings = np.dot(np.dot(U, sigma), Vt) + mean_score.reshape(-1, 1)\n\t\tpreds_df = pd.DataFrame(all_user_predicted_ratings, columns = new_df.columns)\n\t\tpreds_df = preds_df.round(1)\n\n\t\treturn preds_df","def implement_svd(data):\n u, s, v = torch.svd(data) # implement svd\n # note: the u returned by this function only includes the top values.\n # u * s will be equivalent due to the zero terms, but will run more efficiently with this implementation.\n s = torch.diag(s) # turn s into a diagonal matrix\n transformed_matrix = torch.mm(u, s) # u * s\n return l21_reg(s), transformed_matrix # return the L2,1 regularization term and matrix","def fit_svd(self):\n\n # U has the eigenvectors of G.Gt as columns ()\n # S has square roots of the eigenvalues of G.Gt and Gt.G in its diagonal\n # The square roos of the eigenvalues are called singular values\n # V has the eigenvectors of Gt.G as columns ()\n # full_matrices set to false will set the Vt matrix to a shape m x n\n\n U, S, Vt = linalg.svd(self.norm_matrix, full_matrices=False)\n\n # Compute the eigenvalues\n eig_val = (S ** 2)\n\n # Explained_variance tell us how much of the variance in the data each eigen value explains\n explained_variance = eig_val / (self.n_samples - 1)\n # total_var is the total variance in the data\n total_var = explained_variance.sum()\n explained_variance_ratio = explained_variance / total_var\n # The cumulative sum of all ratios\n ratio_cumsum = np.cumsum(explained_variance_ratio)\n\n # We search in the cumsum for the index of the value which, when added, corresponds to the quality_percent\n # The index of the cumsum gives us the components we need to add to explain X quality percent of our data\n n_components = np.searchsorted(ratio_cumsum, self.quality_percent, side='right') + 1\n\n self.components = Vt[:n_components]\n print(\"The principal components have been calculated using svd\", self.components.shape)\n\n return self.components","def R_SVD(U, sigma, V, time, f_fault, tolerance = 0.02, PMItreshold = 1.0, estimate_xi_func=get_SVDxi, estimate_xi_func_params=None):\n\n # Get the search region\n m = sigma.size\n f_fault = np.asanyarray(f_fault)\n dt = time[1] - time[0]\n T0 = np.zeros(f_fault.size, dtype=int)\n T1 = np.zeros(f_fault.size, dtype=int)\n PMI = []\n W = []\n for i in range(0, f_fault.size):\n T0[i] = int( np.floor( (1.0/(f_fault[i]*(1.0 + tolerance)))/dt ) )\n T1[i] = int( np.ceil( (1.0/(f_fault[i]*(1.0 - tolerance)))/dt ) )\n if T1[i] == T0[i]:\n T1[i] += 1\n PMI.append(np.zeros(m))\n W.append(np.zeros(m))\n\n # Calculate PMI for each fault type\n for i in range(0, m):\n if estimate_xi_func_params is None:\n a_i = estimate_xi_func(U, sigma, V, i)\n else:\n a_i = estimate_xi_func(U, sigma, V, i, estimate_xi_func_params)\n a_i = envelope(a_i)\n a_i -= a_i.mean()\n R_a = fftwconvolve(np.flipud(a_i), a_i)\n # Keep positive part\n R_a = R_a[a_i.size-1:]\n # Scale by dividing by number of elements\n R_a = R_a / np.arange(R_a.size, 0, -1)\n # Get T_0\n R_0 = R_a[0]\n # Calculate R and PMI for each fault type\n for k in range(0, f_fault.size):\n # print('T0[%i] = %f, T1[%i] = %f, R_a.size = %i' % (k, T0[k], k, T1[k], R_a.size))\n # print(R_a[481:501])\n R_T = np.max(R_a[T0[k]:T1[k]])\n PMI[k][i] = R_T/(R_0 - R_T)\n\n # Calculate weights\n for k in range(0, f_fault.size):\n temp = np.sum(PMI[k])\n for i in range(0, m):\n if PMI[k][i] > PMItreshold:\n W[k][i] = PMI[k][i]/temp\n\n # Return data\n return PMI, W","def svd_S(T):\n try:\n S = splinalg.svd(T, full_matrices=False, compute_uv=False)\n except splinalg.LinAlgError:\n S = splinalg.svd(T, full_matrices=False, lapack_driver='gesvd', compute_uv=False)\n return S","def svr_kernel(name, kernel, epsilon=None, **kwargs):\n def _name(msg):\n return '%s.%s_%s' % (name, kernel, msg)\n\n hp_space = _svm_hp_space(_name, kernel=kernel, **kwargs)\n hp_space.update(_svr_hp_space(_name, epsilon))\n return scope.sklearn_SVR(**hp_space)","def svd_stability(N=20, display=False):\n B = np.random.randn(N, N) # i.i.d normal matrix\n C = np.random.randn(N, N) # i.i.d normal matrix\n S = np.sort(np.abs(np.random.randn(N)))[::-1] # i.i.d normal vector\n\n # Generating U, V, sigma using QR (Cheap way to get orthogonal matrices)\n U, _ = np.linalg.qr(B) # U\n V, _ = np.linalg.qr(C) # V\n\n A = (U * S) @ V.T # Forming A\n\n U2, S2, VT2 = np.linalg.svd(A)\n\n norm1 = np.linalg.norm(U2 - U)\n norm2 = np.linalg.norm(S2 - S)\n norm3 = np.linalg.norm(VT2 - V.T)\n norm4 = np.linalg.norm(((U2 * S2) @ VT2) - A)\n\n if display:\n print(\"||U2 - U|| = %s\" %norm1)\n print(\"||sigma2 - sigma|| = %s\" %norm2)\n print(\"||V2 - V|| = %s\" %norm3)\n print(\"||(U2 @ S2 @ V2) - A|| = %s\" %norm4)\n return (norm1, norm2, norm3, norm4)","def computesvd(train, K):\n mean = global_mean(train)\n train = standardize(train, mean)\n std = compute_std(train)\n train = div_std(train)\n\n U, s, Vt = sp.linalg.svds(train, k=K)\n\n dim = (len(s), len(s))\n S = np.zeros(dim)\n for i in range(len(s)):\n S[i, i] = mt.sqrt(s[i])\n\n U = sp.lil_matrix(U, dtype=np.float32) # dim(M,k)\n S = sp.lil_matrix(S, dtype=np.float32) # dim(k,k)\n Vt = sp.lil_matrix(Vt, dtype=np.float32) # dim(k,N)\n\n user_features = S.dot(Vt)\n item_features = np.transpose(U.dot(S))\n\n return user_features, item_features, mean, std","def compact_svd(A, tol=1e-6):\r\n eigs, vecs = la.eig(A.conj().T@A)\r\n svs = np.sqrt(eigs)\r\n #sort eigenvalues and eigenvectors accordingly\r\n sorter = list(zip(svs,vecs.T))\r\n sorter.sort(reverse=True, key=lambda tup: tup[0])\r\n svs = [x[0] for x in sorter]\r\n vecs = [x[1] for x in sorter]\r\n #find number of nonzero eigenvalues\r\n r_not = svs.count(0)\r\n r = len(svs) - r_not\r\n svs_1 = np.array(svs[:r])\r\n vecs_1 = np.array(vecs[:r])\r\n u_1 = (A@vecs_1)/svs_1\r\n\r\n return u_1, svs_1, vecs_1.conj().T","def svd(a, full_matrices=1, compute_uv=1):\n return SVD(full_matrices, compute_uv)(a)","def matrix_svd(\n self,\n chis=None,\n eps=0,\n print_errors=\"deprecated\",\n break_degenerate=False,\n degeneracy_eps=1e-6,\n sparse=False,\n trunc_err_func=None,\n ):\n if print_errors != \"deprecated\":\n msg = (\n \"The `print_errors` keyword argument has been deprecated, \"\n \"and has no effect. Rely instead on getting the error as a \"\n \"return value, and print it yourself.\"\n )\n warnings.warn(msg)\n chis = self._matrix_decomp_format_chis(chis, eps)\n maxchi = max(chis)\n assert self.defval == 0\n assert self.invar\n\n # SVD each sector at a time.\n # While doing so, also keep track of a list of all singular values, as\n # well as a heap that gives the negative of the largest singular value\n # in each sector. These will be needed later when deciding how to\n # truncate the decomposition.\n svds = {}\n dims = {}\n minus_next_sings = []\n all_sings = []\n for k, v in self.sects.items():\n if 0 in v.shape:\n shp = v.shape\n m = min(shp)\n u = np.empty((shp[0], m), dtype=self.dtype)\n s = np.empty((m,), dtype=np.float_)\n v = np.empty((m, shp[1]), dtype=self.dtype)\n else:\n if sparse and maxchi < min(v.shape) - 1:\n u, s, v = spsla.svds(\n v, k=maxchi, return_singular_vectors=True\n )\n order = np.argsort(-s)\n u = u[:, order]\n s = s[order]\n v = v[order, :]\n else:\n u, s, v = np.linalg.svd(v, full_matrices=False)\n svd = (s, u, v)\n svds[k] = svd\n dims[k] = 0\n sings = svd[0]\n all_sings.append(sings)\n if 0 not in sings.shape:\n heapq.heappush(minus_next_sings, (-sings[0], k))\n try:\n all_sings = np.concatenate(all_sings)\n except ValueError:\n all_sings = np.array((0,))\n\n if sparse:\n norm_sq = self.norm_sq()\n else:\n norm_sq = None\n\n # Figure out what bond dimension to truncate to, how this bond\n # dimension is distributed over the different sectors, and what the\n # truncation error is.\n chi, dims, rel_err = type(self)._find_trunc_dim(\n all_sings,\n svds,\n minus_next_sings,\n dims,\n chis=chis,\n eps=eps,\n break_degenerate=break_degenerate,\n degeneracy_eps=degeneracy_eps,\n trunc_err_func=trunc_err_func,\n norm_sq=norm_sq,\n )\n\n # Truncate each block and create the dim for the new index.\n new_dim = []\n new_qim = []\n svds = {k: v for k, v in svds.items() if dims[k] > 0}\n for k, v in svds.items():\n d = dims[k]\n if d > 0:\n new_dim.append(d)\n new_qim.append(k[0])\n svds[k] = (v[0][:d], v[1][:, :d], v[2][:d, :])\n else:\n del svds[k]\n\n # Initialize U, S, V.\n d = self.dirs[0]\n U = type(self)(\n [self.shape[0], new_dim],\n qhape=[self.qhape[0], new_qim],\n dirs=[d, -d],\n qodulus=self.qodulus,\n dtype=self.dtype,\n charge=0,\n )\n S = type(self)(\n [new_dim],\n qhape=[new_qim],\n dirs=[d],\n qodulus=self.qodulus,\n dtype=np.float_,\n invar=False,\n charge=0,\n )\n V = type(self)(\n [new_dim, self.shape[1]],\n qhape=[new_qim, self.qhape[1]],\n dirs=[d, self.dirs[1]],\n qodulus=self.qodulus,\n dtype=self.dtype,\n charge=self.charge,\n )\n\n # Set the blocks of U, S and V.\n for k, v in svds.items():\n k_U = (k[0], k[0])\n S[(k[0],)] = v[0]\n U[k_U] = v[1]\n V[k] = v[2]\n\n return U, S, V, rel_err","def pinv_damped(a, l, rcond=1e-15 ):\n # a, wrap = np.linalg._makearray(a)\n # np.linalg._assertNoEmpty2d(a)\n a = a.conjugate()\n u, s, vt = np.linalg.svd(a, 0)\n m = u.shape[0]\n n = vt.shape[1]\n cutoff = rcond*np.maximum.reduce(s)\n # estimate of smallest singular value\n # s_m = 0.00065\n s_m = np.amin(s)\n # size of the singular region\n eps = .10\n if s_m > eps:\n lambda_2 =0\n else:\n lambda_2 = (1-(s_m/eps)**2)*l*l\n # print np.amin(s), lambda_2\n for i in range(min(n, m)):\n if s[i] > cutoff:\n s[i] = s[i]/(s[i]*s[i]+lambda_2)\n # s[i] = s[i]/(s[i]*s[i]+l*l)\n else:\n s[i] = 0.;\n print('singularity: ', s)\n # if s[i] > cutoff:\n # s[i] = s[i]/(s[i]*s[i]+lambda_2)\n # # s[i] = s[i]/(s[i]*s[i]+l*l)\n # else:\n # s[i] = 0.;\n # print('singularity: ', s)\n # print np.maximum.reduce(s)\n res = np.dot(np.transpose(vt), np.multiply(s[:, np.newaxis], np.transpose(u)))\n # return wrap(res)\n return res","def get_kernel_vector(name, kernel, x, d_1, hyps,\n cutoffs=None, hyps_mask=None,\n n_cpus=1, n_sample=100):\n\n size = len(_global_training_data[name])\n\n if (n_cpus is None):\n n_cpus = mp.cpu_count()\n if (n_cpus == 1):\n return get_kernel_vector_unit(\n name, 0, size, x, d_1, kernel, hyps,\n cutoffs, hyps_mask)\n\n block_id, nbatch = partition_c(n_sample, size, n_cpus)\n\n result_queue = mp.Queue()\n children = []\n for wid in range(nbatch):\n s, e = block_id[wid]\n children.append(\n mp.Process(\n target=queue_wrapper,\n args=(result_queue, wid,\n get_kernel_vector_unit,\n (name, s, e, x, d_1, kernel, hyps,\n cutoffs, hyps_mask\n )\n )\n )\n )\n\n # Run child processes.\n for c in children:\n c.start()\n\n # Wait for all results to arrive.\n k12_v = np.zeros(size*3)\n for _ in range(nbatch):\n wid, result_chunk = result_queue.get(block=True)\n s, e = block_id[wid]\n k12_v[s*3:e*3] = result_chunk\n\n # Join child processes (clean up zombies).\n for c in children:\n c.join()\n\n return k12_v","def trace_norm_prox(W, alpha):\n d, T = W.shape\n U, s, V = np.linalg.svd(W, full_matrices=True)\n # U ~ (d, d)\n # s ~ (min(d, T), min(d, T))\n # V ~ (T, T)\n s = soft_thresholding(s, alpha)\n # make the output n1 x n2\n if d >= T:\n st_S = np.vstack((np.diag(s), np.zeros((np.abs(d-T), T))))\n else:\n st_S = np.hstack((np.diag(s), np.zeros((d, np.abs(d-T)))))\n return np.dot(U, np.dot(st_S, V))","def compute_optimal_subspace_projection(G,X,k):\n # center the data\n # G = G - np.mean(G,axis=0)\n # compute the k largest eigens of G\n # U,S,VT = np.linalg.svd(G)\n \n U,S,VT = np.linalg.svd(G/np.sqrt(G.shape[0]))\n # truncated score matrix\n #G = U[:,:k] @ np.diag(S[:k])\n G = G @ (VT.T)[:,:k]\n # project X as well\n X = X @ (VT.T[:,:k])\n print(\"Singular values\", S[:20])\n return G,X, (VT.T[:,:k])","def scv(SP):\n scv = ((np.std(SP,axis=1)/np.mean(SP,axis=1)))\n return scv","def dpp_sw(kernel_matrix, window_size=3, max_length=14, epsilon=1E-10):\r\n item_size = kernel_matrix.shape[0]\r\n v = np.zeros((max_length, max_length))\r\n cis = np.zeros((max_length, item_size))\r\n di2s = np.copy(np.diag(kernel_matrix))\r\n selected_items = list()\r\n selected_item = np.argmax(di2s)\r\n selected_items.append(selected_item)\r\n window_left_index = 0\r\n while len(selected_items) < max_length:\r\n k = len(selected_items) - 1\r\n ci_optimal = cis[window_left_index:k, selected_item]\r\n di_optimal = math.sqrt(di2s[selected_item])\r\n v[k, window_left_index:k] = ci_optimal\r\n v[k, k] = di_optimal\r\n elements = kernel_matrix[selected_item, :]\r\n eis = (elements - np.dot(ci_optimal, cis[window_left_index:k, :])) / di_optimal\r\n cis[k, :] = eis\r\n di2s -= np.square(eis)\r\n if len(selected_items) >= window_size:\r\n window_left_index += 1\r\n for ind in range(window_left_index, k + 1):\r\n t = math.sqrt(v[ind, ind] ** 2 + v[ind, window_left_index - 1] ** 2)\r\n c = t / v[ind, ind]\r\n s = v[ind, window_left_index - 1] / v[ind, ind]\r\n v[ind, ind] = t\r\n v[ind + 1:k + 1, ind] += s * v[ind + 1:k + 1, window_left_index - 1]\r\n v[ind + 1:k + 1, ind] /= c\r\n v[ind + 1:k + 1, window_left_index - 1] *= c\r\n v[ind + 1:k + 1, window_left_index - 1] -= s * v[ind + 1:k + 1, ind]\r\n cis[ind, :] += s * cis[window_left_index - 1, :]\r\n cis[ind, :] /= c\r\n cis[window_left_index - 1, :] *= c\r\n cis[window_left_index - 1, :] -= s * cis[ind, :]\r\n di2s += np.square(cis[window_left_index - 1, :])\r\n di2s[selected_item] = -np.inf\r\n selected_item = np.argmax(di2s)\r\n if di2s[selected_item] < epsilon:\r\n break\r\n selected_items.append(selected_item)\r\n return selected_items","def rebuild_svd(self, U, S, V, k): # [5pts]\n \n N,D = U.shape[0],V.shape[0]\n \n \n if U.ndim == 3:\n Xrebuild = np.zeros((N,D,3))\n for i in range(3):\n U_temp = U[:,0:k,i]\n S_temp = S[:,i]\n S_temp = np.diag(S_temp[0:k])\n V_temp = V[0:k,:,i]\n Xrebuild_temp = U_temp@S_temp@V_temp\n Xrebuild[:,:,i] = Xrebuild_temp\n else:\n U_new = U[:,0:k]\n S_new = np.diag(S[0:k])\n V_new = V[0:k,:]\n Xrebuild = U_new@S_new@V_new\n\n return Xrebuild","def set_gpm_by_svd(self, threshold):\n assert len(self.M) > 0\n\n self.M_vec = []\n self.M_val = []\n\n index = np.arange(len(self.M))\n np.random.shuffle(index)\n\n for k, idx in enumerate(index):\n if k < min(self.mem_batch_size, len(self.M)):\n ox, oy, ot = self.M[idx]\n if k == 0:\n mx = ox.unsqueeze(0)\n else:\n mx = torch.cat((mx, ox.unsqueeze(0)), 0)\n\n # handle gpus if specified\n if self.cuda:\n mx = mx.cuda()\n\n with torch.no_grad():\n rep = self.net.forward(mx, svd=True)\n\n for i in range(len(rep)):\n # rep[i]: n_samples * n_features\n assert rep[i].ndim == 2\n\n u, s, v = torch.svd(rep[i].detach().cpu())\n\n if threshold[i] < 1:\n r = torch.cumsum(s ** 2, 0) / torch.cumsum(s ** 2, 0)[-1]\n k = torch.searchsorted(r, threshold[i]) + 1\n elif threshold[i] > 10:\n # differ with thres = 1\n k = int(threshold[i])\n else:\n k = len(s)\n\n if k > 0:\n self.M_vec.append(v[:, :k].cpu())\n self.M_val.append(s[:k].cpu())","def normalise_kernel(K_temp):\n\n nb_item = K_temp.shape[0]\n K_norm = np.zeros((nb_item, nb_item))\n for i in range(nb_item):\n for j in range(i, nb_item):\n K_norm[i, j] = K_temp[i, j] / math.sqrt(K_temp[i, i] * K_temp[j, j])\n K_norm[j, i] = K_norm[i, j]\n\n return K_norm","def test_svd_smoothing(self):\n\t\t\n\t\t# 819 =~ 4096*0.2\n\t\tself.watcher.SVDSmoothing(layers=[self.fc2_layer])\n\t\tesd = self.watcher.get_ESD(layer=self.fc2_layer) \n\t\tnum_comps = len(esd[esd > 10**-10])\n\t\tself.assertEqual(num_comps, 819)","def gaussian_process_pointwise_variance(kernel, pred_samples, train_samples,\n nugget = 0):\n K_train = kernel(train_samples.T)\n # add small number to diagonal to ensure covariance matrix is\n # positive definite\n ntrain_samples = train_samples.shape[1]\n K_train[np.arange(ntrain_samples), np.arange(ntrain_samples)] += nugget\n k_pred = kernel(train_samples.T, pred_samples.T)\n L = np.linalg.cholesky(K_train)\n tmp = solve_triangular(L, k_pred, lower=True)\n variance = kernel.diag(pred_samples.T) - np.sum(tmp*tmp, axis=0)\n return variance","def visualize_svd():","def invert_L1_svd():","def svd_split(m: np.ndarray):\n Dl, d, Dr = m.shape\n u, s, v = np.linalg.svd(m.reshape(Dl * 2, Dr * d // 2))\n D_middle = min(u.shape[-1], v.shape[0])\n s = np.diag(s[:D_middle])\n\n u = u[:, :D_middle].reshape((Dl, 2, D_middle))\n sv = s.dot(v[:D_middle]).reshape((D_middle, d // 2, Dr))\n return u, sv","def unit_vectors(x):\n xnew = x.copy()\n for v in range(x.shape[-1]):\n xnew[:, v] = x[:, v] / np.linalg.norm(x[:, v])\n return xnew","def compact_svd(A, tol=1e-6):\n #Compute eigenvalues/vectors\n lam, V = la.eig((A.conj().T @ A))\n sig = np.sqrt(lam)\n \n #Sort results\n argB = np.argsort(sig)\n arg = []\n for i in range(0, len(argB)):\n arg.append(argB[len(argB)-1-i])\n sig = sig[arg]\n V = V[:,arg]\n #How many non-zero positive\n r = 0\n for j in range(0, len(sig)):\n if abs(sig[j]) >= tol:\n r += 1\n \n sig1 = sig[:r]\n V1 = np.array(V[:,:r])\n \n# print(np.shape(A))\n# print(np.shape(V1))\n U1 = A@V1\n U1 = U1/sig1\n \n #Return answers\n return U1, sig1, V1.conj().T\n\n raise NotImplementedError(\"Problem 1 Incomplete\")","def svd_spd_decomposition(P):\n\t# Assert Matrix P is symetric\n\tassert check_symmetric(P)\n\n\t# singular value decomposition\n\tU, D, V = np.linalg.svd(P, full_matrices=False)\n\n\t# Create matrix W = Vtsqrt(diagnol(D)) #why Vt?\n\tM = np.dot(np.transpose(V), np.sqrt(np.diag(D)))\n\n\t#print(np.transpose(V))\n\n\treturn M","def truncated_normal(size, threshold=1):\n return truncnorm.rvs(-threshold, threshold, size=size)","def tensor_svd_denoise(data, rank):\n # Case when SVD ranks are fed in the input, call svd_HO function to denoise\n\n if len(data.shape) == 3: # hyperspectral data case, directly feed data to svd_HO function\n [X, _, _] = svd_HO(data, rank)\n if len(data.shape) == 4: # Original 4D STEM data case, unfold reciprocal space dimensions into one dimension then feed to svd_HO function\n data = np.reshape(data, [data.shape[0], data.shape[1], data.shape[2]*data.shape[3]])\n [X, _, _] = svd_HO(data, rank)\n \n return X","def shrink_kernel(self, kernel, up_scale):\n up_scale = torch.tensor(up_scale).float()\n # boundary padding based on the scaling law\n pad_in = (torch.ceil(up_scale**2).int())*((kernel.shape[2]-1)//2)\n pad_h = (torch.ceil(up_scale).int())*((kernel.shape[3]-1)//2)\n pad_w = (torch.ceil(up_scale).int())*((kernel.shape[4]-1)//2)\n padded_kernel = F.pad(kernel, (pad_w, pad_w, pad_h, pad_h, pad_in, pad_in))\n delta = up_scale%1\n \n if delta == 0:\n shrink_factor = 1\n else:\n # shrink_factor for coordinates.\n shrink_factor = (((kernel.shape[4]-1))/(padded_kernel.shape[-1]-1)*(up_scale+1))\n \n # Adjustment to deal with weird filtering on the grid sample function.\n shrink_factor = 1.5*(shrink_factor-0.5)**3 + 0.57 \n\n grid = torch.meshgrid(torch.linspace(-1, 1, kernel.shape[2])*(shrink_factor**2),\n torch.linspace(-1, 1, kernel.shape[3])*shrink_factor, \n torch.linspace(-1, 1, kernel.shape[4])*shrink_factor)\n\n grid = torch.cat([grid[2].unsqueeze(0).unsqueeze(-1), \n grid[1].unsqueeze(0).unsqueeze(-1), \n grid[0].unsqueeze(0).unsqueeze(-1)], dim = -1).repeat(kernel.shape[0],1,1,1,1)\n\n new_kernel = F.grid_sample(padded_kernel, grid.to(device))\n if kernel.shape[-1] - 2*up_scale > 0:\n new_kernel = new_kernel * (kernel.shape[-1]**2/((kernel.shape[-1] - 2*up_scale)**2 + 0.01))\n return new_kernel","def norm_psf(psf_data, psf_ivm):\n psf_sum = fsum(psf_data.flat)\n return psf_data / psf_sum, psf_ivm * psf_sum**2","def truncated_normal(size, lower=-2, upper=2):\r\n\r\n return scipy.stats.truncnorm.rvs(lower, upper, size=size)","def stdfilt(vec, wid=3):\n # 2012-04-05 13:58 IJMC: Created\n\n filt = 0*vec\n if wid<1:\n wid = 1\n \n wid = int(wid)\n\n n = len(vec)\n\n for ii in range(n):\n i0 = np.max([0, ii -wid/2])\n i1 = np.min([n-1, ii + wid/2])\n filt[ii] = np.std(vec[i0:i1+1])\n #print ii, i0, i1\n\n return filt","def _mps_truncateC(self, C, Dmax, tol):\n U, S, V = svd(C)\n tol = max(np.finfo(float).eps, tol)\n keep = min(sum(S > (S[0] * tol)), Dmax)\n projR = V[:keep, :]\n projL = U[:, :keep]\n discarded = np.sqrt(sum(S[keep:]**2)) / S[0]\n S = np.diag(S[:keep])\n return projL, S, projR, keep, discarded","def update_S(self):\n S = svd_S(self.C)\n if self.S[self.pC].size != S.size:\n self.S[self.pC] = self._one_S(S.size)\n dS = (self.S[self.pC] - S)\n dS = np.sqrt(np.sum(dS**2))\n self.S[self.pC] = S\n return dS","def svd_approx(A, k):\n U,s,Vh=la.svd(A,full_matrices=False)\n return U[:,:k].dot(np.diag(s[:k])).dot(Vh[:k,:])","def approximate_svd(A, U, S, V, k=10, params=None):\n\n \n A = lib.adapt(A)\n U = lib.adapt(U)\n S = lib.adapt(S)\n V = lib.adapt(V)\n\n Aobj = A.ptr()\n Uobj = U.ptr()\n Sobj = S.ptr()\n Vobj = V.ptr()\n\n if (Aobj == -1 or Uobj == -1 or Sobj == -1 or Vobj == -1):\n raise errors.InvalidObjectError(\"Invalid/unsupported object passed as A, U, S or V \")\n\n # use default params in case none are provided\n if params == None:\n params = SVDParams()\n params_json = params.str() + '\\0'\n\n lib.callsl(\"sl_approximate_svd\", \\\n A.ctype(), Aobj, \\\n U.ctype(), Uobj, \\\n S.ctype(), Sobj, \\\n V.ctype(), Vobj, \\\n k, params_json, lib.ctxt_obj)\n\n A.ptrcleaner()\n U.ptrcleaner()\n S.ptrcleaner()\n V.ptrcleaner()\n\n return (U.getobj(), S.getobj(), V.getobj())","def reduce_dimensions(feature_vectors_full, model):\n\n if 'fvectors' in model:\n v = np.array(model['fvectors'])\n else:\n #Principal Components Analysis implemented from lab code\n covx = np.cov(feature_vectors_full, rowvar=0)\n N = covx.shape[0]\n w, v = scipy.linalg.eigh(covx, eigvals=(N - 40, N - 1))\n v = np.fliplr(v)\n model['fvectors'] = v.tolist()\n pca_train = np.dot((feature_vectors_full - np.mean(feature_vectors_full)), v)\n return pca_train[:,0:10]","def svd(matrix):\n u = None\n s = None\n v = None\n ### YOUR CODE HERE\n pass\n ### END YOUR CODE\n\n return u, s, v","def svd(matrix, rank=None):\n if matrix.ndim != 2:\n raise ValueError('Input should be a two-dimensional array. matrix.ndim is {} != 2'.format(matrix.ndim))\n dim_1, dim_2 = matrix.shape\n if dim_1 <= dim_2:\n min_dim = dim_1\n else:\n min_dim = dim_2\n\n if rank is None or rank >= min_dim:\n # Default on standard SVD\n U, S, V = scipy.linalg.svd(matrix)\n U, S, V = U[:, :rank], S[:rank], V[:rank, :]\n return U, S, V\n\n else:\n # We can perform a partial SVD\n # First choose whether to use X * X.T or X.T *X\n if dim_1 < dim_2:\n S, U = scipy.sparse.linalg.eigsh(np.dot(matrix, matrix.T), k=rank, which='LM')\n S = np.sqrt(S)\n V = np.dot(matrix.T, U * 1 / S[None, :])\n else:\n S, V = scipy.sparse.linalg.eigsh(np.dot(matrix.T, matrix), k=rank, which='LM')\n S = np.sqrt(S)\n U = np.dot(matrix, V) * 1 / S[None, :]\n\n # WARNING: here, V is still the transpose of what it should be\n U, S, V = U[:, ::-1], S[::-1], V[:, ::-1]\n return U, S, V.T","def reduce_dimensions(feature_vectors_full, model):\n \"\"\"Subtracting the mean of the feature vectors being classified\n as opposed to the average of the test data's feature vectors seems to\n improve performance (I think this is to do with the noise; as on clean pages\n the average for a pixel that's white in all feature vectors would be 255. In\n a noisy image, it'd be lower, so the white areas wouldn't get \"centred\"\n around white by subtracting 255 any more.).\n \"\"\"\n return np.dot(\n (feature_vectors_full - np.mean(feature_vectors_full,axis=0)), \n np.array(model[\"eigenvectors\"]))","def Pkernel(x):\n\n m = (x < 0.) & (x >= 1.)\n x[x < 0.] = np.zeros(np.sum(x < 0.))\n x[x >= 1.] = np.zeros(np.sum(x >= 1.))\n x = np.sqrt(x)\n\n result = np.log(2.) * np.log(2.) - np.pi *np.pi / 6. \\\n + 2. * spence(0.5 + 0.5 * x) - (x + x*x*x) / (1. - x*x) \\\n + (np.log(1. + x) - 2. * np.log(2.)) * np.log(1. - x) \\\n + 0.5 * (np.log(1. - x) * np.log(1. - x) - np.log(1. + x) * np.log(1. + x)) \\\n + 0.5 * (1. + x*x*x*x) / (1. - x*x) * (np.log(1. + x) - np.log(1. - x))\n result[x <= 0.] = np.zeros(np.sum(x <= 0.))\n result[x >= 1.] = np.zeros(np.sum(x >= 1.))\n return result","def std(X,trimming=0):\n \n if trimming==0:\n s = np.power(np.var(X,axis=0),.5)\n s = np.array(s).reshape(-1)\n else: \n var = sps.trim_mean(np.square(X - sps.trim_mean(X,trimming,0)),\n trimming,0)\n s = np.sqrt(var) \n return s","def calc_image_svd(img:list):\r\n img_r = img.swapaxes(1, 2).swapaxes(0, 1)\r\n svd_u, svd_s, svd_vh = np.linalg.svd(img_r, full_matrices=True)\r\n return [svd_u, svd_s, svd_vh]","def preprocess_psf(psf_data, psf_ivm):\n psf_data = fits.getdata(psf_data, ignore_missing_end=True)\n psf_ivm = fits.getdata(psf_ivm, ignore_missing_end=True)\n\n # We don't want zero-weight pixels in the PSF to contribute infinitely to\n # the variance, so we simply set them to 0 in both data and weight map\n badpx = ~np.isfinite(psf_data) | ~np.isfinite(psf_ivm) | (psf_ivm <= 0)\n psf_data[badpx] = 0\n psf_ivm[badpx] = 0\n\n # Normalize the PSF kernel, then return data and variance map\n psf_data, psf_ivm = norm_psf(psf_data, psf_ivm)\n psf_var = np.where(psf_ivm <= 0, 0, 1 / psf_ivm)\n return psf_data, psf_var","def center_and_normalise_kernel(K_temp):\n\n K_temp = KernelCenterer().fit_transform(K_temp)\n nb_item = K_temp.shape[0]\n K_norm = np.zeros((nb_item, nb_item))\n for i in range(nb_item):\n for j in range(i, nb_item):\n K_norm[i, j] = K_temp[i, j] / math.sqrt(K_temp[i, i] * K_temp[j, j])\n K_norm[j, i] = K_norm[i, j]\n\n return K_norm","def normalise(self,data,take_logs:bool=False):\n\n # Normalise vector to sum up to 1\n normalised_vector = data/np.sum(data)\n\n # If take logs is selected, take logs\n if take_logs:\n return np.log(normalised_vector)\n else:\n return normalised_vector","def normalize_data(x):\n mvec = x.mean(0)\n stdvec = x.std(axis=0)\n \n return (x - mvec)/stdvec","def normalize_data(x):\n mvec = x.mean(0)\n stdvec = x.std(axis=0)\n \n return (x - mvec)/stdvec","def test_svd_smoothing_with_model(self):\n\n\t\t\n\t\t# 819 =~ 4096*0.2\n\t\tself.watcher.SVDSmoothing(model=self.model, layers=[self.fc2_layer])\n\t\tesd = self.watcher.get_ESD(layer=self.fc2_layer) \n\t\tnum_comps = len(esd[esd>10**-10])\n\t\tself.assertEqual(num_comps, 819)","def test_svd_smoothing_no_model(self):\n\t\t\n\t\t# 819 =~ 4096*0.2\n\t\tself.watcher.SVDSmoothing(model=self.model, layers=[self.fc2_layer])\n\t\tesd = self.watcher.get_ESD(layer=self.fc2_layer) \n\t\tnum_comps = len(esd[esd>10**-10])\n\t\tself.assertEqual(num_comps, 819)","def get_singular_values(matrix, n):\n singular_values = None\n u, s, v = svd(matrix)\n ### YOUR CODE HERE\n pass\n ### END YOUR CODE\n return singular_values","def test_gauss_kernel():\n\n gauss = gauss_kernel(2, 5)\n\n assert gauss.shape == (5, 5)\n assert gauss[2, 2] == 0.039788735772973836","def standardize(X):\n mu = X.mean(axis=0, keepdims=True)\n s = X.std(axis=0, keepdims=True)\n return (X-mu)/s","def en_kern_vec(name, kernel,\n x, hyps,\n cutoffs=None, hyps_mask=None,\n n_cpus=1, n_sample=100):\n\n training_data = _global_training_data[name]\n size = len(training_data)\n\n if (n_cpus is None):\n n_cpus = mp.cpu_count()\n if (n_cpus == 1):\n return en_kern_vec_unit(\n name, 0, size, x, kernel, hyps,\n cutoffs, hyps_mask)\n\n block_id, nbatch = partition_c(n_sample, size, n_cpus)\n result_queue = mp.Queue()\n children = []\n for wid in range(nbatch):\n children.append(\n mp.Process(\n target=queue_wrapper,\n args=(result_queue, wid,\n en_kern_vec_unit,\n (name, block_id[wid][0], block_id[wid][1], x,\n kernel, hyps, cutoffs, hyps_mask))))\n\n # Run child processes.\n for c in children:\n c.start()\n\n # Wait for all results to arrive.\n k12_v = np.zeros(size*3)\n for _ in range(nbatch):\n wid, result_chunk = result_queue.get(block=True)\n s, e = block_id[wid]\n k12_v[s*3:e*3] = result_chunk\n\n # Join child processes (clean up zombies).\n for c in children:\n c.join()\n\n return k12_v","def svd_approx(A, k):","def run(self, dim):\n conv_mat = np.dot(np.transpose(self._data), self._data)\n u, d, v = np.linalg.svd(conv_mat)\n return u[:, :dim], d[:dim], v[:dim]","def snv(spectra):\n\n return (spectra - np.mean(spectra, axis=0)) / np.std(spectra, axis=0)","def svd_images(imagear):\n n = np.shape(imagear)[1]\n u, s, v = np.linalg.svd(imagear/np.sqrt(n-1),full_matrices=False)\n \n return(u, s, v)","def get_singular_values(matrix, n):\n singular_values = None\n u, s, v = svd(matrix)\n ### YOUR CODE HERE\n singular_values=s[0:n]\n ### END YOUR CODE\n return singular_values","def posdef_eig_svd(mat):\n evals, evecs, _ = tf.svd(mat)\n\n return evals, evecs","def svc_kernel(name, kernel, random_state=None, probability=False, **kwargs):\n def _name(msg):\n return '%s.%s_%s' % (name, kernel, msg)\n\n hp_space = _svm_hp_space(_name, kernel=kernel, **kwargs)\n hp_space.update(_svc_hp_space(_name, random_state, probability))\n return scope.sklearn_SVC(**hp_space)","def normkernel(S, T, n):\n\n k1 = kernel(S, S, n)\n k2 = kernel(T, T, n)\n res = kernel(S, T, n) / sqrt(k1 * k2)\n\n return res","def test_svd_smoothing_no_model(self):\n\t\t\n\t\t# 819 =~ 4096*0.2\n\t\t\n\t\tself.watcher.SVDSmoothing(model=self.model, layers=[21])\n\t\tesd = self.watcher.get_ESD(layer=21) \n\t\tnum_comps = len(esd[esd>10**-10])\n\t\tself.assertEqual(num_comps, 819)","def fit(self, X):\n n_components = X.shape[1] if self.n_components is None else self.n_components\n diff = np.vstack([i[1:] - i[:-1] for i in X])\n cov = scipy.cov(diff, rowvar=0)\n u, _, _ = scipy.linalg.svd(cov, full_matrices=False)\n u = u[:, -n_components:][:, ::-1]\n\n self.weights = u","def normalize(data):\n data = numpy.asmatrix(data)\n std_devs = numpy.std(data, axis=1)\n std_devs[std_devs == 0] = 1 # prevent div by 0\n return (data - numpy.mean(data, axis=1)) / std_devs","def normalize_features(X):\n std = X.std(axis=0)\n std = np.where(std == 0, 1, std) # to avoid division by zero\n x_normed = (X - X.mean(axis=0)) / std\n return x_normed","def svd_approx(A, s):\r\n U, Si, Vh = la.svd(A)\r\n zeros = list(Si).count(0)\r\n #raise error if there are not enough nonzero singular values\r\n if len(Si) - zeros < s:\r\n raise ValueError(\"s > rank(A)\")\r\n #Only save first s singular values for Si\r\n Si_hat = np.diag(Si[:s])\r\n #Save first s columns of U\r\n U_hat = U[:,:s]\r\n #Save first s rows of Vh\r\n Vh_hat = Vh[:s,:]\r\n\r\n # return new A and num of entries needed\r\n return U_hat@Si_hat@Vh_hat, U_hat.size+s+Vh_hat.size","def discretize(vectors, copy=True, max_svd_restarts=30, n_iter_max=20,\n random_state=None):\n\n from scipy.sparse import csc_matrix\n from scipy.linalg import LinAlgError\n\n random_state = check_random_state(random_state)\n\n vectors = as_float_array(vectors, copy=copy)\n\n eps = np.finfo(float).eps\n n_samples, n_components = vectors.shape\n\n # Normalize the eigenvectors to an equal length of a vector of ones.\n # Reorient the eigenvectors to point in the negative direction with respect\n # to the first element. This may have to do with constraining the\n # eigenvectors to lie in a specific quadrant to make the discretization\n # search easier.\n norm_ones = np.sqrt(n_samples)\n for i in range(vectors.shape[1]):\n vectors[:, i] = (vectors[:, i] / np.linalg.norm(vectors[:, i])) \\\n * norm_ones\n if vectors[0, i] != 0:\n vectors[:, i] = -1 * vectors[:, i] * np.sign(vectors[0, i])\n\n # Normalize the rows of the eigenvectors. Samples should lie on the unit\n # hypersphere centered at the origin. This transforms the samples in the\n # embedding space to the space of partition matrices.\n vectors = vectors / np.sqrt((vectors ** 2).sum(axis=1))[:, np.newaxis]\n\n svd_restarts = 0\n has_converged = False\n\n # If there is an exception we try to randomize and rerun SVD again\n # do this max_svd_restarts times.\n while (svd_restarts < max_svd_restarts) and not has_converged:\n\n # Initialize first column of rotation matrix with a row of the\n # eigenvectors\n rotation = np.zeros((n_components, n_components))\n rotation[:, 0] = vectors[random_state.randint(n_samples), :].T\n\n # To initialize the rest of the rotation matrix, find the rows\n # of the eigenvectors that are as orthogonal to each other as\n # possible\n c = np.zeros(n_samples)\n for j in range(1, n_components):\n # Accumulate c to ensure row is as orthogonal as possible to\n # previous picks as well as current one\n c += np.abs(np.dot(vectors, rotation[:, j - 1]))\n rotation[:, j] = vectors[c.argmin(), :].T\n\n last_objective_value = 0.0\n n_iter = 0\n\n while not has_converged:\n n_iter += 1\n\n t_discrete = np.dot(vectors, rotation)\n\n labels = t_discrete.argmax(axis=1)\n vectors_discrete = csc_matrix(\n (np.ones(len(labels)), (np.arange(0, n_samples), labels)),\n shape=(n_samples, n_components))\n\n t_svd = vectors_discrete.T * vectors\n\n try:\n U, S, Vh = np.linalg.svd(t_svd)\n svd_restarts += 1\n except LinAlgError:\n print(\"SVD did not converge, randomizing and trying again\")\n break\n\n ncut_value = 2.0 * (n_samples - S.sum())\n if ((abs(ncut_value - last_objective_value) < eps) or\n (n_iter > n_iter_max)):\n has_converged = True\n else:\n # otherwise calculate rotation and continue\n last_objective_value = ncut_value\n rotation = np.dot(Vh.T, U.T)\n\n if not has_converged:\n raise LinAlgError('SVD did not converge')\n return labels","def center_normalize(x):\n return (x - K.mean(x)) / K.std(x)","def center_normalize(x):\n return (x - K.mean(x)) / K.std(x)","def en_kern_vec_unit(name, s, e, x, kernel,\n hyps, cutoffs=None, hyps_mask=None):\n\n training_data = _global_training_data[name]\n\n ds = [1, 2, 3]\n size = (e-s) * 3\n k_v = np.zeros(size, )\n\n args = from_mask_to_args(hyps, hyps_mask, cutoffs)\n\n for m_index in range(size):\n x_2 = training_data[int(math.floor(m_index / 3))+s]\n d_2 = ds[m_index % 3]\n k_v[m_index] = kernel(x_2, x, d_2, *args)\n\n return k_v","def SVM(x_train, x_test, y_train, kernel):\n svc_model = SVC(kernel=kernel)\n svc_model.fit(x_train, y_train)\n y_pred = svc_model.predict(x_test)\n return y_pred","def svd(matrix, approach):\n\n # Getting the eigenvalues and vectors of transpose(A) * A for V and Sigma\n a = mat_multiply(transpose(matrix), matrix)\n if approach == \"qr\":\n V, sigma, iterations = qr_eig(a)\n else:\n V, sigma, iterations = eig(a)\n\n # Sorting singular values and the colums of V accordingly\n V = transpose(V)\n\n singular_values = list()\n sorted_V = list()\n\n r = 0\n for i in range(rows(sigma)):\n singular_values.append([(sigma[i][i]), i])\n if sigma[i][i] > math.exp(-8):\n r += 1\n\n singular_values.sort(key=first_item, reverse=True)\n\n sigma_r = eye(r)\n sigma_r_inv = eye(r)\n\n # Constructing the sorted U and sigma matrices\n i, j = 0, 0\n for value in singular_values:\n if value[0] > math.exp(-8):\n sorted_V.append(V[value[1]])\n sigma_r[j][j] = value[0] ** (1 / 2)\n sigma_r_inv[j][j] = 1 / (value[0] ** (1 / 2))\n j += 1\n i += 1\n\n # Constructing U by multiplying V and sigma inverse\n sorted_U = mat_multiply(mat_multiply(matrix, transpose(sorted_V)), sigma_r_inv)\n\n return (sorted_U, sigma_r, sorted_V, r, iterations)","def svd_compress_gs(mat, k):\n U, singular_vals, V = np.linalg.svd(mat)\n rank = len(singular_vals)\n print(\"Image rank %r\" % rank)\n if k > rank:\n print(\"k is larger than rank of image %r\" % rank)\n return mat\n # take columns less than k from U\n U_p = U[:, :k]\n # take rows less than k from V\n V_p = V[:k, :]\n # build the new S matrix with top k diagnal elements\n S_p = np.zeros((k, k), mat.dtype)\n for i in range(k):\n S_p[i][i] = singular_vals[i]\n print(\"U_p shape {0}, S_p shape {1}, V_p shape {2}\".format(\n U_p.shape, S_p.shape, V_p.shape))\n compressed = np.dot(np.dot(U_p, S_p), V_p)\n ss = ssim(mat, compressed,\n dynamic_range=compressed.max() - compressed.min())\n print(\"Strucural similarity: %r\" % ss)\n return U_p, S_p, V_p"],"string":"[\n \"def truncated_svd(A,k=None):\",\n \"def svd_shrink(X, tau):\\n U,s,V = np.linalg.svd(X, full_matrices=False)\\n return np.dot(U, np.dot(np.diag(shrink(s, tau)), V))\",\n \"def reduce_svd(embeddings, seed=0):\\n svd = TruncatedSVD(n_components=2, n_iter=10, random_state=seed)\\n return svd.fit_transform(embeddings)\",\n \"def computeTruncatedSVD(docTermMatrix, dim=500):\\r\\n T, S, D = np.linalg.svd(np.transpose(docTermMatrix), full_matrices=False)\\r\\n\\r\\n diagS = np.diag(S)\\r\\n shape = np.shape(diagS)\\r\\n\\r\\n if dim <= shape[0] and dim <= shape[1]:\\r\\n subT = T[:,:dim]\\r\\n subS = diagS[:dim,:dim]\\r\\n subD = np.transpose(D)[:,:dim]\\r\\n else:\\r\\n subT = T\\r\\n subS = diagS\\r\\n subD = np.transpose(D)\\r\\n\\r\\n return subT, subS, subD\",\n \"def truncated_svd(A,k=None):\\n \\n \\n \\n AHA=np.conj(A).T.dot(A)\\n evals,evecs=la.eig(AHA)\\n order=np.argsort(evals)\\n\\n evals=evals[order][::-1].copy()\\n evecs=evecs.T[order][::-1].copy()\\n m,n=AHA.shape\\n \\n tol=1e-12\\n Vh=[]\\n for i in xrange(0,m):\\n\\t\\t if np.abs(evals[i])>=tol:\\n\\t \\t\\tVh+=[evecs[i]]\\n \\n Vh=np.array(Vh)\\n s=np.sqrt(evals[:Vh.shape[0]])\\n U=[]\\n for i in xrange(0,len(s)):\\n U+=[(1./s[i])*A.dot(Vh[i])]\\n U=np.array(U).T\\n \\n return U,s,Vh\",\n \"def tsvd(A, threshold=0.99999, avoid_pathological=True):\\n M,N = A.shape\\n full_matrices = False\\n\\n if is_int(threshold):\\n # Assume specific number is requested\\n r = threshold\\n assert 1 <= r <= max(M,N)\\n if r > min(M,N):\\n full_matrices = True\\n r = min(M,N)\\n\\n U,s,VT = sla.svd(A, full_matrices)\\n\\n if isinstance(threshold,float):\\n # Assume proportion is requested\\n r = truncate_rank(s,threshold,avoid_pathological)\\n\\n # Truncate\\n U = U [:,:r]\\n VT = VT[ :r]\\n s = s [ :r]\\n return U,s,VT\",\n \"def svd(self, X): # [5pts]\\n N,D = X.shape[0],X.shape[1]\\n if X.ndim == 3:\\n U = np.zeros((N,N,3))\\n S = np.zeros((min(N,D),3))\\n V = np.zeros((D,D,3))\\n for i in range(3):\\n U_temp,S_temp,V_temp = np.linalg.svd(X[:,:,i],compute_uv=True, full_matrices=True,hermitian=False)\\n U[:,:,i] = U_temp\\n S[:,i] = S_temp\\n V[:,:,i] = V_temp\\n else:\\n U,S,V = np.linalg.svd(X,compute_uv=True,full_matrices=True, hermitian=False)\\n return U,S,V\",\n \"def test_truncate2():\\n X = rand(5,5,5)\\n T = hosvd(X)\\n k = 3\\n Tk = T.truncate(k)\\n E = X - Tk.asarray()\\n Cdk = T.X\\n Cdk[:k,:k,:k] = 0\\n assert np.allclose(fro_norm(E), fro_norm(Cdk))\",\n \"def svdSoft(A, lmbda, k):\\n if not scipy.sparse.issparse(A): \\n raise ValueError(\\\"A must be a sparse matrix\\\")\\n \\n #U, s, V = scipy.sparse.linalg.svds(A, k)\\n U, s, V = sparsesvd(A, k) \\n U = U.T\\n inds = numpy.flipud(numpy.argsort(s))\\n U, s, V = Util.indSvd(U, s, V, inds) \\n \\n #Soft threshold \\n s = s - lmbda\\n s = numpy.clip(s, 0, numpy.max(s))\\n\\n return U, s, V\",\n \"def update_model(X, U, S, k, n, mu,\\n svdmethod='full',\\n missingmethod='zero'):\\n\\n if len(X) == 0:\\n printt(\\\"Error: No data in X.\\\")\\n return None, None, None, -1, None\\n #print('%d items in X' % X.shape[1])\\n #print('init U:', U)\\n\\n # If there is no previous U, and we just got a single item in X,\\n # set U to all 0's (degenerate SVD),\\n # and return it with mu.\\n # (PR #22 sets first value to 1; see decals implementation)\\n if len(U) == 0 and X.shape[1] == 1:\\n mu = X\\n # Do this no matter what. Let mu get NaNs in it as needed.\\n U = np.zeros_like(mu)\\n U[0] = 1\\n S = np.array([0])\\n n = 1\\n pcts = [1.0]\\n return U, S, mu, n, pcts\\n\\n ###########################################################################\\n # Do full SVD of X if this is requested, regardless of what is in U \\n # Also, if n = 0 or U is empty, start from scratch\\n output_k = False\\n if svdmethod == 'full' or len(U) == 0 or n == 0:\\n if n == 0:\\n if len(U) == 0:\\n printt(\\\"----- initial SVD -----\\\")\\n output_k = True\\n else:\\n # Reshape so we don't have an empty dimension (yay python)\\n U = U.reshape(-1, 1)\\n elif len(U) == 0:\\n printt(\\\"WARNING: N (number of items modeled by U) is %d, not zero, but U is empty!\\\" % n)\\n\\n # Bootstrap\\n if missingmethod == 'ignore':\\n printt(\\\"ERROR: ignore with full is not possible under ordinary circumstances.\\\")\\n printt(\\\"Use --increm-brand to impute for NaNs.\\\")\\n printt(\\\"For now, we are filling NaNs with 0.\\\")\\n X = copy.deepcopy(X)\\n z = np.where(np.isnan(X))\\n X[z] = 0\\n\\n mu = np.mean(X, axis=1).reshape(-1,1)\\n X = X - mu\\n U, S, V = linalg.svd(X, full_matrices=False)\\n printt('Just did full SVD on %d items.' % X.shape[1])\\n #print('X:', X)\\n #print('U:', U)\\n # Reset U to all 0's if we only have one item in X (degenerate SVD)\\n if X.shape[1] == 1:\\n U = np.zeros_like(U)\\n \\n # Keep only the first k components\\n S_full = S\\n S = S[0:k]\\n U = U[:,0:k]\\n\\n # Update n to number of new items in X\\n n = X.shape[1]\\n \\n ###########################################################################\\n # Incremental SVD from Ross\\n elif svdmethod == 'increm-ross':\\n # Incremental SVD from Ross et al. 2008\\n # \\\"Incremental Learning for Robust Visual Tracking\\\"\\n # based on Lim and Ross's sklm.m implementation in MATLAB.\\n\\n # This method DOES NOT handle missing values.\\n if missingmethod == 'ignore':\\n print('ERROR: increm-ross cannot handle missing values.')\\n print('If they are present, try svdmethod=increm-brand')\\n print(' or use missingmethod=zero to zero-fill.')\\n print('If there are no missing values, specify missingmethod=none.')\\n sys.exit(1)\\n\\n n_new = X.shape[1]\\n \\n # Compute mean\\n # Weirdly, the later 'X-mu_new' is MUCH faster if you reshape as shown.\\n # This is because of differences in the way numpy treats a 1d array versus a 2d column.\\n mu_new = np.mean(X, axis=1).reshape(-1,1)\\n\\n # Subtract the mean, append it as a column vector, and update mu\\n # X - mu_new will be zero if X has only 1 item\\n mu_old = mu\\n # New mu is a weighted sum of old and new mus\\n mu = (n * mu_old + n_new * mu_new) / (n + n_new)\\n B = np.hstack((X - mu,\\n math.sqrt(n_new * n/float(n_new+n)) * \\\\\\n (mu_old - mu_new)))\\n printt(\\\"Now tracking mean for %d -> %d items; mu.min %f, mu.max %f \\\" % \\\\\\n (n, n+n_new, np.nanmin(mu), np.nanmax(mu)))\\n n = n + n_new\\n\\n if S.all() == 0:\\n npcs = U.shape[1]\\n diagS = np.zeros((npcs, npcs))\\n else:\\n diagS = np.diag(S)\\n\\n # I don't think this is right. At this point B is the augmented\\n # matrix rather than the single observation.\\n proj = np.dot(U.T, B)\\n reproj_err = B - np.dot(U, proj)\\n\\n # to get orthogonal form of reproj_err\\n # This should return q with dimensions [d(X) by n_new+1], square\\n q, dummy = linalg.qr(reproj_err, mode='full')\\n # print('q.shape should be 7x2: ', q.shape)\\n Q = np.hstack((U, q))\\n\\n # From Ross and Lim, 2008\\n # R = [ [ Sigma, U.T * X ] [ 0, orthog. component of reproj error ] ]\\n k_now = diagS.shape[0]\\n new_dim = k_now + n_new + 1\\n R = np.zeros((new_dim, new_dim))\\n R[0:k_now,0:k_now] = diagS\\n R[0:k_now,k_now:] = proj\\n orthog_reproj_err = np.dot(q.T, reproj_err)\\n R[k_now:, k_now:] = orthog_reproj_err\\n \\n # Perform SVD of R. Then finally update U.\\n U, S, V = linalg.svd(R, full_matrices=False)\\n printt('Just did increm-ross SVD on %d items.' % n)\\n\\n U = np.dot(Q, U)\\n \\n # Keep only the first k components\\n U = U[:,0:min([n,k])]\\n S_full = S\\n S = S[0:min([n,k])]\\n\\n ###########################################################################\\n # Incremental SVD from Brand\\n elif svdmethod == 'increm-brand':\\n # Pulled out James's attempt to handle NaNs into\\n # increm-brand-james.py. Starting over from scratch here.\\n n_new = X.shape[1]\\n\\n if n_new != 1:\\n print(\\\"WARNING: increm-brand will probably only work by adding one item at a time.\\\")\\n input('\\\\nPress enter to continue or ^C/EOF to exit. ')\\n\\n if missingmethod == 'ignore':\\n # 1. Update mu\\n mu_old = mu\\n mu_new = X\\n\\n # Be careful! For any pre-existing NaNs in mu,\\n # let mu_new fill them in. Can't get any worse!\\n naninds = np.where(np.isnan(mu_old))[0]\\n if naninds.size > 0:\\n mu_old[naninds,0] = mu_new[naninds,0]\\n # And likewise for mu_new -- fill with good values from mu_old.\\n naninds = np.where(np.isnan(mu_new))[0]\\n if naninds.size > 0:\\n mu_new[naninds,0] = mu_old[naninds,0]\\n # At this point, the only NaNs that should appear are\\n # values that were NaN for both mu and X to start with.\\n # They will stay NaN and that's okay.\\n \\n # New mu is a weighted sum of old and new mus\\n mu = (n * mu_old + n_new * mu_new) / (n + n_new)\\n printt(\\\"Now tracking mean for %d -> %d items; mu.min %f, mu.max %f \\\" % \\\\\\n (n, n+n_new, np.nanmin(mu), np.nanmax(mu)))\\n n = n + n_new\\n\\n # 2. Subtract off the mean\\n X = X - mu\\n\\n # 3. Compute L, the projection of X onto U\\n # Note: this will only work for a single item in X\\n goodinds = np.where(~np.isnan(X))[0]\\n #print('X: %d of %d are good.' % (len(goodinds), X.shape[0]))\\n\\n diagS = np.diag(S)\\n # This is Brand's method, which involves S:\\n L = np.dot(diagS,\\n np.dot(np.linalg.pinv(np.dot(U[goodinds,:],\\n diagS)),\\n X[goodinds,:]))\\n # Simplified version that does not use S (but is probably wrong):\\n #L = np.dot(U[goodinds,:].T,\\n # X[goodinds,:])\\n # Top row of the Q matrix (eqn 12, Brand 2002)\\n Q1 = np.hstack([diagS, L])\\n\\n # 4. Compute J, the orthogonal basis of H, which is\\n # the component of X orthog to U (i.e., unrepresentable direction)\\n # 5. Compute K, the projection of X onto J (i.e., unrep. content)\\n K = linalg.norm(X[goodinds,:] - np.dot(U[goodinds,:],\\n np.dot(U[goodinds,:].T,\\n X[goodinds,:])))\\n # H = X - UL\\n J = np.zeros((U.shape[0], 1))\\n J[goodinds] = np.dot(K,\\n np.linalg.pinv(X[goodinds,:] -\\n np.dot(U[goodinds,:],\\n L))).T\\n \\n # Bottom row of Q matrix (eqn 12, Brand 2002)\\n Q2 = np.hstack([np.zeros([1, len(S)]), np.array(K).reshape(1,1)])\\n Q = np.vstack([Q1, Q2])\\n\\n # 6. Take the SVD of Q\\n Uq, Sq, Vq = linalg.svd(Q, full_matrices=False)\\n\\n # 7. Update U and S (eqn 4, Brand 2002)\\n # Note: Since J is zero-filled for badinds, now U is too.\\n # Alternatively, we give J NaNs and let them get into U as well.\\n # I think that is a worse idea though.\\n U = np.dot(np.hstack([U, J]), Uq)\\n S = Sq\\n # Updating V requires knowing old V,\\n # but we don't need the new one either so it's okay to skip.\\n\\n printt('Just did increm-brand SVD on %d items.' % n)\\n \\n ############# end ###########\\n \\n else: # No missing values (or not 'ignore')\\n # 1. Update mu\\n mu_old = mu\\n mu_new = X\\n # New mu is a weighted sum of old and new mus\\n mu = (n * mu_old + n_new * mu_new) / (n + n_new)\\n n = n + n_new\\n\\n # 2. Subtract off the mean\\n X = X - mu\\n\\n # 3. Compute L, the projection of X onto U\\n L = np.dot(U.T, X)\\n Q1 = np.hstack([np.diag(S), L])\\n\\n # 4. Compute J, the orthogonal basis of H, which is\\n # the component of X orthog to U (i.e., unrepresentable direction)\\n # 5. Compute K, the projection of X onto J (i.e., unrep. content)\\n JK = X - np.dot(U, L)\\n (J, K) = linalg.qr(JK)\\n\\n Q2 = np.hstack([np.zeros([1, len(S)]), np.array(K).reshape(1,1)])\\n Q = np.vstack([Q1, Q2])\\n\\n # 6. Take the SVD of Q\\n Uq, Sq, Vq = linalg.svd(Q, full_matrices=False)\\n\\n # 7. Update U and S (eqn 4, Brand 2002)\\n U = np.dot(np.hstack([U, J]), Uq)\\n S = Sq\\n # V requires knowing old V,\\n # but we don't need the new one either so it's okay.\\n \\n printt('Just did regular increm SVD on %d items.' % n)\\n\\n # Keep only the first k components\\n U = U[:,0:min([n,k])]\\n S = S[0:min([n,k])]\\n\\n Usum = U.sum(1)\\n\\n\\n ###########################################################################\\n # We have a bad svdmethod, but somehow didn't catch it earlier.\\n else:\\n printt(\\\"504: Bad Gateway in protocol <Skynet_authentication.exe>\\\")\\n return None, None, None, None, None\\n\\n indivpcts = None\\n\\n # This only works if a full SVD was done\\n if (svdmethod == 'full' and output_k and opts['k_var'] == -773038.0):\\n # Calculate percent variance captured by each \\n cumsum = np.cumsum(S_full)\\n #print(cumsum.shape)\\n if cumsum[-1] != 0:\\n indivpcts = S / cumsum[-1]\\n indivpcts = indivpcts[0:k] # truncate to first k\\n cumpercents = cumsum / cumsum[-1]\\n else:\\n indivpcts = []\\n\\n # Calculate percent variance captured\\n if k >= cumsum.shape[0]:\\n printt('Cannot estimate data variance; specified k (%d) exceeds the number of SVs (%d).' % (k, cumsum.shape[0]))\\n else:\\n printt(\\\"Selected value of k=%d captures %5.2f%% of the data variance\\\" % \\\\\\n (k, cumpercents[k-1] * 100))\\n if opts['pause']: input(\\\"Press enter to continue\\\\n\\\")\\n\\n #print('U:', U)\\n #print('mu:', mu)\\n return U, S, mu, n, indivpcts\",\n \"def reduce_to_k_dim(M, k=2): \\n n_iters = 10 # Use this parameter in your call to `TruncatedSVD`\\n M_reduced = None\\n print(\\\"Running Truncated SVD over %i words...\\\" % (M.shape[0]))\\n \\n # YOUR CODE HERE\\n \\n svd = TruncatedSVD(n_components = k, n_iter = 10, random_state = 0)\\n \\n M_reduced = svd.fit_transform(M)\\n \\n #raise NotImplementedError()\\n\\n print(\\\"Done.\\\")\\n return M_reduced\",\n \"def ES_SVD(U, sigma, V, time, f_fault, f_side, PMItreshold, estimate_xi_func=get_SVDxi, estimate_xi_func_params=None):\\n\\n # Get the search region\\n m = sigma.size\\n f_fault = np.asanyarray(f_fault)\\n f_side = np.asanyarray(f_side)\\n dt = time[1] - time[0]\\n Fs = 1.0/dt\\n PMI = [] #PMI is here the envelope score\\n W = []\\n for i in range(0, f_fault.size):\\n PMI.append(np.zeros(m))\\n W.append(np.zeros(m))\\n\\n # Calculate PMI for each fault type\\n for i in range(0, m):\\n if estimate_xi_func_params is None:\\n a_i = estimate_xi_func(U, sigma, V, i)\\n else:\\n a_i = estimate_xi_func(U, sigma, V, i, estimate_xi_func_params)\\n a_i = envelope(a_i)\\n Y, df = fft(a_i, Fs)\\n # Calculate PMI for each fault type\\n for k in range(0, f_fault.size):\\n PMI[k][i] = diagnosefft(Y, df, f_fault[k], 1.0, f_side[k])\\n\\n # Calculate weights\\n for k in range(0, f_fault.size):\\n temp = 0.0\\n for i in range(0, m):\\n if PMI[k][i] > PMItreshold:\\n temp += PMI[k][i]\\n for i in range(0, m):\\n if PMI[k][i] > PMItreshold:\\n W[k][i] = PMI[k][i]/temp\\n\\n # Return data\\n return PMI, W\",\n \"def reduce_dimentions(users):\\n\\tsvd = TruncatedSVD(n_components=300, n_iter=10, random_state=42)\\n\\tsvd.fit(users)\\n\\tusers_svd = svd.transform(users)\\n\\tusers_svd = pd.DataFrame(users_svd, index=users.index)\\n\\treturn users_svd\",\n \"def svd0(A):\\n M,N = A.shape\\n if M>N: return sla.svd(A, full_matrices=True)\\n else: return sla.svd(A, full_matrices=False)\",\n \"def reduce_to_k_dim(M, k=2): \\n n_iters = 10 # Use this parameter in your call to `TruncatedSVD`\\n M_reduced = None\\n print(\\\"Running Truncated SVD over %i words...\\\" % (M.shape[0]))\\n \\n # ------------------\\n # Write your implementation here.\\n svd = TruncatedSVD(n_components=k, n_iter=n_iters)\\n M_reduced = svd.fit_transform(M)\\n \\n # ------------------\\n\\n print(\\\"Done.\\\")\\n return M_reduced\",\n \"def HOSVD(A, k=None, tol=None):\\n\\n d=len(A.shape)\\n\\n if d==2:\\n u, s, vt=svd(A, full_matrices=False)\\n U=[u, vt.T]\\n S=np.diag(s)\\n else:\\n U=[None]*d\\n for j in range(0, d):\\n U[j], s, vt=svd(unfold(A, j), full_matrices=False)\\n\\n S=A.copy()\\n for i in range(0, d):\\n S=nModeProduct(S, U[i].T, i)\\n\\n if k is not None:\\n if isinstance(k, int): # if only one integer is assigned to k\\n k=k*np.ones((len(A.shape),), dtype=int)\\n\\n S=subTensor(S, k=k)\\n for j in range(0, d):\\n U[j]=U[j][:, :k[j]]\\n\\n return S, U\",\n \"def svd(T):\\n try:\\n U, S, V = splinalg.svd(T, full_matrices=False)\\n except splinalg.LinAlgError:\\n U, S, V = splinalg.svd(T, full_matrices=False, lapack_driver='gesvd')\\n maxU, minU = U.max(0), U.min(0)\\n maxV, minV = V.max(1), V.min(1)\\n ind = (np.abs(minU) > maxU) & (np.abs(minV) > maxV)\\n U[:, ind] *= -1\\n V[ind] *= -1\\n return U, S, V\",\n \"def test_truncate():\\n # rank 1 tensor\\n X = outer(rand(3), rand(4), rand(5))\\n T = hosvd(X)\\n assert find_truncation_rank(T.X, 1e-12) == (1,1,1)\\n T1 = T.truncate(1)\\n assert np.allclose(X, T1.asarray())\",\n \"def get_kernel_vector_unit(name, s, e, x, d_1, kernel, hyps,\\n cutoffs, hyps_mask):\\n\\n size = (e-s)\\n ds = [1, 2, 3]\\n\\n args = from_mask_to_args(hyps, hyps_mask, cutoffs)\\n\\n k_v = np.zeros(size*3, )\\n\\n for m_index in range(size):\\n x_2 = _global_training_data[name][m_index+s]\\n for d_2 in ds:\\n k_v[m_index*3+d_2-1] = kernel(x, x_2, d_1, d_2, *args)\\n\\n return k_v\",\n \"def calculate_k_SVD(smooth_spreadsheet_matrix, k):\\n U_unitary_matrix, singular_value, V_unitary_matrix = linalg.svd(smooth_spreadsheet_matrix)\\n S_full_squared_matrix = np.zeros((k, k))\\n np.fill_diagonal(S_full_squared_matrix, np.sqrt(singular_value[:k]))\\n U_unitary_matrix = U_unitary_matrix[:, :k]\\n return U_unitary_matrix, S_full_squared_matrix\",\n \"def svt(X, tau):\\n U, S, Vt = la.svd(X,full_matrices=False)\\n Xs = np.dot(U * st(S,tau), Vt)\\n return Xs\",\n \"def svd(self):\\n U, s, Vh = la.svd(self)\\n S = np.zeros(self.shape)\\n np.fill_diagonal(S, s)\\n return (Matrix(U), Matrix(S), Matrix(Vh))\",\n \"def compute_svd(self,data,k):\\n m, n =data.shape\\n n = self.comm1.allreduce(n)\\n print(m,n)\\n if k==-1:\\n k = min(m,n)\\n args = parse()\\n args.m,args.n,args.k,args.comm = m,n,k,self.comms\\n args.eps = np.finfo(data.dtype).eps\\n if args.m<args.n: args.p_r,args.p_c = 1,self.size\\n dsvd = DistSVD(args, data)\\n singularValues, U, V = dsvd.svd()\\n rel_error = dsvd.rel_error(U, np.diag(singularValues), V)\\n if self.global_rank==0: print('relative error is:', rel_error )\\n return singularValues,U,V,rel_error\",\n \"def svd(matrix):\\n u = None\\n s = None\\n v = None\\n ### YOUR CODE HERE\\n (u,s,v)=np.linalg.svd(matrix)\\n ### END YOUR CODE\\n\\n return u, s, v\",\n \"def get_svd(self,new_df):\\n\\n\\t\\tnew_matrix = new_df.as_matrix()\\n\\t\\t\\n\\t\\t#get the mean score of users\\n\\t\\tmean_score = np.mean(new_matrix,axis = 1)\\n\\t\\tnew_matrix_dm = new_matrix - mean_score.reshape(-1,1)\\n\\t\\t#decompose interaction matrix into U, Vt, Sigma\\n\\t\\tU, sigma, Vt = svds(new_matrix_dm, k = 5)\\n\\t\\tsigma = np.diag(sigma)\\n\\t\\tall_user_predicted_ratings = np.dot(np.dot(U, sigma), Vt) + mean_score.reshape(-1, 1)\\n\\t\\tpreds_df = pd.DataFrame(all_user_predicted_ratings, columns = new_df.columns)\\n\\t\\tpreds_df = preds_df.round(1)\\n\\n\\t\\treturn preds_df\",\n \"def implement_svd(data):\\n u, s, v = torch.svd(data) # implement svd\\n # note: the u returned by this function only includes the top values.\\n # u * s will be equivalent due to the zero terms, but will run more efficiently with this implementation.\\n s = torch.diag(s) # turn s into a diagonal matrix\\n transformed_matrix = torch.mm(u, s) # u * s\\n return l21_reg(s), transformed_matrix # return the L2,1 regularization term and matrix\",\n \"def fit_svd(self):\\n\\n # U has the eigenvectors of G.Gt as columns ()\\n # S has square roots of the eigenvalues of G.Gt and Gt.G in its diagonal\\n # The square roos of the eigenvalues are called singular values\\n # V has the eigenvectors of Gt.G as columns ()\\n # full_matrices set to false will set the Vt matrix to a shape m x n\\n\\n U, S, Vt = linalg.svd(self.norm_matrix, full_matrices=False)\\n\\n # Compute the eigenvalues\\n eig_val = (S ** 2)\\n\\n # Explained_variance tell us how much of the variance in the data each eigen value explains\\n explained_variance = eig_val / (self.n_samples - 1)\\n # total_var is the total variance in the data\\n total_var = explained_variance.sum()\\n explained_variance_ratio = explained_variance / total_var\\n # The cumulative sum of all ratios\\n ratio_cumsum = np.cumsum(explained_variance_ratio)\\n\\n # We search in the cumsum for the index of the value which, when added, corresponds to the quality_percent\\n # The index of the cumsum gives us the components we need to add to explain X quality percent of our data\\n n_components = np.searchsorted(ratio_cumsum, self.quality_percent, side='right') + 1\\n\\n self.components = Vt[:n_components]\\n print(\\\"The principal components have been calculated using svd\\\", self.components.shape)\\n\\n return self.components\",\n \"def R_SVD(U, sigma, V, time, f_fault, tolerance = 0.02, PMItreshold = 1.0, estimate_xi_func=get_SVDxi, estimate_xi_func_params=None):\\n\\n # Get the search region\\n m = sigma.size\\n f_fault = np.asanyarray(f_fault)\\n dt = time[1] - time[0]\\n T0 = np.zeros(f_fault.size, dtype=int)\\n T1 = np.zeros(f_fault.size, dtype=int)\\n PMI = []\\n W = []\\n for i in range(0, f_fault.size):\\n T0[i] = int( np.floor( (1.0/(f_fault[i]*(1.0 + tolerance)))/dt ) )\\n T1[i] = int( np.ceil( (1.0/(f_fault[i]*(1.0 - tolerance)))/dt ) )\\n if T1[i] == T0[i]:\\n T1[i] += 1\\n PMI.append(np.zeros(m))\\n W.append(np.zeros(m))\\n\\n # Calculate PMI for each fault type\\n for i in range(0, m):\\n if estimate_xi_func_params is None:\\n a_i = estimate_xi_func(U, sigma, V, i)\\n else:\\n a_i = estimate_xi_func(U, sigma, V, i, estimate_xi_func_params)\\n a_i = envelope(a_i)\\n a_i -= a_i.mean()\\n R_a = fftwconvolve(np.flipud(a_i), a_i)\\n # Keep positive part\\n R_a = R_a[a_i.size-1:]\\n # Scale by dividing by number of elements\\n R_a = R_a / np.arange(R_a.size, 0, -1)\\n # Get T_0\\n R_0 = R_a[0]\\n # Calculate R and PMI for each fault type\\n for k in range(0, f_fault.size):\\n # print('T0[%i] = %f, T1[%i] = %f, R_a.size = %i' % (k, T0[k], k, T1[k], R_a.size))\\n # print(R_a[481:501])\\n R_T = np.max(R_a[T0[k]:T1[k]])\\n PMI[k][i] = R_T/(R_0 - R_T)\\n\\n # Calculate weights\\n for k in range(0, f_fault.size):\\n temp = np.sum(PMI[k])\\n for i in range(0, m):\\n if PMI[k][i] > PMItreshold:\\n W[k][i] = PMI[k][i]/temp\\n\\n # Return data\\n return PMI, W\",\n \"def svd_S(T):\\n try:\\n S = splinalg.svd(T, full_matrices=False, compute_uv=False)\\n except splinalg.LinAlgError:\\n S = splinalg.svd(T, full_matrices=False, lapack_driver='gesvd', compute_uv=False)\\n return S\",\n \"def svr_kernel(name, kernel, epsilon=None, **kwargs):\\n def _name(msg):\\n return '%s.%s_%s' % (name, kernel, msg)\\n\\n hp_space = _svm_hp_space(_name, kernel=kernel, **kwargs)\\n hp_space.update(_svr_hp_space(_name, epsilon))\\n return scope.sklearn_SVR(**hp_space)\",\n \"def svd_stability(N=20, display=False):\\n B = np.random.randn(N, N) # i.i.d normal matrix\\n C = np.random.randn(N, N) # i.i.d normal matrix\\n S = np.sort(np.abs(np.random.randn(N)))[::-1] # i.i.d normal vector\\n\\n # Generating U, V, sigma using QR (Cheap way to get orthogonal matrices)\\n U, _ = np.linalg.qr(B) # U\\n V, _ = np.linalg.qr(C) # V\\n\\n A = (U * S) @ V.T # Forming A\\n\\n U2, S2, VT2 = np.linalg.svd(A)\\n\\n norm1 = np.linalg.norm(U2 - U)\\n norm2 = np.linalg.norm(S2 - S)\\n norm3 = np.linalg.norm(VT2 - V.T)\\n norm4 = np.linalg.norm(((U2 * S2) @ VT2) - A)\\n\\n if display:\\n print(\\\"||U2 - U|| = %s\\\" %norm1)\\n print(\\\"||sigma2 - sigma|| = %s\\\" %norm2)\\n print(\\\"||V2 - V|| = %s\\\" %norm3)\\n print(\\\"||(U2 @ S2 @ V2) - A|| = %s\\\" %norm4)\\n return (norm1, norm2, norm3, norm4)\",\n \"def computesvd(train, K):\\n mean = global_mean(train)\\n train = standardize(train, mean)\\n std = compute_std(train)\\n train = div_std(train)\\n\\n U, s, Vt = sp.linalg.svds(train, k=K)\\n\\n dim = (len(s), len(s))\\n S = np.zeros(dim)\\n for i in range(len(s)):\\n S[i, i] = mt.sqrt(s[i])\\n\\n U = sp.lil_matrix(U, dtype=np.float32) # dim(M,k)\\n S = sp.lil_matrix(S, dtype=np.float32) # dim(k,k)\\n Vt = sp.lil_matrix(Vt, dtype=np.float32) # dim(k,N)\\n\\n user_features = S.dot(Vt)\\n item_features = np.transpose(U.dot(S))\\n\\n return user_features, item_features, mean, std\",\n \"def compact_svd(A, tol=1e-6):\\r\\n eigs, vecs = la.eig(A.conj().T@A)\\r\\n svs = np.sqrt(eigs)\\r\\n #sort eigenvalues and eigenvectors accordingly\\r\\n sorter = list(zip(svs,vecs.T))\\r\\n sorter.sort(reverse=True, key=lambda tup: tup[0])\\r\\n svs = [x[0] for x in sorter]\\r\\n vecs = [x[1] for x in sorter]\\r\\n #find number of nonzero eigenvalues\\r\\n r_not = svs.count(0)\\r\\n r = len(svs) - r_not\\r\\n svs_1 = np.array(svs[:r])\\r\\n vecs_1 = np.array(vecs[:r])\\r\\n u_1 = (A@vecs_1)/svs_1\\r\\n\\r\\n return u_1, svs_1, vecs_1.conj().T\",\n \"def svd(a, full_matrices=1, compute_uv=1):\\n return SVD(full_matrices, compute_uv)(a)\",\n \"def matrix_svd(\\n self,\\n chis=None,\\n eps=0,\\n print_errors=\\\"deprecated\\\",\\n break_degenerate=False,\\n degeneracy_eps=1e-6,\\n sparse=False,\\n trunc_err_func=None,\\n ):\\n if print_errors != \\\"deprecated\\\":\\n msg = (\\n \\\"The `print_errors` keyword argument has been deprecated, \\\"\\n \\\"and has no effect. Rely instead on getting the error as a \\\"\\n \\\"return value, and print it yourself.\\\"\\n )\\n warnings.warn(msg)\\n chis = self._matrix_decomp_format_chis(chis, eps)\\n maxchi = max(chis)\\n assert self.defval == 0\\n assert self.invar\\n\\n # SVD each sector at a time.\\n # While doing so, also keep track of a list of all singular values, as\\n # well as a heap that gives the negative of the largest singular value\\n # in each sector. These will be needed later when deciding how to\\n # truncate the decomposition.\\n svds = {}\\n dims = {}\\n minus_next_sings = []\\n all_sings = []\\n for k, v in self.sects.items():\\n if 0 in v.shape:\\n shp = v.shape\\n m = min(shp)\\n u = np.empty((shp[0], m), dtype=self.dtype)\\n s = np.empty((m,), dtype=np.float_)\\n v = np.empty((m, shp[1]), dtype=self.dtype)\\n else:\\n if sparse and maxchi < min(v.shape) - 1:\\n u, s, v = spsla.svds(\\n v, k=maxchi, return_singular_vectors=True\\n )\\n order = np.argsort(-s)\\n u = u[:, order]\\n s = s[order]\\n v = v[order, :]\\n else:\\n u, s, v = np.linalg.svd(v, full_matrices=False)\\n svd = (s, u, v)\\n svds[k] = svd\\n dims[k] = 0\\n sings = svd[0]\\n all_sings.append(sings)\\n if 0 not in sings.shape:\\n heapq.heappush(minus_next_sings, (-sings[0], k))\\n try:\\n all_sings = np.concatenate(all_sings)\\n except ValueError:\\n all_sings = np.array((0,))\\n\\n if sparse:\\n norm_sq = self.norm_sq()\\n else:\\n norm_sq = None\\n\\n # Figure out what bond dimension to truncate to, how this bond\\n # dimension is distributed over the different sectors, and what the\\n # truncation error is.\\n chi, dims, rel_err = type(self)._find_trunc_dim(\\n all_sings,\\n svds,\\n minus_next_sings,\\n dims,\\n chis=chis,\\n eps=eps,\\n break_degenerate=break_degenerate,\\n degeneracy_eps=degeneracy_eps,\\n trunc_err_func=trunc_err_func,\\n norm_sq=norm_sq,\\n )\\n\\n # Truncate each block and create the dim for the new index.\\n new_dim = []\\n new_qim = []\\n svds = {k: v for k, v in svds.items() if dims[k] > 0}\\n for k, v in svds.items():\\n d = dims[k]\\n if d > 0:\\n new_dim.append(d)\\n new_qim.append(k[0])\\n svds[k] = (v[0][:d], v[1][:, :d], v[2][:d, :])\\n else:\\n del svds[k]\\n\\n # Initialize U, S, V.\\n d = self.dirs[0]\\n U = type(self)(\\n [self.shape[0], new_dim],\\n qhape=[self.qhape[0], new_qim],\\n dirs=[d, -d],\\n qodulus=self.qodulus,\\n dtype=self.dtype,\\n charge=0,\\n )\\n S = type(self)(\\n [new_dim],\\n qhape=[new_qim],\\n dirs=[d],\\n qodulus=self.qodulus,\\n dtype=np.float_,\\n invar=False,\\n charge=0,\\n )\\n V = type(self)(\\n [new_dim, self.shape[1]],\\n qhape=[new_qim, self.qhape[1]],\\n dirs=[d, self.dirs[1]],\\n qodulus=self.qodulus,\\n dtype=self.dtype,\\n charge=self.charge,\\n )\\n\\n # Set the blocks of U, S and V.\\n for k, v in svds.items():\\n k_U = (k[0], k[0])\\n S[(k[0],)] = v[0]\\n U[k_U] = v[1]\\n V[k] = v[2]\\n\\n return U, S, V, rel_err\",\n \"def pinv_damped(a, l, rcond=1e-15 ):\\n # a, wrap = np.linalg._makearray(a)\\n # np.linalg._assertNoEmpty2d(a)\\n a = a.conjugate()\\n u, s, vt = np.linalg.svd(a, 0)\\n m = u.shape[0]\\n n = vt.shape[1]\\n cutoff = rcond*np.maximum.reduce(s)\\n # estimate of smallest singular value\\n # s_m = 0.00065\\n s_m = np.amin(s)\\n # size of the singular region\\n eps = .10\\n if s_m > eps:\\n lambda_2 =0\\n else:\\n lambda_2 = (1-(s_m/eps)**2)*l*l\\n # print np.amin(s), lambda_2\\n for i in range(min(n, m)):\\n if s[i] > cutoff:\\n s[i] = s[i]/(s[i]*s[i]+lambda_2)\\n # s[i] = s[i]/(s[i]*s[i]+l*l)\\n else:\\n s[i] = 0.;\\n print('singularity: ', s)\\n # if s[i] > cutoff:\\n # s[i] = s[i]/(s[i]*s[i]+lambda_2)\\n # # s[i] = s[i]/(s[i]*s[i]+l*l)\\n # else:\\n # s[i] = 0.;\\n # print('singularity: ', s)\\n # print np.maximum.reduce(s)\\n res = np.dot(np.transpose(vt), np.multiply(s[:, np.newaxis], np.transpose(u)))\\n # return wrap(res)\\n return res\",\n \"def get_kernel_vector(name, kernel, x, d_1, hyps,\\n cutoffs=None, hyps_mask=None,\\n n_cpus=1, n_sample=100):\\n\\n size = len(_global_training_data[name])\\n\\n if (n_cpus is None):\\n n_cpus = mp.cpu_count()\\n if (n_cpus == 1):\\n return get_kernel_vector_unit(\\n name, 0, size, x, d_1, kernel, hyps,\\n cutoffs, hyps_mask)\\n\\n block_id, nbatch = partition_c(n_sample, size, n_cpus)\\n\\n result_queue = mp.Queue()\\n children = []\\n for wid in range(nbatch):\\n s, e = block_id[wid]\\n children.append(\\n mp.Process(\\n target=queue_wrapper,\\n args=(result_queue, wid,\\n get_kernel_vector_unit,\\n (name, s, e, x, d_1, kernel, hyps,\\n cutoffs, hyps_mask\\n )\\n )\\n )\\n )\\n\\n # Run child processes.\\n for c in children:\\n c.start()\\n\\n # Wait for all results to arrive.\\n k12_v = np.zeros(size*3)\\n for _ in range(nbatch):\\n wid, result_chunk = result_queue.get(block=True)\\n s, e = block_id[wid]\\n k12_v[s*3:e*3] = result_chunk\\n\\n # Join child processes (clean up zombies).\\n for c in children:\\n c.join()\\n\\n return k12_v\",\n \"def trace_norm_prox(W, alpha):\\n d, T = W.shape\\n U, s, V = np.linalg.svd(W, full_matrices=True)\\n # U ~ (d, d)\\n # s ~ (min(d, T), min(d, T))\\n # V ~ (T, T)\\n s = soft_thresholding(s, alpha)\\n # make the output n1 x n2\\n if d >= T:\\n st_S = np.vstack((np.diag(s), np.zeros((np.abs(d-T), T))))\\n else:\\n st_S = np.hstack((np.diag(s), np.zeros((d, np.abs(d-T)))))\\n return np.dot(U, np.dot(st_S, V))\",\n \"def compute_optimal_subspace_projection(G,X,k):\\n # center the data\\n # G = G - np.mean(G,axis=0)\\n # compute the k largest eigens of G\\n # U,S,VT = np.linalg.svd(G)\\n \\n U,S,VT = np.linalg.svd(G/np.sqrt(G.shape[0]))\\n # truncated score matrix\\n #G = U[:,:k] @ np.diag(S[:k])\\n G = G @ (VT.T)[:,:k]\\n # project X as well\\n X = X @ (VT.T[:,:k])\\n print(\\\"Singular values\\\", S[:20])\\n return G,X, (VT.T[:,:k])\",\n \"def scv(SP):\\n scv = ((np.std(SP,axis=1)/np.mean(SP,axis=1)))\\n return scv\",\n \"def dpp_sw(kernel_matrix, window_size=3, max_length=14, epsilon=1E-10):\\r\\n item_size = kernel_matrix.shape[0]\\r\\n v = np.zeros((max_length, max_length))\\r\\n cis = np.zeros((max_length, item_size))\\r\\n di2s = np.copy(np.diag(kernel_matrix))\\r\\n selected_items = list()\\r\\n selected_item = np.argmax(di2s)\\r\\n selected_items.append(selected_item)\\r\\n window_left_index = 0\\r\\n while len(selected_items) < max_length:\\r\\n k = len(selected_items) - 1\\r\\n ci_optimal = cis[window_left_index:k, selected_item]\\r\\n di_optimal = math.sqrt(di2s[selected_item])\\r\\n v[k, window_left_index:k] = ci_optimal\\r\\n v[k, k] = di_optimal\\r\\n elements = kernel_matrix[selected_item, :]\\r\\n eis = (elements - np.dot(ci_optimal, cis[window_left_index:k, :])) / di_optimal\\r\\n cis[k, :] = eis\\r\\n di2s -= np.square(eis)\\r\\n if len(selected_items) >= window_size:\\r\\n window_left_index += 1\\r\\n for ind in range(window_left_index, k + 1):\\r\\n t = math.sqrt(v[ind, ind] ** 2 + v[ind, window_left_index - 1] ** 2)\\r\\n c = t / v[ind, ind]\\r\\n s = v[ind, window_left_index - 1] / v[ind, ind]\\r\\n v[ind, ind] = t\\r\\n v[ind + 1:k + 1, ind] += s * v[ind + 1:k + 1, window_left_index - 1]\\r\\n v[ind + 1:k + 1, ind] /= c\\r\\n v[ind + 1:k + 1, window_left_index - 1] *= c\\r\\n v[ind + 1:k + 1, window_left_index - 1] -= s * v[ind + 1:k + 1, ind]\\r\\n cis[ind, :] += s * cis[window_left_index - 1, :]\\r\\n cis[ind, :] /= c\\r\\n cis[window_left_index - 1, :] *= c\\r\\n cis[window_left_index - 1, :] -= s * cis[ind, :]\\r\\n di2s += np.square(cis[window_left_index - 1, :])\\r\\n di2s[selected_item] = -np.inf\\r\\n selected_item = np.argmax(di2s)\\r\\n if di2s[selected_item] < epsilon:\\r\\n break\\r\\n selected_items.append(selected_item)\\r\\n return selected_items\",\n \"def rebuild_svd(self, U, S, V, k): # [5pts]\\n \\n N,D = U.shape[0],V.shape[0]\\n \\n \\n if U.ndim == 3:\\n Xrebuild = np.zeros((N,D,3))\\n for i in range(3):\\n U_temp = U[:,0:k,i]\\n S_temp = S[:,i]\\n S_temp = np.diag(S_temp[0:k])\\n V_temp = V[0:k,:,i]\\n Xrebuild_temp = U_temp@S_temp@V_temp\\n Xrebuild[:,:,i] = Xrebuild_temp\\n else:\\n U_new = U[:,0:k]\\n S_new = np.diag(S[0:k])\\n V_new = V[0:k,:]\\n Xrebuild = U_new@S_new@V_new\\n\\n return Xrebuild\",\n \"def set_gpm_by_svd(self, threshold):\\n assert len(self.M) > 0\\n\\n self.M_vec = []\\n self.M_val = []\\n\\n index = np.arange(len(self.M))\\n np.random.shuffle(index)\\n\\n for k, idx in enumerate(index):\\n if k < min(self.mem_batch_size, len(self.M)):\\n ox, oy, ot = self.M[idx]\\n if k == 0:\\n mx = ox.unsqueeze(0)\\n else:\\n mx = torch.cat((mx, ox.unsqueeze(0)), 0)\\n\\n # handle gpus if specified\\n if self.cuda:\\n mx = mx.cuda()\\n\\n with torch.no_grad():\\n rep = self.net.forward(mx, svd=True)\\n\\n for i in range(len(rep)):\\n # rep[i]: n_samples * n_features\\n assert rep[i].ndim == 2\\n\\n u, s, v = torch.svd(rep[i].detach().cpu())\\n\\n if threshold[i] < 1:\\n r = torch.cumsum(s ** 2, 0) / torch.cumsum(s ** 2, 0)[-1]\\n k = torch.searchsorted(r, threshold[i]) + 1\\n elif threshold[i] > 10:\\n # differ with thres = 1\\n k = int(threshold[i])\\n else:\\n k = len(s)\\n\\n if k > 0:\\n self.M_vec.append(v[:, :k].cpu())\\n self.M_val.append(s[:k].cpu())\",\n \"def normalise_kernel(K_temp):\\n\\n nb_item = K_temp.shape[0]\\n K_norm = np.zeros((nb_item, nb_item))\\n for i in range(nb_item):\\n for j in range(i, nb_item):\\n K_norm[i, j] = K_temp[i, j] / math.sqrt(K_temp[i, i] * K_temp[j, j])\\n K_norm[j, i] = K_norm[i, j]\\n\\n return K_norm\",\n \"def test_svd_smoothing(self):\\n\\t\\t\\n\\t\\t# 819 =~ 4096*0.2\\n\\t\\tself.watcher.SVDSmoothing(layers=[self.fc2_layer])\\n\\t\\tesd = self.watcher.get_ESD(layer=self.fc2_layer) \\n\\t\\tnum_comps = len(esd[esd > 10**-10])\\n\\t\\tself.assertEqual(num_comps, 819)\",\n \"def gaussian_process_pointwise_variance(kernel, pred_samples, train_samples,\\n nugget = 0):\\n K_train = kernel(train_samples.T)\\n # add small number to diagonal to ensure covariance matrix is\\n # positive definite\\n ntrain_samples = train_samples.shape[1]\\n K_train[np.arange(ntrain_samples), np.arange(ntrain_samples)] += nugget\\n k_pred = kernel(train_samples.T, pred_samples.T)\\n L = np.linalg.cholesky(K_train)\\n tmp = solve_triangular(L, k_pred, lower=True)\\n variance = kernel.diag(pred_samples.T) - np.sum(tmp*tmp, axis=0)\\n return variance\",\n \"def visualize_svd():\",\n \"def invert_L1_svd():\",\n \"def svd_split(m: np.ndarray):\\n Dl, d, Dr = m.shape\\n u, s, v = np.linalg.svd(m.reshape(Dl * 2, Dr * d // 2))\\n D_middle = min(u.shape[-1], v.shape[0])\\n s = np.diag(s[:D_middle])\\n\\n u = u[:, :D_middle].reshape((Dl, 2, D_middle))\\n sv = s.dot(v[:D_middle]).reshape((D_middle, d // 2, Dr))\\n return u, sv\",\n \"def unit_vectors(x):\\n xnew = x.copy()\\n for v in range(x.shape[-1]):\\n xnew[:, v] = x[:, v] / np.linalg.norm(x[:, v])\\n return xnew\",\n \"def compact_svd(A, tol=1e-6):\\n #Compute eigenvalues/vectors\\n lam, V = la.eig((A.conj().T @ A))\\n sig = np.sqrt(lam)\\n \\n #Sort results\\n argB = np.argsort(sig)\\n arg = []\\n for i in range(0, len(argB)):\\n arg.append(argB[len(argB)-1-i])\\n sig = sig[arg]\\n V = V[:,arg]\\n #How many non-zero positive\\n r = 0\\n for j in range(0, len(sig)):\\n if abs(sig[j]) >= tol:\\n r += 1\\n \\n sig1 = sig[:r]\\n V1 = np.array(V[:,:r])\\n \\n# print(np.shape(A))\\n# print(np.shape(V1))\\n U1 = A@V1\\n U1 = U1/sig1\\n \\n #Return answers\\n return U1, sig1, V1.conj().T\\n\\n raise NotImplementedError(\\\"Problem 1 Incomplete\\\")\",\n \"def svd_spd_decomposition(P):\\n\\t# Assert Matrix P is symetric\\n\\tassert check_symmetric(P)\\n\\n\\t# singular value decomposition\\n\\tU, D, V = np.linalg.svd(P, full_matrices=False)\\n\\n\\t# Create matrix W = Vtsqrt(diagnol(D)) #why Vt?