\\n <tr><th>Station Name</th><th>Station Number</th><th>State</th></tr>\\n <tr><td>{0:s}</td><td>{1:d}</td><td>{2:s}</td></tr>\\n</table>\\\"\\\"\\\".format(self.description, self.station_number, self.state)\"\n]","isConversational":false},"negative_scores":{"kind":"list like","value":["0.61556923","0.60551417","0.6044294","0.6040636","0.60342383","0.5991108","0.5984696","0.5921795","0.5864797","0.5859054","0.58359385","0.57773423","0.57677716","0.5733748","0.57317203","0.57093257","0.56609064","0.5646776","0.5618573","0.55744916","0.5573128","0.5571736","0.5566074","0.5562642","0.5560332","0.5546981","0.55439764","0.5538401","0.5514115","0.5510847","0.5502132","0.55019087","0.55015755","0.5475911","0.5449036","0.54469234","0.54289716","0.5415606","0.5409098","0.5396077","0.53772104","0.5376238","0.53716314","0.53699505","0.535112","0.5326309","0.53246206","0.5317655","0.53161156","0.53003615","0.5281276","0.52668685","0.526516","0.5254755","0.52543026","0.5245566","0.52365184","0.52343005","0.5232062","0.5230812","0.5230626","0.5226593","0.52229214","0.5222838","0.52105075","0.5208621","0.51997936","0.5195797","0.5186821","0.51839924","0.5179373","0.5177658","0.5177243","0.5174521","0.51743025","0.5173552","0.5166509","0.5166509","0.51659274","0.5160624","0.51542735","0.51407176","0.5118367","0.51155055","0.5113324","0.5112341","0.5106333","0.50960964","0.5093439","0.5088505","0.5087958","0.50714123","0.5067491","0.50674677","0.5062843","0.50609595","0.50582427","0.5050858","0.5044728","0.50413495"],"string":"[\n \"0.61556923\",\n \"0.60551417\",\n \"0.6044294\",\n \"0.6040636\",\n \"0.60342383\",\n \"0.5991108\",\n \"0.5984696\",\n \"0.5921795\",\n \"0.5864797\",\n \"0.5859054\",\n \"0.58359385\",\n \"0.57773423\",\n \"0.57677716\",\n \"0.5733748\",\n \"0.57317203\",\n \"0.57093257\",\n \"0.56609064\",\n \"0.5646776\",\n \"0.5618573\",\n \"0.55744916\",\n \"0.5573128\",\n \"0.5571736\",\n \"0.5566074\",\n \"0.5562642\",\n \"0.5560332\",\n \"0.5546981\",\n \"0.55439764\",\n \"0.5538401\",\n \"0.5514115\",\n \"0.5510847\",\n \"0.5502132\",\n \"0.55019087\",\n \"0.55015755\",\n \"0.5475911\",\n \"0.5449036\",\n \"0.54469234\",\n \"0.54289716\",\n \"0.5415606\",\n \"0.5409098\",\n \"0.5396077\",\n \"0.53772104\",\n \"0.5376238\",\n \"0.53716314\",\n \"0.53699505\",\n \"0.535112\",\n \"0.5326309\",\n \"0.53246206\",\n \"0.5317655\",\n \"0.53161156\",\n \"0.53003615\",\n \"0.5281276\",\n \"0.52668685\",\n \"0.526516\",\n \"0.5254755\",\n \"0.52543026\",\n \"0.5245566\",\n \"0.52365184\",\n \"0.52343005\",\n \"0.5232062\",\n \"0.5230812\",\n \"0.5230626\",\n \"0.5226593\",\n \"0.52229214\",\n \"0.5222838\",\n \"0.52105075\",\n \"0.5208621\",\n \"0.51997936\",\n \"0.5195797\",\n \"0.5186821\",\n \"0.51839924\",\n \"0.5179373\",\n \"0.5177658\",\n \"0.5177243\",\n \"0.5174521\",\n \"0.51743025\",\n \"0.5173552\",\n \"0.5166509\",\n \"0.5166509\",\n \"0.51659274\",\n \"0.5160624\",\n \"0.51542735\",\n \"0.51407176\",\n \"0.5118367\",\n \"0.51155055\",\n \"0.5113324\",\n \"0.5112341\",\n \"0.5106333\",\n \"0.50960964\",\n \"0.5093439\",\n \"0.5088505\",\n \"0.5087958\",\n \"0.50714123\",\n \"0.5067491\",\n \"0.50674677\",\n \"0.5062843\",\n \"0.50609595\",\n \"0.50582427\",\n \"0.5050858\",\n \"0.5044728\",\n \"0.50413495\"\n]","isConversational":false},"document_score":{"kind":"string","value":"0.68484527"},"document_rank":{"kind":"string","value":"0"}}},{"rowIdx":75375,"cells":{"query":{"kind":"string","value":"Extracts url from html"},"document":{"kind":"string","value":"def extract_url(td):\n url = td.find('a',href=True)['href']\n return url"},"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 process_url(self, url, html):\n html = self.getHtmlContent(html, 'content')\n new_urls = self.getValidUrlsFromHtml(html)\n return new_urls","def extractUrl(self, href):\n url = ''\n pattern = re.compile(r'(http[s]?://[^&]+)&', re.U | re.M)\n url_match = pattern.search(href)\n if(url_match and url_match.lastindex > 0):\n url = url_match.group(1)\n\n return url","def extract_real_link(self, text):\n if text.startswith('https://www.google.com/url?'):\n return parse_qs(urlparse(text).query)['url'][0]\n\n return text","def parse(html, url, bases): \n\n soup = BeautifulSoup(html, 'lxml')\n htmlBody = soup.find('body').get_text().strip()\n links = [urljoin(url, l.get('href')) for l in soup.findAll('a')]\n links = [l for l in links if urlparse(l).netloc in bases]\n return url, htmlBody, links","def scrape_google(html_content):\n soup = BeautifulSoup(html_content)\n tag = soup.a\n company_name = tag.get_text()\n url = tag['href']\n #print(company_name,url)\n #return company_name, url\n return url","def get_next_target(html):\n start_link = html.find('<a href=')\n if start_link == -1:\n return None, 0\n start_quote = html.find('\"', start_link)\n end_quote = html.find('\"', start_quote + 1)\n url = html[start_quote+1:end_quote]\n return url, end_quote","def get_img_url_from_html(self, html):\n q = pq(html)\n img_url = q(\"#img\").attr('src')\n return img_url","def extract_url(log_entry):\n pattern = r\"https?://(?:[-\\w.]|(?:%[\\da-fA-F]{2}))+\"\n url = re.findall(pattern, log_entry)[0]\n\n if not url:\n raise URINotFound(log_entry)\n\n return urlparse(url)","def fetch_urls(self, html):\n urls = []\n all_urls = set()\n dom = lh.fromstring(html)\n for href in dom.xpath('//a/@href'):\n url = urljoin(self.base_url, href)\n path = urlparse(url).path\n ext = os.path.splitext(path)[1]\n if bool(ext) and ext not in ['.html', '.htm']:\n continue\n if url not in self.visited_urls and url.startswith(self.base_url):\n urls.append(url)\n if url not in all_urls and url.startswith(self.base_url):\n all_urls.add(url)\n return urls, all_urls","def webpage_miner(url):\n try:\n URL = str(url)\n extractor = Extractor(extractor=\"ArticleExtractor\", url=URL)\n out = extractor.getText()\n return [str(out), url]\n except Exception as e:\n pass","def link_extract(link_text, content):\n h = html5lib.parse(content, namespaceHTMLElements=False)\n candidates = h.findall(\".//a[.='%s']\" % link_text)\n if not candidates:\n return 'NOT MATCHED'\n try:\n return candidates[0].attrib['href']\n except:\n return 'NOT MATCHED'","def get_website_url(url_element):\n if not url_element:\n return ''\n\n web_url = unquote(url_element.find('a', first = True).attrs['href'])\n if 'url=' in web_url :\n web_url = web_url[web_url.find('url=')+4 : web_url.find('&')]\n\n return web_url","def _extract_raw_url(self) -> str:\n soup = BeautifulSoup(self.content.content, self._parser)\n pdf_tag = soup.select_one(self.pdf_tag_selector)\n if pdf_tag is None:\n raise PdfTagNotFoundException(f\"No pdf tag was found in the given content \"\n f\"with the selector: {self.pdf_tag_selector}\")\n raw_url = pdf_tag.attrs.get(self.pdf_tag_attr)\n if raw_url is None:\n raise PdfUrlNotFoundException(f\"No pdf url was found in the pdf tag: {pdf_tag.get_text()} \"\n f\"with the attr {self.pdf_tag_attr}\")\n return raw_url","def parse_links(html):\n\n soup = BeautifulSoup(html, 'lxml')\n content_cards = soup.find_all('a', {'class': 'content-card-link'})\n review_links = [cc.get('href') for cc in content_cards]\n review_links = [review_link.split('/')[-1] for review_link in review_links]\n return review_links","def get_base_url(htmlpage): \n for element in htmlpage.parsed_body:\n if getattr(element, \"tag\", None) == \"base\":\n return element.attributes.get(\"href\") or htmlpage.url\n return htmlpage.url","def __parse_urls_from_response(cls, target_str):\n\t\ttree = html.fromstring(target_str)\n\t\turls = tree.xpath('//a[@class=\"layer-link\"]/@href')\n\t\treturn urls","def _parse_source(self, response):\n return response.url","def _real_extract(self, url):\n pass","def extract_link(url):\n\theaders = {\"Host\": \"www.zomato.com\",\n\t \"User-Agent\": \"Mozilla/5.0 (X11; Ubuntu; Linux x86_64; rv:50.0) Gecko/20100101 Firefox/50.0\",\n\t \"Accept\": \"text/html,application/xhtml+xml,application/xml;q=0.9,*/*;q=0.8\",\n\t \"Accept-Language\": \"en-US,en;q=0.5\",\n\t \"Accept-Encoding\": \"gzip, deflate, br\",\n\t \"Referer\": \"https://www.zomato.com/\",\n\t \"Connection\": \"keep-alive\"}\n\n\tif url.startswith('file'):\n\t\twith open(url.replace('file:\\\\\\\\', ''), encoding='utf-8') as fp:\n\t \t\tpage_source = fp.read()\n\n\telse:\n\t\tr = requests.get(url, headers=headers)\n\t\tif r.status_code == 404:\n\t\t\treturn None\n\t\tpage_source = r.text\n\n\tpage_source = re.sub('<br>', '', page_source)\n\tpage_source = re.sub('<br />', '', page_source)\n\tpage_source = re.sub('<br/>', '', page_source)\n\tsoup = BeautifulSoup(page_source, 'html.parser')\n\n\treturn soup","def url(result):\n return result.entities.get(u'urls')","def UrlParse(url, base_url, root_path):\n ref_dir = url.split(base_url)[1].split(r'/')\n saving_path = copy.deepcopy(root_path)\n print(\"ref split rel dir:{}\".format(ref_dir))\n\n for item in ref_dir[1:-1]:\n saving_path = path.join(saving_path, item)\n\n ref_html = ref_dir[-1]\n return saving_path, ref_html","def _get_url(self, absolute):","def extract_url_from_anchor_tag(text):\n pattern = re.compile(r'(?<=href=\").*?(?=\")')\n matches = pattern.findall(text)\n return matches[0] if matches else ''","def extract_url(message):\n # Returns the first url in a message. If there aren't any returns None\n url_re = \"(?:\\s|^)<(https?://[\\w./?+&+%$!#=\\-_]+)>(?:\\s|$)\"\n captures = re.search(url_re, message)\n\n if captures is not None:\n captures = captures.group(1).strip()\n\n return captures","def core_url(url):\n m = re.search('(http(s)?://(((www|.+\\.blogs)\\.nytimes\\.com.+)(roomfordebate/.+|/$|.html)))', url)\n if m:\n return(m.group(3))\n else:\n return('********url %s not pulled' % url)","def getPaginationHyperlink(html: str) -> str:\n moreLinkPattern = r'\\<tr class=\"morespace\".*?\\<a\\shref=\"(?P<hyperlink>.+?)\"\\sclass=\"morelink\"'\n morelinkCompiledRegex = re.compile(moreLinkPattern, flags=re.IGNORECASE | re.DOTALL)\n matchedRegex = morelinkCompiledRegex.search(html)\n if matchedRegex:\n hyperlink = matchedRegex.group(\"hyperlink\")\n return getCompleteUrl(hyperlink)\n else:\n return str()","def get_url():\n urls = re.findall('http[s]?://(?:[a-zA-Z]|[0-9]|[$-_@.&+]|[!*\\(\\),]|(?:%[0-9a-fA-F][0-9a-fA-F]))+',\n new_tweet)\n return urls","def extract_url_from_result(soup, url, listing_id):\r\n for link in soup.find_all(name='a', class_='prod_container'):\r\n url.append(link.get('href'))\r\n link_text = link.get('href')\r\n\r\n # From URL - extract listing ID\r\n try:\r\n id_regex = re.compile(r'\\d{7}')\r\n id_ = id_regex.search(link_text).group()\r\n listing_id.append(id_)\r\n except:\r\n listing_id.append('Not found')\r\n # print(url)\r\n # print(listing_id)\r\n return url, listing_id","def getLineList(html, lineURL={}, reg = ['<li><a href=.*?>.*?<\\/li>', '>.*?<', '\".*?\"']):\n # Some whitespace character such as \\n\\t should effect regular match\n #so it is a better choice than eliminate it firstly.\n html = re.sub('\\n|\\t', '', html)\n d = re.findall('<div class=.la.><ul>.*?<\\/ul>', html)[0]\n allline = re.findall(reg[0], d)\n for line in allline:\n name = re.findall(reg[1], line[4:-8] )[0][1:-1]\n href = city + re.findall(reg[2], line )[0][1:-1]\n lineURL[name] = href\n return lineURL","def _extract_html(self, url):\n self.response = requests.get(url, timeout=5)\n self.html = BeautifulSoup(self.response.content, \"lxml\") if self.response.ok else None\n # return self.html","def find_url(site, start_url):\n urls=[]\n soup = bs4.BeautifulSoup(site, 'html.parser')\n page_urls=soup.find_all('a', href=True)\n for i in page_urls:\n urls.append(str(i.get(\"href\")))\n return good_url(urls, start_url)","def _extract_download_link(self, response1):\n \n found = re.search('<ul class=\"dataset\">(.*)</ul>', response1.content, re.IGNORECASE)\n link = \"\"\n if found:\n filelist_HTML = found.group(0).strip()\n found_link = re.search('href=\"(.*)\">', found.group(0), re.IGNORECASE)\n if found_link:\n link = found_link.group(1).strip()\n \n self.assertTrue(link!=\"\",\"Could not find any list of files after rendering html '%s'\" % response1.content)\n return link","def parse_html(city, html):\n return city.parse_html(html)","def extract_hit_url(row):\n try:\n row_dict = parse_xml(row)\n if 'ExternalQuestion' in row_dict.keys():\n return row_dict['ExternalQuestion']['ExternalURL']\n elif 'HTMLQuestion' in row_dict.keys():\n return row_dict['HTMLQuestion']['HTMLContent']\n else:\n return row\n except KeyError:\n return row","def getHTML(url): \n return urlopen(url)","def resolve_relative_links(html, base_url):\n import sgmllib\n import urlparse\n import StringIO\n\n class MyParser(sgmllib.SGMLParser):\n def __init__(self):\n sgmllib.SGMLParser.__init__(self)\n self.outhandle = StringIO.StringIO()\n def unknown_starttag(self, tag, attributes):\n contents = [tag]\n for name, value in attributes:\n if name == 'href':\n value = urlparse.urljoin(base_url, value)\n contents.append(\"%s=%s\" % (name, value))\n self.outhandle.write(\"<%s>\" % ' '.join(contents))\n def unknown_endtag(self, tag):\n self.outhandle.write(\"</%s>\" % tag)\n def handle_data(self, data):\n self.outhandle.write(data)\n parser = MyParser()\n parser.feed(html)\n parser.outhandle.seek(0)\n return parser.outhandle.read()","def getStoryURL(self, source):\n URLStart = source.find('href=\"') + 6\n URLEnd = source.find('\">', URLStart)\n url = source[URLStart:URLEnd]\n # Check for \"Ask HN\" links.\n if url[0:4] == \"item\": # \"Ask HN\" links start with \"item\".\n url = \"http://news.ycombinator.com/\" + url\n \n # Change \"&\" to \"&\"\n url = url.replace(\"&\", \"&\")\n \n # Remove 'rel=\"nofollow' from the end of links, since they were causing some bugs.\n if url[len(url)-13:] == \"rel=\\\"nofollow\":\n url = url[:len(url)-13]\n \n # Weird hack for URLs that end in '\" '. Consider removing later if it causes any problems.\n if url[len(url)-2:] == \"\\\" \":\n url = url[:len(url)-2]\n return url","def find_tag_urls(r):\n parser = MyHTMLParser()\n parser.feed(r)\n return parser.url_list","def parse(self, url):\n pass","def get_album_art_url(html):\n\treturn re.findall('img src=\"(.*?)\" width=\"500\"', html)[0]","def __load_webpage(self):\n\n\t\tr = requests.get(self.DRIVE_ATTR_HREF)\n\t\thref_parsed = BS(r.text, \"lxml\")\n\t\treturn href_parsed","def extract(self, url):\n self.initialize()\n return self._real_extract(url)","def _parse_source(self, response) -> str:\n return response.url","def url(self):\r\n return self.urlparts.geturl()","def get_html(url):\n return urllib.request.urlopen(url)","def getValidUrlsFromHtml(self, content):\n a_tags = content.find_all('a')\n urls = []\n for a_tag in a_tags:\n url = a_tag.get('href')\n if self.isUrlValid(url):\n urls.append(self.getFilteredUrl(url.lower()))\n return urls","def handle_url(self, url):\n parse = urlparse.urlparse(url, \"http\")\n # relative url path\n if not parse.netloc:\n parse = urlparse.urlparse(\n urlparse.urljoin(\n self.source_url,\n parse.path))\n return urlparse.urlunparse(parse)","def first_image_url_from_html(html):\n\n pattern = \"<img .*?src=['\\\"](.*?)['\\\"].*?>\"\n match = search(pattern, html)\n\n if match:\n return match.group(1)\n\n return None","def fetchUrl(self, url):\n self.driver.get(url)\n html = self.driver.page_source\n return html","def filter_url_parse_full_links(match):\n url = html.unescape(match.group(1))\n url = html.escape(url)\n punctuation = match.group(2)\n caption = filter_url_trim(url, filter_url_length)\n return '<a href=\"' + url + '\">' + caption + '</a>' + punctuation","def get_url(self, webpage):\n\n try:\n return self.urls_keywords_dict[webpage]['URL']\n except:\n return None","def getHtml(url):\n return urlopen(url)","def extract_text(url, sem):\n with (yield from sem):\n page = yield from get(url)\n\n tree = etree.HTML(page)\n paragraphs = tree.findall('.//*/div[@class=\"entry-content\"]/p')[1:-1]\n return url, b'\\n'.join(map(etree.tostring, paragraphs))","def get_html_source(url):\n # import urllib\n try:\n sock = urllib.urlopen(url)\n html_source = sock.read()\n sock.close()\n return html_source\n except IOError:\n print \"IOError: Not a valid URL\"","def get_href(text, base_url=None):\n m = re.search(r'href\\s*=\\s*[\"\\']?([^\"\\'> ]+)[\"\\'> ]', text, re.I)\n if not m:\n return None\n link = m.group(1).strip()\n if base_url and not link.lower().startswith(\"http\"):\n import urlparse\n link = urlparse.urljoin(base_url, link)\n return link","def extract(self, url):\n if not self.is_tiny(url):\n return url\n # The actual extraction is done in utility method so that\n # result is cached only if is_tiny() check has passed.\n return self._do_extract(url)","def get_all_links(html):\n links = []\n while True:\n url, endpos = get_next_target(html)\n if url:\n links.append(url)\n html = html[endpos:]\n else:\n break\n return links","def get_next_target(page):\n\tstart_link = page.find('<a href=')\n\tif start_link == -1:\n\t\treturn None,0\n\turl_start = page.find('\"',start_link)\n\turl_end = page.find('\"',url_start+1)\n\turl= page[url_start+1:url_end]\n\treturn url, url_end","def extract_url(episode: dict) -> str:\n url = episode['embed_info']\n is_playable = episode['playable']\n episode_num = episode['number']\n #episode might not be playable or url might be empty\n if not is_playable or not url:\n raise Exception(f\"L'episodio {episode_num} non e' scaricabile.\")\n\n url = real_url(episode['embed_info'])\n url = format_url(url, episode['video_type'])\n if 'youtube.com' in url:\n raise Exception(f\"L'episodio {episode_num} e' un video di YouTube che puoi guardare a questo url: \\n{url}\")\n\n return url","def parse_url(url):\n url = urllib.parse.urlparse(url)\n query = urllib.parse.parse_qs(url.query)\n query_ = query.get('dn', query.get('title', ''))[0]\n if url.scheme == \"magnet\":\n return \"magnet:?xt={}\".format(query['xt'][0]), query_\n return \"http://{}{}{}\".format(*url[0:3]), query_","def get_links_from_url(url):\n return [get_base(url)]","def get_html_from_url(url):\n request = requests.get(url)\n data = request.text\n return data","def _format(self, parent: URL, tag: {}):\n\n url = tag.get('href', None)\n if url is None:\n return None\n\n parsed = urlparse(url)\n if parsed.netloc == '':\n parsed = parsed._replace(scheme=parent.scheme)\n parsed = parsed._replace(netloc=parent.host)\n\n return parsed.geturl() if self._is_valid(parsed) else None","def extract_link(self, page_url, element, attribute_name):\n attribute = element.attrs.get(attribute_name, None)\n if attribute is None:\n return None\n\n return urljoin(page_url, attribute, allow_fragments=False)","def extract_text(td):\n text = td.find('a',href=True).text\n return text","def get_url(lines, numlst, keywords):\n\n flag = False\n url = ''\n for k in range(0, GET_NUM):\n num = numlst[k] + 4\n if keywords in lines[num]:\n goodline = lines[num]\n flag = True\n break\n if flag:\n pos = goodline.find('''href=\"''')\n goodline = goodline[pos+6:]\n url = goodline.split('''\">''')[0]\n return url","def url(self):\n return self.data[\"attributes\"][\"URL\"]","def __url_list(self, page):\n url_list = []\n for tag_a in page.find_all('a'):\n href = str(tag_a.get('href'))\n if self.__verify(href):\n url = parse.quote(self.__add_main_site(href), '/:#')\n url_list.append(url)\n return url_list","def extract_links(self, post_text):\n tree = html.fromstring(post_text)\n\n # Map out element types and the attributes we're looking for\n # keep it simple to begin with\n #\n attribs = {\"href\": [\"a\", \"area\"], \"cite\": [\"blockquote\"]}\n\n urls = []\n\n # Iterate over each attribute type\n for attrib in attribs:\n for element in attribs[attrib]:\n # Find elements of type element with attribute\n xpath = \"//{0}[@{1}]\".format(element, attrib)\n for match in tree.xpath(xpath):\n # Remove URL fragments\n u = match.get(\"href\").split(\"#\")[0]\n urls.append(u)\n\n # Make the list unique\n urls = list(set(urls))\n return urls","def htmlFindLinkUrls(page, attrs={}):\n htmlParsePage(page)\n bs = page['parsedHtml']\n elList = bs.findAll('a', attrs=attrs)\n urls = []\n for el in elList:\n if 'href' not in el:\n continue\n url = el['href']\n url = urlparse.urljoin(page['url'], url)\n urls.append(url)\n\n return urls","def get_links(html):\n # a regular expression to extract all links from the webpage\n # re.IGNORECASE 让正则表达式忽略大小写，如[A-Z]也可以匹配小写字母了。\n # link_list = re.findall(r\"(?<=href=\\\").+?(?=\\\")|(?<=href=\\').+?(?=\\')\", content)\n webpage_regex = re.compile('<a href=(.*?)</a>', re.IGNORECASE)\n # list of all links from the webpage\n url_list = webpage_regex.findall(html)\n with open('E:\\淘女郎首页图片list.txt','w') as f:\n for each in url_list:\n f.write(each+'\\n')\n return url_list","def get_links(html):\n # a regular expression to extract all links from thr webpage\n webpage_regex = re.compile('<a[^>] + href = [\"\\'](.*?)[\"\\']]', re.IGNORECASE)\n # list of all links from the webpage\n return webpage_regex.findall(html)","def extract_link_urls(html, page_url):\n pattern = \"\"\"\n <a # beginning of anchor tag\n .+? # stuff before href attribute\n href=\" # beginning of href attribute\n (.+?\\.html?) # contents of href attribute (HTML only)\n \" # end of href attribute\n .+? # stuff after href attribute\n > # end of the anchor tag\n \"\"\"\n\n matches = re.findall(pattern, html, re.VERBOSE)\n\n # Found link URLs that don't leave the site.\n link_urls = []\n\n # Only add intra-site link URLs to the returned list.\n for match in matches:\n if match[:7] != \"http://\":\n link_urls.append(normalize_url(match, page_url))\n\n return link_urls","def extract_url(input_string):\n if input_string:\n url_search = re.search(\n r'http[s]?://(?:[a-zA-Z]|[0-9]|[$-_@.&+]|[!*\\(\\),]|(?:%[0-9a-fA-F][0-9a-fA-F]))+',\n input_string)\n\n if bool(url_search):\n return url_search.group()\n\n return None","def processUrl(url):\n domain = 'http://www.gsmarena.com/'\n if domain not in url:\n url = urllib.parse.urljoin(domain, url)\n return url","def formatURL(self, url):\n pattern = r'(imdb\\.com\\/title\\/(.*/))'\n urls = re.findall(pattern, url)\n urls = urls[0]\n new_url = urls[0]\n new_url = \"https://www.\" + new_url\n title_code = urls[1].replace(\"/\", \"\")\n return new_url","def GetUrlFirst(self):\n self.url = \"https://www.taobao.com/\"\n self.host = \"www.taobao.com\"\n self.referer = \"https://www.taobao.com/\"\n content = self.GetContent()\n __clear__ = '<a href=.*?</a>'\n match = open(self.base_dir_url+\"url_first.html\", 'w')\n try:\n all_link = re.findall(__clear__, content, re.S)\n print \"All links of the web page is: \", len(all_link)\n self.DealUrlFirst(match, all_link)\n except:\n print \"Something wrong is happening!\"\n finally:\n match.close()\n match.close()","async def parse_url(self, url: str, delay: int = 0) -> BeautifulSoup:\n if url != self.driver.current_url:\n self.driver.get(url)\n return BeautifulSoup(self.driver.page_source, 'lxml')","def get_full_url(self):\n full_url = home_page + self.source_link\n return full_url","def get_links(html):\r\n\twebpage_regex = re.compile('<a[^>]+href=[\"\\'](.*?)[\"\\']',re.IGNORECASE)\r\n\t# list of all links from the webpage\r\n\treturn webpage_regex.findall(html)","def get_links_from_body(body):\n return [get_base(url) for url in extract_urls(body)]","def parse_link(self,data,api):\n return REACT_API_DOCS_URL + data.FILE.split('/')[1] + api.find('a',attrs = {'class': 'hash-link'}).attrs['href']","def __expandURL(self, link):\n try:\n return requests.get(link).url\n except Exception:\n return link","def parser(self, page_url, html_cont):\n if page_url is None or html_cont is None:\n return\n soup = BeautifulSoup(html_cont, 'html.parser')\n new_urls = self._get_new_urls(page_url, soup)\n new_data = self._get_new_data(page_url, soup)\n return new_urls, new_data","def url_build(web_url, url_part):\r\n url_full = urljoin(web_url,url_part.get('href'))\r\n return url_full","def get_article_webpage_list(self, news_feed_webpage):\n url_list = list()\n # Use HTML parser to extract appropriates urls\n lemonde_parser = LeMondeHTMLParser()\n lemonde_parser.feed(news_feed_webpage)\n partial_url_list = lemonde_parser.links\n\n\n # add the base url of the website if not present in the article url\n for url in partial_url_list:\n if not 'http' in url:\n url_list.append(self.base_url + url)\n else:\n url_list.append(url)\n\n return url_list","def remove_url(text):\r\n url = re.sub('https?://[A-Za-z0-9./]+', '', text)\r\n return url","def process_link(self, inp):\n url = inp\n try:\n request = urllib2.Request(url)\n request.add_header('User-Agent', self.browsers[randint(0, 28)])\n request.add_header('Accept',\n ('text/html,application/xhtml+xml,'\n 'application/xml;q=0.9,*/*;q=0.8'))\n request.add_header('Accept-Language', 'en-us,en;q=0.5')\n soup = BeautifulSoup(urllib2.urlopen(request).read())\n content_div = soup.findAll(id=\"content\")[0]\n raw_text = clean_html(str(content_div))\n f = open('wiki_text2.txt', 'w')\n f.write(raw_text)\n f.close()\n return self.process_text(raw_text)\n except:\n traceback.print_exc()\n raise \"cant process link :traceback:%s\" % traceback.format_exc()","def extract_next_page(parser):\r\n url = ''\r\n table = parser.table.find_all('table')[1]\r\n tr = table.findAll('tr')\r\n url = url + str(tr[len(tr) - 1].a.get('href'))\r\n\r\n return url","def url( self ):\n return str(self._urlEdit.text())","def find_feed_in_html(html_content: bytes, from_url: str) -> Optional[str]:\n soup = bs4.BeautifulSoup(html_content, 'html.parser')\n for type_ in ('application/atom+xml', 'application/rss+xml'):\n link = soup.find('link', type=type_)\n if not link:\n continue\n\n link = link.get('href')\n if not link:\n continue\n\n return urllib.parse.urljoin(from_url, link)","def get_info_of_url(url):\n pass","def extract(msg):\n\n rgx = url_regex()\n found = re.findall(rgx, msg)\n links_set = Set()\n for link in found:\n # dunno if I like this. don't need seperate groups to make this work.\n links_set.add(link[0].strip())\n return list(links_set)","def test_html_url_link():\n feed = atom.feed_from_annotations([], mock.Mock(), mock.Mock(), html_url=\"html_url\")\n\n assert feed[\"links\"][1] == {\n \"rel\": \"alternate\",\n \"type\": \"text/html\",\n \"href\": \"html_url\",\n }","def get_torrent(html_url):\n sess = HTMLSession()\n r = sess.get(html_url)\n torrent_url = [u for u in r.html.links if u.endswith(\".torrent\")][0]\n torrent_fname = [f for f in torrent_url.split(\"/\") if f.endswith(\".torrent\")][0]\n print(f\"Obtained Torrent File URL:\")\n print(f\"{torrent_url}\")","def __ParseUrl(url):\n return urlparse(url)","def getLineInformation(html, reg=['(?<=<\\/h2><li>).*?<\\/a>', '<.*a.*?>', '查公交上八六八四cn']):\n tmp = re.findall(reg[0], html)[0]\n Info = re.sub(reg[1], '', tmp)\n info = re.sub(reg[2], '', Info)\n return info","def get_html(url):\n req = urllib.request.Request(\n url,\n headers={\n 'User-Agent': 'Python Learning Program',\n 'From': 'hklee310@gmail.com'\n }\n )\n resp = urllib.request.urlopen(req)\n\n if resp.code == 200:\n return resp.read() # returns the html document\n else:\n return None","def find_link(html_content):\n soup = BeautifulSoup(html_content, \"html.parser\")\n paragraphs = soup.find_all('p')\n for p in paragraphs:\n string = ''\n for element in p:\n if type(element) == bs4.element.NavigableString:\n string += element\n elif type(element) == bs4.element.Tag and element.name == 'a':\n if balanced_parenths(string):\n return element\n else:\n string += element.get_text()\n return None","def test_expanded_url(self):\n tweet_object = self.load_tweet('basic')\n tweet_text = self.api.html_for_tweet(tweet_object,\n use_expanded_url=True)\n # Make sure full url is in HTML\n self.assertTrue('http://google.com' in tweet_text)"],"string":"[\n \"def process_url(self, url, html):\\n html = self.getHtmlContent(html, 'content')\\n new_urls = self.getValidUrlsFromHtml(html)\\n return new_urls\",\n \"def extractUrl(self, href):\\n url = ''\\n pattern = re.compile(r'(http[s]?://[^&]+)&', re.U | re.M)\\n url_match = pattern.search(href)\\n if(url_match and url_match.lastindex > 0):\\n url = url_match.group(1)\\n\\n return url\",\n \"def extract_real_link(self, text):\\n if text.startswith('https://www.google.com/url?'):\\n return parse_qs(urlparse(text).query)['url'][0]\\n\\n return text\",\n \"def parse(html, url, bases): \\n\\n soup = BeautifulSoup(html, 'lxml')\\n htmlBody = soup.find('body').get_text().strip()\\n links = [urljoin(url, l.get('href')) for l in soup.findAll('a')]\\n links = [l for l in links if urlparse(l).netloc in bases]\\n return url, htmlBody, links\",\n \"def scrape_google(html_content):\\n soup = BeautifulSoup(html_content)\\n tag = soup.a\\n company_name = tag.get_text()\\n url = tag['href']\\n #print(company_name,url)\\n #return company_name, url\\n return url\",\n \"def get_next_target(html):\\n start_link = html.find('<a href=')\\n if start_link == -1:\\n return None, 0\\n start_quote = html.find('\\\"', start_link)\\n end_quote = html.find('\\\"', start_quote + 1)\\n url = html[start_quote+1:end_quote]\\n return url, end_quote\",\n \"def get_img_url_from_html(self, html):\\n q = pq(html)\\n img_url = q(\\\"#img\\\").attr('src')\\n return img_url\",\n \"def extract_url(log_entry):\\n pattern = r\\\"https?://(?:[-\\\\w.]|(?:%[\\\\da-fA-F]{2}))+\\\"\\n url = re.findall(pattern, log_entry)[0]\\n\\n if not url:\\n raise URINotFound(log_entry)\\n\\n return urlparse(url)\",\n \"def fetch_urls(self, html):\\n urls = []\\n all_urls = set()\\n dom = lh.fromstring(html)\\n for href in dom.xpath('//a/@href'):\\n url = urljoin(self.base_url, href)\\n path = urlparse(url).path\\n ext = os.path.splitext(path)[1]\\n if bool(ext) and ext not in ['.html', '.htm']:\\n continue\\n if url not in self.visited_urls and url.startswith(self.base_url):\\n urls.append(url)\\n if url not in all_urls and url.startswith(self.base_url):\\n all_urls.add(url)\\n return urls, all_urls\",\n \"def webpage_miner(url):\\n try:\\n URL = str(url)\\n extractor = Extractor(extractor=\\\"ArticleExtractor\\\", url=URL)\\n out = extractor.getText()\\n return [str(out), url]\\n except Exception as e:\\n pass\",\n \"def link_extract(link_text, content):\\n h = html5lib.parse(content, namespaceHTMLElements=False)\\n candidates = h.findall(\\\".//a[.='%s']\\\" % link_text)\\n if not candidates:\\n return 'NOT MATCHED'\\n try:\\n return candidates[0].attrib['href']\\n except:\\n return 'NOT MATCHED'\",\n \"def get_website_url(url_element):\\n if not url_element:\\n return ''\\n\\n web_url = unquote(url_element.find('a', first = True).attrs['href'])\\n if 'url=' in web_url :\\n web_url = web_url[web_url.find('url=')+4 : web_url.find('&')]\\n\\n return web_url\",\n \"def _extract_raw_url(self) -> str:\\n soup = BeautifulSoup(self.content.content, self._parser)\\n pdf_tag = soup.select_one(self.pdf_tag_selector)\\n if pdf_tag is None:\\n raise PdfTagNotFoundException(f\\\"No pdf tag was found in the given content \\\"\\n f\\\"with the selector: {self.pdf_tag_selector}\\\")\\n raw_url = pdf_tag.attrs.get(self.pdf_tag_attr)\\n if raw_url is None:\\n raise PdfUrlNotFoundException(f\\\"No pdf url was found in the pdf tag: {pdf_tag.get_text()} \\\"\\n f\\\"with the attr {self.pdf_tag_attr}\\\")\\n return raw_url\",\n \"def parse_links(html):\\n\\n soup = BeautifulSoup(html, 'lxml')\\n content_cards = soup.find_all('a', {'class': 'content-card-link'})\\n review_links = [cc.get('href') for cc in content_cards]\\n review_links = [review_link.split('/')[-1] for review_link in review_links]\\n return review_links\",\n \"def get_base_url(htmlpage): \\n for element in htmlpage.parsed_body:\\n if getattr(element, \\\"tag\\\", None) == \\\"base\\\":\\n return element.attributes.get(\\\"href\\\") or htmlpage.url\\n return htmlpage.url\",\n \"def __parse_urls_from_response(cls, target_str):\\n\\t\\ttree = html.fromstring(target_str)\\n\\t\\turls = tree.xpath('//a[@class=\\\"layer-link\\\"]/@href')\\n\\t\\treturn urls\",\n \"def _parse_source(self, response):\\n return response.url\",\n \"def _real_extract(self, url):\\n pass\",\n \"def extract_link(url):\\n\\theaders = {\\\"Host\\\": \\\"www.zomato.com\\\",\\n\\t \\\"User-Agent\\\": \\\"Mozilla/5.0 (X11; Ubuntu; Linux x86_64; rv:50.0) Gecko/20100101 Firefox/50.0\\\",\\n\\t \\\"Accept\\\": \\\"text/html,application/xhtml+xml,application/xml;q=0.9,*/*;q=0.8\\\",\\n\\t \\\"Accept-Language\\\": \\\"en-US,en;q=0.5\\\",\\n\\t \\\"Accept-Encoding\\\": \\\"gzip, deflate, br\\\",\\n\\t \\\"Referer\\\": \\\"https://www.zomato.com/\\\",\\n\\t \\\"Connection\\\": \\\"keep-alive\\\"}\\n\\n\\tif url.startswith('file'):\\n\\t\\twith open(url.replace('file:\\\\\\\\\\\\\\\\', ''), encoding='utf-8') as fp:\\n\\t \\t\\tpage_source = fp.read()\\n\\n\\telse:\\n\\t\\tr = requests.get(url, headers=headers)\\n\\t\\tif r.status_code == 404:\\n\\t\\t\\treturn None\\n\\t\\tpage_source = r.text\\n\\n\\tpage_source = re.sub('<br>', '', page_source)\\n\\tpage_source = re.sub('<br />', '', page_source)\\n\\tpage_source = re.sub('<br/>', '', page_source)\\n\\tsoup = BeautifulSoup(page_source, 'html.parser')\\n\\n\\treturn soup\",\n \"def url(result):\\n return result.entities.get(u'urls')\",\n \"def UrlParse(url, base_url, root_path):\\n ref_dir = url.split(base_url)[1].split(r'/')\\n saving_path = copy.deepcopy(root_path)\\n print(\\\"ref split rel dir:{}\\\".format(ref_dir))\\n\\n for item in ref_dir[1:-1]:\\n saving_path = path.join(saving_path, item)\\n\\n ref_html = ref_dir[-1]\\n return saving_path, ref_html\",\n \"def _get_url(self, absolute):\",\n \"def extract_url_from_anchor_tag(text):\\n pattern = re.compile(r'(?<=href=\\\").*?(?=\\\")')\\n matches = pattern.findall(text)\\n return matches[0] if matches else ''\",\n \"def extract_url(message):\\n # Returns the first url in a message. If there aren't any returns None\\n url_re = \\\"(?:\\\\s|^)<(https?://[\\\\w./?+&+%$!#=\\\\-_]+)>(?:\\\\s|$)\\\"\\n captures = re.search(url_re, message)\\n\\n if captures is not None:\\n captures = captures.group(1).strip()\\n\\n return captures\",\n \"def core_url(url):\\n m = re.search('(http(s)?://(((www|.+\\\\.blogs)\\\\.nytimes\\\\.com.+)(roomfordebate/.+|/$|.html)))', url)\\n if m:\\n return(m.group(3))\\n else:\\n return('********url %s not pulled' % url)\",\n \"def getPaginationHyperlink(html: str) -> str:\\n moreLinkPattern = r'\\\\<tr class=\\\"morespace\\\".*?\\\\<a\\\\shref=\\\"(?P<hyperlink>.+?)\\\"\\\\sclass=\\\"morelink\\\"'\\n morelinkCompiledRegex = re.compile(moreLinkPattern, flags=re.IGNORECASE | re.DOTALL)\\n matchedRegex = morelinkCompiledRegex.search(html)\\n if matchedRegex:\\n hyperlink = matchedRegex.group(\\\"hyperlink\\\")\\n return getCompleteUrl(hyperlink)\\n else:\\n return str()\",\n \"def get_url():\\n urls = re.findall('http[s]?://(?:[a-zA-Z]|[0-9]|[$-_@.&+]|[!*\\\\(\\\\),]|(?:%[0-9a-fA-F][0-9a-fA-F]))+',\\n new_tweet)\\n return urls\",\n \"def extract_url_from_result(soup, url, listing_id):\\r\\n for link in soup.find_all(name='a', class_='prod_container'):\\r\\n url.append(link.get('href'))\\r\\n link_text = link.get('href')\\r\\n\\r\\n # From URL - extract listing ID\\r\\n try:\\r\\n id_regex = re.compile(r'\\\\d{7}')\\r\\n id_ = id_regex.search(link_text).group()\\r\\n listing_id.append(id_)\\r\\n except:\\r\\n listing_id.append('Not found')\\r\\n # print(url)\\r\\n # print(listing_id)\\r\\n return url, listing_id\",\n \"def getLineList(html, lineURL={}, reg = ['<li><a href=.*?>.*?<\\\\/li>', '>.*?<', '\\\".*?\\\"']):\\n # Some whitespace character such as \\\\n\\\\t should effect regular match\\n #so it is a better choice than eliminate it firstly.\\n html = re.sub('\\\\n|\\\\t', '', html)\\n d = re.findall('<div class=.la.><ul>.*?<\\\\/ul>', html)[0]\\n allline = re.findall(reg[0], d)\\n for line in allline:\\n name = re.findall(reg[1], line[4:-8] )[0][1:-1]\\n href = city + re.findall(reg[2], line )[0][1:-1]\\n lineURL[name] = href\\n return lineURL\",\n \"def _extract_html(self, url):\\n self.response = requests.get(url, timeout=5)\\n self.html = BeautifulSoup(self.response.content, \\\"lxml\\\") if self.response.ok else None\\n # return self.html\",\n \"def find_url(site, start_url):\\n urls=[]\\n soup = bs4.BeautifulSoup(site, 'html.parser')\\n page_urls=soup.find_all('a', href=True)\\n for i in page_urls:\\n urls.append(str(i.get(\\\"href\\\")))\\n return good_url(urls, start_url)\",\n \"def _extract_download_link(self, response1):\\n \\n found = re.search('<ul class=\\\"dataset\\\">(.*)</ul>', response1.content, re.IGNORECASE)\\n link = \\\"\\\"\\n if found:\\n filelist_HTML = found.group(0).strip()\\n found_link = re.search('href=\\\"(.*)\\\">', found.group(0), re.IGNORECASE)\\n if found_link:\\n link = found_link.group(1).strip()\\n \\n self.assertTrue(link!=\\\"\\\",\\\"Could not find any list of files after rendering html '%s'\\\" % response1.content)\\n return link\",\n \"def parse_html(city, html):\\n return city.parse_html(html)\",\n \"def extract_hit_url(row):\\n try:\\n row_dict = parse_xml(row)\\n if 'ExternalQuestion' in row_dict.keys():\\n return row_dict['ExternalQuestion']['ExternalURL']\\n elif 'HTMLQuestion' in row_dict.keys():\\n return row_dict['HTMLQuestion']['HTMLContent']\\n else:\\n return row\\n except KeyError:\\n return row\",\n \"def getHTML(url): \\n return urlopen(url)\",\n \"def resolve_relative_links(html, base_url):\\n import sgmllib\\n import urlparse\\n import StringIO\\n\\n class MyParser(sgmllib.SGMLParser):\\n def __init__(self):\\n sgmllib.SGMLParser.__init__(self)\\n self.outhandle = StringIO.StringIO()\\n def unknown_starttag(self, tag, attributes):\\n contents = [tag]\\n for name, value in attributes:\\n if name == 'href':\\n value = urlparse.urljoin(base_url, value)\\n contents.append(\\\"%s=%s\\\" % (name, value))\\n self.outhandle.write(\\\"<%s>\\\" % ' '.join(contents))\\n def unknown_endtag(self, tag):\\n self.outhandle.write(\\\"</%s>\\\" % tag)\\n def handle_data(self, data):\\n self.outhandle.write(data)\\n parser = MyParser()\\n parser.feed(html)\\n parser.outhandle.seek(0)\\n return parser.outhandle.read()\",\n \"def getStoryURL(self, source):\\n URLStart = source.find('href=\\\"') + 6\\n URLEnd = source.find('\\\">', URLStart)\\n url = source[URLStart:URLEnd]\\n # Check for \\\"Ask HN\\\" links.\\n if url[0:4] == \\\"item\\\": # \\\"Ask HN\\\" links start with \\\"item\\\".\\n url = \\\"http://news.ycombinator.com/\\\" + url\\n \\n # Change \\\"&\\\" to \\\"&\\\"\\n url = url.replace(\\\"&\\\", \\\"&\\\")\\n \\n # Remove 'rel=\\\"nofollow' from the end of links, since they were causing some bugs.\\n if url[len(url)-13:] == \\\"rel=\\\\\\\"nofollow\\\":\\n url = url[:len(url)-13]\\n \\n # Weird hack for URLs that end in '\\\" '. Consider removing later if it causes any problems.\\n if url[len(url)-2:] == \\\"\\\\\\\" \\\":\\n url = url[:len(url)-2]\\n return url\",\n \"def find_tag_urls(r):\\n parser = MyHTMLParser()\\n parser.feed(r)\\n return parser.url_list\",\n \"def parse(self, url):\\n pass\",\n \"def get_album_art_url(html):\\n\\treturn re.findall('img src=\\\"(.*?)\\\" width=\\\"500\\\"', html)[0]\",\n \"def __load_webpage(self):\\n\\n\\t\\tr = requests.get(self.DRIVE_ATTR_HREF)\\n\\t\\thref_parsed = BS(r.text, \\\"lxml\\\")\\n\\t\\treturn href_parsed\",\n \"def extract(self, url):\\n self.initialize()\\n return self._real_extract(url)\",\n \"def _parse_source(self, response) -> str:\\n return response.url\",\n \"def url(self):\\r\\n return self.urlparts.geturl()\",\n \"def get_html(url):\\n return urllib.request.urlopen(url)\",\n \"def getValidUrlsFromHtml(self, content):\\n a_tags = content.find_all('a')\\n urls = []\\n for a_tag in a_tags:\\n url = a_tag.get('href')\\n if self.isUrlValid(url):\\n urls.append(self.getFilteredUrl(url.lower()))\\n return urls\",\n \"def handle_url(self, url):\\n parse = urlparse.urlparse(url, \\\"http\\\")\\n # relative url path\\n if not parse.netloc:\\n parse = urlparse.urlparse(\\n urlparse.urljoin(\\n self.source_url,\\n parse.path))\\n return urlparse.urlunparse(parse)\",\n \"def first_image_url_from_html(html):\\n\\n pattern = \\\"<img .*?src=['\\\\\\\"](.*?)['\\\\\\\"].*?>\\\"\\n match = search(pattern, html)\\n\\n if match:\\n return match.group(1)\\n\\n return None\",\n \"def fetchUrl(self, url):\\n self.driver.get(url)\\n html = self.driver.page_source\\n return html\",\n \"def filter_url_parse_full_links(match):\\n url = html.unescape(match.group(1))\\n url = html.escape(url)\\n punctuation = match.group(2)\\n caption = filter_url_trim(url, filter_url_length)\\n return '<a href=\\\"' + url + '\\\">' + caption + '</a>' + punctuation\",\n \"def get_url(self, webpage):\\n\\n try:\\n return self.urls_keywords_dict[webpage]['URL']\\n except:\\n return None\",\n \"def getHtml(url):\\n return urlopen(url)\",\n \"def extract_text(url, sem):\\n with (yield from sem):\\n page = yield from get(url)\\n\\n tree = etree.HTML(page)\\n paragraphs = tree.findall('.//*/div[@class=\\\"entry-content\\\"]/p')[1:-1]\\n return url, b'\\\\n'.join(map(etree.tostring, paragraphs))\",\n \"def get_html_source(url):\\n # import urllib\\n try:\\n sock = urllib.urlopen(url)\\n html_source = sock.read()\\n sock.close()\\n return html_source\\n except IOError:\\n print \\\"IOError: Not a valid URL\\\"\",\n \"def get_href(text, base_url=None):\\n m = re.search(r'href\\\\s*=\\\\s*[\\\"\\\\']?([^\\\"\\\\'> ]+)[\\\"\\\\'> ]', text, re.I)\\n if not m:\\n return None\\n link = m.group(1).strip()\\n if base_url and not link.lower().startswith(\\\"http\\\"):\\n import urlparse\\n link = urlparse.urljoin(base_url, link)\\n return link\",\n \"def extract(self, url):\\n if not self.is_tiny(url):\\n return url\\n # The actual extraction is done in utility method so that\\n # result is cached only if is_tiny() check has passed.\\n return self._do_extract(url)\",\n \"def get_all_links(html):\\n links = []\\n while True:\\n url, endpos = get_next_target(html)\\n if url:\\n links.append(url)\\n html = html[endpos:]\\n else:\\n break\\n return links\",\n \"def get_next_target(page):\\n\\tstart_link = page.find('<a href=')\\n\\tif start_link == -1:\\n\\t\\treturn None,0\\n\\turl_start = page.find('\\\"',start_link)\\n\\turl_end = page.find('\\\"',url_start+1)\\n\\turl= page[url_start+1:url_end]\\n\\treturn url, url_end\",\n \"def extract_url(episode: dict) -> str:\\n url = episode['embed_info']\\n is_playable = episode['playable']\\n episode_num = episode['number']\\n #episode might not be playable or url might be empty\\n if not is_playable or not url:\\n raise Exception(f\\\"L'episodio {episode_num} non e' scaricabile.\\\")\\n\\n url = real_url(episode['embed_info'])\\n url = format_url(url, episode['video_type'])\\n if 'youtube.com' in url:\\n raise Exception(f\\\"L'episodio {episode_num} e' un video di YouTube che puoi guardare a questo url: \\\\n{url}\\\")\\n\\n return url\",\n \"def parse_url(url):\\n url = urllib.parse.urlparse(url)\\n query = urllib.parse.parse_qs(url.query)\\n query_ = query.get('dn', query.get('title', ''))[0]\\n if url.scheme == \\\"magnet\\\":\\n return \\\"magnet:?xt={}\\\".format(query['xt'][0]), query_\\n return \\\"http://{}{}{}\\\".format(*url[0:3]), query_\",\n \"def get_links_from_url(url):\\n return [get_base(url)]\",\n \"def get_html_from_url(url):\\n request = requests.get(url)\\n data = request.text\\n return data\",\n \"def _format(self, parent: URL, tag: {}):\\n\\n url = tag.get('href', None)\\n if url is None:\\n return None\\n\\n parsed = urlparse(url)\\n if parsed.netloc == '':\\n parsed = parsed._replace(scheme=parent.scheme)\\n parsed = parsed._replace(netloc=parent.host)\\n\\n return parsed.geturl() if self._is_valid(parsed) else None\",\n \"def extract_link(self, page_url, element, attribute_name):\\n attribute = element.attrs.get(attribute_name, None)\\n if attribute is None:\\n return None\\n\\n return urljoin(page_url, attribute, allow_fragments=False)\",\n \"def extract_text(td):\\n text = td.find('a',href=True).text\\n return text\",\n \"def get_url(lines, numlst, keywords):\\n\\n flag = False\\n url = ''\\n for k in range(0, GET_NUM):\\n num = numlst[k] + 4\\n if keywords in lines[num]:\\n goodline = lines[num]\\n flag = True\\n break\\n if flag:\\n pos = goodline.find('''href=\\\"''')\\n goodline = goodline[pos+6:]\\n url = goodline.split('''\\\">''')[0]\\n return url\",\n \"def url(self):\\n return self.data[\\\"attributes\\\"][\\\"URL\\\"]\",\n \"def __url_list(self, page):\\n url_list = []\\n for tag_a in page.find_all('a'):\\n href = str(tag_a.get('href'))\\n if self.__verify(href):\\n url = parse.quote(self.__add_main_site(href), '/:#')\\n url_list.append(url)\\n return url_list\",\n \"def extract_links(self, post_text):\\n tree = html.fromstring(post_text)\\n\\n # Map out element types and the attributes we're looking for\\n # keep it simple to begin with\\n #\\n attribs = {\\\"href\\\": [\\\"a\\\", \\\"area\\\"], \\\"cite\\\": [\\\"blockquote\\\"]}\\n\\n urls = []\\n\\n # Iterate over each attribute type\\n for attrib in attribs:\\n for element in attribs[attrib]:\\n # Find elements of type element with attribute\\n xpath = \\\"//{0}[@{1}]\\\".format(element, attrib)\\n for match in tree.xpath(xpath):\\n # Remove URL fragments\\n u = match.get(\\\"href\\\").split(\\\"#\\\")[0]\\n urls.append(u)\\n\\n # Make the list unique\\n urls = list(set(urls))\\n return urls\",\n \"def htmlFindLinkUrls(page, attrs={}):\\n htmlParsePage(page)\\n bs = page['parsedHtml']\\n elList = bs.findAll('a', attrs=attrs)\\n urls = []\\n for el in elList:\\n if 'href' not in el:\\n continue\\n url = el['href']\\n url = urlparse.urljoin(page['url'], url)\\n urls.append(url)\\n\\n return urls\",\n \"def get_links(html):\\n # a regular expression to extract all links from the webpage\\n # re.IGNORECASE 让正则表达式忽略大小写，如[A-Z]也可以匹配小写字母了。\\n # link_list = re.findall(r\\\"(?<=href=\\\\\\\").+?(?=\\\\\\\")|(?<=href=\\\\').+?(?=\\\\')\\\", content)\\n webpage_regex = re.compile('<a href=(.*?)</a>', re.IGNORECASE)\\n # list of all links from the webpage\\n url_list = webpage_regex.findall(html)\\n with open('E:\\\\淘女郎首页图片list.txt','w') as f:\\n for each in url_list:\\n f.write(each+'\\\\n')\\n return url_list\",\n \"def get_links(html):\\n # a regular expression to extract all links from thr webpage\\n webpage_regex = re.compile('<a[^>] + href = [\\\"\\\\'](.*?)[\\\"\\\\']]', re.IGNORECASE)\\n # list of all links from the webpage\\n return webpage_regex.findall(html)\",\n \"def extract_link_urls(html, page_url):\\n pattern = \\\"\\\"\\\"\\n <a # beginning of anchor tag\\n .+? # stuff before href attribute\\n href=\\\" # beginning of href attribute\\n (.+?\\\\.html?) # contents of href attribute (HTML only)\\n \\\" # end of href attribute\\n .+? # stuff after href attribute\\n > # end of the anchor tag\\n \\\"\\\"\\\"\\n\\n matches = re.findall(pattern, html, re.VERBOSE)\\n\\n # Found link URLs that don't leave the site.\\n link_urls = []\\n\\n # Only add intra-site link URLs to the returned list.\\n for match in matches:\\n if match[:7] != \\\"http://\\\":\\n link_urls.append(normalize_url(match, page_url))\\n\\n return link_urls\",\n \"def extract_url(input_string):\\n if input_string:\\n url_search = re.search(\\n r'http[s]?://(?:[a-zA-Z]|[0-9]|[$-_@.&+]|[!*\\\\(\\\\),]|(?:%[0-9a-fA-F][0-9a-fA-F]))+',\\n input_string)\\n\\n if bool(url_search):\\n return url_search.group()\\n\\n return None\",\n \"def processUrl(url):\\n domain = 'http://www.gsmarena.com/'\\n if domain not in url:\\n url = urllib.parse.urljoin(domain, url)\\n return url\",\n \"def formatURL(self, url):\\n pattern = r'(imdb\\\\.com\\\\/title\\\\/(.*/))'\\n urls = re.findall(pattern, url)\\n urls = urls[0]\\n new_url = urls[0]\\n new_url = \\\"https://www.\\\" + new_url\\n title_code = urls[1].replace(\\\"/\\\", \\\"\\\")\\n return new_url\",\n \"def GetUrlFirst(self):\\n self.url = \\\"https://www.taobao.com/\\\"\\n self.host = \\\"www.taobao.com\\\"\\n self.referer = \\\"https://www.taobao.com/\\\"\\n content = self.GetContent()\\n __clear__ = '<a href=.*?</a>'\\n match = open(self.base_dir_url+\\\"url_first.html\\\", 'w')\\n try:\\n all_link = re.findall(__clear__, content, re.S)\\n print \\\"All links of the web page is: \\\", len(all_link)\\n self.DealUrlFirst(match, all_link)\\n except:\\n print \\\"Something wrong is happening!\\\"\\n finally:\\n match.close()\\n match.close()\",\n \"async def parse_url(self, url: str, delay: int = 0) -> BeautifulSoup:\\n if url != self.driver.current_url:\\n self.driver.get(url)\\n return BeautifulSoup(self.driver.page_source, 'lxml')\",\n \"def get_full_url(self):\\n full_url = home_page + self.source_link\\n return full_url\",\n \"def get_links(html):\\r\\n\\twebpage_regex = re.compile('<a[^>]+href=[\\\"\\\\'](.*?)[\\\"\\\\']',re.IGNORECASE)\\r\\n\\t# list of all links from the webpage\\r\\n\\treturn webpage_regex.findall(html)\",\n \"def get_links_from_body(body):\\n return [get_base(url) for url in extract_urls(body)]\",\n \"def parse_link(self,data,api):\\n return REACT_API_DOCS_URL + data.FILE.split('/')[1] + api.find('a',attrs = {'class': 'hash-link'}).attrs['href']\",\n \"def __expandURL(self, link):\\n try:\\n return requests.get(link).url\\n except Exception:\\n return link\",\n \"def parser(self, page_url, html_cont):\\n if page_url is None or html_cont is None:\\n return\\n soup = BeautifulSoup(html_cont, 'html.parser')\\n new_urls = self._get_new_urls(page_url, soup)\\n new_data = self._get_new_data(page_url, soup)\\n return new_urls, new_data\",\n \"def url_build(web_url, url_part):\\r\\n url_full = urljoin(web_url,url_part.get('href'))\\r\\n return url_full\",\n \"def get_article_webpage_list(self, news_feed_webpage):\\n url_list = list()\\n # Use HTML parser to extract appropriates urls\\n lemonde_parser = LeMondeHTMLParser()\\n lemonde_parser.feed(news_feed_webpage)\\n partial_url_list = lemonde_parser.links\\n\\n\\n # add the base url of the website if not present in the article url\\n for url in partial_url_list:\\n if not 'http' in url:\\n url_list.append(self.base_url + url)\\n else:\\n url_list.append(url)\\n\\n return url_list\",\n \"def remove_url(text):\\r\\n url = re.sub('https?://[A-Za-z0-9./]+', '', text)\\r\\n return url\",\n \"def process_link(self, inp):\\n url = inp\\n try:\\n request = urllib2.Request(url)\\n request.add_header('User-Agent', self.browsers[randint(0, 28)])\\n request.add_header('Accept',\\n ('text/html,application/xhtml+xml,'\\n 'application/xml;q=0.9,*/*;q=0.8'))\\n request.add_header('Accept-Language', 'en-us,en;q=0.5')\\n soup = BeautifulSoup(urllib2.urlopen(request).read())\\n content_div = soup.findAll(id=\\\"content\\\")[0]\\n raw_text = clean_html(str(content_div))\\n f = open('wiki_text2.txt', 'w')\\n f.write(raw_text)\\n f.close()\\n return self.process_text(raw_text)\\n except:\\n traceback.print_exc()\\n raise \\\"cant process link :traceback:%s\\\" % traceback.format_exc()\",\n \"def extract_next_page(parser):\\r\\n url = ''\\r\\n table = parser.table.find_all('table')[1]\\r\\n tr = table.findAll('tr')\\r\\n url = url + str(tr[len(tr) - 1].a.get('href'))\\r\\n\\r\\n return url\",\n \"def url( self ):\\n return str(self._urlEdit.text())\",\n \"def find_feed_in_html(html_content: bytes, from_url: str) -> Optional[str]:\\n soup = bs4.BeautifulSoup(html_content, 'html.parser')\\n for type_ in ('application/atom+xml', 'application/rss+xml'):\\n link = soup.find('link', type=type_)\\n if not link:\\n continue\\n\\n link = link.get('href')\\n if not link:\\n continue\\n\\n return urllib.parse.urljoin(from_url, link)\",\n \"def get_info_of_url(url):\\n pass\",\n \"def extract(msg):\\n\\n rgx = url_regex()\\n found = re.findall(rgx, msg)\\n links_set = Set()\\n for link in found:\\n # dunno if I like this. don't need seperate groups to make this work.\\n links_set.add(link[0].strip())\\n return list(links_set)\",\n \"def test_html_url_link():\\n feed = atom.feed_from_annotations([], mock.Mock(), mock.Mock(), html_url=\\\"html_url\\\")\\n\\n assert feed[\\\"links\\\"][1] == {\\n \\\"rel\\\": \\\"alternate\\\",\\n \\\"type\\\": \\\"text/html\\\",\\n \\\"href\\\": \\\"html_url\\\",\\n }\",\n \"def get_torrent(html_url):\\n sess = HTMLSession()\\n r = sess.get(html_url)\\n torrent_url = [u for u in r.html.links if u.endswith(\\\".torrent\\\")][0]\\n torrent_fname = [f for f in torrent_url.split(\\\"/\\\") if f.endswith(\\\".torrent\\\")][0]\\n print(f\\\"Obtained Torrent File URL:\\\")\\n print(f\\\"{torrent_url}\\\")\",\n \"def __ParseUrl(url):\\n return urlparse(url)\",\n \"def getLineInformation(html, reg=['(?<=<\\\\/h2><li>).*?<\\\\/a>', '<.*a.*?>', '查公交上八六八四cn']):\\n tmp = re.findall(reg[0], html)[0]\\n Info = re.sub(reg[1], '', tmp)\\n info = re.sub(reg[2], '', Info)\\n return info\",\n \"def get_html(url):\\n req = urllib.request.Request(\\n url,\\n headers={\\n 'User-Agent': 'Python Learning Program',\\n 'From': 'hklee310@gmail.com'\\n }\\n )\\n resp = urllib.request.urlopen(req)\\n\\n if resp.code == 200:\\n return resp.read() # returns the html document\\n else:\\n return None\",\n \"def find_link(html_content):\\n soup = BeautifulSoup(html_content, \\\"html.parser\\\")\\n paragraphs = soup.find_all('p')\\n for p in paragraphs:\\n string = ''\\n for element in p:\\n if type(element) == bs4.element.NavigableString:\\n string += element\\n elif type(element) == bs4.element.Tag and element.name == 'a':\\n if balanced_parenths(string):\\n return element\\n else:\\n string += element.get_text()\\n return None\",\n \"def test_expanded_url(self):\\n tweet_object = self.load_tweet('basic')\\n tweet_text = self.api.html_for_tweet(tweet_object,\\n use_expanded_url=True)\\n # Make sure full url is in HTML\\n self.assertTrue('http://google.com' in tweet_text)\"\n]","isConversational":false},"negative_scores":{"kind":"list like","value":["0.6942664","0.694266","0.67256683","0.6684412","0.66800857","0.65427136","0.65086895","0.64643157","0.6385671","0.63537264","0.6315027","0.630541","0.62315476","0.61999756","0.6182553","0.6172405","0.6132446","0.6130918","0.6104548","0.60942876","0.6077041","0.60682946","0.6068219","0.6046267","0.6035202","0.59942573","0.5981191","0.5951945","0.59452933","0.59356356","0.593272","0.591864","0.5911488","0.5889562","0.5887134","0.58869284","0.5884468","0.58743984","0.58737713","0.58727336","0.58622193","0.58592546","0.5851604","0.58465403","0.5842096","0.58403575","0.583267","0.5791722","0.5770199","0.5769711","0.5767315","0.5766842","0.57668316","0.57568586","0.57454777","0.57431954","0.57286304","0.572426","0.5720454","0.5712276","0.5704509","0.56758296","0.5665813","0.565815","0.5658038","0.56521225","0.564333","0.56422615","0.56419796","0.56315935","0.5623344","0.5622833","0.56157905","0.5604626","0.559796","0.55922145","0.55903435","0.55734557","0.55692536","0.5563454","0.5562741","0.5562002","0.5556477","0.55453384","0.55280286","0.5519373","0.55058366","0.5496848","0.549338","0.54888046","0.54821956","0.54786474","0.54733","0.54692304","0.54620355","0.54617363","0.5457741","0.5454919","0.54533505","0.54487675"],"string":"[\n \"0.6942664\",\n \"0.694266\",\n \"0.67256683\",\n \"0.6684412\",\n \"0.66800857\",\n \"0.65427136\",\n \"0.65086895\",\n \"0.64643157\",\n \"0.6385671\",\n \"0.63537264\",\n \"0.6315027\",\n \"0.630541\",\n \"0.62315476\",\n \"0.61999756\",\n \"0.6182553\",\n \"0.6172405\",\n \"0.6132446\",\n \"0.6130918\",\n \"0.6104548\",\n \"0.60942876\",\n \"0.6077041\",\n \"0.60682946\",\n \"0.6068219\",\n \"0.6046267\",\n \"0.6035202\",\n \"0.59942573\",\n \"0.5981191\",\n \"0.5951945\",\n \"0.59452933\",\n \"0.59356356\",\n \"0.593272\",\n \"0.591864\",\n \"0.5911488\",\n \"0.5889562\",\n \"0.5887134\",\n \"0.58869284\",\n \"0.5884468\",\n \"0.58743984\",\n \"0.58737713\",\n \"0.58727336\",\n \"0.58622193\",\n \"0.58592546\",\n \"0.5851604\",\n \"0.58465403\",\n \"0.5842096\",\n \"0.58403575\",\n \"0.583267\",\n \"0.5791722\",\n \"0.5770199\",\n \"0.5769711\",\n \"0.5767315\",\n \"0.5766842\",\n \"0.57668316\",\n \"0.57568586\",\n \"0.57454777\",\n \"0.57431954\",\n \"0.57286304\",\n \"0.572426\",\n \"0.5720454\",\n \"0.5712276\",\n \"0.5704509\",\n \"0.56758296\",\n \"0.5665813\",\n \"0.565815\",\n \"0.5658038\",\n \"0.56521225\",\n \"0.564333\",\n \"0.56422615\",\n \"0.56419796\",\n \"0.56315935\",\n \"0.5623344\",\n \"0.5622833\",\n \"0.56157905\",\n \"0.5604626\",\n \"0.559796\",\n \"0.55922145\",\n \"0.55903435\",\n \"0.55734557\",\n \"0.55692536\",\n \"0.5563454\",\n \"0.5562741\",\n \"0.5562002\",\n \"0.5556477\",\n \"0.55453384\",\n \"0.55280286\",\n \"0.5519373\",\n \"0.55058366\",\n \"0.5496848\",\n \"0.549338\",\n \"0.54888046\",\n \"0.54821956\",\n \"0.54786474\",\n \"0.54733\",\n \"0.54692304\",\n \"0.54620355\",\n \"0.54617363\",\n \"0.5457741\",\n \"0.5454919\",\n \"0.54533505\",\n \"0.54487675\"\n]","isConversational":false},"document_score":{"kind":"string","value":"0.7369381"},"document_rank":{"kind":"string","value":"0"}}},{"rowIdx":75376,"cells":{"query":{"kind":"string","value":"Extracts text from html"},"document":{"kind":"string","value":"def extract_text(td):\n text = td.find('a',href=True).text\n return text"},"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 extract_text(html, guess_punct_space=True):\n sel = cleaned_selector(html)\n return selector_to_text(sel, guess_punct_space=guess_punct_space)","def html_to_text(html):\n s = TextExtractorHTMLParser()\n s.feed(html)\n return s.get_text()","def extract_page_text(html):\n soup = bs4.BeautifulSoup(html)\n\n # Remove <script/> and <style/> content\n for script in soup([\"script\", \"style\"]):\n script.extract()\n\n text = soup.get_text()\n\n # Strip leading and trailing whitespace from each line, then join all the\n # non-empty lines together.\n lines = (line.strip() for line in text.splitlines())\n text = '\\n'.join(line for line in lines if line)\n\n return text","def extractText(html_code):\n html_tree = html.fromstring(html_code)\n chapter_list = html_tree.find_class(\"chapter\")\n chapter_text = chapter_list[0].text_content()\n return chapter_text","def _get_text(raw_html):\n bs = BeautifulSoup(raw_html)\n text_nodes = bs.find_all(_is_text_tag)\n text_elements = [_get_child_text(node) for node in text_nodes]\n return ' '.join(chain(*chain(*text_elements)))","def extract(content):\n parser = MyHTMLParser()\n parser.textBody = []\n parser.feed(content)\n textBody = parser.textBody\n textBody = \" \".join(textBody)\n textBody = textBody.replace('\\xa0', \" \")\n return textBody.strip()","def extractText(text):\n soup = BeautifulSoup(text, 'html.parser')\n for code in soup.find_all('code'):\n code.decompose()\n return soup.get_text()","def extract_all_text(self, url, html_doc):\n self.title_text = self.get_title_words(html_doc)\n self.meta_text = self.get_meta_words(html_doc)\n self.url_text = self.get_url_words(url)\n self.heading_text = self.get_heading_words(html_doc)\n self.body_text = self.get_body_words(html_doc)","def get_txt(url):\n soup = make_soup(url)\n\n # remove scripts and styling\n for script in soup([\"script\", \"style\"]):\n script.extract() # rip it out\n text = soup.get_text()\n\n # clean data\n lines = (line.strip() for line in text.splitlines())\n chunks = (phrase.strip() for line in lines for phrase in line.split(\" \"))\n text = '\\n'.join(chunk for chunk in chunks if chunk)\n\n return text","def remove_html( html):\n return html2txt(html)","def extract_text(url, sem):\n with (yield from sem):\n page = yield from get(url)\n\n tree = etree.HTML(page)\n paragraphs = tree.findall('.//*/div[@class=\"entry-content\"]/p')[1:-1]\n return url, b'\\n'.join(map(etree.tostring, paragraphs))","def _html_text(self, html):\n ee = None\n try: return html.html_text()\n except Exception, e: ee = e; pass\n try: return html.xml_text()\n except Exception, e: print \"HtmlDocument/text\", ee, e; pass\n try: return str(html)\n except Exception, e: print \"HtmlDocument/text\", e; return \" \"","def text_from_html(body):\n soup = BeautifulSoup(body, \"html.parser\")\n texts = soup.findAll(text=True)\n visible_texts = filter(tag_visible, texts)\n return \" \".join(t.strip() for t in visible_texts)","def text_from_html(soup):\n\n texts = soup.findAll(text=True)\n visible_texts = filter(tag_visible, texts)\n\n return u\" \".join(t.strip() for t in visible_texts)","def extractText(postSoup):\n for tag in postSoup.findAll(True):\n if tag.name in (\"code\"):\n tag.extract()\n else:\n tag.hidden=True\n\n return postSoup.renderContents()","def extractContent(content):\n soup = BeautifulSoup(content, 'html.parser')\n return soup.get_text()","def get_html2text(html):\n text_maker = html2text.HTML2Text()\n text_maker.body_width = 0\n return text_maker.handle(html)","def extract_page_text(self, bs_object):\n\n # kill all script and style elements\n for script in bs_object([\"script\", \"style\", \"head\"]):\n script.extract() # rip it out\n\n # get text\n text = bs_object.get_text()\n\n # break into lines and remove leading and trailing space on each\n lines = (line.strip() for line in text.splitlines())\n # break multi-headlines into a line each\n chunks = (phrase.strip() for line in lines for phrase in line.split(\" \"))\n # drop blank lines\n text_list_gen = (chunk for chunk in chunks if chunk)\n text_list = list(text_list_gen)\n # print \"TEXT LIST >>>\\n\", text_list\n \n return text_list","def html_to_text(text):\n # type (str) -> str\n soup = BeautifulSoup(text, \"html.parser\")\n return \"\\n\".join(soup.stripped_strings)","def _get_plain_text(self, url, soup, site):\n print('Get plaint text: ' + url)\n title = str(soup.find(class_=self._title_tags[site]))\n content = str(soup.find(class_=self._content_tags[site]))\n # h = html2text.HTML2Text() # uncomment this segment of code\n # h.ignore_links = True # if you want to get plain text\n # h.ignore_images = True\n # title = h.handle(title)\n # content = h.handle(content)\n if title == None or content == None:\n print('Different website structure: ' + url)\n return ''\n return self._clean(title + content, no_punc=True) # with symbols\n # return title + content # without symbols","def html_to_text(html):\n html_parser = 'html5lib'\n soup = BeautifulSoup(html, html_parser)\n pretty_html = soup.prettify()\n pretty_soup = BeautifulSoup(pretty_html, html_parser)\n text = pretty_soup.get_text()\n lines = [s for s in text.splitlines() if not re.search(r'^\\s*$', s)]\n return os.linesep.join(lines)","def get_only_text(url):\r\n page = urlopen(url)\r\n soup = BeautifulSoup(page, \"lxml\")\r\n text = ' '.join(map(lambda p: p.text, soup.find_all('p')))\r\n return soup.title.text, text","def ExtractText(self, zhtmlstring):\n # Not defined in init due to Python2/Python3 complications.\n # pylint: disable=attribute-defined-outside-init\n self._text = []\n self.feed(zhtmlstring)\n self.close()\n self._text = [line.strip() for line in self._text]\n return ' '.join(self._text)","def extract_text(soup, result):\n if soup:\n for t in soup.children:\n if type(t) == NavigableString:\n # Text content node\n result.append(t)\n elif isinstance(t, NavigableString):\n # Comment, CDATA or other text data: ignore\n pass\n elif t.name in whitespace_tags:\n # Tags that we interpret as whitespace, such as <br> and <img>\n result.append_whitespace()\n elif t.name in block_tags:\n # Nested block tag\n result.begin() # Begin block\n extract_text(t, result)\n result.end() # End block\n elif t.name not in exclude_tags:\n # Non-block tag\n extract_text(t, result)","def get_text_hook(raw):\n soup = bs4.BeautifulSoup(quopri.decodestring(raw), features=\"lxml\")\n return soup.text","def ExtractText(self, selector):\n xpaths = map(self.tree.xpath, selector)\n elements = list(chain.from_iterable(xpaths))\n paragraphs = [e.text_content() for e in elements]\n paragraphs = [s.strip() for s in paragraphs if s and not s == ' ']\n\n return paragraphs","def parseHtmlText(text):\n # text processing\n raw = BeautifulSoup(text.text, 'html.parser').get_text()\n nltk.data.path.append('./nltk_data/') # set the path\n tokens = nltk.word_tokenize(raw)\n text = nltk.Text(tokens)\n # remove punctuation, count raw words\n nonPunct = re.compile('.*[A-Za-z].*')\n raw_words = [w for w in text if nonPunct.match(w)]\n raw_word_count = Counter(raw_words)\n # stop words\n no_stop_words = [w for w in raw_words if w.lower() not in stops]\n no_stop_words_count = Counter(no_stop_words)\n return raw_word_count, no_stop_words_count","def _get_text(self, remove_newlines=True):\n if not self.text:\n url = self.url\n try:\n self.log.debug(\"Try to get content from page {}\".format(url))\n r = requests.get(url)\n except requests.exceptions.RequestException as e:\n self.log.warn(\"Unable to get page content of the url: {url}. \"\n \"The reason: {exc!r}\".format(url=url, exc=e))\n raise ParsingError(e.strerror)\n\n ud = UnicodeDammit(r.content, is_html=True)\n\n enc = ud.original_encoding.lower()\n declared_enc = ud.declared_html_encoding\n if declared_enc:\n declared_enc = declared_enc.lower()\n # possible misregocnition of an encoding\n if (declared_enc and enc != declared_enc):\n detect_dict = chardet.detect(r.content)\n det_conf = detect_dict[\"confidence\"]\n det_enc = detect_dict[\"encoding\"].lower()\n if enc == det_enc and det_conf < THRESHOLD_OF_CHARDETECT:\n enc = declared_enc\n # if page contains any characters that differ from the main\n # encoding we will ignore them\n content = r.content.decode(enc, \"ignore\").encode(enc)\n htmlparser = etree.HTMLParser(encoding=enc)\n root = etree.HTML(content, parser=htmlparser)\n etree.strip_elements(root, html.etree.Comment, \"script\", \"style\")\n text = html.tostring(root, method=\"text\", encoding=\"unicode\")\n\n if remove_newlines:\n self.log.debug(str(type(text)))\n text = re.sub('\\s+', ' ', text)\n self.text = text\n\n return self.text","def strip_html(text):\n soup = BeautifulSoup(text, \"html.parser\")\n return soup.get_text()","def extract_answer_from_html(self, html):\n if html.strip().startswith('<'):\n soup = bs4.BeautifulSoup(html, 'html.parser')\n\n for p in soup.find_all('p'):\n if self.REPLY_RE.match(p.text):\n for el in list(p.previous_elements):\n if isinstance(el, bs4.element.Tag):\n el.decompose()\n p.decompose()\n break\n\n return str(soup)\n else:\n # plain text\n match = self.REPLY_RE.search(html)\n if match:\n return html[match.end(0):]\n\n return html","def getHTMLText(url):\n try:\n r = requests.get(url, timeout = 300)\n r.raise_for_status()\n r.encoding = r.apparent_encoding\n return r.text\n except:\n return \"\"","def extract_raw_text(soup, url):\n \n title_class = \"nom-notice\"\n title = soup.find(class_=title_class)\n raw_infos = {}\n raw_infos['name'] = title.contents[0].replace(u'\\xa0', ' ')\n \n notice = soup.find(class_=\"notice\")\n \n summary = notice.find(class_=\"chapo\")\n if summary is not None:\n first_para = summary.find_all('p', recursive=False)[-1]\n first_para.tag = 'div'\n first_para['class'] = 'summary'\n raw_infos['summary'] = unicode(first_para)\n \n else:\n raw_infos['summary'] = unicode('')\n\n article = notice.find(class_='texte')\n if article is not None:\n article['class'] = 'article'\n raw_infos['article'] = unicode(article)\n \n sources = notice.find(class_='sources')\n raw_infos['sources'] = unicode(sources)\n \n works = notice.find(class_='oeuvres')\n if works is not None:\n works['class'] = 'works'\n raw_infos['works'] = unicode(works)\n \n # In function that writes, encode everything to bytes! .encode('utf-8')\n return raw_infos","def get_gp_text_description(html):\n m = re.search('<div id=\"doc-description-container\"', html)\n desc_section_start = html[m.start():]\n m = re.search('</div>', desc_section_start)\n desc_section = desc_section_start[:m.start()]\n cleaned_desc = filter(lambda x: x in string.printable, desc_section)\n parser = HTMLParser()\n return parser.unescape(nltk.clean_html(cleaned_desc))","def get_text_only(self, soup):\n val = soup.string\n # see if we have a text element\n if val is None:\n conts = soup.contents\n resulttext = ''\n # not text so continue recursing through the tags\n for tag in conts:\n subtext = self.get_text_only(tag)\n resulttext += subtext + '\\n'\n return resulttext\n return val.strip()","def get_text_content(url: str) -> str:\n\n url = _fix_url(url)\n\n return get(url).text","def get_text_from_page(page_content: str) -> str:\n\n soup = BeautifulSoup(page_content, 'lxml')\n\n for tag in soup.findAll([\"script\", \"style\"]):\n tag.extract()\n\n lines = (line.strip() for line in soup.get_text().splitlines())\n\n return '\\n'.join(line for line in lines if line) # remove empty lines","def htmlExtractPart(page, tag, attrs):\n try:\n htmlParsePage(page)\n except UnicodeEncodeError:\n logging.warn('could not parse html')\n return page['data']\n\n bs = page['parsedHtml']\n el = bs.find(tag, attrs=attrs)\n if el != None:\n logging.debug('Successfully stripped html')\n return str(el)\n else:\n logging.debug('Could not strip html')\n return page['data']\n return","def html_text(self):\n return g.markdown_wiki.convert(self.data.text)","def text_of(soup):\n return ''.join([str(x) for x in soup.findAll(text=True)])","def html_to_text(html, target_elements=None):\n soup = BeautifulSoup(html)\n\n for script in soup(\n [\"script\", \"style\"]\n ): # remove all javascript and stylesheet code\n script.extract()\n\n targets = []\n\n if target_elements:\n targets = soup.find_all(target_elements)\n\n if target_elements and len(targets) > 3:\n text = \" \".join([t.text for t in targets])\n else:\n text = soup.get_text()\n\n # break into lines and remove leading and trailing space on each\n lines = (line.strip() for line in text.splitlines())\n # break multi-headlines into a line each\n chunks = (phrase.strip() for line in lines for phrase in line.split(\" \"))\n # drop blank lines\n text = \"\\n\".join(chunk for chunk in chunks if chunk)\n return text","def get_text(self):\n text_element = self.page.find(id=self.text_location)\n return text_element.get_text()","def obtain_text():\n pass","def _get_text(self, element):\n # for text in element.itertext():\n for text in self.iter_main_text(element):\n yield text.strip()","def _extract_tags(html):\n tags = re.findall(r'<[^>]+>', html)\n\n return tags","def _parse_text(self, element):\n try:\n return element.text\n except AttributeError:\n return None","def get_article_text(self, article_webpage):\n lemonde_parser = LeMondeHTMLParser()\n lemonde_parser.feed(article_webpage)\n return lemonde_parser.article_data","def _html(self, text):\r\n html = URL_REGEX.sub(self._parse_urls, text)\r\n html = USERNAME_REGEX.sub(self._parse_users, html)\r\n html = LIST_REGEX.sub(self._parse_lists, html)\r\n return HASHTAG_REGEX.sub(self._parse_tags, html)","def text(html_element):\n return html_element.xpath(\"string()\") or '' # avoid returning None","def get_text(self, selector):\n el = self.locate_element(selector)\n return el.text","def get_from_html_text(resultset, target):\n index = resultset.find(target)+len(target)+2\n return resultset[index:index+140].split(\"'\")[0].lower()","def getText():","def getText():","def getText():","def getText():","def getText():","def scrubHTML( html ):\n parser = StrippingParser()\n parser.feed( html )\n parser.close()\n return parser.result","def strip_html_tags(text):\r\n soup = BeautifulSoup(text, 'lxml')\r\n stripped_text = soup.get_text(separator=\" \")\r\n return stripped_text","def text(self):\n txt = self.web_element.text\n return txt","def extractCode(text):\n soup = BeautifulSoup(text, 'html.parser')\n code_str = str()\n for code in soup.find_all('code'):\n code_without_tags = code.get_text()\n code_str += code_without_tags\n return code_str","def html_to_text(html_message):\r\n process = Popen(\r\n ['lynx', '-stdin', '-display_charset=UTF-8', '-assume_charset=UTF-8', '-dump'],\r\n stdin=PIPE,\r\n stdout=PIPE\r\n )\r\n # use lynx to get plaintext\r\n (plaintext, err_from_stderr) = process.communicate(\r\n input=html_message.encode('utf-8')\r\n )\r\n\r\n if err_from_stderr:\r\n log.info(err_from_stderr)\r\n\r\n return plaintext","def strip_html(inputString):\r\n return BeautifulSoup(inputString, \"html.parser\").text","def __yahoo_parse_text(self, content):\n text = ''\n # Process all paragraphs.\n paragraphs = content.find_all('p')\n for par in paragraphs:\n text += '<p>' + par.getText(separator=' ') + '</p>'\n # Remove all extra whitespace (single space remains).\n text = ' '.join(text.strip().split())\n # Result\n return text","def strip_html_tags(text):\n soup = BeautifulSoup(text, \"html.parser\")\n stripped_text = soup.get_text(separator=\" \")\n return stripped_text","def strip_html_tags(text):\n soup = BeautifulSoup(text, \"html.parser\")\n stripped_text = soup.get_text(separator=\" \")\n return stripped_text","def extract_html(tag):\n s = unescape(remove_all_empty_tags(tag).renderContents())\n if type(s) != unicode:\n return s.decode('utf-8')\n return s","def getLineInformation(html, reg=['(?<=<\\/h2><li>).*?<\\/a>', '<.*a.*?>', '查公交上八六八四cn']):\n tmp = re.findall(reg[0], html)[0]\n Info = re.sub(reg[1], '', tmp)\n info = re.sub(reg[2], '', Info)\n return info","def extract_text(filename):\n\tpdfFileObj = open(filename, \"rb\")\n\tpdfReader = PyPDF2.PdfFileReader(pdfFileObj)\n\n\tmytext = \"\"\n\n\tfor pageNum in range(pdfReader.numPages):\n\t\tpageObj = pdfReader.getPage(pageNum)\n\t\tmytext += pageObj.extractText()\n\n\tpdfFileObj.close()\n\n\treturn mytext","def read_from_web(url):\n\tpage = urlopen(url)\n\tsoup = BeautifulSoup(page, \"lxml\")\n\ttext = ' '.join(map(lambda p: p.text, soup.find_all('p')))\n\treturn soup.title.text, text","def get_text(self):","def extract_code_text(bs, index):\r\n texts = ''\r\n code = ''\r\n try:\r\n for a in bs.find_all('div', class_='post')[index].find_all('p'):\r\n texts += a.text\r\n for a in bs.find_all('div', class_='post')[index].find_all('code'):\r\n code += a.text\r\n except:\r\n pass\r\n return texts, code","def extract_contents(enml):\n return re.search(r'<en-note[^>]*>(.*?)</en-note>', enml, re.S).group(1)","def extract(self, doc, raw_html):\n super(KenyaTodayCrawler, self).extract(doc, raw_html)\n\n soup = BeautifulSoup(raw_html)\n\n # gather title\n doc.title = soup.find(attrs={\"property\":\"og:title\"})['content']\n\n #gather publish date\n date = self.extract_plaintext(soup.select(\"main.content .entry-meta .entry-time\"))\n doc.published_at = self.parse_timestamp(date)\n\n nodes = soup.select(\".content .entry-content p\")\n self.log.info(nodes)\n if len(nodes) > 1:\n doc.summary = self.extract_plaintext(nodes[0:1])\n doc.text = \"\\n\\n\".join(p.text.strip() for p in nodes[2:])\n\n doc.author = Author.unknown()","def get_visible_text(_text):\n #text = _text.decode('utf-8', 'ignore').lower() # Don't get hung up on unicode chars in foreign languages\n text = _text.lower()\n text = re.compile(r'<').sub(' <',text) # These two lines keep words from getting smushed\n text = re.compile(r'>').sub('> ',text) # together when two they are only separated by tags.\n soup = BeautifulSoup(text, 'lxml')\n\n # decompose removes the tag and it's text content completely\n for s in soup(['script','code','style']):\n s.decompose()\n\n text = soup.get_text()\n # compress space to reduce footprint and fit on one line so it neatly fits in csv file\n text = re.compile(r'\\s+').sub(' ',text).strip()\n return text","def _extract_html(self, url):\n self.response = requests.get(url, timeout=5)\n self.html = BeautifulSoup(self.response.content, \"lxml\") if self.response.ok else None\n # return self.html","def parsingconvtext(retrievedtext,customtextlist):\r\n if not retrievedtext: #in case empty text \r\n retrievedtext=changenonetostr(retrievedtext)\r\n newtext=BeautifulSoup(retrievedtext).get_text() \r\n #newtext=changenonetostr(retrievedtext)\r\n #newtext=BeautifulSoup(newtext).get_text() \r\n #remove http links\r\n newtext=re.sub(r'http\\S+', '', newtext)\r\n newtext=re.sub(r'\\r\\r\\r\\n', ' ', newtext)\r\n #remove LL specific text\r\n if customtextlist:\r\n for i in customtextlist:\r\n newtext=re.sub(i, '', newtext)\r\n return newtext","def get_content(response_text):\n soup = BeautifulSoup(response_text, 'html.parser')\n try:\n return soup.find('span', class_='total-entries').get_text()\n except AttributeError:\n return \"Failed to get results\"","def extractText(self, filename):\n file_path = os.path.join(folder_upload, filename)\n file_text = self.textExtractor.get_text(file_path)\n return file_text","def get_inner_text(self, css_selector):\n element = self.driver.find_elements_by_css_selector(css_selector)\n if len(element) > 0:\n information = element[0].get_attribute(\"innerText\").strip()\n return information","def parse_html(html):\n parser = lxml.html.HTMLParser(encoding='utf8')\n return lxml.html.fromstring(html.encode('utf8'), parser=parser)","def get_article_body(url):\n G.go(url)\n text = G.doc.select('////div[@class=\"StandardArticleBody_body\"]').text()\n return text","def process_html(raw_html_text):\n\tbounds_list = pre_proc.get_page_bounds(raw_html_text)\n\n\tprocessed_text_html = ( pre_proc.split_spans(raw_html_text) \t| p(pre_proc.delete_non_textual_elements)\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t| p(pre_proc.delete_headers, bounds_list)\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t| p(pre_proc.delete_vertical_text)\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t| p(pre_proc.sort_html)\n\t\t)\n\treturn processed_text_html","def strip_tags(self, html):\n s = MLStripper()\n s.feed(html)\n return s.get_data()","def remove_html_tags(html_text: str) -> str:\n document = fromstring(html_text)\n text = document.text_content()\n return text.strip()","def parseSearchHtml(self):\n pass","def parseSearchHtml(self):\n pass","def remove_html(txt):\r\n TAG_RE = re.compile(r'<[^>]+>')\r\n return TAG_RE.sub(\"\", txt).strip()","def read_url(url):\n\thtml = urllib.urlopen(url).read().decode('utf8')\n\ttext = plaintext(html)\n\tlines = text.splitlines()\n\tlines = [line for line in lines if line.strip() != '']\n\tlines = [line for line in lines if line.startswith('<') == False]\n\tlines = [line for line in lines if ('{') not in line]\n\n\tprint lines\n \n\ttext ='\\n'.join(line for line in lines)\n\n\treturn text","def get_text(self, selector):\n self._wait_element_localed(self.driver, selector)\n element = self.get_element(selector)\n text = element.text\n\n return text","def get_article_text(article):\n soup = BeautifulSoup(article.content)\n return soup.get_text()","def parse_html(city, html):\n return city.parse_html(html)","def get_text(url):\n try:\n result = requests.get(url, headers=DEFAULT_REQUEST_HEADERS)\n result.raise_for_status()\n except requests.HTTPError as err:\n raise URLGetTextError(err)\n\n return result.text","def get_text(self):\n return self.text[:500]","def get_article_body(url):\n headers = {'User-Agent': 'Codeup Data Science'}\n response = get(url, headers=headers)\n soup = BeautifulSoup(response.content, \"html.parser\") \n return soup.find('div', itemprop='text').text","def parse_text(self, page):\n text = page.find(self.tag_prefix + self.revision_tag).find(self.tag_prefix + self.text_tag).text\n title = page.find(self.tag_prefix + self.title_tag).text\n categories = []\n #\n text = self.parse_archivo(text)\n text = self.parse_foto(text)\n text = self.parse_by_line(text)\n text = self.parse_link(text)\n text = self.parse_url(text)\n text = self.parse_fecha(text)\n text = self.parse_bracketed_word(text)\n #\n if text:\n categories = re.findall(self.category_finder_regex, text)\n #\n text = self.parse_category(text)\n text = self.parse_other_language(text)\n text = self.parse_table_regex(text)\n text = self.parse_ver_fuente(text)\n text = self.remove_extra_text(text)\n text = self.remove_extra_characters(text)\n\n categorias = []\n for cat in categories:\n categorias.append(cat[6])\n\n if text:\n if 'REDIRECT' in text or 'redirect' in text:\n return None\n\n return Article(title=title, content=text, categories=categorias)","def main(html_file):\n \n biglist = name_extractor(html_file)\n text = '\\n'.join(biglist)\n print (text)","def get_text():\n global x\n for i in soup.body(\"aside\", {\"id\": \"text-2\"}):\n x = i.get_text()","def extract_article_text(url):\n pwds = Passwords()\n token = pwds.getPassword('apibot.token')\n\n params = {}\n params['token'] = token\n params['url'] = url\n params['timeout'] = 20000 # timeout in ms\n\n summary_url = 'http://www.diffbot.com/api/article?%s' % urllib.urlencode(\n params)\n code, content, resp = util.get_page(summary_url, max_size=512*1024)\n if code != 200:\n print \"Got %s requesting %s\" % (str(code), summary_url)\n return None\n\n try:\n results = json.loads(content)\n except:\n print code, content, resp\n return \"The summarizer doesn't like this.\"\n if \"title\" in results:\n if \"text\" in results:\n return \"%s\\f%s\" % (results[\"title\"], results[\"text\"])\n else:\n return results[\"title\"]\n elif \"text\" in results:\n return results[\"text\"]\n else:\n print results\n return None","def get_text(element):\n if element.tag.split('}')[-1] == 'h3':\n return \"\\n\" # New section (double newline)\n return re.sub(\"\\s+\", \" \", ((element.text or '') + ''.join(map(get_text, element)) + (element.tail or '')))","def extract_one( html: str, fpath: Path ):\n # %%\n doc = BeautifulSoup( html, features='html.parser')\n\n ret = { 'linkedin_handle': fpath.name.split('.')[0] }\n _parse_top_card( ret, doc )\n # %%\n ret['about'] = _extract_about( doc )\n # if len(ret['about']) < 100 and ret['about'].find('ver más') > 0:\n # print( f\"\\nVer más detected: \\nabout:{ret['about']} fpath={fpath}\" )\n\n ret['about_stats'] = {'about_eng_ratio': _common_english_ratio(ret['about'])}\n # %%\n ret['work_experience'] = _parse_experiences( doc )\n ret['work_stats'] = calc_work_stats( ret['work_experience'])\n # %%\n ret['skills'] = proc_skills_section( doc )\n ret['education'] = _parse_education( doc )\n ret['education_stats'] = _education_stats( ret['education'])\n ret['accomplishments'] = _extract_accomplishments(doc)\n ret['profile_text_stats'] = profile_text_stats( doc )\n # %%\n return ret\n # %%","def get_text(self):\n return self.text"],"string":"[\n \"def extract_text(html, guess_punct_space=True):\\n sel = cleaned_selector(html)\\n return selector_to_text(sel, guess_punct_space=guess_punct_space)\",\n \"def html_to_text(html):\\n s = TextExtractorHTMLParser()\\n s.feed(html)\\n return s.get_text()\",\n \"def extract_page_text(html):\\n soup = bs4.BeautifulSoup(html)\\n\\n # Remove <script/> and <style/> content\\n for script in soup([\\\"script\\\", \\\"style\\\"]):\\n script.extract()\\n\\n text = soup.get_text()\\n\\n # Strip leading and trailing whitespace from each line, then join all the\\n # non-empty lines together.\\n lines = (line.strip() for line in text.splitlines())\\n text = '\\\\n'.join(line for line in lines if line)\\n\\n return text\",\n \"def extractText(html_code):\\n html_tree = html.fromstring(html_code)\\n chapter_list = html_tree.find_class(\\\"chapter\\\")\\n chapter_text = chapter_list[0].text_content()\\n return chapter_text\",\n \"def _get_text(raw_html):\\n bs = BeautifulSoup(raw_html)\\n text_nodes = bs.find_all(_is_text_tag)\\n text_elements = [_get_child_text(node) for node in text_nodes]\\n return ' '.join(chain(*chain(*text_elements)))\",\n \"def extract(content):\\n parser = MyHTMLParser()\\n parser.textBody = []\\n parser.feed(content)\\n textBody = parser.textBody\\n textBody = \\\" \\\".join(textBody)\\n textBody = textBody.replace('\\\\xa0', \\\" \\\")\\n return textBody.strip()\",\n \"def extractText(text):\\n soup = BeautifulSoup(text, 'html.parser')\\n for code in soup.find_all('code'):\\n code.decompose()\\n return soup.get_text()\",\n \"def extract_all_text(self, url, html_doc):\\n self.title_text = self.get_title_words(html_doc)\\n self.meta_text = self.get_meta_words(html_doc)\\n self.url_text = self.get_url_words(url)\\n self.heading_text = self.get_heading_words(html_doc)\\n self.body_text = self.get_body_words(html_doc)\",\n \"def get_txt(url):\\n soup = make_soup(url)\\n\\n # remove scripts and styling\\n for script in soup([\\\"script\\\", \\\"style\\\"]):\\n script.extract() # rip it out\\n text = soup.get_text()\\n\\n # clean data\\n lines = (line.strip() for line in text.splitlines())\\n chunks = (phrase.strip() for line in lines for phrase in line.split(\\\" \\\"))\\n text = '\\\\n'.join(chunk for chunk in chunks if chunk)\\n\\n return text\",\n \"def remove_html( html):\\n return html2txt(html)\",\n \"def extract_text(url, sem):\\n with (yield from sem):\\n page = yield from get(url)\\n\\n tree = etree.HTML(page)\\n paragraphs = tree.findall('.//*/div[@class=\\\"entry-content\\\"]/p')[1:-1]\\n return url, b'\\\\n'.join(map(etree.tostring, paragraphs))\",\n \"def _html_text(self, html):\\n ee = None\\n try: return html.html_text()\\n except Exception, e: ee = e; pass\\n try: return html.xml_text()\\n except Exception, e: print \\\"HtmlDocument/text\\\", ee, e; pass\\n try: return str(html)\\n except Exception, e: print \\\"HtmlDocument/text\\\", e; return \\\" \\\"\",\n \"def text_from_html(body):\\n soup = BeautifulSoup(body, \\\"html.parser\\\")\\n texts = soup.findAll(text=True)\\n visible_texts = filter(tag_visible, texts)\\n return \\\" \\\".join(t.strip() for t in visible_texts)\",\n \"def text_from_html(soup):\\n\\n texts = soup.findAll(text=True)\\n visible_texts = filter(tag_visible, texts)\\n\\n return u\\\" \\\".join(t.strip() for t in visible_texts)\",\n \"def extractText(postSoup):\\n for tag in postSoup.findAll(True):\\n if tag.name in (\\\"code\\\"):\\n tag.extract()\\n else:\\n tag.hidden=True\\n\\n return postSoup.renderContents()\",\n \"def extractContent(content):\\n soup = BeautifulSoup(content, 'html.parser')\\n return soup.get_text()\",\n \"def get_html2text(html):\\n text_maker = html2text.HTML2Text()\\n text_maker.body_width = 0\\n return text_maker.handle(html)\",\n \"def extract_page_text(self, bs_object):\\n\\n # kill all script and style elements\\n for script in bs_object([\\\"script\\\", \\\"style\\\", \\\"head\\\"]):\\n script.extract() # rip it out\\n\\n # get text\\n text = bs_object.get_text()\\n\\n # break into lines and remove leading and trailing space on each\\n lines = (line.strip() for line in text.splitlines())\\n # break multi-headlines into a line each\\n chunks = (phrase.strip() for line in lines for phrase in line.split(\\\" \\\"))\\n # drop blank lines\\n text_list_gen = (chunk for chunk in chunks if chunk)\\n text_list = list(text_list_gen)\\n # print \\\"TEXT LIST >>>\\\\n\\\", text_list\\n \\n return text_list\",\n \"def html_to_text(text):\\n # type (str) -> str\\n soup = BeautifulSoup(text, \\\"html.parser\\\")\\n return \\\"\\\\n\\\".join(soup.stripped_strings)\",\n \"def _get_plain_text(self, url, soup, site):\\n print('Get plaint text: ' + url)\\n title = str(soup.find(class_=self._title_tags[site]))\\n content = str(soup.find(class_=self._content_tags[site]))\\n # h = html2text.HTML2Text() # uncomment this segment of code\\n # h.ignore_links = True # if you want to get plain text\\n # h.ignore_images = True\\n # title = h.handle(title)\\n # content = h.handle(content)\\n if title == None or content == None:\\n print('Different website structure: ' + url)\\n return ''\\n return self._clean(title + content, no_punc=True) # with symbols\\n # return title + content # without symbols\",\n \"def html_to_text(html):\\n html_parser = 'html5lib'\\n soup = BeautifulSoup(html, html_parser)\\n pretty_html = soup.prettify()\\n pretty_soup = BeautifulSoup(pretty_html, html_parser)\\n text = pretty_soup.get_text()\\n lines = [s for s in text.splitlines() if not re.search(r'^\\\\s*$', s)]\\n return os.linesep.join(lines)\",\n \"def get_only_text(url):\\r\\n page = urlopen(url)\\r\\n soup = BeautifulSoup(page, \\\"lxml\\\")\\r\\n text = ' '.join(map(lambda p: p.text, soup.find_all('p')))\\r\\n return soup.title.text, text\",\n \"def ExtractText(self, zhtmlstring):\\n # Not defined in init due to Python2/Python3 complications.\\n # pylint: disable=attribute-defined-outside-init\\n self._text = []\\n self.feed(zhtmlstring)\\n self.close()\\n self._text = [line.strip() for line in self._text]\\n return ' '.join(self._text)\",\n \"def extract_text(soup, result):\\n if soup:\\n for t in soup.children:\\n if type(t) == NavigableString:\\n # Text content node\\n result.append(t)\\n elif isinstance(t, NavigableString):\\n # Comment, CDATA or other text data: ignore\\n pass\\n elif t.name in whitespace_tags:\\n # Tags that we interpret as whitespace, such as <br> and <img>\\n result.append_whitespace()\\n elif t.name in block_tags:\\n # Nested block tag\\n result.begin() # Begin block\\n extract_text(t, result)\\n result.end() # End block\\n elif t.name not in exclude_tags:\\n # Non-block tag\\n extract_text(t, result)\",\n \"def get_text_hook(raw):\\n soup = bs4.BeautifulSoup(quopri.decodestring(raw), features=\\\"lxml\\\")\\n return soup.text\",\n \"def ExtractText(self, selector):\\n xpaths = map(self.tree.xpath, selector)\\n elements = list(chain.from_iterable(xpaths))\\n paragraphs = [e.text_content() for e in elements]\\n paragraphs = [s.strip() for s in paragraphs if s and not s == ' ']\\n\\n return paragraphs\",\n \"def parseHtmlText(text):\\n # text processing\\n raw = BeautifulSoup(text.text, 'html.parser').get_text()\\n nltk.data.path.append('./nltk_data/') # set the path\\n tokens = nltk.word_tokenize(raw)\\n text = nltk.Text(tokens)\\n # remove punctuation, count raw words\\n nonPunct = re.compile('.*[A-Za-z].*')\\n raw_words = [w for w in text if nonPunct.match(w)]\\n raw_word_count = Counter(raw_words)\\n # stop words\\n no_stop_words = [w for w in raw_words if w.lower() not in stops]\\n no_stop_words_count = Counter(no_stop_words)\\n return raw_word_count, no_stop_words_count\",\n \"def _get_text(self, remove_newlines=True):\\n if not self.text:\\n url = self.url\\n try:\\n self.log.debug(\\\"Try to get content from page {}\\\".format(url))\\n r = requests.get(url)\\n except requests.exceptions.RequestException as e:\\n self.log.warn(\\\"Unable to get page content of the url: {url}. \\\"\\n \\\"The reason: {exc!r}\\\".format(url=url, exc=e))\\n raise ParsingError(e.strerror)\\n\\n ud = UnicodeDammit(r.content, is_html=True)\\n\\n enc = ud.original_encoding.lower()\\n declared_enc = ud.declared_html_encoding\\n if declared_enc:\\n declared_enc = declared_enc.lower()\\n # possible misregocnition of an encoding\\n if (declared_enc and enc != declared_enc):\\n detect_dict = chardet.detect(r.content)\\n det_conf = detect_dict[\\\"confidence\\\"]\\n det_enc = detect_dict[\\\"encoding\\\"].lower()\\n if enc == det_enc and det_conf < THRESHOLD_OF_CHARDETECT:\\n enc = declared_enc\\n # if page contains any characters that differ from the main\\n # encoding we will ignore them\\n content = r.content.decode(enc, \\\"ignore\\\").encode(enc)\\n htmlparser = etree.HTMLParser(encoding=enc)\\n root = etree.HTML(content, parser=htmlparser)\\n etree.strip_elements(root, html.etree.Comment, \\\"script\\\", \\\"style\\\")\\n text = html.tostring(root, method=\\\"text\\\", encoding=\\\"unicode\\\")\\n\\n if remove_newlines:\\n self.log.debug(str(type(text)))\\n text = re.sub('\\\\s+', ' ', text)\\n self.text = text\\n\\n return self.text\",\n \"def strip_html(text):\\n soup = BeautifulSoup(text, \\\"html.parser\\\")\\n return soup.get_text()\",\n \"def extract_answer_from_html(self, html):\\n if html.strip().startswith('<'):\\n soup = bs4.BeautifulSoup(html, 'html.parser')\\n\\n for p in soup.find_all('p'):\\n if self.REPLY_RE.match(p.text):\\n for el in list(p.previous_elements):\\n if isinstance(el, bs4.element.Tag):\\n el.decompose()\\n p.decompose()\\n break\\n\\n return str(soup)\\n else:\\n # plain text\\n match = self.REPLY_RE.search(html)\\n if match:\\n return html[match.end(0):]\\n\\n return html\",\n \"def getHTMLText(url):\\n try:\\n r = requests.get(url, timeout = 300)\\n r.raise_for_status()\\n r.encoding = r.apparent_encoding\\n return r.text\\n except:\\n return \\\"\\\"\",\n \"def extract_raw_text(soup, url):\\n \\n title_class = \\\"nom-notice\\\"\\n title = soup.find(class_=title_class)\\n raw_infos = {}\\n raw_infos['name'] = title.contents[0].replace(u'\\\\xa0', ' ')\\n \\n notice = soup.find(class_=\\\"notice\\\")\\n \\n summary = notice.find(class_=\\\"chapo\\\")\\n if summary is not None:\\n first_para = summary.find_all('p', recursive=False)[-1]\\n first_para.tag = 'div'\\n first_para['class'] = 'summary'\\n raw_infos['summary'] = unicode(first_para)\\n \\n else:\\n raw_infos['summary'] = unicode('')\\n\\n article = notice.find(class_='texte')\\n if article is not None:\\n article['class'] = 'article'\\n raw_infos['article'] = unicode(article)\\n \\n sources = notice.find(class_='sources')\\n raw_infos['sources'] = unicode(sources)\\n \\n works = notice.find(class_='oeuvres')\\n if works is not None:\\n works['class'] = 'works'\\n raw_infos['works'] = unicode(works)\\n \\n # In function that writes, encode everything to bytes! .encode('utf-8')\\n return raw_infos\",\n \"def get_gp_text_description(html):\\n m = re.search('<div id=\\\"doc-description-container\\\"', html)\\n desc_section_start = html[m.start():]\\n m = re.search('</div>', desc_section_start)\\n desc_section = desc_section_start[:m.start()]\\n cleaned_desc = filter(lambda x: x in string.printable, desc_section)\\n parser = HTMLParser()\\n return parser.unescape(nltk.clean_html(cleaned_desc))\",\n \"def get_text_only(self, soup):\\n val = soup.string\\n # see if we have a text element\\n if val is None:\\n conts = soup.contents\\n resulttext = ''\\n # not text so continue recursing through the tags\\n for tag in conts:\\n subtext = self.get_text_only(tag)\\n resulttext += subtext + '\\\\n'\\n return resulttext\\n return val.strip()\",\n \"def get_text_content(url: str) -> str:\\n\\n url = _fix_url(url)\\n\\n return get(url).text\",\n \"def get_text_from_page(page_content: str) -> str:\\n\\n soup = BeautifulSoup(page_content, 'lxml')\\n\\n for tag in soup.findAll([\\\"script\\\", \\\"style\\\"]):\\n tag.extract()\\n\\n lines = (line.strip() for line in soup.get_text().splitlines())\\n\\n return '\\\\n'.join(line for line in lines if line) # remove empty lines\",\n \"def htmlExtractPart(page, tag, attrs):\\n try:\\n htmlParsePage(page)\\n except UnicodeEncodeError:\\n logging.warn('could not parse html')\\n return page['data']\\n\\n bs = page['parsedHtml']\\n el = bs.find(tag, attrs=attrs)\\n if el != None:\\n logging.debug('Successfully stripped html')\\n return str(el)\\n else:\\n logging.debug('Could not strip html')\\n return page['data']\\n return\",\n \"def html_text(self):\\n return g.markdown_wiki.convert(self.data.text)\",\n \"def text_of(soup):\\n return ''.join([str(x) for x in soup.findAll(text=True)])\",\n \"def html_to_text(html, target_elements=None):\\n soup = BeautifulSoup(html)\\n\\n for script in soup(\\n [\\\"script\\\", \\\"style\\\"]\\n ): # remove all javascript and stylesheet code\\n script.extract()\\n\\n targets = []\\n\\n if target_elements:\\n targets = soup.find_all(target_elements)\\n\\n if target_elements and len(targets) > 3:\\n text = \\\" \\\".join([t.text for t in targets])\\n else:\\n text = soup.get_text()\\n\\n # break into lines and remove leading and trailing space on each\\n lines = (line.strip() for line in text.splitlines())\\n # break multi-headlines into a line each\\n chunks = (phrase.strip() for line in lines for phrase in line.split(\\\" \\\"))\\n # drop blank lines\\n text = \\\"\\\\n\\\".join(chunk for chunk in chunks if chunk)\\n return text\",\n \"def get_text(self):\\n text_element = self.page.find(id=self.text_location)\\n return text_element.get_text()\",\n \"def obtain_text():\\n pass\",\n \"def _get_text(self, element):\\n # for text in element.itertext():\\n for text in self.iter_main_text(element):\\n yield text.strip()\",\n \"def _extract_tags(html):\\n tags = re.findall(r'<[^>]+>', html)\\n\\n return tags\",\n \"def _parse_text(self, element):\\n try:\\n return element.text\\n except AttributeError:\\n return None\",\n \"def get_article_text(self, article_webpage):\\n lemonde_parser = LeMondeHTMLParser()\\n lemonde_parser.feed(article_webpage)\\n return lemonde_parser.article_data\",\n \"def _html(self, text):\\r\\n html = URL_REGEX.sub(self._parse_urls, text)\\r\\n html = USERNAME_REGEX.sub(self._parse_users, html)\\r\\n html = LIST_REGEX.sub(self._parse_lists, html)\\r\\n return HASHTAG_REGEX.sub(self._parse_tags, html)\",\n \"def text(html_element):\\n return html_element.xpath(\\\"string()\\\") or '' # avoid returning None\",\n \"def get_text(self, selector):\\n el = self.locate_element(selector)\\n return el.text\",\n \"def get_from_html_text(resultset, target):\\n index = resultset.find(target)+len(target)+2\\n return resultset[index:index+140].split(\\\"'\\\")[0].lower()\",\n \"def getText():\",\n \"def getText():\",\n \"def getText():\",\n \"def getText():\",\n \"def getText():\",\n \"def scrubHTML( html ):\\n parser = StrippingParser()\\n parser.feed( html )\\n parser.close()\\n return parser.result\",\n \"def strip_html_tags(text):\\r\\n soup = BeautifulSoup(text, 'lxml')\\r\\n stripped_text = soup.get_text(separator=\\\" \\\")\\r\\n return stripped_text\",\n \"def text(self):\\n txt = self.web_element.text\\n return txt\",\n \"def extractCode(text):\\n soup = BeautifulSoup(text, 'html.parser')\\n code_str = str()\\n for code in soup.find_all('code'):\\n code_without_tags = code.get_text()\\n code_str += code_without_tags\\n return code_str\",\n \"def html_to_text(html_message):\\r\\n process = Popen(\\r\\n ['lynx', '-stdin', '-display_charset=UTF-8', '-assume_charset=UTF-8', '-dump'],\\r\\n stdin=PIPE,\\r\\n stdout=PIPE\\r\\n )\\r\\n # use lynx to get plaintext\\r\\n (plaintext, err_from_stderr) = process.communicate(\\r\\n input=html_message.encode('utf-8')\\r\\n )\\r\\n\\r\\n if err_from_stderr:\\r\\n log.info(err_from_stderr)\\r\\n\\r\\n return plaintext\",\n \"def strip_html(inputString):\\r\\n return BeautifulSoup(inputString, \\\"html.parser\\\").text\",\n \"def __yahoo_parse_text(self, content):\\n text = ''\\n # Process all paragraphs.