list:\n list_of_notes = []\n for note in Note.objects.all():\n list_of_notes.append({\n 'uuid': note.uuid, 'title': note.title,\n 'author': note.author, 'body': note.body, 'created_at': localtime(note.created_at)\n })\n return list_of_notes","def notes(self):\n return reapy.NoteList(self)","def getnotes():","def view_notes(tag_list):\n final_notes = []\n for tag in tag_list:\n all_notes = Tags.objects.get(tag_text=tag)\n notes = all_notes.notes.all()\n for note in notes:\n final_notes.append(note.note_text)\n \n return final_notes","def _get_issue_notes(request, pk):\n if request.user.is_coordinator_or_better:\n note_types = IssueNote.COORDINATOR_NOTE_TYPES\n else:\n note_types = IssueNote.PARALEGAL_NOTE_TYPES\n\n return (\n IssueNote.objects.filter(issue=pk)\n .prefetch_related(\"creator__groups\")\n .filter(note_type__in=note_types)\n .order_by(\"-created_at\")\n .all()\n )","def get_note():\n\n return Note.query.all()","def get_objects_using_notes():\n global objects_using_notes\n\n if not objects_using_notes:\n objects_using_notes = rsh.get_objects_using_notes(get_srcs())\n\n return objects_using_notes","def notes_list():\n if request.method == \"POST\":\n note = str(request.data.get(\"text\", \"\"))\n idx = max(notes.keys()) + 1\n notes[idx] = note\n return note_repr(idx), status.HTTP_201_CREATED\n\n # request.method == 'GET'\n return [note_repr(idx) for idx in sorted(notes.keys())]","def ls(self, count = 200):\n return self._manager.ls_notes(self['id'], count)","def getNotes(self):\n return self._nednotes, self._ongcnotes","def get_notes():\n notes = []\n durs = []\n\n for file in glob.glob(\"D:\\\\anthems\\\\data\\\\*.mid\"):\n midi = converter.parse(file)\n\n print(\"Parsing %s\" % file)\n\n notes_to_parse = None\n\n try:\n s2 = instrument.partitionByInstrument(midi)\n notes_to_parse = s2.parts[0].recurse() \n except:\n notes_to_parse = midi.flat.notes\n \n for element in notes_to_parse:\n if isinstance(element, note.Note):\n notes.append(str(element.pitch))\n durs.append(element.duration.quarterLength)\n elif isinstance(element, chord.Chord):\n notes.append('.'.join(str(n) for n in element.normalOrder))\n durs.append(element.duration.quarterLength)\n\n return notes, durs","def add_notes(self, notes):\n if hasattr(notes, \"notes\"):\n for x in notes.notes:\n self.add_note(x)\n return self.notes\n elif hasattr(notes, \"name\"):\n self.add_note(notes)\n return self.notes\n elif isinstance(notes, six.string_types):\n self.add_note(notes)\n return self.notes\n for x in notes:\n if isinstance(x, list) and len(x) != 1:\n if len(x) == 2:\n self.add_note(x[0], x[1])\n else:\n self.add_note(x[0], x[1], x[2])\n else:\n self.add_note(x)\n return self.notes","def note_list(request):\n user = request.user\n notes = Note.objects.filter(author=user)\n serializer = NoteSerializer(notes, many=True)\n return Response(serializer.data)","def ordered_notes(self, obj, sig_features, tabs):\n supports = dict(\n [(str(support.id), support) for support in obj.all_supports])\n notes = []\n for browsers in sig_features.values():\n for section in tabs:\n for browser in section['browsers']:\n sig_supports = browsers.get(browser, [])\n for sig_support_pk in sig_supports:\n support = supports[sig_support_pk]\n if support.note:\n notes.append(sig_support_pk)\n return OrderedDict((note, i) for i, note in enumerate(notes, 1))","def notes(self):\r\n return notes.Notes(self)","def notes(self):\r\n return notes.Notes(self)","def get_notes(n_notes=3):\n notes = []\n \n for ii, file in enumerate(glob.glob(\"data/maestro-v2.0.0/2004/*.midi\")):\n if ii >= n_notes:\n break\n pickle_file_name = file[:-4] + 'pkl'\n\n if os.path.isfile(pickle_file_name):\n print(f'Reading parsed file: {pickle_file_name}')\n with open(pickle_file_name, 'rb') as handle:\n midi = pickle.load(handle)\n else:\n midi = converter.parse(file)\n\n with open(pickle_file_name, 'wb') as handle:\n print(f'writing parsed file: {pickle_file_name}')\n unserialized_data = pickle.dump(midi, \n handle, \n protocol=pickle.HIGHEST_PROTOCOL\n )\n\n\n print(\"Parsing %s\" % file)\n\n notes_to_parse = None\n try: # file has instrument parts\n s2 = instrument.partitionByInstrument(midi)\n notes_to_parse = s2.parts[0].recurse() \n except: # file has notes in a flat structure\n notes_to_parse = midi.flat.notes\n \n for element in notes_to_parse:\n if isinstance(element, note.Note):\n notes.append(str(element.pitch))\n elif isinstance(element, chord.Chord):\n notes.append('.'.join(str(n) for n in element.normalOrder))\n \n return notes","def get_note_names(self):\n res = []\n for n in self.notes:\n if n.name not in res:\n res.append(n.name)\n return res","def test_list_notes(self):\n pass","def notes(self):\n return notes.Notes(self)","def PublicNotes(self, default=[{}]):\n tmp = self.data.get('metadata', {}).get('public_notes', default)\n return [HEP.SourcedValueObject(i) for i in tmp]","def getNotes(self, *args):\n return _libsbml.SBase_getNotes(self, *args)","def listNoteVersions(self, authenticationToken, noteGuid):\r\n pass","def fNoteLists(self, match):\n att, start_char, g_links, extras = match.groups()\n start_char = start_char or 'a'\n index = '%s%s%s' % (g_links, extras, start_char)\n result = ''\n\n if index not in self.notelist_cache:\n o = []\n if self.notes:\n for seq, info in self.notes.items():\n links = self.makeBackrefLink(info, g_links, start_char)\n atts = ''\n if 'def' in info:\n infoid = info['id']\n atts = info['def']['atts']\n content = info['def']['content']\n li = (\"\"\"\\t<li%s>%s<span id=\"note%s\"> </span>%s</li>\"\"\"\n % (atts, links, infoid, content))\n else:\n li = (\"\"\"\\t<li%s>%s Undefined Note [#%s].<li>\"\"\" %\n (atts, links, info['seq']))\n o.append(li)\n if '+' == extras and self.unreferencedNotes:\n for seq, info in self.unreferencedNotes.items():\n if info['def']:\n atts = info['def']['atts']\n content = info['def']['content']\n li = \"\"\"\\t<li%s>%s</li>\"\"\" % (atts, content)\n o.append(li)\n self.notelist_cache[index] = u\"\\n\".join(o)\n result = self.notelist_cache[index]\n if result:\n list_atts = self.pba(att)\n result = \"\"\"<ol%s>\\n%s\\n</ol>\"\"\" % (list_atts, result)\n return result","def getNotes(self):\n logger.debug(\"Func: getNotes\")\n\n return self._currentNotes","def getNotes(self):\n return self.__notes","def notes(self):\n return self._notes","def __init__(self):\n self.notes = []","def notes(self):\n return self.__notes","def get_notes():\n song_index_to_notes = {}\n\n for file in glob.glob(\"../data/midi/*.mid\"):\n midi = converter.parse(file)\n song_index = int(os.path.splitext(os.path.basename(file))[0])\n #print(\"Parsing %s with an index %d\" % (file, song_index))\n\n notes_to_parse = None\n notes = []\n try: # file has instrument parts\n s2 = instrument.partitionByInstrument(midi)\n notes_to_parse = s2.parts[0].recurse()\n except: # file has notes in a flat structure\n notes_to_parse = midi.flat.notes\n\n for element in notes_to_parse:\n if isinstance(element, note.Note):\n notes.append(str(element.pitch))\n elif isinstance(element, chord.Chord):\n notes.append('.'.join(str(n) for n in element.normalOrder))\n\n song_index_to_notes[song_index] = notes\n\n return song_index_to_notes","def return_markers(self):\r\n ent_file = join(self.filename, self._basename + '.ent')\r\n if not exists(ent_file):\r\n ent_file = join(self.filename, self._basename + '.ent.old')\r\n\r\n try:\r\n ent_notes = _read_ent(ent_file)\r\n\r\n except (FileNotFoundError, PermissionError):\r\n markers = []\r\n\r\n else:\r\n allnote = []\r\n for n in ent_notes:\r\n try:\r\n n['value'].keys()\r\n allnote.append(n['value'])\r\n except AttributeError:\r\n lg.debug('Note of length {} was not '\r\n 'converted to dict'.format(n['length']))\r\n\r\n s_freq = self._hdr['erd']['sample_freq']\r\n pcname = '0CFEBE72-DA20-4b3a-A8AC-CDD41BFE2F0D'\r\n note_time = []\r\n note_name = []\r\n note_note = []\r\n for n in allnote:\r\n if n['Text'] == 'Analyzed Data Note':\r\n continue\r\n if not n['Text']:\r\n continue\r\n if 'User' not in n['Data'].keys():\r\n continue\r\n user1 = n['Data']['User'] == 'Persyst'\r\n user2 = False # n['Data']['User'] == 'eeg'\r\n user3 = n['Data']['User'] == pcname\r\n user4 = n['Data']['User'] == 'XLSpike - Intracranial'\r\n user5 = n['Data']['User'] == 'XLEvent - Intracranial'\r\n if user1 or user2 or user3 or user4 or user5:\r\n continue\r\n if len(n['Data']['User']) == 0:\r\n note_name.append('-unknown-')\r\n else:\r\n note_name.append(n['Data']['User'].split()[0])\r\n note_time.append(n['Stamp'] / s_freq)\r\n note_note.append(n['Text'])\r\n\r\n markers = []\r\n for time, name, note in zip(note_time, note_name, note_note):\r\n m = {'name': note + ' (' + name + ')',\r\n 'start': time,\r\n 'end': time,\r\n 'chan': None,\r\n }\r\n markers.append(m)\r\n\r\n return markers","def get_notes(self, note_limit=200):\n return Note.get_by_person_record_id(\n self.subdomain, self.record_id, limit=note_limit)","async def _view_all_notes(self, ctx: Context):\n\n author = ctx.author\n\n note_infos = []\n\n embed_links = ctx.channel.permissions_for(ctx.guild.me).embed_links\n\n author_str = f\"{author.name}'\"\n\n if author.name[-1].lower() != \"s\":\n author_str += \"s\"\n\n async with self.config.member(author).notes() as notes:\n total = len(notes)\n for page_num, note in enumerate(notes, start=1):\n msg_info = \"\"\n if note[\"author\"]:\n msg_info += _(\"**Author:** {}\").format(note[\"author\"])\n if note[\"channel\"]:\n msg_info += _(\"\\n**Channel:** {}\").format(note[\"channel\"])\n if note[\"jump_url\"]:\n if embed_links:\n msg_info += _(\n \"\\n[Click here to jump to message]({})\"\n ).format(note[\"jump_url\"])\n else:\n msg_info += _(\n \"\\n**Jump To Message:** {}\"\n ).format(note[\"jump_url\"])\n\n note_info = _(\n \"{}\\n\\n**Note:**\\n```{}```\\n**Reason:**\\n```{}```\"\n ).format(\n msg_info,\n note[\"note\"],\n note[\"reason\"]\n ).strip()\n\n if embed_links:\n page = discord.Embed(\n colour=0xff0000,\n description=note_info,\n title=_(\"{} TvM Notes\").format(author_str),\n timestamp=ctx.message.created_at\n )\n\n page.set_footer(\n text=_(\"Page {page_num}/{leng}\").format(\n page_num=page_num, leng=total\n )\n )\n else:\n page = _(\n \"**{author} TvM Notes**\"\n \"\\n\\n{note}\"\n \"\\n{footer}\"\n ).format(\n author=author_str,\n note=note_info,\n footer=_(\"*Page {page_num}/{leng}*\").format(\n page_num=page_num, leng=total\n )\n )\n\n note_infos.append(page)\n\n await menu(ctx, note_infos, DEFAULT_CONTROLS)","def notes(self):\n return NotesTable(self.rpc, self.name)","def notes(self):\n return Notes(self)","def get_scheduled_notes(event):\n def _sort_note_by(note):\n obj = note.object\n if hasattr(obj, 'start_dt'):\n return obj.start_dt, 0\n else:\n return obj.contribution.start_dt, obj.position\n\n tt_entries = (event.timetable_entries\n .filter(TimetableEntry.type != TimetableEntryType.BREAK)\n .options(joinedload('session_block').joinedload('contributions').joinedload('subcontributions'))\n .options(joinedload('contribution').joinedload('subcontributions'))\n .options(noload('break_'))\n .all())\n # build a list of all the objects we need notes for. that way we can query\n # all notes in a single go afterwards instead of making the already-huge\n # timetable query even bigger.\n objects = set()\n for entry in tt_entries:\n objects.add(entry.object)\n if entry.type == TimetableEntryType.CONTRIBUTION:\n objects.update(sc for sc in entry.object.subcontributions if not sc.is_deleted)\n elif entry.type == TimetableEntryType.SESSION_BLOCK:\n for contrib in entry.object.contributions:\n objects.add(contrib)\n objects.update(sc for sc in contrib.subcontributions if not sc.is_deleted)\n notes = [x for x in event.all_notes.filter_by(is_deleted=False) if x.object in objects]\n return sorted(notes, key=_sort_note_by)","def get_todo_list():\n\n # assume that a \"h264\" encoded file is complete\n return models.LibraryItem.objects.filter(h264=False)","def listNoteVersions(self, authenticationToken, noteGuid):\r\n self.send_listNoteVersions(authenticationToken, noteGuid)\r\n return self.recv_listNoteVersions()","def notes(self):\r\n return notes.UserNotes(self)","def all_notes():\n \n return render_template('all_notes.html',colors=music_color,)","def read_notes():\n\n if current_user.is_admin is False:\n notes = Note.query.filter_by(created_by=current_user.id).all()\n else:\n notes = Note.query.all()\n\n return render_template('notes/index.html.j2', notes=notes, title='Notes')","def get_all_notes(user_id, token):\n user = User.get(User.id == user_id).username\n if not validate_token(user, token):\n return HTTPResponse(status=500, body={\"message\":\"Validation error.\"})\n res = []\n for note in Notes.select():\n if note.user.id == user_id:\n new_note = model_to_dict(note)\n res.append({\"id\":new_note['id'], \"title\":new_note['title'],\n \"content\":new_note['content']})\n new_token = generate_token(user)\n body = {\"user_id\":user_id, \"token\":new_token.decode('utf-8'), 'items':res}\n return HTTPResponse(status=200, body=body)","def news(self) -> List[News]:\n return self._news","def reconstruct(notes, resolution):\n # Construct the MusPy Music object\n music = muspy.Music(resolution=resolution, tempos=[muspy.Tempo(0, 100)])\n\n # Append the tracks\n programs = sorted(set(note[-1] for note in notes))\n for program in programs:\n music.tracks.append(muspy.Track(program))\n\n # Append the notes\n for beat, position, pitch, duration, program in notes:\n time = beat * resolution + position\n track_idx = programs.index(program)\n music[track_idx].notes.append(muspy.Note(time, pitch, duration))\n\n return music","def remove_duplicate_notes(self):\n res = []\n for x in self.notes:\n if x not in res:\n res.append(x)\n self.notes = res\n return res","def sort(self):\n self.notes.sort()","def noteList(ownerId, page = 0, count = 15):\n\n query = QUERY_GET_NOTES_LIST\n query = query.format(**{'owner_id':ownerId, 'offset':(page * count), 'count':count})\n\n cursor.execute(query)\n data = cursor.fetchall()\n\n if data == None:\n return False, ERROR_GETTING_NOTES_LIST, 'Failed', None\n\n notesList = []\n\n for noteTuple in data:\n id, title, subject, creationDate, changeDate, ownerId = noteTuple\n noteDict = {\n 'id':id,\n 'title':title,\n 'subject':subject,\n 'creation_date':creationDate,\n 'change_date':changeDate,\n 'owner_id':ownerId\n }\n notesList.append(noteDict)\n\n return True, NO_ERROR, '', notesList","def test_list_note_occurrences(self):\n pass","def notes(self):\r\n return TicketNotes(self)","def extract_notes(bars):\n output = []\n for bar in bars:\n notes = np.array([bar_to_notes(b,i) for i,b in enumerate(bar)])\n\n # Flatten into a single array\n midi_notes = []\n for i in range(notes.shape[1]):\n if all(notes[:,i] == -1):\n midi_notes.append(None)\n else:\n midi_notes.append([n for n in notes[:,i] if n!=-1])\n output.append(midi_notes)\n return output","def starts_with_tonic(a_list):\n key = a_list.track.bars[0].key.name\n note = a_list.get_first_actual_note()\n if note.name == key:\n return []\n else:\n return [note.start]","def midiToNotes(notes):\n names = ['C', 'C#', 'D', 'D#', 'E', 'F', 'F#', 'G', 'G#', 'A', 'A#', 'B']\n chord = []\n \n for note in notes:\n name = names[note%12]\n octave = int((note-12)/12)\n chord.append(name + str(octave))\n \n return chord","def load_org_notes_file():\n nodelist = orgnode.makelist(NOTES_ORG_FILE)\n return nodelist","def todolists(self):\r\n return tdl.Todolists(self)","def notes_in_path(self, path: str) -> List[NoteInfo]:\n check_path = os.path.abspath(path)\n return [n for n in self.notes.values() if n.file_path.startswith(check_path)]","def get_contenu(self):\n return self.objets","def page_list_notes(self, no_history=False):\n\n list_taskid_desc = self.task_store.sort_taskid_list_descending_lamport()\n title = \"woolnote - all notes\"\n page_header_first_text = \"all notes\"\n\n if no_history:\n history_id = self.save_history([])\n else:\n history_id = self.save_history([\"action\"], alt_task_store_name=None)\n\n page_header_list_of_warnings = None\n page_header_small_text = None\n\n if self.error_msg_queue_list:\n page_header_list_of_warnings = self.helper_convert_msg_queue_list_to_list_for_output()\n else:\n try:\n # TODO: use asn library?\n # sha256_fp = read_pem_cert_fingerprint(SSL_CERT_PEM_PATH)\n page_header_small_text = config.SSL_CERT_PEM_FINGERPRINT\n except:\n page_header_small_text = \"cannot get ssl cert sha256\"\n\n return html_page_templates.page_list_notes_template(list_taskid_desc=list_taskid_desc,\n self_sess_action_auth=self.sess_action_auth, title=title,\n history_back_id=history_id, primary_task_store=self.task_store,\n virtual_folders=self.woolnote_config.virtual_folders,\n single_task_line_ids=set(self.woolnote_config.single_note_line_id.keys()),\n page_header_first_text=page_header_first_text,\n page_header_optional_small_second_text=page_header_small_text,\n page_header_optional_list_of_warnings=page_header_list_of_warnings)","def get_notes(notes_type=\"national\", area=None):\n query_menu = get_query_menu(area)\n query = query_menu[notes_type][\"query\"]\n collection = query_menu[notes_type][\"collection\"]\n notes = \"\"\n try:\n doc = next(collection.find(query).sort([(DATE_KEY, -1)]).limit(1))\n notes = doc[NOTE_KEY] if doc[NOTE_KEY] != 0 else None\n except StopIteration:\n app.logger.error(\"While getting notes: no data\")\n return notes if notes is not None and not rubbish_notes(notes) else \"\"","def _on_note(self) -> Iterator:\n return filter(\n lambda x: x.type == constants.NOTE_ON, self.midi_data)","def objects(self):","def do_notes(self, sym):\n # DRY: duplication with Roman.py - FIXME\n if sym == '-':\n # REST\n return []\n note_or_chord = self.do(sym)\n if note_or_chord is None:\n return []\n elif type(note_or_chord) == Chord:\n return note_or_chord.notes\n else:\n return [ note_or_chord ]","def extract_feature_notes(contig, type, start, stop, direction, notes, pos, ref, alt):\n notes = re.sub(\"%\\d+\", \" \", notes)\n desc = re.search(\"Note=(.*?);\", notes)\n id = re.search(\"ID=(.*?);\", notes)\n name = re.search(\"Name=(.*?);\", notes)\n\n if id and name and desc:\n print ('Contig: ' + contig)\n print ('Annotation_type: ' + type)\n print ('Start:Stop:Direction '+ start+':'+stop+':'+direction)\n print (\"Position: \" + pos)\n print ('Ref:Alt: ' + ref + ':'+ alt)\n print(\"ID: \" + id.group(1))\n # print(\"Name: \" + name.group(1))\n print(\"Description: \" + desc.group(1))\n # if name:\n # print(\"Name: \" + name.group(1))\n #\n # if desc:\n # print(\"Desc: \" + desc.group(1))\n\n\n print()","def release_notes(self):\n return self._release_notes","def extract_notes(music, resolution):\n # Check resolution\n assert music.resolution == resolution\n\n # Extract notes\n notes = []\n for track in music:\n if track.is_drum or track.program not in KNOWN_PROGRAMS:\n continue\n for note in track:\n beat, position = divmod(note.time, resolution)\n notes.append(\n (beat, position, note.pitch, note.duration, track.program)\n )\n\n # Deduplicate and sort the notes\n notes = sorted(set(notes))\n\n return np.array(notes)","def bar_to_lilypond_notes(notes):\n lp_notes = []\n if notes[0] is None:\n notes = notes[1:]\n if notes[0] is None:\n lp_notes.append(\"r\")\n for n in notes:\n if n is None:\n continue\n if type(n) is list:\n lp_notes.append([midi_to_lilypond_note(x) for x in n])\n else:\n lp_notes.append(midi_to_lilypond_note(n))\n return lp_notes","def notes(self):\r\n return notes.ForumSuggestionNotes(self)","def getNote(self):\n return (self.noteName[0], self.accidental, self.octave)","def starts_with_tonic_or_fifth(a_list):\n key = a_list.track.bars[0].key\n note = a_list.get_first_actual_note()\n possible_notes = [key.name, Note(key).transpose('5', True).name]\n if note.name in possible_notes:\n return []\n else:\n return [note.start]","def show():\n\n quality_list = []\n\n conn = sqlite3.connect(\"person_database.bd\")\n c = conn.cursor()\n\n c.execute(\"SELECT *, oid FROM person_info\")\n records = c.fetchall()\n\n conn.commit()\n conn.close()\n\n for record in records:\n quality_list.append(str(record[2]) + \" \" + str(record[0]))\n\n return quality_list","def list(self):","def get_notes_from_uuids(dictionary, uuid_list):\n titles = []\n texts = []\n notes = []\n for key in dictionary:\n if dictionary[key][\"content_type\"] == \"Note\" and dictionary[key][\"uuid\"] in uuid_list:\n try:\n titles.append(dictionary[key][\"content\"][\"title\"])\n texts.append(dictionary[key][\"content\"][\"text\"])\n notes = list(zip(titles, texts))\n except KeyError:\n continue\n return notes","def getMaintainerList(self):\n return self.getDocumentedObject().getMaintainerList()","def topics(self):\r\n return contents.Topics(self)","def note_list(self, evarnames=\"\", in_=None):\n evarnames = self._find_vars(evarnames, evars=True, \n unique=True, empty_ok=True)\n if len(evarnames) == 0:\n evarnames = ['_dta'] + self._varlist\n if in_ is not None:\n if isinstance(in_, int):\n in_ = (in_,)\n elif (not isinstance(in_, collections.Iterable)\n or not all(isinstance(n, int) for n in in_)):\n raise TypeError(\"in_ should be int or iterable of int\")\n if any(n <= 0 for n in in_):\n raise ValueError(\"note numbers must be >= 1\")\n for name in evarnames:\n if name not in self._chrdict: continue\n chars = self._chrdict[name]\n if 'note0' not in chars: continue\n nnotes = int(chars['note0'])\n in_range = in_ if in_ is not None else range(1, nnotes + 1)\n note_info = []\n for note_num in in_range:\n note_name = 'note' + str(note_num)\n if note_name in chars:\n note_info.append(note_num)\n if note_info != []:\n note_info.insert(0, name)\n self._display_notes(note_info)","def inverse_notes(note_list):\n raw_output = []\n for note in note_list:\n # We transpose every notes except for Z\n if note.is_pause:\n raw_output.append(f'{note.name}{note.figure}')\n else:\n # Get the index of the note to transpose\n note_name_index = NOTE_NAMES.index(note.name)\n # Get the transposed index according to the amount\n inverted_index = ((len(NOTE_NAMES) - note_name_index) % len(NOTE_NAMES))\n new_note_name = NOTE_NAMES[inverted_index]\n # Create the new note, with the new note's name, and its original figure\n new_note = f'{new_note_name}{note.figure}'\n new_note += 'p' if note.has_point else ''\n raw_output.append(new_note)\n\n return raw_output","def Students_in_majors(l:list,i:list)->list:\n result=[]\n for s in l:\n if s.major in i:\n result.append(s)\n return result","def get_user_visible_notes():\n return grammar.user_visible_notes","def load_working(self, path: str, files: List) -> List[NoteInfo]:\n if not files:\n return self.notes_in_path(path)\n else:\n abs_paths = map(os.path.abspath, files)\n return [self.notes[f] for f in abs_paths if f in self.notes]","def getNoteVersion(self, authenticationToken, noteGuid, updateSequenceNum, withResourcesData, withResourcesRecognition, withResourcesAlternateData):\r\n pass","def todos(self):\r\n return Todos(self)","def get_notes_for_section(self, section, notes_property=\"notes\"):\n return self.get_notes_target(\n self.sections[section], notes_property=notes_property\n )","def showall_incremental (self,\r\n entrylist=None,\r\n highlight=None,\r\n childrentoo=True,\r\n levels=0,\r\n index=None,\r\n beforeafter=10):\r\n\r\n\r\n\r\n if entrylist is None:\r\n\r\n if not self.default_dict['sortbydate']:\r\n entrylist = self.default_dict['indexlist_indexes'].list\r\n else:\r\n entrylist = self.default_dict['indexlist_indexes'].list\r\n entrylist = self.index_sort([Index(a_temp)\r\n for a_temp\r\n in entrylist],\r\n by_date=self.default_dict['sortbydate'],quick=True)\r\n\r\n else:\r\n entrylist = self.index_sort([Index(a_temp)\r\n for a_temp\r\n in entrylist],\r\n by_date=self.default_dict['sortbydate'],quick=False)\r\n\r\n\r\n\r\n if not isinstance(entrylist[0], str):\r\n entrylist = [str(a_temp) for a_temp in entrylist]\r\n\r\n keep_on = True\r\n inp_temp = EMPTYCHAR\r\n while keep_on:\r\n\r\n\r\n if index is None:\r\n index = entrylist[0]\r\n\r\n if index in entrylist:\r\n\r\n indexpoint = entrylist.index(index) # index is the index of the note\r\n else:\r\n indexpoint = 0\r\n begin_point = max([0,indexpoint-beforeafter])\r\n end_point = min([len(entrylist)-1,indexpoint+beforeafter])\r\n\r\n segment = entrylist[begin_point:end_point]\r\n\r\n inc_displaylist = DisplayList (displayobject=display)\r\n header_temp = str(segment[0]) + LONGDASH + str(segment[-1])\r\n\r\n self.showall(entrylist=segment,\r\n highlight=highlight,\r\n childrentoo=childrentoo,\r\n levels=levels,\r\n shortshow=True,\r\n multi=False,\r\n quick=False,\r\n header=header_temp)\r\n\r\n if not inp_temp:\r\n inp_temp = input(queries.MENU_ONE+BLANK)\r\n if inp_temp[0].lower() == 'q':\r\n inp_temp = EMPTYCHAR\r\n if inp_temp:\r\n if inp_temp[0] == '>':\r\n\r\n index = segment[-1]\r\n elif inp_temp[0] == '<':\r\n index = segment[0]\r\n elif inp_temp[0] == '[':\r\n index = entrylist[0]\r\n elif inp_temp[0] == ']':\r\n index = entrylist[-1]\r\n inp_temp = inp_temp[1:]\r\n\r\n else:\r\n keep_on = False","def add_evernote_notes(self, evernote_notes, update=False, log_update_if_unchanged=True):\n new_nids=[]\n action_str_base = ['Add', 'Update'][update]\n action_str = ['Adding', 'Updating'][update]\n action_preposition = ['To', 'In'][update]\n info = stopwatch.ActionInfo(action_str + ' Of', 'Evernote Notes', action_preposition + ' Anki', report_if_empty=False)\n tmr = stopwatch.Timer(evernote_notes, 10, info=info,\n label='Add\\\\Anki-%sEvernoteNotes' % action_str_base)\n\n for ankiNote in evernote_notes:\n try:\n title = ankiNote.FullTitle\n content = decode(ankiNote.Content)\n anki_field_info = {\n FIELDS.TITLE: title,\n FIELDS.CONTENT: content,\n FIELDS.EVERNOTE_GUID: FIELDS.EVERNOTE_GUID_PREFIX + ankiNote.Guid,\n FIELDS.UPDATE_SEQUENCE_NUM: str(ankiNote.UpdateSequenceNum),\n FIELDS.SEE_ALSO: u''\n }\n except Exception:\n log_error(\"Unable to set field info for: Note '%s': '%s'\" % (ankiNote.FullTitle, ankiNote.Guid))\n log_dump(ankiNote.Content, \" NOTE CONTENTS \")\n # log_dump(encode(ankiNote.Content), \" NOTE CONTENTS \")\n raise\n tmr.step(title)\n baseNote = None\n if update:\n baseNote = self.get_anki_note_from_evernote_guid(ankiNote.Guid)\n if not baseNote:\n log_error('Updating note %s: COULD NOT FIND BASE NOTE FOR ANKI NOTE ID' % ankiNote.Guid)\n tmr.reportStatus(EvernoteAPIStatus.MissingDataError)\n continue\n if ankiNote.Tags is None:\n log_error(\"Could note find tags object for note %s: %s. \" % (ankiNote.Guid, ankiNote.FullTitle))\n tmr.reportStatus(EvernoteAPIStatus.MissingDataError)\n continue\n anki_note_prototype = AnkiNotePrototype(self, anki_field_info, ankiNote.TagNames, baseNote,\n notebookGuid=ankiNote.NotebookGuid, count=tmr.count,\n count_update=tmr.counts.updated.completed.val, max_count=tmr.max)\n anki_note_prototype._log_update_if_unchanged_ = log_update_if_unchanged\n nid = tmr.autoStep(anki_note_prototype.update_note() if update else anki_note_prototype.add_note(),\n ankiNote.FullTitle, update)\n if tmr.status.IsSuccess and not update:\n new_nids.append([nid, ankiNote.Guid])\n elif tmr.status.IsError:\n log(\"ANKI ERROR WHILE %s EVERNOTE NOTES: \" % action_str.upper() + str(tmr.status), tmr.label + '-Error')\n tmr.Report()\n if new_nids:\n ankDB().executemany(\"UPDATE {n} SET nid = ? WHERE guid = ?\", new_nids)\n return tmr.counts.success","def get_informatieobjecttypen(\n clients: List[Client] = None,\n) -> List[InformatieObjectType]:\n if clients is None:\n clients = _get_ztc_clients()\n\n catalogi = {cat.url: cat for cat in get_catalogi(clients=clients)}\n iots = _fetch_list(\"informatieobjecttype\", clients, InformatieObjectType)\n\n # resolve relations\n for iot in iots:\n iot.catalogus = catalogi[iot.catalogus]\n\n return iots","def test_get_note(self):\n pass","def _display_notes(self, note_info):\n if note_info == []: return\n evarname = note_info[0]\n chars = self._chrdict[evarname]\n \n if IN_STATA:\n tplt = \"{{text}}{:>3}. {}\"\n print(\"\\n{res}\" + evarname)\n else:\n tplt = \"{:>3}. {}\"\n print(\"\\n\" + evarname)\n \n for num in note_info[1:]:\n print(tplt.format(num, chars['note' + str(num)]))","def midi_to_lilypond_note(note):\n return all_notes[note+4]","def getDependencyList(self):\n return self.getDocumentedObject().getDependencyList()","def get_partlist(self):\n\n # Longdale check\n longdale_user = user_is_ingroup(self.request, \"longdale_user\")\n # REtrieve the correct queryset, sorted on the correct levels\n if longdale_user:\n qs = [prt for prt in Part.objects.all().order_by('corpus__lng', 'corpus__name', 'name')]\n else:\n longdale = \"Longdale\"\n qs = [prt for prt in Part.objects.exclude(Q(name__istartswith=longdale)).order_by('corpus__lng', 'corpus__name', 'name')]\n # REtrieve the correct queryset, sorted on the correct levels\n # qs = [prt for prt in Part.objects.all().order_by('corpus__lng', 'corpus__name', 'name')]\n # Start the output\n html = []\n # Initialize the variables whose changes are important\n lVars = [\"corpus_lng\", \"corpus_name\", \"name\"]\n lFuns = [Part.language, [\"corpus\", \"name\"], [\"name\"]]\n # Get a list of items containing 'first' and 'last' information\n lItem = get_item_list(lVars, lFuns, qs)\n # REturn this list\n return lItem","def get_entries_by_notes(self, notes, regex=False, flags=None,\n group=None, history=False, first=False): \n if self.database is None:\n raise DatabaseNotOpened('No KeePass Database Opened.')\n else:\n return self.database.find_entries_by_notes(notes,\n regex,\n flags,\n group,\n history,\n first)","def GetNotes(self, request, global_params=None):\n config = self.GetMethodConfig('GetNotes')\n return self._RunMethod(\n config, request, global_params=global_params)","def get_all(self):\n pat = re.compile('(<!-- @tk(.*?)-->)',re.M|re.S|re.I)\n for dir in glob.iglob('./**/.book', recursive=True):\n for fname in glob.iglob(os.path.dirname(dir) + '/**/*md', recursive=True):\n with open(fname, 'r') as mkd_file:\n contents = mkd_file.read()\n\n for todo in re.findall(pat,contents):\n t = Todo(todo[1],fname)\n t.context(contents)\n t.github = self.github\n self.todos.append(t)\n\n self.files.append(fname)\n \n return self.todos","def obj_list(self):\n return self._obj_list","def list():","def list():","def get_note (self,\r\n index):\r\n\r\n\r\n\r\n\r\n if self.using_database:\r\n aprint('GETTING NOTE')\r\n value_tuple = (notebookname, str(index),)\r\n db_cursor.execute(\"SELECT note_body \"\r\n +\"FROM notes WHERE notebook=?\"\r\n +\" AND note_index=?;\",\r\n value_tuple)\r\n\r\n res_temp = db_cursor.fetchone()\r\n\r\n if res_temp:\r\n text = res_temp[0].replace(\"''\",\"'\")\r\n else:\r\n text = ''\r\n\r\n db_cursor.execute(\"SELECT user\"\r\n +\" FROM notes\"\r\n +\" WHERE notebook=?\"\r\n +\" AND note_index=?;\",\r\n value_tuple)\r\n res_temp = db_cursor.fetchone()\r\n if res_temp:\r\n\r\n user = res_temp[0]\r\n else:\r\n user = 'USER'\r\n\r\n db_cursor.execute(\"SELECT size\"\r\n +\" FROM notes\"\r\n +\" WHERE notebook=? \"\r\n +\"AND note_index=?;\",\r\n value_tuple)\r\n\r\n res_temp = db_cursor.fetchone()\r\n if res_temp:\r\n size = res_temp[0]\r\n else:\r\n size = 60\r\n db_cursor.execute(\"SELECT timestamp \"\r\n +\"FROM timestamps\"\r\n +\" WHERE notebook=?\"\r\n +\" AND note_index=?\"\r\n +\" ORDER BY timestamp\",\r\n value_tuple)\r\n dates = db_cursor.fetchall()\r\n date_list = [date[0] for date in dates]\r\n db_cursor.execute(\"SELECT keyword\"\r\n +\" FROM all_note_keys\"\r\n +\" WHERE notebook=?\"\r\n +\" AND note_index=?\",\r\n value_tuple)\r\n keyset = db_cursor.fetchall()\r\n keyset = {key[0] for key in keyset}\r\n\r\n metadata = {'user':user,\r\n 'date':date_list,\r\n 'size':size}\r\n return Note(keyset,text,metadata)\r\n\r\n if str(index) in self.note_dict:\r\n return self.note_dict[str(index)]\r\n\r\n return False","def _getListing(self):\n\n # lets assure consistent litsting order\n items = self._items.items()\n items.sort()\n return [ \"%s%s%s: %s\" % (_def_sep, str(x[1]), _def_sep, x[1].__doc__)\n for x in items ]","def exercises_in_object(obj = None):\n if obj is not None and obj.isType() == \"assignments\":\n a_exe = obj.get_exercises()\n else:\n a_exe = Exercise.objects.all()\n \n result = ()\n for exe in a_exe:\n if not exe.name.isspace():\n result += ((exe.id, exe.name),)\n return result","def __repr__(self):\n return str(self.notes)","def note(self, note_id):\r\n return notes.Note(self, note_id)","def find_all(cls):\n return [AuthorModel(a['name'], str(a['_id']))\n for a in cls.db.newsdb.find()]"],"string":"[\n \"def listNotes() -> list:\\n list_of_notes = []\\n for note in Note.objects.all():\\n list_of_notes.append({\\n 'uuid': note.uuid, 'title': note.title,\\n 'author': note.author, 'body': note.body, 'created_at': localtime(note.created_at)\\n })\\n return list_of_notes\",\n \"def notes(self):\\n return reapy.NoteList(self)\",\n \"def getnotes():\",\n \"def view_notes(tag_list):\\n final_notes = []\\n for tag in tag_list:\\n all_notes = Tags.objects.get(tag_text=tag)\\n notes = all_notes.notes.all()\\n for note in notes:\\n final_notes.append(note.note_text)\\n \\n return final_notes\",\n \"def _get_issue_notes(request, pk):\\n if request.user.is_coordinator_or_better:\\n note_types = IssueNote.COORDINATOR_NOTE_TYPES\\n else:\\n note_types = IssueNote.PARALEGAL_NOTE_TYPES\\n\\n return (\\n IssueNote.objects.filter(issue=pk)\\n .prefetch_related(\\\"creator__groups\\\")\\n .filter(note_type__in=note_types)\\n .order_by(\\\"-created_at\\\")\\n .all()\\n )\",\n \"def get_note():\\n\\n return Note.query.all()\",\n \"def get_objects_using_notes():\\n global objects_using_notes\\n\\n if not objects_using_notes:\\n objects_using_notes = rsh.get_objects_using_notes(get_srcs())\\n\\n return objects_using_notes\",\n \"def notes_list():\\n if request.method == \\\"POST\\\":\\n note = str(request.data.get(\\\"text\\\", \\\"\\\"))\\n idx = max(notes.keys()) + 1\\n notes[idx] = note\\n return note_repr(idx), status.HTTP_201_CREATED\\n\\n # request.method == 'GET'\\n return [note_repr(idx) for idx in sorted(notes.keys())]\",\n \"def ls(self, count = 200):\\n return self._manager.ls_notes(self['id'], count)\",\n \"def getNotes(self):\\n return self._nednotes, self._ongcnotes\",\n \"def get_notes():\\n notes = []\\n durs = []\\n\\n for file in glob.glob(\\\"D:\\\\\\\\anthems\\\\\\\\data\\\\\\\\*.mid\\\"):\\n midi = converter.parse(file)\\n\\n print(\\\"Parsing %s\\\" % file)\\n\\n notes_to_parse = None\\n\\n try:\\n s2 = instrument.partitionByInstrument(midi)\\n notes_to_parse = s2.parts[0].recurse() \\n except:\\n notes_to_parse = midi.flat.notes\\n \\n for element in notes_to_parse:\\n if isinstance(element, note.Note):\\n notes.append(str(element.pitch))\\n durs.append(element.duration.quarterLength)\\n elif isinstance(element, chord.Chord):\\n notes.append('.'.join(str(n) for n in element.normalOrder))\\n durs.append(element.duration.quarterLength)\\n\\n return notes, durs\",\n \"def add_notes(self, notes):\\n if hasattr(notes, \\\"notes\\\"):\\n for x in notes.notes:\\n self.add_note(x)\\n return self.notes\\n elif hasattr(notes, \\\"name\\\"):\\n self.add_note(notes)\\n return self.notes\\n elif isinstance(notes, six.string_types):\\n self.add_note(notes)\\n return self.notes\\n for x in notes:\\n if isinstance(x, list) and len(x) != 1:\\n if len(x) == 2:\\n self.add_note(x[0], x[1])\\n else:\\n self.add_note(x[0], x[1], x[2])\\n else:\\n self.add_note(x)\\n return self.notes\",\n \"def note_list(request):\\n user = request.user\\n notes = Note.objects.filter(author=user)\\n serializer = NoteSerializer(notes, many=True)\\n return Response(serializer.data)\",\n \"def ordered_notes(self, obj, sig_features, tabs):\\n supports = dict(\\n [(str(support.id), support) for support in obj.all_supports])\\n notes = []\\n for browsers in sig_features.values():\\n for section in tabs:\\n for browser in section['browsers']:\\n sig_supports = browsers.get(browser, [])\\n for sig_support_pk in sig_supports:\\n support = supports[sig_support_pk]\\n if support.note:\\n notes.append(sig_support_pk)\\n return OrderedDict((note, i) for i, note in enumerate(notes, 1))\",\n \"def notes(self):\\r\\n return notes.Notes(self)\",\n \"def notes(self):\\r\\n return notes.Notes(self)\",\n \"def get_notes(n_notes=3):\\n notes = []\\n \\n for ii, file in enumerate(glob.glob(\\\"data/maestro-v2.0.0/2004/*.midi\\\")):\\n if ii >= n_notes:\\n break\\n pickle_file_name = file[:-4] + 'pkl'\\n\\n if os.path.isfile(pickle_file_name):\\n print(f'Reading parsed file: {pickle_file_name}')\\n with open(pickle_file_name, 'rb') as handle:\\n midi = pickle.load(handle)\\n else:\\n midi = converter.parse(file)\\n\\n with open(pickle_file_name, 'wb') as handle:\\n print(f'writing parsed file: {pickle_file_name}')\\n unserialized_data = pickle.dump(midi, \\n handle, \\n protocol=pickle.HIGHEST_PROTOCOL\\n )\\n\\n\\n print(\\\"Parsing %s\\\" % file)\\n\\n notes_to_parse = None\\n try: # file has instrument parts\\n s2 = instrument.partitionByInstrument(midi)\\n notes_to_parse = s2.parts[0].recurse() \\n except: # file has notes in a flat structure\\n notes_to_parse = midi.flat.notes\\n \\n for element in notes_to_parse:\\n if isinstance(element, note.Note):\\n notes.append(str(element.pitch))\\n elif isinstance(element, chord.Chord):\\n notes.append('.'.join(str(n) for n in element.normalOrder))\\n \\n return notes\",\n \"def get_note_names(self):\\n res = []\\n for n in self.notes:\\n if n.name not in res:\\n res.append(n.name)\\n return res\",\n \"def test_list_notes(self):\\n pass\",\n \"def notes(self):\\n return notes.Notes(self)\",\n \"def PublicNotes(self, default=[{}]):\\n tmp = self.data.get('metadata', {}).get('public_notes', default)\\n return [HEP.SourcedValueObject(i) for i in tmp]\",\n \"def getNotes(self, *args):\\n return _libsbml.SBase_getNotes(self, *args)\",\n \"def listNoteVersions(self, authenticationToken, noteGuid):\\r\\n pass\",\n \"def fNoteLists(self, match):\\n att, start_char, g_links, extras = match.groups()\\n start_char = start_char or 'a'\\n index = '%s%s%s' % (g_links, extras, start_char)\\n result = ''\\n\\n if index not in self.notelist_cache:\\n o = []\\n if self.notes:\\n for seq, info in self.notes.items():\\n links = self.makeBackrefLink(info, g_links, start_char)\\n atts = ''\\n if 'def' in info:\\n infoid = info['id']\\n atts = info['def']['atts']\\n content = info['def']['content']\\n li = (\\\"\\\"\\\"\\\\t<li%s>%s<span id=\\\"note%s\\\"> </span>%s</li>\\\"\\\"\\\"\\n % (atts, links, infoid, content))\\n else:\\n li = (\\\"\\\"\\\"\\\\t<li%s>%s Undefined Note [#%s].<li>\\\"\\\"\\\" %\\n (atts, links, info['seq']))\\n o.append(li)\\n if '+' == extras and self.unreferencedNotes:\\n for seq, info in self.unreferencedNotes.items():\\n if info['def']:\\n atts = info['def']['atts']\\n content = info['def']['content']\\n li = \\\"\\\"\\\"\\\\t<li%s>%s</li>\\\"\\\"\\\" % (atts, content)\\n o.append(li)\\n self.notelist_cache[index] = u\\\"\\\\n\\\".join(o)\\n result = self.notelist_cache[index]\\n if result:\\n list_atts = self.pba(att)\\n result = \\\"\\\"\\\"<ol%s>\\\\n%s\\\\n</ol>\\\"\\\"\\\" % (list_atts, result)\\n return result\",\n \"def getNotes(self):\\n logger.debug(\\\"Func: getNotes\\\")\\n\\n return self._currentNotes\",\n \"def getNotes(self):\\n return self.__notes\",\n \"def notes(self):\\n return self._notes\",\n \"def __init__(self):\\n self.notes = []\",\n \"def notes(self):\\n return self.__notes\",\n \"def get_notes():\\n song_index_to_notes = {}\\n\\n for file in glob.glob(\\\"../data/midi/*.mid\\\"):\\n midi = converter.parse(file)\\n song_index = int(os.path.splitext(os.path.basename(file))[0])\\n #print(\\\"Parsing %s with an index %d\\\" % (file, song_index))\\n\\n notes_to_parse = None\\n notes = []\\n try: # file has instrument parts\\n s2 = instrument.partitionByInstrument(midi)\\n notes_to_parse = s2.parts[0].recurse()\\n except: # file has notes in a flat structure\\n notes_to_parse = midi.flat.notes\\n\\n for element in notes_to_parse:\\n if isinstance(element, note.Note):\\n notes.append(str(element.pitch))\\n elif isinstance(element, chord.Chord):\\n notes.append('.'.join(str(n) for n in element.normalOrder))\\n\\n song_index_to_notes[song_index] = notes\\n\\n return song_index_to_notes\",\n \"def return_markers(self):\\r\\n ent_file = join(self.filename, self._basename + '.ent')\\r\\n if not exists(ent_file):\\r\\n ent_file = join(self.filename, self._basename + '.ent.old')\\r\\n\\r\\n try:\\r\\n ent_notes = _read_ent(ent_file)\\r\\n\\r\\n except (FileNotFoundError, PermissionError):\\r\\n markers = []\\r\\n\\r\\n else:\\r\\n allnote = []\\r\\n for n in ent_notes:\\r\\n try:\\r\\n n['value'].keys()\\r\\n allnote.append(n['value'])\\r\\n except AttributeError:\\r\\n lg.debug('Note of length {} was not '\\r\\n 'converted to dict'.format(n['length']))\\r\\n\\r\\n s_freq = self._hdr['erd']['sample_freq']\\r\\n pcname = '0CFEBE72-DA20-4b3a-A8AC-CDD41BFE2F0D'\\r\\n note_time = []\\r\\n note_name = []\\r\\n note_note = []\\r\\n for n in allnote:\\r\\n if n['Text'] == 'Analyzed Data Note':\\r\\n continue\\r\\n if not n['Text']:\\r\\n continue\\r\\n if 'User' not in n['Data'].keys():\\r\\n continue\\r\\n user1 = n['Data']['User'] == 'Persyst'\\r\\n user2 = False # n['Data']['User'] == 'eeg'\\r\\n user3 = n['Data']['User'] == pcname\\r\\n user4 = n['Data']['User'] == 'XLSpike - Intracranial'\\r\\n user5 = n['Data']['User'] == 'XLEvent - Intracranial'\\r\\n if user1 or user2 or user3 or user4 or user5:\\r\\n continue\\r\\n if len(n['Data']['User']) == 0:\\r\\n note_name.append('-unknown-')\\r\\n else:\\r\\n note_name.append(n['Data']['User'].split()[0])\\r\\n note_time.append(n['Stamp'] / s_freq)\\r\\n note_note.append(n['Text'])\\r\\n\\r\\n markers = []\\r\\n for time, name, note in zip(note_time, note_name, note_note):\\r\\n m = {'name': note + ' (' + name + ')',\\r\\n 'start': time,\\r\\n 'end': time,\\r\\n 'chan': None,\\r\\n }\\r\\n markers.append(m)\\r\\n\\r\\n return markers\",\n \"def get_notes(self, note_limit=200):\\n return Note.get_by_person_record_id(\\n self.subdomain, self.record_id, limit=note_limit)\",\n \"async def _view_all_notes(self, ctx: Context):\\n\\n author = ctx.author\\n\\n note_infos = []\\n\\n embed_links = ctx.channel.permissions_for(ctx.guild.me).embed_links\\n\\n author_str = f\\\"{author.name}'\\\"\\n\\n if author.name[-1].lower() != \\\"s\\\":\\n author_str += \\\"s\\\"\\n\\n async with self.config.member(author).notes() as notes:\\n total = len(notes)\\n for page_num, note in enumerate(notes, start=1):\\n msg_info = \\\"\\\"\\n if note[\\\"author\\\"]:\\n msg_info += _(\\\"**Author:** {}\\\").format(note[\\\"author\\\"])\\n if note[\\\"channel\\\"]:\\n msg_info += _(\\\"\\\\n**Channel:** {}\\\").format(note[\\\"channel\\\"])\\n if note[\\\"jump_url\\\"]:\\n if embed_links:\\n msg_info += _(\\n \\\"\\\\n[Click here to jump to message]({})\\\"\\n ).format(note[\\\"jump_url\\\"])\\n else:\\n msg_info += _(\\n \\\"\\\\n**Jump To Message:** {}\\\"\\n ).format(note[\\\"jump_url\\\"])\\n\\n note_info = _(\\n \\\"{}\\\\n\\\\n**Note:**\\\\n```{}```\\\\n**Reason:**\\\\n```{}```\\\"\\n ).format(\\n msg_info,\\n note[\\\"note\\\"],\\n note[\\\"reason\\\"]\\n ).strip()\\n\\n if embed_links:\\n page = discord.Embed(\\n colour=0xff0000,\\n description=note_info,\\n title=_(\\\"{} TvM Notes\\\").format(author_str),\\n timestamp=ctx.message.created_at\\n )\\n\\n page.set_footer(\\n text=_(\\\"Page {page_num}/{leng}\\\").format(\\n page_num=page_num, leng=total\\n )\\n )\\n else:\\n page = _(\\n \\\"**{author} TvM Notes**\\\"\\n \\\"\\\\n\\\\n{note}\\\"\\n \\\"\\\\n{footer}\\\"\\n ).format(\\n author=author_str,\\n note=note_info,\\n footer=_(\\\"*Page {page_num}/{leng}*\\\").format(\\n page_num=page_num, leng=total\\n )\\n )\\n\\n note_infos.append(page)\\n\\n await menu(ctx, note_infos, DEFAULT_CONTROLS)\",\n \"def notes(self):\\n return NotesTable(self.rpc, self.name)\",\n \"def notes(self):\\n return Notes(self)\",\n \"def get_scheduled_notes(event):\\n def _sort_note_by(note):\\n obj = note.object\\n if hasattr(obj, 'start_dt'):\\n return obj.start_dt, 0\\n else:\\n return obj.contribution.start_dt, obj.position\\n\\n tt_entries = (event.timetable_entries\\n .filter(TimetableEntry.type != TimetableEntryType.BREAK)\\n .options(joinedload('session_block').joinedload('contributions').joinedload('subcontributions'))\\n .options(joinedload('contribution').joinedload('subcontributions'))\\n .options(noload('break_'))\\n .all())\\n # build a list of all the objects we need notes for. that way we can query\\n # all notes in a single go afterwards instead of making the already-huge\\n # timetable query even bigger.\\n objects = set()\\n for entry in tt_entries:\\n objects.add(entry.object)\\n if entry.type == TimetableEntryType.CONTRIBUTION:\\n objects.update(sc for sc in entry.object.subcontributions if not sc.is_deleted)\\n elif entry.type == TimetableEntryType.SESSION_BLOCK:\\n for contrib in entry.object.contributions:\\n objects.add(contrib)\\n objects.update(sc for sc in contrib.subcontributions if not sc.is_deleted)\\n notes = [x for x in event.all_notes.filter_by(is_deleted=False) if x.object in objects]\\n return sorted(notes, key=_sort_note_by)\",\n \"def get_todo_list():\\n\\n # assume that a \\\"h264\\\" encoded file is complete\\n return models.LibraryItem.objects.filter(h264=False)\",\n \"def listNoteVersions(self, authenticationToken, noteGuid):\\r\\n self.send_listNoteVersions(authenticationToken, noteGuid)\\r\\n return self.recv_listNoteVersions()\",\n \"def notes(self):\\r\\n return notes.UserNotes(self)\",\n \"def all_notes():\\n \\n return render_template('all_notes.html',colors=music_color,)\",\n \"def read_notes():\\n\\n if current_user.is_admin is False:\\n notes = Note.query.filter_by(created_by=current_user.id).all()\\n else:\\n notes = Note.query.all()\\n\\n return render_template('notes/index.html.j2', notes=notes, title='Notes')\",\n \"def get_all_notes(user_id, token):\\n user = User.get(User.id == user_id).username\\n if not validate_token(user, token):\\n return HTTPResponse(status=500, body={\\\"message\\\":\\\"Validation error.\\\"})\\n res = []\\n for note in Notes.select():\\n if note.user.id == user_id:\\n new_note = model_to_dict(note)\\n res.append({\\\"id\\\":new_note['id'], \\\"title\\\":new_note['title'],\\n \\\"content\\\":new_note['content']})\\n new_token = generate_token(user)\\n body = {\\\"user_id\\\":user_id, \\\"token\\\":new_token.decode('utf-8'), 'items':res}\\n return HTTPResponse(status=200, body=body)\",\n \"def news(self) -> List[News]:\\n return self._news\",\n \"def reconstruct(notes, resolution):\\n # Construct the MusPy Music object\\n music = muspy.Music(resolution=resolution, tempos=[muspy.Tempo(0, 100)])\\n\\n # Append the tracks\\n programs = sorted(set(note[-1] for note in notes))\\n for program in programs:\\n music.tracks.append(muspy.Track(program))\\n\\n # Append the notes\\n for beat, position, pitch, duration, program in notes:\\n time = beat * resolution + position\\n track_idx = programs.index(program)\\n music[track_idx].notes.append(muspy.Note(time, pitch, duration))\\n\\n return music\",\n \"def remove_duplicate_notes(self):\\n res = []\\n for x in self.notes:\\n if x not in res:\\n res.append(x)\\n self.notes = res\\n return res\",\n \"def sort(self):\\n self.notes.sort()\",\n \"def noteList(ownerId, page = 0, count = 15):\\n\\n query = QUERY_GET_NOTES_LIST\\n query = query.format(**{'owner_id':ownerId, 'offset':(page * count), 'count':count})\\n\\n cursor.execute(query)\\n data = cursor.fetchall()\\n\\n if data == None:\\n return False, ERROR_GETTING_NOTES_LIST, 'Failed', None\\n\\n notesList = []\\n\\n for noteTuple in data:\\n id, title, subject, creationDate, changeDate, ownerId = noteTuple\\n noteDict = {\\n 'id':id,\\n 'title':title,\\n 'subject':subject,\\n 'creation_date':creationDate,\\n 'change_date':changeDate,\\n 'owner_id':ownerId\\n }\\n notesList.append(noteDict)\\n\\n return True, NO_ERROR, '', notesList\",\n \"def test_list_note_occurrences(self):\\n pass\",\n \"def notes(self):\\r\\n return TicketNotes(self)\",\n \"def extract_notes(bars):\\n output = []\\n for bar in bars:\\n notes = np.array([bar_to_notes(b,i) for i,b in enumerate(bar)])\\n\\n # Flatten into a single array\\n midi_notes = []\\n for i in range(notes.shape[1]):\\n if all(notes[:,i] == -1):\\n midi_notes.append(None)\\n else:\\n midi_notes.append([n for n in notes[:,i] if n!=-1])\\n output.append(midi_notes)\\n return output\",\n \"def starts_with_tonic(a_list):\\n key = a_list.track.bars[0].key.name\\n note = a_list.get_first_actual_note()\\n if note.name == key:\\n return []\\n else:\\n return [note.start]\",\n \"def midiToNotes(notes):\\n names = ['C', 'C#', 'D', 'D#', 'E', 'F', 'F#', 'G', 'G#', 'A', 'A#', 'B']\\n chord = []\\n \\n for note in notes:\\n name = names[note%12]\\n octave = int((note-12)/12)\\n chord.append(name + str(octave))\\n \\n return chord\",\n \"def load_org_notes_file():\\n nodelist = orgnode.makelist(NOTES_ORG_FILE)\\n return nodelist\",\n \"def todolists(self):\\r\\n return tdl.Todolists(self)\",\n \"def notes_in_path(self, path: str) -> List[NoteInfo]:\\n check_path = os.path.abspath(path)\\n return [n for n in self.notes.values() if n.file_path.startswith(check_path)]\",\n \"def get_contenu(self):\\n return self.objets\",\n \"def page_list_notes(self, no_history=False):\\n\\n list_taskid_desc = self.task_store.sort_taskid_list_descending_lamport()\\n title = \\\"woolnote - all notes\\\"\\n page_header_first_text = \\\"all notes\\\"\\n\\n if no_history:\\n history_id = self.save_history([])\\n else:\\n history_id = self.save_history([\\\"action\\\"], alt_task_store_name=None)\\n\\n page_header_list_of_warnings = None\\n page_header_small_text = None\\n\\n if self.error_msg_queue_list:\\n page_header_list_of_warnings = self.helper_convert_msg_queue_list_to_list_for_output()\\n else:\\n try:\\n # TODO: use asn library?\\n # sha256_fp = read_pem_cert_fingerprint(SSL_CERT_PEM_PATH)\\n page_header_small_text = config.SSL_CERT_PEM_FINGERPRINT\\n except:\\n page_header_small_text = \\\"cannot get ssl cert sha256\\\"\\n\\n return html_page_templates.page_list_notes_template(list_taskid_desc=list_taskid_desc,\\n self_sess_action_auth=self.sess_action_auth, title=title,\\n history_back_id=history_id, primary_task_store=self.task_store,\\n virtual_folders=self.woolnote_config.virtual_folders,\\n single_task_line_ids=set(self.woolnote_config.single_note_line_id.keys()),\\n page_header_first_text=page_header_first_text,\\n page_header_optional_small_second_text=page_header_small_text,\\n page_header_optional_list_of_warnings=page_header_list_of_warnings)\",\n \"def get_notes(notes_type=\\\"national\\\", area=None):\\n query_menu = get_query_menu(area)\\n query = query_menu[notes_type][\\\"query\\\"]\\n collection = query_menu[notes_type][\\\"collection\\\"]\\n notes = \\\"\\\"\\n try:\\n doc = next(collection.find(query).sort([(DATE_KEY, -1)]).limit(1))\\n notes = doc[NOTE_KEY] if doc[NOTE_KEY] != 0 else None\\n except StopIteration:\\n app.logger.error(\\\"While getting notes: no data\\\")\\n return notes if notes is not None and not rubbish_notes(notes) else \\\"\\\"\",\n \"def _on_note(self) -> Iterator:\\n return filter(\\n lambda x: x.type == constants.NOTE_ON, self.midi_data)\",\n \"def objects(self):\",\n \"def do_notes(self, sym):\\n # DRY: duplication with Roman.py - FIXME\\n if sym == '-':\\n # REST\\n return []\\n note_or_chord = self.do(sym)\\n if note_or_chord is None:\\n return []\\n elif type(note_or_chord) == Chord:\\n return note_or_chord.notes\\n else:\\n return [ note_or_chord ]\",\n \"def extract_feature_notes(contig, type, start, stop, direction, notes, pos, ref, alt):\\n notes = re.sub(\\\"%\\\\d+\\\", \\\" \\\", notes)\\n desc = re.search(\\\"Note=(.*?);\\\", notes)\\n id = re.search(\\\"ID=(.*?);\\\", notes)\\n name = re.search(\\\"Name=(.*?);\\\", notes)\\n\\n if id and name and desc:\\n print ('Contig: ' + contig)\\n print ('Annotation_type: ' + type)\\n print ('Start:Stop:Direction '+ start+':'+stop+':'+direction)\\n print (\\\"Position: \\\" + pos)\\n print ('Ref:Alt: ' + ref + ':'+ alt)\\n print(\\\"ID: \\\" + id.group(1))\\n # print(\\\"Name: \\\" + name.group(1))\\n print(\\\"Description: \\\" + desc.group(1))\\n # if name:\\n # print(\\\"Name: \\\" + name.group(1))\\n #\\n # if desc:\\n # print(\\\"Desc: \\\" + desc.group(1))\\n\\n\\n print()\",\n \"def release_notes(self):\\n return self._release_notes\",\n \"def extract_notes(music, resolution):\\n # Check resolution\\n assert music.resolution == resolution\\n\\n # Extract notes\\n notes = []\\n for track in music:\\n if track.is_drum or track.program not in KNOWN_PROGRAMS:\\n continue\\n for note in track:\\n beat, position = divmod(note.time, resolution)\\n notes.append(\\n (beat, position, note.pitch, note.duration, track.program)\\n )\\n\\n # Deduplicate and sort the notes\\n notes = sorted(set(notes))\\n\\n return np.array(notes)\",\n \"def bar_to_lilypond_notes(notes):\\n lp_notes = []\\n if notes[0] is None:\\n notes = notes[1:]\\n if notes[0] is None:\\n lp_notes.append(\\\"r\\\")\\n for n in notes:\\n if n is None:\\n continue\\n if type(n) is list:\\n lp_notes.append([midi_to_lilypond_note(x) for x in n])\\n else:\\n lp_notes.append(midi_to_lilypond_note(n))\\n return lp_notes\",\n \"def notes(self):\\r\\n return notes.ForumSuggestionNotes(self)\",\n \"def getNote(self):\\n return (self.noteName[0], self.accidental, self.octave)\",\n \"def starts_with_tonic_or_fifth(a_list):\\n key = a_list.track.bars[0].key\\n note = a_list.get_first_actual_note()\\n possible_notes = [key.name, Note(key).transpose('5', True).name]\\n if note.name in possible_notes:\\n return []\\n else:\\n return [note.start]\",\n \"def show():\\n\\n quality_list = []\\n\\n conn = sqlite3.connect(\\\"person_database.bd\\\")\\n c = conn.cursor()\\n\\n c.execute(\\\"SELECT *, oid FROM person_info\\\")\\n records = c.fetchall()\\n\\n conn.commit()\\n conn.close()\\n\\n for record in records:\\n quality_list.append(str(record[2]) + \\\" \\\" + str(record[0]))\\n\\n return quality_list\",\n \"def list(self):\",\n \"def get_notes_from_uuids(dictionary, uuid_list):\\n titles = []\\n texts = []\\n notes = []\\n for key in dictionary:\\n if dictionary[key][\\\"content_type\\\"] == \\\"Note\\\" and dictionary[key][\\\"uuid\\\"] in uuid_list:\\n try:\\n titles.append(dictionary[key][\\\"content\\\"][\\\"title\\\"])\\n texts.append(dictionary[key][\\\"content\\\"][\\\"text\\\"])\\n notes = list(zip(titles, texts))\\n except KeyError:\\n continue\\n return notes\",\n \"def getMaintainerList(self):\\n return self.getDocumentedObject().getMaintainerList()\",\n \"def topics(self):\\r\\n return contents.Topics(self)\",\n \"def note_list(self, evarnames=\\\"\\\", in_=None):\\n evarnames = self._find_vars(evarnames, evars=True, \\n unique=True, empty_ok=True)\\n if len(evarnames) == 0:\\n evarnames = ['_dta'] + self._varlist\\n if in_ is not None:\\n if isinstance(in_, int):\\n in_ = (in_,)\\n elif (not isinstance(in_, collections.Iterable)\\n or not all(isinstance(n, int) for n in in_)):\\n raise TypeError(\\\"in_ should be int or iterable of int\\\")\\n if any(n <= 0 for n in in_):\\n raise ValueError(\\\"note numbers must be >= 1\\\")\\n for name in evarnames:\\n if name not in self._chrdict: continue\\n chars = self._chrdict[name]\\n if 'note0' not in chars: continue\\n nnotes = int(chars['note0'])\\n in_range = in_ if in_ is not None else range(1, nnotes + 1)\\n note_info = []\\n for note_num in in_range:\\n note_name = 'note' + str(note_num)\\n if note_name in chars:\\n note_info.append(note_num)\\n if note_info != []:\\n note_info.insert(0, name)\\n self._display_notes(note_info)\",\n \"def inverse_notes(note_list):\\n raw_output = []\\n for note in note_list:\\n # We transpose every notes except for Z\\n if note.is_pause:\\n raw_output.append(f'{note.name}{note.figure}')\\n else:\\n # Get the index of the note to transpose\\n note_name_index = NOTE_NAMES.index(note.name)\\n # Get the transposed index according to the amount\\n inverted_index = ((len(NOTE_NAMES) - note_name_index) % len(NOTE_NAMES))\\n new_note_name = NOTE_NAMES[inverted_index]\\n # Create the new note, with the new note's name, and its original figure\\n new_note = f'{new_note_name}{note.figure}'\\n new_note += 'p' if note.has_point else ''\\n raw_output.append(new_note)\\n\\n return raw_output\",\n \"def Students_in_majors(l:list,i:list)->list:\\n result=[]\\n for s in l:\\n if s.major in i:\\n result.append(s)\\n return result\",\n \"def get_user_visible_notes():\\n return grammar.user_visible_notes\",\n \"def load_working(self, path: str, files: List) -> List[NoteInfo]:\\n if not files:\\n return self.notes_in_path(path)\\n else:\\n abs_paths = map(os.path.abspath, files)\\n return [self.notes[f] for f in abs_paths if f in self.notes]\",\n \"def getNoteVersion(self, authenticationToken, noteGuid, updateSequenceNum, withResourcesData, withResourcesRecognition, withResourcesAlternateData):\\r\\n pass\",\n \"def todos(self):\\r\\n return Todos(self)\",\n \"def get_notes_for_section(self, section, notes_property=\\\"notes\\\"):\\n return self.get_notes_target(\\n self.sections[section], notes_property=notes_property\\n )\",\n \"def showall_incremental (self,\\r\\n entrylist=None,\\r\\n highlight=None,\\r\\n childrentoo=True,\\r\\n levels=0,\\r\\n index=None,\\r\\n beforeafter=10):\\r\\n\\r\\n\\r\\n\\r\\n if entrylist is None:\\r\\n\\r\\n if not self.default_dict['sortbydate']:\\r\\n entrylist = self.default_dict['indexlist_indexes'].list\\r\\n else:\\r\\n entrylist = self.default_dict['indexlist_indexes'].list\\r\\n entrylist = self.index_sort([Index(a_temp)\\r\\n for a_temp\\r\\n in entrylist],\\r\\n by_date=self.default_dict['sortbydate'],quick=True)\\r\\n\\r\\n else:\\r\\n entrylist = self.index_sort([Index(a_temp)\\r\\n for a_temp\\r\\n in entrylist],\\r\\n by_date=self.default_dict['sortbydate'],quick=False)\\r\\n\\r\\n\\r\\n\\r\\n if not isinstance(entrylist[0], str):\\r\\n entrylist = [str(a_temp) for a_temp in entrylist]\\r\\n\\r\\n keep_on = True\\r\\n inp_temp = EMPTYCHAR\\r\\n while keep_on:\\r\\n\\r\\n\\r\\n if index is None:\\r\\n index = entrylist[0]\\r\\n\\r\\n if index in entrylist:\\r\\n\\r\\n indexpoint = entrylist.index(index) # index is the index of the note\\r\\n else:\\r\\n indexpoint = 0\\r\\n begin_point = max([0,indexpoint-beforeafter])\\r\\n end_point = min([len(entrylist)-1,indexpoint+beforeafter])\\r\\n\\r\\n segment = entrylist[begin_point:end_point]\\r\\n\\r\\n inc_displaylist = DisplayList (displayobject=display)\\r\\n header_temp = str(segment[0]) + LONGDASH + str(segment[-1])\\r\\n\\r\\n self.showall(entrylist=segment,\\r\\n highlight=highlight,\\r\\n childrentoo=childrentoo,\\r\\n levels=levels,\\r\\n shortshow=True,\\r\\n multi=False,\\r\\n quick=False,\\r\\n header=header_temp)\\r\\n\\r\\n if not inp_temp:\\r\\n inp_temp = input(queries.MENU_ONE+BLANK)\\r\\n if inp_temp[0].lower() == 'q':\\r\\n inp_temp = EMPTYCHAR\\r\\n if inp_temp:\\r\\n if inp_temp[0] == '>':\\r\\n\\r\\n index = segment[-1]\\r\\n elif inp_temp[0] == '<':\\r\\n index = segment[0]\\r\\n elif inp_temp[0] == '[':\\r\\n index = entrylist[0]\\r\\n elif inp_temp[0] == ']':\\r\\n index = entrylist[-1]\\r\\n inp_temp = inp_temp[1:]\\r\\n\\r\\n else:\\r\\n keep_on = False\",\n \"def add_evernote_notes(self, evernote_notes, update=False, log_update_if_unchanged=True):\\n new_nids=[]\\n action_str_base = ['Add', 'Update'][update]\\n action_str = ['Adding', 'Updating'][update]\\n action_preposition = ['To', 'In'][update]\\n info = stopwatch.ActionInfo(action_str + ' Of', 'Evernote Notes', action_preposition + ' Anki', report_if_empty=False)\\n tmr = stopwatch.Timer(evernote_notes, 10, info=info,\\n label='Add\\\\\\\\Anki-%sEvernoteNotes' % action_str_base)\\n\\n for ankiNote in evernote_notes:\\n try:\\n title = ankiNote.FullTitle\\n content = decode(ankiNote.Content)\\n anki_field_info = {\\n FIELDS.TITLE: title,\\n FIELDS.CONTENT: content,\\n FIELDS.EVERNOTE_GUID: FIELDS.EVERNOTE_GUID_PREFIX + ankiNote.Guid,\\n FIELDS.UPDATE_SEQUENCE_NUM: str(ankiNote.UpdateSequenceNum),\\n FIELDS.SEE_ALSO: u''\\n }\\n except Exception:\\n log_error(\\\"Unable to set field info for: Note '%s': '%s'\\\" % (ankiNote.FullTitle, ankiNote.Guid))\\n log_dump(ankiNote.Content, \\\" NOTE CONTENTS \\\")\\n # log_dump(encode(ankiNote.Content), \\\" NOTE CONTENTS \\\")\\n raise\\n tmr.step(title)\\n baseNote = None\\n if update:\\n baseNote = self.get_anki_note_from_evernote_guid(ankiNote.Guid)\\n if not baseNote:\\n log_error('Updating note %s: COULD NOT FIND BASE NOTE FOR ANKI NOTE ID' % ankiNote.Guid)\\n tmr.reportStatus(EvernoteAPIStatus.MissingDataError)\\n continue\\n if ankiNote.Tags is None:\\n log_error(\\\"Could note find tags object for note %s: %s. \\\" % (ankiNote.Guid, ankiNote.FullTitle))\\n tmr.reportStatus(EvernoteAPIStatus.MissingDataError)\\n continue\\n anki_note_prototype = AnkiNotePrototype(self, anki_field_info, ankiNote.TagNames, baseNote,\\n notebookGuid=ankiNote.NotebookGuid, count=tmr.count,\\n count_update=tmr.counts.updated.completed.val, max_count=tmr.max)\\n anki_note_prototype._log_update_if_unchanged_ = log_update_if_unchanged\\n nid = tmr.autoStep(anki_note_prototype.update_note() if update else anki_note_prototype.add_note(),\\n ankiNote.FullTitle, update)\\n if tmr.status.IsSuccess and not update:\\n new_nids.append([nid, ankiNote.Guid])\\n elif tmr.status.IsError:\\n log(\\\"ANKI ERROR WHILE %s EVERNOTE NOTES: \\\" % action_str.upper() + str(tmr.status), tmr.label + '-Error')\\n tmr.Report()\\n if new_nids:\\n ankDB().executemany(\\\"UPDATE {n} SET nid = ? WHERE guid = ?\\\", new_nids)\\n return tmr.counts.success\",\n \"def get_informatieobjecttypen(\\n clients: List[Client] = None,\\n) -> List[InformatieObjectType]:\\n if clients is None:\\n clients = _get_ztc_clients()\\n\\n catalogi = {cat.url: cat for cat in get_catalogi(clients=clients)}\\n iots = _fetch_list(\\\"informatieobjecttype\\\", clients, InformatieObjectType)\\n\\n # resolve relations\\n for iot in iots:\\n iot.catalogus = catalogi[iot.catalogus]\\n\\n return iots\",\n \"def test_get_note(self):\\n pass\",\n \"def _display_notes(self, note_info):\\n if note_info == []: return\\n evarname = note_info[0]\\n chars = self._chrdict[evarname]\\n \\n if IN_STATA:\\n tplt = \\\"{{text}}{:>3}. {}\\\"\\n print(\\\"\\\\n{res}\\\" + evarname)\\n else:\\n tplt = \\\"{:>3}. {}\\\"\\n print(\\\"\\\\n\\\" + evarname)\\n \\n for num in note_info[1:]:\\n print(tplt.format(num, chars['note' + str(num)]))\",\n \"def midi_to_lilypond_note(note):\\n return all_notes[note+4]\",\n \"def getDependencyList(self):\\n return self.getDocumentedObject().getDependencyList()\",\n \"def get_partlist(self):\\n\\n # Longdale check\\n longdale_user = user_is_ingroup(self.request, \\\"longdale_user\\\")\\n # REtrieve the correct queryset, sorted on the correct levels\\n if longdale_user:\\n qs = [prt for prt in Part.objects.all().order_by('corpus__lng', 'corpus__name', 'name')]\\n else:\\n longdale = \\\"Longdale\\\"\\n qs = [prt for prt in Part.objects.exclude(Q(name__istartswith=longdale)).order_by('corpus__lng', 'corpus__name', 'name')]\\n # REtrieve the correct queryset, sorted on the correct levels\\n # qs = [prt for prt in Part.objects.all().order_by('corpus__lng', 'corpus__name', 'name')]\\n # Start the output\\n html = []\\n # Initialize the variables whose changes are important\\n lVars = [\\\"corpus_lng\\\", \\\"corpus_name\\\", \\\"name\\\"]\\n lFuns = [Part.language, [\\\"corpus\\\", \\\"name\\\"], [\\\"name\\\"]]\\n # Get a list of items containing 'first' and 'last' information\\n lItem = get_item_list(lVars, lFuns, qs)\\n # REturn this list\\n return lItem\",\n \"def get_entries_by_notes(self, notes, regex=False, flags=None,\\n group=None, history=False, first=False): \\n if self.database is None:\\n raise DatabaseNotOpened('No KeePass Database Opened.')\\n else:\\n return self.database.find_entries_by_notes(notes,\\n regex,\\n flags,\\n group,\\n history,\\n first)\",\n \"def GetNotes(self, request, global_params=None):\\n config = self.GetMethodConfig('GetNotes')\\n return self._RunMethod(\\n config, request, global_params=global_params)\",\n \"def get_all(self):\\n pat = re.compile('(<!-- @tk(.*?)-->)',re.M|re.S|re.I)\\n for dir in glob.iglob('./**/.book', recursive=True):\\n for fname in glob.iglob(os.path.dirname(dir) + '/**/*md', recursive=True):\\n with open(fname, 'r') as mkd_file:\\n contents = mkd_file.read()\\n\\n for todo in re.findall(pat,contents):\\n t = Todo(todo[1],fname)\\n t.context(contents)\\n t.github = self.github\\n self.todos.append(t)\\n\\n self.files.append(fname)\\n \\n return self.todos\",\n \"def obj_list(self):\\n return self._obj_list\",\n \"def list():\",\n \"def list():\",\n \"def get_note (self,\\r\\n index):\\r\\n\\r\\n\\r\\n\\r\\n\\r\\n if self.using_database:\\r\\n aprint('GETTING NOTE')\\r\\n value_tuple = (notebookname, str(index),)\\r\\n db_cursor.execute(\\\"SELECT note_body \\\"\\r\\n +\\\"FROM notes WHERE notebook=?\\\"\\r\\n +\\\" AND note_index=?;\\\",\\r\\n value_tuple)\\r\\n\\r\\n res_temp = db_cursor.fetchone()\\r\\n\\r\\n if res_temp:\\r\\n text = res_temp[0].replace(\\\"''\\\",\\\"'\\\")\\r\\n else:\\r\\n text = ''\\r\\n\\r\\n db_cursor.execute(\\\"SELECT user\\\"\\r\\n +\\\" FROM notes\\\"\\r\\n +\\\" WHERE notebook=?\\\"\\r\\n +\\\" AND note_index=?;\\\",\\r\\n value_tuple)\\r\\n res_temp = db_cursor.fetchone()\\r\\n if res_temp:\\r\\n\\r\\n user = res_temp[0]\\r\\n else:\\r\\n user = 'USER'\\r\\n\\r\\n db_cursor.execute(\\\"SELECT size\\\"\\r\\n +\\\" FROM notes\\\"\\r\\n +\\\" WHERE notebook=? \\\"\\r\\n +\\\"AND note_index=?;\\\",\\r\\n value_tuple)\\r\\n\\r\\n res_temp = db_cursor.fetchone()\\r\\n if res_temp:\\r\\n size = res_temp[0]\\r\\n else:\\r\\n size = 60\\r\\n db_cursor.execute(\\\"SELECT timestamp \\\"\\r\\n +\\\"FROM timestamps\\\"\\r\\n +\\\" WHERE notebook=?\\\"\\r\\n +\\\" AND note_index=?\\\"\\r\\n +\\\" ORDER BY timestamp\\\",\\r\\n value_tuple)\\r\\n dates = db_cursor.fetchall()\\r\\n date_list = [date[0] for date in dates]\\r\\n db_cursor.execute(\\\"SELECT keyword\\\"\\r\\n +\\\" FROM all_note_keys\\\"\\r\\n +\\\" WHERE notebook=?\\\"\\r\\n +\\\" AND note_index=?\\\",\\r\\n value_tuple)\\r\\n keyset = db_cursor.fetchall()\\r\\n keyset = {key[0] for key in keyset}\\r\\n\\r\\n metadata = {'user':user,\\r\\n 'date':date_list,\\r\\n 'size':size}\\r\\n return Note(keyset,text,metadata)\\r\\n\\r\\n if str(index) in self.note_dict:\\r\\n return self.note_dict[str(index)]\\r\\n\\r\\n return False\",\n \"def _getListing(self):\\n\\n # lets assure consistent litsting order\\n items = self._items.items()\\n items.sort()\\n return [ \\\"%s%s%s: %s\\\" % (_def_sep, str(x[1]), _def_sep, x[1].__doc__)\\n for x in items ]\",\n \"def exercises_in_object(obj = None):\\n if obj is not None and obj.isType() == \\\"assignments\\\":\\n a_exe = obj.get_exercises()\\n else:\\n a_exe = Exercise.objects.all()\\n \\n result = ()\\n for exe in a_exe:\\n if not exe.name.isspace():\\n result += ((exe.id, exe.name),)\\n return result\",\n \"def __repr__(self):\\n return str(self.notes)\",\n \"def note(self, note_id):\\r\\n return notes.Note(self, note_id)\",\n \"def find_all(cls):\\n return [AuthorModel(a['name'], str(a['_id']))\\n for a in cls.db.newsdb.find()]\"\n]","isConversational":false},"negative_scores":{"kind":"list like","value":["0.69472075","0.6502322","0.64504087","0.63504153","0.6300085","0.62833136","0.6271847","0.62041575","0.6133791","0.61144036","0.60960543","0.605202","0.6051364","0.60231435","0.6019179","0.6019179","0.60149145","0.59323066","0.5914525","0.59088063","0.59070235","0.58787584","0.58750445","0.57992446","0.5790339","0.57555974","0.571976","0.5680529","0.5641954","0.55868435","0.55830693","0.55722684","0.5559313","0.5551167","0.5548908","0.5462144","0.5457733","0.54352826","0.5424419","0.54239595","0.54099405","0.5374328","0.5353935","0.5352585","0.5330265","0.5329215","0.531712","0.5309133","0.5303966","0.5293722","0.52878046","0.5266749","0.52621406","0.525719","0.5246971","0.5222999","0.52106094","0.5190386","0.5163973","0.5160193","0.5157399","0.51510507","0.51322794","0.5119372","0.5109262","0.51048535","0.50998056","0.5098525","0.50977933","0.50941724","0.5078838","0.5067862","0.5054866","0.5054031","0.5049698","0.5045616","0.50451994","0.5041451","0.50337946","0.5029284","0.50286955","0.5023969","0.5023754","0.5022884","0.5014019","0.50094926","0.5002675","0.50018924","0.49993435","0.49960768","0.49960673","0.49931073","0.49923164","0.49818844","0.49818844","0.49794012","0.49790683","0.49748233","0.497361","0.49696296","0.4963649"],"string":"[\n \"0.69472075\",\n \"0.6502322\",\n \"0.64504087\",\n \"0.63504153\",\n \"0.6300085\",\n \"0.62833136\",\n \"0.6271847\",\n \"0.62041575\",\n \"0.6133791\",\n \"0.61144036\",\n \"0.60960543\",\n \"0.605202\",\n \"0.6051364\",\n \"0.60231435\",\n \"0.6019179\",\n \"0.6019179\",\n \"0.60149145\",\n \"0.59323066\",\n \"0.5914525\",\n \"0.59088063\",\n \"0.59070235\",\n \"0.58787584\",\n \"0.58750445\",\n \"0.57992446\",\n \"0.5790339\",\n \"0.57555974\",\n \"0.571976\",\n \"0.5680529\",\n \"0.5641954\",\n \"0.55868435\",\n \"0.55830693\",\n \"0.55722684\",\n \"0.5559313\",\n \"0.5551167\",\n \"0.5548908\",\n \"0.5462144\",\n \"0.5457733\",\n \"0.54352826\",\n \"0.5424419\",\n \"0.54239595\",\n \"0.54099405\",\n \"0.5374328\",\n \"0.5353935\",\n \"0.5352585\",\n \"0.5330265\",\n \"0.5329215\",\n \"0.531712\",\n \"0.5309133\",\n \"0.5303966\",\n \"0.5293722\",\n \"0.52878046\",\n \"0.5266749\",\n \"0.52621406\",\n \"0.525719\",\n \"0.5246971\",\n \"0.5222999\",\n \"0.52106094\",\n \"0.5190386\",\n \"0.5163973\",\n \"0.5160193\",\n \"0.5157399\",\n \"0.51510507\",\n \"0.51322794\",\n \"0.5119372\",\n \"0.5109262\",\n \"0.51048535\",\n \"0.50998056\",\n \"0.5098525\",\n \"0.50977933\",\n \"0.50941724\",\n \"0.5078838\",\n \"0.5067862\",\n \"0.5054866\",\n \"0.5054031\",\n \"0.5049698\",\n \"0.5045616\",\n \"0.50451994\",\n \"0.5041451\",\n \"0.50337946\",\n \"0.5029284\",\n \"0.50286955\",\n \"0.5023969\",\n \"0.5023754\",\n \"0.5022884\",\n \"0.5014019\",\n \"0.50094926\",\n \"0.5002675\",\n \"0.50018924\",\n \"0.49993435\",\n \"0.49960768\",\n \"0.49960673\",\n \"0.49931073\",\n \"0.49923164\",\n \"0.49818844\",\n \"0.49818844\",\n \"0.49794012\",\n \"0.49790683\",\n \"0.49748233\",\n \"0.497361\",\n \"0.49696296\",\n \"0.4963649\"\n]","isConversational":false},"document_score":{"kind":"string","value":"0.0"},"document_rank":{"kind":"string","value":"-1"}}},{"rowIdx":23777,"cells":{"query":{"kind":"string","value":"Return a list of note objects in minor"},"document":{"kind":"string","value":"def mgChordMinor(value):\n chord = [MgNote(value), MgNote(value) + 3, MgNote(value)+7]\n return chord"},"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 listNotes() -> list:\n list_of_notes = []\n for note in Note.objects.all():\n list_of_notes.append({\n 'uuid': note.uuid, 'title': note.title,\n 'author': note.author, 'body': note.body, 'created_at': localtime(note.created_at)\n })\n return list_of_notes","def notes(self):\n return reapy.NoteList(self)","def getnotes():","def getNotes(self):\n return self._nednotes, self._ongcnotes","def ls(self, count = 200):\n return self._manager.ls_notes(self['id'], count)","def get_notes(n_notes=3):\n notes = []\n \n for ii, file in enumerate(glob.glob(\"data/maestro-v2.0.0/2004/*.midi\")):\n if ii >= n_notes:\n break\n pickle_file_name = file[:-4] + 'pkl'\n\n if os.path.isfile(pickle_file_name):\n print(f'Reading parsed file: {pickle_file_name}')\n with open(pickle_file_name, 'rb') as handle:\n midi = pickle.load(handle)\n else:\n midi = converter.parse(file)\n\n with open(pickle_file_name, 'wb') as handle:\n print(f'writing parsed file: {pickle_file_name}')\n unserialized_data = pickle.dump(midi, \n handle, \n protocol=pickle.HIGHEST_PROTOCOL\n )\n\n\n print(\"Parsing %s\" % file)\n\n notes_to_parse = None\n try: # file has instrument parts\n s2 = instrument.partitionByInstrument(midi)\n notes_to_parse = s2.parts[0].recurse() \n except: # file has notes in a flat structure\n notes_to_parse = midi.flat.notes\n \n for element in notes_to_parse:\n if isinstance(element, note.Note):\n notes.append(str(element.pitch))\n elif isinstance(element, chord.Chord):\n notes.append('.'.join(str(n) for n in element.normalOrder))\n \n return notes","def get_note_names(self):\n res = []\n for n in self.notes:\n if n.name not in res:\n res.append(n.name)\n return res","def notes_list():\n if request.method == \"POST\":\n note = str(request.data.get(\"text\", \"\"))\n idx = max(notes.keys()) + 1\n notes[idx] = note\n return note_repr(idx), status.HTTP_201_CREATED\n\n # request.method == 'GET'\n return [note_repr(idx) for idx in sorted(notes.keys())]","def get_note():\n\n return Note.query.all()","def view_notes(tag_list):\n final_notes = []\n for tag in tag_list:\n all_notes = Tags.objects.get(tag_text=tag)\n notes = all_notes.notes.all()\n for note in notes:\n final_notes.append(note.note_text)\n \n return final_notes","def _get_issue_notes(request, pk):\n if request.user.is_coordinator_or_better:\n note_types = IssueNote.COORDINATOR_NOTE_TYPES\n else:\n note_types = IssueNote.PARALEGAL_NOTE_TYPES\n\n return (\n IssueNote.objects.filter(issue=pk)\n .prefetch_related(\"creator__groups\")\n .filter(note_type__in=note_types)\n .order_by(\"-created_at\")\n .all()\n )","def get_objects_using_notes():\n global objects_using_notes\n\n if not objects_using_notes:\n objects_using_notes = rsh.get_objects_using_notes(get_srcs())\n\n return objects_using_notes","def get_notes():\n notes = []\n durs = []\n\n for file in glob.glob(\"D:\\\\anthems\\\\data\\\\*.mid\"):\n midi = converter.parse(file)\n\n print(\"Parsing %s\" % file)\n\n notes_to_parse = None\n\n try:\n s2 = instrument.partitionByInstrument(midi)\n notes_to_parse = s2.parts[0].recurse() \n except:\n notes_to_parse = midi.flat.notes\n \n for element in notes_to_parse:\n if isinstance(element, note.Note):\n notes.append(str(element.pitch))\n durs.append(element.duration.quarterLength)\n elif isinstance(element, chord.Chord):\n notes.append('.'.join(str(n) for n in element.normalOrder))\n durs.append(element.duration.quarterLength)\n\n return notes, durs","def getNotes(self, *args):\n return _libsbml.SBase_getNotes(self, *args)","def listNoteVersions(self, authenticationToken, noteGuid):\r\n pass","def getNotes(self):\n return self.__notes","def extract_notes(bars):\n output = []\n for bar in bars:\n notes = np.array([bar_to_notes(b,i) for i,b in enumerate(bar)])\n\n # Flatten into a single array\n midi_notes = []\n for i in range(notes.shape[1]):\n if all(notes[:,i] == -1):\n midi_notes.append(None)\n else:\n midi_notes.append([n for n in notes[:,i] if n!=-1])\n output.append(midi_notes)\n return output","def notes(self):\n return self._notes","def add_notes(self, notes):\n if hasattr(notes, \"notes\"):\n for x in notes.notes:\n self.add_note(x)\n return self.notes\n elif hasattr(notes, \"name\"):\n self.add_note(notes)\n return self.notes\n elif isinstance(notes, six.string_types):\n self.add_note(notes)\n return self.notes\n for x in notes:\n if isinstance(x, list) and len(x) != 1:\n if len(x) == 2:\n self.add_note(x[0], x[1])\n else:\n self.add_note(x[0], x[1], x[2])\n else:\n self.add_note(x)\n return self.notes","def notes(self):\n return self.__notes","def get_notes(self, note_limit=200):\n return Note.get_by_person_record_id(\n self.subdomain, self.record_id, limit=note_limit)","def notes(self):\r\n return notes.Notes(self)","def notes(self):\r\n return notes.Notes(self)","def remove_duplicate_notes(self):\n res = []\n for x in self.notes:\n if x not in res:\n res.append(x)\n self.notes = res\n return res","def getNotes(self):\n logger.debug(\"Func: getNotes\")\n\n return self._currentNotes","def test_list_notes(self):\n pass","def fNoteLists(self, match):\n att, start_char, g_links, extras = match.groups()\n start_char = start_char or 'a'\n index = '%s%s%s' % (g_links, extras, start_char)\n result = ''\n\n if index not in self.notelist_cache:\n o = []\n if self.notes:\n for seq, info in self.notes.items():\n links = self.makeBackrefLink(info, g_links, start_char)\n atts = ''\n if 'def' in info:\n infoid = info['id']\n atts = info['def']['atts']\n content = info['def']['content']\n li = (\"\"\"\\t<li%s>%s<span id=\"note%s\"> </span>%s</li>\"\"\"\n % (atts, links, infoid, content))\n else:\n li = (\"\"\"\\t<li%s>%s Undefined Note [#%s].<li>\"\"\" %\n (atts, links, info['seq']))\n o.append(li)\n if '+' == extras and self.unreferencedNotes:\n for seq, info in self.unreferencedNotes.items():\n if info['def']:\n atts = info['def']['atts']\n content = info['def']['content']\n li = \"\"\"\\t<li%s>%s</li>\"\"\" % (atts, content)\n o.append(li)\n self.notelist_cache[index] = u\"\\n\".join(o)\n result = self.notelist_cache[index]\n if result:\n list_atts = self.pba(att)\n result = \"\"\"<ol%s>\\n%s\\n</ol>\"\"\" % (list_atts, result)\n return result","def listNoteVersions(self, authenticationToken, noteGuid):\r\n self.send_listNoteVersions(authenticationToken, noteGuid)\r\n return self.recv_listNoteVersions()","def notes(self):\n return notes.Notes(self)","def noterest_to_mensural(notes, rests, modusminor):\n # Default values for notes according to the mensuration\n b_def = 2048\n\n l_def = modusminor * b_def\n l_imp = 2 * b_def\n l_perf = 3 * b_def\n\n max_def = 2 * l_def\n # The default value of the 'maxima' is double because, actually, there is no maxima in this repertoire\n # Ars Antiqua has no 'maximas' just 'duplex longs'\n\n # Notes's Part:\n # Only breves can be altered (as only longs can be perfect/imperfect)\n # Breves can't be perfect/imperfect (they are always 3 minor-semibreves long)\n for note in notes:\n dur = note.getAttribute('dur').value\n durges_num = int(note.getAttribute('dur.ges').value[:-1])\n\n # For the tied notes:\n # First find its right (contemporary) duration\n if dur == 'TiedNote!':\n # Maximas\n if durges_num == max_def:\n dur = 'maxima'\n # Longas\n elif durges_num in [l_imp, l_perf]:\n dur = 'long'\n # MISTAKE in tie duration\n else:\n print(\"Weird\\n The tied note doesn't seem to be any note in the range of longa to maxima - \" + str(note) + \", its duration is \" + str(durges_num) + \"p\")\n note.getAttribute('dur').setValue(dur)\n\n # Look for the corresponding mensural duration of the notes\n\n # MAXIMA (Duplex Longa)\n if dur == 'maxima':\n mens_dur = 'maxima'\n\n # LONGA\n elif dur == 'long':\n # Default @dur value of the note (Exception: Altered Breve)\n mens_dur = 'longa'\n\n # Looking for 'perfections', 'imperfections' and 'alterations'\n if durges_num == l_perf:\n # Perfection case\n if modusminor == 2:\n note.addAttribute('quality', 'p')\n note.addAttribute('num', '2')\n note.addAttribute('numbase', '3')\n # # And we add a dot of perfection\n # if not note.hasChildren('dot'):\n # dot = MeiElement('dot')\n # note.addChild('dot')\n # dot = note.getChildrenByName('dot')\n # dot.addAttribute('format', 'aug')\n # Default case\n elif modusminor == 3:\n pass\n # Mensuration MISTAKE: 'modusminor'\n else:\n print(\"MISTAKE IN MENSURATION: modusminor\")\n pass\n elif durges_num == l_imp:\n if modusminor == 3:\n # Alteration case - here the @dur attribute changes\n if note.hasAttribute('artic') and note.getAttribute('artic').value == 'stop':\n mens_dur = 'brevis'\n note.addAttribute('quality', 'a')\n note.addAttribute('num', '1')\n note.addAttribute('numbase', '2')\n # Imperfection case\n else:\n note.addAttribute('quality', 'i')\n note.addAttribute('num', '3')\n note.addAttribute('numbase', '2')\n # Default case\n elif modusminor == 2:\n pass\n # Mensuration MISTAKE: 'modusminor'\n else:\n print(\"MISTAKE IN MENSURATION: modusminor\")\n pass\n # MISTAKE on the note's duration\n else:\n print(\"This LONG note \" + str(note) + \" has an inappropriate duration @dur.ges = \" + str(durges_num) + \"p, as it is \" + str(Fraction(durges_num, l_def).numerator) + \"/\" + str(Fraction(durges_num, l_def).denominator) + \" part of its normal value.\")\n\n # BREVIS\n elif dur == 'breve':\n mens_dur = 'brevis'\n\n # SEMIBREVIS\n elif dur == '1':\n mens_dur = 'semibrevis'\n\n # INCORRECT NOTE VALUE\n else:\n if dur != \"TiedNote!\":\n print(\"This note shouldn't be here, as it is larger than a maxima or shorter than a semibrevis! \" + str(note) + \", \" + str(dur) + \", \" + str(durges_num) + \"p\")\n mens_dur = dur\n else:\n print(\"Still tied-note\")\n\n # Change the @dur value to the corresponding mensural note value\n note.getAttribute('dur').setValue(mens_dur)\n\n # Rest's Part:\n # Rests can't be modified from its original value\n # Long-rests don't exist, there only is 1, 2 or 3 breve rests.\n for rest in rests:\n # Due to the mRest part of the code, all the rests have a @dur attribute.\n dur = rest.getAttribute('dur').value\n # Semibreve rest\n if dur == \"1\":\n mens_dur = \"semibrevis\"\n # Check for mistakes in duration (@dur.ges attribute)\n if rest.hasAttribute('dur.ges'):\n durges_num = int(rest.getAttribute('dur.ges').value[:-1])\n if durges_num != 1024:\n print(\"This SEMIBREVE rest \" + str(rest) + \", doesn't have the appropriate @dur.ges value, as it is \" + str(durges_num) + \"p, instead of 1024p\\n\")\n # Breve rest\n elif dur == \"breve\":\n mens_dur = \"brevis\" # 1B rest??????????\n # Check for mistakes in duration (@dur.ges attribute)\n if rest.hasAttribute('dur.ges'):\n durges_num = int(rest.getAttribute('dur.ges').value[:-1])\n if durges_num != 2048:\n print(\"This BREVE rest \" + str(rest) + \", doesn't have the appropriate @dur.ges value, as it is \" + str(durges_num) + \"p, instead of 2048p\\n\")\n # 2-breve and 3-breve rest\n elif dur == \"long\":\n ##########################################################################################################\n mens_dur = \"longa\" # THIS WONT BE HERE, INSTEAD WE WILL USE THE MENS_DUR SPECIFIED IN EACH CONDITION (IF)\n ##########################################################################################################\n if rest.hasAttribute('dur.ges'):\n durges_num = int(rest.getAttribute('dur.ges').value[:-1])\n # 2-breve rest\n if durges_num == l_imp:\n rest.addAttribute('EVENTUALDUR', '2B') # It will be: mens_dur = '2B'\n ###################################################################################################################\n ###### This will go away when the 3B and 2B rests (3-spaces and 2-spaces rests) are implemented in Verovio ########\n if modusminor == 3: # 'imperfected'\n rest.addAttribute('num', '3')\n rest.addAttribute('numbase', '2')\n else: # Default\n pass\n ###################################################################################################################\n # 3-breve rest\n elif durges_num == l_perf:\n rest.addAttribute('EVENTUALDUR', '3B') # It will be: mens_dur = '3B'\n ###################################################################################################################\n ###### This will go away when the 3B and 2B rests (3-spaces and 2-spaces rests) are implemented in Verovio ########\n if modusminor == 2: # 'perfected'\n rest.addAttribute('num', '2')\n rest.addAttribute('numbase', '3')\n else: # Default\n pass\n ###################################################################################################################\n # Check for mistakes in duration (@dur.ges attribute)\n else:\n print(\"This 'LONG' Rest \" + str(rest) + \", doesn't have the appropriate @dur.ges value, as it is \" + str(durges_num) + \"p, instead of \" + str(l_imp) + \"p or \" + str(l_perf) + \"p\")\n print(\"i.e., it isn't a 2-breve or 3-breve rest, instead it is: \" + str(Fraction(durges_num, b_def).numerator) + \"/\" + str(Fraction(durges_num, b_def).denominator) + \" times a BREVE rest\\n\")\n else:\n # 3-breve rest\n if modusminor == 3:\n rest.addAttribute('EVENTUALDUR', '3B')\n # 2-breve rest\n elif modusminor == 2:\n rest.addAttribute('EVENTUALDUR', '2B')\n # Check for mistakes in duration (@dur.ges attribute)\n else:\n print(\"This 'LONG' Rest \" + str(rest) + \", doesn't have the appropriate @dur.ges value\")\n # Mistake in rest's duration (@dur attribute)\n else:\n print(\"This kind of Rest shouldn't be in this repertory \" + str(note) + \", it has a duration of \" + str(dur) + \"\\n\")\n mens_dur = dur\n\n # Change the @dur value to the corresponding mensural note value\n rest.getAttribute('dur').setValue(mens_dur)","def bar_to_lilypond_notes(notes):\n lp_notes = []\n if notes[0] is None:\n notes = notes[1:]\n if notes[0] is None:\n lp_notes.append(\"r\")\n for n in notes:\n if n is None:\n continue\n if type(n) is list:\n lp_notes.append([midi_to_lilypond_note(x) for x in n])\n else:\n lp_notes.append(midi_to_lilypond_note(n))\n return lp_notes","def __init__(self):\n self.notes = []","def add_evernote_notes(self, evernote_notes, update=False, log_update_if_unchanged=True):\n new_nids=[]\n action_str_base = ['Add', 'Update'][update]\n action_str = ['Adding', 'Updating'][update]\n action_preposition = ['To', 'In'][update]\n info = stopwatch.ActionInfo(action_str + ' Of', 'Evernote Notes', action_preposition + ' Anki', report_if_empty=False)\n tmr = stopwatch.Timer(evernote_notes, 10, info=info,\n label='Add\\\\Anki-%sEvernoteNotes' % action_str_base)\n\n for ankiNote in evernote_notes:\n try:\n title = ankiNote.FullTitle\n content = decode(ankiNote.Content)\n anki_field_info = {\n FIELDS.TITLE: title,\n FIELDS.CONTENT: content,\n FIELDS.EVERNOTE_GUID: FIELDS.EVERNOTE_GUID_PREFIX + ankiNote.Guid,\n FIELDS.UPDATE_SEQUENCE_NUM: str(ankiNote.UpdateSequenceNum),\n FIELDS.SEE_ALSO: u''\n }\n except Exception:\n log_error(\"Unable to set field info for: Note '%s': '%s'\" % (ankiNote.FullTitle, ankiNote.Guid))\n log_dump(ankiNote.Content, \" NOTE CONTENTS \")\n # log_dump(encode(ankiNote.Content), \" NOTE CONTENTS \")\n raise\n tmr.step(title)\n baseNote = None\n if update:\n baseNote = self.get_anki_note_from_evernote_guid(ankiNote.Guid)\n if not baseNote:\n log_error('Updating note %s: COULD NOT FIND BASE NOTE FOR ANKI NOTE ID' % ankiNote.Guid)\n tmr.reportStatus(EvernoteAPIStatus.MissingDataError)\n continue\n if ankiNote.Tags is None:\n log_error(\"Could note find tags object for note %s: %s. \" % (ankiNote.Guid, ankiNote.FullTitle))\n tmr.reportStatus(EvernoteAPIStatus.MissingDataError)\n continue\n anki_note_prototype = AnkiNotePrototype(self, anki_field_info, ankiNote.TagNames, baseNote,\n notebookGuid=ankiNote.NotebookGuid, count=tmr.count,\n count_update=tmr.counts.updated.completed.val, max_count=tmr.max)\n anki_note_prototype._log_update_if_unchanged_ = log_update_if_unchanged\n nid = tmr.autoStep(anki_note_prototype.update_note() if update else anki_note_prototype.add_note(),\n ankiNote.FullTitle, update)\n if tmr.status.IsSuccess and not update:\n new_nids.append([nid, ankiNote.Guid])\n elif tmr.status.IsError:\n log(\"ANKI ERROR WHILE %s EVERNOTE NOTES: \" % action_str.upper() + str(tmr.status), tmr.label + '-Error')\n tmr.Report()\n if new_nids:\n ankDB().executemany(\"UPDATE {n} SET nid = ? WHERE guid = ?\", new_nids)\n return tmr.counts.success","def get_scheduled_notes(event):\n def _sort_note_by(note):\n obj = note.object\n if hasattr(obj, 'start_dt'):\n return obj.start_dt, 0\n else:\n return obj.contribution.start_dt, obj.position\n\n tt_entries = (event.timetable_entries\n .filter(TimetableEntry.type != TimetableEntryType.BREAK)\n .options(joinedload('session_block').joinedload('contributions').joinedload('subcontributions'))\n .options(joinedload('contribution').joinedload('subcontributions'))\n .options(noload('break_'))\n .all())\n # build a list of all the objects we need notes for. that way we can query\n # all notes in a single go afterwards instead of making the already-huge\n # timetable query even bigger.\n objects = set()\n for entry in tt_entries:\n objects.add(entry.object)\n if entry.type == TimetableEntryType.CONTRIBUTION:\n objects.update(sc for sc in entry.object.subcontributions if not sc.is_deleted)\n elif entry.type == TimetableEntryType.SESSION_BLOCK:\n for contrib in entry.object.contributions:\n objects.add(contrib)\n objects.update(sc for sc in contrib.subcontributions if not sc.is_deleted)\n notes = [x for x in event.all_notes.filter_by(is_deleted=False) if x.object in objects]\n return sorted(notes, key=_sort_note_by)","def get_notes():\n song_index_to_notes = {}\n\n for file in glob.glob(\"../data/midi/*.mid\"):\n midi = converter.parse(file)\n song_index = int(os.path.splitext(os.path.basename(file))[0])\n #print(\"Parsing %s with an index %d\" % (file, song_index))\n\n notes_to_parse = None\n notes = []\n try: # file has instrument parts\n s2 = instrument.partitionByInstrument(midi)\n notes_to_parse = s2.parts[0].recurse()\n except: # file has notes in a flat structure\n notes_to_parse = midi.flat.notes\n\n for element in notes_to_parse:\n if isinstance(element, note.Note):\n notes.append(str(element.pitch))\n elif isinstance(element, chord.Chord):\n notes.append('.'.join(str(n) for n in element.normalOrder))\n\n song_index_to_notes[song_index] = notes\n\n return song_index_to_notes","def get_note_child_list(self):\n return self.address_list","def return_markers(self):\r\n ent_file = join(self.filename, self._basename + '.ent')\r\n if not exists(ent_file):\r\n ent_file = join(self.filename, self._basename + '.ent.old')\r\n\r\n try:\r\n ent_notes = _read_ent(ent_file)\r\n\r\n except (FileNotFoundError, PermissionError):\r\n markers = []\r\n\r\n else:\r\n allnote = []\r\n for n in ent_notes:\r\n try:\r\n n['value'].keys()\r\n allnote.append(n['value'])\r\n except AttributeError:\r\n lg.debug('Note of length {} was not '\r\n 'converted to dict'.format(n['length']))\r\n\r\n s_freq = self._hdr['erd']['sample_freq']\r\n pcname = '0CFEBE72-DA20-4b3a-A8AC-CDD41BFE2F0D'\r\n note_time = []\r\n note_name = []\r\n note_note = []\r\n for n in allnote:\r\n if n['Text'] == 'Analyzed Data Note':\r\n continue\r\n if not n['Text']:\r\n continue\r\n if 'User' not in n['Data'].keys():\r\n continue\r\n user1 = n['Data']['User'] == 'Persyst'\r\n user2 = False # n['Data']['User'] == 'eeg'\r\n user3 = n['Data']['User'] == pcname\r\n user4 = n['Data']['User'] == 'XLSpike - Intracranial'\r\n user5 = n['Data']['User'] == 'XLEvent - Intracranial'\r\n if user1 or user2 or user3 or user4 or user5:\r\n continue\r\n if len(n['Data']['User']) == 0:\r\n note_name.append('-unknown-')\r\n else:\r\n note_name.append(n['Data']['User'].split()[0])\r\n note_time.append(n['Stamp'] / s_freq)\r\n note_note.append(n['Text'])\r\n\r\n markers = []\r\n for time, name, note in zip(note_time, note_name, note_note):\r\n m = {'name': note + ' (' + name + ')',\r\n 'start': time,\r\n 'end': time,\r\n 'chan': None,\r\n }\r\n markers.append(m)\r\n\r\n return markers","def notes(self):\n return NotesTable(self.rpc, self.name)","def ordered_notes(self, obj, sig_features, tabs):\n supports = dict(\n [(str(support.id), support) for support in obj.all_supports])\n notes = []\n for browsers in sig_features.values():\n for section in tabs:\n for browser in section['browsers']:\n sig_supports = browsers.get(browser, [])\n for sig_support_pk in sig_supports:\n support = supports[sig_support_pk]\n if support.note:\n notes.append(sig_support_pk)\n return OrderedDict((note, i) for i, note in enumerate(notes, 1))","def sb_major_minor(children_of_voiceStaff):\n indices_BrevesOrTuplets = [-1]\n for element in children_of_voiceStaff:\n if (element.name == 'tuplet') or (element.hasAttribute('dur') and (element.getAttribute('dur').value == 'brevis' or element.getAttribute('dur').value == 'longa' or element.getAttribute('dur').value == 'maxima')):\n indices_BrevesOrTuplets.append(children_of_voiceStaff.index(element))\n for i in range(0, len(indices_BrevesOrTuplets)-1):\n start = indices_BrevesOrTuplets[i]\n end = indices_BrevesOrTuplets[i+1]\n number_sb = end - start - 1\n # Case 1: Even number of semibreves\n if number_sb % 2 == 0:\n cont_sb = 0\n for j in range(start+1, end):\n cont_sb = cont_sb + 1\n # 2nd, 4th, 6th, ... semibreve in the sequence; generally, these are the ones that are Major (default case), but there are exceptions\n if cont_sb % 2 == 0:\n previous_sb = children_of_voiceStaff[j-1]\n # The exception: tenuto marks (downward stems) in the previous note (1st, 3rd, 5th, ... semibreve)\n if previous_sb.hasAttribute('artic') and previous_sb.getAttribute('artic').value == 'ten':\n previous_sb.addAttribute('quality', 'major')\n previous_sb.addAttribute('num', '1')\n previous_sb.addAttribute('numbase', '2')\n # The default case:\n else:\n current_sb = children_of_voiceStaff[j]\n current_sb.addAttribute('quality', 'major')\n current_sb.addAttribute('num', '1')\n current_sb.addAttribute('numbase', '2')\n else:\n pass\n # Case 2: Odd number of semibreves\n else:\n # This can (should) only happen when there is a 2:1 tuplet at one end of the sequence of semibreves,\n # so that the whole tuplet is equal to just 1 minor semibreve,\n # and the semibreve that precedes/follows it (ususally has a downward stem to indicate its longer duration in the group) is the Major Semibreve that completes the Perfect Breve.\n # Without this grouping (major semibreve and tuplet), we are left with an even number of semibreves that can be grouped into minor-major pairs, as usual.\n start_element = children_of_voiceStaff[start]\n end_element = children_of_voiceStaff[end]\n # If the 2:1 tuplet precedes of the sequence of semibreves\n if (start_element.name == 'tuplet' and start_element.getAttribute('num').value == '2' and start_element.getAttribute('numbase').value == '1'):\n # The semibreve that follows this 2:1 tuplet should be major (completing the perfection)\n major_sb = children_of_voiceStaff[start + 1]\n major_sb.addAttribute('quality', 'major')\n major_sb.addAttribute('num', '1')\n major_sb.addAttribute('numbase', '2')\n # The other semibreves are grouped into minor-major pairs\n cont_sb = 0\n for j in range(start+2, end):\n cont_sb = cont_sb + 1\n # The second semibreve of each pair: generally it is Major (default case), but there are exceptions\n if cont_sb % 2 == 0:\n previous_sb = children_of_voiceStaff[j-1]\n # The exception: tenuto marks (downward stems) in the previous note (1st, 3rd, 5th, ... semibreve)\n if previous_sb.hasAttribute('artic') and previous_sb.getAttribute('artic').value == 'ten':\n previous_sb.addAttribute('quality', 'major')\n previous_sb.addAttribute('num', '1')\n previous_sb.addAttribute('numbase', '2')\n # The default case:\n else:\n current_sb = children_of_voiceStaff[j]\n current_sb.addAttribute('quality', 'major')\n current_sb.addAttribute('num', '1')\n current_sb.addAttribute('numbase', '2')\n # The first semibreve of each pair (it is generally minor, so we don't make any changes to it)\n else:\n pass\n # If the 2:1 tuplet follows the sequence of semibreves\n elif (end_element.name == 'tuplet' and end_element.getAttribute('num').value == '2' and end_element.getAttribute('numbase').value == '1'):\n # The semibreve that precedes the 2:1 tuplet, should be major (completing the perfection)\n major_sb = children_of_voiceStaff[end - 1]\n major_sb.addAttribute('quality', 'major')\n major_sb.addAttribute('num', '1')\n major_sb.addAttribute('numbase', '2')\n # The other semibreves are grouped into minor-major pairs\n cont_sb = 0\n for j in range(start+1, end-1):\n cont_sb = cont_sb + 1\n # The second semibreve of each pair: generally it is Major (default case), but there are exceptions\n if cont_sb % 2 == 0:\n previous_sb = children_of_voiceStaff[j-1]\n # The exception: tenuto marks (downward stems) in the previous note (1st, 3rd, 5th, ... semibreve)\n if previous_sb.hasAttribute('artic') and previous_sb.getAttribute('artic').value == 'ten':\n previous_sb.addAttribute('quality', 'major')\n previous_sb.addAttribute('num', '1')\n previous_sb.addAttribute('numbase', '2')\n # The default case:\n else:\n current_sb = children_of_voiceStaff[j]\n current_sb.addAttribute('quality', 'major')\n current_sb.addAttribute('num', '1')\n current_sb.addAttribute('numbase', '2')\n # The first semibreve of each pair (it is generally minor, so we don't make any changes to it)\n else:\n pass\n # Mistake case: If there is no tuplet 2:1 at any of the ends of the sequence, there shouldn't be an odd number of semibreves\n else:\n print(\"This shouldn't happen! \\nThere is an odd number of semibreves between two perfect breves (or tuplets that are equivalent to a perfect breve), \\nwhich doesn't allow to form minor-major (or major-minor) pairs of semibreves.\")\n print(\"You can find these breves between the \" + str(start_element.name) + \" with id \" + str(start_element.id) + \" and the \" + str(end_element.name) + \" with id \" + str(end_element.id))","def note_list(self, evarnames=\"\", in_=None):\n evarnames = self._find_vars(evarnames, evars=True, \n unique=True, empty_ok=True)\n if len(evarnames) == 0:\n evarnames = ['_dta'] + self._varlist\n if in_ is not None:\n if isinstance(in_, int):\n in_ = (in_,)\n elif (not isinstance(in_, collections.Iterable)\n or not all(isinstance(n, int) for n in in_)):\n raise TypeError(\"in_ should be int or iterable of int\")\n if any(n <= 0 for n in in_):\n raise ValueError(\"note numbers must be >= 1\")\n for name in evarnames:\n if name not in self._chrdict: continue\n chars = self._chrdict[name]\n if 'note0' not in chars: continue\n nnotes = int(chars['note0'])\n in_range = in_ if in_ is not None else range(1, nnotes + 1)\n note_info = []\n for note_num in in_range:\n note_name = 'note' + str(note_num)\n if note_name in chars:\n note_info.append(note_num)\n if note_info != []:\n note_info.insert(0, name)\n self._display_notes(note_info)","def remove_note(self, note, octave=-1):\n res = []\n for x in self.notes:\n if isinstance(note, six.string_types):\n if x.name != note:\n res.append(x)\n else:\n if x.octave != octave and octave != -1:\n res.append(x)\n else:\n if x != note:\n res.append(x)\n self.notes = res\n return res","async def _view_all_notes(self, ctx: Context):\n\n author = ctx.author\n\n note_infos = []\n\n embed_links = ctx.channel.permissions_for(ctx.guild.me).embed_links\n\n author_str = f\"{author.name}'\"\n\n if author.name[-1].lower() != \"s\":\n author_str += \"s\"\n\n async with self.config.member(author).notes() as notes:\n total = len(notes)\n for page_num, note in enumerate(notes, start=1):\n msg_info = \"\"\n if note[\"author\"]:\n msg_info += _(\"**Author:** {}\").format(note[\"author\"])\n if note[\"channel\"]:\n msg_info += _(\"\\n**Channel:** {}\").format(note[\"channel\"])\n if note[\"jump_url\"]:\n if embed_links:\n msg_info += _(\n \"\\n[Click here to jump to message]({})\"\n ).format(note[\"jump_url\"])\n else:\n msg_info += _(\n \"\\n**Jump To Message:** {}\"\n ).format(note[\"jump_url\"])\n\n note_info = _(\n \"{}\\n\\n**Note:**\\n```{}```\\n**Reason:**\\n```{}```\"\n ).format(\n msg_info,\n note[\"note\"],\n note[\"reason\"]\n ).strip()\n\n if embed_links:\n page = discord.Embed(\n colour=0xff0000,\n description=note_info,\n title=_(\"{} TvM Notes\").format(author_str),\n timestamp=ctx.message.created_at\n )\n\n page.set_footer(\n text=_(\"Page {page_num}/{leng}\").format(\n page_num=page_num, leng=total\n )\n )\n else:\n page = _(\n \"**{author} TvM Notes**\"\n \"\\n\\n{note}\"\n \"\\n{footer}\"\n ).format(\n author=author_str,\n note=note_info,\n footer=_(\"*Page {page_num}/{leng}*\").format(\n page_num=page_num, leng=total\n )\n )\n\n note_infos.append(page)\n\n await menu(ctx, note_infos, DEFAULT_CONTROLS)","def scorepart_to_notes(part, morphetic_pitch=False):\n\n bm = part.beat_map\n\n if morphetic_pitch:\n pitch_attr = attrgetter('morphetic_pitch')\n else:\n pitch_attr = attrgetter('midi_pitch')\n notes = np.array([(bm(n.start.t), bm(n.end.t), pitch_attr(n))\n for n in part.notes], np.float)\n\n ids = np.array([n.id for n in part.notes])\n\n return notes, ids","def notes(self):\n return Notes(self)","def release_notes(self):\n return self._release_notes","def getNote(self):\n return (self.noteName[0], self.accidental, self.octave)","def all_note_by_job_applied_id(job_applied_id):\n\n return Note.query.filter(Note.job_applied_id == job_applied_id, Note.note_category == 'Note' ).all()","def midi_to_lilypond_note(note):\n return all_notes[note+4]","def PublicNotes(self, default=[{}]):\n tmp = self.data.get('metadata', {}).get('public_notes', default)\n return [HEP.SourcedValueObject(i) for i in tmp]","def note_list(request):\n user = request.user\n notes = Note.objects.filter(author=user)\n serializer = NoteSerializer(notes, many=True)\n return Response(serializer.data)","def midiToNotes(notes):\n names = ['C', 'C#', 'D', 'D#', 'E', 'F', 'F#', 'G', 'G#', 'A', 'A#', 'B']\n chord = []\n \n for note in notes:\n name = names[note%12]\n octave = int((note-12)/12)\n chord.append(name + str(octave))\n \n return chord","def business_notes(self):\n return self._business_notes","def parse_pretty_midi_lyrics(midi: PrettyMIDI) -> List[Lyric]:\n return [Lyric(lyric.time, lyric.text) for lyric in midi.lyrics]","def child_show(self,\r\n entrylist=None,\r\n highlight=None,\r\n not_all=False):\r\n\r\n if highlight is None:\r\n highlight = set()\r\n if entrylist is None and not not_all:\r\n entrylist = self.apply_limit(self.find_within(indexfrom=Index(0),orequal=True))\r\n## entrylist = self.apply_limit([a_temp for a_temp\r\n## in sorted([str(Index(a_temp))\r\n## for a_temp in self.indexes()\r\n## if Index(a_temp) > Index(str(0))],\r\n## key=lambda x_temp: Index(x_temp))])\r\n\r\n # if entrylist is , default to all notes,\r\n #with limit applied.\r\n\r\n def recursive_show(dictionary, level=1):\r\n\r\n \"\"\"recursive function to show children of a note\"\"\"\r\n\r\n for k_temp in dictionary:\r\n\r\n\r\n if dictionary[k_temp] == {}:\r\n\r\n if self.default_dict['variablesize']:\r\n if not self.notebook_contains(k_temp):\r\n display.noteprint((alerts.ATTENTION,str(k_temp)+' '+'NOT FOUND'))\r\n else:\r\n display.noteprint(self.show(k_temp,\r\n shortform=(self.shortshow\r\n or len(entrylist) > self.longmax),\r\n yestags=self.tagdefault,\r\n highlight=highlight),\r\n param_width=display.width_needed\r\n (self.show(k_temp),\r\n self.get_metadata_from_note(k_temp)['size'],\r\n leftmargin=self.default_dict['leftmargin']),\r\n param_indent=(level-1)*self.default_dict['indentmultiplier'],\r\n leftmargin=self.default_dict['leftmargin'])\r\n else:\r\n if not self.notebook_contains(k_temp):\r\n display.noteprint((alerts.ATTENTION,str(k_temp)+' '+'NOT FOUND'))\r\n else:\r\n display.noteprint(\r\n self.show(k_temp,\r\n yestags=self.tagdefault,\r\n shortform=(self.shortshow\r\n or len(entrylist) > self.longmax)),\r\n param_width=self.default_dict['size'],\r\n param_indent=(level-1)*self.default_dict['indentmultiplier'],leftmargin=self.default_dict['leftmargin'])\r\n\r\n else:\r\n if self.default_dict['variablesize']:\r\n if not self.notebook_contains(k_temp):\r\n display.noteprint((alerts.ATTENTION,str(k_temp)+' '+'NOT FOUND'))\r\n else:\r\n display.noteprint(\r\n self.show(k_temp,\r\n shortform=(self.shortshow\r\n or len(entrylist) > self.longmax),\r\n yestags=self.tagdefault,\r\n highlight=highlight),\r\n param_width=display.width_needed(self.show(k_temp),\r\n self.get_metadata_from_note(k_temp)['size']),\r\n param_indent=(level-1)*self.default_dict['indentmultiplier'],leftmargin=self.default_dict['leftmargin'])\r\n else:\r\n if not self.notebook_contains(k_temp):\r\n display.noteprint((alerts.ATTENTION,str(k_temp)+' '+'NOT FOUND'))\r\n else:\r\n display.noteprint(self.show\r\n (k_temp, yestags=self.tagdefault,\r\n shortform=(self.shortshow\r\n or len(entrylist) > self.longmax)),\r\n param_width=self.default_dict['size'],\r\n param_indent=(level-1)*self.default_dict['indentmultiplier'],\r\n leftmargin=self.default_dict['leftmargin'])\r\n recursive_show(dictionary[k_temp],\r\n level=level+1)\r\n\r\n\r\n maxlevel = 0\r\n for i_temp in entrylist:\r\n if len(i_temp) > maxlevel:\r\n maxlevel = len(i_temp)\r\n\r\n\r\n tempdict_a = {}\r\n tempdict_b = {}\r\n\r\n for i_temp in range(1, maxlevel+1):\r\n tempdict_a[i_temp] = []\r\n\r\n for i_temp in entrylist:\r\n tempdict_a[len(i_temp)].append(str(i_temp))\r\n\r\n for a_temp in range(1, len(tempdict_a)+1):\r\n #ranges over lengths of indexes\r\n\r\n for b_temp in range(len(tempdict_a[a_temp])):\r\n # ranges through the list of\r\n #indexes for a give length a\r\n if a_temp == 1:\r\n tempdict_b[str(tempdict_a[a_temp][b_temp])] = {}\r\n\r\n\r\n else:\r\n temp = Index(tempdict_a[a_temp][b_temp])\r\n\r\n ancestors = []\r\n while not temp.is_top():\r\n\r\n ancestors = [str(temp.parent())]+ancestors\r\n temp = temp.parent()\r\n\r\n ancestorstring = EMPTYCHAR\r\n for d_temp in ancestors:\r\n ancestorstring += \"['\"+str(d_temp)+\"']\"\r\n\r\n try:\r\n if isinstance(eval('tempdict_b'+\r\n ancestorstring), dict):\r\n\r\n\r\n if isinstance(eval('tempdict_b'+\r\n ancestorstring), dict):\r\n\r\n e_temp = ('tempdict_b'\r\n +ancestorstring\r\n +'={str(tempdict_a[a_temp][b_temp]):{}}')\r\n exec(e_temp)\r\n\r\n else:\r\n e_temp = ('tempdict_b'\r\n +ancestorstring\r\n +'[str(tempdict_a[a_temp][b_temp])]'+'={}')\r\n exec(e_temp)\r\n\r\n except KeyError:\r\n\r\n e_temp = ('tempdict_b'\r\n +ancestorstring+'={}')\r\n try:\r\n exec(e_temp)\r\n except KeyError:\r\n pass\r\n except SyntaxError:\r\n print('Syntax Error')\r\n\r\n e_temp = ('tempdict_b'\r\n +ancestorstring\r\n +'[str(tempdict_a[a_temp][b_temp])]'\r\n +'={}')\r\n try:\r\n exec(e_temp)\r\n except KeyError:\r\n pass\r\n except SyntaxError:\r\n print('Syntax Error')\r\n\r\n\r\n recursive_show(tempdict_b)","def sort(self):\n self.notes.sort()","def _on_note(self) -> Iterator:\n return filter(\n lambda x: x.type == constants.NOTE_ON, self.midi_data)","def get_note_alternatives(self, id):\n notes = self.session.query(models.Note).filter(\n models.Note.conflict_parent_id == id,\n ).all()\n return btype.Note.list >> notes","def internal_notes(self, key, value):\n _internal_notes = self.get(\"internal_notes\", [])\n for v in force_list(value):\n internal_note = {\"value\": clean_val(\"a\", v, str, req=True)}\n if internal_note not in _internal_notes:\n _internal_notes.append(internal_note)\n return _internal_notes","def todolists(self):\r\n return tdl.Todolists(self)","def notes(self):\r\n return TicketNotes(self)","def notes_in_path(self, path: str) -> List[NoteInfo]:\n check_path = os.path.abspath(path)\n return [n for n in self.notes.values() if n.file_path.startswith(check_path)]","def get_todo_list():\n\n # assume that a \"h264\" encoded file is complete\n return models.LibraryItem.objects.filter(h264=False)","def notes(self):\r\n return notes.UserNotes(self)","def remove_pedal(self):\n return Melody([n.remove_pedal() for n in self.notes], nb_bars=self.nb_bars, tags=set(self.tags))","def get_field_notes(self):\n return self._fields_notes","def extract_notes(music, resolution):\n # Check resolution\n assert music.resolution == resolution\n\n # Extract notes\n notes = []\n for track in music:\n if track.is_drum or track.program not in KNOWN_PROGRAMS:\n continue\n for note in track:\n beat, position = divmod(note.time, resolution)\n notes.append(\n (beat, position, note.pitch, note.duration, track.program)\n )\n\n # Deduplicate and sort the notes\n notes = sorted(set(notes))\n\n return np.array(notes)","def getNoteVersion(self, authenticationToken, noteGuid, updateSequenceNum, withResourcesData, withResourcesRecognition, withResourcesAlternateData):\r\n pass","def get_user_visible_notes():\n return grammar.user_visible_notes","def parse_pretty_midi_key_signatures(midi: PrettyMIDI) -> List[KeySignature]:\n key_signatures = []\n for key_signature in midi.key_signature_changes:\n is_minor, root = divmod(key_signature.key_number, 12)\n mode = \"minor\" if is_minor else \"major\"\n key_signatures.append(KeySignature(key_signature.time, root, mode))\n return key_signatures","def extract_melodies(sequence, steps_per_beat=4, min_bars=7,\n min_unique_pitches=5):\n\n # Assume bars contain 4 beats, or quarter notes.\n steps_per_bar = steps_per_beat * 4\n\n # Beats per minute is stored in the tempo change event. If there is no tempo\n # then assume 120 bpm per the MIDI standard.\n bpm = (sequence.tempos[0].bpm if len(sequence.tempos)\n else DEFAULT_BEATS_PER_MINUTE)\n\n # Group note messages into tracks.\n tracks = {}\n for note in sequence.notes:\n if note.instrument not in tracks:\n tracks[note.instrument] = []\n tracks[note.instrument].append(note)\n\n melodies = []\n for track in tracks.values():\n melody = Melody(steps_per_bar)\n\n # Quantize the track into a Melody object.\n # If any notes start at the same time, only one is kept.\n melody.from_notes(track, bpm=bpm, gap=steps_per_bar,\n ignore_polyphonic_notes=True)\n\n # Require a certain melody length.\n if len(melody) - 1 < steps_per_bar * min_bars:\n logging.debug('melody too short')\n continue\n\n # Require a certain number of unique pitches.\n note_histogram = melody.get_note_histogram()\n unique_pitches = np.count_nonzero(note_histogram)\n if unique_pitches < min_unique_pitches:\n logging.debug('melody too simple')\n continue\n\n melodies.append(melody)\n\n return melodies","def processNotes(self, inMeasureElem):\n inNotes = inMeasureElem.findall(\"note\")\n # list to hold array of processed note elements\n lstOutNotes = []\n\n for inNote in inNotes:\n for childNode in inNote:\n outNote = ET.Element(\"note\")\n\n # rest\n inRest = inNote.find(\"rest\")\n if (inRest is not None):\n outRest = ET.Element(\"rest\")\n outNote.append(outRest)\n\n # chord\n inChord = inNote.find(\"chord\")\n if (inChord is not None):\n outChord = ET.Element(\"chord\")\n outNote.append(outChord)\n\n # pitch\n inPitch = inNote.find(\"pitch\")\n if (inPitch is not None):\n outPitch = ET.Element(\"pitch\")\n for childNode in inPitch:\n if (True == (childNode.tag in [\"step\", \"octave\"])):\n pitchSubElem = ET.Element(childNode.tag)\n pitchSubElem.text = childNode.text\n outPitch.append(pitchSubElem)\n # handle the \"alter\" child tag separately even if the alter value is 0 include it otherwise\n # include the original scores alter value\n inPitchAlter = inPitch.find(\"alter\")\n if (inPitchAlter is not None):\n # use sources alter elem\n outPitchAlter = ET.Element(\"alter\")\n outPitchAlter.text = inPitchAlter.text\n outPitch.append(outPitchAlter)\n else:\n # make a zero value alter element\n outPitchAlter = ET.Element(\"alter\")\n outPitchAlter.text = \"0\"\n outPitch.append(outPitchAlter)\n\n outNote.append(outPitch)\n\n # duration\n inDuration = inNote.find(\"duration\")\n if (inDuration is not None):\n outDuration = ET.Element(\"duration\")\n outDurationVal = int(inDuration.text) * self._multiplier\n outDuration.text = str(int(outDurationVal))\n outNote.append(outDuration)\n\n # append the mote to the list of notes\n lstOutNotes.append(outNote)\n\n # finally return the list of notes\n return lstOutNotes","def all_notes():\n \n return render_template('all_notes.html',colors=music_color,)","def get_all_notes(user_id, token):\n user = User.get(User.id == user_id).username\n if not validate_token(user, token):\n return HTTPResponse(status=500, body={\"message\":\"Validation error.\"})\n res = []\n for note in Notes.select():\n if note.user.id == user_id:\n new_note = model_to_dict(note)\n res.append({\"id\":new_note['id'], \"title\":new_note['title'],\n \"content\":new_note['content']})\n new_token = generate_token(user)\n body = {\"user_id\":user_id, \"token\":new_token.decode('utf-8'), 'items':res}\n return HTTPResponse(status=200, body=body)","def page_list_notes(self, no_history=False):\n\n list_taskid_desc = self.task_store.sort_taskid_list_descending_lamport()\n title = \"woolnote - all notes\"\n page_header_first_text = \"all notes\"\n\n if no_history:\n history_id = self.save_history([])\n else:\n history_id = self.save_history([\"action\"], alt_task_store_name=None)\n\n page_header_list_of_warnings = None\n page_header_small_text = None\n\n if self.error_msg_queue_list:\n page_header_list_of_warnings = self.helper_convert_msg_queue_list_to_list_for_output()\n else:\n try:\n # TODO: use asn library?\n # sha256_fp = read_pem_cert_fingerprint(SSL_CERT_PEM_PATH)\n page_header_small_text = config.SSL_CERT_PEM_FINGERPRINT\n except:\n page_header_small_text = \"cannot get ssl cert sha256\"\n\n return html_page_templates.page_list_notes_template(list_taskid_desc=list_taskid_desc,\n self_sess_action_auth=self.sess_action_auth, title=title,\n history_back_id=history_id, primary_task_store=self.task_store,\n virtual_folders=self.woolnote_config.virtual_folders,\n single_task_line_ids=set(self.woolnote_config.single_note_line_id.keys()),\n page_header_first_text=page_header_first_text,\n page_header_optional_small_second_text=page_header_small_text,\n page_header_optional_list_of_warnings=page_header_list_of_warnings)","def generate_message(self) -> List[mido.Message]:\n # check for a None note (which is a \"pause\")\n if self.__note:\n note_value = self.__note + self.__info.octave\n note_velocity = self.__info.volume\n else:\n note_value = 0\n note_velocity = 0\n return [\n mido.Message(\n \"note_on\",\n note=note_value,\n velocity=note_velocity,\n time=NOTE_DURATION,\n ),\n mido.Message(\n \"note_off\",\n note=note_value,\n velocity=note_velocity,\n time=0,\n ),\n ]","def notes(self) -> str:\n return self._notes","def midiToNotesRec(notes, totalnotes, note_names=[]):\n names = ['C', 'C#', 'D', 'D#', 'E', 'F', 'F#', 'G', 'G#', 'A', 'A#', 'B']\n \n #print(note_names)\n \n def recMidiToNotes(notes, note_namesr=[]):\n if notes == []:\n return\n \n head = notes.pop(0)\n name = names[head%12]\n octave = int((head-12)/12)\n note_namesr.append(name + str(octave))\n print('rec names:', note_namesr)\n return note_namesr\n \n if len(note_names) == totalnotes:\n return note_names\n \n midiToNotes(notes, totalnotes, recMidiToNotes(notes, note_names))","def all_jd_by_job_applied_id(job_applied_id): \n return Note.query.filter(Note.job_applied_id == job_applied_id, Note.note_category == 'Job Description' ).order_by(Note.note_date_created.desc()).first()","def reconstruct(notes, resolution):\n # Construct the MusPy Music object\n music = muspy.Music(resolution=resolution, tempos=[muspy.Tempo(0, 100)])\n\n # Append the tracks\n programs = sorted(set(note[-1] for note in notes))\n for program in programs:\n music.tracks.append(muspy.Track(program))\n\n # Append the notes\n for beat, position, pitch, duration, program in notes:\n time = beat * resolution + position\n track_idx = programs.index(program)\n music[track_idx].notes.append(muspy.Note(time, pitch, duration))\n\n return music","def notes(self):\r\n return notes.ForumSuggestionNotes(self)","def displayMelody(self):\r\n print(self.notes)","def notes(self) -> Optional[annotations.NoteData]:\n return load_score(self.notes_path)","def test_list_note_occurrences(self):\n pass","def get_notes(notes_type=\"national\", area=None):\n query_menu = get_query_menu(area)\n query = query_menu[notes_type][\"query\"]\n collection = query_menu[notes_type][\"collection\"]\n notes = \"\"\n try:\n doc = next(collection.find(query).sort([(DATE_KEY, -1)]).limit(1))\n notes = doc[NOTE_KEY] if doc[NOTE_KEY] != 0 else None\n except StopIteration:\n app.logger.error(\"While getting notes: no data\")\n return notes if notes is not None and not rubbish_notes(notes) else \"\"","def load_org_notes_file():\n nodelist = orgnode.makelist(NOTES_ORG_FILE)\n return nodelist","def read_notes():\n\n if current_user.is_admin is False:\n notes = Note.query.filter_by(created_by=current_user.id).all()\n else:\n notes = Note.query.all()\n\n return render_template('notes/index.html.j2', notes=notes, title='Notes')","def get_observations(self):\n # Check hyper_params and criterion\n if len(self.hyper_params) == 0:\n raise AssertionError('!! Hyper-Parameters has not been set.')\n # Check criterion\n if self.criterion is None:\n raise AssertionError(\n '!! Criterion for hyper-parameter searching has not been set.')\n # Fetch notes\n notes = self.summary_fetcher()\n if len(notes) == 0: return []\n # Peel of Note wrapper\n observations = []\n for note in notes:\n # Every note in the note list must contain the criterion\n if self.criterion not in note.criteria: raise AssertionError(\n '!! Every note must contain the criterion `{}`'.format(self.criterion))\n # This note will be ignored if it does not contain all the information\n # in self.hyper_params or the config value is not within the range\n if not all([hp.name in note.configs and hp.within(note.configs[hp.name])\n for hp in self.hyper_params]): continue\n # Gather observation\n od = OrderedDict()\n # self.scroll.hyper_params.values() may have been found themselves\n for hp in self.scroll.hyper_params.values():\n assert isinstance(hp, HyperParameter)\n od[hp] = note.configs[hp.name]\n # Organize the observation list as a list of tuples\n observations.append((od, note.criteria[self.criterion]))\n return observations","def inverse_notes(note_list):\n raw_output = []\n for note in note_list:\n # We transpose every notes except for Z\n if note.is_pause:\n raw_output.append(f'{note.name}{note.figure}')\n else:\n # Get the index of the note to transpose\n note_name_index = NOTE_NAMES.index(note.name)\n # Get the transposed index according to the amount\n inverted_index = ((len(NOTE_NAMES) - note_name_index) % len(NOTE_NAMES))\n new_note_name = NOTE_NAMES[inverted_index]\n # Create the new note, with the new note's name, and its original figure\n new_note = f'{new_note_name}{note.figure}'\n new_note += 'p' if note.has_point else ''\n raw_output.append(new_note)\n\n return raw_output","def remove_notes(self, notes):\n if isinstance(notes, six.string_types):\n return self.remove_note(notes)\n elif hasattr(notes, \"name\"):\n return self.remove_note(notes)\n else:\n for x in notes:\n self.remove_note(x)\n return self.notes","def get_note (self,\r\n index):\r\n\r\n\r\n\r\n\r\n if self.using_database:\r\n aprint('GETTING NOTE')\r\n value_tuple = (notebookname, str(index),)\r\n db_cursor.execute(\"SELECT note_body \"\r\n +\"FROM notes WHERE notebook=?\"\r\n +\" AND note_index=?;\",\r\n value_tuple)\r\n\r\n res_temp = db_cursor.fetchone()\r\n\r\n if res_temp:\r\n text = res_temp[0].replace(\"''\",\"'\")\r\n else:\r\n text = ''\r\n\r\n db_cursor.execute(\"SELECT user\"\r\n +\" FROM notes\"\r\n +\" WHERE notebook=?\"\r\n +\" AND note_index=?;\",\r\n value_tuple)\r\n res_temp = db_cursor.fetchone()\r\n if res_temp:\r\n\r\n user = res_temp[0]\r\n else:\r\n user = 'USER'\r\n\r\n db_cursor.execute(\"SELECT size\"\r\n +\" FROM notes\"\r\n +\" WHERE notebook=? \"\r\n +\"AND note_index=?;\",\r\n value_tuple)\r\n\r\n res_temp = db_cursor.fetchone()\r\n if res_temp:\r\n size = res_temp[0]\r\n else:\r\n size = 60\r\n db_cursor.execute(\"SELECT timestamp \"\r\n +\"FROM timestamps\"\r\n +\" WHERE notebook=?\"\r\n +\" AND note_index=?\"\r\n +\" ORDER BY timestamp\",\r\n value_tuple)\r\n dates = db_cursor.fetchall()\r\n date_list = [date[0] for date in dates]\r\n db_cursor.execute(\"SELECT keyword\"\r\n +\" FROM all_note_keys\"\r\n +\" WHERE notebook=?\"\r\n +\" AND note_index=?\",\r\n value_tuple)\r\n keyset = db_cursor.fetchall()\r\n keyset = {key[0] for key in keyset}\r\n\r\n metadata = {'user':user,\r\n 'date':date_list,\r\n 'size':size}\r\n return Note(keyset,text,metadata)\r\n\r\n if str(index) in self.note_dict:\r\n return self.note_dict[str(index)]\r\n\r\n return False","def do_notes(self, sym):\n # DRY: duplication with Roman.py - FIXME\n if sym == '-':\n # REST\n return []\n note_or_chord = self.do(sym)\n if note_or_chord is None:\n return []\n elif type(note_or_chord) == Chord:\n return note_or_chord.notes\n else:\n return [ note_or_chord ]","def list_motions(self):\n return [x for x,y in self.devices.items() if y.device_type == \"Motion\"]","def ends_with_lt_tonic(a_list):\n key = a_list.track.bars[0].key\n a, b = a_list.notes[-2:]\n lt, tonic = Note(key).transpose('7', True).name, key.name\n\n if (a.name, b.name) == (lt, tonic) and int(b) - int(a) == 1:\n return []\n else:\n return [a.start, b.start]","def get_wall_products(self, wall_bp):\n products = []\n for child in wall_bp.children:\n props = props_closet.get_object_props(child)\n if props.is_closet or props.is_fixed_shelf_and_rod_product_bp:\n child.mv.comment = wall_bp.mv.name_object\n products.append(child)\n products.sort(key=lambda obj: obj.location.x, reverse=False)\n return products","def get_partlist(self):\n\n # Longdale check\n longdale_user = user_is_ingroup(self.request, \"longdale_user\")\n # REtrieve the correct queryset, sorted on the correct levels\n if longdale_user:\n qs = [prt for prt in Part.objects.all().order_by('corpus__lng', 'corpus__name', 'name')]\n else:\n longdale = \"Longdale\"\n qs = [prt for prt in Part.objects.exclude(Q(name__istartswith=longdale)).order_by('corpus__lng', 'corpus__name', 'name')]\n # REtrieve the correct queryset, sorted on the correct levels\n # qs = [prt for prt in Part.objects.all().order_by('corpus__lng', 'corpus__name', 'name')]\n # Start the output\n html = []\n # Initialize the variables whose changes are important\n lVars = [\"corpus_lng\", \"corpus_name\", \"name\"]\n lFuns = [Part.language, [\"corpus\", \"name\"], [\"name\"]]\n # Get a list of items containing 'first' and 'last' information\n lItem = get_item_list(lVars, lFuns, qs)\n # REturn this list\n return lItem","def __repr__(self):\n return str(self.notes)","def get_notes_from_line(line):\n last_char_is_upper = True\n raw_notes_list = []\n current_note = \"\"\n\n for char in line:\n if not last_char_is_upper and char.isupper():\n raw_notes_list.append(current_note)\n current_note = \"\"\n\n current_note += char\n last_char_is_upper = char.isupper()\n\n raw_notes_list.append(current_note)\n return raw_notes_list","def play_notes(self, show_map=False):\n # print(self.programs)\n pianorolls = []\n for track in self.notes:\n pianoroll = np.zeros((track.shape[1] * 3, 128))\n\n for i in range(track.shape[0]):\n note_track = np.array(track[i, :])\n note_track += 1\n notes_pos = np.nonzero(note_track)\n f = 3\n for pos in notes_pos[0]:\n # print(\"Error\", f * pos, f * pos + self.dura_to_timecell(note_track[pos] - 1),\n # self.dura_to_timecell(note_track[pos] - 1))\n pianoroll[f * pos:f * pos + self.dura_to_timecell(note_track[pos] - 1) + 1, 83 - i] = 90\n pianorolls.append(pianoroll)\n\n # print(\"pianoroll\")\n print(self.notes_index)\n tracks = []\n for i in range(len(pianorolls)):\n tracker = Track(pianoroll=pianorolls[i], program=self.programs[i])\n tracks.append(tracker)\n multitrack = Multitrack(tracks=tracks)\n multitrack.write(\"create1.mid\")\n\n if show_map:\n print(\"Show map will not work\")\n # tracker.plot()\n # plt.show()","def update_evernote_notes(self, evernote_notes, log_update_if_unchanged=True):\n return self.add_evernote_notes(evernote_notes, True, log_update_if_unchanged=log_update_if_unchanged)","def listmarkers(repo):\n if not repo.obsstore:\n return {}\n keys = {}\n parts = []\n currentlen = _maxpayload * 2 # ensure we create a new part\n for marker in repo.obsstore:\n nextdata = _encodeonemarker(marker)\n if (len(nextdata) + currentlen > _maxpayload):\n currentpart = []\n currentlen = 0\n parts.append(currentpart)\n currentpart.append(nextdata)\n currentlen += len(nextdata)\n for idx, part in enumerate(reversed(parts)):\n data = ''.join([_pack('>B', _fmversion)] + part)\n keys['dump%i' % idx] = base85.b85encode(data)\n return keys"],"string":"[\n \"def listNotes() -> list:\\n list_of_notes = []\\n for note in Note.objects.all():\\n list_of_notes.append({\\n 'uuid': note.uuid, 'title': note.title,\\n 'author': note.author, 'body': note.body, 'created_at': localtime(note.created_at)\\n })\\n return list_of_notes\",\n \"def notes(self):\\n return reapy.NoteList(self)\",\n \"def getnotes():\",\n \"def getNotes(self):\\n return self._nednotes, self._ongcnotes\",\n \"def ls(self, count = 200):\\n return self._manager.ls_notes(self['id'], count)\",\n \"def get_notes(n_notes=3):\\n notes = []\\n \\n for ii, file in enumerate(glob.glob(\\\"data/maestro-v2.0.0/2004/*.midi\\\")):\\n if ii >= n_notes:\\n break\\n pickle_file_name = file[:-4] + 'pkl'\\n\\n if os.path.isfile(pickle_file_name):\\n print(f'Reading parsed file: {pickle_file_name}')\\n with open(pickle_file_name, 'rb') as handle:\\n midi = pickle.load(handle)\\n else:\\n midi = converter.parse(file)\\n\\n with open(pickle_file_name, 'wb') as handle:\\n print(f'writing parsed file: {pickle_file_name}')\\n unserialized_data = pickle.dump(midi, \\n handle, \\n protocol=pickle.HIGHEST_PROTOCOL\\n )\\n\\n\\n print(\\\"Parsing %s\\\" % file)\\n\\n notes_to_parse = None\\n try: # file has instrument parts\\n s2 = instrument.partitionByInstrument(midi)\\n notes_to_parse = s2.parts[0].recurse() \\n except: # file has notes in a flat structure\\n notes_to_parse = midi.flat.notes\\n \\n for element in notes_to_parse:\\n if isinstance(element, note.Note):\\n notes.append(str(element.pitch))\\n elif isinstance(element, chord.Chord):\\n notes.append('.'.join(str(n) for n in element.normalOrder))\\n \\n return notes\",\n \"def get_note_names(self):\\n res = []\\n for n in self.notes:\\n if n.name not in res:\\n res.append(n.name)\\n return res\",\n \"def notes_list():\\n if request.method == \\\"POST\\\":\\n note = str(request.data.get(\\\"text\\\", \\\"\\\"))\\n idx = max(notes.keys()) + 1\\n notes[idx] = note\\n return note_repr(idx), status.HTTP_201_CREATED\\n\\n # request.method == 'GET'\\n return [note_repr(idx) for idx in sorted(notes.keys())]\",\n \"def get_note():\\n\\n return Note.query.all()\",\n \"def view_notes(tag_list):\\n final_notes = []\\n for tag in tag_list:\\n all_notes = Tags.objects.get(tag_text=tag)\\n notes = all_notes.notes.all()\\n for note in notes:\\n final_notes.append(note.note_text)\\n \\n return final_notes\",\n \"def _get_issue_notes(request, pk):\\n if request.user.is_coordinator_or_better:\\n note_types = IssueNote.COORDINATOR_NOTE_TYPES\\n else:\\n note_types = IssueNote.PARALEGAL_NOTE_TYPES\\n\\n return (\\n IssueNote.objects.filter(issue=pk)\\n .prefetch_related(\\\"creator__groups\\\")\\n .filter(note_type__in=note_types)\\n .order_by(\\\"-created_at\\\")\\n .all()\\n )\",\n \"def get_objects_using_notes():\\n global objects_using_notes\\n\\n if not objects_using_notes:\\n objects_using_notes = rsh.get_objects_using_notes(get_srcs())\\n\\n return objects_using_notes\",\n \"def get_notes():\\n notes = []\\n durs = []\\n\\n for file in glob.glob(\\\"D:\\\\\\\\anthems\\\\\\\\data\\\\\\\\*.mid\\\"):\\n midi = converter.parse(file)\\n\\n print(\\\"Parsing %s\\\" % file)\\n\\n notes_to_parse = None\\n\\n try:\\n s2 = instrument.partitionByInstrument(midi)\\n notes_to_parse = s2.parts[0].recurse() \\n except:\\n notes_to_parse = midi.flat.notes\\n \\n for element in notes_to_parse:\\n if isinstance(element, note.Note):\\n notes.append(str(element.pitch))\\n durs.append(element.duration.quarterLength)\\n elif isinstance(element, chord.Chord):\\n notes.append('.'.join(str(n) for n in element.normalOrder))\\n durs.append(element.duration.quarterLength)\\n\\n return notes, durs\",\n \"def getNotes(self, *args):\\n return _libsbml.SBase_getNotes(self, *args)\",\n \"def listNoteVersions(self, authenticationToken, noteGuid):\\r\\n pass\",\n \"def getNotes(self):\\n return self.__notes\",\n \"def extract_notes(bars):\\n output = []\\n for bar in bars:\\n notes = np.array([bar_to_notes(b,i) for i,b in enumerate(bar)])\\n\\n # Flatten into a single array\\n midi_notes = []\\n for i in range(notes.shape[1]):\\n if all(notes[:,i] == -1):\\n midi_notes.append(None)\\n else:\\n midi_notes.append([n for n in notes[:,i] if n!=-1])\\n output.append(midi_notes)\\n return output\",\n \"def notes(self):\\n return self._notes\",\n \"def add_notes(self, notes):\\n if hasattr(notes, \\\"notes\\\"):\\n for x in notes.notes:\\n self.add_note(x)\\n return self.notes\\n elif hasattr(notes, \\\"name\\\"):\\n self.add_note(notes)\\n return self.notes\\n elif isinstance(notes, six.string_types):\\n self.add_note(notes)\\n return self.notes\\n for x in notes:\\n if isinstance(x, list) and len(x) != 1:\\n if len(x) == 2:\\n self.add_note(x[0], x[1])\\n else:\\n self.add_note(x[0], x[1], x[2])\\n else:\\n self.add_note(x)\\n return self.notes\",\n \"def notes(self):\\n return self.__notes\",\n \"def get_notes(self, note_limit=200):\\n return Note.get_by_person_record_id(\\n self.subdomain, self.record_id, limit=note_limit)\",\n \"def notes(self):\\r\\n return notes.Notes(self)\",\n \"def notes(self):\\r\\n return notes.Notes(self)\",\n \"def remove_duplicate_notes(self):\\n res = []\\n for x in self.notes:\\n if x not in res:\\n res.append(x)\\n self.notes = res\\n return res\",\n \"def getNotes(self):\\n logger.debug(\\\"Func: getNotes\\\")\\n\\n return self._currentNotes\",\n \"def test_list_notes(self):\\n pass\",\n \"def fNoteLists(self, match):\\n att, start_char, g_links, extras = match.groups()\\n start_char = start_char or 'a'\\n index = '%s%s%s' % (g_links, extras, start_char)\\n result = ''\\n\\n if index not in self.notelist_cache:\\n o = []\\n if self.notes:\\n for seq, info in self.notes.items():\\n links = self.makeBackrefLink(info, g_links, start_char)\\n atts = ''\\n if 'def' in info:\\n infoid = info['id']\\n atts = info['def']['atts']\\n content = info['def']['content']\\n li = (\\\"\\\"\\\"\\\\t<li%s>%s<span id=\\\"note%s\\\"> </span>%s</li>\\\"\\\"\\\"\\n % (atts, links, infoid, content))\\n else:\\n li = (\\\"\\\"\\\"\\\\t<li%s>%s Undefined Note [#%s].<li>\\\"\\\"\\\" %\\n (atts, links, info['seq']))\\n o.append(li)\\n if '+' == extras and self.unreferencedNotes:\\n for seq, info in self.unreferencedNotes.items():\\n if info['def']:\\n atts = info['def']['atts']\\n content = info['def']['content']\\n li = \\\"\\\"\\\"\\\\t<li%s>%s</li>\\\"\\\"\\\" % (atts, content)\\n o.append(li)\\n self.notelist_cache[index] = u\\\"\\\\n\\\".join(o)\\n result = self.notelist_cache[index]\\n if result:\\n list_atts = self.pba(att)\\n result = \\\"\\\"\\\"<ol%s>\\\\n%s\\\\n</ol>\\\"\\\"\\\" % (list_atts, result)\\n return result\",\n \"def listNoteVersions(self, authenticationToken, noteGuid):\\r\\n self.send_listNoteVersions(authenticationToken, noteGuid)\\r\\n return self.recv_listNoteVersions()\",\n \"def notes(self):\\n return notes.Notes(self)\",\n \"def noterest_to_mensural(notes, rests, modusminor):\\n # Default values for notes according to the mensuration\\n b_def = 2048\\n\\n l_def = modusminor * b_def\\n l_imp = 2 * b_def\\n l_perf = 3 * b_def\\n\\n max_def = 2 * l_def\\n # The default value of the 'maxima' is double because, actually, there is no maxima in this repertoire\\n # Ars Antiqua has no 'maximas' just 'duplex longs'\\n\\n # Notes's Part:\\n # Only breves can be altered (as only longs can be perfect/imperfect)\\n # Breves can't be perfect/imperfect (they are always 3 minor-semibreves long)\\n for note in notes:\\n dur = note.getAttribute('dur').value\\n durges_num = int(note.getAttribute('dur.ges').value[:-1])\\n\\n # For the tied notes:\\n # First find its right (contemporary) duration\\n if dur == 'TiedNote!':\\n # Maximas\\n if durges_num == max_def:\\n dur = 'maxima'\\n # Longas\\n elif durges_num in [l_imp, l_perf]:\\n dur = 'long'\\n # MISTAKE in tie duration\\n else:\\n print(\\\"Weird\\\\n The tied note doesn't seem to be any note in the range of longa to maxima - \\\" + str(note) + \\\", its duration is \\\" + str(durges_num) + \\\"p\\\")\\n note.getAttribute('dur').setValue(dur)\\n\\n # Look for the corresponding mensural duration of the notes\\n\\n # MAXIMA (Duplex Longa)\\n if dur == 'maxima':\\n mens_dur = 'maxima'\\n\\n # LONGA\\n elif dur == 'long':\\n # Default @dur value of the note (Exception: Altered Breve)\\n mens_dur = 'longa'\\n\\n # Looking for 'perfections', 'imperfections' and 'alterations'\\n if durges_num == l_perf:\\n # Perfection case\\n if modusminor == 2:\\n note.addAttribute('quality', 'p')\\n note.addAttribute('num', '2')\\n note.addAttribute('numbase', '3')\\n # # And we add a dot of perfection\\n # if not note.hasChildren('dot'):\\n # dot = MeiElement('dot')\\n # note.addChild('dot')\\n # dot = note.getChildrenByName('dot')\\n # dot.addAttribute('format', 'aug')\\n # Default case\\n elif modusminor == 3:\\n pass\\n # Mensuration MISTAKE: 'modusminor'\\n else:\\n print(\\\"MISTAKE IN MENSURATION: modusminor\\\")\\n pass\\n elif durges_num == l_imp:\\n if modusminor == 3:\\n # Alteration case - here the @dur attribute changes\\n if note.hasAttribute('artic') and note.getAttribute('artic').value == 'stop':\\n mens_dur = 'brevis'\\n note.addAttribute('quality', 'a')\\n note.addAttribute('num', '1')\\n note.addAttribute('numbase', '2')\\n # Imperfection case\\n else:\\n note.addAttribute('quality', 'i')\\n note.addAttribute('num', '3')\\n note.addAttribute('numbase', '2')\\n # Default case\\n elif modusminor == 2:\\n pass\\n # Mensuration MISTAKE: 'modusminor'\\n else:\\n print(\\\"MISTAKE IN MENSURATION: modusminor\\\")\\n pass\\n # MISTAKE on the note's duration\\n else:\\n print(\\\"This LONG note \\\" + str(note) + \\\" has an inappropriate duration @dur.ges = \\\" + str(durges_num) + \\\"p, as it is \\\" + str(Fraction(durges_num, l_def).numerator) + \\\"/\\\" + str(Fraction(durges_num, l_def).denominator) + \\\" part of its normal value.\\\")\\n\\n # BREVIS\\n elif dur == 'breve':\\n mens_dur = 'brevis'\\n\\n # SEMIBREVIS\\n elif dur == '1':\\n mens_dur = 'semibrevis'\\n\\n # INCORRECT NOTE VALUE\\n else:\\n if dur != \\\"TiedNote!\\\":\\n print(\\\"This note shouldn't be here, as it is larger than a maxima or shorter than a semibrevis! \\\" + str(note) + \\\", \\\" + str(dur) + \\\", \\\" + str(durges_num) + \\\"p\\\")\\n mens_dur = dur\\n else:\\n print(\\\"Still tied-note\\\")\\n\\n # Change the @dur value to the corresponding mensural note value\\n note.getAttribute('dur').setValue(mens_dur)\\n\\n # Rest's Part:\\n # Rests can't be modified from its original value\\n # Long-rests don't exist, there only is 1, 2 or 3 breve rests.\\n for rest in rests:\\n # Due to the mRest part of the code, all the rests have a @dur attribute.\\n dur = rest.getAttribute('dur').value\\n # Semibreve rest\\n if dur == \\\"1\\\":\\n mens_dur = \\\"semibrevis\\\"\\n # Check for mistakes in duration (@dur.ges attribute)\\n if rest.hasAttribute('dur.ges'):\\n durges_num = int(rest.getAttribute('dur.ges').value[:-1])\\n if durges_num != 1024:\\n print(\\\"This SEMIBREVE rest \\\" + str(rest) + \\\", doesn't have the appropriate @dur.ges value, as it is \\\" + str(durges_num) + \\\"p, instead of 1024p\\\\n\\\")\\n # Breve rest\\n elif dur == \\\"breve\\\":\\n mens_dur = \\\"brevis\\\" # 1B rest??????????\\n # Check for mistakes in duration (@dur.ges attribute)\\n if rest.hasAttribute('dur.ges'):\\n durges_num = int(rest.getAttribute('dur.ges').value[:-1])\\n if durges_num != 2048:\\n print(\\\"This BREVE rest \\\" + str(rest) + \\\", doesn't have the appropriate @dur.ges value, as it is \\\" + str(durges_num) + \\\"p, instead of 2048p\\\\n\\\")\\n # 2-breve and 3-breve rest\\n elif dur == \\\"long\\\":\\n ##########################################################################################################\\n mens_dur = \\\"longa\\\" # THIS WONT BE HERE, INSTEAD WE WILL USE THE MENS_DUR SPECIFIED IN EACH CONDITION (IF)\\n ##########################################################################################################\\n if rest.hasAttribute('dur.ges'):\\n durges_num = int(rest.getAttribute('dur.ges').value[:-1])\\n # 2-breve rest\\n if durges_num == l_imp:\\n rest.addAttribute('EVENTUALDUR', '2B') # It will be: mens_dur = '2B'\\n ###################################################################################################################\\n ###### This will go away when the 3B and 2B rests (3-spaces and 2-spaces rests) are implemented in Verovio ########\\n if modusminor == 3: # 'imperfected'\\n rest.addAttribute('num', '3')\\n rest.addAttribute('numbase', '2')\\n else: # Default\\n pass\\n ###################################################################################################################\\n # 3-breve rest\\n elif durges_num == l_perf:\\n rest.addAttribute('EVENTUALDUR', '3B') # It will be: mens_dur = '3B'\\n ###################################################################################################################\\n ###### This will go away when the 3B and 2B rests (3-spaces and 2-spaces rests) are implemented in Verovio ########\\n if modusminor == 2: # 'perfected'\\n rest.addAttribute('num', '2')\\n rest.addAttribute('numbase', '3')\\n else: # Default\\n pass\\n ###################################################################################################################\\n # Check for mistakes in duration (@dur.ges attribute)\\n else:\\n print(\\\"This 'LONG' Rest \\\" + str(rest) + \\\", doesn't have the appropriate @dur.ges value, as it is \\\" + str(durges_num) + \\\"p, instead of \\\" + str(l_imp) + \\\"p or \\\" + str(l_perf) + \\\"p\\\")\\n print(\\\"i.e., it isn't a 2-breve or 3-breve rest, instead it is: \\\" + str(Fraction(durges_num, b_def).numerator) + \\\"/\\\" + str(Fraction(durges_num, b_def).denominator) + \\\" times a BREVE rest\\\\n\\\")\\n else:\\n # 3-breve rest\\n if modusminor == 3:\\n rest.addAttribute('EVENTUALDUR', '3B')\\n # 2-breve rest\\n elif modusminor == 2:\\n rest.addAttribute('EVENTUALDUR', '2B')\\n # Check for mistakes in duration (@dur.ges attribute)\\n else:\\n print(\\\"This 'LONG' Rest \\\" + str(rest) + \\\", doesn't have the appropriate @dur.ges value\\\")\\n # Mistake in rest's duration (@dur attribute)\\n else:\\n print(\\\"This kind of Rest shouldn't be in this repertory \\\" + str(note) + \\\", it has a duration of \\\" + str(dur) + \\\"\\\\n\\\")\\n mens_dur = dur\\n\\n # Change the @dur value to the corresponding mensural note value\\n rest.getAttribute('dur').setValue(mens_dur)\",\n \"def bar_to_lilypond_notes(notes):\\n lp_notes = []\\n if notes[0] is None:\\n notes = notes[1:]\\n if notes[0] is None:\\n lp_notes.append(\\\"r\\\")\\n for n in notes:\\n if n is None:\\n continue\\n if type(n) is list:\\n lp_notes.append([midi_to_lilypond_note(x) for x in n])\\n else:\\n lp_notes.append(midi_to_lilypond_note(n))\\n return lp_notes\",\n \"def __init__(self):\\n self.notes = []\",\n \"def add_evernote_notes(self, evernote_notes, update=False, log_update_if_unchanged=True):\\n new_nids=[]\\n action_str_base = ['Add', 'Update'][update]\\n action_str = ['Adding', 'Updating'][update]\\n action_preposition = ['To', 'In'][update]\\n info = stopwatch.ActionInfo(action_str + ' Of', 'Evernote Notes', action_preposition + ' Anki', report_if_empty=False)\\n tmr = stopwatch.Timer(evernote_notes, 10, info=info,\\n label='Add\\\\\\\\Anki-%sEvernoteNotes' % action_str_base)\\n\\n for ankiNote in evernote_notes:\\n try:\\n title = ankiNote.FullTitle\\n content = decode(ankiNote.Content)\\n anki_field_info = {\\n FIELDS.TITLE: title,\\n FIELDS.CONTENT: content,\\n FIELDS.EVERNOTE_GUID: FIELDS.EVERNOTE_GUID_PREFIX + ankiNote.Guid,\\n FIELDS.UPDATE_SEQUENCE_NUM: str(ankiNote.UpdateSequenceNum),\\n FIELDS.SEE_ALSO: u''\\n }\\n except Exception:\\n log_error(\\\"Unable to set field info for: Note '%s': '%s'\\\" % (ankiNote.FullTitle, ankiNote.Guid))\\n log_dump(ankiNote.Content, \\\" NOTE CONTENTS \\\")\\n # log_dump(encode(ankiNote.Content), \\\" NOTE CONTENTS \\\")\\n raise\\n tmr.step(title)\\n baseNote = None\\n if update:\\n baseNote = self.get_anki_note_from_evernote_guid(ankiNote.Guid)\\n if not baseNote:\\n log_error('Updating note %s: COULD NOT FIND BASE NOTE FOR ANKI NOTE ID' % ankiNote.Guid)\\n tmr.reportStatus(EvernoteAPIStatus.MissingDataError)\\n continue\\n if ankiNote.Tags is None:\\n log_error(\\\"Could note find tags object for note %s: %s. \\\" % (ankiNote.Guid, ankiNote.FullTitle))\\n tmr.reportStatus(EvernoteAPIStatus.MissingDataError)\\n continue\\n anki_note_prototype = AnkiNotePrototype(self, anki_field_info, ankiNote.TagNames, baseNote,\\n notebookGuid=ankiNote.NotebookGuid, count=tmr.count,\\n count_update=tmr.counts.updated.completed.val, max_count=tmr.max)\\n anki_note_prototype._log_update_if_unchanged_ = log_update_if_unchanged\\n nid = tmr.autoStep(anki_note_prototype.update_note() if update else anki_note_prototype.add_note(),\\n ankiNote.FullTitle, update)\\n if tmr.status.IsSuccess and not update:\\n new_nids.append([nid, ankiNote.Guid])\\n elif tmr.status.IsError:\\n log(\\\"ANKI ERROR WHILE %s EVERNOTE NOTES: \\\" % action_str.upper() + str(tmr.status), tmr.label + '-Error')\\n tmr.Report()\\n if new_nids:\\n ankDB().executemany(\\\"UPDATE {n} SET nid = ? WHERE guid = ?\\\", new_nids)\\n return tmr.counts.success\",\n \"def get_scheduled_notes(event):\\n def _sort_note_by(note):\\n obj = note.object\\n if hasattr(obj, 'start_dt'):\\n return obj.start_dt, 0\\n else:\\n return obj.contribution.start_dt, obj.position\\n\\n tt_entries = (event.timetable_entries\\n .filter(TimetableEntry.type != TimetableEntryType.BREAK)\\n .options(joinedload('session_block').joinedload('contributions').joinedload('subcontributions'))\\n .options(joinedload('contribution').joinedload('subcontributions'))\\n .options(noload('break_'))\\n .all())\\n # build a list of all the objects we need notes for. that way we can query\\n # all notes in a single go afterwards instead of making the already-huge\\n # timetable query even bigger.\\n objects = set()\\n for entry in tt_entries:\\n objects.add(entry.object)\\n if entry.type == TimetableEntryType.CONTRIBUTION:\\n objects.update(sc for sc in entry.object.subcontributions if not sc.is_deleted)\\n elif entry.type == TimetableEntryType.SESSION_BLOCK:\\n for contrib in entry.object.contributions:\\n objects.add(contrib)\\n objects.update(sc for sc in contrib.subcontributions if not sc.is_deleted)\\n notes = [x for x in event.all_notes.filter_by(is_deleted=False) if x.object in objects]\\n return sorted(notes, key=_sort_note_by)\",\n \"def get_notes():\\n song_index_to_notes = {}\\n\\n for file in glob.glob(\\\"../data/midi/*.mid\\\"):\\n midi = converter.parse(file)\\n song_index = int(os.path.splitext(os.path.basename(file))[0])\\n #print(\\\"Parsing %s with an index %d\\\" % (file, song_index))\\n\\n notes_to_parse = None\\n notes = []\\n try: # file has instrument parts\\n s2 = instrument.partitionByInstrument(midi)\\n notes_to_parse = s2.parts[0].recurse()\\n except: # file has notes in a flat structure\\n notes_to_parse = midi.flat.notes\\n\\n for element in notes_to_parse:\\n if isinstance(element, note.Note):\\n notes.append(str(element.pitch))\\n elif isinstance(element, chord.Chord):\\n notes.append('.'.join(str(n) for n in element.normalOrder))\\n\\n song_index_to_notes[song_index] = notes\\n\\n return song_index_to_notes\",\n \"def get_note_child_list(self):\\n return self.address_list\",\n \"def return_markers(self):\\r\\n ent_file = join(self.filename, self._basename + '.ent')\\r\\n if not exists(ent_file):\\r\\n ent_file = join(self.filename, self._basename + '.ent.old')\\r\\n\\r\\n try:\\r\\n ent_notes = _read_ent(ent_file)\\r\\n\\r\\n except (FileNotFoundError, PermissionError):\\r\\n markers = []\\r\\n\\r\\n else:\\r\\n allnote = []\\r\\n for n in ent_notes:\\r\\n try:\\r\\n n['value'].keys()\\r\\n allnote.append(n['value'])\\r\\n except AttributeError:\\r\\n lg.debug('Note of length {} was not '\\r\\n 'converted to dict'.format(n['length']))\\r\\n\\r\\n s_freq = self._hdr['erd']['sample_freq']\\r\\n pcname = '0CFEBE72-DA20-4b3a-A8AC-CDD41BFE2F0D'\\r\\n note_time = []\\r\\n note_name = []\\r\\n note_note = []\\r\\n for n in allnote:\\r\\n if n['Text'] == 'Analyzed Data Note':\\r\\n continue\\r\\n if not n['Text']:\\r\\n continue\\r\\n if 'User' not in n['Data'].keys():\\r\\n continue\\r\\n user1 = n['Data']['User'] == 'Persyst'\\r\\n user2 = False # n['Data']['User'] == 'eeg'\\r\\n user3 = n['Data']['User'] == pcname\\r\\n user4 = n['Data']['User'] == 'XLSpike - Intracranial'\\r\\n user5 = n['Data']['User'] == 'XLEvent - Intracranial'\\r\\n if user1 or user2 or user3 or user4 or user5:\\r\\n continue\\r\\n if len(n['Data']['User']) == 0:\\r\\n note_name.append('-unknown-')\\r\\n else:\\r\\n note_name.append(n['Data']['User'].split()[0])\\r\\n note_time.append(n['Stamp'] / s_freq)\\r\\n note_note.append(n['Text'])\\r\\n\\r\\n markers = []\\r\\n for time, name, note in zip(note_time, note_name, note_note):\\r\\n m = {'name': note + ' (' + name + ')',\\r\\n 'start': time,\\r\\n 'end': time,\\r\\n 'chan': None,\\r\\n }\\r\\n markers.append(m)\\r\\n\\r\\n return markers\",\n \"def notes(self):\\n return NotesTable(self.rpc, self.name)\",\n \"def ordered_notes(self, obj, sig_features, tabs):\\n supports = dict(\\n [(str(support.id), support) for support in obj.all_supports])\\n notes = []\\n for browsers in sig_features.values():\\n for section in tabs:\\n for browser in section['browsers']:\\n sig_supports = browsers.get(browser, [])\\n for sig_support_pk in sig_supports:\\n support = supports[sig_support_pk]\\n if support.note:\\n notes.append(sig_support_pk)\\n return OrderedDict((note, i) for i, note in enumerate(notes, 1))\",\n \"def sb_major_minor(children_of_voiceStaff):\\n indices_BrevesOrTuplets = [-1]\\n for element in children_of_voiceStaff:\\n if (element.name == 'tuplet') or (element.hasAttribute('dur') and (element.getAttribute('dur').value == 'brevis' or element.getAttribute('dur').value == 'longa' or element.getAttribute('dur').value == 'maxima')):\\n indices_BrevesOrTuplets.append(children_of_voiceStaff.index(element))\\n for i in range(0, len(indices_BrevesOrTuplets)-1):\\n start = indices_BrevesOrTuplets[i]\\n end = indices_BrevesOrTuplets[i+1]\\n number_sb = end - start - 1\\n # Case 1: Even number of semibreves\\n if number_sb % 2 == 0:\\n cont_sb = 0\\n for j in range(start+1, end):\\n cont_sb = cont_sb + 1\\n # 2nd, 4th, 6th, ... semibreve in the sequence; generally, these are the ones that are Major (default case), but there are exceptions\\n if cont_sb % 2 == 0:\\n previous_sb = children_of_voiceStaff[j-1]\\n # The exception: tenuto marks (downward stems) in the previous note (1st, 3rd, 5th, ... semibreve)\\n if previous_sb.hasAttribute('artic') and previous_sb.getAttribute('artic').value == 'ten':\\n previous_sb.addAttribute('quality', 'major')\\n previous_sb.addAttribute('num', '1')\\n previous_sb.addAttribute('numbase', '2')\\n # The default case:\\n else:\\n current_sb = children_of_voiceStaff[j]\\n current_sb.addAttribute('quality', 'major')\\n current_sb.addAttribute('num', '1')\\n current_sb.addAttribute('numbase', '2')\\n else:\\n pass\\n # Case 2: Odd number of semibreves\\n else:\\n # This can (should) only happen when there is a 2:1 tuplet at one end of the sequence of semibreves,\\n # so that the whole tuplet is equal to just 1 minor semibreve,\\n # and the semibreve that precedes/follows it (ususally has a downward stem to indicate its longer duration in the group) is the Major Semibreve that completes the Perfect Breve.\\n # Without this grouping (major semibreve and tuplet), we are left with an even number of semibreves that can be grouped into minor-major pairs, as usual.\\n start_element = children_of_voiceStaff[start]\\n end_element = children_of_voiceStaff[end]\\n # If the 2:1 tuplet precedes of the sequence of semibreves\\n if (start_element.name == 'tuplet' and start_element.getAttribute('num').value == '2' and start_element.getAttribute('numbase').value == '1'):\\n # The semibreve that follows this 2:1 tuplet should be major (completing the perfection)\\n major_sb = children_of_voiceStaff[start + 1]\\n major_sb.addAttribute('quality', 'major')\\n major_sb.addAttribute('num', '1')\\n major_sb.addAttribute('numbase', '2')\\n # The other semibreves are grouped into minor-major pairs\\n cont_sb = 0\\n for j in range(start+2, end):\\n cont_sb = cont_sb + 1\\n # The second semibreve of each pair: generally it is Major (default case), but there are exceptions\\n if cont_sb % 2 == 0:\\n previous_sb = children_of_voiceStaff[j-1]\\n # The exception: tenuto marks (downward stems) in the previous note (1st, 3rd, 5th, ... semibreve)\\n if previous_sb.hasAttribute('artic') and previous_sb.getAttribute('artic').value == 'ten':\\n previous_sb.addAttribute('quality', 'major')\\n previous_sb.addAttribute('num', '1')\\n previous_sb.addAttribute('numbase', '2')\\n # The default case:\\n else:\\n current_sb = children_of_voiceStaff[j]\\n current_sb.addAttribute('quality', 'major')\\n current_sb.addAttribute('num', '1')\\n current_sb.addAttribute('numbase', '2')\\n # The first semibreve of each pair (it is generally minor, so we don't make any changes to it)\\n else:\\n pass\\n # If the 2:1 tuplet follows the sequence of semibreves\\n elif (end_element.name == 'tuplet' and end_element.getAttribute('num').value == '2' and end_element.getAttribute('numbase').value == '1'):\\n # The semibreve that precedes the 2:1 tuplet, should be major (completing the perfection)\\n major_sb = children_of_voiceStaff[end - 1]\\n major_sb.addAttribute('quality', 'major')\\n major_sb.addAttribute('num', '1')\\n major_sb.addAttribute('numbase', '2')\\n # The other semibreves are grouped into minor-major pairs\\n cont_sb = 0\\n for j in range(start+1, end-1):\\n cont_sb = cont_sb + 1\\n # The second semibreve of each pair: generally it is Major (default case), but there are exceptions\\n if cont_sb % 2 == 0:\\n previous_sb = children_of_voiceStaff[j-1]\\n # The exception: tenuto marks (downward stems) in the previous note (1st, 3rd, 5th, ... semibreve)\\n if previous_sb.hasAttribute('artic') and previous_sb.getAttribute('artic').value == 'ten':\\n previous_sb.addAttribute('quality', 'major')\\n previous_sb.addAttribute('num', '1')\\n previous_sb.addAttribute('numbase', '2')\\n # The default case:\\n else:\\n current_sb = children_of_voiceStaff[j]\\n current_sb.addAttribute('quality', 'major')\\n current_sb.addAttribute('num', '1')\\n current_sb.addAttribute('numbase', '2')\\n # The first semibreve of each pair (it is generally minor, so we don't make any changes to it)\\n else:\\n pass\\n # Mistake case: If there is no tuplet 2:1 at any of the ends of the sequence, there shouldn't be an odd number of semibreves\\n else:\\n print(\\\"This shouldn't happen! \\\\nThere is an odd number of semibreves between two perfect breves (or tuplets that are equivalent to a perfect breve), \\\\nwhich doesn't allow to form minor-major (or major-minor) pairs of semibreves.\\\")\\n print(\\\"You can find these breves between the \\\" + str(start_element.name) + \\\" with id \\\" + str(start_element.id) + \\\" and the \\\" + str(end_element.name) + \\\" with id \\\" + str(end_element.id))\",\n \"def note_list(self, evarnames=\\\"\\\", in_=None):\\n evarnames = self._find_vars(evarnames, evars=True, \\n unique=True, empty_ok=True)\\n if len(evarnames) == 0:\\n evarnames = ['_dta'] + self._varlist\\n if in_ is not None:\\n if isinstance(in_, int):\\n in_ = (in_,)\\n elif (not isinstance(in_, collections.Iterable)\\n or not all(isinstance(n, int) for n in in_)):\\n raise TypeError(\\\"in_ should be int or iterable of int\\\")\\n if any(n <= 0 for n in in_):\\n raise ValueError(\\\"note numbers must be >= 1\\\")\\n for name in evarnames:\\n if name not in self._chrdict: continue\\n chars = self._chrdict[name]\\n if 'note0' not in chars: continue\\n nnotes = int(chars['note0'])\\n in_range = in_ if in_ is not None else range(1, nnotes + 1)\\n note_info = []\\n for note_num in in_range:\\n note_name = 'note' + str(note_num)\\n if note_name in chars:\\n note_info.append(note_num)\\n if note_info != []:\\n note_info.insert(0, name)\\n self._display_notes(note_info)\",\n \"def remove_note(self, note, octave=-1):\\n res = []\\n for x in self.notes:\\n if isinstance(note, six.string_types):\\n if x.name != note:\\n res.append(x)\\n else:\\n if x.octave != octave and octave != -1:\\n res.append(x)\\n else:\\n if x != note:\\n res.append(x)\\n self.notes = res\\n return res\",\n \"async def _view_all_notes(self, ctx: Context):\\n\\n author = ctx.author\\n\\n note_infos = []\\n\\n embed_links = ctx.channel.permissions_for(ctx.guild.me).embed_links\\n\\n author_str = f\\\"{author.name}'\\\"\\n\\n if author.name[-1].lower() != \\\"s\\\":\\n author_str += \\\"s\\\"\\n\\n async with self.config.member(author).notes() as notes:\\n total = len(notes)\\n for page_num, note in enumerate(notes, start=1):\\n msg_info = \\\"\\\"\\n if note[\\\"author\\\"]:\\n msg_info += _(\\\"**Author:** {}\\\").format(note[\\\"author\\\"])\\n if note[\\\"channel\\\"]:\\n msg_info += _(\\\"\\\\n**Channel:** {}\\\").format(note[\\\"channel\\\"])\\n if note[\\\"jump_url\\\"]:\\n if embed_links:\\n msg_info += _(\\n \\\"\\\\n[Click here to jump to message]({})\\\"\\n ).format(note[\\\"jump_url\\\"])\\n else:\\n msg_info += _(\\n \\\"\\\\n**Jump To Message:** {}\\\"\\n ).format(note[\\\"jump_url\\\"])\\n\\n note_info = _(\\n \\\"{}\\\\n\\\\n**Note:**\\\\n```{}```\\\\n**Reason:**\\\\n```{}```\\\"\\n ).format(\\n msg_info,\\n note[\\\"note\\\"],\\n note[\\\"reason\\\"]\\n ).strip()\\n\\n if embed_links:\\n page = discord.Embed(\\n colour=0xff0000,\\n description=note_info,\\n title=_(\\\"{} TvM Notes\\\").format(author_str),\\n timestamp=ctx.message.created_at\\n )\\n\\n page.set_footer(\\n text=_(\\\"Page {page_num}/{leng}\\\").format(\\n page_num=page_num, leng=total\\n )\\n )\\n else:\\n page = _(\\n \\\"**{author} TvM Notes**\\\"\\n \\\"\\\\n\\\\n{note}\\\"\\n \\\"\\\\n{footer}\\\"\\n ).format(\\n author=author_str,\\n note=note_info,\\n footer=_(\\\"*Page {page_num}/{leng}*\\\").format(\\n page_num=page_num, leng=total\\n )\\n )\\n\\n note_infos.append(page)\\n\\n await menu(ctx, note_infos, DEFAULT_CONTROLS)\",\n \"def scorepart_to_notes(part, morphetic_pitch=False):\\n\\n bm = part.beat_map\\n\\n if morphetic_pitch:\\n pitch_attr = attrgetter('morphetic_pitch')\\n else:\\n pitch_attr = attrgetter('midi_pitch')\\n notes = np.array([(bm(n.start.t), bm(n.end.t), pitch_attr(n))\\n for n in part.notes], np.float)\\n\\n ids = np.array([n.id for n in part.notes])\\n\\n return notes, ids\",\n \"def notes(self):\\n return Notes(self)\",\n \"def release_notes(self):\\n return self._release_notes\",\n \"def getNote(self):\\n return (self.noteName[0], self.accidental, self.octave)\",\n \"def all_note_by_job_applied_id(job_applied_id):\\n\\n return Note.query.filter(Note.job_applied_id == job_applied_id, Note.note_category == 'Note' ).all()\",\n \"def midi_to_lilypond_note(note):\\n return all_notes[note+4]\",\n \"def PublicNotes(self, default=[{}]):\\n tmp = self.data.get('metadata', {}).get('public_notes', default)\\n return [HEP.SourcedValueObject(i) for i in tmp]\",\n \"def note_list(request):\\n user = request.user\\n notes = Note.objects.filter(author=user)\\n serializer = NoteSerializer(notes, many=True)\\n return Response(serializer.data)\",\n \"def midiToNotes(notes):\\n names = ['C', 'C#', 'D', 'D#', 'E', 'F', 'F#', 'G', 'G#', 'A', 'A#', 'B']\\n chord = []\\n \\n for note in notes:\\n name = names[note%12]\\n octave = int((note-12)/12)\\n chord.append(name + str(octave))\\n \\n return chord\",\n \"def business_notes(self):\\n return self._business_notes\",\n \"def parse_pretty_midi_lyrics(midi: PrettyMIDI) -> List[Lyric]:\\n return [Lyric(lyric.time, lyric.text) for lyric in midi.lyrics]\",\n \"def child_show(self,\\r\\n entrylist=None,\\r\\n highlight=None,\\r\\n not_all=False):\\r\\n\\r\\n if highlight is None:\\r\\n highlight = set()\\r\\n if entrylist is None and not not_all:\\r\\n entrylist = self.apply_limit(self.find_within(indexfrom=Index(0),orequal=True))\\r\\n## entrylist = self.apply_limit([a_temp for a_temp\\r\\n## in sorted([str(Index(a_temp))\\r\\n## for a_temp in self.indexes()\\r\\n## if Index(a_temp) > Index(str(0))],\\r\\n## key=lambda x_temp: Index(x_temp))])\\r\\n\\r\\n # if entrylist is , default to all notes,\\r\\n #with limit applied.\\r\\n\\r\\n def recursive_show(dictionary, level=1):\\r\\n\\r\\n \\\"\\\"\\\"recursive function to show children of a note\\\"\\\"\\\"\\r\\n\\r\\n for k_temp in dictionary:\\r\\n\\r\\n\\r\\n if dictionary[k_temp] == {}:\\r\\n\\r\\n if self.default_dict['variablesize']:\\r\\n if not self.notebook_contains(k_temp):\\r\\n display.noteprint((alerts.ATTENTION,str(k_temp)+' '+'NOT FOUND'))\\r\\n else:\\r\\n display.noteprint(self.show(k_temp,\\r\\n shortform=(self.shortshow\\r\\n or len(entrylist) > self.longmax),\\r\\n yestags=self.tagdefault,\\r\\n highlight=highlight),\\r\\n param_width=display.width_needed\\r\\n (self.show(k_temp),\\r\\n self.get_metadata_from_note(k_temp)['size'],\\r\\n leftmargin=self.default_dict['leftmargin']),\\r\\n param_indent=(level-1)*self.default_dict['indentmultiplier'],\\r\\n leftmargin=self.default_dict['leftmargin'])\\r\\n else:\\r\\n if not self.notebook_contains(k_temp):\\r\\n display.noteprint((alerts.ATTENTION,str(k_temp)+' '+'NOT FOUND'))\\r\\n else:\\r\\n display.noteprint(\\r\\n self.show(k_temp,\\r\\n yestags=self.tagdefault,\\r\\n shortform=(self.shortshow\\r\\n or len(entrylist) > self.longmax)),\\r\\n param_width=self.default_dict['size'],\\r\\n param_indent=(level-1)*self.default_dict['indentmultiplier'],leftmargin=self.default_dict['leftmargin'])\\r\\n\\r\\n else:\\r\\n if self.default_dict['variablesize']:\\r\\n if not self.notebook_contains(k_temp):\\r\\n display.noteprint((alerts.ATTENTION,str(k_temp)+' '+'NOT FOUND'))\\r\\n else:\\r\\n display.noteprint(\\r\\n self.show(k_temp,\\r\\n shortform=(self.shortshow\\r\\n or len(entrylist) > self.longmax),\\r\\n yestags=self.tagdefault,\\r\\n highlight=highlight),\\r\\n param_width=display.width_needed(self.show(k_temp),\\r\\n self.get_metadata_from_note(k_temp)['size']),\\r\\n param_indent=(level-1)*self.default_dict['indentmultiplier'],leftmargin=self.default_dict['leftmargin'])\\r\\n else:\\r\\n if not self.notebook_contains(k_temp):\\r\\n display.noteprint((alerts.ATTENTION,str(k_temp)+' '+'NOT FOUND'))\\r\\n else:\\r\\n display.noteprint(self.show\\r\\n (k_temp, yestags=self.tagdefault,\\r\\n shortform=(self.shortshow\\r\\n or len(entrylist) > self.longmax)),\\r\\n param_width=self.default_dict['size'],\\r\\n param_indent=(level-1)*self.default_dict['indentmultiplier'],\\r\\n leftmargin=self.default_dict['leftmargin'])\\r\\n recursive_show(dictionary[k_temp],\\r\\n level=level+1)\\r\\n\\r\\n\\r\\n maxlevel = 0\\r\\n for i_temp in entrylist:\\r\\n if len(i_temp) > maxlevel:\\r\\n maxlevel = len(i_temp)\\r\\n\\r\\n\\r\\n tempdict_a = {}\\r\\n tempdict_b = {}\\r\\n\\r\\n for i_temp in range(1, maxlevel+1):\\r\\n tempdict_a[i_temp] = []\\r\\n\\r\\n for i_temp in entrylist:\\r\\n tempdict_a[len(i_temp)].append(str(i_temp))\\r\\n\\r\\n for a_temp in range(1, len(tempdict_a)+1):\\r\\n #ranges over lengths of indexes\\r\\n\\r\\n for b_temp in range(len(tempdict_a[a_temp])):\\r\\n # ranges through the list of\\r\\n #indexes for a give length a\\r\\n if a_temp == 1:\\r\\n tempdict_b[str(tempdict_a[a_temp][b_temp])] = {}\\r\\n\\r\\n\\r\\n else:\\r\\n temp = Index(tempdict_a[a_temp][b_temp])\\r\\n\\r\\n ancestors = []\\r\\n while not temp.is_top():\\r\\n\\r\\n ancestors = [str(temp.parent())]+ancestors\\r\\n temp = temp.parent()\\r\\n\\r\\n ancestorstring = EMPTYCHAR\\r\\n for d_temp in ancestors:\\r\\n ancestorstring += \\\"['\\\"+str(d_temp)+\\\"']\\\"\\r\\n\\r\\n try:\\r\\n if isinstance(eval('tempdict_b'+\\r\\n ancestorstring), dict):\\r\\n\\r\\n\\r\\n if isinstance(eval('tempdict_b'+\\r\\n ancestorstring), dict):\\r\\n\\r\\n e_temp = ('tempdict_b'\\r\\n +ancestorstring\\r\\n +'={str(tempdict_a[a_temp][b_temp]):{}}')\\r\\n exec(e_temp)\\r\\n\\r\\n else:\\r\\n e_temp = ('tempdict_b'\\r\\n +ancestorstring\\r\\n +'[str(tempdict_a[a_temp][b_temp])]'+'={}')\\r\\n exec(e_temp)\\r\\n\\r\\n except KeyError:\\r\\n\\r\\n e_temp = ('tempdict_b'\\r\\n +ancestorstring+'={}')\\r\\n try:\\r\\n exec(e_temp)\\r\\n except KeyError:\\r\\n pass\\r\\n except SyntaxError:\\r\\n print('Syntax Error')\\r\\n\\r\\n e_temp = ('tempdict_b'\\r\\n +ancestorstring\\r\\n +'[str(tempdict_a[a_temp][b_temp])]'\\r\\n +'={}')\\r\\n try:\\r\\n exec(e_temp)\\r\\n except KeyError:\\r\\n pass\\r\\n except SyntaxError:\\r\\n print('Syntax Error')\\r\\n\\r\\n\\r\\n recursive_show(tempdict_b)\",\n \"def sort(self):\\n self.notes.sort()\",\n \"def _on_note(self) -> Iterator:\\n return filter(\\n lambda x: x.type == constants.NOTE_ON, self.midi_data)\",\n \"def get_note_alternatives(self, id):\\n notes = self.session.query(models.Note).filter(\\n models.Note.conflict_parent_id == id,\\n ).all()\\n return btype.Note.list >> notes\",\n \"def internal_notes(self, key, value):\\n _internal_notes = self.get(\\\"internal_notes\\\", [])\\n for v in force_list(value):\\n internal_note = {\\\"value\\\": clean_val(\\\"a\\\", v, str, req=True)}\\n if internal_note not in _internal_notes:\\n _internal_notes.append(internal_note)\\n return _internal_notes\",\n \"def todolists(self):\\r\\n return tdl.Todolists(self)\",\n \"def notes(self):\\r\\n return TicketNotes(self)\",\n \"def notes_in_path(self, path: str) -> List[NoteInfo]:\\n check_path = os.path.abspath(path)\\n return [n for n in self.notes.values() if n.file_path.startswith(check_path)]\",\n \"def get_todo_list():\\n\\n # assume that a \\\"h264\\\" encoded file is complete\\n return models.LibraryItem.objects.filter(h264=False)\",\n \"def notes(self):\\r\\n return notes.UserNotes(self)\",\n \"def remove_pedal(self):\\n return Melody([n.remove_pedal() for n in self.notes], nb_bars=self.nb_bars, tags=set(self.tags))\",\n \"def get_field_notes(self):\\n return self._fields_notes\",\n \"def extract_notes(music, resolution):\\n # Check resolution\\n assert music.resolution == resolution\\n\\n # Extract notes\\n notes = []\\n for track in music:\\n if track.is_drum or track.program not in KNOWN_PROGRAMS:\\n continue\\n for note in track:\\n beat, position = divmod(note.time, resolution)\\n notes.append(\\n (beat, position, note.pitch, note.duration, track.program)\\n )\\n\\n # Deduplicate and sort the notes\\n notes = sorted(set(notes))\\n\\n return np.array(notes)\",\n \"def getNoteVersion(self, authenticationToken, noteGuid, updateSequenceNum, withResourcesData, withResourcesRecognition, withResourcesAlternateData):\\r\\n pass\",\n \"def get_user_visible_notes():\\n return grammar.user_visible_notes\",\n \"def parse_pretty_midi_key_signatures(midi: PrettyMIDI) -> List[KeySignature]:\\n key_signatures = []\\n for key_signature in midi.key_signature_changes:\\n is_minor, root = divmod(key_signature.key_number, 12)\\n mode = \\\"minor\\\" if is_minor else \\\"major\\\"\\n key_signatures.append(KeySignature(key_signature.time, root, mode))\\n return key_signatures\",\n \"def extract_melodies(sequence, steps_per_beat=4, min_bars=7,\\n min_unique_pitches=5):\\n\\n # Assume bars contain 4 beats, or quarter notes.\\n steps_per_bar = steps_per_beat * 4\\n\\n # Beats per minute is stored in the tempo change event. If there is no tempo\\n # then assume 120 bpm per the MIDI standard.\\n bpm = (sequence.tempos[0].bpm if len(sequence.tempos)\\n else DEFAULT_BEATS_PER_MINUTE)\\n\\n # Group note messages into tracks.\\n tracks = {}\\n for note in sequence.notes:\\n if note.instrument not in tracks:\\n tracks[note.instrument] = []\\n tracks[note.instrument].append(note)\\n\\n melodies = []\\n for track in tracks.values():\\n melody = Melody(steps_per_bar)\\n\\n # Quantize the track into a Melody object.\\n # If any notes start at the same time, only one is kept.\\n melody.from_notes(track, bpm=bpm, gap=steps_per_bar,\\n ignore_polyphonic_notes=True)\\n\\n # Require a certain melody length.\\n if len(melody) - 1 < steps_per_bar * min_bars:\\n logging.debug('melody too short')\\n continue\\n\\n # Require a certain number of unique pitches.\\n note_histogram = melody.get_note_histogram()\\n unique_pitches = np.count_nonzero(note_histogram)\\n if unique_pitches < min_unique_pitches:\\n logging.debug('melody too simple')\\n continue\\n\\n melodies.append(melody)\\n\\n return melodies\",\n \"def processNotes(self, inMeasureElem):\\n inNotes = inMeasureElem.findall(\\\"note\\\")\\n # list to hold array of processed note elements\\n lstOutNotes = []\\n\\n for inNote in inNotes:\\n for childNode in inNote:\\n outNote = ET.Element(\\\"note\\\")\\n\\n # rest\\n inRest = inNote.find(\\\"rest\\\")\\n if (inRest is not None):\\n outRest = ET.Element(\\\"rest\\\")\\n outNote.append(outRest)\\n\\n # chord\\n inChord = inNote.find(\\\"chord\\\")\\n if (inChord is not None):\\n outChord = ET.Element(\\\"chord\\\")\\n outNote.append(outChord)\\n\\n # pitch\\n inPitch = inNote.find(\\\"pitch\\\")\\n if (inPitch is not None):\\n outPitch = ET.Element(\\\"pitch\\\")\\n for childNode in inPitch:\\n if (True == (childNode.tag in [\\\"step\\\", \\\"octave\\\"])):\\n pitchSubElem = ET.Element(childNode.tag)\\n pitchSubElem.text = childNode.text\\n outPitch.append(pitchSubElem)\\n # handle the \\\"alter\\\" child tag separately even if the alter value is 0 include it otherwise\\n # include the original scores alter value\\n inPitchAlter = inPitch.find(\\\"alter\\\")\\n if (inPitchAlter is not None):\\n # use sources alter elem\\n outPitchAlter = ET.Element(\\\"alter\\\")\\n outPitchAlter.text = inPitchAlter.text\\n outPitch.append(outPitchAlter)\\n else:\\n # make a zero value alter element\\n outPitchAlter = ET.Element(\\\"alter\\\")\\n outPitchAlter.text = \\\"0\\\"\\n outPitch.append(outPitchAlter)\\n\\n outNote.append(outPitch)\\n\\n # duration\\n inDuration = inNote.find(\\\"duration\\\")\\n if (inDuration is not None):\\n outDuration = ET.Element(\\\"duration\\\")\\n outDurationVal = int(inDuration.text) * self._multiplier\\n outDuration.text = str(int(outDurationVal))\\n outNote.append(outDuration)\\n\\n # append the mote to the list of notes\\n lstOutNotes.append(outNote)\\n\\n # finally return the list of notes\\n return lstOutNotes\",\n \"def all_notes():\\n \\n return render_template('all_notes.html',colors=music_color,)\",\n \"def get_all_notes(user_id, token):\\n user = User.get(User.id == user_id).username\\n if not validate_token(user, token):\\n return HTTPResponse(status=500, body={\\\"message\\\":\\\"Validation error.\\\"})\\n res = []\\n for note in Notes.select():\\n if note.user.id == user_id:\\n new_note = model_to_dict(note)\\n res.append({\\\"id\\\":new_note['id'], \\\"title\\\":new_note['title'],\\n \\\"content\\\":new_note['content']})\\n new_token = generate_token(user)\\n body = {\\\"user_id\\\":user_id, \\\"token\\\":new_token.decode('utf-8'), 'items':res}\\n return HTTPResponse(status=200, body=body)\",\n \"def page_list_notes(self, no_history=False):\\n\\n list_taskid_desc = self.task_store.sort_taskid_list_descending_lamport()\\n title = \\\"woolnote - all notes\\\"\\n page_header_first_text = \\\"all notes\\\"\\n\\n if no_history:\\n history_id = self.save_history([])\\n else:\\n history_id = self.save_history([\\\"action\\\"], alt_task_store_name=None)\\n\\n page_header_list_of_warnings = None\\n page_header_small_text = None\\n\\n if self.error_msg_queue_list:\\n page_header_list_of_warnings = self.helper_convert_msg_queue_list_to_list_for_output()\\n else:\\n try:\\n # TODO: use asn library?\\n # sha256_fp = read_pem_cert_fingerprint(SSL_CERT_PEM_PATH)\\n page_header_small_text = config.SSL_CERT_PEM_FINGERPRINT\\n except:\\n page_header_small_text = \\\"cannot get ssl cert sha256\\\"\\n\\n return html_page_templates.page_list_notes_template(list_taskid_desc=list_taskid_desc,\\n self_sess_action_auth=self.sess_action_auth, title=title,\\n history_back_id=history_id, primary_task_store=self.task_store,\\n virtual_folders=self.woolnote_config.virtual_folders,\\n single_task_line_ids=set(self.woolnote_config.single_note_line_id.keys()),\\n page_header_first_text=page_header_first_text,\\n page_header_optional_small_second_text=page_header_small_text,\\n page_header_optional_list_of_warnings=page_header_list_of_warnings)\",\n \"def generate_message(self) -> List[mido.Message]:\\n # check for a None note (which is a \\\"pause\\\")\\n if self.__note:\\n note_value = self.__note + self.__info.octave\\n note_velocity = self.__info.volume\\n else:\\n note_value = 0\\n note_velocity = 0\\n return [\\n mido.Message(\\n \\\"note_on\\\",\\n note=note_value,\\n velocity=note_velocity,\\n time=NOTE_DURATION,\\n ),\\n mido.Message(\\n \\\"note_off\\\",\\n note=note_value,\\n velocity=note_velocity,\\n time=0,\\n ),\\n ]\",\n \"def notes(self) -> str:\\n return self._notes\",\n \"def midiToNotesRec(notes, totalnotes, note_names=[]):\\n names = ['C', 'C#', 'D', 'D#', 'E', 'F', 'F#', 'G', 'G#', 'A', 'A#', 'B']\\n \\n #print(note_names)\\n \\n def recMidiToNotes(notes, note_namesr=[]):\\n if notes == []:\\n return\\n \\n head = notes.pop(0)\\n name = names[head%12]\\n octave = int((head-12)/12)\\n note_namesr.append(name + str(octave))\\n print('rec names:', note_namesr)\\n return note_namesr\\n \\n if len(note_names) == totalnotes:\\n return note_names\\n \\n midiToNotes(notes, totalnotes, recMidiToNotes(notes, note_names))\",\n \"def all_jd_by_job_applied_id(job_applied_id): \\n return Note.query.filter(Note.job_applied_id == job_applied_id, Note.note_category == 'Job Description' ).order_by(Note.note_date_created.desc()).first()\",\n \"def reconstruct(notes, resolution):\\n # Construct the MusPy Music object\\n music = muspy.Music(resolution=resolution, tempos=[muspy.Tempo(0, 100)])\\n\\n # Append the tracks\\n programs = sorted(set(note[-1] for note in notes))\\n for program in programs:\\n music.tracks.append(muspy.Track(program))\\n\\n # Append the notes\\n for beat, position, pitch, duration, program in notes:\\n time = beat * resolution + position\\n track_idx = programs.index(program)\\n music[track_idx].notes.append(muspy.Note(time, pitch, duration))\\n\\n return music\",\n \"def notes(self):\\r\\n return notes.ForumSuggestionNotes(self)\",\n \"def displayMelody(self):\\r\\n print(self.notes)\",\n \"def notes(self) -> Optional[annotations.NoteData]:\\n return load_score(self.notes_path)\",\n \"def test_list_note_occurrences(self):\\n pass\",\n \"def get_notes(notes_type=\\\"national\\\", area=None):\\n query_menu = get_query_menu(area)\\n query = query_menu[notes_type][\\\"query\\\"]\\n collection = query_menu[notes_type][\\\"collection\\\"]\\n notes = \\\"\\\"\\n try:\\n doc = next(collection.find(query).sort([(DATE_KEY, -1)]).limit(1))\\n notes = doc[NOTE_KEY] if doc[NOTE_KEY] != 0 else None\\n except StopIteration:\\n app.logger.error(\\\"While getting notes: no data\\\")\\n return notes if notes is not None and not rubbish_notes(notes) else \\\"\\\"\",\n \"def load_org_notes_file():\\n nodelist = orgnode.makelist(NOTES_ORG_FILE)\\n return nodelist\",\n \"def read_notes():\\n\\n if current_user.is_admin is False:\\n notes = Note.query.filter_by(created_by=current_user.id).all()\\n else:\\n notes = Note.query.all()\\n\\n return render_template('notes/index.html.j2', notes=notes, title='Notes')\",\n \"def get_observations(self):\\n # Check hyper_params and criterion\\n if len(self.hyper_params) == 0:\\n raise AssertionError('!! Hyper-Parameters has not been set.')\\n # Check criterion\\n if self.criterion is None:\\n raise AssertionError(\\n '!! Criterion for hyper-parameter searching has not been set.')\\n # Fetch notes\\n notes = self.summary_fetcher()\\n if len(notes) == 0: return []\\n # Peel of Note wrapper\\n observations = []\\n for note in notes:\\n # Every note in the note list must contain the criterion\\n if self.criterion not in note.criteria: raise AssertionError(\\n '!! Every note must contain the criterion `{}`'.format(self.criterion))\\n # This note will be ignored if it does not contain all the information\\n # in self.hyper_params or the config value is not within the range\\n if not all([hp.name in note.configs and hp.within(note.configs[hp.name])\\n for hp in self.hyper_params]): continue\\n # Gather observation\\n od = OrderedDict()\\n # self.scroll.hyper_params.values() may have been found themselves\\n for hp in self.scroll.hyper_params.values():\\n assert isinstance(hp, HyperParameter)\\n od[hp] = note.configs[hp.name]\\n # Organize the observation list as a list of tuples\\n observations.append((od, note.criteria[self.criterion]))\\n return observations\",\n \"def inverse_notes(note_list):\\n raw_output = []\\n for note in note_list:\\n # We transpose every notes except for Z\\n if note.is_pause:\\n raw_output.append(f'{note.name}{note.figure}')\\n else:\\n # Get the index of the note to transpose\\n note_name_index = NOTE_NAMES.index(note.name)\\n # Get the transposed index according to the amount\\n inverted_index = ((len(NOTE_NAMES) - note_name_index) % len(NOTE_NAMES))\\n new_note_name = NOTE_NAMES[inverted_index]\\n # Create the new note, with the new note's name, and its original figure\\n new_note = f'{new_note_name}{note.figure}'\\n new_note += 'p' if note.has_point else ''\\n raw_output.append(new_note)\\n\\n return raw_output\",\n \"def remove_notes(self, notes):\\n if isinstance(notes, six.string_types):\\n return self.remove_note(notes)\\n elif hasattr(notes, \\\"name\\\"):\\n return self.remove_note(notes)\\n else:\\n for x in notes:\\n self.remove_note(x)\\n return self.notes\",\n \"def get_note (self,\\r\\n index):\\r\\n\\r\\n\\r\\n\\r\\n\\r\\n if self.using_database:\\r\\n aprint('GETTING NOTE')\\r\\n value_tuple = (notebookname, str(index),)\\r\\n db_cursor.execute(\\\"SELECT note_body \\\"\\r\\n +\\\"FROM notes WHERE notebook=?\\\"\\r\\n +\\\" AND note_index=?;\\\",\\r\\n value_tuple)\\r\\n\\r\\n res_temp = db_cursor.fetchone()\\r\\n\\r\\n if res_temp:\\r\\n text = res_temp[0].replace(\\\"''\\\",\\\"'\\\")\\r\\n else:\\r\\n text = ''\\r\\n\\r\\n db_cursor.execute(\\\"SELECT user\\\"\\r\\n +\\\" FROM notes\\\"\\r\\n +\\\" WHERE notebook=?\\\"\\r\\n +\\\" AND note_index=?;\\\",\\r\\n value_tuple)\\r\\n res_temp = db_cursor.fetchone()\\r\\n if res_temp:\\r\\n\\r\\n user = res_temp[0]\\r\\n else:\\r\\n user = 'USER'\\r\\n\\r\\n db_cursor.execute(\\\"SELECT size\\\"\\r\\n +\\\" FROM notes\\\"\\r\\n +\\\" WHERE notebook=? \\\"\\r\\n +\\\"AND note_index=?;\\\",\\r\\n value_tuple)\\r\\n\\r\\n res_temp = db_cursor.fetchone()\\r\\n if res_temp:\\r\\n size = res_temp[0]\\r\\n else:\\r\\n size = 60\\r\\n db_cursor.execute(\\\"SELECT timestamp \\\"\\r\\n +\\\"FROM timestamps\\\"\\r\\n +\\\" WHERE notebook=?\\\"\\r\\n +\\\" AND note_index=?\\\"\\r\\n +\\\" ORDER BY timestamp\\\",\\r\\n value_tuple)\\r\\n dates = db_cursor.fetchall()\\r\\n date_list = [date[0] for date in dates]\\r\\n db_cursor.execute(\\\"SELECT keyword\\\"\\r\\n +\\\" FROM all_note_keys\\\"\\r\\n +\\\" WHERE notebook=?\\\"\\r\\n +\\\" AND note_index=?\\\",\\r\\n value_tuple)\\r\\n keyset = db_cursor.fetchall()\\r\\n keyset = {key[0] for key in keyset}\\r\\n\\r\\n metadata = {'user':user,\\r\\n 'date':date_list,\\r\\n 'size':size}\\r\\n return Note(keyset,text,metadata)\\r\\n\\r\\n if str(index) in self.note_dict:\\r\\n return self.note_dict[str(index)]\\r\\n\\r\\n return False\",\n \"def do_notes(self, sym):\\n # DRY: duplication with Roman.py - FIXME\\n if sym == '-':\\n # REST\\n return []\\n note_or_chord = self.do(sym)\\n if note_or_chord is None:\\n return []\\n elif type(note_or_chord) == Chord:\\n return note_or_chord.notes\\n else:\\n return [ note_or_chord ]\",\n \"def list_motions(self):\\n return [x for x,y in self.devices.items() if y.device_type == \\\"Motion\\\"]\",\n \"def ends_with_lt_tonic(a_list):\\n key = a_list.track.bars[0].key\\n a, b = a_list.notes[-2:]\\n lt, tonic = Note(key).transpose('7', True).name, key.name\\n\\n if (a.name, b.name) == (lt, tonic) and int(b) - int(a) == 1:\\n return []\\n else:\\n return [a.start, b.start]\",\n \"def get_wall_products(self, wall_bp):\\n products = []\\n for child in wall_bp.children:\\n props = props_closet.get_object_props(child)\\n if props.is_closet or props.is_fixed_shelf_and_rod_product_bp:\\n child.mv.comment = wall_bp.mv.name_object\\n products.append(child)\\n products.sort(key=lambda obj: obj.location.x, reverse=False)\\n return products\",\n \"def get_partlist(self):\\n\\n # Longdale check\\n longdale_user = user_is_ingroup(self.request, \\\"longdale_user\\\")\\n # REtrieve the correct queryset, sorted on the correct levels\\n if longdale_user:\\n qs = [prt for prt in Part.objects.all().order_by('corpus__lng', 'corpus__name', 'name')]\\n else:\\n longdale = \\\"Longdale\\\"\\n qs = [prt for prt in Part.objects.exclude(Q(name__istartswith=longdale)).order_by('corpus__lng', 'corpus__name', 'name')]\\n # REtrieve the correct queryset, sorted on the correct levels\\n # qs = [prt for prt in Part.objects.all().order_by('corpus__lng', 'corpus__name', 'name')]\\n # Start the output\\n html = []\\n # Initialize the variables whose changes are important\\n lVars = [\\\"corpus_lng\\\", \\\"corpus_name\\\", \\\"name\\\"]\\n lFuns = [Part.language, [\\\"corpus\\\", \\\"name\\\"], [\\\"name\\\"]]\\n # Get a list of items containing 'first' and 'last' information\\n lItem = get_item_list(lVars, lFuns, qs)\\n # REturn this list\\n return lItem\",\n \"def __repr__(self):\\n return str(self.notes)\",\n \"def get_notes_from_line(line):\\n last_char_is_upper = True\\n raw_notes_list = []\\n current_note = \\\"\\\"\\n\\n for char in line:\\n if not last_char_is_upper and char.isupper():\\n raw_notes_list.append(current_note)\\n current_note = \\\"\\\"\\n\\n current_note += char\\n last_char_is_upper = char.isupper()\\n\\n raw_notes_list.append(current_note)\\n return raw_notes_list\",\n \"def play_notes(self, show_map=False):\\n # print(self.programs)\\n pianorolls = []\\n for track in self.notes:\\n pianoroll = np.zeros((track.shape[1] * 3, 128))\\n\\n for i in range(track.shape[0]):\\n note_track = np.array(track[i, :])\\n note_track += 1\\n notes_pos = np.nonzero(note_track)\\n f = 3\\n for pos in notes_pos[0]:\\n # print(\\\"Error\\\", f * pos, f * pos + self.dura_to_timecell(note_track[pos] - 1),\\n # self.dura_to_timecell(note_track[pos] - 1))\\n pianoroll[f * pos:f * pos + self.dura_to_timecell(note_track[pos] - 1) + 1, 83 - i] = 90\\n pianorolls.append(pianoroll)\\n\\n # print(\\\"pianoroll\\\")\\n print(self.notes_index)\\n tracks = []\\n for i in range(len(pianorolls)):\\n tracker = Track(pianoroll=pianorolls[i], program=self.programs[i])\\n tracks.append(tracker)\\n multitrack = Multitrack(tracks=tracks)\\n multitrack.write(\\\"create1.mid\\\")\\n\\n if show_map:\\n print(\\\"Show map will not work\\\")\\n # tracker.plot()\\n # plt.show()\",\n \"def update_evernote_notes(self, evernote_notes, log_update_if_unchanged=True):\\n return self.add_evernote_notes(evernote_notes, True, log_update_if_unchanged=log_update_if_unchanged)\",\n \"def listmarkers(repo):\\n if not repo.obsstore:\\n return {}\\n keys = {}\\n parts = []\\n currentlen = _maxpayload * 2 # ensure we create a new part\\n for marker in repo.obsstore:\\n nextdata = _encodeonemarker(marker)\\n if (len(nextdata) + currentlen > _maxpayload):\\n currentpart = []\\n currentlen = 0\\n parts.append(currentpart)\\n currentpart.append(nextdata)\\n currentlen += len(nextdata)\\n for idx, part in enumerate(reversed(parts)):\\n data = ''.join([_pack('>B', _fmversion)] + part)\\n keys['dump%i' % idx] = base85.b85encode(data)\\n return keys\"\n]","isConversational":false},"negative_scores":{"kind":"list like","value":["0.6403214","0.63809764","0.631014","0.62889194","0.61292934","0.61097884","0.605849","0.6053666","0.6009318","0.5971191","0.593564","0.59113747","0.5908884","0.5889423","0.5875394","0.5820836","0.5792839","0.5787869","0.57424015","0.5723736","0.5721369","0.57117254","0.57117254","0.565567","0.56532615","0.5652621","0.56517005","0.5613972","0.5607693","0.5549701","0.5536993","0.5515834","0.549332","0.54780143","0.5475375","0.54380715","0.54340863","0.5342004","0.53159","0.53021723","0.5300455","0.53002846","0.529952","0.52962244","0.5295182","0.5277952","0.5265336","0.5247644","0.5247164","0.5225274","0.5203008","0.52023244","0.5190039","0.51878107","0.51827383","0.5166373","0.51533556","0.51500255","0.5126756","0.5116152","0.51151043","0.51107234","0.5090805","0.5086928","0.50854975","0.5083407","0.5081462","0.5061391","0.5051305","0.5048733","0.50457364","0.50285923","0.5027389","0.5027125","0.5026436","0.50263405","0.50041455","0.5004043","0.50012517","0.49741113","0.49596944","0.4959138","0.49576697","0.49500299","0.49485388","0.49455792","0.49322048","0.49212483","0.49131444","0.4910223","0.4910215","0.49076092","0.49029154","0.4900447","0.48987952","0.48925656","0.48906013","0.48763293","0.48703462","0.48463634","0.48365593"],"string":"[\n \"0.6403214\",\n \"0.63809764\",\n \"0.631014\",\n \"0.62889194\",\n \"0.61292934\",\n \"0.61097884\",\n \"0.605849\",\n \"0.6053666\",\n \"0.6009318\",\n \"0.5971191\",\n \"0.593564\",\n \"0.59113747\",\n \"0.5908884\",\n \"0.5889423\",\n \"0.5875394\",\n \"0.5820836\",\n \"0.5792839\",\n \"0.5787869\",\n \"0.57424015\",\n \"0.5723736\",\n \"0.5721369\",\n \"0.57117254\",\n \"0.57117254\",\n \"0.565567\",\n \"0.56532615\",\n \"0.5652621\",\n \"0.56517005\",\n \"0.5613972\",\n \"0.5607693\",\n \"0.5549701\",\n \"0.5536993\",\n \"0.5515834\",\n \"0.549332\",\n \"0.54780143\",\n \"0.5475375\",\n \"0.54380715\",\n \"0.54340863\",\n \"0.5342004\",\n \"0.53159\",\n \"0.53021723\",\n \"0.5300455\",\n \"0.53002846\",\n \"0.529952\",\n \"0.52962244\",\n \"0.5295182\",\n \"0.5277952\",\n \"0.5265336\",\n \"0.5247644\",\n \"0.5247164\",\n \"0.5225274\",\n \"0.5203008\",\n \"0.52023244\",\n \"0.5190039\",\n \"0.51878107\",\n \"0.51827383\",\n \"0.5166373\",\n \"0.51533556\",\n \"0.51500255\",\n \"0.5126756\",\n \"0.5116152\",\n \"0.51151043\",\n \"0.51107234\",\n \"0.5090805\",\n \"0.5086928\",\n \"0.50854975\",\n \"0.5083407\",\n \"0.5081462\",\n \"0.5061391\",\n \"0.5051305\",\n \"0.5048733\",\n \"0.50457364\",\n \"0.50285923\",\n \"0.5027389\",\n \"0.5027125\",\n \"0.5026436\",\n \"0.50263405\",\n \"0.50041455\",\n \"0.5004043\",\n \"0.50012517\",\n \"0.49741113\",\n \"0.49596944\",\n \"0.4959138\",\n \"0.49576697\",\n \"0.49500299\",\n \"0.49485388\",\n \"0.49455792\",\n \"0.49322048\",\n \"0.49212483\",\n \"0.49131444\",\n \"0.4910223\",\n \"0.4910215\",\n \"0.49076092\",\n \"0.49029154\",\n \"0.4900447\",\n \"0.48987952\",\n \"0.48925656\",\n \"0.48906013\",\n \"0.48763293\",\n \"0.48703462\",\n \"0.48463634\",\n \"0.48365593\"\n]","isConversational":false},"document_score":{"kind":"string","value":"0.0"},"document_rank":{"kind":"string","value":"-1"}}},{"rowIdx":23778,"cells":{"query":{"kind":"string","value":"Return a list of note objects in diminished"},"document":{"kind":"string","value":"def mgChordDiminished(value):\n chord = [MgNote(value), MgNote(value) + 3, MgNote(value) + 6]\n return chord"},"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_note():\n\n return Note.query.all()","def notes(self):\n return reapy.NoteList(self)","def listNotes() -> list:\n list_of_notes = []\n for note in Note.objects.all():\n list_of_notes.append({\n 'uuid': note.uuid, 'title': note.title,\n 'author': note.author, 'body': note.body, 'created_at': localtime(note.created_at)\n })\n return list_of_notes","def ls(self, count = 200):\n return self._manager.ls_notes(self['id'], count)","def get_note_names(self):\n res = []\n for n in self.notes:\n if n.name not in res:\n res.append(n.name)\n return res","def view_notes(tag_list):\n final_notes = []\n for tag in tag_list:\n all_notes = Tags.objects.get(tag_text=tag)\n notes = all_notes.notes.all()\n for note in notes:\n final_notes.append(note.note_text)\n \n return final_notes","def _get_issue_notes(request, pk):\n if request.user.is_coordinator_or_better:\n note_types = IssueNote.COORDINATOR_NOTE_TYPES\n else:\n note_types = IssueNote.PARALEGAL_NOTE_TYPES\n\n return (\n IssueNote.objects.filter(issue=pk)\n .prefetch_related(\"creator__groups\")\n .filter(note_type__in=note_types)\n .order_by(\"-created_at\")\n .all()\n )","def diminish(self):\n for n in self.notes:\n n.diminish()","def getnotes():","def getNotes(self):\n return self._nednotes, self._ongcnotes","def get_notes(self, note_limit=200):\n return Note.get_by_person_record_id(\n self.subdomain, self.record_id, limit=note_limit)","def note_list(request):\n user = request.user\n notes = Note.objects.filter(author=user)\n serializer = NoteSerializer(notes, many=True)\n return Response(serializer.data)","def notes(self):\r\n return notes.Notes(self)","def notes(self):\r\n return notes.Notes(self)","def notes(self):\n return self._notes","def notes(self):\n return notes.Notes(self)","def get_notes():\n notes = []\n durs = []\n\n for file in glob.glob(\"D:\\\\anthems\\\\data\\\\*.mid\"):\n midi = converter.parse(file)\n\n print(\"Parsing %s\" % file)\n\n notes_to_parse = None\n\n try:\n s2 = instrument.partitionByInstrument(midi)\n notes_to_parse = s2.parts[0].recurse() \n except:\n notes_to_parse = midi.flat.notes\n \n for element in notes_to_parse:\n if isinstance(element, note.Note):\n notes.append(str(element.pitch))\n durs.append(element.duration.quarterLength)\n elif isinstance(element, chord.Chord):\n notes.append('.'.join(str(n) for n in element.normalOrder))\n durs.append(element.duration.quarterLength)\n\n return notes, durs","def all_note_by_job_applied_id(job_applied_id):\n\n return Note.query.filter(Note.job_applied_id == job_applied_id, Note.note_category == 'Note' ).all()","def getNotes(self):\n return self.__notes","def DOIs(self, default=[{}]):\n tmp = self.data.get('metadata', {}).get('dois', default)\n return [HEP.DOIObject(i) for i in tmp]","def notes(self):\n return self.__notes","def get_objects_using_notes():\n global objects_using_notes\n\n if not objects_using_notes:\n objects_using_notes = rsh.get_objects_using_notes(get_srcs())\n\n return objects_using_notes","def getNotes(self, *args):\n return _libsbml.SBase_getNotes(self, *args)","def getNotes(self):\n logger.debug(\"Func: getNotes\")\n\n return self._currentNotes","def return_markers(self):\r\n ent_file = join(self.filename, self._basename + '.ent')\r\n if not exists(ent_file):\r\n ent_file = join(self.filename, self._basename + '.ent.old')\r\n\r\n try:\r\n ent_notes = _read_ent(ent_file)\r\n\r\n except (FileNotFoundError, PermissionError):\r\n markers = []\r\n\r\n else:\r\n allnote = []\r\n for n in ent_notes:\r\n try:\r\n n['value'].keys()\r\n allnote.append(n['value'])\r\n except AttributeError:\r\n lg.debug('Note of length {} was not '\r\n 'converted to dict'.format(n['length']))\r\n\r\n s_freq = self._hdr['erd']['sample_freq']\r\n pcname = '0CFEBE72-DA20-4b3a-A8AC-CDD41BFE2F0D'\r\n note_time = []\r\n note_name = []\r\n note_note = []\r\n for n in allnote:\r\n if n['Text'] == 'Analyzed Data Note':\r\n continue\r\n if not n['Text']:\r\n continue\r\n if 'User' not in n['Data'].keys():\r\n continue\r\n user1 = n['Data']['User'] == 'Persyst'\r\n user2 = False # n['Data']['User'] == 'eeg'\r\n user3 = n['Data']['User'] == pcname\r\n user4 = n['Data']['User'] == 'XLSpike - Intracranial'\r\n user5 = n['Data']['User'] == 'XLEvent - Intracranial'\r\n if user1 or user2 or user3 or user4 or user5:\r\n continue\r\n if len(n['Data']['User']) == 0:\r\n note_name.append('-unknown-')\r\n else:\r\n note_name.append(n['Data']['User'].split()[0])\r\n note_time.append(n['Stamp'] / s_freq)\r\n note_note.append(n['Text'])\r\n\r\n markers = []\r\n for time, name, note in zip(note_time, note_name, note_note):\r\n m = {'name': note + ' (' + name + ')',\r\n 'start': time,\r\n 'end': time,\r\n 'chan': None,\r\n }\r\n markers.append(m)\r\n\r\n return markers","def get_all_meals():","def _on_note(self) -> Iterator:\n return filter(\n lambda x: x.type == constants.NOTE_ON, self.midi_data)","async def _view_all_notes(self, ctx: Context):\n\n author = ctx.author\n\n note_infos = []\n\n embed_links = ctx.channel.permissions_for(ctx.guild.me).embed_links\n\n author_str = f\"{author.name}'\"\n\n if author.name[-1].lower() != \"s\":\n author_str += \"s\"\n\n async with self.config.member(author).notes() as notes:\n total = len(notes)\n for page_num, note in enumerate(notes, start=1):\n msg_info = \"\"\n if note[\"author\"]:\n msg_info += _(\"**Author:** {}\").format(note[\"author\"])\n if note[\"channel\"]:\n msg_info += _(\"\\n**Channel:** {}\").format(note[\"channel\"])\n if note[\"jump_url\"]:\n if embed_links:\n msg_info += _(\n \"\\n[Click here to jump to message]({})\"\n ).format(note[\"jump_url\"])\n else:\n msg_info += _(\n \"\\n**Jump To Message:** {}\"\n ).format(note[\"jump_url\"])\n\n note_info = _(\n \"{}\\n\\n**Note:**\\n```{}```\\n**Reason:**\\n```{}```\"\n ).format(\n msg_info,\n note[\"note\"],\n note[\"reason\"]\n ).strip()\n\n if embed_links:\n page = discord.Embed(\n colour=0xff0000,\n description=note_info,\n title=_(\"{} TvM Notes\").format(author_str),\n timestamp=ctx.message.created_at\n )\n\n page.set_footer(\n text=_(\"Page {page_num}/{leng}\").format(\n page_num=page_num, leng=total\n )\n )\n else:\n page = _(\n \"**{author} TvM Notes**\"\n \"\\n\\n{note}\"\n \"\\n{footer}\"\n ).format(\n author=author_str,\n note=note_info,\n footer=_(\"*Page {page_num}/{leng}*\").format(\n page_num=page_num, leng=total\n )\n )\n\n note_infos.append(page)\n\n await menu(ctx, note_infos, DEFAULT_CONTROLS)","def objects(self):","def notes(self):\n return Notes(self)","def get_entries(self):\n return self.find_by_st(\"urn:schemas-denon-com:device:ACT-Denon:1\")","def todolists(self):\r\n return tdl.Todolists(self)","def notes_list():\n if request.method == \"POST\":\n note = str(request.data.get(\"text\", \"\"))\n idx = max(notes.keys()) + 1\n notes[idx] = note\n return note_repr(idx), status.HTTP_201_CREATED\n\n # request.method == 'GET'\n return [note_repr(idx) for idx in sorted(notes.keys())]","def get_observations(self):\n # Check hyper_params and criterion\n if len(self.hyper_params) == 0:\n raise AssertionError('!! Hyper-Parameters has not been set.')\n # Check criterion\n if self.criterion is None:\n raise AssertionError(\n '!! Criterion for hyper-parameter searching has not been set.')\n # Fetch notes\n notes = self.summary_fetcher()\n if len(notes) == 0: return []\n # Peel of Note wrapper\n observations = []\n for note in notes:\n # Every note in the note list must contain the criterion\n if self.criterion not in note.criteria: raise AssertionError(\n '!! Every note must contain the criterion `{}`'.format(self.criterion))\n # This note will be ignored if it does not contain all the information\n # in self.hyper_params or the config value is not within the range\n if not all([hp.name in note.configs and hp.within(note.configs[hp.name])\n for hp in self.hyper_params]): continue\n # Gather observation\n od = OrderedDict()\n # self.scroll.hyper_params.values() may have been found themselves\n for hp in self.scroll.hyper_params.values():\n assert isinstance(hp, HyperParameter)\n od[hp] = note.configs[hp.name]\n # Organize the observation list as a list of tuples\n observations.append((od, note.criteria[self.criterion]))\n return observations","def remove_pedal(self):\n return Melody([n.remove_pedal() for n in self.notes], nb_bars=self.nb_bars, tags=set(self.tags))","def remove_duplicate_notes(self):\n res = []\n for x in self.notes:\n if x not in res:\n res.append(x)\n self.notes = res\n return res","def test_list_notes(self):\n pass","def notes_played(self) -> List[Dict]:\n return [\n dict(note=self._midi_to_note(note.note), time=note.time)\n for note in self._on_note()\n ]","def list(self):\n return self.cell.objects+self.cell.tempObjects","def test_list_note_occurrences(self):\n pass","def get_linked_persons(self, note_limit=200):\n linked_persons = []\n for note in self.get_notes(note_limit):\n person = Person.get(self.subdomain, note.linked_person_record_id)\n if person:\n linked_persons.append(person)\n return linked_persons","def get_user_visible_notes():\n return grammar.user_visible_notes","def getItems(self):\n for object in self.database:\n print(object)","def notes(self):\r\n return notes.UserNotes(self)","def notes(self):\n return NotesTable(self.rpc, self.name)","def get_contenu(self):\n return self.objets","def get_notes(n_notes=3):\n notes = []\n \n for ii, file in enumerate(glob.glob(\"data/maestro-v2.0.0/2004/*.midi\")):\n if ii >= n_notes:\n break\n pickle_file_name = file[:-4] + 'pkl'\n\n if os.path.isfile(pickle_file_name):\n print(f'Reading parsed file: {pickle_file_name}')\n with open(pickle_file_name, 'rb') as handle:\n midi = pickle.load(handle)\n else:\n midi = converter.parse(file)\n\n with open(pickle_file_name, 'wb') as handle:\n print(f'writing parsed file: {pickle_file_name}')\n unserialized_data = pickle.dump(midi, \n handle, \n protocol=pickle.HIGHEST_PROTOCOL\n )\n\n\n print(\"Parsing %s\" % file)\n\n notes_to_parse = None\n try: # file has instrument parts\n s2 = instrument.partitionByInstrument(midi)\n notes_to_parse = s2.parts[0].recurse() \n except: # file has notes in a flat structure\n notes_to_parse = midi.flat.notes\n \n for element in notes_to_parse:\n if isinstance(element, note.Note):\n notes.append(str(element.pitch))\n elif isinstance(element, chord.Chord):\n notes.append('.'.join(str(n) for n in element.normalOrder))\n \n return notes","def GetObjects(self): \r\n return self.model.GetObjects()","def find_notes(\n self, words, notebooks, tags, place,\n limit=const.DEFAULT_LIMIT, order=const.ORDER_UPDATED,\n pinnded=const.NOT_PINNDED,\n ):\n notes = btype.Note.list >> NoteFilterer(self.session)\\\n .by_words(words)\\\n .by_notebooks(notebooks)\\\n .by_tags(tags)\\\n .by_place(place)\\\n .by_pinnded(pinnded)\\\n .order_by(order)\\\n .all()\\\n .limit(limit)\n\n return notes","def extract_notes(bars):\n output = []\n for bar in bars:\n notes = np.array([bar_to_notes(b,i) for i,b in enumerate(bar)])\n\n # Flatten into a single array\n midi_notes = []\n for i in range(notes.shape[1]):\n if all(notes[:,i] == -1):\n midi_notes.append(None)\n else:\n midi_notes.append([n for n in notes[:,i] if n!=-1])\n output.append(midi_notes)\n return output","def all_recruiter_by_job_applied_id(job_applied_id): \n return Note.query.filter(Note.job_applied_id == job_applied_id, Note.note_category == 'Recruiter Contact' ).all()","def get_note_child_list(self):\n return self.address_list","def __init__(self):\n self.notes = []","def get_documents() -> list[Document]:\n g.ledger.changed()\n return [e for e in g.filtered.entries if isinstance(e, Document)]","def add_notes(self, notes):\n if hasattr(notes, \"notes\"):\n for x in notes.notes:\n self.add_note(x)\n return self.notes\n elif hasattr(notes, \"name\"):\n self.add_note(notes)\n return self.notes\n elif isinstance(notes, six.string_types):\n self.add_note(notes)\n return self.notes\n for x in notes:\n if isinstance(x, list) and len(x) != 1:\n if len(x) == 2:\n self.add_note(x[0], x[1])\n else:\n self.add_note(x[0], x[1], x[2])\n else:\n self.add_note(x)\n return self.notes","def get_dois(wt):\n lines = get_wiki_lines(wt, predicate=any)\n return [doi.find_doi_in_text(l) for l in lines]","def all_notes():\n \n return render_template('all_notes.html',colors=music_color,)","def get_notes_from_uuids(dictionary, uuid_list):\n titles = []\n texts = []\n notes = []\n for key in dictionary:\n if dictionary[key][\"content_type\"] == \"Note\" and dictionary[key][\"uuid\"] in uuid_list:\n try:\n titles.append(dictionary[key][\"content\"][\"title\"])\n texts.append(dictionary[key][\"content\"][\"text\"])\n notes = list(zip(titles, texts))\n except KeyError:\n continue\n return notes","def _get_embedded_objects(self):\n return [getattr(self, name) for name, field in self._get_fields().items() if isinstance(field, fields.Object)]","def expose(self):\n arts = [a.art() for a in self.artists]\n return arts","def objects(self):\n\t\treturn self._objects","def list(self):","def persons(self):\r\n return persons.Persons(self)","def get_scheduled_notes(event):\n def _sort_note_by(note):\n obj = note.object\n if hasattr(obj, 'start_dt'):\n return obj.start_dt, 0\n else:\n return obj.contribution.start_dt, obj.position\n\n tt_entries = (event.timetable_entries\n .filter(TimetableEntry.type != TimetableEntryType.BREAK)\n .options(joinedload('session_block').joinedload('contributions').joinedload('subcontributions'))\n .options(joinedload('contribution').joinedload('subcontributions'))\n .options(noload('break_'))\n .all())\n # build a list of all the objects we need notes for. that way we can query\n # all notes in a single go afterwards instead of making the already-huge\n # timetable query even bigger.\n objects = set()\n for entry in tt_entries:\n objects.add(entry.object)\n if entry.type == TimetableEntryType.CONTRIBUTION:\n objects.update(sc for sc in entry.object.subcontributions if not sc.is_deleted)\n elif entry.type == TimetableEntryType.SESSION_BLOCK:\n for contrib in entry.object.contributions:\n objects.add(contrib)\n objects.update(sc for sc in contrib.subcontributions if not sc.is_deleted)\n notes = [x for x in event.all_notes.filter_by(is_deleted=False) if x.object in objects]\n return sorted(notes, key=_sort_note_by)","def getList(self):","def getList(self):","def get_todo_list():\n\n # assume that a \"h264\" encoded file is complete\n return models.LibraryItem.objects.filter(h264=False)","def get_animals(self):\n for animal in self.animals:\n print(animal)","def journals(self):\n return self.search(comp_class=Journal)","def all_jd_by_job_applied_id(job_applied_id): \n return Note.query.filter(Note.job_applied_id == job_applied_id, Note.note_category == 'Job Description' ).order_by(Note.note_date_created.desc()).first()","def PublicNotes(self, default=[{}]):\n tmp = self.data.get('metadata', {}).get('public_notes', default)\n return [HEP.SourcedValueObject(i) for i in tmp]","def get_notes_by_id(self, ids: List[str]) -> pd.Series:\n\n return self.notes[self.notes.apply(lambda n: n.id in ids)]","def get_donnees(self):\r\n \r\n return self._donnees","def all_for(self, observed, signal):\r\n content_type = ContentType.objects.get_for_model(observed)\r\n observed_items = self.filter(content_type=content_type, object_id=observed.id, signal=signal)\r\n return observed_items","def notes_in_path(self, path: str) -> List[NoteInfo]:\n check_path = os.path.abspath(path)\n return [n for n in self.notes.values() if n.file_path.startswith(check_path)]","def get_patients(self):\n return","def get_memos():\n records = [ ]\n for record in collection.find( { \"type\": \"dated_memo\" } ):\n record['date'] = arrow.get(record['date']).isoformat()\n record['time'] = arrow.get(record['date']).timestamp\n record['_id'] = str(record['_id'])\n record['text'] = str(record['text'].encode('ascii','xmlcharrefreplace'), \"utf-8\")\n records.append(record)\n records.sort(key=lambda x: x['time'])\n return records","def notes(self):\r\n return TicketNotes(self)","def read_notes():\n\n if current_user.is_admin is False:\n notes = Note.query.filter_by(created_by=current_user.id).all()\n else:\n notes = Note.query.all()\n\n return render_template('notes/index.html.j2', notes=notes, title='Notes')","def get(self):\r\n\t\treturn list(self)","def note_list(self, evarnames=\"\", in_=None):\n evarnames = self._find_vars(evarnames, evars=True, \n unique=True, empty_ok=True)\n if len(evarnames) == 0:\n evarnames = ['_dta'] + self._varlist\n if in_ is not None:\n if isinstance(in_, int):\n in_ = (in_,)\n elif (not isinstance(in_, collections.Iterable)\n or not all(isinstance(n, int) for n in in_)):\n raise TypeError(\"in_ should be int or iterable of int\")\n if any(n <= 0 for n in in_):\n raise ValueError(\"note numbers must be >= 1\")\n for name in evarnames:\n if name not in self._chrdict: continue\n chars = self._chrdict[name]\n if 'note0' not in chars: continue\n nnotes = int(chars['note0'])\n in_range = in_ if in_ is not None else range(1, nnotes + 1)\n note_info = []\n for note_num in in_range:\n note_name = 'note' + str(note_num)\n if note_name in chars:\n note_info.append(note_num)\n if note_info != []:\n note_info.insert(0, name)\n self._display_notes(note_info)","def readentries(self):\n return list(x for x in self)","def obj_list(self):\n return self._obj_list","def get_object_discussions(self):\n return DSSObjectDiscussions(self.client, self.project_key, \"RECIPE\", self.recipe_name)","def notes(self):\r\n return notes.ForumSuggestionNotes(self)","def do_notes(self, sym):\n # DRY: duplication with Roman.py - FIXME\n if sym == '-':\n # REST\n return []\n note_or_chord = self.do(sym)\n if note_or_chord is None:\n return []\n elif type(note_or_chord) == Chord:\n return note_or_chord.notes\n else:\n return [ note_or_chord ]","def get_notes_by_type(self, typ: type) -> pd.Series:\n\n # doesn't use isinstance() to prevent subtypes from being selected\n return self.notes[self.notes.apply(lambda n: type(n) is typ)]","def get_queryset(self):\n labels = self.kwargs['labels']\n return models.Notes.objects.filter(labels__icontains=labels)","def get_common_food(cls):\n objs = cls.objects\n return objs","def get_all_notes(user_id, token):\n user = User.get(User.id == user_id).username\n if not validate_token(user, token):\n return HTTPResponse(status=500, body={\"message\":\"Validation error.\"})\n res = []\n for note in Notes.select():\n if note.user.id == user_id:\n new_note = model_to_dict(note)\n res.append({\"id\":new_note['id'], \"title\":new_note['title'],\n \"content\":new_note['content']})\n new_token = generate_token(user)\n body = {\"user_id\":user_id, \"token\":new_token.decode('utf-8'), 'items':res}\n return HTTPResponse(status=200, body=body)","def fNoteLists(self, match):\n att, start_char, g_links, extras = match.groups()\n start_char = start_char or 'a'\n index = '%s%s%s' % (g_links, extras, start_char)\n result = ''\n\n if index not in self.notelist_cache:\n o = []\n if self.notes:\n for seq, info in self.notes.items():\n links = self.makeBackrefLink(info, g_links, start_char)\n atts = ''\n if 'def' in info:\n infoid = info['id']\n atts = info['def']['atts']\n content = info['def']['content']\n li = (\"\"\"\\t<li%s>%s<span id=\"note%s\"> </span>%s</li>\"\"\"\n % (atts, links, infoid, content))\n else:\n li = (\"\"\"\\t<li%s>%s Undefined Note [#%s].<li>\"\"\" %\n (atts, links, info['seq']))\n o.append(li)\n if '+' == extras and self.unreferencedNotes:\n for seq, info in self.unreferencedNotes.items():\n if info['def']:\n atts = info['def']['atts']\n content = info['def']['content']\n li = \"\"\"\\t<li%s>%s</li>\"\"\" % (atts, content)\n o.append(li)\n self.notelist_cache[index] = u\"\\n\".join(o)\n result = self.notelist_cache[index]\n if result:\n list_atts = self.pba(att)\n result = \"\"\"<ol%s>\\n%s\\n</ol>\"\"\" % (list_atts, result)\n return result","def search(self, filter):\n return [note for note in self.notes if note.match(filter)]","def get_all_refobjs(self, ):\n return cmds.ls(type=\"jb_reftrack\")","def list(self):\n return self._get_list()","def getListIngr(cls):\n\n # meals = Meals.getMealsByFutureDate(user=session['User'])\n list_ingr = db.session.query(RecipeIngredient).join(Recipe).join(Meals).\\\n join(Ingredient).\\\n filter(func.substr(Meals.date_planned,0,11) >= func.substr(datetime.today(),0,11)).\\\n filter(Meals.recipe_fk==Recipe.recipe_id).\\\n filter(Recipe.recipe_id==RecipeIngredient.recipe_fk).\\\n filter(RecipeIngredient.ingredient_name==Ingredient.name).\\\n filter(Meals.user_fk==session['User']).\\\n order_by(Meals.date_planned).all()\n\n return list_ingr","def remove_tempo(self):\n return Melody([n.remove_tempo() for n in self.notes], nb_bars=self.nb_bars, tags=set(self.tags))","def search(self, filtr):\n return [note for note in self.notes if note.match(filtr)]","def get_note_alternatives(self, id):\n notes = self.session.query(models.Note).filter(\n models.Note.conflict_parent_id == id,\n ).all()\n return btype.Note.list >> notes","def get_notes(notes_type=\"national\", area=None):\n query_menu = get_query_menu(area)\n query = query_menu[notes_type][\"query\"]\n collection = query_menu[notes_type][\"collection\"]\n notes = \"\"\n try:\n doc = next(collection.find(query).sort([(DATE_KEY, -1)]).limit(1))\n notes = doc[NOTE_KEY] if doc[NOTE_KEY] != 0 else None\n except StopIteration:\n app.logger.error(\"While getting notes: no data\")\n return notes if notes is not None and not rubbish_notes(notes) else \"\"","def objects(self):\r\n return self._objects","def _charactersForViewer(self, store, role):\n characters = []\n things = store.query(Thing)\n actors = asAccessibleTo(role, things)\n characters.extend(map(itemFromProxy, actors))\n return characters"],"string":"[\n \"def get_note():\\n\\n return Note.query.all()\",\n \"def notes(self):\\n return reapy.NoteList(self)\",\n \"def listNotes() -> list:\\n list_of_notes = []\\n for note in Note.objects.all():\\n list_of_notes.append({\\n 'uuid': note.uuid, 'title': note.title,\\n 'author': note.author, 'body': note.body, 'created_at': localtime(note.created_at)\\n })\\n return list_of_notes\",\n \"def ls(self, count = 200):\\n return self._manager.ls_notes(self['id'], count)\",\n \"def get_note_names(self):\\n res = []\\n for n in self.notes:\\n if n.name not in res:\\n res.append(n.name)\\n return res\",\n \"def view_notes(tag_list):\\n final_notes = []\\n for tag in tag_list:\\n all_notes = Tags.objects.get(tag_text=tag)\\n notes = all_notes.notes.all()\\n for note in notes:\\n final_notes.append(note.note_text)\\n \\n return final_notes\",\n \"def _get_issue_notes(request, pk):\\n if request.user.is_coordinator_or_better:\\n note_types = IssueNote.COORDINATOR_NOTE_TYPES\\n else:\\n note_types = IssueNote.PARALEGAL_NOTE_TYPES\\n\\n return (\\n IssueNote.objects.filter(issue=pk)\\n .prefetch_related(\\\"creator__groups\\\")\\n .filter(note_type__in=note_types)\\n .order_by(\\\"-created_at\\\")\\n .all()\\n )\",\n \"def diminish(self):\\n for n in self.notes:\\n n.diminish()\",\n \"def getnotes():\",\n \"def getNotes(self):\\n return self._nednotes, self._ongcnotes\",\n \"def get_notes(self, note_limit=200):\\n return Note.get_by_person_record_id(\\n self.subdomain, self.record_id, limit=note_limit)\",\n \"def note_list(request):\\n user = request.user\\n notes = Note.objects.filter(author=user)\\n serializer = NoteSerializer(notes, many=True)\\n return Response(serializer.data)\",\n \"def notes(self):\\r\\n return notes.Notes(self)\",\n \"def notes(self):\\r\\n return notes.Notes(self)\",\n \"def notes(self):\\n return self._notes\",\n \"def notes(self):\\n return notes.Notes(self)\",\n \"def get_notes():\\n notes = []\\n durs = []\\n\\n for file in glob.glob(\\\"D:\\\\\\\\anthems\\\\\\\\data\\\\\\\\*.mid\\\"):\\n midi = converter.parse(file)\\n\\n print(\\\"Parsing %s\\\" % file)\\n\\n notes_to_parse = None\\n\\n try:\\n s2 = instrument.partitionByInstrument(midi)\\n notes_to_parse = s2.parts[0].recurse() \\n except:\\n notes_to_parse = midi.flat.notes\\n \\n for element in notes_to_parse:\\n if isinstance(element, note.Note):\\n notes.append(str(element.pitch))\\n durs.append(element.duration.quarterLength)\\n elif isinstance(element, chord.Chord):\\n notes.append('.'.join(str(n) for n in element.normalOrder))\\n durs.append(element.duration.quarterLength)\\n\\n return notes, durs\",\n \"def all_note_by_job_applied_id(job_applied_id):\\n\\n return Note.query.filter(Note.job_applied_id == job_applied_id, Note.note_category == 'Note' ).all()\",\n \"def getNotes(self):\\n return self.__notes\",\n \"def DOIs(self, default=[{}]):\\n tmp = self.data.get('metadata', {}).get('dois', default)\\n return [HEP.DOIObject(i) for i in tmp]\",\n \"def notes(self):\\n return self.__notes\",\n \"def get_objects_using_notes():\\n global objects_using_notes\\n\\n if not objects_using_notes:\\n objects_using_notes = rsh.get_objects_using_notes(get_srcs())\\n\\n return objects_using_notes\",\n \"def getNotes(self, *args):\\n return _libsbml.SBase_getNotes(self, *args)\",\n \"def getNotes(self):\\n logger.debug(\\\"Func: getNotes\\\")\\n\\n return self._currentNotes\",\n \"def return_markers(self):\\r\\n ent_file = join(self.filename, self._basename + '.ent')\\r\\n if not exists(ent_file):\\r\\n ent_file = join(self.filename, self._basename + '.ent.old')\\r\\n\\r\\n try:\\r\\n ent_notes = _read_ent(ent_file)\\r\\n\\r\\n except (FileNotFoundError, PermissionError):\\r\\n markers = []\\r\\n\\r\\n else:\\r\\n allnote = []\\r\\n for n in ent_notes:\\r\\n try:\\r\\n n['value'].keys()\\r\\n allnote.append(n['value'])\\r\\n except AttributeError:\\r\\n lg.debug('Note of length {} was not '\\r\\n 'converted to dict'.format(n['length']))\\r\\n\\r\\n s_freq = self._hdr['erd']['sample_freq']\\r\\n pcname = '0CFEBE72-DA20-4b3a-A8AC-CDD41BFE2F0D'\\r\\n note_time = []\\r\\n note_name = []\\r\\n note_note = []\\r\\n for n in allnote:\\r\\n if n['Text'] == 'Analyzed Data Note':\\r\\n continue\\r\\n if not n['Text']:\\r\\n continue\\r\\n if 'User' not in n['Data'].keys():\\r\\n continue\\r\\n user1 = n['Data']['User'] == 'Persyst'\\r\\n user2 = False # n['Data']['User'] == 'eeg'\\r\\n user3 = n['Data']['User'] == pcname\\r\\n user4 = n['Data']['User'] == 'XLSpike - Intracranial'\\r\\n user5 = n['Data']['User'] == 'XLEvent - Intracranial'\\r\\n if user1 or user2 or user3 or user4 or user5:\\r\\n continue\\r\\n if len(n['Data']['User']) == 0:\\r\\n note_name.append('-unknown-')\\r\\n else:\\r\\n note_name.append(n['Data']['User'].split()[0])\\r\\n note_time.append(n['Stamp'] / s_freq)\\r\\n note_note.append(n['Text'])\\r\\n\\r\\n markers = []\\r\\n for time, name, note in zip(note_time, note_name, note_note):\\r\\n m = {'name': note + ' (' + name + ')',\\r\\n 'start': time,\\r\\n 'end': time,\\r\\n 'chan': None,\\r\\n }\\r\\n markers.append(m)\\r\\n\\r\\n return markers\",\n \"def get_all_meals():\",\n \"def _on_note(self) -> Iterator:\\n return filter(\\n lambda x: x.type == constants.NOTE_ON, self.midi_data)\",\n \"async def _view_all_notes(self, ctx: Context):\\n\\n author = ctx.author\\n\\n note_infos = []\\n\\n embed_links = ctx.channel.permissions_for(ctx.guild.me).embed_links\\n\\n author_str = f\\\"{author.name}'\\\"\\n\\n if author.name[-1].lower() != \\\"s\\\":\\n author_str += \\\"s\\\"\\n\\n async with self.config.member(author).notes() as notes:\\n total = len(notes)\\n for page_num, note in enumerate(notes, start=1):\\n msg_info = \\\"\\\"\\n if note[\\\"author\\\"]:\\n msg_info += _(\\\"**Author:** {}\\\").format(note[\\\"author\\\"])\\n if note[\\\"channel\\\"]:\\n msg_info += _(\\\"\\\\n**Channel:** {}\\\").format(note[\\\"channel\\\"])\\n if note[\\\"jump_url\\\"]:\\n if embed_links:\\n msg_info += _(\\n \\\"\\\\n[Click here to jump to message]({})\\\"\\n ).format(note[\\\"jump_url\\\"])\\n else:\\n msg_info += _(\\n \\\"\\\\n**Jump To Message:** {}\\\"\\n ).format(note[\\\"jump_url\\\"])\\n\\n note_info = _(\\n \\\"{}\\\\n\\\\n**Note:**\\\\n```{}```\\\\n**Reason:**\\\\n```{}```\\\"\\n ).format(\\n msg_info,\\n note[\\\"note\\\"],\\n note[\\\"reason\\\"]\\n ).strip()\\n\\n if embed_links:\\n page = discord.Embed(\\n colour=0xff0000,\\n description=note_info,\\n title=_(\\\"{} TvM Notes\\\").format(author_str),\\n timestamp=ctx.message.created_at\\n )\\n\\n page.set_footer(\\n text=_(\\\"Page {page_num}/{leng}\\\").format(\\n page_num=page_num, leng=total\\n )\\n )\\n else:\\n page = _(\\n \\\"**{author} TvM Notes**\\\"\\n \\\"\\\\n\\\\n{note}\\\"\\n \\\"\\\\n{footer}\\\"\\n ).format(\\n author=author_str,\\n note=note_info,\\n footer=_(\\\"*Page {page_num}/{leng}*\\\").format(\\n page_num=page_num, leng=total\\n )\\n )\\n\\n note_infos.append(page)\\n\\n await menu(ctx, note_infos, DEFAULT_CONTROLS)\",\n \"def objects(self):\",\n \"def notes(self):\\n return Notes(self)\",\n \"def get_entries(self):\\n return self.find_by_st(\\\"urn:schemas-denon-com:device:ACT-Denon:1\\\")\",\n \"def todolists(self):\\r\\n return tdl.Todolists(self)\",\n \"def notes_list():\\n if request.method == \\\"POST\\\":\\n note = str(request.data.get(\\\"text\\\", \\\"\\\"))\\n idx = max(notes.keys()) + 1\\n notes[idx] = note\\n return note_repr(idx), status.HTTP_201_CREATED\\n\\n # request.method == 'GET'\\n return [note_repr(idx) for idx in sorted(notes.keys())]\",\n \"def get_observations(self):\\n # Check hyper_params and criterion\\n if len(self.hyper_params) == 0:\\n raise AssertionError('!! Hyper-Parameters has not been set.')\\n # Check criterion\\n if self.criterion is None:\\n raise AssertionError(\\n '!! Criterion for hyper-parameter searching has not been set.')\\n # Fetch notes\\n notes = self.summary_fetcher()\\n if len(notes) == 0: return []\\n # Peel of Note wrapper\\n observations = []\\n for note in notes:\\n # Every note in the note list must contain the criterion\\n if self.criterion not in note.criteria: raise AssertionError(\\n '!! Every note must contain the criterion `{}`'.format(self.criterion))\\n # This note will be ignored if it does not contain all the information\\n # in self.hyper_params or the config value is not within the range\\n if not all([hp.name in note.configs and hp.within(note.configs[hp.name])\\n for hp in self.hyper_params]): continue\\n # Gather observation\\n od = OrderedDict()\\n # self.scroll.hyper_params.values() may have been found themselves\\n for hp in self.scroll.hyper_params.values():\\n assert isinstance(hp, HyperParameter)\\n od[hp] = note.configs[hp.name]\\n # Organize the observation list as a list of tuples\\n observations.append((od, note.criteria[self.criterion]))\\n return observations\",\n \"def remove_pedal(self):\\n return Melody([n.remove_pedal() for n in self.notes], nb_bars=self.nb_bars, tags=set(self.tags))\",\n \"def remove_duplicate_notes(self):\\n res = []\\n for x in self.notes:\\n if x not in res:\\n res.append(x)\\n self.notes = res\\n return res\",\n \"def test_list_notes(self):\\n pass\",\n \"def notes_played(self) -> List[Dict]:\\n return [\\n dict(note=self._midi_to_note(note.note), time=note.time)\\n for note in self._on_note()\\n ]\",\n \"def list(self):\\n return self.cell.objects+self.cell.tempObjects\",\n \"def test_list_note_occurrences(self):\\n pass\",\n \"def get_linked_persons(self, note_limit=200):\\n linked_persons = []\\n for note in self.get_notes(note_limit):\\n person = Person.get(self.subdomain, note.linked_person_record_id)\\n if person:\\n linked_persons.append(person)\\n return linked_persons\",\n \"def get_user_visible_notes():\\n return grammar.user_visible_notes\",\n \"def getItems(self):\\n for object in self.database:\\n print(object)\",\n \"def notes(self):\\r\\n return notes.UserNotes(self)\",\n \"def notes(self):\\n return NotesTable(self.rpc, self.name)\",\n \"def get_contenu(self):\\n return self.objets\",\n \"def get_notes(n_notes=3):\\n notes = []\\n \\n for ii, file in enumerate(glob.glob(\\\"data/maestro-v2.0.0/2004/*.midi\\\")):\\n if ii >= n_notes:\\n break\\n pickle_file_name = file[:-4] + 'pkl'\\n\\n if os.path.isfile(pickle_file_name):\\n print(f'Reading parsed file: {pickle_file_name}')\\n with open(pickle_file_name, 'rb') as handle:\\n midi = pickle.load(handle)\\n else:\\n midi = converter.parse(file)\\n\\n with open(pickle_file_name, 'wb') as handle:\\n print(f'writing parsed file: {pickle_file_name}')\\n unserialized_data = pickle.dump(midi, \\n handle, \\n protocol=pickle.HIGHEST_PROTOCOL\\n )\\n\\n\\n print(\\\"Parsing %s\\\" % file)\\n\\n notes_to_parse = None\\n try: # file has instrument parts\\n s2 = instrument.partitionByInstrument(midi)\\n notes_to_parse = s2.parts[0].recurse() \\n except: # file has notes in a flat structure\\n notes_to_parse = midi.flat.notes\\n \\n for element in notes_to_parse:\\n if isinstance(element, note.Note):\\n notes.append(str(element.pitch))\\n elif isinstance(element, chord.Chord):\\n notes.append('.'.join(str(n) for n in element.normalOrder))\\n \\n return notes\",\n \"def GetObjects(self): \\r\\n return self.model.GetObjects()\",\n \"def find_notes(\\n self, words, notebooks, tags, place,\\n limit=const.DEFAULT_LIMIT, order=const.ORDER_UPDATED,\\n pinnded=const.NOT_PINNDED,\\n ):\\n notes = btype.Note.list >> NoteFilterer(self.session)\\\\\\n .by_words(words)\\\\\\n .by_notebooks(notebooks)\\\\\\n .by_tags(tags)\\\\\\n .by_place(place)\\\\\\n .by_pinnded(pinnded)\\\\\\n .order_by(order)\\\\\\n .all()\\\\\\n .limit(limit)\\n\\n return notes\",\n \"def extract_notes(bars):\\n output = []\\n for bar in bars:\\n notes = np.array([bar_to_notes(b,i) for i,b in enumerate(bar)])\\n\\n # Flatten into a single array\\n midi_notes = []\\n for i in range(notes.shape[1]):\\n if all(notes[:,i] == -1):\\n midi_notes.append(None)\\n else:\\n midi_notes.append([n for n in notes[:,i] if n!=-1])\\n output.append(midi_notes)\\n return output\",\n \"def all_recruiter_by_job_applied_id(job_applied_id): \\n return Note.query.filter(Note.job_applied_id == job_applied_id, Note.note_category == 'Recruiter Contact' ).all()\",\n \"def get_note_child_list(self):\\n return self.address_list\",\n \"def __init__(self):\\n self.notes = []\",\n \"def get_documents() -> list[Document]:\\n g.ledger.changed()\\n return [e for e in g.filtered.entries if isinstance(e, Document)]\",\n \"def add_notes(self, notes):\\n if hasattr(notes, \\\"notes\\\"):\\n for x in notes.notes:\\n self.add_note(x)\\n return self.notes\\n elif hasattr(notes, \\\"name\\\"):\\n self.add_note(notes)\\n return self.notes\\n elif isinstance(notes, six.string_types):\\n self.add_note(notes)\\n return self.notes\\n for x in notes:\\n if isinstance(x, list) and len(x) != 1:\\n if len(x) == 2:\\n self.add_note(x[0], x[1])\\n else:\\n self.add_note(x[0], x[1], x[2])\\n else:\\n self.add_note(x)\\n return self.notes\",\n \"def get_dois(wt):\\n lines = get_wiki_lines(wt, predicate=any)\\n return [doi.find_doi_in_text(l) for l in lines]\",\n \"def all_notes():\\n \\n return render_template('all_notes.html',colors=music_color,)\",\n \"def get_notes_from_uuids(dictionary, uuid_list):\\n titles = []\\n texts = []\\n notes = []\\n for key in dictionary:\\n if dictionary[key][\\\"content_type\\\"] == \\\"Note\\\" and dictionary[key][\\\"uuid\\\"] in uuid_list:\\n try:\\n titles.append(dictionary[key][\\\"content\\\"][\\\"title\\\"])\\n texts.append(dictionary[key][\\\"content\\\"][\\\"text\\\"])\\n notes = list(zip(titles, texts))\\n except KeyError:\\n continue\\n return notes\",\n \"def _get_embedded_objects(self):\\n return [getattr(self, name) for name, field in self._get_fields().items() if isinstance(field, fields.Object)]\",\n \"def expose(self):\\n arts = [a.art() for a in self.artists]\\n return arts\",\n \"def objects(self):\\n\\t\\treturn self._objects\",\n \"def list(self):\",\n \"def persons(self):\\r\\n return persons.Persons(self)\",\n \"def get_scheduled_notes(event):\\n def _sort_note_by(note):\\n obj = note.object\\n if hasattr(obj, 'start_dt'):\\n return obj.start_dt, 0\\n else:\\n return obj.contribution.start_dt, obj.position\\n\\n tt_entries = (event.timetable_entries\\n .filter(TimetableEntry.type != TimetableEntryType.BREAK)\\n .options(joinedload('session_block').joinedload('contributions').joinedload('subcontributions'))\\n .options(joinedload('contribution').joinedload('subcontributions'))\\n .options(noload('break_'))\\n .all())\\n # build a list of all the objects we need notes for. that way we can query\\n # all notes in a single go afterwards instead of making the already-huge\\n # timetable query even bigger.\\n objects = set()\\n for entry in tt_entries:\\n objects.add(entry.object)\\n if entry.type == TimetableEntryType.CONTRIBUTION:\\n objects.update(sc for sc in entry.object.subcontributions if not sc.is_deleted)\\n elif entry.type == TimetableEntryType.SESSION_BLOCK:\\n for contrib in entry.object.contributions:\\n objects.add(contrib)\\n objects.update(sc for sc in contrib.subcontributions if not sc.is_deleted)\\n notes = [x for x in event.all_notes.filter_by(is_deleted=False) if x.object in objects]\\n return sorted(notes, key=_sort_note_by)\",\n \"def getList(self):\",\n \"def getList(self):\",\n \"def get_todo_list():\\n\\n # assume that a \\\"h264\\\" encoded file is complete\\n return models.LibraryItem.objects.filter(h264=False)\",\n \"def get_animals(self):\\n for animal in self.animals:\\n print(animal)\",\n \"def journals(self):\\n return self.search(comp_class=Journal)\",\n \"def all_jd_by_job_applied_id(job_applied_id): \\n return Note.query.filter(Note.job_applied_id == job_applied_id, Note.note_category == 'Job Description' ).order_by(Note.note_date_created.desc()).first()\",\n \"def PublicNotes(self, default=[{}]):\\n tmp = self.data.get('metadata', {}).get('public_notes', default)\\n return [HEP.SourcedValueObject(i) for i in tmp]\",\n \"def get_notes_by_id(self, ids: List[str]) -> pd.Series:\\n\\n return self.notes[self.notes.apply(lambda n: n.id in ids)]\",\n \"def get_donnees(self):\\r\\n \\r\\n return self._donnees\",\n \"def all_for(self, observed, signal):\\r\\n content_type = ContentType.objects.get_for_model(observed)\\r\\n observed_items = self.filter(content_type=content_type, object_id=observed.id, signal=signal)\\r\\n return observed_items\",\n \"def notes_in_path(self, path: str) -> List[NoteInfo]:\\n check_path = os.path.abspath(path)\\n return [n for n in self.notes.values() if n.file_path.startswith(check_path)]\",\n \"def get_patients(self):\\n return\",\n \"def get_memos():\\n records = [ ]\\n for record in collection.find( { \\\"type\\\": \\\"dated_memo\\\" } ):\\n record['date'] = arrow.get(record['date']).isoformat()\\n record['time'] = arrow.get(record['date']).timestamp\\n record['_id'] = str(record['_id'])\\n record['text'] = str(record['text'].encode('ascii','xmlcharrefreplace'), \\\"utf-8\\\")\\n records.append(record)\\n records.sort(key=lambda x: x['time'])\\n return records\",\n \"def notes(self):\\r\\n return TicketNotes(self)\",\n \"def read_notes():\\n\\n if current_user.is_admin is False:\\n notes = Note.query.filter_by(created_by=current_user.id).all()\\n else:\\n notes = Note.query.all()\\n\\n return render_template('notes/index.html.j2', notes=notes, title='Notes')\",\n \"def get(self):\\r\\n\\t\\treturn list(self)\",\n \"def note_list(self, evarnames=\\\"\\\", in_=None):\\n evarnames = self._find_vars(evarnames, evars=True, \\n unique=True, empty_ok=True)\\n if len(evarnames) == 0:\\n evarnames = ['_dta'] + self._varlist\\n if in_ is not None:\\n if isinstance(in_, int):\\n in_ = (in_,)\\n elif (not isinstance(in_, collections.Iterable)\\n or not all(isinstance(n, int) for n in in_)):\\n raise TypeError(\\\"in_ should be int or iterable of int\\\")\\n if any(n <= 0 for n in in_):\\n raise ValueError(\\\"note numbers must be >= 1\\\")\\n for name in evarnames:\\n if name not in self._chrdict: continue\\n chars = self._chrdict[name]\\n if 'note0' not in chars: continue\\n nnotes = int(chars['note0'])\\n in_range = in_ if in_ is not None else range(1, nnotes + 1)\\n note_info = []\\n for note_num in in_range:\\n note_name = 'note' + str(note_num)\\n if note_name in chars:\\n note_info.append(note_num)\\n if note_info != []:\\n note_info.insert(0, name)\\n self._display_notes(note_info)\",\n \"def readentries(self):\\n return list(x for x in self)\",\n \"def obj_list(self):\\n return self._obj_list\",\n \"def get_object_discussions(self):\\n return DSSObjectDiscussions(self.client, self.project_key, \\\"RECIPE\\\", self.recipe_name)\",\n \"def notes(self):\\r\\n return notes.ForumSuggestionNotes(self)\",\n \"def do_notes(self, sym):\\n # DRY: duplication with Roman.py - FIXME\\n if sym == '-':\\n # REST\\n return []\\n note_or_chord = self.do(sym)\\n if note_or_chord is None:\\n return []\\n elif type(note_or_chord) == Chord:\\n return note_or_chord.notes\\n else:\\n return [ note_or_chord ]\",\n \"def get_notes_by_type(self, typ: type) -> pd.Series:\\n\\n # doesn't use isinstance() to prevent subtypes from being selected\\n return self.notes[self.notes.apply(lambda n: type(n) is typ)]\",\n \"def get_queryset(self):\\n labels = self.kwargs['labels']\\n return models.Notes.objects.filter(labels__icontains=labels)\",\n \"def get_common_food(cls):\\n objs = cls.objects\\n return objs\",\n \"def get_all_notes(user_id, token):\\n user = User.get(User.id == user_id).username\\n if not validate_token(user, token):\\n return HTTPResponse(status=500, body={\\\"message\\\":\\\"Validation error.