\\n\\tM = np.dot(np.transpose(V), np.sqrt(np.diag(D)))\\n\\n\\t#print(np.transpose(V))\\n\\n\\treturn M\",\n \"def truncated_normal(size, threshold=1):\\n return truncnorm.rvs(-threshold, threshold, size=size)\",\n \"def tensor_svd_denoise(data, rank):\\n # Case when SVD ranks are fed in the input, call svd_HO function to denoise\\n\\n if len(data.shape) == 3: # hyperspectral data case, directly feed data to svd_HO function\\n [X, _, _] = svd_HO(data, rank)\\n if len(data.shape) == 4: # Original 4D STEM data case, unfold reciprocal space dimensions into one dimension then feed to svd_HO function\\n data = np.reshape(data, [data.shape[0], data.shape[1], data.shape[2]*data.shape[3]])\\n [X, _, _] = svd_HO(data, rank)\\n \\n return X\",\n \"def shrink_kernel(self, kernel, up_scale):\\n up_scale = torch.tensor(up_scale).float()\\n # boundary padding based on the scaling law\\n pad_in = (torch.ceil(up_scale**2).int())*((kernel.shape[2]-1)//2)\\n pad_h = (torch.ceil(up_scale).int())*((kernel.shape[3]-1)//2)\\n pad_w = (torch.ceil(up_scale).int())*((kernel.shape[4]-1)//2)\\n padded_kernel = F.pad(kernel, (pad_w, pad_w, pad_h, pad_h, pad_in, pad_in))\\n delta = up_scale%1\\n \\n if delta == 0:\\n shrink_factor = 1\\n else:\\n # shrink_factor for coordinates.\\n shrink_factor = (((kernel.shape[4]-1))/(padded_kernel.shape[-1]-1)*(up_scale+1))\\n \\n # Adjustment to deal with weird filtering on the grid sample function.\\n shrink_factor = 1.5*(shrink_factor-0.5)**3 + 0.57 \\n\\n grid = torch.meshgrid(torch.linspace(-1, 1, kernel.shape[2])*(shrink_factor**2),\\n torch.linspace(-1, 1, kernel.shape[3])*shrink_factor, \\n torch.linspace(-1, 1, kernel.shape[4])*shrink_factor)\\n\\n grid = torch.cat([grid[2].unsqueeze(0).unsqueeze(-1), \\n grid[1].unsqueeze(0).unsqueeze(-1), \\n grid[0].unsqueeze(0).unsqueeze(-1)], dim = -1).repeat(kernel.shape[0],1,1,1,1)\\n\\n new_kernel = F.grid_sample(padded_kernel, grid.to(device))\\n if kernel.shape[-1] - 2*up_scale > 0:\\n new_kernel = new_kernel * (kernel.shape[-1]**2/((kernel.shape[-1] - 2*up_scale)**2 + 0.01))\\n return new_kernel\",\n \"def norm_psf(psf_data, psf_ivm):\\n psf_sum = fsum(psf_data.flat)\\n return psf_data / psf_sum, psf_ivm * psf_sum**2\",\n \"def truncated_normal(size, lower=-2, upper=2):\\r\\n\\r\\n return scipy.stats.truncnorm.rvs(lower, upper, size=size)\",\n \"def stdfilt(vec, wid=3):\\n # 2012-04-05 13:58 IJMC: Created\\n\\n filt = 0*vec\\n if wid<1:\\n wid = 1\\n \\n wid = int(wid)\\n\\n n = len(vec)\\n\\n for ii in range(n):\\n i0 = np.max([0, ii -wid/2])\\n i1 = np.min([n-1, ii + wid/2])\\n filt[ii] = np.std(vec[i0:i1+1])\\n #print ii, i0, i1\\n\\n return filt\",\n \"def _mps_truncateC(self, C, Dmax, tol):\\n U, S, V = svd(C)\\n tol = max(np.finfo(float).eps, tol)\\n keep = min(sum(S > (S[0] * tol)), Dmax)\\n projR = V[:keep, :]\\n projL = U[:, :keep]\\n discarded = np.sqrt(sum(S[keep:]**2)) / S[0]\\n S = np.diag(S[:keep])\\n return projL, S, projR, keep, discarded\",\n \"def update_S(self):\\n S = svd_S(self.C)\\n if self.S[self.pC].size != S.size:\\n self.S[self.pC] = self._one_S(S.size)\\n dS = (self.S[self.pC] - S)\\n dS = np.sqrt(np.sum(dS**2))\\n self.S[self.pC] = S\\n return dS\",\n \"def svd_approx(A, k):\\n U,s,Vh=la.svd(A,full_matrices=False)\\n return U[:,:k].dot(np.diag(s[:k])).dot(Vh[:k,:])\",\n \"def approximate_svd(A, U, S, V, k=10, params=None):\\n\\n \\n A = lib.adapt(A)\\n U = lib.adapt(U)\\n S = lib.adapt(S)\\n V = lib.adapt(V)\\n\\n Aobj = A.ptr()\\n Uobj = U.ptr()\\n Sobj = S.ptr()\\n Vobj = V.ptr()\\n\\n if (Aobj == -1 or Uobj == -1 or Sobj == -1 or Vobj == -1):\\n raise errors.InvalidObjectError(\\\"Invalid/unsupported object passed as A, U, S or V \\\")\\n\\n # use default params in case none are provided\\n if params == None:\\n params = SVDParams()\\n params_json = params.str() + '\\\\0'\\n\\n lib.callsl(\\\"sl_approximate_svd\\\", \\\\\\n A.ctype(), Aobj, \\\\\\n U.ctype(), Uobj, \\\\\\n S.ctype(), Sobj, \\\\\\n V.ctype(), Vobj, \\\\\\n k, params_json, lib.ctxt_obj)\\n\\n A.ptrcleaner()\\n U.ptrcleaner()\\n S.ptrcleaner()\\n V.ptrcleaner()\\n\\n return (U.getobj(), S.getobj(), V.getobj())\",\n \"def reduce_dimensions(feature_vectors_full, model):\\n\\n if 'fvectors' in model:\\n v = np.array(model['fvectors'])\\n else:\\n #Principal Components Analysis implemented from lab code\\n covx = np.cov(feature_vectors_full, rowvar=0)\\n N = covx.shape[0]\\n w, v = scipy.linalg.eigh(covx, eigvals=(N - 40, N - 1))\\n v = np.fliplr(v)\\n model['fvectors'] = v.tolist()\\n pca_train = np.dot((feature_vectors_full - np.mean(feature_vectors_full)), v)\\n return pca_train[:,0:10]\",\n \"def svd(matrix):\\n u = None\\n s = None\\n v = None\\n ### YOUR CODE HERE\\n pass\\n ### END YOUR CODE\\n\\n return u, s, v\",\n \"def svd(matrix, rank=None):\\n if matrix.ndim != 2:\\n raise ValueError('Input should be a two-dimensional array. matrix.ndim is {} != 2'.format(matrix.ndim))\\n dim_1, dim_2 = matrix.shape\\n if dim_1 <= dim_2:\\n min_dim = dim_1\\n else:\\n min_dim = dim_2\\n\\n if rank is None or rank >= min_dim:\\n # Default on standard SVD\\n U, S, V = scipy.linalg.svd(matrix)\\n U, S, V = U[:, :rank], S[:rank], V[:rank, :]\\n return U, S, V\\n\\n else:\\n # We can perform a partial SVD\\n # First choose whether to use X * X.T or X.T *X\\n if dim_1 < dim_2:\\n S, U = scipy.sparse.linalg.eigsh(np.dot(matrix, matrix.T), k=rank, which='LM')\\n S = np.sqrt(S)\\n V = np.dot(matrix.T, U * 1 / S[None, :])\\n else:\\n S, V = scipy.sparse.linalg.eigsh(np.dot(matrix.T, matrix), k=rank, which='LM')\\n S = np.sqrt(S)\\n U = np.dot(matrix, V) * 1 / S[None, :]\\n\\n # WARNING: here, V is still the transpose of what it should be\\n U, S, V = U[:, ::-1], S[::-1], V[:, ::-1]\\n return U, S, V.T\",\n \"def reduce_dimensions(feature_vectors_full, model):\\n \\\"\\\"\\\"Subtracting the mean of the feature vectors being classified\\n as opposed to the average of the test data's feature vectors seems to\\n improve performance (I think this is to do with the noise; as on clean pages\\n the average for a pixel that's white in all feature vectors would be 255. In\\n a noisy image, it'd be lower, so the white areas wouldn't get \\\"centred\\\"\\n around white by subtracting 255 any more.).\\n \\\"\\\"\\\"\\n return np.dot(\\n (feature_vectors_full - np.mean(feature_vectors_full,axis=0)), \\n np.array(model[\\\"eigenvectors\\\"]))\",\n \"def Pkernel(x):\\n\\n m = (x < 0.) & (x >= 1.)\\n x[x < 0.] = np.zeros(np.sum(x < 0.))\\n x[x >= 1.] = np.zeros(np.sum(x >= 1.))\\n x = np.sqrt(x)\\n\\n result = np.log(2.) * np.log(2.) - np.pi *np.pi / 6. \\\\\\n + 2. * spence(0.5 + 0.5 * x) - (x + x*x*x) / (1. - x*x) \\\\\\n + (np.log(1. + x) - 2. * np.log(2.)) * np.log(1. - x) \\\\\\n + 0.5 * (np.log(1. - x) * np.log(1. - x) - np.log(1. + x) * np.log(1. + x)) \\\\\\n + 0.5 * (1. + x*x*x*x) / (1. - x*x) * (np.log(1. + x) - np.log(1. - x))\\n result[x <= 0.] = np.zeros(np.sum(x <= 0.))\\n result[x >= 1.] = np.zeros(np.sum(x >= 1.))\\n return result\",\n \"def std(X,trimming=0):\\n \\n if trimming==0:\\n s = np.power(np.var(X,axis=0),.5)\\n s = np.array(s).reshape(-1)\\n else: \\n var = sps.trim_mean(np.square(X - sps.trim_mean(X,trimming,0)),\\n trimming,0)\\n s = np.sqrt(var) \\n return s\",\n \"def calc_image_svd(img:list):\\r\\n img_r = img.swapaxes(1, 2).swapaxes(0, 1)\\r\\n svd_u, svd_s, svd_vh = np.linalg.svd(img_r, full_matrices=True)\\r\\n return [svd_u, svd_s, svd_vh]\",\n \"def preprocess_psf(psf_data, psf_ivm):\\n psf_data = fits.getdata(psf_data, ignore_missing_end=True)\\n psf_ivm = fits.getdata(psf_ivm, ignore_missing_end=True)\\n\\n # We don't want zero-weight pixels in the PSF to contribute infinitely to\\n # the variance, so we simply set them to 0 in both data and weight map\\n badpx = ~np.isfinite(psf_data) | ~np.isfinite(psf_ivm) | (psf_ivm <= 0)\\n psf_data[badpx] = 0\\n psf_ivm[badpx] = 0\\n\\n # Normalize the PSF kernel, then return data and variance map\\n psf_data, psf_ivm = norm_psf(psf_data, psf_ivm)\\n psf_var = np.where(psf_ivm <= 0, 0, 1 / psf_ivm)\\n return psf_data, psf_var\",\n \"def center_and_normalise_kernel(K_temp):\\n\\n K_temp = KernelCenterer().fit_transform(K_temp)\\n nb_item = K_temp.shape[0]\\n K_norm = np.zeros((nb_item, nb_item))\\n for i in range(nb_item):\\n for j in range(i, nb_item):\\n K_norm[i, j] = K_temp[i, j] / math.sqrt(K_temp[i, i] * K_temp[j, j])\\n K_norm[j, i] = K_norm[i, j]\\n\\n return K_norm\",\n \"def normalise(self,data,take_logs:bool=False):\\n\\n # Normalise vector to sum up to 1\\n normalised_vector = data/np.sum(data)\\n\\n # If take logs is selected, take logs\\n if take_logs:\\n return np.log(normalised_vector)\\n else:\\n return normalised_vector\",\n \"def normalize_data(x):\\n mvec = x.mean(0)\\n stdvec = x.std(axis=0)\\n \\n return (x - mvec)/stdvec\",\n \"def normalize_data(x):\\n mvec = x.mean(0)\\n stdvec = x.std(axis=0)\\n \\n return (x - mvec)/stdvec\",\n \"def test_svd_smoothing_with_model(self):\\n\\n\\t\\t\\n\\t\\t# 819 =~ 4096*0.2\\n\\t\\tself.watcher.SVDSmoothing(model=self.model, layers=[self.fc2_layer])\\n\\t\\tesd = self.watcher.get_ESD(layer=self.fc2_layer) \\n\\t\\tnum_comps = len(esd[esd>10**-10])\\n\\t\\tself.assertEqual(num_comps, 819)\",\n \"def test_svd_smoothing_no_model(self):\\n\\t\\t\\n\\t\\t# 819 =~ 4096*0.2\\n\\t\\tself.watcher.SVDSmoothing(model=self.model, layers=[self.fc2_layer])\\n\\t\\tesd = self.watcher.get_ESD(layer=self.fc2_layer) \\n\\t\\tnum_comps = len(esd[esd>10**-10])\\n\\t\\tself.assertEqual(num_comps, 819)\",\n \"def get_singular_values(matrix, n):\\n singular_values = None\\n u, s, v = svd(matrix)\\n ### YOUR CODE HERE\\n pass\\n ### END YOUR CODE\\n return singular_values\",\n \"def test_gauss_kernel():\\n\\n gauss = gauss_kernel(2, 5)\\n\\n assert gauss.shape == (5, 5)\\n assert gauss[2, 2] == 0.039788735772973836\",\n \"def standardize(X):\\n mu = X.mean(axis=0, keepdims=True)\\n s = X.std(axis=0, keepdims=True)\\n return (X-mu)/s\",\n \"def en_kern_vec(name, kernel,\\n x, hyps,\\n cutoffs=None, hyps_mask=None,\\n n_cpus=1, n_sample=100):\\n\\n training_data = _global_training_data[name]\\n size = len(training_data)\\n\\n if (n_cpus is None):\\n n_cpus = mp.cpu_count()\\n if (n_cpus == 1):\\n return en_kern_vec_unit(\\n name, 0, size, x, kernel, hyps,\\n cutoffs, hyps_mask)\\n\\n block_id, nbatch = partition_c(n_sample, size, n_cpus)\\n result_queue = mp.Queue()\\n children = []\\n for wid in range(nbatch):\\n children.append(\\n mp.Process(\\n target=queue_wrapper,\\n args=(result_queue, wid,\\n en_kern_vec_unit,\\n (name, block_id[wid][0], block_id[wid][1], x,\\n kernel, hyps, cutoffs, hyps_mask))))\\n\\n # Run child processes.\\n for c in children:\\n c.start()\\n\\n # Wait for all results to arrive.\\n k12_v = np.zeros(size*3)\\n for _ in range(nbatch):\\n wid, result_chunk = result_queue.get(block=True)\\n s, e = block_id[wid]\\n k12_v[s*3:e*3] = result_chunk\\n\\n # Join child processes (clean up zombies).\\n for c in children:\\n c.join()\\n\\n return k12_v\",\n \"def svd_approx(A, k):\",\n \"def run(self, dim):\\n conv_mat = np.dot(np.transpose(self._data), self._data)\\n u, d, v = np.linalg.svd(conv_mat)\\n return u[:, :dim], d[:dim], v[:dim]\",\n \"def snv(spectra):\\n\\n return (spectra - np.mean(spectra, axis=0)) / np.std(spectra, axis=0)\",\n \"def svd_images(imagear):\\n n = np.shape(imagear)[1]\\n u, s, v = np.linalg.svd(imagear/np.sqrt(n-1),full_matrices=False)\\n \\n return(u, s, v)\",\n \"def get_singular_values(matrix, n):\\n singular_values = None\\n u, s, v = svd(matrix)\\n ### YOUR CODE HERE\\n singular_values=s[0:n]\\n ### END YOUR CODE\\n return singular_values\",\n \"def posdef_eig_svd(mat):\\n evals, evecs, _ = tf.svd(mat)\\n\\n return evals, evecs\",\n \"def svc_kernel(name, kernel, random_state=None, probability=False, **kwargs):\\n def _name(msg):\\n return '%s.%s_%s' % (name, kernel, msg)\\n\\n hp_space = _svm_hp_space(_name, kernel=kernel, **kwargs)\\n hp_space.update(_svc_hp_space(_name, random_state, probability))\\n return scope.sklearn_SVC(**hp_space)\",\n \"def normkernel(S, T, n):\\n\\n k1 = kernel(S, S, n)\\n k2 = kernel(T, T, n)\\n res = kernel(S, T, n) / sqrt(k1 * k2)\\n\\n return res\",\n \"def test_svd_smoothing_no_model(self):\\n\\t\\t\\n\\t\\t# 819 =~ 4096*0.2\\n\\t\\t\\n\\t\\tself.watcher.SVDSmoothing(model=self.model, layers=[21])\\n\\t\\tesd = self.watcher.get_ESD(layer=21) \\n\\t\\tnum_comps = len(esd[esd>10**-10])\\n\\t\\tself.assertEqual(num_comps, 819)\",\n \"def fit(self, X):\\n n_components = X.shape[1] if self.n_components is None else self.n_components\\n diff = np.vstack([i[1:] - i[:-1] for i in X])\\n cov = scipy.cov(diff, rowvar=0)\\n u, _, _ = scipy.linalg.svd(cov, full_matrices=False)\\n u = u[:, -n_components:][:, ::-1]\\n\\n self.weights = u\",\n \"def normalize(data):\\n data = numpy.asmatrix(data)\\n std_devs = numpy.std(data, axis=1)\\n std_devs[std_devs == 0] = 1 # prevent div by 0\\n return (data - numpy.mean(data, axis=1)) / std_devs\",\n \"def normalize_features(X):\\n std = X.std(axis=0)\\n std = np.where(std == 0, 1, std) # to avoid division by zero\\n x_normed = (X - X.mean(axis=0)) / std\\n return x_normed\",\n \"def svd_approx(A, s):\\r\\n U, Si, Vh = la.svd(A)\\r\\n zeros = list(Si).count(0)\\r\\n #raise error if there are not enough nonzero singular values\\r\\n if len(Si) - zeros < s:\\r\\n raise ValueError(\\\"s > rank(A)\\\")\\r\\n #Only save first s singular values for Si\\r\\n Si_hat = np.diag(Si[:s])\\r\\n #Save first s columns of U\\r\\n U_hat = U[:,:s]\\r\\n #Save first s rows of Vh\\r\\n Vh_hat = Vh[:s,:]\\r\\n\\r\\n # return new A and num of entries needed\\r\\n return U_hat@Si_hat@Vh_hat, U_hat.size+s+Vh_hat.size\",\n \"def discretize(vectors, copy=True, max_svd_restarts=30, n_iter_max=20,\\n random_state=None):\\n\\n from scipy.sparse import csc_matrix\\n from scipy.linalg import LinAlgError\\n\\n random_state = check_random_state(random_state)\\n\\n vectors = as_float_array(vectors, copy=copy)\\n\\n eps = np.finfo(float).eps\\n n_samples, n_components = vectors.shape\\n\\n # Normalize the eigenvectors to an equal length of a vector of ones.\\n # Reorient the eigenvectors to point in the negative direction with respect\\n # to the first element. This may have to do with constraining the\\n # eigenvectors to lie in a specific quadrant to make the discretization\\n # search easier.\\n norm_ones = np.sqrt(n_samples)\\n for i in range(vectors.shape[1]):\\n vectors[:, i] = (vectors[:, i] / np.linalg.norm(vectors[:, i])) \\\\\\n * norm_ones\\n if vectors[0, i] != 0:\\n vectors[:, i] = -1 * vectors[:, i] * np.sign(vectors[0, i])\\n\\n # Normalize the rows of the eigenvectors. Samples should lie on the unit\\n # hypersphere centered at the origin. This transforms the samples in the\\n # embedding space to the space of partition matrices.\\n vectors = vectors / np.sqrt((vectors ** 2).sum(axis=1))[:, np.newaxis]\\n\\n svd_restarts = 0\\n has_converged = False\\n\\n # If there is an exception we try to randomize and rerun SVD again\\n # do this max_svd_restarts times.\\n while (svd_restarts < max_svd_restarts) and not has_converged:\\n\\n # Initialize first column of rotation matrix with a row of the\\n # eigenvectors\\n rotation = np.zeros((n_components, n_components))\\n rotation[:, 0] = vectors[random_state.randint(n_samples), :].T\\n\\n # To initialize the rest of the rotation matrix, find the rows\\n # of the eigenvectors that are as orthogonal to each other as\\n # possible\\n c = np.zeros(n_samples)\\n for j in range(1, n_components):\\n # Accumulate c to ensure row is as orthogonal as possible to\\n # previous picks as well as current one\\n c += np.abs(np.dot(vectors, rotation[:, j - 1]))\\n rotation[:, j] = vectors[c.argmin(), :].T\\n\\n last_objective_value = 0.0\\n n_iter = 0\\n\\n while not has_converged:\\n n_iter += 1\\n\\n t_discrete = np.dot(vectors, rotation)\\n\\n labels = t_discrete.argmax(axis=1)\\n vectors_discrete = csc_matrix(\\n (np.ones(len(labels)), (np.arange(0, n_samples), labels)),\\n shape=(n_samples, n_components))\\n\\n t_svd = vectors_discrete.T * vectors\\n\\n try:\\n U, S, Vh = np.linalg.svd(t_svd)\\n svd_restarts += 1\\n except LinAlgError:\\n print(\\\"SVD did not converge, randomizing and trying again\\\")\\n break\\n\\n ncut_value = 2.0 * (n_samples - S.sum())\\n if ((abs(ncut_value - last_objective_value) < eps) or\\n (n_iter > n_iter_max)):\\n has_converged = True\\n else:\\n # otherwise calculate rotation and continue\\n last_objective_value = ncut_value\\n rotation = np.dot(Vh.T, U.T)\\n\\n if not has_converged:\\n raise LinAlgError('SVD did not converge')\\n return labels\",\n \"def center_normalize(x):\\n return (x - K.mean(x)) / K.std(x)\",\n \"def center_normalize(x):\\n return (x - K.mean(x)) / K.std(x)\",\n \"def en_kern_vec_unit(name, s, e, x, kernel,\\n hyps, cutoffs=None, hyps_mask=None):\\n\\n training_data = _global_training_data[name]\\n\\n ds = [1, 2, 3]\\n size = (e-s) * 3\\n k_v = np.zeros(size, )\\n\\n args = from_mask_to_args(hyps, hyps_mask, cutoffs)\\n\\n for m_index in range(size):\\n x_2 = training_data[int(math.floor(m_index / 3))+s]\\n d_2 = ds[m_index % 3]\\n k_v[m_index] = kernel(x_2, x, d_2, *args)\\n\\n return k_v\",\n \"def SVM(x_train, x_test, y_train, kernel):\\n svc_model = SVC(kernel=kernel)\\n svc_model.fit(x_train, y_train)\\n y_pred = svc_model.predict(x_test)\\n return y_pred\",\n \"def svd(matrix, approach):\\n\\n # Getting the eigenvalues and vectors of transpose(A) * A for V and Sigma\\n a = mat_multiply(transpose(matrix), matrix)\\n if approach == \\\"qr\\\":\\n V, sigma, iterations = qr_eig(a)\\n else:\\n V, sigma, iterations = eig(a)\\n\\n # Sorting singular values and the colums of V accordingly\\n V = transpose(V)\\n\\n singular_values = list()\\n sorted_V = list()\\n\\n r = 0\\n for i in range(rows(sigma)):\\n singular_values.append([(sigma[i][i]), i])\\n if sigma[i][i] > math.exp(-8):\\n r += 1\\n\\n singular_values.sort(key=first_item, reverse=True)\\n\\n sigma_r = eye(r)\\n sigma_r_inv = eye(r)\\n\\n # Constructing the sorted U and sigma matrices\\n i, j = 0, 0\\n for value in singular_values:\\n if value[0] > math.exp(-8):\\n sorted_V.append(V[value[1]])\\n sigma_r[j][j] = value[0] ** (1 / 2)\\n sigma_r_inv[j][j] = 1 / (value[0] ** (1 / 2))\\n j += 1\\n i += 1\\n\\n # Constructing U by multiplying V and sigma inverse\\n sorted_U = mat_multiply(mat_multiply(matrix, transpose(sorted_V)), sigma_r_inv)\\n\\n return (sorted_U, sigma_r, sorted_V, r, iterations)\",\n \"def svd_compress_gs(mat, k):\\n U, singular_vals, V = np.linalg.svd(mat)\\n rank = len(singular_vals)\\n print(\\\"Image rank %r\\\" % rank)\\n if k > rank:\\n print(\\\"k is larger than rank of image %r\\\" % rank)\\n return mat\\n # take columns less than k from U\\n U_p = U[:, :k]\\n # take rows less than k from V\\n V_p = V[:k, :]\\n # build the new S matrix with top k diagnal elements\\n S_p = np.zeros((k, k), mat.dtype)\\n for i in range(k):\\n S_p[i][i] = singular_vals[i]\\n print(\\\"U_p shape {0}, S_p shape {1}, V_p shape {2}\\\".format(\\n U_p.shape, S_p.shape, V_p.shape))\\n compressed = np.dot(np.dot(U_p, S_p), V_p)\\n ss = ssim(mat, compressed,\\n dynamic_range=compressed.max() - compressed.min())\\n print(\\\"Strucural similarity: %r\\\" % ss)\\n return U_p, S_p, V_p\"\n]","isConversational":false},"negative_scores":{"kind":"list like","value":["0.7064768","0.6497113","0.647693","0.63251233","0.6254241","0.6164676","0.61603194","0.60960335","0.60589564","0.5987382","0.59055364","0.58650464","0.57826054","0.57542855","0.573676","0.56839186","0.5663144","0.5609878","0.5581168","0.5574882","0.5572632","0.5565599","0.55464345","0.5502492","0.54823816","0.5477199","0.54482687","0.5443486","0.5436408","0.54319876","0.5404044","0.5403484","0.53800344","0.5371267","0.5370145","0.5358596","0.53287214","0.5320176","0.5307816","0.5306669","0.529265","0.5290347","0.5262317","0.52482957","0.5241507","0.5240983","0.52392143","0.52332073","0.52152115","0.51988906","0.5193681","0.51838505","0.5181282","0.51777184","0.5160726","0.5141776","0.5138939","0.5124286","0.51191443","0.51153547","0.51145256","0.511307","0.50992894","0.5096965","0.5096309","0.5091894","0.509078","0.5085546","0.5068429","0.5064782","0.50547934","0.50471723","0.50359905","0.50359905","0.5031697","0.50283366","0.5017985","0.5011526","0.5007861","0.500476","0.5001999","0.4994764","0.49945492","0.4987969","0.49794447","0.49771428","0.49657902","0.49634546","0.4963213","0.4952644","0.4949157","0.4948496","0.49483415","0.49368173","0.49296823","0.49296823","0.49124804","0.48978993","0.48977342","0.48818505"],"string":"[\n \"0.7064768\",\n \"0.6497113\",\n \"0.647693\",\n \"0.63251233\",\n \"0.6254241\",\n \"0.6164676\",\n \"0.61603194\",\n \"0.60960335\",\n \"0.60589564\",\n \"0.5987382\",\n \"0.59055364\",\n \"0.58650464\",\n \"0.57826054\",\n \"0.57542855\",\n \"0.573676\",\n \"0.56839186\",\n \"0.5663144\",\n \"0.5609878\",\n \"0.5581168\",\n \"0.5574882\",\n \"0.5572632\",\n \"0.5565599\",\n \"0.55464345\",\n \"0.5502492\",\n \"0.54823816\",\n \"0.5477199\",\n \"0.54482687\",\n \"0.5443486\",\n \"0.5436408\",\n \"0.54319876\",\n \"0.5404044\",\n \"0.5403484\",\n \"0.53800344\",\n \"0.5371267\",\n \"0.5370145\",\n \"0.5358596\",\n \"0.53287214\",\n \"0.5320176\",\n \"0.5307816\",\n \"0.5306669\",\n \"0.529265\",\n \"0.5290347\",\n \"0.5262317\",\n \"0.52482957\",\n \"0.5241507\",\n \"0.5240983\",\n \"0.52392143\",\n \"0.52332073\",\n \"0.52152115\",\n \"0.51988906\",\n \"0.5193681\",\n \"0.51838505\",\n \"0.5181282\",\n \"0.51777184\",\n \"0.5160726\",\n \"0.5141776\",\n \"0.5138939\",\n \"0.5124286\",\n \"0.51191443\",\n \"0.51153547\",\n \"0.51145256\",\n \"0.511307\",\n \"0.50992894\",\n \"0.5096965\",\n \"0.5096309\",\n \"0.5091894\",\n \"0.509078\",\n \"0.5085546\",\n \"0.5068429\",\n \"0.5064782\",\n \"0.50547934\",\n \"0.50471723\",\n \"0.50359905\",\n \"0.50359905\",\n \"0.5031697\",\n \"0.50283366\",\n \"0.5017985\",\n \"0.5011526\",\n \"0.5007861\",\n \"0.500476\",\n \"0.5001999\",\n \"0.4994764\",\n \"0.49945492\",\n \"0.4987969\",\n \"0.49794447\",\n \"0.49771428\",\n \"0.49657902\",\n \"0.49634546\",\n \"0.4963213\",\n \"0.4952644\",\n \"0.4949157\",\n \"0.4948496\",\n \"0.49483415\",\n \"0.49368173\",\n \"0.49296823\",\n \"0.49296823\",\n \"0.49124804\",\n \"0.48978993\",\n \"0.48977342\",\n \"0.48818505\"\n]","isConversational":false},"document_score":{"kind":"string","value":"0.6894022"},"document_rank":{"kind":"string","value":"1"}}},{"rowIdx":22692,"cells":{"query":{"kind":"string","value":"Given an array of valid indices, return the corrsesponding array of missing indices."},"document":{"kind":"string","value":"def valid_to_missing(valid, size):\n missing = np.ones(size, dtype=bool)\n try:\n missing[valid] = False\n # In case there is no valid index\n except IndexError:\n pass\n missing = np.flatnonzero(missing)\n return missing"},"metadata":{"kind":"string","value":"{\n \"objective\": {\n \"self\": [],\n \"paired\": [],\n \"triplet\": [\n [\n \"query\",\n \"document\",\n \"negatives\"\n ]\n ]\n }\n}"},"negatives":{"kind":"list like","value":["def non_masked_indices(mask):\n\treturn np.nonzero(np.ravel(mask-1,order='C'))[0]","def _get_nan_indices(*tensors: Tensor) ->Tensor:\n if len(tensors) == 0:\n raise ValueError('Must pass at least one tensor as argument')\n sentinel = tensors[0]\n nan_idxs = torch.zeros(len(sentinel), dtype=torch.bool, device=sentinel.device)\n for tensor in tensors:\n permuted_tensor = tensor.flatten(start_dim=1)\n nan_idxs |= torch.any(torch.isnan(permuted_tensor), dim=1)\n return nan_idxs","def get_valid_indices(labels):\n idxs = (labels != invalid_labels[0])\n for l in invalid_labels[1:]:\n idxs = idxs & (labels != l)\n return idxs","def nonzero_indices(a):\n return (np.nonzero(a)[0])","def exclude_indices(x, excl):\n # ==========================================================================\n return np.array([x[i] for i in np.arange(len(x)) if i not in excl])","def testUnknownIndices(self):\n params = constant_op.constant(((0, 1, 2),))\n indices = array_ops.placeholder(dtypes.int32)\n gather_nd_t = array_ops.gather_nd(params, indices, batch_dims=1)\n shape = gather_nd_t.get_shape()\n self.assertIsNone(shape.ndims)\n self.assertIsNone(tensor_shape.dimension_value(shape[0]))","def masked_indices(mask):\n\treturn np.nonzero(np.ravel(mask,order='C'))[0]","def _exclude_indices(self):\n idx = self._next_idx\n exclude = np.arange(idx - 1, idx + self.obs_len) % self._maxsize\n return exclude","def no_resampling(random_state: np.random.RandomState,\n indices: np.ndarray) -> np.ndarray:\n return indices","def gather_missing_entities(data: List[list], n_ents: int, positions: List[int]) -> np.array:\n\n appeared = np.zeros(n_ents, dtype=np.int)\n for datum in data:\n for pos in positions:\n appeared[datum[pos]] = 1\n\n # Return this removed from range(n_ents)\n return np.arange(n_ents)[appeared == 0]","def conv_idx_numlist_to_missing(self):\n \n ct = 0\n for indx in self.num_list_to_missing:\n self.list_with_missing[indx] = self.num_list[ct]\n ct += 1","def _index_to_nan_fast(data, existing_nans, to_nan):\n index_nan = []\n randgen = (np.random.choice(len(data)) for _ in cnt(start=1))\n for i in range(to_nan):\n ix = next(filter(lambda x: x not in existing_nans and x not in index_nan, randgen))\n index_nan.append(ix)\n data_imp = data.copy()\n data_imp[index_nan] = np.nan\n return data_imp, index_nan","def get_unprescribed_indexes(self):\n total_dof = DOF_PER_NODE_STRUCTURAL * len(self.preprocessor.nodes)\n all_indexes = np.arange(total_dof)\n return np.delete(all_indexes, self.prescribed_indexes)","def data_missing():\n return RaggedArray([[], [-1, 0, 1]], dtype='int16')","def get_unlabeled_idx(X_train, labeled_idx):\n return np.arange(X_train.shape[0])[np.logical_not(np.in1d(np.arange(X_train.shape[0]), labeled_idx))]","def _getNonPrototypeIndices(self, clusters: ndarray) -> ndarray:\n return np.delete(np.arange(self.dataSize), clusters.flatten())","def nonzero_values(a):\r\n return a.flatten()[flatnonzero(a)]","def _get_actor_unfilled_indices(self, actor_index, entries_per_buffer):\n filled_indices = set(\n self._get_replay_buffer_filled_indices(self._replay_buffers, actor_index)\n )\n actor_id_set = set(range(0, entries_per_buffer))\n unfilled_indices = actor_id_set - filled_indices\n return unfilled_indices","def remove_nans(arr):\n not_nan = [i for i in range(len(arr)) if not np.isnan(arr[i])]\n\n return not_nan, arr[not_nan]","def filter_valid(self, keys):\n return np.fromiter(filter(lambda id: id in self.embed.ind, keys), dtype=np.int32)\n # return np.fromiter((key for key in keys if key in self.embed.ind), dtype=np.int32)","def allowed_positions(self, curr_state):\n return [i for i, val in enumerate(curr_state) if np.isnan(val)]","def allowed_positions(self, curr_state):\n return [i for i, val in enumerate(curr_state) if np.isnan(val)]","def allowed_positions(self, curr_state):\n return [i for i, val in enumerate(curr_state) if np.isnan(val)]","def getValidIndicies(self, points):\n try:\n inds = [ i for i in range(points.size) if points.flat[i] in self.maskSet ]\n return inds\n except Exception as error:\n print(\"failed in getValidIndicies\", error)\n return -1","def nonans(array):\n return array[~np.isnan(array)]","def _unite_first_with_all_intersecting_elements(indices):\n first = indices[0]\n new_first = first\n new_others = []\n for idx in indices[1:]:\n if len(first.intersection(idx)) > 0:\n new_first = new_first.union(idx)\n else:\n new_others.append(idx)\n return [new_first] + new_others","def cardinal_indices(self, index):\n cardinals = [\n self.north_index(index),\n self.east_index(index),\n self.south_index(index),\n self.west_index(index)\n ]\n return [i for i in cardinals if 0 < i < (self.size * self.size)]","def _index_to_nan(data, existing_nans, to_nan):\n index_nan = np.random.choice([i for i in range(len(data)) if i not in existing_nans],\n size=to_nan, replace=False)\n data_imp = data.copy()\n data_imp[index_nan] = np.nan\n return data_imp, index_nan","def conv_idx_numlistlist_to_missing(self):\n \n ct = 0\n for indx in self.num_listlist_to_missing:\n self.listlist_with_missing[indx] = self.num_listlist[ct]\n ct += 1","def first_missing_positive_int_linear(nums):\n\t\n\t# Here's the trick: the first missing positive number must be \n\t# between 1 and len(array) + 1 \t\n\ts = set(nums)\n\t\n\tfor i in range(1, len(nums) + 1):\n\t\tif i not in s:\n\t\t\treturn i","def non_zero_idx_val(seq):\n return [(i, v) for i, v in enumerate(seq) if v > 0]","def _find_nonzero_runs(values):\n\n error_checking.assert_is_numpy_array_without_nan(values)\n error_checking.assert_is_numpy_array(values, num_dimensions=1)\n\n zero_flags = numpy.concatenate((\n [True], numpy.equal(values, 0), [True]\n ))\n\n nonzero_flags = numpy.invert(zero_flags)\n differences = numpy.abs(numpy.diff(nonzero_flags))\n index_matrix = numpy.where(differences == 1)[0].reshape(-1, 2)\n\n return index_matrix[:, 0], index_matrix[:, 1] - 1","def get_random_indices(self):\n # col_ix, row_ix = np.random.randint(self.data.shape[1]), np.random.randint(self.data.shape[0])\n # While the selected cell is already corrupted, keep \"rolling the dice\" until we find one that is not corrupted.\n # while self.corrupted_cells_mask.iat[row_ix, col_ix] == 1:\n # col_ix, row_ix = np.random.randint(self.data.shape[1]), np.random.randint(self.data.shape[0])\n # return col_ix, row_ix\n\n non_corrupted_row_col_pairs = []\n for col_idx in range(self.data.shape[1]):\n non_corrupted_cell_row_idx_list = self.data.iloc[:, col_idx].index[\n self.corrupted_cells_mask.iloc[:, col_idx] == 0].tolist()\n non_corrupted_row_col_pairs_for_col_idx = list(\n map(lambda row_index: (self.data.index.get_loc(row_index), col_idx), non_corrupted_cell_row_idx_list))\n non_corrupted_row_col_pairs.extend(non_corrupted_row_col_pairs_for_col_idx)\n\n rand_idx = np.random.randint(len(non_corrupted_row_col_pairs), size=1)[0]\n return non_corrupted_row_col_pairs[rand_idx]","def get_flat_miss_mask(self, df: np.ndarray,\n p_missing: float = None) -> np.array:\n df_size = df.size\n miss_size = int(df_size * p_missing)\n indx = np.arange(df_size)\n flat_miss_mask = np.random.choice(indx, miss_size, replace=False)\n return flat_miss_mask","def filter_nonsense_sequences(sequences):\n\n # filter sequences if contains at least one 'N' character\n good_index = []\n filter_sequences = []\n for i, seq in enumerate(sequences):\n if \"N\" not in seq.upper():\n good_index.append(i)\n filter_sequences.append(seq)\n return np.array(filter_sequences), np.array(good_index)","def _notstaticneighs_get_corestored_by_inds_notslice(self, inds):\n inds = [inds] if type(inds) == int else inds\n idxs = []\n for k in range(len(self.idxs)):\n idxs.append([self.idxs[k][i] for i in inds])\n idxs = np.array(idxs) if type(self.idxs) == np.ndarray else idxs\n\n if self.sp_relative_pos is not None:\n sp_relative_pos = []\n for k in range(len(self.sp_relative_pos)):\n sp_relative_pos += [[self.sp_relative_pos[k][i] for i in inds]]\n else:\n sp_relative_pos = None\n return idxs, sp_relative_pos","def create_jackknife_indexes(data):\n from numpy import arange, delete\n\n index_range = arange(0, len(data))\n return (delete(index_range, i) for i in index_range)","def compute_unary_set_mappings(indexing, counts): \n ret = np.zeros_like(indexing)-1\n for vertex,index in enumerate(indexing):\n if counts[index] == 1:\n ret[index] = vertex\n return ret","def getMissingIds(self):\n return self._missingIds","def missing_seat_ids(taken_seats):\n all_seats = set(range(min(taken_seats), max(taken_seats) + 1))\n return(set(taken_seats).symmetric_difference(all_seats))","def _free_indicies(self):\n return np.logical_not(self._fixed_indicies)","def get_invalid_rows(rows):\n\n invalid_row_indices = []\n for i, row in enumerate(rows):\n is_valid, col_id = is_row_well_formatted(row)\n if not is_valid:\n invalid_row_indices.append((i, col_id))\n\n return invalid_row_indices","def get_valid_indices():\n return [i for i, val in enumerate(all_topics) if val[1] == \"1\"]","def ind_complement(inds, n):\n return tuple(i for i in range(n) if i not in inds)","def _pure_data(data, missing_mask):\n missing_rows = np.where(missing_mask)[0]\n pure_data = np.delete(data, missing_rows, axis=0)\n\n return pure_data","def _frc_indices_for_errors(self, idx_frc=None, idx_rows=None, y_idx=None, idx_original=None):\n idx = [0] * self.nts\n if idx_frc is None:\n if idx_rows is None:\n for i in y_idx:\n idx[i] = range(self.n_hist_points[i], len(self.forecasts[i]))\n\n else:\n for i in y_idx:\n idx[i] = _ravel_idx(self.idxY[i][idx_rows], len(self.forecasts[i]))\n else:\n idx = idx_frc\n\n if idx_original is None:\n return idx\n\n for i in y_idx:\n time_index = self.ts[i].index[idx[i]]\n intersect_mask = np.in1d(time_index, idx_original[i])\n idx[i] = idx[i][intersect_mask]\n\n return idx","def getNonZeroRanges( A, zeroDefVal=0 ):\n dA=np.diff(A)\n N=A.shape[0]\n nzdA,=np.nonzero(dA)\n liRangesAll=np.c_[ np.r_[0,nzdA+1],\n np.r_[nzdA,N-1] +1]\n iiR = A.take(liRangesAll[:,0]) != zeroDefVal\n return liRangesAll[iiR,:]","def CreateIndicesForBasisFilter(indices):\n indices = np.array(indices, dtype = \"uint32\")\n for ind, it in enumerate(indices):\n indices[ind] = ind * it\n return indices","def get_random_indexes(msk, n_idxs):\n pos_idxs = np.array(np.where(msk > 0))\n neg_idxs = np.array(np.where(msk == 0))\n n_pos = pos_idxs.shape[1] # number of positives found in the mask\n n_neg = neg_idxs.shape[1] # number of negatives found in the mask\n n_min = min(n_neg, min(n_idxs, n_pos))\n rnd_idxs_pos = range(n_pos)\n np.random.shuffle(rnd_idxs_pos)\n rnd_idxs_neg = range(n_neg)\n np.random.shuffle(rnd_idxs_neg)\n return pos_idxs[:, rnd_idxs_pos[:n_min]], neg_idxs[:, rnd_idxs_neg[:n_min]]","def bool_2_indices(bool_array):\n if ~isinstance(bool_array, np.ndarray):\n bool_array = np.array(bool_array)\n if bool_array[-1]:\n bool_array[-1] = False\n lims = np.arange(bool_array.size)[bool_array ^ np.roll(bool_array, 1)]\n if len(lims) > 0:\n if lims[-1] == bool_array.size - 1:\n lims[-1] = bool_array.size\n return np.reshape(lims, (len(lims) // 2, 2))\n else:\n return [[np.nan, np.nan]]","def _invert_nonzero(arr):\n arr_inv = arr.copy()\n nz = np.nonzero(arr)\n arr_inv[nz] = 1 / arr[nz]\n return arr_inv","def get_cont_indices(self, X, max_ord):\n indices = np.zeros(X.shape[1]).astype(bool)\n for i, col in enumerate(X.T):\n col_nonan = col[~np.isnan(col)]\n col_unique = np.unique(col_nonan)\n if len(col_unique) > max_ord:\n indices[i] = True\n return indices","def get_vacancy_indices(array):\t\n\tvacancy_indices = np.argwhere(array == 0)\n\treturn vacancy_indices","def missing_bus_indices(net):\n check_results = {}\n bus_indices = set(net.bus.index)\n element_bus_names = {\"ext_grid\": [\"bus\"], \"load\": [\"bus\"], \"gen\": [\"bus\"], \"sgen\": [\"bus\"],\n \"trafo\": [\"lv_bus\", \"hv_bus\"], \"trafo3w\": [\"lv_bus\", \"mv_bus\", \"hv_bus\"],\n \"switch\": [\"bus\", \"element\"], \"line\": [\"from_bus\", \"to_bus\"]}\n for element in element_bus_names.keys():\n element_check = []\n for i, row in net[element].iterrows():\n for bus_name in element_bus_names[element]:\n if row[bus_name] not in bus_indices:\n if not ((element == \"switch\") and (bus_name == \"element\") and (row.et in ['l', 't', 't3'])):\n element_check.append((i, bus_name, row[bus_name]))\n if element_check:\n check_results[element] = element_check\n\n if check_results:\n return check_results","def remove_nans(coords):\n s = np.apply_along_axis(sum,1,np.isnan(coords[1])) == 0\n coords[0] = (np.asarray(coords[0])[s]).tolist()\n coords[1] = coords[1][s,:]","def missingNumber(self, nums):\r\n size = len(nums)\r\n missing_number = len(nums)\r\n for i in range(size):\r\n missing_number ^= (i ^ nums[i])\r\n\r\n return missing_number","def get_agent_indices(array):\t\n\tagent_indices = np.argwhere(array != 0)\n\treturn agent_indices","def get_missing(dicts):\n for d in dicts:\n for k, v in d.items():\n d[k] = set([1, 2, 3, 4, 5, 6, 7, 8, 9]) - set(v)\n return dicts","def vir_indices(self):\n indices = []\n for index,item in enumerate(self):\n if item==0:\n indices.append(index)\n return indices","def connected(index,i):\n adj = concatenate([ where(ind==j)[0] for j in ind[i] if j >= 0 ])\n return unique(adj[adj != i])","def get_missing_seat_id(seat_ids):\n minimum, maximum = min(seat_ids), max(seat_ids)\n\n missing = [s for s in range(minimum, maximum) if s not in seat_ids]\n return missing[0]","def find_idx_boundaries(indices, drop_single_idx=False):\n\n ## Since list slicing counts up to but not including ends, we need to add 1 to all detected end locations\n ends = np.where(np.diff(indices) > 1)[0] + 1\n\n ## Starts and ends will equal each other since list slicing includes start values, but start needs 0 appended\n starts = np.copy(ends)\n if len(starts) == 0 or starts[0] != 0:\n starts = np.insert(starts, 0, 0)\n\n ## np.diff returns an array one smaller than the indices list, so we need to add the last idx to the ends\n if len(ends) == 0 or ends[-1] != len(indices):\n ends = np.insert(ends, len(ends), len(indices))\n\n ## Loop through all continuous idx start & end to see if any are too small (length = 1)\n events = []\n for start, end in zip(starts, ends):\n if end - start < 2:\n if not drop_single_idx:\n raise PipelineException(f\"Disconnected index found at index {start}\")\n else:\n events.append(indices[start:end])\n\n return events","def remove_invalid_idx(self, image_idx_list):\n\n sample_id_list = []\n for sample_id in image_idx_list:\n sample_id = int(sample_id)\n objects = self.get_label(sample_id)\n calib = self.get_calib(sample_id)\n labels, noObjectLabels = kitti_bev_utils.read_labels_for_bevbox(objects)\n if not noObjectLabels:\n labels[:, 1:] = transformation.camera_to_lidar_box(labels[:, 1:], calib.V2C, calib.R0,\n calib.P) # convert rect cam to velo cord\n\n valid_list = []\n for i in range(labels.shape[0]):\n if int(labels[i, 0]) in cnf.CLASS_NAME_TO_ID.values():\n if self.check_point_cloud_range(labels[i, 1:4]):\n valid_list.append(labels[i, 0])\n\n if len(valid_list) > 0:\n sample_id_list.append(sample_id)\n\n return sample_id_list","def find_missing(expected, existing):\n return set(expected).difference(existing)","def locate_nan_rows(arr):\n # Count the number of NaNs in each row\n nan_counts = np.sum(~np.isfinite(arr), axis=1)\n # Trigger on a NaN appearing anywhere in a line/row\n nans, = np.where(nan_counts > 1)\n return frozenset(nans)","def fully_hidden_arrays(self):\n hidden = []\n for m in self.masks():\n invalid = self.get_property(m, '_no_valid_items')\n if invalid: hidden.append(m)\n return hidden","def _fix_uniq_col(self):\n # subgradient; for two boolean arrays, multiplication seems to be the best way \n # (equivalent to logical_and)\n n_covered_col = self.a_csr.dot(np.ones(self.ncols)) \n ifix = np.zeros(self.ncols, dtype=bool)\n if (np.count_nonzero(n_covered_col) != self.mrows):\n raise ValueError(\"There are uncovered rows! Please check your input!\")\n if (np.any(n_covered_col==1)):\n inonzero = self.a_csr[n_covered_col==1,:].nonzero()\n ifix[inonzero[1]] = True\n\n return ifix","def firstMissingPositive(nums):\n\n n = len(nums)\n\n # Base case.\n if 1 not in nums:\n return 1\n\n # nums = [1]\n if n == 1:\n return 2\n\n # Replace negative numbers, zeros,\n # and numbers larger than n by 1s.\n # After this conversion nums will contain \n # only positive numbers.\n for i in range(n):\n if nums[i] <= 0 or nums[i] > n:\n nums[i] = 1\n\n # Use index as a hash key and number sign as a presence detector.\n # For example, if nums[1] is negative that means that number `1`\n # is present in the array. \n # If nums[2] is positive - number 2 is missing.\n for i in range(n): \n a = abs(nums[i])\n # If you meet number a in the array - change the sign of a-th element.\n # Be careful with duplicates : do it only once.\n # [3,4,-1,1]\n if a == n:\n nums[0] = - abs(nums[0])\n else:\n nums[a] = - abs(nums[a])\n \n # Now the index of the first positive number \n # is equal to first missing positive.\n for i in range(1, n):\n if nums[i] > 0:\n return i\n\n if nums[0] > 0:\n return n\n\n return n + 1","def get_nonzero_vectors(motion_vectors):\n if np.shape(motion_vectors)[0] == 0:\n return motion_vectors\n else:\n idx = np.where(np.logical_or(motion_vectors[:, 7] != 0, motion_vectors[:, 8] != 0))[0]\n return motion_vectors[idx, :]","def find_allowed(possible_arrays):\n allowed = [0] * len(possible_arrays[0])\n for array in possible_arrays:\n allowed = [x | y for x, y in zip(allowed, array)] \n return allowed","def create_repeated_indexes(data):\n from numpy import arange\n\n index_range = arange(0, len(data))\n return (index_range for i in index_range)","def create_random_sample_alt(idx_bins,count_array):\n idxs=[]\n for i,x in enumerate(count_array):\n if x > 0:\n idxs.extend([ idx_bins[i][ind] for ind in unique_sample_of_int(len(idx_bins[i])-1,x) ] )\n return idxs","def _staticneighs_get_corestored_by_inds_notslice(self, inds):\n inds = [inds] if type(inds) == int else inds\n idxs = [self.idxs[i] for i in inds]\n idxs = np.array(idxs) if type(self.idxs) == np.ndarray else idxs\n if self.sp_relative_pos is not None:\n sp_relative_pos = [self.sp_relative_pos[i] for i in inds]\n else:\n sp_relative_pos = None\n return idxs, sp_relative_pos","def findIndices(g):\r\n change = [0]\r\n seen = [g[0]]\r\n for i in range(1, len(g)):\r\n if not g[i] in seen:\r\n change.append(i)\r\n seen.append(g[i])\r\n return change","def master_ndindex(self): # itermaster_indices(self):\n return itertools_product(\n *[range(*r) for r in self.location]\n ) # TODO check","def n_ary_mask(d, n, offset):\n return [(i + offset) % n == 0 for i in range(d)]","def hoggar_indices():\n return list(product([0,1], repeat=6))","def indices_using_group_array(group_array):\n unique_elements = np.unique(group_array)\n indices = []\n for unique_element in unique_elements:\n indices.append((unique_element,\n np.flatnonzero(group_array == unique_element)))\n return indices","def test_discrimination_indices(self):\n\n n_items = [10, 20, 30]\n n_factors = [2, 3, 4, 5]\n\n for item in n_items:\n for factor in n_factors:\n diagonal_indices, lower_indices = get_discrimination_indices(item, \n factor)\n lower_size = item * factor - factor * (factor + 1) / 2\n\n self.assertEqual(diagonal_indices[0].size, factor)\n self.assertEqual(diagonal_indices[1].size, factor)\n \n self.assertEqual(lower_indices[0].size, lower_size)\n self.assertEqual(lower_indices[1].size, lower_size)\n\n # Using lower, the rows are flipped, flip them back\n flipped = item - diagonal_indices[0] - 1\n np.testing.assert_equal(flipped, diagonal_indices[1])\n\n test = np.zeros((item, factor))\n test[lower_indices] = 1\n\n self.assertEqual(test.sum(), lower_size)\n\n for ndx1 in range(factor):\n for ndx2 in range(ndx1, factor):\n self.assertEqual(test[item - ndx1 -1, ndx2], 0)","def test_ks_test_empty_indices():\n out = compute_indices_ks_test([], 1000, mode=\"D+\")\n assert all(o is None for o in out)","def least_indices(array: np.ndarray, n: int) -> tuple:\n flat = array.ravel()\n indices = np.argpartition(flat, n)[:n]\n indices = indices[np.argsort(flat[indices])]\n return np.unravel_index(indices, array.shape)","def zero_indexed(array):\n if all(dl == 0 for dl in array.datashape.dim_low):\n return array\n if any(dl < 0 for dl in array.datashape.dim_low):\n raise ValueError(\"Cannot zero_index array: one or more \"\n \"dimensions start < 0\")\n\n ds = array.datashape.copy()\n ds.dim_low = [0] * ds.ndim\n return array.redimension(ds.schema)","def _get_valid_indices(\n self,\n _signals: np.ndarray,\n _labels: np.ndarray,\n window_start_indices: np.ndarray = None,\n elm_start_indices: np.ndarray = None,\n elm_stop_indices: np.ndarray = None,\n valid_t0: np.ndarray = None,\n labels: np.ndarray = None,\n signals: np.ndarray = None,\n ) -> Tuple[\n np.ndarray, np.ndarray, np.ndarray, np.ndarray, np.ndarray, np.ndarray\n ]:\n # allowed indices; time data points which can be used for creating the data chunks\n _valid_t0 = np.ones(_labels.shape, dtype=np.int32)\n _valid_t0[\n -(config.signal_window_size + config.label_look_ahead) + 1 :\n ] = 0\n\n # indices for active elm events in each elm event\n active_elm_events = np.nonzero(_labels >= 0.5)[0]\n\n if signals is None:\n # initialize arrays\n window_start_indices = np.array([0])\n elm_start_indices = active_elm_events[0]\n elm_stop_indices = active_elm_events[-1]\n valid_t0 = _valid_t0\n signals = _signals\n labels = _labels\n else:\n # concat on axis 0 (time dimension)\n last_index = len(labels) - 1\n window_start_indices = np.append(\n window_start_indices, last_index + 1\n )\n elm_start_indices = np.append(\n elm_start_indices, active_elm_events[0] + last_index + 1\n )\n elm_stop_indices = np.append(\n elm_stop_indices, active_elm_events[-1] + last_index + 1\n )\n valid_t0 = np.concatenate([valid_t0, _valid_t0])\n signals = np.concatenate([signals, _signals], axis=0)\n labels = np.concatenate([labels, _labels], axis=0)\n\n return (\n signals,\n labels,\n valid_t0,\n window_start_indices,\n elm_start_indices,\n elm_stop_indices,\n )","def _get_error_index_rows(self):\n index_error_rows = set()\n for series in self:\n index_error_rows.update(set(series.error_values.keys()))\n return sorted(list(index_error_rows))","def test_gap_indices(self):\n r = self.RNA(\"-?A-?NRY-\")\n v = r.gap_indices()\n self.assertEqual(v, array([0, 1, 3, 4, 8]))\n r = self.RNA(\"AC\")\n v = r.gap_indices()\n self.assertEqual(v, array([])) # note: always returns array\n r = self.RNA(\"-?\")\n v = r.gap_indices()\n self.assertEqual(v, array([0, 1]))","def _fixed_indicies(self):\n fixed_inds = self.constraints == 'fixed'\n return fixed_inds","def get_uncut_indices(atoms, orig, vec1, offset=None):\n\n if isinstance(orig, int):\n orig = atoms.get_positions()[orig]\n else:\n orig = np.array(orig)\n if isinstance(vec1, int):\n vec1 = atoms.get_positions()[vec1]\n else:\n vec1 = np.array(vec1)\n\n# if vec2 and isinstance(vec2, int):\n# vec2 = atoms.get_positions()[vec2]\n# elif vec2:\n# vec2 = np.array(vec2)\n# if vec3 and isinstance(vec3, int):\n# vec3 = atoms.get_positions()[vec3]\n# elif vec3:\n# vec3 = np.array(vec3)\n#\n# if vec2:\n# plane_vec = np.cross(vec1-orig, vec2-orig)\n# else:\n# plane_vec = vec1-orig\n#\n# nplane_vec = plane_vec/np.linalg.norm(plane_vec)\n#\n# if vec2 and vec3 and nplane_vec.dot(vec3-orig) < 0:\n# nplane_vec *= -1\n\n plane_vec = vec1 - orig\n nplane_vec = plane_vec/np.linalg.norm(plane_vec)\n\n if offset:\n orig += offset*nplane_vec\n\n atom_indices = [i for i,a in enumerate(atoms) if (a.position - orig).dot(nplane_vec) > 0]\n return atom_indices","def find_missing(nums):\n # calculate sum of all elements\n # in input list\n sum_of_elements = sum(nums)\n\n # There is exactly 1 number missing\n n = len(nums) + 1\n actual_sum = (n * (n + 1)) / 2\n return actual_sum - sum_of_elements","def _to_flat_index(self, idx_in):\n idx_in = tuple([np.array(z, ndmin=1, copy=False) for z in idx_in])\n msk = np.all(np.stack([t < n for t, n in zip(idx_in, self.shape)]), axis=0)\n idx = np.ravel_multi_index(\n tuple([t[msk] for t in idx_in]), self.shape, mode=\"wrap\"\n )\n\n return idx, msk","def _idxs_are_present(self, *args):\n return set(args).issubset(set(range(self.n_atoms)))","def indxmap_diff(Nd): \n\n ndims = len(Nd)\n Ndprod = numpy.prod(Nd)\n mylist = numpy.arange(0, Ndprod).astype(numpy.int32)\n mylist = numpy.reshape(mylist, Nd)\n d_indx = []\n dt_indx = []\n for pp in range(0, ndims):\n d_indx = d_indx + [ numpy.reshape( numpy.roll( mylist, +1 , pp ), (Ndprod,) ,order='C').astype(numpy.int32) ,]\n dt_indx = dt_indx + [ numpy.reshape( numpy.roll( mylist, -1 , pp ) , (Ndprod,) ,order='C').astype(numpy.int32) ,]\n\n return d_indx, dt_indx","def get_missing_test_numbers(a_dict, logged_test_numbers):\n tnum_list = []\n for tname, t_dict in a_dict.iteritems():\n for tpin, r_dict in t_dict.iteritems():\n tnum_list.append(int(r_dict[\"Test number\"]))\n missing = list(set(tnum_list) - set(logged_test_numbers))\n return missing, tnum_list","def prob_cl_indices(self, cl_idxs, cl_losses):\n log.info(\"Finding problematic structures\")\n loss_bound = np.mean(cl_losses) # Initial minimum loss\n loss_step = loss_bound / 500\n loss_bound += loss_step\n idxs = []\n while len(idxs) < 1.5 * self.refine_n_cl:\n log.info(\"Minimum cluster loss : %.4f\", loss_bound)\n cl_idxs_prob = np.concatenate(np.argwhere(cl_losses >= loss_bound))\n clusters = np.array(cl_idxs, dtype=object)[cl_idxs_prob]\n idxs = np.concatenate(clusters)\n loss_bound -= loss_step\n log.info(\"N structures included : %d\\n\", len(idxs))\n return idxs","def diffeo_inverse(a):\n M, N = a.shape[0], a.shape[1]\n result = np.empty_like(a)\n result.fill(-1) # fill invalid data\n many = np.zeros((M, N))\n many.fill(0)\n for i, j in coords_iterate((M, N)):\n i1 = a[i, j, 0]\n j1 = a[i, j, 1]\n result[i1, j1, 0] = i\n result[i1, j1, 1] = j\n many[i1, j1] += 1\n\n num = (many == 0).sum()\n if num:\n fill_invalid(result[:, :, 0], -1)\n fill_invalid(result[:, :, 1], -1)\n\n return result","def data_missing_for_sorting():\n return RaggedArray([[1, 0], [], [0, 0]])","def get_reduced_indices(*indices, axis, keepdims):\n\n # get all indices\n indices_list = list(indices)\n # list of reduction axis: transform negative indices into positive\n # axis in this list wont exist after the reduction\n axis_list = ft_util.refine_reduce_axis(indices_list, list(axis))\n # get indices after reduction\n if keepdims:\n grad_indices_list = [index_i if i not in axis_list else 0 for i, index_i in enumerate(indices_list)]\n else:\n grad_indices_list = [index_i for i, index_i in enumerate(indices_list) if i not in axis_list]\n grad_ind = tuple(grad_indices_list)\n return grad_ind","def period_indices(instantaneous_frequencies, spacing=1, dt=1, time_axis=-1):\n skips = np.ceil(spacing * ((2 * np.pi) / instantaneous_frequencies * dt)).astype(int)\n indices = [np.take(skips, 0, time_axis)]\n indices[-1][indices[-1] >= instantaneous_frequencies.shape[time_axis] - 1] = -1\n while np.any(indices[-1] >= 0):\n indices.append(indices[-1] + np.take(skips, indices[-1], time_axis))\n indices[-1][np.logical_or(indices[-2] < 0, indices[-1] >= instantaneous_frequencies.shape[time_axis] - 1)] = -1\n # the last indices are all -1, so remove them\n indices = np.concatenate(indices[:-1], time_axis)\n # replace negative values with values that will cause index out of bounds if these are used carelessly\n return np.where(indices < 0, instantaneous_frequencies.shape[time_axis], indices)","def get_fixed_array():\n return np.array([[[np.nan, np.nan],\n [np.nan, np.nan],\n [np.nan, np.nan]],\n\n [[3., 7.],\n [2., 4.],\n [1., 16.]],\n\n [[17., 5.],\n [10., 0.],\n [14., 12.]],\n\n [[8., 6.],\n [13., 11.],\n [9., 15.]]])","def _uni_pnts(a):\r\n _, idx = np.unique(a, return_index=True, axis=0)\r\n a = np.concatenate((a[np.sort(idx)], [a[0]]))\r\n return a","def validate_new_inds(selected_inds: list, inds_dict: dict):\n new_set = set(selected_inds)\n train_set = set(inds_dict['train_inds'])\n val_set = set(inds_dict['val_inds'])\n unlabeled_set = set(inds_dict['unlabeled_inds'])\n\n assert len(new_set.intersection(train_set)) == 0, 'Some selected samples are already in the train set'\n assert len(new_set.intersection(val_set)) == 0, 'Some selected samples are already in the val set'\n assert new_set.issubset(unlabeled_set), 'All new selected indices must be in unlabeled_inds'"],"string":"[\n \"def non_masked_indices(mask):\\n\\treturn np.nonzero(np.ravel(mask-1,order='C'))[0]\",\n \"def _get_nan_indices(*tensors: Tensor) ->Tensor:\\n if len(tensors) == 0:\\n raise ValueError('Must pass at least one tensor as argument')\\n sentinel = tensors[0]\\n nan_idxs = torch.zeros(len(sentinel), dtype=torch.bool, device=sentinel.device)\\n for tensor in tensors:\\n permuted_tensor = tensor.flatten(start_dim=1)\\n nan_idxs |= torch.any(torch.isnan(permuted_tensor), dim=1)\\n return nan_idxs\",\n \"def get_valid_indices(labels):\\n idxs = (labels != invalid_labels[0])\\n for l in invalid_labels[1:]:\\n idxs = idxs & (labels != l)\\n return idxs\",\n \"def nonzero_indices(a):\\n return (np.nonzero(a)[0])\",\n \"def exclude_indices(x, excl):\\n # ==========================================================================\\n return np.array([x[i] for i in np.arange(len(x)) if i not in excl])\",\n \"def testUnknownIndices(self):\\n params = constant_op.constant(((0, 1, 2),))\\n indices = array_ops.placeholder(dtypes.int32)\\n gather_nd_t = array_ops.gather_nd(params, indices, batch_dims=1)\\n shape = gather_nd_t.get_shape()\\n self.assertIsNone(shape.ndims)\\n self.assertIsNone(tensor_shape.dimension_value(shape[0]))\",\n \"def masked_indices(mask):\\n\\treturn np.nonzero(np.ravel(mask,order='C'))[0]\",\n \"def _exclude_indices(self):\\n idx = self._next_idx\\n exclude = np.arange(idx - 1, idx + self.obs_len) % self._maxsize\\n return exclude\",\n \"def no_resampling(random_state: np.random.RandomState,\\n indices: np.ndarray) -> np.ndarray:\\n return indices\",\n \"def gather_missing_entities(data: List[list], n_ents: int, positions: List[int]) -> np.array:\\n\\n appeared = np.zeros(n_ents, dtype=np.int)\\n for datum in data:\\n for pos in positions:\\n appeared[datum[pos]] = 1\\n\\n # Return this removed from range(n_ents)\\n return np.arange(n_ents)[appeared == 0]\",\n \"def conv_idx_numlist_to_missing(self):\\n \\n ct = 0\\n for indx in self.num_list_to_missing:\\n self.list_with_missing[indx] = self.num_list[ct]\\n ct += 1\",\n \"def _index_to_nan_fast(data, existing_nans, to_nan):\\n index_nan = []\\n randgen = (np.random.choice(len(data)) for _ in cnt(start=1))\\n for i in range(to_nan):\\n ix = next(filter(lambda x: x not in existing_nans and x not in index_nan, randgen))\\n index_nan.append(ix)\\n data_imp = data.copy()\\n data_imp[index_nan] = np.nan\\n return data_imp, index_nan\",\n \"def get_unprescribed_indexes(self):\\n total_dof = DOF_PER_NODE_STRUCTURAL * len(self.preprocessor.nodes)\\n all_indexes = np.arange(total_dof)\\n return np.delete(all_indexes, self.prescribed_indexes)\",\n \"def data_missing():\\n return RaggedArray([[], [-1, 0, 1]], dtype='int16')\",\n \"def get_unlabeled_idx(X_train, labeled_idx):\\n return np.arange(X_train.shape[0])[np.logical_not(np.in1d(np.arange(X_train.shape[0]), labeled_idx))]\",\n \"def _getNonPrototypeIndices(self, clusters: ndarray) -> ndarray:\\n return np.delete(np.arange(self.dataSize), clusters.flatten())\",\n \"def nonzero_values(a):\\r\\n return a.flatten()[flatnonzero(a)]\",\n \"def _get_actor_unfilled_indices(self, actor_index, entries_per_buffer):\\n filled_indices = set(\\n self._get_replay_buffer_filled_indices(self._replay_buffers, actor_index)\\n )\\n actor_id_set = set(range(0, entries_per_buffer))\\n unfilled_indices = actor_id_set - filled_indices\\n return unfilled_indices\",\n \"def remove_nans(arr):\\n not_nan = [i for i in range(len(arr)) if not np.isnan(arr[i])]\\n\\n return not_nan, arr[not_nan]\",\n \"def filter_valid(self, keys):\\n return np.fromiter(filter(lambda id: id in self.embed.ind, keys), dtype=np.int32)\\n # return np.fromiter((key for key in keys if key in self.embed.ind), dtype=np.int32)\",\n \"def allowed_positions(self, curr_state):\\n return [i for i, val in enumerate(curr_state) if np.isnan(val)]\",\n \"def allowed_positions(self, curr_state):\\n return [i for i, val in enumerate(curr_state) if np.isnan(val)]\",\n \"def allowed_positions(self, curr_state):\\n return [i for i, val in enumerate(curr_state) if np.isnan(val)]\",\n \"def getValidIndicies(self, points):\\n try:\\n inds = [ i for i in range(points.size) if points.flat[i] in self.maskSet ]\\n return inds\\n except Exception as error:\\n print(\\\"failed in getValidIndicies\\\", error)\\n return -1\",\n \"def nonans(array):\\n return array[~np.isnan(array)]\",\n \"def _unite_first_with_all_intersecting_elements(indices):\\n first = indices[0]\\n new_first = first\\n new_others = []\\n for idx in indices[1:]:\\n if len(first.intersection(idx)) > 0:\\n new_first = new_first.union(idx)\\n else:\\n new_others.append(idx)\\n return [new_first] + new_others\",\n \"def cardinal_indices(self, index):\\n cardinals = [\\n self.north_index(index),\\n self.east_index(index),\\n self.south_index(index),\\n self.west_index(index)\\n ]\\n return [i for i in cardinals if 0 < i < (self.size * self.size)]\",\n \"def _index_to_nan(data, existing_nans, to_nan):\\n index_nan = np.random.choice([i for i in range(len(data)) if i not in existing_nans],\\n size=to_nan, replace=False)\\n data_imp = data.copy()\\n data_imp[index_nan] = np.nan\\n return data_imp, index_nan\",\n \"def conv_idx_numlistlist_to_missing(self):\\n \\n ct = 0\\n for indx in self.num_listlist_to_missing:\\n self.listlist_with_missing[indx] = self.num_listlist[ct]\\n ct += 1\",\n \"def first_missing_positive_int_linear(nums):\\n\\t\\n\\t# Here's the trick: the first missing positive number must be \\n\\t# between 1 and len(array) + 1 \\t\\n\\ts = set(nums)\\n\\t\\n\\tfor i in range(1, len(nums) + 1):\\n\\t\\tif i not in s:\\n\\t\\t\\treturn i\",\n \"def non_zero_idx_val(seq):\\n return [(i, v) for i, v in enumerate(seq) if v > 0]\",\n \"def _find_nonzero_runs(values):\\n\\n error_checking.assert_is_numpy_array_without_nan(values)\\n error_checking.assert_is_numpy_array(values, num_dimensions=1)\\n\\n zero_flags = numpy.concatenate((\\n [True], numpy.equal(values, 0), [True]\\n ))\\n\\n nonzero_flags = numpy.invert(zero_flags)\\n differences = numpy.abs(numpy.diff(nonzero_flags))\\n index_matrix = numpy.where(differences == 1)[0].reshape(-1, 2)\\n\\n return index_matrix[:, 0], index_matrix[:, 1] - 1\",\n \"def get_random_indices(self):\\n # col_ix, row_ix = np.random.randint(self.data.shape[1]), np.random.randint(self.data.shape[0])\\n # While the selected cell is already corrupted, keep \\\"rolling the dice\\\" until we find one that is not corrupted.\\n # while self.corrupted_cells_mask.iat[row_ix, col_ix] == 1:\\n # col_ix, row_ix = np.random.randint(self.data.shape[1]), np.random.randint(self.data.shape[0])\\n # return col_ix, row_ix\\n\\n non_corrupted_row_col_pairs = []\\n for col_idx in range(self.data.shape[1]):\\n non_corrupted_cell_row_idx_list = self.data.iloc[:, col_idx].index[\\n self.corrupted_cells_mask.iloc[:, col_idx] == 0].tolist()\\n non_corrupted_row_col_pairs_for_col_idx = list(\\n map(lambda row_index: (self.data.index.get_loc(row_index), col_idx), non_corrupted_cell_row_idx_list))\\n non_corrupted_row_col_pairs.extend(non_corrupted_row_col_pairs_for_col_idx)\\n\\n rand_idx = np.random.randint(len(non_corrupted_row_col_pairs), size=1)[0]\\n return non_corrupted_row_col_pairs[rand_idx]\",\n \"def get_flat_miss_mask(self, df: np.ndarray,\\n p_missing: float = None) -> np.array:\\n df_size = df.size\\n miss_size = int(df_size * p_missing)\\n indx = np.arange(df_size)\\n flat_miss_mask = np.random.choice(indx, miss_size, replace=False)\\n return flat_miss_mask\",\n \"def filter_nonsense_sequences(sequences):\\n\\n # filter sequences if contains at least one 'N' character\\n good_index = []\\n filter_sequences = []\\n for i, seq in enumerate(sequences):\\n if \\\"N\\\" not in seq.upper():\\n good_index.append(i)\\n filter_sequences.append(seq)\\n return np.array(filter_sequences), np.array(good_index)\",\n \"def _notstaticneighs_get_corestored_by_inds_notslice(self, inds):\\n inds = [inds] if type(inds) == int else inds\\n idxs = []\\n for k in range(len(self.idxs)):\\n idxs.append([self.idxs[k][i] for i in inds])\\n idxs = np.array(idxs) if type(self.idxs) == np.ndarray else idxs\\n\\n if self.sp_relative_pos is not None:\\n sp_relative_pos = []\\n for k in range(len(self.sp_relative_pos)):\\n sp_relative_pos += [[self.sp_relative_pos[k][i] for i in inds]]\\n else:\\n sp_relative_pos = None\\n return idxs, sp_relative_pos\",\n \"def create_jackknife_indexes(data):\\n from numpy import arange, delete\\n\\n index_range = arange(0, len(data))\\n return (delete(index_range, i) for i in index_range)\",\n \"def compute_unary_set_mappings(indexing, counts): \\n ret = np.zeros_like(indexing)-1\\n for vertex,index in enumerate(indexing):\\n if counts[index] == 1:\\n ret[index] = vertex\\n return ret\",\n \"def getMissingIds(self):\\n return self._missingIds\",\n \"def missing_seat_ids(taken_seats):\\n all_seats = set(range(min(taken_seats), max(taken_seats) + 1))\\n return(set(taken_seats).symmetric_difference(all_seats))\",\n \"def _free_indicies(self):\\n return np.logical_not(self._fixed_indicies)\",\n \"def get_invalid_rows(rows):\\n\\n invalid_row_indices = []\\n for i, row in enumerate(rows):\\n is_valid, col_id = is_row_well_formatted(row)\\n if not is_valid:\\n invalid_row_indices.append((i, col_id))\\n\\n return invalid_row_indices\",\n \"def get_valid_indices():\\n return [i for i, val in enumerate(all_topics) if val[1] == \\\"1\\\"]\",\n \"def ind_complement(inds, n):\\n return tuple(i for i in range(n) if i not in inds)\",\n \"def _pure_data(data, missing_mask):\\n missing_rows = np.where(missing_mask)[0]\\n pure_data = np.delete(data, missing_rows, axis=0)\\n\\n return pure_data\",\n \"def _frc_indices_for_errors(self, idx_frc=None, idx_rows=None, y_idx=None, idx_original=None):\\n idx = [0] * self.nts\\n if idx_frc is None:\\n if idx_rows is None:\\n for i in y_idx:\\n idx[i] = range(self.n_hist_points[i], len(self.forecasts[i]))\\n\\n else:\\n for i in y_idx:\\n idx[i] = _ravel_idx(self.idxY[i][idx_rows], len(self.forecasts[i]))\\n else:\\n idx = idx_frc\\n\\n if idx_original is None:\\n return idx\\n\\n for i in y_idx:\\n time_index = self.ts[i].index[idx[i]]\\n intersect_mask = np.in1d(time_index, idx_original[i])\\n idx[i] = idx[i][intersect_mask]\\n\\n return idx\",\n \"def getNonZeroRanges( A, zeroDefVal=0 ):\\n dA=np.diff(A)\\n N=A.shape[0]\\n nzdA,=np.nonzero(dA)\\n liRangesAll=np.c_[ np.r_[0,nzdA+1],\\n np.r_[nzdA,N-1] +1]\\n iiR = A.take(liRangesAll[:,0]) != zeroDefVal\\n return liRangesAll[iiR,:]\",\n \"def CreateIndicesForBasisFilter(indices):\\n indices = np.array(indices, dtype = \\\"uint32\\\")\\n for ind, it in enumerate(indices):\\n indices[ind] = ind * it\\n return indices\",\n \"def get_random_indexes(msk, n_idxs):\\n pos_idxs = np.array(np.where(msk > 0))\\n neg_idxs = np.array(np.where(msk == 0))\\n n_pos = pos_idxs.shape[1] # number of positives found in the mask\\n n_neg = neg_idxs.shape[1] # number of negatives found in the mask\\n n_min = min(n_neg, min(n_idxs, n_pos))\\n rnd_idxs_pos = range(n_pos)\\n np.random.shuffle(rnd_idxs_pos)\\n rnd_idxs_neg = range(n_neg)\\n np.random.shuffle(rnd_idxs_neg)\\n return pos_idxs[:, rnd_idxs_pos[:n_min]], neg_idxs[:, rnd_idxs_neg[:n_min]]\",\n \"def bool_2_indices(bool_array):\\n if ~isinstance(bool_array, np.ndarray):\\n bool_array = np.array(bool_array)\\n if bool_array[-1]:\\n bool_array[-1] = False\\n lims = np.arange(bool_array.size)[bool_array ^ np.roll(bool_array, 1)]\\n if len(lims) > 0:\\n if lims[-1] == bool_array.size - 1:\\n lims[-1] = bool_array.size\\n return np.reshape(lims, (len(lims) // 2, 2))\\n else:\\n return [[np.nan, np.nan]]\",\n \"def _invert_nonzero(arr):\\n arr_inv = arr.copy()\\n nz = np.nonzero(arr)\\n arr_inv[nz] = 1 / arr[nz]\\n return arr_inv\",\n \"def get_cont_indices(self, X, max_ord):\\n indices = np.zeros(X.shape[1]).astype(bool)\\n for i, col in enumerate(X.T):\\n col_nonan = col[~np.isnan(col)]\\n col_unique = np.unique(col_nonan)\\n if len(col_unique) > max_ord:\\n indices[i] = True\\n return indices\",\n \"def get_vacancy_indices(array):\\t\\n\\tvacancy_indices = np.argwhere(array == 0)\\n\\treturn vacancy_indices\",\n \"def missing_bus_indices(net):\\n check_results = {}\\n bus_indices = set(net.bus.index)\\n element_bus_names = {\\\"ext_grid\\\": [\\\"bus\\\"], \\\"load\\\": [\\\"bus\\\"], \\\"gen\\\": [\\\"bus\\\"], \\\"sgen\\\": [\\\"bus\\\"],\\n \\\"trafo\\\": [\\\"lv_bus\\\", \\\"hv_bus\\\"], \\\"trafo3w\\\": [\\\"lv_bus\\\", \\\"mv_bus\\\", \\\"hv_bus\\\"],\\n \\\"switch\\\": [\\\"bus\\\", \\\"element\\\"], \\\"line\\\": [\\\"from_bus\\\", \\\"to_bus\\\"]}\\n for element in element_bus_names.keys():\\n element_check = []\\n for i, row in net[element].iterrows():\\n for bus_name in element_bus_names[element]:\\n if row[bus_name] not in bus_indices:\\n if not ((element == \\\"switch\\\") and (bus_name == \\\"element\\\") and (row.et in ['l', 't', 't3'])):\\n element_check.append((i, bus_name, row[bus_name]))\\n if element_check:\\n check_results[element] = element_check\\n\\n if check_results:\\n return check_results\",\n \"def remove_nans(coords):\\n s = np.apply_along_axis(sum,1,np.isnan(coords[1])) == 0\\n coords[0] = (np.asarray(coords[0])[s]).tolist()\\n coords[1] = coords[1][s,:]\",\n \"def missingNumber(self, nums):\\r\\n size = len(nums)\\r\\n missing_number = len(nums)\\r\\n for i in range(size):\\r\\n missing_number ^= (i ^ nums[i])\\r\\n\\r\\n return missing_number\",\n \"def get_agent_indices(array):\\t\\n\\tagent_indices = np.argwhere(array != 0)\\n\\treturn agent_indices\",\n \"def get_missing(dicts):\\n for d in dicts:\\n for k, v in d.items():\\n d[k] = set([1, 2, 3, 4, 5, 6, 7, 8, 9]) - set(v)\\n return dicts\",\n \"def vir_indices(self):\\n indices = []\\n for index,item in enumerate(self):\\n if item==0:\\n indices.append(index)\\n return indices\",\n \"def connected(index,i):\\n adj = concatenate([ where(ind==j)[0] for j in ind[i] if j >= 0 ])\\n return unique(adj[adj != i])\",\n \"def get_missing_seat_id(seat_ids):\\n minimum, maximum = min(seat_ids), max(seat_ids)\\n\\n missing = [s for s in range(minimum, maximum) if s not in seat_ids]\\n return missing[0]\",\n \"def find_idx_boundaries(indices, drop_single_idx=False):\\n\\n ## Since list slicing counts up to but not including ends, we need to add 1 to all detected end locations\\n ends = np.where(np.diff(indices) > 1)[0] + 1\\n\\n ## Starts and ends will equal each other since list slicing includes start values, but start needs 0 appended\\n starts = np.copy(ends)\\n if len(starts) == 0 or starts[0] != 0:\\n starts = np.insert(starts, 0, 0)\\n\\n ## np.diff returns an array one smaller than the indices list, so we need to add the last idx to the ends\\n if len(ends) == 0 or ends[-1] != len(indices):\\n ends = np.insert(ends, len(ends), len(indices))\\n\\n ## Loop through all continuous idx start & end to see if any are too small (length = 1)\\n events = []\\n for start, end in zip(starts, ends):\\n if end - start < 2:\\n if not drop_single_idx:\\n raise PipelineException(f\\\"Disconnected index found at index {start}\\\")\\n else:\\n events.append(indices[start:end])\\n\\n return events\",\n \"def remove_invalid_idx(self, image_idx_list):\\n\\n sample_id_list = []\\n for sample_id in image_idx_list:\\n sample_id = int(sample_id)\\n objects = self.get_label(sample_id)\\n calib = self.get_calib(sample_id)\\n labels, noObjectLabels = kitti_bev_utils.read_labels_for_bevbox(objects)\\n if not noObjectLabels:\\n labels[:, 1:] = transformation.camera_to_lidar_box(labels[:, 1:], calib.V2C, calib.R0,\\n calib.P) # convert rect cam to velo cord\\n\\n valid_list = []\\n for i in range(labels.shape[0]):\\n if int(labels[i, 0]) in cnf.CLASS_NAME_TO_ID.values():\\n if self.check_point_cloud_range(labels[i, 1:4]):\\n valid_list.append(labels[i, 0])\\n\\n if len(valid_list) > 0:\\n sample_id_list.append(sample_id)\\n\\n return sample_id_list\",\n \"def find_missing(expected, existing):\\n return set(expected).difference(existing)\",\n \"def locate_nan_rows(arr):\\n # Count the number of NaNs in each row\\n nan_counts = np.sum(~np.isfinite(arr), axis=1)\\n # Trigger on a NaN appearing anywhere in a line/row\\n nans, = np.where(nan_counts > 1)\\n return frozenset(nans)\",\n \"def fully_hidden_arrays(self):\\n hidden = []\\n for m in self.masks():\\n invalid = self.get_property(m, '_no_valid_items')\\n if invalid: hidden.append(m)\\n return hidden\",\n \"def _fix_uniq_col(self):\\n # subgradient; for two boolean arrays, multiplication seems to be the best way \\n # (equivalent to logical_and)\\n n_covered_col = self.a_csr.dot(np.ones(self.ncols)) \\n ifix = np.zeros(self.ncols, dtype=bool)\\n if (np.count_nonzero(n_covered_col) != self.mrows):\\n raise ValueError(\\\"There are uncovered rows! Please check your input!\\\")\\n if (np.any(n_covered_col==1)):\\n inonzero = self.a_csr[n_covered_col==1,:].nonzero()\\n ifix[inonzero[1]] = True\\n\\n return ifix\",\n \"def firstMissingPositive(nums):\\n\\n n = len(nums)\\n\\n # Base case.\\n if 1 not in nums:\\n return 1\\n\\n # nums = [1]\\n if n == 1:\\n return 2\\n\\n # Replace negative numbers, zeros,\\n # and numbers larger than n by 1s.\\n # After this conversion nums will contain \\n # only positive numbers.