\\n paragraphs = content.find_all('p')\\n for par in paragraphs:\\n text += '<p>' + par.getText(separator=' ') + '</p>'\\n # Remove all extra whitespace (single space remains).\\n text = ' '.join(text.strip().split())\\n # Result\\n return text\",\n \"def strip_html_tags(text):\\n soup = BeautifulSoup(text, \\\"html.parser\\\")\\n stripped_text = soup.get_text(separator=\\\" \\\")\\n return stripped_text\",\n \"def strip_html_tags(text):\\n soup = BeautifulSoup(text, \\\"html.parser\\\")\\n stripped_text = soup.get_text(separator=\\\" \\\")\\n return stripped_text\",\n \"def extract_html(tag):\\n s = unescape(remove_all_empty_tags(tag).renderContents())\\n if type(s) != unicode:\\n return s.decode('utf-8')\\n return s\",\n \"def getLineInformation(html, reg=['(?<=<\\\\/h2><li>).*?<\\\\/a>', '<.*a.*?>', '查公交上八六八四cn']):\\n tmp = re.findall(reg[0], html)[0]\\n Info = re.sub(reg[1], '', tmp)\\n info = re.sub(reg[2], '', Info)\\n return info\",\n \"def extract_text(filename):\\n\\tpdfFileObj = open(filename, \\\"rb\\\")\\n\\tpdfReader = PyPDF2.PdfFileReader(pdfFileObj)\\n\\n\\tmytext = \\\"\\\"\\n\\n\\tfor pageNum in range(pdfReader.numPages):\\n\\t\\tpageObj = pdfReader.getPage(pageNum)\\n\\t\\tmytext += pageObj.extractText()\\n\\n\\tpdfFileObj.close()\\n\\n\\treturn mytext\",\n \"def read_from_web(url):\\n\\tpage = urlopen(url)\\n\\tsoup = BeautifulSoup(page, \\\"lxml\\\")\\n\\ttext = ' '.join(map(lambda p: p.text, soup.find_all('p')))\\n\\treturn soup.title.text, text\",\n \"def get_text(self):\",\n \"def extract_code_text(bs, index):\\r\\n texts = ''\\r\\n code = ''\\r\\n try:\\r\\n for a in bs.find_all('div', class_='post')[index].find_all('p'):\\r\\n texts += a.text\\r\\n for a in bs.find_all('div', class_='post')[index].find_all('code'):\\r\\n code += a.text\\r\\n except:\\r\\n pass\\r\\n return texts, code\",\n \"def extract_contents(enml):\\n return re.search(r'<en-note[^>]*>(.*?)</en-note>', enml, re.S).group(1)\",\n \"def extract(self, doc, raw_html):\\n super(KenyaTodayCrawler, self).extract(doc, raw_html)\\n\\n soup = BeautifulSoup(raw_html)\\n\\n # gather title\\n doc.title = soup.find(attrs={\\\"property\\\":\\\"og:title\\\"})['content']\\n\\n #gather publish date\\n date = self.extract_plaintext(soup.select(\\\"main.content .entry-meta .entry-time\\\"))\\n doc.published_at = self.parse_timestamp(date)\\n\\n nodes = soup.select(\\\".content .entry-content p\\\")\\n self.log.info(nodes)\\n if len(nodes) > 1:\\n doc.summary = self.extract_plaintext(nodes[0:1])\\n doc.text = \\\"\\\\n\\\\n\\\".join(p.text.strip() for p in nodes[2:])\\n\\n doc.author = Author.unknown()\",\n \"def get_visible_text(_text):\\n #text = _text.decode('utf-8', 'ignore').lower() # Don't get hung up on unicode chars in foreign languages\\n text = _text.lower()\\n text = re.compile(r'<').sub(' <',text) # These two lines keep words from getting smushed\\n text = re.compile(r'>').sub('> ',text) # together when two they are only separated by tags.\\n soup = BeautifulSoup(text, 'lxml')\\n\\n # decompose removes the tag and it's text content completely\\n for s in soup(['script','code','style']):\\n s.decompose()\\n\\n text = soup.get_text()\\n # compress space to reduce footprint and fit on one line so it neatly fits in csv file\\n text = re.compile(r'\\\\s+').sub(' ',text).strip()\\n return text\",\n \"def _extract_html(self, url):\\n self.response = requests.get(url, timeout=5)\\n self.html = BeautifulSoup(self.response.content, \\\"lxml\\\") if self.response.ok else None\\n # return self.html\",\n \"def parsingconvtext(retrievedtext,customtextlist):\\r\\n if not retrievedtext: #in case empty text \\r\\n retrievedtext=changenonetostr(retrievedtext)\\r\\n newtext=BeautifulSoup(retrievedtext).get_text() \\r\\n #newtext=changenonetostr(retrievedtext)\\r\\n #newtext=BeautifulSoup(newtext).get_text() \\r\\n #remove http links\\r\\n newtext=re.sub(r'http\\\\S+', '', newtext)\\r\\n newtext=re.sub(r'\\\\r\\\\r\\\\r\\\\n', ' ', newtext)\\r\\n #remove LL specific text\\r\\n if customtextlist:\\r\\n for i in customtextlist:\\r\\n newtext=re.sub(i, '', newtext)\\r\\n return newtext\",\n \"def get_content(response_text):\\n soup = BeautifulSoup(response_text, 'html.parser')\\n try:\\n return soup.find('span', class_='total-entries').get_text()\\n except AttributeError:\\n return \\\"Failed to get results\\\"\",\n \"def extractText(self, filename):\\n file_path = os.path.join(folder_upload, filename)\\n file_text = self.textExtractor.get_text(file_path)\\n return file_text\",\n \"def get_inner_text(self, css_selector):\\n element = self.driver.find_elements_by_css_selector(css_selector)\\n if len(element) > 0:\\n information = element[0].get_attribute(\\\"innerText\\\").strip()\\n return information\",\n \"def parse_html(html):\\n parser = lxml.html.HTMLParser(encoding='utf8')\\n return lxml.html.fromstring(html.encode('utf8'), parser=parser)\",\n \"def get_article_body(url):\\n G.go(url)\\n text = G.doc.select('////div[@class=\\\"StandardArticleBody_body\\\"]').text()\\n return text\",\n \"def process_html(raw_html_text):\\n\\tbounds_list = pre_proc.get_page_bounds(raw_html_text)\\n\\n\\tprocessed_text_html = ( pre_proc.split_spans(raw_html_text) \\t| p(pre_proc.delete_non_textual_elements)\\n\\t\\t\\t\\t\\t\\t\\t\\t\\t\\t\\t\\t\\t\\t\\t\\t\\t| p(pre_proc.delete_headers, bounds_list)\\n\\t\\t\\t\\t\\t\\t\\t\\t\\t\\t\\t\\t\\t\\t\\t\\t\\t| p(pre_proc.delete_vertical_text)\\n\\t\\t\\t\\t\\t\\t\\t\\t\\t\\t\\t\\t\\t\\t\\t\\t\\t| p(pre_proc.sort_html)\\n\\t\\t)\\n\\treturn processed_text_html\",\n \"def strip_tags(self, html):\\n s = MLStripper()\\n s.feed(html)\\n return s.get_data()\",\n \"def remove_html_tags(html_text: str) -> str:\\n document = fromstring(html_text)\\n text = document.text_content()\\n return text.strip()\",\n \"def parseSearchHtml(self):\\n pass\",\n \"def parseSearchHtml(self):\\n pass\",\n \"def remove_html(txt):\\r\\n TAG_RE = re.compile(r'<[^>]+>')\\r\\n return TAG_RE.sub(\\\"\\\", txt).strip()\",\n \"def read_url(url):\\n\\thtml = urllib.urlopen(url).read().decode('utf8')\\n\\ttext = plaintext(html)\\n\\tlines = text.splitlines()\\n\\tlines = [line for line in lines if line.strip() != '']\\n\\tlines = [line for line in lines if line.startswith('<') == False]\\n\\tlines = [line for line in lines if ('{') not in line]\\n\\n\\tprint lines\\n \\n\\ttext ='\\\\n'.join(line for line in lines)\\n\\n\\treturn text\",\n \"def get_text(self, selector):\\n self._wait_element_localed(self.driver, selector)\\n element = self.get_element(selector)\\n text = element.text\\n\\n return text\",\n \"def get_article_text(article):\\n soup = BeautifulSoup(article.content)\\n return soup.get_text()\",\n \"def parse_html(city, html):\\n return city.parse_html(html)\",\n \"def get_text(url):\\n try:\\n result = requests.get(url, headers=DEFAULT_REQUEST_HEADERS)\\n result.raise_for_status()\\n except requests.HTTPError as err:\\n raise URLGetTextError(err)\\n\\n return result.text\",\n \"def get_text(self):\\n return self.text[:500]\",\n \"def get_article_body(url):\\n headers = {'User-Agent': 'Codeup Data Science'}\\n response = get(url, headers=headers)\\n soup = BeautifulSoup(response.content, \\\"html.parser\\\") \\n return soup.find('div', itemprop='text').text\",\n \"def parse_text(self, page):\\n text = page.find(self.tag_prefix + self.revision_tag).find(self.tag_prefix + self.text_tag).text\\n title = page.find(self.tag_prefix + self.title_tag).text\\n categories = []\\n #\\n text = self.parse_archivo(text)\\n text = self.parse_foto(text)\\n text = self.parse_by_line(text)\\n text = self.parse_link(text)\\n text = self.parse_url(text)\\n text = self.parse_fecha(text)\\n text = self.parse_bracketed_word(text)\\n #\\n if text:\\n categories = re.findall(self.category_finder_regex, text)\\n #\\n text = self.parse_category(text)\\n text = self.parse_other_language(text)\\n text = self.parse_table_regex(text)\\n text = self.parse_ver_fuente(text)\\n text = self.remove_extra_text(text)\\n text = self.remove_extra_characters(text)\\n\\n categorias = []\\n for cat in categories:\\n categorias.append(cat[6])\\n\\n if text:\\n if 'REDIRECT' in text or 'redirect' in text:\\n return None\\n\\n return Article(title=title, content=text, categories=categorias)\",\n \"def main(html_file):\\n \\n biglist = name_extractor(html_file)\\n text = '\\\\n'.join(biglist)\\n print (text)\",\n \"def get_text():\\n global x\\n for i in soup.body(\\\"aside\\\", {\\\"id\\\": \\\"text-2\\\"}):\\n x = i.get_text()\",\n \"def extract_article_text(url):\\n pwds = Passwords()\\n token = pwds.getPassword('apibot.token')\\n\\n params = {}\\n params['token'] = token\\n params['url'] = url\\n params['timeout'] = 20000 # timeout in ms\\n\\n summary_url = 'http://www.diffbot.com/api/article?%s' % urllib.urlencode(\\n params)\\n code, content, resp = util.get_page(summary_url, max_size=512*1024)\\n if code != 200:\\n print \\\"Got %s requesting %s\\\" % (str(code), summary_url)\\n return None\\n\\n try:\\n results = json.loads(content)\\n except:\\n print code, content, resp\\n return \\\"The summarizer doesn't like this.\\\"\\n if \\\"title\\\" in results:\\n if \\\"text\\\" in results:\\n return \\\"%s\\\\f%s\\\" % (results[\\\"title\\\"], results[\\\"text\\\"])\\n else:\\n return results[\\\"title\\\"]\\n elif \\\"text\\\" in results:\\n return results[\\\"text\\\"]\\n else:\\n print results\\n return None\",\n \"def get_text(element):\\n if element.tag.split('}')[-1] == 'h3':\\n return \\\"\\\\n\\\" # New section (double newline)\\n return re.sub(\\\"\\\\s+\\\", \\\" \\\", ((element.text or '') + ''.join(map(get_text, element)) + (element.tail or '')))\",\n \"def extract_one( html: str, fpath: Path ):\\n # %%\\n doc = BeautifulSoup( html, features='html.parser')\\n\\n ret = { 'linkedin_handle': fpath.name.split('.')[0] }\\n _parse_top_card( ret, doc )\\n # %%\\n ret['about'] = _extract_about( doc )\\n # if len(ret['about']) < 100 and ret['about'].find('ver más') > 0:\\n # print( f\\\"\\\\nVer más detected: \\\\nabout:{ret['about']} fpath={fpath}\\\" )\\n\\n ret['about_stats'] = {'about_eng_ratio': _common_english_ratio(ret['about'])}\\n # %%\\n ret['work_experience'] = _parse_experiences( doc )\\n ret['work_stats'] = calc_work_stats( ret['work_experience'])\\n # %%\\n ret['skills'] = proc_skills_section( doc )\\n ret['education'] = _parse_education( doc )\\n ret['education_stats'] = _education_stats( ret['education'])\\n ret['accomplishments'] = _extract_accomplishments(doc)\\n ret['profile_text_stats'] = profile_text_stats( doc )\\n # %%\\n return ret\\n # %%\",\n \"def get_text(self):\\n return self.text\"\n]","isConversational":false},"negative_scores":{"kind":"list like","value":["0.7791069","0.7684028","0.7683524","0.7471255","0.7464041","0.74003136","0.7259061","0.7134363","0.7097741","0.7085013","0.7047965","0.69694406","0.69121873","0.69020706","0.6863355","0.6851142","0.680404","0.6749167","0.6742216","0.67198294","0.6711281","0.6697978","0.664609","0.66279405","0.6619066","0.66149694","0.660272","0.6558907","0.6512566","0.65074754","0.6500655","0.64957964","0.643344","0.64013195","0.63571143","0.6344664","0.6332724","0.63105756","0.6308567","0.6296635","0.6287362","0.62820476","0.6259224","0.6220702","0.62096626","0.62002236","0.6174411","0.6149266","0.61363536","0.61235017","0.6113415","0.6113415","0.6113415","0.6113415","0.6113415","0.6104314","0.61030304","0.6092746","0.60878664","0.6075167","0.6054364","0.60496396","0.6045899","0.6045899","0.60448766","0.60445446","0.6040706","0.60380673","0.59917146","0.59716374","0.59695953","0.59594214","0.59592015","0.59558564","0.59521097","0.59451884","0.59228086","0.5914764","0.59103125","0.59087086","0.59053004","0.59033144","0.590048","0.58939886","0.58939886","0.5885958","0.5884138","0.5882717","0.5874825","0.5856216","0.5854651","0.58541983","0.5850785","0.5822226","0.58090305","0.5806748","0.5804309","0.58013725","0.5796504","0.57896584"],"string":"[\n \"0.7791069\",\n \"0.7684028\",\n \"0.7683524\",\n \"0.7471255\",\n \"0.7464041\",\n \"0.74003136\",\n \"0.7259061\",\n \"0.7134363\",\n \"0.7097741\",\n \"0.7085013\",\n \"0.7047965\",\n \"0.69694406\",\n \"0.69121873\",\n \"0.69020706\",\n \"0.6863355\",\n \"0.6851142\",\n \"0.680404\",\n \"0.6749167\",\n \"0.6742216\",\n \"0.67198294\",\n \"0.6711281\",\n \"0.6697978\",\n \"0.664609\",\n \"0.66279405\",\n \"0.6619066\",\n \"0.66149694\",\n \"0.660272\",\n \"0.6558907\",\n \"0.6512566\",\n \"0.65074754\",\n \"0.6500655\",\n \"0.64957964\",\n \"0.643344\",\n \"0.64013195\",\n \"0.63571143\",\n \"0.6344664\",\n \"0.6332724\",\n \"0.63105756\",\n \"0.6308567\",\n \"0.6296635\",\n \"0.6287362\",\n \"0.62820476\",\n \"0.6259224\",\n \"0.6220702\",\n \"0.62096626\",\n \"0.62002236\",\n \"0.6174411\",\n \"0.6149266\",\n \"0.61363536\",\n \"0.61235017\",\n \"0.6113415\",\n \"0.6113415\",\n \"0.6113415\",\n \"0.6113415\",\n \"0.6113415\",\n \"0.6104314\",\n \"0.61030304\",\n \"0.6092746\",\n \"0.60878664\",\n \"0.6075167\",\n \"0.6054364\",\n \"0.60496396\",\n \"0.6045899\",\n \"0.6045899\",\n \"0.60448766\",\n \"0.60445446\",\n \"0.6040706\",\n \"0.60380673\",\n \"0.59917146\",\n \"0.59716374\",\n \"0.59695953\",\n \"0.59594214\",\n \"0.59592015\",\n \"0.59558564\",\n \"0.59521097\",\n \"0.59451884\",\n \"0.59228086\",\n \"0.5914764\",\n \"0.59103125\",\n \"0.59087086\",\n \"0.59053004\",\n \"0.59033144\",\n \"0.590048\",\n \"0.58939886\",\n \"0.58939886\",\n \"0.5885958\",\n \"0.5884138\",\n \"0.5882717\",\n \"0.5874825\",\n \"0.5856216\",\n \"0.5854651\",\n \"0.58541983\",\n \"0.5850785\",\n \"0.5822226\",\n \"0.58090305\",\n \"0.5806748\",\n \"0.5804309\",\n \"0.58013725\",\n \"0.5796504\",\n \"0.57896584\"\n]","isConversational":false},"document_score":{"kind":"string","value":"0.69887584"},"document_rank":{"kind":"string","value":"11"}}},{"rowIdx":75377,"cells":{"query":{"kind":"string","value":"Extract unique ID from url"},"document":{"kind":"string","value":"def extract_id(url):\n trail_id = url.replace('https://www.trailforks.com/trails/','').replace('/','')\n return trail_id"},"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_id(self, url):\n return url.split('/')[-1]","def id_from_url(url):\n return url.split('-')[-1].split('.html')[0]","def _id_from_url(url):\n url = re.sub(r'\\?.*', '', url)\n video_id = url.split('/')[-2]\n return video_id","def get_clean_url(url, unique_id):\n search = f\"(.*{unique_id})\"\n return re.findall(search,url)[0]","def get_id(share_url):\n url = get_redirect_url(share_url)\n id_num = re.findall('(\\d*)\\?', url)[0]\n if id_num.isnumeric():\n return id_num\n else:\n print(\"Something wrong with id number\")","def parse_url_discl_id(cls, url):\n url_query = urlparse(url)[4]\n try:\n return parse_qs(url_query).get('Discl_id', None)[-1]\n except IndexError as e:\n print(e)\n return \"\"","def extract_item_id(url):\n m = re.search('/([0-9]+)\\.htm', url)\n if m is not None:\n return m.group(1)\n else:\n return None","def id_from_url(url: str) -> str:\n parts = RedditBase._url_parts(url)\n try:\n comment_index = parts.index(\"comments\")\n except ValueError:\n raise InvalidURL(url) from None\n\n if len(parts) - 4 != comment_index:\n raise InvalidURL(url)\n return parts[-1]","def get_id_from_url(url):\n doc_id_regex = r'.*docsend.com/view/(?P<doc_id>.*)'\n search = re.search(doc_id_regex, url)\n if search:\n doc_id = search.group('doc_id')\n return doc_id","def torrent_id(url, debug):\n id = url[url.find('tid=')+4:]\n\n if not debug:\n return id\n\n if debug == 'Y':\n print \"ID :\", id\n return id","def get_listing_id(url):\n match = re.search(r\"\\/([\\dA-Za-z]*)_zpid\", url)\n if match:\n return match.group(1)\n else:\n return \"\".join(random.choice(ascii_letters) for _ in range(10))","def shortURLToId(self, shortURL):\n id = 0\n for i in shortURL: \n val_i = ord(i) \n if(val_i >= ord('a') and val_i <= ord('z')): \n id = id*62 + val_i - ord('a') \n elif(val_i >= ord('A') and val_i <= ord('Z')): \n id = id*62 + val_i - ord('Z') + 26\n else: \n id = id*62 + val_i - ord('0') + 52\n return id","def get_pid_from_url(url):\n return re.findall(r'store.lining.com/shop/goods-(\\w+).html\\w*', url)[0]","def get_ch_id(share_url):\n url = get_redirect_url(share_url)\n id_num = re.findall('/(\\d*)/', url)[1]\n if id_num.isnumeric():\n return id_num\n else:\n print(\"Something wrong with id number\")","def find_id(href):\n ID = idRE.search(href)\n if ID:\n return ID.group(1)","def getid(data):\n return int(data.split('/')[-1])","def get_id_from_a(a):\n if a:\n # We split from that and take the rest\n id_ = a['href'].split(\"Id=\")[1]\n\n # We split one more time in case of there is more after the id\n # We take the first part this time\n id_ = id_.split(\"&\")[0]\n\n return id_","def get_lis_id(chamber, url):\n match = re.search(lis_id_patterns[chamber], url)\n if match.groups:\n return match.group(1)","def parse_id(string):\n return string.split('/')[-1]","def get_guid_for_url(self, url):\n index_records = self.get_index_for_url(url=url)\n if len(index_records) == 1:\n guid = index_records[0][\"did\"]\n return guid\n else:\n guids = []\n for index_record in index_records:\n guids.append(index_record[\"did\"])\n return guids","def _get_id(mf, url=None):\n\n\tprops = mf['properties']\n\n\tif 'uid' in props:\n\t\treturn props['uid'][0]\n\telif 'url' in props:\n\t\treturn props['url'][0]\n\telse:\n\t\treturn None","def get_yt_id(url):\n yt = re.search(_YT_PATTERN, url)\n if yt:\n return yt.group(1)","def remove_id(url):\n u = urlparse(url)\n query = parse_qs(u.query, keep_blank_values=True)\n query.pop(\"eo_id\", None)\n u = u._replace(query=urlencode(query, True))\n return urlunparse(u)","def url_to_file_guid(url_id):\r\n\r\n return \"{}-{}-{}-{}-{}\".format(url_id[0:8], url_id[8:12], url_id[12:16], url_id[16:20], url_id[20:])","def get_id(self, resource):\n try:\n return resource.href.split('/')[-1]\n except AttributeError:\n return resource['href'].split('/')[-1]","def get_playlist_id_from_url(url):\n return match1(url, r'youku\\.com/playlist_show/id_([a-zA-Z0-9=]+)')","def _e_to_id(self, e):\n return (e.attrib['href']\n [(e.attrib['href']\n .rfind('/id')+3):]\n .replace('?mt=2', ''))","def _extract_id(self, dirty_id):\n if dirty_id[:1] == \"/\":\n return dirty_id.split(\"/\")[-1]\n else:\n return dirty_id","def get_id_by_url(url):\n urllist = BlogHelper.get_bloglist_all()\n for el in urllist:\n if el.url == url:\n return el.id","def get_video_id(url):\n\n if not url:\n return \"\"\n\n # If URL is embedded\n if \"embed\" in url:\n return url.split(\"/\")[-1]\n\n parse_result = urlparse(url)\n query = parse_qs(parse_result.query)\n return query[\"v\"][0]","def parse_link_to_id(self, playlist_link: str) -> str:\n split_1 = playlist_link.split('/')[4]\n split_2 = split_1.split('?')\n return split_2[0]","def get_id_attribution(link = None):\n log.debug(\"attribution link: \" + repr(link))\n choppedLink = legacy_check(link)\n id = None\n try:\n # First try to get the relevant part, that is encoded\n step1 = choppedLink[3][choppedLink[3].find(\"watch\"):]\n # Then stplit the other encoded params\n step2 = step1[12:].split(\"%\")\n # and get the good part\n step3 = step2[0]\n id = step3 # choppedLink[3][choppedLink[3].find(\"watch\"):][12:].split(\"%\")[0]\n except Exception as e:\n raise e # dont care 'bout issues here. all will be NotImplementedError \n\n # If we havent found a match, then this is not implemented.\n if id == \"\":\n raise Exception(\"no recognised kind of link\")\n\n return id","def parse_image_id(image_ref):\n temp = image_ref.rsplit('/')\n #Return the last item, which is the image id\n return temp[len(temp) - 1]","def get_string_id(self, url):\n\t\tstart = end = 5\n\t\tnum = 10000000\n\n\t\twhile start - 5 < len(url):\n\t\t\tfor i in url[start:end]:\n\t\t\t\tnum += ord(i)\n\t\t\tstart = end\n\t\t\tend += 5\n\n\t\treturn num","def fix_moviedb(url):\n assert url\n\n # get id from the title\n # e.g.: https://www.themoviedb.org/movie/482936-la-quietud\n path = url.split('/')[-1]\n movie_id = int(path.split('-')[0])\n return url, movie_id","def _uri_to_id(cls, uri):\n _, _, identity = uri.rpartition(\"/\")\n return int(identity)","def find_player_id(url):\r\n response = requests.get(url)\r\n result = PLAYER_ID_PATTERN.search(response.text)\r\n return result.group(1)","def getResourceTypeAndId(self, url):\n return url.rsplit('/', 2)[-2:]","def key_id(cls, url: str):\r\n ...","def job_id(driver):\n elem = driver.find_element_by_xpath(\"//meta[@property='og:url']\")\n url = elem.get_attribute(\"content\")\n return url[url.find('/', 34) + 1:]","def get_uuid_from_url(self, request):\n url_parts = request.META.get(\"PATH_INFO\").split(\"/\")\n try:\n given_uuid = str(UUID(url_parts[url_parts.index(\"cost-models\") + 1]))\n except ValueError:\n given_uuid = None\n return given_uuid","def get_urlid(self, url):\r\n\t\tsql = \"select rowid from urllist where url='%s'\" % (url)\r\n\t\tres = self.cur.execute(sql).fetchone()\r\n\t\tif res is None:\r\n\t\t\treturn 0\r\n\t\telse:\r\n\t\t\treturn res[0]","def extract_url_from_result(soup, url, listing_id):\r\n for link in soup.find_all(name='a', class_='prod_container'):\r\n url.append(link.get('href'))\r\n link_text = link.get('href')\r\n\r\n # From URL - extract listing ID\r\n try:\r\n id_regex = re.compile(r'\\d{7}')\r\n id_ = id_regex.search(link_text).group()\r\n listing_id.append(id_)\r\n except:\r\n listing_id.append('Not found')\r\n # print(url)\r\n # print(listing_id)\r\n return url, listing_id","def unique_id() -> str:","def get_id_regular_link(link = None):\n #Legacy compatibility\n choppedLink = legacy_check(link)\n # dont bother if we are none.\n if link == None:\n return link\n\n vid_url_params = choppedLink[3].split(\"&\")\n # Search the id in the list of elements of the url\n vid = search_video_id(vid_url_params)\n\n # And dont forget the links with hashtags #\n vid = vid.split(\"#\")[0]\n\n return vid # change this var names TODO","def get_accession_number(url):\n return url.split(\"/\")[-1]","def extract_id_from_uri(id_or_uri):\n if '/' in id_or_uri:\n return id_or_uri[id_or_uri.rindex('/') + 1:]\n else:\n return id_or_uri","def _get_tweet_number(tweet_url):\n path = urlparse.urlparse(tweet_url)[2]\n number = path.split('/')[-1]\n return '#%s' % (number,)","def _getUidByUrl(self):\n # check for unique id handler tool\n if not PRODUCTS_CMFUID_INSTALLED:\n return\n uidtool = queryUtility(IUniqueIdHandler)\n if uidtool is None:\n return\n\n obj = getUtility(ISiteRoot).restrictedTraverse(self.remote_url)\n return uidtool.register(obj)","def fileId_from_url(url):\r\n raw_fileId = re.findall(\"~[A-z.]+/[0-9]+\", url)[0][1:]\r\n return raw_fileId.replace('/', ':')","def fileId_from_url(url):\r\n raw_fileId = re.findall(\"~[0-z.]+/[0-9]+\", url)[0][1: ]\r\n return raw_fileId.replace('/', ':')","def get_zillow_property_id(url):\n pattern = re.compile(r\"(\\d+)_zpid\")\n zpid = re.search(pattern, url).group(1)\n\n return zpid","def get_current_record_id(self):\n url = self.selenium.get_location()\n for part in url.split(\"/\"):\n oid_match = re.match(OID_REGEX, part)\n if oid_match is not None:\n return oid_match.group(2)\n raise AssertionError(\"Could not parse record id from url: {}\".format(url))","def parse_res_id(response):\n pass","def get_original_asset_id(download_url):\n download_url = download_url.replace(\"https\", \"http\")\n if download_url.startswith(\"http://tile.loc.gov/\"):\n pattern = r\"/service:([A-Za-z0-9:.\\-\\_]+)/|/master/([A-Za-z0-9/]+)([0-9.]+)\"\n asset_id = re.search(pattern, download_url)\n if not asset_id:\n logger.error(\n \"Couldn't find a matching asset ID in download URL %s\", download_url\n )\n raise AssertionError\n else:\n if asset_id.group(1):\n matching_asset_id = asset_id.group(1)\n else:\n matching_asset_id = asset_id.group(2)\n logger.debug(\n \"Found asset ID %s in download URL %s\", matching_asset_id, download_url\n )\n return matching_asset_id\n else:\n logger.warning(\"Download URL doesn't start with tile.loc.gov: %s\", download_url)\n return download_url","def get_row_id_for_short_url(url):\n try:\n return short_url.decode_url(url)\n except:\n return -1","def asinGeturl(url):\n asin = url.split('/')\n for i in asin:\n asinNum = i.strip()\n if len(asinNum) != 10:\n continue\n else:\n asinN = asinNum\n\n return asinN","def get_book_id_from_input_url(self):\n\t #(https:\\/\\/www.goodreads.com\\/book\\/show\\/|http:\\/\\/www.goodreads.com\\/book\\/show\\/)\n # http://www.goodreads.com/book/show/12177850-a-song-of-ice-and-fire\n\n n = 0\n if self.input_url[0:36] == \"https://www.goodreads.com/book/show/\":\n n = 36\n\n remaining_url = self.input_url[n:]\n word_list = re.split('[.-]' ,remaining_url)\n\n if word_list[0].isnumeric():\n self.book_id = word_list[0]\n else:\n # raise the exception when book id is not found in the URL\n raise myException(\"InvalidGoodreadsURL\")\n\n elif self.input_url[0:35] == \"http://www.goodreads.com/book/show/\":\n n=35\n\n remaining_url = self.input_url[n:]\n word_list = re.split('[.-]' ,remaining_url)\n\n if word_list[0].isnumeric():\n self.book_id = word_list[0]\n else:\n # raise the exception when book id is not found in the URL\n raise myException(\"InvalidGoodreadsURL\")\n else:\n # raise exception when the first half of the URL does not match the req\n raise myException(\"InvalidGoodreadsURL\")","def _parse_id(line):\n ablt_pat = re.compile('(?<=2014_)[0-9]{12}(?=.jpg)')\n orig_pat = re.compile('(?<=[0-9]{16}_)[0-9]+')\n mat = ablt_pat.search(line)\n if mat is None: #original image\n mat = orig_pat.search(line)\n assert not mat is None, (\"this line does not contain a COCO image id: {}\" % line )\n return line[mat.start(): mat.end()], 'orig'\n else: #ablated image\n num = line[mat.start(): mat.end()]\n return str(int(num)), 'ablt'","def canvas_course_id(url):\n cid = ''\n match = COURSERE.search(url)\n if match:\n cid = match.group(1)\n return cid","def _get_response_id(self, response):\n if response.url in self._response_id_cache:\n return self._response_id_cache[response.url]\n else:\n response_id = str(uuid.uuid4())\n self._response_id_cache[response.url] = response_id\n return response_id","def unique_id(self) -> str:\n return self.tahoma_device.url","def get_feed_hash(self, url):\n response = self.fetch(\"/feed/info\", {\n \"id\": url,\n })\n\n return response.get(\"id\", None)","def getUniqueID(event):\n\tmatch = reUniqueID.search(event)\n\tif match:\n\t\tresult = match.group(0).split(\" \")[1].rstrip(\"\\r\")\n\t\treturn result\n\telse:\n\t\treturn None","def get_id_shortlink(link = None):\n choppedLink = legacy_check(link)\n id = None\n try:\n id = choppedLink[3] # or -1 instead of 3\n except:\n pass #dont care bout issues here\n return id","def short_url(lastid):\n number = lastid +100000000000\n bs62encoded = base62.encode(number)\n return 'https://abc.com/{id}'.format(id=str(bs62encoded))","def get_id(endpoint):\n _entity, _id = parser_endpoint(endpoint)\n\n return _id","def _shortenUrl(self, url):\n posturi = \"https://www.googleapis.com/urlshortener/v1/url\"\n headers = {'Content-Type' : 'application/json'}\n data = {'longUrl' : url}\n data = json.dumps(data)\n request = urllib2.Request(posturi,data,headers)\n response = urllib2.urlopen(request)\n response_data = response.read()\n shorturi = json.loads(response_data)['id']\n return shorturi","def video_id_from_url(url):\n\n parsed_url = urlparse(url)\n url_params = dict(parse_qsl(parsed_url.query))\n return url_params.get(\"v\", parsed_url.path.split(\"/\")[-1])","def get_epo_id(href):\r\n beginIndex = href.find(\"contextId=\") + len(\"contextId=\")\r\n epoId = href[beginIndex:]\r\n endIndex = 0\r\n nextCharacter = epoId[endIndex]\r\n while nextCharacter.isdigit():\r\n endIndex += 1\r\n nextCharacter = epoId[endIndex]\r\n epoId = epoId[:endIndex]\r\n return epoId","def get_id(novel):\n href = novel.find('td', class_='tal').a['href']\n novel_id = href.split('/')[-1].split('.')[0]\n return novel_id","def getID():","def get_user_id(self, details, response):\n try:\n return urlsplit(response.identity_url).path.rsplit(\"/\",1)[-1]\n except:\n return super(SteamBackend,self).get_user_id(self, details, response)","def get_group_id(url):\n base = 'maven2/'\n pos = url.find(base)\n if pos < 0:\n raise ValueError('Base not found in the URL')\n\n url2 = url[pos+len(base):]\n return url2.replace('/', '.')","def get_maven_id_from_url(url):\n burl = utils.strip_leading_slash(url)\n artifact_id = utils.get_last_url_segment(burl)\n group_id = get_group_id(burl)\n return group_id, artifact_id","def get_yt_list_id(url):\n yt = re.search(_YT_LIST_PATTERN, url)\n if yt:\n return yt.group(1)","def scrape_promed_url(url):\n article_id_regex = re.compile('(id=|\\/post\\/)(?P<id>\\d+\\.?\\d*)')\n parse = article_id_regex.search(url)\n if parse:\n return scrape_promed_id(parse.groupdict().get('id'))\n else:\n raise Exception(\"Couldn't scrape url: \" + url)","def get_type_id(type_url):\n # TODO\n return type_url","def _extract_identifier(self, publication):\n return self._parse_identifier(publication.metadata.identifier)","def _extract_spreadsheet_key_from_url(url):\r\n result = url\r\n\r\n if 'key=' in url:\r\n result = url.split('key=')[-1].split('#')[0].split('&')[0]\r\n\r\n return result","def gen_site_id(self, request):\n # originally, sep. based on scheme + host.\n audience = request.params.get('audience', '')\n if \"http:\" in audience:\n return urlparse.urlparse(audience).netloc\n else:\n if len(audience):\n return audience\n return ''","def extract_instance_name(url):\n return url.rsplit('/', 1)[-1]","def get_id(request, request_type):\n if request_type == \"post\":\n id = request.POST['id']\n else:\n id = request.GET['id']\n id = id.rsplit('_')\n id = int(id[1])\n return id","def extract_id(file_path):\n # An example of file path is AlkEthOH_tripos/AlkEthOH_chain_filt1/AlkEthOH_c555.crd\n return os.path.splitext(os.path.basename(file_path))[0][9:]","def _get_ID(self):\n raw_data = imdb.search_for_title(self.title)\n if len(raw_data) > 1:\n raw_data = raw_data[0] # Pulls the first value of the title (the closest match)\n # if there is more than one\n self.ID = raw_data['imdb_id']","def get_media_id(media_url):\n split_url = media_url.split(\"/\")\n #Media urls of the format https://messaging.bandwidth.com/api/v2/users/123/media/file.png\n if split_url[-2] == \"media\":\n return split_url[-1]\n #Media urls of the format https://messaging.bandwidth.com/api/v2/users/123/media/abc/0/file.png\n else:\n #This is required for now due to the SDK parsing out the `/`s\n return \"%2F\".join(split_url[-3:])","def course_id_from_url(url):\r\n if not url:\r\n return None\r\n\r\n match = COURSE_REGEX.match(url)\r\n\r\n if match is None:\r\n return None\r\n\r\n course_id = match.group('course_id')\r\n\r\n if course_id is None:\r\n return None\r\n\r\n return SlashSeparatedCourseKey.from_deprecated_string(course_id)","def clean_url(url):\n return re.sub(';jsessionid=.+\\?', '?', url).strip()","def uniqueid(self):\n return self.raw.get(\"uniqueid\")","def getIdLink(self):\n return self.urlLink()","def extract_api_name(url):\n host = RE_HOST.sub('\\\\1', url)\n return host","def _parseurl(url):\n tracker1=url\n port=int(re.findall(\"[0-9]+\",tracker1)[0])\n host=re.findall(\"[^0-9]+\",tracker1)[0]\n host=host[:-1]\n host=host[6:]\n return host,port","def test_api_url(self):\n url = 'http://api.shopstyle.com/action/apiVisitRetailer?id=471281504&pid=uid3600-33034440-48'\n assert extract_product_id_from_api_url(url) == '471281504'","def test_get_video_id_from_url(self):\n self.assertEqual(get_video_id_from_url('https://www.youtube.com/embed/DqGwxR_0d1M'),\n 'DqGwxR_0d1M')\n self.assertEqual(get_video_id_from_url('https://youtu.be/DqGwxR_0d1M'), 'DqGwxR_0d1M')\n self.assertEqual(\n get_video_id_from_url('https://www.youtube.com/watch?v=DqGwxR_0d1M&feature=youtu.be'),\n 'DqGwxR_0d1M')\n self.assertEqual(get_video_id_from_url('https://www.youtube.com/watch?v=DqGwxR_0d1M'),\n 'DqGwxR_0d1M')","def get_identifier(self, request):\n return '%s-%s'.format((request.session.session_key, self.flowIdentifier))[:200]","def get_file_guid(file):\r\n\r\n try:\r\n return str(file.url_guid).replace('-', '')\r\n except FileUpload.DoesNotExist:\r\n return ''","def get_video_id(self):\n \n if self.video_id:\n return self.video_id\n \n if not self.original_url:\n return ''\n \n #logger.debug('DAILYMOTION VIDEO FOUND %s' % url)\n \n p = urlparse.urlparse(self.original_url)\n path = p.path\n if path.endswith('/'):\n path = path[:-1]\n path_list = path[1:].split('/')\n \n if len(path_list) == 3 and (p.path.startswith('/embed/video/') or p.path.startswith('/swf/video/')):\n # http://www.dailymotion.com/embed/video/xmp7zw\n return re.sub('_.+', '', path_list[2])\n elif len(path_list) == 2 and (p.path.startswith('/video/') or p.path.startswith('/swf/')):\n # http://www.dailymotion.com/video/xmp7zw_whatever\n # http://www.dailymotion.com/swf/xmp7zw\n return re.sub('_.+', '', path_list[1])\n \n return ''","def process_id_from(self):\r\n return self._tokens[1]","def get_hashid_and_urlid(url):\n existing = db.select('id, hashid', 'ImageURLs', 'url LIKE \"%s\"' % clean_url(url))\n if existing:\n urlid = existing[0][0]\n hashid = existing[0][1]\n return hashid, urlid, False\n\n # Download image\n if url.startswith('//'):\n url = 'http:%s' % url\n logger.debug('Downloading %s ...' % url)\n try:\n image_buffer = web.download(url)\n except Exception as e:\n logger.debug('Failed')\n raise Exception('Unable to download image at %s: %s' % (url, e))\n\n # Get image hash\n try:\n logger.debug('Hashing ...')\n image = image_from_buffer(image_buffer)\n (width, height) = image.size\n image_hash = str(avhash(image))\n except Exception as e:\n logger.debug('Failed')\n raise e\n logger.debug('Indexing ... ')\n\n # Insert image hash into Hashes table\n hashid = db.insert('Hashes', (None, image_hash))\n if hashid == -1:\n # Already exists, need to lookup existing hash\n hashids = db.select('id', 'Hashes', 'hash = \"%s\"' % (image_hash,))\n if not hashids:\n raise Exception('unable to add hash to table, or find hash (wtf?)')\n hashid = hashids[0][0]\n\n # Image attributes\n try:\n filesize = len(image_buffer)\n url = clean_url(url)\n urlid = db.insert('ImageURLs', (None, url, hashid, width, height, filesize))\n create_thumb(image, urlid)\n logger.debug('Done')\n except Exception as e:\n raise e\n return hashid, urlid, True","def extractHostname(self, url):\n return url.split(\"http://\")[1].split('/')[0]"],"string":"[\n \"def get_id(self, url):\\n return url.split('/')[-1]\",\n \"def id_from_url(url):\\n return url.split('-')[-1].split('.html')[0]\",\n \"def _id_from_url(url):\\n url = re.sub(r'\\\\?.*', '', url)\\n video_id = url.split('/')[-2]\\n return video_id\",\n \"def get_clean_url(url, unique_id):\\n search = f\\\"(.*{unique_id})\\\"\\n return re.findall(search,url)[0]\",\n \"def get_id(share_url):\\n url = get_redirect_url(share_url)\\n id_num = re.findall('(\\\\d*)\\\\?', url)[0]\\n if id_num.isnumeric():\\n return id_num\\n else:\\n print(\\\"Something wrong with id number\\\")\",\n \"def parse_url_discl_id(cls, url):\\n url_query = urlparse(url)[4]\\n try:\\n return parse_qs(url_query).get('Discl_id', None)[-1]\\n except IndexError as e:\\n print(e)\\n return \\\"\\\"\",\n \"def extract_item_id(url):\\n m = re.search('/([0-9]+)\\\\.htm', url)\\n if m is not None:\\n return m.group(1)\\n else:\\n return None\",\n \"def id_from_url(url: str) -> str:\\n parts = RedditBase._url_parts(url)\\n try:\\n comment_index = parts.index(\\\"comments\\\")\\n except ValueError:\\n raise InvalidURL(url) from None\\n\\n if len(parts) - 4 != comment_index:\\n raise InvalidURL(url)\\n return parts[-1]\",\n \"def get_id_from_url(url):\\n doc_id_regex = r'.*docsend.com/view/(?P<doc_id>.*)'\\n search = re.search(doc_id_regex, url)\\n if search:\\n doc_id = search.group('doc_id')\\n return doc_id\",\n \"def torrent_id(url, debug):\\n id = url[url.find('tid=')+4:]\\n\\n if not debug:\\n return id\\n\\n if debug == 'Y':\\n print \\\"ID :\\\", id\\n return id\",\n \"def get_listing_id(url):\\n match = re.search(r\\\"\\\\/([\\\\dA-Za-z]*)_zpid\\\", url)\\n if match:\\n return match.group(1)\\n else:\\n return \\\"\\\".join(random.choice(ascii_letters) for _ in range(10))\",\n \"def shortURLToId(self, shortURL):\\n id = 0\\n for i in shortURL: \\n val_i = ord(i) \\n if(val_i >= ord('a') and val_i <= ord('z')): \\n id = id*62 + val_i - ord('a') \\n elif(val_i >= ord('A') and val_i <= ord('Z')): \\n id = id*62 + val_i - ord('Z') + 26\\n else: \\n id = id*62 + val_i - ord('0') + 52\\n return id\",\n \"def get_pid_from_url(url):\\n return re.findall(r'store.lining.com/shop/goods-(\\\\w+).html\\\\w*', url)[0]\",\n \"def get_ch_id(share_url):\\n url = get_redirect_url(share_url)\\n id_num = re.findall('/(\\\\d*)/', url)[1]\\n if id_num.isnumeric():\\n return id_num\\n else:\\n print(\\\"Something wrong with id number\\\")\",\n \"def find_id(href):\\n ID = idRE.search(href)\\n if ID:\\n return ID.group(1)\",\n \"def getid(data):\\n return int(data.split('/')[-1])\",\n \"def get_id_from_a(a):\\n if a:\\n # We split from that and take the rest\\n id_ = a['href'].split(\\\"Id=\\\")[1]\\n\\n # We split one more time in case of there is more after the id\\n # We take the first part this time\\n id_ = id_.split(\\\"&\\\")[0]\\n\\n return id_\",\n \"def get_lis_id(chamber, url):\\n match = re.search(lis_id_patterns[chamber], url)\\n if match.groups:\\n return match.group(1)\",\n \"def parse_id(string):\\n return string.split('/')[-1]\",\n \"def get_guid_for_url(self, url):\\n index_records = self.get_index_for_url(url=url)\\n if len(index_records) == 1:\\n guid = index_records[0][\\\"did\\\"]\\n return guid\\n else:\\n guids = []\\n for index_record in index_records:\\n guids.append(index_record[\\\"did\\\"])\\n return guids\",\n \"def _get_id(mf, url=None):\\n\\n\\tprops = mf['properties']\\n\\n\\tif 'uid' in props:\\n\\t\\treturn props['uid'][0]\\n\\telif 'url' in props:\\n\\t\\treturn props['url'][0]\\n\\telse:\\n\\t\\treturn None\",\n \"def get_yt_id(url):\\n yt = re.search(_YT_PATTERN, url)\\n if yt:\\n return yt.group(1)\",\n \"def remove_id(url):\\n u = urlparse(url)\\n query = parse_qs(u.query, keep_blank_values=True)\\n query.pop(\\\"eo_id\\\", None)\\n u = u._replace(query=urlencode(query, True))\\n return urlunparse(u)\",\n \"def url_to_file_guid(url_id):\\r\\n\\r\\n return \\\"{}-{}-{}-{}-{}\\\".format(url_id[0:8], url_id[8:12], url_id[12:16], url_id[16:20], url_id[20:])\",\n \"def get_id(self, resource):\\n try:\\n return resource.href.split('/')[-1]\\n except AttributeError:\\n return resource['href'].split('/')[-1]\",\n \"def get_playlist_id_from_url(url):\\n return match1(url, r'youku\\\\.com/playlist_show/id_([a-zA-Z0-9=]+)')\",\n \"def _e_to_id(self, e):\\n return (e.attrib['href']\\n [(e.attrib['href']\\n .rfind('/id')+3):]\\n .replace('?mt=2', ''))\",\n \"def _extract_id(self, dirty_id):\\n if dirty_id[:1] == \\\"/\\\":\\n return dirty_id.split(\\\"/\\\")[-1]\\n else:\\n return dirty_id\",\n \"def get_id_by_url(url):\\n urllist = BlogHelper.get_bloglist_all()\\n for el in urllist:\\n if el.url == url:\\n return el.id\",\n \"def get_video_id(url):\\n\\n if not url:\\n return \\\"\\\"\\n\\n # If URL is embedded\\n if \\\"embed\\\" in url:\\n return url.split(\\\"/\\\")[-1]\\n\\n parse_result = urlparse(url)\\n query = parse_qs(parse_result.query)\\n return query[\\\"v\\\"][0]\",\n \"def parse_link_to_id(self, playlist_link: str) -> str:\\n split_1 = playlist_link.split('/')[4]\\n split_2 = split_1.split('?')\\n return split_2[0]\",\n \"def get_id_attribution(link = None):\\n log.debug(\\\"attribution link: \\\" + repr(link))\\n choppedLink = legacy_check(link)\\n id = None\\n try:\\n # First try to get the relevant part, that is encoded\\n step1 = choppedLink[3][choppedLink[3].find(\\\"watch\\\"):]\\n # Then stplit the other encoded params\\n step2 = step1[12:].split(\\\"%\\\")\\n # and get the good part\\n step3 = step2[0]\\n id = step3 # choppedLink[3][choppedLink[3].find(\\\"watch\\\"):][12:].split(\\\"%\\\")[0]\\n except Exception as e:\\n raise e # dont care 'bout issues here. all will be NotImplementedError \\n\\n # If we havent found a match, then this is not implemented.\\n if id == \\\"\\\":\\n raise Exception(\\\"no recognised kind of link\\\")\\n\\n return id\",\n \"def parse_image_id(image_ref):\\n temp = image_ref.rsplit('/')\\n #Return the last item, which is the image id\\n return temp[len(temp) - 1]\",\n \"def get_string_id(self, url):\\n\\t\\tstart = end = 5\\n\\t\\tnum = 10000000\\n\\n\\t\\twhile start - 5 < len(url):\\n\\t\\t\\tfor i in url[start:end]:\\n\\t\\t\\t\\tnum += ord(i)\\n\\t\\t\\tstart = end\\n\\t\\t\\tend += 5\\n\\n\\t\\treturn num\",\n \"def fix_moviedb(url):\\n assert url\\n\\n # get id from the title\\n # e.g.: https://www.themoviedb.org/movie/482936-la-quietud\\n path = url.split('/')[-1]\\n movie_id = int(path.split('-')[0])\\n return url, movie_id\",\n \"def _uri_to_id(cls, uri):\\n _, _, identity = uri.rpartition(\\\"/\\\")\\n return int(identity)\",\n \"def find_player_id(url):\\r\\n response = requests.get(url)\\r\\n result = PLAYER_ID_PATTERN.search(response.text)\\r\\n return result.group(1)\",\n \"def getResourceTypeAndId(self, url):\\n return url.rsplit('/', 2)[-2:]\",\n \"def key_id(cls, url: str):\\r\\n ...\",\n \"def job_id(driver):\\n elem = driver.find_element_by_xpath(\\\"//meta[@property='og:url']\\\")\\n url = elem.get_attribute(\\\"content\\\")\\n return url[url.find('/', 34) + 1:]\",\n \"def get_uuid_from_url(self, request):\\n url_parts = request.META.get(\\\"PATH_INFO\\\").split(\\\"/\\\")\\n try:\\n given_uuid = str(UUID(url_parts[url_parts.index(\\\"cost-models\\\") + 1]))\\n except ValueError:\\n given_uuid = None\\n return given_uuid\",\n \"def get_urlid(self, url):\\r\\n\\t\\tsql = \\\"select rowid from urllist where url='%s'\\\" % (url)\\r\\n\\t\\tres = self.cur.execute(sql).fetchone()\\r\\n\\t\\tif res is None:\\r\\n\\t\\t\\treturn 0\\r\\n\\t\\telse:\\r\\n\\t\\t\\treturn res[0]\",\n \"def extract_url_from_result(soup, url, listing_id):\\r\\n for link in soup.find_all(name='a', class_='prod_container'):\\r\\n url.append(link.get('href'))\\r\\n link_text = link.get('href')\\r\\n\\r\\n # From URL - extract listing ID\\r\\n try:\\r\\n id_regex = re.compile(r'\\\\d{7}')\\r\\n id_ = id_regex.search(link_text).group()\\r\\n listing_id.append(id_)\\r\\n except:\\r\\n listing_id.append('Not found')\\r\\n # print(url)\\r\\n # print(listing_id)\\r\\n return url, listing_id\",\n \"def unique_id() -> str:\",\n \"def get_id_regular_link(link = None):\\n #Legacy compatibility\\n choppedLink = legacy_check(link)\\n # dont bother if we are none.\\n if link == None:\\n return link\\n\\n vid_url_params = choppedLink[3].split(\\\"&\\\")\\n # Search the id in the list of elements of the url\\n vid = search_video_id(vid_url_params)\\n\\n # And dont forget the links with hashtags #\\n vid = vid.split(\\\"#\\\")[0]\\n\\n return vid # change this var names TODO\",\n \"def get_accession_number(url):\\n return url.split(\\\"/\\\")[-1]\",\n \"def extract_id_from_uri(id_or_uri):\\n if '/' in id_or_uri:\\n return id_or_uri[id_or_uri.rindex('/') + 1:]\\n else:\\n return id_or_uri\",\n \"def _get_tweet_number(tweet_url):\\n path = urlparse.urlparse(tweet_url)[2]\\n number = path.split('/')[-1]\\n return '#%s' % (number,)\",\n \"def _getUidByUrl(self):\\n # check for unique id handler tool\\n if not PRODUCTS_CMFUID_INSTALLED:\\n return\\n uidtool = queryUtility(IUniqueIdHandler)\\n if uidtool is None:\\n return\\n\\n obj = getUtility(ISiteRoot).restrictedTraverse(self.remote_url)\\n return uidtool.register(obj)\",\n \"def fileId_from_url(url):\\r\\n raw_fileId = re.findall(\\\"~[A-z.]+/[0-9]+\\\", url)[0][1:]\\r\\n return raw_fileId.replace('/', ':')\",\n \"def fileId_from_url(url):\\r\\n raw_fileId = re.findall(\\\"~[0-z.]+/[0-9]+\\\", url)[0][1: ]\\r\\n return raw_fileId.replace('/', ':')\",\n \"def get_zillow_property_id(url):\\n pattern = re.compile(r\\\"(\\\\d+)_zpid\\\")\\n zpid = re.search(pattern, url).group(1)\\n\\n return zpid\",\n \"def get_current_record_id(self):\\n url = self.selenium.get_location()\\n for part in url.split(\\\"/\\\"):\\n oid_match = re.match(OID_REGEX, part)\\n if oid_match is not None:\\n return oid_match.group(2)\\n raise AssertionError(\\\"Could not parse record id from url: {}\\\".format(url))\",\n \"def parse_res_id(response):\\n pass\",\n \"def get_original_asset_id(download_url):\\n download_url = download_url.replace(\\\"https\\\", \\\"http\\\")\\n if download_url.startswith(\\\"http://tile.loc.gov/\\\"):\\n pattern = r\\\"/service:([A-Za-z0-9:.\\\\-\\\\_]+)/|/master/([A-Za-z0-9/]+)([0-9.]+)\\\"\\n asset_id = re.search(pattern, download_url)\\n if not asset_id:\\n logger.error(\\n \\\"Couldn't find a matching asset ID in download URL %s\\\", download_url\\n )\\n raise AssertionError\\n else:\\n if asset_id.group(1):\\n matching_asset_id = asset_id.group(1)\\n else:\\n matching_asset_id = asset_id.group(2)\\n logger.debug(\\n \\\"Found asset ID %s in download URL %s\\\", matching_asset_id, download_url\\n )\\n return matching_asset_id\\n else:\\n logger.warning(\\\"Download URL doesn't start with tile.loc.gov: %s\\\", download_url)\\n return download_url\",\n \"def get_row_id_for_short_url(url):\\n try:\\n return short_url.decode_url(url)\\n except:\\n return -1\",\n \"def asinGeturl(url):\\n asin = url.split('/')\\n for i in asin:\\n asinNum = i.strip()\\n if len(asinNum) != 10:\\n continue\\n else:\\n asinN = asinNum\\n\\n return asinN\",\n \"def get_book_id_from_input_url(self):\\n\\t #(https:\\\\/\\\\/www.goodreads.com\\\\/book\\\\/show\\\\/|http:\\\\/\\\\/www.goodreads.com\\\\/book\\\\/show\\\\/)\\n # http://www.goodreads.com/book/show/12177850-a-song-of-ice-and-fire\\n\\n n = 0\\n if self.input_url[0:36] == \\\"https://www.goodreads.com/book/show/\\\":\\n n = 36\\n\\n remaining_url = self.input_url[n:]\\n word_list = re.split('[.-]' ,remaining_url)\\n\\n if word_list[0].isnumeric():\\n self.book_id = word_list[0]\\n else:\\n # raise the exception when book id is not found in the URL\\n raise myException(\\\"InvalidGoodreadsURL\\\")\\n\\n elif self.input_url[0:35] == \\\"http://www.goodreads.com/book/show/\\\":\\n n=35\\n\\n remaining_url = self.input_url[n:]\\n word_list = re.split('[.-]' ,remaining_url)\\n\\n if word_list[0].isnumeric():\\n self.book_id = word_list[0]\\n else:\\n # raise the exception when book id is not found in the URL\\n raise myException(\\\"InvalidGoodreadsURL\\\")\\n else:\\n # raise exception when the first half of the URL does not match the req\\n raise myException(\\\"InvalidGoodreadsURL\\\")\",\n \"def _parse_id(line):\\n ablt_pat = re.compile('(?<=2014_)[0-9]{12}(?=.jpg)')\\n orig_pat = re.compile('(?<=[0-9]{16}_)[0-9]+')\\n mat = ablt_pat.search(line)\\n if mat is None: #original image\\n mat = orig_pat.search(line)\\n assert not mat is None, (\\\"this line does not contain a COCO image id: {}\\\" % line )\\n return line[mat.start(): mat.end()], 'orig'\\n else: #ablated image\\n num = line[mat.start(): mat.end()]\\n return str(int(num)), 'ablt'\",\n \"def canvas_course_id(url):\\n cid = ''\\n match = COURSERE.search(url)\\n if match:\\n cid = match.group(1)\\n return cid\",\n \"def _get_response_id(self, response):\\n if response.url in self._response_id_cache:\\n return self._response_id_cache[response.url]\\n else:\\n response_id = str(uuid.uuid4())\\n self._response_id_cache[response.url] = response_id\\n return response_id\",\n \"def unique_id(self) -> str:\\n return self.tahoma_device.url\",\n \"def get_feed_hash(self, url):\\n response = self.fetch(\\\"/feed/info\\\", {\\n \\\"id\\\": url,\\n })\\n\\n return response.get(\\\"id\\\", None)\",\n \"def getUniqueID(event):\\n\\tmatch = reUniqueID.search(event)\\n\\tif match:\\n\\t\\tresult = match.group(0).split(\\\" \\\")[1].rstrip(\\\"\\\\r\\\")\\n\\t\\treturn result\\n\\telse:\\n\\t\\treturn None\",\n \"def get_id_shortlink(link = None):\\n choppedLink = legacy_check(link)\\n id = None\\n try:\\n id = choppedLink[3] # or -1 instead of 3\\n except:\\n pass #dont care bout issues here\\n return id\",\n \"def short_url(lastid):\\n number = lastid +100000000000\\n bs62encoded = base62.encode(number)\\n return 'https://abc.com/{id}'.format(id=str(bs62encoded))\",\n \"def get_id(endpoint):\\n _entity, _id = parser_endpoint(endpoint)\\n\\n return _id\",\n \"def _shortenUrl(self, url):\\n posturi = \\\"https://www.googleapis.com/urlshortener/v1/url\\\"\\n headers = {'Content-Type' : 'application/json'}\\n data = {'longUrl' : url}\\n data = json.dumps(data)\\n request = urllib2.Request(posturi,data,headers)\\n response = urllib2.urlopen(request)\\n response_data = response.read()\\n shorturi = json.loads(response_data)['id']\\n return shorturi\",\n \"def video_id_from_url(url):\\n\\n parsed_url = urlparse(url)\\n url_params = dict(parse_qsl(parsed_url.query))\\n return url_params.get(\\\"v\\\", parsed_url.path.split(\\\"/\\\")[-1])\",\n \"def get_epo_id(href):\\r\\n beginIndex = href.find(\\\"contextId=\\\") + len(\\\"contextId=\\\")\\r\\n epoId = href[beginIndex:]\\r\\n endIndex = 0\\r\\n nextCharacter = epoId[endIndex]\\r\\n while nextCharacter.isdigit():\\r\\n endIndex += 1\\r\\n nextCharacter = epoId[endIndex]\\r\\n epoId = epoId[:endIndex]\\r\\n return epoId\",\n \"def get_id(novel):\\n href = novel.find('td', class_='tal').a['href']\\n novel_id = href.split('/')[-1].split('.')[0]\\n return novel_id\",\n \"def getID():\",\n \"def get_user_id(self, details, response):\\n try:\\n return urlsplit(response.identity_url).path.rsplit(\\\"/\\\",1)[-1]\\n except:\\n return super(SteamBackend,self).get_user_id(self, details, response)\",\n \"def get_group_id(url):\\n base = 'maven2/'\\n pos = url.find(base)\\n if pos < 0:\\n raise ValueError('Base not found in the URL')\\n\\n url2 = url[pos+len(base):]\\n return url2.replace('/', '.')\",\n \"def get_maven_id_from_url(url):\\n burl = utils.strip_leading_slash(url)\\n artifact_id = utils.get_last_url_segment(burl)\\n group_id = get_group_id(burl)\\n return group_id, artifact_id\",\n \"def get_yt_list_id(url):\\n yt = re.search(_YT_LIST_PATTERN, url)\\n if yt:\\n return yt.group(1)\",\n \"def scrape_promed_url(url):\\n article_id_regex = re.compile('(id=|\\\\/post\\\\/)(?P<id>\\\\d+\\\\.?\\\\d*)')\\n parse = article_id_regex.search(url)\\n if parse:\\n return scrape_promed_id(parse.groupdict().get('id'))\\n else:\\n raise Exception(\\\"Couldn't scrape url: \\\" + url)\",\n \"def get_type_id(type_url):\\n # TODO\\n return type_url\",\n \"def _extract_identifier(self, publication):\\n return self._parse_identifier(publication.metadata.identifier)\",\n \"def _extract_spreadsheet_key_from_url(url):\\r\\n result = url\\r\\n\\r\\n if 'key=' in url:\\r\\n result = url.split('key=')[-1].split('#')[0].split('&')[0]\\r\\n\\r\\n return result\",\n \"def gen_site_id(self, request):\\n # originally, sep. based on scheme + host.\\n audience = request.params.get('audience', '')\\n if \\\"http:\\\" in audience:\\n return urlparse.urlparse(audience).netloc\\n else:\\n if len(audience):\\n return audience\\n return ''\",\n \"def extract_instance_name(url):\\n return url.rsplit('/', 1)[-1]\",\n \"def get_id(request, request_type):\\n if request_type == \\\"post\\\":\\n id = request.POST['id']\\n else:\\n id = request.GET['id']\\n id = id.rsplit('_')\\n id = int(id[1])\\n return id\",\n \"def extract_id(file_path):\\n # An example of file path is AlkEthOH_tripos/AlkEthOH_chain_filt1/AlkEthOH_c555.crd\\n return os.path.splitext(os.path.basename(file_path))[0][9:]\",\n \"def _get_ID(self):\\n raw_data = imdb.search_for_title(self.title)\\n if len(raw_data) > 1:\\n raw_data = raw_data[0] # Pulls the first value of the title (the closest match)\\n # if there is more than one\\n self.ID = raw_data['imdb_id']\",\n \"def get_media_id(media_url):\\n split_url = media_url.split(\\\"/\\\")\\n #Media urls of the format https://messaging.bandwidth.com/api/v2/users/123/media/file.png\\n if split_url[-2] == \\\"media\\\":\\n return split_url[-1]\\n #Media urls of the format https://messaging.bandwidth.com/api/v2/users/123/media/abc/0/file.png\\n else:\\n #This is required for now due to the SDK parsing out the `/`s\\n return \\\"%2F\\\".join(split_url[-3:])\",\n \"def course_id_from_url(url):\\r\\n if not url:\\r\\n return None\\r\\n\\r\\n match = COURSE_REGEX.match(url)\\r\\n\\r\\n if match is None:\\r\\n return None\\r\\n\\r\\n course_id = match.group('course_id')\\r\\n\\r\\n if course_id is None:\\r\\n return None\\r\\n\\r\\n return SlashSeparatedCourseKey.from_deprecated_string(course_id)\",\n \"def clean_url(url):\\n return re.sub(';jsessionid=.+\\\\?', '?', url).strip()\",\n \"def uniqueid(self):\\n return self.raw.get(\\\"uniqueid\\\")\",\n \"def getIdLink(self):\\n return self.urlLink()\",\n \"def extract_api_name(url):\\n host = RE_HOST.sub('\\\\\\\\1', url)\\n return host\",\n \"def _parseurl(url):\\n tracker1=url\\n port=int(re.findall(\\\"[0-9]+\\\",tracker1)[0])\\n host=re.findall(\\\"[^0-9]+\\\",tracker1)[0]\\n host=host[:-1]\\n host=host[6:]\\n return host,port\",\n \"def test_api_url(self):\\n url = 'http://api.shopstyle.com/action/apiVisitRetailer?id=471281504&pid=uid3600-33034440-48'\\n assert extract_product_id_from_api_url(url) == '471281504'\",\n \"def test_get_video_id_from_url(self):\\n self.assertEqual(get_video_id_from_url('https://www.youtube.com/embed/DqGwxR_0d1M'),\\n 'DqGwxR_0d1M')\\n self.assertEqual(get_video_id_from_url('https://youtu.be/DqGwxR_0d1M'), 'DqGwxR_0d1M')\\n self.assertEqual(\\n get_video_id_from_url('https://www.youtube.com/watch?v=DqGwxR_0d1M&feature=youtu.be'),\\n 'DqGwxR_0d1M')\\n self.assertEqual(get_video_id_from_url('https://www.youtube.com/watch?v=DqGwxR_0d1M'),\\n 'DqGwxR_0d1M')\",\n \"def get_identifier(self, request):\\n return '%s-%s'.format((request.session.session_key, self.flowIdentifier))[:200]\",\n \"def get_file_guid(file):\\r\\n\\r\\n try:\\r\\n return str(file.url_guid).replace('-', '')\\r\\n except FileUpload.DoesNotExist:\\r\\n return ''\",\n \"def get_video_id(self):\\n \\n if self.video_id:\\n return self.video_id\\n \\n if not self.original_url:\\n return ''\\n \\n #logger.debug('DAILYMOTION VIDEO FOUND %s' % url)\\n \\n p = urlparse.urlparse(self.original_url)\\n path = p.path\\n if path.endswith('/'):\\n path = path[:-1]\\n path_list = path[1:].split('/')\\n \\n if len(path_list) == 3 and (p.path.startswith('/embed/video/') or p.path.startswith('/swf/video/')):\\n # http://www.dailymotion.com/embed/video/xmp7zw\\n return re.sub('_.+', '', path_list[2])\\n elif len(path_list) == 2 and (p.path.startswith('/video/') or p.path.startswith('/swf/')):\\n # http://www.dailymotion.com/video/xmp7zw_whatever\\n # http://www.dailymotion.com/swf/xmp7zw\\n return re.sub('_.+', '', path_list[1])\\n \\n return ''\",\n \"def process_id_from(self):\\r\\n return self._tokens[1]\",\n \"def get_hashid_and_urlid(url):\\n existing = db.select('id, hashid', 'ImageURLs', 'url LIKE \\\"%s\\\"' % clean_url(url))\\n if existing:\\n urlid = existing[0][0]\\n hashid = existing[0][1]\\n return hashid, urlid, False\\n\\n # Download image\\n if url.startswith('//'):\\n url = 'http:%s' % url\\n logger.debug('Downloading %s ...' % url)\\n try:\\n image_buffer = web.download(url)\\n except Exception as e:\\n logger.debug('Failed')\\n raise Exception('Unable to download image at %s: %s' % (url, e))\\n\\n # Get image hash\\n try:\\n logger.debug('Hashing ...')\\n image = image_from_buffer(image_buffer)\\n (width, height) = image.size\\n image_hash = str(avhash(image))\\n except Exception as e:\\n logger.debug('Failed')\\n raise e\\n logger.debug('Indexing ... ')\\n\\n # Insert image hash into Hashes table\\n hashid = db.insert('Hashes', (None, image_hash))\\n if hashid == -1:\\n # Already exists, need to lookup existing hash\\n hashids = db.select('id', 'Hashes', 'hash = \\\"%s\\\"' % (image_hash,))\\n if not hashids:\\n raise Exception('unable to add hash to table, or find hash (wtf?)')\\n hashid = hashids[0][0]\\n\\n # Image attributes\\n try:\\n filesize = len(image_buffer)\\n url = clean_url(url)\\n urlid = db.insert('ImageURLs', (None, url, hashid, width, height, filesize))\\n create_thumb(image, urlid)\\n logger.debug('Done')\\n except Exception as e:\\n raise e\\n return hashid, urlid, True\",\n \"def extractHostname(self, url):\\n return url.split(\\\"http://\\\")[1].split('/')[0]\"\n]","isConversational":false},"negative_scores":{"kind":"list like","value":["0.8380673","0.81195986","0.80006224","0.7679931","0.75390005","0.75193775","0.73941654","0.73696965","0.7283127","0.7201107","0.7174527","0.71031886","0.7055173","0.70551497","0.70354474","0.6988878","0.6982627","0.69719243","0.6964515","0.6903353","0.68871117","0.6881657","0.6850768","0.68409455","0.6763946","0.66883963","0.66862786","0.66528296","0.6597642","0.65764135","0.656152","0.65608245","0.6534134","0.6507599","0.65068394","0.6497861","0.6497159","0.6492813","0.6465371","0.64202255","0.64096534","0.63954353","0.6361782","0.6356396","0.6340133","0.6335305","0.63274014","0.6326907","0.632207","0.6321331","0.6320045","0.6316092","0.6309756","0.6301459","0.6282161","0.62784386","0.62469125","0.624133","0.6239607","0.62183905","0.62180483","0.6209264","0.6204968","0.62008303","0.6200769","0.6193387","0.6184716","0.6168809","0.61644995","0.6161157","0.61578244","0.6145417","0.61422193","0.6139285","0.61310494","0.61202294","0.6108428","0.61081606","0.6077326","0.6061304","0.6021523","0.6021304","0.601732","0.5975533","0.5972155","0.5971253","0.5963148","0.5955514","0.59439033","0.5936099","0.5932049","0.5922203","0.5905487","0.5870081","0.58647823","0.5861856","0.58532166","0.585195","0.585048","0.5843368"],"string":"[\n \"0.8380673\",\n \"0.81195986\",\n \"0.80006224\",\n \"0.7679931\",\n \"0.75390005\",\n \"0.75193775\",\n \"0.73941654\",\n \"0.73696965\",\n \"0.7283127\",\n \"0.7201107\",\n \"0.7174527\",\n \"0.71031886\",\n \"0.7055173\",\n \"0.70551497\",\n \"0.70354474\",\n \"0.6988878\",\n \"0.6982627\",\n \"0.69719243\",\n \"0.6964515\",\n \"0.6903353\",\n \"0.68871117\",\n \"0.6881657\",\n \"0.6850768\",\n \"0.68409455\",\n \"0.6763946\",\n \"0.66883963\",\n \"0.66862786\",\n \"0.66528296\",\n \"0.6597642\",\n \"0.65764135\",\n \"0.656152\",\n \"0.65608245\",\n \"0.6534134\",\n \"0.6507599\",\n \"0.65068394\",\n \"0.6497861\",\n \"0.6497159\",\n \"0.6492813\",\n \"0.6465371\",\n \"0.64202255\",\n \"0.64096534\",\n \"0.63954353\",\n \"0.6361782\",\n \"0.6356396\",\n \"0.6340133\",\n \"0.6335305\",\n \"0.63274014\",\n \"0.6326907\",\n \"0.632207\",\n \"0.6321331\",\n \"0.6320045\",\n \"0.6316092\",\n \"0.6309756\",\n \"0.6301459\",\n \"0.6282161\",\n \"0.62784386\",\n \"0.62469125\",\n \"0.624133\",\n \"0.6239607\",\n \"0.62183905\",\n \"0.62180483\",\n \"0.6209264\",\n \"0.6204968\",\n \"0.62008303\",\n \"0.6200769\",\n \"0.6193387\",\n \"0.6184716\",\n \"0.6168809\",\n \"0.61644995\",\n \"0.6161157\",\n \"0.61578244\",\n \"0.6145417\",\n \"0.61422193\",\n \"0.6139285\",\n \"0.61310494\",\n \"0.61202294\",\n \"0.6108428\",\n \"0.61081606\",\n \"0.6077326\",\n \"0.6061304\",\n \"0.6021523\",\n \"0.6021304\",\n \"0.601732\",\n \"0.5975533\",\n \"0.5972155\",\n \"0.5971253\",\n \"0.5963148\",\n \"0.5955514\",\n \"0.59439033\",\n \"0.5936099\",\n \"0.5932049\",\n \"0.5922203\",\n \"0.5905487\",\n \"0.5870081\",\n \"0.58647823\",\n \"0.5861856\",\n \"0.58532166\",\n \"0.585195\",\n \"0.585048\",\n \"0.5843368\"\n]","isConversational":false},"document_score":{"kind":"string","value":"0.79425615"},"document_rank":{"kind":"string","value":"3"}}},{"rowIdx":75378,"cells":{"query":{"kind":"string","value":"1 sh 0 sz"},"document":{"kind":"string","value":"def _select_market_code(code):\n code = str(code)\n if code[0] in ['5', '6', '9'] or code[:3] in [\"009\", \"126\", \"110\", \"201\", \"202\", \"203\", \"204\"]:\n return 1\n return 0"},"metadata":{"kind":"string","value":"{\n \"objective\": {\n \"self\": [],\n \"paired\": [],\n \"triplet\": [\n [\n \"query\",\n \"document\",\n \"negatives\"\n ]\n ]\n }\n}"},"negatives":{"kind":"list like","value":["def cmd_size(args):","def shar():\n pass","def do_sh(self, none):\n print(\"**** Not Implemented\")","def size(*args):","def make_square(turt,sz):\n for i in range(4):\n turt.forward(sz)\n turt.left(90)","def shopen():\n\tsh('o')","def _s2bl(size):\n return size**2 // 8 + 1","def clRelu(self, size):","def gen_square_subsequent_mask(sz: int) -> torch.Tensor:\n return torch.triu(torch.ones(sz, sz) * float('-inf'), diagonal=1)","def resize_spacing(img_sz,img_sp,factor):\n img_sz_np = np.array(list(img_sz))\n img_sp_np = np.array(list(img_sp))\n new_sz_np = img_sz_np*factor\n new_sp = img_sp_np*(img_sz_np-1)/(new_sz_np-1)\n return tuple(list(new_sp))","def rename_sz(sz):\n sz = str(sz)\n sz_set = sz[0]\n num = sz[1]\n return f\"S{sz_set}_{num}\"","def size(name):","def sh(cmd):\r\n return check_call(cmd, shell=True)","def generate_square_subsequent_mask(sz: int) -> Tensor:\n return torch.triu(torch.ones(sz, sz) * float('-inf'), diagonal=1)","def ksh(i,t,htanses):\n for (zs,ys,zx,yx) in htanses[i]:\n alex.penup()\n alex.goto((zs%m)*20-10*m,(zs//m)*20-10*n)\n alex.pendown()\n alex.goto((ys%m+1)*20-10*m,(ys//m)*20-10*n)\n alex.goto((yx%m+1)*20-10*m,(yx//m+1)*20-10*n)\n alex.goto((zx%m)*20-10*m,(zx//m+1)*20-10*n)\n alex.goto((zs%m)*20-10*m,(zs//m)*20-10*n)\n alex.hideturtle()","def cmd_stor(args):","def shopenall():\n\tsh('oa')","def R_sh(self):\n return self._R_sh","def part1(width, height, size, color):\n pass","def align(): # open EH and fast shutter\n\t#marAuxiliary.closeMarShield()\n\td2in()\n\td3in()\n\tsh('o')","def part1():\n program = read_input()\n root = build_filesystem(program)\n all_sizes = root.make_size_list()\n return sum(size for size in all_sizes if size <= 100000)","def _assign_sizes(self):","def sh(cmd):\n print 'CMD:', cmd\n return check_call(cmd, shell=True)","def size(path):","def h_size(size: int, units: list = None):\n if units is None:\n units = H_SIZE_ABBREVIATIONS\n return f'{size} {units[0]}' if size < 1024 else h_size(size >> 10, units[1:])","def main():\r\n original = SimpleImage(\"images/poppy.png\")\r\n original.show()\r\n # shrink function\r\n after_shrink = shrink('images/poppy.png')\r\n after_shrink.show()","def init_unif(sz):\n bnd = 1 / sqrt(sz[0])\n p = uniform(low=-bnd, high=bnd, size=sz)\n return squeeze(p)","def rotate_snapshots(i=4,tgt=\"tank/vmbak\"):\n #print i, \"%(zfs)s destroy %(tgt)s@last.%(last)s\" % {'zfs': ZFS, 'tgt': tgt, 'last': i}\n cmd = \"%(zfs)s destroy %(tgt)s@last.%(last)s\" % {'zfs': ZFS, 'tgt': tgt, 'last': i}\n cmd_list = [cmd]\n for i in [4,3,2,1]:\n #print i, \"%(zfs)s rename %(tgt)s@last.%(from)s %(tgt)s@last.%(to)s\" % {\n #\t\t\t'zfs':ZFS, 'tgt':tgt, 'from':i-1, 'to':i }\n cmd = \"%(zfs)s rename %(tgt)s@last.%(from)s %(tgt)s@last.%(to)s\" % {\n\t\t\t'zfs':ZFS, 'tgt':tgt, 'from':i-1, 'to':i }\n cmd_list.append(cmd)\n cmd = \"%(zfs)s snap %(tgt)s@last.0\" % {'zfs': ZFS, 'tgt': tgt}\n cmd_list.append(cmd)\n \n #print cmd_list\n for cmd in cmd_list:\n logging.info(cmd)\n subprocess.call(cmd.split(\" \"),stderr=err)","def cosh(a):","def _compute_squeezed_spacing(self,spacing0, dim0, sz0, dimSqueezed):\n spacing = np.zeros(dimSqueezed)\n j = 0\n for i in range(dim0):\n if sz0[i] != 1:\n spacing[j] = spacing0[i]\n j += 1\n return spacing","def make_shack_hartmann(self, N, width, x=0, y=0, z=0, ref=False):\n us = (np.arange(N)-(N-1.0)/2)/N\n vs = us\n spots = [[u*width+x,v*width+y,z,1,u,v,1.0 if ref else 0.5/N,0] for u in us for v in vs]\n self.make_spots(spots)","def sh(s, log_command=True, **kwargs):\n def actually_sh(task=None):\n logger = logging.getLogger(__name__)\n if log_command:\n logger.info(SHELL_COMMAND+s)\n kwargs['shell'] = True\n ret = _sh(s, **kwargs)\n logger.info(\"Execution complete. Stdout: %s\\nStderr: %s\",\n ret[0] or '',\n ret[1] or '')\n return actually_sh","def __create_command_sh(self,jobAttempt):\n with open(jobAttempt.command_script_path,'wb') as f:\n f.write(\"#!/bin/bash\\n\")\n f.write(jobAttempt.command)\n os.system('chmod 700 {0}'.format(jobAttempt.command_script_path))","def drawSquare(t, sz):\n\n t.shape(\"turtle\")\n while 1:\n\t if sz > 200:\n\t \tbreak\n\t for j in range (36):\n\t \tt.left(10)\n\t \tsz = sz + 1 \n\n\t \tif j%2 == 1:\n\t \t\tt.color(\"red\")\n\t \telse:\n\t \t\tt.color(\"blue\")\n\t \tfor i in range(4):\n\t \t\tt.forward(sz)\n\t \t\tt.left(90)\n\t sz = sz + 1","def d(self):\r\n return self.size.z","def generate_image( now ):\n cmd = \"csh mwplot.csh %s\" % (\n now.strftime(\"%Y %m %d %H %M\"),)\n subprocess.call(cmd, shell=True)","def sh(cmd, quiet=False, wd=None, wrap=True, maxbuflen=1000000000, ignore_errors=False, no_venv=False, pyenv_version=None):\n if wd is None: wd = os.getcwd()\n\n try:\n return _rsh(\"127.0.0.1\", cmd, quiet, wd, wrap, maxbuflen, -1, ignore_errors, no_venv, pyenv_version)\n except ShellException as e: # this makes the stacktrace easier to read\n raise ShellException(e.returncode) from None","def __sizeof__(*args):","def __sizeof__(*args):","def __sizeof__(*args):","def __sizeof__(*args):","def __sizeof__(*args):","def __sizeof__(*args):","def __sizeof__(*args):","def __sizeof__(*args):","def __sizeof__(*args):","def __sizeof__(*args):","def __sizeof__(*args):","def __sizeof__(*args):","def __sizeof__(*args):","def __sizeof__(*args):","def __sizeof__(*args):","def __sizeof__(*args):","def __sizeof__(*args):","def __sizeof__(*args):","def __sizeof__(*args):","def cosh(data):\n return _make.cosh(data)","def test_batch_size_pack_size():","def test_resize2fs():\n device = \"/dev/sdX1\"\n mock = MagicMock()\n with patch.dict(disk.__salt__, {\"cmd.run_all\": mock}), patch(\n \"salt.utils.path.which\", MagicMock(return_value=True)\n ):\n disk.resize2fs(device)\n mock.assert_called_once_with(\"resize2fs {}\".format(device), python_shell=False)","def h0(data_1=None, data_2=None):\n\n hsh = SHA512.new()\n hsh.update(b\"3\")\n hsh.update(data_1)\n hsh.update(data_2)\n return hsh","def cmd_stru(args):","def addSquareVignette(size,position,img):\n\n #img[position[1]:min(position[1]+size[1],img.shape[1]),position[0]:min(position[0]+size[0],img.shape[0])]*=0.5\n img[position[1]:position[1]+size[1],position[0]:position[0]+size[0]]*=0.5","def shift(self, t=0, px=0, sx=0):\n t, px, sx = self.convertArgs(t, px, sx)\n self.tile += t \n self.pixel += px \n self.subpixel += sx","def size(self,*args):\n if len(args) == 2:\n x,y = map(int,args)\n else:\n x,y = map(int,args[0])\n self.check_size(x,y)\n self.data['chs'] = '%dx%d'%(x,y)","def calc_size(self):\r\n pass","def sz(self):\n return self.__ph.get('sz', PH_SZ_FULL)","def _shlock(self, fd):\n fcntl.lockf(fd, fcntl.LOCK_SH)","def sh(cmd, sh=False):\n import shlex\n import subprocess as sp\n if not sh: sp.call(shlex.split(cmd)) # \"safe\"\n else: sp.call(cmd, shell=sh) # \"less safe\"","def udcall_map_size(*args):\n return _ida_hexrays.udcall_map_size(*args)","def shclose():\n\tsh('c')","def f(data=None):\n\n hsh = SHA512.new()\n hsh.update(b\"1\")\n hsh.update(data)\n return hsh","def tmp_size(disk):\n tmp_gib = max(5, math.sqrt(disk))\n return int(tmp_gib) * GiB","def testSharpen (name = \"smokey.gif\", degree = 50, amount = 20):\n image = Image(name)\n print(\"Close the image window to see the transformation\")\n image.draw()\n image2 = sharpen(image, degree, amount)\n image2.draw()","def test_03_shred(self, mock_stat, mock_config, mock_verks):\n udocker.Config = mock_config\n udocker.Config.tmpdir = \"/tmp\"\n mock_stat.return_value.st_size = 123\n with mock.patch(BUILTINS + '.open', mock.mock_open()):\n kstore = udocker.KeyStore(\"filename\")\n self.assertTrue(kstore._shred())","def win_add_gmsh(bitsize, binaries_url, program_name, version_str):\n # Needs the user agent header to exist for it to send back the content\n user_agent = ('Mozilla/5.0 (Windows NT 10.0; Win64; x64; rv:56.0) '\n 'Gecko/20100101 Firefox/56.0')\n headers = {'User-Agent': user_agent}\n\n zipfile_regex = '.*%s-%s.*' % (program_name, version_str)\n # Windows 10 64 bit, gmsh version: Gmsh 3.0.7-git-35176e26 hangs\n zipfile_name = zipfile_by_bitsize(binaries_url, headers, zipfile_regex,\n bitsize)\n\n zipfile_url = binaries_url + zipfile_name\n print('Downloading %s from %s' % (program_name, zipfile_url))\n response = requests.get(zipfile_url, stream=True, headers=headers)\n with open(zipfile_name, 'wb') as out_file:\n for chunk in response.iter_content(chunk_size=1024):\n if chunk: # filter out keep-alive new chunks\n out_file.write(chunk)\n print('Unzipping %s' % program_name)\n zip_ref = zipfile.ZipFile(zipfile_name, 'r')\n zipfile_folder_name = os.path.dirname(zip_ref.namelist()[0])\n zip_ref.extractall(None)\n zip_ref.close()\n print('Removing %s zipfile' % program_name)\n os.remove(zipfile_name)\n\n env_path = os.getenv('VIRTUAL_ENV', sys.exec_prefix)\n folder_to = '%s\\Scripts\\%s' % (env_path, zipfile_folder_name)\n exe_loc = '%s\\%s.exe' % (folder_to, program_name)\n exe_link = '%s\\Scripts\\%s.exe' % (env_path, program_name)\n paths = [exe_link, folder_to]\n for path in paths:\n if os.path.isfile(path):\n os.unlink(path)\n elif os.path.isdir(path):\n shutil.rmtree(path)\n print('Installing %s to %s' % (program_name, folder_to))\n shutil.move(zipfile_folder_name, folder_to)\n os.link(exe_loc, exe_link)","def glue(shreds):\n width = sum([s.width for s in shreds])\n height = shreds[0].height\n img = Image.new(\"RGBA\", (width, height))\n x_dest = 0 # x coordinate of insertion point.\n for s in shreds:\n img.paste(s.image, (x_dest, 0))\n x_dest += s.width # Move insertion point for next shred.\n return img","def draw_multicolor_square(t,sz):\r\n for i in [\"red\", \"purple\", \"hotpink\", \"blue\"]:\r\n t.color(i)\r\n t.forward(sz)\r\n t.left(90)","def pack():\n PackCommandExecutor().pack()","def __init__(self, size: int):\r\n self.l = []\r\n self.sz = size\r\n self.sm = 0","def test_gibberish_magnitude(self):\n command_line = [\"filesystem\", \"create\", \"pn\", \"fn\", '--size=\"carbonGiB\"']\n for prefix in [[], [\"--propagate\"]]:\n self.check_system_exit(prefix + command_line, _PARSE_ERROR)","def square(t, seg, size, fib):\n for i in range(seg):\n\n t.fd(size)\n t.left(90)\n t.write(fib)# write the current Fibonacci number next to the square\n t.fd(size)","def msize(path):\n with open(path, \"w\") as w:\n w.write(\"\")\n os.utime(path, (0, 0))\n time.sleep(0.4)\n with open(path, \"w\") as w:\n w.write(\"0\")\n os.utime(path, (0, 0))","def calculate_previous_size(required_hole_size):\n\treturn required_hole_size/8","def size(self):","def part2(width, height):\n pass","def starsize(self, hipid):\n #if hipid<0 or len(self.hip_stars)<=hipid: return 0\n s = self.hip_stars[hipid]\n if s==None: return 0\n #return self.zerosize*(.8**(s[1]))\n #return self.zerosize-s[1]-2\n return self.dimmest_mag-s[1]+1","def getSize(self) -> int:\n ...","def getSize(self) -> int:\n ...","def eamap_size(*args):\n return _ida_hexrays.eamap_size(*args)","def updateSizeHead(self, size): \n self.avatarConfiguration[\"headSize\"] = size\n self.paintHead()\n self.paintHair()\n if (self.avatarConfiguration[\"mask\"]):\n self.generateMask(\"imgUpload.png\")\n self.paintMask()","def test_run_resize(self):\n self.expect_datatore_lookup('SomeBlobKey', True)\n self.expect_open_image('SomeBlobKey', (1600, 1200))\n self.expect_resize(32)\n self.expect_encode_image('SomeImageSize32')\n self.mox.ReplayAll()\n self._environ['PATH_INFO'] += '=s32'\n self.run_request('image/jpeg', 'SomeImageSize32')","def pulp_smash():","def correct_size():\n check50.run(\"./inheritance_test\").stdout(\"size_true.*\").exit(0)","def du(flags='-h'):\n system_command(\"du {0}\".format(flags))","def DrawSplitterSash(*args, **kwargs):\n return _gdi_.RendererNative_DrawSplitterSash(*args, **kwargs)","def run_mash(self):\n if (self.path2 != \"\" and self.path3 != \"\"):\n ###Set up command###\n # <exe path> dist <options> <ref seq> <query seq>\n # Sketch size by default 10000, kmer = 16\n ####################\n\n ###Initialize variables\n ref_file = self.path2\n query_file = self.path3\n self.mash_out_path = os.getcwd() + \"/mash_out.txt\"\n path_to_exe = \"/Users/ddooley/bioinformatics_packages/Mash/mash\"\n cmd = \"\"\n flag_str = \"-s 10000 -k 16 -i\"\n cmd += path_to_exe + \" dist \" + \" \" + flag_str + \" \" + ref_file + \" \" + query_file + \" > \" + self.mash_out_path\n os.system(cmd)\n\n else:\n QtWidgets.QMessageBox.question(self, \"Error!\",\"You must select reference and query .fasta files.\",QtWidgets.QMessageBox.Ok)","def auto(bin_path: str, addr: str, count: int, size: int) -> bool:\n\n return gnu(bin_path, addr, count, size)","def make_shirt(size, text):\n print(\"I'll be making a \" + size + \" shirt with '\" + text + \"' written on\"\n + \" the front of it.\")","def make_shirt(size, message):\n print(\"I need a shirt, size \" + size + \", that says \" + message + \".\")","def size(self) -> int:","def get_size_bytes( path ):\n cmd = [ 'du', '-s', '-B1', '--apparent-size', path ]\n return int( run_cmd( cmd ).split( \"\\t\" )[ 0 ] )"],"string":"[\n \"def cmd_size(args):\",\n \"def shar():\\n pass\",\n \"def do_sh(self, none):\\n print(\\\"**** Not Implemented\\\")\",\n \"def size(*args):\",\n \"def make_square(turt,sz):\\n for i in range(4):\\n turt.forward(sz)\\n turt.left(90)\",\n \"def shopen():\\n\\tsh('o')\",\n \"def _s2bl(size):\\n return size**2 // 8 + 1\",\n \"def clRelu(self, size):\",\n \"def gen_square_subsequent_mask(sz: int) -> torch.Tensor:\\n return torch.triu(torch.ones(sz, sz) * float('-inf'), diagonal=1)\",\n \"def resize_spacing(img_sz,img_sp,factor):\\n img_sz_np = np.array(list(img_sz))\\n img_sp_np = np.array(list(img_sp))\\n new_sz_np = img_sz_np*factor\\n new_sp = img_sp_np*(img_sz_np-1)/(new_sz_np-1)\\n return tuple(list(new_sp))\",\n \"def rename_sz(sz):\\n sz = str(sz)\\n sz_set = sz[0]\\n num = sz[1]\\n return f\\\"S{sz_set}_{num}\\\"\",\n \"def size(name):\",\n \"def sh(cmd):\\r\\n return check_call(cmd, shell=True)\",\n \"def generate_square_subsequent_mask(sz: int) -> Tensor:\\n return torch.triu(torch.ones(sz, sz) * float('-inf'), diagonal=1)\",\n \"def ksh(i,t,htanses):\\n for (zs,ys,zx,yx) in htanses[i]:\\n alex.penup()\\n alex.goto((zs%m)*20-10*m,(zs//m)*20-10*n)\\n alex.pendown()\\n alex.goto((ys%m+1)*20-10*m,(ys//m)*20-10*n)\\n alex.goto((yx%m+1)*20-10*m,(yx//m+1)*20-10*n)\\n alex.goto((zx%m)*20-10*m,(zx//m+1)*20-10*n)\\n alex.goto((zs%m)*20-10*m,(zs//m)*20-10*n)\\n alex.hideturtle()\",\n \"def cmd_stor(args):\",\n \"def shopenall():\\n\\tsh('oa')\",\n \"def R_sh(self):\\n return self._R_sh\",\n \"def part1(width, height, size, color):\\n pass\",\n \"def align(): # open EH and fast shutter\\n\\t#marAuxiliary.closeMarShield()\\n\\td2in()\\n\\td3in()\\n\\tsh('o')\",\n \"def part1():\\n program = read_input()\\n root = build_filesystem(program)\\n all_sizes = root.make_size_list()\\n return sum(size for size in all_sizes if size <= 100000)\",\n \"def _assign_sizes(self):\",\n \"def sh(cmd):\\n print 'CMD:', cmd\\n return check_call(cmd, shell=True)\",\n \"def size(path):\",\n \"def h_size(size: int, units: list = None):\\n if units is None:\\n units = H_SIZE_ABBREVIATIONS\\n return f'{size} {units[0]}' if size < 1024 else h_size(size >> 10, units[1:])\",\n \"def main():\\r\\n original = SimpleImage(\\\"images/poppy.png\\\")\\r\\n original.show()\\r\\n # shrink function\\r\\n after_shrink = shrink('images/poppy.png')\\r\\n after_shrink.show()\",\n \"def init_unif(sz):\\n bnd = 1 / sqrt(sz[0])\\n p = uniform(low=-bnd, high=bnd, size=sz)\\n return squeeze(p)\",\n \"def rotate_snapshots(i=4,tgt=\\\"tank/vmbak\\\"):\\n #print i, \\\"%(zfs)s destroy %(tgt)s@last.%(last)s\\\" % {'zfs': ZFS, 'tgt': tgt, 'last': i}\\n cmd = \\\"%(zfs)s destroy %(tgt)s@last.%(last)s\\\" % {'zfs': ZFS, 'tgt': tgt, 'last': i}\\n cmd_list = [cmd]\\n for i in [4,3,2,1]:\\n #print i, \\\"%(zfs)s rename %(tgt)s@last.%(from)s %(tgt)s@last.%(to)s\\\" % {\\n #\\t\\t\\t'zfs':ZFS, 'tgt':tgt, 'from':i-1, 'to':i }\\n cmd = \\\"%(zfs)s rename %(tgt)s@last.%(from)s %(tgt)s@last.%(to)s\\\" % {\\n\\t\\t\\t'zfs':ZFS, 'tgt':tgt, 'from':i-1, 'to':i }\\n cmd_list.append(cmd)\\n cmd = \\\"%(zfs)s snap %(tgt)s@last.0\\\" % {'zfs': ZFS, 'tgt': tgt}\\n cmd_list.append(cmd)\\n \\n #print cmd_list\\n for cmd in cmd_list:\\n logging.info(cmd)\\n subprocess.call(cmd.split(\\\" \\\"),stderr=err)\",\n \"def cosh(a):\",\n \"def _compute_squeezed_spacing(self,spacing0, dim0, sz0, dimSqueezed):\\n spacing = np.zeros(dimSqueezed)\\n j = 0\\n for i in range(dim0):\\n if sz0[i] != 1:\\n spacing[j] = spacing0[i]\\n j += 1\\n return spacing\",\n \"def make_shack_hartmann(self, N, width, x=0, y=0, z=0, ref=False):\\n us = (np.arange(N)-(N-1.0)/2)/N\\n vs = us\\n spots = [[u*width+x,v*width+y,z,1,u,v,1.0 if ref else 0.5/N,0] for u in us for v in vs]\\n self.make_spots(spots)\",\n \"def sh(s, log_command=True, **kwargs):\\n def actually_sh(task=None):\\n logger = logging.getLogger(__name__)\\n if log_command:\\n logger.info(SHELL_COMMAND+s)\\n kwargs['shell'] = True\\n ret = _sh(s, **kwargs)\\n logger.info(\\\"Execution complete. Stdout: %s\\\\nStderr: %s\\\",\\n ret[0] or '',\\n ret[1] or '')\\n return actually_sh\",\n \"def __create_command_sh(self,jobAttempt):\\n with open(jobAttempt.command_script_path,'wb') as f:\\n f.write(\\\"#!/bin/bash\\\\n\\\")\\n f.write(jobAttempt.command)\\n os.system('chmod 700 {0}'.format(jobAttempt.command_script_path))\",\n \"def drawSquare(t, sz):\\n\\n t.shape(\\\"turtle\\\")\\n while 1:\\n\\t if sz > 200:\\n\\t \\tbreak\\n\\t for j in range (36):\\n\\t \\tt.left(10)\\n\\t \\tsz = sz + 1 \\n\\n\\t \\tif j%2 == 1:\\n\\t \\t\\tt.color(\\\"red\\\")\\n\\t \\telse:\\n\\t \\t\\tt.color(\\\"blue\\\")\\n\\t \\tfor i in range(4):\\n\\t \\t\\tt.forward(sz)\\n\\t \\t\\tt.left(90)\\n\\t sz = sz + 1\",\n \"def d(self):\\r\\n return self.size.z\",\n \"def generate_image( now ):\\n cmd = \\\"csh mwplot.csh %s\\\" % (\\n now.strftime(\\\"%Y %m %d %H %M\\\"),)\\n subprocess.call(cmd, shell=True)\",\n \"def sh(cmd, quiet=False, wd=None, wrap=True, maxbuflen=1000000000, ignore_errors=False, no_venv=False, pyenv_version=None):\\n if wd is None: wd = os.getcwd()\\n\\n try:\\n return _rsh(\\\"127.0.0.1\\\", cmd, quiet, wd, wrap, maxbuflen, -1, ignore_errors, no_venv, pyenv_version)\\n except ShellException as e: # this makes the stacktrace easier to read\\n raise ShellException(e.returncode) from None\",\n \"def __sizeof__(*args):\",\n \"def __sizeof__(*args):\",\n \"def __sizeof__(*args):\",\n \"def __sizeof__(*args):\",\n \"def __sizeof__(*args):\",\n \"def __sizeof__(*args):\",\n \"def __sizeof__(*args):\",\n \"def __sizeof__(*args):\",\n \"def __sizeof__(*args):\",\n \"def __sizeof__(*args):\",\n \"def __sizeof__(*args):\",\n \"def __sizeof__(*args):\",\n \"def __sizeof__(*args):\",\n \"def __sizeof__(*args):\",\n \"def __sizeof__(*args):\",\n \"def __sizeof__(*args):\",\n \"def __sizeof__(*args):\",\n \"def __sizeof__(*args):\",\n \"def __sizeof__(*args):\",\n \"def cosh(data):\\n return _make.cosh(data)\",\n \"def test_batch_size_pack_size():\",\n \"def test_resize2fs():\\n device = \\\"/dev/sdX1\\\"\\n mock = MagicMock()\\n with patch.dict(disk.__salt__, {\\\"cmd.run_all\\\": mock}), patch(\\n \\\"salt.utils.path.which\\\", MagicMock(return_value=True)\\n ):\\n disk.resize2fs(device)\\n mock.assert_called_once_with(\\\"resize2fs {}\\\".format(device), python_shell=False)\",\n \"def h0(data_1=None, data_2=None):\\n\\n hsh = SHA512.new()\\n hsh.update(b\\\"3\\\")\\n hsh.update(data_1)\\n hsh.update(data_2)\\n return hsh\",\n \"def cmd_stru(args):\",\n \"def addSquareVignette(size,position,img):\\n\\n #img[position[1]:min(position[1]+size[1],img.shape[1]),position[0]:min(position[0]+size[0],img.shape[0])]*=0.5\\n img[position[1]:position[1]+size[1],position[0]:position[0]+size[0]]*=0.5\",\n \"def shift(self, t=0, px=0, sx=0):\\n t, px, sx = self.convertArgs(t, px, sx)\\n self.tile += t \\n self.pixel += px \\n self.subpixel += sx\",\n \"def size(self,*args):\\n if len(args) == 2:\\n x,y = map(int,args)\\n else:\\n x,y = map(int,args[0])\\n self.check_size(x,y)\\n self.data['chs'] = '%dx%d'%(x,y)\",\n \"def calc_size(self):\\r\\n pass\",\n \"def sz(self):\\n return self.__ph.get('sz', PH_SZ_FULL)\",\n \"def _shlock(self, fd):\\n fcntl.lockf(fd, fcntl.LOCK_SH)\",\n \"def sh(cmd, sh=False):\\n import shlex\\n import subprocess as sp\\n if not sh: sp.call(shlex.split(cmd)) # \\\"safe\\\"\\n else: sp.call(cmd, shell=sh) # \\\"less safe\\\"\",\n \"def udcall_map_size(*args):\\n return _ida_hexrays.udcall_map_size(*args)\",\n \"def shclose():\\n\\tsh('c')\",\n \"def f(data=None):\\n\\n hsh = SHA512.new()\\n hsh.update(b\\\"1\\\")\\n hsh.update(data)\\n return hsh\",\n \"def tmp_size(disk):\\n tmp_gib = max(5, math.sqrt(disk))\\n return int(tmp_gib) * GiB\",\n \"def testSharpen (name = \\\"smokey.gif\\\", degree = 50, amount = 20):\\n image = Image(name)\\n print(\\\"Close the image window to see the transformation\\\")\\n image.draw()\\n image2 = sharpen(image, degree, amount)\\n image2.draw()\",\n \"def test_03_shred(self, mock_stat, mock_config, mock_verks):\\n udocker.Config = mock_config\\n udocker.Config.tmpdir = \\\"/tmp\\\"\\n mock_stat.return_value.st_size = 123\\n with mock.patch(BUILTINS + '.open', mock.mock_open()):\\n kstore = udocker.KeyStore(\\\"filename\\\")\\n self.assertTrue(kstore._shred())\",\n \"def win_add_gmsh(bitsize, binaries_url, program_name, version_str):\\n # Needs the user agent header to exist for it to send back the content\\n user_agent = ('Mozilla/5.0 (Windows NT 10.0; Win64; x64; rv:56.0) '\\n 'Gecko/20100101 Firefox/56.0')\\n headers = {'User-Agent': user_agent}\\n\\n zipfile_regex = '.*%s-%s.*' % (program_name, version_str)\\n # Windows 10 64 bit, gmsh version: Gmsh 3.0.7-git-35176e26 hangs\\n zipfile_name = zipfile_by_bitsize(binaries_url, headers, zipfile_regex,\\n bitsize)\\n\\n zipfile_url = binaries_url + zipfile_name\\n print('Downloading %s from %s' % (program_name, zipfile_url))\\n response = requests.get(zipfile_url, stream=True, headers=headers)\\n with open(zipfile_name, 'wb') as out_file:\\n for chunk in response.iter_content(chunk_size=1024):\\n if chunk: # filter out keep-alive new chunks\\n out_file.write(chunk)\\n print('Unzipping %s' % program_name)\\n zip_ref = zipfile.ZipFile(zipfile_name, 'r')\\n zipfile_folder_name = os.path.dirname(zip_ref.namelist()[0])\\n zip_ref.extractall(None)\\n zip_ref.close()\\n print('Removing %s zipfile' % program_name)\\n os.remove(zipfile_name)\\n\\n env_path = os.getenv('VIRTUAL_ENV', sys.exec_prefix)\\n folder_to = '%s\\\\Scripts\\\\%s' % (env_path, zipfile_folder_name)\\n exe_loc = '%s\\\\%s.exe' % (folder_to, program_name)\\n exe_link = '%s\\\\Scripts\\\\%s.exe' % (env_path, program_name)\\n paths = [exe_link, folder_to]\\n for path in paths:\\n if os.path.isfile(path):\\n os.unlink(path)\\n elif os.path.isdir(path):\\n shutil.rmtree(path)\\n print('Installing %s to %s' % (program_name, folder_to))\\n shutil.move(zipfile_folder_name, folder_to)\\n os.link(exe_loc, exe_link)\",\n \"def glue(shreds):\\n width = sum([s.width for s in shreds])\\n height = shreds[0].height\\n img = Image.new(\\\"RGBA\\\", (width, height))\\n x_dest = 0 # x coordinate of insertion point.\\n for s in shreds:\\n img.paste(s.image, (x_dest, 0))\\n x_dest += s.width # Move insertion point for next shred.\\n return img\",\n \"def draw_multicolor_square(t,sz):\\r\\n for i in [\\\"red\\\", \\\"purple\\\", \\\"hotpink\\\", \\\"blue\\\"]:\\r\\n t.color(i)\\r\\n t.forward(sz)\\r\\n t.left(90)\",\n \"def pack():\\n PackCommandExecutor().pack()\",\n \"def __init__(self, size: int):\\r\\n self.l = []\\r\\n self.sz = size\\r\\n self.sm = 0\",\n \"def test_gibberish_magnitude(self):\\n command_line = [\\\"filesystem\\\", \\\"create\\\", \\\"pn\\\", \\\"fn\\\", '--size=\\\"carbonGiB\\\"']\\n for prefix in [[], [\\\"--propagate\\\"]]:\\n self.check_system_exit(prefix + command_line, _PARSE_ERROR)\",\n \"def square(t, seg, size, fib):\\n for i in range(seg):\\n\\n t.fd(size)\\n t.left(90)\\n t.write(fib)# write the current Fibonacci number next to the square\\n t.fd(size)\",\n \"def msize(path):\\n with open(path, \\\"w\\\") as w:\\n w.write(\\\"\\\")\\n os.utime(path, (0, 0))\\n time.sleep(0.4)\\n with open(path, \\\"w\\\") as w:\\n w.write(\\\"0\\\")\\n os.utime(path, (0, 0))\",\n \"def calculate_previous_size(required_hole_size):\\n\\treturn required_hole_size/8\",\n \"def size(self):\",\n \"def part2(width, height):\\n pass\",\n \"def starsize(self, hipid):\\n #if hipid<0 or len(self.hip_stars)<=hipid: return 0\\n s = self.hip_stars[hipid]\\n if s==None: return 0\\n #return self.zerosize*(.8**(s[1]))\\n #return self.zerosize-s[1]-2\\n return self.dimmest_mag-s[1]+1\",\n \"def getSize(self) -> int:\\n ...\",\n \"def getSize(self) -> int:\\n ...\",\n \"def eamap_size(*args):\\n return _ida_hexrays.eamap_size(*args)\",\n \"def updateSizeHead(self, size): \\n self.avatarConfiguration[\\\"headSize\\\"] = size\\n self.paintHead()\\n self.paintHair()\\n if (self.avatarConfiguration[\\\"mask\\\"]):\\n self.generateMask(\\\"imgUpload.png\\\")\\n self.paintMask()\",\n \"def test_run_resize(self):\\n self.expect_datatore_lookup('SomeBlobKey', True)\\n self.expect_open_image('SomeBlobKey', (1600, 1200))\\n self.expect_resize(32)\\n self.expect_encode_image('SomeImageSize32')\\n self.mox.ReplayAll()\\n self._environ['PATH_INFO'] += '=s32'\\n self.run_request('image/jpeg', 'SomeImageSize32')\",\n \"def pulp_smash():\",\n \"def correct_size():\\n check50.run(\\\"./inheritance_test\\\").stdout(\\\"size_true.*\\\").exit(0)\",\n \"def du(flags='-h'):\\n system_command(\\\"du {0}\\\".format(flags))\",\n \"def DrawSplitterSash(*args, **kwargs):\\n return _gdi_.RendererNative_DrawSplitterSash(*args, **kwargs)\",\n \"def run_mash(self):\\n if (self.path2 != \\\"\\\" and self.path3 != \\\"\\\"):\\n ###Set up command###\\n # <exe path> dist <options> <ref seq> <query seq>\\n # Sketch size by default 10000, kmer = 16\\n ####################\\n\\n ###Initialize variables\\n ref_file = self.path2\\n query_file = self.path3\\n self.mash_out_path = os.getcwd() + \\\"/mash_out.txt\\\"\\n path_to_exe = \\\"/Users/ddooley/bioinformatics_packages/Mash/mash\\\"\\n cmd = \\\"\\\"\\n flag_str = \\\"-s 10000 -k 16 -i\\\"\\n cmd += path_to_exe + \\\" dist \\\" + \\\" \\\" + flag_str + \\\" \\\" + ref_file + \\\" \\\" + query_file + \\\" > \\\" + self.mash_out_path\\n os.system(cmd)\\n\\n else:\\n QtWidgets.QMessageBox.question(self, \\\"Error!\\\",\\\"You must select reference and query .fasta files.\\\",QtWidgets.QMessageBox.Ok)\",\n \"def auto(bin_path: str, addr: str, count: int, size: int) -> bool:\\n\\n return gnu(bin_path, addr, count, size)\",\n \"def make_shirt(size, text):\\n print(\\\"I'll be making a \\\" + size + \\\" shirt with '\\\" + text + \\\"' written on\\\"\\n + \\\" the front of it.\\\")\",\n \"def make_shirt(size, message):\\n print(\\\"I need a shirt, size \\\" + size + \\\", that says \\\" + message + \\\".\\\")\",\n \"def size(self) -> int:\",\n \"def get_size_bytes( path ):\\n cmd = [ 'du', '-s', '-B1', '--apparent-size', path ]\\n return int( run_cmd( cmd ).split( \\\"\\\\t\\\" )[ 0 ] )\"\n]","isConversational":false},"negative_scores":{"kind":"list like","value":["0.63248545","0.5898294","0.58236194","0.5499801","0.52991474","0.52804255","0.51600564","0.5145358","0.51127124","0.5105524","0.50900805","0.5079548","0.50585145","0.5053135","0.5038149","0.4947009","0.494617","0.49350992","0.49325883","0.4879849","0.487358","0.4867902","0.48654613","0.4849321","0.4804365","0.4791629","0.47811115","0.4768425","0.47678885","0.47615907","0.47269124","0.4713203","0.47125632","0.4703652","0.47008097","0.46988863","0.4671641","0.46694726","0.46694726","0.46694726","0.46694726","0.46694726","0.46694726","0.46694726","0.46694726","0.46694726","0.46694726","0.46694726","0.46694726","0.46694726","0.46694726","0.46694726","0.46694726","0.46694726","0.46694726","0.46694726","0.46686274","0.46662584","0.466057","0.4651302","0.46400258","0.46283576","0.46178752","0.46079805","0.46053067","0.46046048","0.46004647","0.45982817","0.45976925","0.45962632","0.45928746","0.4587599","0.4587328","0.45870346","0.45571843","0.45538673","0.45507059","0.4547212","0.45458448","0.45450613","0.45437267","0.454351","0.4540011","0.45382598","0.4536255","0.45248762","0.45177534","0.45177534","0.45101166","0.4489356","0.44882926","0.44871813","0.44844225","0.44815972","0.44810057","0.44801486","0.44794562","0.4479142","0.4477607","0.44691387","0.44684285"],"string":"[\n \"0.63248545\",\n \"0.5898294\",\n \"0.58236194\",\n \"0.5499801\",\n \"0.52991474\",\n \"0.52804255\",\n \"0.51600564\",\n \"0.5145358\",\n \"0.51127124\",\n \"0.5105524\",\n \"0.50900805\",\n \"0.5079548\",\n \"0.50585145\",\n \"0.5053135\",\n \"0.5038149\",\n \"0.4947009\",\n \"0.494617\",\n \"0.49350992\",\n \"0.49325883\",\n \"0.4879849\",\n \"0.487358\",\n \"0.4867902\",\n \"0.48654613\",\n \"0.4849321\",\n \"0.4804365\",\n \"0.4791629\",\n \"0.47811115\",\n \"0.4768425\",\n \"0.47678885\",\n \"0.47615907\",\n \"0.47269124\",\n \"0.4713203\",\n \"0.47125632\",\n \"0.4703652\",\n \"0.47008097\",\n \"0.46988863\",\n \"0.4671641\",\n \"0.46694726\",\n \"0.46694726\",\n \"0.46694726\",\n \"0.46694726\",\n \"0.46694726\",\n \"0.46694726\",\n \"0.46694726\",\n \"0.46694726\",\n \"0.46694726\",\n \"0.46694726\",\n \"0.46694726\",\n \"0.46694726\",\n \"0.46694726\",\n \"0.46694726\",\n \"0.46694726\",\n \"0.46694726\",\n \"0.46694726\",\n \"0.46694726\",\n \"0.46694726\",\n \"0.46686274\",\n \"0.46662584\",\n \"0.466057\",\n \"0.4651302\",\n \"0.46400258\",\n \"0.46283576\",\n \"0.46178752\",\n \"0.46079805\",\n \"0.46053067\",\n \"0.46046048\",\n \"0.46004647\",\n \"0.45982817\",\n \"0.45976925\",\n \"0.45962632\",\n \"0.45928746\",\n \"0.4587599\",\n \"0.4587328\",\n \"0.45870346\",\n \"0.45571843\",\n \"0.45538673\",\n \"0.45507059\",\n \"0.4547212\",\n \"0.45458448\",\n \"0.45450613\",\n \"0.45437267\",\n \"0.454351\",\n \"0.4540011\",\n \"0.45382598\",\n \"0.4536255\",\n \"0.45248762\",\n \"0.45177534\",\n \"0.45177534\",\n \"0.45101166\",\n \"0.4489356\",\n \"0.44882926\",\n \"0.44871813\",\n \"0.44844225\",\n \"0.44815972\",\n \"0.44810057\",\n \"0.44801486\",\n \"0.44794562\",\n \"0.4479142\",\n \"0.4477607\",\n \"0.44691387\",\n \"0.44684285\"\n]","isConversational":false},"document_score":{"kind":"string","value":"0.0"},"document_rank":{"kind":"string","value":"-1"}}},{"rowIdx":75379,"cells":{"query":{"kind":"string","value":"Allows resource methods to return models, lists of models, and nested dicts including models; calls public_dict() on all objects that provide it."},"document":{"kind":"string","value":"def return_dict(f):\n @wraps(f)\n def wrapper(*args, **kwargs):\n return dictify(f(*args, **kwargs))\n return wrapper"},"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 opt_get_all_models_rest_api():\n return retrieve_all_models()","def on_get_resource(self, req, resp, **params):\n instance = self.get_object(**params)\n resp.json(**instance.as_resource)","def handle_models_request():\n # TODO: add sort and filter by creation/modification date\n return (\n json.dumps({\"models\": {\n k: d[\"data\"] for k, d in app.models.items()\n }}), 200,\n {'ContentType': 'application/json'}\n )","def resources(self):\n return [self]","def resources(self):\r\n return self.page.object_list","def to_representation(self, instance):\n # Load the paginated descendant features\n if instance is None:\n # This happens when OPTIONS is called from browsable API\n return None\n self.add_sources(instance)\n\n ret = OrderedDict()\n fields = self._readable_fields\n\n for field in fields:\n attribute = field.get_attribute(instance)\n assert attribute is not None, (\n 'field.get_attribute return None for instance %s, field %s'\n % (instance, field))\n field_ret = field.to_representation(attribute)\n if isinstance(field, ListSerializer):\n # Wrap lists of related resources in a ReturnList, so that the\n # renderer has access to the serializer\n field_ret = ReturnList(field_ret, serializer=field)\n ret[field.field_name] = field_ret\n\n return ReturnDict(ret, serializer=self)","def _get(self):\n return {\n \"_links\" : self.make_links({\n \"self\" : Root.get_self_url(),\n \"users\" : UserList.get_self_url(),\n \"customers\" : CustomerList.get_self_url(),\n \"tickets\" : TicketList.get_self_url(),\n \"comments\" : CommentList.get_self_url(),\n \"customer_user_associations\" : CustomerUserAssociationList.get_self_url(),\n })\n }","def to_dict(self):\n ret = {}\n for key in dir(self):\n if key.startswith(\"_\"):\n continue\n\n if key in ['id', 'objects', 'pk', 'STRICT']:\n continue\n\n obj = getattr(self, key)\n if callable(obj):\n continue\n ret[key] = obj\n return ret","def api():\n from gluon.contrib.hypermedia import Collection\n rules = {\n '<tablename>': {'GET':{},'POST':{},'PUT':{},'DELETE':{}},\n }\n return Collection(db).process(request,response,rules)","def api():\n from gluon.contrib.hypermedia import Collection\n rules = {\n '<tablename>': {'GET':{},'POST':{},'PUT':{},'DELETE':{}},\n }\n return Collection(db).process(request,response,rules)","def api():\n from gluon.contrib.hypermedia import Collection\n rules = {\n '<tablename>': {'GET':{},'POST':{},'PUT':{},'DELETE':{}},\n }\n return Collection(db).process(request,response,rules)","def _get_embedded_objects(self):\n return [getattr(self, name) for name, field in self._get_fields().items() if isinstance(field, fields.Object)]","def get(cls):\n return {'products': [product.to_json() for product in ProductModel.find_all()]}","def get_objects_data(self):\n return dict(result=self.objects)","def list(self, **kwargs):\n data, self.endpoint = self.data_endpoint(kwargs)\n r = super(Resource, self).list(**data)\n\n # Change display settings and data format for human consumption\n self.configure_display(r)\n return r","def resources(self):\n return self.