\\\"})\\n res = []\\n for note in Notes.select():\\n if note.user.id == user_id:\\n new_note = model_to_dict(note)\\n res.append({\\\"id\\\":new_note['id'], \\\"title\\\":new_note['title'],\\n \\\"content\\\":new_note['content']})\\n new_token = generate_token(user)\\n body = {\\\"user_id\\\":user_id, \\\"token\\\":new_token.decode('utf-8'), 'items':res}\\n return HTTPResponse(status=200, body=body)\",\n \"def fNoteLists(self, match):\\n att, start_char, g_links, extras = match.groups()\\n start_char = start_char or 'a'\\n index = '%s%s%s' % (g_links, extras, start_char)\\n result = ''\\n\\n if index not in self.notelist_cache:\\n o = []\\n if self.notes:\\n for seq, info in self.notes.items():\\n links = self.makeBackrefLink(info, g_links, start_char)\\n atts = ''\\n if 'def' in info:\\n infoid = info['id']\\n atts = info['def']['atts']\\n content = info['def']['content']\\n li = (\\\"\\\"\\\"\\\\t<li%s>%s<span id=\\\"note%s\\\"> </span>%s</li>\\\"\\\"\\\"\\n % (atts, links, infoid, content))\\n else:\\n li = (\\\"\\\"\\\"\\\\t<li%s>%s Undefined Note [#%s].<li>\\\"\\\"\\\" %\\n (atts, links, info['seq']))\\n o.append(li)\\n if '+' == extras and self.unreferencedNotes:\\n for seq, info in self.unreferencedNotes.items():\\n if info['def']:\\n atts = info['def']['atts']\\n content = info['def']['content']\\n li = \\\"\\\"\\\"\\\\t<li%s>%s</li>\\\"\\\"\\\" % (atts, content)\\n o.append(li)\\n self.notelist_cache[index] = u\\\"\\\\n\\\".join(o)\\n result = self.notelist_cache[index]\\n if result:\\n list_atts = self.pba(att)\\n result = \\\"\\\"\\\"<ol%s>\\\\n%s\\\\n</ol>\\\"\\\"\\\" % (list_atts, result)\\n return result\",\n \"def search(self, filter):\\n return [note for note in self.notes if note.match(filter)]\",\n \"def get_all_refobjs(self, ):\\n return cmds.ls(type=\\\"jb_reftrack\\\")\",\n \"def list(self):\\n return self._get_list()\",\n \"def getListIngr(cls):\\n\\n # meals = Meals.getMealsByFutureDate(user=session['User'])\\n list_ingr = db.session.query(RecipeIngredient).join(Recipe).join(Meals).\\\\\\n join(Ingredient).\\\\\\n filter(func.substr(Meals.date_planned,0,11) >= func.substr(datetime.today(),0,11)).\\\\\\n filter(Meals.recipe_fk==Recipe.recipe_id).\\\\\\n filter(Recipe.recipe_id==RecipeIngredient.recipe_fk).\\\\\\n filter(RecipeIngredient.ingredient_name==Ingredient.name).\\\\\\n filter(Meals.user_fk==session['User']).\\\\\\n order_by(Meals.date_planned).all()\\n\\n return list_ingr\",\n \"def remove_tempo(self):\\n return Melody([n.remove_tempo() for n in self.notes], nb_bars=self.nb_bars, tags=set(self.tags))\",\n \"def search(self, filtr):\\n return [note for note in self.notes if note.match(filtr)]\",\n \"def get_note_alternatives(self, id):\\n notes = self.session.query(models.Note).filter(\\n models.Note.conflict_parent_id == id,\\n ).all()\\n return btype.Note.list >> notes\",\n \"def get_notes(notes_type=\\\"national\\\", area=None):\\n query_menu = get_query_menu(area)\\n query = query_menu[notes_type][\\\"query\\\"]\\n collection = query_menu[notes_type][\\\"collection\\\"]\\n notes = \\\"\\\"\\n try:\\n doc = next(collection.find(query).sort([(DATE_KEY, -1)]).limit(1))\\n notes = doc[NOTE_KEY] if doc[NOTE_KEY] != 0 else None\\n except StopIteration:\\n app.logger.error(\\\"While getting notes: no data\\\")\\n return notes if notes is not None and not rubbish_notes(notes) else \\\"\\\"\",\n \"def objects(self):\\r\\n return self._objects\",\n \"def _charactersForViewer(self, store, role):\\n characters = []\\n things = store.query(Thing)\\n actors = asAccessibleTo(role, things)\\n characters.extend(map(itemFromProxy, actors))\\n return characters\"\n]","isConversational":false},"negative_scores":{"kind":"list like","value":["0.6918545","0.68457854","0.67542857","0.6423082","0.6351363","0.633306","0.6201348","0.6194495","0.6162774","0.6150949","0.61312294","0.6117048","0.60784066","0.60784066","0.602897","0.59813625","0.59791195","0.59717655","0.59624857","0.593649","0.590595","0.5825926","0.58186924","0.5785036","0.5767843","0.57359","0.5731705","0.5727749","0.5723146","0.5686318","0.5662658","0.5656062","0.5651167","0.56321555","0.56315184","0.56293184","0.5608781","0.55864275","0.5549463","0.5542501","0.5540498","0.55348843","0.5532931","0.5529944","0.5523522","0.55163985","0.54941726","0.54930866","0.5491013","0.54767144","0.54637605","0.54581565","0.5449573","0.5447407","0.54268074","0.5420258","0.54200894","0.54174954","0.54107064","0.54106015","0.5408196","0.5397371","0.5392167","0.5387888","0.5381068","0.5381068","0.5380341","0.5370158","0.5365927","0.5363541","0.53307676","0.5329608","0.5328404","0.53201514","0.53129286","0.5305123","0.530437","0.5303462","0.5296989","0.5288078","0.52834666","0.5275003","0.5274858","0.5268521","0.5251052","0.5250978","0.5250608","0.52439785","0.5225496","0.52235466","0.52212876","0.5220534","0.5218438","0.5212246","0.5204104","0.5197422","0.51925236","0.51884186","0.51878273","0.5180192","0.5178898"],"string":"[\n \"0.6918545\",\n \"0.68457854\",\n \"0.67542857\",\n \"0.6423082\",\n \"0.6351363\",\n \"0.633306\",\n \"0.6201348\",\n \"0.6194495\",\n \"0.6162774\",\n \"0.6150949\",\n \"0.61312294\",\n \"0.6117048\",\n \"0.60784066\",\n \"0.60784066\",\n \"0.602897\",\n \"0.59813625\",\n \"0.59791195\",\n \"0.59717655\",\n \"0.59624857\",\n \"0.593649\",\n \"0.590595\",\n \"0.5825926\",\n \"0.58186924\",\n \"0.5785036\",\n \"0.5767843\",\n \"0.57359\",\n \"0.5731705\",\n \"0.5727749\",\n \"0.5723146\",\n \"0.5686318\",\n \"0.5662658\",\n \"0.5656062\",\n \"0.5651167\",\n \"0.56321555\",\n \"0.56315184\",\n \"0.56293184\",\n \"0.5608781\",\n \"0.55864275\",\n \"0.5549463\",\n \"0.5542501\",\n \"0.5540498\",\n \"0.55348843\",\n \"0.5532931\",\n \"0.5529944\",\n \"0.5523522\",\n \"0.55163985\",\n \"0.54941726\",\n \"0.54930866\",\n \"0.5491013\",\n \"0.54767144\",\n \"0.54637605\",\n \"0.54581565\",\n \"0.5449573\",\n \"0.5447407\",\n \"0.54268074\",\n \"0.5420258\",\n \"0.54200894\",\n \"0.54174954\",\n \"0.54107064\",\n \"0.54106015\",\n \"0.5408196\",\n \"0.5397371\",\n \"0.5392167\",\n \"0.5387888\",\n \"0.5381068\",\n \"0.5381068\",\n \"0.5380341\",\n \"0.5370158\",\n \"0.5365927\",\n \"0.5363541\",\n \"0.53307676\",\n \"0.5329608\",\n \"0.5328404\",\n \"0.53201514\",\n \"0.53129286\",\n \"0.5305123\",\n \"0.530437\",\n \"0.5303462\",\n \"0.5296989\",\n \"0.5288078\",\n \"0.52834666\",\n \"0.5275003\",\n \"0.5274858\",\n \"0.5268521\",\n \"0.5251052\",\n \"0.5250978\",\n \"0.5250608\",\n \"0.52439785\",\n \"0.5225496\",\n \"0.52235466\",\n \"0.52212876\",\n \"0.5220534\",\n \"0.5218438\",\n \"0.5212246\",\n \"0.5204104\",\n \"0.5197422\",\n \"0.51925236\",\n \"0.51884186\",\n \"0.51878273\",\n \"0.5180192\",\n \"0.5178898\"\n]","isConversational":false},"document_score":{"kind":"string","value":"0.0"},"document_rank":{"kind":"string","value":"-1"}}},{"rowIdx":23779,"cells":{"query":{"kind":"string","value":"Return a list of note objects in augmented"},"document":{"kind":"string","value":"def mgChordAugmented(value):\n chord = [MgNote(value), MgNote(value) + 4, MgNote(value) + 8]\n return chord"},"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 augment(self):\n for n in self.notes:\n n.augment()","def notes(self):\n return reapy.NoteList(self)","def add_notes(self, notes):\n if hasattr(notes, \"notes\"):\n for x in notes.notes:\n self.add_note(x)\n return self.notes\n elif hasattr(notes, \"name\"):\n self.add_note(notes)\n return self.notes\n elif isinstance(notes, six.string_types):\n self.add_note(notes)\n return self.notes\n for x in notes:\n if isinstance(x, list) and len(x) != 1:\n if len(x) == 2:\n self.add_note(x[0], x[1])\n else:\n self.add_note(x[0], x[1], x[2])\n else:\n self.add_note(x)\n return self.notes","def getnotes():","def return_markers(self):\r\n ent_file = join(self.filename, self._basename + '.ent')\r\n if not exists(ent_file):\r\n ent_file = join(self.filename, self._basename + '.ent.old')\r\n\r\n try:\r\n ent_notes = _read_ent(ent_file)\r\n\r\n except (FileNotFoundError, PermissionError):\r\n markers = []\r\n\r\n else:\r\n allnote = []\r\n for n in ent_notes:\r\n try:\r\n n['value'].keys()\r\n allnote.append(n['value'])\r\n except AttributeError:\r\n lg.debug('Note of length {} was not '\r\n 'converted to dict'.format(n['length']))\r\n\r\n s_freq = self._hdr['erd']['sample_freq']\r\n pcname = '0CFEBE72-DA20-4b3a-A8AC-CDD41BFE2F0D'\r\n note_time = []\r\n note_name = []\r\n note_note = []\r\n for n in allnote:\r\n if n['Text'] == 'Analyzed Data Note':\r\n continue\r\n if not n['Text']:\r\n continue\r\n if 'User' not in n['Data'].keys():\r\n continue\r\n user1 = n['Data']['User'] == 'Persyst'\r\n user2 = False # n['Data']['User'] == 'eeg'\r\n user3 = n['Data']['User'] == pcname\r\n user4 = n['Data']['User'] == 'XLSpike - Intracranial'\r\n user5 = n['Data']['User'] == 'XLEvent - Intracranial'\r\n if user1 or user2 or user3 or user4 or user5:\r\n continue\r\n if len(n['Data']['User']) == 0:\r\n note_name.append('-unknown-')\r\n else:\r\n note_name.append(n['Data']['User'].split()[0])\r\n note_time.append(n['Stamp'] / s_freq)\r\n note_note.append(n['Text'])\r\n\r\n markers = []\r\n for time, name, note in zip(note_time, note_name, note_note):\r\n m = {'name': note + ' (' + name + ')',\r\n 'start': time,\r\n 'end': time,\r\n 'chan': None,\r\n }\r\n markers.append(m)\r\n\r\n return markers","def listNotes() -> list:\n list_of_notes = []\n for note in Note.objects.all():\n list_of_notes.append({\n 'uuid': note.uuid, 'title': note.title,\n 'author': note.author, 'body': note.body, 'created_at': localtime(note.created_at)\n })\n return list_of_notes","def get_note():\n\n return Note.query.all()","def view_notes(tag_list):\n final_notes = []\n for tag in tag_list:\n all_notes = Tags.objects.get(tag_text=tag)\n notes = all_notes.notes.all()\n for note in notes:\n final_notes.append(note.note_text)\n \n return final_notes","def get_notes():\n notes = []\n durs = []\n\n for file in glob.glob(\"D:\\\\anthems\\\\data\\\\*.mid\"):\n midi = converter.parse(file)\n\n print(\"Parsing %s\" % file)\n\n notes_to_parse = None\n\n try:\n s2 = instrument.partitionByInstrument(midi)\n notes_to_parse = s2.parts[0].recurse() \n except:\n notes_to_parse = midi.flat.notes\n \n for element in notes_to_parse:\n if isinstance(element, note.Note):\n notes.append(str(element.pitch))\n durs.append(element.duration.quarterLength)\n elif isinstance(element, chord.Chord):\n notes.append('.'.join(str(n) for n in element.normalOrder))\n durs.append(element.duration.quarterLength)\n\n return notes, durs","def get_note_names(self):\n res = []\n for n in self.notes:\n if n.name not in res:\n res.append(n.name)\n return res","def getNotes(self):\n return self._nednotes, self._ongcnotes","def notes(self):\r\n return notes.Notes(self)","def notes(self):\r\n return notes.Notes(self)","def get_objects_using_notes():\n global objects_using_notes\n\n if not objects_using_notes:\n objects_using_notes = rsh.get_objects_using_notes(get_srcs())\n\n return objects_using_notes","def notes_played(self) -> List[Dict]:\n return [\n dict(note=self._midi_to_note(note.note), time=note.time)\n for note in self._on_note()\n ]","def return_augmented_sentences(self) -> list:\n return self.augmented_sentence_list","def notes(self):\n return self._notes","def processNotes(self, inMeasureElem):\n inNotes = inMeasureElem.findall(\"note\")\n # list to hold array of processed note elements\n lstOutNotes = []\n\n for inNote in inNotes:\n for childNode in inNote:\n outNote = ET.Element(\"note\")\n\n # rest\n inRest = inNote.find(\"rest\")\n if (inRest is not None):\n outRest = ET.Element(\"rest\")\n outNote.append(outRest)\n\n # chord\n inChord = inNote.find(\"chord\")\n if (inChord is not None):\n outChord = ET.Element(\"chord\")\n outNote.append(outChord)\n\n # pitch\n inPitch = inNote.find(\"pitch\")\n if (inPitch is not None):\n outPitch = ET.Element(\"pitch\")\n for childNode in inPitch:\n if (True == (childNode.tag in [\"step\", \"octave\"])):\n pitchSubElem = ET.Element(childNode.tag)\n pitchSubElem.text = childNode.text\n outPitch.append(pitchSubElem)\n # handle the \"alter\" child tag separately even if the alter value is 0 include it otherwise\n # include the original scores alter value\n inPitchAlter = inPitch.find(\"alter\")\n if (inPitchAlter is not None):\n # use sources alter elem\n outPitchAlter = ET.Element(\"alter\")\n outPitchAlter.text = inPitchAlter.text\n outPitch.append(outPitchAlter)\n else:\n # make a zero value alter element\n outPitchAlter = ET.Element(\"alter\")\n outPitchAlter.text = \"0\"\n outPitch.append(outPitchAlter)\n\n outNote.append(outPitch)\n\n # duration\n inDuration = inNote.find(\"duration\")\n if (inDuration is not None):\n outDuration = ET.Element(\"duration\")\n outDurationVal = int(inDuration.text) * self._multiplier\n outDuration.text = str(int(outDurationVal))\n outNote.append(outDuration)\n\n # append the mote to the list of notes\n lstOutNotes.append(outNote)\n\n # finally return the list of notes\n return lstOutNotes","def notes(self):\n return self.__notes","def notes(self):\n return notes.Notes(self)","def __init__(self):\n self.notes = []","def get_notes(n_notes=3):\n notes = []\n \n for ii, file in enumerate(glob.glob(\"data/maestro-v2.0.0/2004/*.midi\")):\n if ii >= n_notes:\n break\n pickle_file_name = file[:-4] + 'pkl'\n\n if os.path.isfile(pickle_file_name):\n print(f'Reading parsed file: {pickle_file_name}')\n with open(pickle_file_name, 'rb') as handle:\n midi = pickle.load(handle)\n else:\n midi = converter.parse(file)\n\n with open(pickle_file_name, 'wb') as handle:\n print(f'writing parsed file: {pickle_file_name}')\n unserialized_data = pickle.dump(midi, \n handle, \n protocol=pickle.HIGHEST_PROTOCOL\n )\n\n\n print(\"Parsing %s\" % file)\n\n notes_to_parse = None\n try: # file has instrument parts\n s2 = instrument.partitionByInstrument(midi)\n notes_to_parse = s2.parts[0].recurse() \n except: # file has notes in a flat structure\n notes_to_parse = midi.flat.notes\n \n for element in notes_to_parse:\n if isinstance(element, note.Note):\n notes.append(str(element.pitch))\n elif isinstance(element, chord.Chord):\n notes.append('.'.join(str(n) for n in element.normalOrder))\n \n return notes","def getNotes(self):\n return self.__notes","def notes_to_annotations(self):\n\n for sub_dir, text_name, file_names in anafora.walk(self.xml_dir, self.xml_regex):\n note_path = os.path.join(self.text_dir, text_name)\n xml_path = os.path.join(self.xml_dir, sub_dir, file_names[0])\n ref_data = anafora.AnaforaData.from_file(xml_path)\n\n # collect (annot_start, annot_end, annot_id) tuples\n add_annotations(self.note2times[note_path], ref_data, 'TIMEX3')\n add_annotations(self.note2times[note_path], ref_data, 'SECTIONTIME')\n add_annotations(self.note2times[note_path], ref_data, 'DOCTIME')\n add_annotations(self.note2events[note_path], ref_data, 'EVENT')\n\n # collect (src spans, targ spans, src id, targ id) tuples\n for rel in ref_data.annotations.select_type('TLINK'):\n src = rel.properties['Source']\n targ = rel.properties['Target']\n label = rel.properties['Type']\n if label == 'CONTAINS':\n src_start, src_end = src.spans[0]\n targ_start, targ_end = targ.spans[0]\n self.note2rels[note_path].append(\n (src_start, src_end, targ_start, targ_end, src.id, targ.id))\n\n # sort relation tuples by src arguments' offsets\n # self.note2rels[note_path].sort(key=lambda t: t[0])","def notes(self):\n return Notes(self)","def remove_duplicate_notes(self):\n res = []\n for x in self.notes:\n if x not in res:\n res.append(x)\n self.notes = res\n return res","def fNoteLists(self, match):\n att, start_char, g_links, extras = match.groups()\n start_char = start_char or 'a'\n index = '%s%s%s' % (g_links, extras, start_char)\n result = ''\n\n if index not in self.notelist_cache:\n o = []\n if self.notes:\n for seq, info in self.notes.items():\n links = self.makeBackrefLink(info, g_links, start_char)\n atts = ''\n if 'def' in info:\n infoid = info['id']\n atts = info['def']['atts']\n content = info['def']['content']\n li = (\"\"\"\\t<li%s>%s<span id=\"note%s\"> </span>%s</li>\"\"\"\n % (atts, links, infoid, content))\n else:\n li = (\"\"\"\\t<li%s>%s Undefined Note [#%s].<li>\"\"\" %\n (atts, links, info['seq']))\n o.append(li)\n if '+' == extras and self.unreferencedNotes:\n for seq, info in self.unreferencedNotes.items():\n if info['def']:\n atts = info['def']['atts']\n content = info['def']['content']\n li = \"\"\"\\t<li%s>%s</li>\"\"\" % (atts, content)\n o.append(li)\n self.notelist_cache[index] = u\"\\n\".join(o)\n result = self.notelist_cache[index]\n if result:\n list_atts = self.pba(att)\n result = \"\"\"<ol%s>\\n%s\\n</ol>\"\"\" % (list_atts, result)\n return result","def reconstruct(notes, resolution):\n # Construct the MusPy Music object\n music = muspy.Music(resolution=resolution, tempos=[muspy.Tempo(0, 100)])\n\n # Append the tracks\n programs = sorted(set(note[-1] for note in notes))\n for program in programs:\n music.tracks.append(muspy.Track(program))\n\n # Append the notes\n for beat, position, pitch, duration, program in notes:\n time = beat * resolution + position\n track_idx = programs.index(program)\n music[track_idx].notes.append(muspy.Note(time, pitch, duration))\n\n return music","def extract_notes(bars):\n output = []\n for bar in bars:\n notes = np.array([bar_to_notes(b,i) for i,b in enumerate(bar)])\n\n # Flatten into a single array\n midi_notes = []\n for i in range(notes.shape[1]):\n if all(notes[:,i] == -1):\n midi_notes.append(None)\n else:\n midi_notes.append([n for n in notes[:,i] if n!=-1])\n output.append(midi_notes)\n return output","def notes_list():\n if request.method == \"POST\":\n note = str(request.data.get(\"text\", \"\"))\n idx = max(notes.keys()) + 1\n notes[idx] = note\n return note_repr(idx), status.HTTP_201_CREATED\n\n # request.method == 'GET'\n return [note_repr(idx) for idx in sorted(notes.keys())]","def inverse_notes(note_list):\n raw_output = []\n for note in note_list:\n # We transpose every notes except for Z\n if note.is_pause:\n raw_output.append(f'{note.name}{note.figure}')\n else:\n # Get the index of the note to transpose\n note_name_index = NOTE_NAMES.index(note.name)\n # Get the transposed index according to the amount\n inverted_index = ((len(NOTE_NAMES) - note_name_index) % len(NOTE_NAMES))\n new_note_name = NOTE_NAMES[inverted_index]\n # Create the new note, with the new note's name, and its original figure\n new_note = f'{new_note_name}{note.figure}'\n new_note += 'p' if note.has_point else ''\n raw_output.append(new_note)\n\n return raw_output","def objects(self):","def PublicNotes(self, default=[{}]):\n tmp = self.data.get('metadata', {}).get('public_notes', default)\n return [HEP.SourcedValueObject(i) for i in tmp]","def extract_feature_notes(contig, type, start, stop, direction, notes, pos, ref, alt):\n notes = re.sub(\"%\\d+\", \" \", notes)\n desc = re.search(\"Note=(.*?);\", notes)\n id = re.search(\"ID=(.*?);\", notes)\n name = re.search(\"Name=(.*?);\", notes)\n\n if id and name and desc:\n print ('Contig: ' + contig)\n print ('Annotation_type: ' + type)\n print ('Start:Stop:Direction '+ start+':'+stop+':'+direction)\n print (\"Position: \" + pos)\n print ('Ref:Alt: ' + ref + ':'+ alt)\n print(\"ID: \" + id.group(1))\n # print(\"Name: \" + name.group(1))\n print(\"Description: \" + desc.group(1))\n # if name:\n # print(\"Name: \" + name.group(1))\n #\n # if desc:\n # print(\"Desc: \" + desc.group(1))\n\n\n print()","def test_list_notes(self):\n pass","def note_list(request):\n user = request.user\n notes = Note.objects.filter(author=user)\n serializer = NoteSerializer(notes, many=True)\n return Response(serializer.data)","def expose(self):\n arts = [a.art() for a in self.artists]\n return arts","def extended(self) -> List:\n raise NotImplementedError","def _get_issue_notes(request, pk):\n if request.user.is_coordinator_or_better:\n note_types = IssueNote.COORDINATOR_NOTE_TYPES\n else:\n note_types = IssueNote.PARALEGAL_NOTE_TYPES\n\n return (\n IssueNote.objects.filter(issue=pk)\n .prefetch_related(\"creator__groups\")\n .filter(note_type__in=note_types)\n .order_by(\"-created_at\")\n .all()\n )","def getNotes(self, *args):\n return _libsbml.SBase_getNotes(self, *args)","def append_collectable(self, newnotes: List):\n self.notes.extend(newnotes)","def get_notes():\n song_index_to_notes = {}\n\n for file in glob.glob(\"../data/midi/*.mid\"):\n midi = converter.parse(file)\n song_index = int(os.path.splitext(os.path.basename(file))[0])\n #print(\"Parsing %s with an index %d\" % (file, song_index))\n\n notes_to_parse = None\n notes = []\n try: # file has instrument parts\n s2 = instrument.partitionByInstrument(midi)\n notes_to_parse = s2.parts[0].recurse()\n except: # file has notes in a flat structure\n notes_to_parse = midi.flat.notes\n\n for element in notes_to_parse:\n if isinstance(element, note.Note):\n notes.append(str(element.pitch))\n elif isinstance(element, chord.Chord):\n notes.append('.'.join(str(n) for n in element.normalOrder))\n\n song_index_to_notes[song_index] = notes\n\n return song_index_to_notes","def _on_note(self) -> Iterator:\n return filter(\n lambda x: x.type == constants.NOTE_ON, self.midi_data)","def get_note_alternatives(self, id):\n notes = self.session.query(models.Note).filter(\n models.Note.conflict_parent_id == id,\n ).all()\n return btype.Note.list >> notes","def to_sequence(self, chord, inst):\n time = 0\n sequence = []\n for note in self.notes:\n pitch = chord.to_pitch(note)\n start = time\n end = time + note.duration\n sequence.append([start, end, pitch, chord.to_chord(), inst, note])\n time += note.duration\n return sequence","def get_notes(self, note_limit=200):\n return Note.get_by_person_record_id(\n self.subdomain, self.record_id, limit=note_limit)","def getNotes(self):\n logger.debug(\"Func: getNotes\")\n\n return self._currentNotes","def get_makerNotes(img, field_name='MakerNote'):\n exif_data = img._getexif()\n keys = list(ExifTags.TAGS.keys())\n vals = list(ExifTags.TAGS.values())\n exif_index = vals.index(field_name)\n exif_num = keys[exif_index]\n MakerNote = exif_data[exif_num]\n return MakerNote","def examples(self):\n for obj_ind in range(len(self.objects)):\n yield self.get_object_intent_by_index(obj_ind)","def test_list_note_occurrences(self):\n pass","def notes(self):\r\n return notes.UserNotes(self)","def getMetadataList(self):\n return [ x.toTuple() for x in self.metadata ]","def bar_to_lilypond_notes(notes):\n lp_notes = []\n if notes[0] is None:\n notes = notes[1:]\n if notes[0] is None:\n lp_notes.append(\"r\")\n for n in notes:\n if n is None:\n continue\n if type(n) is list:\n lp_notes.append([midi_to_lilypond_note(x) for x in n])\n else:\n lp_notes.append(midi_to_lilypond_note(n))\n return lp_notes","def remove_pedal(self):\n return Melody([n.remove_pedal() for n in self.notes], nb_bars=self.nb_bars, tags=set(self.tags))","def ordered_notes(self, obj, sig_features, tabs):\n supports = dict(\n [(str(support.id), support) for support in obj.all_supports])\n notes = []\n for browsers in sig_features.values():\n for section in tabs:\n for browser in section['browsers']:\n sig_supports = browsers.get(browser, [])\n for sig_support_pk in sig_supports:\n support = supports[sig_support_pk]\n if support.note:\n notes.append(sig_support_pk)\n return OrderedDict((note, i) for i, note in enumerate(notes, 1))","def get_notes_from_uuids(dictionary, uuid_list):\n titles = []\n texts = []\n notes = []\n for key in dictionary:\n if dictionary[key][\"content_type\"] == \"Note\" and dictionary[key][\"uuid\"] in uuid_list:\n try:\n titles.append(dictionary[key][\"content\"][\"title\"])\n texts.append(dictionary[key][\"content\"][\"text\"])\n notes = list(zip(titles, texts))\n except KeyError:\n continue\n return notes","def _build_experiment_repliseq_embedded_list():\n antibody_embeds = DependencyEmbedder.embed_defaults_for_type(\n base_path='antibody',\n t='antibody')\n return (\n Experiment.embedded_list + antibody_embeds\n )","def get_raw(self):\n \n out_items = []\n for order in self.order_lst:\n out_items += [i.get_metadata() for i in order.get_items()]\n \n return out_items","def ls(self, count = 200):\n return self._manager.ls_notes(self['id'], count)","def _modify_all_notes(self):\n return self._modify_notes_in_time(TimeStep(0.0, MAX_CLIP_LENGTH), self._clip_notes, self._length_offset)","def _get_embedded_objects(self):\n return [getattr(self, name) for name, field in self._get_fields().items() if isinstance(field, fields.Object)]","def transform(self, X: list[DelayedSample]) -> list[DelayedSample]:\n if self.annotation_directory is None:\n return None\n\n annotated_samples = []\n for x in X:\n # we use .key here because .path might not be unique for all\n # samples. Also, the ``bob bio annotate-samples`` command dictates\n # how annotations are stored.\n annotation_file = os.path.join(\n self.annotation_directory, x.key + self.annotation_extension\n )\n\n annotated_samples.append(\n DelayedSample.from_sample(\n x,\n delayed_attributes=dict(\n annotations=lambda: read_annotation_file(\n annotation_file, self.annotation_type\n )\n ),\n )\n )\n\n return annotated_samples","def note_list(self, evarnames=\"\", in_=None):\n evarnames = self._find_vars(evarnames, evars=True, \n unique=True, empty_ok=True)\n if len(evarnames) == 0:\n evarnames = ['_dta'] + self._varlist\n if in_ is not None:\n if isinstance(in_, int):\n in_ = (in_,)\n elif (not isinstance(in_, collections.Iterable)\n or not all(isinstance(n, int) for n in in_)):\n raise TypeError(\"in_ should be int or iterable of int\")\n if any(n <= 0 for n in in_):\n raise ValueError(\"note numbers must be >= 1\")\n for name in evarnames:\n if name not in self._chrdict: continue\n chars = self._chrdict[name]\n if 'note0' not in chars: continue\n nnotes = int(chars['note0'])\n in_range = in_ if in_ is not None else range(1, nnotes + 1)\n note_info = []\n for note_num in in_range:\n note_name = 'note' + str(note_num)\n if note_name in chars:\n note_info.append(note_num)\n if note_info != []:\n note_info.insert(0, name)\n self._display_notes(note_info)","def items(self):\n return iter(self._noise_objs.items())","def snippetList(requeset, format = None):","def _parse(self, array):\n return [self._parse_note(x) for x in array]","def midiToNotesRec(notes, totalnotes, note_names=[]):\n names = ['C', 'C#', 'D', 'D#', 'E', 'F', 'F#', 'G', 'G#', 'A', 'A#', 'B']\n \n #print(note_names)\n \n def recMidiToNotes(notes, note_namesr=[]):\n if notes == []:\n return\n \n head = notes.pop(0)\n name = names[head%12]\n octave = int((head-12)/12)\n note_namesr.append(name + str(octave))\n print('rec names:', note_namesr)\n return note_namesr\n \n if len(note_names) == totalnotes:\n return note_names\n \n midiToNotes(notes, totalnotes, recMidiToNotes(notes, note_names))","def all_note_by_job_applied_id(job_applied_id):\n\n return Note.query.filter(Note.job_applied_id == job_applied_id, Note.note_category == 'Note' ).all()","def get_animals(self):\n for animal in self.animals:\n print(animal)","def notes_to_sound(instrument, track, duration_multiplier=1, srate=None):\n srate = get_srate(srate)\n duration = 1.0\n note_duration = 1.0\n note_on_velocity = 0.7\n note_off_velocity = 0.5\n t = 0.0\n result = []\n for item in track:\n if isinstance(item, Note):\n if item.duration is None:\n item.duration = duration\n else:\n duration = item.duration\n if hasattr(duration, '__getitem__'):\n if len(duration) == 1:\n duration = duration[0]\n else:\n duration, note_duration = duration\n item.duration = note_duration\n else:\n note_duration = duration\n if item.note_on_velocity is None:\n item.note_on_velocity = note_on_velocity\n else:\n note_on_velocity = item.note_on_velocity\n if item.note_off_velocity is None:\n item.note_off_velocity = note_off_velocity\n else:\n note_off_velocity = item.note_off_velocity\n item.multiply_duration(duration_multiplier)\n instrument_sound = instrument(item)\n elif isinstance(item, Rest):\n if item.duration is not None:\n duration = item.duration\n instrument_sound = []\n else:\n if hasattr(item, '__getitem__'):\n pitch = item[0]\n if len(item) > 1:\n duration = item[1]\n if hasattr(duration, '__getitem__'):\n if len(duration) == 1:\n duration = duration[0]\n else:\n duration, note_duration = duration\n else:\n note_duration = duration\n if len(item) > 2:\n note_on_velocity = item[2]\n if len(item) > 3:\n note_off_velocity = item[3]\n else:\n pitch = item\n instrument_sound = instrument(Note(pitch=pitch, duration=note_duration * duration_multiplier, note_on_velocity=note_on_velocity, note_off_velocity=note_off_velocity, srate=srate))\n result = merge(result, instrument_sound, int(t * srate))\n t += duration * duration_multiplier\n return result","def scorepart_to_notes(part, morphetic_pitch=False):\n\n bm = part.beat_map\n\n if morphetic_pitch:\n pitch_attr = attrgetter('morphetic_pitch')\n else:\n pitch_attr = attrgetter('midi_pitch')\n notes = np.array([(bm(n.start.t), bm(n.end.t), pitch_attr(n))\n for n in part.notes], np.float)\n\n ids = np.array([n.id for n in part.notes])\n\n return notes, ids","def diminish(self):\n for n in self.notes:\n n.diminish()","def process_notes_in_song(dict_time_notes, seq_len = 50):\n list_of_dict_keys_time = []\n \n for key in dict_time_notes:\n sample = dict_time_notes[key]\n times = np.unique(np.where(sample > 0)[1])\n index = np.where(sample > 0)\n dict_keys_time = {}\n\n for time in times:\n index_where = np.where(index[1] == time)\n notes = index[0][index_where]\n dict_keys_time[time] = notes\n list_of_dict_keys_time.append(dict_keys_time)\n return list_of_dict_keys_time","def tag_lyrics(data):\r\n\r\n # Initialize list to store tagged lyrics\r\n tagged_documents = []\r\n\r\n # Tag lyrics for all the lyrics in the list\r\n for i, doc in enumerate(data):\r\n\r\n # Tag lyrics\r\n tagged = TaggedDocument(doc, [i])\r\n\r\n # Append tagged lyrics to\r\n tagged_documents.append(tagged)\r\n\r\n return tagged_documents","def model_inputs_and_outputs(self):\n\n # count relation instances\n total_rel_count = 0\n\n for note_path in glob.glob(self.text_dir + 'ID*_clinic_*'):\n\n # some notes weren't annotated\n if note_path not in self.note2rels:\n continue\n\n # to be broken into chunks later\n note_text = open(note_path).read()\n\n # iterate over note chunks\n for chunk_start, chunk_end in self.chunk_generator(note_text):\n\n # each event/time gets a number\n entity_num = 0\n\n # assign a number to each event and time\n time_offsets2num = {}\n event_offsets2num = {}\n\n # t5 i/o\n metadata = []\n rels_in_chunk = []\n\n # look for times and events in this chunk\n for time_start, time_end, time_id in self.note2times[note_path]:\n if time_start >= chunk_start and time_end <= chunk_end:\n time_offsets2num[(time_start, time_end)] = entity_num\n metadata.append('%s|%s' % (entity_num, time_id))\n entity_num += 1\n for event_start, event_end, event_id in self.note2events[note_path]:\n if event_start >= chunk_start and event_end <= chunk_end:\n event_offsets2num[(event_start, event_end)] = entity_num\n metadata.append('%s|%s' % (entity_num, event_id))\n entity_num += 1\n\n # combine time_offsets2num and event_offsets2num\n arg2num = dict(list(time_offsets2num.items()) +\n list(event_offsets2num.items()))\n\n targ2src = {} # map contained events to their containers\n for rel in self.note2rels[note_path]:\n src_start, src_end, targ_start, targ_end, src_id, targ_id = rel\n if src_start >= chunk_start and src_end <= chunk_end and \\\n targ_start >= chunk_start and targ_end <= chunk_end:\n targ2src[(targ_start, targ_end)] = (src_start, src_end)\n\n # map every event / time to its container (or none)\n sorted_args = sorted(arg2num.items(), key=lambda t: t[0][0])\n for (arg_start, arg_end), arg_num in sorted_args:\n if (arg_start, arg_end) in targ2src:\n # this target has a source (container)\n src_start, src_end = targ2src[(arg_start, arg_end)]\n src_num = arg2num[(src_start, src_end)]\n container = src_num\n else:\n container = '_' # no container\n rels_in_chunk.append('c(%s; %s)' % (arg_num, container))\n\n # add seq numbers and markers to events/times\n offset2str = {}\n for (start, end), entity_num in time_offsets2num.items():\n offset2str[start - chunk_start] = '<t> '\n offset2str[end - chunk_start] = '/' + str(entity_num) + ' </t>'\n for (start, end), entity_num in event_offsets2num.items():\n offset2str[start - chunk_start] = '<e> '\n offset2str[end - chunk_start] = '/' + str(entity_num) + ' </e>'\n chunk_text_with_markers = insert_at_offsets(\n note_text[chunk_start:chunk_end],\n offset2str)\n \n metadata_str = '||'.join(metadata)\n input_str = 'task: RELEXT; %s' % chunk_text_with_markers\n if len(rels_in_chunk) > 0:\n output_str = ' '.join(rels_in_chunk)\n else:\n output_str = 'no relations found'\n\n # counts inputs and outputs that t5 cannot handle\n if len(self.tokenizer(input_str).input_ids) > self.max_input_length:\n self.in_over_maxlen += 1\n if len(self.tokenizer(output_str).input_ids) > self.max_input_length:\n self.in_over_maxlen += 1\n\n self.inputs.append(input_str)\n self.outputs.append(output_str)\n self.metadata.append(metadata_str)\n\n print('%d total input/output pairs' % len(self.inputs))\n print('%d total relation instances' % total_rel_count)\n print('%d inputs over maxlen' % self.in_over_maxlen)\n print('%d outputs over maxlen' % self.out_over_maxlen)","def all_notes():\n \n return render_template('all_notes.html',colors=music_color,)","def get_note_child_list(self):\n return self.address_list","def remove_note(self, note, octave=-1):\n res = []\n for x in self.notes:\n if isinstance(note, six.string_types):\n if x.name != note:\n res.append(x)\n else:\n if x.octave != octave and octave != -1:\n res.append(x)\n else:\n if x != note:\n res.append(x)\n self.notes = res\n return res","def getNote(self):\n return (self.noteName[0], self.accidental, self.octave)","def _build_experiment_seq_embedded_list():\n antibody_embeds = DependencyEmbedder.embed_defaults_for_type(\n base_path='antibody',\n t='antibody')\n return (\n Experiment.embedded_list + antibody_embeds\n )","def midiToNotes(notes):\n names = ['C', 'C#', 'D', 'D#', 'E', 'F', 'F#', 'G', 'G#', 'A', 'A#', 'B']\n chord = []\n \n for note in notes:\n name = names[note%12]\n octave = int((note-12)/12)\n chord.append(name + str(octave))\n \n return chord","def notes(self):\r\n return notes.ForumSuggestionNotes(self)","def extract_notes(infile):\n\n # get patient ID\n subj_id = patient_id_from_file(infile)\n \n #get lab_events for this patient\n con = open_db()\n \n query = \\\n \"\"\"\n SELECT i.chartdate, i.charttime, i.description, i.category, i.text\n FROM noteevents i\n WHERE subject_id = {};\n \"\"\".format(subj_id)\n\n notes = pd.read_sql_query(query,con)\n \"\"\" change time stamp to seconds from origin \"\"\"\n \n origin = pd.to_datetime(wfdb.rdheader(infile).base_datetime)\n notes.insert(0, 'time', '')\n for idx, row in notes.iterrows():\n notes['time'].iloc[idx]=int((pd.to_datetime(row['charttime'])-origin).total_seconds())\n del notes['charttime']\n del notes['chartdate']\n\n return (notes)","def items(self):","def add_evernote_notes(self, evernote_notes, update=False, log_update_if_unchanged=True):\n new_nids=[]\n action_str_base = ['Add', 'Update'][update]\n action_str = ['Adding', 'Updating'][update]\n action_preposition = ['To', 'In'][update]\n info = stopwatch.ActionInfo(action_str + ' Of', 'Evernote Notes', action_preposition + ' Anki', report_if_empty=False)\n tmr = stopwatch.Timer(evernote_notes, 10, info=info,\n label='Add\\\\Anki-%sEvernoteNotes' % action_str_base)\n\n for ankiNote in evernote_notes:\n try:\n title = ankiNote.FullTitle\n content = decode(ankiNote.Content)\n anki_field_info = {\n FIELDS.TITLE: title,\n FIELDS.CONTENT: content,\n FIELDS.EVERNOTE_GUID: FIELDS.EVERNOTE_GUID_PREFIX + ankiNote.Guid,\n FIELDS.UPDATE_SEQUENCE_NUM: str(ankiNote.UpdateSequenceNum),\n FIELDS.SEE_ALSO: u''\n }\n except Exception:\n log_error(\"Unable to set field info for: Note '%s': '%s'\" % (ankiNote.FullTitle, ankiNote.Guid))\n log_dump(ankiNote.Content, \" NOTE CONTENTS \")\n # log_dump(encode(ankiNote.Content), \" NOTE CONTENTS \")\n raise\n tmr.step(title)\n baseNote = None\n if update:\n baseNote = self.get_anki_note_from_evernote_guid(ankiNote.Guid)\n if not baseNote:\n log_error('Updating note %s: COULD NOT FIND BASE NOTE FOR ANKI NOTE ID' % ankiNote.Guid)\n tmr.reportStatus(EvernoteAPIStatus.MissingDataError)\n continue\n if ankiNote.Tags is None:\n log_error(\"Could note find tags object for note %s: %s. \" % (ankiNote.Guid, ankiNote.FullTitle))\n tmr.reportStatus(EvernoteAPIStatus.MissingDataError)\n continue\n anki_note_prototype = AnkiNotePrototype(self, anki_field_info, ankiNote.TagNames, baseNote,\n notebookGuid=ankiNote.NotebookGuid, count=tmr.count,\n count_update=tmr.counts.updated.completed.val, max_count=tmr.max)\n anki_note_prototype._log_update_if_unchanged_ = log_update_if_unchanged\n nid = tmr.autoStep(anki_note_prototype.update_note() if update else anki_note_prototype.add_note(),\n ankiNote.FullTitle, update)\n if tmr.status.IsSuccess and not update:\n new_nids.append([nid, ankiNote.Guid])\n elif tmr.status.IsError:\n log(\"ANKI ERROR WHILE %s EVERNOTE NOTES: \" % action_str.upper() + str(tmr.status), tmr.label + '-Error')\n tmr.Report()\n if new_nids:\n ankDB().executemany(\"UPDATE {n} SET nid = ? WHERE guid = ?\", new_nids)\n return tmr.counts.success","def get_extras(self, text=None):\n if text is None:\n text = self.nltk_text(self.text)\n # Tag parts of speech\n tagged = nltk.pos_tag(text)\n # Try for composed NNP / NNPS\n is_proper_noun = False\n text = []\n proper_noun = \"\"\n for (word, tag) in tagged:\n if not is_proper_noun and (tag == 'NNP' or tag == 'NNPS'):\n # Start building a proper noun\n proper_noun = word\n # Set it true\n is_proper_noun = True\n # Add it to annotations anyway\n text.append(word)\n elif tag == 'NNP' or tag == 'NNPS':\n # Previous was proper noun. So it may be combined\n proper_noun += \" \" + word\n # Add the single word to annotations anyway\n text.append(word)\n elif is_proper_noun and tag == 'IN':\n # Add what we have by now to the text\n text.append(proper_noun)\n # Previous was proper noun. So it may be composed\n proper_noun += \" \" + word\n elif is_proper_noun:\n # Add what we have by now to the text\n text.append(proper_noun)\n # Finished with proper noun, so set it false\n is_proper_noun = False\n # Remove duplicates\n seen = {}\n result = []\n for w in text:\n if w in seen:\n continue\n seen[w] = 1\n result.append(w)\n # Eliminate common\n result = [w for w in result if w.lower() not in self.common_words and\n w.lower() not in stopwords.words('english')]\n return result","def extract_notes(music, resolution):\n # Check resolution\n assert music.resolution == resolution\n\n # Extract notes\n notes = []\n for track in music:\n if track.is_drum or track.program not in KNOWN_PROGRAMS:\n continue\n for note in track:\n beat, position = divmod(note.time, resolution)\n notes.append(\n (beat, position, note.pitch, note.duration, track.program)\n )\n\n # Deduplicate and sort the notes\n notes = sorted(set(notes))\n\n return np.array(notes)","def notes(self):\n return NotesTable(self.rpc, self.name)","def add_note(self, note, octave=None, dynamics=None):\n if dynamics is None:\n dynamics = {}\n if isinstance(note, six.string_types):\n if octave is not None:\n note = Note(note, octave, dynamics)\n elif len(self.notes) == 0:\n note = Note(note, 4, dynamics)\n else:\n if Note(note, self.notes[-1].octave) < self.notes[-1]:\n note = Note(note, self.notes[-1].octave + 1, dynamics)\n else:\n note = Note(note, self.notes[-1].octave, dynamics)\n if not hasattr(note, \"name\"):\n raise UnexpectedObjectError(\n \"Object '%s' was not expected. \"\n \"Expecting a mingus.containers.Note object.\" % note\n )\n if note not in self.notes:\n self.notes.append(note)\n self.notes.sort()\n return self.notes","def remove_notes(self, notes):\n if isinstance(notes, six.string_types):\n return self.remove_note(notes)\n elif hasattr(notes, \"name\"):\n return self.remove_note(notes)\n else:\n for x in notes:\n self.remove_note(x)\n return self.notes","def get_all_meals():","def accidentals(a_list):\n key = a_list.track.bars[0].key\n notes_in_key = get_notes(key.name)\n return [\n note for note in a_list\n if note.name not in notes_in_key\n and not note.is_rest\n ]","def label_notes(all_notes_lines):\n# nf = nemo.core.NeuralModuleFactory(backend=nemo.core.Backend.PyTorch, log_dir=None)\n #note_line_queries = notes.split('\\n')\n #note_line_queries = ['pt arrived obtunded not answering questions responding to voice and sternal rub speaking in garbled voice pupils unequal left 3mm and right 2mm brisk bilaterally trauma sicu MD aware currently recieving keppra IV finished dilantin gtt due for level at 08a EEG today LSCTA on 3LNC sats 100 % SBP 90 s to 100 s HR NSR no ectopy 60 s NS @ 75cc continuous +BS no stools rec d lactulose at OSH to recieve PR q4h abd soft non-tender non-distended foley in place draining adequate amt clear yellow urine skin intact left 20G x2 WNL wife Name NI']\n\n# labels_dict = get_vocab(LABELS_DICT)\n# pretrained_bert_model = nemo_nlp.nm.trainables.get_huggingface_model(\n# bert_config=BERT_CONFIG, pretrained_model_name=PRETRAINED_MODEL_NAME\n# )\n\n# tokenizer = nemo.collections.nlp.data.tokenizers.get_tokenizer(\n# tokenizer_name=TOKENIZER,\n# pretrained_model_name=PRETRAINED_MODEL_NAME,\n# tokenizer_model=TOKENIZER_MODEL,\n# )\n# hidden_size = pretrained_bert_model.hidden_size\n\n load_datalayer_begin_time = time.time()\n data_layer = nemo_nlp.nm.data_layers.BertTokenClassificationInferDataLayer(\n queries=all_notes_lines, tokenizer=tokenizer, max_seq_length=MAX_SEQ_LENGTH, batch_size=2000\n )\n load_datalayer_end_time = time.time()\n\n classifier = TokenClassifier(hidden_size=hidden_size, num_classes=len(labels_dict))\n\n input_ids, input_type_ids, input_mask, _, subtokens_mask = data_layer()\n \n load_hidden_states_begin_time = time.time()\n hidden_states = pretrained_bert_model(input_ids=input_ids, token_type_ids=input_type_ids, attention_mask=input_mask)\n load_hidden_states_end_time = time.time()\n load_logits_begin_time = time.time()\n logits = classifier(hidden_states=hidden_states)\n load_logits_end_time = time.time()\n\n ###########################################################################\n\n # Instantiate an optimizer to perform `infer` action\n infer_begin_time = time.time()\n evaluated_tensors = nf.infer(tensors=[logits, subtokens_mask], checkpoint_dir=CHECKPOINT_DIR)\n infer_end_time = time.time()\n\n logits, subtokens_mask = [concatenate(tensors) for tensors in evaluated_tensors]\n\n preds = np.argmax(logits, axis=2) \n all_notes_labeled_lines = []\n\n for i, query in enumerate(all_notes_lines):\n logging.info(f'Query: {query}')\n\n pred = preds[i][subtokens_mask[i] > 0.5]\n words = query.strip().split()\n\n #replaced with logic below instead of raising an error:\n '''\n if len(pred) != len(words):\n logging.info('Preds length: ' + str(len(preds[i])))\n logging.info('subtokens_mask length: ' + str(len(subtokens_mask[i])))\n logging.info('Pred length: ' + str(len(pred)))\n logging.info('words length: ' + str(len(words)))\n logging.info('Preds: ' + str(preds.tolist()))\n logging.info('subtokens_mask: ' + str(subtokens_mask[i]))\n logging.info('Pred:' + str(pred.tolist()))\n logging.info('words:' + str(words))\n\n labeled_note = '__Prediction/Word Mismatch__ pred length: ' + str(len(pred)) + ', words length: ' + str(len(words))\n break\n #raise ValueError('Pred and words must be of the same length')\n \n output = ''\n for j, w in enumerate(words):\n output += w\n label = labels_dict[pred[j]]\n if label != NONE_LABEL:\n label = add_brackets(label)\n output += label\n output += ' '\n labeled_note += '\\n' + output.strip()\n logging.info(f'Combined: {output.strip()}')\n\n '''\n\n if len(pred) == len(words):\n output = ''\n for j, w in enumerate(words):\n output += w\n label = labels_dict[pred[j]]\n if label != NONE_LABEL:\n label = add_brackets(label)\n output += label\n output += ' '\n all_notes_labeled_lines.append(output.strip())\n logging.info(f'Combined: {output.strip()}')\n else:\n all_notes_labeled_lines.append(query)\n pred_length = str(len(pred))\n word_length = str(len(words))\n logging.info(f'__Prediction/Word Length Mismatch__ pred length: {pred_length}, words length: {word_length}')\n logging.info(f'{query}')\n \n\n print(str(load_datalayer_end_time-load_datalayer_begin_time)+' seconds to load the datalayer')\n print(str(load_hidden_states_end_time-load_hidden_states_begin_time)+' seconds to load hidden states')\n print(str(load_logits_end_time-load_logits_begin_time)+' seconds to load logits')\n print(str(infer_end_time-infer_begin_time)+' seconds to run inference')\n\n return all_notes_labeled_lines","def placeNoteLists(self, text):\n if self.notes:\n o = OrderedDict()\n for label, info in self.notes.items():\n if 'seq' in info:\n i = info['seq']\n info['seq'] = label\n o[i] = info\n else:\n self.unreferencedNotes[label] = info\n\n if o:\n # sort o by key\n o = OrderedDict(sorted(o.items(), key=lambda t: t[0]))\n self.notes = o\n text_re = re.compile('<p>notelist(%s)(?:\\:([\\w|%s]))?([\\^!]?)(\\+?)\\.?[\\s]*</p>'\n % (self.c, self.syms), re.U)\n text = text_re.sub(self.fNoteLists, text)\n return text","def records(self):\r\n raise NotImplementedError()","def remove_tempo(self):\n return Melody([n.remove_tempo() for n in self.notes], nb_bars=self.nb_bars, tags=set(self.tags))","def from_notes(self, notes, bpm=120.0, gap=16, ignore_polyphonic_notes=False):\n self._clear()\n\n # Compute quantization steps per second.\n steps_per_second = bpm / 60.0 * self.steps_per_bar / BEATS_PER_BAR\n\n quantize = lambda x: int(math.ceil(x - QUANTIZE_CUTOFF))\n\n # Sort track by note start times.\n notes.sort(key=lambda note: note.start_time)\n for note in notes:\n # Ignore 0 velocity notes.\n if not note.velocity:\n continue\n\n # Quantize the start and end times of the note.\n start_step = quantize(note.start_time * steps_per_second)\n end_step = quantize(note.end_time * steps_per_second)\n if end_step == start_step:\n end_step += 1\n\n # Do not allow notes to start or end in negative time.\n if start_step < 0 or end_step < 0:\n raise BadNoteException(\n 'Got negative note time: start_time = %s, end_time = %s'\n % (note.start_time, note.end_time))\n\n # If start_step comes before or lands on an already added note's start\n # step, we cannot add it. Discard the melody because it is not monophonic.\n if not self._can_add_note(start_step):\n if ignore_polyphonic_notes:\n continue\n else:\n self._clear()\n raise PolyphonicMelodyException()\n\n # If a gap of `gap` or more steps is found, end the melody.\n if (len(self) and\n self._distance_to_last_event(start_step) >= gap):\n break\n\n # Add the note-on and off events to the melody.\n self._add_note(note.pitch, start_step, end_step)\n\n self._write_all_notes()","def _getitem_augmentation(self):\n n_tracks = len(self.tracks)\n track_indices = random.choices(range(n_tracks), k=len(self.sources))\n\n sources = []\n\n for _source, trackID in zip(self.sources, track_indices):\n track = self.tracks[trackID]\n source_path = track['path'][_source]\n track_samples = track['samples_original']\n\n start = random.randint(0, track_samples - self.samples - 1)\n source, _ = torchaudio.load(source_path, frame_offset=start, num_frames=self.samples)\n\n # Apply augmentation\n source = self.augmentation(source)\n sources.append(source.unsqueeze(dim=0))\n \n if type(self.target) is list:\n target = []\n for _target in self.target:\n source_idx = self.sources.index(_target)\n _target = sources[source_idx]\n target.append(_target)\n target = torch.cat(target, dim=0)\n\n sources = torch.cat(sources, dim=0)\n mixture = sources.sum(dim=0, keepdim=True)\n else:\n source_idx = self.sources.index(self.target)\n target = sources[source_idx]\n target = target.squeeze(dim=0)\n\n sources = torch.cat(sources, dim=0)\n mixture = sources.sum(dim=0)\n\n return mixture, target","def annotations(self) -> Iterator['Annotation']:\n return itertools.chain(self.footnotecitations.all(),\n self.cites.all(),\n self.externallinks.all(),\n self.inlinerequirements.all())","def _mixed_documents(self, raw_documents) -> List[str]:\n if not self._ner:\n return raw_documents\n\n mixed_documents = []\n for doc in raw_documents:\n entities = [\n \"{\" + entity[\"type\"] + \"}\" for entity in self._ner.process(doc)]\n if entities:\n mixed_documents.append(\" \".join(entities) + \" \" + doc)\n else:\n mixed_documents.append(doc)\n\n return mixed_documents","def get_observations(self):\n # Check hyper_params and criterion\n if len(self.hyper_params) == 0:\n raise AssertionError('!! Hyper-Parameters has not been set.')\n # Check criterion\n if self.criterion is None:\n raise AssertionError(\n '!! Criterion for hyper-parameter searching has not been set.')\n # Fetch notes\n notes = self.summary_fetcher()\n if len(notes) == 0: return []\n # Peel of Note wrapper\n observations = []\n for note in notes:\n # Every note in the note list must contain the criterion\n if self.criterion not in note.criteria: raise AssertionError(\n '!! Every note must contain the criterion `{}`'.format(self.criterion))\n # This note will be ignored if it does not contain all the information\n # in self.hyper_params or the config value is not within the range\n if not all([hp.name in note.configs and hp.within(note.configs[hp.name])\n for hp in self.hyper_params]): continue\n # Gather observation\n od = OrderedDict()\n # self.scroll.hyper_params.values() may have been found themselves\n for hp in self.scroll.hyper_params.values():\n assert isinstance(hp, HyperParameter)\n od[hp] = note.configs[hp.name]\n # Organize the observation list as a list of tuples\n observations.append((od, note.criteria[self.criterion]))\n return observations"],"string":"[\n \"def augment(self):\\n for n in self.notes:\\n n.augment()\",\n \"def notes(self):\\n return reapy.NoteList(self)\",\n \"def add_notes(self, notes):\\n if hasattr(notes, \\\"notes\\\"):\\n for x in notes.notes:\\n self.add_note(x)\\n return self.notes\\n elif hasattr(notes, \\\"name\\\"):\\n self.add_note(notes)\\n return self.notes\\n elif isinstance(notes, six.string_types):\\n self.add_note(notes)\\n return self.notes\\n for x in notes:\\n if isinstance(x, list) and len(x) != 1:\\n if len(x) == 2:\\n self.add_note(x[0], x[1])\\n else:\\n self.add_note(x[0], x[1], x[2])\\n else:\\n self.add_note(x)\\n return self.notes\",\n \"def getnotes():\",\n \"def return_markers(self):\\r\\n ent_file = join(self.filename, self._basename + '.ent')\\r\\n if not exists(ent_file):\\r\\n ent_file = join(self.filename, self._basename + '.ent.old')\\r\\n\\r\\n try:\\r\\n ent_notes = _read_ent(ent_file)\\r\\n\\r\\n except (FileNotFoundError, PermissionError):\\r\\n markers = []\\r\\n\\r\\n else:\\r\\n allnote = []\\r\\n for n in ent_notes:\\r\\n try:\\r\\n n['value'].keys()\\r\\n allnote.append(n['value'])\\r\\n except AttributeError:\\r\\n lg.debug('Note of length {} was not '\\r\\n 'converted to dict'.format(n['length']))\\r\\n\\r\\n s_freq = self._hdr['erd']['sample_freq']\\r\\n pcname = '0CFEBE72-DA20-4b3a-A8AC-CDD41BFE2F0D'\\r\\n note_time = []\\r\\n note_name = []\\r\\n note_note = []\\r\\n for n in allnote:\\r\\n if n['Text'] == 'Analyzed Data Note':\\r\\n continue\\r\\n if not n['Text']:\\r\\n continue\\r\\n if 'User' not in n['Data'].keys():\\r\\n continue\\r\\n user1 = n['Data']['User'] == 'Persyst'\\r\\n user2 = False # n['Data']['User'] == 'eeg'\\r\\n user3 = n['Data']['User'] == pcname\\r\\n user4 = n['Data']['User'] == 'XLSpike - Intracranial'\\r\\n user5 = n['Data']['User'] == 'XLEvent - Intracranial'\\r\\n if user1 or user2 or user3 or user4 or user5:\\r\\n continue\\r\\n if len(n['Data']['User']) == 0:\\r\\n note_name.append('-unknown-')\\r\\n else:\\r\\n note_name.append(n['Data']['User'].split()[0])\\r\\n note_time.append(n['Stamp'] / s_freq)\\r\\n note_note.append(n['Text'])\\r\\n\\r\\n markers = []\\r\\n for time, name, note in zip(note_time, note_name, note_note):\\r\\n m = {'name': note + ' (' + name + ')',\\r\\n 'start': time,\\r\\n 'end': time,\\r\\n 'chan': None,\\r\\n }\\r\\n markers.append(m)\\r\\n\\r\\n return markers\",\n \"def listNotes() -> list:\\n list_of_notes = []\\n for note in Note.objects.all():\\n list_of_notes.append({\\n 'uuid': note.uuid, 'title': note.title,\\n 'author': note.author, 'body': note.body, 'created_at': localtime(note.created_at)\\n })\\n return list_of_notes\",\n \"def get_note():\\n\\n return Note.query.all()\",\n \"def view_notes(tag_list):\\n final_notes = []\\n for tag in tag_list:\\n all_notes = Tags.objects.get(tag_text=tag)\\n notes = all_notes.notes.all()\\n for note in notes:\\n final_notes.append(note.note_text)\\n \\n return final_notes\",\n \"def get_notes():\\n notes = []\\n durs = []\\n\\n for file in glob.glob(\\\"D:\\\\\\\\anthems\\\\\\\\data\\\\\\\\*.mid\\\"):\\n midi = converter.parse(file)\\n\\n print(\\\"Parsing %s\\\" % file)\\n\\n notes_to_parse = None\\n\\n try:\\n s2 = instrument.partitionByInstrument(midi)\\n notes_to_parse = s2.parts[0].recurse() \\n except:\\n notes_to_parse = midi.flat.notes\\n \\n for element in notes_to_parse:\\n if isinstance(element, note.Note):\\n notes.append(str(element.pitch))\\n durs.append(element.duration.quarterLength)\\n elif isinstance(element, chord.Chord):\\n notes.append('.'.join(str(n) for n in element.normalOrder))\\n durs.append(element.duration.quarterLength)\\n\\n return notes, durs\",\n \"def get_note_names(self):\\n res = []\\n for n in self.notes:\\n if n.name not in res:\\n res.append(n.name)\\n return res\",\n \"def getNotes(self):\\n return self._nednotes, self._ongcnotes\",\n \"def notes(self):\\r\\n return notes.Notes(self)\",\n \"def notes(self):\\r\\n return notes.Notes(self)\",\n \"def get_objects_using_notes():\\n global objects_using_notes\\n\\n if not objects_using_notes:\\n objects_using_notes = rsh.get_objects_using_notes(get_srcs())\\n\\n return objects_using_notes\",\n \"def notes_played(self) -> List[Dict]:\\n return [\\n dict(note=self._midi_to_note(note.note), time=note.time)\\n for note in self._on_note()\\n ]\",\n \"def return_augmented_sentences(self) -> list:\\n return self.augmented_sentence_list\",\n \"def notes(self):\\n return self._notes\",\n \"def processNotes(self, inMeasureElem):\\n inNotes = inMeasureElem.findall(\\\"note\\\")\\n # list to hold array of processed note elements\\n lstOutNotes = []\\n\\n for inNote in inNotes:\\n for childNode in inNote:\\n outNote = ET.Element(\\\"note\\\")\\n\\n # rest\\n inRest = inNote.find(\\\"rest\\\")\\n if (inRest is not None):\\n outRest = ET.Element(\\\"rest\\\")\\n outNote.append(outRest)\\n\\n # chord\\n inChord = inNote.find(\\\"chord\\\")\\n if (inChord is not None):\\n outChord = ET.Element(\\\"chord\\\")\\n outNote.append(outChord)\\n\\n # pitch\\n inPitch = inNote.find(\\\"pitch\\\")\\n if (inPitch is not None):\\n outPitch = ET.Element(\\\"pitch\\\")\\n for childNode in inPitch:\\n if (True == (childNode.tag in [\\\"step\\\", \\\"octave\\\"])):\\n pitchSubElem = ET.Element(childNode.tag)\\n pitchSubElem.text = childNode.text\\n outPitch.append(pitchSubElem)\\n # handle the \\\"alter\\\" child tag separately even if the alter value is 0 include it otherwise\\n # include the original scores alter value\\n inPitchAlter = inPitch.find(\\\"alter\\\")\\n if (inPitchAlter is not None):\\n # use sources alter elem\\n outPitchAlter = ET.Element(\\\"alter\\\")\\n outPitchAlter.text = inPitchAlter.text\\n outPitch.append(outPitchAlter)\\n else:\\n # make a zero value alter element\\n outPitchAlter = ET.Element(\\\"alter\\\")\\n outPitchAlter.text = \\\"0\\\"\\n outPitch.append(outPitchAlter)\\n\\n outNote.append(outPitch)\\n\\n # duration\\n inDuration = inNote.find(\\\"duration\\\")\\n if (inDuration is not None):\\n outDuration = ET.Element(\\\"duration\\\")\\n outDurationVal = int(inDuration.text) * self._multiplier\\n outDuration.text = str(int(outDurationVal))\\n outNote.append(outDuration)\\n\\n # append the mote to the list of notes\\n lstOutNotes.append(outNote)\\n\\n # finally return the list of notes\\n return lstOutNotes\",\n \"def notes(self):\\n return self.__notes\",\n \"def notes(self):\\n return notes.Notes(self)\",\n \"def __init__(self):\\n self.notes = []\",\n \"def get_notes(n_notes=3):\\n notes = []\\n \\n for ii, file in enumerate(glob.glob(\\\"data/maestro-v2.0.0/2004/*.midi\\\")):\\n if ii >= n_notes:\\n break\\n pickle_file_name = file[:-4] + 'pkl'\\n\\n if os.path.isfile(pickle_file_name):\\n print(f'Reading parsed file: {pickle_file_name}')\\n with open(pickle_file_name, 'rb') as handle:\\n midi = pickle.load(handle)\\n else:\\n midi = converter.parse(file)\\n\\n with open(pickle_file_name, 'wb') as handle:\\n print(f'writing parsed file: {pickle_file_name}')\\n unserialized_data = pickle.dump(midi, \\n handle, \\n protocol=pickle.HIGHEST_PROTOCOL\\n )\\n\\n\\n print(\\\"Parsing %s\\\" % file)\\n\\n notes_to_parse = None\\n try: # file has instrument parts\\n s2 = instrument.partitionByInstrument(midi)\\n notes_to_parse = s2.parts[0].recurse() \\n except: # file has notes in a flat structure\\n notes_to_parse = midi.flat.notes\\n \\n for element in notes_to_parse:\\n if isinstance(element, note.Note):\\n notes.append(str(element.pitch))\\n elif isinstance(element, chord.Chord):\\n notes.append('.'.join(str(n) for n in element.normalOrder))\\n \\n return notes\",\n \"def getNotes(self):\\n return self.__notes\",\n \"def notes_to_annotations(self):\\n\\n for sub_dir, text_name, file_names in anafora.walk(self.xml_dir, self.xml_regex):\\n note_path = os.path.join(self.text_dir, text_name)\\n xml_path = os.path.join(self.xml_dir, sub_dir, file_names[0])\\n ref_data = anafora.AnaforaData.from_file(xml_path)\\n\\n # collect (annot_start, annot_end, annot_id) tuples\\n add_annotations(self.note2times[note_path], ref_data, 'TIMEX3')\\n add_annotations(self.note2times[note_path], ref_data, 'SECTIONTIME')\\n add_annotations(self.note2times[note_path], ref_data, 'DOCTIME')\\n add_annotations(self.note2events[note_path], ref_data, 'EVENT')\\n\\n # collect (src spans, targ spans, src id, targ id) tuples\\n for rel in ref_data.annotations.select_type('TLINK'):\\n src = rel.properties['Source']\\n targ = rel.properties['Target']\\n label = rel.properties['Type']\\n if label == 'CONTAINS':\\n src_start, src_end = src.spans[0]\\n targ_start, targ_end = targ.spans[0]\\n self.note2rels[note_path].append(\\n (src_start, src_end, targ_start, targ_end, src.id, targ.id))\\n\\n # sort relation tuples by src arguments' offsets\\n # self.note2rels[note_path].sort(key=lambda t: t[0])\",\n \"def notes(self):\\n return Notes(self)\",\n \"def remove_duplicate_notes(self):\\n res = []\\n for x in self.notes:\\n if x not in res:\\n res.append(x)\\n self.notes = res\\n return res\",\n \"def fNoteLists(self, match):\\n att, start_char, g_links, extras = match.groups()\\n start_char = start_char or 'a'\\n index = '%s%s%s' % (g_links, extras, start_char)\\n result = ''\\n\\n if index not in self.notelist_cache:\\n o = []\\n if self.notes:\\n for seq, info in self.notes.items():\\n links = self.makeBackrefLink(info, g_links, start_char)\\n atts = ''\\n if 'def' in info:\\n infoid = info['id']\\n atts = info['def']['atts']\\n content = info['def']['content']\\n li = (\\\"\\\"\\\"\\\\t<li%s>%s<span id=\\\"note%s\\\"> </span>%s</li>\\\"\\\"\\\"\\n % (atts, links, infoid, content))\\n else:\\n li = (\\\"\\\"\\\"\\\\t<li%s>%s Undefined Note [#%s].<li>\\\"\\\"\\\" %\\n (atts, links, info['seq']))\\n o.append(li)\\n if '+' == extras and self.unreferencedNotes:\\n for seq, info in self.unreferencedNotes.items():\\n if info['def']:\\n atts = info['def']['atts']\\n content = info['def']['content']\\n li = \\\"\\\"\\\"\\\\t<li%s>%s</li>\\\"\\\"\\\" % (atts, content)\\n o.append(li)\\n self.notelist_cache[index] = u\\\"\\\\n\\\".join(o)\\n result = self.notelist_cache[index]\\n if result:\\n list_atts = self.pba(att)\\n result = \\\"\\\"\\\"<ol%s>\\\\n%s\\\\n</ol>\\\"\\\"\\\" % (list_atts, result)\\n return result\",\n \"def reconstruct(notes, resolution):\\n # Construct the MusPy Music object\\n music = muspy.Music(resolution=resolution, tempos=[muspy.Tempo(0, 100)])\\n\\n # Append the tracks\\n programs = sorted(set(note[-1] for note in notes))\\n for program in programs:\\n music.tracks.append(muspy.Track(program))\\n\\n # Append the notes\\n for beat, position, pitch, duration, program in notes:\\n time = beat * resolution + position\\n track_idx = programs.index(program)\\n music[track_idx].notes.append(muspy.Note(time, pitch, duration))\\n\\n return music\",\n \"def extract_notes(bars):\\n output = []\\n for bar in bars:\\n notes = np.array([bar_to_notes(b,i) for i,b in enumerate(bar)])\\n\\n # Flatten into a single array\\n midi_notes = []\\n for i in range(notes.shape[1]):\\n if all(notes[:,i] == -1):\\n midi_notes.append(None)\\n else:\\n midi_notes.append([n for n in notes[:,i] if n!=-1])\\n output.append(midi_notes)\\n return output\",\n \"def notes_list():\\n if request.method == \\\"POST\\\":\\n note = str(request.data.get(\\\"text\\\", \\\"\\\"))\\n idx = max(notes.keys()) + 1\\n notes[idx] = note\\n return note_repr(idx), status.HTTP_201_CREATED\\n\\n # request.method == 'GET'\\n return [note_repr(idx) for idx in sorted(notes.keys())]\",\n \"def inverse_notes(note_list):\\n raw_output = []\\n for note in note_list:\\n # We transpose every notes except for Z\\n if note.is_pause:\\n raw_output.append(f'{note.name}{note.figure}')\\n else:\\n # Get the index of the note to transpose\\n note_name_index = NOTE_NAMES.index(note.name)\\n # Get the transposed index according to the amount\\n inverted_index = ((len(NOTE_NAMES) - note_name_index) % len(NOTE_NAMES))\\n new_note_name = NOTE_NAMES[inverted_index]\\n # Create the new note, with the new note's name, and its original figure\\n new_note = f'{new_note_name}{note.figure}'\\n new_note += 'p' if note.has_point else ''\\n raw_output.append(new_note)\\n\\n return raw_output\",\n \"def objects(self):\",\n \"def PublicNotes(self, default=[{}]):\\n tmp = self.data.get('metadata', {}).get('public_notes', default)\\n return [HEP.SourcedValueObject(i) for i in tmp]\",\n \"def extract_feature_notes(contig, type, start, stop, direction, notes, pos, ref, alt):\\n notes = re.sub(\\\"%\\\\d+\\\", \\\" \\\", notes)\\n desc = re.search(\\\"Note=(.*?);\\\", notes)\\n id = re.search(\\\"ID=(.*?);\\\", notes)\\n name = re.search(\\\"Name=(.*?);\\\", notes)\\n\\n if id and name and desc:\\n print ('Contig: ' + contig)\\n print ('Annotation_type: ' + type)\\n print ('Start:Stop:Direction '+ start+':'+stop+':'+direction)\\n print (\\\"Position: \\\" + pos)\\n print ('Ref:Alt: ' + ref + ':'+ alt)\\n print(\\\"ID: \\\" + id.group(1))\\n # print(\\\"Name: \\\" + name.group(1))\\n print(\\\"Description: \\\" + desc.group(1))\\n # if name:\\n # print(\\\"Name: \\\" + name.group(1))\\n #\\n # if desc:\\n # print(\\\"Desc: \\\" + desc.group(1))\\n\\n\\n print()\",\n \"def test_list_notes(self):\\n pass\",\n \"def note_list(request):\\n user = request.user\\n notes = Note.objects.filter(author=user)\\n serializer = NoteSerializer(notes, many=True)\\n return Response(serializer.data)\",\n \"def expose(self):\\n arts = [a.art() for a in self.artists]\\n return arts\",\n \"def extended(self) -> List:\\n raise NotImplementedError\",\n \"def _get_issue_notes(request, pk):\\n if request.user.is_coordinator_or_better:\\n note_types = IssueNote.COORDINATOR_NOTE_TYPES\\n else:\\n note_types = IssueNote.PARALEGAL_NOTE_TYPES\\n\\n return (\\n IssueNote.objects.filter(issue=pk)\\n .prefetch_related(\\\"creator__groups\\\")\\n .filter(note_type__in=note_types)\\n .order_by(\\\"-created_at\\\")\\n .all()\\n )\",\n \"def getNotes(self, *args):\\n return _libsbml.SBase_getNotes(self, *args)\",\n \"def append_collectable(self, newnotes: List):\\n self.notes.extend(newnotes)\",\n \"def get_notes():\\n song_index_to_notes = {}\\n\\n for file in glob.glob(\\\"../data/midi/*.mid\\\"):\\n midi = converter.parse(file)\\n song_index = int(os.path.splitext(os.path.basename(file))[0])\\n #print(\\\"Parsing %s with an index %d\\\" % (file, song_index))\\n\\n notes_to_parse = None\\n notes = []\\n try: # file has instrument parts\\n s2 = instrument.partitionByInstrument(midi)\\n notes_to_parse = s2.parts[0].recurse()\\n except: # file has notes in a flat structure\\n notes_to_parse = midi.flat.notes\\n\\n for element in notes_to_parse:\\n if isinstance(element, note.Note):\\n notes.append(str(element.pitch))\\n elif isinstance(element, chord.Chord):\\n notes.append('.'.join(str(n) for n in element.normalOrder))\\n\\n song_index_to_notes[song_index] = notes\\n\\n return song_index_to_notes\",\n \"def _on_note(self) -> Iterator:\\n return filter(\\n lambda x: x.type == constants.NOTE_ON, self.midi_data)\",\n \"def get_note_alternatives(self, id):\\n notes = self.session.query(models.Note).filter(\\n models.Note.conflict_parent_id == id,\\n ).all()\\n return btype.Note.list >> notes\",\n \"def to_sequence(self, chord, inst):\\n time = 0\\n sequence = []\\n for note in self.notes:\\n pitch = chord.to_pitch(note)\\n start = time\\n end = time + note.duration\\n sequence.append([start, end, pitch, chord.to_chord(), inst, note])\\n time += note.duration\\n return sequence\",\n \"def get_notes(self, note_limit=200):\\n return Note.get_by_person_record_id(\\n self.subdomain, self.record_id, limit=note_limit)\",\n \"def getNotes(self):\\n logger.debug(\\\"Func: getNotes\\\")\\n\\n return self._currentNotes\",\n \"def get_makerNotes(img, field_name='MakerNote'):\\n exif_data = img._getexif()\\n keys = list(ExifTags.TAGS.keys())\\n vals = list(ExifTags.TAGS.values())\\n exif_index = vals.index(field_name)\\n exif_num = keys[exif_index]\\n MakerNote = exif_data[exif_num]\\n return MakerNote\",\n \"def examples(self):\\n for obj_ind in range(len(self.objects)):\\n yield self.get_object_intent_by_index(obj_ind)\",\n \"def test_list_note_occurrences(self):\\n pass\",\n \"def notes(self):\\r\\n return notes.UserNotes(self)\",\n \"def getMetadataList(self):\\n return [ x.toTuple() for x in self.metadata ]\",\n \"def bar_to_lilypond_notes(notes):\\n lp_notes = []\\n if notes[0] is None:\\n notes = notes[1:]\\n if notes[0] is None:\\n lp_notes.append(\\\"r\\\")\\n for n in notes:\\n if n is None:\\n continue\\n if type(n) is list:\\n lp_notes.append([midi_to_lilypond_note(x) for x in n])\\n else:\\n lp_notes.append(midi_to_lilypond_note(n))\\n return lp_notes\",\n \"def remove_pedal(self):\\n return Melody([n.remove_pedal() for n in self.notes], nb_bars=self.nb_bars, tags=set(self.tags))\",\n \"def ordered_notes(self, obj, sig_features, tabs):\\n supports = dict(\\n [(str(support.id), support) for support in obj.all_supports])\\n notes = []\\n for browsers in sig_features.values():\\n for section in tabs:\\n for browser in section['browsers']:\\n sig_supports = browsers.get(browser, [])\\n for sig_support_pk in sig_supports:\\n support = supports[sig_support_pk]\\n if support.note:\\n notes.append(sig_support_pk)\\n return OrderedDict((note, i) for i, note in enumerate(notes, 1))\",\n \"def get_notes_from_uuids(dictionary, uuid_list):\\n titles = []\\n texts = []\\n notes = []\\n for key in dictionary:\\n if dictionary[key][\\\"content_type\\\"] == \\\"Note\\\" and dictionary[key][\\\"uuid\\\"] in uuid_list:\\n try:\\n titles.append(dictionary[key][\\\"content\\\"][\\\"title\\\"])\\n texts.append(dictionary[key][\\\"content\\\"][\\\"text\\\"])\\n notes = list(zip(titles, texts))\\n except KeyError:\\n continue\\n return notes\",\n \"def _build_experiment_repliseq_embedded_list():\\n antibody_embeds = DependencyEmbedder.embed_defaults_for_type(\\n base_path='antibody',\\n t='antibody')\\n return (\\n Experiment.embedded_list + antibody_embeds\\n )\",\n \"def get_raw(self):\\n \\n out_items = []\\n for order in self.order_lst:\\n out_items += [i.get_metadata() for i in order.get_items()]\\n \\n return out_items\",\n \"def ls(self, count = 200):\\n return self._manager.ls_notes(self['id'], count)\",\n \"def _modify_all_notes(self):\\n return self._modify_notes_in_time(TimeStep(0.0, MAX_CLIP_LENGTH), self._clip_notes, self._length_offset)\",\n \"def _get_embedded_objects(self):\\n return [getattr(self, name) for name, field in self._get_fields().items() if isinstance(field, fields.Object)]\",\n \"def transform(self, X: list[DelayedSample]) -> list[DelayedSample]:\\n if self.annotation_directory is None:\\n return None\\n\\n annotated_samples = []\\n for x in X:\\n # we use .key here because .path might not be unique for all\\n # samples. Also, the ``bob bio annotate-samples`` command dictates\\n # how annotations are stored.\\n annotation_file = os.path.join(\\n self.annotation_directory, x.key + self.annotation_extension\\n )\\n\\n annotated_samples.append(\\n DelayedSample.from_sample(\\n x,\\n delayed_attributes=dict(\\n annotations=lambda: read_annotation_file(\\n annotation_file, self.annotation_type\\n )\\n ),\\n )\\n )\\n\\n return annotated_samples\",\n \"def note_list(self, evarnames=\\\"\\\", in_=None):\\n evarnames = self._find_vars(evarnames, evars=True, \\n unique=True, empty_ok=True)\\n if len(evarnames) == 0:\\n evarnames = ['_dta'] + self._varlist\\n if in_ is not None:\\n if isinstance(in_, int):\\n in_ = (in_,)\\n elif (not isinstance(in_, collections.Iterable)\\n or not all(isinstance(n, int) for n in in_)):\\n raise TypeError(\\\"in_ should be int or iterable of int\\\")\\n if any(n <= 0 for n in in_):\\n raise ValueError(\\\"note numbers must be >= 1\\\")\\n for name in evarnames:\\n if name not in self._chrdict: continue\\n chars = self._chrdict[name]\\n if 'note0' not in chars: continue\\n nnotes = int(chars['note0'])\\n in_range = in_ if in_ is not None else range(1, nnotes + 1)\\n note_info = []\\n for note_num in in_range:\\n note_name = 'note' + str(note_num)\\n if note_name in chars:\\n note_info.append(note_num)\\n if note_info != []:\\n note_info.insert(0, name)\\n self._display_notes(note_info)\",\n \"def items(self):\\n return iter(self._noise_objs.items())\",\n \"def snippetList(requeset, format = None):\",\n \"def _parse(self, array):\\n return [self._parse_note(x) for x in array]\",\n \"def midiToNotesRec(notes, totalnotes, note_names=[]):\\n names = ['C', 'C#', 'D', 'D#', 'E', 'F', 'F#', 'G', 'G#', 'A', 'A#', 'B']\\n \\n #print(note_names)\\n \\n def recMidiToNotes(notes, note_namesr=[]):\\n if notes == []:\\n return\\n \\n head = notes.pop(0)\\n name = names[head%12]\\n octave = int((head-12)/12)\\n note_namesr.append(name + str(octave))\\n print('rec names:', note_namesr)\\n return note_namesr\\n \\n if len(note_names) == totalnotes:\\n return note_names\\n \\n midiToNotes(notes, totalnotes, recMidiToNotes(notes, note_names))\",\n \"def all_note_by_job_applied_id(job_applied_id):\\n\\n return Note.query.filter(Note.job_applied_id == job_applied_id, Note.note_category == 'Note' ).all()\",\n \"def get_animals(self):\\n for animal in self.animals:\\n print(animal)\",\n \"def notes_to_sound(instrument, track, duration_multiplier=1, srate=None):\\n srate = get_srate(srate)\\n duration = 1.0\\n note_duration = 1.0\\n note_on_velocity = 0.7\\n note_off_velocity = 0.5\\n t = 0.0\\n result = []\\n for item in track:\\n if isinstance(item, Note):\\n if item.duration is None:\\n item.duration = duration\\n else:\\n duration = item.duration\\n if hasattr(duration, '__getitem__'):\\n if len(duration) == 1:\\n duration = duration[0]\\n else:\\n duration, note_duration = duration\\n item.duration = note_duration\\n else:\\n note_duration = duration\\n if item.note_on_velocity is None:\\n item.note_on_velocity = note_on_velocity\\n else:\\n note_on_velocity = item.note_on_velocity\\n if item.note_off_velocity is None:\\n item.note_off_velocity = note_off_velocity\\n else:\\n note_off_velocity = item.note_off_velocity\\n item.multiply_duration(duration_multiplier)\\n instrument_sound = instrument(item)\\n elif isinstance(item, Rest):\\n if item.duration is not None:\\n duration = item.duration\\n instrument_sound = []\\n else:\\n if hasattr(item, '__getitem__'):\\n pitch = item[0]\\n if len(item) > 1:\\n duration = item[1]\\n if hasattr(duration, '__getitem__'):\\n if len(duration) == 1:\\n duration = duration[0]\\n else:\\n duration, note_duration = duration\\n else:\\n note_duration = duration\\n if len(item) > 2:\\n note_on_velocity = item[2]\\n if len(item) > 3:\\n note_off_velocity = item[3]\\n else:\\n pitch = item\\n instrument_sound = instrument(Note(pitch=pitch, duration=note_duration * duration_multiplier, note_on_velocity=note_on_velocity, note_off_velocity=note_off_velocity, srate=srate))\\n result = merge(result, instrument_sound, int(t * srate))\\n t += duration * duration_multiplier\\n return result\",\n \"def scorepart_to_notes(part, morphetic_pitch=False):\\n\\n bm = part.beat_map\\n\\n if morphetic_pitch:\\n pitch_attr = attrgetter('morphetic_pitch')\\n else:\\n pitch_attr = attrgetter('midi_pitch')\\n notes = np.array([(bm(n.start.t), bm(n.end.t), pitch_attr(n))\\n for n in part.notes], np.float)\\n\\n ids = np.array([n.id for n in part.notes])\\n\\n return notes, ids\",\n \"def diminish(self):\\n for n in self.notes:\\n n.diminish()\",\n \"def process_notes_in_song(dict_time_notes, seq_len = 50):\\n list_of_dict_keys_time = []\\n \\n for key in dict_time_notes:\\n sample = dict_time_notes[key]\\n times = np.unique(np.where(sample > 0)[1])\\n index = np.where(sample > 0)\\n dict_keys_time = {}\\n\\n for time in times:\\n index_where = np.where(index[1] == time)\\n notes = index[0][index_where]\\n dict_keys_time[time] = notes\\n list_of_dict_keys_time.append(dict_keys_time)\\n return list_of_dict_keys_time\",\n \"def tag_lyrics(data):\\r\\n\\r\\n # Initialize list to store tagged lyrics\\r\\n tagged_documents = []\\r\\n\\r\\n # Tag lyrics for all the lyrics in the list\\r\\n for i, doc in enumerate(data):\\r\\n\\r\\n # Tag lyrics\\r\\n tagged = TaggedDocument(doc, [i])\\r\\n\\r\\n # Append tagged lyrics to\\r\\n tagged_documents.append(tagged)\\r\\n\\r\\n return tagged_documents\",\n \"def model_inputs_and_outputs(self):\\n\\n # count relation instances\\n total_rel_count = 0\\n\\n for note_path in glob.glob(self.text_dir + 'ID*_clinic_*'):\\n\\n # some notes weren't annotated\\n if note_path not in self.note2rels:\\n continue\\n\\n # to be broken into chunks later\\n note_text = open(note_path).read()\\n\\n # iterate over note chunks\\n for chunk_start, chunk_end in self.chunk_generator(note_text):\\n\\n # each event/time gets a number\\n entity_num = 0\\n\\n # assign a number to each event and time\\n time_offsets2num = {}\\n event_offsets2num = {}\\n\\n # t5 i/o\\n metadata = []\\n rels_in_chunk = []\\n\\n # look for times and events in this chunk\\n for time_start, time_end, time_id in self.note2times[note_path]:\\n if time_start >= chunk_start and time_end <= chunk_end:\\n time_offsets2num[(time_start, time_end)] = entity_num\\n metadata.append('%s|%s' % (entity_num, time_id))\\n entity_num += 1\\n for event_start, event_end, event_id in self.note2events[note_path]:\\n if event_start >= chunk_start and event_end <= chunk_end:\\n event_offsets2num[(event_start, event_end)] = entity_num\\n metadata.append('%s|%s' % (entity_num, event_id))\\n entity_num += 1\\n\\n # combine time_offsets2num and event_offsets2num\\n arg2num = dict(list(time_offsets2num.items()) +\\n list(event_offsets2num.items()))\\n\\n targ2src = {} # map contained events to their containers\\n for rel in self.note2rels[note_path]:\\n src_start, src_end, targ_start, targ_end, src_id, targ_id = rel\\n if src_start >= chunk_start and src_end <= chunk_end and \\\\\\n targ_start >= chunk_start and targ_end <= chunk_end:\\n targ2src[(targ_start, targ_end)] = (src_start, src_end)\\n\\n # map every event / time to its container (or none)\\n sorted_args = sorted(arg2num.items(), key=lambda t: t[0][0])\\n for (arg_start, arg_end), arg_num in sorted_args:\\n if (arg_start, arg_end) in targ2src:\\n # this target has a source (container)\\n src_start, src_end = targ2src[(arg_start, arg_end)]\\n src_num = arg2num[(src_start, src_end)]\\n container = src_num\\n else:\\n container = '_' # no container\\n rels_in_chunk.append('c(%s; %s)' % (arg_num, container))\\n\\n # add seq numbers and markers to events/times\\n offset2str = {}\\n for (start, end), entity_num in time_offsets2num.items():\\n offset2str[start - chunk_start] = '<t> '\\n offset2str[end - chunk_start] = '/' + str(entity_num) + ' </t>'\\n for (start, end), entity_num in event_offsets2num.items():\\n offset2str[start - chunk_start] = '<e> '\\n offset2str[end - chunk_start] = '/' + str(entity_num) + ' </e>'\\n chunk_text_with_markers = insert_at_offsets(\\n note_text[chunk_start:chunk_end],\\n offset2str)\\n \\n metadata_str = '||'.join(metadata)\\n input_str = 'task: RELEXT; %s' % chunk_text_with_markers\\n if len(rels_in_chunk) > 0:\\n output_str = ' '.join(rels_in_chunk)\\n else:\\n output_str = 'no relations found'\\n\\n # counts inputs and outputs that t5 cannot handle\\n if len(self.tokenizer(input_str).input_ids) > self.max_input_length:\\n self.in_over_maxlen += 1\\n if len(self.tokenizer(output_str).input_ids) > self.max_input_length:\\n self.in_over_maxlen += 1\\n\\n self.inputs.append(input_str)\\n self.outputs.append(output_str)\\n self.metadata.append(metadata_str)\\n\\n print('%d total input/output pairs' % len(self.inputs))\\n print('%d total relation instances' % total_rel_count)\\n print('%d inputs over maxlen' % self.in_over_maxlen)\\n print('%d outputs over maxlen' % self.out_over_maxlen)\",\n \"def all_notes():\\n \\n return render_template('all_notes.html',colors=music_color,)\",\n \"def get_note_child_list(self):\\n return self.address_list\",\n \"def remove_note(self, note, octave=-1):\\n res = []\\n for x in self.notes:\\n if isinstance(note, six.string_types):\\n if x.name != note:\\n res.append(x)\\n else:\\n if x.octave != octave and octave != -1:\\n res.append(x)\\n else:\\n if x != note:\\n res.append(x)\\n self.notes = res\\n return res\",\n \"def getNote(self):\\n return (self.noteName[0], self.accidental, self.octave)\",\n \"def _build_experiment_seq_embedded_list():\\n antibody_embeds = DependencyEmbedder.embed_defaults_for_type(\\n base_path='antibody',\\n t='antibody')\\n return (\\n Experiment.embedded_list + antibody_embeds\\n )\",\n \"def midiToNotes(notes):\\n names = ['C', 'C#', 'D', 'D#', 'E', 'F', 'F#', 'G', 'G#', 'A', 'A#', 'B']\\n chord = []\\n \\n for note in notes:\\n name = names[note%12]\\n octave = int((note-12)/12)\\n chord.append(name + str(octave))\\n \\n return chord\",\n \"def notes(self):\\r\\n return notes.ForumSuggestionNotes(self)\",\n \"def extract_notes(infile):\\n\\n # get patient ID\\n subj_id = patient_id_from_file(infile)\\n \\n #get lab_events for this patient\\n con = open_db()\\n \\n query = \\\\\\n \\\"\\\"\\\"\\n SELECT i.chartdate, i.charttime, i.description, i.category, i.text\\n FROM noteevents i\\n WHERE subject_id = {};\\n \\\"\\\"\\\".format(subj_id)\\n\\n notes = pd.read_sql_query(query,con)\\n \\\"\\\"\\\" change time stamp to seconds from origin \\\"\\\"\\\"\\n \\n origin = pd.to_datetime(wfdb.rdheader(infile).base_datetime)\\n notes.insert(0, 'time', '')\\n for idx, row in notes.iterrows():\\n notes['time'].iloc[idx]=int((pd.to_datetime(row['charttime'])-origin).total_seconds())\\n del notes['charttime']\\n del notes['chartdate']\\n\\n return (notes)\",\n \"def items(self):\",\n \"def add_evernote_notes(self, evernote_notes, update=False, log_update_if_unchanged=True):\\n new_nids=[]\\n action_str_base = ['Add', 'Update'][update]\\n action_str = ['Adding', 'Updating'][update]\\n action_preposition = ['To', 'In'][update]\\n info = stopwatch.ActionInfo(action_str + ' Of', 'Evernote Notes', action_preposition + ' Anki', report_if_empty=False)\\n tmr = stopwatch.Timer(evernote_notes, 10, info=info,\\n label='Add\\\\\\\\Anki-%sEvernoteNotes' % action_str_base)\\n\\n for ankiNote in evernote_notes:\\n try:\\n title = ankiNote.FullTitle\\n content = decode(ankiNote.Content)\\n anki_field_info = {\\n FIELDS.TITLE: title,\\n FIELDS.CONTENT: content,\\n FIELDS.EVERNOTE_GUID: FIELDS.EVERNOTE_GUID_PREFIX + ankiNote.Guid,\\n FIELDS.UPDATE_SEQUENCE_NUM: str(ankiNote.UpdateSequenceNum),\\n FIELDS.SEE_ALSO: u''\\n }\\n except Exception:\\n log_error(\\\"Unable to set field info for: Note '%s': '%s'\\\" % (ankiNote.FullTitle, ankiNote.Guid))\\n log_dump(ankiNote.Content, \\\" NOTE CONTENTS \\\")\\n # log_dump(encode(ankiNote.Content), \\\" NOTE CONTENTS \\\")\\n raise\\n tmr.step(title)\\n baseNote = None\\n if update:\\n baseNote = self.get_anki_note_from_evernote_guid(ankiNote.Guid)\\n if not baseNote:\\n log_error('Updating note %s: COULD NOT FIND BASE NOTE FOR ANKI NOTE ID' % ankiNote.Guid)\\n tmr.reportStatus(EvernoteAPIStatus.MissingDataError)\\n continue\\n if ankiNote.Tags is None:\\n log_error(\\\"Could note find tags object for note %s: %s. \\\" % (ankiNote.Guid, ankiNote.FullTitle))\\n tmr.reportStatus(EvernoteAPIStatus.MissingDataError)\\n continue\\n anki_note_prototype = AnkiNotePrototype(self, anki_field_info, ankiNote.TagNames, baseNote,\\n notebookGuid=ankiNote.NotebookGuid, count=tmr.count,\\n count_update=tmr.counts.updated.completed.val, max_count=tmr.max)\\n anki_note_prototype._log_update_if_unchanged_ = log_update_if_unchanged\\n nid = tmr.autoStep(anki_note_prototype.update_note() if update else anki_note_prototype.add_note(),\\n ankiNote.FullTitle, update)\\n if tmr.status.IsSuccess and not update:\\n new_nids.append([nid, ankiNote.Guid])\\n elif tmr.status.IsError:\\n log(\\\"ANKI ERROR WHILE %s EVERNOTE NOTES: \\\" % action_str.upper() + str(tmr.status), tmr.label + '-Error')\\n tmr.Report()\\n if new_nids:\\n ankDB().executemany(\\\"UPDATE {n} SET nid = ? WHERE guid = ?\\\", new_nids)\\n return tmr.counts.success\",\n \"def get_extras(self, text=None):\\n if text is None:\\n text = self.nltk_text(self.text)\\n # Tag parts of speech\\n tagged = nltk.pos_tag(text)\\n # Try for composed NNP / NNPS\\n is_proper_noun = False\\n text = []\\n proper_noun = \\\"\\\"\\n for (word, tag) in tagged:\\n if not is_proper_noun and (tag == 'NNP' or tag == 'NNPS'):\\n # Start building a proper noun\\n proper_noun = word\\n # Set it true\\n is_proper_noun = True\\n # Add it to annotations anyway\\n text.append(word)\\n elif tag == 'NNP' or tag == 'NNPS':\\n # Previous was proper noun. So it may be combined\\n proper_noun += \\\" \\\" + word\\n # Add the single word to annotations anyway\\n text.append(word)\\n elif is_proper_noun and tag == 'IN':\\n # Add what we have by now to the text\\n text.append(proper_noun)\\n # Previous was proper noun. So it may be composed\\n proper_noun += \\\" \\\" + word\\n elif is_proper_noun:\\n # Add what we have by now to the text\\n text.append(proper_noun)\\n # Finished with proper noun, so set it false\\n is_proper_noun = False\\n # Remove duplicates\\n seen = {}\\n result = []\\n for w in text:\\n if w in seen:\\n continue\\n seen[w] = 1\\n result.append(w)\\n # Eliminate common\\n result = [w for w in result if w.lower() not in self.common_words and\\n w.lower() not in stopwords.words('english')]\\n return result\",\n \"def extract_notes(music, resolution):\\n # Check resolution\\n assert music.resolution == resolution\\n\\n # Extract notes\\n notes = []\\n for track in music:\\n if track.is_drum or track.program not in KNOWN_PROGRAMS:\\n continue\\n for note in track:\\n beat, position = divmod(note.time, resolution)\\n notes.append(\\n (beat, position, note.pitch, note.duration, track.program)\\n )\\n\\n # Deduplicate and sort the notes\\n notes = sorted(set(notes))\\n\\n return np.array(notes)\",\n \"def notes(self):\\n return NotesTable(self.rpc, self.name)\",\n \"def add_note(self, note, octave=None, dynamics=None):\\n if dynamics is None:\\n dynamics = {}\\n if isinstance(note, six.string_types):\\n if octave is not None:\\n note = Note(note, octave, dynamics)\\n elif len(self.notes) == 0:\\n note = Note(note, 4, dynamics)\\n else:\\n if Note(note, self.notes[-1].octave) < self.notes[-1]:\\n note = Note(note, self.notes[-1].octave + 1, dynamics)\\n else:\\n note = Note(note, self.notes[-1].octave, dynamics)\\n if not hasattr(note, \\\"name\\\"):\\n raise UnexpectedObjectError(\\n \\\"Object '%s' was not expected. \\\"\\n \\\"Expecting a mingus.containers.Note object.\\\" % note\\n )\\n if note not in self.notes:\\n self.notes.append(note)\\n self.notes.sort()\\n return self.notes\",\n \"def remove_notes(self, notes):\\n if isinstance(notes, six.string_types):\\n return self.remove_note(notes)\\n elif hasattr(notes, \\\"name\\\"):\\n return self.remove_note(notes)\\n else:\\n for x in notes:\\n self.remove_note(x)\\n return self.notes\",\n \"def get_all_meals():\",\n \"def accidentals(a_list):\\n key = a_list.track.bars[0].key\\n notes_in_key = get_notes(key.name)\\n return [\\n note for note in a_list\\n if note.name not in notes_in_key\\n and not note.is_rest\\n ]\",\n \"def label_notes(all_notes_lines):\\n# nf = nemo.core.NeuralModuleFactory(backend=nemo.core.Backend.PyTorch, log_dir=None)\\n #note_line_queries = notes.split('\\\\n')\\n #note_line_queries = ['pt arrived obtunded not answering questions responding to voice and sternal rub speaking in garbled voice pupils unequal left 3mm and right 2mm brisk bilaterally trauma sicu MD aware currently recieving keppra IV finished dilantin gtt due for level at 08a EEG today LSCTA on 3LNC sats 100 % SBP 90 s to 100 s HR NSR no ectopy 60 s NS @ 75cc continuous +BS no stools rec d lactulose at OSH to recieve PR q4h abd soft non-tender non-distended foley in place draining adequate amt clear yellow urine skin intact left 20G x2 WNL wife Name NI']\\n\\n# labels_dict = get_vocab(LABELS_DICT)\\n# pretrained_bert_model = nemo_nlp.nm.trainables.get_huggingface_model(\\n# bert_config=BERT_CONFIG, pretrained_model_name=PRETRAINED_MODEL_NAME\\n# )\\n\\n# tokenizer = nemo.collections.nlp.data.tokenizers.get_tokenizer(\\n# tokenizer_name=TOKENIZER,\\n# pretrained_model_name=PRETRAINED_MODEL_NAME,\\n# tokenizer_model=TOKENIZER_MODEL,\\n# )\\n# hidden_size = pretrained_bert_model.hidden_size\\n\\n load_datalayer_begin_time = time.time()\\n data_layer = nemo_nlp.nm.data_layers.BertTokenClassificationInferDataLayer(\\n queries=all_notes_lines, tokenizer=tokenizer, max_seq_length=MAX_SEQ_LENGTH, batch_size=2000\\n )\\n load_datalayer_end_time = time.time()\\n\\n classifier = TokenClassifier(hidden_size=hidden_size, num_classes=len(labels_dict))\\n\\n input_ids, input_type_ids, input_mask, _, subtokens_mask = data_layer()\\n \\n load_hidden_states_begin_time = time.time()\\n hidden_states = pretrained_bert_model(input_ids=input_ids, token_type_ids=input_type_ids, attention_mask=input_mask)\\n load_hidden_states_end_time = time.time()\\n load_logits_begin_time = time.time()\\n logits = classifier(hidden_states=hidden_states)\\n load_logits_end_time = time.time()\\n\\n ###########################################################################\\n\\n # Instantiate an optimizer to perform `infer` action\\n infer_begin_time = time.time()\\n evaluated_tensors = nf.infer(tensors=[logits, subtokens_mask], checkpoint_dir=CHECKPOINT_DIR)\\n infer_end_time = time.time()\\n\\n logits, subtokens_mask = [concatenate(tensors) for tensors in evaluated_tensors]\\n\\n preds = np.argmax(logits, axis=2) \\n all_notes_labeled_lines = []\\n\\n for i, query in enumerate(all_notes_lines):\\n logging.info(f'Query: {query}')\\n\\n pred = preds[i][subtokens_mask[i] > 0.5]\\n words = query.strip().split()\\n\\n #replaced with logic below instead of raising an error:\\n '''\\n if len(pred) != len(words):\\n logging.info('Preds length: ' + str(len(preds[i])))\\n logging.info('subtokens_mask length: ' + str(len(subtokens_mask[i])))\\n logging.info('Pred length: ' + str(len(pred)))\\n logging.info('words length: ' + str(len(words)))\\n logging.info('Preds: ' + str(preds.tolist()))\\n logging.info('subtokens_mask: ' + str(subtokens_mask[i]))\\n logging.info('Pred:' + str(pred.tolist()))\\n logging.info('words:' + str(words))\\n\\n labeled_note = '__Prediction/Word Mismatch__ pred length: ' + str(len(pred)) + ', words length: ' + str(len(words))\\n break\\n #raise ValueError('Pred and words must be of the same length')\\n \\n output = ''\\n for j, w in enumerate(words):\\n output += w\\n label = labels_dict[pred[j]]\\n if label != NONE_LABEL:\\n label = add_brackets(label)\\n output += label\\n output += ' '\\n labeled_note += '\\\\n' + output.strip()\\n logging.info(f'Combined: {output.strip()}')\\n\\n '''\\n\\n if len(pred) == len(words):\\n output = ''\\n for j, w in enumerate(words):\\n output += w\\n label = labels_dict[pred[j]]\\n if label != NONE_LABEL:\\n label = add_brackets(label)\\n output += label\\n output += ' '\\n all_notes_labeled_lines.append(output.strip())\\n logging.info(f'Combined: {output.strip()}')\\n else:\\n all_notes_labeled_lines.append(query)\\n pred_length = str(len(pred))\\n word_length = str(len(words))\\n logging.info(f'__Prediction/Word Length Mismatch__ pred length: {pred_length}, words length: {word_length}')\\n logging.info(f'{query}')\\n \\n\\n print(str(load_datalayer_end_time-load_datalayer_begin_time)+' seconds to load the datalayer')\\n print(str(load_hidden_states_end_time-load_hidden_states_begin_time)+' seconds to load hidden states')\\n print(str(load_logits_end_time-load_logits_begin_time)+' seconds to load logits')\\n print(str(infer_end_time-infer_begin_time)+' seconds to run inference')\\n\\n return all_notes_labeled_lines\",\n \"def placeNoteLists(self, text):\\n if self.notes:\\n o = OrderedDict()\\n for label, info in self.notes.items():\\n if 'seq' in info:\\n i = info['seq']\\n info['seq'] = label\\n o[i] = info\\n else:\\n self.unreferencedNotes[label] = info\\n\\n if o:\\n # sort o by key\\n o = OrderedDict(sorted(o.items(), key=lambda t: t[0]))\\n self.notes = o\\n text_re = re.compile('<p>notelist(%s)(?:\\\\:([\\\\w|%s]))?([\\\\^!]?)(\\\\+?)\\\\.?[\\\\s]*</p>'\\n % (self.c, self.syms), re.U)\\n text = text_re.sub(self.fNoteLists, text)\\n return text\",\n \"def records(self):\\r\\n raise NotImplementedError()\",\n \"def remove_tempo(self):\\n return Melody([n.remove_tempo() for n in self.notes], nb_bars=self.nb_bars, tags=set(self.tags))\",\n \"def from_notes(self, notes, bpm=120.0, gap=16, ignore_polyphonic_notes=False):\\n self._clear()\\n\\n # Compute quantization steps per second.\\n steps_per_second = bpm / 60.0 * self.steps_per_bar / BEATS_PER_BAR\\n\\n quantize = lambda x: int(math.ceil(x - QUANTIZE_CUTOFF))\\n\\n # Sort track by note start times.\\n notes.sort(key=lambda note: note.start_time)\\n for note in notes:\\n # Ignore 0 velocity notes.\\n if not note.velocity:\\n continue\\n\\n # Quantize the start and end times of the note.\\n start_step = quantize(note.start_time * steps_per_second)\\n end_step = quantize(note.end_time * steps_per_second)\\n if end_step == start_step:\\n end_step += 1\\n\\n # Do not allow notes to start or end in negative time.\\n if start_step < 0 or end_step < 0:\\n raise BadNoteException(\\n 'Got negative note time: start_time = %s, end_time = %s'\\n % (note.start_time, note.end_time))\\n\\n # If start_step comes before or lands on an already added note's start\\n # step, we cannot add it. Discard the melody because it is not monophonic.\\n if not self._can_add_note(start_step):\\n if ignore_polyphonic_notes:\\n continue\\n else:\\n self._clear()\\n raise PolyphonicMelodyException()\\n\\n # If a gap of `gap` or more steps is found, end the melody.\\n if (len(self) and\\n self._distance_to_last_event(start_step) >= gap):\\n break\\n\\n # Add the note-on and off events to the melody.\\n self._add_note(note.pitch, start_step, end_step)\\n\\n self._write_all_notes()\",\n \"def _getitem_augmentation(self):\\n n_tracks = len(self.tracks)\\n track_indices = random.choices(range(n_tracks), k=len(self.sources))\\n\\n sources = []\\n\\n for _source, trackID in zip(self.sources, track_indices):\\n track = self.tracks[trackID]\\n source_path = track['path'][_source]\\n track_samples = track['samples_original']\\n\\n start = random.randint(0, track_samples - self.samples - 1)\\n source, _ = torchaudio.load(source_path, frame_offset=start, num_frames=self.samples)\\n\\n # Apply augmentation\\n source = self.augmentation(source)\\n sources.append(source.unsqueeze(dim=0))\\n \\n if type(self.target) is list:\\n target = []\\n for _target in self.target:\\n source_idx = self.sources.index(_target)\\n _target = sources[source_idx]\\n target.append(_target)\\n target = torch.cat(target, dim=0)\\n\\n sources = torch.cat(sources, dim=0)\\n mixture = sources.sum(dim=0, keepdim=True)\\n else:\\n source_idx = self.sources.index(self.target)\\n target = sources[source_idx]\\n target = target.squeeze(dim=0)\\n\\n sources = torch.cat(sources, dim=0)\\n mixture = sources.sum(dim=0)\\n\\n return mixture, target\",\n \"def annotations(self) -> Iterator['Annotation']:\\n return itertools.chain(self.footnotecitations.all(),\\n self.cites.all(),\\n self.externallinks.all(),\\n self.inlinerequirements.all())\",\n \"def _mixed_documents(self, raw_documents) -> List[str]:\\n if not self._ner:\\n return raw_documents\\n\\n mixed_documents = []\\n for doc in raw_documents:\\n entities = [\\n \\\"{\\\" + entity[\\\"type\\\"] + \\\"}\\\" for entity in self._ner.process(doc)]\\n if entities:\\n mixed_documents.append(\\\" \\\".join(entities) + \\\" \\\" + doc)\\n else:\\n mixed_documents.append(doc)\\n\\n return mixed_documents\",\n \"def get_observations(self):\\n # Check hyper_params and criterion\\n if len(self.hyper_params) == 0:\\n raise AssertionError('!! Hyper-Parameters has not been set.')\\n # Check criterion\\n if self.criterion is None:\\n raise AssertionError(\\n '!! Criterion for hyper-parameter searching has not been set.')\\n # Fetch notes\\n notes = self.summary_fetcher()\\n if len(notes) == 0: return []\\n # Peel of Note wrapper\\n observations = []\\n for note in notes:\\n # Every note in the note list must contain the criterion\\n if self.criterion not in note.criteria: raise AssertionError(\\n '!! Every note must contain the criterion `{}`'.format(self.criterion))\\n # This note will be ignored if it does not contain all the information\\n # in self.hyper_params or the config value is not within the range\\n if not all([hp.name in note.configs and hp.within(note.configs[hp.name])\\n for hp in self.hyper_params]): continue\\n # Gather observation\\n od = OrderedDict()\\n # self.scroll.hyper_params.values() may have been found themselves\\n for hp in self.scroll.hyper_params.values():\\n assert isinstance(hp, HyperParameter)\\n od[hp] = note.configs[hp.name]\\n # Organize the observation list as a list of tuples\\n observations.append((od, note.criteria[self.criterion]))\\n return observations\"\n]","isConversational":false},"negative_scores":{"kind":"list like","value":["0.7549341","0.6924999","0.64609313","0.6448983","0.6341835","0.6265768","0.6224044","0.61404175","0.6124106","0.6078764","0.60639954","0.6008974","0.6008974","0.60029846","0.59637725","0.59542704","0.5905439","0.5901639","0.58900833","0.58588296","0.58077854","0.5797133","0.5789488","0.5757178","0.5730626","0.5703335","0.56392765","0.56314266","0.56143224","0.55930483","0.55814135","0.5578179","0.55757564","0.5567998","0.55632156","0.5558758","0.5542764","0.55382633","0.55296856","0.5529461","0.5517582","0.55052775","0.5504211","0.5490391","0.5489668","0.5446942","0.5422216","0.5369864","0.5369835","0.53614813","0.5350441","0.53485423","0.53325474","0.532488","0.5313811","0.52951884","0.52942824","0.5288759","0.52836543","0.5262247","0.52590734","0.52436477","0.523289","0.52209246","0.5219923","0.5208198","0.5182663","0.51773286","0.51615256","0.51596993","0.51532054","0.5143974","0.5140918","0.51371837","0.5133934","0.5118805","0.51179165","0.5114498","0.511318","0.5106521","0.51039946","0.51033634","0.5098857","0.50917923","0.5091195","0.5089794","0.5087343","0.5085954","0.507226","0.5071177","0.5066879","0.50616497","0.50602305","0.50567293","0.50501853","0.50495356","0.5049181","0.5041997","0.5037585","0.50355935","0.50332886"],"string":"[\n \"0.7549341\",\n \"0.6924999\",\n \"0.64609313\",\n \"0.6448983\",\n \"0.6341835\",\n \"0.6265768\",\n \"0.6224044\",\n \"0.61404175\",\n \"0.6124106\",\n \"0.6078764\",\n \"0.60639954\",\n \"0.6008974\",\n \"0.6008974\",\n \"0.60029846\",\n \"0.59637725\",\n \"0.59542704\",\n \"0.5905439\",\n \"0.5901639\",\n \"0.58900833\",\n \"0.58588296\",\n \"0.58077854\",\n \"0.5797133\",\n \"0.5789488\",\n \"0.5757178\",\n \"0.5730626\",\n \"0.5703335\",\n \"0.56392765\",\n \"0.56314266\",\n \"0.56143224\",\n \"0.55930483\",\n \"0.55814135\",\n \"0.5578179\",\n \"0.55757564\",\n \"0.5567998\",\n \"0.55632156\",\n \"0.5558758\",\n \"0.5542764\",\n \"0.55382633\",\n \"0.55296856\",\n \"0.5529461\",\n \"0.5517582\",\n \"0.55052775\",\n \"0.5504211\",\n \"0.5490391\",\n \"0.5489668\",\n \"0.5446942\",\n \"0.5422216\",\n \"0.5369864\",\n \"0.5369835\",\n \"0.53614813\",\n \"0.5350441\",\n \"0.53485423\",\n \"0.53325474\",\n \"0.532488\",\n \"0.5313811\",\n \"0.52951884\",\n \"0.52942824\",\n \"0.5288759\",\n \"0.52836543\",\n \"0.5262247\",\n \"0.52590734\",\n \"0.52436477\",\n \"0.523289\",\n \"0.52209246\",\n \"0.5219923\",\n \"0.5208198\",\n \"0.5182663\",\n \"0.51773286\",\n \"0.51615256\",\n \"0.51596993\",\n \"0.51532054\",\n \"0.5143974\",\n \"0.5140918\",\n \"0.51371837\",\n \"0.5133934\",\n \"0.5118805\",\n \"0.51179165\",\n \"0.5114498\",\n \"0.511318\",\n \"0.5106521\",\n \"0.51039946\",\n \"0.51033634\",\n \"0.5098857\",\n \"0.50917923\",\n \"0.5091195\",\n \"0.5089794\",\n \"0.5087343\",\n \"0.5085954\",\n \"0.507226\",\n \"0.5071177\",\n \"0.5066879\",\n \"0.50616497\",\n \"0.50602305\",\n \"0.50567293\",\n \"0.50501853\",\n \"0.50495356\",\n \"0.5049181\",\n \"0.5041997\",\n \"0.5037585\",\n \"0.50355935\",\n \"0.50332886\"\n]","isConversational":false},"document_score":{"kind":"string","value":"0.0"},"document_rank":{"kind":"string","value":"-1"}}},{"rowIdx":23780,"cells":{"query":{"kind":"string","value":"Return a list of note based on chord"},"document":{"kind":"string","value":"def mgChord(value, chord):\n ret = None\n if chord == 'M':\n ret = mgChordMajor(value)\n elif chord == 'm':\n ret = mgChordMinor(value)\n elif chord == 'dim':\n ret = mgChordDiminished(value)\n elif chord == 'aug':\n ret = mgChordAugmented(value)\n\n return ret"},"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_chord(key: str, starting_note: int, chord: str) -> List[int]:\n assert key in constants.NOTES_FOR_KEY, f\"Invalid key: {key}.\"\n assert (\n starting_note in constants.NOTES_FOR_KEY[key]\n ), f\"Note {starting_note} not in key {key}.\"\n assert chord in constants.STEPS_FOR_CHORD, f\"Invalid chord: {chord}.\"\n return [starting_note + i for i in constants.STEPS_FOR_CHORD[chord]]","def midiToNotes(notes):\n names = ['C', 'C#', 'D', 'D#', 'E', 'F', 'F#', 'G', 'G#', 'A', 'A#', 'B']\n chord = []\n \n for note in notes:\n name = names[note%12]\n octave = int((note-12)/12)\n chord.append(name + str(octave))\n \n return chord","def to_sequence(self, chord, inst):\n time = 0\n sequence = []\n for note in self.notes:\n pitch = chord.to_pitch(note)\n start = time\n end = time + note.duration\n sequence.append([start, end, pitch, chord.to_chord(), inst, note])\n time += note.duration\n return sequence","def getChord(self, c, velocity, isdrum, isgrace):\n\n c = re.split(\"[, ]+\", c)\n\n if not c:\n error(\"You must specify the first note in a solo line\")\n\n \"\"\" Convert the note part into a series of midi values\n Notes can be a single note, or a series of notes. And\n each note can be a letter a-g (or r), a '#,&,n' plus\n a series of '+'s or '-'s. Drum solos must have each\n note separated by ' ' or ','s: \"Snare1,KickDrum1,44\".\n\n Each chunk could be:\n - a midi value (44)\n - a drum note ( KickDrum1)\n - a single note (g#) (g&-)\n - Or groups with spaces/commas (f 100) (44 , KickDrum) (a,b c)\n \"\"\"\n\n events = [] # array for each note event\n\n for cc in c:\n if not cc or not cc[0]:\n continue\n if '/' in cc:\n if cc.count('/') > 1:\n error(\"%s: Only 1 '/velocity' permitted. You can separate \" \\\n \"notes in the chord with ',' or ' ' and it'll work.\" % \\\n self.name)\n cc, newvel = cc.split('/')\n if not newvel:\n error(\"%s: expecting 'volume' after '/'\" % self.name)\n\n if not cc:\n error(\"%s: Volume '/' must immediately follow note.\" % self.name)\n\n thisvel = stoi(newvel)\n\n if thisvel < 0 or thisvel > 127:\n error(\"%s: Velocity must be 0..127, not '%s'.\" % (self.name, newvel))\n else:\n thisvel = velocity\n\n if cc[0] == 'r':\n if isgrace:\n warning(\"%s: Grace note is a rest (ignored).\" % self.name)\n \n if events or len(cc) > 1:\n error(\"%s: Rests and notes cannot be combined.\" % self.name)\n else:\n events.append( NoteEvent(None, 0, isgrace)) # note event with no pitch\n\n elif cc[0] in \"1234567890\":\n n = stoi(cc, \"%s: Note values must be integer or literal.\" % \\\n self.name)\n if n<0 or n>127:\n error(\"%s: Midi notes must be 0..127, not '%s'\" % \\\n (self.name, n))\n\n # if using value we fake-adjust octave,\n # it (and transpose) is set later.\n\n if not isdrum:\n n -= self.octave[self.seq]\n \n events.append(NoteEvent(n, thisvel, isgrace))\n\n\n elif isdrum: # drum must be a value, * or drum-name\n if cc == '*':\n events.append( NoteEvent(self.drumTone, thisvel, isgrace ))\n else:\n events.append( NoteEvent(int(MMA.translate.dtable.get(cc)), thisvel, isgrace) )\n\n else: # must be a note(s) in std. notation\n cc = list(cc)\n while cc:\n name = cc.pop(0)\n\n if not name in self.midiNotes:\n error(\"%s: Encountered illegal note name '%s'\"\n % (self.name, name))\n\n n = self.midiNotes[ name ] # name is string, n is value\n \n # Parse out a \"#', '&' or 'n' accidental.\n\n if cc and cc[0] in accValues:\n i = cc.pop(0)\n self.acc[name] = accValues[i]\n\n n += self.acc[name] # accidental adjust (from above or keysig)\n\n # Parse out +/- (or series) for octave\n\n while cc and cc[0] in '+-':\n a = cc.pop(0)\n if a == '+':\n n += 12\n else:\n n -= 12\n\n events.append( NoteEvent(n, thisvel, isgrace) )\n \n\n return events","def do_notes(self, sym):\n # DRY: duplication with Roman.py - FIXME\n if sym == '-':\n # REST\n return []\n note_or_chord = self.do(sym)\n if note_or_chord is None:\n return []\n elif type(note_or_chord) == Chord:\n return note_or_chord.notes\n else:\n return [ note_or_chord ]","def build_chord_names(bar_notes: List[List[str]]):\n bar_available_chords = []\n # find corresponding chords for each bar\n for one_bar in bar_notes:\n chord_names = chords_from_note_names(one_bar)\n # print(\"===> chord name of choices %s from notes %s\" % (chord_names, one_bar))\n bar_available_chords.append(chord_names)\n\n # now each bar may have several available chord\n # we want them to be different and want to use as many as possible different chords\n bar_chords = []\n used = set([])\n for i in range(len(bar_available_chords)):\n available_chords = bar_available_chords[i]\n if i == 0:\n # use directly first chord as reference\n bar_chords.append(available_chords[0])\n used.add(available_chords[0])\n continue\n\n if len(available_chords) > 0:\n # now we want to make a difference\n hit = False\n for chord in available_chords:\n if chord in used:\n continue\n bar_chords.append(chord)\n used.add(chord)\n hit = True\n break\n\n # all chords are used before,\n # try not to be the same with the first one\n if hit is False:\n last_one = bar_chords[len(bar_chords) - 1]\n for chord in available_chords:\n if chord != last_one:\n bar_chords.append(chord)\n used.add(chord)\n hit = True\n break\n # still false ? that is weird but we don't have much choice here\n if hit is False:\n bar_chords.append(available_chords[0])\n used.add(available_chords[0])\n return bar_chords","def get_random_chord(self) -> []:\n return self.get_random_chords(1)[0]","def randNotes(self, value, chord):\n aChord = mgChord(value, chord)\n self.data = []\n for i in range(len(self.rhythm)):\n note = random.choice(aChord).copy()\n note.setDuration(self.rhythm[i])\n self.data.append(note)","def get_notes():\n notes = []\n durs = []\n\n for file in glob.glob(\"D:\\\\anthems\\\\data\\\\*.mid\"):\n midi = converter.parse(file)\n\n print(\"Parsing %s\" % file)\n\n notes_to_parse = None\n\n try:\n s2 = instrument.partitionByInstrument(midi)\n notes_to_parse = s2.parts[0].recurse() \n except:\n notes_to_parse = midi.flat.notes\n \n for element in notes_to_parse:\n if isinstance(element, note.Note):\n notes.append(str(element.pitch))\n durs.append(element.duration.quarterLength)\n elif isinstance(element, chord.Chord):\n notes.append('.'.join(str(n) for n in element.normalOrder))\n durs.append(element.duration.quarterLength)\n\n return notes, durs","def add_chord(meas, chord_name):\r\n \r\n for note in CHORDS[chord_name]:\r\n meas.NewNote(0, 64, note, 5)","def notes2trk(notes):\n\n trk = MidiTrack()\n\n for i, note in enumerate(notes):\n if note[0] == 's': # Message for a silence\n trk.append(Message(\"note_on\", note=0, velocity=0, time=0))\n trk.append(Message(\"note_off\", note=0, velocity=0, time=note[1]))\n continue\n\n nlist = note[0].split(' ')\n\n for c_n in nlist: # Add the note on message\n n_nb, v_on, v_off = char2note(c_n)\n if len(note)==2:\n trk.append(Message(\"note_on\", note=n_nb, velocity=v_on, time=0))\n else:\n trk.append(Message(\"note_on\", note=n_nb, velocity=note[2],\n time=0))\n if len(note) == 4:\n trk.append(Message(\"note_off\", note=n_nb, velocity=v_off,\n time=note[1]))\n else:\n trk.append(Message(\"note_off\", note=n_nb, velocity=v_off,\n time=note[1]))\n for c_n in nlist[:-1]: # Add the note off message\n n_nb, v_on, v_off = char2note(c_n)\n if len(note) == 4: # For short note\n trk.append(Message(\"note_off\", note=n_nb, velocity=note[3],\n time=0))\n else:\n trk.append(Message(\"note_off\", note=n_nb, velocity=v_off,\n time=0))\n\n return trk","def random_note_in_chord_and_vocal_range(relPitchList, key, vocal_range, prev_note):\n\n\t# for rests, can only be a rest\n\tif relPitchList == -1:\n\t\treturn [note.Rest()]\n\n\t# extract_range\n\tlowest_note, highest_note = vocal_range\n\n\tif (not isinstance(prev_note, note.Rest)) and (prev_note is not None):\n\n\t\t# restrict intervals to a sixth\n\t\tif prev_note.pitch.transpose(-7) > lowest_note:\n\t\t\tlowest_note = prev_note.pitch.transpose(-7)\n\n\t\tif prev_note.pitch.transpose(7) < highest_note:\n\t\t\thighest_note = prev_note.pitch.transpose(7)\n\n\t# turn relative Pitch into a note.Note\n\tnotes = []\n\n\n\tfor relPitch in relPitchList:\n\t\tif relPitch != 0:\n\t\t\tinterval_ = interval.ChromaticInterval(relPitch)\n\t\t\tnotes.append(note.Note(interval_.transposePitch(key.getTonic()), quarterLength=1))\n\t\telse:\n\t\t\t# needed as no transposition by 0 possible\n\t\t\tnotes.append(note.Note(key.getTonic(), quarterLength=1))\n\t \n\t# get all notes in right octave\n\toctave_corrected_notes = []\n\tfor n in notes:\n\n\t\t# possible octaves\n\t\tlow_octave = lowest_note.octave\n\t\thigh_octave = highest_note.octave\n \n\t\t# select random octave\n\t\t# if only one choice available\n\t\toctave_choice = np.arange(low_octave, high_octave + 1)\n\n\t\t# loop throughpossible octaves\n\t\tfor o in octave_choice:\n\t\t\tnote_ = copy.deepcopy(n)\n\t\t\tnote_.octave = o\n\t\t\tif (note_.pitch >= lowest_note) and (note_.pitch <= highest_note):\n\t\t\t\toctave_corrected_notes.append(note_)\n\n\treturn octave_corrected_notes","def mgChordAugmented(value):\n chord = [MgNote(value), MgNote(value) + 4, MgNote(value) + 8]\n return chord","def get_next_chord_conditional(self, chord, note):\n chords = self.key.c_chord_given_c_note(note[:-1])\n idxs = []\n for j in range(len(chords)):\n for i in range(len(self.key.chords)):\n if chords[j] == self.key.chords[i]:\n idxs.append(i)\n\n idxs = list(set(idxs))\n\n chord = self.specified_chord_to_chord(chord)\n cond_dist_dict = self.chord_chord_map[chord] # Gets list of candidate chords\n next_chords = sorted(cond_dist_dict.keys())\n next_probs = [cond_dist_dict[next_chord] for next_chord in next_chords]\n\n true_next_chords = [next_chords[int(i)] for i in idxs]\n true_next_probs = [next_probs[int(i)] for i in idxs]\n\n allsum = sum(true_next_probs)\n\n for i in range(len(true_next_probs)):\n true_next_probs[i] = true_next_probs[i] / allsum\n\n\n cond_dist = stats.rv_discrete(name='cond_dist', values=(true_next_chords, true_next_probs))\n return self.key.chords[cond_dist.rvs(size=1)-1]","def chords_from_note_names(note_names: List[str]):\n pos_set = set([])\n for note_name in note_names:\n pos_set.add(c_major_chord_pos[note_name])\n\n # we have their pos, try to find the ideal chord position\n match_of_chord = {}\n for order, members in major_progression.items():\n hit = 0\n for s in pos_set:\n if s in members:\n hit += 1\n match_of_chord[order] = hit\n\n # if we have same hits\n # may be the first highest hit with the first note name\n highest_orders = []\n max_hits = max(match_of_chord.values())\n for order, hits in match_of_chord.items():\n if hits == max_hits:\n highest_orders.append(order)\n\n # return the names of highest order of choice\n chord_names = []\n for order in highest_orders:\n for name, that_order in c_major_chord_pos.items():\n if order == that_order:\n chord_names.append(name)\n break\n return chord_names","def mgChordMajor(value):\n chord = [MgNote(value), MgNote(value) + 4, MgNote(value) + 7]\n return chord","def ks_chord(chord, N, alpha, f_s):\n y = np.zeros(N)\n # the chord is a dictionary: pitch => gain\n for note, gain in chord.items():\n # create an initial random-filled KS buffer the note\n x = np.random.randn(int(np.round(float(f_s) / note_freq(note))))\n y = y + gain * KS(x, N, alpha)\n return y","def mgChordDiminished(value):\n chord = [MgNote(value), MgNote(value) + 3, MgNote(value) + 6]\n return chord","def chordmode():\n for token in consume():\n if source.inSelection and isinstance(token, tokenizer.Pitch):\n transpose(token, 0)","def getnotes():","def voicings(chord_str, strings=(4, 9, 2, 7, 11, 4), fingers=4, root_string=0, mute_above=False):\n gen = FingeringsGenerator(strings=strings, fingers=fingers)\n chord = parse_chord_name(chord_str)\n res = []\n for voicing in gen.generate(chord, root_string=root_string, mute_above=mute_above):\n q = QueryResult(voicing, chord)\n res.append(q)\n res.sort()\n return res","def play_chord(self, velocity, timing): #timing = number of ms between successive notes\n self.l.append(self.midi.note_on(self.l.chord_channel, self.chord[0], velocity))\n for i, n in enumerate(self.chord[1:], start=1):\n if timing:\n self.pending.append((n, velocity, time.ticks_ms()+timing*i))\n else:\n self.l.append(self.midi.note_on(self.l.chord_channel, n, velocity))","def get_notes(n_notes=3):\n notes = []\n \n for ii, file in enumerate(glob.glob(\"data/maestro-v2.0.0/2004/*.midi\")):\n if ii >= n_notes:\n break\n pickle_file_name = file[:-4] + 'pkl'\n\n if os.path.isfile(pickle_file_name):\n print(f'Reading parsed file: {pickle_file_name}')\n with open(pickle_file_name, 'rb') as handle:\n midi = pickle.load(handle)\n else:\n midi = converter.parse(file)\n\n with open(pickle_file_name, 'wb') as handle:\n print(f'writing parsed file: {pickle_file_name}')\n unserialized_data = pickle.dump(midi, \n handle, \n protocol=pickle.HIGHEST_PROTOCOL\n )\n\n\n print(\"Parsing %s\" % file)\n\n notes_to_parse = None\n try: # file has instrument parts\n s2 = instrument.partitionByInstrument(midi)\n notes_to_parse = s2.parts[0].recurse() \n except: # file has notes in a flat structure\n notes_to_parse = midi.flat.notes\n \n for element in notes_to_parse:\n if isinstance(element, note.Note):\n notes.append(str(element.pitch))\n elif isinstance(element, chord.Chord):\n notes.append('.'.join(str(n) for n in element.normalOrder))\n \n return notes","def extract_notes(bars):\n output = []\n for bar in bars:\n notes = np.array([bar_to_notes(b,i) for i,b in enumerate(bar)])\n\n # Flatten into a single array\n midi_notes = []\n for i in range(notes.shape[1]):\n if all(notes[:,i] == -1):\n midi_notes.append(None)\n else:\n midi_notes.append([n for n in notes[:,i] if n!=-1])\n output.append(midi_notes)\n return output","def determine(self, shorthand=False):\n return chords.determine(self.get_note_names(), shorthand)","def loadChords(self, choFile):\n self.chords = []\n with open(choFile, 'r') as f:\n for line in f:\n for match in re.findall(\"\\[.*?\\]\", line):\n chord = match[1:-1]\n if re.search(chordRE, chord) is not None:\n self.chords.append(chord)\n else:\n print(chord + \" is not a chord\")","def chord_generator(iterable):\n return (CHORDS[chord_symbol] for chord_symbol in iterable)","def f(chord):\r\n m, length, dim = chord.shape\r\n chord2 = chord.reshape([m*length, dim])\r\n weighted_chords = np.dot(chord2, chord_id2sign)\r\n return weighted_chords.reshape(m, length, 12)","def mgChordMinor(value):\n chord = [MgNote(value), MgNote(value) + 3, MgNote(value)+7]\n return chord","def getLine(self, pat):\n \n sc=self.seq\n\n \"\"\" Get a COPY of the keysignature note table (a dict). \n As a bar is processed the table is updated. There is one difference\n here---in real music an accidental for a note in a given octave does\n not effect the following same-named notes in different octaves.\n In this routine IT DOES.\n\n FollowKey=Off is usually (user) set when using a sequence pattern.\n \"\"\"\n\n if self.followKey: # this is the default\n self.acc=keySig.accList.copy()\n else: # disable the feature. Useful for sequence patterns!\n self.acc = {'a': 0, 'c': 0, 'b': 0, 'e': 0, 'd': 0, 'g': 0, 'f': 0}\n\n \n # list of notename to midivalues\n\n self.midiNotes = {'c':0, 'd':2, 'e':4, 'f':5, 'g':7, 'a':9, 'b':11, 'r':None }\n\n\n \"\"\" The initial string is in the format \"1ab;4c;;4r;\". The trailing\n ';' is important and needed. If we don't have this requirement\n we can't tell if the last note is a repeat of the previous. For\n example, if we have coded \"2a;2a;\" as \"2a;;\" and we didn't\n have the 'must end with ;' rule, we end up with \"2a;\" and\n then we make this into 2 notes...or do we? Easiest just to\n insist that all bars end with a \";\".\n \"\"\"\n\n if not pat.endswith(';'):\n error(\"All Solo strings must end with a ';'\")\n \n # Get the end of the bar in ticks. If we're doing a truncate\n # use the temporary bar length, otherwise figure it out.\n\n if MMA.truncate.length:\n barEnd = MMA.truncate.length\n else:\n barEnd = gbl.BperQ*gbl.QperBar\n\n duration = self.getNoteLen('4') # default note length\n velocity = 90 # intial/default velocity for solo notes\n articulation = 1 # additional articulation for solo notes\n\n notes={} # NoteEvent list, keys == offset\n \n if self.drumType:\n isdrum = 1\n lastc = str(self.drumTone)\n else:\n isdrum = None\n lastc = '' # last parsed note \n\n # convert pat to a list\n pat = [x.strip() for x in pat.split(';')[:-1]]\n\n # set initial offset into bar. This compensates for the previous\n # bar ending in a ~ and this one starting with ~.\n # This special case bumps the initial bar offset\n\n if pat[0].startswith(\"~\"):\n if not self.endTilde or self.endTilde[1] != gbl.tickOffset:\n error(\"Previous line did not end with '~'\")\n else:\n pat[0] = pat[0][1:].strip()\n offset = self.endTilde[0]\n else:\n offset = 0\n\n lastOffset = None\n\n # Strip off trailing ~. This permits long notes to end past the\n # current barend. Note, flag set for the next bar to test for\n # a leading ~.\n\n if pat[-1].endswith(\"~\"):\n self.endTilde = [1, gbl.tickOffset + barEnd ]\n pat[-1] = pat[-1][:-1].strip()\n else:\n self.endTilde = []\n\n ##################################################\n # Now we can parse each chunk of the solo string.\n\n for a in pat:\n \"\"\" If we find a \"<>\" we just ignore that. It's useful when\n multiple continuation bars are needed with ~.\n \"\"\"\n \n accentVol = None\n accentDur = None\n isgrace = False\n\n if a == '<>':\n continue\n\n \"\"\" Next, strip out all '<SPECIAL=xx>' settings.\n\n VOLUME: If no option is set, we assume VOLUME. The default\n velocity setting was set before the loop (==90) and is\n changed here for the duration of the current bar/riff.\n The set velocity will still be modified by the global\n and track volume adjustments.\n \n DURATION: Duration or articulation setting is defaulted to 100.\n Changing it here will do so for the duration of the\n bar/riff. Note, the track ARTICULATION is still applied.\n\n ISGRACE: grace note indication and offset. Offset is optional.\n \n OFFSET: change the current offset into the bar. Can be negative\n which forces overlapping notes.\n\n \"\"\"\n\n a, vls = pextract(a, \"<\", \">\")\n\n if vls:\n if len(vls) > 1:\n error(\"Only 1 <modifier> is permitted per note-set\")\n\n vls = vls[0].split(',')\n for vv in vls:\n\n vv = vv.upper().strip()\n\n if vv == 'GRACE':\n vv='GRACE=2'\n\n if not '=' in vv:\n vv = \"VOLUME=\" + vv\n \n vc,vo = vv.split('=', 1) # note: it's already uppercase!\n\n if vc == 'VOLUME':\n if vo in MMA.volume.vols: # arg was a volume 'FF, 'mp', etc.\n velocity *= MMA.volume.vols[vo]\n else:\n error(\"%s: No volume '%s'.\" % (self.name, vo))\n \n elif vc == 'GRACE':\n isgrace = stof(vo, \"%s: Expecting a value, not %s.\" % (self.name, vo))\n if isgrace <= 0:\n error(\"%s: Offset modifier must be greater than 0.\" \\\n % self.name)\n \n elif vc == 'OFFSET':\n offset = stoi(vo, \"%s: Offset expecting integer, not %s.\" \\\n % (self.name, vo))\n\n if offset < 0:\n error(\"%s: Offset must be positive.\" % self.name)\n\n if offset >= barEnd:\n error(\"%s: Offset has been set past the end of the bar.\" \\\n % self.name )\n\n\n elif vc == 'ARTICULATE':\n articulation = stoi(vo, \"%s: Articulation expecting integer,\"\n \" not %s.\" % (self.name, vo))\n \n if articulation < 1 or articulation >200:\n error(\"%s: Articulation must be 1..200, not %s.\" % \\\n (self.name, vo) )\n articulation /= 100.\n\n else:\n error(\"%s: Unknown command '%s'.\" % (self.name, vv))\n\n if offset >= barEnd:\n error(\"Attempt to start Solo note '%s' after end of bar\" % a)\n\n \"\"\" Split the chord chunk into a note length and notes. Each\n part of this is optional and defaults to the previously\n parsed value.\n \"\"\"\n\n i = 0\n while i < len(a):\n if not a[i] in '1234567890.+-tT':\n break\n else:\n i += 1\n\n if i:\n duration = self.getNoteLen(a[0:i].replace(' ', '') )\n a = a[i:].strip()\n\n\n # next item might be an accent string. \n\n i = 0\n while i < len(a):\n if not a[i] in \"!-_^&\":\n break\n else:\n i += 1\n\n if i:\n c = a[0:i]\n accentVol = 1\n accentDur = 1\n\n accentDur -= c.count('!') * .2\n accentDur += c.count('-') * .2\n accentDur += c.count('_') * .2\n accentVol += c.count('^') * .2\n accentVol -= c.count('&') * .2\n\n if accentDur<.1: accentDur = .1\n if accentDur>2: accentDur = 2\n if accentVol<.1: accentVol = .1\n if accentVol>2: accentVol = 2\n a = a[i:]\n\n # Now we get to look at pitches.\n\n if not a or a=='' or a==' ':\n a=lastc\n evts = self.getChord(a, velocity, isdrum, isgrace) # get chord\n \n for e in evts:\n e.velocity = self.adjustVolume(e.defvelocity, offset)\n if accentVol:\n e.velocity *= accentVol\n e.duration = duration\n\n if accentDur:\n e.articulation = articulation * accentDur\n else:\n e.articulation = articulation \n\n lastc = a # save last chord for next loop\n\n\n # add note event(s) to note{}\n\n if not offset in notes:\n notes[offset] = []\n notes[offset].extend(evts)\n\n lastOffset = offset\n if not e.isgrace:\n offset += duration\n\n\n if offset <= barEnd:\n if self.endTilde:\n error(\"Tilde at end of bar has no effect\")\n\n else:\n if self.endTilde:\n self.endTilde[0]=offset-barEnd\n else:\n warning(\"%s, end of last note overlaps end of bar by %g \"\n \"beat(s).\" % (self.name, (offset-barEnd)/float(gbl.BperQ)))\n\n if MMA.swing.mode:\n notes = MMA.swing.swingSolo(notes)\n\n return notes","def degrees_on_beats(chorale):\n \n\t# key signature\n\tkey = chorale.analyze('key') # may need to put this elsewhere to deal with modulation\n\n\t# create output stream\n\toutstream = stream.Stream()\n\n\t# chordify the chorale\n\tcc = chorale.chordify() # <- will be replaced by predicted chords\n\n\toutstream = extract_utils.to_crotchet_stream(cc, chord_flag=True)\n \n\t# roman numeral output stream\n\trn_outstream = []\n\n\t# convert chords to roman numerals\n\tfor bc in outstream.recurse().getElementsByClass([chord.Chord, note.Rest]):\n\t\tif bc.isRest:\n\t\t bc = -1\n\t\telse:\n\t\t bc = relOrderedPitchClassesString(bc.orderedPitchClasses, key)\n\t\trn_outstream.append(bc)\n \n\treturn rn_outstream","def get_diatonic_chord(mode, root_step, tension) -> [int]:\n chord = []\n mode_idx = ChordMdConsts.MODE.index(mode)\n mode_name = ChordMdConsts.MODE[mode_idx]\n mode_seq = ChordMdConsts.MODE_SEQS[mode_name]\n __tone_num = min(5, 3 + tension)\n\n for i in range(__tone_num):\n scalepos = (i * 2) % len(mode_seq)\n tonepos = mode_seq[scalepos]\n if (i * 2) >= len(mode_seq):\n tonepos += 12\n chord.append(tonepos)\n return chord","def process_notes_in_song(dict_time_notes, seq_len = 50):\n list_of_dict_keys_time = []\n \n for key in dict_time_notes:\n sample = dict_time_notes[key]\n times = np.unique(np.where(sample > 0)[1])\n index = np.where(sample > 0)\n dict_keys_time = {}\n\n for time in times:\n index_where = np.where(index[1] == time)\n notes = index[0][index_where]\n dict_keys_time[time] = notes\n list_of_dict_keys_time.append(dict_keys_time)\n return list_of_dict_keys_time","def create_melody(inst, chord_progression):\r\n \r\n for chord in chord_progression:\r\n \r\n meas = inst.NewMeasure()\r\n last_note = random.choice(CHORDS[chord])\r\n pos = 0\r\n \r\n while pos < 64:\r\n dur = random.choice([8, 8, 16, 16]) \r\n last_note = get_rdm_note(chord, last_note)\r\n if random.randint(1,10) <= 8: # 20% silence\r\n meas.NewNote(pos, dur, last_note, 5)\r\n pos += dur","def bar_to_lilypond_notes(notes):\n lp_notes = []\n if notes[0] is None:\n notes = notes[1:]\n if notes[0] is None:\n lp_notes.append(\"r\")\n for n in notes:\n if n is None:\n continue\n if type(n) is list:\n lp_notes.append([midi_to_lilypond_note(x) for x in n])\n else:\n lp_notes.append(midi_to_lilypond_note(n))\n return lp_notes","def compo_test():\n trk = MidiTrack()\n b = mid.ticks_per_beat\n t = int(b/3)\n notes = [['c4', b], ['s', b], ['c4', t], ['e4', t], ['g4', t], ['c4', b]]\n trk = notes2trk(trk, notes)\n return trk","def extract_notes(music, resolution):\n # Check resolution\n assert music.resolution == resolution\n\n # Extract notes\n notes = []\n for track in music:\n if track.is_drum or track.program not in KNOWN_PROGRAMS:\n continue\n for note in track:\n beat, position = divmod(note.time, resolution)\n notes.append(\n (beat, position, note.pitch, note.duration, track.program)\n )\n\n # Deduplicate and sort the notes\n notes = sorted(set(notes))\n\n return np.array(notes)","def _on_note(self) -> Iterator:\n return filter(\n lambda x: x.type == constants.NOTE_ON, self.midi_data)","def scorepart_to_notes(part, morphetic_pitch=False):\n\n bm = part.beat_map\n\n if morphetic_pitch:\n pitch_attr = attrgetter('morphetic_pitch')\n else:\n pitch_attr = attrgetter('midi_pitch')\n notes = np.array([(bm(n.start.t), bm(n.end.t), pitch_attr(n))\n for n in part.notes], np.float)\n\n ids = np.array([n.id for n in part.notes])\n\n return notes, ids","def create_bar(self, chord, key, midi):\n if self.default:\n return [(MEASURE_LENGTH, midi)]\n degree_map = self.get_degree_map(chord, key, midi)\n # map 1 3 5 to the chord degrees\n chord_degree_map = {1: chord.get_chord_pitches()[0], 3: chord.get_chord_pitches()[1], 5: chord.get_chord_pitches()[2]}\n if len(degree_map) == 0:\n print \"RETURNING DEFAULT BECAUSE NO DEGREE MAP\"\n return [(MEASURE_LENGTH, midi)] # return this note as a whole note as this rhythm template doesn't work\n\n measure = []\n for note in self.rhythm_template:\n real_degree = chord_degree_map[note[1]]\n midi = degree_map[real_degree]\n measure.append((note[0], midi))\n return measure","def get_notes():\n song_index_to_notes = {}\n\n for file in glob.glob(\"../data/midi/*.mid\"):\n midi = converter.parse(file)\n song_index = int(os.path.splitext(os.path.basename(file))[0])\n #print(\"Parsing %s with an index %d\" % (file, song_index))\n\n notes_to_parse = None\n notes = []\n try: # file has instrument parts\n s2 = instrument.partitionByInstrument(midi)\n notes_to_parse = s2.parts[0].recurse()\n except: # file has notes in a flat structure\n notes_to_parse = midi.flat.notes\n\n for element in notes_to_parse:\n if isinstance(element, note.Note):\n notes.append(str(element.pitch))\n elif isinstance(element, chord.Chord):\n notes.append('.'.join(str(n) for n in element.normalOrder))\n\n song_index_to_notes[song_index] = notes\n\n return song_index_to_notes","def notes_played(self) -> List[Dict]:\n return [\n dict(note=self._midi_to_note(note.note), time=note.time)\n for note in self._on_note()\n ]","def extractSolo(ln, rptcount):\n\n a = ln.count('{')\n b = ln.count('}')\n\n if a != b:\n error(\"Mismatched {}s for solo found in chord line\")\n\n if a:\n if rptcount > 1:\n error(\"Bars with both repeat count and solos are not permitted\")\n\n ln, solo = pextract(ln, '{', '}')\n \n if len(solo) > len(autoSoloTracks):\n error(\"Too many melody/solo riffs in chord line. %s used, \"\n \"only %s defined\" % (len(solo), len(autoSoloTracks)) )\n\n\n firstSolo = solo[0][:] # save for autoharmony tracks\n \n \"\"\" We have the solo information. Now we loop though each \"solo\" and:\n 1. Ensure or Create a MMA track for the solo\n 2. Push the solo data into a Riff for the given track.\n \"\"\"\n\n for s, trk in zip(solo, autoSoloTracks):\n if not s: continue # skip placeholder/empty tracks\n MMA.alloc.trackAlloc(trk, 1)\n t = gbl.tnames[trk]\n if t.riff:\n error(\"%s: Attempt to add {} solo when the track \"\n \"has pending RIFF data.\" % t.name)\n t.setRiff( s.strip() )\n\n \"\"\" After all the solo data is interpreted and sent to the\n correct track, we check any leftover tracks. If any of these\n tracks are empty of data AND are harmonyonly the note\n data from the first track is interpeted again for that\n track. Tricky: the max() is needed since harmonyonly can\n have different setting for each bar...this way\n the copy is done if ANY bar in the seq has harmonyonly set.\n \"\"\"\n\n for t in autoSoloTracks[1:]:\n if t in gbl.tnames and not gbl.tnames[t].riff \\\n and max(gbl.tnames[t].harmonyOnly):\n gbl.tnames[t].setRiff( firstSolo[:] )\n\n if gbl.debug:\n print \"%s duplicated to %s for HarmonyOnly.\" % (trk, t)\n\n return ln","def c_major_octave_chord(chord_name: str, octave: int):\n pitch = note_name_octave_to_pitch(chord_name, octave)\n if pitch is None:\n raise Exception('unable to find %s%s note' % (chord_name, octave))\n\n # build the major chord\n pos = c_major_chord_pos[chord_name]\n pitches = [pitch]\n tmp = copy(pitch)\n for gap in major_progression_pitch_gap[pos]:\n tmp += gap\n pitches.append(tmp)\n return pitches","def parseChordTuple(self, notes=None, size=4, deltatimes=None, deltatolerance=0.02, debug=False):\n\n #for note in notes:\n #if note not in self._chordmemory:\n #self._chordmemory.append(note)\n #for deltatime in deltatimes:\n #if deltatime not in self._deltamemory:\n #self._deltamemory.append(deltatime)\n \n #if len(self._chordmemory) > size:\n #self._chordmemory = self._chordmemory[1:]\n #if len(self._deltamemory) > size:\n #self._deltamemory = self._deltamemory[1:]\n \n self._chordmemory = notes \n self._deltamemory = deltatimes\n #if debug:\n #print('b chordmem: ', self._chordmemory, 'b deltamem', self._deltamemory)\n \n #if len(self._chordmemory) > 1:\n #average = np.average(np.diff(sorted(self._deltamemory)))\n #if debug:\n #print('average:', average)\n #if average > deltatolerance:\n #self._chordmemory.pop(0)\n #self._deltamemory.pop(0)\n ##self._chordmemory = []\n ##self._deltamemory = []\n\n if len(self._chordmemory) == size:\n average = np.average(np.diff(sorted(self._deltamemory)))\n \n if average < deltatolerance:\n chord = self._chordmemory\n self._chordmemory = []\n self._deltamemory = []\n if debug:\n print('detected!', chord)\n return True, chord\n #else:\n #chord = self._chordmemory\n #self._chordmemory = []\n #self._deltamemory = [] \n \n if debug:\n print('chordmem: ', self._chordmemory, 'deltamem', \n self._deltamemory, 'average', average)\n\n\n return False, None","def find_strings(staff, guitar, *, allow_open=True, reverse=False,\n window_size=7, distinct_only=False):\n cursor = IOCursor(staff=staff, guitar=guitar)\n while not cursor.after_end():\n if cursor.at_chord():\n cursor.current_output = find_multi_fingering(\n frets_matrix=cursor.get_all_frets(),\n guitar=guitar,\n )\n logger.info(json.dumps({\n \"found\": \"chord\",\n \"out\": cursor.current_output,\n \"move\": \"R\",\n }))\n cursor.to_right()\n elif cursor.at_possible_note():\n logger.info(json.dumps({\"found\": \"melody\"}))\n for dist_range in count(3):\n logger.info(json.dumps({\n \"processing\": \"melody\",\n \"dist_range\": dist_range,\n }))\n if AdaptiveFretFinderMelody(\n cursor=cursor,\n guitar=guitar,\n dist_range=dist_range,\n allow_open=allow_open,\n reverse=reverse,\n window_size=window_size,\n distinct_only=distinct_only,\n ).run():\n logger.info(json.dumps({\n \"processed\": \"melody\",\n \"dist_range\": dist_range,\n }))\n break # Finished in an \"accept\" state\n else: # A rest or an impossible note\n logger.info(json.dumps({\n \"found\": \"rest\" if cursor.at_rest() else \"unknown\",\n \"move\": \"R\",\n }))\n cursor.to_right()\n cursor.freeze_left() # \"Store\" the new result\n return cursor.output_tape","def get_full_context(chords_seq):\n chord , context = [],[]\n m_before = context_size\n empty_before = 0\n m_after = context_size\n empty_after = 0\n\n size = len(chords_seq)\n for i in range(size):\n # the neighborhood of chords at the beginning or at the end of a sequence is smaller\n if i < m_before:\n m_before = i\n empty_before = context_size - m_before\n elif size - i <= m_after:\n m_after = size - i - 1\n empty_after = context_size - m_after\n\n neighborhood = []\n\n for j in range(empty_before):\n neighborhood.append([])\n if (m_before > 0):\n neighborhood.extend(map(list, chords_seq[(i - m_before):i]))\n if (m_after > 0):\n neighborhood.extend(map(list, chords_seq[(i + 1):(i + m_after + 1)]))\n for j in range(empty_after):\n neighborhood.append([])\n\n #for context_chord in neighborhood:\n # context_chord.append(EOS_ID)\n\n chord.append(list(chords_seq[i]))\n context.append(neighborhood)\n m_before = context_size\n m_after = context_size\n empty_after = 0\n empty_before = 0\n\n return (chord,context)","def load_chords(fhandle: TextIO) -> annotations.ChordData:\n begs = [] # timestamps of chord beginnings\n ends = [] # timestamps of chord endings\n chords = [] # chord labels\n\n reader = csv.reader(fhandle, delimiter=\"\\t\")\n for line in reader:\n begs.append(float(line[0]))\n ends.append(float(line[1]))\n chords.append(line[2])\n\n return annotations.ChordData(np.array([begs, ends]).T, \"s\", chords, \"harte\")","def export_phenotype(chords):\n import music21\n partupper = music21.stream.Part()\n m = music21.stream.Measure()\n for _chord in chords:\n c = music21.chord.Chord(_chord)\n m.append(c)\n partupper.append(m)\n partupper.write('midi', './sample.mid')","def notes(self):\n return reapy.NoteList(self)","def __scrape_chords(html):\n\n soup = BeautifulSoup(html, 'html.parser')\n \n try:\n chords = []\n for element in soup.find_all('span', attrs={\"class\": \"_3bHP1 _3ffP6\"}):\n\n chord = element.get_text()\n chord = equiChord(chord)\n chords.append(chord)\n\n return chords\n except TypeError:\n return []","def midiToNotesRec(notes, totalnotes, note_names=[]):\n names = ['C', 'C#', 'D', 'D#', 'E', 'F', 'F#', 'G', 'G#', 'A', 'A#', 'B']\n \n #print(note_names)\n \n def recMidiToNotes(notes, note_namesr=[]):\n if notes == []:\n return\n \n head = notes.pop(0)\n name = names[head%12]\n octave = int((head-12)/12)\n note_namesr.append(name + str(octave))\n print('rec names:', note_namesr)\n return note_namesr\n \n if len(note_names) == totalnotes:\n return note_names\n \n midiToNotes(notes, totalnotes, recMidiToNotes(notes, note_names))","def get_notes_from_line(line):\n last_char_is_upper = True\n raw_notes_list = []\n current_note = \"\"\n\n for char in line:\n if not last_char_is_upper and char.isupper():\n raw_notes_list.append(current_note)\n current_note = \"\"\n\n current_note += char\n last_char_is_upper = char.isupper()\n\n raw_notes_list.append(current_note)\n return raw_notes_list","def first_notes_and_song():\n # Construct a robot, put it in safe mode.\n # Make it play two notes, then a song, then disconnect.\n\n time.sleep(2) # In case the robot has recently been shutdown.\n\n port = 'sim' # Or use YOUR laptop's COM number\n hal = new_create.Create(port)\n hal.toSafeMode() # So you can try this out on a table if you want.\n\n #-------------------------------------------------------------------\n # playNote(pitch, duration):\n #\n # Notes range from 31 (low G, 49.0 Hz) to 127 (high G, 12543.9 Hz).\n # They can be played from as little as 1/64th of a second to as\n # long as 255/64 seconds (i.e., almost 4 seconds).\n # The playNote method takes TWO (2) arguments:\n # -- The note (should be in the range 31 to 127, inclusive)\n # -- The duration to play that note, in 64th's of a second\n # (must be between 0 and 255, inclusive)\n #-------------------------------------------------------------------\n\n hal.playNote(31, 255) # Very long, low note.\n time.sleep(5.0) # See next example for why we need sleeps.\n\n hal.playNote(110, 1) # Very short, high note.\n time.sleep(1.0)\n\n #-------------------------------------------------------------------\n # playSong(LIST of pitch-duration TWO-TUPLEs)\n #\n # If you want to play a sequence of notes, it is almost always\n # better to use the playSong method instead of doing a\n # sequence of calls to playNote. The playSong method takes\n # ONE (1) argument. That argument must be:\n # -- a LIST\n # -- with no more than 16 items\n # -- where each item is a 2-tuple\n # whose first element is the pitch (31 to 127)\n # and whose second element is the duration (1 to 255)\n # -- where pitch and duration are as in playNote.\n #-------------------------------------------------------------------\n\n song = [(60, 8), (64, 8), (67, 8), (72, 8)] # a quick C chord\n hal.playSong(song)\n time.sleep(2.0)\n\n hal.shutdown()","def get_and_split_note_lists(composition):\n lists = create_note_lists(composition)\n\n # create a dict of all tracks with notes in them\n n = {}\n for voice in lists:\n if len(lists[voice]):\n n[voice] = lists[voice]\n\n descending_voices = [x for x in ['Soprano', 'Alto', 'Tenor', 'Bass'] if x in n]\n ascending_voices = [x for x in['Bass', 'Tenor', 'Alto', 'Soprano'] if x in n]\n\n # find the high voice\n for voice in descending_voices:\n high_voice = n[voice]\n break\n\n # find the low voice\n for voice in ascending_voices:\n low_voice = n[voice]\n break\n\n # find the inner voices\n inner_voices = []\n for voice in ['Tenor', 'Alto']:\n if voice in n and n[voice] not in [high_voice, low_voice]:\n inner_voices.append(n[voice])\n\n # find all possible combinations of voices\n voice_combos = []\n for i,x in enumerate(descending_voices):\n for y in descending_voices[i+1:]:\n voice_combos.append((x, y))\n\n return n, high_voice, low_voice, inner_voices, voice_combos","def get_onehot2chordnotes_transcoder():\r\n chord_id2sign = np.load('csv/chord-1hot-signatures-rev.npy')\r\n\r\n def f(chord):\r\n \"\"\"\r\n Translate from 1-hot array of dim {DIM} back to superimposed repr of dim=12\r\n :param chord: 1-hot representation of chords in (M, T, Dim)\r\n :return: chord signature in (M, T, 12)\r\n \"\"\"\r\n m, length, dim = chord.shape\r\n chord2 = chord.reshape([m*length, dim])\r\n index = np.argmax(chord2, axis=1)\r\n new_chord = chord_id2sign[index, :]\r\n return new_chord.reshape(m, length, 12)\r\n return f","def getNotes(self):\n return self._nednotes, self._ongcnotes","async def chord(ctx, root:str,length=3,):\n if length < 2:\n length = 2\n elif length > 5:\n length = 5\n chord = Tempo.generateChordProgression(root)\n await ctx.send(chord)","def addHarmony(self, notes, ctable):\n \n sc=self.seq\n\n harmony = self.harmony[sc]\n harmOnly = self.harmonyOnly[sc]\n \n \n for offset in notes:\n nn = notes[offset]\n\n if (len(nn) == 1) and (nn[0].pitch != None) and (nn[0].isgrace != True):\n tb = self.getChordInPos(offset, ctable)\n\n if tb.chordZ:\n continue\n\n h = MMA.harmony.harmonize(harmony, nn[0].pitch, tb.chord.bnoteList)\n\n duration = nn[0].duration\n articulation = nn[0].articulation\n velocity = nn[0].defvelocity\n\n if harmOnly: # remove melody note if harmony only\n nn.pop(0) # DON'T use nn=[] that would release the ptr.\n\n for n in h:\n e = NoteEvent(n,\n self.adjustVolume(velocity * self.harmonyVolume[sc], offset), False)\n e.duration = duration\n e.articulation = articulation\n nn.append(e)","def get_vec_for_hand(hand_notes):\n # vec = np.zeros(300, dtype=np.int8)\n vec = zeros(hands=1)\n note_pattern = r'(\\d+)\\.*([A-Ga-g]+#?)' # treats dotted quarters as quarters, etc\n notes = re.findall(note_pattern, hand_notes) # this is currently ignoring rests! TODO fix\n # print(f'found {len(notes)} matches')\n for i in range(len(notes)):\n if i >= 3:\n # print(f\"note {hand_notes}: {len(notes)} notes found in hand...\")\n break\n duration, note = notes[i]\n if note not in note_to_ind_map:\n # note doesn't exist on piano because of upward transposition; \n # so transpose down\n note = note[1:]\n note_ind = note_to_ind_map.get(note) + i*100\n duration_ind = duration_to_ind_map.get(duration, duration_to_ind_map[\"4\"]) + i*100\n # default to quarter note I guess\n vec[note_ind] = 1 # if appears twice somehow, is still 1\n vec[duration_ind] = 1\n return vec","def do(self, sym):\n # The dash is a bit of a notation hack, it's there because \"C4 major\"\n # would look like two symbols, so we need to have no whitespace\n # between them\n\n if sym is None or sym == '-':\n # REST:\n return chord([])\n if '-' in sym:\n return chord(sym.replace(\"-\",\" \"))\n elif \",\" in sym:\n return chord(sym.split(\",\"))\n else:\n return note(sym)","def _createCadence(self, harmonicRhythm):\n random = RandomManager.getActive()\n\n # choose cadence to apply\n cadences = self.chordProfile.getCadences()\n\n #TODO: Choose cadence in more intelligent way\n cadence = random.choice(cadences)\n\n scale = self.chordProfile.getScale().getName()\n cadenceChordProgression = []\n\n reversedHarmonicRhythm = reversed(harmonicRhythm[:])\n # create as many cadence\n for count, durationObj in enumerate(reversedHarmonicRhythm):\n duration = durationObj.getDuration()\n code = cadence[-count+1]\n chord = Chord(code, duration=duration, scale=scale, octave=4)\n\n if count >= len(cadence):\n return cadenceChordProgression\n\n # prepend chord\n cadenceChordProgression.insert(0, chord)\n return cadenceChordProgression","def to_cnv(chord_list):\r\n\r\n cnv_list = []\r\n err_list = []\r\n for c in chord_list:\r\n if c in ProgCreate.chord_dict.keys():\r\n cnv_list.append(ProgCreate.chord_dict[c])\r\n elif c not in ProgCreate.chord_dict.keys():\r\n err_list.append(''.join([\"#\" if x == \"s\" else x for x in c]))\r\n return cnv_list, err_list","def get_note_names(self):\n res = []\n for n in self.notes:\n if n.name not in res:\n res.append(n.name)\n return res","def get_next_chord(self, chord):\n chord = self.specified_chord_to_chord(chord)\n assert validate_chord(chord), \"not a valid chord!\"\n\n cond_dist_dict = self.chord_chord_map[chord] # Gets list of candidate chords\n\n # Prepare for the random draw\n next_chords = cond_dist_dict.keys()\n next_probs = [cond_dist_dict[next_chord] for next_chord in next_chords]\n cond_dist = stats.rv_discrete(name='cond_dist', values=(next_chords, next_probs))\n\n return self.key.chords[cond_dist.rvs(size=1)-1]","def _nextNotes(self, prev_note, high_note, low_note, voice):\n distance = 1\n high_bound = prev_note.distance(high_note)\n low_bound = prev_note.distance(low_note)\n bound = max([high_bound, low_bound])\n # notes = [prev_note]\n notes = []\n if voice in [1, 2]:\n notes.append(prev_note)\n while distance <= bound:\n if distance <= high_bound:\n total_note_value = prev_note.getTotalValue() + 1 * distance\n note = Note(value2note[total_note_value % 12], total_note_value//12)\n notes.append(note)\n\n if distance <= low_bound:\n total_note_value = prev_note.getTotalValue() - 1 * distance\n note = Note(value2note[total_note_value % 12], total_note_value//12)\n print(prev_note, note)\n notes.append(note)\n \n distance += 1\n\n return notes","def f(chord):\r\n m, length, dim = chord.shape\r\n chord2 = chord.reshape([m*length, dim])\r\n index = np.argmax(chord2, axis=1)\r\n new_chord = chord_id2sign[index, :]\r\n return new_chord.reshape(m, length, 12)","def _compose(track, notes, deltat=461.09, velocity=48, threshold=1.):\n LEN, dim = notes.shape\n track.append(MetaMessage('set_tempo', tempo=500000))\n\n # notes to abs time list\n times = []\n actions = []\n T = 0\n for ind, line in enumerate(notes):\n for note in range(dim):\n if notes[ind, note] >= threshold and \\\n (ind == 0 or notes[ind-1, note] < threshold):\n times.append(T)\n actions.append(('note_on', note))\n\n T += int(deltat)\n\n for note in range(dim):\n if (notes[ind, note] < threshold and\n notes[ind-1, note] >= threshold) or \\\n (ind == LEN-1 and notes[ind, note] >= threshold):\n times.append(T)\n actions.append(('note_off', note))\n\n for i in range(len(times)-1, 0, -1):\n times[i] = times[i] - times[i-1]\n\n for t, a in zip(times, actions):\n if a[0] == 'note_on':\n track.append(Message('note_on',\n note=a[1],\n velocity=velocity,\n time=t))\n else:\n track.append(Message('note_off',\n note=a[1],\n velocity=0,\n time=t))","def parseChord(self, note, size=4, deltatime=0, deltatolerance=0.03, debug=False):\n \n self._chordmemory.append(note)\n self._deltamemory.append(deltatime)\n \n if len(self._chordmemory) > size:\n self._chordmemory = self._chordmemory[1:]\n if len(self._deltamemory) > size:\n self._deltamemory = self._deltamemory[1:]\n \n if debug:\n print('b chordmem: ', self._chordmemory, 'b deltamem', self._deltamemory)\n \n if len(self._chordmemory) > 1:\n average = np.average(np.diff(self._deltamemory))\n if debug:\n print('average:', average)\n if average > deltatolerance:\n self._chordmemory.pop(0)\n self._deltamemory.pop(0)\n #self._chordmemory = []\n #self._deltamemory = []\n\n if len(self._chordmemory) == size:\n average = np.average(np.diff(self._deltamemory))\n \n if average < deltatolerance:\n chord = self._chordmemory\n self._chordmemory = []\n self._deltamemory = []\n return True, chord\n #else:\n #chord = self._chordmemory\n #self._chordmemory = []\n #self._deltamemory = [] \n \n if debug:\n print('chordmem: ', self._chordmemory, 'deltamem', \n self._deltamemory)\n\n\n return False, None","def followAdjust(self, notes, ctable):\n \n sc=self.seq\n\n for offset in notes:\n nn = notes[offset]\n\n if len(nn) == 1 and nn[0].pitch != None:\n tb = self.getChordInPos(offset, ctable)\n\n if tb.chordZ:\n continue\n\n nn[0].pitch += ( tb.chord.rootNote + self.rootChord)\n\n return notes","def note_card_paths() -> List[str]:\n\n pkg = \"music_flash_cards.cards.chromatic_note_cards\"\n\n note_cards = []\n\n for note_card_name in contents(pkg):\n if is_resource(pkg, note_card_name) and note_card_name.endswith(\".png\"):\n with path(pkg, note_card_name) as note_card_path:\n note_cards.append(str(note_card_path))\n return note_cards","def get_notes_from_uuids(dictionary, uuid_list):\n titles = []\n texts = []\n notes = []\n for key in dictionary:\n if dictionary[key][\"content_type\"] == \"Note\" and dictionary[key][\"uuid\"] in uuid_list:\n try:\n titles.append(dictionary[key][\"content\"][\"title\"])\n texts.append(dictionary[key][\"content\"][\"text\"])\n notes = list(zip(titles, texts))\n except KeyError:\n continue\n return notes","def generateNotes():\r\n fs = 44100 # hertz\r\n seconds = 3 # Note duration of 3 seconds\r\n noteNames = [\"C4\", \"D4\", \"E4\", \"F4\", \"G4\", \"A4\", \"B4\"]\r\n for noteName in noteNames:\r\n myNote = music21.note.Note(noteName)\r\n noteFrequency = myNote.pitch.frequency\r\n # Generate array with seconds*sample_rate steps, ranging between 0 and seconds\r\n t = np.linspace(0, seconds, seconds * fs, False)\r\n\r\n # Generate a 440 Hz sine wave\r\n sound = np.sin(noteFrequency * t * 2 * np.pi)\r\n\r\n # Ensure that highest value is in 16-bit range\r\n audio = sound * (2**15 - 1) / np.max(np.abs(sound))\r\n # Convert to 16-bit data\r\n audio = audio.astype(np.int16)\r\n\r\n # Start playback\r\n play_obj = sa.play_buffer(audio, 1, 2, fs)\r\n\r\n # Wait for playback to finish before exiting\r\n play_obj.wait_done()\r\n\r\n #Write sound to file\r\n sf.write('assets/patterns/'+noteName+'.wav', audio, fs)","def comp_pattern_generator(iterable):\n for chord in iterable:\n yield (600, chord)","def play_notes(self, show_map=False):\n # print(self.programs)\n pianorolls = []\n for track in self.notes:\n pianoroll = np.zeros((track.shape[1] * 3, 128))\n\n for i in range(track.shape[0]):\n note_track = np.array(track[i, :])\n note_track += 1\n notes_pos = np.nonzero(note_track)\n f = 3\n for pos in notes_pos[0]:\n # print(\"Error\", f * pos, f * pos + self.dura_to_timecell(note_track[pos] - 1),\n # self.dura_to_timecell(note_track[pos] - 1))\n pianoroll[f * pos:f * pos + self.dura_to_timecell(note_track[pos] - 1) + 1, 83 - i] = 90\n pianorolls.append(pianoroll)\n\n # print(\"pianoroll\")\n print(self.notes_index)\n tracks = []\n for i in range(len(pianorolls)):\n tracker = Track(pianoroll=pianorolls[i], program=self.programs[i])\n tracks.append(tracker)\n multitrack = Multitrack(tracks=tracks)\n multitrack.write(\"create1.mid\")\n\n if show_map:\n print(\"Show map will not work\")\n # tracker.plot()\n # plt.show()","def keychord(m) -> str:\n return \"-\".join(m.modifier_list + [m.any_key])","def _find_channels(note):\r\n id_ch = note.index('ChanNames')\r\n chan_beg = note.index('(', id_ch)\r\n chan_end = note.index(')', chan_beg)\r\n note_with_chan = note[chan_beg + 1:chan_end]\r\n return [x.strip('\" ') for x in note_with_chan.split(',')]","def starts_with_tonic(a_list):\n key = a_list.track.bars[0].key.name\n note = a_list.get_first_actual_note()\n if note.name == key:\n return []\n else:\n return [note.start]","def randMelody(self, value, chord):\n self.randRhythm()\n self.randNotes(value, chord)","def inverse_notes(note_list):\n raw_output = []\n for note in note_list:\n # We transpose every notes except for Z\n if note.is_pause:\n raw_output.append(f'{note.name}{note.figure}')\n else:\n # Get the index of the note to transpose\n note_name_index = NOTE_NAMES.index(note.name)\n # Get the transposed index according to the amount\n inverted_index = ((len(NOTE_NAMES) - note_name_index) % len(NOTE_NAMES))\n new_note_name = NOTE_NAMES[inverted_index]\n # Create the new note, with the new note's name, and its original figure\n new_note = f'{new_note_name}{note.figure}'\n new_note += 'p' if note.has_point else ''\n raw_output.append(new_note)\n\n return raw_output","def parse_pretty_midi_lyrics(midi: PrettyMIDI) -> List[Lyric]:\n return [Lyric(lyric.time, lyric.text) for lyric in midi.lyrics]","def from_chord_shorthand(self, shorthand):\n self.empty()\n self.add_notes(chords.from_shorthand(shorthand))\n return self","def midi_to_lilypond_note(note):\n return all_notes[note+4]","def get_pitches(midi, part_id=None):\n try: # file has instrument parts\n s2 = instrument.partitionByInstrument(midi)\n print(len(s2))\n if part_id is not None:\n notes = s2.parts[part_id].recurse()\n pitches = [c.pitches for c in notes]\n pitch_arr = np.array(pitches)[None]\n ps_arr = np.array([pitch.ps for pitch in pitches])\n else:\n pitch_arr = []\n ps_arr = []\n for part in s2:\n notes = part.recurse()\n pitches = [c.pitches for c in notes]\n pss = [pitch.ps for pitch in pitches]\n pitch_arr.append(pitches)\n ps_arr.append(pss)\n pitch_arr = np.array(pitch_arr)\n ps_arr = np.array(ps_arr)\n except: # file has notes in a flat structure\n notes = midi.flat.notes\n pitch_arr = np.array([c.pitches for c in notes])\n ps_arr = np.array([pitch.ps for pitch in pitches])\n return pitch_arr, ps_arr","def __init__(self):\n \n # initialize Music-Code\n self.m = MusicCode(bpm=120)\n \n # waveform types\n self.waveform_types = ['sine', 'tri', 'saw1', 'saw2', 'square', 'sine-tri', 'sine-saw', 'sine-square', 'saw-square', 'tri-saw', 'tri-square']\n \n # chord labels\n self.all_chord_labels = self.m.all_chords[:73]\n\n # select 4 octaves for root notes\n excess_notes_list = list(self.m.freq_note_table_sharp.values()) \n \n all_root_notes = []\n for item in excess_notes_list:\n if ('0' in item) or ('1' in item) or ('5' in item) or ('6' in item) or ('7' in item):\n pass\n else:\n all_root_notes.append(item)\n \n all_root_notes.sort() \n self.all_root_notes = all_root_notes","def starts_with_tonic_or_fifth(a_list):\n key = a_list.track.bars[0].key\n note = a_list.get_first_actual_note()\n possible_notes = [key.name, Note(key).transpose('5', True).name]\n if note.name in possible_notes:\n return []\n else:\n return [note.start]","def add_notes(self, notes):\n if hasattr(notes, \"notes\"):\n for x in notes.notes:\n self.add_note(x)\n return self.notes\n elif hasattr(notes, \"name\"):\n self.add_note(notes)\n return self.notes\n elif isinstance(notes, six.string_types):\n self.add_note(notes)\n return self.notes\n for x in notes:\n if isinstance(x, list) and len(x) != 1:\n if len(x) == 2:\n self.add_note(x[0], x[1])\n else:\n self.add_note(x[0], x[1], x[2])\n else:\n self.add_note(x)\n return self.notes","def _initCandidateTriads(self):\n codes = list(self.chordProfile.getScores())\n candidateTriads = [Chord(code) for code in codes]\n return candidateTriads","def get_chromatic(self, tonic, octave=None, n_octaves=None,\n descending=None, dont_repeat_tonic=None):\n\n global notes3, notes2\n\n tonic_index = self._get_chromatic_idx(tonic)\n\n if tonic == 'F' or 'b' in tonic:\n notes = deque(notes3)\n else:\n notes = deque(notes2)\n\n notes.rotate(-(tonic_index))\n\n if n_octaves:\n chromatic = notes * n_octaves\n else:\n chromatic = notes\n\n # FIXME: check if this works on descending\n if not dont_repeat_tonic:\n chromatic.append(chromatic[0])\n\n if descending:\n chromatic.reverse()\n\n if octave:\n chromatic = self._append_octave_to_scale(scale=chromatic,\n starting_octave=octave,\n descending=descending)\n\n return chromatic","def generate_message(self) -> List[mido.Message]:\n # check for a None note (which is a \"pause\")\n if self.__note:\n note_value = self.__note + self.__info.octave\n note_velocity = self.__info.volume\n else:\n note_value = 0\n note_velocity = 0\n return [\n mido.Message(\n \"note_on\",\n note=note_value,\n velocity=note_velocity,\n time=NOTE_DURATION,\n ),\n mido.Message(\n \"note_off\",\n note=note_value,\n velocity=note_velocity,\n time=0,\n ),\n ]","def __getCounterClock(self, chord):\n lookupTable = {\n 'C': 'F', 'Am': 'Dm', 'G': 'C', 'Em': 'Am', 'D': 'G',\n 'Bm': 'Em', 'A': 'D', 'F#m': 'Bm', 'E': 'A', 'C#m': 'F#m' ,\n 'B': 'E', 'G#m': 'C#m', 'F#': 'B', 'Gb': 'B', 'Ebm': 'G#m',\n 'Db': 'F#', 'Bbm': 'Ebm', 'Ab': 'Db', 'Fm': 'Bbm', 'Eb': 'Ab',\n 'Cm': 'Fm', 'Bb': 'Eb', 'Gm': 'Cm', 'F': 'Bb', 'Dm': 'Gm'\n }\n\n return lookupTable[chord]","def get_chord_midi_nums(chord_intervals, root) -> [int]:\n midi_nums = []\n midi_root = ChordMdConsts.MIDI_C3 + root # [TODO]\n for i in chord_intervals:\n scalepos = (i * 2) % 7 # [TODO] Magic Number\n if (i * 2) >= 7:\n scalepos += 12\n midi_nums.append(midi_root + scalepos)\n \n return midi_nums","def get_note():\n\n return Note.query.all()","def pattern_to_notes(pattern):\n pattern_notes = []\n for (i, m) in zip(range(0, 6), FRET_POSITION.findall(pattern)):\n pos = m[0].strip()\n if pos != 'x':\n pattern_notes.append(FRET_NOTES[i][int(pos) % 12])\n return pattern_notes","def notes_in_path(self, path: str) -> List[NoteInfo]:\n check_path = os.path.abspath(path)\n return [n for n in self.notes.values() if n.file_path.startswith(check_path)]","def return_markers(self):\r\n ent_file = join(self.filename, self._basename + '.ent')\r\n if not exists(ent_file):\r\n ent_file = join(self.filename, self._basename + '.ent.old')\r\n\r\n try:\r\n ent_notes = _read_ent(ent_file)\r\n\r\n except (FileNotFoundError, PermissionError):\r\n markers = []\r\n\r\n else:\r\n allnote = []\r\n for n in ent_notes:\r\n try:\r\n n['value'].keys()\r\n allnote.append(n['value'])\r\n except AttributeError:\r\n lg.debug('Note of length {} was not '\r\n 'converted to dict'.format(n['length']))\r\n\r\n s_freq = self._hdr['erd']['sample_freq']\r\n pcname = '0CFEBE72-DA20-4b3a-A8AC-CDD41BFE2F0D'\r\n note_time = []\r\n note_name = []\r\n note_note = []\r\n for n in allnote:\r\n if n['Text'] == 'Analyzed Data Note':\r\n continue\r\n if not n['Text']:\r\n continue\r\n if 'User' not in n['Data'].keys():\r\n continue\r\n user1 = n['Data']['User'] == 'Persyst'\r\n user2 = False # n['Data']['User'] == 'eeg'\r\n user3 = n['Data']['User'] == pcname\r\n user4 = n['Data']['User'] == 'XLSpike - Intracranial'\r\n user5 = n['Data']['User'] == 'XLEvent - Intracranial'\r\n if user1 or user2 or user3 or user4 or user5:\r\n continue\r\n if len(n['Data']['User']) == 0:\r\n note_name.append('-unknown-')\r\n else:\r\n note_name.append(n['Data']['User'].split()[0])\r\n note_time.append(n['Stamp'] / s_freq)\r\n note_note.append(n['Text'])\r\n\r\n markers = []\r\n for time, name, note in zip(note_time, note_name, note_note):\r\n m = {'name': note + ' (' + name + ')',\r\n 'start': time,\r\n 'end': time,\r\n 'chan': None,\r\n }\r\n markers.append(m)\r\n\r\n return markers","def getNote(self):\n return (self.noteName[0], self.accidental, self.octave)","def acquire_song_chords(title, artist):\r\n #search for song in database, and demand that it has no chords (otherwise an exception is thrown by db)\r\n song = Song.objects.get(title=title, artist=artist, chords__isnull=True) \r\n chord_vector = get_chords(song.title, song.artist)\r\n for index,chord in enumerate(chord_vector):\r\n root, notes = decode(chord)\r\n #instead of creating a huge amount of duplicated Chord instances, \r\n #create a new instance only for a first appearing chord in the db.\r\n chord, is_new = Chord.objects.get_or_create(root=root, notes=str(notes), symbol = chord) \r\n# if is_new: print \"created new chord instance - \", root, notes \r\n #create the chord_index relationship\r\n Song_chord_index.objects.create(song=song, chord=chord, index=index) \r\n song.previously_failed_chords = False #if this attempt succeeded, remove bad flag\r","def chordfinisher(*args, **kwargs): # pylint: disable=W0613\n return \"SYNC\"","def get_notes_for_instrument(self, instrument, notes_property=\"notes\"):\n return getattr(self.tracks[self.instruments[instrument]], notes_property)"],"string":"[\n \"def get_chord(key: str, starting_note: int, chord: str) -> List[int]:\\n assert key in constants.NOTES_FOR_KEY, f\\\"Invalid key: {key}.\\\"\\n assert (\\n starting_note in constants.NOTES_FOR_KEY[key]\\n ), f\\\"Note {starting_note} not in key {key}.\\\"\\n assert chord in constants.STEPS_FOR_CHORD, f\\\"Invalid chord: {chord}.\\\"\\n return [starting_note + i for i in constants.STEPS_FOR_CHORD[chord]]\",\n \"def midiToNotes(notes):\\n names = ['C', 'C#', 'D', 'D#', 'E', 'F', 'F#', 'G', 'G#', 'A', 'A#', 'B']\\n chord = []\\n \\n for note in notes:\\n name = names[note%12]\\n octave = int((note-12)/12)\\n chord.append(name + str(octave))\\n \\n return chord\",\n \"def to_sequence(self, chord, inst):\\n time = 0\\n sequence = []\\n for note in self.notes:\\n pitch = chord.to_pitch(note)\\n start = time\\n end = time + note.duration\\n sequence.append([start, end, pitch, chord.to_chord(), inst, note])\\n time += note.duration\\n return sequence\",\n \"def getChord(self, c, velocity, isdrum, isgrace):\\n\\n c = re.split(\\\"[, ]+\\\", c)\\n\\n if not c:\\n error(\\\"You must specify the first note in a solo line\\\")\\n\\n \\\"\\\"\\\" Convert the note part into a series of midi values\\n Notes can be a single note, or a series of notes. And\\n each note can be a letter a-g (or r), a '#,&,n' plus\\n a series of '+'s or '-'s. Drum solos must have each\\n note separated by ' ' or ','s: \\\"Snare1,KickDrum1,44\\\".\\n\\n Each chunk could be:\\n - a midi value (44)\\n - a drum note ( KickDrum1)\\n - a single note (g#) (g&-)\\n - Or groups with spaces/commas (f 100) (44 , KickDrum) (a,b c)\\n \\\"\\\"\\\"\\n\\n events = [] # array for each note event\\n\\n for cc in c:\\n if not cc or not cc[0]:\\n continue\\n if '/' in cc:\\n if cc.count('/') > 1:\\n error(\\\"%s: Only 1 '/velocity' permitted. You can separate \\\" \\\\\\n \\\"notes in the chord with ',' or ' ' and it'll work.\\\" % \\\\\\n self.name)\\n cc, newvel = cc.split('/')\\n if not newvel:\\n error(\\\"%s: expecting 'volume' after '/'\\\" % self.name)\\n\\n if not cc:\\n error(\\\"%s: Volume '/' must immediately follow note.\\\" % self.name)\\n\\n thisvel = stoi(newvel)\\n\\n if thisvel < 0 or thisvel > 127:\\n error(\\\"%s: Velocity must be 0..127, not '%s'.\\\" % (self.name, newvel))\\n else:\\n thisvel = velocity\\n\\n if cc[0] == 'r':\\n if isgrace:\\n warning(\\\"%s: Grace note is a rest (ignored).\\\" % self.name)\\n \\n if events or len(cc) > 1:\\n error(\\\"%s: Rests and notes cannot be combined.\\\" % self.name)\\n else:\\n events.append( NoteEvent(None, 0, isgrace)) # note event with no pitch\\n\\n elif cc[0] in \\\"1234567890\\\":\\n n = stoi(cc, \\\"%s: Note values must be integer or literal.\\\" % \\\\\\n self.name)\\n if n<0 or n>127:\\n error(\\\"%s: Midi notes must be 0..127, not '%s'\\\" % \\\\\\n (self.name, n))\\n\\n # if using value we fake-adjust octave,\\n # it (and transpose) is set later.\\n\\n if not isdrum:\\n n -= self.octave[self.seq]\\n \\n events.append(NoteEvent(n, thisvel, isgrace))\\n\\n\\n elif isdrum: # drum must be a value, * or drum-name\\n if cc == '*':\\n events.append( NoteEvent(self.drumTone, thisvel, isgrace ))\\n else:\\n events.append( NoteEvent(int(MMA.translate.dtable.get(cc)), thisvel, isgrace) )\\n\\n else: # must be a note(s) in std. notation\\n cc = list(cc)\\n while cc:\\n name = cc.pop(0)\\n\\n if not name in self.midiNotes:\\n error(\\\"%s: Encountered illegal note name '%s'\\\"\\n % (self.name, name))\\n\\n n = self.midiNotes[ name ] # name is string, n is value\\n \\n # Parse out a \\\"#', '&' or 'n' accidental.\\n\\n if cc and cc[0] in accValues:\\n i = cc.pop(0)\\n self.acc[name] = accValues[i]\\n\\n n += self.acc[name] # accidental adjust (from above or keysig)\\n\\n # Parse out +/- (or series) for octave\\n\\n while cc and cc[0] in '+-':\\n a = cc.pop(0)\\n if a == '+':\\n n += 12\\n else:\\n n -= 12\\n\\n events.append( NoteEvent(n, thisvel, isgrace) )\\n \\n\\n return events\",\n \"def do_notes(self, sym):\\n # DRY: duplication with Roman.py - FIXME\\n if sym == '-':\\n # REST\\n return []\\n note_or_chord = self.do(sym)\\n if note_or_chord is None:\\n return []\\n elif type(note_or_chord) == Chord:\\n return note_or_chord.notes\\n else:\\n return [ note_or_chord ]\",\n \"def build_chord_names(bar_notes: List[List[str]]):\\n bar_available_chords = []\\n # find corresponding chords for each bar\\n for one_bar in bar_notes:\\n chord_names = chords_from_note_names(one_bar)\\n # print(\\\"===> chord name of choices %s from notes %s\\\" % (chord_names, one_bar))\\n bar_available_chords.append(chord_names)\\n\\n # now each bar may have several available chord\\n # we want them to be different and want to use as many as possible different chords\\n bar_chords = []\\n used = set([])\\n for i in range(len(bar_available_chords)):\\n available_chords = bar_available_chords[i]\\n if i == 0:\\n # use directly first chord as reference\\n bar_chords.append(available_chords[0])\\n used.add(available_chords[0])\\n continue\\n\\n if len(available_chords) > 0:\\n # now we want to make a difference\\n hit = False\\n for chord in available_chords:\\n if chord in used:\\n continue\\n bar_chords.append(chord)\\n used.add(chord)\\n hit = True\\n break\\n\\n # all chords are used before,\\n # try not to be the same with the first one\\n if hit is False:\\n last_one = bar_chords[len(bar_chords) - 1]\\n for chord in available_chords:\\n if chord != last_one:\\n bar_chords.append(chord)\\n used.add(chord)\\n hit = True\\n break\\n # still false ? that is weird but we don't have much choice here\\n if hit is False:\\n bar_chords.append(available_chords[0])\\n used.add(available_chords[0])\\n return bar_chords\",\n \"def get_random_chord(self) -> []:\\n return self.get_random_chords(1)[0]\",\n \"def randNotes(self, value, chord):\\n aChord = mgChord(value, chord)\\n self.data = []\\n for i in range(len(self.rhythm)):\\n note = random.choice(aChord).copy()\\n note.setDuration(self.rhythm[i])\\n self.data.append(note)\",\n \"def get_notes():\\n notes = []\\n durs = []\\n\\n for file in glob.glob(\\\"D:\\\\\\\\anthems\\\\\\\\data\\\\\\\\*.mid\\\"):\\n midi = converter.parse(file)\\n\\n print(\\\"Parsing %s\\\" % file)\\n\\n notes_to_parse = None\\n\\n try:\\n s2 = instrument.partitionByInstrument(midi)\\n notes_to_parse = s2.parts[0].recurse() \\n except:\\n notes_to_parse = midi.flat.notes\\n \\n for element in notes_to_parse:\\n if isinstance(element, note.Note):\\n notes.append(str(element.pitch))\\n durs.append(element.duration.quarterLength)\\n elif isinstance(element, chord.Chord):\\n notes.append('.'.join(str(n) for n in element.normalOrder))\\n durs.append(element.duration.quarterLength)\\n\\n return notes, durs\",\n \"def add_chord(meas, chord_name):\\r\\n \\r\\n for note in CHORDS[chord_name]:\\r\\n meas.NewNote(0, 64, note, 5)\",\n \"def notes2trk(notes):\\n\\n trk = MidiTrack()\\n\\n for i, note in enumerate(notes):\\n if note[0] == 's': # Message for a silence\\n trk.append(Message(\\\"note_on\\\", note=0, velocity=0, time=0))\\n trk.append(Message(\\\"note_off\\\", note=0, velocity=0, time=note[1]))\\n continue\\n\\n nlist = note[0].split(' ')\\n\\n for c_n in nlist: # Add the note on message\\n n_nb, v_on, v_off = char2note(c_n)\\n if len(note)==2:\\n trk.append(Message(\\\"note_on\\\", note=n_nb, velocity=v_on, time=0))\\n else:\\n trk.append(Message(\\\"note_on\\\", note=n_nb, velocity=note[2],\\n time=0))\\n if len(note) == 4:\\n trk.append(Message(\\\"note_off\\\", note=n_nb, velocity=v_off,\\n time=note[1]))\\n else:\\n trk.append(Message(\\\"note_off\\\", note=n_nb, velocity=v_off,\\n time=note[1]))\\n for c_n in nlist[:-1]: # Add the note off message\\n n_nb, v_on, v_off = char2note(c_n)\\n if len(note) == 4: # For short note\\n trk.append(Message(\\\"note_off\\\", note=n_nb, velocity=note[3],\\n time=0))\\n else:\\n trk.append(Message(\\\"note_off\\\", note=n_nb, velocity=v_off,\\n time=0))\\n\\n return trk\",\n \"def random_note_in_chord_and_vocal_range(relPitchList, key, vocal_range, prev_note):\\n\\n\\t# for rests, can only be a rest\\n\\tif relPitchList == -1:\\n\\t\\treturn [note.Rest()]\\n\\n\\t# extract_range\\n\\tlowest_note, highest_note = vocal_range\\n\\n\\tif (not isinstance(prev_note, note.Rest)) and (prev_note is not None):\\n\\n\\t\\t# restrict intervals to a sixth\\n\\t\\tif prev_note.pitch.transpose(-7) > lowest_note:\\n\\t\\t\\tlowest_note = prev_note.pitch.transpose(-7)\\n\\n\\t\\tif prev_note.pitch.transpose(7) < highest_note:\\n\\t\\t\\thighest_note = prev_note.pitch.transpose(7)\\n\\n\\t# turn relative Pitch into a note.Note\\n\\tnotes = []\\n\\n\\n\\tfor relPitch in relPitchList:\\n\\t\\tif relPitch != 0:\\n\\t\\t\\tinterval_ = interval.ChromaticInterval(relPitch)\\n\\t\\t\\tnotes.append(note.Note(interval_.transposePitch(key.getTonic()), quarterLength=1))\\n\\t\\telse:\\n\\t\\t\\t# needed as no transposition by 0 possible\\n\\t\\t\\tnotes.append(note.Note(key.getTonic(), quarterLength=1))\\n\\t \\n\\t# get all notes in right octave\\n\\toctave_corrected_notes = []\\n\\tfor n in notes:\\n\\n\\t\\t# possible octaves\\n\\t\\tlow_octave = lowest_note.octave\\n\\t\\thigh_octave = highest_note.octave\\n \\n\\t\\t# select random octave\\n\\t\\t# if only one choice available\\n\\t\\toctave_choice = np.arange(low_octave, high_octave + 1)\\n\\n\\t\\t# loop throughpossible octaves\\n\\t\\tfor o in octave_choice:\\n\\t\\t\\tnote_ = copy.deepcopy(n)\\n\\t\\t\\tnote_.octave = o\\n\\t\\t\\tif (note_.pitch >= lowest_note) and (note_.pitch <= highest_note):\\n\\t\\t\\t\\toctave_corrected_notes.append(note_)\\n\\n\\treturn octave_corrected_notes\",\n \"def mgChordAugmented(value):\\n chord = [MgNote(value), MgNote(value) + 4, MgNote(value) + 8]\\n return chord\",\n \"def get_next_chord_conditional(self, chord, note):\\n chords = self.key.c_chord_given_c_note(note[:-1])\\n idxs = []\\n for j in range(len(chords)):\\n for i in range(len(self.key.chords)):\\n if chords[j] == self.key.chords[i]:\\n idxs.append(i)\\n\\n idxs = list(set(idxs))\\n\\n chord = self.specified_chord_to_chord(chord)\\n cond_dist_dict = self.chord_chord_map[chord] # Gets list of candidate chords\\n next_chords = sorted(cond_dist_dict.keys())\\n next_probs = [cond_dist_dict[next_chord] for next_chord in next_chords]\\n\\n true_next_chords = [next_chords[int(i)] for i in idxs]\\n true_next_probs = [next_probs[int(i)] for i in idxs]\\n\\n allsum = sum(true_next_probs)\\n\\n for i in range(len(true_next_probs)):\\n true_next_probs[i] = true_next_probs[i] / allsum\\n\\n\\n cond_dist = stats.rv_discrete(name='cond_dist', values=(true_next_chords, true_next_probs))\\n return self.key.chords[cond_dist.rvs(size=1)-1]\",\n \"def chords_from_note_names(note_names: List[str]):\\n pos_set = set([])\\n for note_name in note_names:\\n pos_set.add(c_major_chord_pos[note_name])\\n\\n # we have their pos, try to find the ideal chord position\\n match_of_chord = {}\\n for order, members in major_progression.items():\\n hit = 0\\n for s in pos_set:\\n if s in members:\\n hit += 1\\n match_of_chord[order] = hit\\n\\n # if we have same hits\\n # may be the first highest hit with the first note name\\n highest_orders = []\\n max_hits = max(match_of_chord.values())\\n for order, hits in match_of_chord.items():\\n if hits == max_hits:\\n highest_orders.append(order)\\n\\n # return the names of highest order of choice\\n chord_names = []\\n for order in highest_orders:\\n for name, that_order in c_major_chord_pos.items():\\n if order == that_order:\\n chord_names.append(name)\\n break\\n return chord_names\",\n \"def mgChordMajor(value):\\n chord = [MgNote(value), MgNote(value) + 4, MgNote(value) + 7]\\n return chord\",\n \"def ks_chord(chord, N, alpha, f_s):\\n y = np.zeros(N)\\n # the chord is a dictionary: pitch => gain\\n for note, gain in chord.items():\\n # create an initial random-filled KS buffer the note\\n x = np.random.randn(int(np.round(float(f_s) / note_freq(note))))\\n y = y + gain * KS(x, N, alpha)\\n return y\",\n \"def mgChordDiminished(value):\\n chord = [MgNote(value), MgNote(value) + 3, MgNote(value) + 6]\\n return chord\",\n \"def chordmode():\\n for token in consume():\\n if source.inSelection and isinstance(token, tokenizer.Pitch):\\n transpose(token, 0)\",\n \"def getnotes():\",\n \"def voicings(chord_str, strings=(4, 9, 2, 7, 11, 4), fingers=4, root_string=0, mute_above=False):\\n gen = FingeringsGenerator(strings=strings, fingers=fingers)\\n chord = parse_chord_name(chord_str)\\n res = []\\n for voicing in gen.generate(chord, root_string=root_string, mute_above=mute_above):\\n q = QueryResult(voicing, chord)\\n res.append(q)\\n res.sort()\\n return res\",\n \"def play_chord(self, velocity, timing): #timing = number of ms between successive notes\\n self.l.append(self.midi.note_on(self.l.chord_channel, self.chord[0], velocity))\\n for i, n in enumerate(self.chord[1:], start=1):\\n if timing:\\n self.pending.append((n, velocity, time.ticks_ms()+timing*i))\\n else:\\n self.l.append(self.midi.note_on(self.l.chord_channel, n, velocity))\",\n \"def get_notes(n_notes=3):\\n notes = []\\n \\n for ii, file in enumerate(glob.glob(\\\"data/maestro-v2.0.0/2004/*.midi\\\")):\\n if ii >= n_notes:\\n break\\n pickle_file_name = file[:-4] + 'pkl'\\n\\n if os.path.isfile(pickle_file_name):\\n print(f'Reading parsed file: {pickle_file_name}')\\n with open(pickle_file_name, 'rb') as handle:\\n midi = pickle.load(handle)\\n else:\\n midi = converter.parse(file)\\n\\n with open(pickle_file_name, 'wb') as handle:\\n print(f'writing parsed file: {pickle_file_name}')\\n unserialized_data = pickle.dump(midi, \\n handle, \\n protocol=pickle.HIGHEST_PROTOCOL\\n )\\n\\n\\n print(\\\"Parsing %s\\\" % file)\\n\\n notes_to_parse = None\\n try: # file has instrument parts\\n s2 = instrument.partitionByInstrument(midi)\\n notes_to_parse = s2.parts[0].recurse() \\n except: # file has notes in a flat structure\\n notes_to_parse = midi.flat.notes\\n \\n for element in notes_to_parse:\\n if isinstance(element, note.Note):\\n notes.append(str(element.pitch))\\n elif isinstance(element, chord.Chord):\\n notes.append('.'.join(str(n) for n in element.normalOrder))\\n \\n return notes\",\n \"def extract_notes(bars):\\n output = []\\n for bar in bars:\\n notes = np.array([bar_to_notes(b,i) for i,b in enumerate(bar)])\\n\\n # Flatten into a single array\\n midi_notes = []\\n for i in range(notes.shape[1]):\\n if all(notes[:,i] == -1):\\n midi_notes.append(None)\\n else:\\n midi_notes.append([n for n in notes[:,i] if n!=-1])\\n output.append(midi_notes)\\n return output\",\n \"def determine(self, shorthand=False):\\n return chords.determine(self.get_note_names(), shorthand)\",\n \"def loadChords(self, choFile):\\n self.chords = []\\n with open(choFile, 'r') as f:\\n for line in f:\\n for match in re.findall(\\\"\\\\[.*?\\\\]\\\", line):\\n chord = match[1:-1]\\n if re.search(chordRE, chord) is not None:\\n self.chords.append(chord)\\n else:\\n print(chord + \\\" is not a chord\\\")\",\n \"def chord_generator(iterable):\\n return (CHORDS[chord_symbol] for chord_symbol in iterable)\",\n \"def f(chord):\\r\\n m, length, dim = chord.shape\\r\\n chord2 = chord.reshape([m*length, dim])\\r\\n weighted_chords = np.dot(chord2, chord_id2sign)\\r\\n return weighted_chords.reshape(m, length, 12)\",\n \"def mgChordMinor(value):\\n chord = [MgNote(value), MgNote(value) + 3, MgNote(value)+7]\\n return chord\",\n \"def getLine(self, pat):\\n \\n sc=self.seq\\n\\n \\\"\\\"\\\" Get a COPY of the keysignature note table (a dict). \\n As a bar is processed the table is updated. There is one difference\\n here---in real music an accidental for a note in a given octave does\\n not effect the following same-named notes in different octaves.\\n In this routine IT DOES.\\n\\n FollowKey=Off is usually (user) set when using a sequence pattern.\\n \\\"\\\"\\\"\\n\\n if self.followKey: # this is the default\\n self.acc=keySig.accList.copy()\\n else: # disable the feature. Useful for sequence patterns!\\n self.acc = {'a': 0, 'c': 0, 'b': 0, 'e': 0, 'd': 0, 'g': 0, 'f': 0}\\n\\n \\n # list of notename to midivalues\\n\\n self.midiNotes = {'c':0, 'd':2, 'e':4, 'f':5, 'g':7, 'a':9, 'b':11, 'r':None }\\n\\n\\n \\\"\\\"\\\" The initial string is in the format \\\"1ab;4c;;4r;\\\". The trailing\\n ';' is important and needed. If we don't have this requirement\\n we can't tell if the last note is a repeat of the previous. For\\n example, if we have coded \\\"2a;2a;\\\" as \\\"2a;;\\\" and we didn't\\n have the 'must end with ;' rule, we end up with \\\"2a;\\\" and\\n then we make this into 2 notes...or do we? Easiest just to\\n insist that all bars end with a \\\";\\\".\\n \\\"\\\"\\\"\\n\\n if not pat.endswith(';'):\\n error(\\\"All Solo strings must end with a ';'\\\")\\n \\n # Get the end of the bar in ticks. If we're doing a truncate\\n # use the temporary bar length, otherwise figure it out.\\n\\n if MMA.truncate.length:\\n barEnd = MMA.truncate.length\\n else:\\n barEnd = gbl.BperQ*gbl.QperBar\\n\\n duration = self.getNoteLen('4') # default note length\\n velocity = 90 # intial/default velocity for solo notes\\n articulation = 1 # additional articulation for solo notes\\n\\n notes={} # NoteEvent list, keys == offset\\n \\n if self.drumType:\\n isdrum = 1\\n lastc = str(self.drumTone)\\n else:\\n isdrum = None\\n lastc = '' # last parsed note \\n\\n # convert pat to a list\\n pat = [x.strip() for x in pat.split(';')[:-1]]\\n\\n # set initial offset into bar. This compensates for the previous\\n # bar ending in a ~ and this one starting with ~.\\n # This special case bumps the initial bar offset\\n\\n if pat[0].startswith(\\\"~\\\"):\\n if not self.endTilde or self.endTilde[1] != gbl.tickOffset:\\n error(\\\"Previous line did not end with '~'\\\")\\n else:\\n pat[0] = pat[0][1:].strip()\\n offset = self.endTilde[0]\\n else:\\n offset = 0\\n\\n lastOffset = None\\n\\n # Strip off trailing ~. This permits long notes to end past the\\n # current barend. Note, flag set for the next bar to test for\\n # a leading ~.\\n\\n if pat[-1].endswith(\\\"~\\\"):\\n self.endTilde = [1, gbl.tickOffset + barEnd ]\\n pat[-1] = pat[-1][:-1].strip()\\n else:\\n self.endTilde = []\\n\\n ##################################################\\n # Now we can parse each chunk of the solo string.\\n\\n for a in pat:\\n \\\"\\\"\\\" If we find a \\\"<>\\\" we just ignore that. It's useful when\\n multiple continuation bars are needed with ~.\\n \\\"\\\"\\\"\\n \\n accentVol = None\\n accentDur = None\\n isgrace = False\\n\\n if a == '<>':\\n continue\\n\\n \\\"\\\"\\\" Next, strip out all '<SPECIAL=xx>' settings.\\n\\n VOLUME: If no option is set, we assume VOLUME. The default\\n velocity setting was set before the loop (==90) and is\\n changed here for the duration of the current bar/riff.\\n The set velocity will still be modified by the global\\n and track volume adjustments.\\n \\n DURATION: Duration or articulation setting is defaulted to 100.\\n Changing it here will do so for the duration of the\\n bar/riff. Note, the track ARTICULATION is still applied.\\n\\n ISGRACE: grace note indication and offset. Offset is optional.\\n \\n OFFSET: change the current offset into the bar. Can be negative\\n which forces overlapping notes.\\n\\n \\\"\\\"\\\"\\n\\n a, vls = pextract(a, \\\"<\\\", \\\">\\\")\\n\\n if vls:\\n if len(vls) > 1:\\n error(\\\"Only 1 <modifier> is permitted per note-set\\\")\\n\\n vls = vls[0].split(',')\\n for vv in vls:\\n\\n vv = vv.upper().strip()\\n\\n if vv == 'GRACE':\\n vv='GRACE=2'\\n\\n if not '=' in vv:\\n vv = \\\"VOLUME=\\\" + vv\\n \\n vc,vo = vv.split('=', 1) # note: it's already uppercase!\\n\\n if vc == 'VOLUME':\\n if vo in MMA.volume.vols: # arg was a volume 'FF, 'mp', etc.\\n velocity *= MMA.volume.vols[vo]\\n else:\\n error(\\\"%s: No volume '%s'.\\\" % (self.name, vo))\\n \\n elif vc == 'GRACE':\\n isgrace = stof(vo, \\\"%s: Expecting a value, not %s.\\\" % (self.name, vo))\\n if isgrace <= 0:\\n error(\\\"%s: Offset modifier must be greater than 0.\\\" \\\\\\n % self.name)\\n \\n elif vc == 'OFFSET':\\n offset = stoi(vo, \\\"%s: Offset expecting integer, not %s.\\\" \\\\\\n % (self.name, vo))\\n\\n if offset < 0:\\n error(\\\"%s: Offset must be positive.\\\" % self.name)\\n\\n if offset >= barEnd:\\n error(\\\"%s: Offset has been set past the end of the bar.\\\" \\\\\\n % self.name )\\n\\n\\n elif vc == 'ARTICULATE':\\n articulation = stoi(vo, \\\"%s: Articulation expecting integer,\\\"\\n \\\" not %s.\\\" % (self.name, vo))\\n \\n if articulation < 1 or articulation >200:\\n error(\\\"%s: Articulation must be 1..200, not %s.\\\" % \\\\\\n (self.name, vo) )\\n articulation /= 100.\\n\\n else:\\n error(\\\"%s: Unknown command '%s'.\\\" % (self.name, vv))\\n\\n if offset >= barEnd:\\n error(\\\"Attempt to start Solo note '%s' after end of bar\\\" % a)\\n\\n \\\"\\\"\\\" Split the chord chunk into a note length and notes. Each\\n part of this is optional and defaults to the previously\\n parsed value.\\n \\\"\\\"\\\"\\n\\n i = 0\\n while i < len(a):\\n if not a[i] in '1234567890.+-tT':\\n break\\n else:\\n i += 1\\n\\n if i:\\n duration = self.getNoteLen(a[0:i].replace(' ', '') )\\n a = a[i:].strip()\\n\\n\\n # next item might be an accent string. \\n\\n i = 0\\n while i < len(a):\\n if not a[i] in \\\"!-_^&\\\":\\n break\\n else:\\n i += 1\\n\\n if i:\\n c = a[0:i]\\n accentVol = 1\\n accentDur = 1\\n\\n accentDur -= c.count('!') * .2\\n accentDur += c.count('-') * .2\\n accentDur += c.count('_') * .2\\n accentVol += c.count('^') * .2\\n accentVol -= c.count('&') * .2\\n\\n if accentDur<.1: accentDur = .1\\n if accentDur>2: accentDur = 2\\n if accentVol<.1: accentVol = .1\\n if accentVol>2: accentVol = 2\\n a = a[i:]\\n\\n # Now we get to look at pitches.\\n\\n if not a or a=='' or a==' ':\\n a=lastc\\n evts = self.getChord(a, velocity, isdrum, isgrace) # get chord\\n \\n for e in evts:\\n e.velocity = self.adjustVolume(e.defvelocity, offset)\\n if accentVol:\\n e.velocity *= accentVol\\n e.duration = duration\\n\\n if accentDur:\\n e.articulation = articulation * accentDur\\n else:\\n e.articulation = articulation \\n\\n lastc = a # save last chord for next loop\\n\\n\\n # add note event(s) to note{}\\n\\n if not offset in notes:\\n notes[offset] = []\\n notes[offset].extend(evts)\\n\\n lastOffset = offset\\n if not e.isgrace:\\n offset += duration\\n\\n\\n if offset <= barEnd:\\n if self.endTilde:\\n error(\\\"Tilde at end of bar has no effect\\\")\\n\\n else:\\n if self.endTilde:\\n self.endTilde[0]=offset-barEnd\\n else:\\n warning(\\\"%s, end of last note overlaps end of bar by %g \\\"\\n \\\"beat(s).\\\" % (self.name, (offset-barEnd)/float(gbl.BperQ)))\\n\\n if MMA.swing.mode:\\n notes = MMA.swing.swingSolo(notes)\\n\\n return notes\",\n \"def degrees_on_beats(chorale):\\n \\n\\t# key signature\\n\\tkey = chorale.analyze('key') # may need to put this elsewhere to deal with modulation\\n\\n\\t# create output stream\\n\\toutstream = stream.Stream()\\n\\n\\t# chordify the chorale\\n\\tcc = chorale.chordify() # <- will be replaced by predicted chords\\n\\n\\toutstream = extract_utils.to_crotchet_stream(cc, chord_flag=True)\\n \\n\\t# roman numeral output stream\\n\\trn_outstream = []\\n\\n\\t# convert chords to roman numerals\\n\\tfor bc in outstream.recurse().getElementsByClass([chord.Chord, note.Rest]):\\n\\t\\tif bc.isRest:\\n\\t\\t bc = -1\\n\\t\\telse:\\n\\t\\t bc = relOrderedPitchClassesString(bc.orderedPitchClasses, key)\\n\\t\\trn_outstream.append(bc)\\n \\n\\treturn rn_outstream\",\n \"def get_diatonic_chord(mode, root_step, tension) -> [int]:\\n chord = []\\n mode_idx = ChordMdConsts.MODE.index(mode)\\n mode_name = ChordMdConsts.MODE[mode_idx]\\n mode_seq = ChordMdConsts.MODE_SEQS[mode_name]\\n __tone_num = min(5, 3 + tension)\\n\\n for i in range(__tone_num):\\n scalepos = (i * 2) % len(mode_seq)\\n tonepos = mode_seq[scalepos]\\n if (i * 2) >= len(mode_seq):\\n tonepos += 12\\n chord.append(tonepos)\\n return chord\",\n \"def process_notes_in_song(dict_time_notes, seq_len = 50):\\n list_of_dict_keys_time = []\\n \\n for key in dict_time_notes:\\n sample = dict_time_notes[key]\\n times = np.unique(np.where(sample > 0)[1])\\n index = np.where(sample > 0)\\n dict_keys_time = {}\\n\\n for time in times:\\n index_where = np.where(index[1] == time)\\n notes = index[0][index_where]\\n dict_keys_time[time] = notes\\n list_of_dict_keys_time.append(dict_keys_time)\\n return list_of_dict_keys_time\",\n \"def create_melody(inst, chord_progression):\\r\\n \\r\\n for chord in chord_progression:\\r\\n \\r\\n meas = inst.NewMeasure()\\r\\n last_note = random.choice(CHORDS[chord])\\r\\n pos = 0\\r\\n \\r\\n while pos < 64:\\r\\n dur = random.choice([8, 8, 16, 16]) \\r\\n last_note = get_rdm_note(chord, last_note)\\r\\n if random.randint(1,10) <= 8: # 20% silence\\r\\n meas.NewNote(pos, dur, last_note, 5)\\r\\n pos += dur\",\n \"def bar_to_lilypond_notes(notes):\\n lp_notes = []\\n if notes[0] is None:\\n notes = notes[1:]\\n if notes[0] is None:\\n lp_notes.append(\\\"r\\\")\\n for n in notes:\\n if n is None:\\n continue\\n if type(n) is list:\\n lp_notes.append([midi_to_lilypond_note(x) for x in n])\\n else:\\n lp_notes.append(midi_to_lilypond_note(n))\\n return lp_notes\",\n \"def compo_test():\\n trk = MidiTrack()\\n b = mid.ticks_per_beat\\n t = int(b/3)\\n notes = [['c4', b], ['s', b], ['c4', t], ['e4', t], ['g4', t], ['c4', b]]\\n trk = notes2trk(trk, notes)\\n return trk\",\n \"def extract_notes(music, resolution):\\n # Check resolution\\n assert music.resolution == resolution\\n\\n # Extract notes\\n notes = []\\n for track in music:\\n if track.is_drum or track.program not in KNOWN_PROGRAMS:\\n continue\\n for note in track:\\n beat, position = divmod(note.time, resolution)\\n notes.append(\\n (beat, position, note.pitch, note.duration, track.program)\\n )\\n\\n # Deduplicate and sort the notes\\n notes = sorted(set(notes))\\n\\n return np.array(notes)\",\n \"def _on_note(self) -> Iterator:\\n return filter(\\n lambda x: x.type == constants.NOTE_ON, self.midi_data)\",\n \"def scorepart_to_notes(part, morphetic_pitch=False):\\n\\n bm = part.beat_map\\n\\n if morphetic_pitch:\\n pitch_attr = attrgetter('morphetic_pitch')\\n else:\\n pitch_attr = attrgetter('midi_pitch')\\n notes = np.array([(bm(n.start.t), bm(n.end.t), pitch_attr(n))\\n for n in part.notes], np.float)\\n\\n ids = np.array([n.id for n in part.notes])\\n\\n return notes, ids\",\n \"def create_bar(self, chord, key, midi):\\n if self.default:\\n return [(MEASURE_LENGTH, midi)]\\n degree_map = self.get_degree_map(chord, key, midi)\\n # map 1 3 5 to the chord degrees\\n chord_degree_map = {1: chord.get_chord_pitches()[0], 3: chord.get_chord_pitches()[1], 5: chord.get_chord_pitches()[2]}\\n if len(degree_map) == 0:\\n print \\\"RETURNING DEFAULT BECAUSE NO DEGREE MAP\\\"\\n return [(MEASURE_LENGTH, midi)] # return this note as a whole note as this rhythm template doesn't work\\n\\n measure = []\\n for note in self.rhythm_template:\\n real_degree = chord_degree_map[note[1]]\\n midi = degree_map[real_degree]\\n measure.append((note[0], midi))\\n return measure\",\n \"def get_notes():\\n song_index_to_notes = {}\\n\\n for file in glob.glob(\\\"../data/midi/*.mid\\\"):\\n midi = converter.parse(file)\\n song_index = int(os.path.splitext(os.path.basename(file))[0])\\n #print(\\\"Parsing %s with an index %d\\\" % (file, song_index))\\n\\n notes_to_parse = None\\n notes = []\\n try: # file has instrument parts\\n s2 = instrument.partitionByInstrument(midi)\\n notes_to_parse = s2.parts[0].recurse()\\n except: # file has notes in a flat structure\\n notes_to_parse = midi.flat.notes\\n\\n for element in notes_to_parse:\\n if isinstance(element, note.Note):\\n notes.append(str(element.pitch))\\n elif isinstance(element, chord.Chord):\\n notes.append('.'.join(str(n) for n in element.normalOrder))\\n\\n song_index_to_notes[song_index] = notes\\n\\n return song_index_to_notes\",\n \"def notes_played(self) -> List[Dict]:\\n return [\\n dict(note=self._midi_to_note(note.note), time=note.time)\\n for note in self._on_note()\\n ]\",\n \"def extractSolo(ln, rptcount):\\n\\n a = ln.count('{')\\n b = ln.count('}')\\n\\n if a != b:\\n error(\\\"Mismatched {}s for solo found in chord line\\\")\\n\\n if a:\\n if rptcount > 1:\\n error(\\\"Bars with both repeat count and solos are not permitted\\\")\\n\\n ln, solo = pextract(ln, '{', '}')\\n \\n if len(solo) > len(autoSoloTracks):\\n error(\\\"Too many melody/solo riffs in chord line. %s used, \\\"\\n \\\"only %s defined\\\" % (len(solo), len(autoSoloTracks)) )\\n\\n\\n firstSolo = solo[0][:] # save for autoharmony tracks\\n \\n \\\"\\\"\\\" We have the solo information. Now we loop though each \\\"solo\\\" and:\\n 1. Ensure or Create a MMA track for the solo\\n 2. Push the solo data into a Riff for the given track.\\n \\\"\\\"\\\"\\n\\n for s, trk in zip(solo, autoSoloTracks):\\n if not s: continue # skip placeholder/empty tracks\\n MMA.alloc.trackAlloc(trk, 1)\\n t = gbl.tnames[trk]\\n if t.riff:\\n error(\\\"%s: Attempt to add {} solo when the track \\\"\\n \\\"has pending RIFF data.\\\" % t.name)\\n t.setRiff( s.strip() )\\n\\n \\\"\\\"\\\" After all the solo data is interpreted and sent to the\\n correct track, we check any leftover tracks. If any of these\\n tracks are empty of data AND are harmonyonly the note\\n data from the first track is interpeted again for that\\n track. Tricky: the max() is needed since harmonyonly can\\n have different setting for each bar...this way\\n the copy is done if ANY bar in the seq has harmonyonly set.\\n \\\"\\\"\\\"\\n\\n for t in autoSoloTracks[1:]:\\n if t in gbl.tnames and not gbl.tnames[t].riff \\\\\\n and max(gbl.tnames[t].harmonyOnly):\\n gbl.tnames[t].setRiff( firstSolo[:] )\\n\\n if gbl.debug:\\n print \\\"%s duplicated to %s for HarmonyOnly.\\\" % (trk, t)\\n\\n return ln\",\n \"def c_major_octave_chord(chord_name: str, octave: int):\\n pitch = note_name_octave_to_pitch(chord_name, octave)\\n if pitch is None:\\n raise Exception('unable to find %s%s note' % (chord_name, octave))\\n\\n # build the major chord\\n pos = c_major_chord_pos[chord_name]\\n pitches = [pitch]\\n tmp = copy(pitch)\\n for gap in major_progression_pitch_gap[pos]:\\n tmp += gap\\n pitches.append(tmp)\\n return pitches\",\n \"def parseChordTuple(self, notes=None, size=4, deltatimes=None, deltatolerance=0.02, debug=False):\\n\\n #for note in notes:\\n #if note not in self._chordmemory:\\n #self._chordmemory.append(note)\\n #for deltatime in deltatimes:\\n #if deltatime not in self._deltamemory:\\n #self._deltamemory.append(deltatime)\\n \\n #if len(self._chordmemory) > size:\\n #self._chordmemory = self._chordmemory[1:]\\n #if len(self._deltamemory) > size:\\n #self._deltamemory = self._deltamemory[1:]\\n \\n self._chordmemory = notes \\n self._deltamemory = deltatimes\\n #if debug:\\n #print('b chordmem: ', self._chordmemory, 'b deltamem', self._deltamemory)\\n \\n #if len(self._chordmemory) > 1:\\n #average = np.average(np.diff(sorted(self._deltamemory)))\\n #if debug:\\n #print('average:', average)\\n #if average > deltatolerance:\\n #self._chordmemory.pop(0)\\n #self._deltamemory.pop(0)\\n ##self._chordmemory = []\\n ##self._deltamemory = []\\n\\n if len(self._chordmemory) == size:\\n average = np.average(np.diff(sorted(self._deltamemory)))\\n \\n if average < deltatolerance:\\n chord = self._chordmemory\\n self._chordmemory = []\\n self._deltamemory = []\\n if debug:\\n print('detected!', chord)\\n return True, chord\\n #else:\\n #chord = self._chordmemory\\n #self._chordmemory = []\\n #self._deltamemory = [] \\n \\n if debug:\\n print('chordmem: ', self._chordmemory, 'deltamem', \\n self._deltamemory, 'average', average)\\n\\n\\n return False, None\",\n \"def find_strings(staff, guitar, *, allow_open=True, reverse=False,\\n window_size=7, distinct_only=False):\\n cursor = IOCursor(staff=staff, guitar=guitar)\\n while not cursor.after_end():\\n if cursor.at_chord():\\n cursor.current_output = find_multi_fingering(\\n frets_matrix=cursor.get_all_frets(),\\n guitar=guitar,\\n )\\n logger.info(json.dumps({\\n \\\"found\\\": \\\"chord\\\",\\n \\\"out\\\": cursor.current_output,\\n \\\"move\\\": \\\"R\\\",\\n }))\\n cursor.to_right()\\n elif cursor.at_possible_note():\\n logger.info(json.dumps({\\\"found\\\": \\\"melody\\\"}))\\n for dist_range in count(3):\\n logger.info(json.dumps({\\n \\\"processing\\\": \\\"melody\\\",\\n \\\"dist_range\\\": dist_range,\\n }))\\n if AdaptiveFretFinderMelody(\\n cursor=cursor,\\n guitar=guitar,\\n dist_range=dist_range,\\n allow_open=allow_open,\\n reverse=reverse,\\n window_size=window_size,\\n distinct_only=distinct_only,\\n ).run():\\n logger.info(json.dumps({\\n \\\"processed\\\": \\\"melody\\\",\\n \\\"dist_range\\\": dist_range,\\n }))\\n break # Finished in an \\\"accept\\\" state\\n else: # A rest or an impossible note\\n logger.info(json.dumps({\\n \\\"found\\\": \\\"rest\\\" if cursor.at_rest() else \\\"unknown\\\",\\n \\\"move\\\": \\\"R\\\",\\n }))\\n cursor.to_right()\\n cursor.freeze_left() # \\\"Store\\\" the new result\\n return cursor.output_tape\",\n \"def get_full_context(chords_seq):\\n chord , context = [],[]\\n m_before = context_size\\n empty_before = 0\\n m_after = context_size\\n empty_after = 0\\n\\n size = len(chords_seq)\\n for i in range(size):\\n # the neighborhood of chords at the beginning or at the end of a sequence is smaller\\n if i < m_before:\\n m_before = i\\n empty_before = context_size - m_before\\n elif size - i <= m_after:\\n m_after = size - i - 1\\n empty_after = context_size - m_after\\n\\n neighborhood = []\\n\\n for j in range(empty_before):\\n neighborhood.append([])\\n if (m_before > 0):\\n neighborhood.extend(map(list, chords_seq[(i - m_before):i]))\\n if (m_after > 0):\\n neighborhood.extend(map(list, chords_seq[(i + 1):(i + m_after + 1)]))\\n for j in range(empty_after):\\n neighborhood.append([])\\n\\n #for context_chord in neighborhood:\\n # context_chord.append(EOS_ID)\\n\\n chord.append(list(chords_seq[i]))\\n context.append(neighborhood)\\n m_before = context_size\\n m_after = context_size\\n empty_after = 0\\n empty_before = 0\\n\\n return (chord,context)\",\n \"def load_chords(fhandle: TextIO) -> annotations.ChordData:\\n begs = [] # timestamps of chord beginnings\\n ends = [] # timestamps of chord endings\\n chords = [] # chord labels\\n\\n reader = csv.reader(fhandle, delimiter=\\\"\\\\t\\\")\\n for line in reader:\\n begs.append(float(line[0]))\\n ends.append(float(line[1]))\\n chords.append(line[2])\\n\\n return annotations.ChordData(np.array([begs, ends]).T, \\\"s\\\", chords, \\\"harte\\\")\",\n \"def export_phenotype(chords):\\n import music21\\n partupper = music21.stream.Part()\\n m = music21.stream.Measure()\\n for _chord in chords:\\n c = music21.chord.Chord(_chord)\\n m.append(c)\\n partupper.append(m)\\n partupper.write('midi', './sample.mid')\",\n \"def notes(self):\\n return reapy.NoteList(self)\",\n \"def __scrape_chords(html):\\n\\n soup = BeautifulSoup(html, 'html.parser')\\n \\n try:\\n chords = []\\n for element in soup.find_all('span', attrs={\\\"class\\\": \\\"_3bHP1 _3ffP6\\\"}):\\n\\n chord = element.get_text()\\n chord = equiChord(chord)\\n chords.append(chord)\\n\\n return chords\\n except TypeError:\\n return []\",\n \"def midiToNotesRec(notes, totalnotes, note_names=[]):\\n names = ['C', 'C#', 'D', 'D#', 'E', 'F', 'F#', 'G', 'G#', 'A', 'A#', 'B']\\n \\n #print(note_names)\\n \\n def recMidiToNotes(notes, note_namesr=[]):\\n if notes == []:\\n return\\n \\n head = notes.pop(0)\\n name = names[head%12]\\n octave = int((head-12)/12)\\n note_namesr.append(name + str(octave))\\n print('rec names:', note_namesr)\\n return note_namesr\\n \\n if len(note_names) == totalnotes:\\n return note_names\\n \\n midiToNotes(notes, totalnotes, recMidiToNotes(notes, note_names))\",\n \"def get_notes_from_line(line):\\n last_char_is_upper = True\\n raw_notes_list = []\\n current_note = \\\"\\\"\\n\\n for char in line:\\n if not last_char_is_upper and char.isupper():\\n raw_notes_list.append(current_note)\\n current_note = \\\"\\\"\\n\\n current_note += char\\n last_char_is_upper = char.isupper()\\n\\n raw_notes_list.append(current_note)\\n return raw_notes_list\",\n \"def first_notes_and_song():\\n # Construct a robot, put it in safe mode.\\n # Make it play two notes, then a song, then disconnect.\\n\\n time.sleep(2) # In case the robot has recently been shutdown.\\n\\n port = 'sim' # Or use YOUR laptop's COM number\\n hal = new_create.Create(port)\\n hal.toSafeMode() # So you can try this out on a table if you want.\\n\\n #-------------------------------------------------------------------\\n # playNote(pitch, duration):\\n #\\n # Notes range from 31 (low G, 49.0 Hz) to 127 (high G, 12543.9 Hz).\\n # They can be played from as little as 1/64th of a second to as\\n # long as 255/64 seconds (i.e., almost 4 seconds).\\n # The playNote method takes TWO (2) arguments:\\n # -- The note (should be in the range 31 to 127, inclusive)\\n # -- The duration to play that note, in 64th's of a second\\n # (must be between 0 and 255, inclusive)\\n #-------------------------------------------------------------------\\n\\n hal.playNote(31, 255) # Very long, low note.\\n time.sleep(5.0) # See next example for why we need sleeps.\\n\\n hal.playNote(110, 1) # Very short, high note.\\n time.sleep(1.0)\\n\\n #-------------------------------------------------------------------\\n # playSong(LIST of pitch-duration TWO-TUPLEs)\\n #\\n # If you want to play a sequence of notes, it is almost always\\n # better to use the playSong method instead of doing a\\n # sequence of calls to playNote. The playSong method takes\\n # ONE (1) argument. That argument must be:\\n # -- a LIST\\n # -- with no more than 16 items\\n # -- where each item is a 2-tuple\\n # whose first element is the pitch (31 to 127)\\n # and whose second element is the duration (1 to 255)\\n # -- where pitch and duration are as in playNote.\\n #-------------------------------------------------------------------\\n\\n song = [(60, 8), (64, 8), (67, 8), (72, 8)] # a quick C chord\\n hal.playSong(song)\\n time.sleep(2.0)\\n\\n hal.shutdown()\",\n \"def get_and_split_note_lists(composition):\\n lists = create_note_lists(composition)\\n\\n # create a dict of all tracks with notes in them\\n n = {}\\n for voice in lists:\\n if len(lists[voice]):\\n n[voice] = lists[voice]\\n\\n descending_voices = [x for x in ['Soprano', 'Alto', 'Tenor', 'Bass'] if x in n]\\n ascending_voices = [x for x in['Bass', 'Tenor', 'Alto', 'Soprano'] if x in n]\\n\\n # find the high voice\\n for voice in descending_voices:\\n high_voice = n[voice]\\n break\\n\\n # find the low voice\\n for voice in ascending_voices:\\n low_voice = n[voice]\\n break\\n\\n # find the inner voices\\n inner_voices = []\\n for voice in ['Tenor', 'Alto']:\\n if voice in n and n[voice] not in [high_voice, low_voice]:\\n inner_voices.append(n[voice])\\n\\n # find all possible combinations of voices\\n voice_combos = []\\n for i,x in enumerate(descending_voices):\\n for y in descending_voices[i+1:]:\\n voice_combos.append((x, y))\\n\\n return n, high_voice, low_voice, inner_voices, voice_combos\",\n \"def get_onehot2chordnotes_transcoder():\\r\\n chord_id2sign = np.load('csv/chord-1hot-signatures-rev.npy')\\r\\n\\r\\n def f(chord):\\r\\n \\\"\\\"\\\"\\r\\n Translate from 1-hot array of dim {DIM} back to superimposed repr of dim=12\\r\\n :param chord: 1-hot representation of chords in (M, T, Dim)\\r\\n :return: chord signature in (M, T, 12)\\r\\n \\\"\\\"\\\"\\r\\n m, length, dim = chord.shape\\r\\n chord2 = chord.reshape([m*length, dim])\\r\\n index = np.argmax(chord2, axis=1)\\r\\n new_chord = chord_id2sign[index, :]\\r\\n return new_chord.reshape(m, length, 12)\\r\\n return f\",\n \"def getNotes(self):\\n return self._nednotes, self._ongcnotes\",\n \"async def chord(ctx, root:str,length=3,):\\n if length < 2:\\n length = 2\\n elif length > 5:\\n length = 5\\n chord = Tempo.generateChordProgression(root)\\n await ctx.send(chord)\",\n \"def addHarmony(self, notes, ctable):\\n \\n sc=self.seq\\n\\n harmony = self.harmony[sc]\\n harmOnly = self.harmonyOnly[sc]\\n \\n \\n for offset in notes:\\n nn = notes[offset]\\n\\n if (len(nn) == 1) and (nn[0].pitch != None) and (nn[0].isgrace != True):\\n tb = self.getChordInPos(offset, ctable)\\n\\n if tb.chordZ:\\n continue\\n\\n h = MMA.harmony.harmonize(harmony, nn[0].pitch, tb.chord.bnoteList)\\n\\n duration = nn[0].duration\\n articulation = nn[0].articulation\\n velocity = nn[0].defvelocity\\n\\n if harmOnly: # remove melody note if harmony only\\n nn.pop(0) # DON'T use nn=[] that would release the ptr.\\n\\n for n in h:\\n e = NoteEvent(n,\\n self.adjustVolume(velocity * self.harmonyVolume[sc], offset), False)\\n e.duration = duration\\n e.articulation = articulation\\n nn.append(e)\",\n \"def get_vec_for_hand(hand_notes):\\n # vec = np.zeros(300, dtype=np.int8)\\n vec = zeros(hands=1)\\n note_pattern = r'(\\\\d+)\\\\.*([A-Ga-g]+#?)' # treats dotted quarters as quarters, etc\\n notes = re.findall(note_pattern, hand_notes) # this is currently ignoring rests! TODO fix\\n # print(f'found {len(notes)} matches')\\n for i in range(len(notes)):\\n if i >= 3:\\n # print(f\\\"note {hand_notes}: {len(notes)} notes found in hand...\\\")\\n break\\n duration, note = notes[i]\\n if note not in note_to_ind_map:\\n # note doesn't exist on piano because of upward transposition; \\n # so transpose down\\n note = note[1:]\\n note_ind = note_to_ind_map.get(note) + i*100\\n duration_ind = duration_to_ind_map.get(duration, duration_to_ind_map[\\\"4\\\"]) + i*100\\n # default to quarter note I guess\\n vec[note_ind] = 1 # if appears twice somehow, is still 1\\n vec[duration_ind] = 1\\n return vec\",\n \"def do(self, sym):\\n # The dash is a bit of a notation hack, it's there because \\\"C4 major\\\"\\n # would look like two symbols, so we need to have no whitespace\\n # between them\\n\\n if sym is None or sym == '-':\\n # REST:\\n return chord([])\\n if '-' in sym:\\n return chord(sym.replace(\\\"-\\\",\\\" \\\"))\\n elif \\\",\\\" in sym:\\n return chord(sym.split(\\\",\\\"))\\n else:\\n return note(sym)\",\n \"def _createCadence(self, harmonicRhythm):\\n random = RandomManager.getActive()\\n\\n # choose cadence to apply\\n cadences = self.chordProfile.getCadences()\\n\\n #TODO: Choose cadence in more intelligent way\\n cadence = random.choice(cadences)\\n\\n scale = self.chordProfile.getScale().getName()\\n cadenceChordProgression = []\\n\\n reversedHarmonicRhythm = reversed(harmonicRhythm[:])\\n # create as many cadence\\n for count, durationObj in enumerate(reversedHarmonicRhythm):\\n duration = durationObj.getDuration()\\n code = cadence[-count+1]\\n chord = Chord(code, duration=duration, scale=scale, octave=4)\\n\\n if count >= len(cadence):\\n return cadenceChordProgression\\n\\n # prepend chord\\n cadenceChordProgression.insert(0, chord)\\n return cadenceChordProgression\",\n \"def to_cnv(chord_list):\\r\\n\\r\\n cnv_list = []\\r\\n err_list = []\\r\\n for c in chord_list:\\r\\n if c in ProgCreate.chord_dict.keys():\\r\\n cnv_list.append(ProgCreate.chord_dict[c])\\r\\n elif c not in ProgCreate.chord_dict.keys():\\r\\n err_list.append(''.join([\\\"#\\\" if x == \\\"s\\\" else x for x in c]))\\r\\n return cnv_list, err_list\",\n \"def get_note_names(self):\\n res = []\\n for n in self.notes:\\n if n.name not in res:\\n res.append(n.name)\\n return res\",\n \"def get_next_chord(self, chord):\\n chord = self.specified_chord_to_chord(chord)\\n assert validate_chord(chord), \\\"not a valid chord!\\\"\\n\\n cond_dist_dict = self.chord_chord_map[chord] # Gets list of candidate chords\\n\\n # Prepare for the random draw\\n next_chords = cond_dist_dict.keys()\\n next_probs = [cond_dist_dict[next_chord] for next_chord in next_chords]\\n cond_dist = stats.rv_discrete(name='cond_dist', values=(next_chords, next_probs))\\n\\n return self.key.chords[cond_dist.rvs(size=1)-1]\",\n \"def _nextNotes(self, prev_note, high_note, low_note, voice):\\n distance = 1\\n high_bound = prev_note.distance(high_note)\\n low_bound = prev_note.distance(low_note)\\n bound = max([high_bound, low_bound])\\n # notes = [prev_note]\\n notes = []\\n if voice in [1, 2]:\\n notes.append(prev_note)\\n while distance <= bound:\\n if distance <= high_bound:\\n total_note_value = prev_note.getTotalValue() + 1 * distance\\n note = Note(value2note[total_note_value % 12], total_note_value//12)\\n notes.append(note)\\n\\n if distance <= low_bound:\\n total_note_value = prev_note.getTotalValue() - 1 * distance\\n note = Note(value2note[total_note_value % 12], total_note_value//12)\\n print(prev_note, note)\\n notes.append(note)\\n \\n distance += 1\\n\\n return notes\",\n \"def f(chord):\\r\\n m, length, dim = chord.shape\\r\\n chord2 = chord.reshape([m*length, dim])\\r\\n index = np.argmax(chord2, axis=1)\\r\\n new_chord = chord_id2sign[index, :]\\r\\n return new_chord.reshape(m, length, 12)\",\n \"def _compose(track, notes, deltat=461.09, velocity=48, threshold=1.):\\n LEN, dim = notes.shape\\n track.append(MetaMessage('set_tempo', tempo=500000))\\n\\n # notes to abs time list\\n times = []\\n actions = []\\n T = 0\\n for ind, line in enumerate(notes):\\n for note in range(dim):\\n if notes[ind, note] >= threshold and \\\\\\n (ind == 0 or notes[ind-1, note] < threshold):\\n times.append(T)\\n actions.append(('note_on', note))\\n\\n T += int(deltat)\\n\\n for note in range(dim):\\n if (notes[ind, note] < threshold and\\n notes[ind-1, note] >= threshold) or \\\\\\n (ind == LEN-1 and notes[ind, note] >= threshold):\\n times.append(T)\\n actions.append(('note_off', note))\\n\\n for i in range(len(times)-1, 0, -1):\\n times[i] = times[i] - times[i-1]\\n\\n for t, a in zip(times, actions):\\n if a[0] == 'note_on':\\n track.append(Message('note_on',\\n note=a[1],\\n velocity=velocity,\\n time=t))\\n else:\\n track.append(Message('note_off',\\n note=a[1],\\n velocity=0,\\n time=t))\",\n \"def parseChord(self, note, size=4, deltatime=0, deltatolerance=0.03, debug=False):\\n \\n self._chordmemory.append(note)\\n self._deltamemory.append(deltatime)\\n \\n if len(self._chordmemory) > size:\\n self._chordmemory = self._chordmemory[1:]\\n if len(self._deltamemory) > size:\\n self._deltamemory = self._deltamemory[1:]\\n \\n if debug:\\n print('b chordmem: ', self._chordmemory, 'b deltamem', self._deltamemory)\\n \\n if len(self._chordmemory) > 1:\\n average = np.average(np.diff(self._deltamemory))\\n if debug:\\n print('average:', average)\\n if average > deltatolerance:\\n self._chordmemory.pop(0)\\n self._deltamemory.pop(0)\\n #self._chordmemory = []\\n #self._deltamemory = []\\n\\n if len(self._chordmemory) == size:\\n average = np.average(np.diff(self._deltamemory))\\n \\n if average < deltatolerance:\\n chord = self._chordmemory\\n self._chordmemory = []\\n self._deltamemory = []\\n return True, chord\\n #else:\\n #chord = self._chordmemory\\n #self._chordmemory = []\\n #self._deltamemory = [] \\n \\n if debug:\\n print('chordmem: ', self._chordmemory, 'deltamem', \\n self._deltamemory)\\n\\n\\n return False, None\",\n \"def followAdjust(self, notes, ctable):\\n \\n sc=self.seq\\n\\n for offset in notes:\\n nn = notes[offset]\\n\\n if len(nn) == 1 and nn[0].pitch != None:\\n tb = self.getChordInPos(offset, ctable)\\n\\n if tb.chordZ:\\n continue\\n\\n nn[0].pitch += ( tb.chord.rootNote + self.rootChord)\\n\\n return notes\",\n \"def note_card_paths() -> List[str]:\\n\\n pkg = \\\"music_flash_cards.cards.chromatic_note_cards\\\"\\n\\n note_cards = []\\n\\n for note_card_name in contents(pkg):\\n if is_resource(pkg, note_card_name) and note_card_name.endswith(\\\".png\\\"):\\n with path(pkg, note_card_name) as note_card_path:\\n note_cards.append(str(note_card_path))\\n return note_cards\",\n \"def get_notes_from_uuids(dictionary, uuid_list):\\n titles = []\\n texts = []\\n notes = []\\n for key in dictionary:\\n if dictionary[key][\\\"content_type\\\"] == \\\"Note\\\" and dictionary[key][\\\"uuid\\\"] in uuid_list:\\n try:\\n titles.append(dictionary[key][\\\"content\\\"][\\\"title\\\"])\\n texts.append(dictionary[key][\\\"content\\\"][\\\"text\\\"])\\n notes = list(zip(titles, texts))\\n except KeyError:\\n continue\\n return notes\",\n \"def generateNotes():\\r\\n fs = 44100 # hertz\\r\\n seconds = 3 # Note duration of 3 seconds\\r\\n noteNames = [\\\"C4\\\", \\\"D4\\\", \\\"E4\\\", \\\"F4\\\", \\\"G4\\\", \\\"A4\\\", \\\"B4\\\"]\\r\\n for noteName in noteNames:\\r\\n myNote = music21.note.Note(noteName)\\r\\n noteFrequency = myNote.pitch.frequency\\r\\n # Generate array with seconds*sample_rate steps, ranging between 0 and seconds\\r\\n t = np.linspace(0, seconds, seconds * fs, False)\\r\\n\\r\\n # Generate a 440 Hz sine wave\\r\\n sound = np.sin(noteFrequency * t * 2 * np.pi)\\r\\n\\r\\n # Ensure that highest value is in 16-bit range\\r\\n audio = sound * (2**15 - 1) / np.max(np.abs(sound))\\r\\n # Convert to 16-bit data\\r\\n audio = audio.astype(np.int16)\\r\\n\\r\\n # Start playback\\r\\n play_obj = sa.play_buffer(audio, 1, 2, fs)\\r\\n\\r\\n # Wait for playback to finish before exiting\\r\\n play_obj.wait_done()\\r\\n\\r\\n #Write sound to file\\r\\n sf.write('assets/patterns/'+noteName+'.wav', audio, fs)\",\n \"def comp_pattern_generator(iterable):\\n for chord in iterable:\\n yield (600, chord)\",\n \"def play_notes(self, show_map=False):\\n # print(self.programs)\\n pianorolls = []\\n for track in self.notes:\\n pianoroll = np.zeros((track.shape[1] * 3, 128))\\n\\n for i in range(track.shape[0]):\\n note_track = np.array(track[i, :])\\n note_track += 1\\n notes_pos = np.nonzero(note_track)\\n f = 3\\n for pos in notes_pos[0]:\\n # print(\\\"Error\\\", f * pos, f * pos + self.dura_to_timecell(note_track[pos] - 1),\\n # self.dura_to_timecell(note_track[pos] - 1))\\n pianoroll[f * pos:f * pos + self.dura_to_timecell(note_track[pos] - 1) + 1, 83 - i] = 90\\n pianorolls.append(pianoroll)\\n\\n # print(\\\"pianoroll\\\")\\n print(self.notes_index)\\n tracks = []\\n for i in range(len(pianorolls)):\\n tracker = Track(pianoroll=pianorolls[i], program=self.programs[i])\\n tracks.append(tracker)\\n multitrack = Multitrack(tracks=tracks)\\n multitrack.write(\\\"create1.mid\\\")\\n\\n if show_map:\\n print(\\\"Show map will not work\\\")\\n # tracker.plot()\\n # plt.show()\",\n \"def keychord(m) -> str:\\n return \\\"-\\\".join(m.modifier_list + [m.any_key])\",\n \"def _find_channels(note):\\r\\n id_ch = note.index('ChanNames')\\r\\n chan_beg = note.index('(', id_ch)\\r\\n chan_end = note.index(')', chan_beg)\\r\\n note_with_chan = note[chan_beg + 1:chan_end]\\r\\n return [x.strip('\\\" ') for x in note_with_chan.split(',')]\",\n \"def starts_with_tonic(a_list):\\n key = a_list.track.bars[0].key.name\\n note = a_list.get_first_actual_note()\\n if note.name == key:\\n return []\\n else:\\n return [note.start]\",\n \"def randMelody(self, value, chord):\\n self.randRhythm()\\n self.randNotes(value, chord)\",\n \"def inverse_notes(note_list):\\n raw_output = []\\n for note in note_list:\\n # We transpose every notes except for Z\\n if note.is_pause:\\n raw_output.append(f'{note.name}{note.figure}')\\n else:\\n # Get the index of the note to transpose\\n note_name_index = NOTE_NAMES.index(note.name)\\n # Get the transposed index according to the amount\\n inverted_index = ((len(NOTE_NAMES) - note_name_index) % len(NOTE_NAMES))\\n new_note_name = NOTE_NAMES[inverted_index]\\n # Create the new note, with the new note's name, and its original figure\\n new_note = f'{new_note_name}{note.figure}'\\n new_note += 'p' if note.has_point else ''\\n raw_output.append(new_note)\\n\\n return raw_output\",\n \"def parse_pretty_midi_lyrics(midi: PrettyMIDI) -> List[Lyric]:\\n return [Lyric(lyric.time, lyric.text) for lyric in midi.lyrics]\",\n \"def from_chord_shorthand(self, shorthand):\\n self.empty()\\n self.add_notes(chords.from_shorthand(shorthand))\\n return self\",\n \"def midi_to_lilypond_note(note):\\n return all_notes[note+4]\",\n \"def get_pitches(midi, part_id=None):\\n try: # file has instrument parts\\n s2 = instrument.partitionByInstrument(midi)\\n print(len(s2))\\n if part_id is not None:\\n notes = s2.parts[part_id].recurse()\\n pitches = [c.pitches for c in notes]\\n pitch_arr = np.array(pitches)[None]\\n ps_arr = np.array([pitch.ps for pitch in pitches])\\n else:\\n pitch_arr = []\\n ps_arr = []\\n for part in s2:\\n notes = part.recurse()\\n pitches = [c.pitches for c in notes]\\n pss = [pitch.ps for pitch in pitches]\\n pitch_arr.append(pitches)\\n ps_arr.append(pss)\\n pitch_arr = np.array(pitch_arr)\\n ps_arr = np.array(ps_arr)\\n except: # file has notes in a flat structure\\n notes = midi.flat.notes\\n pitch_arr = np.array([c.pitches for c in notes])\\n ps_arr = np.array([pitch.ps for pitch in pitches])\\n return pitch_arr, ps_arr\",\n \"def __init__(self):\\n \\n # initialize Music-Code\\n self.m = MusicCode(bpm=120)\\n \\n # waveform types\\n self.waveform_types = ['sine', 'tri', 'saw1', 'saw2', 'square', 'sine-tri', 'sine-saw', 'sine-square', 'saw-square', 'tri-saw', 'tri-square']\\n \\n # chord labels\\n self.all_chord_labels = self.m.all_chords[:73]\\n\\n # select 4 octaves for root notes\\n excess_notes_list = list(self.m.freq_note_table_sharp.values()) \\n \\n all_root_notes = []\\n for item in excess_notes_list:\\n if ('0' in item) or ('1' in item) or ('5' in item) or ('6' in item) or ('7' in item):\\n pass\\n else:\\n all_root_notes.append(item)\\n \\n all_root_notes.sort() \\n self.all_root_notes = all_root_notes\",\n \"def starts_with_tonic_or_fifth(a_list):\\n key = a_list.track.bars[0].key\\n note = a_list.get_first_actual_note()\\n possible_notes = [key.name, Note(key).transpose('5', True).name]\\n if note.name in possible_notes:\\n return []\\n else:\\n return [note.start]\",\n \"def add_notes(self, notes):\\n if hasattr(notes, \\\"notes\\\"):\\n for x in notes.notes:\\n self.add_note(x)\\n return self.notes\\n elif hasattr(notes, \\\"name\\\"):\\n self.add_note(notes)\\n return self.notes\\n elif isinstance(notes, six.string_types):\\n self.add_note(notes)\\n return self.notes\\n for x in notes:\\n if isinstance(x, list) and len(x) != 1:\\n if len(x) == 2:\\n self.add_note(x[0], x[1])\\n else:\\n self.add_note(x[0], x[1], x[2])\\n else:\\n self.add_note(x)\\n return self.notes\",\n \"def _initCandidateTriads(self):\\n codes = list(self.chordProfile.getScores())\\n candidateTriads = [Chord(code) for code in codes]\\n return candidateTriads\",\n \"def get_chromatic(self, tonic, octave=None, n_octaves=None,\\n descending=None, dont_repeat_tonic=None):\\n\\n global notes3, notes2\\n\\n tonic_index = self._get_chromatic_idx(tonic)\\n\\n if tonic == 'F' or 'b' in tonic:\\n notes = deque(notes3)\\n else:\\n notes = deque(notes2)\\n\\n notes.rotate(-(tonic_index))\\n\\n if n_octaves:\\n chromatic = notes * n_octaves\\n else:\\n chromatic = notes\\n\\n # FIXME: check if this works on descending\\n if not dont_repeat_tonic:\\n chromatic.append(chromatic[0])\\n\\n if descending:\\n chromatic.reverse()\\n\\n if octave:\\n chromatic = self._append_octave_to_scale(scale=chromatic,\\n starting_octave=octave,\\n descending=descending)\\n\\n return chromatic\",\n \"def generate_message(self) -> List[mido.Message]:\\n # check for a None note (which is a \\\"pause\\\")\\n if self.__note:\\n note_value = self.__note + self.__info.octave\\n note_velocity = self.__info.volume\\n else:\\n note_value = 0\\n note_velocity = 0\\n return [\\n mido.Message(\\n \\\"note_on\\\",\\n note=note_value,\\n velocity=note_velocity,\\n time=NOTE_DURATION,\\n ),\\n mido.Message(\\n \\\"note_off\\\",\\n note=note_value,\\n velocity=note_velocity,\\n time=0,\\n ),\\n ]\",\n \"def __getCounterClock(self, chord):\\n lookupTable = {\\n 'C': 'F', 'Am': 'Dm', 'G': 'C', 'Em': 'Am', 'D': 'G',\\n 'Bm': 'Em', 'A': 'D', 'F#m': 'Bm', 'E': 'A', 'C#m': 'F#m' ,\\n 'B': 'E', 'G#m': 'C#m', 'F#': 'B', 'Gb': 'B', 'Ebm': 'G#m',\\n 'Db': 'F#', 'Bbm': 'Ebm', 'Ab': 'Db', 'Fm': 'Bbm', 'Eb': 'Ab',\\n 'Cm': 'Fm', 'Bb': 'Eb', 'Gm': 'Cm', 'F': 'Bb', 'Dm': 'Gm'\\n }\\n\\n return lookupTable[chord]\",\n \"def get_chord_midi_nums(chord_intervals, root) -> [int]:\\n midi_nums = []\\n midi_root = ChordMdConsts.MIDI_C3 + root # [TODO]\\n for i in chord_intervals:\\n scalepos = (i * 2) % 7 # [TODO] Magic Number\\n if (i * 2) >= 7:\\n scalepos += 12\\n midi_nums.append(midi_root + scalepos)\\n \\n return midi_nums\",\n \"def get_note():\\n\\n return Note.query.all()\",\n \"def pattern_to_notes(pattern):\\n pattern_notes = []\\n for (i, m) in zip(range(0, 6), FRET_POSITION.findall(pattern)):\\n pos = m[0].strip()\\n if pos != 'x':\\n pattern_notes.append(FRET_NOTES[i][int(pos) % 12])\\n return pattern_notes\",\n \"def notes_in_path(self, path: str) -> List[NoteInfo]:\\n check_path = os.path.abspath(path)\\n return [n for n in self.notes.values() if n.file_path.startswith(check_path)]\",\n \"def return_markers(self):\\r\\n ent_file = join(self.filename, self._basename + '.ent')\\r\\n if not exists(ent_file):\\r\\n ent_file = join(self.filename, self._basename + '.ent.old')\\r\\n\\r\\n try:\\r\\n ent_notes = _read_ent(ent_file)\\r\\n\\r\\n except (FileNotFoundError, PermissionError):\\r\\n markers = []\\r\\n\\r\\n else:\\r\\n allnote = []\\r\\n for n in ent_notes:\\r\\n try:\\r\\n n['value'].keys()\\r\\n allnote.append(n['value'])\\r\\n except AttributeError:\\r\\n lg.debug('Note of length {} was not '\\r\\n 'converted to dict'.format(n['length']))\\r\\n\\r\\n s_freq = self._hdr['erd']['sample_freq']\\r\\n pcname = '0CFEBE72-DA20-4b3a-A8AC-CDD41BFE2F0D'\\r\\n note_time = []\\r\\n note_name = []\\r\\n note_note = []\\r\\n for n in allnote:\\r\\n if n['Text'] == 'Analyzed Data Note':\\r\\n continue\\r\\n if not n['Text']:\\r\\n continue\\r\\n if 'User' not in n['Data'].keys():\\r\\n continue\\r\\n user1 = n['Data']['User'] == 'Persyst'\\r\\n user2 = False # n['Data']['User'] == 'eeg'\\r\\n user3 = n['Data']['User'] == pcname\\r\\n user4 = n['Data']['User'] == 'XLSpike - Intracranial'\\r\\n user5 = n['Data']['User'] == 'XLEvent - Intracranial'\\r\\n if user1 or user2 or user3 or user4 or user5:\\r\\n continue\\r\\n if len(n['Data']['User']) == 0:\\r\\n note_name.append('-unknown-')\\r\\n else:\\r\\n note_name.append(n['Data']['User'].split()[0])\\r\\n note_time.append(n['Stamp'] / s_freq)\\r\\n note_note.append(n['Text'])\\r\\n\\r\\n markers = []\\r\\n for time, name, note in zip(note_time, note_name, note_note):\\r\\n m = {'name': note + ' (' + name + ')',\\r\\n 'start': time,\\r\\n 'end': time,\\r\\n 'chan': None,\\r\\n }\\r\\n markers.append(m)\\r\\n\\r\\n return markers\",\n \"def getNote(self):\\n return (self.noteName[0], self.accidental, self.octave)\",\n \"def acquire_song_chords(title, artist):\\r\\n #search for song in database, and demand that it has no chords (otherwise an exception is thrown by db)\\r\\n song = Song.objects.get(title=title, artist=artist, chords__isnull=True) \\r\\n chord_vector = get_chords(song.title, song.artist)\\r\\n for index,chord in enumerate(chord_vector):\\r\\n root, notes = decode(chord)\\r\\n #instead of creating a huge amount of duplicated Chord instances, \\r\\n #create a new instance only for a first appearing chord in the db.\\r\\n chord, is_new = Chord.objects.get_or_create(root=root, notes=str(notes), symbol = chord) \\r\\n# if is_new: print \\\"created new chord instance - \\\", root, notes \\r\\n #create the chord_index relationship\\r\\n Song_chord_index.objects.create(song=song, chord=chord, index=index) \\r\\n song.previously_failed_chords = False #if this attempt succeeded, remove bad flag\\r\",\n \"def chordfinisher(*args, **kwargs): # pylint: disable=W0613\\n return \\\"SYNC\\\"\",\n \"def get_notes_for_instrument(self, instrument, notes_property=\\\"notes\\\"):\\n return getattr(self.tracks[self.instruments[instrument]], notes_property)\"\n]","isConversational":false},"negative_scores":{"kind":"list like","value":["0.74840385","0.71133006","0.6987371","0.6944393","0.69076145","0.662363","0.6594826","0.6532851","0.641009","0.62176156","0.6167714","0.61373204","0.61218643","0.6079255","0.60533977","0.59837556","0.5976758","0.5965469","0.5926441","0.5900762","0.5892764","0.5888494","0.5878069","0.58461946","0.58337873","0.58209425","0.5803599","0.57691205","0.576329","0.575961","0.5751598","0.5728602","0.57163984","0.56886315","0.5650857","0.5648649","0.56415635","0.5627614","0.56258404","0.56208426","0.55638623","0.5561826","0.5559092","0.5555257","0.5548849","0.5544766","0.55433595","0.5532358","0.55246556","0.551552","0.5501357","0.5500495","0.549681","0.54910654","0.54906493","0.5471883","0.5459844","0.5450414","0.5446487","0.54447085","0.5443248","0.54404247","0.5439193","0.54359555","0.5433962","0.54157335","0.5414075","0.5402332","0.53806025","0.53783804","0.5370207","0.5370009","0.5337354","0.5328841","0.53106904","0.5300291","0.52831095","0.5275026","0.52618045","0.5250431","0.52462167","0.524566","0.5239654","0.5237061","0.52263963","0.5221547","0.5198219","0.5191301","0.5177727","0.51716465","0.5167712","0.51632875","0.5160758","0.51461643","0.5127952","0.5118162","0.5115537","0.51044333","0.5100666","0.50995123"],"string":"[\n \"0.74840385\",\n \"0.71133006\",\n \"0.6987371\",\n \"0.6944393\",\n \"0.69076145\",\n \"0.662363\",\n \"0.6594826\",\n \"0.6532851\",\n \"0.641009\",\n \"0.62176156\",\n \"0.6167714\",\n \"0.61373204\",\n \"0.61218643\",\n \"0.6079255\",\n \"0.60533977\",\n \"0.59837556\",\n \"0.5976758\",\n \"0.5965469\",\n \"0.5926441\",\n \"0.5900762\",\n \"0.5892764\",\n \"0.5888494\",\n \"0.5878069\",\n \"0.58461946\",\n \"0.58337873\",\n \"0.58209425\",\n \"0.5803599\",\n \"0.57691205\",\n \"0.576329\",\n \"0.575961\",\n \"0.5751598\",\n \"0.5728602\",\n \"0.57163984\",\n \"0.56886315\",\n \"0.5650857\",\n \"0.5648649\",\n \"0.56415635\",\n \"0.5627614\",\n \"0.56258404\",\n \"0.56208426\",\n \"0.55638623\",\n \"0.5561826\",\n \"0.5559092\",\n \"0.5555257\",\n \"0.5548849\",\n \"0.5544766\",\n \"0.55433595\",\n \"0.5532358\",\n \"0.55246556\",\n \"0.551552\",\n \"0.5501357\",\n \"0.5500495\",\n \"0.549681\",\n \"0.54910654\",\n \"0.54906493\",\n \"0.5471883\",\n \"0.5459844\",\n \"0.5450414\",\n \"0.5446487\",\n \"0.54447085\",\n \"0.5443248\",\n \"0.54404247\",\n \"0.5439193\",\n \"0.54359555\",\n \"0.5433962\",\n \"0.54157335\",\n \"0.5414075\",\n \"0.5402332\",\n \"0.53806025\",\n \"0.53783804\",\n \"0.5370207\",\n \"0.5370009\",\n \"0.5337354\",\n \"0.5328841\",\n \"0.53106904\",\n \"0.5300291\",\n \"0.52831095\",\n \"0.5275026\",\n \"0.52618045\",\n \"0.5250431\",\n \"0.52462167\",\n \"0.524566\",\n \"0.5239654\",\n \"0.5237061\",\n \"0.52263963\",\n \"0.5221547\",\n \"0.5198219\",\n \"0.5191301\",\n \"0.5177727\",\n \"0.51716465\",\n \"0.5167712\",\n \"0.51632875\",\n \"0.5160758\",\n \"0.51461643\",\n \"0.5127952\",\n \"0.5118162\",\n \"0.5115537\",\n \"0.51044333\",\n \"0.5100666\",\n \"0.50995123\"\n]","isConversational":false},"document_score":{"kind":"string","value":"0.5303932"},"document_rank":{"kind":"string","value":"75"}}},{"rowIdx":23781,"cells":{"query":{"kind":"string","value":"Random rhythm and notes based on time"},"document":{"kind":"string","value":"def randMelody(self, value, chord):\n self.randRhythm()\n self.randNotes(value, chord)"},"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 randNotes(self, value, chord):\n aChord = mgChord(value, chord)\n self.data = []\n for i in range(len(self.rhythm)):\n note = random.choice(aChord).copy()\n note.setDuration(self.rhythm[i])\n self.data.append(note)","def create_melody(inst, chord_progression):\r\n \r\n for chord in chord_progression:\r\n \r\n meas = inst.NewMeasure()\r\n last_note = random.choice(CHORDS[chord])\r\n pos = 0\r\n \r\n while pos < 64:\r\n dur = random.choice([8, 8, 16, 16]) \r\n last_note = get_rdm_note(chord, last_note)\r\n if random.randint(1,10) <= 8: # 20% silence\r\n meas.NewNote(pos, dur, last_note, 5)\r\n pos += dur","def make_music_rand():\n pass","def gen_melody(scale, timings, note_deviance):\n melody = [random.sample(scale, 1)[0] for i in range(len(timings))]\n\n px = np.random.rand(len(timings))\n idx = np.argwhere(px <= note_deviance)\n direction = [-1, 1]\n for i in idx:\n melody[i[0]] = melody[i[0]] + random.sample(direction, 1)[0]\n return melody","def get_rdm_note(chord, last_note):\r\n \r\n if random.randint(1,10) <= 6:\r\n # chord note\r\n return random.choice(CHORDS[chord]) \r\n else:\r\n # scale note \r\n n = C_SCALE.index(last_note)\r\n return C_SCALE[n+random.choice(range(-n, 6-n)[2:5])]","def sample_times(times, num_examples):\n sample = random.sample(times, num_examples)\n make_hist(sample, 10, 'Sample of Size ' + str(num_examples),\n 'Minutes to Complete Race', 'Number of Runners')","def get_random_time_between_tts():\n return random.randint(TIME_BETWEEN_TTS_LOWER, TIME_BETWEEN_TTS_UPPER)","def generate_line_durations(\n n_measures: int,\n duration_weights: Dict[float, float],\n valid_rhythmic_patterns: List[List[float]],\n end_with_whole_note: bool = True\n) -> List[float]:\n current_time = 0\n line_durations = []\n current_measure_durations = []\n total_time = n_measures - int(end_with_whole_note)\n\n while current_time < total_time:\n appropriate_durations = select_appropriate_durations(\n current_time, total_time, current_measure_durations,\n valid_rhythmic_patterns\n )\n duration = random.choices(\n appropriate_durations,\n [duration_weights[x] for x in appropriate_durations]\n )[0]\n current_time += duration\n current_measure_durations = update_current_measure_durations(\n current_measure_durations, duration\n )\n line_durations.append(duration)\n\n if end_with_whole_note:\n line_durations.append(1.0)\n return line_durations","def _rhythm_obs_proc(pattern):\n # We asign the endpoint of the hypothesis.\n pattern.hypothesis.end.value = pattern.evidence[o.QRS][-1].time.value","def time_per_part():\r\n return random.normalvariate(PT_MEAN, PT_SIGMA)","def getStatusMessage():\n\n now = datetime.datetime.now()\n hour = now.hour\n mood = Sentience.getPrimaryMood()\n exp_mood = Sentience.getExposedPositivity()\n\n random.seed((time.time()//86400*86400))\n\n #sleeping\n if not (9 <= hour < 21) and (mood <= 0.5 or not 7 <= hour < 23):\n if exp_mood < -0.1:\n return random.choice([\n \"bleh\",\n \"not sleeping well\",\n \"why's chat so noisy\",\n \"can't sleep\",\n \"do not disturb pls thx\",\n ])\n\n if mood < 0:\n return random.choice([\n \"crying myself to sleep rn\",\n \":(\",\n \"had a nightmare\",\n \"can't sleep\",\n \"._.\"\n ])\n\n return random.choice([\n \"zzz...\",\n \"sweet dreams\",\n \"good night\",\n \"sleeping...\",\n \"having some rest\"\n ])\n\n if Sentience.isExposedPositivityOverloaded():\n return random.choice([\n \"i'm done\",\n \"too much\"\n \"goodbye\",\n \"tired\",\n \"need rest\",\n ])\n\n #happy\n if mood >= 0.7:\n return random.choice([\n \":D\",\n \"great day\",\n \"happy happy\",\n \"hehe\",\n \"good times\",\n \"yay\",\n \"what's up\",\n \"happiness\",\n \"nice day\",\n ])\n #moody-ish\n if mood >= 0.4:\n return random.choice([\n \"hmm\",\n \"yeet\",\n \"bleh\",\n \"oh\",\n \"moody rn\",\n \"nothing\"\n ])\n #more moody\n if mood >= -0.3:\n return random.choice([\n \"moody rn\",\n \"not happy\",\n \"i'm fine.\",\n \"bleh\",\n \"._.\",\n \":(\",\n ])\n #very unhappy\n return random.choice([\n \"sad\",\n \"cries\",\n \"roar\",\n \":_(\",\n \">:(\",\n \"mad\",\n \"angry\",\n \"I'M FINE.\",\n \"bleh\",\n \"no\",\n ])","def tempo_r(mid, beats, rs):\n bt = mid.ticks_per_beat\n trk = MidiTrack()\n trk.name = \"Tempo variation\"\n trk.append(MetaMessage(\"set_tempo\",\n tempo=beats[0],\n time=0))\n\n for i, beat in enumerate(beats):\n r = rs[i]\n if r == 0: # For the deterministic case\n tempo_r = beat\n else:\n tempo_r = rd.randint(beat-int(beat*r), beat + int(beat*r)) + 1\n trk.append(MetaMessage(\"set_tempo\",\n time=bt,\n tempo=tempo_r))\n\n mid.tracks.append(trk)\n return mid","def time(is_train):\n context = composition.Context()\n start_minutes = random.randint(1, 24*60 - 1)\n while True:\n duration_minutes = random.randint(1, 12*60 - 1)\n if train_test_split.is_train(duration_minutes) == is_train:\n break\n end_minutes = start_minutes + duration_minutes\n\n def format_12hr(minutes):\n \"\"\"Format minutes from midnight in 12 hr format.\"\"\"\n hours = (minutes // 60) % 24\n minutes %= 60\n #am_pm = 'AM' if hours < 12 else 'PM'\n #hours = (hours - 1) % 12 + 1\n return '{}:{:02}'.format(hours, minutes)\n #return '{}:{:02} {}'.format(hours, minutes, am_pm)\n\n start = format_12hr(start_minutes)\n end = format_12hr(end_minutes)\n\n which_question = random.randint(0, 3)\n if which_question == 0:\n # Question: What is start = end - duration?