\\n for i in range(n):\\n if nums[i] <= 0 or nums[i] > n:\\n nums[i] = 1\\n\\n # Use index as a hash key and number sign as a presence detector.\\n # For example, if nums[1] is negative that means that number `1`\\n # is present in the array. \\n # If nums[2] is positive - number 2 is missing.\\n for i in range(n): \\n a = abs(nums[i])\\n # If you meet number a in the array - change the sign of a-th element.\\n # Be careful with duplicates : do it only once.\\n # [3,4,-1,1]\\n if a == n:\\n nums[0] = - abs(nums[0])\\n else:\\n nums[a] = - abs(nums[a])\\n \\n # Now the index of the first positive number \\n # is equal to first missing positive.\\n for i in range(1, n):\\n if nums[i] > 0:\\n return i\\n\\n if nums[0] > 0:\\n return n\\n\\n return n + 1\",\n \"def get_nonzero_vectors(motion_vectors):\\n if np.shape(motion_vectors)[0] == 0:\\n return motion_vectors\\n else:\\n idx = np.where(np.logical_or(motion_vectors[:, 7] != 0, motion_vectors[:, 8] != 0))[0]\\n return motion_vectors[idx, :]\",\n \"def find_allowed(possible_arrays):\\n allowed = [0] * len(possible_arrays[0])\\n for array in possible_arrays:\\n allowed = [x | y for x, y in zip(allowed, array)] \\n return allowed\",\n \"def create_repeated_indexes(data):\\n from numpy import arange\\n\\n index_range = arange(0, len(data))\\n return (index_range for i in index_range)\",\n \"def create_random_sample_alt(idx_bins,count_array):\\n idxs=[]\\n for i,x in enumerate(count_array):\\n if x > 0:\\n idxs.extend([ idx_bins[i][ind] for ind in unique_sample_of_int(len(idx_bins[i])-1,x) ] )\\n return idxs\",\n \"def _staticneighs_get_corestored_by_inds_notslice(self, inds):\\n inds = [inds] if type(inds) == int else inds\\n idxs = [self.idxs[i] for i in inds]\\n idxs = np.array(idxs) if type(self.idxs) == np.ndarray else idxs\\n if self.sp_relative_pos is not None:\\n sp_relative_pos = [self.sp_relative_pos[i] for i in inds]\\n else:\\n sp_relative_pos = None\\n return idxs, sp_relative_pos\",\n \"def findIndices(g):\\r\\n change = [0]\\r\\n seen = [g[0]]\\r\\n for i in range(1, len(g)):\\r\\n if not g[i] in seen:\\r\\n change.append(i)\\r\\n seen.append(g[i])\\r\\n return change\",\n \"def master_ndindex(self): # itermaster_indices(self):\\n return itertools_product(\\n *[range(*r) for r in self.location]\\n ) # TODO check\",\n \"def n_ary_mask(d, n, offset):\\n return [(i + offset) % n == 0 for i in range(d)]\",\n \"def hoggar_indices():\\n return list(product([0,1], repeat=6))\",\n \"def indices_using_group_array(group_array):\\n unique_elements = np.unique(group_array)\\n indices = []\\n for unique_element in unique_elements:\\n indices.append((unique_element,\\n np.flatnonzero(group_array == unique_element)))\\n return indices\",\n \"def test_discrimination_indices(self):\\n\\n n_items = [10, 20, 30]\\n n_factors = [2, 3, 4, 5]\\n\\n for item in n_items:\\n for factor in n_factors:\\n diagonal_indices, lower_indices = get_discrimination_indices(item, \\n factor)\\n lower_size = item * factor - factor * (factor + 1) / 2\\n\\n self.assertEqual(diagonal_indices[0].size, factor)\\n self.assertEqual(diagonal_indices[1].size, factor)\\n \\n self.assertEqual(lower_indices[0].size, lower_size)\\n self.assertEqual(lower_indices[1].size, lower_size)\\n\\n # Using lower, the rows are flipped, flip them back\\n flipped = item - diagonal_indices[0] - 1\\n np.testing.assert_equal(flipped, diagonal_indices[1])\\n\\n test = np.zeros((item, factor))\\n test[lower_indices] = 1\\n\\n self.assertEqual(test.sum(), lower_size)\\n\\n for ndx1 in range(factor):\\n for ndx2 in range(ndx1, factor):\\n self.assertEqual(test[item - ndx1 -1, ndx2], 0)\",\n \"def test_ks_test_empty_indices():\\n out = compute_indices_ks_test([], 1000, mode=\\\"D+\\\")\\n assert all(o is None for o in out)\",\n \"def least_indices(array: np.ndarray, n: int) -> tuple:\\n flat = array.ravel()\\n indices = np.argpartition(flat, n)[:n]\\n indices = indices[np.argsort(flat[indices])]\\n return np.unravel_index(indices, array.shape)\",\n \"def zero_indexed(array):\\n if all(dl == 0 for dl in array.datashape.dim_low):\\n return array\\n if any(dl < 0 for dl in array.datashape.dim_low):\\n raise ValueError(\\\"Cannot zero_index array: one or more \\\"\\n \\\"dimensions start < 0\\\")\\n\\n ds = array.datashape.copy()\\n ds.dim_low = [0] * ds.ndim\\n return array.redimension(ds.schema)\",\n \"def _get_valid_indices(\\n self,\\n _signals: np.ndarray,\\n _labels: np.ndarray,\\n window_start_indices: np.ndarray = None,\\n elm_start_indices: np.ndarray = None,\\n elm_stop_indices: np.ndarray = None,\\n valid_t0: np.ndarray = None,\\n labels: np.ndarray = None,\\n signals: np.ndarray = None,\\n ) -> Tuple[\\n np.ndarray, np.ndarray, np.ndarray, np.ndarray, np.ndarray, np.ndarray\\n ]:\\n # allowed indices; time data points which can be used for creating the data chunks\\n _valid_t0 = np.ones(_labels.shape, dtype=np.int32)\\n _valid_t0[\\n -(config.signal_window_size + config.label_look_ahead) + 1 :\\n ] = 0\\n\\n # indices for active elm events in each elm event\\n active_elm_events = np.nonzero(_labels >= 0.5)[0]\\n\\n if signals is None:\\n # initialize arrays\\n window_start_indices = np.array([0])\\n elm_start_indices = active_elm_events[0]\\n elm_stop_indices = active_elm_events[-1]\\n valid_t0 = _valid_t0\\n signals = _signals\\n labels = _labels\\n else:\\n # concat on axis 0 (time dimension)\\n last_index = len(labels) - 1\\n window_start_indices = np.append(\\n window_start_indices, last_index + 1\\n )\\n elm_start_indices = np.append(\\n elm_start_indices, active_elm_events[0] + last_index + 1\\n )\\n elm_stop_indices = np.append(\\n elm_stop_indices, active_elm_events[-1] + last_index + 1\\n )\\n valid_t0 = np.concatenate([valid_t0, _valid_t0])\\n signals = np.concatenate([signals, _signals], axis=0)\\n labels = np.concatenate([labels, _labels], axis=0)\\n\\n return (\\n signals,\\n labels,\\n valid_t0,\\n window_start_indices,\\n elm_start_indices,\\n elm_stop_indices,\\n )\",\n \"def _get_error_index_rows(self):\\n index_error_rows = set()\\n for series in self:\\n index_error_rows.update(set(series.error_values.keys()))\\n return sorted(list(index_error_rows))\",\n \"def test_gap_indices(self):\\n r = self.RNA(\\\"-?A-?NRY-\\\")\\n v = r.gap_indices()\\n self.assertEqual(v, array([0, 1, 3, 4, 8]))\\n r = self.RNA(\\\"AC\\\")\\n v = r.gap_indices()\\n self.assertEqual(v, array([])) # note: always returns array\\n r = self.RNA(\\\"-?\\\")\\n v = r.gap_indices()\\n self.assertEqual(v, array([0, 1]))\",\n \"def _fixed_indicies(self):\\n fixed_inds = self.constraints == 'fixed'\\n return fixed_inds\",\n \"def get_uncut_indices(atoms, orig, vec1, offset=None):\\n\\n if isinstance(orig, int):\\n orig = atoms.get_positions()[orig]\\n else:\\n orig = np.array(orig)\\n if isinstance(vec1, int):\\n vec1 = atoms.get_positions()[vec1]\\n else:\\n vec1 = np.array(vec1)\\n\\n# if vec2 and isinstance(vec2, int):\\n# vec2 = atoms.get_positions()[vec2]\\n# elif vec2:\\n# vec2 = np.array(vec2)\\n# if vec3 and isinstance(vec3, int):\\n# vec3 = atoms.get_positions()[vec3]\\n# elif vec3:\\n# vec3 = np.array(vec3)\\n#\\n# if vec2:\\n# plane_vec = np.cross(vec1-orig, vec2-orig)\\n# else:\\n# plane_vec = vec1-orig\\n#\\n# nplane_vec = plane_vec/np.linalg.norm(plane_vec)\\n#\\n# if vec2 and vec3 and nplane_vec.dot(vec3-orig) < 0:\\n# nplane_vec *= -1\\n\\n plane_vec = vec1 - orig\\n nplane_vec = plane_vec/np.linalg.norm(plane_vec)\\n\\n if offset:\\n orig += offset*nplane_vec\\n\\n atom_indices = [i for i,a in enumerate(atoms) if (a.position - orig).dot(nplane_vec) > 0]\\n return atom_indices\",\n \"def find_missing(nums):\\n # calculate sum of all elements\\n # in input list\\n sum_of_elements = sum(nums)\\n\\n # There is exactly 1 number missing\\n n = len(nums) + 1\\n actual_sum = (n * (n + 1)) / 2\\n return actual_sum - sum_of_elements\",\n \"def _to_flat_index(self, idx_in):\\n idx_in = tuple([np.array(z, ndmin=1, copy=False) for z in idx_in])\\n msk = np.all(np.stack([t < n for t, n in zip(idx_in, self.shape)]), axis=0)\\n idx = np.ravel_multi_index(\\n tuple([t[msk] for t in idx_in]), self.shape, mode=\\\"wrap\\\"\\n )\\n\\n return idx, msk\",\n \"def _idxs_are_present(self, *args):\\n return set(args).issubset(set(range(self.n_atoms)))\",\n \"def indxmap_diff(Nd): \\n\\n ndims = len(Nd)\\n Ndprod = numpy.prod(Nd)\\n mylist = numpy.arange(0, Ndprod).astype(numpy.int32)\\n mylist = numpy.reshape(mylist, Nd)\\n d_indx = []\\n dt_indx = []\\n for pp in range(0, ndims):\\n d_indx = d_indx + [ numpy.reshape( numpy.roll( mylist, +1 , pp ), (Ndprod,) ,order='C').astype(numpy.int32) ,]\\n dt_indx = dt_indx + [ numpy.reshape( numpy.roll( mylist, -1 , pp ) , (Ndprod,) ,order='C').astype(numpy.int32) ,]\\n\\n return d_indx, dt_indx\",\n \"def get_missing_test_numbers(a_dict, logged_test_numbers):\\n tnum_list = []\\n for tname, t_dict in a_dict.iteritems():\\n for tpin, r_dict in t_dict.iteritems():\\n tnum_list.append(int(r_dict[\\\"Test number\\\"]))\\n missing = list(set(tnum_list) - set(logged_test_numbers))\\n return missing, tnum_list\",\n \"def prob_cl_indices(self, cl_idxs, cl_losses):\\n log.info(\\\"Finding problematic structures\\\")\\n loss_bound = np.mean(cl_losses) # Initial minimum loss\\n loss_step = loss_bound / 500\\n loss_bound += loss_step\\n idxs = []\\n while len(idxs) < 1.5 * self.refine_n_cl:\\n log.info(\\\"Minimum cluster loss : %.4f\\\", loss_bound)\\n cl_idxs_prob = np.concatenate(np.argwhere(cl_losses >= loss_bound))\\n clusters = np.array(cl_idxs, dtype=object)[cl_idxs_prob]\\n idxs = np.concatenate(clusters)\\n loss_bound -= loss_step\\n log.info(\\\"N structures included : %d\\\\n\\\", len(idxs))\\n return idxs\",\n \"def diffeo_inverse(a):\\n M, N = a.shape[0], a.shape[1]\\n result = np.empty_like(a)\\n result.fill(-1) # fill invalid data\\n many = np.zeros((M, N))\\n many.fill(0)\\n for i, j in coords_iterate((M, N)):\\n i1 = a[i, j, 0]\\n j1 = a[i, j, 1]\\n result[i1, j1, 0] = i\\n result[i1, j1, 1] = j\\n many[i1, j1] += 1\\n\\n num = (many == 0).sum()\\n if num:\\n fill_invalid(result[:, :, 0], -1)\\n fill_invalid(result[:, :, 1], -1)\\n\\n return result\",\n \"def data_missing_for_sorting():\\n return RaggedArray([[1, 0], [], [0, 0]])\",\n \"def get_reduced_indices(*indices, axis, keepdims):\\n\\n # get all indices\\n indices_list = list(indices)\\n # list of reduction axis: transform negative indices into positive\\n # axis in this list wont exist after the reduction\\n axis_list = ft_util.refine_reduce_axis(indices_list, list(axis))\\n # get indices after reduction\\n if keepdims:\\n grad_indices_list = [index_i if i not in axis_list else 0 for i, index_i in enumerate(indices_list)]\\n else:\\n grad_indices_list = [index_i for i, index_i in enumerate(indices_list) if i not in axis_list]\\n grad_ind = tuple(grad_indices_list)\\n return grad_ind\",\n \"def period_indices(instantaneous_frequencies, spacing=1, dt=1, time_axis=-1):\\n skips = np.ceil(spacing * ((2 * np.pi) / instantaneous_frequencies * dt)).astype(int)\\n indices = [np.take(skips, 0, time_axis)]\\n indices[-1][indices[-1] >= instantaneous_frequencies.shape[time_axis] - 1] = -1\\n while np.any(indices[-1] >= 0):\\n indices.append(indices[-1] + np.take(skips, indices[-1], time_axis))\\n indices[-1][np.logical_or(indices[-2] < 0, indices[-1] >= instantaneous_frequencies.shape[time_axis] - 1)] = -1\\n # the last indices are all -1, so remove them\\n indices = np.concatenate(indices[:-1], time_axis)\\n # replace negative values with values that will cause index out of bounds if these are used carelessly\\n return np.where(indices < 0, instantaneous_frequencies.shape[time_axis], indices)\",\n \"def get_fixed_array():\\n return np.array([[[np.nan, np.nan],\\n [np.nan, np.nan],\\n [np.nan, np.nan]],\\n\\n [[3., 7.],\\n [2., 4.],\\n [1., 16.]],\\n\\n [[17., 5.],\\n [10., 0.],\\n [14., 12.]],\\n\\n [[8., 6.],\\n [13., 11.],\\n [9., 15.]]])\",\n \"def _uni_pnts(a):\\r\\n _, idx = np.unique(a, return_index=True, axis=0)\\r\\n a = np.concatenate((a[np.sort(idx)], [a[0]]))\\r\\n return a\",\n \"def validate_new_inds(selected_inds: list, inds_dict: dict):\\n new_set = set(selected_inds)\\n train_set = set(inds_dict['train_inds'])\\n val_set = set(inds_dict['val_inds'])\\n unlabeled_set = set(inds_dict['unlabeled_inds'])\\n\\n assert len(new_set.intersection(train_set)) == 0, 'Some selected samples are already in the train set'\\n assert len(new_set.intersection(val_set)) == 0, 'Some selected samples are already in the val set'\\n assert new_set.issubset(unlabeled_set), 'All new selected indices must be in unlabeled_inds'\"\n]","isConversational":false},"negative_scores":{"kind":"list like","value":["0.6300259","0.6108775","0.6091231","0.60477185","0.6002121","0.5978218","0.59119964","0.58613497","0.58460724","0.5842929","0.57560515","0.5704897","0.5615569","0.55952543","0.55720913","0.55479383","0.5543988","0.55328596","0.5526036","0.55213183","0.54728156","0.54728156","0.54728156","0.5470916","0.5445985","0.54443336","0.54180694","0.54119307","0.53775924","0.53767806","0.53660005","0.5346127","0.5328876","0.5320129","0.5318","0.5310348","0.53018093","0.5283208","0.5282062","0.52813786","0.5273954","0.52550834","0.5253982","0.524483","0.52364814","0.52322525","0.52305406","0.52233434","0.5196608","0.51956093","0.51880133","0.51846945","0.5174616","0.5169243","0.5127386","0.51071286","0.5082922","0.5082877","0.5074748","0.50550187","0.50470716","0.5046033","0.5041982","0.5040383","0.5024275","0.5023409","0.5022828","0.5015319","0.50110877","0.50025403","0.50025254","0.50004697","0.4992241","0.49892285","0.49860564","0.49846655","0.4984289","0.49766928","0.4969109","0.49675834","0.4954434","0.49516025","0.49471763","0.49318108","0.49317348","0.4928179","0.4923437","0.49160612","0.49154165","0.49144018","0.49079338","0.4907543","0.49068213","0.49067056","0.49025562","0.48894483","0.4882759","0.48798907","0.48749602","0.48673186"],"string":"[\n \"0.6300259\",\n \"0.6108775\",\n \"0.6091231\",\n \"0.60477185\",\n \"0.6002121\",\n \"0.5978218\",\n \"0.59119964\",\n \"0.58613497\",\n \"0.58460724\",\n \"0.5842929\",\n \"0.57560515\",\n \"0.5704897\",\n \"0.5615569\",\n \"0.55952543\",\n \"0.55720913\",\n \"0.55479383\",\n \"0.5543988\",\n \"0.55328596\",\n \"0.5526036\",\n \"0.55213183\",\n \"0.54728156\",\n \"0.54728156\",\n \"0.54728156\",\n \"0.5470916\",\n \"0.5445985\",\n \"0.54443336\",\n \"0.54180694\",\n \"0.54119307\",\n \"0.53775924\",\n \"0.53767806\",\n \"0.53660005\",\n \"0.5346127\",\n \"0.5328876\",\n \"0.5320129\",\n \"0.5318\",\n \"0.5310348\",\n \"0.53018093\",\n \"0.5283208\",\n \"0.5282062\",\n \"0.52813786\",\n \"0.5273954\",\n \"0.52550834\",\n \"0.5253982\",\n \"0.524483\",\n \"0.52364814\",\n \"0.52322525\",\n \"0.52305406\",\n \"0.52233434\",\n \"0.5196608\",\n \"0.51956093\",\n \"0.51880133\",\n \"0.51846945\",\n \"0.5174616\",\n \"0.5169243\",\n \"0.5127386\",\n \"0.51071286\",\n \"0.5082922\",\n \"0.5082877\",\n \"0.5074748\",\n \"0.50550187\",\n \"0.50470716\",\n \"0.5046033\",\n \"0.5041982\",\n \"0.5040383\",\n \"0.5024275\",\n \"0.5023409\",\n \"0.5022828\",\n \"0.5015319\",\n \"0.50110877\",\n \"0.50025403\",\n \"0.50025254\",\n \"0.50004697\",\n \"0.4992241\",\n \"0.49892285\",\n \"0.49860564\",\n \"0.49846655\",\n \"0.4984289\",\n \"0.49766928\",\n \"0.4969109\",\n \"0.49675834\",\n \"0.4954434\",\n \"0.49516025\",\n \"0.49471763\",\n \"0.49318108\",\n \"0.49317348\",\n \"0.4928179\",\n \"0.4923437\",\n \"0.49160612\",\n \"0.49154165\",\n \"0.49144018\",\n \"0.49079338\",\n \"0.4907543\",\n \"0.49068213\",\n \"0.49067056\",\n \"0.49025562\",\n \"0.48894483\",\n \"0.4882759\",\n \"0.48798907\",\n \"0.48749602\",\n \"0.48673186\"\n]","isConversational":false},"document_score":{"kind":"string","value":"0.610664"},"document_rank":{"kind":"string","value":"2"}}},{"rowIdx":22693,"cells":{"query":{"kind":"string","value":"r\"\"\"Get absolute path and set attributes."},"document":{"kind":"string","value":"def _init_before_open(self, read_meth='read', open_as_binary=True, **kwargs):\n super(FileComm, self)._init_before_open(**kwargs)\n # Process file class keywords\n if not hasattr(self, '_fd'):\n self._fd = None\n if read_meth not in ['read', 'readline']:\n raise ValueError(\"read_meth '%s' not supported.\" % read_meth)\n self.read_meth = read_meth\n self.platform_newline = platform._newline\n if self.in_temp:\n self.address = os.path.join(tempfile.gettempdir(), self.address)\n self.address = os.path.abspath(self.address)\n self.open_as_binary = open_as_binary\n self._series_index = 0\n # Put string attributes in the correct format\n if self.open_as_binary:\n func_conv = backwards.as_bytes\n else:\n func_conv = backwards.as_unicode\n for k in self._attr_conv:\n v = getattr(self, k)\n if v is not None:\n setattr(self, k, func_conv(v))"},"metadata":{"kind":"string","value":"{\n \"objective\": {\n \"self\": [],\n \"paired\": [],\n \"triplet\": [\n [\n \"query\",\n \"document\",\n \"negatives\"\n ]\n ]\n }\n}"},"negatives":{"kind":"list like","value":["def get_path(self):\n raise NotImplementedError(\"This asset does not support absolute paths\")","def abspath(self):\n if self.__abspath is None:\n self.__abspath = pbxpath.abspath(self)\n return self.__abspath","def _get_absolute(self, path: Path) -> Path:\n return path.expanduser().absolute()","def get_absolute_path(self):\n\t\treturn call_sdk_function('PrlFsEntry_GetAbsolutePath', self.handle)","def __init__(self, path):\n self.path = os.path.abspath(path)","def realpath(self):\n return self.__class__(os.path.realpath(self.strpath))","def get_path(self):\n return self.path","def full_path(self):\n return os.path.abspath(self.path)","def _abs_path(self, path):\n\n debug(\"current cache: %s\", self._cache)\n\n #save path in argument\n arg_path = path\n try:\n #try to return whats in cache:\n debug(\"trying to access %s path in cache\", arg_path)\n return self._cache[arg_path]\n except KeyError:\n debug(\"%s not found in cache\", arg_path)\n #normalize path:\n path = os.path.expanduser(path)\n path = os.path.expandvars(path)\n path = os.path.normpath(path)\n #save the result in the cache:\n self._cache[arg_path] = path\n debug(\"stored %s in cache\", self._cache[arg_path])\n return path","def abspath(self, path):\n return DataSource.abspath(self, self._fullpath(path))","def path(self):\n ...","def _absPath(self, relpath):\n\n # Pass through URIs and absolute paths.\n if self.isUrl(relpath) or relpath[0] == '/':\n return relpath\n\n # This won't deal with ~user/ syntax, but it's much less\n # common anyway.\n if relpath.startswith('~/') and 'HOME' in os.environ:\n return os.path.join(os.environ['HOME'], relpath[2:])\n\n if self._configFileStack:\n relativeTo = os.path.dirname(self._configFileStack[-1])\n else:\n relativeTo = os.getcwd()\n\n if self.isUrl(relativeTo):\n parts = urlparse.urlsplit(relativeTo)\n return urlparse.urlunsplit((parts.scheme, parts.netloc, os.path.normpath(os.path.join(parts.path, relpath)), parts.query, parts.fragment))\n return os.path.normpath(os.path.join(relativeTo, relpath))","def __get_path(self):\n return self.path","def path(self, name):\n raise NotImplementedError(\"This backend doesn't support absolute paths.\")","def path(self, name):\n raise NotImplementedError(\"This backend doesn't support absolute paths.\")","def _set_path(self, address):\n self._fullpath = address\n # http://stackoverflow.com/questions/8384737\n split_path = ntpath.split(address)\n self._filename = split_path[1] or ntpath.basename(split_path[0])","def GetAttrib( self ):\n attrib = GameNodePath.GetAttrib( self )\n \n if self.GetModified():\n attrib['path'] = self.GetPath()\n \n return attrib","def __set__(self, obj, val):\n try:\n self._resolve(val)\n except IOError, e:\n Parameterized(name=\"%s.%s\"%(obj.name,self._attrib_name)).warning('%s'%(e.args[0]))\n\n super(Path,self).__set__(obj,val)","def absolute_physical_path(self) -> str:\n return self._path","def as_pathlib(self):\n return Path(self.absolute)","def _get_as_path(self):\n return self.__as_path","def absolute(self):\n if self.relative == '':\n return self.root # don't join in this case as that appends trailing '/'\n return os.path.join(self.root, self.relative)","def setPath(*args):","def get_path(self, path):\n return abspath(join(self.origin, *path))","def abspath(self, path):\n # We do this here to reduce the 'import numpy' initial import time.\n from urllib.parse import urlparse\n\n # TODO: This should be more robust. Handles case where path includes\n # the destpath, but not other sub-paths. Failing case:\n # path = /home/guido/datafile.txt\n # destpath = /home/alex/\n # upath = self.abspath(path)\n # upath == '/home/alex/home/guido/datafile.txt'\n\n # handle case where path includes self._destpath\n splitpath = path.split(self._destpath, 2)\n if len(splitpath) > 1:\n path = splitpath[1]\n scheme, netloc, upath, uparams, uquery, ufrag = urlparse(path)\n netloc = self._sanitize_relative_path(netloc)\n upath = self._sanitize_relative_path(upath)\n return os.path.join(self._destpath, netloc, upath)","def _abs_path(rel_path):\n return os.path.join(BASE_DIR, rel_path)","def __set_full_path_of_file(self, value):\n self.full_path_of_file = value","def get_abs_path(self, value):\n return os.path.abspath(os.path.expanduser(os.path.expandvars(value)))","def load_path_url():\n web.ctx.path_url = web.ctx.home + web.ctx.path","def realpath(self):\n return pbxpath.realpath(self.project(), self.abspath())","def getattr(self, path, fh=None):\n artist_dir_m = self.artist_dir.match(path)\n artist_album_dir_m = self.artist_album_dir.match(path)\n artist_album_track_m = self.artist_album_track.match(path)\n playlist_dir_m = self.playlist_dir.match(path)\n playlist_track_m = self.playlist_track.match(path)\n\n # Default to a directory\n st = {\n 'st_mode': (S_IFDIR | 0o755),\n 'st_nlink': 2}\n date = 0 # Make the date really old, so that cp -u works correctly.\n st['st_ctime'] = st['st_mtime'] = st['st_atime'] = date\n\n if path == '/':\n pass\n elif path == '/artists':\n pass\n elif path == '/playlists':\n pass\n elif artist_dir_m:\n pass\n elif artist_album_dir_m:\n parts = artist_album_dir_m.groupdict()\n artist = self.library.artists_by_name[parts['artist']]\n album = artist.albums[parts['album']]\n #st['st_size'] = len(artist.albums)\n \n elif artist_album_track_m:\n parts = artist_album_track_m.groupdict()\n artist = self.library.artists_by_name[parts['artist']]\n album = artist.albums[parts['album']]\n track = album.tracks[parts['title']]\n return track.get_attr()\n \n elif playlist_dir_m:\n pass\n \n elif playlist_track_m:\n parts = playlist_track_m.groupdict()\n playlist = self.library.playlists[parts['playlist']]\n track = playlist.tracks[parts['title']]\n return track.get_attr()\n else:\n raise FuseOSError(ENOENT)\n \n return st","def base_path(self):\n return Path(self.path)","def __init__ (self, relPath) :\n self.m_path = \"\"\n \n dataPath = os.getenv(\"SIT_DATA\")\n if not dataPath: return\n \n for dir in dataPath.split(':'):\n path = os.path.join(dir, relPath)\n if os.path.exists(path):\n self.m_path = path\n break","def get_absolute_path(*args):\n directory = os.path.dirname(os.path.abspath(__file__))\n return os.path.join(directory, *args)","def real_absolute_path(path):\n return os.path.realpath(absolute_path(path))","def force_absolute(base, path):\n if os.path.abspath(path) and os.path.exists(path):\n return path\n else:\n return path_format(base + path)","def absolute(self, key):\n path = self.relative(key)\n if self.root:\n path = os.path.join(self.root, path)\n return absolute(path)","def get_abs_path(path):\r\n abs_path = lib_path.abspath(path)\r\n return abs_path","def set_basedir(self, host, path):","def to_response_data(self) -> typing.Any:\n return pathlib.Path(self.value[\"absolute\"]).expanduser().absolute().read_text()","def realpath(self, path):\n return os.path.realpath(path)","def __init__(self): \n self.path=os.getcwd()+'/'","def getAbsolutePath(relPath):\n currDir = os.path.dirname(__file__)\n return os.path.join(currDir, relPath)","def getattr(self, path, fh=None):\n\n if path != \"/\":\n raise FuseOSError(ENOENT)\n\n attrs = super(IndexView, self).getattr(path, fh)\n attrs.update({\"st_mode\": S_IFDIR | 0o555, \"st_nlink\": 2})\n\n return attrs","def abspath(self, ref):\n \n directory, path = get_location(self.directory, ref.strip(),\n current=dirname(self.relative))\n path = join_fb_root(join(directory, path))\n return path","def __get__(self, obj, objtype):\n raw_path = super(Path,self).__get__(obj,objtype)\n return self._resolve(raw_path)","def get_absolute_path(self):\n if self.datafile and self.datafile.storage.exists(self.datafile.path):\n return self.datafile.path\n else:\n return None","def make_relative(self,basepath = None):\n __make_relative__(run_object=self,basepath=basepath)","def attribute(self, apath):\n return data.Attribute(self, apath)","def get_full_path(self):\n try:\n full_path = os.path.abspath(self.FILENAME)\n return full_path\n except Exception as e:\n raise SystemExit(f\"Could not complete operation: {e}\")","def full_path(self):\n fullpath = os.path.join(self.path, self.name)\n if self.path == \"\":\n fullpath = self.name\n return fullpath","def _set_rel_paths(self):\n if self.working_dir is not None:\n self._rel_working_dir = os.path.relpath(self.working_dir)\n if self.alignment is not None:\n self._rel_alignment = os.path.relpath(self.alignment, \n self.working_dir)\n if self.out_file is not None:\n self._rel_out_file = os.path.relpath(self.out_file, \n self.working_dir)","def resolved(rpath):\r\n return realpath(abspath(rpath))","def get_absolute_pathname(self):\n return os.path.join(settings.PRIVATE_STORAGE_ROOT, self.get_relative_pathname())","def inputpathabs(self):\n return os.path.abspath(self.inputpath)","def get_path(self):\n return self.path","def __fspath__(self):\n raise NotImplementedError","def absolute_path(path):\n return os.path.abspath(os.path.normpath(path))","def path(self):\r\n raise NotImplementedError()","def get_path(self):\n try:\n return self._file.path\n except AttributeError:\n return os.path.abspath(self._file.name)","def test_set_path_1(self):\n self.file.touch()\n # Since using tempfile, there is an added quirk.\n # the tempfile path may be a symlink, so passing it through set path\n # will resolve the symlink, changing the path, and breaking the test.\n self.file = self.file.resolve()\n output = basic.set_path(self.file, kind=\"file\", expect=True)\n with self.subTest():\n self.assertIsInstance(output, Path)\n with self.subTest():\n self.assertEqual(str(self.file), str(output))","def path(self):\n return self.path","def _set_path(self):\n self.path = self._get_path()\n self.depth = self.get_depth()\n\n self.save()","def path(self):\n return self.chroot","def _get_origin_path(self):\n return self.__origin_path","def __init__(self, realpath, config):\n self.__dict__['_config'] = config\n self.__dict__['_realpath'] = realpath","def path(self):\n return self.file_path()","def test_get_absolute_path():\n eq_(get_absolute_path(\"http://foo.com/bar/baz\", \"../foo\"), \"/bar/foo\")\n eq_(get_absolute_path(\"http://foo.com/bar/baz\", \"/foo\"), \"/foo\")","def getPath(obj):","def local_to_extern_path(self, path: PurePath) -> PurePath:\n return self.path_extern_supervisor / path.relative_to(self.path_supervisor)","def path(self):\r\n return self.chroot","def path(self):\r\n return self.chroot","def absolute_path(path):\n return os.path.abspath(\n os.path.join(\n os.path.dirname(__file__),\n \"..\",\n path\n )\n )","def path(self) :\n return self.m_path","def set_local_path(self):\n return HERE","def _get_attrib(self, attrib_path: str, binfo: dict) -> str:\n apath = attrib_path.split('.')\n return self._get_attrib_by_path(apath, binfo)","def _get_url(self, absolute):","def getpath(self, path):\n return self._join(path)","def abspath(path: str) -> str:\n pass","def get_path():\n return path.abspath(path.dirname(path.dirname(__file__)))","def realpath(path: str) -> str:\n pass","def _expand(self):\n return Path(os.path.expandvars(self)).expanduser().resolve()","def absPath(path):\n return os.path.join(os.path.dirname(os.path.abspath(__file__)), path)","def systemPathToFileUrl(self, path):\n from unohelper import systemPathToFileUrl\n return systemPathToFileUrl(path)","def path( self ) :\n\n return( self.__path )","def _get_path(): # THIS IS JUST FOR GETTING THE FILE\n return os.path.dirname(os.path.abspath(__file__)) + '/'","def get_resources_abs_path() -> pathlib.Path:\n return PathManager._ROOT.joinpath(\n PathManager._TILINGS_GUI, PathManager._RESOURCES\n )","def _setJob_getPath(job, shot=False):\n\tjobpath = j.getPath(job, translate=True)\n\n\tif shot:\n\t\tpath = os_wrapper.absolutePath(\"%s/$IC_SHOTSDIR/%s\" % (jobpath, shot))\n\telse:\n\t\tpath = os_wrapper.absolutePath(\"%s/$IC_SHOTSDIR\" % jobpath)\n\n\treturn path","def _get_absolute_path(path):\n abs_path = Path(os.path.abspath(path))\n\n if is_protected_path(abs_path):\n raise errors.ParameterError(f\"Path '{path}' is protected.\")\n\n try:\n abs_path.relative_to(project_context.path)\n except ValueError:\n raise errors.ParameterError(f\"Path '{path}' is outside the project.\")\n\n return abs_path","def resourcePath(self,relative_path):\n base_path = getattr(sys, '_MEIPASS', os.path.dirname(os.path.dirname(os.path.abspath(__file__))))\n output = base_path + relative_path\n return output","def get_realpath(cls, path_str):\n if path_str.startswith('/'):\n return path_str\n return os.path.abspath(os.path.join(cls.apollo_root, path_str))","def getattr(self, path):\n\n\t\tprint '*** getattr', path\n\n\t\t#depth = getDepth(path) # depth of path, zero-based from root\n\t\t#pathparts = getParts(path) # the actual parts of the path\n\n\t\t#return -errno.ENOSYS\n\t\t\n\t\tself.files = self.snake.apache_get_groups()\n\t\tprint \"self.files:\", self.files\n\t\t\n\t\tst = ObjectStat()\n\t\t\n\t\tparts = path.split( '/' )\n\t\tif len( parts ) > 1:\n\t\t\tfn = parts[ 1 ]\n\t\telse:\n\t\t\tfn = ''\n\t\t\n\t\tif fn == '':\n\t\t\tprint \"returing stats\"\n\t\t\tst.st_nlink += len( self.files )\n\t\t\treturn st\n\t\t\n\t\telif fn not in self.files:\n\t\t\tprint \"No such file! (%s)\"%fn\n\t\t\treturn -errno.ENOENT\n\t\telse:\n\t\t\tprint \"Returning stats..\"\n\t\t\tst.st_mode = stat.S_IFREG | 0755\n\t\t\t\n\t\t\tself.content[ fn ] = self.snake.apache_get_content( fn )\n\t\t\tst.st_size = len( self.content[ fn ] )\n\t\t\t\n\t\t\treturn st","def to_absolute_path(path):\n if not os.path.isabs(path):\n return os.path.join(os.getcwd(), path)\n else:\n return path","def path(self):\n return self._path","def path(self):\n return self._path","def path(self):\n return self._path","def path(self):\n return self._path","def path(self):\n return self._path","def path(self):\n return self._path","def path(self):\n return self._path","def path(self):\n return self._path"],"string":"[\n \"def get_path(self):\\n raise NotImplementedError(\\\"This asset does not support absolute paths\\\")\",\n \"def abspath(self):\\n if self.__abspath is None:\\n self.__abspath = pbxpath.abspath(self)\\n return self.__abspath\",\n \"def _get_absolute(self, path: Path) -> Path:\\n return path.expanduser().absolute()\",\n \"def get_absolute_path(self):\\n\\t\\treturn call_sdk_function('PrlFsEntry_GetAbsolutePath', self.handle)\",\n \"def __init__(self, path):\\n self.path = os.path.abspath(path)\",\n \"def realpath(self):\\n return self.__class__(os.path.realpath(self.strpath))\",\n \"def get_path(self):\\n return self.path\",\n \"def full_path(self):\\n return os.path.abspath(self.path)\",\n \"def _abs_path(self, path):\\n\\n debug(\\\"current cache: %s\\\", self._cache)\\n\\n #save path in argument\\n arg_path = path\\n try:\\n #try to return whats in cache:\\n debug(\\\"trying to access %s path in cache\\\", arg_path)\\n return self._cache[arg_path]\\n except KeyError:\\n debug(\\\"%s not found in cache\\\", arg_path)\\n #normalize path:\\n path = os.path.expanduser(path)\\n path = os.path.expandvars(path)\\n path = os.path.normpath(path)\\n #save the result in the cache:\\n self._cache[arg_path] = path\\n debug(\\\"stored %s in cache\\\", self._cache[arg_path])\\n return path\",\n \"def abspath(self, path):\\n return DataSource.abspath(self, self._fullpath(path))\",\n \"def path(self):\\n ...\",\n \"def _absPath(self, relpath):\\n\\n # Pass through URIs and absolute paths.\\n if self.isUrl(relpath) or relpath[0] == '/':\\n return relpath\\n\\n # This won't deal with ~user/ syntax, but it's much less\\n # common anyway.\\n if relpath.startswith('~/') and 'HOME' in os.environ:\\n return os.path.join(os.environ['HOME'], relpath[2:])\\n\\n if self._configFileStack:\\n relativeTo = os.path.dirname(self._configFileStack[-1])\\n else:\\n relativeTo = os.getcwd()\\n\\n if self.isUrl(relativeTo):\\n parts = urlparse.urlsplit(relativeTo)\\n return urlparse.urlunsplit((parts.scheme, parts.netloc, os.path.normpath(os.path.join(parts.path, relpath)), parts.query, parts.fragment))\\n return os.path.normpath(os.path.join(relativeTo, relpath))\",\n \"def __get_path(self):\\n return self.path\",\n \"def path(self, name):\\n raise NotImplementedError(\\\"This backend doesn't support absolute paths.\\\")\",\n \"def path(self, name):\\n raise NotImplementedError(\\\"This backend doesn't support absolute paths.\\\")\",\n \"def _set_path(self, address):\\n self._fullpath = address\\n # http://stackoverflow.com/questions/8384737\\n split_path = ntpath.split(address)\\n self._filename = split_path[1] or ntpath.basename(split_path[0])\",\n \"def GetAttrib( self ):\\n attrib = GameNodePath.GetAttrib( self )\\n \\n if self.GetModified():\\n attrib['path'] = self.GetPath()\\n \\n return attrib\",\n \"def __set__(self, obj, val):\\n try:\\n self._resolve(val)\\n except IOError, e:\\n Parameterized(name=\\\"%s.%s\\\"%(obj.name,self._attrib_name)).warning('%s'%(e.args[0]))\\n\\n super(Path,self).__set__(obj,val)\",\n \"def absolute_physical_path(self) -> str:\\n return self._path\",\n \"def as_pathlib(self):\\n return Path(self.absolute)\",\n \"def _get_as_path(self):\\n return self.__as_path\",\n \"def absolute(self):\\n if self.relative == '':\\n return self.root # don't join in this case as that appends trailing '/'\\n return os.path.join(self.root, self.relative)\",\n \"def setPath(*args):\",\n \"def get_path(self, path):\\n return abspath(join(self.origin, *path))\",\n \"def abspath(self, path):\\n # We do this here to reduce the 'import numpy' initial import time.\\n from urllib.parse import urlparse\\n\\n # TODO: This should be more robust. Handles case where path includes\\n # the destpath, but not other sub-paths. Failing case:\\n # path = /home/guido/datafile.txt\\n # destpath = /home/alex/\\n # upath = self.abspath(path)\\n # upath == '/home/alex/home/guido/datafile.txt'\\n\\n # handle case where path includes self._destpath\\n splitpath = path.split(self._destpath, 2)\\n if len(splitpath) > 1:\\n path = splitpath[1]\\n scheme, netloc, upath, uparams, uquery, ufrag = urlparse(path)\\n netloc = self._sanitize_relative_path(netloc)\\n upath = self._sanitize_relative_path(upath)\\n return os.path.join(self._destpath, netloc, upath)\",\n \"def _abs_path(rel_path):\\n return os.path.join(BASE_DIR, rel_path)\",\n \"def __set_full_path_of_file(self, value):\\n self.full_path_of_file = value\",\n \"def get_abs_path(self, value):\\n return os.path.abspath(os.path.expanduser(os.path.expandvars(value)))\",\n \"def load_path_url():\\n web.ctx.path_url = web.ctx.home + web.ctx.path\",\n \"def realpath(self):\\n return pbxpath.realpath(self.project(), self.abspath())\",\n \"def getattr(self, path, fh=None):\\n artist_dir_m = self.artist_dir.match(path)\\n artist_album_dir_m = self.artist_album_dir.match(path)\\n artist_album_track_m = self.artist_album_track.match(path)\\n playlist_dir_m = self.playlist_dir.match(path)\\n playlist_track_m = self.playlist_track.match(path)\\n\\n # Default to a directory\\n st = {\\n 'st_mode': (S_IFDIR | 0o755),\\n 'st_nlink': 2}\\n date = 0 # Make the date really old, so that cp -u works correctly.\\n st['st_ctime'] = st['st_mtime'] = st['st_atime'] = date\\n\\n if path == '/':\\n pass\\n elif path == '/artists':\\n pass\\n elif path == '/playlists':\\n pass\\n elif artist_dir_m:\\n pass\\n elif artist_album_dir_m:\\n parts = artist_album_dir_m.groupdict()\\n artist = self.library.artists_by_name[parts['artist']]\\n album = artist.albums[parts['album']]\\n #st['st_size'] = len(artist.albums)\\n \\n elif artist_album_track_m:\\n parts = artist_album_track_m.groupdict()\\n artist = self.library.artists_by_name[parts['artist']]\\n album = artist.albums[parts['album']]\\n track = album.tracks[parts['title']]\\n return track.get_attr()\\n \\n elif playlist_dir_m:\\n pass\\n \\n elif playlist_track_m:\\n parts = playlist_track_m.groupdict()\\n playlist = self.library.playlists[parts['playlist']]\\n track = playlist.tracks[parts['title']]\\n return track.get_attr()\\n else:\\n raise FuseOSError(ENOENT)\\n \\n return st\",\n \"def base_path(self):\\n return Path(self.path)\",\n \"def __init__ (self, relPath) :\\n self.m_path = \\\"\\\"\\n \\n dataPath = os.getenv(\\\"SIT_DATA\\\")\\n if not dataPath: return\\n \\n for dir in dataPath.split(':'):\\n path = os.path.join(dir, relPath)\\n if os.path.exists(path):\\n self.m_path = path\\n break\",\n \"def get_absolute_path(*args):\\n directory = os.path.dirname(os.path.abspath(__file__))\\n return os.path.join(directory, *args)\",\n \"def real_absolute_path(path):\\n return os.path.realpath(absolute_path(path))\",\n \"def force_absolute(base, path):\\n if os.path.abspath(path) and os.path.exists(path):\\n return path\\n else:\\n return path_format(base + path)\",\n \"def absolute(self, key):\\n path = self.relative(key)\\n if self.root:\\n path = os.path.join(self.root, path)\\n return absolute(path)\",\n \"def get_abs_path(path):\\r\\n abs_path = lib_path.abspath(path)\\r\\n return abs_path\",\n \"def set_basedir(self, host, path):\",\n \"def to_response_data(self) -> typing.Any:\\n return pathlib.Path(self.value[\\\"absolute\\\"]).expanduser().absolute().read_text()\",\n \"def realpath(self, path):\\n return os.path.realpath(path)\",\n \"def __init__(self): \\n self.path=os.getcwd()+'/'\",\n \"def getAbsolutePath(relPath):\\n currDir = os.path.dirname(__file__)\\n return os.path.join(currDir, relPath)\",\n \"def getattr(self, path, fh=None):\\n\\n if path != \\\"/\\\":\\n raise FuseOSError(ENOENT)\\n\\n attrs = super(IndexView, self).getattr(path, fh)\\n attrs.update({\\\"st_mode\\\": S_IFDIR | 0o555, \\\"st_nlink\\\": 2})\\n\\n return attrs\",\n \"def abspath(self, ref):\\n \\n directory, path = get_location(self.directory, ref.strip(),\\n current=dirname(self.relative))\\n path = join_fb_root(join(directory, path))\\n return path\",\n \"def __get__(self, obj, objtype):\\n raw_path = super(Path,self).__get__(obj,objtype)\\n return self._resolve(raw_path)\",\n \"def get_absolute_path(self):\\n if self.datafile and self.datafile.storage.exists(self.datafile.path):\\n return self.datafile.path\\n else:\\n return None\",\n \"def make_relative(self,basepath = None):\\n __make_relative__(run_object=self,basepath=basepath)\",\n \"def attribute(self, apath):\\n return data.Attribute(self, apath)\",\n \"def get_full_path(self):\\n try:\\n full_path = os.path.abspath(self.FILENAME)\\n return full_path\\n except Exception as e:\\n raise SystemExit(f\\\"Could not complete operation: {e}\\\")\",\n \"def full_path(self):\\n fullpath = os.path.join(self.path, self.name)\\n if self.path == \\\"\\\":\\n fullpath = self.name\\n return fullpath\",\n \"def _set_rel_paths(self):\\n if self.working_dir is not None:\\n self._rel_working_dir = os.path.relpath(self.working_dir)\\n if self.alignment is not None:\\n self._rel_alignment = os.path.relpath(self.alignment, \\n self.working_dir)\\n if self.out_file is not None:\\n self._rel_out_file = os.path.relpath(self.out_file, \\n self.working_dir)\",\n \"def resolved(rpath):\\r\\n return realpath(abspath(rpath))\",\n \"def get_absolute_pathname(self):\\n return os.path.join(settings.PRIVATE_STORAGE_ROOT, self.get_relative_pathname())\",\n \"def inputpathabs(self):\\n return os.path.abspath(self.inputpath)\",\n \"def get_path(self):\\n return self.path\",\n \"def __fspath__(self):\\n raise NotImplementedError\",\n \"def absolute_path(path):\\n return os.path.abspath(os.path.normpath(path))\",\n \"def path(self):\\r\\n raise NotImplementedError()\",\n \"def get_path(self):\\n try:\\n return self._file.path\\n except AttributeError:\\n return os.path.abspath(self._file.name)\",\n \"def test_set_path_1(self):\\n self.file.touch()\\n # Since using tempfile, there is an added quirk.\\n # the tempfile path may be a symlink, so passing it through set path\\n # will resolve the symlink, changing the path, and breaking the test.\\n self.file = self.file.resolve()\\n output = basic.set_path(self.file, kind=\\\"file\\\", expect=True)\\n with self.subTest():\\n self.assertIsInstance(output, Path)\\n with self.subTest():\\n self.assertEqual(str(self.file), str(output))\",\n \"def path(self):\\n return self.path\",\n \"def _set_path(self):\\n self.path = self._get_path()\\n self.depth = self.get_depth()\\n\\n self.save()\",\n \"def path(self):\\n return self.chroot\",\n \"def _get_origin_path(self):\\n return self.__origin_path\",\n \"def __init__(self, realpath, config):\\n self.__dict__['_config'] = config\\n self.__dict__['_realpath'] = realpath\",\n \"def path(self):\\n return self.file_path()\",\n \"def test_get_absolute_path():\\n eq_(get_absolute_path(\\\"http://foo.com/bar/baz\\\", \\\"../foo\\\"), \\\"/bar/foo\\\")\\n eq_(get_absolute_path(\\\"http://foo.com/bar/baz\\\", \\\"/foo\\\"), \\\"/foo\\\")\",\n \"def getPath(obj):\",\n \"def local_to_extern_path(self, path: PurePath) -> PurePath:\\n return self.path_extern_supervisor / path.relative_to(self.path_supervisor)\",\n \"def path(self):\\r\\n return self.chroot\",\n \"def path(self):\\r\\n return self.chroot\",\n \"def absolute_path(path):\\n return os.path.abspath(\\n os.path.join(\\n os.path.dirname(__file__),\\n \\\"..\\\",\\n path\\n )\\n )\",\n \"def path(self) :\\n return self.m_path\",\n \"def set_local_path(self):\\n return HERE\",\n \"def _get_attrib(self, attrib_path: str, binfo: dict) -> str:\\n apath = attrib_path.split('.')\\n return self._get_attrib_by_path(apath, binfo)\",\n \"def _get_url(self, absolute):\",\n \"def getpath(self, path):\\n return self._join(path)\",\n \"def abspath(path: str) -> str:\\n pass\",\n \"def get_path():\\n return path.abspath(path.dirname(path.dirname(__file__)))\",\n \"def realpath(path: str) -> str:\\n pass\",\n \"def _expand(self):\\n return Path(os.path.expandvars(self)).expanduser().resolve()\",\n \"def absPath(path):\\n return os.path.join(os.path.dirname(os.path.abspath(__file__)), path)\",\n \"def systemPathToFileUrl(self, path):\\n from unohelper import systemPathToFileUrl\\n return systemPathToFileUrl(path)\",\n \"def path( self ) :\\n\\n return( self.__path )\",\n \"def _get_path(): # THIS IS JUST FOR GETTING THE FILE\\n return os.path.dirname(os.path.abspath(__file__)) + '/'\",\n \"def get_resources_abs_path() -> pathlib.Path:\\n return PathManager._ROOT.joinpath(\\n PathManager._TILINGS_GUI, PathManager._RESOURCES\\n )\",\n \"def _setJob_getPath(job, shot=False):\\n\\tjobpath = j.getPath(job, translate=True)\\n\\n\\tif shot:\\n\\t\\tpath = os_wrapper.absolutePath(\\\"%s/$IC_SHOTSDIR/%s\\\" % (jobpath, shot))\\n\\telse:\\n\\t\\tpath = os_wrapper.absolutePath(\\\"%s/$IC_SHOTSDIR\\\" % jobpath)\\n\\n\\treturn path\",\n \"def _get_absolute_path(path):\\n abs_path = Path(os.path.abspath(path))\\n\\n if is_protected_path(abs_path):\\n raise errors.ParameterError(f\\\"Path '{path}' is protected.\\\")\\n\\n try:\\n abs_path.relative_to(project_context.path)\\n except ValueError:\\n raise errors.ParameterError(f\\\"Path '{path}' is outside the project.\\\")\\n\\n return abs_path\",\n \"def resourcePath(self,relative_path):\\n base_path = getattr(sys, '_MEIPASS', os.path.dirname(os.path.dirname(os.path.abspath(__file__))))\\n output = base_path + relative_path\\n return output\",\n \"def get_realpath(cls, path_str):\\n if path_str.startswith('/'):\\n return path_str\\n return os.path.abspath(os.path.join(cls.apollo_root, path_str))\",\n \"def getattr(self, path):\\n\\n\\t\\tprint '*** getattr', path\\n\\n\\t\\t#depth = getDepth(path) # depth of path, zero-based from root\\n\\t\\t#pathparts = getParts(path) # the actual parts of the path\\n\\n\\t\\t#return -errno.ENOSYS\\n\\t\\t\\n\\t\\tself.files = self.snake.apache_get_groups()\\n\\t\\tprint \\\"self.files:\\\", self.files\\n\\t\\t\\n\\t\\tst = ObjectStat()\\n\\t\\t\\n\\t\\tparts = path.split( '/' )\\n\\t\\tif len( parts ) > 1:\\n\\t\\t\\tfn = parts[ 1 ]\\n\\t\\telse:\\n\\t\\t\\tfn = ''\\n\\t\\t\\n\\t\\tif fn == '':\\n\\t\\t\\tprint \\\"returing stats\\\"\\n\\t\\t\\tst.st_nlink += len( self.files )\\n\\t\\t\\treturn st\\n\\t\\t\\n\\t\\telif fn not in self.files:\\n\\t\\t\\tprint \\\"No such file! (%s)\\\"%fn\\n\\t\\t\\treturn -errno.ENOENT\\n\\t\\telse:\\n\\t\\t\\tprint \\\"Returning stats..\\\"\\n\\t\\t\\tst.st_mode = stat.S_IFREG | 0755\\n\\t\\t\\t\\n\\t\\t\\tself.content[ fn ] = self.snake.apache_get_content( fn )\\n\\t\\t\\tst.st_size = len( self.content[ fn ] )\\n\\t\\t\\t\\n\\t\\t\\treturn st\",\n \"def to_absolute_path(path):\\n if not os.path.isabs(path):\\n return os.path.join(os.getcwd(), path)\\n else:\\n return path\",\n \"def path(self):\\n return self._path\",\n \"def path(self):\\n return self._path\",\n \"def path(self):\\n return self._path\",\n \"def path(self):\\n return self._path\",\n \"def path(self):\\n return self._path\",\n \"def path(self):\\n return self._path\",\n \"def path(self):\\n return self._path\",\n \"def path(self):\\n return self._path\"\n]","isConversational":false},"negative_scores":{"kind":"list like","value":["0.68317145","0.66353893","0.6506451","0.6336473","0.62895834","0.6220344","0.6210958","0.6178998","0.61444104","0.6140882","0.61189973","0.60873955","0.604279","0.60294616","0.60294616","0.60211664","0.6019854","0.60156524","0.5973667","0.59265673","0.5923986","0.5910918","0.5886842","0.58456945","0.58451223","0.58437806","0.58390504","0.58242995","0.5810505","0.580805","0.5800775","0.57978797","0.5789685","0.57758474","0.57634306","0.5763101","0.5747373","0.57445467","0.5741118","0.5737641","0.5730495","0.5725523","0.57236594","0.57062757","0.56873786","0.56809586","0.56660146","0.5659729","0.56539226","0.5646459","0.56385","0.5621547","0.56167924","0.5613712","0.5612957","0.5603978","0.5599976","0.55995274","0.55931795","0.5577428","0.5567966","0.55653834","0.5556778","0.55509937","0.5517935","0.5509745","0.5498254","0.5496799","0.5494764","0.5489131","0.5488587","0.5488587","0.54852265","0.54808587","0.5479274","0.54664135","0.54555124","0.5447228","0.54462606","0.54376364","0.5430468","0.54244936","0.5410986","0.5410379","0.54088396","0.5408483","0.54060143","0.5402996","0.5400164","0.5390933","0.5386481","0.5379938","0.53778034","0.5374226","0.5374226","0.5374226","0.5374226","0.5374226","0.5374226","0.5374226","0.5374226"],"string":"[\n \"0.68317145\",\n \"0.66353893\",\n \"0.6506451\",\n \"0.6336473\",\n \"0.62895834\",\n \"0.6220344\",\n \"0.6210958\",\n \"0.6178998\",\n \"0.61444104\",\n \"0.6140882\",\n \"0.61189973\",\n \"0.60873955\",\n \"0.604279\",\n \"0.60294616\",\n \"0.60294616\",\n \"0.60211664\",\n \"0.6019854\",\n \"0.60156524\",\n \"0.5973667\",\n \"0.59265673\",\n \"0.5923986\",\n \"0.5910918\",\n \"0.5886842\",\n \"0.58456945\",\n \"0.58451223\",\n \"0.58437806\",\n \"0.58390504\",\n \"0.58242995\",\n \"0.5810505\",\n \"0.580805\",\n \"0.5800775\",\n \"0.57978797\",\n \"0.5789685\",\n \"0.57758474\",\n \"0.57634306\",\n \"0.5763101\",\n \"0.5747373\",\n \"0.57445467\",\n \"0.5741118\",\n \"0.5737641\",\n \"0.5730495\",\n \"0.5725523\",\n \"0.57236594\",\n \"0.57062757\",\n \"0.56873786\",\n \"0.56809586\",\n \"0.56660146\",\n \"0.5659729\",\n \"0.56539226\",\n \"0.5646459\",\n \"0.56385\",\n \"0.5621547\",\n \"0.56167924\",\n \"0.5613712\",\n \"0.5612957\",\n \"0.5603978\",\n \"0.5599976\",\n \"0.55995274\",\n \"0.55931795\",\n \"0.5577428\",\n \"0.5567966\",\n \"0.55653834\",\n \"0.5556778\",\n \"0.55509937\",\n \"0.5517935\",\n \"0.5509745\",\n \"0.5498254\",\n \"0.5496799\",\n \"0.5494764\",\n \"0.5489131\",\n \"0.5488587\",\n \"0.5488587\",\n \"0.54852265\",\n \"0.54808587\",\n \"0.5479274\",\n \"0.54664135\",\n \"0.54555124\",\n \"0.5447228\",\n \"0.54462606\",\n \"0.54376364\",\n \"0.5430468\",\n \"0.54244936\",\n \"0.5410986\",\n \"0.5410379\",\n \"0.54088396\",\n \"0.5408483\",\n \"0.54060143\",\n \"0.5402996\",\n \"0.5400164\",\n \"0.5390933\",\n \"0.5386481\",\n \"0.5379938\",\n \"0.53778034\",\n \"0.5374226\",\n \"0.5374226\",\n \"0.5374226\",\n \"0.5374226\",\n \"0.5374226\",\n \"0.5374226\",\n \"0.5374226\",\n \"0.5374226\"\n]","isConversational":false},"document_score":{"kind":"string","value":"0.0"},"document_rank":{"kind":"string","value":"-1"}}},{"rowIdx":22694,"cells":{"query":{"kind":"string","value":"r\"\"\"Method to return a dictionary of testing options for this class."},"document":{"kind":"string","value":"def get_testing_options(cls, read_meth='read', open_as_binary=True, **kwargs):\n out = super(FileComm, cls).get_testing_options(**kwargs)\n out['kwargs']['read_meth'] = read_meth\n out['kwargs']['open_as_binary'] = open_as_binary\n if (read_meth == 'read') and isinstance(out['recv'][0], backwards.bytes_type):\n out['recv'] = [b''.join(out['recv'])]\n if not open_as_binary:\n out['contents'] = out['contents'].replace(\n backwards.match_stype(out['contents'], '\\n'),\n backwards.match_stype(out['contents'], platform._newline))\n return out"},"metadata":{"kind":"string","value":"{\n \"objective\": {\n \"self\": [],\n \"paired\": [],\n \"triplet\": [\n [\n \"query\",\n \"document\",\n \"negatives\"\n ]\n ]\n }\n}"},"negatives":{"kind":"list like","value":["def get_testing_options(cls):\n return [{'error': [(1, NotImplementedError)],\n 'kwargs': {'initial_state': {'a': int(1)}}}]","def get_testing_options(cls, **kwargs):\n msg = ['test', 1.0]\n count = 3\n objects = [msg for _ in range(count)]\n out = {'kwargs': {'address': msg, 'count': count},\n 'send': objects,\n 'msg': msg,\n 'objects': objects,\n 'recv': objects}\n return out","def get_options(cls):\n return {\n \"name\": str,\n ConfigOption(\"install_files\", default=None): Or(None, list),\n ConfigOption(\"timeout\", default=300): int,\n ConfigOption(\"log_regexps\", default=None): Or(None, list),\n ConfigOption(\"stdout_regexps\", default=None): Or(None, list),\n ConfigOption(\"stderr_regexps\", default=None): Or(None, list),\n ConfigOption(\"file_logger\", default=None): Or(None, str),\n ConfigOption(\"async_start\", default=False): bool,\n ConfigOption(\"report_errors_from_logs\", default=False): bool,\n ConfigOption(\"error_logs_max_lines\", default=10): int,\n ConfigOption(\"path_cleanup\", default=True): bool,\n ConfigOption(\"pre_start\", default=None): validate_func(\"driver\"),\n ConfigOption(\"post_start\", default=None): validate_func(\"driver\"),\n ConfigOption(\"pre_stop\", default=None): validate_func(\"driver\"),\n ConfigOption(\"post_stop\", default=None): validate_func(\"driver\"),\n }","def options(self):\n options_to_report = dict()\n for cls in inspect.getmro(type(self)):\n parameter_names, _, _, defaults, _, _, _ = inspect.getfullargspec(cls.__init__)\n if defaults:\n class_options = {parameter_name: getattr(self, '_' + parameter_name) for\n parameter_name in parameter_names[-len(defaults):]}\n options_to_report.update(class_options)\n options_to_report.pop('mcmc_moves')\n return options_to_report","def options(self):\n return self.__options","def get_options(self):\n\t\treturn self.options","def options(self):\r\n return self._options","def _get_options(self):\n return self.options","def _options(self):\n return","def options(self):\n options = {\n o.name: getattr(self, o.name)\n for o in _OPTIONS\n }\n return options","def get_options(self):\n return []","def get_simulation_options(self):\n return self.opts","def default_options(cls) -> Dict:\n return {}","def tests(self):\n\n return {\n 'variable_boolean': self.variable_boolean\n }","def _get_options(self) -> Dict[str, Any]:\n # TODO: handle holidays as well\n return {\n \"growth\": self.growth,\n \"changepoints\": self.changepoints and list(self.changepoints.astype('str')),\n \"n_changepoints\": self.n_changepoints,\n \"changepoint_range\": self.changepoint_range,\n \"changepoint_prior_scale\": self.changepoint_prior_scale,\n \"mcmc_samples\": self.mcmc_samples,\n \"interval_width\": self.interval_width,\n \"uncertainty_samples\": self.uncertainty_samples,\n \"yearly_seasonality\": self.yearly_seasonality,\n \"weekly_seasonality\": self.weekly_seasonality,\n \"daily_seasonality\": self.daily_seasonality,\n \"seasonality_mode\": self.seasonality_mode,\n \"seasonality_prior_scale\": self.seasonality_prior_scale,\n\n \"seasonalities\": self.seasonalities,\n \"extra_regressors\": self.extra_regressors\n }","def options(self) -> Mapping[str, str]:\n return pulumi.get(self, \"options\")","def options(self):\n return self._options","def options(self):\n return self._options","def options(self):\n return self._options","def options(self):\n return self._options","def options(self):\n return self._options","def test_options_flags(self):\n opts = []\n\n # Handle \"--failed\" as a special case: we want to re-run only\n # the tests that failed within our Django apps\n # This sets the --last-failed flag for the pytest command, so this\n # functionality is the same as described in the pytest documentation\n if self.failed_only:\n opts.append(\"--last-failed\")\n\n # This makes it so we use pytest's fail-fast feature in two cases.\n # Case 1: --fail-fast is passed as an arg in the paver command\n # Case 2: The environment variable TESTS_FAIL_FAST is set as True\n env_fail_fast_set = (\n 'TESTS_FAIL_FAST' in os.environ and os.environ['TEST_FAIL_FAST']\n )\n\n if self.fail_fast or env_fail_fast_set:\n opts.append(\"--exitfirst\")\n\n if self.with_wtw:\n opts.extend([\n '--wtw',\n f'{COVERAGE_CACHE_BASEPATH}/{WHO_TESTS_WHAT_DIFF}',\n '--wtwdb',\n f'{COVERAGE_CACHE_BASEPATH}/{COVERAGE_CACHE_BASELINE}'\n ])\n\n return opts","def get_options():\n user_options = {}\n user_options['surface'] = {'label': 'Surface',\n 'type': 'stringList',\n 'default': 'bcc100',\n 'values': surface_selections}\n\n user_options['metal'] = {'label': 'Metal',\n 'type': 'string',\n 'default': 'Au'}\n\n user_options['a'] = {'label': 'Lattice Constant',\n 'type': 'float',\n 'precision': 3,\n 'suffix': 'Å'}\n\n user_options['size-x'] = {'label': 'Size X',\n 'type': 'integer',\n 'default': 5}\n\n user_options['size-y'] = {'label': 'Size Y',\n 'type': 'integer',\n 'default': 5}\n\n user_options['size-z'] = {'label': 'Size Z',\n 'type': 'integer',\n 'default': 3}\n\n user_options['vacuum'] = {'label': 'Vacuum distance',\n 'type': 'float',\n 'precision': 1,\n 'suffix': 'Å'}\n\n user_options['orthogonal'] = {'label': 'Orthogonal',\n 'type': 'stringList',\n 'default': 'True',\n 'values': ['True', 'False']}\n\n return {'userOptions': user_options }","def default_options(cls):\n options_to_report = dict()\n for c in inspect.getmro(cls):\n parameter_names, _, _, defaults, _, _, _ = inspect.getfullargspec(c.__init__)\n if defaults:\n class_options = {parameter_name: defaults[index] for (index, parameter_name) in\n enumerate(parameter_names[-len(defaults):])}\n options_to_report.update(class_options)\n options_to_report.pop('mcmc_moves')\n return options_to_report","def get_options(cls, mode):\n return dict(\n (key, properties[mode])\n for key, properties in cls.__register.items()\n if mode in properties\n )","def get_options(self):\r\n return self._option_values","def options(self):\n result = []\n for typ in type(self).mro():\n result.extend(k for k, v in typ.__dict__.items()\n if isinstance(v, Option))\n return dict((o, getattr(self, o)) for o in result)","def to_dict(self):\n import tc\n opts_dict = {}\n for k, v in self.__class__.__dict__.iteritems():\n if isinstance(v, tc.TC):\n opts_dict.update({k: self.__getattribute__(k)})\n return opts_dict","def get_options(self):\n return self._scoped_options","def options(self):\n return list(self._moptions.keys())","def create_options(self):\n return []","def getTestingOptionsFromIni(cfg=DEFAULT_CONFIG_FILE):\n Config = ConfigParser.ConfigParser()\n Config.read(cfg)\n section = \"testing-options\"\n FAILS_ON_UNTESTED_ELEMENT = Config.getboolean(section, \"FAILS_ON_UNTESTED_ELEMENT\")\n TEST_VERBOSE_MODE = Config.getboolean(section, \"TEST_VERBOSE_MODE\")\n return {\n 'FAILS_ON_UNTESTED_ELEMENT': FAILS_ON_UNTESTED_ELEMENT,\n 'TEST_VERBOSE_MODE': TEST_VERBOSE_MODE,\n }","def getTestingOptionsFromIni(cfg=DEFAULT_CONFIG_FILE):\n Config = ConfigParser.ConfigParser()\n Config.read(cfg)\n section = \"testing-options\"\n FAILS_ON_UNTESTED_ELEMENT = Config.getboolean(section, \"FAILS_ON_UNTESTED_ELEMENT\")\n TEST_VERBOSE_MODE = Config.getboolean(section, \"TEST_VERBOSE_MODE\")\n return {\n 'FAILS_ON_UNTESTED_ELEMENT': FAILS_ON_UNTESTED_ELEMENT,\n 'TEST_VERBOSE_MODE': TEST_VERBOSE_MODE,\n }","def get_options(self, panel=\"\"):\n return dict()","async def get_options(self):","def get_options(self) -> dict:\n assert self.task\n task_options = {\n **self.task.get_task_options(),\n **self.expr.task_expr_options,\n **self.task_options,\n }\n return task_options","def define_options(self) -> Optional[Any]:\n return {\n 'basename': OptionDef(required=True, default_value='promtail', allowed_types=[str]),\n 'namespace': OptionDef(required=True, default_value='monitoring', allowed_types=[str]),\n 'config': {\n 'prometheus_annotation': OptionDef(required=True, default_value=False, allowed_types=[bool]),\n 'promtail_config': OptionDef(allowed_types=[str, ConfigFile]),\n 'loki_url': OptionDef(allowed_types=[str]),\n 'authorization': {\n 'serviceaccount_create': OptionDef(required=True, default_value=True, allowed_types=[bool]),\n 'serviceaccount_use': OptionDef(allowed_types=[str]),\n 'roles_create': OptionDef(required=True, default_value=True, allowed_types=[bool]),\n 'roles_bind': OptionDef(required=True, default_value=True, allowed_types=[bool]),\n },\n },\n 'container': {\n 'promtail': OptionDef(required=True, default_value='grafana/promtail:2.0.0', allowed_types=[str]),\n },\n 'kubernetes': {\n 'resources': {\n 'daemonset': OptionDef(allowed_types=[Mapping]),\n }\n },\n }","def get_options(cls):\n for option in cls._general_options.items():\n yield option\n for option in cls._specific_options.items():\n yield option","def common_options(self):\n return self._common_options","def get_testing_options(cls):\n out = super(ObjSerialize, cls).get_testing_options()\n obj = ObjDict({'vertices': [{'x': float(0), 'y': float(0), 'z': float(0)},\n {'x': float(0), 'y': float(0), 'z': float(1)},\n {'x': float(0), 'y': float(1), 'z': float(1)}],\n 'faces': [[{'vertex_index': int(0)},\n {'vertex_index': int(1)},\n {'vertex_index': int(2)}]]})\n out['objects'] = [obj, obj]\n out['contents'] = (b'# Author ygg_auto\\n'\n + b'# Generated by yggdrasil\\n\\n'\n + b'v 0.0000 0.0000 0.0000\\n'\n + b'v 0.0000 0.0000 1.0000\\n'\n + b'v 0.0000 1.0000 1.0000\\n'\n + b'v 0.0000 0.0000 0.0000\\n'\n + b'v 0.0000 0.0000 1.0000\\n'\n + b'v 0.0000 1.0000 1.0000\\n'\n + b'f 1// 2// 3//\\n'\n + b'f 4// 5// 6//\\n')\n return out","def test_options_structure(self):\r\n deploy = self.wsgiDeploy()\r\n expected_keys = self.DEFAULTS.keys()\r\n actual_keys = deploy.options.keys()\r\n self.assertListEqual(expected_keys, actual_keys)","def _generate_options(self, **kwargs: Any) -> dict:\n raise NotImplementedError","def options(self):\n\t\treturn self.config_parser.options(self.section_name)","def get_all_options(self): \n return self._options.items()","def default_options(cls) -> Dict:\n options = super().default_options()\n # scaling factor for temperature adaptation\n options['eta'] = 100\n # controls the adaptation degeneration velocity of the temperature\n # adaption.\n options['nu'] = 1e3\n\n return options","def get_settings(self):\n return [('test_environment', self.test_environment),\n ('base_data_dir', self.base_data_dir),\n ('locale', self.locale)]","def get_settings(self):\n return {\n \"game_name\": self.game_name,\n \"n_epochs\": self.n_epochs,\n \"n_episodes\": self.n_episodes,\n \"n_frames\": self.n_frames,\n \"agent\": self.agent.get_settings(),\n \"results_dir\": self.results_dir,\n \"use_minimal_action_set\": self.use_minimal_action_set,\n }","def get_options(cls, player, context={}):\n\t\traise NotImplementedError()","def initialize_options(self):\n self.all = False\n self.coverage = False\n super(test, self).initialize_options()","def _get_options(ret):\n attrs = {\"host\": \"host\", \"port\": \"port\", \"skip\": \"skip_on_error\", \"mode\": \"mode\"}\n\n _options = salt.returners.get_returner_options(\n __virtualname__, ret, attrs, __salt__=__salt__, __opts__=__opts__\n )\n return _options","def _options(self):\r\n xmi_file = self.tb_xmi_file_name.GetValue()\r\n topic = self.tb_pragma.GetValue()\r\n package = self.tb_package.GetValue()\r\n header = self.tb_file_header.GetValue()\r\n target_folder = self.tb_target_folder.GetValue()\r\n encoding = self.tb_encoding.GetValue()\r\n \r\n return {\"topic\" : topic, \r\n \"package\" : package, \r\n \"header\" : header, \r\n \"target_folder\" : target_folder,\r\n \"encoding\" : encoding,\r\n \"xmi_file\" : xmi_file}","def create_options_for_test_runner(self):\n self.options = turtle.Turtle(\n verbosity=VERBOSITY_NORMAL,\n summary_mode=False,\n )","def get_test_preferences(self, name: str) -> Dict[str, Any]:\n return self.raw_config.get(name, {})","def metric_options(self):\n return Optimizer.list_method_options(self.metric_creator.method_dict)","def create_options():\n optparser = optparse.OptionParser()\n optparser.add_option(\"-f\", \"--filename\", type=\"string\",\n help=\"execute a single unit test file\")\n optparser.add_option(\"-s\", \"--subprocess\", action=\"store_true\",\n default=False,\n help=\"run everything in an own subprocess \"\n \"(default: use a single process)\")\n optparser.add_option(\"-t\", \"--timeout\", type=\"int\", default=70,\n help=\"Timout for subprocesses before being killed \"\n \"(default: 70s per file)\")\n optparser.add_option(\"-v\", \"--verbose\", action=\"store_true\", default=False,\n help=\"be verbose and print anything instantly\")\n optparser.add_option(\"-r\", \"--random\", action=\"store_true\", default=False,\n help=\"randomize the order of tests\")\n optparser.add_option(\"-S\", \"--seed\", type=\"int\",\n help=\"seed the randomizer(useful to \"\n \"recreate earlier randomized test cases)\")\n optparser.add_option(\"-i\", \"--interactive\", action=\"callback\",\n callback=include_tag,\n callback_args=(\"interactive\",),\n help=\"also execute interactive tests\")\n optparser.add_option(\"-e\", \"--exclude\", action=\"callback\",\n callback=exclude_tag, type=\"string\",\n help=\"exclude test containing the tag\")\n optparser.add_option(\"-l\", \"--listtags\", action=\"callback\",\n callback=list_tags,\n help=\"lists all available tags and exits\")\n optparser.add_option(\"--logfile\", type=\"string\",\n help=\"save output to log file\")\n optkeys = [\"filename\",\n \"subprocess\",\n \"timeout\",\n \"random\",\n \"seed\",\n \"verbose\"\n ]\n return optparser, optkeys","def _get_default_options():\n return {\n \"library_folders\": [],\n \"verbose\": False,\n \"check_balanced\": True,\n \"mtime_check\": True,\n \"cache\": False,\n \"codegen\": False,\n \"expand_mx\": False,\n \"unroll_loops\": True,\n \"inline_functions\": True,\n \"expand_vectors\": False,\n \"resolve_parameter_values\": False,\n \"replace_parameter_expressions\": False,\n \"replace_constant_expressions\": False,\n \"eliminate_constant_assignments\": False,\n \"replace_parameter_values\": False,\n \"replace_constant_values\": False,\n \"eliminable_variable_expression\": None,\n \"factor_and_simplify_equations\": False,\n \"detect_aliases\": False,\n \"allow_derivative_aliases\": True,\n \"reduce_affine_expression\": False,\n }","def optionHelp(self):\n return {}","def get_options(self):\n additional_data = self.get_additional_data()\n options_out = []\n for key, value in additional_data['DIMENSION_VALUES'].items():\n key_label = ' '.join(key.split('_')).strip().title()\n data = {'specification': key_label, 'specification_key': key, 'choices': value}\n options_out.append(data)\n return options_out","def read_all_options(self, test_case=None):\n args = self.get_parsed_cmd_args(test_case)\n\n Options.validate_methods(args[\"methods\"])\n\n self.read_config_file(args[\"config_file\"])\n\n for option in self.options:\n if args[option] not in [None, []]:\n self.options[option] = args[option]\n\n if option in self.method_options:\n method, method_option = self.method_options[option]\n Options.available_methods()[method].options[method_option] = args[option]\n\n #remove duplicate\n for option in [\"methods\", \"packages\"]:\n self.options[option] = list(set(self.options[option]))\n\n return self.options","def runoptions(self):\n # outstanding = self.missing_required()\n # if outstanding:\n # raise TypeError('Module missing required parameter: %s' % ', '.join(outstanding))\n return self._runopts","def settings(self) -> Dict[str, Any]:\n return {}","def options(self):\n pclass_options = self.get_product_class().options.all()\n return pclass_options | self.product_options.all()","def options(self) -> Union[object, Sequence]:\n return self._options","def _getOptions(self):\n args = []\n for iname, value in self.options:\n args.append('-' + iname)\n if value != 'true':\n args.append(value)\n return args","def as_dict(self) -> Dict[str, Any]:\n return {\n 'enabled': self._enabled,\n 'maxStackDepth': self._maxStackDepth,\n }","def _get_test(self, config):\n expected_options = {'goodpassword', 'badpassword'}\n _warn_on_extra(set(config.options('test')) - expected_options -\n self.defaults, 'test section option(s)')\n\n get = partial(config.get, 'test')\n\n self.goodpassword = get('GOODPASSWORD')\n self.badpassword = get('BADPASSWORD')","def test_dict(self):\n if getattr(self, \"_test_dict\", None) is None:\n tests = dict()\n\n # build test for each notebook\n for notebook, nbpath in zip(self._nbnames, self._nbpaths):\n tests[\"test_\" + notebook] = get_test(\n notebook, nbpath, timeout=self.timeout\n )\n self._test_dict = tests\n return self._test_dict","def get_options(self):\n option_list = []\n if self.can_analyze():\n option_list.append((EpOp.TASK_ANALYZE, None))\n\n option_tup = self.predict_option()\n if option_tup:\n option_list.append(option_tup)\n\n option_tup = self.check_option()\n if option_tup:\n option_list.append(option_tup)\n\n return option_list","def options(self) -> tuple: # UserOptions\n return self._options","def get_general_options(cls):\n for option in cls._general_options.items():\n yield option","def get_config(self):\n return {\"name\": self.name, \"tunable\": self.tunable}","def get_options(self):\n return (\n Option('-H', '--host',\n dest='host',\n default=self.host,\n help='IP address or hostname of the Glancesync server.'),\n\n Option('-p', '--port',\n dest='port',\n type=int,\n default=self.port,\n help='Port in which the GlanceSync server is running'),\n\n Option('-w', '--workers',\n dest='workers',\n type=int,\n default=self.workers,\n help='Number of concurrent workers to be launched, usually 2*core numbers+1.'),\n )","def initialize_options(self):","def static_opts(ftype, **kwargs):\n\n opts = dict()\n\n if ftype == 'sequential_feature_selector':\n # check if we got the features\n features = kwargs.pop('features', None)\n if features is not None:\n opts['hidden_layer_sizes'] = (features.shape[0], features.shape[1])\n\n if ftype == 'vote':\n # check if we got the training data\n X_train = kwargs.pop('X_train', None)\n if X_train is not None:\n # TODO: check dimensions!\n opts['hidden_layer_sizes'] = (X_train.shape[1], X_train.shape[1])\n\n return opts","def _default_options(cls):\n default_options = super()._default_options()\n default_options.data_processor = dp.DataProcessor(\n input_key=\"counts\",\n data_actions=[dp.Probability(\"1\"), dp.BasisExpectationValue()],\n )\n default_options.plotter.set_figure_options(\n xlabel=\"Flat top width\",\n ylabel=\"Pauli expectation values\",\n xval_unit=\"s\",\n ylim=(-1, 1),\n )\n default_options.data_subfit_map = {\n \"x\": {\"meas_basis\": \"x\"},\n \"y\": {\"meas_basis\": \"y\"},\n \"z\": {\"meas_basis\": \"z\"},\n }\n\n return default_options","def _opt_config(self):\n return self._opt_method.config","def get_specific_options(cls):\n for option in cls._specific_options.items():\n yield option","def _parse_options(options):\n opts = dict()\n for attr in dir(options):\n if attr.startswith(\"__\"):\n continue\n opts[attr] = getattr(options, attr)\n return opts","def support(self):\n return {}","def settings(self):\n return {}","def test_new_options(self):\n obj = Game.new(self._creator, self._ds, {\n 'sixes': True,\n 'spec_allow_chat': False,\n 'win_amount': 400\n })\n self.assertTrue(obj.options.sixes,\n \"game.options.sixes didn't set.\")\n self.assertFalse(obj.options.spec_allow_chat,\n \"game.options.spec_allow_chat didn't set.\")\n self.assertEqual(obj.options.win_amount, 400,\n \"game.options.win_amount didn't set.\")","def get_options_and_reporter(method, init_points, init_vals):\n reporter = get_reporter('default')\n if method in ['GA', 'randGA']:\n options = load_options(ga_optimiser.ga_opt_args, reporter=reporter)\n else:\n raise ValueError('Unknown method %s.'