__resources","def resources(self):\n\n return self.FIXTURE.resources_collection(self)","def get(self):\r\n return get_all()","def data(self):\n ret = super(serializers.ListSerializer, self).data\n return ReturnDict(ret, serializer=self)","def get_resources():\n user_id = session[\"email\"]\n resources = fm.get_resources(user_id)\n returned_val = dict(resources=resources)\n return jsonify(returned_val)","def get(self, request, **resources):\r\n\r\n instance = resources.get(self._meta.name)\r\n if not instance is None:\r\n return instance\r\n\r\n return self.paginate(\r\n request, self.get_collection(request, **resources))","def get_resources(self, obj):\n return WorkshopResourceSerializer(obj.resources, many=True).data","def get_resources():\n return Response(f\"{Resource.get_all_resources()}\", 200, mimetype='text/plain')","def get_serializer(self, *args, **kwargs):\n serializer_class = self.get_serializer_class()\n kwargs['context'] = self.get_serializer_context()\n return serializer_class(many=True, *args, **kwargs)","def _instantiateResource(self, res):\n\n\n try:\n pagination = vsdModels.Pagination(**res)\n pagination.validate() #will fail if it doesno't have totalCount\n return pagination\n except:\n resourcetype, oid = self.getResourceTypeAndId(res['selfUrl'])\n if resourcetype == 'objects':\n return vsdModels.APIObject._create(res)\n #e.g FolderLinks\n model = vsdModels.resourceTypes[resourcetype](**res)\n return model","def get(self, *args, **kwargs):\n self.before_get(args, kwargs)\n\n qs = QSManager(request.args, self.schema)\n objects_count, objects = self._data_layer.get_collection(qs, kwargs)\n\n schema_kwargs = getattr(self, 'get_schema_kwargs', dict())\n schema_kwargs.update({'many': True})\n\n schema = compute_schema(self.schema,\n schema_kwargs,\n qs,\n qs.include)\n\n result = schema.dump(objects).data\n\n view_kwargs = request.view_args if getattr(self, 'view_kwargs', None) is True else dict()\n add_pagination_links(result,\n objects_count,\n qs,\n url_for(self.view, **view_kwargs))\n\n result.update({'meta': {'count': objects_count}})\n\n self.after_get(result)\n return result","def expose_models(app, HOST=\"localhost\", PORT=5000, API_PREFIX=\"/api\"):\n\n api = SAFRSAPI(app, host=HOST, port=PORT)\n api.expose_object(models.Category)\n api.expose_object(models.CustomerCustomerDemo)\n api.expose_object(models.OrderDetail)\n api.expose_object(models.Order)\n api.expose_object(models.Customer)\n api.expose_object(models.CustomerDemographic)\n api.expose_object(models.EmployeeAudit)\n api.expose_object(models.EmployeeTerritory)\n api.expose_object(models.Employee)\n api.expose_object(models.Product)\n api.expose_object(models.Region)\n api.expose_object(models.Shipper)\n api.expose_object(models.Supplier)\n api.expose_object(models.Territory)\n return api","def get_objects_data(self):\n return dict(items=self.objects)","def list(self, filter=None, _check_permissions=True, together='', _jsoned=True):\r\n\r\n join = None\r\n if filter:\r\n for k, v in filter.iteritems():\r\n if None in v:\r\n filter[k] = None\r\n query = reduce(and_,\r\n (self.table[field].belongs(value) if type(value) is list else (self.table[field] == value)\r\n for field, value in filter.iteritems()))\r\n else:\r\n query = None\r\n if _check_permissions:\r\n query, join = self.CASQuery(query, verb='list')\r\n fields = (self.table.ALL,) if self.virtual_fields else self.visible_fields\r\n # objects = self.db.executesql(self.db(query)._select(*fields,join=join),as_dict=True)\r\n objects = self.sql(query, *fields, left=join, as_dict=self.virtual_fields)\r\n if self.virtual_fields and objects:\r\n # calcolo tutti i virtual fields\r\n for obj, field in product(objects, [self.table[field] for field in self.virtual_fields]):\r\n obj[field.name] = field.f(obj)\r\n\r\n vn = partial(zip, self.visible_names + self.virtual_fields)\r\n get_vn = itemgetter(*(self.visible_names + self.virtual_fields))\r\n objects = map(dict, map(vn, map(get_vn, objects)))\r\n # print objects\r\n ret = {self.name: dict(results=objects, totalResults=len(objects), )}\r\n if together:\r\n if 'permissions' in together:\r\n ret.setdefault('PERMISSIONS', {}).update(\r\n self.my_perms(ids=map(itemgetter('id'), objects)).get('PERMISSIONS', {}))\r\n\r\n # results = {self.name : objects}\r\n for resource, redest, t, field in self.find_model_path(together):\r\n # print resource, redest, field,t\r\n if t == 'm':\r\n if resource in ret:\r\n obs = map(itemgetter('id'), ret[resource]['results'])\r\n ret.setdefault('TOMANY', {})['%s_%s' % (redest, field.name)] = obs\r\n if obs:\r\n ret.update(resource_manager.resource(redest).list(filter={field.name: obs}, _jsoned=False))\r\n elif t == '1':\r\n if resource in ret:\r\n obs = list(set(map(itemgetter(field.name), ret[resource]['results'])))\r\n # ret.setdefault('TOONE',{})['%s_%s' % (resource,field.name)] = obs\r\n if obs:\r\n ret.update(resource_manager.resource(redest).list(filter={'id': obs}, _jsoned=False))\r\n elif t == 'M':\r\n if resource in ret:\r\n first = 0 if field else 1\r\n m2m_idx = '%s/%s|%s' % (resource, redest, first)\r\n obs = map(itemgetter('id'), ret[resource]['results'])\r\n ret.setdefault('MANYTOMANY', {}).setdefault(m2m_idx, []).extend(obs)\r\n if obs:\r\n resource_manager.m2m((resource, redest)).list(resource_manager.resource(redest),\r\n collection=obs)\r\n res = current.response.text\r\n ret.setdefault('m2m', {}).update(res['m2m'])\r\n obs = list(set(map(itemgetter(1 - first), imap(itemgetter('add'), res['m2m'][m2m_idx]))))\r\n # ret.setdefault('TOMANY',{})[redest] = obs\r\n if obs:\r\n res = resource_manager.resource(redest).list(filter=dict(id=obs), _jsoned=False)\r\n ret.update(res)\r\n if self.private_args:\r\n if objects:\r\n ret.update(self.private_args.list(map(itemgetter(self.field_order.index('id')), objects)))\r\n\r\n current.response.text = ret\r\n return ret","def get_objects_data(self):\n return dict(objects=self.objects)","def get_resources(self):\n return []","def retrieve(self,request,*args,**kwargs):\n response=super(RetrieveAPIView,self).retrieve(request,*args,**kwargs)\n return self.fetch_related(request,response,*args,**kwargs)","def get_objects_data(self):\n pass","def to_dict(self) -> Dict:\n _dict = {}\n if hasattr(self, 'resources') and self.resources is not None:\n _dict['resources'] = [x.to_dict() for x in self.resources]\n return _dict","def general_serializer(instance):\n # get the serializer for this model\n serializer_class = eval(instance.target_model.__name__ + 'Serializer')\n # use this for create, update and retrieve, since we only need special serialization to display less in list and\n # to communicate with python\n if instance.action in ['retrieve', 'create', 'update']:\n # if it's the detail view, just return the standard serializer\n return serializer_class\n elif instance.action == 'from_python':\n # copy the declared fields from the detail serializer\n PythonSerializer._declared_fields = serializer_class._declared_fields.copy()\n # also the fields\n PythonSerializer.Meta.fields = serializer_class.Meta.fields.copy()\n # get fields\n model_fields = instance.target_model._meta.get_fields()\n # copy the extra_kwargs\n PythonSerializer.Meta.extra_kwargs = serializer_class.Meta.extra_kwargs.copy()\n # and the model\n PythonSerializer.Meta.model = instance.target_model\n # turn the relations into text fields, except the m2m field since the automatic serialization works better\n for fields in model_fields:\n if fields.is_relation:\n\n if (not isinstance(fields, models.ManyToManyField)) and \\\n (not isinstance(fields, models.ManyToManyRel)):\n PythonSerializer._declared_fields[fields.name] = serializers.StringRelatedField()\n else:\n PythonSerializer._declared_fields[fields.name] = serializers.StringRelatedField(many=True)\n\n return PythonSerializer\n else: # if not, modify it to remove unnecessary fields from the list view\n\n # copy the attributes to the generalized serializer\n GeneralSerializer._declared_fields = serializer_class._declared_fields.copy()\n GeneralSerializer.Meta.fields = serializer_class.Meta.fields.copy()\n GeneralSerializer.Meta.extra_kwargs = serializer_class.Meta.extra_kwargs.copy()\n GeneralSerializer.Meta.model = instance.target_model\n # allocate a list of the fields to remove from the list view\n remove_fields = []\n # for all the fields in the serializer\n for fields in GeneralSerializer.Meta.fields:\n\n # remove the id field (since it's not in declared_fields)\n if fields in ['id', 'slug']:\n # eliminate the field from the serializer\n remove_fields.append(fields)\n continue\n if instance.target_model.__name__ != 'Mouse' and fields == 'mouse':\n # remove the current mouse extra_kwargs so it displays\n del GeneralSerializer.Meta.extra_kwargs[fields]\n continue\n # remove the fields that have been assigned as read only\n if (fields in serializer_class._declared_fields.keys()) and \\\n (('read_only=True' in str(GeneralSerializer._declared_fields[fields])) or\n ('ReadOnly' in str(GeneralSerializer._declared_fields[fields]))):\n\n # eliminate the field from the serializer\n remove_fields.append(fields)\n # remove the field from declared fields\n del GeneralSerializer._declared_fields[fields]\n continue\n\n GeneralSerializer.Meta.extra_kwargs[fields] = {'write_only': True}\n\n # remove the read only fields\n GeneralSerializer.Meta.fields = [el for el in GeneralSerializer.Meta.fields if el not in remove_fields]\n # overwrite url kwargs, since it is set by default to read only\n GeneralSerializer.Meta.extra_kwargs['url'] = {'lookup_field': instance.lookup_field}\n # put the mouse entry at the top\n if 'mouse' in GeneralSerializer.Meta.fields:\n GeneralSerializer.Meta.fields.remove('mouse')\n GeneralSerializer.Meta.fields = ['mouse'] + GeneralSerializer.Meta.fields\n return GeneralSerializer","def get():\n\n return {'model_ids': mgmt.get_model_ids()}","def opt_model_create_rest_api():\n request_json = request.get_json()\n OptimModelRequestAPI(request_json).validate()\n return create_model_data(request_json)","def get_serializers(self):\n\n if not self.mapping:\n self.mapping = {\n 'dc': {\n 'obj': 'dc_id'\n },\n 'dc__basic': {\n 'serializer': DCSerializer,\n 'kwargs': {\n 'kind': 'basic'\n },\n 'obj': 'dc',\n 'eager_loading': self.setup_eager_loading_datacenter\n },\n 'dc__details': {\n 'serializer': DCSerializer,\n 'kwargs': {\n 'kind': 'basic'\n },\n 'obj': 'dc',\n 'eager_loading': self.setup_eager_loading_datacenter\n }\n }","def get_list(self, request, **kwargs):\n # :TODO modify top_level_serializer or pass a list with self as\n # argument?\n registry = {getattr(self._meta , 'resource_name'): self}\n content = serializers.top_level_serializer(registry)\n response = HttpResponse(\n content = content,\n content_type = 'application/xml')\n response = add_das_headers(response)\n return response","def get_list(self, request, **kwargs):\n # :TODO modify top_level_serializer or pass a list with self as\n # argument?\n registry = {getattr(self._meta , 'resource_name'): self}\n content = serializers.top_level_serializer(registry)\n response = HttpResponse(\n content = content,\n content_type = 'application/xml')\n response = add_das_headers(response)\n return response","def GetObjects(self): \r\n return self.model.GetObjects()","def worldobjects(self):\n return dict( self.domain.objects.items() | self.problem.objects.items() )","def default_serializer(obj):\n if issubclass(type(obj), BaseModel):\n return obj.dict(by_alias=True)\n\n raise TypeError","def resources(self):","def test_related_resource(read_only):\n class ProjectSerializer(serializers.ModelSerializer):\n class Meta:\n model = ProjectWithCustomID\n fields = ('custom_id', 'name', 'archived', 'members', 'owner_member')\n read_only_fields = ['members', 'owner_member'] if read_only else []\n\n class ProjectViewSet(mixins.RetrieveModelMixin, viewsets.GenericViewSet):\n queryset = ProjectWithCustomID.objects.all()\n serializer_class = ProjectSerializer\n renderer_classes = [renderers.JSONRenderer]\n parser_classes = [parsers.JSONParser]\n swagger_schema = base.BasicSwaggerAutoSchema\n\n router = routers.DefaultRouter()\n router.register(r'projects', ProjectViewSet, **compatibility._basename_or_base_name('projects'))\n\n generator = OpenAPISchemaGenerator(info=openapi.Info(title=\"\", default_version=\"\"), patterns=router.urls)\n\n swagger = generator.get_schema(request=None, public=True)\n\n response_schema = swagger['paths']['/projects/{custom_id}/']['get']['responses']['200']['schema']['properties']\n assert 'id' in response_schema['data']['properties']\n assert response_schema['data']['properties']['id']['type'] == 'string'\n assert response_schema['data']['properties']['id']['format'] == 'int32'\n assert 'type' in response_schema['data']['properties']\n assert 'attributes' in response_schema['data']['properties']\n assert list(response_schema['data']['properties']['attributes']['properties'].keys()) == ['name', 'archived']\n assert 'relationships' in response_schema['data']['properties']\n relationships_schema = response_schema['data']['properties']['relationships']['properties']\n assert list(relationships_schema.keys()) == ['members', 'owner-member']\n members_schema = relationships_schema['members']['properties']\n assert members_schema['data']['items']['properties']['id']['type'] == 'string'\n assert members_schema['data']['items']['properties']['id']['format'] == 'int64'\n assert members_schema['data']['items']['properties']['type']['pattern'] == 'member-with-custom-ids'\n owner_member_schema = relationships_schema['owner-member']['properties']\n assert owner_member_schema['data']['properties']['id']['type'] == 'string'\n assert owner_member_schema['data']['properties']['id']['format'] == 'int64'\n assert owner_member_schema['data']['properties']['type']['pattern'] == 'member-with-custom-ids'","def get():\n\n # OPTION #1 - my preferable\n # sql = \"select * from director\"\n # if not (res := db.engine.execute(sql).fetchall()):\n # raise NotFoundError\n # return jsonify([dict(i) for i in res])\n\n # OPTION #2 - I don't like ORM queries, so it's just to meet the lesson topic\n if not (res := Director.query.all()):\n raise NotFoundError\n return jsonify(DirectorSchema(many=True).dump(res))","def list(self,request,*args,**kwargs):\n response=super(ListAPIView,self).list(request,*args,**kwargs)\n #add applied_filters to the response which is set when filter_queryset method is called\n response=self.addAppliedFilters(response)\n #fetch data from the related views\n return self.fetch_related(request,response,*args,**kwargs)","def index(self, req):\n return self._get_models(req, is_detail=False)","def _generate_implicit_api_resource(self):\n return ImplicitHttpApiResource().to_dict()","async def get_resources(self, **kwargs) -> dict:\n resources = await self.request.get(self._base_path, **kwargs)\n self._sanitize_resources(resources)\n return resources","def GetModels(self, request, context):\n context.code(beta_interfaces.StatusCode.UNIMPLEMENTED)","def get(self, *args, **kwargs):\n self.before_get(args, kwargs)\n\n relationship_field, model_relationship_field, related_type_, related_id_field = self._get_relationship_data()\n related_view = self.schema._declared_fields[relationship_field].related_view\n related_view_kwargs = self.schema._declared_fields[relationship_field].related_view_kwargs\n\n obj, data = self._data_layer.get_relationship(model_relationship_field,\n related_type_,\n related_id_field,\n kwargs)\n\n for key, value in copy(related_view_kwargs).items():\n if isinstance(value, str) and value.startswith('<') and value.endswith('>'):\n tmp_obj = obj\n for field in value[1:-1].split('.'):\n tmp_obj = getattr(tmp_obj, field)\n related_view_kwargs[key] = tmp_obj\n\n result = {'links': {'self': request.path,\n 'related': url_for(related_view, **related_view_kwargs)},\n 'data': data}\n\n qs = QSManager(request.args, self.schema)\n if qs.include:\n schema = compute_schema(self.schema, dict(), qs, qs.include)\n\n serialized_obj = schema.dump(obj)\n result['included'] = serialized_obj.data.get('included', dict())\n\n self.after_get(result)\n return result","def test_resource_serialization(self):\n\n # Construct dict forms of any model objects needed in order to build this model.\n\n attribute_model = {} # Attribute\n attribute_model['name'] = 'testString'\n attribute_model['value'] = 'testString'\n\n # Construct a json representation of a Resource model\n resource_model_json = {}\n resource_model_json['attributes'] = [attribute_model]\n\n # Construct a model instance of Resource by calling from_dict on the json representation\n resource_model = Resource.from_dict(resource_model_json)\n assert resource_model != False\n\n # Construct a model instance of Resource by calling from_dict on the json representation\n resource_model_dict = Resource.from_dict(resource_model_json).__dict__\n resource_model2 = Resource(**resource_model_dict)\n\n # Verify the model instances are equivalent\n assert resource_model == resource_model2\n\n # Convert model instance back to dict and verify no loss of data\n resource_model_json2 = resource_model.to_dict()\n assert resource_model_json2 == resource_model_json","def _openapi_json(self):\n # We don't use Flask.jsonify here as it would sort the keys\n # alphabetically while we want to preserve the order.\n from pprint import pprint\n pprint(self.to_dict())\n return current_app.response_class(json.dumps(self.to_dict(), indent=4),\n mimetype='application/json')","def to_api_object(self):\n visible_to = self.visibleTo.all()\n visible_to_list = []\n for visible in visible_to:\n visible_to_list.append(\"http://\"+visible.author_uid)\n\n # We only get the first 5 comments\n # Get the comments, be aware that comments might not be returned if the foreign author of the comment is unavailable\n comments_list = [comment.to_api_object() for comment in self.comment_set.all().order_by(\"-published\")[:5]]\n filtered_comments_list = [comment for comment in comments_list if 'error' not in comment['author']]\n\n\n\n return {\n \"title\": self.title,\n \"source\": self.source,\n \"origin\": self.origin,\n \"description\": self.description,\n \"contentType\": self.contentType,\n \"content\": self.content,\n \"author\": self.author.to_api_object(),\n \"categories\": [category.name for category in self.categories.all()],\n \"count\": len(filtered_comments_list),\n \"size\": self.size,\n \"next\": settings.HOST_URI + \"/posts/\" + str(self.id.hex) + \"/comments\",\n \"comments\": filtered_comments_list,\n \"published\": self.published,\n \"id\": str(self.id.hex),\n \"visibility\": self.visibility,\n \"visibleTo\": visible_to_list,\n \"unlisted\": self.unlisted\n }","def test_get_api_resources(self):\n pass","def __json__(self):\n filtered_dict = dict()\n\n for k, item in six.iteritems(self.__dict__):\n if k.startswith('_'):\n continue\n\n if hasattr(item, '__json__'):\n filtered_dict[k] = item.__json__\n else:\n filtered_dict[k] = serialize_obj(item)\n\n return filtered_dict","def getAllAPI():\n list_strain = StrainAPI().get_all()\n schema = StrainSchema()\n results = schema.load(list_strain, many=True)\n return results","def list(cls) -> t.Iterable[SDict]:\n endpoint: t.Optional[str] = cls.endpoint\n\n def process_field(v):\n if isinstance(v, dict):\n return json.dumps(v, indent=True)\n return v\n\n while endpoint:\n r = Resource(endpoint=endpoint)\n items = r.get()\n # filter the items, ordering as needed\n for x in items.results:\n yield {k: process_field(x[k]) for k in cls.list_fields if k in x}\n endpoint = items.next if items.next else None","def detail(self, req):\n return self._get_models(req, is_detail=True)","def object_get(self, request):\n _view = _object_view(self, request)\n queried = ObjectPostings(self.cdb_object_id, request.params.mixed()).query()\n postings = [request.view(obj) for obj in queried[0]]\n\n _view.update({\n \"postings\": postings,\n \"result_complete\": queried[1]\n })\n return _view","def safe_json(self, context):\n serialize_context = dict()\n for key, obj in context.items():\n if isinstance(obj.__class__, ModelBase):\n if hasattr(obj, 'serialize') and callable(getattr(obj, 'serialize')):\n serialize_context[key] = obj.serialize()\n else:\n serialize_context[key] = model_to_dict(obj)\n elif isinstance(obj, QuerySet):\n serialize_context[key] = [o.serialize() for o in obj if hasattr(o, 'serialize')]\n if len(serialize_context[key]) != len(obj):\n serialize_context[key] = [model_to_dict(o) for o in obj]\n elif key == 'extra':\n serialize_context[key] = obj\n # elif key == 'view':\n # continue\n # else:\n # serialize_context[key] = obj\n return dict(success=True, data=serialize_context)","def realized(self):\n result = {\"Object\": True}\n for key in self.__dict__:\n if isinstance(self.__dict__[key], Object):\n result[key] = self.__dict__[key].asdict()\n else:\n result[key] = self.__dict__[key]\n return result","def process_resource_api(self, resources, resource, api, context):\n pass","def make_runtime_viewset(self, **kwargs):\n\n class RunTimeViewset(viewsets.ModelViewSet):\n if self.permission_classes:\n permission_classes = self.permission_classes\n serializer_class = self.serializer_class\n model = self.model\n lookup_field = self.lookup_field\n\n def get_permissions(self, **kwargs):\n _permissions = self.model.get_api_permissions(**kwargs)\n if isinstance(_permissions, list):\n self.permission_classes = _permissions\n if isinstance(_permissions, dict):\n if self.action in _permissions.keys():\n self.permission_classes = _permissions[self.action]\n return super(RunTimeViewset, self).get_permissions(**kwargs)\n\n def get_queryset(self, *args, **kwargs):\n _prefetch_related_fields = self.model.api_prefetch_related_fields()\n _select_related_fields = self.model.api_select_related_fields()\n queryset = self.model.get_api_queryset(*args, **kwargs)\n if len(_prefetch_related_fields) > 0:\n queryset = queryset.prefetch_related(\n *_prefetch_related_fields)\n if len(_select_related_fields) > 0:\n queryset = queryset.select_related(*_select_related_fields)\n return queryset\n\n def list(self, request, **kwargs):\n self.queryset = self.get_queryset(request=request, **kwargs)\n request = kwargs.get('request', self.request)\n try:\n search_enabled = bool(eval(request.GET.get('search', '0')))\n if search_enabled:\n _filters = parse_filters(\n model=self.model, request=request)\n self.queryset = self.queryset.filter(_filters)\n except Exception as err:\n self.queryset = self.queryset\n page = self.paginate_queryset(self.queryset)\n if page is not None:\n serializer = self.get_serializer(page, many=True)\n return self.get_paginated_response(serializer.data)\n\n serializer = self.get_serializer(self.queryset, many=True)\n return Response(serializer.data)\n\n def create(self, request, *args, **kwargs):\n before_post_api.send(\n sender=self.model, requested_data=request.data)\n serializer = self.serializer_class(data=request.data)\n if serializer.is_valid(raise_exception=True):\n instance = serializer.save()\n after_post_api.send(\n sender=self.model, instance=instance, requested_data=request.data)\n return Response(serializer.data)\n return Response(serializer.data)\n\n def retrieve(self, request, *args, **kwargs):\n return super(RunTimeViewset, self).retrieve(request, *args, **kwargs)\n\n def update(self, request, *args, **kwargs):\n instance = self.get_object()\n before_put_api.send(\n sender=self.model, instance=instance, requested_data=request.data)\n serializer = self.serializer_class(\n data=request.data, instance=instance)\n if serializer.is_valid(raise_exception=True):\n instance = serializer.save()\n after_put_api.send(\n sender=self.model, instance=instance, requested_data=request.data)\n return Response(serializer.data)\n return Response(serializer.data)\n\n def partial_update(self, request, *args, **kwargs):\n instance = self.get_object()\n before_patch_api.send(\n sender=self.model, instance=instance, requested_data=request.data)\n serializer = self.serializer_class(\n data=request.data, instance=instance, partial=True)\n if serializer.is_valid(raise_exception=True):\n instance = serializer.save()\n after_patch_api.send(\n sender=self.model, instance=instance, requested_data=request.data)\n return Response(serializer.data)\n return Response(serializer.data)\n\n def destroy(self, request, *args, **kwargs):\n return super(RunTimeViewset, self).destroy(request=request, *args, **kwargs)\n\n return class_factory(\n class_name=\"{}RuntimeViewset\".format(self.model.__name__),\n base_classes=(RunTimeViewset,)\n )","def to_representation(self, data):\n # Dealing with nested relationships, data can be a Manager,\n # so, first get a queryset from the Manager if needed\n\n iterable = data.all() if isinstance(data, models.Manager) else data\n \n if self.parent is None:\n post = [\n self.child.to_representation(item) for item in iterable\n ]\n normalized_dict = OrderedDict()\n normalized_dict[self.child.Meta.model_name] = ReturnList(post, serializer=self)\n result = [normalized_dict]\n for normalized_item in self.child.Meta.normalized_fields:\n if normalized_item in self.instancelist_dict:\n normalized_dict[normalized_item] = \\\n ReturnList(self.make_normalized_item_list(normalized_item), serializer=self)\n return result\n\n if self.field_name in self.child.Meta.normalized_fields:\n result = [ item.id for item in iterable ]\n parent = self.root\n if not self.field_name in parent.instancelist_dict:\n parent.instancelist_dict[self.field_name] = []\n parent.instance_repr_dict[self.field_name] = self.child._readable_fields\n parent.instancelist_dict[self.field_name] = \\\n list(set(parent.instancelist_dict[self.field_name]) | set(iterable))\n else:\n result = [\n self.child.to_representation(item) for item in iterable\n ]\n\n return result","def json(self):\n if self.resource:\n return jsonify(self.resource)\n return jsonify({'id': self.id})","def ng_get(self, request, *args, **kwargs):\r\n return self.build_json_response(self.get_object())","def process_resource_listing_api(self, resources, listing_api, context):\n pass","def getObject(self, resource):\n resource = self.parseUrl(resource, 'objects')\n\n res = self.getRequest(resource)\n obj = vsdModels.APIObject._create(res)\n return obj","def get_list(self, request, **kwargs):\n # TODO: Uncached for now. Invalidation that works for everyone may be\n # impossible.\n objects = self.obj_get_list(request=request, **self.remove_api_resource_names(kwargs))\n sorted_objects = self.apply_sorting(objects, options=request.GET)\n\n paginator = self._meta.paginator_class(request.GET, sorted_objects, resource_uri=self.get_resource_uri(),\n limit=self._meta.limit, max_limit=self._meta.max_limit, collection_name=self._meta.collection_name)\n to_be_serialized = paginator.page()\n\n # Dehydrate the bundles in preparation for serialization.\n bundles = [self.build_bundle(obj=obj, request=request) for obj in to_be_serialized['objects']]\n to_be_serialized['objects'] = [self.full_dehydrate(bundle) for bundle in bundles]\n to_be_serialized = self.alter_list_data_to_serialize(request, to_be_serialized)\n\n self.add_shit_to_meta(request, to_be_serialized)\n\n return self.create_response(request, to_be_serialized)","def obj_get_list(self, request=None, **kwargs):\n filters = None\n\n if hasattr(request, 'GET'):\n filters = request.GET\n\n applicable_filters = self.build_filters(filters=filters)\n applicable_filters.update(kwargs)\n\n try:\n return self.get_object_list(request).filter(**applicable_filters)\n except ValueError, e:\n raise NotFound(\"Invalid resource lookup data provided (mismatched type).\")","def dict(self):\n return objToDict(self)","def __json_light__(self):\n filtered_dict = dict()\n\n for k, item in six.iteritems(self.__dict__):\n if k.startswith('_') or k == 'annotations':\n continue\n\n if hasattr(item, '__json__'):\n filtered_dict[k] = item.__json__\n else:\n filtered_dict[k] = serialize_obj(item)\n\n return filtered_dict","def public(self, page=None, per_page=None, sort=None):\r\n params = base.get_params(None, locals())\r\n url = '{0}/public'.format(self.get_url())\r\n request = http.Request('GET', url, params)\r\n\r\n return request, parsers.parse_json","def instances(request): #pylint: disable=unused-argument\n response = list()\n # Retrieve filtered parameter on GET. It's used to display all instances or just user instances\n # In all cases, if user is not superuser, only user instances are displayed\n if 'filtered' in request.GET:\n filtered = ast.literal_eval(request.GET['filtered'])\n else:\n # By default, we filter based on user_id\n filtered = True\n # By default, we just list user instances, not all instances\n if not request.user.is_superuser:\n # If user is superuser and user are requesting admin view of instances\n # We ask a full list of instances\n filtered = True\n\n # We retrieve data from backend\n data_instances = BACKEND.instances(request, filtered)\n data_users = BACKEND.users()\n data_projects = BACKEND.projects()\n\n # We merge user and project information w/ instances\n for data_instance in data_instances:\n try:\n project = \"{name}\".format(\n **data_projects[data_instances[data_instance]['project_id']])\n except KeyError:\n project = data_instances[data_instance]['project_id']\n\n try:\n user = \"{name}\".format(\n **data_users[data_instances[data_instance]['user_id']])\n except KeyError:\n user = data_instances[data_instance]['user_id']\n response.append({\n 'id': data_instances[data_instance]['id'],\n 'name': data_instances[data_instance]['name'],\n 'created_at': data_instances[data_instance]['created_at'],\n 'lease_end': data_instances[data_instance]['lease_end'],\n 'project': project,\n 'user': user\n })\n return JsonResponse(response, safe=False)","def _request_model(self, instance, success, get_embedded=True):\n coll = self.get_collection('_model')\n if get_embedded:\n callback = partial(self._get_embedded_model_names,\n instance=instance,\n success=success)\n else:\n callback = success\n\n try:\n instance['_model']\n except KeyError:\n raise tornado.web.HTTPError(400, 'Missing model key')\n coll.find_one({'_id': instance['_model']},\n callback=callback)","def obj_get_list(self, request=None, **kwargs):\n filters = {}\n if hasattr(request, 'GET'):\n # Grab a mutable copy.\n filters = request.GET.copy()\n\n # Update with the provided kwargs.\n filters.update(kwargs)\n if \"user\" in filters and \"media\" in filters:\n try:\n image_uuid_list = []\n wordbox_uuid_list = []\n for item in filters['media'].split(','):\n # use kwarg \"resource\" field to be more general rather\n # than take the 2 element\n try:\n if item.split('/')[2] == 'image':\n image_uuid_list.append(uuid_re.findall(item)[0])\n elif item.split('/')[2] == 'wordbox':\n wordbox_uuid_list.append(uuid_re.findall(item)[0])\n except IndexError:\n raise BadRequest(\"Invalid uuid or resource URI.\")\n userprofile = UserProfile.objects.get(\n uuid=uuid_from_uri(filters['user']))\n im = userprofile.image_collection_set.filter(\n is_active=True, uuid__in=image_uuid_list)\n wb = userprofile.wordbox_collection_set.\\\n filter(is_active=True, uuid__in=wordbox_uuid_list)\n base_object_list = sorted(chain(im, wb),\n key=attrgetter('created_time'))[::-1]\n return self.apply_authorization_limits(request,\n base_object_list)\n except ValueError:\n raise BadRequest(\"Invalid resource lookup data provided \"\n \"(mismatched type).\")\n elif \"user\" in filters and len(filters) == 1:\n try:\n userprofile = UserProfile.objects.get(\n uuid=uuid_from_uri(filters['user']))\n im = userprofile.image_collection_set.filter(is_active=True)\n wb = userprofile.wordbox_collection_set.filter(is_active=True)\n base_object_list = sorted(chain(im, wb),\n key=attrgetter('created_time'))[::-1]\n return self.apply_authorization_limits(request,\n base_object_list)\n except ValueError:\n raise BadRequest(\"Invalid resource lookup data provided \"\n \"(mismatched type).\")\n else:\n raise BadRequest(\"Invalid filtering parameter(s)\")","def retrieve(self, request, pk=None, **kwargs):\n serializer = self.serializer_class(self.get_object())\n return Response(serializer.data, status=status.HTTP_200_OK)","def _get_models(self, req, is_detail):\n context = req.environ['meteos.context']\n\n search_opts = {}\n search_opts.update(req.GET)\n\n # Remove keys that are not related to model attrs\n search_opts.pop('limit', None)\n search_opts.pop('offset', None)\n sort_key = search_opts.pop('sort_key', 'created_at')\n sort_dir = search_opts.pop('sort_dir', 'desc')\n\n models = self.engine_api.get_all_models(\n context, search_opts=search_opts, sort_key=sort_key,\n sort_dir=sort_dir)\n\n limited_list = common.limited(models, req)\n\n if is_detail:\n models = self._view_builder.detail_list(req, limited_list)\n else:\n models = self._view_builder.summary_list(req, limited_list)\n return models","def obj_get_list(self, bundle, **kwargs):\n filters = {}\n if hasattr(bundle.request, 'GET'):\n # Grab a mutable copy.\n filters = bundle.request.GET.copy()\n\n # Update with the provided kwargs.\n filters.update(kwargs)\n channel = False\n if 'owner' in filters:\n channel = True\n applicable_filters = self.build_filters(filters=filters)\n\n try:\n objects = self.apply_filters(bundle.request, applicable_filters)\n self.objects_returned = len(objects)\n if len(objects) == 1 and applicable_filters and not channel:\n obj = objects[0]\n obj.views = obj.views + 1\n obj.lastview_date = timezone.now()\n obj.save(update_fields=['views', 'lastview_date'])\n return self.authorized_read_list(objects, bundle)\n except ValueError:\n raise BadRequest(\"Invalid resource lookup data provided (mismatched type).\")","def resources(self) -> Generator[Tuple[str, str, Dict[str, Any]], None, None]:\n for path, methods in self.data[\"paths\"].items():\n for method in methods:\n if method.lower() not in HTTP_VERBS:\n continue\n\n yield method, path, methods","def _get_models(self):\n # Fast-path already loaded\n if self.__models is not None:\n return self.__models\n # Initialize the dictionary\n self.__models = dict()\n # Populate this dictionary with TorchVision's models\n for name in dir(torchvision.models):\n if len(name) == 0 or name[0] == \"_\": # Ignore \"protected\" members\n continue\n builder = getattr(torchvision.models, name)\n if isinstance(builder, types.FunctionType): # Heuristic\n self.__models[f\"torchvision-{name.lower()}\"] = builder\n # Dynamically add the custom models from subdirectory 'models/'\n def add_custom_models(name, module, _):\n nonlocal self\n # Check if has exports, fallback otherwise\n exports = getattr(module, \"__all__\", None)\n if exports is None:\n tools.warning(f\"Model module {name!r} does not provide '__all__'; falling back to '__dict__' for name discovery\")\n exports = (name for name in dir(module) if len(name) > 0 and name[0] != \"_\")\n # Register the association 'name -> constructor' for all the models\n exported = False\n for model in exports:\n # Check model name type\n if not isinstance(model, str):\n tools.warning(f\"Model module {name!r} exports non-string name {model!r}; ignored\")\n continue\n # Recover instance from name\n constructor = getattr(module, model, None)\n # Check instance is callable (it's only an heuristic...)\n if not callable(constructor):\n continue\n # Register callable with composite name\n exported = True\n fullname = f\"{name}-{model}\"\n if fullname in self.__models:\n tools.warning(f\"Unable to make available model {model!r} from module {name!r}, as the name {fullname!r} already exists\")\n continue\n self.__models[fullname] = constructor\n if not exported:\n tools.warning(f\"Model module {name!r} does not export any valid constructor name through '__all__'\")\n with tools.Context(\"models\", None):\n tools.import_directory(pathlib.Path(__file__).parent / \"models\", {\"__package__\": f\"{__package__}.models\"}, post=add_custom_models)\n # Return the dictionary\n return self.__models","def subresources(self):\n return self._get_related_resources(True)","def test_all_returns_dict(self):\n self.assertIs(type(models.storage.all()), dict)","def test_all_returns_dict(self):\n self.assertIs(type(models.storage.all()), dict)","def get_all(self) -> tuple:\n items = self.model.get_all()\n\n return {'items': list_map(items)}, 200","def all(cls):\n api = BuslineAPI()\n try:\n objects = api.all()\n except ApiException:\n objects = cls.objects.all()\n return objects","def to_dict(self) -> Dict:\n _dict = {}\n if hasattr(self, 'type') and self.type is not None:\n _dict['type'] = self.type\n if hasattr(self, 'roles') and self.roles is not None:\n _dict['roles'] = [x.to_dict() for x in self.roles]\n if hasattr(self, 'resources') and self.resources is not None:\n _dict['resources'] = [x.to_dict() for x in self.resources]\n return _dict","def get_serializer(self, *args, **kwargs):\n kwargs['context'] = self.get_serializer_context()\n realm = kwargs['context'].get('realm', None)\n if realm is not None:\n serializer_class = ItemSerializer\n else:\n serializer_class = self.get_serializer_class()\n return serializer_class(*args, **kwargs)","def get_objects(self) -> Response:\n tags = [tag for tag in request.args.get(\"tags\", \"\").split(\",\") if tag]\n # filter types\n types = [type_ for type_ in request.args.get(\"types\", \"\").split(\",\") if type_]\n\n try:\n tagged_objects = TagDAO.get_tagged_objects_for_tags(tags, types)\n result = [\n self.object_entity_response_schema.dump(tagged_object)\n for tagged_object in tagged_objects\n ]\n return self.response(200, result=result)\n except TagInvalidError as ex:\n return self.response_422(message=ex.normalized_messages())\n except TagCreateFailedError as ex:\n logger.error(\n \"Error creating model %s: %s\",\n self.__class__.__name__,\n str(ex),\n exc_info=True,\n )\n return self.response_422(message=str(ex))","def get(self,request, format=None):\n all_projects=Project.objects.all()\n serializers = ProjectSerializer(all_projects, many=True)\n return Response(serializers.data)","def serialise(self):\n return {\n 'id': self.id,\n 'name': self.name,\n 'items': [i.serialise for i in self.items]\n }","def process_model(self, resources, resource, model, context):\n pass","def _get(self, request_obj):\n return self._execute_action(request_obj, [AbstractModelGetAction, AbstractModelItemGetAction], 'GET')","def get(self, args):\n query = self.create_query()\n query = self.add_filter(query, args).distinct(self.Meta.model.id)\n total = query.count()\n\n # return\n query = self.add_sort(query, args)\n query = self.add_extend_field(query, args)\n data = self.add_pagination(query, args).all()\n return {\"list\": data, 'total': total, **args}","def data(self):\n return { # TODO Actually query for this shit\n \"foo\": self.__name__,\n \"url\": f\"{self.request.resource_url(self)}\",\n }","def wrapper(resource, request, ** kwargs):\n # ckech if everything is ok, before proceding\n resource.method_check(request, allowed=expected_methods)\n resource.is_authenticated(request)\n resource.throttle_check(request)\n\n # call the decorated method\n result = func(resource, request, **kwargs)\n\n # if a single response is expected\n if single:\n if returns_extra_data:\n objt = result[0]\n else:\n objt = result\n bundle = resource.build_bundle(obj=objt, request=request)\n to_be_serialized = resource.full_dehydrate(bundle)\n if returns_extra_data:\n to_be_serialized.data.update(result[1])\n else: # if we are expecting an array of objects\n # we need to paginante\n paginator = resource._meta.paginator_class(\n request.GET,\n result,\n resource_uri=resource.get_resource_uri(),\n limit=resource._meta.limit,\n max_limit=resource._meta.max_limit,\n collection_name=resource._meta.collection_name)\n\n to_be_serialized = paginator.page()\n\n bundles = [resource.build_bundle(obj=obj, request=request)\n for obj in to_be_serialized['objects']]\n\n to_be_serialized['objects'] = [resource.full_dehydrate(bnd)\n for bnd in bundles]\n\n resource.log_throttled_access(request)\n return resource.create_response(request, to_be_serialized)","def getSerializer():","def docter_list(self, request, **dict):\n\t\tdata = self.get_serializer(self.get_queryset(), many=True).data\n\t\treturn Response(data, status.HTTP_200_OK)","def get_resources(self, request, **resources):\r\n\r\n if self.parent:\r\n resources = self.parent.get_resources(request, **resources)\r\n\r\n pks = (\r\n resources.get(self._meta.name) or\r\n request.REQUEST.getlist(self._meta.name) or\r\n getattr(request, 'data', None) and request.data.get(\r\n self._meta.name))\r\n\r\n if not pks or self._meta.queryset is None:\r\n return resources\r\n\r\n pks = as_tuple(pks)\r\n\r\n try:\r\n if len(pks) == 1:\r\n resources[self._meta.name] = self._meta.queryset.get(pk=pks[0])\r\n\r\n else:\r\n resources[self._meta.name] = self._meta.queryset.filter(\r\n pk__in=pks)\r\n\r\n except (ObjectDoesNotExist, ValueError, AssertionError):\r\n raise HttpError(\"Resource not found.\",\r\n status=status.HTTP_404_NOT_FOUND)\r\n\r\n except MultipleObjectsReturned:\r\n raise HttpError(\"Resources conflict.\",\r\n status=status.HTTP_409_CONFLICT)\r\n\r\n return resources"],"string":"[\n \"def opt_get_all_models_rest_api():\\n return retrieve_all_models()\",\n \"def on_get_resource(self, req, resp, **params):\\n instance = self.get_object(**params)\\n resp.json(**instance.as_resource)\",\n \"def handle_models_request():\\n # TODO: add sort and filter by creation/modification date\\n return (\\n json.dumps({\\\"models\\\": {\\n k: d[\\\"data\\\"] for k, d in app.models.items()\\n }}), 200,\\n {'ContentType': 'application/json'}\\n )\",\n \"def resources(self):\\n return [self]\",\n \"def resources(self):\\r\\n return self.page.object_list\",\n \"def to_representation(self, instance):\\n # Load the paginated descendant features\\n if instance is None:\\n # This happens when OPTIONS is called from browsable API\\n return None\\n self.add_sources(instance)\\n\\n ret = OrderedDict()\\n fields = self._readable_fields\\n\\n for field in fields:\\n attribute = field.get_attribute(instance)\\n assert attribute is not None, (\\n 'field.get_attribute return None for instance %s, field %s'\\n % (instance, field))\\n field_ret = field.to_representation(attribute)\\n if isinstance(field, ListSerializer):\\n # Wrap lists of related resources in a ReturnList, so that the\\n # renderer has access to the serializer\\n field_ret = ReturnList(field_ret, serializer=field)\\n ret[field.field_name] = field_ret\\n\\n return ReturnDict(ret, serializer=self)\",\n \"def _get(self):\\n return {\\n \\\"_links\\\" : self.make_links({\\n \\\"self\\\" : Root.get_self_url(),\\n \\\"users\\\" : UserList.get_self_url(),\\n \\\"customers\\\" : CustomerList.get_self_url(),\\n \\\"tickets\\\" : TicketList.get_self_url(),\\n \\\"comments\\\" : CommentList.get_self_url(),\\n \\\"customer_user_associations\\\" : CustomerUserAssociationList.get_self_url(),\\n })\\n }\",\n \"def to_dict(self):\\n ret = {}\\n for key in dir(self):\\n if key.startswith(\\\"_\\\"):\\n continue\\n\\n if key in ['id', 'objects', 'pk', 'STRICT']:\\n continue\\n\\n obj = getattr(self, key)\\n if callable(obj):\\n continue\\n ret[key] = obj\\n return ret\",\n \"def api():\\n from gluon.contrib.hypermedia import Collection\\n rules = {\\n '<tablename>': {'GET':{},'POST':{},'PUT':{},'DELETE':{}},\\n }\\n return Collection(db).process(request,response,rules)\",\n \"def api():\\n from gluon.contrib.hypermedia import Collection\\n rules = {\\n '<tablename>': {'GET':{},'POST':{},'PUT':{},'DELETE':{}},\\n }\\n return Collection(db).process(request,response,rules)\",\n \"def api():\\n from gluon.contrib.hypermedia import Collection\\n rules = {\\n '<tablename>': {'GET':{},'POST':{},'PUT':{},'DELETE':{}},\\n }\\n return Collection(db).process(request,response,rules)\",\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 get(cls):\\n return {'products': [product.to_json() for product in ProductModel.find_all()]}\",\n \"def get_objects_data(self):\\n return dict(result=self.objects)\",\n \"def list(self, **kwargs):\\n data, self.endpoint = self.data_endpoint(kwargs)\\n r = super(Resource, self).list(**data)\\n\\n # Change display settings and data format for human consumption\\n self.configure_display(r)\\n return r\",\n \"def resources(self):\\n return self.__resources\",\n \"def resources(self):\\n\\n return self.FIXTURE.resources_collection(self)\",\n \"def get(self):\\r\\n return get_all()\",\n \"def data(self):\\n ret = super(serializers.ListSerializer, self).data\\n return ReturnDict(ret, serializer=self)\",\n \"def get_resources():\\n user_id = session[\\\"email\\\"]\\n resources = fm.get_resources(user_id)\\n returned_val = dict(resources=resources)\\n return jsonify(returned_val)\",\n \"def get(self, request, **resources):\\r\\n\\r\\n instance = resources.get(self._meta.name)\\r\\n if not instance is None:\\r\\n return instance\\r\\n\\r\\n return self.paginate(\\r\\n request, self.get_collection(request, **resources))\",\n \"def get_resources(self, obj):\\n return WorkshopResourceSerializer(obj.resources, many=True).data\",\n \"def get_resources():\\n return Response(f\\\"{Resource.get_all_resources()}\\\", 200, mimetype='text/plain')\",\n \"def get_serializer(self, *args, **kwargs):\\n serializer_class = self.get_serializer_class()\\n kwargs['context'] = self.get_serializer_context()\\n return serializer_class(many=True, *args, **kwargs)\",\n \"def _instantiateResource(self, res):\\n\\n\\n try:\\n pagination = vsdModels.Pagination(**res)\\n pagination.validate() #will fail if it doesno't have totalCount\\n return pagination\\n except:\\n resourcetype, oid = self.getResourceTypeAndId(res['selfUrl'])\\n if resourcetype == 'objects':\\n return vsdModels.APIObject._create(res)\\n #e.g FolderLinks\\n model = vsdModels.resourceTypes[resourcetype](**res)\\n return model\",\n \"def get(self, *args, **kwargs):\\n self.before_get(args, kwargs)\\n\\n qs = QSManager(request.args, self.schema)\\n objects_count, objects = self._data_layer.get_collection(qs, kwargs)\\n\\n schema_kwargs = getattr(self, 'get_schema_kwargs', dict())\\n schema_kwargs.update({'many': True})\\n\\n schema = compute_schema(self.schema,\\n schema_kwargs,\\n qs,\\n qs.include)\\n\\n result = schema.dump(objects).data\\n\\n view_kwargs = request.view_args if getattr(self, 'view_kwargs', None) is True else dict()\\n add_pagination_links(result,\\n objects_count,\\n qs,\\n url_for(self.view, **view_kwargs))\\n\\n result.update({'meta': {'count': objects_count}})\\n\\n self.after_get(result)\\n return result\",\n \"def expose_models(app, HOST=\\\"localhost\\\", PORT=5000, API_PREFIX=\\\"/api\\\"):\\n\\n api = SAFRSAPI(app, host=HOST, port=PORT)\\n api.expose_object(models.Category)\\n api.expose_object(models.CustomerCustomerDemo)\\n api.expose_object(models.OrderDetail)\\n api.expose_object(models.Order)\\n api.expose_object(models.Customer)\\n api.expose_object(models.CustomerDemographic)\\n api.expose_object(models.EmployeeAudit)\\n api.expose_object(models.EmployeeTerritory)\\n api.expose_object(models.Employee)\\n api.expose_object(models.Product)\\n api.expose_object(models.Region)\\n api.expose_object(models.Shipper)\\n api.expose_object(models.Supplier)\\n api.expose_object(models.Territory)\\n return api\",\n \"def get_objects_data(self):\\n return dict(items=self.objects)\",\n \"def list(self, filter=None, _check_permissions=True, together='', _jsoned=True):\\r\\n\\r\\n join = None\\r\\n if filter:\\r\\n for k, v in filter.iteritems():\\r\\n if None in v:\\r\\n filter[k] = None\\r\\n query = reduce(and_,\\r\\n (self.table[field].belongs(value) if type(value) is list else (self.table[field] == value)\\r\\n for field, value in filter.iteritems()))\\r\\n else:\\r\\n query = None\\r\\n if _check_permissions:\\r\\n query, join = self.CASQuery(query, verb='list')\\r\\n fields = (self.table.ALL,) if self.virtual_fields else self.visible_fields\\r\\n # objects = self.db.executesql(self.db(query)._select(*fields,join=join),as_dict=True)\\r\\n objects = self.sql(query, *fields, left=join, as_dict=self.virtual_fields)\\r\\n if self.virtual_fields and objects:\\r\\n # calcolo tutti i virtual fields\\r\\n for obj, field in product(objects, [self.table[field] for field in self.virtual_fields]):\\r\\n obj[field.name] = field.f(obj)\\r\\n\\r\\n vn = partial(zip, self.visible_names + self.virtual_fields)\\r\\n get_vn = itemgetter(*(self.visible_names + self.virtual_fields))\\r\\n objects = map(dict, map(vn, map(get_vn, objects)))\\r\\n # print objects\\r\\n ret = {self.name: dict(results=objects, totalResults=len(objects), )}\\r\\n if together:\\r\\n if 'permissions' in together:\\r\\n ret.setdefault('PERMISSIONS', {}).update(\\r\\n self.my_perms(ids=map(itemgetter('id'), objects)).get('PERMISSIONS', {}))\\r\\n\\r\\n # results = {self.name : objects}\\r\\n for resource, redest, t, field in self.find_model_path(together):\\r\\n # print resource, redest, field,t\\r\\n if t == 'm':\\r\\n if resource in ret:\\r\\n obs = map(itemgetter('id'), ret[resource]['results'])\\r\\n ret.setdefault('TOMANY', {})['%s_%s' % (redest, field.name)] = obs\\r\\n if obs:\\r\\n ret.update(resource_manager.resource(redest).list(filter={field.name: obs}, _jsoned=False))\\r\\n elif t == '1':\\r\\n if resource in ret:\\r\\n obs = list(set(map(itemgetter(field.name), ret[resource]['results'])))\\r\\n # ret.setdefault('TOONE',{})['%s_%s' % (resource,field.name)] = obs\\r\\n if obs:\\r\\n ret.update(resource_manager.resource(redest).list(filter={'id': obs}, _jsoned=False))\\r\\n elif t == 'M':\\r\\n if resource in ret:\\r\\n first = 0 if field else 1\\r\\n m2m_idx = '%s/%s|%s' % (resource, redest, first)\\r\\n obs = map(itemgetter('id'), ret[resource]['results'])\\r\\n ret.setdefault('MANYTOMANY', {}).setdefault(m2m_idx, []).extend(obs)\\r\\n if obs:\\r\\n resource_manager.m2m((resource, redest)).list(resource_manager.resource(redest),\\r\\n collection=obs)\\r\\n res = current.response.text\\r\\n ret.setdefault('m2m', {}).update(res['m2m'])\\r\\n obs = list(set(map(itemgetter(1 - first), imap(itemgetter('add'), res['m2m'][m2m_idx]))))\\r\\n # ret.setdefault('TOMANY',{})[redest] = obs\\r\\n if obs:\\r\\n res = resource_manager.resource(redest).list(filter=dict(id=obs), _jsoned=False)\\r\\n ret.update(res)\\r\\n if self.private_args:\\r\\n if objects:\\r\\n ret.update(self.private_args.list(map(itemgetter(self.field_order.index('id')), objects)))\\r\\n\\r\\n current.response.text = ret\\r\\n return ret\",\n \"def get_objects_data(self):\\n return dict(objects=self.objects)\",\n \"def get_resources(self):\\n return []\",\n \"def retrieve(self,request,*args,**kwargs):\\n response=super(RetrieveAPIView,self).retrieve(request,*args,**kwargs)\\n return self.fetch_related(request,response,*args,**kwargs)\",\n \"def get_objects_data(self):\\n pass\",\n \"def to_dict(self) -> Dict:\\n _dict = {}\\n if hasattr(self, 'resources') and self.resources is not None:\\n _dict['resources'] = [x.to_dict() for x in self.resources]\\n return _dict\",\n \"def general_serializer(instance):\\n # get the serializer for this model\\n serializer_class = eval(instance.target_model.__name__ + 'Serializer')\\n # use this for create, update and retrieve, since we only need special serialization to display less in list and\\n # to communicate with python\\n if instance.action in ['retrieve', 'create', 'update']:\\n # if it's the detail view, just return the standard serializer\\n return serializer_class\\n elif instance.action == 'from_python':\\n # copy the declared fields from the detail serializer\\n PythonSerializer._declared_fields = serializer_class._declared_fields.copy()\\n # also the fields\\n PythonSerializer.Meta.fields = serializer_class.Meta.fields.copy()\\n # get fields\\n model_fields = instance.target_model._meta.get_fields()\\n # copy the extra_kwargs\\n PythonSerializer.Meta.extra_kwargs = serializer_class.Meta.extra_kwargs.copy()\\n # and the model\\n PythonSerializer.Meta.model = instance.target_model\\n # turn the relations into text fields, except the m2m field since the automatic serialization works better\\n for fields in model_fields:\\n if fields.is_relation:\\n\\n if (not isinstance(fields, models.ManyToManyField)) and \\\\\\n (not isinstance(fields, models.ManyToManyRel)):\\n PythonSerializer._declared_fields[fields.name] = serializers.StringRelatedField()\\n else:\\n PythonSerializer._declared_fields[fields.name] = serializers.StringRelatedField(many=True)\\n\\n return PythonSerializer\\n else: # if not, modify it to remove unnecessary fields from the list view\\n\\n # copy the attributes to the generalized serializer\\n GeneralSerializer._declared_fields = serializer_class._declared_fields.copy()\\n GeneralSerializer.Meta.fields = serializer_class.Meta.fields.copy()\\n GeneralSerializer.Meta.extra_kwargs = serializer_class.Meta.extra_kwargs.copy()\\n GeneralSerializer.Meta.model = instance.target_model\\n # allocate a list of the fields to remove from the list view\\n remove_fields = []\\n # for all the fields in the serializer\\n for fields in GeneralSerializer.Meta.fields:\\n\\n # remove the id field (since it's not in declared_fields)\\n if fields in ['id', 'slug']:\\n # eliminate the field from the serializer\\n remove_fields.append(fields)\\n continue\\n if instance.target_model.__name__ != 'Mouse' and fields == 'mouse':\\n # remove the current mouse extra_kwargs so it displays\\n del GeneralSerializer.Meta.extra_kwargs[fields]\\n continue\\n # remove the fields that have been assigned as read only\\n if (fields in serializer_class._declared_fields.keys()) and \\\\\\n (('read_only=True' in str(GeneralSerializer._declared_fields[fields])) or\\n ('ReadOnly' in str(GeneralSerializer._declared_fields[fields]))):\\n\\n # eliminate the field from the serializer\\n remove_fields.append(fields)\\n # remove the field from declared fields\\n del GeneralSerializer._declared_fields[fields]\\n continue\\n\\n GeneralSerializer.Meta.extra_kwargs[fields] = {'write_only': True}\\n\\n # remove the read only fields\\n GeneralSerializer.Meta.fields = [el for el in GeneralSerializer.Meta.fields if el not in remove_fields]\\n # overwrite url kwargs, since it is set by default to read only\\n GeneralSerializer.Meta.extra_kwargs['url'] = {'lookup_field': instance.lookup_field}\\n # put the mouse entry at the top\\n if 'mouse' in GeneralSerializer.Meta.fields:\\n GeneralSerializer.Meta.fields.remove('mouse')\\n GeneralSerializer.Meta.fields = ['mouse'] + GeneralSerializer.Meta.fields\\n return GeneralSerializer\",\n \"def get():\\n\\n return {'model_ids': mgmt.get_model_ids()}\",\n \"def opt_model_create_rest_api():\\n request_json = request.get_json()\\n OptimModelRequestAPI(request_json).validate()\\n return create_model_data(request_json)\",\n \"def get_serializers(self):\\n\\n if not self.mapping:\\n self.mapping = {\\n 'dc': {\\n 'obj': 'dc_id'\\n },\\n 'dc__basic': {\\n 'serializer': DCSerializer,\\n 'kwargs': {\\n 'kind': 'basic'\\n },\\n 'obj': 'dc',\\n 'eager_loading': self.setup_eager_loading_datacenter\\n },\\n 'dc__details': {\\n 'serializer': DCSerializer,\\n 'kwargs': {\\n 'kind': 'basic'\\n },\\n 'obj': 'dc',\\n 'eager_loading': self.setup_eager_loading_datacenter\\n }\\n }\",\n \"def get_list(self, request, **kwargs):\\n # :TODO modify top_level_serializer or pass a list with self as\\n # argument?\\n registry = {getattr(self._meta , 'resource_name'): self}\\n content = serializers.top_level_serializer(registry)\\n response = HttpResponse(\\n content = content,\\n content_type = 'application/xml')\\n response = add_das_headers(response)\\n return response\",\n \"def get_list(self, request, **kwargs):\\n # :TODO modify top_level_serializer or pass a list with self as\\n # argument?\\n registry = {getattr(self._meta , 'resource_name'): self}\\n content = serializers.top_level_serializer(registry)\\n response = HttpResponse(\\n content = content,\\n content_type = 'application/xml')\\n response = add_das_headers(response)\\n return response\",\n \"def GetObjects(self): \\r\\n return self.model.GetObjects()\",\n \"def worldobjects(self):\\n return dict( self.domain.objects.items() | self.problem.objects.items() )\",\n \"def default_serializer(obj):\\n if issubclass(type(obj), BaseModel):\\n return obj.dict(by_alias=True)\\n\\n raise TypeError\",\n \"def resources(self):\",\n \"def test_related_resource(read_only):\\n class ProjectSerializer(serializers.ModelSerializer):\\n class Meta:\\n model = ProjectWithCustomID\\n fields = ('custom_id', 'name', 'archived', 'members', 'owner_member')\\n read_only_fields = ['members', 'owner_member'] if read_only else []\\n\\n class ProjectViewSet(mixins.RetrieveModelMixin, viewsets.GenericViewSet):\\n queryset = ProjectWithCustomID.objects.all()\\n serializer_class = ProjectSerializer\\n renderer_classes = [renderers.JSONRenderer]\\n parser_classes = [parsers.JSONParser]\\n swagger_schema = base.BasicSwaggerAutoSchema\\n\\n router = routers.DefaultRouter()\\n router.register(r'projects', ProjectViewSet, **compatibility._basename_or_base_name('projects'))\\n\\n generator = OpenAPISchemaGenerator(info=openapi.Info(title=\\\"\\\", default_version=\\\"\\\"), patterns=router.urls)\\n\\n swagger = generator.get_schema(request=None, public=True)\\n\\n response_schema = swagger['paths']['/projects/{custom_id}/']['get']['responses']['200']['schema']['properties']\\n assert 'id' in response_schema['data']['properties']\\n assert response_schema['data']['properties']['id']['type'] == 'string'\\n assert response_schema['data']['properties']['id']['format'] == 'int32'\\n assert 'type' in response_schema['data']['properties']\\n assert 'attributes' in response_schema['data']['properties']\\n assert list(response_schema['data']['properties']['attributes']['properties'].keys()) == ['name', 'archived']\\n assert 'relationships' in response_schema['data']['properties']\\n relationships_schema = response_schema['data']['properties']['relationships']['properties']\\n assert list(relationships_schema.keys()) == ['members', 'owner-member']\\n members_schema = relationships_schema['members']['properties']\\n assert members_schema['data']['items']['properties']['id']['type'] == 'string'\\n assert members_schema['data']['items']['properties']['id']['format'] == 'int64'\\n assert members_schema['data']['items']['properties']['type']['pattern'] == 'member-with-custom-ids'\\n owner_member_schema = relationships_schema['owner-member']['properties']\\n assert owner_member_schema['data']['properties']['id']['type'] == 'string'\\n assert owner_member_schema['data']['properties']['id']['format'] == 'int64'\\n assert owner_member_schema['data']['properties']['type']['pattern'] == 'member-with-custom-ids'\",\n \"def get():\\n\\n # OPTION #1 - my preferable\\n # sql = \\\"select * from director\\\"\\n # if not (res := db.engine.execute(sql).fetchall()):\\n # raise NotFoundError\\n # return jsonify([dict(i) for i in res])\\n\\n # OPTION #2 - I don't like ORM queries, so it's just to meet the lesson topic\\n if not (res := Director.query.all()):\\n raise NotFoundError\\n return jsonify(DirectorSchema(many=True).dump(res))\",\n \"def list(self,request,*args,**kwargs):\\n response=super(ListAPIView,self).list(request,*args,**kwargs)\\n #add applied_filters to the response which is set when filter_queryset method is called\\n response=self.addAppliedFilters(response)\\n #fetch data from the related views\\n return self.fetch_related(request,response,*args,**kwargs)\",\n \"def index(self, req):\\n return self._get_models(req, is_detail=False)\",\n \"def _generate_implicit_api_resource(self):\\n return ImplicitHttpApiResource().to_dict()\",\n \"async def get_resources(self, **kwargs) -> dict:\\n resources = await self.request.get(self._base_path, **kwargs)\\n self._sanitize_resources(resources)\\n return resources\",\n \"def GetModels(self, request, context):\\n context.code(beta_interfaces.StatusCode.UNIMPLEMENTED)\",\n \"def get(self, *args, **kwargs):\\n self.before_get(args, kwargs)\\n\\n relationship_field, model_relationship_field, related_type_, related_id_field = self._get_relationship_data()\\n related_view = self.schema._declared_fields[relationship_field].related_view\\n related_view_kwargs = self.schema._declared_fields[relationship_field].related_view_kwargs\\n\\n obj, data = self._data_layer.get_relationship(model_relationship_field,\\n related_type_,\\n related_id_field,\\n kwargs)\\n\\n for key, value in copy(related_view_kwargs).items():\\n if isinstance(value, str) and value.startswith('<') and value.endswith('>'):\\n tmp_obj = obj\\n for field in value[1:-1].split('.'):\\n tmp_obj = getattr(tmp_obj, field)\\n related_view_kwargs[key] = tmp_obj\\n\\n result = {'links': {'self': request.path,\\n 'related': url_for(related_view, **related_view_kwargs)},\\n 'data': data}\\n\\n qs = QSManager(request.args, self.schema)\\n if qs.include:\\n schema = compute_schema(self.schema, dict(), qs, qs.include)\\n\\n serialized_obj = schema.dump(obj)\\n result['included'] = serialized_obj.data.get('included', dict())\\n\\n self.after_get(result)\\n return result\",\n \"def test_resource_serialization(self):\\n\\n # Construct dict forms of any model objects needed in order to build this model.\\n\\n attribute_model = {} # Attribute\\n attribute_model['name'] = 'testString'\\n attribute_model['value'] = 'testString'\\n\\n # Construct a json representation of a Resource model\\n resource_model_json = {}\\n resource_model_json['attributes'] = [attribute_model]\\n\\n # Construct a model instance of Resource by calling from_dict on the json representation\\n resource_model = Resource.from_dict(resource_model_json)\\n assert resource_model != False\\n\\n # Construct a model instance of Resource by calling from_dict on the json representation\\n resource_model_dict = Resource.from_dict(resource_model_json).__dict__\\n resource_model2 = Resource(**resource_model_dict)\\n\\n # Verify the model instances are equivalent\\n assert resource_model == resource_model2\\n\\n # Convert model instance back to dict and verify no loss of data\\n resource_model_json2 = resource_model.to_dict()\\n assert resource_model_json2 == resource_model_json\",\n \"def _openapi_json(self):\\n # We don't use Flask.jsonify here as it would sort the keys\\n # alphabetically while we want to preserve the order.\\n from pprint import pprint\\n pprint(self.to_dict())\\n return current_app.response_class(json.dumps(self.to_dict(), indent=4),\\n mimetype='application/json')\",\n \"def to_api_object(self):\\n visible_to = self.visibleTo.all()\\n visible_to_list = []\\n for visible in visible_to:\\n visible_to_list.append(\\\"http://\\\"+visible.author_uid)\\n\\n # We only get the first 5 comments\\n # Get the comments, be aware that comments might not be returned if the foreign author of the comment is unavailable\\n comments_list = [comment.to_api_object() for comment in self.comment_set.all().order_by(\\\"-published\\\")[:5]]\\n filtered_comments_list = [comment for comment in comments_list if 'error' not in comment['author']]\\n\\n\\n\\n return {\\n \\\"title\\\": self.title,\\n \\\"source\\\": self.source,\\n \\\"origin\\\": self.origin,\\n \\\"description\\\": self.description,\\n \\\"contentType\\\": self.contentType,\\n \\\"content\\\": self.content,\\n \\\"author\\\": self.author.to_api_object(),\\n \\\"categories\\\": [category.name for category in self.categories.all()],\\n \\\"count\\\": len(filtered_comments_list),\\n \\\"size\\\": self.size,\\n \\\"next\\\": settings.HOST_URI + \\\"/posts/\\\" + str(self.id.hex) + \\\"/comments\\\",\\n \\\"comments\\\": filtered_comments_list,\\n \\\"published\\\": self.published,\\n \\\"id\\\": str(self.id.hex),\\n \\\"visibility\\\": self.visibility,\\n \\\"visibleTo\\\": visible_to_list,\\n \\\"unlisted\\\": self.unlisted\\n }\",\n \"def test_get_api_resources(self):\\n pass\",\n \"def __json__(self):\\n filtered_dict = dict()\\n\\n for k, item in six.iteritems(self.__dict__):\\n if k.startswith('_'):\\n continue\\n\\n if hasattr(item, '__json__'):\\n filtered_dict[k] = item.__json__\\n else:\\n filtered_dict[k] = serialize_obj(item)\\n\\n return filtered_dict\",\n \"def getAllAPI():\\n list_strain = StrainAPI().get_all()\\n schema = StrainSchema()\\n results = schema.load(list_strain, many=True)\\n return results\",\n \"def list(cls) -> t.Iterable[SDict]:\\n endpoint: t.Optional[str] = cls.endpoint\\n\\n def process_field(v):\\n if isinstance(v, dict):\\n return json.dumps(v, indent=True)\\n return v\\n\\n while endpoint:\\n r = Resource(endpoint=endpoint)\\n items = r.get()\\n # filter the items, ordering as needed\\n for x in items.results:\\n yield {k: process_field(x[k]) for k in cls.list_fields if k in x}\\n endpoint = items.next if items.next else None\",\n \"def detail(self, req):\\n return self._get_models(req, is_detail=True)\",\n \"def object_get(self, request):\\n _view = _object_view(self, request)\\n queried = ObjectPostings(self.cdb_object_id, request.params.mixed()).query()\\n postings = [request.view(obj) for obj in queried[0]]\\n\\n _view.update({\\n \\\"postings\\\": postings,\\n \\\"result_complete\\\": queried[1]\\n })\\n return _view\",\n \"def safe_json(self, context):\\n serialize_context = dict()\\n for key, obj in context.items():\\n if isinstance(obj.__class__, ModelBase):\\n if hasattr(obj, 'serialize') and callable(getattr(obj, 'serialize')):\\n serialize_context[key] = obj.serialize()\\n else:\\n serialize_context[key] = model_to_dict(obj)\\n elif isinstance(obj, QuerySet):\\n serialize_context[key] = [o.serialize() for o in obj if hasattr(o, 'serialize')]\\n if len(serialize_context[key]) != len(obj):\\n serialize_context[key] = [model_to_dict(o) for o in obj]\\n elif key == 'extra':\\n serialize_context[key] = obj\\n # elif key == 'view':\\n # continue\\n # else:\\n # serialize_context[key] = obj\\n return dict(success=True, data=serialize_context)\",\n \"def realized(self):\\n result = {\\\"Object\\\": True}\\n for key in self.__dict__:\\n if isinstance(self.__dict__[key], Object):\\n result[key] = self.__dict__[key].asdict()\\n else:\\n result[key] = self.__dict__[key]\\n return result\",\n \"def process_resource_api(self, resources, resource, api, context):\\n pass\",\n \"def make_runtime_viewset(self, **kwargs):\\n\\n class RunTimeViewset(viewsets.ModelViewSet):\\n if self.permission_classes:\\n permission_classes = self.permission_classes\\n serializer_class = self.serializer_class\\n model = self.model\\n lookup_field = self.lookup_field\\n\\n def get_permissions(self, **kwargs):\\n _permissions = self.model.get_api_permissions(**kwargs)\\n if isinstance(_permissions, list):\\n self.permission_classes = _permissions\\n if isinstance(_permissions, dict):\\n if self.action in _permissions.keys():\\n self.permission_classes = _permissions[self.action]\\n return super(RunTimeViewset, self).get_permissions(**kwargs)\\n\\n def get_queryset(self, *args, **kwargs):\\n _prefetch_related_fields = self.model.api_prefetch_related_fields()\\n _select_related_fields = self.model.api_select_related_fields()\\n queryset = self.model.get_api_queryset(*args, **kwargs)\\n if len(_prefetch_related_fields) > 0:\\n queryset = queryset.prefetch_related(\\n *_prefetch_related_fields)\\n if len(_select_related_fields) > 0:\\n queryset = queryset.select_related(*_select_related_fields)\\n return queryset\\n\\n def list(self, request, **kwargs):\\n self.queryset = self.get_queryset(request=request, **kwargs)\\n request = kwargs.get('request', self.request)\\n try:\\n search_enabled = bool(eval(request.GET.get('search', '0')))\\n if search_enabled:\\n _filters = parse_filters(\\n model=self.model, request=request)\\n self.queryset = self.queryset.filter(_filters)\\n except Exception as err:\\n self.queryset = self.queryset\\n page = self.paginate_queryset(self.queryset)\\n if page is not None:\\n serializer = self.get_serializer(page, many=True)\\n return self.get_paginated_response(serializer.data)\\n\\n serializer = self.get_serializer(self.queryset, many=True)\\n return Response(serializer.data)\\n\\n def create(self, request, *args, **kwargs):\\n before_post_api.send(\\n sender=self.model, requested_data=request.data)\\n serializer = self.serializer_class(data=request.data)\\n if serializer.is_valid(raise_exception=True):\\n instance = serializer.save()\\n after_post_api.send(\\n sender=self.model, instance=instance, requested_data=request.data)\\n return Response(serializer.data)\\n return Response(serializer.data)\\n\\n def retrieve(self, request, *args, **kwargs):\\n return super(RunTimeViewset, self).retrieve(request, *args, **kwargs)\\n\\n def update(self, request, *args, **kwargs):\\n instance = self.get_object()\\n before_put_api.send(\\n sender=self.model, instance=instance, requested_data=request.data)\\n serializer = self.serializer_class(\\n data=request.data, instance=instance)\\n if serializer.is_valid(raise_exception=True):\\n instance = serializer.save()\\n after_put_api.send(\\n sender=self.model, instance=instance, requested_data=request.data)\\n return Response(serializer.data)\\n return Response(serializer.data)\\n\\n def partial_update(self, request, *args, **kwargs):\\n instance = self.get_object()\\n before_patch_api.send(\\n sender=self.model, instance=instance, requested_data=request.data)\\n serializer = self.serializer_class(\\n data=request.data, instance=instance, partial=True)\\n if serializer.is_valid(raise_exception=True):\\n instance = serializer.save()\\n after_patch_api.send(\\n sender=self.model, instance=instance, requested_data=request.data)\\n return Response(serializer.data)\\n return Response(serializer.data)\\n\\n def destroy(self, request, *args, **kwargs):\\n return super(RunTimeViewset, self).destroy(request=request, *args, **kwargs)\\n\\n return class_factory(\\n class_name=\\\"{}RuntimeViewset\\\".format(self.model.__name__),\\n base_classes=(RunTimeViewset,)\\n )\",\n \"def to_representation(self, data):\\n # Dealing with nested relationships, data can be a Manager,\\n # so, first get a queryset from the Manager if needed\\n\\n iterable = data.all() if isinstance(data, models.Manager) else data\\n \\n if self.parent is None:\\n post = [\\n self.child.to_representation(item) for item in iterable\\n ]\\n normalized_dict = OrderedDict()\\n normalized_dict[self.child.Meta.model_name] = ReturnList(post, serializer=self)\\n result = [normalized_dict]\\n for normalized_item in self.child.Meta.normalized_fields:\\n if normalized_item in self.instancelist_dict:\\n normalized_dict[normalized_item] = \\\\\\n ReturnList(self.make_normalized_item_list(normalized_item), serializer=self)\\n return result\\n\\n if self.field_name in self.child.Meta.normalized_fields:\\n result = [ item.id for item in iterable ]\\n parent = self.root\\n if not self.field_name in parent.instancelist_dict:\\n parent.instancelist_dict[self.field_name] = []\\n parent.instance_repr_dict[self.field_name] = self.child._readable_fields\\n parent.instancelist_dict[self.field_name] = \\\\\\n list(set(parent.instancelist_dict[self.field_name]) | set(iterable))\\n else:\\n result = [\\n self.child.to_representation(item) for item in iterable\\n ]\\n\\n return result\",\n \"def json(self):\\n if self.resource:\\n return jsonify(self.resource)\\n return jsonify({'id': self.id})\",\n \"def ng_get(self, request, *args, **kwargs):\\r\\n return self.build_json_response(self.get_object())\",\n \"def process_resource_listing_api(self, resources, listing_api, context):\\n pass\",\n \"def getObject(self, resource):\\n resource = self.parseUrl(resource, 'objects')\\n\\n res = self.getRequest(resource)\\n obj = vsdModels.APIObject._create(res)\\n return obj\",\n \"def get_list(self, request, **kwargs):\\n # TODO: Uncached for now. Invalidation that works for everyone may be\\n # impossible.\\n objects = self.obj_get_list(request=request, **self.remove_api_resource_names(kwargs))\\n sorted_objects = self.apply_sorting(objects, options=request.GET)\\n\\n paginator = self._meta.paginator_class(request.GET, sorted_objects, resource_uri=self.get_resource_uri(),\\n limit=self._meta.limit, max_limit=self._meta.max_limit, collection_name=self._meta.collection_name)\\n to_be_serialized = paginator.page()\\n\\n # Dehydrate the bundles in preparation for serialization.\\n bundles = [self.build_bundle(obj=obj, request=request) for obj in to_be_serialized['objects']]\\n to_be_serialized['objects'] = [self.full_dehydrate(bundle) for bundle in bundles]\\n to_be_serialized = self.alter_list_data_to_serialize(request, to_be_serialized)\\n\\n self.add_shit_to_meta(request, to_be_serialized)\\n\\n return self.create_response(request, to_be_serialized)\",\n \"def obj_get_list(self, request=None, **kwargs):\\n filters = None\\n\\n if hasattr(request, 'GET'):\\n filters = request.GET\\n\\n applicable_filters = self.build_filters(filters=filters)\\n applicable_filters.update(kwargs)\\n\\n try:\\n return self.get_object_list(request).filter(**applicable_filters)\\n except ValueError, e:\\n raise NotFound(\\\"Invalid resource lookup data provided (mismatched type).\\\")\",\n \"def dict(self):\\n return objToDict(self)\",\n \"def __json_light__(self):\\n filtered_dict = dict()\\n\\n for k, item in six.iteritems(self.__dict__):\\n if k.startswith('_') or k == 'annotations':\\n continue\\n\\n if hasattr(item, '__json__'):\\n filtered_dict[k] = item.__json__\\n else:\\n filtered_dict[k] = serialize_obj(item)\\n\\n return filtered_dict\",\n \"def public(self, page=None, per_page=None, sort=None):\\r\\n params = base.get_params(None, locals())\\r\\n url = '{0}/public'.format(self.get_url())\\r\\n request = http.Request('GET', url, params)\\r\\n\\r\\n return request, parsers.parse_json\",\n \"def instances(request): #pylint: disable=unused-argument\\n response = list()\\n # Retrieve filtered parameter on GET. It's used to display all instances or just user instances\\n # In all cases, if user is not superuser, only user instances are displayed\\n if 'filtered' in request.GET:\\n filtered = ast.literal_eval(request.GET['filtered'])\\n else:\\n # By default, we filter based on user_id\\n filtered = True\\n # By default, we just list user instances, not all instances\\n if not request.user.is_superuser:\\n # If user is superuser and user are requesting admin view of instances\\n # We ask a full list of instances\\n filtered = True\\n\\n # We retrieve data from backend\\n data_instances = BACKEND.instances(request, filtered)\\n data_users = BACKEND.users()\\n data_projects = BACKEND.projects()\\n\\n # We merge user and project information w/ instances\\n for data_instance in data_instances:\\n try:\\n project = \\\"{name}\\\".format(\\n **data_projects[data_instances[data_instance]['project_id']])\\n except KeyError:\\n project = data_instances[data_instance]['project_id']\\n\\n try:\\n user = \\\"{name}\\\".format(\\n **data_users[data_instances[data_instance]['user_id']])\\n except KeyError:\\n user = data_instances[data_instance]['user_id']\\n response.append({\\n 'id': data_instances[data_instance]['id'],\\n 'name': data_instances[data_instance]['name'],\\n 'created_at': data_instances[data_instance]['created_at'],\\n 'lease_end': data_instances[data_instance]['lease_end'],\\n 'project': project,\\n 'user': user\\n })\\n return JsonResponse(response, safe=False)\",\n \"def _request_model(self, instance, success, get_embedded=True):\\n coll = self.get_collection('_model')\\n if get_embedded:\\n callback = partial(self._get_embedded_model_names,\\n instance=instance,\\n success=success)\\n else:\\n callback = success\\n\\n try:\\n instance['_model']\\n except KeyError:\\n raise tornado.web.HTTPError(400, 'Missing model key')\\n coll.find_one({'_id': instance['_model']},\\n callback=callback)\",\n \"def obj_get_list(self, request=None, **kwargs):\\n filters = {}\\n if hasattr(request, 'GET'):\\n # Grab a mutable copy.\\n filters = request.GET.copy()\\n\\n # Update with the provided kwargs.\\n filters.update(kwargs)\\n if \\\"user\\\" in filters and \\\"media\\\" in filters:\\n try:\\n image_uuid_list = []\\n wordbox_uuid_list = []\\n for item in filters['media'].split(','):\\n # use kwarg \\\"resource\\\" field to be more general rather\\n # than take the 2 element\\n try:\\n if item.split('/')[2] == 'image':\\n image_uuid_list.append(uuid_re.findall(item)[0])\\n elif item.split('/')[2] == 'wordbox':\\n wordbox_uuid_list.append(uuid_re.findall(item)[0])\\n except IndexError:\\n raise BadRequest(\\\"Invalid uuid or resource URI.\\\")\\n userprofile = UserProfile.objects.get(\\n uuid=uuid_from_uri(filters['user']))\\n im = userprofile.image_collection_set.filter(\\n is_active=True, uuid__in=image_uuid_list)\\n wb = userprofile.wordbox_collection_set.\\\\\\n filter(is_active=True, uuid__in=wordbox_uuid_list)\\n base_object_list = sorted(chain(im, wb),\\n key=attrgetter('created_time'))[::-1]\\n return self.apply_authorization_limits(request,\\n base_object_list)\\n except ValueError:\\n raise BadRequest(\\\"Invalid resource lookup data provided \\\"\\n \\\"(mismatched type).\\\")\\n elif \\\"user\\\" in filters and len(filters) == 1:\\n try:\\n userprofile = UserProfile.objects.get(\\n uuid=uuid_from_uri(filters['user']))\\n im = userprofile.image_collection_set.filter(is_active=True)\\n wb = userprofile.wordbox_collection_set.filter(is_active=True)\\n base_object_list = sorted(chain(im, wb),\\n key=attrgetter('created_time'))[::-1]\\n return self.apply_authorization_limits(request,\\n base_object_list)\\n except ValueError:\\n raise BadRequest(\\\"Invalid resource lookup data provided \\\"\\n \\\"(mismatched type).\\\")\\n else:\\n raise BadRequest(\\\"Invalid filtering parameter(s)\\\")\",\n \"def retrieve(self, request, pk=None, **kwargs):\\n serializer = self.serializer_class(self.get_object())\\n return Response(serializer.data, status=status.HTTP_200_OK)\",\n \"def _get_models(self, req, is_detail):\\n context = req.environ['meteos.context']\\n\\n search_opts = {}\\n search_opts.update(req.GET)\\n\\n # Remove keys that are not related to model attrs\\n search_opts.pop('limit', None)\\n search_opts.pop('offset', None)\\n sort_key = search_opts.pop('sort_key', 'created_at')\\n sort_dir = search_opts.pop('sort_dir', 'desc')\\n\\n models = self.engine_api.get_all_models(\\n context, search_opts=search_opts, sort_key=sort_key,\\n sort_dir=sort_dir)\\n\\n limited_list = common.limited(models, req)\\n\\n if is_detail:\\n models = self._view_builder.detail_list(req, limited_list)\\n else:\\n models = self._view_builder.summary_list(req, limited_list)\\n return models\",\n \"def obj_get_list(self, bundle, **kwargs):\\n filters = {}\\n if hasattr(bundle.request, 'GET'):\\n # Grab a mutable copy.\\n filters = bundle.request.GET.copy()\\n\\n # Update with the provided kwargs.\\n filters.update(kwargs)\\n channel = False\\n if 'owner' in filters:\\n channel = True\\n applicable_filters = self.build_filters(filters=filters)\\n\\n try:\\n objects = self.apply_filters(bundle.request, applicable_filters)\\n self.objects_returned = len(objects)\\n if len(objects) == 1 and applicable_filters and not channel:\\n obj = objects[0]\\n obj.views = obj.views + 1\\n obj.lastview_date = timezone.now()\\n obj.save(update_fields=['views', 'lastview_date'])\\n return self.authorized_read_list(objects, bundle)\\n except ValueError:\\n raise BadRequest(\\\"Invalid resource lookup data provided (mismatched type).\\\")\",\n \"def resources(self) -> Generator[Tuple[str, str, Dict[str, Any]], None, None]:\\n for path, methods in self.data[\\\"paths\\\"].items():\\n for method in methods:\\n if method.lower() not in HTTP_VERBS:\\n continue\\n\\n yield method, path, methods\",\n \"def _get_models(self):\\n # Fast-path already loaded\\n if self.__models is not None:\\n return self.__models\\n # Initialize the dictionary\\n self.__models = dict()\\n # Populate this dictionary with TorchVision's models\\n for name in dir(torchvision.models):\\n if len(name) == 0 or name[0] == \\\"_\\\": # Ignore \\\"protected\\\" members\\n continue\\n builder = getattr(torchvision.models, name)\\n if isinstance(builder, types.FunctionType): # Heuristic\\n self.__models[f\\\"torchvision-{name.lower()}\\\"] = builder\\n # Dynamically add the custom models from subdirectory 'models/'\\n def add_custom_models(name, module, _):\\n nonlocal self\\n # Check if has exports, fallback otherwise\\n exports = getattr(module, \\\"__all__\\\", None)\\n if exports is None:\\n tools.warning(f\\\"Model module {name!r} does not provide '__all__'; falling back to '__dict__' for name discovery\\\")\\n exports = (name for name in dir(module) if len(name) > 0 and name[0] != \\\"_\\\")\\n # Register the association 'name -> constructor' for all the models\\n exported = False\\n for model in exports:\\n # Check model name type\\n if not isinstance(model, str):\\n tools.warning(f\\\"Model module {name!r} exports non-string name {model!r}; ignored\\\")\\n continue\\n # Recover instance from name\\n constructor = getattr(module, model, None)\\n # Check instance is callable (it's only an heuristic...)\\n if not callable(constructor):\\n continue\\n # Register callable with composite name\\n exported = True\\n fullname = f\\\"{name}-{model}\\\"\\n if fullname in self.__models:\\n tools.warning(f\\\"Unable to make available model {model!r} from module {name!r}, as the name {fullname!r} already exists\\\")\\n continue\\n self.__models[fullname] = constructor\\n if not exported:\\n tools.warning(f\\\"Model module {name!r} does not export any valid constructor name through '__all__'\\\")\\n with tools.Context(\\\"models\\\", None):\\n tools.import_directory(pathlib.Path(__file__).parent / \\\"models\\\", {\\\"__package__\\\": f\\\"{__package__}.models\\\"}, post=add_custom_models)\\n # Return the dictionary\\n return self.__models\",\n \"def subresources(self):\\n return self._get_related_resources(True)\",\n \"def test_all_returns_dict(self):\\n self.assertIs(type(models.storage.all()), dict)\",\n \"def test_all_returns_dict(self):\\n self.assertIs(type(models.storage.all()), dict)\",\n \"def get_all(self) -> tuple:\\n items = self.model.get_all()\\n\\n return {'items': list_map(items)}, 200\",\n \"def all(cls):\\n api = BuslineAPI()\\n try:\\n objects = api.all()\\n except ApiException:\\n objects = cls.objects.all()\\n return objects\",\n \"def to_dict(self) -> Dict:\\n _dict = {}\\n if hasattr(self, 'type') and self.type is not None:\\n _dict['type'] = self.type\\n if hasattr(self, 'roles') and self.roles is not None:\\n _dict['roles'] = [x.to_dict() for x in self.roles]\\n if hasattr(self, 'resources') and self.resources is not None:\\n _dict['resources'] = [x.to_dict() for x in self.resources]\\n return _dict\",\n \"def get_serializer(self, *args, **kwargs):\\n kwargs['context'] = self.get_serializer_context()\\n realm = kwargs['context'].get('realm', None)\\n if realm is not None:\\n serializer_class = ItemSerializer\\n else:\\n serializer_class = self.get_serializer_class()\\n return serializer_class(*args, **kwargs)\",\n \"def get_objects(self) -> Response:\\n tags = [tag for tag in request.args.get(\\\"tags\\\", \\\"\\\").split(\\\",\\\") if tag]\\n # filter types\\n types = [type_ for type_ in request.args.get(\\\"types\\\", \\\"\\\").split(\\\",\\\") if type_]\\n\\n try:\\n tagged_objects = TagDAO.get_tagged_objects_for_tags(tags, types)\\n result = [\\n self.object_entity_response_schema.dump(tagged_object)\\n for tagged_object in tagged_objects\\n ]\\n return self.response(200, result=result)\\n except TagInvalidError as ex:\\n return self.response_422(message=ex.normalized_messages())\\n except TagCreateFailedError as ex:\\n logger.error(\\n \\\"Error creating model %s: %s\\\",\\n self.__class__.__name__,\\n str(ex),\\n exc_info=True,\\n )\\n return self.response_422(message=str(ex))\",\n \"def get(self,request, format=None):\\n all_projects=Project.objects.all()\\n serializers = ProjectSerializer(all_projects, many=True)\\n return Response(serializers.data)\",\n \"def serialise(self):\\n return {\\n 'id': self.id,\\n 'name': self.name,\\n 'items': [i.serialise for i in self.items]\\n }\",\n \"def process_model(self, resources, resource, model, context):\\n pass\",\n \"def _get(self, request_obj):\\n return self._execute_action(request_obj, [AbstractModelGetAction, AbstractModelItemGetAction], 'GET')\",\n \"def get(self, args):\\n query = self.create_query()\\n query = self.add_filter(query, args).distinct(self.Meta.model.id)\\n total = query.count()\\n\\n # return\\n query = self.add_sort(query, args)\\n query = self.add_extend_field(query, args)\\n data = self.add_pagination(query, args).all()\\n return {\\\"list\\\": data, 'total': total, **args}\",\n \"def data(self):\\n return { # TODO Actually query for this shit\\n \\\"foo\\\": self.__name__,\\n \\\"url\\\": f\\\"{self.request.resource_url(self)}\\\",\\n }\",\n \"def wrapper(resource, request, ** kwargs):\\n # ckech if everything is ok, before proceding\\n resource.method_check(request, allowed=expected_methods)\\n resource.is_authenticated(request)\\n resource.throttle_check(request)\\n\\n # call the decorated method\\n result = func(resource, request, **kwargs)\\n\\n # if a single response is expected\\n if single:\\n if returns_extra_data:\\n objt = result[0]\\n else:\\n objt = result\\n bundle = resource.build_bundle(obj=objt, request=request)\\n to_be_serialized = resource.full_dehydrate(bundle)\\n if returns_extra_data:\\n to_be_serialized.data.update(result[1])\\n else: # if we are expecting an array of objects\\n # we need to paginante\\n paginator = resource._meta.paginator_class(\\n request.GET,\\n result,\\n resource_uri=resource.get_resource_uri(),\\n limit=resource._meta.limit,\\n max_limit=resource._meta.max_limit,\\n collection_name=resource._meta.collection_name)\\n\\n to_be_serialized = paginator.page()\\n\\n bundles = [resource.build_bundle(obj=obj, request=request)\\n for obj in to_be_serialized['objects']]\\n\\n to_be_serialized['objects'] = [resource.full_dehydrate(bnd)\\n for bnd in bundles]\\n\\n resource.log_throttled_access(request)\\n return resource.create_response(request, to_be_serialized)\",\n \"def getSerializer():\",\n \"def docter_list(self, request, **dict):\\n\\t\\tdata = self.get_serializer(self.get_queryset(), many=True).data\\n\\t\\treturn Response(data, status.HTTP_200_OK)\",\n \"def get_resources(self, request, **resources):\\r\\n\\r\\n if self.parent:\\r\\n resources = self.parent.get_resources(request, **resources)\\r\\n\\r\\n pks = (\\r\\n resources.get(self._meta.name) or\\r\\n request.REQUEST.getlist(self._meta.name) or\\r\\n getattr(request, 'data', None) and request.data.get(\\r\\n self._meta.name))\\r\\n\\r\\n if not pks or self._meta.queryset is None:\\r\\n return resources\\r\\n\\r\\n pks = as_tuple(pks)\\r\\n\\r\\n try:\\r\\n if len(pks) == 1:\\r\\n resources[self._meta.name] = self._meta.queryset.get(pk=pks[0])\\r\\n\\r\\n else:\\r\\n resources[self._meta.name] = self._meta.queryset.filter(\\r\\n pk__in=pks)\\r\\n\\r\\n except (ObjectDoesNotExist, ValueError, AssertionError):\\r\\n raise HttpError(\\\"Resource not found.\\\",\\r\\n status=status.HTTP_404_NOT_FOUND)\\r\\n\\r\\n except MultipleObjectsReturned:\\r\\n raise HttpError(\\\"Resources conflict.\\\",\\r\\n status=status.HTTP_409_CONFLICT)\\r\\n\\r\\n return resources\"\n]","isConversational":false},"negative_scores":{"kind":"list like","value":["0.6702457","0.65570825","0.6285609","0.6211514","0.6006195","0.5948634","0.59445965","0.5880195","0.5846682","0.5846682","0.5846682","0.58263266","0.58077884","0.57976645","0.5784321","0.5751144","0.5739015","0.57262045","0.5678348","0.56772375","0.56757516","0.5675335","0.5672052","0.56681323","0.566245","0.5658193","0.56540775","0.56402373","0.5638458","0.563141","0.5614074","0.56043833","0.5596179","0.5585133","0.5581939","0.5543986","0.5534151","0.5521215","0.5520045","0.5520045","0.5518144","0.55159086","0.55140346","0.5499684","0.54988456","0.54921967","0.54921407","0.54854774","0.54809755","0.54687154","0.5462916","0.54450446","0.5443582","0.5423167","0.5421991","0.54195905","0.54195654","0.54150015","0.54139","0.5408863","0.54087883","0.5405524","0.54031783","0.5402737","0.5399978","0.53980803","0.53916","0.5391533","0.5385948","0.53738225","0.5372511","0.536562","0.5365162","0.5359724","0.53585327","0.53566676","0.53466034","0.53410256","0.53371227","0.53358454","0.53298324","0.5319519","0.5318488","0.5312967","0.5309007","0.5309007","0.53054094","0.5301006","0.52952236","0.52906805","0.5285285","0.52844226","0.5283888","0.5278127","0.5276711","0.5268972","0.5263886","0.5259327","0.52589023","0.5253211","0.5251921"],"string":"[\n \"0.6702457\",\n \"0.65570825\",\n \"0.6285609\",\n \"0.6211514\",\n \"0.6006195\",\n \"0.5948634\",\n \"0.59445965\",\n \"0.5880195\",\n \"0.5846682\",\n \"0.5846682\",\n \"0.5846682\",\n \"0.58263266\",\n \"0.58077884\",\n \"0.57976645\",\n \"0.5784321\",\n \"0.5751144\",\n \"0.5739015\",\n \"0.57262045\",\n \"0.5678348\",\n \"0.56772375\",\n \"0.56757516\",\n \"0.5675335\",\n \"0.5672052\",\n \"0.56681323\",\n \"0.566245\",\n \"0.5658193\",\n \"0.56540775\",\n \"0.56402373\",\n \"0.5638458\",\n \"0.563141\",\n \"0.5614074\",\n \"0.56043833\",\n \"0.5596179\",\n \"0.5585133\",\n \"0.5581939\",\n \"0.5543986\",\n \"0.5534151\",\n \"0.5521215\",\n \"0.5520045\",\n \"0.5520045\",\n \"0.5518144\",\n \"0.55159086\",\n \"0.55140346\",\n \"0.5499684\",\n \"0.54988456\",\n \"0.54921967\",\n \"0.54921407\",\n \"0.54854774\",\n \"0.54809755\",\n \"0.54687154\",\n \"0.5462916\",\n \"0.54450446\",\n \"0.5443582\",\n \"0.5423167\",\n \"0.5421991\",\n \"0.54195905\",\n \"0.54195654\",\n \"0.54150015\",\n \"0.54139\",\n \"0.5408863\",\n \"0.54087883\",\n \"0.5405524\",\n \"0.54031783\",\n \"0.5402737\",\n \"0.5399978\",\n \"0.53980803\",\n \"0.53916\",\n \"0.5391533\",\n \"0.5385948\",\n \"0.53738225\",\n \"0.5372511\",\n \"0.536562\",\n \"0.5365162\",\n \"0.5359724\",\n \"0.53585327\",\n \"0.53566676\",\n \"0.53466034\",\n \"0.53410256\",\n \"0.53371227\",\n \"0.53358454\",\n \"0.53298324\",\n \"0.5319519\",\n \"0.5318488\",\n \"0.5312967\",\n \"0.5309007\",\n \"0.5309007\",\n \"0.53054094\",\n \"0.5301006\",\n \"0.52952236\",\n \"0.52906805\",\n \"0.5285285\",\n \"0.52844226\",\n \"0.5283888\",\n \"0.5278127\",\n \"0.5276711\",\n \"0.5268972\",\n \"0.5263886\",\n \"0.5259327\",\n \"0.52589023\",\n \"0.5253211\",\n \"0.5251921\"\n]","isConversational":false},"document_score":{"kind":"string","value":"0.0"},"document_rank":{"kind":"string","value":"-1"}}},{"rowIdx":75380,"cells":{"query":{"kind":"string","value":"Moves get/post arguments into the arg list."},"document":{"kind":"string","value":"def extend_with_args(f):\n @wraps(f)\n def wrapper(*args, **kwargs):\n if request.get_json():\n kwargs.update(request.get_json())\n\n # WARNING: This may get us into trouble when it comes to\n # list-style \"get\" args.\n kwargs.update({k: v for k, v in request.args.iteritems()})\n\n # \"auth\" is special -- it's an authentication token, not an\n # argument.\n if \"auth\" in kwargs:\n kwargs.pop(\"auth\")\n\n # This is JUST for the 1.0 sidebar app.\n if \"access_token\" in kwargs:\n kwargs.pop(\"access_token\")\n\n return f(*args, **kwargs)\n return wrapper"},"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 _transform_args(self) -> None:\n self.args = None if self.args == [] else self.args","def _set_arguments(self):\n self._arguments = []","def _set_args(self, args):\n if not args:\n self.arg = None\n elif len(args) == 1:\n self.arg = args[0]\n else:\n self.arg = args","def _update_args_and_kargs(self):\n if self.kwargs:\n self.value.append(self.args)\n self.value.append(self.kwargs)\n else:\n if self.args:\n self.value.append(self.args)","def arguments(args_to_pop=None) :\n posname, kwname, args = inspect.getargvalues(inspect.stack()[1][0])[-3:]\n posargs = args.pop(posname, [])\n args.update(args.pop(kwname, []))\n if args_to_pop is not None :\n for arg in args_to_pop :\n args.pop(arg)\n return args, posargs","def preprocess_arguments(self, *args, **kwargs):\n return (args, kwargs)","def update_args(self, args):\n self.args = self.parser.parse_args(args)","def _post_argument_parsing(self):\n pass","def add_args(self):\n raise NotImplementedError","def args(self, args):\n\n self._args = args","def _parse_args(self):\n parser = argparse.ArgumentParser()\n _, args = parser.parse_known_args()\n self.args = [a for a in args if a != '']","def change_args(self, args):\n self.modified_args = args","def normalize_args(self, args):\n return args","def override__args(self,arg,value):\n self.__args[arg] = value","def _get_args_for_run(obj, args, kwargs):\n new_args = []\n for arg in args:\n new_arg = _handle_arg(obj, arg)\n if new_arg is not None:\n new_args.append(new_arg)\n\n for _, value in kwargs.items():\n new_value = _handle_arg(obj, value)\n if new_value is not None:\n new_args.append(new_value)\n\n return new_args","def args_extract(self, args, kwargs):\n # make popable (can't pop tuple of args)\n args = list(args)\n\n def getarg(name, num):\n if args and len(args) > num:\n return args.pop(num)\n elif kwargs.get('files'):\n return kwargs.pop('files')\n return None\n\n # First to not affect data = args.pop(0)\n files = getarg('files', 1)\n data = getarg('data', 0)\n\n # make mutable if something\n if files:\n files = MultiValueDict(files)\n if data:\n data = MultiValueDict(data)\n\n return data, files, args, kwargs","def get_args(self):\r\n return self.args","def pre_forward(self, *args, **kwargs):\n self.allocate_parameter()\n return args, kwargs","def _parse_args(self, prepared_args):\n pass","def args(self):\n return self._args.copy()","def _add_pos_args(self, *args):\n arg_array = self._body.setdefault('args', [])\n arg_array.extend(args)","def get_assign_args(self, arguments):\n pass","def _get_method_args(self, method, request, params):\n idx = 0\n\n if method.__servicemethod__['store_arg']:\n params.insert(idx, method.__servicemethod__['store'])\n idx += 1\n\n if method.__servicemethod__['request_arg']:\n params.insert(idx, request)\n\n return params","def move_arg_attributes(self, arg, old_node, new_node):\n arg.metaattrs[\"deref\"] = new_node.ast.metaattrs[\"deref\"]\n new_node.ast.metaattrs[\"deref\"] = None\n \n c_attrs = new_node.ast.attrs\n attrs = arg.attrs\n for name in [\"owner\", \"free_pattern\"]:\n if c_attrs[name]:\n attrs[name] = c_attrs[name]\n del c_attrs[name]","def _pop_tq_add_args(self, kwargs):\n return {\n x: kwargs.pop('_' + x, None) for x in self.QUEUE_ARGS\n }","def add_args(self, parser):","def _input_args(self, args: List[str]):\n assert self._call is None, f\"You need to specify all inputs before calling `{self._call}`\"\n assert isinstance(args, list), f\"{args} is a {type(args)}, expected a list of strings!\"\n assert len(args) > 0, f\"Expected a non-empty argument list!\"\n assert all(isinstance(a, str) for a in args), f\"Expected a list of strings, not {[type(a) for a in args]}!\"\n # all arguments could potentially be filenames that we write to, so let's just add them\n self._write_files |= set(args)\n # add dummy argument zero\n args = [\"\"] + args\n # allocate args in memory\n arg_strings = [self._str(a, \"arg\") for a in args]\n # allocate a pointer array for argv\n self.data += [f\"argv: .word \" + \" \".join(\"0\" for _ in range(len(args)))]\n # load argc and argv\n self._args += [\"\", \"# argument count in a0\", f\"li a0, {len(args)}\"]\n self._args += [\"\", \"# load pointers to argument strings into argv\", f\"la a1, argv\"]\n for ii, aa in enumerate(arg_strings):\n self._args += [f\"la t1, {aa}\", f\"sw t1, {ii * 4}(a1)\"]","def sort_call_args(self, *args):\n return args","def _extract_args(self, func):\n sig = inspect.signature(func)\n\n # Backwards compatibility\n if len(sig.parameters) == 1:\n ((name, parameter),) = sig.parameters.items()\n if (\n parameter.kind is parameter.POSITIONAL_OR_KEYWORD\n and parameter.annotation in (parameter.empty, argparse.Namespace)\n ):\n self._require_namespace = name\n return\n\n for name, parameter in sig.parameters.items():\n if parameter.annotation is argparse.Namespace:\n self._require_namespace = name\n else:\n arg = Argument.from_parameter(name, parameter)\n action = arg.register_with_proxy(self)\n self._args.append((name, action.dest))","def sync_set_args(self, **kwargs):\n self.original_arguments = kwargs\n Args = namedtuple('Args', [k for k, v in self.arguments.items()])\n Args.__new__.__defaults__ = (None,) * len(self.arguments.items())\n\n valid = {}\n for k, arg in self.arguments.items():\n val = kwargs.get(k, None)\n if val is None and arg.required:\n raise Exception('{0} is required'.format(k))\n\n if arg.options and val not in arg.options:\n raise Exception('{0} provided for {1}. Expected {2}'.format(\n val,\n k,\n arg.options\n ))\n\n if callable(arg.validator):\n val = arg.validator(val, k)\n\n valid[k] = val\n\n self._args = Args(**valid)","def args(self, args):\n self._instructions_setter('ARG', args)","def args(self):\n\t\tret = []\n\t\tfor argname in self._arg_names:\n\t\t\tret += [self._args[argname]]\n\t\treturn ret","def _get_arguments(self, rargs):\r\n\r\n args = []\r\n i = 0\r\n count = len(rargs)\r\n while i < count and not self._is_opt(rargs[i]):\r\n args.append(rargs[i])\r\n i += 1\r\n\r\n return args","def _clean_args(*args):\n newargs = []\n for chk in args:\n if chk is None:\n break\n newargs.append(chk)\n return newargs","def _pre_argument_parsing(self):\n pass","def _process_args(self, largs, rargs, values):\n while rargs:\n arg = rargs[0]\n try:\n if arg[0:2] == \"--\" and len(arg) > 2:\n # process a single long option (possibly with value(s))\n # the superclass code pops the arg off rargs\n self._process_long_opt(rargs, values)\n elif arg[:1] == \"-\" and len(arg) > 1:\n # process a cluster of short options (possibly with\n # value(s) for the last one only)\n # the superclass code pops the arg off rargs\n self._process_short_opts(rargs, values)\n else:\n # it's either a non-default option or an arg\n # either way, add it to the args list so we can keep\n # dealing with options\n del rargs[0]\n raise Exception\n except:\n largs.append(arg)","def __parse_args(self):\n _method_name = '__parse_args'\n\n if self.__raw_args is not None and len(self.__raw_args) > 0:\n if isinstance(self.__raw_args, list):\n arguments = self.__raw_args\n else:\n arguments = self.__raw_args.split()\n\n for argument in arguments:\n if self.__client_server_regex.match(argument):\n self.__client_server_args.append(argument)\n elif self.__x_args_size_regex.match(argument):\n self.__process_x_size_arg(argument)\n elif self.__x_args_value_regex.match(argument):\n self.__process_x_value_arg(argument)\n elif self.__x_args_other_regex.match(argument):\n self.__process_x_other_arg(argument)\n elif self.__xx_args_switch_regex.match(argument):\n self.__process_xx_switch_arg(argument)\n elif self.__xx_args_value_regex.match(argument):\n self.__process_xx_value_arg(argument)\n elif self.__sys_props_regex.match(argument):\n self.__process_sys_prop_arg(argument)\n else:\n self._logger.finer('WLSDPLY-08300', argument, class_name=self._class_name, method_name=_method_name)\n self.__unsorted_args.append(argument)","def _get_args(item):\n args = item.get('args')\n if args and not isinstance(args, collections.Sequence):\n args = (args,)\n return args","def get_args(self):\n req_argv = self._ptr.contents.argv\n args = []\n if bool(req_argv):\n i = 0\n while 1:\n s = bytestostr(req_argv[i])\n i += 1\n if s == None:\n break\n args.append(s)\n return args","def parse_args(self):\n return Args(self.args)","def extra_args(self):\n return []","def args(self):\n return self._parse_args","def map_parameters(arguments, *args):\n for arg in args:\n if arg in ['clear', 'copy', 'fromkeys', 'get', 'items', 'keys', 'pop',\n 'popitem', 'setdefault', 'update', 'values', 'format',\n 'type']:\n Console.error(f'`{arg}` is predefined method.'\n f' Use `arguments[\"--{arg}\"]` in your code')\n raise ValueError(f\"{arg} already used in arguments\")\n elif arg in arguments:\n Console.error(f'`{arg}` is already used in arguments.'