\n template = random.choice([\n 'Che ore sono se mancano {duration} minuti alle {end}?',\n ])\n return example.Problem(\n question=example.question(\n context, template, duration=duration_minutes, end=end),\n answer=start)\n elif which_question == 1:\n # Question: What is end = start + duration?\n template = random.choice([\n 'Che ore sono se sono passati {duration} minuti dalle {start}?',\n ])\n return example.Problem(\n question=example.question(\n context, template, duration=duration_minutes, start=start),\n answer=end)\n else:\n # Question: What is duration = end - start?\n template = random.choice([\n 'Quanti minuti ci sono tra le {start} e le {end}?',\n ])\n return example.Problem(\n question=example.question(context, template, start=start, end=end),\n answer=duration_minutes)","def generate_horror_title():\n d666 = random.randint(1, 666)\n if d666 <= 111:\n #the adj noun\n return \"The \" + horror_adj[random.randint(0, len(horror_adj) - 1)] + \" \" + horror_noun[random.randint(0, len(horror_noun) - 1)]\n elif d666 > 111 and d666 <= 222: \n #noun of noun\n return horror_noun[random.randint(0, len(horror_noun) - 1)] + \" of \" + horror_noun[random.randint(0, len(horror_noun) - 1)]\n elif d666 > 222 and d666 < 444: \n #the adj noun of verb \n return \"The \" + horror_adj[random.randint(0, len(horror_adj) - 1)] + \" \" + horror_noun[random.randint(0, len(horror_noun) - 1)] + \" of \" + horror_verb[random.randint(0, len(horror_verb) - 1)]\n elif d666 >= 444 and d666 < 555: \n #noun of noun\n return horror_noun[random.randint(0, len(horror_noun) - 1)] + \" of \" + horror_noun[random.randint(0, len(horror_noun) - 1)]\n elif d666 >= 555:\n #verb of the adj noun\n return horror_verb[random.randint(0, len(horror_verb) - 1)] + \" of the \" + horror_adj[random.randint(0, len(horror_adj) - 1)] + \" \" + horror_noun[random.randint(0, len(horror_noun) - 1)]","def __init__(self, length):\r\n # Receive how many notes should be in the melody\r\n self.length = length\r\n # Initialize size of melody's note list\r\n self.notes = [0] * self.length\r\n \r\n noteList = [0, 2, 4, 5, 7, 9, 11, 12, 14, 16, 17, 19, 21, 23, 24]\r\n \r\n # randomly assign notes to each index in the melody's\r\n # note list\r\n i = 0\r\n while i < self.length:\r\n self.notes[i] = random.choice(noteList)\r\n i += 1","def rhythm_data_generator(patient_generator, frame_size=2048, samples_per_patient=1):\n for _, (signal, labels) in patient_generator:\n num_segments, segment_size = signal.shape\n patient_rhythm_labels = labels['rtype'] # note: variables in a .npz file are only loaded when accessed\n for _ in range(samples_per_patient):\n # randomly choose a frame that lies within the segment i.e. no zero-padding is necessary\n segment_index = np.random.randint(num_segments)\n frame_start = np.random.randint(segment_size - frame_size)\n frame_end = frame_start + frame_size\n x = signal[segment_index, frame_start:frame_end]\n x = np.expand_dims(x, axis=1) # add channel dimension\n # calculate the durations of each rhythm in the frame and determine the final label\n rhythm_ends, rhythm_labels = patient_rhythm_labels[segment_index]\n frame_rhythm_durations, frame_rhythm_labels = get_rhythm_durations(\n rhythm_ends, rhythm_labels, frame_start, frame_end)\n y = get_rhythm_label(frame_rhythm_durations, frame_rhythm_labels)\n yield x, y","def generate_random_line(\n line_durations: List[float],\n pitches: List[ScaleElement]\n) -> List[PieceElement]:\n melodic_line = []\n current_time = 0\n for duration in line_durations:\n scale_element = random.choice(pitches)\n piece_element = PieceElement(\n note=scale_element.note,\n position_in_semitones=scale_element.position_in_semitones,\n position_in_degrees=scale_element.position_in_degrees,\n degree=scale_element.degree,\n start_time=current_time,\n duration=duration\n )\n melodic_line.append(piece_element)\n current_time += duration\n return melodic_line","def randomize_trajectory(self):\n self.angle = randint(-360, 360)\n self.speed = randint(1, 5)/2.5","def get_timings(beat, max_length, rand_beat):\n BEATS = [int(beat/4),\n int(beat/2),\n int(beat),\n int(beat*2),\n int(beat*4)]\n\n total_steps = beat * max_length\n timings = [beat] * max_length\n\n if rand_beat == True:\n timings = []\n while total_steps > 0:\n duration = random.sample(BEATS, 1)[0]\n total_steps -= duration\n timings.append(duration)\n\n return timings","def mon_maker():\r\n random_mon = {'eyeratio':0.2, 'eyeL':30,\r\n 'mouthratio':0.8, 'mouthL':30,\r\n 'headL':40, 'headA':15,\r\n 'cheekL':25, 'cheekA':45,\r\n 'chinL': 30, 'chinA':90\r\n }\r\n return random_mon","def generateNotes():\r\n fs = 44100 # hertz\r\n seconds = 3 # Note duration of 3 seconds\r\n noteNames = [\"C4\", \"D4\", \"E4\", \"F4\", \"G4\", \"A4\", \"B4\"]\r\n for noteName in noteNames:\r\n myNote = music21.note.Note(noteName)\r\n noteFrequency = myNote.pitch.frequency\r\n # Generate array with seconds*sample_rate steps, ranging between 0 and seconds\r\n t = np.linspace(0, seconds, seconds * fs, False)\r\n\r\n # Generate a 440 Hz sine wave\r\n sound = np.sin(noteFrequency * t * 2 * np.pi)\r\n\r\n # Ensure that highest value is in 16-bit range\r\n audio = sound * (2**15 - 1) / np.max(np.abs(sound))\r\n # Convert to 16-bit data\r\n audio = audio.astype(np.int16)\r\n\r\n # Start playback\r\n play_obj = sa.play_buffer(audio, 1, 2, fs)\r\n\r\n # Wait for playback to finish before exiting\r\n play_obj.wait_done()\r\n\r\n #Write sound to file\r\n sf.write('assets/patterns/'+noteName+'.wav', audio, fs)","def make_note_sound(self, freq, time, amplitude=1):\n note = sin(2 * pi * freq * amplitude *\n linspace(0, time, time * self.RATE))\n freq = utils.reverse_dictionary(self.FREQUENCY_MAP)[freq]\n print freq, time, amplitude, len(note)\n return note / np.max(np.abs(note), axis=0) # Normalize the note","def time(self):\n\n self.timing = True\n self.scramble()\n\n self.disp = False","def buildMelodyNotes(m, bars):\n\n # Get the notes part of the scale specified in Melody object\n s = generate_scale(m.getKeySignature(), m.getScale(), m.getOctave())\n\n if bars < MIN_BARS or bars > MAX_BARS:\n bars = random.randint(MIN_BARS, 4)\n\n # Get a random number of notes value between the number of bars (so\n # that there is roughly one note per bar) and SMALLEST_NOTE //\n # OTHER_NOTE (so that each note would be roughly OTHER_NOTE long)\n numNotes = random.randint(4, max(1, bars * (SMALLEST_NOTE //\n DOTTED_EIGHTH_NOTE)))\n seq = []\n # randomize notes in scale by choosing {numNotes} random notes\n for i in range(numNotes):\n seq.append(random.choice(s))\n\n # randomize length of each notes using getRandomStructure function\n lengths, barSpace = getEqualStructure(bars, numNotes)\n\n # add the random notes to generate the melody\n for i in range(numNotes):\n if seq[i] == -1:\n m.addNote(Note(0, lengths[i], vel=0))\n else:\n m.addNote(Note(seq[i], lengths[i]))\n\n if barSpace > 0:\n # Add rest just in case last note does not extend to end of bar\n m.addNote(Note(0, barSpace, vel=0))","def single_introduction(end):\n return [random.randint(0,end)]","def totem_random():\n random_head()\n random_head()\n random_head()","def randomize(data, length=0):\r\n longitud = 0 if length == 0 else 12 - length\r\n return data + \"-\" + str(time.time()).replace(\".\", \"\")[longitud:]","def seconds (self):\n if self.counter == self.times:\n return None\n\n self.counter += 1\n return random.uniform(self.lower, self.upper)","def migration_time(rand):\n ta=np.random.uniform(4,8)*1e4*yr\n K=np.power(10,rand)\n te=ta/K\n return ta, te","def generate_round():\n prog_len = random.randint(_MIN_LEN, _MAX_LEN)\n diff = random.randint(_MIN_DIFF, _MAX_DIFF)\n start = random.randint(0, _MAX_START)\n prog = _generate_progression(prog_len, diff, start)\n missing_position = random.randint(0, len(prog) - 1)\n missing_element = prog[missing_position]\n prog[missing_position] = '..'\n prog = list(map(str, prog))\n string_question = ' '.join(prog)\n return string_question, missing_element","def test_random_note_pitch(common_scales, all_pitches):\n\n for key, scales in common_scales.items():\n note = random_note_pitch(scales)\n assert note in all_pitches\n assert isinstance(note, (int, str)) is True","def copyRhythmParams(self, note):\n self.setDuration(note.getDurationNoDot())\n self.setBeatNum(note.getBeatNum())\n self.setTie(note.getTie())\n self.setDot(note.getDot())\n return","def generate_fantasy_title():\n d20 = random.randint(1, 20)\n if d20 <= 4:\n #genetive noun\n return fantasy_genetive[random.randint(0, len(fantasy_genetive) - 1)] + \" \" + fantasy_noun[random.randint(0, len(fantasy_noun) - 1)]\n elif d20 > 4 and d20 < 13: \n #The adj noun\n return \"The \" + fantasy_adj[random.randint(0, len(fantasy_adj) - 1)] + \" \" + fantasy_noun[random.randint(0, len(fantasy_noun) - 1)]\n elif d20 >= 13:\n #something of something\n return fantasy_noun[random.randint(0, len(fantasy_noun) - 1)] + \" of \" + fantasy_what_is_this[random.randint(0, len(fantasy_what_is_this) - 1)]","def story_command(update: Update, context: CallbackContext) -> None:\n update.message.reply_text(get_random_story(intro=choice([0, 6])))","def tempo_gen(tempo=120, srate=None):\n dt = tempo / (60 * get_srate(srate))\n return (i * dt for i in count())","def generate_sentence(self, t=20):\n result = [\"START\", \"START\"]\n\n for i in range(t-3):\n if result[-1] == \"STOP\":\n break\n\n match = {}\n for k,v in self.trigramcounts.items():\n if k[0] == result[-2] and k[1] == result[-1]:\n match[k[-1]] = v\n r = np.random.choice(list(match.keys()), p=np.array(list(match.values())) / np.sum(np.array(list(match.values()))))\n result.append(r)\n\n return result","def createMelody(instrument: int = INSTRUMENT_NP_NS_WITHOUT_CATEGORY,\n instrument_cat: str = \"\", bpm: int = -1, scale: str = \"\",\n octave: int = -1, time_sig: Tuple[int, int] = (4,4),\n key_sig: str = \"\", bars: int = -1, makeEmpty: bool = False):\n\n # instrument - MIDI number of instrument or type of random instrument\n # instrument_cat - Category of random instrument (if specified)\n # bpm - The beats per minute of the melody\n # scale - The scale that the melody is based on\n # octave - The octave that the melody is based on\n # time_sig - The time signature that the melody is played in\n # key_sig - The key that the melody is based on\n # bars - The number of bars/measures that the melody spans over\n # makeEmpty - Whether the melody should generate random notes or not\n\n if instrument < 0:\n randInstrument = chooseRandomInstrument(instrument, instrument_cat)\n else:\n randInstrument = instrument\n\n if bpm < LOWEST_BPM or bpm > HIGHEST_BPM:\n randBPM = random.randint(LOWEST_BPM, HIGHEST_BPM)\n else:\n randBPM = bpm\n\n if scale == \"\":\n randScale = get_random_scale()\n else:\n randScale = scale\n\n possibleOctaves = getSuitableOctaves(randInstrument)\n if octave in possibleOctaves:\n randOctave = octave\n elif octave != -2:\n if octave > possibleOctaves[-1]:\n randOctave = possibleOctaves[-1]\n else:\n randOctave = possibleOctaves[0]\n else:\n randOctave = random.choice(possibleOctaves)\n\n if key_sig == \"\":\n randKey = random.choice([\"C\", \"C#\", \"D\", \"D#\", \"E\", \"F\", \"F#\", \"G\",\n \"G#\", \"A\", \"A#\", \"B\"])\n else:\n randKey = flatsToSharps(key_sig)\n\n randMelody = Melody([], instrument=randInstrument, bpm=randBPM,\n scale=randScale, octave=randOctave, time_sig=time_sig,\n key_sig=randKey)\n\n if not makeEmpty:\n buildMelodyNotes(randMelody, bars)\n\n return randMelody","def add_time(t):\n\n times.append(t)\n\n # update number display to show real time\n number_display.time = t\n number_display.update()\n\n # generate new scramble and update scramble_image\n new_scramble = generate_scramble(int(settings['puzzle']),\n int(settings['scramble-length']))\n scrambles.append(new_scramble)\n scramble_image.clear()\n scramble_image.chars = char(new_scramble)\n\n ao5, ao12 = update_stats()\n\n with open(session_file.string, 'a') as f:\n if len(times) == 1:\n f.write(f'{add_zero(t)}\\t{ao5}\\t{ao12}\\t{new_scramble}')\n else:\n f.write(f'\\n{add_zero(t)}\\t{ao5}\\t{ao12}\\t{new_scramble}')","def random_timed_observations(count=100):\n # Generate a list of random, unique Observations\n observations = []\n time = datetime.fromtimestamp(0)\n for _ in range(count):\n time += timedelta(minutes=random.randint(1, 59))\n observations.append(Observation(\"Skeletor\", \"Starbucks\", str(time)))\n\n # Shuffle up the Observations\n random.shuffle(observations)\n\n return observations","def metric_modulation(\n metronome_mark=((1, 4), 60),\n left_note=(abjad.Note(\"c'8\")),\n right_note=(abjad.Note(\"c'8.\")),\n modulated_beat=(abjad.Note(\"c'4\")),\n rounded=None,\n font_size=None,\n leaf_scale=(0.6, 0.6),\n raise_value=None,\n):\n tempo_note = abjad.Note()\n tempo_note.written_duration = metronome_mark[0]\n left_speed_multiplier = compare_speed(l_note=tempo_note, r_note=left_note)\n left_speed = metronome_mark[1] * left_speed_multiplier\n modulation_speed = calculate_metric_modulation(\n l_tempo=left_speed, l_note=left_note, r_note=right_note\n )\n returned_speed = float(modulation_speed * compare_speed(left_note, modulated_beat))\n if font_size is not None:\n size = rf\" \\override #'(font-size . {font_size})\"\n else:\n size = r\" \\huge\"\n if returned_speed % 1 == 0.0:\n met = abjad.MetronomeMark.make_tempo_equation_markup(\n abjad.get.duration(modulated_beat), int(returned_speed)\n )\n met_string = met.string[8:]\n mod = abjad.MetricModulation(\n left_rhythm=left_note, right_rhythm=right_note, scale=leaf_scale\n )\n mod_string = abjad.lilypond(mod)[8:]\n if raise_value is None:\n mark = abjad.LilyPondLiteral(\n [\n r\"^ \\markup {\",\n size,\n r\" \\concat {\",\n f\" {met_string}\",\n r\" \\hspace #1\",\n r\" \\upright [\",\n f\" {mod_string}\",\n r\" \\hspace #0.5\",\n r\" \\upright ]\",\n r\" }\",\n r\"}\",\n ],\n site=\"after\",\n )\n else:\n mark = abjad.LilyPondLiteral(\n [\n r\"^ \\markup {\",\n size,\n rf\" \\raise #{raise_value} \\with-dimensions-from \\null\",\n r\" \\concat {\",\n f\" {met_string}\",\n r\" \\hspace #1\",\n r\" \\upright [\",\n f\" {mod_string}\",\n r\" \\hspace #0.5\",\n r\" \\upright ]\",\n r\" }\",\n r\"}\",\n ],\n site=\"after\",\n )\n return mark\n else:\n if rounded is True:\n met = abjad.MetronomeMark.make_tempo_equation_markup(\n abjad.get.duration(modulated_beat),\n quicktions.Fraction(round(returned_speed)).limit_denominator(),\n )\n met_string = met.string[8:]\n mod = abjad.MetricModulation(\n left_rhythm=left_note, right_rhythm=right_note, scale=leaf_scale\n )\n mod_string = abjad.lilypond(mod)[8:]\n if raise_value is None:\n mark = abjad.LilyPondLiteral(\n [\n r\"^ \\markup {\",\n size,\n r\" \\concat {\",\n \" c.\",\n r\" \\hspace #1\",\n f\" {met_string}\",\n r\" \\hspace #1\",\n r\" \\upright [\",\n f\" {mod_string}\",\n r\" \\hspace #0.5\",\n r\" \\upright ]\",\n r\" }\",\n r\"}\",\n ],\n site=\"after\",\n )\n else:\n mark = abjad.LilyPondLiteral(\n [\n r\"^ \\markup {\",\n size,\n rf\" \\raise #{raise_value} \\with-dimensions-from \\null\",\n r\" \\concat {\",\n \" c.\",\n r\" \\hspace #1\",\n f\" {met_string}\",\n r\" \\hspace #1\",\n r\" \\upright [\",\n f\" {mod_string}\",\n r\" \\hspace #0.5\",\n r\" \\upright ]\",\n r\" }\",\n r\"}\",\n ],\n site=\"after\",\n )\n else:\n met = abjad.MetronomeMark.make_tempo_equation_markup(\n abjad.get.duration(modulated_beat),\n quicktions.Fraction(returned_speed).limit_denominator(),\n )\n met_string = met.string[8:]\n mod = abjad.MetricModulation(\n left_rhythm=left_note, right_rhythm=right_note, scale=leaf_scale\n )\n mod_string = abjad.lilypond(mod)[8:]\n if raise_value is None:\n mark = abjad.LilyPondLiteral(\n [\n r\"^ \\markup {\",\n size,\n r\" \\concat {\",\n f\" {met_string}\",\n r\" \\hspace #1\",\n r\" \\upright [\",\n f\" {mod_string}\",\n r\" \\hspace #0.5\",\n r\" \\upright ]\",\n r\" }\",\n r\"}\",\n ],\n site=\"after\",\n )\n else:\n mark = abjad.LilyPondLiteral(\n [\n r\"^ \\markup {\",\n size,\n rf\" \\raise #{raise_value} \\with-dimensions-from \\null\",\n r\" \\concat {\",\n f\" {met_string}\",\n r\" \\hspace #1\",\n r\" \\upright [\",\n f\" {mod_string}\",\n r\" \\hspace #0.5\",\n r\" \\upright ]\",\n r\" }\",\n r\"}\",\n ],\n site=\"after\",\n )\n return mark","def generate_romance_title():\n d69 = random.randint(1, 69)\n if d69 <= 35:\n #the adj noun\n return \"The \" + romance_adj[random.randint(0, len(romance_adj) - 1)] + \" \" + romance_noun[random.randint(0, len(romance_noun) - 1)]\n elif d69 > 35:\n #noun of the adj noun\n return romance_noun[random.randint(0, len(romance_noun) - 1)] + \" of the \" + romance_adj[random.randint(0, len(romance_adj) - 1)] + \" \" + romance_noun[random.randint(0, len(romance_noun) - 1)]","def mimic_lecture(mimic_dict, starting_word=None, limit=10):\r\n\r\n if not starting_word:\r\n word = ''\r\n else:\r\n word = starting_word\r\n\r\n line_label = (divmod(x*3, 60) for x in numGen())\r\n text = word\r\n text = str(next(line_label)) + word\r\n line_mark = 0\r\n line_words = 1\r\n while line_mark < limit:\r\n if word not in mimic_dict:\r\n word = \"\"\r\n new_word = random.choice(mimic_dict[word])\r\n\r\n if line_words == 0:\r\n rand_silence = random.randint(1,10)\r\n if rand_silence <= 2:\r\n text = text + \"\\n\" + str(next(line_label))\r\n line_words = 0\r\n line_mark += 1\r\n word = new_word\r\n continue\r\n\r\n text = text + \" \" + new_word\r\n rand = random.randint(3,5)\r\n if line_words >= rand:\r\n text = text + \"\\n\" + str(next(line_label))\r\n line_words = 0\r\n line_mark += 1\r\n else:\r\n line_words += 1\r\n word = new_word\r\n return text","def trial(self, clock, window, shapes, mouse, keys, text_color, wait_time, warning_time, exp, count, ser):\n\n # Default Value Set Up for Timing #\n global stimulus_beg_time\n stimulus_beg_time = -1\n global in_between_time\n in_between_time = -1\n global total_stimuli_time\n total_stimuli_time = -1\n\n # Text values\n count_label = visual.TextStim(window, units='norm', text=u'', pos = [0, -0.6], height=0.2, color=text_color,\n colorSpace='rgb255',alignHoriz='center', alignVert='center')\n\n second_label = visual.TextStim(window, units='norm', text=u'Speak color of blocks',\n pos=[0,0.3], height=0.1, color=text_color, colorSpace='rgb255',alignHoriz='center',\n alignVert='center')\n\n done_label = visual.TextStim(window, units='norm', text=u'Done', pos=[0,-0.25], height=0.1, color=text_color,\n colorSpace='rgb255',alignHoriz='center', alignVert='center')\n\n done_button = visual.Rect(window, width=0.5, height=0.25, lineColor=(0, 0, 0), lineWidth=2,\n lineColorSpace='rgb', pos=(0, -0.25))\n\n next_label = visual.TextStim(window, units='norm', text=u'Speech Round', pos=[0,0], height=0.1, color=text_color,\n colorSpace='rgb',alignHoriz='center', alignVert='center')\n\n BLOCK_LIST = [second_label, done_button, done_label]\n\n # Display round name\n helper.displayNewRound(window, next_label)\n\n # Microphone Set Up #\n microphone.switchOn(sampleRate=16000)\n name = \"speech_exp_%d.wav\" %count\n mic = microphone.AdvAudioCapture(filename=name)\n # todo: can edit marker to output as sync signal; played when recording starts\n # marker currently set to not output any sound on onset of recording\n mic.setMarker(tone=5000, secs=0.015, volume=0.0)\n\n # Block Sequence Display #\n print \"%f BEGIN BLOCK SEQUENCE\" %(clock.getTime())\n ser.write(\"Begin Sequence\")\n global in_between_time\n in_between_time = helper.drawSequence(window, shapes, clock)\n ser.write(\"End Sequence\")\n print \"%f END BLOCK SEQUENCE\" %(clock.getTime())\n\n # for block interaction #\n self.hub.clearEvents()\n start_time = clock.getTime()\n timeout_counter = 0\n self.hub.clearEvents()\n\n # store time right when clicking stimuli is presented for reference\n window.callOnFlip(track_speech_time, clock, mouse)\n window.flip()\n\n # records for length of specified wait time\n mic.record(wait_time, block=False)\n ser.write(\"Start\")\n while mic.recorder.running:\n [s.draw() for s in BLOCK_LIST]\n count_label.draw()\n window.flip()\n timeout_counter += 1\n\n ## FOR MOUSE-CLICK END: ##\n # buttons, times = mouse.getPressed(getTime=True)\n # if mouse.isPressedIn(done_button, buttons=[0]):\n # break\n\n ## FOR KEYBOARD END: ##\n events = keys.getKeys()\n if len(events) != 0:\n break\n\n # adjust countdown value, to be displayed with the next flip\n if timeout_counter >= ((wait_time - warning_time)*60) and timeout_counter % 60 == 0:\n count_label.setText(((wait_time*60)-timeout_counter)/60)\n\n # turn off microphone and saves the audio file automatically\n ser.write(\"Finish\")\n microphone.switchOff()\n finish_time = clock.getTime()\n\n # once the round is finished, use previous counters to calculate total time spent and individual click times\n total_stimuli_time = finish_time - speech_beg_time\n print \"\\n%f\" %(finish_time)\n print \"%f TOTAL TIME TO FINISH ROUND\" %(total_stimuli_time)\n\n # save data in the experiment file\n exp.addData(\"stimulus_begin_time\", speech_beg_time)\n exp.addData(\"in_between_time\", in_between_time)\n exp.addData(\"total_stimuli_time\", total_stimuli_time)\n exp.addData(\"time1\", start_time)\n exp.addData(\"time2\", finish_time)\n\n # return status code based on correctness of sequence\n if timeout_counter == wait_time*60:\n return 2\n if timeout_counter < wait_time*60: # assume finished normally by clicking button\n return 0\n return -1","def time_to_failure():\r\n return random.expovariate(BREAK_MEAN)","def random_note_in_chord_and_vocal_range(relPitchList, key, vocal_range, prev_note):\n\n\t# for rests, can only be a rest\n\tif relPitchList == -1:\n\t\treturn [note.Rest()]\n\n\t# extract_range\n\tlowest_note, highest_note = vocal_range\n\n\tif (not isinstance(prev_note, note.Rest)) and (prev_note is not None):\n\n\t\t# restrict intervals to a sixth\n\t\tif prev_note.pitch.transpose(-7) > lowest_note:\n\t\t\tlowest_note = prev_note.pitch.transpose(-7)\n\n\t\tif prev_note.pitch.transpose(7) < highest_note:\n\t\t\thighest_note = prev_note.pitch.transpose(7)\n\n\t# turn relative Pitch into a note.Note\n\tnotes = []\n\n\n\tfor relPitch in relPitchList:\n\t\tif relPitch != 0:\n\t\t\tinterval_ = interval.ChromaticInterval(relPitch)\n\t\t\tnotes.append(note.Note(interval_.transposePitch(key.getTonic()), quarterLength=1))\n\t\telse:\n\t\t\t# needed as no transposition by 0 possible\n\t\t\tnotes.append(note.Note(key.getTonic(), quarterLength=1))\n\t \n\t# get all notes in right octave\n\toctave_corrected_notes = []\n\tfor n in notes:\n\n\t\t# possible octaves\n\t\tlow_octave = lowest_note.octave\n\t\thigh_octave = highest_note.octave\n \n\t\t# select random octave\n\t\t# if only one choice available\n\t\toctave_choice = np.arange(low_octave, high_octave + 1)\n\n\t\t# loop throughpossible octaves\n\t\tfor o in octave_choice:\n\t\t\tnote_ = copy.deepcopy(n)\n\t\t\tnote_.octave = o\n\t\t\tif (note_.pitch >= lowest_note) and (note_.pitch <= highest_note):\n\t\t\t\toctave_corrected_notes.append(note_)\n\n\treturn octave_corrected_notes","def write_music(self, length, bpm=75, initial_note=\"C\",\n initial_rhythm=\"0.25\", initial_key=\"F\"):\n # Translate the string key into an index for our note generator\n key_number = self.FREQUENCY_LIST.index(initial_key)\n print key_number\n\n rhythms = self.generate_rhythms(\n self.learner.ngrams[\"rhythms\"], initial_rhythm, bpm, length)\n notes = self.generate_notes(\n self.learner.ngrams[\"notes\"], initial_note, len(rhythms),\n key_number)\n\n rhythms = map(lambda x: float(60) * x / bpm, rhythms)\n notes = map(lambda x: self.FREQUENCY_MAP[x], notes)\n\n for i, n in enumerate(notes):\n self.write_note(self.make_note_sound(notes[i], rhythms[i]))","def random_midi_clip(self, midi):\n pm = PrettyMIDI(midi_file=midi)\n # Randomly select a note to start\n # This ensures that a note will\n # start in the first half of the clip\n start = max(\n np.random.choice(\n pm.instruments[0].notes\n ).start - np.random.random() * .5 * self.excerpt_length,\n 0\n )\n end = start + self.excerpt_length\n new_notes = []\n for note in pm.instruments[0].notes:\n if note.end < start or note.start > end:\n continue\n note.end = min(end, note.end) - start\n note.start = max(start, note.start) - start\n new_notes.append(note)\n pm.instruments[0].notes = new_notes\n return pm, start","def generate_singlesine(time = 0, samples_nb = 1000, rep_frequency = 10 , pulse_frequency = 50, amplitude = 1 , edge = 1, phase_offset = 0, noise = 0):\r\n\r\n\tif edge not in [0,1]:\r\n\t\tprint(colorama.Back.RED + colorama.Style.BRIGHT + \"ERROR: invalid phase (either 0 for a rising or a 1 for a falling edge) , exit.\"+ colorama.Style.NORMAL + colorama.Back.RESET)\r\n\t\t# Return code for error (empty input file):\r\n\t\tsys.exit(10)\r\n\r\n\r\n\t#Creating empty lists for t and y\r\n\tt = np.zeros(samples_nb)\r\n\r\n\tif noise == 0:\r\n\t\ty = np.zeros(samples_nb)\r\n\telse:\r\n\t\ty = np.random.normal(0, noise, samples_nb)\r\n\r\n\t#Determining the interval limits of t\r\n\tt_limit =1/float(rep_frequency*2)\r\n\r\n\t#Updating the t interval\r\n\tt = np.arange(-samples_nb/2,samples_nb/2)/float(samples_nb*rep_frequency) + 1/float(samples_nb*rep_frequency)\r\n\r\n\r\n\t#calculating the time_shift\r\n\t#delta_t = phase_offset/(2*np.pi*pulse_frequency)\r\n\tdelta_t = phase_offset/(2*np.pi*rep_frequency)\r\n\r\n\t#Setting the pulse amplitude\r\n\ta_pulse = amplitude\r\n\tif edge == 1:\r\n\t\ta_pulse *= -1\r\n\r\n\t#Calculating the pulse limits\r\n\tp_limit = 1/float(2*pulse_frequency)\r\n\tp_interval = list ([-p_limit,p_limit])\r\n\r\n\r\n\tfor n in range (0,len(t)) :\r\n\t\tif (t[n] + delta_t) > p_interval[0] and (t[n] + delta_t) <= p_interval[1]:\r\n\t\t\ty[n] += a_pulse * np.sin(2*np.pi*pulse_frequency*(t[n]+delta_t))\r\n\r\n\r\n\r\n\t#plt.plot(t,y)\r\n\t#plt.show()\r\n\r\n\tresult = {}\r\n\tresult ['time'] = time\r\n\tresult ['t'] = t\r\n\tresult ['y'] = y\r\n\r\n\treturn result","def generate_synth_data(n):","def meme_rand():\n img = random.choice(imgs)\n quote = random.choice(quotes)\n path = meme.make_meme(img, quote.body, quote.author)\n return render_template('meme.html', path=path)","def RandomDelay():\r\n sleep(random())","def trial(length, height):\n screen.refresh()\n global stimList\n global oddLength\n global oddHeight\n currentLength = int(maxLength / 4)\n currentHeight = int(maxHeight / 4)\n for i in range(stimAmt):\n if i == oddLocation:\n oddLength = currentLength\n oddHeight = currentHeight\n stimList.append(\n pg.draw.rect(\n screen.fg,\n PgTools.rand_color(),\n (currentLength, currentHeight, length, height,),\n )\n )\n PgTools.rand_pattern(\n screen.fg,\n (\n currentLength,\n currentHeight,\n ),\n (length, height),\n i=(randint(0, 2), randint(0, 1)),\n )\n if randShapes:\n PgTools.rand_shape(screen.fg, (currentLength, currentHeight),(length, height), oddSeed)\n else:\n stimList.append(\n pg.draw.rect(\n screen.fg,\n color,\n (currentLength, currentHeight, length, height,),\n )\n )\n PgTools.rand_pattern(\n screen.fg,\n (\n currentLength,\n currentHeight,\n ),\n (length, height),\n patColor,\n randNums,\n )\n if randShapes:\n PgTools.rand_shape(screen.fg, (currentLength, currentHeight),(length, height), regSeed)\n currentLength += maxLength / 4\n currentLength = int(currentLength)\n if (i + 1) % 3 == 0:\n currentLength = maxLength / 4\n currentLength = int(currentLength)\n currentHeight += maxHeight / 4\n currentHeight= int(currentHeight)","def first_notes_and_song():\n # Construct a robot, put it in safe mode.\n # Make it play two notes, then a song, then disconnect.\n\n time.sleep(2) # In case the robot has recently been shutdown.\n\n port = 'sim' # Or use YOUR laptop's COM number\n hal = new_create.Create(port)\n hal.toSafeMode() # So you can try this out on a table if you want.\n\n #-------------------------------------------------------------------\n # playNote(pitch, duration):\n #\n # Notes range from 31 (low G, 49.0 Hz) to 127 (high G, 12543.9 Hz).\n # They can be played from as little as 1/64th of a second to as\n # long as 255/64 seconds (i.e., almost 4 seconds).\n # The playNote method takes TWO (2) arguments:\n # -- The note (should be in the range 31 to 127, inclusive)\n # -- The duration to play that note, in 64th's of a second\n # (must be between 0 and 255, inclusive)\n #-------------------------------------------------------------------\n\n hal.playNote(31, 255) # Very long, low note.\n time.sleep(5.0) # See next example for why we need sleeps.\n\n hal.playNote(110, 1) # Very short, high note.\n time.sleep(1.0)\n\n #-------------------------------------------------------------------\n # playSong(LIST of pitch-duration TWO-TUPLEs)\n #\n # If you want to play a sequence of notes, it is almost always\n # better to use the playSong method instead of doing a\n # sequence of calls to playNote. The playSong method takes\n # ONE (1) argument. That argument must be:\n # -- a LIST\n # -- with no more than 16 items\n # -- where each item is a 2-tuple\n # whose first element is the pitch (31 to 127)\n # and whose second element is the duration (1 to 255)\n # -- where pitch and duration are as in playNote.\n #-------------------------------------------------------------------\n\n song = [(60, 8), (64, 8), (67, 8), (72, 8)] # a quick C chord\n hal.playSong(song)\n time.sleep(2.0)\n\n hal.shutdown()","def note():","def random_body():\n extra = random.randint(1, 9)\n sequence = (hex(rand32()) for _ in range(extra))\n return hex(id_) + '\\n' + '\\n'.join(sequence)","def create_song(self, lnmn, lnmx):\n # decide on the length of the song\n nlng = random.randint(lnmn, lnmx)\n\n # load the database\n lns = self.read_database()\n\n # randomly pick nlng lines\n rsong = []\n for i in range(nlng):\n j = random.randint(0,len(lns)-1)\n rsong.append(lns[j])\n\n return rsong","def generate_notes(model, network_input, pitch_names, n_vocab):\n \n # Pick a random sequence from the input as a starting point for the prediction\n random_start = np.random.randint(0, len(network_input)-1)\n\n # Create a dictionary to map note integers to pitches\n int_to_note = dict((number, note) for number, note in enumerate(pitch_names))\n\n # Choose a random sequence of notes to use as the network seed\n pattern = network_input[random_start]\n \n # Create an empty list to record the prediction output for each successive note\n prediction_output = []\n\n # Generate 500 notes\n for num_notes in range(500):\n \n # Reshape the input into a format compatible with LSTM layers\n prediction_input = np.reshape(pattern, (1, len(pattern), 1))\n \n # Normalize the network input by dividing by n_vocab (number of unique notes, rests, and chords)\n prediction_input = prediction_input / float(n_vocab)\n\n # Predict the next note, given an input sequence of notes\n prediction = model.predict(prediction_input, verbose=0)\n\n # Add each generated note to prediction_output\n index = np.argmax(prediction)\n result = int_to_note[index]\n prediction_output.append(result)\n\n # Update the pattern list to include to the generated note, used in next for-loop loop\n pattern.append(index)\n pattern = pattern[1:len(pattern)]\n\n return prediction_output","def pick_time(time_1,time_2,type=\"uniform\"):\n\tif type==\"uniform\":\n\t\treturn np.random.uniform(low=time_1,high=time_2)\n\tif type==\"gaussian\":\n\t\tcenter = 0.5*(time_2-time_1);\n\t\tsig = 0.5*(time_2-center);\n\t\tt = np.random.uniform(low=time_1,high=time_2)\n\t\th = np.exp(-((t-center)**2)/(2.0*sig**2))\n\t\twhile (np.random.rand()>h):\n\t\t\tt = np.random.uniform(low=time_1,high=time_2)\n\t\t\th = np.exp(-((t-center)**2)/(2.0*sig**2))\n\t\treturn t","def _prev_rhythm_tconst(pattern, rhythm):\n BASIC_TCONST(pattern, rhythm)\n tnet = pattern.last_tnet\n tnet.set_equal(pattern.hypothesis.start, rhythm.end)","def get_time(self):\n for _ in range(5):\n current_time = datetime.datetime.now().strftime('%H:%M:%S')\n self.logger.info(current_time)\n num: int = self.rand_num_gen.get_number()\n display = current_time + f\": {num}\\n\"\n yield display.encode()\n sleep(1)\n try:\n raise RuntimeError(\"something unexpected happened!\")\n except:\n trace_info = traceback.format_exc()\n self.logger.error(\"uncaught exception: %s\", trace_info)\n yield trace_info","def random_pause():\n pause_time = random.uniform(0, 0.5)\n sleep(pause_time)","def wishMe():\n hour = int(datetime.datetime.now().hour)\n if 0 <= hour < 12:\n speak(\"Good Morning Boss\")\n\n elif 12 <= hour < 18:\n speak(\"Good Afternoon Boss\")\n\n else:\n speak(\"Good Evening Boss!\")\n\n speak(\"This is mayaa appointed as your Assistant\")","def meme_rand():\n img = random.choice(imgs)\n quotes_list_one = random.choice(quotes)\n quote = random.choice(quotes_list_one)\n if quote and img:\n path = meme.make_meme(img, quote.body, quote.author)\n return render_template('meme.html', path=path)\n else:\n path = meme.make_meme('./_data/photos/dog/xander_1.jpg', \"Quote\", \"Author\")\n return render_template('meme.html', path=path)","def time(self):\r\n time = datetime.datetime.now().strftime(\"%I:%M:%S\")\r\n self.speak(\"the current time is\")\r\n self.speak(time)","def r(w,rangestart,rangeend):\r\n if w == 'r':\r\n print(random.random(rangestart , rangeend))\r\n if w == 'ri':\r\n print(random.randint(rangestart,rangeend))","def beat_stripper(timings, removal_proportion):\n phrase_len = len(timings)\n n_to_rm = int(phrase_len * removal_proportion)\n rm_idx = np.random.randint(0, phrase_len-1, n_to_rm)\n\n timings_stripped = []\n for i, hit in enumerate(timings):\n if not i in rm_idx:\n timings_stripped.append(hit)\n else:\n timings_stripped.append(hit + hit)\n return timings_stripped","def newsample(news, ratio):\n if ratio == 0:\n return []\n if ratio > len(news):\n return news + [0] * (ratio - len(news))\n else:\n return random.sample(news, ratio)","def __call__(self, audio):\n if type(self.stretch) in (int, float):\n return librosa.effects.time_stretch(audio, self.stretch)\n else:\n low, hi = self.stretch\n s = np.random.choice(np.linspace(low, hi, 100))\n return librosa.effects.time_stretch(audio, s)","def generateRandomePlainText():\n randomPlainTextArray = [random.choice('0123456789abcdef')\n for n in range(24)]\n randomPlainText = \"\".join(randomPlainTextArray)\n return randomPlainText","def sample_next(self, time, samples, errors):\n if self.previous_value is None:\n output = self.start_value\n else:\n time_diff = time - self.previous_time\n noise = np.random.normal(loc=0.0, scale=1.0, size=1)\n output = (np.power(self.ar_param, time_diff))*self.previous_value+\\\n self.sigma*np.sqrt(1-np.power(self.ar_param, time_diff))*noise\n self.previous_time = time\n self.previous_value = output\n return output","def createMelody(song, outputSongFileName, timing=4):\n wavInput = (())\n wavInput1 = (())\n wavInput2 = (())\n wavInput3 = (())\n\n # Remove the beginning and end portions of the canvas that are blank\n while song[0] == ['R','R','R','R']:\n del song[0]\n while song[-1] == ['R','R','R','R']:\n del song[-1]\n\n for notesList in song:\n\n remove_dup(notesList)\n\n notesNum = []\n for i in range(len(notesList)):\n if (notesList[i].upper() == 'R'):\n notesNum.append('')\n elif (notesList[i].upper() == 'A' or notesList[i].upper() == 'B'):\n notesNum.append('3')\n else:\n notesNum.append('4')\n\n wavInput = ((notesList[0].lower() + str(notesNum[0]), timing),) + wavInput\n wavInput1 = ((notesList[1].lower() + str(notesNum[1]), timing),) + wavInput1\n wavInput2 = ((notesList[2].lower() + str(notesNum[2]), timing),) + wavInput2\n wavInput3 = ((notesList[3].lower() + str(notesNum[3]), timing),) + wavInput3\n\n\n wavInput = wavInput[::-1]\n wavInput1 = wavInput1[::-1]\n wavInput2 = wavInput2[::-1]\n wavInput3 = wavInput3[::-1]\n\n wavNames = [\".wav1.wav\",\".wav2.wav\",\".wav3.wav\",\".wav4.wav\"]\n wavInputs = [wavInput,wavInput1,wavInput2,wavInput3]\n\n validWavInputs = []\n\n for i in range(len(wavInputs)):\n if isAllRests(wavInputs[i]) == False:\n validWavInputs.append(wavInputs[i])\n\n validWavNames = wavNames[:len(validWavInputs)]\n\n call(['python','GenerateWavFiles.py',str(validWavNames) + \"@\" + str(validWavInputs)])\n\n sounds = []\n for i in range(len(validWavNames)):\n sounds.append(AudioSegment.from_wav(validWavNames[i]))\n\n combined = sounds[0]\n for i in range(1, len(sounds)):\n combined = combined.overlay(sounds[i])\n\n combined.export(outputSongFileName, format='wav')","def random_text(self, n=100):\n # choose a random prefix (not weighted by frequency)\n start = random.choice(list(self.suffix_map.keys()))\n #print(\">>DEBUG | start is\", start)\n \n for i in range(n):\n #print(\">> DEBUG | i is\", n)\n suffixes = self.suffix_map.get(start, None)\n #print(\">> DEBUG | suffixes is\", suffixes)\n if suffixes == None:\n # if the start isn't in map, we got to the end of the\n # original text, so we have to start again.\n #print(\">> DEBUG | start isn't in map\")\n random_text(n-i)\n return\n\n # choose a random suffix\n word = random.choice(suffixes)\n #print(\">> DEBUG | word is\", word)\n print(word, end=' ')\n start = self.shift(start, word)","def generate(self, curr_time):\n status = 200\n new_time = self._get_next_time(curr_time)\n if self.error_dist.random() < self.error_prob:\n status = 500\n\n return new_time, \"{:.4f} {} {} {}\".format(new_time, self.verb, self.resource, status)","def sim(p=0.5, ph=0.7, pl=0.8, n=20,sd=70):\n random.seed(sd)\n te=np.array(random.sample(range(1000),n-1))/1000#random draw\n random.seed(sd)\n rea=random.choices('HL',weights=(p,1-p),k=1) #initial realization at t0\n if rea[0]=='H':\n jp=p\n else:\n jp=round((1-p),2) \n for i in range(n-1): #compute realization from t1 to tn\n temp=te[i]\n if rea[-1]=='L': #using probability of low to low to be compared with draw\n if temp<=pl:\n rea.append('L')\n jp=jp*pl\n else:\n rea.append('H')\n jp=jp*round((1-pl), 2)\n else: #using probability of high to high to be compared with draw\n if temp<=ph:\n rea.append('H')\n jp=jp*ph\n else:\n rea.append('L')\n jp=jp*round((1-ph), 2)\n return (rea, jp)","def sensorInfo(timeDelay = .5):\n cycle = time.time()\n while (time.time() - cycle < timeDelay):\n pass\n return random.randint(1,10000)","def choose_piece_duration(self):\n self.piece_duration = random.randint(900, 1100)","def get_monthday_song():\n current_day = day_of_month()\n print(f\"MONTHDAY:{current_day}\")\n if (current_day == 1):\n return random.choice([ \\\n \"One of Us\", \\\n \"One - Harry Nilsson\", \\\n \"One More Night \", \\\n \"One - Metallica\", \\\n \"The One - Backstreet Boys\", \\\n \"1 Step Forward, 3 Steps Back\"])\n elif (current_day == 2):\n return random.choice([\"It Takes Two\", \"Two Black Cadillacs\"])\n elif (current_day == 3):\n return random.choice([\"Three Little Birds\"])\n elif (current_day == 4):\n return random.choice([\"Four Mintues\", \"Four Five Seconds\"])\n elif (current_day == 5):\n return random.choice([\"Mambo Number 5\", \"I Got 5 on It\", \"Five More Minutes\"])\n elif (current_day == 6):\n return random.choice([\"Six Feet Under\"])\n elif (current_day == 7):\n return random.choice([ \\\n \"7 Years\", \\\n \"Seven Bridges Road\", \\\n \"Seven Seas of Rhye\", \\\n \"Seven Nation Army\", \\\n \"7 things\"])\n elif (current_day == 8):\n return random.choice([\"Eight Days a Week\", \"Eight Miles High\", \"8 out of 10\"])\n elif (current_day == 9):\n return random.choice([\"Love Potion Number 9\"])\n elif (current_day == 13):\n return random.choice([\n \"13 - LANY\", \\\n \"Floor 13 - Machine Gun Kelly\", \\\n \"13 - DDG\", \\\n \"Thirteen - Johnny Cash\"])\n elif (current_day == 14):\n return random.choice([\"14,400 Minutes\", \"March 15th\", \"14 Miles From Home\"])\n elif (current_day == 15):\n return random.choice([\"Fifteen - Taylor Swift\"])\n elif (current_day == 16):\n return random.choice([\"Summer Sixteen\", \"Sixteen Tons\", \"Sixteen\"])\n elif (current_day == 17):\n return random.choice([\"Edge of Seventeen\"])\n elif (current_day == 21):\n return random.choice([\"Twenty One - Khalid\"])\n elif (current_day == 22):\n return random.choice([\"22 - Taylor Swift\", \"Twenty Two and Some Change\", \"Twentytwo - Thea\"])\n elif (current_day == 23):\n return random.choice([\"23 Sam Hunt\", \"23 Mike Will\", \"23 Chayce Beckham\"])\n elif (current_day == 24):\n return random.choice([\"24 - Kanye\"])\n else:\n return \"\"","async def pornx(ctx, n: int = 1):\r\n msg = [\"{}\".format(text) for text in [random.choice(data) for _ in range(0,n)]]\r\n await bot.say('\\n'.join(msg))","def gen():\r\n\r\n output = \"\"\r\n frame = 12\r\n prior = 0\r\n second = 0\r\n\r\n while frame > 0:\r\n frame -= .5\r\n roll = randrange(11 - prior)\r\n\r\n # If strike is rolled:\r\n if roll == 10:\r\n output += \"X\"\r\n frame -= .5\r\n prior = 0\r\n second = 0\r\n\r\n # If spare is rolled:\r\n elif second == 1 and roll + prior == 10:\r\n output += \"/\"\r\n prior = 0\r\n second = 0\r\n\r\n # If not strike or spare:\r\n else:\r\n output += str(roll)\r\n prior = roll\r\n second = 1 - second\r\n\r\n # Deliminate frames:\r\n if second == 0:\r\n output += \"-\"\r\n\r\n return output[:-1]","def schedule_paragraph():","def gen_time(self):\n t_min = self.times['Min']\n t_max = self.times['Max']\n t_sig = self.times['Mean']\n # generating activity time based on truncnorm distribution\n # see StatDistExp jupyter notebook for more details\n # if in test sample, activity time mean/max brought 25% closer\n # the min\n if self.activity in self.test_sample:\n t_max = min(int(t_min + (t_max - t_min)*3/4), t_max-1)\n t_sig = min(int(t_min + (t_sig - t_min)*3/4), t_sig-1)\n t_max = max(t_max, 1)\n t_sig = max(t_sig, 1)\n t_scale = min(t_sig-t_min, t_max-t_sig)/3*2\n #accounting for when activity time is always 1 day\n if t_min == t_max == t_sig:\n self.sample = t_min\n else:\n #this is the normal case generation of activity time\n self.sample = truncnorm.rvs(a=t_min-t_sig, b=t_max-t_sig, scale=t_scale, size=1)[0]\n self.sample += t_sig\n self.sample = int(self.sample)","def wishMe():\n hour = int(dt.datetime.now().hour)\n if hour>= 0 and hour<12:\n speak(\"Good Morning\")\n elif hour>=12 and hour<18:\n speak(\"Good afternoon!\")\n else:\n speak(\"Good Evening\")\n\n speak(\"I am your personal assistant! How may I help you?\")","def generate_notes(model, network_input, n_vocab):\n # pick a random sequence from the input as a starting point for the prediction\n start = np.random.randint(0, len(network_input)-1)\n \n # Get pitch names and store in a dictionary\n pitchnames = sorted(set(item for item in notes))\n int_to_note = dict((number, note) for number, note in enumerate(pitchnames))\n\n pattern = network_input[start]\n prediction_output = []\n\n # generate 500 notes\n for note_index in range(500):\n prediction_input = np.reshape(pattern, (1, len(pattern), 1))\n prediction_input = prediction_input / float(n_vocab)\n\n prediction = model.predict(prediction_input, verbose=0)\n\n index = np.argmax(prediction)\n result = int_to_note[index]\n prediction_output.append(result)\n \n pattern = np.append(pattern,index)\n pattern = pattern[1:len(pattern)]\n\n return prediction_output","def gen_dynstring(self):\n # a timestamp plus something semi random\n return '%s.%s' % (time.strftime('%m%d%H%M%S', (time.localtime())),\n random.randint(1, 100000))","def note(f, start, end, fade=.01):\n\n def sampler(t):\n seconds = t / sampler_rate\n if seconds < start:\n return 0\n elif seconds > end:\n return 0\n elif seconds < start + fade:\n return (seconds - start) / fade * f(t)\n elif seconds > end - fade:\n return (end - seconds) / fade * f(t)\n else:\n return f(t)\n\n return sampler","def tempo(beats, trk=None, bt=480):\n if not trk:\n trk = MidiTrack()\n trk.name = \"Tempo variation\"\n trk.append(MetaMessage(\"set_tempo\",\n tempo=beats[0],\n time=0))\n\n for i, beat in enumerate(beats):\n trk.append(MetaMessage(\"set_tempo\",\n time=bt,\n tempo=beat))\n\n return trk","def randomHelmet():\n return random.choice(HELMETS)","def sample(self):\n seq = []\n for i in range(self._pwm.shape[1]):\n p = numpy.array(self._pwm[:, i], dtype=numpy.float64)\n p /= p.sum()\n seq.extend(numpy.random.choice(self.alphabet, p=p))\n return \"\".join(seq)","def rand(self):\n raise NotImplementedError","def generate_question_and_answer(): # noqa: WPS210\n start_number = random.randint(1, 100)\n progression_step = random.randint(1, 10)\n progression_length = random.randint(5, 10)\n progression = generate_progression(\n start_number, progression_step, progression_length,\n )\n return hide_number(progression)","def elapseTime(self, gameState):\n newParticleList = []\n # Pretend each particle is a ghost, and set its position semi-randomly based on how\n # likely the ghost is to move to that position\n for particle in self.particles:\n newPosDist = self.getPositionDistribution(gameState, particle)\n newParticleList.append(util.sample(newPosDist))\n self.particles = newParticleList","def lf():\n return random.sample(font_list, 25)","def _get_next_time(self, curr_time):\n return curr_time + self.time_dist.random()","def random():\r\n return R.NextDouble()","def generate_midi(config_folder, score_source, number_of_version, duration_gen, rhythmic_reconstruction, logger_generate):\n\n logger_generate.info(\"#############################################\")\n logger_generate.info(\"Orchestrating piano score : \" + score_source)\n ############################################################\n # Load model, config and data\n ############################################################\n\n ########################\n # Load config and model\n parameters = pkl.load(open(config_folder + '/script_parameters.pkl', 'rb'))\n model_parameters = pkl.load(open(config_folder + '/model_params.pkl', 'rb'))\n # Set a minimum seed size, because for very short models you don't event see the beginning\n seed_size = max(model_parameters['temporal_order'], 10) - 1\n quantization = parameters['quantization']\n temporal_granularity = parameters['temporal_granularity']\n instru_mapping = parameters['instru_mapping']\n ########################\n\n ########################\n # Load data\n if re.search(r'mid$', score_source):\n pr_piano, event_piano, duration_piano, name_piano, pr_orch, instru_orch, duration = load_solo(score_source, quantization, parameters[\"binarize_piano\"], temporal_granularity)\n else:\n pr_piano, event_piano, duration_piano, name_piano, pr_orch, instru_orch, duration = load_from_pair(score_source, quantization, parameters[\"binarize_piano\"], parameters[\"binarize_orch\"], temporal_granularity)\n ########################\n\n ########################\n # Shorten\n # Keep only the beginning of the pieces (let's say a 100 events)\n pr_piano = extract_pianoroll_part(pr_piano, 0, duration_gen)\n if parameters[\"duration_piano\"]:\n duration_piano = np.asarray(duration_piano[:duration_gen])\n else:\n duration_piano = None\n event_piano = event_piano[:duration_gen]\n pr_orch = extract_pianoroll_part(pr_orch, 0, duration_gen)\n ########################\n\n ########################\n # Instanciate piano pianoroll\n N_piano = instru_mapping['Piano']['index_max']\n pr_piano_gen = np.zeros((duration_gen, N_piano), dtype=np.float32)\n pr_piano_gen = build_data_aux.cast_small_pr_into_big_pr(pr_piano, {}, 0, duration_gen, instru_mapping, pr_piano_gen)\n pr_piano_gen_flat = pr_piano_gen.sum(axis=1)\n silence_piano = [e for e in range(duration_gen) if pr_piano_gen_flat[e]== 0]\n ########################\n\n ########################\n # Instanciate orchestra pianoroll with orchestra seed\n N_orchestra = parameters['N_orchestra']\n if pr_orch:\n pr_orchestra_gen = np.zeros((seed_size, N_orchestra), dtype=np.float32)\n orch_seed_beginning = {k: v[:seed_size] for k, v in pr_orch.items()}\n pr_orchestra_gen = build_data_aux.cast_small_pr_into_big_pr(orch_seed_beginning, instru_orch, 0, seed_size, instru_mapping, pr_orchestra_gen)\n pr_orchestra_truth = np.zeros((duration_gen, N_orchestra), dtype=np.float32)\n pr_orchestra_truth = build_data_aux.cast_small_pr_into_big_pr(pr_orch, instru_orch, 0, duration_gen, instru_mapping, pr_orchestra_truth)\n else:\n pr_orchestra_gen = None\n pr_orchestra_truth = None\n ########################\n \n #######################################\n # Embed piano\n time_embedding = time.time()\n if parameters['embedded_piano']:\n # Load model\n embedding_path = parameters[\"embedding_path\"]\n embedding_model = embedDenseNet(380, 12, (1500,500), 100, 1500, 2, 3, 12, 0.5, 0, False, True)\n embedding_model.load_state_dict(torch.load(embedding_path))\n\n # Build embedding (no need to batch here, len(pr_piano_gen) is sufficiently small)\n piano_resize_emb = np.zeros((len(pr_piano_gen), 1, 128)) # Embeddings accetp size 128 samples\n piano_resize_emb[:, 0, instru_mapping['Piano']['pitch_min']:instru_mapping['Piano']['pitch_max']] = pr_piano_gen\n piano_resize_emb_TT = torch.tensor(piano_resize_emb)\n piano_embedded_TT = embedding_model(piano_resize_emb_TT.float(), 0)\n pr_piano_gen_embedded = piano_embedded_TT.numpy()\n else:\n pr_piano_gen_embedded = pr_piano_gen\n time_embedding = time.time() - time_embedding\n #######################################\n\n ########################\n # Inputs' normalization\n normalizer = pkl.load(open(os.path.join(config_folder, 'normalizer.pkl'), 'rb'))\n if parameters[\"embedded_piano\"]: # When using embedding, no normalization\n pr_piano_gen_norm = pr_piano_gen_embedded\n else:\n pr_piano_gen_norm = normalizer.transform(pr_piano_gen_embedded)\n ########################\n \n ########################\n # Store folder\n string = re.split(r'/', name_piano)[-1]\n name_track = re.sub('piano_solo.mid', '', string)\n generated_folder = config_folder + '/generation_reference_example/' + name_track\n if not os.path.isdir(generated_folder):\n os.makedirs(generated_folder)\n ########################\n\n ########################\n # Get trainer\n with open(os.path.join(config_folder, 'which_trainer'), 'r') as ff:\n which_trainer = ff.read()\n # Trainer\n if which_trainer == 'standard_trainer':\n from LOP.Scripts.standard_learning.standard_trainer import Standard_trainer as Trainer\n kwargs_trainer = {'temporal_order': model_parameters[\"temporal_order\"]}\n elif which_trainer == 'NADE_trainer':\n from LOP.Scripts.NADE_learning.NADE_trainer import NADE_trainer as Trainer\n kwargs_trainer = {'temporal_order': model_parameters[\"temporal_order\"], 'num_ordering': model_parameters[\"num_ordering\"]}\n else:\n raise Exception(\"Undefined trainer\")\n trainer = Trainer(**kwargs_trainer)\n ########################\n\n ############################################################\n # Generate\n ############################################################\n time_generate_0 = time.time()\n generated_sequences = {}\n for measure_name in parameters['save_measures']:\n model_path = 'model_' + measure_name\n generated_sequences[measure_name] = generate(trainer, pr_piano_gen_norm, silence_piano, duration_piano, config_folder, model_path, pr_orchestra_gen, batch_size=number_of_version)\n time_generate_1 = time.time()\n logger_generate.info('TTT : Generating data took {} seconds'.format(time_generate_1-time_generate_0))\n\n ############################################################\n # Reconstruct and write\n ############################################################\n def reconstruct_write_aux(generated_sequences, prefix):\n for write_counter in range(generated_sequences.shape[0]):\n # To distinguish when seed stop, insert a sustained note\n this_seq = generated_sequences[write_counter] * 127\n this_seq[:seed_size, 0] = 20\n # Reconstruct\n if rhythmic_reconstruction:\n pr_orchestra_clean = from_event_to_frame(this_seq, event_piano)\n else:\n pr_orchestra_clean = this_seq\n pr_orchestra = instrument_reconstruction(pr_orchestra_clean, instru_mapping)\n # Write\n write_path = generated_folder + '/' + prefix + '_' + str(write_counter) + '_generated.mid'\n if rhythmic_reconstruction:\n write_midi(pr_orchestra, quantization, write_path, tempo=80)\n else:\n write_midi(pr_orchestra, 1, write_path, tempo=80)\n return\n\n for measure_name in parameters[\"save_measures\"]:\n reconstruct_write_aux(generated_sequences[measure_name], measure_name)\n \n ############################################################\n ############################################################\n # Write original orchestration and piano scores, but reconstructed version, just to check\n if rhythmic_reconstruction:\n A = from_event_to_frame(pr_piano_gen, event_piano)\n else:\n A = pr_piano_gen\n B = A * 127\n piano_reconstructed = instrument_reconstruction_piano(B, instru_mapping)\n write_path = generated_folder + '/piano_reconstructed.mid'\n if rhythmic_reconstruction:\n write_midi(piano_reconstructed, quantization, write_path, tempo=80)\n else:\n write_midi(piano_reconstructed, 1, write_path, tempo=80)\n #\n if rhythmic_reconstruction:\n A = from_event_to_frame(pr_orchestra_truth, event_piano)\n else:\n A = pr_orchestra_truth\n B = A * 127\n orchestra_reconstructed = instrument_reconstruction(B, instru_mapping)\n write_path = generated_folder + '/orchestra_reconstructed.mid'\n if rhythmic_reconstruction:\n write_midi(orchestra_reconstructed, quantization, write_path, tempo=80)\n else:\n write_midi(orchestra_reconstructed, 1, write_path, tempo=80)\n ############################################################\n ############################################################","def mock_tweet():\n count = random.randint(70, 140)\n return ''.join([random.choice(string.letters) for i in xrange(count)])","def get_weekday_song():\n current_day = day_of_week()\n print(f\"WEEKDAY:{current_day}\")\n if (current_day == \"Monday\"):\n return random.choice([ \\\n \"Monday - Imagine Dragons\", \\\n \"Monday Morning - Quinn XCII\", \\\n \"Monday Mornin' Missin' You - Blake Shelton\", \\\n \"Monday Morning - Fleetwood Mac\", \\\n \"Monday, Monday\", \\\n \"Every Day is a Monday\"])\n elif (current_day == \"Tuesday\"):\n return random.choice([ \\\n \"Tuesdays\", \\\n \"Tuesday (feat Drake)\", \\\n \"Tuesday's Gone\", \\\n \"Tuesday I'll Be Gone\", \\\n \"Taco Tuesday - Migos\", \\\n \"Taco Tuesday - Lil Jon\", \\\n \"Tuesday Afternoon\"])\n elif (current_day == \"Wednesday\"):\n return random.choice([ \\\n \"Wednesday Morning - Macklemore\", \\\n \"Wednesday Night Interlude - Drake\", \\\n \"Wednesday Morning, 3AM\"])\n elif (current_day == \"Thursday\"):\n return random.choice([ \\\n \"Thursday - The Weeknd\", \\\n \"Thursday - Jess Glyne\", \\\n \"(Thursday) Here's Why I Did Not Go to Work Today\", \\\n \"Like a Summer Thursday\", \\\n \"Sweet Thursday\"])\n elif (current_day == \"Friday\"):\n return random.choice([ \\\n \"Friday Night - Eric Paslay \", \\\n \"Last Friday Night\", \\\n \"Finally Friday - George Jones\", \\\n \"Friday Rascall Flatts\", \\\n \"I Gotta Feeling\", \\\n \"Friday Night in Dixie\", \\\n \"Fridays Child\", \\\n \"Hymn for the Weekend\", \\\n \"Friday Night Fish Fry\", \\\n \"Friday Night - Lady A\", \\\n \"Hello Friday - Flo Rida\"])\n elif (current_day == \"Saturday\"):\n return random.choice([ \\\n \"Louisiana Saturday Night\", \\\n \"American Saturday Night\", \\\n \"Small Town Saturday Night\", \\\n \"Satuday Night's Alright\", \\\n \"Saturday in the Park\", \\\n \"Saturday - Twenty One Pilots\", \\\n \"Saturday Nights - Khalid\", \\\n \"Saturday Sun - Vance Joy\"])\n elif (current_day == \"Sunday\"):\n return random.choice([ \\\n \"Sunday Candy\", \\\n \"Sunday Morning - Parmalee\", \\\n \"Sunday Morning - Maroon 5\", \\\n \"Sunday Best\", \\\n \"Sunday\", \\\n \"Closed on Sunday\", \\\n \"Raining on Sunday\", \\\n \"A Month of Sundays\", \\\n \"That's What I Love About Sunday\", \\\n \"Sunday Drive\", \\\n \"Another Sunday in the South - Miranda Lambert\", \\\n \"Sunday - Sia\", \\\n \"Sunday Morning - Maroon 5\", \\\n \"A Month of Sundays - Don Henly\", \\\n \"Lazing on a Sunday Afternoon - Queen\", \\\n \"Sunday Morning Coming Down\", \\\n \"Blue Sunday - The Doors\", \\\n \"A Sunday Kind of Love - Etta James\"])","def getRandomEvent(p = 0.5):\n if random.random() < p: return 1\n else: return 0","def random_timed_observation_timeline(count=100):\n # Generate some observations\n observations = random_timed_observations(count)\n\n # Add the observations to our timeline\n timeline = ObservationTimeline()\n for t in observations:\n timeline.add(t)\n\n return timeline","def play_tone(freq=440, duration=0.01):\n tone(board.A0, freq, duration)"],"string":"[\n \"def randNotes(self, value, chord):\\n aChord = mgChord(value, chord)\\n self.data = []\\n for i in range(len(self.rhythm)):\\n note = random.choice(aChord).copy()\\n note.setDuration(self.rhythm[i])\\n self.data.append(note)\",\n \"def create_melody(inst, chord_progression):\\r\\n \\r\\n for chord in chord_progression:\\r\\n \\r\\n meas = inst.NewMeasure()\\r\\n last_note = random.choice(CHORDS[chord])\\r\\n pos = 0\\r\\n \\r\\n while pos < 64:\\r\\n dur = random.choice([8, 8, 16, 16]) \\r\\n last_note = get_rdm_note(chord, last_note)\\r\\n if random.randint(1,10) <= 8: # 20% silence\\r\\n meas.NewNote(pos, dur, last_note, 5)\\r\\n pos += dur\",\n \"def make_music_rand():\\n pass\",\n \"def gen_melody(scale, timings, note_deviance):\\n melody = [random.sample(scale, 1)[0] for i in range(len(timings))]\\n\\n px = np.random.rand(len(timings))\\n idx = np.argwhere(px <= note_deviance)\\n direction = [-1, 1]\\n for i in idx:\\n melody[i[0]] = melody[i[0]] + random.sample(direction, 1)[0]\\n return melody\",\n \"def get_rdm_note(chord, last_note):\\r\\n \\r\\n if random.randint(1,10) <= 6:\\r\\n # chord note\\r\\n return random.choice(CHORDS[chord]) \\r\\n else:\\r\\n # scale note \\r\\n n = C_SCALE.index(last_note)\\r\\n return C_SCALE[n+random.choice(range(-n, 6-n)[2:5])]\",\n \"def sample_times(times, num_examples):\\n sample = random.sample(times, num_examples)\\n make_hist(sample, 10, 'Sample of Size ' + str(num_examples),\\n 'Minutes to Complete Race', 'Number of Runners')\",\n \"def get_random_time_between_tts():\\n return random.randint(TIME_BETWEEN_TTS_LOWER, TIME_BETWEEN_TTS_UPPER)\",\n \"def generate_line_durations(\\n n_measures: int,\\n duration_weights: Dict[float, float],\\n valid_rhythmic_patterns: List[List[float]],\\n end_with_whole_note: bool = True\\n) -> List[float]:\\n current_time = 0\\n line_durations = []\\n current_measure_durations = []\\n total_time = n_measures - int(end_with_whole_note)\\n\\n while current_time < total_time:\\n appropriate_durations = select_appropriate_durations(\\n current_time, total_time, current_measure_durations,\\n valid_rhythmic_patterns\\n )\\n duration = random.choices(\\n appropriate_durations,\\n [duration_weights[x] for x in appropriate_durations]\\n )[0]\\n current_time += duration\\n current_measure_durations = update_current_measure_durations(\\n current_measure_durations, duration\\n )\\n line_durations.append(duration)\\n\\n if end_with_whole_note:\\n line_durations.append(1.0)\\n return line_durations\",\n \"def _rhythm_obs_proc(pattern):\\n # We asign the endpoint of the hypothesis.\\n pattern.hypothesis.end.value = pattern.evidence[o.QRS][-1].time.value\",\n \"def time_per_part():\\r\\n return random.normalvariate(PT_MEAN, PT_SIGMA)\",\n \"def getStatusMessage():\\n\\n now = datetime.datetime.now()\\n hour = now.hour\\n mood = Sentience.getPrimaryMood()\\n exp_mood = Sentience.getExposedPositivity()\\n\\n random.seed((time.time()//86400*86400))\\n\\n #sleeping\\n if not (9 <= hour < 21) and (mood <= 0.5 or not 7 <= hour < 23):\\n if exp_mood < -0.1:\\n return random.choice([\\n \\\"bleh\\\",\\n \\\"not sleeping well\\\",\\n \\\"why's chat so noisy\\\",\\n \\\"can't sleep\\\",\\n \\\"do not disturb pls thx\\\",\\n ])\\n\\n if mood < 0:\\n return random.choice([\\n \\\"crying myself to sleep rn\\\",\\n \\\":(\\\",\\n \\\"had a nightmare\\\",\\n \\\"can't sleep\\\",\\n \\\"._.\\\"\\n ])\\n\\n return random.choice([\\n \\\"zzz...\\\",\\n \\\"sweet dreams\\\",\\n \\\"good night\\\",\\n \\\"sleeping...\\\",\\n \\\"having some rest\\\"\\n ])\\n\\n if Sentience.isExposedPositivityOverloaded():\\n return random.choice([\\n \\\"i'm done\\\",\\n \\\"too much\\\"\\n \\\"goodbye\\\",\\n \\\"tired\\\",\\n \\\"need rest\\\",\\n ])\\n\\n #happy\\n if mood >= 0.7:\\n return random.choice([\\n \\\":D\\\",\\n \\\"great day\\\",\\n \\\"happy happy\\\",\\n \\\"hehe\\\",\\n \\\"good times\\\",\\n \\\"yay\\\",\\n \\\"what's up\\\",\\n \\\"happiness\\\",\\n \\\"nice day\\\",\\n ])\\n #moody-ish\\n if mood >= 0.4:\\n return random.choice([\\n \\\"hmm\\\",\\n \\\"yeet\\\",\\n \\\"bleh\\\",\\n \\\"oh\\\",\\n \\\"moody rn\\\",\\n \\\"nothing\\\"\\n ])\\n #more moody\\n if mood >= -0.3:\\n return random.choice([\\n \\\"moody rn\\\",\\n \\\"not happy\\\",\\n \\\"i'm fine.\\\",\\n \\\"bleh\\\",\\n \\\"._.\\\",\\n \\\":(\\\",\\n ])\\n #very unhappy\\n return random.choice([\\n \\\"sad\\\",\\n \\\"cries\\\",\\n \\\"roar\\\",\\n \\\":_(\\\",\\n \\\">:(\\\",\\n \\\"mad\\\",\\n \\\"angry\\\",\\n \\\"I'M FINE.\\\",\\n \\\"bleh\\\",\\n \\\"no\\\",\\n ])\",\n \"def tempo_r(mid, beats, rs):\\n bt = mid.ticks_per_beat\\n trk = MidiTrack()\\n trk.name = \\\"Tempo variation\\\"\\n trk.append(MetaMessage(\\\"set_tempo\\\",\\n tempo=beats[0],\\n time=0))\\n\\n for i, beat in enumerate(beats):\\n r = rs[i]\\n if r == 0: # For the deterministic case\\n tempo_r = beat\\n else:\\n tempo_r = rd.randint(beat-int(beat*r), beat + int(beat*r)) + 1\\n trk.append(MetaMessage(\\\"set_tempo\\\",\\n time=bt,\\n tempo=tempo_r))\\n\\n mid.tracks.append(trk)\\n return mid\",\n \"def time(is_train):\\n context = composition.Context()\\n start_minutes = random.randint(1, 24*60 - 1)\\n while True:\\n duration_minutes = random.randint(1, 12*60 - 1)\\n if train_test_split.is_train(duration_minutes) == is_train:\\n break\\n end_minutes = start_minutes + duration_minutes\\n\\n def format_12hr(minutes):\\n \\\"\\\"\\\"Format minutes from midnight in 12 hr format.\\\"\\\"\\\"\\n hours = (minutes // 60) % 24\\n minutes %= 60\\n #am_pm = 'AM' if hours < 12 else 'PM'\\n #hours = (hours - 1) % 12 + 1\\n return '{}:{:02}'.format(hours, minutes)\\n #return '{}:{:02} {}'.format(hours, minutes, am_pm)\\n\\n start = format_12hr(start_minutes)\\n end = format_12hr(end_minutes)\\n\\n which_question = random.randint(0, 3)\\n if which_question == 0:\\n # Question: What is start = end - duration?\\n template = random.choice([\\n 'Che ore sono se mancano {duration} minuti alle {end}?',\\n ])\\n return example.Problem(\\n question=example.question(\\n context, template, duration=duration_minutes, end=end),\\n answer=start)\\n elif which_question == 1:\\n # Question: What is end = start + duration?\\n template = random.choice([\\n 'Che ore sono se sono passati {duration} minuti dalle {start}?',\\n ])\\n return example.Problem(\\n question=example.question(\\n context, template, duration=duration_minutes, start=start),\\n answer=end)\\n else:\\n # Question: What is duration = end - start?\\n template = random.choice([\\n 'Quanti minuti ci sono tra le {start} e le {end}?',\\n ])\\n return example.Problem(\\n question=example.question(context, template, start=start, end=end),\\n answer=duration_minutes)\",\n \"def generate_horror_title():\\n d666 = random.randint(1, 666)\\n if d666 <= 111:\\n #the adj noun\\n return \\\"The \\\" + horror_adj[random.randint(0, len(horror_adj) - 1)] + \\\" \\\" + horror_noun[random.randint(0, len(horror_noun) - 1)]\\n elif d666 > 111 and d666 <= 222: \\n #noun of noun\\n return horror_noun[random.randint(0, len(horror_noun) - 1)] + \\\" of \\\" + horror_noun[random.randint(0, len(horror_noun) - 1)]\\n elif d666 > 222 and d666 < 444: \\n #the adj noun of verb \\n return \\\"The \\\" + horror_adj[random.randint(0, len(horror_adj) - 1)] + \\\" \\\" + horror_noun[random.randint(0, len(horror_noun) - 1)] + \\\" of \\\" + horror_verb[random.randint(0, len(horror_verb) - 1)]\\n elif d666 >= 444 and d666 < 555: \\n #noun of noun\\n return horror_noun[random.randint(0, len(horror_noun) - 1)] + \\\" of \\\" + horror_noun[random.randint(0, len(horror_noun) - 1)]\\n elif d666 >= 555:\\n #verb of the adj noun\\n return horror_verb[random.randint(0, len(horror_verb) - 1)] + \\\" of the \\\" + horror_adj[random.randint(0, len(horror_adj) - 1)] + \\\" \\\" + horror_noun[random.randint(0, len(horror_noun) - 1)]\",\n \"def __init__(self, length):\\r\\n # Receive how many notes should be in the melody\\r\\n self.length = length\\r\\n # Initialize size of melody's note list\\r\\n self.notes = [0] * self.length\\r\\n \\r\\n noteList = [0, 2, 4, 5, 7, 9, 11, 12, 14, 16, 17, 19, 21, 23, 24]\\r\\n \\r\\n # randomly assign notes to each index in the melody's\\r\\n # note list\\r\\n i = 0\\r\\n while i < self.length:\\r\\n self.notes[i] = random.choice(noteList)\\r\\n i += 1\",\n \"def rhythm_data_generator(patient_generator, frame_size=2048, samples_per_patient=1):\\n for _, (signal, labels) in patient_generator:\\n num_segments, segment_size = signal.shape\\n patient_rhythm_labels = labels['rtype'] # note: variables in a .npz file are only loaded when accessed\\n for _ in range(samples_per_patient):\\n # randomly choose a frame that lies within the segment i.e. no zero-padding is necessary\\n segment_index = np.random.randint(num_segments)\\n frame_start = np.random.randint(segment_size - frame_size)\\n frame_end = frame_start + frame_size\\n x = signal[segment_index, frame_start:frame_end]\\n x = np.expand_dims(x, axis=1) # add channel dimension\\n # calculate the durations of each rhythm in the frame and determine the final label\\n rhythm_ends, rhythm_labels = patient_rhythm_labels[segment_index]\\n frame_rhythm_durations, frame_rhythm_labels = get_rhythm_durations(\\n rhythm_ends, rhythm_labels, frame_start, frame_end)\\n y = get_rhythm_label(frame_rhythm_durations, frame_rhythm_labels)\\n yield x, y\",\n \"def generate_random_line(\\n line_durations: List[float],\\n pitches: List[ScaleElement]\\n) -> List[PieceElement]:\\n melodic_line = []\\n current_time = 0\\n for duration in line_durations:\\n scale_element = random.choice(pitches)\\n piece_element = PieceElement(\\n note=scale_element.note,\\n position_in_semitones=scale_element.position_in_semitones,\\n position_in_degrees=scale_element.position_in_degrees,\\n degree=scale_element.degree,\\n start_time=current_time,\\n duration=duration\\n )\\n melodic_line.append(piece_element)\\n current_time += duration\\n return melodic_line\",\n \"def randomize_trajectory(self):\\n self.angle = randint(-360, 360)\\n self.speed = randint(1, 5)/2.5\",\n \"def get_timings(beat, max_length, rand_beat):\\n BEATS = [int(beat/4),\\n int(beat/2),\\n int(beat),\\n int(beat*2),\\n int(beat*4)]\\n\\n total_steps = beat * max_length\\n timings = [beat] * max_length\\n\\n if rand_beat == True:\\n timings = []\\n while total_steps > 0:\\n duration = random.sample(BEATS, 1)[0]\\n total_steps -= duration\\n timings.append(duration)\\n\\n return timings\",\n \"def mon_maker():\\r\\n random_mon = {'eyeratio':0.2, 'eyeL':30,\\r\\n 'mouthratio':0.8, 'mouthL':30,\\r\\n 'headL':40, 'headA':15,\\r\\n 'cheekL':25, 'cheekA':45,\\r\\n 'chinL': 30, 'chinA':90\\r\\n }\\r\\n return random_mon\",\n \"def generateNotes():\\r\\n fs = 44100 # hertz\\r\\n seconds = 3 # Note duration of 3 seconds\\r\\n noteNames = [\\\"C4\\\", \\\"D4\\\", \\\"E4\\\", \\\"F4\\\", \\\"G4\\\", \\\"A4\\\", \\\"B4\\\"]\\r\\n for noteName in noteNames:\\r\\n myNote = music21.note.Note(noteName)\\r\\n noteFrequency = myNote.pitch.frequency\\r\\n # Generate array with seconds*sample_rate steps, ranging between 0 and seconds\\r\\n t = np.linspace(0, seconds, seconds * fs, False)\\r\\n\\r\\n # Generate a 440 Hz sine wave\\r\\n sound = np.sin(noteFrequency * t * 2 * np.pi)\\r\\n\\r\\n # Ensure that highest value is in 16-bit range\\r\\n audio = sound * (2**15 - 1) / np.max(np.abs(sound))\\r\\n # Convert to 16-bit data\\r\\n audio = audio.astype(np.int16)\\r\\n\\r\\n # Start playback\\r\\n play_obj = sa.play_buffer(audio, 1, 2, fs)\\r\\n\\r\\n # Wait for playback to finish before exiting\\r\\n play_obj.wait_done()\\r\\n\\r\\n #Write sound to file\\r\\n sf.write('assets/patterns/'+noteName+'.wav', audio, fs)\",\n \"def make_note_sound(self, freq, time, amplitude=1):\\n note = sin(2 * pi * freq * amplitude *\\n linspace(0, time, time * self.RATE))\\n freq = utils.reverse_dictionary(self.FREQUENCY_MAP)[freq]\\n print freq, time, amplitude, len(note)\\n return note / np.max(np.abs(note), axis=0) # Normalize the note\",\n \"def time(self):\\n\\n self.timing = True\\n self.scramble()\\n\\n self.disp = False\",\n \"def buildMelodyNotes(m, bars):\\n\\n # Get the notes part of the scale specified in Melody object\\n s = generate_scale(m.getKeySignature(), m.getScale(), m.getOctave())\\n\\n if bars < MIN_BARS or bars > MAX_BARS:\\n bars = random.randint(MIN_BARS, 4)\\n\\n # Get a random number of notes value between the number of bars (so\\n # that there is roughly one note per bar) and SMALLEST_NOTE //\\n # OTHER_NOTE (so that each note would be roughly OTHER_NOTE long)\\n numNotes = random.randint(4, max(1, bars * (SMALLEST_NOTE //\\n DOTTED_EIGHTH_NOTE)))\\n seq = []\\n # randomize notes in scale by choosing {numNotes} random notes\\n for i in range(numNotes):\\n seq.append(random.choice(s))\\n\\n # randomize length of each notes using getRandomStructure function\\n lengths, barSpace = getEqualStructure(bars, numNotes)\\n\\n # add the random notes to generate the melody\\n for i in range(numNotes):\\n if seq[i] == -1:\\n m.addNote(Note(0, lengths[i], vel=0))\\n else:\\n m.addNote(Note(seq[i], lengths[i]))\\n\\n if barSpace > 0:\\n # Add rest just in case last note does not extend to end of bar\\n m.addNote(Note(0, barSpace, vel=0))\",\n \"def single_introduction(end):\\n return [random.randint(0,end)]\",\n \"def totem_random():\\n random_head()\\n random_head()\\n random_head()\",\n \"def randomize(data, length=0):\\r\\n longitud = 0 if length == 0 else 12 - length\\r\\n return data + \\\"-\\\" + str(time.time()).replace(\\\".\\\", \\\"\\\")[longitud:]\",\n \"def seconds (self):\\n if self.counter == self.times:\\n return None\\n\\n self.counter += 1\\n return random.uniform(self.lower, self.upper)\",\n \"def migration_time(rand):\\n ta=np.random.uniform(4,8)*1e4*yr\\n K=np.power(10,rand)\\n te=ta/K\\n return ta, te\",\n \"def generate_round():\\n prog_len = random.randint(_MIN_LEN, _MAX_LEN)\\n diff = random.randint(_MIN_DIFF, _MAX_DIFF)\\n start = random.randint(0, _MAX_START)\\n prog = _generate_progression(prog_len, diff, start)\\n missing_position = random.randint(0, len(prog) - 1)\\n missing_element = prog[missing_position]\\n prog[missing_position] = '..'\\n prog = list(map(str, prog))\\n string_question = ' '.join(prog)\\n return string_question, missing_element\",\n \"def test_random_note_pitch(common_scales, all_pitches):\\n\\n for key, scales in common_scales.items():\\n note = random_note_pitch(scales)\\n assert note in all_pitches\\n assert isinstance(note, (int, str)) is True\",\n \"def copyRhythmParams(self, note):\\n self.setDuration(note.getDurationNoDot())\\n self.setBeatNum(note.getBeatNum())\\n self.setTie(note.getTie())\\n self.setDot(note.getDot())\\n return\",\n \"def generate_fantasy_title():\\n d20 = random.randint(1, 20)\\n if d20 <= 4:\\n #genetive noun\\n return fantasy_genetive[random.randint(0, len(fantasy_genetive) - 1)] + \\\" \\\" + fantasy_noun[random.randint(0, len(fantasy_noun) - 1)]\\n elif d20 > 4 and d20 < 13: \\n #The adj noun\\n return \\\"The \\\" + fantasy_adj[random.randint(0, len(fantasy_adj) - 1)] + \\\" \\\" + fantasy_noun[random.randint(0, len(fantasy_noun) - 1)]\\n elif d20 >= 13:\\n #something of something\\n return fantasy_noun[random.randint(0, len(fantasy_noun) - 1)] + \\\" of \\\" + fantasy_what_is_this[random.randint(0, len(fantasy_what_is_this) - 1)]\",\n \"def story_command(update: Update, context: CallbackContext) -> None:\\n update.message.reply_text(get_random_story(intro=choice([0, 6])))\",\n \"def tempo_gen(tempo=120, srate=None):\\n dt = tempo / (60 * get_srate(srate))\\n return (i * dt for i in count())\",\n \"def generate_sentence(self, t=20):\\n result = [\\\"START\\\", \\\"START\\\"]\\n\\n for i in range(t-3):\\n if result[-1] == \\\"STOP\\\":\\n break\\n\\n match = {}\\n for k,v in self.trigramcounts.items():\\n if k[0] == result[-2] and k[1] == result[-1]:\\n match[k[-1]] = v\\n r = np.random.choice(list(match.keys()), p=np.array(list(match.values())) / np.sum(np.array(list(match.values()))))\\n result.append(r)\\n\\n return result\",\n \"def createMelody(instrument: int = INSTRUMENT_NP_NS_WITHOUT_CATEGORY,\\n instrument_cat: str = \\\"\\\", bpm: int = -1, scale: str = \\\"\\\",\\n octave: int = -1, time_sig: Tuple[int, int] = (4,4),\\n key_sig: str = \\\"\\\", bars: int = -1, makeEmpty: bool = False):\\n\\n # instrument - MIDI number of instrument or type of random instrument\\n # instrument_cat - Category of random instrument (if specified)\\n # bpm - The beats per minute of the melody\\n # scale - The scale that the melody is based on\\n # octave - The octave that the melody is based on\\n # time_sig - The time signature that the melody is played in\\n # key_sig - The key that the melody is based on\\n # bars - The number of bars/measures that the melody spans over\\n # makeEmpty - Whether the melody should generate random notes or not\\n\\n if instrument < 0:\\n randInstrument = chooseRandomInstrument(instrument, instrument_cat)\\n else:\\n randInstrument = instrument\\n\\n if bpm < LOWEST_BPM or bpm > HIGHEST_BPM:\\n randBPM = random.randint(LOWEST_BPM, HIGHEST_BPM)\\n else:\\n randBPM = bpm\\n\\n if scale == \\\"\\\":\\n randScale = get_random_scale()\\n else:\\n randScale = scale\\n\\n possibleOctaves = getSuitableOctaves(randInstrument)\\n if octave in possibleOctaves:\\n randOctave = octave\\n elif octave != -2:\\n if octave > possibleOctaves[-1]:\\n randOctave = possibleOctaves[-1]\\n else:\\n randOctave = possibleOctaves[0]\\n else:\\n randOctave = random.choice(possibleOctaves)\\n\\n if key_sig == \\\"\\\":\\n randKey = random.choice([\\\"C\\\", \\\"C#\\\", \\\"D\\\", \\\"D#\\\", \\\"E\\\", \\\"F\\\", \\\"F#\\\", \\\"G\\\",\\n \\\"G#\\\", \\\"A\\\", \\\"A#\\\", \\\"B\\\"])\\n else:\\n randKey = flatsToSharps(key_sig)\\n\\n randMelody = Melody([], instrument=randInstrument, bpm=randBPM,\\n scale=randScale, octave=randOctave, time_sig=time_sig,\\n key_sig=randKey)\\n\\n if not makeEmpty:\\n buildMelodyNotes(randMelody, bars)\\n\\n return randMelody\",\n \"def add_time(t):\\n\\n times.append(t)\\n\\n # update number display to show real time\\n number_display.time = t\\n number_display.update()\\n\\n # generate new scramble and update scramble_image\\n new_scramble = generate_scramble(int(settings['puzzle']),\\n int(settings['scramble-length']))\\n scrambles.append(new_scramble)\\n scramble_image.clear()\\n scramble_image.chars = char(new_scramble)\\n\\n ao5, ao12 = update_stats()\\n\\n with open(session_file.string, 'a') as f:\\n if len(times) == 1:\\n f.write(f'{add_zero(t)}\\\\t{ao5}\\\\t{ao12}\\\\t{new_scramble}')\\n else:\\n f.write(f'\\\\n{add_zero(t)}\\\\t{ao5}\\\\t{ao12}\\\\t{new_scramble}')\",\n \"def random_timed_observations(count=100):\\n # Generate a list of random, unique Observations\\n observations = []\\n time = datetime.fromtimestamp(0)\\n for _ in range(count):\\n time += timedelta(minutes=random.randint(1, 59))\\n observations.append(Observation(\\\"Skeletor\\\", \\\"Starbucks\\\", str(time)))\\n\\n # Shuffle up the Observations\\n random.shuffle(observations)\\n\\n return observations\",\n \"def metric_modulation(\\n metronome_mark=((1, 4), 60),\\n left_note=(abjad.Note(\\\"c'8\\\")),\\n right_note=(abjad.Note(\\\"c'8.\\\")),\\n modulated_beat=(abjad.Note(\\\"c'4\\\")),\\n rounded=None,\\n font_size=None,\\n leaf_scale=(0.6, 0.6),\\n raise_value=None,\\n):\\n tempo_note = abjad.Note()\\n tempo_note.written_duration = metronome_mark[0]\\n left_speed_multiplier = compare_speed(l_note=tempo_note, r_note=left_note)\\n left_speed = metronome_mark[1] * left_speed_multiplier\\n modulation_speed = calculate_metric_modulation(\\n l_tempo=left_speed, l_note=left_note, r_note=right_note\\n )\\n returned_speed = float(modulation_speed * compare_speed(left_note, modulated_beat))\\n if font_size is not None:\\n size = rf\\\" \\\\override #'(font-size . {font_size})\\\"\\n else:\\n size = r\\\" \\\\huge\\\"\\n if returned_speed % 1 == 0.0:\\n met = abjad.MetronomeMark.make_tempo_equation_markup(\\n abjad.get.duration(modulated_beat), int(returned_speed)\\n )\\n met_string = met.string[8:]\\n mod = abjad.MetricModulation(\\n left_rhythm=left_note, right_rhythm=right_note, scale=leaf_scale\\n )\\n mod_string = abjad.lilypond(mod)[8:]\\n if raise_value is None:\\n mark = abjad.LilyPondLiteral(\\n [\\n r\\\"^ \\\\markup {\\\",\\n size,\\n r\\\" \\\\concat {\\\",\\n f\\\" {met_string}\\\",\\n r\\\" \\\\hspace #1\\\",\\n r\\\" \\\\upright [\\\",\\n f\\\" {mod_string}\\\",\\n r\\\" \\\\hspace #0.5\\\",\\n r\\\" \\\\upright ]\\\",\\n r\\\" }\\\",\\n r\\\"}\\\",\\n ],\\n site=\\\"after\\\",\\n )\\n else:\\n mark = abjad.LilyPondLiteral(\\n [\\n r\\\"^ \\\\markup {\\\",\\n size,\\n rf\\\" \\\\raise #{raise_value} \\\\with-dimensions-from \\\\null\\\",\\n r\\\" \\\\concat {\\\",\\n f\\\" {met_string}\\\",\\n r\\\" \\\\hspace #1\\\",\\n r\\\" \\\\upright [\\\",\\n f\\\" {mod_string}\\\",\\n r\\\" \\\\hspace #0.5\\\",\\n r\\\" \\\\upright ]\\\",\\n r\\\" }\\\",\\n r\\\"}\\\",\\n ],\\n site=\\\"after\\\",\\n )\\n return mark\\n else:\\n if rounded is True:\\n met = abjad.MetronomeMark.make_tempo_equation_markup(\\n abjad.get.duration(modulated_beat),\\n quicktions.Fraction(round(returned_speed)).limit_denominator(),\\n )\\n met_string = met.string[8:]\\n mod = abjad.MetricModulation(\\n left_rhythm=left_note, right_rhythm=right_note, scale=leaf_scale\\n )\\n mod_string = abjad.lilypond(mod)[8:]\\n if raise_value is None:\\n mark = abjad.LilyPondLiteral(\\n [\\n r\\\"^ \\\\markup {\\\",\\n size,\\n r\\\" \\\\concat {\\\",\\n \\\" c.\\\",\\n r\\\" \\\\hspace #1\\\",\\n f\\\" {met_string}\\\",\\n r\\\" \\\\hspace #1\\\",\\n r\\\" \\\\upright [\\\",\\n f\\\" {mod_string}\\\",\\n r\\\" \\\\hspace #0.5\\\",\\n r\\\" \\\\upright ]\\\",\\n r\\\" }\\\",\\n r\\\"}\\\",\\n ],\\n site=\\\"after\\\",\\n )\\n else:\\n mark = abjad.LilyPondLiteral(\\n [\\n r\\\"^ \\\\markup {\\\",\\n size,\\n rf\\\" \\\\raise #{raise_value} \\\\with-dimensions-from \\\\null\\\",\\n r\\\" \\\\concat {\\\",\\n \\\" c.\\\",\\n r\\\" \\\\hspace #1\\\",\\n f\\\" {met_string}\\\",\\n r\\\" \\\\hspace #1\\\",\\n r\\\" \\\\upright [\\\",\\n f\\\" {mod_string}\\\",\\n r\\\" \\\\hspace #0.5\\\",\\n r\\\" \\\\upright ]\\\",\\n r\\\" }\\\",\\n r\\\"}\\\",\\n ],\\n site=\\\"after\\\",\\n )\\n else:\\n met = abjad.MetronomeMark.make_tempo_equation_markup(\\n abjad.get.duration(modulated_beat),\\n quicktions.Fraction(returned_speed).limit_denominator(),\\n )\\n met_string = met.string[8:]\\n mod = abjad.MetricModulation(\\n left_rhythm=left_note, right_rhythm=right_note, scale=leaf_scale\\n )\\n mod_string = abjad.lilypond(mod)[8:]\\n if raise_value is None:\\n mark = abjad.LilyPondLiteral(\\n [\\n r\\\"^ \\\\markup {\\\",\\n size,\\n r\\\" \\\\concat {\\\",\\n f\\\" {met_string}\\\",\\n r\\\" \\\\hspace #1\\\",\\n r\\\" \\\\upright [\\\",\\n f\\\" {mod_string}\\\",\\n r\\\" \\\\hspace #0.5\\\",\\n r\\\" \\\\upright ]\\\",\\n r\\\" }\\\",\\n r\\\"}\\\",\\n ],\\n site=\\\"after\\\",\\n )\\n else:\\n mark = abjad.LilyPondLiteral(\\n [\\n r\\\"^ \\\\markup {\\\",\\n size,\\n rf\\\" \\\\raise #{raise_value} \\\\with-dimensions-from \\\\null\\\",\\n r\\\" \\\\concat {\\\",\\n f\\\" {met_string}\\\",\\n r\\\" \\\\hspace #1\\\",\\n r\\\" \\\\upright [\\\",\\n f\\\" {mod_string}\\\",\\n r\\\" \\\\hspace #0.5\\\",\\n r\\\" \\\\upright ]\\\",\\n r\\\" }\\\",\\n r\\\"}\\\",\\n ],\\n site=\\\"after\\\",\\n )\\n return mark\",\n \"def generate_romance_title():\\n d69 = random.randint(1, 69)\\n if d69 <= 35:\\n #the adj noun\\n return \\\"The \\\" + romance_adj[random.randint(0, len(romance_adj) - 1)] + \\\" \\\" + romance_noun[random.randint(0, len(romance_noun) - 1)]\\n elif d69 > 35:\\n #noun of the adj noun\\n return romance_noun[random.randint(0, len(romance_noun) - 1)] + \\\" of the \\\" + romance_adj[random.randint(0, len(romance_adj) - 1)] + \\\" \\\" + romance_noun[random.randint(0, len(romance_noun) - 1)]\",\n \"def mimic_lecture(mimic_dict, starting_word=None, limit=10):\\r\\n\\r\\n if not starting_word:\\r\\n word = ''\\r\\n else:\\r\\n word = starting_word\\r\\n\\r\\n line_label = (divmod(x*3, 60) for x in numGen())\\r\\n text = word\\r\\n text = str(next(line_label)) + word\\r\\n line_mark = 0\\r\\n line_words = 1\\r\\n while line_mark < limit:\\r\\n if word not in mimic_dict:\\r\\n word = \\\"\\\"\\r\\n new_word = random.choice(mimic_dict[word])\\r\\n\\r\\n if line_words == 0:\\r\\n rand_silence = random.randint(1,10)\\r\\n if rand_silence <= 2:\\r\\n text = text + \\\"\\\\n\\\" + str(next(line_label))\\r\\n line_words = 0\\r\\n line_mark += 1\\r\\n word = new_word\\r\\n continue\\r\\n\\r\\n text = text + \\\" \\\" + new_word\\r\\n rand = random.randint(3,5)\\r\\n if line_words >= rand:\\r\\n text = text + \\\"\\\\n\\\" + str(next(line_label))\\r\\n line_words = 0\\r\\n line_mark += 1\\r\\n else:\\r\\n line_words += 1\\r\\n word = new_word\\r\\n return text\",\n \"def trial(self, clock, window, shapes, mouse, keys, text_color, wait_time, warning_time, exp, count, ser):\\n\\n # Default Value Set Up for Timing #\\n global stimulus_beg_time\\n stimulus_beg_time = -1\\n global in_between_time\\n in_between_time = -1\\n global total_stimuli_time\\n total_stimuli_time = -1\\n\\n # Text values\\n count_label = visual.TextStim(window, units='norm', text=u'', pos = [0, -0.6], height=0.2, color=text_color,\\n colorSpace='rgb255',alignHoriz='center', alignVert='center')\\n\\n second_label = visual.TextStim(window, units='norm', text=u'Speak color of blocks',\\n pos=[0,0.3], height=0.1, color=text_color, colorSpace='rgb255',alignHoriz='center',\\n alignVert='center')\\n\\n done_label = visual.TextStim(window, units='norm', text=u'Done', pos=[0,-0.25], height=0.1, color=text_color,\\n colorSpace='rgb255',alignHoriz='center', alignVert='center')\\n\\n done_button = visual.Rect(window, width=0.5, height=0.25, lineColor=(0, 0, 0), lineWidth=2,\\n lineColorSpace='rgb', pos=(0, -0.25))\\n\\n next_label = visual.TextStim(window, units='norm', text=u'Speech Round', pos=[0,0], height=0.1, color=text_color,\\n colorSpace='rgb',alignHoriz='center', alignVert='center')\\n\\n BLOCK_LIST = [second_label, done_button, done_label]\\n\\n # Display round name\\n helper.displayNewRound(window, next_label)\\n\\n # Microphone Set Up #\\n microphone.switchOn(sampleRate=16000)\\n name = \\\"speech_exp_%d.wav\\\" %count\\n mic = microphone.AdvAudioCapture(filename=name)\\n # todo: can edit marker to output as sync signal; played when recording starts\\n # marker currently set to not output any sound on onset of recording\\n mic.setMarker(tone=5000, secs=0.015, volume=0.0)\\n\\n # Block Sequence Display #\\n print \\\"%f BEGIN BLOCK SEQUENCE\\\" %(clock.getTime())\\n ser.write(\\\"Begin Sequence\\\")\\n global in_between_time\\n in_between_time = helper.drawSequence(window, shapes, clock)\\n ser.write(\\\"End Sequence\\\")\\n print \\\"%f END BLOCK SEQUENCE\\\" %(clock.getTime())\\n\\n # for block interaction #\\n self.hub.clearEvents()\\n start_time = clock.getTime()\\n timeout_counter = 0\\n self.hub.clearEvents()\\n\\n # store time right when clicking stimuli is presented for reference\\n window.callOnFlip(track_speech_time, clock, mouse)\\n window.flip()\\n\\n # records for length of specified wait time\\n mic.record(wait_time, block=False)\\n ser.write(\\\"Start\\\")\\n while mic.recorder.running:\\n [s.draw() for s in BLOCK_LIST]\\n count_label.draw()\\n window.flip()\\n timeout_counter += 1\\n\\n ## FOR MOUSE-CLICK END: ##\\n # buttons, times = mouse.getPressed(getTime=True)\\n # if mouse.isPressedIn(done_button, buttons=[0]):\\n # break\\n\\n ## FOR KEYBOARD END: ##\\n events = keys.getKeys()\\n if len(events) != 0:\\n break\\n\\n # adjust countdown value, to be displayed with the next flip\\n if timeout_counter >= ((wait_time - warning_time)*60) and timeout_counter % 60 == 0:\\n count_label.setText(((wait_time*60)-timeout_counter)/60)\\n\\n # turn off microphone and saves the audio file automatically\\n ser.write(\\\"Finish\\\")\\n microphone.switchOff()\\n finish_time = clock.getTime()\\n\\n # once the round is finished, use previous counters to calculate total time spent and individual click times\\n total_stimuli_time = finish_time - speech_beg_time\\n print \\\"\\\\n%f\\\" %(finish_time)\\n print \\\"%f TOTAL TIME TO FINISH ROUND\\\" %(total_stimuli_time)\\n\\n # save data in the experiment file\\n exp.addData(\\\"stimulus_begin_time\\\", speech_beg_time)\\n exp.addData(\\\"in_between_time\\\", in_between_time)\\n exp.addData(\\\"total_stimuli_time\\\", total_stimuli_time)\\n exp.addData(\\\"time1\\\", start_time)\\n exp.addData(\\\"time2\\\", finish_time)\\n\\n # return status code based on correctness of sequence\\n if timeout_counter == wait_time*60:\\n return 2\\n if timeout_counter < wait_time*60: # assume finished normally by clicking button\\n return 0\\n return -1\",\n \"def time_to_failure():\\r\\n return random.expovariate(BREAK_MEAN)\",\n \"def random_note_in_chord_and_vocal_range(relPitchList, key, vocal_range, prev_note):\\n\\n\\t# for rests, can only be a rest\\n\\tif relPitchList == -1:\\n\\t\\treturn [note.Rest()]\\n\\n\\t# extract_range\\n\\tlowest_note, highest_note = vocal_range\\n\\n\\tif (not isinstance(prev_note, note.Rest)) and (prev_note is not None):\\n\\n\\t\\t# restrict intervals to a sixth\\n\\t\\tif prev_note.pitch.transpose(-7) > lowest_note:\\n\\t\\t\\tlowest_note = prev_note.pitch.transpose(-7)\\n\\n\\t\\tif prev_note.pitch.transpose(7) < highest_note:\\n\\t\\t\\thighest_note = prev_note.pitch.transpose(7)\\n\\n\\t# turn relative Pitch into a note.Note\\n\\tnotes = []\\n\\n\\n\\tfor relPitch in relPitchList:\\n\\t\\tif relPitch != 0:\\n\\t\\t\\tinterval_ = interval.ChromaticInterval(relPitch)\\n\\t\\t\\tnotes.append(note.Note(interval_.transposePitch(key.getTonic()), quarterLength=1))\\n\\t\\telse:\\n\\t\\t\\t# needed as no transposition by 0 possible\\n\\t\\t\\tnotes.append(note.Note(key.getTonic(), quarterLength=1))\\n\\t \\n\\t# get all notes in right octave\\n\\toctave_corrected_notes = []\\n\\tfor n in notes:\\n\\n\\t\\t# possible octaves\\n\\t\\tlow_octave = lowest_note.octave\\n\\t\\thigh_octave = highest_note.octave\\n \\n\\t\\t# select random octave\\n\\t\\t# if only one choice available\\n\\t\\toctave_choice = np.arange(low_octave, high_octave + 1)\\n\\n\\t\\t# loop throughpossible octaves\\n\\t\\tfor o in octave_choice:\\n\\t\\t\\tnote_ = copy.deepcopy(n)\\n\\t\\t\\tnote_.octave = o\\n\\t\\t\\tif (note_.pitch >= lowest_note) and (note_.pitch <= highest_note):\\n\\t\\t\\t\\toctave_corrected_notes.append(note_)\\n\\n\\treturn octave_corrected_notes\",\n \"def write_music(self, length, bpm=75, initial_note=\\\"C\\\",\\n initial_rhythm=\\\"0.25\\\", initial_key=\\\"F\\\"):\\n # Translate the string key into an index for our note generator\\n key_number = self.FREQUENCY_LIST.index(initial_key)\\n print key_number\\n\\n rhythms = self.generate_rhythms(\\n self.learner.ngrams[\\\"rhythms\\\"], initial_rhythm, bpm, length)\\n notes = self.generate_notes(\\n self.learner.ngrams[\\\"notes\\\"], initial_note, len(rhythms),\\n key_number)\\n\\n rhythms = map(lambda x: float(60) * x / bpm, rhythms)\\n notes = map(lambda x: self.FREQUENCY_MAP[x], notes)\\n\\n for i, n in enumerate(notes):\\n self.write_note(self.make_note_sound(notes[i], rhythms[i]))\",\n \"def random_midi_clip(self, midi):\\n pm = PrettyMIDI(midi_file=midi)\\n # Randomly select a note to start\\n # This ensures that a note will\\n # start in the first half of the clip\\n start = max(\\n np.random.choice(\\n pm.instruments[0].notes\\n ).start - np.random.random() * .5 * self.excerpt_length,\\n 0\\n )\\n end = start + self.excerpt_length\\n new_notes = []\\n for note in pm.instruments[0].notes:\\n if note.end < start or note.start > end:\\n continue\\n note.end = min(end, note.end) - start\\n note.start = max(start, note.start) - start\\n new_notes.append(note)\\n pm.instruments[0].notes = new_notes\\n return pm, start\",\n \"def generate_singlesine(time = 0, samples_nb = 1000, rep_frequency = 10 , pulse_frequency = 50, amplitude = 1 , edge = 1, phase_offset = 0, noise = 0):\\r\\n\\r\\n\\tif edge not in [0,1]:\\r\\n\\t\\tprint(colorama.Back.RED + colorama.Style.BRIGHT + \\\"ERROR: invalid phase (either 0 for a rising or a 1 for a falling edge) , exit.\\\"+ colorama.Style.NORMAL + colorama.Back.RESET)\\r\\n\\t\\t# Return code for error (empty input file):\\r\\n\\t\\tsys.exit(10)\\r\\n\\r\\n\\r\\n\\t#Creating empty lists for t and y\\r\\n\\tt = np.zeros(samples_nb)\\r\\n\\r\\n\\tif noise == 0:\\r\\n\\t\\ty = np.zeros(samples_nb)\\r\\n\\telse:\\r\\n\\t\\ty = np.random.normal(0, noise, samples_nb)\\r\\n\\r\\n\\t#Determining the interval limits of t\\r\\n\\tt_limit =1/float(rep_frequency*2)\\r\\n\\r\\n\\t#Updating the t interval\\r\\n\\tt = np.arange(-samples_nb/2,samples_nb/2)/float(samples_nb*rep_frequency) + 1/float(samples_nb*rep_frequency)\\r\\n\\r\\n\\r\\n\\t#calculating the time_shift\\r\\n\\t#delta_t = phase_offset/(2*np.pi*pulse_frequency)\\r\\n\\tdelta_t = phase_offset/(2*np.pi*rep_frequency)\\r\\n\\r\\n\\t#Setting the pulse amplitude\\r\\n\\ta_pulse = amplitude\\r\\n\\tif edge == 1:\\r\\n\\t\\ta_pulse *= -1\\r\\n\\r\\n\\t#Calculating the pulse limits\\r\\n\\tp_limit = 1/float(2*pulse_frequency)\\r\\n\\tp_interval = list ([-p_limit,p_limit])\\r\\n\\r\\n\\r\\n\\tfor n in range (0,len(t)) :\\r\\n\\t\\tif (t[n] + delta_t) > p_interval[0] and (t[n] + delta_t) <= p_interval[1]:\\r\\n\\t\\t\\ty[n] += a_pulse * np.sin(2*np.pi*pulse_frequency*(t[n]+delta_t))\\r\\n\\r\\n\\r\\n\\r\\n\\t#plt.plot(t,y)\\r\\n\\t#plt.show()\\r\\n\\r\\n\\tresult = {}\\r\\n\\tresult ['time'] = time\\r\\n\\tresult ['t'] = t\\r\\n\\tresult ['y'] = y\\r\\n\\r\\n\\treturn result\",\n \"def generate_synth_data(n):\",\n \"def meme_rand():\\n img = random.choice(imgs)\\n quote = random.choice(quotes)\\n path = meme.make_meme(img, quote.body, quote.author)\\n return render_template('meme.html', path=path)\",\n \"def RandomDelay():\\r\\n sleep(random())\",\n \"def trial(length, height):\\n screen.refresh()\\n global stimList\\n global oddLength\\n global oddHeight\\n currentLength = int(maxLength / 4)\\n currentHeight = int(maxHeight / 4)\\n for i in range(stimAmt):\\n if i == oddLocation:\\n oddLength = currentLength\\n oddHeight = currentHeight\\n stimList.append(\\n pg.draw.rect(\\n screen.fg,\\n PgTools.rand_color(),\\n (currentLength, currentHeight, length, height,),\\n )\\n )\\n PgTools.rand_pattern(\\n screen.fg,\\n (\\n currentLength,\\n currentHeight,\\n ),\\n (length, height),\\n i=(randint(0, 2), randint(0, 1)),\\n )\\n if randShapes:\\n PgTools.rand_shape(screen.fg, (currentLength, currentHeight),(length, height), oddSeed)\\n else:\\n stimList.append(\\n pg.draw.rect(\\n screen.fg,\\n color,\\n (currentLength, currentHeight, length, height,),\\n )\\n )\\n PgTools.rand_pattern(\\n screen.fg,\\n (\\n currentLength,\\n currentHeight,\\n ),\\n (length, height),\\n patColor,\\n randNums,\\n )\\n if randShapes:\\n PgTools.rand_shape(screen.fg, (currentLength, currentHeight),(length, height), regSeed)\\n currentLength += maxLength / 4\\n currentLength = int(currentLength)\\n if (i + 1) % 3 == 0:\\n currentLength = maxLength / 4\\n currentLength = int(currentLength)\\n currentHeight += maxHeight / 4\\n currentHeight= int(currentHeight)\",\n \"def first_notes_and_song():\\n # Construct a robot, put it in safe mode.\\n # Make it play two notes, then a song, then disconnect.\\n\\n time.sleep(2) # In case the robot has recently been shutdown.\\n\\n port = 'sim' # Or use YOUR laptop's COM number\\n hal = new_create.Create(port)\\n hal.toSafeMode() # So you can try this out on a table if you want.\\n\\n #-------------------------------------------------------------------\\n # playNote(pitch, duration):\\n #\\n # Notes range from 31 (low G, 49.0 Hz) to 127 (high G, 12543.9 Hz).\\n # They can be played from as little as 1/64th of a second to as\\n # long as 255/64 seconds (i.e., almost 4 seconds).\\n # The playNote method takes TWO (2) arguments:\\n # -- The note (should be in the range 31 to 127, inclusive)\\n # -- The duration to play that note, in 64th's of a second\\n # (must be between 0 and 255, inclusive)\\n #-------------------------------------------------------------------\\n\\n hal.playNote(31, 255) # Very long, low note.\\n time.sleep(5.0) # See next example for why we need sleeps.\\n\\n hal.playNote(110, 1) # Very short, high note.\\n time.sleep(1.0)\\n\\n #-------------------------------------------------------------------\\n # playSong(LIST of pitch-duration TWO-TUPLEs)\\n #\\n # If you want to play a sequence of notes, it is almost always\\n # better to use the playSong method instead of doing a\\n # sequence of calls to playNote. The playSong method takes\\n # ONE (1) argument. That argument must be:\\n # -- a LIST\\n # -- with no more than 16 items\\n # -- where each item is a 2-tuple\\n # whose first element is the pitch (31 to 127)\\n # and whose second element is the duration (1 to 255)\\n # -- where pitch and duration are as in playNote.\\n #-------------------------------------------------------------------\\n\\n song = [(60, 8), (64, 8), (67, 8), (72, 8)] # a quick C chord\\n hal.playSong(song)\\n time.sleep(2.0)\\n\\n hal.shutdown()\",\n \"def note():\",\n \"def random_body():\\n extra = random.randint(1, 9)\\n sequence = (hex(rand32()) for _ in range(extra))\\n return hex(id_) + '\\\\n' + '\\\\n'.join(sequence)\",\n \"def create_song(self, lnmn, lnmx):\\n # decide on the length of the song\\n nlng = random.randint(lnmn, lnmx)\\n\\n # load the database\\n lns = self.read_database()\\n\\n # randomly pick nlng lines\\n rsong = []\\n for i in range(nlng):\\n j = random.randint(0,len(lns)-1)\\n rsong.append(lns[j])\\n\\n return rsong\",\n \"def generate_notes(model, network_input, pitch_names, n_vocab):\\n \\n # Pick a random sequence from the input as a starting point for the prediction\\n random_start = np.random.randint(0, len(network_input)-1)\\n\\n # Create a dictionary to map note integers to pitches\\n int_to_note = dict((number, note) for number, note in enumerate(pitch_names))\\n\\n # Choose a random sequence of notes to use as the network seed\\n pattern = network_input[random_start]\\n \\n # Create an empty list to record the prediction output for each successive note\\n prediction_output = []\\n\\n # Generate 500 notes\\n for num_notes in range(500):\\n \\n # Reshape the input into a format compatible with LSTM layers\\n prediction_input = np.reshape(pattern, (1, len(pattern), 1))\\n \\n # Normalize the network input by dividing by n_vocab (number of unique notes, rests, and chords)\\n prediction_input = prediction_input / float(n_vocab)\\n\\n # Predict the next note, given an input sequence of notes\\n prediction = model.predict(prediction_input, verbose=0)\\n\\n # Add each generated note to prediction_output\\n index = np.argmax(prediction)\\n result = int_to_note[index]\\n prediction_output.append(result)\\n\\n # Update the pattern list to include to the generated note, used in next for-loop loop\\n pattern.append(index)\\n pattern = pattern[1:len(pattern)]\\n\\n return prediction_output\",\n \"def pick_time(time_1,time_2,type=\\\"uniform\\\"):\\n\\tif type==\\\"uniform\\\":\\n\\t\\treturn np.random.uniform(low=time_1,high=time_2)\\n\\tif type==\\\"gaussian\\\":\\n\\t\\tcenter = 0.5*(time_2-time_1);\\n\\t\\tsig = 0.5*(time_2-center);\\n\\t\\tt = np.random.uniform(low=time_1,high=time_2)\\n\\t\\th = np.exp(-((t-center)**2)/(2.0*sig**2))\\n\\t\\twhile (np.random.rand()>h):\\n\\t\\t\\tt = np.random.uniform(low=time_1,high=time_2)\\n\\t\\t\\th = np.exp(-((t-center)**2)/(2.0*sig**2))\\n\\t\\treturn t\",\n \"def _prev_rhythm_tconst(pattern, rhythm):\\n BASIC_TCONST(pattern, rhythm)\\n tnet = pattern.last_tnet\\n tnet.set_equal(pattern.hypothesis.start, rhythm.end)\",\n \"def get_time(self):\\n for _ in range(5):\\n current_time = datetime.datetime.now().strftime('%H:%M:%S')\\n self.logger.info(current_time)\\n num: int = self.rand_num_gen.get_number()\\n display = current_time + f\\\": {num}\\\\n\\\"\\n yield display.encode()\\n sleep(1)\\n try:\\n raise RuntimeError(\\\"something unexpected happened!\\\")\\n except:\\n trace_info = traceback.format_exc()\\n self.logger.error(\\\"uncaught exception: %s\\\", trace_info)\\n yield trace_info\",\n \"def random_pause():\\n pause_time = random.uniform(0, 0.5)\\n sleep(pause_time)\",\n \"def wishMe():\\n hour = int(datetime.datetime.now().hour)\\n if 0 <= hour < 12:\\n speak(\\\"Good Morning Boss\\\")\\n\\n elif 12 <= hour < 18:\\n speak(\\\"Good Afternoon Boss\\\")\\n\\n else:\\n speak(\\\"Good Evening Boss!\\\")\\n\\n speak(\\\"This is mayaa appointed as your Assistant\\\")\",\n \"def meme_rand():\\n img = random.choice(imgs)\\n quotes_list_one = random.choice(quotes)\\n quote = random.choice(quotes_list_one)\\n if quote and img:\\n path = meme.make_meme(img, quote.body, quote.author)\\n return render_template('meme.html', path=path)\\n else:\\n path = meme.make_meme('./_data/photos/dog/xander_1.jpg', \\\"Quote\\\", \\\"Author\\\")\\n return render_template('meme.html', path=path)\",\n \"def time(self):\\r\\n time = datetime.datetime.now().strftime(\\\"%I:%M:%S\\\")\\r\\n self.speak(\\\"the current time is\\\")\\r\\n self.speak(time)\",\n \"def r(w,rangestart,rangeend):\\r\\n if w == 'r':\\r\\n print(random.random(rangestart , rangeend))\\r\\n if w == 'ri':\\r\\n print(random.randint(rangestart,rangeend))\",\n \"def beat_stripper(timings, removal_proportion):\\n phrase_len = len(timings)\\n n_to_rm = int(phrase_len * removal_proportion)\\n rm_idx = np.random.randint(0, phrase_len-1, n_to_rm)\\n\\n timings_stripped = []\\n for i, hit in enumerate(timings):\\n if not i in rm_idx:\\n timings_stripped.append(hit)\\n else:\\n timings_stripped.append(hit + hit)\\n return timings_stripped\",\n \"def newsample(news, ratio):\\n if ratio == 0:\\n return []\\n if ratio > len(news):\\n return news + [0] * (ratio - len(news))\\n else:\\n return random.sample(news, ratio)\",\n \"def __call__(self, audio):\\n if type(self.stretch) in (int, float):\\n return librosa.effects.time_stretch(audio, self.stretch)\\n else:\\n low, hi = self.stretch\\n s = np.random.choice(np.linspace(low, hi, 100))\\n return librosa.effects.time_stretch(audio, s)\",\n \"def generateRandomePlainText():\\n randomPlainTextArray = [random.choice('0123456789abcdef')\\n for n in range(24)]\\n randomPlainText = \\\"\\\".join(randomPlainTextArray)\\n return randomPlainText\",\n \"def sample_next(self, time, samples, errors):\\n if self.previous_value is None:\\n output = self.start_value\\n else:\\n time_diff = time - self.previous_time\\n noise = np.random.normal(loc=0.0, scale=1.0, size=1)\\n output = (np.power(self.ar_param, time_diff))*self.previous_value+\\\\\\n self.sigma*np.sqrt(1-np.power(self.ar_param, time_diff))*noise\\n self.previous_time = time\\n self.previous_value = output\\n return output\",\n \"def createMelody(song, outputSongFileName, timing=4):\\n wavInput = (())\\n wavInput1 = (())\\n wavInput2 = (())\\n wavInput3 = (())\\n\\n # Remove the beginning and end portions of the canvas that are blank\\n while song[0] == ['R','R','R','R']:\\n del song[0]\\n while song[-1] == ['R','R','R','R']:\\n del song[-1]\\n\\n for notesList in song:\\n\\n remove_dup(notesList)\\n\\n notesNum = []\\n for i in range(len(notesList)):\\n if (notesList[i].upper() == 'R'):\\n notesNum.append('')\\n elif (notesList[i].upper() == 'A' or notesList[i].upper() == 'B'):\\n notesNum.append('3')\\n else:\\n notesNum.append('4')\\n\\n wavInput = ((notesList[0].lower() + str(notesNum[0]), timing),) + wavInput\\n wavInput1 = ((notesList[1].lower() + str(notesNum[1]), timing),) + wavInput1\\n wavInput2 = ((notesList[2].lower() + str(notesNum[2]), timing),) + wavInput2\\n wavInput3 = ((notesList[3].lower() + str(notesNum[3]), timing),) + wavInput3\\n\\n\\n wavInput = wavInput[::-1]\\n wavInput1 = wavInput1[::-1]\\n wavInput2 = wavInput2[::-1]\\n wavInput3 = wavInput3[::-1]\\n\\n wavNames = [\\\".wav1.wav\\\",\\\".wav2.wav\\\",\\\".wav3.wav\\\",\\\".wav4.wav\\\"]\\n wavInputs = [wavInput,wavInput1,wavInput2,wavInput3]\\n\\n validWavInputs = []\\n\\n for i in range(len(wavInputs)):\\n if isAllRests(wavInputs[i]) == False:\\n validWavInputs.append(wavInputs[i])\\n\\n validWavNames = wavNames[:len(validWavInputs)]\\n\\n call(['python','GenerateWavFiles.py',str(validWavNames) + \\\"@\\\" + str(validWavInputs)])\\n\\n sounds = []\\n for i in range(len(validWavNames)):\\n sounds.append(AudioSegment.from_wav(validWavNames[i]))\\n\\n combined = sounds[0]\\n for i in range(1, len(sounds)):\\n combined = combined.overlay(sounds[i])\\n\\n combined.export(outputSongFileName, format='wav')\",\n \"def random_text(self, n=100):\\n # choose a random prefix (not weighted by frequency)\\n start = random.choice(list(self.suffix_map.keys()))\\n #print(\\\">>DEBUG | start is\\\", start)\\n \\n for i in range(n):\\n #print(\\\">> DEBUG | i is\\\", n)\\n suffixes = self.suffix_map.get(start, None)\\n #print(\\\">> DEBUG | suffixes is\\\", suffixes)\\n if suffixes == None:\\n # if the start isn't in map, we got to the end of the\\n # original text, so we have to start again.\\n #print(\\\">> DEBUG | start isn't in map\\\")\\n random_text(n-i)\\n return\\n\\n # choose a random suffix\\n word = random.choice(suffixes)\\n #print(\\\">> DEBUG | word is\\\", word)\\n print(word, end=' ')\\n start = self.shift(start, word)\",\n \"def generate(self, curr_time):\\n status = 200\\n new_time = self._get_next_time(curr_time)\\n if self.error_dist.random() < self.error_prob:\\n status = 500\\n\\n return new_time, \\\"{:.4f} {} {} {}\\\".format(new_time, self.verb, self.resource, status)\",\n \"def sim(p=0.5, ph=0.7, pl=0.8, n=20,sd=70):\\n random.seed(sd)\\n te=np.array(random.sample(range(1000),n-1))/1000#random draw\\n random.seed(sd)\\n rea=random.choices('HL',weights=(p,1-p),k=1) #initial realization at t0\\n if rea[0]=='H':\\n jp=p\\n else:\\n jp=round((1-p),2) \\n for i in range(n-1): #compute realization from t1 to tn\\n temp=te[i]\\n if rea[-1]=='L': #using probability of low to low to be compared with draw\\n if temp<=pl:\\n rea.append('L')\\n jp=jp*pl\\n else:\\n rea.append('H')\\n jp=jp*round((1-pl), 2)\\n else: #using probability of high to high to be compared with draw\\n if temp<=ph:\\n rea.append('H')\\n jp=jp*ph\\n else:\\n rea.append('L')\\n jp=jp*round((1-ph), 2)\\n return (rea, jp)\",\n \"def sensorInfo(timeDelay = .5):\\n cycle = time.time()\\n while (time.time() - cycle < timeDelay):\\n pass\\n return random.randint(1,10000)\",\n \"def choose_piece_duration(self):\\n self.piece_duration = random.randint(900, 1100)\",\n \"def get_monthday_song():\\n current_day = day_of_month()\\n print(f\\\"MONTHDAY:{current_day}\\\")\\n if (current_day == 1):\\n return random.choice([ \\\\\\n \\\"One of Us\\\", \\\\\\n \\\"One - Harry Nilsson\\\", \\\\\\n \\\"One More Night \\\", \\\\\\n \\\"One - Metallica\\\", \\\\\\n \\\"The One - Backstreet Boys\\\", \\\\\\n \\\"1 Step Forward, 3 Steps Back\\\"])\\n elif (current_day == 2):\\n return random.choice([\\\"It Takes Two\\\", \\\"Two Black Cadillacs\\\"])\\n elif (current_day == 3):\\n return random.choice([\\\"Three Little Birds\\\"])\\n elif (current_day == 4):\\n return random.choice([\\\"Four Mintues\\\", \\\"Four Five Seconds\\\"])\\n elif (current_day == 5):\\n return random.choice([\\\"Mambo Number 5\\\", \\\"I Got 5 on It\\\", \\\"Five More Minutes\\\"])\\n elif (current_day == 6):\\n return random.choice([\\\"Six Feet Under\\\"])\\n elif (current_day == 7):\\n return random.choice([ \\\\\\n \\\"7 Years\\\", \\\\\\n \\\"Seven Bridges Road\\\", \\\\\\n \\\"Seven Seas of Rhye\\\", \\\\\\n \\\"Seven Nation Army\\\", \\\\\\n \\\"7 things\\\"])\\n elif (current_day == 8):\\n return random.choice([\\\"Eight Days a Week\\\", \\\"Eight Miles High\\\", \\\"8 out of 10\\\"])\\n elif (current_day == 9):\\n return random.choice([\\\"Love Potion Number 9\\\"])\\n elif (current_day == 13):\\n return random.choice([\\n \\\"13 - LANY\\\", \\\\\\n \\\"Floor 13 - Machine Gun Kelly\\\", \\\\\\n \\\"13 - DDG\\\", \\\\\\n \\\"Thirteen - Johnny Cash\\\"])\\n elif (current_day == 14):\\n return random.choice([\\\"14,400 Minutes\\\", \\\"March 15th\\\", \\\"14 Miles From Home\\\"])\\n elif (current_day == 15):\\n return random.choice([\\\"Fifteen - Taylor Swift\\\"])\\n elif (current_day == 16):\\n return random.choice([\\\"Summer Sixteen\\\", \\\"Sixteen Tons\\\", \\\"Sixteen\\\"])\\n elif (current_day == 17):\\n return random.choice([\\\"Edge of Seventeen\\\"])\\n elif (current_day == 21):\\n return random.choice([\\\"Twenty One - Khalid\\\"])\\n elif (current_day == 22):\\n return random.choice([\\\"22 - Taylor Swift\\\", \\\"Twenty Two and Some Change\\\", \\\"Twentytwo - Thea\\\"])\\n elif (current_day == 23):\\n return random.choice([\\\"23 Sam Hunt\\\", \\\"23 Mike Will\\\", \\\"23 Chayce Beckham\\\"])\\n elif (current_day == 24):\\n return random.choice([\\\"24 - Kanye\\\"])\\n else:\\n return \\\"\\\"\",\n \"async def pornx(ctx, n: int = 1):\\r\\n msg = [\\\"{}\\\".format(text) for text in [random.choice(data) for _ in range(0,n)]]\\r\\n await bot.say('\\\\n'.join(msg))\",\n \"def gen():\\r\\n\\r\\n output = \\\"\\\"\\r\\n frame = 12\\r\\n prior = 0\\r\\n second = 0\\r\\n\\r\\n while frame > 0:\\r\\n frame -= .5\\r\\n roll = randrange(11 - prior)\\r\\n\\r\\n # If strike is rolled:\\r\\n if roll == 10:\\r\\n output += \\\"X\\\"\\r\\n frame -= .5\\r\\n prior = 0\\r\\n second = 0\\r\\n\\r\\n # If spare is rolled:\\r\\n elif second == 1 and roll + prior == 10:\\r\\n output += \\\"/\\\"\\r\\n prior = 0\\r\\n second = 0\\r\\n\\r\\n # If not strike or spare:\\r\\n else:\\r\\n output += str(roll)\\r\\n prior = roll\\r\\n second = 1 - second\\r\\n\\r\\n # Deliminate frames:\\r\\n if second == 0:\\r\\n output += \\\"-\\\"\\r\\n\\r\\n return output[:-1]\",\n \"def schedule_paragraph():\",\n \"def gen_time(self):\\n t_min = self.times['Min']\\n t_max = self.times['Max']\\n t_sig = self.times['Mean']\\n # generating activity time based on truncnorm distribution\\n # see StatDistExp jupyter notebook for more details\\n # if in test sample, activity time mean/max brought 25% closer\\n # the min\\n if self.activity in self.test_sample:\\n t_max = min(int(t_min + (t_max - t_min)*3/4), t_max-1)\\n t_sig = min(int(t_min + (t_sig - t_min)*3/4), t_sig-1)\\n t_max = max(t_max, 1)\\n t_sig = max(t_sig, 1)\\n t_scale = min(t_sig-t_min, t_max-t_sig)/3*2\\n #accounting for when activity time is always 1 day\\n if t_min == t_max == t_sig:\\n self.sample = t_min\\n else:\\n #this is the normal case generation of activity time\\n self.sample = truncnorm.rvs(a=t_min-t_sig, b=t_max-t_sig, scale=t_scale, size=1)[0]\\n self.sample += t_sig\\n self.sample = int(self.sample)\",\n \"def wishMe():\\n hour = int(dt.datetime.now().hour)\\n if hour>= 0 and hour<12:\\n speak(\\\"Good Morning\\\")\\n elif hour>=12 and hour<18:\\n speak(\\\"Good afternoon!\\\")\\n else:\\n speak(\\\"Good Evening\\\")\\n\\n speak(\\\"I am your personal assistant! How may I help you?\\\")\",\n \"def generate_notes(model, network_input, n_vocab):\\n # pick a random sequence from the input as a starting point for the prediction\\n start = np.random.randint(0, len(network_input)-1)\\n \\n # Get pitch names and store in a dictionary\\n pitchnames = sorted(set(item for item in notes))\\n int_to_note = dict((number, note) for number, note in enumerate(pitchnames))\\n\\n pattern = network_input[start]\\n prediction_output = []\\n\\n # generate 500 notes\\n for note_index in range(500):\\n prediction_input = np.reshape(pattern, (1, len(pattern), 1))\\n prediction_input = prediction_input / float(n_vocab)\\n\\n prediction = model.predict(prediction_input, verbose=0)\\n\\n index = np.argmax(prediction)\\n result = int_to_note[index]\\n prediction_output.append(result)\\n \\n pattern = np.append(pattern,index)\\n pattern = pattern[1:len(pattern)]\\n\\n return prediction_output\",\n \"def gen_dynstring(self):\\n # a timestamp plus something semi random\\n return '%s.%s' % (time.strftime('%m%d%H%M%S', (time.localtime())),\\n random.randint(1, 100000))\",\n \"def note(f, start, end, fade=.01):\\n\\n def sampler(t):\\n seconds = t / sampler_rate\\n if seconds < start:\\n return 0\\n elif seconds > end:\\n return 0\\n elif seconds < start + fade:\\n return (seconds - start) / fade * f(t)\\n elif seconds > end - fade:\\n return (end - seconds) / fade * f(t)\\n else:\\n return f(t)\\n\\n return sampler\",\n \"def tempo(beats, trk=None, bt=480):\\n if not trk:\\n trk = MidiTrack()\\n trk.name = \\\"Tempo variation\\\"\\n trk.append(MetaMessage(\\\"set_tempo\\\",\\n tempo=beats[0],\\n time=0))\\n\\n for i, beat in enumerate(beats):\\n trk.append(MetaMessage(\\\"set_tempo\\\",\\n time=bt,\\n tempo=beat))\\n\\n return trk\",\n \"def randomHelmet():\\n return random.choice(HELMETS)\",\n \"def sample(self):\\n seq = []\\n for i in range(self._pwm.shape[1]):\\n p = numpy.array(self._pwm[:, i], dtype=numpy.float64)\\n p /= p.sum()\\n seq.extend(numpy.random.choice(self.alphabet, p=p))\\n return \\\"\\\".join(seq)\",\n \"def rand(self):\\n raise NotImplementedError\",\n \"def generate_question_and_answer(): # noqa: WPS210\\n start_number = random.randint(1, 100)\\n progression_step = random.randint(1, 10)\\n progression_length = random.randint(5, 10)\\n progression = generate_progression(\\n start_number, progression_step, progression_length,\\n )\\n return hide_number(progression)\",\n \"def elapseTime(self, gameState):\\n newParticleList = []\\n # Pretend each particle is a ghost, and set its position semi-randomly based on how\\n # likely the ghost is to move to that position\\n for particle in self.particles:\\n newPosDist = self.getPositionDistribution(gameState, particle)\\n newParticleList.append(util.sample(newPosDist))\\n self.particles = newParticleList\",\n \"def lf():\\n return random.sample(font_list, 25)\",\n \"def _get_next_time(self, curr_time):\\n return curr_time + self.time_dist.random()\",\n \"def random():\\r\\n return R.NextDouble()\",\n \"def generate_midi(config_folder, score_source, number_of_version, duration_gen, rhythmic_reconstruction, logger_generate):\\n\\n logger_generate.info(\\\"#############################################\\\")\\n logger_generate.info(\\\"Orchestrating piano score : \\\" + score_source)\\n ############################################################\\n # Load model, config and data\\n ############################################################\\n\\n ########################\\n # Load config and model\\n parameters = pkl.load(open(config_folder + '/script_parameters.pkl', 'rb'))\\n model_parameters = pkl.load(open(config_folder + '/model_params.pkl', 'rb'))\\n # Set a minimum seed size, because for very short models you don't event see the beginning\\n seed_size = max(model_parameters['temporal_order'], 10) - 1\\n quantization = parameters['quantization']\\n temporal_granularity = parameters['temporal_granularity']\\n instru_mapping = parameters['instru_mapping']\\n ########################\\n\\n ########################\\n # Load data\\n if re.search(r'mid$', score_source):\\n pr_piano, event_piano, duration_piano, name_piano, pr_orch, instru_orch, duration = load_solo(score_source, quantization, parameters[\\\"binarize_piano\\\"], temporal_granularity)\\n else:\\n pr_piano, event_piano, duration_piano, name_piano, pr_orch, instru_orch, duration = load_from_pair(score_source, quantization, parameters[\\\"binarize_piano\\\"], parameters[\\\"binarize_orch\\\"], temporal_granularity)\\n ########################\\n\\n ########################\\n # Shorten\\n # Keep only the beginning of the pieces (let's say a 100 events)\\n pr_piano = extract_pianoroll_part(pr_piano, 0, duration_gen)\\n if parameters[\\\"duration_piano\\\"]:\\n duration_piano = np.asarray(duration_piano[:duration_gen])\\n else:\\n duration_piano = None\\n event_piano = event_piano[:duration_gen]\\n pr_orch = extract_pianoroll_part(pr_orch, 0, duration_gen)\\n ########################\\n\\n ########################\\n # Instanciate piano pianoroll\\n N_piano = instru_mapping['Piano']['index_max']\\n pr_piano_gen = np.zeros((duration_gen, N_piano), dtype=np.float32)\\n pr_piano_gen = build_data_aux.cast_small_pr_into_big_pr(pr_piano, {}, 0, duration_gen, instru_mapping, pr_piano_gen)\\n pr_piano_gen_flat = pr_piano_gen.sum(axis=1)\\n silence_piano = [e for e in range(duration_gen) if pr_piano_gen_flat[e]== 0]\\n ########################\\n\\n ########################\\n # Instanciate orchestra pianoroll with orchestra seed\\n N_orchestra = parameters['N_orchestra']\\n if pr_orch:\\n pr_orchestra_gen = np.zeros((seed_size, N_orchestra), dtype=np.float32)\\n orch_seed_beginning = {k: v[:seed_size] for k, v in pr_orch.items()}\\n pr_orchestra_gen = build_data_aux.cast_small_pr_into_big_pr(orch_seed_beginning, instru_orch, 0, seed_size, instru_mapping, pr_orchestra_gen)\\n pr_orchestra_truth = np.zeros((duration_gen, N_orchestra), dtype=np.float32)\\n pr_orchestra_truth = build_data_aux.cast_small_pr_into_big_pr(pr_orch, instru_orch, 0, duration_gen, instru_mapping, pr_orchestra_truth)\\n else:\\n pr_orchestra_gen = None\\n pr_orchestra_truth = None\\n ########################\\n \\n #######################################\\n # Embed piano\\n time_embedding = time.time()\\n if parameters['embedded_piano']:\\n # Load model\\n embedding_path = parameters[\\\"embedding_path\\\"]\\n embedding_model = embedDenseNet(380, 12, (1500,500), 100, 1500, 2, 3, 12, 0.5, 0, False, True)\\n embedding_model.load_state_dict(torch.load(embedding_path))\\n\\n # Build embedding (no need to batch here, len(pr_piano_gen) is sufficiently small)\\n piano_resize_emb = np.zeros((len(pr_piano_gen), 1, 128)) # Embeddings accetp size 128 samples\\n piano_resize_emb[:, 0, instru_mapping['Piano']['pitch_min']:instru_mapping['Piano']['pitch_max']] = pr_piano_gen\\n piano_resize_emb_TT = torch.tensor(piano_resize_emb)\\n piano_embedded_TT = embedding_model(piano_resize_emb_TT.float(), 0)\\n pr_piano_gen_embedded = piano_embedded_TT.numpy()\\n else:\\n pr_piano_gen_embedded = pr_piano_gen\\n time_embedding = time.time() - time_embedding\\n #######################################\\n\\n ########################\\n # Inputs' normalization\\n normalizer = pkl.load(open(os.path.join(config_folder, 'normalizer.pkl'), 'rb'))\\n if parameters[\\\"embedded_piano\\\"]: # When using embedding, no normalization\\n pr_piano_gen_norm = pr_piano_gen_embedded\\n else:\\n pr_piano_gen_norm = normalizer.transform(pr_piano_gen_embedded)\\n ########################\\n \\n ########################\\n # Store folder\\n string = re.split(r'/', name_piano)[-1]\\n name_track = re.sub('piano_solo.mid', '', string)\\n generated_folder = config_folder + '/generation_reference_example/' + name_track\\n if not os.path.isdir(generated_folder):\\n os.makedirs(generated_folder)\\n ########################\\n\\n ########################\\n # Get trainer\\n with open(os.path.join(config_folder, 'which_trainer'), 'r') as ff:\\n which_trainer = ff.read()\\n # Trainer\\n if which_trainer == 'standard_trainer':\\n from LOP.Scripts.standard_learning.standard_trainer import Standard_trainer as Trainer\\n kwargs_trainer = {'temporal_order': model_parameters[\\\"temporal_order\\\"]}\\n elif which_trainer == 'NADE_trainer':\\n from LOP.Scripts.NADE_learning.NADE_trainer import NADE_trainer as Trainer\\n kwargs_trainer = {'temporal_order': model_parameters[\\\"temporal_order\\\"], 'num_ordering': model_parameters[\\\"num_ordering\\\"]}\\n else:\\n raise Exception(\\\"Undefined trainer\\\")\\n trainer = Trainer(**kwargs_trainer)\\n ########################\\n\\n ############################################################\\n # Generate\\n ############################################################\\n time_generate_0 = time.time()\\n generated_sequences = {}\\n for measure_name in parameters['save_measures']:\\n model_path = 'model_' + measure_name\\n generated_sequences[measure_name] = generate(trainer, pr_piano_gen_norm, silence_piano, duration_piano, config_folder, model_path, pr_orchestra_gen, batch_size=number_of_version)\\n time_generate_1 = time.time()\\n logger_generate.info('TTT : Generating data took {} seconds'.format(time_generate_1-time_generate_0))\\n\\n ############################################################\\n # Reconstruct and write\\n ############################################################\\n def reconstruct_write_aux(generated_sequences, prefix):\\n for write_counter in range(generated_sequences.shape[0]):\\n # To distinguish when seed stop, insert a sustained note\\n this_seq = generated_sequences[write_counter] * 127\\n this_seq[:seed_size, 0] = 20\\n # Reconstruct\\n if rhythmic_reconstruction:\\n pr_orchestra_clean = from_event_to_frame(this_seq, event_piano)\\n else:\\n pr_orchestra_clean = this_seq\\n pr_orchestra = instrument_reconstruction(pr_orchestra_clean, instru_mapping)\\n # Write\\n write_path = generated_folder + '/' + prefix + '_' + str(write_counter) + '_generated.mid'\\n if rhythmic_reconstruction:\\n write_midi(pr_orchestra, quantization, write_path, tempo=80)\\n else:\\n write_midi(pr_orchestra, 1, write_path, tempo=80)\\n return\\n\\n for measure_name in parameters[\\\"save_measures\\\"]:\\n reconstruct_write_aux(generated_sequences[measure_name], measure_name)\\n \\n ############################################################\\n ############################################################\\n # Write original orchestration and piano scores, but reconstructed version, just to check\\n if rhythmic_reconstruction:\\n A = from_event_to_frame(pr_piano_gen, event_piano)\\n else:\\n A = pr_piano_gen\\n B = A * 127\\n piano_reconstructed = instrument_reconstruction_piano(B, instru_mapping)\\n write_path = generated_folder + '/piano_reconstructed.mid'\\n if rhythmic_reconstruction:\\n write_midi(piano_reconstructed, quantization, write_path, tempo=80)\\n else:\\n write_midi(piano_reconstructed, 1, write_path, tempo=80)\\n #\\n if rhythmic_reconstruction:\\n A = from_event_to_frame(pr_orchestra_truth, event_piano)\\n else:\\n A = pr_orchestra_truth\\n B = A * 127\\n orchestra_reconstructed = instrument_reconstruction(B, instru_mapping)\\n write_path = generated_folder + '/orchestra_reconstructed.mid'\\n if rhythmic_reconstruction:\\n write_midi(orchestra_reconstructed, quantization, write_path, tempo=80)\\n else:\\n write_midi(orchestra_reconstructed, 1, write_path, tempo=80)\\n ############################################################\\n ############################################################\",\n \"def mock_tweet():\\n count = random.randint(70, 140)\\n return ''.join([random.choice(string.letters) for i in xrange(count)])\",\n \"def get_weekday_song():\\n current_day = day_of_week()\\n print(f\\\"WEEKDAY:{current_day}\\\")\\n if (current_day == \\\"Monday\\\"):\\n return random.choice([ \\\\\\n \\\"Monday - Imagine Dragons\\\", \\\\\\n \\\"Monday Morning - Quinn XCII\\\", \\\\\\n \\\"Monday Mornin' Missin' You - Blake Shelton\\\", \\\\\\n \\\"Monday Morning - Fleetwood Mac\\\", \\\\\\n \\\"Monday, Monday\\\", \\\\\\n \\\"Every Day is a Monday\\\"])\\n elif (current_day == \\\"Tuesday\\\"):\\n return random.choice([ \\\\\\n \\\"Tuesdays\\\", \\\\\\n \\\"Tuesday (feat Drake)\\\", \\\\\\n \\\"Tuesday's Gone\\\", \\\\\\n \\\"Tuesday I'll Be Gone\\\", \\\\\\n \\\"Taco Tuesday - Migos\\\", \\\\\\n \\\"Taco Tuesday - Lil Jon\\\", \\\\\\n \\\"Tuesday Afternoon\\\"])\\n elif (current_day == \\\"Wednesday\\\"):\\n return random.choice([ \\\\\\n \\\"Wednesday Morning - Macklemore\\\", \\\\\\n \\\"Wednesday Night Interlude - Drake\\\", \\\\\\n \\\"Wednesday Morning, 3AM\\\"])\\n elif (current_day == \\\"Thursday\\\"):\\n return random.choice([ \\\\\\n \\\"Thursday - The Weeknd\\\", \\\\\\n \\\"Thursday - Jess Glyne\\\", \\\\\\n \\\"(Thursday) Here's Why I Did Not Go to Work Today\\\", \\\\\\n \\\"Like a Summer Thursday\\\", \\\\\\n \\\"Sweet Thursday\\\"])\\n elif (current_day == \\\"Friday\\\"):\\n return random.choice([ \\\\\\n \\\"Friday Night - Eric Paslay \\\", \\\\\\n \\\"Last Friday Night\\\", \\\\\\n \\\"Finally Friday - George Jones\\\", \\\\\\n \\\"Friday Rascall Flatts\\\", \\\\\\n \\\"I Gotta Feeling\\\", \\\\\\n \\\"Friday Night in Dixie\\\", \\\\\\n \\\"Fridays Child\\\", \\\\\\n \\\"Hymn for the Weekend\\\", \\\\\\n \\\"Friday Night Fish Fry\\\", \\\\\\n \\\"Friday Night - Lady A\\\", \\\\\\n \\\"Hello Friday - Flo Rida\\\"])\\n elif (current_day == \\\"Saturday\\\"):\\n return random.choice([ \\\\\\n \\\"Louisiana Saturday Night\\\", \\\\\\n \\\"American Saturday Night\\\", \\\\\\n \\\"Small Town Saturday Night\\\", \\\\\\n \\\"Satuday Night's Alright\\\", \\\\\\n \\\"Saturday in the Park\\\", \\\\\\n \\\"Saturday - Twenty One Pilots\\\", \\\\\\n \\\"Saturday Nights - Khalid\\\", \\\\\\n \\\"Saturday Sun - Vance Joy\\\"])\\n elif (current_day == \\\"Sunday\\\"):\\n return random.choice([ \\\\\\n \\\"Sunday Candy\\\", \\\\\\n \\\"Sunday Morning - Parmalee\\\", \\\\\\n \\\"Sunday Morning - Maroon 5\\\", \\\\\\n \\\"Sunday Best\\\", \\\\\\n \\\"Sunday\\\", \\\\\\n \\\"Closed on Sunday\\\", \\\\\\n \\\"Raining on Sunday\\\", \\\\\\n \\\"A Month of Sundays\\\", \\\\\\n \\\"That's What I Love About Sunday\\\", \\\\\\n \\\"Sunday Drive\\\", \\\\\\n \\\"Another Sunday in the South - Miranda Lambert\\\", \\\\\\n \\\"Sunday - Sia\\\", \\\\\\n \\\"Sunday Morning - Maroon 5\\\", \\\\\\n \\\"A Month of Sundays - Don Henly\\\", \\\\\\n \\\"Lazing on a Sunday Afternoon - Queen\\\", \\\\\\n \\\"Sunday Morning Coming Down\\\", \\\\\\n \\\"Blue Sunday - The Doors\\\", \\\\\\n \\\"A Sunday Kind of Love - Etta James\\\"])\",\n \"def getRandomEvent(p = 0.5):\\n if random.random() < p: return 1\\n else: return 0\",\n \"def random_timed_observation_timeline(count=100):\\n # Generate some observations\\n observations = random_timed_observations(count)\\n\\n # Add the observations to our timeline\\n timeline = ObservationTimeline()\\n for t in observations:\\n timeline.add(t)\\n\\n return timeline\",\n \"def play_tone(freq=440, duration=0.01):\\n tone(board.A0, freq, duration)\"\n]","isConversational":false},"negative_scores":{"kind":"list like","value":["0.70728934","0.6706153","0.6540487","0.6391081","0.6068149","0.60672534","0.60280246","0.60105956","0.59780055","0.5953066","0.59486765","0.5922868","0.57505494","0.5716557","0.5710636","0.57026637","0.5684732","0.56817174","0.5633969","0.5596766","0.556031","0.5557936","0.5541022","0.5512192","0.54990596","0.54879117","0.54747665","0.5471065","0.5451147","0.5436061","0.5404646","0.5402685","0.54016906","0.5401058","0.53881145","0.5377731","0.5358731","0.534259","0.532857","0.5327394","0.53161705","0.5310289","0.53076947","0.5306511","0.53033084","0.5299385","0.5288913","0.5279642","0.527664","0.5266924","0.5260885","0.5255932","0.52400845","0.52312106","0.5228834","0.52158326","0.5213251","0.52081764","0.5198821","0.5197697","0.5195478","0.518371","0.5183585","0.51807904","0.51806164","0.51783866","0.51673716","0.51658916","0.51656514","0.51626617","0.514506","0.51432854","0.51414114","0.51388466","0.5133093","0.51324767","0.5130465","0.5121971","0.5115497","0.5114159","0.5109704","0.5103722","0.50995","0.5096198","0.5088936","0.50878906","0.50877255","0.5086181","0.5074453","0.50528663","0.5048723","0.50462884","0.5040683","0.5038418","0.50343037","0.5028014","0.5027186","0.50260687","0.5024471","0.50228053"],"string":"[\n \"0.70728934\",\n \"0.6706153\",\n \"0.6540487\",\n \"0.6391081\",\n \"0.6068149\",\n \"0.60672534\",\n \"0.60280246\",\n \"0.60105956\",\n \"0.59780055\",\n \"0.5953066\",\n \"0.59486765\",\n \"0.5922868\",\n \"0.57505494\",\n \"0.5716557\",\n \"0.5710636\",\n \"0.57026637\",\n \"0.5684732\",\n \"0.56817174\",\n \"0.5633969\",\n \"0.5596766\",\n \"0.556031\",\n \"0.5557936\",\n \"0.5541022\",\n \"0.5512192\",\n \"0.54990596\",\n \"0.54879117\",\n \"0.54747665\",\n \"0.5471065\",\n \"0.5451147\",\n \"0.5436061\",\n \"0.5404646\",\n \"0.5402685\",\n \"0.54016906\",\n \"0.5401058\",\n \"0.53881145\",\n \"0.5377731\",\n \"0.5358731\",\n \"0.534259\",\n \"0.532857\",\n \"0.5327394\",\n \"0.53161705\",\n \"0.5310289\",\n \"0.53076947\",\n \"0.5306511\",\n \"0.53033084\",\n \"0.5299385\",\n \"0.5288913\",\n \"0.5279642\",\n \"0.527664\",\n \"0.5266924\",\n \"0.5260885\",\n \"0.5255932\",\n \"0.52400845\",\n \"0.52312106\",\n \"0.5228834\",\n \"0.52158326\",\n \"0.5213251\",\n \"0.52081764\",\n \"0.5198821\",\n \"0.5197697\",\n \"0.5195478\",\n \"0.518371\",\n \"0.5183585\",\n \"0.51807904\",\n \"0.51806164\",\n \"0.51783866\",\n \"0.51673716\",\n \"0.51658916\",\n \"0.51656514\",\n \"0.51626617\",\n \"0.514506\",\n \"0.51432854\",\n \"0.51414114\",\n \"0.51388466\",\n \"0.5133093\",\n \"0.51324767\",\n \"0.5130465\",\n \"0.5121971\",\n \"0.5115497\",\n \"0.5114159\",\n \"0.5109704\",\n \"0.5103722\",\n \"0.50995\",\n \"0.5096198\",\n \"0.5088936\",\n \"0.50878906\",\n \"0.50877255\",\n \"0.5086181\",\n \"0.5074453\",\n \"0.50528663\",\n \"0.5048723\",\n \"0.50462884\",\n \"0.5040683\",\n \"0.5038418\",\n \"0.50343037\",\n \"0.5028014\",\n \"0.5027186\",\n \"0.50260687\",\n \"0.5024471\",\n \"0.50228053\"\n]","isConversational":false},"document_score":{"kind":"string","value":"0.72257495"},"document_rank":{"kind":"string","value":"0"}}},{"rowIdx":23782,"cells":{"query":{"kind":"string","value":"Generate random rhythm, based on chord It must have rhythm before"},"document":{"kind":"string","value":"def randNotes(self, value, chord):\n aChord = mgChord(value, chord)\n self.data = []\n for i in range(len(self.rhythm)):\n note = random.choice(aChord).copy()\n note.setDuration(self.rhythm[i])\n self.data.append(note)"},"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 randMelody(self, value, chord):\n self.randRhythm()\n self.randNotes(value, chord)","def get_rdm_note(chord, last_note):\r\n \r\n if random.randint(1,10) <= 6:\r\n # chord note\r\n return random.choice(CHORDS[chord]) \r\n else:\r\n # scale note \r\n n = C_SCALE.index(last_note)\r\n return C_SCALE[n+random.choice(range(-n, 6-n)[2:5])]","def create_melody(inst, chord_progression):\r\n \r\n for chord in chord_progression:\r\n \r\n meas = inst.NewMeasure()\r\n last_note = random.choice(CHORDS[chord])\r\n pos = 0\r\n \r\n while pos < 64:\r\n dur = random.choice([8, 8, 16, 16]) \r\n last_note = get_rdm_note(chord, last_note)\r\n if random.randint(1,10) <= 8: # 20% silence\r\n meas.NewNote(pos, dur, last_note, 5)\r\n pos += dur","def make_music_rand():\n pass","def generate_horror_title():\n d666 = random.randint(1, 666)\n if d666 <= 111:\n #the adj noun\n return \"The \" + horror_adj[random.randint(0, len(horror_adj) - 1)] + \" \" + horror_noun[random.randint(0, len(horror_noun) - 1)]\n elif d666 > 111 and d666 <= 222: \n #noun of noun\n return horror_noun[random.randint(0, len(horror_noun) - 1)] + \" of \" + horror_noun[random.randint(0, len(horror_noun) - 1)]\n elif d666 > 222 and d666 < 444: \n #the adj noun of verb \n return \"The \" + horror_adj[random.randint(0, len(horror_adj) - 1)] + \" \" + horror_noun[random.randint(0, len(horror_noun) - 1)] + \" of \" + horror_verb[random.randint(0, len(horror_verb) - 1)]\n elif d666 >= 444 and d666 < 555: \n #noun of noun\n return horror_noun[random.randint(0, len(horror_noun) - 1)] + \" of \" + horror_noun[random.randint(0, len(horror_noun) - 1)]\n elif d666 >= 555:\n #verb of the adj noun\n return horror_verb[random.randint(0, len(horror_verb) - 1)] + \" of the \" + horror_adj[random.randint(0, len(horror_adj) - 1)] + \" \" + horror_noun[random.randint(0, len(horror_noun) - 1)]","def get_random_chord_type(self) -> str:\n i = random.randint(0, len(self._chord_types) - 1)\n return self._chord_types[i]","def generateHarmonyPitchMU(self, harmonicRhythm, harmonicComplexity,\n minMajRatio, structureLevelMU):\n # decide whether to have cadence\n cadenceProbDict = self.chordProfile.getCadenceProb()\n cadenceProb = cadenceProbDict[structureLevelMU]\n random = RandomManager.getActive()\n r = random.random()\n\n cadenceChordProgression = []\n if r <= cadenceProb:\n cadenceChordProgression = self._createCadence(harmonicRhythm)\n if len(cadenceChordProgression) == len(harmonicRhythm):\n return cadenceChordProgression\n\n # remove as many durations from harmonicRhythm as the\n # number of chords used for the cadence\n harmonicRhythm = harmonicRhythm[:-len(cadenceChordProgression)]\n\n chordProgression = []\n previousTriadCode = None\n chordIndex = None\n\n # step through all durations forming the harmonic rhythm to assign\n # chord\n for durationObj in harmonicRhythm:\n duration = durationObj.getDuration()\n scale = self.chordProfile.getScale().getName()\n metricalAccentLevel = durationObj.getMetricalAccent()\n\n # calculate scores\n scores = self._calcMetrics(previousTriadCode, chordIndex, metricalAccentLevel,\n harmonicComplexity, minMajRatio)\n\n # choose triad\n chord, chordIndex = self._decideTriad(scores, durationObj)\n code = chord.getCode()\n\n # get probability of adding dissonant thirds\n dissonanceProb = self._calcDissonanceProb(harmonicComplexity,\n metricalAccentLevel)\n r = random.random()\n\n # decide whether to apply dissonance\n if r <= dissonanceProb:\n\n # add dissonance(s)\n code = self._decideDissonance(chord)\n\n # create new chord\n newChord = Chord(code, duration=duration, scale=scale,\n octave=4)\n\n # append chord to progression\n chordProgression.append(newChord)\n\n # update previous code and triad code\n previousCode = newChord.getCode()\n previousTriadCode = previousCode[:3]\n\n # add up chord progression and chords for cadence\n chordProgression += cadenceChordProgression\n\n s = self._realizeM21Sequence(chordProgression)\n s.show(\"midi\")\n\n return chordProgression","def _prev_rhythm_tconst(pattern, rhythm):\n BASIC_TCONST(pattern, rhythm)\n tnet = pattern.last_tnet\n tnet.set_equal(pattern.hypothesis.start, rhythm.end)","async def chord(ctx, root:str,length=3,):\n if length < 2:\n length = 2\n elif length > 5:\n length = 5\n chord = Tempo.generateChordProgression(root)\n await ctx.send(chord)","def rhythm_data_generator(patient_generator, frame_size=2048, samples_per_patient=1):\n for _, (signal, labels) in patient_generator:\n num_segments, segment_size = signal.shape\n patient_rhythm_labels = labels['rtype'] # note: variables in a .npz file are only loaded when accessed\n for _ in range(samples_per_patient):\n # randomly choose a frame that lies within the segment i.e. no zero-padding is necessary\n segment_index = np.random.randint(num_segments)\n frame_start = np.random.randint(segment_size - frame_size)\n frame_end = frame_start + frame_size\n x = signal[segment_index, frame_start:frame_end]\n x = np.expand_dims(x, axis=1) # add channel dimension\n # calculate the durations of each rhythm in the frame and determine the final label\n rhythm_ends, rhythm_labels = patient_rhythm_labels[segment_index]\n frame_rhythm_durations, frame_rhythm_labels = get_rhythm_durations(\n rhythm_ends, rhythm_labels, frame_start, frame_end)\n y = get_rhythm_label(frame_rhythm_durations, frame_rhythm_labels)\n yield x, y","def tempo_r(mid, beats, rs):\n bt = mid.ticks_per_beat\n trk = MidiTrack()\n trk.name = \"Tempo variation\"\n trk.append(MetaMessage(\"set_tempo\",\n tempo=beats[0],\n time=0))\n\n for i, beat in enumerate(beats):\n r = rs[i]\n if r == 0: # For the deterministic case\n tempo_r = beat\n else:\n tempo_r = rd.randint(beat-int(beat*r), beat + int(beat*r)) + 1\n trk.append(MetaMessage(\"set_tempo\",\n time=bt,\n tempo=tempo_r))\n\n mid.tracks.append(trk)\n return mid","def _prev_rhythm_gconst(_, rhythm):\n # A bigeminy cannot be preceded by another bigeminy or an extrasystole.\n verify(not isinstance(rhythm, (o.Bigeminy, o.Extrasystole)))","def _rhythm_obs_proc(pattern):\n # We asign the endpoint of the hypothesis.\n pattern.hypothesis.end.value = pattern.evidence[o.QRS][-1].time.value","def generate_round():\n prog_len = random.randint(_MIN_LEN, _MAX_LEN)\n diff = random.randint(_MIN_DIFF, _MAX_DIFF)\n start = random.randint(0, _MAX_START)\n prog = _generate_progression(prog_len, diff, start)\n missing_position = random.randint(0, len(prog) - 1)\n missing_element = prog[missing_position]\n prog[missing_position] = '..'\n prog = list(map(str, prog))\n string_question = ' '.join(prog)\n return string_question, missing_element","def mgChordAugmented(value):\n chord = [MgNote(value), MgNote(value) + 4, MgNote(value) + 8]\n return chord","def random_note_in_chord_and_vocal_range(relPitchList, key, vocal_range, prev_note):\n\n\t# for rests, can only be a rest\n\tif relPitchList == -1:\n\t\treturn [note.Rest()]\n\n\t# extract_range\n\tlowest_note, highest_note = vocal_range\n\n\tif (not isinstance(prev_note, note.Rest)) and (prev_note is not None):\n\n\t\t# restrict intervals to a sixth\n\t\tif prev_note.pitch.transpose(-7) > lowest_note:\n\t\t\tlowest_note = prev_note.pitch.transpose(-7)\n\n\t\tif prev_note.pitch.transpose(7) < highest_note:\n\t\t\thighest_note = prev_note.pitch.transpose(7)\n\n\t# turn relative Pitch into a note.Note\n\tnotes = []\n\n\n\tfor relPitch in relPitchList:\n\t\tif relPitch != 0:\n\t\t\tinterval_ = interval.ChromaticInterval(relPitch)\n\t\t\tnotes.append(note.Note(interval_.transposePitch(key.getTonic()), quarterLength=1))\n\t\telse:\n\t\t\t# needed as no transposition by 0 possible\n\t\t\tnotes.append(note.Note(key.getTonic(), quarterLength=1))\n\t \n\t# get all notes in right octave\n\toctave_corrected_notes = []\n\tfor n in notes:\n\n\t\t# possible octaves\n\t\tlow_octave = lowest_note.octave\n\t\thigh_octave = highest_note.octave\n \n\t\t# select random octave\n\t\t# if only one choice available\n\t\toctave_choice = np.arange(low_octave, high_octave + 1)\n\n\t\t# loop throughpossible octaves\n\t\tfor o in octave_choice:\n\t\t\tnote_ = copy.deepcopy(n)\n\t\t\tnote_.octave = o\n\t\t\tif (note_.pitch >= lowest_note) and (note_.pitch <= highest_note):\n\t\t\t\toctave_corrected_notes.append(note_)\n\n\treturn octave_corrected_notes","def get_random_chord(self) -> []:\n return self.get_random_chords(1)[0]","def get_random_phrase():\n return random.choices(PHRASES, WEIGHTS, k=1)[0]","def gen_melody(scale, timings, note_deviance):\n melody = [random.sample(scale, 1)[0] for i in range(len(timings))]\n\n px = np.random.rand(len(timings))\n idx = np.argwhere(px <= note_deviance)\n direction = [-1, 1]\n for i in idx:\n melody[i[0]] = melody[i[0]] + random.sample(direction, 1)[0]\n return melody","def generate_line_durations(\n n_measures: int,\n duration_weights: Dict[float, float],\n valid_rhythmic_patterns: List[List[float]],\n end_with_whole_note: bool = True\n) -> List[float]:\n current_time = 0\n line_durations = []\n current_measure_durations = []\n total_time = n_measures - int(end_with_whole_note)\n\n while current_time < total_time:\n appropriate_durations = select_appropriate_durations(\n current_time, total_time, current_measure_durations,\n valid_rhythmic_patterns\n )\n duration = random.choices(\n appropriate_durations,\n [duration_weights[x] for x in appropriate_durations]\n )[0]\n current_time += duration\n current_measure_durations = update_current_measure_durations(\n current_measure_durations, duration\n )\n line_durations.append(duration)\n\n if end_with_whole_note:\n line_durations.append(1.0)\n return line_durations","def mgChordDiminished(value):\n chord = [MgNote(value), MgNote(value) + 3, MgNote(value) + 6]\n return chord","def generate_fantasy_title():\n d20 = random.randint(1, 20)\n if d20 <= 4:\n #genetive noun\n return fantasy_genetive[random.randint(0, len(fantasy_genetive) - 1)] + \" \" + fantasy_noun[random.randint(0, len(fantasy_noun) - 1)]\n elif d20 > 4 and d20 < 13: \n #The adj noun\n return \"The \" + fantasy_adj[random.randint(0, len(fantasy_adj) - 1)] + \" \" + fantasy_noun[random.randint(0, len(fantasy_noun) - 1)]\n elif d20 >= 13:\n #something of something\n return fantasy_noun[random.randint(0, len(fantasy_noun) - 1)] + \" of \" + fantasy_what_is_this[random.randint(0, len(fantasy_what_is_this) - 1)]","def sample(self):\n seq = []\n for i in range(self._pwm.shape[1]):\n p = numpy.array(self._pwm[:, i], dtype=numpy.float64)\n p /= p.sum()\n seq.extend(numpy.random.choice(self.alphabet, p=p))\n return \"\".join(seq)","def generate_sentence(self, t=20):\n result = [\"START\", \"START\"]\n\n for i in range(t-3):\n if result[-1] == \"STOP\":\n break\n\n match = {}\n for k,v in self.trigramcounts.items():\n if k[0] == result[-2] and k[1] == result[-1]:\n match[k[-1]] = v\n r = np.random.choice(list(match.keys()), p=np.array(list(match.values())) / np.sum(np.array(list(match.values()))))\n result.append(r)\n\n return result","def mgChord(value, chord):\n ret = None\n if chord == 'M':\n ret = mgChordMajor(value)\n elif chord == 'm':\n ret = mgChordMinor(value)\n elif chord == 'dim':\n ret = mgChordDiminished(value)\n elif chord == 'aug':\n ret = mgChordAugmented(value)\n\n return ret","def randomHelmet():\n return random.choice(HELMETS)","def rand_gen(below, baseline):\n\treturn secrets.randbelow(below)/ baseline","def getRandomRarity():\n r = random.randint(1,100)\n if r <= Rarities.IMPOSIBIL:\n return \"IMPOSIBIL\"\n elif r <= Rarities.LEGENDAR:\n return \"LEGENDAR\"\n elif r <= Rarities.EPIC:\n return \"EPIC\"\n else:\n return \"COMUN\"","def _createCadence(self, harmonicRhythm):\n random = RandomManager.getActive()\n\n # choose cadence to apply\n cadences = self.chordProfile.getCadences()\n\n #TODO: Choose cadence in more intelligent way\n cadence = random.choice(cadences)\n\n scale = self.chordProfile.getScale().getName()\n cadenceChordProgression = []\n\n reversedHarmonicRhythm = reversed(harmonicRhythm[:])\n # create as many cadence\n for count, durationObj in enumerate(reversedHarmonicRhythm):\n duration = durationObj.getDuration()\n code = cadence[-count+1]\n chord = Chord(code, duration=duration, scale=scale, octave=4)\n\n if count >= len(cadence):\n return cadenceChordProgression\n\n # prepend chord\n cadenceChordProgression.insert(0, chord)\n return cadenceChordProgression","def random():\n length = 10**np.random.uniform(2, 6)\n radius = 10**np.random.uniform(1, 3)\n axis_ratio = 10**np.random.uniform(-1, 1)\n kuhn_length = 10**np.random.uniform(-2, -0.7)*length # at least 10 segments\n pars = dict(\n length=length,\n radius=radius,\n axis_ratio=axis_ratio,\n kuhn_length=kuhn_length,\n )\n return pars","def choose_guard(self):\n\n\t\tg = randint(1, 3)\n\n\t\tif g == 1:\n\t\t\treturn 'h'\n\t\tif g == 2:\n\t\t\treturn 't'\n\t\tif g == 3:\n\t\t\treturn 'l'","def mon_maker():\r\n random_mon = {'eyeratio':0.2, 'eyeL':30,\r\n 'mouthratio':0.8, 'mouthL':30,\r\n 'headL':40, 'headA':15,\r\n 'cheekL':25, 'cheekA':45,\r\n 'chinL': 30, 'chinA':90\r\n }\r\n return random_mon","def get_next_chord(chord):\r\n \r\n num = C_SCALE.index(chord)\r\n candidates = F_PROG[num-1]\r\n return C_SCALE[random.choice(candidates)]","def last_word_dict(self, rhyme_dict):\n scheme = {1: 'A', 2: 'B', 3: 'A', 4: 'B', 5: 'C', 6: 'D', 7: 'C', 8: 'D', 9: 'E', 10: 'F', 11: 'E', 12: 'F', 13: 'G', 14: 'G'}\n last_word_dict={}\n\n\n \"\"\"for i in range(1,15):\n temp = []\n if i in [1,2,5,6,9,10,13]: #lines with a new rhyme\n for k in rhyme_dict[scheme[i]].keys():\n temp.append(k)\n if i in [3,4,7,8,11,12,14]: #lines with an old line\n for k in rhyme_dict[scheme[i]].keys():\n temp += rhyme_dict[scheme[i]][k]\n #last_word_dict[i]=[*{*temp}]\n last_word_dict[i] = temp\"\"\"\n first_rhymes = []\n for i in range(1,15):\n if i in [1, 2, 5, 6, 9, 10, 13]: # lines with a new rhyme -> pick a random key\n last_word_dict[i] = [random.choice(list(rhyme_dict[scheme[i]].keys()))] #NB ensure it doesnt pick the same as another one\n while not self.suitable_last_word(last_word_dict[i][0]) or last_word_dict[i][0] in first_rhymes or any(rhyme_dict['A'][last_word_dict[i][0]] in rhyme_dict['A'][word] for word in first_rhymes):\n last_word_dict[i] = [random.choice(list(rhyme_dict[scheme[i]].keys()))]\n first_rhymes.append(last_word_dict[i][0])\n if i in [3, 4, 7, 8, 11, 12, 14]: # lines with an old rhyme -> pick a random value corresponding to key of rhyming couplet\n letter = scheme[i]\n pair = last_word_dict[i-2][0]\n if i == 14:\n pair = last_word_dict[13][0]\n last_word_dict[i] = [word for word in rhyme_dict[letter][pair] if self.suitable_last_word(word)]\n return last_word_dict","def totem_random():\n random_head()\n random_head()\n random_head()","def diceroll(self, mess, args):\n return random.randint(1, 6)","def get_random_phrase(self):\n return random.choice(self.phrases)","def getStatusMessage():\n\n now = datetime.datetime.now()\n hour = now.hour\n mood = Sentience.getPrimaryMood()\n exp_mood = Sentience.getExposedPositivity()\n\n random.seed((time.time()//86400*86400))\n\n #sleeping\n if not (9 <= hour < 21) and (mood <= 0.5 or not 7 <= hour < 23):\n if exp_mood < -0.1:\n return random.choice([\n \"bleh\",\n \"not sleeping well\",\n \"why's chat so noisy\",\n \"can't sleep\",\n \"do not disturb pls thx\",\n ])\n\n if mood < 0:\n return random.choice([\n \"crying myself to sleep rn\",\n \":(\",\n \"had a nightmare\",\n \"can't sleep\",\n \"._.\"\n ])\n\n return random.choice([\n \"zzz...\",\n \"sweet dreams\",\n \"good night\",\n \"sleeping...\",\n \"having some rest\"\n ])\n\n if Sentience.isExposedPositivityOverloaded():\n return random.choice([\n \"i'm done\",\n \"too much\"\n \"goodbye\",\n \"tired\",\n \"need rest\",\n ])\n\n #happy\n if mood >= 0.7:\n return random.choice([\n \":D\",\n \"great day\",\n \"happy happy\",\n \"hehe\",\n \"good times\",\n \"yay\",\n \"what's up\",\n \"happiness\",\n \"nice day\",\n ])\n #moody-ish\n if mood >= 0.4:\n return random.choice([\n \"hmm\",\n \"yeet\",\n \"bleh\",\n \"oh\",\n \"moody rn\",\n \"nothing\"\n ])\n #more moody\n if mood >= -0.3:\n return random.choice([\n \"moody rn\",\n \"not happy\",\n \"i'm fine.\",\n \"bleh\",\n \"._.\",\n \":(\",\n ])\n #very unhappy\n return random.choice([\n \"sad\",\n \"cries\",\n \"roar\",\n \":_(\",\n \">:(\",\n \"mad\",\n \"angry\",\n \"I'M FINE.\",\n \"bleh\",\n \"no\",\n ])","def generate_romance_title():\n d69 = random.randint(1, 69)\n if d69 <= 35:\n #the adj noun\n return \"The \" + romance_adj[random.randint(0, len(romance_adj) - 1)] + \" \" + romance_noun[random.randint(0, len(romance_noun) - 1)]\n elif d69 > 35:\n #noun of the adj noun\n return romance_noun[random.randint(0, len(romance_noun) - 1)] + \" of the \" + romance_adj[random.randint(0, len(romance_adj) - 1)] + \" \" + romance_noun[random.randint(0, len(romance_noun) - 1)]","def tone(n, base_freq=440.0):\n # -2 -1 0 1 2 3 4 5 6 7 8 9 10 11 12\n # G G# A A# B C C# D D# E F F# G G# A\n # G Ab A Bb B C Db D Eb E F Gb G Ab A\n return base_freq * 2 ** (n/12)","def random_iteration(r, w):\n density = 1.225\n blade_number = 6\n wind_v = round(-168.75*r**2-36.75*r+20.05 , 2)\n blade_v = r*w\n rel_v = round(math.sqrt(blade_v**2 + wind_v**2),2)\n arctan = round(math.degrees(math.atan2(wind_v, blade_v)), 2)\n # cl is a random float in range 1.0 to 1.7\n cl = uniform(1.0, 1.7)\n # aoa is a random float in range 10 to 15\n aoa = uniform(10, 15)\n treeHit = 0\n Re = get_re(r, cl, aoa, arctan, rel_v, blade_number)[0]\n new_cl, new_a, new_cd = get_max_cl(Re, r)\n new_Re, new_chord = get_re(r, round(new_cl, 2), round(new_a, 2), arctan, rel_v, blade_number)\n re_devi = abs((new_Re - Re) / Re)\n # iterate until Re_deviation goes under 5%\n while re_devi > 0.05:\n Re = new_Re\n new_cl, new_a, new_cd = get_max_cl(new_Re, r)\n new_Re, new_chord = get_re(r, new_cl, new_a, arctan, rel_v, blade_number)\n re_devi = abs((new_Re - Re) / Re)\n treeHit += 1\n # stop iteration over 10 times\n if treeHit > 10:\n break\n force_reference = 0.5 * density * rel_v**2 * 0.0125 * round(new_chord, 3)\n return {\n \"r\": r,\n \"arctan\": arctan, \n \"chord\": round(new_chord, 3), \n \"aoa\": new_a, \n \"cl\": new_cl, \n \"cd\": new_cd, \n \"Re\": new_Re,\n \"lift\": new_cl * force_reference, \n \"drag\": new_cd * force_reference,\n \"torque\": r * (new_cl * force_reference * math.sin(math.radians(arctan - new_a)) - new_cd * force_reference * math.cos(math.radians(arctan - new_a)))\n }","def randomize_trajectory(self):\n self.angle = randint(-360, 360)\n self.speed = randint(1, 5)/2.5","def generate_synth_data(n):","def generate_random_line(\n line_durations: List[float],\n pitches: List[ScaleElement]\n) -> List[PieceElement]:\n melodic_line = []\n current_time = 0\n for duration in line_durations:\n scale_element = random.choice(pitches)\n piece_element = PieceElement(\n note=scale_element.note,\n position_in_semitones=scale_element.position_in_semitones,\n position_in_degrees=scale_element.position_in_degrees,\n degree=scale_element.degree,\n start_time=current_time,\n duration=duration\n )\n melodic_line.append(piece_element)\n current_time += duration\n return melodic_line","def next_chord(prev, scale):\n # Choose which pitch should be replaced\n replace = random.sample(range(len(prev)), 1)[0]\n\n next = []\n for i, pitch in enumerate(prev):\n prev_pitchspaces = [p.ps for p in prev]\n if i == replace:\n # Possible replacement pitches are within a minor third of the\n # pitch being replaced, not in the previous chord, and not already\n # in the next chord.\n options = scale\n\n # Not more than a major third lower\n options = [p for p in options if p.ps > pitch.ps - 4]\n\n # Not more than a major third higher\n options = [p for p in options if p.ps < pitch.ps + 4]\n\n # Not in the previous chord\n options = [p for p in options if p.ps not in prev_pitchspaces]\n\n # Not in this chord\n next_pitchspaces = [p.ps for p in next]\n options = [p for p in options if p.ps not in next_pitchspaces]\n\n # Choose the replacement pitch from the options\n choice = random.choice(options)\n next.append(Pitch(choice.ps))\n else:\n next.append(Pitch(pitch.ps))\n return next","def mimic_lecture(mimic_dict, starting_word=None, limit=10):\r\n\r\n if not starting_word:\r\n word = ''\r\n else:\r\n word = starting_word\r\n\r\n line_label = (divmod(x*3, 60) for x in numGen())\r\n text = word\r\n text = str(next(line_label)) + word\r\n line_mark = 0\r\n line_words = 1\r\n while line_mark < limit:\r\n if word not in mimic_dict:\r\n word = \"\"\r\n new_word = random.choice(mimic_dict[word])\r\n\r\n if line_words == 0:\r\n rand_silence = random.randint(1,10)\r\n if rand_silence <= 2:\r\n text = text + \"\\n\" + str(next(line_label))\r\n line_words = 0\r\n line_mark += 1\r\n word = new_word\r\n continue\r\n\r\n text = text + \" \" + new_word\r\n rand = random.randint(3,5)\r\n if line_words >= rand:\r\n text = text + \"\\n\" + str(next(line_label))\r\n line_words = 0\r\n line_mark += 1\r\n else:\r\n line_words += 1\r\n word = new_word\r\n return text","def getChord(self, c, velocity, isdrum, isgrace):\n\n c = re.split(\"[, ]+\", c)\n\n if not c:\n error(\"You must specify the first note in a solo line\")\n\n \"\"\" Convert the note part into a series of midi values\n Notes can be a single note, or a series of notes. And\n each note can be a letter a-g (or r), a '#,&,n' plus\n a series of '+'s or '-'s. Drum solos must have each\n note separated by ' ' or ','s: \"Snare1,KickDrum1,44\".\n\n Each chunk could be:\n - a midi value (44)\n - a drum note ( KickDrum1)\n - a single note (g#) (g&-)\n - Or groups with spaces/commas (f 100) (44 , KickDrum) (a,b c)\n \"\"\"\n\n events = [] # array for each note event\n\n for cc in c:\n if not cc or not cc[0]:\n continue\n if '/' in cc:\n if cc.count('/') > 1:\n error(\"%s: Only 1 '/velocity' permitted. You can separate \" \\\n \"notes in the chord with ',' or ' ' and it'll work.\" % \\\n self.name)\n cc, newvel = cc.split('/')\n if not newvel:\n error(\"%s: expecting 'volume' after '/'\" % self.name)\n\n if not cc:\n error(\"%s: Volume '/' must immediately follow note.\" % self.name)\n\n thisvel = stoi(newvel)\n\n if thisvel < 0 or thisvel > 127:\n error(\"%s: Velocity must be 0..127, not '%s'.\" % (self.name, newvel))\n else:\n thisvel = velocity\n\n if cc[0] == 'r':\n if isgrace:\n warning(\"%s: Grace note is a rest (ignored).\" % self.name)\n \n if events or len(cc) > 1:\n error(\"%s: Rests and notes cannot be combined.\" % self.name)\n else:\n events.append( NoteEvent(None, 0, isgrace)) # note event with no pitch\n\n elif cc[0] in \"1234567890\":\n n = stoi(cc, \"%s: Note values must be integer or literal.\" % \\\n self.name)\n if n<0 or n>127:\n error(\"%s: Midi notes must be 0..127, not '%s'\" % \\\n (self.name, n))\n\n # if using value we fake-adjust octave,\n # it (and transpose) is set later.\n\n if not isdrum:\n n -= self.octave[self.seq]\n \n events.append(NoteEvent(n, thisvel, isgrace))\n\n\n elif isdrum: # drum must be a value, * or drum-name\n if cc == '*':\n events.append( NoteEvent(self.drumTone, thisvel, isgrace ))\n else:\n events.append( NoteEvent(int(MMA.translate.dtable.get(cc)), thisvel, isgrace) )\n\n else: # must be a note(s) in std. notation\n cc = list(cc)\n while cc:\n name = cc.pop(0)\n\n if not name in self.midiNotes:\n error(\"%s: Encountered illegal note name '%s'\"\n % (self.name, name))\n\n n = self.midiNotes[ name ] # name is string, n is value\n \n # Parse out a \"#', '&' or 'n' accidental.\n\n if cc and cc[0] in accValues:\n i = cc.pop(0)\n self.acc[name] = accValues[i]\n\n n += self.acc[name] # accidental adjust (from above or keysig)\n\n # Parse out +/- (or series) for octave\n\n while cc and cc[0] in '+-':\n a = cc.pop(0)\n if a == '+':\n n += 12\n else:\n n -= 12\n\n events.append( NoteEvent(n, thisvel, isgrace) )\n \n\n return events","def sonify_mu_rhythm(self, mu_rhythm):\r\n channel = int(input(\"Enter the channel number for which you want to do the mu rhythm sonification: \\n 1 -> FC5,\\n 2 -> FC1,\\n \\\r\n3 -> C3,\\n 4 -> CP5,\\n 5 -> CP1,\\n 6 -> FC2,\\n 7 -> FC6,\\n 8 -> C4,\\n 9 -> CP2,\\n 10 -> CP6\\nInsert number:\"))\r\n fs = 659.25\t # E5 note\r\n data = np.sin(2. * np.pi * fs * self.time)\r\n coeff = np.zeros(self.n_samples)\r\n samp_freq_conversion = int(np.ceil(self.audio_sample_rate / self.eeg_sampling_frequency))\r\n i = 0\r\n for samp in mu_rhythm[channel-1]:\r\n for j in range(i*samp_freq_conversion, (i+1)*samp_freq_conversion): \r\n if j < self.n_samples:\r\n coeff[j] = 0.001 + samp*1e10\r\n print('Mu rhythm sonification: ', i, ' out of ', len(mu_rhythm[channel-1]))\r\n i += 1\r\n print('Mu rhythm sonification: ', i, ' out of ', len(mu_rhythm[channel-1]))\r\n data = data * coeff\r\n return data","def repeat_rythm(self, rythm, dev):\n return rythm","def mgChordMajor(value):\n chord = [MgNote(value), MgNote(value) + 4, MgNote(value) + 7]\n return chord","def pure_tone(freq: float, sr: float = 128, dur: float = 4, device=None):\n time = torch.arange(int(sr * dur), device=device).float() / sr\n return torch.cos(2 * math.pi * freq * time)","def triplet():\n ends = digraphs + list(consonants)\n spaces = [ends, list(vowels), ends]\n bit = \"\".join([random.sample(x, 1)[0] for x in spaces])\n if random.randint(0, 1) == 1:\n bit = bit.title()\n return bit","def test_random_note_pitch(common_scales, all_pitches):\n\n for key, scales in common_scales.items():\n note = random_note_pitch(scales)\n assert note in all_pitches\n assert isinstance(note, (int, str)) is True","def getRandomAngle():\r\n\treturn random.random() * math.pi * 2","def write_music(self, length, bpm=75, initial_note=\"C\",\n initial_rhythm=\"0.25\", initial_key=\"F\"):\n # Translate the string key into an index for our note generator\n key_number = self.FREQUENCY_LIST.index(initial_key)\n print key_number\n\n rhythms = self.generate_rhythms(\n self.learner.ngrams[\"rhythms\"], initial_rhythm, bpm, length)\n notes = self.generate_notes(\n self.learner.ngrams[\"notes\"], initial_note, len(rhythms),\n key_number)\n\n rhythms = map(lambda x: float(60) * x / bpm, rhythms)\n notes = map(lambda x: self.FREQUENCY_MAP[x], notes)\n\n for i, n in enumerate(notes):\n self.write_note(self.make_note_sound(notes[i], rhythms[i]))","def generate_RME():\n RME = [\"ogre\", \"goblin\", \"gnoll\", \"orc\", \"personal injury lawyer\"]\n monster = random.choice(RME)\n return monster","def get_random_time_between_tts():\n return random.randint(TIME_BETWEEN_TTS_LOWER, TIME_BETWEEN_TTS_UPPER)","def gen():\r\n\r\n output = \"\"\r\n frame = 12\r\n prior = 0\r\n second = 0\r\n\r\n while frame > 0:\r\n frame -= .5\r\n roll = randrange(11 - prior)\r\n\r\n # If strike is rolled:\r\n if roll == 10:\r\n output += \"X\"\r\n frame -= .5\r\n prior = 0\r\n second = 0\r\n\r\n # If spare is rolled:\r\n elif second == 1 and roll + prior == 10:\r\n output += \"/\"\r\n prior = 0\r\n second = 0\r\n\r\n # If not strike or spare:\r\n else:\r\n output += str(roll)\r\n prior = roll\r\n second = 1 - second\r\n\r\n # Deliminate frames:\r\n if second == 0:\r\n output += \"-\"\r\n\r\n return output[:-1]","def goals():\n rand_nmr = random.random()\n if rand_nmr < 0.5:\n return 1\n elif rand_nmr < 0.8:\n return 2\n elif rand_nmr < 0.97:\n return 3\n else:\n return 4","def mgChordMinor(value):\n chord = [MgNote(value), MgNote(value) + 3, MgNote(value)+7]\n return chord","def trial(length, height):\n screen.refresh()\n global stimList\n global oddLength\n global oddHeight\n currentLength = int(maxLength / 4)\n currentHeight = int(maxHeight / 4)\n for i in range(stimAmt):\n if i == oddLocation:\n oddLength = currentLength\n oddHeight = currentHeight\n stimList.append(\n pg.draw.rect(\n screen.fg,\n PgTools.rand_color(),\n (currentLength, currentHeight, length, height,),\n )\n )\n PgTools.rand_pattern(\n screen.fg,\n (\n currentLength,\n currentHeight,\n ),\n (length, height),\n i=(randint(0, 2), randint(0, 1)),\n )\n if randShapes:\n PgTools.rand_shape(screen.fg, (currentLength, currentHeight),(length, height), oddSeed)\n else:\n stimList.append(\n pg.draw.rect(\n screen.fg,\n color,\n (currentLength, currentHeight, length, height,),\n )\n )\n PgTools.rand_pattern(\n screen.fg,\n (\n currentLength,\n currentHeight,\n ),\n (length, height),\n patColor,\n randNums,\n )\n if randShapes:\n PgTools.rand_shape(screen.fg, (currentLength, currentHeight),(length, height), regSeed)\n currentLength += maxLength / 4\n currentLength = int(currentLength)\n if (i + 1) % 3 == 0:\n currentLength = maxLength / 4\n currentLength = int(currentLength)\n currentHeight += maxHeight / 4\n currentHeight= int(currentHeight)","def lf():\n return random.sample(font_list, 25)","def spinWheel():\n\tt = random.randrange(8)\n\tif t == 7:\n\t\treturn \"00\"\n\treturn str(t)","def random():\r\n return R.NextDouble()","def get_next_chord(self, chord):\n chord = self.specified_chord_to_chord(chord)\n assert validate_chord(chord), \"not a valid chord!\"\n\n cond_dist_dict = self.chord_chord_map[chord] # Gets list of candidate chords\n\n # Prepare for the random draw\n next_chords = cond_dist_dict.keys()\n next_probs = [cond_dist_dict[next_chord] for next_chord in next_chords]\n cond_dist = stats.rv_discrete(name='cond_dist', values=(next_chords, next_probs))\n\n return self.key.chords[cond_dist.rvs(size=1)-1]","def random_mutate(dna: str) -> str:\r\n result = \"\"\r\n for c in range(DNA_SIZE):\r\n if random.randrange(0, 100, 1) == 1:\r\n result += random_charachter()\r\n else:\r\n result += dna[c]\r\n return result","def r(w,rangestart,rangeend):\r\n if w == 'r':\r\n print(random.random(rangestart , rangeend))\r\n if w == 'ri':\r\n print(random.randint(rangestart,rangeend))","def sample_quat(self):\n if self.z_rotation:\n rot_angle = np.random.uniform(high=2 * np.pi, low=0)\n return [np.cos(rot_angle / 2), 0, 0, np.sin(rot_angle / 2)]\n return [1, 0, 0, 0]","def ninja_chirp(total_chirp):\n return \"chirp\" + (\"-chirp\" * (total_chirp - 1))","def punctuate_rythm(self, rythm): \n # TO BE IMPLEMENTED\n return 0.5","async def pornx(ctx, n: int = 1):\r\n msg = [\"{}\".format(text) for text in [random.choice(data) for _ in range(0,n)]]\r\n await bot.say('\\n'.join(msg))","def _random_id(n):\n ''.join(choice(alphanums) for i in range(n))","async def tapir(self):\n tapir_list = self.config.get('tapirs', [])\n tapir = tapir_list[random.randrange(len(tapir_list))]\n try:\n await self.bot.say(tapir)\n except:\n await self.bot.whisper(tapir)","def get_round():\n progression = []\n start = randint(1, 50)\n step = randint(1, 10)\n stop = start + step * PROGRESSION_LENGTH\n\n for i in range(start, stop, step):\n progression.append(str(i))\n\n remove_index = randint(0, PROGRESSION_LENGTH - 1)\n true_answer = progression[remove_index]\n progression[remove_index] = '..'\n\n return (' '.join(progression), true_answer)","def choose_piece_duration(self):\n self.piece_duration = random.randint(900, 1100)","def make_transition_probs(self):\n n = len(self.speakers) # TODO why this line ???\n transitions = np.random.randint(5, size=(n, n)) + 1\n transitions += transitions.transpose()\n for i in range(0, math.floor(n / 2)):\n s1 = np.random.randint(n)\n s2 = np.random.randint(n)\n transitions[s1][s2] += 10\n transitions[s2][s1] += 8\n return(transitions)","def direction_correction(self):\n self.directions.monster = random.uniform(self.directions.monster * self.get_monster_sensitivity(),\n self.directions.monster * (1 + (1 - self.get_monster_sensitivity())))\n self.directions.food = random.uniform(self.directions.food * self.get_food_sensitivity(),\n self.directions.food * (1 + (1 - self.get_food_sensitivity())))\n self.directions.water = random.uniform(self.directions.water * self.get_water_sensitivity(),\n self.directions.water * (1 + (1 - self.get_water_sensitivity())))","def buildMelodyNotes(m, bars):\n\n # Get the notes part of the scale specified in Melody object\n s = generate_scale(m.getKeySignature(), m.getScale(), m.getOctave())\n\n if bars < MIN_BARS or bars > MAX_BARS:\n bars = random.randint(MIN_BARS, 4)\n\n # Get a random number of notes value between the number of bars (so\n # that there is roughly one note per bar) and SMALLEST_NOTE //\n # OTHER_NOTE (so that each note would be roughly OTHER_NOTE long)\n numNotes = random.randint(4, max(1, bars * (SMALLEST_NOTE //\n DOTTED_EIGHTH_NOTE)))\n seq = []\n # randomize notes in scale by choosing {numNotes} random notes\n for i in range(numNotes):\n seq.append(random.choice(s))\n\n # randomize length of each notes using getRandomStructure function\n lengths, barSpace = getEqualStructure(bars, numNotes)\n\n # add the random notes to generate the melody\n for i in range(numNotes):\n if seq[i] == -1:\n m.addNote(Note(0, lengths[i], vel=0))\n else:\n m.addNote(Note(seq[i], lengths[i]))\n\n if barSpace > 0:\n # Add rest just in case last note does not extend to end of bar\n m.addNote(Note(0, barSpace, vel=0))","def randomize_value(self) -> None:","def to_cms_rhythm(channel):\n ret = []\n for i, x in enumerate(self.pitches[channel]):\n if x > 0:\n ret.append(x)\n else:\n ret.append(-1)\n return ret","def create_challenge():\n\treturn os.urandom(12)","def generate():\n repl_count = 0\n\n phrase = choice(BLACK_CARDS)\n while '_' in phrase:\n repl_count += 1\n phrase = phrase.replace('_', choice(WHITE_CARDS), 1)\n\n if repl_count == 0:\n phrase += \" \" + choice(WHITE_CARDS)\n\n return phrase","def copyRhythmParams(self, note):\n self.setDuration(note.getDurationNoDot())\n self.setBeatNum(note.getBeatNum())\n self.setTie(note.getTie())\n self.setDot(note.getDot())\n return","def generate_pink_noise(duration: float, f_sample: float) -> np.ndarray:\r\n # calculate the number of time points and the time difference between them\r\n length = duration * f_sample\r\n delta_t = 1 / f_sample\r\n # get the angular frequency axis in [omega]\r\n f_k = sc.fft.fftfreq(length, d=delta_t)/(2 * np.pi)\r\n # calculate the frequency coefficients based on the noise color\r\n # pink noise has power spectral density of 1/f -> the amplitude has 1/sqrt(f)\r\n C_k = np.ndarray(length)\r\n C_k[1:] = 1 / np.sqrt(np.abs(f_k[1:]))\r\n # no dc\r\n C_k[0] = 0\r\n # generate the signal from the frequency coefficients\r\n return generate_signal_from_spectrum(C_k)","async def dice( con, min1=1, max1=6):\n r = random.randint(min1, max1)\n await bot.send_message(con.message.channel, \"**{}**\".format(r))","def make_pattern(self):\n probability = random.SystemRandom().random()\n if probability < 0.1:\n _pattern = [0 for x in range(32)]\n elif probability > 0.5:\n pattern_num = SECURE_RANDOM.choice(CLASSIC)\n _probability = random.SystemRandom().random()\n if _probability > 0.80:\n _pattern = [1 if random.SystemRandom().random() < pattern_num/32 else 0 for x in range(1,33)]\n elif _probability < 0.40:\n _offset = random.SystemRandom().randint(2, 16)\n _pattern = [1 if (x == _offset) or (x % pattern_num == _offset) else 0 for x in range(1,33)]\n else:\n _pattern = [1 if (x == 1) or (x % pattern_num == 1) else 0 for x in range(1,33)]\n else:\n pattern_num = SECURE_RANDOM.choice(INNER_CLASSIC)\n _probability = random.SystemRandom().random()\n if _probability > 0.50:\n _pattern = [1 if (x == 1) or (x % pattern_num == 1) else 0 for x in range(1,33)]\n else:\n _pattern = [1 if random.SystemRandom().random() < pattern_num/32 else 0 for x in range(1,33)]\n\n if not self.global_swing:\n _probability = random.SystemRandom().random()\n if _probability > 0.3:\n _pattern.extend([random.SystemRandom().uniform(0.01, 0.5), random.SystemRandom().randint(1, 14), 0])\n else:\n _pattern.extend([0,1,0])\n else: \n _pattern.extend([0,1,1]) \n\n return _pattern","def randomize_herbs(self):\n random.shuffle(self.herbivores)","def sample_sonnet_syl_and_rhyme(hmm, obs_map, rhyme_dict, n_syl = 10):\n sonnetLines = []\n r_sonnetLines = []\n sonnet = ''\n sonnet_length = 14\n count = 0\n syl_counts = syllable_dict()\n # print(syl_counts)\n \n while count < sonnet_length:\n # Pick a random word from the rhyming dictionary that the line has to start with. \n \n start_word = np.random.choice(list(rhyme_dict.keys()))\n rhyme_word = np.random.choice(rhyme_dict[start_word])\n line1 = sample_sentence_syl(hmm, obs_map, rhyme_dict, start_word, n_syl)\n line2 = sample_sentence_syl(hmm, obs_map, rhyme_dict, rhyme_word, n_syl)\n (worked1, nline1) = make_line(line1, n_syl, syl_counts)\n (worked2, nline2) = make_line(line2, n_syl, syl_counts)\n if worked1 and worked2:\n sonnetLines.append(nline1)\n sonnetLines.append(nline2)\n count += 2\n\n # Now flip the order of each line.\n for line in sonnetLines:\n line_reversed = ' '.join(reversed(line.split(' '))).capitalize()\n r_sonnetLines.append(line_reversed + '\\n')\n\n # Rearrange 7 couplets into a sonnet.\n for stanza in range(0, 3):\n idx = [0, 1, 2, 3]\n for i in range(len(idx)):\n idx[i] += stanza * 4\n sonnet += r_sonnetLines[idx[0]] + r_sonnetLines[idx[2]] + r_sonnetLines[idx[1]] + r_sonnetLines[idx[3]]\n\n for line_num in range(12, 14):\n sonnet += r_sonnetLines[line_num]\n\n return sonnet","def rand(self):\n raise NotImplementedError","def ks_chord(chord, N, alpha, f_s):\n y = np.zeros(N)\n # the chord is a dictionary: pitch => gain\n for note, gain in chord.items():\n # create an initial random-filled KS buffer the note\n x = np.random.randn(int(np.round(float(f_s) / note_freq(note))))\n y = y + gain * KS(x, N, alpha)\n return y","def beat_stripper(timings, removal_proportion):\n phrase_len = len(timings)\n n_to_rm = int(phrase_len * removal_proportion)\n rm_idx = np.random.randint(0, phrase_len-1, n_to_rm)\n\n timings_stripped = []\n for i, hit in enumerate(timings):\n if not i in rm_idx:\n timings_stripped.append(hit)\n else:\n timings_stripped.append(hit + hit)\n return timings_stripped","def generate_sound_samples(chord_frequencies, frequency_weights):\n samples = 0\n for i in range(len(frequency_weights)):\n samples = samples + (frequency_weights[i]) * (np.sin(2 * np.pi * np.arange(\n fs * duration) * chord_frequencies[i] / fs)).astype(np.float32)\n return samples","def generate_question_and_answer(): # noqa: WPS210\n start_number = random.randint(1, 100)\n progression_step = random.randint(1, 10)\n progression_length = random.randint(5, 10)\n progression = generate_progression(\n start_number, progression_step, progression_length,\n )\n return hide_number(progression)","def randomize(data, length=0):\r\n longitud = 0 if length == 0 else 12 - length\r\n return data + \"-\" + str(time.time()).replace(\".\", \"\")[longitud:]","def getRandom(self) -> int:","def getRandom(self) -> int:","def single_introduction(end):\n return [random.randint(0,end)]","def random_schechter(ntot,mstar,alpha,mfaint,mbright):\n lfaint = flux(mfaint-mstar)\n lbright = flux(mbright-mstar)\n if alpha > 0:\n print \"random_schechter: alpha must be negative\"\n sys.exit()\n smax = schechter(lfaint,alpha)\n result = zeros((ntot),\"float\")\n n = 0\n while n < ntot:\n x = random.uniform(lfaint,lbright)\n s = schechter(x,alpha)/smax\n y = random.random() \n if y < s:\n result[n] = mag(x)+mstar\n n = n+1\n return result","def generate_midi(config_folder, score_source, number_of_version, duration_gen, rhythmic_reconstruction, logger_generate):\n\n logger_generate.info(\"#############################################\")\n logger_generate.info(\"Orchestrating piano score : \" + score_source)\n ############################################################\n # Load model, config and data\n ############################################################\n\n ########################\n # Load config and model\n parameters = pkl.load(open(config_folder + '/script_parameters.pkl', 'rb'))\n model_parameters = pkl.load(open(config_folder + '/model_params.pkl', 'rb'))\n # Set a minimum seed size, because for very short models you don't event see the beginning\n seed_size = max(model_parameters['temporal_order'], 10) - 1\n quantization = parameters['quantization']\n temporal_granularity = parameters['temporal_granularity']\n instru_mapping = parameters['instru_mapping']\n ########################\n\n ########################\n # Load data\n if re.search(r'mid$', score_source):\n pr_piano, event_piano, duration_piano, name_piano, pr_orch, instru_orch, duration = load_solo(score_source, quantization, parameters[\"binarize_piano\"], temporal_granularity)\n else:\n pr_piano, event_piano, duration_piano, name_piano, pr_orch, instru_orch, duration = load_from_pair(score_source, quantization, parameters[\"binarize_piano\"], parameters[\"binarize_orch\"], temporal_granularity)\n ########################\n\n ########################\n # Shorten\n # Keep only the beginning of the pieces (let's say a 100 events)\n pr_piano = extract_pianoroll_part(pr_piano, 0, duration_gen)\n if parameters[\"duration_piano\"]:\n duration_piano = np.asarray(duration_piano[:duration_gen])\n else:\n duration_piano = None\n event_piano = event_piano[:duration_gen]\n pr_orch = extract_pianoroll_part(pr_orch, 0, duration_gen)\n ########################\n\n ########################\n # Instanciate piano pianoroll\n N_piano = instru_mapping['Piano']['index_max']\n pr_piano_gen = np.zeros((duration_gen, N_piano), dtype=np.float32)\n pr_piano_gen = build_data_aux.cast_small_pr_into_big_pr(pr_piano, {}, 0, duration_gen, instru_mapping, pr_piano_gen)\n pr_piano_gen_flat = pr_piano_gen.sum(axis=1)\n silence_piano = [e for e in range(duration_gen) if pr_piano_gen_flat[e]== 0]\n ########################\n\n ########################\n # Instanciate orchestra pianoroll with orchestra seed\n N_orchestra = parameters['N_orchestra']\n if pr_orch:\n pr_orchestra_gen = np.zeros((seed_size, N_orchestra), dtype=np.float32)\n orch_seed_beginning = {k: v[:seed_size] for k, v in pr_orch.items()}\n pr_orchestra_gen = build_data_aux.cast_small_pr_into_big_pr(orch_seed_beginning, instru_orch, 0, seed_size, instru_mapping, pr_orchestra_gen)\n pr_orchestra_truth = np.zeros((duration_gen, N_orchestra), dtype=np.float32)\n pr_orchestra_truth = build_data_aux.cast_small_pr_into_big_pr(pr_orch, instru_orch, 0, duration_gen, instru_mapping, pr_orchestra_truth)\n else:\n pr_orchestra_gen = None\n pr_orchestra_truth = None\n ########################\n \n #######################################\n # Embed piano\n time_embedding = time.time()\n if parameters['embedded_piano']:\n # Load model\n embedding_path = parameters[\"embedding_path\"]\n embedding_model = embedDenseNet(380, 12, (1500,500), 100, 1500, 2, 3, 12, 0.5, 0, False, True)\n embedding_model.load_state_dict(torch.load(embedding_path))\n\n # Build embedding (no need to batch here, len(pr_piano_gen) is sufficiently small)\n piano_resize_emb = np.zeros((len(pr_piano_gen), 1, 128)) # Embeddings accetp size 128 samples\n piano_resize_emb[:, 0, instru_mapping['Piano']['pitch_min']:instru_mapping['Piano']['pitch_max']] = pr_piano_gen\n piano_resize_emb_TT = torch.tensor(piano_resize_emb)\n piano_embedded_TT = embedding_model(piano_resize_emb_TT.float(), 0)\n pr_piano_gen_embedded = piano_embedded_TT.numpy()\n else:\n pr_piano_gen_embedded = pr_piano_gen\n time_embedding = time.time() - time_embedding\n #######################################\n\n ########################\n # Inputs' normalization\n normalizer = pkl.load(open(os.path.join(config_folder, 'normalizer.pkl'), 'rb'))\n if parameters[\"embedded_piano\"]: # When using embedding, no normalization\n pr_piano_gen_norm = pr_piano_gen_embedded\n else:\n pr_piano_gen_norm = normalizer.transform(pr_piano_gen_embedded)\n ########################\n \n ########################\n # Store folder\n string = re.split(r'/', name_piano)[-1]\n name_track = re.sub('piano_solo.mid', '', string)\n generated_folder = config_folder + '/generation_reference_example/' + name_track\n if not os.path.isdir(generated_folder):\n os.makedirs(generated_folder)\n ########################\n\n ########################\n # Get trainer\n with open(os.path.join(config_folder, 'which_trainer'), 'r') as ff:\n which_trainer = ff.read()\n # Trainer\n if which_trainer == 'standard_trainer':\n from LOP.Scripts.standard_learning.standard_trainer import Standard_trainer as Trainer\n kwargs_trainer = {'temporal_order': model_parameters[\"temporal_order\"]}\n elif which_trainer == 'NADE_trainer':\n from LOP.Scripts.NADE_learning.NADE_trainer import NADE_trainer as Trainer\n kwargs_trainer = {'temporal_order': model_parameters[\"temporal_order\"], 'num_ordering': model_parameters[\"num_ordering\"]}\n else:\n raise Exception(\"Undefined trainer\")\n trainer = Trainer(**kwargs_trainer)\n ########################\n\n ############################################################\n # Generate\n ############################################################\n time_generate_0 = time.time()\n generated_sequences = {}\n for measure_name in parameters['save_measures']:\n model_path = 'model_' + measure_name\n generated_sequences[measure_name] = generate(trainer, pr_piano_gen_norm, silence_piano, duration_piano, config_folder, model_path, pr_orchestra_gen, batch_size=number_of_version)\n time_generate_1 = time.time()\n logger_generate.info('TTT : Generating data took {} seconds'.format(time_generate_1-time_generate_0))\n\n ############################################################\n # Reconstruct and write\n ############################################################\n def reconstruct_write_aux(generated_sequences, prefix):\n for write_counter in range(generated_sequences.shape[0]):\n # To distinguish when seed stop, insert a sustained note\n this_seq = generated_sequences[write_counter] * 127\n this_seq[:seed_size, 0] = 20\n # Reconstruct\n if rhythmic_reconstruction:\n pr_orchestra_clean = from_event_to_frame(this_seq, event_piano)\n else:\n pr_orchestra_clean = this_seq\n pr_orchestra = instrument_reconstruction(pr_orchestra_clean, instru_mapping)\n # Write\n write_path = generated_folder + '/' + prefix + '_' + str(write_counter) + '_generated.mid'\n if rhythmic_reconstruction:\n write_midi(pr_orchestra, quantization, write_path, tempo=80)\n else:\n write_midi(pr_orchestra, 1, write_path, tempo=80)\n return\n\n for measure_name in parameters[\"save_measures\"]:\n reconstruct_write_aux(generated_sequences[measure_name], measure_name)\n \n ############################################################\n ############################################################\n # Write original orchestration and piano scores, but reconstructed version, just to check\n if rhythmic_reconstruction:\n A = from_event_to_frame(pr_piano_gen, event_piano)\n else:\n A = pr_piano_gen\n B = A * 127\n piano_reconstructed = instrument_reconstruction_piano(B, instru_mapping)\n write_path = generated_folder + '/piano_reconstructed.mid'\n if rhythmic_reconstruction:\n write_midi(piano_reconstructed, quantization, write_path, tempo=80)\n else:\n write_midi(piano_reconstructed, 1, write_path, tempo=80)\n #\n if rhythmic_reconstruction:\n A = from_event_to_frame(pr_orchestra_truth, event_piano)\n else:\n A = pr_orchestra_truth\n B = A * 127\n orchestra_reconstructed = instrument_reconstruction(B, instru_mapping)\n write_path = generated_folder + '/orchestra_reconstructed.mid'\n if rhythmic_reconstruction:\n write_midi(orchestra_reconstructed, quantization, write_path, tempo=80)\n else:\n write_midi(orchestra_reconstructed, 1, write_path, tempo=80)\n ############################################################\n ############################################################","def random_sample(prob):\n def _random_sample_xducer(step):\n def _random_sample_step(r=Missing, x=Missing):\n if r is Missing: return step()\n if x is Missing:\n return step(r)\n return step(r, x) if random() < prob else r\n return _random_sample_step\n return _random_sample_xducer"],"string":"[\n \"def randMelody(self, value, chord):\\n self.randRhythm()\\n self.randNotes(value, chord)\",\n \"def get_rdm_note(chord, last_note):\\r\\n \\r\\n if random.randint(1,10) <= 6:\\r\\n # chord note\\r\\n return random.choice(CHORDS[chord]) \\r\\n else:\\r\\n # scale note \\r\\n n = C_SCALE.index(last_note)\\r\\n return C_SCALE[n+random.choice(range(-n, 6-n)[2:5])]\",\n \"def create_melody(inst, chord_progression):\\r\\n \\r\\n for chord in chord_progression:\\r\\n \\r\\n meas = inst.NewMeasure()\\r\\n last_note = random.choice(CHORDS[chord])\\r\\n pos = 0\\r\\n \\r\\n while pos < 64:\\r\\n dur = random.choice([8, 8, 16, 16]) \\r\\n last_note = get_rdm_note(chord, last_note)\\r\\n if random.randint(1,10) <= 8: # 20% silence\\r\\n meas.NewNote(pos, dur, last_note, 5)\\r\\n pos += dur\",\n \"def make_music_rand():\\n pass\",\n \"def generate_horror_title():\\n d666 = random.randint(1, 666)\\n if d666 <= 111:\\n #the adj noun\\n return \\\"The \\\" + horror_adj[random.randint(0, len(horror_adj) - 1)] + \\\" \\\" + horror_noun[random.randint(0, len(horror_noun) - 1)]\\n elif d666 > 111 and d666 <= 222: \\n #noun of noun\\n return horror_noun[random.randint(0, len(horror_noun) - 1)] + \\\" of \\\" + horror_noun[random.randint(0, len(horror_noun) - 1)]\\n elif d666 > 222 and d666 < 444: \\n #the adj noun of verb \\n return \\\"The \\\" + horror_adj[random.randint(0, len(horror_adj) - 1)] + \\\" \\\" + horror_noun[random.randint(0, len(horror_noun) - 1)] + \\\" of \\\" + horror_verb[random.randint(0, len(horror_verb) - 1)]\\n elif d666 >= 444 and d666 < 555: \\n #noun of noun\\n return horror_noun[random.randint(0, len(horror_noun) - 1)] + \\\" of \\\" + horror_noun[random.randint(0, len(horror_noun) - 1)]\\n elif d666 >= 555:\\n #verb of the adj noun\\n return horror_verb[random.randint(0, len(horror_verb) - 1)] + \\\" of the \\\" + horror_adj[random.randint(0, len(horror_adj) - 1)] + \\\" \\\" + horror_noun[random.randint(0, len(horror_noun) - 1)]\",\n \"def get_random_chord_type(self) -> str:\\n i = random.randint(0, len(self._chord_types) - 1)\\n return self._chord_types[i]\",\n \"def generateHarmonyPitchMU(self, harmonicRhythm, harmonicComplexity,\\n minMajRatio, structureLevelMU):\\n # decide whether to have cadence\\n cadenceProbDict = self.chordProfile.getCadenceProb()\\n cadenceProb = cadenceProbDict[structureLevelMU]\\n random = RandomManager.getActive()\\n r = random.random()\\n\\n cadenceChordProgression = []\\n if r <= cadenceProb:\\n cadenceChordProgression = self._createCadence(harmonicRhythm)\\n if len(cadenceChordProgression) == len(harmonicRhythm):\\n return cadenceChordProgression\\n\\n # remove as many durations from harmonicRhythm as the\\n # number of chords used for the cadence\\n harmonicRhythm = harmonicRhythm[:-len(cadenceChordProgression)]\\n\\n chordProgression = []\\n previousTriadCode = None\\n chordIndex = None\\n\\n # step through all durations forming the harmonic rhythm to assign\\n # chord\\n for durationObj in harmonicRhythm:\\n duration = durationObj.getDuration()\\n scale = self.chordProfile.getScale().getName()\\n metricalAccentLevel = durationObj.getMetricalAccent()\\n\\n # calculate scores\\n scores = self._calcMetrics(previousTriadCode, chordIndex, metricalAccentLevel,\\n harmonicComplexity, minMajRatio)\\n\\n # choose triad\\n chord, chordIndex = self._decideTriad(scores, durationObj)\\n code = chord.getCode()\\n\\n # get probability of adding dissonant thirds\\n dissonanceProb = self._calcDissonanceProb(harmonicComplexity,\\n metricalAccentLevel)\\n r = random.random()\\n\\n # decide whether to apply dissonance\\n if r <= dissonanceProb:\\n\\n # add dissonance(s)\\n code = self._decideDissonance(chord)\\n\\n # create new chord\\n newChord = Chord(code, duration=duration, scale=scale,\\n octave=4)\\n\\n # append chord to progression\\n chordProgression.append(newChord)\\n\\n # update previous code and triad code\\n previousCode = newChord.getCode()\\n previousTriadCode = previousCode[:3]\\n\\n # add up chord progression and chords for cadence\\n chordProgression += cadenceChordProgression\\n\\n s = self._realizeM21Sequence(chordProgression)\\n s.show(\\\"midi\\\")\\n\\n return chordProgression\",\n \"def _prev_rhythm_tconst(pattern, rhythm):\\n BASIC_TCONST(pattern, rhythm)\\n tnet = pattern.last_tnet\\n tnet.set_equal(pattern.hypothesis.start, rhythm.end)\",\n \"async def chord(ctx, root:str,length=3,):\\n if length < 2:\\n length = 2\\n elif length > 5:\\n length = 5\\n chord = Tempo.generateChordProgression(root)\\n await ctx.send(chord)\",\n \"def rhythm_data_generator(patient_generator, frame_size=2048, samples_per_patient=1):\\n for _, (signal, labels) in patient_generator:\\n num_segments, segment_size = signal.shape\\n patient_rhythm_labels = labels['rtype'] # note: variables in a .npz file are only loaded when accessed\\n for _ in range(samples_per_patient):\\n # randomly choose a frame that lies within the segment i.e. no zero-padding is necessary\\n segment_index = np.random.randint(num_segments)\\n frame_start = np.random.randint(segment_size - frame_size)\\n frame_end = frame_start + frame_size\\n x = signal[segment_index, frame_start:frame_end]\\n x = np.expand_dims(x, axis=1) # add channel dimension\\n # calculate the durations of each rhythm in the frame and determine the final label\\n rhythm_ends, rhythm_labels = patient_rhythm_labels[segment_index]\\n frame_rhythm_durations, frame_rhythm_labels = get_rhythm_durations(\\n rhythm_ends, rhythm_labels, frame_start, frame_end)\\n y = get_rhythm_label(frame_rhythm_durations, frame_rhythm_labels)\\n yield x, y\",\n \"def tempo_r(mid, beats, rs):\\n bt = mid.ticks_per_beat\\n trk = MidiTrack()\\n trk.name = \\\"Tempo variation\\\"\\n trk.append(MetaMessage(\\\"set_tempo\\\",\\n tempo=beats[0],\\n time=0))\\n\\n for i, beat in enumerate(beats):\\n r = rs[i]\\n if r == 0: # For the deterministic case\\n tempo_r = beat\\n else:\\n tempo_r = rd.randint(beat-int(beat*r), beat + int(beat*r)) + 1\\n trk.append(MetaMessage(\\\"set_tempo\\\",\\n time=bt,\\n tempo=tempo_r))\\n\\n mid.tracks.append(trk)\\n return mid\",\n \"def _prev_rhythm_gconst(_, rhythm):\\n # A bigeminy cannot be preceded by another bigeminy or an extrasystole.\\n verify(not isinstance(rhythm, (o.Bigeminy, o.Extrasystole)))\",\n \"def _rhythm_obs_proc(pattern):\\n # We asign the endpoint of the hypothesis.\\n pattern.hypothesis.end.value = pattern.evidence[o.QRS][-1].time.value\",\n \"def generate_round():\\n prog_len = random.randint(_MIN_LEN, _MAX_LEN)\\n diff = random.randint(_MIN_DIFF, _MAX_DIFF)\\n start = random.randint(0, _MAX_START)\\n prog = _generate_progression(prog_len, diff, start)\\n missing_position = random.randint(0, len(prog) - 1)\\n missing_element = prog[missing_position]\\n prog[missing_position] = '..'\\n prog = list(map(str, prog))\\n string_question = ' '.join(prog)\\n return string_question, missing_element\",\n \"def mgChordAugmented(value):\\n chord = [MgNote(value), MgNote(value) + 4, MgNote(value) + 8]\\n return chord\",\n \"def random_note_in_chord_and_vocal_range(relPitchList, key, vocal_range, prev_note):\\n\\n\\t# for rests, can only be a rest\\n\\tif relPitchList == -1:\\n\\t\\treturn [note.Rest()]\\n\\n\\t# extract_range\\n\\tlowest_note, highest_note = vocal_range\\n\\n\\tif (not isinstance(prev_note, note.Rest)) and (prev_note is not None):\\n\\n\\t\\t# restrict intervals to a sixth\\n\\t\\tif prev_note.pitch.transpose(-7) > lowest_note:\\n\\t\\t\\tlowest_note = prev_note.pitch.transpose(-7)\\n\\n\\t\\tif prev_note.pitch.transpose(7) < highest_note:\\n\\t\\t\\thighest_note = prev_note.pitch.transpose(7)\\n\\n\\t# turn relative Pitch into a note.Note\\n\\tnotes = []\\n\\n\\n\\tfor relPitch in relPitchList:\\n\\t\\tif relPitch != 0:\\n\\t\\t\\tinterval_ = interval.ChromaticInterval(relPitch)\\n\\t\\t\\tnotes.append(note.Note(interval_.transposePitch(key.getTonic()), quarterLength=1))\\n\\t\\telse:\\n\\t\\t\\t# needed as no transposition by 0 possible\\n\\t\\t\\tnotes.append(note.Note(key.getTonic(), quarterLength=1))\\n\\t \\n\\t# get all notes in right octave\\n\\toctave_corrected_notes = []\\n\\tfor n in notes:\\n\\n\\t\\t# possible octaves\\n\\t\\tlow_octave = lowest_note.octave\\n\\t\\thigh_octave = highest_note.octave\\n \\n\\t\\t# select random octave\\n\\t\\t# if only one choice available\\n\\t\\toctave_choice = np.arange(low_octave, high_octave + 1)\\n\\n\\t\\t# loop throughpossible octaves\\n\\t\\tfor o in octave_choice:\\n\\t\\t\\tnote_ = copy.deepcopy(n)\\n\\t\\t\\tnote_.octave = o\\n\\t\\t\\tif (note_.pitch >= lowest_note) and (note_.pitch <= highest_note):\\n\\t\\t\\t\\toctave_corrected_notes.append(note_)\\n\\n\\treturn octave_corrected_notes\",\n \"def get_random_chord(self) -> []:\\n return self.get_random_chords(1)[0]\",\n \"def get_random_phrase():\\n return random.choices(PHRASES, WEIGHTS, k=1)[0]\",\n \"def gen_melody(scale, timings, note_deviance):\\n melody = [random.sample(scale, 1)[0] for i in range(len(timings))]\\n\\n px = np.random.rand(len(timings))\\n idx = np.argwhere(px <= note_deviance)\\n direction = [-1, 1]\\n for i in idx:\\n melody[i[0]] = melody[i[0]] + random.sample(direction, 1)[0]\\n return melody\",\n \"def generate_line_durations(\\n n_measures: int,\\n duration_weights: Dict[float, float],\\n valid_rhythmic_patterns: List[List[float]],\\n end_with_whole_note: bool = True\\n) -> List[float]:\\n current_time = 0\\n line_durations = []\\n current_measure_durations = []\\n total_time = n_measures - int(end_with_whole_note)\\n\\n while current_time < total_time:\\n appropriate_durations = select_appropriate_durations(\\n current_time, total_time, current_measure_durations,\\n valid_rhythmic_patterns\\n )\\n duration = random.choices(\\n appropriate_durations,\\n [duration_weights[x] for x in appropriate_durations]\\n )[0]\\n current_time += duration\\n current_measure_durations = update_current_measure_durations(\\n current_measure_durations, duration\\n )\\n line_durations.append(duration)\\n\\n if end_with_whole_note:\\n line_durations.append(1.0)\\n return line_durations\",\n \"def mgChordDiminished(value):\\n chord = [MgNote(value), MgNote(value) + 3, MgNote(value) + 6]\\n return chord\",\n \"def generate_fantasy_title():\\n d20 = random.randint(1, 20)\\n if d20 <= 4:\\n #genetive noun\\n return fantasy_genetive[random.randint(0, len(fantasy_genetive) - 1)] + \\\" \\\" + fantasy_noun[random.randint(0, len(fantasy_noun) - 1)]\\n elif d20 > 4 and d20 < 13: \\n #The adj noun\\n return \\\"The \\\" + fantasy_adj[random.randint(0, len(fantasy_adj) - 1)] + \\\" \\\" + fantasy_noun[random.randint(0, len(fantasy_noun) - 1)]\\n elif d20 >= 13:\\n #something of something\\n return fantasy_noun[random.randint(0, len(fantasy_noun) - 1)] + \\\" of \\\" + fantasy_what_is_this[random.randint(0, len(fantasy_what_is_this) - 1)]\",\n \"def sample(self):\\n seq = []\\n for i in range(self._pwm.shape[1]):\\n p = numpy.array(self._pwm[:, i], dtype=numpy.float64)\\n p /= p.sum()\\n seq.extend(numpy.random.choice(self.alphabet, p=p))\\n return \\\"\\\".join(seq)\",\n \"def generate_sentence(self, t=20):\\n result = [\\\"START\\\", \\\"START\\\"]\\n\\n for i in range(t-3):\\n if result[-1] == \\\"STOP\\\":\\n break\\n\\n match = {}\\n for k,v in self.trigramcounts.items():\\n if k[0] == result[-2] and k[1] == result[-1]:\\n match[k[-1]] = v\\n r = np.random.choice(list(match.keys()), p=np.array(list(match.values())) / np.sum(np.array(list(match.values()))))\\n result.append(r)\\n\\n return result\",\n \"def mgChord(value, chord):\\n ret = None\\n if chord == 'M':\\n ret = mgChordMajor(value)\\n elif chord == 'm':\\n ret = mgChordMinor(value)\\n elif chord == 'dim':\\n ret = mgChordDiminished(value)\\n elif chord == 'aug':\\n ret = mgChordAugmented(value)\\n\\n return ret\",\n \"def randomHelmet():\\n return random.choice(HELMETS)\",\n \"def rand_gen(below, baseline):\\n\\treturn secrets.randbelow(below)/ baseline\",\n \"def getRandomRarity():\\n r = random.randint(1,100)\\n if r <= Rarities.IMPOSIBIL:\\n return \\\"IMPOSIBIL\\\"\\n elif r <= Rarities.LEGENDAR:\\n return \\\"LEGENDAR\\\"\\n elif r <= Rarities.EPIC:\\n return \\\"EPIC\\\"\\n else:\\n return \\\"COMUN\\\"\",\n \"def _createCadence(self, harmonicRhythm):\\n random = RandomManager.getActive()\\n\\n # choose cadence to apply\\n cadences = self.chordProfile.getCadences()\\n\\n #TODO: Choose cadence in more intelligent way\\n cadence = random.choice(cadences)\\n\\n scale = self.chordProfile.getScale().getName()\\n cadenceChordProgression = []\\n\\n reversedHarmonicRhythm = reversed(harmonicRhythm[:])\\n # create as many cadence\\n for count, durationObj in enumerate(reversedHarmonicRhythm):\\n duration = durationObj.getDuration()\\n code = cadence[-count+1]\\n chord = Chord(code, duration=duration, scale=scale, octave=4)\\n\\n if count >= len(cadence):\\n return cadenceChordProgression\\n\\n # prepend chord\\n cadenceChordProgression.insert(0, chord)\\n return cadenceChordProgression\",\n \"def random():\\n length = 10**np.random.uniform(2, 6)\\n radius = 10**np.random.uniform(1, 3)\\n axis_ratio = 10**np.random.uniform(-1, 1)\\n kuhn_length = 10**np.random.uniform(-2, -0.7)*length # at least 10 segments\\n pars = dict(\\n length=length,\\n radius=radius,\\n axis_ratio=axis_ratio,\\n kuhn_length=kuhn_length,\\n )\\n return pars\",\n \"def choose_guard(self):\\n\\n\\t\\tg = randint(1, 3)\\n\\n\\t\\tif g == 1:\\n\\t\\t\\treturn 'h'\\n\\t\\tif g == 2:\\n\\t\\t\\treturn 't'\\n\\t\\tif g == 3:\\n\\t\\t\\treturn 'l'\",\n \"def mon_maker():\\r\\n random_mon = {'eyeratio':0.2, 'eyeL':30,\\r\\n 'mouthratio':0.8, 'mouthL':30,\\r\\n 'headL':40, 'headA':15,\\r\\n 'cheekL':25, 'cheekA':45,\\r\\n 'chinL': 30, 'chinA':90\\r\\n }\\r\\n return random_mon\",\n \"def get_next_chord(chord):\\r\\n \\r\\n num = C_SCALE.index(chord)\\r\\n candidates = F_PROG[num-1]\\r\\n return C_SCALE[random.choice(candidates)]\",\n \"def last_word_dict(self, rhyme_dict):\\n scheme = {1: 'A', 2: 'B', 3: 'A', 4: 'B', 5: 'C', 6: 'D', 7: 'C', 8: 'D', 9: 'E', 10: 'F', 11: 'E', 12: 'F', 13: 'G', 14: 'G'}\\n last_word_dict={}\\n\\n\\n \\\"\\\"\\\"for i in range(1,15):\\n temp = []\\n if i in [1,2,5,6,9,10,13]: #lines with a new rhyme\\n for k in rhyme_dict[scheme[i]].keys():\\n temp.append(k)\\n if i in [3,4,7,8,11,12,14]: #lines with an old line\\n for k in rhyme_dict[scheme[i]].keys():\\n temp += rhyme_dict[scheme[i]][k]\\n #last_word_dict[i]=[*{*temp}]\\n last_word_dict[i] = temp\\\"\\\"\\\"\\n first_rhymes = []\\n for i in range(1,15):\\n if i in [1, 2, 5, 6, 9, 10, 13]: # lines with a new rhyme -> pick a random key\\n last_word_dict[i] = [random.choice(list(rhyme_dict[scheme[i]].keys()))] #NB ensure it doesnt pick the same as another one\\n while not self.suitable_last_word(last_word_dict[i][0]) or last_word_dict[i][0] in first_rhymes or any(rhyme_dict['A'][last_word_dict[i][0]] in rhyme_dict['A'][word] for word in first_rhymes):\\n last_word_dict[i] = [random.choice(list(rhyme_dict[scheme[i]].keys()))]\\n first_rhymes.append(last_word_dict[i][0])\\n if i in [3, 4, 7, 8, 11, 12, 14]: # lines with an old rhyme -> pick a random value corresponding to key of rhyming couplet\\n letter = scheme[i]\\n pair = last_word_dict[i-2][0]\\n if i == 14:\\n pair = last_word_dict[13][0]\\n last_word_dict[i] = [word for word in rhyme_dict[letter][pair] if self.suitable_last_word(word)]\\n return last_word_dict\",\n \"def totem_random():\\n random_head()\\n random_head()\\n random_head()\",\n \"def diceroll(self, mess, args):\\n return random.randint(1, 6)\",\n \"def get_random_phrase(self):\\n return random.choice(self.phrases)\",\n \"def getStatusMessage():\\n\\n now = datetime.datetime.now()\\n hour = now.hour\\n mood = Sentience.getPrimaryMood()\\n exp_mood = Sentience.getExposedPositivity()\\n\\n random.seed((time.time()//86400*86400))\\n\\n #sleeping\\n if not (9 <= hour < 21) and (mood <= 0.5 or not 7 <= hour < 23):\\n if exp_mood < -0.1:\\n return random.choice([\\n \\\"bleh\\\",\\n \\\"not sleeping well\\\",\\n \\\"why's chat so noisy\\\",\\n \\\"can't sleep\\\",\\n \\\"do not disturb pls thx\\\",\\n ])\\n\\n if mood < 0:\\n return random.choice([\\n \\\"crying myself to sleep rn\\\",\\n \\\":(\\\",\\n \\\"had a nightmare\\\",\\n \\\"can't sleep\\\",\\n \\\"._.\\\"\\n ])\\n\\n return random.choice([\\n \\\"zzz...\\\",\\n \\\"sweet dreams\\\",\\n \\\"good night\\\",\\n \\\"sleeping...\\\",\\n \\\"having some rest\\\"\\n ])\\n\\n if Sentience.isExposedPositivityOverloaded():\\n return random.choice([\\n \\\"i'm done\\\",\\n \\\"too much\\\"\\n \\\"goodbye\\\",\\n \\\"tired\\\",\\n \\\"need rest\\\",\\n ])\\n\\n #happy\\n if mood >= 0.7:\\n return random.choice([\\n \\\":D\\\",\\n \\\"great day\\\",\\n \\\"happy happy\\\",\\n \\\"hehe\\\",\\n \\\"good times\\\",\\n \\\"yay\\\",\\n \\\"what's up\\\",\\n \\\"happiness\\\",\\n \\\"nice day\\\",\\n ])\\n #moody-ish\\n if mood >= 0.4:\\n return random.choice([\\n \\\"hmm\\\",\\n \\\"yeet\\\",\\n \\\"bleh\\\",\\n \\\"oh\\\",\\n \\\"moody rn\\\",\\n \\\"nothing\\\"\\n ])\\n #more moody\\n if mood >= -0.3:\\n return random.choice([\\n \\\"moody rn\\\",\\n \\\"not happy\\\",\\n \\\"i'm fine.\\\",\\n \\\"bleh\\\",\\n \\\"._.\\\",\\n \\\":(\\\",\\n ])\\n #very unhappy\\n return random.choice([\\n \\\"sad\\\",\\n \\\"cries\\\",\\n \\\"roar\\\",\\n \\\":_(\\\",\\n \\\">:(\\\",\\n \\\"mad\\\",\\n \\\"angry\\\",\\n \\\"I'M FINE.\\\",\\n \\\"bleh\\\",\\n \\\"no\\\",\\n ])\",\n \"def generate_romance_title():\\n d69 = random.randint(1, 69)\\n if d69 <= 35:\\n #the adj noun\\n return \\\"The \\\" + romance_adj[random.randint(0, len(romance_adj) - 1)] + \\\" \\\" + romance_noun[random.randint(0, len(romance_noun) - 1)]\\n elif d69 > 35:\\n #noun of the adj noun\\n return romance_noun[random.randint(0, len(romance_noun) - 1)] + \\\" of the \\\" + romance_adj[random.randint(0, len(romance_adj) - 1)] + \\\" \\\" + romance_noun[random.randint(0, len(romance_noun) - 1)]\",\n \"def tone(n, base_freq=440.0):\\n # -2 -1 0 1 2 3 4 5 6 7 8 9 10 11 12\\n # G G# A A# B C C# D D# E F F# G G# A\\n # G Ab A Bb B C Db D Eb E F Gb G Ab A\\n return base_freq * 2 ** (n/12)\",\n \"def random_iteration(r, w):\\n density = 1.225\\n blade_number = 6\\n wind_v = round(-168.75*r**2-36.75*r+20.05 , 2)\\n blade_v = r*w\\n rel_v = round(math.sqrt(blade_v**2 + wind_v**2),2)\\n arctan = round(math.degrees(math.atan2(wind_v, blade_v)), 2)\\n # cl is a random float in range 1.0 to 1.7\\n cl = uniform(1.0, 1.7)\\n # aoa is a random float in range 10 to 15\\n aoa = uniform(10, 15)\\n treeHit = 0\\n Re = get_re(r, cl, aoa, arctan, rel_v, blade_number)[0]\\n new_cl, new_a, new_cd = get_max_cl(Re, r)\\n new_Re, new_chord = get_re(r, round(new_cl, 2), round(new_a, 2), arctan, rel_v, blade_number)\\n re_devi = abs((new_Re - Re) / Re)\\n # iterate until Re_deviation goes under 5%\\n while re_devi > 0.05:\\n Re = new_Re\\n new_cl, new_a, new_cd = get_max_cl(new_Re, r)\\n new_Re, new_chord = get_re(r, new_cl, new_a, arctan, rel_v, blade_number)\\n re_devi = abs((new_Re - Re) / Re)\\n treeHit += 1\\n # stop iteration over 10 times\\n if treeHit > 10:\\n break\\n force_reference = 0.5 * density * rel_v**2 * 0.0125 * round(new_chord, 3)\\n return {\\n \\\"r\\\": r,\\n \\\"arctan\\\": arctan, \\n \\\"chord\\\": round(new_chord, 3), \\n \\\"aoa\\\": new_a, \\n \\\"cl\\\": new_cl, \\n \\\"cd\\\": new_cd, \\n \\\"Re\\\": new_Re,\\n \\\"lift\\\": new_cl * force_reference, \\n \\\"drag\\\": new_cd * force_reference,\\n \\\"torque\\\": r * (new_cl * force_reference * math.sin(math.radians(arctan - new_a)) - new_cd * force_reference * math.cos(math.radians(arctan - new_a)))\\n }\",\n \"def randomize_trajectory(self):\\n self.angle = randint(-360, 360)\\n self.speed = randint(1, 5)/2.5\",\n \"def generate_synth_data(n):\",\n \"def generate_random_line(\\n line_durations: List[float],\\n pitches: List[ScaleElement]\\n) -> List[PieceElement]:\\n melodic_line = []\\n current_time = 0\\n for duration in line_durations:\\n scale_element = random.choice(pitches)\\n piece_element = PieceElement(\\n note=scale_element.note,\\n position_in_semitones=scale_element.position_in_semitones,\\n position_in_degrees=scale_element.position_in_degrees,\\n degree=scale_element.degree,\\n start_time=current_time,\\n duration=duration\\n )\\n melodic_line.append(piece_element)\\n current_time += duration\\n return melodic_line\",\n \"def next_chord(prev, scale):\\n # Choose which pitch should be replaced\\n replace = random.sample(range(len(prev)), 1)[0]\\n\\n next = []\\n for i, pitch in enumerate(prev):\\n prev_pitchspaces = [p.ps for p in prev]\\n if i == replace:\\n # Possible replacement pitches are within a minor third of the\\n # pitch being replaced, not in the previous chord, and not already\\n # in the next chord.\\n options = scale\\n\\n # Not more than a major third lower\\n options = [p for p in options if p.ps > pitch.ps - 4]\\n\\n # Not more than a major third higher\\n options = [p for p in options if p.ps < pitch.ps + 4]\\n\\n # Not in the previous chord\\n options = [p for p in options if p.ps not in prev_pitchspaces]\\n\\n # Not in this chord\\n next_pitchspaces = [p.ps for p in next]\\n options = [p for p in options if p.ps not in next_pitchspaces]\\n\\n # Choose the replacement pitch from the options\\n choice = random.choice(options)\\n next.append(Pitch(choice.ps))\\n else:\\n next.append(Pitch(pitch.ps))\\n return next\",\n \"def mimic_lecture(mimic_dict, starting_word=None, limit=10):\\r\\n\\r\\n if not starting_word:\\r\\n word = ''\\r\\n else:\\r\\n word = starting_word\\r\\n\\r\\n line_label = (divmod(x*3, 60) for x in numGen())\\r\\n text = word\\r\\n text = str(next(line_label)) + word\\r\\n line_mark = 0\\r\\n line_words = 1\\r\\n while line_mark < limit:\\r\\n if word not in mimic_dict:\\r\\n word = \\\"\\\"\\r\\n new_word = random.choice(mimic_dict[word])\\r\\n\\r\\n if line_words == 0:\\r\\n rand_silence = random.randint(1,10)\\r\\n if rand_silence <= 2:\\r\\n text = text + \\\"\\\\n\\\" + str(next(line_label))\\r\\n line_words = 0\\r\\n line_mark += 1\\r\\n word = new_word\\r\\n continue\\r\\n\\r\\n text = text + \\\" \\\" + new_word\\r\\n rand = random.randint(3,5)\\r\\n if line_words >= rand:\\r\\n text = text + \\\"\\\\n\\\" + str(next(line_label))\\r\\n line_words = 0\\r\\n line_mark += 1\\r\\n else:\\r\\n line_words += 1\\r\\n word = new_word\\r\\n return text\",\n \"def getChord(self, c, velocity, isdrum, isgrace):\\n\\n c = re.split(\\\"[, ]+\\\", c)\\n\\n if not c:\\n error(\\\"You must specify the first note in a solo line\\\")\\n\\n \\\"\\\"\\\" Convert the note part into a series of midi values\\n Notes can be a single note, or a series of notes. And\\n each note can be a letter a-g (or r), a '#,&,n' plus\\n a series of '+'s or '-'s. Drum solos must have each\\n note separated by ' ' or ','s: \\\"Snare1,KickDrum1,44\\\".\\n\\n Each chunk could be:\\n - a midi value (44)\\n - a drum note ( KickDrum1)\\n - a single note (g#) (g&-)\\n - Or groups with spaces/commas (f 100) (44 , KickDrum) (a,b c)\\n \\\"\\\"\\\"\\n\\n events = [] # array for each note event\\n\\n for cc in c:\\n if not cc or not cc[0]:\\n continue\\n if '/' in cc:\\n if cc.count('/') > 1:\\n error(\\\"%s: Only 1 '/velocity' permitted. You can separate \\\" \\\\\\n \\\"notes in the chord with ',' or ' ' and it'll work.\\\" % \\\\\\n self.name)\\n cc, newvel = cc.split('/')\\n if not newvel:\\n error(\\\"%s: expecting 'volume' after '/'\\\" % self.name)\\n\\n if not cc:\\n error(\\\"%s: Volume '/' must immediately follow note.\\\" % self.name)\\n\\n thisvel = stoi(newvel)\\n\\n if thisvel < 0 or thisvel > 127:\\n error(\\\"%s: Velocity must be 0..127, not '%s'.\\\" % (self.name, newvel))\\n else:\\n thisvel = velocity\\n\\n if cc[0] == 'r':\\n if isgrace:\\n warning(\\\"%s: Grace note is a rest (ignored).\\\" % self.name)\\n \\n if events or len(cc) > 1:\\n error(\\\"%s: Rests and notes cannot be combined.\\\" % self.name)\\n else:\\n events.append( NoteEvent(None, 0, isgrace)) # note event with no pitch\\n\\n elif cc[0] in \\\"1234567890\\\":\\n n = stoi(cc, \\\"%s: Note values must be integer or literal.\\\" % \\\\\\n self.name)\\n if n<0 or n>127:\\n error(\\\"%s: Midi notes must be 0..127, not '%s'\\\" % \\\\\\n (self.name, n))\\n\\n # if using value we fake-adjust octave,\\n # it (and transpose) is set later.\\n\\n if not isdrum:\\n n -= self.octave[self.seq]\\n \\n events.append(NoteEvent(n, thisvel, isgrace))\\n\\n\\n elif isdrum: # drum must be a value, * or drum-name\\n if cc == '*':\\n events.append( NoteEvent(self.drumTone, thisvel, isgrace ))\\n else:\\n events.append( NoteEvent(int(MMA.translate.dtable.get(cc)), thisvel, isgrace) )\\n\\n else: # must be a note(s) in std. notation\\n cc = list(cc)\\n while cc:\\n name = cc.pop(0)\\n\\n if not name in self.midiNotes:\\n error(\\\"%s: Encountered illegal note name '%s'\\\"\\n % (self.name, name))\\n\\n n = self.midiNotes[ name ] # name is string, n is value\\n \\n # Parse out a \\\"#', '&' or 'n' accidental.\\n\\n if cc and cc[0] in accValues:\\n i = cc.pop(0)\\n self.acc[name] = accValues[i]\\n\\n n += self.acc[name] # accidental adjust (from above or keysig)\\n\\n # Parse out +/- (or series) for octave\\n\\n while cc and cc[0] in '+-':\\n a = cc.pop(0)\\n if a == '+':\\n n += 12\\n else:\\n n -= 12\\n\\n events.append( NoteEvent(n, thisvel, isgrace) )\\n \\n\\n return events\",\n \"def sonify_mu_rhythm(self, mu_rhythm):\\r\\n channel = int(input(\\\"Enter the channel number for which you want to do the mu rhythm sonification: \\\\n 1 -> FC5,\\\\n 2 -> FC1,\\\\n \\\\\\r\\n3 -> C3,\\\\n 4 -> CP5,\\\\n 5 -> CP1,\\\\n 6 -> FC2,\\\\n 7 -> FC6,\\\\n 8 -> C4,\\\\n 9 -> CP2,\\\\n 10 -> CP6\\\\nInsert number:\\\"))\\r\\n fs = 659.25\\t # E5 note\\r\\n data = np.sin(2. * np.pi * fs * self.time)\\r\\n coeff = np.zeros(self.n_samples)\\r\\n samp_freq_conversion = int(np.ceil(self.audio_sample_rate / self.eeg_sampling_frequency))\\r\\n i = 0\\r\\n for samp in mu_rhythm[channel-1]:\\r\\n for j in range(i*samp_freq_conversion, (i+1)*samp_freq_conversion): \\r\\n if j < self.n_samples:\\r\\n coeff[j] = 0.001 + samp*1e10\\r\\n print('Mu rhythm sonification: ', i, ' out of ', len(mu_rhythm[channel-1]))\\r\\n i += 1\\r\\n print('Mu rhythm sonification: ', i, ' out of ', len(mu_rhythm[channel-1]))\\r\\n data = data * coeff\\r\\n return data\",\n \"def repeat_rythm(self, rythm, dev):\\n return rythm\",\n \"def mgChordMajor(value):\\n chord = [MgNote(value), MgNote(value) + 4, MgNote(value) + 7]\\n return chord\",\n \"def pure_tone(freq: float, sr: float = 128, dur: float = 4, device=None):\\n time = torch.arange(int(sr * dur), device=device).float() / sr\\n return torch.cos(2 * math.pi * freq * time)\",\n \"def triplet():\\n ends = digraphs + list(consonants)\\n spaces = [ends, list(vowels), ends]\\n bit = \\\"\\\".join([random.sample(x, 1)[0] for x in spaces])\\n if random.randint(0, 1) == 1:\\n bit = bit.title()\\n return bit\",\n \"def test_random_note_pitch(common_scales, all_pitches):\\n\\n for key, scales in common_scales.items():\\n note = random_note_pitch(scales)\\n assert note in all_pitches\\n assert isinstance(note, (int, str)) is True\",\n \"def getRandomAngle():\\r\\n\\treturn random.random() * math.pi * 2\",\n \"def write_music(self, length, bpm=75, initial_note=\\\"C\\\",\\n initial_rhythm=\\\"0.25\\\", initial_key=\\\"F\\\"):\\n # Translate the string key into an index for our note generator\\n key_number = self.FREQUENCY_LIST.index(initial_key)\\n print key_number\\n\\n rhythms = self.generate_rhythms(\\n self.learner.ngrams[\\\"rhythms\\\"], initial_rhythm, bpm, length)\\n notes = self.generate_notes(\\n self.learner.ngrams[\\\"notes\\\"], initial_note, len(rhythms),\\n key_number)\\n\\n rhythms = map(lambda x: float(60) * x / bpm, rhythms)\\n notes = map(lambda x: self.FREQUENCY_MAP[x], notes)\\n\\n for i, n in enumerate(notes):\\n self.write_note(self.make_note_sound(notes[i], rhythms[i]))\",\n \"def generate_RME():\\n RME = [\\\"ogre\\\", \\\"goblin\\\", \\\"gnoll\\\", \\\"orc\\\", \\\"personal injury lawyer\\\"]\\n monster = random.choice(RME)\\n return monster\",\n \"def get_random_time_between_tts():\\n return random.randint(TIME_BETWEEN_TTS_LOWER, TIME_BETWEEN_TTS_UPPER)\",\n \"def gen():\\r\\n\\r\\n output = \\\"\\\"\\r\\n frame = 12\\r\\n prior = 0\\r\\n second = 0\\r\\n\\r\\n while frame > 0:\\r\\n frame -= .5\\r\\n roll = randrange(11 - prior)\\r\\n\\r\\n # If strike is rolled:\\r\\n if roll == 10:\\r\\n output += \\\"X\\\"\\r\\n frame -= .5\\r\\n prior = 0\\r\\n second = 0\\r\\n\\r\\n # If spare is rolled:\\r\\n elif second == 1 and roll + prior == 10:\\r\\n output += \\\"/\\\"\\r\\n prior = 0\\r\\n second = 0\\r\\n\\r\\n # If not strike or spare:\\r\\n else:\\r\\n output += str(roll)\\r\\n prior = roll\\r\\n second = 1 - second\\r\\n\\r\\n # Deliminate frames:\\r\\n if second == 0:\\r\\n output += \\\"-\\\"\\r\\n\\r\\n return output[:-1]\",\n \"def goals():\\n rand_nmr = random.random()\\n if rand_nmr < 0.5:\\n return 1\\n elif rand_nmr < 0.8:\\n return 2\\n elif rand_nmr < 0.97:\\n return 3\\n else:\\n return 4\",\n \"def mgChordMinor(value):\\n chord = [MgNote(value), MgNote(value) + 3, MgNote(value)+7]\\n return chord\",\n \"def trial(length, height):\\n screen.refresh()\\n global stimList\\n global oddLength\\n global oddHeight\\n currentLength = int(maxLength / 4)\\n currentHeight = int(maxHeight / 4)\\n for i in range(stimAmt):\\n if i == oddLocation:\\n oddLength = currentLength\\n oddHeight = currentHeight\\n stimList.append(\\n pg.draw.rect(\\n screen.fg,\\n PgTools.rand_color(),\\n (currentLength, currentHeight, length, height,),\\n )\\n )\\n PgTools.rand_pattern(\\n screen.fg,\\n (\\n currentLength,\\n currentHeight,\\n ),\\n (length, height),\\n i=(randint(0, 2), randint(0, 1)),\\n )\\n if randShapes:\\n PgTools.rand_shape(screen.fg, (currentLength, currentHeight),(length, height), oddSeed)\\n else:\\n stimList.append(\\n pg.draw.rect(\\n screen.fg,\\n color,\\n (currentLength, currentHeight, length, height,),\\n )\\n )\\n PgTools.rand_pattern(\\n screen.fg,\\n (\\n currentLength,\\n currentHeight,\\n ),\\n (length, height),\\n patColor,\\n randNums,\\n )\\n if randShapes:\\n PgTools.rand_shape(screen.fg, (currentLength, currentHeight),(length, height), regSeed)\\n currentLength += maxLength / 4\\n currentLength = int(currentLength)\\n if (i + 1) % 3 == 0:\\n currentLength = maxLength / 4\\n currentLength = int(currentLength)\\n currentHeight += maxHeight / 4\\n currentHeight= int(currentHeight)\",\n \"def lf():\\n return random.sample(font_list, 25)\",\n \"def spinWheel():\\n\\tt = random.randrange(8)\\n\\tif t == 7:\\n\\t\\treturn \\\"00\\\"\\n\\treturn str(t)\",\n \"def random():\\r\\n return R.NextDouble()\",\n \"def get_next_chord(self, chord):\\n chord = self.specified_chord_to_chord(chord)\\n assert validate_chord(chord), \\\"not a valid chord!\\\"\\n\\n cond_dist_dict = self.chord_chord_map[chord] # Gets list of candidate chords\\n\\n # Prepare for the random draw\\n next_chords = cond_dist_dict.keys()\\n next_probs = [cond_dist_dict[next_chord] for next_chord in next_chords]\\n cond_dist = stats.rv_discrete(name='cond_dist', values=(next_chords, next_probs))\\n\\n return self.key.chords[cond_dist.rvs(size=1)-1]\",\n \"def random_mutate(dna: str) -> str:\\r\\n result = \\\"\\\"\\r\\n for c in range(DNA_SIZE):\\r\\n if random.randrange(0, 100, 1) == 1:\\r\\n result += random_charachter()\\r\\n else:\\r\\n result += dna[c]\\r\\n return result\",\n \"def r(w,rangestart,rangeend):\\r\\n if w == 'r':\\r\\n print(random.random(rangestart , rangeend))\\r\\n if w == 'ri':\\r\\n print(random.randint(rangestart,rangeend))\",\n \"def sample_quat(self):\\n if self.z_rotation:\\n rot_angle = np.random.uniform(high=2 * np.pi, low=0)\\n return [np.cos(rot_angle / 2), 0, 0, np.sin(rot_angle / 2)]\\n return [1, 0, 0, 0]\",\n \"def ninja_chirp(total_chirp):\\n return \\\"chirp\\\" + (\\\"-chirp\\\" * (total_chirp - 1))\",\n \"def punctuate_rythm(self, rythm): \\n # TO BE IMPLEMENTED\\n return 0.5\",\n \"async def pornx(ctx, n: int = 1):\\r\\n msg = [\\\"{}\\\".format(text) for text in [random.choice(data) for _ in range(0,n)]]\\r\\n await bot.say('\\\\n'.join(msg))\",\n \"def _random_id(n):\\n ''.join(choice(alphanums) for i in range(n))\",\n \"async def tapir(self):\\n tapir_list = self.config.get('tapirs', [])\\n tapir = tapir_list[random.randrange(len(tapir_list))]\\n try:\\n await self.bot.say(tapir)\\n except:\\n await self.bot.whisper(tapir)\",\n \"def get_round():\\n progression = []\\n start = randint(1, 50)\\n step = randint(1, 10)\\n stop = start + step * PROGRESSION_LENGTH\\n\\n for i in range(start, stop, step):\\n progression.append(str(i))\\n\\n remove_index = randint(0, PROGRESSION_LENGTH - 1)\\n true_answer = progression[remove_index]\\n progression[remove_index] = '..'\\n\\n return (' '.join(progression), true_answer)\",\n \"def choose_piece_duration(self):\\n self.piece_duration = random.randint(900, 1100)\",\n \"def make_transition_probs(self):\\n n = len(self.speakers) # TODO why this line ???\\n transitions = np.random.randint(5, size=(n, n)) + 1\\n transitions += transitions.transpose()\\n for i in range(0, math.floor(n / 2)):\\n s1 = np.random.randint(n)\\n s2 = np.random.randint(n)\\n transitions[s1][s2] += 10\\n transitions[s2][s1] += 8\\n return(transitions)\",\n \"def direction_correction(self):\\n self.directions.monster = random.uniform(self.directions.monster * self.get_monster_sensitivity(),\\n self.directions.monster * (1 + (1 - self.get_monster_sensitivity())))\\n self.directions.food = random.uniform(self.directions.food * self.get_food_sensitivity(),\\n self.directions.food * (1 + (1 - self.get_food_sensitivity())))\\n self.directions.water = random.uniform(self.directions.water * self.get_water_sensitivity(),\\n self.directions.water * (1 + (1 - self.get_water_sensitivity())))\",\n \"def buildMelodyNotes(m, bars):\\n\\n # Get the notes part of the scale specified in Melody object\\n s = generate_scale(m.getKeySignature(), m.getScale(), m.getOctave())\\n\\n if bars < MIN_BARS or bars > MAX_BARS:\\n bars = random.randint(MIN_BARS, 4)\\n\\n # Get a random number of notes value between the number of bars (so\\n # that there is roughly one note per bar) and SMALLEST_NOTE //\\n # OTHER_NOTE (so that each note would be roughly OTHER_NOTE long)\\n numNotes = random.randint(4, max(1, bars * (SMALLEST_NOTE //\\n DOTTED_EIGHTH_NOTE)))\\n seq = []\\n # randomize notes in scale by choosing {numNotes} random notes\\n for i in range(numNotes):\\n seq.append(random.choice(s))\\n\\n # randomize length of each notes using getRandomStructure function\\n lengths, barSpace = getEqualStructure(bars, numNotes)\\n\\n # add the random notes to generate the melody\\n for i in range(numNotes):\\n if seq[i] == -1:\\n m.addNote(Note(0, lengths[i], vel=0))\\n else:\\n m.addNote(Note(seq[i], lengths[i]))\\n\\n if barSpace > 0:\\n # Add rest just in case last note does not extend to end of bar\\n m.addNote(Note(0, barSpace, vel=0))\",\n \"def randomize_value(self) -> None:\",\n \"def to_cms_rhythm(channel):\\n ret = []\\n for i, x in enumerate(self.pitches[channel]):\\n if x > 0:\\n ret.append(x)\\n else:\\n ret.append(-1)\\n return ret\",\n \"def create_challenge():\\n\\treturn os.urandom(12)\",\n \"def generate():\\n repl_count = 0\\n\\n phrase = choice(BLACK_CARDS)\\n while '_' in phrase:\\n repl_count += 1\\n phrase = phrase.replace('_', choice(WHITE_CARDS), 1)\\n\\n if repl_count == 0:\\n phrase += \\\" \\\" + choice(WHITE_CARDS)\\n\\n return phrase\",\n \"def copyRhythmParams(self, note):\\n self.setDuration(note.getDurationNoDot())\\n self.setBeatNum(note.getBeatNum())\\n self.setTie(note.getTie())\\n self.setDot(note.getDot())\\n return\",\n \"def generate_pink_noise(duration: float, f_sample: float) -> np.ndarray:\\r\\n # calculate the number of time points and the time difference between them\\r\\n length = duration * f_sample\\r\\n delta_t = 1 / f_sample\\r\\n # get the angular frequency axis in [omega]\\r\\n f_k = sc.fft.fftfreq(length, d=delta_t)/(2 * np.pi)\\r\\n # calculate the frequency coefficients based on the noise color\\r\\n # pink noise has power spectral density of 1/f -> the amplitude has 1/sqrt(f)\\r\\n C_k = np.ndarray(length)\\r\\n C_k[1:] = 1 / np.sqrt(np.abs(f_k[1:]))\\r\\n # no dc\\r\\n C_k[0] = 0\\r\\n # generate the signal from the frequency coefficients\\r\\n return generate_signal_from_spectrum(C_k)\",\n \"async def dice( con, min1=1, max1=6):\\n r = random.randint(min1, max1)\\n await bot.send_message(con.message.channel, \\\"**{}**\\\".format(r))\",\n \"def make_pattern(self):\\n probability = random.SystemRandom().random()\\n if probability < 0.1:\\n _pattern = [0 for x in range(32)]\\n elif probability > 0.5:\\n pattern_num = SECURE_RANDOM.choice(CLASSIC)\\n _probability = random.SystemRandom().random()\\n if _probability > 0.80:\\n _pattern = [1 if random.SystemRandom().random() < pattern_num/32 else 0 for x in range(1,33)]\\n elif _probability < 0.40:\\n _offset = random.SystemRandom().randint(2, 16)\\n _pattern = [1 if (x == _offset) or (x % pattern_num == _offset) else 0 for x in range(1,33)]\\n else:\\n _pattern = [1 if (x == 1) or (x % pattern_num == 1) else 0 for x in range(1,33)]\\n else:\\n pattern_num = SECURE_RANDOM.choice(INNER_CLASSIC)\\n _probability = random.SystemRandom().random()\\n if _probability > 0.50:\\n _pattern = [1 if (x == 1) or (x % pattern_num == 1) else 0 for x in range(1,33)]\\n else:\\n _pattern = [1 if random.SystemRandom().random() < pattern_num/32 else 0 for x in range(1,33)]\\n\\n if not self.global_swing:\\n _probability = random.SystemRandom().random()\\n if _probability > 0.3:\\n _pattern.extend([random.SystemRandom().uniform(0.01, 0.5), random.SystemRandom().randint(1, 14), 0])\\n else:\\n _pattern.extend([0,1,0])\\n else: \\n _pattern.extend([0,1,1]) \\n\\n return _pattern\",\n \"def randomize_herbs(self):\\n random.shuffle(self.herbivores)\",\n \"def sample_sonnet_syl_and_rhyme(hmm, obs_map, rhyme_dict, n_syl = 10):\\n sonnetLines = []\\n r_sonnetLines = []\\n sonnet = ''\\n sonnet_length = 14\\n count = 0\\n syl_counts = syllable_dict()\\n # print(syl_counts)\\n \\n while count < sonnet_length:\\n # Pick a random word from the rhyming dictionary that the line has to start with. \\n \\n start_word = np.random.choice(list(rhyme_dict.keys()))\\n rhyme_word = np.random.choice(rhyme_dict[start_word])\\n line1 = sample_sentence_syl(hmm, obs_map, rhyme_dict, start_word, n_syl)\\n line2 = sample_sentence_syl(hmm, obs_map, rhyme_dict, rhyme_word, n_syl)\\n (worked1, nline1) = make_line(line1, n_syl, syl_counts)\\n (worked2, nline2) = make_line(line2, n_syl, syl_counts)\\n if worked1 and worked2:\\n sonnetLines.append(nline1)\\n sonnetLines.append(nline2)\\n count += 2\\n\\n # Now flip the order of each line.\\n for line in sonnetLines:\\n line_reversed = ' '.join(reversed(line.split(' '))).capitalize()\\n r_sonnetLines.append(line_reversed + '\\\\n')\\n\\n # Rearrange 7 couplets into a sonnet.\\n for stanza in range(0, 3):\\n idx = [0, 1, 2, 3]\\n for i in range(len(idx)):\\n idx[i] += stanza * 4\\n sonnet += r_sonnetLines[idx[0]] + r_sonnetLines[idx[2]] + r_sonnetLines[idx[1]] + r_sonnetLines[idx[3]]\\n\\n for line_num in range(12, 14):\\n sonnet += r_sonnetLines[line_num]\\n\\n return sonnet\",\n \"def rand(self):\\n raise NotImplementedError\",\n \"def ks_chord(chord, N, alpha, f_s):\\n y = np.zeros(N)\\n # the chord is a dictionary: pitch => gain\\n for note, gain in chord.items():\\n # create an initial random-filled KS buffer the note\\n x = np.random.randn(int(np.round(float(f_s) / note_freq(note))))\\n y = y + gain * KS(x, N, alpha)\\n return y\",\n \"def beat_stripper(timings, removal_proportion):\\n phrase_len = len(timings)\\n n_to_rm = int(phrase_len * removal_proportion)\\n rm_idx = np.random.randint(0, phrase_len-1, n_to_rm)\\n\\n timings_stripped = []\\n for i, hit in enumerate(timings):\\n if not i in rm_idx:\\n timings_stripped.append(hit)\\n else:\\n timings_stripped.append(hit + hit)\\n return timings_stripped\",\n \"def generate_sound_samples(chord_frequencies, frequency_weights):\\n samples = 0\\n for i in range(len(frequency_weights)):\\n samples = samples + (frequency_weights[i]) * (np.sin(2 * np.pi * np.arange(\\n fs * duration) * chord_frequencies[i] / fs)).astype(np.float32)\\n return samples\",\n \"def generate_question_and_answer(): # noqa: WPS210\\n start_number = random.randint(1, 100)\\n progression_step = random.randint(1, 10)\\n progression_length = random.randint(5, 10)\\n progression = generate_progression(\\n start_number, progression_step, progression_length,\\n )\\n return hide_number(progression)\",\n \"def randomize(data, length=0):\\r\\n longitud = 0 if length == 0 else 12 - length\\r\\n return data + \\\"-\\\" + str(time.time()).replace(\\\".\\\", \\\"\\\")[longitud:]\",\n \"def getRandom(self) -> int:\",\n \"def getRandom(self) -> int:\",\n \"def single_introduction(end):\\n return [random.randint(0,end)]\",\n \"def random_schechter(ntot,mstar,alpha,mfaint,mbright):\\n lfaint = flux(mfaint-mstar)\\n lbright = flux(mbright-mstar)\\n if alpha > 0:\\n print \\\"random_schechter: alpha must be negative\\\"\\n sys.exit()\\n smax = schechter(lfaint,alpha)\\n result = zeros((ntot),\\\"float\\\")\\n n = 0\\n while n < ntot:\\n x = random.uniform(lfaint,lbright)\\n s = schechter(x,alpha)/smax\\n y = random.random() \\n if y < s:\\n result[n] = mag(x)+mstar\\n n = n+1\\n return result\",\n \"def generate_midi(config_folder, score_source, number_of_version, duration_gen, rhythmic_reconstruction, logger_generate):\\n\\n logger_generate.info(\\\"#############################################\\\")\\n logger_generate.info(\\\"Orchestrating piano score : \\\" + score_source)\\n ############################################################\\n # Load model, config and data\\n ############################################################\\n\\n ########################\\n # Load config and model\\n parameters = pkl.load(open(config_folder + '/script_parameters.pkl', 'rb'))\\n model_parameters = pkl.load(open(config_folder + '/model_params.pkl', 'rb'))\\n # Set a minimum seed size, because for very short models you don't event see the beginning\\n seed_size = max(model_parameters['temporal_order'], 10) - 1\\n quantization = parameters['quantization']\\n temporal_granularity = parameters['temporal_granularity']\\n instru_mapping = parameters['instru_mapping']\\n ########################\\n\\n ########################\\n # Load data\\n if re.search(r'mid$', score_source):\\n pr_piano, event_piano, duration_piano, name_piano, pr_orch, instru_orch, duration = load_solo(score_source, quantization, parameters[\\\"binarize_piano\\\"], temporal_granularity)\\n else:\\n pr_piano, event_piano, duration_piano, name_piano, pr_orch, instru_orch, duration = load_from_pair(score_source, quantization, parameters[\\\"binarize_piano\\\"], parameters[\\\"binarize_orch\\\"], temporal_granularity)\\n ########################\\n\\n ########################\\n # Shorten\\n # Keep only the beginning of the pieces (let's say a 100 events)\\n pr_piano = extract_pianoroll_part(pr_piano, 0, duration_gen)\\n if parameters[\\\"duration_piano\\\"]:\\n duration_piano = np.asarray(duration_piano[:duration_gen])\\n else:\\n duration_piano = None\\n event_piano = event_piano[:duration_gen]\\n pr_orch = extract_pianoroll_part(pr_orch, 0, duration_gen)\\n ########################\\n\\n ########################\\n # Instanciate piano pianoroll\\n N_piano = instru_mapping['Piano']['index_max']\\n pr_piano_gen = np.zeros((duration_gen, N_piano), dtype=np.float32)\\n pr_piano_gen = build_data_aux.cast_small_pr_into_big_pr(pr_piano, {}, 0, duration_gen, instru_mapping, pr_piano_gen)\\n pr_piano_gen_flat = pr_piano_gen.sum(axis=1)\\n silence_piano = [e for e in range(duration_gen) if pr_piano_gen_flat[e]== 0]\\n ########################\\n\\n ########################\\n # Instanciate orchestra pianoroll with orchestra seed\\n N_orchestra = parameters['N_orchestra']\\n if pr_orch:\\n pr_orchestra_gen = np.zeros((seed_size, N_orchestra), dtype=np.float32)\\n orch_seed_beginning = {k: v[:seed_size] for k, v in pr_orch.items()}\\n pr_orchestra_gen = build_data_aux.cast_small_pr_into_big_pr(orch_seed_beginning, instru_orch, 0, seed_size, instru_mapping, pr_orchestra_gen)\\n pr_orchestra_truth = np.zeros((duration_gen, N_orchestra), dtype=np.float32)\\n pr_orchestra_truth = build_data_aux.cast_small_pr_into_big_pr(pr_orch, instru_orch, 0, duration_gen, instru_mapping, pr_orchestra_truth)\\n else:\\n pr_orchestra_gen = None\\n pr_orchestra_truth = None\\n ########################\\n \\n #######################################\\n # Embed piano\\n time_embedding = time.time()\\n if parameters['embedded_piano']:\\n # Load model\\n embedding_path = parameters[\\\"embedding_path\\\"]\\n embedding_model = embedDenseNet(380, 12, (1500,500), 100, 1500, 2, 3, 12, 0.5, 0, False, True)\\n embedding_model.load_state_dict(torch.load(embedding_path))\\n\\n # Build embedding (no need to batch here, len(pr_piano_gen) is sufficiently small)\\n piano_resize_emb = np.zeros((len(pr_piano_gen), 1, 128)) # Embeddings accetp size 128 samples\\n piano_resize_emb[:, 0, instru_mapping['Piano']['pitch_min']:instru_mapping['Piano']['pitch_max']] = pr_piano_gen\\n piano_resize_emb_TT = torch.tensor(piano_resize_emb)\\n piano_embedded_TT = embedding_model(piano_resize_emb_TT.float(), 0)\\n pr_piano_gen_embedded = piano_embedded_TT.numpy()\\n else:\\n pr_piano_gen_embedded = pr_piano_gen\\n time_embedding = time.time() - time_embedding\\n #######################################\\n\\n ########################\\n # Inputs' normalization\\n normalizer = pkl.load(open(os.path.join(config_folder, 'normalizer.pkl'), 'rb'))\\n if parameters[\\\"embedded_piano\\\"]: # When using embedding, no normalization\\n pr_piano_gen_norm = pr_piano_gen_embedded\\n else:\\n pr_piano_gen_norm = normalizer.transform(pr_piano_gen_embedded)\\n ########################\\n \\n ########################\\n # Store folder\\n string = re.split(r'/', name_piano)[-1]\\n name_track = re.sub('piano_solo.mid', '', string)\\n generated_folder = config_folder + '/generation_reference_example/' + name_track\\n if not os.path.isdir(generated_folder):\\n os.makedirs(generated_folder)\\n ########################\\n\\n ########################\\n # Get trainer\\n with open(os.path.join(config_folder, 'which_trainer'), 'r') as ff:\\n which_trainer = ff.read()\\n # Trainer\\n if which_trainer == 'standard_trainer':\\n from LOP.Scripts.standard_learning.standard_trainer import Standard_trainer as Trainer\\n kwargs_trainer = {'temporal_order': model_parameters[\\\"temporal_order\\\"]}\\n elif which_trainer == 'NADE_trainer':\\n from LOP.Scripts.NADE_learning.NADE_trainer import NADE_trainer as Trainer\\n kwargs_trainer = {'temporal_order': model_parameters[\\\"temporal_order\\\"], 'num_ordering': model_parameters[\\\"num_ordering\\\"]}\\n else:\\n raise Exception(\\\"Undefined trainer\\\")\\n trainer = Trainer(**kwargs_trainer)\\n ########################\\n\\n ############################################################\\n # Generate\\n ############################################################\\n time_generate_0 = time.time()\\n generated_sequences = {}\\n for measure_name in parameters['save_measures']:\\n model_path = 'model_' + measure_name\\n generated_sequences[measure_name] = generate(trainer, pr_piano_gen_norm, silence_piano, duration_piano, config_folder, model_path, pr_orchestra_gen, batch_size=number_of_version)\\n time_generate_1 = time.time()\\n logger_generate.info('TTT : Generating data took {} seconds'.format(time_generate_1-time_generate_0))\\n\\n ############################################################\\n # Reconstruct and write\\n ############################################################\\n def reconstruct_write_aux(generated_sequences, prefix):\\n for write_counter in range(generated_sequences.shape[0]):\\n # To distinguish when seed stop, insert a sustained note\\n this_seq = generated_sequences[write_counter] * 127\\n this_seq[:seed_size, 0] = 20\\n # Reconstruct\\n if rhythmic_reconstruction:\\n pr_orchestra_clean = from_event_to_frame(this_seq, event_piano)\\n else:\\n pr_orchestra_clean = this_seq\\n pr_orchestra = instrument_reconstruction(pr_orchestra_clean, instru_mapping)\\n # Write\\n write_path = generated_folder + '/' + prefix + '_' + str(write_counter) + '_generated.mid'\\n if rhythmic_reconstruction:\\n write_midi(pr_orchestra, quantization, write_path, tempo=80)\\n else:\\n write_midi(pr_orchestra, 1, write_path, tempo=80)\\n return\\n\\n for measure_name in parameters[\\\"save_measures\\\"]:\\n reconstruct_write_aux(generated_sequences[measure_name], measure_name)\\n \\n ############################################################\\n ############################################################\\n # Write original orchestration and piano scores, but reconstructed version, just to check\\n if rhythmic_reconstruction:\\n A = from_event_to_frame(pr_piano_gen, event_piano)\\n else:\\n A = pr_piano_gen\\n B = A * 127\\n piano_reconstructed = instrument_reconstruction_piano(B, instru_mapping)\\n write_path = generated_folder + '/piano_reconstructed.mid'\\n if rhythmic_reconstruction:\\n write_midi(piano_reconstructed, quantization, write_path, tempo=80)\\n else:\\n write_midi(piano_reconstructed, 1, write_path, tempo=80)\\n #\\n if rhythmic_reconstruction:\\n A = from_event_to_frame(pr_orchestra_truth, event_piano)\\n else:\\n A = pr_orchestra_truth\\n B = A * 127\\n orchestra_reconstructed = instrument_reconstruction(B, instru_mapping)\\n write_path = generated_folder + '/orchestra_reconstructed.mid'\\n if rhythmic_reconstruction:\\n write_midi(orchestra_reconstructed, quantization, write_path, tempo=80)\\n else:\\n write_midi(orchestra_reconstructed, 1, write_path, tempo=80)\\n ############################################################\\n ############################################################\",\n \"def random_sample(prob):\\n def _random_sample_xducer(step):\\n def _random_sample_step(r=Missing, x=Missing):\\n if r is Missing: return step()\\n if x is Missing:\\n return step(r)\\n return step(r, x) if random() < prob else r\\n return _random_sample_step\\n return _random_sample_xducer\"\n]","isConversational":false},"negative_scores":{"kind":"list like","value":["0.7600951","0.6710161","0.6659425","0.6366649","0.6244833","0.59357905","0.59273636","0.5922246","0.58458304","0.5795843","0.5778329","0.5752907","0.57455","0.5733294","0.5724642","0.57119113","0.57089704","0.5679657","0.5665693","0.5636343","0.55583066","0.5549534","0.5535191","0.55293566","0.5519769","0.5503923","0.5491074","0.5450109","0.54429084","0.5441284","0.5396905","0.5395996","0.53876495","0.5377288","0.5376604","0.5360883","0.53596103","0.5357555","0.53477114","0.53464943","0.5335702","0.5323207","0.53222716","0.5314086","0.52912736","0.52817863","0.5270988","0.5238631","0.5224204","0.52022976","0.51936555","0.51914066","0.5188285","0.51842403","0.518245","0.5176774","0.51671803","0.51576865","0.51523757","0.51410097","0.51312995","0.5126347","0.51179236","0.5110419","0.5107696","0.50977135","0.50943756","0.50943035","0.5091882","0.5070609","0.50628793","0.5061944","0.50518274","0.5051463","0.5046858","0.5037731","0.5027985","0.50231594","0.50110865","0.50109386","0.49985427","0.49981958","0.49956092","0.4994719","0.4989251","0.49843976","0.49841273","0.49796575","0.4976226","0.49746403","0.49737352","0.49676841","0.49639884","0.49620792","0.49619973","0.49619973","0.4961074","0.4946914","0.49455878","0.49322325"],"string":"[\n \"0.7600951\",\n \"0.6710161\",\n \"0.6659425\",\n \"0.6366649\",\n \"0.6244833\",\n \"0.59357905\",\n \"0.59273636\",\n \"0.5922246\",\n \"0.58458304\",\n \"0.5795843\",\n \"0.5778329\",\n \"0.5752907\",\n \"0.57455\",\n \"0.5733294\",\n \"0.5724642\",\n \"0.57119113\",\n \"0.57089704\",\n \"0.5679657\",\n \"0.5665693\",\n \"0.5636343\",\n \"0.55583066\",\n \"0.5549534\",\n \"0.5535191\",\n \"0.55293566\",\n \"0.5519769\",\n \"0.5503923\",\n \"0.5491074\",\n \"0.5450109\",\n \"0.54429084\",\n \"0.5441284\",\n \"0.5396905\",\n \"0.5395996\",\n \"0.53876495\",\n \"0.5377288\",\n \"0.5376604\",\n \"0.5360883\",\n \"0.53596103\",\n \"0.5357555\",\n \"0.53477114\",\n \"0.53464943\",\n \"0.5335702\",\n \"0.5323207\",\n \"0.53222716\",\n \"0.5314086\",\n \"0.52912736\",\n \"0.52817863\",\n \"0.5270988\",\n \"0.5238631\",\n \"0.5224204\",\n \"0.52022976\",\n \"0.51936555\",\n \"0.51914066\",\n \"0.5188285\",\n \"0.51842403\",\n \"0.518245\",\n \"0.5176774\",\n \"0.51671803\",\n \"0.51576865\",\n \"0.51523757\",\n \"0.51410097\",\n \"0.51312995\",\n \"0.5126347\",\n \"0.51179236\",\n \"0.5110419\",\n \"0.5107696\",\n \"0.50977135\",\n \"0.50943756\",\n \"0.50943035\",\n \"0.5091882\",\n \"0.5070609\",\n \"0.50628793\",\n \"0.5061944\",\n \"0.50518274\",\n \"0.5051463\",\n \"0.5046858\",\n \"0.5037731\",\n \"0.5027985\",\n \"0.50231594\",\n \"0.50110865\",\n \"0.50109386\",\n \"0.49985427\",\n \"0.49981958\",\n \"0.49956092\",\n \"0.4994719\",\n \"0.4989251\",\n \"0.49843976\",\n \"0.49841273\",\n \"0.49796575\",\n \"0.4976226\",\n \"0.49746403\",\n \"0.49737352\",\n \"0.49676841\",\n \"0.49639884\",\n \"0.49620792\",\n \"0.49619973\",\n \"0.49619973\",\n \"0.4961074\",\n \"0.4946914\",\n \"0.49455878\",\n \"0.49322325\"\n]","isConversational":false},"document_score":{"kind":"string","value":"0.70778203"},"document_rank":{"kind":"string","value":"1"}}},{"rowIdx":23783,"cells":{"query":{"kind":"string","value":"Get the duration remain, within this bar list, values of rhythm int"},"document":{"kind":"string","value":"def durationRemain(self, l=None):\n if l is None:\n l = self.rhythm\n full = float(self.time.upper)/self.time.lower\n s = 0\n for i in range(len(l)):\n s += 1.0 / l[i]\n return full - s"},"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 getDuration(self):\n return (self._get_int('duration'), self._attributes.getDivisions())","def get_dur(self):\n return [char.get_dur() for char in self.string]","def duration(self):\r\n return self.t2 - self.t1","def duration(self) -> float:\n return self.delta_t * len(self)","def duration(self) -> float:\n return self.delta_t * len(self)","def getDuration(self):\n if self.getDot():\n return self.duration*1.5\n else:\n return self.duration","def getDuration(self):\n #return np.sum(self.subintinfo['TSUBINT']) #This is constant.