%(method))\n options.pre_eval_points = init_points\n options.pre_eval_vals = init_vals\n options.pre_eval_true_vals = init_vals\n return options, reporter","def initialize_options(self):\n pass","def options(self): # pragma: no cover\r\n return ''","def get_test_specific_options(test, BLIND=False, ignore_others=True, ignore_labels=None):\n # Need to change when the total test options changed\n # Color key Experiment 1: (Groups 5-8 are unusually flexible; group 1,6-8 are different strains than 2-5)\n exp1 = { #\"0.0\": r'Controls',\n \"1.0\": r\"Controls WT/SAL male P60-90 Bl/6J/CR\",\n \"2.0\": r\"FI male P60 Taconic\",\n \"3.0\": r\"FR male P60 Taconic\",\n \"4.0\": r\"ALG male P60 Taconic\",\n \"5.0\": r\"ALS male P60 Taconic\",\n \"6.0\": r\"5d COC test at P90 Bl/6CR\",\n \"7.0\": r\"BDNF met/met Ron tested at P60\",\n \"8.0\": r\"P26 males WT Bl/6CR\"}\n # Color Key Experiment 2 data (focusing on angel's mice and bdnf/trkb manipulations) P40-60 ages\n exp2 = {\"1.0\": r\"Controls VEH/SAL/WT\",\n \"2.0\": r\"acute NMPP1pump\",\n \"3.0\": r\"chronic NMPP1pump\",\n \"4.0\": r\"BDNF Val/Val Ron\",\n \"5.0\": r\"P1-23 NMPP1H20\",\n \"6.0\": r\"P1-40 NMPP1H20\",\n \"7.0\": r\"BDNF Met/Met Ron\"}\n if not ignore_others:\n exp1[\"-1.0\"] = r\"OTHERS\"\n exp2[\"-1.0\"] = r\"OTHERS\"\n\n exp1_params = {'UMAP': {'n_neighbors': 10,\n 'min_dist': 0.8,\n 'n_components': 3,\n 'metric': 'euclidean'}}\n exp2_params = {}\n\n TEST_LABEL_ALIAS = {\n 'exp1_label_FI_AL_M': None if BLIND else exp1,\n 'exp2_Angel': None if BLIND else exp2,\n 'age': None,\n 'RL_age': None,\n 'RL_treat_sex': None,\n 'RL_treat': None,\n 'RL_sex': None\n }\n\n IGNORE_LABELS = {\n 'exp1_label_FI_AL_M': ['-1.0', '0.0', '1.0', '3.0', '4.0', '6.0', '7.0'],\n 'exp2_Angel': ['-1.0', '1.0'],\n 'age': None,\n 'RL_age': None,\n 'RL_treat_sex': ['ALS_F', 'FI_F', 'FR_F'],\n 'RL_treat': None,\n 'RL_sex': None\n }\n\n DIM_PARAMS = {\n 'exp1_label_FI_AL_M': exp1_params,\n 'exp2_Angel': exp2_params,\n 'age': {},\n 'RL_age': {},\n 'RL_treat_sex': {},\n 'RL_treat': {},\n 'RL_sex': {}\n }\n\n return TEST_LABEL_ALIAS[test], IGNORE_LABELS[test] if ignore_labels is None else ignore_labels,\\\n DIM_PARAMS[test]","def data_dict(self) -> dict:\n return self.design.renderers.gds.options","def test_report_options_dict(self, api_instance: Reports):\n report_type = \"_GET_MERCHANT_LISTINGS_ALL_DATA_\"\n report_options = {\"custom\": True, \"somethingelse\": \"abc\"}\n params = api_instance.request_report(\n report_type=report_type,\n report_options=report_options,\n )\n self.assert_common_params(params, action=\"RequestReport\")\n assert params[\"ReportType\"] == \"_GET_MERCHANT_LISTINGS_ALL_DATA_\"\n # Cannot assume the order of the options dict passed on older versions\n # of Python, so two possible outputs are used:\n # Further, the final result should be encoded once before being sent,\n # resulting in the following URL-encoded strings.\n options_possible = (\n \"custom=true;somethingelse=abc\",\n \"somethingelse=abc;custom=true\",\n )\n assert params[\"ReportOptions\"] in options_possible","def get_config(self):\n return {}","def get_args_dict(class_, options: Options) -> dict:\n\n argspec = getfullargspec(class_.__init__)\n init_args = argspec.args\n init_args.pop(0) # self\n result = {k: v for k, v in options.items() if k in init_args}\n\n positional_args = init_args[:-len(argspec.defaults)]\n\n missing_args = [a for a in positional_args if a not in options]\n if missing_args:\n raise BadConfigError(\n f'Some required parameters are missing in \"{options[\"name\"]}\" config: ' +\n ', '.join(missing_args)\n )\n return result","def define_options(self):\n return {\n 'basename': OptionDef(required=True, default_value='keycloak', allowed_types=[str]),\n 'namespace': OptionDef(required=True, default_value='default', allowed_types=[str]),\n 'config': {\n 'service_port': OptionDef(required=True, default_value=8080, allowed_types=[int]),\n 'realm_import': OptionDef(format=OptionDefFormat.KDATA_VOLUME, allowed_types=[str, bytes, KData_Secret]),\n 'proxy_address_forwarding': OptionDef(format=OptionDefFormat.KDATA_ENV,\n allowed_types=[bool, *KDataHelper_Env.allowed_kdata()]),\n 'frontend_url': OptionDef(allowed_types=[str]),\n 'admin': {\n 'user': OptionDef(format=OptionDefFormat.KDATA_ENV, allowed_types=[str, *KDataHelper_Env.allowed_kdata()]),\n 'password': OptionDef(format=OptionDefFormat.KDATA_ENV, allowed_types=[str, KData_Secret]),\n },\n 'db': {\n 'vendor': OptionDef(format=OptionDefFormat.KDATA_ENV, allowed_types=[str, *KDataHelper_Env.allowed_kdata()]),\n 'addr': OptionDef(format=OptionDefFormat.KDATA_ENV, allowed_types=[str, *KDataHelper_Env.allowed_kdata()]),\n 'port': OptionDef(format=OptionDefFormat.KDATA_ENV, allowed_types=[int, *KDataHelper_Env.allowed_kdata()]),\n 'database': OptionDef(format=OptionDefFormat.KDATA_ENV, allowed_types=[str, *KDataHelper_Env.allowed_kdata()]),\n 'schema': OptionDef(format=OptionDefFormat.KDATA_ENV, allowed_types=[str, *KDataHelper_Env.allowed_kdata()]),\n 'user': OptionDef(format=OptionDefFormat.KDATA_ENV, allowed_types=[str, *KDataHelper_Env.allowed_kdata()]),\n 'password': OptionDef(format=OptionDefFormat.KDATA_ENV, allowed_types=[str, KData_Secret]),\n },\n },\n 'container': {\n 'keycloak': OptionDef(required=True, default_value='quay.io/keycloak/keycloak:11.0.2', allowed_types=[str]),\n },\n 'kubernetes': {\n 'resources': {\n 'deployment': OptionDef(allowed_types=[Mapping]),\n }\n },\n }","def get_config(self) -> Dict[str, Any]:\n return {\n 'num_classes': self.num_classes,\n 'name': self.name,\n 'dtype': self.dtype,\n 'sparse_y_true': self.sparse_y_true,\n 'sparse_y_pred': self.sparse_y_pred,\n 'axis': self.axis,\n }","def get_switching_options(self):\n\n return self._switch_opt_infos.iterkeys()","def training_opts(self):\n return self._training_opts","def requested_config_vals():\n return {} # no extra values needed","def _create_options(self):\n self._OPTIONS = {}","def get_test_params(cls, parameter_set=\"default\"):\n params1 = {}\n params2 = {\"p_threshold\": 0.1, \"regression\": \"ctt\", \"nlags\": 5}\n\n return [params1, params2]","def get_method_options(prob, capital_type):\n methods = prob.methods\n all_method_options = {}\n for meth in methods:\n if meth == 'rand':\n curr_options = load_options(cp_random_optimiser_args)\n elif meth == 'ga':\n curr_options = load_options(cpga_opt_args)\n elif meth.startswith('dragonfly'):\n if meth.startswith('dragonfly-mf'):\n curr_options = load_options(get_all_mf_cp_gp_bandit_args())\n else:\n curr_options = load_options(get_all_cp_gp_bandit_args())\n meth_parts = meth.split('_')\n if len(meth_parts) == 2:\n curr_options.acq_opt_method = meth_parts[-1]\n elif meth in ['hyperopt', 'gpyopt', 'smac', 'spearmint']:\n curr_options = load_options(blackbox_opt_args)\n curr_options = Namespace(redo_evals_for_true_val=True)\n else:\n raise ValueError('Unknown method %s.'%(meth))\n # Some additional data we will need for Spearmint\n if meth == 'spearmint':\n # Swap in the following after you download and install Spearmint\n curr_options.exp_dir = '/home/karun/boss/e3_cp/Spearmint/' + \\\n prob.study_name.split('-')[0]\n curr_options.pkg_dir = '/home/karun/Spearmint/spearmint' \n curr_options.capital_type = capital_type\n all_method_options[meth] = curr_options\n return all_method_options","def test_list_options(self):\n pass","def initialize_options(self):\n pass","def initialize_options(self):\n pass","def make_options(self):\n if self.pdf_generator == 'pisa':\n return {}\n\n toc_msg = _(u'label_toc',\n default=u'Table of content')\n\n options = {'book': self.getUse_book_style(),\n 'margin-top': self.getMargin_top(),\n 'margin-right': self.getMargin_right(),\n 'margin-bottom': self.getMargin_bottom(),\n 'margin-left': self.getMargin_left(),\n }\n\n ac_cookie = self.REQUEST.cookies.get('__ac', None)\n if self.getAllow_cookie() and ac_cookie is not None:\n options['cookie'] = ['__ac', ac_cookie]\n\n return options"],"string":"[\n \"def get_testing_options(cls):\\n return [{'error': [(1, NotImplementedError)],\\n 'kwargs': {'initial_state': {'a': int(1)}}}]\",\n \"def get_testing_options(cls, **kwargs):\\n msg = ['test', 1.0]\\n count = 3\\n objects = [msg for _ in range(count)]\\n out = {'kwargs': {'address': msg, 'count': count},\\n 'send': objects,\\n 'msg': msg,\\n 'objects': objects,\\n 'recv': objects}\\n return out\",\n \"def get_options(cls):\\n return {\\n \\\"name\\\": str,\\n ConfigOption(\\\"install_files\\\", default=None): Or(None, list),\\n ConfigOption(\\\"timeout\\\", default=300): int,\\n ConfigOption(\\\"log_regexps\\\", default=None): Or(None, list),\\n ConfigOption(\\\"stdout_regexps\\\", default=None): Or(None, list),\\n ConfigOption(\\\"stderr_regexps\\\", default=None): Or(None, list),\\n ConfigOption(\\\"file_logger\\\", default=None): Or(None, str),\\n ConfigOption(\\\"async_start\\\", default=False): bool,\\n ConfigOption(\\\"report_errors_from_logs\\\", default=False): bool,\\n ConfigOption(\\\"error_logs_max_lines\\\", default=10): int,\\n ConfigOption(\\\"path_cleanup\\\", default=True): bool,\\n ConfigOption(\\\"pre_start\\\", default=None): validate_func(\\\"driver\\\"),\\n ConfigOption(\\\"post_start\\\", default=None): validate_func(\\\"driver\\\"),\\n ConfigOption(\\\"pre_stop\\\", default=None): validate_func(\\\"driver\\\"),\\n ConfigOption(\\\"post_stop\\\", default=None): validate_func(\\\"driver\\\"),\\n }\",\n \"def options(self):\\n options_to_report = dict()\\n for cls in inspect.getmro(type(self)):\\n parameter_names, _, _, defaults, _, _, _ = inspect.getfullargspec(cls.__init__)\\n if defaults:\\n class_options = {parameter_name: getattr(self, '_' + parameter_name) for\\n parameter_name in parameter_names[-len(defaults):]}\\n options_to_report.update(class_options)\\n options_to_report.pop('mcmc_moves')\\n return options_to_report\",\n \"def options(self):\\n return self.__options\",\n \"def get_options(self):\\n\\t\\treturn self.options\",\n \"def options(self):\\r\\n return self._options\",\n \"def _get_options(self):\\n return self.options\",\n \"def _options(self):\\n return\",\n \"def options(self):\\n options = {\\n o.name: getattr(self, o.name)\\n for o in _OPTIONS\\n }\\n return options\",\n \"def get_options(self):\\n return []\",\n \"def get_simulation_options(self):\\n return self.opts\",\n \"def default_options(cls) -> Dict:\\n return {}\",\n \"def tests(self):\\n\\n return {\\n 'variable_boolean': self.variable_boolean\\n }\",\n \"def _get_options(self) -> Dict[str, Any]:\\n # TODO: handle holidays as well\\n return {\\n \\\"growth\\\": self.growth,\\n \\\"changepoints\\\": self.changepoints and list(self.changepoints.astype('str')),\\n \\\"n_changepoints\\\": self.n_changepoints,\\n \\\"changepoint_range\\\": self.changepoint_range,\\n \\\"changepoint_prior_scale\\\": self.changepoint_prior_scale,\\n \\\"mcmc_samples\\\": self.mcmc_samples,\\n \\\"interval_width\\\": self.interval_width,\\n \\\"uncertainty_samples\\\": self.uncertainty_samples,\\n \\\"yearly_seasonality\\\": self.yearly_seasonality,\\n \\\"weekly_seasonality\\\": self.weekly_seasonality,\\n \\\"daily_seasonality\\\": self.daily_seasonality,\\n \\\"seasonality_mode\\\": self.seasonality_mode,\\n \\\"seasonality_prior_scale\\\": self.seasonality_prior_scale,\\n\\n \\\"seasonalities\\\": self.seasonalities,\\n \\\"extra_regressors\\\": self.extra_regressors\\n }\",\n \"def options(self) -> Mapping[str, str]:\\n return pulumi.get(self, \\\"options\\\")\",\n \"def options(self):\\n return self._options\",\n \"def options(self):\\n return self._options\",\n \"def options(self):\\n return self._options\",\n \"def options(self):\\n return self._options\",\n \"def options(self):\\n return self._options\",\n \"def test_options_flags(self):\\n opts = []\\n\\n # Handle \\\"--failed\\\" as a special case: we want to re-run only\\n # the tests that failed within our Django apps\\n # This sets the --last-failed flag for the pytest command, so this\\n # functionality is the same as described in the pytest documentation\\n if self.failed_only:\\n opts.append(\\\"--last-failed\\\")\\n\\n # This makes it so we use pytest's fail-fast feature in two cases.\\n # Case 1: --fail-fast is passed as an arg in the paver command\\n # Case 2: The environment variable TESTS_FAIL_FAST is set as True\\n env_fail_fast_set = (\\n 'TESTS_FAIL_FAST' in os.environ and os.environ['TEST_FAIL_FAST']\\n )\\n\\n if self.fail_fast or env_fail_fast_set:\\n opts.append(\\\"--exitfirst\\\")\\n\\n if self.with_wtw:\\n opts.extend([\\n '--wtw',\\n f'{COVERAGE_CACHE_BASEPATH}/{WHO_TESTS_WHAT_DIFF}',\\n '--wtwdb',\\n f'{COVERAGE_CACHE_BASEPATH}/{COVERAGE_CACHE_BASELINE}'\\n ])\\n\\n return opts\",\n \"def get_options():\\n user_options = {}\\n user_options['surface'] = {'label': 'Surface',\\n 'type': 'stringList',\\n 'default': 'bcc100',\\n 'values': surface_selections}\\n\\n user_options['metal'] = {'label': 'Metal',\\n 'type': 'string',\\n 'default': 'Au'}\\n\\n user_options['a'] = {'label': 'Lattice Constant',\\n 'type': 'float',\\n 'precision': 3,\\n 'suffix': 'Å'}\\n\\n user_options['size-x'] = {'label': 'Size X',\\n 'type': 'integer',\\n 'default': 5}\\n\\n user_options['size-y'] = {'label': 'Size Y',\\n 'type': 'integer',\\n 'default': 5}\\n\\n user_options['size-z'] = {'label': 'Size Z',\\n 'type': 'integer',\\n 'default': 3}\\n\\n user_options['vacuum'] = {'label': 'Vacuum distance',\\n 'type': 'float',\\n 'precision': 1,\\n 'suffix': 'Å'}\\n\\n user_options['orthogonal'] = {'label': 'Orthogonal',\\n 'type': 'stringList',\\n 'default': 'True',\\n 'values': ['True', 'False']}\\n\\n return {'userOptions': user_options }\",\n \"def default_options(cls):\\n options_to_report = dict()\\n for c in inspect.getmro(cls):\\n parameter_names, _, _, defaults, _, _, _ = inspect.getfullargspec(c.__init__)\\n if defaults:\\n class_options = {parameter_name: defaults[index] for (index, parameter_name) in\\n enumerate(parameter_names[-len(defaults):])}\\n options_to_report.update(class_options)\\n options_to_report.pop('mcmc_moves')\\n return options_to_report\",\n \"def get_options(cls, mode):\\n return dict(\\n (key, properties[mode])\\n for key, properties in cls.__register.items()\\n if mode in properties\\n )\",\n \"def get_options(self):\\r\\n return self._option_values\",\n \"def options(self):\\n result = []\\n for typ in type(self).mro():\\n result.extend(k for k, v in typ.__dict__.items()\\n if isinstance(v, Option))\\n return dict((o, getattr(self, o)) for o in result)\",\n \"def to_dict(self):\\n import tc\\n opts_dict = {}\\n for k, v in self.__class__.__dict__.iteritems():\\n if isinstance(v, tc.TC):\\n opts_dict.update({k: self.__getattribute__(k)})\\n return opts_dict\",\n \"def get_options(self):\\n return self._scoped_options\",\n \"def options(self):\\n return list(self._moptions.keys())\",\n \"def create_options(self):\\n return []\",\n \"def getTestingOptionsFromIni(cfg=DEFAULT_CONFIG_FILE):\\n Config = ConfigParser.ConfigParser()\\n Config.read(cfg)\\n section = \\\"testing-options\\\"\\n FAILS_ON_UNTESTED_ELEMENT = Config.getboolean(section, \\\"FAILS_ON_UNTESTED_ELEMENT\\\")\\n TEST_VERBOSE_MODE = Config.getboolean(section, \\\"TEST_VERBOSE_MODE\\\")\\n return {\\n 'FAILS_ON_UNTESTED_ELEMENT': FAILS_ON_UNTESTED_ELEMENT,\\n 'TEST_VERBOSE_MODE': TEST_VERBOSE_MODE,\\n }\",\n \"def getTestingOptionsFromIni(cfg=DEFAULT_CONFIG_FILE):\\n Config = ConfigParser.ConfigParser()\\n Config.read(cfg)\\n section = \\\"testing-options\\\"\\n FAILS_ON_UNTESTED_ELEMENT = Config.getboolean(section, \\\"FAILS_ON_UNTESTED_ELEMENT\\\")\\n TEST_VERBOSE_MODE = Config.getboolean(section, \\\"TEST_VERBOSE_MODE\\\")\\n return {\\n 'FAILS_ON_UNTESTED_ELEMENT': FAILS_ON_UNTESTED_ELEMENT,\\n 'TEST_VERBOSE_MODE': TEST_VERBOSE_MODE,\\n }\",\n \"def get_options(self, panel=\\\"\\\"):\\n return dict()\",\n \"async def get_options(self):\",\n \"def get_options(self) -> dict:\\n assert self.task\\n task_options = {\\n **self.task.get_task_options(),\\n **self.expr.task_expr_options,\\n **self.task_options,\\n }\\n return task_options\",\n \"def define_options(self) -> Optional[Any]:\\n return {\\n 'basename': OptionDef(required=True, default_value='promtail', allowed_types=[str]),\\n 'namespace': OptionDef(required=True, default_value='monitoring', allowed_types=[str]),\\n 'config': {\\n 'prometheus_annotation': OptionDef(required=True, default_value=False, allowed_types=[bool]),\\n 'promtail_config': OptionDef(allowed_types=[str, ConfigFile]),\\n 'loki_url': OptionDef(allowed_types=[str]),\\n 'authorization': {\\n 'serviceaccount_create': OptionDef(required=True, default_value=True, allowed_types=[bool]),\\n 'serviceaccount_use': OptionDef(allowed_types=[str]),\\n 'roles_create': OptionDef(required=True, default_value=True, allowed_types=[bool]),\\n 'roles_bind': OptionDef(required=True, default_value=True, allowed_types=[bool]),\\n },\\n },\\n 'container': {\\n 'promtail': OptionDef(required=True, default_value='grafana/promtail:2.0.0', allowed_types=[str]),\\n },\\n 'kubernetes': {\\n 'resources': {\\n 'daemonset': OptionDef(allowed_types=[Mapping]),\\n }\\n },\\n }\",\n \"def get_options(cls):\\n for option in cls._general_options.items():\\n yield option\\n for option in cls._specific_options.items():\\n yield option\",\n \"def common_options(self):\\n return self._common_options\",\n \"def get_testing_options(cls):\\n out = super(ObjSerialize, cls).get_testing_options()\\n obj = ObjDict({'vertices': [{'x': float(0), 'y': float(0), 'z': float(0)},\\n {'x': float(0), 'y': float(0), 'z': float(1)},\\n {'x': float(0), 'y': float(1), 'z': float(1)}],\\n 'faces': [[{'vertex_index': int(0)},\\n {'vertex_index': int(1)},\\n {'vertex_index': int(2)}]]})\\n out['objects'] = [obj, obj]\\n out['contents'] = (b'# Author ygg_auto\\\\n'\\n + b'# Generated by yggdrasil\\\\n\\\\n'\\n + b'v 0.0000 0.0000 0.0000\\\\n'\\n + b'v 0.0000 0.0000 1.0000\\\\n'\\n + b'v 0.0000 1.0000 1.0000\\\\n'\\n + b'v 0.0000 0.0000 0.0000\\\\n'\\n + b'v 0.0000 0.0000 1.0000\\\\n'\\n + b'v 0.0000 1.0000 1.0000\\\\n'\\n + b'f 1// 2// 3//\\\\n'\\n + b'f 4// 5// 6//\\\\n')\\n return out\",\n \"def test_options_structure(self):\\r\\n deploy = self.wsgiDeploy()\\r\\n expected_keys = self.DEFAULTS.keys()\\r\\n actual_keys = deploy.options.keys()\\r\\n self.assertListEqual(expected_keys, actual_keys)\",\n \"def _generate_options(self, **kwargs: Any) -> dict:\\n raise NotImplementedError\",\n \"def options(self):\\n\\t\\treturn self.config_parser.options(self.section_name)\",\n \"def get_all_options(self): \\n return self._options.items()\",\n \"def default_options(cls) -> Dict:\\n options = super().default_options()\\n # scaling factor for temperature adaptation\\n options['eta'] = 100\\n # controls the adaptation degeneration velocity of the temperature\\n # adaption.\\n options['nu'] = 1e3\\n\\n return options\",\n \"def get_settings(self):\\n return [('test_environment', self.test_environment),\\n ('base_data_dir', self.base_data_dir),\\n ('locale', self.locale)]\",\n \"def get_settings(self):\\n return {\\n \\\"game_name\\\": self.game_name,\\n \\\"n_epochs\\\": self.n_epochs,\\n \\\"n_episodes\\\": self.n_episodes,\\n \\\"n_frames\\\": self.n_frames,\\n \\\"agent\\\": self.agent.get_settings(),\\n \\\"results_dir\\\": self.results_dir,\\n \\\"use_minimal_action_set\\\": self.use_minimal_action_set,\\n }\",\n \"def get_options(cls, player, context={}):\\n\\t\\traise NotImplementedError()\",\n \"def initialize_options(self):\\n self.all = False\\n self.coverage = False\\n super(test, self).initialize_options()\",\n \"def _get_options(ret):\\n attrs = {\\\"host\\\": \\\"host\\\", \\\"port\\\": \\\"port\\\", \\\"skip\\\": \\\"skip_on_error\\\", \\\"mode\\\": \\\"mode\\\"}\\n\\n _options = salt.returners.get_returner_options(\\n __virtualname__, ret, attrs, __salt__=__salt__, __opts__=__opts__\\n )\\n return _options\",\n \"def _options(self):\\r\\n xmi_file = self.tb_xmi_file_name.GetValue()\\r\\n topic = self.tb_pragma.GetValue()\\r\\n package = self.tb_package.GetValue()\\r\\n header = self.tb_file_header.GetValue()\\r\\n target_folder = self.tb_target_folder.GetValue()\\r\\n encoding = self.tb_encoding.GetValue()\\r\\n \\r\\n return {\\\"topic\\\" : topic, \\r\\n \\\"package\\\" : package, \\r\\n \\\"header\\\" : header, \\r\\n \\\"target_folder\\\" : target_folder,\\r\\n \\\"encoding\\\" : encoding,\\r\\n \\\"xmi_file\\\" : xmi_file}\",\n \"def create_options_for_test_runner(self):\\n self.options = turtle.Turtle(\\n verbosity=VERBOSITY_NORMAL,\\n summary_mode=False,\\n )\",\n \"def get_test_preferences(self, name: str) -> Dict[str, Any]:\\n return self.raw_config.get(name, {})\",\n \"def metric_options(self):\\n return Optimizer.list_method_options(self.metric_creator.method_dict)\",\n \"def create_options():\\n optparser = optparse.OptionParser()\\n optparser.add_option(\\\"-f\\\", \\\"--filename\\\", type=\\\"string\\\",\\n help=\\\"execute a single unit test file\\\")\\n optparser.add_option(\\\"-s\\\", \\\"--subprocess\\\", action=\\\"store_true\\\",\\n default=False,\\n help=\\\"run everything in an own subprocess \\\"\\n \\\"(default: use a single process)\\\")\\n optparser.add_option(\\\"-t\\\", \\\"--timeout\\\", type=\\\"int\\\", default=70,\\n help=\\\"Timout for subprocesses before being killed \\\"\\n \\\"(default: 70s per file)\\\")\\n optparser.add_option(\\\"-v\\\", \\\"--verbose\\\", action=\\\"store_true\\\", default=False,\\n help=\\\"be verbose and print anything instantly\\\")\\n optparser.add_option(\\\"-r\\\", \\\"--random\\\", action=\\\"store_true\\\", default=False,\\n help=\\\"randomize the order of tests\\\")\\n optparser.add_option(\\\"-S\\\", \\\"--seed\\\", type=\\\"int\\\",\\n help=\\\"seed the randomizer(useful to \\\"\\n \\\"recreate earlier randomized test cases)\\\")\\n optparser.add_option(\\\"-i\\\", \\\"--interactive\\\", action=\\\"callback\\\",\\n callback=include_tag,\\n callback_args=(\\\"interactive\\\",),\\n help=\\\"also execute interactive tests\\\")\\n optparser.add_option(\\\"-e\\\", \\\"--exclude\\\", action=\\\"callback\\\",\\n callback=exclude_tag, type=\\\"string\\\",\\n help=\\\"exclude test containing the tag\\\")\\n optparser.add_option(\\\"-l\\\", \\\"--listtags\\\", action=\\\"callback\\\",\\n callback=list_tags,\\n help=\\\"lists all available tags and exits\\\")\\n optparser.add_option(\\\"--logfile\\\", type=\\\"string\\\",\\n help=\\\"save output to log file\\\")\\n optkeys = [\\\"filename\\\",\\n \\\"subprocess\\\",\\n \\\"timeout\\\",\\n \\\"random\\\",\\n \\\"seed\\\",\\n \\\"verbose\\\"\\n ]\\n return optparser, optkeys\",\n \"def _get_default_options():\\n return {\\n \\\"library_folders\\\": [],\\n \\\"verbose\\\": False,\\n \\\"check_balanced\\\": True,\\n \\\"mtime_check\\\": True,\\n \\\"cache\\\": False,\\n \\\"codegen\\\": False,\\n \\\"expand_mx\\\": False,\\n \\\"unroll_loops\\\": True,\\n \\\"inline_functions\\\": True,\\n \\\"expand_vectors\\\": False,\\n \\\"resolve_parameter_values\\\": False,\\n \\\"replace_parameter_expressions\\\": False,\\n \\\"replace_constant_expressions\\\": False,\\n \\\"eliminate_constant_assignments\\\": False,\\n \\\"replace_parameter_values\\\": False,\\n \\\"replace_constant_values\\\": False,\\n \\\"eliminable_variable_expression\\\": None,\\n \\\"factor_and_simplify_equations\\\": False,\\n \\\"detect_aliases\\\": False,\\n \\\"allow_derivative_aliases\\\": True,\\n \\\"reduce_affine_expression\\\": False,\\n }\",\n \"def optionHelp(self):\\n return {}\",\n \"def get_options(self):\\n additional_data = self.get_additional_data()\\n options_out = []\\n for key, value in additional_data['DIMENSION_VALUES'].items():\\n key_label = ' '.join(key.split('_')).strip().title()\\n data = {'specification': key_label, 'specification_key': key, 'choices': value}\\n options_out.append(data)\\n return options_out\",\n \"def read_all_options(self, test_case=None):\\n args = self.get_parsed_cmd_args(test_case)\\n\\n Options.validate_methods(args[\\\"methods\\\"])\\n\\n self.read_config_file(args[\\\"config_file\\\"])\\n\\n for option in self.options:\\n if args[option] not in [None, []]:\\n self.options[option] = args[option]\\n\\n if option in self.method_options:\\n method, method_option = self.method_options[option]\\n Options.available_methods()[method].options[method_option] = args[option]\\n\\n #remove duplicate\\n for option in [\\\"methods\\\", \\\"packages\\\"]:\\n self.options[option] = list(set(self.options[option]))\\n\\n return self.options\",\n \"def runoptions(self):\\n # outstanding = self.missing_required()\\n # if outstanding:\\n # raise TypeError('Module missing required parameter: %s' % ', '.join(outstanding))\\n return self._runopts\",\n \"def settings(self) -> Dict[str, Any]:\\n return {}\",\n \"def options(self):\\n pclass_options = self.get_product_class().options.all()\\n return pclass_options | self.product_options.all()\",\n \"def options(self) -> Union[object, Sequence]:\\n return self._options\",\n \"def _getOptions(self):\\n args = []\\n for iname, value in self.options:\\n args.append('-' + iname)\\n if value != 'true':\\n args.append(value)\\n return args\",\n \"def as_dict(self) -> Dict[str, Any]:\\n return {\\n 'enabled': self._enabled,\\n 'maxStackDepth': self._maxStackDepth,\\n }\",\n \"def _get_test(self, config):\\n expected_options = {'goodpassword', 'badpassword'}\\n _warn_on_extra(set(config.options('test')) - expected_options -\\n self.defaults, 'test section option(s)')\\n\\n get = partial(config.get, 'test')\\n\\n self.goodpassword = get('GOODPASSWORD')\\n self.badpassword = get('BADPASSWORD')\",\n \"def test_dict(self):\\n if getattr(self, \\\"_test_dict\\\", None) is None:\\n tests = dict()\\n\\n # build test for each notebook\\n for notebook, nbpath in zip(self._nbnames, self._nbpaths):\\n tests[\\\"test_\\\" + notebook] = get_test(\\n notebook, nbpath, timeout=self.timeout\\n )\\n self._test_dict = tests\\n return self._test_dict\",\n \"def get_options(self):\\n option_list = []\\n if self.can_analyze():\\n option_list.append((EpOp.TASK_ANALYZE, None))\\n\\n option_tup = self.predict_option()\\n if option_tup:\\n option_list.append(option_tup)\\n\\n option_tup = self.check_option()\\n if option_tup:\\n option_list.append(option_tup)\\n\\n return option_list\",\n \"def options(self) -> tuple: # UserOptions\\n return self._options\",\n \"def get_general_options(cls):\\n for option in cls._general_options.items():\\n yield option\",\n \"def get_config(self):\\n return {\\\"name\\\": self.name, \\\"tunable\\\": self.tunable}\",\n \"def get_options(self):\\n return (\\n Option('-H', '--host',\\n dest='host',\\n default=self.host,\\n help='IP address or hostname of the Glancesync server.'),\\n\\n Option('-p', '--port',\\n dest='port',\\n type=int,\\n default=self.port,\\n help='Port in which the GlanceSync server is running'),\\n\\n Option('-w', '--workers',\\n dest='workers',\\n type=int,\\n default=self.workers,\\n help='Number of concurrent workers to be launched, usually 2*core numbers+1.'),\\n )\",\n \"def initialize_options(self):\",\n \"def static_opts(ftype, **kwargs):\\n\\n opts = dict()\\n\\n if ftype == 'sequential_feature_selector':\\n # check if we got the features\\n features = kwargs.pop('features', None)\\n if features is not None:\\n opts['hidden_layer_sizes'] = (features.shape[0], features.shape[1])\\n\\n if ftype == 'vote':\\n # check if we got the training data\\n X_train = kwargs.pop('X_train', None)\\n if X_train is not None:\\n # TODO: check dimensions!\\n opts['hidden_layer_sizes'] = (X_train.shape[1], X_train.shape[1])\\n\\n return opts\",\n \"def _default_options(cls):\\n default_options = super()._default_options()\\n default_options.data_processor = dp.DataProcessor(\\n input_key=\\\"counts\\\",\\n data_actions=[dp.Probability(\\\"1\\\"), dp.BasisExpectationValue()],\\n )\\n default_options.plotter.set_figure_options(\\n xlabel=\\\"Flat top width\\\",\\n ylabel=\\\"Pauli expectation values\\\",\\n xval_unit=\\\"s\\\",\\n ylim=(-1, 1),\\n )\\n default_options.data_subfit_map = {\\n \\\"x\\\": {\\\"meas_basis\\\": \\\"x\\\"},\\n \\\"y\\\": {\\\"meas_basis\\\": \\\"y\\\"},\\n \\\"z\\\": {\\\"meas_basis\\\": \\\"z\\\"},\\n }\\n\\n return default_options\",\n \"def _opt_config(self):\\n return self._opt_method.config\",\n \"def get_specific_options(cls):\\n for option in cls._specific_options.items():\\n yield option\",\n \"def _parse_options(options):\\n opts = dict()\\n for attr in dir(options):\\n if attr.startswith(\\\"__\\\"):\\n continue\\n opts[attr] = getattr(options, attr)\\n return opts\",\n \"def support(self):\\n return {}\",\n \"def settings(self):\\n return {}\",\n \"def test_new_options(self):\\n obj = Game.new(self._creator, self._ds, {\\n 'sixes': True,\\n 'spec_allow_chat': False,\\n 'win_amount': 400\\n })\\n self.assertTrue(obj.options.sixes,\\n \\\"game.options.sixes didn't set.\\\")\\n self.assertFalse(obj.options.spec_allow_chat,\\n \\\"game.options.spec_allow_chat didn't set.\\\")\\n self.assertEqual(obj.options.win_amount, 400,\\n \\\"game.options.win_amount didn't set.\\\")\",\n \"def get_options_and_reporter(method, init_points, init_vals):\\n reporter = get_reporter('default')\\n if method in ['GA', 'randGA']:\\n options = load_options(ga_optimiser.ga_opt_args, reporter=reporter)\\n else:\\n raise ValueError('Unknown method %s.'%(method))\\n options.pre_eval_points = init_points\\n options.pre_eval_vals = init_vals\\n options.pre_eval_true_vals = init_vals\\n return options, reporter\",\n \"def initialize_options(self):\\n pass\",\n \"def options(self): # pragma: no cover\\r\\n return ''\",\n \"def get_test_specific_options(test, BLIND=False, ignore_others=True, ignore_labels=None):\\n # Need to change when the total test options changed\\n # Color key Experiment 1: (Groups 5-8 are unusually flexible; group 1,6-8 are different strains than 2-5)\\n exp1 = { #\\\"0.0\\\": r'Controls',\\n \\\"1.0\\\": r\\\"Controls WT/SAL male P60-90 Bl/6J/CR\\\",\\n \\\"2.0\\\": r\\\"FI male P60 Taconic\\\",\\n \\\"3.0\\\": r\\\"FR male P60 Taconic\\\",\\n \\\"4.0\\\": r\\\"ALG male P60 Taconic\\\",\\n \\\"5.0\\\": r\\\"ALS male P60 Taconic\\\",\\n \\\"6.0\\\": r\\\"5d COC test at P90 Bl/6CR\\\",\\n \\\"7.0\\\": r\\\"BDNF met/met Ron tested at P60\\\",\\n \\\"8.0\\\": r\\\"P26 males WT Bl/6CR\\\"}\\n # Color Key Experiment 2 data (focusing on angel's mice and bdnf/trkb manipulations) P40-60 ages\\n exp2 = {\\\"1.0\\\": r\\\"Controls VEH/SAL/WT\\\",\\n \\\"2.0\\\": r\\\"acute NMPP1pump\\\",\\n \\\"3.0\\\": r\\\"chronic NMPP1pump\\\",\\n \\\"4.0\\\": r\\\"BDNF Val/Val Ron\\\",\\n \\\"5.0\\\": r\\\"P1-23 NMPP1H20\\\",\\n \\\"6.0\\\": r\\\"P1-40 NMPP1H20\\\",\\n \\\"7.0\\\": r\\\"BDNF Met/Met Ron\\\"}\\n if not ignore_others:\\n exp1[\\\"-1.0\\\"] = r\\\"OTHERS\\\"\\n exp2[\\\"-1.0\\\"] = r\\\"OTHERS\\\"\\n\\n exp1_params = {'UMAP': {'n_neighbors': 10,\\n 'min_dist': 0.8,\\n 'n_components': 3,\\n 'metric': 'euclidean'}}\\n exp2_params = {}\\n\\n TEST_LABEL_ALIAS = {\\n 'exp1_label_FI_AL_M': None if BLIND else exp1,\\n 'exp2_Angel': None if BLIND else exp2,\\n 'age': None,\\n 'RL_age': None,\\n 'RL_treat_sex': None,\\n 'RL_treat': None,\\n 'RL_sex': None\\n }\\n\\n IGNORE_LABELS = {\\n 'exp1_label_FI_AL_M': ['-1.0', '0.0', '1.0', '3.0', '4.0', '6.0', '7.0'],\\n 'exp2_Angel': ['-1.0', '1.0'],\\n 'age': None,\\n 'RL_age': None,\\n 'RL_treat_sex': ['ALS_F', 'FI_F', 'FR_F'],\\n 'RL_treat': None,\\n 'RL_sex': None\\n }\\n\\n DIM_PARAMS = {\\n 'exp1_label_FI_AL_M': exp1_params,\\n 'exp2_Angel': exp2_params,\\n 'age': {},\\n 'RL_age': {},\\n 'RL_treat_sex': {},\\n 'RL_treat': {},\\n 'RL_sex': {}\\n }\\n\\n return TEST_LABEL_ALIAS[test], IGNORE_LABELS[test] if ignore_labels is None else ignore_labels,\\\\\\n DIM_PARAMS[test]\",\n \"def data_dict(self) -> dict:\\n return self.design.renderers.gds.options\",\n \"def test_report_options_dict(self, api_instance: Reports):\\n report_type = \\\"_GET_MERCHANT_LISTINGS_ALL_DATA_\\\"\\n report_options = {\\\"custom\\\": True, \\\"somethingelse\\\": \\\"abc\\\"}\\n params = api_instance.request_report(\\n report_type=report_type,\\n report_options=report_options,\\n )\\n self.assert_common_params(params, action=\\\"RequestReport\\\")\\n assert params[\\\"ReportType\\\"] == \\\"_GET_MERCHANT_LISTINGS_ALL_DATA_\\\"\\n # Cannot assume the order of the options dict passed on older versions\\n # of Python, so two possible outputs are used:\\n # Further, the final result should be encoded once before being sent,\\n # resulting in the following URL-encoded strings.\\n options_possible = (\\n \\\"custom=true;somethingelse=abc\\\",\\n \\\"somethingelse=abc;custom=true\\\",\\n )\\n assert params[\\\"ReportOptions\\\"] in options_possible\",\n \"def get_config(self):\\n return {}\",\n \"def get_args_dict(class_, options: Options) -> dict:\\n\\n argspec = getfullargspec(class_.__init__)\\n init_args = argspec.args\\n init_args.pop(0) # self\\n result = {k: v for k, v in options.items() if k in init_args}\\n\\n positional_args = init_args[:-len(argspec.defaults)]\\n\\n missing_args = [a for a in positional_args if a not in options]\\n if missing_args:\\n raise BadConfigError(\\n f'Some required parameters are missing in \\\"{options[\\\"name\\\"]}\\\" config: ' +\\n ', '.join(missing_args)\\n )\\n return result\",\n \"def define_options(self):\\n return {\\n 'basename': OptionDef(required=True, default_value='keycloak', allowed_types=[str]),\\n 'namespace': OptionDef(required=True, default_value='default', allowed_types=[str]),\\n 'config': {\\n 'service_port': OptionDef(required=True, default_value=8080, allowed_types=[int]),\\n 'realm_import': OptionDef(format=OptionDefFormat.KDATA_VOLUME, allowed_types=[str, bytes, KData_Secret]),\\n 'proxy_address_forwarding': OptionDef(format=OptionDefFormat.KDATA_ENV,\\n allowed_types=[bool, *KDataHelper_Env.allowed_kdata()]),\\n 'frontend_url': OptionDef(allowed_types=[str]),\\n 'admin': {\\n 'user': OptionDef(format=OptionDefFormat.KDATA_ENV, allowed_types=[str, *KDataHelper_Env.allowed_kdata()]),\\n 'password': OptionDef(format=OptionDefFormat.KDATA_ENV, allowed_types=[str, KData_Secret]),\\n },\\n 'db': {\\n 'vendor': OptionDef(format=OptionDefFormat.KDATA_ENV, allowed_types=[str, *KDataHelper_Env.allowed_kdata()]),\\n 'addr': OptionDef(format=OptionDefFormat.KDATA_ENV, allowed_types=[str, *KDataHelper_Env.allowed_kdata()]),\\n 'port': OptionDef(format=OptionDefFormat.KDATA_ENV, allowed_types=[int, *KDataHelper_Env.allowed_kdata()]),\\n 'database': OptionDef(format=OptionDefFormat.KDATA_ENV, allowed_types=[str, *KDataHelper_Env.allowed_kdata()]),\\n 'schema': OptionDef(format=OptionDefFormat.KDATA_ENV, allowed_types=[str, *KDataHelper_Env.allowed_kdata()]),\\n 'user': OptionDef(format=OptionDefFormat.KDATA_ENV, allowed_types=[str, *KDataHelper_Env.allowed_kdata()]),\\n 'password': OptionDef(format=OptionDefFormat.KDATA_ENV, allowed_types=[str, KData_Secret]),\\n },\\n },\\n 'container': {\\n 'keycloak': OptionDef(required=True, default_value='quay.io/keycloak/keycloak:11.0.2', allowed_types=[str]),\\n },\\n 'kubernetes': {\\n 'resources': {\\n 'deployment': OptionDef(allowed_types=[Mapping]),\\n }\\n },\\n }\",\n \"def get_config(self) -> Dict[str, Any]:\\n return {\\n 'num_classes': self.num_classes,\\n 'name': self.name,\\n 'dtype': self.dtype,\\n 'sparse_y_true': self.sparse_y_true,\\n 'sparse_y_pred': self.sparse_y_pred,\\n 'axis': self.axis,\\n }\",\n \"def get_switching_options(self):\\n\\n return self._switch_opt_infos.iterkeys()\",\n \"def training_opts(self):\\n return self._training_opts\",\n \"def requested_config_vals():\\n return {} # no extra values needed\",\n \"def _create_options(self):\\n self._OPTIONS = {}\",\n \"def get_test_params(cls, parameter_set=\\\"default\\\"):\\n params1 = {}\\n params2 = {\\\"p_threshold\\\": 0.1, \\\"regression\\\": \\\"ctt\\\", \\\"nlags\\\": 5}\\n\\n return [params1, params2]\",\n \"def get_method_options(prob, capital_type):\\n methods = prob.methods\\n all_method_options = {}\\n for meth in methods:\\n if meth == 'rand':\\n curr_options = load_options(cp_random_optimiser_args)\\n elif meth == 'ga':\\n curr_options = load_options(cpga_opt_args)\\n elif meth.startswith('dragonfly'):\\n if meth.startswith('dragonfly-mf'):\\n curr_options = load_options(get_all_mf_cp_gp_bandit_args())\\n else:\\n curr_options = load_options(get_all_cp_gp_bandit_args())\\n meth_parts = meth.split('_')\\n if len(meth_parts) == 2:\\n curr_options.acq_opt_method = meth_parts[-1]\\n elif meth in ['hyperopt', 'gpyopt', 'smac', 'spearmint']:\\n curr_options = load_options(blackbox_opt_args)\\n curr_options = Namespace(redo_evals_for_true_val=True)\\n else:\\n raise ValueError('Unknown method %s.'%(meth))\\n # Some additional data we will need for Spearmint\\n if meth == 'spearmint':\\n # Swap in the following after you download and install Spearmint\\n curr_options.exp_dir = '/home/karun/boss/e3_cp/Spearmint/' + \\\\\\n prob.study_name.split('-')[0]\\n curr_options.pkg_dir = '/home/karun/Spearmint/spearmint' \\n curr_options.capital_type = capital_type\\n all_method_options[meth] = curr_options\\n return all_method_options\",\n \"def test_list_options(self):\\n pass\",\n \"def initialize_options(self):\\n pass\",\n \"def initialize_options(self):\\n pass\",\n \"def make_options(self):\\n if self.pdf_generator == 'pisa':\\n return {}\\n\\n toc_msg = _(u'label_toc',\\n default=u'Table of content')\\n\\n options = {'book': self.getUse_book_style(),\\n 'margin-top': self.getMargin_top(),\\n 'margin-right': self.getMargin_right(),\\n 'margin-bottom': self.getMargin_bottom(),\\n 'margin-left': self.getMargin_left(),\\n }\\n\\n ac_cookie = self.REQUEST.cookies.get('__ac', None)\\n if self.getAllow_cookie() and ac_cookie is not None:\\n options['cookie'] = ['__ac', ac_cookie]\\n\\n return options\"\n]","isConversational":false},"negative_scores":{"kind":"list like","value":["0.79496187","0.7280603","0.7255419","0.70370495","0.6979806","0.69656146","0.6909307","0.6908418","0.6870743","0.68551254","0.6849841","0.68234015","0.6817497","0.67941236","0.67408985","0.6725303","0.6684944","0.6684944","0.6684944","0.6684944","0.6684944","0.6624876","0.6604378","0.6525962","0.6497216","0.6426927","0.6424721","0.6417624","0.6416635","0.63925034","0.63648015","0.63474286","0.63474286","0.63299584","0.6324186","0.6321044","0.62596565","0.62540156","0.62322646","0.62292683","0.6226919","0.62222016","0.62194455","0.6214779","0.6208915","0.6184129","0.6132542","0.6120797","0.6096366","0.6063649","0.6058681","0.6044308","0.6038704","0.60312694","0.602998","0.60019875","0.5983963","0.5962757","0.5959978","0.5948386","0.59288013","0.59200937","0.59173965","0.59018534","0.58913684","0.5890849","0.5886275","0.5875377","0.587316","0.5867881","0.5834655","0.5830026","0.5828821","0.5819702","0.5817354","0.57989377","0.57983816","0.5796523","0.5793188","0.57888544","0.5787467","0.57772875","0.5777152","0.57506394","0.57490426","0.57474715","0.5747327","0.57407767","0.5739773","0.5738945","0.5725327","0.5724994","0.5721052","0.57142293","0.5713921","0.5713784","0.57115537","0.5708655","0.57070374","0.57070374","0.570214"],"string":"[\n \"0.79496187\",\n \"0.7280603\",\n \"0.7255419\",\n \"0.70370495\",\n \"0.6979806\",\n \"0.69656146\",\n \"0.6909307\",\n \"0.6908418\",\n \"0.6870743\",\n \"0.68551254\",\n \"0.6849841\",\n \"0.68234015\",\n \"0.6817497\",\n \"0.67941236\",\n \"0.67408985\",\n \"0.6725303\",\n \"0.6684944\",\n \"0.6684944\",\n \"0.6684944\",\n \"0.6684944\",\n \"0.6684944\",\n \"0.6624876\",\n \"0.6604378\",\n \"0.6525962\",\n \"0.6497216\",\n \"0.6426927\",\n \"0.6424721\",\n \"0.6417624\",\n \"0.6416635\",\n \"0.63925034\",\n \"0.63648015\",\n \"0.63474286\",\n \"0.63474286\",\n \"0.63299584\",\n \"0.6324186\",\n \"0.6321044\",\n \"0.62596565\",\n \"0.62540156\",\n \"0.62322646\",\n \"0.62292683\",\n \"0.6226919\",\n \"0.62222016\",\n \"0.62194455\",\n \"0.6214779\",\n \"0.6208915\",\n \"0.6184129\",\n \"0.6132542\",\n \"0.6120797\",\n \"0.6096366\",\n \"0.6063649\",\n \"0.6058681\",\n \"0.6044308\",\n \"0.6038704\",\n \"0.60312694\",\n \"0.602998\",\n \"0.60019875\",\n \"0.5983963\",\n \"0.5962757\",\n \"0.5959978\",\n \"0.5948386\",\n \"0.59288013\",\n \"0.59200937\",\n \"0.59173965\",\n \"0.59018534\",\n \"0.58913684\",\n \"0.5890849\",\n \"0.5886275\",\n \"0.5875377\",\n \"0.587316\",\n \"0.5867881\",\n \"0.5834655\",\n \"0.5830026\",\n \"0.5828821\",\n \"0.5819702\",\n \"0.5817354\",\n \"0.57989377\",\n \"0.57983816\",\n \"0.5796523\",\n \"0.5793188\",\n \"0.57888544\",\n \"0.5787467\",\n \"0.57772875\",\n \"0.5777152\",\n \"0.57506394\",\n \"0.57490426\",\n \"0.57474715\",\n \"0.5747327\",\n \"0.57407767\",\n \"0.5739773\",\n \"0.5738945\",\n \"0.5725327\",\n \"0.5724994\",\n \"0.5721052\",\n \"0.57142293\",\n \"0.5713921\",\n \"0.5713784\",\n \"0.57115537\",\n \"0.5708655\",\n \"0.57070374\",\n \"0.57070374\",\n \"0.570214\"\n]","isConversational":false},"document_score":{"kind":"string","value":"0.0"},"document_rank":{"kind":"string","value":"-1"}}},{"rowIdx":22695,"cells":{"query":{"kind":"string","value":"r\"\"\"Determine if the necessary libraries are installed for this communication class."},"document":{"kind":"string","value":"def is_installed(cls, language=None):\n # Filesystem is implied\n return True"},"metadata":{"kind":"string","value":"{\n \"objective\": {\n \"self\": [],\n \"paired\": [],\n \"triplet\": [\n [\n \"query\",\n \"document\",\n \"negatives\"\n ]\n ]\n }\n}"},"negatives":{"kind":"list like","value":["def _is_installed(self):\n return self._system.is_library(os.path.join(self.get_install_path(), \"lib/libG4event\")) or \\\n self._system.is_library(os.path.join(self.get_install_path(), \"lib64/libG4event\"))","def check_requirements():\n process_output = subprocess.check_output([sys.executable, '-m', 'pip', 'freeze'])\n installed_packages = [r.decode().split('==')[0] for r in process_output.split()]\n if 'pandas' and 'matplotlib' in installed_packages:\n return True\n else:\n print('You don`t have one of required libralies\\n'\n 'I can`t create histogram\\n'\n 'Required libralies: \\n'\n '->pandas\\n'\n '->matplotlib\\n')\n return False","def _is_rpm_all_lib_include_files_installed(self):\n return False","def is_installed(cls):\n return find_spec_or_loader(cls.module) is not None","def _sufficient_deps(cls, deps):\n if cls.MODEL_PACKAGE is None:\n return True\n else:\n for d in deps.conda:\n if cls.MODEL_PACKAGE in d:\n return True\n for d in deps.pip:\n if cls.MODEL_PACKAGE in d:\n return True\n return False","def install_dependencies(self):\n return False","def _is_rpm_all_lib_include_files_installed(self):\n return self.rpm.is_package_installed('rpm-devel')","def _is_installed(self):\n header = self._system.file_exists(\"curl.h\", os.path.join(self.get_install_path(), \n \"include/curl\"))\n lib = self._system.library_exists(\"libcurl\", os.path.join(self.get_install_path(), \"lib\"))\n config = self._system.file_exists(\"curl-config\", os.path.join(self.get_install_path(), \"bin\"))\n return header and lib and config","def is_lib_available(library):\n try:\n __import__(library)\n return True\n except ImportError:\n return False","def is_installed(self):\n try:\n self.import_solver()\n return True\n except ImportError:\n return False","def dependencies_met():\n # Check Java VM command line runner.\n try:\n Popen(['java'], shell=False, stderr=PIPE).communicate()[1]\n except:\n print 'Dependecy unmet. Java virtual machine command line runner not ' \\\n 'found.'\n return False\n # Check selenium-server.jar is ready to run.\n output = Popen(('java -jar %s -unrecognized_argument' % SELENIUM_RC_PATH\n ).split(), shell=False, stderr=PIPE).communicate()[1]\n if not re.search('Usage: java -jar selenium-server.jar', output):\n print 'Dependecy unmet. Selenium RC server (selenium-server.jar) not ' \\\n 'found.'\n return False\n # Check selenium RC python driver is available.\n try:\n import selenium\n except:\n print 'Dependecy unmet. Selenium RC python driver (selenium.py) not ' \\\n 'found.'\n return False\n # Check CherryPy wsgi server is available.\n try:\n import wsgiserver\n except:\n print 'Dependecy unmet. CherryPy wsgi server (wsgiserver.py) not found.'\n return False\n # Check fixture support is implemented for the database engine.\n if not settings.DATABASE_ENGINE in ['sqlite3', 'postgresql_psycopg2']:\n print 'Dependecy unmet. Fixture support for database engine %s not ' \\\n 'implemented.' % settings.DATABASE_ENGINE\n return False\n return True","def is_installed(self):\n pass","def installable(self):\n for pm_name in self._provider_package:\n if helpers[pm_name]:\n return True\n return False","def is_installed():\n # type: () -> bool\n try:\n # noinspection PyUnresolvedReferences\n from ccurl import Curl as CCurl\n except ImportError:\n return False\n else:\n # noinspection PyUnresolvedReferences\n from iota.crypto import Curl\n return issubclass(Curl, CCurl)","def check_missing_dep():\n global MISSING_PACKAGES, INSTALLED_PACKAGES, ENABLE_CUDA\n if ENABLE_CUDA and IS_MACOS:\n REQUIRED_PACKAGES.extend(MACOS_REQUIRED_PACKAGES)\n MISSING_PACKAGES = []\n for pkg in REQUIRED_PACKAGES:\n key = pkg.split(\"==\")[0]\n if key not in INSTALLED_PACKAGES:\n MISSING_PACKAGES.append(pkg)\n continue\n else:\n if len(pkg.split(\"==\")) > 1:\n if pkg.split(\"==\")[1] != INSTALLED_PACKAGES.get(key):\n MISSING_PACKAGES.append(pkg)\n continue","def checkRequiredDependencies(self):\n \n # skip dependency check for downloading only\n if( self.downloadOnly ):\n return\n\n # hard dependencies\n for req in self.reqmodules:\n if( self.parent.module(req) == None ):\n # check if there is an auto detected module\n if( self.parent.module(req, True) == None ):\n self.abort( self.name + \" requires \" + req \\\n + \" and it wasn't found in your config file!!\" )\n else:\n # use auto detected module\n self.parent.use( self.parent.module(req, True) )\n self.parent.module( req ).init()\n\n print self.name + \": auto-detected \" + req + \" version \" + self.parent.module( req ).version\n \n # build only dependencies\n if( self.mode == \"install\" ):\n mods = self.reqmodules_buildonly + self.reqmodules_external\n for req in mods:\n if( self.parent.module(req) == None ):\n # check if there is an auto detected module\n if( self.parent.module(req, True) == None ):\n self.abort( req + \" not found in your config file!! \" + self.name \\\n + \" cannot be built without \" + req )\n else:\n # use auto detected module\n self.parent.use( self.parent.module(req, True) )\n self.parent.module( req ).init()\n\n print \" - \" + self.name + \": auto-detected \" + req + \" version \" + self.parent.module( req ).version","def check_dependencies(cls):\n\n missing = []\n for name in cls.DEPENDENCIES:\n try:\n import_module(name)\n except ModuleNotFoundError:\n missing.append(name)\n\n if any(missing):\n msg = ('The sup3r stitching module depends on the following '\n 'special dependencies that were not found in the active '\n 'environment: {}'.format(missing))\n logger.error(msg)\n raise ModuleNotFoundError(msg)","def packages_required(package_names):\n # info(\"packages_required(%s)\" % repr(package_names))\n # noinspection PyBroadException\n try:\n result = True\n\n # info(package_names)\n # info(__pip_list)\n for requirement in [Requirement(name) for name in package_names]:\n if requirement.supported_python():\n pkg_name = requirement.package\n if pkg_name.lower() not in __pip_list:\n try:\n # info('__import__(\"{name}\")'.format(name=pkg_name))\n __import__(pkg_name)\n except ImportError:\n info(pkg_name + \" not installed!\")\n missing_modules.append(pkg_name)\n result = False\n return result\n except Exception:\n return False","def is_installed(self):\n return not self.dont_install","def check_requirements(self):\n try:\n __import__('requests')\n except ImportError:\n raise Warning(\n \"requests Python library is required for freemobile notifications. \")","def has_composed_rpm_bulid_libs(self):\n return self.version_info >= (4, 9, 0)","def verify_package_status(self):\n # rpm-libs is required for /usr/lib64/librpm*.so\n self.rpm.verify_packages_installed(['rpm-libs'])\n\n # Check RPM so files to build the Python binding.\n message_format = '''\nRPM: {0} or\nRPM download tool (dnf-plugins-core (dnf) or yum-utils (yum)) required.\nInstall any of those.\n'''\n if self.rpm.has_composed_rpm_bulid_libs():\n if (not self.rpm.is_package_installed('rpm-build-libs')\n and not self.rpm.is_downloadable()):\n raise InstallError(message_format.format('rpm-build-libs'))\n else:\n # All the needed so files are included in rpm-libs package.\n pass","def _check_install(self):\n\n try:\n # Get chrome version\n chrome_version = subprocess.run(\"google-chrome --version\", shell=True, capture_output=True, text=True, check=True).stdout\n chrome_version_number = chrome_version.split(' ')[2]\n chrome_version_number = '.'.join(chrome_version_number.split('.')[0:3])\n\n # Get driver version\n driver_version = subprocess.run(\"chromedriver --version\", shell=True, capture_output=True, text=True, check=True).stdout\n driver_version_number = driver_version.split(' ')[1]\n driver_version_number = '.'.join(driver_version_number.split('.')[0:3])\n\n # https://chromedriver.chromium.org/downloads/version-selection\n return True if chrome_version_number == driver_version_number else False\n\n # If there is an exception, that means the install is missing\n except subprocess.CalledProcessError:\n return False","def available():\n if \"MojoPipeline\" not in h2o.cluster().list_core_extensions():\n print(\"Cannot use MOJO Pipelines - runtime was not found.\")\n return False\n else:\n return True","def _resolve_libraries(self):\n # Merge any custom libs needed by rules, etc\n libs_to_install = self.REQUIRED_LIBS.union(\n set(self.config['global']['general'].get('third_party_libraries', []))\n )\n\n LOGGER.info('Installing libraries: %s', ', '.join(libs_to_install))\n pip_command = ['pip', 'install']\n pip_command.extend(libs_to_install)\n pip_command.extend(['--no-cache-dir', '--upgrade', '--target', self.temp_package_path])\n\n # Return True if the pip command is successfully run\n return run_command(pip_command, cwd=self.temp_package_path, quiet=True)","def check_dependencies():\n required_found = True\n recommended_found = True\n print 'Checking dependencies ...\\n'\n print 'Required dependencies:'\n try:\n import Image\n assert Image.VERSION >= '1.1.5'\n print ' Python Imaging Library ....... OK'\n except ImportError:\n print ' !!! Python Imaging Library ... Not found'\n required_found = False\n except AssertionError:\n print ' !!! Python Imaging Library ... version', Image.VERSION,\n print 'found'\n print ' !!! Python Imaging Library 1.1.5 or higher is required'\n required_found = False\n if not required_found:\n print '\\nCould not find all required dependencies!'\n print 'Please install them and try again.'\n sys.exit(1)\n print","def is_installed(self):\r\n return bool(self.pip_requirement.satisfied_by)","def is_library_installed(name):\n\n return pm.is_library_installed(name)","def is_library_installed(name):\n\n return __item_installed(name, dtf.core.item.TYPE_LIBRARY)","def check_dependencies():\n check_python_version()\n\n dependencies = [\"sqlite3\"]\n\n for dependency in dependencies:\n try:\n __import__(dependency)\n except ImportError as e:\n raise CuckooStartupError(\"Unable to import \\\"%s\\\"\" % dependency)\n\n return True","def _check_dependencies(self):\n imgmin = exists('imgmin')\n image_optim = exists('image_optim')\n\n if not imgmin or not image_optim:\n puts(p('Dependencies have not been installed:'))\n\n message = 'imgmin - https://github.com/rflynn/imgmin'\n message = s('✓ ' + message) if imgmin else e('✗ ' + message)\n puts(message)\n\n message = 'image_optim - http://rubygems.org/gems/image_optim'\n message = s('✓ ' + message) if image_optim else e('✗ ' + message)\n puts(message)\n\n sys.exit(0)","def has_setup(self):\n\n return hasattr(self._module, 'setup')","def __bool__(self):\n return self.installed","def test_is_installed():\n assert _is_installed('coverage') is True # regular dependency\n assert _is_installed('pytest') is True # dev dependency\n assert _is_installed('missing') is False # missing dependency","def check_system_dependencies():\n out_info(\"Checking System Dependencies...\")\n check_cmake()\n if OS_VERSION[0] == \"Windows\":\n check_visual_studio()\n check_cplus_plus()\n if OS_VERSION[0] == \"Linux\":\n check_gcc()\n check_gpp()","def is_python_binding_installed(self):\n is_installed = False\n is_install_error = False\n\n try:\n is_installed = self.is_python_binding_installed_on_pip()\n except InstallError:\n # Consider a case of pip is not installed in old Python (<= 2.6).\n is_install_error = True\n if not is_installed or is_install_error:\n for rpm_dir in self.python_lib_rpm_dirs:\n init_py = os.path.join(rpm_dir, '__init__.py')\n if os.path.isfile(init_py):\n is_installed = True\n break\n\n return is_installed","def test_dependencies_are_installed(self):\n installed = [p['id'] for p in self.qi.listInstalledProducts()]\n self.assertIn('plone.restapi', installed)\n self.assertIn('plone.app.contenttypes', installed)\n self.assertIn('plone.app.multilingual', installed)","def test_dependencies_installed(self):\n installer = getattr(self.portal, 'portal_quickinstaller')\n self.assertTrue(installer.isProductInstalled('plone.app.dexterity'))","def check_pkg_consistency():\n pass","def is_installed(self):\n cmd = 'cargo install --list | grep \"^{}\"'\n\n if not run(cmd.format(self.package), stdout=Capture(),\n stderr=Capture()).returncode:\n return True\n\n return False","def _package_available(package_name: str) -> bool:\n try:\n return find_spec(package_name) is not None\n except ModuleNotFoundError:\n return False","async def _async_has_devices(opp: OpenPeerPower) -> bool:\n # TODO Check if there are any devices that can be discovered in the network.\n devices = await opp.async_add_executor_job(my_pypi_dependency.discover)\n return len(devices) > 0","def _get_dependencies():\n return config.check_driver_dependencies(__virtualname__, {\"XenAPI\": HAS_XEN_API})","def glogging_available():\n package_check = False\n\n # Is the glogging package available?\n try:\n from gunicorn import glogging\n except ImportError:\n pass\n else:\n package_check = True\n \n return package_check","def check_deps(self):\n\t\tfor plugin in self.__plugins.values():\n\t\t\tif plugin.require:\n\t\t\t\tfor req in plugin.require.split(','):\n\t\t\t\t\tn, v = req.split(':')\n\t\t\t\t\tif not n in self.__plugins or \\\n\t\t\t\t\tv != self.__plugins[n].version:\n\t\t\t\t\t\treturn False\n\t\treturn True","def already_installed(lib_spec, options, working_set=None):\n if 'path' in options:\n return False\n\n if not working_set:\n working_set=pkg_resources.WorkingSet(sys.path)\n\n try:\n working_set.require(lib_spec)\n except pkg_resources.ResolutionError:\n return False\n else:\n return True","def get_dependencies():\n return config.check_driver_dependencies(\n __virtualname__, {\"profitbricks\": HAS_PROFITBRICKS}\n )","def template_library_installed(self) -> Optional[bool]:\n return pulumi.get(self, \"template_library_installed\")","def check_prerequisites() -> None:\n # check black code formatter is installed\n if not is_installed(\"black\"):\n raise FileNotFoundError(\n \"Cannot find black code formatter! To install, please follow this link: https://black.readthedocs.io/en/stable/installation_and_usage.html\"\n )\n\n # check isort code formatter is installed\n if not is_installed(\"isort\"):\n raise FileNotFoundError(\n \"Cannot find isort code formatter! To install, please follow this link: https://pycqa.github.io/isort/#installing-isort\"\n )\n\n # check protolint code formatter is installed\n if subprocess.call(f\"{base_protolint_command()} version\", shell=True) != 0: # nosec\n raise FileNotFoundError(\n \"Cannot find protolint protocol buffer schema file linter! To install, please follow this link: https://github.com/yoheimuta/protolint.\"\n )\n\n # check protocol buffer compiler is installed\n if not is_installed(\"protoc\"):\n raise FileNotFoundError(\n \"Cannot find protocol buffer compiler! To install, please follow this link: https://developers.google.com/protocol-buffers/\"\n )","def installed(self) -> bool:\n return self._installed","def requirement_missing(script):\n if \"requires\" in script:\n if script[\"requires\"] is None:\n return False\n for package in script[\"requires\"].split():\n try:\n pkg_resources.working_set.require(package)\n except Exception:\n return True\n return False","def check_libraries(env):\n # Detect OS X python installation, and attempt to correct for it.\n if os.uname()[0] == 'Darwin':\n env.Replace(SHLINKFLAGS='$LINKFLAGS -bundle -flat_namespace -undefined suppress')\n env.Replace(SHLIBSUFFIX='.so')\n if os.path.isdir('/opt/local'):\n env.Append(\n LIBPATH=['/opt/local/lib'],\n CPPPATH=['/opt/local/include']\n )\n\n # Detect the presence of necessary dependencies.\n conf = Configure(env)\n\n if not conf.CheckLibWithHeader('m', 'math.h', 'c'):\n print \"Can't find standard math libraries.\"\n Exit(1)\n\n if not conf.CheckLibWithHeader('python%s' % python_version,\n 'Python.h', 'c'):\n print \"Can't find python %s.\" % python_version\n Exit(1)\n\n env = conf.Finish()\n\n return env","def check_requirements():\n\n # Which programs are reqired?\n required_programs = ['virtualbox', 'vagrant']\n\n # Make sure the required programs are installed.\n for program in required_programs:\n\n # What's the path to the executable?\n try:\n subprocess.check_output(['which', program])\n except subprocess.CalledProcessError:\n message = \"Please install \" + program + \" before proceeding.\"\n Utilities.log(message)\n exit(1)","def test_packages_present(self):\n packages = [\"ca-certificates\", \"sudo\", \"wget\", \"unzip\"]\n for pkg in packages:\n with self.subTest(package=pkg):\n self.assertTrue(self.host.package(pkg).is_installed)","def is_installed(self):\n cmd = 'conda list {} | grep \"^{}\"'\n\n if not run(cmd.format(self.package, self.package), stdout=Capture(),\n stderr=Capture()).returncode:\n return True\n\n return False","def check_multiprocessing():\n\n try:\n import multiprocessing\n except ImportError:\n return False\n return True","def check_install():\n if platform.dist()[0] not in ['fedora', 'redhat', 'centos']:\n print \"{} not supported\".format(platform.dist()[0])\n sys.exit(1)\n print \"\\ndetected {} {} ...\".format(platform.dist()[0], platform.dist()[1])\n\n import yum\n # Remove loggin. Taken from: https://stackoverflow.com/a/46716482\n from yum.logginglevels import __NO_LOGGING\n yumloggers = [\n 'yum.filelogging.RPMInstallCallback', 'yum.verbose.Repos',\n 'yum.verbose.plugin', 'yum.Depsolve', 'yum.verbose', 'yum.plugin',\n 'yum.Repos', 'yum', 'yum.verbose.YumBase', 'yum.filelogging',\n 'yum.verbose.YumPlugins', 'yum.RepoStorage', 'yum.YumBase',\n 'yum.filelogging.YumBase', 'yum.verbose.Depsolve'\n ]\n for loggername in yumloggers:\n logger = logging.getLogger(loggername)\n logger.setLevel(__NO_LOGGING)\n\n yumbase = yum.YumBase()\n pkg = 'Percona-XtraDB-Cluster-server-<%= @percona_major_version %>'\n if yumbase.rpmdb.searchNevra(name=pkg):\n pkg_list = yumbase.rpmdb.searchNevra(name=pkg)\n print 'detected {} ...'.format(pkg_list[0])\n else:\n print \"{}{} not installed{}\".format(RED, pkg, WHITE)\n sys.exit(1)\n return 'percona'","def is_mnc_installed(self):\n return self.is_installed('minicap') and self.is_installed('minicap.so')","def has_set_up_py_in(self):\n return (self.version_info >= (4, 10))","def checkCUDAisAvailable():\n # some possible lib names \n libnames = ('libcuda.so', 'libcuda.dylib', 'cuda.dll')\n libsOk = True\n for libname in libnames:\n try:\n cuda = ctypes.CDLL(libname)\n except OSError:\n continue\n else:\n break\n else:\n libsOk = False\n return libsOk","def required():\n kernel = __salt__['grains.item']('os') # pylint: disable=E0602,E0603\n\n # Disable rebooting for HDP clusters until that works reliably\n hadoop_distro = __salt__['pillar.get']('hadoop.distro') # pylint: disable=E0602,E0603\n if hadoop_distro == 'HDP':\n return False\n\n if kernel['os'] == \"CentOS\" or kernel['os'] == \"RedHat\":\n try:\n current_version = __salt__['cmd.run']('uname -r') # pylint: disable=E0602,E0603\n latest_version = __salt__['cmd.run']('rpm -q --last kernel') # pylint: disable=E0602,E0603\n latest_version = latest_version.split(\" \")\n latest_version = [\n version for version in latest_version if 'kernel' in version]\n latest_version = str(latest_version[0]).strip('kernel-') # pylint: disable=E1310\n if current_version == latest_version:\n return False\n except: # pylint: disable=W0702\n return False\n return True\n\n return __salt__['file.file_exists']('/var/run/reboot-required') # pylint: disable=E0602,E0603","def is_installed(self):\n\n package_manager = self.get_available_package_manager()\n command = self.CHECKER_COMMANDS[package_manager]\n package = self.package[package_manager]\n return not run(command.format(package),\n stdout=Capture(), stderr=Capture()).returncode","def has_torch_package() -> bool:\n try:\n import torch # pyright: reportMissingImports=false\n\n return True\n except ImportError:\n Logger(log_folder=None).warning_once(\n \"`torch` not available. Some functionality may be missing.\"\n )\n return False","def is_installed(cls, language=None):\n if language == 'python':\n return True\n return False","def _check_required_if_provider(self):\n return","def dependency_check(dependency_set=CORE, exit_on_failure=True):\n verify_python_version()\n \n disable_warnings()\n\n platform = get_current_platform()\n\n #\n # Check for missing python modules\n #\n failed_deps = []\n pip_distributions = pip.get_installed_distributions()\n \n for w3af_req in platform.PIP_PACKAGES[dependency_set]:\n for dist in pip_distributions:\n if w3af_req.package_name.lower() == dist.project_name.lower():\n\n w3af_req_version = str(Version(w3af_req.package_version))\n dist_version = str(dist.version)\n\n if w3af_req_version == dist_version:\n # It's installed and the version matches!\n break\n else:\n failed_deps.append(w3af_req)\n\n #\n # Check for missing operating system packages\n #\n missing_os_packages = []\n for os_package in platform.SYSTEM_PACKAGES[dependency_set]:\n if not platform.os_package_is_installed(os_package):\n missing_os_packages.append(os_package)\n \n os_packages = list(set(missing_os_packages))\n\n # All installed?\n if not failed_deps and not os_packages:\n # False means: do not exit()\n enable_warnings()\n return False\n\n generate_requirements_txt(failed_deps)\n script_path = generate_helper_script(platform.PKG_MANAGER_CMD, os_packages,\n platform.PIP_CMD, failed_deps)\n\n #\n # Report the missing system packages\n #\n msg = ('w3af\\'s requirements are not met, one or more third-party'\n ' libraries need to be installed.\\n\\n')\n \n if os_packages:\n missing_pkgs = ' '.join(os_packages)\n \n msg += ('On %s systems please install the following operating'\n ' system packages before running the pip installer:\\n'\n ' %s %s\\n')\n print(msg % (platform.SYSTEM_NAME, platform.PKG_MANAGER_CMD,\n missing_pkgs))\n \n #\n # Report all missing python modules\n # \n if failed_deps:\n # pylint: disable=E1101\n msg = ('Your python installation needs the following modules'\n ' to run w3af:\\n')\n msg += ' ' + ' '.join([fdep.module_name for fdep in failed_deps])\n print(msg)\n print('\\n')\n # pylint: enable=E1101\n \n #\n # Report missing pip packages\n #\n not_git_pkgs = [fdep for fdep in failed_deps if not fdep.is_git]\n git_pkgs = [fdep.git_src for fdep in failed_deps if fdep.is_git]\n \n msg = ('After installing any missing operating system packages, use'\n ' pip to install the remaining modules:\\n')\n \n if not_git_pkgs:\n cmd = generate_pip_install_non_git(platform.PIP_CMD, not_git_pkgs)\n msg += ' %s\\n' % cmd\n \n if git_pkgs:\n for missing_git_pkg in git_pkgs:\n msg += ' %s\\n' % generate_pip_install_git(platform.PIP_CMD,\n missing_git_pkg)\n \n print(msg)\n \n msg = 'A script with these commands has been created for you at %s'\n print(msg % script_path)\n \n enable_warnings()\n platform.after_hook()\n \n if exit_on_failure:\n sys.exit(1)\n else:\n return True","def is_setup(self):\n return self._market_data_sock_info.ready.is_set() and \\\n self._orders_sock_info.ready.is_set()","def is_third_party(self) -> bool:\n for third_party_import_string in self.third_party_import_strings:\n if self.source.startswith(third_party_import_string):\n return True\n\n return False","def check_dependencies():\n\n # Check for python version\n print(\"Python location : {}\".format(sys.executable))\n print(\"Python version : {}\".format(sys.version))\n if sys.version_info[0] < 3:\n warnings.warn(\n \"WARNING : Using python 2. This Python version is no longer maintained. Use at your own risk.\"\n )\n\n # Check FSL installation\n try:\n print(f\"Your fsl directory is located here: {os.environ['FSLDIR']}\")\n except KeyError:\n raise AssertionError(\n \"You do not have FSL installed! See installation instructions here: https://fsl.fmrib.ox.ac.uk/fsl/fslwiki/FslInstallation\"\n )\n\n # Check AFNI installation\n try:\n print(\n f\"Your AFNI directory is located here: {subprocess.check_output('which afni', shell=True, universal_newlines=True)}\"\n )\n except subprocess.CalledProcessError:\n raise AssertionError(\n \"You do not have AFNI installed! See installation instructions here: https://afni.nimh.nih.gov/pub/dist/doc/htmldoc/background_install/main_toc.html\"\n )","def test_list_available_libraries(self):\n _ = LibraryFactory.create(modulestore=self.store)\n all_libraries = self.tools.list_available_libraries()\n assert all_libraries\n assert len(all_libraries) == 1","def check_import():\n print('[GenHub] Checking Python modules.')\n\n basemod = [('yaml', 'pyyaml'), ('pycurl', 'pycurl')]\n devmod = ['pep8', 'pytest', 'pytest-cov', 'coverage']\n\n packages = dict()\n for importname, packagename in basemod:\n try:\n importlib.import_module(importname)\n packages[packagename] = True\n except ImportError:\n packages[packagename] = False\n for packagename in devmod:\n try:\n importlib.import_module(packagename)\n packages[packagename] = True\n except ImportError:\n packages[packagename] = False\n\n rundep = False\n for pkg in packages:\n char = '+'\n msg = 'Installed.'\n if packages[pkg] is False:\n char = '-'\n msg = 'Not installed!'\n rundep = True\n print('%c package %-12s: %s' % (char, pkg, msg))\n if rundep is True:\n print('Please install these dependencies before proceding')\n print('')","def check_required_commands(self):\n # always check for `virtualenv` manager\n venv_avail = utils.check_command_avail('virtualenv')\n if not venv_avail:\n raise Exception(\"Unable to find the `virtualenv` executable on \"\n \"the system. Is virtualenv on the PATH?\")\n # check for git as well but only fail if packages are actually\n # marked to be installed from source\n git_avail = utils.check_command_avail('git')\n if not git_avail and self.has_source():\n raise Exception(\"Unable to find `git` on the system but some \"\n \"packages are defined to be installed from \"\n \"source. Either install git or switch to \"\n \"non-source installation!\")","def list_support_required(self):\n\t\treturn self.typemanager.has_lists","def is_available():","def checkLibraries(env):\n # Detect OS X python installation, and attempt to correct for it.\n if os.uname()[0] == 'Darwin':\n env.Replace(SHLINKFLAGS='$LINKFLAGS -bundle -flat_namespace -undefined suppress')\n env.Replace(SHLIBSUFFIX='.so')\n\n # Detect the presence of necessary dependencies.\n conf = Configure(env)\n\n if not conf.CheckLibWithHeader('m', 'math.h', 'c'):\n print \"Can't find standard math libraries.\"\n Exit(1)\n\n env = conf.Finish()\n\n return env","def has_icecube_package() -> bool:\n try:\n import icecube # pyright: reportMissingImports=false\n from icecube import icetray, dataio\n\n return True\n except ImportError:\n Logger(log_folder=None).warning_once(\n \"`icecube` not available. Some functionality may be missing.\",\n )\n return False","def checkDependencies(check=True):\n modules = []\n f = open(CONST_REQUIREMENTS_FILE)\n for line in f:\n if line.find('#'):\n modules.append([line[:line.index('=')], (line[line.index('=')+2:]).strip()])\n f.close()\n\n for module in modules:\n try:\n __import__(module[0])\n except ImportError: \n if query_user_bool(\"Missing module %s.\" \\\n \" Do you wish to install it?\" % module[0]):\n subprocess.call([\"pip2\", \"install\", \"%s==%s\" %\n (module[0], module[1])])\n \n else:\n return False\n return True","def check_supported_features(self):","def requirements():\n if fabric.api.sudo(\"grep 'release 7' /etc/redhat-release\",quiet=True).succeeded:\n \tprint blue(\"This is a Centos/RedHat 7 server. Please install AIDE.\")\n \treturn 1\n if not rpm_is_installed('glibc.*i686'):\n print red(\"GlibC i686 is not installed\")\n if not file_exists(\"/usr/local/tripwire/tfs/bin/tripwire\", use_sudo=True):\n print red(\"Tripwire is not installed\")","def not_installed(self) -> bool:\n return pulumi.get(self, \"not_installed\")","def enabled():\n installed = installedVersion()\n required = MIN_DCM2NIIX_VERSION\n return ((installed is not None) and\n (compareVersions(installed, required) >= 0))","def DEPENDENCIES(self):\n pass","def is_setup(self):\n if self.pocs is None:\n print_warning('POCS has not been setup. Please run `setup_pocs`')\n return False\n return True","def checkOptionalDependencies(self):\n \n # skip dependency check for downloading only\n if( self.downloadOnly ):\n return\n\n # soft dependencies\n failed = []\n for opt in self.optmodules:\n mod = self.parent.module(opt)\n if( mod == None ):\n failed.append(opt)\n \n # remove soft dependencies that were not found\n self.buildWithout(failed)","def check_requirements():\n # Fail hard if Python does not have minimum required version\n if sys.version_info < (3, 3) and sys.version_info[:2] != (2, 7):\n raise SystemExit('PyInstaller requires at least Python 2.7 or 3.3+.')\n\n if is_win:\n try:\n from PyInstaller.utils.win32 import winutils\n pywintypes = winutils.import_pywin32_module('pywintypes')\n except ImportError:\n raise SystemExit('PyInstaller cannot check for assembly dependencies.\\n'\n 'Please install PyWin32.\\n\\n'\n 'pip install pypiwin32\\n')","def detect_if_qlm():\n try:\n importlib.import_module(\"qat.linalg\")\n print(\"=> Detected a QLM installation. <=\")\n return True\n except ModuleNotFoundError:\n print(\"=> No QLM installation detected, adding myQLM to the dependencies. <=\")\n return False","def is_distribute_installed():\n with settings(hide('running', 'warnings', 'stderr', 'stdout'), warn_only=True):\n res = run('easy_install --version')\n return res.succeeded and (res.find('distribute') >= 0)","def pyoptsparse_installed():\n # type: () -> bool\n try:\n from openmdao.api import pyOptSparseDriver\n except ImportError:\n print(PyOptSparseImportError().msg)\n return False\n return True","def self_check(self):\n out = \"Loaded components\\n\"\n for package_name, package in sorted(self.packages.items()):\n out += \"\\t%s:\\n\" % package_name\n for c, fd in sorted(package.components.iteritems()):\n out += \"\\t\\t%s (%s)\\n\" % (c, fd.filename)\n\n LOG.info(out)\n\n for p in self.packages.values():\n for f in p.files:\n for id in f.requires:\n # This throws if it doesn't find something.