\n f' Use `arguments[\"--{arg}\"]` in your code')\n raise ValueError(f\"{arg} already used in arguments\")\n else:\n flag = \"--\" + arg\n if flag in arguments:\n value = arguments[flag]\n else:\n value = None\n arguments[arg] = value","def _sorted_args(self):\n return self.args","def _process_args(self, largs, rargs, values):\n while rargs:\n arg = rargs[0]\n try:\n if arg[0:2] == \"--\" and len(arg) > 2:\n # process a single long option (possibly with value(s))\n # the superclass code pops the arg off rargs\n self._process_long_opt(rargs, values)\n elif arg[:1] == \"-\" and len(arg) > 1:\n # process a cluster of short options (possibly with\n # value(s) for the last one only)\n # the superclass code pops the arg off rargs\n self._process_short_opts(rargs, values)\n else:\n # it's either a non-default option or an arg\n # either way, add it to the args list so we can keep\n # dealing with options\n del rargs[0]\n raise Exception\n except:\n largs.append(arg) # pylint: disable-msg=W0702","def get_args(self) -> List[str]:\n return self.content.split()[1:]","def add_extra_args(self):\n pass","def __getnewargs__(self):\n return ()","def __arg_list(self):\n args = []\n try:\n arg = self.__arg()\n args.append(arg)\n if arg.token.endswith(\"...\"):\n return args\n\n while True:\n self.match_value(Punctuator, \",\")\n\n arg = self.__arg()\n if arg.token.endswith(\"...\"):\n return args\n\n args.append(arg)\n except ParseError:\n return args","def parse_args(self, args):\n raise Exception(\"Not implemented\")","def convert_args(self):\n try:\n spec = _action_args_dict[self.action]\n except KeyError:\n raise Exception('Invalid action: ' + str(action))\n\n def parse_arg(type_, token, name):\n \"\"\"Converts token to type type_.\n \n This includes special cases for some argument types,\n such as Card, bool, and NoneType.\n \"\"\"\n if type_ is Card and token is not None and type(token) is not Card:\n try:\n arg = Card(int(token))\n except ValueError:\n raise GameActionError(\n 'Error converting \"{0}\" argument: {1} token: \"{2}\"'\n .format(name, str(type_), token))\n\n return arg\n\n else:\n return token\n\n\n req_tokens = self.args[:spec.n_req_args]\n req_args = []\n\n for i, tok, (typ, name) in izip(count(), req_tokens, spec.required_arg_specs):\n try:\n arg = parse_arg(typ, tok, name)\n except GameActionError, e:\n # If we failed in parse_arg, message can be appended for details\n raise\n #raise GameActionError(\n # 'Could not convert argument {0}. '.format(i) + e.message)\n\n req_args.append(arg)\n\n\n extended_tokens = self.args[spec.n_req_args:]\n extended_args = []\n \n for i, tok in izip(count(spec.n_req_args), extended_tokens):\n typ, name = spec.extended_arg_spec\n try:\n arg = parse_arg(typ, tok, name)\n except GameActionError, e:\n # If we failed in parse_arg, message can be appended for details\n raise GameActionError(\n 'Could not convert argument {0}. '.format(i) + e.message)\n\n extended_args.append(arg)\n\n self.args = req_args + extended_args","def arglist(self) -> List:\n return self.argv[1:]","def get_args( self, **kwargs ):\n args = []\n for at in self.arg_types:\n args.append( kwargs[at] )\n return args","def getPositionalArgs():","def parseArgs(self, args, **vars):\n argList = []\n for token in self.argLexer.finditer(args):\n for tokenType, tokenValue in list(token.groupdict().items()):\n if tokenValue is not None:\n argList.append(getattr(self, 'argtoken_' +\n tokenType)(tokenValue, vars))\n return argList","def initArgs(self, args):\n ignore = ['self', 'kwargs', 'args']\n for k,v in args.iteritems():\n if k not in ignore: \n setattr(self, k, v)\n pass","async def set_args(self, **kwargs):\n self.original_arguments = kwargs\n Args = namedtuple('Args', [k for k, v in self.arguments.items()])\n Args.__new__.__defaults__ = (None,) * len(self.arguments.items())\n\n valid = {}\n for k, arg in self.arguments.items():\n val = kwargs.get(k, None)\n if val is None and arg.required:\n raise Exception('{0} is required'.format(k))\n\n if arg.options and val not in arg.options:\n raise Exception('{0} provided for {1}. Expected {2}'.format(\n val,\n k,\n arg.options\n ))\n\n if callable(arg.validator):\n val = arg.validator(val, k)\n\n valid[k] = val\n\n self._args = Args(**valid)","def reverse_args(self, /, *args, **kwargs):\n return self._func(*args[::-1], **kwargs)","def forward(self, *args, **kwargs) -> Dict[str, Any]:\n pass","def fetch_arguments(op_def, arg, ws):\n return [fetch_argument(op_def, desc, ws) for desc in arg.strings]","def _bake(self, *args, **kwargs):\n self._args = self.build_arguments(*args, **kwargs)","def get_unsorted_args_list(self):\n return self.__unsorted_args","def set_func_args(self, *args, **kwargs):\n self._func_args = args \n self._func_kw_args = kwargs","def create_argument_list(self):\n raise NotImplementedError","def post(self):\n args = parser.parse_args()","def parse_arguments(self):\n \n for arg in sys.argv[1:]:\n (key, sep, value) = arg.partition(\"=\")\n if sep != \"=\":\n raise ProcessorError(\"Illegal argument '%s'\" % arg)\n self.update_data(key, value)","def __arg_list(self):\n arg = self.__arg()\n args = [arg]\n try:\n while not self.eol():\n self.match_value(Punctuator, \",\")\n arg = self.__arg()\n args.append(arg)\n except ParseError:\n pass\n return args","def parse_arguments(args):","def list_args(args):\n run_list_args(args)","def process_cache_arguments(self, args):\r\n pass","def create_arg_list(self):\n\n sim = self.sim\n\n py_kernel_args = sim.kernel_args # Python variables that are passed into the kernel\n gen_kernel_args = sim.ctx_info['kernel_arguments'] # A list of needed kernel arguments from kernel autogen (Mako)\n\n list_for_kernel = gen_kernel_args[self.short_name]\n\n python_args_needed = [z[0] for z in list_for_kernel]\n\n self.arg_list = [py_kernel_args[z] for z in python_args_needed]\n\n # Loop over the arg_list...if the argument is a function, call it!\n for i in range(len(self.arg_list)):\n value = self.arg_list[i]\n if inspect.isfunction(value):\n self.arg_list[i] = value()\n\n additional_cl_args = [sim.queue, self.kernel_global_size, self.kernel_local_size]\n\n self.arg_list = additional_cl_args + self.arg_list","def parse(self, arg_list):\n\n if self._meta.ignore_unknown_arguments is True:\n args, unknown = self.parse_known_args(arg_list)\n self.parsed_args = args\n self.unknown_args = unknown\n else:\n args = self.parse_args(arg_list)\n self.parsed_args = args\n return self.parsed_args","def args(self) -> List[str]:\n return self.__args","def set_params(self, *arg):\n pass","def popArg(args):\n if len(args) == 0:\n return (None, args)\n nextArg = args[0]\n args = args[1:]\n return (nextArg, args)","def parse_args():\n parser = argparse.ArgumentParser(description=\n \"Reverse archival closing of issues of the specified repo, Note, repo \"\n \"MUST be manually unarchived before this script\")\n parser.add_argument('repo', help = \"owner/repo to unarchive\", action = 'store')\n parser.add_argument('--token', help = \"PAT to access github. Needs Write access to the repos\",\n action='store')\n parser.add_argument('-q', help = 'DO NOT print, or request confirmations', dest = 'quiet',\n action='store_true', default = False)\n args = parser.parse_args()\n if args.token is None:\n args.token = getpass('Please enter your GitHub token: ')\n return args","def parse_args(self):\n return self.__process_args__(self.parser.parse_args())","def positional(self, *args, **kwargs):\n return self.parser.add_argument(*args, **kwargs, nargs=\"*\")","def unpack_args(kwargs):\n return [v for p in zip(list(kwargs.keys()), list(kwargs.values())) for v in p]","def _prepare_input_for_pytorch(args, kwargs):\n if isinstance(args, (torch.Tensor, dict)):\n args = (args,)\n # In-place operators will update input tensor data as well.\n # Thus inputs are replicated before every forward call.\n args = copy.deepcopy(args)\n if kwargs:\n kwargs = copy.deepcopy(kwargs)\n else:\n kwargs = {}\n return args, kwargs","def get_list_cmd_args(self):\r\n return self.get_args(OSPL.list)","def _add_pos_args(self, *args):\n arg_array = self._params.setdefault(\"positional_parameters\", [])\n # couchbase++ wants all args JSONified\n json_args = [json.dumps(arg) for arg in args]\n arg_array.extend(json_args)","def add_args(*args):\n num_args = len(args)\n for i in range(0,num_args,2):\n add_arg(args[i], args[i+1])","def args(self):\n return self._args","def args(self):\n return self._args","def args(self):\n return self._args","def set_call_arguments(self, node, args):\n node.args = args.args\n node.keywords = args.keywords\n node.starargs = args.starargs\n node.kwargs = args.kwargs","def parse_cmd_args(self, args):\n\t\tfor k, v in vars(args).items():\n\t\t\tif k == \"cfg\":\n\t\t\t\tcontinue\t\t\t\t# reserved keyword (for loading settings)\n\n\t\t\tif v is None:\n\t\t\t\tcontinue\t\t\t\t# None stands for not set\n\n\t\t\tif not hasattr(self, k):\n\t\t\t\traise RuntimeError(\"[ERROR] Not recognized argument from comamnd line: {}.\".format(k))\n\t\t\tsetattr(self, k, v)","def get_args(cls) -> List[argparse.ArgumentParser]:\n raise NotImplementedError()","def get_additional_arguments(obj):\n args = ''\n\n try:\n # If the object has arguments parse them to a string and replace the values from the lookup table\n if obj.args:\n # Parse the list to a string\n args = ' '.join(obj.args)\n\n # Replace the items in the string with entries from the lookup table\n for k, v in config.lookup_table:\n args = args.replace(k, v)\n\n # Reset the args in the object to reset its state\n obj.args = None\n except AttributeError:\n # No additional args present so we can just skip the parsing\n pass\n\n return args","def args(self):\n return self._args_[: self.nargs()]","def get_args( self, ):\r\n for iarg in sys.argv[1:]:\r\n #print iarg\r\n argsplits = iarg.split(\"=\")\r\n parm_name = argsplits[0]\r\n parm_value = argsplits[1]\r\n\r\n # so far only one is captured\r\n if parm_name == \"parameters\":\r\n self.parmeters_x = parm_value #\r\n msg = \"command line arg >>{iarg}\" # log file not open but use alt\r\n AppGlobal.logger.info( msg )\r\n else:\r\n msg = \"no parmeter extensions\"\r\n AppGlobal.logger.info( msg )\r\n return","def prepost_hook_too_many_parameters(self, param) -> None:\n pass","def getArgs():\n cache = _arg_cache\n if not cache:\n cache.append(parseArgs())\n return cache[0]","def _repopulate_required_arguments(self, arguments):\n fixed_arguments = []\n original_arguments = self.arguments.split()\n\n original_argument_index = 0\n argument_index = 0\n\n while original_argument_index < len(original_arguments):\n original_argument = original_arguments[original_argument_index]\n if (argument_index < len(arguments) and\n original_argument == arguments[argument_index]):\n argument_index += 1\n fixed_arguments.append(original_argument)\n elif (original_argument in self.required_arguments or\n original_argument.split('=')[0] in self.required_arguments or\n '\"' in original_argument or \"'\" in original_argument):\n fixed_arguments.append(original_argument)\n\n original_argument_index += 1\n\n return fixed_arguments","def prepare_arguments(self, parser):\n pass","def _add_argument(self, args=''):\n\n sys.argv += args.split(' ')","def get_args(inst):\n if is_estimator(inst):\n args = inspect.getargspec(inst.update).args\n args = [arg for arg in args if arg != 'self' and arg != 'X']\n else:\n args = inspect.getargspec(inst).args\n ignore_args = {'self', 'X', 'y', 'pattern', 'normalizer', 'coef'}\n args = [arg for arg in args if arg not in ignore_args]\n\n return args","def pop(self, arguments):\n puts_err(colored.red(\"Not implemented!\"))","def add_arguments(self, parser):","def _make_args(self, args, defaults=[], vararg=None, kwonlyargs=[],\n kw_defaults=[], kwarg=None):\n # On Python 2 convert vararg and kwarg to raw name, raise error using\n # lineno stored on the node and lexer from self.\n # On Python 3.3 extract name and annotation\n # After should be straight forward\n raise NotImplementedError()"],"string":"[\n \"def _transform_args(self) -> None:\\n self.args = None if self.args == [] else self.args\",\n \"def _set_arguments(self):\\n self._arguments = []\",\n \"def _set_args(self, args):\\n if not args:\\n self.arg = None\\n elif len(args) == 1:\\n self.arg = args[0]\\n else:\\n self.arg = args\",\n \"def _update_args_and_kargs(self):\\n if self.kwargs:\\n self.value.append(self.args)\\n self.value.append(self.kwargs)\\n else:\\n if self.args:\\n self.value.append(self.args)\",\n \"def arguments(args_to_pop=None) :\\n posname, kwname, args = inspect.getargvalues(inspect.stack()[1][0])[-3:]\\n posargs = args.pop(posname, [])\\n args.update(args.pop(kwname, []))\\n if args_to_pop is not None :\\n for arg in args_to_pop :\\n args.pop(arg)\\n return args, posargs\",\n \"def preprocess_arguments(self, *args, **kwargs):\\n return (args, kwargs)\",\n \"def update_args(self, args):\\n self.args = self.parser.parse_args(args)\",\n \"def _post_argument_parsing(self):\\n pass\",\n \"def add_args(self):\\n raise NotImplementedError\",\n \"def args(self, args):\\n\\n self._args = args\",\n \"def _parse_args(self):\\n parser = argparse.ArgumentParser()\\n _, args = parser.parse_known_args()\\n self.args = [a for a in args if a != '']\",\n \"def change_args(self, args):\\n self.modified_args = args\",\n \"def normalize_args(self, args):\\n return args\",\n \"def override__args(self,arg,value):\\n self.__args[arg] = value\",\n \"def _get_args_for_run(obj, args, kwargs):\\n new_args = []\\n for arg in args:\\n new_arg = _handle_arg(obj, arg)\\n if new_arg is not None:\\n new_args.append(new_arg)\\n\\n for _, value in kwargs.items():\\n new_value = _handle_arg(obj, value)\\n if new_value is not None:\\n new_args.append(new_value)\\n\\n return new_args\",\n \"def args_extract(self, args, kwargs):\\n # make popable (can't pop tuple of args)\\n args = list(args)\\n\\n def getarg(name, num):\\n if args and len(args) > num:\\n return args.pop(num)\\n elif kwargs.get('files'):\\n return kwargs.pop('files')\\n return None\\n\\n # First to not affect data = args.pop(0)\\n files = getarg('files', 1)\\n data = getarg('data', 0)\\n\\n # make mutable if something\\n if files:\\n files = MultiValueDict(files)\\n if data:\\n data = MultiValueDict(data)\\n\\n return data, files, args, kwargs\",\n \"def get_args(self):\\r\\n return self.args\",\n \"def pre_forward(self, *args, **kwargs):\\n self.allocate_parameter()\\n return args, kwargs\",\n \"def _parse_args(self, prepared_args):\\n pass\",\n \"def args(self):\\n return self._args.copy()\",\n \"def _add_pos_args(self, *args):\\n arg_array = self._body.setdefault('args', [])\\n arg_array.extend(args)\",\n \"def get_assign_args(self, arguments):\\n pass\",\n \"def _get_method_args(self, method, request, params):\\n idx = 0\\n\\n if method.__servicemethod__['store_arg']:\\n params.insert(idx, method.__servicemethod__['store'])\\n idx += 1\\n\\n if method.__servicemethod__['request_arg']:\\n params.insert(idx, request)\\n\\n return params\",\n \"def move_arg_attributes(self, arg, old_node, new_node):\\n arg.metaattrs[\\\"deref\\\"] = new_node.ast.metaattrs[\\\"deref\\\"]\\n new_node.ast.metaattrs[\\\"deref\\\"] = None\\n \\n c_attrs = new_node.ast.attrs\\n attrs = arg.attrs\\n for name in [\\\"owner\\\", \\\"free_pattern\\\"]:\\n if c_attrs[name]:\\n attrs[name] = c_attrs[name]\\n del c_attrs[name]\",\n \"def _pop_tq_add_args(self, kwargs):\\n return {\\n x: kwargs.pop('_' + x, None) for x in self.QUEUE_ARGS\\n }\",\n \"def add_args(self, parser):\",\n \"def _input_args(self, args: List[str]):\\n assert self._call is None, f\\\"You need to specify all inputs before calling `{self._call}`\\\"\\n assert isinstance(args, list), f\\\"{args} is a {type(args)}, expected a list of strings!\\\"\\n assert len(args) > 0, f\\\"Expected a non-empty argument list!\\\"\\n assert all(isinstance(a, str) for a in args), f\\\"Expected a list of strings, not {[type(a) for a in args]}!\\\"\\n # all arguments could potentially be filenames that we write to, so let's just add them\\n self._write_files |= set(args)\\n # add dummy argument zero\\n args = [\\\"\\\"] + args\\n # allocate args in memory\\n arg_strings = [self._str(a, \\\"arg\\\") for a in args]\\n # allocate a pointer array for argv\\n self.data += [f\\\"argv: .word \\\" + \\\" \\\".join(\\\"0\\\" for _ in range(len(args)))]\\n # load argc and argv\\n self._args += [\\\"\\\", \\\"# argument count in a0\\\", f\\\"li a0, {len(args)}\\\"]\\n self._args += [\\\"\\\", \\\"# load pointers to argument strings into argv\\\", f\\\"la a1, argv\\\"]\\n for ii, aa in enumerate(arg_strings):\\n self._args += [f\\\"la t1, {aa}\\\", f\\\"sw t1, {ii * 4}(a1)\\\"]\",\n \"def sort_call_args(self, *args):\\n return args\",\n \"def _extract_args(self, func):\\n sig = inspect.signature(func)\\n\\n # Backwards compatibility\\n if len(sig.parameters) == 1:\\n ((name, parameter),) = sig.parameters.items()\\n if (\\n parameter.kind is parameter.POSITIONAL_OR_KEYWORD\\n and parameter.annotation in (parameter.empty, argparse.Namespace)\\n ):\\n self._require_namespace = name\\n return\\n\\n for name, parameter in sig.parameters.items():\\n if parameter.annotation is argparse.Namespace:\\n self._require_namespace = name\\n else:\\n arg = Argument.from_parameter(name, parameter)\\n action = arg.register_with_proxy(self)\\n self._args.append((name, action.dest))\",\n \"def sync_set_args(self, **kwargs):\\n self.original_arguments = kwargs\\n Args = namedtuple('Args', [k for k, v in self.arguments.items()])\\n Args.__new__.__defaults__ = (None,) * len(self.arguments.items())\\n\\n valid = {}\\n for k, arg in self.arguments.items():\\n val = kwargs.get(k, None)\\n if val is None and arg.required:\\n raise Exception('{0} is required'.format(k))\\n\\n if arg.options and val not in arg.options:\\n raise Exception('{0} provided for {1}. Expected {2}'.format(\\n val,\\n k,\\n arg.options\\n ))\\n\\n if callable(arg.validator):\\n val = arg.validator(val, k)\\n\\n valid[k] = val\\n\\n self._args = Args(**valid)\",\n \"def args(self, args):\\n self._instructions_setter('ARG', args)\",\n \"def args(self):\\n\\t\\tret = []\\n\\t\\tfor argname in self._arg_names:\\n\\t\\t\\tret += [self._args[argname]]\\n\\t\\treturn ret\",\n \"def _get_arguments(self, rargs):\\r\\n\\r\\n args = []\\r\\n i = 0\\r\\n count = len(rargs)\\r\\n while i < count and not self._is_opt(rargs[i]):\\r\\n args.append(rargs[i])\\r\\n i += 1\\r\\n\\r\\n return args\",\n \"def _clean_args(*args):\\n newargs = []\\n for chk in args:\\n if chk is None:\\n break\\n newargs.append(chk)\\n return newargs\",\n \"def _pre_argument_parsing(self):\\n pass\",\n \"def _process_args(self, largs, rargs, values):\\n while rargs:\\n arg = rargs[0]\\n try:\\n if arg[0:2] == \\\"--\\\" and len(arg) > 2:\\n # process a single long option (possibly with value(s))\\n # the superclass code pops the arg off rargs\\n self._process_long_opt(rargs, values)\\n elif arg[:1] == \\\"-\\\" and len(arg) > 1:\\n # process a cluster of short options (possibly with\\n # value(s) for the last one only)\\n # the superclass code pops the arg off rargs\\n self._process_short_opts(rargs, values)\\n else:\\n # it's either a non-default option or an arg\\n # either way, add it to the args list so we can keep\\n # dealing with options\\n del rargs[0]\\n raise Exception\\n except:\\n largs.append(arg)\",\n \"def __parse_args(self):\\n _method_name = '__parse_args'\\n\\n if self.__raw_args is not None and len(self.__raw_args) > 0:\\n if isinstance(self.__raw_args, list):\\n arguments = self.__raw_args\\n else:\\n arguments = self.__raw_args.split()\\n\\n for argument in arguments:\\n if self.__client_server_regex.match(argument):\\n self.__client_server_args.append(argument)\\n elif self.__x_args_size_regex.match(argument):\\n self.__process_x_size_arg(argument)\\n elif self.__x_args_value_regex.match(argument):\\n self.__process_x_value_arg(argument)\\n elif self.__x_args_other_regex.match(argument):\\n self.__process_x_other_arg(argument)\\n elif self.__xx_args_switch_regex.match(argument):\\n self.__process_xx_switch_arg(argument)\\n elif self.__xx_args_value_regex.match(argument):\\n self.__process_xx_value_arg(argument)\\n elif self.__sys_props_regex.match(argument):\\n self.__process_sys_prop_arg(argument)\\n else:\\n self._logger.finer('WLSDPLY-08300', argument, class_name=self._class_name, method_name=_method_name)\\n self.__unsorted_args.append(argument)\",\n \"def _get_args(item):\\n args = item.get('args')\\n if args and not isinstance(args, collections.Sequence):\\n args = (args,)\\n return args\",\n \"def get_args(self):\\n req_argv = self._ptr.contents.argv\\n args = []\\n if bool(req_argv):\\n i = 0\\n while 1:\\n s = bytestostr(req_argv[i])\\n i += 1\\n if s == None:\\n break\\n args.append(s)\\n return args\",\n \"def parse_args(self):\\n return Args(self.args)\",\n \"def extra_args(self):\\n return []\",\n \"def args(self):\\n return self._parse_args\",\n \"def map_parameters(arguments, *args):\\n for arg in args:\\n if arg in ['clear', 'copy', 'fromkeys', 'get', 'items', 'keys', 'pop',\\n 'popitem', 'setdefault', 'update', 'values', 'format',\\n 'type']:\\n Console.error(f'`{arg}` is predefined method.'\\n f' Use `arguments[\\\"--{arg}\\\"]` in your code')\\n raise ValueError(f\\\"{arg} already used in arguments\\\")\\n elif arg in arguments:\\n Console.error(f'`{arg}` is already used in arguments.'\\n f' Use `arguments[\\\"--{arg}\\\"]` in your code')\\n raise ValueError(f\\\"{arg} already used in arguments\\\")\\n else:\\n flag = \\\"--\\\" + arg\\n if flag in arguments:\\n value = arguments[flag]\\n else:\\n value = None\\n arguments[arg] = value\",\n \"def _sorted_args(self):\\n return self.args\",\n \"def _process_args(self, largs, rargs, values):\\n while rargs:\\n arg = rargs[0]\\n try:\\n if arg[0:2] == \\\"--\\\" and len(arg) > 2:\\n # process a single long option (possibly with value(s))\\n # the superclass code pops the arg off rargs\\n self._process_long_opt(rargs, values)\\n elif arg[:1] == \\\"-\\\" and len(arg) > 1:\\n # process a cluster of short options (possibly with\\n # value(s) for the last one only)\\n # the superclass code pops the arg off rargs\\n self._process_short_opts(rargs, values)\\n else:\\n # it's either a non-default option or an arg\\n # either way, add it to the args list so we can keep\\n # dealing with options\\n del rargs[0]\\n raise Exception\\n except:\\n largs.append(arg) # pylint: disable-msg=W0702\",\n \"def get_args(self) -> List[str]:\\n return self.content.split()[1:]\",\n \"def add_extra_args(self):\\n pass\",\n \"def __getnewargs__(self):\\n return ()\",\n \"def __arg_list(self):\\n args = []\\n try:\\n arg = self.__arg()\\n args.append(arg)\\n if arg.token.endswith(\\\"...\\\"):\\n return args\\n\\n while True:\\n self.match_value(Punctuator, \\\",\\\")\\n\\n arg = self.__arg()\\n if arg.token.endswith(\\\"...\\\"):\\n return args\\n\\n args.append(arg)\\n except ParseError:\\n return args\",\n \"def parse_args(self, args):\\n raise Exception(\\\"Not implemented\\\")\",\n \"def convert_args(self):\\n try:\\n spec = _action_args_dict[self.action]\\n except KeyError:\\n raise Exception('Invalid action: ' + str(action))\\n\\n def parse_arg(type_, token, name):\\n \\\"\\\"\\\"Converts token to type type_.\\n \\n This includes special cases for some argument types,\\n such as Card, bool, and NoneType.\\n \\\"\\\"\\\"\\n if type_ is Card and token is not None and type(token) is not Card:\\n try:\\n arg = Card(int(token))\\n except ValueError:\\n raise GameActionError(\\n 'Error converting \\\"{0}\\\" argument: {1} token: \\\"{2}\\\"'\\n .format(name, str(type_), token))\\n\\n return arg\\n\\n else:\\n return token\\n\\n\\n req_tokens = self.args[:spec.n_req_args]\\n req_args = []\\n\\n for i, tok, (typ, name) in izip(count(), req_tokens, spec.required_arg_specs):\\n try:\\n arg = parse_arg(typ, tok, name)\\n except GameActionError, e:\\n # If we failed in parse_arg, message can be appended for details\\n raise\\n #raise GameActionError(\\n # 'Could not convert argument {0}. '.format(i) + e.message)\\n\\n req_args.append(arg)\\n\\n\\n extended_tokens = self.args[spec.n_req_args:]\\n extended_args = []\\n \\n for i, tok in izip(count(spec.n_req_args), extended_tokens):\\n typ, name = spec.extended_arg_spec\\n try:\\n arg = parse_arg(typ, tok, name)\\n except GameActionError, e:\\n # If we failed in parse_arg, message can be appended for details\\n raise GameActionError(\\n 'Could not convert argument {0}. '.format(i) + e.message)\\n\\n extended_args.append(arg)\\n\\n self.args = req_args + extended_args\",\n \"def arglist(self) -> List:\\n return self.argv[1:]\",\n \"def get_args( self, **kwargs ):\\n args = []\\n for at in self.arg_types:\\n args.append( kwargs[at] )\\n return args\",\n \"def getPositionalArgs():\",\n \"def parseArgs(self, args, **vars):\\n argList = []\\n for token in self.argLexer.finditer(args):\\n for tokenType, tokenValue in list(token.groupdict().items()):\\n if tokenValue is not None:\\n argList.append(getattr(self, 'argtoken_' +\\n tokenType)(tokenValue, vars))\\n return argList\",\n \"def initArgs(self, args):\\n ignore = ['self', 'kwargs', 'args']\\n for k,v in args.iteritems():\\n if k not in ignore: \\n setattr(self, k, v)\\n pass\",\n \"async def set_args(self, **kwargs):\\n self.original_arguments = kwargs\\n Args = namedtuple('Args', [k for k, v in self.arguments.items()])\\n Args.__new__.__defaults__ = (None,) * len(self.arguments.items())\\n\\n valid = {}\\n for k, arg in self.arguments.items():\\n val = kwargs.get(k, None)\\n if val is None and arg.required:\\n raise Exception('{0} is required'.format(k))\\n\\n if arg.options and val not in arg.options:\\n raise Exception('{0} provided for {1}. Expected {2}'.format(\\n val,\\n k,\\n arg.options\\n ))\\n\\n if callable(arg.validator):\\n val = arg.validator(val, k)\\n\\n valid[k] = val\\n\\n self._args = Args(**valid)\",\n \"def reverse_args(self, /, *args, **kwargs):\\n return self._func(*args[::-1], **kwargs)\",\n \"def forward(self, *args, **kwargs) -> Dict[str, Any]:\\n pass\",\n \"def fetch_arguments(op_def, arg, ws):\\n return [fetch_argument(op_def, desc, ws) for desc in arg.strings]\",\n \"def _bake(self, *args, **kwargs):\\n self._args = self.build_arguments(*args, **kwargs)\",\n \"def get_unsorted_args_list(self):\\n return self.__unsorted_args\",\n \"def set_func_args(self, *args, **kwargs):\\n self._func_args = args \\n self._func_kw_args = kwargs\",\n \"def create_argument_list(self):\\n raise NotImplementedError\",\n \"def post(self):\\n args = parser.parse_args()\",\n \"def parse_arguments(self):\\n \\n for arg in sys.argv[1:]:\\n (key, sep, value) = arg.partition(\\\"=\\\")\\n if sep != \\\"=\\\":\\n raise ProcessorError(\\\"Illegal argument '%s'\\\" % arg)\\n self.update_data(key, value)\",\n \"def __arg_list(self):\\n arg = self.__arg()\\n args = [arg]\\n try:\\n while not self.eol():\\n self.match_value(Punctuator, \\\",\\\")\\n arg = self.__arg()\\n args.append(arg)\\n except ParseError:\\n pass\\n return args\",\n \"def parse_arguments(args):\",\n \"def list_args(args):\\n run_list_args(args)\",\n \"def process_cache_arguments(self, args):\\r\\n pass\",\n \"def create_arg_list(self):\\n\\n sim = self.sim\\n\\n py_kernel_args = sim.kernel_args # Python variables that are passed into the kernel\\n gen_kernel_args = sim.ctx_info['kernel_arguments'] # A list of needed kernel arguments from kernel autogen (Mako)\\n\\n list_for_kernel = gen_kernel_args[self.short_name]\\n\\n python_args_needed = [z[0] for z in list_for_kernel]\\n\\n self.arg_list = [py_kernel_args[z] for z in python_args_needed]\\n\\n # Loop over the arg_list...if the argument is a function, call it!\\n for i in range(len(self.arg_list)):\\n value = self.arg_list[i]\\n if inspect.isfunction(value):\\n self.arg_list[i] = value()\\n\\n additional_cl_args = [sim.queue, self.kernel_global_size, self.kernel_local_size]\\n\\n self.arg_list = additional_cl_args + self.arg_list\",\n \"def parse(self, arg_list):\\n\\n if self._meta.ignore_unknown_arguments is True:\\n args, unknown = self.parse_known_args(arg_list)\\n self.parsed_args = args\\n self.unknown_args = unknown\\n else:\\n args = self.parse_args(arg_list)\\n self.parsed_args = args\\n return self.parsed_args\",\n \"def args(self) -> List[str]:\\n return self.__args\",\n \"def set_params(self, *arg):\\n pass\",\n \"def popArg(args):\\n if len(args) == 0:\\n return (None, args)\\n nextArg = args[0]\\n args = args[1:]\\n return (nextArg, args)\",\n \"def parse_args():\\n parser = argparse.ArgumentParser(description=\\n \\\"Reverse archival closing of issues of the specified repo, Note, repo \\\"\\n \\\"MUST be manually unarchived before this script\\\")\\n parser.add_argument('repo', help = \\\"owner/repo to unarchive\\\", action = 'store')\\n parser.add_argument('--token', help = \\\"PAT to access github. Needs Write access to the repos\\\",\\n action='store')\\n parser.add_argument('-q', help = 'DO NOT print, or request confirmations', dest = 'quiet',\\n action='store_true', default = False)\\n args = parser.parse_args()\\n if args.token is None:\\n args.token = getpass('Please enter your GitHub token: ')\\n return args\",\n \"def parse_args(self):\\n return self.__process_args__(self.parser.parse_args())\",\n \"def positional(self, *args, **kwargs):\\n return self.parser.add_argument(*args, **kwargs, nargs=\\\"*\\\")\",\n \"def unpack_args(kwargs):\\n return [v for p in zip(list(kwargs.keys()), list(kwargs.values())) for v in p]\",\n \"def _prepare_input_for_pytorch(args, kwargs):\\n if isinstance(args, (torch.Tensor, dict)):\\n args = (args,)\\n # In-place operators will update input tensor data as well.\\n # Thus inputs are replicated before every forward call.\\n args = copy.deepcopy(args)\\n if kwargs:\\n kwargs = copy.deepcopy(kwargs)\\n else:\\n kwargs = {}\\n return args, kwargs\",\n \"def get_list_cmd_args(self):\\r\\n return self.get_args(OSPL.list)\",\n \"def _add_pos_args(self, *args):\\n arg_array = self._params.setdefault(\\\"positional_parameters\\\", [])\\n # couchbase++ wants all args JSONified\\n json_args = [json.dumps(arg) for arg in args]\\n arg_array.extend(json_args)\",\n \"def add_args(*args):\\n num_args = len(args)\\n for i in range(0,num_args,2):\\n add_arg(args[i], args[i+1])\",\n \"def args(self):\\n return self._args\",\n \"def args(self):\\n return self._args\",\n \"def args(self):\\n return self._args\",\n \"def set_call_arguments(self, node, args):\\n node.args = args.args\\n node.keywords = args.keywords\\n node.starargs = args.starargs\\n node.kwargs = args.kwargs\",\n \"def parse_cmd_args(self, args):\\n\\t\\tfor k, v in vars(args).items():\\n\\t\\t\\tif k == \\\"cfg\\\":\\n\\t\\t\\t\\tcontinue\\t\\t\\t\\t# reserved keyword (for loading settings)\\n\\n\\t\\t\\tif v is None:\\n\\t\\t\\t\\tcontinue\\t\\t\\t\\t# None stands for not set\\n\\n\\t\\t\\tif not hasattr(self, k):\\n\\t\\t\\t\\traise RuntimeError(\\\"[ERROR] Not recognized argument from comamnd line: {}.\\\".format(k))\\n\\t\\t\\tsetattr(self, k, v)\",\n \"def get_args(cls) -> List[argparse.ArgumentParser]:\\n raise NotImplementedError()\",\n \"def get_additional_arguments(obj):\\n args = ''\\n\\n try:\\n # If the object has arguments parse them to a string and replace the values from the lookup table\\n if obj.args:\\n # Parse the list to a string\\n args = ' '.join(obj.args)\\n\\n # Replace the items in the string with entries from the lookup table\\n for k, v in config.lookup_table:\\n args = args.replace(k, v)\\n\\n # Reset the args in the object to reset its state\\n obj.args = None\\n except AttributeError:\\n # No additional args present so we can just skip the parsing\\n pass\\n\\n return args\",\n \"def args(self):\\n return self._args_[: self.nargs()]\",\n \"def get_args( self, ):\\r\\n for iarg in sys.argv[1:]:\\r\\n #print iarg\\r\\n argsplits = iarg.split(\\\"=\\\")\\r\\n parm_name = argsplits[0]\\r\\n parm_value = argsplits[1]\\r\\n\\r\\n # so far only one is captured\\r\\n if parm_name == \\\"parameters\\\":\\r\\n self.parmeters_x = parm_value #\\r\\n msg = \\\"command line arg >>{iarg}\\\" # log file not open but use alt\\r\\n AppGlobal.logger.info( msg )\\r\\n else:\\r\\n msg = \\\"no parmeter extensions\\\"\\r\\n AppGlobal.logger.info( msg )\\r\\n return\",\n \"def prepost_hook_too_many_parameters(self, param) -> None:\\n pass\",\n \"def getArgs():\\n cache = _arg_cache\\n if not cache:\\n cache.append(parseArgs())\\n return cache[0]\",\n \"def _repopulate_required_arguments(self, arguments):\\n fixed_arguments = []\\n original_arguments = self.arguments.split()\\n\\n original_argument_index = 0\\n argument_index = 0\\n\\n while original_argument_index < len(original_arguments):\\n original_argument = original_arguments[original_argument_index]\\n if (argument_index < len(arguments) and\\n original_argument == arguments[argument_index]):\\n argument_index += 1\\n fixed_arguments.append(original_argument)\\n elif (original_argument in self.required_arguments or\\n original_argument.split('=')[0] in self.required_arguments or\\n '\\\"' in original_argument or \\\"'\\\" in original_argument):\\n fixed_arguments.append(original_argument)\\n\\n original_argument_index += 1\\n\\n return fixed_arguments\",\n \"def prepare_arguments(self, parser):\\n pass\",\n \"def _add_argument(self, args=''):\\n\\n sys.argv += args.split(' ')\",\n \"def get_args(inst):\\n if is_estimator(inst):\\n args = inspect.getargspec(inst.update).args\\n args = [arg for arg in args if arg != 'self' and arg != 'X']\\n else:\\n args = inspect.getargspec(inst).args\\n ignore_args = {'self', 'X', 'y', 'pattern', 'normalizer', 'coef'}\\n args = [arg for arg in args if arg not in ignore_args]\\n\\n return args\",\n \"def pop(self, arguments):\\n puts_err(colored.red(\\\"Not implemented!\\\"))\",\n \"def add_arguments(self, parser):\",\n \"def _make_args(self, args, defaults=[], vararg=None, kwonlyargs=[],\\n kw_defaults=[], kwarg=None):\\n # On Python 2 convert vararg and kwarg to raw name, raise error using\\n # lineno stored on the node and lexer from self.\\n # On Python 3.3 extract name and annotation\\n # After should be straight forward\\n raise NotImplementedError()\"\n]","isConversational":false},"negative_scores":{"kind":"list like","value":["0.7024696","0.62392586","0.6223757","0.61863637","0.6133016","0.6104633","0.6030061","0.599919","0.59782875","0.5964543","0.59543717","0.5943862","0.58861876","0.58676785","0.58403295","0.5811097","0.57581645","0.5723659","0.57110935","0.5691672","0.5687727","0.56471574","0.5634761","0.5596134","0.55662596","0.5547842","0.5527746","0.5524724","0.55186945","0.55131227","0.55104303","0.5499743","0.54948914","0.54884905","0.5474858","0.5471704","0.5469055","0.5456895","0.54537666","0.5452064","0.5451102","0.5450676","0.5449012","0.5447223","0.5438074","0.5428483","0.54151535","0.5403033","0.5398406","0.5398128","0.5394609","0.536109","0.5347123","0.5342833","0.5341328","0.5335529","0.5332527","0.53230923","0.5321399","0.5319868","0.5319468","0.53109235","0.5305616","0.5304103","0.53008205","0.5288531","0.52793515","0.52695495","0.5259273","0.52566683","0.52361804","0.5232623","0.52255344","0.5220549","0.5212677","0.52122873","0.5209832","0.52087295","0.52083725","0.52052844","0.5204488","0.5204031","0.5189197","0.5183786","0.5183786","0.5183786","0.51787025","0.51650965","0.51585937","0.51572984","0.51492923","0.51485425","0.51477295","0.5117564","0.5110872","0.5108907","0.51074696","0.51053226","0.50954413","0.5090461","0.5079076"],"string":"[\n \"0.7024696\",\n \"0.62392586\",\n \"0.6223757\",\n \"0.61863637\",\n \"0.6133016\",\n \"0.6104633\",\n \"0.6030061\",\n \"0.599919\",\n \"0.59782875\",\n \"0.5964543\",\n \"0.59543717\",\n \"0.5943862\",\n \"0.58861876\",\n \"0.58676785\",\n \"0.58403295\",\n \"0.5811097\",\n \"0.57581645\",\n \"0.5723659\",\n \"0.57110935\",\n \"0.5691672\",\n \"0.5687727\",\n \"0.56471574\",\n \"0.5634761\",\n \"0.5596134\",\n \"0.55662596\",\n \"0.5547842\",\n \"0.5527746\",\n \"0.5524724\",\n \"0.55186945\",\n \"0.55131227\",\n \"0.55104303\",\n \"0.5499743\",\n \"0.54948914\",\n \"0.54884905\",\n \"0.5474858\",\n \"0.5471704\",\n \"0.5469055\",\n \"0.5456895\",\n \"0.54537666\",\n \"0.5452064\",\n \"0.5451102\",\n \"0.5450676\",\n \"0.5449012\",\n \"0.5447223\",\n \"0.5438074\",\n \"0.5428483\",\n \"0.54151535\",\n \"0.5403033\",\n \"0.5398406\",\n \"0.5398128\",\n \"0.5394609\",\n \"0.536109\",\n \"0.5347123\",\n \"0.5342833\",\n \"0.5341328\",\n \"0.5335529\",\n \"0.5332527\",\n \"0.53230923\",\n \"0.5321399\",\n \"0.5319868\",\n \"0.5319468\",\n \"0.53109235\",\n \"0.5305616\",\n \"0.5304103\",\n \"0.53008205\",\n \"0.5288531\",\n \"0.52793515\",\n \"0.52695495\",\n \"0.5259273\",\n \"0.52566683\",\n \"0.52361804\",\n \"0.5232623\",\n \"0.52255344\",\n \"0.5220549\",\n \"0.5212677\",\n \"0.52122873\",\n \"0.5209832\",\n \"0.52087295\",\n \"0.52083725\",\n \"0.52052844\",\n \"0.5204488\",\n \"0.5204031\",\n \"0.5189197\",\n \"0.5183786\",\n \"0.5183786\",\n \"0.5183786\",\n \"0.51787025\",\n \"0.51650965\",\n \"0.51585937\",\n \"0.51572984\",\n \"0.51492923\",\n \"0.51485425\",\n \"0.51477295\",\n \"0.5117564\",\n \"0.5110872\",\n \"0.5108907\",\n \"0.51074696\",\n \"0.51053226\",\n \"0.50954413\",\n \"0.5090461\",\n \"0.5079076\"\n]","isConversational":false},"document_score":{"kind":"string","value":"0.0"},"document_rank":{"kind":"string","value":"-1"}}},{"rowIdx":75381,"cells":{"query":{"kind":"string","value":"Assignment of users to queues"},"document":{"kind":"string","value":"def cli() -> None:\n pass # pragma: no cover"},"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 update_user(self, queue: SubnetQueue, *args):","def task_queue_users():\n users = firebase_admin.auth.list_users().iterate_all()\n for user in users:\n if user.disabled: # skip disabled users\n continue\n # add user task\n add_task(url_for(\"task_queue_podcasts\"), {\"user_uid\": user.uid})\n\n return OK_RESPONSE","def sync(self):\n for subscription in self.getSubscriptionList():\n #user_id = subscription.getZopeUser()\n #uf = self.getPortalObject().acl_users\n #user = uf.getUserById(user_id).__of__(uf)\n #newSecurityManager(None, user)\n subscription.activate(activity='SQLQueue',\n tag=subscription.getId(),\n priority=ACTIVITY_PRIORITY\n ).SubSync(subscription.getPath())","def list_command(ctx: click.Context, user_ids: Tuple[int], queue_ids: Tuple[int]) -> None:\n with RossumClient(context=ctx.obj) as rossum:\n queue_users = rossum.get_queues((USERS,), users=user_ids)\n\n user_queues: Dict[int, List[List[Optional[str]]]] = {}\n for queue in queue_users:\n if queue_ids and int(queue[\"id\"]) not in queue_ids:\n continue\n for user in queue[\"users\"]:\n user_id = int(user[\"id\"])\n if user_ids and user_id not in user_ids:\n continue\n\n if user_id not in user_queues:\n user_queues[user_id] = [[user[\"id\"], user[\"username\"], queue[\"id\"], queue[\"name\"]]]\n else:\n user_queues[user_id].append([None, None, queue[\"id\"], queue[\"name\"]])\n user_queues = dict(sorted(user_queues.items()))\n click.echo(\n tabulate(\n chain.from_iterable(user_queues.values()),\n headers=[\"id\", \"username\", \"queue id\", \"queue name\"],\n )\n )","def add_user_range(self, queue_list: VectorQueue):","def __init__(self):\n self.queues=[]","def process_queue(host_user_list, user_name, user_list):\n\n total_nodes = 0\n total_cores = 0\n disabled_cores = 0\n used_cores = 0\n free_cores = 0\n empty_nodes = 0\n disabled_nodes = 0\n \n node_strings= []\n\n for hostgroup in host_user_list:\n # Obtain totals for one hostgroup, and contine a summation\n tc, uc, tn, en, dc,dn, nl = process_host(hostgroup.replace('@',''))\n\n # Do not want to recount same nodes. This needs to be looked at for efficiency.\n nl_strings = [n.name for n in nl]\n\n if nl_strings[0] in node_strings:\n continue\n\n # Sum to get totals for whole queue which user has access to\n total_cores += tc \n used_cores += uc \n total_nodes += tn\n empty_nodes += en\n disabled_cores += dc\n disabled_nodes += dn\n node_strings= node_strings + nl\n\n # Send totals to be printed to screen\n print_host_info(total_cores, used_cores, total_nodes, empty_nodes, \"Long Queue\", disabled_cores, \n disabled_nodes)\n sys.exit(0)","def init_email_queue():\n g.setdefault('email_queue', [])","def _add_user_to_queue(self, user):\n now = datetime.now(tz=pytz.utc)\n HatQueue.create(user_id=user, start_time=now)\n return HatQueue.select().where(HatQueue.end_time.is_null(True), HatQueue.start_time < now).count()","def find_queue_usersets(queue, user_list):\n\n sq = subprocess.getoutput(\"qconf -sq {0}\".format(queue))\n \n #splicing the sq output up to the user_list point, we don't need the rest of the garbarge before that\n sq = sq[sq.find('user_lists') + 9 :sq.find('xuser')]\n sq = (((sq.replace('\\n', '')).replace(' ', '')).replace('\\\\', '')).replace('],', '')\n host_user_list = []\n sq = sq.split('[')\n hostg_list = []\n for line in sq:\n if line.find('@') != (-1):\n #host-groups in sq output have '@', so that's what we're looking for\n hostg_list.append(line)\n for line in hostg_list:\n for ul in user_list:\n if ul in line:\n host_user_list.append(line.split('=')[0])\n return host_user_list","def enqueue(self, obj, user, songs, length=None):\n item = _QueueItem(obj, user, songs, length)\n if not user in self.user_order:\n user_position = 0\n if not self.user_order:\n self.user_skip[user] = 0\n else:\n for u in self.user_order:\n if self.user_skip[u] == 0:\n break\n user_position += 1\n self.user_skip[user] = 1\n\n self.user_order.insert(user_position, user)\n self.user_queue[user] = []\n\n self.user_queue[user].append(item)\n self.obj_item[obj] = item\n self.nonempty.set()","def users(self, users):\n\n self._users = users","def users(self, users):\n\n self._users = users","def users(self, users):\n\n self._users = users","def __init__(self):\n self.queue1 = []\n self.queue2 = []","def __init__(self):\n self.queue1 = []\n self.queue2 = []","def __init__(self, queue, usercallback):\n self.queue = queue\n self.usercallback = usercallback","def create_queue(q_settings):\r\n db = get_db()\r\n cursor = db.cursor()\r\n cursor.execute(INSERT_QUEUE)\r\n q_settings['qid'] = cursor.lastrowid\r\n cursor.execute(INSERT_QUEUE_SETTINGS, qsettings_dict_to_db_tuple(q_settings))\r\n cursor.close()\r\n db.commit()\r\n permissions.add_permission_list(get_uids(q_settings['admins']), q_settings['qid'], permissions.ADMIN)\r\n if q_settings.has_key('managers'):\r\n permissions.add_permission_list(get_uids(q_settings['managers']), q_settings['qid'], permissions.MANAGER)\r\n if q_settings.has_key('blocked_users'):\r\n permissions.add_permission_list(get_uids(q_settings['blocked_users']), q_settings['qid'], permissions.BLOCKED_USER)\r\n return q_settings['qid']","def set_turn_queue(queue: Dict[EntityID, int]):\n store.turn_queue = queue","def setup_queues_and_bindings(self):\n self._channel.exchange_declare(self.setup_queue, exchange=self.exchange, passive=True)","def test_list_users(self, rabbitmq):\n assert rabbitmq.list_users() == [('user', ['administrator'])]\n rabbitmq.exec_rabbitmqctl('add_user', ['new_user', 'new_pass'])\n assert rabbitmq.list_users() == [\n ('new_user', ['']),\n ('user', ['administrator']),\n ]","def _add_users_to_role(self, users, rolename):\n role = Role.objects.get(name=rolename, course_id=self.course.id)\n for user in users:\n role.users.add(user)","def change_user_mailing_lists(sender, instance, action, reverse, model, pk_set, **kwargs):\n\tmail = instance.associated_user.email\n\tusername = instance.associated_user.first_name+\" \"+instance.associated_user.last_name\n\t#if groups are going to be added\n\tif action == \"post_add\":\n\t\tgroups = instance.groups_as_string\n\t\tgroups = groups.split(\", \")\n\t\t#put all added groups_as_string\n\t\tfor group in groups:\n\t\t \trequests.post(\"https://api.mailgun.net/v3/lists/{}@arenbergorkest.be/members\".format(group),\n\t\t auth=('api', settings.MAILGUN_API_KEY),\n\t\t data={'subscribed': True,\n\t\t \t 'name':username,\n\t\t 'address': mail})\n\t#if groups are going to be removed\n\tif action == \"pre_clear\": \n\t\t#put the removed groups from a set in a list\n\t\tprevious = UserProfile.objects.get(pk=instance.pk)\n\t\tgrplst = previous.groups_as_string.split(\", \")\n\t\t#loop over list\n\t\tfor grp in grplst:\n\t\t\trequests.delete(\"https://api.mailgun.net/v3/lists/{}@arenbergorkest.be/members/{}\".format(grp,mail),auth=('api', settings.MAILGUN_API_KEY))","def new_queue(self, params, maxUsers=1):\n self.spotify.reset_queue()\n\n #Extract parameters\n mood = params[0]\n users = []\n for i in range(maxUsers):\n if len(params[i + 1]) > 0:\n users.append(params[i + 1])\n\n #Add default host if only one guest is present\n # if len(users) == 1:\n # users.append('Paolo')\n\n #Lists to load\n names = []\n for n in self.listIDs.keys():\n for u in users:\n if len(mood) > 0:\n if u + ':' + mood in n:\n names.append(n)\n else:\n if 'top:' + u in n:\n names.append(n)\n\n\n self.add_playlist(names)","def _get_queues(self):\n return self.__queues","def _get_queues(self):\n return self.__queues","def _get_queues(self):\n return self.__queues","def _get_queues(self):\n return self.__queues","def _get_queues(self):\n return self.__queues","def _get_queues(self):\n return self.__queues","def _get_queues(self):\n return self.__queues","def _get_queues(self):\n return self.__queues","def _get_queues(self):\n return self.__queues","def modify_queue_settings(q_settings):\r\n db = get_db()\r\n db.execute(UPDATE_QUEUE_SETTINGS, qsettings_dict_to_db_tuple_modify(q_settings))\r\n db.commit()\r\n permissions.update_permissions(q_settings['qid'],\r\n get_uids(q_settings['admins']),\r\n get_uids(q_settings['managers']) if q_settings.has_key('managers') else None,\r\n get_uids(q_settings['blocked_users']) if q_settings.has_key('blocked_users') else None)","def assign_vote(from_userid, to_userid, group):","def requires_matching_queues(self, requires_matching_queues):\n \n self._requires_matching_queues = requires_matching_queues","def setup(self):\n logging.info(\"ReqMgr setup ...\")\n def doQuery(urlQuery):\n logging.info(\"Query: '%s' ...\" % urlQuery)\n r = None\n try:\n r = self.restSender.put(urlQuery)\n except Exception as ex:\n print \"exception\"\n print str(ex) \n print \"response:\", r\n queries = [\"user/zmaxa?email=testinguser@my.com\",\n \"group/DATAOPS\",\n \"group/DATAOPS/zmaxa\",\n urllib.quote(\"team/\" + TEAM_NAME),\n \"version/%s\" % \"CMSSW_3_5_8\"]\n for q in queries:\n doQuery(q)\n logging.info(\"ReqMgr setup finished, listing known users ...\")\n q = \"user/\"\n r = self.restSender.get(q)\n print r","def assign_task(user_name, task_name, work_server_ip):\r\n\r\n database_handler.update_records(\"current_tasks\",\r\n {\"server_ip\": work_server_ip, \"Task_status\": TaskStatusNames.in_progress.value},\r\n condition=\"Task_name=$? and user_name=$?\", code_args=[task_name, user_name])","def __init__(self): \n self.queue = []","def __init__(self, users, messages):\n self.users = users\n self.messages = messages\n # addd dict with each user as key and there corresponidn gmessages","def _mp_learn_user_mix(queue, ids, args):\n alpha, multinomials, val_mat, num_em_iter, tol = args\n for i in ids:\n # The way the global em is implemented, allows me to simply call it with the i_val_data and it will only\n # compute the \\pi as a function of that user.\n i_val_data = convert_sparse_to_coo(val_mat[i])\n if len(i_val_data) > 0:\n\n # The learning method treats the multinomials as matrices. So I have to wrap it in an array.\n # All the rows in i_val_data are going to be 0 because I'm converting a single row_vector.\n user_multinomials = []\n for m in multinomials:\n if m.shape[0] == 1:\n user_multinomials.append(m)\n else:\n user_multinomials.append(m[i])\n i_pi, i_ll = _learn_global_mixture_weights(alpha, user_multinomials, i_val_data, num_em_iter, tol)\n\n queue.put((i, i_pi, i_ll))\n\n queue.put('$')","def queue_emails(spoofs, message, count):\n\t\tqueues = []\n\t\tnumber_of_spoofs = len(spoofs)\n\t\tmessages_per_queue = count // number_of_spoofs\n\t\textra_to_distribute = count - (messages_per_queue * number_of_spoofs)\n\t\tbatch = Batch(size=count, complete=0)\n\t\tbatch.save()\n\t\tpk = batch.pk\n\n\t\t# going deep into each queue\n\t\tfor x in range(number_of_spoofs):\n\n\t\t\tspoof = spoofs[x]\n\t\t\tmessage['From'] = spoof.username\n\t\t\tqueue = Queue(spoof.username, connection=Redis())\n\t\t\tqueues.append(queue)\n\n\t\t\tfor y in range(messages_per_queue):\n\t\t\t\tqueue.enqueue_call(func=send, args=spoof.task_arguments + (message, pk))\n\n\t\t# panning across each queue\n\t\tfor x in range(extra_to_distribute):\n\t\t\tspoof = spoofs[x]\n\t\t\tmessage['From'] = spoof.username\n\t\t\tqueue = queues[x]\n\t\t\tqueue.enqueue_call(func=send ,args=(spoof.task_arguments + (message, pk)))\n\n\t\treturn pk","def _add_to_whisper_queue(self, user, message):\n whisper_tuple = (user, message)\n self.whisper_message_queue.appendleft(whisper_tuple)","def process_user(user_name):\n \n try:\n user_pwd = getpwnam(user_name)\n except KeyError:\n print('Error: User {0} is not recognized.'.format(user_name))\n sys.exit(25)\n \n qstat = subprocess.getoutput(\"qstat -f\").split('-'.center(81, '-')) #81 -'s\n \n node_list = []\n pending_jobs = ''\n pending_search = '#'.center(79, '#') #denotes pending jobs in qstat 79 #'s\n #Weeding out nonessential nodes\n for node in qstat:\n if user_name in (node.split()):\n if pending_search in node: #Taking pending jobs out\n if \".crc.nd.edu\" in node:\n # This means its the last node. We must only accept up tp the pending jobs ONLY. Below we are doing that and taking out an\n # Additional newline by stripping it but adding one back in to keep formatting correct. (there were two instead of one).\n tempNode = (node[:node.find(pending_search)].rstrip())+'\\n'\n if user_name in tempNode:\n node_list.append(tempNode)\n pending_jobs += (node[node.find(pending_search):]) #reaping pending jobs\n else:\n node_list.append(node)\n \n final_list = []\n \n numU_jobs = 0 # Will hold the number of jobs attributed to the specified user\n numU_cores = 0 # The number of cores the user is currently using. Starts at 0 and counts up as jobs encountered.\n \n for host in node_list:\n # Grabbing the node's name in qstat and making a Node() instance of it\n temp_node = Node((host.split()[0]))\n host_used_cores = host.split()[2].split('/')[1]\n host_total_cores = host.split()[2].split('/')[2]\n # If within the first line of the node there is a 'd' at the end, disable it\n if len(host.split('\\n')[0]) == 6 and host.split()[5] == 'd':\n temp_node.set_disabled_switch(True)\n disabled_cores += int(host_total_cores)\n else: \n temp_node.set_disabled_switch(False)\n \n temp_node.set_cores(host_total_cores, host_used_cores)\n # In qstat -F, qf:min_cpu . . . . is the last item before the jobs are listed, \n # 28 is how many char's that string is (don't want it)\n node_stat= host[host.find('qf:min_cpu_interval=00:05:00') + 28\\\n :host.find('\\n---------------------------------------------------------------------------------\\n')]\n \"\"\"Possibly do a host.split('\\n') and join the rest of 2 - end\"\"\"\n\n # There is always an extra '\\n' in here, so subtract 1 to get rid of it\n num_jobs = len(node_stat.split('\\n')) -1\n # If there are any jobs, parse them and gather info\n if num_jobs > 0:\n # Python is non-inclusive for the right operand, and we want to \n # skip another extra '\\n' so start at 1, and want to go num_jobs\n for i in range(1, num_jobs + 1):\n info = node_stat.split('\\n')[i].split()\n temp_job = Job(info[2], info[3], info[7])\n temp_job.set_id(info[0])\n temp_job.set_priority(info[1])\n temp_node.add_job(temp_job)\n if info[3] == user_name:\n numU_jobs += 1 #info[3] is the user-name of job, if == spec. user, increment user_jobs\n numU_cores += int(info[7]) # info[7] is the number of cores occupied by the user's job\n \n final_list.append(temp_node)\n \n pending_list = []\n if len(pending_jobs): #As long as the user has pending jobs T if len != 0\n p_lines = pending_jobs.split('\\n')\n pending_list.append((p_lines[0] + '\\n' + p_lines[1] + '\\n' + p_lines[2] + '\\n'))\n for i in range(3, len(p_lines)):\n if p_lines[i].find(user_name) != (-1):\n pending_list.append(p_lines[i])\n \n if len(sys.argv) == 4:\n if sys.argv[3] == '--details':\n print_detailed_user(final_list, pending_list, user_name, numU_jobs, numU_cores)\n else:\n print('Error: Arg syntax error with: ' + sys.argv[3])\n show_usage(23)\n else:\n print_short_user(final_list, pending_list, user_name, numU_jobs, numU_cores)","def _manager_main(self, queue):\r\n for task in self._task_generator():\r\n queue.put(task)","def _get_njobs_in_queue(self, username):","def __init__(self):\r\n self.queue = []","def __init__(self):\r\n self.queue = []","def __init__(self, user, queueTwoMin, queueTenMin, queueHour, queueAlerts, queueTermination, testDic = None):\n # Start Time, keep track of the elapsed time\n self.originalTime = time.time()\n # User that uses the monitoring app, must exist !\n self.user = user\n\n # Queue to transmit all data\n self.queueTwoMin = queueTwoMin\n self.queueTenMin = queueTenMin\n self.queueHour = queueHour\n self.queueAlerts = queueAlerts\n\n # Queue for termination\n self.queueTermination = queueTermination\n\n # Alert Storage, to check whether raised alert are to be sent\n self.alertsDic = {}\n if testDic:\n self.alertsDic = testDic\n self.mailer = mailSender.MailSender(mailSender.mailrecipient)\n\n # Start monitoring\n self.monitorAll()","def add_to_queue(self, items):\n\n for i in items:\n self.r.rpush(self.joblist, i)","def store(self, queue: Queue):\n\n validate = RegisterValidator(self.request).register()\n if validate.check():\n validate.check_exists()\n\n if not validate.check():\n self.request.session.flash('validation', json.dumps(validate.errors()))\n return self.request.redirect_to('register')\n\n # register the user\n password = bcrypt_password(self.request.input('password'))\n\n auth.AUTH['model'].create(\n name=self.request.input('username'),\n password=password,\n email=self.request.input('email'),\n )\n\n # login the user\n # redirect to the homepage\n if Auth(self.request).login(self.request.input(auth.AUTH['model'].__auth__), self.request.input('password')):\n queue.push(WelcomeEmailJob, args=[self.request.input('email')])\n return self.request.redirect('/home')\n\n return self.request.redirect('/register')","def _insert_into_player_queue_spreadsheet(self, username, times_played, player_queue):\n spreadsheet_name, _ = self.spreadsheets['player_queue']\n gc = gspread.authorize(self.credentials)\n sheet = gc.open(spreadsheet_name)\n ws = sheet.worksheet('Player Queue')\n\n records = ws.get_all_records()\n records = records[1:] # We don't want the blank space\n for i, tup in enumerate(player_queue):\n try:\n if records[i]['User'] != tup[0]:\n ws.insert_row([username, times_played], index=i+3)\n break\n except IndexError:\n ws.insert_row([username, times_played], index=i+3)","def __init__(self):\n self.queue = []","def __init__(self):\n self.queue = []","def __init__(self):\n self.queue = []","def __init__(self):\n self.queue = []","def __init__(self):\n self.queue = []","async def shush(self, ctx):\n author = ctx.message.author\n channel = author.voice.channel\n members = channel.members\n for member in members:\n user = ctx.guild.get_member(member.id)\n await user.edit(mute=True, deafen=True)\n\n embed = await embeds.generate_embed(ctx, author, members,\n description=\":white_check_mark: Successfully shushed the following users:\",\n title=channel.name)\n await ctx.send(embed=embed)","def assigned_user(self, assigned_user):\n self._assigned_user = assigned_user","def test_ipcrm_queues():\n IPCComm.ipcrm_queues()\n nt.assert_equal(len(IPCComm.ipc_queues()), 0)\n mq = IPCComm.get_queue()\n nt.assert_equal(len(IPCComm.ipc_queues()), 1)\n IPCComm.ipcrm_queues(str(mq.key))\n nt.assert_equal(len(IPCComm.ipc_queues()), 0)","def run(self):\n\t\tmessage = 'user thread {0} started'.format(self.ident)\n\t\tlogger.queue.put({'type':'notice', 'source':'user', 'message':message})\n\t\tstack.add('users',self.ident)\n\n\t\t# Run our main loop to process messages in the message queue\n\t\twhile self.more:\n\t\t\t# Check if there is a packet to process\n\t\t\ttry:\n\t\t\t\tdata\t\t= stack['users'][self.ident].get(self.block,self.wait)\n\t\t\texcept queue.Empty:\n\t\t\t\tpass\n\t\t\telse:\n\t\t\t\tself.process(data)\n\t\t\tfinally:\n\t\t\t\t# Reset our queue parser to its default values\n\t\t\t\tself.block\t= False\n\t\t\t\tself.wait\t= 1\n\n\t\t# When the run loop is broken, clean up any queues and data we have\n\t\tstack.remove('users',self.ident)","def activate_user_setup():\n\n projects_list = app.load_config(current_user.get_id())\n active_projects_list = []\n for project in projects_list:\n if project['_active']:\n active_projects_list.append(project)\n app.active_users[current_user.get_id()] = active_projects_list\n try:\n requests.post(\"http://localhost:9090\",\n json={\"active_users\": app.active_users})\n except:\n logger.info('Unable to send updated list of active users.')\n return Response('500')\n logger.info('Activated messages for user '\n + current_user.get_id() + '.')\n return Response('200')","def __init__(self, users: List[dict], redis_url=None, lock_name='user-clients'):\n import redis\n\n for user in users:\n user['count'] = 0\n\n self._key = 'bdc_collection_builder:users'\n\n if redis_url is None:\n redis_url = current_app.config.get('REDIS_URL', os.getenv('REDIS_URL'))\n\n if redis_url is None:\n raise RuntimeError('Parallel support requires Redis instance. Make sure to export REDIS_URL.')\n\n self._cache = redis.Redis.from_url(redis_url)\n self._lock = self._cache.lock(lock_name)\n self.users = users","def __init__(self):\n self._queue_items = []","def __init__(self):\n self.q1 = queue()\n self.q2 = queue()","def getResponsibleUsers():","def myself(self, mess, args):\n user = self.get_sender_username(mess)\n if user in self.users:\n self.message_queue.append('_%s %s_' % (self.users[user], args))\n self.log.info( '%s says %s in third person.' % (user, args))","def _put(self, item, queue):","def add_user(\n self, imsi: str, sqn_slice_max: dict, backup_network_ids: dict\n ) -> None:\n self.config[\"users\"][imsi] = {\n \"k\": \"465B5CE8B199B49FAA5F0A2EE238A6BC\",\n \"opc\": \"E8ED289DEBA952E4283B54E88E6183CA\",\n \"sqn_slice_max\": copy.deepcopy(sqn_slice_max),\n \"backup_network_ids\": copy.deepcopy(backup_network_ids),\n }","def queue(users, label, extra_context=None, on_site=True):\r\n if extra_context is None:\r\n extra_context = {}\r\n if isinstance(users, QuerySet):\r\n users = [row[\"pk\"] for row in users.values(\"pk\")]\r\n else:\r\n users = [user.pk for user in users]\r\n notices = []\r\n for user in users:\r\n notices.append((user, label, extra_context, on_site))\r\n NoticeQueueBatch(pickled_data=pickle.dumps(notices).encode(\"base64\")).save()","def setUp(self, connections=2):\n self._threads = [Consumer(messages=self.messages) for i in xrange(0, connections)]\n\n map(lambda tq: tq[0].add_queue(QUEUE_NAME.format(number=tq[1])),\n izip(cycle(self._threads), xrange(0, self.queues)))","def assign_passengers(self):\n\n # Update the state of the parallel server after every assignment.\n self.parallel_server.update_state()\n\n # While the assignment agent's queue is not empty and there is space\n # to assign passengers in the parallel block...\n while self.parallel_server.has_space_in_a_server_queue is True and \\\n len(self.queue) > 0:\n\n # Pop the first passenger in line and assign to the shortest queue.\n tmp = self.queue.popleft()\n self.parallel_server.min_queue.queue.append(tmp)\n\n # Update the state of the parallel server after every assignment.\n self.parallel_server.update_state()","def assign(self, available_workers):\n \n status = self.getStatus()\n\n assert len(available_workers) == 1\n worker = available_workers[0]\n assignment = {}\n\n w_id = str(worker.id)\n task_id = self.task_id\n\n #tracks \n worker_assignments_var = redis_get_worker_assignments_var(task_id, w_id)\n\n print \"WORKER ID:\", w_id\n print \"STATUS:\", status\n print \"ASSIGNMENTS FOR WORKER SO FAR:\", app.redis.smembers(worker_assignments_var)\n\n\n # sort questions by pomdp expected reward...\n # XXX this isn't quite what we want...\n # want to sort by value of getting another label\n # so we don't have all workers getting assigned to the same question\n unfinished_unsorted_qs = [(q,v) for (q,v) in status.iteritems() if v['best_action_str'] == 'create-another-job']\n # NOTE REVERSE ORDER\n sorted_qs = sorted(unfinished_unsorted_qs, key=lambda x:x[1]['best_expected_reward'], reverse=True)\n print \"sorted_qs\", sorted_qs\n# print \"worker %s has done the following questions\" % w_id\n# for (q_id,er) in sorted_qs:\n# if app.redis.sismember(worker_assignments_var, q_id):\n# print \"+\", q_id\n# else:\n# print \"-\", q_id\n\n for idx in range(len(sorted_qs)):\n q_id,expected_reward = sorted_qs[idx]\n\n if not app.redis.sismember(worker_assignments_var, q_id):\n assignment[w_id] = q_id\n print \"assignment=\", assignment\n app.redis.sadd(worker_assignments_var, q_id)\n return assignment\n\n #if here no assignment was made to our worker!\n assert len(assignment) == 0\n print \"no assignment made yet\"\n\n #NOTE POMDP doesn't think there are any questions available to the worker \n #that need another label, but let's give them an assignment anyway\n #Pick question where submitting would have worst expected reward \n # (implying it may benefit from another label)\n finished_qs = [(q,v) for (q,v) in status.iteritems() if v['best_action_str'] != 'create-another-job']\n sorted_finished_qs = sorted(finished_qs, key=lambda x:x[1]['best_expected_reward']) # no reverse\n for idx in range(len(sorted_finished_qs)):\n q_id,expected_reward = sorted_finished_qs[idx]\n\n if not app.redis.sismember(worker_assignments_var, q_id):\n assignment[w_id] = q_id\n print \"gave worker a finished q assignment=\", assignment\n app.redis.sadd(worker_assignments_var, q_id)\n return assignment\n\n return assignment","def __init__(self):\n self.subscribers = {}\n self.followers = {}\n self.nackables = {}\n self.threads = []","def send_msg(self, my_queue, my_msg):","def invite_site_users(users):\n #group(run_cron.s(item) for item in sites).delay()\n pass","def set_user(self, args):\n permissions = \"\"\n\n permissions = ' '.join(args['<permission>'])\n\n config = self.config\n config[args['<mask>']] = permissions\n self.bot.db[self.key] = config","def admin_message_queue(self, user, nick, channel, rest):\n message = \"Error loading messages\"\n with self.getDbSession() as db_session:\n count = db_session.query(func.count(Message.id)).scalar()\n message = \"%d messages pending delivery\" % count\n self.reply(channel, nick, message)","def _setup_tubes(self):\n chan = self.channel\n inp = self.config[self.MODULE_NAME]['amqp']['in']\n out = self.config[self.MODULE_NAME]['amqp']['out']\n if inp['exchange']:\n log.info('generating Input Queue'+ str(inp))\n chan.exchange_declare(**inp)\n self.qname = chan.queue_declare(exclusive=True).queue\n chan.queue_bind(exchange=inp['exchange'],queue=self.qname)\n self.consume = lambda cb : chan.basic_consume(cb,queue=self.qname,no_ack=True)\n self.start_loop = lambda : pika.asyncore_loop()\n\n if out['exchange']:\n log.info('generating Output Exchange'+ str(out))\n chan.exchange_declare(**out)\n self.publish = lambda msg: self.channel.basic_publish(exchange=out['exchange'],routing_key='',body=msg)","def set_items(self) -> None:\n if not self.__repeat:\n self._username: str = input(\"Introduzca el nombre del usuario: \")\n self._ordered_items = self.get_rights()[0]\n if not self._ordered_items:\n if self.__option == 1:\n self.print_error(\"El usuario ya tiene todos los permisos.\")\n return\n self.print_error(\"El usuario no tiene ningún permiso.\")\n return","def sync(self):\n\t\tmanagers.storage.write_object_async(VDOM_CONFIG[\"USER-MANAGER-STORAGE-RECORD\"], self.users)","def _update(self, data):\n if len(data) > 0:\n for q in self._queues.values():\n q.put(data)","def queue_on_project(self, queue_for_bfs, list_tasks_project):\n if not len(queue_for_bfs):\n return\n else:\n task = queue_for_bfs.popleft()\n for key_sub in task.subtasks:\n sub = self.get_task(key_sub)\n list_tasks_project.append(sub)\n queue_for_bfs.append(sub)\n self.save_task(sub)\n\n self.queue_on_project(queue_for_bfs, list_tasks_project)","def assigned_by_user(self, assigned_by_user):\n\n self._assigned_by_user = assigned_by_user","def setup_queues():\n sqs = boto.connect_sqs()\n sqs.create_queue('mls_parse_requests')\n sqs.create_queue('mls_fetcher')","def queueStatusAll():","def test_resource_user_resource_set_user_groups_for_user_put(self):\n pass","def users_items_collector(self, user_id, values):\r\n pass","def send_assignee_emails(self):\n\n assignees = list(set([obj.assignee for obj in self.stalled_nf_issues])) # Assignees from New Features\n assignees.extend(list(set([obj.assignee for obj in self.stalled_st_issues]))) # Add assignees from Sub-tasks\n recipients = self.config.get(\"recipients\", \"emails\").split(\"\\n\") # [recipients] section in .ini file\n\n for assignee in assignees:\n assignee_issues = [] # List of IssueClass objects\n # Get all stalled New feature issues for this assignee\n for item in self.stalled_nf_issues + self.stalled_st_issues:\n if item.assignee == assignee:\n# if item.assignee == \"ashih\":\n assignee_issues.append(item)\n assignee_email = item.assignee_email\n \n if len(assignee_issues):\n html_table = '<table style=\"font-size:12px\">'\n html_table += self.make_time_in_status_rows(assignee_issues)\n html_table += '</table>' # Closing table tag\n #recipients.append(assignee_email)\n print \"Sending email to: %s\" % recipients\n self.send_email(recipients, html_table, assignee)","def enqueue(tup):","def test01_assigned_users(self):\n print_ln('test_assigned_users')\n \n try:\n rList = review.find_roles(Role(name='py-role*'))\n for rle in rList: \n print_ln(\"Assigned users role=\" + rle.name)\n uList = review.assigned_users(rle)\n for user in uList: \n print_ln(\"Assigned user=\" + user, 1)\n except Exception as e:\n self.fail('test_assigned_users failed, exception=' + e.msg)","def set_permissions(queue_url, label, permissionlist, accountidlist):\n client = boto3.client('sqs')\n try:\n client.add_permission(QueueUrl=queue_url, Label=label,\n AWSAccountIds=accountidlist, Actions=permissionlist)\n except ClientError as e:\n if e.response['Error']['Code'] == 'InvalidParameterValue':\n raise AlreadyExistsException(e.response['Error']['Message'])\n raise","def assigned_to_changed(self, ar):\n # self.add_change_watcher(self.assigned_to)\n\n if (self.assigned_to is not None and\n self.assigned_to != ar.user and\n dd.is_installed('notify')):\n ctx = dict(user=ar.user, what=ar.obj2memo(self))\n def msg(user, mm):\n subject = _(\"{user} has assigned you to ticket: {what}\").format(**ctx)\n return (subject , tostring(E.span(subject)))\n\n mt = rt.models.notify.MessageTypes.tickets\n\n rt.models.notify.Message.emit_notification(\n ar, self, mt, msg,\n [(self.assigned_to, self.assigned_to.mail_mode)]\n )","def get_user_message_queue(self,user_id,order=0):\n key = key_gen(user_id)\n result = self.redis.lrange(key,0,-1)\n if result != []:\n for index, item in enumerate(result):\n item = eval(item)\n result[index] = self.get_message_detail(item['message_id'])\n if order == 1:\n result.reverse()\n return result","def setusers(self, users=None):\n if users:\n self.users = users\n return\n import jsb.lib.users as u\n if not u.users: u.users_boot()\n self.users = u.users","def queue_enqueue(queue, targets):\n\n enqueued = []\n for target in map(lambda x: x.strip(), targets):\n if target != '':\n enqueued.append({'target': target, 'queue_id': queue.id})\n if enqueued:\n db.session.execute(Target.__table__.insert().values(enqueued))\n db.session.commit()","def _subscribe_users(self):\n group = self.env.ref('odoo_magento2_ept.group_connector_manager')\n if not group:\n return\n companies = self.mapped('company_id')\n domain = [('groups_id', '=', group.id)]\n if companies:\n domain.append(('company_id', 'child_of', companies.ids))\n users = self.env['res.users'].search(domain)\n self.message_subscribe_users(user_ids=users.ids)","def enqueue(self,value):\n pass","def find_host_groups(user_name, detail_switch, queue_switch):\n \n # Ensure user is a crc user (check /etc/passwd)\n try:\n user_pwd = getpwnam(user_name)\n except KeyError:\n print(\"Error: User {0} is not recognized.\".format(user_name))\n sys.exit(25)\n\n all_user_lists = subprocess.getoutput(\"qconf -sul\").split('\\n')\n \n user_list = []\n #Going through each element of all_user_lists and finding detailed output for that element\n for ul in all_user_lists:\n u_list = subprocess.getoutput(\"qconf -su \" + ul)\n if u_list.find(user_name) != (-1):\n user_list.append(ul)\n \n queue_list= []\n if queue_switch:\n queue_list = ['long']\n else:\n queue_list = ['long', 'hpc', 'gpu', 'gpu-debug']\n \n host_user_list = []\n\n # Run through each queue\n for searchQ in queue_list:\n host_user_list = host_user_list + find_queue_usersets(searchQ, user_list)\n\n # Everyone has access to general access, adding in case of queue_switch\n host_user_list.append('@' + GENERAL_ACCESS_QUEUE_HOSTGROUP)\n\n if queue_switch:\n process_queue(host_user_list, user_name, user_list)\n\n # Everyone has access to debug\n host_user_list.append('@' + DEBUG_QUEUE_HOSTGROUP)\n \n if detail_switch:\n print_duser_host(host_user_list, user_name, user_list)\n else:\n print_user_host(host_user_list, user_name, user_list)\n sys.exit()","def _migrate_users(correct_course_key, role, lower_org):\r\n for user in orm['auth.user'].objects.filter(groups=group).all():\r\n entry = orm['student.courseaccessrole'](\r\n role=role, user=user,\r\n org=correct_course_key.org, course_id=correct_course_key\r\n )\r\n try:\r\n entry.save()\r\n except IntegrityError:\r\n # already stored\r\n pass\r\n orgs[lower_org] = correct_course_key.org","def add_users(self, *users):\r\n pass"],"string":"[\n \"def update_user(self, queue: SubnetQueue, *args):\",\n \"def task_queue_users():\\n users = firebase_admin.auth.list_users().iterate_all()\\n for user in users:\\n if user.disabled: # skip disabled users\\n continue\\n # add user task\\n add_task(url_for(\\\"task_queue_podcasts\\\"), {\\\"user_uid\\\": user.uid})\\n\\n return OK_RESPONSE\",\n \"def sync(self):\\n for subscription in self.getSubscriptionList():\\n #user_id = subscription.getZopeUser()\\n #uf = self.getPortalObject().acl_users\\n #user = uf.getUserById(user_id).__of__(uf)\\n #newSecurityManager(None, user)\\n subscription.activate(activity='SQLQueue',\\n tag=subscription.getId(),\\n priority=ACTIVITY_PRIORITY\\n ).SubSync(subscription.getPath())\",\n \"def list_command(ctx: click.Context, user_ids: Tuple[int], queue_ids: Tuple[int]) -> None:\\n with RossumClient(context=ctx.obj) as rossum:\\n queue_users = rossum.get_queues((USERS,), users=user_ids)\\n\\n user_queues: Dict[int, List[List[Optional[str]]]] = {}\\n for queue in queue_users:\\n if queue_ids and int(queue[\\\"id\\\"]) not in queue_ids:\\n continue\\n for user in queue[\\\"users\\\"]:\\n user_id = int(user[\\\"id\\\"])\\n if user_ids and user_id not in user_ids:\\n continue\\n\\n if user_id not in user_queues:\\n user_queues[user_id] = [[user[\\\"id\\\"], user[\\\"username\\\"], queue[\\\"id\\\"], queue[\\\"name\\\"]]]\\n else:\\n user_queues[user_id].append([None, None, queue[\\\"id\\\"], queue[\\\"name\\\"]])\\n user_queues = dict(sorted(user_queues.items()))\\n click.echo(\\n tabulate(\\n chain.from_iterable(user_queues.values()),\\n headers=[\\\"id\\\", \\\"username\\\", \\\"queue id\\\", \\\"queue name\\\"],\\n )\\n )\",\n \"def add_user_range(self, queue_list: VectorQueue):\",\n \"def __init__(self):\\n self.queues=[]\",\n \"def process_queue(host_user_list, user_name, user_list):\\n\\n total_nodes = 0\\n total_cores = 0\\n disabled_cores = 0\\n used_cores = 0\\n free_cores = 0\\n empty_nodes = 0\\n disabled_nodes = 0\\n \\n node_strings= []\\n\\n for hostgroup in host_user_list:\\n # Obtain totals for one hostgroup, and contine a summation\\n tc, uc, tn, en, dc,dn, nl = process_host(hostgroup.replace('@',''))\\n\\n # Do not want to recount same nodes. This needs to be looked at for efficiency.\\n nl_strings = [n.name for n in nl]\\n\\n if nl_strings[0] in node_strings:\\n continue\\n\\n # Sum to get totals for whole queue which user has access to\\n total_cores += tc \\n used_cores += uc \\n total_nodes += tn\\n empty_nodes += en\\n disabled_cores += dc\\n disabled_nodes += dn\\n node_strings= node_strings + nl\\n\\n # Send totals to be printed to screen\\n print_host_info(total_cores, used_cores, total_nodes, empty_nodes, \\\"Long Queue\\\", disabled_cores, \\n disabled_nodes)\\n sys.exit(0)\",\n \"def init_email_queue():\\n g.setdefault('email_queue', [])\",\n \"def _add_user_to_queue(self, user):\\n now = datetime.now(tz=pytz.utc)\\n HatQueue.create(user_id=user, start_time=now)\\n return HatQueue.select().where(HatQueue.end_time.is_null(True), HatQueue.start_time < now).count()\",\n \"def find_queue_usersets(queue, user_list):\\n\\n sq = subprocess.getoutput(\\\"qconf -sq {0}\\\".format(queue))\\n \\n #splicing the sq output up to the user_list point, we don't need the rest of the garbarge before that\\n sq = sq[sq.find('user_lists') + 9 :sq.find('xuser')]\\n sq = (((sq.replace('\\\\n', '')).replace(' ', '')).replace('\\\\\\\\', '')).replace('],', '')\\n host_user_list = []\\n sq = sq.split('[')\\n hostg_list = []\\n for line in sq:\\n if line.find('@') != (-1):\\n #host-groups in sq output have '@', so that's what we're looking for\\n hostg_list.append(line)\\n for line in hostg_list:\\n for ul in user_list:\\n if ul in line:\\n host_user_list.append(line.split('=')[0])\\n return host_user_list\",\n \"def enqueue(self, obj, user, songs, length=None):\\n item = _QueueItem(obj, user, songs, length)\\n if not user in self.user_order:\\n user_position = 0\\n if not self.user_order:\\n self.user_skip[user] = 0\\n else:\\n for u in self.user_order:\\n if self.user_skip[u] == 0:\\n break\\n user_position += 1\\n self.user_skip[user] = 1\\n\\n self.user_order.insert(user_position, user)\\n self.user_queue[user] = []\\n\\n self.user_queue[user].append(item)\\n self.obj_item[obj] = item\\n self.nonempty.set()\",\n \"def users(self, users):\\n\\n self._users = users\",\n \"def users(self, users):\\n\\n self._users = users\",\n \"def users(self, users):\\n\\n self._users = users\",\n \"def __init__(self):\\n self.queue1 = []\\n self.queue2 = []\",\n \"def __init__(self):\\n self.queue1 = []\\n self.queue2 = []\",\n \"def __init__(self, queue, usercallback):\\n self.queue = queue\\n self.usercallback = usercallback\",\n \"def create_queue(q_settings):\\r\\n db = get_db()\\r\\n cursor = db.cursor()\\r\\n cursor.execute(INSERT_QUEUE)\\r\\n q_settings['qid'] = cursor.lastrowid\\r\\n cursor.execute(INSERT_QUEUE_SETTINGS, qsettings_dict_to_db_tuple(q_settings))\\r\\n cursor.close()\\r\\n db.commit()\\r\\n permissions.add_permission_list(get_uids(q_settings['admins']), q_settings['qid'], permissions.ADMIN)\\r\\n if q_settings.has_key('managers'):\\r\\n permissions.add_permission_list(get_uids(q_settings['managers']), q_settings['qid'], permissions.MANAGER)\\r\\n if q_settings.has_key('blocked_users'):\\r\\n permissions.add_permission_list(get_uids(q_settings['blocked_users']), q_settings['qid'], permissions.BLOCKED_USER)\\r\\n return q_settings['qid']\",\n \"def set_turn_queue(queue: Dict[EntityID, int]):\\n store.turn_queue = queue\",\n \"def setup_queues_and_bindings(self):\\n self._channel.exchange_declare(self.setup_queue, exchange=self.exchange, passive=True)\",\n \"def test_list_users(self, rabbitmq):\\n assert rabbitmq.list_users() == [('user', ['administrator'])]\\n rabbitmq.exec_rabbitmqctl('add_user', ['new_user', 'new_pass'])\\n assert rabbitmq.list_users() == [\\n ('new_user', ['']),\\n ('user', ['administrator']),\\n ]\",\n \"def _add_users_to_role(self, users, rolename):\\n role = Role.objects.get(name=rolename, course_id=self.course.id)\\n for user in users:\\n role.users.add(user)\",\n \"def change_user_mailing_lists(sender, instance, action, reverse, model, pk_set, **kwargs):\\n\\tmail = instance.associated_user.email\\n\\tusername = instance.associated_user.first_name+\\\" \\\"+instance.associated_user.last_name\\n\\t#if groups are going to be added\\n\\tif action == \\\"post_add\\\":\\n\\t\\tgroups = instance.groups_as_string\\n\\t\\tgroups = groups.split(\\\", \\\")\\n\\t\\t#put all added groups_as_string\\n\\t\\tfor group in groups:\\n\\t\\t \\trequests.post(\\\"https://api.mailgun.net/v3/lists/{}@arenbergorkest.be/members\\\".format(group),\\n\\t\\t auth=('api', settings.MAILGUN_API_KEY),\\n\\t\\t data={'subscribed': True,\\n\\t\\t \\t 'name':username,\\n\\t\\t 'address': mail})\\n\\t#if groups are going to be removed\\n\\tif action == \\\"pre_clear\\\": \\n\\t\\t#put the removed groups from a set in a list\\n\\t\\tprevious = UserProfile.objects.get(pk=instance.pk)\\n\\t\\tgrplst = previous.groups_as_string.split(\\\", \\\")\\n\\t\\t#loop over list\\n\\t\\tfor grp in grplst:\\n\\t\\t\\trequests.delete(\\\"https://api.mailgun.net/v3/lists/{}@arenbergorkest.be/members/{}\\\".format(grp,mail),auth=('api', settings.MAILGUN_API_KEY))\",\n \"def new_queue(self, params, maxUsers=1):\\n self.spotify.reset_queue()\\n\\n #Extract parameters\\n mood = params[0]\\n users = []\\n for i in range(maxUsers):\\n if len(params[i + 1]) > 0:\\n users.append(params[i + 1])\\n\\n #Add default host if only one guest is present\\n # if len(users) == 1:\\n # users.append('Paolo')\\n\\n #Lists to load\\n names = []\\n for n in self.listIDs.keys():\\n for u in users:\\n if len(mood) > 0:\\n if u + ':' + mood in n:\\n names.append(n)\\n else:\\n if 'top:' + u in n:\\n names.append(n)\\n\\n\\n self.add_playlist(names)\",\n \"def _get_queues(self):\\n return self.__queues\",\n \"def _get_queues(self):\\n return self.__queues\",\n \"def _get_queues(self):\\n return self.__queues\",\n \"def _get_queues(self):\\n return self.__queues\",\n \"def _get_queues(self):\\n return self.__queues\",\n \"def _get_queues(self):\\n return self.__queues\",\n \"def _get_queues(self):\\n return self.__queues\",\n \"def _get_queues(self):\\n return self.__queues\",\n \"def _get_queues(self):\\n return self.__queues\",\n \"def modify_queue_settings(q_settings):\\r\\n db = get_db()\\r\\n db.execute(UPDATE_QUEUE_SETTINGS, qsettings_dict_to_db_tuple_modify(q_settings))\\r\\n db.commit()\\r\\n permissions.update_permissions(q_settings['qid'],\\r\\n get_uids(q_settings['admins']),\\r\\n get_uids(q_settings['managers']) if q_settings.has_key('managers') else None,\\r\\n get_uids(q_settings['blocked_users']) if q_settings.has_key('blocked_users') else None)\",\n \"def assign_vote(from_userid, to_userid, group):\",\n \"def requires_matching_queues(self, requires_matching_queues):\\n \\n self._requires_matching_queues = requires_matching_queues\",\n \"def setup(self):\\n logging.info(\\\"ReqMgr setup ...\\\")\\n def doQuery(urlQuery):\\n logging.info(\\\"Query: '%s' ...\\\" % urlQuery)\\n r = None\\n try:\\n r = self.restSender.put(urlQuery)\\n except Exception as ex:\\n print \\\"exception\\\"\\n print str(ex) \\n print \\\"response:\\\", r\\n queries = [\\\"user/zmaxa?email=testinguser@my.com\\\",\\n \\\"group/DATAOPS\\\",\\n \\\"group/DATAOPS/zmaxa\\\",\\n urllib.quote(\\\"team/\\\" + TEAM_NAME),\\n \\\"version/%s\\\" % \\\"CMSSW_3_5_8\\\"]\\n for q in queries:\\n doQuery(q)\\n logging.info(\\\"ReqMgr setup finished, listing known users ...\\\")\\n q = \\\"user/\\\"\\n r = self.restSender.get(q)\\n print r\",\n \"def assign_task(user_name, task_name, work_server_ip):\\r\\n\\r\\n database_handler.update_records(\\\"current_tasks\\\",\\r\\n {\\\"server_ip\\\": work_server_ip, \\\"Task_status\\\": TaskStatusNames.in_progress.value},\\r\\n condition=\\\"Task_name=$? and user_name=$?\\\", code_args=[task_name, user_name])\",\n \"def __init__(self): \\n self.queue = []\",\n \"def __init__(self, users, messages):\\n self.users = users\\n self.messages = messages\\n # addd dict with each user as key and there corresponidn gmessages\",\n \"def _mp_learn_user_mix(queue, ids, args):\\n alpha, multinomials, val_mat, num_em_iter, tol = args\\n for i in ids:\\n # The way the global em is implemented, allows me to simply call it with the i_val_data and it will only\\n # compute the \\\\pi as a function of that user.\\n i_val_data = convert_sparse_to_coo(val_mat[i])\\n if len(i_val_data) > 0:\\n\\n # The learning method treats the multinomials as matrices. So I have to wrap it in an array.\\n # All the rows in i_val_data are going to be 0 because I'm converting a single row_vector.\\n user_multinomials = []\\n for m in multinomials:\\n if m.shape[0] == 1:\\n user_multinomials.append(m)\\n else:\\n user_multinomials.append(m[i])\\n i_pi, i_ll = _learn_global_mixture_weights(alpha, user_multinomials, i_val_data, num_em_iter, tol)\\n\\n queue.put((i, i_pi, i_ll))\\n\\n queue.put('$')\",\n \"def queue_emails(spoofs, message, count):\\n\\t\\tqueues = []\\n\\t\\tnumber_of_spoofs = len(spoofs)\\n\\t\\tmessages_per_queue = count // number_of_spoofs\\n\\t\\textra_to_distribute = count - (messages_per_queue * number_of_spoofs)\\n\\t\\tbatch = Batch(size=count, complete=0)\\n\\t\\tbatch.save()\\n\\t\\tpk = batch.pk\\n\\n\\t\\t# going deep into each queue\\n\\t\\tfor x in range(number_of_spoofs):\\n\\n\\t\\t\\tspoof = spoofs[x]\\n\\t\\t\\tmessage['From'] = spoof.username\\n\\t\\t\\tqueue = Queue(spoof.username, connection=Redis())\\n\\t\\t\\tqueues.append(queue)\\n\\n\\t\\t\\tfor y in range(messages_per_queue):\\n\\t\\t\\t\\tqueue.enqueue_call(func=send, args=spoof.task_arguments + (message, pk))\\n\\n\\t\\t# panning across each queue\\n\\t\\tfor x in range(extra_to_distribute):\\n\\t\\t\\tspoof = spoofs[x]\\n\\t\\t\\tmessage['From'] = spoof.username\\n\\t\\t\\tqueue = queues[x]\\n\\t\\t\\tqueue.enqueue_call(func=send ,args=(spoof.task_arguments + (message, pk)))\\n\\n\\t\\treturn pk\",\n \"def _add_to_whisper_queue(self, user, message):\\n whisper_tuple = (user, message)\\n self.whisper_message_queue.appendleft(whisper_tuple)\",\n \"def process_user(user_name):\\n \\n try:\\n user_pwd = getpwnam(user_name)\\n except KeyError:\\n print('Error: User {0} is not recognized.'.format(user_name))\\n sys.exit(25)\\n \\n qstat = subprocess.getoutput(\\\"qstat -f\\\").split('-'.center(81, '-')) #81 -'s\\n \\n node_list = []\\n pending_jobs = ''\\n pending_search = '#'.center(79, '#') #denotes pending jobs in qstat 79 #'s\\n #Weeding out nonessential nodes\\n for node in qstat:\\n if user_name in (node.split()):\\n if pending_search in node: #Taking pending jobs out\\n if \\\".crc.nd.edu\\\" in node:\\n # This means its the last node. We must only accept up tp the pending jobs ONLY. Below we are doing that and taking out an\\n # Additional newline by stripping it but adding one back in to keep formatting correct. (there were two instead of one).\\n tempNode = (node[:node.find(pending_search)].rstrip())+'\\\\n'\\n if user_name in tempNode:\\n node_list.append(tempNode)\\n pending_jobs += (node[node.find(pending_search):]) #reaping pending jobs\\n else:\\n node_list.append(node)\\n \\n final_list = []\\n \\n numU_jobs = 0 # Will hold the number of jobs attributed to the specified user\\n numU_cores = 0 # The number of cores the user is currently using. Starts at 0 and counts up as jobs encountered.\\n \\n for host in node_list:\\n # Grabbing the node's name in qstat and making a Node() instance of it\\n temp_node = Node((host.split()[0]))\\n host_used_cores = host.split()[2].split('/')[1]\\n host_total_cores = host.split()[2].split('/')[2]\\n # If within the first line of the node there is a 'd' at the end, disable it\\n if len(host.split('\\\\n')[0]) == 6 and host.split()[5] == 'd':\\n temp_node.set_disabled_switch(True)\\n disabled_cores += int(host_total_cores)\\n else: \\n temp_node.set_disabled_switch(False)\\n \\n temp_node.set_cores(host_total_cores, host_used_cores)\\n # In qstat -F, qf:min_cpu . . . . is the last item before the jobs are listed, \\n # 28 is how many char's that string is (don't want it)\\n node_stat= host[host.find('qf:min_cpu_interval=00:05:00') + 28\\\\\\n :host.find('\\\\n---------------------------------------------------------------------------------\\\\n')]\\n \\\"\\\"\\\"Possibly do a host.split('\\\\n') and join the rest of 2 - end\\\"\\\"\\\"\\n\\n # There is always an extra '\\\\n' in here, so subtract 1 to get rid of it\\n num_jobs = len(node_stat.split('\\\\n')) -1\\n # If there are any jobs, parse them and gather info\\n if num_jobs > 0:\\n # Python is non-inclusive for the right operand, and we want to \\n # skip another extra '\\\\n' so start at 1, and want to go num_jobs\\n for i in range(1, num_jobs + 1):\\n info = node_stat.split('\\\\n')[i].split()\\n temp_job = Job(info[2], info[3], info[7])\\n temp_job.set_id(info[0])\\n temp_job.set_priority(info[1])\\n temp_node.add_job(temp_job)\\n if info[3] == user_name:\\n numU_jobs += 1 #info[3] is the user-name of job, if == spec. user, increment user_jobs\\n numU_cores += int(info[7]) # info[7] is the number of cores occupied by the user's job\\n \\n final_list.append(temp_node)\\n \\n pending_list = []\\n if len(pending_jobs): #As long as the user has pending jobs T if len != 0\\n p_lines = pending_jobs.split('\\\\n')\\n pending_list.append((p_lines[0] + '\\\\n' + p_lines[1] + '\\\\n' + p_lines[2] + '\\\\n'))\\n for i in range(3, len(p_lines)):\\n if p_lines[i].find(user_name) != (-1):\\n pending_list.append(p_lines[i])\\n \\n if len(sys.argv) == 4:\\n if sys.argv[3] == '--details':\\n print_detailed_user(final_list, pending_list, user_name, numU_jobs, numU_cores)\\n else:\\n print('Error: Arg syntax error with: ' + sys.argv[3])\\n show_usage(23)\\n else:\\n print_short_user(final_list, pending_list, user_name, numU_jobs, numU_cores)\",\n \"def _manager_main(self, queue):\\r\\n for task in self._task_generator():\\r\\n queue.put(task)\",\n \"def _get_njobs_in_queue(self, username):\",\n \"def __init__(self):\\r\\n self.queue = []\",\n \"def __init__(self):\\r\\n self.queue = []\",\n \"def __init__(self, user, queueTwoMin, queueTenMin, queueHour, queueAlerts, queueTermination, testDic = None):\\n # Start Time, keep track of the elapsed time\\n self.originalTime = time.time()\\n # User that uses the monitoring app, must exist !\\n self.user = user\\n\\n # Queue to transmit all data\\n self.queueTwoMin = queueTwoMin\\n self.queueTenMin = queueTenMin\\n self.queueHour = queueHour\\n self.queueAlerts = queueAlerts\\n\\n # Queue for termination\\n self.queueTermination = queueTermination\\n\\n # Alert Storage, to check whether raised alert are to be sent\\n self.alertsDic = {}\\n if testDic:\\n self.alertsDic = testDic\\n self.mailer = mailSender.MailSender(mailSender.mailrecipient)\\n\\n # Start monitoring\\n self.monitorAll()\",\n \"def add_to_queue(self, items):\\n\\n for i in items:\\n self.r.rpush(self.joblist, i)\",\n \"def store(self, queue: Queue):\\n\\n validate = RegisterValidator(self.request).register()\\n if validate.check():\\n validate.check_exists()\\n\\n if not validate.check():\\n self.request.session.flash('validation', json.dumps(validate.errors()))\\n return self.request.redirect_to('register')\\n\\n # register the user\\n password = bcrypt_password(self.request.input('password'))\\n\\n auth.AUTH['model'].create(\\n name=self.request.input('username'),\\n password=password,\\n email=self.request.input('email'),\\n )\\n\\n # login the user\\n # redirect to the homepage\\n if Auth(self.request).login(self.request.input(auth.AUTH['model'].__auth__), self.request.input('password')):\\n queue.push(WelcomeEmailJob, args=[self.request.input('email')])\\n return self.request.redirect('/home')\\n\\n return self.request.redirect('/register')\",\n \"def _insert_into_player_queue_spreadsheet(self, username, times_played, player_queue):\\n spreadsheet_name, _ = self.spreadsheets['player_queue']\\n gc = gspread.authorize(self.credentials)\\n sheet = gc.open(spreadsheet_name)\\n ws = sheet.worksheet('Player Queue')\\n\\n records = ws.get_all_records()\\n records = records[1:] # We don't want the blank space\\n for i, tup in enumerate(player_queue):\\n try:\\n if records[i]['User'] != tup[0]:\\n ws.insert_row([username, times_played], index=i+3)\\n break\\n except IndexError:\\n ws.insert_row([username, times_played], index=i+3)\",\n \"def __init__(self):\\n self.queue = []\",\n \"def __init__(self):\\n self.queue = []\",\n \"def __init__(self):\\n self.queue = []\",\n \"def __init__(self):\\n self.queue = []\",\n \"def __init__(self):\\n self.queue = []\",\n \"async def shush(self, ctx):\\n author = ctx.message.author\\n channel = author.voice.channel\\n members = channel.members\\n for member in members:\\n user = ctx.guild.get_member(member.id)\\n await user.edit(mute=True, deafen=True)\\n\\n embed = await embeds.generate_embed(ctx, author, members,\\n description=\\\":white_check_mark: Successfully shushed the following users:\\\",\\n title=channel.name)\\n await ctx.send(embed=embed)\",\n \"def assigned_user(self, assigned_user):\\n self._assigned_user = assigned_user\",\n \"def test_ipcrm_queues():\\n IPCComm.ipcrm_queues()\\n nt.assert_equal(len(IPCComm.ipc_queues()), 0)\\n mq = IPCComm.get_queue()\\n nt.assert_equal(len(IPCComm.ipc_queues()), 1)\\n IPCComm.ipcrm_queues(str(mq.key))\\n nt.assert_equal(len(IPCComm.ipc_queues()), 0)\",\n \"def run(self):\\n\\t\\tmessage = 'user thread {0} started'.format(self.ident)\\n\\t\\tlogger.queue.put({'type':'notice', 'source':'user', 'message':message})\\n\\t\\tstack.add('users',self.ident)\\n\\n\\t\\t# Run our main loop to process messages in the message queue\\n\\t\\twhile self.more:\\n\\t\\t\\t# Check if there is a packet to process\\n\\t\\t\\ttry:\\n\\t\\t\\t\\tdata\\t\\t= stack['users'][self.ident].get(self.block,self.wait)\\n\\t\\t\\texcept queue.Empty:\\n\\t\\t\\t\\tpass\\n\\t\\t\\telse:\\n\\t\\t\\t\\tself.process(data)\\n\\t\\t\\tfinally:\\n\\t\\t\\t\\t# Reset our queue parser to its default values\\n\\t\\t\\t\\tself.block\\t= False\\n\\t\\t\\t\\tself.wait\\t= 1\\n\\n\\t\\t# When the run loop is broken, clean up any queues and data we have\\n\\t\\tstack.remove('users',self.ident)\",\n \"def activate_user_setup():\\n\\n projects_list = app.load_config(current_user.get_id())\\n active_projects_list = []\\n for project in projects_list:\\n if project['_active']:\\n active_projects_list.append(project)\\n app.active_users[current_user.get_id()] = active_projects_list\\n try:\\n requests.post(\\\"http://localhost:9090\\\",\\n json={\\\"active_users\\\": app.active_users})\\n except:\\n logger.info('Unable to send updated list of active users.')\\n return Response('500')\\n logger.info('Activated messages for user '\\n + current_user.get_id() + '.')\\n return Response('200')\",\n \"def __init__(self, users: List[dict], redis_url=None, lock_name='user-clients'):\\n import redis\\n\\n for user in users:\\n user['count'] = 0\\n\\n self._key = 'bdc_collection_builder:users'\\n\\n if redis_url is None:\\n redis_url = current_app.config.get('REDIS_URL', os.getenv('REDIS_URL'))\\n\\n if redis_url is None:\\n raise RuntimeError('Parallel support requires Redis instance. Make sure to export REDIS_URL.')\\n\\n self._cache = redis.Redis.from_url(redis_url)\\n self._lock = self._cache.lock(lock_name)\\n self.users = users\",\n \"def __init__(self):\\n self._queue_items = []\",\n \"def __init__(self):\\n self.q1 = queue()\\n self.q2 = queue()\",\n \"def getResponsibleUsers():\",\n \"def myself(self, mess, args):\\n user = self.get_sender_username(mess)\\n if user in self.users:\\n self.message_queue.append('_%s %s_' % (self.users[user], args))\\n self.log.info( '%s says %s in third person.' % (user, args))\",\n \"def _put(self, item, queue):\",\n \"def add_user(\\n self, imsi: str, sqn_slice_max: dict, backup_network_ids: dict\\n ) -> None:\\n self.config[\\\"users\\\"][imsi] = {\\n \\\"k\\\": \\\"465B5CE8B199B49FAA5F0A2EE238A6BC\\\",\\n \\\"opc\\\": \\\"E8ED289DEBA952E4283B54E88E6183CA\\\",\\n \\\"sqn_slice_max\\\": copy.deepcopy(sqn_slice_max),\\n \\\"backup_network_ids\\\": copy.deepcopy(backup_network_ids),\\n }\",\n \"def queue(users, label, extra_context=None, on_site=True):\\r\\n if extra_context is None:\\r\\n extra_context = {}\\r\\n if isinstance(users, QuerySet):\\r\\n users = [row[\\\"pk\\\"] for row in users.values(\\\"pk\\\")]\\r\\n else:\\r\\n users = [user.pk for user in users]\\r\\n notices = []\\r\\n for user in users:\\r\\n notices.append((user, label, extra_context, on_site))\\r\\n NoticeQueueBatch(pickled_data=pickle.dumps(notices).encode(\\\"base64\\\")).save()\",\n \"def setUp(self, connections=2):\\n self._threads = [Consumer(messages=self.messages) for i in xrange(0, connections)]\\n\\n map(lambda tq: tq[0].add_queue(QUEUE_NAME.format(number=tq[1])),\\n izip(cycle(self._threads), xrange(0, self.queues)))\",\n \"def assign_passengers(self):\\n\\n # Update the state of the parallel server after every assignment.\\n self.parallel_server.update_state()\\n\\n # While the assignment agent's queue is not empty and there is space\\n # to assign passengers in the parallel block...\\n while self.parallel_server.has_space_in_a_server_queue is True and \\\\\\n len(self.queue) > 0:\\n\\n # Pop the first passenger in line and assign to the shortest queue.\\n tmp = self.queue.popleft()\\n self.parallel_server.min_queue.queue.append(tmp)\\n\\n # Update the state of the parallel server after every assignment.\\n self.parallel_server.update_state()\",\n \"def assign(self, available_workers):\\n \\n status = self.getStatus()\\n\\n assert len(available_workers) == 1\\n worker = available_workers[0]\\n assignment = {}\\n\\n w_id = str(worker.id)\\n task_id = self.task_id\\n\\n #tracks \\n worker_assignments_var = redis_get_worker_assignments_var(task_id, w_id)\\n\\n print \\\"WORKER ID:\\\", w_id\\n print \\\"STATUS:\\\", status\\n print \\\"ASSIGNMENTS FOR WORKER SO FAR:\\\", app.redis.smembers(worker_assignments_var)\\n\\n\\n # sort questions by pomdp expected reward...\\n # XXX this isn't quite what we want...\\n # want to sort by value of getting another label\\n # so we don't have all workers getting assigned to the same question\\n unfinished_unsorted_qs = [(q,v) for (q,v) in status.iteritems() if v['best_action_str'] == 'create-another-job']\\n # NOTE REVERSE ORDER\\n sorted_qs = sorted(unfinished_unsorted_qs, key=lambda x:x[1]['best_expected_reward'], reverse=True)\\n print \\\"sorted_qs\\\", sorted_qs\\n# print \\\"worker %s has done the following questions\\\" % w_id\\n# for (q_id,er) in sorted_qs:\\n# if app.redis.sismember(worker_assignments_var, q_id):\\n# print \\\"+\\\", q_id\\n# else:\\n# print \\\"-\\\", q_id\\n\\n for idx in range(len(sorted_qs)):\\n q_id,expected_reward = sorted_qs[idx]\\n\\n if not app.redis.sismember(worker_assignments_var, q_id):\\n assignment[w_id] = q_id\\n print \\\"assignment=\\\", assignment\\n app.redis.sadd(worker_assignments_var, q_id)\\n return assignment\\n\\n #if here no assignment was made to our worker!\\n assert len(assignment) == 0\\n print \\\"no assignment made yet\\\"\\n\\n #NOTE POMDP doesn't think there are any questions available to the worker \\n #that need another label, but let's give them an assignment anyway\\n #Pick question where submitting would have worst expected reward \\n # (implying it may benefit from another label)\\n finished_qs = [(q,v) for (q,v) in status.iteritems() if v['best_action_str'] != 'create-another-job']\\n sorted_finished_qs = sorted(finished_qs, key=lambda x:x[1]['best_expected_reward']) # no reverse\\n for idx in range(len(sorted_finished_qs)):\\n q_id,expected_reward = sorted_finished_qs[idx]\\n\\n if not app.redis.sismember(worker_assignments_var, q_id):\\n assignment[w_id] = q_id\\n print \\\"gave worker a finished q assignment=\\\", assignment\\n app.redis.sadd(worker_assignments_var, q_id)\\n return assignment\\n\\n return assignment\",\n \"def __init__(self):\\n self.subscribers = {}\\n self.followers = {}\\n self.nackables = {}\\n self.threads = []\",\n \"def send_msg(self, my_queue, my_msg):\",\n \"def invite_site_users(users):\\n #group(run_cron.s(item) for item in sites).delay()\\n pass\",\n \"def set_user(self, args):\\n permissions = \\\"\\\"\\n\\n permissions = ' '.join(args['<permission>'])\\n\\n config = self.config\\n config[args['<mask>']] = permissions\\n self.bot.db[self.key] = config\",\n \"def admin_message_queue(self, user, nick, channel, rest):\\n message = \\\"Error loading messages\\\"\\n with self.getDbSession() as db_session:\\n count = db_session.query(func.count(Message.id)).scalar()\\n message = \\\"%d messages pending delivery\\\" % count\\n self.reply(channel, nick, message)\",\n \"def _setup_tubes(self):\\n chan = self.channel\\n inp = self.config[self.MODULE_NAME]['amqp']['in']\\n out = self.config[self.MODULE_NAME]['amqp']['out']\\n if inp['exchange']:\\n log.info('generating Input Queue'+ str(inp))\\n chan.exchange_declare(**inp)\\n self.qname = chan.queue_declare(exclusive=True).queue\\n chan.queue_bind(exchange=inp['exchange'],queue=self.qname)\\n self.consume = lambda cb : chan.basic_consume(cb,queue=self.qname,no_ack=True)\\n self.start_loop = lambda : pika.asyncore_loop()\\n\\n if out['exchange']:\\n log.info('generating Output Exchange'+ str(out))\\n chan.exchange_declare(**out)\\n self.publish = lambda msg: self.channel.basic_publish(exchange=out['exchange'],routing_key='',body=msg)\",\n \"def set_items(self) -> None:\\n if not self.