\n return np.sum(self.getSubintinfo('TSUBINT')) #This is constant.","def _get_dur(inst):\n for fil, sig in inst['localization'].items():\n ke = sorted([int(i) for i in sig.keys()], key=int)\n if (len(ke) != 2):\n log(0, \"Error: Instance has two ranges\\n%s\" % (str(inst)))\n exit(1)\n dur = ke[1] - ke[0]\n assert dur > 0, \"Duration <= 0\"\n return(dur)","def get_metrical_duration_ticks(self):\n return int(sum([d[0]/d[1] for d in self.metrical_durations.flattened()]) * self.rhythm_denominator)","def bar_to_lilypond_duration(durations):\n size = len(durations)\n durations = [d for d in durations if d is not None]\n # Convert to lilypond note durations\n def is_pow2(n):\n return ((n & (n-1)) == 0)\n def compute_lp_duration(d,s):\n # If it's a power of 2\n if is_pow2(d):\n return str(int(size/d))\n # If it's a multiple of 3\n if d%3 == 0:\n if is_pow2(int(d/3)):\n return str(int(size/(d/3*2)))+\".\"\n # Otherwise, it's a tied note. Split into factors.\n # Test all possible splittings\n for i in range(1,int(d/2)+1):\n d1 = compute_lp_duration(d-i,s)\n d2 = compute_lp_duration(i,s)\n if d1 is None or d2 is None:\n continue\n if type(d1) is not list:\n d1 = [d1]\n if type(d2) is not list:\n d2 = [d2]\n return d1+d2\n return None\n lp_durations = [compute_lp_duration(d,size) for d in durations]\n return lp_durations","def get_duration(self):\n frame_dur = self.get_frame_duration()\n num_frames = self.get_num_frames()\n motion_dur = frame_dur * (num_frames - 1)\n return motion_dur","def _get_duration(self):\n try:\n dur = self.im.info[\"duration\"] / 1000.0\n except KeyError:\n dur = DEFAULT_DURATION / 1000.0 \n\n return dur","def get_duration(self):\n duration = 0\n\n for entry in self.entries:\n duration += entry.get_duration()\n return duration","def Duration(self):\r\n\t\treturn self._get_attribute('duration')","def duration(self):\n pass","def duration(self):\n pass","def get_duration(self):\n return float(self.time.iloc[-1] - self.time.iloc[0])","def getDurations(self):\n return self.durations","def Duration(self):\n\t\treturn self._get_attribute('duration')","def find_duration(data):\n t = [i[0] for i in data]\n duration = t[len(t) - 1] - t[0]\n logging.info('Calculated duration: %s', duration)\n return duration","def get_rhythm_durations(indices, labels=None, start=0, end=None):\n if end is None:\n end = indices[-1]\n if start >= end:\n raise ValueError('`end` must be greater than `start`')\n # find the first rhythm label after the beginning of the frame\n start_index = np.searchsorted(indices, start, side='right')\n # find the first rhythm label after or exactly at the end of the frame\n end_index = np.searchsorted(indices, end, side='left') + 1\n frame_indices = indices[start_index:end_index]\n # compute the duration of each rhythm adjusted for the beginning and end of the frame\n frame_rhythm_durations = np.diff(frame_indices[:-1], prepend=start, append=end)\n if labels is None:\n return frame_rhythm_durations\n else:\n frame_labels = labels[start_index:end_index]\n return frame_rhythm_durations, frame_labels","def duration(self):\n return self._end - self._begin","def duration(self) -> int:\n return 0","def getDuration(self):\n return _osgAnimation.Motion_getDuration(self)","def durations_per_type(self):\n pass","def get_duration(self):\n seconds = self.duration.total_seconds()\n mins, secs = divmod(seconds, 60)\n return int(mins), int(secs)","def create_event_dur_score(self):\n for inst in self.instruments:\n #[rest/midipitch, dur, vel]\n inst_score=[]\n running_clock = 0\n for n, note in enumerate(inst.notes):\n freq = mp_to_adjusted_freq(note[0], self.ratios)\n if type(freq) != int: freq = np.asscalar(freq)\n if type(note[0]) != int: inst.notes[n][0] = np.asscalar(note[0])\n if type(note[1]) != int: inst.notes[n][1] = np.asscalar(note[1])\n if type(note[2]) != int: inst.notes[n][2] = np.asscalar(note[2])\n # if type(note[3]) != int: inst.notes[n][3] = np.asscalar(note[3])\n if note[1] != running_clock:\n inst_score.append(['Rest()', note[1] - running_clock, 0])\n inst_score.append([freq, note[2], note[3]])\n running_clock = note[1] + note[2]\n inst.event_dur_score = inst_score","def increment_duration(syllables, note_attrs):\n syllables[-1][-1][-1]['duration'] += note_attrs['duration']\n return syllables","def getDuration(self):\n return _osgAnimation.Animation_getDuration(self)","def duration(self) -> str:\n return pulumi.get(self, \"duration\")","def total_duration(self):\r\n # XXX: bug in duration after slicing - attr_onread should be reset\r\n # after slicing\r\n #return self.duration.sum()\r\n return (self.stop - self.start).sum()","def get_duration(self):\n return self.duration","def to_length_secs(self):\n return (self.bpm / 60.0) / self.period","def duration(self):\n return self._get('duration')","def duration(self):\r\n return self.stop - self.start","def duration(self) -> float:\n return float(len(self.__samples))/float(self.__rate)","def duration(self):\n return self._get(\"duration\")","def getDurationNoDot(self):\n return self.duration","def get_duration(self):\n dur = 0\n for clu in self._clusters:\n dur += self._clusters[clu].get_duration()\n return dur","def pulse_width_percent(self) -> float:","def duration_in_seconds(self):\n return self.get_data(\"duration_in_seconds\")","def _get_duration(self):\n durations = [series_episode.duration for series_episode in SeriesEpisode.objects.filter(series=self)]\n return reduce(lambda x, y: x + y, durations) if len(durations) > 0 else 0","def sum_syllable_durations(syllable_tuple):\n# if syllable_tuple[0] == None:\n# return\n total_duration = 0\n for note in syllable_tuple[-1]:\n total_duration += note['duration']\n return total_duration","def _getLilyDuration(self, alignBeat = None):\n # To tie notes, we can simply replace the duration with power-of-two\n # durations (or dotted durations) with tildes (~) between them.\n if self.duration == None:\n return \"\"\n if self._isBasisDuration():\n return int(1/self.getDurationNoDot())\n length = self.duration # non-reciprocal units\n nNotes = self.parseBeatLength(length)\n nNotes = [x for x in nNotes] # Need to convert tuple to a list to modify contents\n #print(\"1: {}\\n1/2: {}\\n1/4: {}\\n1/8: {}\\n1/16: {}\\n1/32: {}\\n1/64: {}\".format(\n # nNotes[0], nNotes[1], nNotes[2], nNotes[3], nNotes[4], nNotes[5], nNotes[6]))\n ll = []\n candot = False\n tieSymbol = \"\" # This will be populated later (this hack is to work around the dotted note)\n if alignBeat != None: # If we're considering the alignment beat,\n nBeats = self.parseBeatLength(alignBeat) # Parse the alignment beat count\n shortestBeat = 0\n for n in range(len(nBeats)): # Find the shortest non-zero beat duration\n if nBeats[-(n+1)] > 0: # i.e. if we're on beat 2.5 of 4/4 (alignBeat = 1/4 + 1/4 + 1/8 = 1/2 + 1/8,\n shortestBeat = len(nBeats) - (n + 1) # the shortest beat is 1/8\n if nNotes[shortestBeat] > 0: # If the note duration includes this shortest beat, let's start with that one\n ll.append(str(2**shortestBeat)) # Append it to the lily list\n nNotes[shortestBeat] = 0 # Then blank it out of the loop\n tieSymbol = self._lilyTie # Set the tie symbol for subsequent items in the list\n \n for n in range(len(nNotes)): # Walk through the breakdown of the note duration, starting from whole notes\n if nNotes[n] > 0: # If a duration exists,\n if candot: # If the last duration exists, we can dot it!\n candot = False # Then we turn off dotting so we don't end up with double-dots\n ll.append('.') # (those are silly)\n else:\n candot = True # The next note can be a dot if present\n durationInt = 2**n # 4 for quarter note, 8 for eighth note, etc...\n ll.append(\"{}{}\".format(tieSymbol, str(durationInt)))\n tieSymbol = self._lilyTie # Once the first symbol has been added to lily list, set the tie symbol\n else:\n candot = False # The next note cannot be a dot\n return \"\".join(ll)","def duration(self):\n if self._exc_end and self._inc_begin:\n return self._exc_end - self._inc_begin\n return 0","def duration(self):\n return self.sound.nframes","def duration(self):\n return self.end - self.start","def lookback_duration(self) -> str:\n return pulumi.get(self, \"lookback_duration\")","def period(self) -> int:","def delay(self):\r\n return self.relative_phases / (2 * np.pi * self.frequencies)","def delay(self):\r\n return self.relative_phases / (2 * np.pi * self.frequencies)","def get_pulseDuration(self):\n # res\n if self._cacheExpiration <= YAPI.GetTickCount():\n if self.load(YAPI._yapiContext.GetCacheValidity()) != YAPI.SUCCESS:\n return YPwmOutput.PULSEDURATION_INVALID\n res = self._pulseDuration\n return res","def duration(self):\n # type: () -> int\n return self._duration","def duration(self):\r\n\t\treturn (self.globEnd - self.globStart)","def get_duration_steps(self):\n return {\n # acc. to ATV-A 121 chap. 5.2 (till 2012)\n ATV: (60 * 3, 60 * 48),\n # acc. to DWA-A 531 chap. 5.2.1\n DWA_adv: (60 * 3, 60 * 24),\n # acc. to DWA-A 531 chap. 5.2.1\n DWA: (60, 60 * 12)\n }[self.worksheet]","def duration(self):\n return float('{0:.2f}'.format(self.end_time - self.start_time))","def _get_display_duration(self):\n duration = self.duration\n return '%dmn' % duration if duration < 60 else '%dh%dmn' % (duration / 60, duration % 60)","def duration_in_seconds(self):\n \"Should not set track length\"\n return self.duration / float(self.samplerate)","def duration(self):\n self._current_duration = time.perf_counter() - self._duration_start\n return round(self._current_duration, 4)","def duty_ns(self, duration: Optional[int]):","def get_duration(self):\n return self._duration","def _calculate_heat_duration(self, dur):\n factor = 0\n\n if dur >= 0xFC:\n durval = 0xFF #Max duration\n else:\n while dur > 0x3F:\n dur = dur / 4\n factor += 1\n durval = dur + (factor * 64)\n\n return durval","def time_remaining(self):\n elapsed_time = time.time() - self.start_time\n self.progressbar['value'] = progressbar.current\n time_remaining = round((1 - progressbar.current) * elapsed_time)\n # Disabled for Demo due to confusion\n # if time_remaining < 60:\n # self.progress_label.config(text=f'Estimated Time Remaining: {time_remaining} seconds')\n # elif 3600 > time_remaining > 60:\n # time_remaining = round(time_remaining / 60)\n # self.progress_label.config(text=f'Estimated TIme Remaining: {time_remaining} minutes')\n # elif time_remaining > 3600:\n # time_remaining = dt.timedelta(seconds=time_remaining)\n # self.progress_label.config(text=f'Estimated Time Remaining: {time_remaining}')","def getDurationMs(self):\n return self.durationMs","def elapsed_human(self):\n intervals = (\n ('weeks', 604800),\n ('days', 86400),\n ('hours', 3600),\n ('mins', 60),\n ('secs', 1),\n )\n seconds = self.elapsed\n result = []\n for name, count in intervals:\n value = int(seconds // count)\n if value:\n seconds -= value * count\n if value == 1:\n name = name.rstrip('s')\n result.append(\"{} {}\".format(value, name))\n return ', '.join(result)","def talk_durations(self):\n c = self.conn.cursor()\n\n c.execute('''SELECT strftime('%s', MAX(`date`)) - strftime('%s', MIN(`date`)) AS duration\n FROM messages\n WHERE gmail_labels LIKE '%Chat%'\n GROUP BY gmail_thread_id\n HAVING duration > 0;''')\n\n data = {'<= 1 min.': 0, '1 - 10 mins.': 0,\n '10 - 30 mins.': 0, '30 mins. - 1 hr.': 0,\n '> 1 hr.': 0}\n for row in c.fetchall():\n if row[0] <= 60:\n data['<= 1 min.'] += 1\n elif row[0] <= 600:\n data['1 - 10 mins.'] += 1\n elif row[0] <= 1800:\n data['10 - 30 mins.'] += 1\n elif row[0] <= 3600:\n data['30 mins. - 1 hr.'] += 1\n else:\n data['> 1 hr.'] += 1\n\n trace = pgo.Pie(\n labels=data.keys(),\n values=data.values(),\n marker=dict(\n colors=[\n self.config.get('color', 'primary'),\n self.config.get('color', 'secondary'),\n ]\n )\n )\n\n layout_args = plotly_default_layout_options()\n layout_args['title'] = 'Chat Durations'\n del layout_args['xaxis']\n del layout_args['yaxis']\n\n layout = pgo.Layout(**layout_args)\n\n return plotly_output(pgo.Figure(data=[trace], layout=layout))","def duration_display(self):\n return self.duration_poster or self.duration","def duration_bin(self):\n return self._duration_bin","def duration(self) -> datetime.timedelta:\n return self._duration","def duration(self):\n window_length = self.window_length\n if self.window_length is None:\n warnings.warn(\n \"spectrogram must have window_length attribute to\"\n \" accurately calculate duration. Approximating duration.\"\n )\n return self.times[-1]\n else:\n return self.times[-1] + window_length / 2","def duration(self):\n return self.end_abs - self.start","def duration(self):\n if hasattr(self, 'completed_on') and hasattr(self, 'started_on'):\n diff = (self.completed_on - self.started_on)\n minutes, seconds = divmod(diff.seconds, 60)\n return \"{:0>2d}m {:0>2d}s\".format(minutes, seconds)\n else:\n return None","def duration(self):\n return self._duration","def duration(self):\n return self._duration","def duration(self):\n return self._duration","def duration(self):\n return self._duration","def duration(self):\n return self._duration","def duration(self):\n return self._duration","def duration(self):\n return self._duration","def duration(self):\n return self._duration","def read_releaseResourcesMeasuredDuration(self):\n # PROTECTED REGION ID(CspSubElementSubarray.releaseResourcesMeasuredDuration_read) ENABLED START #\n return self._cmd_measured_duration[\"releaseresources\"]\n # PROTECTED REGION END # // CspSubElementSubarray.releaseResourcesMeasuredDuration_read","def duration(self) -> float:\n return self._stop - self._start if self._stop is not None else None","def ticks_remaining(self):\n raise NotImplementedError(\"ticks_remaining() was not implemented in a subclass of TurnListItem.\")","def get_remaining_playback_time(self):\n\n return self._current_playback[\"item\"][\"duration_ms\"] - self._current_playback[\"progress_ms\"]","def duration(self):\n # duration is the difference between the midpoints\n value = self.__end.get_midpoint() - self.__begin.get_midpoint()\n\n # vagueness of the duration is based on begin/end radius values\n vagueness = 0\n if self.__begin.get_radius() is not None:\n vagueness += self.__begin.get_radius()\n if self.__end.get_radius() is not None:\n vagueness += self.__end.get_radius()\n\n return sppasDuration(value, vagueness)","def duration(self) -> float:\n return self.endTime()-self.startTime()","def duration(self):\n return timedelta(days=randrange(2, 30))","def get_annotation_duration(annotation):\n return ANNOTATION_INITIAL_PAUSE_TIME + len(annotation[\"text\"]) * 0.2","def metric_modulation(\n metronome_mark=((1, 4), 60),\n left_note=(abjad.Note(\"c'8\")),\n right_note=(abjad.Note(\"c'8.\")),\n modulated_beat=(abjad.Note(\"c'4\")),\n rounded=None,\n font_size=None,\n leaf_scale=(0.6, 0.6),\n raise_value=None,\n):\n tempo_note = abjad.Note()\n tempo_note.written_duration = metronome_mark[0]\n left_speed_multiplier = compare_speed(l_note=tempo_note, r_note=left_note)\n left_speed = metronome_mark[1] * left_speed_multiplier\n modulation_speed = calculate_metric_modulation(\n l_tempo=left_speed, l_note=left_note, r_note=right_note\n )\n returned_speed = float(modulation_speed * compare_speed(left_note, modulated_beat))\n if font_size is not None:\n size = rf\" \\override #'(font-size . {font_size})\"\n else:\n size = r\" \\huge\"\n if returned_speed % 1 == 0.0:\n met = abjad.MetronomeMark.make_tempo_equation_markup(\n abjad.get.duration(modulated_beat), int(returned_speed)\n )\n met_string = met.string[8:]\n mod = abjad.MetricModulation(\n left_rhythm=left_note, right_rhythm=right_note, scale=leaf_scale\n )\n mod_string = abjad.lilypond(mod)[8:]\n if raise_value is None:\n mark = abjad.LilyPondLiteral(\n [\n r\"^ \\markup {\",\n size,\n r\" \\concat {\",\n f\" {met_string}\",\n r\" \\hspace #1\",\n r\" \\upright [\",\n f\" {mod_string}\",\n r\" \\hspace #0.5\",\n r\" \\upright ]\",\n r\" }\",\n r\"}\",\n ],\n site=\"after\",\n )\n else:\n mark = abjad.LilyPondLiteral(\n [\n r\"^ \\markup {\",\n size,\n rf\" \\raise #{raise_value} \\with-dimensions-from \\null\",\n r\" \\concat {\",\n f\" {met_string}\",\n r\" \\hspace #1\",\n r\" \\upright [\",\n f\" {mod_string}\",\n r\" \\hspace #0.5\",\n r\" \\upright ]\",\n r\" }\",\n r\"}\",\n ],\n site=\"after\",\n )\n return mark\n else:\n if rounded is True:\n met = abjad.MetronomeMark.make_tempo_equation_markup(\n abjad.get.duration(modulated_beat),\n quicktions.Fraction(round(returned_speed)).limit_denominator(),\n )\n met_string = met.string[8:]\n mod = abjad.MetricModulation(\n left_rhythm=left_note, right_rhythm=right_note, scale=leaf_scale\n )\n mod_string = abjad.lilypond(mod)[8:]\n if raise_value is None:\n mark = abjad.LilyPondLiteral(\n [\n r\"^ \\markup {\",\n size,\n r\" \\concat {\",\n \" c.\",\n r\" \\hspace #1\",\n f\" {met_string}\",\n r\" \\hspace #1\",\n r\" \\upright [\",\n f\" {mod_string}\",\n r\" \\hspace #0.5\",\n r\" \\upright ]\",\n r\" }\",\n r\"}\",\n ],\n site=\"after\",\n )\n else:\n mark = abjad.LilyPondLiteral(\n [\n r\"^ \\markup {\",\n size,\n rf\" \\raise #{raise_value} \\with-dimensions-from \\null\",\n r\" \\concat {\",\n \" c.\",\n r\" \\hspace #1\",\n f\" {met_string}\",\n r\" \\hspace #1\",\n r\" \\upright [\",\n f\" {mod_string}\",\n r\" \\hspace #0.5\",\n r\" \\upright ]\",\n r\" }\",\n r\"}\",\n ],\n site=\"after\",\n )\n else:\n met = abjad.MetronomeMark.make_tempo_equation_markup(\n abjad.get.duration(modulated_beat),\n quicktions.Fraction(returned_speed).limit_denominator(),\n )\n met_string = met.string[8:]\n mod = abjad.MetricModulation(\n left_rhythm=left_note, right_rhythm=right_note, scale=leaf_scale\n )\n mod_string = abjad.lilypond(mod)[8:]\n if raise_value is None:\n mark = abjad.LilyPondLiteral(\n [\n r\"^ \\markup {\",\n size,\n r\" \\concat {\",\n f\" {met_string}\",\n r\" \\hspace #1\",\n r\" \\upright [\",\n f\" {mod_string}\",\n r\" \\hspace #0.5\",\n r\" \\upright ]\",\n r\" }\",\n r\"}\",\n ],\n site=\"after\",\n )\n else:\n mark = abjad.LilyPondLiteral(\n [\n r\"^ \\markup {\",\n size,\n rf\" \\raise #{raise_value} \\with-dimensions-from \\null\",\n r\" \\concat {\",\n f\" {met_string}\",\n r\" \\hspace #1\",\n r\" \\upright [\",\n f\" {mod_string}\",\n r\" \\hspace #0.5\",\n r\" \\upright ]\",\n r\" }\",\n r\"}\",\n ],\n site=\"after\",\n )\n return mark","def get_duration(f):\n return 0","def get_tim(self):\n return self.sum(axis=0)","def duration(self):\n with audioread.audio_open(self.path) as f:\n return f.duration","def seconds_remaining(self):\n pass","def duration(self) -> Optional[str]:\n return pulumi.get(self, \"duration\")","def duration(self) -> Optional[str]:\n return pulumi.get(self, \"duration\")","def get_rhythm_label(durations, labels):\n # sum up the durations of each rhythm\n summed_durations = np.zeros(len(ds_rhythm_names))\n for label in ds_rhythm_names:\n summed_durations[label] = durations[labels == label].sum()\n longest_hp_rhythm = np.argmax(summed_durations[_HI_PRIO_RHYTHMS])\n if summed_durations[_HI_PRIO_RHYTHMS][longest_hp_rhythm] > 0:\n y = _HI_PRIO_RHYTHMS[longest_hp_rhythm]\n else:\n longest_lp_rhythm = np.argmax(summed_durations[_LO_PRIO_RHYTHMS])\n # handle the case of no detected rhythm\n if summed_durations[_LO_PRIO_RHYTHMS][longest_lp_rhythm] > 0:\n y = _LO_PRIO_RHYTHMS[longest_lp_rhythm]\n else:\n y = 0 # undefined rhythm\n return y","def half_duration(self):\n if self._interval is not None:\n a, b = self._interval\n return (b - a) * .5\n else:\n return self.interval_duration * .5","def npulses(self):\n return self.header.pulse_count","def get_duration(period, aor, e):\n return 0.25 * period * np.sqrt(1 - e**2) / aor","def taper_ratio(self) -> float:\n return self.xsecs[-1].chord / self.xsecs[0].chord"],"string":"[\n \"def getDuration(self):\\n return (self._get_int('duration'), self._attributes.getDivisions())\",\n \"def get_dur(self):\\n return [char.get_dur() for char in self.string]\",\n \"def duration(self):\\r\\n return self.t2 - self.t1\",\n \"def duration(self) -> float:\\n return self.delta_t * len(self)\",\n \"def duration(self) -> float:\\n return self.delta_t * len(self)\",\n \"def getDuration(self):\\n if self.getDot():\\n return self.duration*1.5\\n else:\\n return self.duration\",\n \"def getDuration(self):\\n #return np.sum(self.subintinfo['TSUBINT']) #This is constant.\\n return np.sum(self.getSubintinfo('TSUBINT')) #This is constant.\",\n \"def _get_dur(inst):\\n for fil, sig in inst['localization'].items():\\n ke = sorted([int(i) for i in sig.keys()], key=int)\\n if (len(ke) != 2):\\n log(0, \\\"Error: Instance has two ranges\\\\n%s\\\" % (str(inst)))\\n exit(1)\\n dur = ke[1] - ke[0]\\n assert dur > 0, \\\"Duration <= 0\\\"\\n return(dur)\",\n \"def get_metrical_duration_ticks(self):\\n return int(sum([d[0]/d[1] for d in self.metrical_durations.flattened()]) * self.rhythm_denominator)\",\n \"def bar_to_lilypond_duration(durations):\\n size = len(durations)\\n durations = [d for d in durations if d is not None]\\n # Convert to lilypond note durations\\n def is_pow2(n):\\n return ((n & (n-1)) == 0)\\n def compute_lp_duration(d,s):\\n # If it's a power of 2\\n if is_pow2(d):\\n return str(int(size/d))\\n # If it's a multiple of 3\\n if d%3 == 0:\\n if is_pow2(int(d/3)):\\n return str(int(size/(d/3*2)))+\\\".\\\"\\n # Otherwise, it's a tied note. Split into factors.\\n # Test all possible splittings\\n for i in range(1,int(d/2)+1):\\n d1 = compute_lp_duration(d-i,s)\\n d2 = compute_lp_duration(i,s)\\n if d1 is None or d2 is None:\\n continue\\n if type(d1) is not list:\\n d1 = [d1]\\n if type(d2) is not list:\\n d2 = [d2]\\n return d1+d2\\n return None\\n lp_durations = [compute_lp_duration(d,size) for d in durations]\\n return lp_durations\",\n \"def get_duration(self):\\n frame_dur = self.get_frame_duration()\\n num_frames = self.get_num_frames()\\n motion_dur = frame_dur * (num_frames - 1)\\n return motion_dur\",\n \"def _get_duration(self):\\n try:\\n dur = self.im.info[\\\"duration\\\"] / 1000.0\\n except KeyError:\\n dur = DEFAULT_DURATION / 1000.0 \\n\\n return dur\",\n \"def get_duration(self):\\n duration = 0\\n\\n for entry in self.entries:\\n duration += entry.get_duration()\\n return duration\",\n \"def Duration(self):\\r\\n\\t\\treturn self._get_attribute('duration')\",\n \"def duration(self):\\n pass\",\n \"def duration(self):\\n pass\",\n \"def get_duration(self):\\n return float(self.time.iloc[-1] - self.time.iloc[0])\",\n \"def getDurations(self):\\n return self.durations\",\n \"def Duration(self):\\n\\t\\treturn self._get_attribute('duration')\",\n \"def find_duration(data):\\n t = [i[0] for i in data]\\n duration = t[len(t) - 1] - t[0]\\n logging.info('Calculated duration: %s', duration)\\n return duration\",\n \"def get_rhythm_durations(indices, labels=None, start=0, end=None):\\n if end is None:\\n end = indices[-1]\\n if start >= end:\\n raise ValueError('`end` must be greater than `start`')\\n # find the first rhythm label after the beginning of the frame\\n start_index = np.searchsorted(indices, start, side='right')\\n # find the first rhythm label after or exactly at the end of the frame\\n end_index = np.searchsorted(indices, end, side='left') + 1\\n frame_indices = indices[start_index:end_index]\\n # compute the duration of each rhythm adjusted for the beginning and end of the frame\\n frame_rhythm_durations = np.diff(frame_indices[:-1], prepend=start, append=end)\\n if labels is None:\\n return frame_rhythm_durations\\n else:\\n frame_labels = labels[start_index:end_index]\\n return frame_rhythm_durations, frame_labels\",\n \"def duration(self):\\n return self._end - self._begin\",\n \"def duration(self) -> int:\\n return 0\",\n \"def getDuration(self):\\n return _osgAnimation.Motion_getDuration(self)\",\n \"def durations_per_type(self):\\n pass\",\n \"def get_duration(self):\\n seconds = self.duration.total_seconds()\\n mins, secs = divmod(seconds, 60)\\n return int(mins), int(secs)\",\n \"def create_event_dur_score(self):\\n for inst in self.instruments:\\n #[rest/midipitch, dur, vel]\\n inst_score=[]\\n running_clock = 0\\n for n, note in enumerate(inst.notes):\\n freq = mp_to_adjusted_freq(note[0], self.ratios)\\n if type(freq) != int: freq = np.asscalar(freq)\\n if type(note[0]) != int: inst.notes[n][0] = np.asscalar(note[0])\\n if type(note[1]) != int: inst.notes[n][1] = np.asscalar(note[1])\\n if type(note[2]) != int: inst.notes[n][2] = np.asscalar(note[2])\\n # if type(note[3]) != int: inst.notes[n][3] = np.asscalar(note[3])\\n if note[1] != running_clock:\\n inst_score.append(['Rest()', note[1] - running_clock, 0])\\n inst_score.append([freq, note[2], note[3]])\\n running_clock = note[1] + note[2]\\n inst.event_dur_score = inst_score\",\n \"def increment_duration(syllables, note_attrs):\\n syllables[-1][-1][-1]['duration'] += note_attrs['duration']\\n return syllables\",\n \"def getDuration(self):\\n return _osgAnimation.Animation_getDuration(self)\",\n \"def duration(self) -> str:\\n return pulumi.get(self, \\\"duration\\\")\",\n \"def total_duration(self):\\r\\n # XXX: bug in duration after slicing - attr_onread should be reset\\r\\n # after slicing\\r\\n #return self.duration.sum()\\r\\n return (self.stop - self.start).sum()\",\n \"def get_duration(self):\\n return self.duration\",\n \"def to_length_secs(self):\\n return (self.bpm / 60.0) / self.period\",\n \"def duration(self):\\n return self._get('duration')\",\n \"def duration(self):\\r\\n return self.stop - self.start\",\n \"def duration(self) -> float:\\n return float(len(self.__samples))/float(self.__rate)\",\n \"def duration(self):\\n return self._get(\\\"duration\\\")\",\n \"def getDurationNoDot(self):\\n return self.duration\",\n \"def get_duration(self):\\n dur = 0\\n for clu in self._clusters:\\n dur += self._clusters[clu].get_duration()\\n return dur\",\n \"def pulse_width_percent(self) -> float:\",\n \"def duration_in_seconds(self):\\n return self.get_data(\\\"duration_in_seconds\\\")\",\n \"def _get_duration(self):\\n durations = [series_episode.duration for series_episode in SeriesEpisode.objects.filter(series=self)]\\n return reduce(lambda x, y: x + y, durations) if len(durations) > 0 else 0\",\n \"def sum_syllable_durations(syllable_tuple):\\n# if syllable_tuple[0] == None:\\n# return\\n total_duration = 0\\n for note in syllable_tuple[-1]:\\n total_duration += note['duration']\\n return total_duration\",\n \"def _getLilyDuration(self, alignBeat = None):\\n # To tie notes, we can simply replace the duration with power-of-two\\n # durations (or dotted durations) with tildes (~) between them.\\n if self.duration == None:\\n return \\\"\\\"\\n if self._isBasisDuration():\\n return int(1/self.getDurationNoDot())\\n length = self.duration # non-reciprocal units\\n nNotes = self.parseBeatLength(length)\\n nNotes = [x for x in nNotes] # Need to convert tuple to a list to modify contents\\n #print(\\\"1: {}\\\\n1/2: {}\\\\n1/4: {}\\\\n1/8: {}\\\\n1/16: {}\\\\n1/32: {}\\\\n1/64: {}\\\".format(\\n # nNotes[0], nNotes[1], nNotes[2], nNotes[3], nNotes[4], nNotes[5], nNotes[6]))\\n ll = []\\n candot = False\\n tieSymbol = \\\"\\\" # This will be populated later (this hack is to work around the dotted note)\\n if alignBeat != None: # If we're considering the alignment beat,\\n nBeats = self.parseBeatLength(alignBeat) # Parse the alignment beat count\\n shortestBeat = 0\\n for n in range(len(nBeats)): # Find the shortest non-zero beat duration\\n if nBeats[-(n+1)] > 0: # i.e. if we're on beat 2.5 of 4/4 (alignBeat = 1/4 + 1/4 + 1/8 = 1/2 + 1/8,\\n shortestBeat = len(nBeats) - (n + 1) # the shortest beat is 1/8\\n if nNotes[shortestBeat] > 0: # If the note duration includes this shortest beat, let's start with that one\\n ll.append(str(2**shortestBeat)) # Append it to the lily list\\n nNotes[shortestBeat] = 0 # Then blank it out of the loop\\n tieSymbol = self._lilyTie # Set the tie symbol for subsequent items in the list\\n \\n for n in range(len(nNotes)): # Walk through the breakdown of the note duration, starting from whole notes\\n if nNotes[n] > 0: # If a duration exists,\\n if candot: # If the last duration exists, we can dot it!\\n candot = False # Then we turn off dotting so we don't end up with double-dots\\n ll.append('.') # (those are silly)\\n else:\\n candot = True # The next note can be a dot if present\\n durationInt = 2**n # 4 for quarter note, 8 for eighth note, etc...\\n ll.append(\\\"{}{}\\\".format(tieSymbol, str(durationInt)))\\n tieSymbol = self._lilyTie # Once the first symbol has been added to lily list, set the tie symbol\\n else:\\n candot = False # The next note cannot be a dot\\n return \\\"\\\".join(ll)\",\n \"def duration(self):\\n if self._exc_end and self._inc_begin:\\n return self._exc_end - self._inc_begin\\n return 0\",\n \"def duration(self):\\n return self.sound.nframes\",\n \"def duration(self):\\n return self.end - self.start\",\n \"def lookback_duration(self) -> str:\\n return pulumi.get(self, \\\"lookback_duration\\\")\",\n \"def period(self) -> int:\",\n \"def delay(self):\\r\\n return self.relative_phases / (2 * np.pi * self.frequencies)\",\n \"def delay(self):\\r\\n return self.relative_phases / (2 * np.pi * self.frequencies)\",\n \"def get_pulseDuration(self):\\n # res\\n if self._cacheExpiration <= YAPI.GetTickCount():\\n if self.load(YAPI._yapiContext.GetCacheValidity()) != YAPI.SUCCESS:\\n return YPwmOutput.PULSEDURATION_INVALID\\n res = self._pulseDuration\\n return res\",\n \"def duration(self):\\n # type: () -> int\\n return self._duration\",\n \"def duration(self):\\r\\n\\t\\treturn (self.globEnd - self.globStart)\",\n \"def get_duration_steps(self):\\n return {\\n # acc. to ATV-A 121 chap. 5.2 (till 2012)\\n ATV: (60 * 3, 60 * 48),\\n # acc. to DWA-A 531 chap. 5.2.1\\n DWA_adv: (60 * 3, 60 * 24),\\n # acc. to DWA-A 531 chap. 5.2.1\\n DWA: (60, 60 * 12)\\n }[self.worksheet]\",\n \"def duration(self):\\n return float('{0:.2f}'.format(self.end_time - self.start_time))\",\n \"def _get_display_duration(self):\\n duration = self.duration\\n return '%dmn' % duration if duration < 60 else '%dh%dmn' % (duration / 60, duration % 60)\",\n \"def duration_in_seconds(self):\\n \\\"Should not set track length\\\"\\n return self.duration / float(self.samplerate)\",\n \"def duration(self):\\n self._current_duration = time.perf_counter() - self._duration_start\\n return round(self._current_duration, 4)\",\n \"def duty_ns(self, duration: Optional[int]):\",\n \"def get_duration(self):\\n return self._duration\",\n \"def _calculate_heat_duration(self, dur):\\n factor = 0\\n\\n if dur >= 0xFC:\\n durval = 0xFF #Max duration\\n else:\\n while dur > 0x3F:\\n dur = dur / 4\\n factor += 1\\n durval = dur + (factor * 64)\\n\\n return durval\",\n \"def time_remaining(self):\\n elapsed_time = time.time() - self.start_time\\n self.progressbar['value'] = progressbar.current\\n time_remaining = round((1 - progressbar.current) * elapsed_time)\\n # Disabled for Demo due to confusion\\n # if time_remaining < 60:\\n # self.progress_label.config(text=f'Estimated Time Remaining: {time_remaining} seconds')\\n # elif 3600 > time_remaining > 60:\\n # time_remaining = round(time_remaining / 60)\\n # self.progress_label.config(text=f'Estimated TIme Remaining: {time_remaining} minutes')\\n # elif time_remaining > 3600:\\n # time_remaining = dt.timedelta(seconds=time_remaining)\\n # self.progress_label.config(text=f'Estimated Time Remaining: {time_remaining}')\",\n \"def getDurationMs(self):\\n return self.durationMs\",\n \"def elapsed_human(self):\\n intervals = (\\n ('weeks', 604800),\\n ('days', 86400),\\n ('hours', 3600),\\n ('mins', 60),\\n ('secs', 1),\\n )\\n seconds = self.elapsed\\n result = []\\n for name, count in intervals:\\n value = int(seconds // count)\\n if value:\\n seconds -= value * count\\n if value == 1:\\n name = name.rstrip('s')\\n result.append(\\\"{} {}\\\".format(value, name))\\n return ', '.join(result)\",\n \"def talk_durations(self):\\n c = self.conn.cursor()\\n\\n c.execute('''SELECT strftime('%s', MAX(`date`)) - strftime('%s', MIN(`date`)) AS duration\\n FROM messages\\n WHERE gmail_labels LIKE '%Chat%'\\n GROUP BY gmail_thread_id\\n HAVING duration > 0;''')\\n\\n data = {'<= 1 min.': 0, '1 - 10 mins.': 0,\\n '10 - 30 mins.': 0, '30 mins. - 1 hr.': 0,\\n '> 1 hr.': 0}\\n for row in c.fetchall():\\n if row[0] <= 60:\\n data['<= 1 min.'] += 1\\n elif row[0] <= 600:\\n data['1 - 10 mins.'] += 1\\n elif row[0] <= 1800:\\n data['10 - 30 mins.'] += 1\\n elif row[0] <= 3600:\\n data['30 mins. - 1 hr.'] += 1\\n else:\\n data['> 1 hr.'] += 1\\n\\n trace = pgo.Pie(\\n labels=data.keys(),\\n values=data.values(),\\n marker=dict(\\n colors=[\\n self.config.get('color', 'primary'),\\n self.config.get('color', 'secondary'),\\n ]\\n )\\n )\\n\\n layout_args = plotly_default_layout_options()\\n layout_args['title'] = 'Chat Durations'\\n del layout_args['xaxis']\\n del layout_args['yaxis']\\n\\n layout = pgo.Layout(**layout_args)\\n\\n return plotly_output(pgo.Figure(data=[trace], layout=layout))\",\n \"def duration_display(self):\\n return self.duration_poster or self.duration\",\n \"def duration_bin(self):\\n return self._duration_bin\",\n \"def duration(self) -> datetime.timedelta:\\n return self._duration\",\n \"def duration(self):\\n window_length = self.window_length\\n if self.window_length is None:\\n warnings.warn(\\n \\\"spectrogram must have window_length attribute to\\\"\\n \\\" accurately calculate duration. Approximating duration.\\\"\\n )\\n return self.times[-1]\\n else:\\n return self.times[-1] + window_length / 2\",\n \"def duration(self):\\n return self.end_abs - self.start\",\n \"def duration(self):\\n if hasattr(self, 'completed_on') and hasattr(self, 'started_on'):\\n diff = (self.completed_on - self.started_on)\\n minutes, seconds = divmod(diff.seconds, 60)\\n return \\\"{:0>2d}m {:0>2d}s\\\".format(minutes, seconds)\\n else:\\n return None\",\n \"def duration(self):\\n return self._duration\",\n \"def duration(self):\\n return self._duration\",\n \"def duration(self):\\n return self._duration\",\n \"def duration(self):\\n return self._duration\",\n \"def duration(self):\\n return self._duration\",\n \"def duration(self):\\n return self._duration\",\n \"def duration(self):\\n return self._duration\",\n \"def duration(self):\\n return self._duration\",\n \"def read_releaseResourcesMeasuredDuration(self):\\n # PROTECTED REGION ID(CspSubElementSubarray.releaseResourcesMeasuredDuration_read) ENABLED START #\\n return self._cmd_measured_duration[\\\"releaseresources\\\"]\\n # PROTECTED REGION END # // CspSubElementSubarray.releaseResourcesMeasuredDuration_read\",\n \"def duration(self) -> float:\\n return self._stop - self._start if self._stop is not None else None\",\n \"def ticks_remaining(self):\\n raise NotImplementedError(\\\"ticks_remaining() was not implemented in a subclass of TurnListItem.\\\")\",\n \"def get_remaining_playback_time(self):\\n\\n return self._current_playback[\\\"item\\\"][\\\"duration_ms\\\"] - self._current_playback[\\\"progress_ms\\\"]\",\n \"def duration(self):\\n # duration is the difference between the midpoints\\n value = self.__end.get_midpoint() - self.__begin.get_midpoint()\\n\\n # vagueness of the duration is based on begin/end radius values\\n vagueness = 0\\n if self.__begin.get_radius() is not None:\\n vagueness += self.__begin.get_radius()\\n if self.__end.get_radius() is not None:\\n vagueness += self.__end.get_radius()\\n\\n return sppasDuration(value, vagueness)\",\n \"def duration(self) -> float:\\n return self.endTime()-self.startTime()\",\n \"def duration(self):\\n return timedelta(days=randrange(2, 30))\",\n \"def get_annotation_duration(annotation):\\n return ANNOTATION_INITIAL_PAUSE_TIME + len(annotation[\\\"text\\\"]) * 0.2\",\n \"def metric_modulation(\\n metronome_mark=((1, 4), 60),\\n left_note=(abjad.Note(\\\"c'8\\\")),\\n right_note=(abjad.Note(\\\"c'8.\\\")),\\n modulated_beat=(abjad.Note(\\\"c'4\\\")),\\n rounded=None,\\n font_size=None,\\n leaf_scale=(0.6, 0.6),\\n raise_value=None,\\n):\\n tempo_note = abjad.Note()\\n tempo_note.written_duration = metronome_mark[0]\\n left_speed_multiplier = compare_speed(l_note=tempo_note, r_note=left_note)\\n left_speed = metronome_mark[1] * left_speed_multiplier\\n modulation_speed = calculate_metric_modulation(\\n l_tempo=left_speed, l_note=left_note, r_note=right_note\\n )\\n returned_speed = float(modulation_speed * compare_speed(left_note, modulated_beat))\\n if font_size is not None:\\n size = rf\\\" \\\\override #'(font-size . {font_size})\\\"\\n else:\\n size = r\\\" \\\\huge\\\"\\n if returned_speed % 1 == 0.0:\\n met = abjad.MetronomeMark.make_tempo_equation_markup(\\n abjad.get.duration(modulated_beat), int(returned_speed)\\n )\\n met_string = met.string[8:]\\n mod = abjad.MetricModulation(\\n left_rhythm=left_note, right_rhythm=right_note, scale=leaf_scale\\n )\\n mod_string = abjad.lilypond(mod)[8:]\\n if raise_value is None:\\n mark = abjad.LilyPondLiteral(\\n [\\n r\\\"^ \\\\markup {\\\",\\n size,\\n r\\\" \\\\concat {\\\",\\n f\\\" {met_string}\\\",\\n r\\\" \\\\hspace #1\\\",\\n r\\\" \\\\upright [\\\",\\n f\\\" {mod_string}\\\",\\n r\\\" \\\\hspace #0.5\\\",\\n r\\\" \\\\upright ]\\\",\\n r\\\" }\\\",\\n r\\\"}\\\",\\n ],\\n site=\\\"after\\\",\\n )\\n else:\\n mark = abjad.LilyPondLiteral(\\n [\\n r\\\"^ \\\\markup {\\\",\\n size,\\n rf\\\" \\\\raise #{raise_value} \\\\with-dimensions-from \\\\null\\\",\\n r\\\" \\\\concat {\\\",\\n f\\\" {met_string}\\\",\\n r\\\" \\\\hspace #1\\\",\\n r\\\" \\\\upright [\\\",\\n f\\\" {mod_string}\\\",\\n r\\\" \\\\hspace #0.5\\\",\\n r\\\" \\\\upright ]\\\",\\n r\\\" }\\\",\\n r\\\"}\\\",\\n ],\\n site=\\\"after\\\",\\n )\\n return mark\\n else:\\n if rounded is True:\\n met = abjad.MetronomeMark.make_tempo_equation_markup(\\n abjad.get.duration(modulated_beat),\\n quicktions.Fraction(round(returned_speed)).limit_denominator(),\\n )\\n met_string = met.string[8:]\\n mod = abjad.MetricModulation(\\n left_rhythm=left_note, right_rhythm=right_note, scale=leaf_scale\\n )\\n mod_string = abjad.lilypond(mod)[8:]\\n if raise_value is None:\\n mark = abjad.LilyPondLiteral(\\n [\\n r\\\"^ \\\\markup {\\\",\\n size,\\n r\\\" \\\\concat {\\\",\\n \\\" c.\\\",\\n r\\\" \\\\hspace #1\\\",\\n f\\\" {met_string}\\\",\\n r\\\" \\\\hspace #1\\\",\\n r\\\" \\\\upright [\\\",\\n f\\\" {mod_string}\\\",\\n r\\\" \\\\hspace #0.5\\\",\\n r\\\" \\\\upright ]\\\",\\n r\\\" }\\\",\\n r\\\"}\\\",\\n ],\\n site=\\\"after\\\",\\n )\\n else:\\n mark = abjad.LilyPondLiteral(\\n [\\n r\\\"^ \\\\markup {\\\",\\n size,\\n rf\\\" \\\\raise #{raise_value} \\\\with-dimensions-from \\\\null\\\",\\n r\\\" \\\\concat {\\\",\\n \\\" c.\\\",\\n r\\\" \\\\hspace #1\\\",\\n f\\\" {met_string}\\\",\\n r\\\" \\\\hspace #1\\\",\\n r\\\" \\\\upright [\\\",\\n f\\\" {mod_string}\\\",\\n r\\\" \\\\hspace #0.5\\\",\\n r\\\" \\\\upright ]\\\",\\n r\\\" }\\\",\\n r\\\"}\\\",\\n ],\\n site=\\\"after\\\",\\n )\\n else:\\n met = abjad.MetronomeMark.make_tempo_equation_markup(\\n abjad.get.duration(modulated_beat),\\n quicktions.Fraction(returned_speed).limit_denominator(),\\n )\\n met_string = met.string[8:]\\n mod = abjad.MetricModulation(\\n left_rhythm=left_note, right_rhythm=right_note, scale=leaf_scale\\n )\\n mod_string = abjad.lilypond(mod)[8:]\\n if raise_value is None:\\n mark = abjad.LilyPondLiteral(\\n [\\n r\\\"^ \\\\markup {\\\",\\n size,\\n r\\\" \\\\concat {\\\",\\n f\\\" {met_string}\\\",\\n r\\\" \\\\hspace #1\\\",\\n r\\\" \\\\upright [\\\",\\n f\\\" {mod_string}\\\",\\n r\\\" \\\\hspace #0.5\\\",\\n r\\\" \\\\upright ]\\\",\\n r\\\" }\\\",\\n r\\\"}\\\",\\n ],\\n site=\\\"after\\\",\\n )\\n else:\\n mark = abjad.LilyPondLiteral(\\n [\\n r\\\"^ \\\\markup {\\\",\\n size,\\n rf\\\" \\\\raise #{raise_value} \\\\with-dimensions-from \\\\null\\\",\\n r\\\" \\\\concat {\\\",\\n f\\\" {met_string}\\\",\\n r\\\" \\\\hspace #1\\\",\\n r\\\" \\\\upright [\\\",\\n f\\\" {mod_string}\\\",\\n r\\\" \\\\hspace #0.5\\\",\\n r\\\" \\\\upright ]\\\",\\n r\\\" }\\\",\\n r\\\"}\\\",\\n ],\\n site=\\\"after\\\",\\n )\\n return mark\",\n \"def get_duration(f):\\n return 0\",\n \"def get_tim(self):\\n return self.sum(axis=0)\",\n \"def duration(self):\\n with audioread.audio_open(self.path) as f:\\n return f.duration\",\n \"def seconds_remaining(self):\\n pass\",\n \"def duration(self) -> Optional[str]:\\n return pulumi.get(self, \\\"duration\\\")\",\n \"def duration(self) -> Optional[str]:\\n return pulumi.get(self, \\\"duration\\\")\",\n \"def get_rhythm_label(durations, labels):\\n # sum up the durations of each rhythm\\n summed_durations = np.zeros(len(ds_rhythm_names))\\n for label in ds_rhythm_names:\\n summed_durations[label] = durations[labels == label].sum()\\n longest_hp_rhythm = np.argmax(summed_durations[_HI_PRIO_RHYTHMS])\\n if summed_durations[_HI_PRIO_RHYTHMS][longest_hp_rhythm] > 0:\\n y = _HI_PRIO_RHYTHMS[longest_hp_rhythm]\\n else:\\n longest_lp_rhythm = np.argmax(summed_durations[_LO_PRIO_RHYTHMS])\\n # handle the case of no detected rhythm\\n if summed_durations[_LO_PRIO_RHYTHMS][longest_lp_rhythm] > 0:\\n y = _LO_PRIO_RHYTHMS[longest_lp_rhythm]\\n else:\\n y = 0 # undefined rhythm\\n return y\",\n \"def half_duration(self):\\n if self._interval is not None:\\n a, b = self._interval\\n return (b - a) * .5\\n else:\\n return self.interval_duration * .5\",\n \"def npulses(self):\\n return self.header.pulse_count\",\n \"def get_duration(period, aor, e):\\n return 0.25 * period * np.sqrt(1 - e**2) / aor\",\n \"def taper_ratio(self) -> float:\\n return self.xsecs[-1].chord / self.xsecs[0].chord\"\n]","isConversational":false},"negative_scores":{"kind":"list like","value":["0.65641594","0.65380937","0.6458796","0.64535576","0.64535576","0.6451906","0.6380517","0.6314581","0.62721646","0.625763","0.6241156","0.6210288","0.6162527","0.61441207","0.6135919","0.6135919","0.6128094","0.61246526","0.6111688","0.6027541","0.6024031","0.600552","0.59731317","0.5967308","0.5964228","0.5941234","0.5936502","0.58956796","0.58623606","0.5854704","0.58543915","0.5831836","0.58136386","0.5813013","0.5804135","0.5803166","0.57979774","0.5766127","0.57595885","0.5756712","0.575594","0.57338274","0.571924","0.5716588","0.570945","0.56970704","0.566473","0.56595814","0.56492424","0.5644328","0.5644328","0.5643029","0.564147","0.56404865","0.5633435","0.55865794","0.55781883","0.5565429","0.55654234","0.55646557","0.55531013","0.5550916","0.55359626","0.5527146","0.5521564","0.5510919","0.55060184","0.54974914","0.5497274","0.5493405","0.549268","0.5490278","0.5482819","0.5482819","0.5482819","0.5482819","0.5482819","0.5482819","0.5482819","0.5482819","0.5472368","0.5466678","0.54664767","0.5461392","0.54608226","0.54404825","0.54364955","0.5416788","0.54130834","0.54099673","0.5403656","0.5394974","0.5389704","0.5388357","0.5388357","0.53856117","0.5383511","0.5378136","0.5370709","0.5357655"],"string":"[\n \"0.65641594\",\n \"0.65380937\",\n \"0.6458796\",\n \"0.64535576\",\n \"0.64535576\",\n \"0.6451906\",\n \"0.6380517\",\n \"0.6314581\",\n \"0.62721646\",\n \"0.625763\",\n \"0.6241156\",\n \"0.6210288\",\n \"0.6162527\",\n \"0.61441207\",\n \"0.6135919\",\n \"0.6135919\",\n \"0.6128094\",\n \"0.61246526\",\n \"0.6111688\",\n \"0.6027541\",\n \"0.6024031\",\n \"0.600552\",\n \"0.59731317\",\n \"0.5967308\",\n \"0.5964228\",\n \"0.5941234\",\n \"0.5936502\",\n \"0.58956796\",\n \"0.58623606\",\n \"0.5854704\",\n \"0.58543915\",\n \"0.5831836\",\n \"0.58136386\",\n \"0.5813013\",\n \"0.5804135\",\n \"0.5803166\",\n \"0.57979774\",\n \"0.5766127\",\n \"0.57595885\",\n \"0.5756712\",\n \"0.575594\",\n \"0.57338274\",\n \"0.571924\",\n \"0.5716588\",\n \"0.570945\",\n \"0.56970704\",\n \"0.566473\",\n \"0.56595814\",\n \"0.56492424\",\n \"0.5644328\",\n \"0.5644328\",\n \"0.5643029\",\n \"0.564147\",\n \"0.56404865\",\n \"0.5633435\",\n \"0.55865794\",\n \"0.55781883\",\n \"0.5565429\",\n \"0.55654234\",\n \"0.55646557\",\n \"0.55531013\",\n \"0.5550916\",\n \"0.55359626\",\n \"0.5527146\",\n \"0.5521564\",\n \"0.5510919\",\n \"0.55060184\",\n \"0.54974914\",\n \"0.5497274\",\n \"0.5493405\",\n \"0.549268\",\n \"0.5490278\",\n \"0.5482819\",\n \"0.5482819\",\n \"0.5482819\",\n \"0.5482819\",\n \"0.5482819\",\n \"0.5482819\",\n \"0.5482819\",\n \"0.5482819\",\n \"0.5472368\",\n \"0.5466678\",\n \"0.54664767\",\n \"0.5461392\",\n \"0.54608226\",\n \"0.54404825\",\n \"0.54364955\",\n \"0.5416788\",\n \"0.54130834\",\n \"0.54099673\",\n \"0.5403656\",\n \"0.5394974\",\n \"0.5389704\",\n \"0.5388357\",\n \"0.5388357\",\n \"0.53856117\",\n \"0.5383511\",\n \"0.5378136\",\n \"0.5370709\",\n \"0.5357655\"\n]","isConversational":false},"document_score":{"kind":"string","value":"0.6915621"},"document_rank":{"kind":"string","value":"0"}}},{"rowIdx":23784,"cells":{"query":{"kind":"string","value":"Check a SIMULATED phylogeny for consistency with its backbone source tree and a taxonomy. The SIMULATED phylogeny should have been generated by the tact_add_taxa script. All phylogenies should be in Newick format."},"document":{"kind":"string","value":"def main(simulated, backbone, taxonomy, output, cores, chunksize):\n pool = multiprocessing.Pool(processes=cores)\n click.echo(\"Using %d parallel cores\" % cores, err=True)\n taxonomy = dendropy.Tree.get_from_path(taxonomy, schema=\"newick\")\n tn = taxonomy.taxon_namespace\n click.echo(\"Taxonomy OK\", err=True)\n\n r1 = pool.apply_async(get_tree, [backbone, tn])\n r2 = pool.apply_async(get_tree, [simulated, tn])\n\n backbone = r1.get()\n click.echo(\"Backbone OK\", err=True)\n simulated = r2.get()\n click.echo(\"Simulated OK\", err=True)\n\n bb_tips = get_tip_labels(backbone)\n st_tips = get_tip_labels(simulated)\n\n # Start calculating ASAP\n wrap = functools.partial(analyze_taxon, bb_tips, st_tips, backbone, simulated)\n nnodes = len(taxonomy.internal_nodes(exclude_seed_node=True))\n if chunksize is None:\n chunksize = max(5, math.ceil(nnodes / cores / 10))\n # We use preorder because the root is going to take the longest to\n # run calculations. Allocating things to cores takes a non-negigible\n # amount of time so we want the root to be running for the longest.\n it = pool.imap_unordered(wrap, taxonomy.preorder_internal_node_iter(exclude_seed_node=True), chunksize=chunksize)\n\n writer = csv.writer(output)\n writer.writerow(\n \"node taxonomy_tips backbone_tips simulated_tips backbone_monophyletic simulated_monophyletic backbone_birth simulated_birth backbone_death simulated_death warnings\".split()\n )\n\n with click.progressbar(it, width=12, length=nnodes) as prog:\n for result in prog:\n if result:\n writer.writerow(result)"},"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_calc_shared_phylotypes(self):\r\n\r\n observed = calc_shared_phylotypes(self.biom_as_string)\r\n expected = \"\"\"\\tS1\\tS2\\tS3\r\nS1\\t5\\t2\\t3\r\nS2\\t2\\t2\\t1\r\nS3\\t3\\t1\\t3\\n\"\"\"\r\n self.assertEqual(observed, expected)","def test_check_tree_exact_match(self):\r\n\r\n fasta_labels = ['seq1_1', 'seq1_2', 'seq2_3', 'seq3_4']\r\n\r\n actual_subset_results = check_tree_exact_match(fasta_labels,\r\n self.sample_tree_3tips_fp)\r\n\r\n # Should find all and give True, True result\r\n\r\n self.assertEqual(actual_subset_results, [True, True])\r\n\r\n # Should get tips not found in fasta labels with 5 tip tree\r\n\r\n fasta_labels = ['seq1_1', 'seq1_2', 'seq2_3', 'seq3_4']\r\n\r\n actual_subset_results = check_tree_exact_match(fasta_labels,\r\n self.sample_tree_5tips_fp)\r\n\r\n # Should find all and give True result\r\n\r\n self.assertEqual(actual_subset_results, [True, ['seq5', 'seq4']])\r\n\r\n # Change two of the fasta labels to not match tree tips\r\n\r\n fasta_labels = ['seq1_1', 'seqX_2', 'seq2_3', 'seqY_4']\r\n\r\n actual_subset_results = check_tree_exact_match(fasta_labels,\r\n self.sample_tree_5tips_fp)\r\n\r\n # Should find seqX and seqY as not being a subset\r\n\r\n self.assertEqual(actual_subset_results, [['seqX', 'seqY'],\r\n ['seq3', 'seq5', 'seq4']])","def check_phylogeny(dis):\n\n birds = ('Gallus_gallus', 'Taeniopygia_guttata')\n bird_dis = dis['Gallus_gallus']['Taeniopygia_guttata']\n for host in dis:\n if host not in birds:\n for bird in birds:\n new_dis = dis[bird][host]\n if new_dis <= bird_dis:\n return False\n return True","def test_call_phylogenetic(self):\r\n c = AlphaDiversityCalc(metric=PD_whole_tree,\r\n is_phylogenetic=True)\r\n assert_almost_equal(c(data_path=self.otu_table1_fp, tree_path=self.tree1,\r\n taxon_names=self.otu_table1.ObservationIds,\r\n sample_names=self.otu_table1.SampleIds),\r\n [13, 17, 0])","def compare_trees(tree1, tree2):\n \tresponse = {}\n \tstart_time = time.time()\n \ttry:\t\n \t\ttns = dendropy.TaxonNamespace() \t\n \t\n \t\ttree_obj1 = dendropy.Tree.get(data=tree1, schema=\"newick\",taxon_namespace=tns)\n \t\ttree_obj2 = dendropy.Tree.get(data=tree2, schema=\"newick\",taxon_namespace=tns)\n\n \t\ttree_obj1.encode_bipartitions()\n \t\ttree_obj2.encode_bipartitions()\n\n \t\t#-----------------------------------------------------------\n \t\t#This method returns the symmetric distance between two trees. \n \t\t#The symmetric distance between two trees is the sum of the number of splits found in one of the trees but not the other. \n \t\t#It is common to see this statistic called the Robinson-Foulds distance\n\n \t\tareSame = True if treecompare.symmetric_difference(tree_obj1, tree_obj2) == 0 else False\n \t\tstatus = 200\n \t\tmessage = \"Success\"\n \t\tresponse['are_same_tree'] = areSame\n \n \texcept Exception, e:\n \t\tif \"Incomplete or improperly-terminated tree statement\" in str(e): #invalid: \"((A,B),C,D));\" valid: ((A,B),(C,D)); \n \t\t\tmessage = \"NewickReaderIncompleteTreeStatementError: \" + str(e)\n \t \t\tstatus = 400\n \t\telif \"Unbalanced parentheses at tree statement\" in str(e): #invalid: \"((A,B),(C,D);\" valid: ((A,B),(C,D)); \n \t\t\tmessage = \"NewickReaderMalformedStatementError: \"+str(e) \n \t \t\tstatus = 400\n \t\telif \"Multiple occurrences of the same taxa\" in str(e): #invalid: \"((A,B),(C,C));\" valid: ((A,B),(C,D));\n \t\t\tmessage = \"NewickReaderDuplicateTaxonError: \"+str(e)\n \t \t\tstatus = 400\n \t\telif \"Unexpected end of stream\" in str(e): # invalid: \"((A,B),(C,D))\" valid: ((A,B),(C,D));\n \t\t\tmessage = \"UnexpectedEndOfStreamError: \"+str(e)\n \t \t\tstatus = 400\n \t\telse:\n \t\t\tmessage = \"Error: Failed to compare trees. \"+str(e)\n \t \t\tstatus = 500\n \t \t\n \tresponse['status_code'] = status\n \tresponse['message'] = message\n\n \tend_time = time.time()\n \texecution_time = end_time-start_time\n #service result creation time\n \tcreation_time = datetime.datetime.now().isoformat()\n \tmeta_data = {'creation_time': creation_time, 'execution_time': float('{:4.2f}'.format(execution_time)), 'source_urls':[\"http://dendropy.org/library/treecompare.html#module-dendropy.calculate.treecompare\"] }\n\n \tresponse['meta_data'] = meta_data\n \tprint response\n \treturn response","def test_contains():\n atom = ATOMClassifier(X_class, y_class, random_state=1)\n atom.run(\"Tree\")\n assert \"alcohol\" in atom.tree","def check_taxa(taxa: Dict[str, AphiaInfo], cache: AphiaCacheInterface = None) -> None:\n\n check_fields(taxa)\n detect_lsid(taxa)\n detect_external(taxa)\n match_obis(taxa, cache)\n match_worms(taxa)\n check_annotated_list(taxa)\n fetch(taxa, cache)","def checkTree(self, root):\n\t\tif len(self.graph.nodes()) > 100: # TODO why this arbitrary limit on the size of the tree?\n\t\t\tNJTree.logger.debug(\"Too big graph detected and arbitrarly split\")\n\t\t\tself.OK = \"false\"\n\t\t\treturn self.OK\n\t\t# check species of root node neighbors\n\t\tedge = root[1]\n# \t\tloss = root[2]\n\t\tset_species = set([])\n\t\tspecies = []\n# \t\tedge_weight = self.graph[edge[0]][edge[1]]['homology_weight']\n\t\t# TODO what is the point of having twice the results?\n\t\tset_species.add(self.rootedTree.node[edge[0]]['species'])\n\t\tset_species.add(self.rootedTree.node[edge[1]]['species'])\n\t\tspecies.append(self.rootedTree.node[edge[0]]['species'])\n\t\tspecies.append(self.rootedTree.node[edge[1]]['species'])\n\t\tif len(species) == 1 or len(set_species) == 1 and (self.mrca not in set_species):\n\t\t\t# all leaf nodes get added to orphan pile\n\t\t\t# NJTree.logger.debug(\"Orphan in checkTree still happens\")\n\t\t\tself.OK = \"orphan\"\n\t\t\treturn self.OK\n\t\tif len(species) == 2:\n\t\t\tif self.mrca in species:\n\t\t\t\tself.OK = \"false\"\n\t\t\t\treturn self.OK\n\t\t\telse:\n\t\t\t\tself.OK = \"true\"\n\t\t\t\treturn self.OK\n\t\t# should add a verification that there are no more than 2 species","def check_tree_exact_match(fasta_labels,\r\n tree_fp):\r\n\r\n # Need to get modified fasta labels with underscore stripped\r\n\r\n raw_fasta_labels = set([label.split('_')[0] for label in fasta_labels])\r\n\r\n tree_f = open(tree_fp, \"U\")\r\n\r\n tree = DndParser(tree_f)\r\n\r\n # Get a set of tree tip names\r\n tree_tips = set(tree.getTipNames())\r\n\r\n labels_not_in_tips = []\r\n\r\n for curr_label in raw_fasta_labels:\r\n if curr_label not in tree_tips:\r\n labels_not_in_tips.append(curr_label)\r\n\r\n # Return True if all found in tree tips\r\n if len(labels_not_in_tips) == 0:\r\n labels_not_in_tips = True\r\n\r\n tips_not_in_labels = []\r\n\r\n for curr_tip in tree_tips:\r\n if curr_tip not in raw_fasta_labels:\r\n tips_not_in_labels.append(curr_tip)\r\n\r\n if len(tips_not_in_labels) == 0:\r\n tips_not_in_labels = True\r\n\r\n return [labels_not_in_tips, tips_not_in_labels]","def check_X_dominant(variant, family, strict=False):\n path = os.path.abspath(os.getcwd())\n output_log = os.path.join(path, \"variants_inheritance_patterns.csv\")\n\n if os.path.isfile(output_log):\n output = open(output_log, \"a\")\n else:\n output = open(output_log, \"w\")\n\n affected = False\n genotyped = False\n\n for individual in family.individuals:\n # Get the genotype for this variant for this individual\n individual_genotype = variant['genotypes'][individual]\n \n if strict:\n if not individual_genotype.genotyped:\n return False\n # The case where the individual is healthy\n if family.individuals[individual].healthy:\n # Healthy womans can be carriers but not homozygote:\n if individual_genotype.genotyped:\n if family.individuals[individual].sex == 2:\n if individual_genotype.homo_alt:\n return False\n # Males can not carry the variant:\n elif family.individuals[individual].sex == 1:\n if individual_genotype.has_variant:\n return False\n \n # The case when the individual is sick\n elif family.individuals[individual].affected:\n affected = True\n # If the individual is sick and homozygote ref it \n # can not be x-linked-dominant\n if individual_genotype.genotyped:\n genotyped = True\n if individual_genotype.homo_ref:\n return False\n\n if affected and genotyped:\n output.write(\"{},Mother heterozigous alt or father without variant and offspring is homozygote or heterozygote alternative\\n\".format(variant.get('variant_id', None)))\n elif affected:\n output.write(\"{},Mother heterozigous alt or father without variant and offspring is not genotyped\\n\".format(variant.get('variant_id', None)))\n else:\n output.write(\"{},Parents without variant and no affected individual\\n\".format(variant.get('variant_id', None)))\n output.close()\n return True","def test_make_compatible_taxa_summaries(self):\r\n exp = [(['Even1'], ['foo'], array([[0.5]])), (['Even1'], ['foo'],\r\n array([[0.1]]))]\r\n obs = _make_compatible_taxa_summaries(self.taxa_summary5,\r\n self.taxa_summary6)\r\n self.compare_multiple_level_array(obs, exp)","def taxonomic_consistency(self, taxonomy, report_errors=True):\n\n expected_parent = {}\n for genome_id, taxa in taxonomy.items():\n if taxa[0] == 'd__Viruses' or '[P]' in taxa[0]:\n # *** This is a HACK. It would be far better to enforce\n # a taxonomically consistent taxonomy, but\n # the viral taxonomy at IMG is currently not consistent\n continue\n\n for r in range(1, len(taxa)):\n if len(taxa[r]) == 3:\n break\n\n if taxa[r] in expected_parent:\n if report_errors:\n if taxa[r - 1] != expected_parent[taxa[r]]:\n self.logger.warning('Provided taxonomy is not taxonomically consistent.')\n self.logger.warning(\n 'Genome %s indicates the parent of %s is %s.' % (genome_id, taxa[r], taxa[r - 1]))\n self.logger.warning(\n 'The parent of this taxa was previously indicated as %s.' % (expected_parent[taxa[r]]))\n # return None\n\n expected_parent[taxa[r]] = taxa[r - 1]\n\n return expected_parent","def test_tree_support(self):\r\n master_tree = parse_newick('((a:2,b:3)ab:2,(c:1,d:2)cd:7)rt;')\r\n \"\"\"\r\n /-------.5 /-a\r\n ---1| \\-b\r\n \\------.5 /-c\r\n \\-d\r\n \"\"\"\r\n t2 = parse_newick('((a:2,b:3,c:33)ho:2,d:7);') # abc are siblings\r\n\r\n tc.tree_support(master_tree, t2)\r\n assert_almost_equal(\r\n master_tree.getNodeMatchingName('rt').bootstrap_support, 1.0)","def match_taxon(name):\n taxid = None\n scientific_name = None\n automatic_replacement = False\n iname = name.strip().lower().replace(' ', ' ')\n\n if name:\n # 1. 'name' matches the scientific name of a virus:\n if name in scientific_names:\n taxid = scientific_names[name]\n scientific_name = name\n # 2. 'name' is a close case-insensitive match for a virus:\n elif iname in lowercase_names:\n taxid = lowercase_names[iname]\n scientific_name = taxid_names[taxid]\n automatic_replacement = True\n # 3. 'name' is the exact synonym of some taxon\n elif name in synonyms:\n taxid = synonyms[name]\n scientific_name = taxid_names[taxid]\n # 4. 'name' is a substring of exactly one scientific name:\n else:\n matches = []\n for scientific_name in scientific_names.keys():\n if name in scientific_name:\n matches.append(scientific_name)\n if len(matches) > 1:\n break\n if len(matches) == 1:\n scientific_name = matches[0]\n taxid = scientific_names[scientific_name]\n\n return (name, taxid, scientific_name, automatic_replacement)","def has_duplicates(tree):\n taxa = [tip.name for tip in tree.tips()]\n if '' in taxa or None in taxa:\n raise ValueError('Empty taxon name(s) found.')\n return len(set(taxa)) < len(taxa)","def test_nti(self):\r\n # using the input of the distance matrix generated from this tree\r\n # '(((sp1:.06,sp2:.1)A:.031,(sp3:.001,sp4:.01)B:.2)AB:.4,((sp5:.03,sp6:.02)C:.13,(sp7:.01,sp8:.005)D:.1)CD:.3)root;'\r\n # using this tree and this clumping we get almost exact agreement\r\n # between phylocom and relatedness.py\r\n distmat = array(\r\n [[0., 0.16, 0.292, 0.301, 0.951, 0.941, 0.901, 0.896],\r\n [0.16,\r\n 0.,\r\n 0.332,\r\n 0.341,\r\n 0.991,\r\n 0.981,\r\n 0.941,\r\n 0.936],\r\n [0.292,\r\n 0.332,\r\n 0.,\r\n 0.011,\r\n 1.061,\r\n 1.051,\r\n 1.011,\r\n 1.006],\r\n [0.301,\r\n 0.341,\r\n 0.011,\r\n 0.,\r\n 1.07,\r\n 1.06,\r\n 1.02,\r\n 1.015],\r\n [0.951,\r\n 0.991,\r\n 1.061,\r\n 1.07,\r\n 0.,\r\n 0.05,\r\n 0.27,\r\n 0.265],\r\n [0.941,\r\n 0.981,\r\n 1.051,\r\n 1.06,\r\n 0.05,\r\n 0.,\r\n 0.26,\r\n 0.255],\r\n [0.901,\r\n 0.941,\r\n 1.011,\r\n 1.02,\r\n 0.27,\r\n 0.26,\r\n 0.,\r\n 0.015],\r\n [0.896, 0.936, 1.006, 1.015, 0.265, 0.255, 0.015, 0.]])\r\n\r\n seed(0)\r\n obs_nti = nti(distmat, list(arange(8)), [0, 1, 3, 6], 1000)\r\n assert_almost_equal(-1.2046544711672049, obs_nti)","def test_make_compatible_taxa_summaries_already_compatible(self):\r\n # Using two compatible ts.\r\n obs = _make_compatible_taxa_summaries(self.taxa_summary4,\r\n self.taxa_summary5)\r\n self.compare_multiple_level_array(obs, (self.taxa_summary4, self.taxa_summary5))\r\n\r\n # Using the same ts twice.\r\n obs = _make_compatible_taxa_summaries(self.taxa_summary4,\r\n self.taxa_summary4)\r\n self.compare_multiple_level_array(obs, (self.taxa_summary4, self.taxa_summary4))\r\n\r\n obs = _make_compatible_taxa_summaries(self.taxa_summary5,\r\n self.taxa_summary5)\r\n self.compare_multiple_level_array(obs, (self.taxa_summary5, self.taxa_summary5))","def testTaxaData(self):\n try:\n numEukaryota = 0\n numBacteria = 0\n numVirus = 0\n numArchaea = 0\n numOther = 0\n numUnclass = 0\n logger.info(\"Loading taxonomy data\")\n tU = TaxonomyUtils()\n logger.info(\"Done loading taxonomy data\")\n iCount = 0\n entryD = self.__mU.doImport(self.__instanceSavePath, fmt=\"pickle\")\n for entryId in entryD:\n for entityId, eD in entryD[entryId][\"selected_polymer_entities\"].items():\n taxId = eD[\"ncbi_taxonomy_id\"] if \"ncbi_taxonomy_id\" in eD else None\n if taxId is None:\n logger.debug(\"Missing taxId entryId %s entityId %s\", entryId, entityId)\n continue\n # lin = tU.getLineage(taxId)\n # nmL = tU.getLineageNames(taxId)\n ok1 = tU.isEukaryota(taxId)\n if ok1:\n numEukaryota += 1\n ok3 = tU.isVirus(taxId)\n if ok3:\n numVirus += 1\n ok2 = tU.isBacteria(taxId)\n if ok2:\n numBacteria += 1\n #\n ok4 = tU.isArchaea(taxId)\n if ok4:\n numArchaea += 1\n #\n ok5 = tU.isOther(taxId)\n if ok5:\n numOther += 1\n #\n ok6 = tU.isUnclassified(taxId)\n if ok6:\n numUnclass += 1\n\n if ok1 and (ok1 and ok2):\n logger.info(\"taxid %r conflicting lineage\", taxId)\n #\n if not ok1 and not ok2 and not ok3 and not ok4 and not ok5 and not ok6:\n logger.info(\"unassigned taxid %r\", taxId)\n\n logger.debug(\"taxId %r entryId %s entityId %s\", taxId, entryId, entityId)\n iCount += 1\n # if iCount > 5000:\n # break\n logger.info(\"Eukaryota %d Bacteria %d Virus %d Archaea %d Other/Syn %r Unclass %d\", numEukaryota, numBacteria, numVirus, numArchaea, numOther, numUnclass)\n except Exception as e:\n logger.exception(\"Failing with %s\", str(e))\n self.fail()","def test_check_tree_subset(self):\r\n\r\n fasta_labels = ['seq1_1', 'seq1_2', 'seq2_3', 'seq3_4']\r\n\r\n actual_subset_results = check_tree_subset(fasta_labels,\r\n self.sample_tree_3tips_fp)\r\n\r\n # Should find all and give True result\r\n\r\n self.assertEqual(actual_subset_results, True)\r\n\r\n # Should also get same results with 5 tip tree\r\n\r\n fasta_labels = ['seq1_1', 'seq1_2', 'seq2_3', 'seq3_4']\r\n\r\n actual_subset_results = check_tree_subset(fasta_labels,\r\n self.sample_tree_5tips_fp)\r\n\r\n # Should find all and give True result\r\n\r\n self.assertEqual(actual_subset_results, True)\r\n\r\n # Change two of the fasta labels to not match tree tips\r\n\r\n fasta_labels = ['seq1_1', 'seqX_2', 'seq2_3', 'seqY_4']\r\n\r\n actual_subset_results = check_tree_subset(fasta_labels,\r\n self.sample_tree_5tips_fp)\r\n\r\n # Should find seqX and seqY as not being a subset\r\n\r\n self.assertEqual(actual_subset_results, ['seqX', 'seqY'])","def validate(self, taxonomy,\n check_prefixes,\n check_ranks,\n check_hierarchy,\n check_species,\n check_group_names,\n check_duplicate_names,\n check_capitalization,\n report_errors=True):\n\n # check for incomplete taxonomy strings or unexpected rank prefixes\n invalid_ranks = {}\n invalid_prefixes = {}\n invalid_species_name = {}\n invalid_group_name = {}\n invalid_capitalization = set()\n for taxon_id, taxa in taxonomy.items():\n if check_ranks:\n if len(taxa) != len(Taxonomy.rank_prefixes):\n invalid_ranks[taxon_id] = ';'.join(taxa)\n continue\n\n if check_prefixes:\n for r, taxon in enumerate(taxa):\n if taxon[0:3] != Taxonomy.rank_prefixes[r]:\n invalid_prefixes[taxon_id] = [taxon, ';'.join(taxa)]\n break\n\n if check_group_names:\n for taxon in taxa:\n canonical_taxon = ' '.join([t.strip() for t in re.split('_[A-Z]+(?= |$)', taxon[3:])]).strip()\n if canonical_taxon and re.match('^[a-zA-Z0-9- ]+$', canonical_taxon) is None:\n if not taxon.startswith('s__') or check_species:\n invalid_group_name[taxon_id] = [taxon, 'Taxon contains invalid characters']\n\n if check_species:\n genus_index = Taxonomy.rank_index['g__']\n species_index = Taxonomy.rank_index['s__']\n if len(taxa) > species_index:\n species_name = taxa[species_index]\n valid, error_msg = self.validate_species_name(species_name, require_full=True, require_prefix=True)\n if not valid:\n invalid_species_name[taxon_id] = [species_name, error_msg]\n\n if species_name != 's__':\n genus_name = taxa[genus_index]\n generic_name = species_name.split()[0]\n if genus_name[3:] != generic_name[3:]:\n invalid_species_name[taxon_id] = [species_name,\n 'Genus and generic names do not match: %s' % genus_name]\n\n if check_capitalization:\n for taxon in taxa:\n if taxon[3].islower():\n invalid_capitalization.add(taxon)\n\n # check for duplicate names\n invalid_duplicate_name = []\n if check_duplicate_names:\n invalid_duplicate_name = self.duplicate_names(taxonomy, check_species)\n\n # check for inconsistencies in the taxonomic hierarchy\n invalid_hierarchies = defaultdict(set)\n missing_parent = set()\n if check_hierarchy:\n expected_parent = self.taxonomic_consistency(taxonomy, False)\n\n for taxon_id, taxa in taxonomy.items():\n for r in range(1, len(taxa)):\n if len(taxa[r]) == 3:\n continue\n\n if r == self.rank_index['s__'] and not check_species:\n continue\n\n if taxa[r] not in expected_parent:\n missing_parent.add(taxa[r])\n elif taxa[r - 1] != expected_parent[taxa[r]]:\n invalid_hierarchies[taxa[r]].add(taxa[r - 1])\n invalid_hierarchies[taxa[r]].add(expected_parent[taxa[r]])\n\n if report_errors:\n if len(invalid_ranks):\n print('')\n print('Taxonomy contains too few ranks:')\n for taxon_id, taxa_str in invalid_ranks.items():\n print('%s\\t%s' % (taxon_id, taxa_str))\n\n if len(invalid_prefixes):\n print('')\n print('Taxonomy contains an invalid rank prefix:')\n for taxon_id, info in invalid_prefixes.items():\n print('%s\\t%s\\t%s' % (taxon_id, info[0], info[1]))\n\n if len(invalid_group_name):\n print('')\n print('Taxa containing invalid characters:')\n for taxon_id, err_msg in invalid_group_name.items():\n print('%s\\t%s\\t%s' % (taxon_id, err_msg[0], err_msg[1]))\n\n if len(invalid_species_name):\n print('')\n print('Taxonomy contains invalid species names:')\n for taxon_id, info in invalid_species_name.items():\n print('%s\\t%s\\t%s' % (taxon_id, info[0], info[1]))\n\n if len(invalid_duplicate_name):\n print('')\n print('Taxonomy contains identical taxon names in multiple lineages:')\n for duplicate_name in invalid_duplicate_name.keys():\n print('%s' % duplicate_name)\n\n if len(missing_parent):\n print('')\n print('Taxonomy contains taxa with an undefined parent:')\n for taxon in missing_parent:\n print('%s' % taxon)\n\n if len(invalid_hierarchies):\n print('')\n print('Taxonomy contains taxa with multiple parents:')\n for child_taxon, parent_taxa in invalid_hierarchies.items():\n print('%s\\t%s' % (child_taxon, ', '.join(parent_taxa)))\n\n if len(invalid_capitalization):\n print('')\n print('Taxa do not start with a capital letter:')\n for taxon in invalid_capitalization:\n print('{}'.format(taxon))\n\n return invalid_ranks, invalid_prefixes, invalid_species_name, invalid_hierarchies, invalid_group_name, invalid_capitalization","def tree_support(master, subsampled_tree):\r\n master_tipnames = set(master.getTipNames())\r\n subsampled_tree_trimmed = copy.deepcopy(subsampled_tree)\r\n\r\n def delete_test(node):\r\n if not node.isTip():\r\n return False\r\n else:\r\n return (node.Name not in master_tipnames)\r\n subsampled_tree_trimmed.removeDeleted(delete_test)\r\n subsampled_tree_trimmed.prune()\r\n\r\n # subsampled_tree_nodes_names is a list of lists.\r\n # each elem is list of tip names for a node\r\n subsampled_tree_nodes_names = []\r\n for node in subsampled_tree_trimmed.iterNontips(include_self=True):\r\n subsampled_tree_nodes_names.append(node.getTipNames())\r\n\r\n # now a list of sets, each set is tip names for a specific node\r\n subsampled_tree_nodes_names = map(set, subsampled_tree_nodes_names)\r\n\r\n for master_node in master.iterNontips(include_self=True):\r\n if set(master_node.getTipNames()) in subsampled_tree_nodes_names:\r\n try:\r\n master_node.bootstrap_support += 1\r\n except AttributeError:\r\n master_node.bootstrap_support = 1","def draw_tre_and_aln(aln_region, tre_str, og, target_sp):\n #site, aa, score=ps_site\n\n # get leaves from tree string\n tstax = getleaf(tre_str)\n\n ts = TreeStyle()\n ts.margin_left = 5\n ts.margin_right = 30\n ts.margin_top = 20\n ts.tree_width = 50\n\n aln_region_str = aln_region.format(\"fasta\")\n\n t = PhyloTree(tre_str, alignment=aln_region_str, alg_format=\"fasta\")\n\n # interfact\n def _set_style(t):\n # input an t with alignment, add label to it\n \"\"\"\n info = TextFace(\"{}\\nCodon:{}\\nScore:{}\".format(og,site,score), fsize=8, fgcolor='black', ftype='Arial')\n info.margin_top = 10\n info.margin_right = 20\n info.margin_left = 5\n t.add_face(info, column=0, position=\"branch-bottom\")\n #t.add_face(TextFace(\"Codon:{}\".format(site)),column=0,position=\"branch-bottom\")\n \"\"\"\n ## label the longbranch\n nstyle = NodeStyle()\n # red line\n #nstyle[\"bgcolor\"] = \"DarkSeaGreen\"\n #nstyle[\"bgcolor\"] = \"LightSalmon\"\n nstyle[\"hz_line_type\"] = 0\n #nstyle[\"hz_line_color\"] = \"#ff0000\"\n for tst in tstax:\n tsnode = t.get_leaves_by_name(name=tst)[0]\n tsnode.set_style(nstyle)\n # add #1 to target species\n if target_sp in tsnode.name:\n tsnode.name = tsnode.name + \"_#1\"\n return t\n t = _set_style(t)\n ## add AA alignment\n def _trans_aln(aln_region):\n # input an coding aln\n aa_aln_str = \"\"\n for seq_obj in aln_region:\n dna = Seq(str(seq_obj.seq))\n aa_s = dna.translate(gap=\"-\")\n aa_aln_str += '>{}\\n{}\\n'.format(seq_obj.id, aa_s)\n #print(aa_aln_str)\n return aa_aln_str\n\n aa_aln_region_str = _trans_aln(aln_region)\n t_aa = PhyloTree(tre_str, alignment=aa_aln_region_str, alg_format=\"fasta\")\n t_aa = _set_style(t_aa)\n\n return t, t_aa, ts","def build_tree(self, genes_share_one_alignment):\r\n species_name = self.species\r\n fun_built_tree = getattr(SSTree, species_name)\r\n return fun_built_tree(genes_share_one_alignment)","def check_X_recessive(variant, family, strict=False):\n path = os.path.abspath(os.getcwd())\n output_log = os.path.join(path, \"variants_inheritance_patterns.csv\")\n\n if os.path.isfile(output_log):\n output = open(output_log, \"a\")\n else:\n output = open(output_log, \"w\")\n\n affected = False\n genotyped = False\n female = False\n \n for individual in family.individuals:\n # Get the genotype for this variant for this individual\n individual_genotype = variant['genotypes'][individual]\n \n if strict:\n if not individual_genotype.genotyped:\n return False\n # The case where the individual is healthy\n if family.individuals[individual].healthy:\n # If individual is healthy and homozygote alternative \n # the variant can not be deleterious:\n if individual_genotype.genotyped:\n if individual_genotype.homo_alt:\n return False\n # If individual is male it can not have the variant at all\n if family.individuals[individual].sex == 1:\n if individual_genotype.has_variant:\n return False\n \n # The case when the individual is sick\n elif family.individuals[individual].affected:\n affected = True\n # If the individual is sick and homozygote ref it can not be x-recessive\n if individual_genotype.genotyped:\n genotyped = True\n if individual_genotype.homo_ref:\n return False\n # Women have to be hom alt to be sick (almost allways carriers)\n elif family.individuals[individual].sex == 2:\n female = True\n if not individual_genotype.homo_alt:\n return False\n if affected and genotyped and female:\n output.write(\"{},Parents are not genotyped or is female het or is male without variant and offspring is female homozygote alternative\\n\".format(variant.get('variant_id', None)))\n elif affected and genotyped:\n output.write(\"{},Parents are not genotyped or is female het or is male without variant and offspring is male with heterozygous variant\\n\".format(variant.get('variant_id', None)))\n elif affected:\n output.write(\"{},Parents are not genotyped or is female het or is male without variant and offspring is not genotyped\\n\".format(variant.get('variant_id', None)))\n else:\n output.write(\"{},Parents without variant and no affected individual\\n\".format(variant.get('variant_id', None)))\n output.close()\n return True","def aglycon_single_tax(df_NP):\n # **seperate aglycons with at least two different entries in taxonomy**\n index_Unique_Tax = [ind for ind, tax_list in enumerate(df_NP.taxonomy) if len(tax_list) == 1]\n df_Without_Double = df_NP.iloc[index_Unique_Tax[:]]\n #df_Without_Double\n # **check for 'double' or 'triple' entries in taxonomy**\n index_double_or_triple = [ind for ind, tax_list in enumerate(df_Without_Double.taxonomy) if 'double' not in tax_list and 'triple' not in tax_list]\n df_Without_Double_or_Triple = df_Without_Double.iloc[index_double_or_triple[:]]\n #df_Without_Double_or_Triple\n # **------for taxonomy prediction------**\n df_Without_Double_or_Triple.to_pickle(\"output_data/df_all_aglycons_with_single_taxonomy.pkl\")\n # **------end for taxonomy prediction------**","def test_taxonomy(self):\n #ncbi\n params = self.default_params.copy()\n params[\"db_prefix\"] = self.results_dir + \"test_taxonomy_ncbi\"\n params[\"taxonomy\"] = \"ncbi\"\n params[\"taxonomy_files\"] = data_dir + \"build-custom/taxdump.tar.gz\"\n params[\"ncbi_file_info\"] = data_dir + \"build-custom/assembly_summary.txt\"\n params[\"genome_size_files\"] = data_dir + \"build-custom/species_genome_size.txt.gz\"\n cfg = Config(\"build-custom\", **params)\n self.assertTrue(run_ganon(cfg, params[\"db_prefix\"]), \"ganon build-custom run failed\")\n res = build_sanity_check_and_parse(vars(cfg))\n self.assertIsNotNone(res, \"ganon build-custom sanity check failed\")\n\n #gtdb\n params = self.default_params.copy()\n params[\"db_prefix\"] = self.results_dir + \"test_taxonomy_gtdb\"\n params[\"taxonomy\"] = \"gtdb\"\n params[\"taxonomy_files\"] = [data_dir + \"build-custom/ar53_taxonomy.tsv.gz\",\n data_dir + \"build-custom/bac120_taxonomy.tsv.gz\"]\n params[\"genome_size_files\"] = [data_dir + \"build-custom/ar53_metadata.tar.gz\",\n data_dir + \"build-custom/bac120_metadata.tar.gz\"]\n cfg = Config(\"build-custom\", **params)\n self.assertTrue(run_ganon(cfg, params[\"db_prefix\"]), \"ganon build-custom run failed\")\n res = build_sanity_check_and_parse(vars(cfg))\n self.assertIsNotNone(res, \"ganon build-custom sanity check failed\")\n\n #skip\n params = self.default_params.copy()\n params[\"db_prefix\"] = self.results_dir + \"test_taxonomy_skip\"\n params[\"taxonomy\"] = \"skip\"\n params[\"taxonomy_files\"] = \"\"\n cfg = Config(\"build-custom\", **params)\n self.assertTrue(run_ganon(cfg, params[\"db_prefix\"]), \"ganon build-custom run failed\")\n res = build_sanity_check_and_parse(vars(cfg))\n self.assertIsNotNone(res, \"ganon build-custom sanity check failed\")","def findDiscripancies(taxonomy):\n i = 0\n for entry in taxonomy:\n if entry['parentName'] != None:\n print entry['nodeName']\n if entry['nodeName'].lower() == entry['parentName'].lower():\n i += 1\n print \"No of same nodes = {} \" .format(i)","def test_write_summarize_taxa(self):\r\n # Classic format.\r\n write_summarize_taxa(self.taxa_summary, self.taxa_header, self.tmp_fp1)\r\n obs = open(self.tmp_fp1).read()\r\n exp = '\\n'.join(['Taxon\\tfoo\\tbar\\tfoobar',\r\n 'a;b;c\\t0\\t1\\t2',\r\n 'd;e;f\\t3\\t4\\t5\\n'])\r\n self.assertEqual(obs, exp)\r\n self.files_to_remove.append(self.tmp_fp1)\r\n\r\n # BIOM format.\r\n write_summarize_taxa(self.taxa_summary, self.taxa_header, self.tmp_fp2,\r\n file_format='biom')\r\n exp = parse_classic_table_to_rich_table(exp.split('\\n'), None, None,\r\n None, SparseTaxonTable)\r\n obs = open(self.tmp_fp2).read()\r\n obs = parse_biom_table(obs)\r\n self.assertEqual(obs, exp)\r\n self.files_to_remove.append(self.tmp_fp2)","def test_leafs():\n leafs = ['s6675', 's136315', 's10765', 's106594', 's131']\n for leaf in leafs:\n synset = germanet_data.get_synset_by_id(leaf)\n np.testing.assert_equal(synset.is_root(), False)\n np.testing.assert_equal(synset.is_leaf(), True)","def _uniqueness_check(self, cls, unique_in = None, **attr):\n # under the same datasource, only 1 subsystem, 1 neuropil, 1 tract of the name can exist\n # under the same neuropil, only 1 neuron of the name can exist\n # multiple (collections of) synapses can exist between two neurons\n if cls == 'Species':\n tmp = self.sql_query(\n \"\"\"select from Species where (name = \"{name}\" or \"{name}\" in synonyms) and stage = \"{stage}\" and sex = \"{sex}\" \"\"\".format(\n name = attr['name'], stage = attr['stage'], sex = attr['sex']))\n if len(tmp):\n objs = tmp.node_objs\n if attr['name'] in [obj.name for obj in objs]:\n raise NodeAlreadyExistError(\"\"\"Species {name} at {stage} stage ({sex}) already exists with rid = {rid}\"\"\".format(\n name = attr['name'], stage = attr['stage'], sex = attr['sex'], rid = objs[0]._id))\n else:\n for obj in objs:\n if attr['name'] in obj.synonyms:\n raise NodeAlreadyExistError(\n \"\"\"Species {name} (as its synonym) at {stage} stage ({sex}) already exists with rid = {rid}, use name {formalname} instead\"\"\".format(\n name = attr['name'], stage = attr['stage'], sex = attr['sex'], rid = obj._id, formalname = obj.name))\n elif cls == 'DataSource':\n objs = self.find_objs('DataSource', name=attr['name'], version=attr['version'])\n #if self.exists(cls, name = attr['name'], version = attr['version']):\n if len(objs):\n raise NodeAlreadyExistError(\"\"\"{} Node with attributes {} already exists with rid = {}\"\"\".format(\n cls, ', '.join([\"\"\"{} = {}\"\"\".format(key, value) \\\n for key, value in attr.items()]), objs[0]._id))\n elif cls == 'Neurotransmitter':\n tmp = self.sql_query(\n \"\"\"select from Neurotransmitter where name = \"{name}\" or \"{name}\" in synonyms\"\"\".format(\n name = attr['name']))\n if len(tmp):\n objs = tmp.node_objs\n if attr['name'] in [obj.name for obj in objs]:\n raise NodeAlreadyExistError(\"\"\"Neurotransmitter {name} already exists with rid = {rid}\"\"\".format(\n name = attr['name'], rid = objs[0]._id))\n return objs\n else:\n for obj in objs:\n if attr['name'] in obj.synonyms:\n raise NodeAlreadyExistError(\n \"\"\"Neurotransmitter {name} (as its synonym) already exists with rid = {rid}, use name {formalname} instead\"\"\".format(\n name = attr['name'], rid = obj._id, formalname = obj.name))\n elif cls in ['Subsystem', 'Neuropil', 'Subregion', 'Tract']:\n # TODO: synonyms are not checked against existing names and synonyms\n if not isinstance(unique_in, models.DataSource):\n raise TypeError('To check the uniqueness of a {} instance, unique_in must be a DataSource object'.format(cls))\n tmp = self.sql_query(\n \"\"\"select from (select from {cls} where name = \"{name}\" or \"{name}\" in synonyms) let $q = (select from (select expand($parent.$parent.current.in('Owns'))) where @class='{ucls}' and @rid = {rid}) where $q.size() = 1\"\"\".format(\n rid = unique_in._id, cls = cls, name = attr['name'], ucls = unique_in.element_type))\n if len(tmp):\n objs = tmp.node_objs\n if attr['name'] in [obj.name for obj in objs]:\n raise NodeAlreadyExistError(\"\"\"{cls} {name} already exists under DataSource {ds} version {version}, rid = {rid}\"\"\".format(\n cls = cls, name = attr['name'],\n ds = unique_in.name,\n version = unique_in.version, rid = objs[0]._id))\n else:\n for obj in objs:\n if attr['name'] in obj.synonyms:\n raise NodeAlreadyExistError(\n \"\"\"{cls} {name} already exists as a synonym of {cls} {formalname} under DataSource {ds} version {version}, rid = {rid}\"\"\".format(\n cls = cls, name = attr['name'], formalname = obj.name,\n ds = unique_in.name,\n version = unique_in.version, rid = obj._id))\n # Alternatively, try:\n # tmp = self.sql_query(\n # \"\"\"select from {cls} where name = \"{name}\" or \"{name}\" in synonyms\"\"\".format(\n # cls = cls, name = attr['name']))\n # ds = tmp.owned_by(cls = 'DataSource').has(rid = datasource)\n # if len(ds):\n # tmp1 = tmp.has(name = attr['name'])\n # if len(tmp1.owned_by(cls = 'DataSource').has(rid = datasource)):\n # raise NodeAlreadyExistError(\"\"\"{cls} {name} already exists under DataSource {ds} version {version}\"\"\".format(\n # cls = cls, name = attr['name'],\n # ds = datasource.name,\n # version = datasource.version))\n # else:\n # all_synonym_objs = (tmp - tmp1).node_objs\n # for obj in objs:\n # if len(QueryWrapper.from_rids(obj._id).has(cls = 'DataSource').has(rid = datasource)):\n # raise NodeAlreadyExistError(\n # \"\"\"{cls} {name} already exists as a synonym of {cls} {formalname} under DataSource {ds} version {version}\"\"\".format(\n # cls = cls, name = attr['name'], formalname = obj.name,\n # ds = datasource.name,\n # version = datasource.version))\n\n # Alternatively 2, try: (will be slow when it has a lot of Owns edges)\n # tmp = sql_query(\n # \"\"\"\n # select from (select expand(out('Owns')[@class = \"{cls}\"]) from {rid}) where name = \"{name}\" or \"{name}\" in synonyms\n # \"\"\"\n # )\n # elif cls in ['Subregion']:\n # if not isinstance(unique_in, models.Neuropil):\n # raise TypeError('To check the uniqueness of a {} instance, unique_in must be a Neuropil object'.format(cls))\n # tmp = self.sql_query(\n # \"\"\"select from (select from {cls} where name = \"{name}\" or \"{name}\" in synonyms) let $q = (select from (select expand($parent.$parent.current.in('Owns'))) where @class='ucls' and @rid = {rid}) where $q.size() = 1\"\"\".format(\n # rid = unique_in._id, cls = cls, name = attr['name'], ucls = unique_in.element_type))\n # if len(tmp):\n # objs = tmp.node_objs\n # if attr['name'] in [obj.name for obj in objs]:\n # raise NodeAlreadyExistError(\"\"\"{cls} {name} already exists under Neuropil {ds}\"\"\".format(\n # cls = cls, name = attr['name'],\n # ds = unique_in.name))\n # else:\n # for obj in objs:\n # if name in obj.synonyms:\n # raise NodeAlreadyExistError(\n # \"\"\"{cls} {name} already exists as a synonym of {cls} {formalname} under Neuropil {ds}\"\"\".format(\n # cls = cls, name = attr['name'], formalname = obj.name,\n # ds = unique_in.name))\n elif cls in ['Neuron', 'NeuronFragment']:\n # TODO: synonyms are not checked against existing names and synonyms\n if not isinstance(unique_in, models.DataSource):\n raise TypeError('To check the uniqueness of a {} instance, unique_in must be a DataSource object'.format(cls))\n tmp = self.sql_query(\n \"\"\"select from (select from {cls} where uname = \"{name}\") let $q = (select from (select expand($parent.$parent.current.in('Owns'))) where @class='{ucls}' and @rid = {rid}) where $q.size() = 1\"\"\".format(\n rid = unique_in._id, cls = cls, name = attr['name'], ucls = unique_in.element_type))\n if len(tmp):\n objs = tmp.node_objs\n raise NodeAlreadyExistError(\"\"\"{cls} {name} already exists with rid = {rid}, under DataSource {ds} version {version}\"\"\".format(\n cls = cls, name = attr['name'], rid = objs[0]._id,\n ds = unique_in.name,\n version = unique_in.version))\n elif cls == 'Circuit':\n if not isinstance(unique_in, models.DataSource):\n raise TypeError('To check the uniqueness of a {} instance, unique_in must be a DataSource object'.format(cls))\n tmp = self.sql_query(\n \"\"\"select from (select from {cls} where name = \"{name}\") let $q = (select from (select expand($parent.$parent.current.in('Owns'))) where @class='{ucls}' and @rid = {rid}) where $q.size() = 1\"\"\".format(\n rid = unique_in._id, cls = cls, name = attr['name'], ucls = unique_in.element_type))\n if len(tmp):\n objs = tmp.node_objs\n if attr['name'] in [obj.name for obj in objs]:\n raise NodeAlreadyExistError(\"\"\"{cls} {name} already exists under DataSource {ds} version {version}, rid = {rid}\"\"\".format(\n cls = cls, name = attr['name'],\n ds = unique_in.name,\n version = unique_in.version, rid = objs[0]._id))\n elif cls == 'ArborizationData':\n if not isinstance(unique_in, (models.Neuron, models.Synapse)):\n raise TypeError('To check the uniqueness of a ArborizationData instance, unique_in must be a Neuron or a Synapse object')\n tmp = self.sql_query(\n \"\"\"select from (select expand(out(HasData)) from {rid}) where @class = 'ArborizationData' \"\"\".format(rid = unique_in._id))\n if len(tmp):\n raise NodeAlreadyExistError(\"\"\"ArborizationData already exists for {node} {uname} with rid = {rid}. Use NeuroArch.update_{node}_arborization to update the record\"\"\".format(\n node = unique_in.element_type.lower(), rid = tmp.node_objs[0]._id, uname = unique_in.uname))\n else:\n raise TypeError('Model type not understood.')\n return True","def test_level_sequence_taxrank(self):\n # --level genus NCBI\n params = self.default_params.copy()\n params[\"db_prefix\"] = self.results_dir + \"test_level_sequence_genus_ncbi\"\n params[\"input_target\"] = \"sequence\"\n params[\"level\"] = \"genus\"\n params[\"taxonomy\"] = \"ncbi\"\n params[\"taxonomy_files\"] = data_dir + \"build-custom/taxdump.tar.gz\"\n params[\"ncbi_sequence_info\"] = data_dir + \"build-custom/nucl_gb.accession2taxid.gz\"\n params[\"genome_size_files\"] = data_dir + \"build-custom/species_genome_size.txt.gz\"\n cfg = Config(\"build-custom\", **params)\n self.assertTrue(run_ganon(cfg, params[\"db_prefix\"]), \"ganon build-custom run failed\")\n res = build_sanity_check_and_parse(vars(cfg), skipped_targets=True) # may skip targets without genus entry\n self.assertIsNotNone(res, \"ganon build-custom sanity check failed\")\n # Tax must not have \"species\" (filtered out)\n self.assertFalse(\"species\" in res[\"tax\"]._ranks.values(), \"rank found\")","def validate_taxon(cell):\n (name, taxid, scientific_name, automatic_replacement) = match_taxon(cell.value or '')\n\n # Update the cell\n if is_virus(taxid):\n if name == scientific_name:\n cell.fill = greenFill\n elif automatic_replacement:\n cell.comment = Comment('Automatically replaced \"%s\" with \"%s\".'\n % (name, scientific_name), author)\n cell.value = scientific_name\n cell.fill = blueFill\n else:\n cell.comment = Comment('Suggestion: ' + scientific_name, author)\n cell.fill = orangeFill\n return scientific_name\n elif taxid:\n cell.comment = Comment('Not the name of a virus', author)\n cell.fill = redFill\n else:\n cell.comment = Comment('Not found in NCBI Taxonomy', author)\n cell.fill = darkRedFill","def test_big_family(self):\n\n self.taxon_tester('Staphylinidae')","def test_make_taxa_long(self):\n basic_test_runner(self, 'taxonomy', rank='all')","def test_level_file_taxrank(self):\n\n # --level genus NCBI\n params = self.default_params.copy()\n params[\"db_prefix\"] = self.results_dir + \"test_level_file_genus_ncbi\"\n params[\"input_target\"] = \"file\"\n params[\"level\"] = \"genus\"\n params[\"taxonomy\"] = \"ncbi\"\n params[\"taxonomy_files\"] = data_dir + \"build-custom/taxdump.tar.gz\"\n params[\"ncbi_file_info\"] = data_dir + \"build-custom/assembly_summary.txt\"\n params[\"genome_size_files\"] = data_dir + \"build-custom/species_genome_size.txt.gz\"\n cfg = Config(\"build-custom\", **params)\n self.assertTrue(run_ganon(cfg, params[\"db_prefix\"]), \"ganon build-custom run failed\")\n res = build_sanity_check_and_parse(vars(cfg), skipped_targets=True) # may skip targets without genus entry\n self.assertIsNotNone(res, \"ganon build-custom sanity check failed\")\n # Tax must not have \"species\" (filtered out)\n self.assertFalse(\"species\" in res[\"tax\"]._ranks.values(), \"rank found\")\n\n # --level genus GTDB\n params = self.default_params.copy()\n params[\"db_prefix\"] = self.results_dir + \"test_level_file_genus_gtdb\"\n params[\"input_target\"] = \"file\"\n params[\"level\"] = \"genus\"\n params[\"taxonomy\"] = \"gtdb\"\n params[\"taxonomy_files\"] = [data_dir + \"build-custom/ar53_taxonomy.tsv.gz\",\n data_dir + \"build-custom/bac120_taxonomy.tsv.gz\"]\n params[\"genome_size_files\"] = [data_dir + \"build-custom/ar53_metadata.tar.gz\",\n data_dir + \"build-custom/bac120_metadata.tar.gz\"]\n cfg = Config(\"build-custom\", **params)\n self.assertTrue(run_ganon(cfg, params[\"db_prefix\"]), \"ganon build-custom run failed\")\n res = build_sanity_check_and_parse(vars(cfg))\n self.assertIsNotNone(res, \"ganon build-custom sanity check failed\")\n # Tax must not have \"species\" (filtered out)\n self.assertFalse(\"species\" in res[\"tax\"]._ranks.values(), \"rank found\")","def assert_sample_labelled_trees_equal(tree, dpy_tree):\n for sample in tree.samples():\n dpy_node = dpy_tree.find_node_with_taxon_label(f\"n{sample}\")\n # Check the branch length paths to root are equal\n p1 = []\n u = sample\n while tree.parent(u) != tskit.NULL:\n p1.append(tree.branch_length(u))\n u = tree.parent(u)\n p2 = []\n while dpy_node.parent_node is not None:\n p2.append(dpy_node.edge_length)\n dpy_node = dpy_node.parent_node\n assert len(p1) == len(p2)\n np.testing.assert_array_almost_equal(p1, p2)","def is_good_cyc(path, G, bamfile):\n\n sing_nodes = set()\n for node in path:\n if node[-1] == \"'\": node = node[:-1]\n sing_nodes.add(node)\n if len(sing_nodes)==0: return True\n\n non_path_dominated_nodes = 0\n\n for nd in sing_nodes:\n mate_tigs = get_contigs_of_mates(nd, bamfile, G)\n # NOTE: ^ this only gets mates that are reachable from nd in G\n logger.info(\"\\tNode: %s\" % nd)\n logger.info(\"\\t\\tMates: %s\" % \", \".join(mate_tigs))\n\n # need to check against F and R versions of path nodes\n path_rc = [rc_node(x) for x in path]\n num_mates_in_path = sum([1 for x in mate_tigs if (x in path or x in path_rc)])\n num_mates_not_in_path = len(mate_tigs)-num_mates_in_path\n if num_mates_in_path < num_mates_not_in_path:\n ########### if len(mate_tigs)>1 and num_mates_in_path < num_mates_not_in_path:\n non_path_dominated_nodes += 1\n if float(non_path_dominated_nodes)/float(len(sing_nodes)) > PARAMS.GOOD_CYC_DOMINATED_THRESH:\n logger.info(\"Too many nodes with majority of mates not on path\")\n return False\n else: return True","def test_regen(self, tmpdir, treantclass):\n with tmpdir.as_cwd():\n C1 = treantclass('regen')\n C1.tags.add('fantastic')\n C2 = treantclass('regen') # should be regen of C1\n assert 'fantastic' in C2.tags\n\n # they point to the same file, but they are not the same object\n assert C1 is not C2","def test_check_isomorphism(self):\n mol1 = Molecule(smiles='[O-][N+]#N')\n mol2 = Molecule(smiles='[N-]=[N+]=O')\n self.assertTrue(converter.check_isomorphism(mol1, mol2))","def test_taxa_with_special_characters(self):\r\n fd, taxonomy_fp = mkstemp()\r\n close(fd)\r\n f = open(taxonomy_fp, \"w\")\r\n f.write(rdp_id_to_taxonomy_special_chars)\r\n f.close()\r\n self._paths_to_clean_up.append(taxonomy_fp)\r\n\r\n app = RdpTaxonAssigner({\r\n 'id_to_taxonomy_fp': taxonomy_fp,\r\n 'reference_sequences_fp': self.reference_seqs_file.name,\r\n })\r\n res = app(self.tmp_seq_filepath)\r\n obs_lineage, obs_confidence = res['X67228 some description']\r\n\r\n self.assertEqual(obs_lineage, (\r\n \"Bacteria;Proteobacteria;Alphaproteobacteria;Rhizobiales<What;\"\r\n \"Rhizobiaceae&Huh?;Rhizobium\"))\r\n self.assertEqual(obs_confidence, 1.0)","def cladistic(tree, taxa):\n tips = []\n taxa = set(taxa)\n for tip in tree.tips():\n if tip.name in taxa:\n tips.append(tip)\n n = len(taxa)\n if len(tips) < n:\n raise ValueError('Taxa not found in the tree.')\n return ('uni' if n == 1 else\n ('mono' if len(tree.lca(tips).subset()) == n else\n 'poly'))","def read_from_tree(self, tree, warnings=True):\n\n if isinstance(tree, str):\n tree = dendropy.Tree.get_from_path(tree,\n schema='newick',\n rooting=\"force-rooted\",\n preserve_underscores=True)\n\n taxonomy = {}\n for leaf in tree.leaf_node_iter():\n taxa = []\n\n node = leaf.parent_node\n while node:\n if node.label:\n taxa_str = node.label\n if ':' in taxa_str:\n taxa_str = taxa_str.split(':')[1]\n\n if not is_float(taxa_str):\n if taxa_str[-1] == ';':\n taxa_str = taxa_str[:-1]\n\n # check for concatenated ranks of the form: p__Crenarchaeota__c__Thermoprotei\n for prefix in Taxonomy.rank_prefixes:\n split_str = '__' + prefix\n if split_str in taxa_str:\n taxa_str = taxa_str.replace(split_str, ';' + prefix)\n\n # appears to be an internal label and not simply a support value\n taxa = [x.strip() for x in taxa_str.split(';')] + taxa\n node = node.parent_node\n\n if warnings and len(taxa) > 7:\n self.logger.warning(\n 'Invalid taxonomy string read from tree for taxon %s: %s' % (leaf.taxon.label, ';'.join(taxa)))\n\n # check if genus name should be appended to species label\n if len(taxa) == 7:\n genus = taxa[5][3:]\n species = taxa[6][3:]\n if genus not in species and len(species.split()) == 1:\n taxa[6] = 's__' + genus + ' ' + species\n\n taxa = self.fill_trailing_ranks(taxa)\n taxonomy[leaf.taxon.label] = taxa\n\n return taxonomy","def are_two_trees_incompatible(tree1, tree2):\n leaves1 = get_leaf_set(tree1)\n leaves2 = get_leaf_set(tree2)\n shared = list(leaves1.intersection(leaves2))\n\n taxa = dendropy.TaxonNamespace(shared) # CRITICAL!!!\n\n # No topological information\n if len(shared) < 4:\n return False\n\n # Move trees onto shared leaf set\n tree1.retain_taxa_with_labels(shared)\n tree1.migrate_taxon_namespace(taxa)\n tree1.is_rooted = False\n tree1.collapse_basal_bifurcation()\n tree1.update_bipartitions()\n\n tree2.retain_taxa_with_labels(shared)\n tree2.migrate_taxon_namespace(taxa)\n tree2.is_rooted = False\n tree2.collapse_basal_bifurcation()\n tree2.update_bipartitions()\n\n # Check for compatibility\n [fp, fn] = false_positives_and_negatives(tree1, tree2)\n if fp > 0 or fn > 0:\n return True\n else:\n return False","def test_genbank_to_genome_taxonomy(self):\n result = self.gfu.genbank_to_genome(self.ctx, {\n 'workspace_name': self.ws_name,\n 'generate_ids_if_needed': 'true', # why is this a string\n 'taxon_id': '3702',\n 'file': {\n 'path': f\"{_DATA_PATH}/wigglesworthia/genome.gb\"\n },\n 'genome_name': str(uuid4()),\n })\n ('result', result)\n ref = result[0]['genome_ref']\n self.assertTrue(ref, 'Genome ref exists')\n info = result[0]['genome_info']\n typ = info[2]\n self.assertTrue(typ.startswith('KBaseGenomes.Genome'))\n info_details = info[-1]\n self.assertEqual(info_details['Taxonomy'], (\n \"cellular organisms;Eukaryota;Viridiplantae;\"\n \"Streptophyta;Streptophytina;Embryophyta;Tracheophyta;\"\n \"Euphyllophyta;Spermatophyta;Magnoliopsida;Mesangiospermae;\"\n \"eudicotyledons;Gunneridae;Pentapetalae;rosids;malvids;\"\n \"Brassicales;Brassicaceae;Camelineae;Arabidopsis\"\n ))\n self.assertEqual(info_details['Size'], '697724')\n self.assertEqual(info_details['Source'], 'Genbank')\n self.assertEqual(info_details['Name'], 'Wigglesworthia glossinidia endosymbiont of Glossina brevipalpis')\n self.assertEqual(info_details['GC content'], '0.22479')\n self.assertEqual(info_details['Genetic code'], '11')\n self.assertEqual(info_details['Number of Genome Level Warnings'], '1')\n self.assertEqual(info_details['Source ID'], 'BA000021')\n self.assertEqual(info_details['Number of Protein Encoding Genes'], '20')\n self.assertEqual(info_details['Domain'], 'Eukaryota')\n self.assertTrue(info_details['Assembly Object'])\n self.assertEqual(info_details['Number contigs'], '1')\n self.assertEqual(info_details['Number of CDS'], '20')\n self.assertTrue(info_details['MD5'])","def test_multitype_path():\n egfr = Agent('EGFR', db_refs={'HGNC':'3236'})\n grb2 = Agent('GRB2', db_refs={'HGNC': '4566'})\n grb2_egfr = Agent('GRB2', bound_conditions=[BoundCondition(egfr)],\n db_refs={'HGNC': '4566'})\n sos1 = Agent('SOS1', db_refs={'HGNC':'11187'}, )\n sos1_grb2 = Agent('SOS1', bound_conditions=[BoundCondition(grb2)],\n db_refs={'HGNC':'11187'}, )\n kras = Agent('KRAS', db_refs={'HGNC':'6407'})\n kras_g = Agent('KRAS', activity=ActivityCondition('gtpbound', True),\n db_refs={'HGNC': '6407'})\n braf = Agent('BRAF', db_refs={'HGNC':'1097'})\n\n def check_stmts(stmts, paths):\n pa = PysbAssembler()\n pa.add_statements(stmts)\n pa.make_model(policies='one_step')\n stmts_to_check = [\n Activation(egfr, kras, 'gtpbound'),\n Activation(egfr, braf, 'kinase')\n ]\n mc = ModelChecker(pa.model, stmts_to_check)\n results = mc.check_model()\n assert len(results) == len(stmts_to_check)\n assert isinstance(results[0], tuple)\n assert results[0][1].paths == paths[0]\n assert results[1][1].paths == paths[1]\n # Check with the ActiveForm\n stmts1 = [\n Complex([egfr, grb2]),\n Complex([sos1, grb2_egfr]),\n ActiveForm(sos1_grb2, 'activity', True),\n Activation(sos1_grb2, kras, 'gtpbound'),\n Activation(kras_g, braf, 'kinase')\n ]\n check_stmts(stmts1, ([[('EGFR_GRB2_bind', 1), ('SOS1_GRB2_EGFR_bind', 1),\n ('SOS1_GRB2_activates_KRAS_gtpbound', 1),\n ('KRAS_gtpbound_active_obs', 1)]],\n [[('EGFR_GRB2_bind', 1), ('SOS1_GRB2_EGFR_bind', 1),\n ('SOS1_GRB2_activates_KRAS_gtpbound', 1),\n ('KRAS_activates_BRAF_kinase', 1),\n ('BRAF_kinase_active_obs', 1)]]))\n # Check without the ActiveForm\n stmts2 = [\n Complex([egfr, grb2]),\n Complex([sos1, grb2_egfr]),\n RasGef(sos1_grb2, kras),\n Activation(kras_g, braf, 'kinase')\n ]\n check_stmts(stmts2, ([[('EGFR_GRB2_bind', 1), ('SOS1_GRB2_EGFR_bind', 1),\n ('SOS1_GRB2_activates_KRAS', 1),\n ('KRAS_gtpbound_active_obs', 1)]],\n [[('EGFR_GRB2_bind', 1), ('SOS1_GRB2_EGFR_bind', 1),\n ('SOS1_GRB2_activates_KRAS', 1),\n ('KRAS_activates_BRAF_kinase', 1),\n ('BRAF_kinase_active_obs', 1)]]))","def testComplexRichness(self):\n self.assertGreaterEqual(self.tree1.get_species_richness(), self.tree2.get_species_richness())\n self.assertGreaterEqual(self.tree3.get_species_richness(), self.tree2.get_species_richness())\n self.assertGreaterEqual(self.tree1.get_species_richness(), self.tree4.get_species_richness())","def test_nxgraph(self):\n self._build_sample_graph()\n # Adding singleton\n sg = self.skill_graph.add(Skill.build('g', ''))\n skill_map = SkillMap.load(self.course)\n nxgraph = SkillMapMetrics(skill_map).nxgraph\n self.assertIsInstance(nxgraph, DiGraph)\n successors = skill_map.build_successors()\n # Check nodes\n self.assertEqual(len(nxgraph), len(successors))\n for skill in successors:\n self.assertIn(skill, nxgraph.nodes(),\n msg='Node {} not found in nx graph.'.format(skill))\n # Check edges\n original_edges = sum(len(dst) for dst in successors.values())\n self.assertEqual(len(nxgraph.edges()), original_edges)\n for src, dst in nxgraph.edges_iter():\n self.assertIn(src, successors)\n self.assertIn(dst, successors[src],\n msg='Extra {},{} edge in nx graph.'.format(src, dst))","def compare_balanced_tree(G, node1:str, node2:str, traversed1:list, traversed2:list):\n logger.debug(f\"checking symmtrical connections for nodes: {node1}, {node2}\")\n tree1 = set(get_next_level(G,[node1]))\n tree2 = set(get_next_level(G,[node2]))\n traversed1.append(node1)\n traversed2.append(node2)\n if tree1==tree2:\n return True\n while(len(list(tree1))== len(list(tree2)) > 0):\n logger.debug(f\"tree1 {tree1} tree2 {tree2} traversed1 {traversed1} traversed2 {traversed2}\")\n tree1 = set(tree1) - set(traversed1)\n tree2 = set(tree2) - set(traversed2)\n\n if tree1.intersection(tree2) or len(list(tree1))== len(list(tree2))==0:\n return True\n else:\n traversed1+=list(tree1)\n traversed2+=list(tree2)\n tree1=set(get_next_level(G,tree1))\n tree2=set(get_next_level(G,tree2))\n\n logger.debug(f\"Non symmetrical branches for nets: {node1}, {node2}\")\n return False","def init_taxon():\n if not exists('./data/taxdmp.zip'):\n ftp = FTP('ftp.ncbi.nih.gov')\n ftp.login()\n ftp.cwd('pub/taxonomy')\n ftp.retrbinary('RETR taxdmp.zip', open('./data/taxdmp.zip', 'wb').write)\n ftp.quit\n with ZipFile('./data/taxdmp.zip', 'r') as dumpfile:\n dumpfile.extractall(path='./data/')\n taxon_id = dict()\n data = list()\n name = dict()\n specie = list()\n son = dict()\n greatson = dict()\n parent = dict()\n rank = dict()\n global taxon\n taxon = list()\n with open('./data/names.dmp', 'r') as dumpfile:\n raw = dumpfile.read().split(sep='\\n')\n raw.pop()\n for record in raw:\n add = record.replace('\\t', '').split(sep='|')\n if add[0] not in name or add[2] == 'scientific name':\n name[add[0]] = add[1]\n with open('./data/nodes.dmp', 'r') as dumpfile:\n raw = dumpfile.read().split(sep='\\n')\n raw.pop()\n for record in raw:\n add = record.replace('\\t', '').split(sep='|')\n # 1696063|Sarcocystis corvusi||scientific name|\n taxon_id[add[0]] = add[1]\n rank[add[0]] = add[3]\n if add[2] == 'species':\n specie.append(add[0])\n for specie in specie:\n record = [specie, ]\n while taxon_id[specie] != '1':\n record.append(taxon_id[specie])\n specie = taxon_id[specie]\n # if '33090' in record:\n # record.pop()\n # record.pop()\n data.append(record)\n for data in data:\n for n in range(len(data)):\n if data[n] not in parent:\n parent[data[n]] = data[(n + 1):]\n if n == 0:\n continue\n if data[n] not in son:\n son[data[n]] = {data[n - 1], }\n else:\n son[data[n]].add(data[n - 1])\n if data[n] not in greatson:\n greatson[data[n]] = {data[0], }\n else:\n greatson[data[n]].add(data[0])\n for specie in name.items():\n if specie[0] not in son:\n son[specie[0]] = set()\n if specie[0] not in parent:\n parent[specie[0]] = list()\n if specie[0] not in greatson:\n greatson[specie[0]] = set()\n record = [specie[0], name[specie[0]], rank[specie[0]], son[specie[0]], parent[specie[0]], greatson[specie[0]]]\n taxon.append(record)\n\n con = sqlite3.connect('./data/DB')\n cur = con.cursor()\n cur.execute(\n 'CREATE TABLE IF NOT EXISTS taxon (Id TEXT, Name TEXT, Rank TEXT, Son TEXT, Parent TEXT, GreatSon TEXT);')\n for line in taxon:\n son = ' '.join(line[3])\n parent = ' '.join(line[4])\n greatson = ' '.join(line[5])\n cur.execute('INSERT INTO taxon (Id, Name, Rank, Son, Parent, GreatSon) VALUES (?, ?, ?, ?, ?, ?);',\n (line[0], line[1], line[2], son, parent, greatson))\n con.commit()\n cur.close()\n con.close()\n print('Done.\\n')","def test_make_taxonomy(self):\n basic_test_runner(self, 'taxonomy')","def read_mothur_taxonomy(f):\n for n, line in enumerate(open(f)):\n if line.startswith('#'):\n continue\n\n line = line.rstrip()\n name, taxa = line.rstrip().split('\\t')\n skip = False\n for word in EXCLUDE:\n if word in taxa:\n skip = True\n break\n\n if skip:\n continue \n\n # the parsing of taxa works for both mothur output and this\n taxa = taxa.rstrip(';') # for mothur classfy.seqs output\n lis = taxa.split(';')\n lis2 = []\n for item in lis:\n item = item.strip() # for copyrigher copy table ' ;' separater\n if item.endswith(')'):\n item = item.rsplit('(', 1)[0].strip()\n\n # remove taxon level prefix, e.g. 'p__Firmicutes'\n if '__' in item:\n item = item.split('__', 1)[1]\n\n #item = item.strip('\"')\n\n # green gene taxonomy has sapce\n item = item.replace(' ', '_')\n\n item = item.lower()\n if item in ['', 'unknown', 'other', 'unassigned']:\n item = 'Unclassifed'\n\n item = item.capitalize()\n lis2.append(item)\n\n length = len(lis2)\n assert length == LEVELS, 'levels ({}) is not ({})'.format(\n length, LEVELS)\n yield tuple(lis2)","def check_tree_subset(fasta_labels,\r\n tree_fp):\r\n\r\n # Need to get modified fasta labels with underscore stripped\r\n\r\n raw_fasta_labels = set([label.split('_')[0] for label in fasta_labels])\r\n\r\n tree_f = open(tree_fp, \"U\")\r\n\r\n tree = DndParser(tree_f)\r\n\r\n # Get a set of tree tip names\r\n tree_tips = set(tree.getTipNames())\r\n\r\n labels_not_in_tips = []\r\n\r\n for curr_label in raw_fasta_labels:\r\n if curr_label not in tree_tips:\r\n labels_not_in_tips.append(curr_label)\r\n\r\n # Return True if all found in tree tips\r\n if len(labels_not_in_tips) == 0:\r\n labels_not_in_tips = True\r\n\r\n return labels_not_in_tips","def update_tip_names(tree, taxdict):\n\n list_nodes = []\n uniprot_mapping = pd.DataFrame(columns=['taxid', 'name', 'uniprot'])\n\n counter = 0\n for node in tree.traverse(\"postorder\"):\n current_name = node.name\n\n if 'NMR' in current_name:\n new_name = \"Heterocephalus_glaber\"\n node.name = new_name\n list_nodes.append(node.name)\n taxid = \"NA\" \n uniprot_mapping.loc[counter] = (taxid, new_name, \"UP000006813\")\n counter += 1\n\n elif 'Nfurzer' in current_name:\n new_name = \"Nothobranchius_furzeri\"\n node.name = new_name\n list_nodes.append(node.name)\n taxid = \"NA\"\n uniprot_mapping.loc[counter] = (taxid, new_name, new_name)\n counter += 1\n\n elif 'TAX' in current_name:\n taxid = current_name[3:].split('x')[0]\n new_name = taxdict.get(taxid, taxid) \n node.name = new_name \n list_nodes.append(node.name)\n unip = get_uniprot(taxid, accession)\n uniprot_mapping.loc[counter] = (taxid, new_name, unip)\n counter += 1\n\n\n \n tree.write(outfile=\"../../data/tree/tree.nw\")\n\n nodes_df = pd.DataFrame(list_nodes)\n nodes_df.to_csv(\"../../data/tree/tree_list_nodes.txt\", index=False, header=False)\n\n uniprot_mapping.to_csv(\"../../data/tree/tree_uniprot.txt\", sep='\\t', index=False, header=True)\n\n return tree, list_nodes","def test_consistency(self):\n def compare_func(obj, node):\n # number of children must be consistent\n self.assertEqual(\n len(obj.children),\n len(obj._children)\n )\n\n # obj.html equals node\n self.assertEqual(obj._html, node)\n\n # coordinates\n self.assertEqual(\n obj._coordinates,\n obj.coordinates,\n self.expected[\"coordinates\"][obj.id or \"document\"]\n )\n\n # confidence\n self.assertAlmostEqual(\n obj.confidence,\n self.expected[\"confidence\"][obj.id or \"document\"]\n )\n\n self.recursively_compare_tree_against_html(compare_func)","def test_change(self):\n # Folder must be root to load in make_net properly\n if os.getcwd().split('\\\\')[-1] == 'tests': os.chdir('..')\n \n # Create parents and deepcopy everything (just to be sure)\n cfg = Config().genome\n gene1, gene2 = get_simple_node_gene(0, cfg)\n gene1_act = deepcopy(gene1.activation)\n gene1_agg = deepcopy(gene1.aggregation)\n gene1_bias = deepcopy(gene1.bias)\n gene2_act = deepcopy(gene2.activation)\n gene2_agg = deepcopy(gene2.aggregation)\n gene2_bias = deepcopy(gene2.bias)\n \n # Perform crossover and mutations\n gene3 = gene1.crossover(other=gene2, cfg=cfg, ratio=0.5)\n gene3.activation = 'c'\n gene3.aggregation = 'c'\n gene3.bias = -10\n \n # Check for unchanged parents\n self.assertEqual(gene1.activation, gene1_act)\n self.assertEqual(gene1.aggregation, gene1_agg)\n self.assertEqual(gene1.bias, gene1_bias)\n self.assertEqual(gene2.activation, gene2_act)\n self.assertEqual(gene2.aggregation, gene2_agg)\n self.assertEqual(gene2.bias, gene2_bias)","def test_calc_shared_phylotypes_pairwise(self):\r\n\r\n #self.assertEqual(_calc_shared_phylotypes_pairwise(self.otu_table, 0, 0), 5)\r\n #self.assertEqual(_calc_shared_phylotypes_pairwise(self.otu_table, 0, 1), 2)\r\n #self.assertEqual(_calc_shared_phylotypes_pairwise(self.otu_table, 0, 2), 3)\r\n #self.assertEqual(_calc_shared_phylotypes_pairwise(self.otu_table, 2, 2), 3)\r\n self.assertEqual(\r\n _calc_shared_phylotypes_pairwise(\r\n self.otu_table,\r\n 'S1',\r\n 'S1'),\r\n 5)\r\n self.assertEqual(\r\n _calc_shared_phylotypes_pairwise(\r\n self.otu_table,\r\n 'S1',\r\n 'S2'),\r\n 2)\r\n self.assertEqual(\r\n _calc_shared_phylotypes_pairwise(\r\n self.otu_table,\r\n 'S1',\r\n 'S3'),\r\n 3)\r\n self.assertEqual(\r\n _calc_shared_phylotypes_pairwise(\r\n self.otu_table,\r\n 'S3',\r\n 'S3'),\r\n 3)","def test_client_tax_information_update(self):\n pass","def test_distinguish_path_polarity1():\n Monomer('A')\n Monomer('B', ['act'], {'act' :['y', 'n']})\n Monomer('C', ['T185'], {'T185':['u', 'p']})\n Parameter('k', 1)\n Rule('A_activate_B', A() + B(act='n') >> A() + B(act='y'), k)\n Rule('B_dephos_C', B(act='y') + C(T185='p') >>\n B(act='y') + C(T185='u'), k)\n Initial(A(), k)\n Initial(B(act='y'), k)\n Initial(C(T185='p'), k)\n Annotation(A, 'http://identifiers.org/hgnc/HGNC:1')\n Annotation(B, 'http://identifiers.org/hgnc/HGNC:2')\n Annotation(C, 'http://identifiers.org/hgnc/HGNC:3')\n Annotation('A_activate_B', 'A', 'rule_has_subject')\n Annotation('A_activate_B', 'B', 'rule_has_object')\n Annotation('B_dephos_C', 'B', 'rule_has_subject')\n Annotation('B_dephos_C', 'C', 'rule_has_object')\n C.site_annotations = [\n Annotation(('T185', 'p'), 'phosphorylation', 'is_modification'),\n Annotation('T185', 'T', 'is_residue'),\n Annotation('T185', '185', 'is_position'),\n ]\n # Create the model checker\n stmts = _path_polarity_stmt_list()\n mc = ModelChecker(model, stmts)\n results = mc.check_model()\n assert len(results) == len(stmts)\n assert isinstance(results[0], tuple)\n path_results = [res[1] for res in results]\n assert path_results[0].paths == []\n assert path_results[1].paths == [[('A_activate_B', 1), ('B_dephos_C', 1),\n ('C_T185_p_obs', -1)]]\n assert path_results[2].paths == []\n assert path_results[3].paths == [[('B_dephos_C', 1), ('C_T185_p_obs', -1)]]","def test_grounded_modified_enzyme():\n mek_s202 = Agent('MEK1', mods=[ModCondition('phosphorylation', 'S', '202')],\n db_refs={'HGNC': '6840'})\n mek_phos = Agent('MEK1', mods=[ModCondition('phosphorylation', None, None)],\n db_refs={'HGNC': '6840'})\n erk = Agent('ERK2', db_refs={'HGNC': '6871'})\n stmt_to_model = Phosphorylation(mek_s202, erk, None, None)\n stmt_to_check = Phosphorylation(mek_phos, erk, None, None)\n pa = PysbAssembler()\n pa.add_statements([stmt_to_model])\n pa.make_model(policies='one_step')\n mc = ModelChecker(pa.model, [stmt_to_check])\n results = mc.check_model()\n assert len(results) == 1\n assert results[0][0] == stmt_to_check\n assert results[0][1].paths == \\\n [[('MEK1_phosphoS202_phosphorylation_ERK2_phospho', 1),\n ('ERK2_phospho_p_obs', 1)]]","def test_taxa_chao1(self):\n table_factory = DataTableFactory(PACKET_DIR)\n chao1 = table_factory.taxa_alpha_diversity(metric='chao1', rarefy=1000 * 1000)\n self.assertTrue((chao1 > 0).all())","def test_make_core_taxa(self):\n basic_test_runner(self, 'core_taxa', nrows=-1)","def test_taxa_alpha_div_genus(self):\n table_factory = DataTableFactory(PACKET_DIR)\n entropy = table_factory.taxa_alpha_diversity(rank='genus')\n self.assertTrue((entropy > 0).all())","def test_written(self):\n\n checkit=subprocess.run([\"python\", \"../../taxonomy/src_files/rdp_lineage_to_tax.py\", \"-i\", \"../resource_files/rdp_test_taxonomy.csv\", \"-o\", \"test_rdp_taxonomy\"], capture_output=True, text=True)\n \n # is the folder there\n self.assertTrue(os.path.exists(os.path.exists(\"../processed_files/rdp_prep_taxonomy\")))\n \n # there should be 2 files in there\n files_in_dir=os.listdir(\"../processed_files/rdp_prep_taxonomy\")\n self.assertEqual(len(files_in_dir), 2)\n \n for x in files_in_dir:\n if x.split('.')[-1]=='txt':\n taxonomy_file=x\n \n # does the test match the provided actual output\n # rdp_team_taxonomy_check can be found on https://github.com/rdpstaff/classifier/issues/18\n self.assertTrue(filecmp.cmp(\"../resource_files/rdp_team_taxonomy_check.txt\", \"../processed_files/rdp_prep_taxonomy/{}\".format(taxonomy_file)))\n \n shutil.rmtree(\"../processed_files/rdp_prep_taxonomy\")","def test_create_gene_ontology(self):\n\n # Here are mappings for just a few yeast genes.\n\n mapping = {}\n mapping['STE7'] = ['GO:0000187']\n mapping['PBS2'] = ['GO:0000187']\n mapping['NOP8'] = [\n 'GO:0003676', 'GO:0003723', 'GO:0042254', 'GO:0005634', 'GO:0005730'\n ]\n\n # Build the ontology, then see if it looks correct.\n\n root = dc.models.tensorgraph.models.ontology.create_gene_ontology(\n mapping, min_node_features=1)\n assert len(root.feature_ids) == 0\n\n def find_features(node, features):\n features.update(node.feature_ids)\n for child in node.children:\n find_features(child, features)\n\n all_features = set()\n find_features(root, all_features)\n assert len(all_features) == 3\n for key in mapping:\n assert key in all_features","def test_tree_search(self) -> None:\n class DumbModel(GymNet):\n count: int = 0\n\n def initial_inference(self, observations: np.ndarray) -> typing.Tuple[np.ndarray, np.ndarray, float]:\n s, pi, v = super().initial_inference(observations)\n self.count += 1\n return np.ones_like(s) * self.count, np.array([6/8 - 1e-8, 2/8 + 1e-8]), 0\n\n def recurrent_inference(self, latent_state: np.ndarray, action: int) -> typing.Tuple[float, np.ndarray]:\n r, s, pi, v = super().recurrent_inference(latent_state, action)\n self.count += 1\n return 0, np.ones_like(latent_state) * self.count, np.array([6/8 - 1e-8, 2/8 + 1e-8]), action\n\n memory_net = self.net\n memory_search = self.mcts\n\n # Swap class variables\n self.net = DumbModel(self.g, self.config.net_args)\n self.mcts = MuZeroMCTS(self.g, self.net, self.config.args)\n\n # No discounting and no exploration to ensure deterministic behaviour.\n self.config.args.gamma = 1\n self.config.args.exploration_fraction = 0\n\n # Experiment 1\n self.config.args.numMCTSSims = 4\n pi_1, v_1 = self.mcts.runMCTS(np.zeros(4), np.ones(2))\n np.testing.assert_array_almost_equal(pi_1, [1/2, 1/2])\n np.testing.assert_almost_equal(v_1, 1/4)\n self.mcts.clear_tree()\n\n # Experiment 2\n self.config.args.numMCTSSims = 8\n pi_2, v_2 = self.mcts.runMCTS(np.zeros(4), np.ones(2))\n np.testing.assert_array_almost_equal(pi_2, [5/8, 3/8])\n np.testing.assert_almost_equal(v_2, 1/4)\n self.mcts.clear_tree()\n\n # Undo class variables swap\n self.net = memory_net\n self.mcts = memory_search","def checkOntoSimilarity(ontology_id):\n # print('checkOntoSimilarity() =>', ontology_id)\n url = cfg.ontology_sim + '/status'\n res = requests.post(url, json={'ontologyId': ontology_id})\n resp = res.json()\n resp['statusCode'] = res.status_code\n return resp #resp['statusCode'] = 200 if ontology exists and 404 otherwise","def is_virus(taxid):\n return taxid and taxid != '1' and (taxid == '10239' or is_virus(parents[taxid]))","def siblinghood_sim_exact(siblinghood_1, siblinghood_2):\n return SubstructureAgreement.substructure_sim_exact(siblinghood_1, siblinghood_2)","def identify_multiple_taxa(cazy_data, multiple_taxa_logger):\n multiple_taxa_gbk = []\n\n for genbank_accession in tqdm(\n cazy_data,\n total=len(list(cazy_data.keys())), desc='Searching for multiple taxa annotations',\n ):\n\n if len(cazy_data[genbank_accession]['taxonomy']) > 1:\n multiple_taxa_gbk.append(genbank_accession)\n\n for tax_tuple in cazy_data[genbank_accession]['taxonomy']:\n multiple_taxa_logger.warning(\n f\"{genbank_accession}\\t{tax_tuple.kingdom}\\t{tax_tuple.organism}\"\n )\n \n else:\n cazy_data[genbank_accession]['organism'] = list(cazy_data[genbank_accession]['taxonomy'])[0]\n\n return multiple_taxa_gbk","def check_nodes_match_nptg_data(cls, gdf, named_area):\n #\n check_name = \"check_nodes_match_nptg_data\"\n # list of all geographic admin areas\n admin_areas = [\n \"Aberdeen\",\n \"Aberdeenshire\",\n \"Angus\",\n \"Argyll & Bute\",\n \"Bath & North East Somerset\",\n \"Bedford\",\n \"Blackburn with Darwen\",\n \"Blackpool\",\n \"Blaenau Gwent\",\n \"Bournemouth\",\n \"Bracknell Forest\",\n \"Bridgend\",\n \"Brighton and Hove\",\n \"Bristol\",\n \"Buckinghamshire\",\n \"Caerphilly\",\n \"Cambridgeshire\",\n \"Cardiff\",\n \"Carmarthenshire\",\n \"Central Bedfordshire\",\n \"Ceredigion\",\n \"Cheshire East\",\n \"Cheshire West & Chester\",\n \"Clackmannanshire\",\n \"Conwy\",\n \"Cornwall\",\n \"Cumbria\",\n \"Darlington\",\n \"Denbighshire\",\n \"Derby\",\n \"Derbyshire\",\n \"Devon\",\n \"Dorset\",\n \"Dumfries & Galloway\",\n \"Dundee\",\n \"Durham\",\n \"East Ayrshire\",\n \"East Dunbartonshire\",\n \"East Lothian\",\n \"East Renfrewshire\",\n \"East Riding of Yorkshire\",\n \"East Sussex\",\n \"Edinburgh\",\n \"Essex\",\n \"Falkirk\",\n \"Fife\",\n \"Flintshire\",\n \"Glasgow\",\n \"Gloucestershire\",\n \"Greater London\",\n \"Greater Manchester\",\n \"Gwynedd\",\n \"Halton\",\n \"Hampshire\",\n \"Hartlepool\",\n \"Herefordshire\",\n \"Hertfordshire\",\n \"Highland\",\n \"Inverclyde\",\n \"Isle of Anglesey\",\n \"Isle of Wight\",\n \"Kent\",\n \"Kingston upon Hull\",\n \"Lancashire\",\n \"Leicester\",\n \"Leicestershire\",\n \"Lincolnshire\",\n \"Luton\",\n \"Medway\",\n \"Merseyside\",\n \"Merthyr Tydfil\",\n \"Middlesbrough\",\n \"Midlothian\",\n \"Milton Keynes\",\n \"Monmouthshire\",\n \"Moray\",\n \"Neath Port Talbot\",\n \"Newport\",\n \"Norfolk\",\n \"North Ayrshire\",\n \"North East Lincolnshire\",\n \"North Lanarkshire\",\n \"North Lincolnshire\",\n \"North Somerset\",\n \"North Yorkshire\",\n \"Northamptonshire\",\n \"Northumberland\",\n \"Nottingham\",\n \"Nottinghamshire\",\n \"Orkney Islands\",\n \"Oxfordshire\",\n \"Pembrokeshire\",\n \"Perth & Kinross\",\n \"Peterborough\",\n \"Plymouth\",\n \"Poole\",\n \"Portsmouth\",\n \"Powys\",\n \"Reading\",\n \"Redcar & Cleveland\",\n \"Renfrewshire\",\n \"Rhondda Cynon Taff\",\n \"Rutland\",\n \"Scottish Borders\",\n \"Shetland Islands\",\n \"Shropshire\",\n \"Slough\",\n \"Somerset\",\n \"South Ayrshire\",\n \"South Gloucestershire\",\n \"South Lanarkshire\",\n \"South Yorkshire\",\n \"Southampton\",\n \"Southend-on-Sea\",\n \"Staffordshire\",\n \"Stirling\",\n \"Stockton-on-Tees\",\n \"Stoke-on-Trent\",\n \"Suffolk\",\n \"Surrey\",\n \"Swansea\",\n \"Swindon\",\n \"Telford & Wrekin\",\n \"Thurrock\",\n \"Torbay\",\n \"Torfaen\",\n \"Tyne & Wear\",\n \"Vale of Glamorgan\",\n \"Warrington\",\n \"Warwickshire\",\n \"West Berkshire\",\n \"West Dunbartonshire\",\n \"West Lothian\",\n \"West Midlands\",\n \"West Sussex\",\n \"West Yorkshire\",\n \"Western Isles\",\n \"Wiltshire\",\n \"Windsor & Maidenhead\",\n \"Wokingham\",\n \"Worcestershire\",\n \"Wrexham\",\n \"York\",\n ]\n\n # TODO get the admin areas from the nodes file, compare against the list of\n # area names\n # nptg values\n adjanct_locals = etl.load_gazette_adjanct_localities()\n admin_codes = etl.naptan_gazette_admin_area_codes()\n districts = etl.naptan_gazette_districts()\n localities = etl.naptan_gazette_localities()\n locality_alternate = etl.load_gazette_localities_alternative_names()\n locality_hierarch = etl.load_gazette_locality_hierarchy()\n plusbusmap = etl.load_gazette_plusbus_mapping()\n plusbuszone = etl.load_gazette_plusbus_zones()\n regions = etl.naptan_gazette_region()\n\n # node values\n node_locs = gdf[\"LocalityName\"].unique()\n # get nptg localities,\n nptg_locs = localities[\"LocalityName\"].unique()\n # TODO filter to nptg to nodes, get all the localities in nptg for\n # this area\n # get the unique area code for this admin area.\n area_admin_code = node_locs[\"AdminCode\"].unique()\n # check the area admin code in the nptg file for the corresponding\n # localities.\n missing_localities = nptg_locs[~nptg_locs.AdminCode.isin(area_admin_code)]\n # check if locality is\n df3 = gaz_locs[gaz_locs.LocalityName.isin(gdf.LocalityName)]\n # get all the localities\n # TODO list the localities in nptg but not nodes\n\n # TODO plot sample on map\n # TODO write unused localities in given area to file.\n report_failing_nodes(\n gdf,\n check_name,\n )\n return","def check_observation(self, observation):\n\n # Cell parts available\n neuron_parts_avail = [neurite_type.name for neurite_type in nm.NEURITE_TYPES[1:]]\n neuron_parts_avail.append('neuron')\n\n # Cell features available\n cell_feats_avail = nm.fst.NEURONFEATURES.keys()\n\n # Neurite features available\n neurite_feats_avail = list(nm.fst.NEURITEFEATURES.keys())\n neurite_feats_extra = ['neurite_field_diameter', 'neurite_largest_extent', 'neurite_shortest_extent',\n 'neurite_X_extent', 'neurite_Y_extent', 'neurite_Z_extent']\n neurite_feats_avail.extend(neurite_feats_extra)\n\n # Statistical modes available\n stat_modes = ['min', 'max', 'median', 'mean', 'total', 'std']\n\n # morph_stats's nomenclature constraints to specify observation files\n \"\"\"\n self.neuroM_morph_stats_doc(neuron_parts_avail,\n cell_feats_avail, neurite_feats_avail, neurite_feats_extra,\n stat_modes)\n \"\"\"\n\n # print \"Checking observation file compliance with NeuroM's ('fst' module) nomenclature...\"\n for dict1 in observation.values(): # Dict. with cell's part-features dictionary pairs for each cell\n for key2, dict2 in list(dict1.items()): # Dict. with feature name-value pairs for each cell part:\n # neuron, apical_dendrite, basal_dendrite or axon\n assert (key2 in neuron_parts_avail), \\\n f\"{key2} is not permitted for neuron parts. Please, use one in the following \\\n list:\\n {neuron_parts_avail}\"\n\n for key3 in dict2.keys():\n feat_name, stat_mode = key3.split('_', 1)[1], key3.split('_', 1)[0]\n if key2 == 'neuron':\n # Checking the NeuroM features for the cell\n assert (feat_name in cell_feats_avail), \\\n f\"{feat_name} is not permitted for cells. Please, use one in the following \\\n list:\\n {sorted(cell_feats_avail)}\"\n # Checking the statistical mode for the cell features\n assert (stat_mode in stat_modes), \\\n f\"{stat_mode} is not permitted for statistical modes. Please, use one in \\\n the following list:\\n {stat_modes}\"\n elif feat_name in nm.fst.NEURITEFEATURES.keys():\n assert (stat_mode in stat_modes), \\\n f\"{stat_mode} is not permitted for statistical modes. Please, use one in \\\n the following \\list:\\n {stat_modes}\"\n else:\n # Checking the extra-NeuroM features for Neurites, if any\n assert (key3 in neurite_feats_extra), \\\n f\"{key3} is not permitted for neurites. Please, use one in the following \\\n list:\\n {sorted(neurite_feats_avail)}\"","def read_mothur_cons_taxonomy(f):\n d_otu2tax = {}\n for n, line in enumerate(open(f)):\n # the first header line does not startswith \"#\" in v1.33\n if line.startswith('OTU\\tSize\\tTaxonomy'):\n continue\n\n if line.startswith('#'):\n continue\n\n line = line.rstrip()\n otu, num, taxa = line.rstrip().split('\\t')\n skip = False\n for word in EXCLUDE:\n if word in taxa:\n skip = True\n break\n\n if skip:\n continue \n\n # the parsing of taxa works for both mothur output and this\n taxa = taxa.rstrip(';') # for mothur classfy.seqs output\n lis = taxa.split(';')\n lis2 = []\n for item in lis:\n item = item.strip() # for copyrigher copy table ' ;' separater\n if item.endswith(')'):\n item = item.rsplit('(', 1)[0].strip()\n\n # remove taxon level prefix, e.g. 'p__Firmicutes'\n if '__' in item:\n item = item.split('__', 1)[1]\n\n #item = item.strip('\"')\n\n # green gene taxonomy has sapce\n item = item.replace(' ', '_')\n\n item = item.lower()\n if item in ['unknown', 'unclassified', 'other', 'unassigned']:\n item = 'Unclassifed'\n\n item = item.capitalize()\n lis2.append(item)\n\n length = len(lis2)\n mes = 'levels number ({}) is not ({}): {}'\n assert length == LEVELS, mes.format(\n length, LEVELS, repr(lis2))\n d_otu2tax[otu] = tuple(lis2)\n\n return d_otu2tax","def isNodeAGizmo(aNode):\n if type(aNode) == 'Gizmo':\n return True\n else:\n return False","def test_check_tipos(self):\n assert 2 in SuplementoRegimen.tipos(), \"Falta el tipo de regimen 2\"\n assert 3 in SuplementoRegimen.tipos(), \"Falta el tipo de regimen 3\"","def validate_prophyle_nhx_tree(tree, verbose=True, throw_exceptions=True, output_fo=sys.stderr):\n\n assert isinstance(tree, ete3.Tree), tree\n\n error = False\n\n existing_names_set = set()\n\n names_with_separator = []\n\n without_name = []\n empty_name = []\n forbidden_name = []\n\n duplicates = []\n\n for i, node in enumerate(tree.traverse(\"postorder\")):\n noname = True\n try:\n nname = node.name\n noname = False\n except AttributeError:\n without_name.append((i, None))\n\n if not noname:\n if nname == '':\n empty_name.append((i, nname))\n error = True\n if nname in set(['A', '0']):\n forbidden_name.append((i, nname))\n error = True\n if nname in existing_names_set:\n duplicates.append((i, nname))\n error = True\n if \"@\" in nname:\n names_with_separator.append((i, nname))\n error = True\n existing_names_set.add(nname)\n\n def _format_node_list(node_list):\n return \", \".join(map(str, node_list))\n\n def _error_report(node_list, message):\n if len(node_list) > 0:\n print(\n \" * {} node(s) {}: {}\".format(\n len(node_list),\n message,\n _format_node_list(node_list),\n ), file=output_fo\n )\n\n if verbose:\n if error:\n print(\"Errors:\".format(), file=output_fo)\n\n _error_report(without_name, \"without name\")\n _error_report(forbidden_name, \"forbidden_name\")\n _error_report(empty_name, \"with empty name\")\n _error_report(duplicates, \"with a duplicate name\")\n _error_report(names_with_separator, \"with a name containing '@'\")\n\n if throw_exceptions:\n if error:\n error(\"Invalid tree. The format of the tree is not correct. See the messages above.\")\n\n if error:\n return False\n else:\n return True","def test_isomorphism_match(data):\n\n reference = data.draw(ISO_BUILDER)\n nodes = data.draw(st.sets(st.sampled_from(list(reference.nodes)),\n max_size=len(reference)))\n graph = reference.subgraph(nodes)\n\n note((\"Reference nodes\", reference.nodes(data=True)))\n note((\"Reference edges\", reference.edges))\n note((\"Graph nodes\", graph.nodes(data=True)))\n note((\"Graph edges\", graph.edges))\n\n node_match = nx.isomorphism.categorical_node_match('element', None)\n matcher = nx.isomorphism.GraphMatcher(reference, graph, node_match=node_match)\n expected = make_into_set(matcher.subgraph_isomorphisms_iter())\n found = make_into_set(vermouth.graph_utils.isomorphism(reference, graph))\n\n note((\"Found\", found))\n note((\"Expected\", expected))\n\n if not expected:\n event(\"Not subgraphs\")\n if found == expected:\n event(\"Exact match\")\n\n assert found <= expected","def check_a11y_relation(filename, tree):\n global widgets_ignored, ids, label_for_elm, labelled_by_elm, description_for_elm, described_by_elm, mnemonic_for_elm\n\n def check_elm(orphan_root, obj, orphan_labels, orphan_widgets):\n \"\"\"\n Check one element, knowing that orphan_labels/widgets tell whether\n there are orphan labels and widgets within orphan_root\n \"\"\"\n\n oid = obj.attrib.get('id')\n klass = obj.attrib.get('class')\n\n # \"Don't care\" special case\n if klass in widgets_ignored:\n return\n for suffix in widgets_suffixignored:\n if klass[-len(suffix):] == suffix:\n return\n\n # Widgets usual do not strictly require a label, i.e. a labelled parent\n # is enough for context, but some do always need one.\n requires_label = klass in widgets_needlabel\n\n if oid is not None:\n # Check that ids are unique\n if oid in ids_dup:\n if ids[oid] == obj:\n # We are the first, warn\n duplicates = tree.findall(\".//object[@id='\" + oid + \"']\")\n err(filename, tree, obj, \"duplicate-id\", \"has the same id as other elements \" + elms_names_lines(duplicates))\n\n # Check label-for and their dual labelled-by\n label_for = check_rels(filename, tree, root, obj, \"label-for\", \"labelled-by\")\n\n # Check labelled-by and its dual label-for\n labelled_by = check_rels(filename, tree, root, obj, \"labelled-by\", \"label-for\")\n\n # Check label-for and their dual labelled-by\n description_for = check_rels(filename, tree, root, obj, \"description-for\", \"described-by\")\n\n # Check labelled-by and its dual label-for\n described_by = check_rels(filename, tree, root, obj, \"described-by\", \"description-for\")\n\n # Should have only one label\n if len(labelled_by) >= 1:\n if oid in mnemonic_for_elm:\n warn(filename, tree, obj, \"labelled-by-and-mnemonic\",\n \"has both a mnemonic \" + elm_name_line(mnemonic_for_elm[oid][0]) + \"and labelled-by relation\")\n if len(labelled_by) > 1:\n warn(filename, tree, obj, \"multiple-labelled-by\", \"has multiple labelled-by relations\")\n if oid in label_for_elm:\n if len(label_for_elm[oid]) > 1:\n warn(filename, tree, obj, \"duplicate-label-for\", \"is referenced by multiple label-for \" + elms_names_lines(label_for_elm[oid]))\n if oid in mnemonic_for_elm:\n if len(mnemonic_for_elm[oid]) > 1:\n warn(filename, tree, obj, \"duplicate-mnemonic\", \"is referenced by multiple mnemonic_widget \" + elms_names_lines(mnemonic_for_elm[oid]))\n\n # Check member-of\n member_of = check_rels(filename, tree, root, obj, \"member-of\")\n\n # Labels special case\n if klass in widgets_labels:\n properties = check_props(filename, tree, root, obj, \"mnemonic_widget\") + \\\n check_props(filename, tree, root, obj, \"mnemonic-widget\")\n if len(properties) > 1:\n err(filename, tree, obj, \"multiple-mnemonic\", \"has multiple mnemonic_widgets properties\"\n \"%s\" % elms_lines(properties))\n\n # Emit orphaning warnings\n if warn_orphan_labels or orphan_widgets:\n is_orphan_label(filename, tree, root, obj, orphan_root, True)\n\n # We are done with the label\n return\n\n # Not a label, will perhaps need one\n\n # Emit orphaning warnings\n is_orphan_widget(filename, tree, root, obj, orphan_labels, orphan_root, True)\n\n root = tree.getroot()\n\n # Flush ids and relations from previous files\n ids = {}\n ids_dup = {}\n labelled_by_elm = {}\n label_for_elm = {}\n described_by_elm = {}\n description_for_elm = {}\n mnemonic_for_elm = {}\n\n # First pass to get links into hash tables, no warning, just record duplicates\n for obj in root.iter('object'):\n oid = obj.attrib.get('id')\n if oid is not None:\n if oid not in ids:\n ids[oid] = obj\n else:\n ids_dup[oid] = True\n\n labelled_by = obj.findall(\"accessibility/relation[@type='labelled-by']\")\n labelled_by += obj.findall(\"accessibility/relation[@name='labelled-by']\")\n for rel in labelled_by:\n target = rel.attrib.get('target')\n if target is not None:\n if target not in labelled_by_elm:\n labelled_by_elm[target] = [ obj ]\n else:\n labelled_by_elm[target].append(obj)\n\n label_for = obj.findall(\"accessibility/relation[@type='label-for']\")\n label_for += obj.findall(\"accessibility/relation[@name='label-for']\")\n for rel in label_for:\n target = rel.attrib.get('target')\n if target is not None:\n if target not in label_for_elm:\n label_for_elm[target] = [ obj ]\n else:\n label_for_elm[target].append(obj)\n\n described_by = obj.findall(\"accessibility/relation[@type='described-by']\")\n described_by += obj.findall(\"accessibility/relation[@name='described-by']\")\n for rel in described_by:\n target = rel.attrib.get('target')\n if target is not None:\n if target not in described_by_elm:\n described_by_elm[target] = [ obj ]\n else:\n described_by_elm[target].append(obj)\n\n description_for = obj.findall(\"accessibility/relation[@type='description-for']\")\n description_for += obj.findall(\"accessibility/relation[@name='description-for']\")\n for rel in description_for:\n target = rel.attrib.get('target')\n if target is not None:\n if target not in description_for_elm:\n description_for_elm[target] = [ obj ]\n else:\n description_for_elm[target].append(obj)\n\n mnemonic_for = obj.findall(\"property[@name='mnemonic_widget']\") + \\\n obj.findall(\"property[@name='mnemonic-widget']\")\n for rel in mnemonic_for:\n target = rel.text\n if target is not None:\n if target not in mnemonic_for_elm:\n mnemonic_for_elm[target] = [ obj ]\n else:\n mnemonic_for_elm[target].append(obj)\n\n # Second pass, recursive depth-first, to be able to efficiently know whether\n # there are orphan labels within a part of the tree.\n def recurse(orphan_root, obj, orphan_labels, orphan_widgets):\n if obj == root or is_labelled_parent(obj):\n orphan_root = obj\n orphan_labels, orphan_widgets = orphan_items(filename, tree, root, obj)\n\n if obj.tag == 'object':\n check_elm(orphan_root, obj, orphan_labels, orphan_widgets)\n\n for o in obj:\n recurse(orphan_root, o, orphan_labels, orphan_widgets)\n\n recurse(root, root, False, False)","def test_change(self):\n # Folder must be root to load in make_net properly\n if os.getcwd().split('\\\\')[-1] == 'tests': os.chdir('..')\n \n # Create parents and deepcopy everything (just to be sure)\n cfg = Config().genome\n gene1, gene2 = get_output_node_gene(0, cfg)\n gene1_act = deepcopy(gene1.activation)\n gene1_agg = deepcopy(gene1.aggregation)\n gene1_bias = deepcopy(gene1.bias)\n gene2_act = deepcopy(gene2.activation)\n gene2_agg = deepcopy(gene2.aggregation)\n gene2_bias = deepcopy(gene2.bias)\n \n # Perform crossover and mutations\n gene3 = gene1.crossover(other=gene2, cfg=cfg, ratio=0.5)\n gene3.activation = 'c'\n gene3.aggregation = 'c'\n gene3.bias = -10\n \n # Check for unchanged parents\n self.assertEqual(gene1.activation, gene1_act)\n self.assertEqual(gene1.aggregation, gene1_agg)\n self.assertEqual(gene1.bias, gene1_bias)\n self.assertEqual(gene2.activation, gene2_act)\n self.assertEqual(gene2.aggregation, gene2_agg)\n self.assertEqual(gene2.bias, gene2_bias)","def can_be_linked(self):\n # upstream trees of tested treed should nad share trees with downstream trees of current tree\n tested_tree_upstream_trees = {t.name for t in self.upstream_trees()}\n current_tree_downstream_trees = {p.node_tree.name for p in bpy.context.space_data.path}\n shared_trees = tested_tree_upstream_trees & current_tree_downstream_trees\n return not shared_trees","def substructure_sim_exact(subtrees_1, subtrees_2):\n assert(len(subtrees_1) == len(subtrees_2))\n n = len(subtrees_1)\n f1 = np.zeros(n)\n for i in range(n):\n f1[i] = subtrees_1[i] == subtrees_2[i] # calculate the number of matching pairs\n\n return float(np.count_nonzero(f1)) / float(len(f1))","def test_call_phylogenetic_escaped_names(self):\r\n\r\n c = AlphaDiversityCalc(metric=PD_whole_tree, is_phylogenetic=True)\r\n expected = [13., 17., 0.]\r\n\r\n non_escaped_result = c(data_path=self.otu_table1_fp,\r\n tree_path=self.tree1,\r\n taxon_names=self.otu_table1.ObservationIds,\r\n sample_names=self.otu_table1.SampleIds)\r\n\r\n escaped_result = c(data_path=self.otu_table2_fp,\r\n tree_path=self.tree2,\r\n taxon_names=self.otu_table2.ObservationIds,\r\n sample_names=self.otu_table2.SampleIds)\r\n\r\n assert_almost_equal(non_escaped_result, expected)\r\n assert_almost_equal(escaped_result, expected)\r\n assert_almost_equal(non_escaped_result, escaped_result)","def tree(n, names):\n \n t = Tree()\n t.populate(n, names_library=names.keys(), random_branches=True, support_range=(0.7, 1))\n t.write(format=2, outfile='fake.tree.newick', dist_formatter='%0.4g', support_formatter='%0.4g')\n with open('fake.tree.notation.tsv', 'w') as o:\n o.write('#TaxaID\\tName\\n')\n o.writelines('{}\\t{}\\n'.format(k, v) for k, v in names.items())","def test_equivalency(self):\n def compare_func(obj, node):\n # same id\n self.assertEqual(obj.id, node.get(\"id\"))\n\n # same html\n self.assertEqual(obj.html.prettify, node.prettify)\n\n # parents have same id (only for non-root elements)\n if not obj == self.document.root:\n self.assertEqual(obj.parent.id, node.parent.get(\"id\"))\n\n # same number of children\n child_nodes = self.get_children_of_node(node)\n self.assertEqual(len(obj.children), len(child_nodes))\n\n # children have same ids\n for (child_obj, child_node) in zip(obj.children, child_nodes):\n self.assertEqual(child_obj.id, child_node.get(\"id\"))\n\n self.recursively_compare_tree_against_html(compare_func)","def check_subtree(node):\r\n if node:\r\n node_loc = node.location\r\n self.assertFalse(\r\n modulestore().has_item(node_loc.version_agnostic())\r\n )\r\n self.assertTrue(modulestore().has_item(node_loc.course_agnostic()))\r\n if node.has_children:\r\n for sub in node.get_children():\r\n check_subtree(sub)","def compare_trees(first_soup: HTMLStrip, second_soup: HTMLStrip) -> float:\n first_tree = Tree.from_soup_object(first_soup.file_name, first_soup.original_soup)\n second_tree = Tree.from_soup_object(second_soup.file_name, second_soup.original_soup)\n\n common_paths_size: int = first_tree.num_of_common_paths(second_tree)\n target_size: int = second_tree.total_num_of_paths()\n similarity = float(common_paths_size)*100/target_size\n print(f'{similarity:.2f}')\n return similarity","def test_distinguish_path_polarity2():\n Monomer('A')\n Monomer('B', ['act'], {'act' :['y', 'n']})\n Monomer('C', ['T185'], {'T185':['u', 'p']})\n Parameter('k', 1)\n Rule('A_inhibit_B', A() + B(act='y') >> A() + B(act='n'), k)\n Rule('B_dephos_C', B(act='y') + C(T185='p') >>\n B(act='y') + C(T185='u'), k)\n Initial(A(), k)\n Initial(B(act='y'), k)\n Initial(C(T185='p'), k)\n Annotation(A, 'http://identifiers.org/hgnc/HGNC:1')\n Annotation(B, 'http://identifiers.org/hgnc/HGNC:2')\n Annotation(C, 'http://identifiers.org/hgnc/HGNC:3')\n Annotation('A_inhibit_B', 'A', 'rule_has_subject')\n Annotation('A_inhibit_B', 'B', 'rule_has_object')\n Annotation('B_dephos_C', 'B', 'rule_has_subject')\n Annotation('B_dephos_C', 'C', 'rule_has_object')\n C.site_annotations = [\n Annotation(('T185', 'p'), 'phosphorylation', 'is_modification'),\n Annotation('T185', 'T', 'is_residue'),\n Annotation('T185', '185', 'is_position'),\n ]\n # Create the model checker\n stmts = _path_polarity_stmt_list()\n mc = ModelChecker(model, stmts)\n results = mc.check_model()\n assert len(results) == len(stmts)\n assert isinstance(results[0], tuple)\n assert results[0][1].paths == [[('A_inhibit_B', 1), ('B_dephos_C', -1),\n ('C_T185_p_obs', 1)]]\n assert results[1][1].paths == []\n assert results[2][1].paths == [[('A_inhibit_B', 1), ('B_dephos_C', -1),\n ('C_T185_p_obs', 1)]]\n assert results[3][1].paths == [[('B_dephos_C', 1), ('C_T185_p_obs', -1)]]","def distanceToTree_deprec(self,taxonomic_forest,update_inner_attributes=True):\n #Thankfully the distance is symmetric by definition so order doesn't matter.\n Jacobian = []\n logger.warning('[FOR DEVELOPER] This is not optimized. Should be necessary to use attr_t1 and attr_t2 with name_id (integer)')\n for i in settings.TAXONOMIC_TREE_KEYS:\n F1 = []\n for j in settings.TAXONOMIC_TREE_KEYS:\n d = self.forest[i].robinson_foulds(taxonomic_forest[j],unrooted_trees=True)[0]\n F1.append(float(d))\n Jacobian.append(F1)\n if update_inner_attributes:\n self.JacobiM = np.matrix(Jacobian)\n self.Jacobi = np.linalg.det(self.JacobiM)\n return np.matrix(Jacobian)","def test01(self):\n\n t = tree(\"a\", [tree(\"b\"), tree(\"c\")]);\n self.assertTrue(self.isTree(t))","def legitimate_mark_changes(self, verbose=False):\n if self._undirected:\n raise ValueError('Only defined for DMAGs')\n\n disc_paths = self.discriminating_paths()\n\n mark_changes_dir = set()\n for i, j in self._directed:\n if verbose: print(f'{i}->{j} => {i}<->{j} ?')\n # FIRST CONDITIONS\n parents_condition = all(self.has_directed(parent, j) for parent in self._parents[i])\n if not parents_condition:\n if verbose: print('Failed parents condition')\n continue\n spouses_condition = all(self.has_any_edge(spouse, j) for spouse in self._spouses[i])\n if not spouses_condition:\n if verbose: print('Failed spouses condition')\n continue\n\n # SECOND CONDITION\n disc_paths_for_i = [path for path in disc_paths.keys() if path[-2] == i]\n disc_paths_condition = all(path[-1] != j for path in disc_paths_for_i)\n if not disc_paths_condition:\n if verbose: print('Failed discriminating path condition')\n continue\n\n # FINAL CONDITION\n if i in self.ancestors_of(j, exclude_arcs={(i, j)}):\n if verbose: print('Failed ancestral condition')\n continue\n\n if verbose: print('Passed')\n mark_changes_dir.add((i, j))\n\n mark_changes_bidir = set()\n for i, j in self._bidirected | set(map(reversed, self._bidirected)):\n if verbose: print(f'{i}<->{j} => {i}->{j} ?')\n # FIRST CONDITIONS\n parents_condition = all(self.has_directed(parent, j) for parent in self._parents[i])\n if not parents_condition:\n if verbose: print('Failed parents condition')\n continue\n spouses_condition = all(self.has_any_edge(spouse, j) for spouse in self._spouses[i] if spouse != j)\n if not spouses_condition:\n if verbose: print('Failed spouses condition')\n continue\n\n # SECOND CONDITION\n disc_paths_for_i = [path for path in disc_paths.keys() if path[-2] == i]\n disc_paths_condition = all(path[-1] != j for path in disc_paths_for_i)\n if not disc_paths_condition:\n if verbose: print('Failed discriminating path condition')\n continue\n\n # FINAL CONDITION\n if i in self.ancestors_of(j):\n if verbose: print('Failed ancestral condition')\n continue\n\n if verbose: print('Passed')\n mark_changes_bidir.add((i, j))\n\n return mark_changes_dir, mark_changes_bidir","def test_consistent_ids(self) -> None:\n bnode = BNode()\n g0_ts: _TripleSet = {\n (bnode, FOAF.name, Literal(\"Golan Trevize\")),\n (bnode, RDF.type, FOAF.Person),\n }\n bnode = BNode()\n g1_ts: _TripleSet = {\n (bnode, FOAF.name, Literal(\"Janov Pelorat\")),\n (bnode, RDF.type, FOAF.Person),\n }\n\n g0 = Graph()\n g0 += g0_ts\n cg0 = to_canonical_graph(g0)\n cg0_ts = GraphHelper.triple_set(cg0)\n\n g1 = Graph()\n g1 += g1_ts\n cg1 = to_canonical_graph(g1)\n cg1_ts = GraphHelper.triple_set(cg1)\n\n assert cg0_ts.issubset(\n cg1_ts\n ), \"canonical triple set cg0_ts should be a subset of canonical triple set cg1_ts\"","def write_phylogeny_pages(outfile: TextIO, genera_tree: list, species_tree: list, do_print: bool,\n refdict: dict) -> None:\n\n def split_html_tree(intree: list) -> Tuple[list, list]:\n \"\"\"\n take a phy2html output file and split it into style and body sections\n \"\"\"\n instyle = False\n inbody = False\n style_list = []\n body_list = []\n for inline in intree:\n if \"<style>\" in inline:\n instyle = True\n elif \"</style>\" in inline:\n instyle = False\n elif \"<body>\" in inline:\n inbody = True\n elif \"</body>\" in inline:\n inbody = False\n elif instyle:\n style_list.append(inline)\n elif inbody:\n body_list.append(inline)\n return style_list, body_list\n\n def add_link_to_genus(inline: str) -> str:\n if \"id=\\\"genera_taxon\" in inline:\n i = inline.find(\">\")\n start = inline[:i+1]\n tname = inline[i+1:]\n j = tname.find(\"<\")\n end = tname[j:]\n tname = tname[:j]\n tname = create_taxon_link(\"genus\", tname, do_print=do_print, include_rank=False)\n return start + tname + end\n else:\n return inline\n\n # --- main function code ---\n gen_style, gen_body = split_html_tree(genera_tree)\n sp_style, sp_body = split_html_tree(species_tree)\n\n if do_print:\n start_page_division(outfile, \"base_page\")\n else:\n common_header_part1(outfile, \"Fiddler Crab Phylogeny\")\n outfile.write(\" <style>\\n\")\n for line in gen_style:\n outfile.write(line)\n outfile.write(\"\\n\")\n for line in sp_style:\n outfile.write(line)\n outfile.write(\"\\n\")\n outfile.write(\" .phylogeny_grid { padding: 30px; }\\n\") # add a little extra padding\n outfile.write(\" </style>\\n\")\n\n if not do_print:\n common_header_part2(outfile)\n\n outfile.write(\" <header id=\\\"\" + init_data().tree_url + \"\\\">\\n\")\n outfile.write(\" <h1 class=\\\"bookmark1\\\">Phylogeny</h1>\\n\")\n outfile.write(\" </header>\\n\")\n outfile.write(\"\\n\")\n outfile.write(\" <p>\\n\")\n outfile.write(\" The phylogeny of fiddler crabs is still largely unresolved. Two trees are shown below: one \"\n \"of just the genera and one including all species. The tree of genera is fairly solid, \"\n \"but the species tree is a rough estimate with many polytomies. Both are predominantly based on the \"\n \"work of \" + format_reference_cite(refdict[\"Shih2016.2\"], do_print, AUTHOR_PAREN) + \".\\n\")\n outfile.write(\" </p>\\n\")\n outfile.write(\"\\n\")\n outfile.write(\" <section class=\\\"spsection\\\">\\n\")\n outfile.write(\" <h2 class=\\\"bookmark2\\\">Genera Phylogeny</h2>\\n\")\n for line in gen_body:\n outfile.write(add_link_to_genus(line))\n outfile.write(\" </section>\\n\")\n outfile.write(\" <section class=\\\"spsection\\\">\\n\")\n outfile.write(\" <h2 class=\\\"bookmark2\\\">Species Phylogeny</h2>\\n\")\n for line in sp_body:\n outfile.write(replace_species_in_string(line, True, do_print))\n outfile.write(\" </section>\\n\")\n outfile.write(\"\\n\")\n if do_print:\n end_page_division(outfile)\n else:\n common_html_footer(outfile)","def test_change(self):\n # Folder must be root to load in make_net properly\n if os.getcwd().split('\\\\')[-1] == 'tests': os.chdir('..')\n \n # Create parents and deepcopy everything (just to be sure)\n cfg = Config().genome\n gene1, gene2 = get_connection_genes((-1, 0), cfg)\n gene1_en = deepcopy(gene1.enabled)\n gene1_w = deepcopy(gene1.weight)\n gene2_en = deepcopy(gene2.enabled)\n gene2_w = deepcopy(gene2.weight)\n \n # Perform crossover and mutations\n gene3 = gene1.crossover(other=gene2, cfg=cfg, ratio=0.5)\n gene3.enabled = False\n gene3.weight = -10\n \n # Check for unchanged parents\n self.assertEqual(gene1.enabled, gene1_en)\n self.assertEqual(gene1.weight, gene1_w)\n self.assertEqual(gene2.enabled, gene2_en)\n self.assertEqual(gene2.weight, gene2_w)","def test_compare_taxa_summaries_no_permutations(self):\r\n # Verified with R's cor.test function.\r\n exp = ('# Correlation coefficient: pearson.\\n# The parametric '\r\n 'p-value(s) were calculated using a one-sided (positive '\r\n 'association) test of significance using a '\r\n 't-distribution.\\n# The '\r\n 'confidence interval(s) were constructed at a confidence level of '\r\n '70.0% using Fisher\\'s z-transformation (see Sokal and Rohlf 3rd '\r\n 'edition pg. 575). The confidence interval(s) are two-sided.\\n# '\r\n 'Number of samples that matched between the taxa summary files: 1'\r\n '\\nCorrelation coefficient\\tParametric p-value\\tNonparametric '\r\n 'p-value\\tCI (lower)\\tCI (upper)\\n-0.6264\\t0.8132\\tN/A\\t-0.9438\\t'\r\n '0.2922\\n')\r\n\r\n obs = compare_taxa_summaries(self.taxa_summary_paired1,\r\n self.taxa_summary_paired5, 'paired', 'pearson',\r\n tail_type='high', num_permutations=0, confidence_level=0.70,\r\n perform_detailed_comparisons=False,\r\n sample_id_map=self.taxa_summary_paired_samp_id_map2)\r\n obs = (obs[2])\r\n self.assertEqual(obs, exp)","def test_canonize_neighborhood_same_graph(nauty, ref_graph, ref_graph2):\n key = nauty.canonize_neighborhood(ref_graph, 2, 1)\n key2 = nauty.canonize_neighborhood(ref_graph2, 3, 1)\n assert key == key2","def test_markAsTree(self):\n x = t.Rule(\"foo\", t.List(\n t.Exactly(\"x\")))\n g = t.Grammar(\"TestGrammar\", True, [x])\n self.assertIn(\"\\n tree = True\\n\", writePython(g))","def x_dom(self, min_depth=0, min_gq=0):\n affecteds = self.affecteds\n if len(affecteds) == 0:\n warn(\"WARNING: no affecteds in family %s. skipping\\n\" % self.family_id)\n return False\n\n try:\n # affected males het or hom_alt\n af = reduce(op.and_, [(s.gt_types == HET) | (s.gt_types == HOM_ALT) for s in affecteds if s.sex == 'male'])\n # affected females must be het\n af = reduce(op.and_, [s.gt_types == HET for s in affecteds if s.sex == 'female'], af)\n except TypeError:\n # affected females must be het\n try:\n af = reduce(op.and_, [s.gt_types == HET for s in affecteds if s.sex == 'female'])\n except TypeError:\n af = None\n\n # unaffecteds must be hom_ref\n if len(self.unaffecteds) > 0:\n un = reduce(op.and_, [s.gt_types == HOM_REF for s in self.unaffecteds])\n else:\n un = None\n\n def is_parent(a, b):\n if a is None or b is None: return False\n return a.sample_id == b.sample_id and a.family_id == b.family_id\n\n for parent in self.affecteds:\n kids = [k for k in self.subjects if is_parent(parent, k.dad)]\n for kid in kids:\n # girls of affected dad must be affected\n if kid.sex == 'female' and parent.sex == 'male' and not kid.affected:\n warn(\"WARNING: unaffected female kid of affected dad \" +\n \"in family: %s. not x-linked dominant. skipping %s\\n\" % (kid.family_id, parent.name))\n return False\n\n #. boys of affected dad must be unaffected\n if kid.sex == 'male' and parent.sex == 'male' and kid.affected:\n warn(\"WARNING: affected male kid of affected dad in \" +\n \"family: %s. not x-linked dominant. skipping %s\\n\" % (kid.family_id, parent.name))\n return False\n\n #. mothers of affected males must be het (and affected)\n k = parent\n if k.sex == 'male' and k.mom and not k.mom.affected:\n return False\n\n #. at least 1 parent of affected females must be het (and affected).\n if k.sex == 'female':\n if k.mom and k.dad and not any((k.mom.affected, k.dad.affected)):\n return False\n\n depth = self._restrict_to_min_depth(min_depth)\n quals = self._restrict_to_min_gq(min_gq)\n return combine_and(af, un, depth, quals)","def test_deep_equals(obja, objb, isequal):\n\n objatree = wo.typedtree(obja)\n objbtree = wo.typedtree(objb)\n match = objatree == objbtree\n ok = match == isequal\n\n if ok:\n s = \"pass\"\n else:\n s = \"fail\"\n\n print(f\"{obja} == {objb} is {match} : {s}\")\n return ok","def test_tree_intersection_name_exists():\n assert tree_intersection","def testStratifiedAxioms(self):\n \n a1a = Parser.parse_as(strat1a.split(\"\\n\"), axioms.Axiom, self.prob)\n a1b = Parser.parse_as(strat1b.split(\"\\n\"), axioms.Axiom, self.prob)\n a2 = Parser.parse_as(strat2.split(\"\\n\"), axioms.Axiom, self.prob)\n \n self.dom.axioms = [a1a, a1b, a2]\n self.dom.stratify_axioms()\n\n self.assert_(a1a.predicate in self.dom.stratification[1])\n self.assert_(a1b.predicate in self.dom.stratification[1])\n self.assert_(a2.predicate in self.dom.stratification[2])\n \n state = State.from_problem(self.prob).get_extended_state()\n\n oc1 = StateVariable(self.prob.predicates[\"occupied\"][0], [self.prob[\"pos1\"]])\n oc2 = StateVariable(self.prob.predicates[\"occupied\"][0], [self.prob[\"pos2\"]])\n oc3 = StateVariable(self.prob.predicates[\"occupied\"][0], [self.prob[\"apt1\"]])\n oc4 = StateVariable(self.prob.predicates[\"occupied\"][0], [self.prob[\"apt2\"]])\n\n self.assertEqual(state[oc1], TRUE)\n self.assertEqual(state[oc2], TRUE)\n self.assertEqual(state[oc3], FALSE)\n self.assertEqual(state[oc4], TRUE)\n\n int1 = StateVariable(self.prob.predicates[\"interesting\"][0], [self.prob[\"pos1\"]])\n int2 = StateVariable(self.prob.predicates[\"interesting\"][0], [self.prob[\"pos2\"]])\n int3 = StateVariable(self.prob.predicates[\"interesting\"][0], [self.prob[\"apt1\"]])\n int4 = StateVariable(self.prob.predicates[\"interesting\"][0], [self.prob[\"apt2\"]])\n \n self.assertEqual(state[int1], TRUE)\n self.assertEqual(state[int2], TRUE)\n self.assertEqual(state[int3], FALSE)\n self.assertEqual(state[int4], TRUE)\n \n free1 = StateVariable(self.prob.predicates[\"free\"][0], [self.prob[\"pos1\"]])\n free2 = StateVariable(self.prob.predicates[\"free\"][0], [self.prob[\"pos2\"]])\n free3 = StateVariable(self.prob.predicates[\"free\"][0], [self.prob[\"apt1\"]])\n free4 = StateVariable(self.prob.predicates[\"free\"][0], [self.prob[\"apt2\"]])\n\n # print \"-----------------_\"\n # print state[free1]\n # print state\n # print \"-----------------\"\n # print state[free1]\n self.assertEqual(state[free1], FALSE)\n self.assertEqual(state[free2], FALSE)\n self.assertEqual(state[free3], TRUE)\n self.assertEqual(state[free4], FALSE)","def printGeneTree(self):\n align = AlignIO.read(self.newPhylip, 'phylip') # Reads created .phy file containing the SeqRecord\n #print (align) # prints concatenated allignments\n calculator = DistanceCalculator('identity')\n dm = calculator.get_distance(align)# Calculate the distance matrix\n print('\\n======================================== DISTANCE MATRIX =======================================\\n')\n print(dm,\"\\n\\n\") # Print the distance Matrix\n constructor = DistanceTreeConstructor() # Construct the phylogenetic tree using UPGMA algorithm\n tree = constructor.upgma(dm)\n print('\\n========================================= GENE TREE ===========================================\\n')\n Phylo.draw(tree) # Draw the phylogenetic tree (must install matplotlib to use this formatting)\n Phylo.draw_ascii(tree) # Print the phylogenetic tree in terminal","def test_client_tax_information_retrieve(self):\n pass"],"string":"[\n \"def test_calc_shared_phylotypes(self):\\r\\n\\r\\n observed = calc_shared_phylotypes(self.biom_as_string)\\r\\n expected = \\\"\\\"\\\"\\\\tS1\\\\tS2\\\\tS3\\r\\nS1\\\\t5\\\\t2\\\\t3\\r\\nS2\\\\t2\\\\t2\\\\t1\\r\\nS3\\\\t3\\\\t1\\\\t3\\\\n\\\"\\\"\\\"\\r\\n self.assertEqual(observed, expected)\",\n \"def test_check_tree_exact_match(self):\\r\\n\\r\\n fasta_labels = ['seq1_1', 'seq1_2', 'seq2_3', 'seq3_4']\\r\\n\\r\\n actual_subset_results = check_tree_exact_match(fasta_labels,\\r\\n self.sample_tree_3tips_fp)\\r\\n\\r\\n # Should find all and give True, True result\\r\\n\\r\\n self.assertEqual(actual_subset_results, [True, True])\\r\\n\\r\\n # Should get tips not found in fasta labels with 5 tip tree\\r\\n\\r\\n fasta_labels = ['seq1_1', 'seq1_2', 'seq2_3', 'seq3_4']\\r\\n\\r\\n actual_subset_results = check_tree_exact_match(fasta_labels,\\r\\n self.sample_tree_5tips_fp)\\r\\n\\r\\n # Should find all and give True result\\r\\n\\r\\n self.assertEqual(actual_subset_results, [True, ['seq5', 'seq4']])\\r\\n\\r\\n # Change two of the fasta labels to not match tree tips\\r\\n\\r\\n fasta_labels = ['seq1_1', 'seqX_2', 'seq2_3', 'seqY_4']\\r\\n\\r\\n actual_subset_results = check_tree_exact_match(fasta_labels,\\r\\n self.sample_tree_5tips_fp)\\r\\n\\r\\n # Should find seqX and seqY as not being a subset\\r\\n\\r\\n self.assertEqual(actual_subset_results, [['seqX', 'seqY'],\\r\\n ['seq3', 'seq5', 'seq4']])\",\n \"def check_phylogeny(dis):\\n\\n birds = ('Gallus_gallus', 'Taeniopygia_guttata')\\n bird_dis = dis['Gallus_gallus']['Taeniopygia_guttata']\\n for host in dis:\\n if host not in birds:\\n for bird in birds:\\n new_dis = dis[bird][host]\\n if new_dis <= bird_dis:\\n return False\\n return True\",\n \"def test_call_phylogenetic(self):\\r\\n c = AlphaDiversityCalc(metric=PD_whole_tree,\\r\\n is_phylogenetic=True)\\r\\n assert_almost_equal(c(data_path=self.otu_table1_fp, tree_path=self.tree1,\\r\\n taxon_names=self.otu_table1.ObservationIds,\\r\\n sample_names=self.otu_table1.SampleIds),\\r\\n [13, 17, 0])\",\n \"def compare_trees(tree1, tree2):\\n \\tresponse = {}\\n \\tstart_time = time.time()\\n \\ttry:\\t\\n \\t\\ttns = dendropy.TaxonNamespace() \\t\\n \\t\\n \\t\\ttree_obj1 = dendropy.Tree.get(data=tree1, schema=\\\"newick\\\",taxon_namespace=tns)\\n \\t\\ttree_obj2 = dendropy.Tree.get(data=tree2, schema=\\\"newick\\\",taxon_namespace=tns)\\n\\n \\t\\ttree_obj1.encode_bipartitions()\\n \\t\\ttree_obj2.encode_bipartitions()\\n\\n \\t\\t#-----------------------------------------------------------\\n \\t\\t#This method returns the symmetric distance between two trees. \\n \\t\\t#The symmetric distance between two trees is the sum of the number of splits found in one of the trees but not the other. \\n \\t\\t#It is common to see this statistic called the Robinson-Foulds distance\\n\\n \\t\\tareSame = True if treecompare.symmetric_difference(tree_obj1, tree_obj2) == 0 else False\\n \\t\\tstatus = 200\\n \\t\\tmessage = \\\"Success\\\"\\n \\t\\tresponse['are_same_tree'] = areSame\\n \\n \\texcept Exception, e:\\n \\t\\tif \\\"Incomplete or improperly-terminated tree statement\\\" in str(e): #invalid: \\\"((A,B),C,D));\\\" valid: ((A,B),(C,D)); \\n \\t\\t\\tmessage = \\\"NewickReaderIncompleteTreeStatementError: \\\" + str(e)\\n \\t \\t\\tstatus = 400\\n \\t\\telif \\\"Unbalanced parentheses at tree statement\\\" in str(e): #invalid: \\\"((A,B),(C,D);\\\" valid: ((A,B),(C,D)); \\n \\t\\t\\tmessage = \\\"NewickReaderMalformedStatementError: \\\"+str(e) \\n \\t \\t\\tstatus = 400\\n \\t\\telif \\\"Multiple occurrences of the same taxa\\\" in str(e): #invalid: \\\"((A,B),(C,C));\\\" valid: ((A,B),(C,D));\\n \\t\\t\\tmessage = \\\"NewickReaderDuplicateTaxonError: \\\"+str(e)\\n \\t \\t\\tstatus = 400\\n \\t\\telif \\\"Unexpected end of stream\\\" in str(e): # invalid: \\\"((A,B),(C,D))\\\" valid: ((A,B),(C,D));\\n \\t\\t\\tmessage = \\\"UnexpectedEndOfStreamError: \\\"+str(e)\\n \\t \\t\\tstatus = 400\\n \\t\\telse:\\n \\t\\t\\tmessage = \\\"Error: Failed to compare trees. \\\"+str(e)\\n \\t \\t\\tstatus = 500\\n \\t \\t\\n \\tresponse['status_code'] = status\\n \\tresponse['message'] = message\\n\\n \\tend_time = time.time()\\n \\texecution_time = end_time-start_time\\n #service result creation time\\n \\tcreation_time = datetime.datetime.now().isoformat()\\n \\tmeta_data = {'creation_time': creation_time, 'execution_time': float('{:4.2f}'.format(execution_time)), 'source_urls':[\\\"http://dendropy.org/library/treecompare.html#module-dendropy.calculate.treecompare\\\"] }\\n\\n \\tresponse['meta_data'] = meta_data\\n \\tprint response\\n \\treturn response\",\n \"def test_contains():\\n atom = ATOMClassifier(X_class, y_class, random_state=1)\\n atom.run(\\\"Tree\\\")\\n assert \\\"alcohol\\\" in atom.tree\",\n \"def check_taxa(taxa: Dict[str, AphiaInfo], cache: AphiaCacheInterface = None) -> None:\\n\\n check_fields(taxa)\\n detect_lsid(taxa)\\n detect_external(taxa)\\n match_obis(taxa, cache)\\n match_worms(taxa)\\n check_annotated_list(taxa)\\n fetch(taxa, cache)\",\n \"def checkTree(self, root):\\n\\t\\tif len(self.graph.nodes()) > 100: # TODO why this arbitrary limit on the size of the tree?\\n\\t\\t\\tNJTree.logger.debug(\\\"Too big graph detected and arbitrarly split\\\")\\n\\t\\t\\tself.OK = \\\"false\\\"\\n\\t\\t\\treturn self.OK\\n\\t\\t# check species of root node neighbors\\n\\t\\tedge = root[1]\\n# \\t\\tloss = root[2]\\n\\t\\tset_species = set([])\\n\\t\\tspecies = []\\n# \\t\\tedge_weight = self.graph[edge[0]][edge[1]]['homology_weight']\\n\\t\\t# TODO what is the point of having twice the results?\\n\\t\\tset_species.add(self.rootedTree.node[edge[0]]['species'])\\n\\t\\tset_species.add(self.rootedTree.node[edge[1]]['species'])\\n\\t\\tspecies.append(self.rootedTree.node[edge[0]]['species'])\\n\\t\\tspecies.append(self.rootedTree.node[edge[1]]['species'])\\n\\t\\tif len(species) == 1 or len(set_species) == 1 and (self.mrca not in set_species):\\n\\t\\t\\t# all leaf nodes get added to orphan pile\\n\\t\\t\\t# NJTree.logger.debug(\\\"Orphan in checkTree still happens\\\")\\n\\t\\t\\tself.OK = \\\"orphan\\\"\\n\\t\\t\\treturn self.OK\\n\\t\\tif len(species) == 2:\\n\\t\\t\\tif self.mrca in species:\\n\\t\\t\\t\\tself.OK = \\\"false\\\"\\n\\t\\t\\t\\treturn self.OK\\n\\t\\t\\telse:\\n\\t\\t\\t\\tself.OK = \\\"true\\\"\\n\\t\\t\\t\\treturn self.OK\\n\\t\\t# should add a verification that there are no more than 2 species\",\n \"def check_tree_exact_match(fasta_labels,\\r\\n tree_fp):\\r\\n\\r\\n # Need to get modified fasta labels with underscore stripped\\r\\n\\r\\n raw_fasta_labels = set([label.split('_')[0] for label in fasta_labels])\\r\\n\\r\\n tree_f = open(tree_fp, \\\"U\\\")\\r\\n\\r\\n tree = DndParser(tree_f)\\r\\n\\r\\n # Get a set of tree tip names\\r\\n tree_tips = set(tree.getTipNames())\\r\\n\\r\\n labels_not_in_tips = []\\r\\n\\r\\n for curr_label in raw_fasta_labels:\\r\\n if curr_label not in tree_tips:\\r\\n labels_not_in_tips.append(curr_label)\\r\\n\\r\\n # Return True if all found in tree tips\\r\\n if len(labels_not_in_tips) == 0:\\r\\n labels_not_in_tips = True\\r\\n\\r\\n tips_not_in_labels = []\\r\\n\\r\\n for curr_tip in tree_tips:\\r\\n if curr_tip not in raw_fasta_labels:\\r\\n tips_not_in_labels.append(curr_tip)\\r\\n\\r\\n if len(tips_not_in_labels) == 0:\\r\\n tips_not_in_labels = True\\r\\n\\r\\n return [labels_not_in_tips, tips_not_in_labels]\",\n \"def check_X_dominant(variant, family, strict=False):\\n path = os.path.abspath(os.getcwd())\\n output_log = os.path.join(path, \\\"variants_inheritance_patterns.csv\\\")\\n\\n if os.path.isfile(output_log):\\n output = open(output_log, \\\"a\\\")\\n else:\\n output = open(output_log, \\\"w\\\")\\n\\n affected = False\\n genotyped = False\\n\\n for individual in family.individuals:\\n # Get the genotype for this variant for this individual\\n individual_genotype = variant['genotypes'][individual]\\n \\n if strict:\\n if not individual_genotype.genotyped:\\n return False\\n # The case where the individual is healthy\\n if family.individuals[individual].healthy:\\n # Healthy womans can be carriers but not homozygote:\\n if individual_genotype.genotyped:\\n if family.individuals[individual].sex == 2:\\n if individual_genotype.homo_alt:\\n return False\\n # Males can not carry the variant:\\n elif family.individuals[individual].sex == 1:\\n if individual_genotype.has_variant:\\n return False\\n \\n # The case when the individual is sick\\n elif family.individuals[individual].affected:\\n affected = True\\n # If the individual is sick and homozygote ref it \\n # can not be x-linked-dominant\\n if individual_genotype.genotyped:\\n genotyped = True\\n if individual_genotype.homo_ref:\\n return False\\n\\n if affected and genotyped:\\n output.write(\\\"{},Mother heterozigous alt or father without variant and offspring is homozygote or heterozygote alternative\\\\n\\\".format(variant.get('variant_id', None)))\\n elif affected:\\n output.write(\\\"{},Mother heterozigous alt or father without variant and offspring is not genotyped\\\\n\\\".format(variant.get('variant_id', None)))\\n else:\\n output.write(\\\"{},Parents without variant and no affected individual\\\\n\\\".format(variant.get('variant_id', None)))\\n output.close()\\n return True\",\n \"def test_make_compatible_taxa_summaries(self):\\r\\n exp = [(['Even1'], ['foo'], array([[0.5]])), (['Even1'], ['foo'],\\r\\n array([[0.1]]))]\\r\\n obs = _make_compatible_taxa_summaries(self.taxa_summary5,\\r\\n self.taxa_summary6)\\r\\n self.compare_multiple_level_array(obs, exp)\",\n \"def taxonomic_consistency(self, taxonomy, report_errors=True):\\n\\n expected_parent = {}\\n for genome_id, taxa in taxonomy.items():\\n if taxa[0] == 'd__Viruses' or '[P]' in taxa[0]:\\n # *** This is a HACK. It would be far better to enforce\\n # a taxonomically consistent taxonomy, but\\n # the viral taxonomy at IMG is currently not consistent\\n continue\\n\\n for r in range(1, len(taxa)):\\n if len(taxa[r]) == 3:\\n break\\n\\n if taxa[r] in expected_parent:\\n if report_errors:\\n if taxa[r - 1] != expected_parent[taxa[r]]:\\n self.logger.warning('Provided taxonomy is not taxonomically consistent.')\\n self.logger.warning(\\n 'Genome %s indicates the parent of %s is %s.' % (genome_id, taxa[r], taxa[r - 1]))\\n self.logger.warning(\\n 'The parent of this taxa was previously indicated as %s.' % (expected_parent[taxa[r]]))\\n # return None\\n\\n expected_parent[taxa[r]] = taxa[r - 1]\\n\\n return expected_parent\",\n \"def test_tree_support(self):\\r\\n master_tree = parse_newick('((a:2,b:3)ab:2,(c:1,d:2)cd:7)rt;')\\r\\n \\\"\\\"\\\"\\r\\n /-------.5 /-a\\r\\n ---1| \\\\-b\\r\\n \\\\------.5 /-c\\r\\n \\\\-d\\r\\n \\\"\\\"\\\"\\r\\n t2 = parse_newick('((a:2,b:3,c:33)ho:2,d:7);') # abc are siblings\\r\\n\\r\\n tc.tree_support(master_tree, t2)\\r\\n assert_almost_equal(\\r\\n master_tree.getNodeMatchingName('rt').bootstrap_support, 1.0)\",\n \"def match_taxon(name):\\n taxid = None\\n scientific_name = None\\n automatic_replacement = False\\n iname = name.strip().lower().replace(' ', ' ')\\n\\n if name:\\n # 1. 'name' matches the scientific name of a virus:\\n if name in scientific_names:\\n taxid = scientific_names[name]\\n scientific_name = name\\n # 2. 'name' is a close case-insensitive match for a virus:\\n elif iname in lowercase_names:\\n taxid = lowercase_names[iname]\\n scientific_name = taxid_names[taxid]\\n automatic_replacement = True\\n # 3. 'name' is the exact synonym of some taxon\\n elif name in synonyms:\\n taxid = synonyms[name]\\n scientific_name = taxid_names[taxid]\\n # 4. 'name' is a substring of exactly one scientific name:\\n else:\\n matches = []\\n for scientific_name in scientific_names.keys():\\n if name in scientific_name:\\n matches.append(scientific_name)\\n if len(matches) > 1:\\n break\\n if len(matches) == 1:\\n scientific_name = matches[0]\\n taxid = scientific_names[scientific_name]\\n\\n return (name, taxid, scientific_name, automatic_replacement)\",\n \"def has_duplicates(tree):\\n taxa = [tip.name for tip in tree.tips()]\\n if '' in taxa or None in taxa:\\n raise ValueError('Empty taxon name(s) found.')\\n return len(set(taxa)) < len(taxa)\",\n \"def test_nti(self):\\r\\n # using the input of the distance matrix generated from this tree\\r\\n # '(((sp1:.06,sp2:.1)A:.031,(sp3:.001,sp4:.01)B:.2)AB:.4,((sp5:.03,sp6:.02)C:.13,(sp7:.01,sp8:.005)D:.1)CD:.3)root;'\\r\\n # using this tree and this clumping we get almost exact agreement\\r\\n # between phylocom and relatedness.py\\r\\n distmat = array(\\r\\n [[0., 0.16, 0.292, 0.301, 0.951, 0.941, 0.901, 0.896],\\r\\n [0.16,\\r\\n 0.,\\r\\n 0.332,\\r\\n 0.341,\\r\\n 0.991,\\r\\n 0.981,\\r\\n 0.941,\\r\\n 0.936],\\r\\n [0.292,\\r\\n 0.332,\\r\\n 0.,\\r\\n 0.011,\\r\\n 1.061,\\r\\n 1.051,\\r\\n 1.011,\\r\\n 1.006],\\r\\n [0.301,\\r\\n 0.341,\\r\\n 0.011,\\r\\n 0.,\\r\\n 1.07,\\r\\n 1.06,\\r\\n 1.02,\\r\\n 1.015],\\r\\n [0.951,\\r\\n 0.991,\\r\\n 1.061,\\r\\n 1.07,\\r\\n 0.,\\r\\n 0.05,\\r\\n 0.27,\\r\\n 0.265],\\r\\n [0.941,\\r\\n 0.981,\\r\\n 1.051,\\r\\n 1.06,\\r\\n 0.05,\\r\\n 0.,\\r\\n 0.26,\\r\\n 0.255],\\r\\n [0.901,\\r\\n 0.941,\\r\\n 1.011,\\r\\n 1.02,\\r\\n 0.27,\\r\\n 0.26,\\r\\n 0.,\\r\\n 0.015],\\r\\n [0.896, 0.936, 1.006, 1.015, 0.265, 0.255, 0.015, 0.]])\\r\\n\\r\\n seed(0)\\r\\n obs_nti = nti(distmat, list(arange(8)), [0, 1, 3, 6], 1000)\\r\\n assert_almost_equal(-1.2046544711672049, obs_nti)\",\n \"def test_make_compatible_taxa_summaries_already_compatible(self):\\r\\n # Using two compatible ts.\\r\\n obs = _make_compatible_taxa_summaries(self.taxa_summary4,\\r\\n self.taxa_summary5)\\r\\n self.compare_multiple_level_array(obs, (self.taxa_summary4, self.taxa_summary5))\\r\\n\\r\\n # Using the same ts twice.\\r\\n obs = _make_compatible_taxa_summaries(self.taxa_summary4,\\r\\n self.taxa_summary4)\\r\\n self.compare_multiple_level_array(obs, (self.taxa_summary4, self.taxa_summary4))\\r\\n\\r\\n obs = _make_compatible_taxa_summaries(self.taxa_summary5,\\r\\n self.taxa_summary5)\\r\\n self.compare_multiple_level_array(obs, (self.taxa_summary5, self.taxa_summary5))\",\n \"def testTaxaData(self):\\n try:\\n numEukaryota = 0\\n numBacteria = 0\\n numVirus = 0\\n numArchaea = 0\\n numOther = 0\\n numUnclass = 0\\n logger.info(\\\"Loading taxonomy data\\\")\\n tU = TaxonomyUtils()\\n logger.info(\\\"Done loading taxonomy data\\\")\\n iCount = 0\\n entryD = self.__mU.doImport(self.__instanceSavePath, fmt=\\\"pickle\\\")\\n for entryId in entryD:\\n for entityId, eD in entryD[entryId][\\\"selected_polymer_entities\\\"].items():\\n taxId = eD[\\\"ncbi_taxonomy_id\\\"] if \\\"ncbi_taxonomy_id\\\" in eD else None\\n if taxId is None:\\n logger.debug(\\\"Missing taxId entryId %s entityId %s\\\", entryId, entityId)\\n continue\\n # lin = tU.getLineage(taxId)\\n # nmL = tU.getLineageNames(taxId)\\n ok1 = tU.isEukaryota(taxId)\\n if ok1:\\n numEukaryota += 1\\n ok3 = tU.isVirus(taxId)\\n if ok3:\\n numVirus += 1\\n ok2 = tU.isBacteria(taxId)\\n if ok2:\\n numBacteria += 1\\n #\\n ok4 = tU.isArchaea(taxId)\\n if ok4:\\n numArchaea += 1\\n #\\n ok5 = tU.isOther(taxId)\\n if ok5:\\n numOther += 1\\n #\\n ok6 = tU.isUnclassified(taxId)\\n if ok6:\\n numUnclass += 1\\n\\n if ok1 and (ok1 and ok2):\\n logger.info(\\\"taxid %r conflicting lineage\\\", taxId)\\n #\\n if not ok1 and not ok2 and not ok3 and not ok4 and not ok5 and not ok6:\\n logger.info(\\\"unassigned taxid %r\\\", taxId)\\n\\n logger.debug(\\\"taxId %r entryId %s entityId %s\\\", taxId, entryId, entityId)\\n iCount += 1\\n # if iCount > 5000:\\n # break\\n logger.info(\\\"Eukaryota %d Bacteria %d Virus %d Archaea %d Other/Syn %r Unclass %d\\\", numEukaryota, numBacteria, numVirus, numArchaea, numOther, numUnclass)\\n except Exception as e:\\n logger.exception(\\\"Failing with %s\\\", str(e))\\n self.fail()\",\n \"def test_check_tree_subset(self):\\r\\n\\r\\n fasta_labels = ['seq1_1', 'seq1_2', 'seq2_3', 'seq3_4']\\r\\n\\r\\n actual_subset_results = check_tree_subset(fasta_labels,\\r\\n self.sample_tree_3tips_fp)\\r\\n\\r\\n # Should find all and give True result\\r\\n\\r\\n self.assertEqual(actual_subset_results, True)\\r\\n\\r\\n # Should also get same results with 5 tip tree\\r\\n\\r\\n fasta_labels = ['seq1_1', 'seq1_2', 'seq2_3', 'seq3_4']\\r\\n\\r\\n actual_subset_results = check_tree_subset(fasta_labels,\\r\\n self.sample_tree_5tips_fp)\\r\\n\\r\\n # Should find all and give True result\\r\\n\\r\\n self.assertEqual(actual_subset_results, True)\\r\\n\\r\\n # Change two of the fasta labels to not match tree tips\\r\\n\\r\\n fasta_labels = ['seq1_1', 'seqX_2', 'seq2_3', 'seqY_4']\\r\\n\\r\\n actual_subset_results = check_tree_subset(fasta_labels,\\r\\n self.sample_tree_5tips_fp)\\r\\n\\r\\n # Should find seqX and seqY as not being a subset\\r\\n\\r\\n self.assertEqual(actual_subset_results, ['seqX', 'seqY'])\",\n \"def validate(self, taxonomy,\\n check_prefixes,\\n check_ranks,\\n check_hierarchy,\\n check_species,\\n check_group_names,\\n check_duplicate_names,\\n check_capitalization,\\n report_errors=True):\\n\\n # check for incomplete taxonomy strings or unexpected rank prefixes\\n invalid_ranks = {}\\n invalid_prefixes = {}\\n invalid_species_name = {}\\n invalid_group_name = {}\\n invalid_capitalization = set()\\n for taxon_id, taxa in taxonomy.items():\\n if check_ranks:\\n if len(taxa) != len(Taxonomy.rank_prefixes):\\n invalid_ranks[taxon_id] = ';'.join(taxa)\\n continue\\n\\n if check_prefixes:\\n for r, taxon in enumerate(taxa):\\n if taxon[0:3] != Taxonomy.rank_prefixes[r]:\\n invalid_prefixes[taxon_id] = [taxon, ';'.join(taxa)]\\n break\\n\\n if check_group_names:\\n for taxon in taxa:\\n canonical_taxon = ' '.join([t.strip() for t in re.split('_[A-Z]+(?= |$)', taxon[3:])]).strip()\\n if canonical_taxon and re.match('^[a-zA-Z0-9- ]+$', canonical_taxon) is None:\\n if not taxon.startswith('s__') or check_species:\\n invalid_group_name[taxon_id] = [taxon, 'Taxon contains invalid characters']\\n\\n if check_species:\\n genus_index = Taxonomy.rank_index['g__']\\n species_index = Taxonomy.rank_index['s__']\\n if len(taxa) > species_index:\\n species_name = taxa[species_index]\\n valid, error_msg = self.validate_species_name(species_name, require_full=True, require_prefix=True)\\n if not valid:\\n invalid_species_name[taxon_id] = [species_name, error_msg]\\n\\n if species_name != 's__':\\n genus_name = taxa[genus_index]\\n generic_name = species_name.split()[0]\\n if genus_name[3:] != generic_name[3:]:\\n invalid_species_name[taxon_id] = [species_name,\\n 'Genus and generic names do not match: %s' % genus_name]\\n\\n if check_capitalization:\\n for taxon in taxa:\\n if taxon[3].islower():\\n invalid_capitalization.add(taxon)\\n\\n # check for duplicate names\\n invalid_duplicate_name = []\\n if check_duplicate_names:\\n invalid_duplicate_name = self.duplicate_names(taxonomy, check_species)\\n\\n # check for inconsistencies in the taxonomic hierarchy\\n invalid_hierarchies = defaultdict(set)\\n missing_parent = set()\\n if check_hierarchy:\\n expected_parent = self.taxonomic_consistency(taxonomy, False)\\n\\n for taxon_id, taxa in taxonomy.items():\\n for r in range(1, len(taxa)):\\n if len(taxa[r]) == 3:\\n continue\\n\\n if r == self.rank_index['s__'] and not check_species:\\n continue\\n\\n if taxa[r] not in expected_parent:\\n missing_parent.add(taxa[r])\\n elif taxa[r - 1] != expected_parent[taxa[r]]:\\n invalid_hierarchies[taxa[r]].add(taxa[r - 1])\\n invalid_hierarchies[taxa[r]].add(expected_parent[taxa[r]])\\n\\n if report_errors:\\n if len(invalid_ranks):\\n print('')\\n print('Taxonomy contains too few ranks:')\\n for taxon_id, taxa_str in invalid_ranks.items():\\n print('%s\\\\t%s' % (taxon_id, taxa_str))\\n\\n if len(invalid_prefixes):\\n print('')\\n print('Taxonomy contains an invalid rank prefix:')\\n for taxon_id, info in invalid_prefixes.items():\\n print('%s\\\\t%s\\\\t%s' % (taxon_id, info[0], info[1]))\\n\\n if len(invalid_group_name):\\n print('')\\n print('Taxa containing invalid characters:')\\n for taxon_id, err_msg in invalid_group_name.items():\\n print('%s\\\\t%s\\\\t%s' % (taxon_id, err_msg[0], err_msg[1]))\\n\\n if len(invalid_species_name):\\n print('')\\n print('Taxonomy contains invalid species names:')\\n for taxon_id, info in invalid_species_name.items():\\n print('%s\\\\t%s\\\\t%s' % (taxon_id, info[0], info[1]))\\n\\n if len(invalid_duplicate_name):\\n print('')\\n print('Taxonomy contains identical taxon names in multiple lineages:')\\n for duplicate_name in invalid_duplicate_name.keys():\\n print('%s' % duplicate_name)\\n\\n if len(missing_parent):\\n print('')\\n print('Taxonomy contains taxa with an undefined parent:')\\n for taxon in missing_parent:\\n print('%s' % taxon)\\n\\n if len(invalid_hierarchies):\\n print('')\\n print('Taxonomy contains taxa with multiple parents:')\\n for child_taxon, parent_taxa in invalid_hierarchies.items():\\n print('%s\\\\t%s' % (child_taxon, ', '.join(parent_taxa)))\\n\\n if len(invalid_capitalization):\\n print('')\\n print('Taxa do not start with a capital letter:')\\n for taxon in invalid_capitalization:\\n print('{}'.format(taxon))\\n\\n return invalid_ranks, invalid_prefixes, invalid_species_name, invalid_hierarchies, invalid_group_name, invalid_capitalization\",\n \"def tree_support(master, subsampled_tree):\\r\\n master_tipnames = set(master.getTipNames())\\r\\n subsampled_tree_trimmed = copy.deepcopy(subsampled_tree)\\r\\n\\r\\n def delete_test(node):\\r\\n if not node.isTip():\\r\\n return False\\r\\n else:\\r\\n return (node.Name not in master_tipnames)\\r\\n subsampled_tree_trimmed.removeDeleted(delete_test)\\r\\n subsampled_tree_trimmed.prune()\\r\\n\\r\\n # subsampled_tree_nodes_names is a list of lists.\\r\\n # each elem is list of tip names for a node\\r\\n subsampled_tree_nodes_names = []\\r\\n for node in subsampled_tree_trimmed.iterNontips(include_self=True):\\r\\n subsampled_tree_nodes_names.append(node.getTipNames())\\r\\n\\r\\n # now a list of sets, each set is tip names for a specific node\\r\\n subsampled_tree_nodes_names = map(set, subsampled_tree_nodes_names)\\r\\n\\r\\n for master_node in master.iterNontips(include_self=True):\\r\\n if set(master_node.getTipNames()) in subsampled_tree_nodes_names:\\r\\n try:\\r\\n master_node.bootstrap_support += 1\\r\\n except AttributeError:\\r\\n master_node.bootstrap_support = 1\",\n \"def draw_tre_and_aln(aln_region, tre_str, og, target_sp):\\n #site, aa, score=ps_site\\n\\n # get leaves from tree string\\n tstax = getleaf(tre_str)\\n\\n ts = TreeStyle()\\n ts.margin_left = 5\\n ts.margin_right = 30\\n ts.margin_top = 20\\n ts.tree_width = 50\\n\\n aln_region_str = aln_region.format(\\\"fasta\\\")\\n\\n t = PhyloTree(tre_str, alignment=aln_region_str, alg_format=\\\"fasta\\\")\\n\\n # interfact\\n def _set_style(t):\\n # input an t with alignment, add label to it\\n \\\"\\\"\\\"\\n info = TextFace(\\\"{}\\\\nCodon:{}\\\\nScore:{}\\\".format(og,site,score), fsize=8, fgcolor='black', ftype='Arial')\\n info.margin_top = 10\\n info.margin_right = 20\\n info.margin_left = 5\\n t.add_face(info, column=0, position=\\\"branch-bottom\\\")\\n #t.add_face(TextFace(\\\"Codon:{}\\\".format(site)),column=0,position=\\\"branch-bottom\\\")\\n \\\"\\\"\\\"\\n ## label the longbranch\\n nstyle = NodeStyle()\\n # red line\\n #nstyle[\\\"bgcolor\\\"] = \\\"DarkSeaGreen\\\"\\n #nstyle[\\\"bgcolor\\\"] = \\\"LightSalmon\\\"\\n nstyle[\\\"hz_line_type\\\"] = 0\\n #nstyle[\\\"hz_line_color\\\"] = \\\"#ff0000\\\"\\n for tst in tstax:\\n tsnode = t.get_leaves_by_name(name=tst)[0]\\n tsnode.set_style(nstyle)\\n # add #1 to target species\\n if target_sp in tsnode.name:\\n tsnode.name = tsnode.name + \\\"_#1\\\"\\n return t\\n t = _set_style(t)\\n ## add AA alignment\\n def _trans_aln(aln_region):\\n # input an coding aln\\n aa_aln_str = \\\"\\\"\\n for seq_obj in aln_region:\\n dna = Seq(str(seq_obj.seq))\\n aa_s = dna.translate(gap=\\\"-\\\")\\n aa_aln_str += '>{}\\\\n{}\\\\n'.format(seq_obj.id, aa_s)\\n #print(aa_aln_str)\\n return aa_aln_str\\n\\n aa_aln_region_str = _trans_aln(aln_region)\\n t_aa = PhyloTree(tre_str, alignment=aa_aln_region_str, alg_format=\\\"fasta\\\")\\n t_aa = _set_style(t_aa)\\n\\n return t, t_aa, ts\",\n \"def build_tree(self, genes_share_one_alignment):\\r\\n species_name = self.species\\r\\n fun_built_tree = getattr(SSTree, species_name)\\r\\n return fun_built_tree(genes_share_one_alignment)\",\n \"def check_X_recessive(variant, family, strict=False):\\n path = os.path.abspath(os.getcwd())\\n output_log = os.path.join(path, \\\"variants_inheritance_patterns.csv\\\")\\n\\n if os.path.isfile(output_log):\\n output = open(output_log, \\\"a\\\")\\n else:\\n output = open(output_log, \\\"w\\\")\\n\\n affected = False\\n genotyped = False\\n female = False\\n \\n for individual in family.individuals:\\n # Get the genotype for this variant for this individual\\n individual_genotype = variant['genotypes'][individual]\\n \\n if strict:\\n if not individual_genotype.genotyped:\\n return False\\n # The case where the individual is healthy\\n if family.individuals[individual].healthy:\\n # If individual is healthy and homozygote alternative \\n # the variant can not be deleterious:\\n if individual_genotype.genotyped:\\n if individual_genotype.homo_alt:\\n return False\\n # If individual is male it can not have the variant at all\\n if family.individuals[individual].sex == 1:\\n if individual_genotype.has_variant:\\n return False\\n \\n # The case when the individual is sick\\n elif family.individuals[individual].affected:\\n affected = True\\n # If the individual is sick and homozygote ref it can not be x-recessive\\n if individual_genotype.genotyped:\\n genotyped = True\\n if individual_genotype.homo_ref:\\n return False\\n # Women have to be hom alt to be sick (almost allways carriers)\\n elif family.individuals[individual].sex == 2:\\n female = True\\n if not individual_genotype.homo_alt:\\n return False\\n if affected and genotyped and female:\\n output.write(\\\"{},Parents are not genotyped or is female het or is male without variant and offspring is female homozygote alternative\\\\n\\\".format(variant.get('variant_id', None)))\\n elif affected and genotyped:\\n output.write(\\\"{},Parents are not genotyped or is female het or is male without variant and offspring is male with heterozygous variant\\\\n\\\".format(variant.get('variant_id', None)))\\n elif affected:\\n output.write(\\\"{},Parents are not genotyped or is female het or is male without variant and offspring is not genotyped\\\\n\\\".format(variant.get('variant_id', None)))\\n else:\\n output.write(\\\"{},Parents without variant and no affected individual\\\\n\\\".format(variant.get('variant_id', None)))\\n output.close()\\n return True\",\n \"def aglycon_single_tax(df_NP):\\n # **seperate aglycons with at least two different entries in taxonomy**\\n index_Unique_Tax = [ind for ind, tax_list in enumerate(df_NP.taxonomy) if len(tax_list) == 1]\\n df_Without_Double = df_NP.iloc[index_Unique_Tax[:]]\\n #df_Without_Double\\n # **check for 'double' or 'triple' entries in taxonomy**\\n index_double_or_triple = [ind for ind, tax_list in enumerate(df_Without_Double.taxonomy) if 'double' not in tax_list and 'triple' not in tax_list]\\n df_Without_Double_or_Triple = df_Without_Double.iloc[index_double_or_triple[:]]\\n #df_Without_Double_or_Triple\\n # **------for taxonomy prediction------**\\n df_Without_Double_or_Triple.to_pickle(\\\"output_data/df_all_aglycons_with_single_taxonomy.pkl\\\")\\n # **------end for taxonomy prediction------**\",\n \"def test_taxonomy(self):\\n #ncbi\\n params = self.default_params.copy()\\n params[\\\"db_prefix\\\"] = self.results_dir + \\\"test_taxonomy_ncbi\\\"\\n params[\\\"taxonomy\\\"] = \\\"ncbi\\\"\\n params[\\\"taxonomy_files\\\"] = data_dir + \\\"build-custom/taxdump.tar.gz\\\"\\n params[\\\"ncbi_file_info\\\"] = data_dir + \\\"build-custom/assembly_summary.txt\\\"\\n params[\\\"genome_size_files\\\"] = data_dir + \\\"build-custom/species_genome_size.txt.gz\\\"\\n cfg = Config(\\\"build-custom\\\", **params)\\n self.assertTrue(run_ganon(cfg, params[\\\"db_prefix\\\"]), \\\"ganon build-custom run failed\\\")\\n res = build_sanity_check_and_parse(vars(cfg))\\n self.assertIsNotNone(res, \\\"ganon build-custom sanity check failed\\\")\\n\\n #gtdb\\n params = self.default_params.copy()\\n params[\\\"db_prefix\\\"] = self.results_dir + \\\"test_taxonomy_gtdb\\\"\\n params[\\\"taxonomy\\\"] = \\\"gtdb\\\"\\n params[\\\"taxonomy_files\\\"] = [data_dir + \\\"build-custom/ar53_taxonomy.tsv.gz\\\",\\n data_dir + \\\"build-custom/bac120_taxonomy.tsv.gz\\\"]\\n params[\\\"genome_size_files\\\"] = [data_dir + \\\"build-custom/ar53_metadata.tar.gz\\\",\\n data_dir + \\\"build-custom/bac120_metadata.tar.gz\\\"]\\n cfg = Config(\\\"build-custom\\\", **params)\\n self.assertTrue(run_ganon(cfg, params[\\\"db_prefix\\\"]), \\\"ganon build-custom run failed\\\")\\n res = build_sanity_check_and_parse(vars(cfg))\\n self.assertIsNotNone(res, \\\"ganon build-custom sanity check failed\\\")\\n\\n #skip\\n params = self.default_params.copy()\\n params[\\\"db_prefix\\\"] = self.results_dir + \\\"test_taxonomy_skip\\\"\\n params[\\\"taxonomy\\\"] = \\\"skip\\\"\\n params[\\\"taxonomy_files\\\"] = \\\"\\\"\\n cfg = Config(\\\"build-custom\\\", **params)\\n self.assertTrue(run_ganon(cfg, params[\\\"db_prefix\\\"]), \\\"ganon build-custom run failed\\\")\\n res = build_sanity_check_and_parse(vars(cfg))\\n self.assertIsNotNone(res, \\\"ganon build-custom sanity check failed\\\")\",\n \"def findDiscripancies(taxonomy):\\n i = 0\\n for entry in taxonomy:\\n if entry['parentName'] != None:\\n print entry['nodeName']\\n if entry['nodeName'].lower() == entry['parentName'].lower():\\n i += 1\\n print \\\"No of same nodes = {} \\\" .format(i)\",\n \"def test_write_summarize_taxa(self):\\r\\n # Classic format.\\r\\n write_summarize_taxa(self.taxa_summary, self.taxa_header, self.tmp_fp1)\\r\\n obs = open(self.tmp_fp1).read()\\r\\n exp = '\\\\n'.join(['Taxon\\\\tfoo\\\\tbar\\\\tfoobar',\\r\\n 'a;b;c\\\\t0\\\\t1\\\\t2',\\r\\n 'd;e;f\\\\t3\\\\t4\\\\t5\\\\n'])\\r\\n self.assertEqual(obs, exp)\\r\\n self.files_to_remove.append(self.tmp_fp1)\\r\\n\\r\\n # BIOM format.\\r\\n write_summarize_taxa(self.taxa_summary, self.taxa_header, self.tmp_fp2,\\r\\n file_format='biom')\\r\\n exp = parse_classic_table_to_rich_table(exp.split('\\\\n'), None, None,\\r\\n None, SparseTaxonTable)\\r\\n obs = open(self.tmp_fp2).read()\\r\\n obs = parse_biom_table(obs)\\r\\n self.assertEqual(obs, exp)\\r\\n self.files_to_remove.append(self.tmp_fp2)\",\n \"def test_leafs():\\n leafs = ['s6675', 's136315', 's10765', 's106594', 's131']\\n for leaf in leafs:\\n synset = germanet_data.get_synset_by_id(leaf)\\n np.testing.assert_equal(synset.is_root(), False)\\n np.testing.assert_equal(synset.is_leaf(), True)\",\n \"def _uniqueness_check(self, cls, unique_in = None, **attr):\\n # under the same datasource, only 1 subsystem, 1 neuropil, 1 tract of the name can exist\\n # under the same neuropil, only 1 neuron of the name can exist\\n # multiple (collections of) synapses can exist between two neurons\\n if cls == 'Species':\\n tmp = self.sql_query(\\n \\\"\\\"\\\"select from Species where (name = \\\"{name}\\\" or \\\"{name}\\\" in synonyms) and stage = \\\"{stage}\\\" and sex = \\\"{sex}\\\" \\\"\\\"\\\".format(\\n name = attr['name'], stage = attr['stage'], sex = attr['sex']))\\n if len(tmp):\\n objs = tmp.node_objs\\n if attr['name'] in [obj.name for obj in objs]:\\n raise NodeAlreadyExistError(\\\"\\\"\\\"Species {name} at {stage} stage ({sex}) already exists with rid = {rid}\\\"\\\"\\\".format(\\n name = attr['name'], stage = attr['stage'], sex = attr['sex'], rid = objs[0]._id))\\n else:\\n for obj in objs:\\n if attr['name'] in obj.synonyms:\\n raise NodeAlreadyExistError(\\n \\\"\\\"\\\"Species {name} (as its synonym) at {stage} stage ({sex}) already exists with rid = {rid}, use name {formalname} instead\\\"\\\"\\\".format(\\n name = attr['name'], stage = attr['stage'], sex = attr['sex'], rid = obj._id, formalname = obj.name))\\n elif cls == 'DataSource':\\n objs = self.find_objs('DataSource', name=attr['name'], version=attr['version'])\\n #if self.exists(cls, name = attr['name'], version = attr['version']):\\n if len(objs):\\n raise NodeAlreadyExistError(\\\"\\\"\\\"{} Node with attributes {} already exists with rid = {}\\\"\\\"\\\".format(\\n cls, ', '.join([\\\"\\\"\\\"{} = {}\\\"\\\"\\\".format(key, value) \\\\\\n for key, value in attr.items()]), objs[0]._id))\\n elif cls == 'Neurotransmitter':\\n tmp = self.sql_query(\\n \\\"\\\"\\\"select from Neurotransmitter where name = \\\"{name}\\\" or \\\"{name}\\\" in synonyms\\\"\\\"\\\".format(\\n name = attr['name']))\\n if len(tmp):\\n objs = tmp.node_objs\\n if attr['name'] in [obj.name for obj in objs]:\\n raise NodeAlreadyExistError(\\\"\\\"\\\"Neurotransmitter {name} already exists with rid = {rid}\\\"\\\"\\\".format(\\n name = attr['name'], rid = objs[0]._id))\\n return objs\\n else:\\n for obj in objs:\\n if attr['name'] in obj.synonyms:\\n raise NodeAlreadyExistError(\\n \\\"\\\"\\\"Neurotransmitter {name} (as its synonym) already exists with rid = {rid}, use name {formalname} instead\\\"\\\"\\\".format(\\n name = attr['name'], rid = obj._id, formalname = obj.name))\\n elif cls in ['Subsystem', 'Neuropil', 'Subregion', 'Tract']:\\n # TODO: synonyms are not checked against existing names and synonyms\\n if not isinstance(unique_in, models.DataSource):\\n raise TypeError('To check the uniqueness of a {} instance, unique_in must be a DataSource object'.format(cls))\\n tmp = self.sql_query(\\n \\\"\\\"\\\"select from (select from {cls} where name = \\\"{name}\\\" or \\\"{name}\\\" in synonyms) let $q = (select from (select expand($parent.$parent.current.in('Owns'))) where @class='{ucls}' and @rid = {rid}) where $q.size() = 1\\\"\\\"\\\".format(\\n rid = unique_in._id, cls = cls, name = attr['name'], ucls = unique_in.element_type))\\n if len(tmp):\\n objs = tmp.node_objs\\n if attr['name'] in [obj.name for obj in objs]:\\n raise NodeAlreadyExistError(\\\"\\\"\\\"{cls} {name} already exists under DataSource {ds} version {version}, rid = {rid}\\\"\\\"\\\".format(\\n cls = cls, name = attr['name'],\\n ds = unique_in.name,\\n version = unique_in.version, rid = objs[0]._id))\\n else:\\n for obj in objs:\\n if attr['name'] in obj.synonyms:\\n raise NodeAlreadyExistError(\\n \\\"\\\"\\\"{cls} {name} already exists as a synonym of {cls} {formalname} under DataSource {ds} version {version}, rid = {rid}\\\"\\\"\\\".format(\\n cls = cls, name = attr['name'], formalname = obj.name,\\n ds = unique_in.name,\\n version = unique_in.version, rid = obj._id))\\n # Alternatively, try:\\n # tmp = self.sql_query(\\n # \\\"\\\"\\\"select from {cls} where name = \\\"{name}\\\" or \\\"{name}\\\" in synonyms\\\"\\\"\\\".format(\\n # cls = cls, name = attr['name']))\\n # ds = tmp.owned_by(cls = 'DataSource').has(rid = datasource)\\n # if len(ds):\\n # tmp1 = tmp.has(name = attr['name'])\\n # if len(tmp1.owned_by(cls = 'DataSource').has(rid = datasource)):\\n # raise NodeAlreadyExistError(\\\"\\\"\\\"{cls} {name} already exists under DataSource {ds} version {version}\\\"\\\"\\\".format(\\n # cls = cls, name = attr['name'],\\n # ds = datasource.name,\\n # version = datasource.version))\\n # else:\\n # all_synonym_objs = (tmp - tmp1).node_objs\\n # for obj in objs:\\n # if len(QueryWrapper.from_rids(obj._id).has(cls = 'DataSource').has(rid = datasource)):\\n # raise NodeAlreadyExistError(\\n # \\\"\\\"\\\"{cls} {name} already exists as a synonym of {cls} {formalname} under DataSource {ds} version {version}\\\"\\\"\\\".format(\\n # cls = cls, name = attr['name'], formalname = obj.name,\\n # ds = datasource.name,\\n # version = datasource.version))\\n\\n # Alternatively 2, try: (will be slow when it has a lot of Owns edges)\\n # tmp = sql_query(\\n # \\\"\\\"\\\"\\n # select from (select expand(out('Owns')[@class = \\\"{cls}\\\"]) from {rid}) where name = \\\"{name}\\\" or \\\"{name}\\\" in synonyms\\n # \\\"\\\"\\\"\\n # )\\n # elif cls in ['Subregion']:\\n # if not isinstance(unique_in, models.Neuropil):\\n # raise TypeError('To check the uniqueness of a {} instance, unique_in must be a Neuropil object'.format(cls))\\n # tmp = self.sql_query(\\n # \\\"\\\"\\\"select from (select from {cls} where name = \\\"{name}\\\" or \\\"{name}\\\" in synonyms) let $q = (select from (select expand($parent.$parent.current.in('Owns'))) where @class='ucls' and @rid = {rid}) where $q.size() = 1\\\"\\\"\\\".format(\\n # rid = unique_in._id, cls = cls, name = attr['name'], ucls = unique_in.element_type))\\n # if len(tmp):\\n # objs = tmp.node_objs\\n # if attr['name'] in [obj.name for obj in objs]:\\n # raise NodeAlreadyExistError(\\\"\\\"\\\"{cls} {name} already exists under Neuropil {ds}\\\"\\\"\\\".format(\\n # cls = cls, name = attr['name'],\\n # ds = unique_in.name))\\n # else:\\n # for obj in objs:\\n # if name in obj.synonyms:\\n # raise NodeAlreadyExistError(\\n # \\\"\\\"\\\"{cls} {name} already exists as a synonym of {cls} {formalname} under Neuropil {ds}\\\"\\\"\\\".format(\\n # cls = cls, name = attr['name'], formalname = obj.name,\\n # ds = unique_in.name))\\n elif cls in ['Neuron', 'NeuronFragment']:\\n # TODO: synonyms are not checked against existing names and synonyms\\n if not isinstance(unique_in, models.DataSource):\\n raise TypeError('To check the uniqueness of a {} instance, unique_in must be a DataSource object'.format(cls))\\n tmp = self.sql_query(\\n \\\"\\\"\\\"select from (select from {cls} where uname = \\\"{name}\\\") let $q = (select from (select expand($parent.$parent.current.in('Owns'))) where @class='{ucls}' and @rid = {rid}) where $q.size() = 1\\\"\\\"\\\".format(\\n rid = unique_in._id, cls = cls, name = attr['name'], ucls = unique_in.element_type))\\n if len(tmp):\\n objs = tmp.node_objs\\n raise NodeAlreadyExistError(\\\"\\\"\\\"{cls} {name} already exists with rid = {rid}, under DataSource {ds} version {version}\\\"\\\"\\\".format(\\n cls = cls, name = attr['name'], rid = objs[0]._id,\\n ds = unique_in.name,\\n version = unique_in.version))\\n elif cls == 'Circuit':\\n if not isinstance(unique_in, models.DataSource):\\n raise TypeError('To check the uniqueness of a {} instance, unique_in must be a DataSource object'.format(cls))\\n tmp = self.sql_query(\\n \\\"\\\"\\\"select from (select from {cls} where name = \\\"{name}\\\") let $q = (select from (select expand($parent.$parent.current.in('Owns'))) where @class='{ucls}' and @rid = {rid}) where $q.size() = 1\\\"\\\"\\\".format(\\n rid = unique_in._id, cls = cls, name = attr['name'], ucls = unique_in.element_type))\\n if len(tmp):\\n objs = tmp.node_objs\\n if attr['name'] in [obj.name for obj in objs]:\\n raise NodeAlreadyExistError(\\\"\\\"\\\"{cls} {name} already exists under DataSource {ds} version {version}, rid = {rid}\\\"\\\"\\\".format(\\n cls = cls, name = attr['name'],\\n ds = unique_in.name,\\n version = unique_in.version, rid = objs[0]._id))\\n elif cls == 'ArborizationData':\\n if not isinstance(unique_in, (models.Neuron, models.Synapse)):\\n raise TypeError('To check the uniqueness of a ArborizationData instance, unique_in must be a Neuron or a Synapse object')\\n tmp = self.sql_query(\\n \\\"\\\"\\\"select from (select expand(out(HasData)) from {rid}) where @class = 'ArborizationData' \\\"\\\"\\\".format(rid = unique_in._id))\\n if len(tmp):\\n raise NodeAlreadyExistError(\\\"\\\"\\\"ArborizationData already exists for {node} {uname} with rid = {rid}. Use NeuroArch.update_{node}_arborization to update the record\\\"\\\"\\\".format(\\n node = unique_in.element_type.lower(), rid = tmp.node_objs[0]._id, uname = unique_in.uname))\\n else:\\n raise TypeError('Model type not understood.')\\n return True\",\n \"def test_level_sequence_taxrank(self):\\n # --level genus NCBI\\n params = self.default_params.copy()\\n params[\\\"db_prefix\\\"] = self.results_dir + \\\"test_level_sequence_genus_ncbi\\\"\\n params[\\\"input_target\\\"] = \\\"sequence\\\"\\n params[\\\"level\\\"] = \\\"genus\\\"\\n params[\\\"taxonomy\\\"] = \\\"ncbi\\\"\\n params[\\\"taxonomy_files\\\"] = data_dir + \\\"build-custom/taxdump.tar.gz\\\"\\n params[\\\"ncbi_sequence_info\\\"] = data_dir + \\\"build-custom/nucl_gb.accession2taxid.gz\\\"\\n params[\\\"genome_size_files\\\"] = data_dir + \\\"build-custom/species_genome_size.txt.gz\\\"\\n cfg = Config(\\\"build-custom\\\", **params)\\n self.assertTrue(run_ganon(cfg, params[\\\"db_prefix\\\"]), \\\"ganon build-custom run failed\\\")\\n res = build_sanity_check_and_parse(vars(cfg), skipped_targets=True) # may skip targets without genus entry\\n self.assertIsNotNone(res, \\\"ganon build-custom sanity check failed\\\")\\n # Tax must not have \\\"species\\\" (filtered out)\\n self.assertFalse(\\\"species\\\" in res[\\\"tax\\\"]._ranks.values(), \\\"rank found\\\")\",\n \"def validate_taxon(cell):\\n (name, taxid, scientific_name, automatic_replacement) = match_taxon(cell.value or '')\\n\\n # Update the cell\\n if is_virus(taxid):\\n if name == scientific_name:\\n cell.fill = greenFill\\n elif automatic_replacement:\\n cell.comment = Comment('Automatically replaced \\\"%s\\\" with \\\"%s\\\".'\\n % (name, scientific_name), author)\\n cell.value = scientific_name\\n cell.fill = blueFill\\n else:\\n cell.comment = Comment('Suggestion: ' + scientific_name, author)\\n cell.fill = orangeFill\\n return scientific_name\\n elif taxid:\\n cell.comment = Comment('Not the name of a virus', author)\\n cell.fill = redFill\\n else:\\n cell.comment = Comment('Not found in NCBI Taxonomy', author)\\n cell.fill = darkRedFill\",\n \"def test_big_family(self):\\n\\n self.taxon_tester('Staphylinidae')\",\n \"def test_make_taxa_long(self):\\n basic_test_runner(self, 'taxonomy', rank='all')\",\n \"def test_level_file_taxrank(self):\\n\\n # --level genus NCBI\\n params = self.default_params.copy()\\n params[\\\"db_prefix\\\"] = self.results_dir + \\\"test_level_file_genus_ncbi\\\"\\n params[\\\"input_target\\\"] = \\\"file\\\"\\n params[\\\"level\\\"] = \\\"genus\\\"\\n params[\\\"taxonomy\\\"] = \\\"ncbi\\\"\\n params[\\\"taxonomy_files\\\"] = data_dir + \\\"build-custom/taxdump.tar.gz\\\"\\n params[\\\"ncbi_file_info\\\"] = data_dir + \\\"build-custom/assembly_summary.txt\\\"\\n params[\\\"genome_size_files\\\"] = data_dir + \\\"build-custom/species_genome_size.txt.gz\\\"\\n cfg = Config(\\\"build-custom\\\", **params)\\n self.assertTrue(run_ganon(cfg, params[\\\"db_prefix\\\"]), \\\"ganon build-custom run failed\\\")\\n res = build_sanity_check_and_parse(vars(cfg), skipped_targets=True) # may skip targets without genus entry\\n self.assertIsNotNone(res, \\\"ganon build-custom sanity check failed\\\")\\n # Tax must not have \\\"species\\\" (filtered out)\\n self.assertFalse(\\\"species\\\" in res[\\\"tax\\\"]._ranks.values(), \\\"rank found\\\")\\n\\n # --level genus GTDB\\n params = self.default_params.copy()\\n params[\\\"db_prefix\\\"] = self.results_dir + \\\"test_level_file_genus_gtdb\\\"\\n params[\\\"input_target\\\"] = \\\"file\\\"\\n params[\\\"level\\\"] = \\\"genus\\\"\\n params[\\\"taxonomy\\\"] = \\\"gtdb\\\"\\n params[\\\"taxonomy_files\\\"] = [data_dir + \\\"build-custom/ar53_taxonomy.tsv.gz\\\",\\n data_dir + \\\"build-custom/bac120_taxonomy.tsv.gz\\\"]\\n params[\\\"genome_size_files\\\"] = [data_dir + \\\"build-custom/ar53_metadata.tar.gz\\\",\\n data_dir + \\\"build-custom/bac120_metadata.tar.gz\\\"]\\n cfg = Config(\\\"build-custom\\\", **params)\\n self.assertTrue(run_ganon(cfg, params[\\\"db_prefix\\\"]), \\\"ganon build-custom run failed\\\")\\n res = build_sanity_check_and_parse(vars(cfg))\\n self.assertIsNotNone(res, \\\"ganon build-custom sanity check failed\\\")\\n # Tax must not have \\\"species\\\" (filtered out)\\n self.assertFalse(\\\"species\\\" in res[\\\"tax\\\"]._ranks.values(), \\\"rank found\\\")\",\n \"def assert_sample_labelled_trees_equal(tree, dpy_tree):\\n for sample in tree.samples():\\n dpy_node = dpy_tree.find_node_with_taxon_label(f\\\"n{sample}\\\")\\n # Check the branch length paths to root are equal\\n p1 = []\\n u = sample\\n while tree.parent(u) != tskit.NULL:\\n p1.append(tree.branch_length(u))\\n u = tree.parent(u)\\n p2 = []\\n while dpy_node.parent_node is not None:\\n p2.append(dpy_node.edge_length)\\n dpy_node = dpy_node.parent_node\\n assert len(p1) == len(p2)\\n np.testing.assert_array_almost_equal(p1, p2)\",\n \"def is_good_cyc(path, G, bamfile):\\n\\n sing_nodes = set()\\n for node in path:\\n if node[-1] == \\\"'\\\": node = node[:-1]\\n sing_nodes.add(node)\\n if len(sing_nodes)==0: return True\\n\\n non_path_dominated_nodes = 0\\n\\n for nd in sing_nodes:\\n mate_tigs = get_contigs_of_mates(nd, bamfile, G)\\n # NOTE: ^ this only gets mates that are reachable from nd in G\\n logger.info(\\\"\\\\tNode: %s\\\" % nd)\\n logger.info(\\\"\\\\t\\\\tMates: %s\\\" % \\\", \\\".join(mate_tigs))\\n\\n # need to check against F and R versions of path nodes\\n path_rc = [rc_node(x) for x in path]\\n num_mates_in_path = sum([1 for x in mate_tigs if (x in path or x in path_rc)])\\n num_mates_not_in_path = len(mate_tigs)-num_mates_in_path\\n if num_mates_in_path < num_mates_not_in_path:\\n ########### if len(mate_tigs)>1 and num_mates_in_path < num_mates_not_in_path:\\n non_path_dominated_nodes += 1\\n if float(non_path_dominated_nodes)/float(len(sing_nodes)) > PARAMS.GOOD_CYC_DOMINATED_THRESH:\\n logger.info(\\\"Too many nodes with majority of mates not on path\\\")\\n return False\\n else: return True\",\n \"def test_regen(self, tmpdir, treantclass):\\n with tmpdir.as_cwd():\\n C1 = treantclass('regen')\\n C1.tags.add('fantastic')\\n C2 = treantclass('regen') # should be regen of C1\\n assert 'fantastic' in C2.tags\\n\\n # they point to the same file, but they are not the same object\\n assert C1 is not C2\",\n \"def test_check_isomorphism(self):\\n mol1 = Molecule(smiles='[O-][N+]#N')\\n mol2 = Molecule(smiles='[N-]=[N+]=O')\\n self.assertTrue(converter.check_isomorphism(mol1, mol2))\",\n \"def test_taxa_with_special_characters(self):\\r\\n fd, taxonomy_fp = mkstemp()\\r\\n close(fd)\\r\\n f = open(taxonomy_fp, \\\"w\\\")\\r\\n f.write(rdp_id_to_taxonomy_special_chars)\\r\\n f.close()\\r\\n self._paths_to_clean_up.append(taxonomy_fp)\\r\\n\\r\\n app = RdpTaxonAssigner({\\r\\n 'id_to_taxonomy_fp': taxonomy_fp,\\r\\n 'reference_sequences_fp': self.reference_seqs_file.name,\\r\\n })\\r\\n res = app(self.tmp_seq_filepath)\\r\\n obs_lineage, obs_confidence = res['X67228 some description']\\r\\n\\r\\n self.assertEqual(obs_lineage, (\\r\\n \\\"Bacteria;Proteobacteria;Alphaproteobacteria;Rhizobiales<What;\\\"\\r\\n \\\"Rhizobiaceae&Huh?;Rhizobium\\\"))\\r\\n self.assertEqual(obs_confidence, 1.0)\",\n \"def cladistic(tree, taxa):\\n tips = []\\n taxa = set(taxa)\\n for tip in tree.tips():\\n if tip.name in taxa:\\n tips.append(tip)\\n n = len(taxa)\\n if len(tips) < n:\\n raise ValueError('Taxa not found in the tree.')\\n return ('uni' if n == 1 else\\n ('mono' if len(tree.lca(tips).subset()) == n else\\n 'poly'))\",\n \"def read_from_tree(self, tree, warnings=True):\\n\\n if isinstance(tree, str):\\n tree = dendropy.Tree.get_from_path(tree,\\n schema='newick',\\n rooting=\\\"force-rooted\\\",\\n preserve_underscores=True)\\n\\n taxonomy = {}\\n for leaf in tree.leaf_node_iter():\\n taxa = []\\n\\n node = leaf.parent_node\\n while node:\\n if node.label:\\n taxa_str = node.label\\n if ':' in taxa_str:\\n taxa_str = taxa_str.split(':')[1]\\n\\n if not is_float(taxa_str):\\n if taxa_str[-1] == ';':\\n taxa_str = taxa_str[:-1]\\n\\n # check for concatenated ranks of the form: p__Crenarchaeota__c__Thermoprotei\\n for prefix in Taxonomy.rank_prefixes:\\n split_str = '__' + prefix\\n if split_str in taxa_str:\\n taxa_str = taxa_str.replace(split_str, ';' + prefix)\\n\\n # appears to be an internal label and not simply a support value\\n taxa = [x.strip() for x in taxa_str.split(';')] + taxa\\n node = node.parent_node\\n\\n if warnings and len(taxa) > 7:\\n self.logger.warning(\\n 'Invalid taxonomy string read from tree for taxon %s: %s' % (leaf.taxon.label, ';'.join(taxa)))\\n\\n # check if genus name should be appended to species label\\n if len(taxa) == 7:\\n genus = taxa[5][3:]\\n species = taxa[6][3:]\\n if genus not in species and len(species.split()) == 1:\\n taxa[6] = 's__' + genus + ' ' + species\\n\\n taxa = self.fill_trailing_ranks(taxa)\\n taxonomy[leaf.taxon.label] = taxa\\n\\n return taxonomy\",\n \"def are_two_trees_incompatible(tree1, tree2):\\n leaves1 = get_leaf_set(tree1)\\n leaves2 = get_leaf_set(tree2)\\n shared = list(leaves1.intersection(leaves2))\\n\\n taxa = dendropy.TaxonNamespace(shared) # CRITICAL!!!\\n\\n # No topological information\\n if len(shared) < 4:\\n return False\\n\\n # Move trees onto shared leaf set\\n tree1.retain_taxa_with_labels(shared)\\n tree1.migrate_taxon_namespace(taxa)\\n tree1.is_rooted = False\\n tree1.collapse_basal_bifurcation()\\n tree1.update_bipartitions()\\n\\n tree2.retain_taxa_with_labels(shared)\\n tree2.migrate_taxon_namespace(taxa)\\n tree2.is_rooted = False\\n tree2.collapse_basal_bifurcation()\\n tree2.update_bipartitions()\\n\\n # Check for compatibility\\n [fp, fn] = false_positives_and_negatives(tree1, tree2)\\n if fp > 0 or fn > 0:\\n return True\\n else:\\n return False\",\n \"def test_genbank_to_genome_taxonomy(self):\\n result = self.gfu.genbank_to_genome(self.ctx, {\\n 'workspace_name': self.ws_name,\\n 'generate_ids_if_needed': 'true', # why is this a string\\n 'taxon_id': '3702',\\n 'file': {\\n 'path': f\\\"{_DATA_PATH}/wigglesworthia/genome.gb\\\"\\n },\\n 'genome_name': str(uuid4()),\\n })\\n ('result', result)\\n ref = result[0]['genome_ref']\\n self.assertTrue(ref, 'Genome ref exists')\\n info = result[0]['genome_info']\\n typ = info[2]\\n self.assertTrue(typ.startswith('KBaseGenomes.Genome'))\\n info_details = info[-1]\\n self.assertEqual(info_details['Taxonomy'], (\\n \\\"cellular organisms;Eukaryota;Viridiplantae;\\\"\\n \\\"Streptophyta;Streptophytina;Embryophyta;Tracheophyta;\\\"\\n \\\"Euphyllophyta;Spermatophyta;Magnoliopsida;Mesangiospermae;\\\"\\n \\\"eudicotyledons;Gunneridae;Pentapetalae;rosids;malvids;\\\"\\n \\\"Brassicales;Brassicaceae;Camelineae;Arabidopsis\\\"\\n ))\\n self.assertEqual(info_details['Size'], '697724')\\n self.assertEqual(info_details['Source'], 'Genbank')\\n self.assertEqual(info_details['Name'], 'Wigglesworthia glossinidia endosymbiont of Glossina brevipalpis')\\n self.assertEqual(info_details['GC content'], '0.22479')\\n self.assertEqual(info_details['Genetic code'], '11')\\n self.assertEqual(info_details['Number of Genome Level Warnings'], '1')\\n self.assertEqual(info_details['Source ID'], 'BA000021')\\n self.assertEqual(info_details['Number of Protein Encoding Genes'], '20')\\n self.assertEqual(info_details['Domain'], 'Eukaryota')\\n self.assertTrue(info_details['Assembly Object'])\\n self.assertEqual(info_details['Number contigs'], '1')\\n self.assertEqual(info_details['Number of CDS'], '20')\\n self.assertTrue(info_details['MD5'])\",\n \"def test_multitype_path():\\n egfr = Agent('EGFR', db_refs={'HGNC':'3236'})\\n grb2 = Agent('GRB2', db_refs={'HGNC': '4566'})\\n grb2_egfr = Agent('GRB2', bound_conditions=[BoundCondition(egfr)],\\n db_refs={'HGNC': '4566'})\\n sos1 = Agent('SOS1', db_refs={'HGNC':'11187'}, )\\n sos1_grb2 = Agent('SOS1', bound_conditions=[BoundCondition(grb2)],\\n db_refs={'HGNC':'11187'}, )\\n kras = Agent('KRAS', db_refs={'HGNC':'6407'})\\n kras_g = Agent('KRAS', activity=ActivityCondition('gtpbound', True),\\n db_refs={'HGNC': '6407'})\\n braf = Agent('BRAF', db_refs={'HGNC':'1097'})\\n\\n def check_stmts(stmts, paths):\\n pa = PysbAssembler()\\n pa.add_statements(stmts)\\n pa.make_model(policies='one_step')\\n stmts_to_check = [\\n Activation(egfr, kras, 'gtpbound'),\\n Activation(egfr, braf, 'kinase')\\n ]\\n mc = ModelChecker(pa.model, stmts_to_check)\\n results = mc.check_model()\\n assert len(results) == len(stmts_to_check)\\n assert isinstance(results[0], tuple)\\n assert results[0][1].paths == paths[0]\\n assert results[1][1].paths == paths[1]\\n # Check with the ActiveForm\\n stmts1 = [\\n Complex([egfr, grb2]),\\n Complex([sos1, grb2_egfr]),\\n ActiveForm(sos1_grb2, 'activity', True),\\n Activation(sos1_grb2, kras, 'gtpbound'),\\n Activation(kras_g, braf, 'kinase')\\n ]\\n check_stmts(stmts1, ([[('EGFR_GRB2_bind', 1), ('SOS1_GRB2_EGFR_bind', 1),\\n ('SOS1_GRB2_activates_KRAS_gtpbound', 1),\\n ('KRAS_gtpbound_active_obs', 1)]],\\n [[('EGFR_GRB2_bind', 1), ('SOS1_GRB2_EGFR_bind', 1),\\n ('SOS1_GRB2_activates_KRAS_gtpbound', 1),\\n ('KRAS_activates_BRAF_kinase', 1),\\n ('BRAF_kinase_active_obs', 1)]]))\\n # Check without the ActiveForm\\n stmts2 = [\\n Complex([egfr, grb2]),\\n Complex([sos1, grb2_egfr]),\\n RasGef(sos1_grb2, kras),\\n Activation(kras_g, braf, 'kinase')\\n ]\\n check_stmts(stmts2, ([[('EGFR_GRB2_bind', 1), ('SOS1_GRB2_EGFR_bind', 1),\\n ('SOS1_GRB2_activates_KRAS', 1),\\n ('KRAS_gtpbound_active_obs', 1)]],\\n [[('EGFR_GRB2_bind', 1), ('SOS1_GRB2_EGFR_bind', 1),\\n ('SOS1_GRB2_activates_KRAS', 1),\\n ('KRAS_activates_BRAF_kinase', 1),\\n ('BRAF_kinase_active_obs', 1)]]))\",\n \"def testComplexRichness(self):\\n self.assertGreaterEqual(self.tree1.get_species_richness(), self.tree2.get_species_richness())\\n self.assertGreaterEqual(self.tree3.get_species_richness(), self.tree2.get_species_richness())\\n self.assertGreaterEqual(self.tree1.get_species_richness(), self.tree4.get_species_richness())\",\n \"def test_nxgraph(self):\\n self._build_sample_graph()\\n # Adding singleton\\n sg = self.skill_graph.add(Skill.build('g', ''))\\n skill_map = SkillMap.load(self.course)\\n nxgraph = SkillMapMetrics(skill_map).nxgraph\\n self.assertIsInstance(nxgraph, DiGraph)\\n successors = skill_map.build_successors()\\n # Check nodes\\n self.assertEqual(len(nxgraph), len(successors))\\n for skill in successors:\\n self.assertIn(skill, nxgraph.nodes(),\\n msg='Node {} not found in nx graph.'.format(skill))\\n # Check edges\\n original_edges = sum(len(dst) for dst in successors.values())\\n self.assertEqual(len(nxgraph.edges()), original_edges)\\n for src, dst in nxgraph.edges_iter():\\n self.assertIn(src, successors)\\n self.assertIn(dst, successors[src],\\n msg='Extra {},{} edge in nx graph.'.format(src, dst))\",\n \"def compare_balanced_tree(G, node1:str, node2:str, traversed1:list, traversed2:list):\\n logger.debug(f\\\"checking symmtrical connections for nodes: {node1}, {node2}\\\")\\n tree1 = set(get_next_level(G,[node1]))\\n tree2 = set(get_next_level(G,[node2]))\\n traversed1.append(node1)\\n traversed2.append(node2)\\n if tree1==tree2:\\n return True\\n while(len(list(tree1))== len(list(tree2)) > 0):\\n logger.debug(f\\\"tree1 {tree1} tree2 {tree2} traversed1 {traversed1} traversed2 {traversed2}\\\")\\n tree1 = set(tree1) - set(traversed1)\\n tree2 = set(tree2) - set(traversed2)\\n\\n if tree1.intersection(tree2) or len(list(tree1))== len(list(tree2))==0:\\n return True\\n else:\\n traversed1+=list(tree1)\\n traversed2+=list(tree2)\\n tree1=set(get_next_level(G,tree1))\\n tree2=set(get_next_level(G,tree2))\\n\\n logger.debug(f\\\"Non symmetrical branches for nets: {node1}, {node2}\\\")\\n return False\",\n \"def init_taxon():\\n if not exists('./data/taxdmp.zip'):\\n ftp = FTP('ftp.ncbi.nih.gov')\\n ftp.login()\\n ftp.cwd('pub/taxonomy')\\n ftp.retrbinary('RETR taxdmp.zip', open('./data/taxdmp.zip', 'wb').write)\\n ftp.quit\\n with ZipFile('./data/taxdmp.zip', 'r') as dumpfile:\\n dumpfile.extractall(path='./data/')\\n taxon_id = dict()\\n data = list()\\n name = dict()\\n specie = list()\\n son = dict()\\n greatson = dict()\\n parent = dict()\\n rank = dict()\\n global taxon\\n taxon = list()\\n with open('./data/names.dmp', 'r') as dumpfile:\\n raw = dumpfile.read().split(sep='\\\\n')\\n raw.pop()\\n for record in raw:\\n add = record.replace('\\\\t', '').split(sep='|')\\n if add[0] not in name or add[2] == 'scientific name':\\n name[add[0]] = add[1]\\n with open('./data/nodes.dmp', 'r') as dumpfile:\\n raw = dumpfile.read().split(sep='\\\\n')\\n raw.pop()\\n for record in raw:\\n add = record.replace('\\\\t', '').split(sep='|')\\n # 1696063|Sarcocystis corvusi||scientific name|\\n taxon_id[add[0]] = add[1]\\n rank[add[0]] = add[3]\\n if add[2] == 'species':\\n specie.append(add[0])\\n for specie in specie:\\n record = [specie, ]\\n while taxon_id[specie] != '1':\\n record.append(taxon_id[specie])\\n specie = taxon_id[specie]\\n # if '33090' in record:\\n # record.pop()\\n # record.pop()\\n data.append(record)\\n for data in data:\\n for n in range(len(data)):\\n if data[n] not in parent:\\n parent[data[n]] = data[(n + 1):]\\n if n == 0:\\n continue\\n if data[n] not in son:\\n son[data[n]] = {data[n - 1], }\\n else:\\n son[data[n]].add(data[n - 1])\\n if data[n] not in greatson:\\n greatson[data[n]] = {data[0], }\\n else:\\n greatson[data[n]].add(data[0])\\n for specie in name.items():\\n if specie[0] not in son:\\n son[specie[0]] = set()\\n if specie[0] not in parent:\\n parent[specie[0]] = list()\\n if specie[0] not in greatson:\\n greatson[specie[0]] = set()\\n record = [specie[0], name[specie[0]], rank[specie[0]], son[specie[0]], parent[specie[0]], greatson[specie[0]]]\\n taxon.append(record)\\n\\n con = sqlite3.connect('./data/DB')\\n cur = con.cursor()\\n cur.execute(\\n 'CREATE TABLE IF NOT EXISTS taxon (Id TEXT, Name TEXT, Rank TEXT, Son TEXT, Parent TEXT, GreatSon TEXT);')\\n for line in taxon:\\n son = ' '.join(line[3])\\n parent = ' '.join(line[4])\\n greatson = ' '.join(line[5])\\n cur.execute('INSERT INTO taxon (Id, Name, Rank, Son, Parent, GreatSon) VALUES (?, ?, ?, ?, ?, ?);',\\n (line[0], line[1], line[2], son, parent, greatson))\\n con.commit()\\n cur.close()\\n con.close()\\n print('Done.\\\\n')\",\n \"def test_make_taxonomy(self):\\n basic_test_runner(self, 'taxonomy')\",\n \"def read_mothur_taxonomy(f):\\n for n, line in enumerate(open(f)):\\n if line.startswith('#'):\\n continue\\n\\n line = line.rstrip()\\n name, taxa = line.rstrip().split('\\\\t')\\n skip = False\\n for word in EXCLUDE:\\n if word in taxa:\\n skip = True\\n break\\n\\n if skip:\\n continue \\n\\n # the parsing of taxa works for both mothur output and this\\n taxa = taxa.rstrip(';') # for mothur classfy.seqs output\\n lis = taxa.split(';')\\n lis2 = []\\n for item in lis:\\n item = item.strip() # for copyrigher copy table ' ;' separater\\n if item.endswith(')'):\\n item = item.rsplit('(', 1)[0].strip()\\n\\n # remove taxon level prefix, e.g. 'p__Firmicutes'\\n if '__' in item:\\n item = item.split('__', 1)[1]\\n\\n #item = item.strip('\\\"')\\n\\n # green gene taxonomy has sapce\\n item = item.replace(' ', '_')\\n\\n item = item.lower()\\n if item in ['', 'unknown', 'other', 'unassigned']:\\n item = 'Unclassifed'\\n\\n item = item.capitalize()\\n lis2.append(item)\\n\\n length = len(lis2)\\n assert length == LEVELS, 'levels ({}) is not ({})'.format(\\n length, LEVELS)\\n yield tuple(lis2)\",\n \"def check_tree_subset(fasta_labels,\\r\\n tree_fp):\\r\\n\\r\\n # Need to get modified fasta labels with underscore stripped\\r\\n\\r\\n raw_fasta_labels = set([label.split('_')[0] for label in fasta_labels])\\r\\n\\r\\n tree_f = open(tree_fp, \\\"U\\\")\\r\\n\\r\\n tree = DndParser(tree_f)\\r\\n\\r\\n # Get a set of tree tip names\\r\\n tree_tips = set(tree.getTipNames())\\r\\n\\r\\n labels_not_in_tips = []\\r\\n\\r\\n for curr_label in raw_fasta_labels:\\r\\n if curr_label not in tree_tips:\\r\\n labels_not_in_tips.append(curr_label)\\r\\n\\r\\n # Return True if all found in tree tips\\r\\n if len(labels_not_in_tips) == 0:\\r\\n labels_not_in_tips = True\\r\\n\\r\\n return labels_not_in_tips\",\n \"def update_tip_names(tree, taxdict):\\n\\n list_nodes = []\\n uniprot_mapping = pd.DataFrame(columns=['taxid', 'name', 'uniprot'])\\n\\n counter = 0\\n for node in tree.traverse(\\\"postorder\\\"):\\n current_name = node.name\\n\\n if 'NMR' in current_name:\\n new_name = \\\"Heterocephalus_glaber\\\"\\n node.name = new_name\\n list_nodes.append(node.name)\\n taxid = \\\"NA\\\" \\n uniprot_mapping.loc[counter] = (taxid, new_name, \\\"UP000006813\\\")\\n counter += 1\\n\\n elif 'Nfurzer' in current_name:\\n new_name = \\\"Nothobranchius_furzeri\\\"\\n node.name = new_name\\n list_nodes.append(node.name)\\n taxid = \\\"NA\\\"\\n uniprot_mapping.loc[counter] = (taxid, new_name, new_name)\\n counter += 1\\n\\n elif 'TAX' in current_name:\\n taxid = current_name[3:].split('x')[0]\\n new_name = taxdict.get(taxid, taxid) \\n node.name = new_name \\n list_nodes.append(node.name)\\n unip = get_uniprot(taxid, accession)\\n uniprot_mapping.loc[counter] = (taxid, new_name, unip)\\n counter += 1\\n\\n\\n \\n tree.write(outfile=\\\"../../data/tree/tree.nw\\\")\\n\\n nodes_df = pd.DataFrame(list_nodes)\\n nodes_df.to_csv(\\\"../../data/tree/tree_list_nodes.txt\\\", index=False, header=False)\\n\\n uniprot_mapping.to_csv(\\\"../../data/tree/tree_uniprot.txt\\\", sep='\\\\t', index=False, header=True)\\n\\n return tree, list_nodes\",\n \"def test_consistency(self):\\n def compare_func(obj, node):\\n # number of children must be consistent\\n self.assertEqual(\\n len(obj.children),\\n len(obj._children)\\n )\\n\\n # obj.html equals node\\n self.assertEqual(obj._html, node)\\n\\n # coordinates\\n self.assertEqual(\\n obj._coordinates,\\n obj.coordinates,\\n self.expected[\\\"coordinates\\\"][obj.id or \\\"document\\\"]\\n )\\n\\n # confidence\\n self.assertAlmostEqual(\\n obj.confidence,\\n self.expected[\\\"confidence\\\"][obj.id or \\\"document\\\"]\\n )\\n\\n self.recursively_compare_tree_against_html(compare_func)\",\n \"def test_change(self):\\n # Folder must be root to load in make_net properly\\n if os.getcwd().split('\\\\\\\\')[-1] == 'tests': os.chdir('..')\\n \\n # Create parents and deepcopy everything (just to be sure)\\n cfg = Config().genome\\n gene1, gene2 = get_simple_node_gene(0, cfg)\\n gene1_act = deepcopy(gene1.activation)\\n gene1_agg = deepcopy(gene1.aggregation)\\n gene1_bias = deepcopy(gene1.bias)\\n gene2_act = deepcopy(gene2.activation)\\n gene2_agg = deepcopy(gene2.aggregation)\\n gene2_bias = deepcopy(gene2.bias)\\n \\n # Perform crossover and mutations\\n gene3 = gene1.crossover(other=gene2, cfg=cfg, ratio=0.5)\\n gene3.activation = 'c'\\n gene3.aggregation = 'c'\\n gene3.bias = -10\\n \\n # Check for unchanged parents\\n self.assertEqual(gene1.activation, gene1_act)\\n self.assertEqual(gene1.aggregation, gene1_agg)\\n self.assertEqual(gene1.bias, gene1_bias)\\n self.assertEqual(gene2.activation, gene2_act)\\n self.assertEqual(gene2.aggregation, gene2_agg)\\n self.assertEqual(gene2.bias, gene2_bias)\",\n \"def test_calc_shared_phylotypes_pairwise(self):\\r\\n\\r\\n #self.assertEqual(_calc_shared_phylotypes_pairwise(self.otu_table, 0, 0), 5)\\r\\n #self.assertEqual(_calc_shared_phylotypes_pairwise(self.otu_table, 0, 1), 2)\\r\\n #self.assertEqual(_calc_shared_phylotypes_pairwise(self.otu_table, 0, 2), 3)\\r\\n #self.assertEqual(_calc_shared_phylotypes_pairwise(self.otu_table, 2, 2), 3)\\r\\n self.assertEqual(\\r\\n _calc_shared_phylotypes_pairwise(\\r\\n self.otu_table,\\r\\n 'S1',\\r\\n 'S1'),\\r\\n 5)\\r\\n self.assertEqual(\\r\\n _calc_shared_phylotypes_pairwise(\\r\\n self.otu_table,\\r\\n 'S1',\\r\\n 'S2'),\\r\\n 2)\\r\\n self.assertEqual(\\r\\n _calc_shared_phylotypes_pairwise(\\r\\n self.otu_table,\\r\\n 'S1',\\r\\n 'S3'),\\r\\n 3)\\r\\n self.assertEqual(\\r\\n _calc_shared_phylotypes_pairwise(\\r\\n self.otu_table,\\r\\n 'S3',\\r\\n 'S3'),\\r\\n 3)\",\n \"def test_client_tax_information_update(self):\\n pass\",\n \"def test_distinguish_path_polarity1():\\n Monomer('A')\\n Monomer('B', ['act'], {'act' :['y', 'n']})\\n Monomer('C', ['T185'], {'T185':['u', 'p']})\\n Parameter('k', 1)\\n Rule('A_activate_B', A() + B(act='n') >> A() + B(act='y'), k)\\n Rule('B_dephos_C', B(act='y') + C(T185='p') >>\\n B(act='y') + C(T185='u'), k)\\n Initial(A(), k)\\n Initial(B(act='y'), k)\\n Initial(C(T185='p'), k)\\n Annotation(A, 'http://identifiers.org/hgnc/HGNC:1')\\n Annotation(B, 'http://identifiers.org/hgnc/HGNC:2')\\n Annotation(C, 'http://identifiers.org/hgnc/HGNC:3')\\n Annotation('A_activate_B', 'A', 'rule_has_subject')\\n Annotation('A_activate_B', 'B', 'rule_has_object')\\n Annotation('B_dephos_C', 'B', 'rule_has_subject')\\n Annotation('B_dephos_C', 'C', 'rule_has_object')\\n C.site_annotations = [\\n Annotation(('T185', 'p'), 'phosphorylation', 'is_modification'),\\n Annotation('T185', 'T', 'is_residue'),\\n Annotation('T185', '185', 'is_position'),\\n ]\\n # Create the model checker\\n stmts = _path_polarity_stmt_list()\\n mc = ModelChecker(model, stmts)\\n results = mc.check_model()\\n assert len(results) == len(stmts)\\n assert isinstance(results[0], tuple)\\n path_results = [res[1] for res in results]\\n assert path_results[0].paths == []\\n assert path_results[1].paths == [[('A_activate_B', 1), ('B_dephos_C', 1),\\n ('C_T185_p_obs', -1)]]\\n assert path_results[2].paths == []\\n assert path_results[3].paths == [[('B_dephos_C', 1), ('C_T185_p_obs', -1)]]\",\n \"def test_grounded_modified_enzyme():\\n mek_s202 = Agent('MEK1', mods=[ModCondition('phosphorylation', 'S', '202')],\\n db_refs={'HGNC': '6840'})\\n mek_phos = Agent('MEK1', mods=[ModCondition('phosphorylation', None, None)],\\n db_refs={'HGNC': '6840'})\\n erk = Agent('ERK2', db_refs={'HGNC': '6871'})\\n stmt_to_model = Phosphorylation(mek_s202, erk, None, None)\\n stmt_to_check = Phosphorylation(mek_phos, erk, None, None)\\n pa = PysbAssembler()\\n pa.add_statements([stmt_to_model])\\n pa.make_model(policies='one_step')\\n mc = ModelChecker(pa.model, [stmt_to_check])\\n results = mc.check_model()\\n assert len(results) == 1\\n assert results[0][0] == stmt_to_check\\n assert results[0][1].paths == \\\\\\n [[('MEK1_phosphoS202_phosphorylation_ERK2_phospho', 1),\\n ('ERK2_phospho_p_obs', 1)]]\",\n \"def test_taxa_chao1(self):\\n table_factory = DataTableFactory(PACKET_DIR)\\n chao1 = table_factory.taxa_alpha_diversity(metric='chao1', rarefy=1000 * 1000)\\n self.assertTrue((chao1 > 0).all())\",\n \"def test_make_core_taxa(self):\\n basic_test_runner(self, 'core_taxa', nrows=-1)\",\n \"def test_taxa_alpha_div_genus(self):\\n table_factory = DataTableFactory(PACKET_DIR)\\n entropy = table_factory.taxa_alpha_diversity(rank='genus')\\n self.assertTrue((entropy > 0).all())\",\n \"def test_written(self):\\n\\n checkit=subprocess.run([\\\"python\\\", \\\"../../taxonomy/src_files/rdp_lineage_to_tax.py\\\", \\\"-i\\\", \\\"../resource_files/rdp_test_taxonomy.csv\\\", \\\"-o\\\", \\\"test_rdp_taxonomy\\\"], capture_output=True, text=True)\\n \\n # is the folder there\\n self.assertTrue(os.path.exists(os.path.exists(\\\"../processed_files/rdp_prep_taxonomy\\\")))\\n \\n # there should be 2 files in there\\n files_in_dir=os.listdir(\\\"../processed_files/rdp_prep_taxonomy\\\")\\n self.assertEqual(len(files_in_dir), 2)\\n \\n for x in files_in_dir:\\n if x.split('.')[-1]=='txt':\\n taxonomy_file=x\\n \\n # does the test match the provided actual output\\n # rdp_team_taxonomy_check can be found on https://github.com/rdpstaff/classifier/issues/18\\n self.assertTrue(filecmp.cmp(\\\"../resource_files/rdp_team_taxonomy_check.txt\\\", \\\"../processed_files/rdp_prep_taxonomy/{}\\\".format(taxonomy_file)))\\n \\n shutil.rmtree(\\\"../processed_files/rdp_prep_taxonomy\\\")\",\n \"def test_create_gene_ontology(self):\\n\\n # Here are mappings for just a few yeast genes.\\n\\n mapping = {}\\n mapping['STE7'] = ['GO:0000187']\\n mapping['PBS2'] = ['GO:0000187']\\n mapping['NOP8'] = [\\n 'GO:0003676', 'GO:0003723', 'GO:0042254', 'GO:0005634', 'GO:0005730'\\n ]\\n\\n # Build the ontology, then see if it looks correct.\\n\\n root = dc.models.tensorgraph.models.ontology.create_gene_ontology(\\n mapping, min_node_features=1)\\n assert len(root.feature_ids) == 0\\n\\n def find_features(node, features):\\n features.update(node.feature_ids)\\n for child in node.children:\\n find_features(child, features)\\n\\n all_features = set()\\n find_features(root, all_features)\\n assert len(all_features) == 3\\n for key in mapping:\\n assert key in all_features\",\n \"def test_tree_search(self) -> None:\\n class DumbModel(GymNet):\\n count: int = 0\\n\\n def initial_inference(self, observations: np.ndarray) -> typing.Tuple[np.ndarray, np.ndarray, float]:\\n s, pi, v = super().initial_inference(observations)\\n self.count += 1\\n return np.ones_like(s) * self.count, np.array([6/8 - 1e-8, 2/8 + 1e-8]), 0\\n\\n def recurrent_inference(self, latent_state: np.ndarray, action: int) -> typing.Tuple[float, np.ndarray]:\\n r, s, pi, v = super().recurrent_inference(latent_state, action)\\n self.count += 1\\n return 0, np.ones_like(latent_state) * self.count, np.array([6/8 - 1e-8, 2/8 + 1e-8]), action\\n\\n memory_net = self.net\\n memory_search = self.mcts\\n\\n # Swap class variables\\n self.net = DumbModel(self.g, self.config.net_args)\\n self.mcts = MuZeroMCTS(self.g, self.net, self.config.args)\\n\\n # No discounting and no exploration to ensure deterministic behaviour.