\n try:\n self.get(id)\n except:\n LOG.exception(\"Error in: \" + f.filename)\n raise","def no_prereq_install():\n return str2bool(os.environ.get('NO_PREREQ_INSTALL', 'False'))","def _check_reqs():\n errors = []\n\n if not HAS_TTP:\n errors.append(missing_required_lib(\"ttp\"))\n\n return {\"errors\": errors}","def test_powerlaw_package_available(self):\n\t\timport importlib\n\n\t\ttry:\n\t\t\timportlib.import_module('powerlaw')\n\t\texcept ImportError:\n\t\t\tself.fail(\"Failed to import powerlaw package\")\n\n\t\treturn","def check_req_utils():\n utils = (['dmenu', 'gpg', 'pass', 'xclip', 'exo-open', 'pkill'])\n for util in utils:\n if find_executable(util) is None:\n print(\"ERROR: Util '{}' is missing, install it before proceeding! Exiting!\".format(util))\n sys.exit(1)","def exists():\n return PYTHON_VERSION is not None","def needsResolution (self):\n return self.__unresolvedComponents is not None","def check_library(self, **kw):\n\tself.check(\n\t\tcompile_filename = [],\n\t\tfeatures = 'link_lib_test',\n\t\tmsg = 'Checking for libraries',\n\t\t)","def _check_all_systems_ready(self):\n raise NotImplementedError()","def is_setup_connected(self):\n return bool(self.get_target_namespace())","def check_requirements():\n debug(\"check_requirements\")\n needed = Requirements(Project).find_missing_requirements()\n if needed:\n info(\"Please add the following to your %s file:\\n\" % 'requirements.txt')\n info(\"\\n\".join(str(needed)))\n else:\n info(\"Your %s includes all known herringlib task requirements\" % 'requirements.txt')","def runipy_available():\n try:\n import runipy\n except ImportError:\n return False\n return True"],"string":"[\n \"def _is_installed(self):\\n return self._system.is_library(os.path.join(self.get_install_path(), \\\"lib/libG4event\\\")) or \\\\\\n self._system.is_library(os.path.join(self.get_install_path(), \\\"lib64/libG4event\\\"))\",\n \"def check_requirements():\\n process_output = subprocess.check_output([sys.executable, '-m', 'pip', 'freeze'])\\n installed_packages = [r.decode().split('==')[0] for r in process_output.split()]\\n if 'pandas' and 'matplotlib' in installed_packages:\\n return True\\n else:\\n print('You don`t have one of required libralies\\\\n'\\n 'I can`t create histogram\\\\n'\\n 'Required libralies: \\\\n'\\n '->pandas\\\\n'\\n '->matplotlib\\\\n')\\n return False\",\n \"def _is_rpm_all_lib_include_files_installed(self):\\n return False\",\n \"def is_installed(cls):\\n return find_spec_or_loader(cls.module) is not None\",\n \"def _sufficient_deps(cls, deps):\\n if cls.MODEL_PACKAGE is None:\\n return True\\n else:\\n for d in deps.conda:\\n if cls.MODEL_PACKAGE in d:\\n return True\\n for d in deps.pip:\\n if cls.MODEL_PACKAGE in d:\\n return True\\n return False\",\n \"def install_dependencies(self):\\n return False\",\n \"def _is_rpm_all_lib_include_files_installed(self):\\n return self.rpm.is_package_installed('rpm-devel')\",\n \"def _is_installed(self):\\n header = self._system.file_exists(\\\"curl.h\\\", os.path.join(self.get_install_path(), \\n \\\"include/curl\\\"))\\n lib = self._system.library_exists(\\\"libcurl\\\", os.path.join(self.get_install_path(), \\\"lib\\\"))\\n config = self._system.file_exists(\\\"curl-config\\\", os.path.join(self.get_install_path(), \\\"bin\\\"))\\n return header and lib and config\",\n \"def is_lib_available(library):\\n try:\\n __import__(library)\\n return True\\n except ImportError:\\n return False\",\n \"def is_installed(self):\\n try:\\n self.import_solver()\\n return True\\n except ImportError:\\n return False\",\n \"def dependencies_met():\\n # Check Java VM command line runner.\\n try:\\n Popen(['java'], shell=False, stderr=PIPE).communicate()[1]\\n except:\\n print 'Dependecy unmet. Java virtual machine command line runner not ' \\\\\\n 'found.'\\n return False\\n # Check selenium-server.jar is ready to run.\\n output = Popen(('java -jar %s -unrecognized_argument' % SELENIUM_RC_PATH\\n ).split(), shell=False, stderr=PIPE).communicate()[1]\\n if not re.search('Usage: java -jar selenium-server.jar', output):\\n print 'Dependecy unmet. Selenium RC server (selenium-server.jar) not ' \\\\\\n 'found.'\\n return False\\n # Check selenium RC python driver is available.\\n try:\\n import selenium\\n except:\\n print 'Dependecy unmet. Selenium RC python driver (selenium.py) not ' \\\\\\n 'found.'\\n return False\\n # Check CherryPy wsgi server is available.\\n try:\\n import wsgiserver\\n except:\\n print 'Dependecy unmet. CherryPy wsgi server (wsgiserver.py) not found.'\\n return False\\n # Check fixture support is implemented for the database engine.\\n if not settings.DATABASE_ENGINE in ['sqlite3', 'postgresql_psycopg2']:\\n print 'Dependecy unmet. Fixture support for database engine %s not ' \\\\\\n 'implemented.' % settings.DATABASE_ENGINE\\n return False\\n return True\",\n \"def is_installed(self):\\n pass\",\n \"def installable(self):\\n for pm_name in self._provider_package:\\n if helpers[pm_name]:\\n return True\\n return False\",\n \"def is_installed():\\n # type: () -> bool\\n try:\\n # noinspection PyUnresolvedReferences\\n from ccurl import Curl as CCurl\\n except ImportError:\\n return False\\n else:\\n # noinspection PyUnresolvedReferences\\n from iota.crypto import Curl\\n return issubclass(Curl, CCurl)\",\n \"def check_missing_dep():\\n global MISSING_PACKAGES, INSTALLED_PACKAGES, ENABLE_CUDA\\n if ENABLE_CUDA and IS_MACOS:\\n REQUIRED_PACKAGES.extend(MACOS_REQUIRED_PACKAGES)\\n MISSING_PACKAGES = []\\n for pkg in REQUIRED_PACKAGES:\\n key = pkg.split(\\\"==\\\")[0]\\n if key not in INSTALLED_PACKAGES:\\n MISSING_PACKAGES.append(pkg)\\n continue\\n else:\\n if len(pkg.split(\\\"==\\\")) > 1:\\n if pkg.split(\\\"==\\\")[1] != INSTALLED_PACKAGES.get(key):\\n MISSING_PACKAGES.append(pkg)\\n continue\",\n \"def checkRequiredDependencies(self):\\n \\n # skip dependency check for downloading only\\n if( self.downloadOnly ):\\n return\\n\\n # hard dependencies\\n for req in self.reqmodules:\\n if( self.parent.module(req) == None ):\\n # check if there is an auto detected module\\n if( self.parent.module(req, True) == None ):\\n self.abort( self.name + \\\" requires \\\" + req \\\\\\n + \\\" and it wasn't found in your config file!!\\\" )\\n else:\\n # use auto detected module\\n self.parent.use( self.parent.module(req, True) )\\n self.parent.module( req ).init()\\n\\n print self.name + \\\": auto-detected \\\" + req + \\\" version \\\" + self.parent.module( req ).version\\n \\n # build only dependencies\\n if( self.mode == \\\"install\\\" ):\\n mods = self.reqmodules_buildonly + self.reqmodules_external\\n for req in mods:\\n if( self.parent.module(req) == None ):\\n # check if there is an auto detected module\\n if( self.parent.module(req, True) == None ):\\n self.abort( req + \\\" not found in your config file!! \\\" + self.name \\\\\\n + \\\" cannot be built without \\\" + req )\\n else:\\n # use auto detected module\\n self.parent.use( self.parent.module(req, True) )\\n self.parent.module( req ).init()\\n\\n print \\\" - \\\" + self.name + \\\": auto-detected \\\" + req + \\\" version \\\" + self.parent.module( req ).version\",\n \"def check_dependencies(cls):\\n\\n missing = []\\n for name in cls.DEPENDENCIES:\\n try:\\n import_module(name)\\n except ModuleNotFoundError:\\n missing.append(name)\\n\\n if any(missing):\\n msg = ('The sup3r stitching module depends on the following '\\n 'special dependencies that were not found in the active '\\n 'environment: {}'.format(missing))\\n logger.error(msg)\\n raise ModuleNotFoundError(msg)\",\n \"def packages_required(package_names):\\n # info(\\\"packages_required(%s)\\\" % repr(package_names))\\n # noinspection PyBroadException\\n try:\\n result = True\\n\\n # info(package_names)\\n # info(__pip_list)\\n for requirement in [Requirement(name) for name in package_names]:\\n if requirement.supported_python():\\n pkg_name = requirement.package\\n if pkg_name.lower() not in __pip_list:\\n try:\\n # info('__import__(\\\"{name}\\\")'.format(name=pkg_name))\\n __import__(pkg_name)\\n except ImportError:\\n info(pkg_name + \\\" not installed!\\\")\\n missing_modules.append(pkg_name)\\n result = False\\n return result\\n except Exception:\\n return False\",\n \"def is_installed(self):\\n return not self.dont_install\",\n \"def check_requirements(self):\\n try:\\n __import__('requests')\\n except ImportError:\\n raise Warning(\\n \\\"requests Python library is required for freemobile notifications. \\\")\",\n \"def has_composed_rpm_bulid_libs(self):\\n return self.version_info >= (4, 9, 0)\",\n \"def verify_package_status(self):\\n # rpm-libs is required for /usr/lib64/librpm*.so\\n self.rpm.verify_packages_installed(['rpm-libs'])\\n\\n # Check RPM so files to build the Python binding.\\n message_format = '''\\nRPM: {0} or\\nRPM download tool (dnf-plugins-core (dnf) or yum-utils (yum)) required.\\nInstall any of those.\\n'''\\n if self.rpm.has_composed_rpm_bulid_libs():\\n if (not self.rpm.is_package_installed('rpm-build-libs')\\n and not self.rpm.is_downloadable()):\\n raise InstallError(message_format.format('rpm-build-libs'))\\n else:\\n # All the needed so files are included in rpm-libs package.\\n pass\",\n \"def _check_install(self):\\n\\n try:\\n # Get chrome version\\n chrome_version = subprocess.run(\\\"google-chrome --version\\\", shell=True, capture_output=True, text=True, check=True).stdout\\n chrome_version_number = chrome_version.split(' ')[2]\\n chrome_version_number = '.'.join(chrome_version_number.split('.')[0:3])\\n\\n # Get driver version\\n driver_version = subprocess.run(\\\"chromedriver --version\\\", shell=True, capture_output=True, text=True, check=True).stdout\\n driver_version_number = driver_version.split(' ')[1]\\n driver_version_number = '.'.join(driver_version_number.split('.')[0:3])\\n\\n # https://chromedriver.chromium.org/downloads/version-selection\\n return True if chrome_version_number == driver_version_number else False\\n\\n # If there is an exception, that means the install is missing\\n except subprocess.CalledProcessError:\\n return False\",\n \"def available():\\n if \\\"MojoPipeline\\\" not in h2o.cluster().list_core_extensions():\\n print(\\\"Cannot use MOJO Pipelines - runtime was not found.\\\")\\n return False\\n else:\\n return True\",\n \"def _resolve_libraries(self):\\n # Merge any custom libs needed by rules, etc\\n libs_to_install = self.REQUIRED_LIBS.union(\\n set(self.config['global']['general'].get('third_party_libraries', []))\\n )\\n\\n LOGGER.info('Installing libraries: %s', ', '.join(libs_to_install))\\n pip_command = ['pip', 'install']\\n pip_command.extend(libs_to_install)\\n pip_command.extend(['--no-cache-dir', '--upgrade', '--target', self.temp_package_path])\\n\\n # Return True if the pip command is successfully run\\n return run_command(pip_command, cwd=self.temp_package_path, quiet=True)\",\n \"def check_dependencies():\\n required_found = True\\n recommended_found = True\\n print 'Checking dependencies ...\\\\n'\\n print 'Required dependencies:'\\n try:\\n import Image\\n assert Image.VERSION >= '1.1.5'\\n print ' Python Imaging Library ....... OK'\\n except ImportError:\\n print ' !!! Python Imaging Library ... Not found'\\n required_found = False\\n except AssertionError:\\n print ' !!! Python Imaging Library ... version', Image.VERSION,\\n print 'found'\\n print ' !!! Python Imaging Library 1.1.5 or higher is required'\\n required_found = False\\n if not required_found:\\n print '\\\\nCould not find all required dependencies!'\\n print 'Please install them and try again.'\\n sys.exit(1)\\n print\",\n \"def is_installed(self):\\r\\n return bool(self.pip_requirement.satisfied_by)\",\n \"def is_library_installed(name):\\n\\n return pm.is_library_installed(name)\",\n \"def is_library_installed(name):\\n\\n return __item_installed(name, dtf.core.item.TYPE_LIBRARY)\",\n \"def check_dependencies():\\n check_python_version()\\n\\n dependencies = [\\\"sqlite3\\\"]\\n\\n for dependency in dependencies:\\n try:\\n __import__(dependency)\\n except ImportError as e:\\n raise CuckooStartupError(\\\"Unable to import \\\\\\\"%s\\\\\\\"\\\" % dependency)\\n\\n return True\",\n \"def _check_dependencies(self):\\n imgmin = exists('imgmin')\\n image_optim = exists('image_optim')\\n\\n if not imgmin or not image_optim:\\n puts(p('Dependencies have not been installed:'))\\n\\n message = 'imgmin - https://github.com/rflynn/imgmin'\\n message = s('✓ ' + message) if imgmin else e('✗ ' + message)\\n puts(message)\\n\\n message = 'image_optim - http://rubygems.org/gems/image_optim'\\n message = s('✓ ' + message) if image_optim else e('✗ ' + message)\\n puts(message)\\n\\n sys.exit(0)\",\n \"def has_setup(self):\\n\\n return hasattr(self._module, 'setup')\",\n \"def __bool__(self):\\n return self.installed\",\n \"def test_is_installed():\\n assert _is_installed('coverage') is True # regular dependency\\n assert _is_installed('pytest') is True # dev dependency\\n assert _is_installed('missing') is False # missing dependency\",\n \"def check_system_dependencies():\\n out_info(\\\"Checking System Dependencies...\\\")\\n check_cmake()\\n if OS_VERSION[0] == \\\"Windows\\\":\\n check_visual_studio()\\n check_cplus_plus()\\n if OS_VERSION[0] == \\\"Linux\\\":\\n check_gcc()\\n check_gpp()\",\n \"def is_python_binding_installed(self):\\n is_installed = False\\n is_install_error = False\\n\\n try:\\n is_installed = self.is_python_binding_installed_on_pip()\\n except InstallError:\\n # Consider a case of pip is not installed in old Python (<= 2.6).\\n is_install_error = True\\n if not is_installed or is_install_error:\\n for rpm_dir in self.python_lib_rpm_dirs:\\n init_py = os.path.join(rpm_dir, '__init__.py')\\n if os.path.isfile(init_py):\\n is_installed = True\\n break\\n\\n return is_installed\",\n \"def test_dependencies_are_installed(self):\\n installed = [p['id'] for p in self.qi.listInstalledProducts()]\\n self.assertIn('plone.restapi', installed)\\n self.assertIn('plone.app.contenttypes', installed)\\n self.assertIn('plone.app.multilingual', installed)\",\n \"def test_dependencies_installed(self):\\n installer = getattr(self.portal, 'portal_quickinstaller')\\n self.assertTrue(installer.isProductInstalled('plone.app.dexterity'))\",\n \"def check_pkg_consistency():\\n pass\",\n \"def is_installed(self):\\n cmd = 'cargo install --list | grep \\\"^{}\\\"'\\n\\n if not run(cmd.format(self.package), stdout=Capture(),\\n stderr=Capture()).returncode:\\n return True\\n\\n return False\",\n \"def _package_available(package_name: str) -> bool:\\n try:\\n return find_spec(package_name) is not None\\n except ModuleNotFoundError:\\n return False\",\n \"async def _async_has_devices(opp: OpenPeerPower) -> bool:\\n # TODO Check if there are any devices that can be discovered in the network.\\n devices = await opp.async_add_executor_job(my_pypi_dependency.discover)\\n return len(devices) > 0\",\n \"def _get_dependencies():\\n return config.check_driver_dependencies(__virtualname__, {\\\"XenAPI\\\": HAS_XEN_API})\",\n \"def glogging_available():\\n package_check = False\\n\\n # Is the glogging package available?\\n try:\\n from gunicorn import glogging\\n except ImportError:\\n pass\\n else:\\n package_check = True\\n \\n return package_check\",\n \"def check_deps(self):\\n\\t\\tfor plugin in self.__plugins.values():\\n\\t\\t\\tif plugin.require:\\n\\t\\t\\t\\tfor req in plugin.require.split(','):\\n\\t\\t\\t\\t\\tn, v = req.split(':')\\n\\t\\t\\t\\t\\tif not n in self.__plugins or \\\\\\n\\t\\t\\t\\t\\tv != self.__plugins[n].version:\\n\\t\\t\\t\\t\\t\\treturn False\\n\\t\\treturn True\",\n \"def already_installed(lib_spec, options, working_set=None):\\n if 'path' in options:\\n return False\\n\\n if not working_set:\\n working_set=pkg_resources.WorkingSet(sys.path)\\n\\n try:\\n working_set.require(lib_spec)\\n except pkg_resources.ResolutionError:\\n return False\\n else:\\n return True\",\n \"def get_dependencies():\\n return config.check_driver_dependencies(\\n __virtualname__, {\\\"profitbricks\\\": HAS_PROFITBRICKS}\\n )\",\n \"def template_library_installed(self) -> Optional[bool]:\\n return pulumi.get(self, \\\"template_library_installed\\\")\",\n \"def check_prerequisites() -> None:\\n # check black code formatter is installed\\n if not is_installed(\\\"black\\\"):\\n raise FileNotFoundError(\\n \\\"Cannot find black code formatter! To install, please follow this link: https://black.readthedocs.io/en/stable/installation_and_usage.html\\\"\\n )\\n\\n # check isort code formatter is installed\\n if not is_installed(\\\"isort\\\"):\\n raise FileNotFoundError(\\n \\\"Cannot find isort code formatter! To install, please follow this link: https://pycqa.github.io/isort/#installing-isort\\\"\\n )\\n\\n # check protolint code formatter is installed\\n if subprocess.call(f\\\"{base_protolint_command()} version\\\", shell=True) != 0: # nosec\\n raise FileNotFoundError(\\n \\\"Cannot find protolint protocol buffer schema file linter! To install, please follow this link: https://github.com/yoheimuta/protolint.\\\"\\n )\\n\\n # check protocol buffer compiler is installed\\n if not is_installed(\\\"protoc\\\"):\\n raise FileNotFoundError(\\n \\\"Cannot find protocol buffer compiler! To install, please follow this link: https://developers.google.com/protocol-buffers/\\\"\\n )\",\n \"def installed(self) -> bool:\\n return self._installed\",\n \"def requirement_missing(script):\\n if \\\"requires\\\" in script:\\n if script[\\\"requires\\\"] is None:\\n return False\\n for package in script[\\\"requires\\\"].split():\\n try:\\n pkg_resources.working_set.require(package)\\n except Exception:\\n return True\\n return False\",\n \"def check_libraries(env):\\n # Detect OS X python installation, and attempt to correct for it.\\n if os.uname()[0] == 'Darwin':\\n env.Replace(SHLINKFLAGS='$LINKFLAGS -bundle -flat_namespace -undefined suppress')\\n env.Replace(SHLIBSUFFIX='.so')\\n if os.path.isdir('/opt/local'):\\n env.Append(\\n LIBPATH=['/opt/local/lib'],\\n CPPPATH=['/opt/local/include']\\n )\\n\\n # Detect the presence of necessary dependencies.\\n conf = Configure(env)\\n\\n if not conf.CheckLibWithHeader('m', 'math.h', 'c'):\\n print \\\"Can't find standard math libraries.\\\"\\n Exit(1)\\n\\n if not conf.CheckLibWithHeader('python%s' % python_version,\\n 'Python.h', 'c'):\\n print \\\"Can't find python %s.\\\" % python_version\\n Exit(1)\\n\\n env = conf.Finish()\\n\\n return env\",\n \"def check_requirements():\\n\\n # Which programs are reqired?\\n required_programs = ['virtualbox', 'vagrant']\\n\\n # Make sure the required programs are installed.\\n for program in required_programs:\\n\\n # What's the path to the executable?\\n try:\\n subprocess.check_output(['which', program])\\n except subprocess.CalledProcessError:\\n message = \\\"Please install \\\" + program + \\\" before proceeding.\\\"\\n Utilities.log(message)\\n exit(1)\",\n \"def test_packages_present(self):\\n packages = [\\\"ca-certificates\\\", \\\"sudo\\\", \\\"wget\\\", \\\"unzip\\\"]\\n for pkg in packages:\\n with self.subTest(package=pkg):\\n self.assertTrue(self.host.package(pkg).is_installed)\",\n \"def is_installed(self):\\n cmd = 'conda list {} | grep \\\"^{}\\\"'\\n\\n if not run(cmd.format(self.package, self.package), stdout=Capture(),\\n stderr=Capture()).returncode:\\n return True\\n\\n return False\",\n \"def check_multiprocessing():\\n\\n try:\\n import multiprocessing\\n except ImportError:\\n return False\\n return True\",\n \"def check_install():\\n if platform.dist()[0] not in ['fedora', 'redhat', 'centos']:\\n print \\\"{} not supported\\\".format(platform.dist()[0])\\n sys.exit(1)\\n print \\\"\\\\ndetected {} {} ...\\\".format(platform.dist()[0], platform.dist()[1])\\n\\n import yum\\n # Remove loggin. Taken from: https://stackoverflow.com/a/46716482\\n from yum.logginglevels import __NO_LOGGING\\n yumloggers = [\\n 'yum.filelogging.RPMInstallCallback', 'yum.verbose.Repos',\\n 'yum.verbose.plugin', 'yum.Depsolve', 'yum.verbose', 'yum.plugin',\\n 'yum.Repos', 'yum', 'yum.verbose.YumBase', 'yum.filelogging',\\n 'yum.verbose.YumPlugins', 'yum.RepoStorage', 'yum.YumBase',\\n 'yum.filelogging.YumBase', 'yum.verbose.Depsolve'\\n ]\\n for loggername in yumloggers:\\n logger = logging.getLogger(loggername)\\n logger.setLevel(__NO_LOGGING)\\n\\n yumbase = yum.YumBase()\\n pkg = 'Percona-XtraDB-Cluster-server-<%= @percona_major_version %>'\\n if yumbase.rpmdb.searchNevra(name=pkg):\\n pkg_list = yumbase.rpmdb.searchNevra(name=pkg)\\n print 'detected {} ...'.format(pkg_list[0])\\n else:\\n print \\\"{}{} not installed{}\\\".format(RED, pkg, WHITE)\\n sys.exit(1)\\n return 'percona'\",\n \"def is_mnc_installed(self):\\n return self.is_installed('minicap') and self.is_installed('minicap.so')\",\n \"def has_set_up_py_in(self):\\n return (self.version_info >= (4, 10))\",\n \"def checkCUDAisAvailable():\\n # some possible lib names \\n libnames = ('libcuda.so', 'libcuda.dylib', 'cuda.dll')\\n libsOk = True\\n for libname in libnames:\\n try:\\n cuda = ctypes.CDLL(libname)\\n except OSError:\\n continue\\n else:\\n break\\n else:\\n libsOk = False\\n return libsOk\",\n \"def required():\\n kernel = __salt__['grains.item']('os') # pylint: disable=E0602,E0603\\n\\n # Disable rebooting for HDP clusters until that works reliably\\n hadoop_distro = __salt__['pillar.get']('hadoop.distro') # pylint: disable=E0602,E0603\\n if hadoop_distro == 'HDP':\\n return False\\n\\n if kernel['os'] == \\\"CentOS\\\" or kernel['os'] == \\\"RedHat\\\":\\n try:\\n current_version = __salt__['cmd.run']('uname -r') # pylint: disable=E0602,E0603\\n latest_version = __salt__['cmd.run']('rpm -q --last kernel') # pylint: disable=E0602,E0603\\n latest_version = latest_version.split(\\\" \\\")\\n latest_version = [\\n version for version in latest_version if 'kernel' in version]\\n latest_version = str(latest_version[0]).strip('kernel-') # pylint: disable=E1310\\n if current_version == latest_version:\\n return False\\n except: # pylint: disable=W0702\\n return False\\n return True\\n\\n return __salt__['file.file_exists']('/var/run/reboot-required') # pylint: disable=E0602,E0603\",\n \"def is_installed(self):\\n\\n package_manager = self.get_available_package_manager()\\n command = self.CHECKER_COMMANDS[package_manager]\\n package = self.package[package_manager]\\n return not run(command.format(package),\\n stdout=Capture(), stderr=Capture()).returncode\",\n \"def has_torch_package() -> bool:\\n try:\\n import torch # pyright: reportMissingImports=false\\n\\n return True\\n except ImportError:\\n Logger(log_folder=None).warning_once(\\n \\\"`torch` not available. Some functionality may be missing.\\\"\\n )\\n return False\",\n \"def is_installed(cls, language=None):\\n if language == 'python':\\n return True\\n return False\",\n \"def _check_required_if_provider(self):\\n return\",\n \"def dependency_check(dependency_set=CORE, exit_on_failure=True):\\n verify_python_version()\\n \\n disable_warnings()\\n\\n platform = get_current_platform()\\n\\n #\\n # Check for missing python modules\\n #\\n failed_deps = []\\n pip_distributions = pip.get_installed_distributions()\\n \\n for w3af_req in platform.PIP_PACKAGES[dependency_set]:\\n for dist in pip_distributions:\\n if w3af_req.package_name.lower() == dist.project_name.lower():\\n\\n w3af_req_version = str(Version(w3af_req.package_version))\\n dist_version = str(dist.version)\\n\\n if w3af_req_version == dist_version:\\n # It's installed and the version matches!\\n break\\n else:\\n failed_deps.append(w3af_req)\\n\\n #\\n # Check for missing operating system packages\\n #\\n missing_os_packages = []\\n for os_package in platform.SYSTEM_PACKAGES[dependency_set]:\\n if not platform.os_package_is_installed(os_package):\\n missing_os_packages.append(os_package)\\n \\n os_packages = list(set(missing_os_packages))\\n\\n # All installed?\\n if not failed_deps and not os_packages:\\n # False means: do not exit()\\n enable_warnings()\\n return False\\n\\n generate_requirements_txt(failed_deps)\\n script_path = generate_helper_script(platform.PKG_MANAGER_CMD, os_packages,\\n platform.PIP_CMD, failed_deps)\\n\\n #\\n # Report the missing system packages\\n #\\n msg = ('w3af\\\\'s requirements are not met, one or more third-party'\\n ' libraries need to be installed.\\\\n\\\\n')\\n \\n if os_packages:\\n missing_pkgs = ' '.join(os_packages)\\n \\n msg += ('On %s systems please install the following operating'\\n ' system packages before running the pip installer:\\\\n'\\n ' %s %s\\\\n')\\n print(msg % (platform.SYSTEM_NAME, platform.PKG_MANAGER_CMD,\\n missing_pkgs))\\n \\n #\\n # Report all missing python modules\\n # \\n if failed_deps:\\n # pylint: disable=E1101\\n msg = ('Your python installation needs the following modules'\\n ' to run w3af:\\\\n')\\n msg += ' ' + ' '.join([fdep.module_name for fdep in failed_deps])\\n print(msg)\\n print('\\\\n')\\n # pylint: enable=E1101\\n \\n #\\n # Report missing pip packages\\n #\\n not_git_pkgs = [fdep for fdep in failed_deps if not fdep.is_git]\\n git_pkgs = [fdep.git_src for fdep in failed_deps if fdep.is_git]\\n \\n msg = ('After installing any missing operating system packages, use'\\n ' pip to install the remaining modules:\\\\n')\\n \\n if not_git_pkgs:\\n cmd = generate_pip_install_non_git(platform.PIP_CMD, not_git_pkgs)\\n msg += ' %s\\\\n' % cmd\\n \\n if git_pkgs:\\n for missing_git_pkg in git_pkgs:\\n msg += ' %s\\\\n' % generate_pip_install_git(platform.PIP_CMD,\\n missing_git_pkg)\\n \\n print(msg)\\n \\n msg = 'A script with these commands has been created for you at %s'\\n print(msg % script_path)\\n \\n enable_warnings()\\n platform.after_hook()\\n \\n if exit_on_failure:\\n sys.exit(1)\\n else:\\n return True\",\n \"def is_setup(self):\\n return self._market_data_sock_info.ready.is_set() and \\\\\\n self._orders_sock_info.ready.is_set()\",\n \"def is_third_party(self) -> bool:\\n for third_party_import_string in self.third_party_import_strings:\\n if self.source.startswith(third_party_import_string):\\n return True\\n\\n return False\",\n \"def check_dependencies():\\n\\n # Check for python version\\n print(\\\"Python location : {}\\\".format(sys.executable))\\n print(\\\"Python version : {}\\\".format(sys.version))\\n if sys.version_info[0] < 3:\\n warnings.warn(\\n \\\"WARNING : Using python 2. This Python version is no longer maintained. Use at your own risk.\\\"\\n )\\n\\n # Check FSL installation\\n try:\\n print(f\\\"Your fsl directory is located here: {os.environ['FSLDIR']}\\\")\\n except KeyError:\\n raise AssertionError(\\n \\\"You do not have FSL installed! See installation instructions here: https://fsl.fmrib.ox.ac.uk/fsl/fslwiki/FslInstallation\\\"\\n )\\n\\n # Check AFNI installation\\n try:\\n print(\\n f\\\"Your AFNI directory is located here: {subprocess.check_output('which afni', shell=True, universal_newlines=True)}\\\"\\n )\\n except subprocess.CalledProcessError:\\n raise AssertionError(\\n \\\"You do not have AFNI installed! See installation instructions here: https://afni.nimh.nih.gov/pub/dist/doc/htmldoc/background_install/main_toc.html\\\"\\n )\",\n \"def test_list_available_libraries(self):\\n _ = LibraryFactory.create(modulestore=self.store)\\n all_libraries = self.tools.list_available_libraries()\\n assert all_libraries\\n assert len(all_libraries) == 1\",\n \"def check_import():\\n print('[GenHub] Checking Python modules.')\\n\\n basemod = [('yaml', 'pyyaml'), ('pycurl', 'pycurl')]\\n devmod = ['pep8', 'pytest', 'pytest-cov', 'coverage']\\n\\n packages = dict()\\n for importname, packagename in basemod:\\n try:\\n importlib.import_module(importname)\\n packages[packagename] = True\\n except ImportError:\\n packages[packagename] = False\\n for packagename in devmod:\\n try:\\n importlib.import_module(packagename)\\n packages[packagename] = True\\n except ImportError:\\n packages[packagename] = False\\n\\n rundep = False\\n for pkg in packages:\\n char = '+'\\n msg = 'Installed.'\\n if packages[pkg] is False:\\n char = '-'\\n msg = 'Not installed!'\\n rundep = True\\n print('%c package %-12s: %s' % (char, pkg, msg))\\n if rundep is True:\\n print('Please install these dependencies before proceding')\\n print('')\",\n \"def check_required_commands(self):\\n # always check for `virtualenv` manager\\n venv_avail = utils.check_command_avail('virtualenv')\\n if not venv_avail:\\n raise Exception(\\\"Unable to find the `virtualenv` executable on \\\"\\n \\\"the system. Is virtualenv on the PATH?\\\")\\n # check for git as well but only fail if packages are actually\\n # marked to be installed from source\\n git_avail = utils.check_command_avail('git')\\n if not git_avail and self.has_source():\\n raise Exception(\\\"Unable to find `git` on the system but some \\\"\\n \\\"packages are defined to be installed from \\\"\\n \\\"source. Either install git or switch to \\\"\\n \\\"non-source installation!\\\")\",\n \"def list_support_required(self):\\n\\t\\treturn self.typemanager.has_lists\",\n \"def is_available():\",\n \"def checkLibraries(env):\\n # Detect OS X python installation, and attempt to correct for it.\\n if os.uname()[0] == 'Darwin':\\n env.Replace(SHLINKFLAGS='$LINKFLAGS -bundle -flat_namespace -undefined suppress')\\n env.Replace(SHLIBSUFFIX='.so')\\n\\n # Detect the presence of necessary dependencies.\\n conf = Configure(env)\\n\\n if not conf.CheckLibWithHeader('m', 'math.h', 'c'):\\n print \\\"Can't find standard math libraries.\\\"\\n Exit(1)\\n\\n env = conf.Finish()\\n\\n return env\",\n \"def has_icecube_package() -> bool:\\n try:\\n import icecube # pyright: reportMissingImports=false\\n from icecube import icetray, dataio\\n\\n return True\\n except ImportError:\\n Logger(log_folder=None).warning_once(\\n \\\"`icecube` not available. Some functionality may be missing.\\\",\\n )\\n return False\",\n \"def checkDependencies(check=True):\\n modules = []\\n f = open(CONST_REQUIREMENTS_FILE)\\n for line in f:\\n if line.find('#'):\\n modules.append([line[:line.index('=')], (line[line.index('=')+2:]).strip()])\\n f.close()\\n\\n for module in modules:\\n try:\\n __import__(module[0])\\n except ImportError: \\n if query_user_bool(\\\"Missing module %s.\\\" \\\\\\n \\\" Do you wish to install it?\\\" % module[0]):\\n subprocess.call([\\\"pip2\\\", \\\"install\\\", \\\"%s==%s\\\" %\\n (module[0], module[1])])\\n \\n else:\\n return False\\n return True\",\n \"def check_supported_features(self):\",\n \"def requirements():\\n if fabric.api.sudo(\\\"grep 'release 7' /etc/redhat-release\\\",quiet=True).succeeded:\\n \\tprint blue(\\\"This is a Centos/RedHat 7 server. Please install AIDE.\\\")\\n \\treturn 1\\n if not rpm_is_installed('glibc.*i686'):\\n print red(\\\"GlibC i686 is not installed\\\")\\n if not file_exists(\\\"/usr/local/tripwire/tfs/bin/tripwire\\\", use_sudo=True):\\n print red(\\\"Tripwire is not installed\\\")\",\n \"def not_installed(self) -> bool:\\n return pulumi.get(self, \\\"not_installed\\\")\",\n \"def enabled():\\n installed = installedVersion()\\n required = MIN_DCM2NIIX_VERSION\\n return ((installed is not None) and\\n (compareVersions(installed, required) >= 0))\",\n \"def DEPENDENCIES(self):\\n pass\",\n \"def is_setup(self):\\n if self.pocs is None:\\n print_warning('POCS has not been setup. Please run `setup_pocs`')\\n return False\\n return True\",\n \"def checkOptionalDependencies(self):\\n \\n # skip dependency check for downloading only\\n if( self.downloadOnly ):\\n return\\n\\n # soft dependencies\\n failed = []\\n for opt in self.optmodules:\\n mod = self.parent.module(opt)\\n if( mod == None ):\\n failed.append(opt)\\n \\n # remove soft dependencies that were not found\\n self.buildWithout(failed)\",\n \"def check_requirements():\\n # Fail hard if Python does not have minimum required version\\n if sys.version_info < (3, 3) and sys.version_info[:2] != (2, 7):\\n raise SystemExit('PyInstaller requires at least Python 2.7 or 3.3+.')\\n\\n if is_win:\\n try:\\n from PyInstaller.utils.win32 import winutils\\n pywintypes = winutils.import_pywin32_module('pywintypes')\\n except ImportError:\\n raise SystemExit('PyInstaller cannot check for assembly dependencies.\\\\n'\\n 'Please install PyWin32.\\\\n\\\\n'\\n 'pip install pypiwin32\\\\n')\",\n \"def detect_if_qlm():\\n try:\\n importlib.import_module(\\\"qat.linalg\\\")\\n print(\\\"=> Detected a QLM installation. <=\\\")\\n return True\\n except ModuleNotFoundError:\\n print(\\\"=> No QLM installation detected, adding myQLM to the dependencies. <=\\\")\\n return False\",\n \"def is_distribute_installed():\\n with settings(hide('running', 'warnings', 'stderr', 'stdout'), warn_only=True):\\n res = run('easy_install --version')\\n return res.succeeded and (res.find('distribute') >= 0)\",\n \"def pyoptsparse_installed():\\n # type: () -> bool\\n try:\\n from openmdao.api import pyOptSparseDriver\\n except ImportError:\\n print(PyOptSparseImportError().msg)\\n return False\\n return True\",\n \"def self_check(self):\\n out = \\\"Loaded components\\\\n\\\"\\n for package_name, package in sorted(self.packages.items()):\\n out += \\\"\\\\t%s:\\\\n\\\" % package_name\\n for c, fd in sorted(package.components.iteritems()):\\n out += \\\"\\\\t\\\\t%s (%s)\\\\n\\\" % (c, fd.filename)\\n\\n LOG.info(out)\\n\\n for p in self.packages.values():\\n for f in p.files:\\n for id in f.requires:\\n # This throws if it doesn't find something.\\n try:\\n self.get(id)\\n except:\\n LOG.exception(\\\"Error in: \\\" + f.filename)\\n raise\",\n \"def no_prereq_install():\\n return str2bool(os.environ.get('NO_PREREQ_INSTALL', 'False'))\",\n \"def _check_reqs():\\n errors = []\\n\\n if not HAS_TTP:\\n errors.append(missing_required_lib(\\\"ttp\\\"))\\n\\n return {\\\"errors\\\": errors}\",\n \"def test_powerlaw_package_available(self):\\n\\t\\timport importlib\\n\\n\\t\\ttry:\\n\\t\\t\\timportlib.import_module('powerlaw')\\n\\t\\texcept ImportError:\\n\\t\\t\\tself.fail(\\\"Failed to import powerlaw package\\\")\\n\\n\\t\\treturn\",\n \"def check_req_utils():\\n utils = (['dmenu', 'gpg', 'pass', 'xclip', 'exo-open', 'pkill'])\\n for util in utils:\\n if find_executable(util) is None:\\n print(\\\"ERROR: Util '{}' is missing, install it before proceeding! Exiting!\\\".format(util))\\n sys.exit(1)\",\n \"def exists():\\n return PYTHON_VERSION is not None\",\n \"def needsResolution (self):\\n return self.__unresolvedComponents is not None\",\n \"def check_library(self, **kw):\\n\\tself.check(\\n\\t\\tcompile_filename = [],\\n\\t\\tfeatures = 'link_lib_test',\\n\\t\\tmsg = 'Checking for libraries',\\n\\t\\t)\",\n \"def _check_all_systems_ready(self):\\n raise NotImplementedError()\",\n \"def is_setup_connected(self):\\n return bool(self.get_target_namespace())\",\n \"def check_requirements():\\n debug(\\\"check_requirements\\\")\\n needed = Requirements(Project).find_missing_requirements()\\n if needed:\\n info(\\\"Please add the following to your %s file:\\\\n\\\" % 'requirements.txt')\\n info(\\\"\\\\n\\\".join(str(needed)))\\n else:\\n info(\\\"Your %s includes all known herringlib task requirements\\\" % 'requirements.txt')\",\n \"def runipy_available():\\n try:\\n import runipy\\n except ImportError:\\n return False\\n return True\"\n]","isConversational":false},"negative_scores":{"kind":"list like","value":["0.77119476","0.7107023","0.70856094","0.69626397","0.69026285","0.68549293","0.68545127","0.6821311","0.6820046","0.68049216","0.6733924","0.6677433","0.6667949","0.6651116","0.65795135","0.65275687","0.6507767","0.6464986","0.6459156","0.6447705","0.64221764","0.6418463","0.63992983","0.6388802","0.63707364","0.63363236","0.6325816","0.63154227","0.6309567","0.630948","0.6308225","0.62920725","0.6266254","0.6225911","0.61857253","0.61510986","0.6141804","0.6110594","0.6091485","0.60846245","0.6082131","0.60678375","0.60572064","0.6052707","0.6046948","0.6035045","0.6011657","0.6002685","0.5992703","0.5978664","0.59752846","0.5956483","0.59539384","0.59532565","0.59491646","0.5935644","0.59311694","0.59293824","0.59284717","0.59281415","0.5914746","0.5902991","0.5901474","0.5896566","0.5892459","0.5874723","0.58692545","0.58613896","0.58435243","0.5837677","0.582598","0.582075","0.5819588","0.5815965","0.5807981","0.580473","0.5799671","0.57991844","0.5797533","0.5796606","0.57943517","0.5792763","0.57781065","0.57554555","0.57525456","0.57454985","0.5741994","0.57383657","0.5724753","0.5716199","0.5714951","0.5714795","0.57140595","0.5703085","0.56973064","0.5695614","0.5694656","0.5694091","0.56834924","0.5682397"],"string":"[\n \"0.77119476\",\n \"0.7107023\",\n \"0.70856094\",\n \"0.69626397\",\n \"0.69026285\",\n \"0.68549293\",\n \"0.68545127\",\n \"0.6821311\",\n \"0.6820046\",\n \"0.68049216\",\n \"0.6733924\",\n \"0.6677433\",\n \"0.6667949\",\n \"0.6651116\",\n \"0.65795135\",\n \"0.65275687\",\n \"0.6507767\",\n \"0.6464986\",\n \"0.6459156\",\n \"0.6447705\",\n \"0.64221764\",\n \"0.6418463\",\n \"0.63992983\",\n \"0.6388802\",\n \"0.63707364\",\n \"0.63363236\",\n \"0.6325816\",\n \"0.63154227\",\n \"0.6309567\",\n \"0.630948\",\n \"0.6308225\",\n \"0.62920725\",\n \"0.6266254\",\n \"0.6225911\",\n \"0.61857253\",\n \"0.61510986\",\n \"0.6141804\",\n \"0.6110594\",\n \"0.6091485\",\n \"0.60846245\",\n \"0.6082131\",\n \"0.60678375\",\n \"0.60572064\",\n \"0.6052707\",\n \"0.6046948\",\n \"0.6035045\",\n \"0.6011657\",\n \"0.6002685\",\n \"0.5992703\",\n \"0.5978664\",\n \"0.59752846\",\n \"0.5956483\",\n \"0.59539384\",\n \"0.59532565\",\n \"0.59491646\",\n \"0.5935644\",\n \"0.59311694\",\n \"0.59293824\",\n \"0.59284717\",\n \"0.59281415\",\n \"0.5914746\",\n \"0.5902991\",\n \"0.5901474\",\n \"0.5896566\",\n \"0.5892459\",\n \"0.5874723\",\n \"0.58692545\",\n \"0.58613896\",\n \"0.58435243\",\n \"0.5837677\",\n \"0.582598\",\n \"0.582075\",\n \"0.5819588\",\n \"0.5815965\",\n \"0.5807981\",\n \"0.580473\",\n \"0.5799671\",\n \"0.57991844\",\n \"0.5797533\",\n \"0.5796606\",\n \"0.57943517\",\n \"0.5792763\",\n \"0.57781065\",\n \"0.57554555\",\n \"0.57525456\",\n \"0.57454985\",\n \"0.5741994\",\n \"0.57383657\",\n \"0.5724753\",\n \"0.5716199\",\n \"0.5714951\",\n \"0.5714795\",\n \"0.57140595\",\n \"0.5703085\",\n \"0.56973064\",\n \"0.5695614\",\n \"0.5694656\",\n \"0.5694091\",\n \"0.56834924\",\n \"0.5682397\"\n]","isConversational":false},"document_score":{"kind":"string","value":"0.57553107"},"document_rank":{"kind":"string","value":"84"}}},{"rowIdx":22696,"cells":{"query":{"kind":"string","value":"r\"\"\"Close a registry entry."},"document":{"kind":"string","value":"def close_registry_entry(cls, value):\n out = False\n if not value.closed: # pragma: debug\n value.close()\n out = True\n return out"},"metadata":{"kind":"string","value":"{\n \"objective\": {\n \"self\": [],\n \"paired\": [],\n \"triplet\": [\n [\n \"query\",\n \"document\",\n \"negatives\"\n ]\n ]\n }\n}"},"negatives":{"kind":"list like","value":["def close(self):\n self.__exit__(None, None, None)","def close(self):\n self.__exit__(None, None, None)","def close(self):\n \n self.__exit__(None, None, None)\n return","def __del__(self):\n for key_path_prefix, registry_file in iter(self._registry_files.items()):\n self._registry_files[key_path_prefix] = None\n if registry_file:\n registry_file.Close()","def close(self):\n self.exit()","def close():\n sys.exit()","def close_registrar(self):\n self.registrar.destroy()","def delete_registry_value(hive, key, value):\n access = winreg.KEY_ALL_ACCESS\n with winreg.OpenKeyEx(hive, key, 0, access) as k:\n winreg.DeleteValue(k, value)","def close(self):\n\n self.r.close()","def __exit__(self, type, value, traceback):\n self.close()","def __exit__(self, type, value, traceback):\n self.close()","def __exit__(self, type, value, traceback):\n self.close()","def __exit__(self, type, value, traceback):\n self.close()","def __exit__(self, type, value, traceback):\n self.close()","def close(self):\n\t\t_moRedis.delete(self.__id)","def close(self):\n self.call('close')","def __exit__(self, type_, value, traceback):\n self.close()","def close():","def close(self) -> None:\n ...","def close(self) -> None:\n ...","def exit(self):\n self.close()","def close_and_exit(self):\n self.close()\n sys.exit(1)","def Close(self):","def close(self):\n self._command = \"close\"","def __exit__(self, type, value, traceback):\n\n self.close()","def close(self):\n ...","def close(self):\n ...","def close(self):\n ckresult(_dll.FMOD_System_Close(self._ptr))","def close( self ):\n self.__del__()","def close(self):\n self.__CheckOpen('close')\n self.__closed = True","def close(self):\n self._close = True","def close(self):","def close(self):","def close(self):","def close(self):","def close(self):","def close(self):","def close(self):","def close(self):","def close(self):","def close(self):","def close(self) -> None:\n self._close_impl(invalidate=False)","def close(self):\n self.handle.close()","def close(self) -> None:","def close(self) -> None:","def close(self) -> None:","def close(self) -> None:","def close(self) -> None:","def close(self) -> None:","def close(self) -> None:","def close(self) -> None:","def close(self):\n self.k2000.close()","def close(self):\r\n pass","def close(self):\n pass","def close(self):\n pass","def close(self):\n pass","def close(self):\n pass","def close(self):\n pass","def close(self):\n pass","def close(self):\n pass","def close(self):\n pass","def close(self):\n pass","def close(self):\n pass","def close(self):\n pass","def close(self):\n pass","def close(self):\n pass","def close(self):\n pass","def close(self):\n pass","def close(self):\n pass","def close(self):\n pass","def close(self):\n pass","def close(self):\n pass","def close(self):\n pass","def close(self):\n pass","def close(self):\n pass","def close(self):\n pass","def close(self):\n pass","def close(self):\n pass","def close(self):\n pass","def close(self):\n pass","def close(self):\n pass","def close (self):\n pass","def close(self):\r\n pass","def close(self):\r\n pass","def close(self):\r\n pass","def __exit__(self, type, value, traceback) :\n if self.spec :\n self.handle.close()\n self.handle = None","def closeEvent(self, event):\n\n sys.exit()","def close(self):\n self.__closed = True","def close(self):\n self.__closed = True","def close (self):\n pass\n #TODO: implement more realistic closing semantics","def close(self):\n self.closed = True","def close(cls):\n pass","def close(*args):","def close(*args):","def close(*args):","def close(*args):","def close(*args):","def close(self) -> None:\r\n pass","def close():\n\trfm.close()","def close(self) -> None:\n pass"],"string":"[\n \"def close(self):\\n self.__exit__(None, None, None)\",\n \"def close(self):\\n self.__exit__(None, None, None)\",\n \"def close(self):\\n \\n self.__exit__(None, None, None)\\n return\",\n \"def __del__(self):\\n for key_path_prefix, registry_file in iter(self._registry_files.items()):\\n self._registry_files[key_path_prefix] = None\\n if registry_file:\\n registry_file.Close()\",\n \"def close(self):\\n self.exit()\",\n \"def close():\\n sys.exit()\",\n \"def close_registrar(self):\\n self.registrar.destroy()\",\n \"def delete_registry_value(hive, key, value):\\n access = winreg.KEY_ALL_ACCESS\\n with winreg.OpenKeyEx(hive, key, 0, access) as k:\\n winreg.DeleteValue(k, value)\",\n \"def close(self):\\n\\n self.r.close()\",\n \"def __exit__(self, type, value, traceback):\\n self.close()\",\n \"def __exit__(self, type, value, traceback):\\n self.close()\",\n \"def __exit__(self, type, value, traceback):\\n self.close()\",\n \"def __exit__(self, type, value, traceback):\\n self.close()\",\n \"def __exit__(self, type, value, traceback):\\n self.close()\",\n \"def close(self):\\n\\t\\t_moRedis.delete(self.__id)\",\n \"def close(self):\\n self.call('close')\",\n \"def __exit__(self, type_, value, traceback):\\n self.close()\",\n \"def close():\",\n \"def close(self) -> None:\\n ...\",\n \"def close(self) -> None:\\n ...\",\n \"def exit(self):\\n self.close()\",\n \"def close_and_exit(self):\\n self.close()\\n sys.exit(1)\",\n \"def Close(self):\",\n \"def close(self):\\n self._command = \\\"close\\\"\",\n \"def __exit__(self, type, value, traceback):\\n\\n self.close()\",\n \"def close(self):\\n ...\",\n \"def close(self):\\n ...\",\n \"def close(self):\\n ckresult(_dll.FMOD_System_Close(self._ptr))\",\n \"def close( self ):\\n self.__del__()\",\n \"def close(self):\\n self.__CheckOpen('close')\\n self.__closed = True\",\n \"def close(self):\\n self._close = True\",\n \"def close(self):\",\n \"def close(self):\",\n \"def close(self):\",\n \"def close(self):\",\n \"def close(self):\",\n \"def close(self):\",\n \"def close(self):\",\n \"def close(self):\",\n \"def close(self):\",\n \"def close(self):\",\n \"def close(self) -> None:\\n self._close_impl(invalidate=False)\",\n \"def close(self):\\n self.handle.close()\",\n \"def close(self) -> None:\",\n \"def close(self) -> None:\",\n \"def close(self) -> None:\",\n \"def close(self) -> None:\",\n \"def close(self) -> None:\",\n \"def close(self) -> None:\",\n \"def close(self) -> None:\",\n \"def close(self) -> None:\",\n \"def close(self):\\n self.k2000.close()\",\n \"def close(self):\\r\\n pass\",\n \"def close(self):\\n pass\",\n \"def close(self):\\n pass\",\n \"def close(self):\\n pass\",\n \"def close(self):\\n pass\",\n \"def close(self):\\n pass\",\n \"def close(self):\\n pass\",\n \"def close(self):\\n pass\",\n \"def close(self):\\n pass\",\n \"def close(self):\\n pass\",\n \"def close(self):\\n pass\",\n \"def close(self):\\n pass\",\n \"def close(self):\\n pass\",\n \"def close(self):\\n pass\",\n \"def close(self):\\n pass\",\n \"def close(self):\\n pass\",\n \"def close(self):\\n pass\",\n \"def close(self):\\n pass\",\n \"def close(self):\\n pass\",\n \"def close(self):\\n pass\",\n \"def close(self):\\n pass\",\n \"def close(self):\\n pass\",\n \"def close(self):\\n pass\",\n \"def close(self):\\n pass\",\n \"def close(self):\\n pass\",\n \"def close(self):\\n pass\",\n \"def close(self):\\n pass\",\n \"def close(self):\\n pass\",\n \"def close(self):\\n pass\",\n \"def close (self):\\n pass\",\n \"def close(self):\\r\\n pass\",\n \"def close(self):\\r\\n pass\",\n \"def close(self):\\r\\n pass\",\n \"def __exit__(self, type, value, traceback) :\\n if self.spec :\\n self.handle.close()\\n self.handle = None\",\n \"def closeEvent(self, event):\\n\\n sys.exit()\",\n \"def close(self):\\n self.__closed = True\",\n \"def close(self):\\n self.__closed = True\",\n \"def close (self):\\n pass\\n #TODO: implement more realistic closing semantics\",\n \"def close(self):\\n self.closed = True\",\n \"def close(cls):\\n pass\",\n \"def close(*args):\",\n \"def close(*args):\",\n \"def close(*args):\",\n \"def close(*args):\",\n \"def close(*args):\",\n \"def close(self) -> None:\\r\\n pass\",\n \"def close():\\n\\trfm.close()\",\n \"def close(self) -> None:\\n pass\"\n]","isConversational":false},"negative_scores":{"kind":"list like","value":["0.62410325","0.62410325","0.6005659","0.6003081","0.59938776","0.5955254","0.592907","0.58687013","0.57975334","0.5778289","0.5778289","0.5778289","0.5778289","0.5778289","0.57693046","0.57626265","0.5747791","0.5740424","0.5720488","0.5720488","0.5716972","0.57169664","0.57117724","0.5696709","0.5694895","0.5683764","0.5683764","0.5681563","0.56668615","0.56611246","0.5641216","0.5614596","0.5614596","0.5614596","0.5614596","0.5614596","0.5614596","0.5614596","0.5614596","0.5614596","0.5614596","0.5613158","0.55981207","0.5584843","0.5584843","0.5584843","0.5584843","0.5584843","0.5584843","0.5584843","0.5584843","0.55692685","0.5568613","0.55554265","0.55554265","0.55554265","0.55554265","0.55554265","0.55554265","0.55554265","0.55554265","0.55554265","0.55554265","0.55554265","0.55554265","0.55554265","0.55554265","0.55554265","0.55554265","0.55554265","0.55554265","0.55554265","0.55554265","0.55554265","0.55554265","0.55554265","0.55554265","0.55554265","0.55554265","0.55554265","0.55554265","0.55513126","0.55508965","0.55508965","0.55508965","0.55417436","0.5535562","0.5531005","0.5531005","0.55249006","0.55060875","0.55027163","0.55023956","0.55023956","0.55023956","0.55023956","0.55023956","0.55023646","0.5491982","0.5491908"],"string":"[\n \"0.62410325\",\n \"0.62410325\",\n \"0.6005659\",\n \"0.6003081\",\n \"0.59938776\",\n \"0.5955254\",\n \"0.592907\",\n \"0.58687013\",\n \"0.57975334\",\n \"0.5778289\",\n \"0.5778289\",\n \"0.5778289\",\n \"0.5778289\",\n \"0.5778289\",\n \"0.57693046\",\n \"0.57626265\",\n \"0.5747791\",\n \"0.5740424\",\n \"0.5720488\",\n \"0.5720488\",\n \"0.5716972\",\n \"0.57169664\",\n \"0.57117724\",\n \"0.5696709\",\n \"0.5694895\",\n \"0.5683764\",\n \"0.5683764\",\n \"0.5681563\",\n \"0.56668615\",\n \"0.56611246\",\n \"0.5641216\",\n \"0.5614596\",\n \"0.5614596\",\n \"0.5614596\",\n \"0.5614596\",\n \"0.5614596\",\n \"0.5614596\",\n \"0.5614596\",\n \"0.5614596\",\n \"0.5614596\",\n \"0.5614596\",\n \"0.5613158\",\n \"0.55981207\",\n \"0.5584843\",\n \"0.5584843\",\n \"0.5584843\",\n \"0.5584843\",\n \"0.5584843\",\n \"0.5584843\",\n \"0.5584843\",\n \"0.5584843\",\n \"0.55692685\",\n \"0.5568613\",\n \"0.55554265\",\n \"0.55554265\",\n \"0.55554265\",\n \"0.55554265\",\n \"0.55554265\",\n \"0.55554265\",\n \"0.55554265\",\n \"0.55554265\",\n \"0.55554265\",\n \"0.55554265\",\n \"0.55554265\",\n \"0.55554265\",\n \"0.55554265\",\n \"0.55554265\",\n \"0.55554265\",\n \"0.55554265\",\n \"0.55554265\",\n \"0.55554265\",\n \"0.55554265\",\n \"0.55554265\",\n \"0.55554265\",\n \"0.55554265\",\n \"0.55554265\",\n \"0.55554265\",\n \"0.55554265\",\n \"0.55554265\",\n \"0.55554265\",\n \"0.55554265\",\n \"0.55513126\",\n \"0.55508965\",\n \"0.55508965\",\n \"0.55508965\",\n \"0.55417436\",\n \"0.5535562\",\n \"0.5531005\",\n \"0.5531005\",\n \"0.55249006\",\n \"0.55060875\",\n \"0.55027163\",\n \"0.55023956\",\n \"0.55023956\",\n \"0.55023956\",\n \"0.55023956\",\n \"0.55023956\",\n \"0.55023646\",\n \"0.5491982\",\n \"0.5491908\"\n]","isConversational":false},"document_score":{"kind":"string","value":"0.73456883"},"document_rank":{"kind":"string","value":"0"}}},{"rowIdx":22697,"cells":{"query":{"kind":"string","value":"r\"\"\"Initialize communication with new queue."},"document":{"kind":"string","value":"def new_comm_kwargs(cls, *args, **kwargs):\n kwargs.setdefault('address', 'file.txt')\n return args, kwargs"},"metadata":{"kind":"string","value":"{\n \"objective\": {\n \"self\": [],\n \"paired\": [],\n \"triplet\": [\n [\n \"query\",\n \"document\",\n \"negatives\"\n ]\n ]\n }\n}"},"negatives":{"kind":"list like","value":["def on_init(self, queue=None, **kwargs):\n self.queue = queue if queue else Queue()","def setup_queue(self):\n self.logger.info('declaring queue %s', self.queue)\n if self.otq:\n self._channel.queue_declare(self.on_queue_declareok, self.queue, auto_delete=True)\n else:\n self._channel.queue_declare(self.on_queue_declareok, self.queue)","def __init__(self) -> None:\n self._queue = []","def __init__(self, queue_id=None):\n super().__init__()\n self.queue_id = queue_id","def __init__(self, address, queue_name):\n self.connection = pika.BlockingConnection(\n pika.ConnectionParameters(address))\n self.queue_name = queue_name\n\n # create the channel\n self.channel = self.connection.channel()\n\n # declare the queue\n self.channel.queue_declare(queue=queue_name, durable=True)\n\n logging.info(\"Message Broker connected to {0}\".format(address))","def __init__(self, config, queue_name):\n self.work_queue_client = WorkQueueClient(config, queue_name)","def __init__(self, config, queue_name):\n self.work_queue_client = WorkQueueClient(config, queue_name)","def connect(self):\n # @TODO: implement SSL\n connection = pika.BlockingConnection(self.params)\n channel = connection.channel()\n channel.queue_declare(queue=self.queue_name, durable=True)\n\n self.connection = connection\n self.channel = channel","async def declare(self) -> 'Queue':\n # we are relying to this in other functions\n self._channel = await self._backend.channel()\n self.log.debug(\"Channel acquired CHANNEL%i\",\n self._channel.channel_number)\n\n if self.exchange:\n await self.declare_exchange()\n\n if self.name is not None:\n await self.declare_queue()\n\n if self.exchange:\n await self.bind_queue()\n\n return self","def __init__(self):\n self.queue = Queue()","def __init__(self):\n self.queues=[]","def __init__(self):\n self._queue_items = []","def __init__(self, queue: Queue):\n super().__init__()\n self._cursor = 0\n self._queue = queue\n self._all_transferred = threading.Event()","def __init__(self, queue_name: str, connection_name: str):\n\n self.queue_name = queue_name\n self.connection_name = connection_name\n self.connection = CONNECTION_MANAGER.get_connection(connection_name)","def start(self):\n self._connect()\n self._init_exchange()\n self._init_queue()\n self._bind_queue()","def setup_queues_and_bindings(self):\n self._channel.exchange_declare(self.setup_queue, exchange=self.exchange, passive=True)","async def _connect(self):\n self._connection = await connect_robust(self._connection_string)\n self._channel = await self._connection.channel()\n await self._channel.declare_queue(self._queue, durable=True, arguments={'x-max-priority': 10})","def __init__(self):\n self.queue = []","def __init__(self):\n self.queue = []","def __init__(self):\n self.queue = []","def __init__(self):\n self.queue = []","def __init__(self):\n self.queue = []","def __init__(self):\n self.queue = []\n self.queue.append(Queue())\n self.queue.append(Queue())\n self.tag = 0 # using to record which queue contain the data","def __init__(self, queue_id):\n self.queue_id = queue_id\n self.action_type = 'set_queue'","def setUpClass(cls):\n cls.queue = RabbitQueue(QUEUE_CONN_PARAMS)","def __init__(self):\n self.messageSet = set()\n self.messageQueue = deque()","def perform_setup():\n global credentials, connection, channel\n credentials = pika.PlainCredentials('guest', 'guest') # AUTH via Default guest user on RabbitMQ\n connection = pika.BlockingConnection(pika.ConnectionParameters(\"127.0.0.1\", 5672, '/', credentials)) # Using rabbit-mq container name to access the RabbitMQ container from other containers\n channel = connection.channel()\n channel.queue_declare(queue='poll', durable=True)","def __init__(self):\n self._queue = defaultdict(\n OrderedDict\n ) # type: Dict[OrderedDict[TelnetCommand, ExpectedResponse]]","def __init__(self, config):\n self.config = config\n self.received_messages = []\n self.processed_messages = []\n\n self.setup()","def setup_queue(self, method_frame):\n logger.info('Declaring queue %s', self.queue_name)\n # self._channel.queue_declare(self.on_queue_declareok, queue_name)\n\n self._channel.queue_declare(self.on_queue_declareok, exclusive=False, durable=True, queue=self.queue_name)","def __init__(self):\n self.shared_counter = 0\n self.queue = list()\n self.queue_size = 0","def __init__(self):\r\n self.queue = []","def __init__(self):\r\n self.queue = []","def __init__(self):\n self.data = Queue()","def __init__(self, queueLength):\r\n self.queueLength = queueLength\r\n self.data = []\r\n return","def __init__(self,\r\n network_queue,\r\n logic_queue,\r\n port):\r\n current_class = self.__class__\r\n thread_name = current_class.__name__\r\n super(current_class, self).__init__(name=thread_name)\r\n self.logger = logging.getLogger('network')\r\n\r\n self.network_queue = network_queue\r\n self.logic_queue = logic_queue\r\n\r\n self.sockets = {}\r\n\r\n self.running = True","def __init__(self, sock_obj, cmd_channel):\n asyncore.dispatcher.__init__(self, sock_obj)\n # we toss the use of the asynchat's \"simple producer\" and\n # replace it with a pure deque, which the original fifo\n # was a wrapping of\n self.producer_fifo = deque()\n\n self.cmd_channel = cmd_channel\n self.file_obj = None\n self.receive = False\n self.transfer_finished = False\n self.tot_bytes_sent = 0\n self.tot_bytes_received = 0","def __init__(self):\n Queue.__init__(self)","def __init__(self):\n self.connection = pika.BlockingConnection(\n pika.ConnectionParameters(host='localhost'))\n self.channel = self.connection.channel()","def __init__(self, queue):\n super(Worker, self).__init__()\n self.queue = queue\n logger.debug(\"Worker thread started.\")\n self.start()","def __init__(self,\r\n network_queue,\r\n logic_queue,\r\n port):\r\n current_class = self.__class__\r\n thread_name = current_class.__name__\r\n super(current_class, self).__init__(name=thread_name)\r\n self.logger = logging.getLogger('network')\r\n\r\n self.network_queue = network_queue\r\n self.logic_queue = logic_queue\r\n self.port = port\r\n\r\n self.server = socket.socket(socket.AF_INET, socket.SOCK_STREAM)\r\n self.sockets = {}\r\n\r\n # dict to store incomplete messages\r\n # keys are sockets, values are pairs of:\r\n # (received data (str), number of missing bytes (int))\r\n self.messages = {}","def __init__(self):\n self.l_queue, self.r_queue = [], []","def _init(self, conf, **kwargs):\n self.conf = conf\n self.rpc_listener = None\n self.transport = kwargs.pop('transport')\n self.target = kwargs.pop('target')\n self.endpoints = kwargs.pop('endpoints')\n self.flush = kwargs.pop('flush')\n self.kwargs = kwargs\n self.RPC = self.target.topic_class\n self.name = \"{}, topic({})\".format(RPCSVRNAME, self.target.topic)\n\n self.messaging_owner_condition = {\n \"owner\": socket.gethostname()\n }\n\n self.enqueued_status_condition = {\n \"status\": message.Message.ENQUEUED\n }\n\n self.working_status_condition = {\n \"status\": message.Message.WORKING\n }\n\n if self.flush:\n self._flush_enqueued()","def __init__(self):\n self.push_queue = []\n self.pop_queue = []","def __init__(self, pyrps, queue, consumer_id):\n self.pyrps = pyrps\n self.queue = queue\n self.consumer_id = consumer_id","def init_connect_mq(self):\n try:\n mq_username = Configs.mq_username\n mq_pwd = Configs.mq_pwd\n mq_ip_addr = Configs.mq_ip_addr\n mq_port_num = Configs.mq_port_num\n mq_vhost = Configs.mq_vhost\n\n mq_credentials = pika.PlainCredentials(mq_username, mq_pwd)\n mq_connection = pika.BlockingConnection(\n pika.ConnectionParameters(host=mq_ip_addr, port=mq_port_num, virtual_host=mq_vhost,\n credentials=mq_credentials))\n # connect to mq channel\n self.mq_channel = mq_connection.channel()\n self.mq_channel.exchange_declare(exchange=Configs.mq_exchange_name, exchange_type='topic', durable='true')\n # self.mq_channel.queue_declare(queue='test', durable=False, arguments={'x-message-ttl': 10000})\n self.mq_conn_flag = True\n print(\" ************** MQ Connect Success ************** \")\n except Exception as e:\n print(e)","def init_email_queue():\n g.setdefault('email_queue', [])","def __init__(self, parallel_server):\n self.queue = deque()\n self.parallel_server = parallel_server","def __init__(self, conf, transport, target):\n self.conf = conf\n self.transport = transport\n self.target = target\n self.RPC = self.target.topic_class\n\n # introduced as a quick means to cache messages\n # with the aim of preventing unnecessary communication\n # across conductor components.\n # self.message_cache = dict()","def __init__(self, mq_choice=\"zmq\"):\n self.mq = mq_choice\n func = getattr(self, \"_init_{}\".format(self.mq))\n func()","def __init__(self): \n self.queue = []","def preProcess(self, msg):\n\n # open connection\n self.conn = Connection(\n user=self.user, password=self.password,\n vhost=self.vhost, host=self.host,\n heartbeat=self.heartbeat, debug=self.debug)\n\n # create AMQP channel\n self.channel = self.conn.channel()\n self.channel.exchange.declare(self.exchange, self.exchange_type)\n self.channel.queue.declare(self.queue, self.auto_delete)\n self.channel.queue.bind(self.queue, self.exchange, self.routing_key)","def preProcess(self, msg):\n\n # open connection\n self.conn = Connection(\n user=self.user, password=self.password,\n vhost=self.vhost, host=self.host,\n heartbeat=self.heartbeat, debug=self.debug)\n\n # create AMQP channel\n self.channel = self.conn.channel()\n self.channel.exchange.declare(self.exchange, self.exchange_type)\n self.channel.queue.declare(self.queue, self.auto_delete)\n self.channel.queue.bind(self.queue, self.exchange, self.routing_key)","def preProcess(self, msg):\n\n # open connection\n self.conn = Connection(\n user=self.user, password=self.password,\n vhost=self.vhost, host=self.host,\n heartbeat=self.heartbeat, debug=self.debug)\n\n # create AMQP channel\n self.channel = self.conn.channel()\n self.channel.exchange.declare(self.exchange, self.exchange_type)\n self.channel.queue.declare(self.queue, self.auto_delete)\n self.channel.queue.bind(self.queue, self.exchange, self.routing_key)","def __init__(self):\n self._data_queue = []\n self._access_queue_lock = Lock()","def init_queues(self):\n for step in self.get_children():\n if step.kind == StepKinds.queue:\n step.init_object(self.context, None)","def connect(self):\n self._connect()\n if self.conn is not None:\n try:\n self.chan.queue_declare(queue=self.rq, durable=True, exclusive=False, auto_delete=False)\n self.chan.queue_bind(queue=self.rq, exchange=self.exch, routing_key=self.RKEY)\n self.chan.basic_consume(queue=self.rq, no_ack=True, callback=self._amqpCallback, consumer_tag=self.ctag)\n except:\n self.closeConn()","def __init__(self, queue_name, **kwargs):\n super(Queue, self).__init__(**kwargs)\n self.value = queue_name","def __init__(self, routing_key):\n self.routing_key = routing_key\n self.connection = pika.BlockingConnection(pika.ConnectionParameters(host=rabbitmq_hostname))\n self.channel = self.connection.channel()\n result = self.channel.queue_declare(queue='', exclusive=True, durable=True)\n self.callback_queue = result.method.queue\n\n self.channel.basic_consume(\n queue=self.callback_queue,\n on_message_callback=self.on_response,\n auto_ack=True\n )","def setupTcp(self):\n \tself.tcpManager = QueuedConnectionManager()\n \tself.tcpReader = QueuedConnectionReader(self.tcpManager, 0)\n \tself.tcpWriter = ConnectionWriter(self.tcpManager, 0)","def __init__(self, queue: Queue):\n logging.Handler.__init__(self)\n self.queue = queue","def _setup_communication(self):\n state = self.ui.checkBox_comm.checkState()\n if state:\n try:\n sys.path.append(\"..\")\n from zmq_interface.gui_interface import ZmqInterface\n except ImportError as e:\n self.write_text(\"ZMQ interface failed to import. No remote control for this session.\")\n self.disable_visualizer()\n return\n try:\n ##TODO: let user specify ports\n self.com = ZmqInterface(rep_port=REPLY_PORT,\n gui_handle=self)\n except Exception as e:\n #traceback.print_exc(file=sys.stdout)\n self.write_text(\"ZMQ interface failed to start. No remote control for this session. Reason: %s\" % e)\n self.disable_visualizer()\n return\n self.start = self._start_session\n self.stop = self._stop_session\n self.load_config = self._load_state\n self.save_config = self._save_state\n self.com_timer = QtCore.QTimer()\n self.com_timer.timeout.connect(self._check_coms)\n self.com_timer.start(200)\n self.write_text(\"ZMQ interface set up. Reply port on %s\" % self.com.rep_port)\n self.enable_visualizer()\n else:\n if self.com:\n self.com.close()\n if self.com_timer:\n self.com_timer.stop()\n self.com = None\n self.com_timer = None\n self.enable_visualizer()\n self.write_text(\"ZMQ interface closed.\")","def __init__(self, params={}):\n self.lt_ses = lt.session() # pylint: disable=no-member\n self.lt_ses.listen_on(6881, 6891)\n\n self.params = params\n self.queue = deque()\n self.stream_thread = None\n self.handle = None","def on_channel_open(new_channel):\n global channel\n channel = new_channel\n channel.queue_declare(queue=\"test\", durable=True, exclusive=False, auto_delete=False, callback=on_queue_declared)","def __init__(self, framer):\n ModbusClientProtocol.__init__(self, framer)\n log.debug(\"Beginning the processing loop\")\n reactor.callLater(CLIENT_DELAY, self.fetch_holding_registers)","def __init__(self, connection, exchange, queue, routing_key,\n sub_worker, thread_support=True, no_declare=True):\n # Reduce logging from amqp module\n setup_logging(loglevel='INFO', loggers=['amqp'])\n logger.debug(f\"Initializating a new listener for exchange/queue: {exchange}/{queue}...\")\n self.connection = connection\n self.exchange = Exchange(exchange, 'direct',\n durable=True, no_declare=no_declare)\n self.queue = Queue(queue, exchange=self.exchange,\n routing_key=routing_key, no_declare=no_declare)\n self.queues = [self.queue]\n self.no_declare = no_declare\n logger.info(f\"New listener initialized for exchange/queue: {exchange}/{queue}...\")\n logger.debug(f\"Importing sub_worker module: {sub_worker}...\")\n self.sub_worker = sub_worker\n self.thread_support = thread_support\n mod_name = '.'.join(self.sub_worker.split(\".\")[:-1])\n try:\n self.sub_worker_mod = importlib.import_module(mod_name)\n except ModuleNotFoundError as e:\n logger.error(f\"Cannot import the sub worker module named {mod_name}: ModuleNotFoundError\")\n sys.exit(-1)\n except Exception as e:\n logger.error(f\"Cannot import the sub worker module named {mod_name}: \" + str(e))\n sys.exit(-1)","def _setup_tubes(self):\n chan = self.channel\n inp = self.config[self.MODULE_NAME]['amqp']['in']\n out = self.config[self.MODULE_NAME]['amqp']['out']\n if inp['exchange']:\n log.info('generating Input Queue'+ str(inp))\n chan.exchange_declare(**inp)\n self.qname = chan.queue_declare(exclusive=True).queue\n chan.queue_bind(exchange=inp['exchange'],queue=self.qname)\n self.consume = lambda cb : chan.basic_consume(cb,queue=self.qname,no_ack=True)\n self.start_loop = lambda : pika.asyncore_loop()\n\n if out['exchange']:\n log.info('generating Output Exchange'+ str(out))\n chan.exchange_declare(**out)\n self.publish = lambda msg: self.channel.basic_publish(exchange=out['exchange'],routing_key='',body=msg)","def declare_queue(self):\n\n self._channel.queue_declare(queue=self._queue_name, durable=True)\n print(\"Queue declared....\")","def __init__(self, queue, usercallback):\n self.queue = queue\n self.usercallback = usercallback","def __init__(self, initial=[]):\n self.the_queue = copy.copy(initial)","def __init__(self, queue):\n logging.Handler.__init__(self)\n self.queue = queue","def __init__(self, msgQueue, readPeriod=0.5):\n\n threading.Thread.__init__(self)\n\n self.shutdownEvent = threading.Event()\n\n self._msgQueue = msgQueue\n self._readPeriod = readPeriod\n\n # Initialize GPS (Python 3 version info found at https://learn.adafruit.com/adafruit-ultimate-gps-on-the-raspberry-pi/using-your-gps)\n self._gpsSession = gps.gps('localhost', '2947')\n self._gpsSession.stream(gps.WATCH_ENABLE | gps.WATCH_NEWSTYLE)","def __init__(self):\n \n # Initialize logger\n self._log = logging.getLogger(\"OemGateway\")\n \n # Initialize variables\n self._data_buffer = []\n self._last_send = time.time()\n self._settings = {}","def __init__(self):\n self.queue = deque()\n self.aux_queue = deque()","def __init__(self, store=None):\n self.sockets = []\n self.poller = zmq.core.poll.Poller()\n self.mh = MessageHandler()\n self.store = store","def __init__(self):\n self.queue = deque()","def __init__(self):\n self.queue = deque()","def __init__(self):\n self.queue = deque()","def __init__(self):\n self.queue = deque()","def init(self):\n self.dispatcher.start()\n self.replyer.start()","def __init__(self):\n # NOTE: DO not modify this method\n self.items = SimpleQueue()","def __init__(self, threaded=False):\n self._message_listeners = {}\n self._threaded = threaded","def __init__(self, queue, debug=False):\n super(Blue, self).__init__()\n # set bluetooth to be in visible and pairing mode\n os.system('hciconfig hci0 piscan')\n self.queue = queue\n self.__stop = threading.Event()\n self.lock = threading.RLock()\n self.old_data = None\n self.write_okay = True\n if not debug:\n self.__server_sock = BluetoothSocket(RFCOMM)\n self.__server_sock.bind((\"\", PORT_ANY))\n self.__server_sock.listen(1)\n\n advertise_service(self.__server_sock, self.__name,\n service_id=self.__uuid,\n service_classes=[self.__uuid, SERIAL_PORT_CLASS],\n profiles=[SERIAL_PORT_PROFILE])\n self.start()","def setup_queue(self, channel, queue_name):\n logger.info('Declaring queue %s', queue_name)\n channel.queue_declare(queue = queue_name, \n durable = True,\n auto_delete = False)","def setup(self):\n self.context = zmq.Context()\n self.sub_socket = self.context.socket(zmq.SUB)\n if self.filter:\n self.sub_socket.setsockopt(zmq.SUBSCRIBE, self.filter)\n self.sub_socket.connect('tcp://'+self.host+':'+str(self.com_port))\n return self","def __init__(self):\n self.dequeue = DeQueue()","def setup_amq(self):\n self.setup_amq_cluster_operator()\n self.setup_amq_kafka_persistent()\n self.setup_amq_kafka_connect()\n self.setup_amq_kafka_bridge()\n self.amq_is_setup = True\n return self","def connect(self):\n try:\n self.conn = amqp.Connection(insist=True, **self.config)\n self.chan = self.conn.channel()\n self.chan.queue_declare(queue=self.RQU, durable=False, exclusive=False, auto_delete=True)\n self.chan.exchange_declare(exchange=self.EXCH, type=\"topic\", durable=True, auto_delete=False,)\n self.chan.queue_bind(queue=self.RQU, exchange=self.EXCH, routing_key=self.RKEY)\n self.chan.basic_consume(queue=self.RQU, no_ack=True, callback=self.amqpCallback, consumer_tag=\"ctag\")\n except:\n self.closeConn()","def __init__(self, buf=None, *args, **kwargs):\n super(Message, self).__init__(buf, *args, **kwargs)\n self.__initialized = True","def __init__(self):\r\n self.queue = []\r\n self.current = False","def __init__(self, topic, address, group=None, message_type=MessageFlagType.DIRECT):\n self._response_lock = asyncio.Lock()\n self._direct_response = asyncio.Queue()\n super().