__repeat:\\n self._username: str = input(\\\"Introduzca el nombre del usuario: \\\")\\n self._ordered_items = self.get_rights()[0]\\n if not self._ordered_items:\\n if self.__option == 1:\\n self.print_error(\\\"El usuario ya tiene todos los permisos.\\\")\\n return\\n self.print_error(\\\"El usuario no tiene ningún permiso.\\\")\\n return\",\n \"def sync(self):\\n\\t\\tmanagers.storage.write_object_async(VDOM_CONFIG[\\\"USER-MANAGER-STORAGE-RECORD\\\"], self.users)\",\n \"def _update(self, data):\\n if len(data) > 0:\\n for q in self._queues.values():\\n q.put(data)\",\n \"def queue_on_project(self, queue_for_bfs, list_tasks_project):\\n if not len(queue_for_bfs):\\n return\\n else:\\n task = queue_for_bfs.popleft()\\n for key_sub in task.subtasks:\\n sub = self.get_task(key_sub)\\n list_tasks_project.append(sub)\\n queue_for_bfs.append(sub)\\n self.save_task(sub)\\n\\n self.queue_on_project(queue_for_bfs, list_tasks_project)\",\n \"def assigned_by_user(self, assigned_by_user):\\n\\n self._assigned_by_user = assigned_by_user\",\n \"def setup_queues():\\n sqs = boto.connect_sqs()\\n sqs.create_queue('mls_parse_requests')\\n sqs.create_queue('mls_fetcher')\",\n \"def queueStatusAll():\",\n \"def test_resource_user_resource_set_user_groups_for_user_put(self):\\n pass\",\n \"def users_items_collector(self, user_id, values):\\r\\n pass\",\n \"def send_assignee_emails(self):\\n\\n assignees = list(set([obj.assignee for obj in self.stalled_nf_issues])) # Assignees from New Features\\n assignees.extend(list(set([obj.assignee for obj in self.stalled_st_issues]))) # Add assignees from Sub-tasks\\n recipients = self.config.get(\\\"recipients\\\", \\\"emails\\\").split(\\\"\\\\n\\\") # [recipients] section in .ini file\\n\\n for assignee in assignees:\\n assignee_issues = [] # List of IssueClass objects\\n # Get all stalled New feature issues for this assignee\\n for item in self.stalled_nf_issues + self.stalled_st_issues:\\n if item.assignee == assignee:\\n# if item.assignee == \\\"ashih\\\":\\n assignee_issues.append(item)\\n assignee_email = item.assignee_email\\n \\n if len(assignee_issues):\\n html_table = '<table style=\\\"font-size:12px\\\">'\\n html_table += self.make_time_in_status_rows(assignee_issues)\\n html_table += '</table>' # Closing table tag\\n #recipients.append(assignee_email)\\n print \\\"Sending email to: %s\\\" % recipients\\n self.send_email(recipients, html_table, assignee)\",\n \"def enqueue(tup):\",\n \"def test01_assigned_users(self):\\n print_ln('test_assigned_users')\\n \\n try:\\n rList = review.find_roles(Role(name='py-role*'))\\n for rle in rList: \\n print_ln(\\\"Assigned users role=\\\" + rle.name)\\n uList = review.assigned_users(rle)\\n for user in uList: \\n print_ln(\\\"Assigned user=\\\" + user, 1)\\n except Exception as e:\\n self.fail('test_assigned_users failed, exception=' + e.msg)\",\n \"def set_permissions(queue_url, label, permissionlist, accountidlist):\\n client = boto3.client('sqs')\\n try:\\n client.add_permission(QueueUrl=queue_url, Label=label,\\n AWSAccountIds=accountidlist, Actions=permissionlist)\\n except ClientError as e:\\n if e.response['Error']['Code'] == 'InvalidParameterValue':\\n raise AlreadyExistsException(e.response['Error']['Message'])\\n raise\",\n \"def assigned_to_changed(self, ar):\\n # self.add_change_watcher(self.assigned_to)\\n\\n if (self.assigned_to is not None and\\n self.assigned_to != ar.user and\\n dd.is_installed('notify')):\\n ctx = dict(user=ar.user, what=ar.obj2memo(self))\\n def msg(user, mm):\\n subject = _(\\\"{user} has assigned you to ticket: {what}\\\").format(**ctx)\\n return (subject , tostring(E.span(subject)))\\n\\n mt = rt.models.notify.MessageTypes.tickets\\n\\n rt.models.notify.Message.emit_notification(\\n ar, self, mt, msg,\\n [(self.assigned_to, self.assigned_to.mail_mode)]\\n )\",\n \"def get_user_message_queue(self,user_id,order=0):\\n key = key_gen(user_id)\\n result = self.redis.lrange(key,0,-1)\\n if result != []:\\n for index, item in enumerate(result):\\n item = eval(item)\\n result[index] = self.get_message_detail(item['message_id'])\\n if order == 1:\\n result.reverse()\\n return result\",\n \"def setusers(self, users=None):\\n if users:\\n self.users = users\\n return\\n import jsb.lib.users as u\\n if not u.users: u.users_boot()\\n self.users = u.users\",\n \"def queue_enqueue(queue, targets):\\n\\n enqueued = []\\n for target in map(lambda x: x.strip(), targets):\\n if target != '':\\n enqueued.append({'target': target, 'queue_id': queue.id})\\n if enqueued:\\n db.session.execute(Target.__table__.insert().values(enqueued))\\n db.session.commit()\",\n \"def _subscribe_users(self):\\n group = self.env.ref('odoo_magento2_ept.group_connector_manager')\\n if not group:\\n return\\n companies = self.mapped('company_id')\\n domain = [('groups_id', '=', group.id)]\\n if companies:\\n domain.append(('company_id', 'child_of', companies.ids))\\n users = self.env['res.users'].search(domain)\\n self.message_subscribe_users(user_ids=users.ids)\",\n \"def enqueue(self,value):\\n pass\",\n \"def find_host_groups(user_name, detail_switch, queue_switch):\\n \\n # Ensure user is a crc user (check /etc/passwd)\\n try:\\n user_pwd = getpwnam(user_name)\\n except KeyError:\\n print(\\\"Error: User {0} is not recognized.\\\".format(user_name))\\n sys.exit(25)\\n\\n all_user_lists = subprocess.getoutput(\\\"qconf -sul\\\").split('\\\\n')\\n \\n user_list = []\\n #Going through each element of all_user_lists and finding detailed output for that element\\n for ul in all_user_lists:\\n u_list = subprocess.getoutput(\\\"qconf -su \\\" + ul)\\n if u_list.find(user_name) != (-1):\\n user_list.append(ul)\\n \\n queue_list= []\\n if queue_switch:\\n queue_list = ['long']\\n else:\\n queue_list = ['long', 'hpc', 'gpu', 'gpu-debug']\\n \\n host_user_list = []\\n\\n # Run through each queue\\n for searchQ in queue_list:\\n host_user_list = host_user_list + find_queue_usersets(searchQ, user_list)\\n\\n # Everyone has access to general access, adding in case of queue_switch\\n host_user_list.append('@' + GENERAL_ACCESS_QUEUE_HOSTGROUP)\\n\\n if queue_switch:\\n process_queue(host_user_list, user_name, user_list)\\n\\n # Everyone has access to debug\\n host_user_list.append('@' + DEBUG_QUEUE_HOSTGROUP)\\n \\n if detail_switch:\\n print_duser_host(host_user_list, user_name, user_list)\\n else:\\n print_user_host(host_user_list, user_name, user_list)\\n sys.exit()\",\n \"def _migrate_users(correct_course_key, role, lower_org):\\r\\n for user in orm['auth.user'].objects.filter(groups=group).all():\\r\\n entry = orm['student.courseaccessrole'](\\r\\n role=role, user=user,\\r\\n org=correct_course_key.org, course_id=correct_course_key\\r\\n )\\r\\n try:\\r\\n entry.save()\\r\\n except IntegrityError:\\r\\n # already stored\\r\\n pass\\r\\n orgs[lower_org] = correct_course_key.org\",\n \"def add_users(self, *users):\\r\\n pass\"\n]","isConversational":false},"negative_scores":{"kind":"list like","value":["0.7112401","0.65540063","0.6523784","0.6046179","0.5905534","0.58399224","0.5835143","0.57973343","0.57812953","0.5747986","0.570831","0.5543441","0.5543441","0.5543441","0.5516011","0.5516011","0.551184","0.55012774","0.54841727","0.54561526","0.5411173","0.5353801","0.52877283","0.5279604","0.5247368","0.5247368","0.5247368","0.5247368","0.5247368","0.5247368","0.5247368","0.5247368","0.5247368","0.52470607","0.5244776","0.52416605","0.52305365","0.5222412","0.5209977","0.5207914","0.52070314","0.5198858","0.5196542","0.5190945","0.5187917","0.517604","0.5175705","0.5175705","0.5154333","0.51513463","0.5150819","0.51463413","0.5123257","0.5123257","0.5123257","0.5123257","0.5123257","0.51116693","0.5100935","0.5100092","0.5091084","0.507043","0.5068894","0.50666064","0.50605243","0.5060358","0.5055134","0.50465596","0.5037081","0.50258625","0.50154567","0.50091046","0.5005835","0.49993682","0.49978805","0.4996941","0.49911848","0.49888518","0.49864966","0.49850857","0.4977762","0.497693","0.49749207","0.4967755","0.4965694","0.49647307","0.49618113","0.49573198","0.4953559","0.4945872","0.49453163","0.4936449","0.49307096","0.49238765","0.49227384","0.49211922","0.49153364","0.49150136","0.49131966","0.49126554","0.49107134"],"string":"[\n \"0.7112401\",\n \"0.65540063\",\n \"0.6523784\",\n \"0.6046179\",\n \"0.5905534\",\n \"0.58399224\",\n \"0.5835143\",\n \"0.57973343\",\n \"0.57812953\",\n \"0.5747986\",\n \"0.570831\",\n \"0.5543441\",\n \"0.5543441\",\n \"0.5543441\",\n \"0.5516011\",\n \"0.5516011\",\n \"0.551184\",\n \"0.55012774\",\n \"0.54841727\",\n \"0.54561526\",\n \"0.5411173\",\n \"0.5353801\",\n \"0.52877283\",\n \"0.5279604\",\n \"0.5247368\",\n \"0.5247368\",\n \"0.5247368\",\n \"0.5247368\",\n \"0.5247368\",\n \"0.5247368\",\n \"0.5247368\",\n \"0.5247368\",\n \"0.5247368\",\n \"0.52470607\",\n \"0.5244776\",\n \"0.52416605\",\n \"0.52305365\",\n \"0.5222412\",\n \"0.5209977\",\n \"0.5207914\",\n \"0.52070314\",\n \"0.5198858\",\n \"0.5196542\",\n \"0.5190945\",\n \"0.5187917\",\n \"0.517604\",\n \"0.5175705\",\n \"0.5175705\",\n \"0.5154333\",\n \"0.51513463\",\n \"0.5150819\",\n \"0.51463413\",\n \"0.5123257\",\n \"0.5123257\",\n \"0.5123257\",\n \"0.5123257\",\n \"0.5123257\",\n \"0.51116693\",\n \"0.5100935\",\n \"0.5100092\",\n \"0.5091084\",\n \"0.507043\",\n \"0.5068894\",\n \"0.50666064\",\n \"0.50605243\",\n \"0.5060358\",\n \"0.5055134\",\n \"0.50465596\",\n \"0.5037081\",\n \"0.50258625\",\n \"0.50154567\",\n \"0.50091046\",\n \"0.5005835\",\n \"0.49993682\",\n \"0.49978805\",\n \"0.4996941\",\n \"0.49911848\",\n \"0.49888518\",\n \"0.49864966\",\n \"0.49850857\",\n \"0.4977762\",\n \"0.497693\",\n \"0.49749207\",\n \"0.4967755\",\n \"0.4965694\",\n \"0.49647307\",\n \"0.49618113\",\n \"0.49573198\",\n \"0.4953559\",\n \"0.4945872\",\n \"0.49453163\",\n \"0.4936449\",\n \"0.49307096\",\n \"0.49238765\",\n \"0.49227384\",\n \"0.49211922\",\n \"0.49153364\",\n \"0.49150136\",\n \"0.49131966\",\n \"0.49126554\",\n \"0.49107134\"\n]","isConversational":false},"document_score":{"kind":"string","value":"0.0"},"document_rank":{"kind":"string","value":"-1"}}},{"rowIdx":75382,"cells":{"query":{"kind":"string","value":"List all users and their assignments to queues."},"document":{"kind":"string","value":"def list_command(ctx: click.Context, user_ids: Tuple[int], queue_ids: Tuple[int]) -> None:\n with RossumClient(context=ctx.obj) as rossum:\n queue_users = rossum.get_queues((USERS,), users=user_ids)\n\n user_queues: Dict[int, List[List[Optional[str]]]] = {}\n for queue in queue_users:\n if queue_ids and int(queue[\"id\"]) not in queue_ids:\n continue\n for user in queue[\"users\"]:\n user_id = int(user[\"id\"])\n if user_ids and user_id not in user_ids:\n continue\n\n if user_id not in user_queues:\n user_queues[user_id] = [[user[\"id\"], user[\"username\"], queue[\"id\"], queue[\"name\"]]]\n else:\n user_queues[user_id].append([None, None, queue[\"id\"], queue[\"name\"]])\n user_queues = dict(sorted(user_queues.items()))\n click.echo(\n tabulate(\n chain.from_iterable(user_queues.values()),\n headers=[\"id\", \"username\", \"queue id\", \"queue name\"],\n )\n )"},"metadata":{"kind":"string","value":"{\n \"objective\": {\n \"self\": [],\n \"paired\": [],\n \"triplet\": [\n [\n \"query\",\n \"document\",\n \"negatives\"\n ]\n ]\n }\n}"},"negatives":{"kind":"list like","value":["def task_queue_users():\n users = firebase_admin.auth.list_users().iterate_all()\n for user in users:\n if user.disabled: # skip disabled users\n continue\n # add user task\n add_task(url_for(\"task_queue_podcasts\"), {\"user_uid\": user.uid})\n\n return OK_RESPONSE","def list_users(self):\n raise NotImplementedError","def user_list(ctx):\n data = ctx.obj.get_all_users()\n output_json_data(data)","def get_all_users():","def list_users(self):\n return self.get_admin(\"users\")","def user_list():\n if session['user_admin'] == False:\n abort(403)\n\n # Retrieve all Users\n sqa_sess = sqa_session()\n users = sqa_sess.query(User).all()\n\n return render_template('admin/user_list.html', users=users)","def find_queue_usersets(queue, user_list):\n\n sq = subprocess.getoutput(\"qconf -sq {0}\".format(queue))\n \n #splicing the sq output up to the user_list point, we don't need the rest of the garbarge before that\n sq = sq[sq.find('user_lists') + 9 :sq.find('xuser')]\n sq = (((sq.replace('\\n', '')).replace(' ', '')).replace('\\\\', '')).replace('],', '')\n host_user_list = []\n sq = sq.split('[')\n hostg_list = []\n for line in sq:\n if line.find('@') != (-1):\n #host-groups in sq output have '@', so that's what we're looking for\n hostg_list.append(line)\n for line in hostg_list:\n for ul in user_list:\n if ul in line:\n host_user_list.append(line.split('=')[0])\n return host_user_list","def fetch_all_users():\n url = \"{}/workspace/{}/users\".format(V1_API_URL, WORKSPACE_ID)\n responses = requests.get(url, headers=HEADERS)\n return [\n {\n \"acronym\": user[\"name\"].lower(),\n \"clockify_id\": user[\"id\"],\n \"email\": user[\"email\"].lower(),\n }\n for user in responses.json()\n ]","def __list_all_users(self):\n\n usernames_dict = get_data.get_usernames_dict()\n if len(usernames_dict) > 0:\n first_str = 'user'\n second_str = 'posts scraped'\n descriptor = '{:<40} {}'\n print('')\n print(descriptor.format(first_str, second_str))\n print(descriptor.format(len(first_str) * '-', len(second_str) * '-'))\n for number, username in usernames_dict.items():\n space_str = ' ' if len(str(number)) > 1 else ' '\n first = '[' + space_str + str(number) + '] ' + username\n second = str(get_data.get_user_post_count(username))\n print(descriptor.format(first, second))\n else:\n print('no users found in the database')","def do_user_list(cs, args):\n _, users = cs.users.list()\n fields = ['user_id', 'username', 'email', 'realname', 'comment']\n utils.print_list(users, fields, sortby=args.sortby)","def get_queryset(self):\n if self.request.user.is_staff:\n return WaitQueue.objects.all()\n return WaitQueue.objects.filter(user=self.request.user)","def do_get_all_users(self, *args):\n self.user_data = self.connection_obj.get_all()\n self.__class__.print_func(self, self.user_data)","def get_queue_list(self):\n return self.manager.get_queue_list()","def display_users(cls):\n return cls.user_list","def test_list_users(self, rabbitmq):\n assert rabbitmq.list_users() == [('user', ['administrator'])]\n rabbitmq.exec_rabbitmqctl('add_user', ['new_user', 'new_pass'])\n assert rabbitmq.list_users() == [\n ('new_user', ['']),\n ('user', ['administrator']),\n ]","def _list_users(self):\n users = fileIO.load_json(\"users.json\")\n print(\"The list of users is as follows:\")\n for i in users:\n print(users[i][\"name\"])\n self._list_user_settings(users)","def list_users():\n check_admin()\n results = User.query.order_by(-User.id)\n return render_template('user_list.html', users=results)","def user_list(server_object, client, address, command_args):\n\n\tmsg = \"\"\n\n\t#: Create a formatted string of all the users.\n\tfor usr in server_object.usrs.values():\n\t\tmsg += usr + '\\n'\n\n\tclient.send(msg.encode())","def list_users(self, user=None):\n from expfactory.database.models import Participant\n\n participants = Participant.query.all()\n users = []\n for user in participants:\n users.append(self.print_user(user))\n return users","def users(self):\n return self.get_data(\"users\")","def get_all_users():\n return jsonify(admin.get_all_users(current_app.scoped_session()))","def list_users(ctx, project_name):\n project = ctx.obj.groups.byName[project_name].get().data\n pprint(ctx.obj.groups[project.id].databaseUsers.get().data)","def sync(self):\n for subscription in self.getSubscriptionList():\n #user_id = subscription.getZopeUser()\n #uf = self.getPortalObject().acl_users\n #user = uf.getUserById(user_id).__of__(uf)\n #newSecurityManager(None, user)\n subscription.activate(activity='SQLQueue',\n tag=subscription.getId(),\n priority=ACTIVITY_PRIORITY\n ).SubSync(subscription.getPath())","def users(self):\n return self._users","def all_users():\n\n users = crud.get_users()\n\n return render_template('all_users.html', users=users)","def list_users(BrokerId=None, MaxResults=None, NextToken=None):\n pass","def get_users(self):\r\n\t\tlogger.debug(\"Fetch users\")\r\n\t\t\r\n\t\treturn login.get_users()","def print_users(self):\n for user in self.users.values():\n print(user)","def list_all_users():\n session_id = request.args.get('session-id', None)\n user_id = request.args.get('user-id', None)\n users_list = get_users_list()\n if check_authentication(session_id, user_id) and is_admin_user(user_id):\n return render_template('admin_area.html', user=user_id, session_id=session_id, users_list=users_list)\n else:\n return render_template('home.html', cars_list=get_cars_preview(), news_list=get_news_list(), authjs=False,\n preview_length=get_cars_preview().__len__(), del_session_cookie=True)","def list_accounts(self, **kwargs):\r\n if 'mask' not in kwargs:\r\n items = [\r\n 'id',\r\n 'name',\r\n 'status',\r\n 'nodes',\r\n ]\r\n kwargs['mask'] = \"mask[%s]\" % ','.join(items)\r\n\r\n return self.client['Account'].getMessageQueueAccounts(**kwargs)","def _messages_list(self, queue):\n\n return queue.messages()","def print_users(self):\n for i, item in enumerate(self.users):\n print(\"{}. {}\".format(i, item.name))","def listusers():\n\n try:\n users = User.query.order_by(User.email).all()\n click.echo(\n tabulate(\n [\n [u.username, u.email, \"admin\" if u.is_admin else None]\n for u in users\n ]\n )\n )\n except OperationalError:\n click.echo(\"Tabela de usuários inexistente...\")","def list( self, mess, args):\n user = self.get_sender_username(mess)\n args = args.replace(' ', '_')\n if user in self.users:\n user_list = 'All these users are subscribed - \\n'\n user_list += '\\n'.join(['%s :: %s' %(u, self.users[u]) for u in sorted(self.users)])\n if self.invited.keys():\n user_list += '\\n The following users are invited - \\n'\n user_list += '\\n'.join(self.invited.keys())\n self.log.info( '%s checks list of users.' % user)\n return user_list","def get(self):\n return get_all_users()","def get(self):\n return get_all_users()","def get(self):\n return get_all_users()","def get(self):\n return get_all_users()","def users(self) -> pulumi.Input[Sequence[pulumi.Input['BrokerUserArgs']]]:\n return pulumi.get(self, \"users\")","def list_users(self):\n _url = \"http://\" + self.host_ip + \":35357/v2.0/users\"\n _body = None\n _headers = {'Content-type': 'application/json',\n 'x-auth-token': self.cloud_admin_info['token_project']}\n\n response = self.request(\"GET\", _url, _headers, _body)\n if response is None:\n LOG_OBJ.error(\" no response from Server\")\n return response\n if response.status not in [200, 201, 202, 203, 204]:\n LOG_OBJ.error(\n \"get user list Failed with status %s \" %\n response.status)\n return response.status\n\n output = json.loads(response.data)\n LOG_OBJ.info(\"users List : %s\")\n return output[\"users\"]","def listUsers(self):\n return self._client.listUsers()","def list_users(item):\n users = User.load_all_users(item)\n for user in users:\n print(user.username)","def users(self) -> List[str]:\n return self._users","def list_queues():\n url = urlparse(Config.RABBIT_MQ_URL)\n response = requests.get(f'http://{url.hostname}:{15672}/api/queues?columns=name,messages,'\\\n 'messages_ready,messages_unacknowledged',\n auth=(url.username, url.password))\n\n tasks = dict()\n\n for task in response.json():\n if 'cube' in task['name']:\n tasks[task['name']] = dict(total=task['messages'],\n ready=task['messages_ready'],\n unacked=task['messages_unacknowledged'])\n\n return tasks","def list(self, *args, **kwargs):\n users = User.objects.filter(is_superuser=False)\n return self.list_by(users, self.serializer_class)","def user_available_boards(user):\n if not user.is_authenticated:\n return []\n\n return Board.objects.for_user(user)","def user_list():\n\n users = User.query.all()\n\n return render_template(\"user_list.html\", users=users)","def query(self, *args, **kwargs) -> List[str]:\r\n self.logger.info(\"Returning Manual Users\")\r\n\r\n return kwargs['users']","def user_list(request_dict):\n users = User.query.all()\n users_list = list()\n for user in users:\n users_list.append(user)\n\n return JSONTools.user_list_reply(users_list)","def users(self) -> pulumi.Output[Sequence['outputs.BrokerUser']]:\n return pulumi.get(self, \"users\")","def user_list():\n\n users = User.query.all()\n \n return render_template(\"user_list.html\", users=users)","def user_list():\n\n users = User.query.all()\n return render_template(\"user_list.html\", users=users)","def user_list():\n\n users = User.query.all()\n return render_template(\"user_list.html\", users=users)","def user_list():\n\n users = User.query.all()\n return render_template(\"user_list.html\", users=users)","def user_list():\n\n users = User.query.all()\n return render_template(\"user_list.html\", users=users)","def user_list():\n\n users = User.query.all()\n return render_template(\"user_list.html\", users=users)","def user_list():\n\n users = User.query.all()\n return render_template(\"user_list.html\", users=users)","def get_list():\r\n qry = ImportQueue.query\r\n qry = qry.order_by(ImportQueue.id)\r\n return qry.all()","def user_list():\n\n users = User.query.all()\n return render_template(\"/user_list.html\", users=users)","def test_list_resources(self, rabbitmq):\n assert rabbitmq.list_vhosts() == ['/vhost']\n assert rabbitmq.list_users() == [('user', ['administrator'])]\n assert rabbitmq.list_queues() == []","def GetQueueList(handler, query):\n json_config = {}\n if 'TiVo' in query:\n tivoIP = query['TiVo'][0]\n with active_tivos_lock:\n if tivoIP in active_tivos:\n with active_tivos[tivoIP]['lock']:\n json_config['urls'] = [ status['url'] for status in active_tivos[tivoIP]['queue'] ]\n\n handler.send_json(json.dumps(json_config))","def list_user(self, username):\n return self.get_admin(u\"users/{}\".format(username))","def get_users(self):\n return self.execute(TABELLE['users']['select']['all'])","def all_users(self):\n distinct_users = list(self.client.smartsleep.attendees.distinct(\"userId\"))\n return distinct_users","def test_admin_user_list_all_users(self):\n response = self.client.get(CONSTS.USER_ADMIN_LIST)\n self.assertEqual(response.status_code, status.HTTP_200_OK)\n self.assertEqual(response.data, self.users.data)","def read_all(self):\n for user_id in self.user_ids:\n self.read(user_id)","def get_users(self):\n res = self.conn.cursor().execute('SELECT id,email,username FROM users')\n return res.fetchall()","def user_list_all(message=''):\n return Response(render_template('employee/user/list.html',\n users=User.query.all(),\n message=message),\n mimetype='text/html')","def user_list(request):\r\n params = request.params\r\n order = params.get('order', None)\r\n limit = params.get('limit', None)\r\n user_list = UserMgr.get_list(order=order, limit=limit)\r\n ret = {\r\n 'count': len(user_list),\r\n 'users': [dict(h) for h in user_list],\r\n }\r\n return _api_response(request, ret)","def users(self) -> Optional[pulumi.Input[Sequence[pulumi.Input['BrokerUserArgs']]]]:\n return pulumi.get(self, \"users\")","def process_queue(host_user_list, user_name, user_list):\n\n total_nodes = 0\n total_cores = 0\n disabled_cores = 0\n used_cores = 0\n free_cores = 0\n empty_nodes = 0\n disabled_nodes = 0\n \n node_strings= []\n\n for hostgroup in host_user_list:\n # Obtain totals for one hostgroup, and contine a summation\n tc, uc, tn, en, dc,dn, nl = process_host(hostgroup.replace('@',''))\n\n # Do not want to recount same nodes. This needs to be looked at for efficiency.\n nl_strings = [n.name for n in nl]\n\n if nl_strings[0] in node_strings:\n continue\n\n # Sum to get totals for whole queue which user has access to\n total_cores += tc \n used_cores += uc \n total_nodes += tn\n empty_nodes += en\n disabled_cores += dc\n disabled_nodes += dn\n node_strings= node_strings + nl\n\n # Send totals to be printed to screen\n print_host_info(total_cores, used_cores, total_nodes, empty_nodes, \"Long Queue\", disabled_cores, \n disabled_nodes)\n sys.exit(0)","def get_users_admins_list(self, session):\n\n users = session.query(User.chat_id).all()\n return users","def list_users(access_token):\n request_url = OKTA_URL + \"api/v1/users\"\n headers = {\"Authorization\": \"Bearer \" + access_token}\n group_request = requests.get(request_url, headers=headers).json()\n return group_request","def get_users(self):\n return self.get_all_dbusers()","def get_all_users():\n token = request.headers.get('token')\n\n # Token Validation\n token_valid, response = is_token_valid(token)\n if not token_valid:\n return response\n token_username = response\n\n # Privilege handling\n if token_username != 'admin':\n return jsonify({'message': \"You aren't allowed to access this\"}), 404\n\n return jsonify(list(Users.values())), 200","async def list_users(self) -> List[int]:\n return [\n # row[0]\n # async for row in self.conn.execute(\n # \"select userid from tg_users\",\n # )\n ]","def get_lists(self, request):\n target_user = User.objects.filter(email=request.POST['email'])\n if not target_user.exists():\n # In this case we don't want to return to the initial page\n return JsonResponse({\n 'msg': \"ERROR: The user doesn't exist\"\n })\n\n requests = Request.objects.get_active_by_user(target_user.first())\n borrowings = Borrowing.objects.get_active_by_user(target_user.first())\n html = render_to_string(\"include/hardware_admin_user.html\", {\n 'requests': requests,\n 'borrowings': borrowings\n })\n return JsonResponse({\n 'content': html\n })","def getResponsibleUsers():","def get_all_users(self):\n \n sql = \"select * from users\"\n return self._query_all(sql)","def users(self):\r\n return resources.Users(self)","def users():\n access_token = session['access_token']\n return \"%s\" % list_users(access_token)","def get_current_user_lists():\n return TaskList.get_user_lists(users.get_current_user())","def _get_users_list(self):\n return self.users['user_id'].tolist()","def user_list(self):\n self.cur.execute(\"SELECT username FROM users\")\n users = []\n for username in self.cur.fetchall():\n users.append(username[0])\n return users","def list_users():\n\ttry:\n\t\tusers_call = sc.api_call(\"users.list\")\n\t\tusers = []\n\t\tif users_call.get('ok'):\n\t\t\treturn users_call['members']\n\texcept:\n\t\tprint(\"users error\")\n\treturn None","def get_users():\n users = functions.users()\n return users","def get_user_lists(user):\n if not user: return []\n memberships = db.Query(TaskListMember).filter('user =', user)\n return [m.task_list for m in memberships]","def list(self):\n self.background_scheduler.print_jobs()","def _users_list(self):\n result = self.slack.api_call(\"users.list\", presence=0)\n\n if not result.get(\"ok\"):\n logging.error(result['error'])\n return None\n\n return result['members']","def show_users():\n users_list = []\n all_users = storage.all('User')\n for obj in all_users.values():\n users_list.append(obj.to_dict())\n return jsonify(users_list)","def list_users():\n\n db_users = User.query.all()\n\n return render_template(\"list_users.html\", headline=\"Blogly Users\", users=db_users)","def get_users_list(self, session):\n\n users = session.query(User.chat_id).filter(User.is_admin==False).all()\n return users","def users(self):\r\n return resource.Users(self)","def get_all_users():\n users = []\n for mv in storage.all(\"User\").values():\n users.append(mv.to_dict())\n return jsonify(users)","def view_list_users(self, user):\r\n return user.realm._users.keys()","def get_all_users():\n return Users.query.all()","def get_users(self):\n # remove some user media fields that we can't submit back\n def clean_media(entry):\n entry.pop(\"mediaid\", None)\n entry.pop(\"userid\", None)\n entry.pop(\"description\", None)\n return entry\n zabbix_users = self.conn.user.get(selectMedias=\"extend\", selectUsrgrps=\"extend\")\n zabbix_users = {user[\"alias\"].lower(): User(\n id=user[\"userid\"],\n name=user[\"name\"],\n surname=user[\"surname\"],\n alias=user[\"alias\"],\n groups=set(g[\"usrgrpid\"] for g in user[\"usrgrps\"]),\n media=[clean_media(entry) for entry in user[\"medias\"]],\n ) for user in zabbix_users}\n return zabbix_users","def list(self, user_ids: Optional[List[UserId]]) -> List[U]:\n ...","def queue_list(self, req):\n project_id = req._headers.get('X-Project-ID')\n\n LOG.debug(u'Queue list - project: %(project)s',\n {'project': project_id})\n\n kwargs = {}\n\n if req._body.get('marker') is not None:\n kwargs['marker'] = req._body.get('marker')\n\n if req._body.get('limit') is not None:\n kwargs['limit'] = req._body.get('limit')\n\n if req._body.get('detailed') is not None:\n kwargs['detailed'] = req._body.get('detailed')\n\n try:\n self._validate.queue_listing(**kwargs)\n results = self._queue_controller.list(\n project=project_id, **kwargs)\n except validation.ValidationFailed as ex:\n LOG.debug(ex)\n headers = {'status': 400}\n return api_utils.error_response(req, ex, headers)\n except storage_errors.ExceptionBase as ex:\n LOG.exception(ex)\n error = 'Queues could not be listed.'\n headers = {'status': 503}\n return api_utils.error_response(req, ex, error, headers)\n\n # Buffer list of queues\n queues = list(next(results))\n\n # Got some. Prepare the response.\n body = {'queues': queues}\n headers = {'status': 200}\n\n resp = response.Response(req, body, headers)\n\n return resp","def getUsers(self) -> List[bbUser.bbUser]:\n return list(self.users.values())"],"string":"[\n \"def task_queue_users():\\n users = firebase_admin.auth.list_users().iterate_all()\\n for user in users:\\n if user.disabled: # skip disabled users\\n continue\\n # add user task\\n add_task(url_for(\\\"task_queue_podcasts\\\"), {\\\"user_uid\\\": user.uid})\\n\\n return OK_RESPONSE\",\n \"def list_users(self):\\n raise NotImplementedError\",\n \"def user_list(ctx):\\n data = ctx.obj.get_all_users()\\n output_json_data(data)\",\n \"def get_all_users():\",\n \"def list_users(self):\\n return self.get_admin(\\\"users\\\")\",\n \"def user_list():\\n if session['user_admin'] == False:\\n abort(403)\\n\\n # Retrieve all Users\\n sqa_sess = sqa_session()\\n users = sqa_sess.query(User).all()\\n\\n return render_template('admin/user_list.html', users=users)\",\n \"def find_queue_usersets(queue, user_list):\\n\\n sq = subprocess.getoutput(\\\"qconf -sq {0}\\\".format(queue))\\n \\n #splicing the sq output up to the user_list point, we don't need the rest of the garbarge before that\\n sq = sq[sq.find('user_lists') + 9 :sq.find('xuser')]\\n sq = (((sq.replace('\\\\n', '')).replace(' ', '')).replace('\\\\\\\\', '')).replace('],', '')\\n host_user_list = []\\n sq = sq.split('[')\\n hostg_list = []\\n for line in sq:\\n if line.find('@') != (-1):\\n #host-groups in sq output have '@', so that's what we're looking for\\n hostg_list.append(line)\\n for line in hostg_list:\\n for ul in user_list:\\n if ul in line:\\n host_user_list.append(line.split('=')[0])\\n return host_user_list\",\n \"def fetch_all_users():\\n url = \\\"{}/workspace/{}/users\\\".format(V1_API_URL, WORKSPACE_ID)\\n responses = requests.get(url, headers=HEADERS)\\n return [\\n {\\n \\\"acronym\\\": user[\\\"name\\\"].lower(),\\n \\\"clockify_id\\\": user[\\\"id\\\"],\\n \\\"email\\\": user[\\\"email\\\"].lower(),\\n }\\n for user in responses.json()\\n ]\",\n \"def __list_all_users(self):\\n\\n usernames_dict = get_data.get_usernames_dict()\\n if len(usernames_dict) > 0:\\n first_str = 'user'\\n second_str = 'posts scraped'\\n descriptor = '{:<40} {}'\\n print('')\\n print(descriptor.format(first_str, second_str))\\n print(descriptor.format(len(first_str) * '-', len(second_str) * '-'))\\n for number, username in usernames_dict.items():\\n space_str = ' ' if len(str(number)) > 1 else ' '\\n first = '[' + space_str + str(number) + '] ' + username\\n second = str(get_data.get_user_post_count(username))\\n print(descriptor.format(first, second))\\n else:\\n print('no users found in the database')\",\n \"def do_user_list(cs, args):\\n _, users = cs.users.list()\\n fields = ['user_id', 'username', 'email', 'realname', 'comment']\\n utils.print_list(users, fields, sortby=args.sortby)\",\n \"def get_queryset(self):\\n if self.request.user.is_staff:\\n return WaitQueue.objects.all()\\n return WaitQueue.objects.filter(user=self.request.user)\",\n \"def do_get_all_users(self, *args):\\n self.user_data = self.connection_obj.get_all()\\n self.__class__.print_func(self, self.user_data)\",\n \"def get_queue_list(self):\\n return self.manager.get_queue_list()\",\n \"def display_users(cls):\\n return cls.user_list\",\n \"def test_list_users(self, rabbitmq):\\n assert rabbitmq.list_users() == [('user', ['administrator'])]\\n rabbitmq.exec_rabbitmqctl('add_user', ['new_user', 'new_pass'])\\n assert rabbitmq.list_users() == [\\n ('new_user', ['']),\\n ('user', ['administrator']),\\n ]\",\n \"def _list_users(self):\\n users = fileIO.load_json(\\\"users.json\\\")\\n print(\\\"The list of users is as follows:\\\")\\n for i in users:\\n print(users[i][\\\"name\\\"])\\n self._list_user_settings(users)\",\n \"def list_users():\\n check_admin()\\n results = User.query.order_by(-User.id)\\n return render_template('user_list.html', users=results)\",\n \"def user_list(server_object, client, address, command_args):\\n\\n\\tmsg = \\\"\\\"\\n\\n\\t#: Create a formatted string of all the users.\\n\\tfor usr in server_object.usrs.values():\\n\\t\\tmsg += usr + '\\\\n'\\n\\n\\tclient.send(msg.encode())\",\n \"def list_users(self, user=None):\\n from expfactory.database.models import Participant\\n\\n participants = Participant.query.all()\\n users = []\\n for user in participants:\\n users.append(self.print_user(user))\\n return users\",\n \"def users(self):\\n return self.get_data(\\\"users\\\")\",\n \"def get_all_users():\\n return jsonify(admin.get_all_users(current_app.scoped_session()))\",\n \"def list_users(ctx, project_name):\\n project = ctx.obj.groups.byName[project_name].get().data\\n pprint(ctx.obj.groups[project.id].databaseUsers.get().data)\",\n \"def sync(self):\\n for subscription in self.getSubscriptionList():\\n #user_id = subscription.getZopeUser()\\n #uf = self.getPortalObject().acl_users\\n #user = uf.getUserById(user_id).__of__(uf)\\n #newSecurityManager(None, user)\\n subscription.activate(activity='SQLQueue',\\n tag=subscription.getId(),\\n priority=ACTIVITY_PRIORITY\\n ).SubSync(subscription.getPath())\",\n \"def users(self):\\n return self._users\",\n \"def all_users():\\n\\n users = crud.get_users()\\n\\n return render_template('all_users.html', users=users)\",\n \"def list_users(BrokerId=None, MaxResults=None, NextToken=None):\\n pass\",\n \"def get_users(self):\\r\\n\\t\\tlogger.debug(\\\"Fetch users\\\")\\r\\n\\t\\t\\r\\n\\t\\treturn login.get_users()\",\n \"def print_users(self):\\n for user in self.users.values():\\n print(user)\",\n \"def list_all_users():\\n session_id = request.args.get('session-id', None)\\n user_id = request.args.get('user-id', None)\\n users_list = get_users_list()\\n if check_authentication(session_id, user_id) and is_admin_user(user_id):\\n return render_template('admin_area.html', user=user_id, session_id=session_id, users_list=users_list)\\n else:\\n return render_template('home.html', cars_list=get_cars_preview(), news_list=get_news_list(), authjs=False,\\n preview_length=get_cars_preview().__len__(), del_session_cookie=True)\",\n \"def list_accounts(self, **kwargs):\\r\\n if 'mask' not in kwargs:\\r\\n items = [\\r\\n 'id',\\r\\n 'name',\\r\\n 'status',\\r\\n 'nodes',\\r\\n ]\\r\\n kwargs['mask'] = \\\"mask[%s]\\\" % ','.join(items)\\r\\n\\r\\n return self.client['Account'].getMessageQueueAccounts(**kwargs)\",\n \"def _messages_list(self, queue):\\n\\n return queue.messages()\",\n \"def print_users(self):\\n for i, item in enumerate(self.users):\\n print(\\\"{}. {}\\\".format(i, item.name))\",\n \"def listusers():\\n\\n try:\\n users = User.query.order_by(User.email).all()\\n click.echo(\\n tabulate(\\n [\\n [u.username, u.email, \\\"admin\\\" if u.is_admin else None]\\n for u in users\\n ]\\n )\\n )\\n except OperationalError:\\n click.echo(\\\"Tabela de usuários inexistente...\\\")\",\n \"def list( self, mess, args):\\n user = self.get_sender_username(mess)\\n args = args.replace(' ', '_')\\n if user in self.users:\\n user_list = 'All these users are subscribed - \\\\n'\\n user_list += '\\\\n'.join(['%s :: %s' %(u, self.users[u]) for u in sorted(self.users)])\\n if self.invited.keys():\\n user_list += '\\\\n The following users are invited - \\\\n'\\n user_list += '\\\\n'.join(self.invited.keys())\\n self.log.info( '%s checks list of users.' % user)\\n return user_list\",\n \"def get(self):\\n return get_all_users()\",\n \"def get(self):\\n return get_all_users()\",\n \"def get(self):\\n return get_all_users()\",\n \"def get(self):\\n return get_all_users()\",\n \"def users(self) -> pulumi.Input[Sequence[pulumi.Input['BrokerUserArgs']]]:\\n return pulumi.get(self, \\\"users\\\")\",\n \"def list_users(self):\\n _url = \\\"http://\\\" + self.host_ip + \\\":35357/v2.0/users\\\"\\n _body = None\\n _headers = {'Content-type': 'application/json',\\n 'x-auth-token': self.cloud_admin_info['token_project']}\\n\\n response = self.request(\\\"GET\\\", _url, _headers, _body)\\n if response is None:\\n LOG_OBJ.error(\\\" no response from Server\\\")\\n return response\\n if response.status not in [200, 201, 202, 203, 204]:\\n LOG_OBJ.error(\\n \\\"get user list Failed with status %s \\\" %\\n response.status)\\n return response.status\\n\\n output = json.loads(response.data)\\n LOG_OBJ.info(\\\"users List : %s\\\")\\n return output[\\\"users\\\"]\",\n \"def listUsers(self):\\n return self._client.listUsers()\",\n \"def list_users(item):\\n users = User.load_all_users(item)\\n for user in users:\\n print(user.username)\",\n \"def users(self) -> List[str]:\\n return self._users\",\n \"def list_queues():\\n url = urlparse(Config.RABBIT_MQ_URL)\\n response = requests.get(f'http://{url.hostname}:{15672}/api/queues?columns=name,messages,'\\\\\\n 'messages_ready,messages_unacknowledged',\\n auth=(url.username, url.password))\\n\\n tasks = dict()\\n\\n for task in response.json():\\n if 'cube' in task['name']:\\n tasks[task['name']] = dict(total=task['messages'],\\n ready=task['messages_ready'],\\n unacked=task['messages_unacknowledged'])\\n\\n return tasks\",\n \"def list(self, *args, **kwargs):\\n users = User.objects.filter(is_superuser=False)\\n return self.list_by(users, self.serializer_class)\",\n \"def user_available_boards(user):\\n if not user.is_authenticated:\\n return []\\n\\n return Board.objects.for_user(user)\",\n \"def user_list():\\n\\n users = User.query.all()\\n\\n return render_template(\\\"user_list.html\\\", users=users)\",\n \"def query(self, *args, **kwargs) -> List[str]:\\r\\n self.logger.info(\\\"Returning Manual Users\\\")\\r\\n\\r\\n return kwargs['users']\",\n \"def user_list(request_dict):\\n users = User.query.all()\\n users_list = list()\\n for user in users:\\n users_list.append(user)\\n\\n return JSONTools.user_list_reply(users_list)\",\n \"def users(self) -> pulumi.Output[Sequence['outputs.BrokerUser']]:\\n return pulumi.get(self, \\\"users\\\")\",\n \"def user_list():\\n\\n users = User.query.all()\\n \\n return render_template(\\\"user_list.html\\\", users=users)\",\n \"def user_list():\\n\\n users = User.query.all()\\n return render_template(\\\"user_list.html\\\", users=users)\",\n \"def user_list():\\n\\n users = User.query.all()\\n return render_template(\\\"user_list.html\\\", users=users)\",\n \"def user_list():\\n\\n users = User.query.all()\\n return render_template(\\\"user_list.html\\\", users=users)\",\n \"def user_list():\\n\\n users = User.query.all()\\n return render_template(\\\"user_list.html\\\", users=users)\",\n \"def user_list():\\n\\n users = User.query.all()\\n return render_template(\\\"user_list.html\\\", users=users)\",\n \"def user_list():\\n\\n users = User.query.all()\\n return render_template(\\\"user_list.html\\\", users=users)\",\n \"def get_list():\\r\\n qry = ImportQueue.query\\r\\n qry = qry.order_by(ImportQueue.id)\\r\\n return qry.all()\",\n \"def user_list():\\n\\n users = User.query.all()\\n return render_template(\\\"/user_list.html\\\", users=users)\",\n \"def test_list_resources(self, rabbitmq):\\n assert rabbitmq.list_vhosts() == ['/vhost']\\n assert rabbitmq.list_users() == [('user', ['administrator'])]\\n assert rabbitmq.list_queues() == []\",\n \"def GetQueueList(handler, query):\\n json_config = {}\\n if 'TiVo' in query:\\n tivoIP = query['TiVo'][0]\\n with active_tivos_lock:\\n if tivoIP in active_tivos:\\n with active_tivos[tivoIP]['lock']:\\n json_config['urls'] = [ status['url'] for status in active_tivos[tivoIP]['queue'] ]\\n\\n handler.send_json(json.dumps(json_config))\",\n \"def list_user(self, username):\\n return self.get_admin(u\\\"users/{}\\\".format(username))\",\n \"def get_users(self):\\n return self.execute(TABELLE['users']['select']['all'])\",\n \"def all_users(self):\\n distinct_users = list(self.client.smartsleep.attendees.distinct(\\\"userId\\\"))\\n return distinct_users\",\n \"def test_admin_user_list_all_users(self):\\n response = self.client.get(CONSTS.USER_ADMIN_LIST)\\n self.assertEqual(response.status_code, status.HTTP_200_OK)\\n self.assertEqual(response.data, self.users.data)\",\n \"def read_all(self):\\n for user_id in self.user_ids:\\n self.read(user_id)\",\n \"def get_users(self):\\n res = self.conn.cursor().execute('SELECT id,email,username FROM users')\\n return res.fetchall()\",\n \"def user_list_all(message=''):\\n return Response(render_template('employee/user/list.html',\\n users=User.query.all(),\\n message=message),\\n mimetype='text/html')\",\n \"def user_list(request):\\r\\n params = request.params\\r\\n order = params.get('order', None)\\r\\n limit = params.get('limit', None)\\r\\n user_list = UserMgr.get_list(order=order, limit=limit)\\r\\n ret = {\\r\\n 'count': len(user_list),\\r\\n 'users': [dict(h) for h in user_list],\\r\\n }\\r\\n return _api_response(request, ret)\",\n \"def users(self) -> Optional[pulumi.Input[Sequence[pulumi.Input['BrokerUserArgs']]]]:\\n return pulumi.get(self, \\\"users\\\")\",\n \"def process_queue(host_user_list, user_name, user_list):\\n\\n total_nodes = 0\\n total_cores = 0\\n disabled_cores = 0\\n used_cores = 0\\n free_cores = 0\\n empty_nodes = 0\\n disabled_nodes = 0\\n \\n node_strings= []\\n\\n for hostgroup in host_user_list:\\n # Obtain totals for one hostgroup, and contine a summation\\n tc, uc, tn, en, dc,dn, nl = process_host(hostgroup.replace('@',''))\\n\\n # Do not want to recount same nodes. This needs to be looked at for efficiency.\\n nl_strings = [n.name for n in nl]\\n\\n if nl_strings[0] in node_strings:\\n continue\\n\\n # Sum to get totals for whole queue which user has access to\\n total_cores += tc \\n used_cores += uc \\n total_nodes += tn\\n empty_nodes += en\\n disabled_cores += dc\\n disabled_nodes += dn\\n node_strings= node_strings + nl\\n\\n # Send totals to be printed to screen\\n print_host_info(total_cores, used_cores, total_nodes, empty_nodes, \\\"Long Queue\\\", disabled_cores, \\n disabled_nodes)\\n sys.exit(0)\",\n \"def get_users_admins_list(self, session):\\n\\n users = session.query(User.chat_id).all()\\n return users\",\n \"def list_users(access_token):\\n request_url = OKTA_URL + \\\"api/v1/users\\\"\\n headers = {\\\"Authorization\\\": \\\"Bearer \\\" + access_token}\\n group_request = requests.get(request_url, headers=headers).json()\\n return group_request\",\n \"def get_users(self):\\n return self.get_all_dbusers()\",\n \"def get_all_users():\\n token = request.headers.get('token')\\n\\n # Token Validation\\n token_valid, response = is_token_valid(token)\\n if not token_valid:\\n return response\\n token_username = response\\n\\n # Privilege handling\\n if token_username != 'admin':\\n return jsonify({'message': \\\"You aren't allowed to access this\\\"}), 404\\n\\n return jsonify(list(Users.values())), 200\",\n \"async def list_users(self) -> List[int]:\\n return [\\n # row[0]\\n # async for row in self.conn.execute(\\n # \\\"select userid from tg_users\\\",\\n # )\\n ]\",\n \"def get_lists(self, request):\\n target_user = User.objects.filter(email=request.POST['email'])\\n if not target_user.exists():\\n # In this case we don't want to return to the initial page\\n return JsonResponse({\\n 'msg': \\\"ERROR: The user doesn't exist\\\"\\n })\\n\\n requests = Request.objects.get_active_by_user(target_user.first())\\n borrowings = Borrowing.objects.get_active_by_user(target_user.first())\\n html = render_to_string(\\\"include/hardware_admin_user.html\\\", {\\n 'requests': requests,\\n 'borrowings': borrowings\\n })\\n return JsonResponse({\\n 'content': html\\n })\",\n \"def getResponsibleUsers():\",\n \"def get_all_users(self):\\n \\n sql = \\\"select * from users\\\"\\n return self._query_all(sql)\",\n \"def users(self):\\r\\n return resources.Users(self)\",\n \"def users():\\n access_token = session['access_token']\\n return \\\"%s\\\" % list_users(access_token)\",\n \"def get_current_user_lists():\\n return TaskList.get_user_lists(users.get_current_user())\",\n \"def _get_users_list(self):\\n return self.users['user_id'].tolist()\",\n \"def user_list(self):\\n self.cur.execute(\\\"SELECT username FROM users\\\")\\n users = []\\n for username in self.cur.fetchall():\\n users.append(username[0])\\n return users\",\n \"def list_users():\\n\\ttry:\\n\\t\\tusers_call = sc.api_call(\\\"users.list\\\")\\n\\t\\tusers = []\\n\\t\\tif users_call.get('ok'):\\n\\t\\t\\treturn users_call['members']\\n\\texcept:\\n\\t\\tprint(\\\"users error\\\")\\n\\treturn None\",\n \"def get_users():\\n users = functions.users()\\n return users\",\n \"def get_user_lists(user):\\n if not user: return []\\n memberships = db.Query(TaskListMember).filter('user =', user)\\n return [m.task_list for m in memberships]\",\n \"def list(self):\\n self.background_scheduler.print_jobs()\",\n \"def _users_list(self):\\n result = self.slack.api_call(\\\"users.list\\\", presence=0)\\n\\n if not result.get(\\\"ok\\\"):\\n logging.error(result['error'])\\n return None\\n\\n return result['members']\",\n \"def show_users():\\n users_list = []\\n all_users = storage.all('User')\\n for obj in all_users.values():\\n users_list.append(obj.to_dict())\\n return jsonify(users_list)\",\n \"def list_users():\\n\\n db_users = User.query.all()\\n\\n return render_template(\\\"list_users.html\\\", headline=\\\"Blogly Users\\\", users=db_users)\",\n \"def get_users_list(self, session):\\n\\n users = session.query(User.chat_id).filter(User.is_admin==False).all()\\n return users\",\n \"def users(self):\\r\\n return resource.Users(self)\",\n \"def get_all_users():\\n users = []\\n for mv in storage.all(\\\"User\\\").values():\\n users.append(mv.to_dict())\\n return jsonify(users)\",\n \"def view_list_users(self, user):\\r\\n return user.realm._users.keys()\",\n \"def get_all_users():\\n return Users.query.all()\",\n \"def get_users(self):\\n # remove some user media fields that we can't submit back\\n def clean_media(entry):\\n entry.pop(\\\"mediaid\\\", None)\\n entry.pop(\\\"userid\\\", None)\\n entry.pop(\\\"description\\\", None)\\n return entry\\n zabbix_users = self.conn.user.get(selectMedias=\\\"extend\\\", selectUsrgrps=\\\"extend\\\")\\n zabbix_users = {user[\\\"alias\\\"].lower(): User(\\n id=user[\\\"userid\\\"],\\n name=user[\\\"name\\\"],\\n surname=user[\\\"surname\\\"],\\n alias=user[\\\"alias\\\"],\\n groups=set(g[\\\"usrgrpid\\\"] for g in user[\\\"usrgrps\\\"]),\\n media=[clean_media(entry) for entry in user[\\\"medias\\\"]],\\n ) for user in zabbix_users}\\n return zabbix_users\",\n \"def list(self, user_ids: Optional[List[UserId]]) -> List[U]:\\n ...\",\n \"def queue_list(self, req):\\n project_id = req._headers.get('X-Project-ID')\\n\\n LOG.debug(u'Queue list - project: %(project)s',\\n {'project': project_id})\\n\\n kwargs = {}\\n\\n if req._body.get('marker') is not None:\\n kwargs['marker'] = req._body.get('marker')\\n\\n if req._body.get('limit') is not None:\\n kwargs['limit'] = req._body.get('limit')\\n\\n if req._body.get('detailed') is not None:\\n kwargs['detailed'] = req._body.get('detailed')\\n\\n try:\\n self._validate.queue_listing(**kwargs)\\n results = self._queue_controller.list(\\n project=project_id, **kwargs)\\n except validation.ValidationFailed as ex:\\n LOG.debug(ex)\\n headers = {'status': 400}\\n return api_utils.error_response(req, ex, headers)\\n except storage_errors.ExceptionBase as ex:\\n LOG.exception(ex)\\n error = 'Queues could not be listed.'\\n headers = {'status': 503}\\n return api_utils.error_response(req, ex, error, headers)\\n\\n # Buffer list of queues\\n queues = list(next(results))\\n\\n # Got some. Prepare the response.\\n body = {'queues': queues}\\n headers = {'status': 200}\\n\\n resp = response.Response(req, body, headers)\\n\\n return resp\",\n \"def getUsers(self) -> List[bbUser.bbUser]:\\n return list(self.users.values())\"\n]","isConversational":false},"negative_scores":{"kind":"list like","value":["0.6420194","0.62280095","0.61567736","0.6114229","0.6111416","0.60904527","0.6056923","0.59933186","0.59414256","0.5924326","0.5871818","0.58335733","0.5831743","0.58315027","0.5815715","0.5800357","0.5722068","0.57204676","0.5710141","0.57079506","0.57015085","0.56970286","0.56766","0.5658875","0.56494224","0.563503","0.56320816","0.5628136","0.5627083","0.56143683","0.56140065","0.5605264","0.55839723","0.5582163","0.55809265","0.55809265","0.55809265","0.55809265","0.5575939","0.5571857","0.5566355","0.5557711","0.55533946","0.5536255","0.55229896","0.55030435","0.5496838","0.54918617","0.54847324","0.54807776","0.5471762","0.5468883","0.5468883","0.5468883","0.5468883","0.5468883","0.5468883","0.54659873","0.54577106","0.5456817","0.5448394","0.54480577","0.5442001","0.5441452","0.54339445","0.5432382","0.5430704","0.54296535","0.5416319","0.5416094","0.5413631","0.5412264","0.54080683","0.5407276","0.5404691","0.54017055","0.53948873","0.5387642","0.5385595","0.5382434","0.53821903","0.5379837","0.53792274","0.5373516","0.5367073","0.5366076","0.53653187","0.5364187","0.53577095","0.535681","0.535619","0.5354738","0.5346396","0.53389657","0.5332097","0.53289205","0.5328745","0.53215045","0.53205466","0.53202116"],"string":"[\n \"0.6420194\",\n \"0.62280095\",\n \"0.61567736\",\n \"0.6114229\",\n \"0.6111416\",\n \"0.60904527\",\n \"0.6056923\",\n \"0.59933186\",\n \"0.59414256\",\n \"0.5924326\",\n \"0.5871818\",\n \"0.58335733\",\n \"0.5831743\",\n \"0.58315027\",\n \"0.5815715\",\n \"0.5800357\",\n \"0.5722068\",\n \"0.57204676\",\n \"0.5710141\",\n \"0.57079506\",\n \"0.57015085\",\n \"0.56970286\",\n \"0.56766\",\n \"0.5658875\",\n \"0.56494224\",\n \"0.563503\",\n \"0.56320816\",\n \"0.5628136\",\n \"0.5627083\",\n \"0.56143683\",\n \"0.56140065\",\n \"0.5605264\",\n \"0.55839723\",\n \"0.5582163\",\n \"0.55809265\",\n \"0.55809265\",\n \"0.55809265\",\n \"0.55809265\",\n \"0.5575939\",\n \"0.5571857\",\n \"0.5566355\",\n \"0.5557711\",\n \"0.55533946\",\n \"0.5536255\",\n \"0.55229896\",\n \"0.55030435\",\n \"0.5496838\",\n \"0.54918617\",\n \"0.54847324\",\n \"0.54807776\",\n \"0.5471762\",\n \"0.5468883\",\n \"0.5468883\",\n \"0.5468883\",\n \"0.5468883\",\n \"0.5468883\",\n \"0.5468883\",\n \"0.54659873\",\n \"0.54577106\",\n \"0.5456817\",\n \"0.5448394\",\n \"0.54480577\",\n \"0.5442001\",\n \"0.5441452\",\n \"0.54339445\",\n \"0.5432382\",\n \"0.5430704\",\n \"0.54296535\",\n \"0.5416319\",\n \"0.5416094\",\n \"0.5413631\",\n \"0.5412264\",\n \"0.54080683\",\n \"0.5407276\",\n \"0.5404691\",\n \"0.54017055\",\n \"0.53948873\",\n \"0.5387642\",\n \"0.5385595\",\n \"0.5382434\",\n \"0.53821903\",\n \"0.5379837\",\n \"0.53792274\",\n \"0.5373516\",\n \"0.5367073\",\n \"0.5366076\",\n \"0.53653187\",\n \"0.5364187\",\n \"0.53577095\",\n \"0.535681\",\n \"0.535619\",\n \"0.5354738\",\n \"0.5346396\",\n \"0.53389657\",\n \"0.5332097\",\n \"0.53289205\",\n \"0.5328745\",\n \"0.53215045\",\n \"0.53205466\",\n \"0.53202116\"\n]","isConversational":false},"document_score":{"kind":"string","value":"0.71933156"},"document_rank":{"kind":"string","value":"0"}}},{"rowIdx":75383,"cells":{"query":{"kind":"string","value":"Helper Function for isvalid, Checks if s1, s2 are a valid bracket pair"},"document":{"kind":"string","value":"def isValidPair(self,s1,s2):\n if (s1 == '(' and s2 == ')'):\n return True\n if (s1 == '[' and s2 == ']'):\n return True\n if (s1 == '{' and s2 == '}'):\n return True\n return False"},"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 match(str1, str2):\n if ( (str2 == '[' and str1 == ']')\n or (str2 == '{' and str1 == '}')\n or (str2 == '(' and str1 == ')')):\n return True\n return False","def multi_bracket_validation(input):\n brackets = Stack()\n for i in input:\n if i == '(' or i == '[' or i == '{':\n brackets.push(i)\n elif i == ')' or i == ']' or i == '}':\n if brackets._size == 0:\n return False\n elif i == ')' and brackets.top.val == '(':\n brackets.pop()\n elif i == ']' and brackets.top.val == '[':\n brackets.pop()\n elif i == '}' and brackets.top.val == '{':\n brackets.pop()\n else:\n return False\n\n if brackets._size != 0:\n return False\n return True","def bracket_validator(brackets):\n bracket_stack = []\n for b in brackets:\n if b == '(' or b == '{' or b == '[':\n bracket_stack.append(b)\n\n elif b == ')' or b == '}' or b == ']':\n top = bracket_stack.pop()\n\n if b != bracket_match(top):\n return False\n\n return True","def _check_brackets(tokens: List[str]) -> True:\r\n brackets = []\r\n for token in tokens:\r\n if token in \"([\":\r\n brackets.append(token)\r\n elif token in \")]\":\r\n if not brackets:\r\n raise ExtraBracket(token)\r\n closing_bracket = brackets.pop()\r\n if ((token == \"(\" and closing_bracket != \")\") or\r\n (token == \"[\" and closing_bracket != \"]\")):\r\n raise BracketsMismatch(token, closing_bracket)\r\n if brackets:\r\n raise NoClosingBracket(brackets[0])\r\n return True","def isValid(self, s: str) -> bool:\n open_brackets = [] # stack of open but not closed brackets\n for char in s:\n if char in ['(', '[', '{']:\n open_brackets.append(char)\n else:\n try:\n complement = open_brackets.pop()\n except IndexError:\n return False\n else:\n if complement + char not in ['()', '[]', '{}']:\n return False\n return not len(open_brackets)","def bracketed (phrase,bracketing='()'):\r\n\r\n level = 0\r\n left_point = None\r\n right_point = None\r\n \r\n\r\n for count,char in enumerate(phrase):\r\n\r\n if char == bracketing[0]:\r\n if level == 0:\r\n left_point = count\r\n level+=1\r\n if char == bracketing[1]:\r\n level-=1\r\n if level == 0:\r\n right_point = count\r\n if not (left_point is None) and (not right_point is None) and left_point == 0 and right_point == len(phrase)-1:\r\n return True\r\n return False","def isValid(self, s):\n # 1\n if not s:\n return True\n\n # 2\n if not len(s) % 2 == 0:\n return False\n\n comp = {\n ')': '(',\n ']': '[',\n '}': '{',\n }\n stack = []\n\n for char in s:\n # 3\n if char in comp:\n popped = stack.pop() if stack else '@'\n if popped != comp[char]:\n return False\n # 2\n else:\n stack.append(char)\n\n return not stack","def check_matching_brackets(s, opening=\"([{\", closing=\")]}\"):\n\n stack = Stack()\n\n for item in s:\n # Stores any opening character\n if item in opening:\n stack.push(item)\n else:\n # if a closing bracket is found and we try to remove the matching opening bracket from the top of the stack\n # in case the top of the stack is not a matching bracket, the string is not properly nested\n if stack.length() < 1 or stack.pop() is not opening[closing.index(item)]:\n return 0\n\n return 0 if stack.length() > 0 else 1","def bracketed (self,phrase):\r\n\r\n level = 0\r\n left_point = None\r\n right_point = None\r\n \r\n\r\n for count,char in enumerate(phrase):\r\n\r\n if char == '(':\r\n if level == 0:\r\n left_point = count\r\n level+=1\r\n if char == ')':\r\n level-=1\r\n if level == 0:\r\n right_point = count\r\n if not (left_point is None) and (not right_point is None) and left_point == 0 and right_point == len(phrase)-1:\r\n return True\r\n return False","def BalancedBrackets(Str):\n stack = []\n for parenthesis in Str:\n if parenthesis == '{' or parenthesis == '(' or parenthesis == '[':\n stack.append(parenthesis)\n elif parenthesis == '}' or parenthesis == ')' or parenthesis == ']':\n if len(stack) < 1:\n return False\n last_item = stack.pop()\n if not Compare(last_item, parenthesis):\n return False\n if len(stack) != 0:\n return False\n\n return True","def bracket_match(bracket):\n if bracket == '(':\n return ')'\n if bracket == '[':\n return ']'\n if bracket == '{':\n return '}'","def isValid(self, s: str) -> bool:\n stack = list()\n for c in s:\n if c in Solution.corresponding_parenthesis:\n stack.append(Solution.corresponding_parenthesis[c])\n elif not stack or stack.pop() != c:\n return False\n return not stack","def is_well_formed(s):\n # TODO - you fill in here.\n s = list(s)\n op = []\n l = len(s)\n for i in range(l):\n if s[i] in ['(','{','[']:\n op.append(s[i])\n else: \n if len(op) == 0:\n return False\n temp = op.pop() \n if s[i] == ')':\n if temp != '(':\n return False\n elif s[i] == '}':\n if temp != '{':\n return False\n else:\n if temp != '[':\n return False\n if len(op) != 0:\n return False\n return True","def syntaxValid(s):\n\tif not s or type(s) != str or s[0] != '[':\n\t\treturn False\n\n\t#using stack to check for matching brackets\n\t#push '[' and pop for ']'\n\tbracketStack = []\n\tfor i in range(len(s)):\n\t\tcurrentChar = s[i]\n\t\tif currentChar == '[':\n\t\t\t#check for there is a delimiter before open bracket or trying to double nest\n\t\t\tif i != 0 and s[i-1] == '[' or i > 1 and s[i-2] != ',':\n\t\t\t\treturn False\n\t\t\tbracketStack.append('[')\n\t\telif currentChar == ']':\n\t\t\t#check if stack is prematurely empty or no delimiter after closed bracket\n\t\t\tif not bracketStack or (i != len(s)-1 and s[i+1] != ']' and s[i+1] != ','):\n\t\t\t\treturn False \n\t\t\telse: \n\t\t\t\tbracketStack.pop()\n\t\telif currentChar == ',':\n\t\t\t#check if delimiter is invalid\n\t\t\tif i == 0 or i == len(s)-1 or s[i-1] == '[' or s[i+1] != ' ':\n\t\t\t\treturn False\n\t#if the stack is empty, we have matched brackets, hence s is valid\n\treturn len(bracketStack) == 0","def parentheses_balanced(astr):\n\n if astr == '':\n return True\n\n start = 0\n end = len(astr) - 1\n found_open = False\n found_closed = False\n\n while True:\n\n while found_open is False:\n if astr[start] == '(':\n found_open = True\n elif astr[start] == ')':\n return 'Invalid'\n elif start >= end:\n return 'Valid'\n\n start += 1\n \n found_open = False\n\n while found_closed is False:\n if astr[end] == ')':\n found_closed = True\n elif astr[end] == '(' or end <= start:\n return 'Invalid'\n\n end -= 1\n\n found_closed = False","def valid_parenthesis(string):\n if len(string) % 2:\n print(\"Invalid Parenthesis\")\n return\n stack = []\n bracket_hash = {\"}\": \"{\", \")\": \"(\", \"]\": \"[\"}\n\n for bracket in string:\n if bracket in bracket_hash:\n if stack:\n top = stack.pop()\n if bracket_hash[bracket] is not top:\n print(\"Invalid Parenthesis\")\n return\n else:\n top = \"\"\n else:\n if bracket in bracket_hash.values():\n stack.append(bracket)\n else:\n print(\"Invalid String\")\n return\n\n if not stack:\n print(\"Valid Parenthesis\")\n return","def has_balanced_parens(string):","def isbracket(token):\n\n # Token is a bracket\n return token == \"[\"","def end_other(s_1, s_2):\n str_1 = s_1[-3:]\n str_2 = s_2[-3:]\n\n if(str_1.lower() == s_2.lower()):\n \n isValid = True\n elif(str_2.lower() == s_1.lower()):\n isValid = True\n else:\n isValid = False\n return isValid","def check(s1, s2):\n\n l1 = len(s1)\n l2 = len(s2)\n\n if len(s1) == len(s2):\n return check_update(s1, s2)\n elif l1 + 1 == l2:\n return check_insert(s1, s2)\n elif l1 - 1 == l2:\n return check_insert(s2, s1)\n else:\n return False","def group_check(s):\n parenthesis = ['(',')']\n brackets = ['[', ']']\n squirely = ['{', '}']\n parenthesis_num0 = 0\n brackets_num0 = 0\n squirely_num0 = 0\n parenthesis_num1 = 0\n brackets_num1 = 0\n squirely_num1 = 0\n expect = []\n for i in enumerate(s):\n if i == parenthesis[0]:\n parenthesis_num0+=1\n expect.append(parenthesis_num1)\n elif i == brackets[0]:\n brackets_num0+=1\n expect.append(brackets_num1)\n elif i == squirely[0]:\n squirely_num0+=1\n expect.append(squirely_num1)\n elif i == parenthesis[1]:\n parenthesis_num1+=1\n if parenthesis_num1>parenthesis_num0:\n return False\n if i == expect[:-1]:\n expect.pop(len(expect)-1)\n else:\n return False\n elif i == brackets[1]:\n brackets_num1+=1\n if brackets_num1>brackets_num0:\n return False\n if i == expect[:-1]:\n expect.pop(len(expect)-1)\n else:\n return False\n elif i == squirely[1]:\n squirely_num1+=1\n if squirely_num1>squirely_num0:\n return False\n if i == expect[:-1]:\n expect.pop(len(expect)-1)\n else:\n return False\n if parenthesis_num0 == parenthesis_num1 and brackets_num0 == brackets_num1 and squirely_num0 == squirely_num1:\n return True\n else:\n return False","def is_valid(s):\n in_str = False\n bb = 0\n for c in s:\n if c == '(' and not in_str:\n bb += 1\n elif c == ')' and not in_str:\n bb -= 1\n if bb < 0:\n return False\n elif c == '\\\"':\n in_str = not in_str\n return bb == 0","def isValid(self, s: str) -> bool:\n stack = []\n \n mapping = {\n \")\":\"(\",\n \"}\":\"{\",\n \"]\":\"[\"\n }\n \n for char in s:\n if char not in mapping:\n stack.append(char)\n \n else:\n top_element = stack.pop() if stack else \"#\"\n \n if mapping[char] != top_element:\n return False\n \n return not stack","def checkString(self,newick):\n\t\tisBalanced=True\n\t\tstack=[]\n\t\tfoundComma=False\n\t\tfor char in newick:\n\t\t\tif char == \"(\":\n\t\t\t\tstack.append(char)\n\t\t\telif char == \",\":\n\t\t\t\tfoundComma=True\n\t\t\telif char == \")\":\n\t\t\t\tif foundComma==True:\n\t\t\t\t\tstack.pop()\n\t\t\t\t\tfoundComma=False\n\t\t\t\telse:\n\t\t\t\t\tisBalanced=False\n\t\t\t\t\tbreak\n\t\t\telse:\n\t\t\t\tpass\n\t\tif len(stack) != 0:\n\t\t\tisBalanced=False\n\t\treturn isBalanced","def valid_parentheses(parens):\n\n # offset = 0\n # for p in parens:\n # if p == \"(\":\n # offset += 1\n # if p == \")\":\n # offset -= 1\n # return True if offset == 0 else False\n\n num_of_opens = 0\n for p in parens:\n if p == \"(\":\n num_of_opens += 1\n if p == \")\":\n num_of_opens -= 1\n if num_of_opens < 0:\n return False\n return True if num_of_opens == 0 else False","def checkBrackets(Code):\r\n \r\n left = 0; right = 0;\r\n for item in Code:\r\n if \"(\" in item: left+= item.count(\"(\");\r\n if \")\" in item: right += item.count(\")\");\r\n if left != right: raise Exception(\"incorrect number of brackets.\");","def is_matched2(expression):\n\n balanced = {\"{\": \"}\",\n \"[\": \"]\",\n \"(\": \")\"\n }\n stack = []\n\n if len(expression) < 2 or len(expression) % 2 != 0:\n return False\n\n\n for i in expression:\n\n if i in balanced.keys():\n stack.append(balanced[i])\n\n elif len(stack) > 0 and i == stack[-1]:\n stack.pop()\n\n else:\n return False\n\n return True","def valid_parentheses(parens):\n while len(parens) >= 1:\n if parens[0] == ')' or parens[-1] == '(':\n return False\n index = parens.find('()')\n parens = parens[:index] + parens[(index + 2):]\n return True","def Stringchecker(s1, s2):\r\n\r\n if len(s1) != len(s2) or len(set(s1)) < len(set(s2)):\r\n return False\r\n d = dict()\r\n for idx,c in enumerate(s1):\r\n if not d.get(c):\r\n d[c] = s2[idx]\r\n elif d[c] != s2[idx]:\r\n return False\r\n return True","def test_allow_bracket_multiplemix(self):\n val = DwcaValidator(yaml.load(self.yaml_allow6, Loader=yaml.FullLoader),\n error_handler=WhipErrorHandler)\n document = {'sex': 'male'}\n self.assertTrue(val.validate(document))\n\n document = {'sex': 'female'}\n self.assertTrue(val.validate(document))\n\n document = {'sex': 'male, female'}\n self.assertTrue(val.validate(document))\n\n document = {'sex': 'male,female'}\n self.assertFalse(val.validate(document))","def isBalanced(s):\n left = list(s)\n right = []\n\n result = 'YES'\n# breakpoint()\n while left or right: # if not empty, enter loop\n if (not left) and right:\n # left is empty but right is not empty, we have a problem\n result = 'NO'\n break\n\n else:\n x = left.pop()\n if x in set('})]'):\n right.append(x)\n elif x == '{' and not right:\n # cannot have hanging opening brackets; right.pop()\n # when right is empty generates error\n result = 'NO'\n break\n\n elif x == '{' and right[-1] == '}':\n right.pop()\n elif x == '(' and not right:\n result = 'NO'\n break\n\n elif x == '(' and right[-1] == ')':\n right.pop()\n elif x == '[' and not right:\n result = 'NO'\n break\n elif x == '[' and (right[-1] == ']'):\n right.pop()\n else:\n result = 'NO'\n break\n print(result)\n return result","def is_pair(a, b):\n\n\tif (not a.isalpha() or not b.isalpha()):\n\t\treturn False\n\n\ttest = ord(a) - ord(b)\n\tif (test == 32 or test == -32):\n\t\treturn True\n\t\n\treturn False","def parse_bracketed(s, left, right):\n if s.startswith(left) and s.endswith(right):\n return normalize(s[len(left) : -len(right)])\n else:\n return None","def brackets_match(string):\n expected = Stack()\n for character in string:\n if character == \"(\":\n expected.push(\")\")\n elif character == \"[\":\n expected.push(\"]\")\n elif character in \")]\":\n if expected and character == expected.top():\n expected.pop()\n else:\n return False\n return expected.is_empty()","def par_checker(s):\n lparens = \"([{\" # otevírací závorky\n rparens = \")]}\" # uzavírací závorky (ve stejném pořadí)\n stack = Stack()\n for c in s:\n if c in lparens:\n stack.push(c)\n for i in range(len(rparens)):\n if c == rparens[i]:\n if stack.is_empty() or stack.pop() != lparens[i]: # líné vyhodnocení\n return False\n return stack.is_empty()","def is_valid(self, string) -> bool:\n while '()' in string or '{}' in string or '[]' in string:\n string = string.replace('()', '').replace('[]', '').replace('{}', '')\n\n return len(string) == 0","def matches(open_paran, close_paran):\n opens = \"([{\"\n closes = \")]}\"\n return opens.index(open_paran) == closes.index(close_paran)","def check_valid(indexes):\n # Check if any substrings has any versions that are the opposite of it anywhere in that line.\n valid = False\n for k, v in indexes.items():\n swapped = k[::-1]\n other = indexes.get(swapped)\n # Check to see if the swapped version exists in the dictionary.\n if other:\n # 'aaa' case, these are invalid matches, don't bother checking further.\n if k == swapped:\n continue\n # single occurence case\n if len(v) == 1 and len(other) == 1:\n # Case where both occur inside or outsid brackets.\n if (int(v[0]) % 2) == (int(other[0]) % 2):\n continue\n else:\n valid = True\n else:\n # Use sets to eliminate duplicates in the same chunk.\n v_s = set(v)\n other_s = set(other)\n possible_combinations = [(x % 2, y % 2) for x in v_s for y in other_s]\n # For a pairing to be valid, one part needs to be in an even chunk and the other in an odd ([]) chunk.\n if (1, 0) in possible_combinations or (0, 1) in possible_combinations:\n valid = True\n return valid","def _has_matched_brackets(text: str) -> bool:\n open_bracket_stack = []\n for index, _ in enumerate(text):\n if text[index] == \"[\":\n open_bracket_stack.append(index)\n elif text[index] == \"]\":\n if len(open_bracket_stack) == 0:\n return False\n open_bracket_stack.pop()\n return len(open_bracket_stack) == 0","def test_should_raise_in_case_of_wrong_parenthesising(self):\n validator = CharCombinationValidator()\n\n with self.assertRaises(FormulaValidationError):\n validator(self.wrong_opening_closing_parenthesis)","def has_balanced_parens(phrase):\n\n# result = False\n\n# open_count = 0\n# close_count = 0\n\n# for char in phrase:\n# if char == \"(\":\n# open_count += 1\n\n# if char == \")\":\n# close_count += 1\n \n# if open_count < close_count:\n# return result \n\n\n# if open_count == close_count:\n# result = True\n\n \n# #open has to come first \n# return result\n\n # pair = \"()\"\n\n # if char == \"(\": #no open parenthesis then false\n# ( ) (\n\n# )(\n #for every open parenthesis there has to be a closed parenthesis \n\n\n parenthesis = []\n result = True\n\n for char in phrase:\n if char == \"(\":\n parenthesis.append(char)\n if char == \")\" and len(parenthesis) < 1: #seeing a closed before an open \n result = False\n if char == \")\" and len(parenthesis) >= 1:\n parenthesis.pop()\n \n\n if len(parenthesis) == 0:\n return result \n\n else: \n result = False \n return result","def is_balanced_other_solution(exp):\n closing = [\"}\", \")\", \"]\"]\n stack = MyStack()\n for character in exp:\n if character in closing:\n if stack.is_empty():\n return False\n top_element = stack.pop()\n if character == \"}\" and top_element != \"{\":\n return False\n if character == \")\" and top_element != \"(\":\n return False\n if character == \"]\" and top_element != \"[\":\n return False\n else:\n stack.push(character)\n if not stack.is_empty():\n return False\n return True","def validate_expression(str):\n stack = []\n pushChars, popChars = \"([\", \")]\"\n for c in str:\n if c in pushChars:\n stack.append(c)\n elif c in popChars:\n if not len(stack):\n raise ValidationError('La expresion tiene corchetes \\'[ ]\\' o parentesis \\'( )\\' sin cerrar')\n else:\n stackTop = stack.pop()\n balancingBracket = pushChars[popChars.index(c)]\n if stackTop != balancingBracket:\n raise ValidationError('La expresion tiene corchetes \\'[ ]\\' o parentesis \\'( )\\' sin cerrar')\n\n if len(stack):\n raise ValidationError('La expresion tiene corchetes \\'[ ]\\' o parentesis \\'( )\\' sin cerrar')","def test_match_multiple_brackets_right_context():\r\n runmatch(lcode)","def test_sqpp_nested_U1_or_SL2(self):\n self.assertEqual(self.parser.parse_query('(U(1) or SL(2,Z))'),\n ['+', 'u(1) | sl(2,z)'])","def _between_symbols(string, c1, c2):\n for char in [c1, c2]:\n if char not in string:\n raise ValueError(\"Couldn't find character {} in string {}\".format(\n char, string))\n return string[string.index(c1)+1:string.index(c2)]","def multi_bracket_validation(input):\n return recursion(iter(input))","def _match_brace(string, start_pos, pair='[]'):\n depth = 1\n if string[start_pos] != pair[0]:\n return None\n for index, char in enumerate(string[start_pos + 1:]):\n if char == pair[0]:\n depth += 1\n elif char == pair[1]:\n depth -= 1\n if depth == 0:\n return start_pos + index + 1\n return None","def is_pair_allowed(a, b):\n if a == complementary(b):\n return True\n if a == 'G' and b == 'U' or a == 'U' and b == 'G':\n return True\n return False","def matches(open, close):\n\n dic = {\"(\":\")\", \"{\":\"}\", \"[\":\"]\"}\n return dic[open] == close","def q(segmentation, s1, s2):\n index1 = find_index(segmentation, s1)\n index2 = find_index(segmentation, s2)\n return index1 == index2","def containsBrackets(Code):\r\n \r\n for item in Code:\r\n if '(' in item or ')' in item:\r\n return True;\r\n return False;","def isValid(self, s: str) -> bool:\n st = []\n\n for char in s:\n if (len(st) != 0):\n e = st[-1]\n if (self.isValidPair(e,char)):\n st.pop()\n continue\n st.append(char)\n return (len(st)==0)","def has_balanced_parens(string):\n\n parens = 0\n\n for char in string:\n if char == \"(\":\n parens += 1\n elif char == \")\":\n parens -= 1\n\n if parens < 0:\n return False\n\n return parens == 0","def _should_be_pair(s_list):\n assert(isa(s_list, List))\n return str(s_list).find(' . ') > 0","def test_validate_begin_equals_end():\n with pytest.raises(InvalidSegmentError):\n _validate([[1, 2], [5, 5]])","def _checkParentheses(tokens):\n\n\tparentheses_counter = 0\n\tfor token in tokens:\n\t\tif token.type == _Token.OPENING_PARENTHESIS:\n\t\t\tparentheses_counter += 1\n\t\telif token.type == _Token.CLOSING_PARENTHESIS:\n\t\t\tparentheses_counter -= 1\n\n\t\tif parentheses_counter < 0:\n\t\t\traise ParserError(\"Unmatching closing parenthesis\", token.start, token.end)\n\n\tif parentheses_counter > 0:\n\t\traise ParserError(\"Missing closing parenthesis\", tokens[-1].start, tokens[-1].end)","def compatible_grammars(sig1, sig2):\n return (\n sig1.keys() == sig2.keys()\n and\n all([regex_equivalent(sig1[rule], sig2[rule]) for rule in sig1.keys()])\n )","def is_substring_enclosed_in_brackets(sub, string):\n # to get (...)\n pattern = re.compile(\"\\([^\\)]*\\)\")\n # to get: (...(...)...)\n pattern2 = re.compile(\"\\([^\\)]*\\([^\\)]*\\)[^\\)]*\\)\")\n # to get: (...(...(...)...)...)\n pattern3 = re.compile(\"\\([^\\)]*\\([^\\)]*\\([^\\)]*\\)[^\\)]*\\)[^\\)]*\\)\")\n\n string = str(string)\n search = re.findall(pattern, string)\n search2 = re.findall(pattern2, string)\n search3 = re.findall(pattern3, string)\n\n search3 = set(search + search2 + search3)\n if any(str(sub) in s for s in search3): # or any(str(sub) in s for s in search2):\n return True\n return False","def test_allow_bracket_multiple(self):\n val = DwcaValidator(yaml.load(self.yaml_allow5, Loader=yaml.FullLoader),\n error_handler=WhipErrorHandler)\n document = {'sex': 'male'}\n self.assertTrue(val.validate(document))\n\n document = {'sex': 'female'}\n self.assertTrue(val.validate(document))\n\n document = {'sex': 'male, female'}\n self.assertFalse(val.validate(document))","def checkend(s):\n\n x = s[0]\n y = s[-1]\n\n if x == y:\n return True \n else:\n return False","def compare(lst1):\n brackets = list(\"{[()]}\")\n check = []\n for i in range(len(lst1)):\n check.append(lst1[i] in brackets)\n return all(check)","def is_rotation(s1, s2):\n\tif s1 is None or s2 is None:\n\t\traise ValueError('Invalid input')\n\n\t# Check if:\n\t# a. their lengths match\n\t# b. s1 is a substring of s2 appended to s2\n\treturn len(s1) == len(s2) and _is_substring(s2+s2, s1)","def is_balanced(par_str):\n\n left_par = \"{[(\"\n right_par = \"}])\"\n\n if len(par_str) % 2 != 0:\n return False\n \n # prevents in index error\n if par_str[0] in right_par:\n return False\n \n while len(par_str) < 0:\n for ind, par in par_str:\n if par not in left_par and par[ind] == par[ind - 1]:\n par_str.pop(ind)\n par_str.pop(ind - 1)\n print(par_str)\n break\n else:\n return False\n return True","def test_brackets_success(self):\n found = False\n pyint = Interpreter()\n try:\n pyint.run(code=BF_CORRECT_BRACK)\n except SystemExit: \n found = True\n self.assertFalse(found)","def test_allow_bracket(self):\n val = DwcaValidator(yaml.load(self.yaml_allow4, Loader=yaml.FullLoader),\n error_handler=WhipErrorHandler)\n document = {'sex': 'male'}\n self.assertTrue(val.validate(document))\n\n document = {'sex': 'female'}\n self.assertFalse(val.validate(document))","def balanced_string(string):\n stack = create_stack()\n pos = 0\n while pos < len(string):\n if string[pos] in '[{(':\n stack.push(string[pos])\n elif string[pos] in ']})':\n pair = stack.pop()\n if not match(string[pos], pair):\n return False\n pos = pos+1\n #return stack.length()\n if stack.length() == 0:\n return True\n else:\n return False","def one_edit(str1: str, str2: str):\n chars = dict()\n for i in range(max(len(str1), len(str2))):\n if i < len(str1):\n chars[str1[i]] = True if str1[i] in chars else False\n if i < len(str2):\n chars[str2[i]] = True if str2[i] in chars else False\n\n almost_not_okey = False\n for val in chars.values():\n if not val:\n if not almost_not_okey:\n almost_not_okey = True\n else:\n return False\n return True","def _check_separate(gti0, gti1):\n gti0_start = gti0[:, 0]\n gti0_end = gti0[:, 1]\n gti1_start = gti1[:, 0]\n gti1_end = gti1[:, 1]\n\n if (gti0_end[-1] <= gti1_start[0]) or (gti1_end[-1] <= gti0_start[0]):\n return True\n\n for g in gti1.flatten():\n for g0, g1 in zip(gti0[:, 0], gti0[:, 1]):\n if (g <= g1) and (g >= g0):\n return False\n for g in gti0.flatten():\n for g0, g1 in zip(gti1[:, 0], gti1[:, 1]):\n if (g <= g1) and (g >= g0):\n return False\n return True","def bracketsBalance(exp):\n stk = LinkedStack()\n for ch in exp:\n if ch in ['[' '(']:\n stk.push(ch)\n elif ch in [']', ')']:\n if stk.isEmpty():\n return False\n chFromStack = stk.pop()\n if ch == ']' and chFromStack != '[' or \\\n ch == ')' and chFromStack != '(':\n return False\n return stk.isEmpty()","def if2symbols(symbol1, symbol2, reel):\n for i in range(len(reel)-2):\n if reel[i] == symbol1 and reel[i+1] == symbol2:\n return True\n return False","def validate(name, bracket, bracket_side, bfr):\n\n return bfr[bracket.begin:bracket.end].islower()","def verify_name_syntax(sv, name, here, argtext, last):\r\n if name.find(Equal)!=-1: # \"=\" is not allowed in names\r\n print(\"\\n\", Err_equal_in_name, \"\\n\", name) # *** Illegal character in name: \"+ Equal +\" *** \r\n raise ReferenceError\r\n\r\n if not name or here==0: # name may not start with a bracket\r\n print(\"\\n\", Err_empty_name) # *** Syntax error: empty name *** \r\n print(name)\r\n if num>2: # common source of empty name error\r\n print(Help_continuation+Mline+\"' ):\") # you may have meant (with continuation character '\"+Mline):\r\n print(lines[num-3].strip(Space)+Col, Mline, Crlf, name) # suggested correction\r\n raise ReferenceError\r\n\r\n if argtext: # name is a function or a dict\r\n fun=name[:here]\r\n if fun in Internal_Functions: \r\n print(\"\\n\", Err_redef_internal_func) # *** Error: You cannot define an internal function *** \r\n print(fun, \"in\", fun+Obr+argtext+Cbr)\r\n raise ReferenceError\r\n \r\n if name[last:]: # name must end with closing bracket after args\r\n print(\"\\n\", Err_text_after_args) # *** Syntax error: text found after arguments *** \r\n print(name)\r\n raise ReferenceError","def validate_pairs(pairs, historical_pairs):\n if pairs is None:\n return False\n for p in pairs:\n if p in historical_pairs:\n return False\n return True","def _is_substring(s1, s2):\n\treturn s1.find(s2) != -1","def __check_if_symbol_is_over(rect1, rect2):\n\n rect_center_x_coord = rect1[4][0]\n rect2_center_x_coord = rect2[4][0]\n rect2_width = rect2[5]\n rect1_center_y_coord = rect1[4][1]\n rect2_center_y_coord = rect2[4][1]\n\n leftmost_x_coord = rect2_center_x_coord - (rect2_width // 2)\n rightmost_y_coord = rect2_center_x_coord + (rect2_width // 2)\n if (\n leftmost_x_coord <= rect_center_x_coord <= rightmost_y_coord\n and\n rect1_center_y_coord < rect2_center_y_coord\n ):\n return True\n else:\n return False","def parentheses_are_uneven(input_string):\n pcounter = 0\n for char in input_string:\n if char == '(':\n pcounter += 1\n elif char == ')':\n pcounter -= 1\n if pcounter != 0:\n return False\n else:\n return True","def check_validated_mapks(res, st1):\n assert len(res) == 2\n valid_stmts = res[0]\n mapped_stmts = res[1]\n assert isinstance(valid_stmts, list)\n assert isinstance(mapped_stmts, list)\n assert len(valid_stmts) == 0\n assert len(mapped_stmts) == 1\n mapped_stmt = mapped_stmts[0]\n assert isinstance(mapped_stmt, MappedStatement)\n assert mapped_stmt.original_stmt == st1\n assert isinstance(mapped_stmt.mapped_mods, list)\n assert len(mapped_stmt.mapped_mods) == 4\n ms = mapped_stmt.mapped_stmt\n assert isinstance(ms, Statement)\n agents = ms.agent_list()\n assert len(agents) == 2\n agent1 = agents[0]\n agent2 = agents[1]\n validate_mapk1(agent1)\n assert agent2.mods[0].matches(ModCondition('phosphorylation', 'T', '202'))\n assert agent2.mods[1].matches(ModCondition('phosphorylation', 'Y', '204'))\n assert unicode_strs((res, st1))","def brackets(result):\n return len(re.findall(r'\"', result.text)) == 2","def test_sqpp_paren_open_only_failure(self):\n self.failUnlessRaises(SyntaxError,\n self.parser.parse_query,\"(expr\")","def check_slice(self, m1, m2):\n\n if m1 == \"U'\" and m2 == \"D\" or m2 == \"U'\" and m1 == \"D\":\n return \"E' y'\"\n if m1 == \"U\" and m2 == \"D'\" or m2 == \"U\" and m1 == \"D'\":\n return \"E y\"\n if m1 == \"R\" and m2 == \"L'\" or m2 == \"R\" and m1 == \"L'\":\n return \"M x\"\n if m1 == \"R'\" and m2 == \"L\" or m2 == \"R'\" and m1 == \"L\":\n return \"M' x'\"\n if m1 == \"F\" and m2 == \"B'\" or m2 == \"F\" and m1 == \"B'\":\n return \"S' z\"\n if m1 == \"F'\" and m2 == \"B\" or m2 == \"F'\" and m1 == \"B\":\n return \"S z'\"\n\n return None","def validate_balanced_parenthesis(s):\n positions = [] # container for position of unbalanced parenthesis\n # counter for comments\n k = 0 # 0 = no comment range\n comment_open1 = False # comment indicator for /* */ comments\n comment_open2 = False # comment indicator for -- comments\n quote_open1 = False # quote '\n quote_open2 = False # quote \"\n for i, c in enumerate(s):\n if c == \"(\" and k == 0:\n positions.append(i)\n elif c == \")\" and k == 0:\n if len(positions) == 0:\n return {\n \"exit_code\": 1,\n \"val_lines\": find_line_number(s, [i]),\n \"total_lines\": count_lines(s)\n }\n else:\n positions.pop()\n elif (\n s[i:i+2] == \"/*\" and\n not comment_open1 and\n not comment_open2 and\n not quote_open1 and\n not quote_open2\n ): # if there is an opening comment /*\n k += 1\n comment_open1 = True\n elif (\n s[i:i+2] == \"*/\" and\n comment_open1 and\n not comment_open2 and\n not quote_open1 and\n not quote_open2\n ): # if there is a closing comment */\n k -= 1\n comment_open1 = False\n elif (\n s[i:i+2] == \"--\" and\n not comment_open1 and\n not comment_open2 and\n not quote_open1 and\n not quote_open2\n ): # if there is an opening comment --\n k += 1\n comment_open2 = True\n elif (\n (c == \"\\n\" or s[i:i+3] == \"[c]\") and\n not comment_open1 and\n comment_open2 and\n not quote_open1 and\n not quote_open2\n ): # if the -- comment ends\n k -= 1\n comment_open2 = False\n elif (\n c == \"'\" and\n not comment_open1 and\n not comment_open2 and\n not quote_open1 and\n not quote_open2\n ): # if opening quote '\n k += 1\n quote_open1 = True\n elif (\n c == \"'\" and\n not comment_open1 and\n not comment_open2 and\n quote_open1 and\n not quote_open2\n ): # if opening quote '\n k -= 1\n quote_open1 = False\n elif (\n c == '\"' and\n not comment_open1 and\n not comment_open2 and\n not quote_open1 and\n quote_open2\n ): # if opening quote '\n k += 1\n quote_open2 = True\n elif (\n c == '\"' and\n not comment_open1 and\n not comment_open2 and\n not quote_open1 and\n quote_open2\n ): # if opening quote '\n k -= 1\n quote_open2 = False\n if len(positions) == 0:\n return {\n \"exit_code\": 0,\n \"total_lines\": count_lines(s)\n }\n else:\n return {\n \"exit_code\": 1,\n \"val_lines\": find_line_number(s, positions),\n \"total_lines\": count_lines(s)\n }","def checkValidString(self, string: str) -> bool:\n @lru_cache(None)\n def dp(index, diff):\n \"\"\"\n index is the index of string\n diff the counts of '(' - counts of ')'\n \"\"\"\n\n if index == len_s:\n return diff == 0\n if abs(diff) > len_s - index:\n return False\n c = string[index]\n index += 1\n if c == '(':\n return dp(index, diff + 1)\n elif c == ')':\n if diff - 1 < 0:\n return False\n return dp(index, diff - 1)\n else:\n return dp(index, diff + 1) or dp(index, diff - 1) or dp(index, diff)\n\n len_s = len(string)\n return dp(0, 0)","def match_parentheses(string_characters):\n left_parenteses = \"({[\"\n right_parenteses = \")}]\"\n S = LinkedListStack()\n\n for character in string_characters:\n if character in left_parenteses:\n S.push(character)\n elif character in right_parenteses:\n if S._is_empty():\n return False\n if right_parenteses.index(character) != left_parenteses.index(S.pop().value):\n return False\n return S._is_empty()","def test_valid_region_syntax():\n reg_str1 = 'circle[[18h12m24s, -23d11m00s], [2.3arcsec,4.5arcsec]'\n with pytest.raises(CRTFRegionParserError) as excinfo:\n Regions.parse(reg_str1, format='crtf')\n\n estr = (\"Not in proper format: ('2.3arcsec', '4.5arcsec') should be \"\n \"a single length\")\n assert estr in str(excinfo.value)\n\n reg_str2 = ('symbol[[32.1423deg, 12.1412deg], 12deg], linewidth=2, '\n 'coord=J2000, symsize=2')\n with pytest.raises(CRTFRegionParserError) as excinfo:\n Regions.parse(reg_str2, format='crtf')\n estr = 'Not in proper format: \"12deg\" should be a symbol'\n assert estr in str(excinfo.value)\n\n reg_str3 = 'circle[[18h12m24s, -23d11m00s]'\n with pytest.raises(CRTFRegionParserError) as excinfo:\n Regions.parse(reg_str3, format='crtf')\n estr = ('Does not contain expected number of parameters for the region '\n '\"circle\"')\n assert estr in str(excinfo.value)\n\n reg_str4 = 'poly[[1, 2], [4, 5]]'\n with pytest.raises(CRTFRegionParserError) as excinfo:\n Regions.parse(reg_str4, format='crtf')\n assert 'polygon should have >= 3 coordinates' in str(excinfo.value)\n\n reg_str6 = 'rotbox[[12h01m34.1s, 12d23m33s], [3arcmin,], 12deg]'\n with pytest.raises(CRTFRegionParserError) as excinfo:\n Regions.parse(reg_str6, format='crtf')\n assert \"('3arcmin', '') should be a pair of lengths\" in str(excinfo.value)","def hasMidBrackets(Code):\r\n \r\n l = len(Code);\r\n \r\n # Skip past the end brackets.\r\n left, right = endBrackets(Code)[0], endBrackets(Code)[1];\r\n left += 1; right -= 1;\r\n \r\n # Then keep going until either references a bracket or right < left.\r\n while right >= left: \r\n if Code[left] in brackets: return True;\r\n if Code[right] in brackets: return True;\r\n left += 1; right -= 1;\r\n \r\n # Return False if right < left.\r\n return False;","def strings_differ(string1, string2):\n if len(string1) != len(string2):\n return True\n invalid_bits = 0\n for a, b in zip(string1, string2):\n invalid_bits += a != b\n return invalid_bits != 0","def test_brackets_tall(self):\r\n self._each_parens(r'\\left[x^y\\right]', 'x^y', '[', tall=True)","def judge(self, s1, s2):\n if len(s2) < len(s1):\n return False\n index_of_s1 = 0\n index_of_s2 = 0\n while index_of_s1 < len(s1) and index_of_s2 < len(s2):\n if s1[index_of_s1] == s2[index_of_s2]:\n index_of_s1 += 1\n index_of_s2 += 1\n else:\n index_of_s2 += 1\n return True if index_of_s1 == len(s1) else False","def match_pair(expr, pair=(r'{', r'}'), start=0):\n\n beg = pair[0]\n fin = pair[1]\n\n # find first opening\n sstart = expr.find(beg, start)\n\n count = 0\n\n if beg == fin:\n eend = expr.find(fin, sstart + 1)\n return sstart, eend\n\n p = re.compile('(' + beg + '|' + fin + ')', re.M)\n ps = re.compile(beg, re.M)\n\n iterator = p.finditer(expr, start)\n\n for match in iterator:\n if ps.match(match.group()):\n count += 1\n else:\n count += -1\n\n if count == 0:\n return sstart, match.end()\n\n return None","def validate(string):\n \n tokens = string.split()\n \n # Remembers if the previous token was an operator\n opflag = True\n \n ## Highly inefficient validity checking begins here ##\n \n # List of operators as they would appear in the infix expression\n operators = ['+', '-', '*', '/', '^', 'sqrt']\n \n # First and foremost, detect all unary minus signs and mark them as such\n for i in xrange(len(tokens)):\n # A unary minus is a minus operator which occurs after another operator\n # or after an open parenthesis.\n if tokens[i] in operators or tokens[i] == '(':\n if opflag:\n if tokens[i] == '-':\n tokens[i] = 'u-'\n # Leave opflag true to allow cascading of unary minuses\n elif tokens[i] in ['sqrt', '(']:\n # These operators can be cascaded, so leave them alone\n # Also, leave opflag true to handle a subsequent u-\n pass\n else:\n # Any other operator must be caught\n raise ExpressionError('Operators cannot be cascaded!')\n # We found an operator, but opflag isn't true. Set it.\n else:\n opflag = True\n else:\n # We found something other than an operator, or a ')'. If opflag is\n # false, and the token is not ')', then we have two adjacent\n # variables/numbers. This is also an invalid combination\n if not opflag and tokens[i] != ')':\n raise ExpressionError('Adjacent operands with no operator!')\n # Otherwise, unset opflag\n else:\n opflag = False\n \n # Check whether parentheses match\n s = Stack()\n for token in tokens:\n if token == '(':\n s.push(token)\n elif token == ')':\n if s.pop() != '(':\n raise ExpressionError('Parentheses do not match')\n if not s.is_empty():\n raise ExpressionError('Parentheses do not match')\n \n return tokens","def check_two_spots(a, b, c):\n\n if a != \"\" and b != \"\" and c == \"\":\n return False\n if a != \"\" and b == \"\" and c != \"\":\n return False\n if a == \"\" and b != \"\" and c != \"\":\n return False\n return True","def is_minpair(first, second, corpus_context, segment_pairs, environment_filter):\n first = getattr(first, corpus_context.sequence_type)\n second = getattr(second, corpus_context.sequence_type)\n\n if len(first) != len(second):\n return False\n\n has_difference = False\n for i in range(len(first)):\n if first[i] == second[i]:\n continue\n elif (conflateable(first[i], second[i], segment_pairs)\n and fits_environment(first, second, i, environment_filter)):\n has_difference = True\n continue\n else:\n return False\n\n if has_difference:\n return True","def is_balanced(exp: str) -> bool:\n if len(exp) % 2 != 0:\n print(\"expression is not balanced because it doesn't have at least 1 closing parenthesis;\")\n print(\"its not a balanced expression; the length is odd\")\n return False\n\n if len(exp) == 0:\n print(\"empty expression, balance\")\n return True\n\n open_list = [\"[\", \"{\", \"(\"]\n closed_list = [\"]\", \"}\", \")\"]\n s = MyStack()\n for i in range(len(exp) // 2):\n if exp[i] in open_list:\n s.push(exp[i])\n\n for i in range(len(exp) // 2, len(exp)):\n close_pos = closed_list.index(exp[i])\n open_pos = open_list.index(s.pop())\n if close_pos == open_pos:\n continue\n else:\n return False\n\n return True","def check_str_cov(str_fun, str_cov, shape_X1, shape_X2=None):\n\n assert isinstance(str_fun, str)\n assert isinstance(str_cov, str)\n assert isinstance(shape_X1, tuple)\n assert shape_X2 is None or isinstance(shape_X2, tuple)\n\n if str_cov in constants.ALLOWED_GP_COV_BASE:\n assert len(shape_X1) == 2\n if shape_X2 is not None:\n assert len(shape_X2) == 2\n elif str_cov in constants.ALLOWED_GP_COV_SET:\n assert len(shape_X1) == 3\n if shape_X2 is not None:\n assert len(shape_X2) == 3\n elif str_cov in constants.ALLOWED_GP_COV: # pragma: no cover\n raise ValueError('{}: missing conditions for str_cov.'.format(str_fun))\n else:\n raise ValueError('{}: invalid str_cov.'.format(str_fun))\n return","def valid_expression(expression):\n OPERATORS= '+*/-'\n if no_operators(expression) != True:\n return no_operators(expression)\n if no_paranthesis(expression) != True:\n return no_paranthesis(expression)\n if no_numbers(expression) != True:\n return no_numbers(expression)\n if invalid_characters(expression) != True:\n return invalid_characters(expression)\n if match_paranthesis(expression) == False:\n raise NotValidExpression('Not a valid expression, brackets mismatched.')\n number_operators = 0\n number_paranthesis = 0\n for i in expression:\n if i in OPERATORS:\n number_operators += 1\n elif i == '(' or i == ')':\n number_paranthesis +=1\n expression1 = expression[1:(len(expression) - 1)] # checks if the expression without the first and last character is valid\n if match_paranthesis(expression1) == False and ('(' in expression1 or ')' in expression1):\n raise NotValidExpression('Not a valid expression, brackets mismatched.') # if it is not, raises an appropiate error\n for i in range(0, len(expression) - 1):\n #Checks if an operator is missing,if there exists a number followed by ( or if there is a )before the number\n if expression[i] not in OPERATORS and expression[i] not in '()':\n if expression[i + 1] == '(':\n raise NotValidExpression('Not a valid expression, operator missing.')\n elif expression[i] in OPERATORS and expression[i + 1] in OPERATORS + ')' :\n raise NotValidExpression('Not a valid expression, wrong placement of operators')\n #Checks if an operator is placed wrongly , before ) or next to another operator\n if expression[i+1] not in OPERATORS and expression[i + 1] not in '()':\n if expression[i] == ')':\n raise NotValidExpression('Not a valid expression, operator missing.')\n elif expression[i+1] in OPERATORS and expression[i] in OPERATORS + '(':\n raise NotValidExpression('Not a valid expression, wrong placement of operators')\n if 2*number_operators != number_paranthesis: # an expression is valid only if the number of paranthesis is equal to the double of the number of operators\n raise NotValidExpression('Not a valid expression, wrong number of operands.')