\\n self.config.args.gamma = 1\\n self.config.args.exploration_fraction = 0\\n\\n # Experiment 1\\n self.config.args.numMCTSSims = 4\\n pi_1, v_1 = self.mcts.runMCTS(np.zeros(4), np.ones(2))\\n np.testing.assert_array_almost_equal(pi_1, [1/2, 1/2])\\n np.testing.assert_almost_equal(v_1, 1/4)\\n self.mcts.clear_tree()\\n\\n # Experiment 2\\n self.config.args.numMCTSSims = 8\\n pi_2, v_2 = self.mcts.runMCTS(np.zeros(4), np.ones(2))\\n np.testing.assert_array_almost_equal(pi_2, [5/8, 3/8])\\n np.testing.assert_almost_equal(v_2, 1/4)\\n self.mcts.clear_tree()\\n\\n # Undo class variables swap\\n self.net = memory_net\\n self.mcts = memory_search\",\n \"def checkOntoSimilarity(ontology_id):\\n # print('checkOntoSimilarity() =>', ontology_id)\\n url = cfg.ontology_sim + '/status'\\n res = requests.post(url, json={'ontologyId': ontology_id})\\n resp = res.json()\\n resp['statusCode'] = res.status_code\\n return resp #resp['statusCode'] = 200 if ontology exists and 404 otherwise\",\n \"def is_virus(taxid):\\n return taxid and taxid != '1' and (taxid == '10239' or is_virus(parents[taxid]))\",\n \"def siblinghood_sim_exact(siblinghood_1, siblinghood_2):\\n return SubstructureAgreement.substructure_sim_exact(siblinghood_1, siblinghood_2)\",\n \"def identify_multiple_taxa(cazy_data, multiple_taxa_logger):\\n multiple_taxa_gbk = []\\n\\n for genbank_accession in tqdm(\\n cazy_data,\\n total=len(list(cazy_data.keys())), desc='Searching for multiple taxa annotations',\\n ):\\n\\n if len(cazy_data[genbank_accession]['taxonomy']) > 1:\\n multiple_taxa_gbk.append(genbank_accession)\\n\\n for tax_tuple in cazy_data[genbank_accession]['taxonomy']:\\n multiple_taxa_logger.warning(\\n f\\\"{genbank_accession}\\\\t{tax_tuple.kingdom}\\\\t{tax_tuple.organism}\\\"\\n )\\n \\n else:\\n cazy_data[genbank_accession]['organism'] = list(cazy_data[genbank_accession]['taxonomy'])[0]\\n\\n return multiple_taxa_gbk\",\n \"def check_nodes_match_nptg_data(cls, gdf, named_area):\\n #\\n check_name = \\\"check_nodes_match_nptg_data\\\"\\n # list of all geographic admin areas\\n admin_areas = [\\n \\\"Aberdeen\\\",\\n \\\"Aberdeenshire\\\",\\n \\\"Angus\\\",\\n \\\"Argyll & Bute\\\",\\n \\\"Bath & North East Somerset\\\",\\n \\\"Bedford\\\",\\n \\\"Blackburn with Darwen\\\",\\n \\\"Blackpool\\\",\\n \\\"Blaenau Gwent\\\",\\n \\\"Bournemouth\\\",\\n \\\"Bracknell Forest\\\",\\n \\\"Bridgend\\\",\\n \\\"Brighton and Hove\\\",\\n \\\"Bristol\\\",\\n \\\"Buckinghamshire\\\",\\n \\\"Caerphilly\\\",\\n \\\"Cambridgeshire\\\",\\n \\\"Cardiff\\\",\\n \\\"Carmarthenshire\\\",\\n \\\"Central Bedfordshire\\\",\\n \\\"Ceredigion\\\",\\n \\\"Cheshire East\\\",\\n \\\"Cheshire West & Chester\\\",\\n \\\"Clackmannanshire\\\",\\n \\\"Conwy\\\",\\n \\\"Cornwall\\\",\\n \\\"Cumbria\\\",\\n \\\"Darlington\\\",\\n \\\"Denbighshire\\\",\\n \\\"Derby\\\",\\n \\\"Derbyshire\\\",\\n \\\"Devon\\\",\\n \\\"Dorset\\\",\\n \\\"Dumfries & Galloway\\\",\\n \\\"Dundee\\\",\\n \\\"Durham\\\",\\n \\\"East Ayrshire\\\",\\n \\\"East Dunbartonshire\\\",\\n \\\"East Lothian\\\",\\n \\\"East Renfrewshire\\\",\\n \\\"East Riding of Yorkshire\\\",\\n \\\"East Sussex\\\",\\n \\\"Edinburgh\\\",\\n \\\"Essex\\\",\\n \\\"Falkirk\\\",\\n \\\"Fife\\\",\\n \\\"Flintshire\\\",\\n \\\"Glasgow\\\",\\n \\\"Gloucestershire\\\",\\n \\\"Greater London\\\",\\n \\\"Greater Manchester\\\",\\n \\\"Gwynedd\\\",\\n \\\"Halton\\\",\\n \\\"Hampshire\\\",\\n \\\"Hartlepool\\\",\\n \\\"Herefordshire\\\",\\n \\\"Hertfordshire\\\",\\n \\\"Highland\\\",\\n \\\"Inverclyde\\\",\\n \\\"Isle of Anglesey\\\",\\n \\\"Isle of Wight\\\",\\n \\\"Kent\\\",\\n \\\"Kingston upon Hull\\\",\\n \\\"Lancashire\\\",\\n \\\"Leicester\\\",\\n \\\"Leicestershire\\\",\\n \\\"Lincolnshire\\\",\\n \\\"Luton\\\",\\n \\\"Medway\\\",\\n \\\"Merseyside\\\",\\n \\\"Merthyr Tydfil\\\",\\n \\\"Middlesbrough\\\",\\n \\\"Midlothian\\\",\\n \\\"Milton Keynes\\\",\\n \\\"Monmouthshire\\\",\\n \\\"Moray\\\",\\n \\\"Neath Port Talbot\\\",\\n \\\"Newport\\\",\\n \\\"Norfolk\\\",\\n \\\"North Ayrshire\\\",\\n \\\"North East Lincolnshire\\\",\\n \\\"North Lanarkshire\\\",\\n \\\"North Lincolnshire\\\",\\n \\\"North Somerset\\\",\\n \\\"North Yorkshire\\\",\\n \\\"Northamptonshire\\\",\\n \\\"Northumberland\\\",\\n \\\"Nottingham\\\",\\n \\\"Nottinghamshire\\\",\\n \\\"Orkney Islands\\\",\\n \\\"Oxfordshire\\\",\\n \\\"Pembrokeshire\\\",\\n \\\"Perth & Kinross\\\",\\n \\\"Peterborough\\\",\\n \\\"Plymouth\\\",\\n \\\"Poole\\\",\\n \\\"Portsmouth\\\",\\n \\\"Powys\\\",\\n \\\"Reading\\\",\\n \\\"Redcar & Cleveland\\\",\\n \\\"Renfrewshire\\\",\\n \\\"Rhondda Cynon Taff\\\",\\n \\\"Rutland\\\",\\n \\\"Scottish Borders\\\",\\n \\\"Shetland Islands\\\",\\n \\\"Shropshire\\\",\\n \\\"Slough\\\",\\n \\\"Somerset\\\",\\n \\\"South Ayrshire\\\",\\n \\\"South Gloucestershire\\\",\\n \\\"South Lanarkshire\\\",\\n \\\"South Yorkshire\\\",\\n \\\"Southampton\\\",\\n \\\"Southend-on-Sea\\\",\\n \\\"Staffordshire\\\",\\n \\\"Stirling\\\",\\n \\\"Stockton-on-Tees\\\",\\n \\\"Stoke-on-Trent\\\",\\n \\\"Suffolk\\\",\\n \\\"Surrey\\\",\\n \\\"Swansea\\\",\\n \\\"Swindon\\\",\\n \\\"Telford & Wrekin\\\",\\n \\\"Thurrock\\\",\\n \\\"Torbay\\\",\\n \\\"Torfaen\\\",\\n \\\"Tyne & Wear\\\",\\n \\\"Vale of Glamorgan\\\",\\n \\\"Warrington\\\",\\n \\\"Warwickshire\\\",\\n \\\"West Berkshire\\\",\\n \\\"West Dunbartonshire\\\",\\n \\\"West Lothian\\\",\\n \\\"West Midlands\\\",\\n \\\"West Sussex\\\",\\n \\\"West Yorkshire\\\",\\n \\\"Western Isles\\\",\\n \\\"Wiltshire\\\",\\n \\\"Windsor & Maidenhead\\\",\\n \\\"Wokingham\\\",\\n \\\"Worcestershire\\\",\\n \\\"Wrexham\\\",\\n \\\"York\\\",\\n ]\\n\\n # TODO get the admin areas from the nodes file, compare against the list of\\n # area names\\n # nptg values\\n adjanct_locals = etl.load_gazette_adjanct_localities()\\n admin_codes = etl.naptan_gazette_admin_area_codes()\\n districts = etl.naptan_gazette_districts()\\n localities = etl.naptan_gazette_localities()\\n locality_alternate = etl.load_gazette_localities_alternative_names()\\n locality_hierarch = etl.load_gazette_locality_hierarchy()\\n plusbusmap = etl.load_gazette_plusbus_mapping()\\n plusbuszone = etl.load_gazette_plusbus_zones()\\n regions = etl.naptan_gazette_region()\\n\\n # node values\\n node_locs = gdf[\\\"LocalityName\\\"].unique()\\n # get nptg localities,\\n nptg_locs = localities[\\\"LocalityName\\\"].unique()\\n # TODO filter to nptg to nodes, get all the localities in nptg for\\n # this area\\n # get the unique area code for this admin area.\\n area_admin_code = node_locs[\\\"AdminCode\\\"].unique()\\n # check the area admin code in the nptg file for the corresponding\\n # localities.\\n missing_localities = nptg_locs[~nptg_locs.AdminCode.isin(area_admin_code)]\\n # check if locality is\\n df3 = gaz_locs[gaz_locs.LocalityName.isin(gdf.LocalityName)]\\n # get all the localities\\n # TODO list the localities in nptg but not nodes\\n\\n # TODO plot sample on map\\n # TODO write unused localities in given area to file.\\n report_failing_nodes(\\n gdf,\\n check_name,\\n )\\n return\",\n \"def check_observation(self, observation):\\n\\n # Cell parts available\\n neuron_parts_avail = [neurite_type.name for neurite_type in nm.NEURITE_TYPES[1:]]\\n neuron_parts_avail.append('neuron')\\n\\n # Cell features available\\n cell_feats_avail = nm.fst.NEURONFEATURES.keys()\\n\\n # Neurite features available\\n neurite_feats_avail = list(nm.fst.NEURITEFEATURES.keys())\\n neurite_feats_extra = ['neurite_field_diameter', 'neurite_largest_extent', 'neurite_shortest_extent',\\n 'neurite_X_extent', 'neurite_Y_extent', 'neurite_Z_extent']\\n neurite_feats_avail.extend(neurite_feats_extra)\\n\\n # Statistical modes available\\n stat_modes = ['min', 'max', 'median', 'mean', 'total', 'std']\\n\\n # morph_stats's nomenclature constraints to specify observation files\\n \\\"\\\"\\\"\\n self.neuroM_morph_stats_doc(neuron_parts_avail,\\n cell_feats_avail, neurite_feats_avail, neurite_feats_extra,\\n stat_modes)\\n \\\"\\\"\\\"\\n\\n # print \\\"Checking observation file compliance with NeuroM's ('fst' module) nomenclature...\\\"\\n for dict1 in observation.values(): # Dict. with cell's part-features dictionary pairs for each cell\\n for key2, dict2 in list(dict1.items()): # Dict. with feature name-value pairs for each cell part:\\n # neuron, apical_dendrite, basal_dendrite or axon\\n assert (key2 in neuron_parts_avail), \\\\\\n f\\\"{key2} is not permitted for neuron parts. Please, use one in the following \\\\\\n list:\\\\n {neuron_parts_avail}\\\"\\n\\n for key3 in dict2.keys():\\n feat_name, stat_mode = key3.split('_', 1)[1], key3.split('_', 1)[0]\\n if key2 == 'neuron':\\n # Checking the NeuroM features for the cell\\n assert (feat_name in cell_feats_avail), \\\\\\n f\\\"{feat_name} is not permitted for cells. Please, use one in the following \\\\\\n list:\\\\n {sorted(cell_feats_avail)}\\\"\\n # Checking the statistical mode for the cell features\\n assert (stat_mode in stat_modes), \\\\\\n f\\\"{stat_mode} is not permitted for statistical modes. Please, use one in \\\\\\n the following list:\\\\n {stat_modes}\\\"\\n elif feat_name in nm.fst.NEURITEFEATURES.keys():\\n assert (stat_mode in stat_modes), \\\\\\n f\\\"{stat_mode} is not permitted for statistical modes. Please, use one in \\\\\\n the following \\\\list:\\\\n {stat_modes}\\\"\\n else:\\n # Checking the extra-NeuroM features for Neurites, if any\\n assert (key3 in neurite_feats_extra), \\\\\\n f\\\"{key3} is not permitted for neurites. Please, use one in the following \\\\\\n list:\\\\n {sorted(neurite_feats_avail)}\\\"\",\n \"def read_mothur_cons_taxonomy(f):\\n d_otu2tax = {}\\n for n, line in enumerate(open(f)):\\n # the first header line does not startswith \\\"#\\\" in v1.33\\n if line.startswith('OTU\\\\tSize\\\\tTaxonomy'):\\n continue\\n\\n if line.startswith('#'):\\n continue\\n\\n line = line.rstrip()\\n otu, num, taxa = line.rstrip().split('\\\\t')\\n skip = False\\n for word in EXCLUDE:\\n if word in taxa:\\n skip = True\\n break\\n\\n if skip:\\n continue \\n\\n # the parsing of taxa works for both mothur output and this\\n taxa = taxa.rstrip(';') # for mothur classfy.seqs output\\n lis = taxa.split(';')\\n lis2 = []\\n for item in lis:\\n item = item.strip() # for copyrigher copy table ' ;' separater\\n if item.endswith(')'):\\n item = item.rsplit('(', 1)[0].strip()\\n\\n # remove taxon level prefix, e.g. 'p__Firmicutes'\\n if '__' in item:\\n item = item.split('__', 1)[1]\\n\\n #item = item.strip('\\\"')\\n\\n # green gene taxonomy has sapce\\n item = item.replace(' ', '_')\\n\\n item = item.lower()\\n if item in ['unknown', 'unclassified', 'other', 'unassigned']:\\n item = 'Unclassifed'\\n\\n item = item.capitalize()\\n lis2.append(item)\\n\\n length = len(lis2)\\n mes = 'levels number ({}) is not ({}): {}'\\n assert length == LEVELS, mes.format(\\n length, LEVELS, repr(lis2))\\n d_otu2tax[otu] = tuple(lis2)\\n\\n return d_otu2tax\",\n \"def isNodeAGizmo(aNode):\\n if type(aNode) == 'Gizmo':\\n return True\\n else:\\n return False\",\n \"def test_check_tipos(self):\\n assert 2 in SuplementoRegimen.tipos(), \\\"Falta el tipo de regimen 2\\\"\\n assert 3 in SuplementoRegimen.tipos(), \\\"Falta el tipo de regimen 3\\\"\",\n \"def validate_prophyle_nhx_tree(tree, verbose=True, throw_exceptions=True, output_fo=sys.stderr):\\n\\n assert isinstance(tree, ete3.Tree), tree\\n\\n error = False\\n\\n existing_names_set = set()\\n\\n names_with_separator = []\\n\\n without_name = []\\n empty_name = []\\n forbidden_name = []\\n\\n duplicates = []\\n\\n for i, node in enumerate(tree.traverse(\\\"postorder\\\")):\\n noname = True\\n try:\\n nname = node.name\\n noname = False\\n except AttributeError:\\n without_name.append((i, None))\\n\\n if not noname:\\n if nname == '':\\n empty_name.append((i, nname))\\n error = True\\n if nname in set(['A', '0']):\\n forbidden_name.append((i, nname))\\n error = True\\n if nname in existing_names_set:\\n duplicates.append((i, nname))\\n error = True\\n if \\\"@\\\" in nname:\\n names_with_separator.append((i, nname))\\n error = True\\n existing_names_set.add(nname)\\n\\n def _format_node_list(node_list):\\n return \\\", \\\".join(map(str, node_list))\\n\\n def _error_report(node_list, message):\\n if len(node_list) > 0:\\n print(\\n \\\" * {} node(s) {}: {}\\\".format(\\n len(node_list),\\n message,\\n _format_node_list(node_list),\\n ), file=output_fo\\n )\\n\\n if verbose:\\n if error:\\n print(\\\"Errors:\\\".format(), file=output_fo)\\n\\n _error_report(without_name, \\\"without name\\\")\\n _error_report(forbidden_name, \\\"forbidden_name\\\")\\n _error_report(empty_name, \\\"with empty name\\\")\\n _error_report(duplicates, \\\"with a duplicate name\\\")\\n _error_report(names_with_separator, \\\"with a name containing '@'\\\")\\n\\n if throw_exceptions:\\n if error:\\n error(\\\"Invalid tree. The format of the tree is not correct. See the messages above.\\\")\\n\\n if error:\\n return False\\n else:\\n return True\",\n \"def test_isomorphism_match(data):\\n\\n reference = data.draw(ISO_BUILDER)\\n nodes = data.draw(st.sets(st.sampled_from(list(reference.nodes)),\\n max_size=len(reference)))\\n graph = reference.subgraph(nodes)\\n\\n note((\\\"Reference nodes\\\", reference.nodes(data=True)))\\n note((\\\"Reference edges\\\", reference.edges))\\n note((\\\"Graph nodes\\\", graph.nodes(data=True)))\\n note((\\\"Graph edges\\\", graph.edges))\\n\\n node_match = nx.isomorphism.categorical_node_match('element', None)\\n matcher = nx.isomorphism.GraphMatcher(reference, graph, node_match=node_match)\\n expected = make_into_set(matcher.subgraph_isomorphisms_iter())\\n found = make_into_set(vermouth.graph_utils.isomorphism(reference, graph))\\n\\n note((\\\"Found\\\", found))\\n note((\\\"Expected\\\", expected))\\n\\n if not expected:\\n event(\\\"Not subgraphs\\\")\\n if found == expected:\\n event(\\\"Exact match\\\")\\n\\n assert found <= expected\",\n \"def check_a11y_relation(filename, tree):\\n global widgets_ignored, ids, label_for_elm, labelled_by_elm, description_for_elm, described_by_elm, mnemonic_for_elm\\n\\n def check_elm(orphan_root, obj, orphan_labels, orphan_widgets):\\n \\\"\\\"\\\"\\n Check one element, knowing that orphan_labels/widgets tell whether\\n there are orphan labels and widgets within orphan_root\\n \\\"\\\"\\\"\\n\\n oid = obj.attrib.get('id')\\n klass = obj.attrib.get('class')\\n\\n # \\\"Don't care\\\" special case\\n if klass in widgets_ignored:\\n return\\n for suffix in widgets_suffixignored:\\n if klass[-len(suffix):] == suffix:\\n return\\n\\n # Widgets usual do not strictly require a label, i.e. a labelled parent\\n # is enough for context, but some do always need one.\\n requires_label = klass in widgets_needlabel\\n\\n if oid is not None:\\n # Check that ids are unique\\n if oid in ids_dup:\\n if ids[oid] == obj:\\n # We are the first, warn\\n duplicates = tree.findall(\\\".//object[@id='\\\" + oid + \\\"']\\\")\\n err(filename, tree, obj, \\\"duplicate-id\\\", \\\"has the same id as other elements \\\" + elms_names_lines(duplicates))\\n\\n # Check label-for and their dual labelled-by\\n label_for = check_rels(filename, tree, root, obj, \\\"label-for\\\", \\\"labelled-by\\\")\\n\\n # Check labelled-by and its dual label-for\\n labelled_by = check_rels(filename, tree, root, obj, \\\"labelled-by\\\", \\\"label-for\\\")\\n\\n # Check label-for and their dual labelled-by\\n description_for = check_rels(filename, tree, root, obj, \\\"description-for\\\", \\\"described-by\\\")\\n\\n # Check labelled-by and its dual label-for\\n described_by = check_rels(filename, tree, root, obj, \\\"described-by\\\", \\\"description-for\\\")\\n\\n # Should have only one label\\n if len(labelled_by) >= 1:\\n if oid in mnemonic_for_elm:\\n warn(filename, tree, obj, \\\"labelled-by-and-mnemonic\\\",\\n \\\"has both a mnemonic \\\" + elm_name_line(mnemonic_for_elm[oid][0]) + \\\"and labelled-by relation\\\")\\n if len(labelled_by) > 1:\\n warn(filename, tree, obj, \\\"multiple-labelled-by\\\", \\\"has multiple labelled-by relations\\\")\\n if oid in label_for_elm:\\n if len(label_for_elm[oid]) > 1:\\n warn(filename, tree, obj, \\\"duplicate-label-for\\\", \\\"is referenced by multiple label-for \\\" + elms_names_lines(label_for_elm[oid]))\\n if oid in mnemonic_for_elm:\\n if len(mnemonic_for_elm[oid]) > 1:\\n warn(filename, tree, obj, \\\"duplicate-mnemonic\\\", \\\"is referenced by multiple mnemonic_widget \\\" + elms_names_lines(mnemonic_for_elm[oid]))\\n\\n # Check member-of\\n member_of = check_rels(filename, tree, root, obj, \\\"member-of\\\")\\n\\n # Labels special case\\n if klass in widgets_labels:\\n properties = check_props(filename, tree, root, obj, \\\"mnemonic_widget\\\") + \\\\\\n check_props(filename, tree, root, obj, \\\"mnemonic-widget\\\")\\n if len(properties) > 1:\\n err(filename, tree, obj, \\\"multiple-mnemonic\\\", \\\"has multiple mnemonic_widgets properties\\\"\\n \\\"%s\\\" % elms_lines(properties))\\n\\n # Emit orphaning warnings\\n if warn_orphan_labels or orphan_widgets:\\n is_orphan_label(filename, tree, root, obj, orphan_root, True)\\n\\n # We are done with the label\\n return\\n\\n # Not a label, will perhaps need one\\n\\n # Emit orphaning warnings\\n is_orphan_widget(filename, tree, root, obj, orphan_labels, orphan_root, True)\\n\\n root = tree.getroot()\\n\\n # Flush ids and relations from previous files\\n ids = {}\\n ids_dup = {}\\n labelled_by_elm = {}\\n label_for_elm = {}\\n described_by_elm = {}\\n description_for_elm = {}\\n mnemonic_for_elm = {}\\n\\n # First pass to get links into hash tables, no warning, just record duplicates\\n for obj in root.iter('object'):\\n oid = obj.attrib.get('id')\\n if oid is not None:\\n if oid not in ids:\\n ids[oid] = obj\\n else:\\n ids_dup[oid] = True\\n\\n labelled_by = obj.findall(\\\"accessibility/relation[@type='labelled-by']\\\")\\n labelled_by += obj.findall(\\\"accessibility/relation[@name='labelled-by']\\\")\\n for rel in labelled_by:\\n target = rel.attrib.get('target')\\n if target is not None:\\n if target not in labelled_by_elm:\\n labelled_by_elm[target] = [ obj ]\\n else:\\n labelled_by_elm[target].append(obj)\\n\\n label_for = obj.findall(\\\"accessibility/relation[@type='label-for']\\\")\\n label_for += obj.findall(\\\"accessibility/relation[@name='label-for']\\\")\\n for rel in label_for:\\n target = rel.attrib.get('target')\\n if target is not None:\\n if target not in label_for_elm:\\n label_for_elm[target] = [ obj ]\\n else:\\n label_for_elm[target].append(obj)\\n\\n described_by = obj.findall(\\\"accessibility/relation[@type='described-by']\\\")\\n described_by += obj.findall(\\\"accessibility/relation[@name='described-by']\\\")\\n for rel in described_by:\\n target = rel.attrib.get('target')\\n if target is not None:\\n if target not in described_by_elm:\\n described_by_elm[target] = [ obj ]\\n else:\\n described_by_elm[target].append(obj)\\n\\n description_for = obj.findall(\\\"accessibility/relation[@type='description-for']\\\")\\n description_for += obj.findall(\\\"accessibility/relation[@name='description-for']\\\")\\n for rel in description_for:\\n target = rel.attrib.get('target')\\n if target is not None:\\n if target not in description_for_elm:\\n description_for_elm[target] = [ obj ]\\n else:\\n description_for_elm[target].append(obj)\\n\\n mnemonic_for = obj.findall(\\\"property[@name='mnemonic_widget']\\\") + \\\\\\n obj.findall(\\\"property[@name='mnemonic-widget']\\\")\\n for rel in mnemonic_for:\\n target = rel.text\\n if target is not None:\\n if target not in mnemonic_for_elm:\\n mnemonic_for_elm[target] = [ obj ]\\n else:\\n mnemonic_for_elm[target].append(obj)\\n\\n # Second pass, recursive depth-first, to be able to efficiently know whether\\n # there are orphan labels within a part of the tree.\\n def recurse(orphan_root, obj, orphan_labels, orphan_widgets):\\n if obj == root or is_labelled_parent(obj):\\n orphan_root = obj\\n orphan_labels, orphan_widgets = orphan_items(filename, tree, root, obj)\\n\\n if obj.tag == 'object':\\n check_elm(orphan_root, obj, orphan_labels, orphan_widgets)\\n\\n for o in obj:\\n recurse(orphan_root, o, orphan_labels, orphan_widgets)\\n\\n recurse(root, root, False, False)\",\n \"def test_change(self):\\n # Folder must be root to load in make_net properly\\n if os.getcwd().split('\\\\\\\\')[-1] == 'tests': os.chdir('..')\\n \\n # Create parents and deepcopy everything (just to be sure)\\n cfg = Config().genome\\n gene1, gene2 = get_output_node_gene(0, cfg)\\n gene1_act = deepcopy(gene1.activation)\\n gene1_agg = deepcopy(gene1.aggregation)\\n gene1_bias = deepcopy(gene1.bias)\\n gene2_act = deepcopy(gene2.activation)\\n gene2_agg = deepcopy(gene2.aggregation)\\n gene2_bias = deepcopy(gene2.bias)\\n \\n # Perform crossover and mutations\\n gene3 = gene1.crossover(other=gene2, cfg=cfg, ratio=0.5)\\n gene3.activation = 'c'\\n gene3.aggregation = 'c'\\n gene3.bias = -10\\n \\n # Check for unchanged parents\\n self.assertEqual(gene1.activation, gene1_act)\\n self.assertEqual(gene1.aggregation, gene1_agg)\\n self.assertEqual(gene1.bias, gene1_bias)\\n self.assertEqual(gene2.activation, gene2_act)\\n self.assertEqual(gene2.aggregation, gene2_agg)\\n self.assertEqual(gene2.bias, gene2_bias)\",\n \"def can_be_linked(self):\\n # upstream trees of tested treed should nad share trees with downstream trees of current tree\\n tested_tree_upstream_trees = {t.name for t in self.upstream_trees()}\\n current_tree_downstream_trees = {p.node_tree.name for p in bpy.context.space_data.path}\\n shared_trees = tested_tree_upstream_trees & current_tree_downstream_trees\\n return not shared_trees\",\n \"def substructure_sim_exact(subtrees_1, subtrees_2):\\n assert(len(subtrees_1) == len(subtrees_2))\\n n = len(subtrees_1)\\n f1 = np.zeros(n)\\n for i in range(n):\\n f1[i] = subtrees_1[i] == subtrees_2[i] # calculate the number of matching pairs\\n\\n return float(np.count_nonzero(f1)) / float(len(f1))\",\n \"def test_call_phylogenetic_escaped_names(self):\\r\\n\\r\\n c = AlphaDiversityCalc(metric=PD_whole_tree, is_phylogenetic=True)\\r\\n expected = [13., 17., 0.]\\r\\n\\r\\n non_escaped_result = c(data_path=self.otu_table1_fp,\\r\\n tree_path=self.tree1,\\r\\n taxon_names=self.otu_table1.ObservationIds,\\r\\n sample_names=self.otu_table1.SampleIds)\\r\\n\\r\\n escaped_result = c(data_path=self.otu_table2_fp,\\r\\n tree_path=self.tree2,\\r\\n taxon_names=self.otu_table2.ObservationIds,\\r\\n sample_names=self.otu_table2.SampleIds)\\r\\n\\r\\n assert_almost_equal(non_escaped_result, expected)\\r\\n assert_almost_equal(escaped_result, expected)\\r\\n assert_almost_equal(non_escaped_result, escaped_result)\",\n \"def tree(n, names):\\n \\n t = Tree()\\n t.populate(n, names_library=names.keys(), random_branches=True, support_range=(0.7, 1))\\n t.write(format=2, outfile='fake.tree.newick', dist_formatter='%0.4g', support_formatter='%0.4g')\\n with open('fake.tree.notation.tsv', 'w') as o:\\n o.write('#TaxaID\\\\tName\\\\n')\\n o.writelines('{}\\\\t{}\\\\n'.format(k, v) for k, v in names.items())\",\n \"def test_equivalency(self):\\n def compare_func(obj, node):\\n # same id\\n self.assertEqual(obj.id, node.get(\\\"id\\\"))\\n\\n # same html\\n self.assertEqual(obj.html.prettify, node.prettify)\\n\\n # parents have same id (only for non-root elements)\\n if not obj == self.document.root:\\n self.assertEqual(obj.parent.id, node.parent.get(\\\"id\\\"))\\n\\n # same number of children\\n child_nodes = self.get_children_of_node(node)\\n self.assertEqual(len(obj.children), len(child_nodes))\\n\\n # children have same ids\\n for (child_obj, child_node) in zip(obj.children, child_nodes):\\n self.assertEqual(child_obj.id, child_node.get(\\\"id\\\"))\\n\\n self.recursively_compare_tree_against_html(compare_func)\",\n \"def check_subtree(node):\\r\\n if node:\\r\\n node_loc = node.location\\r\\n self.assertFalse(\\r\\n modulestore().has_item(node_loc.version_agnostic())\\r\\n )\\r\\n self.assertTrue(modulestore().has_item(node_loc.course_agnostic()))\\r\\n if node.has_children:\\r\\n for sub in node.get_children():\\r\\n check_subtree(sub)\",\n \"def compare_trees(first_soup: HTMLStrip, second_soup: HTMLStrip) -> float:\\n first_tree = Tree.from_soup_object(first_soup.file_name, first_soup.original_soup)\\n second_tree = Tree.from_soup_object(second_soup.file_name, second_soup.original_soup)\\n\\n common_paths_size: int = first_tree.num_of_common_paths(second_tree)\\n target_size: int = second_tree.total_num_of_paths()\\n similarity = float(common_paths_size)*100/target_size\\n print(f'{similarity:.2f}')\\n return similarity\",\n \"def test_distinguish_path_polarity2():\\n Monomer('A')\\n Monomer('B', ['act'], {'act' :['y', 'n']})\\n Monomer('C', ['T185'], {'T185':['u', 'p']})\\n Parameter('k', 1)\\n Rule('A_inhibit_B', A() + B(act='y') >> A() + B(act='n'), k)\\n Rule('B_dephos_C', B(act='y') + C(T185='p') >>\\n B(act='y') + C(T185='u'), k)\\n Initial(A(), k)\\n Initial(B(act='y'), k)\\n Initial(C(T185='p'), k)\\n Annotation(A, 'http://identifiers.org/hgnc/HGNC:1')\\n Annotation(B, 'http://identifiers.org/hgnc/HGNC:2')\\n Annotation(C, 'http://identifiers.org/hgnc/HGNC:3')\\n Annotation('A_inhibit_B', 'A', 'rule_has_subject')\\n Annotation('A_inhibit_B', 'B', 'rule_has_object')\\n Annotation('B_dephos_C', 'B', 'rule_has_subject')\\n Annotation('B_dephos_C', 'C', 'rule_has_object')\\n C.site_annotations = [\\n Annotation(('T185', 'p'), 'phosphorylation', 'is_modification'),\\n Annotation('T185', 'T', 'is_residue'),\\n Annotation('T185', '185', 'is_position'),\\n ]\\n # Create the model checker\\n stmts = _path_polarity_stmt_list()\\n mc = ModelChecker(model, stmts)\\n results = mc.check_model()\\n assert len(results) == len(stmts)\\n assert isinstance(results[0], tuple)\\n assert results[0][1].paths == [[('A_inhibit_B', 1), ('B_dephos_C', -1),\\n ('C_T185_p_obs', 1)]]\\n assert results[1][1].paths == []\\n assert results[2][1].paths == [[('A_inhibit_B', 1), ('B_dephos_C', -1),\\n ('C_T185_p_obs', 1)]]\\n assert results[3][1].paths == [[('B_dephos_C', 1), ('C_T185_p_obs', -1)]]\",\n \"def distanceToTree_deprec(self,taxonomic_forest,update_inner_attributes=True):\\n #Thankfully the distance is symmetric by definition so order doesn't matter.\\n Jacobian = []\\n logger.warning('[FOR DEVELOPER] This is not optimized. Should be necessary to use attr_t1 and attr_t2 with name_id (integer)')\\n for i in settings.TAXONOMIC_TREE_KEYS:\\n F1 = []\\n for j in settings.TAXONOMIC_TREE_KEYS:\\n d = self.forest[i].robinson_foulds(taxonomic_forest[j],unrooted_trees=True)[0]\\n F1.append(float(d))\\n Jacobian.append(F1)\\n if update_inner_attributes:\\n self.JacobiM = np.matrix(Jacobian)\\n self.Jacobi = np.linalg.det(self.JacobiM)\\n return np.matrix(Jacobian)\",\n \"def test01(self):\\n\\n t = tree(\\\"a\\\", [tree(\\\"b\\\"), tree(\\\"c\\\")]);\\n self.assertTrue(self.isTree(t))\",\n \"def legitimate_mark_changes(self, verbose=False):\\n if self._undirected:\\n raise ValueError('Only defined for DMAGs')\\n\\n disc_paths = self.discriminating_paths()\\n\\n mark_changes_dir = set()\\n for i, j in self._directed:\\n if verbose: print(f'{i}->{j} => {i}<->{j} ?')\\n # FIRST CONDITIONS\\n parents_condition = all(self.has_directed(parent, j) for parent in self._parents[i])\\n if not parents_condition:\\n if verbose: print('Failed parents condition')\\n continue\\n spouses_condition = all(self.has_any_edge(spouse, j) for spouse in self._spouses[i])\\n if not spouses_condition:\\n if verbose: print('Failed spouses condition')\\n continue\\n\\n # SECOND CONDITION\\n disc_paths_for_i = [path for path in disc_paths.keys() if path[-2] == i]\\n disc_paths_condition = all(path[-1] != j for path in disc_paths_for_i)\\n if not disc_paths_condition:\\n if verbose: print('Failed discriminating path condition')\\n continue\\n\\n # FINAL CONDITION\\n if i in self.ancestors_of(j, exclude_arcs={(i, j)}):\\n if verbose: print('Failed ancestral condition')\\n continue\\n\\n if verbose: print('Passed')\\n mark_changes_dir.add((i, j))\\n\\n mark_changes_bidir = set()\\n for i, j in self._bidirected | set(map(reversed, self._bidirected)):\\n if verbose: print(f'{i}<->{j} => {i}->{j} ?')\\n # FIRST CONDITIONS\\n parents_condition = all(self.has_directed(parent, j) for parent in self._parents[i])\\n if not parents_condition:\\n if verbose: print('Failed parents condition')\\n continue\\n spouses_condition = all(self.has_any_edge(spouse, j) for spouse in self._spouses[i] if spouse != j)\\n if not spouses_condition:\\n if verbose: print('Failed spouses condition')\\n continue\\n\\n # SECOND CONDITION\\n disc_paths_for_i = [path for path in disc_paths.keys() if path[-2] == i]\\n disc_paths_condition = all(path[-1] != j for path in disc_paths_for_i)\\n if not disc_paths_condition:\\n if verbose: print('Failed discriminating path condition')\\n continue\\n\\n # FINAL CONDITION\\n if i in self.ancestors_of(j):\\n if verbose: print('Failed ancestral condition')\\n continue\\n\\n if verbose: print('Passed')\\n mark_changes_bidir.add((i, j))\\n\\n return mark_changes_dir, mark_changes_bidir\",\n \"def test_consistent_ids(self) -> None:\\n bnode = BNode()\\n g0_ts: _TripleSet = {\\n (bnode, FOAF.name, Literal(\\\"Golan Trevize\\\")),\\n (bnode, RDF.type, FOAF.Person),\\n }\\n bnode = BNode()\\n g1_ts: _TripleSet = {\\n (bnode, FOAF.name, Literal(\\\"Janov Pelorat\\\")),\\n (bnode, RDF.type, FOAF.Person),\\n }\\n\\n g0 = Graph()\\n g0 += g0_ts\\n cg0 = to_canonical_graph(g0)\\n cg0_ts = GraphHelper.triple_set(cg0)\\n\\n g1 = Graph()\\n g1 += g1_ts\\n cg1 = to_canonical_graph(g1)\\n cg1_ts = GraphHelper.triple_set(cg1)\\n\\n assert cg0_ts.issubset(\\n cg1_ts\\n ), \\\"canonical triple set cg0_ts should be a subset of canonical triple set cg1_ts\\\"\",\n \"def write_phylogeny_pages(outfile: TextIO, genera_tree: list, species_tree: list, do_print: bool,\\n refdict: dict) -> None:\\n\\n def split_html_tree(intree: list) -> Tuple[list, list]:\\n \\\"\\\"\\\"\\n take a phy2html output file and split it into style and body sections\\n \\\"\\\"\\\"\\n instyle = False\\n inbody = False\\n style_list = []\\n body_list = []\\n for inline in intree:\\n if \\\"<style>\\\" in inline:\\n instyle = True\\n elif \\\"</style>\\\" in inline:\\n instyle = False\\n elif \\\"<body>\\\" in inline:\\n inbody = True\\n elif \\\"</body>\\\" in inline:\\n inbody = False\\n elif instyle:\\n style_list.append(inline)\\n elif inbody:\\n body_list.append(inline)\\n return style_list, body_list\\n\\n def add_link_to_genus(inline: str) -> str:\\n if \\\"id=\\\\\\\"genera_taxon\\\" in inline:\\n i = inline.find(\\\">\\\")\\n start = inline[:i+1]\\n tname = inline[i+1:]\\n j = tname.find(\\\"<\\\")\\n end = tname[j:]\\n tname = tname[:j]\\n tname = create_taxon_link(\\\"genus\\\", tname, do_print=do_print, include_rank=False)\\n return start + tname + end\\n else:\\n return inline\\n\\n # --- main function code ---\\n gen_style, gen_body = split_html_tree(genera_tree)\\n sp_style, sp_body = split_html_tree(species_tree)\\n\\n if do_print:\\n start_page_division(outfile, \\\"base_page\\\")\\n else:\\n common_header_part1(outfile, \\\"Fiddler Crab Phylogeny\\\")\\n outfile.write(\\\" <style>\\\\n\\\")\\n for line in gen_style:\\n outfile.write(line)\\n outfile.write(\\\"\\\\n\\\")\\n for line in sp_style:\\n outfile.write(line)\\n outfile.write(\\\"\\\\n\\\")\\n outfile.write(\\\" .phylogeny_grid { padding: 30px; }\\\\n\\\") # add a little extra padding\\n outfile.write(\\\" </style>\\\\n\\\")\\n\\n if not do_print:\\n common_header_part2(outfile)\\n\\n outfile.write(\\\" <header id=\\\\\\\"\\\" + init_data().tree_url + \\\"\\\\\\\">\\\\n\\\")\\n outfile.write(\\\" <h1 class=\\\\\\\"bookmark1\\\\\\\">Phylogeny</h1>\\\\n\\\")\\n outfile.write(\\\" </header>\\\\n\\\")\\n outfile.write(\\\"\\\\n\\\")\\n outfile.write(\\\" <p>\\\\n\\\")\\n outfile.write(\\\" The phylogeny of fiddler crabs is still largely unresolved. Two trees are shown below: one \\\"\\n \\\"of just the genera and one including all species. The tree of genera is fairly solid, \\\"\\n \\\"but the species tree is a rough estimate with many polytomies. Both are predominantly based on the \\\"\\n \\\"work of \\\" + format_reference_cite(refdict[\\\"Shih2016.2\\\"], do_print, AUTHOR_PAREN) + \\\".\\\\n\\\")\\n outfile.write(\\\" </p>\\\\n\\\")\\n outfile.write(\\\"\\\\n\\\")\\n outfile.write(\\\" <section class=\\\\\\\"spsection\\\\\\\">\\\\n\\\")\\n outfile.write(\\\" <h2 class=\\\\\\\"bookmark2\\\\\\\">Genera Phylogeny</h2>\\\\n\\\")\\n for line in gen_body:\\n outfile.write(add_link_to_genus(line))\\n outfile.write(\\\" </section>\\\\n\\\")\\n outfile.write(\\\" <section class=\\\\\\\"spsection\\\\\\\">\\\\n\\\")\\n outfile.write(\\\" <h2 class=\\\\\\\"bookmark2\\\\\\\">Species Phylogeny</h2>\\\\n\\\")\\n for line in sp_body:\\n outfile.write(replace_species_in_string(line, True, do_print))\\n outfile.write(\\\" </section>\\\\n\\\")\\n outfile.write(\\\"\\\\n\\\")\\n if do_print:\\n end_page_division(outfile)\\n else:\\n common_html_footer(outfile)\",\n \"def test_change(self):\\n # Folder must be root to load in make_net properly\\n if os.getcwd().split('\\\\\\\\')[-1] == 'tests': os.chdir('..')\\n \\n # Create parents and deepcopy everything (just to be sure)\\n cfg = Config().genome\\n gene1, gene2 = get_connection_genes((-1, 0), cfg)\\n gene1_en = deepcopy(gene1.enabled)\\n gene1_w = deepcopy(gene1.weight)\\n gene2_en = deepcopy(gene2.enabled)\\n gene2_w = deepcopy(gene2.weight)\\n \\n # Perform crossover and mutations\\n gene3 = gene1.crossover(other=gene2, cfg=cfg, ratio=0.5)\\n gene3.enabled = False\\n gene3.weight = -10\\n \\n # Check for unchanged parents\\n self.assertEqual(gene1.enabled, gene1_en)\\n self.assertEqual(gene1.weight, gene1_w)\\n self.assertEqual(gene2.enabled, gene2_en)\\n self.assertEqual(gene2.weight, gene2_w)\",\n \"def test_compare_taxa_summaries_no_permutations(self):\\r\\n # Verified with R's cor.test function.\\r\\n exp = ('# Correlation coefficient: pearson.\\\\n# The parametric '\\r\\n 'p-value(s) were calculated using a one-sided (positive '\\r\\n 'association) test of significance using a '\\r\\n 't-distribution.\\\\n# The '\\r\\n 'confidence interval(s) were constructed at a confidence level of '\\r\\n '70.0% using Fisher\\\\'s z-transformation (see Sokal and Rohlf 3rd '\\r\\n 'edition pg. 575). The confidence interval(s) are two-sided.\\\\n# '\\r\\n 'Number of samples that matched between the taxa summary files: 1'\\r\\n '\\\\nCorrelation coefficient\\\\tParametric p-value\\\\tNonparametric '\\r\\n 'p-value\\\\tCI (lower)\\\\tCI (upper)\\\\n-0.6264\\\\t0.8132\\\\tN/A\\\\t-0.9438\\\\t'\\r\\n '0.2922\\\\n')\\r\\n\\r\\n obs = compare_taxa_summaries(self.taxa_summary_paired1,\\r\\n self.taxa_summary_paired5, 'paired', 'pearson',\\r\\n tail_type='high', num_permutations=0, confidence_level=0.70,\\r\\n perform_detailed_comparisons=False,\\r\\n sample_id_map=self.taxa_summary_paired_samp_id_map2)\\r\\n obs = (obs[2])\\r\\n self.assertEqual(obs, exp)\",\n \"def test_canonize_neighborhood_same_graph(nauty, ref_graph, ref_graph2):\\n key = nauty.canonize_neighborhood(ref_graph, 2, 1)\\n key2 = nauty.canonize_neighborhood(ref_graph2, 3, 1)\\n assert key == key2\",\n \"def test_markAsTree(self):\\n x = t.Rule(\\\"foo\\\", t.List(\\n t.Exactly(\\\"x\\\")))\\n g = t.Grammar(\\\"TestGrammar\\\", True, [x])\\n self.assertIn(\\\"\\\\n tree = True\\\\n\\\", writePython(g))\",\n \"def x_dom(self, min_depth=0, min_gq=0):\\n affecteds = self.affecteds\\n if len(affecteds) == 0:\\n warn(\\\"WARNING: no affecteds in family %s. skipping\\\\n\\\" % self.family_id)\\n return False\\n\\n try:\\n # affected males het or hom_alt\\n af = reduce(op.and_, [(s.gt_types == HET) | (s.gt_types == HOM_ALT) for s in affecteds if s.sex == 'male'])\\n # affected females must be het\\n af = reduce(op.and_, [s.gt_types == HET for s in affecteds if s.sex == 'female'], af)\\n except TypeError:\\n # affected females must be het\\n try:\\n af = reduce(op.and_, [s.gt_types == HET for s in affecteds if s.sex == 'female'])\\n except TypeError:\\n af = None\\n\\n # unaffecteds must be hom_ref\\n if len(self.unaffecteds) > 0:\\n un = reduce(op.and_, [s.gt_types == HOM_REF for s in self.unaffecteds])\\n else:\\n un = None\\n\\n def is_parent(a, b):\\n if a is None or b is None: return False\\n return a.sample_id == b.sample_id and a.family_id == b.family_id\\n\\n for parent in self.affecteds:\\n kids = [k for k in self.subjects if is_parent(parent, k.dad)]\\n for kid in kids:\\n # girls of affected dad must be affected\\n if kid.sex == 'female' and parent.sex == 'male' and not kid.affected:\\n warn(\\\"WARNING: unaffected female kid of affected dad \\\" +\\n \\\"in family: %s. not x-linked dominant. skipping %s\\\\n\\\" % (kid.family_id, parent.name))\\n return False\\n\\n #. boys of affected dad must be unaffected\\n if kid.sex == 'male' and parent.sex == 'male' and kid.affected:\\n warn(\\\"WARNING: affected male kid of affected dad in \\\" +\\n \\\"family: %s. not x-linked dominant. skipping %s\\\\n\\\" % (kid.family_id, parent.name))\\n return False\\n\\n #. mothers of affected males must be het (and affected)\\n k = parent\\n if k.sex == 'male' and k.mom and not k.mom.affected:\\n return False\\n\\n #. at least 1 parent of affected females must be het (and affected).\\n if k.sex == 'female':\\n if k.mom and k.dad and not any((k.mom.affected, k.dad.affected)):\\n return False\\n\\n depth = self._restrict_to_min_depth(min_depth)\\n quals = self._restrict_to_min_gq(min_gq)\\n return combine_and(af, un, depth, quals)\",\n \"def test_deep_equals(obja, objb, isequal):\\n\\n objatree = wo.typedtree(obja)\\n objbtree = wo.typedtree(objb)\\n match = objatree == objbtree\\n ok = match == isequal\\n\\n if ok:\\n s = \\\"pass\\\"\\n else:\\n s = \\\"fail\\\"\\n\\n print(f\\\"{obja} == {objb} is {match} : {s}\\\")\\n return ok\",\n \"def test_tree_intersection_name_exists():\\n assert tree_intersection\",\n \"def testStratifiedAxioms(self):\\n \\n a1a = Parser.parse_as(strat1a.split(\\\"\\\\n\\\"), axioms.Axiom, self.prob)\\n a1b = Parser.parse_as(strat1b.split(\\\"\\\\n\\\"), axioms.Axiom, self.prob)\\n a2 = Parser.parse_as(strat2.split(\\\"\\\\n\\\"), axioms.Axiom, self.prob)\\n \\n self.dom.axioms = [a1a, a1b, a2]\\n self.dom.stratify_axioms()\\n\\n self.assert_(a1a.predicate in self.dom.stratification[1])\\n self.assert_(a1b.predicate in self.dom.stratification[1])\\n self.assert_(a2.predicate in self.dom.stratification[2])\\n \\n state = State.from_problem(self.prob).get_extended_state()\\n\\n oc1 = StateVariable(self.prob.predicates[\\\"occupied\\\"][0], [self.prob[\\\"pos1\\\"]])\\n oc2 = StateVariable(self.prob.predicates[\\\"occupied\\\"][0], [self.prob[\\\"pos2\\\"]])\\n oc3 = StateVariable(self.prob.predicates[\\\"occupied\\\"][0], [self.prob[\\\"apt1\\\"]])\\n oc4 = StateVariable(self.prob.predicates[\\\"occupied\\\"][0], [self.prob[\\\"apt2\\\"]])\\n\\n self.assertEqual(state[oc1], TRUE)\\n self.assertEqual(state[oc2], TRUE)\\n self.assertEqual(state[oc3], FALSE)\\n self.assertEqual(state[oc4], TRUE)\\n\\n int1 = StateVariable(self.prob.predicates[\\\"interesting\\\"][0], [self.prob[\\\"pos1\\\"]])\\n int2 = StateVariable(self.prob.predicates[\\\"interesting\\\"][0], [self.prob[\\\"pos2\\\"]])\\n int3 = StateVariable(self.prob.predicates[\\\"interesting\\\"][0], [self.prob[\\\"apt1\\\"]])\\n int4 = StateVariable(self.prob.predicates[\\\"interesting\\\"][0], [self.prob[\\\"apt2\\\"]])\\n \\n self.assertEqual(state[int1], TRUE)\\n self.assertEqual(state[int2], TRUE)\\n self.assertEqual(state[int3], FALSE)\\n self.assertEqual(state[int4], TRUE)\\n \\n free1 = StateVariable(self.prob.predicates[\\\"free\\\"][0], [self.prob[\\\"pos1\\\"]])\\n free2 = StateVariable(self.prob.predicates[\\\"free\\\"][0], [self.prob[\\\"pos2\\\"]])\\n free3 = StateVariable(self.prob.predicates[\\\"free\\\"][0], [self.prob[\\\"apt1\\\"]])\\n free4 = StateVariable(self.prob.predicates[\\\"free\\\"][0], [self.prob[\\\"apt2\\\"]])\\n\\n # print \\\"-----------------_\\\"\\n # print state[free1]\\n # print state\\n # print \\\"-----------------\\\"\\n # print state[free1]\\n self.assertEqual(state[free1], FALSE)\\n self.assertEqual(state[free2], FALSE)\\n self.assertEqual(state[free3], TRUE)\\n self.assertEqual(state[free4], FALSE)\",\n \"def printGeneTree(self):\\n align = AlignIO.read(self.newPhylip, 'phylip') # Reads created .phy file containing the SeqRecord\\n #print (align) # prints concatenated allignments\\n calculator = DistanceCalculator('identity')\\n dm = calculator.get_distance(align)# Calculate the distance matrix\\n print('\\\\n======================================== DISTANCE MATRIX =======================================\\\\n')\\n print(dm,\\\"\\\\n\\\\n\\\") # Print the distance Matrix\\n constructor = DistanceTreeConstructor() # Construct the phylogenetic tree using UPGMA algorithm\\n tree = constructor.upgma(dm)\\n print('\\\\n========================================= GENE TREE ===========================================\\\\n')\\n Phylo.draw(tree) # Draw the phylogenetic tree (must install matplotlib to use this formatting)\\n Phylo.draw_ascii(tree) # Print the phylogenetic tree in terminal\",\n \"def test_client_tax_information_retrieve(self):\\n pass\"\n]","isConversational":false},"negative_scores":{"kind":"list like","value":["0.5643234","0.5496658","0.5485725","0.54545605","0.5438682","0.5353483","0.53417605","0.5323132","0.52515745","0.52508986","0.52369666","0.51995516","0.5156391","0.5128606","0.51240194","0.5093063","0.50914335","0.5089271","0.5042017","0.5032846","0.5017457","0.50071","0.4964126","0.49313855","0.491306","0.49077463","0.49032027","0.49023056","0.48719183","0.48447728","0.48310077","0.48151004","0.4809347","0.4807863","0.47966257","0.47951412","0.47700801","0.47553357","0.47372407","0.4731636","0.47311804","0.47237155","0.47194797","0.47125694","0.4702699","0.46992177","0.4696352","0.46929318","0.4692521","0.46901327","0.4688831","0.4685334","0.46620765","0.4661543","0.465562","0.46525186","0.46475497","0.4641776","0.46389347","0.46373475","0.46336922","0.46318835","0.46300766","0.46298262","0.46270418","0.46200687","0.461666","0.46139276","0.46126524","0.46115732","0.46071732","0.46039373","0.45973173","0.45792356","0.45738295","0.45727268","0.45723248","0.45713967","0.4569173","0.4560139","0.45556244","0.45484364","0.45420113","0.45387837","0.45374495","0.4537207","0.45362058","0.45357776","0.45356783","0.4526625","0.45241","0.45214698","0.45196477","0.4513456","0.4505681","0.45005426","0.4495827","0.44946194","0.4483436","0.44833833","0.4480697"],"string":"[\n \"0.5643234\",\n \"0.5496658\",\n \"0.5485725\",\n \"0.54545605\",\n \"0.5438682\",\n \"0.5353483\",\n \"0.53417605\",\n \"0.5323132\",\n \"0.52515745\",\n \"0.52508986\",\n \"0.52369666\",\n \"0.51995516\",\n \"0.5156391\",\n \"0.5128606\",\n \"0.51240194\",\n \"0.5093063\",\n \"0.50914335\",\n \"0.5089271\",\n \"0.5042017\",\n \"0.5032846\",\n \"0.5017457\",\n \"0.50071\",\n \"0.4964126\",\n \"0.49313855\",\n \"0.491306\",\n \"0.49077463\",\n \"0.49032027\",\n \"0.49023056\",\n \"0.48719183\",\n \"0.48447728\",\n \"0.48310077\",\n \"0.48151004\",\n \"0.4809347\",\n \"0.4807863\",\n \"0.47966257\",\n \"0.47951412\",\n \"0.47700801\",\n \"0.47553357\",\n \"0.47372407\",\n \"0.4731636\",\n \"0.47311804\",\n \"0.47237155\",\n \"0.47194797\",\n \"0.47125694\",\n \"0.4702699\",\n \"0.46992177\",\n \"0.4696352\",\n \"0.46929318\",\n \"0.4692521\",\n \"0.46901327\",\n \"0.4688831\",\n \"0.4685334\",\n \"0.46620765\",\n \"0.4661543\",\n \"0.465562\",\n \"0.46525186\",\n \"0.46475497\",\n \"0.4641776\",\n \"0.46389347\",\n \"0.46373475\",\n \"0.46336922\",\n \"0.46318835\",\n \"0.46300766\",\n \"0.46298262\",\n \"0.46270418\",\n \"0.46200687\",\n \"0.461666\",\n \"0.46139276\",\n \"0.46126524\",\n \"0.46115732\",\n \"0.46071732\",\n \"0.46039373\",\n \"0.45973173\",\n \"0.45792356\",\n \"0.45738295\",\n \"0.45727268\",\n \"0.45723248\",\n \"0.45713967\",\n \"0.4569173\",\n \"0.4560139\",\n \"0.45556244\",\n \"0.45484364\",\n \"0.45420113\",\n \"0.45387837\",\n \"0.45374495\",\n \"0.4537207\",\n \"0.45362058\",\n \"0.45357776\",\n \"0.45356783\",\n \"0.4526625\",\n \"0.45241\",\n \"0.45214698\",\n \"0.45196477\",\n \"0.4513456\",\n \"0.4505681\",\n \"0.45005426\",\n \"0.4495827\",\n \"0.44946194\",\n \"0.4483436\",\n \"0.44833833\",\n \"0.4480697\"\n]","isConversational":false},"document_score":{"kind":"string","value":"0.0"},"document_rank":{"kind":"string","value":"-1"}}},{"rowIdx":23785,"cells":{"query":{"kind":"string","value":"Reshape a numpy array, which is input_shape=(height, width), as opposed to input_shape=(width, height) for cv2"},"document":{"kind":"string","value":"def np_resize(img, input_shape):\n height, width = input_shape\n return cv2.resize(img, (width, height))"},"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 img_reshape(self, input_img):\n _img = np.transpose(input_img, (1, 2, 0)) \n _img = np.flipud(_img)\n _img = np.reshape(_img, (1, img_dim[0], img_dim[1], img_dim[2]))\n return _img","def numpyReshape(array):\n return np.array(array, dtype = float).reshape(1, len(array))","def flattenImage(input_array):\r\n shp = np.size(input_array)\r\n return np.reshape(input_array, (shp,))","def reshape(arr):\r\n reshape_arr = np.empty((3,240,320),dtype='float32')\r\n reshape_arr[0,:,:] = arr[:,:,0]\r\n reshape_arr[1,:,:] = arr[:,:,1]\r\n reshape_arr[2,:,:] = arr[:,:,2]\r\n return reshape_arr","def reshape(input):\n\n input = input / 255\n input = trans.resize(input, (args.size, args.size))\n input = np.reshape(input, input.shape + (1,))\n input = np.reshape(input, (1,) + input.shape)\n return input","def reshape(x, shape):\n return Reshape(shape)(x)","def reshape_raw_images(raw_im, \n resize_shape,\n ):\n _reshaped_im = np.array([cv2.resize(_lr, tuple(resize_shape[-2:]), \n interpolation=cv2.INTER_AREA) for _lr in raw_im])\n return _reshaped_im","def shape_new_img(image_path):\n desired_size = 64\n img = cv2.imread(image_path, cv2.IMREAD_GRAYSCALE)\n new_img_array = cv2.resize(img, (desired_size, desired_size))\n finalarray = new_img_array.reshape(1, desired_size, desired_size, 1)\n\n return finalarray","def _reshape(self, arr: np.ndarray) -> np.ndarray:\n return arr.reshape(self.TileHeight.value, self.TileWidth.value, self.bands,)","def resize_arr(arr, W, H, mode=cv2.INTER_LINEAR):\n arr = arr.transpose(1,2,0)\n arr = cv2.resize(arr, (W, H), mode)\n if len(arr.shape) < 3:\n arr = np.expand_dims(arr, 2)\n arr = arr.transpose(2,0,1)\n return arr","def preprocess(img: 'np.ndarray') -> 'np.ndarray':\n return cv2.resize(img, (45, 45), interpolation=cv2.INTER_AREA)[:,:,:3]","def img_reshape(self, img):\n # reshape image to have a leading 1 dimension\n img = numpy.asarray(img, dtype='float32') / 256.\n img_shape = img.shape\n img_reshaped = img.reshape(1, img_shape[0], img_shape[1], 3)\n return img_reshaped","def np_resize(img,input_shape,graystyle=False):\n height,width = input_shape\n resized_img = cv2.resize(img,(width,height))\n\n if graystyle:\n resized_img = resized_img[...,None]\n return resized_img","def flatten_image(x):\n *batch_shape, h, w, c = x.shape\n return x.reshape((*batch_shape, h * w * c))","def _reshape_channels(x):\n assert x.dim() == 4\n batch_size, nc, h, w = x.size()\n x_t = x.view(batch_size, nc, -1).transpose(1, 2).contiguous()\n x_t = x_t.view(batch_size, h, w, nc)\n return x_t","def reshape_image(img_as_np, to_shape=(256, 256)):\n\n # Trim original image to have an even number of pixels (in both axes)\n if np.shape(img_as_np)[0] % 2 != 0:\n img_as_np = img_as_np[0:np.shape(img_as_np)[0] - 1, :]\n if np.shape(img_as_np)[1] % 2 != 0:\n img_as_np = img_as_np[:, 0:np.shape(img_as_np)[1] - 1]\n\n # Define old and intermediate shapes\n ori_shape = np.shape(img_as_np)\n int_shape = (max(ori_shape), max(ori_shape))\n x_pad, y_pad = (max([0, int_shape[0] - ori_shape[0]]) // 2,\n max([0, int_shape[1] - ori_shape[1]]) // 2)\n\n # Pad the image\n new_img_as_np = np.zeros(int_shape)\n new_img_as_np[x_pad:int_shape[0] - x_pad, y_pad:int_shape[0] - y_pad] = \\\n img_as_np[:, :]\n\n # Resample the image to the required size\n new_img = Image.fromarray(new_img_as_np.astype(np.uint8))\n new_img_res = new_img.resize(to_shape, resample=Image.BILINEAR)\n\n return new_img_res","def reshape(x, shape):\n if x.shape == shape:\n return chainer.as_variable(x)\n y, = Reshape(shape).apply((x,))\n return y","def reshape_pixel_array(self, pixel_arr):\n reshaped_pixel_arr = []\n n = 28\n while n <= len(pixel_arr):\n reshaped_pixel_arr.append(pixel_arr[n-28:n])\n n+=28\n\n return reshaped_pixel_arr","def resize_img_to_array(img, img_shape=(244, 244)):\n # Code from https://gist.github.com/yinleon/8a6bf8c2f3226a521680f930f3d4339f\n img_array = np.array(\n img.resize(\n img_shape,\n Image.ANTIALIAS\n )\n )\n\n return img_array","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 _reshape_feature(self, X, size):\n new_shape = (X.shape[0],) + size + (X.shape[-1],)\n return X.reshape(new_shape)","def reshape(self, *shape):\n return F.Reshape.apply(self, shape)","def reshape(data):\n return K.reshape(x=data, shape=(K.shape(data)[0], 1, reshape_size))","def _reshape_output(self, output):\n output = np.transpose(output, [0, 2, 3, 1])\n _, height, width, _ = output.shape\n dim1, dim2 = height, width\n dim3 = 3\n # There are CATEGORY_NUM=80 object categories:\n dim4 = (4 + 1 + CATEGORY_NUM)\n return np.reshape(output, (dim1, dim2, dim3, dim4))","def reshape_to_batch(array):\n if len(array.shape) == 2:\n array = numpy.expand_dims(array, axis=2)\n array = numpy.expand_dims(array, axis=0)\n return array","def preprocessing(image, w, h):\n image = cv2.resize(image, (w, h))\n image = np.transpose(image, (2, 0, 1))\n image = image.reshape(1, 3, h, w)\n \n return image","def _fit_array_to_image(base_shape, array: np.ndarray) -> np.ndarray:\n shape = list(array.shape)\n for i, el in enumerate(base_shape):\n if el == 1 and el != shape[i]:\n shape.insert(i, 1)\n elif el != shape[i]:\n raise ValueError(f\"Wrong array shape {shape} for {base_shape}\")\n if len(shape) != len(base_shape):\n raise ValueError(f\"Wrong array shape {shape} for {base_shape}\")\n return np.reshape(array, shape)","def reshape_output_shape(input_shape):\n shape_1 = input_shape[0]\n shape_2 = 384\n return(shape_1, shape_2)","def flatten_numpy(ndarray):\n return np.reshape(ndarray, (-1,), 'F')","def data_reshape(image):\n image_mat = []\n if image.shape[-1] == 3:\n for x, i in enumerate(image):\n for y, j in enumerate(i):\n image_mat.append([x, y, j[0], j[1], j[2]])\n else:\n for x, i in enumerate(image):\n for y, j in enumerate(i):\n image_mat.append([x, y, j])\n return np.array(image_mat)","def reshape(x, shape):\n return float(x) if shape is None else jnp.reshape(x, shape)","def reconstructImage(self,arr):\n\t\tarr = arr * 256\n\t\tarr = np.array(np.round(arr),dtype=np.uint8)\n\t\t#arr = np.array(arr,dtype=np.uint8)\n\n\t\t# We need to transpose the array because we flatten X by columns\n\t\t#arr = arr.T\n\t\t#a = arr.reshape((self.width, self.height,3))\n\t\t\n\t\tif self.mode == 'L':\n\t\t\ta = arr.reshape((self.width, self.height))\n\t\telse:\n\t\t\ta = arr.reshape((self.width, self.height,3))\n\n\t\t#a = arr.reshape((3,self.width, self.height))\t\t\n\t\t#a = arr.transpose(0, 3, 1, 2)\n\n\t\tim = Image.fromarray(a,mode=self.mode)\n\n\t\treturn im","def mat2im(X, shape):\n return X.reshape(shape)","def resh(x):\r\n a = x.shape[0]\r\n b = x.shape[1]\r\n return x.reshape(a*b, 1), a, b","def transform_image(img, new_shape):\n img = img[0:720, 0:720]\n img = cv2.resize(img, (new_shape, new_shape)) / 255\n img = np.expand_dims(img, axis=0) # 3D to 4D\n return img","def _reshape(self, data):\n\n\t\td = np.zeros((32,32,3))\n\t\td_r = data[0:1024].reshape(32,32)\n\t\td_g = data[1024:2048].reshape(32,32)\n\t\td_b = data[2048:].reshape(32,32)\n\n\t\tfor h in range(32):\n\t\t for w in range(32):\n\t\t for c in range(3):\n\n\t\t if c == 0 : d[h,w,c] = d_r[h,w]\n\t\t elif c == 1 : d[h,w,c] = d_g[h,w]\n\t\t else : d[h,w,c] = d_b[h,w]\n\n\t\tarray = np.array(d, dtype=np.uint8)\n\t\timg = Image.fromarray(array)\n\t\ttemp = img.resize(size = (64,64))\n\t\td = image.img_to_array(temp)\n\n\t\t#plt.imshow(d)\n\t\t#plt.show()\n\t\treturn d","def blockshaped(arr, nrows, ncols):\r\n\t h, w = arr.shape\r\n\t return (arr.reshape(h//nrows, nrows, -1, ncols)\r\n\t .swapaxes(1,2)\r\n\t .reshape(-1, nrows, ncols))","def image_to_array(self, img):\n x = np.asarray(img, dtype=self.dtype)\n if len(x.shape) == 3:\n if self.channels_first:\n x = x.transpose(2, 0, 1)\n elif len(x.shape) == 2:\n if self.channels_first:\n x = x.reshape((1, x.shape[0], x.shape[1]))\n else:\n x = x.reshape((x.shape[0], x.shape[1], 1))\n else:\n raise ValueError('Unsupported image shape: ', x.shape)\n return x","def format_data(img_path, size):\n img_color = cv2.imread(img_path)\n img_color = img_color[:, :, ::-1]\n img_color = cv2.resize(img_color, (size, size), interpolation=cv2.INTER_AREA)\n img_color = img_color.reshape((1, size, size, 3))\\\n #.transpose(0, 3, 1, 2)\n\n return img_color","def img_to_array(img, path=True):\n global width, height\n\n if path:\n img = Image.open(img)\n img_arr = np.array(img) / 255.0\n img_arr = img_arr.reshape(width, height, channels)\n \n return img_arr","def _reshape(self, data):\n\n\t\t\td = np.zeros((32,32,3))\n\t\t\td_r = data[0:1024].reshape(32,32)\n\t\t\td_g = data[1024:2048].reshape(32,32)\n\t\t\td_b = data[2048:].reshape(32,32)\n\n\t\t\tfor h in range(32):\n\t\t\t for w in range(32):\n\t\t\t for c in range(3):\n\n\t\t\t if c == 0 : d[h,w,c] = d_r[h,w]\n\t\t\t elif c == 1 : d[h,w,c] = d_g[h,w]\n\t\t\t else : d[h,w,c] = d_b[h,w]\n\n\t\t\tarray = np.array(d, dtype=np.uint8)\n\t\t\timg = Image.fromarray(array)\n\t\t\ttemp = img.resize(size = (64,64))\n\t\t\td = image.img_to_array(temp)\n\n\t\t\t#plt.imshow(d)\n\t\t\t#plt.show()\n\t\t\treturn d","def reshape_input(input_data, input_size, single=True, warning=False):\n with suppress(Exception):\n input_data = torch.from_numpy(input_data)\n\n if input_size is None:\n if warning is True:\n print(\"No size was given and no reshaping can occur\")\n return input_data\n\n # Reshape the data regardless of batch size\n start = len(input_data)\n\n alternate = list(input_size)\n alternate[0] = start\n alternate = tuple(alternate)\n\n try:\n if single:\n input_data = input_data.reshape(alternate)\n else:\n input_data = input_data.reshape(input_size)\n except Exception:\n if warning is True:\n print(\"Warning: Data loss is possible during resizing.\")\n if single:\n input_data = input_data.resize_(alternate)\n else:\n input_data = input_data.resize_(input_size)\n return input_data","def reshape_output_shape_0(input_shape): \n shape_1 = input_shape[0]\n shape_2 = input_shape[1]\n shape_3 = input_shape[2]\n return(shape_1, shape_2, shape_3, 1)","def _np_resize_image(image, size, dtype='int'):\n if dtype == 'int':\n _size = (size[1], size[0]) # (H,W) to (W,H)\n return cv2.resize(image.astype('uint8'),\n _size,\n interpolation=cv2.INTER_LINEAR)\n elif dtype == 'float':\n return skresize(image,\n size,\n order=0,\n mode='constant',\n preserve_range=True)\n else:\n raise NotImplementedError(f\"'{dtype}' is not a valid dtype.\")","def flatten(self, arr):\n shape = arr.shape\n return arr.reshape(shape[0] * shape[1], *shape[2:])","def resize_image(arr, img_height, img_width):\n arr_pil = Image.fromarray((arr * 255).astype(np.uint8)) # Convert to a PIL Python Image Library format\n out = arr_pil.resize((img_height, img_width))\n a = image.img_to_array(out)\n return a","def output_reshape(ct):\n return np.moveaxis(ct, 1, -1)","def reshape_to_cv_format(image, rescale=False):\n reshape = False\n if rescale:\n image = rescale_image_0255(image)\n if image.shape[0] == 3 or image.shape[0] == 1:\n reshape = True\n if image.shape[0] == 1 and image.shape[1] == 3: # color 1L, 3L, h, w\n reshapeVector = (image.shape[2], image.shape[3], image.shape[1]) \n elif image.shape[0] == 3: # single row color 3L, h, w \n reshapeVector = (image.shape[1], image.shape[2], image.shape[0]) \n elif image.shape[0] == 1 and image.shape[1] == 1: # grayscale 1L, 1L, h, w \n reshapeVector = (image.shape[2], image.shape[3])\n else: # single row grayscale 1L, h, w\n reshapeVector = (image.shape[1], image.shape[2])\n image = image.reshape(reshapeVector)\n return image, reshape","def reshape_masks(masks, \n resize_shape,\n ):\n _reshaped_masks = np.array([cv2.resize(_lr, tuple(resize_shape[-2:]), \n interpolation=cv2.INTER_NEAREST) for _lr in masks])\n return _reshaped_masks","def split_to_tiles(array: np.ndarray, tile_height: int, tile_width: int) -> np.ndarray:\n arr_height, arr_width, *dimensions = array.shape\n nchannels = dimensions[0] if dimensions else 1\n new_shape = get_shape_for_tile_split(\n arr_height, arr_width, nchannels, tile_height, tile_width\n )\n return array.reshape(new_shape).swapaxes(1, 2)","def reshape(self, shape, ndim=None):\r\n\r\n if ndim is not None:\r\n if not isinstance(ndim, int):\r\n raise ValueError(\"Expected ndim to be an integer, is \" +\r\n str(type(ndim)))\r\n\r\n return theano.tensor.basic.reshape(self, shape, ndim=ndim)","def split(array, nrows, ncols):\r\n r, h = array.shape\r\n return (array.reshape(h//nrows, nrows, -1, ncols)\r\n .swapaxes(1, 2)\r\n .reshape(-1, nrows, ncols))","def resizeImages(img_path,size=(50,50)):\n image = cv2.imread(img_path)\n resized = cv2.resize(image, size, interpolation=cv2.INTER_CUBIC)\n img=np.array([resized.flatten()])\n #print('resized image')\n return img","def preprocess(self, image, input_shape):\n # image = cv2.cvtColor(image, cv2.COLOR_BGR2RGB)\n h, w, _ = image.shape\n\n input_h, input_w = input_shape\n scale = min(float(input_w) / float(w), float(input_h) / float(h))\n nw = int(w * scale)\n nh = int(h * scale)\n\n image = cv2.resize(image, (nw, nh))\n\n new_image = np.zeros((input_h, input_w, 3), np.float32)\n new_image.fill(128)\n bh, bw, _ = new_image.shape\n new_image[int((bh - nh) / 2):(nh + int((bh - nh) / 2)), int((bw - nw) / 2):(nw + int((bw - nw) / 2)), :] = image\n\n new_image /= 255.\n new_image = np.expand_dims(new_image, 0) # Add batch dimension.\n return new_image","def flatten(x_tensor):\n with tf.name_scope('input_reshape'):\n x = x_tensor.get_shape().as_list()[1]\n y = x_tensor.get_shape().as_list()[2]\n z = x_tensor.get_shape().as_list()[3]\n image_shaped_input = tf.reshape(x_tensor, [-1, x*y*z])\n return image_shaped_input","def reshape_image(raw_data, image_shape,\n orig_axes=(0, 1, 'c'), dest_axes=(0, 1, 'c')):\n\n if raw_data.ndim != len(image_shape) + 1:\n image_tensor = raw_data.reshape((-1,) + image_shape)\n else:\n image_tensor = raw_data\n\n rval = image_tensor.transpose(\n (0,) + tuple(orig_axes.index(x) + 1 for x in dest_axes)\n )\n\n return rval[::-1, ::-1]","def expand_data(X, orig_shape=(256, 256)):\n\n return X.reshape((-1, *orig_shape, X.shape[-1]))","def reshape(module, array, shape):\n _import_modules()\n if module in [np, ma, torch, jnp]:\n return array.reshape(shape)\n if module == tf:\n return tf.reshape(array, shape)\n raise UnknownModuleException(f\"Module {module.__name__} not supported.\")","def unblockshaped(arr, h, w):\n n, nrows, ncols = arr.shape\n return (arr.reshape(h//nrows, -1, nrows, ncols)\n .swapaxes(1,2)\n .reshape(h, w))","def preprocess_feature(x, image_width, image_height, image_depth):\n x_max = np.max(x)\n x = x/x_max\n return x.reshape(1, image_width, image_height, image_depth)","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 reshape(tensor, newshape):\n raise NotImplementedError","def resize(image, size):\n return np.array(Image.fromarray(image).resize(size))","def blockshaped(arr, nrows, ncols):\n h, w = arr.shape\n return (arr.reshape(h//nrows, nrows, -1, ncols)\n .swapaxes(1,2)\n .reshape(-1, nrows, ncols))","def blockshaped(arr, nrows, ncols):\n h, w = arr.shape\n return (arr.reshape(h//nrows, nrows, -1, ncols)\n .swapaxes(1,2)\n .reshape(-1, nrows, ncols))","def blockshaped(arr, nrows, ncols):\n h, w = arr.shape\n return (arr.reshape(h//nrows, nrows, -1, ncols)\n .swapaxes(1,2)\n .reshape(-1, nrows, ncols))","def _reshape(self, data):\n batch_size, height, width, n_channels = data.shape\n if self._grid_height:\n grid_height = self._grid_height\n else:\n grid_height = int(math.floor(math.sqrt(batch_size)))\n\n grid_width = int(math.ceil(batch_size/grid_height))\n\n if n_channels == 1:\n data = np.tile(data, (1, 1, 1, 3))\n n_channels = 3\n\n if n_channels != 3:\n raise ValueError('Image batch must have either 1 or 3 channels, but '\n 'was {}'.format(n_channels))\n\n shape = (height * grid_height, width * grid_width, n_channels)\n buf = np.full(shape, 255, dtype=np.uint8)\n multiplier = 1 if data.dtype in (np.int32, np.int64) else 255\n\n for k in range(batch_size):\n i = k // grid_width\n j = k % grid_width\n arr = data[k]\n x, y = i * height, j * width\n buf[x:x + height, y:y + width, :] = np.clip(\n multiplier * arr, 0, 255).astype(np.uint8)\n\n if self._zoom > 1:\n buf = buf.repeat(self._zoom, axis=0).repeat(self._zoom, axis=1)\n return buf","def reshape_normalise(img):\n\t# The image shape is expected to match the input of VGG19\n\timg = np.resize(img, (1, CONFIG.IMAGE_HEIGHT, CONFIG.IMAGE_WIDTH, CONFIG.COLOR_CHANNELS)).astype('float32')\n\timg -= CONFIG.MEAN_PIXEL\n\treturn img","def preprocess_(image, input_shape):\n if isinstance(image, str):\n filename = image\n image = Image.open(image)\n if isinstance(image, np.ndarray):\n image = Image.fromarray(image)\n assert (isinstance(image, Image.Image)) \n\n w_ori, h_ori = image.size\n h, w = input_shape\n image = image.convert(\"RGB\")\n\n if w_ori > h or h_ori > w:\n scale = max(1.0*w_ori / w, 1.0*h_ori / h)\n image.thumbnail((w,h), Image.BILINEAR)\n else:\n scale = 1\n\n image = image.crop((0, 0, w, h))\n img_data = np.array(image).reshape((1, h, w, 3))\n img_data = img_data / 256. - 0.5\n\n return img_data, scale, w_ori, h_ori","def reshape_var(var):\n dims = np.shape(var)\n nx = dims[0]\n ny = dims[1]\n nz = dims[2]\n\n var_2d = var.reshape(nx * ny, nz)\n return var_2d","def get_array(self, scale=1):\n array = cv2.imread(str(self.path), self.read_type)\n\n # resize original image so it can be be scaled without fractions\n x_extra = array.shape[0] % self.scaling\n y_extra = array.shape[1] % self.scaling\n\n x_extra = self.scaling - x_extra if x_extra != 0 else x_extra\n y_extra = self.scaling - y_extra if y_extra != 0 else y_extra\n\n padded_array = cv2.resize(\n array, (int(array.shape[1] + y_extra), int(array.shape[0] + x_extra))\n )\n\n # scale image\n resized_array = cv2.resize(\n padded_array,\n (int(padded_array.shape[1] * scale), int(padded_array.shape[0] * scale)),\n )\n\n # cv2 reads in array as BGR, tensorboard shows as RGB\n if not self.greyscale:\n x = np.copy(resized_array)\n resized_array[:, :, 0] = x[:, :, 2]\n resized_array[:, :, 2] = x[:, :, 0]\n\n # cv2.imshow('image',array)\n # cv2.waitKey(0)\n # cv2.destroyAllWindows()\n\n if self.greyscale:\n resized_array = np.expand_dims(resized_array, 2)\n return resized_array","def reshape(self, newshape):\n\n return ArrayCoordinates1d(self.coordinates.reshape(newshape), **self.properties)","def _reshape_like(mat: Tensor, shape: Tuple[int]) -> Tensor:\n return mat.reshape(-1, *shape)","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 resize_tensor(tensor, new_shape):\n channels = tensor.shape[0]\n new_tensor = np.zeros(shape=(channels,) + new_shape)\n for i in range(0, channels):\n new_tensor[i] = cv2.resize(tensor[i], dsize=new_shape[::-1])\n\n return new_tensor","def preprocess_image(self, image_path, input_size):\n img = tf.io.read_file(image_path)\n img = tf.io.decode_image(img, channels=3)\n img = tf.image.convert_image_dtype(img, tf.uint8)\n resized_img = tf.image.resize(img, input_size)\n resized_img = resized_img[tf.newaxis, :]\n return resized_img","def preprocess_image(image_path, input_size):\n img = tf.io.read_file(image_path)\n img = tf.io.decode_image(img, channels=3)\n img = tf.image.convert_image_dtype(img, tf.uint8)\n original_image = img\n resized_img = tf.image.resize(img, input_size)\n resized_img = resized_img[tf.newaxis, :]\n return resized_img, original_image","def postprocess(X, image_shape=(28, 28)):\n return tf.reshape(X, image_shape) * 255","def convert_reshape(g, op, block):\n\n input_shape = op.input(\"Shape\")\n input_shape_tensor = op.input(\"ShapeTensor\")\n data = g.get_node(op.input(\"X\")[0])\n if input_shape:\n new_shape = g.get_node(input_shape[0])\n elif input_shape_tensor:\n new_shape = []\n for shape_name in input_shape_tensor:\n shape = g.get_node(shape_name)\n if len(infer_shape(shape)) == 0:\n shape = _op.reshape(shape, [-1])\n new_shape.append(shape)\n new_shape = _op.concatenate(new_shape, axis=0)\n new_shape, infered = try_infer_value(new_shape, parameters=g.get_params())\n if infered:\n new_shape = new_shape.tolist()\n else:\n new_shape = op.attr(\"shape\")\n out = _op.reshape(data, new_shape)\n g.add_node(op.output(\"Out\")[0], out)","def resized_crop(img, i, j, h, w, size, interpolation=cv2.INTER_LINEAR):\n assert _is_numpy_image(img), 'img should be nparray Image'\n img = crop(img, i, j, h, w)\n img = resize(img, size, interpolation)\n return img","def preserve_shape(func):\n @wraps(func)\n def wrapped_function(img, *args, **kwargs):\n shape = img.shape\n result = func(img, *args, **kwargs)\n result = result.reshape(shape)\n return result\n\n return wrapped_function","def _reshape_output_batch(self, number, output):\n #tt = cutotime('reshape')\n #tt.start()\n output = output.reshape(self.output_shapes[number]) # batch, h, w, 3, (5 + 80)\n #tt.stop()\n return output","def transform( img, shape, keep_aspect = False, verbose = 0 ):\n for i in range( 3 ):\n img[:, :, i] = cv2.equalizeHist(img[:, :, i])\n img2 = resize(img, shape, keep_aspect, verbose = verbose )\n return img2","def rescale_image(image: np.ndarray, scale: float) -> np.ndarray:\n (height, width) = image.shape[:2]\n new_dims = (int(width * scale), int(height * scale))\n return cv2.resize(image, new_dims, interpolation=cv2.INTER_CUBIC)","def to_2d_array(self):\n return reshape_fns.to_2d(self._obj, raw=True)","def reshape_dataset(self, dataset, params):\n assert hasattr(params, \"vectorize_data\"), (\n \"Model params must set vectorize_data.\")\n for key in dataset.keys():\n if dataset[key] is None:\n continue\n dataset[key].images = dp.reshape_data(dataset[key].images, params.vectorize_data)[0]\n dataset[key].shape = dataset[key].images.shape\n return dataset","def flatten_image(data):\n\t# print(img.shape[0])\n\t# print(img.shape[1])\n\t# cv2.imshow('image',img)\n\t# gray = cv2.cvtColor(img,cv2.COLOR_BGR2GRAY)\n\t# plt.imshow(gray)\n\t# plt.show()\n\t# X_normalized = preprocessing.normalize(img, norm='l2')\n\t\n\t# s = img.shape[0] * img.shape[1]\n\t# img_wide = img.reshape((1, s,-1))\t\n\t# img_wide = np.rollaxis(X_normalized, axis=1, start=0)\n\t# plt.imshow(img_wide[0])\n\t# plt.show()\n\t# print(X_normalized)\n\tnsamples, nx, ny = data.shape\n\td2_train_dataset = data.reshape((nsamples,nx*ny))\n\treturn d2_train_dataset","def reshape(a, shape=None, name=None):\n if K.is_sparse(a):\n reshape_op = tf.sparse.reshape\n else:\n reshape_op = tf.reshape\n\n return reshape_op(a, shape=shape, name=name)","def resize(img, size):\n img = cv2.resize(img, tuple(size[::-1]))\n return img","def preprocess_image(image, height, width):\n image = np.uint8(\n resize(rgb2gray(image), [height, width], mode='constant')*255)\n image = np.reshape(image, [height, width, 1])/255.\n return image","def _data_reshape(self, data):\n data_offset = [int(size / 2) for size in data.shape[1:]]\n data_diff = [int(size / 2) for size in self.shape]\n data_diff_min = data_diff\n data_diff_max = []\n for i, elem in enumerate(data_diff):\n if self.shape[i] % 2 == 0:\n data_diff_max.append(elem)\n else:\n data_diff_max.append(elem + 1)\n data = data[:, (data_offset[0] - data_diff_min[0]):(data_offset[0] + data_diff_max[0]),\n (data_offset[1] - data_diff_min[1]):(data_offset[1] + data_diff_max[1]),\n (data_offset[2] - data_diff_min[2]):(data_offset[2] + data_diff_max[2])]\n\n if data.shape[1] == 1:\n data = data.reshape(data.shape[0], data.shape[2], data.shape[3])\n return data","def get_image_array(image_input,\n width, height,\n imgNorm=\"sub_mean\", ordering='channels_last'):\n\n if type(image_input) is np.ndarray:\n # It is already an array, use it as it is\n img = image_input\n elif isinstance(image_input, six.string_types):\n if not os.path.isfile(image_input):\n raise DataLoaderError(\"get_image_array: path {0} doesn't exist\"\n .format(image_input))\n img = cv2.imread(image_input, 1)\n else:\n raise DataLoaderError(\"get_image_array: Can't process input type {0}\"\n .format(str(type(image_input))))\n\n if imgNorm == \"sub_and_divide\":\n img = np.float32(cv2.resize(img, (width, height))) / 127.5 - 1\n elif imgNorm == \"sub_mean\":\n img = cv2.resize(img, (width, height))\n img = img.astype(np.float32)\n img[:, :, 0] -= 103.939\n img[:, :, 1] -= 116.779\n img[:, :, 2] -= 123.68\n img = img[:, :, ::-1]\n elif imgNorm == \"divide\":\n img = cv2.resize(img, (width, height))\n img = img.astype(np.float32)\n img = img/255.0\n\n if ordering == 'channels_first':\n img = np.rollaxis(img, 2, 0)\n return img","def get_image_array(image_input,\n width, height,\n imgNorm=\"sub_mean\", ordering='channels_last'):\n\n if type(image_input) is np.ndarray:\n # It is already an array, use it as it is\n img = image_input\n elif isinstance(image_input, six.string_types):\n if not os.path.isfile(image_input):\n raise DataLoaderError(\"get_image_array: path {0} doesn't exist\"\n .format(image_input))\n img = cv2.imread(image_input, 1)\n else:\n raise DataLoaderError(\"get_image_array: Can't process input type {0}\"\n .format(str(type(image_input))))\n\n if imgNorm == \"sub_and_divide\":\n img = np.float32(cv2.resize(img, (width, height))) / 127.5 - 1\n elif imgNorm == \"sub_mean\":\n img = cv2.resize(img, (width, height))\n img = img.astype(np.float32)\n img[:, :, 0] -= 103.939\n img[:, :, 1] -= 116.779\n img[:, :, 2] -= 123.68\n img = img[:, :, ::-1]\n elif imgNorm == \"divide\":\n img = cv2.resize(img, (width, height))\n img = img.astype(np.float32)\n img = img/255.0\n\n if ordering == 'channels_first':\n img = np.rollaxis(img, 2, 0)\n return img","def get_image_array(image_input,\n width, height,\n imgNorm=\"sub_mean\", ordering='channels_first'):\n\n if type(image_input) is np.ndarray:\n # It is already an array, use it as it is\n img = image_input\n elif isinstance(image_input, six.string_types):\n if not os.path.isfile(image_input):\n raise DataLoaderError(\"get_image_array: path {0} doesn't exist\"\n .format(image_input))\n img = cv2.imread(image_input, 1)\n else:\n raise DataLoaderError(\"get_image_array: Can't process input type {0}\"\n .format(str(type(image_input))))\n\n if imgNorm == \"sub_and_divide\":\n img = np.float32(cv2.resize(img, (width, height))) / 127.5 - 1\n elif imgNorm == \"sub_mean\":\n img = cv2.resize(img, (width, height))\n img = img.astype(np.float32)\n img[:, :, 0] -= 103.939\n img[:, :, 1] -= 116.779\n img[:, :, 2] -= 123.68\n img = img[:, :, ::-1]\n elif imgNorm == \"divide\":\n img = cv2.resize(img, (width, height))\n img = img.astype(np.float32)\n img = img/255.0\n\n if ordering == 'channels_first':\n img = np.rollaxis(img, 2, 0)\n return img","def get_image_array(image_input,\n width, height,\n imgNorm=\"sub_mean\", ordering='channels_last'):\n if type(image_input) is np.ndarray:\n # It is already an array, use it as it is\n img = image_input\n elif isinstance(image_input, six.string_types):\n if not os.path.isfile(image_input):\n raise DataLoaderError(\"get_image_array: path {0} doesn't exist\"\n .format(image_input))\n img = cv2.imread(image_input, 1)\n else:\n raise DataLoaderError(\"get_image_array: Can't process input type {0}\"\n .format(str(type(image_input))))\n if imgNorm == \"sub_and_divide\":\n img = np.float32(cv2.resize(img, (width, height))) / 127.5 - 1\n elif imgNorm == \"sub_mean\":\n img = cv2.resize(img, (width, height))\n img = img.astype(np.float32)\n img[:, :, 0] -= 103.939\n img[:, :, 1] -= 116.779\n img[:, :, 2] -= 123.68\n img = img[:, :, ::-1]\n elif imgNorm == \"divide\":\n img = cv2.resize(img, (width, height))\n img = img.astype(np.float32)\n img = img/255.0\n if ordering == 'channels_first':\n img = np.rollaxis(img, 2, 0)\n return img","def scale_down(image:np.array)->np.array:\n src = image\n scale_percent = 25\n width = int(src.shape[1] * scale_percent / 100)\n height = int(src.shape[0] * scale_percent / 100)\n dsize = (width, height)\n output = cv2.resize(src, dsize)\n return output","def preprocess_image(image, model_image_size):\n #resized_image = cv2.resize(image, tuple(reversed(model_image_size)), cv2.INTER_AREA)\n resized_image = letterbox_resize(image, tuple(reversed(model_image_size)))\n image_data = np.asarray(resized_image).astype('float32')\n image_data = normalize_image(image_data)\n image_data = np.expand_dims(image_data, 0) # Add batch dimension.\n return image_data","def image_resize(image: Image, size: (int, int)) -> array:\n image = Image.fromarray(uint8(image))\n\n return array(image.resize(size))","def preprocess(input_image, input_mask, input_size=128, keep_ratio=True):\n if keep_ratio == False:\n input_image_ = cv2.resize(input_image, (input_size, input_size))\n input_mask_ = cv2.resize(input_mask, (input_size, input_size))\n else:\n input_image_ = np.zeros((input_size, input_size, 3))\n h, w, _ = input_image.shape\n ratio = min(input_size/h, input_size/w)\n scaled_image = cv2.resize(input_image, None, None, ratio, ratio)\n \n new_h, new_w, _ = scaled_image.shape\n offset_h, offset_w = (input_size-new_h)//2, (input_size - new_w)//2\n input_image_[offset_h: offset_h + new_h, offset_w: offset_w + new_w, :] = scaled_image\n\n\n input_mask_ = np.zeros((input_size, input_size))\n scaled_mask = cv2.resize(input_mask, None, None, ratio, ratio)\n input_mask_[offset_h: offset_h + new_h, offset_w: offset_w + new_w] = scaled_mask\n\n input_image_ = input_image_/255.0\n return input_image_, input_mask_"],"string":"[\n \"def img_reshape(self, input_img):\\n _img = np.transpose(input_img, (1, 2, 0)) \\n _img = np.flipud(_img)\\n _img = np.reshape(_img, (1, img_dim[0], img_dim[1], img_dim[2]))\\n return _img\",\n \"def numpyReshape(array):\\n return np.array(array, dtype = float).reshape(1, len(array))\",\n \"def flattenImage(input_array):\\r\\n shp = np.size(input_array)\\r\\n return np.reshape(input_array, (shp,))\",\n \"def reshape(arr):\\r\\n reshape_arr = np.empty((3,240,320),dtype='float32')\\r\\n reshape_arr[0,:,:] = arr[:,:,0]\\r\\n reshape_arr[1,:,:] = arr[:,:,1]\\r\\n reshape_arr[2,:,:] = arr[:,:,2]\\r\\n return reshape_arr\",\n \"def reshape(input):\\n\\n input = input / 255\\n input = trans.resize(input, (args.size, args.size))\\n input = np.reshape(input, input.shape + (1,))\\n input = np.reshape(input, (1,) + input.shape)\\n return input\",\n \"def reshape(x, shape):\\n return Reshape(shape)(x)\",\n \"def reshape_raw_images(raw_im, \\n resize_shape,\\n ):\\n _reshaped_im = np.array([cv2.resize(_lr, tuple(resize_shape[-2:]), \\n interpolation=cv2.INTER_AREA) for _lr in raw_im])\\n return _reshaped_im\",\n \"def shape_new_img(image_path):\\n desired_size = 64\\n img = cv2.imread(image_path, cv2.IMREAD_GRAYSCALE)\\n new_img_array = cv2.resize(img, (desired_size, desired_size))\\n finalarray = new_img_array.reshape(1, desired_size, desired_size, 1)\\n\\n return finalarray\",\n \"def _reshape(self, arr: np.ndarray) -> np.ndarray:\\n return arr.reshape(self.TileHeight.value, self.TileWidth.value, self.bands,)\",\n \"def resize_arr(arr, W, H, mode=cv2.INTER_LINEAR):\\n arr = arr.transpose(1,2,0)\\n arr = cv2.resize(arr, (W, H), mode)\\n if len(arr.shape) < 3:\\n arr = np.expand_dims(arr, 2)\\n arr = arr.transpose(2,0,1)\\n return arr\",\n \"def preprocess(img: 'np.ndarray') -> 'np.ndarray':\\n return cv2.resize(img, (45, 45), interpolation=cv2.INTER_AREA)[:,:,:3]\",\n \"def img_reshape(self, img):\\n # reshape image to have a leading 1 dimension\\n img = numpy.asarray(img, dtype='float32') / 256.\\n img_shape = img.shape\\n img_reshaped = img.reshape(1, img_shape[0], img_shape[1], 3)\\n return img_reshaped\",\n \"def np_resize(img,input_shape,graystyle=False):\\n height,width = input_shape\\n resized_img = cv2.resize(img,(width,height))\\n\\n if graystyle:\\n resized_img = resized_img[...,None]\\n return resized_img\",\n \"def flatten_image(x):\\n *batch_shape, h, w, c = x.shape\\n return x.reshape((*batch_shape, h * w * c))\",\n \"def _reshape_channels(x):\\n assert x.dim() == 4\\n batch_size, nc, h, w = x.size()\\n x_t = x.view(batch_size, nc, -1).transpose(1, 2).contiguous()\\n x_t = x_t.view(batch_size, h, w, nc)\\n return x_t\",\n \"def reshape_image(img_as_np, to_shape=(256, 256)):\\n\\n # Trim original image to have an even number of pixels (in both axes)\\n if np.shape(img_as_np)[0] % 2 != 0:\\n img_as_np = img_as_np[0:np.shape(img_as_np)[0] - 1, :]\\n if np.shape(img_as_np)[1] % 2 != 0:\\n img_as_np = img_as_np[:, 0:np.shape(img_as_np)[1] - 1]\\n\\n # Define old and intermediate shapes\\n ori_shape = np.shape(img_as_np)\\n int_shape = (max(ori_shape), max(ori_shape))\\n x_pad, y_pad = (max([0, int_shape[0] - ori_shape[0]]) // 2,\\n max([0, int_shape[1] - ori_shape[1]]) // 2)\\n\\n # Pad the image\\n new_img_as_np = np.zeros(int_shape)\\n new_img_as_np[x_pad:int_shape[0] - x_pad, y_pad:int_shape[0] - y_pad] = \\\\\\n img_as_np[:, :]\\n\\n # Resample the image to the required size\\n new_img = Image.fromarray(new_img_as_np.astype(np.uint8))\\n new_img_res = new_img.resize(to_shape, resample=Image.BILINEAR)\\n\\n return new_img_res\",\n \"def reshape(x, shape):\\n if x.shape == shape:\\n return chainer.as_variable(x)\\n y, = Reshape(shape).apply((x,))\\n return y\",\n \"def reshape_pixel_array(self, pixel_arr):\\n reshaped_pixel_arr = []\\n n = 28\\n while n <= len(pixel_arr):\\n reshaped_pixel_arr.append(pixel_arr[n-28:n])\\n n+=28\\n\\n return reshaped_pixel_arr\",\n \"def resize_img_to_array(img, img_shape=(244, 244)):\\n # Code from https://gist.github.com/yinleon/8a6bf8c2f3226a521680f930f3d4339f\\n img_array = np.array(\\n img.resize(\\n img_shape,\\n Image.ANTIALIAS\\n )\\n )\\n\\n return img_array\",\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 _reshape_feature(self, X, size):\\n new_shape = (X.shape[0],) + size + (X.shape[-1],)\\n return X.reshape(new_shape)\",\n \"def reshape(self, *shape):\\n return F.Reshape.apply(self, shape)\",\n \"def reshape(data):\\n return K.reshape(x=data, shape=(K.shape(data)[0], 1, reshape_size))\",\n \"def _reshape_output(self, output):\\n output = np.transpose(output, [0, 2, 3, 1])\\n _, height, width, _ = output.shape\\n dim1, dim2 = height, width\\n dim3 = 3\\n # There are CATEGORY_NUM=80 object categories:\\n dim4 = (4 + 1 + CATEGORY_NUM)\\n return np.reshape(output, (dim1, dim2, dim3, dim4))\",\n \"def reshape_to_batch(array):\\n if len(array.shape) == 2:\\n array = numpy.expand_dims(array, axis=2)\\n array = numpy.expand_dims(array, axis=0)\\n return array\",\n \"def preprocessing(image, w, h):\\n image = cv2.resize(image, (w, h))\\n image = np.transpose(image, (2, 0, 1))\\n image = image.reshape(1, 3, h, w)\\n \\n return image\",\n \"def _fit_array_to_image(base_shape, array: np.ndarray) -> np.ndarray:\\n shape = list(array.shape)\\n for i, el in enumerate(base_shape):\\n if el == 1 and el != shape[i]:\\n shape.insert(i, 1)\\n elif el != shape[i]:\\n raise ValueError(f\\\"Wrong array shape {shape} for {base_shape}\\\")\\n if len(shape) != len(base_shape):\\n raise ValueError(f\\\"Wrong array shape {shape} for {base_shape}\\\")\\n return np.reshape(array, shape)\",\n \"def reshape_output_shape(input_shape):\\n shape_1 = input_shape[0]\\n shape_2 = 384\\n return(shape_1, shape_2)\",\n \"def flatten_numpy(ndarray):\\n return np.reshape(ndarray, (-1,), 'F')\",\n \"def data_reshape(image):\\n image_mat = []\\n if image.shape[-1] == 3:\\n for x, i in enumerate(image):\\n for y, j in enumerate(i):\\n image_mat.append([x, y, j[0], j[1], j[2]])\\n else:\\n for x, i in enumerate(image):\\n for y, j in enumerate(i):\\n image_mat.append([x, y, j])\\n return np.array(image_mat)\",\n \"def reshape(x, shape):\\n return float(x) if shape is None else jnp.reshape(x, shape)\",\n \"def reconstructImage(self,arr):\\n\\t\\tarr = arr * 256\\n\\t\\tarr = np.array(np.round(arr),dtype=np.uint8)\\n\\t\\t#arr = np.array(arr,dtype=np.uint8)\\n\\n\\t\\t# We need to transpose the array because we flatten X by columns\\n\\t\\t#arr = arr.T\\n\\t\\t#a = arr.reshape((self.width, self.height,3))\\n\\t\\t\\n\\t\\tif self.mode == 'L':\\n\\t\\t\\ta = arr.reshape((self.width, self.height))\\n\\t\\telse:\\n\\t\\t\\ta = arr.reshape((self.width, self.height,3))\\n\\n\\t\\t#a = arr.reshape((3,self.width, self.height))\\t\\t\\n\\t\\t#a = arr.transpose(0, 3, 1, 2)\\n\\n\\t\\tim = Image.fromarray(a,mode=self.mode)\\n\\n\\t\\treturn im\",\n \"def mat2im(X, shape):\\n return X.reshape(shape)\",\n \"def resh(x):\\r\\n a = x.shape[0]\\r\\n b = x.shape[1]\\r\\n return x.reshape(a*b, 1), a, b\",\n \"def transform_image(img, new_shape):\\n img = img[0:720, 0:720]\\n img = cv2.resize(img, (new_shape, new_shape)) / 255\\n img = np.expand_dims(img, axis=0) # 3D to 4D\\n return img\",\n \"def _reshape(self, data):\\n\\n\\t\\td = np.zeros((32,32,3))\\n\\t\\td_r = data[0:1024].reshape(32,32)\\n\\t\\td_g = data[1024:2048].reshape(32,32)\\n\\t\\td_b = data[2048:].reshape(32,32)\\n\\n\\t\\tfor h in range(32):\\n\\t\\t for w in range(32):\\n\\t\\t for c in range(3):\\n\\n\\t\\t if c == 0 : d[h,w,c] = d_r[h,w]\\n\\t\\t elif c == 1 : d[h,w,c] = d_g[h,w]\\n\\t\\t else : d[h,w,c] = d_b[h,w]\\n\\n\\t\\tarray = np.array(d, dtype=np.uint8)\\n\\t\\timg = Image.fromarray(array)\\n\\t\\ttemp = img.resize(size = (64,64))\\n\\t\\td = image.img_to_array(temp)\\n\\n\\t\\t#plt.imshow(d)\\n\\t\\t#plt.show()\\n\\t\\treturn d\",\n \"def blockshaped(arr, nrows, ncols):\\r\\n\\t h, w = arr.shape\\r\\n\\t return (arr.reshape(h//nrows, nrows, -1, ncols)\\r\\n\\t .swapaxes(1,2)\\r\\n\\t .reshape(-1, nrows, ncols))\",\n \"def image_to_array(self, img):\\n x = np.asarray(img, dtype=self.dtype)\\n if len(x.shape) == 3:\\n if self.channels_first:\\n x = x.transpose(2, 0, 1)\\n elif len(x.shape) == 2:\\n if self.channels_first:\\n x = x.reshape((1, x.shape[0], x.shape[1]))\\n else:\\n x = x.reshape((x.shape[0], x.shape[1], 1))\\n else:\\n raise ValueError('Unsupported image shape: ', x.shape)\\n return x\",\n \"def format_data(img_path, size):\\n img_color = cv2.imread(img_path)\\n img_color = img_color[:, :, ::-1]\\n img_color = cv2.resize(img_color, (size, size), interpolation=cv2.INTER_AREA)\\n img_color = img_color.reshape((1, size, size, 3))\\\\\\n #.transpose(0, 3, 1, 2)\\n\\n return img_color\",\n \"def img_to_array(img, path=True):\\n global width, height\\n\\n if path:\\n img = Image.open(img)\\n img_arr = np.array(img) / 255.0\\n img_arr = img_arr.reshape(width, height, channels)\\n \\n return img_arr\",\n \"def _reshape(self, data):\\n\\n\\t\\t\\td = np.zeros((32,32,3))\\n\\t\\t\\td_r = data[0:1024].reshape(32,32)\\n\\t\\t\\td_g = data[1024:2048].reshape(32,32)\\n\\t\\t\\td_b = data[2048:].reshape(32,32)\\n\\n\\t\\t\\tfor h in range(32):\\n\\t\\t\\t for w in range(32):\\n\\t\\t\\t for c in range(3):\\n\\n\\t\\t\\t if c == 0 : d[h,w,c] = d_r[h,w]\\n\\t\\t\\t elif c == 1 : d[h,w,c] = d_g[h,w]\\n\\t\\t\\t else : d[h,w,c] = d_b[h,w]\\n\\n\\t\\t\\tarray = np.array(d, dtype=np.uint8)\\n\\t\\t\\timg = Image.fromarray(array)\\n\\t\\t\\ttemp = img.resize(size = (64,64))\\n\\t\\t\\td = image.img_to_array(temp)\\n\\n\\t\\t\\t#plt.imshow(d)\\n\\t\\t\\t#plt.show()\\n\\t\\t\\treturn d\",\n \"def reshape_input(input_data, input_size, single=True, warning=False):\\n with suppress(Exception):\\n input_data = torch.from_numpy(input_data)\\n\\n if input_size is None:\\n if warning is True:\\n print(\\\"No size was given and no reshaping can occur\\\")\\n return input_data\\n\\n # Reshape the data regardless of batch size\\n start = len(input_data)\\n\\n alternate = list(input_size)\\n alternate[0] = start\\n alternate = tuple(alternate)\\n\\n try:\\n if single:\\n input_data = input_data.reshape(alternate)\\n else:\\n input_data = input_data.reshape(input_size)\\n except Exception:\\n if warning is True:\\n print(\\\"Warning: Data loss is possible during resizing.\\\")\\n if single:\\n input_data = input_data.resize_(alternate)\\n else:\\n input_data = input_data.resize_(input_size)\\n return input_data\",\n \"def reshape_output_shape_0(input_shape): \\n shape_1 = input_shape[0]\\n shape_2 = input_shape[1]\\n shape_3 = input_shape[2]\\n return(shape_1, shape_2, shape_3, 1)\",\n \"def _np_resize_image(image, size, dtype='int'):\\n if dtype == 'int':\\n _size = (size[1], size[0]) # (H,W) to (W,H)\\n return cv2.resize(image.astype('uint8'),\\n _size,\\n interpolation=cv2.INTER_LINEAR)\\n elif dtype == 'float':\\n return skresize(image,\\n size,\\n order=0,\\n mode='constant',\\n preserve_range=True)\\n else:\\n raise NotImplementedError(f\\\"'{dtype}' is not a valid dtype.\\\")\",\n \"def flatten(self, arr):\\n shape = arr.shape\\n return arr.reshape(shape[0] * shape[1], *shape[2:])\",\n \"def resize_image(arr, img_height, img_width):\\n arr_pil = Image.fromarray((arr * 255).astype(np.uint8)) # Convert to a PIL Python Image Library format\\n out = arr_pil.resize((img_height, img_width))\\n a = image.img_to_array(out)\\n return a\",\n \"def output_reshape(ct):\\n return np.moveaxis(ct, 1, -1)\",\n \"def reshape_to_cv_format(image, rescale=False):\\n reshape = False\\n if rescale:\\n image = rescale_image_0255(image)\\n if image.shape[0] == 3 or image.shape[0] == 1:\\n reshape = True\\n if image.shape[0] == 1 and image.shape[1] == 3: # color 1L, 3L, h, w\\n reshapeVector = (image.shape[2], image.shape[3], image.shape[1]) \\n elif image.shape[0] == 3: # single row color 3L, h, w \\n reshapeVector = (image.shape[1], image.shape[2], image.shape[0]) \\n elif image.shape[0] == 1 and image.shape[1] == 1: # grayscale 1L, 1L, h, w \\n reshapeVector = (image.shape[2], image.shape[3])\\n else: # single row grayscale 1L, h, w\\n reshapeVector = (image.shape[1], image.shape[2])\\n image = image.reshape(reshapeVector)\\n return image, reshape\",\n \"def reshape_masks(masks, \\n resize_shape,\\n ):\\n _reshaped_masks = np.array([cv2.resize(_lr, tuple(resize_shape[-2:]), \\n interpolation=cv2.INTER_NEAREST) for _lr in masks])\\n return _reshaped_masks\",\n \"def split_to_tiles(array: np.ndarray, tile_height: int, tile_width: int) -> np.ndarray:\\n arr_height, arr_width, *dimensions = array.shape\\n nchannels = dimensions[0] if dimensions else 1\\n new_shape = get_shape_for_tile_split(\\n arr_height, arr_width, nchannels, tile_height, tile_width\\n )\\n return array.reshape(new_shape).swapaxes(1, 2)\",\n \"def reshape(self, shape, ndim=None):\\r\\n\\r\\n if ndim is not None:\\r\\n if not isinstance(ndim, int):\\r\\n raise ValueError(\\\"Expected ndim to be an integer, is \\\" +\\r\\n str(type(ndim)))\\r\\n\\r\\n return theano.tensor.basic.reshape(self, shape, ndim=ndim)\",\n \"def split(array, nrows, ncols):\\r\\n r, h = array.shape\\r\\n return (array.reshape(h//nrows, nrows, -1, ncols)\\r\\n .swapaxes(1, 2)\\r\\n .reshape(-1, nrows, ncols))\",\n \"def resizeImages(img_path,size=(50,50)):\\n image = cv2.imread(img_path)\\n resized = cv2.resize(image, size, interpolation=cv2.INTER_CUBIC)\\n img=np.array([resized.flatten()])\\n #print('resized image')\\n return img\",\n \"def preprocess(self, image, input_shape):\\n # image = cv2.cvtColor(image, cv2.COLOR_BGR2RGB)\\n h, w, _ = image.shape\\n\\n input_h, input_w = input_shape\\n scale = min(float(input_w) / float(w), float(input_h) / float(h))\\n nw = int(w * scale)\\n nh = int(h * scale)\\n\\n image = cv2.resize(image, (nw, nh))\\n\\n new_image = np.zeros((input_h, input_w, 3), np.float32)\\n new_image.fill(128)\\n bh, bw, _ = new_image.shape\\n new_image[int((bh - nh) / 2):(nh + int((bh - nh) / 2)), int((bw - nw) / 2):(nw + int((bw - nw) / 2)), :] = image\\n\\n new_image /= 255.\\n new_image = np.expand_dims(new_image, 0) # Add batch dimension.\\n return new_image\",\n \"def flatten(x_tensor):\\n with tf.name_scope('input_reshape'):\\n x = x_tensor.get_shape().as_list()[1]\\n y = x_tensor.get_shape().as_list()[2]\\n z = x_tensor.get_shape().as_list()[3]\\n image_shaped_input = tf.reshape(x_tensor, [-1, x*y*z])\\n return image_shaped_input\",\n \"def reshape_image(raw_data, image_shape,\\n orig_axes=(0, 1, 'c'), dest_axes=(0, 1, 'c')):\\n\\n if raw_data.ndim != len(image_shape) + 1:\\n image_tensor = raw_data.reshape((-1,) + image_shape)\\n else:\\n image_tensor = raw_data\\n\\n rval = image_tensor.transpose(\\n (0,) + tuple(orig_axes.index(x) + 1 for x in dest_axes)\\n )\\n\\n return rval[::-1, ::-1]\",\n \"def expand_data(X, orig_shape=(256, 256)):\\n\\n return X.reshape((-1, *orig_shape, X.shape[-1]))\",\n \"def reshape(module, array, shape):\\n _import_modules()\\n if module in [np, ma, torch, jnp]:\\n return array.reshape(shape)\\n if module == tf:\\n return tf.reshape(array, shape)\\n raise UnknownModuleException(f\\\"Module {module.__name__} not supported.\\\")\",\n \"def unblockshaped(arr, h, w):\\n n, nrows, ncols = arr.shape\\n return (arr.reshape(h//nrows, -1, nrows, ncols)\\n .swapaxes(1,2)\\n .reshape(h, w))\",\n \"def preprocess_feature(x, image_width, image_height, image_depth):\\n x_max = np.max(x)\\n x = x/x_max\\n return x.reshape(1, image_width, image_height, image_depth)\",\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 reshape(tensor, newshape):\\n raise NotImplementedError\",\n \"def resize(image, size):\\n return np.array(Image.fromarray(image).resize(size))\",\n \"def blockshaped(arr, nrows, ncols):\\n h, w = arr.shape\\n return (arr.reshape(h//nrows, nrows, -1, ncols)\\n .swapaxes(1,2)\\n .reshape(-1, nrows, ncols))\",\n \"def blockshaped(arr, nrows, ncols):\\n h, w = arr.shape\\n return (arr.reshape(h//nrows, nrows, -1, ncols)\\n .swapaxes(1,2)\\n .reshape(-1, nrows, ncols))\",\n \"def blockshaped(arr, nrows, ncols):\\n h, w = arr.shape\\n return (arr.reshape(h//nrows, nrows, -1, ncols)\\n .swapaxes(1,2)\\n .reshape(-1, nrows, ncols))\",\n \"def _reshape(self, data):\\n batch_size, height, width, n_channels = data.shape\\n if self._grid_height:\\n grid_height = self._grid_height\\n else:\\n grid_height = int(math.floor(math.sqrt(batch_size)))\\n\\n grid_width = int(math.ceil(batch_size/grid_height))\\n\\n if n_channels == 1:\\n data = np.tile(data, (1, 1, 1, 3))\\n n_channels = 3\\n\\n if n_channels != 3:\\n raise ValueError('Image batch must have either 1 or 3 channels, but '\\n 'was {}'.format(n_channels))\\n\\n shape = (height * grid_height, width * grid_width, n_channels)\\n buf = np.full(shape, 255, dtype=np.uint8)\\n multiplier = 1 if data.dtype in (np.int32, np.int64) else 255\\n\\n for k in range(batch_size):\\n i = k // grid_width\\n j = k % grid_width\\n arr = data[k]\\n x, y = i * height, j * width\\n buf[x:x + height, y:y + width, :] = np.clip(\\n multiplier * arr, 0, 255).astype(np.uint8)\\n\\n if self._zoom > 1:\\n buf = buf.repeat(self._zoom, axis=0).repeat(self._zoom, axis=1)\\n return buf\",\n \"def reshape_normalise(img):\\n\\t# The image shape is expected to match the input of VGG19\\n\\timg = np.resize(img, (1, CONFIG.IMAGE_HEIGHT, CONFIG.IMAGE_WIDTH, CONFIG.COLOR_CHANNELS)).astype('float32')\\n\\timg -= CONFIG.MEAN_PIXEL\\n\\treturn img\",\n \"def preprocess_(image, input_shape):\\n if isinstance(image, str):\\n filename = image\\n image = Image.open(image)\\n if isinstance(image, np.ndarray):\\n image = Image.fromarray(image)\\n assert (isinstance(image, Image.Image)) \\n\\n w_ori, h_ori = image.size\\n h, w = input_shape\\n image = image.convert(\\\"RGB\\\")\\n\\n if w_ori > h or h_ori > w:\\n scale = max(1.0*w_ori / w, 1.0*h_ori / h)\\n image.thumbnail((w,h), Image.BILINEAR)\\n else:\\n scale = 1\\n\\n image = image.crop((0, 0, w, h))\\n img_data = np.array(image).reshape((1, h, w, 3))\\n img_data = img_data / 256. - 0.5\\n\\n return img_data, scale, w_ori, h_ori\",\n \"def reshape_var(var):\\n dims = np.shape(var)\\n nx = dims[0]\\n ny = dims[1]\\n nz = dims[2]\\n\\n var_2d = var.reshape(nx * ny, nz)\\n return var_2d\",\n \"def get_array(self, scale=1):\\n array = cv2.imread(str(self.path), self.read_type)\\n\\n # resize original image so it can be be scaled without fractions\\n x_extra = array.shape[0] % self.scaling\\n y_extra = array.shape[1] % self.scaling\\n\\n x_extra = self.scaling - x_extra if x_extra != 0 else x_extra\\n y_extra = self.scaling - y_extra if y_extra != 0 else y_extra\\n\\n padded_array = cv2.resize(\\n array, (int(array.shape[1] + y_extra), int(array.shape[0] + x_extra))\\n )\\n\\n # scale image\\n resized_array = cv2.resize(\\n padded_array,\\n (int(padded_array.shape[1] * scale), int(padded_array.shape[0] * scale)),\\n )\\n\\n # cv2 reads in array as BGR, tensorboard shows as RGB\\n if not self.greyscale:\\n x = np.copy(resized_array)\\n resized_array[:, :, 0] = x[:, :, 2]\\n resized_array[:, :, 2] = x[:, :, 0]\\n\\n # cv2.imshow('image',array)\\n # cv2.waitKey(0)\\n # cv2.destroyAllWindows()\\n\\n if self.greyscale:\\n resized_array = np.expand_dims(resized_array, 2)\\n return resized_array\",\n \"def reshape(self, newshape):\\n\\n return ArrayCoordinates1d(self.coordinates.reshape(newshape), **self.properties)\",\n \"def _reshape_like(mat: Tensor, shape: Tuple[int]) -> Tensor:\\n return mat.reshape(-1, *shape)\",\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 resize_tensor(tensor, new_shape):\\n channels = tensor.shape[0]\\n new_tensor = np.zeros(shape=(channels,) + new_shape)\\n for i in range(0, channels):\\n new_tensor[i] = cv2.resize(tensor[i], dsize=new_shape[::-1])\\n\\n return new_tensor\",\n \"def preprocess_image(self, image_path, input_size):\\n img = tf.io.read_file(image_path)\\n img = tf.io.decode_image(img, channels=3)\\n img = tf.image.convert_image_dtype(img, tf.uint8)\\n resized_img = tf.image.resize(img, input_size)\\n resized_img = resized_img[tf.newaxis, :]\\n return resized_img\",\n \"def preprocess_image(image_path, input_size):\\n img = tf.io.read_file(image_path)\\n img = tf.io.decode_image(img, channels=3)\\n img = tf.image.convert_image_dtype(img, tf.uint8)\\n original_image = img\\n resized_img = tf.image.resize(img, input_size)\\n resized_img = resized_img[tf.newaxis, :]\\n return resized_img, original_image\",\n \"def postprocess(X, image_shape=(28, 28)):\\n return tf.reshape(X, image_shape) * 255\",\n \"def convert_reshape(g, op, block):\\n\\n input_shape = op.input(\\\"Shape\\\")\\n input_shape_tensor = op.input(\\\"ShapeTensor\\\")\\n data = g.get_node(op.input(\\\"X\\\")[0])\\n if input_shape:\\n new_shape = g.get_node(input_shape[0])\\n elif input_shape_tensor:\\n new_shape = []\\n for shape_name in input_shape_tensor:\\n shape = g.get_node(shape_name)\\n if len(infer_shape(shape)) == 0:\\n shape = _op.reshape(shape, [-1])\\n new_shape.append(shape)\\n new_shape = _op.concatenate(new_shape, axis=0)\\n new_shape, infered = try_infer_value(new_shape, parameters=g.get_params())\\n if infered:\\n new_shape = new_shape.tolist()\\n else:\\n new_shape = op.attr(\\\"shape\\\")\\n out = _op.reshape(data, new_shape)\\n g.add_node(op.output(\\\"Out\\\")[0], out)\",\n \"def resized_crop(img, i, j, h, w, size, interpolation=cv2.INTER_LINEAR):\\n assert _is_numpy_image(img), 'img should be nparray Image'\\n img = crop(img, i, j, h, w)\\n img = resize(img, size, interpolation)\\n return img\",\n \"def preserve_shape(func):\\n @wraps(func)\\n def wrapped_function(img, *args, **kwargs):\\n shape = img.shape\\n result = func(img, *args, **kwargs)\\n result = result.reshape(shape)\\n return result\\n\\n return wrapped_function\",\n \"def _reshape_output_batch(self, number, output):\\n #tt = cutotime('reshape')\\n #tt.start()\\n output = output.reshape(self.output_shapes[number]) # batch, h, w, 3, (5 + 80)\\n #tt.stop()\\n return output\",\n \"def transform( img, shape, keep_aspect = False, verbose = 0 ):\\n for i in range( 3 ):\\n img[:, :, i] = cv2.equalizeHist(img[:, :, i])\\n img2 = resize(img, shape, keep_aspect, verbose = verbose )\\n return img2\",\n \"def rescale_image(image: np.ndarray, scale: float) -> np.ndarray:\\n (height, width) = image.shape[:2]\\n new_dims = (int(width * scale), int(height * scale))\\n return cv2.resize(image, new_dims, interpolation=cv2.INTER_CUBIC)\",\n \"def to_2d_array(self):\\n return reshape_fns.to_2d(self._obj, raw=True)\",\n \"def reshape_dataset(self, dataset, params):\\n assert hasattr(params, \\\"vectorize_data\\\"), (\\n \\\"Model params must set vectorize_data.\\\")\\n for key in dataset.keys():\\n if dataset[key] is None:\\n continue\\n dataset[key].images = dp.reshape_data(dataset[key].images, params.vectorize_data)[0]\\n dataset[key].shape = dataset[key].images.shape\\n return dataset\",\n \"def flatten_image(data):\\n\\t# print(img.shape[0])\\n\\t# print(img.shape[1])\\n\\t# cv2.imshow('image',img)\\n\\t# gray = cv2.cvtColor(img,cv2.COLOR_BGR2GRAY)\\n\\t# plt.imshow(gray)\\n\\t# plt.show()\\n\\t# X_normalized = preprocessing.normalize(img, norm='l2')\\n\\t\\n\\t# s = img.shape[0] * img.shape[1]\\n\\t# img_wide = img.reshape((1, s,-1))\\t\\n\\t# img_wide = np.rollaxis(X_normalized, axis=1, start=0)\\n\\t# plt.imshow(img_wide[0])\\n\\t# plt.show()\\n\\t# print(X_normalized)\\n\\tnsamples, nx, ny = data.shape\\n\\td2_train_dataset = data.reshape((nsamples,nx*ny))\\n\\treturn d2_train_dataset\",\n \"def reshape(a, shape=None, name=None):\\n if K.is_sparse(a):\\n reshape_op = tf.sparse.reshape\\n else:\\n reshape_op = tf.reshape\\n\\n return reshape_op(a, shape=shape, name=name)\",\n \"def resize(img, size):\\n img = cv2.resize(img, tuple(size[::-1]))\\n return img\",\n \"def preprocess_image(image, height, width):\\n image = np.uint8(\\n resize(rgb2gray(image), [height, width], mode='constant')*255)\\n image = np.reshape(image, [height, width, 1])/255.\\n return image\",\n \"def _data_reshape(self, data):\\n data_offset = [int(size / 2) for size in data.shape[1:]]\\n data_diff = [int(size / 2) for size in self.shape]\\n data_diff_min = data_diff\\n data_diff_max = []\\n for i, elem in enumerate(data_diff):\\n if self.shape[i] % 2 == 0:\\n data_diff_max.append(elem)\\n else:\\n data_diff_max.append(elem + 1)\\n data = data[:, (data_offset[0] - data_diff_min[0]):(data_offset[0] + data_diff_max[0]),\\n (data_offset[1] - data_diff_min[1]):(data_offset[1] + data_diff_max[1]),\\n (data_offset[2] - data_diff_min[2]):(data_offset[2] + data_diff_max[2])]\\n\\n if data.shape[1] == 1:\\n data = data.reshape(data.shape[0], data.shape[2], data.shape[3])\\n return data\",\n \"def get_image_array(image_input,\\n width, height,\\n imgNorm=\\\"sub_mean\\\", ordering='channels_last'):\\n\\n if type(image_input) is np.ndarray:\\n # It is already an array, use it as it is\\n img = image_input\\n elif isinstance(image_input, six.string_types):\\n if not os.path.isfile(image_input):\\n raise DataLoaderError(\\\"get_image_array: path {0} doesn't exist\\\"\\n .format(image_input))\\n img = cv2.imread(image_input, 1)\\n else:\\n raise DataLoaderError(\\\"get_image_array: Can't process input type {0}\\\"\\n .format(str(type(image_input))))\\n\\n if imgNorm == \\\"sub_and_divide\\\":\\n img = np.float32(cv2.resize(img, (width, height))) / 127.5 - 1\\n elif imgNorm == \\\"sub_mean\\\":\\n img = cv2.resize(img, (width, height))\\n img = img.astype(np.float32)\\n img[:, :, 0] -= 103.939\\n img[:, :, 1] -= 116.779\\n img[:, :, 2] -= 123.68\\n img = img[:, :, ::-1]\\n elif imgNorm == \\\"divide\\\":\\n img = cv2.resize(img, (width, height))\\n img = img.astype(np.float32)\\n img = img/255.0\\n\\n if ordering == 'channels_first':\\n img = np.rollaxis(img, 2, 0)\\n return img\",\n \"def get_image_array(image_input,\\n width, height,\\n imgNorm=\\\"sub_mean\\\", ordering='channels_last'):\\n\\n if type(image_input) is np.ndarray:\\n # It is already an array, use it as it is\\n img = image_input\\n elif isinstance(image_input, six.string_types):\\n if not os.path.isfile(image_input):\\n raise DataLoaderError(\\\"get_image_array: path {0} doesn't exist\\\"\\n .format(image_input))\\n img = cv2.imread(image_input, 1)\\n else:\\n raise DataLoaderError(\\\"get_image_array: Can't process input type {0}\\\"\\n .format(str(type(image_input))))\\n\\n if imgNorm == \\\"sub_and_divide\\\":\\n img = np.float32(cv2.resize(img, (width, height))) / 127.5 - 1\\n elif imgNorm == \\\"sub_mean\\\":\\n img = cv2.resize(img, (width, height))\\n img = img.astype(np.float32)\\n img[:, :, 0] -= 103.939\\n img[:, :, 1] -= 116.779\\n img[:, :, 2] -= 123.68\\n img = img[:, :, ::-1]\\n elif imgNorm == \\\"divide\\\":\\n img = cv2.resize(img, (width, height))\\n img = img.astype(np.float32)\\n img = img/255.0\\n\\n if ordering == 'channels_first':\\n img = np.rollaxis(img, 2, 0)\\n return img\",\n \"def get_image_array(image_input,\\n width, height,\\n imgNorm=\\\"sub_mean\\\", ordering='channels_first'):\\n\\n if type(image_input) is np.ndarray:\\n # It is already an array, use it as it is\\n img = image_input\\n elif isinstance(image_input, six.string_types):\\n if not os.path.isfile(image_input):\\n raise DataLoaderError(\\\"get_image_array: path {0} doesn't exist\\\"\\n .format(image_input))\\n img = cv2.imread(image_input, 1)\\n else:\\n raise DataLoaderError(\\\"get_image_array: Can't process input type {0}\\\"\\n .format(str(type(image_input))))\\n\\n if imgNorm == \\\"sub_and_divide\\\":\\n img = np.float32(cv2.resize(img, (width, height))) / 127.5 - 1\\n elif imgNorm == \\\"sub_mean\\\":\\n img = cv2.resize(img, (width, height))\\n img = img.astype(np.float32)\\n img[:, :, 0] -= 103.939\\n img[:, :, 1] -= 116.779\\n img[:, :, 2] -= 123.68\\n img = img[:, :, ::-1]\\n elif imgNorm == \\\"divide\\\":\\n img = cv2.resize(img, (width, height))\\n img = img.astype(np.float32)\\n img = img/255.0\\n\\n if ordering == 'channels_first':\\n img = np.rollaxis(img, 2, 0)\\n return img\",\n \"def get_image_array(image_input,\\n width, height,\\n imgNorm=\\\"sub_mean\\\", ordering='channels_last'):\\n if type(image_input) is np.ndarray:\\n # It is already an array, use it as it is\\n img = image_input\\n elif isinstance(image_input, six.string_types):\\n if not os.path.isfile(image_input):\\n raise DataLoaderError(\\\"get_image_array: path {0} doesn't exist\\\"\\n .format(image_input))\\n img = cv2.imread(image_input, 1)\\n else:\\n raise DataLoaderError(\\\"get_image_array: Can't process input type {0}\\\"\\n .format(str(type(image_input))))\\n if imgNorm == \\\"sub_and_divide\\\":\\n img = np.float32(cv2.resize(img, (width, height))) / 127.5 - 1\\n elif imgNorm == \\\"sub_mean\\\":\\n img = cv2.resize(img, (width, height))\\n img = img.astype(np.float32)\\n img[:, :, 0] -= 103.939\\n img[:, :, 1] -= 116.779\\n img[:, :, 2] -= 123.68\\n img = img[:, :, ::-1]\\n elif imgNorm == \\\"divide\\\":\\n img = cv2.resize(img, (width, height))\\n img = img.astype(np.float32)\\n img = img/255.0\\n if ordering == 'channels_first':\\n img = np.rollaxis(img, 2, 0)\\n return img\",\n \"def scale_down(image:np.array)->np.array:\\n src = image\\n scale_percent = 25\\n width = int(src.shape[1] * scale_percent / 100)\\n height = int(src.shape[0] * scale_percent / 100)\\n dsize = (width, height)\\n output = cv2.resize(src, dsize)\\n return output\",\n \"def preprocess_image(image, model_image_size):\\n #resized_image = cv2.resize(image, tuple(reversed(model_image_size)), cv2.INTER_AREA)\\n resized_image = letterbox_resize(image, tuple(reversed(model_image_size)))\\n image_data = np.asarray(resized_image).astype('float32')\\n image_data = normalize_image(image_data)\\n image_data = np.expand_dims(image_data, 0) # Add batch dimension.\\n return image_data\",\n \"def image_resize(image: Image, size: (int, int)) -> array:\\n image = Image.fromarray(uint8(image))\\n\\n return array(image.resize(size))\",\n \"def preprocess(input_image, input_mask, input_size=128, keep_ratio=True):\\n if keep_ratio == False:\\n input_image_ = cv2.resize(input_image, (input_size, input_size))\\n input_mask_ = cv2.resize(input_mask, (input_size, input_size))\\n else:\\n input_image_ = np.zeros((input_size, input_size, 3))\\n h, w, _ = input_image.shape\\n ratio = min(input_size/h, input_size/w)\\n scaled_image = cv2.resize(input_image, None, None, ratio, ratio)\\n \\n new_h, new_w, _ = scaled_image.shape\\n offset_h, offset_w = (input_size-new_h)//2, (input_size - new_w)//2\\n input_image_[offset_h: offset_h + new_h, offset_w: offset_w + new_w, :] = scaled_image\\n\\n\\n input_mask_ = np.zeros((input_size, input_size))\\n scaled_mask = cv2.resize(input_mask, None, None, ratio, ratio)\\n input_mask_[offset_h: offset_h + new_h, offset_w: offset_w + new_w] = scaled_mask\\n\\n input_image_ = input_image_/255.0\\n return input_image_, input_mask_\"\n]","isConversational":false},"negative_scores":{"kind":"list like","value":["0.75424194","0.75378686","0.75035334","0.7405382","0.7378981","0.7117111","0.70272535","0.6998127","0.6938943","0.6895453","0.68916667","0.6878874","0.6737582","0.667375","0.6638827","0.6624398","0.6608408","0.65904003","0.654305","0.65211385","0.64932525","0.64708745","0.6468186","0.6450826","0.6432837","0.64241195","0.6421818","0.6413301","0.63934374","0.6388426","0.6319238","0.6289288","0.6246786","0.6209988","0.6188208","0.61725426","0.6145759","0.61336786","0.6117133","0.61162186","0.611478","0.6095883","0.60770416","0.6072552","0.60439014","0.6010284","0.60012424","0.5992353","0.5985846","0.5965372","0.5961634","0.5959277","0.59566486","0.59278876","0.5908884","0.5908776","0.5889713","0.58879787","0.5859525","0.58567137","0.58474094","0.5845013","0.5843377","0.5842842","0.5842842","0.5842842","0.5831594","0.58291334","0.5820552","0.58082664","0.5797539","0.57750404","0.5771505","0.5768775","0.5767837","0.5767216","0.57572865","0.5753825","0.5748266","0.5741828","0.5738225","0.5734297","0.5728374","0.5727153","0.5705373","0.5690678","0.5672874","0.5666346","0.5652711","0.56509405","0.563868","0.56335115","0.56335115","0.56325966","0.56301194","0.56239694","0.56235516","0.56234336","0.56051046"],"string":"[\n \"0.75424194\",\n \"0.75378686\",\n \"0.75035334\",\n \"0.7405382\",\n \"0.7378981\",\n \"0.7117111\",\n \"0.70272535\",\n \"0.6998127\",\n \"0.6938943\",\n \"0.6895453\",\n \"0.68916667\",\n \"0.6878874\",\n \"0.6737582\",\n \"0.667375\",\n \"0.6638827\",\n \"0.6624398\",\n \"0.6608408\",\n \"0.65904003\",\n \"0.654305\",\n \"0.65211385\",\n \"0.64932525\",\n \"0.64708745\",\n \"0.6468186\",\n \"0.6450826\",\n \"0.6432837\",\n \"0.64241195\",\n \"0.6421818\",\n \"0.6413301\",\n \"0.63934374\",\n \"0.6388426\",\n \"0.6319238\",\n \"0.6289288\",\n \"0.6246786\",\n \"0.6209988\",\n \"0.6188208\",\n \"0.61725426\",\n \"0.6145759\",\n \"0.61336786\",\n \"0.6117133\",\n \"0.61162186\",\n \"0.611478\",\n \"0.6095883\",\n \"0.60770416\",\n \"0.6072552\",\n \"0.60439014\",\n \"0.6010284\",\n \"0.60012424\",\n \"0.5992353\",\n \"0.5985846\",\n \"0.5965372\",\n \"0.5961634\",\n \"0.5959277\",\n \"0.59566486\",\n \"0.59278876\",\n \"0.5908884\",\n \"0.5908776\",\n \"0.5889713\",\n \"0.58879787\",\n \"0.5859525\",\n \"0.58567137\",\n \"0.58474094\",\n \"0.5845013\",\n \"0.5843377\",\n \"0.5842842\",\n \"0.5842842\",\n \"0.5842842\",\n \"0.5831594\",\n \"0.58291334\",\n \"0.5820552\",\n \"0.58082664\",\n \"0.5797539\",\n \"0.57750404\",\n \"0.5771505\",\n \"0.5768775\",\n \"0.5767837\",\n \"0.5767216\",\n \"0.57572865\",\n \"0.5753825\",\n \"0.5748266\",\n \"0.5741828\",\n \"0.5738225\",\n \"0.5734297\",\n \"0.5728374\",\n \"0.5727153\",\n \"0.5705373\",\n \"0.5690678\",\n \"0.5672874\",\n \"0.5666346\",\n \"0.5652711\",\n \"0.56509405\",\n \"0.563868\",\n \"0.56335115\",\n \"0.56335115\",\n \"0.56325966\",\n \"0.56301194\",\n \"0.56239694\",\n \"0.56235516\",\n \"0.56234336\",\n \"0.56051046\"\n]","isConversational":false},"document_score":{"kind":"string","value":"0.72959924"},"document_rank":{"kind":"string","value":"5"}}},{"rowIdx":23786,"cells":{"query":{"kind":"string","value":"Compute MD5 hash of the data_path (dir or file) for data versioning."},"document":{"kind":"string","value":"def hash_data(data_path: Union[str, Path], chunk_size: int = 65536) -> str:\n if Path(data_path).is_dir():\n hash = _hash_dir(data_path, chunk_size)\n elif Path(data_path).is_file():\n hash = _hash_file(data_path, chunk_size)\n else:\n raise ValueError(f\"{data_path} is neither directory nor file.\")\n return hash.hexdigest()"},"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 python_repo_hash_md5(root_dir: str, *, verbose: bool = False):\n m = hashlib.md5()\n for e in _collect_entries(root_dir, '.'):\n if verbose:\n log_info('Processing e', e)\n m.update(\n f\"path={e['path']}\\tisdir={e['isdir']}\\tsize={e['size']}\\tmode={e['mode']:03o}\\tmtime={e['mtime']}\\n\"\n .encode('UTF-8'))\n\n return m.hexdigest()","def calculate_md5_of_dir(self, verbose=0):\n directory = self.cfg['sharing_path']\n if verbose:\n start = time.time()\n md5Hash = hashlib.md5()\n if not os.path.exists(directory):\n self.stop(1, 'Error during calculate md5! Impossible to find \"{}\" in user folder'.format(directory))\n\n for root, dirs, files in os.walk(directory, followlinks=False):\n for names in files:\n filepath = os.path.join(root, names)\n rel_path = self.relativize_path(filepath)\n if rel_path in self.client_snapshot:\n md5Hash.update(self.client_snapshot[rel_path][1])\n md5Hash.update(hashlib.md5(filepath).hexdigest())\n else:\n hashed_file = self.hash_file(filepath)\n if hashed_file:\n md5Hash.update(hashed_file)\n md5Hash.update(hashlib.md5(filepath).hexdigest())\n else:\n print \"can't hash file: \", filepath\n\n if verbose:\n stop = time.time()\n print stop - start\n return md5Hash.hexdigest()","def _md5sum(data):\n hash = hashlib.md5()\n hash.update(six.b(data))\n hash_hex = hash.hexdigest()\n return hash_hex","def svn_fs_file_md5_checksum(*args):\r\n return _fs.svn_fs_file_md5_checksum(*args)","def md5(self):\n return md5file(self.abspath)","def md5_hash(self) -> str:\n\n ordered_model_data = sort_dictionary(self.data, recursive=True)\n\n return md5(json.dumps(ordered_model_data).encode(\"utf-8\")).hexdigest()","def data_checksum(self, node):\n cmd = f\"find {RedpandaService.DATA_DIR} -type f -exec md5sum '{{}}' \\; -exec stat -c %s '{{}}' \\;\"\n lines = node.account.ssh_output(cmd)\n tokens = lines.split()\n return {\n tokens[ix + 1].decode(): (tokens[ix].decode(), int(tokens[ix + 2]))\n for ix in range(0, len(tokens), 3)\n }","def checksum(path):\n with open(path, 'r') as f:\n return md5(f.read()).digest()","def calc_file_md5(file_path):\n hash_md5 = str()\n method = hashlib.md5()\n if not os.path.exists(file_path):\n logger.error(\"File(%s) don not exist, can not calculation file hash\" % file_path)\n return hash_md5\n\n with open(file_path, 'rb') as f:\n for chunk in read_chunks(f, 1024 * 1024):\n method.update(chunk)\n return method.hexdigest()","def calculate(d):\r\n\r\n # Set correct slashes for the OS\r\n if sys.platform == 'windows':\r\n slash = '\\\\'\r\n elif sys.platform == 'linux':\r\n slash = '/'\r\n else:\r\n print('#Error. Unknown platform.')\r\n return\r\n\r\n print('Files in the current directory and their md5-hashes:\\n')\r\n i = 0\r\n assert i == 0, '#Error. Variable i != 0.'\r\n\r\n for i in range(len(d[2])): # Go through the list of files\r\n full_path = d[0]+slash+d[2][i]\r\n print(full_path) # Get the list of files with full paths\r\n print(md5(full_path))\r\n size(full_path)","def md5Hash(pathAndFilename, blockSize=8192):\n hashcode = hashlib.md5()\n with open(pathAndFilename, \"rb\" ) as f:\n block = f.read(blockSize)\n while len(block)>0:\n hashcode.update(block)\n block = f.read(blockSize)\n return hashcode.hexdigest()","def md5_hexdigest(data):\n\n if not (data and isinstance(data, six.text_type)):\n raise Exception(\"invalid data to be hashed: %s\", repr(data))\n\n encoded_data = data.encode(\"utf-8\")\n\n if not new_md5:\n m = md5.new() # nosec\n else:\n m = md5()\n m.update(encoded_data)\n\n return m.hexdigest()","def get_md5(data):\n if hasattr(data, \"read\") and hasattr(data, 'seek'):\n data.seek(0)\n m = md5()\n chunk = data.read(1024*1024) # 1Mb\n f_size = 0\n while(chunk):\n f_size += len(chunk)\n m.update(chunk)\n chunk = data.read(1024*1024)\n data.seek(0)\n return m.hexdigest(), f_size\n else: # normal str\n m = md5()\n f_size = len(data)\n m.update(data)\n return m.hexdigest(), f_size","def md5_hash(file_path):\n with open(file_path, 'rb') as fp:\n return md5(fp.read()).hexdigest()","def data_hash(self):\n md5 = hashlib.md5()\n # text header row\n for column in self.columns:\n md5.update(hxl.datatypes.normalise_space(column.header).encode('utf-8'))\n # hashtag row\n for column in self.columns:\n md5.update(hxl.datatypes.normalise_space(column.display_tag).encode('utf-8'))\n # data rows\n for row in self:\n for value in row:\n md5.update(hxl.datatypes.normalise_space(value).encode('utf-8'))\n return md5.hexdigest()","def md5(dir):\n\n # ugly way to avoid circular imports\n from . import settings\n\n files = [ \n settings.DATA['nation']['file_name'],\n settings.DATA['regions']['file_name'],\n settings.DATA['provinces']['file_name'],\n ]\n\n hash_md5 = hashlib.md5()\n for f in files:\n with open(dir+'/'+f, \"rb\") as f:\n for chunk in iter(lambda: f.read(4096), b\"\"):\n hash_md5.update(chunk)\n \n return hash_md5.hexdigest()","def md5_checksum(file_path):\n with open(file_path, 'rb') as fh:\n m = hashlib.md5()\n while True:\n data = fh.read(8192)\n if not data:\n break\n m.update(data)\n return m.hexdigest()","def MD5(self) -> _n_0_t_3[_n_0_t_9]:","def aws_md5(data):\n hasher = hashlib.new(\"md5\")\n if hasattr(data, \"read\"):\n data.seek(0)\n while True:\n chunk = data.read(8192)\n if not chunk:\n break\n hasher.update(chunk)\n data.seek(0)\n else:\n hasher.update(data)\n return b64encode(hasher.digest()).decode(\"ascii\")","def _md5_of_file(path):\n md5 = hashlib.md5()\n\n with open(path, 'rb') as f:\n for chunk in iter(lambda: f.read(4096), b''):\n md5.update(chunk)\n\n return md5.hexdigest()","def md5_sum_file(path):\n with open(path, 'rb') as f:\n m = hashlib.md5()\n while True:\n data = f.read(8192)\n if not data:\n break\n m.update(data)\n return m.hexdigest()","def _get_local_md5(self, blocksize=2**20):\n m = hashlib.md5()\n with open(self.dst, \"rb\") as f:\n buf = f.read(blocksize)\n while buf:\n m.update(buf)\n buf = f.read(blocksize)\n return m.hexdigest()","def hash_file(path: str) -> str:\n return _hash_file(path, hashlib.md5()).hexdigest()","def sha256Sum(self, data):\n data = str(data)\n m = hashlib.sha256()\n if os.path.isfile(data):\n try:\n f = file(data, 'rb')\n except:\n return 'ERROR: unable to open %s' % data\n while True:\n d = f.read(8096)\n if not d:\n break\n m.update(d)\n f.close()\n # Otherwise it could be either 1) a directory 2) miscellaneous data (like json)\n else:\n m.update(data)\n return m.hexdigest()","def static_file_hash(filepath):\n hasher = hashlib.md5() # nosec: B303\n\n with contextlib.closing(open(filepath, 'rb')) as file:\n hasher.update(file.read())\n return hasher.hexdigest()","def hash_directory(path):\n if not os.path.isdir(path):\n raise ValueError(\n \"The given path `{}` is not a directory.\".format(path))\n\n md5 = hashlib.md5()\n\n for root, _, files in os.walk(path):\n for fil in files:\n md5.update(hash_file(os.path.join(root, fil)).encode(\"utf-8\"))\n\n return \"{}\".format(md5.hexdigest())","def local_md5(filepath, blocksize=65536):\n hasher = hashlib.md5()\n with open(filepath, 'rb') as source:\n buf = source.read(blocksize)\n while len(buf) > 0:\n hasher.update(buf)\n buf = source.read(blocksize)\n return hasher.hexdigest()","def GetFileMd5(file_path):\n return binascii.hexlify(GetFileHashes(file_path, do_md5=True)['md5'])","def md5hash(self) -> pulumi.Output[str]:\n return pulumi.get(self, \"md5hash\")","def md5(path):\n with open(path, 'rb') as f:\n md5hash = hashlib.md5()\n for chunk in iter(lambda: f.read(4096), b''):\n md5hash.update(chunk)\n return md5hash.hexdigest()","def calculate_md5sum_of_a_file(context, file_name, file_path):\n command = \"md5sum \" + file_path + \"/\" + file_name + \" | awk {'print $1'}\"\n return context.cme_session.send_ssh_command(command=command)","def get_all_hashes(data_dir):\n file_hashes = {}\n for root, dirs, files in os.walk(data_dir):\n for file_name in files:\n file_path = os.path.join(root, file_name)\n file_hashes[file_path] = generate_file_md5(file_path)\n return file_hashes","def get_data_dependency_hash(cls, data_dir, dataset_class):\n\n file_hashes = []\n\n for file in sorted(dataset_class.files):\n\n if not os.path.isfile('%sraw/%s' % (data_dir, file)):\n raise IOError('The following file that was referenced in your Dataset class does not exist: %s' % file)\n\n tar_hash = MantraHashed.get_256_hash_from_file('%sraw/%s' % (data_dir, file))\n file_hashes.append(tar_hash)\n\n return MantraHashed.get_256_hash_from_string(''.join(file_hashes))","def CalcMD5(filepath):\n with open(filepath,'rb') as f:\n md5obj = hashlib.md5()\n md5obj.update(f.read())\n return md5obj.hexdigest()","def _md5(input):\n m = hashlib.md5()\n m.update(input)\n return m.hexdigest()","def file_field_md5(source_file, blocksize=65536):\n hasher = hashlib.md5()\n if source_file.closed:\n source_file.open('rb')\n buf = source_file.read(blocksize)\n while len(buf) > 0:\n hasher.update(buf)\n buf = source_file.read(blocksize)\n source_file.seek(0)\n return hasher.hexdigest()","def get_md5(location: str, ignore_hidden_files: bool=True) -> Optional[str]:\n if not os.path.exists(location):\n return None\n if os.path.isfile(location):\n with open(location, \"rb\") as file:\n content = file.read()\n return hashlib.md5(content).hexdigest()\n else:\n return dirhash(location, \"md5\", ignore_hidden=ignore_hidden_files)","def checksum(data=b\"\"):\n hasher = get_hasher(DEFAULT_CHECKSUM_ALGO)\n hasher.update(data)\n return hasher","def hash_file(path):\n if not os.path.isfile(path):\n raise ValueError(\"The given path `{}` is not a file.\".format(path))\n\n md5 = hashlib.md5()\n\n with open(path, 'rb') as file_:\n while True:\n data = file_.read(65536)\n if not data:\n break\n md5.update(data)\n\n return \"{}\".format(md5.hexdigest())","def md5(file_path):\r\n\r\n hasher = hashlib.md5()\r\n with Profiler():\r\n with open(file_path, 'rb') as f:\r\n while True:\r\n buf = f.read(BLOCKSIZE)\r\n if not buf:\r\n break\r\n while len(buf) > 0:\r\n hasher.update(buf)\r\n buf = f.read(BLOCKSIZE)\r\n md5_hash = (hasher.hexdigest()).upper()\r\n return md5_hash","def md5(self):\n if self.source_file is None:\n return None\n if self._md5 is None:\n self._md5 = file_field_md5(self.source_file)\n # try: # Locally stored file\n # self._md5 = local_md5(self.source_file.path)\n # except NotImplementedError: # AWS S3 storage\n # self._md5 = s3_md5(self.source_file.file.key)\n return self._md5","def generate_hash(self):\r\n\r\n hash_list = []\r\n for root, dirs, files in os.walk(self.options['source']):\r\n for f in sorted([f for f in files if not f.startswith('.')]):\r\n hash_list.append(os.path.join(root, f))\r\n hash_list.append(str(os.path.getmtime(os.path.join(root, f))))\r\n hash_list = ''.join(hash_list)\r\n\r\n if sys.version < '3':\r\n return hashlib.sha1(hash_list).hexdigest()\r\n return hashlib.sha1(hash_list.encode('utf-8')).hexdigest()","def digest(dirname, glob=None):\n md5 = hashlib.md5()\n if glob is None:\n fnames = [fname for _, fname in list_files(Path(dirname))]\n for fname in sorted(fnames):\n fname = os.path.join(dirname, fname)\n md5.update(open(fname, 'rb').read())\n else:\n fnames = Path(dirname).glob(glob)\n for fname in sorted(fnames):\n md5.update(fname.open('rb').read())\n return md5.hexdigest()","def calc_file_hash(filepath):\n with open(filepath, 'rb') as f:\n return md5(f.read()).hexdigest()","def md5sum(file_name):\n f = open(file_name, mode='rb')\n h = hashlib.md5()\n h.update(f.read())\n return h.hexdigest()","def get_contents_hash(self):\n md5 = hashlib.md5()\n with closing(self.open()) as handle:\n for chunk in handle.chunks():\n md5.update(chunk)\n return md5.hexdigest()","def _hash_file_or_dir(path: str, md5: Hash) -> Hash:\n if isinstance(path, str) and path.lower().startswith(\"file://\"):\n path = unquote(urlparse(path).path)\n md5.update(path.encode())\n if Path(path).is_dir():\n md5 = _hash_dir(path, md5)\n elif Path(path).is_file():\n md5 = _hash_file(path, md5)\n return md5","def ondisk_digest(self):\n with open(self.rename_phase_src) as f:\n return hasher(f.read()).hexdigest()","def _calculate_hash(self) -> str:\n data_str = str(self.version) + str(self.index) + self.pre_hash + str(self.timestamp) + str(self.data)\n return sha256(data_str.encode('utf-8')).hexdigest()","def md5_of_bytes(data: bytes) -> bytes:\n return hashlib.md5(data).digest()","def compute_digest(path):\n hash = hashlib.sha512()\n for part in DiskCrawler.partial_reader(path, 4 * 1024 * 1024):\n hash.update(part)\n return hash.digest()","def md5sum_file(filepath):\n hasher = hashlib.md5()\n with open(filepath, 'rb') as infile:\n for chunk in util.chunk_reader(infile):\n hasher.update(chunk)\n return hasher.hexdigest()","def hex_md5_of_bytes(data: bytes) -> str:\n return hashlib.md5(data).hexdigest()","def hash_file_md5(file_path, binary=False, buffer_size=65536):\n return hash_file(file_path, hash_type=hashlib.md5, binary=binary, buffer_size=buffer_size)","def md5_sum(content):\r\n md5_hash = hashlib.md5(content).hexdigest()\r\n return md5_hash","def checksum(self, filepath) -> str:\n if os.path.exists(filepath):\n hash_md5 = md5()\n with open(filepath, \"rb\") as f:\n for chunk in iter(lambda: f.read(4096), b\"\"):\n hash_md5.update(chunk)\n return urlsafe_b64encode(hash_md5.digest()).decode('utf-8')\n\n return \"\"","def get_md5(self, line):\n m = hashlib.md5()\n m.update(str(line).encode('utf-8'))\n return m.hexdigest()","def dir_hash(cls, d):\r\n names = sorted(f for f in cls._iter_files(d) if not f.endswith('.pyc'))\r\n def stream_factory(name):\r\n return open(os.path.join(d, name), 'rb')\r\n return cls._compute_hash(names, stream_factory)","def compute(self):\n self.checksum = self.get_files_hashes_in_path()\n self.real_checksum = self.checksum\n # This appends the filename when checksum was made for a single file.\n # We need to get this when testing the consistency on the moment of\n # restore.\n if self.count == 1:\n self.checksum = self.real_checksum + os.path.basename(self.path)\n return self.checksum","def get_md5(self):\n self.md5sum = ''\n return self.md5sum","def get_checksum(path: Union[Path, str]) -> str:\n path = Path(path)\n if not (path.is_file() or path.is_dir()):\n msg.fail(f\"Can't get checksum for {path}: not a file or directory\", exits=1)\n if path.is_file():\n return hashlib.md5(Path(path).read_bytes()).hexdigest()\n else:\n # TODO: this is currently pretty slow\n dir_checksum = hashlib.md5()\n for sub_file in sorted(fp for fp in path.rglob(\"*\") if fp.is_file()):\n dir_checksum.update(sub_file.read_bytes())\n return dir_checksum.hexdigest()","def __hash_md5__(self, text):\n key = hashlib.md5()\n key.update(text.encode('utf-8'))\n return key.digest()","def _hash_dir(directory: Union[str, Path], md5: Hash) -> Hash:\n if not Path(directory).is_dir():\n raise ValueError(str(directory) + \" is not a valid directory\")\n for path in sorted(Path(directory).iterdir()):\n md5.update(path.name.encode())\n if path.is_file():\n md5 = _hash_file(path, md5)\n elif path.is_dir():\n md5 = _hash_dir(path, md5)\n return md5","def get_md5sum(host, fqpath):\n command = \"md5sum %s\" % fqpath\n rcode, rout, rerr = g.run(host, command)\n\n if rcode == 0:\n return rout.strip()\n\n g.log.error('md5sum failed: %s' % rerr)\n return None","def md5(file_path):\n hash_md5 = hashlib.md5()\n with open(file_path, 'rb') as fhandle:\n for chunk in iter(lambda: fhandle.read(4096), b''):\n hash_md5.update(chunk)\n return hash_md5.hexdigest()","def md5(file_path):\n hash_md5 = hashlib.md5()\n with open(file_path, 'rb') as fhandle:\n for chunk in iter(lambda: fhandle.read(4096), b''):\n hash_md5.update(chunk)\n return hash_md5.hexdigest()","def checksumFile(filename):\n return md5File(filename)","def _hash_data(data, alg=\"sha256\"):\n m = hashlib.new(alg)\n m.update(data)\n return m.hexdigest()","def compute_checksum(filename):\n cmd = 'md5sum ' + filename\n return pipe(cmd)","def calc_md5(code):\n md5 = hashlib.md5()\n md5.update(code)\n return md5.hexdigest()","def get_data_hash(args):\n pass","def get_partial_md5(self, nbytes):\n return md5file(abspath=self.abspath, nbytes=nbytes)","def hash(self):\r\n hash_list = []\r\n for image in self.images:\r\n hash_list.append(os.path.relpath(image.path))\r\n hash_list.append(image._image_data)\r\n\r\n for key, value in self.config.iteritems():\r\n hash_list.append(key)\r\n hash_list.append(value)\r\n\r\n if sys.version < '3':\r\n return hashlib.sha1(''.join(map(str, hash_list))).hexdigest()[:10]\r\n return hashlib.sha1(''.join(map(str, hash_list)).encode('utf-8')).hexdigest()[:10]","def __md5_hash(txt) -> str:\n\n return md5_crypt.hash(txt)","def calc_md5(string):\n\treturn md5(string).hexdigest()","def get_file_hash (fullpath) : \n\n # This bit was sourced from Stack Overflow via Google, specifically:\n # http://stackoverflow.com/questions/1131220/get-md5-hash-of-a-files-without-open-it-in-python\n\n md5 = hashlib.md5()\n with open(fullpath,'rb') as f: \n for chunk in iter(lambda: f.read(512*md5.block_size), ''): \n md5.update(chunk)\n # Hexdigest is the safe varchar(32) style output\n return md5.hexdigest()","def get_file_hash(file_path):\n with open(file_path, 'rb') as f:\n file_name = os.path.basename(file_path)\n to_hash = f.read() + file_name.encode('utf-8')\n new_hash = hashlib.md5(to_hash).hexdigest()\n return new_hash","def file_digest(path, algo=hashlib.md5):\n checksum = algo()\n with open(path, 'rb') as f:\n for chunk in iter(lambda: f.read(4096), b\"\"):\n checksum.update(chunk)\n return checksum.hexdigest()","def __hash__(self):\n return hash(self._full_path)","def _hash_cache_dir():\n import hashlib\n\n out = OrderedDict(python_version=python_version)\n\n try:\n import shapely\n out['shapely_version'] = shapely.__version__\n out['shapely_file'] = shapely.__file__\n except ImportError:\n pass\n try:\n import fiona\n out['fiona_version'] = fiona.__version__\n out['fiona_file'] = fiona.__file__\n except ImportError:\n pass\n try:\n import pandas\n out['pandas_version'] = pandas.__version__\n out['pandas_file'] = pandas.__file__\n except ImportError:\n pass\n try:\n import geopandas\n out['geopandas_version'] = geopandas.__version__\n out['geopandas_file'] = geopandas.__file__\n except ImportError:\n pass\n try:\n import osgeo\n out['osgeo_version'] = osgeo.__version__\n out['osgeo_file'] = osgeo.__file__\n except ImportError:\n pass\n try:\n import pyproj\n out['pyproj_version'] = pyproj.__version__\n out['pyproj_file'] = pyproj.__file__\n except ImportError:\n pass\n try:\n import salem\n out['salem_version'] = salem.__version__\n out['salem_file'] = salem.__file__\n except ImportError:\n pass\n\n # ok, now make a dummy str that we will hash\n strout = ''\n for k, v in out.items():\n strout += k + v\n strout = 'salem_hash_' + hashlib.md5(strout.encode()).hexdigest()\n dirout = os.path.join(cache_dir, 'cache', strout)\n return dirout","def generate_content_hash(source_path):\n\n sha256 = hashlib.sha256()\n\n if os.path.isdir(source_path):\n source_dir = source_path\n for source_file in list_files(source_dir):\n update_hash(sha256, source_dir, source_file)\n else:\n source_dir = os.path.dirname(source_path)\n source_file = source_path\n update_hash(sha256, source_dir, source_file)\n\n return sha256","def md5(filename: str) -> str:\n # using md5 for speed\n _hash = hashlib.md5()\n # open file for reading in binary mode\n with open(filename,'rb') as file:\n for block in iter(lambda: file.read(1024), b\"\"):\n _hash.update(block)\n return _hash.hexdigest()","def md5(val):\n return hashlib.md5(val).hexdigest()","def file_md5(file_path):\n log.debug(\"Calculating md5 for %s\", file_path)\n start_ms = time.time() * 10**3\n fp = open(file_path, 'rb')\n try:\n # returns tuple (md5_hex, md5_base64, size)\n md5, _, _ = boto.utils.compute_md5(fp)\n finally:\n fp.close()\n duration_ms = (time.time() * 10**3) - start_ms\n log.debug(\"Calculated hash %s for %s in %s ms\", md5, file_path, \n duration_ms)\n return md5","def hash(self):\n block = 1024 * 1024 * 4 # 4 MB.\n hasher = hashlib.sha256()\n\n with open(self.path, \"rb\") as f:\n while True:\n chunk = f.read(block)\n if not chunk:\n break\n hasher.update(hashlib.sha256(chunk).digest())\n\n digest = hasher.hexdigest()\n pdbox.debug(\"Hash for %s: %s\" % (self.path, digest))\n return digest","def count_md5hash_file(file_path):\n hash_md5 = hashlib.md5()\n with open(file_path, \"rb\") as f:\n for chunk in iter(lambda: f.read(4096), b\"\"):\n hash_md5.update(chunk)\n return hash_md5.hexdigest()","def fsum(fpath):\n import hashlib\n import codecs\n with codecs.open(fpath, \"r\", \"utf-8\") as filep:\n buff = filep.read()\n cksum = hashlib.md5(buff.encode(\"utf-8\"))\n return cksum.hexdigest()","def _update_hdf5_file(self, field_name, saveformat, data, timestep, t):\n assert saveformat == \"hdf5\"\n fullname, metadata = self._get_datafile_name(field_name, saveformat, timestep)\n\n # Create \"good enough\" hash. This is done to avoid data corruption when restarted from\n # different number of processes, different distribution or different function space\n local_hash = sha1()\n local_hash.update(str(data.function_space().mesh().num_cells()))\n local_hash.update(str(data.function_space().ufl_element()))\n local_hash.update(str(data.function_space().dim()))\n local_hash.update(str(MPI.size(mpi_comm_world())))\n\n # Global hash (same on all processes), 10 digits long\n global_hash = MPI.sum(mpi_comm_world(), int(local_hash.hexdigest(), 16))\n global_hash = str(int(global_hash%1e10)).zfill(10)\n\n #key = (field_name, saveformat)\n #datafile = self._datafile_cache.get(key)\n #if datafile is None:\n # datafile = HDF5File(mpi_comm_world(), fullname, 'w')\n # self._datafile_cache[key] = datafile\n\n # Open HDF5File\n if not os.path.isfile(fullname):\n datafile = HDF5File(mpi_comm_world(), fullname, 'w')\n else:\n datafile = HDF5File(mpi_comm_world(), fullname, 'a')\n\n # Write to hash-dataset if not yet done\n if not datafile.has_dataset(global_hash) or not datafile.has_dataset(global_hash+\"/\"+field_name):\n datafile.write(data, str(global_hash)+\"/\"+field_name)\n\n if not datafile.has_dataset(\"Mesh\"):\n datafile.write(data.function_space().mesh(), \"Mesh\")\n\n # Write vector to file\n # TODO: Link vector when function has been written to hash\n datafile.write(data.vector(), field_name+str(timestep)+\"/vector\")\n\n # HDF5File.close is broken in 1.4\n if dolfin_version() == \"1.4.0+\":\n datafile.close()\n del datafile\n # Link information about function space from hash-dataset\n hdf5_link(fullname, str(global_hash)+\"/\"+field_name+\"/x_cell_dofs\", field_name+str(timestep)+\"/x_cell_dofs\")\n hdf5_link(fullname, str(global_hash)+\"/\"+field_name+\"/cell_dofs\", field_name+str(timestep)+\"/cell_dofs\")\n hdf5_link(fullname, str(global_hash)+\"/\"+field_name+\"/cells\", field_name+str(timestep)+\"/cells\")\n\n return metadata","def md5hash(self) -> Optional[pulumi.Input[str]]:\n return pulumi.get(self, \"md5hash\")","def md5checksum(file_name):\n from hashlib import md5\n hash_md5 = md5()\n with open(file_name, \"rb\") as f:\n for chunk in iter(lambda: f.read(32768), b\"\"):\n hash_md5.update(chunk)\n return hash_md5.hexdigest()","def getHash(Data):\n\treturn getMD5ForIterable(getJSON(Data, sort_keys=True)) # #Note: JSON output has to be sorted by keys, for the output to be deterministic.","def get_md5_from_path(path):\n if not os.path.isdir(path):\n return _md5_of_file(path)\n\n # make sure that path looks like foo/bar, not foo/bar/ or foo/bar/./. .\n # This makes it easier to munge the outputs from os.walk().\n\n while path.endswith('/.'):\n path = path[:-2]\n\n if path[-1] == '/':\n path = path[:-1]\n\n def all_files():\n for dirname, dirs, files in os.walk(path):\n for f in files:\n yield dirname + '/' + f\n\n md5 = hashlib.md5()\n plen = len(path)\n\n try:\n # NOTE: this is not threadsafe. This will *probably* never come back\n # to bite us in the ass ...\n prevlocale = locale.getlocale(locale.LC_COLLATE)\n locale.setlocale(locale.LC_COLLATE, 'C')\n\n for f in sorted(all_files()):\n subhash = _md5_of_file(f).encode(\"utf-8\")\n md5.update(subhash) # this is the hex digest, like we want\n md5.update(' .'.encode(\"utf-8\")) # compat with command-line approach\n md5.update(f[plen:].encode(\"utf-8\"))\n md5.update('\\n'.encode(\"utf-8\"))\n finally:\n locale.setlocale(locale.LC_COLLATE, prevlocale)\n\n return md5.hexdigest()","def md5_hash_file(filepath: str) -> str:\n block_size = 64 * 1024\n md5 = hashlib.md5()\n with open(filepath, \"rb\") as file_handler:\n while True:\n data = file_handler.read(block_size)\n if not data:\n break\n md5.update(data)\n return md5.hexdigest()","def _asset_hash(path: str) -> str:\n full_path = THEME_PATH / \"static\" / path\n digest = hashlib.sha1(full_path.read_bytes()).hexdigest()\n\n return f\"_static/{path}?digest={digest}\"","def get_md5(text):\n return hashlib.md5(text).hexdigest()","def get_hash(path: Path) -> str:\n m = hashlib.sha256()\n m.update(path.read_bytes())\n return m.hexdigest()","def _hash_file_content(self, path):\n hasher = hashlib.sha1()\n with open(path, 'rb') as file:\n buffer = file.read(self.hash_block_size)\n while len(buffer) > 0:\n hasher.update(buffer)\n buffer = file.read(self.hash_block_size)\n return hasher.hexdigest()","def count_files_md5hash_indir(self, dir_path):\n for file_name in os.listdir(dir_path):\n file_path = \"{}/{}\".format(dir_path, file_name)\n self.md5hash.add(count_md5hash_file(file_path))","def md5(self):\n\t\tfrom utils import get_md5\n\t\t# from hashlib import md5\n\t\t# m = md5()\n\t\t# m.update(str(self.html))\n\t\t# return m.hexdigest()\n\t\treturn get_md5(str(self.html))","def secret_hash(data):\n\n passwords_hash = hashlib.md5(data.encode(\"UTF-8\")).hexdigest()\n \n return passwords_hash"],"string":"[\n \"def python_repo_hash_md5(root_dir: str, *, verbose: bool = False):\\n m = hashlib.md5()\\n for e in _collect_entries(root_dir, '.'):\\n if verbose:\\n log_info('Processing e', e)\\n m.update(\\n f\\\"path={e['path']}\\\\tisdir={e['isdir']}\\\\tsize={e['size']}\\\\tmode={e['mode']:03o}\\\\tmtime={e['mtime']}\\\\n\\\"\\n .encode('UTF-8'))\\n\\n return m.hexdigest()\",\n \"def calculate_md5_of_dir(self, verbose=0):\\n directory = self.cfg['sharing_path']\\n if verbose:\\n start = time.time()\\n md5Hash = hashlib.md5()\\n if not os.path.exists(directory):\\n self.stop(1, 'Error during calculate md5! Impossible to find \\\"{}\\\" in user folder'.format(directory))\\n\\n for root, dirs, files in os.walk(directory, followlinks=False):\\n for names in files:\\n filepath = os.path.join(root, names)\\n rel_path = self.relativize_path(filepath)\\n if rel_path in self.client_snapshot:\\n md5Hash.update(self.client_snapshot[rel_path][1])\\n md5Hash.update(hashlib.md5(filepath).hexdigest())\\n else:\\n hashed_file = self.hash_file(filepath)\\n if hashed_file:\\n md5Hash.update(hashed_file)\\n md5Hash.update(hashlib.md5(filepath).hexdigest())\\n else:\\n print \\\"can't hash file: \\\", filepath\\n\\n if verbose:\\n stop = time.time()\\n print stop - start\\n return md5Hash.hexdigest()\",\n \"def _md5sum(data):\\n hash = hashlib.md5()\\n hash.update(six.b(data))\\n hash_hex = hash.hexdigest()\\n return hash_hex\",\n \"def svn_fs_file_md5_checksum(*args):\\r\\n return _fs.svn_fs_file_md5_checksum(*args)\",\n \"def md5(self):\\n return md5file(self.abspath)\",\n \"def md5_hash(self) -> str:\\n\\n ordered_model_data = sort_dictionary(self.data, recursive=True)\\n\\n return md5(json.dumps(ordered_model_data).encode(\\\"utf-8\\\")).hexdigest()\",\n \"def data_checksum(self, node):\\n cmd = f\\\"find {RedpandaService.DATA_DIR} -type f -exec md5sum '{{}}' \\\\; -exec stat -c %s '{{}}' \\\\;\\\"\\n lines = node.account.ssh_output(cmd)\\n tokens = lines.split()\\n return {\\n tokens[ix + 1].decode(): (tokens[ix].decode(), int(tokens[ix + 2]))\\n for ix in range(0, len(tokens), 3)\\n }\",\n \"def checksum(path):\\n with open(path, 'r') as f:\\n return md5(f.read()).digest()\",\n \"def calc_file_md5(file_path):\\n hash_md5 = str()\\n method = hashlib.md5()\\n if not os.path.exists(file_path):\\n logger.error(\\\"File(%s) don not exist, can not calculation file hash\\\" % file_path)\\n return hash_md5\\n\\n with open(file_path, 'rb') as f:\\n for chunk in read_chunks(f, 1024 * 1024):\\n method.update(chunk)\\n return method.hexdigest()\",\n \"def calculate(d):\\r\\n\\r\\n # Set correct slashes for the OS\\r\\n if sys.platform == 'windows':\\r\\n slash = '\\\\\\\\'\\r\\n elif sys.platform == 'linux':\\r\\n slash = '/'\\r\\n else:\\r\\n print('#Error. Unknown platform.')\\r\\n return\\r\\n\\r\\n print('Files in the current directory and their md5-hashes:\\\\n')\\r\\n i = 0\\r\\n assert i == 0, '#Error. Variable i != 0.'\\r\\n\\r\\n for i in range(len(d[2])): # Go through the list of files\\r\\n full_path = d[0]+slash+d[2][i]\\r\\n print(full_path) # Get the list of files with full paths\\r\\n print(md5(full_path))\\r\\n size(full_path)\",\n \"def md5Hash(pathAndFilename, blockSize=8192):\\n hashcode = hashlib.md5()\\n with open(pathAndFilename, \\\"rb\\\" ) as f:\\n block = f.read(blockSize)\\n while len(block)>0:\\n hashcode.update(block)\\n block = f.read(blockSize)\\n return hashcode.hexdigest()\",\n \"def md5_hexdigest(data):\\n\\n if not (data and isinstance(data, six.text_type)):\\n raise Exception(\\\"invalid data to be hashed: %s\\\", repr(data))\\n\\n encoded_data = data.encode(\\\"utf-8\\\")\\n\\n if not new_md5:\\n m = md5.new() # nosec\\n else:\\n m = md5()\\n m.update(encoded_data)\\n\\n return m.hexdigest()\",\n \"def get_md5(data):\\n if hasattr(data, \\\"read\\\") and hasattr(data, 'seek'):\\n data.seek(0)\\n m = md5()\\n chunk = data.read(1024*1024) # 1Mb\\n f_size = 0\\n while(chunk):\\n f_size += len(chunk)\\n m.update(chunk)\\n chunk = data.read(1024*1024)\\n data.seek(0)\\n return m.hexdigest(), f_size\\n else: # normal str\\n m = md5()\\n f_size = len(data)\\n m.update(data)\\n return m.hexdigest(), f_size\",\n \"def md5_hash(file_path):\\n with open(file_path, 'rb') as fp:\\n return md5(fp.read()).hexdigest()\",\n \"def data_hash(self):\\n md5 = hashlib.md5()\\n # text header row\\n for column in self.columns:\\n md5.update(hxl.datatypes.normalise_space(column.header).encode('utf-8'))\\n # hashtag row\\n for column in self.columns:\\n md5.update(hxl.datatypes.normalise_space(column.display_tag).encode('utf-8'))\\n # data rows\\n for row in self:\\n for value in row:\\n md5.update(hxl.datatypes.normalise_space(value).encode('utf-8'))\\n return md5.hexdigest()\",\n \"def md5(dir):\\n\\n # ugly way to avoid circular imports\\n from . import settings\\n\\n files = [ \\n settings.DATA['nation']['file_name'],\\n settings.DATA['regions']['file_name'],\\n settings.DATA['provinces']['file_name'],\\n ]\\n\\n hash_md5 = hashlib.md5()\\n for f in files:\\n with open(dir+'/'+f, \\\"rb\\\") as f:\\n for chunk in iter(lambda: f.read(4096), b\\\"\\\"):\\n hash_md5.update(chunk)\\n \\n return hash_md5.hexdigest()\",\n \"def md5_checksum(file_path):\\n with open(file_path, 'rb') as fh:\\n m = hashlib.md5()\\n while True:\\n data = fh.read(8192)\\n if not data:\\n break\\n m.update(data)\\n return m.hexdigest()\",\n \"def MD5(self) -> _n_0_t_3[_n_0_t_9]:\",\n \"def aws_md5(data):\\n hasher = hashlib.new(\\\"md5\\\")\\n if hasattr(data, \\\"read\\\"):\\n data.seek(0)\\n while True:\\n chunk = data.read(8192)\\n if not chunk:\\n break\\n hasher.update(chunk)\\n data.seek(0)\\n else:\\n hasher.update(data)\\n return b64encode(hasher.digest()).decode(\\\"ascii\\\")\",\n \"def _md5_of_file(path):\\n md5 = hashlib.md5()\\n\\n with open(path, 'rb') as f:\\n for chunk in iter(lambda: f.read(4096), b''):\\n md5.update(chunk)\\n\\n return md5.hexdigest()\",\n \"def md5_sum_file(path):\\n with open(path, 'rb') as f:\\n m = hashlib.md5()\\n while True:\\n data = f.read(8192)\\n if not data:\\n break\\n m.update(data)\\n return m.hexdigest()\",\n \"def _get_local_md5(self, blocksize=2**20):\\n m = hashlib.md5()\\n with open(self.dst, \\\"rb\\\") as f:\\n buf = f.read(blocksize)\\n while buf:\\n m.update(buf)\\n buf = f.read(blocksize)\\n return m.hexdigest()\",\n \"def hash_file(path: str) -> str:\\n return _hash_file(path, hashlib.md5()).hexdigest()\",\n \"def sha256Sum(self, data):\\n data = str(data)\\n m = hashlib.sha256()\\n if os.path.isfile(data):\\n try:\\n f = file(data, 'rb')\\n except:\\n return 'ERROR: unable to open %s' % data\\n while True:\\n d = f.read(8096)\\n if not d:\\n break\\n m.update(d)\\n f.close()\\n # Otherwise it could be either 1) a directory 2) miscellaneous data (like json)\\n else:\\n m.update(data)\\n return m.hexdigest()\",\n \"def static_file_hash(filepath):\\n hasher = hashlib.md5() # nosec: B303\\n\\n with contextlib.closing(open(filepath, 'rb')) as file:\\n hasher.update(file.read())\\n return hasher.hexdigest()\",\n \"def hash_directory(path):\\n if not os.path.isdir(path):\\n raise ValueError(\\n \\\"The given path `{}` is not a directory.\\\".format(path))\\n\\n md5 = hashlib.md5()\\n\\n for root, _, files in os.walk(path):\\n for fil in files:\\n md5.update(hash_file(os.path.join(root, fil)).encode(\\\"utf-8\\\"))\\n\\n return \\\"{}\\\".format(md5.hexdigest())\",\n \"def local_md5(filepath, blocksize=65536):\\n hasher = hashlib.md5()\\n with open(filepath, 'rb') as source:\\n buf = source.read(blocksize)\\n while len(buf) > 0:\\n hasher.update(buf)\\n buf = source.read(blocksize)\\n return hasher.hexdigest()\",\n \"def GetFileMd5(file_path):\\n return binascii.hexlify(GetFileHashes(file_path, do_md5=True)['md5'])\",\n \"def md5hash(self) -> pulumi.Output[str]:\\n return pulumi.get(self, \\\"md5hash\\\")\",\n \"def md5(path):\\n with open(path, 'rb') as f:\\n md5hash = hashlib.md5()\\n for chunk in iter(lambda: f.read(4096), b''):\\n md5hash.update(chunk)\\n return md5hash.hexdigest()\",\n \"def calculate_md5sum_of_a_file(context, file_name, file_path):\\n command = \\\"md5sum \\\" + file_path + \\\"/\\\" + file_name + \\\" | awk {'print $1'}\\\"\\n return context.cme_session.send_ssh_command(command=command)\",\n \"def get_all_hashes(data_dir):\\n file_hashes = {}\\n for root, dirs, files in os.walk(data_dir):\\n for file_name in files:\\n file_path = os.path.join(root, file_name)\\n file_hashes[file_path] = generate_file_md5(file_path)\\n return file_hashes\",\n \"def get_data_dependency_hash(cls, data_dir, dataset_class):\\n\\n file_hashes = []\\n\\n for file in sorted(dataset_class.files):\\n\\n if not os.path.isfile('%sraw/%s' % (data_dir, file)):\\n raise IOError('The following file that was referenced in your Dataset class does not exist: %s' % file)\\n\\n tar_hash = MantraHashed.get_256_hash_from_file('%sraw/%s' % (data_dir, file))\\n file_hashes.append(tar_hash)\\n\\n return MantraHashed.get_256_hash_from_string(''.join(file_hashes))\",\n \"def CalcMD5(filepath):\\n with open(filepath,'rb') as f:\\n md5obj = hashlib.md5()\\n md5obj.update(f.read())\\n return md5obj.hexdigest()\",\n \"def _md5(input):\\n m = hashlib.md5()\\n m.update(input)\\n return m.hexdigest()\",\n \"def file_field_md5(source_file, blocksize=65536):\\n hasher = hashlib.md5()\\n if source_file.closed:\\n source_file.open('rb')\\n buf = source_file.read(blocksize)\\n while len(buf) > 0:\\n hasher.update(buf)\\n buf = source_file.read(blocksize)\\n source_file.seek(0)\\n return hasher.hexdigest()\",\n \"def get_md5(location: str, ignore_hidden_files: bool=True) -> Optional[str]:\\n if not os.path.exists(location):\\n return None\\n if os.path.isfile(location):\\n with open(location, \\\"rb\\\") as file:\\n content = file.read()\\n return hashlib.md5(content).hexdigest()\\n else:\\n return dirhash(location, \\\"md5\\\", ignore_hidden=ignore_hidden_files)\",\n \"def checksum(data=b\\\"\\\"):\\n hasher = get_hasher(DEFAULT_CHECKSUM_ALGO)\\n hasher.update(data)\\n return hasher\",\n \"def hash_file(path):\\n if not os.path.isfile(path):\\n raise ValueError(\\\"The given path `{}` is not a file.\\\".format(path))\\n\\n md5 = hashlib.md5()\\n\\n with open(path, 'rb') as file_:\\n while True:\\n data = file_.read(65536)\\n if not data:\\n break\\n md5.update(data)\\n\\n return \\\"{}\\\".format(md5.hexdigest())\",\n \"def md5(file_path):\\r\\n\\r\\n hasher = hashlib.md5()\\r\\n with Profiler():\\r\\n with open(file_path, 'rb') as f:\\r\\n while True:\\r\\n buf = f.read(BLOCKSIZE)\\r\\n if not buf:\\r\\n break\\r\\n while len(buf) > 0:\\r\\n hasher.update(buf)\\r\\n buf = f.read(BLOCKSIZE)\\r\\n md5_hash = (hasher.hexdigest()).upper()\\r\\n return md5_hash\",\n \"def md5(self):\\n if self.source_file is None:\\n return None\\n if self._md5 is None:\\n self._md5 = file_field_md5(self.source_file)\\n # try: # Locally stored file\\n # self._md5 = local_md5(self.source_file.path)\\n # except NotImplementedError: # AWS S3 storage\\n # self._md5 = s3_md5(self.source_file.file.key)\\n return self._md5\",\n \"def generate_hash(self):\\r\\n\\r\\n hash_list = []\\r\\n for root, dirs, files in os.walk(self.options['source']):\\r\\n for f in sorted([f for f in files if not f.startswith('.')]):\\r\\n hash_list.append(os.path.join(root, f))\\r\\n hash_list.append(str(os.path.getmtime(os.path.join(root, f))))\\r\\n hash_list = ''.join(hash_list)\\r\\n\\r\\n if sys.version < '3':\\r\\n return hashlib.sha1(hash_list).hexdigest()\\r\\n return hashlib.sha1(hash_list.encode('utf-8')).hexdigest()\",\n \"def digest(dirname, glob=None):\\n md5 = hashlib.md5()\\n if glob is None:\\n fnames = [fname for _, fname in list_files(Path(dirname))]\\n for fname in sorted(fnames):\\n fname = os.path.join(dirname, fname)\\n md5.update(open(fname, 'rb').read())\\n else:\\n fnames = Path(dirname).glob(glob)\\n for fname in sorted(fnames):\\n md5.update(fname.open('rb').read())\\n return md5.hexdigest()\",\n \"def calc_file_hash(filepath):\\n with open(filepath, 'rb') as f:\\n return md5(f.read()).hexdigest()\",\n \"def md5sum(file_name):\\n f = open(file_name, mode='rb')\\n h = hashlib.md5()\\n h.update(f.read())\\n return h.hexdigest()\",\n \"def get_contents_hash(self):\\n md5 = hashlib.md5()\\n with closing(self.open()) as handle:\\n for chunk in handle.chunks():\\n md5.update(chunk)\\n return md5.hexdigest()\",\n \"def _hash_file_or_dir(path: str, md5: Hash) -> Hash:\\n if isinstance(path, str) and path.lower().startswith(\\\"file://\\\"):\\n path = unquote(urlparse(path).path)\\n md5.update(path.encode())\\n if Path(path).is_dir():\\n md5 = _hash_dir(path, md5)\\n elif Path(path).is_file():\\n md5 = _hash_file(path, md5)\\n return md5\",\n \"def ondisk_digest(self):\\n with open(self.rename_phase_src) as f:\\n return hasher(f.read()).hexdigest()\",\n \"def _calculate_hash(self) -> str:\\n data_str = str(self.version) + str(self.index) + self.pre_hash + str(self.timestamp) + str(self.data)\\n return sha256(data_str.encode('utf-8')).hexdigest()\",\n \"def md5_of_bytes(data: bytes) -> bytes:\\n return hashlib.md5(data).digest()\",\n \"def compute_digest(path):\\n hash = hashlib.sha512()\\n for part in DiskCrawler.partial_reader(path, 4 * 1024 * 1024):\\n hash.update(part)\\n return hash.digest()\",\n \"def md5sum_file(filepath):\\n hasher = hashlib.md5()\\n with open(filepath, 'rb') as infile:\\n for chunk in util.chunk_reader(infile):\\n hasher.update(chunk)\\n return hasher.hexdigest()\",\n \"def hex_md5_of_bytes(data: bytes) -> str:\\n return hashlib.md5(data).hexdigest()\",\n \"def hash_file_md5(file_path, binary=False, buffer_size=65536):\\n return hash_file(file_path, hash_type=hashlib.md5, binary=binary, buffer_size=buffer_size)\",\n \"def md5_sum(content):\\r\\n md5_hash = hashlib.md5(content).hexdigest()\\r\\n return md5_hash\",\n \"def checksum(self, filepath) -> str:\\n if os.path.exists(filepath):\\n hash_md5 = md5()\\n with open(filepath, \\\"rb\\\") as f:\\n for chunk in iter(lambda: f.read(4096), b\\\"\\\"):\\n hash_md5.update(chunk)\\n return urlsafe_b64encode(hash_md5.digest()).decode('utf-8')\\n\\n return \\\"\\\"\",\n \"def get_md5(self, line):\\n m = hashlib.md5()\\n m.update(str(line).encode('utf-8'))\\n return m.hexdigest()\",\n \"def dir_hash(cls, d):\\r\\n names = sorted(f for f in cls._iter_files(d) if not f.endswith('.pyc'))\\r\\n def stream_factory(name):\\r\\n return open(os.path.join(d, name), 'rb')\\r\\n return cls._compute_hash(names, stream_factory)\",\n \"def compute(self):\\n self.checksum = self.get_files_hashes_in_path()\\n self.real_checksum = self.checksum\\n # This appends the filename when checksum was made for a single file.\\n # We need to get this when testing the consistency on the moment of\\n # restore.\\n if self.count == 1:\\n self.checksum = self.real_checksum + os.path.basename(self.path)\\n return self.checksum\",\n \"def get_md5(self):\\n self.md5sum = ''\\n return self.md5sum\",\n \"def get_checksum(path: Union[Path, str]) -> str:\\n path = Path(path)\\n if not (path.is_file() or path.is_dir()):\\n msg.fail(f\\\"Can't get checksum for {path}: not a file or directory\\\", exits=1)\\n if path.is_file():\\n return hashlib.md5(Path(path).read_bytes()).hexdigest()\\n else:\\n # TODO: this is currently pretty slow\\n dir_checksum = hashlib.md5()\\n for sub_file in sorted(fp for fp in path.rglob(\\\"*\\\") if fp.is_file()):\\n dir_checksum.update(sub_file.read_bytes())\\n return dir_checksum.hexdigest()\",\n \"def __hash_md5__(self, text):\\n key = hashlib.md5()\\n key.update(text.encode('utf-8'))\\n return key.digest()\",\n \"def _hash_dir(directory: Union[str, Path], md5: Hash) -> Hash:\\n if not Path(directory).is_dir():\\n raise ValueError(str(directory) + \\\" is not a valid directory\\\")\\n for path in sorted(Path(directory).iterdir()):\\n md5.update(path.name.encode())\\n if path.is_file():\\n md5 = _hash_file(path, md5)\\n elif path.is_dir():\\n md5 = _hash_dir(path, md5)\\n return md5\",\n \"def get_md5sum(host, fqpath):\\n command = \\\"md5sum %s\\\" % fqpath\\n rcode, rout, rerr = g.run(host, command)\\n\\n if rcode == 0:\\n return rout.strip()\\n\\n g.log.error('md5sum failed: %s' % rerr)\\n return None\",\n \"def md5(file_path):\\n hash_md5 = hashlib.md5()\\n with open(file_path, 'rb') as fhandle:\\n for chunk in iter(lambda: fhandle.read(4096), b''):\\n hash_md5.update(chunk)\\n return hash_md5.hexdigest()\",\n \"def md5(file_path):\\n hash_md5 = hashlib.md5()\\n with open(file_path, 'rb') as fhandle:\\n for chunk in iter(lambda: fhandle.read(4096), b''):\\n hash_md5.update(chunk)\\n return hash_md5.hexdigest()\",\n \"def checksumFile(filename):\\n return md5File(filename)\",\n \"def _hash_data(data, alg=\\\"sha256\\\"):\\n m = hashlib.new(alg)\\n m.update(data)\\n return m.hexdigest()\",\n \"def compute_checksum(filename):\\n cmd = 'md5sum ' + filename\\n return pipe(cmd)\",\n \"def calc_md5(code):\\n md5 = hashlib.md5()\\n md5.update(code)\\n return md5.hexdigest()\",\n \"def get_data_hash(args):\\n pass\",\n \"def get_partial_md5(self, nbytes):\\n return md5file(abspath=self.abspath, nbytes=nbytes)\",\n \"def hash(self):\\r\\n hash_list = []\\r\\n for image in self.images:\\r\\n hash_list.append(os.path.relpath(image.path))\\r\\n hash_list.append(image._image_data)\\r\\n\\r\\n for key, value in self.config.iteritems():\\r\\n hash_list.append(key)\\r\\n hash_list.append(value)\\r\\n\\r\\n if sys.version < '3':\\r\\n return hashlib.sha1(''.join(map(str, hash_list))).hexdigest()[:10]\\r\\n return hashlib.sha1(''.join(map(str, hash_list)).encode('utf-8')).hexdigest()[:10]\",\n \"def __md5_hash(txt) -> str:\\n\\n return md5_crypt.hash(txt)\",\n \"def calc_md5(string):\\n\\treturn md5(string).hexdigest()\",\n \"def get_file_hash (fullpath) : \\n\\n # This bit was sourced from Stack Overflow via Google, specifically:\\n # http://stackoverflow.com/questions/1131220/get-md5-hash-of-a-files-without-open-it-in-python\\n\\n md5 = hashlib.md5()\\n with open(fullpath,'rb') as f: \\n for chunk in iter(lambda: f.read(512*md5.block_size), ''): \\n md5.update(chunk)\\n # Hexdigest is the safe varchar(32) style output\\n return md5.hexdigest()\",\n \"def get_file_hash(file_path):\\n with open(file_path, 'rb') as f:\\n file_name = os.path.basename(file_path)\\n to_hash = f.read() + file_name.encode('utf-8')\\n new_hash = hashlib.md5(to_hash).hexdigest()\\n return new_hash\",\n \"def file_digest(path, algo=hashlib.md5):\\n checksum = algo()\\n with open(path, 'rb') as f:\\n for chunk in iter(lambda: f.read(4096), b\\\"\\\"):\\n checksum.update(chunk)\\n return checksum.hexdigest()\",\n \"def __hash__(self):\\n return hash(self._full_path)\",\n \"def _hash_cache_dir():\\n import hashlib\\n\\n out = OrderedDict(python_version=python_version)\\n\\n try:\\n import shapely\\n out['shapely_version'] = shapely.__version__\\n out['shapely_file'] = shapely.__file__\\n except ImportError:\\n pass\\n try:\\n import fiona\\n out['fiona_version'] = fiona.__version__\\n out['fiona_file'] = fiona.__file__\\n except ImportError:\\n pass\\n try:\\n import pandas\\n out['pandas_version'] = pandas.__version__\\n out['pandas_file'] = pandas.__file__\\n except ImportError:\\n pass\\n try:\\n import geopandas\\n out['geopandas_version'] = geopandas.__version__\\n out['geopandas_file'] = geopandas.__file__\\n except ImportError:\\n pass\\n try:\\n import osgeo\\n out['osgeo_version'] = osgeo.__version__\\n out['osgeo_file'] = osgeo.__file__\\n except ImportError:\\n pass\\n try:\\n import pyproj\\n out['pyproj_version'] = pyproj.__version__\\n out['pyproj_file'] = pyproj.__file__\\n except ImportError:\\n pass\\n try:\\n import salem\\n out['salem_version'] = salem.__version__\\n out['salem_file'] = salem.__file__\\n except ImportError:\\n pass\\n\\n # ok, now make a dummy str that we will hash\\n strout = ''\\n for k, v in out.items():\\n strout += k + v\\n strout = 'salem_hash_' + hashlib.md5(strout.encode()).hexdigest()\\n dirout = os.path.join(cache_dir, 'cache', strout)\\n return dirout\",\n \"def generate_content_hash(source_path):\\n\\n sha256 = hashlib.sha256()\\n\\n if os.path.isdir(source_path):\\n source_dir = source_path\\n for source_file in list_files(source_dir):\\n update_hash(sha256, source_dir, source_file)\\n else:\\n source_dir = os.path.dirname(source_path)\\n source_file = source_path\\n update_hash(sha256, source_dir, source_file)\\n\\n return sha256\",\n \"def md5(filename: str) -> str:\\n # using md5 for speed\\n _hash = hashlib.md5()\\n # open file for reading in binary mode\\n with open(filename,'rb') as file:\\n for block in iter(lambda: file.read(1024), b\\\"\\\"):\\n _hash.update(block)\\n return _hash.hexdigest()\",\n \"def md5(val):\\n return hashlib.md5(val).hexdigest()\",\n \"def file_md5(file_path):\\n log.debug(\\\"Calculating md5 for %s\\\", file_path)\\n start_ms = time.time() * 10**3\\n fp = open(file_path, 'rb')\\n try:\\n # returns tuple (md5_hex, md5_base64, size)\\n md5, _, _ = boto.utils.compute_md5(fp)\\n finally:\\n fp.close()\\n duration_ms = (time.time() * 10**3) - start_ms\\n log.debug(\\\"Calculated hash %s for %s in %s ms\\\", md5, file_path, \\n duration_ms)\\n return md5\",\n \"def hash(self):\\n block = 1024 * 1024 * 4 # 4 MB.\\n hasher = hashlib.sha256()\\n\\n with open(self.path, \\\"rb\\\") as f:\\n while True:\\n chunk = f.read(block)\\n if not chunk:\\n break\\n hasher.update(hashlib.sha256(chunk).digest())\\n\\n digest = hasher.hexdigest()\\n pdbox.debug(\\\"Hash for %s: %s\\\" % (self.path, digest))\\n return digest\",\n \"def count_md5hash_file(file_path):\\n hash_md5 = hashlib.md5()\\n with open(file_path, \\\"rb\\\") as f:\\n for chunk in iter(lambda: f.read(4096), b\\\"\\\"):\\n hash_md5.update(chunk)\\n return hash_md5.hexdigest()\",\n \"def fsum(fpath):\\n import hashlib\\n import codecs\\n with codecs.open(fpath, \\\"r\\\", \\\"utf-8\\\") as filep:\\n buff = filep.read()\\n cksum = hashlib.md5(buff.encode(\\\"utf-8\\\"))\\n return cksum.hexdigest()\",\n \"def _update_hdf5_file(self, field_name, saveformat, data, timestep, t):\\n assert saveformat == \\\"hdf5\\\"\\n fullname, metadata = self._get_datafile_name(field_name, saveformat, timestep)\\n\\n # Create \\\"good enough\\\" hash. This is done to avoid data corruption when restarted from\\n # different number of processes, different distribution or different function space\\n local_hash = sha1()\\n local_hash.update(str(data.function_space().mesh().num_cells()))\\n local_hash.update(str(data.function_space().ufl_element()))\\n local_hash.update(str(data.function_space().dim()))\\n local_hash.update(str(MPI.size(mpi_comm_world())))\\n\\n # Global hash (same on all processes), 10 digits long\\n global_hash = MPI.sum(mpi_comm_world(), int(local_hash.hexdigest(), 16))\\n global_hash = str(int(global_hash%1e10)).zfill(10)\\n\\n #key = (field_name, saveformat)\\n #datafile = self._datafile_cache.get(key)\\n #if datafile is None:\\n # datafile = HDF5File(mpi_comm_world(), fullname, 'w')\\n # self._datafile_cache[key] = datafile\\n\\n # Open HDF5File\\n if not os.path.isfile(fullname):\\n datafile = HDF5File(mpi_comm_world(), fullname, 'w')\\n else:\\n datafile = HDF5File(mpi_comm_world(), fullname, 'a')\\n\\n # Write to hash-dataset if not yet done\\n if not datafile.has_dataset(global_hash) or not datafile.has_dataset(global_hash+\\\"/\\\"+field_name):\\n datafile.write(data, str(global_hash)+\\\"/\\\"+field_name)\\n\\n if not datafile.has_dataset(\\\"Mesh\\\"):\\n datafile.write(data.function_space().mesh(), \\\"Mesh\\\")\\n\\n # Write vector to file\\n # TODO: Link vector when function has been written to hash\\n datafile.write(data.vector(), field_name+str(timestep)+\\\"/vector\\\")\\n\\n # HDF5File.close is broken in 1.4\\n if dolfin_version() == \\\"1.4.0+\\\":\\n datafile.close()\\n del datafile\\n # Link information about function space from hash-dataset\\n hdf5_link(fullname, str(global_hash)+\\\"/\\\"+field_name+\\\"/x_cell_dofs\\\", field_name+str(timestep)+\\\"/x_cell_dofs\\\")\\n hdf5_link(fullname, str(global_hash)+\\\"/\\\"+field_name+\\\"/cell_dofs\\\", field_name+str(timestep)+\\\"/cell_dofs\\\")\\n hdf5_link(fullname, str(global_hash)+\\\"/\\\"+field_name+\\\"/cells\\\", field_name+str(timestep)+\\\"/cells\\\")\\n\\n return metadata\",\n \"def md5hash(self) -> Optional[pulumi.Input[str]]:\\n return pulumi.get(self, \\\"md5hash\\\")\",\n \"def md5checksum(file_name):\\n from hashlib import md5\\n hash_md5 = md5()\\n with open(file_name, \\\"rb\\\") as f:\\n for chunk in iter(lambda: f.read(32768), b\\\"\\\"):\\n hash_md5.update(chunk)\\n return hash_md5.hexdigest()\",\n \"def getHash(Data):\\n\\treturn getMD5ForIterable(getJSON(Data, sort_keys=True)) # #Note: JSON output has to be sorted by keys, for the output to be deterministic.\",\n \"def get_md5_from_path(path):\\n if not os.path.isdir(path):\\n return _md5_of_file(path)\\n\\n # make sure that path looks like foo/bar, not foo/bar/ or foo/bar/./. .\\n # This makes it easier to munge the outputs from os.walk().\\n\\n while path.endswith('/.'):\\n path = path[:-2]\\n\\n if path[-1] == '/':\\n path = path[:-1]\\n\\n def all_files():\\n for dirname, dirs, files in os.walk(path):\\n for f in files:\\n yield dirname + '/' + f\\n\\n md5 = hashlib.md5()\\n plen = len(path)\\n\\n try:\\n # NOTE: this is not threadsafe. This will *probably* never come back\\n # to bite us in the ass ...\\n prevlocale = locale.getlocale(locale.LC_COLLATE)\\n locale.setlocale(locale.LC_COLLATE, 'C')\\n\\n for f in sorted(all_files()):\\n subhash = _md5_of_file(f).encode(\\\"utf-8\\\")\\n md5.update(subhash) # this is the hex digest, like we want\\n md5.update(' .'.encode(\\\"utf-8\\\")) # compat with command-line approach\\n md5.update(f[plen:].encode(\\\"utf-8\\\"))\\n md5.update('\\\\n'.encode(\\\"utf-8\\\"))\\n finally:\\n locale.setlocale(locale.LC_COLLATE, prevlocale)\\n\\n return md5.hexdigest()\",\n \"def md5_hash_file(filepath: str) -> str:\\n block_size = 64 * 1024\\n md5 = hashlib.md5()\\n with open(filepath, \\\"rb\\\") as file_handler:\\n while True:\\n data = file_handler.read(block_size)\\n if not data:\\n break\\n md5.update(data)\\n return md5.hexdigest()\",\n \"def _asset_hash(path: str) -> str:\\n full_path = THEME_PATH / \\\"static\\\" / path\\n digest = hashlib.sha1(full_path.read_bytes()).hexdigest()\\n\\n return f\\\"_static/{path}?digest={digest}\\\"\",\n \"def get_md5(text):\\n return hashlib.md5(text).hexdigest()\",\n \"def get_hash(path: Path) -> str:\\n m = hashlib.sha256()\\n m.update(path.read_bytes())\\n return m.hexdigest()\",\n \"def _hash_file_content(self, path):\\n hasher = hashlib.sha1()\\n with open(path, 'rb') as file:\\n buffer = file.read(self.hash_block_size)\\n while len(buffer) > 0:\\n hasher.update(buffer)\\n buffer = file.read(self.hash_block_size)\\n return hasher.hexdigest()\",\n \"def count_files_md5hash_indir(self, dir_path):\\n for file_name in os.listdir(dir_path):\\n file_path = \\\"{}/{}\\\".format(dir_path, file_name)\\n self.md5hash.add(count_md5hash_file(file_path))\",\n \"def md5(self):\\n\\t\\tfrom utils import get_md5\\n\\t\\t# from hashlib import md5\\n\\t\\t# m = md5()\\n\\t\\t# m.update(str(self.html))\\n\\t\\t# return m.hexdigest()\\n\\t\\treturn get_md5(str(self.html))\",\n \"def secret_hash(data):\\n\\n passwords_hash = hashlib.md5(data.encode(\\\"UTF-8\\\")).hexdigest()\\n \\n return passwords_hash\"\n]","isConversational":false},"negative_scores":{"kind":"list like","value":["0.6850441","0.6710063","0.6619043","0.654432","0.6535877","0.6533921","0.644331","0.6342902","0.6323048","0.6295428","0.6286576","0.62413317","0.6216765","0.6169943","0.61495596","0.6140576","0.61181694","0.6096366","0.60879916","0.6078742","0.60649794","0.6051122","0.60456747","0.6030513","0.60283333","0.6022939","0.60052115","0.599215","0.5989495","0.5983932","0.59552073","0.5943392","0.5936492","0.59340614","0.5915724","0.591462","0.59125406","0.59102744","0.59073514","0.589981","0.5887095","0.5863508","0.5849253","0.584615","0.58444935","0.58355904","0.58284223","0.5819996","0.5819856","0.5818068","0.5802936","0.5790066","0.5785358","0.57803065","0.5768894","0.57641226","0.5761271","0.576096","0.5758278","0.5756527","0.57490486","0.57457125","0.57331944","0.5728951","0.5725121","0.5725121","0.5722926","0.571258","0.5709265","0.57031626","0.5687729","0.5682561","0.5666608","0.566224","0.5642101","0.5639046","0.56368715","0.562344","0.56228036","0.5616493","0.56140596","0.56105655","0.5600838","0.5594477","0.5592449","0.5590957","0.5589436","0.5570709","0.5565941","0.55629295","0.55607605","0.5560471","0.5553415","0.5553116","0.5550647","0.55499953","0.55484253","0.5543358","0.55166036","0.5515596"],"string":"[\n \"0.6850441\",\n \"0.6710063\",\n \"0.6619043\",\n \"0.654432\",\n \"0.6535877\",\n \"0.6533921\",\n \"0.644331\",\n \"0.6342902\",\n \"0.6323048\",\n \"0.6295428\",\n \"0.6286576\",\n \"0.62413317\",\n \"0.6216765\",\n \"0.6169943\",\n \"0.61495596\",\n \"0.6140576\",\n \"0.61181694\",\n \"0.6096366\",\n \"0.60879916\",\n \"0.6078742\",\n \"0.60649794\",\n \"0.6051122\",\n \"0.60456747\",\n \"0.6030513\",\n \"0.60283333\",\n \"0.6022939\",\n \"0.60052115\",\n \"0.599215\",\n \"0.5989495\",\n \"0.5983932\",\n \"0.59552073\",\n \"0.5943392\",\n \"0.5936492\",\n \"0.59340614\",\n \"0.5915724\",\n \"0.591462\",\n \"0.59125406\",\n \"0.59102744\",\n \"0.59073514\",\n \"0.589981\",\n \"0.5887095\",\n \"0.5863508\",\n \"0.5849253\",\n \"0.584615\",\n \"0.58444935\",\n \"0.58355904\",\n \"0.58284223\",\n \"0.5819996\",\n \"0.5819856\",\n \"0.5818068\",\n \"0.5802936\",\n \"0.5790066\",\n \"0.5785358\",\n \"0.57803065\",\n \"0.5768894\",\n \"0.57641226\",\n \"0.5761271\",\n \"0.576096\",\n \"0.5758278\",\n \"0.5756527\",\n \"0.57490486\",\n \"0.57457125\",\n \"0.57331944\",\n \"0.5728951\",\n \"0.5725121\",\n \"0.5725121\",\n \"0.5722926\",\n \"0.571258\",\n \"0.5709265\",\n \"0.57031626\",\n \"0.5687729\",\n \"0.5682561\",\n \"0.5666608\",\n \"0.566224\",\n \"0.5642101\",\n \"0.5639046\",\n \"0.56368715\",\n \"0.562344\",\n \"0.56228036\",\n \"0.5616493\",\n \"0.56140596\",\n \"0.56105655\",\n \"0.5600838\",\n \"0.5594477\",\n \"0.5592449\",\n \"0.5590957\",\n \"0.5589436\",\n \"0.5570709\",\n \"0.5565941\",\n \"0.55629295\",\n \"0.55607605\",\n \"0.5560471\",\n \"0.5553415\",\n \"0.5553116\",\n \"0.5550647\",\n \"0.55499953\",\n \"0.55484253\",\n \"0.5543358\",\n \"0.55166036\",\n \"0.5515596\"\n]","isConversational":false},"document_score":{"kind":"string","value":"0.6991578"},"document_rank":{"kind":"string","value":"0"}}},{"rowIdx":23787,"cells":{"query":{"kind":"string","value":"Display information about pet"},"document":{"kind":"string","value":"def describe_pets(animal_type, pet_name):\n print(f\"\\nI have a {animal_type}.\")\n print(f\"My {animal_type}'s name is {pet_name.title()}\")"},"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 show_pet(self):\n pet = self.pet_factory.get_pet()\n print \"We have a lovely {}\".format(pet)\n print \"It says {}\".format(pet.speak())\n print \"We also have {}\".format(self.pet_factory.get_food())","def show_pet(self):\n pet = self.pet_factory.get_pet()\n\n print(\"this is a lovely \", pet)\n print(\"It says \", pet.speak())\n print(\"It eats \", self.pet_factory.get_food())","def describe_pet(animal_type, pet_name):\n print(\"\\nI have a \" + animal_type + \".\")\n print(\"My \" + animal_type + \"'s name is \" + pet_name.title() + \".\")","def describe_pet(animal_type, pet_name):\n print(\"\\nI have a \" + animal_type + \".\")\n print(\"My \" + animal_type + \"'s name is \" + pet_name.title() + \".\")","def describe_pet(animal_type, pet_name):\n print(\"\\nI have a \" + animal_type + \".\")\n print(\"My \" + animal_type + \"'s name is \" + pet_name.title() + \".\")","def describe_pet(animal_type, pet_name):\n print(\"\\nI have a \" + animal_type + \".\")\n print(\"My \" + animal_type + \"'s name is \" + pet_name.title() + \".\")","def describe_pet(animal_type, pet_name):\r\n print(f\"\\nI have a {animal_type}.\")\r\n print(f\"My {animal_type}'s name is {pet_name.title()}.\")","def describe_pet(animal_type, pet_name):\n print(f\"\\nI have a {animal_type}.\")\n print(f\"My {animal_type}'s name is {pet_name.title()}.\")","def describe_pet(animal_type, pet_name):\n print(f\"\\nI have a {animal_type}.\")\n print(f\"My {animal_type}'s name is {pet_name.title()}.\")","def describe_pet(animal_type='dog',pet_name):\n\tprint(\"\\nI have a \" + animal_type + \".\")\n\tprint(\"My \" + animal_type + \"'s name is \" + pet_name.title() + \".\")","def describe_pet(pet_name, animal_type='tiger'):\n\tprint(f\"\\nI have a {animal_type}.\")\n\tprint(f\"My {animal_type}'s name is {pet_name}.\")","def describe_pet(pet_name, animal_type='dog'):\n print(\"\\nI have a \" + animal_type + \".\")\n print(\"My \" + animal_type + \"'s name is \" + pet_name.title() + \".\")","def describe_pet(animal, name):\r\n print(\"\\nI have a \" + animal + \".\")\r\n print(\"Its name is \" + name + \".\")","def describe_pet(pet_name, animal_type='dog'):\n print(\"\\nI have a \" + animal_type + \".\")\n print(\"My \" + animal_type + \"'s name is \" + pet_name.title() + \".\")\n \n # A dog named Willie.","def describe_pet(animal_type,pet_name):\n print(\"\\nI have a \"+animal_type+\".\")\n print(\"\\tMy \"+animal_type+\"'s name is \"+pet_name+\".\")","def describe_pet(name, animal='dog'):\r\n print(\"\\nI have a \" + animal + \".\")\r\n print(\"Its name is \" + name + \".\")","def describe_pet(pet_name, animal_type='dog'):\n print(f\"\\nI have a(n) {animal_type}.\")\n print(f\"My {animal_type}'s name is {pet_name.title()}.\")","def describe_pet(pet_name, animal_type = 'dog'):\r\n print(f\"\\nI have a {animal_type}.\")\r\n print(f\"My {animal_type}'s name is {pet_name.title()}.\")","def describe_pet(pet_name,animal_type = 'dog'):\n\tprint(\"I have a \" + animal_type + \".\")\n\tprint(\"My \" + animal_type + \"'s name is \" + pet_name.title() + \".\\n\")","def describe_pets(animal_type,name):\n print(\"I have a \" + animal_type + \".\")\n print(\"My \" + animal_type + \"'s name is \" + name.title())","def show_pet(pet_id):\n \n pet = Pet.query.get_or_404(pet_id)\n form = EditPetForm(obj=pet)\n\n if form.validate_on_submit():\n notes = form.notes.data\n photo_url = form.photo_url.data\n available = form.available.data\n\n pet.notes = notes\n pet.photo_url = photo_url\n pet.available = available\n\n db.session.commit()\n flash(f\"notes: {notes}, photo_url={photo_url}, available={pet.available}\")\n \n return redirect(f'/{pet_id}')\n\n p = dict(name=pet.name, species=pet.species, photo_url=pet.photo_url, age=pet.age, notes=pet.notes, available=pet.available)\n\n\n return render_template('pet_edit_form.html', pet=p, form=form)","def pet_display(pet_id):\n pet = Pets.query.get_or_404(pet_id)\n form = EditPetForm(obj=pet)\n if form.validate_on_submit():\n pet.photo_url = form.photo_url.data\n pet.notes = form.notes.data\n pet.available = form.available.data\n db.session.commit()\n flash(\"Succesfully updated\")\n return redirect(f'/{pet_id}')\n else:\n return render_template('display.html', pet=pet, form=form)","def show_pet_details(id):\n \n pet = Pet.query.get_or_404(id)\n form = EditPetForm(obj=pet)\n\n if form.validate_on_submit():\n pet.notes = form.notes.data\n pet.available = form.available.data\n pet.photo_url = form.photo_url.data\n db.session.commit()\n return redirect('/')\n\n else:\n return render_template('/pet_edit.html', form = form, pet = pet)","def describe_pet2(pet_name, animal_type='dog'):\n print(f\"\\nI have a {animal_type}.\")\n print(f\"My {animal_type}'s name is {pet_name.title()}.\")","def show_add_pet():\n\n form = PetForm()\n\n if form.validate_on_submit():\n name = form.name.data\n species = form.species.data\n photo_url = form.photo_url.data\n age = form.age.data\n notes = form.notes.data\n available = form.available.data\n\n pet = Pet(name=name, species=species, photo_url=photo_url, age=age, notes=notes, available=available)\n\n db.session.add(pet)\n db.session.commit()\n\n return redirect(\"/\")\n\n else:\n return render_template('add_pet.html', form=form)","def pets():\n \n pets_owned = db.execute(\"SELECT pets.id, pet_types.imgsrc, pet_types.pet_type, pets.created, pets.exp, pets.name, users.active_pet_id FROM owners JOIN pets ON pets.id = owners.pet_id JOIN pet_types ON pets.type = pet_types.id JOIN users ON users.id = owners.owner_id WHERE owner_id = ?\", (session_get_int(\"user_id\"), )).fetchall()\n return render_template(\"list.html\", pets_owned=pets_owned)","def show_edit_pet(pet_id):\n pet = Pet.query.get(pet_id)\n form = EditPet(obj=pet)\n\n if form.validate_on_submit():\n pet.photo_url = form.photo_url.data\n pet.notes = form.notes.data\n pet.available = form.available.data\n\n db.session.add(pet)\n db.session.commit()\n\n return redirect(\"/\")\n\n else:\n return render_template('edit_pet.html', form=form, pet=pet)","def show_and_edit_pet_page(pet_id):\n \n pet = Pet.query.get(pet_id)\n\n form = EditPetPage(obj=pet)\n\n if form.validate_on_submit():\n pet.photo_url = form.photo_url.data\n pet.notes = form.notes.data\n pet.available = form.available.data\n\n db.session.commit()\n\n return redirect('/')\n\n else:\n return render_template('display_pet.html', pet=pet, form=form)","def show_edit_pet(id):\r\n pet = Pet.query.get_or_404(id)\r\n form = EditPetForm(obj=pet)\r\n\r\n if form.validate_on_submit():\r\n pet.photo_url = form.photo_url.data\r\n pet.notes = form.notes.data\r\n pet.available = form.available.data\r\n db.session.commit()\r\n\r\n return redirect('/')\r\n\r\n else:\r\n return render_template(\"pet_profile.html\", form=form, pet=pet)","def get(self, pet_id):\n app.logger.info(\"Request to Retrieve a pet with id [%s]\", pet_id)\n pet = Pet.find(pet_id)\n if not pet:\n abort(status.HTTP_404_NOT_FOUND, \"Pet with id '{}' was not found.\".format(pet_id))\n return pet.serialize(), status.HTTP_200_OK","def _view_animal(self):\n print(repr(self.animals[self.park_location]))","def parterre_info(id):\n parterre = get_parterre(id)\n return render_template(\n \"parterre-info.html\",\n parterre = parterre,\n title = parterre.get_name(),\n capteurs = get_capteurs_parterre(id))","def add_information_about_new_pet(self, auth_key: json, name: str, animal_type: str, age: int, pet_photo: str) -> json:\n\n data = MultipartEncoder(\n fields={\n 'name': name,\n 'animal_type': animal_type,\n 'age': age,\n 'pet_photo': (pet_photo, open(pet_photo, 'rb'), 'image/jpeg')\n })\n headers = {'auth_key': auth_key['key'], 'Content-Type': data.content_type}\n\n res = requests.post(self.base_url + 'api/pets', headers=headers, data=data)\n status = res.status_code\n\n result = \"\"\n try:\n result = res.json()\n except:\n result = res.text\n\n return status, result","def show_homepage():\n\n pets = Pet.query.all()\n rando_pet = get_info_random_pet()\n\n name = rando_pet['petfinder']['pet']['name']['$t']\n age = rando_pet['petfinder']['pet']['age']['$t']\n image = rando_pet['petfinder']['pet']['media']['photos']['photo'][0]['$t']\n\n return render_template('homepage.html', pets=pets,\n name=name, age=age, image=image)","def get_pet_user_info():\n\n return db.session.query(User.email, Pet.pet_name, Pet.pet_type, Pet.pet_breed, \n Pet.pet_color, Pet.pet.status, Pet.last_address).filter(User.user_id == Pet.user_id).all()","def test_get_pets(self):\n response = self.client.open(\n '/pet',\n method='GET')\n self.assert200(response,\n 'Response body is : ' + response.data.decode('utf-8'))","def update_information_about_pet(self, auth_key: json, pet_id: str, name: str, animal_type: str, age: int) -> json:\n\n data = {\n 'name': name,\n 'animal_type': animal_type,\n 'age': age\n }\n headers = {'auth_key': auth_key['key']}\n\n res = requests.put(self.base_url + f'api/pets/{pet_id}', headers=headers, data=data)\n status = res.status_code\n\n result = \"\"\n try:\n result = res.json()\n except:\n result = res.text\n\n return status, result","def view_animal(self):\n self._view_animal()","def get_random_pet():\n resp = HTTP_request.get(' https://api.petfinder.com/v2/animals',\n params={\n \"limit\": 100,\n },\n headers={\"Authorization\": f\"Bearer {pet_finder_token}\"})\n\n pets = resp.json()[\"animals\"]\n\n random_pet = random.choice(pets)\n\n return {\"name\": random_pet[\"name\"], \"age\": random_pet[\"age\"], \"photo_url\": random_pet[\"photos\"][0][\"medium\"]}","def add_information_about_new_pet_without_photo(self, auth_key: json, name: str, animal_type: str, age: int) -> json:\n\n data = {\n 'name': name,\n 'animal_type': animal_type,\n 'age': age\n }\n headers = {'auth_key': auth_key['key']}\n\n res = requests.post(self.base_url + f'api/create_pet_simple', headers=headers, data=data)\n status = res.status_code\n\n result = \"\"\n try:\n result = res.json()\n except:\n result = res.text\n\n return status, result","def load_animal_info(rescue_id, animal_id):\n\n animal_info = c.get_animal(animal_id)\n title = animal_info.name\n\n return render_template('animal_info.html', animal_info=animal_info,\n title=title)","def pokemonInfo():\n pokemonId = request.args.get('id')\n user_email = request.args.get('user_email')\n if pokemonId is None:\n return jsonify(message=\"No pokemon ID specified\"), 404\n else:\n pokemon = session.query(Pokemon).filter_by(id=pokemonId).first()\n if pokemon is None:\n return jsonify(message=\"Nothing found\"), 404\n else:\n # Verify if the user is authorized to modify or delete this entry\n if pokemon.user is not None and pokemon.user.email == user_email:\n return jsonify(pokemon=pokemon.getJSON(), authorized=True)\n else:\n return jsonify(pokemon=pokemon.getJSON())","def view_pokemon(self, team_name: str, team_choice: str) -> None:\n\n print(f\"\\n\\u001b[1m\\u001b[4mTeam\\u001b[0m: \\u001b[7m {team_name} \\u001b[0m\")\n print(f\"\\n\\u001b[4mPokémon Slot #{int(team_choice)}\\u001b[0m\\n\\n\")\n print(f\"\\u001b[1mName\\u001b[0m: {self.name}\")\n print(f\"\\u001b[1mPokédex ID:\\u001b[0m {self.id}\\n\")\n print(f\"\\u001b[1mHeight\\u001b[0m: {self.height} decimetres\")\n print(f\"\\u001b[1mWeight\\u001b[0m: {self.weight} hectograms\\n\")\n\n if len(self.types) == 2:\n print(f\"\\u001b[1mTypes\\u001b[0m: {self.types[0]}\")\n print(f\" {self.types[1]}\")\n else:\n print(f\"\\u001b[1mType\\u001b[0m: {self.types[0]}\")\n\n print(\"\")\n print(\"\\u001b[1mAbilities\\u001b[0m:\")\n if len(self.abilities) > 0:\n for ability in self.abilities:\n print(f\" - \\u001b[4m{ability}\\u001b[0m:\")\n print(f\" {self.abilities[ability]}\")\n else:\n print(\" This Pokémon has no abilities.\")\n\n print(\"\")\n print(\"\\u001b[1mCurrent Move Set\\u001b[0m:\")\n if len(self.move_set) > 0:\n for move in self.move_set:\n print(f\" - {move.name}\")\n else:\n print(\" This Pokémon cannot learn any moves.\")\n\n print(\"\\n\")","def test_get_pet_by_id(self):\n response = self.client.open(\n '/pet/{petId}'.format(pet_id=789),\n method='GET')\n self.assert200(response,\n 'Response body is : ' + response.data.decode('utf-8'))","def add_pet():\r\n form = AddPetForm()\r\n if form.validate_on_submit():\r\n name = form.name.data\r\n species = form.species.data\r\n photo_url = form.photo_url.data\r\n age = form.age.data\r\n notes = form.notes.data\r\n\r\n new_pet = Pet(name=name, species=species, photo_url=photo_url, age=age, notes=notes)\r\n db.session.add(new_pet)\r\n db.session.commit()\r\n return redirect('/')\r\n\r\n else:\r\n return render_template(\"add_pet.html\", form=form)","def show_pet_with_edit_form(pet_id):\n pet = Pet.query.get_or_404(pet_id)\n form = PetFormEdit(obj=pet)\n if form.validate_on_submit():\n pet.photo_url = form.photo_url.data\n pet.notes = form.notes.data\n pet.available = form.available.data\n \n db.session.commit()\n return redirect('/')\n else:\n return render_template('pet.html', pet=pet, form=form)","def add_pet(name, age, animal='dog'):\n print('You have a %d year old %s named %s' % (age, animal, name))","def view_edit_pet(id):\n pet = Pet.query.get_or_404(id)\n form = PetEditForm(obj=pet)\n if form.validate_on_submit():\n form.populate_obj(pet)\n db.session.commit()\n\n flash(f\"Updated {pet.species} named {pet.name}\")\n return redirect(f'/{id}')\n else:\n return render_template(\"pet_edit_form.html\", form=form, pet=pet)","def add_pet():\n form = AddPet()\n if form.validate_on_submit():\n name = form.name.data\n species = form.species.data\n photo_url = form.photo_url.data\n age = form.age.data\n notes = form.notes.data\n\n new_pet = Pet(name=name,\n species=species,\n photo_url=photo_url,\n age=age,\n notes=notes)\n\n db.session.add(new_pet)\n db.session.commit()\n return redirect('/')\n else:\n return render_template('/add_form.html', form=form)","async def info(self, ctx: \"IceTeaContext\", *, otag: TagConverter):\n tag: models.Tag = otag\n if not tag.alias:\n embed = discord.Embed(description=f\"{ctx.message.guild.name} ``{tag.title}`` tag information\")\n user = ctx.guild.get_member(tag.author)\n embed.set_author(name=user.display_name, icon_url=user.avatar_url)\n embed.add_field(name=\"Tag name\", value=tag.title)\n embed.add_field(name=\"Amount used\", value=str(tag.count))\n embed.timestamp = tag.created\n await ctx.send(embed=embed)\n else:\n embed = discord.Embed(description=f\"{ctx.message.guild.name} ``{tag.title}`` alias information\")\n user = ctx.guild.get_member(tag.author)\n embed.add_field(name=\"Author\", value=user or \"Unknown\")\n embed.add_field(name=\"Amount used\", value=str(tag.count))\n embed.timestamp = tag.created\n await ctx.send(embed=embed)","def pet_detail_edit(pet_id):\n\n pet = Pet.query.get_or_404(pet_id)\n form = PetEditForm(obj=pet)\n\n if form.validate_on_submit():\n pet.photo_url = form.photo_url.data\n pet.notes = form.notes.data\n pet.available = form.available.data\n\n db.session.commit()\n flash(f\"Pet{pet_id} updated!\")\n return redirect(f\"/{pet_id}\")\n\n else:\n return render_template(\"pet_detail.html\", form=form, pet=pet)","def info():\n print(\"Made using the OOP RPG game creator (c) Claire.\\n\")","def plante_info(id):\n plante = get_plante(id)\n return render_template(\n \"plante-info.html\",\n plante = plante,\n title = plante.get_name(),\n parterre = get_parterre(plante.get_parterre()))","def add_pet():\n\n form = AddPetForm()\n\n if form.validate_on_submit():\n name = form.name.data\n species = form.species.data\n photo_url = form.photo_url.data\n age = form.age.data\n notes = form.notes.data\n\n pet = Pet(name=name, \n species=species, \n photo_url=photo_url, \n age=age, \n notes=notes)\n db.session.add(pet)\n db.session.commit()\n \n return redirect('/')\n\n else:\n return render_template(\n \"add_pet_form.html\", form=form)","def petedit():\n if request.method == \"POST\":\n pet_id = request.form.get(\"pet_id\")\n rename = request.form.get(\"rename\")\n rows = db.execute(\"SELECT count(*) as count FROM owners WHERE owner_id = ? AND pet_id = ?\",\n (session_get_int(\"user_id\"), pet_id, )).fetchall()\n # Confirmed user owns this pet\n if rows[0]['count'] == 1:\n exp = db.execute(\"SELECT exp FROM pets WHERE id = ?\", (pet_id, )).fetchall()\n if (int(exp[0]['exp']) >= 100):\n db.execute(\"UPDATE pets SET name = ? WHERE id = ?\", (rename, pet_id, ))\n con.commit()\n\n # Put updated info into session for pet\n set_active_pet_in_session(session_get_int(\"user_id\"))\n flash(\"Pet renamed to \" + rename)\n else:\n return apology(\"Your pet needs at least 100 experience to be renamed!\", 403)\n\n return redirect('/pets/edit/?id='+ str(pet_id))\n else:\n pet_id = int(request.args.get('id'))\n\n # This ensures the current user owns the pet being renamed\n pet_info = db.execute(\"SELECT pets.id, pet_types.imgsrc, pet_types.pet_type, pets.created, pets.exp, pets.name, users.active_pet_id FROM owners JOIN pets ON pets.id = owners.pet_id JOIN pet_types ON pets.type = pet_types.id JOIN users ON users.id = owners.owner_id WHERE owner_id = ? AND pet_id = ?\", \n (session_get_int(\"user_id\"), pet_id, )).fetchall()\n if len(pet_info) == 1:\n return render_template(\"petedit.html\", pet_info=pet_info)\n else:\n return apology(\"Error getting pet info\", 403)","def cat_details(cat_id, shelter_id):\n\n shelter = petfinder.shelter_data_map(shelter_id)\n shelter = list(shelter.values())\n cat = petfinder.cat_data_map(cat_id)\n cat = list(cat.values())\n\n return render_template('more_details.html',\n shelter=shelter,\n cat=cat)\n\n #if user selects <3 to favorite a cat then redirct to the login page","def select_pets_by_person(conn, person_id):\n sql = \"\"\"\n SELECT person.first_name \n || ' ' \n || person.last_name AS `Owner`, \n pet.name AS `Pet Name`, \n pet.breed AS `Pet Breed`, \n pet.age AS `Pet Age` \n FROM person_pet \n INNER JOIN person \n ON person.id = person_pet.person_id \n INNER JOIN pet \n ON pet.id = person_pet.pet_id \n WHERE person.id = ? \n \"\"\"\n cur = conn.cursor()\n try:\n cur.execute(sql, (person_id,))\n data = cur.fetchall()\n if data:\n name = data[0][0]\n print \"\\nQuerying for pets belonging to {}\\n\".format(name)\n print sql_pp(cur, data)\n except OperationalError, msg:\n print \"SQL error {} while running our code\".format(msg)","def info():\n print 'Loading info page'\n\n team_list = datastore.get_all_teams(engine)\n\n return render_template('info.html', rows=team_list)","def display(animal):\n for name, valeur in animal.items(): # boucle contenant deux variables pour le nom et la valeur de chaque clef dans le dictionaire\n print(\"donnée de votre animal: {} : {}\".format(name,valeur))","def plants (plant_name, plant_type):\n print (f\"\\n{plant_name.title()} is a {plant_type}. \\n\")","def add_pet_form():\n\n form = AddPetForm()\n \n if form.validate_on_submit():\n name = form.name.data\n species = form.species.data\n photo_url = form.photo_url.data\n age = form.age.data\n notes = form.notes.data\n available = form.available.data\n\n new_pet = Pet(name=name, species=species, photo_url=photo_url, age=age, notes=notes, available=available)\n db.session.add(new_pet)\n db.session.commit()\n\n flash(f\"Added {name}, species: {species}, age: {age}, notes: {notes}, photo_url={photo_url}, available={available}\")\n \n return redirect(\"/add\")\n\n else:\n return render_template(\n \"pet_add_form.html\", form=form)","def pokemon():\n # TODO: Add form, nature and habitat to displayed table.\n color = request.form.get(\"pokecolor\")\n pokemon_of_color = []\n pokemon_with_data = []\n if color:\n pokemon_of_color = get_pokemon_of_color(color)\n # This is not a good implementation, because after getting the Pokemon\n # with a certain color, now I need to get each species to extract the\n # forms.\n for poke in pokemon_of_color:\n print(\"Extending Species: {0}\".format(poke))\n extended_poke = {}\n extended_poke[\"name\"] = poke\n extended_poke[\"forms\"] = get_forms_of_pokemon_species(species=poke)\n pokemon_with_data.append(extended_poke)\n return render_template(\"pokemon.html.j2\", pokemon=pokemon_with_data)","def show_myhero(self):\n description = (self.name + ' Level is: ' + str(self.level) + ' Age is: ' + str(\n self.age) + ' Rank is: ' + self.rank + ' health is: ' + str(self.health)).title()\n print(description)","def capteur_info(id):\n capteur = get_capteur(id)\n return render_template(\n \"capteur-info.html\",\n capteur = capteur,\n title = capteur.get_name(),\n parterre = get_parterre(capteur.get_parterre()),\n mesure = get_typeMesure(capteur.get_typeMesure()))","def show_home_page():\n\n pet_list = Pet.query.all()\n return render_template('index.html', pet_list=pet_list)","def add_pet():\n form = PetAddForm()\n if form.validate_on_submit():\n pet = Pet()\n form.populate_obj(pet)\n db.session.add(pet)\n db.session.commit()\n\n flash(f\"Added {form.species.data} named {form.name.data}\")\n return redirect('/')\n else:\n return render_template(\"add_pet_form.html\", form=form)","def create_pet(pet_name, pet_type, pet_breed, pet_gender, \n pet_color, pet_status, pet_image, last_address):\n\n pet = Pet(pet_name=pet_name,\n pet_type=pet_type, \n pet_breed=pet_breed, \n pet_gender=pet_gender,\n pet_color=pet_color,\n pet_status=pet_status,\n pet_image=pet_image,\n last_address=last_address)\n\n db.session.add(pet)\n db.session.commit()\n\n return pet","def show_myhero(self):\n description = (self.name + ' Level is: ' + str(self.level) + ' Age is: ' + str(\n self.age) + ' Rank is: ' + self.rank + ' health is: ' + str(self.health) + ' magic is: ' + str(self.__magic)).title()\n print(description)","def shelterGetPets(url, URL_JSON_KEY, shelter_id):\n \n method = \"shelter.getPets?\"\n count = \"&count=100\"\n url+= method + URL_JSON_KEY + shelter_id + count\n petJson = urlopen(url)\n petInfo = load(reader(petJson))\n return petInfo","def show_info(self):\n txt = \"Brand: %s\\nModel: %s\\nHostname: %s\\n\"%(self.brand, self.model, self.hostname)\n return txt","def add_pet():\n form = AddPetForm()\n\n print(form.validate_on_submit())\n for value in form:\n print(value.data)\n\n if form.validate_on_submit():\n name = form.name.data\n species = form.species.data\n photo_url = form.photo_url.data\n age = form.age.data\n notes = form.notes.data\n new_pet = Pets(name=name,\n species=species,\n photo_url=photo_url,\n age=age,\n notes=notes)\n\n db.session.add(new_pet)\n db.session.commit()\n flash(f\"Succesfully registered {new_pet.name}\")\n return redirect('/')\n else:\n return render_template('add.html', form=form)","def pet_add_form():\n\n form = PetAddForm()\n\n if form.validate_on_submit():\n name = form.name.data\n species = form.species.data\n photo_url = form.photo_url.data\n age = form.age.data\n notes = form.notes.data\n\n flash(f\"Added a {species} called {name}\")\n\n pet = Pet(\n name=name,\n species=species,\n photo_url=photo_url if photo_url != '' else None,\n age=age,\n notes=notes\n )\n\n db.session.add(pet)\n db.session.commit()\n\n return redirect(\"/\")\n\n else:\n return render_template(\"pet_add_form.html\", form=form)","def show_info(self): \n color= Fore.WHITE\n print(f\"\"\" {color} \nNombre: {self.name} \nRuta: {self.route }\nFecha de salida: {self.departure_date}\"\"\")\n print(\"<\"*8, \">\"*8)\n print(\"El precio por habitacion es:\")\n for key, value in self.prize.items():\n color_value= (Fore.GREEN + str(value))\n color_key= Fore.WHITE + \"Habitacion\" + \" \" + key\n print(f\"\"\" {color_key} : {color_value}$ \"\"\")\n \n print(Fore.WHITE + \"<\"*8, \">\"*8)\n for floor, info in self.floors_info.items():\n piso=(Fore.WHITE + floor)\n print(f\" {piso}:{info} \")\n \n \n print(\"<\"*8, \">\"*8)\n print(\"Capacidad por tipo de habitacion: \")\n for key, value in self.room_capacity.items():\n print(f\"Habitacion {key}: {value} personas \",\"\\t\")\n return \"\"","async def _me_pets(self, ctx):\n if has_post_permission(ctx.guild.id, ctx.channel.id):\n messages = ch.print_pets(ctx.user_object)\n await self.paginate(ctx, messages)","def ProfService_detail(request, pk):\n ProfService = get_object_or_404(PServices, pk=pk)\n \n return render(request, \"ProfService_detail.html\", {\"ProfService\": ProfService})","def show_person(uuid=None, fanchart=False):\n t0 = time.time()\n uuid = request.args.get(\"uuid\", uuid)\n fanchart_shown = request.args.get(\"fanchart\", fanchart)\n dbg = request.args.get(\"debug\", None)\n u_context = UserContext(user_session, current_user, request)\n\n with PersonReaderTx(\"read_tx\", u_context) as service:\n result = service.get_person_data(uuid)\n\n # result {'person':PersonBl, 'objs':{uniq_id:obj}, 'jscode':str, 'root':{root_type,root_user,batch_id}}\n if Status.has_failed(result):\n flash(f'{result.get(\"statustext\",\"error\")}', \"error\")\n person = result.get(\"person\")\n objs = result.get(\"objs\", [])\n print(f\"# Person with {len(objs)} objects\")\n jscode = result.get(\"jscode\", \"\")\n # Batch or Audit node data like {'root_type', 'root_user', 'id'}\n person.root = result.get(\"root\")\n\n stk_logger(u_context, f\"-> bp.scene.routes.show_person n={len(objs)}\")\n\n last_year_allowed = datetime.now().year - shareds.PRIVACY_LIMIT\n may_edit = current_user.has_role(\"audit\") # or current_user.has_role('admin')\n # may_edit = 0\n return render_template(\n \"/scene/person.html\",\n person=person,\n obj=objs,\n jscode=jscode,\n menuno=12,\n debug=dbg,\n last_year_allowed=last_year_allowed,\n elapsed=time.time() - t0,\n user_context=u_context,\n may_edit=may_edit,\n fanchart_shown=fanchart_shown,\n )","def print_food(self):\n for dish in self.food:\n print(dish.get_name())","def show_data(self, ):\r\n return print('society_name : {}\\n'\r\n 'flat : {}\\n'\r\n 'house_no : {}\\n'\r\n 'no_of_members : {}\\n'\r\n 'income : {}\\n '\r\n .format(self.society_name, self.flat, self.house_no, self.no_of_members, self.income))","def info(self, id):","def display_profile(self):\n print(f\"Id: {self._id}\")\n print(f\"username: {self.username}\")\n print(f\"name: {self.name}\")\n print(f\"contact: {self.contact}\")\n print(f\"address: {self.address}\")","def adopt():\n if request.method == \"POST\":\n # Check if user bought a pet\n if not request.form.get(\"pet_type\"):\n return apology(\"must choose pet type\", 403)\n pet_type_id = int(request.form.get(\"pet_type\"))\n petname = db.execute(\n \"SELECT pet_type FROM pet_types WHERE id = ?\", (pet_type_id, )).fetchall()\n\n # Create pet with default name as pet type\n petid = (db.execute(\"INSERT INTO pets(type, name, exp, created) VALUES (?, ?, ?, CURRENT_TIMESTAMP)\",\n (pet_type_id, petname[0]['pet_type'], 0 ))).lastrowid\n con.commit()\n\n # Add owner to pet\n db.execute(\"INSERT INTO owners(owner_id, pet_id) VALUES (?, ?)\",\n (session_get_int(\"user_id\"), petid))\n con.commit()\n\n # Set as user's active pet\n db.execute(\"UPDATE users SET active_pet_id = ? WHERE id = ?\",\n (petid, session_get_int(\"user_id\") ))\n con.commit()\n set_active_pet_in_session(session_get_int(\"user_id\"))\n return redirect(\"/pets\")\n\n else:\n rows = db.execute(\"SELECT * FROM pet_types\").fetchall()\n if len(rows) < 1:\n return apology(\"no pets\", 403)\n return render_template(\"adopt.html\", pet_types=rows)","def __repr__(self):\n return \"{} hp:{:.1f} stunned: {} potions: {}\".format(self.name, self.hitpoints,\n self.stunned, self.potions)","def __str__(self):\n return (\n 'Name:\\t{}\\nType:\\t{}\\nAge:\\t{}'.format(self.__name,\n self.__animal_type,\n self.__age))","def get_pet(pet=\"dog\"):\n pets = dict(dog=Dog(\"Hope\"))\n return pets[pet]","def getStatus(url, URL_JSON_KEY,pet_id):\n \n method = \"pet.get?\"\n petId = \"&id=\"+pet_id\n url+= method + URL_JSON_KEY + petId\n petJson = urlopen(url)\n petsInfo = load(reader(petJson))\n message = petsInfo['petfinder']['header']['status']['code']['$t']\n if message == '100':\n status = petsInfo['petfinder']['pet']['status']['$t']\n return status\n elif message =='201':\n status = \"Animal Removed\"\n return status\n else:\n print(\"Something went wrong. Sorry.\")","def printPokemon():\n print(\" _ \")\n print(\" _ __ ___ | | _____ _ __ ___ ___ _ __ \")\n print(\" | '_ \\ / _ \\| |/ / _ \\ '_ ` _ \\ / _ \\| '_ \\ \")\n print(\" | |_) | (_) | < __/ | | | | | (_) | | | |\")\n print(\" | .__/ \\___/|_|\\_\\___|_| |_| |_|\\___/|_| |_|\")\n print(\" |_| \")","def get_description(self):\n print(\"This Iron door.\")","def get(self):\n app.logger.info('Request to list Pets...')\n pets = []\n category = request.args.get('category')\n name = request.args.get('name')\n available = request.args.get('available')\n if category:\n app.logger.info('Filtering by category: %s', category)\n pets = Pet.find_by_category(category)\n elif name:\n app.logger.info('Filtering by name:%s', name)\n pets = Pet.find_by_name(name)\n elif available:\n app.logger.info('Filtering by available: %s', available)\n is_available = available.lower() in ['yes', 'y', 'true', 't', '1']\n pets = Pet.find_by_availability(is_available)\n else:\n pets = Pet.all()\n\n app.logger.info('[%s] Pets returned', len(pets))\n results = [pet.serialize() for pet in pets]\n return results, status.HTTP_200_OK","def showInfo(p,personDict):\n info1 = personDict['EnterpriseID'][p[0]]\n info2 = personDict['EnterpriseID'][p[1]]\n print (\"Person A:\",info1)\n print (\"Person B:\",info2)","def info(self):","def info(self):","def get(self):\n app.logger.info('Request to list Pets...')\n pets = []\n category = request.args.get('category')\n name = request.args.get('name')\n available = request.args.get('available')\n if category:\n app.logger.info('Filtering by category: %s', category)\n pets = Pet.find_by_category(category)\n elif name:\n app.logger.info('Filtering by name:%s', name)\n pets = Pet.find_by_name(name)\n elif available:\n app.logger.info('Filtering by available: %s', available)\n pets = Pet.find_by_availability(available)\n else:\n pets = Pet.all()\n\n app.logger.info('[%s] Pets returned', len(pets))\n results = [pet.serialize() for pet in pets]\n return results, status.HTTP_200_OK","def show_user_info(self):\n name = self.get_user_name()\n print(f'Name: {name.title()}')\n print(f'Age: {self.age}')\n print(f'Gender: {self.gender.title()}')\n print(f'Mobile: {self.m_number}')","def info(id):\n sql = \"select distinct name, description, stars, url, last_push_date, repo_id, created_date, avatar from python_repos where repo_id=\"+id\n db = get_db()\n cursor = db.execute(sql)\n repo_info = cursor.fetchall()\n return render_template('repo.html',info=repo_info)","def player_information():\n if request.method == 'POST':\n result = request.form\n if request.files:\n playerImage = request.files['playerImage'].read()\n else:\n playerImage = None\n player = Player(player_image=playerImage, player_fname=result['player_first_name'],\n player_lname=result['player_last_name'], team_id=result['team_selected'])\n db.session.add(player)\n db.session.commit()\n teams = get_team()\n if teams:\n return render_template('team-players.html', teams=teams)\n else:\n return render_template('team-players.html')","def get_info(self):\n if self.own_home:\n return print(f'Hi! My name is {self.name}, I\\'m {self.age}. Currently I have {self.own_home} house')\n return print(f'Hi! My name is {self.name}, I\\'m {self.age}. I don\\'t have any home now!')","def home():\n pets = Pets.query.all()\n return render_template('home.html', pets=pets)","async def info(self, ctx, *, tag):\n try:\n self.fetch_tag(ctx, tag)\n except Exception as error:\n return await ctx.send(error)\n data = self._tag_dict[ctx.guild.id][tag]\n author = self.bot.get_user(data['author']) or await self.bot.fetch_user(data['author'])\n embed = discord.Embed(colour=self.bot.colour)\n embed.title = tag\n embed.description = f\"<:author:734991429843157042> **{author}**\\n\"\n embed.description += f\"Uses: **{data['uses']}**\\n\"\n embed.description += f\"ID: **{data['id']}**\"\n embed.set_author(name=str(author), icon_url=author.avatar_url)\n await ctx.send(embed=embed)","def displayInfo(self, model):\n\t\ttaglist = []\n\t\tfor tag in model.tags:\n\t\t\ttaglist.append(tag.tagname)\n\t\tself.infoText.SetPage(infoTemplate.render(model=model, tags= ','.join(taglist)))"],"string":"[\n \"def show_pet(self):\\n pet = self.pet_factory.get_pet()\\n print \\\"We have a lovely {}\\\".format(pet)\\n print \\\"It says {}\\\".format(pet.speak())\\n print \\\"We also have {}\\\".format(self.pet_factory.get_food())\",\n \"def show_pet(self):\\n pet = self.pet_factory.get_pet()\\n\\n print(\\\"this is a lovely \\\", pet)\\n print(\\\"It says \\\", pet.speak())\\n print(\\\"It eats \\\", self.pet_factory.get_food())\",\n \"def describe_pet(animal_type, pet_name):\\n print(\\\"\\\\nI have a \\\" + animal_type + \\\".\\\")\\n print(\\\"My \\\" + animal_type + \\\"'s name is \\\" + pet_name.title() + \\\".\\\")\",\n \"def describe_pet(animal_type, pet_name):\\n print(\\\"\\\\nI have a \\\" + animal_type + \\\".\\\")\\n print(\\\"My \\\" + animal_type + \\\"'s name is \\\" + pet_name.title() + \\\".\\\")\",\n \"def describe_pet(animal_type, pet_name):\\n print(\\\"\\\\nI have a \\\" + animal_type + \\\".\\\")\\n print(\\\"My \\\" + animal_type + \\\"'s name is \\\" + pet_name.title() + \\\".\\\")\",\n \"def describe_pet(animal_type, pet_name):\\n print(\\\"\\\\nI have a \\\" + animal_type + \\\".\\\")\\n print(\\\"My \\\" + animal_type + \\\"'s name is \\\" + pet_name.title() + \\\".\\\")\",\n \"def describe_pet(animal_type, pet_name):\\r\\n print(f\\\"\\\\nI have a {animal_type}.\\\")\\r\\n print(f\\\"My {animal_type}'s name is {pet_name.title()}.\\\")\",\n \"def describe_pet(animal_type, pet_name):\\n print(f\\\"\\\\nI have a {animal_type}.\\\")\\n print(f\\\"My {animal_type}'s name is {pet_name.title()}.\\\")\",\n \"def describe_pet(animal_type, pet_name):\\n print(f\\\"\\\\nI have a {animal_type}.\\\")\\n print(f\\\"My {animal_type}'s name is {pet_name.title()}.\\\")\",\n \"def describe_pet(animal_type='dog',pet_name):\\n\\tprint(\\\"\\\\nI have a \\\" + animal_type + \\\".\\\")\\n\\tprint(\\\"My \\\" + animal_type + \\\"'s name is \\\" + pet_name.title() + \\\".\\\")\",\n \"def describe_pet(pet_name, animal_type='tiger'):\\n\\tprint(f\\\"\\\\nI have a {animal_type}.\\\")\\n\\tprint(f\\\"My {animal_type}'s name is {pet_name}.\\\")\",\n \"def describe_pet(pet_name, animal_type='dog'):\\n print(\\\"\\\\nI have a \\\" + animal_type + \\\".\\\")\\n print(\\\"My \\\" + animal_type + \\\"'s name is \\\" + pet_name.title() + \\\".\\\")\",\n \"def describe_pet(animal, name):\\r\\n print(\\\"\\\\nI have a \\\" + animal + \\\".\\\")\\r\\n print(\\\"Its name is \\\" + name + \\\".\\\")\",\n \"def describe_pet(pet_name, animal_type='dog'):\\n print(\\\"\\\\nI have a \\\" + animal_type + \\\".\\\")\\n print(\\\"My \\\" + animal_type + \\\"'s name is \\\" + pet_name.title() + \\\".\\\")\\n \\n # A dog named Willie.\",\n \"def describe_pet(animal_type,pet_name):\\n print(\\\"\\\\nI have a \\\"+animal_type+\\\".\\\")\\n print(\\\"\\\\tMy \\\"+animal_type+\\\"'s name is \\\"+pet_name+\\\".\\\")\",\n \"def describe_pet(name, animal='dog'):\\r\\n print(\\\"\\\\nI have a \\\" + animal + \\\".\\\")\\r\\n print(\\\"Its name is \\\" + name + \\\".\\\")\",\n \"def describe_pet(pet_name, animal_type='dog'):\\n print(f\\\"\\\\nI have a(n) {animal_type}.\\\")\\n print(f\\\"My {animal_type}'s name is {pet_name.title()}.\\\")\",\n \"def describe_pet(pet_name, animal_type = 'dog'):\\r\\n print(f\\\"\\\\nI have a {animal_type}.\\\")\\r\\n print(f\\\"My {animal_type}'s name is {pet_name.title()}.\\\")\",\n \"def describe_pet(pet_name,animal_type = 'dog'):\\n\\tprint(\\\"I have a \\\" + animal_type + \\\".\\\")\\n\\tprint(\\\"My \\\" + animal_type + \\\"'s name is \\\" + pet_name.title() + \\\".\\\\n\\\")\",\n \"def describe_pets(animal_type,name):\\n print(\\\"I have a \\\" + animal_type + \\\".\\\")\\n print(\\\"My \\\" + animal_type + \\\"'s name is \\\" + name.title())\",\n \"def show_pet(pet_id):\\n \\n pet = Pet.query.get_or_404(pet_id)\\n form = EditPetForm(obj=pet)\\n\\n if form.validate_on_submit():\\n notes = form.notes.data\\n photo_url = form.photo_url.data\\n available = form.available.data\\n\\n pet.notes = notes\\n pet.photo_url = photo_url\\n pet.available = available\\n\\n db.session.commit()\\n flash(f\\\"notes: {notes}, photo_url={photo_url}, available={pet.available}\\\")\\n \\n return redirect(f'/{pet_id}')\\n\\n p = dict(name=pet.name, species=pet.species, photo_url=pet.photo_url, age=pet.age, notes=pet.notes, available=pet.available)\\n\\n\\n return render_template('pet_edit_form.html', pet=p, form=form)\",\n \"def pet_display(pet_id):\\n pet = Pets.query.get_or_404(pet_id)\\n form = EditPetForm(obj=pet)\\n if form.validate_on_submit():\\n pet.photo_url = form.photo_url.data\\n pet.notes = form.notes.data\\n pet.available = form.available.data\\n db.session.commit()\\n flash(\\\"Succesfully updated\\\")\\n return redirect(f'/{pet_id}')\\n else:\\n return render_template('display.html', pet=pet, form=form)\",\n \"def show_pet_details(id):\\n \\n pet = Pet.query.get_or_404(id)\\n form = EditPetForm(obj=pet)\\n\\n if form.validate_on_submit():\\n pet.notes = form.notes.data\\n pet.available = form.available.data\\n pet.photo_url = form.photo_url.data\\n db.session.commit()\\n return redirect('/')\\n\\n else:\\n return render_template('/pet_edit.html', form = form, pet = pet)\",\n \"def describe_pet2(pet_name, animal_type='dog'):\\n print(f\\\"\\\\nI have a {animal_type}.\\\")\\n print(f\\\"My {animal_type}'s name is {pet_name.title()}.\\\")\",\n \"def show_add_pet():\\n\\n form = PetForm()\\n\\n if form.validate_on_submit():\\n name = form.name.data\\n species = form.species.data\\n photo_url = form.photo_url.data\\n age = form.age.data\\n notes = form.notes.data\\n available = form.available.data\\n\\n pet = Pet(name=name, species=species, photo_url=photo_url, age=age, notes=notes, available=available)\\n\\n db.session.add(pet)\\n db.session.commit()\\n\\n return redirect(\\\"/\\\")\\n\\n else:\\n return render_template('add_pet.html', form=form)\",\n \"def pets():\\n \\n pets_owned = db.execute(\\\"SELECT pets.id, pet_types.imgsrc, pet_types.pet_type, pets.created, pets.exp, pets.name, users.active_pet_id FROM owners JOIN pets ON pets.id = owners.pet_id JOIN pet_types ON pets.type = pet_types.id JOIN users ON users.id = owners.owner_id WHERE owner_id = ?\\\", (session_get_int(\\\"user_id\\\"), )).fetchall()\\n return render_template(\\\"list.html\\\", pets_owned=pets_owned)\",\n \"def show_edit_pet(pet_id):\\n pet = Pet.query.get(pet_id)\\n form = EditPet(obj=pet)\\n\\n if form.validate_on_submit():\\n pet.photo_url = form.photo_url.data\\n pet.notes = form.notes.data\\n pet.available = form.available.data\\n\\n db.session.add(pet)\\n db.session.commit()\\n\\n return redirect(\\\"/\\\")\\n\\n else:\\n return render_template('edit_pet.html', form=form, pet=pet)\",\n \"def show_and_edit_pet_page(pet_id):\\n \\n pet = Pet.query.get(pet_id)\\n\\n form = EditPetPage(obj=pet)\\n\\n if form.validate_on_submit():\\n pet.photo_url = form.photo_url.data\\n pet.notes = form.notes.data\\n pet.available = form.available.data\\n\\n db.session.commit()\\n\\n return redirect('/')\\n\\n else:\\n return render_template('display_pet.html', pet=pet, form=form)\",\n \"def show_edit_pet(id):\\r\\n pet = Pet.query.get_or_404(id)\\r\\n form = EditPetForm(obj=pet)\\r\\n\\r\\n if form.validate_on_submit():\\r\\n pet.photo_url = form.photo_url.data\\r\\n pet.notes = form.notes.data\\r\\n pet.available = form.available.data\\r\\n db.session.commit()\\r\\n\\r\\n return redirect('/')\\r\\n\\r\\n else:\\r\\n return render_template(\\\"pet_profile.html\\\", form=form, pet=pet)\",\n \"def get(self, pet_id):\\n app.logger.info(\\\"Request to Retrieve a pet with id [%s]\\\", pet_id)\\n pet = Pet.find(pet_id)\\n if not pet:\\n abort(status.HTTP_404_NOT_FOUND, \\\"Pet with id '{}' was not found.\\\".format(pet_id))\\n return pet.serialize(), status.HTTP_200_OK\",\n \"def _view_animal(self):\\n print(repr(self.animals[self.park_location]))\",\n \"def parterre_info(id):\\n parterre = get_parterre(id)\\n return render_template(\\n \\\"parterre-info.html\\\",\\n parterre = parterre,\\n title = parterre.get_name(),\\n capteurs = get_capteurs_parterre(id))\",\n \"def add_information_about_new_pet(self, auth_key: json, name: str, animal_type: str, age: int, pet_photo: str) -> json:\\n\\n data = MultipartEncoder(\\n fields={\\n 'name': name,\\n 'animal_type': animal_type,\\n 'age': age,\\n 'pet_photo': (pet_photo, open(pet_photo, 'rb'), 'image/jpeg')\\n })\\n headers = {'auth_key': auth_key['key'], 'Content-Type': data.content_type}\\n\\n res = requests.post(self.base_url + 'api/pets', headers=headers, data=data)\\n status = res.status_code\\n\\n result = \\\"\\\"\\n try:\\n result = res.json()\\n except:\\n result = res.text\\n\\n return status, result\",\n \"def show_homepage():\\n\\n pets = Pet.query.all()\\n rando_pet = get_info_random_pet()\\n\\n name = rando_pet['petfinder']['pet']['name']['$t']\\n age = rando_pet['petfinder']['pet']['age']['$t']\\n image = rando_pet['petfinder']['pet']['media']['photos']['photo'][0]['$t']\\n\\n return render_template('homepage.html', pets=pets,\\n name=name, age=age, image=image)\",\n \"def get_pet_user_info():\\n\\n return db.session.query(User.email, Pet.pet_name, Pet.pet_type, Pet.pet_breed, \\n Pet.pet_color, Pet.pet.status, Pet.last_address).filter(User.user_id == Pet.user_id).all()\",\n \"def test_get_pets(self):\\n response = self.client.open(\\n '/pet',\\n method='GET')\\n self.assert200(response,\\n 'Response body is : ' + response.data.decode('utf-8'))\",\n \"def update_information_about_pet(self, auth_key: json, pet_id: str, name: str, animal_type: str, age: int) -> json:\\n\\n data = {\\n 'name': name,\\n 'animal_type': animal_type,\\n 'age': age\\n }\\n headers = {'auth_key': auth_key['key']}\\n\\n res = requests.put(self.base_url + f'api/pets/{pet_id}', headers=headers, data=data)\\n status = res.status_code\\n\\n result = \\\"\\\"\\n try:\\n result = res.json()\\n except:\\n result = res.text\\n\\n return status, result\",\n \"def view_animal(self):\\n self._view_animal()\",\n \"def get_random_pet():\\n resp = HTTP_request.get(' https://api.petfinder.com/v2/animals',\\n params={\\n \\\"limit\\\": 100,\\n },\\n headers={\\\"Authorization\\\": f\\\"Bearer {pet_finder_token}\\\"})\\n\\n pets = resp.json()[\\\"animals\\\"]\\n\\n random_pet = random.choice(pets)\\n\\n return {\\\"name\\\": random_pet[\\\"name\\\"], \\\"age\\\": random_pet[\\\"age\\\"], \\\"photo_url\\\": random_pet[\\\"photos\\\"][0][\\\"medium\\\"]}\",\n \"def add_information_about_new_pet_without_photo(self, auth_key: json, name: str, animal_type: str, age: int) -> json:\\n\\n data = {\\n 'name': name,\\n 'animal_type': animal_type,\\n 'age': age\\n }\\n headers = {'auth_key': auth_key['key']}\\n\\n res = requests.post(self.base_url + f'api/create_pet_simple', headers=headers, data=data)\\n status = res.status_code\\n\\n result = \\\"\\\"\\n try:\\n result = res.json()\\n except:\\n result = res.text\\n\\n return status, result\",\n \"def load_animal_info(rescue_id, animal_id):\\n\\n animal_info = c.get_animal(animal_id)\\n title = animal_info.name\\n\\n return render_template('animal_info.html', animal_info=animal_info,\\n title=title)\",\n \"def pokemonInfo():\\n pokemonId = request.args.get('id')\\n user_email = request.args.get('user_email')\\n if pokemonId is None:\\n return jsonify(message=\\\"No pokemon ID specified\\\"), 404\\n else:\\n pokemon = session.query(Pokemon).filter_by(id=pokemonId).first()\\n if pokemon is None:\\n return jsonify(message=\\\"Nothing found\\\"), 404\\n else:\\n # Verify if the user is authorized to modify or delete this entry\\n if pokemon.user is not None and pokemon.user.email == user_email:\\n return jsonify(pokemon=pokemon.getJSON(), authorized=True)\\n else:\\n return jsonify(pokemon=pokemon.getJSON())\",\n \"def view_pokemon(self, team_name: str, team_choice: str) -> None:\\n\\n print(f\\\"\\\\n\\\\u001b[1m\\\\u001b[4mTeam\\\\u001b[0m: \\\\u001b[7m {team_name} \\\\u001b[0m\\\")\\n print(f\\\"\\\\n\\\\u001b[4mPokémon Slot #{int(team_choice)}\\\\u001b[0m\\\\n\\\\n\\\")\\n print(f\\\"\\\\u001b[1mName\\\\u001b[0m: {self.name}\\\")\\n print(f\\\"\\\\u001b[1mPokédex ID:\\\\u001b[0m {self.id}\\\\n\\\")\\n print(f\\\"\\\\u001b[1mHeight\\\\u001b[0m: {self.height} decimetres\\\")\\n print(f\\\"\\\\u001b[1mWeight\\\\u001b[0m: {self.weight} hectograms\\\\n\\\")\\n\\n if len(self.types) == 2:\\n print(f\\\"\\\\u001b[1mTypes\\\\u001b[0m: {self.types[0]}\\\")\\n print(f\\\" {self.types[1]}\\\")\\n else:\\n print(f\\\"\\\\u001b[1mType\\\\u001b[0m: {self.types[0]}\\\")\\n\\n print(\\\"\\\")\\n print(\\\"\\\\u001b[1mAbilities\\\\u001b[0m:\\\")\\n if len(self.abilities) > 0:\\n for ability in self.abilities:\\n print(f\\\" - \\\\u001b[4m{ability}\\\\u001b[0m:\\\")\\n print(f\\\" {self.abilities[ability]}\\\")\\n else:\\n print(\\\" This Pokémon has no abilities.\\\")\\n\\n print(\\\"\\\")\\n print(\\\"\\\\u001b[1mCurrent Move Set\\\\u001b[0m:\\\")\\n if len(self.move_set) > 0:\\n for move in self.move_set:\\n print(f\\\" - {move.name}\\\")\\n else:\\n print(\\\" This Pokémon cannot learn any moves.\\\")\\n\\n print(\\\"\\\\n\\\")\",\n \"def test_get_pet_by_id(self):\\n response = self.client.open(\\n '/pet/{petId}'.format(pet_id=789),\\n method='GET')\\n self.assert200(response,\\n 'Response body is : ' + response.data.decode('utf-8'))\",\n \"def add_pet():\\r\\n form = AddPetForm()\\r\\n if form.validate_on_submit():\\r\\n name = form.name.data\\r\\n species = form.species.data\\r\\n photo_url = form.photo_url.data\\r\\n age = form.age.data\\r\\n notes = form.notes.data\\r\\n\\r\\n new_pet = Pet(name=name, species=species, photo_url=photo_url, age=age, notes=notes)\\r\\n db.session.add(new_pet)\\r\\n db.session.commit()\\r\\n return redirect('/')\\r\\n\\r\\n else:\\r\\n return render_template(\\\"add_pet.html\\\", form=form)\",\n \"def show_pet_with_edit_form(pet_id):\\n pet = Pet.query.get_or_404(pet_id)\\n form = PetFormEdit(obj=pet)\\n if form.validate_on_submit():\\n pet.photo_url = form.photo_url.data\\n pet.notes = form.notes.data\\n pet.available = form.available.data\\n \\n db.session.commit()\\n return redirect('/')\\n else:\\n return render_template('pet.html', pet=pet, form=form)\",\n \"def add_pet(name, age, animal='dog'):\\n print('You have a %d year old %s named %s' % (age, animal, name))\",\n \"def view_edit_pet(id):\\n pet = Pet.query.get_or_404(id)\\n form = PetEditForm(obj=pet)\\n if form.validate_on_submit():\\n form.populate_obj(pet)\\n db.session.commit()\\n\\n flash(f\\\"Updated {pet.species} named {pet.name}\\\")\\n return redirect(f'/{id}')\\n else:\\n return render_template(\\\"pet_edit_form.html\\\", form=form, pet=pet)\",\n \"def add_pet():\\n form = AddPet()\\n if form.validate_on_submit():\\n name = form.name.data\\n species = form.species.data\\n photo_url = form.photo_url.data\\n age = form.age.data\\n notes = form.notes.data\\n\\n new_pet = Pet(name=name,\\n species=species,\\n photo_url=photo_url,\\n age=age,\\n notes=notes)\\n\\n db.session.add(new_pet)\\n db.session.commit()\\n return redirect('/')\\n else:\\n return render_template('/add_form.html', form=form)\",\n \"async def info(self, ctx: \\\"IceTeaContext\\\", *, otag: TagConverter):\\n tag: models.Tag = otag\\n if not tag.alias:\\n embed = discord.Embed(description=f\\\"{ctx.message.guild.name} ``{tag.title}`` tag information\\\")\\n user = ctx.guild.get_member(tag.author)\\n embed.set_author(name=user.display_name, icon_url=user.avatar_url)\\n embed.add_field(name=\\\"Tag name\\\", value=tag.title)\\n embed.add_field(name=\\\"Amount used\\\", value=str(tag.count))\\n embed.timestamp = tag.created\\n await ctx.send(embed=embed)\\n else:\\n embed = discord.Embed(description=f\\\"{ctx.message.guild.name} ``{tag.title}`` alias information\\\")\\n user = ctx.guild.get_member(tag.author)\\n embed.add_field(name=\\\"Author\\\", value=user or \\\"Unknown\\\")\\n embed.add_field(name=\\\"Amount used\\\", value=str(tag.count))\\n embed.timestamp = tag.created\\n await ctx.send(embed=embed)\",\n \"def pet_detail_edit(pet_id):\\n\\n pet = Pet.query.get_or_404(pet_id)\\n form = PetEditForm(obj=pet)\\n\\n if form.validate_on_submit():\\n pet.photo_url = form.photo_url.data\\n pet.notes = form.notes.data\\n pet.available = form.available.data\\n\\n db.session.commit()\\n flash(f\\\"Pet{pet_id} updated!\\\")\\n return redirect(f\\\"/{pet_id}\\\")\\n\\n else:\\n return render_template(\\\"pet_detail.html\\\", form=form, pet=pet)\",\n \"def info():\\n print(\\\"Made using the OOP RPG game creator (c) Claire.\\\\n\\\")\",\n \"def plante_info(id):\\n plante = get_plante(id)\\n return render_template(\\n \\\"plante-info.html\\\",\\n plante = plante,\\n title = plante.get_name(),\\n parterre = get_parterre(plante.get_parterre()))\",\n \"def add_pet():\\n\\n form = AddPetForm()\\n\\n if form.validate_on_submit():\\n name = form.name.data\\n species = form.species.data\\n photo_url = form.photo_url.data\\n age = form.age.data\\n notes = form.notes.data\\n\\n pet = Pet(name=name, \\n species=species, \\n photo_url=photo_url, \\n age=age, \\n notes=notes)\\n db.session.add(pet)\\n db.session.commit()\\n \\n return redirect('/')\\n\\n else:\\n return render_template(\\n \\\"add_pet_form.html\\\", form=form)\",\n \"def petedit():\\n if request.method == \\\"POST\\\":\\n pet_id = request.form.get(\\\"pet_id\\\")\\n rename = request.form.get(\\\"rename\\\")\\n rows = db.execute(\\\"SELECT count(*) as count FROM owners WHERE owner_id = ? AND pet_id = ?\\\",\\n (session_get_int(\\\"user_id\\\"), pet_id, )).fetchall()\\n # Confirmed user owns this pet\\n if rows[0]['count'] == 1:\\n exp = db.execute(\\\"SELECT exp FROM pets WHERE id = ?\\\", (pet_id, )).fetchall()\\n if (int(exp[0]['exp']) >= 100):\\n db.execute(\\\"UPDATE pets SET name = ? WHERE id = ?\\\", (rename, pet_id, ))\\n con.commit()\\n\\n # Put updated info into session for pet\\n set_active_pet_in_session(session_get_int(\\\"user_id\\\"))\\n flash(\\\"Pet renamed to \\\" + rename)\\n else:\\n return apology(\\\"Your pet needs at least 100 experience to be renamed!\\\", 403)\\n\\n return redirect('/pets/edit/?id='+ str(pet_id))\\n else:\\n pet_id = int(request.args.get('id'))\\n\\n # This ensures the current user owns the pet being renamed\\n pet_info = db.execute(\\\"SELECT pets.id, pet_types.imgsrc, pet_types.pet_type, pets.created, pets.exp, pets.name, users.active_pet_id FROM owners JOIN pets ON pets.id = owners.pet_id JOIN pet_types ON pets.type = pet_types.id JOIN users ON users.id = owners.owner_id WHERE owner_id = ? AND pet_id = ?\\\", \\n (session_get_int(\\\"user_id\\\"), pet_id, )).fetchall()\\n if len(pet_info) == 1:\\n return render_template(\\\"petedit.html\\\", pet_info=pet_info)\\n else:\\n return apology(\\\"Error getting pet info\\\", 403)\",\n \"def cat_details(cat_id, shelter_id):\\n\\n shelter = petfinder.shelter_data_map(shelter_id)\\n shelter = list(shelter.values())\\n cat = petfinder.cat_data_map(cat_id)\\n cat = list(cat.values())\\n\\n return render_template('more_details.html',\\n shelter=shelter,\\n cat=cat)\\n\\n #if user selects <3 to favorite a cat then redirct to the login page\",\n \"def select_pets_by_person(conn, person_id):\\n sql = \\\"\\\"\\\"\\n SELECT person.first_name \\n || ' ' \\n || person.last_name AS `Owner`, \\n pet.name AS `Pet Name`, \\n pet.breed AS `Pet Breed`, \\n pet.age AS `Pet Age` \\n FROM person_pet \\n INNER JOIN person \\n ON person.id = person_pet.person_id \\n INNER JOIN pet \\n ON pet.id = person_pet.pet_id \\n WHERE person.id = ? \\n \\\"\\\"\\\"\\n cur = conn.cursor()\\n try:\\n cur.execute(sql, (person_id,))\\n data = cur.fetchall()\\n if data:\\n name = data[0][0]\\n print \\\"\\\\nQuerying for pets belonging to {}\\\\n\\\".format(name)\\n print sql_pp(cur, data)\\n except OperationalError, msg:\\n print \\\"SQL error {} while running our code\\\".format(msg)\",\n \"def info():\\n print 'Loading info page'\\n\\n team_list = datastore.get_all_teams(engine)\\n\\n return render_template('info.html', rows=team_list)\",\n \"def display(animal):\\n for name, valeur in animal.items(): # boucle contenant deux variables pour le nom et la valeur de chaque clef dans le dictionaire\\n print(\\\"donnée de votre animal: {} : {}\\\".format(name,valeur))\",\n \"def plants (plant_name, plant_type):\\n print (f\\\"\\\\n{plant_name.title()} is a {plant_type}. \\\\n\\\")\",\n \"def add_pet_form():\\n\\n form = AddPetForm()\\n \\n if form.validate_on_submit():\\n name = form.name.data\\n species = form.species.data\\n photo_url = form.photo_url.data\\n age = form.age.data\\n notes = form.notes.data\\n available = form.available.data\\n\\n new_pet = Pet(name=name, species=species, photo_url=photo_url, age=age, notes=notes, available=available)\\n db.session.add(new_pet)\\n db.session.commit()\\n\\n flash(f\\\"Added {name}, species: {species}, age: {age}, notes: {notes}, photo_url={photo_url}, available={available}\\\")\\n \\n return redirect(\\\"/add\\\")\\n\\n else:\\n return render_template(\\n \\\"pet_add_form.html\\\", form=form)\",\n \"def pokemon():\\n # TODO: Add form, nature and habitat to displayed table.\\n color = request.form.get(\\\"pokecolor\\\")\\n pokemon_of_color = []\\n pokemon_with_data = []\\n if color:\\n pokemon_of_color = get_pokemon_of_color(color)\\n # This is not a good implementation, because after getting the Pokemon\\n # with a certain color, now I need to get each species to extract the\\n # forms.\\n for poke in pokemon_of_color:\\n print(\\\"Extending Species: {0}\\\".format(poke))\\n extended_poke = {}\\n extended_poke[\\\"name\\\"] = poke\\n extended_poke[\\\"forms\\\"] = get_forms_of_pokemon_species(species=poke)\\n pokemon_with_data.append(extended_poke)\\n return render_template(\\\"pokemon.html.j2\\\", pokemon=pokemon_with_data)\",\n \"def show_myhero(self):\\n description = (self.name + ' Level is: ' + str(self.level) + ' Age is: ' + str(\\n self.age) + ' Rank is: ' + self.rank + ' health is: ' + str(self.health)).title()\\n print(description)\",\n \"def capteur_info(id):\\n capteur = get_capteur(id)\\n return render_template(\\n \\\"capteur-info.html\\\",\\n capteur = capteur,\\n title = capteur.get_name(),\\n parterre = get_parterre(capteur.get_parterre()),\\n mesure = get_typeMesure(capteur.get_typeMesure()))\",\n \"def show_home_page():\\n\\n pet_list = Pet.query.all()\\n return render_template('index.html', pet_list=pet_list)\",\n \"def add_pet():\\n form = PetAddForm()\\n if form.validate_on_submit():\\n pet = Pet()\\n form.populate_obj(pet)\\n db.session.add(pet)\\n db.session.commit()\\n\\n flash(f\\\"Added {form.species.data} named {form.name.data}\\\")\\n return redirect('/')\\n else:\\n return render_template(\\\"add_pet_form.html\\\", form=form)\",\n \"def create_pet(pet_name, pet_type, pet_breed, pet_gender, \\n pet_color, pet_status, pet_image, last_address):\\n\\n pet = Pet(pet_name=pet_name,\\n pet_type=pet_type, \\n pet_breed=pet_breed, \\n pet_gender=pet_gender,\\n pet_color=pet_color,\\n pet_status=pet_status,\\n pet_image=pet_image,\\n last_address=last_address)\\n\\n db.session.add(pet)\\n db.session.commit()\\n\\n return pet\",\n \"def show_myhero(self):\\n description = (self.name + ' Level is: ' + str(self.level) + ' Age is: ' + str(\\n self.age) + ' Rank is: ' + self.rank + ' health is: ' + str(self.health) + ' magic is: ' + str(self.__magic)).title()\\n print(description)\",\n \"def shelterGetPets(url, URL_JSON_KEY, shelter_id):\\n \\n method = \\\"shelter.getPets?\\\"\\n count = \\\"&count=100\\\"\\n url+= method + URL_JSON_KEY + shelter_id + count\\n petJson = urlopen(url)\\n petInfo = load(reader(petJson))\\n return petInfo\",\n \"def show_info(self):\\n txt = \\\"Brand: %s\\\\nModel: %s\\\\nHostname: %s\\\\n\\\"%(self.brand, self.model, self.hostname)\\n return txt\",\n \"def add_pet():\\n form = AddPetForm()\\n\\n print(form.validate_on_submit())\\n for value in form:\\n print(value.data)\\n\\n if form.validate_on_submit():\\n name = form.name.data\\n species = form.species.data\\n photo_url = form.photo_url.data\\n age = form.age.data\\n notes = form.notes.data\\n new_pet = Pets(name=name,\\n species=species,\\n photo_url=photo_url,\\n age=age,\\n notes=notes)\\n\\n db.session.add(new_pet)\\n db.session.commit()\\n flash(f\\\"Succesfully registered {new_pet.name}\\\")\\n return redirect('/')\\n else:\\n return render_template('add.html', form=form)\",\n \"def pet_add_form():\\n\\n form = PetAddForm()\\n\\n if form.validate_on_submit():\\n name = form.name.data\\n species = form.species.data\\n photo_url = form.photo_url.data\\n age = form.age.data\\n notes = form.notes.data\\n\\n flash(f\\\"Added a {species} called {name}\\\")\\n\\n pet = Pet(\\n name=name,\\n species=species,\\n photo_url=photo_url if photo_url != '' else None,\\n age=age,\\n notes=notes\\n )\\n\\n db.session.add(pet)\\n db.session.commit()\\n\\n return redirect(\\\"/\\\")\\n\\n else:\\n return render_template(\\\"pet_add_form.html\\\", form=form)\",\n \"def show_info(self): \\n color= Fore.WHITE\\n print(f\\\"\\\"\\\" {color} \\nNombre: {self.name} \\nRuta: {self.route }\\nFecha de salida: {self.departure_date}\\\"\\\"\\\")\\n print(\\\"<\\\"*8, \\\">\\\"*8)\\n print(\\\"El precio por habitacion es:\\\")\\n for key, value in self.prize.items():\\n color_value= (Fore.GREEN + str(value))\\n color_key= Fore.WHITE + \\\"Habitacion\\\" + \\\" \\\" + key\\n print(f\\\"\\\"\\\" {color_key} : {color_value}$ \\\"\\\"\\\")\\n \\n print(Fore.WHITE + \\\"<\\\"*8, \\\">\\\"*8)\\n for floor, info in self.floors_info.items():\\n piso=(Fore.WHITE + floor)\\n print(f\\\" {piso}:{info} \\\")\\n \\n \\n print(\\\"<\\\"*8, \\\">\\\"*8)\\n print(\\\"Capacidad por tipo de habitacion: \\\")\\n for key, value in self.room_capacity.items():\\n print(f\\\"Habitacion {key}: {value} personas \\\",\\\"\\\\t\\\")\\n return \\\"\\\"\",\n \"async def _me_pets(self, ctx):\\n if has_post_permission(ctx.guild.id, ctx.channel.id):\\n messages = ch.print_pets(ctx.user_object)\\n await self.paginate(ctx, messages)\",\n \"def ProfService_detail(request, pk):\\n ProfService = get_object_or_404(PServices, pk=pk)\\n \\n return render(request, \\\"ProfService_detail.html\\\", {\\\"ProfService\\\": ProfService})\",\n \"def show_person(uuid=None, fanchart=False):\\n t0 = time.time()\\n uuid = request.args.get(\\\"uuid\\\", uuid)\\n fanchart_shown = request.args.get(\\\"fanchart\\\", fanchart)\\n dbg = request.args.get(\\\"debug\\\", None)\\n u_context = UserContext(user_session, current_user, request)\\n\\n with PersonReaderTx(\\\"read_tx\\\", u_context) as service:\\n result = service.get_person_data(uuid)\\n\\n # result {'person':PersonBl, 'objs':{uniq_id:obj}, 'jscode':str, 'root':{root_type,root_user,batch_id}}\\n if Status.has_failed(result):\\n flash(f'{result.get(\\\"statustext\\\",\\\"error\\\")}', \\\"error\\\")\\n person = result.get(\\\"person\\\")\\n objs = result.get(\\\"objs\\\", [])\\n print(f\\\"# Person with {len(objs)} objects\\\")\\n jscode = result.get(\\\"jscode\\\", \\\"\\\")\\n # Batch or Audit node data like {'root_type', 'root_user', 'id'}\\n person.root = result.get(\\\"root\\\")\\n\\n stk_logger(u_context, f\\\"-> bp.scene.routes.show_person n={len(objs)}\\\")\\n\\n last_year_allowed = datetime.now().year - shareds.PRIVACY_LIMIT\\n may_edit = current_user.has_role(\\\"audit\\\") # or current_user.has_role('admin')\\n # may_edit = 0\\n return render_template(\\n \\\"/scene/person.html\\\",\\n person=person,\\n obj=objs,\\n jscode=jscode,\\n menuno=12,\\n debug=dbg,\\n last_year_allowed=last_year_allowed,\\n elapsed=time.time() - t0,\\n user_context=u_context,\\n may_edit=may_edit,\\n fanchart_shown=fanchart_shown,\\n )\",\n \"def print_food(self):\\n for dish in self.food:\\n print(dish.get_name())\",\n \"def show_data(self, ):\\r\\n return print('society_name : {}\\\\n'\\r\\n 'flat : {}\\\\n'\\r\\n 'house_no : {}\\\\n'\\r\\n 'no_of_members : {}\\\\n'\\r\\n 'income : {}\\\\n '\\r\\n .format(self.society_name, self.flat, self.house_no, self.no_of_members, self.income))\",\n \"def info(self, id):\",\n \"def display_profile(self):\\n print(f\\\"Id: {self._id}\\\")\\n print(f\\\"username: {self.username}\\\")\\n print(f\\\"name: {self.name}\\\")\\n print(f\\\"contact: {self.contact}\\\")\\n print(f\\\"address: {self.address}\\\")\",\n \"def adopt():\\n if request.method == \\\"POST\\\":\\n # Check if user bought a pet\\n if not request.form.get(\\\"pet_type\\\"):\\n return apology(\\\"must choose pet type\\\", 403)\\n pet_type_id = int(request.form.get(\\\"pet_type\\\"))\\n petname = db.execute(\\n \\\"SELECT pet_type FROM pet_types WHERE id = ?