__init__(topic, address=address, group=group, message_type=message_type)","def initzmq(self):\n\n if \"topics\" not in self.configData:\n raise Exception(\"Topics not found in %s\" % self.configPath)\n\n for topic in self.configData['topics']:\n addr = self.gen_address(topic['protocol'], topic['address'],\n topic['port'])\n socket = self.build_socket(topic['paradigm'], topic['topic'], addr)\n self.topics[topic['name']] = socket","def setup(self, dictParams):\n global connack\n # we dont use globals here now - instead use config.py which is imported where needed to \n # give cross-module scope\n #global globalScope.incomingMessageBuffer\n global broker_public_key\n \n hostname = dictParams[\"sess\"]\n name = dictParams[\"dest\"]\n port = dictParams[\"port\"]\n protocol = dictParams[\"protoVer\"]\n broker = dictParams[\"broker\"]\n # this is a Singleton\n self.receiver = dictParams[\"RcvdMsg\"]\n timeout = dictParams[\"timeout\"]\n oldtimestamp = dictParams[\"oldtimestamp\"]\n anotherParam = dictParams[\"anotherParam\"]\n \n self.brokerName = broker\n\n # Setting clean_session = False means that subsciption information and \n # queued messages are retained after the client disconnects. It is suitable\n # in an environment where disconnects are frequent.\n # client_id is any unique identifier so our own fqdn should be alright to use\n mqtt_client = mqtt.Client(protocol=self.protocol, client_id=self.myName, clean_session=False)\n mqtt_client.on_connect = self.on_connect\n mqtt_client.on_message = self.on_message\n mqtt_client.on_publish = self.on_publish\n mqtt_client.on_disconnect = self.on_disconnect\n mqtt_client.on_subscribe = self.on_subscribe\n mqtt_client.on_unsubscribe = self.on_unsubscribe\n mqtt_client.on_log = self.on_log\n \n \n # Set the LWT\n # If the client disconnects without calling disconnect, the broker will\n # publish this message on its behalf\n # retain should be set to true\n mqtt_client.will_set(self.STATUS, \n self.status_message(STATUS_DISCONNECTED_UNGRACE), \n qos=QosType.FLAG_QOS_ATMOSTONCE.value, retain=True) \n\n # Connect to the broker\n # keepalive is maximum number of seconds allowed between communications\n # with the broker. If no other messages are sent, the client will send a\n # ping request at this interval\n # set it high for devices that are disconnected for small periods of time, also for debugging\n # set it extremely high for devices that are out to sea for days\n keepalive=1800\n try:\n logging.info('Attempting to connect to broker at ' + self.brokerName)\n mqtt_client.connect(self.brokerName, self.port, keepalive)\n except:\n logging.error(\"ERROR - could not connect to broker at \" + self.brokerName)\n return False\n else:\n logging.info(\"INFO - all OK - no problem on attempt to connect to broker at \" + self.brokerName) \n \n \n # Force function to block until connack is sent from the broker, or timeout\n connack = False\n start_time = time.time()\n while not connack:\n time.sleep(0.1)\n mqtt_client.loop()\n \n if time.time() - start_time > float(timeout):\n raise MqttTimeOutError(\"The program timed out while trying to connect to the broker!\")\n break\n \n # When connected, subscribe to the relevant channels\n mqtt_client.subscribe([(self.PUBLIC, 1), (self.PROTECTED, 1),\n (self.PRIVATE, 1), (self.PINGREQ, 1),\n (self.PINGACK, 1), (self.HANDSHAKE, 1)\n ])\n \n self.client = mqtt_client\n \n # init the globalScope.incomingMessageBuffer - this is now done in the config.py\n \n \n # Do a blocking call\n broker_public_key = None\n self.client.publish(self.STATUS, \n self.status_message(STATUS_CONNECTED), \n qos=QosType.FLAG_QOS_ATLEASTONCE.value)\n\n # TODO dont know what this does and it was getting stuck in the loop so i got rid of it \n# while self.broker_public_key == None:\n# time.sleep(0.1)\n# mqtt_client.loop()\n# # Check the message buffer\n# if globalScope.incomingMessageBuffer != []:\n# for message in globalScope.incomingMessageBuffer:\n# if message.topic == self.HANDSHAKE:\n# # Check whether it is a broker key message.\n# try:\n# payload = json.loads(message.payload.decode())\n## disabled for now\n## self.broker_public_key = payload['public_key']\n## print(self.broker_public_key)\n# self.broker_public_key = json.loads(message.payload.decode())\n# except:\n# pass\n# globalScope.incomingMessageBuffer = []\n \n\n # Start the loop. This method is preferable to repeatedly calling loop\n # since it handles reconnections automatically. It is non-blocking and \n # handles interactions with the broker in the background.\n logging.debug('DEBUG - Starting loop')\n try:\n# mqtt_client.loop()\n self.client.loop_start()\n except:\n logging.error(\"ERROR - failure of loop_start\")\n \n return True","def __init__(self):\n queue = collections.deque()","def declare(self):\n self.channel.queue_declare(queue='files_to_database')","def __init__(self, queue):\r\n logging.Handler.__init__(self)\r\n self.queue = queue","def setup(self):\n global log_th, conf_th, header_th, command_w_th\n self.conf_th_ic = conf_th\n self.header_th_ic = header_th\n self.command_w_th_inc = command_w_th\n self.hostname = conf_th.get_item(q_key='general').get('hostname')\n self.std_recv_size = int(conf_th.get_item(q_key='general').get('std_recv_size'))\n self.data_recv_size = int(conf_th.get_item(q_key='general').get('data_recv_size'))\n self.mail_save_enable = int(conf_th.get_item(q_key='general').get('mail_save_enable'))\n self.mail_save_path = conf_th.get_item(q_key='general').get('mail_save_path')\n self.no_answer = int(conf_th.get_item(q_key='general').get('no_answer'))\n self.sleep_between = int(conf_th.get_item(q_key='general').get('sleep_between'))\n self.message_id = library.q_id_generate(size=16)\n self.client_ip = tuple(self.client_address).__getitem__(0)\n self.client_port = int(tuple(self.client_address).__getitem__(1))\n # Running\n self.header_th_ic.write_header(ip=self.client_ip, qid=self.message_id)\n message = '220 ' + self.hostname\n self.func_sender(message)\n log_th.log_info('{} connected to {} thread'.format(self.client_ip, threading.current_thread().name))","def __init__(self, amqp_url):\n self._connection = None\n self._channel = None\n self._url = amqp_url","def __init__(self):\n self.q1 = queue()\n self.q2 = queue()","def __init__(self, lsfQueue, jobName=\"\", async=False):\n self.lsfQueue = lsfQueue\n self.jobName = jobName\n self.async = async\n self.jobid = None\n self.cmd = None","def __init__(self, topic):\n self._topic = topic\n\n # Because this is Music-specific, the server is\n # built-in to the API class, stored as the transport.\n # Thus, unlike oslo.messaging, there is no server\n # specified for a target. There also isn't an\n # exchange, namespace, or version at the moment.\n\n # Dynamically create a message class for this topic.\n self._topic_class = base.create_dynamic_model(\n keyspace=CONF.messaging_server.keyspace,\n baseclass=message.Message, classname=self.topic)\n\n if not self._topic_class:\n raise RuntimeError(\"Error setting the topic class for the messaging layer.\")"],"string":"[\n \"def on_init(self, queue=None, **kwargs):\\n self.queue = queue if queue else Queue()\",\n \"def setup_queue(self):\\n self.logger.info('declaring queue %s', self.queue)\\n if self.otq:\\n self._channel.queue_declare(self.on_queue_declareok, self.queue, auto_delete=True)\\n else:\\n self._channel.queue_declare(self.on_queue_declareok, self.queue)\",\n \"def __init__(self) -> None:\\n self._queue = []\",\n \"def __init__(self, queue_id=None):\\n super().__init__()\\n self.queue_id = queue_id\",\n \"def __init__(self, address, queue_name):\\n self.connection = pika.BlockingConnection(\\n pika.ConnectionParameters(address))\\n self.queue_name = queue_name\\n\\n # create the channel\\n self.channel = self.connection.channel()\\n\\n # declare the queue\\n self.channel.queue_declare(queue=queue_name, durable=True)\\n\\n logging.info(\\\"Message Broker connected to {0}\\\".format(address))\",\n \"def __init__(self, config, queue_name):\\n self.work_queue_client = WorkQueueClient(config, queue_name)\",\n \"def __init__(self, config, queue_name):\\n self.work_queue_client = WorkQueueClient(config, queue_name)\",\n \"def connect(self):\\n # @TODO: implement SSL\\n connection = pika.BlockingConnection(self.params)\\n channel = connection.channel()\\n channel.queue_declare(queue=self.queue_name, durable=True)\\n\\n self.connection = connection\\n self.channel = channel\",\n \"async def declare(self) -> 'Queue':\\n # we are relying to this in other functions\\n self._channel = await self._backend.channel()\\n self.log.debug(\\\"Channel acquired CHANNEL%i\\\",\\n self._channel.channel_number)\\n\\n if self.exchange:\\n await self.declare_exchange()\\n\\n if self.name is not None:\\n await self.declare_queue()\\n\\n if self.exchange:\\n await self.bind_queue()\\n\\n return self\",\n \"def __init__(self):\\n self.queue = Queue()\",\n \"def __init__(self):\\n self.queues=[]\",\n \"def __init__(self):\\n self._queue_items = []\",\n \"def __init__(self, queue: Queue):\\n super().__init__()\\n self._cursor = 0\\n self._queue = queue\\n self._all_transferred = threading.Event()\",\n \"def __init__(self, queue_name: str, connection_name: str):\\n\\n self.queue_name = queue_name\\n self.connection_name = connection_name\\n self.connection = CONNECTION_MANAGER.get_connection(connection_name)\",\n \"def start(self):\\n self._connect()\\n self._init_exchange()\\n self._init_queue()\\n self._bind_queue()\",\n \"def setup_queues_and_bindings(self):\\n self._channel.exchange_declare(self.setup_queue, exchange=self.exchange, passive=True)\",\n \"async def _connect(self):\\n self._connection = await connect_robust(self._connection_string)\\n self._channel = await self._connection.channel()\\n await self._channel.declare_queue(self._queue, durable=True, arguments={'x-max-priority': 10})\",\n \"def __init__(self):\\n self.queue = []\",\n \"def __init__(self):\\n self.queue = []\",\n \"def __init__(self):\\n self.queue = []\",\n \"def __init__(self):\\n self.queue = []\",\n \"def __init__(self):\\n self.queue = []\",\n \"def __init__(self):\\n self.queue = []\\n self.queue.append(Queue())\\n self.queue.append(Queue())\\n self.tag = 0 # using to record which queue contain the data\",\n \"def __init__(self, queue_id):\\n self.queue_id = queue_id\\n self.action_type = 'set_queue'\",\n \"def setUpClass(cls):\\n cls.queue = RabbitQueue(QUEUE_CONN_PARAMS)\",\n \"def __init__(self):\\n self.messageSet = set()\\n self.messageQueue = deque()\",\n \"def perform_setup():\\n global credentials, connection, channel\\n credentials = pika.PlainCredentials('guest', 'guest') # AUTH via Default guest user on RabbitMQ\\n connection = pika.BlockingConnection(pika.ConnectionParameters(\\\"127.0.0.1\\\", 5672, '/', credentials)) # Using rabbit-mq container name to access the RabbitMQ container from other containers\\n channel = connection.channel()\\n channel.queue_declare(queue='poll', durable=True)\",\n \"def __init__(self):\\n self._queue = defaultdict(\\n OrderedDict\\n ) # type: Dict[OrderedDict[TelnetCommand, ExpectedResponse]]\",\n \"def __init__(self, config):\\n self.config = config\\n self.received_messages = []\\n self.processed_messages = []\\n\\n self.setup()\",\n \"def setup_queue(self, method_frame):\\n logger.info('Declaring queue %s', self.queue_name)\\n # self._channel.queue_declare(self.on_queue_declareok, queue_name)\\n\\n self._channel.queue_declare(self.on_queue_declareok, exclusive=False, durable=True, queue=self.queue_name)\",\n \"def __init__(self):\\n self.shared_counter = 0\\n self.queue = list()\\n self.queue_size = 0\",\n \"def __init__(self):\\r\\n self.queue = []\",\n \"def __init__(self):\\r\\n self.queue = []\",\n \"def __init__(self):\\n self.data = Queue()\",\n \"def __init__(self, queueLength):\\r\\n self.queueLength = queueLength\\r\\n self.data = []\\r\\n return\",\n \"def __init__(self,\\r\\n network_queue,\\r\\n logic_queue,\\r\\n port):\\r\\n current_class = self.__class__\\r\\n thread_name = current_class.__name__\\r\\n super(current_class, self).__init__(name=thread_name)\\r\\n self.logger = logging.getLogger('network')\\r\\n\\r\\n self.network_queue = network_queue\\r\\n self.logic_queue = logic_queue\\r\\n\\r\\n self.sockets = {}\\r\\n\\r\\n self.running = True\",\n \"def __init__(self, sock_obj, cmd_channel):\\n asyncore.dispatcher.__init__(self, sock_obj)\\n # we toss the use of the asynchat's \\\"simple producer\\\" and\\n # replace it with a pure deque, which the original fifo\\n # was a wrapping of\\n self.producer_fifo = deque()\\n\\n self.cmd_channel = cmd_channel\\n self.file_obj = None\\n self.receive = False\\n self.transfer_finished = False\\n self.tot_bytes_sent = 0\\n self.tot_bytes_received = 0\",\n \"def __init__(self):\\n Queue.__init__(self)\",\n \"def __init__(self):\\n self.connection = pika.BlockingConnection(\\n pika.ConnectionParameters(host='localhost'))\\n self.channel = self.connection.channel()\",\n \"def __init__(self, queue):\\n super(Worker, self).__init__()\\n self.queue = queue\\n logger.debug(\\\"Worker thread started.\\\")\\n self.start()\",\n \"def __init__(self,\\r\\n network_queue,\\r\\n logic_queue,\\r\\n port):\\r\\n current_class = self.__class__\\r\\n thread_name = current_class.__name__\\r\\n super(current_class, self).__init__(name=thread_name)\\r\\n self.logger = logging.getLogger('network')\\r\\n\\r\\n self.network_queue = network_queue\\r\\n self.logic_queue = logic_queue\\r\\n self.port = port\\r\\n\\r\\n self.server = socket.socket(socket.AF_INET, socket.SOCK_STREAM)\\r\\n self.sockets = {}\\r\\n\\r\\n # dict to store incomplete messages\\r\\n # keys are sockets, values are pairs of:\\r\\n # (received data (str), number of missing bytes (int))\\r\\n self.messages = {}\",\n \"def __init__(self):\\n self.l_queue, self.r_queue = [], []\",\n \"def _init(self, conf, **kwargs):\\n self.conf = conf\\n self.rpc_listener = None\\n self.transport = kwargs.pop('transport')\\n self.target = kwargs.pop('target')\\n self.endpoints = kwargs.pop('endpoints')\\n self.flush = kwargs.pop('flush')\\n self.kwargs = kwargs\\n self.RPC = self.target.topic_class\\n self.name = \\\"{}, topic({})\\\".format(RPCSVRNAME, self.target.topic)\\n\\n self.messaging_owner_condition = {\\n \\\"owner\\\": socket.gethostname()\\n }\\n\\n self.enqueued_status_condition = {\\n \\\"status\\\": message.Message.ENQUEUED\\n }\\n\\n self.working_status_condition = {\\n \\\"status\\\": message.Message.WORKING\\n }\\n\\n if self.flush:\\n self._flush_enqueued()\",\n \"def __init__(self):\\n self.push_queue = []\\n self.pop_queue = []\",\n \"def __init__(self, pyrps, queue, consumer_id):\\n self.pyrps = pyrps\\n self.queue = queue\\n self.consumer_id = consumer_id\",\n \"def init_connect_mq(self):\\n try:\\n mq_username = Configs.mq_username\\n mq_pwd = Configs.mq_pwd\\n mq_ip_addr = Configs.mq_ip_addr\\n mq_port_num = Configs.mq_port_num\\n mq_vhost = Configs.mq_vhost\\n\\n mq_credentials = pika.PlainCredentials(mq_username, mq_pwd)\\n mq_connection = pika.BlockingConnection(\\n pika.ConnectionParameters(host=mq_ip_addr, port=mq_port_num, virtual_host=mq_vhost,\\n credentials=mq_credentials))\\n # connect to mq channel\\n self.mq_channel = mq_connection.channel()\\n self.mq_channel.exchange_declare(exchange=Configs.mq_exchange_name, exchange_type='topic', durable='true')\\n # self.mq_channel.queue_declare(queue='test', durable=False, arguments={'x-message-ttl': 10000})\\n self.mq_conn_flag = True\\n print(\\\" ************** MQ Connect Success ************** \\\")\\n except Exception as e:\\n print(e)\",\n \"def init_email_queue():\\n g.setdefault('email_queue', [])\",\n \"def __init__(self, parallel_server):\\n self.queue = deque()\\n self.parallel_server = parallel_server\",\n \"def __init__(self, conf, transport, target):\\n self.conf = conf\\n self.transport = transport\\n self.target = target\\n self.RPC = self.target.topic_class\\n\\n # introduced as a quick means to cache messages\\n # with the aim of preventing unnecessary communication\\n # across conductor components.\\n # self.message_cache = dict()\",\n \"def __init__(self, mq_choice=\\\"zmq\\\"):\\n self.mq = mq_choice\\n func = getattr(self, \\\"_init_{}\\\".format(self.mq))\\n func()\",\n \"def __init__(self): \\n self.queue = []\",\n \"def preProcess(self, msg):\\n\\n # open connection\\n self.conn = Connection(\\n user=self.user, password=self.password,\\n vhost=self.vhost, host=self.host,\\n heartbeat=self.heartbeat, debug=self.debug)\\n\\n # create AMQP channel\\n self.channel = self.conn.channel()\\n self.channel.exchange.declare(self.exchange, self.exchange_type)\\n self.channel.queue.declare(self.queue, self.auto_delete)\\n self.channel.queue.bind(self.queue, self.exchange, self.routing_key)\",\n \"def preProcess(self, msg):\\n\\n # open connection\\n self.conn = Connection(\\n user=self.user, password=self.password,\\n vhost=self.vhost, host=self.host,\\n heartbeat=self.heartbeat, debug=self.debug)\\n\\n # create AMQP channel\\n self.channel = self.conn.channel()\\n self.channel.exchange.declare(self.exchange, self.exchange_type)\\n self.channel.queue.declare(self.queue, self.auto_delete)\\n self.channel.queue.bind(self.queue, self.exchange, self.routing_key)\",\n \"def preProcess(self, msg):\\n\\n # open connection\\n self.conn = Connection(\\n user=self.user, password=self.password,\\n vhost=self.vhost, host=self.host,\\n heartbeat=self.heartbeat, debug=self.debug)\\n\\n # create AMQP channel\\n self.channel = self.conn.channel()\\n self.channel.exchange.declare(self.exchange, self.exchange_type)\\n self.channel.queue.declare(self.queue, self.auto_delete)\\n self.channel.queue.bind(self.queue, self.exchange, self.routing_key)\",\n \"def __init__(self):\\n self._data_queue = []\\n self._access_queue_lock = Lock()\",\n \"def init_queues(self):\\n for step in self.get_children():\\n if step.kind == StepKinds.queue:\\n step.init_object(self.context, None)\",\n \"def connect(self):\\n self._connect()\\n if self.conn is not None:\\n try:\\n self.chan.queue_declare(queue=self.rq, durable=True, exclusive=False, auto_delete=False)\\n self.chan.queue_bind(queue=self.rq, exchange=self.exch, routing_key=self.RKEY)\\n self.chan.basic_consume(queue=self.rq, no_ack=True, callback=self._amqpCallback, consumer_tag=self.ctag)\\n except:\\n self.closeConn()\",\n \"def __init__(self, queue_name, **kwargs):\\n super(Queue, self).__init__(**kwargs)\\n self.value = queue_name\",\n \"def __init__(self, routing_key):\\n self.routing_key = routing_key\\n self.connection = pika.BlockingConnection(pika.ConnectionParameters(host=rabbitmq_hostname))\\n self.channel = self.connection.channel()\\n result = self.channel.queue_declare(queue='', exclusive=True, durable=True)\\n self.callback_queue = result.method.queue\\n\\n self.channel.basic_consume(\\n queue=self.callback_queue,\\n on_message_callback=self.on_response,\\n auto_ack=True\\n )\",\n \"def setupTcp(self):\\n \\tself.tcpManager = QueuedConnectionManager()\\n \\tself.tcpReader = QueuedConnectionReader(self.tcpManager, 0)\\n \\tself.tcpWriter = ConnectionWriter(self.tcpManager, 0)\",\n \"def __init__(self, queue: Queue):\\n logging.Handler.__init__(self)\\n self.queue = queue\",\n \"def _setup_communication(self):\\n state = self.ui.checkBox_comm.checkState()\\n if state:\\n try:\\n sys.path.append(\\\"..\\\")\\n from zmq_interface.gui_interface import ZmqInterface\\n except ImportError as e:\\n self.write_text(\\\"ZMQ interface failed to import. No remote control for this session.\\\")\\n self.disable_visualizer()\\n return\\n try:\\n ##TODO: let user specify ports\\n self.com = ZmqInterface(rep_port=REPLY_PORT,\\n gui_handle=self)\\n except Exception as e:\\n #traceback.print_exc(file=sys.stdout)\\n self.write_text(\\\"ZMQ interface failed to start. No remote control for this session. Reason: %s\\\" % e)\\n self.disable_visualizer()\\n return\\n self.start = self._start_session\\n self.stop = self._stop_session\\n self.load_config = self._load_state\\n self.save_config = self._save_state\\n self.com_timer = QtCore.QTimer()\\n self.com_timer.timeout.connect(self._check_coms)\\n self.com_timer.start(200)\\n self.write_text(\\\"ZMQ interface set up. Reply port on %s\\\" % self.com.rep_port)\\n self.enable_visualizer()\\n else:\\n if self.com:\\n self.com.close()\\n if self.com_timer:\\n self.com_timer.stop()\\n self.com = None\\n self.com_timer = None\\n self.enable_visualizer()\\n self.write_text(\\\"ZMQ interface closed.\\\")\",\n \"def __init__(self, params={}):\\n self.lt_ses = lt.session() # pylint: disable=no-member\\n self.lt_ses.listen_on(6881, 6891)\\n\\n self.params = params\\n self.queue = deque()\\n self.stream_thread = None\\n self.handle = None\",\n \"def on_channel_open(new_channel):\\n global channel\\n channel = new_channel\\n channel.queue_declare(queue=\\\"test\\\", durable=True, exclusive=False, auto_delete=False, callback=on_queue_declared)\",\n \"def __init__(self, framer):\\n ModbusClientProtocol.__init__(self, framer)\\n log.debug(\\\"Beginning the processing loop\\\")\\n reactor.callLater(CLIENT_DELAY, self.fetch_holding_registers)\",\n \"def __init__(self, connection, exchange, queue, routing_key,\\n sub_worker, thread_support=True, no_declare=True):\\n # Reduce logging from amqp module\\n setup_logging(loglevel='INFO', loggers=['amqp'])\\n logger.debug(f\\\"Initializating a new listener for exchange/queue: {exchange}/{queue}...\\\")\\n self.connection = connection\\n self.exchange = Exchange(exchange, 'direct',\\n durable=True, no_declare=no_declare)\\n self.queue = Queue(queue, exchange=self.exchange,\\n routing_key=routing_key, no_declare=no_declare)\\n self.queues = [self.queue]\\n self.no_declare = no_declare\\n logger.info(f\\\"New listener initialized for exchange/queue: {exchange}/{queue}...\\\")\\n logger.debug(f\\\"Importing sub_worker module: {sub_worker}...\\\")\\n self.sub_worker = sub_worker\\n self.thread_support = thread_support\\n mod_name = '.'.join(self.sub_worker.split(\\\".\\\")[:-1])\\n try:\\n self.sub_worker_mod = importlib.import_module(mod_name)\\n except ModuleNotFoundError as e:\\n logger.error(f\\\"Cannot import the sub worker module named {mod_name}: ModuleNotFoundError\\\")\\n sys.exit(-1)\\n except Exception as e:\\n logger.error(f\\\"Cannot import the sub worker module named {mod_name}: \\\" + str(e))\\n sys.exit(-1)\",\n \"def _setup_tubes(self):\\n chan = self.channel\\n inp = self.config[self.MODULE_NAME]['amqp']['in']\\n out = self.config[self.MODULE_NAME]['amqp']['out']\\n if inp['exchange']:\\n log.info('generating Input Queue'+ str(inp))\\n chan.exchange_declare(**inp)\\n self.qname = chan.queue_declare(exclusive=True).queue\\n chan.queue_bind(exchange=inp['exchange'],queue=self.qname)\\n self.consume = lambda cb : chan.basic_consume(cb,queue=self.qname,no_ack=True)\\n self.start_loop = lambda : pika.asyncore_loop()\\n\\n if out['exchange']:\\n log.info('generating Output Exchange'+ str(out))\\n chan.exchange_declare(**out)\\n self.publish = lambda msg: self.channel.basic_publish(exchange=out['exchange'],routing_key='',body=msg)\",\n \"def declare_queue(self):\\n\\n self._channel.queue_declare(queue=self._queue_name, durable=True)\\n print(\\\"Queue declared....\\\")\",\n \"def __init__(self, queue, usercallback):\\n self.queue = queue\\n self.usercallback = usercallback\",\n \"def __init__(self, initial=[]):\\n self.the_queue = copy.copy(initial)\",\n \"def __init__(self, queue):\\n logging.Handler.__init__(self)\\n self.queue = queue\",\n \"def __init__(self, msgQueue, readPeriod=0.5):\\n\\n threading.Thread.__init__(self)\\n\\n self.shutdownEvent = threading.Event()\\n\\n self._msgQueue = msgQueue\\n self._readPeriod = readPeriod\\n\\n # Initialize GPS (Python 3 version info found at https://learn.adafruit.com/adafruit-ultimate-gps-on-the-raspberry-pi/using-your-gps)\\n self._gpsSession = gps.gps('localhost', '2947')\\n self._gpsSession.stream(gps.WATCH_ENABLE | gps.WATCH_NEWSTYLE)\",\n \"def __init__(self):\\n \\n # Initialize logger\\n self._log = logging.getLogger(\\\"OemGateway\\\")\\n \\n # Initialize variables\\n self._data_buffer = []\\n self._last_send = time.time()\\n self._settings = {}\",\n \"def __init__(self):\\n self.queue = deque()\\n self.aux_queue = deque()\",\n \"def __init__(self, store=None):\\n self.sockets = []\\n self.poller = zmq.core.poll.Poller()\\n self.mh = MessageHandler()\\n self.store = store\",\n \"def __init__(self):\\n self.queue = deque()\",\n \"def __init__(self):\\n self.queue = deque()\",\n \"def __init__(self):\\n self.queue = deque()\",\n \"def __init__(self):\\n self.queue = deque()\",\n \"def init(self):\\n self.dispatcher.start()\\n self.replyer.start()\",\n \"def __init__(self):\\n # NOTE: DO not modify this method\\n self.items = SimpleQueue()\",\n \"def __init__(self, threaded=False):\\n self._message_listeners = {}\\n self._threaded = threaded\",\n \"def __init__(self, queue, debug=False):\\n super(Blue, self).__init__()\\n # set bluetooth to be in visible and pairing mode\\n os.system('hciconfig hci0 piscan')\\n self.queue = queue\\n self.__stop = threading.Event()\\n self.lock = threading.RLock()\\n self.old_data = None\\n self.write_okay = True\\n if not debug:\\n self.__server_sock = BluetoothSocket(RFCOMM)\\n self.__server_sock.bind((\\\"\\\", PORT_ANY))\\n self.__server_sock.listen(1)\\n\\n advertise_service(self.__server_sock, self.__name,\\n service_id=self.__uuid,\\n service_classes=[self.__uuid, SERIAL_PORT_CLASS],\\n profiles=[SERIAL_PORT_PROFILE])\\n self.start()\",\n \"def setup_queue(self, channel, queue_name):\\n logger.info('Declaring queue %s', queue_name)\\n channel.queue_declare(queue = queue_name, \\n durable = True,\\n auto_delete = False)\",\n \"def setup(self):\\n self.context = zmq.Context()\\n self.sub_socket = self.context.socket(zmq.SUB)\\n if self.filter:\\n self.sub_socket.setsockopt(zmq.SUBSCRIBE, self.filter)\\n self.sub_socket.connect('tcp://'+self.host+':'+str(self.com_port))\\n return self\",\n \"def __init__(self):\\n self.dequeue = DeQueue()\",\n \"def setup_amq(self):\\n self.setup_amq_cluster_operator()\\n self.setup_amq_kafka_persistent()\\n self.setup_amq_kafka_connect()\\n self.setup_amq_kafka_bridge()\\n self.amq_is_setup = True\\n return self\",\n \"def connect(self):\\n try:\\n self.conn = amqp.Connection(insist=True, **self.config)\\n self.chan = self.conn.channel()\\n self.chan.queue_declare(queue=self.RQU, durable=False, exclusive=False, auto_delete=True)\\n self.chan.exchange_declare(exchange=self.EXCH, type=\\\"topic\\\", durable=True, auto_delete=False,)\\n self.chan.queue_bind(queue=self.RQU, exchange=self.EXCH, routing_key=self.RKEY)\\n self.chan.basic_consume(queue=self.RQU, no_ack=True, callback=self.amqpCallback, consumer_tag=\\\"ctag\\\")\\n except:\\n self.closeConn()\",\n \"def __init__(self, buf=None, *args, **kwargs):\\n super(Message, self).__init__(buf, *args, **kwargs)\\n self.__initialized = True\",\n \"def __init__(self):\\r\\n self.queue = []\\r\\n self.current = False\",\n \"def __init__(self, topic, address, group=None, message_type=MessageFlagType.DIRECT):\\n self._response_lock = asyncio.Lock()\\n self._direct_response = asyncio.Queue()\\n super().__init__(topic, address=address, group=group, message_type=message_type)\",\n \"def initzmq(self):\\n\\n if \\\"topics\\\" not in self.configData:\\n raise Exception(\\\"Topics not found in %s\\\" % self.configPath)\\n\\n for topic in self.configData['topics']:\\n addr = self.gen_address(topic['protocol'], topic['address'],\\n topic['port'])\\n socket = self.build_socket(topic['paradigm'], topic['topic'], addr)\\n self.topics[topic['name']] = socket\",\n \"def setup(self, dictParams):\\n global connack\\n # we dont use globals here now - instead use config.py which is imported where needed to \\n # give cross-module scope\\n #global globalScope.incomingMessageBuffer\\n global broker_public_key\\n \\n hostname = dictParams[\\\"sess\\\"]\\n name = dictParams[\\\"dest\\\"]\\n port = dictParams[\\\"port\\\"]\\n protocol = dictParams[\\\"protoVer\\\"]\\n broker = dictParams[\\\"broker\\\"]\\n # this is a Singleton\\n self.receiver = dictParams[\\\"RcvdMsg\\\"]\\n timeout = dictParams[\\\"timeout\\\"]\\n oldtimestamp = dictParams[\\\"oldtimestamp\\\"]\\n anotherParam = dictParams[\\\"anotherParam\\\"]\\n \\n self.brokerName = broker\\n\\n # Setting clean_session = False means that subsciption information and \\n # queued messages are retained after the client disconnects. It is suitable\\n # in an environment where disconnects are frequent.\\n # client_id is any unique identifier so our own fqdn should be alright to use\\n mqtt_client = mqtt.Client(protocol=self.protocol, client_id=self.myName, clean_session=False)\\n mqtt_client.on_connect = self.on_connect\\n mqtt_client.on_message = self.on_message\\n mqtt_client.on_publish = self.on_publish\\n mqtt_client.on_disconnect = self.on_disconnect\\n mqtt_client.on_subscribe = self.on_subscribe\\n mqtt_client.on_unsubscribe = self.on_unsubscribe\\n mqtt_client.on_log = self.on_log\\n \\n \\n # Set the LWT\\n # If the client disconnects without calling disconnect, the broker will\\n # publish this message on its behalf\\n # retain should be set to true\\n mqtt_client.will_set(self.STATUS, \\n self.status_message(STATUS_DISCONNECTED_UNGRACE), \\n qos=QosType.FLAG_QOS_ATMOSTONCE.value, retain=True) \\n\\n # Connect to the broker\\n # keepalive is maximum number of seconds allowed between communications\\n # with the broker. If no other messages are sent, the client will send a\\n # ping request at this interval\\n # set it high for devices that are disconnected for small periods of time, also for debugging\\n # set it extremely high for devices that are out to sea for days\\n keepalive=1800\\n try:\\n logging.info('Attempting to connect to broker at ' + self.brokerName)\\n mqtt_client.connect(self.brokerName, self.port, keepalive)\\n except:\\n logging.error(\\\"ERROR - could not connect to broker at \\\" + self.brokerName)\\n return False\\n else:\\n logging.info(\\\"INFO - all OK - no problem on attempt to connect to broker at \\\" + self.brokerName) \\n \\n \\n # Force function to block until connack is sent from the broker, or timeout\\n connack = False\\n start_time = time.time()\\n while not connack:\\n time.sleep(0.1)\\n mqtt_client.loop()\\n \\n if time.time() - start_time > float(timeout):\\n raise MqttTimeOutError(\\\"The program timed out while trying to connect to the broker!\\\")\\n break\\n \\n # When connected, subscribe to the relevant channels\\n mqtt_client.subscribe([(self.PUBLIC, 1), (self.PROTECTED, 1),\\n (self.PRIVATE, 1), (self.PINGREQ, 1),\\n (self.PINGACK, 1), (self.HANDSHAKE, 1)\\n ])\\n \\n self.client = mqtt_client\\n \\n # init the globalScope.incomingMessageBuffer - this is now done in the config.py\\n \\n \\n # Do a blocking call\\n broker_public_key = None\\n self.client.publish(self.STATUS, \\n self.status_message(STATUS_CONNECTED), \\n qos=QosType.FLAG_QOS_ATLEASTONCE.value)\\n\\n # TODO dont know what this does and it was getting stuck in the loop so i got rid of it \\n# while self.broker_public_key == None:\\n# time.sleep(0.1)\\n# mqtt_client.loop()\\n# # Check the message buffer\\n# if globalScope.incomingMessageBuffer != []:\\n# for message in globalScope.incomingMessageBuffer:\\n# if message.topic == self.HANDSHAKE:\\n# # Check whether it is a broker key message.\\n# try:\\n# payload = json.loads(message.payload.decode())\\n## disabled for now\\n## self.broker_public_key = payload['public_key']\\n## print(self.broker_public_key)\\n# self.broker_public_key = json.loads(message.payload.decode())\\n# except:\\n# pass\\n# globalScope.incomingMessageBuffer = []\\n \\n\\n # Start the loop. This method is preferable to repeatedly calling loop\\n # since it handles reconnections automatically. It is non-blocking and \\n # handles interactions with the broker in the background.\\n logging.debug('DEBUG - Starting loop')\\n try:\\n# mqtt_client.loop()\\n self.client.loop_start()\\n except:\\n logging.error(\\\"ERROR - failure of loop_start\\\")\\n \\n return True\",\n \"def __init__(self):\\n queue = collections.deque()\",\n \"def declare(self):\\n self.channel.queue_declare(queue='files_to_database')\",\n \"def __init__(self, queue):\\r\\n logging.Handler.__init__(self)\\r\\n self.queue = queue\",\n \"def setup(self):\\n global log_th, conf_th, header_th, command_w_th\\n self.conf_th_ic = conf_th\\n self.header_th_ic = header_th\\n self.command_w_th_inc = command_w_th\\n self.hostname = conf_th.get_item(q_key='general').get('hostname')\\n self.std_recv_size = int(conf_th.get_item(q_key='general').get('std_recv_size'))\\n self.data_recv_size = int(conf_th.get_item(q_key='general').get('data_recv_size'))\\n self.mail_save_enable = int(conf_th.get_item(q_key='general').get('mail_save_enable'))\\n self.mail_save_path = conf_th.get_item(q_key='general').get('mail_save_path')\\n self.no_answer = int(conf_th.get_item(q_key='general').get('no_answer'))\\n self.sleep_between = int(conf_th.get_item(q_key='general').get('sleep_between'))\\n self.message_id = library.q_id_generate(size=16)\\n self.client_ip = tuple(self.client_address).__getitem__(0)\\n self.client_port = int(tuple(self.client_address).__getitem__(1))\\n # Running\\n self.header_th_ic.write_header(ip=self.client_ip, qid=self.message_id)\\n message = '220 ' + self.hostname\\n self.func_sender(message)\\n log_th.log_info('{} connected to {} thread'.format(self.client_ip, threading.current_thread().name))\",\n \"def __init__(self, amqp_url):\\n self._connection = None\\n self._channel = None\\n self._url = amqp_url\",\n \"def __init__(self):\\n self.q1 = queue()\\n self.q2 = queue()\",\n \"def __init__(self, lsfQueue, jobName=\\\"\\\", async=False):\\n self.lsfQueue = lsfQueue\\n self.jobName = jobName\\n self.async = async\\n self.jobid = None\\n self.cmd = None\",\n \"def __init__(self, topic):\\n self._topic = topic\\n\\n # Because this is Music-specific, the server is\\n # built-in to the API class, stored as the transport.\\n # Thus, unlike oslo.messaging, there is no server\\n # specified for a target. There also isn't an\\n # exchange, namespace, or version at the moment.\\n\\n # Dynamically create a message class for this topic.\\n self._topic_class = base.create_dynamic_model(\\n keyspace=CONF.messaging_server.keyspace,\\n baseclass=message.Message, classname=self.topic)\\n\\n if not self._topic_class:\\n raise RuntimeError(\\\"Error setting the topic class for the messaging layer.\\\")\"\n]","isConversational":false},"negative_scores":{"kind":"list like","value":["0.74446756","0.71945274","0.6970091","0.6969268","0.6967559","0.6944674","0.6944674","0.69317365","0.6866296","0.67447776","0.6695959","0.6673099","0.6663105","0.6660289","0.66565484","0.66547716","0.6641651","0.662688","0.662688","0.662688","0.662688","0.662688","0.66056186","0.659599","0.6583802","0.65774083","0.6572097","0.6570421","0.65492195","0.65350723","0.65201527","0.65155536","0.65155536","0.65140057","0.64770055","0.6446091","0.64174503","0.6413532","0.64133716","0.6412314","0.64103013","0.6405916","0.6403532","0.639967","0.6393287","0.6391302","0.63588905","0.63469446","0.634133","0.6338595","0.63336015","0.63192976","0.63192976","0.63192976","0.63175493","0.6288361","0.62784857","0.6270591","0.6256499","0.6254235","0.6253233","0.62516785","0.6248308","0.62482876","0.624725","0.62404513","0.6239804","0.6229242","0.6223766","0.6213334","0.6201073","0.6199076","0.619311","0.6191119","0.6187436","0.61854863","0.61854863","0.61854863","0.61854863","0.61791515","0.617641","0.61677915","0.6161832","0.61605096","0.6148911","0.6146733","0.6146498","0.6138339","0.61366296","0.613109","0.6126816","0.6107343","0.6104814","0.6104365","0.6099961","0.6055588","0.60553205","0.60498345","0.60450774","0.60382897","0.60361207"],"string":"[\n \"0.74446756\",\n \"0.71945274\",\n \"0.6970091\",\n \"0.6969268\",\n \"0.6967559\",\n \"0.6944674\",\n \"0.6944674\",\n \"0.69317365\",\n \"0.6866296\",\n \"0.67447776\",\n \"0.6695959\",\n \"0.6673099\",\n \"0.6663105\",\n \"0.6660289\",\n \"0.66565484\",\n \"0.66547716\",\n \"0.6641651\",\n \"0.662688\",\n \"0.662688\",\n \"0.662688\",\n \"0.662688\",\n \"0.662688\",\n \"0.66056186\",\n \"0.659599\",\n \"0.6583802\",\n \"0.65774083\",\n \"0.6572097\",\n \"0.6570421\",\n \"0.65492195\",\n \"0.65350723\",\n \"0.65201527\",\n \"0.65155536\",\n \"0.65155536\",\n \"0.65140057\",\n \"0.64770055\",\n \"0.6446091\",\n \"0.64174503\",\n \"0.6413532\",\n \"0.64133716\",\n \"0.6412314\",\n \"0.64103013\",\n \"0.6405916\",\n \"0.6403532\",\n \"0.639967\",\n \"0.6393287\",\n \"0.6391302\",\n \"0.63588905\",\n \"0.63469446\",\n \"0.634133\",\n \"0.6338595\",\n \"0.63336015\",\n \"0.63192976\",\n \"0.63192976\",\n \"0.63192976\",\n \"0.63175493\",\n \"0.6288361\",\n \"0.62784857\",\n \"0.6270591\",\n \"0.6256499\",\n \"0.6254235\",\n \"0.6253233\",\n \"0.62516785\",\n \"0.6248308\",\n \"0.62482876\",\n \"0.624725\",\n \"0.62404513\",\n \"0.6239804\",\n \"0.6229242\",\n \"0.6223766\",\n \"0.6213334\",\n \"0.6201073\",\n \"0.6199076\",\n \"0.619311\",\n \"0.6191119\",\n \"0.6187436\",\n \"0.61854863\",\n \"0.61854863\",\n \"0.61854863\",\n \"0.61854863\",\n \"0.61791515\",\n \"0.617641\",\n \"0.61677915\",\n \"0.6161832\",\n \"0.61605096\",\n \"0.6148911\",\n \"0.6146733\",\n \"0.6146498\",\n \"0.6138339\",\n \"0.61366296\",\n \"0.613109\",\n \"0.6126816\",\n \"0.6107343\",\n \"0.6104814\",\n \"0.6104365\",\n \"0.6099961\",\n \"0.6055588\",\n \"0.60553205\",\n \"0.60498345\",\n \"0.60450774\",\n \"0.60382897\",\n \"0.60361207\"\n]","isConversational":false},"document_score":{"kind":"string","value":"0.0"},"document_rank":{"kind":"string","value":"-1"}}},{"rowIdx":22698,"cells":{"query":{"kind":"string","value":"r\"\"\"Get keyword arguments to initialize communication with opposite comm object."},"document":{"kind":"string","value":"def opp_comm_kwargs(self):\n kwargs = super(FileComm, self).opp_comm_kwargs()\n kwargs['newline'] = self.newline\n kwargs['open_as_binary'] = self.open_as_binary\n kwargs['is_series'] = self.is_series\n return kwargs"},"metadata":{"kind":"string","value":"{\n \"objective\": {\n \"self\": [],\n \"paired\": [],\n \"triplet\": [\n [\n \"query\",\n \"document\",\n \"negatives\"\n ]\n ]\n }\n}"},"negatives":{"kind":"list like","value":["def new_comm_kwargs(cls, *args, **kwargs):\n kwargs.setdefault('address', 'file.txt')\n return args, kwargs","def driver_kwargs(self):\n out = super(TestCisRpcServer, self).driver_kwargs\n out['comm'] = 'ClientComm'\n return out","def driver_kwargs(self):\n out = super(TestCisRpcSplit, self).driver_kwargs\n # Reversed\n out['icomm_kwargs'] = dict(name=self.ocomm_name)\n out['ocomm_kwargs'] = dict(name=self.icomm_name)\n return out","def __init__(self, *args, **kwds):\n if args or kwds:\n super(KomodoSpeechRecCommand, self).__init__(*args, **kwds)\n #message fields cannot be None, assign default values for those that are\n if self.header is None:\n self.header = std_msgs.msg.Header()\n if self.cmd is None:\n self.cmd = ''\n if self.cat is None:\n self.cat = ''\n else:\n self.header = std_msgs.msg.Header()\n self.cmd = ''\n self.cat = ''","def __init__(**params):","def __init__(self, *args, **kwds):\n if args or kwds:\n super(robot_GetStateRequest, self).__init__(*args, **kwds)","def driver_kwargs(self):\n out = super(TestCisRpc, self).driver_kwargs\n out['comm'] = 'RPCComm'\n out['format_str'] = self.fmt_str\n return out","def __init__(self, *args, **kwds):\n if args or kwds:\n super(DrivetrainCommand, self).__init__(*args, **kwds)\n #message fields cannot be None, assign default values for those that are\n if self.header is None:\n self.header = std_msgs.msg._Header.Header()\n if self.gear is None:\n self.gear = 0\n if self.front_diff is None:\n self.front_diff = 0\n if self.rear_diff is None:\n self.rear_diff = 0\n else:\n self.header = std_msgs.msg._Header.Header()\n self.gear = 0\n self.front_diff = 0\n self.rear_diff = 0","def __init__(self, *args, **kwds):\n if args or kwds:\n super(AlignModelSrvRequest, self).__init__(*args, **kwds)\n #message fields cannot be None, assign default values for those that are\n if self.model is None:\n self.model = articulation_msgs.msg.ModelMsg()\n if self.data is None:\n self.data = articulation_msgs.msg.ModelMsg()\n else:\n self.model = articulation_msgs.msg.ModelMsg()\n self.data = articulation_msgs.msg.ModelMsg()","def get_mandatory_arguments(self):\n args, varargs, keyword, defaults = inspect.getargspec(self.exec_obj)\n\n if defaults is not None:\n args = args[:-len(defaults)]\n\n if args.count('self') > 0:\n args.remove('self')\n return args","def oic_pre_construct(self, cli_info, request_args=None, **kwargs):\n for prop in self.msg_type.c_param.keys():\n if prop in request_args:\n continue\n try:\n request_args[prop] = cli_info.behaviour[prop]\n except KeyError:\n pass\n\n if \"post_logout_redirect_uris\" not in request_args:\n try:\n request_args[\n \"post_logout_redirect_uris\"] = \\\n cli_info.post_logout_redirect_uris\n except AttributeError:\n pass\n\n if \"redirect_uris\" not in request_args:\n try:\n request_args[\"redirect_uris\"] = cli_info.redirect_uris\n except AttributeError:\n raise MissingRequiredAttribute(\"redirect_uris\", request_args)\n\n try:\n if cli_info.provider_info[\n 'require_request_uri_registration'] is True:\n request_args['request_uris'] = cli_info.generate_request_uris(\n cli_info.requests_dir)\n except KeyError:\n pass\n\n return request_args, {}","def connection_arguments(self):\n filtered_args = ['name', 'metrics']\n\n # make sure we make a copy of this global so it is thread-safe\n args = dict(DEFAULT_CONNECT_ARGS)\n\n for key in set(self.config) - set(filtered_args):\n if key == 'dbname':\n args['database'] = self.config[key]\n else:\n args[key] = self.config[key]\n return args","def __init__(self, args, kwargs):\n self._args_dec = list(args)\n self._kwargs_dec = dict(kwargs)","def get_mandatory_args(self):\n raise NotImplementedError(\"ICallable.get_mandatory_args\")","def driver_kwargs(self):\n out = super(TestCisRpcClient, self).driver_kwargs\n out['comm'] = 'ServerComm'\n out['response_kwargs'] = {'format_str': self.fmt_str}\n return out","def extra_target_arguments(self):\n return {}","def __init__(self, *args, **kwds):\n if args or kwds:\n super(Interaction, self).__init__(*args, **kwds)\n #message fields cannot be None, assign default values for those that are\n if self.name is None:\n self.name = ''\n if self.compatibility is None:\n self.compatibility = ''\n if self.display_name is None:\n self.display_name = ''\n if self.description is None:\n self.description = ''\n if self.namespace is None:\n self.namespace = ''\n if self.icon is None:\n self.icon = rocon_std_msgs.msg.Icon()\n if self.remappings is None:\n self.remappings = []\n if self.parameters is None:\n self.parameters = ''\n if self.max is None:\n self.max = 0\n if self.pairing is None:\n self.pairing = rocon_interaction_msgs.msg.Pairing()\n if self.hash is None:\n self.hash = 0\n if self.role is None:\n self.role = ''\n else:\n self.name = ''\n self.compatibility = ''\n self.display_name = ''\n self.description = ''\n self.namespace = ''\n self.icon = rocon_std_msgs.msg.Icon()\n self.remappings = []\n self.parameters = ''\n self.max = 0\n self.pairing = rocon_interaction_msgs.msg.Pairing()\n self.hash = 0\n self.role = ''","def __init__ (self, *args, **kw):\n self.__args = args\n self.__kw = kw","def __init__(self, connection, topic, args, kargs):\n self.__args__ = args\n self.__kargs__ = kargs\n self.__topic__ = topic\n self.__conn__ = connection","def getInitParams(self):\n paramDict = super().getInitParams()\n paramDict['p'] = self.p\n return paramDict","def __init__(self, *args, **kwds):\n if args or kwds:\n super(RobotDescription, self).__init__(*args, **kwds)\n #message fields cannot be None, assign default values for those that are\n if self.header is None:\n self.header = std_msgs.msg.Header()\n if self.robot_id is None:\n self.robot_id = opil_v2.msg.Id()\n if self.agv_msg is None:\n self.agv_msg = opil_v2.msg.RobotDescriptionAGV()\n else:\n self.header = std_msgs.msg.Header()\n self.robot_id = opil_v2.msg.Id()\n self.agv_msg = opil_v2.msg.RobotDescriptionAGV()","def __init__(self, message: str = None, **kwargs):\n self.message = message\n self.kwargs = kwargs\n self.args = {}\n self.args['message'] = self.message\n self.args['args'] = self.kwargs","def __init__(self, *args, **kwds):\n if args or kwds:\n super(GetPlanningSceneRequest, self).__init__(*args, **kwds)\n #message fields cannot be None, assign default values for those that are\n if self.planning_scene_diff is None:\n self.planning_scene_diff = arm_navigation_msgs.msg.PlanningScene()\n if self.operations is None:\n self.operations = arm_navigation_msgs.msg.OrderedCollisionOperations()\n else:\n self.planning_scene_diff = arm_navigation_msgs.msg.PlanningScene()\n self.operations = arm_navigation_msgs.msg.OrderedCollisionOperations()","def __init__(self, *args, **kwargs):\n self.args = args\n self.kwargs = kwargs","def __init__(self, *args, **kwds):\n if args or kwds:\n super(observationRPY, self).__init__(*args, **kwds)\n #message fields cannot be None, assign default values for those that are\n if self.position is None:\n self.position = pcl_segment.msg.positionRPY()\n if self.is_Known is None:\n self.is_Known = False\n else:\n self.position = pcl_segment.msg.positionRPY()\n self.is_Known = False","def _get_init_args(self):\n signature = inspect.signature(self.__init__)\n parameters = signature.parameters\n args = [arg for arg, p in parameters.items()\n if p.kind is p.POSITIONAL_OR_KEYWORD]\n\n return {arg: getattr(self, arg) for arg in args if arg != 'self'}","def _get_init_args(self):\n\n return dict(enum=self.enum, dflt=self._defname,\n base=self.base, shape=self.shape)","def define_parameters(self):","def __init__(self, *args, **kwargs):\n super().__init__(*args, **kwargs)\n self.expected_cont_uuid = None\n self.daos_cmd = None","def _invocation_params(self) -> Dict[str, Any]:\n return self._default_params","def __init__(self, ignored_args = [], **common_options):\n self.ignored_args = ignored_args\n self.common_options = self._fix_argparse_dicts(common_options)","def __init__(self, params_dict):\n\n self._params = []\n \"\"\"\n Initialized by the command line parser with all positional parameters\n supplied on the command line\n \"\"\"\n\n if not params_dict:\n return\n self._param_text = params_dict.get(\"params\")\n self._help_text = params_dict.get(\"text\")","def __init__(self, *args, **kwargs):\n self._args = args\n self._kwargs = kwargs","def initArgs(self, args):\n ignore = ['self', 'kwargs', 'args']\n for k,v in args.iteritems():\n if k not in ignore: \n setattr(self, k, v)\n pass","def __getinitargs__NOT__(self):\n if self._idevice:\n return self._idevice, self.path\n else:\n return self._package, self.path","def _first_init(self, **kwargs):\n return kwargs","def _first_init(self, **kwargs):\n return kwargs","def __init__(self, **kwargs):\n self.__kwargs = kwargs","def getPositionalArgs():","def get_interface_initialization_kwargs(self, **kwargs) -> dict:\n return {\n key: value\n for key, value in kwargs.items()\n if not self.input_definitions.get(key=key).run_method_input\n }","def base_arguments(self):\n raise NotImplementedError()","def __init__(self, **keywords):\n # Set cord with the normal method if it was passed. Set atoms if\n # atomlist was passed. init() if both were passed.\n if keywords.has_key(\"cord\"):\n self.setcord(keywords[\"cord\"])\n if keywords.has_key(\"weights\"):\n self.setweights(keywords[\"weights\"])\n if keywords.has_key(\"cord\") and keywords.has_key(\"weights\"):\n self.init()","def __init__(self, *args, **kwds):\n if args or kwds:\n super(SetElectromotorsControlRequest, self).__init__(*args, **kwds)\n #message fields cannot be None, assign default values for those that are\n if self.ctrl is None:\n self.ctrl = actuators_canopen.msg.ElectromotorsState()\n else:\n self.ctrl = actuators_canopen.msg.ElectromotorsState()","def get_connection_params(self, session, **kwargs):\n return {}","def __init__(self):\n self.params = arcpy.GetParameterInfo()","def __init__(self, *args: Any, **kwargs: Any) -> None:\n kwargs[\"aux_params\"] = None\n super().__init__(*args, **kwargs)","def __init__(self, *args, **kwds):\n if args or kwds:\n super(ModifyParametersRequest, self).__init__(*args, **kwds)\n #message fields cannot be None, assign default values for those that are\n if self.major_ax is None:\n self.major_ax = 0.\n if self.minor_ax is None:\n self.minor_ax = 0.\n if self.coup_strength is None:\n self.coup_strength = 0.\n if self.limit_cycle is None:\n self.limit_cycle = 0.\n if self.forward_velocity is None:\n self.forward_velocity = 0.\n if self.curvature is None:\n self.curvature = 0.\n if self.x_offset is None:\n self.x_offset = []\n if self.y_offset is None:\n self.y_offset = []\n if self.coupling_1 is None:\n self.coupling_1 = []\n if self.coupling_2 is None:\n self.coupling_2 = []\n if self.coupling_3 is None:\n self.coupling_3 = []\n if self.coupling_4 is None:\n self.coupling_4 = []\n if self.coupling_5 is None:\n self.coupling_5 = []\n if self.coupling_6 is None:\n self.coupling_6 = []\n else:\n self.major_ax = 0.\n self.minor_ax = 0.\n self.coup_strength = 0.\n self.limit_cycle = 0.\n self.forward_velocity = 0.\n self.curvature = 0.\n self.x_offset = []\n self.y_offset = []\n self.coupling_1 = []\n self.coupling_2 = []\n self.coupling_3 = []\n self.coupling_4 = []\n self.coupling_5 = []\n self.coupling_6 = []","def __init__(self, sent: int, received: int):\n super().__init__(sent, received)\n self.sent = sent\n self.received = received","def __init__(self, *args, **kwds):\n if args or kwds:\n super(pid_control_reqRequest, self).__init__(*args, **kwds)\n #message fields cannot be None, assign default values for those that are\n if self.yaw is None:\n self.yaw = 0.\n else:\n self.yaw = 0.","def __init__(self, *args, **kwargs):\n super().__init__(*args, **kwargs)\n self.__args = args\n self.__kwargs = kwargs","def _ex_connection_class_kwargs(self):\r\n kwargs = {}\r\n if not self._host_argument_set:\r\n kwargs['host'] = API_ENDPOINTS[self.region]['host']\r\n\r\n return kwargs","def default_hparams():\n return {\n \"name\": \"forward_connector\"\n }","def initialize(self, *args, **kwargs):","def get_arguments():\n\tparser.add_argument('-i', '--interface', help='interface to affect')\n\tparser.add_argument('-m','--mac', help='mac to allocate')\n\n\targs = parser.parse_args()\n\tinterface = args.interface\n\tmac = args.mac\n\treturn (interface, mac)","def params(self, **kwargs):\n return kwargs","def __init__(self, args=False):\n self.args = args","def initialize(self, **kwargs):","def __init__(self, *args, **kwargs):\n super().__init__(*args, **kwargs)\n\n # Add common arguments\n self.add_argument(\n \"--debug\",\n action=\"store_true\",\n default=False,\n help=\"debug mode: show debug messages\",\n )\n self.add_argument(\n \"-s\",\n \"--server\",\n required=True,\n help='Galène server to connect to, e.g. \"wss://galene.example.com/ws\"',\n )\n self.add_argument(\n \"-g\",\n \"--group\",\n required=True,\n help=\"Join this group\",\n )\n self.add_argument(\n \"-u\",\n \"--username\",\n required=True,\n help=\"Group username\",\n )\n self.add_argument(\n \"-p\",\n \"--password\",\n help=\"Group password\",\n )","def __init__(self, *args, **kwds):\n if args or kwds:\n super(g_sens, self).__init__(*args, **kwds)\n #message fields cannot be None, assign default values for those that are\n if self.Front_sens is None:\n self.Front_sens = motor_sc.msg.sens9ax()\n if self.Rear_sens is None:\n self.Rear_sens = motor_sc.msg.sens9ax()\n else:\n self.Front_sens = motor_sc.msg.sens9ax()\n self.Rear_sens = motor_sc.msg.sens9ax()","def __init__(self, **kwargs):\n # Handle whatever kwargs we want here\n self.io_mapping = kwargs.get(\"io_mapping\", [])\n\n self.create_inputs = kwargs.get(\"create_inputs\", [])\n self.min_completion_fraction = kwargs.get(\"min_completion_fraction\", 1.0)\n\n # Now pass all of them to the parent class\n super(self.__class__, self).__init__(**kwargs)","def __init__(self, *args, **kwds):\n if args or kwds:\n super(RotationMatrix, self).__init__(*args, **kwds)\n #message fields cannot be None, assign default values for those that are\n if self.c0 is None:\n self.c0 = bh_motion.msg.Vector3()\n if self.c1 is None:\n self.c1 = bh_motion.msg.Vector3()\n if self.c2 is None:\n self.c2 = bh_motion.msg.Vector3()\n else:\n self.c0 = bh_motion.msg.Vector3()\n self.c1 = bh_motion.msg.Vector3()\n self.c2 = bh_motion.msg.Vector3()","def get_default_kwargs(self, **defaults):\n return super().get_default_kwargs(**{\"port\": 22})","def init_params(self):\n blah","def getInitParams(self):\n paramDict = super().getInitParams()\n paramDict['method'] = self.method\n paramDict['dimension'] = self.dimension\n paramDict['rank'] = self.rank\n paramDict['mu'] = self.mu\n paramDict['covariance'] = self.covariance\n return paramDict","def get_kwargs(self):\n return {}","def get_cli_arguments(self):\n pass","def set_params(self, **kwargs) -> NoReturn:\n pass","def __getinitargs__(self):\n return (self.cutout,)","def __init__(self, other=None, **kw):\n self.__doc__ = None # Make BaseRequest objects unpublishable\n if other is None:\n other = kw\n else:\n other.update(kw)\n self.other = other","def init(self, *args, **kwds):\n pass","def __init__(self, parms, body, env):\n self.parms = parms\n self.body = body\n self.env = env","def init_pos_parms(self):\n\n ## init_pos_parms()\n parms = {}\n\n # length axis\n parms['length_attribute_road'] = ('top', 'cl', 'bottom')[self.segment]\n parms['length_attribute_artifact'] = ('top', 'cl', 'bottom')[self.pos_length]\n\n # width axis\n if type(self.pos_width) is int:\n parms['width_road_rect'] = self.road.lanes[self.pos_width]\n parms['width_attribute_road'] = 'cw'\n parms['width_attribute_artifact'] = 'cw'\n else:\n parms['width_road_rect'] = self.road\n pos_parms = {'l': ('left', 'right'),\n 'c': ('cw', 'cw'),\n 'r': ('right', 'left')}[self.pos_width]\n parms['width_attribute_road'] = pos_parms[0]\n parms['width_attribute_artifact'] = pos_parms[1]\n return parms","def __init__(self, **parameters):\n self.parameters = parameters","def __init__(self, *args, **kwds):\n if args or kwds:\n super(Cmd_WF, self).__init__(*args, **kwds)\n #message fields cannot be None, assign default values for those that are\n if self.enable_steering is None:\n self.enable_steering = False\n if self.enable_braking is None:\n self.enable_braking = False\n if self.enable_driving is None:\n self.enable_driving = False\n if self.enable_Estop is None:\n self.enable_Estop = False\n if self.enable_gear is None:\n self.enable_gear = False\n if self.sw_deg is None:\n self.sw_deg = 0.\n if self.sw_rad is None:\n self.sw_rad = 0.\n if self.speed_ms is None:\n self.speed_ms = 0.\n if self.speed_kms is None:\n self.speed_kms = 0.\n if self.ax_ms2 is None:\n self.ax_ms2 = 0.\n if self.omega_rad is None:\n self.omega_rad = 0.\n if self.gear_mode is None:\n self.gear_mode = 0\n if self.steering is None:\n self.steering = 0.\n if self.speed is None:\n self.speed = 0.\n else:\n self.enable_steering = False\n self.enable_braking = False\n self.enable_driving = False\n self.enable_Estop = False\n self.enable_gear = False\n self.sw_deg = 0.\n self.sw_rad = 0.\n self.speed_ms = 0.\n self.speed_kms = 0.\n self.ax_ms2 = 0.\n self.omega_rad = 0.\n self.gear_mode = 0\n self.steering = 0.\n self.speed = 0.","def set_params(self, **kwargs):\n ...","def initialize(self, args):\n\t\tpass","def __init__(self, *args, **kwds):\n if args or kwds:\n super(ConductorGraph, self).__init__(*args, **kwds)\n #message fields cannot be None, assign default values for those that are\n if self.pending is None:\n self.pending = []\n if self.available is None:\n self.available = []\n if self.missing is None:\n self.missing = []\n if self.gone is None:\n self.gone = []\n else:\n self.pending = []\n self.available = []\n self.missing = []\n self.gone = []","def initialize_comm(self, config_options):\n try:\n self.comm = MPI.COMM_WORLD\n self.comm.Set_errhandler(MPI.ERRORS_ARE_FATAL)\n except AttributeError as ae:\n config_options.errMsg = \"Unable to initialize the MPI Communicator object\"\n raise ae\n\n try:\n self.size = self.comm.Get_size()\n except MPI.Exception as mpi_exception:\n config_options.errMsg = \"Unable to retrieve the MPI size.\"\n raise mpi_exception\n\n try:\n self.rank = self.comm.Get_rank()\n except MPI.Exception as mpi_exception:\n config_options.errMsg = \"Unable to retrieve the MPI processor rank.\"\n raise mpi_exception","def getInitParams(self):\n paramDict = super().getInitParams()\n paramDict['functionType'] = self.functionType\n paramDict['dataFilename'] = self.dataFilename\n paramDict['workingDir'] = self.workingDir\n return paramDict","def set_params(self, *arg):\n pass","def __init__(self, *args, **kwds):\n if args or kwds:\n super(FeedbackMsg, self).__init__(*args, **kwds)\n #message fields cannot be None, assign default values for those that are\n if self.name is None:\n self.name = []\n if self.position is None:\n self.position = []\n if self.velocity is None:\n self.velocity = []\n if self.effort is None:\n self.effort = []\n if self.position_command is None:\n self.position_command = []\n if self.velocity_command is None:\n self.velocity_command = []\n if self.effort_command is None:\n self.effort_command = []\n if self.accelerometer is None:\n self.accelerometer = []\n if self.gyro is None:\n self.gyro = []\n if self.orientation is None:\n self.orientation = []\n if self.deflection is None:\n self.deflection = []\n if self.deflection_velocity is None:\n self.deflection_velocity = []\n if self.motor_velocity is None:\n self.motor_velocity = []\n if self.motor_current is None:\n self.motor_current = []\n if self.motor_winding_current is None:\n self.motor_winding_current = []\n if self.motor_sensor_temperature is None:\n self.motor_sensor_temperature = []\n if self.motor_winding_temperature is None:\n self.motor_winding_temperature = []\n if self.motor_housing_temperature is None:\n self.motor_housing_temperature = []\n if self.board_temperature is None:\n self.board_temperature = []\n if self.processor_temperature is None:\n self.processor_temperature = []\n if self.voltage is None:\n self.voltage = []\n if self.led_color is None:\n self.led_color = []\n if self.sequence_number is None:\n self.sequence_number = []\n if self.receive_time is None:\n self.receive_time = []\n if self.transmit_time is None:\n self.transmit_time = []\n if self.hardware_receive_time is None:\n self.hardware_receive_time = []\n if self.hardware_transmit_time is None:\n self.hardware_transmit_time = []\n else:\n self.name = []\n self.position = []\n self.velocity = []\n self.effort = []\n self.position_command = []\n self.velocity_command = []\n self.effort_command = []\n self.accelerometer = []\n self.gyro = []\n self.orientation = []\n self.deflection = []\n self.deflection_velocity = []\n self.motor_velocity = []\n self.motor_current = []\n self.motor_winding_current = []\n self.motor_sensor_temperature = []\n self.motor_winding_temperature = []\n self.motor_housing_temperature = []\n self.board_temperature = []\n self.processor_temperature = []\n self.voltage = []\n self.led_color = []\n self.sequence_number = []\n self.receive_time = []\n self.transmit_time = []\n self.hardware_receive_time = []\n self.hardware_transmit_time = []","def pykwarg(self):\n return self._pykwarg","def __init__(self, *args, **kwds):\n if args or kwds:\n super(pid_control_reqResponse, self).__init__(*args, **kwds)\n #message fields cannot be None, assign default values for those that are\n if self.FL_vel is None:\n self.FL_vel = 0\n if self.FR_vel is None:\n self.FR_vel = 0\n if self.BL_vel is None:\n self.BL_vel = 0\n if self.BR_vel is None:\n self.BR_vel = 0\n else:\n self.FL_vel = 0\n self.FR_vel = 0\n self.BL_vel = 0\n self.BR_vel = 0","def getInitParams(self):\n return {}","def getInitParams(self):\n paramDict = BoostDistribution.getInitParams(self)\n paramDict['p'] = self.p\n return paramDict","def getInitParams(self):\n paramDict = BoostDistribution.getInitParams(self)\n paramDict['p'] = self.p\n return paramDict","def params():\n raise NotImplementedError","def __init__(*args):","def __init__(*args):","def __init__(*args):","def __init__(*args):","def __init__(*args):","def __init__(*args):","def __init__(*args):","def __init__(*args):","def __init__(*args):","def __init__(*args):","def __init__(*args):","def __init__(*args):","def __init__(*args):"],"string":"[\n \"def new_comm_kwargs(cls, *args, **kwargs):\\n kwargs.setdefault('address', 'file.txt')\\n return args, kwargs\",\n \"def driver_kwargs(self):\\n out = super(TestCisRpcServer, self).driver_kwargs\\n out['comm'] = 'ClientComm'\\n return out\",\n \"def driver_kwargs(self):\\n out = super(TestCisRpcSplit, self).driver_kwargs\\n # Reversed\\n out['icomm_kwargs'] = dict(name=self.ocomm_name)\\n out['ocomm_kwargs'] = dict(name=self.icomm_name)\\n return out\",\n \"def __init__(self, *args, **kwds):\\n if args or kwds:\\n super(KomodoSpeechRecCommand, self).__init__(*args, **kwds)\\n #message fields cannot be None, assign default values for those that are\\n if self.header is None:\\n self.header = std_msgs.msg.Header()\\n if self.cmd is None:\\n self.cmd = ''\\n if self.cat is None:\\n self.cat = ''\\n else:\\n self.header = std_msgs.msg.Header()\\n self.cmd = ''\\n self.cat = ''\",\n \"def __init__(**params):\",\n \"def __init__(self, *args, **kwds):\\n if args or kwds:\\n super(robot_GetStateRequest, self).__init__(*args, **kwds)\",\n \"def driver_kwargs(self):\\n out = super(TestCisRpc, self).driver_kwargs\\n out['comm'] = 'RPCComm'\\n out['format_str'] = self.fmt_str\\n return out\",\n \"def __init__(self, *args, **kwds):\\n if args or kwds:\\n super(DrivetrainCommand, self).__init__(*args, **kwds)\\n #message fields cannot be None, assign default values for those that are\\n if self.header is None:\\n self.header = std_msgs.msg._Header.Header()\\n if self.gear is None:\\n self.gear = 0\\n if self.front_diff is None:\\n self.front_diff = 0\\n if self.rear_diff is None:\\n self.rear_diff = 0\\n else:\\n self.header = std_msgs.msg._Header.Header()\\n self.gear = 0\\n self.front_diff = 0\\n self.rear_diff = 0\",\n \"def __init__(self, *args, **kwds):\\n if args or kwds:\\n super(AlignModelSrvRequest, self).__init__(*args, **kwds)\\n #message fields cannot be None, assign default values for those that are\\n if self.model is None:\\n self.model = articulation_msgs.msg.ModelMsg()\\n if self.data is None:\\n self.data = articulation_msgs.msg.ModelMsg()\\n else:\\n self.model = articulation_msgs.msg.ModelMsg()\\n self.data = articulation_msgs.msg.ModelMsg()\",\n \"def get_mandatory_arguments(self):\\n args, varargs, keyword, defaults = inspect.getargspec(self.exec_obj)\\n\\n if defaults is not None:\\n args = args[:-len(defaults)]\\n\\n if args.count('self') > 0:\\n args.remove('self')\\n return args\",\n \"def oic_pre_construct(self, cli_info, request_args=None, **kwargs):\\n for prop in self.msg_type.c_param.keys():\\n if prop in request_args:\\n continue\\n try:\\n request_args[prop] = cli_info.behaviour[prop]\\n except KeyError:\\n pass\\n\\n if \\\"post_logout_redirect_uris\\\" not in request_args:\\n try:\\n request_args[\\n \\\"post_logout_redirect_uris\\\"] = \\\\\\n cli_info.post_logout_redirect_uris\\n except AttributeError:\\n pass\\n\\n if \\\"redirect_uris\\\" not in request_args:\\n try:\\n request_args[\\\"redirect_uris\\\"] = cli_info.redirect_uris\\n except AttributeError:\\n raise MissingRequiredAttribute(\\\"redirect_uris\\\", request_args)\\n\\n try:\\n if cli_info.provider_info[\\n 'require_request_uri_registration'] is True:\\n request_args['request_uris'] = cli_info.generate_request_uris(\\n cli_info.requests_dir)\\n except KeyError:\\n pass\\n\\n return request_args, {}\",\n \"def connection_arguments(self):\\n filtered_args = ['name', 'metrics']\\n\\n # make sure we make a copy of this global so it is thread-safe\\n args = dict(DEFAULT_CONNECT_ARGS)\\n\\n for key in set(self.config) - set(filtered_args):\\n if key == 'dbname':\\n args['database'] = self.config[key]\\n else:\\n args[key] = self.config[key]\\n return args\",\n \"def __init__(self, args, kwargs):\\n self._args_dec = list(args)\\n self._kwargs_dec = dict(kwargs)\",\n \"def get_mandatory_args(self):\\n raise NotImplementedError(\\\"ICallable.get_mandatory_args\\\")\",\n \"def driver_kwargs(self):\\n out = super(TestCisRpcClient, self).driver_kwargs\\n out['comm'] = 'ServerComm'\\n out['response_kwargs'] = {'format_str': self.fmt_str}\\n return out\",\n \"def extra_target_arguments(self):\\n return {}\",\n \"def __init__(self, *args, **kwds):\\n if args or kwds:\\n super(Interaction, self).__init__(*args, **kwds)\\n #message fields cannot be None, assign default values for those that are\\n if self.name is None:\\n self.name = ''\\n if self.compatibility is None:\\n self.compatibility = ''\\n if self.display_name is None:\\n self.display_name = ''\\n if self.description is None:\\n self.description = ''\\n if self.namespace is None:\\n self.namespace = ''\\n if self.icon is None:\\n self.icon = rocon_std_msgs.msg.Icon()\\n if self.remappings is None:\\n self.remappings = []\\n if self.parameters is None:\\n self.parameters = ''\\n if self.max is None:\\n self.max = 0\\n if self.pairing is None:\\n self.pairing = rocon_interaction_msgs.msg.Pairing()\\n if self.hash is None:\\n self.hash = 0\\n if self.role is None:\\n self.role = ''\\n else:\\n self.name = ''\\n self.compatibility = ''\\n self.display_name = ''\\n self.description = ''\\n self.namespace = ''\\n self.icon = rocon_std_msgs.msg.Icon()\\n self.remappings = []\\n self.parameters = ''\\n self.max = 0\\n self.pairing = rocon_interaction_msgs.msg.Pairing()\\n self.hash = 0\\n self.role = ''\",\n \"def __init__ (self, *args, **kw):\\n self.__args = args\\n self.__kw = kw\",\n \"def __init__(self, connection, topic, args, kargs):\\n self.__args__ = args\\n self.__kargs__ = kargs\\n self.__topic__ = topic\\n self.__conn__ = connection\",\n \"def getInitParams(self):\\n paramDict = super().getInitParams()\\n paramDict['p'] = self.p\\n return paramDict\",\n \"def __init__(self, *args, **kwds):\\n if args or kwds:\\n super(RobotDescription, self).__init__(*args, **kwds)\\n #message fields cannot be None, assign default values for those that are\\n if self.header is None:\\n self.header = std_msgs.msg.Header()\\n if self.robot_id is None:\\n self.robot_id = opil_v2.msg.Id()\\n if self.agv_msg is None:\\n self.agv_msg = opil_v2.msg.RobotDescriptionAGV()\\n else:\\n self.header = std_msgs.msg.Header()\\n self.robot_id = opil_v2.msg.Id()\\n self.agv_msg = opil_v2.msg.RobotDescriptionAGV()\",\n \"def __init__(self, message: str = None, **kwargs):\\n self.message = message\\n self.kwargs = kwargs\\n self.args = {}\\n self.args['message'] = self.message\\n self.args['args'] = self.kwargs\",\n \"def __init__(self, *args, **kwds):\\n if args or kwds:\\n super(GetPlanningSceneRequest, self).__init__(*args, **kwds)\\n #message fields cannot be None, assign default values for those that are\\n if self.planning_scene_diff is None:\\n self.planning_scene_diff = arm_navigation_msgs.msg.PlanningScene()\\n if self.operations is None:\\n self.operations = arm_navigation_msgs.msg.OrderedCollisionOperations()\\n else:\\n self.planning_scene_diff = arm_navigation_msgs.msg.PlanningScene()\\n self.operations = arm_navigation_msgs.msg.OrderedCollisionOperations()\",\n \"def __init__(self, *args, **kwargs):\\n self.args = args\\n self.kwargs = kwargs\",\n \"def __init__(self, *args, **kwds):\\n if args or kwds:\\n super(observationRPY, self).__init__(*args, **kwds)\\n #message fields cannot be None, assign default values for those that are\\n if self.position is None:\\n self.position = pcl_segment.msg.positionRPY()\\n if self.is_Known is None:\\n self.is_Known = False\\n else:\\n self.position = pcl_segment.msg.positionRPY()\\n self.is_Known = False\",\n \"def _get_init_args(self):\\n signature = inspect.signature(self.__init__)\\n parameters = signature.parameters\\n args = [arg for arg, p in parameters.items()\\n if p.kind is p.POSITIONAL_OR_KEYWORD]\\n\\n return {arg: getattr(self, arg) for arg in args if arg != 'self'}\",\n \"def _get_init_args(self):\\n\\n return dict(enum=self.enum, dflt=self._defname,\\n base=self.base, shape=self.shape)\",\n \"def define_parameters(self):\",\n \"def __init__(self, *args, **kwargs):\\n super().__init__(*args, **kwargs)\\n self.expected_cont_uuid = None\\n self.daos_cmd = None\",\n \"def _invocation_params(self) -> Dict[str, Any]:\\n return self._default_params\",\n \"def __init__(self, ignored_args = [], **common_options):\\n self.ignored_args = ignored_args\\n self.common_options = self._fix_argparse_dicts(common_options)\",\n \"def __init__(self, params_dict):\\n\\n self._params = []\\n \\\"\\\"\\\"\\n Initialized by the command line parser with all positional parameters\\n supplied on the command line\\n \\\"\\\"\\\"\\n\\n if not params_dict:\\n return\\n self._param_text = params_dict.get(\\\"params\\\")\\n self._help_text = params_dict.get(\\\"text\\\")\",\n \"def __init__(self, *args, **kwargs):\\n self._args = args\\n self._kwargs = kwargs\",\n \"def initArgs(self, args):\\n ignore = ['self', 'kwargs', 'args']\\n for k,v in args.iteritems():\\n if k not in ignore: \\n setattr(self, k, v)\\n pass\",\n \"def __getinitargs__NOT__(self):\\n if self._idevice:\\n return self._idevice, self.path\\n else:\\n return self._package, self.path\",\n \"def _first_init(self, **kwargs):\\n return kwargs\",\n \"def _first_init(self, **kwargs):\\n return kwargs\",\n \"def __init__(self, **kwargs):\\n self.__kwargs = kwargs\",\n \"def getPositionalArgs():\",\n \"def get_interface_initialization_kwargs(self, **kwargs) -> dict:\\n return {\\n key: value\\n for key, value in kwargs.items()\\n if not self.input_definitions.get(key=key).run_method_input\\n }\",\n \"def base_arguments(self):\\n raise NotImplementedError()\",\n \"def __init__(self, **keywords):\\n # Set cord with the normal method if it was passed. Set atoms if\\n # atomlist was passed. init() if both were passed.\\n if keywords.has_key(\\\"cord\\\"):\\n self.setcord(keywords[\\\"cord\\\"])\\n if keywords.has_key(\\\"weights\\\"):\\n self.setweights(keywords[\\\"weights\\\"])\\n if keywords.has_key(\\\"cord\\\") and keywords.has_key(\\\"weights\\\"):\\n self.init()\",\n \"def __init__(self, *args, **kwds):\\n if args or kwds:\\n super(SetElectromotorsControlRequest, self).