\n return True","def shipvalidator(point1: tuple, point2: tuple, board: list):\n valid = True\n # Is horizontal\n if point1[0] == point2[0]:\n # No collisions\n for i in range(min(point1[1], point2[1]), max(point1[1], point2[1])):\n if board[point1[0]][i] != \" \":\n valid = False\n # Is vertical\n elif point1[1] == point2[1]:\n # Doesn't overlap\n for i in range(min(point1[0], point2[0]), max(point1[0], point2[0])):\n if board[i][point1[1]] != \" \":\n valid = False\n else:\n valid = False\n return valid","def validate(self, s):\n if len(s) == 0:\n return False\n if s in self.whitelist:\n return True\n if s in self.blacklist:\n return False\n\n # SQL Types are rarely used\n if 't' in s and 'f(t' not in s and 'At' not in s:\n return False\n\n if '1nf' in s:\n return False\n if 's1o' in s:\n return False\n if 'oo' in s:\n return False\n if 'v,s' in s:\n return False\n if 's,v' in s:\n return False\n if 'v,v' in s:\n return False\n if 'v,1' in s:\n return False\n if 'v,n' in s:\n return False\n if 'n,v' in s:\n return False\n if '1,v' in s:\n return False\n if 'Eo(' in s:\n return False\n if '(o(' in s:\n return False\n if '(o1' in s:\n return False\n if '(on' in s:\n return False\n if '(os' in s:\n return False\n if '(of' in s:\n return False\n if '(ov' in s:\n return False\n if 'B(n)' in s:\n return False\n if 'oso' in s:\n return False\n if 'o1o' in s:\n return False\n if 'ono' in s:\n return False\n\n # only 1 special case for this\n # 1;foo:goto foo\n # 1;n:k\n # the 'foo' can only be a 'n' type\n if ':' in s and not 'n:' in s:\n return False\n\n if '11' in s:\n return False\n\n if '))' in s:\n return False\n if '((' in s:\n return False\n if 'v1' in s:\n return False\n\n if 'nv' in s and ';T' not in s:\n return False\n if 'nn' in s and ';T' not in s:\n return False\n\n # select @version foo is legit\n # but unlikely anywhere else\n if 'vn' in s and 'Evn' not in s:\n return False\n\n if 'oE' in s:\n return False\n\n if 'A1' in s:\n return False\n if 'An' in s:\n return False\n if 'A(1' in s:\n return False\n\n if 'vov' in s:\n return False\n if 'vo1' in s:\n return False\n if 'von' in s:\n return False\n\n if 'ns' in s:\n if 'U' in s:\n return True\n if 'T' in s:\n return True\n return False\n\n if 'sn' in s:\n # that is... Tsn is ok\n if s.find('T') != -1 and s.find('T') < s.find('sn'):\n return True\n return False\n\n # select foo (as) bar is only nn type i know\n if 'nn' in s and 'Enn' not in s and ';T' not in s:\n return False\n\n if ',o' in s:\n return False\n\n if 'kk' in s and 'Tkk' not in s:\n return False\n\n if 'ss' in s:\n return False\n\n if 'ff' in s:\n return False\n\n if '1no' in s:\n return False\n\n if 'kno' in s:\n return False\n\n if 'nEk' in s:\n return False\n\n if 'n(n' in s:\n return False\n if '1so' in s:\n return False\n if '1s1' in s:\n return False\n if 'noo' in s:\n return False\n if 'ooo' in s:\n return False\n\n if 'vvv' in s:\n return False\n\n if '1vn' in s:\n return False\n if '1n1' in s:\n return False\n if '&1n' in s:\n return False\n if '&1v' in s:\n return False\n if '&1s' in s:\n return False\n if 'nnk' in s:\n return False\n if 'n1f' in s:\n return False\n # folded away\n if s.startswith('('):\n return False\n\n if '&o' in s:\n return False\n\n if '1,1' in s:\n return False\n if '1,s' in s:\n return False\n if '1,n' in s:\n return False\n if 's,1' in s:\n return False\n if 's,s' in s:\n return False\n if 's,n' in s:\n return False\n if 'n,1' in s:\n return False\n if 'n,s' in s:\n return False\n if 'n,n' in s:\n return False\n if '1o1' in s:\n return False\n if '1on' in s:\n return False\n if 'no1' in s:\n return False\n if 'non' in s:\n return False\n if '1(v' in s:\n return False\n if '1(n' in s:\n return False\n if '1(s' in s:\n return False\n if '1(1' in s:\n return False\n if 's(s' in s:\n return False\n if 's(n' in s:\n return False\n if 's(1' in s:\n return False\n if 's(v' in s:\n return False\n if 'v(s' in s:\n return False\n if 'v(n' in s:\n return False\n if 'v(1' in s:\n return False\n if 'v(v' in s:\n return False\n\n if s.startswith('n('):\n return False\n\n if s.startswith('vs'):\n return False\n\n if s.startswith('o'):\n return False\n\n if ')(' in s:\n return False\n\n # need to investigate T(vv) to see\n # if it's correct\n if 'vv' in s and s != 'T(vv)':\n return False\n\n # unlikely to be sqli but case FP\n if s in ('so1n)', 'sonoE'):\n return False\n\n return True","def forbid_consecutive_interval(s1, s2, interval):\n for (na,n1), (nb, n2) in util.pairwise(tools.iter_melodies(s1,s2,all=True)):\n nap, nbp, n1p, n2p = [util.to_pitch(x) for x in (na, nb, n1, n2)]\n if nap.isQualifiedInterval(interval).With(n1p) and nbp.isQualifiedInterval(interval).With(n2p):\n error.warn(f\"Two {interval} in a row are forbidden\",na,nb,n1,n2,f\"in {s1.title} and {s2.title}\")","def test_s2():\n vc = vtec.parse(EX1)\n assert vc[0].s2() == \"TO.W\""],"string":"[\n \"def match(str1, str2):\\n if ( (str2 == '[' and str1 == ']')\\n or (str2 == '{' and str1 == '}')\\n or (str2 == '(' and str1 == ')')):\\n return True\\n return False\",\n \"def multi_bracket_validation(input):\\n brackets = Stack()\\n for i in input:\\n if i == '(' or i == '[' or i == '{':\\n brackets.push(i)\\n elif i == ')' or i == ']' or i == '}':\\n if brackets._size == 0:\\n return False\\n elif i == ')' and brackets.top.val == '(':\\n brackets.pop()\\n elif i == ']' and brackets.top.val == '[':\\n brackets.pop()\\n elif i == '}' and brackets.top.val == '{':\\n brackets.pop()\\n else:\\n return False\\n\\n if brackets._size != 0:\\n return False\\n return True\",\n \"def bracket_validator(brackets):\\n bracket_stack = []\\n for b in brackets:\\n if b == '(' or b == '{' or b == '[':\\n bracket_stack.append(b)\\n\\n elif b == ')' or b == '}' or b == ']':\\n top = bracket_stack.pop()\\n\\n if b != bracket_match(top):\\n return False\\n\\n return True\",\n \"def _check_brackets(tokens: List[str]) -> True:\\r\\n brackets = []\\r\\n for token in tokens:\\r\\n if token in \\\"([\\\":\\r\\n brackets.append(token)\\r\\n elif token in \\\")]\\\":\\r\\n if not brackets:\\r\\n raise ExtraBracket(token)\\r\\n closing_bracket = brackets.pop()\\r\\n if ((token == \\\"(\\\" and closing_bracket != \\\")\\\") or\\r\\n (token == \\\"[\\\" and closing_bracket != \\\"]\\\")):\\r\\n raise BracketsMismatch(token, closing_bracket)\\r\\n if brackets:\\r\\n raise NoClosingBracket(brackets[0])\\r\\n return True\",\n \"def isValid(self, s: str) -> bool:\\n open_brackets = [] # stack of open but not closed brackets\\n for char in s:\\n if char in ['(', '[', '{']:\\n open_brackets.append(char)\\n else:\\n try:\\n complement = open_brackets.pop()\\n except IndexError:\\n return False\\n else:\\n if complement + char not in ['()', '[]', '{}']:\\n return False\\n return not len(open_brackets)\",\n \"def bracketed (phrase,bracketing='()'):\\r\\n\\r\\n level = 0\\r\\n left_point = None\\r\\n right_point = None\\r\\n \\r\\n\\r\\n for count,char in enumerate(phrase):\\r\\n\\r\\n if char == bracketing[0]:\\r\\n if level == 0:\\r\\n left_point = count\\r\\n level+=1\\r\\n if char == bracketing[1]:\\r\\n level-=1\\r\\n if level == 0:\\r\\n right_point = count\\r\\n if not (left_point is None) and (not right_point is None) and left_point == 0 and right_point == len(phrase)-1:\\r\\n return True\\r\\n return False\",\n \"def isValid(self, s):\\n # 1\\n if not s:\\n return True\\n\\n # 2\\n if not len(s) % 2 == 0:\\n return False\\n\\n comp = {\\n ')': '(',\\n ']': '[',\\n '}': '{',\\n }\\n stack = []\\n\\n for char in s:\\n # 3\\n if char in comp:\\n popped = stack.pop() if stack else '@'\\n if popped != comp[char]:\\n return False\\n # 2\\n else:\\n stack.append(char)\\n\\n return not stack\",\n \"def check_matching_brackets(s, opening=\\\"([{\\\", closing=\\\")]}\\\"):\\n\\n stack = Stack()\\n\\n for item in s:\\n # Stores any opening character\\n if item in opening:\\n stack.push(item)\\n else:\\n # if a closing bracket is found and we try to remove the matching opening bracket from the top of the stack\\n # in case the top of the stack is not a matching bracket, the string is not properly nested\\n if stack.length() < 1 or stack.pop() is not opening[closing.index(item)]:\\n return 0\\n\\n return 0 if stack.length() > 0 else 1\",\n \"def bracketed (self,phrase):\\r\\n\\r\\n level = 0\\r\\n left_point = None\\r\\n right_point = None\\r\\n \\r\\n\\r\\n for count,char in enumerate(phrase):\\r\\n\\r\\n if char == '(':\\r\\n if level == 0:\\r\\n left_point = count\\r\\n level+=1\\r\\n if char == ')':\\r\\n level-=1\\r\\n if level == 0:\\r\\n right_point = count\\r\\n if not (left_point is None) and (not right_point is None) and left_point == 0 and right_point == len(phrase)-1:\\r\\n return True\\r\\n return False\",\n \"def BalancedBrackets(Str):\\n stack = []\\n for parenthesis in Str:\\n if parenthesis == '{' or parenthesis == '(' or parenthesis == '[':\\n stack.append(parenthesis)\\n elif parenthesis == '}' or parenthesis == ')' or parenthesis == ']':\\n if len(stack) < 1:\\n return False\\n last_item = stack.pop()\\n if not Compare(last_item, parenthesis):\\n return False\\n if len(stack) != 0:\\n return False\\n\\n return True\",\n \"def bracket_match(bracket):\\n if bracket == '(':\\n return ')'\\n if bracket == '[':\\n return ']'\\n if bracket == '{':\\n return '}'\",\n \"def isValid(self, s: str) -> bool:\\n stack = list()\\n for c in s:\\n if c in Solution.corresponding_parenthesis:\\n stack.append(Solution.corresponding_parenthesis[c])\\n elif not stack or stack.pop() != c:\\n return False\\n return not stack\",\n \"def is_well_formed(s):\\n # TODO - you fill in here.\\n s = list(s)\\n op = []\\n l = len(s)\\n for i in range(l):\\n if s[i] in ['(','{','[']:\\n op.append(s[i])\\n else: \\n if len(op) == 0:\\n return False\\n temp = op.pop() \\n if s[i] == ')':\\n if temp != '(':\\n return False\\n elif s[i] == '}':\\n if temp != '{':\\n return False\\n else:\\n if temp != '[':\\n return False\\n if len(op) != 0:\\n return False\\n return True\",\n \"def syntaxValid(s):\\n\\tif not s or type(s) != str or s[0] != '[':\\n\\t\\treturn False\\n\\n\\t#using stack to check for matching brackets\\n\\t#push '[' and pop for ']'\\n\\tbracketStack = []\\n\\tfor i in range(len(s)):\\n\\t\\tcurrentChar = s[i]\\n\\t\\tif currentChar == '[':\\n\\t\\t\\t#check for there is a delimiter before open bracket or trying to double nest\\n\\t\\t\\tif i != 0 and s[i-1] == '[' or i > 1 and s[i-2] != ',':\\n\\t\\t\\t\\treturn False\\n\\t\\t\\tbracketStack.append('[')\\n\\t\\telif currentChar == ']':\\n\\t\\t\\t#check if stack is prematurely empty or no delimiter after closed bracket\\n\\t\\t\\tif not bracketStack or (i != len(s)-1 and s[i+1] != ']' and s[i+1] != ','):\\n\\t\\t\\t\\treturn False \\n\\t\\t\\telse: \\n\\t\\t\\t\\tbracketStack.pop()\\n\\t\\telif currentChar == ',':\\n\\t\\t\\t#check if delimiter is invalid\\n\\t\\t\\tif i == 0 or i == len(s)-1 or s[i-1] == '[' or s[i+1] != ' ':\\n\\t\\t\\t\\treturn False\\n\\t#if the stack is empty, we have matched brackets, hence s is valid\\n\\treturn len(bracketStack) == 0\",\n \"def parentheses_balanced(astr):\\n\\n if astr == '':\\n return True\\n\\n start = 0\\n end = len(astr) - 1\\n found_open = False\\n found_closed = False\\n\\n while True:\\n\\n while found_open is False:\\n if astr[start] == '(':\\n found_open = True\\n elif astr[start] == ')':\\n return 'Invalid'\\n elif start >= end:\\n return 'Valid'\\n\\n start += 1\\n \\n found_open = False\\n\\n while found_closed is False:\\n if astr[end] == ')':\\n found_closed = True\\n elif astr[end] == '(' or end <= start:\\n return 'Invalid'\\n\\n end -= 1\\n\\n found_closed = False\",\n \"def valid_parenthesis(string):\\n if len(string) % 2:\\n print(\\\"Invalid Parenthesis\\\")\\n return\\n stack = []\\n bracket_hash = {\\\"}\\\": \\\"{\\\", \\\")\\\": \\\"(\\\", \\\"]\\\": \\\"[\\\"}\\n\\n for bracket in string:\\n if bracket in bracket_hash:\\n if stack:\\n top = stack.pop()\\n if bracket_hash[bracket] is not top:\\n print(\\\"Invalid Parenthesis\\\")\\n return\\n else:\\n top = \\\"\\\"\\n else:\\n if bracket in bracket_hash.values():\\n stack.append(bracket)\\n else:\\n print(\\\"Invalid String\\\")\\n return\\n\\n if not stack:\\n print(\\\"Valid Parenthesis\\\")\\n return\",\n \"def has_balanced_parens(string):\",\n \"def isbracket(token):\\n\\n # Token is a bracket\\n return token == \\\"[\\\"\",\n \"def end_other(s_1, s_2):\\n str_1 = s_1[-3:]\\n str_2 = s_2[-3:]\\n\\n if(str_1.lower() == s_2.lower()):\\n \\n isValid = True\\n elif(str_2.lower() == s_1.lower()):\\n isValid = True\\n else:\\n isValid = False\\n return isValid\",\n \"def check(s1, s2):\\n\\n l1 = len(s1)\\n l2 = len(s2)\\n\\n if len(s1) == len(s2):\\n return check_update(s1, s2)\\n elif l1 + 1 == l2:\\n return check_insert(s1, s2)\\n elif l1 - 1 == l2:\\n return check_insert(s2, s1)\\n else:\\n return False\",\n \"def group_check(s):\\n parenthesis = ['(',')']\\n brackets = ['[', ']']\\n squirely = ['{', '}']\\n parenthesis_num0 = 0\\n brackets_num0 = 0\\n squirely_num0 = 0\\n parenthesis_num1 = 0\\n brackets_num1 = 0\\n squirely_num1 = 0\\n expect = []\\n for i in enumerate(s):\\n if i == parenthesis[0]:\\n parenthesis_num0+=1\\n expect.append(parenthesis_num1)\\n elif i == brackets[0]:\\n brackets_num0+=1\\n expect.append(brackets_num1)\\n elif i == squirely[0]:\\n squirely_num0+=1\\n expect.append(squirely_num1)\\n elif i == parenthesis[1]:\\n parenthesis_num1+=1\\n if parenthesis_num1>parenthesis_num0:\\n return False\\n if i == expect[:-1]:\\n expect.pop(len(expect)-1)\\n else:\\n return False\\n elif i == brackets[1]:\\n brackets_num1+=1\\n if brackets_num1>brackets_num0:\\n return False\\n if i == expect[:-1]:\\n expect.pop(len(expect)-1)\\n else:\\n return False\\n elif i == squirely[1]:\\n squirely_num1+=1\\n if squirely_num1>squirely_num0:\\n return False\\n if i == expect[:-1]:\\n expect.pop(len(expect)-1)\\n else:\\n return False\\n if parenthesis_num0 == parenthesis_num1 and brackets_num0 == brackets_num1 and squirely_num0 == squirely_num1:\\n return True\\n else:\\n return False\",\n \"def is_valid(s):\\n in_str = False\\n bb = 0\\n for c in s:\\n if c == '(' and not in_str:\\n bb += 1\\n elif c == ')' and not in_str:\\n bb -= 1\\n if bb < 0:\\n return False\\n elif c == '\\\\\\\"':\\n in_str = not in_str\\n return bb == 0\",\n \"def isValid(self, s: str) -> bool:\\n stack = []\\n \\n mapping = {\\n \\\")\\\":\\\"(\\\",\\n \\\"}\\\":\\\"{\\\",\\n \\\"]\\\":\\\"[\\\"\\n }\\n \\n for char in s:\\n if char not in mapping:\\n stack.append(char)\\n \\n else:\\n top_element = stack.pop() if stack else \\\"#\\\"\\n \\n if mapping[char] != top_element:\\n return False\\n \\n return not stack\",\n \"def checkString(self,newick):\\n\\t\\tisBalanced=True\\n\\t\\tstack=[]\\n\\t\\tfoundComma=False\\n\\t\\tfor char in newick:\\n\\t\\t\\tif char == \\\"(\\\":\\n\\t\\t\\t\\tstack.append(char)\\n\\t\\t\\telif char == \\\",\\\":\\n\\t\\t\\t\\tfoundComma=True\\n\\t\\t\\telif char == \\\")\\\":\\n\\t\\t\\t\\tif foundComma==True:\\n\\t\\t\\t\\t\\tstack.pop()\\n\\t\\t\\t\\t\\tfoundComma=False\\n\\t\\t\\t\\telse:\\n\\t\\t\\t\\t\\tisBalanced=False\\n\\t\\t\\t\\t\\tbreak\\n\\t\\t\\telse:\\n\\t\\t\\t\\tpass\\n\\t\\tif len(stack) != 0:\\n\\t\\t\\tisBalanced=False\\n\\t\\treturn isBalanced\",\n \"def valid_parentheses(parens):\\n\\n # offset = 0\\n # for p in parens:\\n # if p == \\\"(\\\":\\n # offset += 1\\n # if p == \\\")\\\":\\n # offset -= 1\\n # return True if offset == 0 else False\\n\\n num_of_opens = 0\\n for p in parens:\\n if p == \\\"(\\\":\\n num_of_opens += 1\\n if p == \\\")\\\":\\n num_of_opens -= 1\\n if num_of_opens < 0:\\n return False\\n return True if num_of_opens == 0 else False\",\n \"def checkBrackets(Code):\\r\\n \\r\\n left = 0; right = 0;\\r\\n for item in Code:\\r\\n if \\\"(\\\" in item: left+= item.count(\\\"(\\\");\\r\\n if \\\")\\\" in item: right += item.count(\\\")\\\");\\r\\n if left != right: raise Exception(\\\"incorrect number of brackets.\\\");\",\n \"def is_matched2(expression):\\n\\n balanced = {\\\"{\\\": \\\"}\\\",\\n \\\"[\\\": \\\"]\\\",\\n \\\"(\\\": \\\")\\\"\\n }\\n stack = []\\n\\n if len(expression) < 2 or len(expression) % 2 != 0:\\n return False\\n\\n\\n for i in expression:\\n\\n if i in balanced.keys():\\n stack.append(balanced[i])\\n\\n elif len(stack) > 0 and i == stack[-1]:\\n stack.pop()\\n\\n else:\\n return False\\n\\n return True\",\n \"def valid_parentheses(parens):\\n while len(parens) >= 1:\\n if parens[0] == ')' or parens[-1] == '(':\\n return False\\n index = parens.find('()')\\n parens = parens[:index] + parens[(index + 2):]\\n return True\",\n \"def Stringchecker(s1, s2):\\r\\n\\r\\n if len(s1) != len(s2) or len(set(s1)) < len(set(s2)):\\r\\n return False\\r\\n d = dict()\\r\\n for idx,c in enumerate(s1):\\r\\n if not d.get(c):\\r\\n d[c] = s2[idx]\\r\\n elif d[c] != s2[idx]:\\r\\n return False\\r\\n return True\",\n \"def test_allow_bracket_multiplemix(self):\\n val = DwcaValidator(yaml.load(self.yaml_allow6, Loader=yaml.FullLoader),\\n error_handler=WhipErrorHandler)\\n document = {'sex': 'male'}\\n self.assertTrue(val.validate(document))\\n\\n document = {'sex': 'female'}\\n self.assertTrue(val.validate(document))\\n\\n document = {'sex': 'male, female'}\\n self.assertTrue(val.validate(document))\\n\\n document = {'sex': 'male,female'}\\n self.assertFalse(val.validate(document))\",\n \"def isBalanced(s):\\n left = list(s)\\n right = []\\n\\n result = 'YES'\\n# breakpoint()\\n while left or right: # if not empty, enter loop\\n if (not left) and right:\\n # left is empty but right is not empty, we have a problem\\n result = 'NO'\\n break\\n\\n else:\\n x = left.pop()\\n if x in set('})]'):\\n right.append(x)\\n elif x == '{' and not right:\\n # cannot have hanging opening brackets; right.pop()\\n # when right is empty generates error\\n result = 'NO'\\n break\\n\\n elif x == '{' and right[-1] == '}':\\n right.pop()\\n elif x == '(' and not right:\\n result = 'NO'\\n break\\n\\n elif x == '(' and right[-1] == ')':\\n right.pop()\\n elif x == '[' and not right:\\n result = 'NO'\\n break\\n elif x == '[' and (right[-1] == ']'):\\n right.pop()\\n else:\\n result = 'NO'\\n break\\n print(result)\\n return result\",\n \"def is_pair(a, b):\\n\\n\\tif (not a.isalpha() or not b.isalpha()):\\n\\t\\treturn False\\n\\n\\ttest = ord(a) - ord(b)\\n\\tif (test == 32 or test == -32):\\n\\t\\treturn True\\n\\t\\n\\treturn False\",\n \"def parse_bracketed(s, left, right):\\n if s.startswith(left) and s.endswith(right):\\n return normalize(s[len(left) : -len(right)])\\n else:\\n return None\",\n \"def brackets_match(string):\\n expected = Stack()\\n for character in string:\\n if character == \\\"(\\\":\\n expected.push(\\\")\\\")\\n elif character == \\\"[\\\":\\n expected.push(\\\"]\\\")\\n elif character in \\\")]\\\":\\n if expected and character == expected.top():\\n expected.pop()\\n else:\\n return False\\n return expected.is_empty()\",\n \"def par_checker(s):\\n lparens = \\\"([{\\\" # otevírací závorky\\n rparens = \\\")]}\\\" # uzavírací závorky (ve stejném pořadí)\\n stack = Stack()\\n for c in s:\\n if c in lparens:\\n stack.push(c)\\n for i in range(len(rparens)):\\n if c == rparens[i]:\\n if stack.is_empty() or stack.pop() != lparens[i]: # líné vyhodnocení\\n return False\\n return stack.is_empty()\",\n \"def is_valid(self, string) -> bool:\\n while '()' in string or '{}' in string or '[]' in string:\\n string = string.replace('()', '').replace('[]', '').replace('{}', '')\\n\\n return len(string) == 0\",\n \"def matches(open_paran, close_paran):\\n opens = \\\"([{\\\"\\n closes = \\\")]}\\\"\\n return opens.index(open_paran) == closes.index(close_paran)\",\n \"def check_valid(indexes):\\n # Check if any substrings has any versions that are the opposite of it anywhere in that line.\\n valid = False\\n for k, v in indexes.items():\\n swapped = k[::-1]\\n other = indexes.get(swapped)\\n # Check to see if the swapped version exists in the dictionary.\\n if other:\\n # 'aaa' case, these are invalid matches, don't bother checking further.\\n if k == swapped:\\n continue\\n # single occurence case\\n if len(v) == 1 and len(other) == 1:\\n # Case where both occur inside or outsid brackets.\\n if (int(v[0]) % 2) == (int(other[0]) % 2):\\n continue\\n else:\\n valid = True\\n else:\\n # Use sets to eliminate duplicates in the same chunk.\\n v_s = set(v)\\n other_s = set(other)\\n possible_combinations = [(x % 2, y % 2) for x in v_s for y in other_s]\\n # For a pairing to be valid, one part needs to be in an even chunk and the other in an odd ([]) chunk.\\n if (1, 0) in possible_combinations or (0, 1) in possible_combinations:\\n valid = True\\n return valid\",\n \"def _has_matched_brackets(text: str) -> bool:\\n open_bracket_stack = []\\n for index, _ in enumerate(text):\\n if text[index] == \\\"[\\\":\\n open_bracket_stack.append(index)\\n elif text[index] == \\\"]\\\":\\n if len(open_bracket_stack) == 0:\\n return False\\n open_bracket_stack.pop()\\n return len(open_bracket_stack) == 0\",\n \"def test_should_raise_in_case_of_wrong_parenthesising(self):\\n validator = CharCombinationValidator()\\n\\n with self.assertRaises(FormulaValidationError):\\n validator(self.wrong_opening_closing_parenthesis)\",\n \"def has_balanced_parens(phrase):\\n\\n# result = False\\n\\n# open_count = 0\\n# close_count = 0\\n\\n# for char in phrase:\\n# if char == \\\"(\\\":\\n# open_count += 1\\n\\n# if char == \\\")\\\":\\n# close_count += 1\\n \\n# if open_count < close_count:\\n# return result \\n\\n\\n# if open_count == close_count:\\n# result = True\\n\\n \\n# #open has to come first \\n# return result\\n\\n # pair = \\\"()\\\"\\n\\n # if char == \\\"(\\\": #no open parenthesis then false\\n# ( ) (\\n\\n# )(\\n #for every open parenthesis there has to be a closed parenthesis \\n\\n\\n parenthesis = []\\n result = True\\n\\n for char in phrase:\\n if char == \\\"(\\\":\\n parenthesis.append(char)\\n if char == \\\")\\\" and len(parenthesis) < 1: #seeing a closed before an open \\n result = False\\n if char == \\\")\\\" and len(parenthesis) >= 1:\\n parenthesis.pop()\\n \\n\\n if len(parenthesis) == 0:\\n return result \\n\\n else: \\n result = False \\n return result\",\n \"def is_balanced_other_solution(exp):\\n closing = [\\\"}\\\", \\\")\\\", \\\"]\\\"]\\n stack = MyStack()\\n for character in exp:\\n if character in closing:\\n if stack.is_empty():\\n return False\\n top_element = stack.pop()\\n if character == \\\"}\\\" and top_element != \\\"{\\\":\\n return False\\n if character == \\\")\\\" and top_element != \\\"(\\\":\\n return False\\n if character == \\\"]\\\" and top_element != \\\"[\\\":\\n return False\\n else:\\n stack.push(character)\\n if not stack.is_empty():\\n return False\\n return True\",\n \"def validate_expression(str):\\n stack = []\\n pushChars, popChars = \\\"([\\\", \\\")]\\\"\\n for c in str:\\n if c in pushChars:\\n stack.append(c)\\n elif c in popChars:\\n if not len(stack):\\n raise ValidationError('La expresion tiene corchetes \\\\'[ ]\\\\' o parentesis \\\\'( )\\\\' sin cerrar')\\n else:\\n stackTop = stack.pop()\\n balancingBracket = pushChars[popChars.index(c)]\\n if stackTop != balancingBracket:\\n raise ValidationError('La expresion tiene corchetes \\\\'[ ]\\\\' o parentesis \\\\'( )\\\\' sin cerrar')\\n\\n if len(stack):\\n raise ValidationError('La expresion tiene corchetes \\\\'[ ]\\\\' o parentesis \\\\'( )\\\\' sin cerrar')\",\n \"def test_match_multiple_brackets_right_context():\\r\\n runmatch(lcode)\",\n \"def test_sqpp_nested_U1_or_SL2(self):\\n self.assertEqual(self.parser.parse_query('(U(1) or SL(2,Z))'),\\n ['+', 'u(1) | sl(2,z)'])\",\n \"def _between_symbols(string, c1, c2):\\n for char in [c1, c2]:\\n if char not in string:\\n raise ValueError(\\\"Couldn't find character {} in string {}\\\".format(\\n char, string))\\n return string[string.index(c1)+1:string.index(c2)]\",\n \"def multi_bracket_validation(input):\\n return recursion(iter(input))\",\n \"def _match_brace(string, start_pos, pair='[]'):\\n depth = 1\\n if string[start_pos] != pair[0]:\\n return None\\n for index, char in enumerate(string[start_pos + 1:]):\\n if char == pair[0]:\\n depth += 1\\n elif char == pair[1]:\\n depth -= 1\\n if depth == 0:\\n return start_pos + index + 1\\n return None\",\n \"def is_pair_allowed(a, b):\\n if a == complementary(b):\\n return True\\n if a == 'G' and b == 'U' or a == 'U' and b == 'G':\\n return True\\n return False\",\n \"def matches(open, close):\\n\\n dic = {\\\"(\\\":\\\")\\\", \\\"{\\\":\\\"}\\\", \\\"[\\\":\\\"]\\\"}\\n return dic[open] == close\",\n \"def q(segmentation, s1, s2):\\n index1 = find_index(segmentation, s1)\\n index2 = find_index(segmentation, s2)\\n return index1 == index2\",\n \"def containsBrackets(Code):\\r\\n \\r\\n for item in Code:\\r\\n if '(' in item or ')' in item:\\r\\n return True;\\r\\n return False;\",\n \"def isValid(self, s: str) -> bool:\\n st = []\\n\\n for char in s:\\n if (len(st) != 0):\\n e = st[-1]\\n if (self.isValidPair(e,char)):\\n st.pop()\\n continue\\n st.append(char)\\n return (len(st)==0)\",\n \"def has_balanced_parens(string):\\n\\n parens = 0\\n\\n for char in string:\\n if char == \\\"(\\\":\\n parens += 1\\n elif char == \\\")\\\":\\n parens -= 1\\n\\n if parens < 0:\\n return False\\n\\n return parens == 0\",\n \"def _should_be_pair(s_list):\\n assert(isa(s_list, List))\\n return str(s_list).find(' . ') > 0\",\n \"def test_validate_begin_equals_end():\\n with pytest.raises(InvalidSegmentError):\\n _validate([[1, 2], [5, 5]])\",\n \"def _checkParentheses(tokens):\\n\\n\\tparentheses_counter = 0\\n\\tfor token in tokens:\\n\\t\\tif token.type == _Token.OPENING_PARENTHESIS:\\n\\t\\t\\tparentheses_counter += 1\\n\\t\\telif token.type == _Token.CLOSING_PARENTHESIS:\\n\\t\\t\\tparentheses_counter -= 1\\n\\n\\t\\tif parentheses_counter < 0:\\n\\t\\t\\traise ParserError(\\\"Unmatching closing parenthesis\\\", token.start, token.end)\\n\\n\\tif parentheses_counter > 0:\\n\\t\\traise ParserError(\\\"Missing closing parenthesis\\\", tokens[-1].start, tokens[-1].end)\",\n \"def compatible_grammars(sig1, sig2):\\n return (\\n sig1.keys() == sig2.keys()\\n and\\n all([regex_equivalent(sig1[rule], sig2[rule]) for rule in sig1.keys()])\\n )\",\n \"def is_substring_enclosed_in_brackets(sub, string):\\n # to get (...)\\n pattern = re.compile(\\\"\\\\([^\\\\)]*\\\\)\\\")\\n # to get: (...(...)...)\\n pattern2 = re.compile(\\\"\\\\([^\\\\)]*\\\\([^\\\\)]*\\\\)[^\\\\)]*\\\\)\\\")\\n # to get: (...(...(...)...)...)\\n pattern3 = re.compile(\\\"\\\\([^\\\\)]*\\\\([^\\\\)]*\\\\([^\\\\)]*\\\\)[^\\\\)]*\\\\)[^\\\\)]*\\\\)\\\")\\n\\n string = str(string)\\n search = re.findall(pattern, string)\\n search2 = re.findall(pattern2, string)\\n search3 = re.findall(pattern3, string)\\n\\n search3 = set(search + search2 + search3)\\n if any(str(sub) in s for s in search3): # or any(str(sub) in s for s in search2):\\n return True\\n return False\",\n \"def test_allow_bracket_multiple(self):\\n val = DwcaValidator(yaml.load(self.yaml_allow5, Loader=yaml.FullLoader),\\n error_handler=WhipErrorHandler)\\n document = {'sex': 'male'}\\n self.assertTrue(val.validate(document))\\n\\n document = {'sex': 'female'}\\n self.assertTrue(val.validate(document))\\n\\n document = {'sex': 'male, female'}\\n self.assertFalse(val.validate(document))\",\n \"def checkend(s):\\n\\n x = s[0]\\n y = s[-1]\\n\\n if x == y:\\n return True \\n else:\\n return False\",\n \"def compare(lst1):\\n brackets = list(\\\"{[()]}\\\")\\n check = []\\n for i in range(len(lst1)):\\n check.append(lst1[i] in brackets)\\n return all(check)\",\n \"def is_rotation(s1, s2):\\n\\tif s1 is None or s2 is None:\\n\\t\\traise ValueError('Invalid input')\\n\\n\\t# Check if:\\n\\t# a. their lengths match\\n\\t# b. s1 is a substring of s2 appended to s2\\n\\treturn len(s1) == len(s2) and _is_substring(s2+s2, s1)\",\n \"def is_balanced(par_str):\\n\\n left_par = \\\"{[(\\\"\\n right_par = \\\"}])\\\"\\n\\n if len(par_str) % 2 != 0:\\n return False\\n \\n # prevents in index error\\n if par_str[0] in right_par:\\n return False\\n \\n while len(par_str) < 0:\\n for ind, par in par_str:\\n if par not in left_par and par[ind] == par[ind - 1]:\\n par_str.pop(ind)\\n par_str.pop(ind - 1)\\n print(par_str)\\n break\\n else:\\n return False\\n return True\",\n \"def test_brackets_success(self):\\n found = False\\n pyint = Interpreter()\\n try:\\n pyint.run(code=BF_CORRECT_BRACK)\\n except SystemExit: \\n found = True\\n self.assertFalse(found)\",\n \"def test_allow_bracket(self):\\n val = DwcaValidator(yaml.load(self.yaml_allow4, Loader=yaml.FullLoader),\\n error_handler=WhipErrorHandler)\\n document = {'sex': 'male'}\\n self.assertTrue(val.validate(document))\\n\\n document = {'sex': 'female'}\\n self.assertFalse(val.validate(document))\",\n \"def balanced_string(string):\\n stack = create_stack()\\n pos = 0\\n while pos < len(string):\\n if string[pos] in '[{(':\\n stack.push(string[pos])\\n elif string[pos] in ']})':\\n pair = stack.pop()\\n if not match(string[pos], pair):\\n return False\\n pos = pos+1\\n #return stack.length()\\n if stack.length() == 0:\\n return True\\n else:\\n return False\",\n \"def one_edit(str1: str, str2: str):\\n chars = dict()\\n for i in range(max(len(str1), len(str2))):\\n if i < len(str1):\\n chars[str1[i]] = True if str1[i] in chars else False\\n if i < len(str2):\\n chars[str2[i]] = True if str2[i] in chars else False\\n\\n almost_not_okey = False\\n for val in chars.values():\\n if not val:\\n if not almost_not_okey:\\n almost_not_okey = True\\n else:\\n return False\\n return True\",\n \"def _check_separate(gti0, gti1):\\n gti0_start = gti0[:, 0]\\n gti0_end = gti0[:, 1]\\n gti1_start = gti1[:, 0]\\n gti1_end = gti1[:, 1]\\n\\n if (gti0_end[-1] <= gti1_start[0]) or (gti1_end[-1] <= gti0_start[0]):\\n return True\\n\\n for g in gti1.flatten():\\n for g0, g1 in zip(gti0[:, 0], gti0[:, 1]):\\n if (g <= g1) and (g >= g0):\\n return False\\n for g in gti0.flatten():\\n for g0, g1 in zip(gti1[:, 0], gti1[:, 1]):\\n if (g <= g1) and (g >= g0):\\n return False\\n return True\",\n \"def bracketsBalance(exp):\\n stk = LinkedStack()\\n for ch in exp:\\n if ch in ['[' '(']:\\n stk.push(ch)\\n elif ch in [']', ')']:\\n if stk.isEmpty():\\n return False\\n chFromStack = stk.pop()\\n if ch == ']' and chFromStack != '[' or \\\\\\n ch == ')' and chFromStack != '(':\\n return False\\n return stk.isEmpty()\",\n \"def if2symbols(symbol1, symbol2, reel):\\n for i in range(len(reel)-2):\\n if reel[i] == symbol1 and reel[i+1] == symbol2:\\n return True\\n return False\",\n \"def validate(name, bracket, bracket_side, bfr):\\n\\n return bfr[bracket.begin:bracket.end].islower()\",\n \"def verify_name_syntax(sv, name, here, argtext, last):\\r\\n if name.find(Equal)!=-1: # \\\"=\\\" is not allowed in names\\r\\n print(\\\"\\\\n\\\", Err_equal_in_name, \\\"\\\\n\\\", name) # *** Illegal character in name: \\\"+ Equal +\\\" *** \\r\\n raise ReferenceError\\r\\n\\r\\n if not name or here==0: # name may not start with a bracket\\r\\n print(\\\"\\\\n\\\", Err_empty_name) # *** Syntax error: empty name *** \\r\\n print(name)\\r\\n if num>2: # common source of empty name error\\r\\n print(Help_continuation+Mline+\\\"' ):\\\") # you may have meant (with continuation character '\\\"+Mline):\\r\\n print(lines[num-3].strip(Space)+Col, Mline, Crlf, name) # suggested correction\\r\\n raise ReferenceError\\r\\n\\r\\n if argtext: # name is a function or a dict\\r\\n fun=name[:here]\\r\\n if fun in Internal_Functions: \\r\\n print(\\\"\\\\n\\\", Err_redef_internal_func) # *** Error: You cannot define an internal function *** \\r\\n print(fun, \\\"in\\\", fun+Obr+argtext+Cbr)\\r\\n raise ReferenceError\\r\\n \\r\\n if name[last:]: # name must end with closing bracket after args\\r\\n print(\\\"\\\\n\\\", Err_text_after_args) # *** Syntax error: text found after arguments *** \\r\\n print(name)\\r\\n raise ReferenceError\",\n \"def validate_pairs(pairs, historical_pairs):\\n if pairs is None:\\n return False\\n for p in pairs:\\n if p in historical_pairs:\\n return False\\n return True\",\n \"def _is_substring(s1, s2):\\n\\treturn s1.find(s2) != -1\",\n \"def __check_if_symbol_is_over(rect1, rect2):\\n\\n rect_center_x_coord = rect1[4][0]\\n rect2_center_x_coord = rect2[4][0]\\n rect2_width = rect2[5]\\n rect1_center_y_coord = rect1[4][1]\\n rect2_center_y_coord = rect2[4][1]\\n\\n leftmost_x_coord = rect2_center_x_coord - (rect2_width // 2)\\n rightmost_y_coord = rect2_center_x_coord + (rect2_width // 2)\\n if (\\n leftmost_x_coord <= rect_center_x_coord <= rightmost_y_coord\\n and\\n rect1_center_y_coord < rect2_center_y_coord\\n ):\\n return True\\n else:\\n return False\",\n \"def parentheses_are_uneven(input_string):\\n pcounter = 0\\n for char in input_string:\\n if char == '(':\\n pcounter += 1\\n elif char == ')':\\n pcounter -= 1\\n if pcounter != 0:\\n return False\\n else:\\n return True\",\n \"def check_validated_mapks(res, st1):\\n assert len(res) == 2\\n valid_stmts = res[0]\\n mapped_stmts = res[1]\\n assert isinstance(valid_stmts, list)\\n assert isinstance(mapped_stmts, list)\\n assert len(valid_stmts) == 0\\n assert len(mapped_stmts) == 1\\n mapped_stmt = mapped_stmts[0]\\n assert isinstance(mapped_stmt, MappedStatement)\\n assert mapped_stmt.original_stmt == st1\\n assert isinstance(mapped_stmt.mapped_mods, list)\\n assert len(mapped_stmt.mapped_mods) == 4\\n ms = mapped_stmt.mapped_stmt\\n assert isinstance(ms, Statement)\\n agents = ms.agent_list()\\n assert len(agents) == 2\\n agent1 = agents[0]\\n agent2 = agents[1]\\n validate_mapk1(agent1)\\n assert agent2.mods[0].matches(ModCondition('phosphorylation', 'T', '202'))\\n assert agent2.mods[1].matches(ModCondition('phosphorylation', 'Y', '204'))\\n assert unicode_strs((res, st1))\",\n \"def brackets(result):\\n return len(re.findall(r'\\\"', result.text)) == 2\",\n \"def test_sqpp_paren_open_only_failure(self):\\n self.failUnlessRaises(SyntaxError,\\n self.parser.parse_query,\\\"(expr\\\")\",\n \"def check_slice(self, m1, m2):\\n\\n if m1 == \\\"U'\\\" and m2 == \\\"D\\\" or m2 == \\\"U'\\\" and m1 == \\\"D\\\":\\n return \\\"E' y'\\\"\\n if m1 == \\\"U\\\" and m2 == \\\"D'\\\" or m2 == \\\"U\\\" and m1 == \\\"D'\\\":\\n return \\\"E y\\\"\\n if m1 == \\\"R\\\" and m2 == \\\"L'\\\" or m2 == \\\"R\\\" and m1 == \\\"L'\\\":\\n return \\\"M x\\\"\\n if m1 == \\\"R'\\\" and m2 == \\\"L\\\" or m2 == \\\"R'\\\" and m1 == \\\"L\\\":\\n return \\\"M' x'\\\"\\n if m1 == \\\"F\\\" and m2 == \\\"B'\\\" or m2 == \\\"F\\\" and m1 == \\\"B'\\\":\\n return \\\"S' z\\\"\\n if m1 == \\\"F'\\\" and m2 == \\\"B\\\" or m2 == \\\"F'\\\" and m1 == \\\"B\\\":\\n return \\\"S z'\\\"\\n\\n return None\",\n \"def validate_balanced_parenthesis(s):\\n positions = [] # container for position of unbalanced parenthesis\\n # counter for comments\\n k = 0 # 0 = no comment range\\n comment_open1 = False # comment indicator for /* */ comments\\n comment_open2 = False # comment indicator for -- comments\\n quote_open1 = False # quote '\\n quote_open2 = False # quote \\\"\\n for i, c in enumerate(s):\\n if c == \\\"(\\\" and k == 0:\\n positions.append(i)\\n elif c == \\\")\\\" and k == 0:\\n if len(positions) == 0:\\n return {\\n \\\"exit_code\\\": 1,\\n \\\"val_lines\\\": find_line_number(s, [i]),\\n \\\"total_lines\\\": count_lines(s)\\n }\\n else:\\n positions.pop()\\n elif (\\n s[i:i+2] == \\\"/*\\\" and\\n not comment_open1 and\\n not comment_open2 and\\n not quote_open1 and\\n not quote_open2\\n ): # if there is an opening comment /*\\n k += 1\\n comment_open1 = True\\n elif (\\n s[i:i+2] == \\\"*/\\\" and\\n comment_open1 and\\n not comment_open2 and\\n not quote_open1 and\\n not quote_open2\\n ): # if there is a closing comment */\\n k -= 1\\n comment_open1 = False\\n elif (\\n s[i:i+2] == \\\"--\\\" and\\n not comment_open1 and\\n not comment_open2 and\\n not quote_open1 and\\n not quote_open2\\n ): # if there is an opening comment --\\n k += 1\\n comment_open2 = True\\n elif (\\n (c == \\\"\\\\n\\\" or s[i:i+3] == \\\"[c]\\\") and\\n not comment_open1 and\\n comment_open2 and\\n not quote_open1 and\\n not quote_open2\\n ): # if the -- comment ends\\n k -= 1\\n comment_open2 = False\\n elif (\\n c == \\\"'\\\" and\\n not comment_open1 and\\n not comment_open2 and\\n not quote_open1 and\\n not quote_open2\\n ): # if opening quote '\\n k += 1\\n quote_open1 = True\\n elif (\\n c == \\\"'\\\" and\\n not comment_open1 and\\n not comment_open2 and\\n quote_open1 and\\n not quote_open2\\n ): # if opening quote '\\n k -= 1\\n quote_open1 = False\\n elif (\\n c == '\\\"' and\\n not comment_open1 and\\n not comment_open2 and\\n not quote_open1 and\\n quote_open2\\n ): # if opening quote '\\n k += 1\\n quote_open2 = True\\n elif (\\n c == '\\\"' and\\n not comment_open1 and\\n not comment_open2 and\\n not quote_open1 and\\n quote_open2\\n ): # if opening quote '\\n k -= 1\\n quote_open2 = False\\n if len(positions) == 0:\\n return {\\n \\\"exit_code\\\": 0,\\n \\\"total_lines\\\": count_lines(s)\\n }\\n else:\\n return {\\n \\\"exit_code\\\": 1,\\n \\\"val_lines\\\": find_line_number(s, positions),\\n \\\"total_lines\\\": count_lines(s)\\n }\",\n \"def checkValidString(self, string: str) -> bool:\\n @lru_cache(None)\\n def dp(index, diff):\\n \\\"\\\"\\\"\\n index is the index of string\\n diff the counts of '(' - counts of ')'\\n \\\"\\\"\\\"\\n\\n if index == len_s:\\n return diff == 0\\n if abs(diff) > len_s - index:\\n return False\\n c = string[index]\\n index += 1\\n if c == '(':\\n return dp(index, diff + 1)\\n elif c == ')':\\n if diff - 1 < 0:\\n return False\\n return dp(index, diff - 1)\\n else:\\n return dp(index, diff + 1) or dp(index, diff - 1) or dp(index, diff)\\n\\n len_s = len(string)\\n return dp(0, 0)\",\n \"def match_parentheses(string_characters):\\n left_parenteses = \\\"({[\\\"\\n right_parenteses = \\\")}]\\\"\\n S = LinkedListStack()\\n\\n for character in string_characters:\\n if character in left_parenteses:\\n S.push(character)\\n elif character in right_parenteses:\\n if S._is_empty():\\n return False\\n if right_parenteses.index(character) != left_parenteses.index(S.pop().value):\\n return False\\n return S._is_empty()\",\n \"def test_valid_region_syntax():\\n reg_str1 = 'circle[[18h12m24s, -23d11m00s], [2.3arcsec,4.5arcsec]'\\n with pytest.raises(CRTFRegionParserError) as excinfo:\\n Regions.parse(reg_str1, format='crtf')\\n\\n estr = (\\\"Not in proper format: ('2.3arcsec', '4.5arcsec') should be \\\"\\n \\\"a single length\\\")\\n assert estr in str(excinfo.value)\\n\\n reg_str2 = ('symbol[[32.1423deg, 12.1412deg], 12deg], linewidth=2, '\\n 'coord=J2000, symsize=2')\\n with pytest.raises(CRTFRegionParserError) as excinfo:\\n Regions.parse(reg_str2, format='crtf')\\n estr = 'Not in proper format: \\\"12deg\\\" should be a symbol'\\n assert estr in str(excinfo.value)\\n\\n reg_str3 = 'circle[[18h12m24s, -23d11m00s]'\\n with pytest.raises(CRTFRegionParserError) as excinfo:\\n Regions.parse(reg_str3, format='crtf')\\n estr = ('Does not contain expected number of parameters for the region '\\n '\\\"circle\\\"')\\n assert estr in str(excinfo.value)\\n\\n reg_str4 = 'poly[[1, 2], [4, 5]]'\\n with pytest.raises(CRTFRegionParserError) as excinfo:\\n Regions.parse(reg_str4, format='crtf')\\n assert 'polygon should have >= 3 coordinates' in str(excinfo.value)\\n\\n reg_str6 = 'rotbox[[12h01m34.1s, 12d23m33s], [3arcmin,], 12deg]'\\n with pytest.raises(CRTFRegionParserError) as excinfo:\\n Regions.parse(reg_str6, format='crtf')\\n assert \\\"('3arcmin', '') should be a pair of lengths\\\" in str(excinfo.value)\",\n \"def hasMidBrackets(Code):\\r\\n \\r\\n l = len(Code);\\r\\n \\r\\n # Skip past the end brackets.\\r\\n left, right = endBrackets(Code)[0], endBrackets(Code)[1];\\r\\n left += 1; right -= 1;\\r\\n \\r\\n # Then keep going until either references a bracket or right < left.\\r\\n while right >= left: \\r\\n if Code[left] in brackets: return True;\\r\\n if Code[right] in brackets: return True;\\r\\n left += 1; right -= 1;\\r\\n \\r\\n # Return False if right < left.\\r\\n return False;\",\n \"def strings_differ(string1, string2):\\n if len(string1) != len(string2):\\n return True\\n invalid_bits = 0\\n for a, b in zip(string1, string2):\\n invalid_bits += a != b\\n return invalid_bits != 0\",\n \"def test_brackets_tall(self):\\r\\n self._each_parens(r'\\\\left[x^y\\\\right]', 'x^y', '[', tall=True)\",\n \"def judge(self, s1, s2):\\n if len(s2) < len(s1):\\n return False\\n index_of_s1 = 0\\n index_of_s2 = 0\\n while index_of_s1 < len(s1) and index_of_s2 < len(s2):\\n if s1[index_of_s1] == s2[index_of_s2]:\\n index_of_s1 += 1\\n index_of_s2 += 1\\n else:\\n index_of_s2 += 1\\n return True if index_of_s1 == len(s1) else False\",\n \"def match_pair(expr, pair=(r'{', r'}'), start=0):\\n\\n beg = pair[0]\\n fin = pair[1]\\n\\n # find first opening\\n sstart = expr.find(beg, start)\\n\\n count = 0\\n\\n if beg == fin:\\n eend = expr.find(fin, sstart + 1)\\n return sstart, eend\\n\\n p = re.compile('(' + beg + '|' + fin + ')', re.M)\\n ps = re.compile(beg, re.M)\\n\\n iterator = p.finditer(expr, start)\\n\\n for match in iterator:\\n if ps.match(match.group()):\\n count += 1\\n else:\\n count += -1\\n\\n if count == 0:\\n return sstart, match.end()\\n\\n return None\",\n \"def validate(string):\\n \\n tokens = string.split()\\n \\n # Remembers if the previous token was an operator\\n opflag = True\\n \\n ## Highly inefficient validity checking begins here ##\\n \\n # List of operators as they would appear in the infix expression\\n operators = ['+', '-', '*', '/', '^', 'sqrt']\\n \\n # First and foremost, detect all unary minus signs and mark them as such\\n for i in xrange(len(tokens)):\\n # A unary minus is a minus operator which occurs after another operator\\n # or after an open parenthesis.\\n if tokens[i] in operators or tokens[i] == '(':\\n if opflag:\\n if tokens[i] == '-':\\n tokens[i] = 'u-'\\n # Leave opflag true to allow cascading of unary minuses\\n elif tokens[i] in ['sqrt', '(']:\\n # These operators can be cascaded, so leave them alone\\n # Also, leave opflag true to handle a subsequent u-\\n pass\\n else:\\n # Any other operator must be caught\\n raise ExpressionError('Operators cannot be cascaded!')\\n # We found an operator, but opflag isn't true. Set it.\\n else:\\n opflag = True\\n else:\\n # We found something other than an operator, or a ')'. If opflag is\\n # false, and the token is not ')', then we have two adjacent\\n # variables/numbers. This is also an invalid combination\\n if not opflag and tokens[i] != ')':\\n raise ExpressionError('Adjacent operands with no operator!')\\n # Otherwise, unset opflag\\n else:\\n opflag = False\\n \\n # Check whether parentheses match\\n s = Stack()\\n for token in tokens:\\n if token == '(':\\n s.push(token)\\n elif token == ')':\\n if s.pop() != '(':\\n raise ExpressionError('Parentheses do not match')\\n if not s.is_empty():\\n raise ExpressionError('Parentheses do not match')\\n \\n return tokens\",\n \"def check_two_spots(a, b, c):\\n\\n if a != \\\"\\\" and b != \\\"\\\" and c == \\\"\\\":\\n return False\\n if a != \\\"\\\" and b == \\\"\\\" and c != \\\"\\\":\\n return False\\n if a == \\\"\\\" and b != \\\"\\\" and c != \\\"\\\":\\n return False\\n return True\",\n \"def is_minpair(first, second, corpus_context, segment_pairs, environment_filter):\\n first = getattr(first, corpus_context.sequence_type)\\n second = getattr(second, corpus_context.sequence_type)\\n\\n if len(first) != len(second):\\n return False\\n\\n has_difference = False\\n for i in range(len(first)):\\n if first[i] == second[i]:\\n continue\\n elif (conflateable(first[i], second[i], segment_pairs)\\n and fits_environment(first, second, i, environment_filter)):\\n has_difference = True\\n continue\\n else:\\n return False\\n\\n if has_difference:\\n return True\",\n \"def is_balanced(exp: str) -> bool:\\n if len(exp) % 2 != 0:\\n print(\\\"expression is not balanced because it doesn't have at least 1 closing parenthesis;\\\")\\n print(\\\"its not a balanced expression; the length is odd\\\")\\n return False\\n\\n if len(exp) == 0:\\n print(\\\"empty expression, balance\\\")\\n return True\\n\\n open_list = [\\\"[\\\", \\\"{\\\", \\\"(\\\"]\\n closed_list = [\\\"]\\\", \\\"}\\\", \\\")\\\"]\\n s = MyStack()\\n for i in range(len(exp) // 2):\\n if exp[i] in open_list:\\n s.push(exp[i])\\n\\n for i in range(len(exp) // 2, len(exp)):\\n close_pos = closed_list.index(exp[i])\\n open_pos = open_list.index(s.pop())\\n if close_pos == open_pos:\\n continue\\n else:\\n return False\\n\\n return True\",\n \"def check_str_cov(str_fun, str_cov, shape_X1, shape_X2=None):\\n\\n assert isinstance(str_fun, str)\\n assert isinstance(str_cov, str)\\n assert isinstance(shape_X1, tuple)\\n assert shape_X2 is None or isinstance(shape_X2, tuple)\\n\\n if str_cov in constants.ALLOWED_GP_COV_BASE:\\n assert len(shape_X1) == 2\\n if shape_X2 is not None:\\n assert len(shape_X2) == 2\\n elif str_cov in constants.ALLOWED_GP_COV_SET:\\n assert len(shape_X1) == 3\\n if shape_X2 is not None:\\n assert len(shape_X2) == 3\\n elif str_cov in constants.ALLOWED_GP_COV: # pragma: no cover\\n raise ValueError('{}: missing conditions for str_cov.'.format(str_fun))\\n else:\\n raise ValueError('{}: invalid str_cov.'.format(str_fun))\\n return\",\n \"def valid_expression(expression):\\n OPERATORS= '+*/-'\\n if no_operators(expression) != True:\\n return no_operators(expression)\\n if no_paranthesis(expression) != True:\\n return no_paranthesis(expression)\\n if no_numbers(expression) != True:\\n return no_numbers(expression)\\n if invalid_characters(expression) != True:\\n return invalid_characters(expression)\\n if match_paranthesis(expression) == False:\\n raise NotValidExpression('Not a valid expression, brackets mismatched.')\\n number_operators = 0\\n number_paranthesis = 0\\n for i in expression:\\n if i in OPERATORS:\\n number_operators += 1\\n elif i == '(' or i == ')':\\n number_paranthesis +=1\\n expression1 = expression[1:(len(expression) - 1)] # checks if the expression without the first and last character is valid\\n if match_paranthesis(expression1) == False and ('(' in expression1 or ')' in expression1):\\n raise NotValidExpression('Not a valid expression, brackets mismatched.') # if it is not, raises an appropiate error\\n for i in range(0, len(expression) - 1):\\n #Checks if an operator is missing,if there exists a number followed by ( or if there is a )before the number\\n if expression[i] not in OPERATORS and expression[i] not in '()':\\n if expression[i + 1] == '(':\\n raise NotValidExpression('Not a valid expression, operator missing.')\\n elif expression[i] in OPERATORS and expression[i + 1] in OPERATORS + ')' :\\n raise NotValidExpression('Not a valid expression, wrong placement of operators')\\n #Checks if an operator is placed wrongly , before ) or next to another operator\\n if expression[i+1] not in OPERATORS and expression[i + 1] not in '()':\\n if expression[i] == ')':\\n raise NotValidExpression('Not a valid expression, operator missing.')\\n elif expression[i+1] in OPERATORS and expression[i] in OPERATORS + '(':\\n raise NotValidExpression('Not a valid expression, wrong placement of operators')\\n if 2*number_operators != number_paranthesis: # an expression is valid only if the number of paranthesis is equal to the double of the number of operators\\n raise NotValidExpression('Not a valid expression, wrong number of operands.')\\n return True\",\n \"def shipvalidator(point1: tuple, point2: tuple, board: list):\\n valid = True\\n # Is horizontal\\n if point1[0] == point2[0]:\\n # No collisions\\n for i in range(min(point1[1], point2[1]), max(point1[1], point2[1])):\\n if board[point1[0]][i] != \\\" \\\":\\n valid = False\\n # Is vertical\\n elif point1[1] == point2[1]:\\n # Doesn't overlap\\n for i in range(min(point1[0], point2[0]), max(point1[0], point2[0])):\\n if board[i][point1[1]] != \\\" \\\":\\n valid = False\\n else:\\n valid = False\\n return valid\",\n \"def validate(self, s):\\n if len(s) == 0:\\n return False\\n if s in self.whitelist:\\n return True\\n if s in self.blacklist:\\n return False\\n\\n # SQL Types are rarely used\\n if 't' in s and 'f(t' not in s and 'At' not in s:\\n return False\\n\\n if '1nf' in s:\\n return False\\n if 's1o' in s:\\n return False\\n if 'oo' in s:\\n return False\\n if 'v,s' in s:\\n return False\\n if 's,v' in s:\\n return False\\n if 'v,v' in s:\\n return False\\n if 'v,1' in s:\\n return False\\n if 'v,n' in s:\\n return False\\n if 'n,v' in s:\\n return False\\n if '1,v' in s:\\n return False\\n if 'Eo(' in s:\\n return False\\n if '(o(' in s:\\n return False\\n if '(o1' in s:\\n return False\\n if '(on' in s:\\n return False\\n if '(os' in s:\\n return False\\n if '(of' in s:\\n return False\\n if '(ov' in s:\\n return False\\n if 'B(n)' in s:\\n return False\\n if 'oso' in s:\\n return False\\n if 'o1o' in s:\\n return False\\n if 'ono' in s:\\n return False\\n\\n # only 1 special case for this\\n # 1;foo:goto foo\\n # 1;n:k\\n # the 'foo' can only be a 'n' type\\n if ':' in s and not 'n:' in s:\\n return False\\n\\n if '11' in s:\\n return False\\n\\n if '))' in s:\\n return False\\n if '((' in s:\\n return False\\n if 'v1' in s:\\n return False\\n\\n if 'nv' in s and ';T' not in s:\\n return False\\n if 'nn' in s and ';T' not in s:\\n return False\\n\\n # select @version foo is legit\\n # but unlikely anywhere else\\n if 'vn' in s and 'Evn' not in s:\\n return False\\n\\n if 'oE' in s:\\n return False\\n\\n if 'A1' in s:\\n return False\\n if 'An' in s:\\n return False\\n if 'A(1' in s:\\n return False\\n\\n if 'vov' in s:\\n return False\\n if 'vo1' in s:\\n return False\\n if 'von' in s:\\n return False\\n\\n if 'ns' in s:\\n if 'U' in s:\\n return True\\n if 'T' in s:\\n return True\\n return False\\n\\n if 'sn' in s:\\n # that is... Tsn is ok\\n if s.find('T') != -1 and s.find('T') < s.find('sn'):\\n return True\\n return False\\n\\n # select foo (as) bar is only nn type i know\\n if 'nn' in s and 'Enn' not in s and ';T' not in s:\\n return False\\n\\n if ',o' in s:\\n return False\\n\\n if 'kk' in s and 'Tkk' not in s:\\n return False\\n\\n if 'ss' in s:\\n return False\\n\\n if 'ff' in s:\\n return False\\n\\n if '1no' in s:\\n return False\\n\\n if 'kno' in s:\\n return False\\n\\n if 'nEk' in s:\\n return False\\n\\n if 'n(n' in s:\\n return False\\n if '1so' in s:\\n return False\\n if '1s1' in s:\\n return False\\n if 'noo' in s:\\n return False\\n if 'ooo' in s:\\n return False\\n\\n if 'vvv' in s:\\n return False\\n\\n if '1vn' in s:\\n return False\\n if '1n1' in s:\\n return False\\n if '&1n' in s:\\n return False\\n if '&1v' in s:\\n return False\\n if '&1s' in s:\\n return False\\n if 'nnk' in s:\\n return False\\n if 'n1f' in s:\\n return False\\n # folded away\\n if s.startswith('('):\\n return False\\n\\n if '&o' in s:\\n return False\\n\\n if '1,1' in s:\\n return False\\n if '1,s' in s:\\n return False\\n if '1,n' in s:\\n return False\\n if 's,1' in s:\\n return False\\n if 's,s' in s:\\n return False\\n if 's,n' in s:\\n return False\\n if 'n,1' in s:\\n return False\\n if 'n,s' in s:\\n return False\\n if 'n,n' in s:\\n return False\\n if '1o1' in s:\\n return False\\n if '1on' in s:\\n return False\\n if 'no1' in s:\\n return False\\n if 'non' in s:\\n return False\\n if '1(v' in s:\\n return False\\n if '1(n' in s:\\n return False\\n if '1(s' in s:\\n return False\\n if '1(1' in s:\\n return False\\n if 's(s' in s:\\n return False\\n if 's(n' in s:\\n return False\\n if 's(1' in s:\\n return False\\n if 's(v' in s:\\n return False\\n if 'v(s' in s:\\n return False\\n if 'v(n' in s:\\n return False\\n if 'v(1' in s:\\n return False\\n if 'v(v' in s:\\n return False\\n\\n if s.startswith('n('):\\n return False\\n\\n if s.startswith('vs'):\\n return False\\n\\n if s.startswith('o'):\\n return False\\n\\n if ')(' in s:\\n return False\\n\\n # need to investigate T(vv) to see\\n # if it's correct\\n if 'vv' in s and s != 'T(vv)':\\n return False\\n\\n # unlikely to be sqli but case FP\\n if s in ('so1n)', 'sonoE'):\\n return False\\n\\n return True\",\n \"def forbid_consecutive_interval(s1, s2, interval):\\n for (na,n1), (nb, n2) in util.pairwise(tools.iter_melodies(s1,s2,all=True)):\\n nap, nbp, n1p, n2p = [util.to_pitch(x) for x in (na, nb, n1, n2)]\\n if nap.isQualifiedInterval(interval).With(n1p) and nbp.isQualifiedInterval(interval).With(n2p):\\n error.warn(f\\\"Two {interval} in a row are forbidden\\\",na,nb,n1,n2,f\\\"in {s1.title} and {s2.title}\\\")\",\n \"def test_s2():\\n vc = vtec.parse(EX1)\\n assert vc[0].s2() == \\\"TO.W\\\"\"\n]","isConversational":false},"negative_scores":{"kind":"list like","value":["0.6902342","0.6578745","0.64985824","0.6201826","0.6157199","0.61534166","0.6116992","0.60981786","0.60610473","0.5993438","0.5981416","0.5980033","0.5964657","0.59297395","0.58786607","0.5873677","0.5793385","0.57868797","0.57779795","0.5727495","0.57272065","0.5716562","0.5687896","0.5670781","0.5663771","0.56333303","0.5603899","0.5588771","0.55872566","0.5567595","0.55490476","0.5547332","0.5536543","0.55338895","0.5515118","0.5487953","0.54796785","0.5478178","0.54716146","0.5447112","0.54453737","0.54414916","0.5419772","0.5419567","0.5414459","0.5403655","0.5402479","0.5391476","0.5388694","0.53730804","0.5371622","0.533973","0.5335716","0.53203034","0.529827","0.52933604","0.52830106","0.52785295","0.52729684","0.52668893","0.5263705","0.52634037","0.52632636","0.525652","0.5252305","0.5219756","0.52037704","0.52000725","0.5197195","0.5193392","0.51853764","0.5184849","0.51787674","0.5175202","0.51631427","0.5148325","0.5114591","0.5109257","0.51046014","0.5103419","0.5098298","0.508325","0.50764877","0.5075167","0.5072815","0.5068484","0.50669974","0.5063036","0.5055111","0.50527525","0.50495845","0.50442225","0.50380135","0.5030317","0.5020386","0.50059444","0.4999814","0.49987853","0.49982083","0.49938256"],"string":"[\n \"0.6902342\",\n \"0.6578745\",\n \"0.64985824\",\n \"0.6201826\",\n \"0.6157199\",\n \"0.61534166\",\n \"0.6116992\",\n \"0.60981786\",\n \"0.60610473\",\n \"0.5993438\",\n \"0.5981416\",\n \"0.5980033\",\n \"0.5964657\",\n \"0.59297395\",\n \"0.58786607\",\n \"0.5873677\",\n \"0.5793385\",\n \"0.57868797\",\n \"0.57779795\",\n \"0.5727495\",\n \"0.57272065\",\n \"0.5716562\",\n \"0.5687896\",\n \"0.5670781\",\n \"0.5663771\",\n \"0.56333303\",\n \"0.5603899\",\n \"0.5588771\",\n \"0.55872566\",\n \"0.5567595\",\n \"0.55490476\",\n \"0.5547332\",\n \"0.5536543\",\n \"0.55338895\",\n \"0.5515118\",\n \"0.5487953\",\n \"0.54796785\",\n \"0.5478178\",\n \"0.54716146\",\n \"0.5447112\",\n \"0.54453737\",\n \"0.54414916\",\n \"0.5419772\",\n \"0.5419567\",\n \"0.5414459\",\n \"0.5403655\",\n \"0.5402479\",\n \"0.5391476\",\n \"0.5388694\",\n \"0.53730804\",\n \"0.5371622\",\n \"0.533973\",\n \"0.5335716\",\n \"0.53203034\",\n \"0.529827\",\n \"0.52933604\",\n \"0.52830106\",\n \"0.52785295\",\n \"0.52729684\",\n \"0.52668893\",\n \"0.5263705\",\n \"0.52634037\",\n \"0.52632636\",\n \"0.525652\",\n \"0.5252305\",\n \"0.5219756\",\n \"0.52037704\",\n \"0.52000725\",\n \"0.5197195\",\n \"0.5193392\",\n \"0.51853764\",\n \"0.5184849\",\n \"0.51787674\",\n \"0.5175202\",\n \"0.51631427\",\n \"0.5148325\",\n \"0.5114591\",\n \"0.5109257\",\n \"0.51046014\",\n \"0.5103419\",\n \"0.5098298\",\n \"0.508325\",\n \"0.50764877\",\n \"0.5075167\",\n \"0.5072815\",\n \"0.5068484\",\n \"0.50669974\",\n \"0.5063036\",\n \"0.5055111\",\n \"0.50527525\",\n \"0.50495845\",\n \"0.50442225\",\n \"0.50380135\",\n \"0.5030317\",\n \"0.5020386\",\n \"0.50059444\",\n \"0.4999814\",\n \"0.49987853\",\n \"0.49982083\",\n \"0.49938256\"\n]","isConversational":false},"document_score":{"kind":"string","value":"0.86355895"},"document_rank":{"kind":"string","value":"0"}}},{"rowIdx":75384,"cells":{"query":{"kind":"string","value":"Checks if s contains valid ({[]}) pair in correct order"},"document":{"kind":"string","value":"def isValid(self, s: str) -> bool:\n st = []\n\n for char in s:\n if (len(st) != 0):\n e = st[-1]\n if (self.isValidPair(e,char)):\n st.pop()\n continue\n st.append(char)\n return (len(st)==0)"},"metadata":{"kind":"string","value":"{\n \"objective\": {\n \"self\": [],\n \"paired\": [],\n \"triplet\": [\n [\n \"query\",\n \"document\",\n \"negatives\"\n ]\n ]\n }\n}"},"negatives":{"kind":"list like","value":["def isValid(self, s):\n # 1\n if not s:\n return True\n\n # 2\n if not len(s) % 2 == 0:\n return False\n\n comp = {\n ')': '(',\n ']': '[',\n '}': '{',\n }\n stack = []\n\n for char in s:\n # 3\n if char in comp:\n popped = stack.pop() if stack else '@'\n if popped != comp[char]:\n return False\n # 2\n else:\n stack.append(char)\n\n return not stack","def isValidPair(self,s1,s2):\n if (s1 == '(' and s2 == ')'):\n return True\n if (s1 == '[' and s2 == ']'):\n return True\n if (s1 == '{' and s2 == '}'):\n return True\n return False","def isValid(self, s: str) -> bool:\n stack = list()\n for c in s:\n if c in Solution.corresponding_parenthesis:\n stack.append(Solution.corresponding_parenthesis[c])\n elif not stack or stack.pop() != c:\n return False\n return not stack","def is_well_formed(s):\n # TODO - you fill in here.\n s = list(s)\n op = []\n l = len(s)\n for i in range(l):\n if s[i] in ['(','{','[']:\n op.append(s[i])\n else: \n if len(op) == 0:\n return False\n temp = op.pop() \n if s[i] == ')':\n if temp != '(':\n return False\n elif s[i] == '}':\n if temp != '{':\n return False\n else:\n if temp != '[':\n return False\n if len(op) != 0:\n return False\n return True","def is_valid(s):\n in_str = False\n bb = 0\n for c in s:\n if c == '(' and not in_str:\n bb += 1\n elif c == ')' and not in_str:\n bb -= 1\n if bb < 0:\n return False\n elif c == '\\\"':\n in_str = not in_str\n return bb == 0","def par_checker(s):\n lparens = \"([{\" # otevírací závorky\n rparens = \")]}\" # uzavírací závorky (ve stejném pořadí)\n stack = Stack()\n for c in s:\n if c in lparens:\n stack.push(c)\n for i in range(len(rparens)):\n if c == rparens[i]:\n if stack.is_empty() or stack.pop() != lparens[i]: # líné vyhodnocení\n return False\n return stack.is_empty()","def isValid(self, s: str) -> bool:\n stack = []\n \n mapping = {\n \")\":\"(\",\n \"}\":\"{\",\n \"]\":\"[\"\n }\n \n for char in s:\n if char not in mapping:\n stack.append(char)\n \n else:\n top_element = stack.pop() if stack else \"#\"\n \n if mapping[char] != top_element:\n return False\n \n return not stack","def isValid(self, s: str) -> bool:\n open_brackets = [] # stack of open but not closed brackets\n for char in s:\n if char in ['(', '[', '{']:\n open_brackets.append(char)\n else:\n try:\n complement = open_brackets.pop()\n except IndexError:\n return False\n else:\n if complement + char not in ['()', '[]', '{}']:\n return False\n return not len(open_brackets)","def is_valid(self, string) -> bool:\n while '()' in string or '{}' in string or '[]' in string:\n string = string.replace('()', '').replace('[]', '').replace('{}', '')\n\n return len(string) == 0","def _should_be_pair(s_list):\n assert(isa(s_list, List))\n return str(s_list).find(' . ') > 0","def check(self, s: str, mem: dict):\n dp = [False for _ in range(len(s)+1)]\n dp[0] = True\n for i in range(1, len(s)+1):\n for j in range(i):\n if dp[j] and s[j:i] in mem:\n dp[i] = True\n return dp[-1]","def check_valid(indexes):\n # Check if any substrings has any versions that are the opposite of it anywhere in that line.\n valid = False\n for k, v in indexes.items():\n swapped = k[::-1]\n other = indexes.get(swapped)\n # Check to see if the swapped version exists in the dictionary.\n if other:\n # 'aaa' case, these are invalid matches, don't bother checking further.\n if k == swapped:\n continue\n # single occurence case\n if len(v) == 1 and len(other) == 1:\n # Case where both occur inside or outsid brackets.\n if (int(v[0]) % 2) == (int(other[0]) % 2):\n continue\n else:\n valid = True\n else:\n # Use sets to eliminate duplicates in the same chunk.\n v_s = set(v)\n other_s = set(other)\n possible_combinations = [(x % 2, y % 2) for x in v_s for y in other_s]\n # For a pairing to be valid, one part needs to be in an even chunk and the other in an odd ([]) chunk.\n if (1, 0) in possible_combinations or (0, 1) in possible_combinations:\n valid = True\n return valid","def multi_bracket_validation(input):\n brackets = Stack()\n for i in input:\n if i == '(' or i == '[' or i == '{':\n brackets.push(i)\n elif i == ')' or i == ']' or i == '}':\n if brackets._size == 0:\n return False\n elif i == ')' and brackets.top.val == '(':\n brackets.pop()\n elif i == ']' and brackets.top.val == '[':\n brackets.pop()\n elif i == '}' and brackets.top.val == '{':\n brackets.pop()\n else:\n return False\n\n if brackets._size != 0:\n return False\n return True","def valid_format(s):\n if len(s) > 7:\n return False\n elif '0' in s and len(s) == 1:\n return True\n elif s[0] == '0' and len(s) > 1:\n return False\n elif introcs.isalpha(s):\n return False\n elif (len(s) > 3) and (introcs.count_str(s, ',') == 0):\n return False\n elif introcs.count_str(s, ',') == 0:\n return introcs.isdecimal(s)\n elif introcs.count_str(s, ',') > 1:\n return False\n elif ',' in s and introcs.count_str(s,',') == 1:\n comma_check = s[introcs.find_str(s, ',')+1:]\n before_comma_check = s[:introcs.find_str(s, ',')]\n introcs.isdecimal(before_comma_check)\n return (True if len(comma_check) == 3 else False) and introcs.isdecimal(before_comma_check)","def is_sqrs_valid(bd):\n for sqr in sqrs:\n seen = []\n for num in nums:\n if bd[sqr[num]] == \" \":\n continue\n elif bd[sqr[num]] not in seen:\n seen += [bd[sqr[num]]]\n else:\n return False\n else:\n continue\n return True","def group_check(s):\n parenthesis = ['(',')']\n brackets = ['[', ']']\n squirely = ['{', '}']\n parenthesis_num0 = 0\n brackets_num0 = 0\n squirely_num0 = 0\n parenthesis_num1 = 0\n brackets_num1 = 0\n squirely_num1 = 0\n expect = []\n for i in enumerate(s):\n if i == parenthesis[0]:\n parenthesis_num0+=1\n expect.append(parenthesis_num1)\n elif i == brackets[0]:\n brackets_num0+=1\n expect.append(brackets_num1)\n elif i == squirely[0]:\n squirely_num0+=1\n expect.append(squirely_num1)\n elif i == parenthesis[1]:\n parenthesis_num1+=1\n if parenthesis_num1>parenthesis_num0:\n return False\n if i == expect[:-1]:\n expect.pop(len(expect)-1)\n else:\n return False\n elif i == brackets[1]:\n brackets_num1+=1\n if brackets_num1>brackets_num0:\n return False\n if i == expect[:-1]:\n expect.pop(len(expect)-1)\n else:\n return False\n elif i == squirely[1]:\n squirely_num1+=1\n if squirely_num1>squirely_num0:\n return False\n if i == expect[:-1]:\n expect.pop(len(expect)-1)\n else:\n return False\n if parenthesis_num0 == parenthesis_num1 and brackets_num0 == brackets_num1 and squirely_num0 == squirely_num1:\n return True\n else:\n return False","def syntaxValid(s):\n\tif not s or type(s) != str or s[0] != '[':\n\t\treturn False\n\n\t#using stack to check for matching brackets\n\t#push '[' and pop for ']'\n\tbracketStack = []\n\tfor i in range(len(s)):\n\t\tcurrentChar = s[i]\n\t\tif currentChar == '[':\n\t\t\t#check for there is a delimiter before open bracket or trying to double nest\n\t\t\tif i != 0 and s[i-1] == '[' or i > 1 and s[i-2] != ',':\n\t\t\t\treturn False\n\t\t\tbracketStack.append('[')\n\t\telif currentChar == ']':\n\t\t\t#check if stack is prematurely empty or no delimiter after closed bracket\n\t\t\tif not bracketStack or (i != len(s)-1 and s[i+1] != ']' and s[i+1] != ','):\n\t\t\t\treturn False \n\t\t\telse: \n\t\t\t\tbracketStack.pop()\n\t\telif currentChar == ',':\n\t\t\t#check if delimiter is invalid\n\t\t\tif i == 0 or i == len(s)-1 or s[i-1] == '[' or s[i+1] != ' ':\n\t\t\t\treturn False\n\t#if the stack is empty, we have matched brackets, hence s is valid\n\treturn len(bracketStack) == 0","def _can_be_list(pair):\n assert(isa(pair, Pair))\n return str(pair).find(' . ') < 0","def is_special(s):\n for part in xrange(1, 3**len(s)):\n p = part\n sa = 0\n ca = 0\n sb = 0\n cb = 0\n for i, x in enumerate(s):\n if p%3 == 1:\n sa += x\n ca += 1\n elif p%3 == 2:\n sb += x\n cb += 1\n p = p//3\n if ca == 0 or cb == 0:\n continue\n if sa == sb:\n return False\n if ca > cb and sa <= sb:\n return False\n if cb > ca and sb <= sa:\n return False\n return True","def coord_valid(s, e):\n s = coord_fixer(s) # 0-index\n e = coord_fixer(e) # 1-index\n e = 1 if e == 0 else e # \n s, e = [s, e] if (int(s) < int(e)) else [e, s]\n return [str(s), str(e)]","def check_series(s: pd.Series) -> bool:\n\n error_string = (\n \"There are non-token cells (every cell should be a list of words/tokens) in the given Series.\"\n \" See help(hero.HeroSeries) for more information.\"\n )\n\n def is_list_of_strings(cell):\n return all(isinstance(x, str) for x in cell) and isinstance(\n cell, (list, tuple)\n )\n\n if not is_list_of_strings(s.iloc[0]) or s.index.nlevels != 1:\n raise TypeError(error_string)","def property_3(string):\n pairs = [ 'ab', 'cd', 'pq', 'xy' ]\n for pair in pairs:\n if pair in string:\n return False\n return True","def checkString(self,newick):\n\t\tisBalanced=True\n\t\tstack=[]\n\t\tfoundComma=False\n\t\tfor char in newick:\n\t\t\tif char == \"(\":\n\t\t\t\tstack.append(char)\n\t\t\telif char == \",\":\n\t\t\t\tfoundComma=True\n\t\t\telif char == \")\":\n\t\t\t\tif foundComma==True:\n\t\t\t\t\tstack.pop()\n\t\t\t\t\tfoundComma=False\n\t\t\t\telse:\n\t\t\t\t\tisBalanced=False\n\t\t\t\t\tbreak\n\t\t\telse:\n\t\t\t\tpass\n\t\tif len(stack) != 0:\n\t\t\tisBalanced=False\n\t\treturn isBalanced","def validate_insert(self, s, internal=True):\n super(FieldSet, self).validate_insert(s, internal) # mandatory check\n if s and s not in [d[0] for d in self.details]:\n valid = []\n for k,v in self.details:\n valid.append(\"%s=%s\" % (k, v))\n raise FilemanError(\"\"\"Value [%s] is not valid. must be one of: %s\"\"\" % (s, \", \".join(valid)))","def isAlternativeString(s):\n L = list(set(s))\n if len(L) != 2:\n return False\n for i in range(len(s) - 1):\n if s[i] == s[i + 1]:\n return False\n return True","def check_matching_brackets(s, opening=\"([{\", closing=\")]}\"):\n\n stack = Stack()\n\n for item in s:\n # Stores any opening character\n if item in opening:\n stack.push(item)\n else:\n # if a closing bracket is found and we try to remove the matching opening bracket from the top of the stack\n # in case the top of the stack is not a matching bracket, the string is not properly nested\n if stack.length() < 1 or stack.pop() is not opening[closing.index(item)]:\n return 0\n\n return 0 if stack.length() > 0 else 1","def checkValidString(self, string: str) -> bool:\n @lru_cache(None)\n def dp(index, diff):\n \"\"\"\n index is the index of string\n diff the counts of '(' - counts of ')'\n \"\"\"\n\n if index == len_s:\n return diff == 0\n if abs(diff) > len_s - index:\n return False\n c = string[index]\n index += 1\n if c == '(':\n return dp(index, diff + 1)\n elif c == ')':\n if diff - 1 < 0:\n return False\n return dp(index, diff - 1)\n else:\n return dp(index, diff + 1) or dp(index, diff - 1) or dp(index, diff)\n\n len_s = len(string)\n return dp(0, 0)","def is_substring_enclosed_in_brackets(sub, string):\n # to get (...)\n pattern = re.compile(\"\\([^\\)]*\\)\")\n # to get: (...(...)...)\n pattern2 = re.compile(\"\\([^\\)]*\\([^\\)]*\\)[^\\)]*\\)\")\n # to get: (...(...(...)...)...)\n pattern3 = re.compile(\"\\([^\\)]*\\([^\\)]*\\([^\\)]*\\)[^\\)]*\\)[^\\)]*\\)\")\n\n string = str(string)\n search = re.findall(pattern, string)\n search2 = re.findall(pattern2, string)\n search3 = re.findall(pattern3, string)\n\n search3 = set(search + search2 + search3)\n if any(str(sub) in s for s in search3): # or any(str(sub) in s for s in search2):\n return True\n return False","def validate(self, s):\n if len(s) == 0:\n return False\n if s in self.whitelist:\n return True\n if s in self.blacklist:\n return False\n\n # SQL Types are rarely used\n if 't' in s and 'f(t' not in s and 'At' not in s:\n return False\n\n if '1nf' in s:\n return False\n if 's1o' in s:\n return False\n if 'oo' in s:\n return False\n if 'v,s' in s:\n return False\n if 's,v' in s:\n return False\n if 'v,v' in s:\n return False\n if 'v,1' in s:\n return False\n if 'v,n' in s:\n return False\n if 'n,v' in s:\n return False\n if '1,v' in s:\n return False\n if 'Eo(' in s:\n return False\n if '(o(' in s:\n return False\n if '(o1' in s:\n return False\n if '(on' in s:\n return False\n if '(os' in s:\n return False\n if '(of' in s:\n return False\n if '(ov' in s:\n return False\n if 'B(n)' in s:\n return False\n if 'oso' in s:\n return False\n if 'o1o' in s:\n return False\n if 'ono' in s:\n return False\n\n # only 1 special case for this\n # 1;foo:goto foo\n # 1;n:k\n # the 'foo' can only be a 'n' type\n if ':' in s and not 'n:' in s:\n return False\n\n if '11' in s:\n return False\n\n if '))' in s:\n return False\n if '((' in s:\n return False\n if 'v1' in s:\n return False\n\n if 'nv' in s and ';T' not in s:\n return False\n if 'nn' in s and ';T' not in s:\n return False\n\n # select @version foo is legit\n # but unlikely anywhere else\n if 'vn' in s and 'Evn' not in s:\n return False\n\n if 'oE' in s:\n return False\n\n if 'A1' in s:\n return False\n if 'An' in s:\n return False\n if 'A(1' in s:\n return False\n\n if 'vov' in s:\n return False\n if 'vo1' in s:\n return False\n if 'von' in s:\n return False\n\n if 'ns' in s:\n if 'U' in s:\n return True\n if 'T' in s:\n return True\n return False\n\n if 'sn' in s:\n # that is... Tsn is ok\n if s.find('T') != -1 and s.find('T') < s.find('sn'):\n return True\n return False\n\n # select foo (as) bar is only nn type i know\n if 'nn' in s and 'Enn' not in s and ';T' not in s:\n return False\n\n if ',o' in s:\n return False\n\n if 'kk' in s and 'Tkk' not in s:\n return False\n\n if 'ss' in s:\n return False\n\n if 'ff' in s:\n return False\n\n if '1no' in s:\n return False\n\n if 'kno' in s:\n return False\n\n if 'nEk' in s:\n return False\n\n if 'n(n' in s:\n return False\n if '1so' in s:\n return False\n if '1s1' in s:\n return False\n if 'noo' in s:\n return False\n if 'ooo' in s:\n return False\n\n if 'vvv' in s:\n return False\n\n if '1vn' in s:\n return False\n if '1n1' in s:\n return False\n if '&1n' in s:\n return False\n if '&1v' in s:\n return False\n if '&1s' in s:\n return False\n if 'nnk' in s:\n return False\n if 'n1f' in s:\n return False\n # folded away\n if s.startswith('('):\n return False\n\n if '&o' in s:\n return False\n\n if '1,1' in s:\n return False\n if '1,s' in s:\n return False\n if '1,n' in s:\n return False\n if 's,1' in s:\n return False\n if 's,s' in s:\n return False\n if 's,n' in s:\n return False\n if 'n,1' in s:\n return False\n if 'n,s' in s:\n return False\n if 'n,n' in s:\n return False\n if '1o1' in s:\n return False\n if '1on' in s:\n return False\n if 'no1' in s:\n return False\n if 'non' in s:\n return False\n if '1(v' in s:\n return False\n if '1(n' in s:\n return False\n if '1(s' in s:\n return False\n if '1(1' in s:\n return False\n if 's(s' in s:\n return False\n if 's(n' in s:\n return False\n if 's(1' in s:\n return False\n if 's(v' in s:\n return False\n if 'v(s' in s:\n return False\n if 'v(n' in s:\n return False\n if 'v(1' in s:\n return False\n if 'v(v' in s:\n return False\n\n if s.startswith('n('):\n return False\n\n if s.startswith('vs'):\n return False\n\n if s.startswith('o'):\n return False\n\n if ')(' in s:\n return False\n\n # need to investigate T(vv) to see\n # if it's correct\n if 'vv' in s and s != 'T(vv)':\n return False\n\n # unlikely to be sqli but case FP\n if s in ('so1n)', 'sonoE'):\n return False\n\n return True","def BalancedBrackets(Str):\n stack = []\n for parenthesis in Str:\n if parenthesis == '{' or parenthesis == '(' or parenthesis == '[':\n stack.append(parenthesis)\n elif parenthesis == '}' or parenthesis == ')' or parenthesis == ']':\n if len(stack) < 1:\n return False\n last_item = stack.pop()\n if not Compare(last_item, parenthesis):\n return False\n if len(stack) != 0:\n return False\n\n return True","def string_is_index_list(inp: str):\n inp = inp.strip()\n return len((inp)) > 0 and all([x in [\" \", \":\", \"-\"] or x.isdigit() for x in inp])","def dict_check34(_str1):\n\t_len=len(_str1)\n\t_list1=[(i,j,k) for i in range(5) for j in range(5) for k in range(5)]\n\t_list0=[]\n\tfor i in range(len(_list1)):\n\t\t#Take different length\n\t\t_current=_list1[i]\n\t\tif _len>=sum(_current) and sum(_list1[i])!=0:\n\t\t\t_list2=[]\n\t\t\t_n1=_current[0]\n\t\t\t_n2=_current[1]\n\t\t\t_n3=_current[2]\n\t\t\t_list2.append(_str1[:_n1])\n\t\t\t_list2.append(_str1[_n1:_n1+_n2])\n\t\t\t_list2.append(_str1[_n1+_n2:_n1+_n2+_n3])\n\t\telse:\n\t\t\tcontinue\n\t\tn=0\n\t\tfor j in range(3):\n\t\t\tif _list2[j] in _dict_ori or _list2[j]==\"\":\n\t\t\t\tn+=1\n\t\tif n==3:\n\t\t\t_list0.append(_list2)\n\treturn(_list0)","def validate(s):\n\n # base case: square is 1 or 0\n if s == 1 or s == 0:\n return True\n\n # list of length 4\n if isinstance(s, list) and len(s) == 4:\n\n # idea one: fail fast\n for i in s:\n if not validate(i):\n return False\n return True\n\n # idea 2: \"and\" the results ALSO fail fast\n # return (validate(s[0]) and \n # validate(s[1]) and \n # validate(s[2]) and \n # validate(s[3]))\n # OR\n # return all([validate(i) for i in s])\n\n # idea 3: multiply the results: will not return boolean\n # return (validate(s[0]) * \n # validate(s[1]) * \n # validate(s[2]) * \n # validate(s[3]))\n\n # not one of our numbers or list of length 4\n # another base case\n return False","def uniqueCheckString(aList):\r\n check = ','\r\n for v in aList:\r\n if f',{v},' in check:\r\n return True\r\n check = f'{check}{v},'\r\n return False","def check(s,l):\n if len(s)==1:\n if s[0] in l:\n return False\n else:\n return True\n else:\n if s[0] in l:\n return False\n else:\n l.append(s[0])\n return check(s[1:],l)","def valid_variant(s, is_coding=True):\n _validate_str(s)\n if s == WILD_TYPE_VARIANT:\n return True\n else:\n if is_coding:\n for mut in s.split(\", \"):\n match = re_coding.match(mut)\n if match is None:\n return False\n return True\n else:\n for mut in s.split(\", \"):\n match = re_noncoding.match(mut)\n if match is None:\n return False\n return True","def is_tuple_consists_of_strings(an_array):\n return isinstance(an_array, tuple) and is_array_type(an_array, str)","def bracket_validator(brackets):\n bracket_stack = []\n for b in brackets:\n if b == '(' or b == '{' or b == '[':\n bracket_stack.append(b)\n\n elif b == ')' or b == '}' or b == ']':\n top = bracket_stack.pop()\n\n if b != bracket_match(top):\n return False\n\n return True","def is_valid_string_listing(listing):\n assert listing is not None\n listing = str(listing)\n if len(listing) == 0:\n return True\n list_object = json.loads(listing)\n return not any(key not in listing_keys\n for key in list_object)","def complete(s):\n in_str = False\n bb = 0\n for i, c in enumerate(s):\n if c == '(' and not in_str:\n bb += 1\n elif c == ')' and not in_str:\n bb -= 1\n if bb < 0:\n raise ValueError('Insufficient open brackets in ' + repr(s))\n elif c == '\\\"':\n in_str = not in_str\n return s + ')' * bb","def valid_parenthesis(string):\n if len(string) % 2:\n print(\"Invalid Parenthesis\")\n return\n stack = []\n bracket_hash = {\"}\": \"{\", \")\": \"(\", \"]\": \"[\"}\n\n for bracket in string:\n if bracket in bracket_hash:\n if stack:\n top = stack.pop()\n if bracket_hash[bracket] is not top:\n print(\"Invalid Parenthesis\")\n return\n else:\n top = \"\"\n else:\n if bracket in bracket_hash.values():\n stack.append(bracket)\n else:\n print(\"Invalid String\")\n return\n\n if not stack:\n print(\"Valid Parenthesis\")\n return","def is_item_substring(msg, i):\n if msg[i] == \"(\" and i + 4 <= len(msg):\n return msg[i:i + 4] == \"({})\"\n else:\n return False","def _checkSSFormatArg(ssformat):\n if ssformat == '':\n raise ShortStrException('ssformat argument cannot be the empty string')\n\n if not isinstance(ssformat, str):\n raise ShortStrException('ssformat argument must be a string with only characters *, c, l, u, and d')\n\n for c in ssformat:\n if c not in '*clud':\n raise ShortStrException('ssformat argument must be a string with only characters *, c, l, u, and d')","def minAddToMakeValid(self, S):\n stack = []\n for char in S:\n if char == '(':\n stack.append(char)\n if char == ')':\n if len(stack):\n if stack[-1] == '(':\n stack.pop()\n else:\n stack.append(char)\n else:\n stack.append(char)\n return len(stack)","def is_it_possible(e, s, e_to_seps):\n\n for u in e:\n num = 0\n seps = set()\n # Count the edges that need to be added and the number of separators that can be involved in their addition\n for w in s:\n\n if w < u:\n uw = (w, u)\n else:\n uw = (u, w)\n\n if uw in e_to_seps:\n num += 1\n seps.update(e_to_seps[uw])\n\n if num > len(seps):\n return False\n\n return True","def multiCheck(word, multi_exception):\n length = 0\n a = False\n b = \"\"\n for keys in multi_exception:\n if len(keys) > length:\n length = len(keys)\n for i in range(1, length + 1):\n if word[-i:] in multi_exception:\n a = True\n b = word[-i:]\n return a, b","def __call__(self, s):\n state = self._initial\n try:\n for sym in s:\n state = self._trans_matrix[state][self._syms_to_indices[sym]]\n except KeyError:\n raise NotInAlphabetError(sym) from None\n return state in self._accepting","def validate_expression(str):\n stack = []\n pushChars, popChars = \"([\", \")]\"\n for c in str:\n if c in pushChars:\n stack.append(c)\n elif c in popChars:\n if not len(stack):\n raise ValidationError('La expresion tiene corchetes \\'[ ]\\' o parentesis \\'( )\\' sin cerrar')\n else:\n stackTop = stack.pop()\n balancingBracket = pushChars[popChars.index(c)]\n if stackTop != balancingBracket:\n raise ValidationError('La expresion tiene corchetes \\'[ ]\\' o parentesis \\'( )\\' sin cerrar')\n\n if len(stack):\n raise ValidationError('La expresion tiene corchetes \\'[ ]\\' o parentesis \\'( )\\' sin cerrar')","def _valid_(s) :\n return LoKi.Dicts.MCFinderDicts.valid (s)","def is_unique3(test_str):\n\n for i in range(len(test_str) - 1):\n for j in range(i+1, len(test_str)):\n if test_str[i] == test_str[j]:\n return False\n return True","def validate_state(self, state):\n assert isinstance(state, str), \"`state` must be of type str\"\n assert len(state) == 9, \"`state` must have exactly 9 elements\"\n\n state = state.upper()\n assert set(state) <= {\n \"1\",\n \"2\",\n \"_\",\n }, \"`state` elements must be in {'1', '2', '_'}\"\n assert (\n 0 <= self.counter[\"1\"] - self.counter[\"2\"] <= 1\n ), \"Invalid state: Players must alternate.\"","def has_balanced_parens(string):","def validate_X(X: List[str]):\n _check_string_list(X)","def checkvalid(s: bytes, m: bytes, pk: bytes) -> None:\n if len(s) != b // 4:\n raise ValueError(\"signature length is wrong\")\n\n if len(pk) != b // 8:\n raise ValueError(\"public-key length is wrong\")\n\n R = decodepoint(s[: b // 8])\n A = decodepoint(pk)\n S = decodeint(s[b // 8 : b // 4])\n h = Hint(encodepoint(R) + pk + m)\n\n (x1, y1, z1, _) = P = scalarmult_B(S)\n (x2, y2, z2, _) = Q = edwards_add(R, scalarmult(A, h))\n\n if (\n not isoncurve(P)\n or not isoncurve(Q)\n or (x1 * z2 - x2 * z1) % q != 0\n or (y1 * z2 - y2 * z1) % q != 0\n ):\n raise SignatureMismatch(\"signature does not pass verification\")","def _not_valid_(s) :\n return not s.valid()","def isBalanced(string):\n assert type(string)==str\n if any(a not in '(*)' for a in string):\n raise AssertionError\n string = list(string) #Converts the inputted list to a string.\n #Loops through the list, checks for opening and closing parentheses and removes them from the list.\n k = 0\n while True:\n if k>=len(string)-1 or len(string)==0:\n break\n if string[k]=='(':\n if ')' in string[k:]:\n b = string[k:].index(')')\n c = string.pop(k)\n d = string.pop(k+b-1)\n else:\n break\n else:\n k+=1\n continue\n \n #Checks if the list is empty and returns True\n if string==[]:\n return True\n #Loops through the list, checks for opening parentheses as '*' and closing parentheses and removes them from the list.\n k = 0\n while True:\n if k>=len(string)-1 or len(string)==0:\n break\n if string[k]=='*':\n if ')' in string[k:]:\n b = string[k:].index(')')\n c = string.pop(k)\n d = string.pop(k+b-1)\n else:\n break\n else:\n k+=1\n continue\n \n #Checks if the list is empty and returns True \n if string==[]:\n return True\n \n #Loops through the list, checks for opening parentheses and closing parentheses as '*'and removes them from the list.\n k = 0\n while True:\n if k>=len(string)-1 or len(string)==0:\n break\n if string[k]=='(':\n if '*' in string[k:]:\n b = string[k:].index('*')\n c = string.pop(k)\n d = string.pop(k+b-1)\n else:\n break\n else:\n k+=1\n continue\n \n #Checks if the list is empty and returns True\n if string==[]:\n return True\n #Checks if the list contains only asterix and returns True.\n if not any(a not in '*' for a in string):\n return True\n return False","def validate(value):\n\n found_repeat = False\n for element in range(len(value)-1):\n search = value[element:element+2]\n reg_result = re.findall(search, value)\n if len(reg_result) >= 2:\n found_repeat = True\n continue\n\n if not found_repeat:\n return False\n\n found_sandwich = False\n for element in range(len(value)-2):\n if value[element] == value[element+2]:\n found_sandwich = True\n break\n\n if not found_sandwich:\n return False\n\n return True","def is_palin(s):\r\n if s == \"\":\r\n return True\r\n if s[0] == s[-1]:\r\n return is_palin(s[1:-1])\r\n else:\r\n return False","def check_string(s, strings):\n for string in strings:\n if string not in s:\n return False\n return True","def extract_pairs(raw):\n try:\n pairs = list(set([(p.split(\"-\")[0].strip().upper(), p.split(\"-\")[1].strip().upper()) for p in raw.split(\",\")]))\n except IndexError as e:\n raise IndexError(\"Invalid pair\")\n\n for x, y in pairs:\n if not (len(x) > 1 and len(y) > 1):\n raise Exception(f'Invalid pair: {x}-{y}')\n \n if len(pairs) is 0:\n raise Exception(\"No valid pairs\")\n\n return pairs","def property_2(string):\n for letter in al:\n pair = letter + letter\n if pair in string:\n return True\n return False","def check_series(s: pd.Series) -> bool:\n\n error_string = (\n \"The input Series should consist only of strings in every cell.\"\n \" See help(hero.HeroSeries) for more information.\"\n )\n\n if not isinstance(s.iloc[0], str) or s.index.nlevels != 1:\n raise TypeError(error_string)","def validate_strand(strand: str) -> bool:\n strand = strand.upper()\n count = dict(Counter(strand))\n for k in count.keys():\n if k not in NUCLEOTIDES:\n raise Exception(\"Invalid DNA sequence\")\n return True","def check_type(s: pd.Series) -> Tuple[bool, str]:\n\n error_string = (\n \"should be TokenSeries: there are non-token cells (every cell should be a list of words/tokens) in the given Series.\"\n \" See help(hero.HeroTypes) for more information.\"\n )\n\n def is_list_of_strings(cell):\n return all(isinstance(x, str) for x in cell) and isinstance(\n cell, (list, tuple)\n )\n\n try:\n first_non_nan_value = s.loc[s.first_valid_index()]\n if not is_list_of_strings(first_non_nan_value):\n return False, error_string\n except KeyError: # Only NaNs in Series -> same warning applies\n return False, error_string\n\n return True, \"\"","def _match_brace(string, start_pos, pair='[]'):\n depth = 1\n if string[start_pos] != pair[0]:\n return None\n for index, char in enumerate(string[start_pos + 1:]):\n if char == pair[0]:\n depth += 1\n elif char == pair[1]:\n depth -= 1\n if depth == 0:\n return start_pos + index + 1\n return None","def validate_pairs(pairs, historical_pairs):\n if pairs is None:\n return False\n for p in pairs:\n if p in historical_pairs:\n return False\n return True","def is_balanced(par_str):\n\n left_par = \"{[(\"\n right_par = \"}])\"\n\n if len(par_str) % 2 != 0:\n return False\n \n # prevents in index error\n if par_str[0] in right_par:\n return False\n \n while len(par_str) < 0:\n for ind, par in par_str:\n if par not in left_par and par[ind] == par[ind - 1]:\n par_str.pop(ind)\n par_str.pop(ind - 1)\n print(par_str)\n break\n else:\n return False\n return True","def brackets_match(string):\n expected = Stack()\n for character in string:\n if character == \"(\":\n expected.push(\")\")\n elif character == \"[\":\n expected.push(\"]\")\n elif character in \")]\":\n if expected and character == expected.top():\n expected.pop()\n else:\n return False\n return expected.is_empty()","def isValid(self, s):\n for valid in self.validTargets:\n if (valid[0] in s):\n return True\n return False","def list_check(listz):\n isValid = False\n x = 0\n position = ''\n\n #checking if characters contains 023 and extracting them \n\n while (x < len(listz)):\n if(listz.__contains__(0)):\n position = position + str(listz[listz.index(0)])\n if(listz.__contains__(2)):\n position = position + str(listz[listz.index(2)])\n if(listz.__contains__(3)):\n position = position + str(listz[listz.index(3)])\n x = len(listz) + 1\n\n#making sure its the requered sequence\n\n if(position == '023'):\n isValid = True\n x = x + 1\n return isValid","def _check_params(self):\n\t\tstrange_param_helper = False\n\t\tfor param in self.params:\n\t\t\n\t\t\t# It could be that the param encapsulates several values (e.g., \"FLUX_RADIUS(10)\")\n\t\t\t# So we have to dissect this\n\t\t\tmatch = re.compile(\"(\\w*)\\(\\d*\\)\").match(param)\n\t\t\tif match:\n\t\t\t\tcleanparam = match.group(1)\n\t\t\telse:\n\t\t\t\tcleanparam = param\n\t\t\t\t\n\t\t\tif cleanparam not in self.fullparamlist:\n\t\t\t\tlogger.warning(\"Parameter '%s' seems strange and might be unknown to SExtractor\" \\\n % (param))\n\t\t\t\tstrange_param_helper = True\n\t\t\t\t\n\t\tif strange_param_helper:\n\t\t\tlogger.warning(\"Known parameters are: %s\" % (self.fullparamtxt))","def question1(s, t):\n if type(t) == str and type(s) == str and len(s) >= len(t):\n for letter in t:\n if letter not in s:\n return False\n return True\n else:\n return False","def validate(data, badchars):\n assert(all(b not in data for b in badchars))","def is_valid_posse(posse: 'Tuple'):\n # todo: write this as iterator through test types\n # could use test types as blocks for lambda calculus\n # for test in attribute tests:\n\n # print('testing posse: %s' % str(posse))\n\n prefixes = set()\n cases = set()\n lengths = set()\n letters = set()\n\n for member in posse:\n # prefix validity\n prefixes.add(member[0])\n\n # case validity\n if ord(member[1]) in THIRD_CASE:\n cases.add(2)\n elif member[1].isupper():\n cases.add(1)\n else:\n cases.add(0)\n\n # length validity\n lengths.add(len(member[1:]))\n\n # letter validity\n # print('letter validity for %s' % member)\n for letter_type in LETTER_SETS:\n if ord(member[1]) in LETTER_SETS[letter_type]:\n letters.add(letter_type)\n\n prefix_is_valid = len(prefixes) == 1 or len(prefixes) == 3\n case_is_valid = len(cases) == 1 or len(cases) == 3\n length_is_valid = len(lengths) == 1 or len(lengths) == 3\n letter_is_valid = len(letters) == 1 or len(letters) == 3\n\n # print('prefix_is_valid: %s' % prefix_is_valid)\n # print('case_is_valid: %s' % case_is_valid)\n # print('length_is_valid: %s' % length_is_valid)\n # print('letter_is_valid: %s' % letter_is_valid)\n\n return all((prefix_is_valid,\n case_is_valid,\n length_is_valid,\n letter_is_valid))","def is_rows_valid(bd):\n for row in rows:\n seen = []\n for num in nums:\n if bd[row[num]] == \" \":\n continue\n elif bd[row[num]] not in seen:\n seen += [bd[row[num]]]\n else:\n return False\n else:\n continue\n return True","def test_string(self, s):\n\n data = s.split(' ')\n\n origin = ' '.join(data[0:-1])\n if not origin:\n return False\n \n origin_hashed = self.hash_with_salt(origin)\n\n return origin_hashed == s","def balanced_string(string):\n stack = create_stack()\n pos = 0\n while pos < len(string):\n if string[pos] in '[{(':\n stack.push(string[pos])\n elif string[pos] in ']})':\n pair = stack.pop()\n if not match(string[pos], pair):\n return False\n pos = pos+1\n #return stack.length()\n if stack.length() == 0:\n return True\n else:\n return False","def _check_brackets(tokens: List[str]) -> True:\r\n brackets = []\r\n for token in tokens:\r\n if token in \"([\":\r\n brackets.append(token)\r\n elif token in \")]\":\r\n if not brackets:\r\n raise ExtraBracket(token)\r\n closing_bracket = brackets.pop()\r\n if ((token == \"(\" and closing_bracket != \")\") or\r\n (token == \"[\" and closing_bracket != \"]\")):\r\n raise BracketsMismatch(token, closing_bracket)\r\n if brackets:\r\n raise NoClosingBracket(brackets[0])\r\n return True","def is_selector_seq(cls, s):\n\n assert np.iterable(s)\n for tokens in s:\n\n # The selector must contain sequences of tokens:\n if not np.iterable(tokens):\n return False\n\n # Each token must either be a string, integer, slice,\n # list of strings, or list of integers:\n for token in tokens:\n if type(token) == list:\n # token_types = set(map(type, token))\n # if not (token_types.issubset([str, int])):\n if not all(map(\n lambda x: isinstance(x, (int, long, basestring)),\n token)):\n return False\n elif not isinstance(token, (slice, basestring, int, long)):\n # elif not type(token) not in [slice, str, int]:\n return False\n\n # All tokens are valid:\n return True","def __valid_token_format(self, token):\n if len(token) != self.TOKEN_LENGTH * 2:\n return False\n for c in token:\n if c not in '01234567890abcdef':\n return False\n return True","def checkend(s):\n\n x = s[0]\n y = s[-1]\n\n if x == y:\n return True \n else:\n return False","def is_matched(expression):\n\n balance = []\n\n for char in expression:\n if char == \"{\" or char == \"[\" or char == \"(\":\n balance.append(char)\n\n elif char == \"}\":\n if balance[-1] == \"{\":\n balance.pop()\n else:\n return False\n\n elif char == \"]\":\n if balance[-1] == \"[\":\n balance.pop()\n else:\n return False\n\n elif char == \")\":\n if balance[-1] == \"(\":\n balance.pop()\n else:\n return False\n\n if len(balance) == 0:\n return True","def is_pal(string):\n a = reversed(list(string))\n if list(a) == list(string):\n return True\n else:\n return False","def _has_matched_brackets(text: str) -> bool:\n open_bracket_stack = []\n for index, _ in enumerate(text):\n if text[index] == \"[\":\n open_bracket_stack.append(index)\n elif text[index] == \"]\":\n if len(open_bracket_stack) == 0:\n return False\n open_bracket_stack.pop()\n return len(open_bracket_stack) == 0","def valid_parentheses(parens):\n while len(parens) >= 1:\n if parens[0] == ')' or parens[-1] == '(':\n return False\n index = parens.find('()')\n parens = parens[:index] + parens[(index + 2):]\n return True","def _checkForSlicesInKey(self, key):\n if isinstance(key, tuple):\n for i, v in enumerate(key):\n if isinstance(v, slice):\n raise PyTextCanvasException('Use parentheses when specifying slices, i.e. spam[(0, 0):(9, 9)] not spam[0, 0:9, 9].')","def _is_well_formed(l):\n\tif _is_symbol(l):\n\t\treturn 1\n\tif (type(l) == types.TupleType and len(l) == 2\n\t\t\tand l[0] == neg and _is_well_formed(l[1])):\n\t\treturn 1\n\tif (type(l) == types.TupleType and len(l) == 3\n\t\t\tand _is_binary(l[1])\n\t\t\tand _is_well_formed(l[0]) and _is_well_formed(l[2])):\n\t\treturn 1\n\treturn 0","def all_simple (phrase):\r\n\r\n\r\n for x in phrase:\r\n if (x not in self.operations and not (isinstance(x,(int,type(ListType()),float,bool) or (isinstance(x,str) and quoted(x)))) or self.current_register.contains(x)):\r\n return False\r\n return True","def bracketed (self,phrase):\r\n\r\n level = 0\r\n left_point = None\r\n right_point = None\r\n \r\n\r\n for count,char in enumerate(phrase):\r\n\r\n if char == '(':\r\n if level == 0:\r\n left_point = count\r\n level+=1\r\n if char == ')':\r\n level-=1\r\n if level == 0:\r\n right_point = count\r\n if not (left_point is None) and (not right_point is None) and left_point == 0 and right_point == len(phrase)-1:\r\n return True\r\n return False","def CheckSeq(Seq):\n OkNucleo = (\"A\", \"C\", \"G\", \"T\")\n for i in Seq:\n if i not in OkNucleo:\n raise InputError(Seq,\"malformed input\")","def is_valid(n):\n\tif type(n) == int:\n\t\tn = str(n)\n\tfor index, c in enumerate(n):\n\t\tif index == 0:\n\t\t\tcontinue\n\t\tif n[index - 1] > n[index]:\n\t\t\treturn False\n\treturn True","def is_unique_n_dict(string: str) -> bool:\n\n store = {}\n\n for letter in string:\n if letter in store:\n return False\n store[letter] = 1\n\n return True","def valid_parentheses(parens):\n\n # offset = 0\n # for p in parens:\n # if p == \"(\":\n # offset += 1\n # if p == \")\":\n # offset -= 1\n # return True if offset == 0 else False\n\n num_of_opens = 0\n for p in parens:\n if p == \"(\":\n num_of_opens += 1\n if p == \")\":\n num_of_opens -= 1\n if num_of_opens < 0:\n return False\n return True if num_of_opens == 0 else False","def verify_CS(self, raw):\n total = 0\n for b in raw[0:-1]:\n total += b\n\n return total == raw[-1]","def naughty_nice_check2(string: str) -> bool:\n letters = 'abcdefghijklmnopqrstuvwxyz'\n # All combinations of two letters, search for single-separated\n # has_double_doubles = False\n # for two_letter_combo in itertools.product(letters, letters):\n # x, y = two_letter_combo\n # if re.findall('{0}.*{0}'.format(x, y), string):\n # has_double_doubles = True\n # break\n has_double_doubles = any(\n re.findall('{0}.*{0}'.format(x + y), string) for x, y in itertools.product(letters, letters)\n )\n\n has_separated_letter = any(\n re.findall('{0}.{0}'.format(letter), string) for letter in letters\n )\n\n return has_double_doubles and has_separated_letter","def check_filter_str(inputStr):\n if(not(isinstance(inputStr, str))):\n return False\n\n acceptedFields = ['remote', 'local', 'flow', 'eventval', 'irule']\n\n inputList = inputStr.split('|')\n myList = []\n for index, item in enumerate(inputList):\n inputList[index] = item.split('&')\n myList += inputList[index]\n\n\n for item in myList:\n if(item.count('(') != 1):\n return False\n if(item.count(')') != 1):\n return False\n if(item.count('=') != 2):\n return False\n\n item = item.replace('(', '')\n item = item.replace(')', '')\n\n equivList = item.split('==')\n if(equivList[0] not in acceptedFields):\n return False\n\n if((equivList[0] == 'remote') or (equivList[0] == 'local')):\n if(equivList[1].count(':') != 2):\n return False\n\n if(equivList[0] == 'flow'):\n if(equivList[1].count('-') != 1):\n return False\n splitVal = equivList[1].split('-')\n for val in splitVal:\n if(val.count(':') != 2):\n return False\n\n return True","def validate_coords(coords):\n coords = list(coords)\n if coords != sorted(coords):\n raise ValueError(\"The argument 'coords' must be sorted.\")\n if any(c not in \"xyz\" for c in coords):\n raise ValueError(\"The argument 'coords' may only contain \" \"'x', 'y', or 'z'.\")\n return coords","def validate_starr(p_stt_arr):\n station = 0\n totg = 0\n length_gas = len(p_stt_arr)\n for index in p_stt_arr:\n if ':' in index:\n station += 1\n g, c = index.split(':')\n totg += int(g) - int(c)\n if totg < 0:\n raise Exception('Impossible')\n if ((station + 1) == length_gas) and (int(c) > 0):\n station = 1\n return station","def valid(phrase):\n words = []\n series_of_words = phrase.split(' ')\n words.append(series_of_words.pop())\n for word in series_of_words:\n if word in words:\n return False\n words.append(word)\n return True","def barcode_is_valid(s):\n return (bool(re.match(r'^[ATGC]*$',s))\n or barcode_is_10xgenomics(s))"],"string":"[\n \"def isValid(self, s):\\n # 1\\n if not s:\\n return True\\n\\n # 2\\n if not len(s) % 2 == 0:\\n return False\\n\\n comp = {\\n ')': '(',\\n ']': '[',\\n '}': '{',\\n }\\n stack = []\\n\\n for char in s:\\n # 3\\n if char in comp:\\n popped = stack.pop() if stack else '@'\\n if popped != comp[char]:\\n return False\\n # 2\\n else:\\n stack.append(char)\\n\\n return not stack\",\n \"def isValidPair(self,s1,s2):\\n if (s1 == '(' and s2 == ')'):\\n return True\\n if (s1 == '[' and s2 == ']'):\\n return True\\n if (s1 == '{' and s2 == '}'):\\n return True\\n return False\",\n \"def isValid(self, s: str) -> bool:\\n stack = list()\\n for c in s:\\n if c in Solution.corresponding_parenthesis:\\n stack.append(Solution.corresponding_parenthesis[c])\\n elif not stack or stack.pop() != c:\\n return False\\n return not stack\",\n \"def is_well_formed(s):\\n # TODO - you fill in here.\\n s = list(s)\\n op = []\\n l = len(s)\\n for i in range(l):\\n if s[i] in ['(','{','[']:\\n op.append(s[i])\\n else: \\n if len(op) == 0:\\n return False\\n temp = op.pop() \\n if s[i] == ')':\\n if temp != '(':\\n return False\\n elif s[i] == '}':\\n if temp != '{':\\n return False\\n else:\\n if temp != '[':\\n return False\\n if len(op) != 0:\\n return False\\n return True\",\n \"def is_valid(s):\\n in_str = False\\n bb = 0\\n for c in s:\\n if c == '(' and not in_str:\\n bb += 1\\n elif c == ')' and not in_str:\\n bb -= 1\\n if bb < 0:\\n return False\\n elif c == '\\\\\\\"':\\n in_str = not in_str\\n return bb == 0\",\n \"def par_checker(s):\\n lparens = \\\"([{\\\" # otevírací závorky\\n rparens = \\\")]}\\\" # uzavírací závorky (ve stejném pořadí)\\n stack = Stack()\\n for c in s:\\n if c in lparens:\\n stack.push(c)\\n for i in range(len(rparens)):\\n if c == rparens[i]:\\n if stack.is_empty() or stack.pop() != lparens[i]: # líné vyhodnocení\\n return False\\n return stack.is_empty()\",\n \"def isValid(self, s: str) -> bool:\\n stack = []\\n \\n mapping = {\\n \\\")\\\":\\\"(\\\",\\n \\\"}\\\":\\\"{\\\",\\n \\\"]\\\":\\\"[\\\"\\n }\\n \\n for char in s:\\n if char not in mapping:\\n stack.append(char)\\n \\n else:\\n top_element = stack.pop() if stack else \\\"#\\\"\\n \\n if mapping[char] != top_element:\\n return False\\n \\n return not stack\",\n \"def isValid(self, s: str) -> bool:\\n open_brackets = [] # stack of open but not closed brackets\\n for char in s:\\n if char in ['(', '[', '{']:\\n open_brackets.append(char)\\n else:\\n try:\\n complement = open_brackets.pop()\\n except IndexError:\\n return False\\n else:\\n if complement + char not in ['()', '[]', '{}']:\\n return False\\n return not len(open_brackets)\",\n \"def is_valid(self, string) -> bool:\\n while '()' in string or '{}' in string or '[]' in string:\\n string = string.replace('()', '').replace('[]', '').replace('{}', '')\\n\\n return len(string) == 0\",\n \"def _should_be_pair(s_list):\\n assert(isa(s_list, List))\\n return str(s_list).find(' . ') > 0\",\n \"def check(self, s: str, mem: dict):\\n dp = [False for _ in range(len(s)+1)]\\n dp[0] = True\\n for i in range(1, len(s)+1):\\n for j in range(i):\\n if dp[j] and s[j:i] in mem:\\n dp[i] = True\\n return dp[-1]\",\n \"def check_valid(indexes):\\n # Check if any substrings has any versions that are the opposite of it anywhere in that line.\\n valid = False\\n for k, v in indexes.items():\\n swapped = k[::-1]\\n other = indexes.get(swapped)\\n # Check to see if the swapped version exists in the dictionary.\\n if other:\\n # 'aaa' case, these are invalid matches, don't bother checking further.\\n if k == swapped:\\n continue\\n # single occurence case\\n if len(v) == 1 and len(other) == 1:\\n # Case where both occur inside or outsid brackets.\\n if (int(v[0]) % 2) == (int(other[0]) % 2):\\n continue\\n else:\\n valid = True\\n else:\\n # Use sets to eliminate duplicates in the same chunk.\\n v_s = set(v)\\n other_s = set(other)\\n possible_combinations = [(x % 2, y % 2) for x in v_s for y in other_s]\\n # For a pairing to be valid, one part needs to be in an even chunk and the other in an odd ([]) chunk.\\n if (1, 0) in possible_combinations or (0, 1) in possible_combinations:\\n valid = True\\n return valid\",\n \"def multi_bracket_validation(input):\\n brackets = Stack()\\n for i in input:\\n if i == '(' or i == '[' or i == '{':\\n brackets.push(i)\\n elif i == ')' or i == ']' or i == '}':\\n if brackets._size == 0:\\n return False\\n elif i == ')' and brackets.top.val == '(':\\n brackets.pop()\\n elif i == ']' and brackets.top.val == '[':\\n brackets.pop()\\n elif i == '}' and brackets.top.val == '{':\\n brackets.pop()\\n else:\\n return False\\n\\n if brackets._size != 0:\\n return False\\n return True\",\n \"def valid_format(s):\\n if len(s) > 7:\\n return False\\n elif '0' in s and len(s) == 1:\\n return True\\n elif s[0] == '0' and len(s) > 1:\\n return False\\n elif introcs.isalpha(s):\\n return False\\n elif (len(s) > 3) and (introcs.count_str(s, ',') == 0):\\n return False\\n elif introcs.count_str(s, ',') == 0:\\n return introcs.isdecimal(s)\\n elif introcs.count_str(s, ',') > 1:\\n return False\\n elif ',' in s and introcs.count_str(s,',') == 1:\\n comma_check = s[introcs.find_str(s, ',')+1:]\\n before_comma_check = s[:introcs.find_str(s, ',')]\\n introcs.isdecimal(before_comma_check)\\n return (True if len(comma_check) == 3 else False) and introcs.isdecimal(before_comma_check)\",\n \"def is_sqrs_valid(bd):\\n for sqr in sqrs:\\n seen = []\\n for num in nums:\\n if bd[sqr[num]] == \\\" \\\":\\n continue\\n elif bd[sqr[num]] not in seen:\\n seen += [bd[sqr[num]]]\\n else:\\n return False\\n else:\\n continue\\n return True\",\n \"def group_check(s):\\n parenthesis = ['(',')']\\n brackets = ['[', ']']\\n squirely = ['{', '}']\\n parenthesis_num0 = 0\\n brackets_num0 = 0\\n squirely_num0 = 0\\n parenthesis_num1 = 0\\n brackets_num1 = 0\\n squirely_num1 = 0\\n expect = []\\n for i in enumerate(s):\\n if i == parenthesis[0]:\\n parenthesis_num0+=1\\n expect.append(parenthesis_num1)\\n elif i == brackets[0]:\\n brackets_num0+=1\\n expect.append(brackets_num1)\\n elif i == squirely[0]:\\n squirely_num0+=1\\n expect.append(squirely_num1)\\n elif i == parenthesis[1]:\\n parenthesis_num1+=1\\n if parenthesis_num1>parenthesis_num0:\\n return False\\n if i == expect[:-1]:\\n expect.pop(len(expect)-1)\\n else:\\n return False\\n elif i == brackets[1]:\\n brackets_num1+=1\\n if brackets_num1>brackets_num0:\\n return False\\n if i == expect[:-1]:\\n expect.pop(len(expect)-1)\\n else:\\n return False\\n elif i == squirely[1]:\\n squirely_num1+=1\\n if squirely_num1>squirely_num0:\\n return False\\n if i == expect[:-1]:\\n expect.pop(len(expect)-1)\\n else:\\n return False\\n if parenthesis_num0 == parenthesis_num1 and brackets_num0 == brackets_num1 and squirely_num0 == squirely_num1:\\n return True\\n else:\\n return False\",\n \"def syntaxValid(s):\\n\\tif not s or type(s) != str or s[0] != '[':\\n\\t\\treturn False\\n\\n\\t#using stack to check for matching brackets\\n\\t#push '[' and pop for ']'\\n\\tbracketStack = []\\n\\tfor i in range(len(s)):\\n\\t\\tcurrentChar = s[i]\\n\\t\\tif currentChar == '[':\\n\\t\\t\\t#check for there is a delimiter before open bracket or trying to double nest\\n\\t\\t\\tif i != 0 and s[i-1] == '[' or i > 1 and s[i-2] != ',':\\n\\t\\t\\t\\treturn False\\n\\t\\t\\tbracketStack.append('[')\\n\\t\\telif currentChar == ']':\\n\\t\\t\\t#check if stack is prematurely empty or no delimiter after closed bracket\\n\\t\\t\\tif not bracketStack or (i != len(s)-1 and s[i+1] != ']' and s[i+1] != ','):\\n\\t\\t\\t\\treturn False \\n\\t\\t\\telse: \\n\\t\\t\\t\\tbracketStack.pop()\\n\\t\\telif currentChar == ',':\\n\\t\\t\\t#check if delimiter is invalid\\n\\t\\t\\tif i == 0 or i == len(s)-1 or s[i-1] == '[' or s[i+1] != ' ':\\n\\t\\t\\t\\treturn False\\n\\t#if the stack is empty, we have matched brackets, hence s is valid\\n\\treturn len(bracketStack) == 0\",\n \"def _can_be_list(pair):\\n assert(isa(pair, Pair))\\n return str(pair).find(' . ') < 0\",\n \"def is_special(s):\\n for part in xrange(1, 3**len(s)):\\n p = part\\n sa = 0\\n ca = 0\\n sb = 0\\n cb = 0\\n for i, x in enumerate(s):\\n if p%3 == 1:\\n sa += x\\n ca += 1\\n elif p%3 == 2:\\n sb += x\\n cb += 1\\n p = p//3\\n if ca == 0 or cb == 0:\\n continue\\n if sa == sb:\\n return False\\n if ca > cb and sa <= sb:\\n return False\\n if cb > ca and sb <= sa:\\n return False\\n return True\",\n \"def coord_valid(s, e):\\n s = coord_fixer(s) # 0-index\\n e = coord_fixer(e) # 1-index\\n e = 1 if e == 0 else e # \\n s, e = [s, e] if (int(s) < int(e)) else [e, s]\\n return [str(s), str(e)]\",\n \"def check_series(s: pd.Series) -> bool:\\n\\n error_string = (\\n \\\"There are non-token cells (every cell should be a list of words/tokens) in the given Series.