\\\", (pet_type_id, )).fetchall()\\n\\n # Create pet with default name as pet type\\n petid = (db.execute(\\\"INSERT INTO pets(type, name, exp, created) VALUES (?, ?, ?, CURRENT_TIMESTAMP)\\\",\\n (pet_type_id, petname[0]['pet_type'], 0 ))).lastrowid\\n con.commit()\\n\\n # Add owner to pet\\n db.execute(\\\"INSERT INTO owners(owner_id, pet_id) VALUES (?, ?)\\\",\\n (session_get_int(\\\"user_id\\\"), petid))\\n con.commit()\\n\\n # Set as user's active pet\\n db.execute(\\\"UPDATE users SET active_pet_id = ? WHERE id = ?\\\",\\n (petid, session_get_int(\\\"user_id\\\") ))\\n con.commit()\\n set_active_pet_in_session(session_get_int(\\\"user_id\\\"))\\n return redirect(\\\"/pets\\\")\\n\\n else:\\n rows = db.execute(\\\"SELECT * FROM pet_types\\\").fetchall()\\n if len(rows) < 1:\\n return apology(\\\"no pets\\\", 403)\\n return render_template(\\\"adopt.html\\\", pet_types=rows)\",\n \"def __repr__(self):\\n return \\\"{} hp:{:.1f} stunned: {} potions: {}\\\".format(self.name, self.hitpoints,\\n self.stunned, self.potions)\",\n \"def __str__(self):\\n return (\\n 'Name:\\\\t{}\\\\nType:\\\\t{}\\\\nAge:\\\\t{}'.format(self.__name,\\n self.__animal_type,\\n self.__age))\",\n \"def get_pet(pet=\\\"dog\\\"):\\n pets = dict(dog=Dog(\\\"Hope\\\"))\\n return pets[pet]\",\n \"def getStatus(url, URL_JSON_KEY,pet_id):\\n \\n method = \\\"pet.get?\\\"\\n petId = \\\"&id=\\\"+pet_id\\n url+= method + URL_JSON_KEY + petId\\n petJson = urlopen(url)\\n petsInfo = load(reader(petJson))\\n message = petsInfo['petfinder']['header']['status']['code']['$t']\\n if message == '100':\\n status = petsInfo['petfinder']['pet']['status']['$t']\\n return status\\n elif message =='201':\\n status = \\\"Animal Removed\\\"\\n return status\\n else:\\n print(\\\"Something went wrong. Sorry.\\\")\",\n \"def printPokemon():\\n print(\\\" _ \\\")\\n print(\\\" _ __ ___ | | _____ _ __ ___ ___ _ __ \\\")\\n print(\\\" | '_ \\\\ / _ \\\\| |/ / _ \\\\ '_ ` _ \\\\ / _ \\\\| '_ \\\\ \\\")\\n print(\\\" | |_) | (_) | < __/ | | | | | (_) | | | |\\\")\\n print(\\\" | .__/ \\\\___/|_|\\\\_\\\\___|_| |_| |_|\\\\___/|_| |_|\\\")\\n print(\\\" |_| \\\")\",\n \"def get_description(self):\\n print(\\\"This Iron door.\\\")\",\n \"def get(self):\\n app.logger.info('Request to list Pets...')\\n pets = []\\n category = request.args.get('category')\\n name = request.args.get('name')\\n available = request.args.get('available')\\n if category:\\n app.logger.info('Filtering by category: %s', category)\\n pets = Pet.find_by_category(category)\\n elif name:\\n app.logger.info('Filtering by name:%s', name)\\n pets = Pet.find_by_name(name)\\n elif available:\\n app.logger.info('Filtering by available: %s', available)\\n is_available = available.lower() in ['yes', 'y', 'true', 't', '1']\\n pets = Pet.find_by_availability(is_available)\\n else:\\n pets = Pet.all()\\n\\n app.logger.info('[%s] Pets returned', len(pets))\\n results = [pet.serialize() for pet in pets]\\n return results, status.HTTP_200_OK\",\n \"def showInfo(p,personDict):\\n info1 = personDict['EnterpriseID'][p[0]]\\n info2 = personDict['EnterpriseID'][p[1]]\\n print (\\\"Person A:\\\",info1)\\n print (\\\"Person B:\\\",info2)\",\n \"def info(self):\",\n \"def info(self):\",\n \"def get(self):\\n app.logger.info('Request to list Pets...')\\n pets = []\\n category = request.args.get('category')\\n name = request.args.get('name')\\n available = request.args.get('available')\\n if category:\\n app.logger.info('Filtering by category: %s', category)\\n pets = Pet.find_by_category(category)\\n elif name:\\n app.logger.info('Filtering by name:%s', name)\\n pets = Pet.find_by_name(name)\\n elif available:\\n app.logger.info('Filtering by available: %s', available)\\n pets = Pet.find_by_availability(available)\\n else:\\n pets = Pet.all()\\n\\n app.logger.info('[%s] Pets returned', len(pets))\\n results = [pet.serialize() for pet in pets]\\n return results, status.HTTP_200_OK\",\n \"def show_user_info(self):\\n name = self.get_user_name()\\n print(f'Name: {name.title()}')\\n print(f'Age: {self.age}')\\n print(f'Gender: {self.gender.title()}')\\n print(f'Mobile: {self.m_number}')\",\n \"def info(id):\\n sql = \\\"select distinct name, description, stars, url, last_push_date, repo_id, created_date, avatar from python_repos where repo_id=\\\"+id\\n db = get_db()\\n cursor = db.execute(sql)\\n repo_info = cursor.fetchall()\\n return render_template('repo.html',info=repo_info)\",\n \"def player_information():\\n if request.method == 'POST':\\n result = request.form\\n if request.files:\\n playerImage = request.files['playerImage'].read()\\n else:\\n playerImage = None\\n player = Player(player_image=playerImage, player_fname=result['player_first_name'],\\n player_lname=result['player_last_name'], team_id=result['team_selected'])\\n db.session.add(player)\\n db.session.commit()\\n teams = get_team()\\n if teams:\\n return render_template('team-players.html', teams=teams)\\n else:\\n return render_template('team-players.html')\",\n \"def get_info(self):\\n if self.own_home:\\n return print(f'Hi! My name is {self.name}, I\\\\'m {self.age}. Currently I have {self.own_home} house')\\n return print(f'Hi! My name is {self.name}, I\\\\'m {self.age}. I don\\\\'t have any home now!')\",\n \"def home():\\n pets = Pets.query.all()\\n return render_template('home.html', pets=pets)\",\n \"async def info(self, ctx, *, tag):\\n try:\\n self.fetch_tag(ctx, tag)\\n except Exception as error:\\n return await ctx.send(error)\\n data = self._tag_dict[ctx.guild.id][tag]\\n author = self.bot.get_user(data['author']) or await self.bot.fetch_user(data['author'])\\n embed = discord.Embed(colour=self.bot.colour)\\n embed.title = tag\\n embed.description = f\\\"<:author:734991429843157042> **{author}**\\\\n\\\"\\n embed.description += f\\\"Uses: **{data['uses']}**\\\\n\\\"\\n embed.description += f\\\"ID: **{data['id']}**\\\"\\n embed.set_author(name=str(author), icon_url=author.avatar_url)\\n await ctx.send(embed=embed)\",\n \"def displayInfo(self, model):\\n\\t\\ttaglist = []\\n\\t\\tfor tag in model.tags:\\n\\t\\t\\ttaglist.append(tag.tagname)\\n\\t\\tself.infoText.SetPage(infoTemplate.render(model=model, tags= ','.join(taglist)))\"\n]","isConversational":false},"negative_scores":{"kind":"list like","value":["0.814422","0.81127334","0.7560209","0.7560209","0.7560209","0.7560209","0.7532049","0.7495639","0.7495639","0.7481198","0.74389154","0.7438613","0.7393894","0.7373882","0.7327594","0.7288435","0.7232166","0.7204182","0.7189433","0.71606326","0.6922216","0.6914054","0.6829571","0.678672","0.6761083","0.65555423","0.6408576","0.64032227","0.6334595","0.628814","0.6278628","0.6256026","0.61743444","0.6161504","0.6108322","0.6032514","0.6003543","0.59961563","0.599161","0.59755623","0.5947048","0.5941656","0.59175944","0.5907323","0.5907015","0.5894077","0.5867727","0.58512723","0.5850068","0.5840473","0.58214223","0.578853","0.57840693","0.57460135","0.57058364","0.570507","0.5676385","0.56659985","0.5656396","0.5630068","0.5625555","0.5599401","0.55917","0.5579285","0.55579174","0.55449843","0.5528939","0.5523459","0.5509166","0.5493441","0.5479511","0.54789287","0.54766345","0.5456067","0.5448645","0.541864","0.5414118","0.5410661","0.5407467","0.5407269","0.5402634","0.5391949","0.53878677","0.5386316","0.5382473","0.5373532","0.5364868","0.53584474","0.5354693","0.53533375","0.53533375","0.5349497","0.5337187","0.5330489","0.5326229","0.531124","0.5305145","0.53051364","0.5303378"],"string":"[\n \"0.814422\",\n \"0.81127334\",\n \"0.7560209\",\n \"0.7560209\",\n \"0.7560209\",\n \"0.7560209\",\n \"0.7532049\",\n \"0.7495639\",\n \"0.7495639\",\n \"0.7481198\",\n \"0.74389154\",\n \"0.7438613\",\n \"0.7393894\",\n \"0.7373882\",\n \"0.7327594\",\n \"0.7288435\",\n \"0.7232166\",\n \"0.7204182\",\n \"0.7189433\",\n \"0.71606326\",\n \"0.6922216\",\n \"0.6914054\",\n \"0.6829571\",\n \"0.678672\",\n \"0.6761083\",\n \"0.65555423\",\n \"0.6408576\",\n \"0.64032227\",\n \"0.6334595\",\n \"0.628814\",\n \"0.6278628\",\n \"0.6256026\",\n \"0.61743444\",\n \"0.6161504\",\n \"0.6108322\",\n \"0.6032514\",\n \"0.6003543\",\n \"0.59961563\",\n \"0.599161\",\n \"0.59755623\",\n \"0.5947048\",\n \"0.5941656\",\n \"0.59175944\",\n \"0.5907323\",\n \"0.5907015\",\n \"0.5894077\",\n \"0.5867727\",\n \"0.58512723\",\n \"0.5850068\",\n \"0.5840473\",\n \"0.58214223\",\n \"0.578853\",\n \"0.57840693\",\n \"0.57460135\",\n \"0.57058364\",\n \"0.570507\",\n \"0.5676385\",\n \"0.56659985\",\n \"0.5656396\",\n \"0.5630068\",\n \"0.5625555\",\n \"0.5599401\",\n \"0.55917\",\n \"0.5579285\",\n \"0.55579174\",\n \"0.55449843\",\n \"0.5528939\",\n \"0.5523459\",\n \"0.5509166\",\n \"0.5493441\",\n \"0.5479511\",\n \"0.54789287\",\n \"0.54766345\",\n \"0.5456067\",\n \"0.5448645\",\n \"0.541864\",\n \"0.5414118\",\n \"0.5410661\",\n \"0.5407467\",\n \"0.5407269\",\n \"0.5402634\",\n \"0.5391949\",\n \"0.53878677\",\n \"0.5386316\",\n \"0.5382473\",\n \"0.5373532\",\n \"0.5364868\",\n \"0.53584474\",\n \"0.5354693\",\n \"0.53533375\",\n \"0.53533375\",\n \"0.5349497\",\n \"0.5337187\",\n \"0.5330489\",\n \"0.5326229\",\n \"0.531124\",\n \"0.5305145\",\n \"0.53051364\",\n \"0.5303378\"\n]","isConversational":false},"document_score":{"kind":"string","value":"0.75088006"},"document_rank":{"kind":"string","value":"8"}}},{"rowIdx":23788,"cells":{"query":{"kind":"string","value":"Returns a normalized data. If the data embed a numpy data or a dataset it is returned. Else returns the input data."},"document":{"kind":"string","value":"def _normalizeData(data):\n if isinstance(data, H5Node):\n if data.is_broken:\n return None\n return data.h5py_object\n return data"},"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 normalizeData(self, data):\n return _normalizeData(data)","def normalizeData(self, data):\n return _normalizeData(data)","def normalized_data(self):\n return self._data","def normalize_dataset(self):","def normalized_data(self):\n return self._normalization_constant * self._data","def _prep_data(self, data, func_input_dtype):\n if func_input_dtype in (None, 'DataArray'):\n return data\n if func_input_dtype == 'Dataset':\n # TODO: add logic that creates a single Dataset comprising all of\n # the DataArray objects in `data`.\n raise NotImplementedError(\"func_input_dtype of `Dataset` not yet \"\n \"implemented.\")\n if func_input_dtype == 'numpy':\n self.coords = data[0].coords\n return [d.values for d in data]","def preprocess_data(self, data, scale_data=True):\n print(\"preprocess_data not implemented\")\n return data","def normalize_data(data):\n mean = np.mean(data)\n std = np.std(data)\n return (data - mean) / std","def _preprocess(self, data, normalize=False) -> np.ndarray:\n \n preprocessor = StandardScaler() if not normalize else Normalizer()\n\n data = preprocessor.fit_transform(data)\n \n return data","def normalize_data(self, data):\n self.find_mean_std(data)\n return (data - self._data_mean) / self._data_std","def unNormalizeData(normalizedData, data_mean, data_std, dimensions_to_ignore):\n T = normalizedData.shape[0]\n D = data_mean.shape[0]\n\n origData = np.zeros((T, D), dtype=np.float32)\n dimensions_to_use = []\n for i in range(D):\n if i in dimensions_to_ignore:\n continue\n dimensions_to_use.append(i)\n dimensions_to_use = np.array(dimensions_to_use)\n\n origData[:, dimensions_to_use] = normalizedData\n\n # potentially ineficient, but only done once per experimentdata_conversions\n stdMat = data_std.reshape((1, D))\n stdMat = np.repeat(stdMat, T, axis=0)\n meanMat = data_mean.reshape((1, D))\n meanMat = np.repeat(meanMat, T, axis=0)\n origData = np.multiply(origData, stdMat) + meanMat\n return origData","def normalize(dataset):\n return normalize_standard_deviation(normalize_mean(dataset))","def _convert_data(self, data):\n if isinstance(data, Tensor):\n data = data.asnumpy()\n elif isinstance(data, list):\n data = np.array(data)\n elif isinstance(data, np.ndarray):\n pass\n else:\n raise TypeError('Input data type must be tensor, list or numpy.ndarray')\n return data","def normalize_data(data, n=1):\n\n if isinstance(data, str):\n # TODO: could Antti comment on this?\n # numpy array initialization works unintuitively with strings\n data = np.array([[data]], dtype=object)\n else:\n data = np.atleast_1d(data)\n\n if data.ndim == 1:\n if data.shape[0] == n:\n data = data[:, None]\n else:\n data = data[None, :]\n if n > 1:\n data = np.vstack((data, ) * n)\n else:\n if data.shape[0] != n:\n data = data[None, :]\n if n > 1:\n data = np.vstack((data, ) * n)\n return data","def normalise(raw_data, normalise_by_column=False):\n data = raw_data\n if normalise_by_column:\n #normaliza valores usando o maximo de cada coluna\n col_maxes = raw_data.max(axis=0)\n #divide cada valor pelo maximo correspondente de cada coluna\n data = raw_data / col_maxes[np.newaxis,:] \n else:\n #divide todos os valores pelo maximo do dataset (tudo na mesma escala)\n data = raw_data / raw_data.max()\n\n return data","def denormalise_0_1(value_or_array, array_min, array_max):\n if isinstance(value_or_array, list):\n raise ValueError('this function accepts arraylike data, not a list. '\n 'Please check data or convert list to numpy array')\n elif isinstance(value_or_array, float):\n denormalised = value_or_array * (array_max - array_min) + array_min\n elif isinstance(value_or_array, np.ndarray):\n denormalised = value_or_array * (array_max - array_min) + array_min\n elif isinstance(value_or_array, pd.Series):\n denormalised = value_or_array * (array_max - array_min) + array_min\n else:\n sys.stdout.write(\"Unknown datatype. denormalise_0_1 has been given an input that does not appear to be \"\n \"an int, float, np.ndarray or pandas Series\\n\"\n \"Attempting to process as if it is arraylike.....\")\n return denormalised","def normalize(data):\n # normalize data and return\n # https://stackoverflow.com/questions/29661574/normalize-numpy-array-columns-in-python\n return (data - data.min(axis=0)) / data.ptp(axis=0)","def _convert_data(data, inf_ok=False, min_len=1):\n # If it's scalar, convert to array\n if np.isscalar(data):\n data = np.array([data], dtype=float)\n\n # Convert data to NumPy array\n data = np.array(data, dtype=float)\n\n # Make sure it is 1D\n if len(data.shape) != 1:\n raise RuntimeError(\"Input must be a 1D array or Pandas series.\")\n\n # Remove NaNs\n data = data[~np.isnan(data)]\n\n # Check for infinite entries\n if not inf_ok and np.isinf(data).any():\n raise RuntimeError(\"All entries must be finite.\")\n\n # Check to minimal length\n if len(data) < min_len:\n raise RuntimeError(\n \"Array must have at least {0:d} non-NaN entries.\".format(min_len)\n )\n\n return data","def canonicalize(data):\n\n if isinstance(data, np.ndarray):\n if data.dtype == np.uint8:\n data = data.astype(np.float32) / 255.0\n data = torch.from_numpy(np.ascontiguousarray(data))\n\n elif isinstance(data, torch.Tensor):\n if data.dtype == torch.uint8:\n data = data.float() / 255.0\n\n else:\n raise NotImplementedError()\n\n return data","def _canonize_input(self, dataset):\n unpack = lambda x: x\n if isinstance(dataset, _tc.SArray):\n dataset = _tc.SFrame({self.feature: dataset})\n elif isinstance(dataset, _tc.Image):\n dataset = _tc.SFrame({self.feature: [dataset]})\n unpack = lambda x: x[0]\n return dataset, unpack","def _check_and_transform_input(self, data):\n if isinstance(data, list):\n if np.array(data).shape == (len(data),):\n if len(data) == 1:\n data = np.array(data).reshape(1, 1)\n data = np.array(data).reshape(len(data), 1)\n else:\n data = np.concatenate(data).reshape(len(data), -1)\n else:\n raise TypeError('Input data should be of type list, but found type {}'.format(type(data)))\n\n return data","def _norm_data(data):\n if data is None:\n return data\n data_min = np.min(data)\n c_norm = np.max(data) - data_min\n return (data - data_min) / c_norm if (c_norm != 0) else (data - data_min)","def _format_data(self, data: np.ndarray) -> np.ndarray:\n if self._validate:\n if len(data.shape) <= 1:\n raise DataProcessorError(\n \"The data should be an array with at least two dimensions.\"\n )\n\n return data","def normalize_data(data=None):\n # Data pre-processing\n n = data.shape[0]\n for i in range(n):\n xx = data[i,:,:]\n xx -= np.mean(xx) # Centering in 0\n xx /= np.linalg.norm(xx) # Normalizing to 1\n data[i] = xx # Affect value\n return data","def normalize(data):\n data_range = data.max() - data.min()\n #if data_range == 0.:\n # sys.exit(\"data.max() - data.min() == 0. !\")\n if stddev != 0.:\n data = (data - data.min()) / data_range\n\n return data","def normalize_data(data):\n if data.element_spec[0].shape[2] == 1:\n data = data.map(lambda x, y: (tf.image.grayscale_to_rgb(\n tf.image.resize(x, [32, 32])), y))\n else:\n data = data.map(lambda x, y: (tf.image.resize(x, [32, 32]), y))\n normalization_layer = tf.keras.layers.experimental.preprocessing.Rescaling(1. / 255)\n normalized_ds = data.map(lambda x, y: (normalization_layer(x), y))\n return normalized_ds","def norm_data(self):\n if (self.nrows, self.ncolumns) < self.data.shape:\n self.data = self.data[0:self.nrows, 0:self.ncolumns]\n if self.data.dtype != np.float64:\n self.data = self.data.astype(np.float64)\n self.meanval = self.data.mean()\n self.stdval = self.data.std()","def _de_transform(self, data):\r\n mean, variance = self._input_statistics.overall_feature_moments\r\n return data * variance + mean","def _raw_data(self):\n data = self.datasource.as_array()\n if not isinstance(data, np.ndarray):\n raise TypeError(\"The data you try to load is no numpy array!\")\n if data.ndim != 2:\n raise ValueError(\"The data array you try to load does not have 2 \"\n \"dimensions!\")\n data = data.copy(order='C')\n ident = hashlib.md5(data).hexdigest()\n if self._dataident is None:\n self._dataident = ident\n elif self._dataident != ident:\n raise ValueError(\"The data you try to load from '%s' has changed \"\n \"since the last time. Please, check the \"\n \"datasource of the record '%s'.\"\n % (self.datasource.name, self.name))\n\n return data","def normalize(dataset):\n minVals = dataset.min(axis=0)\n maxVals = dataset.max(axis=0)\n factors = maxVals-minVals\n num = dataset.shape[0]\n norm_data = (dataset - np.tile(minVals,(num,1)))/np.tile(factors,(num,1)) \n return norm_data","def standardization(input_data):\n\n # Insert debugging assertions\n assert type(input_data) is np.ndarray, \"The 'input_data' must be numpy array.\"\n\n # Get the mean values and the standard deviations of the input numpy array along the axis \n Mean = np.mean(input_data, axis = 0)\n Std = np.std(input_data, axis = 0)\n\n # Standardization \n standardized_input_data = (input_data - Mean) / (Std + sys.float_info.min)\n\n # Return standardized input data\n return standardized_input_data","def load_own_data():\n data = Data()\n features = data.read_selected_features()\n dataframe = data.get_dataframe_with(features)\n return normalize(dataframe.values)","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 _transform(self, data):\r\n mean, variance = self._input_statistics.overall_feature_moments\r\n return (data - mean) / variance","def _validate_input(self, data: Union[np.ndarray, pd.DataFrame, pd.Series],\n expected_dim: int, inference: bool = False) -> np.ndarray:\n allowed_types = (\n np.ndarray,\n pd.core.frame.DataFrame,\n pd.core.frame.Series\n )\n\n if type(data) not in allowed_types:\n raise TypeError('Supported input types: np.ndarray, '\n 'pd.core.frame.DataFrame, pd.core.frame.Series got'\n ' {}'.format(type(data)))\n\n if isinstance(data, pd.DataFrame) or isinstance(data, pd.Series):\n data = data.values\n\n if data.size == 0:\n raise ValueError('Empty array passed to fit() or predict()')\n\n if data.ndim > expected_dim:\n raise ValueError('Data with incorrect number of dimensions '\n 'passed to fit() or predict(). Max dim is '\n '{}, got {}'.format(expected_dim, data.ndim))\n\n if not np.issubdtype(data.dtype, np.number):\n raise ValueError('Non numeric value found in data')\n\n if not np.isfinite(data).all():\n raise ValueError('Data contains nan or inf')\n\n if inference:\n # additional checks on prediction time\n if not self._fitted:\n raise ValueError('Fit the model first.')\n\n if self._ndim == 2 and data.shape[-1] != self._shape[-1]:\n raise ValueError('Number of features does not match'\n ' data model was trained on. Expected'\n ' {}, got {}'\n .format(self._shape[-1], data.shape[-1]))\n\n return data","def min_max_normalization(input_data):\n\n # Insert debugging assertions\n assert type(input_data) is np.ndarray, \"The 'input_data' must be numpy array.\"\n\n # Get the minimum and maximun values of the input numpy array along the axis \n Max = np.max(input_data, axis = 0)\n Min = np.min(input_data, axis = 0)\n\n # Min-max normalization \n normalized_input_data = (input_data - Min) / (Max - Min + sys.float_info.min)\n\n # Return normalized input data\n return normalized_input_data","def normalize(self):\n d = learning_utils.convert_data_to_2d(self._data)\n d = learning_utils.normalize_2d(d)\n self._data = learning_utils.convert_data_to_1d(d)","def normalize_data(self):\n self.x_mean, self.x_std = du.get_mean_std(self.x_train)\n self.x_train = du.normalize(self.x_train, self.x_mean, self.x_std)\n if self.x_test is not None and self.y_test is not None:\n self.x_test = du.normalize(self.x_test, self.x_mean, self.x_std)\n self.normalized_data = True","def normalize(data):\n\n\t#return [float(x) / pow(2, 15) for x in data]\n\n\tl = [float(x) / pow(2, 15) for x in data]\n\treturn np.asarray(l)","def _handle_input_data(data):\n data = np.asarray(data)\n if np.ndim(data) == 1:\n d_rows = 1\n d_cols = len(data)\n data = data.reshape((1, data.shape[0]))\n elif np.ndim(data) == 2:\n d_rows = data.shape[0]\n d_cols = data.shape[1]\n else:\n raise ValueError(\"Incorrect dimensionality of data. Must be <= 2\")\n return data, d_rows, d_cols","def _convert_data(self, data):\n if isinstance(data, np.ndarray):\n data = data.astype(float_save(), copy=False)\n if self.compress:\n return KaldiCompressedMatrix.compress(data, self.compression_method)\n return KaldiMatrix(data)\n\n if isinstance(data, KaldiMatrix):\n if self.compress:\n return KaldiCompressedMatrix.compress(data, self.compression_method)\n return data\n\n if isinstance(data, KaldiCompressedMatrix):\n if not self.compress:\n return data.to_matrix()\n return data\n\n raise ValueError(\"Data is not ndarray or KaldiMatrix\")","def handle_input(data, device=None):\n if type(data) == tuple:\n x, y = data\n x = torch.tensor(x, dtype=torch.float)\n y = torch.tensor(y, dtype=torch.float)\n if not device is None:\n x = x.to(device)\n y = y.to(device)\n return x, y\n if type(data) == np.ndarray:\n x = torch.tensor(data, dtype=torch.float)\n if not device is None:\n x = x.to(device)\n return x\n else:\n return data","def scale(self, data: np.ndarray):\n if self.scale_type == \"min_max\":\n scaled_data = (data - self.predictor_min) / (\n self.predictor_max - self.predictor_mean\n )\n elif self.scale_type == \"normalize\":\n scaled_data = (data - self.predictor_mean) / (\n self.predictor_max - self.predictor_min\n )\n elif self.scale_type == \"standardize\":\n scaled_data = (data - self.predictor_mean) / self.predictor_std\n elif self.scale_type == \"scale\":\n scaled_data = data - self.predictor_mean\n else:\n scaled_data = data\n return scaled_data","def unscale_data(self, data):\n return (data + self.mean)*self.std","def process(self, data):\n data = np.atleast_2d(data)\n\n if self.orientation == 'row':\n return data\n else:\n return data.T","def _preprocess(self, data):\n\n # pipeline: first call the previous statistics:\n if self.previous_statistics is not None:\n data = self.previous_statistics.statistics(data)\n # the first of the statistics need to take list as input, in order to match the API. Then actually the\n # transformations work on np.arrays. In fact the first statistic transforms the list to array. Therefore, the\n # following code needs to be called only if the self statistic is the first, i.e. it does not have a\n # previous_statistic element.\n else:\n data = self._check_and_transform_input(data)\n\n return data","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 _convert_dataset(self):\n allowed_vars = ['x', 'y', 'z', 'tvu', 'thu']\n dtyp = [(varname, self.data[varname].dtype) for varname in allowed_vars if varname in self.data]\n empty_struct = np.empty(len(self.data['x']), dtype=dtyp)\n for varname, vartype in dtyp:\n empty_struct[varname] = self.data[varname].values\n self.data = empty_struct","def _process(self, data: np.ndarray) -> np.ndarray:\n return data[..., 0] * self.scale","def _process(self, data: np.ndarray) -> np.ndarray:\n return data[..., 1] * self.scale","def _normalize(empty, user_data):\n if isinstance(user_data, UserData):\n return user_data.to_dict()\n if isinstance(user_data, dict):\n return _dict_to_dict(empty, user_data)\n if isinstance(user_data, (bytes, bytearray)):\n return _bytes_to_dict(empty, user_data)\n if user_data is None:\n return empty\n raise ValueError","def _process(self, data: np.ndarray) -> np.ndarray:\n nominals = unp.nominal_values(data)\n min_y, max_y = np.min(nominals), np.max(nominals)\n\n return (data - min_y) / (max_y - min_y)","def normalize(data):\n data = lowercase(data)\n data = remove_punct(data)\n data = remove_apostrophes(data)\n data = remove_stopwords(data)\n data = num_to_words(data)\n data = lemmatize(data)\n data = stemming(data)\n data = remove_punct(data)\n data = num_to_words(data)\n data = lemmatize(data)\n data = stemming(data)\n data = remove_punct(data) #done again to remove hyphens produced by num2words\n data = remove_stopwords(data) #done agan to remove stopwords produced by num2words\n return data","def _format_data(self, data: np.ndarray) -> np.ndarray:\n if self._validate:\n if len(data.shape) <= self._axis:\n raise DataProcessorError(\n f\"Cannot average the {len(data.shape)} dimensional \"\n f\"array along axis {self._axis}.\"\n )\n\n return data","def normalizeData(pre_signal):\n\n if sp.any(sp.isnan(pre_signal)):\n print('there are NaNs in the data matrix, making them zero')\n\n pre_signal[sp.isnan(pre_signal)] = 0\n mean_vector = sp.mean(pre_signal, axis=0, keepdims=True)\n normed_signal = pre_signal - mean_vector\n norm_vector = sp.linalg.norm(normed_signal, axis=0, keepdims=True)\n norm_vector[norm_vector == 0] = 1e-116\n normed_signal = normed_signal / norm_vector\n\n return normed_signal, mean_vector, norm_vector","def norm_data(data):\n return (data-np.min(data))/(np.max(data)-np.min(data))","def _format_data(self, data: np.ndarray) -> np.ndarray:\n self._n_shots = 0\n\n # identify shape\n try:\n # level1 single-shot data\n self._n_circs, self._n_shots, self._n_slots, self._n_iq = data.shape\n except ValueError as ex:\n raise DataProcessorError(\n f\"The data given to {self.__class__.__name__} does not have the shape of \"\n \"single-shot IQ data; expecting a 4D array.\"\n ) from ex\n\n if self._validate:\n if data.shape[-1] != 2:\n raise DataProcessorError(\n f\"IQ data given to {self.__class__.__name__} must be a multi-dimensional array\"\n \"of dimension [d0, d1, ..., 2] in which the last dimension \"\n \"corresponds to IQ elements.\"\n f\"Input data contains element with length {data.shape[-1]} != 2.\"\n )\n\n if self._validate:\n if isinstance(self._discriminator, list):\n if self._n_slots != len(self._discriminator):\n raise DataProcessorError(\n f\"The Discriminator node has {len(self._discriminator)} which does \"\n f\"not match the {self._n_slots} slots in the data.\"\n )\n\n return unp.nominal_values(data)","def convert_dataset(dataset: h5py.Dataset, refs: h5py.Group):\n # all MATLAB variables have the attribute MATLAB_class\n # if this is not present, it is not convertible\n if has_refs(dataset):\n mtype = \"cell_with_refs\"\n else:\n try:\n mtype = dataset.attrs[\"MATLAB_class\"].decode()\n except KeyError:\n raise ValueError(f\"{dataset} is not a MATLAB type.\")\n\n if mtype == \"cell_with_refs\":\n cell = []\n for ref in dataset:\n row = []\n for r in ref:\n entry = convert_dataset(refs.get(r), refs=refs)\n row.append(entry)\n cell.append(row)\n return cell\n\n elif mtype == \"char\":\n if len(dataset) == 0:\n return \"\"\n elif isinstance(dataset[0], np.ndarray):\n return \"\".join([chr(x[0]) for x in dataset]).replace(\"\\x00\", \"\")\n else:\n return \"\".join([chr(x) for x in dataset]).replace(\"\\x00\", \"\")\n\n elif mtype == \"bool\":\n return bool(dataset)\n\n elif mtype == \"logical\":\n arr = np.array(dataset, dtype=bool).T.squeeze()\n if arr.size == 1:\n arr = bool(arr)\n return arr\n\n elif mtype == \"canonical empty\":\n return None\n\n # complex numbers need to be filtered out separately\n elif \"imag\" in str(dataset.dtype):\n if dataset.attrs[\"MATLAB_class\"] == b\"single\":\n dtype = np.complex64\n else:\n dtype = np.complex128\n arr = np.array(dataset)\n arr = (arr[\"real\"] + arr[\"imag\"] * 1j).astype(dtype)\n return arr.T.squeeze()\n\n # if it is none of the above, we can convert to numpy array\n elif mtype in (\n \"double\",\n \"single\",\n \"int8\",\n \"int16\",\n \"int32\",\n \"int64\",\n \"uint8\",\n \"uint16\",\n \"uint32\",\n \"uint64\",\n \"cell\",\n ):\n if mtype == \"cell\":\n print(dataset)\n arr = np.array(dataset, dtype=dataset.dtype)\n return arr.T.squeeze()\n\n else:\n raise ValueError(f\"Data type not supported: {mtype}, {dataset.dtype}.\")","def load_data():\n\n # https://docs.scipy.org/doc/numpy-1.13.0/reference/generated/numpy.ndarray.astype.html\n data = np.load(\"phase3-data/data_selected_1980_2010.npy\").astype(float)\n\n return normalize(data)","def __prepare(self, data):\n #print(\"Running Prepare data\")\n #print(data)\n #print(type(data))\n if len(data) > 1:\n if type(data[0]) == np.ndarray:\n return np.concatenate(data)\n else:\n return torch.cat(data).cpu().numpy()\n else:\n return data[0].cpu().numpy()","def _process(self, data: np.ndarray) -> np.ndarray:","def _process(self, data: np.ndarray) -> np.ndarray:","def parse(\n self, data: Union[pd.DataFrame, pd.Series, str], verbose: int = 0\n ) -> pd.DataFrame:\n\n data = self.__transform_data(data)\n data = self.norm.normalize(data)\n data = self.find.find_all(data, verbose)\n data = data.drop(\"name_norm\", axis=1)\n return data","def normalize_mean(dataset):\n normalized_dataset = np.array(dataset)\n return normalized_dataset - np.mean(normalized_dataset)","def _to_arraylike(data):\n _load_objects()\n if data is None:\n raise ValueError('Cannot convert None data.')\n return None\n if not isinstance(data, (ndarray, DataArray, DataFrame, Series, Index)):\n data = np.asarray(data)\n if not np.iterable(data):\n data = np.atleast_1d(data)\n return data","def standardize(data):\n stddev = data.std()\n #if stddev == 0.:\n # sys.exit(\"data.std() == 0. !\")\n if stddev != 0.:\n data = (data - data.mean()) / (data.std())\n\n return data","def _normalize(\n ds: xr.Dataset,\n *,\n dim: Sequence[str],\n kind: str = ADDITIVE,\n) -> xr.Dataset:\n if \"norm\" in ds:\n norm = ds.norm\n else:\n norm = ds.data.mean(dim=dim)\n norm.attrs[\"_group_apply_reshape\"] = True\n\n return xr.Dataset(\n dict(data=apply_correction(ds.data, invert(norm, kind), kind), norm=norm)\n )","def convertData(data):\n\n return data","def _normalize(self, dataset):\n if self.max is None: # if we are normalizing the training set\n self.max, self.min = dataset.max(), dataset.min() # find max, min value for each columns\n for row in dataset.index: # for each row in dataset\n for col in self.features: # for each feature in the instance (exclude target)\n dataset.at[row, col] = (dataset.at[row, col] - self.min[col]) / (self.max[col] - self.min[col]) if col != \"Bias\" else 1","def normalize(data):\n min = np.min(data)\n if min:\n data = data + min\n return old_div(data,np.max(data))\n else: # if min is 0\n return old_div(data,np.max(data))","def transform(self, data):\n data -= self.mean\n if 0.0 in self.std:\n self.std = np.where(self.std == 0.0, 1.0, self.std)\n data /= self.std\n return data","def normalize_multivariate_data(data, scaling_values=None):\n normed_data = np.zeros(data.shape, dtype=data.dtype)\n scale_cols = [\"mean\", \"std\"]\n if scaling_values is None:\n scaling_values = pd.DataFrame(np.zeros((data.shape[-1], len(scale_cols)), dtype=np.float32),\n columns=scale_cols)\n for i in range(data.shape[-1]):\n scaling_values.loc[i, [\"mean\", \"std\"]] = [data[:, :, :, i].mean(), data[:, :, :, i].std()]\n normed_data[:, :, :, i] = (data[:, :, :, i] - scaling_values.loc[i, \"mean\"]) / scaling_values.loc[i, \"std\"]\n return normed_data, scaling_values","def normalize_multivariate_data(data, scaling_values=None):\n normed_data = np.zeros(data.shape, dtype=data.dtype)\n scale_cols = [\"mean\", \"std\"]\n if scaling_values is None:\n scaling_values = pd.DataFrame(np.zeros((data.shape[-1], len(scale_cols)), dtype=np.float32),\n columns=scale_cols)\n for i in range(data.shape[-1]):\n scaling_values.loc[i, [\"mean\", \"std\"]] = [data[:, :, :, i].mean(), data[:, :, :, i].std()]\n normed_data[:, :, :, i] = (data[:, :, :, i] - scaling_values.loc[i, \"mean\"]) / scaling_values.loc[i, \"std\"]\n return normed_data, scaling_values","def normalize_data(img):\n nor = np.linalg.norm(img, axis = 1)\n nor = np.reshape(nor, (len(img), 1))\n img = np.divide(img, nor)\n return img","def transform(self, data):\n return self.fit_transform(data, fitting=False)","def normalize(dataset, tests=None):\n # ensure that all values are of the same length\n try:\n exp_length = len(dataset[0])\n for val in dataset:\n assert len(val) == exp_length\n if(tests):\n for val in tests:\n assert len(val) == exp_length\n except TypeError:\n print(\"Error: Value in dataset is not list\")\n except AssertionError:\n print(\"Error: Values in dataset are not all of the same length\")\n\n attribs = list(zip(*dataset))\n new_attribs = []\n\n for attr in attribs:\n attr_norm = []\n for val in attr:\n attr_norm.append((val - min(attr)) / ((max(attr) - min(attr)) * 1.0))\n new_attribs.append(attr_norm)\n\n dataset_norm = list(map(list, zip(*new_attribs)))\n\n if(tests):\n attribs_test = list(zip(*tests))\n new_attribs_test = []\n for i, attr in enumerate(attribs_test):\n attr_norm = []\n for val in attr:\n attr_norm.append((val - min(attribs[i])) / ((max(attribs[i]) - min(attribs[i])) * 1.0))\n new_attribs_test.append(attr_norm)\n\n tests_norm = list(map(list, zip(*new_attribs_test)))\n\n if(tests):\n return dataset_norm, tests_norm\n else:\n return dataset_norm","def _format_data(self, data: np.ndarray) -> np.ndarray:\n self._n_circs = 0\n self._n_shots = 0\n self._n_slots = 0\n self._n_iq = 0\n\n # identify shape\n try:\n # level1 single-shot data\n self._n_circs, self._n_shots, self._n_slots, self._n_iq = data.shape\n except ValueError:\n try:\n # level1 data averaged over shots\n self._n_circs, self._n_slots, self._n_iq = data.shape\n except ValueError as ex:\n raise DataProcessorError(\n f\"Data given to {self.__class__.__name__} is not likely level1 data.\"\n ) from ex\n\n if self._validate:\n if self._n_iq != 2:\n raise DataProcessorError(\n f\"IQ data given to {self.__class__.__name__} does not have two-dimensions \"\n f\"(I and Q). Instead, {self._n_iq} dimensions were found.\"\n )\n\n return data","def process(self, data):\n # get truncation parameters from config or container defaults\n self._get_params(type(data))\n\n if self.weight_dataset is None:\n self.weight_dataset = [None] * len(self.dataset)\n\n for dset, wgt in zip(self.dataset, self.weight_dataset):\n old_shape = data[dset].local_shape\n val = np.ndarray.reshape(data[dset][:], data[dset][:].size)\n if wgt is None:\n if np.iscomplexobj(data[dset]):\n data[dset][:].real = bit_truncate_fixed(\n val.real, self.fixed_precision\n ).reshape(old_shape)\n data[dset][:].imag = bit_truncate_fixed(\n val.imag, self.fixed_precision\n ).reshape(old_shape)\n else:\n data[dset][:] = bit_truncate_fixed(\n val, self.fixed_precision\n ).reshape(old_shape)\n else:\n if data[dset][:].shape != data[wgt][:].shape:\n raise pipeline.PipelineRuntimeError(\n \"Dataset and weight arrays must have same shape ({} != {})\".format(\n data[dset].shape, data[wgt].shape\n )\n )\n invvar = np.ndarray.reshape(data[wgt][:], data[dset][:].size)\n if np.iscomplexobj(data[dset]):\n data[dset][:].real = bit_truncate_weights(\n val.real,\n invvar * 2.0 / self.variance_increase,\n self.fixed_precision,\n ).reshape(old_shape)\n data[dset][:].imag = bit_truncate_weights(\n val.imag,\n invvar * 2.0 / self.variance_increase,\n self.fixed_precision,\n ).reshape(old_shape)\n else:\n data[dset][:] = bit_truncate_weights(\n val, invvar / self.variance_increase, self.fixed_precision\n ).reshape(old_shape)\n\n return data","def normalizeData(meanAndStd, dataset):\n\n for i in range(len(dataset)):\n for j in range(len(dataset[i])-1):\n mean = meanAndStd[j][\"mean\"]\n std = meanAndStd[j][\"std\"]\n dataset[i][j] = (dataset[i][j] - mean)/std","def check_input_and_return_data(self, input_name):\n\n #Check to see if data is given by a file\n if isinstance(self.input_data['reservoir'][input_name], str):\n #Get filename\n filename = self.input_data['reservoir'][input_name]\n #Load data \n data = np.loadtxt(filename, dtype=np.double)\n \n #Check to see if data is given by a list\n elif isinstance(self.input_data['reservoir'][input_name], (list, tuple)):\n #Turn the list into numpy array\n data = np.array(self.input_data['reservoir'][input_name], \n dtype=np.double)\n\n #data is a constant array (homogeneous)\n else:\n ngrids = self.input_data['numerical']['number of grids']\n data = (self.input_data['reservoir'][input_name] * \n np.ones(ngrids))\n return data","def _format_data(self, data: np.ndarray) -> np.ndarray:\n self._n_shots = 0\n\n # identify shape\n try:\n # level1 single-shot data\n self._n_circs, self._n_shots, self._n_slots, self._n_iq = data.shape\n except ValueError:\n try:\n # level1 data averaged over shots\n self._n_circs, self._n_slots, self._n_iq = data.shape\n except ValueError as ex:\n raise DataProcessorError(\n f\"Data given to {self.__class__.__name__} is not likely level1 data.\"\n ) from ex\n\n if self._validate:\n if data.shape[-1] != 2:\n raise DataProcessorError(\n f\"IQ data given to {self.__class__.__name__} must be a multi-dimensional array\"\n \"of dimension [d0, d1, ..., 2] in which the last dimension \"\n \"corresponds to IQ elements.\"\n f\"Input data contains element with length {data.shape[-1]} != 2.\"\n )\n\n return data","def transform(self, dataset: NumpyOrPandas) -> NumpyDataset:\n # checks here\n super().transform(dataset)\n # convert to accepted dtype and get attributes\n dataset = dataset.to_pandas()\n df = dataset.data\n\n # transform\n roles = NumericRole()\n outputs = []\n for n, i in enumerate(df.columns):\n new_arr = self.dicts[i][\"fg\"].transform(df[i].values)\n output = dataset.empty().to_numpy()\n output.set_data(new_arr, self.dicts[i][\"feats\"], roles)\n outputs.append(output)\n # create resulted\n return dataset.empty().to_numpy().concat(outputs)","def _to_ndarray(data):\n return np.atleast_1d(getattr(data, 'values', data))","def normalize(\n ds: xr.Dataset,\n *,\n dim: str,\n kind: str = ADDITIVE,\n) -> xr.Dataset:\n\n if \"norm\" in ds:\n norm = invert(ds.norm, kind)\n else:\n norm = invert(ds.data.mean(dim=dim), kind)\n\n return xr.Dataset(dict(data=apply_correction(ds.data, norm, kind)))","def normalise(dataset):\n # Scale images to the [0, 1] range\n dataset = dataset.astype(\"float32\") / 255\n # Make sure images have shape (28, 28, 1)\n return np.expand_dims(dataset, -1)","def normalize2(data):\n return old_div(data,np.max([np.max(data),-1.0*np.min(data)]))","def get_normalizer(data):\n scaler = StandardScaler().fit(data)\n return scaler","def _normalize_column(self, data, coerce_dtype=None, store_categories=True):\n if coerce_dtype is not None:\n coerce_dtype = np.dtype(coerce_dtype)\n\n if np.isscalar(data):\n array = np.array([data], dtype=coerce_dtype)\n dtype = data.dtype\n fillvalue = None\n\n elif is_categorical(data):\n if store_categories:\n cats = data.cat.categories\n enum_dict = dict(zip(cats, range(len(cats))))\n array = data.cat.codes\n dtype = h5py.special_dtype(enum=(array.dtype, enum_dict))\n fillvalue = -1\n else:\n array = data.cat.codes\n dtype = coerce_dtype or array.dtype\n fillvalue = -1\n\n elif data.dtype in (object, str, bytes):\n data = np.asarray(data)\n dtype = np.dtype(\"S\")\n array = np.array(data, dtype=dtype)\n fillvalue = None\n\n else:\n array = np.asarray(data)\n dtype = data.dtype\n fillvalue = None\n\n return array, dtype, fillvalue","def transform(self, data):\n unflattened = [unflatten_vec(d) for d in data]\n return numpy.array([self.signature(uf)\n for uf in unflattened])","def normalize(self):\n self._data /= self.norm()","def _check_data(data, dset_attrs):\n dtype = dset_attrs['dtype']\n float_to_int = (np.issubdtype(dtype, np.integer)\n and np.issubdtype(data.dtype, np.floating))\n if float_to_int:\n attrs = dset_attrs['attrs']\n scale_factor = [c for c in attrs if 'scale_factor' in c][0]\n scale_factor = attrs[scale_factor]\n\n # apply scale factor and dtype\n data = np.multiply(data, scale_factor)\n if np.issubdtype(dtype, np.integer):\n data = np.round(data)\n\n data = data.astype(dtype)\n\n return data","def _to_numpy_ndarray(cls, data):\n if isinstance(data, np.ndarray):\n return data\n arr = np.array(data, dtype=np.float)\n if len(arr.shape) == 1:\n arr = np.reshape(arr, newshape=(1, arr.shape[0]))\n return arr","def standardize_data(data):\n return (data - np.mean(data, axis=0)) / (np.std(data, axis=0) + 10 ** -16)","def dataNormalize(data):\n tr = re.sub('<(.*)>|\\\\n', '', data.prettify())\n return re.sub(' +', ',', tr).split(',')","def standardize_input_data(data,\n names,\n shapes=None,\n check_batch_axis=True,\n exception_prefix=''):\n try:\n data_len = len(data)\n except TypeError:\n # For instance if data is `None` or a symbolic Tensor.\n data_len = None\n\n if not names:\n if data_len and not isinstance(data, dict):\n raise ValueError(\n 'Error when checking model ' + exception_prefix + ': '\n 'expected no data, but got:', data)\n return []\n if data is None:\n return [None for _ in range(len(names))]\n\n if isinstance(data, dict):\n try:\n data = [\n data[x].values\n if data[x].__class__.__name__ == 'DataFrame' else data[x]\n for x in names\n ]\n except KeyError as e:\n raise ValueError('No data provided for \"' + e.args[0] + '\". Need data '\n 'for each key in: ' + str(names))\n elif isinstance(data, (list, tuple)):\n if isinstance(data[0], (list, tuple)):\n data = [np.asarray(d) for d in data]\n elif len(names) == 1 and isinstance(data[0], (float, int)):\n data = [np.asarray(data)]\n else:\n data = [\n x.values if x.__class__.__name__ == 'DataFrame' else x for x in data\n ]\n else:\n data = data.values if data.__class__.__name__ == 'DataFrame' else data\n data = [data]\n\n if shapes is not None:\n data = [\n standardize_single_array(x, shape) for (x, shape) in zip(data, shapes)\n ]\n else:\n data = [standardize_single_array(x) for x in data]\n\n if len(data) != len(names):\n if data and hasattr(data[0], 'shape'):\n raise ValueError('Error when checking model ' + exception_prefix +\n ': the list of Numpy arrays that you are passing to '\n 'your model is not the size the model expected. '\n 'Expected to see ' + str(len(names)) + ' array(s), ' +\n 'for inputs ' + str(names) + ' but instead got the '\n 'following list of ' + str(len(data)) + ' arrays: ' +\n str(data)[:200] + '...')\n elif len(names) > 1:\n raise ValueError('Error when checking model ' + exception_prefix +\n ': you are passing a list as input to your model, '\n 'but the model expects a list of ' + str(len(names)) +\n ' Numpy arrays instead. The list you passed was: ' +\n str(data)[:200])\n elif len(data) == 1 and not hasattr(data[0], 'shape'):\n raise TypeError('Error when checking model ' + exception_prefix +\n ': data should be a Numpy array, or list/dict of '\n 'Numpy arrays. Found: ' + str(data)[:200] + '...')\n elif len(names) == 1:\n data = [np.asarray(data)]\n\n # Check shapes compatibility.\n if shapes:\n for i in range(len(names)):\n if shapes[i] is not None:\n if tensor_util.is_tf_type(data[i]):\n tensorshape = data[i].shape\n if not tensorshape:\n continue\n data_shape = tuple(tensorshape.as_list())\n elif is_composite_or_composite_value(data[i]):\n tensorshape = get_composite_shape(data[i])\n data_shape = tuple(tensorshape.as_list())\n else:\n data_shape = data[i].shape\n\n shape = shapes[i]\n if len(data_shape) != len(shape):\n raise ValueError('Error when checking ' + exception_prefix +\n ': expected ' + names[i] + ' to have ' +\n str(len(shape)) + ' dimensions, but got array '\n 'with shape ' + str(data_shape))\n if not check_batch_axis:\n data_shape = data_shape[1:]\n shape = shape[1:]\n for dim, ref_dim in zip(data_shape, shape):\n if ref_dim != dim and ref_dim is not None and dim is not None:\n raise ValueError('Error when checking ' + exception_prefix +\n ': expected ' + names[i] + ' to have shape ' +\n str(shape) + ' but got array with shape ' +\n str(data_shape))\n return data","def _normalizeComplex(data):\n if hasattr(data, \"dtype\"):\n isComplex = numpy.issubdtype(data.dtype, numpy.complexfloating)\n else:\n isComplex = isinstance(data, numbers.Complex)\n if isComplex:\n data = numpy.absolute(data)\n return data","def _validate_input_data(self):\n\n if type(self.data) in [np.ndarray, da.Array]:\n if not self.data.dtype.names:\n raise ValueError('QuadTree: numpy array provided for data, but no names were found, array must be a structured array')\n if 'x' not in self.data.dtype.names or 'y' not in self.data.dtype.names:\n raise ValueError('QuadTree: numpy structured array provided for data, but \"x\" or \"y\" not found in variable names')\n self.layernames = [self.rev_layer_lookup[var] for var in self.data.dtype.names if var in ['z', 'tvu']]\n elif type(self.data) == xr.Dataset:\n if 'x' not in self.data:\n raise ValueError('QuadTree: xarray Dataset provided for data, but \"x\" or \"y\" not found in variable names')\n if len(self.data.dims) > 1:\n raise ValueError('QuadTree: xarray Dataset provided for data, but found multiple dimensions, must be one dimensional: {}'.format(self.data.dims))\n self.layernames = [self.rev_layer_lookup[var] for var in self.data if var in ['z', 'tvu']]\n self._convert_dataset() # internally we just convert xarray dataset to numpy for ease of use\n else:\n raise ValueError('QuadTree: numpy structured array or dask array with \"x\" and \"y\" as variable must be provided')","def normalize(self, image, transpose=False, data_type=None):\n return normalize(image, self.mean, self.std, transpose)","def scale_data(x: np.ndarray, scaler=None) -> Tuple[np.ndarray, object]:\n original_shape = x.shape\n\n # reshape data to 2d array\n x = x.reshape((x.shape[0], -1))\n\n if scaler is None:\n scaler = StandardScaler().fit(x)\n\n x = scaler.transform(x)\n\n # reshape back\n x = x.reshape(original_shape)\n return x, scaler"],"string":"[\n \"def normalizeData(self, data):\\n return _normalizeData(data)\",\n \"def normalizeData(self, data):\\n return _normalizeData(data)\",\n \"def normalized_data(self):\\n return self._data\",\n \"def normalize_dataset(self):\",\n \"def normalized_data(self):\\n return self._normalization_constant * self._data\",\n \"def _prep_data(self, data, func_input_dtype):\\n if func_input_dtype in (None, 'DataArray'):\\n return data\\n if func_input_dtype == 'Dataset':\\n # TODO: add logic that creates a single Dataset comprising all of\\n # the DataArray objects in `data`.\\n raise NotImplementedError(\\\"func_input_dtype of `Dataset` not yet \\\"\\n \\\"implemented.\\\")\\n if func_input_dtype == 'numpy':\\n self.coords = data[0].coords\\n return [d.values for d in data]\",\n \"def preprocess_data(self, data, scale_data=True):\\n print(\\\"preprocess_data not implemented\\\")\\n return data\",\n \"def normalize_data(data):\\n mean = np.mean(data)\\n std = np.std(data)\\n return (data - mean) / std\",\n \"def _preprocess(self, data, normalize=False) -> np.ndarray:\\n \\n preprocessor = StandardScaler() if not normalize else Normalizer()\\n\\n data = preprocessor.fit_transform(data)\\n \\n return data\",\n \"def normalize_data(self, data):\\n self.find_mean_std(data)\\n return (data - self._data_mean) / self._data_std\",\n \"def unNormalizeData(normalizedData, data_mean, data_std, dimensions_to_ignore):\\n T = normalizedData.shape[0]\\n D = data_mean.shape[0]\\n\\n origData = np.zeros((T, D), dtype=np.float32)\\n dimensions_to_use = []\\n for i in range(D):\\n if i in dimensions_to_ignore:\\n continue\\n dimensions_to_use.append(i)\\n dimensions_to_use = np.array(dimensions_to_use)\\n\\n origData[:, dimensions_to_use] = normalizedData\\n\\n # potentially ineficient, but only done once per experimentdata_conversions\\n stdMat = data_std.reshape((1, D))\\n stdMat = np.repeat(stdMat, T, axis=0)\\n meanMat = data_mean.reshape((1, D))\\n meanMat = np.repeat(meanMat, T, axis=0)\\n origData = np.multiply(origData, stdMat) + meanMat\\n return origData\",\n \"def normalize(dataset):\\n return normalize_standard_deviation(normalize_mean(dataset))\",\n \"def _convert_data(self, data):\\n if isinstance(data, Tensor):\\n data = data.asnumpy()\\n elif isinstance(data, list):\\n data = np.array(data)\\n elif isinstance(data, np.ndarray):\\n pass\\n else:\\n raise TypeError('Input data type must be tensor, list or numpy.ndarray')\\n return data\",\n \"def normalize_data(data, n=1):\\n\\n if isinstance(data, str):\\n # TODO: could Antti comment on this?\\n # numpy array initialization works unintuitively with strings\\n data = np.array([[data]], dtype=object)\\n else:\\n data = np.atleast_1d(data)\\n\\n if data.ndim == 1:\\n if data.shape[0] == n:\\n data = data[:, None]\\n else:\\n data = data[None, :]\\n if n > 1:\\n data = np.vstack((data, ) * n)\\n else:\\n if data.shape[0] != n:\\n data = data[None, :]\\n if n > 1:\\n data = np.vstack((data, ) * n)\\n return data\",\n \"def normalise(raw_data, normalise_by_column=False):\\n data = raw_data\\n if normalise_by_column:\\n #normaliza valores usando o maximo de cada coluna\\n col_maxes = raw_data.max(axis=0)\\n #divide cada valor pelo maximo correspondente de cada coluna\\n data = raw_data / col_maxes[np.newaxis,:] \\n else:\\n #divide todos os valores pelo maximo do dataset (tudo na mesma escala)\\n data = raw_data / raw_data.max()\\n\\n return data\",\n \"def denormalise_0_1(value_or_array, array_min, array_max):\\n if isinstance(value_or_array, list):\\n raise ValueError('this function accepts arraylike data, not a list. '\\n 'Please check data or convert list to numpy array')\\n elif isinstance(value_or_array, float):\\n denormalised = value_or_array * (array_max - array_min) + array_min\\n elif isinstance(value_or_array, np.ndarray):\\n denormalised = value_or_array * (array_max - array_min) + array_min\\n elif isinstance(value_or_array, pd.Series):\\n denormalised = value_or_array * (array_max - array_min) + array_min\\n else:\\n sys.stdout.write(\\\"Unknown datatype. denormalise_0_1 has been given an input that does not appear to be \\\"\\n \\\"an int, float, np.ndarray or pandas Series\\\\n\\\"\\n \\\"Attempting to process as if it is arraylike.....\\\")\\n return denormalised\",\n \"def normalize(data):\\n # normalize data and return\\n # https://stackoverflow.com/questions/29661574/normalize-numpy-array-columns-in-python\\n return (data - data.min(axis=0)) / data.ptp(axis=0)\",\n \"def _convert_data(data, inf_ok=False, min_len=1):\\n # If it's scalar, convert to array\\n if np.isscalar(data):\\n data = np.array([data], dtype=float)\\n\\n # Convert data to NumPy array\\n data = np.array(data, dtype=float)\\n\\n # Make sure it is 1D\\n if len(data.shape) != 1:\\n raise RuntimeError(\\\"Input must be a 1D array or Pandas series.\\\")\\n\\n # Remove NaNs\\n data = data[~np.isnan(data)]\\n\\n # Check for infinite entries\\n if not inf_ok and np.isinf(data).any():\\n raise RuntimeError(\\\"All entries must be finite.\\\")\\n\\n # Check to minimal length\\n if len(data) < min_len:\\n raise RuntimeError(\\n \\\"Array must have at least {0:d} non-NaN entries.\\\".format(min_len)\\n )\\n\\n return data\",\n \"def canonicalize(data):\\n\\n if isinstance(data, np.ndarray):\\n if data.dtype == np.uint8:\\n data = data.astype(np.float32) / 255.0\\n data = torch.from_numpy(np.ascontiguousarray(data))\\n\\n elif isinstance(data, torch.Tensor):\\n if data.dtype == torch.uint8:\\n data = data.float() / 255.0\\n\\n else:\\n raise NotImplementedError()\\n\\n return data\",\n \"def _canonize_input(self, dataset):\\n unpack = lambda x: x\\n if isinstance(dataset, _tc.SArray):\\n dataset = _tc.SFrame({self.feature: dataset})\\n elif isinstance(dataset, _tc.Image):\\n dataset = _tc.SFrame({self.feature: [dataset]})\\n unpack = lambda x: x[0]\\n return dataset, unpack\",\n \"def _check_and_transform_input(self, data):\\n if isinstance(data, list):\\n if np.array(data).shape == (len(data),):\\n if len(data) == 1:\\n data = np.array(data).reshape(1, 1)\\n data = np.array(data).reshape(len(data), 1)\\n else:\\n data = np.concatenate(data).reshape(len(data), -1)\\n else:\\n raise TypeError('Input data should be of type list, but found type {}'.format(type(data)))\\n\\n return data\",\n \"def _norm_data(data):\\n if data is None:\\n return data\\n data_min = np.min(data)\\n c_norm = np.max(data) - data_min\\n return (data - data_min) / c_norm if (c_norm != 0) else (data - data_min)\",\n \"def _format_data(self, data: np.ndarray) -> np.ndarray:\\n if self._validate:\\n if len(data.shape) <= 1:\\n raise DataProcessorError(\\n \\\"The data should be an array with at least two dimensions.\\\"\\n )\\n\\n return data\",\n \"def normalize_data(data=None):\\n # Data pre-processing\\n n = data.shape[0]\\n for i in range(n):\\n xx = data[i,:,:]\\n xx -= np.mean(xx) # Centering in 0\\n xx /= np.linalg.norm(xx) # Normalizing to 1\\n data[i] = xx # Affect value\\n return data\",\n \"def normalize(data):\\n data_range = data.max() - data.min()\\n #if data_range == 0.:\\n # sys.exit(\\\"data.max() - data.min() == 0. !\\\")\\n if stddev != 0.:\\n data = (data - data.min()) / data_range\\n\\n return data\",\n \"def normalize_data(data):\\n if data.element_spec[0].shape[2] == 1:\\n data = data.map(lambda x, y: (tf.image.grayscale_to_rgb(\\n tf.image.resize(x, [32, 32])), y))\\n else:\\n data = data.map(lambda x, y: (tf.image.resize(x, [32, 32]), y))\\n normalization_layer = tf.keras.layers.experimental.preprocessing.Rescaling(1. / 255)\\n normalized_ds = data.map(lambda x, y: (normalization_layer(x), y))\\n return normalized_ds\",\n \"def norm_data(self):\\n if (self.nrows, self.ncolumns) < self.data.shape:\\n self.data = self.data[0:self.nrows, 0:self.ncolumns]\\n if self.data.dtype != np.float64:\\n self.data = self.data.astype(np.float64)\\n self.meanval = self.data.mean()\\n self.stdval = self.data.std()\",\n \"def _de_transform(self, data):\\r\\n mean, variance = self._input_statistics.overall_feature_moments\\r\\n return data * variance + mean\",\n \"def _raw_data(self):\\n data = self.datasource.as_array()\\n if not isinstance(data, np.ndarray):\\n raise TypeError(\\\"The data you try to load is no numpy array!\\\")\\n if data.ndim != 2:\\n raise ValueError(\\\"The data array you try to load does not have 2 \\\"\\n \\\"dimensions!\\\")\\n data = data.copy(order='C')\\n ident = hashlib.md5(data).hexdigest()\\n if self._dataident is None:\\n self._dataident = ident\\n elif self._dataident != ident:\\n raise ValueError(\\\"The data you try to load from '%s' has changed \\\"\\n \\\"since the last time. Please, check the \\\"\\n \\\"datasource of the record '%s'.\\\"\\n % (self.datasource.name, self.name))\\n\\n return data\",\n \"def normalize(dataset):\\n minVals = dataset.min(axis=0)\\n maxVals = dataset.max(axis=0)\\n factors = maxVals-minVals\\n num = dataset.shape[0]\\n norm_data = (dataset - np.tile(minVals,(num,1)))/np.tile(factors,(num,1)) \\n return norm_data\",\n \"def standardization(input_data):\\n\\n # Insert debugging assertions\\n assert type(input_data) is np.ndarray, \\\"The 'input_data' must be numpy array.\\\"\\n\\n # Get the mean values and the standard deviations of the input numpy array along the axis \\n Mean = np.mean(input_data, axis = 0)\\n Std = np.std(input_data, axis = 0)\\n\\n # Standardization \\n standardized_input_data = (input_data - Mean) / (Std + sys.float_info.min)\\n\\n # Return standardized input data\\n return standardized_input_data\",\n \"def load_own_data():\\n data = Data()\\n features = data.read_selected_features()\\n dataframe = data.get_dataframe_with(features)\\n return normalize(dataframe.values)\",\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 _transform(self, data):\\r\\n mean, variance = self._input_statistics.overall_feature_moments\\r\\n return (data - mean) / variance\",\n \"def _validate_input(self, data: Union[np.ndarray, pd.DataFrame, pd.Series],\\n expected_dim: int, inference: bool = False) -> np.ndarray:\\n allowed_types = (\\n np.ndarray,\\n pd.core.frame.DataFrame,\\n pd.core.frame.Series\\n )\\n\\n if type(data) not in allowed_types:\\n raise TypeError('Supported input types: np.ndarray, '\\n 'pd.core.frame.DataFrame, pd.core.frame.Series got'\\n ' {}'.format(type(data)))\\n\\n if isinstance(data, pd.DataFrame) or isinstance(data, pd.Series):\\n data = data.values\\n\\n if data.size == 0:\\n raise ValueError('Empty array passed to fit() or predict()')\\n\\n if data.ndim > expected_dim:\\n raise ValueError('Data with incorrect number of dimensions '\\n 'passed to fit() or predict(). Max dim is '\\n '{}, got {}'.format(expected_dim, data.ndim))\\n\\n if not np.issubdtype(data.dtype, np.number):\\n raise ValueError('Non numeric value found in data')\\n\\n if not np.isfinite(data).all():\\n raise ValueError('Data contains nan or inf')\\n\\n if inference:\\n # additional checks on prediction time\\n if not self._fitted:\\n raise ValueError('Fit the model first.')\\n\\n if self._ndim == 2 and data.shape[-1] != self._shape[-1]:\\n raise ValueError('Number of features does not match'\\n ' data model was trained on. Expected'\\n ' {}, got {}'\\n .format(self._shape[-1], data.shape[-1]))\\n\\n return data\",\n \"def min_max_normalization(input_data):\\n\\n # Insert debugging assertions\\n assert type(input_data) is np.ndarray, \\\"The 'input_data' must be numpy array.\\\"\\n\\n # Get the minimum and maximun values of the input numpy array along the axis \\n Max = np.max(input_data, axis = 0)\\n Min = np.min(input_data, axis = 0)\\n\\n # Min-max normalization \\n normalized_input_data = (input_data - Min) / (Max - Min + sys.float_info.min)\\n\\n # Return normalized input data\\n return normalized_input_data\",\n \"def normalize(self):\\n d = learning_utils.convert_data_to_2d(self._data)\\n d = learning_utils.normalize_2d(d)\\n self._data = learning_utils.convert_data_to_1d(d)\",\n \"def normalize_data(self):\\n self.x_mean, self.x_std = du.get_mean_std(self.x_train)\\n self.x_train = du.normalize(self.x_train, self.x_mean, self.x_std)\\n if self.x_test is not None and self.y_test is not None:\\n self.x_test = du.normalize(self.x_test, self.x_mean, self.x_std)\\n self.normalized_data = True\",\n \"def normalize(data):\\n\\n\\t#return [float(x) / pow(2, 15) for x in data]\\n\\n\\tl = [float(x) / pow(2, 15) for x in data]\\n\\treturn np.asarray(l)\",\n \"def _handle_input_data(data):\\n data = np.asarray(data)\\n if np.ndim(data) == 1:\\n d_rows = 1\\n d_cols = len(data)\\n data = data.reshape((1, data.shape[0]))\\n elif np.ndim(data) == 2:\\n d_rows = data.shape[0]\\n d_cols = data.shape[1]\\n else:\\n raise ValueError(\\\"Incorrect dimensionality of data. Must be <= 2\\\")\\n return data, d_rows, d_cols\",\n \"def _convert_data(self, data):\\n if isinstance(data, np.ndarray):\\n data = data.astype(float_save(), copy=False)\\n if self.compress:\\n return KaldiCompressedMatrix.compress(data, self.compression_method)\\n return KaldiMatrix(data)\\n\\n if isinstance(data, KaldiMatrix):\\n if self.compress:\\n return KaldiCompressedMatrix.compress(data, self.compression_method)\\n return data\\n\\n if isinstance(data, KaldiCompressedMatrix):\\n if not self.compress:\\n return data.to_matrix()\\n return data\\n\\n raise ValueError(\\\"Data is not ndarray or KaldiMatrix\\\")\",\n \"def handle_input(data, device=None):\\n if type(data) == tuple:\\n x, y = data\\n x = torch.tensor(x, dtype=torch.float)\\n y = torch.tensor(y, dtype=torch.float)\\n if not device is None:\\n x = x.to(device)\\n y = y.to(device)\\n return x, y\\n if type(data) == np.ndarray:\\n x = torch.tensor(data, dtype=torch.float)\\n if not device is None:\\n x = x.to(device)\\n return x\\n else:\\n return data\",\n \"def scale(self, data: np.ndarray):\\n if self.scale_type == \\\"min_max\\\":\\n scaled_data = (data - self.predictor_min) / (\\n self.predictor_max - self.predictor_mean\\n )\\n elif self.scale_type == \\\"normalize\\\":\\n scaled_data = (data - self.predictor_mean) / (\\n self.predictor_max - self.predictor_min\\n )\\n elif self.scale_type == \\\"standardize\\\":\\n scaled_data = (data - self.predictor_mean) / self.predictor_std\\n elif self.scale_type == \\\"scale\\\":\\n scaled_data = data - self.predictor_mean\\n else:\\n scaled_data = data\\n return scaled_data\",\n \"def unscale_data(self, data):\\n return (data + self.mean)*self.std\",\n \"def process(self, data):\\n data = np.atleast_2d(data)\\n\\n if self.orientation == 'row':\\n return data\\n else:\\n return data.T\",\n \"def _preprocess(self, data):\\n\\n # pipeline: first call the previous statistics:\\n if self.previous_statistics is not None:\\n data = self.previous_statistics.statistics(data)\\n # the first of the statistics need to take list as input, in order to match the API. Then actually the\\n # transformations work on np.arrays. In fact the first statistic transforms the list to array. Therefore, the\\n # following code needs to be called only if the self statistic is the first, i.e. it does not have a\\n # previous_statistic element.\\n else:\\n data = self._check_and_transform_input(data)\\n\\n return data\",\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 _convert_dataset(self):\\n allowed_vars = ['x', 'y', 'z', 'tvu', 'thu']\\n dtyp = [(varname, self.data[varname].dtype) for varname in allowed_vars if varname in self.data]\\n empty_struct = np.empty(len(self.data['x']), dtype=dtyp)\\n for varname, vartype in dtyp:\\n empty_struct[varname] = self.data[varname].values\\n self.data = empty_struct\",\n \"def _process(self, data: np.ndarray) -> np.ndarray:\\n return data[..., 0] * self.scale\",\n \"def _process(self, data: np.ndarray) -> np.ndarray:\\n return data[..., 1] * self.scale\",\n \"def _normalize(empty, user_data):\\n if isinstance(user_data, UserData):\\n return user_data.to_dict()\\n if isinstance(user_data, dict):\\n return _dict_to_dict(empty, user_data)\\n if isinstance(user_data, (bytes, bytearray)):\\n return _bytes_to_dict(empty, user_data)\\n if user_data is None:\\n return empty\\n raise ValueError\",\n \"def _process(self, data: np.ndarray) -> np.ndarray:\\n nominals = unp.nominal_values(data)\\n min_y, max_y = np.min(nominals), np.max(nominals)\\n\\n return (data - min_y) / (max_y - min_y)\",\n \"def normalize(data):\\n data = lowercase(data)\\n data = remove_punct(data)\\n data = remove_apostrophes(data)\\n data = remove_stopwords(data)\\n data = num_to_words(data)\\n data = lemmatize(data)\\n data = stemming(data)\\n data = remove_punct(data)\\n data = num_to_words(data)\\n data = lemmatize(data)\\n data = stemming(data)\\n data = remove_punct(data) #done again to remove hyphens produced by num2words\\n data = remove_stopwords(data) #done agan to remove stopwords produced by num2words\\n return data\",\n \"def _format_data(self, data: np.ndarray) -> np.ndarray:\\n if self._validate:\\n if len(data.shape) <= self._axis:\\n raise DataProcessorError(\\n f\\\"Cannot average the {len(data.shape)} dimensional \\\"\\n f\\\"array along axis {self._axis}.\\\"\\n )\\n\\n return data\",\n \"def normalizeData(pre_signal):\\n\\n if sp.any(sp.isnan(pre_signal)):\\n print('there are NaNs in the data matrix, making them zero')\\n\\n pre_signal[sp.isnan(pre_signal)] = 0\\n mean_vector = sp.mean(pre_signal, axis=0, keepdims=True)\\n normed_signal = pre_signal - mean_vector\\n norm_vector = sp.linalg.norm(normed_signal, axis=0, keepdims=True)\\n norm_vector[norm_vector == 0] = 1e-116\\n normed_signal = normed_signal / norm_vector\\n\\n return normed_signal, mean_vector, norm_vector\",\n \"def norm_data(data):\\n return (data-np.min(data))/(np.max(data)-np.min(data))\",\n \"def _format_data(self, data: np.ndarray) -> np.ndarray:\\n self._n_shots = 0\\n\\n # identify shape\\n try:\\n # level1 single-shot data\\n self._n_circs, self._n_shots, self._n_slots, self._n_iq = data.shape\\n except ValueError as ex:\\n raise DataProcessorError(\\n f\\\"The data given to {self.__class__.__name__} does not have the shape of \\\"\\n \\\"single-shot IQ data; expecting a 4D array.\\\"\\n ) from ex\\n\\n if self._validate:\\n if data.shape[-1] != 2:\\n raise DataProcessorError(\\n f\\\"IQ data given to {self.__class__.__name__} must be a multi-dimensional array\\\"\\n \\\"of dimension [d0, d1, ..., 2] in which the last dimension \\\"\\n \\\"corresponds to IQ elements.\\\"\\n f\\\"Input data contains element with length {data.shape[-1]} != 2.\\\"\\n )\\n\\n if self._validate:\\n if isinstance(self._discriminator, list):\\n if self._n_slots != len(self._discriminator):\\n raise DataProcessorError(\\n f\\\"The Discriminator node has {len(self._discriminator)} which does \\\"\\n f\\\"not match the {self._n_slots} slots in the data.\\\"\\n )\\n\\n return unp.nominal_values(data)\",\n \"def convert_dataset(dataset: h5py.Dataset, refs: h5py.Group):\\n # all MATLAB variables have the attribute MATLAB_class\\n # if this is not present, it is not convertible\\n if has_refs(dataset):\\n mtype = \\\"cell_with_refs\\\"\\n else:\\n try:\\n mtype = dataset.attrs[\\\"MATLAB_class\\\"].decode()\\n except KeyError:\\n raise ValueError(f\\\"{dataset} is not a MATLAB type.\\\")\\n\\n if mtype == \\\"cell_with_refs\\\":\\n cell = []\\n for ref in dataset:\\n row = []\\n for r in ref:\\n entry = convert_dataset(refs.get(r), refs=refs)\\n row.append(entry)\\n cell.append(row)\\n return cell\\n\\n elif mtype == \\\"char\\\":\\n if len(dataset) == 0:\\n return \\\"\\\"\\n elif isinstance(dataset[0], np.ndarray):\\n return \\\"\\\".join([chr(x[0]) for x in dataset]).replace(\\\"\\\\x00\\\", \\\"\\\")\\n else:\\n return \\\"\\\".join([chr(x) for x in dataset]).replace(\\\"\\\\x00\\\", \\\"\\\")\\n\\n elif mtype == \\\"bool\\\":\\n return bool(dataset)\\n\\n elif mtype == \\\"logical\\\":\\n arr = np.array(dataset, dtype=bool).T.squeeze()\\n if arr.size == 1:\\n arr = bool(arr)\\n return arr\\n\\n elif mtype == \\\"canonical empty\\\":\\n return None\\n\\n # complex numbers need to be filtered out separately\\n elif \\\"imag\\\" in str(dataset.dtype):\\n if dataset.attrs[\\\"MATLAB_class\\\"] == b\\\"single\\\":\\n dtype = np.complex64\\n else:\\n dtype = np.complex128\\n arr = np.array(dataset)\\n arr = (arr[\\\"real\\\"] + arr[\\\"imag\\\"] * 1j).astype(dtype)\\n return arr.T.squeeze()\\n\\n # if it is none of the above, we can convert to numpy array\\n elif mtype in (\\n \\\"double\\\",\\n \\\"single\\\",\\n \\\"int8\\\",\\n \\\"int16\\\",\\n \\\"int32\\\",\\n \\\"int64\\\",\\n \\\"uint8\\\",\\n \\\"uint16\\\",\\n \\\"uint32\\\",\\n \\\"uint64\\\",\\n \\\"cell\\\",\\n ):\\n if mtype == \\\"cell\\\":\\n print(dataset)\\n arr = np.array(dataset, dtype=dataset.dtype)\\n return arr.T.squeeze()\\n\\n else:\\n raise ValueError(f\\\"Data type not supported: {mtype}, {dataset.dtype}.\\\")\",\n \"def load_data():\\n\\n # https://docs.scipy.org/doc/numpy-1.13.0/reference/generated/numpy.ndarray.astype.html\\n data = np.load(\\\"phase3-data/data_selected_1980_2010.npy\\\").astype(float)\\n\\n return normalize(data)\",\n \"def __prepare(self, data):\\n #print(\\\"Running Prepare data\\\")\\n #print(data)\\n #print(type(data))\\n if len(data) > 1:\\n if type(data[0]) == np.ndarray:\\n return np.concatenate(data)\\n else:\\n return torch.cat(data).cpu().numpy()\\n else:\\n return data[0].cpu().numpy()\",\n \"def _process(self, data: np.ndarray) -> np.ndarray:\",\n \"def _process(self, data: np.ndarray) -> np.ndarray:\",\n \"def parse(\\n self, data: Union[pd.DataFrame, pd.Series, str], verbose: int = 0\\n ) -> pd.DataFrame:\\n\\n data = self.__transform_data(data)\\n data = self.norm.normalize(data)\\n data = self.find.find_all(data, verbose)\\n data = data.drop(\\\"name_norm\\\", axis=1)\\n return data\",\n \"def normalize_mean(dataset):\\n normalized_dataset = np.array(dataset)\\n return normalized_dataset - np.mean(normalized_dataset)\",\n \"def _to_arraylike(data):\\n _load_objects()\\n if data is None:\\n raise ValueError('Cannot convert None data.')\\n return None\\n if not isinstance(data, (ndarray, DataArray, DataFrame, Series, Index)):\\n data = np.asarray(data)\\n if not np.iterable(data):\\n data = np.atleast_1d(data)\\n return data\",\n \"def standardize(data):\\n stddev = data.std()\\n #if stddev == 0.:\\n # sys.exit(\\\"data.std() == 0. !\\\")\\n if stddev != 0.:\\n data = (data - data.mean()) / (data.std())\\n\\n return data\",\n \"def _normalize(\\n ds: xr.Dataset,\\n *,\\n dim: Sequence[str],\\n kind: str = ADDITIVE,\\n) -> xr.Dataset:\\n if \\\"norm\\\" in ds:\\n norm = ds.norm\\n else:\\n norm = ds.data.mean(dim=dim)\\n norm.attrs[\\\"_group_apply_reshape\\\"] = True\\n\\n return xr.Dataset(\\n dict(data=apply_correction(ds.data, invert(norm, kind), kind), norm=norm)\\n )\",\n \"def convertData(data):\\n\\n return data\",\n \"def _normalize(self, dataset):\\n if self.max is None: # if we are normalizing the training set\\n self.max, self.min = dataset.max(), dataset.min() # find max, min value for each columns\\n for row in dataset.index: # for each row in dataset\\n for col in self.features: # for each feature in the instance (exclude target)\\n dataset.at[row, col] = (dataset.at[row, col] - self.min[col]) / (self.max[col] - self.min[col]) if col != \\\"Bias\\\" else 1\",\n \"def normalize(data):\\n min = np.min(data)\\n if min:\\n data = data + min\\n return old_div(data,np.max(data))\\n else: # if min is 0\\n return old_div(data,np.max(data))\",\n \"def transform(self, data):\\n data -= self.mean\\n if 0.0 in self.std:\\n self.std = np.where(self.std == 0.0, 1.0, self.std)\\n data /= self.std\\n return data\",\n \"def normalize_multivariate_data(data, scaling_values=None):\\n normed_data = np.zeros(data.shape, dtype=data.dtype)\\n scale_cols = [\\\"mean\\\", \\\"std\\\"]\\n if scaling_values is None:\\n scaling_values = pd.DataFrame(np.zeros((data.shape[-1], len(scale_cols)), dtype=np.float32),\\n columns=scale_cols)\\n for i in range(data.shape[-1]):\\n scaling_values.loc[i, [\\\"mean\\\", \\\"std\\\"]] = [data[:, :, :, i].mean(), data[:, :, :, i].std()]\\n normed_data[:, :, :, i] = (data[:, :, :, i] - scaling_values.loc[i, \\\"mean\\\"]) / scaling_values.loc[i, \\\"std\\\"]\\n return normed_data, scaling_values\",\n \"def normalize_multivariate_data(data, scaling_values=None):\\n normed_data = np.zeros(data.shape, dtype=data.dtype)\\n scale_cols = [\\\"mean\\\", \\\"std\\\"]\\n if scaling_values is None:\\n scaling_values = pd.DataFrame(np.zeros((data.shape[-1], len(scale_cols)), dtype=np.float32),\\n columns=scale_cols)\\n for i in range(data.shape[-1]):\\n scaling_values.loc[i, [\\\"mean\\\", \\\"std\\\"]] = [data[:, :, :, i].mean(), data[:, :, :, i].std()]\\n normed_data[:, :, :, i] = (data[:, :, :, i] - scaling_values.loc[i, \\\"mean\\\"]) / scaling_values.loc[i, \\\"std\\\"]\\n return normed_data, scaling_values\",\n \"def normalize_data(img):\\n nor = np.linalg.norm(img, axis = 1)\\n nor = np.reshape(nor, (len(img), 1))\\n img = np.divide(img, nor)\\n return img\",\n \"def transform(self, data):\\n return self.fit_transform(data, fitting=False)\",\n \"def normalize(dataset, tests=None):\\n # ensure that all values are of the same length\\n try:\\n exp_length = len(dataset[0])\\n for val in dataset:\\n assert len(val) == exp_length\\n if(tests):\\n for val in tests:\\n assert len(val) == exp_length\\n except TypeError:\\n print(\\\"Error: Value in dataset is not list\\\")\\n except AssertionError:\\n print(\\\"Error: Values in dataset are not all of the same length\\\")\\n\\n attribs = list(zip(*dataset))\\n new_attribs = []\\n\\n for attr in attribs:\\n attr_norm = []\\n for val in attr:\\n attr_norm.append((val - min(attr)) / ((max(attr) - min(attr)) * 1.0))\\n new_attribs.append(attr_norm)\\n\\n dataset_norm = list(map(list, zip(*new_attribs)))\\n\\n if(tests):\\n attribs_test = list(zip(*tests))\\n new_attribs_test = []\\n for i, attr in enumerate(attribs_test):\\n attr_norm = []\\n for val in attr:\\n attr_norm.append((val - min(attribs[i])) / ((max(attribs[i]) - min(attribs[i])) * 1.0))\\n new_attribs_test.append(attr_norm)\\n\\n tests_norm = list(map(list, zip(*new_attribs_test)))\\n\\n if(tests):\\n return dataset_norm, tests_norm\\n else:\\n return dataset_norm\",\n \"def _format_data(self, data: np.ndarray) -> np.ndarray:\\n self._n_circs = 0\\n self._n_shots = 0\\n self._n_slots = 0\\n self._n_iq = 0\\n\\n # identify shape\\n try:\\n # level1 single-shot data\\n self._n_circs, self._n_shots, self._n_slots, self._n_iq = data.shape\\n except ValueError:\\n try:\\n # level1 data averaged over shots\\n self._n_circs, self._n_slots, self._n_iq = data.shape\\n except ValueError as ex:\\n raise DataProcessorError(\\n f\\\"Data given to {self.__class__.__name__} is not likely level1 data.\\\"\\n ) from ex\\n\\n if self._validate:\\n if self._n_iq != 2:\\n raise DataProcessorError(\\n f\\\"IQ data given to {self.__class__.__name__} does not have two-dimensions \\\"\\n f\\\"(I and Q). Instead, {self._n_iq} dimensions were found.\\\"\\n )\\n\\n return data\",\n \"def process(self, data):\\n # get truncation parameters from config or container defaults\\n self._get_params(type(data))\\n\\n if self.weight_dataset is None:\\n self.weight_dataset = [None] * len(self.dataset)\\n\\n for dset, wgt in zip(self.dataset, self.weight_dataset):\\n old_shape = data[dset].local_shape\\n val = np.ndarray.reshape(data[dset][:], data[dset][:].size)\\n if wgt is None:\\n if np.iscomplexobj(data[dset]):\\n data[dset][:].real = bit_truncate_fixed(\\n val.real, self.fixed_precision\\n ).reshape(old_shape)\\n data[dset][:].imag = bit_truncate_fixed(\\n val.imag, self.fixed_precision\\n ).reshape(old_shape)\\n else:\\n data[dset][:] = bit_truncate_fixed(\\n val, self.fixed_precision\\n ).reshape(old_shape)\\n else:\\n if data[dset][:].shape != data[wgt][:].shape:\\n raise pipeline.PipelineRuntimeError(\\n \\\"Dataset and weight arrays must have same shape ({} != {})\\\".format(\\n data[dset].shape, data[wgt].shape\\n )\\n )\\n invvar = np.ndarray.reshape(data[wgt][:], data[dset][:].size)\\n if np.iscomplexobj(data[dset]):\\n data[dset][:].real = bit_truncate_weights(\\n val.real,\\n invvar * 2.0 / self.variance_increase,\\n self.fixed_precision,\\n ).reshape(old_shape)\\n data[dset][:].imag = bit_truncate_weights(\\n val.imag,\\n invvar * 2.0 / self.variance_increase,\\n self.fixed_precision,\\n ).reshape(old_shape)\\n else:\\n data[dset][:] = bit_truncate_weights(\\n val, invvar / self.variance_increase, self.fixed_precision\\n ).reshape(old_shape)\\n\\n return data\",\n \"def normalizeData(meanAndStd, dataset):\\n\\n for i in range(len(dataset)):\\n for j in range(len(dataset[i])-1):\\n mean = meanAndStd[j][\\\"mean\\\"]\\n std = meanAndStd[j][\\\"std\\\"]\\n dataset[i][j] = (dataset[i][j] - mean)/std\",\n \"def check_input_and_return_data(self, input_name):\\n\\n #Check to see if data is given by a file\\n if isinstance(self.input_data['reservoir'][input_name], str):\\n #Get filename\\n filename = self.input_data['reservoir'][input_name]\\n #Load data \\n data = np.loadtxt(filename, dtype=np.double)\\n \\n #Check to see if data is given by a list\\n elif isinstance(self.input_data['reservoir'][input_name], (list, tuple)):\\n #Turn the list into numpy array\\n data = np.array(self.input_data['reservoir'][input_name], \\n dtype=np.double)\\n\\n #data is a constant array (homogeneous)\\n else:\\n ngrids = self.input_data['numerical']['number of grids']\\n data = (self.input_data['reservoir'][input_name] * \\n np.ones(ngrids))\\n return data\",\n \"def _format_data(self, data: np.ndarray) -> np.ndarray:\\n self._n_shots = 0\\n\\n # identify shape\\n try:\\n # level1 single-shot data\\n self._n_circs, self._n_shots, self._n_slots, self._n_iq = data.shape\\n except ValueError:\\n try:\\n # level1 data averaged over shots\\n self._n_circs, self._n_slots, self._n_iq = data.shape\\n except ValueError as ex:\\n raise DataProcessorError(\\n f\\\"Data given to {self.__class__.__name__} is not likely level1 data.\\\"\\n ) from ex\\n\\n if self._validate:\\n if data.shape[-1] != 2:\\n raise DataProcessorError(\\n f\\\"IQ data given to {self.__class__.__name__} must be a multi-dimensional array\\\"\\n \\\"of dimension [d0, d1, ..., 2] in which the last dimension \\\"\\n \\\"corresponds to IQ elements.\\\"\\n f\\\"Input data contains element with length {data.shape[-1]} != 2.\\\"\\n )\\n\\n return data\",\n \"def transform(self, dataset: NumpyOrPandas) -> NumpyDataset:\\n # checks here\\n super().transform(dataset)\\n # convert to accepted dtype and get attributes\\n dataset = dataset.to_pandas()\\n df = dataset.data\\n\\n # transform\\n roles = NumericRole()\\n outputs = []\\n for n, i in enumerate(df.columns):\\n new_arr = self.dicts[i][\\\"fg\\\"].transform(df[i].values)\\n output = dataset.empty().to_numpy()\\n output.set_data(new_arr, self.dicts[i][\\\"feats\\\"], roles)\\n outputs.append(output)\\n # create resulted\\n return dataset.empty().to_numpy().concat(outputs)\",\n \"def _to_ndarray(data):\\n return np.atleast_1d(getattr(data, 'values', data))\",\n \"def normalize(\\n ds: xr.Dataset,\\n *,\\n dim: str,\\n kind: str = ADDITIVE,\\n) -> xr.Dataset:\\n\\n if \\\"norm\\\" in ds:\\n norm = invert(ds.norm, kind)\\n else:\\n norm = invert(ds.data.mean(dim=dim), kind)\\n\\n return xr.Dataset(dict(data=apply_correction(ds.data, norm, kind)))\",\n \"def normalise(dataset):\\n # Scale images to the [0, 1] range\\n dataset = dataset.astype(\\\"float32\\\") / 255\\n # Make sure images have shape (28, 28, 1)\\n return np.expand_dims(dataset, -1)\",\n \"def normalize2(data):\\n return old_div(data,np.max([np.max(data),-1.0*np.min(data)]))\",\n \"def get_normalizer(data):\\n scaler = StandardScaler().fit(data)\\n return scaler\",\n \"def _normalize_column(self, data, coerce_dtype=None, store_categories=True):\\n if coerce_dtype is not None:\\n coerce_dtype = np.dtype(coerce_dtype)\\n\\n if np.isscalar(data):\\n array = np.array([data], dtype=coerce_dtype)\\n dtype = data.dtype\\n fillvalue = None\\n\\n elif is_categorical(data):\\n if store_categories:\\n cats = data.cat.categories\\n enum_dict = dict(zip(cats, range(len(cats))))\\n array = data.cat.codes\\n dtype = h5py.special_dtype(enum=(array.dtype, enum_dict))\\n fillvalue = -1\\n else:\\n array = data.cat.codes\\n dtype = coerce_dtype or array.dtype\\n fillvalue = -1\\n\\n elif data.dtype in (object, str, bytes):\\n data = np.asarray(data)\\n dtype = np.dtype(\\\"S\\\")\\n array = np.array(data, dtype=dtype)\\n fillvalue = None\\n\\n else:\\n array = np.asarray(data)\\n dtype = data.dtype\\n fillvalue = None\\n\\n return array, dtype, fillvalue\",\n \"def transform(self, data):\\n unflattened = [unflatten_vec(d) for d in data]\\n return numpy.array([self.signature(uf)\\n for uf in unflattened])\",\n \"def normalize(self):\\n self._data /= self.norm()\",\n \"def _check_data(data, dset_attrs):\\n dtype = dset_attrs['dtype']\\n float_to_int = (np.issubdtype(dtype, np.integer)\\n and np.issubdtype(data.dtype, np.floating))\\n if float_to_int:\\n attrs = dset_attrs['attrs']\\n scale_factor = [c for c in attrs if 'scale_factor' in c][0]\\n scale_factor = attrs[scale_factor]\\n\\n # apply scale factor and dtype\\n data = np.multiply(data, scale_factor)\\n if np.issubdtype(dtype, np.integer):\\n data = np.round(data)\\n\\n data = data.astype(dtype)\\n\\n return data\",\n \"def _to_numpy_ndarray(cls, data):\\n if isinstance(data, np.ndarray):\\n return data\\n arr = np.array(data, dtype=np.float)\\n if len(arr.shape) == 1:\\n arr = np.reshape(arr, newshape=(1, arr.shape[0]))\\n return arr\",\n \"def standardize_data(data):\\n return (data - np.mean(data, axis=0)) / (np.std(data, axis=0) + 10 ** -16)\",\n \"def dataNormalize(data):\\n tr = re.sub('<(.*)>|\\\\\\\\n', '', data.prettify())\\n return re.sub(' +', ',', tr).split(',')\",\n \"def standardize_input_data(data,\\n names,\\n shapes=None,\\n check_batch_axis=True,\\n exception_prefix=''):\\n try:\\n data_len = len(data)\\n except TypeError:\\n # For instance if data is `None` or a symbolic Tensor.\\n data_len = None\\n\\n if not names:\\n if data_len and not isinstance(data, dict):\\n raise ValueError(\\n 'Error when checking model ' + exception_prefix + ': '\\n 'expected no data, but got:', data)\\n return []\\n if data is None:\\n return [None for _ in range(len(names))]\\n\\n if isinstance(data, dict):\\n try:\\n data = [\\n data[x].values\\n if data[x].__class__.__name__ == 'DataFrame' else data[x]\\n for x in names\\n ]\\n except KeyError as e:\\n raise ValueError('No data provided for \\\"' + e.args[0] + '\\\". Need data '\\n 'for each key in: ' + str(names))\\n elif isinstance(data, (list, tuple)):\\n if isinstance(data[0], (list, tuple)):\\n data = [np.asarray(d) for d in data]\\n elif len(names) == 1 and isinstance(data[0], (float, int)):\\n data = [np.asarray(data)]\\n else:\\n data = [\\n x.values if x.__class__.__name__ == 'DataFrame' else x for x in data\\n ]\\n else:\\n data = data.values if data.__class__.__name__ == 'DataFrame' else data\\n data = [data]\\n\\n if shapes is not None:\\n data = [\\n standardize_single_array(x, shape) for (x, shape) in zip(data, shapes)\\n ]\\n else:\\n data = [standardize_single_array(x) for x in data]\\n\\n if len(data) != len(names):\\n if data and hasattr(data[0], 'shape'):\\n raise ValueError('Error when checking model ' + exception_prefix +\\n ': the list of Numpy arrays that you are passing to '\\n 'your model is not the size the model expected. '\\n 'Expected to see ' + str(len(names)) + ' array(s), ' +\\n 'for inputs ' + str(names) + ' but instead got the '\\n 'following list of ' + str(len(data)) + ' arrays: ' +\\n str(data)[:200] + '...')\\n elif len(names) > 1:\\n raise ValueError('Error when checking model ' + exception_prefix +\\n ': you are passing a list as input to your model, '\\n 'but the model expects a list of ' + str(len(names)) +\\n ' Numpy arrays instead. The list you passed was: ' +\\n str(data)[:200])\\n elif len(data) == 1 and not hasattr(data[0], 'shape'):\\n raise TypeError('Error when checking model ' + exception_prefix +\\n ': data should be a Numpy array, or list/dict of '\\n 'Numpy arrays. Found: ' + str(data)[:200] + '...')\\n elif len(names) == 1:\\n data = [np.asarray(data)]\\n\\n # Check shapes compatibility.\\n if shapes:\\n for i in range(len(names)):\\n if shapes[i] is not None:\\n if tensor_util.is_tf_type(data[i]):\\n tensorshape = data[i].shape\\n if not tensorshape:\\n continue\\n data_shape = tuple(tensorshape.as_list())\\n elif is_composite_or_composite_value(data[i]):\\n tensorshape = get_composite_shape(data[i])\\n data_shape = tuple(tensorshape.as_list())\\n else:\\n data_shape = data[i].shape\\n\\n shape = shapes[i]\\n if len(data_shape) != len(shape):\\n raise ValueError('Error when checking ' + exception_prefix +\\n ': expected ' + names[i] + ' to have ' +\\n str(len(shape)) + ' dimensions, but got array '\\n 'with shape ' + str(data_shape))\\n if not check_batch_axis:\\n data_shape = data_shape[1:]\\n shape = shape[1:]\\n for dim, ref_dim in zip(data_shape, shape):\\n if ref_dim != dim and ref_dim is not None and dim is not None:\\n raise ValueError('Error when checking ' + exception_prefix +\\n ': expected ' + names[i] + ' to have shape ' +\\n str(shape) + ' but got array with shape ' +\\n str(data_shape))\\n return data\",\n \"def _normalizeComplex(data):\\n if hasattr(data, \\\"dtype\\\"):\\n isComplex = numpy.issubdtype(data.dtype, numpy.complexfloating)\\n else:\\n isComplex = isinstance(data, numbers.Complex)\\n if isComplex:\\n data = numpy.absolute(data)\\n return data\",\n \"def _validate_input_data(self):\\n\\n if type(self.data) in [np.ndarray, da.Array]:\\n if not self.data.dtype.names:\\n raise ValueError('QuadTree: numpy array provided for data, but no names were found, array must be a structured array')\\n if 'x' not in self.data.dtype.names or 'y' not in self.data.dtype.names:\\n raise ValueError('QuadTree: numpy structured array provided for data, but \\\"x\\\" or \\\"y\\\" not found in variable names')\\n self.layernames = [self.rev_layer_lookup[var] for var in self.data.dtype.names if var in ['z', 'tvu']]\\n elif type(self.data) == xr.Dataset:\\n if 'x' not in self.data:\\n raise ValueError('QuadTree: xarray Dataset provided for data, but \\\"x\\\" or \\\"y\\\" not found in variable names')\\n if len(self.data.dims) > 1:\\n raise ValueError('QuadTree: xarray Dataset provided for data, but found multiple dimensions, must be one dimensional: {}'.format(self.data.dims))\\n self.layernames = [self.rev_layer_lookup[var] for var in self.data if var in ['z', 'tvu']]\\n self._convert_dataset() # internally we just convert xarray dataset to numpy for ease of use\\n else:\\n raise ValueError('QuadTree: numpy structured array or dask array with \\\"x\\\" and \\\"y\\\" as variable must be provided')\",\n \"def normalize(self, image, transpose=False, data_type=None):\\n return normalize(image, self.mean, self.std, transpose)\",\n \"def scale_data(x: np.ndarray, scaler=None) -> Tuple[np.ndarray, object]:\\n original_shape = x.shape\\n\\n # reshape data to 2d array\\n x = x.reshape((x.shape[0], -1))\\n\\n if scaler is None:\\n scaler = StandardScaler().fit(x)\\n\\n x = scaler.transform(x)\\n\\n # reshape back\\n x = x.reshape(original_shape)\\n return x, scaler\"\n]","isConversational":false},"negative_scores":{"kind":"list like","value":["0.72648424","0.72648424","0.71091664","0.7035169","0.6851011","0.6670504","0.6645314","0.6523729","0.6430414","0.64292383","0.6396991","0.6373839","0.63612485","0.63536185","0.6324898","0.6320754","0.6300311","0.6296813","0.629189","0.6264069","0.6258603","0.6223888","0.61500144","0.61459136","0.6131614","0.61211455","0.6103099","0.6088974","0.6082629","0.6072452","0.6040251","0.60364705","0.6032708","0.6030999","0.60306185","0.6010635","0.600311","0.600121","0.5990764","0.5987272","0.59806776","0.59680927","0.59606165","0.5959473","0.5954855","0.5946862","0.59408253","0.59408253","0.59260005","0.5924354","0.59128815","0.5886978","0.58763796","0.5871822","0.5860432","0.58577365","0.5849719","0.5843995","0.5822025","0.5817995","0.5777172","0.57761234","0.57761234","0.57759875","0.57682246","0.5747629","0.57327914","0.5710851","0.5703104","0.56940246","0.56930506","0.568805","0.56876713","0.56876713","0.5683493","0.565768","0.5655443","0.56550056","0.5653012","0.56452024","0.56442356","0.5642971","0.56388277","0.562664","0.5618044","0.5613669","0.5602638","0.560142","0.55970734","0.55910206","0.5590202","0.55868596","0.5585338","0.557708","0.5567672","0.5554315","0.5548759","0.55439264","0.5541081","0.5538125"],"string":"[\n \"0.72648424\",\n \"0.72648424\",\n \"0.71091664\",\n \"0.7035169\",\n \"0.6851011\",\n \"0.6670504\",\n \"0.6645314\",\n \"0.6523729\",\n \"0.6430414\",\n \"0.64292383\",\n \"0.6396991\",\n \"0.6373839\",\n \"0.63612485\",\n \"0.63536185\",\n \"0.6324898\",\n \"0.6320754\",\n \"0.6300311\",\n \"0.6296813\",\n \"0.629189\",\n \"0.6264069\",\n \"0.6258603\",\n \"0.6223888\",\n \"0.61500144\",\n \"0.61459136\",\n \"0.6131614\",\n \"0.61211455\",\n \"0.6103099\",\n \"0.6088974\",\n \"0.6082629\",\n \"0.6072452\",\n \"0.6040251\",\n \"0.60364705\",\n \"0.6032708\",\n \"0.6030999\",\n \"0.60306185\",\n \"0.6010635\",\n \"0.600311\",\n \"0.600121\",\n \"0.5990764\",\n \"0.5987272\",\n \"0.59806776\",\n \"0.59680927\",\n \"0.59606165\",\n \"0.5959473\",\n \"0.5954855\",\n \"0.5946862\",\n \"0.59408253\",\n \"0.59408253\",\n \"0.59260005\",\n \"0.5924354\",\n \"0.59128815\",\n \"0.5886978\",\n \"0.58763796\",\n \"0.5871822\",\n \"0.5860432\",\n \"0.58577365\",\n \"0.5849719\",\n \"0.5843995\",\n \"0.5822025\",\n \"0.5817995\",\n \"0.5777172\",\n \"0.57761234\",\n \"0.57761234\",\n \"0.57759875\",\n \"0.57682246\",\n \"0.5747629\",\n \"0.57327914\",\n \"0.5710851\",\n \"0.5703104\",\n \"0.56940246\",\n \"0.56930506\",\n \"0.568805\",\n \"0.56876713\",\n \"0.56876713\",\n \"0.5683493\",\n \"0.565768\",\n \"0.5655443\",\n \"0.56550056\",\n \"0.5653012\",\n \"0.56452024\",\n \"0.56442356\",\n \"0.5642971\",\n \"0.56388277\",\n \"0.562664\",\n \"0.5618044\",\n \"0.5613669\",\n \"0.5602638\",\n \"0.560142\",\n \"0.55970734\",\n \"0.55910206\",\n \"0.5590202\",\n \"0.55868596\",\n \"0.5585338\",\n \"0.557708\",\n \"0.5567672\",\n \"0.5554315\",\n \"0.5548759\",\n \"0.55439264\",\n \"0.5541081\",\n \"0.5538125\"\n]","isConversational":false},"document_score":{"kind":"string","value":"0.6096874"},"document_rank":{"kind":"string","value":"27"}}},{"rowIdx":23789,"cells":{"query":{"kind":"string","value":"Returns a normalized complex data. If the data is a numpy data with complex, returns the absolute value. Else returns the input data."},"document":{"kind":"string","value":"def _normalizeComplex(data):\n if hasattr(data, \"dtype\"):\n isComplex = numpy.issubdtype(data.dtype, numpy.complexfloating)\n else:\n isComplex = isinstance(data, numbers.Complex)\n if isComplex:\n data = numpy.absolute(data)\n return data"},"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 complex_abs(data):\n assert data.size(-1) == 2\n return (data ** 2).sum(dim=-1).sqrt()","def complex_abs(data):\n assert data.size(-1) == 2\n return (data ** 2).sum(dim=-1).sqrt()","def complex_abs(data):\n assert data.size(-1) == 2\n return (data ** 2).sum(dim=-1).sqrt()","def complex_abs_sq(data):\n assert data.size(-1) == 2\n return (data ** 2).sum(dim=-1)","def abs(data):\n return _make.abs(data)","def complex_normalize(c):\n mag = complex_magnitude(c)\n mag = mag if mag > 0 else 1\n return c / mag","def __complex__(self) -> complex:\n return self._translate_in_type(complex, self.integer, self.float_num)","def complex_normalize(X, axis=-1):\n mags = np.linalg.norm(np.abs(X), axis=axis, keepdims=True)\n return X / mags","def _norm_data(data):\n if data is None:\n return data\n data_min = np.min(data)\n c_norm = np.max(data) - data_min\n return (data - data_min) / c_norm if (c_norm != 0) else (data - data_min)","def test_op_abs_offload_array_complex(self):\n\n device = pymic.devices[0]\n stream = device.get_default_stream()\n a = numpy.arange(1.0, 4711.0 * 1024, dtype=complex)\n\n old_a = numpy.empty_like(a)\n old_a[:] = a[:]\n expect = abs(a)\n\n offl_a = stream.bind(a)\n offl_r = abs(offl_a)\n r = offl_r.update_host().array\n stream.sync()\n\n self.assertTrue((a == old_a).all(),\n \"Input array operand 1 must not be modified: \"\n \"{0} should be {1}\".format(a, old_a))\n self.assertTrue((r == expect).all(),\n \"Array contains unexpected values: \"\n \"{0} should be {1}\".format(r, expect))","def __abs__(self):\r\n return (self._real.fma(self._real, self._imag*self._imag)).sqrt()","def get_value(self):\n return complex(*self.points[0, :2])","def real_of_complex(z):\n return np.vstack((np.real(z[:,0]),np.imag(z[:,0]),np.real(z[:,1]),np.imag(z[:,1]))).T","def __complex__(self):\n return complex(self._reNum, self._imNum)","def test_op_isub_scalar_complex(self):\n\n device = pymic.devices[0]\n stream = device.get_default_stream()\n a = numpy.arange(1, 4711 * 1024, dtype=complex)\n s = complex(1.2, -1.3)\n\n old = numpy.empty_like(a)\n old[:] = a[:]\n expect = a - s\n\n offl_a = stream.bind(a)\n offl_a -= s\n offl_a.update_host()\n r = offl_a.array\n stream.sync()\n\n self.assertTrue((r != old).all(),\n \"Input array operand must be modified: \"\n \"{0} should be {1}\".format(r, old))\n self.assertTrue((r == expect).all(),\n \"Array contains unexpected values: \"\n \"{0} should be {1}\".format(r, expect))","def dcomplex(self):\n return self._dcomplex","def to_float_complex(self):\r\n return eval(str(self))","def copy_abs(self):\r\n return (self._real.fma(self._real, self._imag*self._imag)).sqrt()","def complex_value(self) -> global___Expression.ComplexValue:","def __complex__(self):\n return complex(self.q[0], self.q[1])","def test_op_reverse_complex(self):\n\n device = pymic.devices[0]\n stream = device.get_default_stream()\n a = numpy.arange(1.0, 4711.0 * 1024, dtype=complex)\n old_a = numpy.empty_like(a)\n old_a[:] = a[:]\n expect = numpy.array(a[::-1])\n\n offl_a = stream.bind(a)\n offl_r = offl_a.reverse()\n offl_r.update_host()\n stream.sync()\n r = offl_r.array\n\n self.assertTrue((a == old_a).all(),\n \"Input array operand 1 must not be modified: \"\n \"{0} should be {1}\".format(a, old_a))\n self.assertTrue((r == expect).all(),\n \"Array contains unexpected values: \"\n \"{0} should be {1}\".format(a, r))","def complex(real, imag):","def test_op_isub_array_complex(self):\n\n device = pymic.devices[0]\n stream = device.get_default_stream()\n a = numpy.arange(1, 4711 * 1024, dtype=complex)\n o = a + complex(1.2, -1.3)\n\n old_a = numpy.empty_like(a)\n old_o = numpy.empty_like(o)\n old_a[:] = a[:]\n old_o[:] = o[:]\n expect = a - o\n\n offl_a = stream.bind(a)\n offl_o = stream.bind(o)\n offl_a -= o\n offl_a.update_host()\n r = offl_a.array\n stream.sync()\n\n self.assertTrue((r != old_a).all(),\n \"Array operand must be modified: \"\n \"{0} should be {1}\".format(a, old_a))\n self.assertTrue((o == old_o).all(),\n \"Input array operand 2 must not be modified: \"\n \"{0} should be {1}\".format(o, old_o))\n self.assertTrue((r == expect).all(),\n \"Array contains unexpected values: \"\n \"{0} should be {1}\".format(r, expect))","def _get_complex_null_value(complex):\n return _COMPLEX_NULL_VALUE","def complex(self, real=0.0, imag=0.0):\n if imag == 0.0 and real == 0.0:\n return complex_zero","def test_op_sub_scalar_complex(self):\n\n device = pymic.devices[0]\n stream = device.get_default_stream()\n a = numpy.arange(1.0, 4711.0 * 1024, dtype=complex)\n s = complex(1.3, 1.3)\n\n old = numpy.empty_like(a)\n old[:] = a[:]\n expect = a - s\n\n offl_a = stream.bind(a)\n offl_r = offl_a - s\n offl_a.update_host()\n r = offl_r.update_host().array\n stream.sync()\n\n self.assertEqual(r.shape, a.shape)\n self.assertEqual(r.dtype, a.dtype)\n self.assertTrue((a == old).all(),\n \"Input array operand must not be modified: \"\n \"{0} should be {1}\".format(a, old))\n self.assertTrue((r == expect).all(),\n \"Array contains unexpected values: \"\n \"{0} should be {1}\".format(r, expect))","def _cmplx_to_complex_ ( s ) :\n return complex ( s.real , s.imag )","def normalize(complex_vec, axis=None):\n cv_mag_vector = complex_magnitude(complex_vec)\n return vector_normalize(cv_mag_vector, axis=axis)","def test_parserval_complex_2d():\n\ta, b, c = np.meshgrid([1j, 1, 1, 1j], [0, 1j, 1j], [0, 1, 1, 1])\n\tdummy_array = xr.DataArray(a * b * c, dims=['x', 'y', 'z'])\n\tchunked_array = dummy_array.chunk(chunks={'x': 2, 'y': 2, 'z': 2})\n\tchunked_array_zeromean = chunked_array - chunked_array.mean(dim=['y', 'z'])\n\tspec = xfft.fft(chunked_array_zeromean, dim=['y', 'z'], sym=True)\n\tassert np.allclose(np.var(a * b * c, axis=(1, 2)),\n\t xfft.ps(spec).sum(dim=['f_y','f_z']))","def test_op_fillfrom_complex(self):\n\n device = pymic.devices[0]\n stream = device.get_default_stream()\n a = numpy.arange(1.0, 4711.0 * 1024, dtype=complex)\n offl_r = stream.empty_like(a)\n offl_r.fillfrom(a)\n offl_r.update_host()\n stream.sync()\n r = offl_r.array\n self.assertTrue((a == r).all(),\n \"Array contains unexpected values: \"\n \"{0} should be {1}\".format(a, r))","def abs(number):\n if isinstance(number,(int,float,complex)): return builtins.abs(number)\n elif isinstance(number,(numpy.float64,numpy.complex128)): return numpy.abs(number)\n else: raise error(\"field_traits.abs executed on unavailable type\")","def _cmplx_to_complex_ ( s ) :\n return complex ( s.real () , s.imag () )","def normalized_data(self):\n return self._normalization_constant * self._data","def complexify(self,z):\r\n \r\n import numpy as np\r\n \r\n if not np.iscomplex(z).any():\r\n if len(z.shape) != 2 or z.shape[1] != 2:\r\n raise Exception('Shape format not understood. Provide shape vertices either as a complex vector, or as a N-by-2 real numpy array.')\r\n else:\r\n z = z[:,0] + 1j*z[:,1]\r\n \r\n return z","def complexify(self,z):\r\n \r\n import numpy as np\r\n \r\n if not np.iscomplex(z).any():\r\n if len(z.shape) != 2 or z.shape[1] != 2:\r\n raise Exception('Shape format not understood. Provide shape vertices either as a complex vector, or as a N-by-2 real numpy array.')\r\n else:\r\n z = z[:,0] + 1j*z[:,1]\r\n \r\n return z","def complexify(self,z):\r\n \r\n import numpy as np\r\n \r\n if not np.iscomplex(z).any():\r\n if len(z.shape) != 2 or z.shape[1] != 2:\r\n raise Exception('Shape format not understood. Provide shape vertices either as a complex vector, or as a N-by-2 real numpy array.')\r\n else:\r\n z = z[:,0] + 1j*z[:,1]\r\n \r\n return z","def complexify(self,z):\r\n \r\n import numpy as np\r\n \r\n if not np.iscomplex(z).any():\r\n if len(z.shape) != 2 or z.shape[1] != 2:\r\n raise Exception('Shape format not understood. Provide shape vertices either as a complex vector, or as a N-by-2 real numpy array.')\r\n else:\r\n z = z[:,0] + 1j*z[:,1]\r\n \r\n return z","def complexify(self,z):\r\n \r\n import numpy as np\r\n \r\n if not np.iscomplex(z).any():\r\n if len(z.shape) != 2 or z.shape[1] != 2:\r\n raise Exception('Shape format not understood. Provide shape vertices either as a complex vector, or as a N-by-2 real numpy array.')\r\n else:\r\n z = z[:,0] + 1j*z[:,1]\r\n \r\n return z","def complexify(self,z):\r\n \r\n import numpy as np\r\n \r\n if not np.iscomplex(z).any():\r\n if len(z.shape) != 2 or z.shape[1] != 2:\r\n raise Exception('Shape format not understood. Provide shape vertices either as a complex vector, or as a N-by-2 real numpy array.')\r\n else:\r\n z = z[:,0] + 1j*z[:,1]\r\n \r\n return z","def complexify(self,z):\r\n \r\n import numpy as np\r\n \r\n if not np.iscomplex(z).any():\r\n if len(z.shape) != 2 or z.shape[1] != 2:\r\n raise Exception('Shape format not understood. Provide shape vertices either as a complex vector, or as a N-by-2 real numpy array.')\r\n else:\r\n z = z[:,0] + 1j*z[:,1]\r\n \r\n return z","def complexify(self,z):\r\n \r\n import numpy as np\r\n \r\n if not np.iscomplex(z).any():\r\n if len(z.shape) != 2 or z.shape[1] != 2:\r\n raise Exception('Shape format not understood. Provide shape vertices either as a complex vector, or as a N-by-2 real numpy array.')\r\n else:\r\n z = z[:,0] + 1j*z[:,1]\r\n \r\n return z","def complexify(self,z):\r\n \r\n import numpy as np\r\n \r\n if not np.iscomplex(z).any():\r\n if len(z.shape) != 2 or z.shape[1] != 2:\r\n raise Exception('Shape format not understood. Provide shape vertices either as a complex vector, or as a N-by-2 real numpy array.')\r\n else:\r\n z = z[:,0] + 1j*z[:,1]\r\n \r\n return z","def complexify(self,z):\r\n \r\n import numpy as np\r\n \r\n if not np.iscomplex(z).any():\r\n if len(z.shape) != 2 or z.shape[1] != 2:\r\n raise Exception('Shape format not understood. Provide shape vertices either as a complex vector, or as a N-by-2 real numpy array.')\r\n else:\r\n z = z[:,0] + 1j*z[:,1]\r\n \r\n return z","def normalize_data(data=None):\n # Data pre-processing\n n = data.shape[0]\n for i in range(n):\n xx = data[i,:,:]\n xx -= np.mean(xx) # Centering in 0\n xx /= np.linalg.norm(xx) # Normalizing to 1\n data[i] = xx # Affect value\n return data","def complex(real=0.0, imag=0.0):\n if imag == 0.0 and real == 0.0:\n return complex_zero","def test_op_zero_complex(self):\n\n device = pymic.devices[0]\n stream = device.get_default_stream()\n a = numpy.arange(1.0, 4711.0 * 1024, dtype=complex)\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 + 0.0j,\n \"Array should be all zeros.\")","def _de_transform(self, data):\r\n mean, variance = self._input_statistics.overall_feature_moments\r\n return data * variance + mean","def test_op_sub_array_complex(self):\n\n device = pymic.devices[0]\n stream = device.get_default_stream()\n a = numpy.arange(1.0, 4711.0 * 1024, dtype=complex)\n o = a + complex(1.3, -1.4)\n\n old_a = numpy.empty_like(a)\n old_o = numpy.empty_like(o)\n old_a[:] = a[:]\n old_o[:] = o[:]\n expect = a - o\n\n offl_a = stream.bind(a)\n offl_r = offl_a - o\n offl_a.update_host()\n r = offl_r.update_host().array\n stream.sync()\n\n self.assertEqual(r.shape, a.shape)\n self.assertEqual(r.dtype, a.dtype)\n self.assertTrue((a == old_a).all(),\n \"Input array operand 1 must not be modified: \"\n \"{0} should be {1}\".format(a, old_a))\n self.assertTrue((o == old_o).all(),\n \"Input array operand 2 must not be modified: \"\n \"{0} should be {1}\".format(o, old_o))\n self.assertTrue((r == expect).all(),\n \"Array contains unexpected values: \"\n \"{0} should be {1}\".format(r, expect))","def complex_matrix_to_real(m):\n a, b = np.real(m), np.imag(m)\n return np.vstack([np.hstack([a,b]), np.hstack([-b, a])])","def norm_data(data):\n return (data-np.min(data))/(np.max(data)-np.min(data))","def test_op_one_complex(self):\n\n device = pymic.devices[0]\n stream = device.get_default_stream()\n a = numpy.arange(1.0, 4711.0 * 1024, dtype=complex)\n offl_a = stream.bind(a)\n offl_a.one()\n offl_a.update_host()\n stream.sync()\n self.assertTrue((a == complex(1.0, 0.0)).all(),\n \"Array should be all one.\" + str(a))","def normalize_data(data):\n mean = np.mean(data)\n std = np.std(data)\n return (data - mean) / std","def abs(self):\n return math.sqrt(self['real'] * self['real'] + self['imaginary'] * self['imaginary'])","def toComplex(self):\n return (self._points[0].value(),\n self._points[1].value(),\n self._points[2].value())","def complex_difference(c_1,c_2):\n return c_1 - c_2","def numpy_scalar(data):\r\n\r\n # handle case where data is numpy.array([])\r\n if data.ndim > 0 and (len(data.shape) == 0 or\r\n __builtins__['max'](data.shape) == 0):\r\n assert numpy.all(numpy.array([]) == data)\r\n raise EmptyConstantError()\r\n try:\r\n numpy.complex(data) # works for all numeric scalars\r\n return data\r\n except Exception:\r\n raise NotScalarConstantError(\r\n 'v.data is non-numeric, non-scalar, or has more than one'\r\n ' unique value', data)","def _complex(real, imag):\n real = np.asarray(real)\n imag = np.asarray(imag)\n cplx = 1j * imag \n return cplx + real","def test_op_imul_array_complex(self):\n\n device = pymic.devices[0]\n stream = device.get_default_stream()\n a = numpy.arange(1, 4711 * 1024, dtype=complex)\n o = a + complex(1.2, -1.3)\n\n old_a = numpy.empty_like(a)\n old_o = numpy.empty_like(o)\n old_a[:] = a[:]\n old_o[:] = o[:]\n expect = a * o\n\n offl_a = stream.bind(a)\n offl_o = stream.bind(o)\n offl_a *= o\n offl_a.update_host()\n r = offl_a.array\n stream.sync()\n\n self.assertTrue((r != old_a).all(),\n \"Array operand must be modified: \"\n \"{0} should be {1}\".format(a, old_a))\n self.assertTrue((o == old_o).all(),\n \"Input array operand 2 must not be modified: \"\n \"{0} should be {1}\".format(o, old_o))\n self.assertTrue((r == expect).all(),\n \"Array contains unexpected values: \"\n \"{0} should be {1}\".format(r, expect))","def test_op_imul_scalar_complex(self):\n\n device = pymic.devices[0]\n stream = device.get_default_stream()\n a = numpy.arange(1, 4711 * 1024, dtype=complex)\n s = complex(1.2, -1.3)\n\n old = numpy.empty_like(a)\n old[:] = a[:]\n expect = a * s\n\n offl_a = stream.bind(a)\n offl_a *= s\n offl_a.update_host()\n r = offl_a.array\n stream.sync()\n\n self.assertTrue((r != old).all(),\n \"Input array operand must be modified: \"\n \"{0} should be {1}\".format(r, old))\n self.assertTrue((r == expect).all(),\n \"Array contains unexpected values: \"\n \"{0} should be {1}\".format(r, expect))","def complex_magnitude(c):\n return (c * c.conjugate()) ** 0.5","def complex_inverse(c1,cr):","def tensor_to_complex_np(data):\n data = data.numpy()\n return data[..., 0] + 1j * data[..., 1]","def _process(self, data: np.array) -> np.array:\n # pylint: disable=no-member\n return unp.sqrt(data[..., 0] ** 2 + data[..., 1] ** 2) * self.scale","def normalized_data(self):\n return self._data","def normalize(data):\n # normalize data and return\n # https://stackoverflow.com/questions/29661574/normalize-numpy-array-columns-in-python\n return (data - data.min(axis=0)) / data.ptp(axis=0)","def arg(self):\r\n if self.is_zero():\r\n raise InvalidOperationError(\r\n 'argument of a complex number is not defined at 0'\r\n )\r\n return atan2(self._imag, self._real)","def normalize(data):\n\n\t#return [float(x) / pow(2, 15) for x in data]\n\n\tl = [float(x) / pow(2, 15) for x in data]\n\treturn np.asarray(l)","def _transform(self, data):\r\n mean, variance = self._input_statistics.overall_feature_moments\r\n return (data - mean) / variance","def _cmplx_sub_ ( s , o ) :\n return (-o ) + complex ( s )","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 normalizeData(pre_signal):\n\n if sp.any(sp.isnan(pre_signal)):\n print('there are NaNs in the data matrix, making them zero')\n\n pre_signal[sp.isnan(pre_signal)] = 0\n mean_vector = sp.mean(pre_signal, axis=0, keepdims=True)\n normed_signal = pre_signal - mean_vector\n norm_vector = sp.linalg.norm(normed_signal, axis=0, keepdims=True)\n norm_vector[norm_vector == 0] = 1e-116\n normed_signal = normed_signal / norm_vector\n\n return normed_signal, mean_vector, norm_vector","def test_op_fill_complex(self):\n\n device = pymic.devices[0]\n stream = device.get_default_stream()\n value = complex(7342.0, -2137.0)\n a = numpy.arange(1.0, 4711.0 * 1024, dtype=complex)\n expect = numpy.empty_like(a)\n expect[:] = value\n offl_a = stream.bind(a)\n offl_a.fill(value)\n offl_a.update_host()\n stream.sync()\n self.assertTrue((a == expect).all(),\n \"Array contains unexpected values: \"\n \"{0} should be {1}\".format(a, expect))","def test_op_mul_array_complex(self):\n\n device = pymic.devices[0]\n stream = device.get_default_stream()\n a = numpy.arange(1.0, 4711.0 * 1024, dtype=complex)\n o = a + complex(-1.6, -2.2)\n\n old_a = numpy.empty_like(a)\n old_o = numpy.empty_like(o)\n old_a[:] = a[:]\n old_o[:] = o[:]\n expect = a * o\n\n offl_a = stream.bind(a)\n offl_r = offl_a * o\n offl_a.update_host()\n r = offl_r.update_host().array\n stream.sync()\n\n self.assertEqual(r.shape, a.shape)\n self.assertEqual(r.dtype, a.dtype)\n self.assertTrue((a == old_a).all(),\n \"Input array operand 1 must not be modified: \"\n \"{0} should be {1}\".format(a, old_a))\n self.assertTrue((o == old_o).all(),\n \"Input array operand 2 must not be modified: \"\n \"{0} should be {1}\".format(o, old_o))\n self.assertTrue((r == expect).all(),\n \"Array contains unexpected values: \"\n \"{0} should be {1}\".format(r, expect))","def norm(self):\n if self._coord_format != constants.MatrixCoordinateDefault:\n self._logger.error(\"invalid coordinate format\")\n raise NotImplementedError(\"invalid coordinate format\")\n\n if self._dtype == complex:\n def __map(m):\n return m[2].real ** 2 + m[2].imag ** 2\n else:\n def __map(m):\n return m[2] ** 2\n\n n = self._data.map(\n __map\n ).reduce(\n lambda a, b: a + b\n )\n\n return math.sqrt(n)","def test_op_isub_offload_array_complex(self):\n\n device = pymic.devices[0]\n stream = device.get_default_stream()\n a = numpy.arange(1, 4711 * 1024, dtype=complex)\n o = a + complex(1.2, -1.3)\n\n old_a = numpy.empty_like(a)\n old_o = numpy.empty_like(o)\n old_a[:] = a[:]\n old_o[:] = o[:]\n expect = a - o\n\n offl_a = stream.bind(a)\n offl_o = stream.bind(o)\n offl_a -= offl_o\n offl_a.update_host()\n r = offl_a.array\n stream.sync()\n\n self.assertTrue((r != old_a).all(),\n \"Array operand must be modified: \"\n \"{0} should be {1}\".format(a, old_a))\n self.assertTrue((o == old_o).all(),\n \"Input array operand 2 must not be modified: \"\n \"{0} should be {1}\".format(o, old_o))\n self.assertTrue((r == expect).all(),\n \"Array contains unexpected values: \"\n \"{0} should be {1}\".format(r, expect))","def test_parseval_real_2d():\n\ta = np.mgrid[:5, :5, :5][0]\n\tdummy_array = xr.DataArray(a, dims=['x', 'y', 'z'])\n\tchunked_array = dummy_array.chunk(chunks={'x': 2, 'y': 2, 'z': 2})\n\tchunked_array_zeromean = chunked_array - chunked_array.mean(dim=['y', 'z'])\n\tspec = xfft.fft(chunked_array_zeromean, dim=['y', 'z'])\n\tassert np.allclose(np.var(a, axis=(1, 2)),\n\t xfft.ps(spec).sum(dim=['f_y','f_z']))","def real(self):\n res = self._defer_unary_elementwise(np.real)\n res.dtype = np.float_\n return res","def getData(self, copy=True, mode=None):\n if mode is None:\n return super(ComplexField3D, self).getData(copy=copy)\n else:\n return self._convertComplexData(self._data, mode)","def calculate_abs(self):\n ref_spectra_raw = np.array(self.raw_data['spectrum_0'].attrs['reference'])\n self.ref_spectra_arr = np.subtract(ref_spectra_raw,self.back_spectra_arr)\n abs=-np.log10(self.pre_proc_data.div(self.ref_spectra_arr))\n self.abs_data=abs\n return self.abs_data","def __neg__(self):\n return Complex(-self._reNum, -self._imNum)","def normalize(data):\n min = np.min(data)\n if min:\n data = data + min\n return old_div(data,np.max(data))\n else: # if min is 0\n return old_div(data,np.max(data))","def unscale_data(self, data):\n return (data + self.mean)*self.std","def __abs__(self):\n\t\tval = abs(self.val)\n\t\tif 0 in self.val:\n\t\t\traise ValueError(\"Absolute value is not differentiable at 0.\")\n\n\t\tder_copy = np.copy(self.der)\n\t\tif len(der_copy.shape):\n\t\t\tfor i, val_i in enumerate(self.val):\n\t\t\t\tif val_i < 0:\n\t\t\t\t\tder_copy[i] = -1 * der_copy[i]\n\t\treturn Var(val, der_copy)","def test_parserval_complex_1d():\n\ta = np.exp(2j * np.pi * np.arange(8) / 8)\n\tdummy_array = xr.DataArray(a, dims=['x'])\n\tchunked_array = dummy_array.chunk(chunks={'x': 2})\n\tspec = xfft.fft(chunked_array, dim=['x'], detrend='mean')\n\tassert np.var(a) == xfft.ps(spec).sum()","def np_safe_abs(x):\n try:\n return linalg.norm(x,2)\n except ValueError:\n return abs(x)","def get_complex_type(self):\n import numpy\n return numpy.complex128","def __abs__(self) -> numbers.Number:\n\n return np.sqrt(abs(self.mag2()))","def get_data_radiant(data):\n try:\n if not isinstance(data, np.ndarray):\n raise AttributeError(\"The given data should be of type numpy array\")\n\n return np.arctan2(data[:, 1].max() - data[:, 1].min(),\n data[:, 0].max() - data[:, 0].min())\n except AttributeError as err:\n LOGGER.error(err)","def normalize2(data):\n return old_div(data,np.max([np.max(data),-1.0*np.min(data)]))","def magnitude(X):\r\n r = np.real(X)\r\n i = np.imag(X)\r\n return np.sqrt(r * r + i * i);","def canonicalize(data):\n\n if isinstance(data, np.ndarray):\n if data.dtype == np.uint8:\n data = data.astype(np.float32) / 255.0\n data = torch.from_numpy(np.ascontiguousarray(data))\n\n elif isinstance(data, torch.Tensor):\n if data.dtype == torch.uint8:\n data = data.float() / 255.0\n\n else:\n raise NotImplementedError()\n\n return data","def normalizeData(self, data):\n return _normalizeData(data)","def normalizeData(self, data):\n return _normalizeData(data)","def real(self):\n return self.__array_ufunc__(np.real, \"__call__\", self)","def process(self, data):\n return data - np.mean(data)","def toReal(self):\n\n\t\tif self.space==\"real\":\n\t\t\tpass \n\t\telse:\n\t\t\tself.data = fftengine.irfft2(self.data)\n\t\t\tself.space = \"real\"","def normalize_data(self, data):\n self.find_mean_std(data)\n return (data - self._data_mean) / self._data_std","def normalized(self):\n d = self.magnitude()\n if d:\n return type(self)(self.x / d, self.y / d, self.z / d)\n return self.copy()","def test_op_imul_offload_array_complex(self):\n\n device = pymic.devices[0]\n stream = device.get_default_stream()\n a = numpy.arange(1, 4711 * 1024, dtype=complex)\n o = a + complex(1.2, -1.3)\n\n old_a = numpy.empty_like(a)\n old_o = numpy.empty_like(o)\n old_a[:] = a[:]\n old_o[:] = o[:]\n expect = a * o\n\n offl_a = stream.bind(a)\n offl_o = stream.bind(o)\n offl_a *= offl_o\n offl_a.update_host()\n r = offl_a.array\n stream.sync()\n\n self.assertTrue((r != old_a).all(),\n \"Array operand must be modified: \"\n \"{0} should be {1}\".format(a, old_a))\n self.assertTrue((o == old_o).all(),\n \"Input array operand 2 must not be modified: \"\n \"{0} should be {1}\".format(o, old_o))\n self.assertTrue((r == expect).all(),\n \"Array contains unexpected values: \"\n \"{0} should be {1}\".format(r, expect))","def unNormalizeData(normalizedData, data_mean, data_std, dimensions_to_ignore):\n T = normalizedData.shape[0]\n D = data_mean.shape[0]\n\n origData = np.zeros((T, D), dtype=np.float32)\n dimensions_to_use = []\n for i in range(D):\n if i in dimensions_to_ignore:\n continue\n dimensions_to_use.append(i)\n dimensions_to_use = np.array(dimensions_to_use)\n\n origData[:, dimensions_to_use] = normalizedData\n\n # potentially ineficient, but only done once per experimentdata_conversions\n stdMat = data_std.reshape((1, D))\n stdMat = np.repeat(stdMat, T, axis=0)\n meanMat = data_mean.reshape((1, D))\n meanMat = np.repeat(meanMat, T, axis=0)\n origData = np.multiply(origData, stdMat) + meanMat\n return origData"],"string":"[\n \"def complex_abs(data):\\n assert data.size(-1) == 2\\n return (data ** 2).sum(dim=-1).sqrt()\",\n \"def complex_abs(data):\\n assert data.size(-1) == 2\\n return (data ** 2).sum(dim=-1).sqrt()\",\n \"def complex_abs(data):\\n assert data.size(-1) == 2\\n return (data ** 2).sum(dim=-1).sqrt()\",\n \"def complex_abs_sq(data):\\n assert data.size(-1) == 2\\n return (data ** 2).sum(dim=-1)\",\n \"def abs(data):\\n return _make.abs(data)\",\n \"def complex_normalize(c):\\n mag = complex_magnitude(c)\\n mag = mag if mag > 0 else 1\\n return c / mag\",\n \"def __complex__(self) -> complex:\\n return self._translate_in_type(complex, self.integer, self.float_num)\",\n \"def complex_normalize(X, axis=-1):\\n mags = np.linalg.norm(np.abs(X), axis=axis, keepdims=True)\\n return X / mags\",\n \"def _norm_data(data):\\n if data is None:\\n return data\\n data_min = np.min(data)\\n c_norm = np.max(data) - data_min\\n return (data - data_min) / c_norm if (c_norm != 0) else (data - data_min)\",\n \"def test_op_abs_offload_array_complex(self):\\n\\n device = pymic.devices[0]\\n stream = device.get_default_stream()\\n a = numpy.arange(1.0, 4711.0 * 1024, dtype=complex)\\n\\n old_a = numpy.empty_like(a)\\n old_a[:] = a[:]\\n expect = abs(a)\\n\\n offl_a = stream.bind(a)\\n offl_r = abs(offl_a)\\n r = offl_r.update_host().array\\n stream.sync()\\n\\n self.assertTrue((a == old_a).all(),\\n \\\"Input array operand 1 must not be modified: \\\"\\n \\\"{0} should be {1}\\\".format(a, old_a))\\n self.assertTrue((r == expect).all(),\\n \\\"Array contains unexpected values: \\\"\\n \\\"{0} should be {1}\\\".format(r, expect))\",\n \"def __abs__(self):\\r\\n return (self._real.fma(self._real, self._imag*self._imag)).sqrt()\",\n \"def get_value(self):\\n return complex(*self.points[0, :2])\",\n \"def real_of_complex(z):\\n return np.vstack((np.real(z[:,0]),np.imag(z[:,0]),np.real(z[:,1]),np.imag(z[:,1]))).T\",\n \"def __complex__(self):\\n return complex(self._reNum, self._imNum)\",\n \"def test_op_isub_scalar_complex(self):\\n\\n device = pymic.devices[0]\\n stream = device.get_default_stream()\\n a = numpy.arange(1, 4711 * 1024, dtype=complex)\\n s = complex(1.2, -1.3)\\n\\n old = numpy.empty_like(a)\\n old[:] = a[:]\\n expect = a - s\\n\\n offl_a = stream.bind(a)\\n offl_a -= s\\n offl_a.update_host()\\n r = offl_a.array\\n stream.sync()\\n\\n self.assertTrue((r != old).all(),\\n \\\"Input array operand must be modified: \\\"\\n \\\"{0} should be {1}\\\".format(r, old))\\n self.assertTrue((r == expect).all(),\\n \\\"Array contains unexpected values: \\\"\\n \\\"{0} should be {1}\\\".format(r, expect))\",\n \"def dcomplex(self):\\n return self._dcomplex\",\n \"def to_float_complex(self):\\r\\n return eval(str(self))\",\n \"def copy_abs(self):\\r\\n return (self._real.fma(self._real, self._imag*self._imag)).sqrt()\",\n \"def complex_value(self) -> global___Expression.ComplexValue:\",\n \"def __complex__(self):\\n return complex(self.q[0], self.q[1])\",\n \"def test_op_reverse_complex(self):\\n\\n device = pymic.devices[0]\\n stream = device.get_default_stream()\\n a = numpy.arange(1.0, 4711.0 * 1024, dtype=complex)\\n old_a = numpy.empty_like(a)\\n old_a[:] = a[:]\\n expect = numpy.array(a[::-1])\\n\\n offl_a = stream.bind(a)\\n offl_r = offl_a.reverse()\\n offl_r.update_host()\\n stream.sync()\\n r = offl_r.array\\n\\n self.assertTrue((a == old_a).all(),\\n \\\"Input array operand 1 must not be modified: \\\"\\n \\\"{0} should be {1}\\\".format(a, old_a))\\n self.assertTrue((r == expect).all(),\\n \\\"Array contains unexpected values: \\\"\\n \\\"{0} should be {1}\\\".format(a, r))\",\n \"def complex(real, imag):\",\n \"def test_op_isub_array_complex(self):\\n\\n device = pymic.devices[0]\\n stream = device.get_default_stream()\\n a = numpy.arange(1, 4711 * 1024, dtype=complex)\\n o = a + complex(1.2, -1.3)\\n\\n old_a = numpy.empty_like(a)\\n old_o = numpy.empty_like(o)\\n old_a[:] = a[:]\\n old_o[:] = o[:]\\n expect = a - o\\n\\n offl_a = stream.bind(a)\\n offl_o = stream.bind(o)\\n offl_a -= o\\n offl_a.update_host()\\n r = offl_a.array\\n stream.sync()\\n\\n self.assertTrue((r != old_a).all(),\\n \\\"Array operand must be modified: \\\"\\n \\\"{0} should be {1}\\\".format(a, old_a))\\n self.assertTrue((o == old_o).all(),\\n \\\"Input array operand 2 must not be modified: \\\"\\n \\\"{0} should be {1}\\\".format(o, old_o))\\n self.assertTrue((r == expect).all(),\\n \\\"Array contains unexpected values: \\\"\\n \\\"{0} should be {1}\\\".format(r, expect))\",\n \"def _get_complex_null_value(complex):\\n return _COMPLEX_NULL_VALUE\",\n \"def complex(self, real=0.0, imag=0.0):\\n if imag == 0.0 and real == 0.0:\\n return complex_zero\",\n \"def test_op_sub_scalar_complex(self):\\n\\n device = pymic.devices[0]\\n stream = device.get_default_stream()\\n a = numpy.arange(1.0, 4711.0 * 1024, dtype=complex)\\n s = complex(1.3, 1.3)\\n\\n old = numpy.empty_like(a)\\n old[:] = a[:]\\n expect = a - s\\n\\n offl_a = stream.bind(a)\\n offl_r = offl_a - s\\n offl_a.update_host()\\n r = offl_r.update_host().array\\n stream.sync()\\n\\n self.assertEqual(r.shape, a.shape)\\n self.assertEqual(r.dtype, a.dtype)\\n self.assertTrue((a == old).all(),\\n \\\"Input array operand must not be modified: \\\"\\n \\\"{0} should be {1}\\\".format(a, old))\\n self.assertTrue((r == expect).all(),\\n \\\"Array contains unexpected values: \\\"\\n \\\"{0} should be {1}\\\".format(r, expect))\",\n \"def _cmplx_to_complex_ ( s ) :\\n return complex ( s.real , s.imag )\",\n \"def normalize(complex_vec, axis=None):\\n cv_mag_vector = complex_magnitude(complex_vec)\\n return vector_normalize(cv_mag_vector, axis=axis)\",\n \"def test_parserval_complex_2d():\\n\\ta, b, c = np.meshgrid([1j, 1, 1, 1j], [0, 1j, 1j], [0, 1, 1, 1])\\n\\tdummy_array = xr.DataArray(a * b * c, dims=['x', 'y', 'z'])\\n\\tchunked_array = dummy_array.chunk(chunks={'x': 2, 'y': 2, 'z': 2})\\n\\tchunked_array_zeromean = chunked_array - chunked_array.mean(dim=['y', 'z'])\\n\\tspec = xfft.fft(chunked_array_zeromean, dim=['y', 'z'], sym=True)\\n\\tassert np.allclose(np.var(a * b * c, axis=(1, 2)),\\n\\t xfft.ps(spec).sum(dim=['f_y','f_z']))\",\n \"def test_op_fillfrom_complex(self):\\n\\n device = pymic.devices[0]\\n stream = device.get_default_stream()\\n a = numpy.arange(1.0, 4711.0 * 1024, dtype=complex)\\n offl_r = stream.empty_like(a)\\n offl_r.fillfrom(a)\\n offl_r.update_host()\\n stream.sync()\\n r = offl_r.array\\n self.assertTrue((a == r).all(),\\n \\\"Array contains unexpected values: \\\"\\n \\\"{0} should be {1}\\\".format(a, r))\",\n \"def abs(number):\\n if isinstance(number,(int,float,complex)): return builtins.abs(number)\\n elif isinstance(number,(numpy.float64,numpy.complex128)): return numpy.abs(number)\\n else: raise error(\\\"field_traits.abs executed on unavailable type\\\")\",\n \"def _cmplx_to_complex_ ( s ) :\\n return complex ( s.real () , s.imag () )\",\n \"def normalized_data(self):\\n return self._normalization_constant * self._data\",\n \"def complexify(self,z):\\r\\n \\r\\n import numpy as np\\r\\n \\r\\n if not np.iscomplex(z).any():\\r\\n if len(z.shape) != 2 or z.shape[1] != 2:\\r\\n raise Exception('Shape format not understood. Provide shape vertices either as a complex vector, or as a N-by-2 real numpy array.')\\r\\n else:\\r\\n z = z[:,0] + 1j*z[:,1]\\r\\n \\r\\n return z\",\n \"def complexify(self,z):\\r\\n \\r\\n import numpy as np\\r\\n \\r\\n if not np.iscomplex(z).any():\\r\\n if len(z.shape) != 2 or z.shape[1] != 2:\\r\\n raise Exception('Shape format not understood. Provide shape vertices either as a complex vector, or as a N-by-2 real numpy array.')\\r\\n else:\\r\\n z = z[:,0] + 1j*z[:,1]\\r\\n \\r\\n return z\",\n \"def complexify(self,z):\\r\\n \\r\\n import numpy as np\\r\\n \\r\\n if not np.iscomplex(z).any():\\r\\n if len(z.shape) != 2 or z.shape[1] != 2:\\r\\n raise Exception('Shape format not understood. Provide shape vertices either as a complex vector, or as a N-by-2 real numpy array.')\\r\\n else:\\r\\n z = z[:,0] + 1j*z[:,1]\\r\\n \\r\\n return z\",\n \"def complexify(self,z):\\r\\n \\r\\n import numpy as np\\r\\n \\r\\n if not np.iscomplex(z).any():\\r\\n if len(z.shape) != 2 or z.shape[1] != 2:\\r\\n raise Exception('Shape format not understood. Provide shape vertices either as a complex vector, or as a N-by-2 real numpy array.')\\r\\n else:\\r\\n z = z[:,0] + 1j*z[:,1]\\r\\n \\r\\n return z\",\n \"def complexify(self,z):\\r\\n \\r\\n import numpy as np\\r\\n \\r\\n if not np.iscomplex(z).any():\\r\\n if len(z.shape) != 2 or z.shape[1] != 2:\\r\\n raise Exception('Shape format not understood. Provide shape vertices either as a complex vector, or as a N-by-2 real numpy array.')\\r\\n else:\\r\\n z = z[:,0] + 1j*z[:,1]\\r\\n \\r\\n return z\",\n \"def complexify(self,z):\\r\\n \\r\\n import numpy as np\\r\\n \\r\\n if not np.iscomplex(z).any():\\r\\n if len(z.shape) != 2 or z.shape[1] != 2:\\r\\n raise Exception('Shape format not understood. Provide shape vertices either as a complex vector, or as a N-by-2 real numpy array.')\\r\\n else:\\r\\n z = z[:,0] + 1j*z[:,1]\\r\\n \\r\\n return z\",\n \"def complexify(self,z):\\r\\n \\r\\n import numpy as np\\r\\n \\r\\n if not np.iscomplex(z).any():\\r\\n if len(z.shape) != 2 or z.shape[1] != 2:\\r\\n raise Exception('Shape format not understood. Provide shape vertices either as a complex vector, or as a N-by-2 real numpy array.')\\r\\n else:\\r\\n z = z[:,0] + 1j*z[:,1]\\r\\n \\r\\n return z\",\n \"def complexify(self,z):\\r\\n \\r\\n import numpy as np\\r\\n \\r\\n if not np.iscomplex(z).any():\\r\\n if len(z.shape) != 2 or z.shape[1] != 2:\\r\\n raise Exception('Shape format not understood. Provide shape vertices either as a complex vector, or as a N-by-2 real numpy array.')\\r\\n else:\\r\\n z = z[:,0] + 1j*z[:,1]\\r\\n \\r\\n return z\",\n \"def complexify(self,z):\\r\\n \\r\\n import numpy as np\\r\\n \\r\\n if not np.iscomplex(z).any():\\r\\n if len(z.shape) != 2 or z.shape[1] != 2:\\r\\n raise Exception('Shape format not understood. Provide shape vertices either as a complex vector, or as a N-by-2 real numpy array.')\\r\\n else:\\r\\n z = z[:,0] + 1j*z[:,1]\\r\\n \\r\\n return z\",\n \"def complexify(self,z):\\r\\n \\r\\n import numpy as np\\r\\n \\r\\n if not np.iscomplex(z).any():\\r\\n if len(z.shape) != 2 or z.shape[1] != 2:\\r\\n raise Exception('Shape format not understood. Provide shape vertices either as a complex vector, or as a N-by-2 real numpy array.')\\r\\n else:\\r\\n z = z[:,0] + 1j*z[:,1]\\r\\n \\r\\n return z\",\n \"def normalize_data(data=None):\\n # Data pre-processing\\n n = data.shape[0]\\n for i in range(n):\\n xx = data[i,:,:]\\n xx -= np.mean(xx) # Centering in 0\\n xx /= np.linalg.norm(xx) # Normalizing to 1\\n data[i] = xx # Affect value\\n return data\",\n \"def complex(real=0.0, imag=0.0):\\n if imag == 0.0 and real == 0.0:\\n return complex_zero\",\n \"def test_op_zero_complex(self):\\n\\n device = pymic.devices[0]\\n stream = device.get_default_stream()\\n a = numpy.arange(1.0, 4711.0 * 1024, dtype=complex)\\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 + 0.0j,\\n \\\"Array should be all zeros.\\\")\",\n \"def _de_transform(self, data):\\r\\n mean, variance = self._input_statistics.overall_feature_moments\\r\\n return data * variance + mean\",\n \"def test_op_sub_array_complex(self):\\n\\n device = pymic.devices[0]\\n stream = device.get_default_stream()\\n a = numpy.arange(1.0, 4711.0 * 1024, dtype=complex)\\n o = a + complex(1.3, -1.4)\\n\\n old_a = numpy.empty_like(a)\\n old_o = numpy.empty_like(o)\\n old_a[:] = a[:]\\n old_o[:] = o[:]\\n expect = a - o\\n\\n offl_a = stream.bind(a)\\n offl_r = offl_a - o\\n offl_a.update_host()\\n r = offl_r.update_host().array\\n stream.sync()\\n\\n self.assertEqual(r.shape, a.shape)\\n self.assertEqual(r.dtype, a.dtype)\\n self.assertTrue((a == old_a).all(),\\n \\\"Input array operand 1 must not be modified: \\\"\\n \\\"{0} should be {1}\\\".format(a, old_a))\\n self.assertTrue((o == old_o).all(),\\n \\\"Input array operand 2 must not be modified: \\\"\\n \\\"{0} should be {1}\\\".format(o, old_o))\\n self.assertTrue((r == expect).all(),\\n \\\"Array contains unexpected values: \\\"\\n \\\"{0} should be {1}\\\".format(r, expect))\",\n \"def complex_matrix_to_real(m):\\n a, b = np.real(m), np.imag(m)\\n return np.vstack([np.hstack([a,b]), np.hstack([-b, a])])\",\n \"def norm_data(data):\\n return (data-np.min(data))/(np.max(data)-np.min(data))\",\n \"def test_op_one_complex(self):\\n\\n device = pymic.devices[0]\\n stream = device.get_default_stream()\\n a = numpy.arange(1.0, 4711.0 * 1024, dtype=complex)\\n offl_a = stream.bind(a)\\n offl_a.one()\\n offl_a.update_host()\\n stream.sync()\\n self.assertTrue((a == complex(1.0, 0.0)).all(),\\n \\\"Array should be all one.\\\" + str(a))\",\n \"def normalize_data(data):\\n mean = np.mean(data)\\n std = np.std(data)\\n return (data - mean) / std\",\n \"def abs(self):\\n return math.sqrt(self['real'] * self['real'] + self['imaginary'] * self['imaginary'])\",\n \"def toComplex(self):\\n return (self._points[0].value(),\\n self._points[1].value(),\\n self._points[2].value())\",\n \"def complex_difference(c_1,c_2):\\n return c_1 - c_2\",\n \"def numpy_scalar(data):\\r\\n\\r\\n # handle case where data is numpy.array([])\\r\\n if data.ndim > 0 and (len(data.shape) == 0 or\\r\\n __builtins__['max'](data.shape) == 0):\\r\\n assert numpy.all(numpy.array([]) == data)\\r\\n raise EmptyConstantError()\\r\\n try:\\r\\n numpy.complex(data) # works for all numeric scalars\\r\\n return data\\r\\n except Exception:\\r\\n raise NotScalarConstantError(\\r\\n 'v.data is non-numeric, non-scalar, or has more than one'\\r\\n ' unique value', data)\",\n \"def _complex(real, imag):\\n real = np.asarray(real)\\n imag = np.asarray(imag)\\n cplx = 1j * imag \\n return cplx + real\",\n \"def test_op_imul_array_complex(self):\\n\\n device = pymic.devices[0]\\n stream = device.get_default_stream()\\n a = numpy.arange(1, 4711 * 1024, dtype=complex)\\n o = a + complex(1.2, -1.3)\\n\\n old_a = numpy.empty_like(a)\\n old_o = numpy.empty_like(o)\\n old_a[:] = a[:]\\n old_o[:] = o[:]\\n expect = a * o\\n\\n offl_a = stream.bind(a)\\n offl_o = stream.bind(o)\\n offl_a *= o\\n offl_a.update_host()\\n r = offl_a.array\\n stream.sync()\\n\\n self.assertTrue((r != old_a).all(),\\n \\\"Array operand must be modified: \\\"\\n \\\"{0} should be {1}\\\".format(a, old_a))\\n self.assertTrue((o == old_o).all(),\\n \\\"Input array operand 2 must not be modified: \\\"\\n \\\"{0} should be {1}\\\".format(o, old_o))\\n self.assertTrue((r == expect).all(),\\n \\\"Array contains unexpected values: \\\"\\n \\\"{0} should be {1}\\\".format(r, expect))\",\n \"def test_op_imul_scalar_complex(self):\\n\\n device = pymic.devices[0]\\n stream = device.get_default_stream()\\n a = numpy.arange(1, 4711 * 1024, dtype=complex)\\n s = complex(1.2, -1.3)\\n\\n old = numpy.empty_like(a)\\n old[:] = a[:]\\n expect = a * s\\n\\n offl_a = stream.bind(a)\\n offl_a *= s\\n offl_a.update_host()\\n r = offl_a.array\\n stream.sync()\\n\\n self.assertTrue((r != old).all(),\\n \\\"Input array operand must be modified: \\\"\\n \\\"{0} should be {1}\\\".format(r, old))\\n self.assertTrue((r == expect).all(),\\n \\\"Array contains unexpected values: \\\"\\n \\\"{0} should be {1}\\\".format(r, expect))\",\n \"def complex_magnitude(c):\\n return (c * c.conjugate()) ** 0.5\",\n \"def complex_inverse(c1,cr):\",\n \"def tensor_to_complex_np(data):\\n data = data.numpy()\\n return data[..., 0] + 1j * data[..., 1]\",\n \"def _process(self, data: np.array) -> np.array:\\n # pylint: disable=no-member\\n return unp.sqrt(data[..., 0] ** 2 + data[..., 1] ** 2) * self.scale\",\n \"def normalized_data(self):\\n return self._data\",\n \"def normalize(data):\\n # normalize data and return\\n # https://stackoverflow.com/questions/29661574/normalize-numpy-array-columns-in-python\\n return (data - data.min(axis=0)) / data.ptp(axis=0)\",\n \"def arg(self):\\r\\n if self.is_zero():\\r\\n raise InvalidOperationError(\\r\\n 'argument of a complex number is not defined at 0'\\r\\n )\\r\\n return atan2(self._imag, self._real)\",\n \"def normalize(data):\\n\\n\\t#return [float(x) / pow(2, 15) for x in data]\\n\\n\\tl = [float(x) / pow(2, 15) for x in data]\\n\\treturn np.asarray(l)\",\n \"def _transform(self, data):\\r\\n mean, variance = self._input_statistics.overall_feature_moments\\r\\n return (data - mean) / variance\",\n \"def _cmplx_sub_ ( s , o ) :\\n return (-o ) + complex ( s )\",\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 normalizeData(pre_signal):\\n\\n if sp.any(sp.isnan(pre_signal)):\\n print('there are NaNs in the data matrix, making them zero')\\n\\n pre_signal[sp.isnan(pre_signal)] = 0\\n mean_vector = sp.mean(pre_signal, axis=0, keepdims=True)\\n normed_signal = pre_signal - mean_vector\\n norm_vector = sp.linalg.norm(normed_signal, axis=0, keepdims=True)\\n norm_vector[norm_vector == 0] = 1e-116\\n normed_signal = normed_signal / norm_vector\\n\\n return normed_signal, mean_vector, norm_vector\",\n \"def test_op_fill_complex(self):\\n\\n device = pymic.devices[0]\\n stream = device.get_default_stream()\\n value = complex(7342.0, -2137.0)\\n a = numpy.arange(1.0, 4711.0 * 1024, dtype=complex)\\n expect = numpy.empty_like(a)\\n expect[:] = value\\n offl_a = stream.bind(a)\\n offl_a.fill(value)\\n offl_a.update_host()\\n stream.sync()\\n self.assertTrue((a == expect).all(),\\n \\\"Array contains unexpected values: \\\"\\n \\\"{0} should be {1}\\\".format(a, expect))\",\n \"def test_op_mul_array_complex(self):\\n\\n device = pymic.devices[0]\\n stream = device.get_default_stream()\\n a = numpy.arange(1.0, 4711.0 * 1024, dtype=complex)\\n o = a + complex(-1.6, -2.2)\\n\\n old_a = numpy.empty_like(a)\\n old_o = numpy.empty_like(o)\\n old_a[:] = a[:]\\n old_o[:] = o[:]\\n expect = a * o\\n\\n offl_a = stream.bind(a)\\n offl_r = offl_a * o\\n offl_a.update_host()\\n r = offl_r.update_host().array\\n stream.sync()\\n\\n self.assertEqual(r.shape, a.shape)\\n self.assertEqual(r.dtype, a.dtype)\\n self.assertTrue((a == old_a).all(),\\n \\\"Input array operand 1 must not be modified: \\\"\\n \\\"{0} should be {1}\\\".format(a, old_a))\\n self.assertTrue((o == old_o).all(),\\n \\\"Input array operand 2 must not be modified: \\\"\\n \\\"{0} should be {1}\\\".format(o, old_o))\\n self.assertTrue((r == expect).all(),\\n \\\"Array contains unexpected values: \\\"\\n \\\"{0} should be {1}\\\".format(r, expect))\",\n \"def norm(self):\\n if self._coord_format != constants.MatrixCoordinateDefault:\\n self._logger.error(\\\"invalid coordinate format\\\")\\n raise NotImplementedError(\\\"invalid coordinate format\\\")\\n\\n if self._dtype == complex:\\n def __map(m):\\n return m[2].real ** 2 + m[2].imag ** 2\\n else:\\n def __map(m):\\n return m[2] ** 2\\n\\n n = self._data.map(\\n __map\\n ).reduce(\\n lambda a, b: a + b\\n )\\n\\n return math.sqrt(n)\",\n \"def test_op_isub_offload_array_complex(self):\\n\\n device = pymic.devices[0]\\n stream = device.get_default_stream()\\n a = numpy.arange(1, 4711 * 1024, dtype=complex)\\n o = a + complex(1.2, -1.3)\\n\\n old_a = numpy.empty_like(a)\\n old_o = numpy.empty_like(o)\\n old_a[:] = a[:]\\n old_o[:] = o[:]\\n expect = a - o\\n\\n offl_a = stream.bind(a)\\n offl_o = stream.bind(o)\\n offl_a -= offl_o\\n offl_a.update_host()\\n r = offl_a.array\\n stream.sync()\\n\\n self.assertTrue((r != old_a).all(),\\n \\\"Array operand must be modified: \\\"\\n \\\"{0} should be {1}\\\".format(a, old_a))\\n self.assertTrue((o == old_o).all(),\\n \\\"Input array operand 2 must not be modified: \\\"\\n \\\"{0} should be {1}\\\".format(o, old_o))\\n self.assertTrue((r == expect).all(),\\n \\\"Array contains unexpected values: \\\"\\n \\\"{0} should be {1}\\\".format(r, expect))\",\n \"def test_parseval_real_2d():\\n\\ta = np.mgrid[:5, :5, :5][0]\\n\\tdummy_array = xr.DataArray(a, dims=['x', 'y', 'z'])\\n\\tchunked_array = dummy_array.chunk(chunks={'x': 2, 'y': 2, 'z': 2})\\n\\tchunked_array_zeromean = chunked_array - chunked_array.mean(dim=['y', 'z'])\\n\\tspec = xfft.fft(chunked_array_zeromean, dim=['y', 'z'])\\n\\tassert np.allclose(np.var(a, axis=(1, 2)),\\n\\t xfft.ps(spec).sum(dim=['f_y','f_z']))\",\n \"def real(self):\\n res = self._defer_unary_elementwise(np.real)\\n res.dtype = np.float_\\n return res\",\n \"def getData(self, copy=True, mode=None):\\n if mode is None:\\n return super(ComplexField3D, self).getData(copy=copy)\\n else:\\n return self._convertComplexData(self._data, mode)\",\n \"def calculate_abs(self):\\n ref_spectra_raw = np.array(self.raw_data['spectrum_0'].attrs['reference'])\\n self.ref_spectra_arr = np.subtract(ref_spectra_raw,self.back_spectra_arr)\\n abs=-np.log10(self.pre_proc_data.div(self.ref_spectra_arr))\\n self.abs_data=abs\\n return self.abs_data\",\n \"def __neg__(self):\\n return Complex(-self._reNum, -self._imNum)\",\n \"def normalize(data):\\n min = np.min(data)\\n if min:\\n data = data + min\\n return old_div(data,np.max(data))\\n else: # if min is 0\\n return old_div(data,np.max(data))\",\n \"def unscale_data(self, data):\\n return (data + self.mean)*self.std\",\n \"def __abs__(self):\\n\\t\\tval = abs(self.val)\\n\\t\\tif 0 in self.val:\\n\\t\\t\\traise ValueError(\\\"Absolute value is not differentiable at 0.\\\")\\n\\n\\t\\tder_copy = np.copy(self.der)\\n\\t\\tif len(der_copy.shape):\\n\\t\\t\\tfor i, val_i in enumerate(self.val):\\n\\t\\t\\t\\tif val_i < 0:\\n\\t\\t\\t\\t\\tder_copy[i] = -1 * der_copy[i]\\n\\t\\treturn Var(val, der_copy)\",\n \"def test_parserval_complex_1d():\\n\\ta = np.exp(2j * np.pi * np.arange(8) / 8)\\n\\tdummy_array = xr.DataArray(a, dims=['x'])\\n\\tchunked_array = dummy_array.chunk(chunks={'x': 2})\\n\\tspec = xfft.fft(chunked_array, dim=['x'], detrend='mean')\\n\\tassert np.var(a) == xfft.ps(spec).sum()\",\n \"def np_safe_abs(x):\\n try:\\n return linalg.norm(x,2)\\n except ValueError:\\n return abs(x)\",\n \"def get_complex_type(self):\\n import numpy\\n return numpy.complex128\",\n \"def __abs__(self) -> numbers.Number:\\n\\n return np.sqrt(abs(self.mag2()))\",\n \"def get_data_radiant(data):\\n try:\\n if not isinstance(data, np.ndarray):\\n raise AttributeError(\\\"The given data should be of type numpy array\\\")\\n\\n return np.arctan2(data[:, 1].max() - data[:, 1].min(),\\n data[:, 0].max() - data[:, 0].min())\\n except AttributeError as err:\\n LOGGER.error(err)\",\n \"def normalize2(data):\\n return old_div(data,np.max([np.max(data),-1.0*np.min(data)]))\",\n \"def magnitude(X):\\r\\n r = np.real(X)\\r\\n i = np.imag(X)\\r\\n return np.sqrt(r * r + i * i);\",\n \"def canonicalize(data):\\n\\n if isinstance(data, np.ndarray):\\n if data.dtype == np.uint8:\\n data = data.astype(np.float32) / 255.0\\n data = torch.from_numpy(np.ascontiguousarray(data))\\n\\n elif isinstance(data, torch.Tensor):\\n if data.dtype == torch.uint8:\\n data = data.float() / 255.0\\n\\n else:\\n raise NotImplementedError()\\n\\n return data\",\n \"def normalizeData(self, data):\\n return _normalizeData(data)\",\n \"def normalizeData(self, data):\\n return _normalizeData(data)\",\n \"def real(self):\\n return self.__array_ufunc__(np.real, \\\"__call__\\\", self)\",\n \"def process(self, data):\\n return data - np.mean(data)\",\n \"def toReal(self):\\n\\n\\t\\tif self.space==\\\"real\\\":\\n\\t\\t\\tpass \\n\\t\\telse:\\n\\t\\t\\tself.data = fftengine.irfft2(self.data)\\n\\t\\t\\tself.space = \\\"real\\\"\",\n \"def normalize_data(self, data):\\n self.find_mean_std(data)\\n return (data - self._data_mean) / self._data_std\",\n \"def normalized(self):\\n d = self.magnitude()\\n if d:\\n return type(self)(self.x / d, self.y / d, self.z / d)\\n return self.copy()\",\n \"def test_op_imul_offload_array_complex(self):\\n\\n device = pymic.devices[0]\\n stream = device.get_default_stream()\\n a = numpy.arange(1, 4711 * 1024, dtype=complex)\\n o = a + complex(1.2, -1.3)\\n\\n old_a = numpy.empty_like(a)\\n old_o = numpy.empty_like(o)\\n old_a[:] = a[:]\\n old_o[:] = o[:]\\n expect = a * o\\n\\n offl_a = stream.bind(a)\\n offl_o = stream.bind(o)\\n offl_a *= offl_o\\n offl_a.update_host()\\n r = offl_a.array\\n stream.sync()\\n\\n self.assertTrue((r != old_a).all(),\\n \\\"Array operand must be modified: \\\"\\n \\\"{0} should be {1}\\\".format(a, old_a))\\n self.assertTrue((o == old_o).all(),\\n \\\"Input array operand 2 must not be modified: \\\"\\n \\\"{0} should be {1}\\\".format(o, old_o))\\n self.assertTrue((r == expect).all(),\\n \\\"Array contains unexpected values: \\\"\\n \\\"{0} should be {1}\\\".format(r, expect))\",\n \"def unNormalizeData(normalizedData, data_mean, data_std, dimensions_to_ignore):\\n T = normalizedData.shape[0]\\n D = data_mean.shape[0]\\n\\n origData = np.zeros((T, D), dtype=np.float32)\\n dimensions_to_use = []\\n for i in range(D):\\n if i in dimensions_to_ignore:\\n continue\\n dimensions_to_use.append(i)\\n dimensions_to_use = np.array(dimensions_to_use)\\n\\n origData[:, dimensions_to_use] = normalizedData\\n\\n # potentially ineficient, but only done once per experimentdata_conversions\\n stdMat = data_std.reshape((1, D))\\n stdMat = np.repeat(stdMat, T, axis=0)\\n meanMat = data_mean.reshape((1, D))\\n meanMat = np.repeat(meanMat, T, axis=0)\\n origData = np.multiply(origData, stdMat) + meanMat\\n return origData\"\n]","isConversational":false},"negative_scores":{"kind":"list like","value":["0.766307","0.766307","0.766307","0.661879","0.6422132","0.6198529","0.6017067","0.5993175","0.5902083","0.58823895","0.5855812","0.57926065","0.57813406","0.5773507","0.5769934","0.57515544","0.5712033","0.56957585","0.568994","0.5641625","0.5637767","0.5625738","0.5615207","0.5614494","0.5612818","0.5542472","0.55367124","0.55245197","0.55121094","0.5500521","0.5496167","0.54773074","0.54713565","0.54616326","0.54616326","0.54616326","0.54616326","0.54616326","0.54616326","0.54616326","0.54616326","0.54616326","0.54616326","0.5461327","0.5460604","0.5411068","0.5390507","0.53690326","0.53642845","0.53558546","0.535442","0.53509426","0.5342851","0.53371507","0.5333988","0.5333464","0.5298818","0.5298485","0.5254942","0.5249701","0.5244853","0.52438056","0.5243488","0.52389663","0.5234501","0.52307236","0.52176327","0.5199526","0.51786464","0.5176231","0.51731104","0.51651716","0.5154403","0.5146559","0.51447666","0.51157135","0.50941455","0.5092748","0.50905854","0.50804245","0.50733286","0.50708634","0.5063736","0.5061462","0.5059288","0.5052496","0.5036388","0.5033562","0.5016531","0.50113034","0.50029176","0.5002658","0.5002658","0.49954036","0.4995378","0.49933836","0.49854743","0.49843666","0.4972575","0.49722934"],"string":"[\n \"0.766307\",\n \"0.766307\",\n \"0.766307\",\n \"0.661879\",\n \"0.6422132\",\n \"0.6198529\",\n \"0.6017067\",\n \"0.5993175\",\n \"0.5902083\",\n \"0.58823895\",\n \"0.5855812\",\n \"0.57926065\",\n \"0.57813406\",\n \"0.5773507\",\n \"0.5769934\",\n \"0.57515544\",\n \"0.5712033\",\n \"0.56957585\",\n \"0.568994\",\n \"0.5641625\",\n \"0.5637767\",\n \"0.5625738\",\n \"0.5615207\",\n \"0.5614494\",\n \"0.5612818\",\n \"0.5542472\",\n \"0.55367124\",\n \"0.55245197\",\n \"0.55121094\",\n \"0.5500521\",\n \"0.5496167\",\n \"0.54773074\",\n \"0.54713565\",\n \"0.54616326\",\n \"0.54616326\",\n \"0.54616326\",\n \"0.54616326\",\n \"0.54616326\",\n \"0.54616326\",\n \"0.54616326\",\n \"0.54616326\",\n \"0.54616326\",\n \"0.54616326\",\n \"0.5461327\",\n \"0.5460604\",\n \"0.5411068\",\n \"0.5390507\",\n \"0.53690326\",\n \"0.53642845\",\n \"0.53558546\",\n \"0.535442\",\n \"0.53509426\",\n \"0.5342851\",\n \"0.53371507\",\n \"0.5333988\",\n \"0.5333464\",\n \"0.5298818\",\n \"0.5298485\",\n \"0.5254942\",\n \"0.5249701\",\n \"0.5244853\",\n \"0.52438056\",\n \"0.5243488\",\n \"0.52389663\",\n \"0.5234501\",\n \"0.52307236\",\n \"0.52176327\",\n \"0.5199526\",\n \"0.51786464\",\n \"0.5176231\",\n \"0.51731104\",\n \"0.51651716\",\n \"0.5154403\",\n \"0.5146559\",\n \"0.51447666\",\n \"0.51157135\",\n \"0.50941455\",\n \"0.5092748\",\n \"0.50905854\",\n \"0.50804245\",\n \"0.50733286\",\n \"0.50708634\",\n \"0.5063736\",\n \"0.5061462\",\n \"0.5059288\",\n \"0.5052496\",\n \"0.5036388\",\n \"0.5033562\",\n \"0.5016531\",\n \"0.50113034\",\n \"0.50029176\",\n \"0.5002658\",\n \"0.5002658\",\n \"0.49954036\",\n \"0.4995378\",\n \"0.49933836\",\n \"0.49854743\",\n \"0.49843666\",\n \"0.4972575\",\n \"0.49722934\"\n]","isConversational":false},"document_score":{"kind":"string","value":"0.87394947"},"document_rank":{"kind":"string","value":"0"}}},{"rowIdx":23790,"cells":{"query":{"kind":"string","value":"Returns a normalized data if the embed a numpy or a dataset. Else returns the data."},"document":{"kind":"string","value":"def normalizeData(self, data):\n return _normalizeData(data)"},"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 normalize_dataset(self):","def denormalise_0_1(value_or_array, array_min, array_max):\n if isinstance(value_or_array, list):\n raise ValueError('this function accepts arraylike data, not a list. '\n 'Please check data or convert list to numpy array')\n elif isinstance(value_or_array, float):\n denormalised = value_or_array * (array_max - array_min) + array_min\n elif isinstance(value_or_array, np.ndarray):\n denormalised = value_or_array * (array_max - array_min) + array_min\n elif isinstance(value_or_array, pd.Series):\n denormalised = value_or_array * (array_max - array_min) + array_min\n else:\n sys.stdout.write(\"Unknown datatype. denormalise_0_1 has been given an input that does not appear to be \"\n \"an int, float, np.ndarray or pandas Series\\n\"\n \"Attempting to process as if it is arraylike.....\")\n return denormalised","def convert_dataset(dataset: h5py.Dataset, refs: h5py.Group):\n # all MATLAB variables have the attribute MATLAB_class\n # if this is not present, it is not convertible\n if has_refs(dataset):\n mtype = \"cell_with_refs\"\n else:\n try:\n mtype = dataset.attrs[\"MATLAB_class\"].decode()\n except KeyError:\n raise ValueError(f\"{dataset} is not a MATLAB type.\")\n\n if mtype == \"cell_with_refs\":\n cell = []\n for ref in dataset:\n row = []\n for r in ref:\n entry = convert_dataset(refs.get(r), refs=refs)\n row.append(entry)\n cell.append(row)\n return cell\n\n elif mtype == \"char\":\n if len(dataset) == 0:\n return \"\"\n elif isinstance(dataset[0], np.ndarray):\n return \"\".join([chr(x[0]) for x in dataset]).replace(\"\\x00\", \"\")\n else:\n return \"\".join([chr(x) for x in dataset]).replace(\"\\x00\", \"\")\n\n elif mtype == \"bool\":\n return bool(dataset)\n\n elif mtype == \"logical\":\n arr = np.array(dataset, dtype=bool).T.squeeze()\n if arr.size == 1:\n arr = bool(arr)\n return arr\n\n elif mtype == \"canonical empty\":\n return None\n\n # complex numbers need to be filtered out separately\n elif \"imag\" in str(dataset.dtype):\n if dataset.attrs[\"MATLAB_class\"] == b\"single\":\n dtype = np.complex64\n else:\n dtype = np.complex128\n arr = np.array(dataset)\n arr = (arr[\"real\"] + arr[\"imag\"] * 1j).astype(dtype)\n return arr.T.squeeze()\n\n # if it is none of the above, we can convert to numpy array\n elif mtype in (\n \"double\",\n \"single\",\n \"int8\",\n \"int16\",\n \"int32\",\n \"int64\",\n \"uint8\",\n \"uint16\",\n \"uint32\",\n \"uint64\",\n \"cell\",\n ):\n if mtype == \"cell\":\n print(dataset)\n arr = np.array(dataset, dtype=dataset.dtype)\n return arr.T.squeeze()\n\n else:\n raise ValueError(f\"Data type not supported: {mtype}, {dataset.dtype}.\")","def normalized_data(self):\n return self._data","def _prep_data(self, data, func_input_dtype):\n if func_input_dtype in (None, 'DataArray'):\n return data\n if func_input_dtype == 'Dataset':\n # TODO: add logic that creates a single Dataset comprising all of\n # the DataArray objects in `data`.\n raise NotImplementedError(\"func_input_dtype of `Dataset` not yet \"\n \"implemented.\")\n if func_input_dtype == 'numpy':\n self.coords = data[0].coords\n return [d.values for d in data]","def _convert_data(self, data):\n if isinstance(data, Tensor):\n data = data.asnumpy()\n elif isinstance(data, list):\n data = np.array(data)\n elif isinstance(data, np.ndarray):\n pass\n else:\n raise TypeError('Input data type must be tensor, list or numpy.ndarray')\n return data","def normalized_data(self):\n return self._normalization_constant * self._data","def _convert_dataset(self):\n allowed_vars = ['x', 'y', 'z', 'tvu', 'thu']\n dtyp = [(varname, self.data[varname].dtype) for varname in allowed_vars if varname in self.data]\n empty_struct = np.empty(len(self.data['x']), dtype=dtyp)\n for varname, vartype in dtyp:\n empty_struct[varname] = self.data[varname].values\n self.data = empty_struct","def _canonize_input(self, dataset):\n unpack = lambda x: x\n if isinstance(dataset, _tc.SArray):\n dataset = _tc.SFrame({self.feature: dataset})\n elif isinstance(dataset, _tc.Image):\n dataset = _tc.SFrame({self.feature: [dataset]})\n unpack = lambda x: x[0]\n return dataset, unpack","def _raw_data(self):\n data = self.datasource.as_array()\n if not isinstance(data, np.ndarray):\n raise TypeError(\"The data you try to load is no numpy array!\")\n if data.ndim != 2:\n raise ValueError(\"The data array you try to load does not have 2 \"\n \"dimensions!\")\n data = data.copy(order='C')\n ident = hashlib.md5(data).hexdigest()\n if self._dataident is None:\n self._dataident = ident\n elif self._dataident != ident:\n raise ValueError(\"The data you try to load from '%s' has changed \"\n \"since the last time. Please, check the \"\n \"datasource of the record '%s'.\"\n % (self.datasource.name, self.name))\n\n return data","def normalise(raw_data, normalise_by_column=False):\n data = raw_data\n if normalise_by_column:\n #normaliza valores usando o maximo de cada coluna\n col_maxes = raw_data.max(axis=0)\n #divide cada valor pelo maximo correspondente de cada coluna\n data = raw_data / col_maxes[np.newaxis,:] \n else:\n #divide todos os valores pelo maximo do dataset (tudo na mesma escala)\n data = raw_data / raw_data.max()\n\n return data","def __prepare(self, data):\n #print(\"Running Prepare data\")\n #print(data)\n #print(type(data))\n if len(data) > 1:\n if type(data[0]) == np.ndarray:\n return np.concatenate(data)\n else:\n return torch.cat(data).cpu().numpy()\n else:\n return data[0].cpu().numpy()","def _check_data(data, dset_attrs):\n dtype = dset_attrs['dtype']\n float_to_int = (np.issubdtype(dtype, np.integer)\n and np.issubdtype(data.dtype, np.floating))\n if float_to_int:\n attrs = dset_attrs['attrs']\n scale_factor = [c for c in attrs if 'scale_factor' in c][0]\n scale_factor = attrs[scale_factor]\n\n # apply scale factor and dtype\n data = np.multiply(data, scale_factor)\n if np.issubdtype(dtype, np.integer):\n data = np.round(data)\n\n data = data.astype(dtype)\n\n return data","def _normalizeData(data):\n if isinstance(data, H5Node):\n if data.is_broken:\n return None\n return data.h5py_object\n return data","def __prepare_dataset(dataset, verbose=True):\r\n supported_datasets = ['coco', 'voc']\r\n if isinstance(dataset, ExternalDataset):\r\n if dataset.dataset_type.lower() not in supported_datasets:\r\n raise UserWarning(\"ExternalDataset dataset_type must be one of: \", supported_datasets)\r\n\r\n dataset_root = dataset.path\r\n\r\n if verbose:\r\n print(\"Loading {} type dataset...\".format(dataset.dataset_type))\r\n\r\n if dataset.dataset_type.lower() == 'voc':\r\n from gluoncv.data import VOCDetection\r\n\r\n dataset = VOCDetection(root=dataset_root,\r\n splits=[(2007, 'trainval'), (2012, 'trainval')])\r\n\r\n elif dataset.dataset_type.lower() == 'coco':\r\n from gluoncv.data import COCODetection\r\n\r\n dataset = COCODetection(root=dataset_root,\r\n splits=['instances_train2017'])\r\n if verbose:\r\n print(\"ExternalDataset loaded.\")\r\n return dataset\r\n elif isinstance(dataset, DetectionDataset) or issubclass(type(dataset), DetectionDataset):\r\n dataset.set_image_transform(ImageToNDArrayTransform())\r\n dataset.set_target_transform(BoundingBoxListToNumpyArray())\r\n return dataset\r\n else:\r\n raise ValueError(\"Dataset type {} not supported\".format(type(dataset)))","def normalize_value(value: Any) -> Optional[Union[np.ndarray, List[Any], Tuple[Any]]]:\n if value is None:\n # Exclude None from falling through to blanket np.asarray conversion.\n return value\n\n if isinstance(value, (list, tuple, dict)):\n return value\n\n array = np.asarray(value)\n # TODO(#5359): Move into the function abi.\n if isinstance(value, (bool, int, float)):\n # Manually convert ints and floats to 32 bits.\n if array.dtype == np.float64:\n array = array.astype(np.float32)\n elif array.dtype == np.int64:\n array = array.astype(np.int32)\n\n return array","def preprocess_data(self, data, scale_data=True):\n print(\"preprocess_data not implemented\")\n return data","def canonicalize(data):\n\n if isinstance(data, np.ndarray):\n if data.dtype == np.uint8:\n data = data.astype(np.float32) / 255.0\n data = torch.from_numpy(np.ascontiguousarray(data))\n\n elif isinstance(data, torch.Tensor):\n if data.dtype == torch.uint8:\n data = data.float() / 255.0\n\n else:\n raise NotImplementedError()\n\n return data","def process(self, data):\n # get truncation parameters from config or container defaults\n self._get_params(type(data))\n\n if self.weight_dataset is None:\n self.weight_dataset = [None] * len(self.dataset)\n\n for dset, wgt in zip(self.dataset, self.weight_dataset):\n old_shape = data[dset].local_shape\n val = np.ndarray.reshape(data[dset][:], data[dset][:].size)\n if wgt is None:\n if np.iscomplexobj(data[dset]):\n data[dset][:].real = bit_truncate_fixed(\n val.real, self.fixed_precision\n ).reshape(old_shape)\n data[dset][:].imag = bit_truncate_fixed(\n val.imag, self.fixed_precision\n ).reshape(old_shape)\n else:\n data[dset][:] = bit_truncate_fixed(\n val, self.fixed_precision\n ).reshape(old_shape)\n else:\n if data[dset][:].shape != data[wgt][:].shape:\n raise pipeline.PipelineRuntimeError(\n \"Dataset and weight arrays must have same shape ({} != {})\".format(\n data[dset].shape, data[wgt].shape\n )\n )\n invvar = np.ndarray.reshape(data[wgt][:], data[dset][:].size)\n if np.iscomplexobj(data[dset]):\n data[dset][:].real = bit_truncate_weights(\n val.real,\n invvar * 2.0 / self.variance_increase,\n self.fixed_precision,\n ).reshape(old_shape)\n data[dset][:].imag = bit_truncate_weights(\n val.imag,\n invvar * 2.0 / self.variance_increase,\n self.fixed_precision,\n ).reshape(old_shape)\n else:\n data[dset][:] = bit_truncate_weights(\n val, invvar / self.variance_increase, self.fixed_precision\n ).reshape(old_shape)\n\n return data","def test_single_null(self):\n dset = self.f.create_dataset('x', (1,), dtype='i1')\n out = dset[()]\n self.assertIsInstance(out, np.ndarray)\n self.assertEqual(out.shape, (1,))","def load_own_data():\n data = Data()\n features = data.read_selected_features()\n dataframe = data.get_dataframe_with(features)\n return normalize(dataframe.values)","def normalize(dataset):\n return normalize_standard_deviation(normalize_mean(dataset))","def astype(self, dtype, device=None, *_args, **_kwargs):\n if isinstance(dtype, str):\n mod_str, *dtype = dtype.split(\".\", 1)\n dtype = mod_str if not dtype else dtype[0]\n else:\n mod_str = getattr(dtype, \"__module__\", \"torch\")\n mod_str = look_up_option(mod_str, {\"torch\", \"numpy\", \"np\"}, default=\"numpy\")\n\n out_type: type[torch.Tensor] | type[np.ndarray] | None\n if mod_str == \"torch\":\n out_type = torch.Tensor\n elif mod_str in (\"numpy\", \"np\"):\n out_type = np.ndarray\n else:\n out_type = None\n return self.get_array(output_type=out_type, dtype=dtype, device=device)","def is_pyvista_dataset(obj):\n return isinstance(obj, (pyvista.DataSet, pyvista.MultiBlock))","def unNormalizeData(normalizedData, data_mean, data_std, dimensions_to_ignore):\n T = normalizedData.shape[0]\n D = data_mean.shape[0]\n\n origData = np.zeros((T, D), dtype=np.float32)\n dimensions_to_use = []\n for i in range(D):\n if i in dimensions_to_ignore:\n continue\n dimensions_to_use.append(i)\n dimensions_to_use = np.array(dimensions_to_use)\n\n origData[:, dimensions_to_use] = normalizedData\n\n # potentially ineficient, but only done once per experimentdata_conversions\n stdMat = data_std.reshape((1, D))\n stdMat = np.repeat(stdMat, T, axis=0)\n meanMat = data_mean.reshape((1, D))\n meanMat = np.repeat(meanMat, T, axis=0)\n origData = np.multiply(origData, stdMat) + meanMat\n return origData","def _validate_input(self, data: Union[np.ndarray, pd.DataFrame, pd.Series],\n expected_dim: int, inference: bool = False) -> np.ndarray:\n allowed_types = (\n np.ndarray,\n pd.core.frame.DataFrame,\n pd.core.frame.Series\n )\n\n if type(data) not in allowed_types:\n raise TypeError('Supported input types: np.ndarray, '\n 'pd.core.frame.DataFrame, pd.core.frame.Series got'\n ' {}'.format(type(data)))\n\n if isinstance(data, pd.DataFrame) or isinstance(data, pd.Series):\n data = data.values\n\n if data.size == 0:\n raise ValueError('Empty array passed to fit() or predict()')\n\n if data.ndim > expected_dim:\n raise ValueError('Data with incorrect number of dimensions '\n 'passed to fit() or predict(). Max dim is '\n '{}, got {}'.format(expected_dim, data.ndim))\n\n if not np.issubdtype(data.dtype, np.number):\n raise ValueError('Non numeric value found in data')\n\n if not np.isfinite(data).all():\n raise ValueError('Data contains nan or inf')\n\n if inference:\n # additional checks on prediction time\n if not self._fitted:\n raise ValueError('Fit the model first.')\n\n if self._ndim == 2 and data.shape[-1] != self._shape[-1]:\n raise ValueError('Number of features does not match'\n ' data model was trained on. Expected'\n ' {}, got {}'\n .format(self._shape[-1], data.shape[-1]))\n\n return data","def GetNoDataValue(self):\n result = super(Band, self).GetNoDataValue()\n if result is not None:\n return self.NumPyDataType(result)","def is_dataset(self):\n return self._dataset is not None","def __prepare_val_dataset(dataset, save_prefix='tmp', data_shape=512, verbose=True):\r\n supported_datasets = ['coco', 'voc']\r\n if isinstance(dataset, ExternalDataset):\r\n if dataset.dataset_type.lower() not in supported_datasets:\r\n raise UserWarning(\"dataset_type must be one of: \", supported_datasets)\r\n\r\n dataset_root = dataset.path\r\n\r\n if dataset.dataset_type.lower() == 'voc':\r\n from gluoncv.data import VOCDetection\r\n\r\n dataset = VOCDetection(root=dataset_root,\r\n splits=[(2007, 'test')])\r\n val_metric = VOC07MApMetric(iou_thresh=0.5, class_names=dataset.classes)\r\n return dataset, val_metric\r\n elif dataset.dataset_type.lower() == 'coco':\r\n from gluoncv.data import COCODetection\r\n\r\n dataset = COCODetection(root=dataset_root, splits='instances_val2017',\r\n skip_empty=False)\r\n val_metric = COCODetectionMetric(\r\n dataset, os.path.join(save_prefix, 'eval'), cleanup=False, data_shape=(data_shape, data_shape))\r\n return dataset, val_metric\r\n elif isinstance(dataset, DetectionDataset) or issubclass(type(dataset), DetectionDataset):\r\n eval_metric = DetectionDatasetCOCOEval(dataset.classes, data_shape)\r\n dataset.set_image_transform(ImageToNDArrayTransform())\r\n dataset.set_target_transform(BoundingBoxListToNumpyArray())\r\n return dataset, eval_metric\r\n else:\r\n print(\"Dataset type {} not supported\".format(type(dataset)))\r\n return dataset, None","def get_dataset(self, dataset_path=None, normalize=True, return_original=False):\n if dataset_path is None:\n dataset_path = self.dir\n \n if \"mocap\" in dataset_path.lower():\n print(\"Loading Mocap dataset.\")\n df = get_mocap()\n df_orig = df\n elif \"profi\" in dataset_path.lower():\n print(\"Loading Profiset dataset.\")\n df = get_profiset()\n df_orig = df\n else:\n print(\"Loading CoPhIR dataset.\")\n df_orig, attr_lengths = get_objects_with_indexes(self.labels, f'{dataset_path}/level-{str(self.n_levels)}.txt', f'{dataset_path}/objects.txt')\n if normalize:\n df = scale_per_descriptor(df_orig, self.labels, attr_lengths)\n else:\n df = df_orig\n \n assert df.shape[1] == self.descriptor_values + self.n_levels + len([\"object_id\"])\n logging.info(f\"Loaded dataset of shape: {df.shape}\")\n if return_original:\n return df, df_orig\n else:\n return df","def _normalize(empty, user_data):\n if isinstance(user_data, UserData):\n return user_data.to_dict()\n if isinstance(user_data, dict):\n return _dict_to_dict(empty, user_data)\n if isinstance(user_data, (bytes, bytearray)):\n return _bytes_to_dict(empty, user_data)\n if user_data is None:\n return empty\n raise ValueError","def get_observed(self, data):\r\n\r\n if isinstance(data, mtuq.Dataset):\r\n observed = np.array([_get_polarity(stream) for stream in data])\r\n\r\n elif isinstance(data, list):\r\n observed = np.array(data)\r\n\r\n elif isinstance(data, np.ndarray):\r\n observed = data\r\n\r\n else:\r\n raise TypeError\r\n\r\n return observed","def _convert_data(self, data):\n if isinstance(data, np.ndarray):\n data = data.astype(float_save(), copy=False)\n if self.compress:\n return KaldiCompressedMatrix.compress(data, self.compression_method)\n return KaldiMatrix(data)\n\n if isinstance(data, KaldiMatrix):\n if self.compress:\n return KaldiCompressedMatrix.compress(data, self.compression_method)\n return data\n\n if isinstance(data, KaldiCompressedMatrix):\n if not self.compress:\n return data.to_matrix()\n return data\n\n raise ValueError(\"Data is not ndarray or KaldiMatrix\")","def _normalize(\n ds: xr.Dataset,\n *,\n dim: Sequence[str],\n kind: str = ADDITIVE,\n) -> xr.Dataset:\n if \"norm\" in ds:\n norm = ds.norm\n else:\n norm = ds.data.mean(dim=dim)\n norm.attrs[\"_group_apply_reshape\"] = True\n\n return xr.Dataset(\n dict(data=apply_correction(ds.data, invert(norm, kind), kind), norm=norm)\n )","def DataSet(self):\n if hasattr(self, '_dataset') and self._dataset and self._dataset.Get():\n return WrapDataObject(self._dataset.Get())\n\n return None","def _extract_data(\n data: AnnData, layer: Optional[str] = None, use_raw: bool = True\n) -> Union[np.ndarray, spmatrix]:\n if isinstance(data, AnnData):\n if use_raw:\n if not hasattr(data, \"raw\"):\n raise AttributeError(\"No `.raw` attribute found.\")\n elif data.raw is None:\n raise ValueError(\"Attribute `.raw` is None.\")\n x = data.raw.X\n elif layer is not None:\n if layer not in data.layers:\n raise KeyError(\n f\"Layer `{layer!s}` not found in `adata.layers`. \"\n f\"Valid options are: `{list(data.layers.keys())}`.\"\n )\n x = data.layers[layer]\n else:\n x = data.X\n elif not isinstance(data, (np.ndarray, spmatrix)):\n raise TypeError(\n f\"Expected parameter `data` to be either `anndata.AnnData`, `numpy.ndarray` \"\n f\"or `scipy.sparse.spmatrix`, found `{type(data).__name__!r}`.\"\n )\n else:\n x = data\n\n return x","def check_numpy(x):\n if isinstance(x, torch.Tensor):\n x = x.detach().cpu().numpy()\n x = np.asarray(x)\n assert isinstance(x, np.ndarray)\n return x","def is_dataset(obj):\n return isinstance(obj, (DictDataset, ImageDataset, LabeledImageDataset,\n TupleDataset, DatasetMixin))","def _format_data(self, data: np.ndarray) -> np.ndarray:\n self._n_shots = 0\n\n # identify shape\n try:\n # level1 single-shot data\n self._n_circs, self._n_shots, self._n_slots, self._n_iq = data.shape\n except ValueError as ex:\n raise DataProcessorError(\n f\"The data given to {self.__class__.__name__} does not have the shape of \"\n \"single-shot IQ data; expecting a 4D array.\"\n ) from ex\n\n if self._validate:\n if data.shape[-1] != 2:\n raise DataProcessorError(\n f\"IQ data given to {self.__class__.__name__} must be a multi-dimensional array\"\n \"of dimension [d0, d1, ..., 2] in which the last dimension \"\n \"corresponds to IQ elements.\"\n f\"Input data contains element with length {data.shape[-1]} != 2.\"\n )\n\n if self._validate:\n if isinstance(self._discriminator, list):\n if self._n_slots != len(self._discriminator):\n raise DataProcessorError(\n f\"The Discriminator node has {len(self._discriminator)} which does \"\n f\"not match the {self._n_slots} slots in the data.\"\n )\n\n return unp.nominal_values(data)","def transform(self, dataset: NumpyOrPandas) -> NumpyDataset:\n # checks here\n super().transform(dataset)\n # convert to accepted dtype and get attributes\n dataset = dataset.to_pandas()\n df = dataset.data\n\n # transform\n roles = NumericRole()\n outputs = []\n for n, i in enumerate(df.columns):\n new_arr = self.dicts[i][\"fg\"].transform(df[i].values)\n output = dataset.empty().to_numpy()\n output.set_data(new_arr, self.dicts[i][\"feats\"], roles)\n outputs.append(output)\n # create resulted\n return dataset.empty().to_numpy().concat(outputs)","def handle_input(data, device=None):\n if type(data) == tuple:\n x, y = data\n x = torch.tensor(x, dtype=torch.float)\n y = torch.tensor(y, dtype=torch.float)\n if not device is None:\n x = x.to(device)\n y = y.to(device)\n return x, y\n if type(data) == np.ndarray:\n x = torch.tensor(data, dtype=torch.float)\n if not device is None:\n x = x.to(device)\n return x\n else:\n return data","def load_data():\n\n # https://docs.scipy.org/doc/numpy-1.13.0/reference/generated/numpy.ndarray.astype.html\n data = np.load(\"phase3-data/data_selected_1980_2010.npy\").astype(float)\n\n return normalize(data)","def transform(self, dataset: NumpyOrPandas) -> NumpyDataset:\n # checks here\n super().transform(dataset)\n # convert to accepted dtype and get attributes\n dataset = dataset.to_pandas()\n df = dataset.data\n\n # transform\n roles = NumericRole()\n outputs = []\n\n for n, conlumn_name in enumerate(df.columns):\n if self.cache_dir is not None:\n full_hash = get_textarr_hash(df[conlumn_name]) + get_textarr_hash(self.dicts[conlumn_name][\"feats\"])\n fname = os.path.join(self.cache_dir, full_hash + \".pkl\")\n\n if os.path.exists(fname):\n logger.info3(f\"Load saved dataset for {conlumn_name}\")\n\n with open(fname, \"rb\") as f:\n new_arr = pickle.load(f)\n\n else:\n new_arr = self.dicts[conlumn_name][\"transformer\"].transform(df[conlumn_name])\n with open(fname, \"wb\") as f:\n pickle.dump(new_arr, f)\n else:\n new_arr = self.dicts[conlumn_name][\"transformer\"].transform(df[conlumn_name])\n\n output = dataset.empty().to_numpy()\n output.set_data(new_arr, self.dicts[conlumn_name][\"feats\"], roles)\n outputs.append(output)\n logger.info3(f\"Feature {conlumn_name} transformed\")\n # create resulted\n return dataset.empty().to_numpy().concat(outputs)","def scale(self, data: np.ndarray):\n if self.scale_type == \"min_max\":\n scaled_data = (data - self.predictor_min) / (\n self.predictor_max - self.predictor_mean\n )\n elif self.scale_type == \"normalize\":\n scaled_data = (data - self.predictor_mean) / (\n self.predictor_max - self.predictor_min\n )\n elif self.scale_type == \"standardize\":\n scaled_data = (data - self.predictor_mean) / self.predictor_std\n elif self.scale_type == \"scale\":\n scaled_data = data - self.predictor_mean\n else:\n scaled_data = data\n return scaled_data","def _convert_data(data, inf_ok=False, min_len=1):\n # If it's scalar, convert to array\n if np.isscalar(data):\n data = np.array([data], dtype=float)\n\n # Convert data to NumPy array\n data = np.array(data, dtype=float)\n\n # Make sure it is 1D\n if len(data.shape) != 1:\n raise RuntimeError(\"Input must be a 1D array or Pandas series.\")\n\n # Remove NaNs\n data = data[~np.isnan(data)]\n\n # Check for infinite entries\n if not inf_ok and np.isinf(data).any():\n raise RuntimeError(\"All entries must be finite.\")\n\n # Check to minimal length\n if len(data) < min_len:\n raise RuntimeError(\n \"Array must have at least {0:d} non-NaN entries.\".format(min_len)\n )\n\n return data","def _preprocess(self, data, normalize=False) -> np.ndarray:\n \n preprocessor = StandardScaler() if not normalize else Normalizer()\n\n data = preprocessor.fit_transform(data)\n \n return data","def normalize_data(data, n=1):\n\n if isinstance(data, str):\n # TODO: could Antti comment on this?\n # numpy array initialization works unintuitively with strings\n data = np.array([[data]], dtype=object)\n else:\n data = np.atleast_1d(data)\n\n if data.ndim == 1:\n if data.shape[0] == n:\n data = data[:, None]\n else:\n data = data[None, :]\n if n > 1:\n data = np.vstack((data, ) * n)\n else:\n if data.shape[0] != n:\n data = data[None, :]\n if n > 1:\n data = np.vstack((data, ) * n)\n return data","def test_obsm_values_ara_numpy(self):\n\n self.validator.adata.obsm[\"X_tsne\"] = pd.DataFrame(\n self.validator.adata.obsm[\"X_umap\"], index=self.validator.adata.obs_names\n )\n self.validator.validate_adata()\n self.assertEqual(\n self.validator.errors,\n [\n \"ERROR: All embeddings have to be of 'numpy.ndarray' type, \"\n \"'adata.obsm['X_tsne']' is <class 'pandas.core.frame.DataFrame'>').\"\n ],\n )","def item(self):\n if self.data.shape != ():\n raise RuntimeError(\"Cannot call item on non-scalar type!\")\n return self.data","def _process(self, data: np.ndarray) -> np.ndarray:","def _process(self, data: np.ndarray) -> np.ndarray:","def test_univariate(self):\n data = load_dataset(data_format=\"numpy\", standardize=True)\n assert data.shape[0] > 131, \"Imported time series collection has the wrong shape\"\n assert data.shape[-1] == 1000, \"Imported time series collection has the wrong shape\"","def get_dataset(args):\n\n if args['experiment']['dataset'] == Dataset.mindsets:\n xs, ys, cs = make_mindsets(mindset_sizes=args['dataset']['mindset_sizes'],\n nb_questions=args['dataset']['nb_questions'],\n nb_useless=args['dataset']['nb_useless'],\n noise=args['dataset']['noise'],\n seed=args['experiment']['seed'])\n\n return Data(xs=xs, ys=ys, cs=cs)\n\n if args['experiment']['dataset'] == Dataset.questionnaire_likert:\n xs, ys, cs = make_likert_questionnaire(nb_samples=args['dataset']['nb_samples'],\n nb_features=args['dataset']['nb_features'],\n nb_mindsets=args['dataset']['nb_mindsets'],\n centers=args['dataset']['centers'],\n range_answers=args['dataset']['range_answers'],\n seed=args['experiment']['seed'])\n\n return Data(xs=xs, ys=ys, cs=cs)\n\n if args['experiment']['dataset'] == Dataset.retinal:\n xs, ys = load_RETINAL(root_path=args['root_dir'],\n nb_bins=args['dataset']['nb_bins'],\n max_idx=args['dataset']['max_idx'])\n\n return Data(xs=xs, ys=ys)\n\n if args['experiment']['dataset'] == Dataset.moons:\n xs, ys = make_moons(n_samples=args['dataset']['n_samples'],\n noise=args['dataset']['noise'],\n random_state=args['experiment']['seed'])\n\n return Data(xs=xs, ys=ys)\n\n if args['experiment']['dataset'] == Dataset.breast_cancer_wisconsin:\n xs, ys = load_CANCER(args['dataset']['nb_bins'])\n\n return Data(xs=xs, ys=ys)\n\n if args['experiment']['dataset'] == Dataset.SBM:\n A, ys, G = load_SBM(block_sizes=args['dataset']['block_sizes'],\n p_in=args['dataset']['p'],\n p_out=args['dataset']['q'],\n seed=args['experiment']['seed'])\n\n return Data(ys=ys, A=A, G=G)\n\n if args['experiment']['dataset'] == Dataset.gaussian_mixture:\n xs, ys = make_blobs(n_samples=args['dataset']['blob_sizes'],\n centers=args['dataset']['blob_centers'],\n n_features=args['dataset']['blob_centers'],\n cluster_std=args['dataset']['blob_variances'],\n random_state=args['experiment']['seed'])\n\n return Data(xs=xs, ys=ys)\n\n if args['experiment']['dataset'] == Dataset.LFR:\n A, ys, G = load_LFR(nb_nodes=args['dataset']['nb_nodes'],\n tau1=args['dataset']['tau1'],\n tau2=args['dataset']['tau2'],\n mu=args['dataset']['mu'],\n average_degree=args['dataset']['average_degree'],\n min_community=args['dataset']['min_community'],\n seed=args['experiment']['seed'])\n\n return Data(ys=ys, A=A, G=G)\n\n if args['experiment']['dataset'] == Dataset.wave:\n df = pd.read_csv('datasets/waveform.csv')\n xs = df[df.columns[:-1]].to_numpy()\n ys = df[df.columns[-1]].to_numpy()\n\n return Data(xs=xs, ys=ys)\n\n raise ValueError('Wrong name for a dataset')","def _format_data(self, data: np.ndarray) -> np.ndarray:\n if self._validate:\n if len(data.shape) <= 1:\n raise DataProcessorError(\n \"The data should be an array with at least two dimensions.\"\n )\n\n return data","def _to_arraylike(data):\n _load_objects()\n if data is None:\n raise ValueError('Cannot convert None data.')\n return None\n if not isinstance(data, (ndarray, DataArray, DataFrame, Series, Index)):\n data = np.asarray(data)\n if not np.iterable(data):\n data = np.atleast_1d(data)\n return data","def load_dataset(\n dataset: str\n) -> Union[lmp.dataset.AnalogyDataset, lmp.dataset.LanguageModelDataset]:\n # Type check.\n if not isinstance(dataset, str):\n raise TypeError('`dataset` must be an instance of `str`.')\n\n if dataset == 'news_collection_desc':\n return _preprocess_news_collection(column='desc')\n\n if dataset == 'news_collection_title':\n return _preprocess_news_collection(column='title')\n\n if dataset == 'wiki_train_tokens':\n return _preprocess_wiki_tokens(split='train')\n\n if dataset == 'wiki_valid_tokens':\n return _preprocess_wiki_tokens(split='valid')\n\n if dataset == 'wiki_test_tokens':\n return _preprocess_wiki_tokens(split='test')\n\n if dataset == 'word_test_v1':\n return _preprocess_word_test_v1()\n\n raise ValueError(\n f'dataset `{dataset}` does not support.\\nSupported options:' +\n ''.join(list(map(\n lambda option: f'\\n\\t--dataset {option}',\n [\n 'news_collection_desc',\n 'news_collection_title',\n 'wiki_test_tokens',\n 'wiki_train_tokens',\n 'wiki_valid_tokens',\n 'word_test_v1'\n ]\n )))\n )","def filter(self, data, strict=False, allow_downcast=None):\r\n # Explicit error message when one accidentally uses a Variable as\r\n # input (typical mistake, especially with shared variables).\r\n if isinstance(data, Variable):\r\n raise TypeError(\r\n 'Expected an array-like object, but found a Variable: '\r\n 'maybe you are trying to call a function on a (possibly '\r\n 'shared) variable instead of a numeric array?')\r\n\r\n if ((type(data) is numpy.ndarray)\r\n and (data.dtype == self.numpy_dtype)):\r\n if data.dtype.num != self.numpy_dtype.num:\r\n data = theano._asarray(data, dtype=self.dtype)\r\n # -- now fall through to ndim check\r\n elif((type(data) is numpy.memmap)\r\n and (data.dtype == self.numpy_dtype)):\r\n # numpy.memmap is a \"safe\" subclass of ndarray,\r\n # so we can use it whereever we expect a base ndarray.\r\n # however, casting it would defeat the purpose of not\r\n # loading the whole data into memory\r\n pass\r\n elif strict:\r\n # If any of the two conditions above was not met,\r\n # we raise a meaningful TypeError.\r\n if not (type(data) is numpy.ndarray):\r\n raise TypeError(\"%s expected a ndarray object.\" % self,\r\n data, type(data))\r\n if data.dtype != self.numpy_dtype:\r\n raise TypeError((\"%s expected a ndarray object with \"\r\n \"dtype = %s (got %s).\") % (\r\n self, self.numpy_dtype, data.dtype))\r\n assert False, \"This point should never be reached.\"\r\n else:\r\n if allow_downcast:\r\n # Convert to self.dtype, regardless of the type of data\r\n data = theano._asarray(data, dtype=self.dtype)\r\n # TODO: consider to pad shape with ones to make it consistent\r\n # with self.broadcastable... like vector->row type thing\r\n else:\r\n if isinstance(data, numpy.ndarray):\r\n # Check if self.dtype can accurately represent data\r\n # (do not try to convert the data)\r\n up_dtype = scal.upcast(self.dtype, data.dtype)\r\n if up_dtype == self.dtype:\r\n # Bug in the following line when data is a\r\n # scalar array, see\r\n # http://projects.scipy.org/numpy/ticket/1611\r\n # data = data.astype(self.dtype)\r\n data = theano._asarray(data, dtype=self.dtype)\r\n if up_dtype != self.dtype:\r\n err_msg = (\r\n '%s cannot store a value of dtype %s without '\r\n 'risking loss of precision. If you do not mind '\r\n 'this loss, you can: '\r\n '1) explicitly cast your data to %s, or '\r\n '2) set \"allow_input_downcast=True\" when calling '\r\n '\"function\".'\r\n % (self, data.dtype, self.dtype))\r\n raise TypeError(err_msg, data)\r\n elif (allow_downcast is None and\r\n type(data) is float and\r\n self.dtype == theano.config.floatX):\r\n # Special case where we allow downcasting of Python float\r\n # literals to floatX, even when floatX=='float32'\r\n data = theano._asarray(data, self.dtype)\r\n else:\r\n # data has to be converted.\r\n # Check that this conversion is lossless\r\n converted_data = theano._asarray(data, self.dtype)\r\n # We use the `values_eq` static function from TensorType\r\n # to handle NaN values.\r\n if TensorType.values_eq(numpy.asarray(data),\r\n converted_data,\r\n force_same_dtype=False):\r\n data = converted_data\r\n else:\r\n # Do not print a too long description of data\r\n # (ndarray truncates it, but it's not sure for data)\r\n str_data = str(data)\r\n if len(str_data) > 80:\r\n str_data = str_data[:75] + '(...)'\r\n\r\n err_msg = (\r\n '%s cannot store accurately value %s, '\r\n 'it would be represented as %s. '\r\n 'If you do not mind this precision loss, you can: '\r\n '1) explicitly convert your data to a numpy array '\r\n 'of dtype %s, or '\r\n '2) set \"allow_input_downcast=True\" when calling '\r\n '\"function\".'\r\n % (self, data, converted_data, self.dtype))\r\n raise TypeError(err_msg, data)\r\n\r\n if self.ndim != data.ndim:\r\n raise TypeError(\"Wrong number of dimensions: expected %s,\"\r\n \" got %s with shape %s.\" % (self.ndim, data.ndim,\r\n data.shape))\r\n if not data.flags.aligned:\r\n try:\r\n msg = \"object buffer\" + str(data.data)\r\n except AttributeError:\r\n msg = \"\"\r\n raise TypeError(\"The numpy.ndarray object is not aligned.\"\r\n \" Theano C code does not support that.\",\r\n msg,\r\n \"object shape\", data.shape,\r\n \"object strides\", data.strides,\r\n \"object dtype\", data.dtype)\r\n\r\n i = 0\r\n for b in self.broadcastable:\r\n if b and data.shape[i] != 1:\r\n raise TypeError(\"Non-unit value on shape on a broadcastable\"\r\n \" dimension.\", data.shape, self.broadcastable)\r\n i += 1\r\n if (self.filter_checks_isfinite and\r\n not numpy.all(numpy.isfinite(data))):\r\n raise ValueError(\"non-finite elements not allowed\")\r\n return data","def _normalize_column(self, data, coerce_dtype=None, store_categories=True):\n if coerce_dtype is not None:\n coerce_dtype = np.dtype(coerce_dtype)\n\n if np.isscalar(data):\n array = np.array([data], dtype=coerce_dtype)\n dtype = data.dtype\n fillvalue = None\n\n elif is_categorical(data):\n if store_categories:\n cats = data.cat.categories\n enum_dict = dict(zip(cats, range(len(cats))))\n array = data.cat.codes\n dtype = h5py.special_dtype(enum=(array.dtype, enum_dict))\n fillvalue = -1\n else:\n array = data.cat.codes\n dtype = coerce_dtype or array.dtype\n fillvalue = -1\n\n elif data.dtype in (object, str, bytes):\n data = np.asarray(data)\n dtype = np.dtype(\"S\")\n array = np.array(data, dtype=dtype)\n fillvalue = None\n\n else:\n array = np.asarray(data)\n dtype = data.dtype\n fillvalue = None\n\n return array, dtype, fillvalue","def _prepare(self, data, train=True):\n if data is None:\n return None\n\n if hasattr(data, \"to_tfdataset\"):\n return data.to_tfdataset(train=train)\n else:\n return data","def WrapDataObject(ds):\n if ds.IsA(\"vtkPolyData\"):\n return PolyData(ds)\n elif ds.IsA(\"vtkUnstructuredGrid\"):\n return UnstructuredGrid(ds)\n elif ds.IsA(\"vtkPointSet\"):\n return PointSet(ds)\n elif ds.IsA(\"vtkDataSet\"):\n return DataSet(ds)\n elif ds.IsA(\"vtkCompositeDataSet\"):\n return CompositeDataSet(ds)\n elif ds.IsA(\"vtkTable\"):\n return Table(ds)\n elif ds.IsA(\"vtkMolecule\"):\n return Molecule(ds)\n elif ds.IsA(\"vtkGraph\"):\n return Table(ds)\n elif ds.IsA(\"vtkHyperTreeGrid\"):\n return HyperTreeGrid(ds)\n elif ds.IsA(\"vtkDataObject\"):\n return DataObject(ds)","def process(self, data):\n data = np.atleast_2d(data)\n\n if self.orientation == 'row':\n return data\n else:\n return data.T","def from_homogeneous(x):\n if not isinstance(x, np.ndarray):\n x = np.array(x)\n if len(x.shape) == 1:\n h = x[-1]\n if h != 0:\n x = x/h\n return x[0:-1]\n else:\n return None\n else:\n assert len(x.shape) == 2\n h = np.expand_dims(x[:, -1], axis=1)\n x = x / h\n return x[:, 0:-1]","def required_data_type(self):\n return Data","def wrap(dataset):\n # Return if None\n if dataset is None:\n return\n\n if isinstance(dataset, tuple(pyvista._wrappers.values())):\n # Return object if it is already wrapped\n return dataset\n\n # Check if dataset is a numpy array. We do this first since\n # pyvista_ndarray contains a VTK type that we don't want to\n # directly wrap.\n if isinstance(dataset, (np.ndarray, pyvista.pyvista_ndarray)):\n if dataset.ndim == 1 and dataset.shape[0] == 3:\n return pyvista.PolyData(dataset)\n if dataset.ndim > 1 and dataset.ndim < 3 and dataset.shape[1] == 3:\n return pyvista.PolyData(dataset)\n elif dataset.ndim == 3:\n mesh = pyvista.ImageData(dimensions=dataset.shape)\n if isinstance(dataset, pyvista.pyvista_ndarray):\n # this gets rid of pesky VTK reference since we're raveling this\n dataset = np.array(dataset, copy=False)\n mesh['values'] = dataset.ravel(order='F')\n mesh.active_scalars_name = 'values'\n return mesh\n else:\n raise NotImplementedError('NumPy array could not be wrapped pyvista.')\n\n # wrap VTK arrays as pyvista_ndarray\n if isinstance(dataset, _vtk.vtkDataArray):\n return pyvista.pyvista_ndarray(dataset)\n\n # Check if a dataset is a VTK type\n if hasattr(dataset, 'GetClassName'):\n key = dataset.GetClassName()\n try:\n return pyvista._wrappers[key](dataset)\n except KeyError:\n raise TypeError(f'VTK data type ({key}) is not currently supported by pyvista.')\n return\n\n # wrap meshio\n if is_meshio_mesh(dataset):\n return from_meshio(dataset)\n\n # wrap trimesh\n if dataset.__class__.__name__ == 'Trimesh':\n # trimesh doesn't pad faces\n n_face = dataset.faces.shape[0]\n faces = np.empty((n_face, 4), dataset.faces.dtype)\n faces[:, 1:] = dataset.faces\n faces[:, 0] = 3\n polydata = pyvista.PolyData(np.asarray(dataset.vertices), faces)\n # If the Trimesh object has uv, pass them to the PolyData\n if hasattr(dataset.visual, 'uv'):\n polydata.active_t_coords = np.asarray(dataset.visual.uv)\n return polydata\n\n # otherwise, flag tell the user we can't wrap this object\n raise NotImplementedError(f'Unable to wrap ({type(dataset)}) into a pyvista type.')","def _process(self, data: np.ndarray) -> np.ndarray:\n return data[..., 1] * self.scale","def get_data(dataset: str = None, scale: bool = True) -> tuple:\n \n if dataset not in ['ecoli', 'glass', 'letter-recognition', 'lymphography', 'yeast', \n 'digits', 'breast-cancer', 'wine', 'mnist']:\n raise ValueError(\"Invalid dataset provided.\")\n \n if dataset in dataset in ['ecoli', 'glass', 'letter-recognition', 'lymphography', 'yeast']:\n path = 'https://archive.ics.uci.edu/ml/machine-learning-databases/' \n f = path + dataset + \"/\" + dataset + \".data\"\n \n if dataset in ['ecoli', 'yeast']:\n df = pd.read_table(f, delim_whitespace=True, header=None)\n elif dataset in [ 'glass', 'letter-recognition', 'lymphography']:\n df = pd.read_csv(f, header=None)\n elif dataset == 'digits':\n df = load_digits()\n X = df.data\n y = df.target\n elif dataset == 'breast-cancer':\n df = load_breast_cancer()\n X = df.data\n y = df.target\n elif dataset == 'wine':\n df = load_wine()\n X = df.data\n y = df.target\n \n if dataset == 'ecoli':\n y = preprocessing.LabelEncoder().fit_transform(df.iloc[:,-1])\n X = df.iloc[:,1:8].values\n \n elif dataset == 'glass':\n y = df.iloc[:,-1].values\n X = df.iloc[:, 1:(df.shape[1]-1)].values\n \n elif dataset in ['letter-recognition', 'lymphography']:\n y = preprocessing.LabelEncoder().fit_transform(df.iloc[:,0])\n X = df.iloc[:, 1:(df.shape[1])].values\n \n elif dataset == 'yeast':\n y = preprocessing.LabelEncoder().fit_transform(df.iloc[:,-1])\n X = df.iloc[:,1:9].values\n \n elif dataset == 'mnist':\n X, y = fetch_openml('mnist_784', version=1, return_X_y=True)\n y = y.astype('int64') \n\n if scale==True:\n scaler = preprocessing.StandardScaler()\n X = scaler.fit_transform(X)\n \n return X, y","def norm_data(self):\n if (self.nrows, self.ncolumns) < self.data.shape:\n self.data = self.data[0:self.nrows, 0:self.ncolumns]\n if self.data.dtype != np.float64:\n self.data = self.data.astype(np.float64)\n self.meanval = self.data.mean()\n self.stdval = self.data.std()","def normalize(\n ds: xr.Dataset,\n *,\n dim: str,\n kind: str = ADDITIVE,\n) -> xr.Dataset:\n\n if \"norm\" in ds:\n norm = invert(ds.norm, kind)\n else:\n norm = invert(ds.data.mean(dim=dim), kind)\n\n return xr.Dataset(dict(data=apply_correction(ds.data, norm, kind)))","def test_single_null(setup_teardown_file):\n f = setup_teardown_file[3]\n\n dset = f.create_dataset('x', (1,), dtype='i1')\n out = dset[()]\n assert isinstance(out, np.ndarray)\n assert out.shape == (1,)","def _is_DataArrays(data):\n if isinstance(data, (Dataset, DataArray)):\n return True\n if isinstance(data, Mapping):\n for da in data.values():\n if not isinstance(da, DataArray):\n raise TypeError(\"Please provide List/Mapping of DataArrays\")\n return True\n if isinstance(data, Iterable):\n for da in data:\n if not isinstance(da, DataArray):\n return False\n # raise TypeError(\"Please provide List/Mapping of DataArrays\")\n return True\n return False","def _check_and_transform_input(self, data):\n if isinstance(data, list):\n if np.array(data).shape == (len(data),):\n if len(data) == 1:\n data = np.array(data).reshape(1, 1)\n data = np.array(data).reshape(len(data), 1)\n else:\n data = np.concatenate(data).reshape(len(data), -1)\n else:\n raise TypeError('Input data should be of type list, but found type {}'.format(type(data)))\n\n return data","def _process(self, data: np.ndarray) -> np.ndarray:\n return data[..., 0] * self.scale","def astype(self, dtype):\n return NoneArray","def _format_data(self, data: np.ndarray) -> np.ndarray:\n self._n_shots = 0\n\n # identify shape\n try:\n # level1 single-shot data\n self._n_circs, self._n_shots, self._n_slots, self._n_iq = data.shape\n except ValueError:\n try:\n # level1 data averaged over shots\n self._n_circs, self._n_slots, self._n_iq = data.shape\n except ValueError as ex:\n raise DataProcessorError(\n f\"Data given to {self.__class__.__name__} is not likely level1 data.\"\n ) from ex\n\n if self._validate:\n if data.shape[-1] != 2:\n raise DataProcessorError(\n f\"IQ data given to {self.__class__.__name__} must be a multi-dimensional array\"\n \"of dimension [d0, d1, ..., 2] in which the last dimension \"\n \"corresponds to IQ elements.\"\n f\"Input data contains element with length {data.shape[-1]} != 2.\"\n )\n\n return data","def _to_ndarray(data):\n return np.atleast_1d(getattr(data, 'values', data))","def _normalize_default(value, dtype):\n\n # Create NumPy objects as defaults\n # This is better in order to serialize them as attributes\n if value is None:\n value = 0\n basedtype = dtype.base\n try:\n default = np.array(value, dtype=basedtype)\n except ValueError:\n array = np.array(value)\n if array.shape != basedtype.shape:\n raise\n # Maybe nested dtype with \"scalar\" value.\n default = np.array(value, dtype=basedtype.base)\n # 0-dim arrays will be representented as NumPy scalars\n # (PyTables attribute convention)\n if default.shape == ():\n default = default[()]\n return default","def _format_data(self, data: np.ndarray) -> np.ndarray:\n if self._validate:\n if len(data.shape) <= self._axis:\n raise DataProcessorError(\n f\"Cannot average the {len(data.shape)} dimensional \"\n f\"array along axis {self._axis}.\"\n )\n\n return data","def test_scalar_null(self):\n dset = self.f.create_dataset('x', shape=(), dtype='i1')\n out = dset[()]\n self.assertIsInstance(out, np.int8)","def _normalizeComplex(data):\n if hasattr(data, \"dtype\"):\n isComplex = numpy.issubdtype(data.dtype, numpy.complexfloating)\n else:\n isComplex = isinstance(data, numbers.Complex)\n if isComplex:\n data = numpy.absolute(data)\n return data","def _norm_data(data):\n if data is None:\n return data\n data_min = np.min(data)\n c_norm = np.max(data) - data_min\n return (data - data_min) / c_norm if (c_norm != 0) else (data - data_min)","def _format_data(self, data: np.ndarray) -> np.ndarray:\n self._n_circs = 0\n self._n_shots = 0\n self._n_slots = 0\n self._n_iq = 0\n\n # identify shape\n try:\n # level1 single-shot data\n self._n_circs, self._n_shots, self._n_slots, self._n_iq = data.shape\n except ValueError:\n try:\n # level1 data averaged over shots\n self._n_circs, self._n_slots, self._n_iq = data.shape\n except ValueError as ex:\n raise DataProcessorError(\n f\"Data given to {self.__class__.__name__} is not likely level1 data.\"\n ) from ex\n\n if self._validate:\n if self._n_iq != 2:\n raise DataProcessorError(\n f\"IQ data given to {self.__class__.__name__} does not have two-dimensions \"\n f\"(I and Q). Instead, {self._n_iq} dimensions were found.\"\n )\n\n return data","def _validate_X(X):\n return X if not isinstance(X, pd.DataFrame) else X.as_matrix()","def applyNormalization(ds, reference, target=-1):\n print 'normalization of', ds.title\n # Store reference name for later\n refname = str(reference)\n # Normalization\n reference = getattr(ds,reference)\n\n # check if reference/target is a number\n # TODO: gumpy doesn't allow us to handle a scalar with variance\n # for multiplying arrays, so we can't propagate variance at present\n numericReference = isinstance(reference, (int, long, float))\n \n # check arguments\n if not numericReference:\n if reference.ndim != 1:\n raise AttributeError('reference.ndim != 1')\n if reference.shape[0] != ds.shape[0]:\n raise AttributeError('reference.shape[0] != ds.shape[0] (%d != %d)' % (reference.shape[0],ds.shape[0]))\n\n def do_norm(rs, f, varf):\n # We propagate errors in the data, but not in\n # the ancillary values\n print 'In do_norm, given %f(%f)' % (f,varf)\n # Funny syntax below to make sure we write into the original area,\n # not assign a new value\n rs.var *= f * f\n rs.var += varf * rs * rs\n rs.storage *= f\n try: #These may be absent in some cases\n rs.bm1_counts *= f\n rs.bm2_counts *= f\n rs.bm3_counts *= f\n rs.detector_time *= f\n rs.total_counts *= f\n except AttributeError:\n pass\n \n # normalization\n rs = ds.__copy__()\n copy_metadata_deep(rs,ds) #NeXuS metadata\n rs.copy_cif_metadata(ds) #CIF metadata\n if numericReference and target > 0:\n # We have a single number to refer to for normalisation, so\n # we are effectively scaling everything by a single number\n scale_factor = float(target)/reference\n variance = scale_factor * target/(reference*reference)\n do_norm(rs, scale_factor, variance)\n info_string = \"Data multiplied by %f with variance %f\" % (scale_factor,variance)\n elif not numericReference:\n # Each step has a different value, and we manually perform the\n # error propagation \n reference = Data(reference)\n if target <= 0:\n target = reference.max()\n for i in xrange(rs.shape[0]):\n # handle unexpected zero values\n one_reference = reference[i]\n if one_reference == 0:\n one_reference = 0.1 #so small it is like zero\n print \"Warning: zero monitor counts found at step %d\" % i\n f = float(target)/one_reference\n v = f*target/(one_reference*one_reference)\n # Funny syntax below to make sure we write into the original area,\n # not assign a new value\n tar_shape = [1,rs.shape[1],rs.shape[2]]\n tar_origin = [i,0,0]\n rss = rs.storage.get_section(tar_origin,tar_shape).get_reduced()\n rsv = rs.var.get_section(tar_origin,tar_shape).get_reduced()\n rs.var[i] = rsv*f * f\n rs.var[i] += v * rss * rss\n rs.storage[i] = rs.storage[i]*f\n info_string = \"Data normalised to %f on %s with error propagation assuming counting statistics\" % (float(target),refname)\n else:\n # interesting note - if we get here, we are passed a single reference number\n # and a negative target, meaning that we use the reference as the target and\n # end up multiplying by 1.0, so no need to do anything at all.\n target = reference\n info_string = \"No normalisation applied to data.\"\n rs.add_metadata('_pd_proc_info_data_reduction',info_string, append=True)\n print 'normalized:', ds.title\n return rs,target","def get_data(name):\n\n if name == 'train' or name == 'unlabeled':\n return np.expand_dims(np.load(os.path.join(DATADIR, 'source_d10_train_X.npy')), axis=-1), \\\n np.argmax(np.load(os.path.join(DATADIR, 'source_d10_train_y.npy')), axis=1)\n # custom svhn has only training/validation set\n elif name == 'validation' or name == 'val':\n return np.expand_dims(np.load(os.path.join(DATADIR, 'source_d10_val_X.npy')), axis=-1), \\\n np.argmax(np.load(os.path.join(DATADIR, 'source_d10_val_y.npy')), axis=1)","def test_scalar_null(setup_teardown_file):\n f = setup_teardown_file[3]\n\n dset = f.create_dataset('x', shape=(), dtype='i1')\n out = dset[()]\n\n assert out.dtype == \"int8\"","def _numpy_to_cpu_safe(\n t: Optional[Union[np.ndarray, torch.Tensor]]\n) -> Optional[torch.Tensor]:\n\n if t is not None and isinstance(t, np.ndarray):\n return torch.from_numpy(t)\n return t","def normalize_data(data):\n mean = np.mean(data)\n std = np.std(data)\n return (data - mean) / std","def get_X():\n metadata = get_dataset_metadata(['shape_X', 'type_X'])\n return get_ndarray(name='X_original',\n arr_shape=metadata['shape_X'],\n arr_type=metadata['type_X'])","def check_input_and_return_data(self, input_name):\n\n #Check to see if data is given by a file\n if isinstance(self.input_data['reservoir'][input_name], str):\n #Get filename\n filename = self.input_data['reservoir'][input_name]\n #Load data \n data = np.loadtxt(filename, dtype=np.double)\n \n #Check to see if data is given by a list\n elif isinstance(self.input_data['reservoir'][input_name], (list, tuple)):\n #Turn the list into numpy array\n data = np.array(self.input_data['reservoir'][input_name], \n dtype=np.double)\n\n #data is a constant array (homogeneous)\n else:\n ngrids = self.input_data['numerical']['number of grids']\n data = (self.input_data['reservoir'][input_name] * \n np.ones(ngrids))\n return data","def convert_to_ndarray(entity):\n if isinstance(entity, np.ndarray) and entity.dtype.kind in set('biufc'):\n # entity is numerical ndarray already\n return entity\n if isinstance(entity, np.ndarray) and isinstance(entity.flat[0], qt.Qobj):\n # entity is output from qt.eigenstates\n return convert_esys_to_ndarray(entity)\n if isinstance(entity, list) and isinstance(entity[0], np.ndarray) and isinstance(entity[0].flat[0], qt.Qobj):\n # entity is a list of qt.eigenstates\n return np.asarray([convert_esys_to_ndarray(entry) for entry in entity])\n # possibly we have a list of numerical values or a list of ndarrays\n converted_entity = np.asarray(entity)\n if converted_entity.dtype.kind not in set('biufc'):\n raise TypeError('Unable to convert data to numerical numpy array: ', entity)\n return converted_entity","def parse(\n self, data: Union[pd.DataFrame, pd.Series, str], verbose: int = 0\n ) -> pd.DataFrame:\n\n data = self.__transform_data(data)\n data = self.norm.normalize(data)\n data = self.find.find_all(data, verbose)\n data = data.drop(\"name_norm\", axis=1)\n return data","def _validate_input_data(self):\n\n if type(self.data) in [np.ndarray, da.Array]:\n if not self.data.dtype.names:\n raise ValueError('QuadTree: numpy array provided for data, but no names were found, array must be a structured array')\n if 'x' not in self.data.dtype.names or 'y' not in self.data.dtype.names:\n raise ValueError('QuadTree: numpy structured array provided for data, but \"x\" or \"y\" not found in variable names')\n self.layernames = [self.rev_layer_lookup[var] for var in self.data.dtype.names if var in ['z', 'tvu']]\n elif type(self.data) == xr.Dataset:\n if 'x' not in self.data:\n raise ValueError('QuadTree: xarray Dataset provided for data, but \"x\" or \"y\" not found in variable names')\n if len(self.data.dims) > 1:\n raise ValueError('QuadTree: xarray Dataset provided for data, but found multiple dimensions, must be one dimensional: {}'.format(self.data.dims))\n self.layernames = [self.rev_layer_lookup[var] for var in self.data if var in ['z', 'tvu']]\n self._convert_dataset() # internally we just convert xarray dataset to numpy for ease of use\n else:\n raise ValueError('QuadTree: numpy structured array or dask array with \"x\" and \"y\" as variable must be provided')","def test_data(self):\n\n self.assertIsInstance(self.image.data, np.ndarray)","def get_normalize_layer(dataset: str) -> torch.nn.Module:\n if dataset == \"imagenet\":\n return NormalizeLayer(_IMAGENET_MEAN, _IMAGENET_STDDEV)\n elif dataset == \"cifar10\":\n return NormalizeLayer(_CIFAR10_MEAN, _CIFAR10_STDDEV)\n elif dataset == \"imagenet32\":\n return NormalizeLayer(_CIFAR10_MEAN, _CIFAR10_STDDEV)","def normalize_data(self):\n self.x_mean, self.x_std = du.get_mean_std(self.x_train)\n self.x_train = du.normalize(self.x_train, self.x_mean, self.x_std)\n if self.x_test is not None and self.y_test is not None:\n self.x_test = du.normalize(self.x_test, self.x_mean, self.x_std)\n self.normalized_data = True","def normalize(data):\n # normalize data and return\n # https://stackoverflow.com/questions/29661574/normalize-numpy-array-columns-in-python\n return (data - data.min(axis=0)) / data.ptp(axis=0)","def _check_dataset_name_return_data(self, dataset_name, strict=False):\n if strict:\n _check_dataset_presence(self, dataset_name)\n elif dataset_name is None:\n raise MutantError(\"Cannot use None as dataset name!\")\n return self.by_dataset[dataset_name]","def iris():\n return IrisDataset()","def _match_shape_to_data(self,\n data: 'Union[np.ndarray, tensorflow.Tensor, torch.Tensor, Any, List[Any]]',\n target_type: Literal['X', 'Y']\n ) -> 'Union[np.ndarray, tensorflow.Tensor, torch.Tensor, List[Any]]':\n if self._is_tensor(data):\n return self._match_shape_to_data_tensor(data, target_type)\n return self._match_shape_to_data_any(data)"],"string":"[\n \"def normalize_dataset(self):\",\n \"def denormalise_0_1(value_or_array, array_min, array_max):\\n if isinstance(value_or_array, list):\\n raise ValueError('this function accepts arraylike data, not a list. '\\n 'Please check data or convert list to numpy array')\\n elif isinstance(value_or_array, float):\\n denormalised = value_or_array * (array_max - array_min) + array_min\\n elif isinstance(value_or_array, np.ndarray):\\n denormalised = value_or_array * (array_max - array_min) + array_min\\n elif isinstance(value_or_array, pd.Series):\\n denormalised = value_or_array * (array_max - array_min) + array_min\\n else:\\n sys.stdout.write(\\\"Unknown datatype. denormalise_0_1 has been given an input that does not appear to be \\\"\\n \\\"an int, float, np.ndarray or pandas Series\\\\n\\\"\\n \\\"Attempting to process as if it is arraylike.....\\\")\\n return denormalised\",\n \"def convert_dataset(dataset: h5py.Dataset, refs: h5py.Group):\\n # all MATLAB variables have the attribute MATLAB_class\\n # if this is not present, it is not convertible\\n if has_refs(dataset):\\n mtype = \\\"cell_with_refs\\\"\\n else:\\n try:\\n mtype = dataset.attrs[\\\"MATLAB_class\\\"].decode()\\n except KeyError:\\n raise ValueError(f\\\"{dataset} is not a MATLAB type.\\\")\\n\\n if mtype == \\\"cell_with_refs\\\":\\n cell = []\\n for ref in dataset:\\n row = []\\n for r in ref:\\n entry = convert_dataset(refs.get(r), refs=refs)\\n row.append(entry)\\n cell.append(row)\\n return cell\\n\\n elif mtype == \\\"char\\\":\\n if len(dataset) == 0:\\n return \\\"\\\"\\n elif isinstance(dataset[0], np.ndarray):\\n return \\\"\\\".join([chr(x[0]) for x in dataset]).replace(\\\"\\\\x00\\\", \\\"\\\")\\n else:\\n return \\\"\\\".join([chr(x) for x in dataset]).replace(\\\"\\\\x00\\\", \\\"\\\")\\n\\n elif mtype == \\\"bool\\\":\\n return bool(dataset)\\n\\n elif mtype == \\\"logical\\\":\\n arr = np.array(dataset, dtype=bool).T.squeeze()\\n if arr.size == 1:\\n arr = bool(arr)\\n return arr\\n\\n elif mtype == \\\"canonical empty\\\":\\n return None\\n\\n # complex numbers need to be filtered out separately\\n elif \\\"imag\\\" in str(dataset.dtype):\\n if dataset.attrs[\\\"MATLAB_class\\\"] == b\\\"single\\\":\\n dtype = np.complex64\\n else:\\n dtype = np.complex128\\n arr = np.array(dataset)\\n arr = (arr[\\\"real\\\"] + arr[\\\"imag\\\"] * 1j).astype(dtype)\\n return arr.T.squeeze()\\n\\n # if it is none of the above, we can convert to numpy array\\n elif mtype in (\\n \\\"double\\\",\\n \\\"single\\\",\\n \\\"int8\\\",\\n \\\"int16\\\",\\n \\\"int32\\\",\\n \\\"int64\\\",\\n \\\"uint8\\\",\\n \\\"uint16\\\",\\n \\\"uint32\\\",\\n \\\"uint64\\\",\\n \\\"cell\\\",\\n ):\\n if mtype == \\\"cell\\\":\\n print(dataset)\\n arr = np.array(dataset, dtype=dataset.dtype)\\n return arr.T.squeeze()\\n\\n else:\\n raise ValueError(f\\\"Data type not supported: {mtype}, {dataset.dtype}.\\\")\",\n \"def normalized_data(self):\\n return self._data\",\n \"def _prep_data(self, data, func_input_dtype):\\n if func_input_dtype in (None, 'DataArray'):\\n return data\\n if func_input_dtype == 'Dataset':\\n # TODO: add logic that creates a single Dataset comprising all of\\n # the DataArray objects in `data`.\\n raise NotImplementedError(\\\"func_input_dtype of `Dataset` not yet \\\"\\n \\\"implemented.\\\")\\n if func_input_dtype == 'numpy':\\n self.coords = data[0].coords\\n return [d.values for d in data]\",\n \"def _convert_data(self, data):\\n if isinstance(data, Tensor):\\n data = data.asnumpy()\\n elif isinstance(data, list):\\n data = np.array(data)\\n elif isinstance(data, np.ndarray):\\n pass\\n else:\\n raise TypeError('Input data type must be tensor, list or numpy.ndarray')\\n return data\",\n \"def normalized_data(self):\\n return self._normalization_constant * self._data\",\n \"def _convert_dataset(self):\\n allowed_vars = ['x', 'y', 'z', 'tvu', 'thu']\\n dtyp = [(varname, self.data[varname].dtype) for varname in allowed_vars if varname in self.data]\\n empty_struct = np.empty(len(self.data['x']), dtype=dtyp)\\n for varname, vartype in dtyp:\\n empty_struct[varname] = self.data[varname].values\\n self.data = empty_struct\",\n \"def _canonize_input(self, dataset):\\n unpack = lambda x: x\\n if isinstance(dataset, _tc.SArray):\\n dataset = _tc.SFrame({self.feature: dataset})\\n elif isinstance(dataset, _tc.Image):\\n dataset = _tc.SFrame({self.feature: [dataset]})\\n unpack = lambda x: x[0]\\n return dataset, unpack\",\n \"def _raw_data(self):\\n data = self.datasource.as_array()\\n if not isinstance(data, np.ndarray):\\n raise TypeError(\\\"The data you try to load is no numpy array!\\\")\\n if data.ndim != 2:\\n raise ValueError(\\\"The data array you try to load does not have 2 \\\"\\n \\\"dimensions!\\\")\\n data = data.copy(order='C')\\n ident = hashlib.md5(data).hexdigest()\\n if self._dataident is None:\\n self._dataident = ident\\n elif self._dataident != ident:\\n raise ValueError(\\\"The data you try to load from '%s' has changed \\\"\\n \\\"since the last time. Please, check the \\\"\\n \\\"datasource of the record '%s'.\\\"\\n % (self.datasource.name, self.name))\\n\\n return data\",\n \"def normalise(raw_data, normalise_by_column=False):\\n data = raw_data\\n if normalise_by_column:\\n #normaliza valores usando o maximo de cada coluna\\n col_maxes = raw_data.max(axis=0)\\n #divide cada valor pelo maximo correspondente de cada coluna\\n data = raw_data / col_maxes[np.newaxis,:] \\n else:\\n #divide todos os valores pelo maximo do dataset (tudo na mesma escala)\\n data = raw_data / raw_data.max()\\n\\n return data\",\n \"def __prepare(self, data):\\n #print(\\\"Running Prepare data\\\")\\n #print(data)\\n #print(type(data))\\n if len(data) > 1:\\n if type(data[0]) == np.ndarray:\\n return np.concatenate(data)\\n else:\\n return torch.cat(data).cpu().numpy()\\n else:\\n return data[0].cpu().numpy()\",\n \"def _check_data(data, dset_attrs):\\n dtype = dset_attrs['dtype']\\n float_to_int = (np.issubdtype(dtype, np.integer)\\n and np.issubdtype(data.dtype, np.floating))\\n if float_to_int:\\n attrs = dset_attrs['attrs']\\n scale_factor = [c for c in attrs if 'scale_factor' in c][0]\\n scale_factor = attrs[scale_factor]\\n\\n # apply scale factor and dtype\\n data = np.multiply(data, scale_factor)\\n if np.issubdtype(dtype, np.integer):\\n data = np.round(data)\\n\\n data = data.astype(dtype)\\n\\n return data\",\n \"def _normalizeData(data):\\n if isinstance(data, H5Node):\\n if data.is_broken:\\n return None\\n return data.h5py_object\\n return data\",\n \"def __prepare_dataset(dataset, verbose=True):\\r\\n supported_datasets = ['coco', 'voc']\\r\\n if isinstance(dataset, ExternalDataset):\\r\\n if dataset.dataset_type.lower() not in supported_datasets:\\r\\n raise UserWarning(\\\"ExternalDataset dataset_type must be one of: \\\", supported_datasets)\\r\\n\\r\\n dataset_root = dataset.path\\r\\n\\r\\n if verbose:\\r\\n print(\\\"Loading {} type dataset...\\\".format(dataset.dataset_type))\\r\\n\\r\\n if dataset.dataset_type.lower() == 'voc':\\r\\n from gluoncv.data import VOCDetection\\r\\n\\r\\n dataset = VOCDetection(root=dataset_root,\\r\\n splits=[(2007, 'trainval'), (2012, 'trainval')])\\r\\n\\r\\n elif dataset.dataset_type.lower() == 'coco':\\r\\n from gluoncv.data import COCODetection\\r\\n\\r\\n dataset = COCODetection(root=dataset_root,\\r\\n splits=['instances_train2017'])\\r\\n if verbose:\\r\\n print(\\\"ExternalDataset loaded.\\\")\\r\\n return dataset\\r\\n elif isinstance(dataset, DetectionDataset) or issubclass(type(dataset), DetectionDataset):\\r\\n dataset.set_image_transform(ImageToNDArrayTransform())\\r\\n dataset.set_target_transform(BoundingBoxListToNumpyArray())\\r\\n return dataset\\r\\n else:\\r\\n raise ValueError(\\\"Dataset type {} not supported\\\".format(type(dataset)))\",\n \"def normalize_value(value: Any) -> Optional[Union[np.ndarray, List[Any], Tuple[Any]]]:\\n if value is None:\\n # Exclude None from falling through to blanket np.asarray conversion.\\n return value\\n\\n if isinstance(value, (list, tuple, dict)):\\n return value\\n\\n array = np.asarray(value)\\n # TODO(#5359): Move into the function abi.\\n if isinstance(value, (bool, int, float)):\\n # Manually convert ints and floats to 32 bits.\\n if array.dtype == np.float64:\\n array = array.astype(np.float32)\\n elif array.dtype == np.int64:\\n array = array.astype(np.int32)\\n\\n return array\",\n \"def preprocess_data(self, data, scale_data=True):\\n print(\\\"preprocess_data not implemented\\\")\\n return data\",\n \"def canonicalize(data):\\n\\n if isinstance(data, np.ndarray):\\n if data.dtype == np.uint8:\\n data = data.astype(np.float32) / 255.0\\n data = torch.from_numpy(np.ascontiguousarray(data))\\n\\n elif isinstance(data, torch.Tensor):\\n if data.dtype == torch.uint8:\\n data = data.float() / 255.0\\n\\n else:\\n raise NotImplementedError()\\n\\n return data\",\n \"def process(self, data):\\n # get truncation parameters from config or container defaults\\n self._get_params(type(data))\\n\\n if self.weight_dataset is None:\\n self.weight_dataset = [None] * len(self.dataset)\\n\\n for dset, wgt in zip(self.dataset, self.weight_dataset):\\n old_shape = data[dset].local_shape\\n val = np.ndarray.reshape(data[dset][:], data[dset][:].size)\\n if wgt is None:\\n if np.iscomplexobj(data[dset]):\\n data[dset][:].real = bit_truncate_fixed(\\n val.real, self.fixed_precision\\n ).reshape(old_shape)\\n data[dset][:].imag = bit_truncate_fixed(\\n val.imag, self.fixed_precision\\n ).reshape(old_shape)\\n else:\\n data[dset][:] = bit_truncate_fixed(\\n val, self.fixed_precision\\n ).reshape(old_shape)\\n else:\\n if data[dset][:].shape != data[wgt][:].shape:\\n raise pipeline.PipelineRuntimeError(\\n \\\"Dataset and weight arrays must have same shape ({} != {})\\\".format(\\n data[dset].shape, data[wgt].shape\\n )\\n )\\n invvar = np.ndarray.reshape(data[wgt][:], data[dset][:].size)\\n if np.iscomplexobj(data[dset]):\\n data[dset][:].real = bit_truncate_weights(\\n val.real,\\n invvar * 2.0 / self.variance_increase,\\n self.fixed_precision,\\n ).reshape(old_shape)\\n data[dset][:].imag = bit_truncate_weights(\\n val.imag,\\n invvar * 2.0 / self.variance_increase,\\n self.fixed_precision,\\n ).reshape(old_shape)\\n else:\\n data[dset][:] = bit_truncate_weights(\\n val, invvar / self.variance_increase, self.fixed_precision\\n ).reshape(old_shape)\\n\\n return data\",\n \"def test_single_null(self):\\n dset = self.f.create_dataset('x', (1,), dtype='i1')\\n out = dset[()]\\n self.assertIsInstance(out, np.ndarray)\\n self.assertEqual(out.shape, (1,))\",\n \"def load_own_data():\\n data = Data()\\n features = data.read_selected_features()\\n dataframe = data.get_dataframe_with(features)\\n return normalize(dataframe.values)\",\n \"def normalize(dataset):\\n return normalize_standard_deviation(normalize_mean(dataset))\",\n \"def astype(self, dtype, device=None, *_args, **_kwargs):\\n if isinstance(dtype, str):\\n mod_str, *dtype = dtype.split(\\\".\\\", 1)\\n dtype = mod_str if not dtype else dtype[0]\\n else:\\n mod_str = getattr(dtype, \\\"__module__\\\", \\\"torch\\\")\\n mod_str = look_up_option(mod_str, {\\\"torch\\\", \\\"numpy\\\", \\\"np\\\"}, default=\\\"numpy\\\")\\n\\n out_type: type[torch.Tensor] | type[np.ndarray] | None\\n if mod_str == \\\"torch\\\":\\n out_type = torch.Tensor\\n elif mod_str in (\\\"numpy\\\", \\\"np\\\"):\\n out_type = np.ndarray\\n else:\\n out_type = None\\n return self.get_array(output_type=out_type, dtype=dtype, device=device)\",\n \"def is_pyvista_dataset(obj):\\n return isinstance(obj, (pyvista.DataSet, pyvista.MultiBlock))\",\n \"def unNormalizeData(normalizedData, data_mean, data_std, dimensions_to_ignore):\\n T = normalizedData.shape[0]\\n D = data_mean.shape[0]\\n\\n origData = np.zeros((T, D), dtype=np.float32)\\n dimensions_to_use = []\\n for i in range(D):\\n if i in dimensions_to_ignore:\\n continue\\n dimensions_to_use.append(i)\\n dimensions_to_use = np.array(dimensions_to_use)\\n\\n origData[:, dimensions_to_use] = normalizedData\\n\\n # potentially ineficient, but only done once per experimentdata_conversions\\n stdMat = data_std.reshape((1, D))\\n stdMat = np.repeat(stdMat, T, axis=0)\\n meanMat = data_mean.reshape((1, D))\\n meanMat = np.repeat(meanMat, T, axis=0)\\n origData = np.multiply(origData, stdMat) + meanMat\\n return origData\",\n \"def _validate_input(self, data: Union[np.ndarray, pd.DataFrame, pd.Series],\\n expected_dim: int, inference: bool = False) -> np.ndarray:\\n allowed_types = (\\n np.ndarray,\\n pd.core.frame.DataFrame,\\n pd.core.frame.Series\\n )\\n\\n if type(data) not in allowed_types:\\n raise TypeError('Supported input types: np.ndarray, '\\n 'pd.core.frame.DataFrame, pd.core.frame.Series got'\\n ' {}'.format(type(data)))\\n\\n if isinstance(data, pd.DataFrame) or isinstance(data, pd.Series):\\n data = data.values\\n\\n if data.size == 0:\\n raise ValueError('Empty array passed to fit() or predict()')\\n\\n if data.ndim > expected_dim:\\n raise ValueError('Data with incorrect number of dimensions '\\n 'passed to fit() or predict(). Max dim is '\\n '{}, got {}'.format(expected_dim, data.ndim))\\n\\n if not np.issubdtype(data.dtype, np.number):\\n raise ValueError('Non numeric value found in data')\\n\\n if not np.isfinite(data).all():\\n raise ValueError('Data contains nan or inf')\\n\\n if inference:\\n # additional checks on prediction time\\n if not self._fitted:\\n raise ValueError('Fit the model first.')\\n\\n if self._ndim == 2 and data.shape[-1] != self._shape[-1]:\\n raise ValueError('Number of features does not match'\\n ' data model was trained on. Expected'\\n ' {}, got {}'\\n .format(self._shape[-1], data.shape[-1]))\\n\\n return data\",\n \"def GetNoDataValue(self):\\n result = super(Band, self).GetNoDataValue()\\n if result is not None:\\n return self.NumPyDataType(result)\",\n \"def is_dataset(self):\\n return self._dataset is not None\",\n \"def __prepare_val_dataset(dataset, save_prefix='tmp', data_shape=512, verbose=True):\\r\\n supported_datasets = ['coco', 'voc']\\r\\n if isinstance(dataset, ExternalDataset):\\r\\n if dataset.dataset_type.lower() not in supported_datasets:\\r\\n raise UserWarning(\\\"dataset_type must be one of: \\\", supported_datasets)\\r\\n\\r\\n dataset_root = dataset.path\\r\\n\\r\\n if dataset.dataset_type.lower() == 'voc':\\r\\n from gluoncv.data import VOCDetection\\r\\n\\r\\n dataset = VOCDetection(root=dataset_root,\\r\\n splits=[(2007, 'test')])\\r\\n val_metric = VOC07MApMetric(iou_thresh=0.5, class_names=dataset.classes)\\r\\n return dataset, val_metric\\r\\n elif dataset.dataset_type.lower() == 'coco':\\r\\n from gluoncv.data import COCODetection\\r\\n\\r\\n dataset = COCODetection(root=dataset_root, splits='instances_val2017',\\r\\n skip_empty=False)\\r\\n val_metric = COCODetectionMetric(\\r\\n dataset, os.path.join(save_prefix, 'eval'), cleanup=False, data_shape=(data_shape, data_shape))\\r\\n return dataset, val_metric\\r\\n elif isinstance(dataset, DetectionDataset) or issubclass(type(dataset), DetectionDataset):\\r\\n eval_metric = DetectionDatasetCOCOEval(dataset.classes, data_shape)\\r\\n dataset.set_image_transform(ImageToNDArrayTransform())\\r\\n dataset.set_target_transform(BoundingBoxListToNumpyArray())\\r\\n return dataset, eval_metric\\r\\n else:\\r\\n print(\\\"Dataset type {} not supported\\\".format(type(dataset)))\\r\\n return dataset, None\",\n \"def get_dataset(self, dataset_path=None, normalize=True, return_original=False):\\n if dataset_path is None:\\n dataset_path = self.dir\\n \\n if \\\"mocap\\\" in dataset_path.lower():\\n print(\\\"Loading Mocap dataset.\\\")\\n df = get_mocap()\\n df_orig = df\\n elif \\\"profi\\\" in dataset_path.lower():\\n print(\\\"Loading Profiset dataset.\\\")\\n df = get_profiset()\\n df_orig = df\\n else:\\n print(\\\"Loading CoPhIR dataset.\\\")\\n df_orig, attr_lengths = get_objects_with_indexes(self.labels, f'{dataset_path}/level-{str(self.n_levels)}.txt', f'{dataset_path}/objects.txt')\\n if normalize:\\n df = scale_per_descriptor(df_orig, self.labels, attr_lengths)\\n else:\\n df = df_orig\\n \\n assert df.shape[1] == self.descriptor_values + self.n_levels + len([\\\"object_id\\\"])\\n logging.info(f\\\"Loaded dataset of shape: {df.shape}\\\")\\n if return_original:\\n return df, df_orig\\n else:\\n return df\",\n \"def _normalize(empty, user_data):\\n if isinstance(user_data, UserData):\\n return user_data.to_dict()\\n if isinstance(user_data, dict):\\n return _dict_to_dict(empty, user_data)\\n if isinstance(user_data, (bytes, bytearray)):\\n return _bytes_to_dict(empty, user_data)\\n if user_data is None:\\n return empty\\n raise ValueError\",\n \"def get_observed(self, data):\\r\\n\\r\\n if isinstance(data, mtuq.Dataset):\\r\\n observed = np.array([_get_polarity(stream) for stream in data])\\r\\n\\r\\n elif isinstance(data, list):\\r\\n observed = np.array(data)\\r\\n\\r\\n elif isinstance(data, np.ndarray):\\r\\n observed = data\\r\\n\\r\\n else:\\r\\n raise TypeError\\r\\n\\r\\n return observed\",\n \"def _convert_data(self, data):\\n if isinstance(data, np.ndarray):\\n data = data.astype(float_save(), copy=False)\\n if self.compress:\\n return KaldiCompressedMatrix.compress(data, self.compression_method)\\n return KaldiMatrix(data)\\n\\n if isinstance(data, KaldiMatrix):\\n if self.compress:\\n return KaldiCompressedMatrix.compress(data, self.compression_method)\\n return data\\n\\n if isinstance(data, KaldiCompressedMatrix):\\n if not self.compress:\\n return data.to_matrix()\\n return data\\n\\n raise ValueError(\\\"Data is not ndarray or KaldiMatrix\\\")\",\n \"def _normalize(\\n ds: xr.Dataset,\\n *,\\n dim: Sequence[str],\\n kind: str = ADDITIVE,\\n) -> xr.Dataset:\\n if \\\"norm\\\" in ds:\\n norm = ds.norm\\n else:\\n norm = ds.data.mean(dim=dim)\\n norm.attrs[\\\"_group_apply_reshape\\\"] = True\\n\\n return xr.Dataset(\\n dict(data=apply_correction(ds.data, invert(norm, kind), kind), norm=norm)\\n )\",\n \"def DataSet(self):\\n if hasattr(self, '_dataset') and self._dataset and self._dataset.Get():\\n return WrapDataObject(self._dataset.Get())\\n\\n return None\",\n \"def _extract_data(\\n data: AnnData, layer: Optional[str] = None, use_raw: bool = True\\n) -> Union[np.ndarray, spmatrix]:\\n if isinstance(data, AnnData):\\n if use_raw:\\n if not hasattr(data, \\\"raw\\\"):\\n raise AttributeError(\\\"No `.raw` attribute found.\\\")\\n elif data.raw is None:\\n raise ValueError(\\\"Attribute `.raw` is None.\\\")\\n x = data.raw.X\\n elif layer is not None:\\n if layer not in data.layers:\\n raise KeyError(\\n f\\\"Layer `{layer!s}` not found in `adata.layers`. \\\"\\n f\\\"Valid options are: `{list(data.layers.keys())}`.\\\"\\n )\\n x = data.layers[layer]\\n else:\\n x = data.X\\n elif not isinstance(data, (np.ndarray, spmatrix)):\\n raise TypeError(\\n f\\\"Expected parameter `data` to be either `anndata.AnnData`, `numpy.ndarray` \\\"\\n f\\\"or `scipy.sparse.spmatrix`, found `{type(data).__name__!r}`.\\\"\\n )\\n else:\\n x = data\\n\\n return x\",\n \"def check_numpy(x):\\n if isinstance(x, torch.Tensor):\\n x = x.detach().cpu().numpy()\\n x = np.asarray(x)\\n assert isinstance(x, np.ndarray)\\n return x\",\n \"def is_dataset(obj):\\n return isinstance(obj, (DictDataset, ImageDataset, LabeledImageDataset,\\n TupleDataset, DatasetMixin))\",\n \"def _format_data(self, data: np.ndarray) -> np.ndarray:\\n self._n_shots = 0\\n\\n # identify shape\\n try:\\n # level1 single-shot data\\n self._n_circs, self._n_shots, self._n_slots, self._n_iq = data.shape\\n except ValueError as ex:\\n raise DataProcessorError(\\n f\\\"The data given to {self.__class__.__name__} does not have the shape of \\\"\\n \\\"single-shot IQ data; expecting a 4D array.\\\"\\n ) from ex\\n\\n if self._validate:\\n if data.shape[-1] != 2:\\n raise DataProcessorError(\\n f\\\"IQ data given to {self.__class__.__name__} must be a multi-dimensional array\\\"\\n \\\"of dimension [d0, d1, ..., 2] in which the last dimension \\\"\\n \\\"corresponds to IQ elements.\\\"\\n f\\\"Input data contains element with length {data.shape[-1]} != 2.\\\"\\n )\\n\\n if self._validate:\\n if isinstance(self._discriminator, list):\\n if self._n_slots != len(self._discriminator):\\n raise DataProcessorError(\\n f\\\"The Discriminator node has {len(self._discriminator)} which does \\\"\\n f\\\"not match the {self._n_slots} slots in the data.\\\"\\n )\\n\\n return unp.nominal_values(data)\",\n \"def transform(self, dataset: NumpyOrPandas) -> NumpyDataset:\\n # checks here\\n super().transform(dataset)\\n # convert to accepted dtype and get attributes\\n dataset = dataset.to_pandas()\\n df = dataset.data\\n\\n # transform\\n roles = NumericRole()\\n outputs = []\\n for n, i in enumerate(df.columns):\\n new_arr = self.dicts[i][\\\"fg\\\"].transform(df[i].values)\\n output = dataset.empty().to_numpy()\\n output.set_data(new_arr, self.dicts[i][\\\"feats\\\"], roles)\\n outputs.append(output)\\n # create resulted\\n return dataset.empty().to_numpy().concat(outputs)\",\n \"def handle_input(data, device=None):\\n if type(data) == tuple:\\n x, y = data\\n x = torch.tensor(x, dtype=torch.float)\\n y = torch.tensor(y, dtype=torch.float)\\n if not device is None:\\n x = x.to(device)\\n y = y.to(device)\\n return x, y\\n if type(data) == np.ndarray:\\n x = torch.tensor(data, dtype=torch.float)\\n if not device is None:\\n x = x.to(device)\\n return x\\n else:\\n return data\",\n \"def load_data():\\n\\n # https://docs.scipy.org/doc/numpy-1.13.0/reference/generated/numpy.ndarray.astype.html\\n data = np.load(\\\"phase3-data/data_selected_1980_2010.npy\\\").astype(float)\\n\\n return normalize(data)\",\n \"def transform(self, dataset: NumpyOrPandas) -> NumpyDataset:\\n # checks here\\n super().transform(dataset)\\n # convert to accepted dtype and get attributes\\n dataset = dataset.to_pandas()\\n df = dataset.data\\n\\n # transform\\n roles = NumericRole()\\n outputs = []\\n\\n for n, conlumn_name in enumerate(df.columns):\\n if self.cache_dir is not None:\\n full_hash = get_textarr_hash(df[conlumn_name]) + get_textarr_hash(self.dicts[conlumn_name][\\\"feats\\\"])\\n fname = os.path.join(self.cache_dir, full_hash + \\\".pkl\\\")\\n\\n if os.path.exists(fname):\\n logger.info3(f\\\"Load saved dataset for {conlumn_name}\\\")\\n\\n with open(fname, \\\"rb\\\") as f:\\n new_arr = pickle.load(f)\\n\\n else:\\n new_arr = self.dicts[conlumn_name][\\\"transformer\\\"].transform(df[conlumn_name])\\n with open(fname, \\\"wb\\\") as f:\\n pickle.dump(new_arr, f)\\n else:\\n new_arr = self.dicts[conlumn_name][\\\"transformer\\\"].transform(df[conlumn_name])\\n\\n output = dataset.empty().to_numpy()\\n output.set_data(new_arr, self.dicts[conlumn_name][\\\"feats\\\"], roles)\\n outputs.append(output)\\n logger.info3(f\\\"Feature {conlumn_name} transformed\\\")\\n # create resulted\\n return dataset.empty().to_numpy().concat(outputs)\",\n \"def scale(self, data: np.ndarray):\\n if self.scale_type == \\\"min_max\\\":\\n scaled_data = (data - self.predictor_min) / (\\n self.predictor_max - self.predictor_mean\\n )\\n elif self.scale_type == \\\"normalize\\\":\\n scaled_data = (data - self.predictor_mean) / (\\n self.predictor_max - self.predictor_min\\n )\\n elif self.scale_type == \\\"standardize\\\":\\n scaled_data = (data - self.predictor_mean) / self.predictor_std\\n elif self.scale_type == \\\"scale\\\":\\n scaled_data = data - self.predictor_mean\\n else:\\n scaled_data = data\\n return scaled_data\",\n \"def _convert_data(data, inf_ok=False, min_len=1):\\n # If it's scalar, convert to array\\n if np.isscalar(data):\\n data = np.array([data], dtype=float)\\n\\n # Convert data to NumPy array\\n data = np.array(data, dtype=float)\\n\\n # Make sure it is 1D\\n if len(data.shape) != 1:\\n raise RuntimeError(\\\"Input must be a 1D array or Pandas series.\\\")\\n\\n # Remove NaNs\\n data = data[~np.isnan(data)]\\n\\n # Check for infinite entries\\n if not inf_ok and np.isinf(data).any():\\n raise RuntimeError(\\\"All entries must be finite.\\\")\\n\\n # Check to minimal length\\n if len(data) < min_len:\\n raise RuntimeError(\\n \\\"Array must have at least {0:d} non-NaN entries.\\\".format(min_len)\\n )\\n\\n return data\",\n \"def _preprocess(self, data, normalize=False) -> np.ndarray:\\n \\n preprocessor = StandardScaler() if not normalize else Normalizer()\\n\\n data = preprocessor.fit_transform(data)\\n \\n return data\",\n \"def normalize_data(data, n=1):\\n\\n if isinstance(data, str):\\n # TODO: could Antti comment on this?\\n # numpy array initialization works unintuitively with strings\\n data = np.array([[data]], dtype=object)\\n else:\\n data = np.atleast_1d(data)\\n\\n if data.ndim == 1:\\n if data.shape[0] == n:\\n data = data[:, None]\\n else:\\n data = data[None, :]\\n if n > 1:\\n data = np.vstack((data, ) * n)\\n else:\\n if data.shape[0] != n:\\n data = data[None, :]\\n if n > 1:\\n data = np.vstack((data, ) * n)\\n return data\",\n \"def test_obsm_values_ara_numpy(self):\\n\\n self.validator.adata.obsm[\\\"X_tsne\\\"] = pd.DataFrame(\\n self.validator.adata.obsm[\\\"X_umap\\\"], index=self.validator.adata.obs_names\\n )\\n self.validator.validate_adata()\\n self.assertEqual(\\n self.validator.errors,\\n [\\n \\\"ERROR: All embeddings have to be of 'numpy.ndarray' type, \\\"\\n \\\"'adata.obsm['X_tsne']' is <class 'pandas.core.frame.DataFrame'>').\\\"\\n ],\\n )\",\n \"def item(self):\\n if self.data.shape != ():\\n raise RuntimeError(\\\"Cannot call item on non-scalar type!\\\")\\n return self.data\",\n \"def _process(self, data: np.ndarray) -> np.ndarray:\",\n \"def _process(self, data: np.ndarray) -> np.ndarray:\",\n \"def test_univariate(self):\\n data = load_dataset(data_format=\\\"numpy\\\", standardize=True)\\n assert data.shape[0] > 131, \\\"Imported time series collection has the wrong shape\\\"\\n assert data.shape[-1] == 1000, \\\"Imported time series collection has the wrong shape\\\"\",\n \"def get_dataset(args):\\n\\n if args['experiment']['dataset'] == Dataset.mindsets:\\n xs, ys, cs = make_mindsets(mindset_sizes=args['dataset']['mindset_sizes'],\\n nb_questions=args['dataset']['nb_questions'],\\n nb_useless=args['dataset']['nb_useless'],\\n noise=args['dataset']['noise'],\\n seed=args['experiment']['seed'])\\n\\n return Data(xs=xs, ys=ys, cs=cs)\\n\\n if args['experiment']['dataset'] == Dataset.questionnaire_likert:\\n xs, ys, cs = make_likert_questionnaire(nb_samples=args['dataset']['nb_samples'],\\n nb_features=args['dataset']['nb_features'],\\n nb_mindsets=args['dataset']['nb_mindsets'],\\n centers=args['dataset']['centers'],\\n range_answers=args['dataset']['range_answers'],\\n seed=args['experiment']['seed'])\\n\\n return Data(xs=xs, ys=ys, cs=cs)\\n\\n if args['experiment']['dataset'] == Dataset.retinal:\\n xs, ys = load_RETINAL(root_path=args['root_dir'],\\n nb_bins=args['dataset']['nb_bins'],\\n max_idx=args['dataset']['max_idx'])\\n\\n return Data(xs=xs, ys=ys)\\n\\n if args['experiment']['dataset'] == Dataset.moons:\\n xs, ys = make_moons(n_samples=args['dataset']['n_samples'],\\n noise=args['dataset']['noise'],\\n random_state=args['experiment']['seed'])\\n\\n return Data(xs=xs, ys=ys)\\n\\n if args['experiment']['dataset'] == Dataset.breast_cancer_wisconsin:\\n xs, ys = load_CANCER(args['dataset']['nb_bins'])\\n\\n return Data(xs=xs, ys=ys)\\n\\n if args['experiment']['dataset'] == Dataset.SBM:\\n A, ys, G = load_SBM(block_sizes=args['dataset']['block_sizes'],\\n p_in=args['dataset']['p'],\\n p_out=args['dataset']['q'],\\n seed=args['experiment']['seed'])\\n\\n return Data(ys=ys, A=A, G=G)\\n\\n if args['experiment']['dataset'] == Dataset.gaussian_mixture:\\n xs, ys = make_blobs(n_samples=args['dataset']['blob_sizes'],\\n centers=args['dataset']['blob_centers'],\\n n_features=args['dataset']['blob_centers'],\\n cluster_std=args['dataset']['blob_variances'],\\n random_state=args['experiment']['seed'])\\n\\n return Data(xs=xs, ys=ys)\\n\\n if args['experiment']['dataset'] == Dataset.LFR:\\n A, ys, G = load_LFR(nb_nodes=args['dataset']['nb_nodes'],\\n tau1=args['dataset']['tau1'],\\n tau2=args['dataset']['tau2'],\\n mu=args['dataset']['mu'],\\n average_degree=args['dataset']['average_degree'],\\n min_community=args['dataset']['min_community'],\\n seed=args['experiment']['seed'])\\n\\n return Data(ys=ys, A=A, G=G)\\n\\n if args['experiment']['dataset'] == Dataset.wave:\\n df = pd.read_csv('datasets/waveform.csv')\\n xs = df[df.columns[:-1]].to_numpy()\\n ys = df[df.columns[-1]].to_numpy()\\n\\n return Data(xs=xs, ys=ys)\\n\\n raise ValueError('Wrong name for a dataset')\",\n \"def _format_data(self, data: np.ndarray) -> np.ndarray:\\n if self._validate:\\n if len(data.shape) <= 1:\\n raise DataProcessorError(\\n \\\"The data should be an array with at least two dimensions.