__init__(*args, **kwds)\\n #message fields cannot be None, assign default values for those that are\\n if self.ctrl is None:\\n self.ctrl = actuators_canopen.msg.ElectromotorsState()\\n else:\\n self.ctrl = actuators_canopen.msg.ElectromotorsState()\",\n \"def get_connection_params(self, session, **kwargs):\\n return {}\",\n \"def __init__(self):\\n self.params = arcpy.GetParameterInfo()\",\n \"def __init__(self, *args: Any, **kwargs: Any) -> None:\\n kwargs[\\\"aux_params\\\"] = None\\n super().__init__(*args, **kwargs)\",\n \"def __init__(self, *args, **kwds):\\n if args or kwds:\\n super(ModifyParametersRequest, self).__init__(*args, **kwds)\\n #message fields cannot be None, assign default values for those that are\\n if self.major_ax is None:\\n self.major_ax = 0.\\n if self.minor_ax is None:\\n self.minor_ax = 0.\\n if self.coup_strength is None:\\n self.coup_strength = 0.\\n if self.limit_cycle is None:\\n self.limit_cycle = 0.\\n if self.forward_velocity is None:\\n self.forward_velocity = 0.\\n if self.curvature is None:\\n self.curvature = 0.\\n if self.x_offset is None:\\n self.x_offset = []\\n if self.y_offset is None:\\n self.y_offset = []\\n if self.coupling_1 is None:\\n self.coupling_1 = []\\n if self.coupling_2 is None:\\n self.coupling_2 = []\\n if self.coupling_3 is None:\\n self.coupling_3 = []\\n if self.coupling_4 is None:\\n self.coupling_4 = []\\n if self.coupling_5 is None:\\n self.coupling_5 = []\\n if self.coupling_6 is None:\\n self.coupling_6 = []\\n else:\\n self.major_ax = 0.\\n self.minor_ax = 0.\\n self.coup_strength = 0.\\n self.limit_cycle = 0.\\n self.forward_velocity = 0.\\n self.curvature = 0.\\n self.x_offset = []\\n self.y_offset = []\\n self.coupling_1 = []\\n self.coupling_2 = []\\n self.coupling_3 = []\\n self.coupling_4 = []\\n self.coupling_5 = []\\n self.coupling_6 = []\",\n \"def __init__(self, sent: int, received: int):\\n super().__init__(sent, received)\\n self.sent = sent\\n self.received = received\",\n \"def __init__(self, *args, **kwds):\\n if args or kwds:\\n super(pid_control_reqRequest, self).__init__(*args, **kwds)\\n #message fields cannot be None, assign default values for those that are\\n if self.yaw is None:\\n self.yaw = 0.\\n else:\\n self.yaw = 0.\",\n \"def __init__(self, *args, **kwargs):\\n super().__init__(*args, **kwargs)\\n self.__args = args\\n self.__kwargs = kwargs\",\n \"def _ex_connection_class_kwargs(self):\\r\\n kwargs = {}\\r\\n if not self._host_argument_set:\\r\\n kwargs['host'] = API_ENDPOINTS[self.region]['host']\\r\\n\\r\\n return kwargs\",\n \"def default_hparams():\\n return {\\n \\\"name\\\": \\\"forward_connector\\\"\\n }\",\n \"def initialize(self, *args, **kwargs):\",\n \"def get_arguments():\\n\\tparser.add_argument('-i', '--interface', help='interface to affect')\\n\\tparser.add_argument('-m','--mac', help='mac to allocate')\\n\\n\\targs = parser.parse_args()\\n\\tinterface = args.interface\\n\\tmac = args.mac\\n\\treturn (interface, mac)\",\n \"def params(self, **kwargs):\\n return kwargs\",\n \"def __init__(self, args=False):\\n self.args = args\",\n \"def initialize(self, **kwargs):\",\n \"def __init__(self, *args, **kwargs):\\n super().__init__(*args, **kwargs)\\n\\n # Add common arguments\\n self.add_argument(\\n \\\"--debug\\\",\\n action=\\\"store_true\\\",\\n default=False,\\n help=\\\"debug mode: show debug messages\\\",\\n )\\n self.add_argument(\\n \\\"-s\\\",\\n \\\"--server\\\",\\n required=True,\\n help='Galène server to connect to, e.g. \\\"wss://galene.example.com/ws\\\"',\\n )\\n self.add_argument(\\n \\\"-g\\\",\\n \\\"--group\\\",\\n required=True,\\n help=\\\"Join this group\\\",\\n )\\n self.add_argument(\\n \\\"-u\\\",\\n \\\"--username\\\",\\n required=True,\\n help=\\\"Group username\\\",\\n )\\n self.add_argument(\\n \\\"-p\\\",\\n \\\"--password\\\",\\n help=\\\"Group password\\\",\\n )\",\n \"def __init__(self, *args, **kwds):\\n if args or kwds:\\n super(g_sens, self).__init__(*args, **kwds)\\n #message fields cannot be None, assign default values for those that are\\n if self.Front_sens is None:\\n self.Front_sens = motor_sc.msg.sens9ax()\\n if self.Rear_sens is None:\\n self.Rear_sens = motor_sc.msg.sens9ax()\\n else:\\n self.Front_sens = motor_sc.msg.sens9ax()\\n self.Rear_sens = motor_sc.msg.sens9ax()\",\n \"def __init__(self, **kwargs):\\n # Handle whatever kwargs we want here\\n self.io_mapping = kwargs.get(\\\"io_mapping\\\", [])\\n\\n self.create_inputs = kwargs.get(\\\"create_inputs\\\", [])\\n self.min_completion_fraction = kwargs.get(\\\"min_completion_fraction\\\", 1.0)\\n\\n # Now pass all of them to the parent class\\n super(self.__class__, self).__init__(**kwargs)\",\n \"def __init__(self, *args, **kwds):\\n if args or kwds:\\n super(RotationMatrix, self).__init__(*args, **kwds)\\n #message fields cannot be None, assign default values for those that are\\n if self.c0 is None:\\n self.c0 = bh_motion.msg.Vector3()\\n if self.c1 is None:\\n self.c1 = bh_motion.msg.Vector3()\\n if self.c2 is None:\\n self.c2 = bh_motion.msg.Vector3()\\n else:\\n self.c0 = bh_motion.msg.Vector3()\\n self.c1 = bh_motion.msg.Vector3()\\n self.c2 = bh_motion.msg.Vector3()\",\n \"def get_default_kwargs(self, **defaults):\\n return super().get_default_kwargs(**{\\\"port\\\": 22})\",\n \"def init_params(self):\\n blah\",\n \"def getInitParams(self):\\n paramDict = super().getInitParams()\\n paramDict['method'] = self.method\\n paramDict['dimension'] = self.dimension\\n paramDict['rank'] = self.rank\\n paramDict['mu'] = self.mu\\n paramDict['covariance'] = self.covariance\\n return paramDict\",\n \"def get_kwargs(self):\\n return {}\",\n \"def get_cli_arguments(self):\\n pass\",\n \"def set_params(self, **kwargs) -> NoReturn:\\n pass\",\n \"def __getinitargs__(self):\\n return (self.cutout,)\",\n \"def __init__(self, other=None, **kw):\\n self.__doc__ = None # Make BaseRequest objects unpublishable\\n if other is None:\\n other = kw\\n else:\\n other.update(kw)\\n self.other = other\",\n \"def init(self, *args, **kwds):\\n pass\",\n \"def __init__(self, parms, body, env):\\n self.parms = parms\\n self.body = body\\n self.env = env\",\n \"def init_pos_parms(self):\\n\\n ## init_pos_parms()\\n parms = {}\\n\\n # length axis\\n parms['length_attribute_road'] = ('top', 'cl', 'bottom')[self.segment]\\n parms['length_attribute_artifact'] = ('top', 'cl', 'bottom')[self.pos_length]\\n\\n # width axis\\n if type(self.pos_width) is int:\\n parms['width_road_rect'] = self.road.lanes[self.pos_width]\\n parms['width_attribute_road'] = 'cw'\\n parms['width_attribute_artifact'] = 'cw'\\n else:\\n parms['width_road_rect'] = self.road\\n pos_parms = {'l': ('left', 'right'),\\n 'c': ('cw', 'cw'),\\n 'r': ('right', 'left')}[self.pos_width]\\n parms['width_attribute_road'] = pos_parms[0]\\n parms['width_attribute_artifact'] = pos_parms[1]\\n return parms\",\n \"def __init__(self, **parameters):\\n self.parameters = parameters\",\n \"def __init__(self, *args, **kwds):\\n if args or kwds:\\n super(Cmd_WF, self).__init__(*args, **kwds)\\n #message fields cannot be None, assign default values for those that are\\n if self.enable_steering is None:\\n self.enable_steering = False\\n if self.enable_braking is None:\\n self.enable_braking = False\\n if self.enable_driving is None:\\n self.enable_driving = False\\n if self.enable_Estop is None:\\n self.enable_Estop = False\\n if self.enable_gear is None:\\n self.enable_gear = False\\n if self.sw_deg is None:\\n self.sw_deg = 0.\\n if self.sw_rad is None:\\n self.sw_rad = 0.\\n if self.speed_ms is None:\\n self.speed_ms = 0.\\n if self.speed_kms is None:\\n self.speed_kms = 0.\\n if self.ax_ms2 is None:\\n self.ax_ms2 = 0.\\n if self.omega_rad is None:\\n self.omega_rad = 0.\\n if self.gear_mode is None:\\n self.gear_mode = 0\\n if self.steering is None:\\n self.steering = 0.\\n if self.speed is None:\\n self.speed = 0.\\n else:\\n self.enable_steering = False\\n self.enable_braking = False\\n self.enable_driving = False\\n self.enable_Estop = False\\n self.enable_gear = False\\n self.sw_deg = 0.\\n self.sw_rad = 0.\\n self.speed_ms = 0.\\n self.speed_kms = 0.\\n self.ax_ms2 = 0.\\n self.omega_rad = 0.\\n self.gear_mode = 0\\n self.steering = 0.\\n self.speed = 0.\",\n \"def set_params(self, **kwargs):\\n ...\",\n \"def initialize(self, args):\\n\\t\\tpass\",\n \"def __init__(self, *args, **kwds):\\n if args or kwds:\\n super(ConductorGraph, self).__init__(*args, **kwds)\\n #message fields cannot be None, assign default values for those that are\\n if self.pending is None:\\n self.pending = []\\n if self.available is None:\\n self.available = []\\n if self.missing is None:\\n self.missing = []\\n if self.gone is None:\\n self.gone = []\\n else:\\n self.pending = []\\n self.available = []\\n self.missing = []\\n self.gone = []\",\n \"def initialize_comm(self, config_options):\\n try:\\n self.comm = MPI.COMM_WORLD\\n self.comm.Set_errhandler(MPI.ERRORS_ARE_FATAL)\\n except AttributeError as ae:\\n config_options.errMsg = \\\"Unable to initialize the MPI Communicator object\\\"\\n raise ae\\n\\n try:\\n self.size = self.comm.Get_size()\\n except MPI.Exception as mpi_exception:\\n config_options.errMsg = \\\"Unable to retrieve the MPI size.\\\"\\n raise mpi_exception\\n\\n try:\\n self.rank = self.comm.Get_rank()\\n except MPI.Exception as mpi_exception:\\n config_options.errMsg = \\\"Unable to retrieve the MPI processor rank.\\\"\\n raise mpi_exception\",\n \"def getInitParams(self):\\n paramDict = super().getInitParams()\\n paramDict['functionType'] = self.functionType\\n paramDict['dataFilename'] = self.dataFilename\\n paramDict['workingDir'] = self.workingDir\\n return paramDict\",\n \"def set_params(self, *arg):\\n pass\",\n \"def __init__(self, *args, **kwds):\\n if args or kwds:\\n super(FeedbackMsg, self).__init__(*args, **kwds)\\n #message fields cannot be None, assign default values for those that are\\n if self.name is None:\\n self.name = []\\n if self.position is None:\\n self.position = []\\n if self.velocity is None:\\n self.velocity = []\\n if self.effort is None:\\n self.effort = []\\n if self.position_command is None:\\n self.position_command = []\\n if self.velocity_command is None:\\n self.velocity_command = []\\n if self.effort_command is None:\\n self.effort_command = []\\n if self.accelerometer is None:\\n self.accelerometer = []\\n if self.gyro is None:\\n self.gyro = []\\n if self.orientation is None:\\n self.orientation = []\\n if self.deflection is None:\\n self.deflection = []\\n if self.deflection_velocity is None:\\n self.deflection_velocity = []\\n if self.motor_velocity is None:\\n self.motor_velocity = []\\n if self.motor_current is None:\\n self.motor_current = []\\n if self.motor_winding_current is None:\\n self.motor_winding_current = []\\n if self.motor_sensor_temperature is None:\\n self.motor_sensor_temperature = []\\n if self.motor_winding_temperature is None:\\n self.motor_winding_temperature = []\\n if self.motor_housing_temperature is None:\\n self.motor_housing_temperature = []\\n if self.board_temperature is None:\\n self.board_temperature = []\\n if self.processor_temperature is None:\\n self.processor_temperature = []\\n if self.voltage is None:\\n self.voltage = []\\n if self.led_color is None:\\n self.led_color = []\\n if self.sequence_number is None:\\n self.sequence_number = []\\n if self.receive_time is None:\\n self.receive_time = []\\n if self.transmit_time is None:\\n self.transmit_time = []\\n if self.hardware_receive_time is None:\\n self.hardware_receive_time = []\\n if self.hardware_transmit_time is None:\\n self.hardware_transmit_time = []\\n else:\\n self.name = []\\n self.position = []\\n self.velocity = []\\n self.effort = []\\n self.position_command = []\\n self.velocity_command = []\\n self.effort_command = []\\n self.accelerometer = []\\n self.gyro = []\\n self.orientation = []\\n self.deflection = []\\n self.deflection_velocity = []\\n self.motor_velocity = []\\n self.motor_current = []\\n self.motor_winding_current = []\\n self.motor_sensor_temperature = []\\n self.motor_winding_temperature = []\\n self.motor_housing_temperature = []\\n self.board_temperature = []\\n self.processor_temperature = []\\n self.voltage = []\\n self.led_color = []\\n self.sequence_number = []\\n self.receive_time = []\\n self.transmit_time = []\\n self.hardware_receive_time = []\\n self.hardware_transmit_time = []\",\n \"def pykwarg(self):\\n return self._pykwarg\",\n \"def __init__(self, *args, **kwds):\\n if args or kwds:\\n super(pid_control_reqResponse, self).__init__(*args, **kwds)\\n #message fields cannot be None, assign default values for those that are\\n if self.FL_vel is None:\\n self.FL_vel = 0\\n if self.FR_vel is None:\\n self.FR_vel = 0\\n if self.BL_vel is None:\\n self.BL_vel = 0\\n if self.BR_vel is None:\\n self.BR_vel = 0\\n else:\\n self.FL_vel = 0\\n self.FR_vel = 0\\n self.BL_vel = 0\\n self.BR_vel = 0\",\n \"def getInitParams(self):\\n return {}\",\n \"def getInitParams(self):\\n paramDict = BoostDistribution.getInitParams(self)\\n paramDict['p'] = self.p\\n return paramDict\",\n \"def getInitParams(self):\\n paramDict = BoostDistribution.getInitParams(self)\\n paramDict['p'] = self.p\\n return paramDict\",\n \"def params():\\n raise NotImplementedError\",\n \"def __init__(*args):\",\n \"def __init__(*args):\",\n \"def __init__(*args):\",\n \"def __init__(*args):\",\n \"def __init__(*args):\",\n \"def __init__(*args):\",\n \"def __init__(*args):\",\n \"def __init__(*args):\",\n \"def __init__(*args):\",\n \"def __init__(*args):\",\n \"def __init__(*args):\",\n \"def __init__(*args):\",\n \"def __init__(*args):\"\n]","isConversational":false},"negative_scores":{"kind":"list like","value":["0.6724078","0.65556204","0.64141464","0.6366962","0.611054","0.6110213","0.60796076","0.59395605","0.5830559","0.58100736","0.5667532","0.5665083","0.5630391","0.5616168","0.5616114","0.5589997","0.5575281","0.55713445","0.5564068","0.5554116","0.5552634","0.5537825","0.5535452","0.5518173","0.5512458","0.54954463","0.54764235","0.5473008","0.5470334","0.5464803","0.5454409","0.5452661","0.5447114","0.5443881","0.5431572","0.54298085","0.54298085","0.54254055","0.53938496","0.5391379","0.5383742","0.53681064","0.5367241","0.5345161","0.5341104","0.5337357","0.5333415","0.5300091","0.5297987","0.5295224","0.529209","0.52847713","0.5283887","0.52830565","0.528005","0.52791375","0.527712","0.5276167","0.52715486","0.5267722","0.5266543","0.5264225","0.5262076","0.525345","0.52462566","0.52448833","0.5244353","0.5237205","0.52342385","0.5228185","0.5227568","0.5226739","0.5223327","0.52199805","0.52172047","0.5203138","0.51998496","0.51977205","0.51952493","0.51917493","0.5190975","0.519062","0.51869106","0.5186791","0.51833236","0.51833236","0.51806676","0.51778233","0.51778233","0.51778233","0.51778233","0.51778233","0.51778233","0.51778233","0.51778233","0.51778233","0.51778233","0.51778233","0.51778233","0.51778233"],"string":"[\n \"0.6724078\",\n \"0.65556204\",\n \"0.64141464\",\n \"0.6366962\",\n \"0.611054\",\n \"0.6110213\",\n \"0.60796076\",\n \"0.59395605\",\n \"0.5830559\",\n \"0.58100736\",\n \"0.5667532\",\n \"0.5665083\",\n \"0.5630391\",\n \"0.5616168\",\n \"0.5616114\",\n \"0.5589997\",\n \"0.5575281\",\n \"0.55713445\",\n \"0.5564068\",\n \"0.5554116\",\n \"0.5552634\",\n \"0.5537825\",\n \"0.5535452\",\n \"0.5518173\",\n \"0.5512458\",\n \"0.54954463\",\n \"0.54764235\",\n \"0.5473008\",\n \"0.5470334\",\n \"0.5464803\",\n \"0.5454409\",\n \"0.5452661\",\n \"0.5447114\",\n \"0.5443881\",\n \"0.5431572\",\n \"0.54298085\",\n \"0.54298085\",\n \"0.54254055\",\n \"0.53938496\",\n \"0.5391379\",\n \"0.5383742\",\n \"0.53681064\",\n \"0.5367241\",\n \"0.5345161\",\n \"0.5341104\",\n \"0.5337357\",\n \"0.5333415\",\n \"0.5300091\",\n \"0.5297987\",\n \"0.5295224\",\n \"0.529209\",\n \"0.52847713\",\n \"0.5283887\",\n \"0.52830565\",\n \"0.528005\",\n \"0.52791375\",\n \"0.527712\",\n \"0.5276167\",\n \"0.52715486\",\n \"0.5267722\",\n \"0.5266543\",\n \"0.5264225\",\n \"0.5262076\",\n \"0.525345\",\n \"0.52462566\",\n \"0.52448833\",\n \"0.5244353\",\n \"0.5237205\",\n \"0.52342385\",\n \"0.5228185\",\n \"0.5227568\",\n \"0.5226739\",\n \"0.5223327\",\n \"0.52199805\",\n \"0.52172047\",\n \"0.5203138\",\n \"0.51998496\",\n \"0.51977205\",\n \"0.51952493\",\n \"0.51917493\",\n \"0.5190975\",\n \"0.519062\",\n \"0.51869106\",\n \"0.5186791\",\n \"0.51833236\",\n \"0.51833236\",\n \"0.51806676\",\n \"0.51778233\",\n \"0.51778233\",\n \"0.51778233\",\n \"0.51778233\",\n \"0.51778233\",\n \"0.51778233\",\n \"0.51778233\",\n \"0.51778233\",\n \"0.51778233\",\n \"0.51778233\",\n \"0.51778233\",\n \"0.51778233\",\n \"0.51778233\"\n]","isConversational":false},"document_score":{"kind":"string","value":"0.6149904"},"document_rank":{"kind":"string","value":"4"}}},{"rowIdx":22699,"cells":{"query":{"kind":"string","value":"r\"\"\"Record the current position in the file/series."},"document":{"kind":"string","value":"def record_position(self):\n _rec_pos = self.fd.tell()\n _rec_ind = self._series_index\n return _rec_pos, _rec_ind"},"metadata":{"kind":"string","value":"{\n \"objective\": {\n \"self\": [],\n \"paired\": [],\n \"triplet\": [\n [\n \"query\",\n \"document\",\n \"negatives\"\n ]\n ]\n }\n}"},"negatives":{"kind":"list like","value":["def change_position(self, file_pos, series_index=None):\n if series_index is None:\n series_index = self._series_index\n self.advance_in_series(series_index)\n self.advance_in_file(file_pos)","def record(self, pos):\n self.lasts += (datetime.now(), pos),\n if len(self.lasts) > 10:\n self.lasts.pop(0)","def savepos(self):\n self.out.write(self.csi + \"s\")","def current_pos(self):\n new_ptr = _decode(self._read_fixed_block(0x0020),\n lo_fix_format['current_pos'])\n if new_ptr is None:\n raise ObservationError('current_pos is None')\n if new_ptr == self._current_ptr:\n return self._current_ptr\n if self._current_ptr and new_ptr != self.inc_ptr(self._current_ptr):\n for k in reading_len:\n if (new_ptr - self._current_ptr) == reading_len[k]:\n log.error('changing data format from %s to %s' % (self.data_format, k))\n self.data_format = k\n break\n self._current_ptr = new_ptr\n return self._current_ptr","def pos(self):\n return self.file.tell()","def track(self, offset: int):","def update(self):\n if self._position:\n self._frequency = len(self._position)\n for i in range(len(self._position)):\n # convert from text file\n self._position[i] = float(self._position[i])\n self._recency = [self._position[0]]\n for i in range(1, self._frequency):\n self._recency.append(self._position[i] - self._position[i - 1])\n self._recency.append(1 - self._position[self._frequency - 1])\n self._isUpdated = True","def set_position(self, posicion):\n\n # FIXME: Actualmente no funciona bien\n posicion = int(posicion)\n if posicion != self.posicion:\n self.posicion = posicion\n self.entrada.write('seek %s %i 0\\n' % (posicion, 1))\n self.entrada.flush()","def tell(self):\n return self._seek_pos","def _update_offset_file(self):\n if self.on_update:\n self.on_update()\n offset = self._filehandle().tell()\n inode = stat(self.filename).st_ino\n fh = open(self._offset_file, \"w\")\n fh.write(\"%s\\n%s\\n\" % (inode, offset))\n fh.close()\n self._since_update = 0","def seek(self, *args) -> \"int\":\n return _ida_fpro.qfile_t_seek(self, *args)","def remember_pos(self, directory, position):\n self.dir_pos[directory] = position","def seek(self, loc):\n assert loc == 0\n\n # rewind progress bar\n if self.progressbar:\n self.progressbar.update(-self._fp.tell())\n\n self._fp.seek(loc)","def position(self, value):\r\n self._position = value\r\n\r\n # We need to update this so that we start re-calculating the\r\n # the measurements according to this change in status. That is\r\n # if we just opened, then we cannot have any current or\r\n # power flowing.\r\n self.update_mmtr(datetime.utcnow())","def save(self) -> None:\n self._save_marker = self.get_next()","def current_frame(self, n):\n self.sound.seek(n)\n self._current_frame = n","def tell(self):\n _complain_ifclosed(self._closed)\n return self._position","def set_reader_position(self):\n recorded_position = self.process_application.get_recorded_position(\n self.upstream_name\n )\n self.reader.seek(recorded_position)","def tell(self):\n return self._pos","def tell(self):\n return self.offset","def print_position(self):\n\n while True:\n lat = self.vehicle.location.global_relative_frame.lat\n lon = self.vehicle.location.global_relative_frame.lon\n try:\n self.pathFile.write('{0},{1}'.format(lat, lon))\n time.sleep(1)\n except:\n break\n #print ('{0},{1}'.format(lat, lon))","def tell(self):\n return self._upload_position","def _seek(self, iteration):\n\n # Validate it\n if iteration < 1:\n iteration = 1\n\n # Seek to one iteration before the specified iteration, then run the\n # network for one iteration, so the inspectors will show the right data\n self.iteration = iteration - 1\n self.experiment.position.iter = iteration - 1\n for sensor in self.sensors:\n assert sensor.type == 'VectorFileSensor'\n sensor.setParameter('position', self.iteration)\n self._step()","def set_marker(self, packet):\n if self.file_type == 'csv':\n self.write_data(packet=packet)\n elif self.file_type == 'edf':\n timestamp, code = packet.get_data()\n if self._rectime_offset is None:\n self._rectime_offset = timestamp[0]\n timestamp = timestamp-np.float64(self._rectime_offset)\n self._file_obj.writeAnnotation(timestamp[0], 0.001, str(int(code[0])))","def advance_in_file(self, file_pos):\n if self.is_open:\n try:\n self.fd.seek(file_pos)\n except (AttributeError, ValueError): # pragma: debug\n if self.is_open:\n raise","def SetCurrentPosition(self,pos):\n\n if self.Reverse: pos*=-1\n self.Bus.Transaction(chr(self.Address)+chr(0x40)+struct.pack('@l',pos))","def print_pos(self, byte_offset=-1):\n if self._debug_level > 3:\n if hasattr(self, 'ensemble'):\n k = self.ensemble.k\n else:\n k = 0\n print(' pos: %d, pos_: %d, nbyte: %d, k: %d, byte_offset: %d' %\n (self.f.tell(), self._pos, self._nbyte, k, byte_offset))","def refresh_mark(self):\n current = self.player.current_position()\n if current != None:\n if self.prev_song != None and self.prev_song < len(self.buf):\n self.buf[self.prev_song] = ' ' + self.buf[self.prev_song][1:]\n self.buf[current] = '-' + self.buf[current][1:]\n self.prev_song = current\n # Move cursor to current position.\n vim.current.window.cursor = (current + 1, 1)","def increment(self):\n self.pos += 1\n if self.pos == len(self.progress) - 1:\n self.pos = 0","def seek(self, loc):\n assert loc == 0\n\n # rewind progress bar\n if self.progressbar:\n self.progressbar.update(-self._tell)\n\n self._fp_left.seek(loc)\n self._fp_right.seek(loc)\n self._tell = loc\n self._buf = Buffer()","def _save_increment(self):\n self._set_scenefile_properties_from_ui()\n self._update_successful_save(self.scenefile.save_increment())\n self.version_spinbox.setValue(self.scenefile.version)","def _record(self):\r\n self._plot()\r\n self._csvWriter()\r\n self._logger()","def seek(self, val):\n if self.p:\n self.p.set_position(val/100.0 + self.p.get_position())","def save(self) -> None:\n self._save_marker = self._current","def seeked(self):\n # type: () -> int\n return self._seeked","def _pos_changed(self, timestamp=None, value=None, **kwargs):\n self._set_position(value)","def get_position(self):\n\n return (self._fileobj.tell() - self._pos) * 8 - self._bits","def tell(self):\n return self._offset","def buildSeekIndex(self):\n runs = 0;\n while runs < 100:\n runs += 1\n bytes = self.seekfile.read(FileFlvStreamProvider.BUFFERFILL_CHUNCKSIZE)\n if len(bytes)==0:\n # either the file is finished, or the file is still rendering, either way, nothing to do\n return\n else:\n self.seekbuffer.write(bytes)\n while True:\n self.seekbuffer.transactionStart();\n try:\n pos = self.seekbuffer.bytesRead();\n chunk = self.flvseeker.readChunk()\n except util.bufferedstringstream.BufferedStringStreamInsufficientDataException:\n self.seekbuffer.transactionRollback();\n break\n self.seekbuffer.transactionCommit();\n self.maxtimestamp = max(chunk.time, self.maxtimestamp)\n if (chunk.CHUNKTYPE == util.flv.FLVVideoChunk.CHUNKTYPE and chunk.isKeyFrame) or not self.hasVideo(): # if we don't have video, every frame is a keyframe\n if len(self.aKeyFrame) == 0 or chunk.time > self.aKeyFrame[-1][\"timestamp\"]+200: #we might be replaying a part we checkout out before, make sure that the timestamps stay in order; in addition, we store at most 5 timestamps per second, to save memory\n self.aKeyFrame.append({\"timestamp\":chunk.time, \"filepos\":pos})\n if len(bytes) < FileFlvStreamProvider.BUFFERFILL_CHUNCKSIZE:\n return","def _seek(self, offset):\n assert offset % self.recordsize == 0\n file_number, file_offset = divmod(offset,\n self.filesize - self.header_size)\n self.open(file_number)\n self.fh_raw.seek(file_offset + self.header_size)\n self.offset = offset","def record_line(self, frame, event, arg): # pylint: disable=unused-argument\n if event == 'line':\n if self.prev_timestamp:\n runtime = time.time() - self.prev_timestamp\n self.lines.append([self.prev_path, self.prev_lineno, runtime])\n self.prev_lineno = frame.f_lineno\n self.prev_path = frame.f_code.co_filename\n self.prev_timestamp = time.time()\n return self.record_line","def record(self, step):","def track_position(self):\n return self._track_position","def _write_frame(self : \"animation\",\n frame : \"np.ndarray\"\n ):\n self._writer.append_data(frame)\n self._frame_number += 1\n self._prevFrame = frame","def setPosition(self):\n self.data['pos-x'] = \"%s\" % self.x()\n self.data['pos-y'] = \"%s\" % self.y()","def mark(self): # type: () -> None\n self._marker = self._idx","def get_next_position(self):","def position(self, pos: int):\n self.__pos = pos","def snapshot(self,):\n self.stack.append((self.pos,self.dataptr))","def _increment_file_counter(self):\n self._add_to_file_counter(1)","def record(self):\n # TODO: record the data","def saveCurrentStagePosition(self):\n c= \"/cli:python /app:matrix /sys:1 /cmd:savecurrentposition /dev:stage\"\n self.sendCMDstring(c)","def position(self):\r\n pass","def restorepos(self):\n self.out.write(self.csi + \"u\")","def record_trace(self):\n\n tfname = str(int(time.time())) + \".obd2_reader.trace\"\n self.tf_out = open(tfname, 'a')\n self.RecordTrace = 1\n print \"Recoding trace to:\", tfname","def __setitem__(self, index, value):\n self.position[index] = value","def record(self, time, increment):\n raise NotImplementedError(\"Abstract method not implemented.\")","def stat_beg_file(self, filename):\n\n self.batchvals['numfiles'] += 1\n self.filevals['filename'] = filename\n self.filevals['start_time'] = time.time()\n\n return -1","def tell(self) -> int:\n ...","def set_position(self, idx, pos):\n if self.EMULATOR_MODE:\n return\n if idx >= self.nleaflets or idx < 0:\n raise IndexError('index specified is out of bounds')\n self._fserial.write(self.MAGIC_BYTES + bytes([idx]) + pos.to_bytes(2, byteorder='big', signed=False) )\n self._fserial.reset_input_buffer()","def writePosFilesStep(self): \n \n writeSetOfCoordinates(self._getExtraPath(), self.inputCoordinatesTiltedPairs.get().getUntilted())\n \n writeSetOfCoordinates(self._getExtraPath(), self.inputCoordinatesTiltedPairs.get().getTilted())","def update_position(position):\n pass","def tell(self):\n return self.data_source.tell()","def saveCurrentZPosition(self):\n c= \"/cli:python /app:matrix /sys:1 /cmd:savecurrentposition /dev:zdrive\"\n self.sendCMDstring(c)","def logFile(self):\n\n event = 'stim'\n mStr = '{:013}'.format(self.mouse.tag) + '\\t'\n outPutStr = mStr + \\\n datetime.fromtimestamp(int(time())).isoformat(' ') + '\\t' + event\n print (outPutStr)\n if self.textfp != None:\n outPutStr = mStr + '{:.2f}'.format(time()) + '\\t' + event\n self.textfp.write(outPutStr + '\\n')\n self.textfp.flush()","def output_position(self) -> int:\n return self._output_position","def advance(self):\n self._current_inst += 1\n self._line = self._lines[self._current_inst].strip()","def last_sequence_ind(self, last_sequence_ind):\n self.last_sequence_ind_ = last_sequence_ind\n logging.debug(\n \"Segment - Last sequence ind: {lsi}\".format(\n lsi=self.last_sequence_ind\n )\n )","def setPosition(position):","def seekEnd(self):\n self.oFile.seek(0, 2)","def write(self, file):\n pos = file.tell()\n pickle.dump((self.index, self.meta, self.info), file)\n file.seek(0)\n\n # update the header with the position of the content index.\n file.write(struct.pack('<Q', pos))","def save_history(cube, field, filename): \n\n history.append(cube.attributes['history'])","def update(self):\n self.__token += self.__lines[self.__i]\n self.__i += 1","def update(self, position):\n self.time_since_update = 0\n self.history = []\n self.hits += 1\n self.hit_streak += 1\n self.kf.update(position)","def store_current_position_as_previous(self):\n pos = self.get_current_position()\n self.previous_xloc = pos[0]\n self.previous_yloc = pos[1]\n self.previous_zloc = pos[2]\n return pos","def get_position(self): # maybe encoded in filepath at some point\n result = (self.iter * self.row_step)% self.row_size, self.iter // (self.row_size * self.row_step)* self.col_step\n self.iter += 1\n return result","def update(self, pos):\n\t\tpass","def media_seek_position(self):\n return self._state.get(\"seek\", None)","def sig(self):\n if not self.fin():\n self.column += 1\n if self.column == self.maxcol:\n self.column = 0\n self.row +=1\n if self.row == self.maxfila:\n self.row = -1\n self.column = -1","def tell(self):\n ...","def get_record_position(self, index):\n pos = c_uint()\n ckresult(_dll.FMOD_System_GetRecordPosition(self._ptr, index, byref(pos)))\n return pos.value","async def seek(self, pos: int):\n pos = max(pos, 0) # Prevent seeking before start of track\n await self._bot.lavalink.ws.send(op='seek', guildId=self.guild_id, position=pos)","def calculate_record_number(self):\n return (self._read_state[StateKey.POSITION] - FLAG_RECORD_SIZE) / \\\n self.velocity_record_size","def mark_position(self, node):\n if self.block:\n src_descr = self.source_descr\n pos = (src_descr,) + getpos(node)\n self.block.positions.add(pos)","def seek(self, index: int, /) -> str:\n self.index = index\n return self.current","def output_position(self, output_position: int):\n\n self._output_position = output_position","def mark_saved(self):\n self._saved_position = self._current_undo_command","def seek(self, offset, location=0):\n if (0 <= location <= len(self._tiff)) and (0 <= location+offset <= len(self._tiff)):\n self._offset = location+offset","def save(self):\r\n self.pushes.append((self.token, self.stream, self.line, self.column))","def history_buffer_pos_changed():\n # Only when this buffer has the focus.\n if buffer_mapping.focus_stack[-1] == HISTORY_BUFFER:\n line_no = history_buffer.document.cursor_position_row\n\n if line_no in history_mapping.selected_lines:\n default_lineno = sorted(history_mapping.selected_lines).index(line_no) + \\\n history_mapping.result_line_offset\n\n default_buffer.cursor_position = \\\n default_buffer.document.translate_row_col_to_index(default_lineno, 0)","def file_pointer(self):\n\n try:\n self.__file.seek(self.__file.tell() - 1)\n except Exception as e:\n raise e","def record(position):\n\tlogging.info('Recording position %d', position)\n\twith locked_level(create=True) as level:\n\t\ttry:\n\t\t\tdelay = pickle.load(level)\n\t\t\tlogging.info('Loaded pickle: %r', delay)\n\t\t\tremaining_expected_positions = iter(delay.expected_positions)\n\t\t\tif position == next(remaining_expected_positions, None):\n\t\t\t\t# This is just the feedback from slow dimming that is already in progress\n\t\t\t\tlogging.info('Ignoring feedback')\n\t\t\t\tnext_delay = delay_record.Delay(delay.when, delay.source_position, delay.target_position, list(remaining_expected_positions))\n\t\t\t\texpect(level, next_delay)\n\t\t\t\treturn\n\t\texcept IOError:\n\t\t\t# The file is not open for writing, so there is no expected position yet\n\t\t\tpass\n\n\t\tnext_delay = delay_record.Delay(datetime.datetime.utcnow() + DELAY_DURATION, position, position, [])\n\t\texpect(level, next_delay)","def __position_jaw_current1_cb(self, data):\n self.__position_jaw_current[0] = data.position[0]\n self.__get_jaw_event[0].set()","def position_helper():\n for file_name in file_list[:1]:\n file_bits = file_splitter(file_name)\n line_length = len(max(file_bits, key=len)) + 13\n index = 0\n print('\\n' + ('-' * line_length))\n for x in file_bits:\n print('Index ', str(index), ' = ', file_bits[index])\n index += 1\n print(('-' * line_length) + '\\n')","def number_idx(self, filename):\n with open(filename) as fh:\n firstline = fh.readline()\n parts = firstline.split('\\t')\n # only add if there are 4 parts\n if len(parts) != 4:\n return\n\n count = 1\n def writeline(fho, line, count):\n fho.write(line.rstrip() + '\\t' + str(count) + '\\n')\n\n with open(filename + '.tmp', 'w+b') as fho:\n writeline(fho, firstline, count)\n count += 1\n for line in fh:\n writeline(fho, line, count)\n count += 1\n\n shutil.move(filename + '.tmp', filename)","def record_pose(self):\n drone_pos = client.getMultirotorState().kinematics_estimated.position\n output = \" | \".join([\"x_val: \" + str(drone_pos.x_val), \"y_val: \" + str(drone_pos.y_val), \"z_val: \" + str(drone_pos.z_val)])\n print(output)\n self.log_arr.append(output)\n return output","def tell(self):\n return self.oFile.tell()","def read_recorded(self):\n reader=csv.reader(open(self._filename,\"rb\"),delimiter=',')\n self._headers = reader.next()\n x=list(reader)\n recorded_positions=np.array(x).astype('float')\n self.refine_pos(recorded_positions)\n self._joint_first = recorded_positions[0]\n self._joint_last = recorded_positions[-1]","def on_increased_position(self, order) -> None:\n pass","def position_changed(self, position):\n pass"],"string":"[\n \"def change_position(self, file_pos, series_index=None):\\n if series_index is None:\\n series_index = self._series_index\\n self.advance_in_series(series_index)\\n self.advance_in_file(file_pos)\",\n \"def record(self, pos):\\n self.lasts += (datetime.now(), pos),\\n if len(self.lasts) > 10:\\n self.lasts.pop(0)\",\n \"def savepos(self):\\n self.out.write(self.csi + \\\"s\\\")\",\n \"def current_pos(self):\\n new_ptr = _decode(self._read_fixed_block(0x0020),\\n lo_fix_format['current_pos'])\\n if new_ptr is None:\\n raise ObservationError('current_pos is None')\\n if new_ptr == self._current_ptr:\\n return self._current_ptr\\n if self._current_ptr and new_ptr != self.inc_ptr(self._current_ptr):\\n for k in reading_len:\\n if (new_ptr - self._current_ptr) == reading_len[k]:\\n log.error('changing data format from %s to %s' % (self.data_format, k))\\n self.data_format = k\\n break\\n self._current_ptr = new_ptr\\n return self._current_ptr\",\n \"def pos(self):\\n return self.file.tell()\",\n \"def track(self, offset: int):\",\n \"def update(self):\\n if self._position:\\n self._frequency = len(self._position)\\n for i in range(len(self._position)):\\n # convert from text file\\n self._position[i] = float(self._position[i])\\n self._recency = [self._position[0]]\\n for i in range(1, self._frequency):\\n self._recency.append(self._position[i] - self._position[i - 1])\\n self._recency.append(1 - self._position[self._frequency - 1])\\n self._isUpdated = True\",\n \"def set_position(self, posicion):\\n\\n # FIXME: Actualmente no funciona bien\\n posicion = int(posicion)\\n if posicion != self.posicion:\\n self.posicion = posicion\\n self.entrada.write('seek %s %i 0\\\\n' % (posicion, 1))\\n self.entrada.flush()\",\n \"def tell(self):\\n return self._seek_pos\",\n \"def _update_offset_file(self):\\n if self.on_update:\\n self.on_update()\\n offset = self._filehandle().tell()\\n inode = stat(self.filename).st_ino\\n fh = open(self._offset_file, \\\"w\\\")\\n fh.write(\\\"%s\\\\n%s\\\\n\\\" % (inode, offset))\\n fh.close()\\n self._since_update = 0\",\n \"def seek(self, *args) -> \\\"int\\\":\\n return _ida_fpro.qfile_t_seek(self, *args)\",\n \"def remember_pos(self, directory, position):\\n self.dir_pos[directory] = position\",\n \"def seek(self, loc):\\n assert loc == 0\\n\\n # rewind progress bar\\n if self.progressbar:\\n self.progressbar.update(-self._fp.tell())\\n\\n self._fp.seek(loc)\",\n \"def position(self, value):\\r\\n self._position = value\\r\\n\\r\\n # We need to update this so that we start re-calculating the\\r\\n # the measurements according to this change in status. That is\\r\\n # if we just opened, then we cannot have any current or\\r\\n # power flowing.\\r\\n self.update_mmtr(datetime.utcnow())\",\n \"def save(self) -> None:\\n self._save_marker = self.get_next()\",\n \"def current_frame(self, n):\\n self.sound.seek(n)\\n self._current_frame = n\",\n \"def tell(self):\\n _complain_ifclosed(self._closed)\\n return self._position\",\n \"def set_reader_position(self):\\n recorded_position = self.process_application.get_recorded_position(\\n self.upstream_name\\n )\\n self.reader.seek(recorded_position)\",\n \"def tell(self):\\n return self._pos\",\n \"def tell(self):\\n return self.offset\",\n \"def print_position(self):\\n\\n while True:\\n lat = self.vehicle.location.global_relative_frame.lat\\n lon = self.vehicle.location.global_relative_frame.lon\\n try:\\n self.pathFile.write('{0},{1}'.format(lat, lon))\\n time.sleep(1)\\n except:\\n break\\n #print ('{0},{1}'.format(lat, lon))\",\n \"def tell(self):\\n return self._upload_position\",\n \"def _seek(self, iteration):\\n\\n # Validate it\\n if iteration < 1:\\n iteration = 1\\n\\n # Seek to one iteration before the specified iteration, then run the\\n # network for one iteration, so the inspectors will show the right data\\n self.iteration = iteration - 1\\n self.experiment.position.iter = iteration - 1\\n for sensor in self.sensors:\\n assert sensor.type == 'VectorFileSensor'\\n sensor.setParameter('position', self.iteration)\\n self._step()\",\n \"def set_marker(self, packet):\\n if self.file_type == 'csv':\\n self.write_data(packet=packet)\\n elif self.file_type == 'edf':\\n timestamp, code = packet.get_data()\\n if self._rectime_offset is None:\\n self._rectime_offset = timestamp[0]\\n timestamp = timestamp-np.float64(self._rectime_offset)\\n self._file_obj.writeAnnotation(timestamp[0], 0.001, str(int(code[0])))\",\n \"def advance_in_file(self, file_pos):\\n if self.is_open:\\n try:\\n self.fd.seek(file_pos)\\n except (AttributeError, ValueError): # pragma: debug\\n if self.is_open:\\n raise\",\n \"def SetCurrentPosition(self,pos):\\n\\n if self.Reverse: pos*=-1\\n self.Bus.Transaction(chr(self.Address)+chr(0x40)+struct.pack('@l',pos))\",\n \"def print_pos(self, byte_offset=-1):\\n if self._debug_level > 3:\\n if hasattr(self, 'ensemble'):\\n k = self.ensemble.k\\n else:\\n k = 0\\n print(' pos: %d, pos_: %d, nbyte: %d, k: %d, byte_offset: %d' %\\n (self.f.tell(), self._pos, self._nbyte, k, byte_offset))\",\n \"def refresh_mark(self):\\n current = self.player.current_position()\\n if current != None:\\n if self.prev_song != None and self.prev_song < len(self.buf):\\n self.buf[self.prev_song] = ' ' + self.buf[self.prev_song][1:]\\n self.buf[current] = '-' + self.buf[current][1:]\\n self.prev_song = current\\n # Move cursor to current position.\\n vim.current.window.cursor = (current + 1, 1)\",\n \"def increment(self):\\n self.pos += 1\\n if self.pos == len(self.progress) - 1:\\n self.pos = 0\",\n \"def seek(self, loc):\\n assert loc == 0\\n\\n # rewind progress bar\\n if self.progressbar:\\n self.progressbar.update(-self._tell)\\n\\n self._fp_left.seek(loc)\\n self._fp_right.seek(loc)\\n self._tell = loc\\n self._buf = Buffer()\",\n \"def _save_increment(self):\\n self._set_scenefile_properties_from_ui()\\n self._update_successful_save(self.scenefile.save_increment())\\n self.version_spinbox.setValue(self.scenefile.version)\",\n \"def _record(self):\\r\\n self._plot()\\r\\n self._csvWriter()\\r\\n self._logger()\",\n \"def seek(self, val):\\n if self.p:\\n self.p.set_position(val/100.0 + self.p.get_position())\",\n \"def save(self) -> None:\\n self._save_marker = self._current\",\n \"def seeked(self):\\n # type: () -> int\\n return self._seeked\",\n \"def _pos_changed(self, timestamp=None, value=None, **kwargs):\\n self._set_position(value)\",\n \"def get_position(self):\\n\\n return (self._fileobj.tell() - self._pos) * 8 - self._bits\",\n \"def tell(self):\\n return self._offset\",\n \"def buildSeekIndex(self):\\n runs = 0;\\n while runs < 100:\\n runs += 1\\n bytes = self.seekfile.read(FileFlvStreamProvider.BUFFERFILL_CHUNCKSIZE)\\n if len(bytes)==0:\\n # either the file is finished, or the file is still rendering, either way, nothing to do\\n return\\n else:\\n self.seekbuffer.write(bytes)\\n while True:\\n self.seekbuffer.transactionStart();\\n try:\\n pos = self.seekbuffer.bytesRead();\\n chunk = self.flvseeker.readChunk()\\n except util.bufferedstringstream.BufferedStringStreamInsufficientDataException:\\n self.seekbuffer.transactionRollback();\\n break\\n self.seekbuffer.transactionCommit();\\n self.maxtimestamp = max(chunk.time, self.maxtimestamp)\\n if (chunk.CHUNKTYPE == util.flv.FLVVideoChunk.CHUNKTYPE and chunk.isKeyFrame) or not self.hasVideo(): # if we don't have video, every frame is a keyframe\\n if len(self.aKeyFrame) == 0 or chunk.time > self.aKeyFrame[-1][\\\"timestamp\\\"]+200: #we might be replaying a part we checkout out before, make sure that the timestamps stay in order; in addition, we store at most 5 timestamps per second, to save memory\\n self.aKeyFrame.append({\\\"timestamp\\\":chunk.time, \\\"filepos\\\":pos})\\n if len(bytes) < FileFlvStreamProvider.BUFFERFILL_CHUNCKSIZE:\\n return\",\n \"def _seek(self, offset):\\n assert offset % self.recordsize == 0\\n file_number, file_offset = divmod(offset,\\n self.filesize - self.header_size)\\n self.open(file_number)\\n self.fh_raw.seek(file_offset + self.header_size)\\n self.offset = offset\",\n \"def record_line(self, frame, event, arg): # pylint: disable=unused-argument\\n if event == 'line':\\n if self.prev_timestamp:\\n runtime = time.time() - self.prev_timestamp\\n self.lines.append([self.prev_path, self.prev_lineno, runtime])\\n self.prev_lineno = frame.f_lineno\\n self.prev_path = frame.f_code.co_filename\\n self.prev_timestamp = time.time()\\n return self.record_line\",\n \"def record(self, step):\",\n \"def track_position(self):\\n return self._track_position\",\n \"def _write_frame(self : \\\"animation\\\",\\n frame : \\\"np.ndarray\\\"\\n ):\\n self._writer.append_data(frame)\\n self._frame_number += 1\\n self._prevFrame = frame\",\n \"def setPosition(self):\\n self.data['pos-x'] = \\\"%s\\\" % self.x()\\n self.data['pos-y'] = \\\"%s\\\" % self.y()\",\n \"def mark(self): # type: () -> None\\n self._marker = self._idx\",\n \"def get_next_position(self):\",\n \"def position(self, pos: int):\\n self.__pos = pos\",\n \"def snapshot(self,):\\n self.stack.append((self.pos,self.dataptr))\",\n \"def _increment_file_counter(self):\\n self._add_to_file_counter(1)\",\n \"def record(self):\\n # TODO: record the data\",\n \"def saveCurrentStagePosition(self):\\n c= \\\"/cli:python /app:matrix /sys:1 /cmd:savecurrentposition /dev:stage\\\"\\n self.sendCMDstring(c)\",\n \"def position(self):\\r\\n pass\",\n \"def restorepos(self):\\n self.out.write(self.csi + \\\"u\\\")\",\n \"def record_trace(self):\\n\\n tfname = str(int(time.time())) + \\\".obd2_reader.trace\\\"\\n self.tf_out = open(tfname, 'a')\\n self.RecordTrace = 1\\n print \\\"Recoding trace to:\\\", tfname\",\n \"def __setitem__(self, index, value):\\n self.position[index] = value\",\n \"def record(self, time, increment):\\n raise NotImplementedError(\\\"Abstract method not implemented.\\\")\",\n \"def stat_beg_file(self, filename):\\n\\n self.batchvals['numfiles'] += 1\\n self.filevals['filename'] = filename\\n self.filevals['start_time'] = time.time()\\n\\n return -1\",\n \"def tell(self) -> int:\\n ...\",\n \"def set_position(self, idx, pos):\\n if self.EMULATOR_MODE:\\n return\\n if idx >= self.nleaflets or idx < 0:\\n raise IndexError('index specified is out of bounds')\\n self._fserial.write(self.MAGIC_BYTES + bytes([idx]) + pos.to_bytes(2, byteorder='big', signed=False) )\\n self._fserial.reset_input_buffer()\",\n \"def writePosFilesStep(self): \\n \\n writeSetOfCoordinates(self._getExtraPath(), self.inputCoordinatesTiltedPairs.get().getUntilted())\\n \\n writeSetOfCoordinates(self._getExtraPath(), self.inputCoordinatesTiltedPairs.get().getTilted())\",\n \"def update_position(position):\\n pass\",\n \"def tell(self):\\n return self.data_source.tell()\",\n \"def saveCurrentZPosition(self):\\n c= \\\"/cli:python /app:matrix /sys:1 /cmd:savecurrentposition /dev:zdrive\\\"\\n self.sendCMDstring(c)\",\n \"def logFile(self):\\n\\n event = 'stim'\\n mStr = '{:013}'.format(self.mouse.tag) + '\\\\t'\\n outPutStr = mStr + \\\\\\n datetime.fromtimestamp(int(time())).isoformat(' ') + '\\\\t' + event\\n print (outPutStr)\\n if self.textfp != None:\\n outPutStr = mStr + '{:.2f}'.format(time()) + '\\\\t' + event\\n self.textfp.write(outPutStr + '\\\\n')\\n self.textfp.flush()\",\n \"def output_position(self) -> int:\\n return self._output_position\",\n \"def advance(self):\\n self._current_inst += 1\\n self._line = self._lines[self._current_inst].strip()\",\n \"def last_sequence_ind(self, last_sequence_ind):\\n self.last_sequence_ind_ = last_sequence_ind\\n logging.debug(\\n \\\"Segment - Last sequence ind: {lsi}\\\".format(\\n lsi=self.last_sequence_ind\\n )\\n )\",\n \"def setPosition(position):\",\n \"def seekEnd(self):\\n self.oFile.seek(0, 2)\",\n \"def write(self, file):\\n pos = file.tell()\\n pickle.dump((self.index, self.meta, self.info), file)\\n file.seek(0)\\n\\n # update the header with the position of the content index.\\n file.write(struct.pack('<Q', pos))\",\n \"def save_history(cube, field, filename): \\n\\n history.append(cube.attributes['history'])\",\n \"def update(self):\\n self.__token += self.__lines[self.__i]\\n self.__i += 1\",\n \"def update(self, position):\\n self.time_since_update = 0\\n self.history = []\\n self.hits += 1\\n self.hit_streak += 1\\n self.kf.update(position)\",\n \"def store_current_position_as_previous(self):\\n pos = self.get_current_position()\\n self.previous_xloc = pos[0]\\n self.previous_yloc = pos[1]\\n self.previous_zloc = pos[2]\\n return pos\",\n \"def get_position(self): # maybe encoded in filepath at some point\\n result = (self.iter * self.row_step)% self.row_size, self.iter // (self.row_size * self.row_step)* self.col_step\\n self.iter += 1\\n return result\",\n \"def update(self, pos):\\n\\t\\tpass\",\n \"def media_seek_position(self):\\n return self._state.get(\\\"seek\\\", None)\",\n \"def sig(self):\\n if not self.fin():\\n self.column += 1\\n if self.column == self.maxcol:\\n self.column = 0\\n self.row +=1\\n if self.row == self.maxfila:\\n self.row = -1\\n self.column = -1\",\n \"def tell(self):\\n ...\",\n \"def get_record_position(self, index):\\n pos = c_uint()\\n ckresult(_dll.FMOD_System_GetRecordPosition(self._ptr, index, byref(pos)))\\n return pos.value\",\n \"async def seek(self, pos: int):\\n pos = max(pos, 0) # Prevent seeking before start of track\\n await self._bot.lavalink.ws.send(op='seek', guildId=self.guild_id, position=pos)\",\n \"def calculate_record_number(self):\\n return (self._read_state[StateKey.POSITION] - FLAG_RECORD_SIZE) / \\\\\\n self.velocity_record_size\",\n \"def mark_position(self, node):\\n if self.block:\\n src_descr = self.source_descr\\n pos = (src_descr,) + getpos(node)\\n self.block.positions.add(pos)\",\n \"def seek(self, index: int, /) -> str:\\n self.index = index\\n return self.current\",\n \"def output_position(self, output_position: int):\\n\\n self._output_position = output_position\",\n \"def mark_saved(self):\\n self._saved_position = self._current_undo_command\",\n \"def seek(self, offset, location=0):\\n if (0 <= location <= len(self._tiff)) and (0 <= location+offset <= len(self._tiff)):\\n self._offset = location+offset\",\n \"def save(self):\\r\\n self.pushes.append((self.token, self.stream, self.line, self.column))\",\n \"def history_buffer_pos_changed():\\n # Only when this buffer has the focus.\\n if buffer_mapping.focus_stack[-1] == HISTORY_BUFFER:\\n line_no = history_buffer.document.cursor_position_row\\n\\n if line_no in history_mapping.selected_lines:\\n default_lineno = sorted(history_mapping.selected_lines).index(line_no) + \\\\\\n history_mapping.result_line_offset\\n\\n default_buffer.cursor_position = \\\\\\n default_buffer.document.translate_row_col_to_index(default_lineno, 0)\",\n \"def file_pointer(self):\\n\\n try:\\n self.__file.seek(self.__file.tell() - 1)\\n except Exception as e:\\n raise e\",\n \"def record(position):\\n\\tlogging.info('Recording position %d', position)\\n\\twith locked_level(create=True) as level:\\n\\t\\ttry:\\n\\t\\t\\tdelay = pickle.load(level)\\n\\t\\t\\tlogging.info('Loaded pickle: %r', delay)\\n\\t\\t\\tremaining_expected_positions = iter(delay.expected_positions)\\n\\t\\t\\tif position == next(remaining_expected_positions, None):\\n\\t\\t\\t\\t# This is just the feedback from slow dimming that is already in progress\\n\\t\\t\\t\\tlogging.info('Ignoring feedback')\\n\\t\\t\\t\\tnext_delay = delay_record.Delay(delay.when, delay.source_position, delay.target_position, list(remaining_expected_positions))\\n\\t\\t\\t\\texpect(level, next_delay)\\n\\t\\t\\t\\treturn\\n\\t\\texcept IOError:\\n\\t\\t\\t# The file is not open for writing, so there is no expected position yet\\n\\t\\t\\tpass\\n\\n\\t\\tnext_delay = delay_record.Delay(datetime.datetime.utcnow() + DELAY_DURATION, position, position, [])\\n\\t\\texpect(level, next_delay)\",\n \"def __position_jaw_current1_cb(self, data):\\n self.__position_jaw_current[0] = data.position[0]\\n self.__get_jaw_event[0].set()\",\n \"def position_helper():\\n for file_name in file_list[:1]:\\n file_bits = file_splitter(file_name)\\n line_length = len(max(file_bits, key=len)) + 13\\n index = 0\\n print('\\\\n' + ('-' * line_length))\\n for x in file_bits:\\n print('Index ', str(index), ' = ', file_bits[index])\\n index += 1\\n print(('-' * line_length) + '\\\\n')\",\n \"def number_idx(self, filename):\\n with open(filename) as fh:\\n firstline = fh.readline()\\n parts = firstline.split('\\\\t')\\n # only add if there are 4 parts\\n if len(parts) != 4:\\n return\\n\\n count = 1\\n def writeline(fho, line, count):\\n fho.write(line.rstrip() + '\\\\t' + str(count) + '\\\\n')\\n\\n with open(filename + '.tmp', 'w+b') as fho:\\n writeline(fho, firstline, count)\\n count += 1\\n for line in fh:\\n writeline(fho, line, count)\\n count += 1\\n\\n shutil.move(filename + '.tmp', filename)\",\n \"def record_pose(self):\\n drone_pos = client.getMultirotorState().kinematics_estimated.position\\n output = \\\" | \\\".join([\\\"x_val: \\\" + str(drone_pos.x_val), \\\"y_val: \\\" + str(drone_pos.y_val), \\\"z_val: \\\" + str(drone_pos.z_val)])\\n print(output)\\n self.log_arr.append(output)\\n return output\",\n \"def tell(self):\\n return self.oFile.tell()\",\n \"def read_recorded(self):\\n reader=csv.reader(open(self._filename,\\\"rb\\\"),delimiter=',')\\n self._headers = reader.next()\\n x=list(reader)\\n recorded_positions=np.array(x).astype('float')\\n self.refine_pos(recorded_positions)\\n self._joint_first = recorded_positions[0]\\n self._joint_last = recorded_positions[-1]\",\n \"def on_increased_position(self, order) -> None:\\n pass\",\n \"def position_changed(self, position):\\n pass\"\n]","isConversational":false},"negative_scores":{"kind":"list like","value":["0.66489357","0.66329324","0.6479423","0.64465714","0.61586374","0.61237216","0.6114697","0.610729","0.6095362","0.60406315","0.5951866","0.59466505","0.5869703","0.58657867","0.58627445","0.58404976","0.5832795","0.58294195","0.5811629","0.5791412","0.57500947","0.57394385","0.57351726","0.5733135","0.57191616","0.57077116","0.56901973","0.5670479","0.56697345","0.5636059","0.559934","0.55927545","0.558215","0.5580169","0.55703485","0.5544094","0.5543841","0.5538751","0.55325675","0.553028","0.5526871","0.55256534","0.552175","0.5515409","0.5514753","0.55136317","0.5512924","0.5502873","0.55014426","0.5478546","0.54708266","0.54594463","0.5456406","0.54543966","0.5450749","0.54390734","0.54280317","0.5407133","0.5406796","0.5405104","0.5398952","0.5398273","0.53947353","0.53921306","0.5364042","0.53618133","0.5356609","0.53406227","0.5332508","0.5330925","0.53282684","0.5327544","0.5316768","0.531518","0.53112894","0.53051907","0.52955395","0.52935153","0.52926266","0.528826","0.52881247","0.52873665","0.52826166","0.5278092","0.52758497","0.52753603","0.52730757","0.5272969","0.52679926","0.52590287","0.5258004","0.5252055","0.52392745","0.52391917","0.5235711","0.5231649","0.52253854","0.52174133","0.5208106","0.52027756"],"string":"[\n \"0.66489357\",\n \"0.66329324\",\n \"0.6479423\",\n \"0.64465714\",\n \"0.61586374\",\n \"0.61237216\",\n \"0.6114697\",\n \"0.610729\",\n \"0.6095362\",\n \"0.60406315\",\n \"0.5951866\",\n \"0.59466505\",\n \"0.5869703\",\n \"0.58657867\",\n \"0.58627445\",\n \"0.58404976\",\n \"0.5832795\",\n \"0.58294195\",\n \"0.5811629\",\n \"0.5791412\",\n \"0.57500947\",\n \"0.57394385\",\n \"0.57351726\",\n \"0.5733135\",\n \"0.57191616\",\n \"0.57077116\",\n \"0.56901973\",\n \"0.5670479\",\n \"0.56697345\",\n \"0.5636059\",\n \"0.559934\",\n \"0.55927545\",\n \"0.558215\",\n \"0.5580169\",\n \"0.55703485\",\n \"0.5544094\",\n \"0.5543841\",\n \"0.5538751\",\n \"0.55325675\",\n \"0.553028\",\n \"0.5526871\",\n \"0.55256534\",\n \"0.552175\",\n \"0.5515409\",\n \"0.5514753\",\n \"0.55136317\",\n \"0.5512924\",\n \"0.5502873\",\n \"0.55014426\",\n \"0.5478546\",\n \"0.54708266\",\n \"0.54594463\",\n \"0.5456406\",\n \"0.54543966\",\n \"0.5450749\",\n \"0.54390734\",\n \"0.54280317\",\n \"0.5407133\",\n \"0.5406796\",\n \"0.5405104\",\n \"0.5398952\",\n \"0.5398273\",\n \"0.53947353\",\n \"0.53921306\",\n \"0.5364042\",\n \"0.53618133\",\n \"0.5356609\",\n \"0.53406227\",\n \"0.5332508\",\n \"0.5330925\",\n \"0.53282684\",\n \"0.5327544\",\n \"0.5316768\",\n \"0.531518\",\n \"0.53112894\",\n \"0.53051907\",\n \"0.52955395\",\n \"0.52935153\",\n \"0.52926266\",\n \"0.528826\",\n \"0.52881247\",\n \"0.52873665\",\n \"0.52826166\",\n \"0.5278092\",\n \"0.52758497\",\n \"0.52753603\",\n \"0.52730757\",\n \"0.5272969\",\n \"0.52679926\",\n \"0.52590287\",\n \"0.5258004\",\n \"0.5252055\",\n \"0.52392745\",\n \"0.52391917\",\n \"0.5235711\",\n \"0.5231649\",\n \"0.52253854\",\n \"0.52174133\",\n \"0.5208106\",\n \"0.52027756\"\n]","isConversational":false},"document_score":{"kind":"string","value":"0.7691817"},"document_rank":{"kind":"string","value":"0"}}}],"truncated":false,"partial":true},"paginationData":{"pageIndex":226,"numItemsPerPage":100,"numTotalItems":95772,"offset":22600,"length":100}},"jwt":"eyJhbGciOiJFZERTQSIsImtpZCI6IjVHZDBvd0g5MTM2eDZjc1FvbE1zcktNWUZoRVFoUm5PVVVybEpjOUhaUEEifQ.eyJyZWFkIjp0cnVlLCJwZXJtaXNzaW9ucyI6eyJyZXBvLmNvbnRlbnQucmVhZCI6dHJ1ZX0sImlhdCI6MTc4NjY1MzQxNSwianRpIjoiYjE1NjJiNGMtMmZkZC00NjE0LTk4NDYtYjdjYjNmMDAxODMyIiwic3ViIjoiL2RhdGFzZXRzL25vbWljLWFpL2Nvcm5zdGFjay1weXRob24tdjEiLCJleHAiOjE3ODY2NTcwMTUsImlzcyI6Imh0dHBzOi8vaHVnZ2luZ2ZhY2UuY28ifQ.KblfcJKIEuqhPcrWZ0XY0d5m-WL3BGJHkXmPfn2gcZHn3PVeHOgz-Q9wfJqZKqOO11jxUtkzaqjEIkksfyHIDQ","displayUrls":true,"splitSizeSummaries":[{"config":"default","split":"train","numRows":95772,"numBytesParquet":1928375891}]},"discussionsStats":{"closed":1,"open":1,"total":2},"fullWidth":true,"hasGatedAccess":true,"hasFullAccess":true,"isEmbedded":false,"savedQueries":{"community":[],"user":[]}}">
query stringlengths 9 3.4k | document stringlengths 9 87.4k | metadata dict | negatives listlengths 4 101 | negative_scores listlengths 4 101 | document_score stringlengths 3 10 | document_rank stringclasses 102
values |
|---|---|---|---|---|---|---|
Returns GSoCProfile or GCIProfile which corresponds to the specified entity. | def _getProfileForRole(entity, profile_model):
if isinstance(entity, profile_model):
return entity
if isinstance(entity, OrgAdmin) or isinstance(entity, Mentor):
key_name = entity.program.key().name() + '/' + entity.user.key().name()
else:
key_name = entity.key().name()
parent = entity.user
ret... | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def _getProfileFromUser(self):\n # Make sure user is authenticated\n user = endpoints.get_current_user()\n if not user:\n raise endpoints.UnauthorizedException('Authorization required')\n # Get Profile from datastore\n user_id = user.email()\n p_key = ndb.Key(Pr... | [
"0.60517067",
"0.5931097",
"0.5920047",
"0.57531446",
"0.5752912",
"0.57454103",
"0.5593141",
"0.5578978",
"0.5446997",
"0.54374474",
"0.5414628",
"0.5366052",
"0.5310331",
"0.53044224",
"0.5297717",
"0.528342",
"0.5280851",
"0.5238035",
"0.5170172",
"0.5164785",
"0.51386154"... | 0.67691934 | 0 |
Returns Key instance of the Profile which corresponds to the Role which is represented by the specified Key. | def _getProfileKeyForRoleKey(key, profile_model):
entity = db.get(key)
profile = _getProfileForRole(entity, profile_model)
return profile.key() | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def get_key(self, role):\n\n for key, role_name in self.assignable_roles[0].items():\n if role_name == role.name:\n return key",
"def get_key(self, key):\n ret = None\n qkey = key.__qualname__\n ret = self.get(qkey)\n if not ret:\n # check a... | [
"0.66111445",
"0.58805066",
"0.58505315",
"0.5831204",
"0.5803281",
"0.5803281",
"0.57678306",
"0.5754127",
"0.56673247",
"0.56673247",
"0.5639647",
"0.5624423",
"0.56116235",
"0.56116235",
"0.56116235",
"0.56116235",
"0.56116235",
"0.56116235",
"0.56116235",
"0.56116235",
"0... | 0.828253 | 0 |
Iterates through the entities and updates the references. | def _process(self, start_key, batch_size):
query = self.MODEL.all()
if start_key:
query.filter('__key__ > ', start_key)
try:
entities = query.fetch(batch_size)
if not entities:
# all entities has already been processed
return
for entity in entities:
for fi... | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def update_entities(self):\n raise NotImplementedError()",
"def updateAllEntities():\n entityKeys=list(registeredEntities.keys())\n for currKey in entityKeys:\n try:\n currEntity=registeredEntities[currKey]\n currEntity.update()\n except KeyError:\n #th... | [
"0.6931095",
"0.65709054",
"0.6287238",
"0.61648846",
"0.60134435",
"0.5991761",
"0.59900665",
"0.591261",
"0.58126783",
"0.5805775",
"0.5740838",
"0.5702715",
"0.56896293",
"0.5662672",
"0.5657498",
"0.56457126",
"0.5643056",
"0.5629276",
"0.56268924",
"0.55490804",
"0.55468... | 0.54959655 | 24 |
Starts a task which updates references for a particular model. | def updateReferencesForModel(model):
if model == 'student_proposal':
updater = ReferenceUpdater(StudentProposal, GSoCProfile,
['scope', 'mentor'], ['possible_mentors'])
elif model == 'student_project':
updater = ReferenceUpdater(StudentProject, GSoCProfile,
['mentor', 'student'], ['addition... | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def update(self, task_model):\n raise NotImplementedError()",
"def task_update(self):\n try:\n self.task_stop()\n except:\n pass\n self.update()\n self.task_start()",
"async def update_model(model_updates):\n async for model_update in model_updates:\n... | [
"0.6739131",
"0.57978827",
"0.5764734",
"0.57161385",
"0.56552994",
"0.555894",
"0.5534376",
"0.5498616",
"0.53723985",
"0.5328883",
"0.52869743",
"0.5229465",
"0.52287436",
"0.5193433",
"0.518396",
"0.51796234",
"0.50973964",
"0.5076054",
"0.50661147",
"0.5063656",
"0.505143... | 0.5769493 | 2 |
Starts a bunch of iterative tasks which update references in StudentProjects. | def updateStudentProjectReferences(request):
return updateReferencesForModel('student_project') | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def _update_all_tasks(self) -> None:\n for task in self.tasks:\n task.update()",
"def _update_projects_watch(self, new_projects_list):\n persistent_update_project = retry_children_watch_coroutine(\n '/appscale/projects', self.update_projects\n )\n main_io_loop = IOLoop.instance()\... | [
"0.589292",
"0.58421355",
"0.5682374",
"0.5677322",
"0.56522554",
"0.5562921",
"0.55216223",
"0.5498498",
"0.5461224",
"0.5419146",
"0.5413647",
"0.5395407",
"0.5332223",
"0.5322703",
"0.5321278",
"0.52861494",
"0.52130514",
"0.5199185",
"0.51861364",
"0.51582754",
"0.5149266... | 0.54775155 | 8 |
Starts a bunch of iterative tasks which update references in StudentProposals. | def updateStudentProposalReferences(request):
return updateReferencesForModel('student_proposal') | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def _update_all_tasks(self) -> None:\n for task in self.tasks:\n task.update()",
"def _stage1(self):\n self.start_progress()\n tasks = list(self._chain_dict(self._model.adjust_tasks))\n if len(tasks) == 0:\n self._stage2(self._no_adjustments_case())\n else... | [
"0.5717533",
"0.54796076",
"0.54364556",
"0.53478354",
"0.5293635",
"0.51366085",
"0.5125454",
"0.5119872",
"0.51075053",
"0.5100745",
"0.5065694",
"0.5057105",
"0.5049609",
"0.50482255",
"0.5044344",
"0.5037738",
"0.5019423",
"0.5008759",
"0.50071543",
"0.5003686",
"0.500107... | 0.54240596 | 3 |
Starts a bunch of iterative tasks which update references to various roles. | def updateReferences(request):
# updates student proposals
updateReferencesForModel('student_proposal')
# updates student projects
updateReferencesForModel('student_project')
return http.HttpResponse("Ok") | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"async def actualize(self):\r\n for guild, settings in self.bot.settings.items():\r\n # Grab the roles and their requirements\r\n guild = self.bot.get_guild(guild)\r\n base_member = settings.get(\"rank_basic_member_role_id\")\r\n base_member = guild.get_role(base_m... | [
"0.5954693",
"0.5937825",
"0.585267",
"0.58371156",
"0.58321106",
"0.5830262",
"0.5742774",
"0.5681948",
"0.5625343",
"0.561437",
"0.5614087",
"0.5601306",
"0.5526887",
"0.55241233",
"0.5524092",
"0.5454957",
"0.5360223",
"0.53481555",
"0.5344117",
"0.5330294",
"0.53005415",
... | 0.0 | -1 |
Creates a new IntMachine running given input Intcode progam. | def __init__(
self,
program: Iterable[int],
noun: Optional[int] = None,
verb: Optional[int] = None,
fn_get_input: Optional[Callable[[], int]] = None,
fn_set_output: Optional[Callable[[int], None]] = None
):
self._program: List[i... | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def _interactive_input_fn(hparams, decode_hp):\n num_samples = decode_hp.num_samples if decode_hp.num_samples > 0 else 1\n decode_length = decode_hp.extra_length\n input_type = \"text\"\n p_hparams = hparams.problem_hparams\n has_input = \"inputs\" in p_hparams.modality\n vocabulary = p_hparams.vocabulary[\"... | [
"0.57619214",
"0.5523791",
"0.5497577",
"0.5425302",
"0.5412043",
"0.51995695",
"0.51851875",
"0.5182559",
"0.5148229",
"0.512977",
"0.5129548",
"0.5096928",
"0.5052561",
"0.5028506",
"0.5015094",
"0.4980892",
"0.49586543",
"0.49470448",
"0.49391463",
"0.49381799",
"0.4921976... | 0.5506527 | 2 |
Standard input method if none provided. | def get_raw_input() -> int:
return int(input("> ")) | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def createStandardInput(self):\n\t\traise NotImplementedError(\"\")",
"def sysinput(rockstate):\n raise NotImplementedError",
"def input_helper(prompt):\n if version_info[0] == 2:\n # python2 input is scary - we want raw_input\n return raw_input(prompt)\n else:\n return input(prom... | [
"0.70983446",
"0.7037133",
"0.70268506",
"0.70225686",
"0.70193917",
"0.69750947",
"0.69259167",
"0.6899299",
"0.67370933",
"0.6504721",
"0.64968175",
"0.6495343",
"0.63925487",
"0.63925487",
"0.63355005",
"0.63202924",
"0.6318242",
"0.6313571",
"0.6311044",
"0.62952733",
"0.... | 0.6201655 | 27 |
Standard output method if none provided. | def set_raw_output(val: int) -> None:
print(val) | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def standard_output(self) -> global___Statement.StandardOutput:",
"def print_out():\n pass",
"def printOutput(self):\n pass",
"def __call__(self, *args, **kwargs):\n kwargs.setdefault(\"print_output\", self.PRINT_OUTPUT)\n kwargs.setdefault(\"return_output\", self.RETURN_OUTPUT)\n... | [
"0.7402091",
"0.72167224",
"0.6968697",
"0.6889925",
"0.687557",
"0.6834578",
"0.66962534",
"0.66657144",
"0.6642879",
"0.65625054",
"0.6447009",
"0.6412474",
"0.6398471",
"0.6375641",
"0.62936854",
"0.6277434",
"0.62161696",
"0.61924154",
"0.6149526",
"0.6132663",
"0.6124538... | 0.0 | -1 |
Entend memory to be at least this long by padding with zeros. | def _extend(self, newlen: int) -> None:
diff = newlen - len(self)
if diff > 0:
self.extend([0] * diff) | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def dec_size(self):\r\n self.__length -= 1",
"def zero_pad(data):\n N = len(data)\n pow_2 = np.ceil(np.log2(N))\n return np.pad(data,(0,int((2**pow_2)-N)),'constant')",
"def getSizeOfZeroFill(self) -> int:\n ...",
"def len_unpadded(self) -> int:",
"def __pad(self, data):\n ret... | [
"0.6212953",
"0.6161777",
"0.61475414",
"0.5942525",
"0.58954096",
"0.58908606",
"0.57974887",
"0.57881296",
"0.5772151",
"0.5764062",
"0.5752971",
"0.56805044",
"0.56183845",
"0.55799264",
"0.5576856",
"0.5575887",
"0.5575213",
"0.5573375",
"0.5572014",
"0.555359",
"0.551463... | 0.55716133 | 19 |
Function to concatenate dictionaries | def _join_dicts(dicts):
if dicts is None: # pragma: no cover
return
assembled_dict = {k: v for D in dicts for k, v in D.items()}
return assembled_dict | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def concatDic(dic1, dic2):\n pass",
"def concat_dicts(self, dict1, dict2):\n result = dict()\n for key, value in dict1.items():\n if len(value.shape) == 1:\n result[key] = np.concatenate([value, dict2[key]])\n else:\n result[key] = np.vstack([v... | [
"0.78326815",
"0.76263916",
"0.7547149",
"0.7144526",
"0.7144526",
"0.7033631",
"0.70007455",
"0.6978834",
"0.68688685",
"0.67944866",
"0.6774909",
"0.67653054",
"0.6748235",
"0.6728825",
"0.67226535",
"0.6719533",
"0.6710105",
"0.6710105",
"0.6710105",
"0.6700148",
"0.669384... | 0.71008927 | 5 |
1. Convert ifg phase data into numpy binary files. 2. Save the preread_ifgs dict with information about the ifgs that are later used for fast loading of Ifg files in IfgPart class | def _create_ifg_dict(dest_tifs, params):
ifgs_dict = {}
nifgs = len(dest_tifs)
process_tifs = mpiops.array_split(dest_tifs)
for d in process_tifs:
ifg = shared._prep_ifg(d, params)
ifgs_dict[d] = PrereadIfg(path=d,
nan_fraction=ifg.nan_fraction,
... | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def process_ifgs(ifg_paths, params, rows, cols):\n\n if mpiops.size > 1: # turn of multiprocessing during mpi jobs\n params[cf.PARALLEL] = False\n outdir = params[cf.TMPDIR]\n if not os.path.exists(outdir):\n shared.mkdir_p(outdir)\n\n tiles = mpiops.run_once(get_tiles, ifg_paths[0], row... | [
"0.6318846",
"0.59935594",
"0.58033705",
"0.557617",
"0.5541329",
"0.548497",
"0.54715943",
"0.54680747",
"0.54556257",
"0.5448563",
"0.5441124",
"0.543736",
"0.54259264",
"0.5376901",
"0.53260976",
"0.5311196",
"0.5308559",
"0.5306656",
"0.52739114",
"0.524727",
"0.52424717"... | 0.69364214 | 0 |
MPI wrapper function for MST calculation | def _mst_calc(dest_tifs, params, tiles, preread_ifgs):
process_tiles = mpiops.array_split(tiles)
log.info('Calculating minimum spanning tree matrix')
def _save_mst_tile(tile, i, preread_ifgs):
"""
Convenient inner loop for mst tile saving
"""
mst_tile = mst.mst_multiprocessi... | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def local_mediation(task):\n\tatlas = 'power'\n\tproject='hcp'\n\tknown_membership,network_names,num_nodes,name_int_dict = network_labels(atlas)\n\tsubjects = np.load('%s/dynamic_mod/results/%s_%s_%s_subs_fz.npy' %(homedir,'hcp',task,atlas))\n\tstatic_results = graph_metrics(subjects,task,atlas,run_version='fz')\n... | [
"0.5712891",
"0.56743956",
"0.5644941",
"0.56176066",
"0.5497657",
"0.5445917",
"0.53922945",
"0.53776425",
"0.53776425",
"0.5272448",
"0.5257341",
"0.52249587",
"0.5215293",
"0.52147806",
"0.51855075",
"0.51734966",
"0.51608145",
"0.5157823",
"0.51528966",
"0.5091743",
"0.50... | 0.6318238 | 0 |
Convenient inner loop for mst tile saving | def _save_mst_tile(tile, i, preread_ifgs):
mst_tile = mst.mst_multiprocessing(tile, dest_tifs, preread_ifgs, params)
# locally save the mst_mat
mst_file_process_n = join(params[cf.TMPDIR], 'mst_mat_{}.npy'.format(i))
np.save(file=mst_file_process_n, arr=mst_tile) | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def saveTiles(z, x, y, ntiles, mapname, image, suffix = 'png', imgtype = None):\n for dx in range(0, ntiles):\n tilex = x*ntiles + dx\n ensureDirExists(getTileDir(mapname, z, tilex))\n for dy in range(0, ntiles): \n tiley = y*ntiles + dy\n offsetx = BORDER_WIDTH + d... | [
"0.7104713",
"0.6770546",
"0.66476554",
"0.6564062",
"0.6432503",
"0.63820964",
"0.63444513",
"0.62283266",
"0.62283266",
"0.6224158",
"0.6180002",
"0.6177511",
"0.61530757",
"0.61273086",
"0.61271673",
"0.61231756",
"0.60986423",
"0.6098325",
"0.6088457",
"0.60603994",
"0.60... | 0.73028386 | 0 |
Wrapper for reference pixel calculation | def _ref_pixel_calc(ifg_paths: List[str], params: dict) -> Tuple[int, int]:
lon = params[cf.REFX]
lat = params[cf.REFY]
ifg = Ifg(ifg_paths[0])
ifg.open(readonly=True)
# assume all interferograms have same projection and will share the same transform
transform = ifg.dataset.GetGeoTransform()
... | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def referencepixel(self, *args, **kwargs):\n return _coordsys.coordsys_referencepixel(self, *args, **kwargs)",
"def calc(self, *args, **kwargs):\n return _image.image_calc(self, *args, **kwargs)",
"def pixel(self, x: int, y: int, colour: int, /) -> None:",
"def pixel_ref(self):\n return ... | [
"0.70273024",
"0.6470538",
"0.6387868",
"0.6360936",
"0.6305052",
"0.6197508",
"0.6159541",
"0.6128455",
"0.6084345",
"0.6061778",
"0.60592145",
"0.5950711",
"0.5843533",
"0.5832167",
"0.58209383",
"0.5817011",
"0.5734207",
"0.57130796",
"0.5680172",
"0.5634101",
"0.56332415"... | 0.6673501 | 1 |
MPI wrapper for orbital fit correction | def _orb_fit_calc(multi_paths: List[MultiplePaths], params, preread_ifgs=None) -> None:
if not params[cf.ORBITAL_FIT]:
log.info('Orbital correction not required!')
print('Orbital correction not required!')