\\\"\\n \\\" See help(hero.HeroSeries) for more information.\\\"\\n )\\n\\n def is_list_of_strings(cell):\\n return all(isinstance(x, str) for x in cell) and isinstance(\\n cell, (list, tuple)\\n )\\n\\n if not is_list_of_strings(s.iloc[0]) or s.index.nlevels != 1:\\n raise TypeError(error_string)\",\n \"def property_3(string):\\n pairs = [ 'ab', 'cd', 'pq', 'xy' ]\\n for pair in pairs:\\n if pair in string:\\n return False\\n return True\",\n \"def checkString(self,newick):\\n\\t\\tisBalanced=True\\n\\t\\tstack=[]\\n\\t\\tfoundComma=False\\n\\t\\tfor char in newick:\\n\\t\\t\\tif char == \\\"(\\\":\\n\\t\\t\\t\\tstack.append(char)\\n\\t\\t\\telif char == \\\",\\\":\\n\\t\\t\\t\\tfoundComma=True\\n\\t\\t\\telif char == \\\")\\\":\\n\\t\\t\\t\\tif foundComma==True:\\n\\t\\t\\t\\t\\tstack.pop()\\n\\t\\t\\t\\t\\tfoundComma=False\\n\\t\\t\\t\\telse:\\n\\t\\t\\t\\t\\tisBalanced=False\\n\\t\\t\\t\\t\\tbreak\\n\\t\\t\\telse:\\n\\t\\t\\t\\tpass\\n\\t\\tif len(stack) != 0:\\n\\t\\t\\tisBalanced=False\\n\\t\\treturn isBalanced\",\n \"def validate_insert(self, s, internal=True):\\n super(FieldSet, self).validate_insert(s, internal) # mandatory check\\n if s and s not in [d[0] for d in self.details]:\\n valid = []\\n for k,v in self.details:\\n valid.append(\\\"%s=%s\\\" % (k, v))\\n raise FilemanError(\\\"\\\"\\\"Value [%s] is not valid. must be one of: %s\\\"\\\"\\\" % (s, \\\", \\\".join(valid)))\",\n \"def isAlternativeString(s):\\n L = list(set(s))\\n if len(L) != 2:\\n return False\\n for i in range(len(s) - 1):\\n if s[i] == s[i + 1]:\\n return False\\n return True\",\n \"def check_matching_brackets(s, opening=\\\"([{\\\", closing=\\\")]}\\\"):\\n\\n stack = Stack()\\n\\n for item in s:\\n # Stores any opening character\\n if item in opening:\\n stack.push(item)\\n else:\\n # if a closing bracket is found and we try to remove the matching opening bracket from the top of the stack\\n # in case the top of the stack is not a matching bracket, the string is not properly nested\\n if stack.length() < 1 or stack.pop() is not opening[closing.index(item)]:\\n return 0\\n\\n return 0 if stack.length() > 0 else 1\",\n \"def checkValidString(self, string: str) -> bool:\\n @lru_cache(None)\\n def dp(index, diff):\\n \\\"\\\"\\\"\\n index is the index of string\\n diff the counts of '(' - counts of ')'\\n \\\"\\\"\\\"\\n\\n if index == len_s:\\n return diff == 0\\n if abs(diff) > len_s - index:\\n return False\\n c = string[index]\\n index += 1\\n if c == '(':\\n return dp(index, diff + 1)\\n elif c == ')':\\n if diff - 1 < 0:\\n return False\\n return dp(index, diff - 1)\\n else:\\n return dp(index, diff + 1) or dp(index, diff - 1) or dp(index, diff)\\n\\n len_s = len(string)\\n return dp(0, 0)\",\n \"def is_substring_enclosed_in_brackets(sub, string):\\n # to get (...)\\n pattern = re.compile(\\\"\\\\([^\\\\)]*\\\\)\\\")\\n # to get: (...(...)...)\\n pattern2 = re.compile(\\\"\\\\([^\\\\)]*\\\\([^\\\\)]*\\\\)[^\\\\)]*\\\\)\\\")\\n # to get: (...(...(...)...)...)\\n pattern3 = re.compile(\\\"\\\\([^\\\\)]*\\\\([^\\\\)]*\\\\([^\\\\)]*\\\\)[^\\\\)]*\\\\)[^\\\\)]*\\\\)\\\")\\n\\n string = str(string)\\n search = re.findall(pattern, string)\\n search2 = re.findall(pattern2, string)\\n search3 = re.findall(pattern3, string)\\n\\n search3 = set(search + search2 + search3)\\n if any(str(sub) in s for s in search3): # or any(str(sub) in s for s in search2):\\n return True\\n return False\",\n \"def validate(self, s):\\n if len(s) == 0:\\n return False\\n if s in self.whitelist:\\n return True\\n if s in self.blacklist:\\n return False\\n\\n # SQL Types are rarely used\\n if 't' in s and 'f(t' not in s and 'At' not in s:\\n return False\\n\\n if '1nf' in s:\\n return False\\n if 's1o' in s:\\n return False\\n if 'oo' in s:\\n return False\\n if 'v,s' in s:\\n return False\\n if 's,v' in s:\\n return False\\n if 'v,v' in s:\\n return False\\n if 'v,1' in s:\\n return False\\n if 'v,n' in s:\\n return False\\n if 'n,v' in s:\\n return False\\n if '1,v' in s:\\n return False\\n if 'Eo(' in s:\\n return False\\n if '(o(' in s:\\n return False\\n if '(o1' in s:\\n return False\\n if '(on' in s:\\n return False\\n if '(os' in s:\\n return False\\n if '(of' in s:\\n return False\\n if '(ov' in s:\\n return False\\n if 'B(n)' in s:\\n return False\\n if 'oso' in s:\\n return False\\n if 'o1o' in s:\\n return False\\n if 'ono' in s:\\n return False\\n\\n # only 1 special case for this\\n # 1;foo:goto foo\\n # 1;n:k\\n # the 'foo' can only be a 'n' type\\n if ':' in s and not 'n:' in s:\\n return False\\n\\n if '11' in s:\\n return False\\n\\n if '))' in s:\\n return False\\n if '((' in s:\\n return False\\n if 'v1' in s:\\n return False\\n\\n if 'nv' in s and ';T' not in s:\\n return False\\n if 'nn' in s and ';T' not in s:\\n return False\\n\\n # select @version foo is legit\\n # but unlikely anywhere else\\n if 'vn' in s and 'Evn' not in s:\\n return False\\n\\n if 'oE' in s:\\n return False\\n\\n if 'A1' in s:\\n return False\\n if 'An' in s:\\n return False\\n if 'A(1' in s:\\n return False\\n\\n if 'vov' in s:\\n return False\\n if 'vo1' in s:\\n return False\\n if 'von' in s:\\n return False\\n\\n if 'ns' in s:\\n if 'U' in s:\\n return True\\n if 'T' in s:\\n return True\\n return False\\n\\n if 'sn' in s:\\n # that is... Tsn is ok\\n if s.find('T') != -1 and s.find('T') < s.find('sn'):\\n return True\\n return False\\n\\n # select foo (as) bar is only nn type i know\\n if 'nn' in s and 'Enn' not in s and ';T' not in s:\\n return False\\n\\n if ',o' in s:\\n return False\\n\\n if 'kk' in s and 'Tkk' not in s:\\n return False\\n\\n if 'ss' in s:\\n return False\\n\\n if 'ff' in s:\\n return False\\n\\n if '1no' in s:\\n return False\\n\\n if 'kno' in s:\\n return False\\n\\n if 'nEk' in s:\\n return False\\n\\n if 'n(n' in s:\\n return False\\n if '1so' in s:\\n return False\\n if '1s1' in s:\\n return False\\n if 'noo' in s:\\n return False\\n if 'ooo' in s:\\n return False\\n\\n if 'vvv' in s:\\n return False\\n\\n if '1vn' in s:\\n return False\\n if '1n1' in s:\\n return False\\n if '&1n' in s:\\n return False\\n if '&1v' in s:\\n return False\\n if '&1s' in s:\\n return False\\n if 'nnk' in s:\\n return False\\n if 'n1f' in s:\\n return False\\n # folded away\\n if s.startswith('('):\\n return False\\n\\n if '&o' in s:\\n return False\\n\\n if '1,1' in s:\\n return False\\n if '1,s' in s:\\n return False\\n if '1,n' in s:\\n return False\\n if 's,1' in s:\\n return False\\n if 's,s' in s:\\n return False\\n if 's,n' in s:\\n return False\\n if 'n,1' in s:\\n return False\\n if 'n,s' in s:\\n return False\\n if 'n,n' in s:\\n return False\\n if '1o1' in s:\\n return False\\n if '1on' in s:\\n return False\\n if 'no1' in s:\\n return False\\n if 'non' in s:\\n return False\\n if '1(v' in s:\\n return False\\n if '1(n' in s:\\n return False\\n if '1(s' in s:\\n return False\\n if '1(1' in s:\\n return False\\n if 's(s' in s:\\n return False\\n if 's(n' in s:\\n return False\\n if 's(1' in s:\\n return False\\n if 's(v' in s:\\n return False\\n if 'v(s' in s:\\n return False\\n if 'v(n' in s:\\n return False\\n if 'v(1' in s:\\n return False\\n if 'v(v' in s:\\n return False\\n\\n if s.startswith('n('):\\n return False\\n\\n if s.startswith('vs'):\\n return False\\n\\n if s.startswith('o'):\\n return False\\n\\n if ')(' in s:\\n return False\\n\\n # need to investigate T(vv) to see\\n # if it's correct\\n if 'vv' in s and s != 'T(vv)':\\n return False\\n\\n # unlikely to be sqli but case FP\\n if s in ('so1n)', 'sonoE'):\\n return False\\n\\n return True\",\n \"def BalancedBrackets(Str):\\n stack = []\\n for parenthesis in Str:\\n if parenthesis == '{' or parenthesis == '(' or parenthesis == '[':\\n stack.append(parenthesis)\\n elif parenthesis == '}' or parenthesis == ')' or parenthesis == ']':\\n if len(stack) < 1:\\n return False\\n last_item = stack.pop()\\n if not Compare(last_item, parenthesis):\\n return False\\n if len(stack) != 0:\\n return False\\n\\n return True\",\n \"def string_is_index_list(inp: str):\\n inp = inp.strip()\\n return len((inp)) > 0 and all([x in [\\\" \\\", \\\":\\\", \\\"-\\\"] or x.isdigit() for x in inp])\",\n \"def dict_check34(_str1):\\n\\t_len=len(_str1)\\n\\t_list1=[(i,j,k) for i in range(5) for j in range(5) for k in range(5)]\\n\\t_list0=[]\\n\\tfor i in range(len(_list1)):\\n\\t\\t#Take different length\\n\\t\\t_current=_list1[i]\\n\\t\\tif _len>=sum(_current) and sum(_list1[i])!=0:\\n\\t\\t\\t_list2=[]\\n\\t\\t\\t_n1=_current[0]\\n\\t\\t\\t_n2=_current[1]\\n\\t\\t\\t_n3=_current[2]\\n\\t\\t\\t_list2.append(_str1[:_n1])\\n\\t\\t\\t_list2.append(_str1[_n1:_n1+_n2])\\n\\t\\t\\t_list2.append(_str1[_n1+_n2:_n1+_n2+_n3])\\n\\t\\telse:\\n\\t\\t\\tcontinue\\n\\t\\tn=0\\n\\t\\tfor j in range(3):\\n\\t\\t\\tif _list2[j] in _dict_ori or _list2[j]==\\\"\\\":\\n\\t\\t\\t\\tn+=1\\n\\t\\tif n==3:\\n\\t\\t\\t_list0.append(_list2)\\n\\treturn(_list0)\",\n \"def validate(s):\\n\\n # base case: square is 1 or 0\\n if s == 1 or s == 0:\\n return True\\n\\n # list of length 4\\n if isinstance(s, list) and len(s) == 4:\\n\\n # idea one: fail fast\\n for i in s:\\n if not validate(i):\\n return False\\n return True\\n\\n # idea 2: \\\"and\\\" the results ALSO fail fast\\n # return (validate(s[0]) and \\n # validate(s[1]) and \\n # validate(s[2]) and \\n # validate(s[3]))\\n # OR\\n # return all([validate(i) for i in s])\\n\\n # idea 3: multiply the results: will not return boolean\\n # return (validate(s[0]) * \\n # validate(s[1]) * \\n # validate(s[2]) * \\n # validate(s[3]))\\n\\n # not one of our numbers or list of length 4\\n # another base case\\n return False\",\n \"def uniqueCheckString(aList):\\r\\n check = ','\\r\\n for v in aList:\\r\\n if f',{v},' in check:\\r\\n return True\\r\\n check = f'{check}{v},'\\r\\n return False\",\n \"def check(s,l):\\n if len(s)==1:\\n if s[0] in l:\\n return False\\n else:\\n return True\\n else:\\n if s[0] in l:\\n return False\\n else:\\n l.append(s[0])\\n return check(s[1:],l)\",\n \"def valid_variant(s, is_coding=True):\\n _validate_str(s)\\n if s == WILD_TYPE_VARIANT:\\n return True\\n else:\\n if is_coding:\\n for mut in s.split(\\\", \\\"):\\n match = re_coding.match(mut)\\n if match is None:\\n return False\\n return True\\n else:\\n for mut in s.split(\\\", \\\"):\\n match = re_noncoding.match(mut)\\n if match is None:\\n return False\\n return True\",\n \"def is_tuple_consists_of_strings(an_array):\\n return isinstance(an_array, tuple) and is_array_type(an_array, str)\",\n \"def bracket_validator(brackets):\\n bracket_stack = []\\n for b in brackets:\\n if b == '(' or b == '{' or b == '[':\\n bracket_stack.append(b)\\n\\n elif b == ')' or b == '}' or b == ']':\\n top = bracket_stack.pop()\\n\\n if b != bracket_match(top):\\n return False\\n\\n return True\",\n \"def is_valid_string_listing(listing):\\n assert listing is not None\\n listing = str(listing)\\n if len(listing) == 0:\\n return True\\n list_object = json.loads(listing)\\n return not any(key not in listing_keys\\n for key in list_object)\",\n \"def complete(s):\\n in_str = False\\n bb = 0\\n for i, c in enumerate(s):\\n if c == '(' and not in_str:\\n bb += 1\\n elif c == ')' and not in_str:\\n bb -= 1\\n if bb < 0:\\n raise ValueError('Insufficient open brackets in ' + repr(s))\\n elif c == '\\\\\\\"':\\n in_str = not in_str\\n return s + ')' * bb\",\n \"def valid_parenthesis(string):\\n if len(string) % 2:\\n print(\\\"Invalid Parenthesis\\\")\\n return\\n stack = []\\n bracket_hash = {\\\"}\\\": \\\"{\\\", \\\")\\\": \\\"(\\\", \\\"]\\\": \\\"[\\\"}\\n\\n for bracket in string:\\n if bracket in bracket_hash:\\n if stack:\\n top = stack.pop()\\n if bracket_hash[bracket] is not top:\\n print(\\\"Invalid Parenthesis\\\")\\n return\\n else:\\n top = \\\"\\\"\\n else:\\n if bracket in bracket_hash.values():\\n stack.append(bracket)\\n else:\\n print(\\\"Invalid String\\\")\\n return\\n\\n if not stack:\\n print(\\\"Valid Parenthesis\\\")\\n return\",\n \"def is_item_substring(msg, i):\\n if msg[i] == \\\"(\\\" and i + 4 <= len(msg):\\n return msg[i:i + 4] == \\\"({})\\\"\\n else:\\n return False\",\n \"def _checkSSFormatArg(ssformat):\\n if ssformat == '':\\n raise ShortStrException('ssformat argument cannot be the empty string')\\n\\n if not isinstance(ssformat, str):\\n raise ShortStrException('ssformat argument must be a string with only characters *, c, l, u, and d')\\n\\n for c in ssformat:\\n if c not in '*clud':\\n raise ShortStrException('ssformat argument must be a string with only characters *, c, l, u, and d')\",\n \"def minAddToMakeValid(self, S):\\n stack = []\\n for char in S:\\n if char == '(':\\n stack.append(char)\\n if char == ')':\\n if len(stack):\\n if stack[-1] == '(':\\n stack.pop()\\n else:\\n stack.append(char)\\n else:\\n stack.append(char)\\n return len(stack)\",\n \"def is_it_possible(e, s, e_to_seps):\\n\\n for u in e:\\n num = 0\\n seps = set()\\n # Count the edges that need to be added and the number of separators that can be involved in their addition\\n for w in s:\\n\\n if w < u:\\n uw = (w, u)\\n else:\\n uw = (u, w)\\n\\n if uw in e_to_seps:\\n num += 1\\n seps.update(e_to_seps[uw])\\n\\n if num > len(seps):\\n return False\\n\\n return True\",\n \"def multiCheck(word, multi_exception):\\n length = 0\\n a = False\\n b = \\\"\\\"\\n for keys in multi_exception:\\n if len(keys) > length:\\n length = len(keys)\\n for i in range(1, length + 1):\\n if word[-i:] in multi_exception:\\n a = True\\n b = word[-i:]\\n return a, b\",\n \"def __call__(self, s):\\n state = self._initial\\n try:\\n for sym in s:\\n state = self._trans_matrix[state][self._syms_to_indices[sym]]\\n except KeyError:\\n raise NotInAlphabetError(sym) from None\\n return state in self._accepting\",\n \"def validate_expression(str):\\n stack = []\\n pushChars, popChars = \\\"([\\\", \\\")]\\\"\\n for c in str:\\n if c in pushChars:\\n stack.append(c)\\n elif c in popChars:\\n if not len(stack):\\n raise ValidationError('La expresion tiene corchetes \\\\'[ ]\\\\' o parentesis \\\\'( )\\\\' sin cerrar')\\n else:\\n stackTop = stack.pop()\\n balancingBracket = pushChars[popChars.index(c)]\\n if stackTop != balancingBracket:\\n raise ValidationError('La expresion tiene corchetes \\\\'[ ]\\\\' o parentesis \\\\'( )\\\\' sin cerrar')\\n\\n if len(stack):\\n raise ValidationError('La expresion tiene corchetes \\\\'[ ]\\\\' o parentesis \\\\'( )\\\\' sin cerrar')\",\n \"def _valid_(s) :\\n return LoKi.Dicts.MCFinderDicts.valid (s)\",\n \"def is_unique3(test_str):\\n\\n for i in range(len(test_str) - 1):\\n for j in range(i+1, len(test_str)):\\n if test_str[i] == test_str[j]:\\n return False\\n return True\",\n \"def validate_state(self, state):\\n assert isinstance(state, str), \\\"`state` must be of type str\\\"\\n assert len(state) == 9, \\\"`state` must have exactly 9 elements\\\"\\n\\n state = state.upper()\\n assert set(state) <= {\\n \\\"1\\\",\\n \\\"2\\\",\\n \\\"_\\\",\\n }, \\\"`state` elements must be in {'1', '2', '_'}\\\"\\n assert (\\n 0 <= self.counter[\\\"1\\\"] - self.counter[\\\"2\\\"] <= 1\\n ), \\\"Invalid state: Players must alternate.\\\"\",\n \"def has_balanced_parens(string):\",\n \"def validate_X(X: List[str]):\\n _check_string_list(X)\",\n \"def checkvalid(s: bytes, m: bytes, pk: bytes) -> None:\\n if len(s) != b // 4:\\n raise ValueError(\\\"signature length is wrong\\\")\\n\\n if len(pk) != b // 8:\\n raise ValueError(\\\"public-key length is wrong\\\")\\n\\n R = decodepoint(s[: b // 8])\\n A = decodepoint(pk)\\n S = decodeint(s[b // 8 : b // 4])\\n h = Hint(encodepoint(R) + pk + m)\\n\\n (x1, y1, z1, _) = P = scalarmult_B(S)\\n (x2, y2, z2, _) = Q = edwards_add(R, scalarmult(A, h))\\n\\n if (\\n not isoncurve(P)\\n or not isoncurve(Q)\\n or (x1 * z2 - x2 * z1) % q != 0\\n or (y1 * z2 - y2 * z1) % q != 0\\n ):\\n raise SignatureMismatch(\\\"signature does not pass verification\\\")\",\n \"def _not_valid_(s) :\\n return not s.valid()\",\n \"def isBalanced(string):\\n assert type(string)==str\\n if any(a not in '(*)' for a in string):\\n raise AssertionError\\n string = list(string) #Converts the inputted list to a string.\\n #Loops through the list, checks for opening and closing parentheses and removes them from the list.\\n k = 0\\n while True:\\n if k>=len(string)-1 or len(string)==0:\\n break\\n if string[k]=='(':\\n if ')' in string[k:]:\\n b = string[k:].index(')')\\n c = string.pop(k)\\n d = string.pop(k+b-1)\\n else:\\n break\\n else:\\n k+=1\\n continue\\n \\n #Checks if the list is empty and returns True\\n if string==[]:\\n return True\\n #Loops through the list, checks for opening parentheses as '*' and closing parentheses and removes them from the list.\\n k = 0\\n while True:\\n if k>=len(string)-1 or len(string)==0:\\n break\\n if string[k]=='*':\\n if ')' in string[k:]:\\n b = string[k:].index(')')\\n c = string.pop(k)\\n d = string.pop(k+b-1)\\n else:\\n break\\n else:\\n k+=1\\n continue\\n \\n #Checks if the list is empty and returns True \\n if string==[]:\\n return True\\n \\n #Loops through the list, checks for opening parentheses and closing parentheses as '*'and removes them from the list.\\n k = 0\\n while True:\\n if k>=len(string)-1 or len(string)==0:\\n break\\n if string[k]=='(':\\n if '*' in string[k:]:\\n b = string[k:].index('*')\\n c = string.pop(k)\\n d = string.pop(k+b-1)\\n else:\\n break\\n else:\\n k+=1\\n continue\\n \\n #Checks if the list is empty and returns True\\n if string==[]:\\n return True\\n #Checks if the list contains only asterix and returns True.\\n if not any(a not in '*' for a in string):\\n return True\\n return False\",\n \"def validate(value):\\n\\n found_repeat = False\\n for element in range(len(value)-1):\\n search = value[element:element+2]\\n reg_result = re.findall(search, value)\\n if len(reg_result) >= 2:\\n found_repeat = True\\n continue\\n\\n if not found_repeat:\\n return False\\n\\n found_sandwich = False\\n for element in range(len(value)-2):\\n if value[element] == value[element+2]:\\n found_sandwich = True\\n break\\n\\n if not found_sandwich:\\n return False\\n\\n return True\",\n \"def is_palin(s):\\r\\n if s == \\\"\\\":\\r\\n return True\\r\\n if s[0] == s[-1]:\\r\\n return is_palin(s[1:-1])\\r\\n else:\\r\\n return False\",\n \"def check_string(s, strings):\\n for string in strings:\\n if string not in s:\\n return False\\n return True\",\n \"def extract_pairs(raw):\\n try:\\n pairs = list(set([(p.split(\\\"-\\\")[0].strip().upper(), p.split(\\\"-\\\")[1].strip().upper()) for p in raw.split(\\\",\\\")]))\\n except IndexError as e:\\n raise IndexError(\\\"Invalid pair\\\")\\n\\n for x, y in pairs:\\n if not (len(x) > 1 and len(y) > 1):\\n raise Exception(f'Invalid pair: {x}-{y}')\\n \\n if len(pairs) is 0:\\n raise Exception(\\\"No valid pairs\\\")\\n\\n return pairs\",\n \"def property_2(string):\\n for letter in al:\\n pair = letter + letter\\n if pair in string:\\n return True\\n return False\",\n \"def check_series(s: pd.Series) -> bool:\\n\\n error_string = (\\n \\\"The input Series should consist only of strings in every cell.\\\"\\n \\\" See help(hero.HeroSeries) for more information.\\\"\\n )\\n\\n if not isinstance(s.iloc[0], str) or s.index.nlevels != 1:\\n raise TypeError(error_string)\",\n \"def validate_strand(strand: str) -> bool:\\n strand = strand.upper()\\n count = dict(Counter(strand))\\n for k in count.keys():\\n if k not in NUCLEOTIDES:\\n raise Exception(\\\"Invalid DNA sequence\\\")\\n return True\",\n \"def check_type(s: pd.Series) -> Tuple[bool, str]:\\n\\n error_string = (\\n \\\"should be TokenSeries: there are non-token cells (every cell should be a list of words/tokens) in the given Series.\\\"\\n \\\" See help(hero.HeroTypes) for more information.\\\"\\n )\\n\\n def is_list_of_strings(cell):\\n return all(isinstance(x, str) for x in cell) and isinstance(\\n cell, (list, tuple)\\n )\\n\\n try:\\n first_non_nan_value = s.loc[s.first_valid_index()]\\n if not is_list_of_strings(first_non_nan_value):\\n return False, error_string\\n except KeyError: # Only NaNs in Series -> same warning applies\\n return False, error_string\\n\\n return True, \\\"\\\"\",\n \"def _match_brace(string, start_pos, pair='[]'):\\n depth = 1\\n if string[start_pos] != pair[0]:\\n return None\\n for index, char in enumerate(string[start_pos + 1:]):\\n if char == pair[0]:\\n depth += 1\\n elif char == pair[1]:\\n depth -= 1\\n if depth == 0:\\n return start_pos + index + 1\\n return None\",\n \"def validate_pairs(pairs, historical_pairs):\\n if pairs is None:\\n return False\\n for p in pairs:\\n if p in historical_pairs:\\n return False\\n return True\",\n \"def is_balanced(par_str):\\n\\n left_par = \\\"{[(\\\"\\n right_par = \\\"}])\\\"\\n\\n if len(par_str) % 2 != 0:\\n return False\\n \\n # prevents in index error\\n if par_str[0] in right_par:\\n return False\\n \\n while len(par_str) < 0:\\n for ind, par in par_str:\\n if par not in left_par and par[ind] == par[ind - 1]:\\n par_str.pop(ind)\\n par_str.pop(ind - 1)\\n print(par_str)\\n break\\n else:\\n return False\\n return True\",\n \"def brackets_match(string):\\n expected = Stack()\\n for character in string:\\n if character == \\\"(\\\":\\n expected.push(\\\")\\\")\\n elif character == \\\"[\\\":\\n expected.push(\\\"]\\\")\\n elif character in \\\")]\\\":\\n if expected and character == expected.top():\\n expected.pop()\\n else:\\n return False\\n return expected.is_empty()\",\n \"def isValid(self, s):\\n for valid in self.validTargets:\\n if (valid[0] in s):\\n return True\\n return False\",\n \"def list_check(listz):\\n isValid = False\\n x = 0\\n position = ''\\n\\n #checking if characters contains 023 and extracting them \\n\\n while (x < len(listz)):\\n if(listz.__contains__(0)):\\n position = position + str(listz[listz.index(0)])\\n if(listz.__contains__(2)):\\n position = position + str(listz[listz.index(2)])\\n if(listz.__contains__(3)):\\n position = position + str(listz[listz.index(3)])\\n x = len(listz) + 1\\n\\n#making sure its the requered sequence\\n\\n if(position == '023'):\\n isValid = True\\n x = x + 1\\n return isValid\",\n \"def _check_params(self):\\n\\t\\tstrange_param_helper = False\\n\\t\\tfor param in self.params:\\n\\t\\t\\n\\t\\t\\t# It could be that the param encapsulates several values (e.g., \\\"FLUX_RADIUS(10)\\\")\\n\\t\\t\\t# So we have to dissect this\\n\\t\\t\\tmatch = re.compile(\\\"(\\\\w*)\\\\(\\\\d*\\\\)\\\").match(param)\\n\\t\\t\\tif match:\\n\\t\\t\\t\\tcleanparam = match.group(1)\\n\\t\\t\\telse:\\n\\t\\t\\t\\tcleanparam = param\\n\\t\\t\\t\\t\\n\\t\\t\\tif cleanparam not in self.fullparamlist:\\n\\t\\t\\t\\tlogger.warning(\\\"Parameter '%s' seems strange and might be unknown to SExtractor\\\" \\\\\\n % (param))\\n\\t\\t\\t\\tstrange_param_helper = True\\n\\t\\t\\t\\t\\n\\t\\tif strange_param_helper:\\n\\t\\t\\tlogger.warning(\\\"Known parameters are: %s\\\" % (self.fullparamtxt))\",\n \"def question1(s, t):\\n if type(t) == str and type(s) == str and len(s) >= len(t):\\n for letter in t:\\n if letter not in s:\\n return False\\n return True\\n else:\\n return False\",\n \"def validate(data, badchars):\\n assert(all(b not in data for b in badchars))\",\n \"def is_valid_posse(posse: 'Tuple'):\\n # todo: write this as iterator through test types\\n # could use test types as blocks for lambda calculus\\n # for test in attribute tests:\\n\\n # print('testing posse: %s' % str(posse))\\n\\n prefixes = set()\\n cases = set()\\n lengths = set()\\n letters = set()\\n\\n for member in posse:\\n # prefix validity\\n prefixes.add(member[0])\\n\\n # case validity\\n if ord(member[1]) in THIRD_CASE:\\n cases.add(2)\\n elif member[1].isupper():\\n cases.add(1)\\n else:\\n cases.add(0)\\n\\n # length validity\\n lengths.add(len(member[1:]))\\n\\n # letter validity\\n # print('letter validity for %s' % member)\\n for letter_type in LETTER_SETS:\\n if ord(member[1]) in LETTER_SETS[letter_type]:\\n letters.add(letter_type)\\n\\n prefix_is_valid = len(prefixes) == 1 or len(prefixes) == 3\\n case_is_valid = len(cases) == 1 or len(cases) == 3\\n length_is_valid = len(lengths) == 1 or len(lengths) == 3\\n letter_is_valid = len(letters) == 1 or len(letters) == 3\\n\\n # print('prefix_is_valid: %s' % prefix_is_valid)\\n # print('case_is_valid: %s' % case_is_valid)\\n # print('length_is_valid: %s' % length_is_valid)\\n # print('letter_is_valid: %s' % letter_is_valid)\\n\\n return all((prefix_is_valid,\\n case_is_valid,\\n length_is_valid,\\n letter_is_valid))\",\n \"def is_rows_valid(bd):\\n for row in rows:\\n seen = []\\n for num in nums:\\n if bd[row[num]] == \\\" \\\":\\n continue\\n elif bd[row[num]] not in seen:\\n seen += [bd[row[num]]]\\n else:\\n return False\\n else:\\n continue\\n return True\",\n \"def test_string(self, s):\\n\\n data = s.split(' ')\\n\\n origin = ' '.join(data[0:-1])\\n if not origin:\\n return False\\n \\n origin_hashed = self.hash_with_salt(origin)\\n\\n return origin_hashed == s\",\n \"def balanced_string(string):\\n stack = create_stack()\\n pos = 0\\n while pos < len(string):\\n if string[pos] in '[{(':\\n stack.push(string[pos])\\n elif string[pos] in ']})':\\n pair = stack.pop()\\n if not match(string[pos], pair):\\n return False\\n pos = pos+1\\n #return stack.length()\\n if stack.length() == 0:\\n return True\\n else:\\n return False\",\n \"def _check_brackets(tokens: List[str]) -> True:\\r\\n brackets = []\\r\\n for token in tokens:\\r\\n if token in \\\"([\\\":\\r\\n brackets.append(token)\\r\\n elif token in \\\")]\\\":\\r\\n if not brackets:\\r\\n raise ExtraBracket(token)\\r\\n closing_bracket = brackets.pop()\\r\\n if ((token == \\\"(\\\" and closing_bracket != \\\")\\\") or\\r\\n (token == \\\"[\\\" and closing_bracket != \\\"]\\\")):\\r\\n raise BracketsMismatch(token, closing_bracket)\\r\\n if brackets:\\r\\n raise NoClosingBracket(brackets[0])\\r\\n return True\",\n \"def is_selector_seq(cls, s):\\n\\n assert np.iterable(s)\\n for tokens in s:\\n\\n # The selector must contain sequences of tokens:\\n if not np.iterable(tokens):\\n return False\\n\\n # Each token must either be a string, integer, slice,\\n # list of strings, or list of integers:\\n for token in tokens:\\n if type(token) == list:\\n # token_types = set(map(type, token))\\n # if not (token_types.issubset([str, int])):\\n if not all(map(\\n lambda x: isinstance(x, (int, long, basestring)),\\n token)):\\n return False\\n elif not isinstance(token, (slice, basestring, int, long)):\\n # elif not type(token) not in [slice, str, int]:\\n return False\\n\\n # All tokens are valid:\\n return True\",\n \"def __valid_token_format(self, token):\\n if len(token) != self.TOKEN_LENGTH * 2:\\n return False\\n for c in token:\\n if c not in '01234567890abcdef':\\n return False\\n return True\",\n \"def checkend(s):\\n\\n x = s[0]\\n y = s[-1]\\n\\n if x == y:\\n return True \\n else:\\n return False\",\n \"def is_matched(expression):\\n\\n balance = []\\n\\n for char in expression:\\n if char == \\\"{\\\" or char == \\\"[\\\" or char == \\\"(\\\":\\n balance.append(char)\\n\\n elif char == \\\"}\\\":\\n if balance[-1] == \\\"{\\\":\\n balance.pop()\\n else:\\n return False\\n\\n elif char == \\\"]\\\":\\n if balance[-1] == \\\"[\\\":\\n balance.pop()\\n else:\\n return False\\n\\n elif char == \\\")\\\":\\n if balance[-1] == \\\"(\\\":\\n balance.pop()\\n else:\\n return False\\n\\n if len(balance) == 0:\\n return True\",\n \"def is_pal(string):\\n a = reversed(list(string))\\n if list(a) == list(string):\\n return True\\n else:\\n return False\",\n \"def _has_matched_brackets(text: str) -> bool:\\n open_bracket_stack = []\\n for index, _ in enumerate(text):\\n if text[index] == \\\"[\\\":\\n open_bracket_stack.append(index)\\n elif text[index] == \\\"]\\\":\\n if len(open_bracket_stack) == 0:\\n return False\\n open_bracket_stack.pop()\\n return len(open_bracket_stack) == 0\",\n \"def valid_parentheses(parens):\\n while len(parens) >= 1:\\n if parens[0] == ')' or parens[-1] == '(':\\n return False\\n index = parens.find('()')\\n parens = parens[:index] + parens[(index + 2):]\\n return True\",\n \"def _checkForSlicesInKey(self, key):\\n if isinstance(key, tuple):\\n for i, v in enumerate(key):\\n if isinstance(v, slice):\\n raise PyTextCanvasException('Use parentheses when specifying slices, i.e. spam[(0, 0):(9, 9)] not spam[0, 0:9, 9].')\",\n \"def _is_well_formed(l):\\n\\tif _is_symbol(l):\\n\\t\\treturn 1\\n\\tif (type(l) == types.TupleType and len(l) == 2\\n\\t\\t\\tand l[0] == neg and _is_well_formed(l[1])):\\n\\t\\treturn 1\\n\\tif (type(l) == types.TupleType and len(l) == 3\\n\\t\\t\\tand _is_binary(l[1])\\n\\t\\t\\tand _is_well_formed(l[0]) and _is_well_formed(l[2])):\\n\\t\\treturn 1\\n\\treturn 0\",\n \"def all_simple (phrase):\\r\\n\\r\\n\\r\\n for x in phrase:\\r\\n if (x not in self.operations and not (isinstance(x,(int,type(ListType()),float,bool) or (isinstance(x,str) and quoted(x)))) or self.current_register.contains(x)):\\r\\n return False\\r\\n return True\",\n \"def bracketed (self,phrase):\\r\\n\\r\\n level = 0\\r\\n left_point = None\\r\\n right_point = None\\r\\n \\r\\n\\r\\n for count,char in enumerate(phrase):\\r\\n\\r\\n if char == '(':\\r\\n if level == 0:\\r\\n left_point = count\\r\\n level+=1\\r\\n if char == ')':\\r\\n level-=1\\r\\n if level == 0:\\r\\n right_point = count\\r\\n if not (left_point is None) and (not right_point is None) and left_point == 0 and right_point == len(phrase)-1:\\r\\n return True\\r\\n return False\",\n \"def CheckSeq(Seq):\\n OkNucleo = (\\\"A\\\", \\\"C\\\", \\\"G\\\", \\\"T\\\")\\n for i in Seq:\\n if i not in OkNucleo:\\n raise InputError(Seq,\\\"malformed input\\\")\",\n \"def is_valid(n):\\n\\tif type(n) == int:\\n\\t\\tn = str(n)\\n\\tfor index, c in enumerate(n):\\n\\t\\tif index == 0:\\n\\t\\t\\tcontinue\\n\\t\\tif n[index - 1] > n[index]:\\n\\t\\t\\treturn False\\n\\treturn True\",\n \"def is_unique_n_dict(string: str) -> bool:\\n\\n store = {}\\n\\n for letter in string:\\n if letter in store:\\n return False\\n store[letter] = 1\\n\\n return True\",\n \"def valid_parentheses(parens):\\n\\n # offset = 0\\n # for p in parens:\\n # if p == \\\"(\\\":\\n # offset += 1\\n # if p == \\\")\\\":\\n # offset -= 1\\n # return True if offset == 0 else False\\n\\n num_of_opens = 0\\n for p in parens:\\n if p == \\\"(\\\":\\n num_of_opens += 1\\n if p == \\\")\\\":\\n num_of_opens -= 1\\n if num_of_opens < 0:\\n return False\\n return True if num_of_opens == 0 else False\",\n \"def verify_CS(self, raw):\\n total = 0\\n for b in raw[0:-1]:\\n total += b\\n\\n return total == raw[-1]\",\n \"def naughty_nice_check2(string: str) -> bool:\\n letters = 'abcdefghijklmnopqrstuvwxyz'\\n # All combinations of two letters, search for single-separated\\n # has_double_doubles = False\\n # for two_letter_combo in itertools.product(letters, letters):\\n # x, y = two_letter_combo\\n # if re.findall('{0}.*{0}'.format(x, y), string):\\n # has_double_doubles = True\\n # break\\n has_double_doubles = any(\\n re.findall('{0}.*{0}'.format(x + y), string) for x, y in itertools.product(letters, letters)\\n )\\n\\n has_separated_letter = any(\\n re.findall('{0}.{0}'.format(letter), string) for letter in letters\\n )\\n\\n return has_double_doubles and has_separated_letter\",\n \"def check_filter_str(inputStr):\\n if(not(isinstance(inputStr, str))):\\n return False\\n\\n acceptedFields = ['remote', 'local', 'flow', 'eventval', 'irule']\\n\\n inputList = inputStr.split('|')\\n myList = []\\n for index, item in enumerate(inputList):\\n inputList[index] = item.split('&')\\n myList += inputList[index]\\n\\n\\n for item in myList:\\n if(item.count('(') != 1):\\n return False\\n if(item.count(')') != 1):\\n return False\\n if(item.count('=') != 2):\\n return False\\n\\n item = item.replace('(', '')\\n item = item.replace(')', '')\\n\\n equivList = item.split('==')\\n if(equivList[0] not in acceptedFields):\\n return False\\n\\n if((equivList[0] == 'remote') or (equivList[0] == 'local')):\\n if(equivList[1].count(':') != 2):\\n return False\\n\\n if(equivList[0] == 'flow'):\\n if(equivList[1].count('-') != 1):\\n return False\\n splitVal = equivList[1].split('-')\\n for val in splitVal:\\n if(val.count(':') != 2):\\n return False\\n\\n return True\",\n \"def validate_coords(coords):\\n coords = list(coords)\\n if coords != sorted(coords):\\n raise ValueError(\\\"The argument 'coords' must be sorted.\\\")\\n if any(c not in \\\"xyz\\\" for c in coords):\\n raise ValueError(\\\"The argument 'coords' may only contain \\\" \\\"'x', 'y', or 'z'.\\\")\\n return coords\",\n \"def validate_starr(p_stt_arr):\\n station = 0\\n totg = 0\\n length_gas = len(p_stt_arr)\\n for index in p_stt_arr:\\n if ':' in index:\\n station += 1\\n g, c = index.split(':')\\n totg += int(g) - int(c)\\n if totg < 0:\\n raise Exception('Impossible')\\n if ((station + 1) == length_gas) and (int(c) > 0):\\n station = 1\\n return station\",\n \"def valid(phrase):\\n words = []\\n series_of_words = phrase.split(' ')\\n words.append(series_of_words.pop())\\n for word in series_of_words:\\n if word in words:\\n return False\\n words.append(word)\\n return True\",\n \"def barcode_is_valid(s):\\n return (bool(re.match(r'^[ATGC]*$',s))\\n or barcode_is_10xgenomics(s))\"\n]","isConversational":false},"negative_scores":{"kind":"list like","value":["0.6825853","0.6750236","0.6749478","0.66877943","0.6573299","0.6547619","0.6510734","0.6484433","0.62763965","0.6209836","0.61905116","0.61400807","0.60200226","0.59876984","0.5978047","0.5973066","0.5904061","0.58395123","0.5775908","0.5752234","0.5632644","0.5617982","0.56161314","0.5605394","0.55693674","0.5548481","0.5541787","0.5541171","0.5520408","0.55153835","0.5499608","0.54990876","0.5492941","0.54925126","0.54834104","0.54791963","0.547377","0.5462528","0.54594976","0.54434824","0.543839","0.5436498","0.54247653","0.5398169","0.53249866","0.5323787","0.53189766","0.53189343","0.5313428","0.53115463","0.5311322","0.52955866","0.52951205","0.52936924","0.52929705","0.5276767","0.5262435","0.5260012","0.52567446","0.52553684","0.5251517","0.52491695","0.52489156","0.5243761","0.5242657","0.52374035","0.5224504","0.52205586","0.52173257","0.5216695","0.5214384","0.5205876","0.5200066","0.51921785","0.5184861","0.5182406","0.51792026","0.51686007","0.51638776","0.514758","0.5147333","0.51428586","0.5138422","0.5138127","0.5132422","0.5127928","0.5126533","0.5119679","0.51156557","0.50900006","0.5087531","0.5084597","0.5083192","0.50734776","0.50733745","0.5065725","0.5062805","0.50582564","0.50561035","0.50559217"],"string":"[\n \"0.6825853\",\n \"0.6750236\",\n \"0.6749478\",\n \"0.66877943\",\n \"0.6573299\",\n \"0.6547619\",\n \"0.6510734\",\n \"0.6484433\",\n \"0.62763965\",\n \"0.6209836\",\n \"0.61905116\",\n \"0.61400807\",\n \"0.60200226\",\n \"0.59876984\",\n \"0.5978047\",\n \"0.5973066\",\n \"0.5904061\",\n \"0.58395123\",\n \"0.5775908\",\n \"0.5752234\",\n \"0.5632644\",\n \"0.5617982\",\n \"0.56161314\",\n \"0.5605394\",\n \"0.55693674\",\n \"0.5548481\",\n \"0.5541787\",\n \"0.5541171\",\n \"0.5520408\",\n \"0.55153835\",\n \"0.5499608\",\n \"0.54990876\",\n \"0.5492941\",\n \"0.54925126\",\n \"0.54834104\",\n \"0.54791963\",\n \"0.547377\",\n \"0.5462528\",\n \"0.54594976\",\n \"0.54434824\",\n \"0.543839\",\n \"0.5436498\",\n \"0.54247653\",\n \"0.5398169\",\n \"0.53249866\",\n \"0.5323787\",\n \"0.53189766\",\n \"0.53189343\",\n \"0.5313428\",\n \"0.53115463\",\n \"0.5311322\",\n \"0.52955866\",\n \"0.52951205\",\n \"0.52936924\",\n \"0.52929705\",\n \"0.5276767\",\n \"0.5262435\",\n \"0.5260012\",\n \"0.52567446\",\n \"0.52553684\",\n \"0.5251517\",\n \"0.52491695\",\n \"0.52489156\",\n \"0.5243761\",\n \"0.5242657\",\n \"0.52374035\",\n \"0.5224504\",\n \"0.52205586\",\n \"0.52173257\",\n \"0.5216695\",\n \"0.5214384\",\n \"0.5205876\",\n \"0.5200066\",\n \"0.51921785\",\n \"0.5184861\",\n \"0.5182406\",\n \"0.51792026\",\n \"0.51686007\",\n \"0.51638776\",\n \"0.514758\",\n \"0.5147333\",\n \"0.51428586\",\n \"0.5138422\",\n \"0.5138127\",\n \"0.5132422\",\n \"0.5127928\",\n \"0.5126533\",\n \"0.5119679\",\n \"0.51156557\",\n \"0.50900006\",\n \"0.5087531\",\n \"0.5084597\",\n \"0.5083192\",\n \"0.50734776\",\n \"0.50733745\",\n \"0.5065725\",\n \"0.5062805\",\n \"0.50582564\",\n \"0.50561035\",\n \"0.50559217\"\n]","isConversational":false},"document_score":{"kind":"string","value":"0.69817543"},"document_rank":{"kind":"string","value":"0"}}},{"rowIdx":75385,"cells":{"query":{"kind":"string","value":"Runs a BFS Traversal, Returning a 2D Array representing notes at each level of the tree. One dimension representing each level, and it's array representing nodes at that level. q is a queue of child nodes, starting from the root, down to the end."},"document":{"kind":"string","value":"def levelOrder(self, root: TreeNode) -> List[List[int]]:\n\n result = []\n if(root is None):\n return result\n\n q = deque([root])\n while(q):\n n = len(q)\n level = []\n for i in range(0,n):\n f = q.popleft()\n level.append(f.val)\n\n if (f.left is not None):\n q.append(f.left)\n if (f.right is not None):\n q.append(f.right)\n\n if(len(level) > 0):\n result.append(level[:])\n level.clear()\n return 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 bfs_traversal_queue(self):\n queue = ArrayQueue()\n if not self.root:\n print(\"Tree is empty\")\n return\n queue.enqueue(self.root)\n\n while (queue.length() > 0):\n node = queue.dequeue()\n print(node.data, end=\" \")\n if node.left:\n queue.enqueue(node.left)\n if node.right:\n queue.enqueue(node.right)","def breadth_traverse(self):\n\n output = []\n q = Queue()\n q.enqueue(self.root)\n while q.peek():\n curr = q.dequeue()\n output.append(curr.value)\n if curr.l_child:\n q.enqueue(curr.l_child)\n if curr.r_child:\n q.enqueue(curr.r_child)\n return output","def by_level_traversal(self) -> Queue:\n # initialize Queue objects\n new_q = Queue()\n last_q = Queue()\n\n #binary search tree == empty\n if self.root is None:\n return last_q\n\n #root in enque.q\n new_q.enqueue(self.root)\n\n # iterate for processing\n while not new_q.is_empty():\n working_node = new_q.dequeue()\n if working_node is not None:\n last_q.enqueue(working_node)\n new_q.enqueue(working_node.left)\n new_q.enqueue(working_node.right)\n\n return last_q","def bfs(self):\n level = 0\n q = Queue()\n visit_order = list()\n node = self.get_root()\n q.enq( (node.get_value()[1],level) )\n while(len(q) > 0):\n node, level = q.deq()\n if node == None:\n visit_order.append( (\"<empty>\", level))\n continue\n visit_order.append( (node, level) )\n if type(node) == str:\n q.enq( (None, level +1) )\n q.enq( (None, level +1) )\n continue\n if node.has_left_child():\n q.enq( (node.get_left_child()[1], level +1 ))\n else:\n q.enq( (None, level +1) )\n\n if node.has_right_child():\n q.enq( (node.get_right_child()[1], level +1 ))\n else:\n q.enq( (None, level +1) )\n\n return visit_order","def by_level_traversal(self) -> Queue:\n q = Queue() # Initializing queue\n final_q = Queue()\n if self.root is None: # If tree is empty\n return final_q\n\n q.enqueue(self.root) # Begin process, placing root node in working queue\n\n while not q.is_empty(): # Traversing tree, adding nodes to q\n node = q.dequeue()\n if node is not None:\n final_q.enqueue(node)\n q.enqueue(node.left)\n q.enqueue(node.right)\n\n return final_q","def level_queue(self, root):\n if root.elem is None:\n return None\n myQueue = []\n result = []\n node = root\n myQueue.append(node)\n result.append(node)\n while myQueue:\n node = myQueue.pop(0)\n # print(node.elem)\n if node.lchild is not None:\n myQueue.append(node.lchild)\n result.append(node.lchild)\n if node.rchild is not None:\n myQueue.append(node.rchild)\n result.append(node.rchild)\n return result","def get_bfs(self)->list:\n\t\tqueue=[]\n\t\tbfs=[]\n\t\tqueue.append(self)\n\t\twhile(len(queue)>0):\n\t\t\tnode=queue.pop(0)\n\t\t\tbfs.append(node.data)\n\t\t\tif(node.right!=None):\n\t\t\t\tqueue.append(node.right)\n\t\t\tif(node.left!=None):\n\t\t\t\tqueue.append(node.left)\n\t\treturn bfs","def BFT(tree):\n queue = [tree]\n bft_nodelist = []\n while queue:\n node = queue.pop(0)\n bft_nodelist.append(node)\n queue += node.nodelist\n return bft_nodelist","def zigzak_using_bfs(root):\n current_level = [root]\n next_level = []\n while current_level:\n node = current_level.pop()\n print(node.data, end=\" \")\n if node.right:\n next_level.append(node.right)\n if node.left:\n next_level.append(node.left)\n if not current_level:\n current_level, next_level = next_level, current_level\n print()","def level_order_traversal(self):\r\n level_queue = [self]\r\n next_level_queue = []\r\n curr_level = 0\r\n print (self.root)\r\n while level_queue:\r\n curr_node = level_queue.pop(0)\r\n if curr_node.left:\r\n next_level_queue.append(curr_node.left)\r\n if curr_node.right:\r\n next_level_queue.append(curr_node.right)\r\n if not level_queue:\r\n for nodes in next_level_queue:\r\n print (nodes.root , ' ' , end = '')\r\n print ()\r\n curr_level += 1\r\n level_queue = next_level_queue[:]\r\n next_level_queue = []","def bfs(self, queue, target, targetx,\n targety): # finds BFS path to the finish. if there is no path, will return nothing\n\n '''\n 1. So we have a parent matrix\n 2. This records the parent\n 3. We have a dictionary of cell: parents'''\n if self.map1[queue[0][0]][queue[0][1]] == target:\n return [1]\n\n thisset = {queue[0]}\n traceSet = {queue[0]: None}\n\n flag = False # variable to see if it is possible to reach the goal\n while queue:\n fringe = queue.pop(0) # gets 0, 0 first\n adjs = self.getAdj(fringe[0], fringe[1])\n\n if self.map1[fringe[0]][fringe[1]] == 2:\n print(\"Our attempt has started\")\n\n if self.map1[fringe[0]][fringe[1]] == target:\n print(\"Goal reached\")\n print(\"This is how you go about it\")\n # print(traceSet)\n ans = self.trace(traceSet, targetx, targety)\n path = self.savePath(ans)\n flag = True\n # print(ans.pop())\n break\n\n if self.map1[fringe[0]][fringe[1]] == 0 or self.map1[fringe[0]][fringe[1]] == 3 or self.map1[fringe[0]][fringe[1]] == 4:\n continue\n\n for i in range(len(adjs)):\n if self.legal(adjs[i][0], adjs[i][1]):\n if adjs[i] in thisset:\n continue\n\n thisset.add(adjs[i])\n traceSet[adjs[i]] = fringe\n queue.append(adjs[i])\n if flag is False:\n print(\"No way to goal\")\n return []\n return path","def post_order_traversal(self) -> Queue:\n # initialize Queue\n q = Queue()\n\n #BST == empty\n if self.root is None:\n return q\n\n #recursive helper function return new Queue\n self.post_order_helper(self.root, q)\n return q","def bfs(self):\n\n root = self.root\n dq = collections.deque()\n\n dq.appendleft(root)\n visited = set()\n\n while dq:\n curr = dq.popleft()\n if curr in curr.children:\n curr.children.remove(curr) #Remove child from itself\n\n print(curr.name)\n\n currCh = curr.children\n filteredCh = set()\n\n for child in currCh:\n if child.parent != curr.name:\n continue\n else:\n dq.appendleft(child)\n filteredCh.add(child)\n curr.children = filteredCh","def bfs(self, node: \"BSTNode\") -> Iterable[\"BSTNode\"]:\n queue = [node]\n\n while queue:\n current = queue.pop()\n yield current\n if current.left:\n queue.insert(0, current.left)\n if current.right:\n queue.insert(0, current.right)","def bfs(root):\n lvl = 0\n vals = [[root.data, lvl]]\n roots = [root]\n while roots:\n roots = ([r.right for r in roots if r.right] +\n [r.left for r in roots if r.left])\n lvl += 1\n for r in roots:\n vals.append([r.data, lvl])\n return vals","def _level_order_traversal_old(self, item_index):\n q = Queue()\n q.enqueue(item_index)\n res = []\n while True: # queue not empty\n try:\n node_index = q.dequeue()\n except IndexError:\n break\n res.append(node_index)\n\n for get_child in [self._left_index, self._right_index]:\n try:\n q.enqueue(get_child(node_index))\n except IndexError:\n pass\n return res","def breadthfirst(self):\n import os\n cwd = os.getcwd()\n os.chdir('/Users/raj/Documents/algorithms_in_python/linked_lists/')\n from linked_collections import LinkedQueue\n os.chdir(cwd) # change to cwd\n if not self.is_empty():\n lq = LinkedQueue()\n lq.enqueue(self.root())\n while not lq.is_empty():\n p = lq.dequeue()\n yield p\n for c in self.children(p):\n lq.enqueue(c)","def reverse_level_order(self):\r\n stack = []\r\n queue = [self]\r\n while queue:\r\n curr_node = queue.pop(0)\r\n stack.append(curr_node.root)\r\n if curr_node.right:\r\n queue.append(curr_node.right)\r\n if curr_node.left:\r\n queue.append(curr_node.left)\r\n while stack:\r\n print (stack.pop(), '' , end= '')","def bft_print(self):\n queue = []\n queue.append(self)\n while len(queue):\n current = queue.pop(0)\n print(current.value)\n if current.left:\n queue.append(current.left)\n if current.right:\n queue.append(current.right)","def breadth_first_traversal(self):\n breadth_first = []\n h = self.root.get_height() \n for i in range(h+2): \n self.level = []\n self.print_level(self.root, i + 1) \n breadth_first.append(self.level)\n return breadth_first","def bfs(maze):\n # TODO: Write your code here\n q = queue.Queue()\n q.put(maze.getStart())\n traversed = []\n path = []\n tracker = {maze.getStart(): None} #Tracker needs to contain tuples\n\n while q:\n curr_loc = q.get() \n\n if curr_loc not in traversed: #Add to traversed points list\n traversed.append(curr_loc)\n\n if maze.isObjective(curr_loc[0], curr_loc[1]): #Reached end of maze\n finished = curr_loc \n break\n\n nextpath = maze.getNeighbors(curr_loc[0], curr_loc[1]) #Search neighbor points\n for point in nextpath:\n if point not in traversed and maze.isValidMove(point[0], point[1]):\n q.put(point)\n tracker[point] = curr_loc #Update curr_loc\n\n while finished:\n path.insert(0, finished) \n finished = tracker[finished]\n\n return path","def post_order_traversal(self) -> Queue:\n q = Queue() # Initializing queue\n if self.root is None: # If tree is empty\n return q\n\n self.post_order_helper(self.root, q) # Using helper function if tree isn't empty and returning Queue\n return q","def in_order_traversal(self) -> Queue:\n # initialize Queue\n q = Queue()\n\n #binary search tree is empty\n if self.root is None:\n return q\n\n #recursive helper function (non-empty)return new Queue\n self.in_order_helper(self.root, q)\n return q","def nodes(self):\n root = self.root()\n queue = Queue()\n queue.enqueue(root)\n return self._breadth_first(queue, elements=False)","def breadthFirstSearch(problem):\n \"*** YOUR CODE HERE ***\"\n from util import Queue\n q = Queue()\n mapper = {} #child_point : (parent_point, direction_to_child)\n q.push(problem.getStartState())\n mapper[problem.getStartState()] = None #root\n\n while (not q.isEmpty()):\n point = q.pop()\n\n if (problem.isGoalState(point)):\n c = point\n l = []\n while mapper[c] != None:\n tup = mapper[c]\n l.append(tup[1])\n c = tup[0]\n l.reverse()\n print l\n return l\n\n else:\n for child in problem.getSuccessors(point):\n if (child[0] not in mapper):\n q.push(child[0])\n mapper[child[0]] = (point, child[1])\n\n # util.raiseNotDefined()","def zigzagLevelOrder(self, root):\n if not root:\n return []\n\n # create direction variable\n going_right = False\n\n # initialize a level stack\n level_stack = [root]\n\n # initialize a result array\n result = []\n\n # loop while level stack is not empry\n while level_stack:\n\n # create tmp array\n tmp = []\n\n res_tmp = []\n\n # do the BFS by looping through each element\n while level_stack:\n\n # pop an element from the stack\n node = level_stack.pop()\n\n res_tmp.append(node.val)\n\n # if direction is 1 or going right, take left node first\n if going_right == 1:\n if node.left:\n tmp.append(node.left)\n\n if node.right:\n tmp.append(node.right)\n\n # otherwise take right node first, append to the temp list\n else:\n if node.right:\n tmp.append(node.right)\n\n if node.left:\n tmp.append(node.left)\n\n # append temp list to the result list\n result.append(res_tmp[::-1])\n\n # update level stack\n level_stack = tmp\n\n # change the direction\n going_right = not going_right\n\n return result","def breadthFirstSearch(problem):\n \"*** YOUR CODE HERE ***\"\n visited=[]\n \n node=dict()\n start=problem.getStartState()\n node['parent']=None\n node['direction']=None\n node['state']=start\n \n \n qu.push(node)\n lis.append(node)\n \n print qu.list\n while qu.isEmpty()!=True:\n node=qu.pop()\n pos=node['state']\n visited.append(pos)\n print visited\n if problem.isGoalState(pos):\n print \"found\"\n return getPath(problem,node)\n #break\n suc=problem.getSuccessors(pos)\n if suc ==None:\n continue \n \n print suc\n for step in suc:\n #if step not in dic :\n if step[0] not in visited:\n childnode={}\n childnode['parent']=pos\n childnode['direction']=step[1]\n childnode['state']=step[0]\n qu.push(childnode)\n lis.append(childnode)\n \n\n #util.raiseNotDefined()","def print_binary_tree_bfs(root):\n if not isinstance(root, BinaryTreeNode):\n return\n queue = Queue()\n queue.put(root)\n\n while not queue.empty():\n node = queue.get()\n print(node.val)\n if node.left != None:\n queue.put(node.left)\n if node.right != None:\n queue.put(node.right)","def BFTbin(tree):\n queue = [tree]\n bft_nodelist = []\n while queue:\n node = queue.pop(0)\n bft_nodelist.append(node)\n if node.lnode is not None:\n queue.append(node.lnode)\n if node.rnode is not None:\n queue.append(node.rnode)\n return bft_nodelist","def level_order_1(root: Node):\n if not root:\n return\n temp = root\n que = [temp]\n while len(que) > 0:\n print(que[0].data, end=\" \")\n temp = que.pop(0)\n if temp.left:\n que.append(temp.left)\n if temp.right:\n que.append(temp.right)\n return que","def breadth_first_search(self):\r\n queue = [self.root]\r\n while queue:\r\n node = queue.pop()\r\n yield node\r\n queue.extend(node.children)","def bfs(self):\r\n Q = [self.head()]\r\n visited = []\r\n while Q != []:\r\n cur = Q[0]\r\n visited.append(cur)\r\n Q = Q[1:]\r\n Q.extend([ch for ch in self.get_deps(cur.get_field('index'))])\r\n for x in reversed(visited):\r\n yield x","def breadth_first(self):\n self.breadth_first_list = []\n queque = Queue()\n queque.enqueue(self.root)\n while not queque.is_empty():\n ele = queque.dequeue()\n self.breadth_first_list.append(ele.value)\n if ele.left is not None:\n queque.enqueue(ele.left)\n if ele.right is not None:\n queque.enqueue(ele.right)\n return self.breadth_first_list","def bfs(maze):\n # TODO: Write your code here\n frontier = Queue()\n visited = []\n path = []\n ret = []\n objectives = maze.getObjectives()\n start = State(maze.getStart()[0], maze.getStart()[1], objectives.copy())\n frontier.put(start)\n explored = []\n \n\n while not frontier.empty(): # while frontier queue is not empty\n\n currentState = frontier.get()\n currentCell = currentState.cell()\n objectivesLeft = currentState.objectives()\n\n if objectivesLeft.count(currentCell) != 0:\n\n objectivesLeft.remove(currentCell)\n \n # all objectives found, initialise backtrace and exit loop\n # if len(objectivesLeft) == 0:\n path.append(currentState)\n ret.append(currentCell)\n visited.append(currentState)\n break\n\n # current cell is not objective nor visited\n if visited.count(currentState) == 0:\n explored.append(currentCell)\n neighbors = maze.getNeighbors(currentCell[0], currentCell[1])\n\n for i in neighbors:\n\n neighbor = State(i[0], i[1], objectivesLeft)\n\n # if neighbor is not visited, add it to the frontier\n if visited.count(neighbor) == 0:\n neighbor.setParent(currentState)\n frontier.put(neighbor)\n\n visited.append(currentState)\n\n #backtrace\n while path[0] != start:\n\n currentState = path[0]\n path.insert(0, currentState.parent())\n ret.insert(0, currentState.parent().cell())\n\n return ret","def pre_order_traversal(self) -> Queue:\n # initialize\n q = Queue()\n\n #binary search tree == empty\n if self.root is None:\n return q\n\n #recursive helper return Queue\n self.pre_order_helper(self.root, q)\n return q","def breadthfirst(self):\n if not self.is_empty():\n fringe = LinkedQueue() # known positions not yet yielded\n fringe.enqueue(self.root()) # starting with the root\n while not fringe.is_empty():\n p = fringe.dequeue() # remove from front of the queue\n yield p # report this position\n for c in self.children(p):\n fringe.enqueue(c) # add children to back of queue","def _breadthfirst(self,root, action=lambda nodes: print(nodes)):\n nodes = []\n breadth = Queue()\n visited = []\n\n breadth.enqueue(root)\n visited.append(root)\n\n while breadth.front:\n front = breadth.dequeue()\n nodes.append(front.value)\n\n for child in self._adjacency_list.get(front.value):\n if not child.start_vertex in visited:\n visited.append(child.start_vertex)\n breadth.enqueue(child.start_vertex) \n\n return nodes","def level_order_nodes(root):\n queue = []\n queue.append(root)\n\n while 0 < len(queue):\n next_node = queue.pop(0)\n if next_node.get_left():\n queue.append(next_node.get_left())\n if next_node.get_right():\n queue.append(next_node.get_right())\n yield next_node","def order_bfs(self) -> List[Nodo]:\n\n # Nodos por buscar, es una cola\n pending: List[Nodo] = [self.root]\n # Nodos ya visitados\n visited: List[Nodo] = []\n\n # Mientras la cola tenga items\n while len(pending) > 0:\n # Procesar el primer elemento\n curr = pending.pop(0)\n visited.append(curr)\n\n # Agregar los hijos no visitados del nodo a la cola\n for child in curr.children:\n if child in visited:\n continue\n pending.append(child)\n\n return visited","def bfs(self, root_node_id):\n try: \n status = dict.fromkeys(self._nodes.iterkeys(), 0)\n status[root_node_id] = 1 \n L = []\n q = Queue()\n q.enqueue(root_node_id) \n while not q.is_empty():\n curr_node_id = q.dequeue()\n status[curr_node_id] = -1\n L.append(self._nodes[curr_node_id])\n arcs_list = self._inc[curr_node_id]\n record = arcs_list.get_first_record()\n while record is not None:\n arc = record.element\n if status[arc._head] is 0:\n status[arc._head] = 1\n q.enqueue(arc._head)\n record = record._next \n return L\n except KeyError:\n return []","def get_tree(self):\n current = self\n nodes = []\n while current is not None:\n nodes.append(current)\n current = current.parent\n return nodes # reversed tree path","def level_order_list(root):\n return_list = []\n queue = []\n queue.append(root)\n\n while 0 < len(queue):\n next_node = queue.pop(0)\n if next_node.get_left():\n queue.append(next_node.get_left())\n if next_node.get_right():\n queue.append(next_node.get_right())\n return_list.append(next_node)\n\n return return_list","def generate_list(self):\n\n self.queue.append(self.root_node)\n\n while self.queue:\n node = self.queue.pop(0)\n\n self.rtn_list.append(node.data)\n\n if node.left:\n self.queue.append(node.left)\n\n if node.right:\n self.queue.append(node.right)\n\n return self.rtn_list","def binarizetree(tree):\n queue = [tree]\n while queue:\n node = queue.pop(0)\n queue += node.nodelist\n # Construct binary tree\n if len(node.nodelist) == 2:\n node.lnode = node.nodelist[0]\n node.rnode = node.nodelist[1]\n # Parent node\n node.lnode.pnode = node\n node.rnode.pnode = node\n elif len(node.nodelist) > 2:\n # Remove one node from the nodelist\n node.lnode = node.nodelist.pop(0)\n newnode = SpanNode(node.nodelist[0].prop)\n newnode.nodelist += node.nodelist\n # Right-branching\n node.rnode = newnode\n # Parent node\n node.lnode.pnode = node\n node.rnode.pnode = node\n # Add to the head of the queue\n # So the code will keep branching\n # until the nodelist size is 2\n queue.insert(0, newnode)\n # Clear nodelist for the current node\n node.nodelist = []\n return tree","def breadthFirstSearch(problem):\n \"*** YOUR CODE HERE ***\"\n\n \"\"\"\n ALGORITHM FOR bFS \n Create a queue Q\n enqueue root node to Q\n while Q is not empty:\n dequeu an item v from Q\n mark the item v as visited \n for each node w that is directed from v:\n enqueue w to Q\n \n \n \"\"\"\n\n fringes = util.Queue()\n explored =[]\n fringes.push((problem.getStartState(),[]))\n\n while(not fringes.isEmpty()):\n currentNode,currDir = fringes.pop()\n if problem.isGoalState(currentNode):\n goal = currentNode\n pathToGoal = currDir\n #print \"final path is : \", pathToGoal\n\n break\n # print \"HOraaay goal has been found === > \", currentNode\n\n if not (currentNode in explored):\n explored.append(currentNode)\n for childNode in problem.getSuccessors(currentNode):\n fringes.push((childNode[0],currDir+[childNode[1]]))\n\n\n return pathToGoal","def bft(self, starting_vertex):\n \n \"\"\"\n Plan:\n - Start at given index. Add that index to the Q.\n - While len(Q) is greater than 0:\n - Check if q[0] has children.\n - If so then make sure children have not been visited, then add those children to the Q.\n - If they have been visited, skip over the child and DO NOT add to Q # !! will result in infinite loop !!\n \"\"\"\n\n queue = Q()\n visited = []\n\n queue.add(starting_vertex)\n\n while len(queue):\n current = queue.first()\n children = self.vertices[current]\n \n if len(children) > 0:\n for child in children:\n if child not in visited:\n queue.add(child)\n else: continue\n\n print(current)\n visited.append(current)\n queue.pop()","def print_tree_breadth_first(self):\n\t\tqueue = []\n\t\tqueue.append(self)\n\t\tqueue.append(None)\n\t\twhile len(queue) > 0:\n\t\t\tcurrent = queue.pop(0)\n\t\t\tif current == None:\n\t\t\t\tprint('')\n\t\t\t\tif len(queue) > 0:\n\t\t\t\t\tqueue.append(None)\n\t\t\t\tcontinue\n\t\t\tprint(str(current.value) + ' ', end='')\n\t\t\tif not current.left == None:\n\t\t\t\tqueue.append(current.left)\n\t\t\tif not current.right == None:\n\t\t\t\tqueue.append(current.right)","def breadth_first_traverse(self) -> Generator:\n assist_queue = deque()\n assist_queue.append(self.root_node)\n while assist_queue:\n current_node = assist_queue.popleft()\n yield current_node\n if current_node.children:\n for child in current_node.children:\n assist_queue.append(child)","def bfs(self, start, end):\n\n queue = [start]\n parent = dict()\n\n # Initialize parent dictionary\n for v in iter(self._reachable): parent[v] = None\n parent[start] = start\n\n while len(queue) > 0:\n (x, y) = queue.pop(0)\n if (x, y) == end: break\n\n for v in self.get_reachables(x, y):\n if parent[v] is not None: \n # Vertex v already visited\n continue\n parent[v] = (x, y)\n queue.append(v)\n\n # Reconstruct path\n path = [end]\n vertex = end\n\n while parent[vertex] != vertex:\n if parent[vertex] is None: return []\n path.append(parent[vertex])\n vertex = parent[vertex]\n\n path.reverse()\n return path","def postorder(self):\n\n traversal = []\n self.postorder_helper(self.root, traversal)\n return traversal","def find_solution_bfs(root, expected_jar):\n solution = []\n\n dq = deque()\n # Add root to the collection, hence there is no need for any special\n # handling.\n dq.append(root)\n while dq:\n node = dq.popleft()\n # Whenever we visit a node, add it to the solution 'cause it's been\n # visited.\n solution.append(node)\n if node.contains(expected_jar):\n break\n children = node.get_all_children()\n for child in children:\n dq.append(child)\n return solution","def in_order_traversal(self) -> Queue:\n q = Queue() # Initializing queue\n if self.root is None: # If tree is empty\n return q\n\n self.in_order_helper(self.root, q) # Using helper function if tree isn't empty and returning Queue\n return q","def bfs(self, root_node_id):\n try: \n status = dict.fromkeys(self._nodes.iterkeys(), 0)\n status[root_node_id] = 1 \n L = []\n q = Queue()\n q.enqueue(root_node_id) \n while not q.is_empty():\n curr_node_id = q.dequeue()\n status[curr_node_id] = -1\n L.append(self._nodes[curr_node_id])\n arcs_set = self._inc[curr_node_id]\n for arc in arcs_set:\n if status[arc._head] is 0:\n status[arc._head] = 1\n q.enqueue(arc._head) \n return L\n except KeyError:\n return []","def items_level_order(self):\n # TODO: Create a queue to store nodes not yet traversed in level-order\n queue = ...\n # Create an items list\n items = list()\n # TODO: Enqueue the root node if this tree is not empty\n if ...:\n queue...\n # TODO: Loop until the queue is empty\n while ...:\n # TODO: Dequeue the node at the front of the queue\n node = ...\n # TODO: Add this node's data to the items list\n ...\n # TODO: Enqueue this node's left child if it exists\n if ...:\n ...\n # TODO: Enqueue this node's right child if it exists\n if ...:\n ...\n # Return the items list\n return items","def print_bi_tree(self):\n\n to_print = [self]\n # current = None\n\n while to_print:\n current = to_print.pop(0)\n if current:\n print(f'\\t{current.data}')\n to_print.append(current.left)\n to_print.append(current.right)","def _buildtree(self):\n self.pricetree = np.zeros((self.steps+1,self.steps+1))\n self.pricetree[0][0] = self.p\n for j in range(self.steps):\n for i in range(j+1):\n self.pricetree[j+1][i+1] = self.pricetree[j][i]*self.down\n self.pricetree[j+1][0] = self.pricetree[j][0]*self.up","def bfs(start_node, goal_node, max_depth) -> \"solution path\":\n\td = deque([start_node,[]])\n\texplored = {}\n\tlevel = 0\n\n\t# Return empty path if start is equal to goal\n\tif start_node == goal_node:\n\t\treturn []\n\n\t# Keep exploring while the deque has nodes\n\twhile len(d) > 0:\n\t\tpath = d.popleft()\n\n\t\tif level == 0:\n\t\t\tnode = path\n\t\telse:\n\t\t\t# To keep track of levels an empty node gets popped between levels which will cause an exception\n\t\t\ttry:\n\t\t\t\tnode = path[-1]\n\t\t\texcept Exception:\n\t\t\t\tnode = []\n\t\t\t\tpass\n\n\t\tif len(node) == 0:\n\t\t\tlevel += 1\n\t\t\t# Return empty list if max depth was reached\n\t\t\tif max_depth == level:\n\t\t\t\treturn []\n\t\t\td.append(node)\n\n\t\telse:\n\t\t\tval = getNodeVal(node)\n\t\t\tif val not in explored:\n\n\t\t\t\t# Mark node as explored\n\t\t\t\texplored[val] = True\n\n\t\t\t\tfor row in range(len(node)):\n\t\t\t\t\tfor col in range(len(node)):\n\t\t\t\t\t\tchild = toggle(node, row, col)\n\t\t\t\t\t\tnew_path = list(path)\n\t\t\t\t\t\tif level == 0:\n\t\t\t\t\t\t\tnew_path = [new_path]\n\t\t\t\t\t\tnew_path.append(child)\n\t\t\t\t\t\td.append(new_path)\n\t\t\t\t\t\tif child == goal_node:\n\t\t\t\t\t\t\tlevel+=1\n\t\t\t\t\t\t\treturn new_path\n\t# No solution found\n\treturn []","def bfs(graph, start, goal):\n\n final = []\n agenda = [[start]]\n\n # Process node queue\n while agenda:\n path = agenda.pop(0)\n\n # Exit if a path is found which reaches the goal\n if path[-1] == goal:\n final = path\n break\n\n # Push the new paths onto the queue\n connected = graph.get_connected_nodes(path[-1])\n for node in connected:\n # Ignore previously visited nodes\n if node not in path:\n agenda.append(path + [node])\n\n # Return the final path or initial empty list\n return final","def make_linked_lists(self):\n\t\tif self.is_empty():\n\t\t\treturn []\n\n\t\tqueue = c3.Queue()\n\t\tlists = [] #an empty list of lists\n\t\tlevel = 0\n\t\tqueue.enqueue((1, self._root))\n\t\twhile queue.peek():\n\t\t\tnodelev, node = queue.dequeue()._data\n\t\t\tif (nodelev != level):\n\t\t\t\tlevel = nodelev\n\t\t\t\tlists.append([]) #appends a new empty list for the next level of nodes\n\t\t\tlists[-1].append(node.value())\n\t\t\tif node.lchild():\n\t\t\t\tqueue.enqueue((nodelev + 1, node.lchild()))\n\t\t\tif node.rchild():\n\t\t\t\tqueue.enqueue((nodelev + 1, node.rchild()))\n\t\treturn lists","def BFS(graph, start, end, toPrint=False):\n initPath = [start]\n pathQueue = [initPath]\n if toPrint:\n print('current BFS path:', printPath(pathQueue))\n while len(pathQueue) != 0:\n # get and remove oldest element in pathQueue\n tmpPath = pathQueue.pop(0)\n print('Current BFS path:', printPath(tmpPath))\n lastNode = tmpPath[-1]\n if lastNode == end:\n return tmpPath # Explore all paths with n hops \n # before exploring any path with >n hops\n for nextNode in graph.childrenOf(lastNode):\n if next not in tmpPath:\n newPath = tmpPath + [nextNode]\n pathQueue.append(newPath)\n return None","def bfs(maze):\n # TODO: Write your code here\n\n q = []\n visited = {}\n keys = {}\n selected = None\n q.append(maze.getStart())\n\n while len(q) > 0:\n curr = q.pop(0)\n if maze.isObjective(curr[0], curr[1]):\n selected = curr\n break\n\n neighbors = maze.getNeighbors(curr[0], curr[1])\n\n for n in neighbors:\n if n not in visited:\n visited[n] = True\n q.append(n)\n keys[n] = curr\n\n curr = selected\n path = []\n while curr != maze.getStart():\n path.append(curr)\n curr = keys[curr]\n\n path.append(curr)\n path.reverse() # backtrace\n print(path)\n return path","def binaryTree(r):\r\n return [r, [], []]","def breadthFirstSearch(problem):\n \"*** YOUR CODE HERE ***\"\n class Node:\n def __init__(self, state, parent, action, pathCost):\n self.state = state\n self.parent = parent\n self.action = action\n self.pathCost = pathCost\n\n def solution(self):\n path = list()\n tempNode = self\n while tempNode.state != problem.getStartState():\n path.insert(0, tempNode.action)\n tempNode = tempNode.parent\n return path\n\n\n\n\n def childNode(successor, parent, action, stepCost):\n pathCost = parent.pathCost + stepCost\n child = Node(successor, parent, action, pathCost)\n return child\n\n initialNode = Node(problem.getStartState(), None, None, 0)\n if problem.isGoalState(initialNode.state):\n return initialNode.solution()\n\n frontier = util.Queue() #bfs uses a queue\n frontier.push(initialNode)\n explored = set()\n\n while not frontier.isEmpty() :\n nextNode = frontier.pop() #extract from the start of the queue\n explored.add(nextNode.state)\n for successor, action, stepCost in problem.getSuccessors(nextNode.state):\n child = childNode(successor, nextNode, action, stepCost)\n if child.state not in explored and child not in frontier.list:\n if problem.isGoalState(child.state):\n return child.solution()\n frontier.push(child)\n return []\n util.raiseNotDefined()","def bfs(initial_state, dimension=3):\n\t\n\treturn search(initial_state, Frontier(Queue), dimension)","def printBinaryTree(root):\n level = treeLevel(root)\n q = deque([root])\n while level > 0:\n new_q = deque()\n while q:\n tmp = q.popleft()\n new_q.append(tmp.left)\n new_q.append(tmp.right)\n print(level*' ', tmp.val, end='')\n print('\\n')\n q = new_q\n level -= 1\n return","def bfs(self, root: TreeNode) -> int:\n if not root:\n return 0\n queue = deque([(root, 1)])\n while queue:\n node, level = queue.popleft()\n if not node.left and not node.right:\n return level\n if node.left:\n queue.append((node.left, level + 1))\n if node.right:\n queue.append((node.right, level + 1))\n return -1","def _backtrack_path(self, current=None):\n path = []\n if self.path_found:\n if not current:\n current = self.end_node\n path.append(self.end_node)\n while current.parent:\n path.append(current.parent)\n self.board_array[current.parent.y][current.parent.x] = 4\n current = current.parent\n self.board_array[self.start_node.y][self.start_node.x] = 2\n self.board_array[self.end_node.y][self.end_node.x] = 3\n return path","def bfs(self, start_word, end_word): # great if you know to result is somewhere close to the root/start\n visited = set() # create an empty 'set' to store visisted vertex, set sorts \n\n q = Queue() # create an empty Queue\n q.enqueue([start_word]) # set the starting_vertex with enqueue \n\n while q.size() > 0:\n path = q.dequeue() # dequeue and store first path\n v = path[-1] # store the vertex from the end of path \n\n if v == end_word: # if v is equal to the destination_vertex\n return path # return the path \n\n if v not in visited: # if v has not been visited yet \n visited.add(v) # add v to the vistied set \n\n for neighbor in self.vertices[v]: # loop through the neighbors \n path_copy = list(path) # make a copy of the path \n path_copy.append(neighbor) # append each neighbor to the back of the path copy \n q.enqueue(path_copy) # enqueue the path copy to the queue ","def bfs(self, start):\n output_list = []\n queue = Queue()\n queue.put(start)\n visited = set(start)\n while not queue.empty():\n current_node = queue.get()\n output_list.append(current_node)\n visited.add(current_node)\n for node in self.__graph[current_node].neighbours:\n if node.name not in visited:\n queue.put(node.name)\n return output_list","def bfs(comment, fun):\n cur_level = [comment]\n while cur_level:\n next_level = [] # type: ignore\n for c in cur_level:\n # do not touch original comment to comply with dfs version\n if not c == comment:\n # do something with a comment\n fun(c)\n if c.replies:\n next_level.extend(c.replies)\n # level change\n cur_level = next_level","def bfs(g: nx.Graph, start_node: Any) -> list:\r\n\tx = [start_node]\r\n\tqueue = [start_node]\r\n\ttracks = {node: [] for node in g.nodes}\r\n\twhile queue:\r\n\t\telement = queue.pop(0)\r\n\t\ty = list(g.neighbors(element))\r\n\t\tfor node in y:\r\n\t\t\tif node not in x:\r\n\t\t\t\tx.append(node)\r\n\t\t\t\tqueue.append(node)\r\n\t\t\t\ttracks[node].extend((*tracks[element], element))\r\n\treturn x","def serialize(self, root):\n if not root:\n return []\n\n _queue = [root]\n result = []\n # 按层遍历二叉树\n while _queue:\n node = _queue.pop(0)\n # 无论是否有左右儿子，都压入队列\n # 如果为空用＃进行占位, 但不压入队列\n if node:\n result.append(node.val)\n _queue.append(node.left)\n _queue.append(node.right)\n else:\n result.append('#')\n return result","def BFS(graph, s, n):\n #Implement queue using list. list.pop() to dequeue and list.insert(0,x) to enqueue.\n visited = [False] * n\n queue = []\n \n queue.insert(0, s)\n visited[s] = True\n while(queue):\n v = queue.pop()\n print(v, end= \" \")\n \n for i in range(len(graph[v])):\n if (not visited[graph[v][i]]):\n queue.insert(0, graph[v][i] )\n visited[graph[v][i]] = True","def get_bfs(self, s):\n # create a queue for BFS\n queue = []\n # mark all the vertices as not visited\n visited = [False] * (len(self.graph))\n # mark the start node as visited and enqueue it\n visited[s] = True\n queue.append(s)\n results = []\n\n while queue:\n # dequeue a vertex from queue and append to results.\n p = queue.pop(0)\n results.append(p)\n # get all adjacent vertices of the dequeued vertex s,\n # and for any unvisited adjacent, mark it visited and enqueue it.\n for v in self.graph[p]:\n if visited[v] is False:\n visited[v] = True\n queue.append(v)\n\n return results","def breadthFirstSearch(problem):\n \"*** YOUR CODE HERE ***\"\n #Queue to hold the node along with the path taken from the start node to reach that node\n queue = Queue()\n #Set to hold the node explored.\n explorednode = set()\n # Get the start node.\n startnode = problem.getStartState()\n print startnode\n # Push the starting node on the Queue along with an empty set to know the direction in order to reach the node.\n queue.push((startnode,[]))\n\n # Loop till the queue is empty\n while queue.isEmpty() is not True:\n # Pop the currentnode and the direction from the queue\n currentnode, direction = queue.pop()\n # Check if the currentnode is not in explorednode.\n if currentnode not in explorednode:\n # We will now add the node to set of explored node.\n explorednode.add(currentnode)\n # If the node is the goal. We made it!!\n if problem.isGoalState(currentnode):\n # The direction holds the way to reach till the goal from the start node.\n print currentnode\n return direction\n # Loop for each successor(child) of the current node.\n for (successor, action, stepCost) in problem.getSuccessors(currentnode):\n # If the successor(child) is not explored\n if successor not in explorednode:\n # Add the successor to the queue along with the path to reach it.\n queue.push((successor, direction + [action]))\n util.raiseNotDefined()","def pre_order_traversal(self) -> Queue:\n q = Queue() # Initializing queue\n if self.root is None: # If tree is empty\n return q\n\n self.pre_order_helper(self.root, q)\n return q","def breadth_first_solve(puzzle):\n # instantiating queue using new PuzzleNode (root)\n queue = deque([PuzzleNode(puzzle, [])])\n\n # while puzzle still has moves to make (or is not solved yet)\n while len(queue) > 0:\n\n current = queue.popleft() # update current node\n extensions = current.puzzle.extensions() # gather moves to make\n\n # loop through extensions (breadth)\n for i in range(len(extensions)):\n newNode = PuzzleNode(extensions[i], [], current)\n\n # do not include already traversed nodes\n if newNode not in queue:\n current.children.append(newNode) # add as child of current node\n queue.append(newNode) # add to queue\n\n # if child node is solved, return it\n if newNode.puzzle.is_solved():\n return newNode\n\n\n return None # no solution was found","def print_bfs(self):\n visit_order = self.bfs()\n s = \"Tree (from BFS)\\n\"\n previous_level = -1\n for i in range(len(visit_order)):\n node, level = visit_order[i]\n if level == previous_level:\n s += \" | \" + str(node) \n else:\n s += \"\\n\" + str(node)\n previous_level = level\n \n return s","def create_treelist(dataclass,uri):\n queue=[uri]\n list=[uri]\n while len(queue):\n element=queue[-1]\n if dataclass.is_collection(element):\n childs=dataclass.get_childs(element)\n else:\n childs=[]\n if len(childs):\n list=list+childs\n # update queue\n del queue[-1]\n if len(childs):\n queue=queue+childs\n return list","def bfs(self, starting_vertex, destination_vertex):\n \"\"\" FIFO ir LILO\n Create a queue\n Enqueue PATH to starting Vertex\n Create a set top store visited vertices\n While the queue is NOT empty: e.g. > 0\n Dequeue the first PATH Vertex\n Get Vertex from END of PATH\n Check IF NOT visited:\n Mark as visited\n check if vertex is destination_vertex\n If TRUE, return path\n enqueue PATH to ALL of neighbors \n make COPY of current path\n add neighbor to path copy\n enqueue copy \n \"\"\"\n\n q = Queue() # Create a queue\n q.enqueue([starting_vertex]) # Enqueue starting at vertex into Queue (list)\n visited = set() # Create a set to store visited \n \n while q.size() > 0: # While the queue is NOT empty: \n path = q.dequeue() # Dequeue the first PATH Vertices\n v = path[-1] # Get Vertex from END of PATH\n\n if v not in visited: # Check IF NOT visited:\n visited.add(v) # Mark as visited\n\n if v == destination_vertex: # check if vertex is destination_vertex\n return path # If TRUE, return path, DONE\n\n for n in self.get_neighbors(v): # enqueue PATH to ALL of neighbors\n path_c = path [:] # make COPY of current path\n path_c.append(n) # add neighbor to path copy\n q.enqueue(path_c) # enqueue copy","def bfs(level):\n length = len(queue)\n print \"Length of queue: \" + str(length) + \" at level \" + str(level)\n if length <= 0 or level <= 0:\n return\n i = 0\n while i < length:\n try:\n text = req_obj.get_html_text(queue[0])\n if text is None:\n raise requests.RequestException()\n add_links_to_queue(text, queue[0])\n\n # summary generated using summarizer1\n sum_obj.create_and_index_summary(\n req_obj.get_base_url(), text)\n\n # summary generated using summarizer2\n sum_obj2.create_and_index_summary(\n req_obj.get_base_url(), text)\n on_pg_sum.index_on_page_summary(text, queue[0])\n except requests.RequestException as trace:\n print str(trace) + '\\n'\n er_file.write(queue[0] + '\\n')\n er_file.write(str(trace) + '\\n\\n')\n queue.pop(0)\n i += 1\n bfs(level - 1)","def breadthFirstSearch(problem):\n \"*** YOUR CODE HERE ***\"\n\n queue = util.Queue() # queue for searshing the graph\n visited = [] # keep track of visited nodes\n start =problem.getStartState() # The start node\n queue.push((start, [])) # the sart state and empty path list is pushed to the queue\n \n while queue:\n (vrtx, path) = queue.pop()\n if vrtx not in visited: \n if problem.isGoalState(vrtx):\n return [p[1] for p in path]\n visited.append(vrtx)\n for successor in problem.getSuccessors(vrtx) :\n queue.push((successor[0], path+[successor]))\n\n util.raiseNotDefined()","def __init__(self, dim, points, parent, root, rec):\n import numpy as np\n \n ##Assumption: 0 - left and down, 1 - right and up, 0 - x, 1 - y \n self.node = root\n self.parent = parent\n self.points = points\n self.children = [None, None]\n self.childlist = [[], []]\n self.rec = rec\n \n \n if(parent == None):\n self.depth = 0\n else:\n self.depth = self.parent.depth + 1\n \n self.dim = self.depth%dim\n \n if(self.parent == None): #ROOT - 0, BRANCH - 1, LEAF - 2\n self.type = \"ROOT\"\n \n elif(len(self.points) == 0):\n self.type = \"LEAF\"\n \n else:\n self.type = \"BRANCH\"\n \n self.update_rec()\n self.subdivide(dim)","def breadth_first_list(graph, current=\"a\"):\n queue = []\n queue.append(current)\n while queue:\n current = queue.pop(0)\n print(current)\n for node in graph.get(current):\n queue.append(node)","def depthFirstSearch(problem):\n \"*** YOUR CODE HERE ***\"\n fringeList = util.Stack()\n print \"fringeList\",fringeList\n closedList = {str(problem.getStartState()): ([])} #Hash Map to maintain state to path\n print \"closed list:\", closedList\n isGoalStateArrived = False\n\n # Push start state into fringeList\n fringeList.push((problem.getStartState()))\n\n while not isGoalStateArrived and not fringeList.isEmpty():\n currentNode = fringeList.pop()\n print \"currentNode\",currentNode\n currentNodePath = closedList[str(currentNode)]\n print \"currentNodepath:\",currentNodePath\n # Explore children\n childrenOfCurrentNode = problem.getSuccessors(currentNode)\n print \"childrenOfCurrentNode:\",childrenOfCurrentNode\n for childNode in childrenOfCurrentNode:\n if str(childNode[0]) not in closedList:\n path = copy.copy(currentNodePath)\n path.append(childNode[1])\n print \"child [0] %s, child [1] %s\", childNode[0],childNode[1]\n print \"path \", path\n fringeList.push(childNode[0])\n closedList[str(childNode[0])] = path # Put parent node in closed List\n if problem.isGoalState(childNode[0]):\n isGoalStateArrived = True\n goalState = childNode[0]\n break\n\n if isGoalStateArrived:\n #print closedList[str(problem.getStartState())]\n return closedList[str(goalState)]\n \"util.raiseNotDefined()\"","def children(self):\n n = []\n zi = 0\n zj = 0\n for i in range(self.board_size):\n for j in range(self.board_size):\n if self.arr[i][j] == 0:\n zi = i\n zj = j\n break\n i = zi\n j = zj\n # \"UDLR\" order; that is, [‘Up’, ‘Down’, ‘Left’, ‘Right’]\n if 0 <= i-1: # up\n self.arr[i][j], self.arr[i-1][j] = self.arr[i -\n 1][j], self.arr[i][j] # do the move\n x = state([item for row in self.arr for item in row],\n self) # create a copy\n x.move = \"Up\" # set the movement that leads for this copy\n n.append(x) # add the copy to the list\n # undo the move so we can continue finding the other children\n self.arr[i][j], self.arr[i-1][j] = self.arr[i-1][j], self.arr[i][j]\n if self.board_size > i+1: # down\n self.arr[i][j], self.arr[i+1][j] = self.arr[i+1][j], self.arr[i][j]\n x = state([item for row in self.arr for item in row], self)\n x.move = \"Down\"\n n.append(x)\n self.arr[i][j], self.arr[i+1][j] = self.arr[i+1][j], self.arr[i][j]\n if 0 <= j-1: # left\n self.arr[i][j], self.arr[i][j-1] = self.arr[i][j-1], self.arr[i][j]\n x = state([item for row in self.arr for item in row], self)\n x.move = \"Left\"\n n.append(x)\n self.arr[i][j], self.arr[i][j-1] = self.arr[i][j-1], self.arr[i][j]\n if self.board_size > j+1: # right\n self.arr[i][j], self.arr[i][j+1] = self.arr[i][j+1], self.arr[i][j]\n x = state([item for row in self.arr for item in row], self)\n x.move = \"Right\"\n n.append(x)\n self.arr[i][j], self.arr[i][j+1] = self.arr[i][j+1], self.arr[i][j]\n\n return n","def _breadth_first(self, queue, elements=True):\n while not queue.is_empty():\n node = queue.dequeue()\n if elements:\n yield node.element()\n else:\n yield node\n for child in self.children(node):\n queue.enqueue(child)","def bfs(graph, root, max_depth):\n ###TODO\n pass","def bfs(maze):\n # TODO: Write your code here.\n start = maze.getStart() \n frontier = [] \n path = [] \n dim = maze.getDimensions()\n objs = maze.getObjectives()\n rows = dim[0]\n cols = dim[1]\n visited = {} # visited as a dictionary\n for i in range (0, rows):\n for j in range (0, cols):\n visited[(i,j)] = (-1, -1)\n frontier.append(start)\n visited[(i,j)] = (-2, -2)\n while frontier:\n pt = frontier.pop(0)\n if maze.isObjective(pt[0], pt[1]) == True:\n break\n else:\n list_of_neighbors = maze.getNeighbors(pt[0], pt[1])\n for i in list_of_neighbors:\n if visited.get(i) == (-1, -1): \n frontier.append(i)\n visited[i] = pt \n while pt != start:\n path.append(pt)\n pt = visited.get(pt)\n path.append(start)\n path.reverse()\n return path","def breadthFirstSearch(problem):\n \"*** YOUR CODE HERE ***\"\n result = []\n qu = util.Queue()\n visited = set([])\n current = [problem.getStartState()]\n\n qu.push(current)\n\n while not qu.isEmpty():\n current = qu.pop()\n visited.add(current[-1])\n\n if problem.isGoalState(current[-1]):\n result = current\n break\n\n for each in problem.getSuccessors(current[-1]):\n if each[0] not in visited:\n temp = list(current)\n temp.append(each[0])\n qu.push(temp)\n visited.add(each[0])\n\n path = get_path(result)\n return path\n util.raiseNotDefined()","def build_extended_tree(root, p=1, q=1):\n original_root = root # store for later\n \n # Step 1\n for i in range(p-1):\n node = Node(label=\"*\")\n node.addkid(root)\n root = node\n \n # Steps 2 and 3\n list_of_children = original_root.children\n if(len(list_of_children) == 0):\n q_append_leaf(original_root, q)\n else:\n q_append_non_leaf(original_root, q)\n while(len(list_of_children) > 0):\n temp_list = list()\n for child in list_of_children:\n if(child.label != \"*\"):\n if(len(child.children) == 0):\n q_append_leaf(child, q)\n else:\n q_append_non_leaf(child, q)\n temp_list.extend(child.children)\n list_of_children = temp_list\n return root","def __init__(self): \n self.left, self.right = [], []","def bfs(self, starting_vertex, destination_vertex): # great if you know to result is somewhere close to the root/start\n visited = set() # create an empty 'set' to store visisted vertex, set sorts \n\n q = Queue() # create an empty Queue\n q.enqueue([starting_vertex]) # set the starting_vertex with enqueue \n\n while q.size() > 0:\n path = q.dequeue() # dequeue and store first path\n v = path[-1] # store the vertex from the end of path \n\n if v == destination_vertex: # if v is equal to the destination_vertex\n return path # return the path \n\n if v not in visited: # if v has not been visited yet \n visited.add(v) # add v to the vistied set \n\n for neighbor in self.vertices[v]: # loop through the neighbors \n path_copy = list(path) # make a copy of the path \n path_copy.append(neighbor) # append each neighbor to the back of the path copy \n q.enqueue(path_copy) # enqueue the path copy to the queue ","def level_order_nary2(root):\n if root is None:\n return []\n res = []\n q = []\n q.append(root)\n while len(q) > 0:\n temp = []\n for _ in range(len(q)):\n node = q.pop(0)\n q.extend(node.children)\n temp.append(node.val)\n res.append(temp)\n return res","def traverse_breadth_first(self, fn):\n queue = deque([self.root])\n while len(queue) > 0:\n node = queue.popleft()\n fn(node)\n queue.extend(node.children)","def bft(self, starting_vertex):\n # Create a q and enqueue starting vertex\n qq = Queue()\n qq.enqueue([starting_vertex])\n # Create a set of traversed vertices\n visited = set()\n # eldest = []\n depth_counter = {} \n starter = 0 \n # visited = []\n # While queue is not empty:\n while qq.size() > 0:\n # dequeue/pop the first vertex\n path = qq.dequeue()\n # if not visited\n # print(visited)\n starter += 1\n if path[-1] not in visited:\n # DO THE THING!!!!!!!\n # print(path[-1])\n depth_counter[starter] = path[-1]\n # mark as visited\n visited.add(path[-1])\n # visited.append(path[-1])\n # enqueue all neightbors\n \n if not self.get_neighbors(path[-1]):\n \n if starting_vertex == path[-1]:\n return -1\n else:\n # print(\"eldest ancestor:\",path[-1])\n depth_counter[starter] = path[-1]\n # print(depth_counter)\n # eldest.append(path[-1])\n else:\n # starter += 1\n for next_vert in self.get_neighbors(path[-1]): \n new_path = list(path)\n new_path.append(next_vert)\n qq.enqueue(new_path)\n\n\n return depth_counter[starter]","def bfs(maze, current_node):\n q = collections.deque()\n\n q.append(current_node)\n\n while len(q) > 0:\n current_node = q.popleft()\n maze[current_node.row][current_node.cell] = 1\n yield maze\n\n for neighbour in get_neighbours(maze, current_node):\n if maze[neighbour.row][neighbour.cell] == 2:\n backtrack(maze, neighbour)\n yield maze\n return\n else:\n q.append(neighbour)\n maze[neighbour.row][neighbour.cell] = -2\n\n yield maze\n maze[current_node.row][current_node.cell] = -3\n time.sleep(args.speed)","def breadthFirstSearchPaths(problem):\n #import pdb;pdb.set_trace()\n frontier = util.Queue()\n start_node = problem.getStartState()\n if problem.isGoalState(start_node):\n return ['Stop']\n frontier.push((start_node,()))\n explored = set()\n while True:\n if frontier.isEmpty():\n return []\n node = frontier.pop()\n\n #explored.add(node[0])\n # exploratory code for SUPER-optimal solution:\n # by saving the path in explored, we assure that we explore the same cell even if\n # two different actions go through it:\n explored.add(node)\n for successor in problem.getSuccessors(node[0]):\n nextState, action, cost = successor\n if nextState in explored or nextState in [f[0] for f in frontier.list]:\n continue\n actions = node[1]\n next_actions = actions + (action,)\n new_node = (nextState, next_actions)\n if problem.isGoalState(new_node[0]):\n return new_node[1]\n frontier.push(new_node)\n #print frontier.list\n return []","def get_nodes_by_depth(root):\n queue = deque([root])\n res = []\n while queue:\n next_queue = deque([])\n this_level = []\n while queue:\n parent = queue.popleft()\n this_level.append(parent.data)\n if parent.left:\n next_queue.append(parent.left)\n if parent.right:\n next_queue.append(parent.right)\n res.append(this_level)\n queue = next_queue\n\n return res","def _build_jump_nodes(self):\n self._jump_nodes = []\n Q = Queue()\n Q.enqueue(self._tree.root())\n\n while not Q.is_empty():\n p = Q.dequeue()\n\n if self._tree.height(p) > self._block_size:\n for ch in self._tree.children(p):\n if self._tree.height(ch) <= self._block_size:\n if p not in self._jump_nodes:\n self._jump_nodes.append(p)\n else:\n Q.enqueue(ch)\n\n # Bucket sort should be used for sorting!\n self._jump_nodes.sort(key=lambda p: self._tree.depth(p), reverse=True)","def BFS(self,s,t,parent):\n #mark all vertices as not visited\n visited = [False]*(self.ROWS);\n # initialize a queue\n queue = []\n # add source to q and mark it visited\n queue.append(s)\n visited[s] = True\n #Breadth-first-search\n while queue:\n n = queue.pop(0)\n for index,val in enumerate(self.graph[n]):\n if visited[index] == False and val>0:\n queue.append(index)\n visited[index] = True\n parent[index] = n\n #return True if sink was visted\n if visited[t]:\n return True\n else:\n return False"],"string":"[\n \"def bfs_traversal_queue(self):\\n queue = ArrayQueue()\\n if not self.root:\\n print(\\\"Tree is empty\\\")\\n return\\n queue.enqueue(self.root)\\n\\n while (queue.length() > 0):\\n node = queue.dequeue()\\n print(node.data, end=\\\" \\\")\\n if node.left:\\n queue.enqueue(node.left)\\n if node.right:\\n queue.enqueue(node.right)\",\n \"def breadth_traverse(self):\\n\\n output = []\\n q = Queue()\\n q.enqueue(self.root)\\n while q.peek():\\n curr = q.dequeue()\\n output.append(curr.value)\\n if curr.l_child:\\n q.enqueue(curr.l_child)\\n if curr.r_child:\\n q.enqueue(curr.r_child)\\n return output\",\n \"def by_level_traversal(self) -> Queue:\\n # initialize Queue objects\\n new_q = Queue()\\n last_q = Queue()\\n\\n #binary search tree == empty\\n if self.root is None:\\n return last_q\\n\\n #root in enque.q\\n new_q.enqueue(self.root)\\n\\n # iterate for processing\\n while not new_q.is_empty():\\n working_node = new_q.dequeue()\\n if working_node is not None:\\n last_q.enqueue(working_node)\\n new_q.enqueue(working_node.left)\\n new_q.enqueue(working_node.right)\\n\\n return last_q\",\n \"def bfs(self):\\n level = 0\\n q = Queue()\\n visit_order = list()\\n node = self.get_root()\\n q.enq( (node.get_value()[1],level) )\\n while(len(q) > 0):\\n node, level = q.deq()\\n if node == None:\\n visit_order.append( (\\\"<empty>\\\", level))\\n continue\\n visit_order.append( (node, level) )\\n if type(node) == str:\\n q.enq( (None, level +1) )\\n q.enq( (None, level +1) )\\n continue\\n if node.has_left_child():\\n q.enq( (node.get_left_child()[1], level +1 ))\\n else:\\n q.enq( (None, level +1) )\\n\\n if node.has_right_child():\\n q.enq( (node.get_right_child()[1], level +1 ))\\n else:\\n q.enq( (None, level +1) )\\n\\n return visit_order\",\n \"def by_level_traversal(self) -> Queue:\\n q = Queue() # Initializing queue\\n final_q = Queue()\\n if self.root is None: # If tree is empty\\n return final_q\\n\\n q.enqueue(self.root) # Begin process, placing root node in working queue\\n\\n while not q.is_empty(): # Traversing tree, adding nodes to q\\n node = q.dequeue()\\n if node is not None:\\n final_q.enqueue(node)\\n q.enqueue(node.left)\\n q.enqueue(node.right)\\n\\n return final_q\",\n \"def level_queue(self, root):\\n if root.elem is None:\\n return None\\n myQueue = []\\n result = []\\n node = root\\n myQueue.append(node)\\n result.append(node)\\n while myQueue:\\n node = myQueue.pop(0)\\n # print(node.elem)\\n if node.lchild is not None:\\n myQueue.append(node.lchild)\\n result.append(node.lchild)\\n if node.rchild is not None:\\n myQueue.append(node.rchild)\\n result.append(node.rchild)\\n return result\",\n \"def get_bfs(self)->list:\\n\\t\\tqueue=[]\\n\\t\\tbfs=[]\\n\\t\\tqueue.append(self)\\n\\t\\twhile(len(queue)>0):\\n\\t\\t\\tnode=queue.pop(0)\\n\\t\\t\\tbfs.append(node.data)\\n\\t\\t\\tif(node.right!=None):\\n\\t\\t\\t\\tqueue.append(node.right)\\n\\t\\t\\tif(node.left!=None):\\n\\t\\t\\t\\tqueue.append(node.left)\\n\\t\\treturn bfs\",\n \"def BFT(tree):\\n queue = [tree]\\n bft_nodelist = []\\n while queue:\\n node = queue.pop(0)\\n bft_nodelist.append(node)\\n queue += node.nodelist\\n return bft_nodelist\",\n \"def zigzak_using_bfs(root):\\n current_level = [root]\\n next_level = []\\n while current_level:\\n node = current_level.pop()\\n print(node.data, end=\\\" \\\")\\n if node.right:\\n next_level.append(node.right)\\n if node.left:\\n next_level.append(node.left)\\n if not current_level:\\n current_level, next_level = next_level, current_level\\n print()\",\n \"def level_order_traversal(self):\\r\\n level_queue = [self]\\r\\n next_level_queue = []\\r\\n curr_level = 0\\r\\n print (self.root)\\r\\n while level_queue:\\r\\n curr_node = level_queue.pop(0)\\r\\n if curr_node.left:\\r\\n next_level_queue.append(curr_node.left)\\r\\n if curr_node.right:\\r\\n next_level_queue.append(curr_node.right)\\r\\n if not level_queue:\\r\\n for nodes in next_level_queue:\\r\\n print (nodes.root , ' ' , end = '')\\r\\n print ()\\r\\n curr_level += 1\\r\\n level_queue = next_level_queue[:]\\r\\n next_level_queue = []\",\n \"def bfs(self, queue, target, targetx,\\n targety): # finds BFS path to the finish. if there is no path, will return nothing\\n\\n '''\\n 1. So we have a parent matrix\\n 2. This records the parent\\n 3. We have a dictionary of cell: parents'''\\n if self.map1[queue[0][0]][queue[0][1]] == target:\\n return [1]\\n\\n thisset = {queue[0]}\\n traceSet = {queue[0]: None}\\n\\n flag = False # variable to see if it is possible to reach the goal\\n while queue:\\n fringe = queue.pop(0) # gets 0, 0 first\\n adjs = self.getAdj(fringe[0], fringe[1])\\n\\n if self.map1[fringe[0]][fringe[1]] == 2:\\n print(\\\"Our attempt has started\\\")\\n\\n if self.map1[fringe[0]][fringe[1]] == target:\\n print(\\\"Goal reached\\\")\\n print(\\\"This is how you go about it\\\")\\n # print(traceSet)\\n ans = self.trace(traceSet, targetx, targety)\\n path = self.savePath(ans)\\n flag = True\\n # print(ans.pop())\\n break\\n\\n if self.map1[fringe[0]][fringe[1]] == 0 or self.map1[fringe[0]][fringe[1]] == 3 or self.map1[fringe[0]][fringe[1]] == 4:\\n continue\\n\\n for i in range(len(adjs)):\\n if self.legal(adjs[i][0], adjs[i][1]):\\n if adjs[i] in thisset:\\n continue\\n\\n thisset.add(adjs[i])\\n traceSet[adjs[i]] = fringe\\n queue.append(adjs[i])\\n if flag is False:\\n print(\\\"No way to goal\\\")\\n return []\\n return path\",\n \"def post_order_traversal(self) -> Queue:\\n # initialize Queue\\n q = Queue()\\n\\n #BST == empty\\n if self.root is None:\\n return q\\n\\n #recursive helper function return new Queue\\n self.post_order_helper(self.root, q)\\n return q\",\n \"def bfs(self):\\n\\n root = self.root\\n dq = collections.deque()\\n\\n dq.appendleft(root)\\n visited = set()\\n\\n while dq:\\n curr = dq.popleft()\\n if curr in curr.children:\\n curr.children.remove(curr) #Remove child from itself\\n\\n print(curr.name)\\n\\n currCh = curr.children\\n filteredCh = set()\\n\\n for child in currCh:\\n if child.parent != curr.name:\\n continue\\n else:\\n dq.appendleft(child)\\n filteredCh.add(child)\\n curr.children = filteredCh\",\n \"def bfs(self, node: \\\"BSTNode\\\") -> Iterable[\\\"BSTNode\\\"]:\\n queue = [node]\\n\\n while queue:\\n current = queue.pop()\\n yield current\\n if current.left:\\n queue.insert(0, current.left)\\n if current.right:\\n queue.insert(0, current.right)\",\n \"def bfs(root):\\n lvl = 0\\n vals = [[root.data, lvl]]\\n roots = [root]\\n while roots:\\n roots = ([r.right for r in roots if r.right] +\\n [r.left for r in roots if r.left])\\n lvl += 1\\n for r in roots:\\n vals.append([r.data, lvl])\\n return vals\",\n \"def _level_order_traversal_old(self, item_index):\\n q = Queue()\\n q.enqueue(item_index)\\n res = []\\n while True: # queue not empty\\n try:\\n node_index = q.dequeue()\\n except IndexError:\\n break\\n res.append(node_index)\\n\\n for get_child in [self._left_index, self._right_index]:\\n try:\\n q.enqueue(get_child(node_index))\\n except IndexError:\\n pass\\n return res\",\n \"def breadthfirst(self):\\n import os\\n cwd = os.getcwd()\\n os.chdir('/Users/raj/Documents/algorithms_in_python/linked_lists/')\\n from linked_collections import LinkedQueue\\n os.chdir(cwd) # change to cwd\\n if not self.is_empty():\\n lq = LinkedQueue()\\n lq.enqueue(self.root())\\n while not lq.is_empty():\\n p = lq.dequeue()\\n yield p\\n for c in self.children(p):\\n lq.enqueue(c)\",\n \"def reverse_level_order(self):\\r\\n stack = []\\r\\n queue = [self]\\r\\n while queue:\\r\\n curr_node = queue.pop(0)\\r\\n stack.append(curr_node.root)\\r\\n if curr_node.right:\\r\\n queue.append(curr_node.right)\\r\\n if curr_node.left:\\r\\n queue.append(curr_node.left)\\r\\n while stack:\\r\\n print (stack.pop(), '' , end= '')\",\n \"def bft_print(self):\\n queue = []\\n queue.append(self)\\n while len(queue):\\n current = queue.pop(0)\\n print(current.value)\\n if current.left:\\n queue.append(current.left)\\n if current.right:\\n queue.append(current.right)\",\n \"def breadth_first_traversal(self):\\n breadth_first = []\\n h = self.root.get_height() \\n for i in range(h+2): \\n self.level = []\\n self.print_level(self.root, i + 1) \\n breadth_first.append(self.level)\\n return breadth_first\",\n \"def bfs(maze):\\n # TODO: Write your code here\\n q = queue.Queue()\\n q.put(maze.getStart())\\n traversed = []\\n path = []\\n tracker = {maze.getStart(): None} #Tracker needs to contain tuples\\n\\n while q:\\n curr_loc = q.get() \\n\\n if curr_loc not in traversed: #Add to traversed points list\\n traversed.append(curr_loc)\\n\\n if maze.isObjective(curr_loc[0], curr_loc[1]): #Reached end of maze\\n finished = curr_loc \\n break\\n\\n nextpath = maze.getNeighbors(curr_loc[0], curr_loc[1]) #Search neighbor points\\n for point in nextpath:\\n if point not in traversed and maze.isValidMove(point[0], point[1]):\\n q.put(point)\\n tracker[point] = curr_loc #Update curr_loc\\n\\n while finished:\\n path.insert(0, finished) \\n finished = tracker[finished]\\n\\n return path\",\n \"def post_order_traversal(self) -> Queue:\\n q = Queue() # Initializing queue\\n if self.root is None: # If tree is empty\\n return q\\n\\n self.post_order_helper(self.root, q) # Using helper function if tree isn't empty and returning Queue\\n return q\",\n \"def in_order_traversal(self) -> Queue:\\n # initialize Queue\\n q = Queue()\\n\\n #binary search tree is empty\\n if self.root is None:\\n return q\\n\\n #recursive helper function (non-empty)return new Queue\\n self.in_order_helper(self.root, q)\\n return q\",\n \"def nodes(self):\\n root = self.root()\\n queue = Queue()\\n queue.enqueue(root)\\n return self._breadth_first(queue, elements=False)\",\n \"def breadthFirstSearch(problem):\\n \\\"*** YOUR CODE HERE ***\\\"\\n from util import Queue\\n q = Queue()\\n mapper = {} #child_point : (parent_point, direction_to_child)\\n q.push(problem.getStartState())\\n mapper[problem.getStartState()] = None #root\\n\\n while (not q.isEmpty()):\\n point = q.pop()\\n\\n if (problem.isGoalState(point)):\\n c = point\\n l = []\\n while mapper[c] != None:\\n tup = mapper[c]\\n l.append(tup[1])\\n c = tup[0]\\n l.reverse()\\n print l\\n return l\\n\\n else:\\n for child in problem.getSuccessors(point):\\n if (child[0] not in mapper):\\n q.push(child[0])\\n mapper[child[0]] = (point, child[1])\\n\\n # util.raiseNotDefined()\",\n \"def zigzagLevelOrder(self, root):\\n if not root:\\n return []\\n\\n # create direction variable\\n going_right = False\\n\\n # initialize a level stack\\n level_stack = [root]\\n\\n # initialize a result array\\n result = []\\n\\n # loop while level stack is not empry\\n while level_stack:\\n\\n # create tmp array\\n tmp = []\\n\\n res_tmp = []\\n\\n # do the BFS by looping through each element\\n while level_stack:\\n\\n # pop an element from the stack\\n node = level_stack.pop()\\n\\n res_tmp.append(node.val)\\n\\n # if direction is 1 or going right, take left node first\\n if going_right == 1:\\n if node.left:\\n tmp.append(node.left)\\n\\n if node.right:\\n tmp.append(node.right)\\n\\n # otherwise take right node first, append to the temp list\\n else:\\n if node.right:\\n tmp.append(node.right)\\n\\n if node.left:\\n tmp.append(node.left)\\n\\n # append temp list to the result list\\n result.append(res_tmp[::-1])\\n\\n # update level stack\\n level_stack = tmp\\n\\n # change the direction\\n going_right = not going_right\\n\\n return result\",\n \"def breadthFirstSearch(problem):\\n \\\"*** YOUR CODE HERE ***\\\"\\n visited=[]\\n \\n node=dict()\\n start=problem.getStartState()\\n node['parent']=None\\n node['direction']=None\\n node['state']=start\\n \\n \\n qu.push(node)\\n lis.append(node)\\n \\n print qu.list\\n while qu.isEmpty()!