\\\"\\n )\\n\\n return data\",\n \"def _to_arraylike(data):\\n _load_objects()\\n if data is None:\\n raise ValueError('Cannot convert None data.')\\n return None\\n if not isinstance(data, (ndarray, DataArray, DataFrame, Series, Index)):\\n data = np.asarray(data)\\n if not np.iterable(data):\\n data = np.atleast_1d(data)\\n return data\",\n \"def load_dataset(\\n dataset: str\\n) -> Union[lmp.dataset.AnalogyDataset, lmp.dataset.LanguageModelDataset]:\\n # Type check.\\n if not isinstance(dataset, str):\\n raise TypeError('`dataset` must be an instance of `str`.')\\n\\n if dataset == 'news_collection_desc':\\n return _preprocess_news_collection(column='desc')\\n\\n if dataset == 'news_collection_title':\\n return _preprocess_news_collection(column='title')\\n\\n if dataset == 'wiki_train_tokens':\\n return _preprocess_wiki_tokens(split='train')\\n\\n if dataset == 'wiki_valid_tokens':\\n return _preprocess_wiki_tokens(split='valid')\\n\\n if dataset == 'wiki_test_tokens':\\n return _preprocess_wiki_tokens(split='test')\\n\\n if dataset == 'word_test_v1':\\n return _preprocess_word_test_v1()\\n\\n raise ValueError(\\n f'dataset `{dataset}` does not support.\\\\nSupported options:' +\\n ''.join(list(map(\\n lambda option: f'\\\\n\\\\t--dataset {option}',\\n [\\n 'news_collection_desc',\\n 'news_collection_title',\\n 'wiki_test_tokens',\\n 'wiki_train_tokens',\\n 'wiki_valid_tokens',\\n 'word_test_v1'\\n ]\\n )))\\n )\",\n \"def filter(self, data, strict=False, allow_downcast=None):\\r\\n # Explicit error message when one accidentally uses a Variable as\\r\\n # input (typical mistake, especially with shared variables).\\r\\n if isinstance(data, Variable):\\r\\n raise TypeError(\\r\\n 'Expected an array-like object, but found a Variable: '\\r\\n 'maybe you are trying to call a function on a (possibly '\\r\\n 'shared) variable instead of a numeric array?')\\r\\n\\r\\n if ((type(data) is numpy.ndarray)\\r\\n and (data.dtype == self.numpy_dtype)):\\r\\n if data.dtype.num != self.numpy_dtype.num:\\r\\n data = theano._asarray(data, dtype=self.dtype)\\r\\n # -- now fall through to ndim check\\r\\n elif((type(data) is numpy.memmap)\\r\\n and (data.dtype == self.numpy_dtype)):\\r\\n # numpy.memmap is a \\\"safe\\\" subclass of ndarray,\\r\\n # so we can use it whereever we expect a base ndarray.\\r\\n # however, casting it would defeat the purpose of not\\r\\n # loading the whole data into memory\\r\\n pass\\r\\n elif strict:\\r\\n # If any of the two conditions above was not met,\\r\\n # we raise a meaningful TypeError.\\r\\n if not (type(data) is numpy.ndarray):\\r\\n raise TypeError(\\\"%s expected a ndarray object.\\\" % self,\\r\\n data, type(data))\\r\\n if data.dtype != self.numpy_dtype:\\r\\n raise TypeError((\\\"%s expected a ndarray object with \\\"\\r\\n \\\"dtype = %s (got %s).\\\") % (\\r\\n self, self.numpy_dtype, data.dtype))\\r\\n assert False, \\\"This point should never be reached.\\\"\\r\\n else:\\r\\n if allow_downcast:\\r\\n # Convert to self.dtype, regardless of the type of data\\r\\n data = theano._asarray(data, dtype=self.dtype)\\r\\n # TODO: consider to pad shape with ones to make it consistent\\r\\n # with self.broadcastable... like vector->row type thing\\r\\n else:\\r\\n if isinstance(data, numpy.ndarray):\\r\\n # Check if self.dtype can accurately represent data\\r\\n # (do not try to convert the data)\\r\\n up_dtype = scal.upcast(self.dtype, data.dtype)\\r\\n if up_dtype == self.dtype:\\r\\n # Bug in the following line when data is a\\r\\n # scalar array, see\\r\\n # http://projects.scipy.org/numpy/ticket/1611\\r\\n # data = data.astype(self.dtype)\\r\\n data = theano._asarray(data, dtype=self.dtype)\\r\\n if up_dtype != self.dtype:\\r\\n err_msg = (\\r\\n '%s cannot store a value of dtype %s without '\\r\\n 'risking loss of precision. If you do not mind '\\r\\n 'this loss, you can: '\\r\\n '1) explicitly cast your data to %s, or '\\r\\n '2) set \\\"allow_input_downcast=True\\\" when calling '\\r\\n '\\\"function\\\".'\\r\\n % (self, data.dtype, self.dtype))\\r\\n raise TypeError(err_msg, data)\\r\\n elif (allow_downcast is None and\\r\\n type(data) is float and\\r\\n self.dtype == theano.config.floatX):\\r\\n # Special case where we allow downcasting of Python float\\r\\n # literals to floatX, even when floatX=='float32'\\r\\n data = theano._asarray(data, self.dtype)\\r\\n else:\\r\\n # data has to be converted.\\r\\n # Check that this conversion is lossless\\r\\n converted_data = theano._asarray(data, self.dtype)\\r\\n # We use the `values_eq` static function from TensorType\\r\\n # to handle NaN values.\\r\\n if TensorType.values_eq(numpy.asarray(data),\\r\\n converted_data,\\r\\n force_same_dtype=False):\\r\\n data = converted_data\\r\\n else:\\r\\n # Do not print a too long description of data\\r\\n # (ndarray truncates it, but it's not sure for data)\\r\\n str_data = str(data)\\r\\n if len(str_data) > 80:\\r\\n str_data = str_data[:75] + '(...)'\\r\\n\\r\\n err_msg = (\\r\\n '%s cannot store accurately value %s, '\\r\\n 'it would be represented as %s. '\\r\\n 'If you do not mind this precision loss, you can: '\\r\\n '1) explicitly convert your data to a numpy array '\\r\\n 'of dtype %s, or '\\r\\n '2) set \\\"allow_input_downcast=True\\\" when calling '\\r\\n '\\\"function\\\".'\\r\\n % (self, data, converted_data, self.dtype))\\r\\n raise TypeError(err_msg, data)\\r\\n\\r\\n if self.ndim != data.ndim:\\r\\n raise TypeError(\\\"Wrong number of dimensions: expected %s,\\\"\\r\\n \\\" got %s with shape %s.\\\" % (self.ndim, data.ndim,\\r\\n data.shape))\\r\\n if not data.flags.aligned:\\r\\n try:\\r\\n msg = \\\"object buffer\\\" + str(data.data)\\r\\n except AttributeError:\\r\\n msg = \\\"\\\"\\r\\n raise TypeError(\\\"The numpy.ndarray object is not aligned.\\\"\\r\\n \\\" Theano C code does not support that.\\\",\\r\\n msg,\\r\\n \\\"object shape\\\", data.shape,\\r\\n \\\"object strides\\\", data.strides,\\r\\n \\\"object dtype\\\", data.dtype)\\r\\n\\r\\n i = 0\\r\\n for b in self.broadcastable:\\r\\n if b and data.shape[i] != 1:\\r\\n raise TypeError(\\\"Non-unit value on shape on a broadcastable\\\"\\r\\n \\\" dimension.\\\", data.shape, self.broadcastable)\\r\\n i += 1\\r\\n if (self.filter_checks_isfinite and\\r\\n not numpy.all(numpy.isfinite(data))):\\r\\n raise ValueError(\\\"non-finite elements not allowed\\\")\\r\\n return data\",\n \"def _normalize_column(self, data, coerce_dtype=None, store_categories=True):\\n if coerce_dtype is not None:\\n coerce_dtype = np.dtype(coerce_dtype)\\n\\n if np.isscalar(data):\\n array = np.array([data], dtype=coerce_dtype)\\n dtype = data.dtype\\n fillvalue = None\\n\\n elif is_categorical(data):\\n if store_categories:\\n cats = data.cat.categories\\n enum_dict = dict(zip(cats, range(len(cats))))\\n array = data.cat.codes\\n dtype = h5py.special_dtype(enum=(array.dtype, enum_dict))\\n fillvalue = -1\\n else:\\n array = data.cat.codes\\n dtype = coerce_dtype or array.dtype\\n fillvalue = -1\\n\\n elif data.dtype in (object, str, bytes):\\n data = np.asarray(data)\\n dtype = np.dtype(\\\"S\\\")\\n array = np.array(data, dtype=dtype)\\n fillvalue = None\\n\\n else:\\n array = np.asarray(data)\\n dtype = data.dtype\\n fillvalue = None\\n\\n return array, dtype, fillvalue\",\n \"def _prepare(self, data, train=True):\\n if data is None:\\n return None\\n\\n if hasattr(data, \\\"to_tfdataset\\\"):\\n return data.to_tfdataset(train=train)\\n else:\\n return data\",\n \"def WrapDataObject(ds):\\n if ds.IsA(\\\"vtkPolyData\\\"):\\n return PolyData(ds)\\n elif ds.IsA(\\\"vtkUnstructuredGrid\\\"):\\n return UnstructuredGrid(ds)\\n elif ds.IsA(\\\"vtkPointSet\\\"):\\n return PointSet(ds)\\n elif ds.IsA(\\\"vtkDataSet\\\"):\\n return DataSet(ds)\\n elif ds.IsA(\\\"vtkCompositeDataSet\\\"):\\n return CompositeDataSet(ds)\\n elif ds.IsA(\\\"vtkTable\\\"):\\n return Table(ds)\\n elif ds.IsA(\\\"vtkMolecule\\\"):\\n return Molecule(ds)\\n elif ds.IsA(\\\"vtkGraph\\\"):\\n return Table(ds)\\n elif ds.IsA(\\\"vtkHyperTreeGrid\\\"):\\n return HyperTreeGrid(ds)\\n elif ds.IsA(\\\"vtkDataObject\\\"):\\n return DataObject(ds)\",\n \"def process(self, data):\\n data = np.atleast_2d(data)\\n\\n if self.orientation == 'row':\\n return data\\n else:\\n return data.T\",\n \"def from_homogeneous(x):\\n if not isinstance(x, np.ndarray):\\n x = np.array(x)\\n if len(x.shape) == 1:\\n h = x[-1]\\n if h != 0:\\n x = x/h\\n return x[0:-1]\\n else:\\n return None\\n else:\\n assert len(x.shape) == 2\\n h = np.expand_dims(x[:, -1], axis=1)\\n x = x / h\\n return x[:, 0:-1]\",\n \"def required_data_type(self):\\n return Data\",\n \"def wrap(dataset):\\n # Return if None\\n if dataset is None:\\n return\\n\\n if isinstance(dataset, tuple(pyvista._wrappers.values())):\\n # Return object if it is already wrapped\\n return dataset\\n\\n # Check if dataset is a numpy array. We do this first since\\n # pyvista_ndarray contains a VTK type that we don't want to\\n # directly wrap.\\n if isinstance(dataset, (np.ndarray, pyvista.pyvista_ndarray)):\\n if dataset.ndim == 1 and dataset.shape[0] == 3:\\n return pyvista.PolyData(dataset)\\n if dataset.ndim > 1 and dataset.ndim < 3 and dataset.shape[1] == 3:\\n return pyvista.PolyData(dataset)\\n elif dataset.ndim == 3:\\n mesh = pyvista.ImageData(dimensions=dataset.shape)\\n if isinstance(dataset, pyvista.pyvista_ndarray):\\n # this gets rid of pesky VTK reference since we're raveling this\\n dataset = np.array(dataset, copy=False)\\n mesh['values'] = dataset.ravel(order='F')\\n mesh.active_scalars_name = 'values'\\n return mesh\\n else:\\n raise NotImplementedError('NumPy array could not be wrapped pyvista.')\\n\\n # wrap VTK arrays as pyvista_ndarray\\n if isinstance(dataset, _vtk.vtkDataArray):\\n return pyvista.pyvista_ndarray(dataset)\\n\\n # Check if a dataset is a VTK type\\n if hasattr(dataset, 'GetClassName'):\\n key = dataset.GetClassName()\\n try:\\n return pyvista._wrappers[key](dataset)\\n except KeyError:\\n raise TypeError(f'VTK data type ({key}) is not currently supported by pyvista.')\\n return\\n\\n # wrap meshio\\n if is_meshio_mesh(dataset):\\n return from_meshio(dataset)\\n\\n # wrap trimesh\\n if dataset.__class__.__name__ == 'Trimesh':\\n # trimesh doesn't pad faces\\n n_face = dataset.faces.shape[0]\\n faces = np.empty((n_face, 4), dataset.faces.dtype)\\n faces[:, 1:] = dataset.faces\\n faces[:, 0] = 3\\n polydata = pyvista.PolyData(np.asarray(dataset.vertices), faces)\\n # If the Trimesh object has uv, pass them to the PolyData\\n if hasattr(dataset.visual, 'uv'):\\n polydata.active_t_coords = np.asarray(dataset.visual.uv)\\n return polydata\\n\\n # otherwise, flag tell the user we can't wrap this object\\n raise NotImplementedError(f'Unable to wrap ({type(dataset)}) into a pyvista type.')\",\n \"def _process(self, data: np.ndarray) -> np.ndarray:\\n return data[..., 1] * self.scale\",\n \"def get_data(dataset: str = None, scale: bool = True) -> tuple:\\n \\n if dataset not in ['ecoli', 'glass', 'letter-recognition', 'lymphography', 'yeast', \\n 'digits', 'breast-cancer', 'wine', 'mnist']:\\n raise ValueError(\\\"Invalid dataset provided.\\\")\\n \\n if dataset in dataset in ['ecoli', 'glass', 'letter-recognition', 'lymphography', 'yeast']:\\n path = 'https://archive.ics.uci.edu/ml/machine-learning-databases/' \\n f = path + dataset + \\\"/\\\" + dataset + \\\".data\\\"\\n \\n if dataset in ['ecoli', 'yeast']:\\n df = pd.read_table(f, delim_whitespace=True, header=None)\\n elif dataset in [ 'glass', 'letter-recognition', 'lymphography']:\\n df = pd.read_csv(f, header=None)\\n elif dataset == 'digits':\\n df = load_digits()\\n X = df.data\\n y = df.target\\n elif dataset == 'breast-cancer':\\n df = load_breast_cancer()\\n X = df.data\\n y = df.target\\n elif dataset == 'wine':\\n df = load_wine()\\n X = df.data\\n y = df.target\\n \\n if dataset == 'ecoli':\\n y = preprocessing.LabelEncoder().fit_transform(df.iloc[:,-1])\\n X = df.iloc[:,1:8].values\\n \\n elif dataset == 'glass':\\n y = df.iloc[:,-1].values\\n X = df.iloc[:, 1:(df.shape[1]-1)].values\\n \\n elif dataset in ['letter-recognition', 'lymphography']:\\n y = preprocessing.LabelEncoder().fit_transform(df.iloc[:,0])\\n X = df.iloc[:, 1:(df.shape[1])].values\\n \\n elif dataset == 'yeast':\\n y = preprocessing.LabelEncoder().fit_transform(df.iloc[:,-1])\\n X = df.iloc[:,1:9].values\\n \\n elif dataset == 'mnist':\\n X, y = fetch_openml('mnist_784', version=1, return_X_y=True)\\n y = y.astype('int64') \\n\\n if scale==True:\\n scaler = preprocessing.StandardScaler()\\n X = scaler.fit_transform(X)\\n \\n return X, y\",\n \"def norm_data(self):\\n if (self.nrows, self.ncolumns) < self.data.shape:\\n self.data = self.data[0:self.nrows, 0:self.ncolumns]\\n if self.data.dtype != np.float64:\\n self.data = self.data.astype(np.float64)\\n self.meanval = self.data.mean()\\n self.stdval = self.data.std()\",\n \"def normalize(\\n ds: xr.Dataset,\\n *,\\n dim: str,\\n kind: str = ADDITIVE,\\n) -> xr.Dataset:\\n\\n if \\\"norm\\\" in ds:\\n norm = invert(ds.norm, kind)\\n else:\\n norm = invert(ds.data.mean(dim=dim), kind)\\n\\n return xr.Dataset(dict(data=apply_correction(ds.data, norm, kind)))\",\n \"def test_single_null(setup_teardown_file):\\n f = setup_teardown_file[3]\\n\\n dset = f.create_dataset('x', (1,), dtype='i1')\\n out = dset[()]\\n assert isinstance(out, np.ndarray)\\n assert out.shape == (1,)\",\n \"def _is_DataArrays(data):\\n if isinstance(data, (Dataset, DataArray)):\\n return True\\n if isinstance(data, Mapping):\\n for da in data.values():\\n if not isinstance(da, DataArray):\\n raise TypeError(\\\"Please provide List/Mapping of DataArrays\\\")\\n return True\\n if isinstance(data, Iterable):\\n for da in data:\\n if not isinstance(da, DataArray):\\n return False\\n # raise TypeError(\\\"Please provide List/Mapping of DataArrays\\\")\\n return True\\n return False\",\n \"def _check_and_transform_input(self, data):\\n if isinstance(data, list):\\n if np.array(data).shape == (len(data),):\\n if len(data) == 1:\\n data = np.array(data).reshape(1, 1)\\n data = np.array(data).reshape(len(data), 1)\\n else:\\n data = np.concatenate(data).reshape(len(data), -1)\\n else:\\n raise TypeError('Input data should be of type list, but found type {}'.format(type(data)))\\n\\n return data\",\n \"def _process(self, data: np.ndarray) -> np.ndarray:\\n return data[..., 0] * self.scale\",\n \"def astype(self, dtype):\\n return NoneArray\",\n \"def _format_data(self, data: np.ndarray) -> np.ndarray:\\n self._n_shots = 0\\n\\n # identify shape\\n try:\\n # level1 single-shot data\\n self._n_circs, self._n_shots, self._n_slots, self._n_iq = data.shape\\n except ValueError:\\n try:\\n # level1 data averaged over shots\\n self._n_circs, self._n_slots, self._n_iq = data.shape\\n except ValueError as ex:\\n raise DataProcessorError(\\n f\\\"Data given to {self.__class__.__name__} is not likely level1 data.\\\"\\n ) from ex\\n\\n if self._validate:\\n if data.shape[-1] != 2:\\n raise DataProcessorError(\\n f\\\"IQ data given to {self.__class__.__name__} must be a multi-dimensional array\\\"\\n \\\"of dimension [d0, d1, ..., 2] in which the last dimension \\\"\\n \\\"corresponds to IQ elements.\\\"\\n f\\\"Input data contains element with length {data.shape[-1]} != 2.\\\"\\n )\\n\\n return data\",\n \"def _to_ndarray(data):\\n return np.atleast_1d(getattr(data, 'values', data))\",\n \"def _normalize_default(value, dtype):\\n\\n # Create NumPy objects as defaults\\n # This is better in order to serialize them as attributes\\n if value is None:\\n value = 0\\n basedtype = dtype.base\\n try:\\n default = np.array(value, dtype=basedtype)\\n except ValueError:\\n array = np.array(value)\\n if array.shape != basedtype.shape:\\n raise\\n # Maybe nested dtype with \\\"scalar\\\" value.\\n default = np.array(value, dtype=basedtype.base)\\n # 0-dim arrays will be representented as NumPy scalars\\n # (PyTables attribute convention)\\n if default.shape == ():\\n default = default[()]\\n return default\",\n \"def _format_data(self, data: np.ndarray) -> np.ndarray:\\n if self._validate:\\n if len(data.shape) <= self._axis:\\n raise DataProcessorError(\\n f\\\"Cannot average the {len(data.shape)} dimensional \\\"\\n f\\\"array along axis {self._axis}.\\\"\\n )\\n\\n return data\",\n \"def test_scalar_null(self):\\n dset = self.f.create_dataset('x', shape=(), dtype='i1')\\n out = dset[()]\\n self.assertIsInstance(out, np.int8)\",\n \"def _normalizeComplex(data):\\n if hasattr(data, \\\"dtype\\\"):\\n isComplex = numpy.issubdtype(data.dtype, numpy.complexfloating)\\n else:\\n isComplex = isinstance(data, numbers.Complex)\\n if isComplex:\\n data = numpy.absolute(data)\\n return data\",\n \"def _norm_data(data):\\n if data is None:\\n return data\\n data_min = np.min(data)\\n c_norm = np.max(data) - data_min\\n return (data - data_min) / c_norm if (c_norm != 0) else (data - data_min)\",\n \"def _format_data(self, data: np.ndarray) -> np.ndarray:\\n self._n_circs = 0\\n self._n_shots = 0\\n self._n_slots = 0\\n self._n_iq = 0\\n\\n # identify shape\\n try:\\n # level1 single-shot data\\n self._n_circs, self._n_shots, self._n_slots, self._n_iq = data.shape\\n except ValueError:\\n try:\\n # level1 data averaged over shots\\n self._n_circs, self._n_slots, self._n_iq = data.shape\\n except ValueError as ex:\\n raise DataProcessorError(\\n f\\\"Data given to {self.__class__.__name__} is not likely level1 data.\\\"\\n ) from ex\\n\\n if self._validate:\\n if self._n_iq != 2:\\n raise DataProcessorError(\\n f\\\"IQ data given to {self.__class__.__name__} does not have two-dimensions \\\"\\n f\\\"(I and Q). Instead, {self._n_iq} dimensions were found.\\\"\\n )\\n\\n return data\",\n \"def _validate_X(X):\\n return X if not isinstance(X, pd.DataFrame) else X.as_matrix()\",\n \"def applyNormalization(ds, reference, target=-1):\\n print 'normalization of', ds.title\\n # Store reference name for later\\n refname = str(reference)\\n # Normalization\\n reference = getattr(ds,reference)\\n\\n # check if reference/target is a number\\n # TODO: gumpy doesn't allow us to handle a scalar with variance\\n # for multiplying arrays, so we can't propagate variance at present\\n numericReference = isinstance(reference, (int, long, float))\\n \\n # check arguments\\n if not numericReference:\\n if reference.ndim != 1:\\n raise AttributeError('reference.ndim != 1')\\n if reference.shape[0] != ds.shape[0]:\\n raise AttributeError('reference.shape[0] != ds.shape[0] (%d != %d)' % (reference.shape[0],ds.shape[0]))\\n\\n def do_norm(rs, f, varf):\\n # We propagate errors in the data, but not in\\n # the ancillary values\\n print 'In do_norm, given %f(%f)' % (f,varf)\\n # Funny syntax below to make sure we write into the original area,\\n # not assign a new value\\n rs.var *= f * f\\n rs.var += varf * rs * rs\\n rs.storage *= f\\n try: #These may be absent in some cases\\n rs.bm1_counts *= f\\n rs.bm2_counts *= f\\n rs.bm3_counts *= f\\n rs.detector_time *= f\\n rs.total_counts *= f\\n except AttributeError:\\n pass\\n \\n # normalization\\n rs = ds.__copy__()\\n copy_metadata_deep(rs,ds) #NeXuS metadata\\n rs.copy_cif_metadata(ds) #CIF metadata\\n if numericReference and target > 0:\\n # We have a single number to refer to for normalisation, so\\n # we are effectively scaling everything by a single number\\n scale_factor = float(target)/reference\\n variance = scale_factor * target/(reference*reference)\\n do_norm(rs, scale_factor, variance)\\n info_string = \\\"Data multiplied by %f with variance %f\\\" % (scale_factor,variance)\\n elif not numericReference:\\n # Each step has a different value, and we manually perform the\\n # error propagation \\n reference = Data(reference)\\n if target <= 0:\\n target = reference.max()\\n for i in xrange(rs.shape[0]):\\n # handle unexpected zero values\\n one_reference = reference[i]\\n if one_reference == 0:\\n one_reference = 0.1 #so small it is like zero\\n print \\\"Warning: zero monitor counts found at step %d\\\" % i\\n f = float(target)/one_reference\\n v = f*target/(one_reference*one_reference)\\n # Funny syntax below to make sure we write into the original area,\\n # not assign a new value\\n tar_shape = [1,rs.shape[1],rs.shape[2]]\\n tar_origin = [i,0,0]\\n rss = rs.storage.get_section(tar_origin,tar_shape).get_reduced()\\n rsv = rs.var.get_section(tar_origin,tar_shape).get_reduced()\\n rs.var[i] = rsv*f * f\\n rs.var[i] += v * rss * rss\\n rs.storage[i] = rs.storage[i]*f\\n info_string = \\\"Data normalised to %f on %s with error propagation assuming counting statistics\\\" % (float(target),refname)\\n else:\\n # interesting note - if we get here, we are passed a single reference number\\n # and a negative target, meaning that we use the reference as the target and\\n # end up multiplying by 1.0, so no need to do anything at all.\\n target = reference\\n info_string = \\\"No normalisation applied to data.\\\"\\n rs.add_metadata('_pd_proc_info_data_reduction',info_string, append=True)\\n print 'normalized:', ds.title\\n return rs,target\",\n \"def get_data(name):\\n\\n if name == 'train' or name == 'unlabeled':\\n return np.expand_dims(np.load(os.path.join(DATADIR, 'source_d10_train_X.npy')), axis=-1), \\\\\\n np.argmax(np.load(os.path.join(DATADIR, 'source_d10_train_y.npy')), axis=1)\\n # custom svhn has only training/validation set\\n elif name == 'validation' or name == 'val':\\n return np.expand_dims(np.load(os.path.join(DATADIR, 'source_d10_val_X.npy')), axis=-1), \\\\\\n np.argmax(np.load(os.path.join(DATADIR, 'source_d10_val_y.npy')), axis=1)\",\n \"def test_scalar_null(setup_teardown_file):\\n f = setup_teardown_file[3]\\n\\n dset = f.create_dataset('x', shape=(), dtype='i1')\\n out = dset[()]\\n\\n assert out.dtype == \\\"int8\\\"\",\n \"def _numpy_to_cpu_safe(\\n t: Optional[Union[np.ndarray, torch.Tensor]]\\n) -> Optional[torch.Tensor]:\\n\\n if t is not None and isinstance(t, np.ndarray):\\n return torch.from_numpy(t)\\n return t\",\n \"def normalize_data(data):\\n mean = np.mean(data)\\n std = np.std(data)\\n return (data - mean) / std\",\n \"def get_X():\\n metadata = get_dataset_metadata(['shape_X', 'type_X'])\\n return get_ndarray(name='X_original',\\n arr_shape=metadata['shape_X'],\\n arr_type=metadata['type_X'])\",\n \"def check_input_and_return_data(self, input_name):\\n\\n #Check to see if data is given by a file\\n if isinstance(self.input_data['reservoir'][input_name], str):\\n #Get filename\\n filename = self.input_data['reservoir'][input_name]\\n #Load data \\n data = np.loadtxt(filename, dtype=np.double)\\n \\n #Check to see if data is given by a list\\n elif isinstance(self.input_data['reservoir'][input_name], (list, tuple)):\\n #Turn the list into numpy array\\n data = np.array(self.input_data['reservoir'][input_name], \\n dtype=np.double)\\n\\n #data is a constant array (homogeneous)\\n else:\\n ngrids = self.input_data['numerical']['number of grids']\\n data = (self.input_data['reservoir'][input_name] * \\n np.ones(ngrids))\\n return data\",\n \"def convert_to_ndarray(entity):\\n if isinstance(entity, np.ndarray) and entity.dtype.kind in set('biufc'):\\n # entity is numerical ndarray already\\n return entity\\n if isinstance(entity, np.ndarray) and isinstance(entity.flat[0], qt.Qobj):\\n # entity is output from qt.eigenstates\\n return convert_esys_to_ndarray(entity)\\n if isinstance(entity, list) and isinstance(entity[0], np.ndarray) and isinstance(entity[0].flat[0], qt.Qobj):\\n # entity is a list of qt.eigenstates\\n return np.asarray([convert_esys_to_ndarray(entry) for entry in entity])\\n # possibly we have a list of numerical values or a list of ndarrays\\n converted_entity = np.asarray(entity)\\n if converted_entity.dtype.kind not in set('biufc'):\\n raise TypeError('Unable to convert data to numerical numpy array: ', entity)\\n return converted_entity\",\n \"def parse(\\n self, data: Union[pd.DataFrame, pd.Series, str], verbose: int = 0\\n ) -> pd.DataFrame:\\n\\n data = self.__transform_data(data)\\n data = self.norm.normalize(data)\\n data = self.find.find_all(data, verbose)\\n data = data.drop(\\\"name_norm\\\", axis=1)\\n return data\",\n \"def _validate_input_data(self):\\n\\n if type(self.data) in [np.ndarray, da.Array]:\\n if not self.data.dtype.names:\\n raise ValueError('QuadTree: numpy array provided for data, but no names were found, array must be a structured array')\\n if 'x' not in self.data.dtype.names or 'y' not in self.data.dtype.names:\\n raise ValueError('QuadTree: numpy structured array provided for data, but \\\"x\\\" or \\\"y\\\" not found in variable names')\\n self.layernames = [self.rev_layer_lookup[var] for var in self.data.dtype.names if var in ['z', 'tvu']]\\n elif type(self.data) == xr.Dataset:\\n if 'x' not in self.data:\\n raise ValueError('QuadTree: xarray Dataset provided for data, but \\\"x\\\" or \\\"y\\\" not found in variable names')\\n if len(self.data.dims) > 1:\\n raise ValueError('QuadTree: xarray Dataset provided for data, but found multiple dimensions, must be one dimensional: {}'.format(self.data.dims))\\n self.layernames = [self.rev_layer_lookup[var] for var in self.data if var in ['z', 'tvu']]\\n self._convert_dataset() # internally we just convert xarray dataset to numpy for ease of use\\n else:\\n raise ValueError('QuadTree: numpy structured array or dask array with \\\"x\\\" and \\\"y\\\" as variable must be provided')\",\n \"def test_data(self):\\n\\n self.assertIsInstance(self.image.data, np.ndarray)\",\n \"def get_normalize_layer(dataset: str) -> torch.nn.Module:\\n if dataset == \\\"imagenet\\\":\\n return NormalizeLayer(_IMAGENET_MEAN, _IMAGENET_STDDEV)\\n elif dataset == \\\"cifar10\\\":\\n return NormalizeLayer(_CIFAR10_MEAN, _CIFAR10_STDDEV)\\n elif dataset == \\\"imagenet32\\\":\\n return NormalizeLayer(_CIFAR10_MEAN, _CIFAR10_STDDEV)\",\n \"def normalize_data(self):\\n self.x_mean, self.x_std = du.get_mean_std(self.x_train)\\n self.x_train = du.normalize(self.x_train, self.x_mean, self.x_std)\\n if self.x_test is not None and self.y_test is not None:\\n self.x_test = du.normalize(self.x_test, self.x_mean, self.x_std)\\n self.normalized_data = True\",\n \"def normalize(data):\\n # normalize data and return\\n # https://stackoverflow.com/questions/29661574/normalize-numpy-array-columns-in-python\\n return (data - data.min(axis=0)) / data.ptp(axis=0)\",\n \"def _check_dataset_name_return_data(self, dataset_name, strict=False):\\n if strict:\\n _check_dataset_presence(self, dataset_name)\\n elif dataset_name is None:\\n raise MutantError(\\\"Cannot use None as dataset name!\\\")\\n return self.by_dataset[dataset_name]\",\n \"def iris():\\n return IrisDataset()\",\n \"def _match_shape_to_data(self,\\n data: 'Union[np.ndarray, tensorflow.Tensor, torch.Tensor, Any, List[Any]]',\\n target_type: Literal['X', 'Y']\\n ) -> 'Union[np.ndarray, tensorflow.Tensor, torch.Tensor, List[Any]]':\\n if self._is_tensor(data):\\n return self._match_shape_to_data_tensor(data, target_type)\\n return self._match_shape_to_data_any(data)\"\n]","isConversational":false},"negative_scores":{"kind":"list like","value":["0.6411453","0.6259078","0.6019786","0.59921646","0.57862496","0.5763898","0.5716769","0.571654","0.56771535","0.56653965","0.56437415","0.5640771","0.56243145","0.56201","0.55731094","0.555722","0.5551846","0.55445325","0.55431825","0.5495918","0.5480445","0.54654485","0.5460286","0.54232347","0.54228157","0.5413331","0.54101616","0.5407706","0.5398918","0.53934973","0.5377827","0.5371782","0.5368883","0.5358672","0.53531957","0.53504145","0.5336204","0.5335532","0.53306156","0.5330235","0.5320829","0.53206384","0.53127134","0.5293333","0.52924645","0.52891654","0.5288406","0.5288002","0.52874","0.52708036","0.52708036","0.52663606","0.526175","0.52616924","0.5258673","0.5251613","0.5246853","0.5237172","0.5232961","0.5227883","0.5223738","0.522089","0.52093774","0.519872","0.5196062","0.5191002","0.5183546","0.5183129","0.5174698","0.51678383","0.5164444","0.5163599","0.5152552","0.51516265","0.51478964","0.5144402","0.51436734","0.5133916","0.5126004","0.5123036","0.51167417","0.51161325","0.5115647","0.5107918","0.51078194","0.5097525","0.50918204","0.50879765","0.50789183","0.5073172","0.5069272","0.5067154","0.5063632","0.50606734","0.5059842","0.50597733","0.5055351","0.5040844","0.50321686"],"string":"[\n \"0.6411453\",\n \"0.6259078\",\n \"0.6019786\",\n \"0.59921646\",\n \"0.57862496\",\n \"0.5763898\",\n \"0.5716769\",\n \"0.571654\",\n \"0.56771535\",\n \"0.56653965\",\n \"0.56437415\",\n \"0.5640771\",\n \"0.56243145\",\n \"0.56201\",\n \"0.55731094\",\n \"0.555722\",\n \"0.5551846\",\n \"0.55445325\",\n \"0.55431825\",\n \"0.5495918\",\n \"0.5480445\",\n \"0.54654485\",\n \"0.5460286\",\n \"0.54232347\",\n \"0.54228157\",\n \"0.5413331\",\n \"0.54101616\",\n \"0.5407706\",\n \"0.5398918\",\n \"0.53934973\",\n \"0.5377827\",\n \"0.5371782\",\n \"0.5368883\",\n \"0.5358672\",\n \"0.53531957\",\n \"0.53504145\",\n \"0.5336204\",\n \"0.5335532\",\n \"0.53306156\",\n \"0.5330235\",\n \"0.5320829\",\n \"0.53206384\",\n \"0.53127134\",\n \"0.5293333\",\n \"0.52924645\",\n \"0.52891654\",\n \"0.5288406\",\n \"0.5288002\",\n \"0.52874\",\n \"0.52708036\",\n \"0.52708036\",\n \"0.52663606\",\n \"0.526175\",\n \"0.52616924\",\n \"0.5258673\",\n \"0.5251613\",\n \"0.5246853\",\n \"0.5237172\",\n \"0.5232961\",\n \"0.5227883\",\n \"0.5223738\",\n \"0.522089\",\n \"0.52093774\",\n \"0.519872\",\n \"0.5196062\",\n \"0.5191002\",\n \"0.5183546\",\n \"0.5183129\",\n \"0.5174698\",\n \"0.51678383\",\n \"0.5164444\",\n \"0.5163599\",\n \"0.5152552\",\n \"0.51516265\",\n \"0.51478964\",\n \"0.5144402\",\n \"0.51436734\",\n \"0.5133916\",\n \"0.5126004\",\n \"0.5123036\",\n \"0.51167417\",\n \"0.51161325\",\n \"0.5115647\",\n \"0.5107918\",\n \"0.51078194\",\n \"0.5097525\",\n \"0.50918204\",\n \"0.50879765\",\n \"0.50789183\",\n \"0.5073172\",\n \"0.5069272\",\n \"0.5067154\",\n \"0.5063632\",\n \"0.50606734\",\n \"0.5059842\",\n \"0.50597733\",\n \"0.5055351\",\n \"0.5040844\",\n \"0.50321686\"\n]","isConversational":false},"document_score":{"kind":"string","value":"0.57240814"},"document_rank":{"kind":"string","value":"7"}}},{"rowIdx":23791,"cells":{"query":{"kind":"string","value":"Returns a colormap for this view."},"document":{"kind":"string","value":"def getColormap(self, view):\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 get_colormap(self):\n return colors.colormaps[self.name]","def get_colormap(self):\n return colors.colormaps[self.name]","def get_colormap(self):\n return file_io.load_viscm_colormap(self.path)","def get_colormap(self):\n return file_io.load_viscm_colormap(self.path)","def get_colormap(self):\n return colors.ev_colormaps[self.name]","def cmap(self):\n return self._cmap","def cmap(self):\n return self.pixels.get_cmap()","def get_mpl_colormap(self):\n return mpl.colors.ListedColormap(self.get_colors().astype(float) / 255.0)","def cmap(self):\n return self._palette","def colormap(self):\n palette = [(0, 0, 0), (0, 0, 0), (0, 0, 0), (0, 0, 0), (0, 0, 0),\n (111, 74, 0), (81, 0, 81), (128, 64, 128), (244, 35, 232),\n (250, 170, 160), (230, 150, 140), (70, 70, 70),\n (102, 102, 156), (190, 153, 153), (180, 165, 180),\n (150, 100, 100), (150, 120, 90), (153, 153, 153),\n (153, 153, 153), (250, 170, 30), (220, 220, 0),\n (107, 142, 35), (152, 251, 152), (70, 130, 180),\n (220, 20, 60), (255, 0, 0), (0, 0, 142), (0, 0, 70),\n (0, 60, 100), (0, 0, 90), (0, 0, 110), (0, 80, 100),\n (0, 0, 230), (119, 11, 32), (0, 0, 142)]\n\n num_colors = self[0][1].shape[-1]\n colormap = np.zeros((num_colors, 3), dtype=int)\n for i in range(num_colors):\n colormap[i, ...] = palette[self._update_labels_dict[i]]\n return colormap","def defaultColormap(self):\n colormap = None\n if self.__hooks is not None:\n colormap = self.__hooks.getColormap(self)\n if colormap is None:\n colormap = Colormap(name=\"viridis\")\n return colormap","def getColormapDialog(self, view):\n return None","def getColormapImage(self):\n if self._colormapImage is None and self._colormap is not None:\n image = numpy.zeros((16, 130, 3), dtype=numpy.uint8)\n image[1:-1, 1:-1] = self._colormap.getNColors(image.shape[1] - 2)[:, :3]\n self._colormapImage = convertArrayToQImage(image)\n return self._colormapImage","def get_dict(self):\r\n return self.cmap","def colormap(self, band_idx=1):\n if band_idx < 1 or band_idx > self.dataset.RasterCount:\n raise IndexError(\"band index is out of range\")\n ct = self.dataset.GetRasterBand(band_idx).GetColorTable()\n if ct is None:\n return None\n\n colormap = []\n for i in range(ct.GetCount()):\n colormap.append(ct.GetColorEntry(i))\n\n return colormap","def get_cmap(n, name='jet'):\n return plt.cm.get_cmap(name, n)","def get_cmap(n, name='Paired'):\n return plt.cm.get_cmap(name, n)","def get_cmap(n, name='hsv'):\n return plt.cm.get_cmap(name, n)","def get_cmap(n, name='hsv'):\n return plt.cm.get_cmap(name, n)","def get_cmap(n, name=\"hsv\"):\n return plt.cm.get_cmap(name, n)","def get_cmap(n, name=\"hsv\"):\n return plt.cm.get_cmap(name, n)","def get_cmap(n, name=\"hsv\"):\n return plt.cm.get_cmap(name, n)","def colorspace(self):\n return self._color.colorspace","def colorspace(self):\n return self._color.colorspace","def get_density_cmap():\n # Add completely white color to Reds colormap in Matplotlib\n list_colors = plt.cm.datad['Reds']\n list_colors = list(list_colors)\n list_colors.insert(0, (1, 1, 1))\n list_colors.insert(0, (1, 1, 1))\n lscm = matplotlib.colors.LinearSegmentedColormap.from_list(\"my_Reds\", list_colors)\n return lscm","def cmap(self, background_color='#000000'):\n return get_cmap(self.img.data.max() + 1,\n background_color=background_color)","def get_cmap(cmap=None):\n if cmap:\n if isinstance(cmap, (mpl.colors.Colormap)):\n colormap = cmap\n elif cmap in cmo.cmapnames:\n colormap = cmo.cmap_d[cmap]\n elif cmap in plt.colormaps():\n colormap = plt.get_cmap(cmap)\n else:\n raise ValueError(\n \"Get unrecognised name for the colormap `{}`. Colormaps should be from standard matplotlib set of from cmocean package.\".format(\n cmap\n )\n )\n else:\n colormap = plt.get_cmap(\"Spectral_r\")\n\n return colormap","def _getColormapRange(self):\n item = self.item()\n if item is not None and self._colormap is not None:\n return self._colormap.getColormapRange(item)\n else:\n return 1, 100 # Fallback","def getColorModel(self):\n return self.getModel().getColorModel()","def get_colors(self):\n df = self.reindexed_dataframe()\n\n colormap = cm.get_cmap(self.cmap)\n norm = Normalize(vmin=df.min(), vmax=df.max())\n\n sm = cm.ScalarMappable(norm=norm, cmap=colormap)\n\n return ['rgba' + str(sm.to_rgba(m, bytes = True, alpha = 0.8)) if not np.isnan(m) else 'rgba(128,128,128,1)' for m in df.values]","def linkcolormap(self, linkcolors=\"viridis\"):\n\n if isinstance(linkcolors, list) and len(linkcolors) == self.n:\n # provided a list of color names\n return colors.ListedColormap(linkcolors)\n else:\n # assume it is a colormap name\n return cm.get_cmap(linkcolors, 6)","def get_colormap(level_values: np.ndarray) -> np.ndarray:\n color_dimension = level_values # change to desired fourth dimension\n color_min, color_max = color_dimension.min(), color_dimension.max()\n norm = colors.Normalize(color_min, color_max)\n m = plt.cm.ScalarMappable(norm=norm, cmap=\"Spectral_r\")\n m.set_array([])\n face_colors = m.to_rgba(color_dimension)\n\n return face_colors","def norm(self):\n return self._color_mapper","def colorscale(self):\n return self[\"colorscale\"]","def colorscale(self):\n return self['colorscale']","def _get_csi_colour_scheme():\n\n this_colour_map_object = pyplot.cm.Blues\n this_colour_norm_object = matplotlib.colors.BoundaryNorm(\n LEVELS_FOR_CSI_CONTOURS, this_colour_map_object.N)\n\n rgba_matrix = this_colour_map_object(this_colour_norm_object(\n LEVELS_FOR_CSI_CONTOURS\n ))\n\n colour_list = [\n rgba_matrix[i, ..., :-1] for i in range(rgba_matrix.shape[0])\n ]\n\n colour_map_object = matplotlib.colors.ListedColormap(colour_list)\n colour_map_object.set_under(numpy.full(3, 1.))\n colour_norm_object = matplotlib.colors.BoundaryNorm(\n LEVELS_FOR_CSI_CONTOURS, colour_map_object.N)\n\n return colour_map_object, colour_norm_object","def color(self):\n return self._zoom.color","def get_color(self):\n return COLOR_DICT[self.element]","def create_colormap(seg_map):\n\tcolormap = np.zeros((256, 3), dtype=int)\n\tind = np.arange(256, dtype=int)\n\tfor shift in reversed(range(8)):\n\t\tfor channel in range(3):\n\t\t\tcolormap[:, channel] |= ((ind >> channel) & 1) << shift \n\t\tind >>= 3\n\treturn colormap[seg_map]","def coloraxis(self):\n return self[\"coloraxis\"]","def color(self):\n return self.container['color']","def get_font_cmap(font):\n return font['cmap'].tables[0].cmap","def getColor(self):\n return self.__color","def getColor(self):\n return self.__color","def getColor(self):\n return self.__color","def cmap(num,cmap = plt.cm.gist_earth_r):\n return cmap(np.linspace(0, 1, num))","def _create_colormap(self):\n\n max_count = max(self.data['rects'], key=lambda r: r['count'])['count']\n ranges_list = [\n [\n int(max_count/x[0])+1,\n int(max_count/x[1])+1\n ] for x in [[6, 3], [3, 2], [2, 1]]\n ]\n ranges_list = [[0, 1]] + [[1, ranges_list[0][0]]] + ranges_list\n\n self.data['colormap'] = [\n {\n 'fill': self._get_fill(\n i,\n range(\n ranges_list[i][0],\n ranges_list[i][1]\n )\n ),\n 'range': range(\n ranges_list[i][0],\n ranges_list[i][1]\n )\n } for i in range(0, 5)\n ]","def get_color(self):\n return self._color","def get_color(self):\n return self._color","def getColor(self):\n return self.color","def getColorSpace(self):\n return getI1ProColorSpaceMode()","def get_color(self):\n\n return self._color","def get_color(self):\r\n return self.__color","def color(self):\n return self['color']","def get_color(self):\r\n return self._color","def get_color(self):\n\n return self.color","def getColor(self):\r\n return self.color","def get_color(self):\n return self.color","def colors(self):\r\n\t\treturn self._colors","def display_colormap(self, colormap=None):\n if colormap is None:\n if HTML:\n for colorname in cm._gen_cmap_registry():\n display(colorname, self.show_colormap(colorname))\n else:\n raise Exception(\"you need to install IPython for this function to work\")\n else:\n width, height = (400, 25)\n vector = np.arange(0, 1, 0.01)\n vector = vector.reshape((1, 100))\n cm_hot = cm.get_cmap(colormap)\n vector = cm_hot(vector)\n vector = np.uint8(vector * 255)\n image = Image.fromarray(vector)\n image = image.resize((width, height))\n return image","def create_cityscapes_label_colormap():\r\n colormap = np.zeros((256, 3), dtype=np.uint8)\r\n colormap[0] = [128, 64, 128]\r\n colormap[1] = [244, 35, 232]\r\n colormap[2] = [70, 70, 70]\r\n colormap[3] = [102, 102, 156]\r\n colormap[4] = [190, 153, 153]\r\n colormap[5] = [153, 153, 153]\r\n colormap[6] = [250, 170, 30]\r\n colormap[7] = [220, 220, 0]\r\n colormap[8] = [107, 142, 35]\r\n colormap[9] = [152, 251, 152]\r\n colormap[10] = [70, 130, 180]\r\n colormap[11] = [220, 20, 60]\r\n colormap[12] = [255, 0, 0]\r\n colormap[13] = [0, 0, 142]\r\n colormap[14] = [0, 0, 70]\r\n colormap[15] = [0, 60, 100]\r\n colormap[16] = [0, 80, 100]\r\n colormap[17] = [0, 0, 230]\r\n colormap[18] = [119, 11, 32]\r\n return colormap","def get_colors(self):\n colors = [\"#244486\", \"#A6A6A6\", \"#B12122\"]\n cmap = LinearSegmentedColormap.from_list(\"mycmap\", colors)\n\n color_palette=[cmap(i) for i in np.linspace(0, 1, len(set(self.nodes_list)))]\n return dict(zip(list(set(self.nodes_list)), color_palette))","def color(self):\n return self.__color","def create_label_colormap():\n colormap = np.array([\n [128, 64, 128],\n [244, 35, 232],\n [ 70, 70, 70],\n [102, 102, 156],\n [190, 153, 153],\n [153, 153, 153],\n [250, 170, 30],\n [220, 220, 0],\n [107, 142, 35],\n [152, 251, 152],\n [ 70, 130, 180],\n [220, 20, 60],\n [255, 0, 0],\n [ 0, 0, 142],\n [ 0, 0, 70],\n [ 0, 60, 100],\n [ 0, 80, 100],\n [ 0, 0, 230],\n [119, 11, 32],\n [ 0, 0, 0]], dtype=np.uint8)\n return colormap","def colors(self):\n return self._colors","def color(self):\n return self._color","def color(self):\n return self._color","def get_colors(self):\n x = np.linspace(0, 1, self.length)\n y = x**self.gamma\n\n value = np.linspace(0, 1, len(self.colors))\n r = np.interp(y, value, self.colors[:,0])\n g = np.interp(y, value, self.colors[:,1])\n b = np.interp(y, value, self.colors[:,2])\n\n return np.dstack((r, g, b)).reshape(len(r), 3).astype(np.uint8)","def get_palette(palette_name):\n\n if hasattr(plt.cm, palette_name):\n cmap = getattr(plt.cm, palette_name)\n elif palette_name in list(mpl_cm.cmap_d.keys()):\n cmap = mpl_cm.get_cmap(palette_name)\n else:\n print(\"Error, color option '\", palette_name, \"' not a valid option\")\n sys.exit(1)\n\n return cmap","def color_mode(self):\n return ids_core.Camera.color_mode.__get__(self)","def getColor(self):\n return self._l[2]","def getColorMap(colors):\n # Normalise RGBs\n norm_colors = []\n for color in colors:\n norm_colors.append([val / 255. for val in color])\n # create color map\n cmap = cols.ListedColormap(norm_colors)\n\n return cmap","def __get_color_table(self):\n #Color: Green Yellow Orange Red Distance:\n return [[0.70, 0.15, 0.1, 0.05], # 0\n [0.17, 0.6, 0.17, 0.06], # 1\n [0.06, 0.17, 0.6, 0.17], # 2\n [0.05, 0.12, 0.23, 0.6], # 3\n [0.05, 0.1, 0.15, 0.8]] # >= 4 ","def colors(self):\n return self[\"colors\"]","def colors(self):\n return self[\"colors\"]","def get_colors(nlevels, colormap=None):\n if colormap is None:\n from matplotlib.pyplot import cm\n colormap = cm.rainbow\n return colormap(np.linspace(0, 1, nlevels))","def getColorSpace(self):\n if not self.proxy:\n self.proxy = self.session.service(\"ALVideoRecorder\")\n return self.proxy.getColorSpace()","def get_color(self):\n _lib.caca_get_dither_color.argtypes = [_Dither]\n _lib.caca_get_dither_color.restype = ctypes.c_char_p\n\n return _lib.caca_get_dither_color(self)","def setColourMap(self):\n cmap = self.config['cmap']\n\n pos, colour, mode = colourMaps.colourMaps(cmap)\n\n cmap = pg.ColorMap(pos, colour,mode)\n self.lut = cmap.getLookupTable(0.0, 1.0, 256)\n minsg = np.min(self.sg)\n maxsg = np.max(self.sg)\n self.colourStart = (self.config['brightness'] / 100.0 * self.config['contrast'] / 100.0) * (maxsg - minsg) + minsg\n self.colourEnd = (maxsg - minsg) * (1.0 - self.config['contrast'] / 100.0) + self.colourStart","def setColourMap(self):\n cmap = self.config['cmap']\n\n pos, colour, mode = colourMaps.colourMaps(cmap)\n\n cmap = pg.ColorMap(pos, colour, mode)\n self.lut = cmap.getLookupTable(0.0, 1.0, 256)\n minsg = np.min(self.sg)\n maxsg = np.max(self.sg)\n self.colourStart = (self.config['brightness'] / 100.0 * self.config['contrast'] / 100.0) * (\n maxsg - minsg) + minsg\n self.colourEnd = (maxsg - minsg) * (1.0 - self.config['contrast'] / 100.0) + self.colourStart","def generate_lut(self):\n colormap = self.get_colormap()\n\n if self.test:\n self.print_colormap(self.name, colormap)\n\n if self.centered:\n return self.generate_spi3d_from_colormap(colormap, centered=True)\n else:\n return self.generate_spi3d_from_colormap(colormap, centered=False)","def get_color_map(n):\n jet = plt.get_cmap('jet')\n cNorm = colors.Normalize(vmin=0, vmax=n-1)\n scalarMap = cmx.ScalarMappable(norm=cNorm, cmap=jet)\n outmap = []\n for i in range(n):\n outmap.append( scalarMap.to_rgba(i) )\n return outmap","def generate_colormap(scale_range=(0.0, 1.0), hue_range=(0.8, 0.0),\n saturation_range=(1.0, 1.0), value_range=(0.8, 0.8),\n nan_color=(0.2, 0.2, 0.2, 1.0)):\n lookup_table = vtk.vtkLookupTable()\n lookup_table.SetRange(scale_range)\n\n lookup_table.SetHueRange(hue_range)\n lookup_table.SetSaturationRange(saturation_range)\n lookup_table.SetValueRange(value_range)\n lookup_table.SetNanColor(nan_color)\n lookup_table.Build()\n return lookup_table","def _get_peirce_colour_scheme():\n\n this_colour_map_object = pyplot.cm.Blues\n this_colour_norm_object = matplotlib.colors.BoundaryNorm(\n LEVELS_FOR_PEIRCE_CONTOURS, this_colour_map_object.N)\n\n rgba_matrix = this_colour_map_object(this_colour_norm_object(\n LEVELS_FOR_PEIRCE_CONTOURS\n ))\n\n colour_list = [\n rgba_matrix[i, ..., :-1] for i in range(rgba_matrix.shape[0])\n ]\n\n colour_map_object = matplotlib.colors.ListedColormap(colour_list)\n colour_map_object.set_under(numpy.full(3, 1.))\n colour_norm_object = matplotlib.colors.BoundaryNorm(\n LEVELS_FOR_PEIRCE_CONTOURS, colour_map_object.N)\n\n return colour_map_object, colour_norm_object","def getCMAPResolutions(self):\n try:\n return self._cmapResolution\n except AttributeError:\n pass\n flag = 'CMAP_RESOLUTION'\n if flag not in self._raw_data and self.chamber:\n flag = 'CHARMM_CMAP_RESOLUTION'\n if flag in self._raw_data:\n self._cmapResolution=self._raw_data[flag]\n return self._cmapResolution\n return 0","def color(self):\n return self.COLOR","def autocolorscale(self):\n return self[\"autocolorscale\"]","def _get_color(self):\n return self.__color","def _get_color(self):\n return self.__color","def _get_color(self):\n return self.__color","def _get_color(self):\n return self.__color","def autocolorscale(self):\n return self['autocolorscale']","def _create_correlation_color_mapper(self):\n tints = self.plot_design.contrary_color_tints\n\n no_correlation = [tints[9]]\n small_correlation = [tints[7]] * 2\n medium_correlation = [tints[6]] * 2\n high_correlation = [tints[4]] * 4\n very_high_correlation = [tints[2]] * 1\n palette = no_correlation + small_correlation + medium_correlation + high_correlation + very_high_correlation\n\n linear_correlation = tints[0] # perfect correlations gets it's own color\n cmap = LinearColorMapper(\n palette=palette,\n low=0,\n high=0.9999,\n high_color=linear_correlation\n )\n return cmap","def map_channels(self, map_function):\n return ScreenColor(map_function(self.red), map_function(self.green), map_function(self.blue))","def color(self):\n return self.settings['color']","def get_color(self) -> str:\n return self.color","def _get_colors(num_colors):\n cmap = plt.get_cmap()\n return [cmap(1. * i / num_colors) for i in range(num_colors)]","def getColorMapFlags():\n\treturn colorMap_flag","def get_color(self):\n return self._io.last_state['color']['front-center']","def ListColorMaps(self):\n p.rc('text', usetex=False)\n a=p.outerproduct(numpy.arange(0,1,0.01),numpy.ones(10))\n p.figure(figsize=(10,5))\n p.subplots_adjust(top=0.8,bottom=0.05,left=0.01,right=0.99)\n maps=[m for m in p.cm.datad.keys() if not m.endswith(\"_r\")]\n maps.sort()\n l=len(maps)+1\n i=1\n for m in maps:\n p.subplot(1,l,i)\n p.axis(\"off\")\n p.imshow(a,aspect='auto',cmap=p.get_cmap(m),origin=\"lower\")\n p.title(m,rotation=90,fontsize=10)\n i=i+1\n #savefig(\"colormaps.png\",dpi=100,facecolor='gray')\n p.show()"],"string":"[\n \"def get_colormap(self):\\n return colors.colormaps[self.name]\",\n \"def get_colormap(self):\\n return colors.colormaps[self.name]\",\n \"def get_colormap(self):\\n return file_io.load_viscm_colormap(self.path)\",\n \"def get_colormap(self):\\n return file_io.load_viscm_colormap(self.path)\",\n \"def get_colormap(self):\\n return colors.ev_colormaps[self.name]\",\n \"def cmap(self):\\n return self._cmap\",\n \"def cmap(self):\\n return self.pixels.get_cmap()\",\n \"def get_mpl_colormap(self):\\n return mpl.colors.ListedColormap(self.get_colors().astype(float) / 255.0)\",\n \"def cmap(self):\\n return self._palette\",\n \"def colormap(self):\\n palette = [(0, 0, 0), (0, 0, 0), (0, 0, 0), (0, 0, 0), (0, 0, 0),\\n (111, 74, 0), (81, 0, 81), (128, 64, 128), (244, 35, 232),\\n (250, 170, 160), (230, 150, 140), (70, 70, 70),\\n (102, 102, 156), (190, 153, 153), (180, 165, 180),\\n (150, 100, 100), (150, 120, 90), (153, 153, 153),\\n (153, 153, 153), (250, 170, 30), (220, 220, 0),\\n (107, 142, 35), (152, 251, 152), (70, 130, 180),\\n (220, 20, 60), (255, 0, 0), (0, 0, 142), (0, 0, 70),\\n (0, 60, 100), (0, 0, 90), (0, 0, 110), (0, 80, 100),\\n (0, 0, 230), (119, 11, 32), (0, 0, 142)]\\n\\n num_colors = self[0][1].shape[-1]\\n colormap = np.zeros((num_colors, 3), dtype=int)\\n for i in range(num_colors):\\n colormap[i, ...] = palette[self._update_labels_dict[i]]\\n return colormap\",\n \"def defaultColormap(self):\\n colormap = None\\n if self.__hooks is not None:\\n colormap = self.__hooks.getColormap(self)\\n if colormap is None:\\n colormap = Colormap(name=\\\"viridis\\\")\\n return colormap\",\n \"def getColormapDialog(self, view):\\n return None\",\n \"def getColormapImage(self):\\n if self._colormapImage is None and self._colormap is not None:\\n image = numpy.zeros((16, 130, 3), dtype=numpy.uint8)\\n image[1:-1, 1:-1] = self._colormap.getNColors(image.shape[1] - 2)[:, :3]\\n self._colormapImage = convertArrayToQImage(image)\\n return self._colormapImage\",\n \"def get_dict(self):\\r\\n return self.cmap\",\n \"def colormap(self, band_idx=1):\\n if band_idx < 1 or band_idx > self.dataset.RasterCount:\\n raise IndexError(\\\"band index is out of range\\\")\\n ct = self.dataset.GetRasterBand(band_idx).GetColorTable()\\n if ct is None:\\n return None\\n\\n colormap = []\\n for i in range(ct.GetCount()):\\n colormap.append(ct.GetColorEntry(i))\\n\\n return colormap\",\n \"def get_cmap(n, name='jet'):\\n return plt.cm.get_cmap(name, n)\",\n \"def get_cmap(n, name='Paired'):\\n return plt.cm.get_cmap(name, n)\",\n \"def get_cmap(n, name='hsv'):\\n return plt.cm.get_cmap(name, n)\",\n \"def get_cmap(n, name='hsv'):\\n return plt.cm.get_cmap(name, n)\",\n \"def get_cmap(n, name=\\\"hsv\\\"):\\n return plt.cm.get_cmap(name, n)\",\n \"def get_cmap(n, name=\\\"hsv\\\"):\\n return plt.cm.get_cmap(name, n)\",\n \"def get_cmap(n, name=\\\"hsv\\\"):\\n return plt.cm.get_cmap(name, n)\",\n \"def colorspace(self):\\n return self._color.colorspace\",\n \"def colorspace(self):\\n return self._color.colorspace\",\n \"def get_density_cmap():\\n # Add completely white color to Reds colormap in Matplotlib\\n list_colors = plt.cm.datad['Reds']\\n list_colors = list(list_colors)\\n list_colors.insert(0, (1, 1, 1))\\n list_colors.insert(0, (1, 1, 1))\\n lscm = matplotlib.colors.LinearSegmentedColormap.from_list(\\\"my_Reds\\\", list_colors)\\n return lscm\",\n \"def cmap(self, background_color='#000000'):\\n return get_cmap(self.img.data.max() + 1,\\n background_color=background_color)\",\n \"def get_cmap(cmap=None):\\n if cmap:\\n if isinstance(cmap, (mpl.colors.Colormap)):\\n colormap = cmap\\n elif cmap in cmo.cmapnames:\\n colormap = cmo.cmap_d[cmap]\\n elif cmap in plt.colormaps():\\n colormap = plt.get_cmap(cmap)\\n else:\\n raise ValueError(\\n \\\"Get unrecognised name for the colormap `{}`. Colormaps should be from standard matplotlib set of from cmocean package.\\\".format(\\n cmap\\n )\\n )\\n else:\\n colormap = plt.get_cmap(\\\"Spectral_r\\\")\\n\\n return colormap\",\n \"def _getColormapRange(self):\\n item = self.item()\\n if item is not None and self._colormap is not None:\\n return self._colormap.getColormapRange(item)\\n else:\\n return 1, 100 # Fallback\",\n \"def getColorModel(self):\\n return self.getModel().getColorModel()\",\n \"def get_colors(self):\\n df = self.reindexed_dataframe()\\n\\n colormap = cm.get_cmap(self.cmap)\\n norm = Normalize(vmin=df.min(), vmax=df.max())\\n\\n sm = cm.ScalarMappable(norm=norm, cmap=colormap)\\n\\n return ['rgba' + str(sm.to_rgba(m, bytes = True, alpha = 0.8)) if not np.isnan(m) else 'rgba(128,128,128,1)' for m in df.values]\",\n \"def linkcolormap(self, linkcolors=\\\"viridis\\\"):\\n\\n if isinstance(linkcolors, list) and len(linkcolors) == self.n:\\n # provided a list of color names\\n return colors.ListedColormap(linkcolors)\\n else:\\n # assume it is a colormap name\\n return cm.get_cmap(linkcolors, 6)\",\n \"def get_colormap(level_values: np.ndarray) -> np.ndarray:\\n color_dimension = level_values # change to desired fourth dimension\\n color_min, color_max = color_dimension.min(), color_dimension.max()\\n norm = colors.Normalize(color_min, color_max)\\n m = plt.cm.ScalarMappable(norm=norm, cmap=\\\"Spectral_r\\\")\\n m.set_array([])\\n face_colors = m.to_rgba(color_dimension)\\n\\n return face_colors\",\n \"def norm(self):\\n return self._color_mapper\",\n \"def colorscale(self):\\n return self[\\\"colorscale\\\"]\",\n \"def colorscale(self):\\n return self['colorscale']\",\n \"def _get_csi_colour_scheme():\\n\\n this_colour_map_object = pyplot.cm.Blues\\n this_colour_norm_object = matplotlib.colors.BoundaryNorm(\\n LEVELS_FOR_CSI_CONTOURS, this_colour_map_object.N)\\n\\n rgba_matrix = this_colour_map_object(this_colour_norm_object(\\n LEVELS_FOR_CSI_CONTOURS\\n ))\\n\\n colour_list = [\\n rgba_matrix[i, ..., :-1] for i in range(rgba_matrix.shape[0])\\n ]\\n\\n colour_map_object = matplotlib.colors.ListedColormap(colour_list)\\n colour_map_object.set_under(numpy.full(3, 1.))\\n colour_norm_object = matplotlib.colors.BoundaryNorm(\\n LEVELS_FOR_CSI_CONTOURS, colour_map_object.N)\\n\\n return colour_map_object, colour_norm_object\",\n \"def color(self):\\n return self._zoom.color\",\n \"def get_color(self):\\n return COLOR_DICT[self.element]\",\n \"def create_colormap(seg_map):\\n\\tcolormap = np.zeros((256, 3), dtype=int)\\n\\tind = np.arange(256, dtype=int)\\n\\tfor shift in reversed(range(8)):\\n\\t\\tfor channel in range(3):\\n\\t\\t\\tcolormap[:, channel] |= ((ind >> channel) & 1) << shift \\n\\t\\tind >>= 3\\n\\treturn colormap[seg_map]\",\n \"def coloraxis(self):\\n return self[\\\"coloraxis\\\"]\",\n \"def color(self):\\n return self.container['color']\",\n \"def get_font_cmap(font):\\n return font['cmap'].tables[0].cmap\",\n \"def getColor(self):\\n return self.__color\",\n \"def getColor(self):\\n return self.__color\",\n \"def getColor(self):\\n return self.__color\",\n \"def cmap(num,cmap = plt.cm.gist_earth_r):\\n return cmap(np.linspace(0, 1, num))\",\n \"def _create_colormap(self):\\n\\n max_count = max(self.data['rects'], key=lambda r: r['count'])['count']\\n ranges_list = [\\n [\\n int(max_count/x[0])+1,\\n int(max_count/x[1])+1\\n ] for x in [[6, 3], [3, 2], [2, 1]]\\n ]\\n ranges_list = [[0, 1]] + [[1, ranges_list[0][0]]] + ranges_list\\n\\n self.data['colormap'] = [\\n {\\n 'fill': self._get_fill(\\n i,\\n range(\\n ranges_list[i][0],\\n ranges_list[i][1]\\n )\\n ),\\n 'range': range(\\n ranges_list[i][0],\\n ranges_list[i][1]\\n )\\n } for i in range(0, 5)\\n ]\",\n \"def get_color(self):\\n return self._color\",\n \"def get_color(self):\\n return self._color\",\n \"def getColor(self):\\n return self.color\",\n \"def getColorSpace(self):\\n return getI1ProColorSpaceMode()\",\n \"def get_color(self):\\n\\n return self._color\",\n \"def get_color(self):\\r\\n return self.__color\",\n \"def color(self):\\n return self['color']\",\n \"def get_color(self):\\r\\n return self._color\",\n \"def get_color(self):\\n\\n return self.color\",\n \"def getColor(self):\\r\\n return self.color\",\n \"def get_color(self):\\n return self.color\",\n \"def colors(self):\\r\\n\\t\\treturn self._colors\",\n \"def display_colormap(self, colormap=None):\\n if colormap is None:\\n if HTML:\\n for colorname in cm._gen_cmap_registry():\\n display(colorname, self.show_colormap(colorname))\\n else:\\n raise Exception(\\\"you need to install IPython for this function to work\\\")\\n else:\\n width, height = (400, 25)\\n vector = np.arange(0, 1, 0.01)\\n vector = vector.reshape((1, 100))\\n cm_hot = cm.get_cmap(colormap)\\n vector = cm_hot(vector)\\n vector = np.uint8(vector * 255)\\n image = Image.fromarray(vector)\\n image = image.resize((width, height))\\n return image\",\n \"def create_cityscapes_label_colormap():\\r\\n colormap = np.zeros((256, 3), dtype=np.uint8)\\r\\n colormap[0] = [128, 64, 128]\\r\\n colormap[1] = [244, 35, 232]\\r\\n colormap[2] = [70, 70, 70]\\r\\n colormap[3] = [102, 102, 156]\\r\\n colormap[4] = [190, 153, 153]\\r\\n colormap[5] = [153, 153, 153]\\r\\n colormap[6] = [250, 170, 30]\\r\\n colormap[7] = [220, 220, 0]\\r\\n colormap[8] = [107, 142, 35]\\r\\n colormap[9] = [152, 251, 152]\\r\\n colormap[10] = [70, 130, 180]\\r\\n colormap[11] = [220, 20, 60]\\r\\n colormap[12] = [255, 0, 0]\\r\\n colormap[13] = [0, 0, 142]\\r\\n colormap[14] = [0, 0, 70]\\r\\n colormap[15] = [0, 60, 100]\\r\\n colormap[16] = [0, 80, 100]\\r\\n colormap[17] = [0, 0, 230]\\r\\n colormap[18] = [119, 11, 32]\\r\\n return colormap\",\n \"def get_colors(self):\\n colors = [\\\"#244486\\\", \\\"#A6A6A6\\\", \\\"#B12122\\\"]\\n cmap = LinearSegmentedColormap.from_list(\\\"mycmap\\\", colors)\\n\\n color_palette=[cmap(i) for i in np.linspace(0, 1, len(set(self.nodes_list)))]\\n return dict(zip(list(set(self.nodes_list)), color_palette))\",\n \"def color(self):\\n return self.__color\",\n \"def create_label_colormap():\\n colormap = np.array([\\n [128, 64, 128],\\n [244, 35, 232],\\n [ 70, 70, 70],\\n [102, 102, 156],\\n [190, 153, 153],\\n [153, 153, 153],\\n [250, 170, 30],\\n [220, 220, 0],\\n [107, 142, 35],\\n [152, 251, 152],\\n [ 70, 130, 180],\\n [220, 20, 60],\\n [255, 0, 0],\\n [ 0, 0, 142],\\n [ 0, 0, 70],\\n [ 0, 60, 100],\\n [ 0, 80, 100],\\n [ 0, 0, 230],\\n [119, 11, 32],\\n [ 0, 0, 0]], dtype=np.uint8)\\n return colormap\",\n \"def colors(self):\\n return self._colors\",\n \"def color(self):\\n return self._color\",\n \"def color(self):\\n return self._color\",\n \"def get_colors(self):\\n x = np.linspace(0, 1, self.length)\\n y = x**self.gamma\\n\\n value = np.linspace(0, 1, len(self.colors))\\n r = np.interp(y, value, self.colors[:,0])\\n g = np.interp(y, value, self.colors[:,1])\\n b = np.interp(y, value, self.colors[:,2])\\n\\n return np.dstack((r, g, b)).reshape(len(r), 3).astype(np.uint8)\",\n \"def get_palette(palette_name):\\n\\n if hasattr(plt.cm, palette_name):\\n cmap = getattr(plt.cm, palette_name)\\n elif palette_name in list(mpl_cm.cmap_d.keys()):\\n cmap = mpl_cm.get_cmap(palette_name)\\n else:\\n print(\\\"Error, color option '\\\", palette_name, \\\"' not a valid option\\\")\\n sys.exit(1)\\n\\n return cmap\",\n \"def color_mode(self):\\n return ids_core.Camera.color_mode.__get__(self)\",\n \"def getColor(self):\\n return self._l[2]\",\n \"def getColorMap(colors):\\n # Normalise RGBs\\n norm_colors = []\\n for color in colors:\\n norm_colors.append([val / 255. for val in color])\\n # create color map\\n cmap = cols.ListedColormap(norm_colors)\\n\\n return cmap\",\n \"def __get_color_table(self):\\n #Color: Green Yellow Orange Red Distance:\\n return [[0.70, 0.15, 0.1, 0.05], # 0\\n [0.17, 0.6, 0.17, 0.06], # 1\\n [0.06, 0.17, 0.6, 0.17], # 2\\n [0.05, 0.12, 0.23, 0.6], # 3\\n [0.05, 0.1, 0.15, 0.8]] # >= 4 \",\n \"def colors(self):\\n return self[\\\"colors\\\"]\",\n \"def colors(self):\\n return self[\\\"colors\\\"]\",\n \"def get_colors(nlevels, colormap=None):\\n if colormap is None:\\n from matplotlib.pyplot import cm\\n colormap = cm.rainbow\\n return colormap(np.linspace(0, 1, nlevels))\",\n \"def getColorSpace(self):\\n if not self.proxy:\\n self.proxy = self.session.service(\\\"ALVideoRecorder\\\")\\n return self.proxy.getColorSpace()\",\n \"def get_color(self):\\n _lib.caca_get_dither_color.argtypes = [_Dither]\\n _lib.caca_get_dither_color.restype = ctypes.c_char_p\\n\\n return _lib.caca_get_dither_color(self)\",\n \"def setColourMap(self):\\n cmap = self.config['cmap']\\n\\n pos, colour, mode = colourMaps.colourMaps(cmap)\\n\\n cmap = pg.ColorMap(pos, colour,mode)\\n self.lut = cmap.getLookupTable(0.0, 1.0, 256)\\n minsg = np.min(self.sg)\\n maxsg = np.max(self.sg)\\n self.colourStart = (self.config['brightness'] / 100.0 * self.config['contrast'] / 100.0) * (maxsg - minsg) + minsg\\n self.colourEnd = (maxsg - minsg) * (1.0 - self.config['contrast'] / 100.0) + self.colourStart\",\n \"def setColourMap(self):\\n cmap = self.config['cmap']\\n\\n pos, colour, mode = colourMaps.colourMaps(cmap)\\n\\n cmap = pg.ColorMap(pos, colour, mode)\\n self.lut = cmap.getLookupTable(0.0, 1.0, 256)\\n minsg = np.min(self.sg)\\n maxsg = np.max(self.sg)\\n self.colourStart = (self.config['brightness'] / 100.0 * self.config['contrast'] / 100.0) * (\\n maxsg - minsg) + minsg\\n self.colourEnd = (maxsg - minsg) * (1.0 - self.config['contrast'] / 100.0) + self.colourStart\",\n \"def generate_lut(self):\\n colormap = self.get_colormap()\\n\\n if self.test:\\n self.print_colormap(self.name, colormap)\\n\\n if self.centered:\\n return self.generate_spi3d_from_colormap(colormap, centered=True)\\n else:\\n return self.generate_spi3d_from_colormap(colormap, centered=False)\",\n \"def get_color_map(n):\\n jet = plt.get_cmap('jet')\\n cNorm = colors.Normalize(vmin=0, vmax=n-1)\\n scalarMap = cmx.ScalarMappable(norm=cNorm, cmap=jet)\\n outmap = []\\n for i in range(n):\\n outmap.append( scalarMap.to_rgba(i) )\\n return outmap\",\n \"def generate_colormap(scale_range=(0.0, 1.0), hue_range=(0.8, 0.0),\\n saturation_range=(1.0, 1.0), value_range=(0.8, 0.8),\\n nan_color=(0.2, 0.2, 0.2, 1.0)):\\n lookup_table = vtk.vtkLookupTable()\\n lookup_table.SetRange(scale_range)\\n\\n lookup_table.SetHueRange(hue_range)\\n lookup_table.SetSaturationRange(saturation_range)\\n lookup_table.SetValueRange(value_range)\\n lookup_table.SetNanColor(nan_color)\\n lookup_table.Build()\\n return lookup_table\",\n \"def _get_peirce_colour_scheme():\\n\\n this_colour_map_object = pyplot.cm.Blues\\n this_colour_norm_object = matplotlib.colors.BoundaryNorm(\\n LEVELS_FOR_PEIRCE_CONTOURS, this_colour_map_object.N)\\n\\n rgba_matrix = this_colour_map_object(this_colour_norm_object(\\n LEVELS_FOR_PEIRCE_CONTOURS\\n ))\\n\\n colour_list = [\\n rgba_matrix[i, ..., :-1] for i in range(rgba_matrix.shape[0])\\n ]\\n\\n colour_map_object = matplotlib.colors.ListedColormap(colour_list)\\n colour_map_object.set_under(numpy.full(3, 1.))\\n colour_norm_object = matplotlib.colors.BoundaryNorm(\\n LEVELS_FOR_PEIRCE_CONTOURS, colour_map_object.N)\\n\\n return colour_map_object, colour_norm_object\",\n \"def getCMAPResolutions(self):\\n try:\\n return self._cmapResolution\\n except AttributeError:\\n pass\\n flag = 'CMAP_RESOLUTION'\\n if flag not in self._raw_data and self.chamber:\\n flag = 'CHARMM_CMAP_RESOLUTION'\\n if flag in self._raw_data:\\n self._cmapResolution=self._raw_data[flag]\\n return self._cmapResolution\\n return 0\",\n \"def color(self):\\n return self.COLOR\",\n \"def autocolorscale(self):\\n return self[\\\"autocolorscale\\\"]\",\n \"def _get_color(self):\\n return self.__color\",\n \"def _get_color(self):\\n return self.__color\",\n \"def _get_color(self):\\n return self.__color\",\n \"def _get_color(self):\\n return self.__color\",\n \"def autocolorscale(self):\\n return self['autocolorscale']\",\n \"def _create_correlation_color_mapper(self):\\n tints = self.plot_design.contrary_color_tints\\n\\n no_correlation = [tints[9]]\\n small_correlation = [tints[7]] * 2\\n medium_correlation = [tints[6]] * 2\\n high_correlation = [tints[4]] * 4\\n very_high_correlation = [tints[2]] * 1\\n palette = no_correlation + small_correlation + medium_correlation + high_correlation + very_high_correlation\\n\\n linear_correlation = tints[0] # perfect correlations gets it's own color\\n cmap = LinearColorMapper(\\n palette=palette,\\n low=0,\\n high=0.9999,\\n high_color=linear_correlation\\n )\\n return cmap\",\n \"def map_channels(self, map_function):\\n return ScreenColor(map_function(self.red), map_function(self.green), map_function(self.blue))\",\n \"def color(self):\\n return self.settings['color']\",\n \"def get_color(self) -> str:\\n return self.color\",\n \"def _get_colors(num_colors):\\n cmap = plt.get_cmap()\\n return [cmap(1. * i / num_colors) for i in range(num_colors)]\",\n \"def getColorMapFlags():\\n\\treturn colorMap_flag\",\n \"def get_color(self):\\n return self._io.last_state['color']['front-center']\",\n \"def ListColorMaps(self):\\n p.rc('text', usetex=False)\\n a=p.outerproduct(numpy.arange(0,1,0.01),numpy.ones(10))\\n p.figure(figsize=(10,5))\\n p.subplots_adjust(top=0.8,bottom=0.05,left=0.01,right=0.99)\\n maps=[m for m in p.cm.datad.keys() if not m.endswith(\\\"_r\\\")]\\n maps.sort()\\n l=len(maps)+1\\n i=1\\n for m in maps:\\n p.subplot(1,l,i)\\n p.axis(\\\"off\\\")\\n p.imshow(a,aspect='auto',cmap=p.get_cmap(m),origin=\\\"lower\\\")\\n p.title(m,rotation=90,fontsize=10)\\n i=i+1\\n #savefig(\\\"colormaps.png\\\",dpi=100,facecolor='gray')\\n p.show()\"\n]","isConversational":false},"negative_scores":{"kind":"list like","value":["0.8298571","0.8298571","0.81955755","0.81955755","0.8056556","0.7981543","0.764267","0.76329446","0.756393","0.7444203","0.7027589","0.6996905","0.6950566","0.6934548","0.6839568","0.66553605","0.65015465","0.65008515","0.65008515","0.6488026","0.6488026","0.6488026","0.6443309","0.6443309","0.6436809","0.6324905","0.6262395","0.62276167","0.6214642","0.61982113","0.61126727","0.6080968","0.6019224","0.5993138","0.59862745","0.5974962","0.59669495","0.5954637","0.5936804","0.59193903","0.5874551","0.5873526","0.5868163","0.5868163","0.5868163","0.5843487","0.5843044","0.58302873","0.58302873","0.5827831","0.58197623","0.58162224","0.5787187","0.5771468","0.5770561","0.5764891","0.57537884","0.5745809","0.57413","0.5741277","0.574078","0.5737733","0.57276064","0.5723969","0.57196367","0.5706787","0.5706787","0.56832033","0.5672628","0.56693155","0.5654575","0.5642125","0.5638017","0.5635836","0.5635836","0.56332165","0.56298554","0.56148684","0.56066424","0.56039405","0.55978477","0.5591139","0.5580135","0.5572983","0.5556945","0.5551672","0.55283016","0.55150187","0.55150187","0.55150187","0.55150187","0.5500963","0.54912496","0.5489569","0.5480687","0.54778117","0.547639","0.5475924","0.54561067","0.54532385"],"string":"[\n \"0.8298571\",\n \"0.8298571\",\n \"0.81955755\",\n \"0.81955755\",\n \"0.8056556\",\n \"0.7981543\",\n \"0.764267\",\n \"0.76329446\",\n \"0.756393\",\n \"0.7444203\",\n \"0.7027589\",\n \"0.6996905\",\n \"0.6950566\",\n \"0.6934548\",\n \"0.6839568\",\n \"0.66553605\",\n \"0.65015465\",\n \"0.65008515\",\n \"0.65008515\",\n \"0.6488026\",\n \"0.6488026\",\n \"0.6488026\",\n \"0.6443309\",\n \"0.6443309\",\n \"0.6436809\",\n \"0.6324905\",\n \"0.6262395\",\n \"0.62276167\",\n \"0.6214642\",\n \"0.61982113\",\n \"0.61126727\",\n \"0.6080968\",\n \"0.6019224\",\n \"0.5993138\",\n \"0.59862745\",\n \"0.5974962\",\n \"0.59669495\",\n \"0.5954637\",\n \"0.5936804\",\n \"0.59193903\",\n \"0.5874551\",\n \"0.5873526\",\n \"0.5868163\",\n \"0.5868163\",\n \"0.5868163\",\n \"0.5843487\",\n \"0.5843044\",\n \"0.58302873\",\n \"0.58302873\",\n \"0.5827831\",\n \"0.58197623\",\n \"0.58162224\",\n \"0.5787187\",\n \"0.5771468\",\n \"0.5770561\",\n \"0.5764891\",\n \"0.57537884\",\n \"0.5745809\",\n \"0.57413\",\n \"0.5741277\",\n \"0.574078\",\n \"0.5737733\",\n \"0.57276064\",\n \"0.5723969\",\n \"0.57196367\",\n \"0.5706787\",\n \"0.5706787\",\n \"0.56832033\",\n \"0.5672628\",\n \"0.56693155\",\n \"0.5654575\",\n \"0.5642125\",\n \"0.5638017\",\n \"0.5635836\",\n \"0.5635836\",\n \"0.56332165\",\n \"0.56298554\",\n \"0.56148684\",\n \"0.56066424\",\n \"0.56039405\",\n \"0.55978477\",\n \"0.5591139\",\n \"0.5580135\",\n \"0.5572983\",\n \"0.5556945\",\n \"0.5551672\",\n \"0.55283016\",\n \"0.55150187\",\n \"0.55150187\",\n \"0.55150187\",\n \"0.55150187\",\n \"0.5500963\",\n \"0.54912496\",\n \"0.5489569\",\n \"0.5480687\",\n \"0.54778117\",\n \"0.547639\",\n \"0.5475924\",\n \"0.54561067\",\n \"0.54532385\"\n]","isConversational":false},"document_score":{"kind":"string","value":"0.7707698"},"document_rank":{"kind":"string","value":"6"}}},{"rowIdx":23792,"cells":{"query":{"kind":"string","value":"Returns a color dialog for this view."},"document":{"kind":"string","value":"def getColormapDialog(self, view):\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 defaultColorDialog(self):\n dialog = None\n if self.__hooks is not None:\n dialog = self.__hooks.getColormapDialog(self)\n if dialog is None:\n dialog = ColormapDialog()\n dialog.setModal(False)\n return dialog","def colorPickerDialog(self, current_color=None):\n\t\tcolor_dialog = QtWidgets.QColorDialog()\n\t\t#color_dialog.setOption(QtWidgets.QColorDialog.DontUseNativeDialog)\n\n\t\t# Set current colour\n\t\tif current_color is not None:\n\t\t\tcolor_dialog.setCurrentColor(current_color)\n\n\t\t# Only return a color if valid / dialog accepted\n\t\tif color_dialog.exec_() == color_dialog.Accepted:\n\t\t\tcolor = color_dialog.selectedColor()\n\t\t\treturn color","def createColorWindow():\n colorWindow = g.GraphWin(\"Color\", 150, 500) #Window to show colors to pick\n return colorWindow","def get_color(self):\n self.view.present(\n \"sheet\",\n orientations=ORIENTATIONS,\n )\n self.view.wait_modal()\n return self.rgb","def _color_change_mode(self):\r\n self.dlg.exec_()\r\n self.color = self.dlg.currentColor().name()\r\n self.colorPlate.setStyleSheet(\"background-color: %s;\" % self.color)\r\n self.input_scene.get_stk_color(self.color)\r\n return","def onCustomColorMenu(self, item):\n self.dialog = pyui.dialogs.ColorDialog(self.customColorChosen)\n self.dialog.doModal()\n return 1","def addDialogBox(self, color):\n\t\tself._getSize()\n\t\tobj = nceDialogBox(self.hcenter - 11, 10)\n\t\tobj.rtc = False\n\t\tobj.frame = [\t['╭' + ('─' * 19) + '╮', 3],\n\t\t\t\t\t\t['│' + (' ' * 19) + '│', 3],\n\t\t\t\t\t\t['│' + (' ' * 19) + '│', 3],\n\t\t\t\t\t\t['└' + ('─' * 19) + '╯', 3]]\n\t\tobj.visible = True\n\t\tobj.color = color\n\t\tself.menus.append(obj)\n\t\treturn obj","def GetHandle(self):\n return _XCAFDoc.XCAFDoc_ColorTool_GetHandle(self)","def build(self):\n Config.set('graphics', 'width', '1150')\n Config.set('graphics', 'height', '300')\n return ColorWidget()","def GetHandle(self):\n return _XCAFDoc.XCAFDoc_Color_GetHandle(self)","def colors(self):\r\n return ColorsResource(self)","def color(self):\n return self.settings['color']","def color(self):\n return self.COLOR","def getColor(self):\n return qt.QColor.fromRgbF(*self._color)","def color_picker(self):\n\n\t\tnum_to_select = 4\n\t\tpeg_color_list = [] #creates the list to store the peg object\n\n\t\t#write a for loop to set a loop to select 4 colors from SOLUTION in mm_model\n\t\tfor i in range(num_to_select): #use i just to run the loop, variable is not used elsewhere \n\t\t\t# print(i)\n\t\t\tcolor = random.choice(MasterModel.COLORS)\n\t\t\t# print(color)\n\t\t\t#associate color with peg objects\n\t\t\tpeg = ColorPeg(color)\n\n\t\t\t#append the peg_color list to make a list of peg objects \n\t\t\tpeg_color_list.append(peg)\n\t\t\t# print (peg_color_list)\n\t\t\n\t\t#create object for solution so it can be stored in model.py\n\t\tsolution = Guess(peg_color_list)\n\n\t\t#put solution into the self.guesses dictionary in the model\n\t\tself.model.guesses[\"solution\"] = solution\n\n\n\t\t#Testing Stuff:\n\t\t# for peg in peg_color_list:\n\t\t# \tprint(peg.peg_color)\n\n\t\t# print(self.model.guesses[\"solution\"])","def getColorModel(self):\n return self.getModel().getColorModel()","def GetColor(self, *args):\n return _XCAFDoc.XCAFDoc_ColorTool_GetColor(self, *args)","def getColor(self):\r\n return self.color","def getColor(self):\n return self.color","def ColorTool(*args):\n return _XCAFDoc.XCAFDoc_DocumentTool_ColorTool(*args)","def get_color(self):\n return self.color","def color(self):\n return self.container['color']","def pickAColor():\n color = _tkCall(tkColorChooser.askcolor)\n if color[0] != None:\n return Color(color[0][0], color[0][1], color[0][2])","def pickColour(self):\n colour = QColorDialog.getColor()\n if colour.isValid():\n self.user[\"Colour\"] = colour.name()\n self.ui.l_colour.setText(self.user[\"Colour\"])","def get_color(self):\n\n return self.color","def color(self):\n return self['color']","def get_color(self):\r\n return self._color","def color(self):\n return self._color","def color(self):\n return self._color","def FindColor(self, *args):\n return _XCAFDoc.XCAFDoc_ColorTool_FindColor(self, *args)","def get_color(self):\r\n return self.__color","def _get_color(self):\n return self.__color","def _get_color(self):\n return self.__color","def _get_color(self):\n return self.__color","def _get_color(self):\n return self.__color","def get_color(self):\n return self._color","def get_color(self):\n return self._color","def color(self):\n return self.__color","def __init__(self, parent):\n self.parent = parent\n myStyle = wx.DEFAULT_DIALOG_STYLE|wx.RESIZE_BORDER|wx.TAB_TRAVERSAL\n wx.Dialog.__init__(self, None, wx.ID_ANY, '%s - Choose your colors' % globs.myName, style=myStyle)\n\n self._initialize()\n\n self.panel1.SetSizerAndFit(self.topBoxSizer)\n self.SetClientSize(self.topBoxSizer.GetSize())\n self.Centre()","def getColor(self):\n return self.__color","def getColor(self):\n return self.__color","def getColor(self):\n return self.__color","def open_win(e) -> None:\n widget_sfx()\n r, g, b = parse_color(var.get())\n new_color, tk_color = askcolor(\n color=f\"#{r:02x}{g:02x}{b:02x}\",\n parent=parent.winfo_toplevel(),\n title=str(TRANS_SELECT_TITLE),\n )\n if new_color is not None:\n # On 3.8, these are floats.\n rf, gf, bf = new_color\n var.set(f'{int(rf)} {int(gf)} {int(bf)}')","def handlerSelectFillColor(self):\r\n dialog = QtGui.QColorDialog(self.layerSymbolFillColor)\r\n \r\n if dialog.exec_():\r\n selectedColor = dialog.selectedColor()\r\n sender = self.sender()\r\n if sender.objectName() == 'buttonLayerSymbolFillColor':\r\n self.layerSymbolFillColor = selectedColor\r\n elif sender.objectName() == 'buttonStyleCategorizedFillColor':\r\n self.styleCategorizedColor = selectedColor\r\n elif sender.objectName() == 'buttonStyleGraduatedFillColor':\r\n self.styleGraduatedColor = selectedColor\r\n elif sender.objectName() == 'buttonStyleRuleBasedFillColor':\r\n self.styleRuleBasedColor = selectedColor\r\n sender.setStyleSheet('background-color: {0};'.format(selectedColor.name()))","def get_option_color(cls, option):\n\n if option == 'Grey':\n color = cls.fromRgbF(0.4, 0.4, 0.4)\n elif option == 'Cancel':\n color = cls.fromRgbF(0.7, 0.5, 0.4)\n elif option == 'OK' or option == 'ok':\n color = cls.fromRgbF(0.5, 0.7, 0.8)\n elif option == 'Warning' or option == 'Warn':\n color = cls.fromRgbF(0.7, 0.2, 0.2)\n elif option == 'Collapse':\n color = cls.fromRgbF(0.15, 0.15, 0.15)\n elif option == 'Subtle':\n color = cls.fromRgbF(0.48, 0.48, 0.6)\n else:\n color = cls.fromRgbF(0.48, 0.48, 0.6)\n if option:\n color.ann = option + ' Color'\n return color","def color(self):\n if \"color\" in self._prop_dict:\n return self._prop_dict[\"color\"]\n else:\n return None","def get_color(self):\n return \"yellow\"","def _confirm_color(self, event = None):\n color = self._entry.get().strip()\n if color != \"\":\n self._color = color\n self._window.destroy()","def colors(self) -> Optional[pulumi.Input[Sequence[pulumi.Input['OneDashboardPageWidgetPyColorArgs']]]]:\n return pulumi.get(self, \"colors\")","def GetInstanceColor(self, *args):\n return _XCAFDoc.XCAFDoc_ColorTool_GetInstanceColor(self, *args)","def get_color(self) -> Optional[str]:\n return self.color","def _get_color(self, color):\n qcolor = QtGui.QColor()\n qcolor.setRgb(int(color[:2], base=16),\n int(color[2:4], base=16),\n int(color[4:6], base=16))\n return qcolor","def get_color(self):\n\n return self._color","def abrirDialogoDeConfiguracion(self):\n ventanaEmergente = Toplevel()\n ventanaEmergente.geometry(\"200x320\")\n # Se crea un canvas\n tela = Canvas(ventanaEmergente, height=320, width=200, bg=\"snow\")\n tela.place(x=0, y=0)\n btnColorEspacioDisponible = Button(tela, text=\"Espacio Disponible\", command = lambda :self.cambiarColor(0))\n btnColorEspacioDisponible.place(x=50, y=20)\n btnColorPared = Button(tela, text=\"Paredes\", command = lambda :self.cambiarColor(2))\n btnColorPared.place(x=66, y=50)\n btnColorNodo = Button(tela, text=\"Nodo\", command = lambda :self.cambiarColor(3))\n btnColorNodo.place(x=70, y=80)","def get_color(self) -> str:\r\n return self.color","def color(self) -> Optional[pulumi.Input[str]]:\n return pulumi.get(self, \"color\")","def color(self) -> Optional[pulumi.Input[str]]:\n return pulumi.get(self, \"color\")","def color(self) -> Optional[pulumi.Input[str]]:\n return pulumi.get(self, \"color\")","def color(self) -> Optional[pulumi.Input[str]]:\n return pulumi.get(self, \"color\")","def color(self) -> Optional[pulumi.Input[str]]:\n return pulumi.get(self, \"color\")","def color(self) -> Optional[pulumi.Input[str]]:\n return pulumi.get(self, \"color\")","def color(self) -> Optional[pulumi.Input[str]]:\n return pulumi.get(self, \"color\")","def color(self) -> Optional[pulumi.Input[str]]:\n return pulumi.get(self, \"color\")","def color(self) -> Optional[pulumi.Input[str]]:\n return pulumi.get(self, \"color\")","def color(self) -> Optional[pulumi.Input[str]]:\n return pulumi.get(self, \"color\")","def color(self) -> Optional[pulumi.Input[str]]:\n return pulumi.get(self, \"color\")","def color(self) -> Optional[pulumi.Input[str]]:\n return pulumi.get(self, \"color\")","def color(self) -> Optional[pulumi.Input[str]]:\n return pulumi.get(self, \"color\")","def color(self) -> Optional[pulumi.Input[str]]:\n return pulumi.get(self, \"color\")","def color(self) -> Optional[pulumi.Input[str]]:\n return pulumi.get(self, \"color\")","def color(self) -> Optional[pulumi.Input[str]]:\n return pulumi.get(self, \"color\")","def color(self) -> Optional[pulumi.Input[str]]:\n return pulumi.get(self, \"color\")","def color(self) -> Optional[pulumi.Input[str]]:\n return pulumi.get(self, \"color\")","def color(self) -> Optional[pulumi.Input[str]]:\n return pulumi.get(self, \"color\")","def color(self) -> Optional[pulumi.Input[str]]:\n return pulumi.get(self, \"color\")","def color(self) -> Optional[pulumi.Input[str]]:\n return pulumi.get(self, \"color\")","def color(self) -> Optional[pulumi.Input[str]]:\n return pulumi.get(self, \"color\")","def color(self) -> Optional[pulumi.Input[str]]:\n return pulumi.get(self, \"color\")","def color(self) -> Optional[pulumi.Input[str]]:\n return pulumi.get(self, \"color\")","def color(self) -> Optional[pulumi.Input[str]]:\n return pulumi.get(self, \"color\")","def color(self) -> Optional[pulumi.Input[str]]:\n return pulumi.get(self, \"color\")","def color(self) -> Optional[pulumi.Input[str]]:\n return pulumi.get(self, \"color\")","def color(self) -> Optional[pulumi.Input[str]]:\n return pulumi.get(self, \"color\")","def comdlg32_ChooseColor(jitter, get_str, set_str):\n ret_ad, args = jitter.func_args_stdcall([\"lpcc\"])\n raise RuntimeError('API not implemented')\n jitter.func_ret_stdcall(ret_ad, ret_value)","def get_color(self) -> str:\n return self.color","def conseguir_color(self):\n return self.pluma.conseguir_color()","def select_color(self):\n\t\tresult = tkinter.colorchooser.askcolor(self.center['bg'])\n\t\tif result:\n\t\t\t# 2nd part of result is the color object\n\t\t\tself.center['bg'] = result[1]","def color(self) -> Optional[str]:\n return self.colour","def create_dialog_helper(cls, title, body):\n content = MDLabel(\n font_style='Body1',\n theme_text_color='Secondary',\n text=body,\n size_hint_y=None,\n valign='top')\n content.bind(texture_size=content.setter('size'))\n dialog = cls.create_dialog_content_helper(title, content)\n return dialog","def color_mode(self):\n return ids_core.Camera.color_mode.__get__(self)","def color(self):\n global bordercolor\n bordercolor = QColorDialog.getColor(Qt.green, self)\n self.b_color.setPalette(QPalette(bordercolor))","def ui(self):\n return ui","def color(self):\n return rgba(self.value_of_css_property('color'))","def ChoiceColor(self, request, context):\n context.set_code(grpc.StatusCode.UNIMPLEMENTED)\n context.set_details('Method not implemented!')\n raise NotImplementedError('Method not implemented!')","def get_colour(self):\n return self.colour","def colors(self) -> Optional[pulumi.Input[Sequence[pulumi.Input['OneDashboardPageWidgetAreaColorArgs']]]]:\n return pulumi.get(self, \"colors\")","def _onEdit(self, event):\n index = self.colorlist.GetSelection()\n icol = self._indexTupleToColor(index)\n icd = wx.ColourData()\n icd.SetColour(icol)\n dialog = wx.ColourDialog(self, icd)\n\n if dialog.ShowModal() == wx.ID_OK:\n tup = _colorDataToTuple(dialog.GetColourData())\n self.graphColors[index] = tup\n self._tupleListToStrings()\n self._updateButtons(None)","def createFindDialog(self, c: Cmdr) -> Widget:\n if c:\n g.app.globalFindTabManager = c.findCommands.ftm\n top = c and c.frame.top # top is the DynamicWindow class.\n w = top.findTab # type:ignore\n dialog = QtWidgets.QDialog()\n # Fix #516: Hide the dialog. Never delete it.\n\n def closeEvent(event: Event) -> None:\n event.ignore()\n dialog.hide()\n\n dialog.closeEvent = closeEvent\n layout = QtWidgets.QVBoxLayout(dialog)\n layout.addWidget(w)\n self.attachLeoIcon(dialog)\n dialog.setLayout(layout)\n if c:\n # c.styleSheetManager.set_style_sheets(w=dialog)\n g.app.gui.setFilter(c, dialog, dialog, 'find-dialog')\n # This makes most standard bindings available.\n dialog.setModal(False)\n return dialog","def set_window_colour(self, event):\n rgb_triplet, rgb_string = tkColorChooser.askcolor()\n self.canvas.config(bg = rgb_string)","def colors(self) -> Optional[pulumi.Input[Sequence[pulumi.Input['OneDashboardPageWidgetFunnelColorArgs']]]]:\n return pulumi.get(self, \"colors\")"],"string":"[\n \"def defaultColorDialog(self):\\n dialog = None\\n if self.__hooks is not None:\\n dialog = self.__hooks.getColormapDialog(self)\\n if dialog is None:\\n dialog = ColormapDialog()\\n dialog.setModal(False)\\n return dialog\",\n \"def colorPickerDialog(self, current_color=None):\\n\\t\\tcolor_dialog = QtWidgets.QColorDialog()\\n\\t\\t#color_dialog.setOption(QtWidgets.QColorDialog.DontUseNativeDialog)\\n\\n\\t\\t# Set current colour\\n\\t\\tif current_color is not None:\\n\\t\\t\\tcolor_dialog.setCurrentColor(current_color)\\n\\n\\t\\t# Only return a color if valid / dialog accepted\\n\\t\\tif color_dialog.exec_() == color_dialog.Accepted:\\n\\t\\t\\tcolor = color_dialog.selectedColor()\\n\\t\\t\\treturn color\",\n \"def createColorWindow():\\n colorWindow = g.GraphWin(\\\"Color\\\", 150, 500) #Window to show colors to pick\\n return colorWindow\",\n \"def get_color(self):\\n self.view.present(\\n \\\"sheet\\\",\\n orientations=ORIENTATIONS,\\n )\\n self.view.wait_modal()\\n return self.rgb\",\n \"def _color_change_mode(self):\\r\\n self.dlg.exec_()\\r\\n self.color = self.dlg.currentColor().name()\\r\\n self.colorPlate.setStyleSheet(\\\"background-color: %s;\\\" % self.color)\\r\\n self.input_scene.get_stk_color(self.color)\\r\\n return\",\n \"def onCustomColorMenu(self, item):\\n self.dialog = pyui.dialogs.ColorDialog(self.customColorChosen)\\n self.dialog.doModal()\\n return 1\",\n \"def addDialogBox(self, color):\\n\\t\\tself._getSize()\\n\\t\\tobj = nceDialogBox(self.hcenter - 11, 10)\\n\\t\\tobj.rtc = False\\n\\t\\tobj.frame = [\\t['╭' + ('─' * 19) + '╮', 3],\\n\\t\\t\\t\\t\\t\\t['│' + (' ' * 19) + '│', 3],\\n\\t\\t\\t\\t\\t\\t['│' + (' ' * 19) + '│', 3],\\n\\t\\t\\t\\t\\t\\t['└' + ('─' * 19) + '╯', 3]]\\n\\t\\tobj.visible = True\\n\\t\\tobj.color = color\\n\\t\\tself.menus.append(obj)\\n\\t\\treturn obj\",\n \"def GetHandle(self):\\n return _XCAFDoc.XCAFDoc_ColorTool_GetHandle(self)\",\n \"def build(self):\\n Config.set('graphics', 'width', '1150')\\n Config.set('graphics', 'height', '300')\\n return ColorWidget()\",\n \"def GetHandle(self):\\n return _XCAFDoc.XCAFDoc_Color_GetHandle(self)\",\n \"def colors(self):\\r\\n return ColorsResource(self)\",\n \"def color(self):\\n return self.settings['color']\",\n \"def color(self):\\n return self.COLOR\",\n \"def getColor(self):\\n return qt.QColor.fromRgbF(*self._color)\",\n \"def color_picker(self):\\n\\n\\t\\tnum_to_select = 4\\n\\t\\tpeg_color_list = [] #creates the list to store the peg object\\n\\n\\t\\t#write a for loop to set a loop to select 4 colors from SOLUTION in mm_model\\n\\t\\tfor i in range(num_to_select): #use i just to run the loop, variable is not used elsewhere \\n\\t\\t\\t# print(i)\\n\\t\\t\\tcolor = random.choice(MasterModel.COLORS)\\n\\t\\t\\t# print(color)\\n\\t\\t\\t#associate color with peg objects\\n\\t\\t\\tpeg = ColorPeg(color)\\n\\n\\t\\t\\t#append the peg_color list to make a list of peg objects \\n\\t\\t\\tpeg_color_list.append(peg)\\n\\t\\t\\t# print (peg_color_list)\\n\\t\\t\\n\\t\\t#create object for solution so it can be stored in model.py\\n\\t\\tsolution = Guess(peg_color_list)\\n\\n\\t\\t#put solution into the self.guesses dictionary in the model\\n\\t\\tself.model.guesses[\\\"solution\\\"] = solution\\n\\n\\n\\t\\t#Testing Stuff:\\n\\t\\t# for peg in peg_color_list:\\n\\t\\t# \\tprint(peg.peg_color)\\n\\n\\t\\t# print(self.model.guesses[\\\"solution\\\"])\",\n \"def getColorModel(self):\\n return self.getModel().getColorModel()\",\n \"def GetColor(self, *args):\\n return _XCAFDoc.XCAFDoc_ColorTool_GetColor(self, *args)\",\n \"def getColor(self):\\r\\n return self.color\",\n \"def getColor(self):\\n return self.color\",\n \"def ColorTool(*args):\\n return _XCAFDoc.XCAFDoc_DocumentTool_ColorTool(*args)\",\n \"def get_color(self):\\n return self.color\",\n \"def color(self):\\n return self.container['color']\",\n \"def pickAColor():\\n color = _tkCall(tkColorChooser.askcolor)\\n if color[0] != None:\\n return Color(color[0][0], color[0][1], color[0][2])\",\n \"def pickColour(self):\\n colour = QColorDialog.getColor()\\n if colour.isValid():\\n self.user[\\\"Colour\\\"] = colour.name()\\n self.ui.l_colour.setText(self.user[\\\"Colour\\\"])\",\n \"def get_color(self):\\n\\n return self.color\",\n \"def color(self):\\n return self['color']\",\n \"def get_color(self):\\r\\n return self._color\",\n \"def color(self):\\n return self._color\",\n \"def color(self):\\n return self._color\",\n \"def FindColor(self, *args):\\n return _XCAFDoc.XCAFDoc_ColorTool_FindColor(self, *args)\",\n \"def get_color(self):\\r\\n return self.__color\",\n \"def _get_color(self):\\n return self.__color\",\n \"def _get_color(self):\\n return self.__color\",\n \"def _get_color(self):\\n return self.__color\",\n \"def _get_color(self):\\n return self.__color\",\n \"def get_color(self):\\n return self._color\",\n \"def get_color(self):\\n return self._color\",\n \"def color(self):\\n return self.__color\",\n \"def __init__(self, parent):\\n self.parent = parent\\n myStyle = wx.DEFAULT_DIALOG_STYLE|wx.RESIZE_BORDER|wx.TAB_TRAVERSAL\\n wx.Dialog.__init__(self, None, wx.ID_ANY, '%s - Choose your colors' % globs.myName, style=myStyle)\\n\\n self._initialize()\\n\\n self.panel1.SetSizerAndFit(self.topBoxSizer)\\n self.SetClientSize(self.topBoxSizer.GetSize())\\n self.Centre()\",\n \"def getColor(self):\\n return self.__color\",\n \"def getColor(self):\\n return self.__color\",\n \"def getColor(self):\\n return self.__color\",\n \"def open_win(e) -> None:\\n widget_sfx()\\n r, g, b = parse_color(var.get())\\n new_color, tk_color = askcolor(\\n color=f\\\"#{r:02x}{g:02x}{b:02x}\\\",\\n parent=parent.winfo_toplevel(),\\n title=str(TRANS_SELECT_TITLE),\\n )\\n if new_color is not None:\\n # On 3.8, these are floats.\\n rf, gf, bf = new_color\\n var.set(f'{int(rf)} {int(gf)} {int(bf)}')\",\n \"def handlerSelectFillColor(self):\\r\\n dialog = QtGui.QColorDialog(self.layerSymbolFillColor)\\r\\n \\r\\n if dialog.exec_():\\r\\n selectedColor = dialog.selectedColor()\\r\\n sender = self.sender()\\r\\n if sender.objectName() == 'buttonLayerSymbolFillColor':\\r\\n self.layerSymbolFillColor = selectedColor\\r\\n elif sender.objectName() == 'buttonStyleCategorizedFillColor':\\r\\n self.styleCategorizedColor = selectedColor\\r\\n elif sender.objectName() == 'buttonStyleGraduatedFillColor':\\r\\n self.styleGraduatedColor = selectedColor\\r\\n elif sender.objectName() == 'buttonStyleRuleBasedFillColor':\\r\\n self.styleRuleBasedColor = selectedColor\\r\\n sender.setStyleSheet('background-color: {0};'.format(selectedColor.name()))\",\n \"def get_option_color(cls, option):\\n\\n if option == 'Grey':\\n color = cls.fromRgbF(0.4, 0.4, 0.4)\\n elif option == 'Cancel':\\n color = cls.fromRgbF(0.7, 0.5, 0.4)\\n elif option == 'OK' or option == 'ok':\\n color = cls.fromRgbF(0.5, 0.7, 0.8)\\n elif option == 'Warning' or option == 'Warn':\\n color = cls.fromRgbF(0.7, 0.2, 0.2)\\n elif option == 'Collapse':\\n color = cls.fromRgbF(0.15, 0.15, 0.15)\\n elif option == 'Subtle':\\n color = cls.fromRgbF(0.48, 0.48, 0.6)\\n else:\\n color = cls.fromRgbF(0.48, 0.48, 0.6)\\n if option:\\n color.ann = option + ' Color'\\n return color\",\n \"def color(self):\\n if \\\"color\\\" in self._prop_dict:\\n return self._prop_dict[\\\"color\\\"]\\n else:\\n return None\",\n \"def get_color(self):\\n return \\\"yellow\\\"\",\n \"def _confirm_color(self, event = None):\\n color = self._entry.get().strip()\\n if color != \\\"\\\":\\n self._color = color\\n self._window.destroy()\",\n \"def colors(self) -> Optional[pulumi.Input[Sequence[pulumi.Input['OneDashboardPageWidgetPyColorArgs']]]]:\\n return pulumi.get(self, \\\"colors\\\")\",\n \"def GetInstanceColor(self, *args):\\n return _XCAFDoc.XCAFDoc_ColorTool_GetInstanceColor(self, *args)\",\n \"def get_color(self) -> Optional[str]:\\n return self.color\",\n \"def _get_color(self, color):\\n qcolor = QtGui.QColor()\\n qcolor.setRgb(int(color[:2], base=16),\\n int(color[2:4], base=16),\\n int(color[4:6], base=16))\\n return qcolor\",\n \"def get_color(self):\\n\\n return self._color\",\n \"def abrirDialogoDeConfiguracion(self):\\n ventanaEmergente = Toplevel()\\n ventanaEmergente.geometry(\\\"200x320\\\")\\n # Se crea un canvas\\n tela = Canvas(ventanaEmergente, height=320, width=200, bg=\\\"snow\\\")\\n tela.place(x=0, y=0)\\n btnColorEspacioDisponible = Button(tela, text=\\\"Espacio Disponible\\\", command = lambda :self.cambiarColor(0))\\n btnColorEspacioDisponible.place(x=50, y=20)\\n btnColorPared = Button(tela, text=\\\"Paredes\\\", command = lambda :self.cambiarColor(2))\\n btnColorPared.place(x=66, y=50)\\n btnColorNodo = Button(tela, text=\\\"Nodo\\\", command = lambda :self.cambiarColor(3))\\n btnColorNodo.place(x=70, y=80)\",\n \"def get_color(self) -> str:\\r\\n return self.color\",\n \"def color(self) -> Optional[pulumi.Input[str]]:\\n return pulumi.get(self, \\\"color\\\")\",\n \"def color(self) -> Optional[pulumi.Input[str]]:\\n return pulumi.get(self, \\\"color\\\")\",\n \"def color(self) -> Optional[pulumi.Input[str]]:\\n return pulumi.get(self, \\\"color\\\")\",\n \"def color(self) -> Optional[pulumi.Input[str]]:\\n return pulumi.get(self, \\\"color\\\")\",\n \"def color(self) -> Optional[pulumi.Input[str]]:\\n return pulumi.get(self, \\\"color\\\")\",\n \"def color(self) -> Optional[pulumi.Input[str]]:\\n return pulumi.get(self, \\\"color\\\")\",\n \"def color(self) -> Optional[pulumi.Input[str]]:\\n return pulumi.get(self, \\\"color\\\")\",\n \"def color(self) -> Optional[pulumi.Input[str]]:\\n return pulumi.get(self, \\\"color\\\")\",\n \"def color(self) -> Optional[pulumi.Input[str]]:\\n return pulumi.get(self, \\\"color\\\")\",\n \"def color(self) -> Optional[pulumi.Input[str]]:\\n return pulumi.get(self, \\\"color\\\")\",\n \"def color(self) -> Optional[pulumi.Input[str]]:\\n return pulumi.get(self, \\\"color\\\")\",\n \"def color(self) -> Optional[pulumi.Input[str]]:\\n return pulumi.get(self, \\\"color\\\")\",\n \"def color(self) -> Optional[pulumi.Input[str]]:\\n return pulumi.get(self, \\\"color\\\")\",\n \"def color(self) -> Optional[pulumi.Input[str]]:\\n return pulumi.get(self, \\\"color\\\")\",\n \"def color(self) -> Optional[pulumi.Input[str]]:\\n return pulumi.get(self, \\\"color\\\")\",\n \"def color(self) -> Optional[pulumi.Input[str]]:\\n return pulumi.get(self, \\\"color\\\")\",\n \"def color(self) -> Optional[pulumi.Input[str]]:\\n return pulumi.get(self, \\\"color\\\")\",\n \"def color(self) -> Optional[pulumi.Input[str]]:\\n return pulumi.get(self, \\\"color\\\")\",\n \"def color(self) -> Optional[pulumi.Input[str]]:\\n return pulumi.get(self, \\\"color\\\")\",\n \"def color(self) -> Optional[pulumi.Input[str]]:\\n return pulumi.get(self, \\\"color\\\")\",\n \"def color(self) -> Optional[pulumi.Input[str]]:\\n return pulumi.get(self, \\\"color\\\")\",\n \"def color(self) -> Optional[pulumi.Input[str]]:\\n return pulumi.get(self, \\\"color\\\")\",\n \"def color(self) -> Optional[pulumi.Input[str]]:\\n return pulumi.get(self, \\\"color\\\")\",\n \"def color(self) -> Optional[pulumi.Input[str]]:\\n return pulumi.get(self, \\\"color\\\")\",\n \"def color(self) -> Optional[pulumi.Input[str]]:\\n return pulumi.get(self, \\\"color\\\")\",\n \"def color(self) -> Optional[pulumi.Input[str]]:\\n return pulumi.get(self, \\\"color\\\")\",\n \"def color(self) -> Optional[pulumi.Input[str]]:\\n return pulumi.get(self, \\\"color\\\")\",\n \"def color(self) -> Optional[pulumi.Input[str]]:\\n return pulumi.get(self, \\\"color\\\")\",\n \"def comdlg32_ChooseColor(jitter, get_str, set_str):\\n ret_ad, args = jitter.func_args_stdcall([\\\"lpcc\\\"])\\n raise RuntimeError('API not implemented')\\n jitter.func_ret_stdcall(ret_ad, ret_value)\",\n \"def get_color(self) -> str:\\n return self.color\",\n \"def conseguir_color(self):\\n return self.pluma.conseguir_color()\",\n \"def select_color(self):\\n\\t\\tresult = tkinter.colorchooser.askcolor(self.center['bg'])\\n\\t\\tif result:\\n\\t\\t\\t# 2nd part of result is the color object\\n\\t\\t\\tself.center['bg'] = result[1]\",\n \"def color(self) -> Optional[str]:\\n return self.colour\",\n \"def create_dialog_helper(cls, title, body):\\n content = MDLabel(\\n font_style='Body1',\\n theme_text_color='Secondary',\\n text=body,\\n size_hint_y=None,\\n valign='top')\\n content.bind(texture_size=content.setter('size'))\\n dialog = cls.create_dialog_content_helper(title, content)\\n return dialog\",\n \"def color_mode(self):\\n return ids_core.Camera.color_mode.__get__(self)\",\n \"def color(self):\\n global bordercolor\\n bordercolor = QColorDialog.getColor(Qt.green, self)\\n self.b_color.setPalette(QPalette(bordercolor))\",\n \"def ui(self):\\n return ui\",\n \"def color(self):\\n return rgba(self.value_of_css_property('color'))\",\n \"def ChoiceColor(self, request, context):\\n context.set_code(grpc.StatusCode.UNIMPLEMENTED)\\n context.set_details('Method not implemented!')\\n raise NotImplementedError('Method not implemented!')\",\n \"def get_colour(self):\\n return self.colour\",\n \"def colors(self) -> Optional[pulumi.Input[Sequence[pulumi.Input['OneDashboardPageWidgetAreaColorArgs']]]]:\\n return pulumi.get(self, \\\"colors\\\")\",\n \"def _onEdit(self, event):\\n index = self.colorlist.GetSelection()\\n icol = self._indexTupleToColor(index)\\n icd = wx.ColourData()\\n icd.SetColour(icol)\\n dialog = wx.ColourDialog(self, icd)\\n\\n if dialog.ShowModal() == wx.ID_OK:\\n tup = _colorDataToTuple(dialog.GetColourData())\\n self.graphColors[index] = tup\\n self._tupleListToStrings()\\n self._updateButtons(None)\",\n \"def createFindDialog(self, c: Cmdr) -> Widget:\\n if c:\\n g.app.globalFindTabManager = c.findCommands.ftm\\n top = c and c.frame.top # top is the DynamicWindow class.\\n w = top.findTab # type:ignore\\n dialog = QtWidgets.QDialog()\\n # Fix #516: Hide the dialog. Never delete it.\\n\\n def closeEvent(event: Event) -> None:\\n event.ignore()\\n dialog.hide()\\n\\n dialog.closeEvent = closeEvent\\n layout = QtWidgets.QVBoxLayout(dialog)\\n layout.addWidget(w)\\n self.attachLeoIcon(dialog)\\n dialog.setLayout(layout)\\n if c:\\n # c.styleSheetManager.set_style_sheets(w=dialog)\\n g.app.gui.setFilter(c, dialog, dialog, 'find-dialog')\\n # This makes most standard bindings available.\\n dialog.setModal(False)\\n return dialog\",\n \"def set_window_colour(self, event):\\n rgb_triplet, rgb_string = tkColorChooser.askcolor()\\n self.canvas.config(bg = rgb_string)\",\n \"def colors(self) -> Optional[pulumi.Input[Sequence[pulumi.Input['OneDashboardPageWidgetFunnelColorArgs']]]]:\\n return pulumi.get(self, \\\"colors\\\")\"\n]","isConversational":false},"negative_scores":{"kind":"list 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\"0.75629133\",\n \"0.68842196\",\n \"0.62867564\",\n \"0.6189634\",\n \"0.61299944\",\n \"0.6009618\",\n \"0.59474766\",\n \"0.58456355\",\n \"0.5730012\",\n \"0.5706063\",\n \"0.57012826\",\n \"0.5627111\",\n \"0.55824256\",\n \"0.55625993\",\n \"0.5549173\",\n \"0.5520978\",\n \"0.55189264\",\n \"0.5484155\",\n \"0.546726\",\n \"0.5448726\",\n \"0.5443655\",\n \"0.5420507\",\n \"0.54115045\",\n \"0.53807205\",\n \"0.53508925\",\n \"0.530614\",\n \"0.52909666\",\n \"0.5286275\",\n \"0.5286275\",\n \"0.5276902\",\n \"0.5265661\",\n \"0.5249751\",\n \"0.5249751\",\n \"0.5249751\",\n \"0.5249751\",\n \"0.52488387\",\n \"0.52488387\",\n \"0.52409434\",\n \"0.5235679\",\n \"0.52333057\",\n \"0.52333057\",\n \"0.52333057\",\n \"0.5227849\",\n \"0.52078795\",\n \"0.5185691\",\n \"0.51813555\",\n \"0.5166895\",\n \"0.51550585\",\n \"0.5153839\",\n \"0.51468784\",\n \"0.51398605\",\n \"0.5124245\",\n \"0.5124147\",\n \"0.511932\",\n \"0.51079196\",\n \"0.51035875\",\n \"0.51035875\",\n \"0.51035875\",\n \"0.51035875\",\n \"0.51035875\",\n \"0.51035875\",\n \"0.51035875\",\n \"0.51035875\",\n \"0.51035875\",\n \"0.51035875\",\n \"0.51035875\",\n \"0.51035875\",\n \"0.51035875\",\n \"0.51035875\",\n \"0.51035875\",\n \"0.51035875\",\n \"0.51035875\",\n \"0.51035875\",\n \"0.51035875\",\n \"0.51035875\",\n \"0.51035875\",\n \"0.51035875\",\n \"0.51035875\",\n \"0.51035875\",\n \"0.51035875\",\n \"0.51035875\",\n \"0.51035875\",\n \"0.51035875\",\n \"0.50949997\",\n \"0.50927985\",\n \"0.5092594\",\n \"0.5081813\",\n \"0.5081053\",\n \"0.5076718\",\n \"0.5070743\",\n \"0.505667\",\n \"0.5051744\",\n \"0.50388736\",\n \"0.5031223\",\n \"0.50301164\",\n \"0.50224614\",\n \"0.50079125\",\n \"0.50058573\",\n \"0.50041467\",\n \"0.49935946\"\n]","isConversational":false},"document_score":{"kind":"string","value":"0.62007624"},"document_rank":{"kind":"string","value":"3"}}},{"rowIdx":23793,"cells":{"query":{"kind":"string","value":"Called when the widget of the view was created"},"document":{"kind":"string","value":"def viewWidgetCreated(self, view, plot):\n return"},"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 init_widget(self):","def create_widgets(self):","def on_show_view(self):\n self.setup()","def on_show_view(self):\n self.setup()","def on_show_view(self):\n self.setup()","def create_widgets( self ):","def create_widget(self):\n pass","def onShow(self):\n pass","def init_widget(self):\n super(UiKitTextView, self).init_widget()\n self.init_text()","def getWidget(self):","def on_widget_constructed(callback):\n Widget._widget_construction_callback = callback","def on_draw(self):\n\t\tself.render()","def on_draw(self):\n\t\tself.render()","def on_create(self):","def create_widget(self):\n self.widget = ListView(self.get_context())","def updateWidget(self):\n pass","def prepare_UI(self):","def create(self, parent):\n self.widget = QtCore.QObject(parent)","def onInit(self):\n pass","def update_view(self): \n raise NotImplementedError(\"Widget descendents MUST implement the update_view() method!\")","def __init__(self):\r\n super().__init__()\r\n self.init_ui()","def __init__(self):\n self.view = GuiView(self)\n return","def initializeUI(self):\n self.setStyleSheet(abstyle)\n self.setGeometry(140, 100, 860, 484)\n self.setWindowTitle('Emotions Data View')\n self.setupModelView()","def init_ui(self):\n raise NotImplementedError","def init_ui(self):\n raise NotImplementedError","def create_widget(self):\n self.widget = UILabel()","def on_show_view(self):\n\n # Makes the background darker\n arcade.set_background_color([rgb - 50 for rgb in arcade.color.DARK_BLUE_GRAY])\n\n # Enable the UIManager when the view is showm.\n self.manager.enable()","def on_show_view(self):\n arcade.set_background_color(arcade.color.DARK_BLUE_GRAY)\n\n # Enable the UIManager when the view is showm.\n self.manager.enable()","def init_widget(self):\n super(AndroidListView, self).init_widget()\n d = self.declaration\n\n if d.divider_height >= 0:\n self.set_divider_height(d.divider_height)\n if d.header_dividers:\n self.set_header_dividers(d.header_dividers)\n if d.footer_dividers:\n self.set_footer_dividers(d.footer_dividers)\n if d.items_can_focus:\n self.set_items_can_focus(d.items_can_focus)\n\n w = self.widget\n w.setOnItemClickListener(w.getId())\n w.setOnItemLongClickListener(w.getId())\n w.onItemClick.connect(self.on_item_click)\n w.onItemLongClick.connect(self.on_item_long_click)\n #self.widget.setOnScrollListener(self.widget.getId())\n #self.widget.onScroll.connect(self.on_scroll)\n\n #: Selection listener\n #self.widget.setOnItemSelectedListener(self.widget.getId())\n #self.widget.onItemSelected.connect(self.on_item_selected)\n #self.widget.onNothingSelected.connect(self.on_nothing_selected)","def fromControls(self,widget):","def setup(self):\n self.ui.setup_window()","def on_load(self):","def init_widget(self):\n self._build_config()\n self._raw_toolbar.initToolbar(self.config)","def on_show_view(self):\n self.setup()\n arcade.set_background_color(arcade.color.BLACK)","def process_IN_CREATE(self, event):","def __init__(self):\n Redrawable.all_instantiated_widgets.append(self)","def initialize(self):\n super(QtBaseWidgetComponent, self).initialize()","def __init__(self, parent=None):\n super(Representative, self).__init__(parent)\n self.setupUi(self)","def set_content(self, widget):\n\t\tpass","def create_widget(self):\n item = QNodeItem(self)\n self.widget = item","def onNew(self):\n # Ensure that we can work\n plt = Plot.getPlot()\n if not plt:\n self.updateUI()\n return\n # Get again all the subwidgets (to avoid PySide Pitfalls)\n mw = self.getMainWindow()\n form = mw.findChild(QtGui.QWidget, \"TaskPanel\")\n form.axId = self.widget(QtGui.QSpinBox, \"axesIndex\")\n\n Plot.addNewAxes()\n form.axId.setValue(len(plt.axesList) - 1)\n plt.update()","def on_activate(self):","def afterInit(self):","def init_layout(self):\n pass","def on_draw(self):\n # draw everything","def dlg_initialize(self):\n pass # override","def widgetSetup(self):\n self.master.resizable(0, 0)\n self.master.iconbitmap('logo.ico')\n self.master.title(\"Ejercicio POO\")\n\n self.master.bind(\"<Return>\", lambda e: self.create())\n self.master.bind(\"<Delete>\", lambda e: self.delete())","def init_ui(self):\n raise NotImplementedError(\"This is an abstract method.\")","def __init__(self, parent, **kwargs):\n super(bubblePyHomeWidget, self).__init__(parent, **kwargs)\n self._firstView = True\n\n self.OneBubbleBtn.clicked.connect(self._handleOneBubble)\n self.ManyBubblesBtn.clicked.connect(self._handleManyBubbles)\n\n self._statusBox, statusLayout = self._createElementWidget(self, QtGui.QVBoxLayout, True)\n statusLayout.addStretch()\n\n self._nimbleStatus = NimbleStatusElement(\n self._statusBox,\n disabled=self.mainWindow.appConfig.get(UserConfigEnum.NIMBLE_TEST_STATUS, True) )\n statusLayout.addWidget(self._nimbleStatus)","def on_draw_overlay(self):","def fill_ui(self):\n self.review_type_widget.set_review_type(self.review_type)\n\n if self.reviewer:\n self.reviewer_name_widget.setText(self.reviewer.name)\n\n if self.task:\n self.task_name_widget.setText(\n \"%s (%s) (%s)\"\n % (\n self.task.name,\n \" | \".join(\n [self.task.project.name]\n + [parent_task.name for parent_task in self.task.parents]\n ),\n self.task.id,\n )\n )\n\n # from stalker import Version\n # version = Version.query.filter(Version.task == self.task).order_by(Version.date_created.desc()).first()\n #\n # if version:\n # self.latest_version_widget.version = version","def on_pre_enter(self):\n\n # Setup task list\n self.task_list = SheetView()\n self.task_list_layout = FloatLayout()\n self.task_list.add_widget(self.task_list_layout)\n\n # Tool bar\n self.ids.action_bar.on_action_button = lambda: self.open_component_menu(\n )\n\n self.open_task_list()\n\n self.app.tool_bar.right_action_items.append(\n ['image-filter-center-focus', lambda x: self.to_center()])","def create_widget(self):\n self.widget = QCustomActionGroup(self.parent_widget())","def on_show_view(self):\r\n self.setup()\r\n arcade.set_background_color(BACKGROUND_COLOR)","def CreatePresentation(self, event):\n pass","def init_widget(self):\n super(QtNodeItem, self).init_widget()\n d = self.declaration\n self.set_id(d.id)\n\n self.set_width(d.width)\n self.set_height(d.height)\n self.set_position(d.position)\n self.set_edge_size(d.edge_size)\n self.set_title_height(d.title_height)\n self.set_padding(d.padding)\n\n self.set_show_content_inline(d.show_content_inline)\n self.set_content(d.content)\n\n self.set_name(d.name)\n self.set_color_default(d.color_default)\n self.set_color_selected(d.color_selected)\n self.set_color_title(d.color_title)\n self.set_color_title_background(d.color_title_background)\n self.set_color_background(d.color_background)\n\n self.widget.setFlag(QtWidgets.QGraphicsItem.ItemIsSelectable)\n self.widget.setFlag(QtWidgets.QGraphicsItem.ItemIsMovable)\n self.widget.setFlag(QtWidgets.QGraphicsItem.ItemSendsGeometryChanges)","def create(self, parent):\n self.widget = QFrame(parent)","def on_window_ready(self):\n pass","def post_build(self):","def init_widget(self):\n super(QtViewTable, self).init_widget()\n d = self.declaration\n self.set_table_model(d.table_model)\n self.set_orientation(d.orientation)","def __init__(self, parent=None):\n super(addOne, self).__init__(parent)\n self.setupUi(self)\n \n # 内部自定义初始化操作 ---------------------------------------------------------------------begin\n self.zShow = Fdebug(self);\n self.zShow.show();\n self.verticalLayout.insertWidget(0, self.zShow)","def OnSize(self,event):\r\n\t\t\r\n self.SetupView()\r\n event.Skip()","def widget(self, request, group):","def onViewLog(self):\n view_log.ViewLog(self.root, self.log)","def init_ui(self, dict_info):\n\n data = dict_info['data']\n color = dict_info['color']\n depot = dict_info['depot']\n mtour = dict_info['tour']\n zoomx = dict_info['zoomx']\n zoomy = dict_info['zoomy']\n\n list_appointment = data['appointment']\n\n self.parent.title(\"Simple\")\n self.pack(fill=BOTH, expand=1)\n\n depot, mtour = zoom_before_drawing(\n depot,\n mtour,\n zoomx,\n zoomy)\n\n canvas = Canvas(self)\n\n idx = 0\n\n for tour in mtour:\n tour.insert(0, model.Appointment(depot, 0, -1))\n draw_tour(tour, canvas, translate_to_tkcolor(color[idx]))\n idx += 1\n\n canvas.create_oval(depot.get_x(),\n depot.get_y(),\n depot.get_x()-5,\n depot.get_y()-5,\n outline=\"black\",\n fill=\"green\",\n width=7)\n\n for appointment in list_appointment:\n currentx = appointment.get_x() * zoomx\n currenty = appointment.get_y() * zoomy\n\n canvas.create_oval(\n currentx,\n currenty,\n currentx - 3,\n currenty - 3,\n outline=\"red\",\n fill=\"red\",\n width=5)\n\n canvas.pack(fill=BOTH, expand=1)","def __init__(self):\n self.stack = QWidget()","def on_load(self):\n pass","def on_load(self):\n pass","def init_layout(self):\n super(WxTraitsItem, self).init_layout()\n self.refresh_traits_widget(notify=False)","def init_widget(self):\n super(WxDockPane, self).init_widget()\n d = self.declaration\n self.set_title(d.title)\n self.set_title_bar_visible(d.title_bar_visible)\n self.set_title_bar_orientation(d.title_bar_orientation)\n self.set_closable(d.closable)\n self.set_movable(d.movable)\n self.set_floatable(d.floatable)\n self.set_floating(d.floating)\n self.set_dock_area(d.dock_area)\n self.set_allowed_dock_areas(d.allowed_dock_areas)\n widget = self.widget\n widget.Bind(EVT_DOCK_PANE_FLOATED, self.on_floated)\n widget.Bind(EVT_DOCK_PANE_DOCKED, self.on_docked)\n widget.Bind(EVT_DOCK_PANE_CLOSED, self.on_closed)","def __init__(self, parent: View):\n super().__init__(parent)\n # Crossword title\n self.title = tk.StringVar(self.root)\n self.title_label = tk.Label(self.frame, textvariable=self.title)\n # Crossword author\n self.author = tk.StringVar(self.root)\n self.author_label = tk.Label(self.frame, textvariable=self.author)\n # Dividing line separating the header and other groups\n self.separator = tk.Frame(self.frame)\n # Load\n self.load()","def onShowed(self):\n self.parent.actionTagTwo=\"\"\n pass","def initUI(self):\n # Setting the main layout as Vertical.\n self.mainLayout = QHBoxLayout()\n\n # Create title.\n self.title = QLabel(self.__name + \" : \")\n\n # Add description as tooltip.\n self.title.setToolTip(self.__description)\n\n # Add title to main layout.\n self.mainLayout.addWidget(self.title)\n\n # Create ComboBox.\n self.dropDown = QComboBox()\n\n # Add datas to drop down.\n self.dropDown.addItems(self.__datas)\n\n # Set default index to dropdown.\n self.dropDown.setCurrentIndex(self.__currentValue)\n\n # Connect dropdown with update method.\n self.dropDown.currentIndexChanged.connect(self.changeCurrentValue)\n\n # Add ComboBox to main layout.\n self.mainLayout.addWidget(self.dropDown)\n\n # Add the main layout to the window.\n self.setLayout(self.mainLayout)","def toControls(self,widget):","def on_show_view(self):\n self.setup()\n arcade.set_background_color(arcade.color.BLACK)\n arcade.set_viewport(0, constants.SCREEN_WIDTH - 1, 0, constants.SCREEN_HEIGHT - 1)","def createWidget(self, parent):\n raise NotImplementedError()","def _setup_ui(self):\n\n self.window = ui.Widget()\n self.window.dimensions = ui.normalize_dimension((\n 0, 0,\n self.normalized_screen_resolution[0],\n self.normalized_screen_resolution[1]\n ))\n self.window.background_color = ImageColor.getcolor('#000000', 'RGB')\n\n interface_frame = ui.Widget(parent=self.window)\n interface_frame.dimensions = ui.normalize_dimension((\n self.preview_renderer.window[2],\n 0,\n self.normalized_screen_resolution[0] - self.preview_renderer.window[2],\n self.normalized_screen_resolution[1]\n ))\n interface_frame.background_color = ImageColor.getcolor('#ffffff', 'RGB')\n\n number = ui.LabelWidget(\"\",\n name=NAME_GET_STARTED,\n parent=interface_frame,\n align=\"center\",\n font_color=(0, 0, 0, 255))\n number.dimensions = (\n 5, 5,\n interface_frame.width - 10,\n interface_frame.height - 10\n )","def init_ui(self):\n self.parent.title(\"Roku Player Controller\")\n self.style.theme_use(\"default\")","def on_load(self):\n self.__init__()","def on_activate(self) -> None:","def create_widget(self):\r\n self.root.ids.entriesBox.clear_widgets()\r\n num_place = len(self.place_list.list_places) # Determine the number of places in the list\r\n visited_place = 0\r\n for place in self.place_list.list_places: # Loop from first place to last place\r\n name = place.name\r\n # assert isinstance(place.country)\r\n country = place.country\r\n priority = place.priority\r\n visited = place.is_required\r\n display_text = self.generateDisplayText(name, country, priority,\r\n visited) # Display place's information on the widget\r\n if visited == \"n\":\r\n visited_place += 1\r\n button_color = self.getColor(visited)\r\n else:\r\n button_color = self.getColor(visited)\r\n\r\n temp_button = Button(text=display_text, id=place.name,\r\n background_color=button_color) # Mark the place visited\r\n temp_button.bind(on_release=self.press_entry) # Display message of the GUI status\r\n self.root.ids.entriesBox.add_widget(temp_button)\r\n self.message = \"To visit: {}. visited: {}\".format(num_place - visited_place,\r\n visited_place) # Display number of place visited or not visited\r","def __init__(self, *args, **kwargs):\n\n super(FlirCameraWidget, self).__init__(*args, **kwargs)\n #self.loadForm()\n self.window = Ui_MainWindow()\n self.window.setupUi(self)\n\n self.initUI()\n Styles(self)","def createWidgets(self):\n raise NotImplementedError","def __init__(self, controller):\n super().__init__(controller)\n\n # The hovered input when entering this View.\n self.first_inp = \"s\"\n\n # Initialize selected variable.\n self.selected = None\n\n # Make background graphics.\n self.make_background_graphics()\n\n # Make Buttons.\n self.make_buttons()\n\n # Make the information box. This explains each Button.\n self.make_info_box()\n\n # Initializes popup.\n self.make_popup()\n\n # Map of input to functions.\n enter = self.graphics.ENTER_KEY\n self.controls = {\n # Pressing \"q\" will go back to the main menu.\n \"q\": lambda: Action(\"goto main menu view\", []),\n\n # Movement keys.\n \"w\": lambda: self.move_cursor(Direction.U),\n \"a\": lambda: self.move_cursor(Direction.L),\n \"s\": lambda: self.move_cursor(Direction.D),\n \"d\": lambda: self.move_cursor(Direction.R),\n\n # Repeat the last valid input.\n enter: self.repeat_last_valid_input,\n\n # Click the selected UIElement.\n \"m\": self.click\n }","def onInsert(self):\n self.mainWindow.insert()","def __init__(self, parent=None):\n super(QCTP, self).__init__(parent)\n self.setupUi(self)","def add_widget(self, widget):\n widget.bind(on_touch_down=self.button_touch_down)\n return super(MainGrid, self).add_widget(widget)","def on_run(self):\r\n\r\n\t\tpass","def create(self, parent):\n self.widget = QImageView(parent)","def pre_draw(self):","def create_widgets(self):\n # self.var_spherical = IntVar()\n # self.var_3d = IntVar()\n # self.var_spatial_audio = IntVar()\n # self.button_open[\"command\"] = self.action_open\n # self.button_inject[\"command\"] = self.action_inject\n pass","def initialize(self):\n super(QtWidgetComponent, self).initialize()\n shell = self.shell_obj\n self.set_enabled(shell.enabled)\n if shell.bgcolor:\n self.set_bgcolor(shell.bgcolor)\n if shell.fgcolor:\n self.set_fgcolor(shell.fgcolor)\n if shell.font:\n self.set_font(shell.font)","def _initUI(self) -> None:\n self._createActions()\n self._addActionsToMoveButtons()\n self._createToolBar()\n self._createStatusBar()\n self._createMainContextMenu()","def onInit(self):\n self.list_control = self.getControl(6)\n self.getControl(1).setLabel(self.window_title)\n self.getControl(3).setVisible(False)\n try:\n self.getControl(7).setLabel(xbmc.getLocalizedString(222))\n except Exception:\n pass\n\n self.getControl(5).setVisible(False)\n\n # add our items to the listing and focus the control\n self.list_control.addItems(self.listing)\n self.setFocus(self.list_control)","def _render_callback(self, _sim, _viewer):\n pass","def init_toolbar(self):\n raise NotImplementedError","def __init__widget(self):\n self.__introduction = QtWidgets.QPushButton('使用“WSAD”对应“上下左右”控制\\n贪吃蛇，点击开始游戏！')\n self.add_layout_widget(self.central_widget, self.__introduction)\n self.__introduction.clicked.connect(self.__run)\n self.__ticker.timeout.connect(self.__snake_move)","def __call__(self):\n self.show()","def on_event(self, event):","def render(self):\r\n super().render()"],"string":"[\n \"def init_widget(self):\",\n \"def create_widgets(self):\",\n \"def on_show_view(self):\\n self.setup()\",\n \"def on_show_view(self):\\n self.setup()\",\n \"def on_show_view(self):\\n self.setup()\",\n \"def create_widgets( self ):\",\n \"def create_widget(self):\\n pass\",\n \"def onShow(self):\\n pass\",\n \"def init_widget(self):\\n super(UiKitTextView, self).init_widget()\\n self.init_text()\",\n \"def getWidget(self):\",\n \"def on_widget_constructed(callback):\\n Widget._widget_construction_callback = callback\",\n \"def on_draw(self):\\n\\t\\tself.render()\",\n \"def on_draw(self):\\n\\t\\tself.render()\",\n \"def on_create(self):\",\n \"def create_widget(self):\\n self.widget = ListView(self.get_context())\",\n \"def updateWidget(self):\\n pass\",\n \"def prepare_UI(self):\",\n \"def create(self, parent):\\n self.widget = QtCore.QObject(parent)\",\n \"def onInit(self):\\n pass\",\n \"def update_view(self): \\n raise NotImplementedError(\\\"Widget descendents MUST implement the update_view() method!\\\")\",\n \"def __init__(self):\\r\\n super().__init__()\\r\\n self.init_ui()\",\n \"def __init__(self):\\n self.view = GuiView(self)\\n return\",\n \"def initializeUI(self):\\n self.setStyleSheet(abstyle)\\n self.setGeometry(140, 100, 860, 484)\\n self.setWindowTitle('Emotions Data View')\\n self.setupModelView()\",\n \"def init_ui(self):\\n raise NotImplementedError\",\n \"def init_ui(self):\\n raise NotImplementedError\",\n \"def create_widget(self):\\n self.widget = UILabel()\",\n \"def on_show_view(self):\\n\\n # Makes the background darker\\n arcade.set_background_color([rgb - 50 for rgb in arcade.color.DARK_BLUE_GRAY])\\n\\n # Enable the UIManager when the view is showm.\\n self.manager.enable()\",\n \"def on_show_view(self):\\n arcade.set_background_color(arcade.color.DARK_BLUE_GRAY)\\n\\n # Enable the UIManager when the view is showm.\\n self.manager.enable()\",\n \"def init_widget(self):\\n super(AndroidListView, self).init_widget()\\n d = self.declaration\\n\\n if d.divider_height >= 0:\\n self.set_divider_height(d.divider_height)\\n if d.header_dividers:\\n self.set_header_dividers(d.header_dividers)\\n if d.footer_dividers:\\n self.set_footer_dividers(d.footer_dividers)\\n if d.items_can_focus:\\n self.set_items_can_focus(d.items_can_focus)\\n\\n w = self.widget\\n w.setOnItemClickListener(w.getId())\\n w.setOnItemLongClickListener(w.getId())\\n w.onItemClick.connect(self.on_item_click)\\n w.onItemLongClick.connect(self.on_item_long_click)\\n #self.widget.setOnScrollListener(self.widget.getId())\\n #self.widget.onScroll.connect(self.on_scroll)\\n\\n #: Selection listener\\n #self.widget.setOnItemSelectedListener(self.widget.getId())\\n #self.widget.onItemSelected.connect(self.on_item_selected)\\n #self.widget.onNothingSelected.connect(self.on_nothing_selected)\",\n \"def fromControls(self,widget):\",\n \"def setup(self):\\n self.ui.setup_window()\",\n \"def on_load(self):\",\n \"def init_widget(self):\\n self._build_config()\\n self._raw_toolbar.initToolbar(self.config)\",\n \"def on_show_view(self):\\n self.setup()\\n arcade.set_background_color(arcade.color.BLACK)\",\n \"def process_IN_CREATE(self, event):\",\n \"def __init__(self):\\n Redrawable.all_instantiated_widgets.append(self)\",\n \"def initialize(self):\\n super(QtBaseWidgetComponent, self).initialize()\",\n \"def __init__(self, parent=None):\\n super(Representative, self).__init__(parent)\\n self.setupUi(self)\",\n \"def set_content(self, widget):\\n\\t\\tpass\",\n \"def create_widget(self):\\n item = QNodeItem(self)\\n self.widget = item\",\n \"def onNew(self):\\n # Ensure that we can work\\n plt = Plot.getPlot()\\n if not plt:\\n self.updateUI()\\n return\\n # Get again all the subwidgets (to avoid PySide Pitfalls)\\n mw = self.getMainWindow()\\n form = mw.findChild(QtGui.QWidget, \\\"TaskPanel\\\")\\n form.axId = self.widget(QtGui.QSpinBox, \\\"axesIndex\\\")\\n\\n Plot.addNewAxes()\\n form.axId.setValue(len(plt.axesList) - 1)\\n plt.update()\",\n \"def on_activate(self):\",\n \"def afterInit(self):\",\n \"def init_layout(self):\\n pass\",\n \"def on_draw(self):\\n # draw everything\",\n \"def dlg_initialize(self):\\n pass # override\",\n \"def widgetSetup(self):\\n self.master.resizable(0, 0)\\n self.master.iconbitmap('logo.ico')\\n self.master.title(\\\"Ejercicio POO\\\")\\n\\n self.master.bind(\\\"<Return>\\\", lambda e: self.create())\\n self.master.bind(\\\"<Delete>\\\", lambda e: self.delete())\",\n \"def init_ui(self):\\n raise NotImplementedError(\\\"This is an abstract method.\\\")\",\n \"def __init__(self, parent, **kwargs):\\n super(bubblePyHomeWidget, self).__init__(parent, **kwargs)\\n self._firstView = True\\n\\n self.OneBubbleBtn.clicked.connect(self._handleOneBubble)\\n self.ManyBubblesBtn.clicked.connect(self._handleManyBubbles)\\n\\n self._statusBox, statusLayout = self._createElementWidget(self, QtGui.QVBoxLayout, True)\\n statusLayout.addStretch()\\n\\n self._nimbleStatus = NimbleStatusElement(\\n self._statusBox,\\n disabled=self.mainWindow.appConfig.get(UserConfigEnum.NIMBLE_TEST_STATUS, True) )\\n statusLayout.addWidget(self._nimbleStatus)\",\n \"def on_draw_overlay(self):\",\n \"def fill_ui(self):\\n self.review_type_widget.set_review_type(self.review_type)\\n\\n if self.reviewer:\\n self.reviewer_name_widget.setText(self.reviewer.name)\\n\\n if self.task:\\n self.task_name_widget.setText(\\n \\\"%s (%s) (%s)\\\"\\n % (\\n self.task.name,\\n \\\" | \\\".join(\\n [self.task.project.name]\\n + [parent_task.name for parent_task in self.task.parents]\\n ),\\n self.task.id,\\n )\\n )\\n\\n # from stalker import Version\\n # version = Version.query.filter(Version.task == self.task).order_by(Version.date_created.desc()).first()\\n #\\n # if version:\\n # self.latest_version_widget.version = version\",\n \"def on_pre_enter(self):\\n\\n # Setup task list\\n self.task_list = SheetView()\\n self.task_list_layout = FloatLayout()\\n self.task_list.add_widget(self.task_list_layout)\\n\\n # Tool bar\\n self.ids.action_bar.on_action_button = lambda: self.open_component_menu(\\n )\\n\\n self.open_task_list()\\n\\n self.app.tool_bar.right_action_items.append(\\n ['image-filter-center-focus', lambda x: self.to_center()])\",\n \"def create_widget(self):\\n self.widget = QCustomActionGroup(self.parent_widget())\",\n \"def on_show_view(self):\\r\\n self.setup()\\r\\n arcade.set_background_color(BACKGROUND_COLOR)\",\n \"def CreatePresentation(self, event):\\n pass\",\n \"def init_widget(self):\\n super(QtNodeItem, self).init_widget()\\n d = self.declaration\\n self.set_id(d.id)\\n\\n self.set_width(d.width)\\n self.set_height(d.height)\\n self.set_position(d.position)\\n self.set_edge_size(d.edge_size)\\n self.set_title_height(d.title_height)\\n self.set_padding(d.padding)\\n\\n self.set_show_content_inline(d.show_content_inline)\\n self.set_content(d.content)\\n\\n self.set_name(d.name)\\n self.set_color_default(d.color_default)\\n self.set_color_selected(d.color_selected)\\n self.set_color_title(d.color_title)\\n self.set_color_title_background(d.color_title_background)\\n self.set_color_background(d.color_background)\\n\\n self.widget.setFlag(QtWidgets.QGraphicsItem.ItemIsSelectable)\\n self.widget.setFlag(QtWidgets.QGraphicsItem.ItemIsMovable)\\n self.widget.setFlag(QtWidgets.QGraphicsItem.ItemSendsGeometryChanges)\",\n \"def create(self, parent):\\n self.widget = QFrame(parent)\",\n \"def on_window_ready(self):\\n pass\",\n \"def post_build(self):\",\n \"def init_widget(self):\\n super(QtViewTable, self).init_widget()\\n d = self.declaration\\n self.set_table_model(d.table_model)\\n self.set_orientation(d.orientation)\",\n \"def __init__(self, parent=None):\\n super(addOne, self).__init__(parent)\\n self.setupUi(self)\\n \\n # 内部自定义初始化操作 ---------------------------------------------------------------------begin\\n self.zShow = Fdebug(self);\\n self.zShow.show();\\n self.verticalLayout.insertWidget(0, self.zShow)\",\n \"def OnSize(self,event):\\r\\n\\t\\t\\r\\n self.SetupView()\\r\\n event.Skip()\",\n \"def widget(self, request, group):\",\n \"def onViewLog(self):\\n view_log.ViewLog(self.root, self.log)\",\n \"def init_ui(self, dict_info):\\n\\n data = dict_info['data']\\n color = dict_info['color']\\n depot = dict_info['depot']\\n mtour = dict_info['tour']\\n zoomx = dict_info['zoomx']\\n zoomy = dict_info['zoomy']\\n\\n list_appointment = data['appointment']\\n\\n self.parent.title(\\\"Simple\\\")\\n self.pack(fill=BOTH, expand=1)\\n\\n depot, mtour = zoom_before_drawing(\\n depot,\\n mtour,\\n zoomx,\\n zoomy)\\n\\n canvas = Canvas(self)\\n\\n idx = 0\\n\\n for tour in mtour:\\n tour.insert(0, model.Appointment(depot, 0, -1))\\n draw_tour(tour, canvas, translate_to_tkcolor(color[idx]))\\n idx += 1\\n\\n canvas.create_oval(depot.get_x(),\\n depot.get_y(),\\n depot.get_x()-5,\\n depot.get_y()-5,\\n outline=\\\"black\\\",\\n fill=\\\"green\\\",\\n width=7)\\n\\n for appointment in list_appointment:\\n currentx = appointment.get_x() * zoomx\\n currenty = appointment.get_y() * zoomy\\n\\n canvas.create_oval(\\n currentx,\\n currenty,\\n currentx - 3,\\n currenty - 3,\\n outline=\\\"red\\\",\\n fill=\\\"red\\\",\\n width=5)\\n\\n canvas.pack(fill=BOTH, expand=1)\",\n \"def __init__(self):\\n self.stack = QWidget()\",\n \"def on_load(self):\\n pass\",\n \"def on_load(self):\\n pass\",\n \"def init_layout(self):\\n super(WxTraitsItem, self).init_layout()\\n self.refresh_traits_widget(notify=False)\",\n \"def init_widget(self):\\n super(WxDockPane, self).init_widget()\\n d = self.declaration\\n self.set_title(d.title)\\n self.set_title_bar_visible(d.title_bar_visible)\\n self.set_title_bar_orientation(d.title_bar_orientation)\\n self.set_closable(d.closable)\\n self.set_movable(d.movable)\\n self.set_floatable(d.floatable)\\n self.set_floating(d.floating)\\n self.set_dock_area(d.dock_area)\\n self.set_allowed_dock_areas(d.allowed_dock_areas)\\n widget = self.widget\\n widget.Bind(EVT_DOCK_PANE_FLOATED, self.on_floated)\\n widget.Bind(EVT_DOCK_PANE_DOCKED, self.on_docked)\\n widget.Bind(EVT_DOCK_PANE_CLOSED, self.on_closed)\",\n \"def __init__(self, parent: View):\\n super().__init__(parent)\\n # Crossword title\\n self.title = tk.StringVar(self.root)\\n self.title_label = tk.Label(self.frame, textvariable=self.title)\\n # Crossword author\\n self.author = tk.StringVar(self.root)\\n self.author_label = tk.Label(self.frame, textvariable=self.author)\\n # Dividing line separating the header and other groups\\n self.separator = tk.Frame(self.frame)\\n # Load\\n self.load()\",\n \"def onShowed(self):\\n self.parent.actionTagTwo=\\\"\\\"\\n pass\",\n \"def initUI(self):\\n # Setting the main layout as Vertical.\\n self.mainLayout = QHBoxLayout()\\n\\n # Create title.\\n self.title = QLabel(self.__name + \\\" : \\\")\\n\\n # Add description as tooltip.\\n self.title.setToolTip(self.__description)\\n\\n # Add title to main layout.\\n self.mainLayout.addWidget(self.title)\\n\\n # Create ComboBox.\\n self.dropDown = QComboBox()\\n\\n # Add datas to drop down.\\n self.dropDown.addItems(self.__datas)\\n\\n # Set default index to dropdown.\\n self.dropDown.setCurrentIndex(self.__currentValue)\\n\\n # Connect dropdown with update method.\\n self.dropDown.currentIndexChanged.connect(self.changeCurrentValue)\\n\\n # Add ComboBox to main layout.\\n self.mainLayout.addWidget(self.dropDown)\\n\\n # Add the main layout to the window.\\n self.setLayout(self.mainLayout)\",\n \"def toControls(self,widget):\",\n \"def on_show_view(self):\\n self.setup()\\n arcade.set_background_color(arcade.color.BLACK)\\n arcade.set_viewport(0, constants.SCREEN_WIDTH - 1, 0, constants.SCREEN_HEIGHT - 1)\",\n \"def createWidget(self, parent):\\n raise NotImplementedError()\",\n \"def _setup_ui(self):\\n\\n self.window = ui.Widget()\\n self.window.dimensions = ui.normalize_dimension((\\n 0, 0,\\n self.normalized_screen_resolution[0],\\n self.normalized_screen_resolution[1]\\n ))\\n self.window.background_color = ImageColor.getcolor('#000000', 'RGB')\\n\\n interface_frame = ui.Widget(parent=self.window)\\n interface_frame.dimensions = ui.normalize_dimension((\\n self.preview_renderer.window[2],\\n 0,\\n self.normalized_screen_resolution[0] - self.preview_renderer.window[2],\\n self.normalized_screen_resolution[1]\\n ))\\n interface_frame.background_color = ImageColor.getcolor('#ffffff', 'RGB')\\n\\n number = ui.LabelWidget(\\\"\\\",\\n name=NAME_GET_STARTED,\\n parent=interface_frame,\\n align=\\\"center\\\",\\n font_color=(0, 0, 0, 255))\\n number.dimensions = (\\n 5, 5,\\n interface_frame.width - 10,\\n interface_frame.height - 10\\n )\",\n \"def init_ui(self):\\n self.parent.title(\\\"Roku Player Controller\\\")\\n self.style.theme_use(\\\"default\\\")\",\n \"def on_load(self):\\n self.__init__()\",\n \"def on_activate(self) -> None:\",\n \"def create_widget(self):\\r\\n self.root.ids.entriesBox.clear_widgets()\\r\\n num_place = len(self.place_list.list_places) # Determine the number of places in the list\\r\\n visited_place = 0\\r\\n for place in self.place_list.list_places: # Loop from first place to last place\\r\\n name = place.name\\r\\n # assert isinstance(place.country)\\r\\n country = place.country\\r\\n priority = place.priority\\r\\n visited = place.is_required\\r\\n display_text = self.generateDisplayText(name, country, priority,\\r\\n visited) # Display place's information on the widget\\r\\n if visited == \\\"n\\\":\\r\\n visited_place += 1\\r\\n button_color = self.getColor(visited)\\r\\n else:\\r\\n button_color = self.getColor(visited)\\r\\n\\r\\n temp_button = Button(text=display_text, id=place.name,\\r\\n background_color=button_color) # Mark the place visited\\r\\n temp_button.bind(on_release=self.press_entry) # Display message of the GUI status\\r\\n self.root.ids.entriesBox.add_widget(temp_button)\\r\\n self.message = \\\"To visit: {}. visited: {}\\\".format(num_place - visited_place,\\r\\n visited_place) # Display number of place visited or not visited\\r\",\n \"def __init__(self, *args, **kwargs):\\n\\n super(FlirCameraWidget, self).__init__(*args, **kwargs)\\n #self.loadForm()\\n self.window = Ui_MainWindow()\\n self.window.setupUi(self)\\n\\n self.initUI()\\n Styles(self)\",\n \"def createWidgets(self):\\n raise NotImplementedError\",\n \"def __init__(self, controller):\\n super().__init__(controller)\\n\\n # The hovered input when entering this View.\\n self.first_inp = \\\"s\\\"\\n\\n # Initialize selected variable.\\n self.selected = None\\n\\n # Make background graphics.\\n self.make_background_graphics()\\n\\n # Make Buttons.\\n self.make_buttons()\\n\\n # Make the information box. This explains each Button.\\n self.make_info_box()\\n\\n # Initializes popup.\\n self.make_popup()\\n\\n # Map of input to functions.\\n enter = self.graphics.ENTER_KEY\\n self.controls = {\\n # Pressing \\\"q\\\" will go back to the main menu.\\n \\\"q\\\": lambda: Action(\\\"goto main menu view\\\", []),\\n\\n # Movement keys.\\n \\\"w\\\": lambda: self.move_cursor(Direction.U),\\n \\\"a\\\": lambda: self.move_cursor(Direction.L),\\n \\\"s\\\": lambda: self.move_cursor(Direction.D),\\n \\\"d\\\": lambda: self.move_cursor(Direction.R),\\n\\n # Repeat the last valid input.\\n enter: self.repeat_last_valid_input,\\n\\n # Click the selected UIElement.\\n \\\"m\\\": self.click\\n }\",\n \"def onInsert(self):\\n self.mainWindow.insert()\",\n \"def __init__(self, parent=None):\\n super(QCTP, self).__init__(parent)\\n self.setupUi(self)\",\n \"def add_widget(self, widget):\\n widget.bind(on_touch_down=self.button_touch_down)\\n return super(MainGrid, self).add_widget(widget)\",\n \"def on_run(self):\\r\\n\\r\\n\\t\\tpass\",\n \"def create(self, parent):\\n self.widget = QImageView(parent)\",\n \"def pre_draw(self):\",\n \"def create_widgets(self):\\n # self.var_spherical = IntVar()\\n # self.var_3d = IntVar()\\n # self.var_spatial_audio = IntVar()\\n # self.button_open[\\\"command\\\"] = self.action_open\\n # self.button_inject[\\\"command\\\"] = self.action_inject\\n pass\",\n \"def initialize(self):\\n super(QtWidgetComponent, self).initialize()\\n shell = self.shell_obj\\n self.set_enabled(shell.enabled)\\n if shell.bgcolor:\\n self.set_bgcolor(shell.bgcolor)\\n if shell.fgcolor:\\n self.set_fgcolor(shell.fgcolor)\\n if shell.font:\\n self.set_font(shell.font)\",\n \"def _initUI(self) -> None:\\n self._createActions()\\n self._addActionsToMoveButtons()\\n self._createToolBar()\\n self._createStatusBar()\\n self._createMainContextMenu()\",\n \"def onInit(self):\\n self.list_control = self.getControl(6)\\n self.getControl(1).setLabel(self.window_title)\\n self.getControl(3).setVisible(False)\\n try:\\n self.getControl(7).setLabel(xbmc.getLocalizedString(222))\\n except Exception:\\n pass\\n\\n self.getControl(5).setVisible(False)\\n\\n # add our items to the listing and focus the control\\n self.list_control.addItems(self.listing)\\n self.setFocus(self.list_control)\",\n \"def _render_callback(self, _sim, _viewer):\\n pass\",\n \"def init_toolbar(self):\\n raise NotImplementedError\",\n \"def __init__widget(self):\\n self.__introduction = QtWidgets.QPushButton('使用“WSAD”对应“上下左右”控制\\\\n贪吃蛇，点击开始游戏！')\\n self.add_layout_widget(self.central_widget, self.__introduction)\\n self.__introduction.clicked.connect(self.__run)\\n self.__ticker.timeout.connect(self.__snake_move)\",\n \"def __call__(self):\\n self.show()\",\n \"def on_event(self, event):\",\n \"def render(self):\\r\\n super().render()\"\n]","isConversational":false},"negative_scores":{"kind":"list like","value":["0.7902635","0.7300569","0.7272871","0.7272871","0.7272871","0.71997607","0.7128017","0.7008906","0.6765284","0.66968477","0.6603362","0.6543564","0.6543564","0.65416574","0.651932","0.64497674","0.6448184","0.64066774","0.63004065","0.62705046","0.62472427","0.62237954","0.6223471","0.6220702","0.6220702","0.6216364","0.61887383","0.61881894","0.6173309","0.616524","0.61517537","0.61480236","0.6134704","0.61266047","0.6121295","0.6106766","0.6098424","0.6087604","0.60871816","0.6067187","0.6065093","0.6020914","0.6020083","0.6014957","0.59919745","0.5991919","0.5972043","0.5969252","0.5964516","0.59626263","0.5957027","0.5939844","0.59386724","0.5935465","0.59335124","0.5925625","0.59228075","0.5917534","0.590634","0.58949834","0.58893234","0.58850515","0.588337","0.5878489","0.58705854","0.58703095","0.58687645","0.58687645","0.5863065","0.5854223","0.584608","0.58365464","0.5832142","0.5824681","0.58199936","0.5794621","0.5792245","0.5786056","0.5782749","0.5775541","0.5775478","0.57591295","0.5757407","0.5755566","0.5754593","0.57541305","0.5753911","0.5752202","0.57510716","0.5750702","0.5740241","0.57288903","0.5727228","0.5726501","0.5725928","0.5724252","0.57190424","0.56988215","0.5694415","0.56921154"],"string":"[\n \"0.7902635\",\n \"0.7300569\",\n \"0.7272871\",\n \"0.7272871\",\n \"0.7272871\",\n \"0.71997607\",\n \"0.7128017\",\n \"0.7008906\",\n \"0.6765284\",\n \"0.66968477\",\n \"0.6603362\",\n \"0.6543564\",\n \"0.6543564\",\n \"0.65416574\",\n \"0.651932\",\n \"0.64497674\",\n \"0.6448184\",\n \"0.64066774\",\n \"0.63004065\",\n \"0.62705046\",\n \"0.62472427\",\n \"0.62237954\",\n \"0.6223471\",\n \"0.6220702\",\n \"0.6220702\",\n \"0.6216364\",\n \"0.61887383\",\n \"0.61881894\",\n \"0.6173309\",\n \"0.616524\",\n \"0.61517537\",\n \"0.61480236\",\n \"0.6134704\",\n \"0.61266047\",\n \"0.6121295\",\n \"0.6106766\",\n \"0.6098424\",\n \"0.6087604\",\n \"0.60871816\",\n \"0.6067187\",\n \"0.6065093\",\n \"0.6020914\",\n \"0.6020083\",\n \"0.6014957\",\n \"0.59919745\",\n \"0.5991919\",\n \"0.5972043\",\n \"0.5969252\",\n \"0.5964516\",\n \"0.59626263\",\n \"0.5957027\",\n \"0.5939844\",\n \"0.59386724\",\n \"0.5935465\",\n \"0.59335124\",\n \"0.5925625\",\n \"0.59228075\",\n \"0.5917534\",\n \"0.590634\",\n \"0.58949834\",\n \"0.58893234\",\n \"0.58850515\",\n \"0.588337\",\n \"0.5878489\",\n \"0.58705854\",\n \"0.58703095\",\n \"0.58687645\",\n \"0.58687645\",\n \"0.5863065\",\n \"0.5854223\",\n \"0.584608\",\n \"0.58365464\",\n \"0.5832142\",\n \"0.5824681\",\n \"0.58199936\",\n \"0.5794621\",\n \"0.5792245\",\n \"0.5786056\",\n \"0.5782749\",\n \"0.5775541\",\n \"0.5775478\",\n \"0.57591295\",\n \"0.5757407\",\n \"0.5755566\",\n \"0.5754593\",\n \"0.57541305\",\n \"0.5753911\",\n \"0.5752202\",\n \"0.57510716\",\n \"0.5750702\",\n \"0.5740241\",\n \"0.57288903\",\n \"0.5727228\",\n \"0.5726501\",\n \"0.5725928\",\n \"0.5724252\",\n \"0.57190424\",\n \"0.56988215\",\n \"0.5694415\",\n \"0.56921154\"\n]","isConversational":false},"document_score":{"kind":"string","value":"0.79256094"},"document_rank":{"kind":"string","value":"0"}}},{"rowIdx":23794,"cells":{"query":{"kind":"string","value":"Returns the data viewer hooks used by this view."},"document":{"kind":"string","value":"def getHooks(self):\n return self.__hooks"},"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 hooks(self):\n return tuple(self.__hooks.keys())","def get_extra_lvs_hooks(self) -> List[HammerToolHookAction]:\n return list()","def get_extra_hierarchical_lvs_hooks(self) -> Dict[str, List[HammerToolHookAction]]:\n return dict()","def custom_hooks(self):\n return self.config.options(self.CUSTOM_HOOKS_SECTION, [])","def get_extra_drc_hooks(self) -> List[HammerToolHookAction]:\n return list()","def extension_hooks(self, global_step):\n return []","def extension_hooks(self, global_step):\n return []","def available_hooks(self):\n return [RoutesDevHandlerHook]","def get_extra_hierarchical_drc_hooks(self) -> Dict[str, List[HammerToolHookAction]]:\n return dict()","def getSyscallHooks(self):\n return None","def get_user_hook_info ( self ):\n return [\n ( s.name, [ ( s.event or \"undef\", s.priority ) ] )\n for event, s in self.user_hooks.iter_scripts()\n ]","def get_hooks(name):\n register_all_hooks()\n return _hooks.get(name, [])","def get_application_hooks(cls) -> Optional[AppExtensionHooks]:\n return cls._app_exts","def get_hooks(self, hook: str) -> List[Callable]:\n return [getattr(self, name) for name in self.__plugin_data.hooks.get(hook, ())]","def available_hooks(self):\n return [HtmlMinPostRenderHook]","def dataOverrides(self):\n\t\treturn self._overrides","def list(self):\n return self._post(\n request=ApiActions.LIST.value,\n uri=ApiUri.HOOKS.value,\n ).get('hooks')","def builtin_hooks(self):\n return [k for k, v in self.config.items(self.BUILTIN_HOOKS_SECTION, ())\n if rh.shell.boolean_shell_value(v, None)]","def getRawSyscallHooks(self):\n return None","def process_hooks(self, hooks):\n try:\n enabled_hooks = self.project.HOOKS\n except AttributeError:\n return hooks","def get_extra_hierarchical_synthesis_hooks(self) -> Dict[str, List[HammerToolHookAction]]:\n return dict()","def setHooks(self, hooks):\n super(SelectOneDataView, self).setHooks(hooks)\n if hooks is not None:\n for v in self.__views:\n v.setHooks(hooks)","def functions(self):\n return self.__functions","def hooks(self) -> Sequence['outputs.GetGroupHooksHookResult']:\n return pulumi.get(self, \"hooks\")","def set_hooks(self):\n for layer_idx, layer in enumerate(self.vgg_features):\n if layer_idx in self.layers_to_watch:\n layer.register_forward_hook(self.layer_watch_hooks(layer_idx))","def getViews(self):\n return list(self.__views.keys())","def get_custom_hook_names():\n return list(_CUSTOM_HOOKS)","def game_functions(self):\n\t\treturn self._game_functions","def get_hooks(self, type_: str) -> typing.Iterable:\n hooks = []\n if self._parent:\n hooks.extend(self._parent.get_hooks(type_))\n\n hooks.extend(self._request_hooks.get(type_, []))\n\n return hooks","def setHooks(self, hooks):\n super(SelectManyDataView, self).setHooks(hooks)\n if hooks is not None:\n for v in self.__views:\n v.setHooks(hooks)","def get_extra_synthesis_hooks(self) -> List[HammerToolHookAction]:\n return list()","def get_meta_functions(self):\n return self.meta_functions.values()","def available_hooks(self):\n return [DeploymentDeploymentRegisterHook]","def views(self):\n return self._views","def get_function_hooks(cls, name: str) -> Optional[FuncExtensionHooks]:\n return cls._func_exts.get(name.lower())","def get_view_steps(self):\n return self._data_dict[self.KEY_VIEW_STEPS]","def LDFlags(self):\n return self._g_linkflags","def get_extra_par_hooks(self) -> List[HammerToolHookAction]:\n return list()","def _getHookInformation(self, inifile, path, factory):\n sections = inifile.getSections()\n hookInformationList = list()\n for hookid in sorted(section for section in sections if section.startswith('hook')):\n pylabs.q.logger.log('Found hook %s in %s' % (hookid, inifile, ), 7)\n # Extract the information from the section\n hookInformation = self._extractHookInformation(inifile, hookid)\n if not hookInformation:\n continue\n # Add global information\n hookInformation['extension_path'] = path\n hookInformation['extension_factory'] = factory\n hookInformationList.append(hookInformation)\n return hookInformationList","def hook_def(self) -> HookDefinition:\n return self._hook_def","def getPreviews(self):\n logger.debug(\"Func: getPreviews\")\n \n # return \"ASDFASDF\"\n return sorted(self._currentPreviewsDict.keys())","def get_extra_hierarchical_par_hooks(self) -> Dict[str, List[HammerToolHookAction]]:\n return dict()","def getViews(self):\n return list(self.__views)","def transmission_hooks(cls):\n return []","def find(self):\n extension_hooks = list()\n eggs = find_eggs(self.rootDir)\n factory = EggPMExtensionFactory()\n for egg in eggs:\n # Add egg to path so other parts of pylabs can import its contents\n eggfile = egg.location\n sys.path.append(eggfile)\n for filePointer, path in self._generateExtensionConfigFilePointers(eggfile):\n inifile = pylabs.inifile.IniFile(filePointer)\n hooks = self._getHookInformation(inifile, path, factory)\n extension_hooks.extend(hooks)\n return extension_hooks","def define_log_pre_format_hooks(self):\n return []","def get_functions_seen(self):\n return self._functions_seen","def data():\n return app_views","def keyrefs(self):\r\n return self.data.keys()","def keyrefs(self):\r\n return self.data.keys()","def get_visuals(self):\n return self.scene['visuals']","def getViews(self):\n raise NotImplementedError()","def callable_hooks(self):\n for hook in self.custom_hooks:\n options = rh.hooks.HookOptions(hook,\n self.custom_hook(hook),\n self.tool_paths)\n yield (hook, functools.partial(rh.hooks.check_custom,\n options=options))\n\n for hook in self.builtin_hooks:\n options = rh.hooks.HookOptions(hook,\n self.builtin_hook_option(hook),\n self.tool_paths)\n yield (hook, functools.partial(rh.hooks.BUILTIN_HOOKS[hook],\n options=options))","def get_macros(self):\n return LEVELS.keys()","def get_views(self):\n return self._get_types_from_default_ns(View)","def outlineexplorer_data_list(self):\n return self._editor.outlineexplorer_data_list()","def getFeatureDicts(self):\n pass","def layers(self):\r\n return self._flc.layers","def get_hook_root_info ( self, nonlinked_only=False ):\n if nonlinked_only:\n return [\n ( script.name, [] )\n for script, user_scripts in self.hook_root.iter_linked()\n if not user_scripts\n ]\n else:\n return [\n (\n script.name,\n [ ( s.event or \"undef\", s.priority ) for s in user_scripts ]\n )\n for script, user_scripts in self.hook_root.iter_linked()\n ]","def get_visualizations( self, dataset ):\n\n return [ 'phyloviz' ]","def get_user_iflags(self, *args):\n return _ida_hexrays.cfuncptr_t_get_user_iflags(self, *args)","def setup_hooks(self):\n pass","def available_functions(self):\n return self.config.keys()","def layers(self):\n return self['layers']","def hook_introspect(self):\n # NOTE that this doesn't use @run_async, unlike other hooks, because it has to return right away\n user_state = self.updater.persistence.user_data\n bot_state = self.updater.persistence.bot_data\n return {\"volunteers\": user_state, \"requests\": bot_state}","def traces(self):\n if self._traces is None:\n raise NotImplementedError(\"Weak implementation not supported\")\n else:\n return self._traces","def getFormatterDefs(self): #$NON-NLS-1$\r\n if self.formatterDefs is None:\r\n resouceReg = self.extensionPoint.getPlugin().getResourceRegistry()\r\n self.formatterDefs = []\r\n formatElems = self._getExtensionDefChildNodes(u\"plg:link-formatters/plg:link-formatter\") #$NON-NLS-1$\r\n for formatterElem in formatElems:\r\n formatterDef = ZLinkFormatterDef(formatterElem, resouceReg)\r\n self.formatterDefs.append(formatterDef)\r\n return self.formatterDefs","def getLayers(self):\n return self.__layers","def notifiers(self):\n return self.registry.keys()","def getFrontendData(self):\n return getFrontendData(self)","def get_builtins(self):\n return self.__builtin_index","def event_markers(self):\n return self._event_markers","def get_user_iflags(self, *args):\n return _ida_hexrays.cfunc_t_get_user_iflags(self, *args)","def get_viewer_urls(self):\n return {name: viewer.top.url for name, viewer in self.viewers.items()}","def find(self):\n extension_hooks = list()\n #Find all extension names\n dirs = pylabs.q.system.fs.listDirsInDir(self.rootDir, True,findDirectorySymlinks=True)\n # Use a simple PMExtensionFactory\n factory = PMExtensionFactory()\n for dir in (d for d in dirs if pylabs.q.system.fs.exists(os.path.join(d, self.extensionConfigName))):\n #we found possible extension because extension.cfg file found\n pylabs.q.logger.log('Found extension in %s' % dir, 6)\n # Load extension ini file\n configfilePath = os.path.join(dir, self.extensionConfigName)\n inifile = pylabs.inifile.IniFile(configfilePath)\n path = pylabs.q.system.fs.getDirName(configfilePath)\n hooks = self._getHookInformation(inifile, path, factory)\n extension_hooks.extend(hooks)\n return extension_hooks","def filters(self):\n # easy enough\n return self.dcpl.getFilters()","def get_renderMethods(self):\n return self.render_methods","def plugins(self):\n\n return list(self._renderers.keys())","def metadata_history(self, ds_name):\n return self.runinfos.get(self._infer_fqn(ds_name)).metadata_history._hist_list","def get_data(self):\n return [self.axes]","def hookregistry():\n registry = HookRegistry()\n yield registry","def filters(self):\n\t\treturn self.local_filter","def extension_attributes(self):\n return self._extension_attributes","def extension_attributes(self):\n return self._extension_attributes","def getVars(self):\n return self.__vars","def detectionlists(self):\n return self._sdk_dependencies.detection_lists_module","def get_docs(self):\n return get_view_description(self.callback)","def data_alerts(self):\n return self._data_alerts","def getTools(self):\n return [self.toggleEditMode]","def _handlers(self) -> tuple:\n return self._classname2handlers[self.deco_class.__name__]","def get_current_visuals(self):\n visual_ret = OrderedDict()\n for name in self.visual_names:\n if isinstance(name, str):\n visual_ret[name] = getattr(self, name)\n return visual_ret","def get_current_visuals(self):\n visual_ret = OrderedDict()\n for name in self.visual_names:\n if isinstance(name, str):\n visual_ret[name] = getattr(self, name)\n return visual_ret","def data(self):\n\t\treturn vars(self)","def analysis_rule_plug_ins(self):\n return self._analysis_rule_plug_ins","def get_hook(self) -> QuboleHook:\n return QuboleHook(**self.kwargs)","def get_provisioning_scripts(self):\n\n return self.client.call('Account', 'getPostProvisioningHooks')","def getFeatureDicts(self):\n return [self.data.getWordTagDict(), self.data.tags_trigrams, self.data.tags_bigrams]","def variables(self):\n return self._.vars","def get_fns(self):\n return self.key_to_fn","def _get_hook(self, shader, name):\n assert name in ('pre', 'post')\n key = (shader, name)\n if key in self._hooks:\n return self._hooks[key]\n hook = StatementList()\n if shader == 'vert':\n self.view_program.vert[name] = hook\n elif shader == 'frag':\n self.view_program.frag[name] = hook\n self._hooks[key] = hook\n return hook"],"string":"[\n \"def hooks(self):\\n return tuple(self.__hooks.keys())\",\n \"def get_extra_lvs_hooks(self) -> List[HammerToolHookAction]:\\n return list()\",\n \"def get_extra_hierarchical_lvs_hooks(self) -> Dict[str, List[HammerToolHookAction]]:\\n return dict()\",\n \"def custom_hooks(self):\\n return self.config.options(self.CUSTOM_HOOKS_SECTION, [])\",\n \"def get_extra_drc_hooks(self) -> List[HammerToolHookAction]:\\n return list()\",\n \"def extension_hooks(self, global_step):\\n return []\",\n \"def extension_hooks(self, global_step):\\n return []\",\n \"def available_hooks(self):\\n return [RoutesDevHandlerHook]\",\n \"def get_extra_hierarchical_drc_hooks(self) -> Dict[str, List[HammerToolHookAction]]:\\n return dict()\",\n \"def getSyscallHooks(self):\\n return None\",\n \"def get_user_hook_info ( self ):\\n return [\\n ( s.name, [ ( s.event or \\\"undef\\\", s.priority ) ] )\\n for event, s in self.user_hooks.iter_scripts()\\n ]\",\n \"def get_hooks(name):\\n register_all_hooks()\\n return _hooks.get(name, [])\",\n \"def get_application_hooks(cls) -> Optional[AppExtensionHooks]:\\n return cls._app_exts\",\n \"def get_hooks(self, hook: str) -> List[Callable]:\\n return [getattr(self, name) for name in self.__plugin_data.hooks.get(hook, ())]\",\n \"def available_hooks(self):\\n return [HtmlMinPostRenderHook]\",\n \"def dataOverrides(self):\\n\\t\\treturn self._overrides\",\n \"def list(self):\\n return self._post(\\n request=ApiActions.LIST.value,\\n uri=ApiUri.HOOKS.value,\\n ).get('hooks')\",\n \"def builtin_hooks(self):\\n return [k for k, v in self.config.items(self.BUILTIN_HOOKS_SECTION, ())\\n if rh.shell.boolean_shell_value(v, None)]\",\n \"def getRawSyscallHooks(self):\\n return None\",\n \"def process_hooks(self, hooks):\\n try:\\n enabled_hooks = self.project.HOOKS\\n except AttributeError:\\n return hooks\",\n \"def get_extra_hierarchical_synthesis_hooks(self) -> Dict[str, List[HammerToolHookAction]]:\\n return dict()\",\n \"def setHooks(self, hooks):\\n super(SelectOneDataView, self).setHooks(hooks)\\n if hooks is not None:\\n for v in self.__views:\\n v.setHooks(hooks)\",\n \"def functions(self):\\n return self.__functions\",\n \"def hooks(self) -> Sequence['outputs.GetGroupHooksHookResult']:\\n return pulumi.get(self, \\\"hooks\\\")\",\n \"def set_hooks(self):\\n for layer_idx, layer in enumerate(self.vgg_features):\\n if layer_idx in self.layers_to_watch:\\n layer.register_forward_hook(self.layer_watch_hooks(layer_idx))\",\n \"def getViews(self):\\n return list(self.__views.keys())\",\n \"def get_custom_hook_names():\\n return list(_CUSTOM_HOOKS)\",\n \"def game_functions(self):\\n\\t\\treturn self._game_functions\",\n \"def get_hooks(self, type_: str) -> typing.Iterable:\\n hooks = []\\n if self._parent:\\n hooks.extend(self._parent.get_hooks(type_))\\n\\n hooks.extend(self._request_hooks.get(type_, []))\\n\\n return hooks\",\n \"def setHooks(self, hooks):\\n super(SelectManyDataView, self).setHooks(hooks)\\n if hooks is not None:\\n for v in self.__views:\\n v.setHooks(hooks)\",\n \"def get_extra_synthesis_hooks(self) -> List[HammerToolHookAction]:\\n return list()\",\n \"def get_meta_functions(self):\\n return self.meta_functions.values()\",\n \"def available_hooks(self):\\n return [DeploymentDeploymentRegisterHook]\",\n \"def views(self):\\n return self._views\",\n \"def get_function_hooks(cls, name: str) -> Optional[FuncExtensionHooks]:\\n return cls._func_exts.get(name.lower())\",\n \"def get_view_steps(self):\\n return self._data_dict[self.KEY_VIEW_STEPS]\",\n \"def LDFlags(self):\\n return self._g_linkflags\",\n \"def get_extra_par_hooks(self) -> List[HammerToolHookAction]:\\n return list()\",\n \"def _getHookInformation(self, inifile, path, factory):\\n sections = inifile.getSections()\\n hookInformationList = list()\\n for hookid in sorted(section for section in sections if section.startswith('hook')):\\n pylabs.q.logger.log('Found hook %s in %s' % (hookid, inifile, ), 7)\\n # Extract the information from the section\\n hookInformation = self._extractHookInformation(inifile, hookid)\\n if not hookInformation:\\n continue\\n # Add global information\\n hookInformation['extension_path'] = path\\n hookInformation['extension_factory'] = factory\\n hookInformationList.append(hookInformation)\\n return hookInformationList\",\n \"def hook_def(self) -> HookDefinition:\\n return self._hook_def\",\n \"def getPreviews(self):\\n logger.debug(\\\"Func: getPreviews\\\")\\n \\n # return \\\"ASDFASDF\\\"\\n return sorted(self._currentPreviewsDict.keys())\",\n \"def get_extra_hierarchical_par_hooks(self) -> Dict[str, List[HammerToolHookAction]]:\\n return dict()\",\n \"def getViews(self):\\n return list(self.__views)\",\n \"def transmission_hooks(cls):\\n return []\",\n \"def find(self):\\n extension_hooks = list()\\n eggs = find_eggs(self.rootDir)\\n factory = EggPMExtensionFactory()\\n for egg in eggs:\\n # Add egg to path so other parts of pylabs can import its contents\\n eggfile = egg.location\\n sys.path.append(eggfile)\\n for filePointer, path in self._generateExtensionConfigFilePointers(eggfile):\\n inifile = pylabs.inifile.IniFile(filePointer)\\n hooks = self._getHookInformation(inifile, path, factory)\\n extension_hooks.extend(hooks)\\n return extension_hooks\",\n \"def define_log_pre_format_hooks(self):\\n return []\",\n \"def get_functions_seen(self):\\n return self._functions_seen\",\n \"def data():\\n return app_views\",\n \"def keyrefs(self):\\r\\n return self.data.keys()\",\n \"def keyrefs(self):\\r\\n return self.data.keys()\",\n \"def get_visuals(self):\\n return self.scene['visuals']\",\n \"def getViews(self):\\n raise NotImplementedError()\",\n \"def callable_hooks(self):\\n for hook in self.custom_hooks:\\n options = rh.hooks.HookOptions(hook,\\n self.custom_hook(hook),\\n self.tool_paths)\\n yield (hook, functools.partial(rh.hooks.check_custom,\\n options=options))\\n\\n for hook in self.builtin_hooks:\\n options = rh.hooks.HookOptions(hook,\\n self.builtin_hook_option(hook),\\n self.tool_paths)\\n yield (hook, functools.partial(rh.hooks.BUILTIN_HOOKS[hook],\\n options=options))\",\n \"def get_macros(self):\\n return LEVELS.keys()\",\n \"def get_views(self):\\n return self._get_types_from_default_ns(View)\",\n \"def outlineexplorer_data_list(self):\\n return self._editor.outlineexplorer_data_list()\",\n \"def getFeatureDicts(self):\\n pass\",\n \"def layers(self):\\r\\n return self._flc.layers\",\n \"def get_hook_root_info ( self, nonlinked_only=False ):\\n if nonlinked_only:\\n return [\\n ( script.name, [] )\\n for script, user_scripts in self.hook_root.iter_linked()\\n if not user_scripts\\n ]\\n else:\\n return [\\n (\\n script.name,\\n [ ( s.event or \\\"undef\\\", s.priority ) for s in user_scripts ]\\n )\\n for script, user_scripts in self.hook_root.iter_linked()\\n ]\",\n \"def get_visualizations( self, dataset ):\\n\\n return [ 'phyloviz' ]\",\n \"def get_user_iflags(self, *args):\\n return _ida_hexrays.cfuncptr_t_get_user_iflags(self, *args)\",\n \"def setup_hooks(self):\\n pass\",\n \"def available_functions(self):\\n return self.config.keys()\",\n \"def layers(self):\\n return self['layers']\",\n \"def hook_introspect(self):\\n # NOTE that this doesn't use @run_async, unlike other hooks, because it has to return right away\\n user_state = self.updater.persistence.user_data\\n bot_state = self.updater.persistence.bot_data\\n return {\\\"volunteers\\\": user_state, \\\"requests\\\": bot_state}\",\n \"def traces(self):\\n if self._traces is None:\\n raise NotImplementedError(\\\"Weak implementation not supported\\\")\\n else:\\n return self._traces\",\n \"def getFormatterDefs(self): #$NON-NLS-1$\\r\\n if self.formatterDefs is None:\\r\\n resouceReg = self.extensionPoint.getPlugin().getResourceRegistry()\\r\\n self.formatterDefs = []\\r\\n formatElems = self._getExtensionDefChildNodes(u\\\"plg:link-formatters/plg:link-formatter\\\") #$NON-NLS-1$\\r\\n for formatterElem in formatElems:\\r\\n formatterDef = ZLinkFormatterDef(formatterElem, resouceReg)\\r\\n self.formatterDefs.append(formatterDef)\\r\\n return self.formatterDefs\",\n \"def getLayers(self):\\n return self.__layers\",\n \"def notifiers(self):\\n return self.registry.keys()\",\n \"def getFrontendData(self):\\n return getFrontendData(self)\",\n \"def get_builtins(self):\\n return self.__builtin_index\",\n \"def event_markers(self):\\n return self._event_markers\",\n \"def get_user_iflags(self, *args):\\n return _ida_hexrays.cfunc_t_get_user_iflags(self, *args)\",\n \"def get_viewer_urls(self):\\n return {name: viewer.top.url for name, viewer in self.viewers.items()}\",\n \"def find(self):\\n extension_hooks = list()\\n #Find all extension names\\n dirs = pylabs.q.system.fs.listDirsInDir(self.rootDir, True,findDirectorySymlinks=True)\\n # Use a simple PMExtensionFactory\\n factory = PMExtensionFactory()\\n for dir in (d for d in dirs if pylabs.q.system.fs.exists(os.path.join(d, self.extensionConfigName))):\\n #we found possible extension because extension.cfg file found\\n pylabs.q.logger.log('Found extension in %s' % dir, 6)\\n # Load extension ini file\\n configfilePath = os.path.join(dir, self.extensionConfigName)\\n inifile = pylabs.inifile.IniFile(configfilePath)\\n path = pylabs.q.system.fs.getDirName(configfilePath)\\n hooks = self._getHookInformation(inifile, path, factory)\\n extension_hooks.extend(hooks)\\n return extension_hooks\",\n \"def filters(self):\\n # easy enough\\n return self.dcpl.getFilters()\",\n \"def get_renderMethods(self):\\n return self.render_methods\",\n \"def plugins(self):\\n\\n return list(self._renderers.keys())\",\n \"def metadata_history(self, ds_name):\\n return self.runinfos.get(self._infer_fqn(ds_name)).metadata_history._hist_list\",\n \"def get_data(self):\\n return [self.axes]\",\n \"def hookregistry():\\n registry = HookRegistry()\\n yield registry\",\n \"def filters(self):\\n\\t\\treturn self.local_filter\",\n \"def extension_attributes(self):\\n return self._extension_attributes\",\n \"def extension_attributes(self):\\n return self._extension_attributes\",\n \"def getVars(self):\\n return self.__vars\",\n \"def detectionlists(self):\\n return self._sdk_dependencies.detection_lists_module\",\n \"def get_docs(self):\\n return get_view_description(self.callback)\",\n \"def data_alerts(self):\\n return self._data_alerts\",\n \"def getTools(self):\\n return [self.toggleEditMode]\",\n \"def _handlers(self) -> tuple:\\n return self._classname2handlers[self.deco_class.__name__]\",\n \"def get_current_visuals(self):\\n visual_ret = OrderedDict()\\n for name in self.visual_names:\\n if isinstance(name, str):\\n visual_ret[name] = getattr(self, name)\\n return visual_ret\",\n \"def get_current_visuals(self):\\n visual_ret = OrderedDict()\\n for name in self.visual_names:\\n if isinstance(name, str):\\n visual_ret[name] = getattr(self, name)\\n return visual_ret\",\n \"def data(self):\\n\\t\\treturn vars(self)\",\n \"def analysis_rule_plug_ins(self):\\n return self._analysis_rule_plug_ins\",\n \"def get_hook(self) -> QuboleHook:\\n return QuboleHook(**self.kwargs)\",\n \"def get_provisioning_scripts(self):\\n\\n return self.client.call('Account', 'getPostProvisioningHooks')\",\n \"def getFeatureDicts(self):\\n return [self.data.getWordTagDict(), self.data.tags_trigrams, self.data.tags_bigrams]\",\n \"def variables(self):\\n return self._.vars\",\n \"def get_fns(self):\\n return self.key_to_fn\",\n \"def _get_hook(self, shader, name):\\n assert name in ('pre', 'post')\\n key = (shader, name)\\n if key in self._hooks:\\n return self._hooks[key]\\n hook = StatementList()\\n if shader == 'vert':\\n self.view_program.vert[name] = hook\\n elif shader == 'frag':\\n self.view_program.frag[name] = hook\\n self._hooks[key] = hook\\n return hook\"\n]","isConversational":false},"negative_scores":{"kind":"list like","value":["0.69417256","0.6475532","0.6276654","0.624319","0.61839193","0.60875654","0.60875654","0.6062711","0.6000305","0.59981173","0.59789795","0.5936823","0.5913618","0.58525425","0.5787659","0.57738584","0.577255","0.576733","0.5754267","0.56217355","0.5590773","0.5569066","0.55612904","0.5559603","0.5544965","0.55279243","0.55103725","0.54937005","0.54753315","0.5458202","0.5456737","0.5418876","0.54174185","0.53722525","0.53714347","0.53558797","0.5351672","0.5345525","0.5337644","0.5329987","0.5320048","0.52711195","0.52701515","0.5251194","0.52172804","0.5144853","0.5137229","0.5127971","0.5126153","0.5126153","0.5098304","0.5084875","0.50833917","0.50689816","0.50655437","0.5053259","0.504978","0.5026861","0.5025518","0.4999664","0.49881446","0.49753124","0.49693525","0.4951065","0.494251","0.4914275","0.49012992","0.4898421","0.48925352","0.4891845","0.489006","0.48832917","0.48832875","0.4881342","0.48762125","0.48686582","0.4850181","0.48427382","0.48367965","0.4829026","0.4826332","0.48252788","0.48233396","0.48233396","0.4817476","0.4811282","0.481054","0.48098883","0.4805598","0.48013046","0.4795695","0.4795695","0.47906926","0.4787806","0.47877473","0.4784123","0.47808778","0.47732335","0.47726384","0.47713703"],"string":"[\n \"0.69417256\",\n \"0.6475532\",\n \"0.6276654\",\n \"0.624319\",\n \"0.61839193\",\n \"0.60875654\",\n \"0.60875654\",\n \"0.6062711\",\n \"0.6000305\",\n \"0.59981173\",\n \"0.59789795\",\n \"0.5936823\",\n \"0.5913618\",\n \"0.58525425\",\n \"0.5787659\",\n \"0.57738584\",\n \"0.577255\",\n \"0.576733\",\n \"0.5754267\",\n \"0.56217355\",\n \"0.5590773\",\n \"0.5569066\",\n \"0.55612904\",\n \"0.5559603\",\n \"0.5544965\",\n \"0.55279243\",\n \"0.55103725\",\n \"0.54937005\",\n \"0.54753315\",\n \"0.5458202\",\n \"0.5456737\",\n \"0.5418876\",\n \"0.54174185\",\n \"0.53722525\",\n \"0.53714347\",\n \"0.53558797\",\n \"0.5351672\",\n \"0.5345525\",\n \"0.5337644\",\n \"0.5329987\",\n \"0.5320048\",\n \"0.52711195\",\n \"0.52701515\",\n \"0.5251194\",\n \"0.52172804\",\n \"0.5144853\",\n \"0.5137229\",\n \"0.5127971\",\n \"0.5126153\",\n \"0.5126153\",\n \"0.5098304\",\n \"0.5084875\",\n \"0.50833917\",\n \"0.50689816\",\n \"0.50655437\",\n \"0.5053259\",\n \"0.504978\",\n \"0.5026861\",\n \"0.5025518\",\n \"0.4999664\",\n \"0.49881446\",\n \"0.49753124\",\n \"0.49693525\",\n \"0.4951065\",\n \"0.494251\",\n \"0.4914275\",\n \"0.49012992\",\n \"0.4898421\",\n \"0.48925352\",\n \"0.4891845\",\n \"0.489006\",\n \"0.48832917\",\n \"0.48832875\",\n \"0.4881342\",\n \"0.48762125\",\n \"0.48686582\",\n \"0.4850181\",\n \"0.48427382\",\n \"0.48367965\",\n \"0.4829026\",\n \"0.4826332\",\n \"0.48252788\",\n \"0.48233396\",\n \"0.48233396\",\n \"0.4817476\",\n \"0.4811282\",\n \"0.481054\",\n \"0.48098883\",\n \"0.4805598\",\n \"0.48013046\",\n \"0.4795695\",\n \"0.4795695\",\n \"0.47906926\",\n \"0.4787806\",\n \"0.47877473\",\n \"0.4784123\",\n \"0.47808778\",\n \"0.47732335\",\n \"0.47726384\",\n \"0.47713703\"\n]","isConversational":false},"document_score":{"kind":"string","value":"0.7191292"},"document_rank":{"kind":"string","value":"0"}}},{"rowIdx":23795,"cells":{"query":{"kind":"string","value":"Set the data view hooks to use with this view."},"document":{"kind":"string","value":"def setHooks(self, hooks):\n self.__hooks = hooks"},"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 setHooks(self, hooks):\n super(SelectOneDataView, self).setHooks(hooks)\n if hooks is not None:\n for v in self.__views:\n v.setHooks(hooks)","def setHooks(self, hooks):\n super(SelectManyDataView, self).setHooks(hooks)\n if hooks is not None:\n for v in self.__views:\n v.setHooks(hooks)","def _hook_data(self, data):\n if self.data_hook is not None:\n self.data_hook(data)","def setup_hooks(self):\n pass","def setup_hooks(self) -> None:\n install_hooks(self.record_interaction)","def set_hooks(self):\n for layer_idx, layer in enumerate(self.vgg_features):\n if layer_idx in self.layers_to_watch:\n layer.register_forward_hook(self.layer_watch_hooks(layer_idx))","def addView(self, dataView):\n hooks = self.getHooks()\n if hooks is not None:\n dataView.setHooks(hooks)\n self.__views[dataView] = None","def addView(self, dataView):\n hooks = self.getHooks()\n if hooks is not None:\n dataView.setHooks(hooks)\n self.__views.append(dataView)","def _apply_post_render_hooks(self, data, obj, fmt):\n hooks = self.post_render_hooks.get(fmt,[])\n for hook in hooks:\n try:\n data = hook(data, obj)\n except Exception as e:\n self.param.warning(f\"The post_render_hook {hook!r} could not \"\n f\"be applied:\\n\\n {e}\")\n return data","def inject_data_hook(self, data):\n return data","def __setattr__(self, name, value):\n if name in self._hook_triggers:\n super().__setattr__(name, _HooksList(value))\n else:\n super().__setattr__(name, value)","def dataset_initializer_hook(self):\n iterator = self._dataset.make_initializable_iterator()\n # pylint: disable=protected-access\n hook = estimator_lib._DatasetInitializerHook(iterator)\n self._iterator = iterator\n return hook","def _pre_setup(self):\n apps.clear_cache()\n call_command('migrate', interactive=False, verbosity=0)\n call_command('loaddata', 'initial_data', verbosity=0)\n super(DatatableViewTestCase, self)._pre_setup()","def _post_hooks(self):","def declare_hook(self, fct_name, fct):\r\n self._hooks_fct[fct_name] = fct","def set_hook(self,name,hook):\n\n # At some point in the future, this should validate the hook before it\n # accepts it. Probably at least check that the hook takes the number\n # of args it's supposed to.\n setattr(self.hooks,name,new.instancemethod(hook,self,self.__class__))","def set_data(self, data):\n\n pass","def setData(data, language=None):","def set_predefined_data(self, data: dict) -> None:\n\n metainfo = {\n self.META_KEYS[k]: v\n for (k, v) in data.items() if k in self.META_KEYS\n }\n self.set_metainfo(metainfo)\n\n data = {\n self.DATA_KEYS[k]: v\n for (k, v) in data.items() if k in self.DATA_KEYS\n }\n self.set_tensor_data(data)","def create_default_hooks(self):\n\n self.create_hooks(\"commands\")\n self.create_hooks(\"commands_out\")\n self.create_hooks(\"hooks\")","def tweaks(self) -> None:\n pass","def set_view_steps(self, steps_list):\n self._data_dict[self.KEY_VIEW_STEPS] = steps_list","def setData(self, data):\n self.data = data\n dagPath, components = self.__getGeometryComponents()\n self.setInfluenceWeights(dagPath, components)\n self.setBlendWeights(dagPath, components)\n\n for attr in ['skinningMethod', 'normalizeWeights']:\n cmds.setAttr('%s.%s' % (self.node, attr), self.data[attr])","def __init__(self):\n\t\tself.satchmo_variation_manager = adminviews.variation_manager\n\t\tadminviews.variation_list = self.variation_list\n\t\tadminviews.variation_manager = self.variation_manager\n\t\t# listeners.add_toolbar_context = self.add_my_toolbar_context#(listeners.add_toolbar_context)","def plug_sets(self, plug_sets):\n\n self._plug_sets = plug_sets","def addResetHooks(self, before, after):\n self._beforeResetHook = before\n self._afterResetHook = after","def register_stimulus_set_hook(self, hook):\n\n handle = HookHandle(self._stimulus_set_hooks)\n self._stimulus_set_hooks[handle.id] = hook\n return handle","def set_all_data(self, data):\n self.setData(data, QtCore.Qt.DisplayRole)\n self.setData(data, QtCore.Qt.EditRole)\n self.setData(data, QtCore.Qt.UserRole)\n self.setData(data, QtCore.Qt.ToolTipRole)\n self.setData(data, QtCore.Qt.StatusTipRole)\n self.setData(data, QtCore.Qt.WhatsThisRole)","def makeOverrides(self):\n\t\tself.overridesWithValues = self.dataOverrides","def setData(self, data):\n self.data = data","def setData(self, data):\n self.data = data","def views(self, views):\n\n self._views = views","def data():\n return app_views","def dataOverrides(self):\n\t\treturn self._overrides","def setData(self, data: DataDictBase):\n super().setData(data)\n\n changes = self._analyzeData(data)\n self.plotDataType = determinePlotDataType(data)\n\n self._processPlotTypeOptions()\n self._plotData(adjustSize=True)","def create_hooks(self, hclass):\n for extension in self.db.values():\n self.events.register_callbacks_from_inst(hclass, extension)","def _hook(self):","def setData(self, data):\n return None","def add_traits_meta_data ( self, bases, class_dict, base_traits,\n class_traits, instance_traits, prefix_traits,\n view_elements ):\n class_dict[ BaseTraits ] = base_traits\n class_dict[ ClassTraits ] = class_traits\n class_dict[ InstanceTraits ] = instance_traits\n class_dict[ PrefixTraits ] = prefix_traits\n class_dict[ ViewTraits ] = view_elements","def set_metadata(self, data):\r\n pass","def on_hook(self) -> None:","def on_show_view(self):\n self.setup()","def on_show_view(self):\n self.setup()","def on_show_view(self):\n self.setup()","def setup_method(self):\n MANAGER._tables = {}\n MANAGER._views = {}","def _init_and_add_listeners_to_stage_traits(self):\n self.stages[\"Preprocessing\"].config.tracking_tool = self.stages[\"Diffusion\"].config.tracking_processing_tool\n self.stages[\"Preprocessing\"].config.act_tracking = self.stages[\"Diffusion\"].config.mrtrix_tracking_config.use_act\n self.stages[\"Preprocessing\"].config.gmwmi_seeding = self.stages[\"Diffusion\"].config.mrtrix_tracking_config.seed_from_gmwmi\n self.stages[\"Registration\"].config.tracking_tool = self.stages[\"Diffusion\"].config.tracking_processing_tool\n self.stages[\"Registration\"].config.act_tracking = self.stages[\"Diffusion\"].config.mrtrix_tracking_config.use_act\n self.stages[\"Registration\"].config.gmwmi_seeding = self.stages[\"Diffusion\"].config.mrtrix_tracking_config.seed_from_gmwmi\n\n self.stages[\"Connectome\"].config.on_trait_change(\n self.update_vizualization_layout, \"circular_layout\"\n )\n self.stages[\"Connectome\"].config.on_trait_change(\n self.update_vizualization_logscale, \"log_visualization\"\n )\n self.stages[\"Diffusion\"].config.on_trait_change(\n self.update_outputs_recon, \"recon_processing_tool\"\n )\n self.stages[\"Diffusion\"].config.on_trait_change(\n self.update_tracking_tool, \"tracking_processing_tool\"\n )\n self.stages[\"Diffusion\"].config.mrtrix_tracking_config.on_trait_change(\n self.update_preprocessing_act, \"use_act\"\n )\n self.stages[\"Diffusion\"].config.dipy_tracking_config.on_trait_change(\n self.update_preprocessing_act, \"use_act\"\n )\n self.stages[\"Diffusion\"].config.mrtrix_tracking_config.on_trait_change(\n self.update_preprocessing_gmwmi, \"seed_from_gmwmi\"\n )","def set_action_data(self, action, index):\n self._set_action_enabled(action, index)\n self._set_action_checkable(action, index)\n for args in self.setdataargs:\n self._set_action_attribute(action, index, args)","def attach_data(**kwargs) -> None:\n import time\n\n num_engines = len(kwargs['dview'])\n print(ColorText(\"\\nAdding data to engines ...\").bold())\n print(ColorText(\"\\tWARN: Watch available mem in another terminal window: 'watch free -h'\").warn())\n print(ColorText(\"\\tWARN: If available mem gets too low, kill engines and restart cmh_test.py with fewer engines: 'ipcluster stop'\").warn())\n for key,value in kwargs.items():\n if key != 'dview':\n print(f'\\tLoading {key} ({value.__class__.__name__}) to {num_engines} engines')\n kwargs['dview'][key] = value\n time.sleep(1)\n time.sleep(10)\n\n return None","def set(self, **kwargs: Any) -> None:\n self._overrides.update(kwargs)","def set_references(self, map_data):\n \n pass","def mk_data(self):\n self.data = self.DEFAULTS.copy()\n\n for template in self.raw_data.get('extends', []):\n template_data = self.load_template(template)\n self.data.update(template_data)\n\n self.data.update(self.raw_data)\n\n str_replace(self.data)\n\n if self.data.get('redirect_stderr'):\n self.data.pop('stderr')","def setData(self, data):\n self._data = data","def dispatch_hook(key, hooks, hook_data=None, **kwargs):\n # like requests.hooks.dispatch_hook\n hooks = hooks or dict()\n hooks = hooks.get(key)\n if hooks:\n if hasattr(hooks, '__call__'):\n hooks = [hooks]\n for hook in hooks:\n _hook_data = hook(hook_data, **kwargs)\n if _hook_data is not None:\n hook_data = _hook_data\n return hook_data","def set_data(self, data):\n self.data = data","def SetUserData(self, l):\r\n\r\n self.user_data = l","def set_hook(self, module):\n self.hook = module.register_forward_hook(self.hook_fn)","def setData(self,newData):\r\n pass","def setup_view(self, view, request, *args, **kwargs):\n view.request = request\n view.args = args\n view.kwargs = kwargs\n return view","def custom_hooks(self):\n return self.config.options(self.CUSTOM_HOOKS_SECTION, [])","def setHook(self, module):\n self.hook = module.register_forward_hook(self.hook_fn)","def setData(self, data=None, columns=[]):\n\n olv_columns = []\n for column in columns:\n olv_columns.append(ColumnDefn(column.title(), \"left\", 120, column.lower()))\n self.dataOlv.SetColumns(olv_columns)\n if len(data) > 20:\n self.dataOlv.SetObjects(data[:20])\n else:\n self.dataOlv.SetObjects(data)","def register_mutate_hook(self, hook):\n self._mutate_hooks.add(hook)","def setup(self, ds):\n pass","def __setstate__(self, d):\n self.temp_yaml = None\n self.__dict__.update(d)","def f_set(self, data):\n raise NotImplementedError(\"Should have implemented this.\")","def set_data(self, d):\n self._data = d\n self.is_data_set = True","def custom_data(self, custom_data):\n\n self._custom_data = custom_data","def set_data(self, data):\n self._model.set_data(data)\n self.__refresh()","def set_label_maker_hook(hook):\n\n disassembly.set_user_label_maker_hook(hook)","def init_context_data(self):\n pass","def init_view(self):\n self.view_map = self.ctx.clientmap","def available_hooks(self):\n return [HtmlMinPostRenderHook]","def meta_data(self, meta_data):\n\n self._meta_data = meta_data","def __fill_data_variables(self):\n data_vars = []\n for data_var in self.ts.data.data_vars:\n data_vars.append(data_var)\n\n self.data_vars = Dropdown(\n options=data_vars,\n value=data_vars[0],\n description='Data variables:',\n disabled=False,\n style = {'description_width': 'initial'},\n layout={'width': '400px'},\n )\n\n self.data_vars.observe(self.on_data_vars_change)","def set_data(self, indexer_table):\n\n self.indexer_table = indexer_table","def set_data(self, data):\n self._set_data(data)","def set_view_options(self):\n active_panel = self.get_active_panel()\n # turn all show/hide display options off except for polygons and\n # surfaces\n pm.modelEditor(active_panel, e=1, allObjects=False)\n pm.modelEditor(active_panel, e=1, manipulators=False)\n pm.modelEditor(active_panel, e=1, grid=False)\n\n pm.modelEditor(active_panel, e=1, polymeshes=True)\n pm.modelEditor(active_panel, e=1, nurbsSurfaces=True)\n pm.modelEditor(active_panel, e=1, subdivSurfaces=True)\n pm.modelEditor(active_panel, e=1,\n pluginObjects=('gpuCacheDisplayFilter', True))\n pm.modelEditor(active_panel, e=1, planes=True)\n\n # turn all hud displays off\n hud_flags = pm.headsUpDisplay(lh=1)\n for flag in hud_flags:\n pm.headsUpDisplay(flag, e=1, vis=0)\n\n # set camera options for playblast\n for camera in pm.ls(type='camera'):\n camera.setAttr('overscan', 1)\n camera.setAttr('filmFit', 1)\n camera.setAttr('displayFilmGate', 1)\n camera.setAttr('displayResolution', 0)","def setDataFormat(self, format='yaml'):\n self.dataFormat = format;","def setup(self):\n in_dataset, out_dataset = self.get_datasets()\n \n out_dataset[0].create_dataset(in_dataset[0])\n\n in_pData, out_pData = self.get_plugin_datasets()\n\n in_pData[0].plugin_data_setup( 'PROJECTION',multiple)\n out_pData[0].plugin_data_setup( 'PROJECTION',multiple)","def map_to_app_lifecycle_hooks(self, app):\n app['lifecycle_hooks'] = {}\n form_lifecycle_hooks = self.lifecycle_hooks\n if form_lifecycle_hooks.pre_buildimage.data:\n app['lifecycle_hooks']['pre_buildimage'] = b64encode_utf8(\n form_lifecycle_hooks.pre_buildimage.data.replace('\\r\\n', '\\n'))\n else:\n app['lifecycle_hooks']['pre_buildimage'] = ''\n\n if form_lifecycle_hooks.post_buildimage.data:\n app['lifecycle_hooks']['post_buildimage'] = b64encode_utf8(\n form_lifecycle_hooks.post_buildimage.data.replace('\\r\\n', '\\n'))\n else:\n app['lifecycle_hooks']['post_buildimage'] = ''\n\n if form_lifecycle_hooks.pre_bootstrap.data:\n app['lifecycle_hooks']['pre_bootstrap'] = b64encode_utf8(\n form_lifecycle_hooks.pre_bootstrap.data.replace('\\r\\n', '\\n'))\n else:\n app['lifecycle_hooks']['pre_bootstrap'] = ''\n\n if form_lifecycle_hooks.post_bootstrap.data:\n app['lifecycle_hooks']['post_bootstrap'] = b64encode_utf8(\n form_lifecycle_hooks.post_bootstrap.data.replace('\\r\\n', '\\n'))\n else:\n app['lifecycle_hooks']['post_bootstrap'] = ''","def setup_view(view, request, *args, **kwargs):\n\n view.request = request\n view.args = args\n view.kwargs = kwargs\n return view","def install_kv_cache_hooks(self, cache: Optional[dict] = None):\n cache = {**cache} if cache is not None else {}\n hooks = []\n\n def save_to_cache(module, _, output):\n if module not in cache or output.shape[1] > self.dims.n_text_ctx:\n # save as-is, for the first token or cross attention\n cache[module] = output\n else:\n cache[module] = torch.cat([cache[module], output], dim=1).detach()\n return cache[module]\n\n def install_hooks(layer: nn.Module):\n if isinstance(layer, MultiHeadAttention):\n hooks.append(layer.key.register_forward_hook(save_to_cache))\n hooks.append(layer.value.register_forward_hook(save_to_cache))\n\n self.decoder.apply(install_hooks)\n return cache, hooks","def addhooks(self, hooks, node):\n for hook in hooks:\n self.hooks[hook].append(node)","def setup(self, **kwargs):\n\n for k, v in kwargs.items():\n setattr(self, k, v)","def before_dataobj_create(self, dataobj):","def setup(self):\n # Setup the callbacks.\n self.choosethedatefordisplay.on_change(\n \"value\",\n self.callback_choosethedatefordisplay)","def InitView(self):\n\n self._DisablePlotterOptions()\n\n if (self.localModel.datasetCols == 2):\n self.view.Enable2DRadio()\n\n self.Radio2DClicked(True)\n\n if (self.localModel.datasetCols >= 3):\n self.view.Enable2DRadio()\n self.view.Enable3DRadio()\n self.view.Set3DSelected()\n\n self.Radio3DClicked(True)","def hook_events(self):\n wxMediator.hook_events(self)\n EVT_MINE(self, wxEVT_SOCKET_DATA, self.on_data)\n EVT_MINE(self, wxEVT_NEW_LISTEN_CONN, self.new_listen_conn)\n EVT_MINE(self, wxEVT_NEW_TALK_CONN, self.new_talk_conn)","def setup(self):\n in_dataset, out_dataset = self.get_datasets()\n \n out_dataset[0].create_dataset(in_dataset[0])\n\n in_pData, out_pData = self.get_plugin_datasets()\n\n in_pData[0].plugin_data_setup( '',)\n out_pData[0].plugin_data_setup( 'PROJECTION','multiple')","def controls_setup(self):\n pass","def on_action_set_view(self, content):\n self._view = content['view']\n self.refresh_traits_widget()","def viewer_setup(self):\n pass","def viewer_setup(self):\n pass","def set_data_protected(self): \n pass","def set_tensor_data(self, data: dict) -> None:\n assert isinstance(data,\n dict), f'data should be a `dict` but got {data}'\n for k, v in data.items():\n if k == 'gt_label':\n self.set_gt_label(v)\n elif k == 'prompt':\n self.set_field(v, k, dtype=(str, list))\n else:\n self.set_field(all_to_tensor(v), k, dtype=torch.Tensor)","def set_asyncgen_hooks(*args, **kwargs): # real signature unknown; NOTE: unreliably restored from __doc__\n pass","def hook_adapter_script(self) -> HookAdapterScript:","def set_all_data_internal(self, check_data=True):\n # set blocks\n for key, block in self.blocks.items():\n block.set_all_data_internal(check_data)\n # set sub-packages\n for package in self._packagelist:\n package.set_all_data_internal(check_data)","def SetUserData(self, key, data):\n self._userdata[key] = data","def reset_user_view_options_storage(self):\n self.user_view_options = {\n 'display_flags': {},\n 'hud_flags': {},\n 'camera_flags': {}\n }"],"string":"[\n \"def setHooks(self, hooks):\\n super(SelectOneDataView, self).setHooks(hooks)\\n if hooks is not None:\\n for v in self.__views:\\n v.setHooks(hooks)\",\n \"def setHooks(self, hooks):\\n super(SelectManyDataView, self).setHooks(hooks)\\n if hooks is not None:\\n for v in self.__views:\\n v.setHooks(hooks)\",\n \"def _hook_data(self, data):\\n if self.data_hook is not None:\\n self.data_hook(data)\",\n \"def setup_hooks(self):\\n pass\",\n \"def setup_hooks(self) -> None:\\n install_hooks(self.record_interaction)\",\n \"def set_hooks(self):\\n for layer_idx, layer in enumerate(self.vgg_features):\\n if layer_idx in self.layers_to_watch:\\n layer.register_forward_hook(self.layer_watch_hooks(layer_idx))\",\n \"def addView(self, dataView):\\n hooks = self.getHooks()\\n if hooks is not None:\\n dataView.setHooks(hooks)\\n self.__views[dataView] = None\",\n \"def addView(self, dataView):\\n hooks = self.getHooks()\\n if hooks is not None:\\n dataView.setHooks(hooks)\\n self.__views.append(dataView)\",\n \"def _apply_post_render_hooks(self, data, obj, fmt):\\n hooks = self.post_render_hooks.get(fmt,[])\\n for hook in hooks:\\n try:\\n data = hook(data, obj)\\n except Exception as e:\\n self.param.warning(f\\\"The post_render_hook {hook!r} could not \\\"\\n f\\\"be applied:\\\\n\\\\n {e}\\\")\\n return data\",\n \"def inject_data_hook(self, data):\\n return data\",\n \"def __setattr__(self, name, value):\\n if name in self._hook_triggers:\\n super().__setattr__(name, _HooksList(value))\\n else:\\n super().__setattr__(name, value)\",\n \"def dataset_initializer_hook(self):\\n iterator = self._dataset.make_initializable_iterator()\\n # pylint: disable=protected-access\\n hook = estimator_lib._DatasetInitializerHook(iterator)\\n self._iterator = iterator\\n return hook\",\n \"def _pre_setup(self):\\n apps.clear_cache()\\n call_command('migrate', interactive=False, verbosity=0)\\n call_command('loaddata', 'initial_data', verbosity=0)\\n super(DatatableViewTestCase, self)._pre_setup()\",\n \"def _post_hooks(self):\",\n \"def declare_hook(self, fct_name, fct):\\r\\n self._hooks_fct[fct_name] = fct\",\n \"def set_hook(self,name,hook):\\n\\n # At some point in the future, this should validate the hook before it\\n # accepts it. Probably at least check that the hook takes the number\\n # of args it's supposed to.\\n setattr(self.hooks,name,new.instancemethod(hook,self,self.__class__))\",\n \"def set_data(self, data):\\n\\n pass\",\n \"def setData(data, language=None):\",\n \"def set_predefined_data(self, data: dict) -> None:\\n\\n metainfo = {\\n self.META_KEYS[k]: v\\n for (k, v) in data.items() if k in self.META_KEYS\\n }\\n self.set_metainfo(metainfo)\\n\\n data = {\\n self.DATA_KEYS[k]: v\\n for (k, v) in data.items() if k in self.DATA_KEYS\\n }\\n self.set_tensor_data(data)\",\n \"def create_default_hooks(self):\\n\\n self.create_hooks(\\\"commands\\\")\\n self.create_hooks(\\\"commands_out\\\")\\n self.create_hooks(\\\"hooks\\\")\",\n \"def tweaks(self) -> None:\\n pass\",\n \"def set_view_steps(self, steps_list):\\n self._data_dict[self.KEY_VIEW_STEPS] = steps_list\",\n \"def setData(self, data):\\n self.data = data\\n dagPath, components = self.__getGeometryComponents()\\n self.setInfluenceWeights(dagPath, components)\\n self.setBlendWeights(dagPath, components)\\n\\n for attr in ['skinningMethod', 'normalizeWeights']:\\n cmds.setAttr('%s.%s' % (self.node, attr), self.data[attr])\",\n \"def __init__(self):\\n\\t\\tself.satchmo_variation_manager = adminviews.variation_manager\\n\\t\\tadminviews.variation_list = self.variation_list\\n\\t\\tadminviews.variation_manager = self.variation_manager\\n\\t\\t# listeners.add_toolbar_context = self.add_my_toolbar_context#(listeners.add_toolbar_context)\",\n \"def plug_sets(self, plug_sets):\\n\\n self._plug_sets = plug_sets\",\n \"def addResetHooks(self, before, after):\\n self._beforeResetHook = before\\n self._afterResetHook = after\",\n \"def register_stimulus_set_hook(self, hook):\\n\\n handle = HookHandle(self._stimulus_set_hooks)\\n self._stimulus_set_hooks[handle.id] = hook\\n return handle\",\n \"def set_all_data(self, data):\\n self.setData(data, QtCore.Qt.DisplayRole)\\n self.setData(data, QtCore.Qt.EditRole)\\n self.setData(data, QtCore.Qt.UserRole)\\n self.setData(data, QtCore.Qt.ToolTipRole)\\n self.setData(data, QtCore.Qt.StatusTipRole)\\n self.setData(data, QtCore.Qt.WhatsThisRole)\",\n \"def makeOverrides(self):\\n\\t\\tself.overridesWithValues = self.dataOverrides\",\n \"def setData(self, data):\\n self.data = data\",\n \"def setData(self, data):\\n self.data = data\",\n \"def views(self, views):\\n\\n self._views = views\",\n \"def data():\\n return app_views\",\n \"def dataOverrides(self):\\n\\t\\treturn self._overrides\",\n \"def setData(self, data: DataDictBase):\\n super().setData(data)\\n\\n changes = self._analyzeData(data)\\n self.plotDataType = determinePlotDataType(data)\\n\\n self._processPlotTypeOptions()\\n self._plotData(adjustSize=True)\",\n \"def create_hooks(self, hclass):\\n for extension in self.db.values():\\n self.events.register_callbacks_from_inst(hclass, extension)\",\n \"def _hook(self):\",\n \"def setData(self, data):\\n return None\",\n \"def add_traits_meta_data ( self, bases, class_dict, base_traits,\\n class_traits, instance_traits, prefix_traits,\\n view_elements ):\\n class_dict[ BaseTraits ] = base_traits\\n class_dict[ ClassTraits ] = class_traits\\n class_dict[ InstanceTraits ] = instance_traits\\n class_dict[ PrefixTraits ] = prefix_traits\\n class_dict[ ViewTraits ] = view_elements\",\n \"def set_metadata(self, data):\\r\\n pass\",\n \"def on_hook(self) -> None:\",\n \"def on_show_view(self):\\n self.setup()\",\n \"def on_show_view(self):\\n self.setup()\",\n \"def on_show_view(self):\\n self.setup()\",\n \"def setup_method(self):\\n MANAGER._tables = {}\\n MANAGER._views = {}\",\n \"def _init_and_add_listeners_to_stage_traits(self):\\n self.stages[\\\"Preprocessing\\\"].config.tracking_tool = self.stages[\\\"Diffusion\\\"].config.tracking_processing_tool\\n self.stages[\\\"Preprocessing\\\"].config.act_tracking = self.stages[\\\"Diffusion\\\"].config.mrtrix_tracking_config.use_act\\n self.stages[\\\"Preprocessing\\\"].config.gmwmi_seeding = self.stages[\\\"Diffusion\\\"].config.mrtrix_tracking_config.seed_from_gmwmi\\n self.stages[\\\"Registration\\\"].config.tracking_tool = self.stages[\\\"Diffusion\\\"].config.tracking_processing_tool\\n self.stages[\\\"Registration\\\"].config.act_tracking = self.stages[\\\"Diffusion\\\"].config.mrtrix_tracking_config.use_act\\n self.stages[\\\"Registration\\\"].config.gmwmi_seeding = self.stages[\\\"Diffusion\\\"].config.mrtrix_tracking_config.seed_from_gmwmi\\n\\n self.stages[\\\"Connectome\\\"].config.on_trait_change(\\n self.update_vizualization_layout, \\\"circular_layout\\\"\\n )\\n self.stages[\\\"Connectome\\\"].config.on_trait_change(\\n self.update_vizualization_logscale, \\\"log_visualization\\\"\\n )\\n self.stages[\\\"Diffusion\\\"].config.on_trait_change(\\n self.update_outputs_recon, \\\"recon_processing_tool\\\"\\n )\\n self.stages[\\\"Diffusion\\\"].config.on_trait_change(\\n self.update_tracking_tool, \\\"tracking_processing_tool\\\"\\n )\\n self.stages[\\\"Diffusion\\\"].config.mrtrix_tracking_config.on_trait_change(\\n self.update_preprocessing_act, \\\"use_act\\\"\\n )\\n self.stages[\\\"Diffusion\\\"].config.dipy_tracking_config.on_trait_change(\\n self.update_preprocessing_act, \\\"use_act\\\"\\n )\\n self.stages[\\\"Diffusion\\\"].config.mrtrix_tracking_config.on_trait_change(\\n self.update_preprocessing_gmwmi, \\\"seed_from_gmwmi\\\"\\n )\",\n \"def set_action_data(self, action, index):\\n self._set_action_enabled(action, index)\\n self._set_action_checkable(action, index)\\n for args in self.setdataargs:\\n self._set_action_attribute(action, index, args)\",\n \"def attach_data(**kwargs) -> None:\\n import time\\n\\n num_engines = len(kwargs['dview'])\\n print(ColorText(\\\"\\\\nAdding data to engines ...\\\").bold())\\n print(ColorText(\\\"\\\\tWARN: Watch available mem in another terminal window: 'watch free -h'\\\").warn())\\n print(ColorText(\\\"\\\\tWARN: If available mem gets too low, kill engines and restart cmh_test.py with fewer engines: 'ipcluster stop'\\\").warn())\\n for key,value in kwargs.items():\\n if key != 'dview':\\n print(f'\\\\tLoading {key} ({value.__class__.__name__}) to {num_engines} engines')\\n kwargs['dview'][key] = value\\n time.sleep(1)\\n time.sleep(10)\\n\\n return None\",\n \"def set(self, **kwargs: Any) -> None:\\n self._overrides.update(kwargs)\",\n \"def set_references(self, map_data):\\n \\n pass\",\n \"def mk_data(self):\\n self.data = self.DEFAULTS.copy()\\n\\n for template in self.raw_data.get('extends', []):\\n template_data = self.load_template(template)\\n self.data.update(template_data)\\n\\n self.data.update(self.raw_data)\\n\\n str_replace(self.data)\\n\\n if self.data.get('redirect_stderr'):\\n self.data.pop('stderr')\",\n \"def setData(self, data):\\n self._data = data\",\n \"def dispatch_hook(key, hooks, hook_data=None, **kwargs):\\n # like requests.hooks.dispatch_hook\\n hooks = hooks or dict()\\n hooks = hooks.get(key)\\n if hooks:\\n if hasattr(hooks, '__call__'):\\n hooks = [hooks]\\n for hook in hooks:\\n _hook_data = hook(hook_data, **kwargs)\\n if _hook_data is not None:\\n hook_data = _hook_data\\n return hook_data\",\n \"def set_data(self, data):\\n self.data = data\",\n \"def SetUserData(self, l):\\r\\n\\r\\n self.user_data = l\",\n \"def set_hook(self, module):\\n self.hook = module.register_forward_hook(self.hook_fn)\",\n \"def setData(self,newData):\\r\\n pass\",\n \"def setup_view(self, view, request, *args, **kwargs):\\n view.request = request\\n view.args = args\\n view.kwargs = kwargs\\n return view\",\n \"def custom_hooks(self):\\n return self.config.options(self.CUSTOM_HOOKS_SECTION, [])\",\n \"def setHook(self, module):\\n self.hook = module.register_forward_hook(self.hook_fn)\",\n \"def setData(self, data=None, columns=[]):\\n\\n olv_columns = []\\n for column in columns:\\n olv_columns.append(ColumnDefn(column.title(), \\\"left\\\", 120, column.lower()))\\n self.dataOlv.SetColumns(olv_columns)\\n if len(data) > 20:\\n self.dataOlv.SetObjects(data[:20])\\n else:\\n self.dataOlv.SetObjects(data)\",\n \"def register_mutate_hook(self, hook):\\n self._mutate_hooks.add(hook)\",\n \"def setup(self, ds):\\n pass\",\n \"def __setstate__(self, d):\\n self.temp_yaml = None\\n self.__dict__.update(d)\",\n \"def f_set(self, data):\\n raise NotImplementedError(\\\"Should have implemented this.\\\")\",\n \"def set_data(self, d):\\n self._data = d\\n self.is_data_set = True\",\n \"def custom_data(self, custom_data):\\n\\n self._custom_data = custom_data\",\n \"def set_data(self, data):\\n self._model.set_data(data)\\n self.__refresh()\",\n \"def set_label_maker_hook(hook):\\n\\n disassembly.set_user_label_maker_hook(hook)\",\n \"def init_context_data(self):\\n pass\",\n \"def init_view(self):\\n self.view_map = self.ctx.clientmap\",\n \"def available_hooks(self):\\n return [HtmlMinPostRenderHook]\",\n \"def meta_data(self, meta_data):\\n\\n self._meta_data = meta_data\",\n \"def __fill_data_variables(self):\\n data_vars = []\\n for data_var in self.ts.data.data_vars:\\n data_vars.append(data_var)\\n\\n self.data_vars = Dropdown(\\n options=data_vars,\\n value=data_vars[0],\\n description='Data variables:',\\n disabled=False,\\n style = {'description_width': 'initial'},\\n layout={'width': '400px'},\\n )\\n\\n self.data_vars.observe(self.on_data_vars_change)\",\n \"def set_data(self, indexer_table):\\n\\n self.indexer_table = indexer_table\",\n \"def set_data(self, data):\\n self._set_data(data)\",\n \"def set_view_options(self):\\n active_panel = self.get_active_panel()\\n # turn all show/hide display options off except for polygons and\\n # surfaces\\n pm.modelEditor(active_panel, e=1, allObjects=False)\\n pm.modelEditor(active_panel, e=1, manipulators=False)\\n pm.modelEditor(active_panel, e=1, grid=False)\\n\\n pm.modelEditor(active_panel, e=1, polymeshes=True)\\n pm.modelEditor(active_panel, e=1, nurbsSurfaces=True)\\n pm.modelEditor(active_panel, e=1, subdivSurfaces=True)\\n pm.modelEditor(active_panel, e=1,\\n pluginObjects=('gpuCacheDisplayFilter', True))\\n pm.modelEditor(active_panel, e=1, planes=True)\\n\\n # turn all hud displays off\\n hud_flags = pm.headsUpDisplay(lh=1)\\n for flag in hud_flags:\\n pm.headsUpDisplay(flag, e=1, vis=0)\\n\\n # set camera options for playblast\\n for camera in pm.ls(type='camera'):\\n camera.setAttr('overscan', 1)\\n camera.setAttr('filmFit', 1)\\n camera.setAttr('displayFilmGate', 1)\\n camera.setAttr('displayResolution', 0)\",\n \"def setDataFormat(self, format='yaml'):\\n self.dataFormat = format;\",\n \"def setup(self):\\n in_dataset, out_dataset = self.get_datasets()\\n \\n out_dataset[0].create_dataset(in_dataset[0])\\n\\n in_pData, out_pData = self.get_plugin_datasets()\\n\\n in_pData[0].plugin_data_setup( 'PROJECTION',multiple)\\n out_pData[0].plugin_data_setup( 'PROJECTION',multiple)\",\n \"def map_to_app_lifecycle_hooks(self, app):\\n app['lifecycle_hooks'] = {}\\n form_lifecycle_hooks = self.lifecycle_hooks\\n if form_lifecycle_hooks.pre_buildimage.data:\\n app['lifecycle_hooks']['pre_buildimage'] = b64encode_utf8(\\n form_lifecycle_hooks.pre_buildimage.data.replace('\\\\r\\\\n', '\\\\n'))\\n else:\\n app['lifecycle_hooks']['pre_buildimage'] = ''\\n\\n if form_lifecycle_hooks.post_buildimage.data:\\n app['lifecycle_hooks']['post_buildimage'] = b64encode_utf8(\\n form_lifecycle_hooks.post_buildimage.data.replace('\\\\r\\\\n', '\\\\n'))\\n else:\\n app['lifecycle_hooks']['post_buildimage'] = ''\\n\\n if form_lifecycle_hooks.pre_bootstrap.data:\\n app['lifecycle_hooks']['pre_bootstrap'] = b64encode_utf8(\\n form_lifecycle_hooks.pre_bootstrap.data.replace('\\\\r\\\\n', '\\\\n'))\\n else:\\n app['lifecycle_hooks']['pre_bootstrap'] = ''\\n\\n if form_lifecycle_hooks.post_bootstrap.data:\\n app['lifecycle_hooks']['post_bootstrap'] = b64encode_utf8(\\n form_lifecycle_hooks.post_bootstrap.data.replace('\\\\r\\\\n', '\\\\n'))\\n else:\\n app['lifecycle_hooks']['post_bootstrap'] = ''\",\n \"def setup_view(view, request, *args, **kwargs):\\n\\n view.request = request\\n view.args = args\\n view.kwargs = kwargs\\n return view\",\n \"def install_kv_cache_hooks(self, cache: Optional[dict] = None):\\n cache = {**cache} if cache is not None else {}\\n hooks = []\\n\\n def save_to_cache(module, _, output):\\n if module not in cache or output.shape[1] > self.dims.n_text_ctx:\\n # save as-is, for the first token or cross attention\\n cache[module] = output\\n else:\\n cache[module] = torch.cat([cache[module], output], dim=1).detach()\\n return cache[module]\\n\\n def install_hooks(layer: nn.Module):\\n if isinstance(layer, MultiHeadAttention):\\n hooks.append(layer.key.register_forward_hook(save_to_cache))\\n hooks.append(layer.value.register_forward_hook(save_to_cache))\\n\\n self.decoder.apply(install_hooks)\\n return cache, hooks\",\n \"def addhooks(self, hooks, node):\\n for hook in hooks:\\n self.hooks[hook].append(node)\",\n \"def setup(self, **kwargs):\\n\\n for k, v in kwargs.items():\\n setattr(self, k, v)\",\n \"def before_dataobj_create(self, dataobj):\",\n \"def setup(self):\\n # Setup the callbacks.\\n self.choosethedatefordisplay.on_change(\\n \\\"value\\\",\\n self.callback_choosethedatefordisplay)\",\n \"def InitView(self):\\n\\n self._DisablePlotterOptions()\\n\\n if (self.localModel.datasetCols == 2):\\n self.view.Enable2DRadio()\\n\\n self.Radio2DClicked(True)\\n\\n if (self.localModel.datasetCols >= 3):\\n self.view.Enable2DRadio()\\n self.view.Enable3DRadio()\\n self.view.Set3DSelected()\\n\\n self.Radio3DClicked(True)\",\n \"def hook_events(self):\\n wxMediator.hook_events(self)\\n EVT_MINE(self, wxEVT_SOCKET_DATA, self.on_data)\\n EVT_MINE(self, wxEVT_NEW_LISTEN_CONN, self.new_listen_conn)\\n EVT_MINE(self, wxEVT_NEW_TALK_CONN, self.new_talk_conn)\",\n \"def setup(self):\\n in_dataset, out_dataset = self.get_datasets()\\n \\n out_dataset[0].create_dataset(in_dataset[0])\\n\\n in_pData, out_pData = self.get_plugin_datasets()\\n\\n in_pData[0].plugin_data_setup( '',)\\n out_pData[0].plugin_data_setup( 'PROJECTION','multiple')\",\n \"def controls_setup(self):\\n pass\",\n \"def on_action_set_view(self, content):\\n self._view = content['view']\\n self.refresh_traits_widget()\",\n \"def viewer_setup(self):\\n pass\",\n \"def viewer_setup(self):\\n pass\",\n \"def set_data_protected(self): \\n pass\",\n \"def set_tensor_data(self, data: dict) -> None:\\n assert isinstance(data,\\n dict), f'data should be a `dict` but got {data}'\\n for k, v in data.items():\\n if k == 'gt_label':\\n self.set_gt_label(v)\\n elif k == 'prompt':\\n self.set_field(v, k, dtype=(str, list))\\n else:\\n self.set_field(all_to_tensor(v), k, dtype=torch.Tensor)\",\n \"def set_asyncgen_hooks(*args, **kwargs): # real signature unknown; NOTE: unreliably restored from __doc__\\n pass\",\n \"def hook_adapter_script(self) -> HookAdapterScript:\",\n \"def set_all_data_internal(self, check_data=True):\\n # set blocks\\n for key, block in self.blocks.items():\\n block.set_all_data_internal(check_data)\\n # set sub-packages\\n for package in self._packagelist:\\n package.set_all_data_internal(check_data)\",\n \"def SetUserData(self, key, data):\\n self._userdata[key] = data\",\n \"def reset_user_view_options_storage(self):\\n self.user_view_options = {\\n 'display_flags': {},\\n 'hud_flags': {},\\n 'camera_flags': {}\\n }\"\n]","isConversational":false},"negative_scores":{"kind":"list like","value":["0.7749455","0.762406","0.6071119","0.60160655","0.5796744","0.5793318","0.5742086","0.57116985","0.5485993","0.53418344","0.52981484","0.5218101","0.52134025","0.52093846","0.51925564","0.5186529","0.51696926","0.51528996","0.515082","0.51220757","0.510818","0.5106565","0.50764436","0.5061438","0.50541055","0.5053458","0.49952346","0.49779683","0.49761164","0.49749932","0.49749932","0.49748862","0.49570447","0.49350905","0.4926313","0.492257","0.4917764","0.49110404","0.4906229","0.4904536","0.48947912","0.48922887","0.48922887","0.48922887","0.48836643","0.48827654","0.48751688","0.4869517","0.4868761","0.48655754","0.48585102","0.48530835","0.48343113","0.48286363","0.482572","0.4822624","0.48185933","0.47987506","0.47851247","0.47816554","0.4775602","0.47739676","0.4763223","0.4748832","0.47479808","0.47462216","0.4745246","0.47404143","0.47165173","0.47107413","0.47107255","0.4710551","0.47033656","0.47005767","0.4694872","0.46920153","0.46744812","0.4671571","0.4662118","0.46536225","0.46509263","0.4650802","0.46403322","0.4633","0.4631983","0.46318913","0.4620101","0.4617746","0.46142393","0.46132383","0.46129853","0.46116382","0.46116382","0.46100336","0.46087083","0.46014258","0.4600051","0.45995554","0.4593138","0.4589695"],"string":"[\n \"0.7749455\",\n \"0.762406\",\n \"0.6071119\",\n \"0.60160655\",\n \"0.5796744\",\n \"0.5793318\",\n \"0.5742086\",\n \"0.57116985\",\n \"0.5485993\",\n \"0.53418344\",\n \"0.52981484\",\n \"0.5218101\",\n \"0.52134025\",\n \"0.52093846\",\n \"0.51925564\",\n \"0.5186529\",\n \"0.51696926\",\n \"0.51528996\",\n \"0.515082\",\n \"0.51220757\",\n \"0.510818\",\n \"0.5106565\",\n \"0.50764436\",\n \"0.5061438\",\n \"0.50541055\",\n \"0.5053458\",\n \"0.49952346\",\n \"0.49779683\",\n \"0.49761164\",\n \"0.49749932\",\n \"0.49749932\",\n \"0.49748862\",\n \"0.49570447\",\n \"0.49350905\",\n \"0.4926313\",\n \"0.492257\",\n \"0.4917764\",\n \"0.49110404\",\n \"0.4906229\",\n \"0.4904536\",\n \"0.48947912\",\n \"0.48922887\",\n \"0.48922887\",\n \"0.48922887\",\n \"0.48836643\",\n \"0.48827654\",\n \"0.48751688\",\n \"0.4869517\",\n \"0.4868761\",\n \"0.48655754\",\n \"0.48585102\",\n \"0.48530835\",\n \"0.48343113\",\n \"0.48286363\",\n \"0.482572\",\n \"0.4822624\",\n \"0.48185933\",\n \"0.47987506\",\n \"0.47851247\",\n \"0.47816554\",\n \"0.4775602\",\n \"0.47739676\",\n \"0.4763223\",\n \"0.4748832\",\n \"0.47479808\",\n \"0.47462216\",\n \"0.4745246\",\n \"0.47404143\",\n \"0.47165173\",\n \"0.47107413\",\n \"0.47107255\",\n \"0.4710551\",\n \"0.47033656\",\n \"0.47005767\",\n \"0.4694872\",\n \"0.46920153\",\n \"0.46744812\",\n \"0.4671571\",\n \"0.4662118\",\n \"0.46536225\",\n \"0.46509263\",\n \"0.4650802\",\n \"0.46403322\",\n \"0.4633\",\n \"0.4631983\",\n \"0.46318913\",\n \"0.4620101\",\n \"0.4617746\",\n \"0.46142393\",\n \"0.46132383\",\n \"0.46129853\",\n \"0.46116382\",\n \"0.46116382\",\n \"0.46100336\",\n \"0.46087083\",\n \"0.46014258\",\n \"0.4600051\",\n \"0.45995554\",\n \"0.4593138\",\n \"0.4589695\"\n]","isConversational":false},"document_score":{"kind":"string","value":"0.6966032"},"document_rank":{"kind":"string","value":"2"}}},{"rowIdx":23796,"cells":{"query":{"kind":"string","value":"Returns a default colormap."},"document":{"kind":"string","value":"def defaultColormap(self):\n colormap = None\n if self.__hooks is not None:\n colormap = self.__hooks.getColormap(self)\n if colormap is None:\n colormap = Colormap(name=\"viridis\")\n return colormap"},"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_colormap(self):\n return colors.colormaps[self.name]","def get_colormap(self):\n return colors.colormaps[self.name]","def get_colormap(self):\n return file_io.load_viscm_colormap(self.path)","def get_colormap(self):\n return file_io.load_viscm_colormap(self.path)","def colormap(self):\n palette = [(0, 0, 0), (0, 0, 0), (0, 0, 0), (0, 0, 0), (0, 0, 0),\n (111, 74, 0), (81, 0, 81), (128, 64, 128), (244, 35, 232),\n (250, 170, 160), (230, 150, 140), (70, 70, 70),\n (102, 102, 156), (190, 153, 153), (180, 165, 180),\n (150, 100, 100), (150, 120, 90), (153, 153, 153),\n (153, 153, 153), (250, 170, 30), (220, 220, 0),\n (107, 142, 35), (152, 251, 152), (70, 130, 180),\n (220, 20, 60), (255, 0, 0), (0, 0, 142), (0, 0, 70),\n (0, 60, 100), (0, 0, 90), (0, 0, 110), (0, 80, 100),\n (0, 0, 230), (119, 11, 32), (0, 0, 142)]\n\n num_colors = self[0][1].shape[-1]\n colormap = np.zeros((num_colors, 3), dtype=int)\n for i in range(num_colors):\n colormap[i, ...] = palette[self._update_labels_dict[i]]\n return colormap","def get_mpl_colormap(self):\n return mpl.colors.ListedColormap(self.get_colors().astype(float) / 255.0)","def default_colors():\n # default_colors = [\n # # r, g, b, a\n # [92, 192, 98, 0.5],\n # [90, 155, 212, 0.5],\n # [246, 236, 86, 0.6],\n # [241, 90, 96, 0.4],\n # [255, 117, 0, 0.3],\n # [82, 82, 190, 0.2],\n # ]\n\n default_colors = [\n # r, g, b, a\n [188, 114, 3, 0.5],\n [3, 133, 188, 0.5],\n [155, 9, 118, 0.6],\n [155, 53, 9, 0.4],\n [4, 140, 128, 0.3],\n [140, 8, 8, 0.2],\n ]\n\n default_colors = [\n [i[0] / 255.0, i[1] / 255.0, i[2] / 255.0, i[3]]\n for i in default_colors\n ]\n\n return default_colors","def get_density_cmap():\n # Add completely white color to Reds colormap in Matplotlib\n list_colors = plt.cm.datad['Reds']\n list_colors = list(list_colors)\n list_colors.insert(0, (1, 1, 1))\n list_colors.insert(0, (1, 1, 1))\n lscm = matplotlib.colors.LinearSegmentedColormap.from_list(\"my_Reds\", list_colors)\n return lscm","def get_colormap(self):\n return colors.ev_colormaps[self.name]","def get_cmap(cmap=None):\n if cmap:\n if isinstance(cmap, (mpl.colors.Colormap)):\n colormap = cmap\n elif cmap in cmo.cmapnames:\n colormap = cmo.cmap_d[cmap]\n elif cmap in plt.colormaps():\n colormap = plt.get_cmap(cmap)\n else:\n raise ValueError(\n \"Get unrecognised name for the colormap `{}`. Colormaps should be from standard matplotlib set of from cmocean package.\".format(\n cmap\n )\n )\n else:\n colormap = plt.get_cmap(\"Spectral_r\")\n\n return colormap","def cmap(self):\n return self._cmap","def cmap(self):\n return self._palette","def get_cmap(n, name='Paired'):\n return plt.cm.get_cmap(name, n)","def cmap(self):\n return self.pixels.get_cmap()","def get_cmap(n, name='jet'):\n return plt.cm.get_cmap(name, n)","def cmap(self, background_color='#000000'):\n return get_cmap(self.img.data.max() + 1,\n background_color=background_color)","def get_cmap(n, name='hsv'):\n return plt.cm.get_cmap(name, n)","def get_cmap(n, name='hsv'):\n return plt.cm.get_cmap(name, n)","def create_label_colormap():\n colormap = np.array([\n [128, 64, 128],\n [244, 35, 232],\n [ 70, 70, 70],\n [102, 102, 156],\n [190, 153, 153],\n [153, 153, 153],\n [250, 170, 30],\n [220, 220, 0],\n [107, 142, 35],\n [152, 251, 152],\n [ 70, 130, 180],\n [220, 20, 60],\n [255, 0, 0],\n [ 0, 0, 142],\n [ 0, 0, 70],\n [ 0, 60, 100],\n [ 0, 80, 100],\n [ 0, 0, 230],\n [119, 11, 32],\n [ 0, 0, 0]], dtype=np.uint8)\n return colormap","def get_cmap(n, name=\"hsv\"):\n return plt.cm.get_cmap(name, n)","def get_cmap(n, name=\"hsv\"):\n return plt.cm.get_cmap(name, n)","def get_cmap(n, name=\"hsv\"):\n return plt.cm.get_cmap(name, n)","def cmap(num,cmap = plt.cm.gist_earth_r):\n return cmap(np.linspace(0, 1, num))","def GetDefaultColor(self):\n return wx.BLACK","def default_colour(self):\n colour = self.DEFAULT_COLOUR\n return colour","def getColormapImage(self):\n if self._colormapImage is None and self._colormap is not None:\n image = numpy.zeros((16, 130, 3), dtype=numpy.uint8)\n image[1:-1, 1:-1] = self._colormap.getNColors(image.shape[1] - 2)[:, :3]\n self._colormapImage = convertArrayToQImage(image)\n return self._colormapImage","def getColorMap(colors):\n # Normalise RGBs\n norm_colors = []\n for color in colors:\n norm_colors.append([val / 255. for val in color])\n # create color map\n cmap = cols.ListedColormap(norm_colors)\n\n return cmap","def linkcolormap(self, linkcolors=\"viridis\"):\n\n if isinstance(linkcolors, list) and len(linkcolors) == self.n:\n # provided a list of color names\n return colors.ListedColormap(linkcolors)\n else:\n # assume it is a colormap name\n return cm.get_cmap(linkcolors, 6)","def get_colors(nlevels, colormap=None):\n if colormap is None:\n from matplotlib.pyplot import cm\n colormap = cm.rainbow\n return colormap(np.linspace(0, 1, nlevels))","def test_density_colormap(self):\n cmap = matplotlib.cm.get_cmap('density')\n np.testing.assert_allclose(cmap(0.0), [0.214, 0.152, 0.535, 1], atol=0.001)\n np.testing.assert_allclose(cmap(1.0), [0.988, 0.978, 0.042, 1], atol=0.001)","def create_cityscapes_label_colormap():\r\n colormap = np.zeros((256, 3), dtype=np.uint8)\r\n colormap[0] = [128, 64, 128]\r\n colormap[1] = [244, 35, 232]\r\n colormap[2] = [70, 70, 70]\r\n colormap[3] = [102, 102, 156]\r\n colormap[4] = [190, 153, 153]\r\n colormap[5] = [153, 153, 153]\r\n colormap[6] = [250, 170, 30]\r\n colormap[7] = [220, 220, 0]\r\n colormap[8] = [107, 142, 35]\r\n colormap[9] = [152, 251, 152]\r\n colormap[10] = [70, 130, 180]\r\n colormap[11] = [220, 20, 60]\r\n colormap[12] = [255, 0, 0]\r\n colormap[13] = [0, 0, 142]\r\n colormap[14] = [0, 0, 70]\r\n colormap[15] = [0, 60, 100]\r\n colormap[16] = [0, 80, 100]\r\n colormap[17] = [0, 0, 230]\r\n colormap[18] = [119, 11, 32]\r\n return colormap","def use_default_dict():\n color = defaultdict(lambda: \"#000000\")\n color[\"White\"] = \"#FFFFFF\"\n pp.pprint(color[\"White\"])\n pp.pprint(color[\"Red\"])","def _get_csi_colour_scheme():\n\n this_colour_map_object = pyplot.cm.Blues\n this_colour_norm_object = matplotlib.colors.BoundaryNorm(\n LEVELS_FOR_CSI_CONTOURS, this_colour_map_object.N)\n\n rgba_matrix = this_colour_map_object(this_colour_norm_object(\n LEVELS_FOR_CSI_CONTOURS\n ))\n\n colour_list = [\n rgba_matrix[i, ..., :-1] for i in range(rgba_matrix.shape[0])\n ]\n\n colour_map_object = matplotlib.colors.ListedColormap(colour_list)\n colour_map_object.set_under(numpy.full(3, 1.))\n colour_norm_object = matplotlib.colors.BoundaryNorm(\n LEVELS_FOR_CSI_CONTOURS, colour_map_object.N)\n\n return colour_map_object, colour_norm_object","def discrete_cmap(n_colors: int, base_cmap: str) -> Colormap:\r\n # https://gist.github.com/jakevdp/91077b0cae40f8f8244a\r\n base = plt.cm.get_cmap(base_cmap)\r\n color_list = base(np.linspace(0, 1, n_colors))\r\n cmap_name = base.name + str(n_colors)\r\n\r\n return base.from_list(cmap_name, color_list, n_colors)","def defaultColorDialog(self):\n dialog = None\n if self.__hooks is not None:\n dialog = self.__hooks.getColormapDialog(self)\n if dialog is None:\n dialog = ColormapDialog()\n dialog.setModal(False)\n return dialog","def getColormap(self, view):\n return None","def get_colour_map(self):\n try:\n return {'C# minor' : 'Grey', 'A major' : 'Red', 'D minor' : 'Green',\n 'Eb Purple': 'greenyellow', 'D major' : 'Pink', 'G major' : 'Orange',\n 'G minor': 'goldenrod', 'A minor' : 'indianred', 'C minor' : 'peachpuff',\n 'B minor' : 'deepskyblue', 'Ab Major' : 'firebrick', 'Eb / D# minor' : 'orchid',\n 'Ab major' : 'moccasin', 'G# minor' : 'slateblue', 'Eb major' : 'turquoise',\n 'C major' : 'tomato', 'B major' : 'darkmagenta', 'F major' : 'olivedrab',\n 'F minor' : 'olive', 'Bb major' : 'lightsteelblue', 'Db major' : 'plum',\n 'Bb minor' : 'mediumspringgreen', 'E minor' : 'lightsalmon',\n 'F# / Gb major' : 'gold', 'F# minor' : 'burlywood'}\n\n # If colour not found to match, return grey as a last resort\n except KeyError as e:\n print('Unmatched colour: {0}'.format(e))\n return 'Grey'","def transparent_cmap(cmap, N=255):\n if type(cmap) == str:\n mycmap = plt.get_cmap(cmap)\n else:\n mycmap = cmap\n mycmap._init()\n mycmap._lut[0, -1] = 0\n return mycmap","def _get_peirce_colour_scheme():\n\n this_colour_map_object = pyplot.cm.Blues\n this_colour_norm_object = matplotlib.colors.BoundaryNorm(\n LEVELS_FOR_PEIRCE_CONTOURS, this_colour_map_object.N)\n\n rgba_matrix = this_colour_map_object(this_colour_norm_object(\n LEVELS_FOR_PEIRCE_CONTOURS\n ))\n\n colour_list = [\n rgba_matrix[i, ..., :-1] for i in range(rgba_matrix.shape[0])\n ]\n\n colour_map_object = matplotlib.colors.ListedColormap(colour_list)\n colour_map_object.set_under(numpy.full(3, 1.))\n colour_norm_object = matplotlib.colors.BoundaryNorm(\n LEVELS_FOR_PEIRCE_CONTOURS, colour_map_object.N)\n\n return colour_map_object, colour_norm_object","def generate_colormap(scale_range=(0.0, 1.0), hue_range=(0.8, 0.0),\n saturation_range=(1.0, 1.0), value_range=(0.8, 0.8),\n nan_color=(0.2, 0.2, 0.2, 1.0)):\n lookup_table = vtk.vtkLookupTable()\n lookup_table.SetRange(scale_range)\n\n lookup_table.SetHueRange(hue_range)\n lookup_table.SetSaturationRange(saturation_range)\n lookup_table.SetValueRange(value_range)\n lookup_table.SetNanColor(nan_color)\n lookup_table.Build()\n return lookup_table","def default_border_colors():\n\n default_colors = [\n # r, g, b, a\n [0, 0, 0, 1],\n [0, 0, 0, 1],\n [0, 0, 0, 1],\n [0, 0, 0, 1],\n [0, 0, 0, 1],\n [0, 0, 0, 1],\n ]\n\n return default_colors","def create_colormap(seg_map):\n\tcolormap = np.zeros((256, 3), dtype=int)\n\tind = np.arange(256, dtype=int)\n\tfor shift in reversed(range(8)):\n\t\tfor channel in range(3):\n\t\t\tcolormap[:, channel] |= ((ind >> channel) & 1) << shift \n\t\tind >>= 3\n\treturn colormap[seg_map]","def colormap(cats, mplmap='auto', categorical=None):\n # Should automatically choose the right colormaps for:\n # categorical data\n # sequential data (low, high important)\n # diverging data (low, mid, high important)\n global DEF_SEQUENTIAL\n from matplotlib import cm\n\n if hasattr(cm, 'inferno'):\n DEF_SEQUENTIAL = 'inferno'\n else:\n DEF_SEQUENTIAL = 'BrBG'\n\n # strip units\n units = None # TODO: build a color bar with units\n if hasattr(cats[0], 'magnitude'):\n arr = u.array(cats)\n units = arr.units\n cats = arr.magnitude\n is_categorical = False\n else:\n is_categorical = not isinstance(cats[0], (float, int))\n\n if categorical is not None:\n is_categorical = categorical\n\n if is_categorical:\n values = _map_categories_to_ints(cats)\n if mplmap == 'auto':\n mplmap = DEF_CATEGORICAL\n else:\n values = np.array(list(map(float, cats)))\n if mplmap == 'auto':\n mplmap = DEF_SEQUENTIAL\n\n rgb = _cmap_to_rgb(mplmap, values)\n hexcolors = [webcolors.rgb_to_hex(np.array(c)) for c in rgb]\n return hexcolors","def as_default(self, string):\n return idaapi.COLSTR(string, idaapi.SCOLOR_DEFAULT)","def discrete_cmap(N, base_cmap=None):\n\t# Note that if base_cmap is a string or None, you can simply do\n\t# return plt.cm.get_cmap(base_cmap, N)\n\t# The following works for string, None, or a colormap instance:\n\tbase = plt.cm.get_cmap(base_cmap)\n\tcolor_list = base(np.linspace(0, 1, N))\n\tcmap_name = base.name + str(N)\n\treturn base.from_list(cmap_name, color_list, N)","def discrete_cmap(N, base_cmap=None):\n \n # Note that if base_cmap is a string or None, you can simply do\n # return plt.cm.get_cmap(base_cmap, N)\n # The following works for string, None, or a colormap instance:\n \n base = plt.cm.get_cmap(base_cmap)\n color_list = base(np.linspace(0, 1, N))\n cmap_name = base.name + str(N)\n return base.from_list(cmap_name, color_list, N)","def discrete_cmap(N, base_cmap=None):\n \n # Note that if base_cmap is a string or None, you can simply do\n # return plt.cm.get_cmap(base_cmap, N)\n # The following works for string, None, or a colormap instance:\n \n base = plt.cm.get_cmap(base_cmap)\n color_list = base(np.linspace(0, 1, N))\n cmap_name = base.name + str(N)\n return base.from_list(cmap_name, color_list, N)","def discrete_cmap(N, base_cmap=None):\n \n # Note that if base_cmap is a string or None, you can simply do\n # return plt.cm.get_cmap(base_cmap, N)\n # The following works for string, None, or a colormap instance:\n \n base = plt.cm.get_cmap(base_cmap)\n color_list = base(np.linspace(0, 1, N))\n cmap_name = base.name + str(N)\n return base.from_list(cmap_name, color_list, N)","def discrete_cmap(N, base_cmap=None):\n # Note that if base_cmap is a string or None, you can simply do\n # return plt.cm.get_cmap(base_cmap, N)\n # The following works for string, None, or a colormap instance:\n base = plt.cm.get_cmap(base_cmap)\n color_list = base(np.linspace(0, 1, N))\n cmap_name = base.name + str(N)\n return base.from_list(cmap_name, color_list, N)","def get_palette(palette_name):\n\n if hasattr(plt.cm, palette_name):\n cmap = getattr(plt.cm, palette_name)\n elif palette_name in list(mpl_cm.cmap_d.keys()):\n cmap = mpl_cm.get_cmap(palette_name)\n else:\n print(\"Error, color option '\", palette_name, \"' not a valid option\")\n sys.exit(1)\n\n return cmap","def get_font_cmap(font):\n return font['cmap'].tables[0].cmap","def discrete_cmap(N, base_cmap=None):\n # see https://gist.github.com/jakevdp/91077b0cae40f8f8244a\n base = plt.cm.get_cmap(base_cmap)\n color_list = base(np.linspace(0, 1, N))\n cmap_name = base.name + str(N)\n return base.from_list(cmap_name, color_list, N)","def discrete_cmap(N, base_cmap=None):\n\n # Note that if base_cmap is a string or None, you can simply do\n # return plt.cm.get_cmap(base_cmap, N)\n # The following works for string, None, or a colormap instance:\n\n base = plt.cm.get_cmap(base_cmap)\n color_list = base(np.linspace(0, 1, N))\n cmap_name = base.name + str(N)\n return base.from_list(cmap_name, color_list, N)","def discrete_cmap(N, base_cmap=None):\n\n # Note that if base_cmap is a string or None, you can simply do\n # return plt.cm.get_cmap(base_cmap, N)\n # The following works for string, None, or a colormap instance:\n\n base = plt.cm.get_cmap(base_cmap)\n color_list = base(np.linspace(0, 1, N))\n cmap_name = base.name + str(N)\n return base.from_list(cmap_name, color_list, N)","def discrete_cmap(N, base_cmap=None):\n\n # Note that if base_cmap is a string or None, you can simply do\n # return plt.cm.get_cmap(base_cmap, N)\n # The following works for string, None, or a colormap instance:\n\n base = plt.cm.get_cmap(base_cmap)\n color_list = base(np.linspace(0, 1, N))\n cmap_name = base.name + str(N)\n return base.from_list(cmap_name, color_list, N)","def discrete_cmap(N, base_cmap=None):\n\n # Note that if base_cmap is a string or None, you can simply do\n # return plt.cm.get_cmap(base_cmap, N)\n # The following works for string, None, or a colormap instance:\n\n base = plt.cm.get_cmap(base_cmap)\n color_list = base(np.linspace(0, 1, N))\n cmap_name = base.name + str(N)\n return base.from_list(cmap_name, color_list, N)","def colormap(self, band_idx=1):\n if band_idx < 1 or band_idx > self.dataset.RasterCount:\n raise IndexError(\"band index is out of range\")\n ct = self.dataset.GetRasterBand(band_idx).GetColorTable()\n if ct is None:\n return None\n\n colormap = []\n for i in range(ct.GetCount()):\n colormap.append(ct.GetColorEntry(i))\n\n return colormap","def discrete_cmap(N, base_cmap=None):\n base = plt.get_cmap(base_cmap)\n color_list = base(np.linspace(0, 1, N))\n cmap_name = base.name + str(N)\n return base.from_list(cmap_name, color_list, N)","def get_dict(self):\r\n return self.cmap","def _get_colors(num_colors):\n cmap = plt.get_cmap()\n return [cmap(1. * i / num_colors) for i in range(num_colors)]","def get_colors(self):\n colors = [\"#244486\", \"#A6A6A6\", \"#B12122\"]\n cmap = LinearSegmentedColormap.from_list(\"mycmap\", colors)\n\n color_palette=[cmap(i) for i in np.linspace(0, 1, len(set(self.nodes_list)))]\n return dict(zip(list(set(self.nodes_list)), color_palette))","def binary_cmap(color='red', alpha=1.):\n if color == 'red':\n color_code = (1., 0., 0., alpha)\n elif color == 'green':\n color_code = (0., 1., 0., alpha)\n elif color == 'blue':\n color_code = (0., 0., 1., alpha)\n else:\n color_code = color\n transparent_code = (1., 1., 1., 0.)\n return ListedColormap([transparent_code, color_code])","def uniqueish_color():\n return plt.cm.gist_ncar(np.random.random())","def get_colormap(values, cmap='jet', colorbar=True, log=False):\n by_value = not isinstance(values, int)\n if not by_value:\n colorbar = False\n if by_value:\n cmap = plt.get_cmap(cmap)\n if log:\n colors = [cmap(x) for x in tools.maths.normalise(np.log10(values))]\n else:\n colors = [cmap(x) for x in tools.maths.normalise(np.array(values))]\n else:\n cmap = plt.get_cmap(cmap, values)\n colors = [cmap(x) for x in range(values)]\n if colorbar:\n if log:\n norm = mpl.colors.LogNorm(vmin=min(values), vmax=max(values))\n else:\n norm = mpl.colors.Normalize(vmin=min(values), vmax=max(values))\n sm = plt.cm.ScalarMappable(cmap=cmap, norm=norm)\n sm.set_array([])\n if isinstance(colorbar, str):\n label = colorbar\n else:\n label = ''\n plt.colorbar(sm, label=label)\n return colors","def terrain_cmap_256():\n C = np.array(\n [\n [0, 125, 255],\n [2, 97, 0], # Alternativley [0, 0, 255], for blue at sealevel\n [2, 97, 0],\n [3, 97, 0],\n [4, 97, 0],\n [5, 97, 0],\n [6, 98, 0],\n [7, 98, 0],\n [8, 98, 0],\n [9, 98, 0],\n [10, 98, 0],\n [11, 98, 0],\n [11, 99, 0],\n [12, 99, 0],\n [13, 99, 0],\n [14, 99, 0],\n [15, 99, 0],\n [16, 99, 0],\n [17, 100, 0],\n [18, 100, 0],\n [19, 100, 0],\n [19, 100, 0],\n [20, 100, 0],\n [21, 101, 0],\n [22, 101, 0],\n [23, 101, 0],\n [24, 101, 0],\n [25, 101, 0],\n [26, 102, 0],\n [27, 102, 0],\n [28, 102, 0],\n [28, 102, 0],\n [29, 102, 0],\n [30, 102, 0],\n [31, 103, 0],\n [32, 103, 0],\n [33, 103, 0],\n [34, 103, 0],\n [35, 103, 0],\n [36, 104, 0],\n [37, 104, 0],\n [37, 104, 0],\n [38, 104, 0],\n [39, 104, 0],\n [40, 104, 0],\n [41, 105, 0],\n [42, 105, 0],\n [43, 105, 0],\n [44, 105, 0],\n [45, 105, 0],\n [45, 106, 0],\n [46, 106, 0],\n [47, 106, 0],\n [48, 106, 0],\n [49, 106, 0],\n [50, 106, 0],\n [51, 107, 0],\n [52, 107, 0],\n [53, 107, 0],\n [54, 107, 0],\n [54, 107, 0],\n [55, 108, 0],\n [56, 108, 0],\n [57, 108, 0],\n [58, 108, 0],\n [59, 108, 0],\n [60, 108, 1],\n [61, 109, 1],\n [62, 109, 2],\n [63, 109, 2],\n [64, 109, 3],\n [65, 109, 3],\n [66, 110, 4],\n [67, 110, 4],\n [68, 110, 4],\n [69, 110, 5],\n [70, 110, 5],\n [71, 110, 6],\n [72, 111, 6],\n [73, 111, 7],\n [74, 111, 7],\n [75, 111, 8],\n [76, 111, 8],\n [77, 112, 9],\n [78, 112, 9],\n [79, 112, 10],\n [80, 112, 10],\n [81, 112, 11],\n [82, 112, 11],\n [83, 113, 12],\n [84, 113, 12],\n [85, 113, 13],\n [85, 113, 13],\n [86, 113, 14],\n [87, 114, 14],\n [88, 114, 15],\n [89, 114, 15],\n [90, 114, 16],\n [91, 114, 16],\n [92, 114, 17],\n [93, 115, 17],\n [94, 115, 18],\n [95, 115, 18],\n [96, 115, 19],\n [97, 115, 19],\n [98, 115, 20],\n [99, 116, 20],\n [100, 116, 20],\n [101, 116, 21],\n [102, 116, 21],\n [103, 116, 22],\n [104, 117, 22],\n [105, 117, 23],\n [106, 117, 23],\n [107, 117, 24],\n [108, 117, 24],\n [109, 118, 25],\n [110, 118, 25],\n [111, 118, 26],\n [112, 118, 26],\n [113, 118, 27],\n [114, 118, 27],\n [115, 119, 28],\n [116, 119, 28],\n [117, 119, 29],\n [118, 119, 29],\n [119, 119, 30],\n [120, 120, 30],\n [121, 120, 31],\n [122, 120, 31],\n [123, 120, 32],\n [124, 121, 32],\n [125, 121, 32],\n [126, 121, 33],\n [127, 122, 33],\n [128, 122, 34],\n [129, 122, 34],\n [130, 123, 35],\n [131, 123, 35],\n [132, 123, 36],\n [133, 124, 36],\n [134, 124, 37],\n [135, 124, 37],\n [136, 125, 37],\n [137, 125, 38],\n [138, 125, 38],\n [139, 126, 39],\n [139, 126, 39],\n [140, 126, 40],\n [141, 126, 40],\n [142, 127, 41],\n [143, 127, 41],\n [144, 127, 41],\n [145, 128, 42],\n [146, 128, 42],\n [147, 128, 43],\n [148, 129, 43],\n [149, 129, 44],\n [150, 129, 44],\n [151, 130, 45],\n [152, 130, 45],\n [153, 130, 45],\n [154, 131, 46],\n [155, 131, 46],\n [156, 131, 47],\n [157, 132, 47],\n [158, 132, 48],\n [159, 132, 48],\n [160, 133, 49],\n [161, 133, 49],\n [162, 133, 50],\n [163, 134, 50],\n [164, 134, 50],\n [165, 134, 51],\n [166, 135, 51],\n [167, 135, 52],\n [168, 135, 52],\n [169, 136, 53],\n [170, 136, 53],\n [171, 136, 54],\n [172, 137, 54],\n [173, 137, 54],\n [174, 137, 55],\n [175, 138, 55],\n [176, 138, 56],\n [177, 138, 56],\n [178, 139, 57],\n [179, 139, 57],\n [180, 139, 58],\n [181, 140, 58],\n [182, 140, 58],\n [183, 140, 59],\n [184, 141, 59],\n [185, 142, 62],\n [186, 144, 65],\n [187, 146, 68],\n [188, 147, 71],\n [189, 149, 74],\n [190, 151, 77],\n [192, 153, 80],\n [193, 155, 83],\n [194, 156, 86],\n [195, 158, 90],\n [196, 160, 93],\n [197, 162, 96],\n [198, 164, 99],\n [199, 165, 102],\n [201, 167, 105],\n [202, 169, 108],\n [203, 171, 111],\n [204, 173, 114],\n [205, 174, 117],\n [206, 176, 120],\n [207, 178, 123],\n [208, 180, 126],\n [210, 182, 130],\n [211, 184, 133],\n [212, 185, 136],\n [213, 187, 139],\n [214, 189, 142],\n [215, 191, 145],\n [216, 193, 148],\n [217, 194, 151],\n [219, 196, 154],\n [220, 198, 157],\n [221, 200, 160],\n [222, 202, 163],\n [223, 203, 166],\n [224, 205, 170],\n [225, 207, 173],\n [226, 209, 176],\n [228, 211, 179],\n [229, 212, 182],\n [230, 214, 185],\n [231, 216, 188],\n [232, 218, 191],\n [233, 220, 194],\n [234, 221, 197],\n [235, 223, 200],\n [237, 225, 203],\n [238, 227, 207],\n [239, 229, 210],\n [240, 230, 213],\n [241, 232, 216],\n [242, 234, 219],\n [243, 236, 222],\n [245, 238, 225],\n [246, 240, 228],\n [247, 241, 231],\n [248, 243, 234],\n [249, 245, 237],\n [250, 247, 240],\n [251, 249, 243],\n [252, 250, 247],\n [254, 252, 250],\n [255, 254, 253],\n [255, 255, 255],\n ]\n )\n\n cm = ListedColormap(C / 255.0)\n return cm","def test_colormap_discrete():\n with TestingCanvas(size=size, bgcolor='w') as c:\n idata = np.linspace(255, 0, size[0]*size[1]).astype(np.ubyte)\n data = idata.reshape((size[0], size[1]))\n image = Image(cmap=Colormap(colors=['r', 'g', 'b'],\n interpolation='zero'),\n clim='auto', parent=c.scene)\n image.set_data(data)\n assert_image_approved(c.render(), \"visuals/colormap_rgb.png\")","def discrete_cmap(N, base_cmap=None):\n\n # Note that if base_cmap is a string or None, you can simply do\n # return plt.cm.get_cmap(base_cmap, N)\n # The following works for string, None, or a colormap instance:\n # By Jake VanderPlas\n # License: BSD-style\n\n base = plt.cm.get_cmap(base_cmap)\n color_list = base(np.linspace(0, 1, N))\n cmap_name = base.name + str(N)\n return base.from_list(cmap_name, color_list, N)","def get_cmap(n):\n cmap_fn = plt.cm.get_cmap('hsv', n+1)\n colors = [cmap_fn(i + 1)[:3] for i in range(n)]\n random.shuffle(colors)\n cmap = (np.array(colors) * 255.0).astype(np.uint8)\n return cmap","def get_colormap(level_values: np.ndarray) -> np.ndarray:\n color_dimension = level_values # change to desired fourth dimension\n color_min, color_max = color_dimension.min(), color_dimension.max()\n norm = colors.Normalize(color_min, color_max)\n m = plt.cm.ScalarMappable(norm=norm, cmap=\"Spectral_r\")\n m.set_array([])\n face_colors = m.to_rgba(color_dimension)\n\n return face_colors","def colors_to_cmap(colors):\n colors = np.asarray(colors)\n if colors.shape[1] == 3:\n colors = np.hstack((colors, np.ones((len(colors),1))))\n steps = (0.5 + np.asarray(range(len(colors)-1), dtype=np.float))/(len(colors) - 1)\n return matplotlib.colors.LinearSegmentedColormap(\n 'auto_cmap',\n {clrname: ([(0, col[0], col[0])] + \n [(step, c0, c1) for (step,c0,c1) in zip(steps, col[:-1], col[1:])] + \n [(1, col[-1], col[-1])])\n for (clridx,clrname) in enumerate(['red', 'green', 'blue', 'alpha'])\n for col in [colors[:,clridx]]},\n N=len(colors))","def grain_type_colormap():\n\n colors = [('lime'), # 1, precipitation particles\n ('green'), # 2, Decomposing fragmented PP\n ('yellow'), # 3 Rounded Grains (RG)\n ('orange'), # 4 Faceted Crystals\n ('blue'), # 5 Depth Hoar\n ('gray'), # 6 Surface Hoar\n ('red'), # 7 Melt FOrms\n ('cyan'), # 8 Ice formations\n ('black'), # 9 Rounded faceted particles\n ]\n\n\n cmap_name = 'grain_type_map'\n\n my_cmap = LinearSegmentedColormap.from_list(cmap_name, colors, N=9)\n\n return(my_cmap)","def clr_to_cmap(clr, val=None):\n v = np.loadtxt(clr)\n assert np.all(np.arange(v.shape[0]) == v[:,0])\n if not val is None:\n # subset the colors to match with min/max\n # numpy arr\n #mn = val.amin()\n #mx = val.amax()\n # masked arr\n mn = val.min()\n mx = val.max()\n v = v[mn:(mx+1),:]\n v = v[:,1:]\n cm = mpl.colors.ListedColormap(v / 255)\n return cm","def make_colormap(colors):\n#-------------------------\n from matplotlib.colors import LinearSegmentedColormap, ColorConverter\n from numpy import sort\n z = sort(colors.keys())\n n = len(z)\n z1 = min(z)\n zn = max(z)\n x0 = (z - z1) / (zn - z1)\n CC = ColorConverter()\n R = []\n G = []\n B = []\n for i in range(n):\n #i'th color at level z[i]:\n Ci = colors[z[i]]\n if type(Ci) == str:\n # a hex string of form '#ff0000' for example (for red)\n RGB = CC.to_rgb(Ci)\n else:\n # assume it's an RGB triple already:\n RGB = Ci\n R.append(RGB[0])\n G.append(RGB[1])\n B.append(RGB[2])\n cmap_dict = {}\n cmap_dict['red'] = [(x0[i],R[i],R[i]) for i in range(len(R))]\n cmap_dict['green'] = [(x0[i],G[i],G[i]) for i in range(len(G))]\n cmap_dict['blue'] = [(x0[i],B[i],B[i]) for i in range(len(B))]\n mymap = LinearSegmentedColormap('mymap',cmap_dict)\n return mymap","def discrete_cmap(N, base_cmap=None):\n\n # Note that if base_cmap is a string or None, you can simply do\n # return plt.cm.get_cmap(base_cmap, N)\n # The following works for string, None, or a colormap instance:\n\n base = plt.cm.get_cmap(base_cmap)\n color_list = base(np.linspace(0, 1, N))\n cmap_name = base.name + str(N)\n return matplotlib.colors.LinearSegmentedColormap \\\n .from_list(cmap_name, color_list, N)","def create_colormap(color_list: Sequence[str], n_colors: int) -> NDArrayFloat:\n cmap = LinearSegmentedColormap.from_list(name=\"dummy_name\", colors=color_list)\n colorscale: NDArrayFloat = np.array(\n [cmap(k * 1 / n_colors) for k in range(n_colors)]\n )\n # ignore the 4th alpha channel\n return colorscale[:, :3]","def grayscale_cmap(cmap):\n # cmap = cm.get_cmap(cmap)\n map_colors = cmap(np.arange(cmap.N))\n\n # convert RGBA to perceived grayscale luminance\n # cf. http://alienryderflex.com/hsp.html\n RGB_weight = [0.299, 0.587, 0.114]\n luminance = np.sqrt(np.dot(map_colors[:, :3] ** 2, RGB_weight))\n map_colors[:, :3] = luminance[:, np.newaxis]\n\n return colors.LinearSegmentedColormap.from_list(cmap.name + \"_gray\", map_colors, cmap.N)","def base_color(self):\n return curses.color_pair(3) if self.cycling else curses.color_pair(1)","def terrain_cmap_50():\n C = np.array(\n [\n [2, 97, 0],\n [6, 98, 0],\n [11, 98, 0],\n [16, 99, 0],\n [20, 100, 0],\n [25, 101, 0],\n [30, 102, 0],\n [34, 103, 0],\n [39, 104, 0],\n [44, 105, 0],\n [48, 106, 0],\n [53, 107, 0],\n [58, 108, 0],\n [63, 109, 2],\n [68, 110, 4],\n [73, 111, 7],\n [78, 112, 9],\n [83, 113, 12],\n [88, 114, 15],\n [93, 115, 17],\n [98, 116, 20],\n [103, 116, 22],\n [109, 117, 25],\n [114, 118, 27],\n [119, 119, 30],\n [124, 121, 32],\n [129, 122, 34],\n [134, 124, 37],\n [139, 126, 39],\n [144, 127, 41],\n [149, 129, 44],\n [155, 131, 46],\n [160, 133, 48],\n [165, 134, 51],\n [170, 136, 53],\n [175, 138, 55],\n [180, 139, 58],\n [185, 143, 64],\n [191, 152, 80],\n [197, 162, 96],\n [203, 171, 112],\n [209, 181, 128],\n [215, 190, 144],\n [221, 199, 160],\n [226, 209, 176],\n [232, 218, 192],\n [238, 228, 208],\n [244, 237, 224],\n [250, 246, 240],\n [255, 255, 255],\n ]\n )\n\n cm = ListedColormap(C / 255.0)\n return cm","def create_pascal_label_colormap():\r\n colormap = np.zeros((256, 3), dtype = int)\r\n ind = np.arange(256, dtype=int)\r\n\r\n for shift in reversed(range(8)):\r\n for channel in range(3):\r\n colormap[:, channel] |= ((ind >> channel) & 1) << shift\r\n ind >>= 3\r\n\r\n return colormap","def _get_colors():\n mapping = {\n \"terminal\": -1,\n \"black\": curses.COLOR_BLACK,\n \"red\": curses.COLOR_RED,\n \"green\": curses.COLOR_GREEN,\n \"yellow\": curses.COLOR_YELLOW,\n \"blue\": curses.COLOR_BLUE,\n \"magenta\": curses.COLOR_MAGENTA,\n \"cyan\": curses.COLOR_CYAN,\n \"white\": curses.COLOR_WHITE,\n }\n\n cp = get_config_file()\n\n if \"general\" in cp.sections():\n general = cp[\"general\"]\n else:\n general = {}\n\n defaults = {\n \"color_default_background\": \"black\",\n \"color_default_foreground\": \"white\",\n \"color_correct_background\": \"black\",\n \"color_correct_foreground\": \"green\",\n \"color_wrong_background\": \"red\",\n \"color_wrong_foreground\": \"white\",\n \"color_target_background\": \"magenta\",\n \"color_target_foreground\": \"white\",\n \"color_replay_background\": \"blue\",\n \"color_replay_foreground\": \"white\",\n }\n\n colors = {}\n\n for name, default in defaults.items():\n color = general.get(name, default)\n\n if color not in mapping:\n raise KeyError(f\"Unsupported color {color}\")\n\n colors[name] = mapping[color]\n\n return colors","def _create_colormap(self):\n\n max_count = max(self.data['rects'], key=lambda r: r['count'])['count']\n ranges_list = [\n [\n int(max_count/x[0])+1,\n int(max_count/x[1])+1\n ] for x in [[6, 3], [3, 2], [2, 1]]\n ]\n ranges_list = [[0, 1]] + [[1, ranges_list[0][0]]] + ranges_list\n\n self.data['colormap'] = [\n {\n 'fill': self._get_fill(\n i,\n range(\n ranges_list[i][0],\n ranges_list[i][1]\n )\n ),\n 'range': range(\n ranges_list[i][0],\n ranges_list[i][1]\n )\n } for i in range(0, 5)\n ]","def setDefaultColorSpace(self, defaultColorSpace):\n self.PDFreactorConfiguration.in1[\"defaultColorSpace\"] = defaultColorSpace","def get_color_in_rgb_decimal():\n\n # Grabbing custom colormap from matplotlib\n a = cm.get_cmap('cool', 32)\n b = cm.get_cmap('spring', 32)\n c = cm.get_cmap('autumn_r', 64)\n d = cm.get_cmap('bwr_r', 192)\n e = cm.get_cmap('Greens', 192)\n\n # Adding the colormaps into one stack to have a more comprehensive color spectrum \n newcolors = np.vstack((a(np.linspace(0, 1, 32)), \n b(np.linspace(0, 1, 32)), \n c(np.linspace(0, 1, 64)),\n d(np.linspace(0, 0.5, 192)),\n e(np.linspace(0, 1, 192)),\n ))\n return newcolors","def setColourMap(self):\n cmap = self.config['cmap']\n\n pos, colour, mode = colourMaps.colourMaps(cmap)\n\n cmap = pg.ColorMap(pos, colour,mode)\n self.lut = cmap.getLookupTable(0.0, 1.0, 256)\n minsg = np.min(self.sg)\n maxsg = np.max(self.sg)\n self.colourStart = (self.config['brightness'] / 100.0 * self.config['contrast'] / 100.0) * (maxsg - minsg) + minsg\n self.colourEnd = (maxsg - minsg) * (1.0 - self.config['contrast'] / 100.0) + self.colourStart","def create_pascal_label_colormap():\n colormap = np.zeros((256, 3), dtype=int)\n ind = np.arange(256, dtype=int)\n\n for shift in reversed(range(8)):\n for channel in range(3):\n colormap[:, channel] |= ((ind >> channel) & 1) << shift\n ind >>= 3\n\n return colormap","def create_pascal_label_colormap():\n colormap = np.zeros((256, 3), dtype=int)\n ind = np.arange(256, dtype=int)\n\n for shift in reversed(range(8)):\n for channel in range(3):\n colormap[:, channel] |= ((ind >> channel) & 1) << shift\n ind >>= 3\n\n return colormap","def palette_from_mpl_name(name):\n if name in CMAPS:\n return CMAPS[name]\n\n rgba = plt.get_cmap(name)(np.linspace(0, 1, 256))\n palette = [to_hex(color) for color in rgba]\n return palette","def default_render_color_maker(num:int):\n if num < 7:\n log.warn('Fewer than 7 rendering colors are being generated. This may cause issues if a URDF with a 6+ axis robot is loaded.')