return
log.info('Calculating orbital correction')
ifg_paths = [p.sampled_path... | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def test_MPI_Parallel_Interface(comm):\n\n def printMPI(msg):\n for i in range(comm.Get_size()):\n comm.barrier()\n if comm.Get_rank() == i:\n print(\"Proc {}: {}\".format(i, msg))\n\n n = 10\n\n par = MPI_Objective_Interface(mp.Extended_Rosenbrock, nb_domain_gr... | [
"0.5844359",
"0.5622064",
"0.55996203",
"0.5493118",
"0.5468938",
"0.5443779",
"0.54386663",
"0.53946644",
"0.5390193",
"0.53538936",
"0.5300762",
"0.52867675",
"0.5278852",
"0.5252904",
"0.52269447",
"0.5218884",
"0.52153146",
"0.52118087",
"0.5195961",
"0.5157866",
"0.51551... | 0.54099774 | 7 |
Wrapper for reference phase estimation. | def _ref_phase_estimation(ifg_paths, params, refpx, refpy):
log.info("Calculating reference phase and correcting each interferogram")
if len(ifg_paths) < 2:
raise rpe.ReferencePhaseError(
"At least two interferograms required for reference phase correction ({len_ifg_paths} "
"pro... | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def GetReferencePhase (self, miips_settings=None) :\n\t\tif miips_settings is None :\n\t\t\tmiips_settings = self.GetSettings()\n\t\t\n\t\t# Retrieve the basis type\n\t\tpolynomial_basis = self.polynomial_bases[ miips_settings[\"polynomial_basis\"] ]\n\t\t\n\t\t# Arguments of the basis \n\t\tX = np.linspace(-1., 1... | [
"0.7015458",
"0.6582434",
"0.6338043",
"0.6205949",
"0.5983391",
"0.58729506",
"0.58365",
"0.5791588",
"0.5786385",
"0.5780738",
"0.5776022",
"0.57702374",
"0.57702374",
"0.5757595",
"0.5725867",
"0.5724824",
"0.5707285",
"0.5639593",
"0.5624812",
"0.5580624",
"0.557016",
"... | 0.6133667 | 4 |
Top level function to perform PyRate workflow on given interferograms | def main(params):
mpi_vs_multiprocess_logging("process", params)
ifg_paths = []
for ifg_path in params[cf.INTERFEROGRAM_FILES]:
ifg_paths.append(ifg_path.sampled_path)
rows, cols = params["rows"], params["cols"]
return process_ifgs(ifg_paths, params, rows, cols) | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def func(args):\n\n classifier_stat_list = []\n cf_frames = []\n confidences_list = []\n intents_in_results = pd.Series(dtype='float')\n\n classifier_num = len(args.classifiers_results)\n\n # Prepare labels for each curve\n labels = [os.path.splitext(os.path.basename(file_path))[0]\n ... | [
"0.5867609",
"0.5741518",
"0.5724703",
"0.57064086",
"0.56842136",
"0.5653325",
"0.5587649",
"0.5583598",
"0.5509468",
"0.5485103",
"0.5458944",
"0.5457116",
"0.5422393",
"0.5393152",
"0.5389847",
"0.5385744",
"0.53734523",
"0.53389794",
"0.53318554",
"0.52802575",
"0.5274898... | 0.0 | -1 |
Top level function to perform PyRate workflow on given interferograms | def process_ifgs(ifg_paths, params, rows, cols):
if mpiops.size > 1: # turn of multiprocessing during mpi jobs
params[cf.PARALLEL] = False
outdir = params[cf.TMPDIR]
if not os.path.exists(outdir):
shared.mkdir_p(outdir)
tiles = mpiops.run_once(get_tiles, ifg_paths[0], rows, cols)
... | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def func(args):\n\n classifier_stat_list = []\n cf_frames = []\n confidences_list = []\n intents_in_results = pd.Series(dtype='float')\n\n classifier_num = len(args.classifiers_results)\n\n # Prepare labels for each curve\n labels = [os.path.splitext(os.path.basename(file_path))[0]\n ... | [
"0.5867609",
"0.5741518",
"0.5724703",
"0.57064086",
"0.56842136",
"0.5653325",
"0.5587649",
"0.5583598",
"0.5509468",
"0.5485103",
"0.5458944",
"0.5457116",
"0.5422393",
"0.5393152",
"0.5389847",
"0.5385744",
"0.53734523",
"0.53389794",
"0.53318554",
"0.52802575",
"0.5274898... | 0.0 | -1 |
MPI wrapper for stacking calculation | def _stack_calc(ifg_paths, params, vcmt, tiles, preread_ifgs):
process_tiles = mpiops.array_split(tiles)
log.info('Calculating rate map from stacking')
output_dir = params[cf.TMPDIR]
for t in process_tiles:
log.info('Stacking of tile {}'.format(t.index))
ifg_parts = [shared.IfgPart(p, t,... | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def gather_ps(rank, size, comm, k_allmodels, P21_allmodels, PHII_allmodels,\n first_snap_allmodels, last_snap_allmodels):\n\n def generate_tag(rank):\n tag = int(rank*100)\n\n return tag\n\n # Rank 0 will gather the wavenumber bins/power spectra from all other\n # ranks. \n i... | [
"0.56406677",
"0.54841715",
"0.5319267",
"0.53158826",
"0.52270633",
"0.52124757",
"0.5178584",
"0.51732755",
"0.51422054",
"0.5141755",
"0.5132032",
"0.51277685",
"0.5125442",
"0.51210636",
"0.5116323",
"0.50880647",
"0.508449",
"0.5058797",
"0.505715",
"0.5034772",
"0.50324... | 0.515047 | 8 |
MPI wrapper for maxvar and vcmt computation | def _maxvar_vcm_calc(ifg_paths, params, preread_ifgs):
log.info('Calculating the temporal variance-covariance matrix')
process_indices = mpiops.array_split(range(len(ifg_paths)))
def _get_r_dist(ifg_path):
"""
Get RDIst class object
"""
ifg = Ifg(ifg_path)
ifg.open()... | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def local_max_and_argmax(node):\r\n if node.op == T._max_and_argmax:\r\n if len(node.outputs[1].clients) == 0:\r\n #MaxAndArgmax support variable axis,\r\n #but CAReduce support only constant axis.\r\n if node.inputs[1].data is None:\r\n axis = None\r\n ... | [
"0.6341681",
"0.61969894",
"0.6065083",
"0.60228604",
"0.60162735",
"0.596405",
"0.5831983",
"0.58185774",
"0.57805014",
"0.57521695",
"0.57240343",
"0.57173043",
"0.56774616",
"0.5617856",
"0.5602376",
"0.5560389",
"0.55562276",
"0.5540691",
"0.5516906",
"0.549255",
"0.54219... | 0.80074286 | 0 |
Get RDIst class object | def _get_r_dist(ifg_path):
ifg = Ifg(ifg_path)
ifg.open()
r_dist = vcm_module.RDist(ifg)()
ifg.close()
return r_dist | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def _get_instance(self):",
"def _get_instance(self):",
"def get_class(self):\n\t\treturn self.CLASS",
"def GetInstance():\n pass",
"def getClassObject(self):\n return self.device()",
"def get_object(cls):\n values = cls._METHOD(get_object(TOP_OBJECT))\n return StratisdConstants.... | [
"0.6729752",
"0.6729752",
"0.65443677",
"0.6543258",
"0.6500587",
"0.6476979",
"0.6384267",
"0.63405716",
"0.6319236",
"0.6222948",
"0.6133858",
"0.590584",
"0.58932704",
"0.58908397",
"0.58358616",
"0.5834195",
"0.58051187",
"0.58033943",
"0.5774452",
"0.57590514",
"0.575060... | 0.0 | -1 |
MPI wrapper for time series calculation. | def _timeseries_calc(ifg_paths, params, vcmt, tiles, preread_ifgs):
if params[cf.TIME_SERIES_CAL] == 0:
log.info('Time Series Calculation not required')
return
if params[cf.TIME_SERIES_METHOD] == 1:
log.info('Calculating time series using Laplacian Smoothing method')
elif params[cf.... | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def compute_trajectory():\n pass",
"def mpi_schedule_job_array(csvstore, job_array, mpi_service=MPIService()):\n param_array = job_array.param_array\n job = job_array.job\n try:\n if mpi_service.rank == 0:\n # master\n results = []\n nb_completed_tasks = 0\n ... | [
"0.56803995",
"0.56590575",
"0.53260535",
"0.5320121",
"0.52309686",
"0.5216873",
"0.52014816",
"0.51989406",
"0.5178184",
"0.51628107",
"0.5117995",
"0.5109982",
"0.50846106",
"0.50473",
"0.5044743",
"0.50315577",
"0.49988696",
"0.4984488",
"0.49706215",
"0.49481472",
"0.493... | 0.622448 | 0 |
Performs forward propagation over a pooling layer of a neural network A_prev is a numpy.ndarray of shape (m, h_prev, w_prev, c_prev) containing the output of the previous layer m is the number of examples h_prev is the height of the previous layer w_prev is the width of the previous layer c_prev is the number of channe... | def pool_forward(A_prev, kernel_shape, stride=(1, 1), mode='max'):
sh, sw = stride
kh, kw = kernel_shape
m, h_prev, w_prev, c_prev = A_prev.shape
if mode == 'max':
pool = np.max
else:
pool = np.average
pixy = (((h_prev - kh) // sh) + 1)
pixx = (((w_prev - kw) // sw) + 1)
... | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def pool_forward(A_prev, kernel_shape, stride=(1, 1), mode='max'):\n\n m = A_prev.shape[0]\n image_h = A_prev.shape[1]\n image_w = A_prev.shape[2]\n nc = A_prev.shape[3]\n filter_h = kernel_shape[0]\n filter_w = kernel_shape[1]\n s1 = stride[0]\n s2 = stride[1]\n n_dim1 = int((image_h - ... | [
"0.74333364",
"0.72409993",
"0.70541304",
"0.68746275",
"0.68447983",
"0.6832156",
"0.6613929",
"0.6610399",
"0.65891165",
"0.6552982",
"0.65083486",
"0.6470873",
"0.6430382",
"0.6384204",
"0.63692904",
"0.63500184",
"0.6325443",
"0.6323297",
"0.6311723",
"0.62989324",
"0.628... | 0.70796555 | 2 |
Decorate routes to require login. | def login_required(f):
@wraps(f)
def decorated_function(*args, **kwargs):
if session.get("user_id") is None:
return redirect("/login")
return f(*args, **kwargs)
return decorated_function | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def require_logged_in():\n def handler(f, *args, **kwargs):\n if args[0].current_user is not None:\n return f(*args, **kwargs)\n else:\n raise HTTPFound(args[0].route_url('user.login', _query={'redirect': encode_route(args[0])}))\n return decorator(handler)",
"def login_... | [
"0.7091533",
"0.6892078",
"0.68879133",
"0.6886881",
"0.6870711",
"0.6868814",
"0.6861688",
"0.68355834",
"0.68217534",
"0.68217534",
"0.68066365",
"0.6799305",
"0.6781187",
"0.67807055",
"0.67787105",
"0.67541313",
"0.67410886",
"0.6710316",
"0.6700641",
"0.6659009",
"0.6634... | 0.65288085 | 39 |
Nodes contains the list of Node IDs | def nodes(self):
return self._nodes | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def _node_ids(self, nodes=None): # this function comes from BaseCard.py\n if not nodes:\n nodes = self.nodes\n if isinstance(nodes[0], integer_types):\n node_ids = [node for node in nodes]\n else:\n node_ids = [node.nid for node in nodes]\n assert 0 not... | [
"0.83000004",
"0.8131124",
"0.797375",
"0.7874567",
"0.7622371",
"0.76150525",
"0.76021403",
"0.7440475",
"0.72694916",
"0.72609204",
"0.7224216",
"0.7205625",
"0.71716934",
"0.7168971",
"0.7166156",
"0.7128564",
"0.7105078",
"0.7098094",
"0.7096057",
"0.7057704",
"0.7051385"... | 0.709314 | 21 |
Elements contains the list of Node IDs | def els(self):
return self._els | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def node_ids(self):\n return [self.node_id]",
"def _node_ids(self, nodes=None): # this function comes from BaseCard.py\n if not nodes:\n nodes = self.nodes\n if isinstance(nodes[0], integer_types):\n node_ids = [node for node in nodes]\n else:\n node_... | [
"0.7360851",
"0.7293044",
"0.69521904",
"0.6883702",
"0.6718082",
"0.6672819",
"0.6615255",
"0.6561446",
"0.6528296",
"0.6508696",
"0.64987046",
"0.6453324",
"0.6450563",
"0.64014673",
"0.63438755",
"0.62822205",
"0.62575054",
"0.62286055",
"0.62051105",
"0.619053",
"0.617895... | 0.0 | -1 |
Elements with the associated face orientations are specified as Nx2 numpy array, with the first column being the element Id, and the second column the chosen face orientation | def surfacePairs(self):
return self._els | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def GetElementsFaceNumberingTet(self):\n\n if isinstance(self.face_to_element,np.ndarray):\n if self.face_to_element.shape[0] > 1:\n return self.face_to_element\n\n assert self.elements is not None\n\n # GET ALL FACES FROM ELEMENT CONNECTIVITY\n if self.all_fac... | [
"0.6492938",
"0.6433311",
"0.6385231",
"0.6308946",
"0.60947216",
"0.6033617",
"0.6026896",
"0.5914788",
"0.57787204",
"0.5756701",
"0.5699518",
"0.56663305",
"0.5603247",
"0.5529283",
"0.54977447",
"0.54731786",
"0.54153985",
"0.5397718",
"0.5385123",
"0.53754866",
"0.537321... | 0.0 | -1 |
Nodes contains the list of Node IDs | def nodeset(self):
return self._nodeset | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def _node_ids(self, nodes=None): # this function comes from BaseCard.py\n if not nodes:\n nodes = self.nodes\n if isinstance(nodes[0], integer_types):\n node_ids = [node for node in nodes]\n else:\n node_ids = [node.nid for node in nodes]\n assert 0 not... | [
"0.83000004",
"0.8131124",
"0.797375",
"0.7874567",
"0.7622371",
"0.76150525",
"0.76021403",
"0.7440475",
"0.72694916",
"0.72609204",
"0.7224216",
"0.7205625",
"0.71716934",
"0.7168971",
"0.7166156",
"0.7128564",
"0.7105078",
"0.7098094",
"0.7096057",
"0.709314",
"0.709314",
... | 0.0 | -1 |
Sets the number of simulation threads to use in Calculix | def setNumThreads(cls, numThreads: int):
cls.NUMTHREADS = numThreads | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def setNumThreads(self, num):\r\n self.threads = num",
"def setNumThreads(self, num):\r\n # implement ThreadPool interface\r\n assert not self.prepared, \"You can't change number of threads for working server\"\r\n self.threads = num",
"def setNThreads(self,n):\n assert(n>0)\n ... | [
"0.8256052",
"0.7567058",
"0.75560105",
"0.7523079",
"0.71850544",
"0.71713644",
"0.7045087",
"0.6716871",
"0.6572469",
"0.6557934",
"0.6522405",
"0.64511395",
"0.63847023",
"0.63694143",
"0.6355767",
"0.62587845",
"0.625188",
"0.625188",
"0.62317884",
"0.6220977",
"0.6196904... | 0.7770937 | 1 |
Returns the number of threads used | def getNumThreads(cls) -> int:
return cls.NUMTHREADS | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def getThreads():\r\n return multiprocessing.cpu_count()",
"def nThreads(self):\n return self._c_param.n_threads",
"def getThreads():\n if sys.platform == 'win32':\n return int(os.environ['NUMBER_OF_PROCESSORS'])\n else:\n return int(os.popen('grep -c cores /proc/cpuinfo').read())... | [
"0.87160325",
"0.8365115",
"0.80756944",
"0.8064238",
"0.80488205",
"0.80468506",
"0.80122787",
"0.796875",
"0.79343176",
"0.79194176",
"0.7910496",
"0.78782344",
"0.78163666",
"0.76584685",
"0.76553136",
"0.76175404",
"0.76108193",
"0.7597156",
"0.7580023",
"0.7564992",
"0.7... | 0.86468875 | 1 |
Sets the path for the Calculix executable. Necessary when using Windows where there is not a default installation proceedure for Calculix | def setCalculixPath(cls, calculixPath: str) -> None:
if os.path.isdir(calculixPath) :
cls.CALCULIX_PATH = calculixPath | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def set_vernissagecmd_path(path):\n\n global vernissagecmd_path # Allows us to change the global value of the path.\n if path == 'default': # Change the file path back to the default value.\n vernissagecmd_path = default_vernissagecmd_path\n print('VernissageCmd.exe path changed to {path}'.fo... | [
"0.59972835",
"0.57588685",
"0.57472515",
"0.56947476",
"0.55666745",
"0.54746413",
"0.5436197",
"0.54127634",
"0.5398966",
"0.5395865",
"0.5377132",
"0.5316808",
"0.52898544",
"0.5277046",
"0.5276305",
"0.5267065",
"0.52460194",
"0.5244962",
"0.5238546",
"0.52273226",
"0.522... | 0.7140539 | 0 |
Sets if the output from Calculix should be verbose i.e. printed to the console | def setVerboseOuput(cls, state: bool) -> None:
cls.VERBOSE_OUTPUT = state | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def _verbose(self,text):\n if self.verbose:\n print(text)",
"def verbose():\n GLOBAL['VERBOSE'] = True",
"def enable_verbose(self):\n self.verbose = True",
"def do_verbose(self, arg):\n global verbose\n if verbose == 1:\n verbose = 0\n # prtin a... | [
"0.7576609",
"0.75671554",
"0.7325032",
"0.72009075",
"0.7171362",
"0.71610594",
"0.71219254",
"0.710803",
"0.70517606",
"0.7015005",
"0.6994801",
"0.69857395",
"0.69782174",
"0.6964244",
"0.69489235",
"0.69267154",
"0.6889459",
"0.6883595",
"0.6871422",
"0.6870989",
"0.68132... | 0.7289895 | 3 |
Creates node sets for any RBE connectors used in the simulation | def prepareConnectors(self):
# Kinematic Connectors require creating node sets
# These are created and added to the node set collection prior to writing
numConnectors = 1
for connector in self.connectors:
# Node are created and are an attribute of a Connector
se... | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def create_nodes(self):",
"def gen_nodes(self):\n self.nodes = []\n for i in range(self.num_nodes):\n self.nodes.append(Node(self.fk))",
"def create_exporters(self):\n for node_cfg in self.node_cfg_list:\n self.create_node(node_cfg)",
"def create_nodes(self):\n ... | [
"0.6607326",
"0.6201226",
"0.61612624",
"0.6156707",
"0.6078838",
"0.6066718",
"0.6010416",
"0.598341",
"0.58685356",
"0.5867943",
"0.5858527",
"0.5854881",
"0.5841058",
"0.58386105",
"0.5774203",
"0.5754194",
"0.5719591",
"0.568923",
"0.5685475",
"0.56728554",
"0.56583863",
... | 0.70691216 | 0 |
Writes the input deck for the simulation | def writeInput(self) -> str:
self.init()
self.prepareConnectors()
self.writeHeaders()
self.writeMesh()
self.writeNodeSets()
self.writeElementSets()
self.writeKinematicConnectors()
self.writeMPCs()
self.writeMaterials()
self.writeMaterial... | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def write(self, location: str):\n with open(location, \"w\") as f:\n f.write(self.get_deck())",
"def write_data_card(spec, data_card, channels, path):\n with open(path, \"w\") as f:\n f.write(f\"imax {str(size(data_card.bins))}\" + \"\\n\")\n f.write(\n \"jmax \"\n ... | [
"0.64848584",
"0.61496377",
"0.59714556",
"0.59345895",
"0.5903547",
"0.5831078",
"0.5801172",
"0.57777035",
"0.56310844",
"0.557189",
"0.5561565",
"0.5543363",
"0.5537963",
"0.55217195",
"0.5507108",
"0.54969305",
"0.54969305",
"0.54969305",
"0.54965746",
"0.54940253",
"0.54... | 0.5011826 | 64 |
Routine checks that the analysis has been correctly generated | def checkAnalysis(self) -> bool:
if len(self.materials) == 0:
raise AnalysisError('No material models have been assigned to the analysis')
for material in self.materials:
if not material.isValid():
raise AnalysisError('Material ({:s}) is not valid'.format(materi... | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def check_analysis(self, analysis, ecosystem, package, version):\n try:\n assert analysis is not None, \"Analysis not available\"\n assert \"result\" in analysis, \"Can not find the 'result' node.\"\n result = analysis[\"result\"]\n self.check_recommendation_part(... | [
"0.66156864",
"0.6456809",
"0.64475083",
"0.6370584",
"0.6334549",
"0.63327205",
"0.627546",
"0.6254273",
"0.61636823",
"0.61520344",
"0.6151861",
"0.6145502",
"0.61336225",
"0.6127267",
"0.6127267",
"0.6127267",
"0.6127267",
"0.61148167",
"0.6100177",
"0.60883856",
"0.608783... | 0.6097213 | 19 |
Returns the results obtained after running an analysis | def results(self) -> ResultProcessor:
if self.isAnalysisCompleted():
return ResultProcessor('input')
else:
raise ValueError('Results were not available') | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def getResults():",
"def getTestResults():",
"def _process_results(self):\n self.portfolio.create_backtest_result_dataframe()\n stats = self._show_stats()\n return stats",
"def results(self):\n pass",
"def results(self):\r\n pass",
"def return_results(self):\n\n ... | [
"0.76782405",
"0.7111066",
"0.7080622",
"0.6936419",
"0.68742526",
"0.68452024",
"0.6749717",
"0.67244124",
"0.6706752",
"0.66855854",
"0.667995",
"0.6626502",
"0.6617574",
"0.6617118",
"0.6613596",
"0.66079456",
"0.6602328",
"0.65929186",
"0.6563379",
"0.65618026",
"0.654714... | 0.65645134 | 18 |
Returns if the analysis was completed successfully. | def isAnalysisCompleted(self) -> bool:
return self._analysisCompleted | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def has_result(self):\n return len(self.__analysis_items) > 0",
"def successful(self) -> bool:\n\n return self._successful",
"def has_success(self) -> bool:\n return self._has_success",
"def is_successful(self) -> bool:\n return bool(self.result_state and self.result_state.is_succ... | [
"0.7395358",
"0.7370877",
"0.7368734",
"0.7324297",
"0.7295538",
"0.7222038",
"0.71889675",
"0.71834224",
"0.71548474",
"0.71323526",
"0.7120799",
"0.70954716",
"0.7075373",
"0.7017168",
"0.701042",
"0.7007025",
"0.7000122",
"0.698778",
"0.6979704",
"0.6962313",
"0.69311875",... | 0.80789053 | 0 |
Clears any files generated from the analysis | def clearAnalysis(self, includeResults:bool = False) -> None:
filename = 'input' # Base filename for the analysis
files = [filename + '.inp',
filename + '.cvg',
filename + '.sta']
if includeResults:
files.append(filename + '.frd')
file... | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def clean_files(self):\n self.filenames.clear()",
"def clean():\n clean_files()",
"def clear(self):\r\n shutil.rmtree(self._output_dir, ignore_errors=True)",
"def clear_local_output_directory():\n output_path = '../output/*'\n files = glob.glob(output_path)\n for single_... | [
"0.7968305",
"0.77191716",
"0.7537249",
"0.7515307",
"0.7513296",
"0.7491484",
"0.7484085",
"0.7410033",
"0.734755",
"0.7287088",
"0.72671735",
"0.72499853",
"0.7249866",
"0.7248436",
"0.72048616",
"0.7182686",
"0.7161519",
"0.71290827",
"0.7113421",
"0.7099185",
"0.70814157"... | 0.778449 | 1 |
Performs preanalysis checks on the model and submits the job for Calculix to perform. | def run(self):
# Reset analysis status
self._analysisCompleted = False
print('{:=^60}\n'.format(' RUNNING PRE-ANALYSIS CHECKS '))
self.checkAnalysis()
print('{:=^60}\n'.format(' WRITING INPUT FILE '))
inputDeckContents = self.writeInput()
inputDeckPath = os.pa... | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def process(self):\n\t\tif self.update_check() or self.force_update:\n\t\t\tself.district_check() #pull all local data and regions\n\t\t\tself.fix() #fix data anomalies - e.g add in Bucks.\n\t\t\tself.save_all() #store a copy of the data\n\t\t\tself.ingest() #add data to models\n\t\t\tself.update_totals() #calcula... | [
"0.60384953",
"0.6016247",
"0.60156345",
"0.59840256",
"0.59757525",
"0.5951607",
"0.59254736",
"0.59193635",
"0.5880545",
"0.58239514",
"0.58234215",
"0.5755604",
"0.57518536",
"0.5748295",
"0.5726689",
"0.5719881",
"0.5683315",
"0.5667691",
"0.5627192",
"0.56015015",
"0.560... | 0.57189363 | 16 |
crawl targeted twitter account, save tweets to csv | def crawlAccount(target):
# connect Twitter api
twitter = connectTwitter()
try:
user_timeline = twitter.get_user_timeline(screen_name=target, count=200, include_rts=False, exclude_replies=False)
except TwythonError:
sys.exit('Received 404 for %s. Account does not exist or is banned.' % target)
user_timelin... | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def collect_twitter_sentiment():\r\n # Open/create a file to append data to\r\n csvFile = open(NAME+'_posts.csv', 'a')\r\n # Use csv writer\r\n csvWriter = csv.writer(csvFile)\r\n # Calling the user function with current parameters\r\n results = twitter.user_timeline(id=NAME, count=TWEET_COUNT)\r... | [
"0.70010406",
"0.67850053",
"0.6734272",
"0.66105545",
"0.6591761",
"0.64855397",
"0.64794904",
"0.6473131",
"0.64262015",
"0.64123046",
"0.63488454",
"0.6339719",
"0.6291743",
"0.6237709",
"0.621407",
"0.62131447",
"0.61392415",
"0.6134089",
"0.6120575",
"0.60954064",
"0.606... | 0.7076422 | 0 |
setting up (once) the database_handler object | def setUpClass(cls):
cls.database_connection = DatabaseHandler(database_path)
cls.database_connection.connect()
processing.create_table_if_not_exist(cls.database_connection, table_name)
cls.database_connection.close() | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def db_handler():\n\n pass",
"def init_database(self):\n # init_database(self.engine)",
"def __init__(self):\n self._connection = get_db_connection()",
"def init_database(self):\n init_database(self.engine)",
"def __post_init__(self):\n self.dbase = databases.Database(\n ... | [
"0.78314054",
"0.7627274",
"0.7559624",
"0.754649",
"0.75244814",
"0.7467165",
"0.74635506",
"0.74344593",
"0.73077554",
"0.7241103",
"0.7236283",
"0.72287995",
"0.72168446",
"0.7195054",
"0.71664596",
"0.7162537",
"0.7154164",
"0.7145977",
"0.71009624",
"0.7090436",
"0.70133... | 0.69591 | 23 |
adding an assertion for testing dataframe equality setting up a database_handler object with the dummy database path and connecting it | def setUp(self):
self.addTypeEqualityFunc(pandas.DataFrame, self.assertDataframeEqual)
self.database_connection.connect() | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def test_build_dataframe(self):\n insert_good_data()\n dataframe = get_dataframe()\n # 1 2 3\n self.assertIs(type(dataframe['Total'][0]), numpy.float64)\n self.assertIs(type(dataframe['InvoiceDate'][0]), str)\n self.assertIs(type(dataframe['Count'][0]), numpy.int64)\n ... | [
"0.71694195",
"0.69254637",
"0.6790876",
"0.6677135",
"0.6648234",
"0.6580347",
"0.650768",
"0.6470732",
"0.6430044",
"0.6362951",
"0.6330373",
"0.632379",
"0.6319858",
"0.6303485",
"0.6296251",
"0.6263661",
"0.6252208",
"0.6215653",
"0.6214677",
"0.6211742",
"0.62074625",
... | 0.7636546 | 0 |
tearing down the table, and closing the connection | def tearDown(self):
# in case one of the methods closed the connection
self.database_connection.connect()
self.database_connection.clear_table('''DELETE FROM ''' + table_name)
self.database_connection.close() | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def __close_table(self):\r\n self.talkdb.close()",
"def close(self): # XXX This should stop everything else from working but currently doesn't!\r\n if self._closed:\r\n raise Error('The connection to the database has already been closed.')\r\n self.rollback()\r\n for table ... | [
"0.7777105",
"0.7532934",
"0.7225821",
"0.71760124",
"0.683726",
"0.6809468",
"0.67966264",
"0.6765855",
"0.67586",
"0.6750372",
"0.66682756",
"0.66682756",
"0.66682756",
"0.6660634",
"0.6641654",
"0.6626848",
"0.66202575",
"0.6592834",
"0.65923333",
"0.65890336",
"0.6573911"... | 0.7154105 | 4 |
data is getting here after already tested in main (path, type, table_name) 1. testing process_file process for json, establishing, creating, reading file, sorting, inserting 2. testing correct amount in database of first insertion (4) 3. testing process_file process for csv, establishing, creating, reading file, sortin... | def test_process_file(self):
# 1
self.assertEqual(get_file_reply(files[0][0], files[0][1]), "Inserted 4 Records")
results = self.database_connection.select('''SELECT COUNT(*) FROM ''' + table_name)[0][0]
# 2
self.assertEqual(results, 4)
# csv, renewing connection
... | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def import_data_to_database(self, database_type, data):\n\n if database_type == \"render\":\n connection = sqlite3.connect(self.filepath_render_database)\n pointer = connection.cursor()\n pointer.executemany(\"\"\"\n INSERT INTO render_information\... | [
"0.67387795",
"0.65439796",
"0.64619803",
"0.64615047",
"0.6430858",
"0.6424109",
"0.6397895",
"0.63515615",
"0.63336784",
"0.6242904",
"0.61867195",
"0.616004",
"0.6157133",
"0.61454034",
"0.61321026",
"0.61135757",
"0.60946494",
"0.6084579",
"0.60293835",
"0.6022912",
"0.60... | 0.6500793 | 2 |
1.2.3 checking data type is correct 4. testing sum of columns is correct 5. (added assertion) testing dataframes are the same, one manufactured 6. clearing the database and inserting altered data CustomerId duplicate, checking difference in Count (34) 7. testing raising an exception of AssertionError with dataframe and... | def test_build_dataframe(self):
insert_good_data()
dataframe = get_dataframe()
# 1 2 3
self.assertIs(type(dataframe['Total'][0]), numpy.float64)
self.assertIs(type(dataframe['InvoiceDate'][0]), str)
self.assertIs(type(dataframe['Count'][0]), numpy.int64)
# 4
... | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def test_create_dataframe(dataframe):\n results = True\n rows = dataframe.shape[0]\n column_names = sorted(dataframe.columns)\n column_datatypes = list(dataframe[column_names].dtypes)\n\n # Checks columns match those specified in #1\n if column_names != DATA_COLUMNS:\n raise ValueError(\"D... | [
"0.7033019",
"0.6735344",
"0.66333157",
"0.644243",
"0.6424497",
"0.6372388",
"0.63426524",
"0.6339254",
"0.62987274",
"0.62735826",
"0.62442666",
"0.6217152",
"0.6199997",
"0.617955",
"0.61647725",
"0.61497754",
"0.6148181",
"0.6148095",
"0.6096861",
"0.60874194",
"0.6004757... | 0.78982604 | 0 |
1. testing the build_graph method returns the correct string, and waiting for file to open (less than 1 sec) | def test_build_graph(self):
insert_good_data()
dataframe = get_dataframe()
results = processing.build_graph(dataframe, figure_path, False)
# 1
self.assertEqual(results, "Updated html File and Opened it") | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def testGraphExtract(self):\n graph = Graph2()\n graph.parseFile(TESTFILE)",
"async def get_graph_for_file(\n file_name: str,\n score: int = 0,\n par_length: int = 0,\n co_occ: int = 0,\n target_collection: List[str] = Query([]),\n):\n database = get_db()\n query_graph_result =... | [
"0.6007912",
"0.59982365",
"0.5962933",
"0.5850433",
"0.5817421",
"0.57249165",
"0.57078034",
"0.568576",
"0.5582051",
"0.55256677",
"0.55047196",
"0.54700655",
"0.54588395",
"0.54585487",
"0.5439813",
"0.5436548",
"0.54200363",
"0.54097277",
"0.5380385",
"0.5369311",
"0.5364... | 0.6569553 | 0 |
adding assertion test to this class | def assertDataframeEqual(self, df1, df2, msg='Dataframes are NOT equal'):
try:
pandas.testing.assert_frame_equal(df1, df2)
except AssertionError as e:
raise self.failureException(msg) from e | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def assertion_passed(self, func):",
"def _test(self):",
"def _test(self):",
"def _test(self):",
"def _test(self):",
"def _test(self):",
"def _Assert(self, t):\n self.RaiseError(t, \"Assert not supported\")",
"def test_assert_truth(self):\n\n # Confused? This video should help:\n ... | [
"0.7113474",
"0.7074918",
"0.7074918",
"0.7074918",
"0.7074918",
"0.7074918",
"0.7011479",
"0.6953643",
"0.69323",
"0.6802398",
"0.67889684",
"0.67889684",
"0.67889684",
"0.6664128",
"0.6663575",
"0.66216046",
"0.6594647",
"0.65849733",
"0.6583794",
"0.6566348",
"0.6565654",
... | 0.0 | -1 |
dropping table at the end of all tests | def tearDownClass(cls):
cls.database_connection.connect()
cls.database_connection.clear_table('''DROP TABLE ''' + table_name)
cls.database_connection.close() | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def tearDown(self):\n self.database.truncate_all_tables()",
"def tearDown(self):\n drop_all_tables()\n create_all()",
"def tearDown(self):\n with app.app_context():\n db = app.db.get_db()\n cur = db.cursor()\n with app.open_resource('sql/drop_tests.s... | [
"0.7847522",
"0.77294594",
"0.76650023",
"0.76413673",
"0.76413673",
"0.76196283",
"0.7617512",
"0.7613479",
"0.7605289",
"0.7605084",
"0.7603006",
"0.7583381",
"0.7562543",
"0.7504366",
"0.7504366",
"0.7504366",
"0.7504366",
"0.7504366",
"0.7504366",
"0.7504366",
"0.7504366"... | 0.738027 | 35 |
creating a duplicate dataframe for testing | def get_equal_dataframe() -> pandas.DataFrame:
data = {
'InvoiceDate': ['2009-01', '2012-01'],
'Count': [4, 4],
'Total': [8198.79, 5323.15]
}
return pandas.DataFrame(data, columns=['InvoiceDate', 'Count', 'Total']) | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def test_create_from_dataframe(self):\n self.insert()\n data = self.tbl.select()\n data.index.name = None\n tbl = Table.create(':memory:', \"Foo_2\", data, verbose=True,\n primary_key='id', autoincrement=True)\n self.check(self.idata, tbl.select())",
"... | [
"0.7096513",
"0.68160087",
"0.674574",
"0.6648187",
"0.6643554",
"0.6559037",
"0.6534431",
"0.6526871",
"0.6524183",
"0.65222037",
"0.6514919",
"0.6492036",
"0.64886624",
"0.6478896",
"0.6473863",
"0.64581394",
"0.64479345",
"0.6443508",
"0.64392173",
"0.64355046",
"0.6425424... | 0.63564044 | 24 |
creating a connection, creating table if needed, reading file, ordering columns, and inserting it | def get_file_reply(file_path: str, file_type: str):
database_connection = processing.establish_connection(database_path)
processing.create_table_if_not_exist(database_connection, table_name)
insert_many_query = processing.get_insert_many_query(table_name)
dataframe = None
try:
if "json" in f... | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def _create_connection(db_file):\n try:\n conn = sqlite3.connect(db_file)\n cur = conn.cursor()\n\n # Create a new SQLite table\n cur.execute(\"CREATE TABLE {tn} ({r1}, {r2}, {time} {ft})\"\n .format(tn=TABLE_NAME, r1=INPUT_COLUMN, r2=OUTPUT_COLUMN,\n ... | [
"0.69324505",
"0.6774582",
"0.67507434",
"0.6734306",
"0.66502136",
"0.66429377",
"0.66239434",
"0.6623156",
"0.6599766",
"0.6592246",
"0.65591776",
"0.6539219",
"0.65329206",
"0.65138304",
"0.64928627",
"0.64893234",
"0.6469165",
"0.6468972",
"0.6460597",
"0.64557076",
"0.64... | 0.0 | -1 |
creating a connection, extracting from database and getting dataframe, and after working it, returning merged dataframe | def get_dataframe() -> pandas.DataFrame:
database_connection = processing.establish_connection(database_path)
dataframe = database_connection.to_dataframe(['CustomerId', 'InvoiceDate', 'Total'], table_name)
database_connection.close()
dataframe = processing.get_invoice_date_fixed(dataframe)
analyze_... | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def create_dataframe(connection: sqlite3.Connection) -> pd.DataFrame:\n dataframe = pd.read_sql_query(f\"\"\"\n SELECT\n combined_jobs.id, combined_jobs.company, combined_jobs.link, combined_jobs.location,\n combined_jobs.date, combined_jobs.content, combined_jobs.title, location_ca... | [
"0.71510464",
"0.6742855",
"0.67427015",
"0.672365",
"0.6603794",
"0.6562777",
"0.6477388",
"0.6425082",
"0.6340942",
"0.6312744",
"0.6285037",
"0.6247587",
"0.6245854",
"0.6236883",
"0.6234288",
"0.6230843",
"0.619917",
"0.613783",
"0.6135698",
"0.612881",
"0.61173105",
"0... | 0.6164279 | 17 |
inserting good data into the database prior to dataframe request | def insert_good_data():
get_file_reply(files[0][0], files[0][1])
get_file_reply(files[1][0], files[1][1]) | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def test_inserted_data(client):\n data = pd.read_csv(\"housing.csv\")\n data = format_data_housing(data)\n House.insert_from_pd(data)\n houses: DataFrame = pd.read_sql(\"SELECT * FROM house\", db.engine)\n assert len(houses) == data.shape[0]\n houses = house_results_to_dataframe(houses)\n asse... | [
"0.678737",
"0.6730259",
"0.67274314",
"0.66890144",
"0.66151464",
"0.6559837",
"0.64792013",
"0.6421191",
"0.6408326",
"0.63489366",
"0.63218206",
"0.6304754",
"0.6295192",
"0.6289917",
"0.6281531",
"0.6279984",
"0.6276843",
"0.6270269",
"0.6228473",
"0.621875",
"0.61913466"... | 0.0 | -1 |
inserting bad data into the database prior to dataframe request | def insert_bad_data():
get_file_reply(files[2][0], files[2][1]) | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def addMissingData():\n\n conn = sqlite3.connect(\"./transactions.db\")\n\n person = pd.read_sql(\n \"\"\"\n select * from person;\n \"\"\",\n conn,\n )\n\n record = pd.read_sql(\n \"\"\"\n select * from record;\n \"\"\",\n conn,\n )\n\n tracked = set([_id ... | [
"0.66893804",
"0.6497551",
"0.6349632",
"0.6349105",
"0.6324138",
"0.61940855",
"0.61221445",
"0.6109089",
"0.6084575",
"0.6075451",
"0.606424",
"0.6051254",
"0.59059036",
"0.58634156",
"0.58618003",
"0.5821349",
"0.5786034",
"0.5778522",
"0.5776547",
"0.5775964",
"0.5763927"... | 0.0 | -1 |
clearing the table and inserting the 'bad' data | def get_alter_dataframe(database_connection: DatabaseHandler) -> pandas.DataFrame:
# clearing for bad insertion
database_connection.clear_table('''DELETE FROM ''' + table_name)
insert_bad_data()
return get_dataframe() | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def data_cleaning():\n conn = get_connect()\n conn.execute(\"DELETE FROM championMatchData WHERE kills < 2 AND deaths < 2 AND assists < 2\")\n conn.commit()\n conn.close()\n return",
"def cleanup(self) -> None:\n self.min_insert_size = -1 # ensure everything gets flushed\n self.inser... | [
"0.6611473",
"0.6526312",
"0.6474836",
"0.6459992",
"0.6391733",
"0.63897455",
"0.63642263",
"0.6357135",
"0.6306425",
"0.6286085",
"0.6278254",
"0.62574065",
"0.60844815",
"0.607679",
"0.60704106",
"0.60607165",
"0.60444313",
"0.6043645",
"0.60232717",
"0.6022161",
"0.599655... | 0.0 | -1 |
computes fraction of poi with give messages type and total of that message type | def computeFraction(poi_messages, all_messages):
fraction = 0.
if all_messages != "NaN":
fraction = float(poi_messages)/float(all_messages)
else:
fraction = 0
return fraction | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def computeFraction( poi_messages, all_messages ):\n\n\n ### you fill in this code, so that it returns either\n ### the fraction of all messages to this person that come from POIs\n ### or\n ### the fraction of all messages from this person that are sent to POIs\n ### the same code can b... | [
"0.7224581",
"0.7168596",
"0.7146333",
"0.7096382",
"0.7049188",
"0.6986391",
"0.69323903",
"0.6917504",
"0.6886109",
"0.60604805",
"0.5660145",
"0.5508525",
"0.5426048",
"0.53619677",
"0.53383446",
"0.5319203",
"0.5312553",
"0.5309126",
"0.5298099",
"0.52928567",
"0.52516794... | 0.7001343 | 5 |
Attempt to coerce `val` into a numeric type, or a string stripped of whitespace. | def cast(val):
for func in [int, float, lambda x: x.strip(), lambda x: x]:
try:
return func(val)
except ValueError:
pass | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def as_int_or_float(val):\n if type(val) == str:\n ret_val = float(val) if '.' in val else int(val)\n return ret_val\n return val",
"def parse_numeric(val):\n try: return int(val)\n except: pass\n\n try: return float(val)\n except: pass\n\n return val",
"def _convert_val_to_n... | [
"0.73402566",
"0.7304908",
"0.70569414",
"0.6962173",
"0.6891436",
"0.685007",
"0.6784463",
"0.66131794",
"0.6608134",
"0.6539546",
"0.6484636",
"0.64712876",
"0.6459049",
"0.6445312",
"0.63889277",
"0.63889277",
"0.63718385",
"0.6369737",
"0.63425106",
"0.6337671",
"0.629779... | 0.6828669 | 6 |
Create a (potentially existing) directory without errors. Raise OSError if directory can't be created. If clobber is True, remove dirpath if it exists. | def mkdir(dirpath, clobber=False):
if clobber:
shutil.rmtree(dirpath, ignore_errors=True)
try:
os.mkdir(dirpath)
except OSError:
pass
if not path.exists(dirpath):
raise OSError('Failed to create %s' % dirpath)
return dirpath | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def makedirectory(path):\n\n exist_ok = True\n if not exist_ok and os.path.isdir(path):\n with contextlib.suppress(OSError):\n Path.mkdir(path, parents=True)",
"def mkDir(path):\n if not os.path.exists(path):\n try:\n os.makedirs(path)\n except OSError:\n ... | [
"0.7061584",
"0.70569605",
"0.70226943",
"0.7006338",
"0.699954",
"0.6977039",
"0.6966338",
"0.6966338",
"0.6966338",
"0.6966338",
"0.69507277",
"0.6921338",
"0.68780476",
"0.68686837",
"0.6837868",
"0.6795552",
"0.67695946",
"0.67676157",
"0.67505884",
"0.6743762",
"0.672953... | 0.85963845 | 0 |
Copy fname from package data to outdir/subdir (creating dir if necessary), and return the path to the copy of fname relative to outdir. | def make_local_copy(outdir, subdir, fname):
destdir = path.join(outdir, subdir)
mkdir(destdir)
shutil.copyfile(package_data(fname), path.join(destdir, fname))
return path.join(subdir, fname) | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def package_dest_path(self, package):\n\n if self.destdir is None:\n return self.package_final_path(package)\n else:\n return os.path.join(\n self.destdir,\n self.package_install_space(package).lstrip(os.sep))",
"def create_file_path(fname, direc=... | [
"0.62651706",
"0.6082093",
"0.60650736",
"0.6050992",
"0.5938889",
"0.58145124",
"0.5783093",
"0.5764241",
"0.57486904",
"0.57103425",
"0.57036674",
"0.5678299",
"0.56111586",
"0.56056064",
"0.5599549",
"0.5561045",
"0.5521641",
"0.54854083",
"0.5484105",
"0.5466086",
"0.5459... | 0.7523176 | 0 |
Produce a string with date and time information for a report | def timestamp_now():
return datetime.now().strftime("%A, %B %d, %Y, %I:%M %p") | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def _printable(self):\n toPrint = \"Time Info header. \"\n toPrint += \"timestamp: \" + str(self.datetime) + \" \"\n\n return toPrint",
"def get_time_string(self):\n return f\"{self.year} {self.month:02} \" \\\n f\"{self.start_day:02} {self.start_hour:02} 00 {self.get_durat... | [
"0.6874049",
"0.6570977",
"0.6555926",
"0.6487434",
"0.6411186",
"0.64016986",
"0.6372366",
"0.6368585",
"0.6361905",
"0.6336149",
"0.621202",
"0.62100637",
"0.6179392",
"0.61576116",
"0.6127666",
"0.61024475",
"0.60803306",
"0.60774654",
"0.6076919",
"0.60626364",
"0.6061876... | 0.0 | -1 |
Given a course code, requests the correspnding course page | def get_coursepage(code):
url = 'http://gla.ac.uk/coursecatalogue/course/?code=' + code
print url
coursepage = requests.get(url)
return coursepage | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def get_course_page(self):\n\n print(\"Course URL: {}\".format(self.course_url))\n try:\n self.course_page = BeautifulSoup(requests.get(self.course_url).text, \"lxml\")\n except requests.ConnectionError as e:\n print(\"Error Connecting!\\n\", e)\n sys.exit(1)\n... | [
"0.7398104",
"0.6673765",
"0.66678524",
"0.64198714",
"0.6413772",
"0.63720846",
"0.6232175",
"0.61700314",
"0.6150578",
"0.6142026",
"0.61378634",
"0.60246366",
"0.6009835",
"0.5975768",
"0.5974896",
"0.59581447",
"0.5909168",
"0.58898294",
"0.58715636",
"0.5865942",
"0.5853... | 0.857764 | 0 |
Given a course code, requests the corresponding page and returns only the name of that course. This is used to generate a list of courses which I have opted to disclude with only their course codes | def get_course_title_only(code):
coursepage = get_coursepage(code)
soup = BeautifulSoup(coursepage.content, 'lxml')
title = [soup.find_all('h1')[2].string][0]
return title | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def get_coursepage(code):\n url = 'http://gla.ac.uk/coursecatalogue/course/?code=' + code\n print url\n coursepage = requests.get(url)\n return coursepage",
"def get_course_page(self):\n\n print(\"Course URL: {}\".format(self.course_url))\n try:\n self.course_page = Beautiful... | [
"0.7957394",
"0.65620685",
"0.6373555",
"0.6254304",
"0.6069474",
"0.60259366",
"0.6012922",
"0.6005837",
"0.58794546",
"0.585379",
"0.5835477",
"0.5812423",
"0.5765354",
"0.57308453",
"0.56890464",
"0.5684061",
"0.5674316",
"0.5661891",
"0.56544876",
"0.5581465",
"0.5581465"... | 0.65289664 | 2 |
Creates a dictionary with a headingvalue pair, which is the structure of all the sections in the courses dictionary | def new_dict(heading, value):
value = value.replace('%', '\%').replace('&', '\&').replace(u'\xa0', ' ')
# Currently encoding is causeing me problems - the quick fix below removes
# all the characters that have broken the code so far. This solution is not
# likely to work if more courses were added
v... | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def _section_course_info(course_key, access):\r\n course = get_course_by_id(course_key, depth=None)\r\n\r\n section_data = {\r\n 'section_key': 'course_info',\r\n 'section_display_name': _('Course Info'),\r\n 'access': access,\r\n 'course_id': course_key,\r\n 'course_displa... | [
"0.7070789",
"0.60182756",
"0.6016982",
"0.5990312",
"0.5969372",
"0.59540147",
"0.59495693",
"0.59283185",
"0.58511907",
"0.58379775",
"0.58187705",
"0.5808631",
"0.5805773",
"0.58040214",
"0.5761286",
"0.57347447",
"0.5731346",
"0.5700729",
"0.5679609",
"0.5641057",
"0.5584... | 0.64286023 | 1 |
Each course page has a small info section at the beginning, which I had to extract and formulate in a different way to the main sections. This function constructs the dictionary entries for he course when given a string with all the details required for the info section | def get_info_list(info_string, course):
info_list = []
split_on_newline = info_string.split("\n")
for elem in split_on_newline:
split = elem.split(": ")
for s in split:
info_list.append(s)
info_list = info_list[1:-1]
info_tags = [
'session', 'school', 'credits', '... | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def _section_course_info(course_key, access):\r\n course = get_course_by_id(course_key, depth=None)\r\n\r\n section_data = {\r\n 'section_key': 'course_info',\r\n 'section_display_name': _('Course Info'),\r\n 'access': access,\r\n 'course_id': course_key,\r\n 'course_displa... | [
"0.698141",
"0.6433248",
"0.61811566",
"0.608286",
"0.6082416",
"0.6056426",
"0.6050662",
"0.6001302",
"0.5982004",
"0.59207666",
"0.58908296",
"0.5850639",
"0.5830395",
"0.5818191",
"0.5790374",
"0.577954",
"0.57718724",
"0.5770274",
"0.57416594",
"0.57249576",
"0.57086146",... | 0.7118217 | 0 |
Given a course page, takes the context and parses it to extract all the useful information and construct a dictionary with the information corresponding to assigned names ready to be written into the TeX file | def bsoup(coursepage):
soup = BeautifulSoup(coursepage.content, 'lxml')
h1 = soup.find_all('h1')[2]
html = h1.find_next_siblings()
all_strings = [h1.string]
for div in html:
try:
text = div.get_text()
except:
text = div.string
if text is not None:
... | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def __title_context_dict(data):\n d = {}\n if isinstance(data, str):\n raw_data = __load_data(data)['data']\n else:\n raw_data = data['data']\n for i in range(len(raw_data)):\n title = raw_data[i]['title']\n paragraphs = raw_data[i]['paragraphs']\n text = []\n ... | [
"0.56877637",
"0.56606907",
"0.5630893",
"0.53998905",
"0.53966093",
"0.5361915",
"0.5360787",
"0.5359631",
"0.5314254",
"0.52851605",
"0.5281027",
"0.5259862",
"0.52557594",
"0.52467847",
"0.5240469",
"0.5206612",
"0.5163807",
"0.5151245",
"0.51210815",
"0.5102797",
"0.51018... | 0.52513224 | 13 |
Given a list of course codes, ge5t their corresponding titles and format them in a bulletted TeX list. This is used to indicate in the abstract which courses have been deliberately discluded from the document | def create_not_included_list(codes):
string = '\\begin{itemize}\n'
for code in codes:
title = get_course_title_only(code)
string += '\\item{' + title + '}\n'
string += '\\end{itemize}\n'
return string | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def write_to_latex(codelist, unwanted_courses):\n # TODO: investigate a way to add large amounts of text outside of the\n # function\n abstract01 = \"I created this document to practice parsing html and using\\\n tools like Beautiful Soup which I've previously had little experience\\\n in. A... | [
"0.61776614",
"0.61535215",
"0.5460157",
"0.53928626",
"0.5347271",
"0.5328471",
"0.5140035",
"0.5117348",
"0.51146877",
"0.5056962",
"0.5048898",
"0.50328934",
"0.50134706",
"0.50019485",
"0.49917555",
"0.49796224",
"0.49568665",
"0.4950176",
"0.49257633",
"0.49147525",
"0.4... | 0.66535985 | 0 |
Constructs the entire TeX document from all the courses with key document details (like author and table of contents) | def write_to_latex(codelist, unwanted_courses):
# TODO: investigate a way to add large amounts of text outside of the
# function
abstract01 = "I created this document to practice parsing html and using\
tools like Beautiful Soup which I've previously had little experience\
in. As a result, i... | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def preprocess_courses_corpus():\n soup = None\n with open('courses_corpus.html', 'r') as infile:\n content = infile.read()\n\n soup = BeautifulSoup(content, 'html.parser')\n\n docid = 0\n data = {}\n data['documents'] = []\n\n main_table = soup.find_all(\"div\", attrs={'class': 'course... | [
"0.67228544",
"0.64569527",
"0.5929274",
"0.5925268",
"0.5916192",
"0.564219",
"0.5635638",
"0.5593578",
"0.55859154",
"0.5570419",
"0.5569982",
"0.55561346",
"0.55053335",
"0.5473243",
"0.5456045",
"0.54416245",
"0.5440941",
"0.5430982",
"0.54077095",
"0.5400782",
"0.5395573... | 0.5822862 | 5 |
Provides the special treatment that the info section requires | def latex_info(info):
return '\\textbf{' + info['heading'] + '} ' + info['value'] + ' \\break\n' | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def get_info(self, info):\r\n pass",
"def ExtraInfo(self) -> object:",
"def manage_info():",
"def _get_information(self):\n pass",
"def process_info(self, info):\n return info",
"def info() -> None:",
"def info(self):",
"def info(self):",
"def info(self, *args, **kwargs):",
"... | [
"0.7103027",
"0.69999164",
"0.6913003",
"0.66993517",
"0.6696078",
"0.649746",
"0.6492575",
"0.6492575",
"0.6453869",
"0.64022076",
"0.6401089",
"0.6384137",
"0.63549614",
"0.6325025",
"0.6280559",
"0.62133276",
"0.62043655",
"0.6174107",
"0.6174107",
"0.6089512",
"0.6064964"... | 0.0 | -1 |
Creates a TeX formatted string for a given subsubsection | def latex_subsection(section):
string = '\\subsubsection*{' + section['heading'] + '}\n'
string += section['value'] + '\n'
return string | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def sub(string, subscript):\n return string + \"<sub>\" + subscript + \"</sub>\"",
"def _build_sub(self) -> str:\n return dedent(\n \"\"\"\n @SP\n M=M-1\n A=M\n D=M\n @SP\n M=M-1\n A=M\n M=M-D\n ... | [
"0.65773475",
"0.63128084",
"0.62126887",
"0.6208371",
"0.61632997",
"0.6140767",
"0.6039124",
"0.6012975",
"0.5978812",
"0.5934507",
"0.58059335",
"0.5795126",
"0.569796",
"0.5690162",
"0.5618066",
"0.5618066",
"0.5604984",
"0.5596953",
"0.5590398",
"0.55688125",
"0.55626035... | 0.75560915 | 0 |
Creates a TeX formatted string for a course | def latex_course(course):
basic_info_list = [
'session', 'school', 'credits', 'level', 'offered',
'visiting_students', 'erasmus_students'
]
generic_subsection_list = [
'description', 'timetable', 'requirements_of_entry',
'excluded_courses', 'co_requisites', 'assessment_weight... | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def create_tex(unwanted_courses, wanted_courses=None):\n page = requests.get(\n 'http://gla.ac.uk/coursecatalogue/courselist/' +\n '?code=REG30200000&name=School+of+Computing+Science')\n tree = html.fromstring(page.content)\n spans = tree.xpath('//span/text()')\n codes = []\n if wanted... | [
"0.6946371",
"0.6540668",
"0.63720375",
"0.62898386",
"0.6107234",
"0.6071788",
"0.5923579",
"0.585324",
"0.5844796",
"0.58082366",
"0.5749922",
"0.5556818",
"0.54912657",
"0.53892386",
"0.5379833",
"0.53704685",
"0.53665537",
"0.5356205",
"0.53560764",
"0.5349088",
"0.533566... | 0.7629145 | 0 |
Creates the TeX document from the Computer Science Course Catalog | def create_tex(unwanted_courses, wanted_courses=None):
page = requests.get(
'http://gla.ac.uk/coursecatalogue/courselist/' +
'?code=REG30200000&name=School+of+Computing+Science')
tree = html.fromstring(page.content)
spans = tree.xpath('//span/text()')
codes = []
if wanted_courses is ... | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def makeCourse( xmlFile, genPath, importPaths, commonFiles, rendererContent=True):\n\ttry:\n\n\t\t# parse the command line\n\t\tConfig.add_option('--verbose', help='Set verbosity to maximum', dest='verbosity', default=0, action='store_const', const=2)\n\t\tConfig.add_option('-v', '--verbosity', help='Set the verbo... | [
"0.6260808",
"0.6079598",
"0.5850458",
"0.5665877",
"0.55810404",
"0.55767274",
"0.5510529",
"0.55077094",
"0.550131",
"0.5462771",
"0.53828937",
"0.537524",
"0.53713846",
"0.5369341",
"0.533082",
"0.52978146",
"0.52650625",
"0.52579224",
"0.5252961",
"0.52399004",
"0.5219007... | 0.64500177 | 0 |
Generates a TeX document and then runs the pdflatex command to create a PDF from the TeX | def pdflatex(unwanted_courses):
create_tex(unwanted_courses)
cmd = ['pdflatex', '-interaction', 'nonstopmode', 'courses.tex']
proc = subprocess.Popen(cmd)
proc.communicate()
return None | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def pdf():\n env.file_ext = \".pdf\"\n local(\"pandoc {input_files} -o {output_file}{file_ext} -H {preamble_file} --template {template_file} --bibliography={bib_file} --csl={csl_file} -V fontsize=12pt -V papersize=a4paper -V documentclass:report -N --latex-engine=xelatex\".format(**env))",
"def compile_lat... | [
"0.7378825",
"0.71747476",
"0.7139049",
"0.70846206",
"0.6956496",
"0.6915404",
"0.69058365",
"0.68944186",
"0.67462385",
"0.66899025",
"0.66854495",
"0.66612506",
"0.65569717",
"0.6552778",
"0.65360767",
"0.64799696",
"0.6461581",
"0.6451979",
"0.6432288",
"0.63957024",
"0.6... | 0.7367564 | 1 |
Propagate changes made to models into database schema. | def migrate_db():
Base.metadata.create_all(ENGINE) | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def migrate(self):\n\tpass",
"def setup_schema(self):\n models.Base.metadata.create_all(self.session.bind)",
"def update_model(self):\n pass",
"def model_update(self, db):\n db.session.commit()",
"def post_migrations(self):",
"def update_model(self):\n pass # TODO: Implement ... | [
"0.6670259",
"0.6642882",
"0.66169477",
"0.6522209",
"0.6405807",
"0.6354266",
"0.62821496",
"0.6278887",
"0.6274175",
"0.6015691",
"0.59840304",
"0.5931382",
"0.5882499",
"0.58795726",
"0.58661336",
"0.58658046",
"0.5792609",
"0.57480407",
"0.57398003",
"0.57097906",
"0.5685... | 0.0 | -1 |
Database connection context manager. | def session_scope(expire=True):
if not isinstance(expire, bool):
raise ValueError(f'Expire attr must be bool. Got {type(expire)}')
session = Session()
session.expire_on_commit = expire
try:
yield session
session.commit()
except:
session.rollback()
raise
f... | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def _db_connection(self):\n pass",
"def obtainDatabaseConnection(self):\n\t\tself.databaseConnector = DatabaseConnector()",
"def db_session_context(db_name):\n try:\n db = psycopg2.connect(database=db_name)\n yield db\n finally:\n db.close()",
"def __enter__(self) -> sqlite3... | [
"0.7842984",
"0.77469206",
"0.7391354",
"0.7299444",
"0.7295811",
"0.7281722",
"0.7276145",
"0.7275543",
"0.72531223",
"0.72529674",
"0.72500235",
"0.7196104",
"0.71822184",
"0.71411437",
"0.71265644",
"0.7103985",
"0.70446414",
"0.7011069",
"0.69964695",
"0.6982708",
"0.6979... | 0.0 | -1 |
Tests http request construction. | def test_search(self):
req = http.make_request('http://xxx', 'GET', None, None)
self.assertIsNone(req.data)
req = http.make_request('http://xxx', 'GET', 'ignored', None)
self.assertIsNone(req.data)
req = http.make_request('http://xxx', 'DELETE', None, None)
self.assertI... | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def test_request():\n response = requests.get('http://jsonplaceholder.typicode.com/todos')\n assert response.ok",
"def createRequest(test, url, headers=None):\n request = HTTPRequest(url=url)\n if headers: request.headers=headers\n test.record(request, HTTPRequest.getHttpMethodFilter())\n retur... | [
"0.69102174",
"0.6897086",
"0.67407787",
"0.67172885",
"0.668804",
"0.668527",
"0.66408455",
"0.66260934",
"0.65981567",
"0.6573342",
"0.65649724",
"0.6560071",
"0.6523077",
"0.651958",
"0.65011233",
"0.6492675",
"0.6482599",
"0.64548385",
"0.64541405",
"0.6449433",
"0.644555... | 0.65178454 | 14 |
Given a sparse matrix, set all pixels in missing (invalid) bins to 0. | def erase_missing(signal, valid_rows, valid_cols, sym_upper=True):
if sym_upper and sp.issparse(signal):
if np.any(valid_rows != valid_cols):
raise ValueError(
"Valid rows and columns must be identical with sym_upper=True"
)
if signal.shape[0] != signal.shape[... | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def setZeroes(self, matrix: List[List[int]]) -> None:\n m = len(matrix)\n n = len(matrix[0])\n rows = set()\n columns = set()\n for i in range(m):\n for j in range(n):\n if not matrix[i][j]:\n rows.add(i)\n columns.a... | [
"0.69846714",
"0.69818574",
"0.69084895",
"0.68822354",
"0.68314415",
"0.68032897",
"0.6796617",
"0.6766385",
"0.67445296",
"0.6728862",
"0.6650272",
"0.66448796",
"0.66283685",
"0.66217226",
"0.6617057",
"0.6614921",
"0.65993714",
"0.6568216",
"0.6566557",
"0.65331614",
"0.6... | 0.6212842 | 41 |
Set the nth diagonal of a symmetric 2D numpy array to a fixed value. Operates in place. | def set_mat_diag(mat, diag=0, val=0):
m = mat.shape[0]
step = m + 1
start = diag
end = m ** 2 - diag * m
mat.flat[start:end:step] = val | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def set_diagonal(self, value = 0):\n for d in range(self.size):\n self.write(d, d, value)",
"def set_diag(M,d,idx=0):\n n, m = shape_mat(M)\n if idx >= 0:\n for i, di in enumerate( d ):\n M[i][i+idx] = di\n else:\n for i, di in enumerate( d ):\n M[i-... | [
"0.7125442",
"0.69381803",
"0.66931725",
"0.6662349",
"0.6404038",
"0.6235831",
"0.6171781",
"0.6152024",
"0.60630727",
"0.5889371",
"0.57974076",
"0.5756528",
"0.57523865",
"0.5675084",
"0.5653157",
"0.5643824",
"0.55920154",
"0.5581051",
"0.55746204",
"0.5569762",
"0.554835... | 0.6709417 | 2 |
Trim an upper triangle sparse matrix so that only the first n diagonals are kept. | def diag_trim(mat, n):
if sp.issparse(mat):
if mat.format != "csr":
raise ValueError("input type must be scipy.sparse.csr_matrix")
# Trim diagonals by removing all elements further than n in the
# upper triangle
trimmed = sp.tril(mat, n, format="csr")
trimmed = sp... | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def trim_whitespace(matrix, details, min_gap):\r\n if details == -1:\r\n row = matrix[0, ]\r\n else:\r\n row = matrix[matrix.shape[0] - 1, ]\r\n\r\n min_left = np.argmin(row)\r\n min_right = np.argmin(row[::-1])\r\n\r\n if min_left > min_gap:\r\n ... | [
"0.64127666",
"0.58649224",
"0.57881296",
"0.5781573",
"0.5728801",
"0.57002896",
"0.5696073",
"0.568997",
"0.5671774",
"0.566362",
"0.566362",
"0.566362",
"0.56233585",
"0.5622041",
"0.5614353",
"0.55710584",
"0.55435014",
"0.5482823",
"0.5455417",
"0.54416484",
"0.54353005"... | 0.7814169 | 0 |
Computes genomic distance law by averaging over each diagonal in the upper triangle matrix. If a list of detectable bins is provided, pixels in missing bins will be excluded from the averages. A maximum distance can be specified to define how many diagonals should be computed. | def distance_law(
matrix, detectable_bins=None, max_dist=None, smooth=True, fun=np.nanmean
):
mat_n = matrix.shape[0]
if max_dist is None:
max_dist = mat_n
n_diags = min(mat_n, max_dist + 1)
dist = np.zeros(mat_n)
if detectable_bins is None:
detectable_bins = np.array(range(mat_n... | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def sum_diag(max_lines):\r\n dsum = 1 # sum of diagonals\r\n cpt = 1 # number of lines processed\r\n val = 1 # value of the current place in the square\r\n inc = 0 # the increment between number for one line\r\n \r\n while cpt < max_lines:\r\n cpt += 2\r\n inc += 2\r\n \r\n ... | [
"0.5350398",
"0.52518773",
"0.5155675",
"0.5135908",
"0.50542927",
"0.502655",
"0.5009027",
"0.5004774",
"0.4977624",
"0.49264264",
"0.48782477",
"0.48724344",
"0.4847657",
"0.4825151",
"0.48010293",
"0.4734856",
"0.47299528",
"0.47192916",
"0.47074535",
"0.46959183",
"0.4675... | 0.6471979 | 0 |
Returns lists of detectable indices after excluding low interacting bins based on the proportion of zero pixel values in the matrix bins. | def get_detectable_bins(mat, n_mads=3, inter=False):
matrix = mat.copy()
matrix.eliminate_zeros()
def mad(x): return ss.median_abs_deviation(x, nan_policy="omit")
if not inter:
if matrix.shape[0] != matrix.shape[1]:
raise ValueError("Intrachromosomal matrices must be symmetric.")
... | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def vir_indices(self):\n indices = []\n for index,item in enumerate(self):\n if item==0:\n indices.append(index)\n return indices",
"def get_agent_indices(array):\t\n\tagent_indices = np.argwhere(array != 0)\n\treturn agent_indices",
"def pick_nonzero_bin(binning,... | [
"0.6325455",
"0.63100487",
"0.6245059",
"0.6240828",
"0.62382156",
"0.622966",
"0.6220698",
"0.62201536",
"0.6188076",
"0.6029027",
"0.59354",
"0.5917809",
"0.59095675",
"0.5891025",
"0.5851099",
"0.58345515",
"0.5804097",
"0.5793994",
"0.5785157",
"0.5784481",
"0.5775276",
... | 0.57897145 | 18 |
Detrends a HiC matrix by the distance law. The input matrix should have been normalised beforehandand. | def detrend(
matrix,
detectable_bins=None,
max_dist=None,
smooth=False,
fun=np.nanmean,
max_val=10,
):
matrix = matrix.tocsr()
y = distance_law(
matrix,
detectable_bins=detectable_bins,
max_dist=max_dist,
smooth=smooth,
fun=fun,
)
y[np.isna... | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def det(A):\n # Section 1: Establish n parameter and copy A\n n = len(A)\n AM = A[:]\n\n # Section 2: Row manipulate A into an upper triangle matrix\n for fd in range(n): # fd stands for focus diagonal\n if AM[fd][fd] == 0:\n AM[fd][fd] = 1.0e-18 # Cheating by adding zero + ~zero... | [
"0.58057857",
"0.5708356",
"0.5658325",
"0.56237066",
"0.5622826",
"0.5600156",
"0.5581219",
"0.5456089",
"0.54544467",
"0.54441947",
"0.54128253",
"0.53995603",
"0.5385993",
"0.5369172",
"0.53661954",
"0.5349746",
"0.5349005",
"0.5301684",
"0.52880996",
"0.5278491",
"0.52762... | 0.515411 | 32 |
Z transformation for HiC matrices. | def ztransform(matrix):
mat = matrix.copy()
mu = np.mean(mat.data)
sd = np.std(mat.data)
mat.data -= mu
mat.data /= sd
return mat | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def __call__(self,Zin):\r\n self.Z = np.zeros((self.Nc,))\r\n for ii in range(0,self.Nc):\r\n self.Z[ii] = np.dot(self.W[:,ii],Zin[self.ind[ii,:]])\r\n \r\n return self.Z",
"def zoisite():\n\n rho = 3343.\n\n C = np.zeros((6,6), dtype=float)\n C[0,0] = 279.8; ... | [
"0.657526",
"0.65030193",
"0.6374585",
"0.6241165",
"0.6134121",
"0.6029814",
"0.6002619",
"0.5971881",
"0.59294695",
"0.59294695",
"0.5912561",
"0.59013504",
"0.5897728",
"0.5878301",
"0.5842042",
"0.57775015",
"0.5770409",
"0.5757381",
"0.57313776",
"0.5728551",
"0.5725833"... | 0.55584866 | 36 |
Compute the sum of matrices bins (i.e. rows or columns) using only the upper triangle, assuming symmetrical matrices. | def sum_mat_bins(mat):
# Equivalaent to row or col sum on a full matrix
# Note: mat.sum returns a 'matrix' object. A1 extracts the 1D flat array
# from the matrix
return mat.sum(axis=0).A1 + mat.sum(axis=1).A1 - mat.diagonal(0) | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def row_col_sums(i, b_j, bins, C, n_u):\n s= C[i][i]*n_u[i]*n_u[i]\n for j in range(bins[b_j], bins[b_j+1]):\n if i != j:\n s+= (C[i][j] + C[j][i])*n_u[i]*n_u[j]\n return s",
"def block_sum(i, bins, C, n_u):\n s= 0.0\n for j in range(bins[i], bins[i+1]):\n for k in range(b... | [
"0.7041019",
"0.6156732",
"0.6129798",
"0.5976102",
"0.59643245",
"0.5771837",
"0.57445884",
"0.5654173",
"0.5501301",
"0.5317589",
"0.5288595",
"0.52820784",
"0.5261982",
"0.5255658",
"0.52305037",
"0.5229471",
"0.52255195",
"0.5207664",
"0.5206341",
"0.5196019",
"0.5180939"... | 0.7172675 | 0 |
Bootstrap sampling of contacts in a sparse HiC map. | def subsample_contacts(M, n_contacts):
S = M.data.copy()
# Match cell idx to cumulative number of contacts
cum_counts = np.cumsum(S)
# Total number of contacts to sample
tot_contacts = int(cum_counts[-1])
# Sample desired number of contacts from the range(0, n_contacts) array
sampled_conta... | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def bootstrap(X):\n return X[np.random.choice(list(range(X.shape[0])), size=X.shape[0]), :]",
"def bootstrapping(datasample):\r\n \r\n datasample=df_to_array(datasample)\r\n \r\n boots_indexs=np.random.randint(len(datasample),size=(1,len(datasample)))\r\n \r\n whole_indexs=list(range(len(dat... | [
"0.60307676",
"0.5836601",
"0.57376236",
"0.56982666",
"0.5689801",
"0.5620543",
"0.5541793",
"0.5484117",
"0.54839253",
"0.5435804",
"0.54210573",
"0.53805405",
"0.5368052",
"0.5355705",
"0.5346038",
"0.5340237",
"0.5306805",
"0.53025925",
"0.5296711",
"0.5295456",
"0.528197... | 0.59620684 | 1 |
Adds a frame around input mask, given a kernel. The goal of this frame is define margins around the matrix where the kernel will not perform convolution (denoted by 1). If the matrix is upper symmetric, a margin of half the kernel's width is added below the diagonal and a maximum distance from the diagonal above which ... | def frame_missing_mask(mask, kernel_shape, sym_upper=False, max_dist=None):
if mask.dtype != bool:
raise ValueError("Mask must contain boolean values")
if not sp.issparse(mask):
raise ValueError("Mask must be a sparse matrix")
framed_mask = mask.copy()
ms, ns = mask.shape
mk, nk = k... | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def slidekernelthroughdiagonal(kernel, matrix):\n size_kernel = kernel.shape[0]\n size_matrix = matrix.shape[0]\n result = np.zeros([size_matrix])\n for i in range(size_matrix):\n # Calculate zero padding needed\n padding_b = -min(i - int(size_kernel/2), 0)\n padding_a = -min(size_matrix - int(i + siz... | [
"0.56605875",
"0.5587884",
"0.5477105",
"0.5454366",
"0.5391526",
"0.5310862",
"0.52129424",
"0.509257",
"0.50285244",
"0.5000354",
"0.49889517",
"0.49485168",
"0.49176568",
"0.48939496",
"0.48860693",
"0.4881778",
"0.4866566",
"0.48574573",
"0.4845596",
"0.48219603",
"0.4799... | 0.5740741 | 0 |
Ensure all elements defined as missing by the mask are set to zero in the signal. If this is not the case, raises an error. | def check_missing_mask(signal, mask):
if sp.issparse(mask):
# Check if there are nonzero values in the signal reported as missing
# by the mask
missing_with_signal = np.nonzero(
abs(signal[mask.nonzero()[0], mask.nonzero()[1]]) > 0
)[0]
if len(missing_with_signal... | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def applymask(self,mask):\n self.spec[mask==0]=np.nan",
"def _check_missing_value_mask(self, missing_mask):\n if not missing_mask.any():\n raise ValueError(\"Input matrix is not missing any values\")\n if missing_mask.all():\n raise ValueError(\"Input matrix must have s... | [
"0.7038222",
"0.6567302",
"0.63071376",
"0.6267162",
"0.6249988",
"0.6000625",
"0.5920129",
"0.5898875",
"0.5888325",
"0.5880828",
"0.5875885",
"0.58599186",
"0.5855308",
"0.5855247",
"0.5853476",
"0.5831566",
"0.5795374",
"0.5774761",
"0.5770358",
"0.57691586",
"0.5761126",
... | 0.6792724 | 1 |
Given lists of valid rows and columns, generate a sparse matrix mask with missing pixels denoted as 1 and valid pixels as 0. If a max_dist is provided, upper symmetric matrices will only be flagged up to max_dist pixels from the diagonal. | def make_missing_mask(
shape, valid_rows, valid_cols, max_dist=None, sym_upper=False
):
# Error if the matrix upper symmetric but shape is rectangle or missing
# rows and cols are different
sm, sn = shape
if sym_upper and (sm != sn or len(valid_rows) != len(valid_cols)):
raise ValueError("Re... | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def frame_missing_mask(mask, kernel_shape, sym_upper=False, max_dist=None):\n if mask.dtype != bool:\n raise ValueError(\"Mask must contain boolean values\")\n if not sp.issparse(mask):\n raise ValueError(\"Mask must be a sparse matrix\")\n\n framed_mask = mask.copy()\n ms, ns = mask.shap... | [
"0.5894775",
"0.58664924",
"0.5579",
"0.55251396",
"0.545152",
"0.53834623",
"0.53789914",
"0.5377302",
"0.53023285",
"0.5288333",
"0.51945496",
"0.51902205",
"0.5188426",
"0.51522386",
"0.5135295",
"0.5130921",
"0.5122746",
"0.51174325",
"0.51109743",
"0.508941",
"0.5082233"... | 0.7036244 | 0 |
Adds margin of zeros around an input sparse matrix. | def zero_pad_sparse(mat, margin_h, margin_v, fmt="coo"):
sm, sn = mat.shape
padded_mat = mat.copy()
# Up and down margins initialized with zeros and filled as needed
margin_h_0 = sp.csr_matrix((sm, margin_h), dtype=mat.dtype)
margin_v_0 = sp.csr_matrix((margin_v, sn + 2 * margin_h), dtype=mat.dtype... | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def _pad_with_zeros(self, X, margin):\n newX = np.zeros((X.shape[0] + 2 * margin, X.shape[1] + 2 * margin, X.shape[2]))\n x_offset = margin\n y_offset = margin\n newX[x_offset:X.shape[0] + x_offset, y_offset:X.shape[1] + y_offset, :] = X\n return newX",
"def OffsetSparseMatrix(... | [
"0.6846492",
"0.6157045",
"0.6157045",
"0.6109819",
"0.59839016",
"0.59775317",
"0.59726745",
"0.59028137",
"0.5901642",
"0.5830433",
"0.57942516",
"0.57754564",
"0.5743076",
"0.57350683",
"0.57294387",
"0.57047725",
"0.5701384",
"0.5644425",
"0.5624889",
"0.56114304",
"0.560... | 0.7736398 | 0 |
Crop a kernel matrix to target size horizontally and vertically. If the target size is even, the target size is adjusted to the next integer up. | def crop_kernel(kernel, target_size):
# Use list for mutability
target = [d for d in target_size]
adjusted = False
for dim in range(len(target)):
if not target[dim] % 2:
target[dim] += 1
adjusted = True
if adjusted:
sys.stderr.write(
"WARNING: Crop... | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def crop_to_target(x, target):\n\n if target.ndim==3:\n t_h, t_w = target.shape[1], target.shape[2]\n elif target.ndim==4:\n t_h, t_w = target.shape[2], target.shape[3]\n cr = int((x.shape[2] - t_h) / 2)\n cc = int((x.shape[3] - t_w) / 2)\n x_cropped = x[:, :, cr:cr + t_h, cc:cc + t_w]... | [
"0.657598",
"0.62372273",
"0.6060326",
"0.58836097",
"0.56814396",
"0.5558945",
"0.5557947",
"0.55271006",
"0.54924375",
"0.5463341",
"0.54487395",
"0.5447843",
"0.5445061",
"0.544338",
"0.54312927",
"0.54123294",
"0.54112655",
"0.5396363",
"0.5391804",
"0.53821915",
"0.53773... | 0.78084946 | 0 |
Resize a kernel matrix based on the resolution at which it was defined and the signal resolution. E.g. if a kernel matrix was generated for 10kb and the input signal is 20kb, kernel size will be divided by two. If the kernel is enlarged, pixels are interpolated with a spline of degree 1. Alternatively, a resize factor ... | def resize_kernel(
kernel,
kernel_res=None,
signal_res=None,
factor=None,
min_size=7,
quiet=False,
):
km, kn = kernel.shape
if km != kn:
raise ValueError("kernel must be square.")
if not (km % 2) or not (kn % 2):
raise ValueError("kernel size must be odd.")
if fa... | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def shrink_kernel(self, kernel, up_scale):\n up_scale = torch.tensor(up_scale).float()\n # boundary padding based on the scaling law\n pad_in = (torch.ceil(up_scale**2).int())*((kernel.shape[2]-1)//2)\n pad_h = (torch.ceil(up_scale).int())*((kernel.shape[3]-1)//2)\n pad_w = (torc... | [
"0.6673881",
"0.64376324",
"0.63498217",
"0.6145752",
"0.60997856",
"0.6078737",
"0.5899416",
"0.5899416",
"0.5854729",
"0.5854729",
"0.580574",
"0.58015907",
"0.57691246",
"0.57011503",
"0.56532955",
"0.55739254",
"0.5490137",
"0.5478357",
"0.5461603",
"0.5452446",
"0.529977... | 0.7894084 | 0 |
Performs truncated SVD on an input kernel, returning the singular vectors necessary to retain a given proportion of information contained in the kernel. | def factorise_kernel(kernel, prop_info=0.999):
u, sigma, v = la.svd(kernel)
total_info = np.sum(sigma ** 2)
# Compute min. number of singular vectors to retain enough info
keep_k = np.flatnonzero(np.cumsum(sigma ** 2) > prop_info * total_info)[0] + 1
if keep_k > np.floor(min(kernel.shape) / 2):
... | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def truncated_svd(A,k=None):",
"def svd_shrink(X, tau):\n U,s,V = np.linalg.svd(X, full_matrices=False)\n return np.dot(U, np.dot(np.diag(shrink(s, tau)), V))",
"def reduce_svd(embeddings, seed=0):\n svd = TruncatedSVD(n_components=2, n_iter=10, random_state=seed)\n return svd.fit_transform(embeddi... | [
"0.7064768",
"0.6497113",
"0.647693",
"0.63251233",
"0.6254241",
"0.6164676",
"0.61603194",
"0.60960335",
"0.60589564",
"0.5987382",
"0.59055364",
"0.58650464",
"0.57826054",
"0.57542855",
"0.573676",
"0.56839186",
"0.5663144",
"0.5609878",
"0.5581168",
"0.5574882",
"0.557263... | 0.6894022 | 1 |
Given an array of valid indices, return the corrsesponding array of missing indices. | def valid_to_missing(valid, size):
missing = np.ones(size, dtype=bool)
try:
missing[valid] = False
# In case there is no valid index
except IndexError:
pass
missing = np.flatnonzero(missing)
return missing | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def non_masked_indices(mask):\n\treturn np.nonzero(np.ravel(mask-1,order='C'))[0]",
"def _get_nan_indices(*tensors: Tensor) ->Tensor:\n if len(tensors) == 0:\n raise ValueError('Must pass at least one tensor as argument')\n sentinel = tensors[0]\n nan_idxs = torch.zeros(len(sentinel), dtype=torch... | [
"0.6300259",
"0.6108775",
"0.6091231",
"0.60477185",
"0.6002121",
"0.5978218",
"0.59119964",
"0.58613497",
"0.58460724",
"0.5842929",
"0.57560515",
"0.5704897",
"0.5615569",
"0.55952543",
"0.55720913",
"0.55479383",
"0.5543988",
"0.55328596",
"0.5526036",
"0.55213183",
"0.547... | 0.610664 | 2 |
r"""Get absolute path and set attributes. | def _init_before_open(self, read_meth='read', open_as_binary=True, **kwargs):
super(FileComm, self)._init_before_open(**kwargs)
# Process file class keywords
if not hasattr(self, '_fd'):
self._fd = None
if read_meth not in ['read', 'readline']:
raise ValueError("r... | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def get_path(self):\n raise NotImplementedError(\"This asset does not support absolute paths\")",
"def abspath(self):\n if self.__abspath is None:\n self.__abspath = pbxpath.abspath(self)\n return self.__abspath",
"def _get_absolute(self, path: Path) -> Path:\n return pat... | [
"0.68317145",
"0.66353893",
"0.6506451",
"0.6336473",
"0.62895834",
"0.6220344",
"0.6210958",
"0.6178998",
"0.61444104",
"0.6140882",
"0.61189973",
"0.60873955",
"0.604279",
"0.60294616",
"0.60294616",
"0.60211664",
"0.6019854",
"0.60156524",
"0.5973667",
"0.59265673",
"0.592... | 0.0 | -1 |
r"""Method to return a dictionary of testing options for this class. | def get_testing_options(cls, read_meth='read', open_as_binary=True, **kwargs):
out = super(FileComm, cls).get_testing_options(**kwargs)
out['kwargs']['read_meth'] = read_meth
out['kwargs']['open_as_binary'] = open_as_binary
if (read_meth == 'read') and isinstance(out['recv'][0], backward... | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def get_testing_options(cls):\n return [{'error': [(1, NotImplementedError)],\n 'kwargs': {'initial_state': {'a': int(1)}}}]",
"def get_testing_options(cls, **kwargs):\n msg = ['test', 1.0]\n count = 3\n objects = [msg for _ in range(count)]\n out = {'kwargs': {... | [
"0.79496187",
"0.7280603",
"0.7255419",
"0.70370495",
"0.6979806",
"0.69656146",
"0.6909307",
"0.6908418",
"0.6870743",
"0.68551254",
"0.6849841",
"0.68234015",
"0.6817497",
"0.67941236",
"0.67408985",
"0.6725303",
"0.6684944",
"0.6684944",
"0.6684944",
"0.6684944",
"0.668494... | 0.0 | -1 |
r"""Determine if the necessary libraries are installed for this communication class. | def is_installed(cls, language=None):
# Filesystem is implied
return True | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def _is_installed(self):\n return self._system.is_library(os.path.join(self.get_install_path(), \"lib/libG4event\")) or \\\n self._system.is_library(os.path.join(self.get_install_path(), \"lib64/libG4event\"))",
"def check_requirements():\n process_output = subprocess.check_output([sys.execu... | [
"0.77119476",
"0.7107023",
"0.70856094",
"0.69626397",
"0.69026285",
"0.68549293",
"0.68545127",
"0.6821311",
"0.6820046",
"0.68049216",
"0.6733924",
"0.6677433",
"0.6667949",
"0.6651116",
"0.65795135",
"0.65275687",
"0.6507767",
"0.6464986",
"0.6459156",
"0.6447705",
"0.6422... | 0.57553107 | 84 |
r"""Close a registry entry. | def close_registry_entry(cls, value):
out = False
if not value.closed: # pragma: debug
value.close()
out = True
return out | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def close(self):\n self.__exit__(None, None, None)",
"def close(self):\n self.__exit__(None, None, None)",
"def close(self):\n \n self.__exit__(None, None, None)\n return",
"def __del__(self):\n for key_path_prefix, registry_file in iter(self._registry_files.items()):\n ... | [
"0.62410325",
"0.62410325",
"0.6005659",
"0.6003081",
"0.59938776",
"0.5955254",
"0.592907",
"0.58687013",
"0.57975334",
"0.5778289",
"0.5778289",
"0.5778289",
"0.5778289",
"0.5778289",
"0.57693046",
"0.57626265",
"0.5747791",
"0.5740424",
"0.5720488",
"0.5720488",
"0.5716972... | 0.73456883 | 0 |
r"""Initialize communication with new queue. | def new_comm_kwargs(cls, *args, **kwargs):
kwargs.setdefault('address', 'file.txt')
return args, kwargs | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def on_init(self, queue=None, **kwargs):\n self.queue = queue if queue else Queue()",
"def setup_queue(self):\n self.logger.info('declaring queue %s', self.queue)\n if self.otq:\n self._channel.queue_declare(self.on_queue_declareok, self.queue, auto_delete=True)\n else:\n ... | [
"0.74446756",
"0.71945274",
"0.6970091",
"0.6969268",
"0.6967559",
"0.6944674",
"0.6944674",
"0.69317365",
"0.6866296",
"0.67447776",
"0.6695959",
"0.6673099",
"0.6663105",
"0.6660289",
"0.66565484",
"0.66547716",
"0.6641651",
"0.662688",
"0.662688",
"0.662688",
"0.662688",
... | 0.0 | -1 |
r"""Get keyword arguments to initialize communication with opposite comm object. | def opp_comm_kwargs(self):
kwargs = super(FileComm, self).opp_comm_kwargs()
kwargs['newline'] = self.newline
kwargs['open_as_binary'] = self.open_as_binary
kwargs['is_series'] = self.is_series
return kwargs | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def new_comm_kwargs(cls, *args, **kwargs):\n kwargs.setdefault('address', 'file.txt')\n return args, kwargs",
"def driver_kwargs(self):\n out = super(TestCisRpcServer, self).driver_kwargs\n out['comm'] = 'ClientComm'\n return out",
"def driver_kwargs(self):\n out = sup... | [
"0.6724078",
"0.65556204",
"0.64141464",
"0.6366962",
"0.611054",
"0.6110213",
"0.60796076",
"0.59395605",
"0.5830559",
"0.58100736",
"0.5667532",
"0.5665083",
"0.5630391",
"0.5616168",
"0.5616114",
"0.5589997",
"0.5575281",
"0.55713445",
"0.5564068",
"0.5554116",
"0.5552634"... | 0.6149904 | 4 |
r"""Record the current position in the file/series. | def record_position(self):
_rec_pos = self.fd.tell()
_rec_ind = self._series_index
return _rec_pos, _rec_ind | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def change_position(self, file_pos, series_index=None):\n if series_index is None:\n series_index = self._series_index\n self.advance_in_series(series_index)\n self.advance_in_file(file_pos)",
"def record(self, pos):\n self.lasts += (datetime.now(), pos),\n if len(se... | [
"0.66489357",
"0.66329324",
"0.6479423",
"0.64465714",
"0.61586374",
"0.61237216",
"0.6114697",
"0.610729",
"0.6095362",
"0.60406315",
"0.5951866",
"0.59466505",
"0.5869703",
"0.58657867",
"0.58627445",
"0.58404976",
"0.5832795",
"0.58294195",
"0.5811629",
"0.5791412",
"0.575... | 0.7691817 | 0 |
Subsets and Splits
No community queries yet
The top public SQL queries from the community will appear here once available.