\n\n b = np.linspace(0,255,num).astype(int) # Blue values are always unique\n\n g = [0] * b.size\n r = np.abs(255 - 2*b)\n\n colors = []\n for idx in range(num):\n colors.append([b[idx],g[idx],r[idx]])\n return colors","def setColourMap(self):\n cmap = self.config['cmap']\n\n pos, colour, mode = colourMaps.colourMaps(cmap)\n\n cmap = pg.ColorMap(pos, colour, mode)\n self.lut = cmap.getLookupTable(0.0, 1.0, 256)\n minsg = np.min(self.sg)\n maxsg = np.max(self.sg)\n self.colourStart = (self.config['brightness'] / 100.0 * self.config['contrast'] / 100.0) * (\n maxsg - minsg) + minsg\n self.colourEnd = (maxsg - minsg) * (1.0 - self.config['contrast'] / 100.0) + self.colourStart","def create_pascal_label_colormap():\n colormap = np.zeros((256, 3), dtype=int)\n ind = np.arrange(256, dtype=int)\n\n for shift in reversed(range(8)):\n for channel in range(3):\n colormap[:, channel] |= ((ind >> channel) & 1) << shift\n ind >>=3\n\n return colormap","def create_pascal_label_colormap():\n colormap = np.zeros((256, 3), dtype=int)\n ind = np.arange(256, dtype=int)\n\n for shift in reversed(range(8)):\n for channel in range(3):\n colormap[:, channel] |= ((ind >> channel) & 1) << shift\n ind >>= 3\n\n return colormap","def create_pascal_label_colormap():\n colormap = np.zeros((256, 3), dtype=int)\n ind = np.arange(256, dtype=int)\n\n for shift in reversed(range(8)):\n for channel in range(3):\n colormap[:, channel] |= ((ind >> channel) & 1) << shift\n ind >>= 3\n\n return colormap","def _color_for_labels(label_color, default_color, seq_index):\n if label_color is None:\n if hasattr(default_color, '__getitem__'):\n c = default_color[seq_index]\n else:\n c = default_color\n else:\n c = label_color\n\n return c or 'black'","def assigning_colors():\n rgb_colors = {}\n for name, hex in matplotlib.colors.cnames.items():\n color = []\n # So the values are from 0-255 and not 0-1\n for i in matplotlib.colors.to_rgb(hex):\n color.append(int(i * 255))\n\n color = tuple(color)\n rgb_colors[name] = color\n\n return rgb_colors","def __init__(self):\n self.primary = '#9CC537' # slightly darker than YellowGreen #9acd32, rgb[156/255, 197/255, 55/255]\n self.secondary = '#2E3743' # asphalt, rgb[46/255, 55/255, 67/255]\n self.tertiary = '#9B2B2C' # red'ish, rgb(155, 43, 44)\n self.fourth = '#E57925' # orange'ish, rgb(229, 121, 37)\n self.fifth = '#F2D869' # yellow'ish, rgb(242, 216, 105)\n self.sixth = '#AB8D60'\n self.seventh = '#A4D29F'\n self.eighth = '#6E807B'\n self.ninth = '#3D636F' # blue grey\n self.tenth = '#A49E9D'\n self.eleventh = '#DA9BA6'\n self.primary_10 = '#1F290A' # darkest green, 10% of primary\n self.primary_35 = '#6C9023' # dark green, 35% of primary\n self.primary_80 = '#D7EBAD' # light green, 80% of primary\n self.primary_90 = '#ebf5d6' # light green, 90% of primary\n self.primary_95 = '#F5FAEA' # lightest green, 95% of primary\n self.secondary_70 = '#6d737b' # light asphalt\n\n _col_map_colors = [self.primary_95, # lightest primary\n self.primary, # primary\n self.primary_10] # darkest primary\n self._color_map = self._set_col_map(_col_map_colors)\n\n self.color_list = [self.primary, self.secondary, self.tertiary, self.fourth, self.fifth, self.sixth,\n self.seventh, self.eighth, self.ninth, self.tenth, self.eleventh, self.primary_35]\n\n # set the mpl color cycler to our colors. It has 10 colors\n # mpl.rcParams['axes.prop_cycle']","def get_cmap_levels(colormap,levels):\n \n #Matplotlib colormap name\n if isinstance(colormap,str):\n cmap = mlib.cm.get_cmap(colormap)\n \n #User defined list of colors\n elif isinstance(colormap,list):\n cmap = mcolors.ListedColormap(colormap)\n \n #Dictionary\n elif isinstance(colormap,dict):\n cmap = make_colormap(colormap)\n \n #Otherwise, a cmap was passed\n else:\n cmap = colormap\n \n #Normalize colors relative to levels\n norm = mcolors.Normalize(vmin=0, vmax=len(levels)-1)\n \n #If more than 2 levels were passed, use those for the contour levels\n if len(levels) > 2:\n colors = cmap(norm(np.arange(len(levels)-1)+.5))\n cmap = mcolors.ListedColormap(colors)\n \n #Otherwise, create a list of colors based on levels\n else:\n colors = cmap(norm(np.linspace(0,1,256)))\n cmap = mcolors.LinearSegmentedColormap.from_list('my_colormap',colors)\n \n y0 = min(levels)\n y1 = max(levels)\n dy = (y1-y0)/16\n scalemag = int(np.round_(np.log(dy)/np.log(8),decimals=0))\n dy_scaled = dy*10**-scalemag\n dc = min([1,2,5,10], key=lambda x:abs(x-dy_scaled))\n dc = np.round_(dc*10**scalemag,decimals=-scalemag+1)\n c0 = np.round_(y0/dc,decimals=0)*dc\n c1 = np.round_(y1/dc,decimals=0)*dc\n levels = np.arange(c0,c1+dc*.5,dc)\n \n if scalemag > 0:\n levels = levels.astype(int)\n else:\n levels = np.round_(levels,decimals=-scalemag+1)\n\n #Return colormap and levels\n return cmap, levels","def colormap_masked(ncolors=256, knee_index=None, cmap='inferno', alpha=0.3):\n cm = plt.cm.get_cmap(cmap)(np.linspace(0, 1, ncolors))\n if knee_index is None:\n # Then map to pvals, as -log(p) between 0 and 3.5, and threshold at 0.05\n knee_index = np.argmin(abs(np.linspace(0., 3.5, ncolors)+np.log10(0.05)))\n \n cm[:knee_index, :] = np.c_[cm[:knee_index, 0], cm[:knee_index, 1], cm[:knee_index, 2], alpha*np.ones((len(cm[:knee_index, 1])))]\n return LinearSegmentedColormap.from_list('my_colormap', cm)","def get_cfg_defaults():\n return _C.clone()","def getColormapDialog(self, view):\n return None","def register_cccmacms(cmap='all'):\n \n \n #print 'registering cmaps'\n \n \n \n \n \n \n # define individual colors as RGB triples\n \n \n # from colorwheel.m\n \n \n # =============================================\n \n \n # kem_w20 (20) OR blue2red_w20\n \n \n # blueish at top, white in middle, reddish at bottom\n \n \n \n cpool = np.array([ [153,255,255], \\\n \n \n [204,255,229], \\\n \n \n [240,255,240],\\\n \n \n [204,255,153],\\\n \n \n [178,255,102],\\\n \n \n [216,255,76],\\\n \n \n [255,255,51],\\\n \n \n [255,220,51],\\\n \n \n [255,187,51],\\\n \n \n [255,153,51],\\\n \n \n [255,0,0],\\\n \n \n [204,0,0],\\\n \n \n [153,0,0]], \\\n \n \n dtype=float)\n \n \n \n acccbar = (cpool/255.)\n \n \n thecmap = col.ListedColormap(acccbar,'acccbar')\n \n \n cm.register_cmap(cmap=thecmap)\n\n return"],"string":"[\n \"def get_colormap(self):\\n return colors.colormaps[self.name]\",\n \"def get_colormap(self):\\n return colors.colormaps[self.name]\",\n \"def get_colormap(self):\\n return file_io.load_viscm_colormap(self.path)\",\n \"def get_colormap(self):\\n return file_io.load_viscm_colormap(self.path)\",\n \"def colormap(self):\\n palette = [(0, 0, 0), (0, 0, 0), (0, 0, 0), (0, 0, 0), (0, 0, 0),\\n (111, 74, 0), (81, 0, 81), (128, 64, 128), (244, 35, 232),\\n (250, 170, 160), (230, 150, 140), (70, 70, 70),\\n (102, 102, 156), (190, 153, 153), (180, 165, 180),\\n (150, 100, 100), (150, 120, 90), (153, 153, 153),\\n (153, 153, 153), (250, 170, 30), (220, 220, 0),\\n (107, 142, 35), (152, 251, 152), (70, 130, 180),\\n (220, 20, 60), (255, 0, 0), (0, 0, 142), (0, 0, 70),\\n (0, 60, 100), (0, 0, 90), (0, 0, 110), (0, 80, 100),\\n (0, 0, 230), (119, 11, 32), (0, 0, 142)]\\n\\n num_colors = self[0][1].shape[-1]\\n colormap = np.zeros((num_colors, 3), dtype=int)\\n for i in range(num_colors):\\n colormap[i, ...] = palette[self._update_labels_dict[i]]\\n return colormap\",\n \"def get_mpl_colormap(self):\\n return mpl.colors.ListedColormap(self.get_colors().astype(float) / 255.0)\",\n \"def default_colors():\\n # default_colors = [\\n # # r, g, b, a\\n # [92, 192, 98, 0.5],\\n # [90, 155, 212, 0.5],\\n # [246, 236, 86, 0.6],\\n # [241, 90, 96, 0.4],\\n # [255, 117, 0, 0.3],\\n # [82, 82, 190, 0.2],\\n # ]\\n\\n default_colors = [\\n # r, g, b, a\\n [188, 114, 3, 0.5],\\n [3, 133, 188, 0.5],\\n [155, 9, 118, 0.6],\\n [155, 53, 9, 0.4],\\n [4, 140, 128, 0.3],\\n [140, 8, 8, 0.2],\\n ]\\n\\n default_colors = [\\n [i[0] / 255.0, i[1] / 255.0, i[2] / 255.0, i[3]]\\n for i in default_colors\\n ]\\n\\n return default_colors\",\n \"def get_density_cmap():\\n # Add completely white color to Reds colormap in Matplotlib\\n list_colors = plt.cm.datad['Reds']\\n list_colors = list(list_colors)\\n list_colors.insert(0, (1, 1, 1))\\n list_colors.insert(0, (1, 1, 1))\\n lscm = matplotlib.colors.LinearSegmentedColormap.from_list(\\\"my_Reds\\\", list_colors)\\n return lscm\",\n \"def get_colormap(self):\\n return colors.ev_colormaps[self.name]\",\n \"def get_cmap(cmap=None):\\n if cmap:\\n if isinstance(cmap, (mpl.colors.Colormap)):\\n colormap = cmap\\n elif cmap in cmo.cmapnames:\\n colormap = cmo.cmap_d[cmap]\\n elif cmap in plt.colormaps():\\n colormap = plt.get_cmap(cmap)\\n else:\\n raise ValueError(\\n \\\"Get unrecognised name for the colormap `{}`. Colormaps should be from standard matplotlib set of from cmocean package.\\\".format(\\n cmap\\n )\\n )\\n else:\\n colormap = plt.get_cmap(\\\"Spectral_r\\\")\\n\\n return colormap\",\n \"def cmap(self):\\n return self._cmap\",\n \"def cmap(self):\\n return self._palette\",\n \"def get_cmap(n, name='Paired'):\\n return plt.cm.get_cmap(name, n)\",\n \"def cmap(self):\\n return self.pixels.get_cmap()\",\n \"def get_cmap(n, name='jet'):\\n return plt.cm.get_cmap(name, n)\",\n \"def cmap(self, background_color='#000000'):\\n return get_cmap(self.img.data.max() + 1,\\n background_color=background_color)\",\n \"def get_cmap(n, name='hsv'):\\n return plt.cm.get_cmap(name, n)\",\n \"def get_cmap(n, name='hsv'):\\n return plt.cm.get_cmap(name, n)\",\n \"def create_label_colormap():\\n colormap = np.array([\\n [128, 64, 128],\\n [244, 35, 232],\\n [ 70, 70, 70],\\n [102, 102, 156],\\n [190, 153, 153],\\n [153, 153, 153],\\n [250, 170, 30],\\n [220, 220, 0],\\n [107, 142, 35],\\n [152, 251, 152],\\n [ 70, 130, 180],\\n [220, 20, 60],\\n [255, 0, 0],\\n [ 0, 0, 142],\\n [ 0, 0, 70],\\n [ 0, 60, 100],\\n [ 0, 80, 100],\\n [ 0, 0, 230],\\n [119, 11, 32],\\n [ 0, 0, 0]], dtype=np.uint8)\\n return colormap\",\n \"def get_cmap(n, name=\\\"hsv\\\"):\\n return plt.cm.get_cmap(name, n)\",\n \"def get_cmap(n, name=\\\"hsv\\\"):\\n return plt.cm.get_cmap(name, n)\",\n \"def get_cmap(n, name=\\\"hsv\\\"):\\n return plt.cm.get_cmap(name, n)\",\n \"def cmap(num,cmap = plt.cm.gist_earth_r):\\n return cmap(np.linspace(0, 1, num))\",\n \"def GetDefaultColor(self):\\n return wx.BLACK\",\n \"def default_colour(self):\\n colour = self.DEFAULT_COLOUR\\n return colour\",\n \"def getColormapImage(self):\\n if self._colormapImage is None and self._colormap is not None:\\n image = numpy.zeros((16, 130, 3), dtype=numpy.uint8)\\n image[1:-1, 1:-1] = self._colormap.getNColors(image.shape[1] - 2)[:, :3]\\n self._colormapImage = convertArrayToQImage(image)\\n return self._colormapImage\",\n \"def getColorMap(colors):\\n # Normalise RGBs\\n norm_colors = []\\n for color in colors:\\n norm_colors.append([val / 255. for val in color])\\n # create color map\\n cmap = cols.ListedColormap(norm_colors)\\n\\n return cmap\",\n \"def linkcolormap(self, linkcolors=\\\"viridis\\\"):\\n\\n if isinstance(linkcolors, list) and len(linkcolors) == self.n:\\n # provided a list of color names\\n return colors.ListedColormap(linkcolors)\\n else:\\n # assume it is a colormap name\\n return cm.get_cmap(linkcolors, 6)\",\n \"def get_colors(nlevels, colormap=None):\\n if colormap is None:\\n from matplotlib.pyplot import cm\\n colormap = cm.rainbow\\n return colormap(np.linspace(0, 1, nlevels))\",\n \"def test_density_colormap(self):\\n cmap = matplotlib.cm.get_cmap('density')\\n np.testing.assert_allclose(cmap(0.0), [0.214, 0.152, 0.535, 1], atol=0.001)\\n np.testing.assert_allclose(cmap(1.0), [0.988, 0.978, 0.042, 1], atol=0.001)\",\n \"def create_cityscapes_label_colormap():\\r\\n colormap = np.zeros((256, 3), dtype=np.uint8)\\r\\n colormap[0] = [128, 64, 128]\\r\\n colormap[1] = [244, 35, 232]\\r\\n colormap[2] = [70, 70, 70]\\r\\n colormap[3] = [102, 102, 156]\\r\\n colormap[4] = [190, 153, 153]\\r\\n colormap[5] = [153, 153, 153]\\r\\n colormap[6] = [250, 170, 30]\\r\\n colormap[7] = [220, 220, 0]\\r\\n colormap[8] = [107, 142, 35]\\r\\n colormap[9] = [152, 251, 152]\\r\\n colormap[10] = [70, 130, 180]\\r\\n colormap[11] = [220, 20, 60]\\r\\n colormap[12] = [255, 0, 0]\\r\\n colormap[13] = [0, 0, 142]\\r\\n colormap[14] = [0, 0, 70]\\r\\n colormap[15] = [0, 60, 100]\\r\\n colormap[16] = [0, 80, 100]\\r\\n colormap[17] = [0, 0, 230]\\r\\n colormap[18] = [119, 11, 32]\\r\\n return colormap\",\n \"def use_default_dict():\\n color = defaultdict(lambda: \\\"#000000\\\")\\n color[\\\"White\\\"] = \\\"#FFFFFF\\\"\\n pp.pprint(color[\\\"White\\\"])\\n pp.pprint(color[\\\"Red\\\"])\",\n \"def _get_csi_colour_scheme():\\n\\n this_colour_map_object = pyplot.cm.Blues\\n this_colour_norm_object = matplotlib.colors.BoundaryNorm(\\n LEVELS_FOR_CSI_CONTOURS, this_colour_map_object.N)\\n\\n rgba_matrix = this_colour_map_object(this_colour_norm_object(\\n LEVELS_FOR_CSI_CONTOURS\\n ))\\n\\n colour_list = [\\n rgba_matrix[i, ..., :-1] for i in range(rgba_matrix.shape[0])\\n ]\\n\\n colour_map_object = matplotlib.colors.ListedColormap(colour_list)\\n colour_map_object.set_under(numpy.full(3, 1.))\\n colour_norm_object = matplotlib.colors.BoundaryNorm(\\n LEVELS_FOR_CSI_CONTOURS, colour_map_object.N)\\n\\n return colour_map_object, colour_norm_object\",\n \"def discrete_cmap(n_colors: int, base_cmap: str) -> Colormap:\\r\\n # https://gist.github.com/jakevdp/91077b0cae40f8f8244a\\r\\n base = plt.cm.get_cmap(base_cmap)\\r\\n color_list = base(np.linspace(0, 1, n_colors))\\r\\n cmap_name = base.name + str(n_colors)\\r\\n\\r\\n return base.from_list(cmap_name, color_list, n_colors)\",\n \"def defaultColorDialog(self):\\n dialog = None\\n if self.__hooks is not None:\\n dialog = self.__hooks.getColormapDialog(self)\\n if dialog is None:\\n dialog = ColormapDialog()\\n dialog.setModal(False)\\n return dialog\",\n \"def getColormap(self, view):\\n return None\",\n \"def get_colour_map(self):\\n try:\\n return {'C# minor' : 'Grey', 'A major' : 'Red', 'D minor' : 'Green',\\n 'Eb Purple': 'greenyellow', 'D major' : 'Pink', 'G major' : 'Orange',\\n 'G minor': 'goldenrod', 'A minor' : 'indianred', 'C minor' : 'peachpuff',\\n 'B minor' : 'deepskyblue', 'Ab Major' : 'firebrick', 'Eb / D# minor' : 'orchid',\\n 'Ab major' : 'moccasin', 'G# minor' : 'slateblue', 'Eb major' : 'turquoise',\\n 'C major' : 'tomato', 'B major' : 'darkmagenta', 'F major' : 'olivedrab',\\n 'F minor' : 'olive', 'Bb major' : 'lightsteelblue', 'Db major' : 'plum',\\n 'Bb minor' : 'mediumspringgreen', 'E minor' : 'lightsalmon',\\n 'F# / Gb major' : 'gold', 'F# minor' : 'burlywood'}\\n\\n # If colour not found to match, return grey as a last resort\\n except KeyError as e:\\n print('Unmatched colour: {0}'.format(e))\\n return 'Grey'\",\n \"def transparent_cmap(cmap, N=255):\\n if type(cmap) == str:\\n mycmap = plt.get_cmap(cmap)\\n else:\\n mycmap = cmap\\n mycmap._init()\\n mycmap._lut[0, -1] = 0\\n return mycmap\",\n \"def _get_peirce_colour_scheme():\\n\\n this_colour_map_object = pyplot.cm.Blues\\n this_colour_norm_object = matplotlib.colors.BoundaryNorm(\\n LEVELS_FOR_PEIRCE_CONTOURS, this_colour_map_object.N)\\n\\n rgba_matrix = this_colour_map_object(this_colour_norm_object(\\n LEVELS_FOR_PEIRCE_CONTOURS\\n ))\\n\\n colour_list = [\\n rgba_matrix[i, ..., :-1] for i in range(rgba_matrix.shape[0])\\n ]\\n\\n colour_map_object = matplotlib.colors.ListedColormap(colour_list)\\n colour_map_object.set_under(numpy.full(3, 1.))\\n colour_norm_object = matplotlib.colors.BoundaryNorm(\\n LEVELS_FOR_PEIRCE_CONTOURS, colour_map_object.N)\\n\\n return colour_map_object, colour_norm_object\",\n \"def generate_colormap(scale_range=(0.0, 1.0), hue_range=(0.8, 0.0),\\n saturation_range=(1.0, 1.0), value_range=(0.8, 0.8),\\n nan_color=(0.2, 0.2, 0.2, 1.0)):\\n lookup_table = vtk.vtkLookupTable()\\n lookup_table.SetRange(scale_range)\\n\\n lookup_table.SetHueRange(hue_range)\\n lookup_table.SetSaturationRange(saturation_range)\\n lookup_table.SetValueRange(value_range)\\n lookup_table.SetNanColor(nan_color)\\n lookup_table.Build()\\n return lookup_table\",\n \"def default_border_colors():\\n\\n default_colors = [\\n # r, g, b, a\\n [0, 0, 0, 1],\\n [0, 0, 0, 1],\\n [0, 0, 0, 1],\\n [0, 0, 0, 1],\\n [0, 0, 0, 1],\\n [0, 0, 0, 1],\\n ]\\n\\n return default_colors\",\n \"def create_colormap(seg_map):\\n\\tcolormap = np.zeros((256, 3), dtype=int)\\n\\tind = np.arange(256, dtype=int)\\n\\tfor shift in reversed(range(8)):\\n\\t\\tfor channel in range(3):\\n\\t\\t\\tcolormap[:, channel] |= ((ind >> channel) & 1) << shift \\n\\t\\tind >>= 3\\n\\treturn colormap[seg_map]\",\n \"def colormap(cats, mplmap='auto', categorical=None):\\n # Should automatically choose the right colormaps for:\\n # categorical data\\n # sequential data (low, high important)\\n # diverging data (low, mid, high important)\\n global DEF_SEQUENTIAL\\n from matplotlib import cm\\n\\n if hasattr(cm, 'inferno'):\\n DEF_SEQUENTIAL = 'inferno'\\n else:\\n DEF_SEQUENTIAL = 'BrBG'\\n\\n # strip units\\n units = None # TODO: build a color bar with units\\n if hasattr(cats[0], 'magnitude'):\\n arr = u.array(cats)\\n units = arr.units\\n cats = arr.magnitude\\n is_categorical = False\\n else:\\n is_categorical = not isinstance(cats[0], (float, int))\\n\\n if categorical is not None:\\n is_categorical = categorical\\n\\n if is_categorical:\\n values = _map_categories_to_ints(cats)\\n if mplmap == 'auto':\\n mplmap = DEF_CATEGORICAL\\n else:\\n values = np.array(list(map(float, cats)))\\n if mplmap == 'auto':\\n mplmap = DEF_SEQUENTIAL\\n\\n rgb = _cmap_to_rgb(mplmap, values)\\n hexcolors = [webcolors.rgb_to_hex(np.array(c)) for c in rgb]\\n return hexcolors\",\n \"def as_default(self, string):\\n return idaapi.COLSTR(string, idaapi.SCOLOR_DEFAULT)\",\n \"def discrete_cmap(N, base_cmap=None):\\n\\t# Note that if base_cmap is a string or None, you can simply do\\n\\t# return plt.cm.get_cmap(base_cmap, N)\\n\\t# The following works for string, None, or a colormap instance:\\n\\tbase = plt.cm.get_cmap(base_cmap)\\n\\tcolor_list = base(np.linspace(0, 1, N))\\n\\tcmap_name = base.name + str(N)\\n\\treturn base.from_list(cmap_name, color_list, N)\",\n \"def discrete_cmap(N, base_cmap=None):\\n \\n # Note that if base_cmap is a string or None, you can simply do\\n # return plt.cm.get_cmap(base_cmap, N)\\n # The following works for string, None, or a colormap instance:\\n \\n base = plt.cm.get_cmap(base_cmap)\\n color_list = base(np.linspace(0, 1, N))\\n cmap_name = base.name + str(N)\\n return base.from_list(cmap_name, color_list, N)\",\n \"def discrete_cmap(N, base_cmap=None):\\n \\n # Note that if base_cmap is a string or None, you can simply do\\n # return plt.cm.get_cmap(base_cmap, N)\\n # The following works for string, None, or a colormap instance:\\n \\n base = plt.cm.get_cmap(base_cmap)\\n color_list = base(np.linspace(0, 1, N))\\n cmap_name = base.name + str(N)\\n return base.from_list(cmap_name, color_list, N)\",\n \"def discrete_cmap(N, base_cmap=None):\\n \\n # Note that if base_cmap is a string or None, you can simply do\\n # return plt.cm.get_cmap(base_cmap, N)\\n # The following works for string, None, or a colormap instance:\\n \\n base = plt.cm.get_cmap(base_cmap)\\n color_list = base(np.linspace(0, 1, N))\\n cmap_name = base.name + str(N)\\n return base.from_list(cmap_name, color_list, N)\",\n \"def discrete_cmap(N, base_cmap=None):\\n # Note that if base_cmap is a string or None, you can simply do\\n # return plt.cm.get_cmap(base_cmap, N)\\n # The following works for string, None, or a colormap instance:\\n base = plt.cm.get_cmap(base_cmap)\\n color_list = base(np.linspace(0, 1, N))\\n cmap_name = base.name + str(N)\\n return base.from_list(cmap_name, color_list, N)\",\n \"def get_palette(palette_name):\\n\\n if hasattr(plt.cm, palette_name):\\n cmap = getattr(plt.cm, palette_name)\\n elif palette_name in list(mpl_cm.cmap_d.keys()):\\n cmap = mpl_cm.get_cmap(palette_name)\\n else:\\n print(\\\"Error, color option '\\\", palette_name, \\\"' not a valid option\\\")\\n sys.exit(1)\\n\\n return cmap\",\n \"def get_font_cmap(font):\\n return font['cmap'].tables[0].cmap\",\n \"def discrete_cmap(N, base_cmap=None):\\n # see https://gist.github.com/jakevdp/91077b0cae40f8f8244a\\n base = plt.cm.get_cmap(base_cmap)\\n color_list = base(np.linspace(0, 1, N))\\n cmap_name = base.name + str(N)\\n return base.from_list(cmap_name, color_list, N)\",\n \"def discrete_cmap(N, base_cmap=None):\\n\\n # Note that if base_cmap is a string or None, you can simply do\\n # return plt.cm.get_cmap(base_cmap, N)\\n # The following works for string, None, or a colormap instance:\\n\\n base = plt.cm.get_cmap(base_cmap)\\n color_list = base(np.linspace(0, 1, N))\\n cmap_name = base.name + str(N)\\n return base.from_list(cmap_name, color_list, N)\",\n \"def discrete_cmap(N, base_cmap=None):\\n\\n # Note that if base_cmap is a string or None, you can simply do\\n # return plt.cm.get_cmap(base_cmap, N)\\n # The following works for string, None, or a colormap instance:\\n\\n base = plt.cm.get_cmap(base_cmap)\\n color_list = base(np.linspace(0, 1, N))\\n cmap_name = base.name + str(N)\\n return base.from_list(cmap_name, color_list, N)\",\n \"def discrete_cmap(N, base_cmap=None):\\n\\n # Note that if base_cmap is a string or None, you can simply do\\n # return plt.cm.get_cmap(base_cmap, N)\\n # The following works for string, None, or a colormap instance:\\n\\n base = plt.cm.get_cmap(base_cmap)\\n color_list = base(np.linspace(0, 1, N))\\n cmap_name = base.name + str(N)\\n return base.from_list(cmap_name, color_list, N)\",\n \"def discrete_cmap(N, base_cmap=None):\\n\\n # Note that if base_cmap is a string or None, you can simply do\\n # return plt.cm.get_cmap(base_cmap, N)\\n # The following works for string, None, or a colormap instance:\\n\\n base = plt.cm.get_cmap(base_cmap)\\n color_list = base(np.linspace(0, 1, N))\\n cmap_name = base.name + str(N)\\n return base.from_list(cmap_name, color_list, N)\",\n \"def colormap(self, band_idx=1):\\n if band_idx < 1 or band_idx > self.dataset.RasterCount:\\n raise IndexError(\\\"band index is out of range\\\")\\n ct = self.dataset.GetRasterBand(band_idx).GetColorTable()\\n if ct is None:\\n return None\\n\\n colormap = []\\n for i in range(ct.GetCount()):\\n colormap.append(ct.GetColorEntry(i))\\n\\n return colormap\",\n \"def discrete_cmap(N, base_cmap=None):\\n base = plt.get_cmap(base_cmap)\\n color_list = base(np.linspace(0, 1, N))\\n cmap_name = base.name + str(N)\\n return base.from_list(cmap_name, color_list, N)\",\n \"def get_dict(self):\\r\\n return self.cmap\",\n \"def _get_colors(num_colors):\\n cmap = plt.get_cmap()\\n return [cmap(1. * i / num_colors) for i in range(num_colors)]\",\n \"def get_colors(self):\\n colors = [\\\"#244486\\\", \\\"#A6A6A6\\\", \\\"#B12122\\\"]\\n cmap = LinearSegmentedColormap.from_list(\\\"mycmap\\\", colors)\\n\\n color_palette=[cmap(i) for i in np.linspace(0, 1, len(set(self.nodes_list)))]\\n return dict(zip(list(set(self.nodes_list)), color_palette))\",\n \"def binary_cmap(color='red', alpha=1.):\\n if color == 'red':\\n color_code = (1., 0., 0., alpha)\\n elif color == 'green':\\n color_code = (0., 1., 0., alpha)\\n elif color == 'blue':\\n color_code = (0., 0., 1., alpha)\\n else:\\n color_code = color\\n transparent_code = (1., 1., 1., 0.)\\n return ListedColormap([transparent_code, color_code])\",\n \"def uniqueish_color():\\n return plt.cm.gist_ncar(np.random.random())\",\n \"def get_colormap(values, cmap='jet', colorbar=True, log=False):\\n by_value = not isinstance(values, int)\\n if not by_value:\\n colorbar = False\\n if by_value:\\n cmap = plt.get_cmap(cmap)\\n if log:\\n colors = [cmap(x) for x in tools.maths.normalise(np.log10(values))]\\n else:\\n colors = [cmap(x) for x in tools.maths.normalise(np.array(values))]\\n else:\\n cmap = plt.get_cmap(cmap, values)\\n colors = [cmap(x) for x in range(values)]\\n if colorbar:\\n if log:\\n norm = mpl.colors.LogNorm(vmin=min(values), vmax=max(values))\\n else:\\n norm = mpl.colors.Normalize(vmin=min(values), vmax=max(values))\\n sm = plt.cm.ScalarMappable(cmap=cmap, norm=norm)\\n sm.set_array([])\\n if isinstance(colorbar, str):\\n label = colorbar\\n else:\\n label = ''\\n plt.colorbar(sm, label=label)\\n return colors\",\n \"def terrain_cmap_256():\\n C = np.array(\\n [\\n [0, 125, 255],\\n [2, 97, 0], # Alternativley [0, 0, 255], for blue at sealevel\\n [2, 97, 0],\\n [3, 97, 0],\\n [4, 97, 0],\\n [5, 97, 0],\\n [6, 98, 0],\\n [7, 98, 0],\\n [8, 98, 0],\\n [9, 98, 0],\\n [10, 98, 0],\\n [11, 98, 0],\\n [11, 99, 0],\\n [12, 99, 0],\\n [13, 99, 0],\\n [14, 99, 0],\\n [15, 99, 0],\\n [16, 99, 0],\\n [17, 100, 0],\\n [18, 100, 0],\\n [19, 100, 0],\\n [19, 100, 0],\\n [20, 100, 0],\\n [21, 101, 0],\\n [22, 101, 0],\\n [23, 101, 0],\\n [24, 101, 0],\\n [25, 101, 0],\\n [26, 102, 0],\\n [27, 102, 0],\\n [28, 102, 0],\\n [28, 102, 0],\\n [29, 102, 0],\\n [30, 102, 0],\\n [31, 103, 0],\\n [32, 103, 0],\\n [33, 103, 0],\\n [34, 103, 0],\\n [35, 103, 0],\\n [36, 104, 0],\\n [37, 104, 0],\\n [37, 104, 0],\\n [38, 104, 0],\\n [39, 104, 0],\\n [40, 104, 0],\\n [41, 105, 0],\\n [42, 105, 0],\\n [43, 105, 0],\\n [44, 105, 0],\\n [45, 105, 0],\\n [45, 106, 0],\\n [46, 106, 0],\\n [47, 106, 0],\\n [48, 106, 0],\\n [49, 106, 0],\\n [50, 106, 0],\\n [51, 107, 0],\\n [52, 107, 0],\\n [53, 107, 0],\\n [54, 107, 0],\\n [54, 107, 0],\\n [55, 108, 0],\\n [56, 108, 0],\\n [57, 108, 0],\\n [58, 108, 0],\\n [59, 108, 0],\\n [60, 108, 1],\\n [61, 109, 1],\\n [62, 109, 2],\\n [63, 109, 2],\\n [64, 109, 3],\\n [65, 109, 3],\\n [66, 110, 4],\\n [67, 110, 4],\\n [68, 110, 4],\\n [69, 110, 5],\\n [70, 110, 5],\\n [71, 110, 6],\\n [72, 111, 6],\\n [73, 111, 7],\\n [74, 111, 7],\\n [75, 111, 8],\\n [76, 111, 8],\\n [77, 112, 9],\\n [78, 112, 9],\\n [79, 112, 10],\\n [80, 112, 10],\\n [81, 112, 11],\\n [82, 112, 11],\\n [83, 113, 12],\\n [84, 113, 12],\\n [85, 113, 13],\\n [85, 113, 13],\\n [86, 113, 14],\\n [87, 114, 14],\\n [88, 114, 15],\\n [89, 114, 15],\\n [90, 114, 16],\\n [91, 114, 16],\\n [92, 114, 17],\\n [93, 115, 17],\\n [94, 115, 18],\\n [95, 115, 18],\\n [96, 115, 19],\\n [97, 115, 19],\\n [98, 115, 20],\\n [99, 116, 20],\\n [100, 116, 20],\\n [101, 116, 21],\\n [102, 116, 21],\\n [103, 116, 22],\\n [104, 117, 22],\\n [105, 117, 23],\\n [106, 117, 23],\\n [107, 117, 24],\\n [108, 117, 24],\\n [109, 118, 25],\\n [110, 118, 25],\\n [111, 118, 26],\\n [112, 118, 26],\\n [113, 118, 27],\\n [114, 118, 27],\\n [115, 119, 28],\\n [116, 119, 28],\\n [117, 119, 29],\\n [118, 119, 29],\\n [119, 119, 30],\\n [120, 120, 30],\\n [121, 120, 31],\\n [122, 120, 31],\\n [123, 120, 32],\\n [124, 121, 32],\\n [125, 121, 32],\\n [126, 121, 33],\\n [127, 122, 33],\\n [128, 122, 34],\\n [129, 122, 34],\\n [130, 123, 35],\\n [131, 123, 35],\\n [132, 123, 36],\\n [133, 124, 36],\\n [134, 124, 37],\\n [135, 124, 37],\\n [136, 125, 37],\\n [137, 125, 38],\\n [138, 125, 38],\\n [139, 126, 39],\\n [139, 126, 39],\\n [140, 126, 40],\\n [141, 126, 40],\\n [142, 127, 41],\\n [143, 127, 41],\\n [144, 127, 41],\\n [145, 128, 42],\\n [146, 128, 42],\\n [147, 128, 43],\\n [148, 129, 43],\\n [149, 129, 44],\\n [150, 129, 44],\\n [151, 130, 45],\\n [152, 130, 45],\\n [153, 130, 45],\\n [154, 131, 46],\\n [155, 131, 46],\\n [156, 131, 47],\\n [157, 132, 47],\\n [158, 132, 48],\\n [159, 132, 48],\\n [160, 133, 49],\\n [161, 133, 49],\\n [162, 133, 50],\\n [163, 134, 50],\\n [164, 134, 50],\\n [165, 134, 51],\\n [166, 135, 51],\\n [167, 135, 52],\\n [168, 135, 52],\\n [169, 136, 53],\\n [170, 136, 53],\\n [171, 136, 54],\\n [172, 137, 54],\\n [173, 137, 54],\\n [174, 137, 55],\\n [175, 138, 55],\\n [176, 138, 56],\\n [177, 138, 56],\\n [178, 139, 57],\\n [179, 139, 57],\\n [180, 139, 58],\\n [181, 140, 58],\\n [182, 140, 58],\\n [183, 140, 59],\\n [184, 141, 59],\\n [185, 142, 62],\\n [186, 144, 65],\\n [187, 146, 68],\\n [188, 147, 71],\\n [189, 149, 74],\\n [190, 151, 77],\\n [192, 153, 80],\\n [193, 155, 83],\\n [194, 156, 86],\\n [195, 158, 90],\\n [196, 160, 93],\\n [197, 162, 96],\\n [198, 164, 99],\\n [199, 165, 102],\\n [201, 167, 105],\\n [202, 169, 108],\\n [203, 171, 111],\\n [204, 173, 114],\\n [205, 174, 117],\\n [206, 176, 120],\\n [207, 178, 123],\\n [208, 180, 126],\\n [210, 182, 130],\\n [211, 184, 133],\\n [212, 185, 136],\\n [213, 187, 139],\\n [214, 189, 142],\\n [215, 191, 145],\\n [216, 193, 148],\\n [217, 194, 151],\\n [219, 196, 154],\\n [220, 198, 157],\\n [221, 200, 160],\\n [222, 202, 163],\\n [223, 203, 166],\\n [224, 205, 170],\\n [225, 207, 173],\\n [226, 209, 176],\\n [228, 211, 179],\\n [229, 212, 182],\\n [230, 214, 185],\\n [231, 216, 188],\\n [232, 218, 191],\\n [233, 220, 194],\\n [234, 221, 197],\\n [235, 223, 200],\\n [237, 225, 203],\\n [238, 227, 207],\\n [239, 229, 210],\\n [240, 230, 213],\\n [241, 232, 216],\\n [242, 234, 219],\\n [243, 236, 222],\\n [245, 238, 225],\\n [246, 240, 228],\\n [247, 241, 231],\\n [248, 243, 234],\\n [249, 245, 237],\\n [250, 247, 240],\\n [251, 249, 243],\\n [252, 250, 247],\\n [254, 252, 250],\\n [255, 254, 253],\\n [255, 255, 255],\\n ]\\n )\\n\\n cm = ListedColormap(C / 255.0)\\n return cm\",\n \"def test_colormap_discrete():\\n with TestingCanvas(size=size, bgcolor='w') as c:\\n idata = np.linspace(255, 0, size[0]*size[1]).astype(np.ubyte)\\n data = idata.reshape((size[0], size[1]))\\n image = Image(cmap=Colormap(colors=['r', 'g', 'b'],\\n interpolation='zero'),\\n clim='auto', parent=c.scene)\\n image.set_data(data)\\n assert_image_approved(c.render(), \\\"visuals/colormap_rgb.png\\\")\",\n \"def discrete_cmap(N, base_cmap=None):\\n\\n # Note that if base_cmap is a string or None, you can simply do\\n # return plt.cm.get_cmap(base_cmap, N)\\n # The following works for string, None, or a colormap instance:\\n # By Jake VanderPlas\\n # License: BSD-style\\n\\n base = plt.cm.get_cmap(base_cmap)\\n color_list = base(np.linspace(0, 1, N))\\n cmap_name = base.name + str(N)\\n return base.from_list(cmap_name, color_list, N)\",\n \"def get_cmap(n):\\n cmap_fn = plt.cm.get_cmap('hsv', n+1)\\n colors = [cmap_fn(i + 1)[:3] for i in range(n)]\\n random.shuffle(colors)\\n cmap = (np.array(colors) * 255.0).astype(np.uint8)\\n return cmap\",\n \"def get_colormap(level_values: np.ndarray) -> np.ndarray:\\n color_dimension = level_values # change to desired fourth dimension\\n color_min, color_max = color_dimension.min(), color_dimension.max()\\n norm = colors.Normalize(color_min, color_max)\\n m = plt.cm.ScalarMappable(norm=norm, cmap=\\\"Spectral_r\\\")\\n m.set_array([])\\n face_colors = m.to_rgba(color_dimension)\\n\\n return face_colors\",\n \"def colors_to_cmap(colors):\\n colors = np.asarray(colors)\\n if colors.shape[1] == 3:\\n colors = np.hstack((colors, np.ones((len(colors),1))))\\n steps = (0.5 + np.asarray(range(len(colors)-1), dtype=np.float))/(len(colors) - 1)\\n return matplotlib.colors.LinearSegmentedColormap(\\n 'auto_cmap',\\n {clrname: ([(0, col[0], col[0])] + \\n [(step, c0, c1) for (step,c0,c1) in zip(steps, col[:-1], col[1:])] + \\n [(1, col[-1], col[-1])])\\n for (clridx,clrname) in enumerate(['red', 'green', 'blue', 'alpha'])\\n for col in [colors[:,clridx]]},\\n N=len(colors))\",\n \"def grain_type_colormap():\\n\\n colors = [('lime'), # 1, precipitation particles\\n ('green'), # 2, Decomposing fragmented PP\\n ('yellow'), # 3 Rounded Grains (RG)\\n ('orange'), # 4 Faceted Crystals\\n ('blue'), # 5 Depth Hoar\\n ('gray'), # 6 Surface Hoar\\n ('red'), # 7 Melt FOrms\\n ('cyan'), # 8 Ice formations\\n ('black'), # 9 Rounded faceted particles\\n ]\\n\\n\\n cmap_name = 'grain_type_map'\\n\\n my_cmap = LinearSegmentedColormap.from_list(cmap_name, colors, N=9)\\n\\n return(my_cmap)\",\n \"def clr_to_cmap(clr, val=None):\\n v = np.loadtxt(clr)\\n assert np.all(np.arange(v.shape[0]) == v[:,0])\\n if not val is None:\\n # subset the colors to match with min/max\\n # numpy arr\\n #mn = val.amin()\\n #mx = val.amax()\\n # masked arr\\n mn = val.min()\\n mx = val.max()\\n v = v[mn:(mx+1),:]\\n v = v[:,1:]\\n cm = mpl.colors.ListedColormap(v / 255)\\n return cm\",\n \"def make_colormap(colors):\\n#-------------------------\\n from matplotlib.colors import LinearSegmentedColormap, ColorConverter\\n from numpy import sort\\n z = sort(colors.keys())\\n n = len(z)\\n z1 = min(z)\\n zn = max(z)\\n x0 = (z - z1) / (zn - z1)\\n CC = ColorConverter()\\n R = []\\n G = []\\n B = []\\n for i in range(n):\\n #i'th color at level z[i]:\\n Ci = colors[z[i]]\\n if type(Ci) == str:\\n # a hex string of form '#ff0000' for example (for red)\\n RGB = CC.to_rgb(Ci)\\n else:\\n # assume it's an RGB triple already:\\n RGB = Ci\\n R.append(RGB[0])\\n G.append(RGB[1])\\n B.append(RGB[2])\\n cmap_dict = {}\\n cmap_dict['red'] = [(x0[i],R[i],R[i]) for i in range(len(R))]\\n cmap_dict['green'] = [(x0[i],G[i],G[i]) for i in range(len(G))]\\n cmap_dict['blue'] = [(x0[i],B[i],B[i]) for i in range(len(B))]\\n mymap = LinearSegmentedColormap('mymap',cmap_dict)\\n return mymap\",\n \"def discrete_cmap(N, base_cmap=None):\\n\\n # Note that if base_cmap is a string or None, you can simply do\\n # return plt.cm.get_cmap(base_cmap, N)\\n # The following works for string, None, or a colormap instance:\\n\\n base = plt.cm.get_cmap(base_cmap)\\n color_list = base(np.linspace(0, 1, N))\\n cmap_name = base.name + str(N)\\n return matplotlib.colors.LinearSegmentedColormap \\\\\\n .from_list(cmap_name, color_list, N)\",\n \"def create_colormap(color_list: Sequence[str], n_colors: int) -> NDArrayFloat:\\n cmap = LinearSegmentedColormap.from_list(name=\\\"dummy_name\\\", colors=color_list)\\n colorscale: NDArrayFloat = np.array(\\n [cmap(k * 1 / n_colors) for k in range(n_colors)]\\n )\\n # ignore the 4th alpha channel\\n return colorscale[:, :3]\",\n \"def grayscale_cmap(cmap):\\n # cmap = cm.get_cmap(cmap)\\n map_colors = cmap(np.arange(cmap.N))\\n\\n # convert RGBA to perceived grayscale luminance\\n # cf. http://alienryderflex.com/hsp.html\\n RGB_weight = [0.299, 0.587, 0.114]\\n luminance = np.sqrt(np.dot(map_colors[:, :3] ** 2, RGB_weight))\\n map_colors[:, :3] = luminance[:, np.newaxis]\\n\\n return colors.LinearSegmentedColormap.from_list(cmap.name + \\\"_gray\\\", map_colors, cmap.N)\",\n \"def base_color(self):\\n return curses.color_pair(3) if self.cycling else curses.color_pair(1)\",\n \"def terrain_cmap_50():\\n C = np.array(\\n [\\n [2, 97, 0],\\n [6, 98, 0],\\n [11, 98, 0],\\n [16, 99, 0],\\n [20, 100, 0],\\n [25, 101, 0],\\n [30, 102, 0],\\n [34, 103, 0],\\n [39, 104, 0],\\n [44, 105, 0],\\n [48, 106, 0],\\n [53, 107, 0],\\n [58, 108, 0],\\n [63, 109, 2],\\n [68, 110, 4],\\n [73, 111, 7],\\n [78, 112, 9],\\n [83, 113, 12],\\n [88, 114, 15],\\n [93, 115, 17],\\n [98, 116, 20],\\n [103, 116, 22],\\n [109, 117, 25],\\n [114, 118, 27],\\n [119, 119, 30],\\n [124, 121, 32],\\n [129, 122, 34],\\n [134, 124, 37],\\n [139, 126, 39],\\n [144, 127, 41],\\n [149, 129, 44],\\n [155, 131, 46],\\n [160, 133, 48],\\n [165, 134, 51],\\n [170, 136, 53],\\n [175, 138, 55],\\n [180, 139, 58],\\n [185, 143, 64],\\n [191, 152, 80],\\n [197, 162, 96],\\n [203, 171, 112],\\n [209, 181, 128],\\n [215, 190, 144],\\n [221, 199, 160],\\n [226, 209, 176],\\n [232, 218, 192],\\n [238, 228, 208],\\n [244, 237, 224],\\n [250, 246, 240],\\n [255, 255, 255],\\n ]\\n )\\n\\n cm = ListedColormap(C / 255.0)\\n return cm\",\n \"def create_pascal_label_colormap():\\r\\n colormap = np.zeros((256, 3), dtype = int)\\r\\n ind = np.arange(256, dtype=int)\\r\\n\\r\\n for shift in reversed(range(8)):\\r\\n for channel in range(3):\\r\\n colormap[:, channel] |= ((ind >> channel) & 1) << shift\\r\\n ind >>= 3\\r\\n\\r\\n return colormap\",\n \"def _get_colors():\\n mapping = {\\n \\\"terminal\\\": -1,\\n \\\"black\\\": curses.COLOR_BLACK,\\n \\\"red\\\": curses.COLOR_RED,\\n \\\"green\\\": curses.COLOR_GREEN,\\n \\\"yellow\\\": curses.COLOR_YELLOW,\\n \\\"blue\\\": curses.COLOR_BLUE,\\n \\\"magenta\\\": curses.COLOR_MAGENTA,\\n \\\"cyan\\\": curses.COLOR_CYAN,\\n \\\"white\\\": curses.COLOR_WHITE,\\n }\\n\\n cp = get_config_file()\\n\\n if \\\"general\\\" in cp.sections():\\n general = cp[\\\"general\\\"]\\n else:\\n general = {}\\n\\n defaults = {\\n \\\"color_default_background\\\": \\\"black\\\",\\n \\\"color_default_foreground\\\": \\\"white\\\",\\n \\\"color_correct_background\\\": \\\"black\\\",\\n \\\"color_correct_foreground\\\": \\\"green\\\",\\n \\\"color_wrong_background\\\": \\\"red\\\",\\n \\\"color_wrong_foreground\\\": \\\"white\\\",\\n \\\"color_target_background\\\": \\\"magenta\\\",\\n \\\"color_target_foreground\\\": \\\"white\\\",\\n \\\"color_replay_background\\\": \\\"blue\\\",\\n \\\"color_replay_foreground\\\": \\\"white\\\",\\n }\\n\\n colors = {}\\n\\n for name, default in defaults.items():\\n color = general.get(name, default)\\n\\n if color not in mapping:\\n raise KeyError(f\\\"Unsupported color {color}\\\")\\n\\n colors[name] = mapping[color]\\n\\n return colors\",\n \"def _create_colormap(self):\\n\\n max_count = max(self.data['rects'], key=lambda r: r['count'])['count']\\n ranges_list = [\\n [\\n int(max_count/x[0])+1,\\n int(max_count/x[1])+1\\n ] for x in [[6, 3], [3, 2], [2, 1]]\\n ]\\n ranges_list = [[0, 1]] + [[1, ranges_list[0][0]]] + ranges_list\\n\\n self.data['colormap'] = [\\n {\\n 'fill': self._get_fill(\\n i,\\n range(\\n ranges_list[i][0],\\n ranges_list[i][1]\\n )\\n ),\\n 'range': range(\\n ranges_list[i][0],\\n ranges_list[i][1]\\n )\\n } for i in range(0, 5)\\n ]\",\n \"def setDefaultColorSpace(self, defaultColorSpace):\\n self.PDFreactorConfiguration.in1[\\\"defaultColorSpace\\\"] = defaultColorSpace\",\n \"def get_color_in_rgb_decimal():\\n\\n # Grabbing custom colormap from matplotlib\\n a = cm.get_cmap('cool', 32)\\n b = cm.get_cmap('spring', 32)\\n c = cm.get_cmap('autumn_r', 64)\\n d = cm.get_cmap('bwr_r', 192)\\n e = cm.get_cmap('Greens', 192)\\n\\n # Adding the colormaps into one stack to have a more comprehensive color spectrum \\n newcolors = np.vstack((a(np.linspace(0, 1, 32)), \\n b(np.linspace(0, 1, 32)), \\n c(np.linspace(0, 1, 64)),\\n d(np.linspace(0, 0.5, 192)),\\n e(np.linspace(0, 1, 192)),\\n ))\\n return newcolors\",\n \"def setColourMap(self):\\n cmap = self.config['cmap']\\n\\n pos, colour, mode = colourMaps.colourMaps(cmap)\\n\\n cmap = pg.ColorMap(pos, colour,mode)\\n self.lut = cmap.getLookupTable(0.0, 1.0, 256)\\n minsg = np.min(self.sg)\\n maxsg = np.max(self.sg)\\n self.colourStart = (self.config['brightness'] / 100.0 * self.config['contrast'] / 100.0) * (maxsg - minsg) + minsg\\n self.colourEnd = (maxsg - minsg) * (1.0 - self.config['contrast'] / 100.0) + self.colourStart\",\n \"def create_pascal_label_colormap():\\n colormap = np.zeros((256, 3), dtype=int)\\n ind = np.arange(256, dtype=int)\\n\\n for shift in reversed(range(8)):\\n for channel in range(3):\\n colormap[:, channel] |= ((ind >> channel) & 1) << shift\\n ind >>= 3\\n\\n return colormap\",\n \"def create_pascal_label_colormap():\\n colormap = np.zeros((256, 3), dtype=int)\\n ind = np.arange(256, dtype=int)\\n\\n for shift in reversed(range(8)):\\n for channel in range(3):\\n colormap[:, channel] |= ((ind >> channel) & 1) << shift\\n ind >>= 3\\n\\n return colormap\",\n \"def palette_from_mpl_name(name):\\n if name in CMAPS:\\n return CMAPS[name]\\n\\n rgba = plt.get_cmap(name)(np.linspace(0, 1, 256))\\n palette = [to_hex(color) for color in rgba]\\n return palette\",\n \"def default_render_color_maker(num:int):\\n if num < 7:\\n log.warn('Fewer than 7 rendering colors are being generated. This may cause issues if a URDF with a 6+ axis robot is loaded.')\\n\\n b = np.linspace(0,255,num).astype(int) # Blue values are always unique\\n\\n g = [0] * b.size\\n r = np.abs(255 - 2*b)\\n\\n colors = []\\n for idx in range(num):\\n colors.append([b[idx],g[idx],r[idx]])\\n return colors\",\n \"def setColourMap(self):\\n cmap = self.config['cmap']\\n\\n pos, colour, mode = colourMaps.colourMaps(cmap)\\n\\n cmap = pg.ColorMap(pos, colour, mode)\\n self.lut = cmap.getLookupTable(0.0, 1.0, 256)\\n minsg = np.min(self.sg)\\n maxsg = np.max(self.sg)\\n self.colourStart = (self.config['brightness'] / 100.0 * self.config['contrast'] / 100.0) * (\\n maxsg - minsg) + minsg\\n self.colourEnd = (maxsg - minsg) * (1.0 - self.config['contrast'] / 100.0) + self.colourStart\",\n \"def create_pascal_label_colormap():\\n colormap = np.zeros((256, 3), dtype=int)\\n ind = np.arrange(256, dtype=int)\\n\\n for shift in reversed(range(8)):\\n for channel in range(3):\\n colormap[:, channel] |= ((ind >> channel) & 1) << shift\\n ind >>=3\\n\\n return colormap\",\n \"def create_pascal_label_colormap():\\n colormap = np.zeros((256, 3), dtype=int)\\n ind = np.arange(256, dtype=int)\\n\\n for shift in reversed(range(8)):\\n for channel in range(3):\\n colormap[:, channel] |= ((ind >> channel) & 1) << shift\\n ind >>= 3\\n\\n return colormap\",\n \"def create_pascal_label_colormap():\\n colormap = np.zeros((256, 3), dtype=int)\\n ind = np.arange(256, dtype=int)\\n\\n for shift in reversed(range(8)):\\n for channel in range(3):\\n colormap[:, channel] |= ((ind >> channel) & 1) << shift\\n ind >>= 3\\n\\n return colormap\",\n \"def _color_for_labels(label_color, default_color, seq_index):\\n if label_color is None:\\n if hasattr(default_color, '__getitem__'):\\n c = default_color[seq_index]\\n else:\\n c = default_color\\n else:\\n c = label_color\\n\\n return c or 'black'\",\n \"def assigning_colors():\\n rgb_colors = {}\\n for name, hex in matplotlib.colors.cnames.items():\\n color = []\\n # So the values are from 0-255 and not 0-1\\n for i in matplotlib.colors.to_rgb(hex):\\n color.append(int(i * 255))\\n\\n color = tuple(color)\\n rgb_colors[name] = color\\n\\n return rgb_colors\",\n \"def __init__(self):\\n self.primary = '#9CC537' # slightly darker than YellowGreen #9acd32, rgb[156/255, 197/255, 55/255]\\n self.secondary = '#2E3743' # asphalt, rgb[46/255, 55/255, 67/255]\\n self.tertiary = '#9B2B2C' # red'ish, rgb(155, 43, 44)\\n self.fourth = '#E57925' # orange'ish, rgb(229, 121, 37)\\n self.fifth = '#F2D869' # yellow'ish, rgb(242, 216, 105)\\n self.sixth = '#AB8D60'\\n self.seventh = '#A4D29F'\\n self.eighth = '#6E807B'\\n self.ninth = '#3D636F' # blue grey\\n self.tenth = '#A49E9D'\\n self.eleventh = '#DA9BA6'\\n self.primary_10 = '#1F290A' # darkest green, 10% of primary\\n self.primary_35 = '#6C9023' # dark green, 35% of primary\\n self.primary_80 = '#D7EBAD' # light green, 80% of primary\\n self.primary_90 = '#ebf5d6' # light green, 90% of primary\\n self.primary_95 = '#F5FAEA' # lightest green, 95% of primary\\n self.secondary_70 = '#6d737b' # light asphalt\\n\\n _col_map_colors = [self.primary_95, # lightest primary\\n self.primary, # primary\\n self.primary_10] # darkest primary\\n self._color_map = self._set_col_map(_col_map_colors)\\n\\n self.color_list = [self.primary, self.secondary, self.tertiary, self.fourth, self.fifth, self.sixth,\\n self.seventh, self.eighth, self.ninth, self.tenth, self.eleventh, self.primary_35]\\n\\n # set the mpl color cycler to our colors. It has 10 colors\\n # mpl.rcParams['axes.prop_cycle']\",\n \"def get_cmap_levels(colormap,levels):\\n \\n #Matplotlib colormap name\\n if isinstance(colormap,str):\\n cmap = mlib.cm.get_cmap(colormap)\\n \\n #User defined list of colors\\n elif isinstance(colormap,list):\\n cmap = mcolors.ListedColormap(colormap)\\n \\n #Dictionary\\n elif isinstance(colormap,dict):\\n cmap = make_colormap(colormap)\\n \\n #Otherwise, a cmap was passed\\n else:\\n cmap = colormap\\n \\n #Normalize colors relative to levels\\n norm = mcolors.Normalize(vmin=0, vmax=len(levels)-1)\\n \\n #If more than 2 levels were passed, use those for the contour levels\\n if len(levels) > 2:\\n colors = cmap(norm(np.arange(len(levels)-1)+.5))\\n cmap = mcolors.ListedColormap(colors)\\n \\n #Otherwise, create a list of colors based on levels\\n else:\\n colors = cmap(norm(np.linspace(0,1,256)))\\n cmap = mcolors.LinearSegmentedColormap.from_list('my_colormap',colors)\\n \\n y0 = min(levels)\\n y1 = max(levels)\\n dy = (y1-y0)/16\\n scalemag = int(np.round_(np.log(dy)/np.log(8),decimals=0))\\n dy_scaled = dy*10**-scalemag\\n dc = min([1,2,5,10], key=lambda x:abs(x-dy_scaled))\\n dc = np.round_(dc*10**scalemag,decimals=-scalemag+1)\\n c0 = np.round_(y0/dc,decimals=0)*dc\\n c1 = np.round_(y1/dc,decimals=0)*dc\\n levels = np.arange(c0,c1+dc*.5,dc)\\n \\n if scalemag > 0:\\n levels = levels.astype(int)\\n else:\\n levels = np.round_(levels,decimals=-scalemag+1)\\n\\n #Return colormap and levels\\n return cmap, levels\",\n \"def colormap_masked(ncolors=256, knee_index=None, cmap='inferno', alpha=0.3):\\n cm = plt.cm.get_cmap(cmap)(np.linspace(0, 1, ncolors))\\n if knee_index is None:\\n # Then map to pvals, as -log(p) between 0 and 3.5, and threshold at 0.05\\n knee_index = np.argmin(abs(np.linspace(0., 3.5, ncolors)+np.log10(0.05)))\\n \\n cm[:knee_index, :] = np.c_[cm[:knee_index, 0], cm[:knee_index, 1], cm[:knee_index, 2], alpha*np.ones((len(cm[:knee_index, 1])))]\\n return LinearSegmentedColormap.from_list('my_colormap', cm)\",\n \"def get_cfg_defaults():\\n return _C.clone()\",\n \"def getColormapDialog(self, view):\\n return None\",\n \"def register_cccmacms(cmap='all'):\\n \\n \\n #print 'registering cmaps'\\n \\n \\n \\n \\n \\n \\n # define individual colors as RGB triples\\n \\n \\n # from colorwheel.m\\n \\n \\n # =============================================\\n \\n \\n # kem_w20 (20) OR blue2red_w20\\n \\n \\n # blueish at top, white in middle, reddish at bottom\\n \\n \\n \\n cpool = np.array([ [153,255,255], \\\\\\n \\n \\n [204,255,229], \\\\\\n \\n \\n [240,255,240],\\\\\\n \\n \\n [204,255,153],\\\\\\n \\n \\n [178,255,102],\\\\\\n \\n \\n [216,255,76],\\\\\\n \\n \\n [255,255,51],\\\\\\n \\n \\n [255,220,51],\\\\\\n \\n \\n [255,187,51],\\\\\\n \\n \\n [255,153,51],\\\\\\n \\n \\n [255,0,0],\\\\\\n \\n \\n [204,0,0],\\\\\\n \\n \\n [153,0,0]], \\\\\\n \\n \\n dtype=float)\\n \\n \\n \\n acccbar = (cpool/255.)\\n \\n \\n thecmap = col.ListedColormap(acccbar,'acccbar')\\n \\n \\n cm.register_cmap(cmap=thecmap)\\n\\n return\"\n]","isConversational":false},"negative_scores":{"kind":"list like","value":["0.72189206","0.72189206","0.7079339","0.7079339","0.7058597","0.7002624","0.6966337","0.6905248","0.68922603","0.67234963","0.6715274","0.6709201","0.66109717","0.66100955","0.6520509","0.64768016","0.6442025","0.6442025","0.6424049","0.63886","0.63886","0.63886","0.63851047","0.6250673","0.6247811","0.6201924","0.61323","0.60997665","0.6053372","0.60439646","0.6039153","0.6036733","0.60343385","0.602548","0.60074484","0.6000053","0.5976209","0.5950276","0.5930011","0.59249276","0.591751","0.5870018","0.58517486","0.58160365","0.58094144","0.58013475","0.58013475","0.58013475","0.57803875","0.5766474","0.5754278","0.5725599","0.57204163","0.57204163","0.57204163","0.57204163","0.5706089","0.5701028","0.56900185","0.568482","0.5679187","0.567661","0.5671801","0.56649363","0.5661162","0.5655722","0.5654309","0.5652588","0.5632454","0.56256413","0.56212646","0.5614409","0.56129664","0.55989945","0.55804104","0.55753636","0.557398","0.55719554","0.5565753","0.5560547","0.5548711","0.55465955","0.5542251","0.55416703","0.5534368","0.5534368","0.5520491","0.55096453","0.55048585","0.5497453","0.549498","0.549498","0.5484641","0.5462074","0.5457017","0.54562044","0.5455783","0.54383165","0.54321504","0.5426153"],"string":"[\n \"0.72189206\",\n \"0.72189206\",\n \"0.7079339\",\n \"0.7079339\",\n \"0.7058597\",\n \"0.7002624\",\n \"0.6966337\",\n \"0.6905248\",\n \"0.68922603\",\n \"0.67234963\",\n \"0.6715274\",\n \"0.6709201\",\n \"0.66109717\",\n \"0.66100955\",\n \"0.6520509\",\n \"0.64768016\",\n \"0.6442025\",\n \"0.6442025\",\n \"0.6424049\",\n \"0.63886\",\n \"0.63886\",\n \"0.63886\",\n \"0.63851047\",\n \"0.6250673\",\n \"0.6247811\",\n \"0.6201924\",\n \"0.61323\",\n \"0.60997665\",\n \"0.6053372\",\n \"0.60439646\",\n \"0.6039153\",\n \"0.6036733\",\n \"0.60343385\",\n \"0.602548\",\n \"0.60074484\",\n \"0.6000053\",\n \"0.5976209\",\n \"0.5950276\",\n \"0.5930011\",\n \"0.59249276\",\n \"0.591751\",\n \"0.5870018\",\n \"0.58517486\",\n \"0.58160365\",\n \"0.58094144\",\n \"0.58013475\",\n \"0.58013475\",\n \"0.58013475\",\n \"0.57803875\",\n \"0.5766474\",\n \"0.5754278\",\n \"0.5725599\",\n \"0.57204163\",\n \"0.57204163\",\n \"0.57204163\",\n \"0.57204163\",\n \"0.5706089\",\n \"0.5701028\",\n \"0.56900185\",\n \"0.568482\",\n \"0.5679187\",\n \"0.567661\",\n \"0.5671801\",\n \"0.56649363\",\n \"0.5661162\",\n \"0.5655722\",\n \"0.5654309\",\n \"0.5652588\",\n \"0.5632454\",\n \"0.56256413\",\n \"0.56212646\",\n \"0.5614409\",\n \"0.56129664\",\n \"0.55989945\",\n \"0.55804104\",\n \"0.55753636\",\n \"0.557398\",\n \"0.55719554\",\n \"0.5565753\",\n \"0.5560547\",\n \"0.5548711\",\n \"0.55465955\",\n \"0.5542251\",\n \"0.55416703\",\n \"0.5534368\",\n \"0.5534368\",\n \"0.5520491\",\n \"0.55096453\",\n \"0.55048585\",\n \"0.5497453\",\n \"0.549498\",\n \"0.549498\",\n \"0.5484641\",\n \"0.5462074\",\n \"0.5457017\",\n \"0.54562044\",\n \"0.5455783\",\n \"0.54383165\",\n \"0.54321504\",\n \"0.5426153\"\n]","isConversational":false},"document_score":{"kind":"string","value":"0.83686036"},"document_rank":{"kind":"string","value":"0"}}},{"rowIdx":23797,"cells":{"query":{"kind":"string","value":"Returns a default color dialog."},"document":{"kind":"string","value":"def defaultColorDialog(self):\n dialog = None\n if self.__hooks is not None:\n dialog = self.__hooks.getColormapDialog(self)\n if dialog is None:\n dialog = ColormapDialog()\n dialog.setModal(False)\n return dialog"},"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 colorPickerDialog(self, current_color=None):\n\t\tcolor_dialog = QtWidgets.QColorDialog()\n\t\t#color_dialog.setOption(QtWidgets.QColorDialog.DontUseNativeDialog)\n\n\t\t# Set current colour\n\t\tif current_color is not None:\n\t\t\tcolor_dialog.setCurrentColor(current_color)\n\n\t\t# Only return a color if valid / dialog accepted\n\t\tif color_dialog.exec_() == color_dialog.Accepted:\n\t\t\tcolor = color_dialog.selectedColor()\n\t\t\treturn color","def _color_change_mode(self):\r\n self.dlg.exec_()\r\n self.color = self.dlg.currentColor().name()\r\n self.colorPlate.setStyleSheet(\"background-color: %s;\" % self.color)\r\n self.input_scene.get_stk_color(self.color)\r\n return","def GetDefaultColor(self):\n return wx.BLACK","def createColorWindow():\n colorWindow = g.GraphWin(\"Color\", 150, 500) #Window to show colors to pick\n return colorWindow","def defaultColor(self, p_int=None): # real signature unknown; restored from __doc__ with multiple overloads\r\n pass","def onCustomColorMenu(self, item):\n self.dialog = pyui.dialogs.ColorDialog(self.customColorChosen)\n self.dialog.doModal()\n return 1","def pickColour(self):\n colour = QColorDialog.getColor()\n if colour.isValid():\n self.user[\"Colour\"] = colour.name()\n self.ui.l_colour.setText(self.user[\"Colour\"])","def default_colour(self):\n colour = self.DEFAULT_COLOUR\n return colour","def pickAColor():\n color = _tkCall(tkColorChooser.askcolor)\n if color[0] != None:\n return Color(color[0][0], color[0][1], color[0][2])","def choose_colour(self) -> None:\n self.chosen_colour = QtWidgets.QColorDialog.getColor(Qt.white, self, \"Выберите цвет\")\n self.pen_colour = self.chosen_colour\n self.palette.setColor(QPalette.Background, self.chosen_colour)\n self.ui.frame.setPalette(self.palette)","def addDialogBox(self, color):\n\t\tself._getSize()\n\t\tobj = nceDialogBox(self.hcenter - 11, 10)\n\t\tobj.rtc = False\n\t\tobj.frame = [\t['╭' + ('─' * 19) + '╮', 3],\n\t\t\t\t\t\t['│' + (' ' * 19) + '│', 3],\n\t\t\t\t\t\t['│' + (' ' * 19) + '│', 3],\n\t\t\t\t\t\t['└' + ('─' * 19) + '╯', 3]]\n\t\tobj.visible = True\n\t\tobj.color = color\n\t\tself.menus.append(obj)\n\t\treturn obj","def _confirm_color(self, event = None):\n color = self._entry.get().strip()\n if color != \"\":\n self._color = color\n self._window.destroy()","def open_win(e) -> None:\n widget_sfx()\n r, g, b = parse_color(var.get())\n new_color, tk_color = askcolor(\n color=f\"#{r:02x}{g:02x}{b:02x}\",\n parent=parent.winfo_toplevel(),\n title=str(TRANS_SELECT_TITLE),\n )\n if new_color is not None:\n # On 3.8, these are floats.\n rf, gf, bf = new_color\n var.set(f'{int(rf)} {int(gf)} {int(bf)}')","def select_color(self):\n\t\tresult = tkinter.colorchooser.askcolor(self.center['bg'])\n\t\tif result:\n\t\t\t# 2nd part of result is the color object\n\t\t\tself.center['bg'] = result[1]","def comdlg32_ChooseColor(jitter, get_str, set_str):\n ret_ad, args = jitter.func_args_stdcall([\"lpcc\"])\n raise RuntimeError('API not implemented')\n jitter.func_ret_stdcall(ret_ad, ret_value)","def set_window_colour(self, event):\n rgb_triplet, rgb_string = tkColorChooser.askcolor()\n self.canvas.config(bg = rgb_string)","def showColors(self):\n\t\tcolors = ['white', 'red', 'green', 'orange', 'blue', 'purple', 'cyan', 'lightgrey',\n\t\t\t\t 'darkgrey', 'light red', 'light green', 'yellow', 'light blue', 'purple', 'cyan', 'dark white']\n\t\tmax = curses.COLORS if curses.COLORS <= 16 else 16\n\t\tself.screen.clear()\n\t\tfor c in range(0, max):\n\t\t\tself.wts(c + 2, 1, \"color \" + str(c) + ' : ' + colors[c], c)\n\t\tself.wts(18, 1, \"color 16 : red on white\", 16)\n\t\tself.wts(20, 1, 'Color demo, displaying ' + str(max) + ' colors + 1 special')\n\t\tself.screen.refresh()\n\t\tch = False\n\t\twhile not ch:\n\t\t\tch = self.screen.getch()\n\t\tself.exit('Color demo complete')","def get_color(self):\n self.view.present(\n \"sheet\",\n orientations=ORIENTATIONS,\n )\n self.view.wait_modal()\n return self.rgb","def get_color(self):\n return \"yellow\"","def color(self):\n return self.settings['color']","def color(self):\n global bordercolor\n bordercolor = QColorDialog.getColor(Qt.green, self)\n self.b_color.setPalette(QPalette(bordercolor))","def default_style_sheet(self) -> str:\n # Valid color names: http://www.w3.org/TR/SVG/types.html#ColorKeywords\n g.trace('===== using default style sheet =====')\n return '''\\\n\n /* A QWidget: supports only background attributes.*/\n QSplitter::handle {\n background-color: #CAE1FF; /* Leo's traditional lightSteelBlue1 */\n }\n QSplitter {\n border-color: white;\n background-color: white;\n border-width: 3px;\n border-style: solid;\n }\n QTreeWidget {\n background-color: #ffffec; /* Leo's traditional tree color */\n }\n QsciScintilla {\n background-color: pink;\n }\n '''","def set_colors(self, ):\n try:\n odd = self._parent.settings.get_key('interface.odd_color')\n even = self._parent.settings.get_key('interface.even_color')\n self.dialog.instruments.set_odd_color(odd)\n self.dialog.accounts.set_odd_color(odd)\n self.dialog.instruments.set_even_color(even)\n self.dialog.accounts.set_even_color(even)\n except od_exception_config_key_error:\n pass","def set_color(self):\n new_color = QColorDialog.getColor(QColor(self.config['color']))\n if not new_color.isValid():\n return\n self.config['color'] = new_color.rgb()\n self.paint()","def __init__(self, parent):\n self.parent = parent\n myStyle = wx.DEFAULT_DIALOG_STYLE|wx.RESIZE_BORDER|wx.TAB_TRAVERSAL\n wx.Dialog.__init__(self, None, wx.ID_ANY, '%s - Choose your colors' % globs.myName, style=myStyle)\n\n self._initialize()\n\n self.panel1.SetSizerAndFit(self.topBoxSizer)\n self.SetClientSize(self.topBoxSizer.GetSize())\n self.Centre()","def abrirDialogoDeConfiguracion(self):\n ventanaEmergente = Toplevel()\n ventanaEmergente.geometry(\"200x320\")\n # Se crea un canvas\n tela = Canvas(ventanaEmergente, height=320, width=200, bg=\"snow\")\n tela.place(x=0, y=0)\n btnColorEspacioDisponible = Button(tela, text=\"Espacio Disponible\", command = lambda :self.cambiarColor(0))\n btnColorEspacioDisponible.place(x=50, y=20)\n btnColorPared = Button(tela, text=\"Paredes\", command = lambda :self.cambiarColor(2))\n btnColorPared.place(x=66, y=50)\n btnColorNodo = Button(tela, text=\"Nodo\", command = lambda :self.cambiarColor(3))\n btnColorNodo.place(x=70, y=80)","def getColor(col=None,caption=None):\n if type(col) == tuple:\n col = QtGui.QColor.fromRgb(*col)\n else:\n col = QtGui.QColor(col)\n dia = QtGui.QColorDialog\n #myButton = QtGui.QPushButton('MY')\n #dia.layout()\n col = dia.getColor(col)\n if col.isValid():\n return str(col.name())\n else:\n return None","def getCenterColor():\n return input(\"What color do you want the center color to be?\")","def color(self) -> Optional[pulumi.Input[str]]:\n return pulumi.get(self, \"color\")","def color(self) -> Optional[pulumi.Input[str]]:\n return pulumi.get(self, \"color\")","def color(self) -> Optional[pulumi.Input[str]]:\n return pulumi.get(self, \"color\")","def color(self) -> Optional[pulumi.Input[str]]:\n return pulumi.get(self, \"color\")","def color(self) -> Optional[pulumi.Input[str]]:\n return pulumi.get(self, \"color\")","def color(self) -> Optional[pulumi.Input[str]]:\n return pulumi.get(self, \"color\")","def color(self) -> Optional[pulumi.Input[str]]:\n return pulumi.get(self, \"color\")","def color(self) -> Optional[pulumi.Input[str]]:\n return pulumi.get(self, \"color\")","def color(self) -> Optional[pulumi.Input[str]]:\n return pulumi.get(self, \"color\")","def color(self) -> Optional[pulumi.Input[str]]:\n return pulumi.get(self, \"color\")","def color(self) -> Optional[pulumi.Input[str]]:\n return pulumi.get(self, \"color\")","def color(self) -> Optional[pulumi.Input[str]]:\n return pulumi.get(self, \"color\")","def color(self) -> Optional[pulumi.Input[str]]:\n return pulumi.get(self, \"color\")","def color(self) -> Optional[pulumi.Input[str]]:\n return pulumi.get(self, \"color\")","def color(self) -> Optional[pulumi.Input[str]]:\n return pulumi.get(self, \"color\")","def color(self) -> Optional[pulumi.Input[str]]:\n return pulumi.get(self, \"color\")","def color(self) -> Optional[pulumi.Input[str]]:\n return pulumi.get(self, \"color\")","def color(self) -> Optional[pulumi.Input[str]]:\n return pulumi.get(self, \"color\")","def color(self) -> Optional[pulumi.Input[str]]:\n return pulumi.get(self, \"color\")","def color(self) -> Optional[pulumi.Input[str]]:\n return pulumi.get(self, \"color\")","def color(self) -> Optional[pulumi.Input[str]]:\n return pulumi.get(self, \"color\")","def color(self) -> Optional[pulumi.Input[str]]:\n return pulumi.get(self, \"color\")","def color(self) -> Optional[pulumi.Input[str]]:\n return pulumi.get(self, \"color\")","def color(self) -> Optional[pulumi.Input[str]]:\n return pulumi.get(self, \"color\")","def color(self) -> Optional[pulumi.Input[str]]:\n return pulumi.get(self, \"color\")","def color(self) -> Optional[pulumi.Input[str]]:\n return pulumi.get(self, \"color\")","def color(self) -> Optional[pulumi.Input[str]]:\n return pulumi.get(self, \"color\")","def color(self) -> Optional[pulumi.Input[str]]:\n return pulumi.get(self, \"color\")","def getDefaultDisplayMode(self):\n return \"Shaded\"","def handlerSelectFillColor(self):\r\n dialog = QtGui.QColorDialog(self.layerSymbolFillColor)\r\n \r\n if dialog.exec_():\r\n selectedColor = dialog.selectedColor()\r\n sender = self.sender()\r\n if sender.objectName() == 'buttonLayerSymbolFillColor':\r\n self.layerSymbolFillColor = selectedColor\r\n elif sender.objectName() == 'buttonStyleCategorizedFillColor':\r\n self.styleCategorizedColor = selectedColor\r\n elif sender.objectName() == 'buttonStyleGraduatedFillColor':\r\n self.styleGraduatedColor = selectedColor\r\n elif sender.objectName() == 'buttonStyleRuleBasedFillColor':\r\n self.styleRuleBasedColor = selectedColor\r\n sender.setStyleSheet('background-color: {0};'.format(selectedColor.name()))","def Set_Profile_Color(self):\r\n default= self.system.Get_Color()\r\n color = tkColorChooser.askcolor(default,parent=self.root)\r\n if( color[1] == None ):\r\n return\r\n else:\r\n self.system.Set_Profile_Color( color[1] )","def __init__(self, last_color):\n self._color = last_color\n self._window = tk.Toplevel(width = 200, height = 132)\n self._window.title(\"Color\")\n \n self._initialize()","def get_option_color(cls, option):\n\n if option == 'Grey':\n color = cls.fromRgbF(0.4, 0.4, 0.4)\n elif option == 'Cancel':\n color = cls.fromRgbF(0.7, 0.5, 0.4)\n elif option == 'OK' or option == 'ok':\n color = cls.fromRgbF(0.5, 0.7, 0.8)\n elif option == 'Warning' or option == 'Warn':\n color = cls.fromRgbF(0.7, 0.2, 0.2)\n elif option == 'Collapse':\n color = cls.fromRgbF(0.15, 0.15, 0.15)\n elif option == 'Subtle':\n color = cls.fromRgbF(0.48, 0.48, 0.6)\n else:\n color = cls.fromRgbF(0.48, 0.48, 0.6)\n if option:\n color.ann = option + ' Color'\n return color","def getColormapDialog(self, view):\n return None","def magic_colors(self,parameter_s = ''):\n \n new_scheme = parameter_s.strip()\n if not new_scheme:\n print 'You must specify a color scheme.'\n return\n try:\n self.shell.outputcache.set_colors(new_scheme)\n except:\n warn('Error changing prompt color schemes.\\n'\n + str(sys.exc_info()[1]))\n else:\n self.shell.rc.colors = \\\n self.shell.outputcache.color_table.active_scheme_name\n try:\n self.shell.InteractiveTB.set_colors(scheme = new_scheme)\n self.shell.SyntaxTB.set_colors(scheme = new_scheme)\n except:\n warn('Error changing exception color schemes.\\n'\n + str(sys.exc_info()[1]))\n if self.shell.rc.color_info:\n try:\n self.shell.inspector.set_active_scheme(new_scheme)\n except:\n warn('Error changing object inspector color schemes.\\n'\n + str(sys.exc_info()[1]))\n else:\n self.shell.inspector.set_active_scheme('NoColor')","def default_colors():\n # default_colors = [\n # # r, g, b, a\n # [92, 192, 98, 0.5],\n # [90, 155, 212, 0.5],\n # [246, 236, 86, 0.6],\n # [241, 90, 96, 0.4],\n # [255, 117, 0, 0.3],\n # [82, 82, 190, 0.2],\n # ]\n\n default_colors = [\n # r, g, b, a\n [188, 114, 3, 0.5],\n [3, 133, 188, 0.5],\n [155, 9, 118, 0.6],\n [155, 53, 9, 0.4],\n [4, 140, 128, 0.3],\n [140, 8, 8, 0.2],\n ]\n\n default_colors = [\n [i[0] / 255.0, i[1] / 255.0, i[2] / 255.0, i[3]]\n for i in default_colors\n ]\n\n return default_colors","def test_default_dialogues(self):\n _, dialogue = self.default_dialogues.create(\n counterparty=COUNTERPARTY_AGENT_ADDRESS,\n performative=DefaultMessage.Performative.BYTES,\n content=b\"some_content\",\n )\n assert dialogue.role == DefaultDialogue.Role.AGENT\n assert dialogue.self_address == self.skill.skill_context.agent_address","def GetColor(self, *args):\n return _XCAFDoc.XCAFDoc_ColorTool_GetColor(self, *args)","def __selectColorName(self):\n editor = e5App().getObject(\"ViewManager\").activeWindow()\n if editor is None:\n return\n \n if editor.hasSelectedText():\n currColor = editor.selectedText()\n if currColor not in QColor.colorNames():\n E5MessageBox.critical(\n self.__ui,\n self.tr(\"Color String\"),\n self.tr(\n \"\"\"<p>The selected string <b>{0}</b> is not a\"\"\"\n \"\"\" valid color name. Aborting!</p>\"\"\")\n .format(currColor))\n return\n else:\n currColor = \"\"\n \n from ColorString.ColorSelectionDialog import ColorSelectionDialog\n dlg = ColorSelectionDialog(currColor, self.__ui)\n if dlg.exec_() == QDialog.Accepted:\n colorStr = dlg.getColor()\n editor.beginUndoAction()\n if editor.hasSelectedText():\n editor.replaceSelectedText(colorStr)\n else:\n line, index = editor.getCursorPosition()\n editor.insert(colorStr)\n editor.setCursorPosition(line, index + len(colorStr))\n editor.endUndoAction()","def conseguir_color(self):\n return self.pluma.conseguir_color()","def _init_colors(self):\n self.clr_primary = None\n self.clr_secondary = 'green'\n self.clr_tertiary = 'cyan'\n self.clr_quaternary = 'yellow'\n self.clr_bold = 'cyan'\n self.clr_code = 'cyan'\n self.clr_error = 'red'\n self.clr_header = 'yellow'\n self.clr_link = 'green'\n self.clr_list = 'cyan'\n self.clr_message = None\n self.clr_num_comments = 'green'\n self.clr_num_points = 'green'\n self.clr_tag = 'cyan'\n self.clr_time = 'yellow'\n self.clr_title = None\n self.clr_tooltip = None\n self.clr_user = 'cyan'\n self.clr_view_link = 'magenta'\n self.clr_view_index = 'magenta'","def _color_name(self, color_name):\n try:\n for color in self.config[\"Defaults\"][\"Colors\"]:\n if color == color_name:\n return self.config[\"Defaults\"][\"Colors\"][color]\n print(\n f\"ERROR: Color {color_name} not found in default color list, returning Black\"\n )\n return \"#000000\"\n except KeyError as e:\n print(f\"Error: {e}\")\n print(\n \"config.yml requires minimal config section, please see documentation\"\n )\n sys.exit(1)","def color(self):\n return self.COLOR","def FindColor(self, *args):\n return _XCAFDoc.XCAFDoc_ColorTool_FindColor(self, *args)","def changePopupColour(templateVar):\n\tglobal currentPeaWindow\n\tif templateVar in podTemplate.templateColours and currentPeaWindow:\n\t\tcorrectColour=podTemplate.templateColours[templateVar]\n\t\tcurrentPeaWindow.sectionData[\"Template\"].label.config(fg=correctColour)\n\t\t#currentPeaWindow.status.colour(correctColour)","def colors(theme=None):\n\n if theme == \"dark\":\n return {\n \"selection\": \"1;32;40\",\n \"queued\": \"0;37\",\n }\n\n return {\n \"selection\": \"\",\n \"queued\": \"\",\n }","def color_style():\n if not supports_color():\n style = no_style()\n else:\n environment_color_style = os.environ.get('DJANGO_COLORS', '')\n color_settings = parse_color_setting(environment_color_style)\n if color_settings:\n class Dummy:\n \"\"\"A style object.\"\"\"\n pass\n style = Dummy()\n # The nocolor palette has all available roles.\n # Use that pallete as the basis for populating\n # the palette as defined in the environment.\n for role in PALETTES[NOCOLOR_PALETTE]:\n colour_format = color_settings.get(role, {})\n # pylint: disable-msg=W0142\n setattr(style, role, make_style(**colour_format))\n # For backwards compatibility,\n # set style for ERROR_OUTPUT == ERROR\n style.ERROR_OUTPUT = style.ERROR\n else:\n style = no_style()\n return style","def __init__(self):\n self.colors = (\n 'BLACK', 'RED', 'GREEN', 'YELLOW',\n 'BLUE', 'MAGENTA', 'CYAN', 'WHITE'\n )\n\n self.disable_color = True\n\n if sys.stdout.isatty():\n self.disable_color = False","def color(self) -> Optional[str]:\n return self.colour","def colors(self, parameter_s=''):\n def color_switch_err(name):\n warn('Error changing %s color schemes.\\n%s' %\n (name, sys.exc_info()[1]), stacklevel=2)\n\n\n new_scheme = parameter_s.strip()\n if not new_scheme:\n raise UsageError(\n \"%colors: you must specify a color scheme. See '%colors?'\")\n # local shortcut\n shell = self.shell\n\n # Set shell colour scheme\n try:\n shell.colors = new_scheme\n shell.refresh_style()\n except:\n color_switch_err('shell')\n\n # Set exception colors\n try:\n shell.InteractiveTB.set_colors(scheme = new_scheme)\n shell.SyntaxTB.set_colors(scheme = new_scheme)\n except:\n color_switch_err('exception')\n\n # Set info (for 'object?') colors\n if shell.color_info:\n try:\n shell.inspector.set_active_scheme(new_scheme)\n except:\n color_switch_err('object inspector')\n else:\n shell.inspector.set_active_scheme('NoColor')","def as_default(self, string):\n return idaapi.COLSTR(string, idaapi.SCOLOR_DEFAULT)","def get_color(self):\r\n if self.color:\r\n return \"RED\"\r\n else:\r\n return \"BLACK\"","def getDefaultBrush(*args, **kwargs)->AnyStr:\n pass","def ColorTool(*args):\n return _XCAFDoc.XCAFDoc_DocumentTool_ColorTool(*args)","def use_default_dict():\n color = defaultdict(lambda: \"#000000\")\n color[\"White\"] = \"#FFFFFF\"\n pp.pprint(color[\"White\"])\n pp.pprint(color[\"Red\"])","def __selectHexColor(self):\n editor = e5App().getObject(\"ViewManager\").activeWindow()\n if editor is None:\n return\n \n if editor.hasSelectedText():\n currColor = editor.selectedText()\n if not self.__isValidColor(currColor):\n E5MessageBox.critical(\n self.__ui,\n self.tr(\"Color String\"),\n self.tr(\n \"\"\"<p>The selected string <b>{0}</b> is not a\"\"\"\n \"\"\" valid color string. Aborting!</p>\"\"\")\n .format(currColor))\n return\n \n if currColor.startswith(\"#\"):\n withHash = True\n elif self.__isHexString(currColor):\n withHash = False\n currColor = \"#\" + currColor\n else:\n withHash = True\n initColor = QColor(currColor)\n else:\n withHash = True\n currColor = \"\"\n initColor = QColor()\n \n color = QColorDialog.getColor(\n initColor, self.__ui, self.tr(\"Color String\"))\n if color.isValid():\n colorStr = color.name()\n if not withHash:\n colorStr = colorStr[1:]\n editor.beginUndoAction()\n if editor.hasSelectedText():\n editor.replaceSelectedText(colorStr)\n else:\n line, index = editor.getCursorPosition()\n editor.insert(colorStr)\n editor.setCursorPosition(line, index + len(colorStr))\n editor.endUndoAction()","def color_style():\n if (sys.platform == 'win32' or sys.platform == 'Pocket PC'\n or sys.platform.startswith('java') or not sys.stdout.isatty()):\n return no_style()\n class dummy: pass\n style = dummy()\n style.ERROR = termcolors.make_style(fg='red', opts=('bold',))\n style.ERROR_OUTPUT = termcolors.make_style(fg='red', opts=('bold',))\n style.NOTICE = termcolors.make_style(fg='red')\n style.SQL_FIELD = termcolors.make_style(fg='green', opts=('bold',))\n style.SQL_COLTYPE = termcolors.make_style(fg='green')\n style.SQL_KEYWORD = termcolors.make_style(fg='yellow')\n style.SQL_TABLE = termcolors.make_style(opts=('bold',))\n return style","def base_color(self):\n return curses.color_pair(3) if self.cycling else curses.color_pair(1)","def getPetalColor():\n return input(\"What color do you want the petals to be?\")","def accent_color(self) -> undefined.UndefinedNoneOr[colors.Color]: # noqa: D401 - Imperative mood","def get_color(self) -> Optional[str]:\n return self.color","def initialize_colors(self) -> None:\n curses.init_pair(ColorPair.black_on_white.value, curses.COLOR_BLACK, curses.COLOR_WHITE)\n curses.init_pair(ColorPair.red_on_black.value, curses.COLOR_RED, curses.COLOR_BLACK)\n curses.init_pair(ColorPair.blue_on_black.value, curses.COLOR_BLUE, curses.COLOR_BLACK)\n curses.init_pair(ColorPair.green_on_black.value, curses.COLOR_GREEN, curses.COLOR_BLACK)\n curses.init_pair(ColorPair.white_on_black.value, curses.COLOR_WHITE, curses.COLOR_BLACK)","def __init__(self,baseColor=QtGui.QColor(50,50,50), highlightColor=QtGui.QColor(\"yellow\"), spread=2.5):\n self.palette = QtGui.QPalette()\n self.baseColor = baseColor\n self.highlightColor = highlightColor\n self.spread = spread\n self.generateScheme()\n QtGui.QApplication.setStyle(\"Plastique\")","def getInitialColor(self):\n return self.__initial_color__","def change_colour(self) -> None:\n if self.ui.radioButtonWhite.isChecked():\n self.pen_colour = QColor(Qt.white)\n elif self.ui.radioButtonColour.isChecked():\n self.pen_colour = self.chosen_colour\n else: # Impossible but better to control\n message.show_error(config.PROG_ERR3)\n return None","def colors(self):\r\n return ColorsResource(self)","def color_picker(self):\n\n\t\tnum_to_select = 4\n\t\tpeg_color_list = [] #creates the list to store the peg object\n\n\t\t#write a for loop to set a loop to select 4 colors from SOLUTION in mm_model\n\t\tfor i in range(num_to_select): #use i just to run the loop, variable is not used elsewhere \n\t\t\t# print(i)\n\t\t\tcolor = random.choice(MasterModel.COLORS)\n\t\t\t# print(color)\n\t\t\t#associate color with peg objects\n\t\t\tpeg = ColorPeg(color)\n\n\t\t\t#append the peg_color list to make a list of peg objects \n\t\t\tpeg_color_list.append(peg)\n\t\t\t# print (peg_color_list)\n\t\t\n\t\t#create object for solution so it can be stored in model.py\n\t\tsolution = Guess(peg_color_list)\n\n\t\t#put solution into the self.guesses dictionary in the model\n\t\tself.model.guesses[\"solution\"] = solution\n\n\n\t\t#Testing Stuff:\n\t\t# for peg in peg_color_list:\n\t\t# \tprint(peg.peg_color)\n\n\t\t# print(self.model.guesses[\"solution\"])","def _get_color(self, color_name):\n if not color_name:\n return 0\n\n if color_name == 'ORANGE':\n color = self.COLOR_ORANGE\n else:\n color = getattr(curses, 'COLOR_' + color_name)\n return curses.color_pair(color)","def GetHandle(self):\n return _XCAFDoc.XCAFDoc_ColorTool_GetHandle(self)","def color(self):\n if \"color\" in self._prop_dict:\n return self._prop_dict[\"color\"]\n else:\n return None","def accent_color(self) -> typing.Optional[colors.Color]: # noqa: D401 - Imperative mood","def color(self):\n return self.container['color']"],"string":"[\n \"def colorPickerDialog(self, current_color=None):\\n\\t\\tcolor_dialog = QtWidgets.QColorDialog()\\n\\t\\t#color_dialog.setOption(QtWidgets.QColorDialog.DontUseNativeDialog)\\n\\n\\t\\t# Set current colour\\n\\t\\tif current_color is not None:\\n\\t\\t\\tcolor_dialog.setCurrentColor(current_color)\\n\\n\\t\\t# Only return a color if valid / dialog accepted\\n\\t\\tif color_dialog.exec_() == color_dialog.Accepted:\\n\\t\\t\\tcolor = color_dialog.selectedColor()\\n\\t\\t\\treturn color\",\n \"def _color_change_mode(self):\\r\\n self.dlg.exec_()\\r\\n self.color = self.dlg.currentColor().name()\\r\\n self.colorPlate.setStyleSheet(\\\"background-color: %s;\\\" % self.color)\\r\\n self.input_scene.get_stk_color(self.color)\\r\\n return\",\n \"def GetDefaultColor(self):\\n return wx.BLACK\",\n \"def createColorWindow():\\n colorWindow = g.GraphWin(\\\"Color\\\", 150, 500) #Window to show colors to pick\\n return colorWindow\",\n \"def defaultColor(self, p_int=None): # real signature unknown; restored from __doc__ with multiple overloads\\r\\n pass\",\n \"def onCustomColorMenu(self, item):\\n self.dialog = pyui.dialogs.ColorDialog(self.customColorChosen)\\n self.dialog.doModal()\\n return 1\",\n \"def pickColour(self):\\n colour = QColorDialog.getColor()\\n if colour.isValid():\\n self.user[\\\"Colour\\\"] = colour.name()\\n self.ui.l_colour.setText(self.user[\\\"Colour\\\"])\",\n \"def default_colour(self):\\n colour = self.DEFAULT_COLOUR\\n return colour\",\n \"def pickAColor():\\n color = _tkCall(tkColorChooser.askcolor)\\n if color[0] != None:\\n return Color(color[0][0], color[0][1], color[0][2])\",\n \"def choose_colour(self) -> None:\\n self.chosen_colour = QtWidgets.QColorDialog.getColor(Qt.white, self, \\\"Выберите цвет\\\")\\n self.pen_colour = self.chosen_colour\\n self.palette.setColor(QPalette.Background, self.chosen_colour)\\n self.ui.frame.setPalette(self.palette)\",\n \"def addDialogBox(self, color):\\n\\t\\tself._getSize()\\n\\t\\tobj = nceDialogBox(self.hcenter - 11, 10)\\n\\t\\tobj.rtc = False\\n\\t\\tobj.frame = [\\t['╭' + ('─' * 19) + '╮', 3],\\n\\t\\t\\t\\t\\t\\t['│' + (' ' * 19) + '│', 3],\\n\\t\\t\\t\\t\\t\\t['│' + (' ' * 19) + '│', 3],\\n\\t\\t\\t\\t\\t\\t['└' + ('─' * 19) + '╯', 3]]\\n\\t\\tobj.visible = True\\n\\t\\tobj.color = color\\n\\t\\tself.menus.append(obj)\\n\\t\\treturn obj\",\n \"def _confirm_color(self, event = None):\\n color = self._entry.get().strip()\\n if color != \\\"\\\":\\n self._color = color\\n self._window.destroy()\",\n \"def open_win(e) -> None:\\n widget_sfx()\\n r, g, b = parse_color(var.get())\\n new_color, tk_color = askcolor(\\n color=f\\\"#{r:02x}{g:02x}{b:02x}\\\",\\n parent=parent.winfo_toplevel(),\\n title=str(TRANS_SELECT_TITLE),\\n )\\n if new_color is not None:\\n # On 3.8, these are floats.\\n rf, gf, bf = new_color\\n var.set(f'{int(rf)} {int(gf)} {int(bf)}')\",\n \"def select_color(self):\\n\\t\\tresult = tkinter.colorchooser.askcolor(self.center['bg'])\\n\\t\\tif result:\\n\\t\\t\\t# 2nd part of result is the color object\\n\\t\\t\\tself.center['bg'] = result[1]\",\n \"def comdlg32_ChooseColor(jitter, get_str, set_str):\\n ret_ad, args = jitter.func_args_stdcall([\\\"lpcc\\\"])\\n raise RuntimeError('API not implemented')\\n jitter.func_ret_stdcall(ret_ad, ret_value)\",\n \"def set_window_colour(self, event):\\n rgb_triplet, rgb_string = tkColorChooser.askcolor()\\n self.canvas.config(bg = rgb_string)\",\n \"def showColors(self):\\n\\t\\tcolors = ['white', 'red', 'green', 'orange', 'blue', 'purple', 'cyan', 'lightgrey',\\n\\t\\t\\t\\t 'darkgrey', 'light red', 'light green', 'yellow', 'light blue', 'purple', 'cyan', 'dark white']\\n\\t\\tmax = curses.COLORS if curses.COLORS <= 16 else 16\\n\\t\\tself.screen.clear()\\n\\t\\tfor c in range(0, max):\\n\\t\\t\\tself.wts(c + 2, 1, \\\"color \\\" + str(c) + ' : ' + colors[c], c)\\n\\t\\tself.wts(18, 1, \\\"color 16 : red on white\\\", 16)\\n\\t\\tself.wts(20, 1, 'Color demo, displaying ' + str(max) + ' colors + 1 special')\\n\\t\\tself.screen.refresh()\\n\\t\\tch = False\\n\\t\\twhile not ch:\\n\\t\\t\\tch = self.screen.getch()\\n\\t\\tself.exit('Color demo complete')\",\n \"def get_color(self):\\n self.view.present(\\n \\\"sheet\\\",\\n orientations=ORIENTATIONS,\\n )\\n self.view.wait_modal()\\n return self.rgb\",\n \"def get_color(self):\\n return \\\"yellow\\\"\",\n \"def color(self):\\n return self.settings['color']\",\n \"def color(self):\\n global bordercolor\\n bordercolor = QColorDialog.getColor(Qt.green, self)\\n self.b_color.setPalette(QPalette(bordercolor))\",\n \"def default_style_sheet(self) -> str:\\n # Valid color names: http://www.w3.org/TR/SVG/types.html#ColorKeywords\\n g.trace('===== using default style sheet =====')\\n return '''\\\\\\n\\n /* A QWidget: supports only background attributes.*/\\n QSplitter::handle {\\n background-color: #CAE1FF; /* Leo's traditional lightSteelBlue1 */\\n }\\n QSplitter {\\n border-color: white;\\n background-color: white;\\n border-width: 3px;\\n border-style: solid;\\n }\\n QTreeWidget {\\n background-color: #ffffec; /* Leo's traditional tree color */\\n }\\n QsciScintilla {\\n background-color: pink;\\n }\\n '''\",\n \"def set_colors(self, ):\\n try:\\n odd = self._parent.settings.get_key('interface.odd_color')\\n even = self._parent.settings.get_key('interface.even_color')\\n self.dialog.instruments.set_odd_color(odd)\\n self.dialog.accounts.set_odd_color(odd)\\n self.dialog.instruments.set_even_color(even)\\n self.dialog.accounts.set_even_color(even)\\n except od_exception_config_key_error:\\n pass\",\n \"def set_color(self):\\n new_color = QColorDialog.getColor(QColor(self.config['color']))\\n if not new_color.isValid():\\n return\\n self.config['color'] = new_color.rgb()\\n self.paint()\",\n \"def __init__(self, parent):\\n self.parent = parent\\n myStyle = wx.DEFAULT_DIALOG_STYLE|wx.RESIZE_BORDER|wx.TAB_TRAVERSAL\\n wx.Dialog.__init__(self, None, wx.ID_ANY, '%s - Choose your colors' % globs.myName, style=myStyle)\\n\\n self._initialize()\\n\\n self.panel1.SetSizerAndFit(self.topBoxSizer)\\n self.SetClientSize(self.topBoxSizer.GetSize())\\n self.Centre()\",\n \"def abrirDialogoDeConfiguracion(self):\\n ventanaEmergente = Toplevel()\\n ventanaEmergente.geometry(\\\"200x320\\\")\\n # Se crea un canvas\\n tela = Canvas(ventanaEmergente, height=320, width=200, bg=\\\"snow\\\")\\n tela.place(x=0, y=0)\\n btnColorEspacioDisponible = Button(tela, text=\\\"Espacio Disponible\\\", command = lambda :self.cambiarColor(0))\\n btnColorEspacioDisponible.place(x=50, y=20)\\n btnColorPared = Button(tela, text=\\\"Paredes\\\", command = lambda :self.cambiarColor(2))\\n btnColorPared.place(x=66, y=50)\\n btnColorNodo = Button(tela, text=\\\"Nodo\\\", command = lambda :self.cambiarColor(3))\\n btnColorNodo.place(x=70, y=80)\",\n \"def getColor(col=None,caption=None):\\n if type(col) == tuple:\\n col = QtGui.QColor.fromRgb(*col)\\n else:\\n col = QtGui.QColor(col)\\n dia = QtGui.QColorDialog\\n #myButton = QtGui.QPushButton('MY')\\n #dia.layout()\\n col = dia.getColor(col)\\n if col.isValid():\\n return str(col.name())\\n else:\\n return None\",\n \"def getCenterColor():\\n return input(\\\"What color do you want the center color to be?\\\")\",\n \"def color(self) -> Optional[pulumi.Input[str]]:\\n return pulumi.get(self, \\\"color\\\")\",\n \"def color(self) -> Optional[pulumi.Input[str]]:\\n return pulumi.get(self, \\\"color\\\")\",\n \"def color(self) -> Optional[pulumi.Input[str]]:\\n return pulumi.get(self, \\\"color\\\")\",\n \"def color(self) -> Optional[pulumi.Input[str]]:\\n return pulumi.get(self, \\\"color\\\")\",\n \"def color(self) -> Optional[pulumi.Input[str]]:\\n return pulumi.get(self, \\\"color\\\")\",\n \"def color(self) -> Optional[pulumi.Input[str]]:\\n return pulumi.get(self, \\\"color\\\")\",\n \"def color(self) -> Optional[pulumi.Input[str]]:\\n return pulumi.get(self, \\\"color\\\")\",\n \"def color(self) -> Optional[pulumi.Input[str]]:\\n return pulumi.get(self, \\\"color\\\")\",\n \"def color(self) -> Optional[pulumi.Input[str]]:\\n return pulumi.get(self, \\\"color\\\")\",\n \"def color(self) -> Optional[pulumi.Input[str]]:\\n return pulumi.get(self, \\\"color\\\")\",\n \"def color(self) -> Optional[pulumi.Input[str]]:\\n return pulumi.get(self, \\\"color\\\")\",\n \"def color(self) -> Optional[pulumi.Input[str]]:\\n return pulumi.get(self, \\\"color\\\")\",\n \"def color(self) -> Optional[pulumi.Input[str]]:\\n return pulumi.get(self, \\\"color\\\")\",\n \"def color(self) -> Optional[pulumi.Input[str]]:\\n return pulumi.get(self, \\\"color\\\")\",\n \"def color(self) -> Optional[pulumi.Input[str]]:\\n return pulumi.get(self, \\\"color\\\")\",\n \"def color(self) -> Optional[pulumi.Input[str]]:\\n return pulumi.get(self, \\\"color\\\")\",\n \"def color(self) -> Optional[pulumi.Input[str]]:\\n return pulumi.get(self, \\\"color\\\")\",\n \"def color(self) -> Optional[pulumi.Input[str]]:\\n return pulumi.get(self, \\\"color\\\")\",\n \"def color(self) -> Optional[pulumi.Input[str]]:\\n return pulumi.get(self, \\\"color\\\")\",\n \"def color(self) -> Optional[pulumi.Input[str]]:\\n return pulumi.get(self, \\\"color\\\")\",\n \"def color(self) -> Optional[pulumi.Input[str]]:\\n return pulumi.get(self, \\\"color\\\")\",\n \"def color(self) -> Optional[pulumi.Input[str]]:\\n return pulumi.get(self, \\\"color\\\")\",\n \"def color(self) -> Optional[pulumi.Input[str]]:\\n return pulumi.get(self, \\\"color\\\")\",\n \"def color(self) -> Optional[pulumi.Input[str]]:\\n return pulumi.get(self, \\\"color\\\")\",\n \"def color(self) -> Optional[pulumi.Input[str]]:\\n return pulumi.get(self, \\\"color\\\")\",\n \"def color(self) -> Optional[pulumi.Input[str]]:\\n return pulumi.get(self, \\\"color\\\")\",\n \"def color(self) -> Optional[pulumi.Input[str]]:\\n return pulumi.get(self, \\\"color\\\")\",\n \"def color(self) -> Optional[pulumi.Input[str]]:\\n return pulumi.get(self, \\\"color\\\")\",\n \"def getDefaultDisplayMode(self):\\n return \\\"Shaded\\\"\",\n \"def handlerSelectFillColor(self):\\r\\n dialog = QtGui.QColorDialog(self.layerSymbolFillColor)\\r\\n \\r\\n if dialog.exec_():\\r\\n selectedColor = dialog.selectedColor()\\r\\n sender = self.sender()\\r\\n if sender.objectName() == 'buttonLayerSymbolFillColor':\\r\\n self.layerSymbolFillColor = selectedColor\\r\\n elif sender.objectName() == 'buttonStyleCategorizedFillColor':\\r\\n self.styleCategorizedColor = selectedColor\\r\\n elif sender.objectName() == 'buttonStyleGraduatedFillColor':\\r\\n self.styleGraduatedColor = selectedColor\\r\\n elif sender.objectName() == 'buttonStyleRuleBasedFillColor':\\r\\n self.styleRuleBasedColor = selectedColor\\r\\n sender.setStyleSheet('background-color: {0};'.format(selectedColor.name()))\",\n \"def Set_Profile_Color(self):\\r\\n default= self.system.Get_Color()\\r\\n color = tkColorChooser.askcolor(default,parent=self.root)\\r\\n if( color[1] == None ):\\r\\n return\\r\\n else:\\r\\n self.system.Set_Profile_Color( color[1] )\",\n \"def __init__(self, last_color):\\n self._color = last_color\\n self._window = tk.Toplevel(width = 200, height = 132)\\n self._window.title(\\\"Color\\\")\\n \\n self._initialize()\",\n \"def get_option_color(cls, option):\\n\\n if option == 'Grey':\\n color = cls.fromRgbF(0.4, 0.4, 0.4)\\n elif option == 'Cancel':\\n color = cls.fromRgbF(0.7, 0.5, 0.4)\\n elif option == 'OK' or option == 'ok':\\n color = cls.fromRgbF(0.5, 0.7, 0.8)\\n elif option == 'Warning' or option == 'Warn':\\n color = cls.fromRgbF(0.7, 0.2, 0.2)\\n elif option == 'Collapse':\\n color = cls.fromRgbF(0.15, 0.15, 0.15)\\n elif option == 'Subtle':\\n color = cls.fromRgbF(0.48, 0.48, 0.6)\\n else:\\n color = cls.fromRgbF(0.48, 0.48, 0.6)\\n if option:\\n color.ann = option + ' Color'\\n return color\",\n \"def getColormapDialog(self, view):\\n return None\",\n \"def magic_colors(self,parameter_s = ''):\\n \\n new_scheme = parameter_s.strip()\\n if not new_scheme:\\n print 'You must specify a color scheme.'\\n return\\n try:\\n self.shell.outputcache.set_colors(new_scheme)\\n except:\\n warn('Error changing prompt color schemes.\\\\n'\\n + str(sys.exc_info()[1]))\\n else:\\n self.shell.rc.colors = \\\\\\n self.shell.outputcache.color_table.active_scheme_name\\n try:\\n self.shell.InteractiveTB.set_colors(scheme = new_scheme)\\n self.shell.SyntaxTB.set_colors(scheme = new_scheme)\\n except:\\n warn('Error changing exception color schemes.\\\\n'\\n + str(sys.exc_info()[1]))\\n if self.shell.rc.color_info:\\n try:\\n self.shell.inspector.set_active_scheme(new_scheme)\\n except:\\n warn('Error changing object inspector color schemes.\\\\n'\\n + str(sys.exc_info()[1]))\\n else:\\n self.shell.inspector.set_active_scheme('NoColor')\",\n \"def default_colors():\\n # default_colors = [\\n # # r, g, b, a\\n # [92, 192, 98, 0.5],\\n # [90, 155, 212, 0.5],\\n # [246, 236, 86, 0.6],\\n # [241, 90, 96, 0.4],\\n # [255, 117, 0, 0.3],\\n # [82, 82, 190, 0.2],\\n # ]\\n\\n default_colors = [\\n # r, g, b, a\\n [188, 114, 3, 0.5],\\n [3, 133, 188, 0.5],\\n [155, 9, 118, 0.6],\\n [155, 53, 9, 0.4],\\n [4, 140, 128, 0.3],\\n [140, 8, 8, 0.2],\\n ]\\n\\n default_colors = [\\n [i[0] / 255.0, i[1] / 255.0, i[2] / 255.0, i[3]]\\n for i in default_colors\\n ]\\n\\n return default_colors\",\n \"def test_default_dialogues(self):\\n _, dialogue = self.default_dialogues.create(\\n counterparty=COUNTERPARTY_AGENT_ADDRESS,\\n performative=DefaultMessage.Performative.BYTES,\\n content=b\\\"some_content\\\",\\n )\\n assert dialogue.role == DefaultDialogue.Role.AGENT\\n assert dialogue.self_address == self.skill.skill_context.agent_address\",\n \"def GetColor(self, *args):\\n return _XCAFDoc.XCAFDoc_ColorTool_GetColor(self, *args)\",\n \"def __selectColorName(self):\\n editor = e5App().getObject(\\\"ViewManager\\\").activeWindow()\\n if editor is None:\\n return\\n \\n if editor.hasSelectedText():\\n currColor = editor.selectedText()\\n if currColor not in QColor.colorNames():\\n E5MessageBox.critical(\\n self.__ui,\\n self.tr(\\\"Color String\\\"),\\n self.tr(\\n \\\"\\\"\\\"<p>The selected string <b>{0}</b> is not a\\\"\\\"\\\"\\n \\\"\\\"\\\" valid color name. Aborting!</p>\\\"\\\"\\\")\\n .format(currColor))\\n return\\n else:\\n currColor = \\\"\\\"\\n \\n from ColorString.ColorSelectionDialog import ColorSelectionDialog\\n dlg = ColorSelectionDialog(currColor, self.__ui)\\n if dlg.exec_() == QDialog.Accepted:\\n colorStr = dlg.getColor()\\n editor.beginUndoAction()\\n if editor.hasSelectedText():\\n editor.replaceSelectedText(colorStr)\\n else:\\n line, index = editor.getCursorPosition()\\n editor.insert(colorStr)\\n editor.setCursorPosition(line, index + len(colorStr))\\n editor.endUndoAction()\",\n \"def conseguir_color(self):\\n return self.pluma.conseguir_color()\",\n \"def _init_colors(self):\\n self.clr_primary = None\\n self.clr_secondary = 'green'\\n self.clr_tertiary = 'cyan'\\n self.clr_quaternary = 'yellow'\\n self.clr_bold = 'cyan'\\n self.clr_code = 'cyan'\\n self.clr_error = 'red'\\n self.clr_header = 'yellow'\\n self.clr_link = 'green'\\n self.clr_list = 'cyan'\\n self.clr_message = None\\n self.clr_num_comments = 'green'\\n self.clr_num_points = 'green'\\n self.clr_tag = 'cyan'\\n self.clr_time = 'yellow'\\n self.clr_title = None\\n self.clr_tooltip = None\\n self.clr_user = 'cyan'\\n self.clr_view_link = 'magenta'\\n self.clr_view_index = 'magenta'\",\n \"def _color_name(self, color_name):\\n try:\\n for color in self.config[\\\"Defaults\\\"][\\\"Colors\\\"]:\\n if color == color_name:\\n return self.config[\\\"Defaults\\\"][\\\"Colors\\\"][color]\\n print(\\n f\\\"ERROR: Color {color_name} not found in default color list, returning Black\\\"\\n )\\n return \\\"#000000\\\"\\n except KeyError as e:\\n print(f\\\"Error: {e}\\\")\\n print(\\n \\\"config.yml requires minimal config section, please see documentation\\\"\\n )\\n sys.exit(1)\",\n \"def color(self):\\n return self.COLOR\",\n \"def FindColor(self, *args):\\n return _XCAFDoc.XCAFDoc_ColorTool_FindColor(self, *args)\",\n \"def changePopupColour(templateVar):\\n\\tglobal currentPeaWindow\\n\\tif templateVar in podTemplate.templateColours and currentPeaWindow:\\n\\t\\tcorrectColour=podTemplate.templateColours[templateVar]\\n\\t\\tcurrentPeaWindow.sectionData[\\\"Template\\\"].label.config(fg=correctColour)\\n\\t\\t#currentPeaWindow.status.colour(correctColour)\",\n \"def colors(theme=None):\\n\\n if theme == \\\"dark\\\":\\n return {\\n \\\"selection\\\": \\\"1;32;40\\\",\\n \\\"queued\\\": \\\"0;37\\\",\\n }\\n\\n return {\\n \\\"selection\\\": \\\"\\\",\\n \\\"queued\\\": \\\"\\\",\\n }\",\n \"def color_style():\\n if not supports_color():\\n style = no_style()\\n else:\\n environment_color_style = os.environ.get('DJANGO_COLORS', '')\\n color_settings = parse_color_setting(environment_color_style)\\n if color_settings:\\n class Dummy:\\n \\\"\\\"\\\"A style object.\\\"\\\"\\\"\\n pass\\n style = Dummy()\\n # The nocolor palette has all available roles.\\n # Use that pallete as the basis for populating\\n # the palette as defined in the environment.\\n for role in PALETTES[NOCOLOR_PALETTE]:\\n colour_format = color_settings.get(role, {})\\n # pylint: disable-msg=W0142\\n setattr(style, role, make_style(**colour_format))\\n # For backwards compatibility,\\n # set style for ERROR_OUTPUT == ERROR\\n style.ERROR_OUTPUT = style.ERROR\\n else:\\n style = no_style()\\n return style\",\n \"def __init__(self):\\n self.colors = (\\n 'BLACK', 'RED', 'GREEN', 'YELLOW',\\n 'BLUE', 'MAGENTA', 'CYAN', 'WHITE'\\n )\\n\\n self.disable_color = True\\n\\n if sys.stdout.isatty():\\n self.disable_color = False\",\n \"def color(self) -> Optional[str]:\\n return self.colour\",\n \"def colors(self, parameter_s=''):\\n def color_switch_err(name):\\n warn('Error changing %s color schemes.\\\\n%s' %\\n (name, sys.exc_info()[1]), stacklevel=2)\\n\\n\\n new_scheme = parameter_s.strip()\\n if not new_scheme:\\n raise UsageError(\\n \\\"%colors: you must specify a color scheme. See '%colors?'\\\")\\n # local shortcut\\n shell = self.shell\\n\\n # Set shell colour scheme\\n try:\\n shell.colors = new_scheme\\n shell.refresh_style()\\n except:\\n color_switch_err('shell')\\n\\n # Set exception colors\\n try:\\n shell.InteractiveTB.set_colors(scheme = new_scheme)\\n shell.SyntaxTB.set_colors(scheme = new_scheme)\\n except:\\n color_switch_err('exception')\\n\\n # Set info (for 'object?') colors\\n if shell.color_info:\\n try:\\n shell.inspector.set_active_scheme(new_scheme)\\n except:\\n color_switch_err('object inspector')\\n else:\\n shell.inspector.set_active_scheme('NoColor')\",\n \"def as_default(self, string):\\n return idaapi.COLSTR(string, idaapi.SCOLOR_DEFAULT)\",\n \"def get_color(self):\\r\\n if self.color:\\r\\n return \\\"RED\\\"\\r\\n else:\\r\\n return \\\"BLACK\\\"\",\n \"def getDefaultBrush(*args, **kwargs)->AnyStr:\\n pass\",\n \"def ColorTool(*args):\\n return _XCAFDoc.XCAFDoc_DocumentTool_ColorTool(*args)\",\n \"def use_default_dict():\\n color = defaultdict(lambda: \\\"#000000\\\")\\n color[\\\"White\\\"] = \\\"#FFFFFF\\\"\\n pp.pprint(color[\\\"White\\\"])\\n pp.pprint(color[\\\"Red\\\"])\",\n \"def __selectHexColor(self):\\n editor = e5App().getObject(\\\"ViewManager\\\").activeWindow()\\n if editor is None:\\n return\\n \\n if editor.hasSelectedText():\\n currColor = editor.selectedText()\\n if not self.__isValidColor(currColor):\\n E5MessageBox.critical(\\n self.__ui,\\n self.tr(\\\"Color String\\\"),\\n self.tr(\\n \\\"\\\"\\\"<p>The selected string <b>{0}</b> is not a\\\"\\\"\\\"\\n \\\"\\\"\\\" valid color string. Aborting!</p>\\\"\\\"\\\")\\n .format(currColor))\\n return\\n \\n if currColor.startswith(\\\"#\\\"):\\n withHash = True\\n elif self.__isHexString(currColor):\\n withHash = False\\n currColor = \\\"#\\\" + currColor\\n else:\\n withHash = True\\n initColor = QColor(currColor)\\n else:\\n withHash = True\\n currColor = \\\"\\\"\\n initColor = QColor()\\n \\n color = QColorDialog.getColor(\\n initColor, self.__ui, self.tr(\\\"Color String\\\"))\\n if color.isValid():\\n colorStr = color.name()\\n if not withHash:\\n colorStr = colorStr[1:]\\n editor.beginUndoAction()\\n if editor.hasSelectedText():\\n editor.replaceSelectedText(colorStr)\\n else:\\n line, index = editor.getCursorPosition()\\n editor.insert(colorStr)\\n editor.setCursorPosition(line, index + len(colorStr))\\n editor.endUndoAction()\",\n \"def color_style():\\n if (sys.platform == 'win32' or sys.platform == 'Pocket PC'\\n or sys.platform.startswith('java') or not sys.stdout.isatty()):\\n return no_style()\\n class dummy: pass\\n style = dummy()\\n style.ERROR = termcolors.make_style(fg='red', opts=('bold',))\\n style.ERROR_OUTPUT = termcolors.make_style(fg='red', opts=('bold',))\\n style.NOTICE = termcolors.make_style(fg='red')\\n style.SQL_FIELD = termcolors.make_style(fg='green', opts=('bold',))\\n style.SQL_COLTYPE = termcolors.make_style(fg='green')\\n style.SQL_KEYWORD = termcolors.make_style(fg='yellow')\\n style.SQL_TABLE = termcolors.make_style(opts=('bold',))\\n return style\",\n \"def base_color(self):\\n return curses.color_pair(3) if self.cycling else curses.color_pair(1)\",\n \"def getPetalColor():\\n return input(\\\"What color do you want the petals to be?\\\")\",\n \"def accent_color(self) -> undefined.UndefinedNoneOr[colors.Color]: # noqa: D401 - Imperative mood\",\n \"def get_color(self) -> Optional[str]:\\n return self.color\",\n \"def initialize_colors(self) -> None:\\n curses.init_pair(ColorPair.black_on_white.value, curses.COLOR_BLACK, curses.COLOR_WHITE)\\n curses.init_pair(ColorPair.red_on_black.value, curses.COLOR_RED, curses.COLOR_BLACK)\\n curses.init_pair(ColorPair.blue_on_black.value, curses.COLOR_BLUE, curses.COLOR_BLACK)\\n curses.init_pair(ColorPair.green_on_black.value, curses.COLOR_GREEN, curses.COLOR_BLACK)\\n curses.init_pair(ColorPair.white_on_black.value, curses.COLOR_WHITE, curses.COLOR_BLACK)\",\n \"def __init__(self,baseColor=QtGui.QColor(50,50,50), highlightColor=QtGui.QColor(\\\"yellow\\\"), spread=2.5):\\n self.palette = QtGui.QPalette()\\n self.baseColor = baseColor\\n self.highlightColor = highlightColor\\n self.spread = spread\\n self.generateScheme()\\n QtGui.QApplication.setStyle(\\\"Plastique\\\")\",\n \"def getInitialColor(self):\\n return self.__initial_color__\",\n \"def change_colour(self) -> None:\\n if self.ui.radioButtonWhite.isChecked():\\n self.pen_colour = QColor(Qt.white)\\n elif self.ui.radioButtonColour.isChecked():\\n self.pen_colour = self.chosen_colour\\n else: # Impossible but better to control\\n message.show_error(config.PROG_ERR3)\\n return None\",\n \"def colors(self):\\r\\n return ColorsResource(self)\",\n \"def color_picker(self):\\n\\n\\t\\tnum_to_select = 4\\n\\t\\tpeg_color_list = [] #creates the list to store the peg object\\n\\n\\t\\t#write a for loop to set a loop to select 4 colors from SOLUTION in mm_model\\n\\t\\tfor i in range(num_to_select): #use i just to run the loop, variable is not used elsewhere \\n\\t\\t\\t# print(i)\\n\\t\\t\\tcolor = random.choice(MasterModel.COLORS)\\n\\t\\t\\t# print(color)\\n\\t\\t\\t#associate color with peg objects\\n\\t\\t\\tpeg = ColorPeg(color)\\n\\n\\t\\t\\t#append the peg_color list to make a list of peg objects \\n\\t\\t\\tpeg_color_list.append(peg)\\n\\t\\t\\t# print (peg_color_list)\\n\\t\\t\\n\\t\\t#create object for solution so it can be stored in model.py\\n\\t\\tsolution = Guess(peg_color_list)\\n\\n\\t\\t#put solution into the self.guesses dictionary in the model\\n\\t\\tself.model.guesses[\\\"solution\\\"] = solution\\n\\n\\n\\t\\t#Testing Stuff:\\n\\t\\t# for peg in peg_color_list:\\n\\t\\t# \\tprint(peg.peg_color)\\n\\n\\t\\t# print(self.model.guesses[\\\"solution\\\"])\",\n \"def _get_color(self, color_name):\\n if not color_name:\\n return 0\\n\\n if color_name == 'ORANGE':\\n color = self.COLOR_ORANGE\\n else:\\n color = getattr(curses, 'COLOR_' + color_name)\\n return curses.color_pair(color)\",\n \"def GetHandle(self):\\n return _XCAFDoc.XCAFDoc_ColorTool_GetHandle(self)\",\n \"def color(self):\\n if \\\"color\\\" in self._prop_dict:\\n return self._prop_dict[\\\"color\\\"]\\n else:\\n return None\",\n \"def accent_color(self) -> typing.Optional[colors.Color]: # noqa: D401 - Imperative mood\",\n \"def color(self):\\n return self.container['color']\"\n]","isConversational":false},"negative_scores":{"kind":"list like","value":["0.7305316","0.6629004","0.66000414","0.65310407","0.6420928","0.63252723","0.6239439","0.62127674","0.60073066","0.59559417","0.5938414","0.5874301","0.582238","0.58178926","0.5801812","0.57167405","0.5696698","0.567963","0.56683296","0.5642302","0.56238717","0.56186587","0.558961","0.55782926","0.55780405","0.55506265","0.55038905","0.5491939","0.5489528","0.5489528","0.5489528","0.5489528","0.5489528","0.5489528","0.5489528","0.5489528","0.5489528","0.5489528","0.5489528","0.5489528","0.5489528","0.5489528","0.5489528","0.5489528","0.5489528","0.5489528","0.5489528","0.5489528","0.5489528","0.5489528","0.5489528","0.5489528","0.5489528","0.5489528","0.5489528","0.5489528","0.54878926","0.5485675","0.5482745","0.5479055","0.54742044","0.53831553","0.538227","0.537022","0.5356987","0.5354779","0.53520334","0.5336862","0.5334659","0.5330297","0.5304307","0.52904755","0.5265535","0.5265359","0.5260234","0.52564156","0.52482957","0.5240597","0.5240012","0.52300584","0.52225083","0.52220976","0.52032626","0.5194997","0.51900345","0.5186887","0.5185958","0.5178391","0.51743364","0.51714706","0.51699877","0.5167825","0.5167354","0.51579666","0.5154947","0.51532125","0.5150619","0.51498276","0.51488346","0.5142224"],"string":"[\n \"0.7305316\",\n \"0.6629004\",\n \"0.66000414\",\n \"0.65310407\",\n \"0.6420928\",\n \"0.63252723\",\n \"0.6239439\",\n \"0.62127674\",\n \"0.60073066\",\n \"0.59559417\",\n \"0.5938414\",\n \"0.5874301\",\n \"0.582238\",\n \"0.58178926\",\n \"0.5801812\",\n \"0.57167405\",\n \"0.5696698\",\n \"0.567963\",\n \"0.56683296\",\n \"0.5642302\",\n \"0.56238717\",\n \"0.56186587\",\n \"0.558961\",\n \"0.55782926\",\n \"0.55780405\",\n \"0.55506265\",\n \"0.55038905\",\n \"0.5491939\",\n \"0.5489528\",\n \"0.5489528\",\n \"0.5489528\",\n \"0.5489528\",\n \"0.5489528\",\n \"0.5489528\",\n \"0.5489528\",\n \"0.5489528\",\n \"0.5489528\",\n \"0.5489528\",\n \"0.5489528\",\n \"0.5489528\",\n \"0.5489528\",\n \"0.5489528\",\n \"0.5489528\",\n \"0.5489528\",\n \"0.5489528\",\n \"0.5489528\",\n \"0.5489528\",\n \"0.5489528\",\n \"0.5489528\",\n \"0.5489528\",\n \"0.5489528\",\n \"0.5489528\",\n \"0.5489528\",\n \"0.5489528\",\n \"0.5489528\",\n \"0.5489528\",\n \"0.54878926\",\n \"0.5485675\",\n \"0.5482745\",\n \"0.5479055\",\n \"0.54742044\",\n \"0.53831553\",\n \"0.538227\",\n \"0.537022\",\n \"0.5356987\",\n \"0.5354779\",\n \"0.53520334\",\n \"0.5336862\",\n \"0.5334659\",\n \"0.5330297\",\n \"0.5304307\",\n \"0.52904755\",\n \"0.5265535\",\n \"0.5265359\",\n \"0.5260234\",\n \"0.52564156\",\n \"0.52482957\",\n \"0.5240597\",\n \"0.5240012\",\n \"0.52300584\",\n \"0.52225083\",\n \"0.52220976\",\n \"0.52032626\",\n \"0.5194997\",\n \"0.51900345\",\n \"0.5186887\",\n \"0.5185958\",\n \"0.5178391\",\n \"0.51743364\",\n \"0.51714706\",\n \"0.51699877\",\n \"0.5167825\",\n \"0.5167354\",\n \"0.51579666\",\n \"0.5154947\",\n \"0.51532125\",\n \"0.5150619\",\n \"0.51498276\",\n \"0.51488346\",\n \"0.5142224\"\n]","isConversational":false},"document_score":{"kind":"string","value":"0.8498397"},"document_rank":{"kind":"string","value":"0"}}},{"rowIdx":23798,"cells":{"query":{"kind":"string","value":"Returns the default icon"},"document":{"kind":"string","value":"def icon(self):\n return self.__icon"},"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 icon(self):\n return DEFAULT_ICON","def icon(self):\n return None","def icon(self):\n return None","def icon(self):\n return ICON","def icon(self):\n return ICON","def icon(self):\n return ICON","def icon(self):\n return ICON","def icon(self):\n return ICON","def icon(self):\n return ICON","def icon(self):\n return ICON","def icon(self):\n return ICON","def icon(self):\n return ICON","def icon(self):\n return ICON","def icon(self):\n return self.ICON","def icon(self):\n return self.ICON","def get_icon(self):\n return self.ICON","def get_icon(self):\r\n return get_icon(self.ICON)","def icon(self):\r\n try:\r\n return self.data['icon_url_base']+self.data['icon_url_name']\r\n except KeyError:\r\n return ''","def icon(self):\n return STATUSES.get(self._mower_status, {}).get('icon', DEFAULT_ICON)","def icon(self):\n if \"icon\" in self._typeconf:\n return self._typeconf[\"icon\"]","def get_icon(self):\r\n raise NotImplementedError","def get_icon(self):\n raise NotImplementedError","def getIconPath(self):\n return '/zport/dmd/img/icons/noicon.png'","def icon(self) -> typing.Union[str, None]:\n return self._icon","def icon(self) -> str | None:\n return self._icon","def icon(self):","def icon(self):\n return self._config.get(CONF_ICON)","def icon(self):\n return self._sensor[CONF_ICON]","def icon(self):\r\n return self._icon","def icon(self):\n return self._icon","def icon(self):\n return self._icon","def icon(self):\n return self._icon","def icon(self):\n return self._icon","def icon(self):\n return self._icon","def icon(self):\n return self._icon","def icon(self):\n return self._icon","def icon(self):\n return self._icon","def icon(self):\n return self._icon","def icon(self):\n return self._icon","def icon(self):\n return self._icon","def icon(self):\n return self._icon","def icon(self):\n return self._icon","def icon(self):\n return self._icon","def icon(self):\n return self._icon","def icon(self):\n return self._icon","def icon(self):\n return self._icon","def icon(self):\n return self._icon","def icon(self):\n return self._icon","def icon(self):\n return self._icon","def getIconPath(self):\n try:\n return self.primaryAq().zIcon\n except AttributeError:\n return '/zport/dmd/img/icons/noicon.png'","def get_icon(self):\n\t\treturn QIcon()","def get_icon(self):\n return self._icon","def icon(self) -> str:\n return self._icon","def icon(self) -> str:\n return self._icon","def icon(self) -> str:\n return self._icon","def icon(self):\n if not self.device_class:\n return ICONS.get(self.entity_type)\n return None","def getIcon():\n\treturn \"Animator.png\"","def getIcon(self):\n return \":/icons/Ship_Instance.svg\"","def getIcon(self): #$NON-NLS-1$\r\n iconXPath = self._getIconXPath()\r\n icon = self._getExtensionText(iconXPath)\r\n if icon:\r\n return icon\r\n else:\r\n return None","def icon(self):\n return self.sensor_type[\"icon\"]","def icon(self):\n icons = self._icons.split(\",\")\n return (\n f\"mdi:{icons[0]}\" if self.state != TYPE_RECORD_NEVER else f\"mdi:{icons[1]}\"\n )","def icon(self):\n return ICON_BUS","def icon(self):\n\n if self._playing_tts or self._announce:\n return ICON_TTS\n\n if self._state in [STATE_PAUSED, STATE_UNAVAILABLE, STATE_IDLE, STATE_UNKNOWN]:\n return ICON_DEFAULT\n\n if self._muted:\n return ICON_MUTED\n\n if self._slave_mode or self._is_master:\n return ICON_MULTIROOM\n\n if self._source == \"Bluetooth\":\n return ICON_BLUETOOTH\n\n if self._source == \"DLNA\" or self._source == \"Airplay\" or self._source == \"Spotify\":\n return ICON_PUSHSTREAM\n\n if self._state == STATE_PLAYING:\n return ICON_PLAYING\n\n return ICON_DEFAULT","def get_icon(self):\n try:\n icon = self.icon.fa_icon\n except AttributeError:\n icon = 'fa-globe'\n\n return icon","def icon(self) -> str | None:\n if isinstance(self.wemo, CoffeeMaker):\n return \"mdi:coffee\"\n return None","def icon(self) -> str:\n return ICON_CORAL","def icon(self) -> str:\n return ICON_CORAL","def icon(self):\n return self._metadata[2]","def icon(self):\n if self.device_class:\n return None\n\n return ICONS.get(self.tesla_device.type)","def get_icon(self):\n\n return self._icon","def icon(self) -> str:\n return ICON_SERVER","def icon(self):\n return self.var_icon","def icon(self):\n return self._var_icon","def icon(self):\n return self._var_icon","def icon(self) -> Icon:\n return self._icon","def icon(self):\n return self._sensor_type.icon","def toolIcon(self):\n if self.tmFile is not None:\n iconFile = pFile.conformPath(os.path.join(os.path.dirname(self.tmFile), '__ico__.png'))\n if os.path.exists(iconFile):\n return iconFile","def icon(self):\r\n icon_path = \":/plugins/pdok_services/icon.png\"\r\n icon = QtGui.QIcon(icon_path)\r\n return icon","def icon(self):\n\n # look for icon one level up from this hook's folder in \"icons\" folder\n return os.path.join(\n self.disk_location,\n os.pardir,\n \"icons\",\n \"review.png\"\n )","def icon(self) -> str | None:\n return self._get_sensor_type()[1]","def icon(self):\n if self.zone_variable == \"temperature\":\n return \"mdi:thermometer\"\n if self.zone_variable == \"humidity\":\n return \"mdi:water-percent\"","def icon(self):\n return SENSOR_TYPES[self._type][\"icon\"]","def icon(self) -> str | None:\n value = self.entity_description.icon\n if self.entity_description.key == \"weather\":\n value = self.state\n if value is None:\n value = \"sunny\"\n elif value == \"partlycloudy\":\n value = \"partly-cloudy\"\n value = f\"mdi:weather-{value}\"\n\n return value","def icon(self):\n return SENSOR_TYPES[self.type][2]","def get_icon_class(self):\r\n return self.icon_class","def icon(self) -> str | None:\n if self._pending_state_is_armed is not None:\n return \"mdi:alarm-snooze\"\n elif self._is_armed:\n return \"mdi:alarm\"\n return \"mdi:alarm-off\"","def icon(self):\n value = SENSOR_TYPES[self._type][3]\n if self._type == \"weather\":\n value = self.state\n if value is None:\n value = \"sunny\"\n elif value == \"partlycloudy\":\n value = \"partly-cloudy\"\n value = f\"mdi:weather-{value}\"\n\n return value","def icon(self):\n value = SENSOR_TYPES[self._type][3]\n if self._type == \"weather\":\n value = self.state\n if value is None:\n value = \"sunny\"\n elif value == \"partlycloudy\":\n value = \"partly-cloudy\"\n value = f\"mdi:weather-{value}\"\n\n return value","def icon(self):\n if self._type == 'birth':\n return 'mdi:calendar-star'\n elif self._type == 'wedding':\n return 'mdi:calendar-heart'\n elif self._type == 'memorial':\n return 'mdi:calendar-clock'\n else:\n return 'mdi:calendar-check'","def icon(self) -> str | None:\n icons = self.entity_description.on_off_icons\n return icons[0] if self.is_on else icons[1]","def get_icon(self):\n\t\treturn call_sdk_function('PrlVmCfg_GetIcon', self.handle)","def icon(self):\n if self._sensor_type == DEVICE_TYPE_DOORBELL:\n if self._camera_data[\"event_ring_on\"]:\n return \"mdi:bell-ring-outline\"\n return \"mdi:doorbell-video\"","def icon(self):\n if \"icon\" in self._prop_dict:\n if isinstance(self._prop_dict[\"icon\"], OneDriveObjectBase):\n return self._prop_dict[\"icon\"]\n else :\n self._prop_dict[\"icon\"] = WorkbookIcon(self._prop_dict[\"icon\"])\n return self._prop_dict[\"icon\"]\n\n return None","def get_icon_title(self): # real signature unknown; restored from __doc__\n return \"\"","def icon(self):\n return 'mdi:broom'","def icon(self):\n return SENSOR_TYPES[self._sensor][1]","def icon(self):\n return SENSOR_TYPES[self._sensor][1]","def get_default_icon_size():\n return _QSize(ICON_SIZE, ICON_SIZE)","def icon(self):\n if self._sensor_type == \"battery_level\" and self._state is not None:\n return icon_for_battery_level(\n battery_level=int(self._state), charging=False\n )\n return self._icon","def icon(self) -> Optional[str]:\n if not self.profile_device.icons:\n return None\n\n if not self._icon:\n icon_mime_preference = {\"image/png\": 3, \"image/jpeg\": 2, \"image/gif\": 1}\n icons = [icon for icon in self.profile_device.icons if icon.url]\n icons = sorted(\n icons,\n # Sort by area, then colour depth, then preferred mimetype\n key=lambda icon: (\n icon.width * icon.height,\n icon.depth,\n icon_mime_preference.get(icon.mimetype, 0),\n ),\n reverse=True,\n )\n self._icon = icons[0].url\n\n return self._icon"],"string":"[\n \"def icon(self):\\n return DEFAULT_ICON\",\n \"def icon(self):\\n return None\",\n \"def icon(self):\\n return None\",\n \"def icon(self):\\n return ICON\",\n \"def icon(self):\\n return ICON\",\n \"def icon(self):\\n return ICON\",\n \"def icon(self):\\n return ICON\",\n \"def icon(self):\\n return ICON\",\n \"def icon(self):\\n return ICON\",\n \"def icon(self):\\n return ICON\",\n \"def icon(self):\\n return ICON\",\n \"def icon(self):\\n return ICON\",\n \"def icon(self):\\n return ICON\",\n \"def icon(self):\\n return self.ICON\",\n \"def icon(self):\\n return self.ICON\",\n \"def get_icon(self):\\n return self.ICON\",\n \"def get_icon(self):\\r\\n return get_icon(self.ICON)\",\n \"def icon(self):\\r\\n try:\\r\\n return self.data['icon_url_base']+self.data['icon_url_name']\\r\\n except KeyError:\\r\\n return ''\",\n \"def icon(self):\\n return STATUSES.get(self._mower_status, {}).get('icon', DEFAULT_ICON)\",\n \"def icon(self):\\n if \\\"icon\\\" in self._typeconf:\\n return self._typeconf[\\\"icon\\\"]\",\n \"def get_icon(self):\\r\\n raise NotImplementedError\",\n \"def get_icon(self):\\n raise NotImplementedError\",\n \"def getIconPath(self):\\n return '/zport/dmd/img/icons/noicon.png'\",\n \"def icon(self) -> typing.Union[str, None]:\\n return self._icon\",\n \"def icon(self) -> str | None:\\n return self._icon\",\n \"def icon(self):\",\n \"def icon(self):\\n return self._config.get(CONF_ICON)\",\n \"def icon(self):\\n return self._sensor[CONF_ICON]\",\n \"def icon(self):\\r\\n return self._icon\",\n \"def icon(self):\\n return self._icon\",\n \"def icon(self):\\n return self._icon\",\n \"def icon(self):\\n return self._icon\",\n \"def icon(self):\\n return self._icon\",\n \"def icon(self):\\n return self._icon\",\n \"def icon(self):\\n return self._icon\",\n \"def icon(self):\\n return self._icon\",\n \"def icon(self):\\n return self._icon\",\n \"def icon(self):\\n return self._icon\",\n \"def icon(self):\\n return self._icon\",\n \"def icon(self):\\n return self._icon\",\n \"def icon(self):\\n return self._icon\",\n \"def icon(self):\\n return self._icon\",\n \"def icon(self):\\n return self._icon\",\n \"def icon(self):\\n return self._icon\",\n \"def icon(self):\\n return self._icon\",\n \"def icon(self):\\n return self._icon\",\n \"def icon(self):\\n return self._icon\",\n \"def icon(self):\\n return self._icon\",\n \"def icon(self):\\n return self._icon\",\n \"def getIconPath(self):\\n try:\\n return self.primaryAq().zIcon\\n except AttributeError:\\n return '/zport/dmd/img/icons/noicon.png'\",\n \"def get_icon(self):\\n\\t\\treturn QIcon()\",\n \"def get_icon(self):\\n return self._icon\",\n \"def icon(self) -> str:\\n return self._icon\",\n \"def icon(self) -> str:\\n return self._icon\",\n \"def icon(self) -> str:\\n return self._icon\",\n \"def icon(self):\\n if not self.device_class:\\n return ICONS.get(self.entity_type)\\n return None\",\n \"def getIcon():\\n\\treturn \\\"Animator.png\\\"\",\n \"def getIcon(self):\\n return \\\":/icons/Ship_Instance.svg\\\"\",\n \"def getIcon(self): #$NON-NLS-1$\\r\\n iconXPath = self._getIconXPath()\\r\\n icon = self._getExtensionText(iconXPath)\\r\\n if icon:\\r\\n return icon\\r\\n else:\\r\\n return None\",\n \"def icon(self):\\n return self.sensor_type[\\\"icon\\\"]\",\n \"def icon(self):\\n icons = self._icons.split(\\\",\\\")\\n return (\\n f\\\"mdi:{icons[0]}\\\" if self.state != TYPE_RECORD_NEVER else f\\\"mdi:{icons[1]}\\\"\\n )\",\n \"def icon(self):\\n return ICON_BUS\",\n \"def icon(self):\\n\\n if self._playing_tts or self._announce:\\n return ICON_TTS\\n\\n if self._state in [STATE_PAUSED, STATE_UNAVAILABLE, STATE_IDLE, STATE_UNKNOWN]:\\n return ICON_DEFAULT\\n\\n if self._muted:\\n return ICON_MUTED\\n\\n if self._slave_mode or self._is_master:\\n return ICON_MULTIROOM\\n\\n if self._source == \\\"Bluetooth\\\":\\n return ICON_BLUETOOTH\\n\\n if self._source == \\\"DLNA\\\" or self._source == \\\"Airplay\\\" or self._source == \\\"Spotify\\\":\\n return ICON_PUSHSTREAM\\n\\n if self._state == STATE_PLAYING:\\n return ICON_PLAYING\\n\\n return ICON_DEFAULT\",\n \"def get_icon(self):\\n try:\\n icon = self.icon.fa_icon\\n except AttributeError:\\n icon = 'fa-globe'\\n\\n return icon\",\n \"def icon(self) -> str | None:\\n if isinstance(self.wemo, CoffeeMaker):\\n return \\\"mdi:coffee\\\"\\n return None\",\n \"def icon(self) -> str:\\n return ICON_CORAL\",\n \"def icon(self) -> str:\\n return ICON_CORAL\",\n \"def icon(self):\\n return self._metadata[2]\",\n \"def icon(self):\\n if self.device_class:\\n return None\\n\\n return ICONS.get(self.tesla_device.type)\",\n \"def get_icon(self):\\n\\n return self._icon\",\n \"def icon(self) -> str:\\n return ICON_SERVER\",\n \"def icon(self):\\n return self.var_icon\",\n \"def icon(self):\\n return self._var_icon\",\n \"def icon(self):\\n return self._var_icon\",\n \"def icon(self) -> Icon:\\n return self._icon\",\n \"def icon(self):\\n return self._sensor_type.icon\",\n \"def toolIcon(self):\\n if self.tmFile is not None:\\n iconFile = pFile.conformPath(os.path.join(os.path.dirname(self.tmFile), '__ico__.png'))\\n if os.path.exists(iconFile):\\n return iconFile\",\n \"def icon(self):\\r\\n icon_path = \\\":/plugins/pdok_services/icon.png\\\"\\r\\n icon = QtGui.QIcon(icon_path)\\r\\n return icon\",\n \"def icon(self):\\n\\n # look for icon one level up from this hook's folder in \\\"icons\\\" folder\\n return os.path.join(\\n self.disk_location,\\n os.pardir,\\n \\\"icons\\\",\\n \\\"review.png\\\"\\n )\",\n \"def icon(self) -> str | None:\\n return self._get_sensor_type()[1]\",\n \"def icon(self):\\n if self.zone_variable == \\\"temperature\\\":\\n return \\\"mdi:thermometer\\\"\\n if self.zone_variable == \\\"humidity\\\":\\n return \\\"mdi:water-percent\\\"\",\n \"def icon(self):\\n return SENSOR_TYPES[self._type][\\\"icon\\\"]\",\n \"def icon(self) -> str | None:\\n value = self.entity_description.icon\\n if self.entity_description.key == \\\"weather\\\":\\n value = self.state\\n if value is None:\\n value = \\\"sunny\\\"\\n elif value == \\\"partlycloudy\\\":\\n value = \\\"partly-cloudy\\\"\\n value = f\\\"mdi:weather-{value}\\\"\\n\\n return value\",\n \"def icon(self):\\n return SENSOR_TYPES[self.type][2]\",\n \"def get_icon_class(self):\\r\\n return self.icon_class\",\n \"def icon(self) -> str | None:\\n if self._pending_state_is_armed is not None:\\n return \\\"mdi:alarm-snooze\\\"\\n elif self._is_armed:\\n return \\\"mdi:alarm\\\"\\n return \\\"mdi:alarm-off\\\"\",\n \"def icon(self):\\n value = SENSOR_TYPES[self._type][3]\\n if self._type == \\\"weather\\\":\\n value = self.state\\n if value is None:\\n value = \\\"sunny\\\"\\n elif value == \\\"partlycloudy\\\":\\n value = \\\"partly-cloudy\\\"\\n value = f\\\"mdi:weather-{value}\\\"\\n\\n return value\",\n \"def icon(self):\\n value = SENSOR_TYPES[self._type][3]\\n if self._type == \\\"weather\\\":\\n value = self.state\\n if value is None:\\n value = \\\"sunny\\\"\\n elif value == \\\"partlycloudy\\\":\\n value = \\\"partly-cloudy\\\"\\n value = f\\\"mdi:weather-{value}\\\"\\n\\n return value\",\n \"def icon(self):\\n if self._type == 'birth':\\n return 'mdi:calendar-star'\\n elif self._type == 'wedding':\\n return 'mdi:calendar-heart'\\n elif self._type == 'memorial':\\n return 'mdi:calendar-clock'\\n else:\\n return 'mdi:calendar-check'\",\n \"def icon(self) -> str | None:\\n icons = self.entity_description.on_off_icons\\n return icons[0] if self.is_on else icons[1]\",\n \"def get_icon(self):\\n\\t\\treturn call_sdk_function('PrlVmCfg_GetIcon', self.handle)\",\n \"def icon(self):\\n if self._sensor_type == DEVICE_TYPE_DOORBELL:\\n if self._camera_data[\\\"event_ring_on\\\"]:\\n return \\\"mdi:bell-ring-outline\\\"\\n return \\\"mdi:doorbell-video\\\"\",\n \"def icon(self):\\n if \\\"icon\\\" in self._prop_dict:\\n if isinstance(self._prop_dict[\\\"icon\\\"], OneDriveObjectBase):\\n return self._prop_dict[\\\"icon\\\"]\\n else :\\n self._prop_dict[\\\"icon\\\"] = WorkbookIcon(self._prop_dict[\\\"icon\\\"])\\n return self._prop_dict[\\\"icon\\\"]\\n\\n return None\",\n \"def get_icon_title(self): # real signature unknown; restored from __doc__\\n return \\\"\\\"\",\n \"def icon(self):\\n return 'mdi:broom'\",\n \"def icon(self):\\n return SENSOR_TYPES[self._sensor][1]\",\n \"def icon(self):\\n return SENSOR_TYPES[self._sensor][1]\",\n \"def get_default_icon_size():\\n return _QSize(ICON_SIZE, ICON_SIZE)\",\n \"def icon(self):\\n if self._sensor_type == \\\"battery_level\\\" and self._state is not None:\\n return icon_for_battery_level(\\n battery_level=int(self._state), charging=False\\n )\\n return self._icon\",\n \"def icon(self) -> Optional[str]:\\n if not self.profile_device.icons:\\n return None\\n\\n if not self._icon:\\n icon_mime_preference = {\\\"image/png\\\": 3, \\\"image/jpeg\\\": 2, \\\"image/gif\\\": 1}\\n icons = [icon for icon in self.profile_device.icons if icon.url]\\n icons = sorted(\\n icons,\\n # Sort by area, then colour depth, then preferred mimetype\\n key=lambda icon: (\\n icon.width * icon.height,\\n icon.depth,\\n icon_mime_preference.get(icon.mimetype, 0),\\n ),\\n reverse=True,\\n )\\n self._icon = icons[0].url\\n\\n return self._icon\"\n]","isConversational":false},"negative_scores":{"kind":"list like","value":["0.8951425","0.8020554","0.8020554","0.7916969","0.7916969","0.7916969","0.7916969","0.7916969","0.7916969","0.7916969","0.7916969","0.7916969","0.7916969","0.7746288","0.7746288","0.77171725","0.7693508","0.7670877","0.76192194","0.7585411","0.7554771","0.75528175","0.7551785","0.75234586","0.7516212","0.7413208","0.7403281","0.74016947","0.7370001","0.7357689","0.7357689","0.7357689","0.7357689","0.7357689","0.7357689","0.7357689","0.7357689","0.7357689","0.7357689","0.7357689","0.7357689","0.7357689","0.7357689","0.7357689","0.7357689","0.7357689","0.7357689","0.7357689","0.7357689","0.73463506","0.73367214","0.73308533","0.7324035","0.7324035","0.7324035","0.7262009","0.72298706","0.7213227","0.7179225","0.7176732","0.717472","0.7170466","0.71504503","0.7141775","0.7125949","0.712357","0.712357","0.7123082","0.7109768","0.71053183","0.7097953","0.708948","0.70687443","0.70687443","0.7051065","0.70394486","0.703834","0.7010111","0.70066005","0.6996444","0.6991582","0.6986973","0.69812304","0.69709957","0.69478863","0.6937128","0.693058","0.693058","0.6926495","0.6923406","0.69181705","0.69174135","0.6909344","0.6907781","0.6902572","0.6872898","0.6872898","0.6844412","0.6841692","0.6840376"],"string":"[\n \"0.8951425\",\n \"0.8020554\",\n \"0.8020554\",\n \"0.7916969\",\n \"0.7916969\",\n \"0.7916969\",\n \"0.7916969\",\n \"0.7916969\",\n \"0.7916969\",\n \"0.7916969\",\n \"0.7916969\",\n \"0.7916969\",\n \"0.7916969\",\n \"0.7746288\",\n \"0.7746288\",\n \"0.77171725\",\n \"0.7693508\",\n \"0.7670877\",\n \"0.76192194\",\n \"0.7585411\",\n \"0.7554771\",\n \"0.75528175\",\n \"0.7551785\",\n \"0.75234586\",\n \"0.7516212\",\n \"0.7413208\",\n \"0.7403281\",\n \"0.74016947\",\n \"0.7370001\",\n \"0.7357689\",\n \"0.7357689\",\n \"0.7357689\",\n \"0.7357689\",\n \"0.7357689\",\n \"0.7357689\",\n \"0.7357689\",\n \"0.7357689\",\n \"0.7357689\",\n \"0.7357689\",\n \"0.7357689\",\n \"0.7357689\",\n \"0.7357689\",\n \"0.7357689\",\n \"0.7357689\",\n \"0.7357689\",\n \"0.7357689\",\n \"0.7357689\",\n \"0.7357689\",\n \"0.7357689\",\n \"0.73463506\",\n \"0.73367214\",\n \"0.73308533\",\n \"0.7324035\",\n \"0.7324035\",\n \"0.7324035\",\n \"0.7262009\",\n \"0.72298706\",\n \"0.7213227\",\n \"0.7179225\",\n \"0.7176732\",\n \"0.717472\",\n \"0.7170466\",\n \"0.71504503\",\n \"0.7141775\",\n \"0.7125949\",\n \"0.712357\",\n \"0.712357\",\n \"0.7123082\",\n \"0.7109768\",\n \"0.71053183\",\n \"0.7097953\",\n \"0.708948\",\n \"0.70687443\",\n \"0.70687443\",\n \"0.7051065\",\n \"0.70394486\",\n \"0.703834\",\n \"0.7010111\",\n \"0.70066005\",\n \"0.6996444\",\n \"0.6991582\",\n \"0.6986973\",\n \"0.69812304\",\n \"0.69709957\",\n \"0.69478863\",\n \"0.6937128\",\n \"0.693058\",\n \"0.693058\",\n \"0.6926495\",\n \"0.6923406\",\n \"0.69181705\",\n \"0.69174135\",\n \"0.6909344\",\n \"0.6907781\",\n \"0.6902572\",\n \"0.6872898\",\n \"0.6872898\",\n \"0.6844412\",\n \"0.6841692\",\n \"0.6840376\"\n]","isConversational":false},"document_score":{"kind":"string","value":"0.74875426"},"document_rank":{"kind":"string","value":"25"}}},{"rowIdx":23799,"cells":{"query":{"kind":"string","value":"Returns the default label"},"document":{"kind":"string","value":"def label(self):\n return self.__label"},"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 default_label(self) -> str:\n return self.settings[\"default_label\"]","def Label(self, default=None):\n return self.data.get('label', default)","def Label(self, default=None):\n return self.data.get('label', default)","def _get_label(self):\n return self.label","def default_label(self, label: str):\n self.settings[\"default_label\"] = label","def _get_label ( self ):\n if self._label is not None:\n return self._label\n return self.name","def label(self) -> Optional[str]:\n return self._itempage.labels.get(\"en\", None)","def get_label(self):\n return self.label","def get_label(self):\n return self.label","def get_label(self):\n return self.label","def get_label(self):\n return self.label","def get_label ( self ):\n return self.label","def label(self):\n return ''","def _get_label ( self ):\n if self._label is not None:\n return self._label\n return user_name_for( self.name )","def label(self) -> Optional[pulumi.Input[str]]:\n return pulumi.get(self, \"label\")","def label(self) -> Optional[pulumi.Input[str]]:\n return pulumi.get(self, \"label\")","def label(self) -> Optional[pulumi.Input[str]]:\n return pulumi.get(self, \"label\")","def label(self) -> Optional[pulumi.Input[str]]:\n return pulumi.get(self, \"label\")","def label(self) -> Optional[pulumi.Input[str]]:\n return pulumi.get(self, \"label\")","def label(self) -> Optional[pulumi.Input[str]]:\n return pulumi.get(self, \"label\")","def label(self) -> Optional[pulumi.Input[str]]:\n return pulumi.get(self, \"label\")","def label(self):\n return self.label_","def label(self):\n return '?'","def label(self) -> str:\n return self[\"label\"]","def label(self):\n # type: () -> str\n labels = self.__class__.__labels__\n return force_str(labels.get(self.value, self.name))","def label(self):\r\n return self._label","def first_label(self):\n if self.labels:\n return self.labels[0]\n else:\n return None","def label(self) -> pulumi.Output[str]:\n return pulumi.get(self, \"label\")","def label(self) -> pulumi.Output[str]:\n return pulumi.get(self, \"label\")","def label(self) -> pulumi.Output[str]:\n return pulumi.get(self, \"label\")","def GetLabel(self):\r\n\r\n return self.label","def GetLabel(self):\r\n\r\n return self.label","def label(self):\n return self._label","def label(self):\n return self._label","def label(self):\n return self._label","def label(self):\n return self._label","def label(self):\n return self._label","def label(self):\n return self._label","def label(self):\n return self._label","def label(self):\n return self._label","def label(self):\n return self._label","def label(self):\n return self._label","def get_label(self):\n return self._label","def label(self):\n return self._label_","def get_label(self, ):\n return self.attrs.get(self.AttributeNames.LABEL, None)","def label(self) -> str:\r\n\r\n return self.__label","def _get_label(self):\n if self.model.name == '':\n return \"KPI\"\n return \"KPI: {} ({})\".format(self.model.name, self.model.objective)","def get_labelname(self):\n return self.options['labelname']","def label(self) -> pulumi.Input[str]:\n return pulumi.get(self, \"label\")","def label(self) -> pulumi.Input[str]:\n return pulumi.get(self, \"label\")","def label(self) -> pulumi.Input[str]:\n return pulumi.get(self, \"label\")","def label(self) -> pulumi.Input[str]:\n return pulumi.get(self, \"label\")","def default(self):\n return self.get(name='Unknown')","def label(self):\r\n return self._name","def label_name(self) -> str:\n return pulumi.get(self, \"label_name\")","def label(self) -> str:\n return self._label","def label(self) -> str:\n return self._label","def label(self) -> str:\n return self._label","def label(self) -> str:\n return self._label","def label(self) -> str:\n return self._label","def label(self) -> str:\n return self._label","def label(self) -> str:\n return self._label","def Label(self) -> str:","def first_label(self):\r\n return self.labels.split(',')[0]","def labelName(self):\n if self.isRequired:\n return '%s*' % self.name\n return self.name","def label(self) -> str:\n return self._underlying.label","def get_label(self):\n\n return self._label","def label_name(self) -> pulumi.Input[str]:\n return pulumi.get(self, \"label_name\")","def getDefaultName(self): # real signature unknown; restored from __doc__\n pass","def getDefault():","def GetLabel(self):\n \n return self.label_str","def get_label():\n inp = option_text('Input label name (leave blank for no label):')\n add_to_collected('label', inp)\n OPTIONS['label'] = inp\n return","def GetLabel(self) -> str:\n return self._label","def GetLabel(self):\r\n\r\n return self._label","def get_label(self):\n return self.job[self.label_key]","def get_label(cls) -> str:\n return cls._meta.label_lower.split('.')[-1]","def get_label(settings):","def get_base_label(self):\n return self.function_label","def get_label_name(label):\n\tindex = np.argmax(label)\n\tlabels = ['airplane', 'automobile', 'bird', 'cat', 'deer', 'dog', 'frog', 'horse', 'ship', 'truck']\n\treturn labels[int(index)]","def label(self) -> str: # Enforcing every node defines a label\n pass","def labelit(self, varname):\n \n if not varname:\n return \"\"\n return self.vardict[varname].VariableLabel or varname","def label(self) -> str:\n return self.__parameters.label","def get_default(cls):\n return cls.DESC","def default_name(self):\n return '[' + self.__class__.__name__ + ']'","def id_for_label(self):\n return \"\"","def label(cls) -> str:\n return \"!lobotomy.unknown\"","def to_label(self):\n return self.label","def label(self):\r\n raise NotImplementedError","def get_default(self):\n\n\t\treturn self.__default","def get_current_label(self):\n if self.gt_helper_open:\n return self.gt_helper.get_current_label()\n else:\n return -1","def get_label_id(self) -> Optional[str]:\n\n return None if (self.elements is None or len(self.elements) == 0) else self.elements[0].label_id","def get(cls, key: t.Any, default: t.Optional[t.Any] = None) -> t.Any:\n return cls.__labels__.get(key, default)","def get_label(cls):\r\n return cls._type_name(cls.label)","def label(self):\r\n return self._text","def get_labels(self):\r\n return None","def label(self) -> str:\r\n self._logger.debug(log_message_formatter(\r\n \"get\", f\"{self}\", \"label\"))\r\n return self._name","def _get_label ( self ):\n if self._label is not None:\n return self._label\n return 'Column %d' % (self.index + 1)","def plugin_first_label():\n return \"first\"","def get_name(self):\n return self._label"],"string":"[\n \"def default_label(self) -> str:\\n return self.settings[\\\"default_label\\\"]\",\n \"def Label(self, default=None):\\n return self.data.get('label', default)\",\n \"def Label(self, 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GetLabel(self):\\r\\n\\r\\n return self._label\",\n \"def get_label(self):\\n return self.job[self.label_key]\",\n \"def get_label(cls) -> str:\\n return cls._meta.label_lower.split('.')[-1]\",\n \"def get_label(settings):\",\n \"def get_base_label(self):\\n return self.function_label\",\n \"def get_label_name(label):\\n\\tindex = np.argmax(label)\\n\\tlabels = ['airplane', 'automobile', 'bird', 'cat', 'deer', 'dog', 'frog', 'horse', 'ship', 'truck']\\n\\treturn labels[int(index)]\",\n \"def label(self) -> str: # Enforcing every node defines a label\\n pass\",\n \"def labelit(self, varname):\\n \\n if not varname:\\n return \\\"\\\"\\n return self.vardict[varname].VariableLabel or varname\",\n \"def label(self) -> str:\\n return self.__parameters.label\",\n \"def get_default(cls):\\n return cls.DESC\",\n \"def default_name(self):\\n return '[' + self.__class__.__name__ + ']'\",\n \"def id_for_label(self):\\n return \\\"\\\"\",\n \"def label(cls) -> str:\\n return \\\"!lobotomy.unknown\\\"\",\n \"def to_label(self):\\n return self.label\",\n \"def label(self):\\r\\n raise NotImplementedError\",\n \"def get_default(self):\\n\\n\\t\\treturn self.__default\",\n \"def get_current_label(self):\\n if self.gt_helper_open:\\n return self.gt_helper.get_current_label()\\n else:\\n return -1\",\n \"def get_label_id(self) -> Optional[str]:\\n\\n return None if (self.elements is None or len(self.elements) == 0) else self.elements[0].label_id\",\n \"def get(cls, key: t.Any, default: t.Optional[t.Any] = None) -> t.Any:\\n return cls.__labels__.get(key, default)\",\n \"def get_label(cls):\\r\\n return cls._type_name(cls.label)\",\n \"def label(self):\\r\\n return self._text\",\n \"def get_labels(self):\\r\\n return None\",\n \"def label(self) -> str:\\r\\n self._logger.debug(log_message_formatter(\\r\\n \\\"get\\\", f\\\"{self}\\\", \\\"label\\\"))\\r\\n return self._name\",\n \"def _get_label ( self ):\\n if self._label is not None:\\n return self._label\\n 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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 |
|---|---|---|---|---|---|---|
CQ encodings are independent of speed, so should not be grouped. | def SpeedGroup(self, bitrate):
return 'all' | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def encode(self, seq):",
"def test_decode_qdc(self):\n self.assertEqual(td.qdc(), decoder.decode_qdc(BytesIO(td.qdc(True))))",
"def time_encode(self):\n for ii in range(100):\n for fragment in self.msg.encode_msg(1, 16382):\n pass",
"def decode(self, coded_set):",
"d... | [
"0.6006678",
"0.5760689",
"0.5671513",
"0.55821073",
"0.5502236",
"0.53923804",
"0.53876966",
"0.5364135",
"0.5356846",
"0.5337399",
"0.5313916",
"0.53095436",
"0.5269019",
"0.52463037",
"0.5223725",
"0.51786166",
"0.51786166",
"0.51783174",
"0.5172435",
"0.5169267",
"0.51578... | 0.0 | -1 |
Ensure that goldq and keyq are smaller than fixedq. | def ConfigurationFixups(self, config):
fixed_q_value = config.GetValue('fixed-q')
if int(config.GetValue('gold-q')) > int(fixed_q_value):
config = config.ChangeValue('gold-q', fixed_q_value)
if int(config.GetValue('key-q')) > int(fixed_q_value):
config = config.ChangeValue('key-q', fixed_q_value... | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def blowup(qNew, max_size):\n return (absolute(qNew) > max_size).any()",
"def _sanityCheckKeySizes(other):\n if other.minKeySize < 512:\n raise ValueError(\"minKeySize too small\")\n if other.minKeySize > 16384:\n raise ValueError(\"minKeySize too large\")\n if other... | [
"0.62403035",
"0.6000047",
"0.5806859",
"0.56767833",
"0.55996144",
"0.5595407",
"0.5577116",
"0.5559701",
"0.5514552",
"0.5482072",
"0.54538447",
"0.5435169",
"0.54317313",
"0.5420425",
"0.54120344",
"0.54033726",
"0.53820455",
"0.53723013",
"0.53532654",
"0.5351187",
"0.533... | 0.5455897 | 10 |
Returns a parameter string based on this encoding that has the parameter identified by "name" changed in a way worth testing. If no sensible change is found, returns None. | def _SuggestTweakToName(self, encoding, name):
parameters = encoding.encoder.parameters
value = int(parameters.GetValue(name))
new_value = None
if encoding.result['bitrate'] > encoding.bitrate:
delta = 1
new_value = 63
candidates = range(value + 1, 64)
else:
delta = -1
... | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def get_name(self) -> str:\n # read the original value passed by the command\n name = self.raw_param.get(\"name\")\n\n # this parameter does not need dynamic completion\n # this parameter does not need validation\n return name",
"def getParam(self, params, name):\n retur... | [
"0.622179",
"0.60660815",
"0.6040889",
"0.6040889",
"0.58146137",
"0.5806079",
"0.5795579",
"0.57549846",
"0.5727818",
"0.57237387",
"0.57168627",
"0.5687548",
"0.5619562",
"0.5595139",
"0.556885",
"0.55674666",
"0.55310893",
"0.55302817",
"0.5513008",
"0.551243",
"0.55016464... | 0.57055604 | 11 |
Suggest a tweak based on an encoding result. For fixed QP, suggest increasing minq when bitrate is too high, otherwise suggest decreasing it. If a parameter is already at the limit, go to the next one. | def SuggestTweak(self, encoding):
if not encoding.result:
return None
parameters = self._SuggestTweakToName(encoding, 'fixed-q')
if not parameters:
parameters = self._SuggestTweakToName(encoding, 'gold-q')
if not parameters:
parameters = self._SuggestTweakToName(encoding, 'key-q')
... | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def _SuggestTweakToName(self, encoding, name):\n parameters = encoding.encoder.parameters\n value = int(parameters.GetValue(name))\n new_value = None\n if encoding.result['bitrate'] > encoding.bitrate:\n delta = 1\n new_value = 63\n candidates = range(value + 1, 64)\n else:\n del... | [
"0.78182817",
"0.5859518",
"0.5770075",
"0.5642969",
"0.5607727",
"0.5415742",
"0.52216774",
"0.5206427",
"0.51767814",
"0.5172263",
"0.5101419",
"0.505127",
"0.5038782",
"0.5033367",
"0.49953026",
"0.49904868",
"0.4973912",
"0.49698272",
"0.49614057",
"0.49476328",
"0.493733... | 0.666621 | 1 |
The HttpAgent used to make observations is bound in the constructor. | def agent(self):
return self.__agent | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def __init__(self, agent):\n self.agent = agent",
"def __init__(self, agent: AEA) -> None:\n self._agent = agent\n super().__init__()",
"def __init__(self, agent_name):\n\n self._agent_name = agent_name",
"def __init__(self, gym_env: gym.Env) -> None:\n super().__init__()\n... | [
"0.7351164",
"0.6957303",
"0.6808179",
"0.670577",
"0.6512269",
"0.6495163",
"0.6488134",
"0.6333099",
"0.6304801",
"0.63044655",
"0.62554175",
"0.62005746",
"0.6146556",
"0.61349744",
"0.6131",
"0.61131203",
"0.6107742",
"0.6102221",
"0.6100459",
"0.609825",
"0.6079396",
"... | 0.6539539 | 4 |
Implements helper method to extract observed objects. | def _do_decode_objects(self, content, observation):
decoder = json.JSONDecoder()
try:
doc = decoder.decode(content)
if not isinstance(doc, list):
doc = [doc]
observation.add_all_objects(doc)
except ValueError as ex:
error = 'Invalid JSON in response: %s' % content
loggi... | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def observation(self):\n return {k: observer(self._state)\n for k, observer in self.observers.items()}",
"def _get_observation(self):\n return []",
"def __iter__(self):\n return iter(vars(self.obj))",
"def observe(self) -> dict:\n return OrderedDict((key, o.get()) f... | [
"0.6175112",
"0.61106783",
"0.60204405",
"0.60091937",
"0.5929103",
"0.59056705",
"0.5799155",
"0.5745424",
"0.5730578",
"0.56924415",
"0.56217235",
"0.5579725",
"0.5554065",
"0.55297357",
"0.548012",
"0.5456269",
"0.5448644",
"0.54456645",
"0.5442461",
"0.5431473",
"0.543008... | 0.5350148 | 31 |
Perform the observation using HTTP GET on a path. | def get_url_path(self, path):
self.observer = HttpObjectObserver(self.__agent, path)
observation_builder = jc.ValueObservationVerifierBuilder(
'Get ' + path, strict=self.__strict)
self.verifier_builder.append_verifier_builder(observation_builder)
return observation_builder | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def simulate_get(self, path='/', **kwargs):\n return self.simulate_request('GET', path, **kwargs)",
"def get(self, path):\n return self.request(path, method='GET')",
"def get(self, *path, **data):\n\t\treturn self.request('GET', *path, **data)",
"def simulate_get(self, path='/', **kwargs) -> _R... | [
"0.7288038",
"0.72503275",
"0.7173312",
"0.70527613",
"0.7005011",
"0.69969994",
"0.69610393",
"0.6953752",
"0.6909677",
"0.690561",
"0.6859075",
"0.68457997",
"0.6798055",
"0.67914647",
"0.6736182",
"0.6731656",
"0.6726706",
"0.67003083",
"0.6679551",
"0.66303647",
"0.662056... | 0.6458858 | 29 |
Calibration of the deformable mirror | def m4_calibration(self, commandAmpVector_ForM4Calibration,
nPushPull_ForM4Calibration, maskIndex_ForM4Alignement,
nFrames):
zernike_coef_coma, coma_surface = self._measureComaOnSegmentMask(nFrames)
print(zernike_coef_coma)
self._tt = self._cal.measu... | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def _doCalibration(self):\n self._cmdCalibration(2)",
"def photometric_calibration():\n pass",
"def calibration(self) -> int:",
"def arm_calibration(self):\n self.arm_motor.run_forever(speed_sp=self.MAX_SPEED)\n while not self.touch_sensor.is_pressed:\n time.sleep(0.01)... | [
"0.67941266",
"0.67645663",
"0.65776956",
"0.63947",
"0.6092254",
"0.6069542",
"0.6038276",
"0.59309524",
"0.5876742",
"0.58702165",
"0.58668363",
"0.5824653",
"0.5823095",
"0.58227694",
"0.58188707",
"0.5799465",
"0.5777438",
"0.57686573",
"0.5747901",
"0.57312226",
"0.56957... | 0.0 | -1 |
Fetch the points for a given fish body | def _fish_body_points(cls, fish_num):
# y coordinate is from top of FISH_HEIGHT for a given fish, to bottom of FISH_HEIGHT for a given FISH
left_oval_side = FishTileView.SIZE_MULTIPLIER, \
(fish_num * cls.FISH_HEIGHT) + (cls.FISH_HEIGHT / FishTile.MAX_AMOUNT_FISH)
right... | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def find_body_part(self, body_points, segmentation_image):\n print(\"start the measuring....\")\n body_parts = {}\n shoulders = self.find_shoulders_point(body_points, segmentation_image)\n abdomen = self.find_abdomen_point(body_points)\n chest = self.find_chest_point(body_points)... | [
"0.6347192",
"0.57089156",
"0.5574567",
"0.5518729",
"0.54645497",
"0.5356999",
"0.5321854",
"0.5314515",
"0.53112376",
"0.530583",
"0.52986497",
"0.52113086",
"0.5188562",
"0.51864165",
"0.5180519",
"0.51597506",
"0.5130005",
"0.5108989",
"0.5103025",
"0.50794214",
"0.506578... | 0.64592373 | 0 |
Extracts triples using Stanford CoreNLP OpenIE library via CoreNLPConnector in Java REST API | def openie(text: str) -> List[Tuple[str, str, str]]:
client = env.resolve('servers.java')
verbose.info('Extracting triples using OpenIE at: ' + client['address'], caller=openie)
return requests.get('%s/openie/triples' % client['address'], params={'text': text}).json() | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def extract_triplets(self) -> Iterable[dict]:\n stg_corpus = [txt.strip()+\".\" if txt.strip()[-1]!=\".\" else txt.strip() for txt in self.__corpus__]\n stg_corpus = ' '.join(self.__corpus__)\n\n with StanfordOpenIE() as client:\n triples_corpus = client.annotate(stg_corpus)\n\n ... | [
"0.61961323",
"0.6138691",
"0.55915344",
"0.55132866",
"0.540919",
"0.5384903",
"0.5370869",
"0.5334106",
"0.5277659",
"0.5261717",
"0.5182233",
"0.51706254",
"0.5137889",
"0.50874555",
"0.50837165",
"0.50661874",
"0.5056941",
"0.50455534",
"0.4996862",
"0.4972436",
"0.496881... | 0.6626662 | 0 |
readPDF opens pdf as array to be hundled by opevCV. {{{ When the pdf file has multiple pages, automatically pick first page. | def readPDF(infile, width, grayscale=True):
#To open a pdf file.
imgAllPages = convert_from_path(infile, dpi=100)
img = imgAllPages[0] #pick first page up
img = np.asarray(img)
img = img.take([1,2,0], axis=2) #change color ch. (GBR -> RGB)
#To scale image to designated width.
if img... | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def read_pdf(exp_type='c'):\n # shape file path\n file_name = pdffile_exptype[exp_type]\n file_path_name = os.path.join('data', 'pdf', file_name)\n pdf_file = resource_filename(__name__, file_path_name)\n\n return h5py.File(pdf_file, 'r')",
"def parse_pdf(url):\n pdf_data = urllib2.urlopen(Requ... | [
"0.6721945",
"0.6396926",
"0.63947225",
"0.62901264",
"0.6155976",
"0.6124074",
"0.61084336",
"0.608346",
"0.59989536",
"0.5985878",
"0.5940284",
"0.59238076",
"0.5911455",
"0.58851826",
"0.58835346",
"0.5843061",
"0.58381116",
"0.5762712",
"0.5747169",
"0.5674462",
"0.565503... | 0.6609024 | 1 |
correctMisalign corrects misalignment/misscale of a image {{{ by using two markers on the image. | def correctMisalign(img, marker, center, compus, scope=100):
markerCenter = np.asarray(marker.shape)//2
guide = np.asarray([center, compus])
landmark = np.zeros(guide.shape)
#To run template matching to finder markers
result = cv2.matchTemplate(img, marker, 0)
result = (1-result/np.max(res... | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def align_preprocessed(self, img):\n aligner = FaceAligner(self.args.wing_path, self.args.lm_path, self.args.img_size)\n return aligner.align(img)",
"def resetAlignmentCenter(self):\n cent = self.TiltSeries_._TiltAlignmentParas.cent\n imdimX = self.TiltSeries_._imdimX\n imdimY ... | [
"0.5492849",
"0.5472743",
"0.5366377",
"0.53595704",
"0.53412116",
"0.53201115",
"0.5234939",
"0.5230496",
"0.519625",
"0.5172563",
"0.51371306",
"0.5108179",
"0.5102144",
"0.50905186",
"0.5074283",
"0.50719476",
"0.5055099",
"0.49876216",
"0.4970037",
"0.49581602",
"0.495739... | 0.63359916 | 0 |
checkAnswer checks answers according to {{{ the coordinate in answerList. | def checkAnswer(img, marker, answerList, threshold=110):
markerCenter = np.asarray(marker.shape)//2
width = img.shape[1]
height = img.shape[0]
#To run template matching to find answer markers
resultFinal = cv2.matchTemplate(imgModified, marker, 0)
resultFinal = (1-resultFinal/np.max(resultFina... | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def check_for_answer(list_name, answer):\n\n for item in list_name:\n if item['answer'] == answer:\n return True",
"def check_answer():\r\n global choice, answer_choice, tries, submit_button, total, point, file, split, detail\r\n # getting the answer being submitted and comparing them ... | [
"0.7170723",
"0.67453265",
"0.638104",
"0.6376527",
"0.63755095",
"0.6354558",
"0.6348346",
"0.6266409",
"0.6145809",
"0.6018026",
"0.5969312",
"0.59654385",
"0.58950686",
"0.58929944",
"0.58739173",
"0.57799274",
"0.5771715",
"0.5734414",
"0.57037175",
"0.570233",
"0.5677227... | 0.5611689 | 29 |
Send NDB query result to serialize function if single result, else loop through the query result and serialize records one by one | def filter_results(qry):
result = []
# check if qry is a list (multiple records) or not (single record)
if type(qry) != list:
record = make_ndb_return_data_json_serializable(qry)
return(record)
for q in qry:
result.append(make_ndb_return_data_json_serializable(q))
return(r... | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def query(self,query):\n if self.data is not None:\n qData = cPickle.loads(query)\n results = self.handleQuery(qData)\n qResults = cPickle.dumps(results)\n else:\n results = None\n qResults = cPickle.dumps(results)\n return qResults",
"d... | [
"0.6215104",
"0.5766029",
"0.5742681",
"0.57376796",
"0.56941646",
"0.5663792",
"0.5624949",
"0.5558396",
"0.5548996",
"0.55322397",
"0.55096585",
"0.5504129",
"0.54955757",
"0.54347366",
"0.5416373",
"0.5400906",
"0.536496",
"0.5363302",
"0.53601617",
"0.5337387",
"0.5325546... | 0.5694889 | 4 |
Build a new dict so that the data can be JSON serializable | def make_ndb_return_data_json_serializable(data):
result = data.to_dict()
record = {}
# Populate the new dict with JSON serializiable values
for key in result.iterkeys():
if isinstance(result[key], datetime.datetime):
record[key] = result[key].isoformat()
continue
... | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def as_dict(self) -> dict[str, Any]:\n return {\n \"type\": self.type,\n \"timestamp\": self.timestamp,\n \"data\": self.data or {},\n }",
"def to_dict(self) -> Dict:\n obj = super().to_dict()\n obj['values'] = self.values\n return obj",
"def ... | [
"0.68644804",
"0.67345625",
"0.66815794",
"0.6653987",
"0.661725",
"0.65883857",
"0.6576611",
"0.65699446",
"0.65687215",
"0.6557158",
"0.6544177",
"0.65357584",
"0.6521566",
"0.6508762",
"0.649658",
"0.64861465",
"0.6482451",
"0.6482451",
"0.6480924",
"0.64330506",
"0.642630... | 0.6570601 | 7 |
tv_loss. Deprecated. Please use tensorflow total_variation loss implementation. | def tv_loss(x, name='tv_loss'):
raise NotImplementedError("Please use tensorflow total_variation loss.") | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def tv_loss(input: th.Tensor):\n input = tf.pad(input, (0, 1, 0, 1), \"replicate\")\n x_diff = input[..., :-1, 1:] - input[..., :-1, :-1]\n y_diff = input[..., 1:, :-1] - input[..., :-1, :-1]\n return (x_diff ** 2 + y_diff ** 2).mean([1, 2, 3])",
"def tv_loss(img, tv_weight):\n # Your implementati... | [
"0.71811527",
"0.7058403",
"0.63139117",
"0.62708676",
"0.6116712",
"0.60516834",
"0.6036121",
"0.5978986",
"0.5977184",
"0.59507954",
"0.5913045",
"0.591241",
"0.58793354",
"0.5870999",
"0.5853943",
"0.580963",
"0.5757218",
"0.5752254",
"0.5751082",
"0.57493937",
"0.5742276"... | 0.8830987 | 0 |
Runs loadData from LoadDataModel. Runs also previewData from this class. Shows error warning in GUI if data load does not work. | def loadPreviewData(self):
# parameters for data load from GUI
self.loadDataModel.pathToDataSet = self.entryPath.get()
self.loadDataModel.firstRowIsTitle = bool(self.checkVarRow.get())
self.loadDataModel.firstColIsRowNbr = bool(self.checkVarCol.get())
# if entry field is empty, s... | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def loadPreviewDataforClassification(self):\n # parameters for data load from GUI\n self.loadDataModel.pathToDataSet = self.entryPath.get()\n self.loadDataModel.firstRowIsTitle = bool(self.checkVarRow.get())\n self.loadDataModel.firstColIsRowNbr = bool(self.checkVarCol.get())\n #... | [
"0.7363575",
"0.6815484",
"0.6728596",
"0.65464854",
"0.64301383",
"0.6418219",
"0.6348947",
"0.6315146",
"0.62988985",
"0.6275391",
"0.6239956",
"0.616186",
"0.6159784",
"0.61524343",
"0.6125845",
"0.6116381",
"0.6072893",
"0.6040148",
"0.603637",
"0.6027977",
"0.5955419",
... | 0.7560283 | 0 |
Runs loadData from LoadDataModel. Runs also previewData from this class. Shows error warning in GUI if data load does not work. | def loadPreviewDataforClassification(self):
# parameters for data load from GUI
self.loadDataModel.pathToDataSet = self.entryPath.get()
self.loadDataModel.firstRowIsTitle = bool(self.checkVarRow.get())
self.loadDataModel.firstColIsRowNbr = bool(self.checkVarCol.get())
# if entry ... | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def loadPreviewData(self):\n # parameters for data load from GUI\n self.loadDataModel.pathToDataSet = self.entryPath.get()\n self.loadDataModel.firstRowIsTitle = bool(self.checkVarRow.get())\n self.loadDataModel.firstColIsRowNbr = bool(self.checkVarCol.get())\n # if entry field i... | [
"0.75603956",
"0.68159103",
"0.6730189",
"0.6547364",
"0.6430838",
"0.64175487",
"0.63499826",
"0.6315992",
"0.6298599",
"0.6275572",
"0.62402666",
"0.61616594",
"0.61607134",
"0.61529624",
"0.61271083",
"0.61156225",
"0.60730803",
"0.60393345",
"0.60365355",
"0.6028048",
"0.... | 0.7363604 | 1 |
Set initials and try to set django user before saving | def save(self, *args, **kwargs):
self._set_first_initial()
self._set_user()
super(AbstractHuman, self).save(*args, **kwargs) | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def _set_user(self):\n\n if '' in (self.last_name, self.first_name):\n return\n\n self._set_first_initial()\n\n User = get_user_model()\n try:\n self.user = User.objects.get(\n models.Q(last_name__iexact=self.last_name),\n models.Q(fir... | [
"0.7399037",
"0.6818464",
"0.67360806",
"0.6703886",
"0.6615366",
"0.65040535",
"0.64916104",
"0.6349639",
"0.6300928",
"0.6296971",
"0.6286593",
"0.6262981",
"0.62472683",
"0.62465847",
"0.6244291",
"0.62277496",
"0.6220768",
"0.62102824",
"0.62016064",
"0.61989367",
"0.6194... | 0.7171682 | 1 |
Set author first name initial | def _set_first_initial(self, force=False):
if self.first_initial and not force:
return
self.first_initial = ' '.join([c[0] for c in self.first_name.split()]) | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def author_name(self, author_name):\n\n self._author_name = author_name",
"def first_name(self, name):\n self._first_name = name",
"def first_name_and_initial(self):\n return u\"{} {}\".format(self.pref_first_name(), self.last_name[0])",
"def _get_first_author(self):\n if not len(... | [
"0.7323572",
"0.71771324",
"0.70817894",
"0.7054424",
"0.7022617",
"0.7003764",
"0.6919942",
"0.6896514",
"0.6873318",
"0.68670076",
"0.68632287",
"0.6860131",
"0.68508995",
"0.6757529",
"0.6736017",
"0.67150563",
"0.67113507",
"0.67113507",
"0.670267",
"0.6662656",
"0.666265... | 0.6821622 | 13 |
Return author formated full name, e.g. Maupetit J | def get_formatted_name(self):
return '%s %s' % (self.last_name, self.first_initial) | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def get_author_full_name(self, obj):\n return obj.author.get_full_name()",
"def author(self) -> str:\n return pulumi.get(self, \"author\")",
"def get_short_name(self):\n split = self.name.split(' - ')\n # author, year, and first couple of words of paper title\n return \"{} ({... | [
"0.82585573",
"0.77515906",
"0.7608914",
"0.75679374",
"0.75590986",
"0.736285",
"0.73591524",
"0.73569363",
"0.73169523",
"0.73169523",
"0.7259127",
"0.72563523",
"0.7227451",
"0.7207076",
"0.7200211",
"0.7180581",
"0.71690756",
"0.7159358",
"0.714601",
"0.70617265",
"0.7046... | 0.0 | -1 |
Look for local django user based on human name | def _set_user(self):
if '' in (self.last_name, self.first_name):
return
self._set_first_initial()
User = get_user_model()
try:
self.user = User.objects.get(
models.Q(last_name__iexact=self.last_name),
models.Q(first_name__iexact=... | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def usernameFind(self):\r\n return self.username()",
"def find_user(user_name):\n return User.find_by_user_name(user_name)",
"def _user_from_name_or_email(username_or_email):\r\n username_or_email = strip_if_string(username_or_email)\r\n\r\n if '@' in username_or_email:\r\n return User.o... | [
"0.6567877",
"0.65613717",
"0.6539821",
"0.64463395",
"0.6416921",
"0.63903224",
"0.6369646",
"0.6352932",
"0.6349323",
"0.6345395",
"0.634484",
"0.63361585",
"0.63224024",
"0.63217324",
"0.62943405",
"0.62787086",
"0.6234388",
"0.6217391",
"0.6210545",
"0.6195787",
"0.618779... | 0.5708707 | 99 |
Format entry with a default bibliography style | def __unicode__(self):
# Authors
author_str = '%(last_name)s %(first_initial)s'
s = ', '.join([author_str % a.__dict__ for a in self.get_authors()])
s = ', and '.join(s.rsplit(', ', 1)) # last author case
s += ', '
# Title
s += '"%(title)s", ' % self.__dict__
... | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def format_bib_entry(e: BibDocument):\n if e.bibtex is not None:\n b = e.bibtex\n s = fix_string(b.get('title', b.get('ID', '?'))) + '\\n'\n s += format_author(b.get('author', b.get('editor', '?'))) + ' ' + b.get('year', '')\n if len(e.filepaths) > 0:\n s += ' [PDF]'\n ... | [
"0.7411567",
"0.69221437",
"0.6217476",
"0.61077625",
"0.61064523",
"0.60556054",
"0.5987748",
"0.592009",
"0.590796",
"0.5846889",
"0.58392334",
"0.5790335",
"0.57841504",
"0.575899",
"0.57537985",
"0.57427317",
"0.57351536",
"0.5686892",
"0.5663739",
"0.5643611",
"0.5634517... | 0.0 | -1 |
Get this entry first author | def _get_first_author(self):
if not len(self.get_authors()):
return ''
return self.get_authors()[0] | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def get_author(self):\n return self.author",
"def get_author(self):\n return self.author",
"def author(self):\n return self._data.get('author', None)",
"def author(self):\n return self._author",
"def author(self):\n return self._author",
"def author(self):\n retu... | [
"0.8229262",
"0.8229262",
"0.8032657",
"0.7940489",
"0.7940489",
"0.7940489",
"0.78599817",
"0.7840174",
"0.7684184",
"0.7680722",
"0.7656797",
"0.7656797",
"0.75249034",
"0.7519448",
"0.748793",
"0.7446496",
"0.73990786",
"0.7372625",
"0.7222093",
"0.7180607",
"0.7167988",
... | 0.88237596 | 0 |
Get this entry last author | def _get_last_author(self):
if not len(self.get_authors()):
return ''
return self.get_authors()[-1] | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def get_author(self):\n return self.author",
"def get_author(self):\n return self.author",
"def author(self):\n return self._author",
"def author(self):\n return self._author",
"def author(self):\n return self._author",
"def _get_first_author(self):\n if not len(... | [
"0.77854884",
"0.77854884",
"0.76113564",
"0.76113564",
"0.76113564",
"0.7610408",
"0.75873405",
"0.7484349",
"0.7426977",
"0.73916334",
"0.73916334",
"0.73881865",
"0.72666234",
"0.7250214",
"0.7246593",
"0.71829695",
"0.7149919",
"0.70887244",
"0.70449716",
"0.7027704",
"0.... | 0.876842 | 0 |
Get ordered authors list Note that authorentryrank_set is ordered as expected while the authors queryset is not (M2M with a through case). | def get_authors(self):
return [aer.author for aer in self.authorentryrank_set.all()] | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def author_articles(self):\n return ArticlePage.objects.live().filter(author=self).order_by('-date')",
"def authors(self):\n authors = [\n n.people for n in self.pymbake_person_relationship.all()\n ]\n\n return authors",
"def authors(self):\n user_ids = set(r.autho... | [
"0.67668843",
"0.67430836",
"0.6685066",
"0.6684926",
"0.6559511",
"0.65458703",
"0.65159404",
"0.64794177",
"0.64563096",
"0.64382637",
"0.63679224",
"0.63222766",
"0.6306415",
"0.63006264",
"0.6292419",
"0.6286021",
"0.6283298",
"0.6283292",
"0.6231312",
"0.62176764",
"0.62... | 0.8204957 | 0 |
This is the function which is called from the main method. Redirection handling is done here based on returned values from other functions | def smart_client(uri):
original_uri = uri
http = "1.0"
use_https = check_https(uri)
redir = True
count = 0
u_p = True
while redir:
response_status, \
response_headers, \
highest_http, \
redirect, \
location, \
new_https, ... | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def main():\n inputs = []\n files = set()\n\n args = parseArguments()\n\n # Configure the stdout logger\n logging.basicConfig(format=\"%(filename)s: %(levelname)s: %(message)s\",\n level=logging.DEBUG)\n\n try:\n # Create a list of input format objects\n for gcsv in args.gcs... | [
"0.5755369",
"0.56499326",
"0.5628064",
"0.56229633",
"0.560184",
"0.55084866",
"0.5496683",
"0.5468766",
"0.54514056",
"0.54514056",
"0.53364265",
"0.53364265",
"0.53364265",
"0.53364265",
"0.53364265",
"0.53364265",
"0.53364265",
"0.53364265",
"0.53364265",
"0.53364265",
"0... | 0.0 | -1 |
This method determines the highest http a server can support. This is done over http or https, depending on the parameter. HTTP2 is checked but never used to exchange messages. | def get_highest_http(uri, https, upgrade=True):
highest_http = '1.0'
response_status = ""
redirect = False
location = ""
port = 443 if https else 80
use_https = https
use_upgrade = upgrade
host, path = get_host(uri)
i_p = check_host_name(host)
request_line = "GET "+ path +" HTTP/... | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def get_http_protocol(self):\n if self.cfg.ssl:\n return \"https\"\n else:\n return \"http\"",
"def get_protocol():\n if https():\n protocol = 'https'\n else:\n protocol = 'http'\n return protocol",
"def supports_http_1_1():",
"def get_protocol(self)... | [
"0.6752688",
"0.6329223",
"0.62902206",
"0.6241211",
"0.6171372",
"0.6168764",
"0.61381495",
"0.5726957",
"0.5725487",
"0.57185143",
"0.5554565",
"0.5525395",
"0.55057085",
"0.5468806",
"0.5437173",
"0.543616",
"0.5421312",
"0.5418585",
"0.5397975",
"0.5352422",
"0.5352422",
... | 0.69072604 | 0 |
Passes CL args to smart_client() | def main():
parser = argparse.ArgumentParser()
parser.add_argument("URI")
args = parser.parse_args()
smart_client(args.URI) | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def __init__(self):\n super(BaseCLIClient, self).__init__()\n self.client_type = \"cli\"\n self.set_content_type('raw')\n self.set_accept_type('raw')\n self.execution_type = \"sync\"",
"def client():",
"def __init__(self, args):\n ClientPlugin.__init__(self)\n ... | [
"0.58076566",
"0.5776437",
"0.56957334",
"0.5592278",
"0.5579138",
"0.55543005",
"0.5536592",
"0.55259174",
"0.55259174",
"0.55040467",
"0.55021024",
"0.549863",
"0.5486501",
"0.5452488",
"0.5451785",
"0.54476345",
"0.54291",
"0.53953785",
"0.53953785",
"0.53887117",
"0.53653... | 0.6573514 | 0 |
Used by pipeline. Necessary when dealing with multiple input ports | def FillInputPortInformation(self, port, info):
# all are tables so no need to check port
info.Set(self.INPUT_REQUIRED_DATA_TYPE(), "vtkTable")
return 1 | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def processInputs(self):",
"def __init__(__self__, *,\n from_port: pulumi.Input[int],\n to_port: pulumi.Input[int]):\n pulumi.set(__self__, \"from_port\", from_port)\n pulumi.set(__self__, \"to_port\", to_port)",
"def get_number_of_output_ports(self):\n retu... | [
"0.64459944",
"0.625526",
"0.6248811",
"0.62121683",
"0.6181329",
"0.6100554",
"0.60476756",
"0.5961336",
"0.5931539",
"0.5907093",
"0.58822894",
"0.58620906",
"0.58542335",
"0.5853043",
"0.58156896",
"0.58087647",
"0.58058053",
"0.5786404",
"0.5725807",
"0.5700029",
"0.56195... | 0.0 | -1 |
Used by pipeline to generate output | def RequestData(self, request, inInfo, outInfo):
# Inputs from different ports:
pdi0 = self.GetInputData(inInfo, 0, 0)
pdi1 = self.GetInputData(inInfo, 1, 0)
pdo = self.GetOutputData(outInfo, 0)
pdo.DeepCopy(pdi0)
# Get number of rows
nrows = pdi0.GetNumberOfRow... | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def _generate_output(self):\n raise NotImplementedError()",
"def write_output(self):",
"def outputs(self):\n pass",
"def _populate_output(self):\n pass",
"def collect_output(self):\n pass",
"def collect_output(self):\n pass",
"def output(self):\r\n self.logic ( )\r... | [
"0.7615837",
"0.73535603",
"0.72271895",
"0.7095047",
"0.6970138",
"0.6970138",
"0.69010055",
"0.68070275",
"0.67311907",
"0.6730409",
"0.66625345",
"0.66403157",
"0.6595656",
"0.65816605",
"0.6573563",
"0.6573563",
"0.6573563",
"0.6573563",
"0.65275764",
"0.6520451",
"0.6491... | 0.0 | -1 |
Internal helper to perfrom the reshape | def _Reshape(self, pdi, pdo):
# Get number of columns
cols = pdi.GetNumberOfColumns()
# Get number of rows
rows = pdi.GetColumn(0).GetNumberOfTuples()
if len(self.__names) is not 0:
num = len(self.__names)
if num < self.__ncols:
for i in r... | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def reshape(data):\n return K.reshape(x=data, shape=(K.shape(data)[0], 1, reshape_size))",
"def _reshape(self, arr: np.ndarray) -> np.ndarray:\n return arr.reshape(self.TileHeight.value, self.TileWidth.value, self.bands,)",
"def reshape(self, *shape):\n return F.Reshape.apply(self, sha... | [
"0.7144847",
"0.68330306",
"0.6810042",
"0.663358",
"0.65176374",
"0.6502568",
"0.6428562",
"0.6419787",
"0.6401154",
"0.63567775",
"0.6333695",
"0.63271326",
"0.6304994",
"0.6303932",
"0.6302932",
"0.62272435",
"0.62223023",
"0.61971766",
"0.6173259",
"0.61662495",
"0.615951... | 0.60213363 | 28 |
Set names using a semicolon (;) seperated string or a list of strings | def SetNames(self, names):
# parse the names (a semicolon seperated list of names)
if isinstance(names, str):
names = names.split(';')
if self.__names != names:
self.__names = names
self.Modified() | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def names(self, names):\n\n self._names = names",
"def setName(self,value):\n assert value == None or type(value) == str, repr(value)+' is not a valid name'\n self._name = value",
"def setnames(self, *args, **kwargs):\n return _coordsys.coordsys_setnames(self, *args, **kwargs)",
"... | [
"0.58341724",
"0.5792466",
"0.57665694",
"0.57385945",
"0.57164675",
"0.5659365",
"0.56351304",
"0.5599404",
"0.5597629",
"0.55682",
"0.55471784",
"0.5483656",
"0.53453225",
"0.5342401",
"0.53067094",
"0.52203083",
"0.5213452",
"0.5210753",
"0.52076817",
"0.52076817",
"0.5205... | 0.74215704 | 0 |
Use to append a name to the list of data array names for the output table. | def AddName(self, name):
self.__names.append(name)
self.Modified() | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def append(self, narray, name):\n if narray is NoneArray:\n # if NoneArray, nothing to do.\n return\n\n added = False\n if not isinstance(narray, VTKCompositeDataArray): # Scalar input\n for ds in self.DataSet:\n ds.GetAttributes(self.Association... | [
"0.5988727",
"0.58012074",
"0.56854564",
"0.56484556",
"0.5633105",
"0.5623627",
"0.55826586",
"0.5524948",
"0.54935294",
"0.5478021",
"0.5474341",
"0.54661304",
"0.54569197",
"0.5453789",
"0.54530656",
"0.5418614",
"0.5380218",
"0.5361656",
"0.5359868",
"0.5359625",
"0.53534... | 0.0 | -1 |
Set the number of columns for the output ``vtkTable`` | def SetNumberOfColumns(self, ncols):
if isinstance(ncols, float):
ncols = int(ncols)
if self.__ncols != ncols:
self.__ncols = ncols
self.Modified() | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def setNumColumns(self, num):\n ExportDialog.numColumns = num",
"def setNumCols(serDisplay, cols):\n cmd = array.array('B', (124,0))\n if (cols == 20):\n cmd[1] = 3\n else:\n if (cols != 16):\n print(\"WARNING: num columns of %d not valid - must be 16 or 20. Defaulting t... | [
"0.7280288",
"0.6517732",
"0.634845",
"0.62785757",
"0.6208003",
"0.61394155",
"0.61232775",
"0.61086184",
"0.6093149",
"0.60815024",
"0.6042228",
"0.59893984",
"0.597266",
"0.5961764",
"0.5887591",
"0.58124113",
"0.5785969",
"0.5768652",
"0.57523006",
"0.5723587",
"0.5680898... | 0.6582668 | 1 |
Set the number of rows for the output ``vtkTable`` | def SetNumberOfRows(self, nrows):
if isinstance(nrows, float):
nrows = int(nrows)
if self.__nrows != nrows:
self.__nrows = nrows
self.Modified() | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def set_number_of_rows(self, number_of_rows):\n self.set_value_into_input_field(self.number_of_rows_inputbox_locator, number_of_rows, True)\n global bulk_add_number_of_rows\n bulk_add_number_of_rows = int(number_of_rows)",
"def setNumRows(serDisplay, rows):\n cmd = array.array('B', (124,0... | [
"0.69697976",
"0.69400305",
"0.67426425",
"0.6337014",
"0.62214696",
"0.6137355",
"0.6128068",
"0.6114174",
"0.61094636",
"0.60945934",
"0.59596753",
"0.59596753",
"0.59561414",
"0.5952081",
"0.59400713",
"0.5908973",
"0.5877149",
"0.585046",
"0.5849349",
"0.5849035",
"0.5816... | 0.6433899 | 3 |
Set the reshape order (``'C'`` of ``'F'``) | def SetOrder(self, order):
if self.__order != order:
self.__order = order
self.Modified() | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def output_reshape(ct):\n return np.moveaxis(ct, 1, -1)",
"def _optimizeshape(shape):\n shape.sort()\n if ORDER == 'C':\n shape[:] = shape[::-1]",
"def change_orientation(self):\n self.shape = self.shape.T",
"def reorderEigenspaces(self, *order):\n order = self._reor... | [
"0.56006664",
"0.5536408",
"0.534123",
"0.52923334",
"0.5150682",
"0.5138542",
"0.5106645",
"0.50824875",
"0.5078592",
"0.5048254",
"0.5010744",
"0.49845436",
"0.4983413",
"0.49833164",
"0.49573484",
"0.4957227",
"0.49509206",
"0.49305683",
"0.49204037",
"0.48952043",
"0.4872... | 0.0 | -1 |
Used by pipeline to generate output | def RequestData(self, request, inInfo, outInfo):
# Inputs from different ports:
pdi = self.GetInputData(inInfo, 0, 0)
table = self.GetOutputData(outInfo, 0)
# Note user has to select a single array to save out
field, name = self.__inputArray[0], self.__inputArray[1]
vtk... | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def _generate_output(self):\n raise NotImplementedError()",
"def write_output(self):",
"def outputs(self):\n pass",
"def _populate_output(self):\n pass",
"def collect_output(self):\n pass",
"def collect_output(self):\n pass",
"def output(self):\r\n self.logic ( )\r... | [
"0.76157534",
"0.7353986",
"0.72273624",
"0.70952",
"0.69702864",
"0.69702864",
"0.6901308",
"0.68081224",
"0.67318356",
"0.6731069",
"0.6662458",
"0.6640859",
"0.6595182",
"0.65812975",
"0.65740347",
"0.65740347",
"0.65740347",
"0.65740347",
"0.6528857",
"0.6521222",
"0.6492... | 0.0 | -1 |
Used to set the input array(s) | def SetInputArrayToProcess(self, idx, port, connection, field, name):
if self.__inputArray[0] != field:
self.__inputArray[0] = field
self.Modified()
if self.__inputArray[1] != name:
self.__inputArray[1] = name
self.Modified()
return 1 | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def array(self, src) -> None:\n self.set_array(src)",
"def set_params(self, arr):\n self.arr = arr",
"def setUniformValueArray(self, *__args): # real signature unknown; restored from __doc__ with multiple overloads\n pass",
"def array(self, array):\n\n self._array = array",
"def... | [
"0.7241959",
"0.7219714",
"0.68609273",
"0.6672674",
"0.6511627",
"0.64334154",
"0.64316237",
"0.6391575",
"0.6360614",
"0.6334181",
"0.62941873",
"0.62733847",
"0.6226461",
"0.62186706",
"0.6136797",
"0.6098933",
"0.6078015",
"0.6034682",
"0.6034682",
"0.6034682",
"0.6034682... | 0.66184026 | 6 |
Used to set the input array(s) | def SetInputArrayToProcess(self, idx, port, connection, field, name):
if self.__inputArray[0] != field:
self.__inputArray[0] = field
self.Modified()
if self.__inputArray[1] != name:
self.__inputArray[1] = name
self.Modified()
return 1 | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def array(self, src) -> None:\n self.set_array(src)",
"def set_params(self, arr):\n self.arr = arr",
"def setUniformValueArray(self, *__args): # real signature unknown; restored from __doc__ with multiple overloads\n pass",
"def array(self, array):\n\n self._array = array",
"def... | [
"0.7240696",
"0.721781",
"0.6860336",
"0.6669498",
"0.65123177",
"0.6434082",
"0.64325017",
"0.63909864",
"0.6359503",
"0.63352495",
"0.62932074",
"0.6274183",
"0.6225836",
"0.621846",
"0.61331207",
"0.60983515",
"0.60760087",
"0.6032754",
"0.6032754",
"0.6032754",
"0.6032754... | 0.6619196 | 5 |
Creates new schema or creates new version and updates next_version of previous | def create(self, validated_data):
if validated_data['version'] > 1: # Viewset's get_serializer() will always add 'version'
with transaction.atomic():
current = RecordSchema.objects.get(record_type=validated_data['record_type'],
next... | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"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('Attempt... | [
"0.7260953",
"0.67752755",
"0.6296328",
"0.6292026",
"0.6222661",
"0.61200744",
"0.6024229",
"0.5977015",
"0.5931262",
"0.59245014",
"0.5897773",
"0.58668256",
"0.5809811",
"0.5774801",
"0.5773382",
"0.5700059",
"0.56862545",
"0.5684552",
"0.5684552",
"0.56727153",
"0.5633165... | 0.56990176 | 16 |
Due to the way our merging works, if this schema has any defaults they will clobber potentially useful values in the backing manifest. 227 | def test_does_not_contain_defaults():
to_process = [(CONFIG_JSON_SCHEMA, ())]
while to_process:
schema, route = to_process.pop()
# Check this value
if isinstance(schema, dict):
if 'default' in schema:
raise AssertionError(
'Unexpected defau... | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def merge_schema(self, schema):\n self.validate_schema(schema)\n\n if self.exclusive is False:\n self.exclusive = schema.exclusive\n\n if self.default is None:\n self.default = schema.default",
"def _add_to_schema(self, new: dict):\n self._defaults.update(new)\n ... | [
"0.6092501",
"0.5738518",
"0.5725758",
"0.5545568",
"0.5352203",
"0.5305392",
"0.52949214",
"0.5261467",
"0.5245125",
"0.52413476",
"0.52342093",
"0.52113444",
"0.52074134",
"0.51651245",
"0.51092094",
"0.51080793",
"0.5104977",
"0.5098712",
"0.5086971",
"0.5076587",
"0.50538... | 0.51478 | 14 |
Test case for networking_project_network_create | def test_networking_project_network_create(self):
pass | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def test_03_network_create(self):\n # Validate the following\n # 1. Create a project.\n # 2. Add virtual/direct network resource to the project. User shared\n # network resource for the project\n # 3. Verify any number of Project level Virtual/Direct networks can be\n #... | [
"0.88057435",
"0.83907145",
"0.8311333",
"0.82338715",
"0.8144531",
"0.8073008",
"0.766816",
"0.7591707",
"0.75781333",
"0.75702184",
"0.7456846",
"0.7370858",
"0.7303496",
"0.72163486",
"0.71597195",
"0.7158633",
"0.71257806",
"0.7089581",
"0.69936407",
"0.69620895",
"0.6961... | 0.95674586 | 0 |
Test case for networking_project_network_delete | def test_networking_project_network_delete(self):
pass | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def test_delete_network(self):\n pass",
"def test_networking_project_network_tag_delete(self):\n pass",
"def test_delete__network(self):\n arglist = [\n '--network',\n self.projects[0].id,\n ]\n verifylist = [\n ('service', 'network'),\n ... | [
"0.8734149",
"0.84788615",
"0.8282214",
"0.7911755",
"0.74620235",
"0.74408305",
"0.7362307",
"0.7338039",
"0.72818667",
"0.7161988",
"0.7161988",
"0.71514744",
"0.7089159",
"0.7023079",
"0.70029515",
"0.6991699",
"0.6976583",
"0.68999213",
"0.687359",
"0.6855459",
"0.6838732... | 0.9514106 | 0 |
Test case for networking_project_network_event_get | def test_networking_project_network_event_get(self):
pass | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def test_networking_project_network_event_list(self):\n pass",
"def test_networking_project_network_get(self):\n pass",
"def test_networking_project_network_service_get(self):\n pass",
"def test_get_network(self):\n pass",
"def test_networking_project_network_tag_get(self):\n ... | [
"0.82577205",
"0.79113317",
"0.76856494",
"0.7209927",
"0.7185073",
"0.6887082",
"0.66650337",
"0.66104114",
"0.6588003",
"0.6416295",
"0.64085007",
"0.6167186",
"0.60606116",
"0.595175",
"0.59372723",
"0.58573383",
"0.5803691",
"0.57594985",
"0.5739596",
"0.56881183",
"0.563... | 0.9450455 | 0 |
Test case for networking_project_network_event_list | def test_networking_project_network_event_list(self):
pass | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def test_networking_project_network_event_get(self):\n pass",
"def test_networking_project_network_list(self):\n pass",
"def test_networking_project_network_service_list(self):\n pass",
"def test_networking_project_network_tag_list(self):\n pass",
"def test_networking_project_ne... | [
"0.8299006",
"0.81361306",
"0.7848326",
"0.76107645",
"0.70611805",
"0.68074656",
"0.67305243",
"0.6706901",
"0.64687085",
"0.6259645",
"0.62270015",
"0.6207792",
"0.619327",
"0.6122696",
"0.6068232",
"0.6055929",
"0.60044557",
"0.6001083",
"0.59810936",
"0.5941543",
"0.59415... | 0.95086074 | 0 |
Test case for networking_project_network_get | def test_networking_project_network_get(self):
pass | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def test_networking_project_network_service_get(self):\n pass",
"def test_get_network(self):\n pass",
"def test_networking_project_network_list(self):\n pass",
"def test_networking_project_network_create(self):\n pass",
"def test_networking_project_network_event_get(self):\n ... | [
"0.8632432",
"0.8508769",
"0.8449543",
"0.80295455",
"0.79398155",
"0.78251946",
"0.7792246",
"0.7602196",
"0.7560636",
"0.7488109",
"0.7394283",
"0.71187145",
"0.7039374",
"0.70385844",
"0.70047534",
"0.68530726",
"0.6823287",
"0.681199",
"0.6767339",
"0.67336947",
"0.653972... | 0.9436339 | 0 |
Test case for networking_project_network_list | def test_networking_project_network_list(self):
pass | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def test_networking_project_network_service_list(self):\n pass",
"def test_networking_project_network_get(self):\n pass",
"def test_networking_project_network_event_list(self):\n pass",
"def test_networking_project_network_tag_list(self):\n pass",
"def test_networking_project_ne... | [
"0.8585402",
"0.8372369",
"0.82831913",
"0.8275119",
"0.7899982",
"0.76960224",
"0.755038",
"0.74486506",
"0.7311336",
"0.7203733",
"0.7135034",
"0.708045",
"0.70350575",
"0.70243955",
"0.6986017",
"0.69722104",
"0.6854558",
"0.6749359",
"0.67465365",
"0.6731198",
"0.67179805... | 0.94664603 | 0 |
Test case for networking_project_network_service_get | def test_networking_project_network_service_get(self):
pass | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def test_networking_project_network_get(self):\n pass",
"def test_networking_project_network_service_list(self):\n pass",
"def test_networking_project_network_event_get(self):\n pass",
"def test_get_network(self):\n pass",
"def test_networking_project_network_list(self):\n ... | [
"0.8669004",
"0.83204156",
"0.77490056",
"0.76448596",
"0.7637649",
"0.7419113",
"0.7296355",
"0.72419596",
"0.69356287",
"0.68189347",
"0.672783",
"0.65830845",
"0.6519098",
"0.651618",
"0.6364173",
"0.6340621",
"0.62232184",
"0.62168264",
"0.6199433",
"0.6182356",
"0.616484... | 0.9413343 | 0 |
Test case for networking_project_network_service_list | def test_networking_project_network_service_list(self):
pass | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def test_networking_project_network_list(self):\n pass",
"def test_networking_project_network_service_get(self):\n pass",
"def test_networking_project_network_event_list(self):\n pass",
"def test_networking_project_network_tag_list(self):\n pass",
"def test_networking_project_ne... | [
"0.8442342",
"0.8200452",
"0.79131794",
"0.765145",
"0.7384875",
"0.7134209",
"0.68193364",
"0.67868644",
"0.6717133",
"0.6681138",
"0.6677454",
"0.65660775",
"0.6522769",
"0.64892286",
"0.64892286",
"0.6452811",
"0.6409245",
"0.6376114",
"0.6276882",
"0.62717396",
"0.6192692... | 0.94697046 | 0 |
Test case for networking_project_network_tag_create | def test_networking_project_network_tag_create(self):
pass | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def test_networking_project_network_tag_put(self):\n pass",
"def test_networking_project_network_tag_get(self):\n pass",
"def test_networking_project_network_create(self):\n pass",
"def test_networking_project_network_tag_list(self):\n pass",
"def test_networking_project_network... | [
"0.8415681",
"0.8278862",
"0.827278",
"0.8097047",
"0.7693032",
"0.71981835",
"0.716799",
"0.7052762",
"0.7006196",
"0.68853045",
"0.6870068",
"0.677154",
"0.6724007",
"0.65254825",
"0.6511818",
"0.6463387",
"0.6381934",
"0.6359911",
"0.63259894",
"0.6287146",
"0.62654346",
... | 0.95820755 | 0 |
Test case for networking_project_network_tag_delete | def test_networking_project_network_tag_delete(self):
pass | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def test_networking_project_network_delete(self):\n pass",
"def test_delete_network(self):\n pass",
"def test_networking_project_network_tag_create(self):\n pass",
"def test_delete__network(self):\n arglist = [\n '--network',\n self.projects[0].id,\n ]... | [
"0.85864913",
"0.79074013",
"0.73118925",
"0.72815436",
"0.726035",
"0.70726025",
"0.7015217",
"0.69911855",
"0.6827487",
"0.6809548",
"0.6754534",
"0.6743542",
"0.67119163",
"0.66424894",
"0.66424894",
"0.65924495",
"0.65480644",
"0.6546871",
"0.65056103",
"0.645967",
"0.639... | 0.9531553 | 0 |
Test case for networking_project_network_tag_get | def test_networking_project_network_tag_get(self):
pass | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def test_networking_project_network_tag_list(self):\n pass",
"def test_networking_project_network_tag_create(self):\n pass",
"def test_networking_project_network_get(self):\n pass",
"def test_networking_project_network_tag_put(self):\n pass",
"def test_networking_project_network... | [
"0.81374985",
"0.7996989",
"0.7775014",
"0.7694906",
"0.7312093",
"0.71594137",
"0.7070286",
"0.69871694",
"0.68383783",
"0.67855865",
"0.6660819",
"0.64519733",
"0.62692016",
"0.6119776",
"0.60387516",
"0.59296036",
"0.59206605",
"0.5916272",
"0.59021133",
"0.5760429",
"0.55... | 0.9510173 | 0 |
Test case for networking_project_network_tag_list | def test_networking_project_network_tag_list(self):
pass | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def test_networking_project_network_tag_get(self):\n pass",
"def test_networking_project_network_list(self):\n pass",
"def test_networking_project_network_tag_create(self):\n pass",
"def test_networking_project_network_service_list(self):\n pass",
"def test_networking_project_ne... | [
"0.8407284",
"0.82673967",
"0.8170092",
"0.7694319",
"0.7642346",
"0.7619595",
"0.73755604",
"0.7235602",
"0.7030399",
"0.6982695",
"0.6671765",
"0.66642684",
"0.65270686",
"0.64974034",
"0.6470683",
"0.64488524",
"0.6414432",
"0.6403719",
"0.6192123",
"0.611372",
"0.6030102"... | 0.94968116 | 0 |
Test case for networking_project_network_tag_put | def test_networking_project_network_tag_put(self):
pass | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def test_networking_project_network_tag_create(self):\n pass",
"def test_networking_project_network_tag_get(self):\n pass",
"def test_networking_project_network_tag_delete(self):\n pass",
"def test_networking_project_network_tag_list(self):\n pass",
"def test_aws_service_api_vm_... | [
"0.8162548",
"0.7632924",
"0.7529343",
"0.73087597",
"0.69838846",
"0.67863727",
"0.67096",
"0.6460837",
"0.62958515",
"0.6271951",
"0.6260058",
"0.6142768",
"0.6081592",
"0.60224813",
"0.6007177",
"0.59064764",
"0.5894923",
"0.58403486",
"0.5809004",
"0.5711",
"0.5707141",
... | 0.9519759 | 0 |
Test case for networking_project_network_update | def test_networking_project_network_update(self):
pass | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def test_networking_project_network_get(self):\n pass",
"def test_networking_project_network_create(self):\n pass",
"def test_networking_project_network_list(self):\n pass",
"def test_networking_project_network_tag_put(self):\n pass",
"def test_networking_project_network_delete(... | [
"0.76064444",
"0.74541",
"0.7369837",
"0.69817394",
"0.6963334",
"0.6901382",
"0.69006497",
"0.682339",
"0.6764553",
"0.67619634",
"0.6689635",
"0.6650327",
"0.658505",
"0.658505",
"0.65725785",
"0.65587854",
"0.651825",
"0.6489945",
"0.6476724",
"0.6430147",
"0.6420457",
"... | 0.94251615 | 0 |
Implements FTRL with rescaled gradients and linearithmic regularizer. | def __init__(self, params):
defaults = {}
super(Regralizer, self).__init__(params, defaults) | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def apply_regularization(self, w, loss, gradient, regularization, lambda_, m):\n if regularization == 'l2':\n loss += lambda_ / (2 * m) * np.squeeze(w.T.dot(w))\n gradient += lambda_ / m * w\n elif regularization == 'l1':\n loss += lambda_ / (2 * m) * np.sum(np.abs(w)... | [
"0.6043861",
"0.59705585",
"0.5945837",
"0.5887921",
"0.585111",
"0.5839",
"0.5834819",
"0.582763",
"0.5718077",
"0.57092035",
"0.5691352",
"0.5689245",
"0.5647996",
"0.5641125",
"0.5615204",
"0.56046516",
"0.55929756",
"0.55704355",
"0.55497617",
"0.553497",
"0.5526624",
"... | 0.0 | -1 |
Validate that required_cols are in self.frame | def validate(self):
super().validate()
frame = getattr(self, 'frame', None)
if frame is None:
raise ValueError('Missing columns %s since no frame' % ', '.join(
self.required_cols))
cols = set(list(self.frame))
missing = sorted(self.required_cols - cols... | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def cols_valid(self,\n df: pd.DataFrame,\n req_cols: set) -> bool:\n missing_cols = req_cols.difference(df.columns)\n\n if len(missing_cols) > 0:\n logging.error(f\"{missing_cols} columns required but missing\")\n return False\n\n return Tru... | [
"0.7897676",
"0.7564741",
"0.7366434",
"0.7349003",
"0.7240517",
"0.7036314",
"0.70306194",
"0.69790363",
"0.68797225",
"0.6794021",
"0.66460437",
"0.6629376",
"0.66237783",
"0.65535456",
"0.65107137",
"0.6479833",
"0.64632124",
"0.646052",
"0.6442498",
"0.64324087",
"0.64135... | 0.8550042 | 0 |
Validate that required_cols are in self.frame | def validate(self):
super().validate()
frame = getattr(self, 'frame', None)
if frame is None:
raise ValueError('Missing columns %s since no frame' % ', '.join(
[c[0] for c in self.col_regexps]))
for col_name, c_re in self.col_regexps:
if col_name n... | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def validate(self):\n super().validate()\n frame = getattr(self, 'frame', None)\n if frame is None:\n raise ValueError('Missing columns %s since no frame' % ', '.join(\n self.required_cols))\n cols = set(list(self.frame))\n missing = sorted(self.required... | [
"0.8549901",
"0.7896959",
"0.75633276",
"0.7367074",
"0.7239867",
"0.70361763",
"0.70298463",
"0.6977291",
"0.68799907",
"0.67940336",
"0.6645067",
"0.6627117",
"0.6622096",
"0.65550065",
"0.650822",
"0.64810985",
"0.6462903",
"0.6459277",
"0.6440878",
"0.64331204",
"0.641338... | 0.73491883 | 4 |
Create an S3Backend instance. | def get_backend(cls, backend=None):
return backend if backend else aws.S3Backend(
category=cls.default_category, bucket_name=cls.default_bucket) | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def make_s3(sitename):\n return s3.S3(sitename)",
"def s3_create_bucket(self):\n self.conn.create_bucket(DEFAULT_BUCKET_NAME)",
"def get_s3_client():\n return boto3.resource('s3')",
"def s3_bucket(s3_server): # pylint: disable=redefined-outer-name\n client = s3_server.get_s3_client()\n bu... | [
"0.67337704",
"0.67020965",
"0.64005727",
"0.6341852",
"0.6122109",
"0.60842115",
"0.6067415",
"0.6063343",
"0.6048431",
"0.6045403",
"0.604531",
"0.60147995",
"0.5989435",
"0.596969",
"0.5956802",
"0.5870337",
"0.5861566",
"0.5846471",
"0.58433914",
"0.58393073",
"0.5824821"... | 0.6487786 | 2 |
This function returns train and test data and amount of different classes | def get_data(numbers):
numbers = numbers
n_classes = len(numbers)
z = zipfile.ZipFile('lab3/mnist.pkl.zip', 'r')
k = z.extract('mnist.pkl') # Извлечь файл из архива
with open(k, 'rb') as f:
train_set, _, test_set = pickle.load(f, encoding="bytes")
x_train = train_set[0]
x_te... | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def num_training_examples(self):",
"def get_num_classes(self):",
"def num_classes():\n return NUM_CLASSES",
"def get_num_classes(dataset: str):\n if dataset == \"imagenet\" or dataset == \"kitti\":\n return 1000\n elif dataset == \"cifar10\" or dataset == \"mnist\" or dataset == \"fashion_mni... | [
"0.75494504",
"0.7527431",
"0.7394811",
"0.72946763",
"0.69094735",
"0.6897226",
"0.6870488",
"0.67837375",
"0.668422",
"0.6665446",
"0.6654703",
"0.6577999",
"0.65736794",
"0.65736794",
"0.6543151",
"0.6542344",
"0.6535379",
"0.6532243",
"0.6501701",
"0.6497568",
"0.6497568"... | 0.6272706 | 40 |
This function calculates init p(k|x) and p(k) | def init_parameters(data, n_classes):
N = data.shape[0]
K = n_classes
D = data.shape[1]
init_p_k_x = np.zeros((K,))
matrix = np.random.rand(N, K)
matrix /= matrix.sum(axis=1)[:, np.newaxis]
for i in range(K):
init_p_k_x[i] = sum(matrix[:, i]) / matrix.shape[0]
p_k_x_... | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def initialize_parameters(X: np.ndarray, k):\n idx = np.random.choice(X.shape[0], k, replace=False)\n mu = X[idx]\n sigma = compute_sigma(X, mu)\n pi = np.ones(k) / k\n return mu, sigma, pi",
"def _compute_parameters(self, p, k):\n for i in range(self._.d + 1):\n p[0, i, i] = k[i... | [
"0.66798216",
"0.6609475",
"0.66066784",
"0.6502079",
"0.64455104",
"0.642912",
"0.641136",
"0.6377091",
"0.61414003",
"0.6138719",
"0.612936",
"0.6117516",
"0.610951",
"0.59979415",
"0.5979644",
"0.5978313",
"0.5893618",
"0.58717704",
"0.5866976",
"0.58425736",
"0.5841052",
... | 0.61180997 | 11 |
This function performs Maximization Step of the EMalgorithm | def m_step(data, p_k_x):
N = data.shape[0]
D = data.shape[1]
K = p_k_x.shape[1]
Nk = np.sum(p_k_x, axis=0)
p_i_j_new = np.empty((K, D))
for k in range(K):
p_i_j_new[k] = np.sum(p_k_x[:, k][:, np.newaxis] * data, axis=0) / Nk[k]
return Nk / N, p_i_j_new | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def run_em(self, maxiter=400, tol=1e-4, verbose=True, regularization=0.0):\n self.means = self.means.T\n\n L = None\n for i in xrange(maxiter):\n newL = self._expectation()\n if i == 0 and verbose:\n print(\"Initial NLL =\", -newL)\n\n self._maxi... | [
"0.6474297",
"0.64715934",
"0.63503337",
"0.6318457",
"0.62608415",
"0.6227241",
"0.60013664",
"0.5978136",
"0.58662164",
"0.5829126",
"0.5817829",
"0.58153695",
"0.5807976",
"0.5776075",
"0.5764646",
"0.57643855",
"0.57546365",
"0.5753092",
"0.5744375",
"0.57314605",
"0.5702... | 0.0 | -1 |
This function performs Expectation Step of the EMalgorithm | def e_step(data, p_k, p_i_j):
N = data.shape[0]
K = p_i_j.shape[0]
p_k_x = np.empty((N, K))
for i in range(N):
for k in range(K):
p_k_x[i, k] = np.prod((p_i_j[k] ** data[i]) * ((1 - p_i_j[k]) ** (1 - data[i])))
p_k_x *= p_k
p_k_x /= p_k_x.sum(axis=1)[:, np.newaxis... | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def __em(self, x):\n _, log_resp = self._e_step(x)\n\n pi, mu, var = self._m_step(x, log_resp)\n\n self.__update_pi(pi)\n self.__update_mu(mu)\n self.__update_var(var)",
"def e_step(self):\n # update VMF probabilities (Equation (3))\n logP = np.dot(self.features, ... | [
"0.63739145",
"0.62474275",
"0.61847925",
"0.613631",
"0.6098325",
"0.6009017",
"0.6005586",
"0.5960616",
"0.59229577",
"0.5879187",
"0.5849416",
"0.58455557",
"0.5838416",
"0.58154655",
"0.5804324",
"0.5774335",
"0.57677954",
"0.57566774",
"0.5727094",
"0.5719774",
"0.571922... | 0.0 | -1 |
This function performs EMalgorithm | def bernoulli_em_algorithm(data, n_classes, max_iters=100):
N = data.shape[0]
D = data.shape[1]
K = n_classes
# initializing
init_p_k_x, init_p_k = init_parameters(data, K)
p_k_x = e_step(data, init_p_k, init_p_k_x)
for i in range(max_iters):
# perform M Step
p_k... | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def em_mog(X, k, max_iter=20):\n\n # Initialize variables\n mu = None\n sigma = [np.eye(X.shape[1]) for i in range(k)]\n phi = np.ones([k,])/k\n ll_prev = float('inf')\n start = time.time()\n\n #######################################################################\n # TODO: ... | [
"0.6568218",
"0.65053093",
"0.6457587",
"0.640457",
"0.6350182",
"0.6315953",
"0.6213009",
"0.61301386",
"0.6121796",
"0.60581744",
"0.604855",
"0.60376555",
"0.59813815",
"0.5941793",
"0.59308505",
"0.5916092",
"0.5899629",
"0.58466226",
"0.5839338",
"0.58202195",
"0.5780968... | 0.6138721 | 7 |
This functions calculates predictions for test data and calculates mean squared error | def predict(data, labels, p_k, p_i_j, numbers):
pred = e_step(data, p_k, p_i_j).argmax(axis=1)
for j in range(len(numbers)):
for i in range(len(pred)):
if pred[i] == j:
pred[i] = numbers[j]
print('metrics:', metrics.classification_report(labels, pred))
return p... | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def run_analyses(y_predict_train, y_train, y_predict, y_test):\n # calculate metrics\n _, training_error = output_error(y_predict_train, y_train)\n (precision, recall, f1, _), testing_error = output_error(y_predict, y_test)\n \n # print out metrics\n print 'Average Precision:',... | [
"0.74832344",
"0.73980325",
"0.72532654",
"0.71508014",
"0.71508014",
"0.7138027",
"0.71137744",
"0.710535",
"0.70594305",
"0.70295507",
"0.70254856",
"0.69821894",
"0.69704026",
"0.695812",
"0.6956206",
"0.69433063",
"0.68771243",
"0.68710256",
"0.6835331",
"0.68330276",
"0.... | 0.0 | -1 |
read calibration file returns > dict calibration matrices as 44 numpy arrays | def read_calib_file(filename):
calib = {}
"""calib1 = np.eye(4,4)
calib1[0:3, 3] = [0.27, 0.0, -0.08]
print(calib1)
calib.append(calib1)
calib2 = np.eye(4,4)
calib2[0:3, 3] = [0.27, -0.51, -0.08]
print(calib2)
calib.append(calib2)
calib3 = np.eye(4,4)
calib3[0:3, 3] = [0.27... | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def read_calib_file(self, filepath):\n data = {}\n with open(filepath, 'r') as f:\n for line in f.readlines():\n values = line.split()\n # The only non-float values in these files are dates, which\n # we don't care about anyway\n ... | [
"0.72249043",
"0.7208568",
"0.7208568",
"0.70669484",
"0.70456314",
"0.7041981",
"0.69114566",
"0.68411756",
"0.6807544",
"0.6807203",
"0.6558858",
"0.6471239",
"0.6442276",
"0.63747907",
"0.6323744",
"0.62730527",
"0.61834854",
"0.61613894",
"0.61245453",
"0.6097307",
"0.607... | 0.792746 | 0 |
Convolutional model with dropout for EMNIST experiments. | def create_conv_dropout_model(num_classes: int, seed: Optional[int] = None):
data_format = 'channels_last'
if seed is not None:
tf.random.set_seed(seed)
model = tf.keras.models.Sequential([
tf.keras.layers.Conv2D(
32,
kernel_size=(3, 3),
activation='relu',
data_f... | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def dropout_keras_model():\n\n inputs = tf.keras.Input(shape=(10, 10, 3,))\n x = tf.keras.layers.Conv2D(16, (3, 3))(inputs)\n x = tf.keras.layers.Dropout(rate=.4)(x)\n x = tf.identity(x)\n x = tf.keras.layers.Conv2D(8, (2, 2))(x)\n x = tf.keras.layers.Flatten()(x)\n outputs = tf.keras.layers.D... | [
"0.6676039",
"0.6343185",
"0.6315899",
"0.63006896",
"0.6291182",
"0.62849694",
"0.6277511",
"0.6254841",
"0.6247444",
"0.62473774",
"0.6212494",
"0.6209054",
"0.6208818",
"0.62064856",
"0.6202018",
"0.612881",
"0.6127414",
"0.61184245",
"0.61167794",
"0.6106968",
"0.6086234"... | 0.61856663 | 15 |
Parse the model description string to a keras model builder. | def _parse_model(model: str, num_classes: int) -> Callable[[], tf.keras.Model]:
if model == 'cnn':
keras_model_builder = functools.partial(
create_conv_dropout_model, num_classes=num_classes)
elif model in ['resnet18', 'resnet34', 'resnet50', 'resnet101', 'resnet152']:
keras_model_builder = functool... | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def parse_model_description(model_description: str) -> ModelDescription:\n root = ET.fromstring(model_description)\n\n defaults = _get_attribute_default_values()\n\n # mandatory p.32\n fmi_version = root.get(\"fmiVersion\")\n model_name = root.get(\"modelName\")\n guid = root.get(\"guid\")\n #... | [
"0.6326366",
"0.6091344",
"0.5876037",
"0.56606436",
"0.5658026",
"0.5654233",
"0.562259",
"0.5619373",
"0.56061447",
"0.55622476",
"0.55564666",
"0.5539365",
"0.55305934",
"0.5526856",
"0.5487436",
"0.5468888",
"0.5461886",
"0.5455671",
"0.54494077",
"0.54338133",
"0.5423484... | 0.6771992 | 0 |
Load (unsplitted) EMNIST(like) clientdata from sql database. | def load_custom_emnist_client_data(sql_database: str) -> ClientData:
if sql_database is None:
raise ValueError('sql_database cannot be None.')
return sql_client_data_utils.load_parsed_sql_client_data(
sql_database, element_spec=_ELEMENT_SPEC) | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def load_custom_cifar_client_data(sql_database: str) -> ClientData:\n\n if sql_database is None:\n raise ValueError('sql_database cannot be None.')\n\n return sql_client_data_utils.load_parsed_sql_client_data(\n sql_database, element_spec=_ELEMENT_SPEC)",
"def load_data(client):\n codes = [\"DUB\", ... | [
"0.62082094",
"0.60316384",
"0.5761554",
"0.57444966",
"0.5691772",
"0.5639299",
"0.5636918",
"0.5616825",
"0.5611436",
"0.5602569",
"0.55277586",
"0.5519456",
"0.5478773",
"0.543038",
"0.5419184",
"0.54132307",
"0.5410894",
"0.5410576",
"0.5405717",
"0.5401371",
"0.5387977",... | 0.702729 | 0 |
Create a preprocessing function for EMNIST client datasets. | def _create_preprocess_fn(
num_epochs: int,
batch_size: int,
merge_case: bool,
shuffle_buffer_size: int = emnist_dataset.MAX_CLIENT_DATASET_SIZE,
use_cache: bool = True,
use_prefetch: bool = True,
) -> Callable[[tf.data.Dataset], tf.data.Dataset]:
@tf.function
def merge_mapping(elem):
or... | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def get_preprocess_fn(**preprocessing_kwargs):\n\n def _preprocess_fn(data):\n \"\"\"The preprocessing function that is returned.\"\"\"\n\n # Validate input\n if not isinstance(data, dict) or 'image' not in data:\n raise ValueError('Argument `data` must be a dictionary, '\n 'no... | [
"0.6911369",
"0.6907225",
"0.67146105",
"0.64246845",
"0.63995093",
"0.638671",
"0.63772017",
"0.63574916",
"0.62886465",
"0.6257666",
"0.62195",
"0.60264665",
"0.60262877",
"0.60111964",
"0.60054433",
"0.5993835",
"0.59816",
"0.59801525",
"0.59755504",
"0.5967095",
"0.596003... | 0.7066413 | 0 |
Configuring federated runner spec. | def build_federated_runner_spec(self) -> training_specs.RunnerSpecFederated:
task_spec = self._task_spec
train_preprocess_fn = _create_preprocess_fn(
num_epochs=task_spec.client_epochs_per_round,
batch_size=task_spec.client_batch_size,
merge_case=self._merge_case,
use_cache=Tru... | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def build_federated_runner_spec(self) -> training_specs.RunnerSpecFederated:\n task_spec = self._task_spec\n\n train_preprocess_fn = _create_preprocess_fn(\n num_epochs=task_spec.client_epochs_per_round,\n batch_size=task_spec.client_batch_size,\n use_cache=True,\n use_prefetch=Tr... | [
"0.66514647",
"0.65894425",
"0.60085166",
"0.59400094",
"0.5837415",
"0.5833026",
"0.5818086",
"0.57228833",
"0.57128286",
"0.56866115",
"0.56454456",
"0.56344074",
"0.5603153",
"0.5552686",
"0.5537055",
"0.55300355",
"0.55255353",
"0.5486335",
"0.5477406",
"0.5473784",
"0.54... | 0.66257405 | 1 |
Configuring centralized runner spec. | def build_centralized_runner_spec(
self) -> training_specs.RunnerSpecCentralized:
task_spec = self._task_spec
train_preprocess_fn = _create_preprocess_fn(
num_epochs=1,
batch_size=task_spec.batch_size,
merge_case=self._merge_case,
shuffle_buffer_size=task_spec.centralized... | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def runner_setup():\n runner = ClassicRunner()\n yield runner",
"def test_configurator(self):\n runner = Runner(YamlManifest(manifest))\n run1 = runner.run(JobOptions(resource=\"test1\"))\n assert not run1.unexpectedAbort, run1.unexpectedAbort.getStackTrace()\n assert len(run1.w... | [
"0.71298325",
"0.6979565",
"0.6711335",
"0.6575954",
"0.6558287",
"0.6379455",
"0.636857",
"0.636857",
"0.636857",
"0.6353171",
"0.6317362",
"0.6305636",
"0.6305636",
"0.62795645",
"0.6226063",
"0.61880255",
"0.61236566",
"0.61038077",
"0.6095097",
"0.6086364",
"0.60816413",
... | 0.5579558 | 88 |
Configures federated training for the EMNIST character recognition task. This method will load and preprocess datasets and construct a model used for the task. It then uses `iterative_process_builder` to create an iterative process compatible with `tff.simulation.run_training_process`. | def configure_training_federated(
task_spec: training_specs.TaskSpecFederated,
*, # Caller passes below args by name.
model: str = 'resnet18',
only_digits: bool = False,
merge_case: bool = False,
) -> training_specs.RunnerSpecFederated:
return _EmnistCharacterTask(
task_spec,
model=mo... | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def _train_model(\n self,\n dataset: DatasetEntity,\n ):\n logger.info(\"init data cfg.\")\n self._data_cfg = ConfigDict(data=ConfigDict())\n\n for cfg_key, subset in zip(\n [\"train\", \"val\", \"unlabeled\"],\n [Subset.TRAINING, Subset.VALIDATION, Subse... | [
"0.61181545",
"0.6045724",
"0.6024364",
"0.6009437",
"0.5953014",
"0.59526104",
"0.5929882",
"0.5899533",
"0.58728266",
"0.58654255",
"0.57695735",
"0.5762787",
"0.5755617",
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"0.5687636",
"0.5658742",
"0.5657223",
"0.56548303",
"0.56238604",
"0.56198394",
"0.56055... | 0.61471665 | 0 |
Configures centralized training for the EMNIST character recognition task. | def configure_training_centralized(
task_spec: training_specs.TaskSpecCentralized,
*, # Caller passes below args by name.
model: str = 'resnet18',
only_digits: bool = False,
merge_case: bool = False,
) -> training_specs.RunnerSpecCentralized:
return _EmnistCharacterTask(
task_spec,
mo... | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def set_training_parameters(\n self,\n config: ConfigDict,\n len_train: int,\n len_test: int,\n ):\n self.configure_steps(config, len_train, len_test)\n self.configure_reporting(config)\n self.configure_training_functions(config)",
"def train_start(self):\n ... | [
"0.63909596",
"0.6319854",
"0.6294421",
"0.62634546",
"0.62572944",
"0.62403774",
"0.6204174",
"0.6202169",
"0.620016",
"0.6191305",
"0.6153793",
"0.6125302",
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"0.6086183",
"0.60679245",
"0.6048397",
"0.60145044",
"0.6010601",
"0.5994686",
"0.5976073... | 0.65846765 | 0 |
Fit model that predicts return of credit | def fit_model():
global _HOME_OWNERSHIP
_HOME_OWNERSHIP = {x: i for i, x in enumerate(["rent", "own", "mortgage", "other"])}
df = pd.read_csv(os.path.join(settings.BASE_DIR, "LoanStats3a.csv"), skiprows=1).head(5000)
df = df[df.apply(is_poor_coverage, axis=1)]
df['year_issued'] = df.issue_d.apply(la... | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def fit(self, X):",
"def predict():\n model = LinearRegression().fit(input_data[['x']], input_data['y'])\n future_vals = [[20], [21], [22]]\n return None",
"def fit(self, X_raw, y_made_claim, y_claims_amount):\n\n # YOUR CODE HERE\n\n # Remember to include a line similar to the one below... | [
"0.6555756",
"0.6489909",
"0.64893115",
"0.6393043",
"0.6281668",
"0.6277158",
"0.62597495",
"0.6257928",
"0.62528986",
"0.6236069",
"0.62204945",
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"0.6123758",
"0.61215025",
"0.61070174",
"0.60761875",
"0.60761875",
"0.60761875",
"0.6063148",
"0.6063... | 0.66583115 | 0 |
Return a list of note objects in major | def mgChordMajor(value):
chord = [MgNote(value), MgNote(value) + 4, MgNote(value) + 7]
return chord | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def listNotes() -> list:\n list_of_notes = []\n for note in Note.objects.all():\n list_of_notes.append({\n 'uuid': note.uuid, 'title': note.title,\n 'author': note.author, 'body': note.body, 'created_at': localtime(note.created_at)\n })\n return list_of_notes",
"def n... | [
"0.69472075",
"0.6502322",
"0.64504087",
"0.63504153",
"0.6300085",
"0.62833136",
"0.6271847",
"0.62041575",
"0.6133791",
"0.61144036",
"0.60960543",
"0.605202",
"0.6051364",
"0.60231435",
"0.6019179",
"0.6019179",
"0.60149145",
"0.59323066",
"0.5914525",
"0.59088063",
"0.590... | 0.0 | -1 |
Return a list of note objects in minor | def mgChordMinor(value):
chord = [MgNote(value), MgNote(value) + 3, MgNote(value)+7]
return chord | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def listNotes() -> list:\n list_of_notes = []\n for note in Note.objects.all():\n list_of_notes.append({\n 'uuid': note.uuid, 'title': note.title,\n 'author': note.author, 'body': note.body, 'created_at': localtime(note.created_at)\n })\n return list_of_notes",
"def n... | [
"0.6403214",
"0.63809764",
"0.631014",
"0.62889194",
"0.61292934",
"0.61097884",
"0.605849",
"0.6053666",
"0.6009318",
"0.5971191",
"0.593564",
"0.59113747",
"0.5908884",
"0.5889423",
"0.5875394",
"0.5820836",
"0.5792839",
"0.5787869",
"0.57424015",
"0.5723736",
"0.5721369",
... | 0.0 | -1 |
Return a list of note objects in diminished | def mgChordDiminished(value):
chord = [MgNote(value), MgNote(value) + 3, MgNote(value) + 6]
return chord | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def get_note():\n\n return Note.query.all()",
"def notes(self):\n return reapy.NoteList(self)",
"def listNotes() -> list:\n list_of_notes = []\n for note in Note.objects.all():\n list_of_notes.append({\n 'uuid': note.uuid, 'title': note.title,\n 'author': note.autho... | [
"0.6918545",
"0.68457854",
"0.67542857",
"0.6423082",
"0.6351363",
"0.633306",
"0.6201348",
"0.6194495",
"0.6162774",
"0.6150949",
"0.61312294",
"0.6117048",
"0.60784066",
"0.60784066",
"0.602897",
"0.59813625",
"0.59791195",
"0.59717655",
"0.59624857",
"0.593649",
"0.590595"... | 0.0 | -1 |
Return a list of note objects in augmented | def mgChordAugmented(value):
chord = [MgNote(value), MgNote(value) + 4, MgNote(value) + 8]
return chord | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def augment(self):\n for n in self.notes:\n n.augment()",
"def notes(self):\n return reapy.NoteList(self)",
"def add_notes(self, notes):\n if hasattr(notes, \"notes\"):\n for x in notes.notes:\n self.add_note(x)\n return self.notes\n e... | [
"0.7549341",
"0.6924999",
"0.64609313",
"0.6448983",
"0.6341835",
"0.6265768",
"0.6224044",
"0.61404175",
"0.6124106",
"0.6078764",
"0.60639954",
"0.6008974",
"0.6008974",
"0.60029846",
"0.59637725",
"0.59542704",
"0.5905439",
"0.5901639",
"0.58900833",
"0.58588296",
"0.58077... | 0.0 | -1 |
Return a list of note based on chord | def mgChord(value, chord):
ret = None
if chord == 'M':
ret = mgChordMajor(value)
elif chord == 'm':
ret = mgChordMinor(value)
elif chord == 'dim':
ret = mgChordDiminished(value)
elif chord == 'aug':
ret = mgChordAugmented(value)
return ret | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def get_chord(key: str, starting_note: int, chord: str) -> List[int]:\n assert key in constants.NOTES_FOR_KEY, f\"Invalid key: {key}.\"\n assert (\n starting_note in constants.NOTES_FOR_KEY[key]\n ), f\"Note {starting_note} not in key {key}.\"\n assert chord in constants.STEPS_FOR_CHORD, f\"Inva... | [
"0.74840385",
"0.71133006",
"0.6987371",
"0.6944393",
"0.69076145",
"0.662363",
"0.6594826",
"0.6532851",
"0.641009",
"0.62176156",
"0.6167714",
"0.61373204",
"0.61218643",
"0.6079255",
"0.60533977",
"0.59837556",
"0.5976758",
"0.5965469",
"0.5926441",
"0.5900762",
"0.5892764... | 0.5303932 | 75 |
Random rhythm and notes based on time | def randMelody(self, value, chord):
self.randRhythm()
self.randNotes(value, chord) | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def randNotes(self, value, chord):\n aChord = mgChord(value, chord)\n self.data = []\n for i in range(len(self.rhythm)):\n note = random.choice(aChord).copy()\n note.setDuration(self.rhythm[i])\n self.data.append(note)",
"def create_melody(inst, chord_progres... | [
"0.70728934",
"0.6706153",
"0.6540487",
"0.6391081",
"0.6068149",
"0.60672534",
"0.60280246",
"0.60105956",
"0.59780055",
"0.5953066",
"0.59486765",
"0.5922868",
"0.57505494",
"0.5716557",
"0.5710636",
"0.57026637",
"0.5684732",
"0.56817174",
"0.5633969",
"0.5596766",
"0.5560... | 0.72257495 | 0 |
Generate random rhythm, based on chord It must have rhythm before | def randNotes(self, value, chord):
aChord = mgChord(value, chord)
self.data = []
for i in range(len(self.rhythm)):
note = random.choice(aChord).copy()
note.setDuration(self.rhythm[i])
self.data.append(note) | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def randMelody(self, value, chord):\n self.randRhythm()\n self.randNotes(value, chord)",
"def get_rdm_note(chord, last_note):\r\n \r\n if random.randint(1,10) <= 6:\r\n # chord note\r\n return random.choice(CHORDS[chord]) \r\n else:\r\n # scale note \r\n n ... | [
"0.7600951",
"0.6710161",
"0.6659425",
"0.6366649",
"0.6244833",
"0.59357905",
"0.59273636",
"0.5922246",
"0.58458304",
"0.5795843",
"0.5778329",
"0.5752907",
"0.57455",
"0.5733294",
"0.5724642",
"0.57119113",
"0.57089704",
"0.5679657",
"0.5665693",
"0.5636343",
"0.55583066",... | 0.70778203 | 1 |
Get the duration remain, within this bar list, values of rhythm int | def durationRemain(self, l=None):
if l is None:
l = self.rhythm
full = float(self.time.upper)/self.time.lower
s = 0
for i in range(len(l)):
s += 1.0 / l[i]
return full - s | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def getDuration(self):\n return (self._get_int('duration'), self._attributes.getDivisions())",
"def get_dur(self):\n return [char.get_dur() for char in self.string]",
"def duration(self):\r\n return self.t2 - self.t1",
"def duration(self) -> float:\n return self.delta_t * len(self)",
... | [
"0.65641594",
"0.65380937",
"0.6458796",
"0.64535576",
"0.64535576",
"0.6451906",
"0.6380517",
"0.6314581",
"0.62721646",
"0.625763",
"0.6241156",
"0.6210288",
"0.6162527",
"0.61441207",
"0.6135919",
"0.6135919",
"0.6128094",
"0.61246526",
"0.6111688",
"0.6027541",
"0.6024031... | 0.6915621 | 0 |
Check a SIMULATED phylogeny for consistency with its backbone source tree and a taxonomy. The SIMULATED phylogeny should have been generated by the tact_add_taxa script. All phylogenies should be in Newick format. | def main(simulated, backbone, taxonomy, output, cores, chunksize):
pool = multiprocessing.Pool(processes=cores)
click.echo("Using %d parallel cores" % cores, err=True)
taxonomy = dendropy.Tree.get_from_path(taxonomy, schema="newick")
tn = taxonomy.taxon_namespace
click.echo("Taxonomy OK", err=True)
... | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def test_calc_shared_phylotypes(self):\r\n\r\n observed = calc_shared_phylotypes(self.biom_as_string)\r\n expected = \"\"\"\\tS1\\tS2\\tS3\r\nS1\\t5\\t2\\t3\r\nS2\\t2\\t2\\t1\r\nS3\\t3\\t1\\t3\\n\"\"\"\r\n self.assertEqual(observed, expected)",
"def test_check_tree_exact_match(self):\r\n\r\n... | [
"0.5643234",
"0.5496658",
"0.5485725",
"0.54545605",
"0.5438682",
"0.5353483",
"0.53417605",
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"0.52515745",
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"0.5093063",
"0.50914335",
"0.5089271",
"0.5042017",
"0.5032846",
"0.50174... | 0.0 | -1 |
Reshape a numpy array, which is input_shape=(height, width), as opposed to input_shape=(width, height) for cv2 | def np_resize(img, input_shape):
height, width = input_shape
return cv2.resize(img, (width, height)) | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def img_reshape(self, input_img):\n _img = np.transpose(input_img, (1, 2, 0)) \n _img = np.flipud(_img)\n _img = np.reshape(_img, (1, img_dim[0], img_dim[1], img_dim[2]))\n return _img",
"def numpyReshape(array):\n return np.array(array, dtype = float).reshape(1, len(array))",... | [
"0.75424194",
"0.75378686",
"0.75035334",
"0.7405382",
"0.7378981",
"0.7117111",
"0.70272535",
"0.6998127",
"0.6938943",
"0.6895453",
"0.68916667",
"0.6878874",
"0.6737582",
"0.667375",
"0.6638827",
"0.6624398",
"0.6608408",
"0.65904003",
"0.654305",
"0.65211385",
"0.64932525... | 0.72959924 | 5 |
Compute MD5 hash of the data_path (dir or file) for data versioning. | def hash_data(data_path: Union[str, Path], chunk_size: int = 65536) -> str:
if Path(data_path).is_dir():
hash = _hash_dir(data_path, chunk_size)
elif Path(data_path).is_file():
hash = _hash_file(data_path, chunk_size)
else:
raise ValueError(f"{data_path} is neither directory nor file... | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def python_repo_hash_md5(root_dir: str, *, verbose: bool = False):\n m = hashlib.md5()\n for e in _collect_entries(root_dir, '.'):\n if verbose:\n log_info('Processing e', e)\n m.update(\n f\"path={e['path']}\\tisdir={e['isdir']}\\tsize={e['size']}\\tmode={e['mode']:03o}\\... | [
"0.6850441",
"0.6710063",
"0.6619043",
"0.654432",
"0.6535877",
"0.6533921",
"0.644331",
"0.6342902",
"0.6323048",
"0.6295428",
"0.6286576",
"0.62413317",
"0.6216765",
"0.6169943",
"0.61495596",
"0.6140576",
"0.61181694",
"0.6096366",
"0.60879916",
"0.6078742",
"0.60649794",
... | 0.6991578 | 0 |
Display information about pet | def describe_pets(animal_type, pet_name):
print(f"\nI have a {animal_type}.")
print(f"My {animal_type}'s name is {pet_name.title()}") | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def show_pet(self):\n pet = self.pet_factory.get_pet()\n print \"We have a lovely {}\".format(pet)\n print \"It says {}\".format(pet.speak())\n print \"We also have {}\".format(self.pet_factory.get_food())",
"def show_pet(self):\n pet = self.pet_factory.get_pet()\n\n pri... | [
"0.814422",
"0.81127334",
"0.7560209",
"0.7560209",
"0.7560209",
"0.7560209",
"0.7532049",
"0.7495639",
"0.7495639",
"0.7481198",
"0.74389154",
"0.7438613",
"0.7393894",
"0.7373882",
"0.7327594",
"0.7288435",
"0.7232166",
"0.7204182",
"0.7189433",
"0.71606326",
"0.6922216",
... | 0.75088006 | 8 |
Returns a normalized data. If the data embed a numpy data or a dataset it is returned. Else returns the input data. | def _normalizeData(data):
if isinstance(data, H5Node):
if data.is_broken:
return None
return data.h5py_object
return data | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def normalizeData(self, data):\n return _normalizeData(data)",
"def normalizeData(self, data):\n return _normalizeData(data)",
"def normalized_data(self):\n return self._data",
"def normalize_dataset(self):",
"def normalized_data(self):\n return self._normalization_constant * se... | [
"0.72648424",
"0.72648424",
"0.71091664",
"0.7035169",
"0.6851011",
"0.6670504",
"0.6645314",
"0.6523729",
"0.6430414",
"0.64292383",
"0.6396991",
"0.6373839",
"0.63612485",
"0.63536185",
"0.6324898",
"0.6320754",
"0.6300311",
"0.6296813",
"0.629189",
"0.6264069",
"0.6258603"... | 0.6096874 | 27 |
Returns a normalized complex data. If the data is a numpy data with complex, returns the absolute value. Else returns the input data. | def _normalizeComplex(data):
if hasattr(data, "dtype"):
isComplex = numpy.issubdtype(data.dtype, numpy.complexfloating)
else:
isComplex = isinstance(data, numbers.Complex)
if isComplex:
data = numpy.absolute(data)
return data | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def complex_abs(data):\n assert data.size(-1) == 2\n return (data ** 2).sum(dim=-1).sqrt()",
"def complex_abs(data):\n assert data.size(-1) == 2\n return (data ** 2).sum(dim=-1).sqrt()",
"def complex_abs(data):\n assert data.size(-1) == 2\n return (data ** 2).sum(dim=-1).sqrt()",
"def compl... | [
"0.766307",
"0.766307",
"0.766307",
"0.661879",
"0.6422132",
"0.6198529",
"0.6017067",
"0.5993175",
"0.5902083",
"0.58823895",
"0.5855812",
"0.57926065",
"0.57813406",
"0.5773507",
"0.5769934",
"0.57515544",
"0.5712033",
"0.56957585",
"0.568994",
"0.5641625",
"0.5637767",
"... | 0.87394947 | 0 |
Returns a normalized data if the embed a numpy or a dataset. Else returns the data. | def normalizeData(self, data):
return _normalizeData(data) | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def normalize_dataset(self):",
"def denormalise_0_1(value_or_array, array_min, array_max):\n if isinstance(value_or_array, list):\n raise ValueError('this function accepts arraylike data, not a list. '\n 'Please check data or convert list to numpy array')\n elif isinstance(va... | [
"0.6411453",
"0.6259078",
"0.6019786",
"0.59921646",
"0.57862496",
"0.5763898",
"0.5716769",
"0.571654",
"0.56771535",
"0.56653965",
"0.56437415",
"0.5640771",
"0.56243145",
"0.56201",
"0.55731094",
"0.555722",
"0.5551846",
"0.55445325",
"0.55431825",
"0.5495918",
"0.5480445"... | 0.57240814 | 7 |
Returns a colormap for this view. | def getColormap(self, view):
return None | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def get_colormap(self):\n return colors.colormaps[self.name]",
"def get_colormap(self):\n return colors.colormaps[self.name]",
"def get_colormap(self):\n return file_io.load_viscm_colormap(self.path)",
"def get_colormap(self):\n return file_io.load_viscm_colormap(self.path)",
"d... | [
"0.8298571",
"0.8298571",
"0.81955755",
"0.81955755",
"0.8056556",
"0.7981543",
"0.764267",
"0.76329446",
"0.756393",
"0.7444203",
"0.7027589",
"0.6996905",
"0.6950566",
"0.6934548",
"0.6839568",
"0.66553605",
"0.65015465",
"0.65008515",
"0.65008515",
"0.6488026",
"0.6488026"... | 0.7707698 | 6 |
Returns a color dialog for this view. | def getColormapDialog(self, view):
return None | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def defaultColorDialog(self):\n dialog = None\n if self.__hooks is not None:\n dialog = self.__hooks.getColormapDialog(self)\n if dialog is None:\n dialog = ColormapDialog()\n dialog.setModal(False)\n return dialog",
"def colorPickerDialog(self, curren... | [
"0.75629133",
"0.68842196",
"0.62867564",
"0.6189634",
"0.61299944",
"0.6009618",
"0.59474766",
"0.58456355",
"0.5730012",
"0.5706063",
"0.57012826",
"0.5627111",
"0.55824256",
"0.55625993",
"0.5549173",
"0.5520978",
"0.55189264",
"0.5484155",
"0.546726",
"0.5448726",
"0.5443... | 0.62007624 | 3 |
Called when the widget of the view was created | def viewWidgetCreated(self, view, plot):
return | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def init_widget(self):",
"def create_widgets(self):",
"def on_show_view(self):\n self.setup()",
"def on_show_view(self):\n self.setup()",
"def on_show_view(self):\n self.setup()",
"def create_widgets( self ):",
"def create_widget(self):\n pass",
"def onShow(self):\n ... | [
"0.7902635",
"0.7300569",
"0.7272871",
"0.7272871",
"0.7272871",
"0.71997607",
"0.7128017",
"0.7008906",
"0.6765284",
"0.66968477",
"0.6603362",
"0.6543564",
"0.6543564",
"0.65416574",
"0.651932",
"0.64497674",
"0.6448184",
"0.64066774",
"0.63004065",
"0.62705046",
"0.6247242... | 0.79256094 | 0 |
Returns the data viewer hooks used by this view. | def getHooks(self):
return self.__hooks | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def hooks(self):\n return tuple(self.__hooks.keys())",
"def get_extra_lvs_hooks(self) -> List[HammerToolHookAction]:\n return list()",
"def get_extra_hierarchical_lvs_hooks(self) -> Dict[str, List[HammerToolHookAction]]:\n return dict()",
"def custom_hooks(self):\n return self.con... | [
"0.69417256",
"0.6475532",
"0.6276654",
"0.624319",
"0.61839193",
"0.60875654",
"0.60875654",
"0.6062711",
"0.6000305",
"0.59981173",
"0.59789795",
"0.5936823",
"0.5913618",
"0.58525425",
"0.5787659",
"0.57738584",
"0.577255",
"0.576733",
"0.5754267",
"0.56217355",
"0.5590773... | 0.7191292 | 0 |
Set the data view hooks to use with this view. | def setHooks(self, hooks):
self.__hooks = hooks | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def setHooks(self, hooks):\n super(SelectOneDataView, self).setHooks(hooks)\n if hooks is not None:\n for v in self.__views:\n v.setHooks(hooks)",
"def setHooks(self, hooks):\n super(SelectManyDataView, self).setHooks(hooks)\n if hooks is not None:\n ... | [
"0.7749455",
"0.762406",
"0.6071119",
"0.60160655",
"0.5796744",
"0.5793318",
"0.5742086",
"0.57116985",
"0.5485993",
"0.53418344",
"0.52981484",
"0.5218101",
"0.52134025",
"0.52093846",
"0.51925564",
"0.5186529",
"0.51696926",
"0.51528996",
"0.515082",
"0.51220757",
"0.51081... | 0.6966032 | 2 |
Returns a default colormap. | def defaultColormap(self):
colormap = None
if self.__hooks is not None:
colormap = self.__hooks.getColormap(self)
if colormap is None:
colormap = Colormap(name="viridis")
return colormap | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def get_colormap(self):\n return colors.colormaps[self.name]",
"def get_colormap(self):\n return colors.colormaps[self.name]",
"def get_colormap(self):\n return file_io.load_viscm_colormap(self.path)",
"def get_colormap(self):\n return file_io.load_viscm_colormap(self.path)",
"d... | [
"0.72189206",
"0.72189206",
"0.7079339",
"0.7079339",
"0.7058597",
"0.7002624",
"0.6966337",
"0.6905248",
"0.68922603",
"0.67234963",
"0.6715274",
"0.6709201",
"0.66109717",
"0.66100955",
"0.6520509",
"0.64768016",
"0.6442025",
"0.6442025",
"0.6424049",
"0.63886",
"0.63886",
... | 0.83686036 | 0 |
Returns a default color dialog. | def defaultColorDialog(self):
dialog = None
if self.__hooks is not None:
dialog = self.__hooks.getColormapDialog(self)
if dialog is None:
dialog = ColormapDialog()
dialog.setModal(False)
return dialog | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def colorPickerDialog(self, current_color=None):\n\t\tcolor_dialog = QtWidgets.QColorDialog()\n\t\t#color_dialog.setOption(QtWidgets.QColorDialog.DontUseNativeDialog)\n\n\t\t# Set current colour\n\t\tif current_color is not None:\n\t\t\tcolor_dialog.setCurrentColor(current_color)\n\n\t\t# Only return a color if va... | [
"0.7305316",
"0.6629004",
"0.66000414",
"0.65310407",
"0.6420928",
"0.63252723",
"0.6239439",
"0.62127674",
"0.60073066",
"0.59559417",
"0.5938414",
"0.5874301",
"0.582238",
"0.58178926",
"0.5801812",
"0.57167405",
"0.5696698",
"0.567963",
"0.56683296",
"0.5642302",
"0.562387... | 0.8498397 | 0 |
Returns the default icon | def icon(self):
return self.__icon | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def icon(self):\n return DEFAULT_ICON",
"def icon(self):\n return None",
"def icon(self):\n return None",
"def icon(self):\n return ICON",
"def icon(self):\n return ICON",
"def icon(self):\n return ICON",
"def icon(self):\n return ICON",
"def icon(self... | [
"0.8951425",
"0.8020554",
"0.8020554",
"0.7916969",
"0.7916969",
"0.7916969",
"0.7916969",
"0.7916969",
"0.7916969",
"0.7916969",
"0.7916969",
"0.7916969",
"0.7916969",
"0.7746288",
"0.7746288",
"0.77171725",
"0.7693508",
"0.7670877",
"0.76192194",
"0.7585411",
"0.7554771",
... | 0.74875426 | 25 |
Returns the default label | def label(self):
return self.__label | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def default_label(self) -> str:\n return self.settings[\"default_label\"]",
"def Label(self, default=None):\n return self.data.get('label', default)",
"def Label(self, default=None):\n return self.data.get('label', default)",
"def _get_label(self):\n return self.label",
... | [
"0.88785267",
"0.8536957",
"0.8536957",
"0.7672459",
"0.76072305",
"0.7562238",
"0.7522904",
"0.7505501",
"0.7505501",
"0.7505501",
"0.7505501",
"0.74452853",
"0.7442264",
"0.7435549",
"0.74049765",
"0.74049765",
"0.74049765",
"0.74049765",
"0.74049765",
"0.74049765",
"0.7404... | 0.7432878 | 15 |
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