=True:\\n node=qu.pop()\\n pos=node['state']\\n visited.append(pos)\\n print visited\\n if problem.isGoalState(pos):\\n print \\\"found\\\"\\n return getPath(problem,node)\\n #break\\n suc=problem.getSuccessors(pos)\\n if suc ==None:\\n continue \\n \\n print suc\\n for step in suc:\\n #if step not in dic :\\n if step[0] not in visited:\\n childnode={}\\n childnode['parent']=pos\\n childnode['direction']=step[1]\\n childnode['state']=step[0]\\n qu.push(childnode)\\n lis.append(childnode)\\n \\n\\n #util.raiseNotDefined()\",\n \"def print_binary_tree_bfs(root):\\n if not isinstance(root, BinaryTreeNode):\\n return\\n queue = Queue()\\n queue.put(root)\\n\\n while not queue.empty():\\n node = queue.get()\\n print(node.val)\\n if node.left != None:\\n queue.put(node.left)\\n if node.right != None:\\n queue.put(node.right)\",\n \"def BFTbin(tree):\\n queue = [tree]\\n bft_nodelist = []\\n while queue:\\n node = queue.pop(0)\\n bft_nodelist.append(node)\\n if node.lnode is not None:\\n queue.append(node.lnode)\\n if node.rnode is not None:\\n queue.append(node.rnode)\\n return bft_nodelist\",\n \"def level_order_1(root: Node):\\n if not root:\\n return\\n temp = root\\n que = [temp]\\n while len(que) > 0:\\n print(que[0].data, end=\\\" \\\")\\n temp = que.pop(0)\\n if temp.left:\\n que.append(temp.left)\\n if temp.right:\\n que.append(temp.right)\\n return que\",\n \"def breadth_first_search(self):\\r\\n queue = [self.root]\\r\\n while queue:\\r\\n node = queue.pop()\\r\\n yield node\\r\\n queue.extend(node.children)\",\n \"def bfs(self):\\r\\n Q = [self.head()]\\r\\n visited = []\\r\\n while Q != []:\\r\\n cur = Q[0]\\r\\n visited.append(cur)\\r\\n Q = Q[1:]\\r\\n Q.extend([ch for ch in self.get_deps(cur.get_field('index'))])\\r\\n for x in reversed(visited):\\r\\n yield x\",\n \"def breadth_first(self):\\n self.breadth_first_list = []\\n queque = Queue()\\n queque.enqueue(self.root)\\n while not queque.is_empty():\\n ele = queque.dequeue()\\n self.breadth_first_list.append(ele.value)\\n if ele.left is not None:\\n queque.enqueue(ele.left)\\n if ele.right is not None:\\n queque.enqueue(ele.right)\\n return self.breadth_first_list\",\n \"def bfs(maze):\\n # TODO: Write your code here\\n frontier = Queue()\\n visited = []\\n path = []\\n ret = []\\n objectives = maze.getObjectives()\\n start = State(maze.getStart()[0], maze.getStart()[1], objectives.copy())\\n frontier.put(start)\\n explored = []\\n \\n\\n while not frontier.empty(): # while frontier queue is not empty\\n\\n currentState = frontier.get()\\n currentCell = currentState.cell()\\n objectivesLeft = currentState.objectives()\\n\\n if objectivesLeft.count(currentCell) != 0:\\n\\n objectivesLeft.remove(currentCell)\\n \\n # all objectives found, initialise backtrace and exit loop\\n # if len(objectivesLeft) == 0:\\n path.append(currentState)\\n ret.append(currentCell)\\n visited.append(currentState)\\n break\\n\\n # current cell is not objective nor visited\\n if visited.count(currentState) == 0:\\n explored.append(currentCell)\\n neighbors = maze.getNeighbors(currentCell[0], currentCell[1])\\n\\n for i in neighbors:\\n\\n neighbor = State(i[0], i[1], objectivesLeft)\\n\\n # if neighbor is not visited, add it to the frontier\\n if visited.count(neighbor) == 0:\\n neighbor.setParent(currentState)\\n frontier.put(neighbor)\\n\\n visited.append(currentState)\\n\\n #backtrace\\n while path[0] != start:\\n\\n currentState = path[0]\\n path.insert(0, currentState.parent())\\n ret.insert(0, currentState.parent().cell())\\n\\n return ret\",\n \"def pre_order_traversal(self) -> Queue:\\n # initialize\\n q = Queue()\\n\\n #binary search tree == empty\\n if self.root is None:\\n return q\\n\\n #recursive helper return Queue\\n self.pre_order_helper(self.root, q)\\n return q\",\n \"def breadthfirst(self):\\n if not self.is_empty():\\n fringe = LinkedQueue() # known positions not yet yielded\\n fringe.enqueue(self.root()) # starting with the root\\n while not fringe.is_empty():\\n p = fringe.dequeue() # remove from front of the queue\\n yield p # report this position\\n for c in self.children(p):\\n fringe.enqueue(c) # add children to back of queue\",\n \"def _breadthfirst(self,root, action=lambda nodes: print(nodes)):\\n nodes = []\\n breadth = Queue()\\n visited = []\\n\\n breadth.enqueue(root)\\n visited.append(root)\\n\\n while breadth.front:\\n front = breadth.dequeue()\\n nodes.append(front.value)\\n\\n for child in self._adjacency_list.get(front.value):\\n if not child.start_vertex in visited:\\n visited.append(child.start_vertex)\\n breadth.enqueue(child.start_vertex) \\n\\n return nodes\",\n \"def level_order_nodes(root):\\n queue = []\\n queue.append(root)\\n\\n while 0 < len(queue):\\n next_node = queue.pop(0)\\n if next_node.get_left():\\n queue.append(next_node.get_left())\\n if next_node.get_right():\\n queue.append(next_node.get_right())\\n yield next_node\",\n \"def order_bfs(self) -> List[Nodo]:\\n\\n # Nodos por buscar, es una cola\\n pending: List[Nodo] = [self.root]\\n # Nodos ya visitados\\n visited: List[Nodo] = []\\n\\n # Mientras la cola tenga items\\n while len(pending) > 0:\\n # Procesar el primer elemento\\n curr = pending.pop(0)\\n visited.append(curr)\\n\\n # Agregar los hijos no visitados del nodo a la cola\\n for child in curr.children:\\n if child in visited:\\n continue\\n pending.append(child)\\n\\n return visited\",\n \"def bfs(self, root_node_id):\\n try: \\n status = dict.fromkeys(self._nodes.iterkeys(), 0)\\n status[root_node_id] = 1 \\n L = []\\n q = Queue()\\n q.enqueue(root_node_id) \\n while not q.is_empty():\\n curr_node_id = q.dequeue()\\n status[curr_node_id] = -1\\n L.append(self._nodes[curr_node_id])\\n arcs_list = self._inc[curr_node_id]\\n record = arcs_list.get_first_record()\\n while record is not None:\\n arc = record.element\\n if status[arc._head] is 0:\\n status[arc._head] = 1\\n q.enqueue(arc._head)\\n record = record._next \\n return L\\n except KeyError:\\n return []\",\n \"def get_tree(self):\\n current = self\\n nodes = []\\n while current is not None:\\n nodes.append(current)\\n current = current.parent\\n return nodes # reversed tree path\",\n \"def level_order_list(root):\\n return_list = []\\n queue = []\\n queue.append(root)\\n\\n while 0 < len(queue):\\n next_node = queue.pop(0)\\n if next_node.get_left():\\n queue.append(next_node.get_left())\\n if next_node.get_right():\\n queue.append(next_node.get_right())\\n return_list.append(next_node)\\n\\n return return_list\",\n \"def generate_list(self):\\n\\n self.queue.append(self.root_node)\\n\\n while self.queue:\\n node = self.queue.pop(0)\\n\\n self.rtn_list.append(node.data)\\n\\n if node.left:\\n self.queue.append(node.left)\\n\\n if node.right:\\n self.queue.append(node.right)\\n\\n return self.rtn_list\",\n \"def binarizetree(tree):\\n queue = [tree]\\n while queue:\\n node = queue.pop(0)\\n queue += node.nodelist\\n # Construct binary tree\\n if len(node.nodelist) == 2:\\n node.lnode = node.nodelist[0]\\n node.rnode = node.nodelist[1]\\n # Parent node\\n node.lnode.pnode = node\\n node.rnode.pnode = node\\n elif len(node.nodelist) > 2:\\n # Remove one node from the nodelist\\n node.lnode = node.nodelist.pop(0)\\n newnode = SpanNode(node.nodelist[0].prop)\\n newnode.nodelist += node.nodelist\\n # Right-branching\\n node.rnode = newnode\\n # Parent node\\n node.lnode.pnode = node\\n node.rnode.pnode = node\\n # Add to the head of the queue\\n # So the code will keep branching\\n # until the nodelist size is 2\\n queue.insert(0, newnode)\\n # Clear nodelist for the current node\\n node.nodelist = []\\n return tree\",\n \"def breadthFirstSearch(problem):\\n \\\"*** YOUR CODE HERE ***\\\"\\n\\n \\\"\\\"\\\"\\n ALGORITHM FOR bFS \\n Create a queue Q\\n enqueue root node to Q\\n while Q is not empty:\\n dequeu an item v from Q\\n mark the item v as visited \\n for each node w that is directed from v:\\n enqueue w to Q\\n \\n \\n \\\"\\\"\\\"\\n\\n fringes = util.Queue()\\n explored =[]\\n fringes.push((problem.getStartState(),[]))\\n\\n while(not fringes.isEmpty()):\\n currentNode,currDir = fringes.pop()\\n if problem.isGoalState(currentNode):\\n goal = currentNode\\n pathToGoal = currDir\\n #print \\\"final path is : \\\", pathToGoal\\n\\n break\\n # print \\\"HOraaay goal has been found === > \\\", currentNode\\n\\n if not (currentNode in explored):\\n explored.append(currentNode)\\n for childNode in problem.getSuccessors(currentNode):\\n fringes.push((childNode[0],currDir+[childNode[1]]))\\n\\n\\n return pathToGoal\",\n \"def bft(self, starting_vertex):\\n \\n \\\"\\\"\\\"\\n Plan:\\n - Start at given index. Add that index to the Q.\\n - While len(Q) is greater than 0:\\n - Check if q[0] has children.\\n - If so then make sure children have not been visited, then add those children to the Q.\\n - If they have been visited, skip over the child and DO NOT add to Q # !! will result in infinite loop !!\\n \\\"\\\"\\\"\\n\\n queue = Q()\\n visited = []\\n\\n queue.add(starting_vertex)\\n\\n while len(queue):\\n current = queue.first()\\n children = self.vertices[current]\\n \\n if len(children) > 0:\\n for child in children:\\n if child not in visited:\\n queue.add(child)\\n else: continue\\n\\n print(current)\\n visited.append(current)\\n queue.pop()\",\n \"def print_tree_breadth_first(self):\\n\\t\\tqueue = []\\n\\t\\tqueue.append(self)\\n\\t\\tqueue.append(None)\\n\\t\\twhile len(queue) > 0:\\n\\t\\t\\tcurrent = queue.pop(0)\\n\\t\\t\\tif current == None:\\n\\t\\t\\t\\tprint('')\\n\\t\\t\\t\\tif len(queue) > 0:\\n\\t\\t\\t\\t\\tqueue.append(None)\\n\\t\\t\\t\\tcontinue\\n\\t\\t\\tprint(str(current.value) + ' ', end='')\\n\\t\\t\\tif not current.left == None:\\n\\t\\t\\t\\tqueue.append(current.left)\\n\\t\\t\\tif not current.right == None:\\n\\t\\t\\t\\tqueue.append(current.right)\",\n \"def breadth_first_traverse(self) -> Generator:\\n assist_queue = deque()\\n assist_queue.append(self.root_node)\\n while assist_queue:\\n current_node = assist_queue.popleft()\\n yield current_node\\n if current_node.children:\\n for child in current_node.children:\\n assist_queue.append(child)\",\n \"def bfs(self, start, end):\\n\\n queue = [start]\\n parent = dict()\\n\\n # Initialize parent dictionary\\n for v in iter(self._reachable): parent[v] = None\\n parent[start] = start\\n\\n while len(queue) > 0:\\n (x, y) = queue.pop(0)\\n if (x, y) == end: break\\n\\n for v in self.get_reachables(x, y):\\n if parent[v] is not None: \\n # Vertex v already visited\\n continue\\n parent[v] = (x, y)\\n queue.append(v)\\n\\n # Reconstruct path\\n path = [end]\\n vertex = end\\n\\n while parent[vertex] != vertex:\\n if parent[vertex] is None: return []\\n path.append(parent[vertex])\\n vertex = parent[vertex]\\n\\n path.reverse()\\n return path\",\n \"def postorder(self):\\n\\n traversal = []\\n self.postorder_helper(self.root, traversal)\\n return traversal\",\n \"def find_solution_bfs(root, expected_jar):\\n solution = []\\n\\n dq = deque()\\n # Add root to the collection, hence there is no need for any special\\n # handling.\\n dq.append(root)\\n while dq:\\n node = dq.popleft()\\n # Whenever we visit a node, add it to the solution 'cause it's been\\n # visited.\\n solution.append(node)\\n if node.contains(expected_jar):\\n break\\n children = node.get_all_children()\\n for child in children:\\n dq.append(child)\\n return solution\",\n \"def in_order_traversal(self) -> Queue:\\n q = Queue() # Initializing queue\\n if self.root is None: # If tree is empty\\n return q\\n\\n self.in_order_helper(self.root, q) # Using helper function if tree isn't empty and returning Queue\\n return q\",\n \"def bfs(self, root_node_id):\\n try: \\n status = dict.fromkeys(self._nodes.iterkeys(), 0)\\n status[root_node_id] = 1 \\n L = []\\n q = Queue()\\n q.enqueue(root_node_id) \\n while not q.is_empty():\\n curr_node_id = q.dequeue()\\n status[curr_node_id] = -1\\n L.append(self._nodes[curr_node_id])\\n arcs_set = self._inc[curr_node_id]\\n for arc in arcs_set:\\n if status[arc._head] is 0:\\n status[arc._head] = 1\\n q.enqueue(arc._head) \\n return L\\n except KeyError:\\n return []\",\n \"def items_level_order(self):\\n # TODO: Create a queue to store nodes not yet traversed in level-order\\n queue = ...\\n # Create an items list\\n items = list()\\n # TODO: Enqueue the root node if this tree is not empty\\n if ...:\\n queue...\\n # TODO: Loop until the queue is empty\\n while ...:\\n # TODO: Dequeue the node at the front of the queue\\n node = ...\\n # TODO: Add this node's data to the items list\\n ...\\n # TODO: Enqueue this node's left child if it exists\\n if ...:\\n ...\\n # TODO: Enqueue this node's right child if it exists\\n if ...:\\n ...\\n # Return the items list\\n return items\",\n \"def print_bi_tree(self):\\n\\n to_print = [self]\\n # current = None\\n\\n while to_print:\\n current = to_print.pop(0)\\n if current:\\n print(f'\\\\t{current.data}')\\n to_print.append(current.left)\\n to_print.append(current.right)\",\n \"def _buildtree(self):\\n self.pricetree = np.zeros((self.steps+1,self.steps+1))\\n self.pricetree[0][0] = self.p\\n for j in range(self.steps):\\n for i in range(j+1):\\n self.pricetree[j+1][i+1] = self.pricetree[j][i]*self.down\\n self.pricetree[j+1][0] = self.pricetree[j][0]*self.up\",\n \"def bfs(start_node, goal_node, max_depth) -> \\\"solution path\\\":\\n\\td = deque([start_node,[]])\\n\\texplored = {}\\n\\tlevel = 0\\n\\n\\t# Return empty path if start is equal to goal\\n\\tif start_node == goal_node:\\n\\t\\treturn []\\n\\n\\t# Keep exploring while the deque has nodes\\n\\twhile len(d) > 0:\\n\\t\\tpath = d.popleft()\\n\\n\\t\\tif level == 0:\\n\\t\\t\\tnode = path\\n\\t\\telse:\\n\\t\\t\\t# To keep track of levels an empty node gets popped between levels which will cause an exception\\n\\t\\t\\ttry:\\n\\t\\t\\t\\tnode = path[-1]\\n\\t\\t\\texcept Exception:\\n\\t\\t\\t\\tnode = []\\n\\t\\t\\t\\tpass\\n\\n\\t\\tif len(node) == 0:\\n\\t\\t\\tlevel += 1\\n\\t\\t\\t# Return empty list if max depth was reached\\n\\t\\t\\tif max_depth == level:\\n\\t\\t\\t\\treturn []\\n\\t\\t\\td.append(node)\\n\\n\\t\\telse:\\n\\t\\t\\tval = getNodeVal(node)\\n\\t\\t\\tif val not in explored:\\n\\n\\t\\t\\t\\t# Mark node as explored\\n\\t\\t\\t\\texplored[val] = True\\n\\n\\t\\t\\t\\tfor row in range(len(node)):\\n\\t\\t\\t\\t\\tfor col in range(len(node)):\\n\\t\\t\\t\\t\\t\\tchild = toggle(node, row, col)\\n\\t\\t\\t\\t\\t\\tnew_path = list(path)\\n\\t\\t\\t\\t\\t\\tif level == 0:\\n\\t\\t\\t\\t\\t\\t\\tnew_path = [new_path]\\n\\t\\t\\t\\t\\t\\tnew_path.append(child)\\n\\t\\t\\t\\t\\t\\td.append(new_path)\\n\\t\\t\\t\\t\\t\\tif child == goal_node:\\n\\t\\t\\t\\t\\t\\t\\tlevel+=1\\n\\t\\t\\t\\t\\t\\t\\treturn new_path\\n\\t# No solution found\\n\\treturn []\",\n \"def bfs(graph, start, goal):\\n\\n final = []\\n agenda = [[start]]\\n\\n # Process node queue\\n while agenda:\\n path = agenda.pop(0)\\n\\n # Exit if a path is found which reaches the goal\\n if path[-1] == goal:\\n final = path\\n break\\n\\n # Push the new paths onto the queue\\n connected = graph.get_connected_nodes(path[-1])\\n for node in connected:\\n # Ignore previously visited nodes\\n if node not in path:\\n agenda.append(path + [node])\\n\\n # Return the final path or initial empty list\\n return final\",\n \"def make_linked_lists(self):\\n\\t\\tif self.is_empty():\\n\\t\\t\\treturn []\\n\\n\\t\\tqueue = c3.Queue()\\n\\t\\tlists = [] #an empty list of lists\\n\\t\\tlevel = 0\\n\\t\\tqueue.enqueue((1, self._root))\\n\\t\\twhile queue.peek():\\n\\t\\t\\tnodelev, node = queue.dequeue()._data\\n\\t\\t\\tif (nodelev != level):\\n\\t\\t\\t\\tlevel = nodelev\\n\\t\\t\\t\\tlists.append([]) #appends a new empty list for the next level of nodes\\n\\t\\t\\tlists[-1].append(node.value())\\n\\t\\t\\tif node.lchild():\\n\\t\\t\\t\\tqueue.enqueue((nodelev + 1, node.lchild()))\\n\\t\\t\\tif node.rchild():\\n\\t\\t\\t\\tqueue.enqueue((nodelev + 1, node.rchild()))\\n\\t\\treturn lists\",\n \"def BFS(graph, start, end, toPrint=False):\\n initPath = [start]\\n pathQueue = [initPath]\\n if toPrint:\\n print('current BFS path:', printPath(pathQueue))\\n while len(pathQueue) != 0:\\n # get and remove oldest element in pathQueue\\n tmpPath = pathQueue.pop(0)\\n print('Current BFS path:', printPath(tmpPath))\\n lastNode = tmpPath[-1]\\n if lastNode == end:\\n return tmpPath # Explore all paths with n hops \\n # before exploring any path with >n hops\\n for nextNode in graph.childrenOf(lastNode):\\n if next not in tmpPath:\\n newPath = tmpPath + [nextNode]\\n pathQueue.append(newPath)\\n return None\",\n \"def bfs(maze):\\n # TODO: Write your code here\\n\\n q = []\\n visited = {}\\n keys = {}\\n selected = None\\n q.append(maze.getStart())\\n\\n while len(q) > 0:\\n curr = q.pop(0)\\n if maze.isObjective(curr[0], curr[1]):\\n selected = curr\\n break\\n\\n neighbors = maze.getNeighbors(curr[0], curr[1])\\n\\n for n in neighbors:\\n if n not in visited:\\n visited[n] = True\\n q.append(n)\\n keys[n] = curr\\n\\n curr = selected\\n path = []\\n while curr != maze.getStart():\\n path.append(curr)\\n curr = keys[curr]\\n\\n path.append(curr)\\n path.reverse() # backtrace\\n print(path)\\n return path\",\n \"def binaryTree(r):\\r\\n return [r, [], []]\",\n \"def breadthFirstSearch(problem):\\n \\\"*** YOUR CODE HERE ***\\\"\\n class Node:\\n def __init__(self, state, parent, action, pathCost):\\n self.state = state\\n self.parent = parent\\n self.action = action\\n self.pathCost = pathCost\\n\\n def solution(self):\\n path = list()\\n tempNode = self\\n while tempNode.state != problem.getStartState():\\n path.insert(0, tempNode.action)\\n tempNode = tempNode.parent\\n return path\\n\\n\\n\\n\\n def childNode(successor, parent, action, stepCost):\\n pathCost = parent.pathCost + stepCost\\n child = Node(successor, parent, action, pathCost)\\n return child\\n\\n initialNode = Node(problem.getStartState(), None, None, 0)\\n if problem.isGoalState(initialNode.state):\\n return initialNode.solution()\\n\\n frontier = util.Queue() #bfs uses a queue\\n frontier.push(initialNode)\\n explored = set()\\n\\n while not frontier.isEmpty() :\\n nextNode = frontier.pop() #extract from the start of the queue\\n explored.add(nextNode.state)\\n for successor, action, stepCost in problem.getSuccessors(nextNode.state):\\n child = childNode(successor, nextNode, action, stepCost)\\n if child.state not in explored and child not in frontier.list:\\n if problem.isGoalState(child.state):\\n return child.solution()\\n frontier.push(child)\\n return []\\n util.raiseNotDefined()\",\n \"def bfs(initial_state, dimension=3):\\n\\t\\n\\treturn search(initial_state, Frontier(Queue), dimension)\",\n \"def printBinaryTree(root):\\n level = treeLevel(root)\\n q = deque([root])\\n while level > 0:\\n new_q = deque()\\n while q:\\n tmp = q.popleft()\\n new_q.append(tmp.left)\\n new_q.append(tmp.right)\\n print(level*' ', tmp.val, end='')\\n print('\\\\n')\\n q = new_q\\n level -= 1\\n return\",\n \"def bfs(self, root: TreeNode) -> int:\\n if not root:\\n return 0\\n queue = deque([(root, 1)])\\n while queue:\\n node, level = queue.popleft()\\n if not node.left and not node.right:\\n return level\\n if node.left:\\n queue.append((node.left, level + 1))\\n if node.right:\\n queue.append((node.right, level + 1))\\n return -1\",\n \"def _backtrack_path(self, current=None):\\n path = []\\n if self.path_found:\\n if not current:\\n current = self.end_node\\n path.append(self.end_node)\\n while current.parent:\\n path.append(current.parent)\\n self.board_array[current.parent.y][current.parent.x] = 4\\n current = current.parent\\n self.board_array[self.start_node.y][self.start_node.x] = 2\\n self.board_array[self.end_node.y][self.end_node.x] = 3\\n return path\",\n \"def bfs(self, start_word, end_word): # great if you know to result is somewhere close to the root/start\\n visited = set() # create an empty 'set' to store visisted vertex, set sorts \\n\\n q = Queue() # create an empty Queue\\n q.enqueue([start_word]) # set the starting_vertex with enqueue \\n\\n while q.size() > 0:\\n path = q.dequeue() # dequeue and store first path\\n v = path[-1] # store the vertex from the end of path \\n\\n if v == end_word: # if v is equal to the destination_vertex\\n return path # return the path \\n\\n if v not in visited: # if v has not been visited yet \\n visited.add(v) # add v to the vistied set \\n\\n for neighbor in self.vertices[v]: # loop through the neighbors \\n path_copy = list(path) # make a copy of the path \\n path_copy.append(neighbor) # append each neighbor to the back of the path copy \\n q.enqueue(path_copy) # enqueue the path copy to the queue \",\n \"def bfs(self, start):\\n output_list = []\\n queue = Queue()\\n queue.put(start)\\n visited = set(start)\\n while not queue.empty():\\n current_node = queue.get()\\n output_list.append(current_node)\\n visited.add(current_node)\\n for node in self.__graph[current_node].neighbours:\\n if node.name not in visited:\\n queue.put(node.name)\\n return output_list\",\n \"def bfs(comment, fun):\\n cur_level = [comment]\\n while cur_level:\\n next_level = [] # type: ignore\\n for c in cur_level:\\n # do not touch original comment to comply with dfs version\\n if not c == comment:\\n # do something with a comment\\n fun(c)\\n if c.replies:\\n next_level.extend(c.replies)\\n # level change\\n cur_level = next_level\",\n \"def bfs(g: nx.Graph, start_node: Any) -> list:\\r\\n\\tx = [start_node]\\r\\n\\tqueue = [start_node]\\r\\n\\ttracks = {node: [] for node in g.nodes}\\r\\n\\twhile queue:\\r\\n\\t\\telement = queue.pop(0)\\r\\n\\t\\ty = list(g.neighbors(element))\\r\\n\\t\\tfor node in y:\\r\\n\\t\\t\\tif node not in x:\\r\\n\\t\\t\\t\\tx.append(node)\\r\\n\\t\\t\\t\\tqueue.append(node)\\r\\n\\t\\t\\t\\ttracks[node].extend((*tracks[element], element))\\r\\n\\treturn x\",\n \"def serialize(self, root):\\n if not root:\\n return []\\n\\n _queue = [root]\\n result = []\\n # 按层遍历二叉树\\n while _queue:\\n node = _queue.pop(0)\\n # 无论是否有左右儿子，都压入队列\\n # 如果为空用＃进行占位, 但不压入队列\\n if node:\\n result.append(node.val)\\n _queue.append(node.left)\\n _queue.append(node.right)\\n else:\\n result.append('#')\\n return result\",\n \"def BFS(graph, s, n):\\n #Implement queue using list. list.pop() to dequeue and list.insert(0,x) to enqueue.\\n visited = [False] * n\\n queue = []\\n \\n queue.insert(0, s)\\n visited[s] = True\\n while(queue):\\n v = queue.pop()\\n print(v, end= \\\" \\\")\\n \\n for i in range(len(graph[v])):\\n if (not visited[graph[v][i]]):\\n queue.insert(0, graph[v][i] )\\n visited[graph[v][i]] = True\",\n \"def get_bfs(self, s):\\n # create a queue for BFS\\n queue = []\\n # mark all the vertices as not visited\\n visited = [False] * (len(self.graph))\\n # mark the start node as visited and enqueue it\\n visited[s] = True\\n queue.append(s)\\n results = []\\n\\n while queue:\\n # dequeue a vertex from queue and append to results.\\n p = queue.pop(0)\\n results.append(p)\\n # get all adjacent vertices of the dequeued vertex s,\\n # and for any unvisited adjacent, mark it visited and enqueue it.\\n for v in self.graph[p]:\\n if visited[v] is False:\\n visited[v] = True\\n queue.append(v)\\n\\n return results\",\n \"def breadthFirstSearch(problem):\\n \\\"*** YOUR CODE HERE ***\\\"\\n #Queue to hold the node along with the path taken from the start node to reach that node\\n queue = Queue()\\n #Set to hold the node explored.\\n explorednode = set()\\n # Get the start node.\\n startnode = problem.getStartState()\\n print startnode\\n # Push the starting node on the Queue along with an empty set to know the direction in order to reach the node.\\n queue.push((startnode,[]))\\n\\n # Loop till the queue is empty\\n while queue.isEmpty() is not True:\\n # Pop the currentnode and the direction from the queue\\n currentnode, direction = queue.pop()\\n # Check if the currentnode is not in explorednode.\\n if currentnode not in explorednode:\\n # We will now add the node to set of explored node.\\n explorednode.add(currentnode)\\n # If the node is the goal. We made it!!\\n if problem.isGoalState(currentnode):\\n # The direction holds the way to reach till the goal from the start node.\\n print currentnode\\n return direction\\n # Loop for each successor(child) of the current node.\\n for (successor, action, stepCost) in problem.getSuccessors(currentnode):\\n # If the successor(child) is not explored\\n if successor not in explorednode:\\n # Add the successor to the queue along with the path to reach it.\\n queue.push((successor, direction + [action]))\\n util.raiseNotDefined()\",\n \"def pre_order_traversal(self) -> Queue:\\n q = Queue() # Initializing queue\\n if self.root is None: # If tree is empty\\n return q\\n\\n self.pre_order_helper(self.root, q)\\n return q\",\n \"def breadth_first_solve(puzzle):\\n # instantiating queue using new PuzzleNode (root)\\n queue = deque([PuzzleNode(puzzle, [])])\\n\\n # while puzzle still has moves to make (or is not solved yet)\\n while len(queue) > 0:\\n\\n current = queue.popleft() # update current node\\n extensions = current.puzzle.extensions() # gather moves to make\\n\\n # loop through extensions (breadth)\\n for i in range(len(extensions)):\\n newNode = PuzzleNode(extensions[i], [], current)\\n\\n # do not include already traversed nodes\\n if newNode not in queue:\\n current.children.append(newNode) # add as child of current node\\n queue.append(newNode) # add to queue\\n\\n # if child node is solved, return it\\n if newNode.puzzle.is_solved():\\n return newNode\\n\\n\\n return None # no solution was found\",\n \"def print_bfs(self):\\n visit_order = self.bfs()\\n s = \\\"Tree (from BFS)\\\\n\\\"\\n previous_level = -1\\n for i in range(len(visit_order)):\\n node, level = visit_order[i]\\n if level == previous_level:\\n s += \\\" | \\\" + str(node) \\n else:\\n s += \\\"\\\\n\\\" + str(node)\\n previous_level = level\\n \\n return s\",\n \"def create_treelist(dataclass,uri):\\n queue=[uri]\\n list=[uri]\\n while len(queue):\\n element=queue[-1]\\n if dataclass.is_collection(element):\\n childs=dataclass.get_childs(element)\\n else:\\n childs=[]\\n if len(childs):\\n list=list+childs\\n # update queue\\n del queue[-1]\\n if len(childs):\\n queue=queue+childs\\n return list\",\n \"def bfs(self, starting_vertex, destination_vertex):\\n \\\"\\\"\\\" FIFO ir LILO\\n Create a queue\\n Enqueue PATH to starting Vertex\\n Create a set top store visited vertices\\n While the queue is NOT empty: e.g. > 0\\n Dequeue the first PATH Vertex\\n Get Vertex from END of PATH\\n Check IF NOT visited:\\n Mark as visited\\n check if vertex is destination_vertex\\n If TRUE, return path\\n enqueue PATH to ALL of neighbors \\n make COPY of current path\\n add neighbor to path copy\\n enqueue copy \\n \\\"\\\"\\\"\\n\\n q = Queue() # Create a queue\\n q.enqueue([starting_vertex]) # Enqueue starting at vertex into Queue (list)\\n visited = set() # Create a set to store visited \\n \\n while q.size() > 0: # While the queue is NOT empty: \\n path = q.dequeue() # Dequeue the first PATH Vertices\\n v = path[-1] # Get Vertex from END of PATH\\n\\n if v not in visited: # Check IF NOT visited:\\n visited.add(v) # Mark as visited\\n\\n if v == destination_vertex: # check if vertex is destination_vertex\\n return path # If TRUE, return path, DONE\\n\\n for n in self.get_neighbors(v): # enqueue PATH to ALL of neighbors\\n path_c = path [:] # make COPY of current path\\n path_c.append(n) # add neighbor to path copy\\n q.enqueue(path_c) # enqueue copy\",\n \"def bfs(level):\\n length = len(queue)\\n print \\\"Length of queue: \\\" + str(length) + \\\" at level \\\" + str(level)\\n if length <= 0 or level <= 0:\\n return\\n i = 0\\n while i < length:\\n try:\\n text = req_obj.get_html_text(queue[0])\\n if text is None:\\n raise requests.RequestException()\\n add_links_to_queue(text, queue[0])\\n\\n # summary generated using summarizer1\\n sum_obj.create_and_index_summary(\\n req_obj.get_base_url(), text)\\n\\n # summary generated using summarizer2\\n sum_obj2.create_and_index_summary(\\n req_obj.get_base_url(), text)\\n on_pg_sum.index_on_page_summary(text, queue[0])\\n except requests.RequestException as trace:\\n print str(trace) + '\\\\n'\\n er_file.write(queue[0] + '\\\\n')\\n er_file.write(str(trace) + '\\\\n\\\\n')\\n queue.pop(0)\\n i += 1\\n bfs(level - 1)\",\n \"def breadthFirstSearch(problem):\\n \\\"*** YOUR CODE HERE ***\\\"\\n\\n queue = util.Queue() # queue for searshing the graph\\n visited = [] # keep track of visited nodes\\n start =problem.getStartState() # The start node\\n queue.push((start, [])) # the sart state and empty path list is pushed to the queue\\n \\n while queue:\\n (vrtx, path) = queue.pop()\\n if vrtx not in visited: \\n if problem.isGoalState(vrtx):\\n return [p[1] for p in path]\\n visited.append(vrtx)\\n for successor in problem.getSuccessors(vrtx) :\\n queue.push((successor[0], path+[successor]))\\n\\n util.raiseNotDefined()\",\n \"def __init__(self, dim, points, parent, root, rec):\\n import numpy as np\\n \\n ##Assumption: 0 - left and down, 1 - right and up, 0 - x, 1 - y \\n self.node = root\\n self.parent = parent\\n self.points = points\\n self.children = [None, None]\\n self.childlist = [[], []]\\n self.rec = rec\\n \\n \\n if(parent == None):\\n self.depth = 0\\n else:\\n self.depth = self.parent.depth + 1\\n \\n self.dim = self.depth%dim\\n \\n if(self.parent == None): #ROOT - 0, BRANCH - 1, LEAF - 2\\n self.type = \\\"ROOT\\\"\\n \\n elif(len(self.points) == 0):\\n self.type = \\\"LEAF\\\"\\n \\n else:\\n self.type = \\\"BRANCH\\\"\\n \\n self.update_rec()\\n self.subdivide(dim)\",\n \"def breadth_first_list(graph, current=\\\"a\\\"):\\n queue = []\\n queue.append(current)\\n while queue:\\n current = queue.pop(0)\\n print(current)\\n for node in graph.get(current):\\n queue.append(node)\",\n \"def depthFirstSearch(problem):\\n \\\"*** YOUR CODE HERE ***\\\"\\n fringeList = util.Stack()\\n print \\\"fringeList\\\",fringeList\\n closedList = {str(problem.getStartState()): ([])} #Hash Map to maintain state to path\\n print \\\"closed list:\\\", closedList\\n isGoalStateArrived = False\\n\\n # Push start state into fringeList\\n fringeList.push((problem.getStartState()))\\n\\n while not isGoalStateArrived and not fringeList.isEmpty():\\n currentNode = fringeList.pop()\\n print \\\"currentNode\\\",currentNode\\n currentNodePath = closedList[str(currentNode)]\\n print \\\"currentNodepath:\\\",currentNodePath\\n # Explore children\\n childrenOfCurrentNode = problem.getSuccessors(currentNode)\\n print \\\"childrenOfCurrentNode:\\\",childrenOfCurrentNode\\n for childNode in childrenOfCurrentNode:\\n if str(childNode[0]) not in closedList:\\n path = copy.copy(currentNodePath)\\n path.append(childNode[1])\\n print \\\"child [0] %s, child [1] %s\\\", childNode[0],childNode[1]\\n print \\\"path \\\", path\\n fringeList.push(childNode[0])\\n closedList[str(childNode[0])] = path # Put parent node in closed List\\n if problem.isGoalState(childNode[0]):\\n isGoalStateArrived = True\\n goalState = childNode[0]\\n break\\n\\n if isGoalStateArrived:\\n #print closedList[str(problem.getStartState())]\\n return closedList[str(goalState)]\\n \\\"util.raiseNotDefined()\\\"\",\n \"def children(self):\\n n = []\\n zi = 0\\n zj = 0\\n for i in range(self.board_size):\\n for j in range(self.board_size):\\n if self.arr[i][j] == 0:\\n zi = i\\n zj = j\\n break\\n i = zi\\n j = zj\\n # \\\"UDLR\\\" order; that is, [‘Up’, ‘Down’, ‘Left’, ‘Right’]\\n if 0 <= i-1: # up\\n self.arr[i][j], self.arr[i-1][j] = self.arr[i -\\n 1][j], self.arr[i][j] # do the move\\n x = state([item for row in self.arr for item in row],\\n self) # create a copy\\n x.move = \\\"Up\\\" # set the movement that leads for this copy\\n n.append(x) # add the copy to the list\\n # undo the move so we can continue finding the other children\\n self.arr[i][j], self.arr[i-1][j] = self.arr[i-1][j], self.arr[i][j]\\n if self.board_size > i+1: # down\\n self.arr[i][j], self.arr[i+1][j] = self.arr[i+1][j], self.arr[i][j]\\n x = state([item for row in self.arr for item in row], self)\\n x.move = \\\"Down\\\"\\n n.append(x)\\n self.arr[i][j], self.arr[i+1][j] = self.arr[i+1][j], self.arr[i][j]\\n if 0 <= j-1: # left\\n self.arr[i][j], self.arr[i][j-1] = self.arr[i][j-1], self.arr[i][j]\\n x = state([item for row in self.arr for item in row], self)\\n x.move = \\\"Left\\\"\\n n.append(x)\\n self.arr[i][j], self.arr[i][j-1] = self.arr[i][j-1], self.arr[i][j]\\n if self.board_size > j+1: # right\\n self.arr[i][j], self.arr[i][j+1] = self.arr[i][j+1], self.arr[i][j]\\n x = state([item for row in self.arr for item in row], self)\\n x.move = \\\"Right\\\"\\n n.append(x)\\n self.arr[i][j], self.arr[i][j+1] = self.arr[i][j+1], self.arr[i][j]\\n\\n return n\",\n \"def _breadth_first(self, queue, elements=True):\\n while not queue.is_empty():\\n node = queue.dequeue()\\n if elements:\\n yield node.element()\\n else:\\n yield node\\n for child in self.children(node):\\n queue.enqueue(child)\",\n \"def bfs(graph, root, max_depth):\\n ###TODO\\n pass\",\n \"def bfs(maze):\\n # TODO: Write your code here.\\n start = maze.getStart() \\n frontier = [] \\n path = [] \\n dim = maze.getDimensions()\\n objs = maze.getObjectives()\\n rows = dim[0]\\n cols = dim[1]\\n visited = {} # visited as a dictionary\\n for i in range (0, rows):\\n for j in range (0, cols):\\n visited[(i,j)] = (-1, -1)\\n frontier.append(start)\\n visited[(i,j)] = (-2, -2)\\n while frontier:\\n pt = frontier.pop(0)\\n if maze.isObjective(pt[0], pt[1]) == True:\\n break\\n else:\\n list_of_neighbors = maze.getNeighbors(pt[0], pt[1])\\n for i in list_of_neighbors:\\n if visited.get(i) == (-1, -1): \\n frontier.append(i)\\n visited[i] = pt \\n while pt != start:\\n path.append(pt)\\n pt = visited.get(pt)\\n path.append(start)\\n path.reverse()\\n return path\",\n \"def breadthFirstSearch(problem):\\n \\\"*** YOUR CODE HERE ***\\\"\\n result = []\\n qu = util.Queue()\\n visited = set([])\\n current = [problem.getStartState()]\\n\\n qu.push(current)\\n\\n while not qu.isEmpty():\\n current = qu.pop()\\n visited.add(current[-1])\\n\\n if problem.isGoalState(current[-1]):\\n result = current\\n break\\n\\n for each in problem.getSuccessors(current[-1]):\\n if each[0] not in visited:\\n temp = list(current)\\n temp.append(each[0])\\n qu.push(temp)\\n visited.add(each[0])\\n\\n path = get_path(result)\\n return path\\n util.raiseNotDefined()\",\n \"def build_extended_tree(root, p=1, q=1):\\n original_root = root # store for later\\n \\n # Step 1\\n for i in range(p-1):\\n node = Node(label=\\\"*\\\")\\n node.addkid(root)\\n root = node\\n \\n # Steps 2 and 3\\n list_of_children = original_root.children\\n if(len(list_of_children) == 0):\\n q_append_leaf(original_root, q)\\n else:\\n q_append_non_leaf(original_root, q)\\n while(len(list_of_children) > 0):\\n temp_list = list()\\n for child in list_of_children:\\n if(child.label != \\\"*\\\"):\\n if(len(child.children) == 0):\\n q_append_leaf(child, q)\\n else:\\n q_append_non_leaf(child, q)\\n temp_list.extend(child.children)\\n list_of_children = temp_list\\n return root\",\n \"def __init__(self): \\n self.left, self.right = [], []\",\n \"def bfs(self, starting_vertex, destination_vertex): # great if you know to result is somewhere close to the root/start\\n visited = set() # create an empty 'set' to store visisted vertex, set sorts \\n\\n q = Queue() # create an empty Queue\\n q.enqueue([starting_vertex]) # set the starting_vertex with enqueue \\n\\n while q.size() > 0:\\n path = q.dequeue() # dequeue and store first path\\n v = path[-1] # store the vertex from the end of path \\n\\n if v == destination_vertex: # if v is equal to the destination_vertex\\n return path # return the path \\n\\n if v not in visited: # if v has not been visited yet \\n visited.add(v) # add v to the vistied set \\n\\n for neighbor in self.vertices[v]: # loop through the neighbors \\n path_copy = list(path) # make a copy of the path \\n path_copy.append(neighbor) # append each neighbor to the back of the path copy \\n q.enqueue(path_copy) # enqueue the path copy to the queue \",\n \"def level_order_nary2(root):\\n if root is None:\\n return []\\n res = []\\n q = []\\n q.append(root)\\n while len(q) > 0:\\n temp = []\\n for _ in range(len(q)):\\n node = q.pop(0)\\n q.extend(node.children)\\n temp.append(node.val)\\n res.append(temp)\\n return res\",\n \"def traverse_breadth_first(self, fn):\\n queue = deque([self.root])\\n while len(queue) > 0:\\n node = queue.popleft()\\n fn(node)\\n queue.extend(node.children)\",\n \"def bft(self, starting_vertex):\\n # Create a q and enqueue starting vertex\\n qq = Queue()\\n qq.enqueue([starting_vertex])\\n # Create a set of traversed vertices\\n visited = set()\\n # eldest = []\\n depth_counter = {} \\n starter = 0 \\n # visited = []\\n # While queue is not empty:\\n while qq.size() > 0:\\n # dequeue/pop the first vertex\\n path = qq.dequeue()\\n # if not visited\\n # print(visited)\\n starter += 1\\n if path[-1] not in visited:\\n # DO THE THING!!!!!!!\\n # print(path[-1])\\n depth_counter[starter] = path[-1]\\n # mark as visited\\n visited.add(path[-1])\\n # visited.append(path[-1])\\n # enqueue all neightbors\\n \\n if not self.get_neighbors(path[-1]):\\n \\n if starting_vertex == path[-1]:\\n return -1\\n else:\\n # print(\\\"eldest ancestor:\\\",path[-1])\\n depth_counter[starter] = path[-1]\\n # print(depth_counter)\\n # eldest.append(path[-1])\\n else:\\n # starter += 1\\n for next_vert in self.get_neighbors(path[-1]): \\n new_path = list(path)\\n new_path.append(next_vert)\\n qq.enqueue(new_path)\\n\\n\\n return depth_counter[starter]\",\n \"def bfs(maze, current_node):\\n q = collections.deque()\\n\\n q.append(current_node)\\n\\n while len(q) > 0:\\n current_node = q.popleft()\\n maze[current_node.row][current_node.cell] = 1\\n yield maze\\n\\n for neighbour in get_neighbours(maze, current_node):\\n if maze[neighbour.row][neighbour.cell] == 2:\\n backtrack(maze, neighbour)\\n yield maze\\n return\\n else:\\n q.append(neighbour)\\n maze[neighbour.row][neighbour.cell] = -2\\n\\n yield maze\\n maze[current_node.row][current_node.cell] = -3\\n time.sleep(args.speed)\",\n \"def breadthFirstSearchPaths(problem):\\n #import pdb;pdb.set_trace()\\n frontier = util.Queue()\\n start_node = problem.getStartState()\\n if problem.isGoalState(start_node):\\n return ['Stop']\\n frontier.push((start_node,()))\\n explored = set()\\n while True:\\n if frontier.isEmpty():\\n return []\\n node = frontier.pop()\\n\\n #explored.add(node[0])\\n # exploratory code for SUPER-optimal solution:\\n # by saving the path in explored, we assure that we explore the same cell even if\\n # two different actions go through it:\\n explored.add(node)\\n for successor in problem.getSuccessors(node[0]):\\n nextState, action, cost = successor\\n if nextState in explored or nextState in [f[0] for f in frontier.list]:\\n continue\\n actions = node[1]\\n next_actions = actions + (action,)\\n new_node = (nextState, next_actions)\\n if problem.isGoalState(new_node[0]):\\n return new_node[1]\\n frontier.push(new_node)\\n #print frontier.list\\n return []\",\n \"def get_nodes_by_depth(root):\\n queue = deque([root])\\n res = []\\n while queue:\\n next_queue = deque([])\\n this_level = []\\n while queue:\\n parent = queue.popleft()\\n this_level.append(parent.data)\\n if parent.left:\\n next_queue.append(parent.left)\\n if parent.right:\\n next_queue.append(parent.right)\\n res.append(this_level)\\n queue = next_queue\\n\\n return res\",\n \"def _build_jump_nodes(self):\\n self._jump_nodes = []\\n Q = Queue()\\n Q.enqueue(self._tree.root())\\n\\n while not Q.is_empty():\\n p = Q.dequeue()\\n\\n if self._tree.height(p) > self._block_size:\\n for ch in self._tree.children(p):\\n if self._tree.height(ch) <= self._block_size:\\n if p not in self._jump_nodes:\\n self._jump_nodes.append(p)\\n else:\\n Q.enqueue(ch)\\n\\n # Bucket sort should be used for sorting!\\n self._jump_nodes.sort(key=lambda p: self._tree.depth(p), reverse=True)\",\n \"def BFS(self,s,t,parent):\\n #mark all vertices as not visited\\n visited = [False]*(self.ROWS);\\n # initialize a queue\\n queue = []\\n # add source to q and mark it visited\\n queue.append(s)\\n visited[s] = True\\n #Breadth-first-search\\n while queue:\\n n = queue.pop(0)\\n for index,val in enumerate(self.graph[n]):\\n if visited[index] == False and val>0:\\n queue.append(index)\\n visited[index] = True\\n parent[index] = n\\n #return True if sink was visted\\n if visited[t]:\\n return True\\n else:\\n return False\"\n]","isConversational":false},"negative_scores":{"kind":"list like","value":["0.7717397","0.71512514","0.7137357","0.68200314","0.6652208","0.6628352","0.66126645","0.6381496","0.6362987","0.6343037","0.62802523","0.6268586","0.6243742","0.6185299","0.6096152","0.60347956","0.60323125","0.59980357","0.59462214","0.5933634","0.5902215","0.58894324","0.588607","0.586998","0.5864968","0.5829627","0.58241314","0.5819067","0.58135784","0.58131826","0.5789008","0.57692295","0.5762508","0.57344323","0.57020354","0.5691855","0.568839","0.56856745","0.56784606","0.5667763","0.5651677","0.5638854","0.56258744","0.5615563","0.5609391","0.5607902","0.5598266","0.55951476","0.55908203","0.55888563","0.5555367","0.55328834","0.55291355","0.54998845","0.54956263","0.54944694","0.54881865","0.54824483","0.5481411","0.5477817","0.5456438","0.54505056","0.54406124","0.5432878","0.5406113","0.539998","0.5395797","0.5379206","0.5378332","0.53720737","0.53708524","0.5355402","0.5337946","0.53300333","0.5325737","0.531873","0.53164613","0.5313864","0.5309509","0.5304274","0.5304168","0.5304034","0.52983505","0.5297235","0.5292266","0.5287181","0.5279643","0.5278626","0.5276851","0.5273194","0.5272679","0.52642703","0.52595025","0.5257198","0.5257051","0.5242351","0.5240013","0.5232631","0.5220942","0.5215497","0.5207305"],"string":"[\n \"0.7717397\",\n \"0.71512514\",\n \"0.7137357\",\n \"0.68200314\",\n \"0.6652208\",\n \"0.6628352\",\n \"0.66126645\",\n \"0.6381496\",\n \"0.6362987\",\n \"0.6343037\",\n \"0.62802523\",\n \"0.6268586\",\n \"0.6243742\",\n \"0.6185299\",\n \"0.6096152\",\n \"0.60347956\",\n \"0.60323125\",\n \"0.59980357\",\n \"0.59462214\",\n \"0.5933634\",\n \"0.5902215\",\n \"0.58894324\",\n \"0.588607\",\n \"0.586998\",\n \"0.5864968\",\n \"0.5829627\",\n \"0.58241314\",\n \"0.5819067\",\n \"0.58135784\",\n \"0.58131826\",\n \"0.5789008\",\n \"0.57692295\",\n \"0.5762508\",\n \"0.57344323\",\n \"0.57020354\",\n \"0.5691855\",\n \"0.568839\",\n \"0.56856745\",\n \"0.56784606\",\n \"0.5667763\",\n \"0.5651677\",\n \"0.5638854\",\n \"0.56258744\",\n \"0.5615563\",\n \"0.5609391\",\n \"0.5607902\",\n \"0.5598266\",\n \"0.55951476\",\n \"0.55908203\",\n \"0.55888563\",\n \"0.5555367\",\n \"0.55328834\",\n \"0.55291355\",\n \"0.54998845\",\n \"0.54956263\",\n \"0.54944694\",\n \"0.54881865\",\n \"0.54824483\",\n \"0.5481411\",\n \"0.5477817\",\n \"0.5456438\",\n \"0.54505056\",\n \"0.54406124\",\n \"0.5432878\",\n \"0.5406113\",\n \"0.539998\",\n \"0.5395797\",\n \"0.5379206\",\n \"0.5378332\",\n \"0.53720737\",\n \"0.53708524\",\n \"0.5355402\",\n \"0.5337946\",\n \"0.53300333\",\n \"0.5325737\",\n \"0.531873\",\n \"0.53164613\",\n \"0.5313864\",\n \"0.5309509\",\n \"0.5304274\",\n \"0.5304168\",\n \"0.5304034\",\n \"0.52983505\",\n \"0.5297235\",\n \"0.5292266\",\n \"0.5287181\",\n \"0.5279643\",\n \"0.5278626\",\n \"0.5276851\",\n \"0.5273194\",\n \"0.5272679\",\n \"0.52642703\",\n \"0.52595025\",\n \"0.5257198\",\n \"0.5257051\",\n \"0.5242351\",\n \"0.5240013\",\n \"0.5232631\",\n \"0.5220942\",\n \"0.5215497\",\n \"0.5207305\"\n]","isConversational":false},"document_score":{"kind":"string","value":"0.0"},"document_rank":{"kind":"string","value":"-1"}}},{"rowIdx":75386,"cells":{"query":{"kind":"string","value":"Returns a list with the order traversal of elements in the tree Uses a strategy type pattern with parameters strategy = {PRE,POST,IN} For respective order traversals"},"document":{"kind":"string","value":"def RecursiveTraversal(self, root: TreeNode, strategy: str) -> List[int]:\n\n result = []\n if (root):\n if strategy == 'POST':\n if (root.left is not None):\n result = result + self.RecursiveTraversal(root.left,strategy)\n if (root.right is not None):\n result = result + self.RecursiveTraversal(root.right,strategy)\n result.append(root.val)\n elif strategy == 'PRE':\n result.append(root.val)\n if (root.left is not None):\n result = result + self.RecursiveTraversal(root.left,strategy)\n if (root.right is not None):\n result = result + self.RecursiveTraversal(root.right,strategy)\n elif strategy == 'IN':\n if (root.left is not None):\n result = result + self.RecursiveTraversal(root.left,strategy)\n result.append(root.val)\n if (root.right is not None):\n result = result + self.RecursiveTraversal(root.right,strategy)\n return 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 traverse_postorder(operation):\n\n nodes_postorder = []\n def recurse(node):\n if isinstance(node, Operation):\n for input_node in node.input_nodes:\n recurse(input_node)\n nodes_postorder.append(node)\n\n recurse(operation)\n return nodes_postorder","def traverse_postorder(operation):\n\n nodes_postorder = []\n def recurse(node):\n if isinstance(node, Operation):\n for input_node in node.input_nodes:\n recurse(input_node)\n nodes_postorder.append(node)\n\n recurse(operation)\n return nodes_postorder","def pre_order_traversal(self):\n\n elements = []\n\n ##visit base node\n elements.append(self.data)\n\n ##visit left tree\n if self.left:\n elements += self.left.pre_order_traversal()\n\n #visit right tree\n if self.right:\n elements += self.right.pre_order_traversal()\n\n return elements","def postorder(self):\n\n traversal = []\n self.postorder_helper(self.root, traversal)\n return traversal","def in_order(node):\n result = []\n if node is None:\n return result\n result.extend(in_order(node._lesser))\n result.append(node.value)\n result.extend(in_order(node._greater))\n return result","def StackBasedTraversal(self,root: TreeNode, strategy: str) -> List[int]:\n if(not root):\n return\n\n ans = []\n s1,s2 = [],[]\n s1.append(root)\n\n while(s1):\n x = s1[-1]\n s1.pop()\n s2.append(x)\n\n if(x.left):\n s1.append(x.left)\n\n if(x.right):\n s1.append(x.right)\n\n while(s2):\n y = s2[-1]\n s2.pop()\n ans.append(y.val)\n\n return ans","def inorder(self):\n\n traversal = []\n self.inorder_helper(self.root, traversal)\n return traversal","def in_order_traversal(self):\n elements = []\n\n #visit left tree\n if self.left:\n elements += self.left.in_order_traversal()\n\n #visit base node\n elements.append(self.data)\n\n #visit right tree\n if self.right:\n elements += self.right.in_order_traversal()\n\n return elements","def inorder(self):\n if len(self.children) == 0:\n return ['%s' % self.val]\n params = (' , ').join([' <N> '] * len(self.children))\n inorder = ['%s ( %s ) ' % (self.val, params)]\n for child in self.children:\n inorder += child.inorder()\n\n return inorder","def preorder(self):\n\n traversal = []\n self.preorder_helper(self.root, traversal)\n return traversal","def post_order(self):\n try:\n if not self.root:\n return \"the tree is empty!\"\n else:\n output = []\n\n def order_tree(node):\n if node.left:\n order_tree(node.left)\n if node.right:\n order_tree(node.right)\n nonlocal output\n output += [node.value]\n return output\n final_out = order_tree(self.root)\n return final_out\n except:\n print(\"something went wrong please try again\")","def in_order(self):\n try:\n if not self.root:\n return \"the tree is empty!\"\n else:\n output = []\n\n def order_tree(node):\n if node.left:\n order_tree(node.left)\n nonlocal output\n output += [node.value]\n if node.right:\n order_tree(node.right)\n return output\n return order_tree(self.root)\n except:\n print(\"something went wrong please try again\")","def getNodes(self, strategy = 'DEPTH-FIRST'):\n nodes = []\n queue = [self.root]\n while len(queue) > 0:\n node = queue.pop()\n nodes.append(node)\n # if strategy.upper().startswith('DEPTH'):\n if not node.isLeaf():\n queue.extend(node.children)\n return nodes","def list_postorder(t: Tree) -> list:\n if t.value is None:\n return []\n # elif t.children == []:\n # return [t.value]\n else:\n return sum([list_postorder(s) for s in t.children], []) + [t.value]\n\n # Version 2\n # if t.value is None:\n # return []\n # else:\n # post = []\n # for s in t.children:\n # post.extend(list_postorder(s))\n # return post + [t.value]","def in_order_traverse(self):\n result = []\n\n if not self.node:\n return result\n\n result.extend(self.node.left.in_order_traverse())\n result.append(self.node.key)\n result.extend(self.node.right.in_order_traverse())\n return result","def _inorder_traverse_to_list_helper(self, node):\n\t\tl = []\n\t\tif (node.lchild()):\n\t\t\tl += self._inorder_traverse_to_list_helper(node.lchild())\n\t\tl.append(node.value())\n\t\tif (node.rchild()):\n\t\t\tl += self._inorder_traverse_to_list_helper(node.rchild())\n\t\treturn l","def _preorder_traverse_to_list_helper(self, node, depth):\n\t\t#visit node\n\t\tl = []\n\t\tif (node):\n\t\t\tl.append(node.value())\n\t\telse:\n\t\t\tl.append(None)\n\n\t\t#anon function for this thing\n\t\tfakechild = lambda:self._preorder_traverse_to_list_helper(None, depth + 1)\n\n\t\t#call on children\n\t\tif (node):\n\t\t\tif (node.lchild()):\n\t\t\t\tl += self._preorder_traverse_to_list_helper(node.lchild(), depth + 1)\n\t\t\telse:\n\t\t\t\tif (depth < self._depth):\n\t\t\t\t\t#recurse with None for empty children (lchild)\n\t\t\t\t\tl += fakechild()\n\t\t\tif (node.rchild()):\n\t\t\t\tl += self._preorder_traverse_to_list_helper(node.rchild(), depth + 1)\n\t\t\telse:\n\t\t\t\tif (depth < self._depth):\n\t\t\t\t\t#recurse with None for empty children (rchild)\n\t\t\t\t\tl += fakechild()\n\t\telse:\n\t\t\tif (depth < self._depth):\n\t\t\t\t#recurse with None for empty children (lchild) and (rchild)\n\t\t\t\t#l += fakechild() #need to call twice?\n\t\t\t\tl += fakechild()\n\t\treturn l","def get_ordered_steps(self, ignore_order: bool = True) -> List[Step]:\n if self.order and not ignore_order:\n edges = defaultdict(list)\n in_degree = defaultdict(int)\n for order in self.order:\n for before, after in order.get_ordered_pairs():\n edges[before].append(after)\n in_degree[after] += 1\n q = Queue()\n steps = {step.at_id: step for step in self.steps}\n for step in self.steps:\n if in_degree[step.at_id] == 0:\n q.put(step.at_id)\n ordered_steps = []\n while not q.empty():\n step_id = q.get()\n ordered_steps.append(steps[step_id])\n for edge in edges[step_id]:\n in_degree[edge] -= 1\n if in_degree[edge] == 0:\n q.put(edge)\n\n return ordered_steps\n else:\n return self.steps","def Trees_preOrder_traversal():\n # Python2 ported to Python3 via 2to3-3.7\n # URL:https://www.hackerrank.com/challenges/tree-preorder-traversal/problem\n def preOrder(root):\n # inorder: left root right\n # preorder: root, left, right 5,3,1,4,7,6,8\n # postorder: left,right, root\n # 5\n # 3 7\n # 1 4 6 8\n out = []\n to_proccess = [root]\n while to_proccess:\n node = to_proccess.pop()\n out.append(node.info)\n for child in [node.right, node.left]:\n if child:\n to_proccess.append(child)\n print(\" \".join(map(str, out)))\n\n def preOrder_recursive(root):\n def _preOrder(node):\n out = []\n out.append(node.info)\n for child in [node.left, node.right]:\n if child:\n out.extend(_preOrder(child))\n return out\n print(\" \".join(map(str, _preOrder(root))))","def preorder_traverse_to_list(self):\n\t\tif (not self.root()):\n\t\t\treturn None\n\t\tself._synchronize_attributes()\n\t\treturn self._preorder_traverse_to_list_helper(self.root(), 1)","def tree_to_list_preorder(T):\n return (T.label, ) + \\\n reduce(add, map(tree_to_list_preorder, T, ()))","def post_order_traversal(self, root):\n\n def postorder_traversal_helper(root):\n if root:\n postorder_traversal_helper(root.left)\n postorder_traversal_helper(root.right)\n result.append(root.data)\n\n result = []\n postorder_traversal_helper(root)\n return result","def get_level_order(self, node, more=None):\n if node is not None:\n if more is None:\n more = []\n more += [n for n in node.children if n is not None]\n yield node\n if more:\n for data in self.get_level_order(more[0], more[1:]):\n yield data","def postorder_iteratively(tree):\n visited = []\n path = [tree]\n result = []\n while len(path) > 0:\n t = path[-1]\n if t not in visited:\n if t.right:\n path = path + [t.right]\n if t.left:\n path = path + [t.left]\n visited.append(t)\n else:\n path.pop()\n result.append(t.data)\n\n return result","def postorder(G):\n for v,w,edgetype in search(G):\n if edgetype is reverse:\n yield w","def nodes(self, root=None, order=\"preorder\"):\n methods = {\n \"preorder\": self._preorder_traversal,\n \"inorder\": self._inorder_traversal,\n \"postorder\": self._postorder_traversal,\n \"levelorder\": self._levelorder_traversal,\n \"breadthfirst\": self._levelorder_traversal,\n \"timeasc\": self._timeasc_traversal,\n \"timedesc\": self._timedesc_traversal,\n \"minlex_postorder\": self._minlex_postorder_traversal,\n }\n try:\n iterator = methods[order]\n except KeyError:\n raise ValueError(f\"Traversal ordering '{order}' not supported\")\n\n root = -1 if root is None else root\n return iterator(root)","def pre_order(self):\n def walk(root):\n self.pre_values.append(root.value)\n\n if root.left:\n walk(root.left)\n \n if root.right:\n walk(root.right)\n \n walk(self.root)\n return self.pre_values","def traverse(self, traversal_type):\n if traversal_type == \"preorder\":\n print(self.preorder_recursive(self.root, \"\").rstrip(\"--\"))\n elif traversal_type == \"inorder\":\n print(self.inorder_recursive(self.root, \"\").rstrip(\"--\"))\n elif traversal_type == \"postorder\":\n print(self.postorder_recursive(self.root, \"\").rstrip(\"--\")) \n elif traversal_type == \"levelorder\":\n print(self.levelorder_bfs())","def pre_order_traversal(self, root):\n\n def pre_order_traversal_helper(root):\n if root:\n result.append(root.data)\n pre_order_traversal_helper(root.left)\n pre_order_traversal_helper(root.right)\n\n result = []\n pre_order_traversal_helper(root)\n return result","def sorted_traversal(self):\n\t\tself.__sorted_traversal(self)","def build_order(self):\n seen = set()\n\n def _already_built(node):\n # visit only once\n if node in seen:\n return True\n seen.add(node)\n\n # prune if the result is already computed\n if node.output_ready:\n return True\n\n return False\n\n for target in self.targets:\n if target in seen:\n continue\n for node in target.postorder(prune_fn=_already_built):\n yield node","def post_order(self):\n def walk(root):\n\n if root.left:\n walk(root.left)\n\n if root.right:\n walk(root.right)\n\n self.post_values.append(root.value)\n \n walk(self.root)\n return self.post_values","def postorder(G):\n for v, w, edgetype in search(G):\n if edgetype is reverse:\n yield w","def generate_tree_postorder(node_lst, root_index):","def __travers_in_order(self, node):\n if node.left_child is not None:\n self.__travers_in_order(node.left_child)\n\n print(\"{}\".format(node.data))\n\n if node.right_child is not None:\n self.__travers_in_order(node.right_child)","def make_order(self, root):\n order = []\n if root and isinstance(root[0], dict):\n keys = set()\n for item in root:\n for key in item.keys():\n keys.add(key)\n for key in args.order or []:\n key = self.get_key(key, keys)\n keys.remove(key)\n order.append(key)\n order += sorted(list(keys))\n return order","def in_order(self):\n return (node for node in self.get_in_order(self.root))","def in_order_nodes(root):\n if root.get_left():\n for node in in_order_nodes(root.get_left()):\n yield node\n\n yield root\n\n if root.get_right():\n for node in in_order_nodes(root.get_right()):\n yield node","def levelorder(self):\n return (node for node in self.get_level_order(self.root))","def in_order_traversal(self, root):\n\n def in_order_traversal_helper(root):\n if root:\n in_order_traversal_helper(root.left)\n result.append(root.data)\n in_order_traversal_helper(root.right)\n\n result = []\n in_order_traversal_helper(root)\n return result","def fizzbuzztree(tree):\n tree.in_order(fizz_the_buzz)\n return tree","def post_order(self, verbose=False):\n if not self.root:\n return []\n alist = []\n self.pre_order_helper(self.root, alist, verbose)\n return alist","def inorder_traverse_to_list(self):\n\t\tif (not self.root()):\n\t\t\treturn None\n\t\tself._synchronize_attributes()\n\t\treturn self._inorder_traverse_to_list_helper(self.root())","def preorder_visit(t: Tree, act: Callable[[Tree], Any]) -> None:\n act(t)\n for child in t.children:\n preorder_visit(child, act)","def postorder(self):\n return (node for node in self.get_postorder(self.root))","def preorder(self,root)->list:\n\t\tres=[]\n\t\tif root:\n\t\t\tres.append(root.data)\n\t\t\tres=res+self.preorder(root.left)\n\t\t\tres=res+self.preorder(root.right)\n\t\treturn res","def order_dfs(self) -> List[Nodo]:\n # Nodos por buscar, es una pila\n pending: List[Nodo] = [self.root]\n # Nodos ya visitados\n visited: List[Nodo] = []\n\n # Mientras la pila tenga items\n while len(pending) > 0:\n # Procesar el primer elemento\n curr = pending.pop()\n visited.append(curr)\n\n # Agregar los hijos no visitados del nodo a la pila\n for child in reversed(curr.children):\n if child in visited:\n continue\n pending.append(child)\n\n return visited","def iter_traverse(language_node):\n out = []\n for element in language_node:\n if isinstance(element, LanguageNode):\n out.extend(iter_traverse(element))\n elif isinstance(element, LanguageToken):\n out.append(element)\n return out","def in_order_traversal(self):\n root = self.root\n self.traverse = self.in_order_traversal_node(root)\n return self.traverse","def list_preorder(t: Tree) -> list:\n if t.value is None:\n return []\n else:\n return [t.value] + sum([list_preorder(s) for s in t.children], [])\n\n # if t.value is None:\n # return []\n # elif t.children == []:\n # return [t.value]\n # else:\n # pre = [t.value]\n # for subtree in t.children:\n # pre.extend(list_preorder(subtree))\n # return pre","def postorder_traverse(self):\n\n keys = []\n\n if not self.node:\n return keys\n \n keys.extend(self.node.left.postorder_traverse())\n keys.extend(self.node.right.postorder_traverse())\n keys.append(self.node.vp.index)\n\n return keys","def test_postorder_traversal(depth_one_tree):\n testlist = []\n depth_one_tree.post_order(lambda x: testlist.append(x))\n assert str(testlist) == str([1, 2, 3, 4, 0])","def depth_node_ordering(start_node, end_nodes):\n ordered_list = []\n ordered_set = set()\n working_list = [start_node]\n while working_list != []:\n node = working_list.pop(0)\n if not node in ordered_set:\n ordered_set.add(node)\n ordered_list.append(node)\n if not is_leaf_node(node) and not node in end_nodes:\n for node_op in node.get_inputs():\n working_list.append(node_op)\n return ordered_list","def level_order_list(root):\n return_list = []\n queue = []\n queue.append(root)\n\n while 0 < len(queue):\n next_node = queue.pop(0)\n if next_node.get_left():\n queue.append(next_node.get_left())\n if next_node.get_right():\n queue.append(next_node.get_right())\n return_list.append(next_node)\n\n return return_list","def _iter_child_nodes_in_order(node):\n return _flatten_ast_nodes(_iter_child_nodes_in_order_internal_1(node))","def preorder(self):\n return (node for node in self.get_preorder(self.root))","def pre_order_helper(self, node, alist=[], verbose=False):\n if node:\n if verbose:\n print(node.data)\n alist.append(node)\n pre_order_helper(node.left, alist, verbose)\n pre_order_helper(node.right, alist, verbose)","def bfs(self):\n level = 0\n q = Queue()\n visit_order = list()\n node = self.get_root()\n q.enq( (node.get_value()[1],level) )\n while(len(q) > 0):\n node, level = q.deq()\n if node == None:\n visit_order.append( (\"<empty>\", level))\n continue\n visit_order.append( (node, level) )\n if type(node) == str:\n q.enq( (None, level +1) )\n q.enq( (None, level +1) )\n continue\n if node.has_left_child():\n q.enq( (node.get_left_child()[1], level +1 ))\n else:\n q.enq( (None, level +1) )\n\n if node.has_right_child():\n q.enq( (node.get_right_child()[1], level +1 ))\n else:\n q.enq( (None, level +1) )\n\n return visit_order","def in_order(self, verbose=False):\n if not self.root:\n return []\n alist = []\n self.in_order_helper(self.root, alist, verbose)\n return alist","def in_order(self):\n def walk(root):\n\n if root.left:\n walk(root.left)\n\n self.in_values.append(root.value)\n\n if root.right:\n walk(root.right)\n \n walk(self.root)\n return self.in_values","def _toposort_with_ordered_mech_tuples(self, data):\n result = []\n for dependency_set in toposort(data):\n d_iter = iter(dependency_set)\n result.extend(sorted(dependency_set, key=lambda item : next(d_iter).mechanism.name))\n return result","def postorder_iterator(node):\n for child in node.children:\n yield from postorder_iterator(child)\n yield node","def toposort(adj):\n # Memoize for visited vertex\n used = [0] * len(adj)\n order = []\n # write your code here\n # Traverse through each vertex\n for i in range(len(adj)):\n if not used[i]:\n # If not visited, run dfs\n dfs(adj, used, order, i)\n\n # Reverse the order list to show in descending order\n order.reverse()\n return order","def posisions(self):\n return self.preorder() # return entire preorder iteration","def levelOrder(self, root: 'Node') -> List[List[int]]:\n if not root: return []\n level = []\n waiting = []\n result = []\n level.append(root)\n while level:\n current = []\n while level:\n tmp = level.pop(0)\n if not tmp:\n continue\n current.append(tmp.val)\n waiting.append(tmp)\n if len(current) > 0:\n result.append(current)\n while waiting:\n tmp = waiting.pop(0)\n for ch in tmp.children:\n level.append(ch)\n return result","def inorder(self,root)->list:\n\t\tres=[]\n\t\tif root:\n\t\t\tres=self.inorder(root.left)\n\t\t\tres.append(root.data)\n\t\t\tres=res+self.inorder(root.right)\n\t\treturn res","def postorder(self,root)->list:\n\t\tres=[]\n\t\tif root:\n\t\t\tres=self.postorder(root.left)\n\t\t\tres=res+self.postorder(root.right)\n\t\t\tres.append(root.data)\n\t\treturn res","def _topological_sort_dfs(self) -> Iterator[BaseTransaction]:\n raise NotImplementedError","def preorder(self):\n assert self.is_canonical\n # Names of visited nodes\n visited = set()\n # Names of nodes to visit\n worklist = [LANGUAGE]\n\n result = []\n while len(worklist) > 0:\n successors = []\n for rule_name in worklist:\n if rule_name in visited:\n continue\n result.append(rule_name)\n visited.add(rule_name)\n\n rule = self.rules[rule_name].as_container()\n for rhs in rule:\n phrase = rhs.as_container()\n # Note: this tolerates duplicates among siblings.\n successors.extend([x.content for x in phrase if x.is_symbol_name() and x.content not in visited])\n worklist = successors\n return result","def preorder_iterator(node):\n yield node\n for child in node.children:\n yield from preorder_iterator(child)","def post_order_helper(self, node, alist=[], verbose=False):\n if node:\n pre_order_helper(node.left, alist, verbose)\n pre_order_helper(node.right, alist, verbose)\n if verbose:\n print(node.data)\n alist.append(node)","def inorder(self):\n if not self.is_empty():\n for p in self._subtree_inorder(self.root()):\n yield p","def topological_sort_generator(self):\n from sage.graphs.linearextensions import LinearExtensions\n try:\n return LinearExtensions(self).list()\n except TypeError:\n raise TypeError('Digraph is not acyclic; there is no topological sort (or there was an error in sage/graphs/linearextensions.py).')","def traverse(self, prt):\n lst = []\n for e in self.items:\n if(e is not None):\n prt(e[1])\n lst.append(e[1])\n return lst","def execution_order(self) -> typing.Iterator:\n return self.execution_order_strategy_class(self._steps)","def preorder_traverse(self):\n \n keys = []\n\n if not self.node:\n return keys\n \n keys.append(self.node.vp.index)\n keys.extend(self.node.left.preorder_traverse())\n keys.extend(self.node.right.preorder_traverse())\n\n return keys","def in_order_traverse(self):\n return self.recur_in_order_traverse(self._root)","def preorder(root: Node):\n return [root.data] + preorder(root.left) + preorder(root.right) if root else []","def pre_order_nodes(root):\n yield root\n\n if root.get_left():\n for node in pre_order_nodes(root.get_left()):\n yield node\n\n if root.get_right():\n for node in pre_order_nodes(root.get_right()):\n yield node","def _get_tree(root: spacy.tokens.Token, depth: int, token_filter: types.FunctionType) -> [spacy.tokens.Token]:\n if depth == 0:\n return [root] if token_filter(root) else []\n\n result = []\n # for tokens on the left of the root, whose head is root\n for child in filter(token_filter, root.lefts):\n result += SpacyEventExtractor._get_tree(child, depth - 1, token_filter)\n result.append(root)\n # for tokens on the right of the root, whose head is root\n for child in filter(token_filter, root.rights):\n result += SpacyEventExtractor._get_tree(child, depth - 1, token_filter)\n return result","def inorder(self):\n if not self.is_empty():\n for p in self._sub_tree_inorder(self.root()):\n yield p","def get_order(parent_level):\n subset_lookup = {}\n for entry in [d for d in data if d[0] == parent_level]:\n start = entry[1]\n if start not in subset_lookup:\n subset_lookup[start] = []\n subset_lookup[start].append(entry)\n print(entry)\n pprint(subset_lookup)\n processing_order = []\n for items in reversed(list(subset_lookup.values())):\n for item in items:\n processing_order.append(item)\n processing_order.extend(get_order(item[2]))\n return processing_order","def preorder(self, root):\n if not root:\n return []\n nodes, stack = [], [root]\n while stack:\n node = stack.pop()\n nodes.append(node.val)\n for child in reversed(node.children):\n stack.append(child)\n return nodes","def level_order_iterator(node):\n nodes_ = [node]\n while len(nodes_):\n for node_ in nodes_:\n yield node_\n nodes_ = list(chain(*[node_.children for node_ in nodes_]))","def positions(self):\n return self.preorder() # return entire preorder iteration","def _subtree_postorder(self, p):\n for c in self.children(p):\n for pos in self._subtree_postorder(c):\n yield pos\n yield p","def _subtree_postorder(self, p):\n for c in self.children(p): # for each child c\n for other in self._subtree_postorder(c): # do postorder of c's subtree\n yield other # yielding each to our caller\n yield p # visit p after its subtrees","def pre_order(self):\n for node_data in self._pre_order_helper(self._root):\n yield node_data","def test_preorder_traversal(depth_one_tree):\n testlist = []\n depth_one_tree.pre_order(lambda x: testlist.append(x))\n assert str(testlist) == str([0, 1, 2, 3, 4])","def full_sort(orders: list or tuple) -> list:\r\n\r\n # We set up the topo sort to include the \"before\" and \"after\" objects\r\n # as another element in the sort. These are injected into the to-be\r\n # sorted list, and removed at the end.\r\n\r\n # Setup the topo sort.\r\n input_list = list(orders)\r\n\r\n def input_sorter(a, b) -> int:\r\n if isinstance(a, str):\r\n if isinstance(b, str):\r\n return (a == b and 0) or (a < b and -1) or 1\r\n else:\r\n # Orders always come before strings\r\n return 1\r\n elif isinstance(b, str):\r\n return -1\r\n else:\r\n assert isinstance(a, Order)\r\n assert isinstance(b, Order)\r\n return a - b\r\n\r\n is_visiting = {}\r\n visiting_stack = []\r\n\r\n depends = {}\r\n\r\n # Translate before / after into a strict dependency.\r\n for order in orders:\r\n assert isinstance(order, Order)\r\n depends[order] = list(order.occurs_after)\r\n # We don't need to capture the after in our dependency list -\r\n # if everything marks itself as being after something, but nothing\r\n # is before it, then it isn't necessary for the ordering.\r\n for name in order.occurs_before:\r\n if name in depends:\r\n depends[name].append(order)\r\n else:\r\n depends[name] = [order]\r\n input_list.append(name)\r\n\r\n # Sort the \"natural\" order (non-dependency check)\r\n input_list.sort(key=functools.cmp_to_key(input_sorter))\r\n for dep_list in depends.values():\r\n dep_list.sort(key=functools.cmp_to_key(input_sorter))\r\n\r\n # For each order item, run a Depth First Search-based sort\r\n # By putting the orders in a numbered sort first, we urge the\r\n # search to maintain the correct order.\r\n\r\n sorted_list = []\r\n for val in input_list:\r\n if val not in is_visiting:\r\n Order.__tsort(val, depends, is_visiting,\r\n visiting_stack, sorted_list)\r\n elif is_visiting[val]:\r\n # FIXME make this a warning? Definitely need better debugging.\r\n raise Exception(\"cyclic dependency in orders (FIXME add better debugging)\")\r\n\r\n ret = []\r\n for val in sorted_list:\r\n if isinstance(val, Order):\r\n ret.append(val)\r\n return ret","def post_order_nodes(root):\n if root.get_left():\n for node in post_order_nodes(root.get_left()):\n yield node\n\n if root.get_right():\n for node in post_order_nodes(root.get_right()):\n yield node\n\n yield root","def pre_order(self, verbose=False):\n if not self.root:\n return []\n alist = []\n self.pre_order_helper(self.root, alist, verbose)\n return alist","def preorderTraversal(self, root: TreeNode) -> List[int]:\n def preorder(root,seq):\n if root is None:\n return seq\n seq.append(root.val)\n preorder(root.left,seq)\n preorder(root.right,seq)\n return seq\n \n prelist= []\n return preorder(root,prelist)","def get_ordered_nodes(self):\n nodes = []\n self.build_nodes_list(self.root, nodes)\n return nodes","def get_order_from_tree(ids, tree_text):\r\n tree = parse_newick(tree_text, PhyloNode)\r\n ordered_ids = []\r\n for tip in tree.iterTips():\r\n if tip.Name in ids:\r\n ordered_ids.append(tip.Name)\r\n return names_to_indices(ids, ordered_ids)","def pre_order(self):\n stack = []\n node = self\n while stack or node:\n if node:\n yield node.val\n stack.append(node)\n node = node.left\n else:\n node = stack.pop()\n node = node.right","def post_order(self):\n if self.left is not None:\n for i in self.left.post_order():\n yield i\n if self.right is not None:\n for i in self.right.post_order():\n yield i\n if self.val is not None:\n yield self.val","def postorderTraversal(self, root: TreeNode) -> List[int]:\n stack = []\n postorder = []\n stack.append(root)\n\n while stack:\n node = stack.pop()\n if not node: continue\n right = node.right\n left = node.left\n if not right and not left:\n postorder.append(node.val)\n else:\n node.left = None\n node.right = None\n stack.append(node)\n stack.append(right)\n stack.append(left)\n return postorder","def _walk_ast_nodes_in_order(node):\n # The implementation is basically the same as ``ast.walk``, but:\n # 1. Use a stack instead of a deque. (I.e., depth-first search instead\n # of breadth-first search.)\n # 2. Use _iter_child_nodes_in_order instead of ``ast.iter_child_nodes``.\n todo = [node]\n while todo:\n node = todo.pop()\n yield node\n todo.extend(reversed(list(_iter_child_nodes_in_order(node))))","def post_order(self):\n for node_data in self._post_order_helper(self._root):\n yield node_data"],"string":"[\n \"def traverse_postorder(operation):\\n\\n nodes_postorder = []\\n def recurse(node):\\n if isinstance(node, Operation):\\n for input_node in node.input_nodes:\\n recurse(input_node)\\n nodes_postorder.append(node)\\n\\n recurse(operation)\\n return nodes_postorder\",\n \"def traverse_postorder(operation):\\n\\n nodes_postorder = []\\n def recurse(node):\\n if isinstance(node, Operation):\\n for input_node in node.input_nodes:\\n recurse(input_node)\\n nodes_postorder.append(node)\\n\\n recurse(operation)\\n return nodes_postorder\",\n \"def pre_order_traversal(self):\\n\\n elements = []\\n\\n ##visit base node\\n elements.append(self.data)\\n\\n ##visit left tree\\n if self.left:\\n elements += self.left.pre_order_traversal()\\n\\n #visit right tree\\n if self.right:\\n elements += self.right.pre_order_traversal()\\n\\n return elements\",\n \"def postorder(self):\\n\\n traversal = []\\n self.postorder_helper(self.root, traversal)\\n return traversal\",\n \"def in_order(node):\\n result = []\\n if node is None:\\n return result\\n result.extend(in_order(node._lesser))\\n result.append(node.value)\\n result.extend(in_order(node._greater))\\n return result\",\n \"def StackBasedTraversal(self,root: TreeNode, strategy: str) -> List[int]:\\n if(not root):\\n return\\n\\n ans = []\\n s1,s2 = [],[]\\n s1.append(root)\\n\\n while(s1):\\n x = s1[-1]\\n s1.pop()\\n s2.append(x)\\n\\n if(x.left):\\n s1.append(x.left)\\n\\n if(x.right):\\n s1.append(x.right)\\n\\n while(s2):\\n y = s2[-1]\\n s2.pop()\\n ans.append(y.val)\\n\\n return ans\",\n \"def inorder(self):\\n\\n traversal = []\\n self.inorder_helper(self.root, traversal)\\n return traversal\",\n \"def in_order_traversal(self):\\n elements = []\\n\\n #visit left tree\\n if self.left:\\n elements += self.left.in_order_traversal()\\n\\n #visit base node\\n elements.append(self.data)\\n\\n #visit right tree\\n if self.right:\\n elements += self.right.in_order_traversal()\\n\\n return elements\",\n \"def inorder(self):\\n if len(self.children) == 0:\\n return ['%s' % self.val]\\n params = (' , ').join([' <N> '] * len(self.children))\\n inorder = ['%s ( %s ) ' % (self.val, params)]\\n for child in self.children:\\n inorder += child.inorder()\\n\\n return inorder\",\n \"def preorder(self):\\n\\n traversal = []\\n self.preorder_helper(self.root, traversal)\\n return traversal\",\n \"def post_order(self):\\n try:\\n if not self.root:\\n return \\\"the tree is empty!\\\"\\n else:\\n output = []\\n\\n def order_tree(node):\\n if node.left:\\n order_tree(node.left)\\n if node.right:\\n order_tree(node.right)\\n nonlocal output\\n output += [node.value]\\n return output\\n final_out = order_tree(self.root)\\n return final_out\\n except:\\n print(\\\"something went wrong please try again\\\")\",\n \"def in_order(self):\\n try:\\n if not self.root:\\n return \\\"the tree is empty!\\\"\\n else:\\n output = []\\n\\n def order_tree(node):\\n if node.left:\\n order_tree(node.left)\\n nonlocal output\\n output += [node.value]\\n if node.right:\\n order_tree(node.right)\\n return output\\n return order_tree(self.root)\\n except:\\n print(\\\"something went wrong please try again\\\")\",\n \"def getNodes(self, strategy = 'DEPTH-FIRST'):\\n nodes = []\\n queue = [self.root]\\n while len(queue) > 0:\\n node = queue.pop()\\n nodes.append(node)\\n # if strategy.upper().startswith('DEPTH'):\\n if not node.isLeaf():\\n queue.extend(node.children)\\n return nodes\",\n \"def list_postorder(t: Tree) -> list:\\n if t.value is None:\\n return []\\n # elif t.children == []:\\n # return [t.value]\\n else:\\n return sum([list_postorder(s) for s in t.children], []) + [t.value]\\n\\n # Version 2\\n # if t.value is None:\\n # return []\\n # else:\\n # post = []\\n # for s in t.children:\\n # post.extend(list_postorder(s))\\n # return post + [t.value]\",\n \"def in_order_traverse(self):\\n result = []\\n\\n if not self.node:\\n return result\\n\\n result.extend(self.node.left.in_order_traverse())\\n result.append(self.node.key)\\n result.extend(self.node.right.in_order_traverse())\\n return result\",\n \"def _inorder_traverse_to_list_helper(self, node):\\n\\t\\tl = []\\n\\t\\tif (node.lchild()):\\n\\t\\t\\tl += self._inorder_traverse_to_list_helper(node.lchild())\\n\\t\\tl.append(node.value())\\n\\t\\tif (node.rchild()):\\n\\t\\t\\tl += self._inorder_traverse_to_list_helper(node.rchild())\\n\\t\\treturn l\",\n \"def _preorder_traverse_to_list_helper(self, node, depth):\\n\\t\\t#visit node\\n\\t\\tl = []\\n\\t\\tif (node):\\n\\t\\t\\tl.append(node.value())\\n\\t\\telse:\\n\\t\\t\\tl.append(None)\\n\\n\\t\\t#anon function for this thing\\n\\t\\tfakechild = lambda:self._preorder_traverse_to_list_helper(None, depth + 1)\\n\\n\\t\\t#call on children\\n\\t\\tif (node):\\n\\t\\t\\tif (node.lchild()):\\n\\t\\t\\t\\tl += self._preorder_traverse_to_list_helper(node.lchild(), depth + 1)\\n\\t\\t\\telse:\\n\\t\\t\\t\\tif (depth < self._depth):\\n\\t\\t\\t\\t\\t#recurse with None for empty children (lchild)\\n\\t\\t\\t\\t\\tl += fakechild()\\n\\t\\t\\tif (node.rchild()):\\n\\t\\t\\t\\tl += self._preorder_traverse_to_list_helper(node.rchild(), depth + 1)\\n\\t\\t\\telse:\\n\\t\\t\\t\\tif (depth < self._depth):\\n\\t\\t\\t\\t\\t#recurse with None for empty children (rchild)\\n\\t\\t\\t\\t\\tl += fakechild()\\n\\t\\telse:\\n\\t\\t\\tif (depth < self._depth):\\n\\t\\t\\t\\t#recurse with None for empty children (lchild) and (rchild)\\n\\t\\t\\t\\t#l += fakechild() #need to call twice?\\n\\t\\t\\t\\tl += fakechild()\\n\\t\\treturn l\",\n \"def get_ordered_steps(self, ignore_order: bool = True) -> List[Step]:\\n if self.order and not ignore_order:\\n edges = defaultdict(list)\\n in_degree = defaultdict(int)\\n for order in self.order:\\n for before, after in order.get_ordered_pairs():\\n edges[before].append(after)\\n in_degree[after] += 1\\n q = Queue()\\n steps = {step.at_id: step for step in self.steps}\\n for step in self.steps:\\n if in_degree[step.at_id] == 0:\\n q.put(step.at_id)\\n ordered_steps = []\\n while not q.empty():\\n step_id = q.get()\\n ordered_steps.append(steps[step_id])\\n for edge in edges[step_id]:\\n in_degree[edge] -= 1\\n if in_degree[edge] == 0:\\n q.put(edge)\\n\\n return ordered_steps\\n else:\\n return self.steps\",\n \"def Trees_preOrder_traversal():\\n # Python2 ported to Python3 via 2to3-3.7\\n # URL:https://www.hackerrank.com/challenges/tree-preorder-traversal/problem\\n def preOrder(root):\\n # inorder: left root right\\n # preorder: root, left, right 5,3,1,4,7,6,8\\n # postorder: left,right, root\\n # 5\\n # 3 7\\n # 1 4 6 8\\n out = []\\n to_proccess = [root]\\n while to_proccess:\\n node = to_proccess.pop()\\n out.append(node.info)\\n for child in [node.right, node.left]:\\n if child:\\n to_proccess.append(child)\\n print(\\\" \\\".join(map(str, out)))\\n\\n def preOrder_recursive(root):\\n def _preOrder(node):\\n out = []\\n out.append(node.info)\\n for child in [node.left, node.right]:\\n if child:\\n out.extend(_preOrder(child))\\n return out\\n print(\\\" \\\".join(map(str, _preOrder(root))))\",\n \"def preorder_traverse_to_list(self):\\n\\t\\tif (not self.root()):\\n\\t\\t\\treturn None\\n\\t\\tself._synchronize_attributes()\\n\\t\\treturn self._preorder_traverse_to_list_helper(self.root(), 1)\",\n \"def tree_to_list_preorder(T):\\n return (T.label, ) + \\\\\\n reduce(add, map(tree_to_list_preorder, T, ()))\",\n \"def post_order_traversal(self, root):\\n\\n def postorder_traversal_helper(root):\\n if root:\\n postorder_traversal_helper(root.left)\\n postorder_traversal_helper(root.right)\\n result.append(root.data)\\n\\n result = []\\n postorder_traversal_helper(root)\\n return result\",\n \"def get_level_order(self, node, more=None):\\n if node is not None:\\n if more is None:\\n more = []\\n more += [n for n in node.children if n is not None]\\n yield node\\n if more:\\n for data in self.get_level_order(more[0], more[1:]):\\n yield data\",\n \"def postorder_iteratively(tree):\\n visited = []\\n path = [tree]\\n result = []\\n while len(path) > 0:\\n t = path[-1]\\n if t not in visited:\\n if t.right:\\n path = path + [t.right]\\n if t.left:\\n path = path + [t.left]\\n visited.append(t)\\n else:\\n path.pop()\\n result.append(t.data)\\n\\n return result\",\n \"def postorder(G):\\n for v,w,edgetype in search(G):\\n if edgetype is reverse:\\n yield w\",\n \"def nodes(self, root=None, order=\\\"preorder\\\"):\\n methods = {\\n \\\"preorder\\\": self._preorder_traversal,\\n \\\"inorder\\\": self._inorder_traversal,\\n \\\"postorder\\\": self._postorder_traversal,\\n \\\"levelorder\\\": self._levelorder_traversal,\\n \\\"breadthfirst\\\": self._levelorder_traversal,\\n \\\"timeasc\\\": self._timeasc_traversal,\\n \\\"timedesc\\\": self._timedesc_traversal,\\n \\\"minlex_postorder\\\": self._minlex_postorder_traversal,\\n }\\n try:\\n iterator = methods[order]\\n except KeyError:\\n raise ValueError(f\\\"Traversal ordering '{order}' not supported\\\")\\n\\n root = -1 if root is None else root\\n return iterator(root)\",\n \"def pre_order(self):\\n def walk(root):\\n self.pre_values.append(root.value)\\n\\n if root.left:\\n walk(root.left)\\n \\n if root.right:\\n walk(root.right)\\n \\n walk(self.root)\\n return self.pre_values\",\n \"def traverse(self, traversal_type):\\n if traversal_type == \\\"preorder\\\":\\n print(self.preorder_recursive(self.root, \\\"\\\").rstrip(\\\"--\\\"))\\n elif traversal_type == \\\"inorder\\\":\\n print(self.inorder_recursive(self.root, \\\"\\\").rstrip(\\\"--\\\"))\\n elif traversal_type == \\\"postorder\\\":\\n print(self.postorder_recursive(self.root, \\\"\\\").rstrip(\\\"--\\\")) \\n elif traversal_type == \\\"levelorder\\\":\\n print(self.levelorder_bfs())\",\n \"def pre_order_traversal(self, root):\\n\\n def pre_order_traversal_helper(root):\\n if root:\\n result.append(root.data)\\n pre_order_traversal_helper(root.left)\\n pre_order_traversal_helper(root.right)\\n\\n result = []\\n pre_order_traversal_helper(root)\\n return result\",\n \"def sorted_traversal(self):\\n\\t\\tself.__sorted_traversal(self)\",\n \"def build_order(self):\\n seen = set()\\n\\n def _already_built(node):\\n # visit only once\\n if node in seen:\\n return True\\n seen.add(node)\\n\\n # prune if the result is already computed\\n if node.output_ready:\\n return True\\n\\n return False\\n\\n for target in self.targets:\\n if target in seen:\\n continue\\n for node in target.postorder(prune_fn=_already_built):\\n yield node\",\n \"def post_order(self):\\n def walk(root):\\n\\n if root.left:\\n walk(root.left)\\n\\n if root.right:\\n walk(root.right)\\n\\n self.post_values.append(root.value)\\n \\n walk(self.root)\\n return self.post_values\",\n \"def postorder(G):\\n for v, w, edgetype in search(G):\\n if edgetype is reverse:\\n yield w\",\n \"def generate_tree_postorder(node_lst, root_index):\",\n \"def __travers_in_order(self, node):\\n if node.left_child is not None:\\n self.__travers_in_order(node.left_child)\\n\\n print(\\\"{}\\\".format(node.data))\\n\\n if node.right_child is not None:\\n self.__travers_in_order(node.right_child)\",\n \"def make_order(self, root):\\n order = []\\n if root and isinstance(root[0], dict):\\n keys = set()\\n for item in root:\\n for key in item.keys():\\n keys.add(key)\\n for key in args.order or []:\\n key = self.get_key(key, keys)\\n keys.remove(key)\\n order.append(key)\\n order += sorted(list(keys))\\n return order\",\n \"def in_order(self):\\n return (node for node in self.get_in_order(self.root))\",\n \"def in_order_nodes(root):\\n if root.get_left():\\n for node in in_order_nodes(root.get_left()):\\n yield node\\n\\n yield root\\n\\n if root.get_right():\\n for node in in_order_nodes(root.get_right()):\\n yield node\",\n \"def levelorder(self):\\n return (node for node in self.get_level_order(self.root))\",\n \"def in_order_traversal(self, root):\\n\\n def in_order_traversal_helper(root):\\n if root:\\n in_order_traversal_helper(root.left)\\n result.append(root.data)\\n in_order_traversal_helper(root.right)\\n\\n result = []\\n in_order_traversal_helper(root)\\n return result\",\n \"def fizzbuzztree(tree):\\n tree.in_order(fizz_the_buzz)\\n return tree\",\n \"def post_order(self, verbose=False):\\n if not self.root:\\n return []\\n alist = []\\n self.pre_order_helper(self.root, alist, verbose)\\n return alist\",\n \"def inorder_traverse_to_list(self):\\n\\t\\tif (not self.root()):\\n\\t\\t\\treturn None\\n\\t\\tself._synchronize_attributes()\\n\\t\\treturn self._inorder_traverse_to_list_helper(self.root())\",\n \"def preorder_visit(t: Tree, act: Callable[[Tree], Any]) -> None:\\n act(t)\\n for child in t.children:\\n preorder_visit(child, act)\",\n \"def postorder(self):\\n return (node for node in self.get_postorder(self.root))\",\n \"def preorder(self,root)->list:\\n\\t\\tres=[]\\n\\t\\tif root:\\n\\t\\t\\tres.append(root.data)\\n\\t\\t\\tres=res+self.preorder(root.left)\\n\\t\\t\\tres=res+self.preorder(root.right)\\n\\t\\treturn res\",\n \"def order_dfs(self) -> List[Nodo]:\\n # Nodos por buscar, es una pila\\n pending: List[Nodo] = [self.root]\\n # Nodos ya visitados\\n visited: List[Nodo] = []\\n\\n # Mientras la pila tenga items\\n while len(pending) > 0:\\n # Procesar el primer elemento\\n curr = pending.pop()\\n visited.append(curr)\\n\\n # Agregar los hijos no visitados del nodo a la pila\\n for child in reversed(curr.children):\\n if child in visited:\\n continue\\n pending.append(child)\\n\\n return visited\",\n \"def iter_traverse(language_node):\\n out = []\\n for element in language_node:\\n if isinstance(element, LanguageNode):\\n out.extend(iter_traverse(element))\\n elif isinstance(element, LanguageToken):\\n out.append(element)\\n return out\",\n \"def in_order_traversal(self):\\n root = self.root\\n self.traverse = self.in_order_traversal_node(root)\\n return self.traverse\",\n \"def list_preorder(t: Tree) -> list:\\n if t.value is None:\\n return []\\n else:\\n return [t.value] + sum([list_preorder(s) for s in t.children], [])\\n\\n # if t.value is None:\\n # return []\\n # elif t.children == []:\\n # return [t.value]\\n # else:\\n # pre = [t.value]\\n # for subtree in t.children:\\n # pre.extend(list_preorder(subtree))\\n # return pre\",\n \"def postorder_traverse(self):\\n\\n keys = []\\n\\n if not self.node:\\n return keys\\n \\n keys.extend(self.node.left.postorder_traverse())\\n keys.extend(self.node.right.postorder_traverse())\\n keys.append(self.node.vp.index)\\n\\n return keys\",\n \"def test_postorder_traversal(depth_one_tree):\\n testlist = []\\n depth_one_tree.post_order(lambda x: testlist.append(x))\\n assert str(testlist) == str([1, 2, 3, 4, 0])\",\n \"def depth_node_ordering(start_node, end_nodes):\\n ordered_list = []\\n ordered_set = set()\\n working_list = [start_node]\\n while working_list != []:\\n node = working_list.pop(0)\\n if not node in ordered_set:\\n ordered_set.add(node)\\n ordered_list.append(node)\\n if not is_leaf_node(node) and not node in end_nodes:\\n for node_op in node.get_inputs():\\n working_list.append(node_op)\\n return ordered_list\",\n \"def level_order_list(root):\\n return_list = []\\n queue = []\\n queue.append(root)\\n\\n while 0 < len(queue):\\n next_node = queue.pop(0)\\n if next_node.get_left():\\n queue.append(next_node.get_left())\\n if next_node.get_right():\\n queue.append(next_node.get_right())\\n return_list.append(next_node)\\n\\n return return_list\",\n \"def _iter_child_nodes_in_order(node):\\n return _flatten_ast_nodes(_iter_child_nodes_in_order_internal_1(node))\",\n \"def preorder(self):\\n return (node for node in self.get_preorder(self.root))\",\n \"def pre_order_helper(self, node, alist=[], verbose=False):\\n if node:\\n if verbose:\\n print(node.data)\\n alist.append(node)\\n pre_order_helper(node.left, alist, verbose)\\n pre_order_helper(node.right, alist, verbose)\",\n \"def bfs(self):\\n level = 0\\n q = Queue()\\n visit_order = list()\\n node = self.get_root()\\n q.enq( (node.get_value()[1],level) )\\n while(len(q) > 0):\\n node, level = q.deq()\\n if node == None:\\n visit_order.append( (\\\"<empty>\\\", level))\\n continue\\n visit_order.append( (node, level) )\\n if type(node) == str:\\n q.enq( (None, level +1) )\\n q.enq( (None, level +1) )\\n continue\\n if node.has_left_child():\\n q.enq( (node.get_left_child()[1], level +1 ))\\n else:\\n q.enq( (None, level +1) )\\n\\n if node.has_right_child():\\n q.enq( (node.get_right_child()[1], level +1 ))\\n else:\\n q.enq( (None, level +1) )\\n\\n return visit_order\",\n \"def in_order(self, verbose=False):\\n if not self.root:\\n return []\\n alist = []\\n self.in_order_helper(self.root, alist, verbose)\\n return alist\",\n \"def in_order(self):\\n def walk(root):\\n\\n if root.left:\\n walk(root.left)\\n\\n self.in_values.append(root.value)\\n\\n if root.right:\\n walk(root.right)\\n \\n walk(self.root)\\n return self.in_values\",\n \"def _toposort_with_ordered_mech_tuples(self, data):\\n result = []\\n for dependency_set in toposort(data):\\n d_iter = iter(dependency_set)\\n result.extend(sorted(dependency_set, key=lambda item : next(d_iter).mechanism.name))\\n return result\",\n \"def postorder_iterator(node):\\n for child in node.children:\\n yield from postorder_iterator(child)\\n yield node\",\n \"def toposort(adj):\\n # Memoize for visited vertex\\n used = [0] * len(adj)\\n order = []\\n # write your code here\\n # Traverse through each vertex\\n for i in range(len(adj)):\\n if not used[i]:\\n # If not visited, run dfs\\n dfs(adj, used, order, i)\\n\\n # Reverse the order list to show in descending order\\n order.reverse()\\n return order\",\n \"def posisions(self):\\n return self.preorder() # return entire preorder iteration\",\n \"def levelOrder(self, root: 'Node') -> List[List[int]]:\\n if not root: return []\\n level = []\\n waiting = []\\n result = []\\n level.append(root)\\n while level:\\n current = []\\n while level:\\n tmp = level.pop(0)\\n if not tmp:\\n continue\\n current.append(tmp.val)\\n waiting.append(tmp)\\n if len(current) > 0:\\n result.append(current)\\n while waiting:\\n tmp = waiting.pop(0)\\n for ch in tmp.children:\\n level.append(ch)\\n return result\",\n \"def inorder(self,root)->list:\\n\\t\\tres=[]\\n\\t\\tif root:\\n\\t\\t\\tres=self.inorder(root.left)\\n\\t\\t\\tres.append(root.data)\\n\\t\\t\\tres=res+self.inorder(root.right)\\n\\t\\treturn res\",\n \"def postorder(self,root)->list:\\n\\t\\tres=[]\\n\\t\\tif root:\\n\\t\\t\\tres=self.postorder(root.left)\\n\\t\\t\\tres=res+self.postorder(root.right)\\n\\t\\t\\tres.append(root.data)\\n\\t\\treturn res\",\n \"def _topological_sort_dfs(self) -> Iterator[BaseTransaction]:\\n raise NotImplementedError\",\n \"def preorder(self):\\n assert self.is_canonical\\n # Names of visited nodes\\n visited = set()\\n # Names of nodes to visit\\n worklist = [LANGUAGE]\\n\\n result = []\\n while len(worklist) > 0:\\n successors = []\\n for rule_name in worklist:\\n if rule_name in visited:\\n continue\\n result.append(rule_name)\\n visited.add(rule_name)\\n\\n rule = self.rules[rule_name].as_container()\\n for rhs in rule:\\n phrase = rhs.as_container()\\n # Note: this tolerates duplicates among siblings.\\n successors.extend([x.content for x in phrase if x.is_symbol_name() and x.content not in visited])\\n worklist = successors\\n return result\",\n \"def preorder_iterator(node):\\n yield node\\n for child in node.children:\\n yield from preorder_iterator(child)\",\n \"def post_order_helper(self, node, alist=[], verbose=False):\\n if node:\\n pre_order_helper(node.left, alist, verbose)\\n pre_order_helper(node.right, alist, verbose)\\n if verbose:\\n print(node.data)\\n alist.append(node)\",\n \"def inorder(self):\\n if not self.is_empty():\\n for p in self._subtree_inorder(self.root()):\\n yield p\",\n \"def topological_sort_generator(self):\\n from sage.graphs.linearextensions import LinearExtensions\\n try:\\n return LinearExtensions(self).list()\\n except TypeError:\\n raise TypeError('Digraph is not acyclic; there is no topological sort (or there was an error in sage/graphs/linearextensions.py).')\",\n \"def traverse(self, prt):\\n lst = []\\n for e in self.items:\\n if(e is not None):\\n prt(e[1])\\n lst.append(e[1])\\n return lst\",\n \"def execution_order(self) -> typing.Iterator:\\n return self.execution_order_strategy_class(self._steps)\",\n \"def preorder_traverse(self):\\n \\n keys = []\\n\\n if not self.node:\\n return keys\\n \\n keys.append(self.node.vp.index)\\n keys.extend(self.node.left.preorder_traverse())\\n keys.extend(self.node.right.preorder_traverse())\\n\\n return keys\",\n \"def in_order_traverse(self):\\n return self.recur_in_order_traverse(self._root)\",\n \"def preorder(root: Node):\\n return [root.data] + preorder(root.left) + preorder(root.right) if root else []\",\n \"def pre_order_nodes(root):\\n yield root\\n\\n if root.get_left():\\n for node in pre_order_nodes(root.get_left()):\\n yield node\\n\\n if root.get_right():\\n for node in pre_order_nodes(root.get_right()):\\n yield node\",\n \"def _get_tree(root: spacy.tokens.Token, depth: int, token_filter: types.FunctionType) -> [spacy.tokens.Token]:\\n if depth == 0:\\n return [root] if token_filter(root) else []\\n\\n result = []\\n # for tokens on the left of the root, whose head is root\\n for child in filter(token_filter, root.lefts):\\n result += SpacyEventExtractor._get_tree(child, depth - 1, token_filter)\\n result.append(root)\\n # for tokens on the right of the root, whose head is root\\n for child in filter(token_filter, root.rights):\\n result += SpacyEventExtractor._get_tree(child, depth - 1, token_filter)\\n return result\",\n \"def inorder(self):\\n if not self.is_empty():\\n for p in self._sub_tree_inorder(self.root()):\\n yield p\",\n \"def get_order(parent_level):\\n subset_lookup = {}\\n for entry in [d for d in data if d[0] == parent_level]:\\n start = entry[1]\\n if start not in subset_lookup:\\n subset_lookup[start] = []\\n subset_lookup[start].append(entry)\\n print(entry)\\n pprint(subset_lookup)\\n processing_order = []\\n for items in reversed(list(subset_lookup.values())):\\n for item in items:\\n processing_order.append(item)\\n processing_order.extend(get_order(item[2]))\\n return processing_order\",\n \"def preorder(self, root):\\n if not root:\\n return []\\n nodes, stack = [], [root]\\n while stack:\\n node = stack.pop()\\n nodes.append(node.val)\\n for child in reversed(node.children):\\n stack.append(child)\\n return nodes\",\n \"def level_order_iterator(node):\\n nodes_ = [node]\\n while len(nodes_):\\n for node_ in nodes_:\\n yield node_\\n nodes_ = list(chain(*[node_.children for node_ in nodes_]))\",\n \"def positions(self):\\n return self.preorder() # return entire preorder iteration\",\n \"def _subtree_postorder(self, p):\\n for c in self.children(p):\\n for pos in self._subtree_postorder(c):\\n yield pos\\n yield p\",\n \"def _subtree_postorder(self, p):\\n for c in self.children(p): # for each child c\\n for other in self._subtree_postorder(c): # do postorder of c's subtree\\n yield other # yielding each to our caller\\n yield p # visit p after its subtrees\",\n \"def pre_order(self):\\n for node_data in self._pre_order_helper(self._root):\\n yield node_data\",\n \"def test_preorder_traversal(depth_one_tree):\\n testlist = []\\n depth_one_tree.pre_order(lambda x: testlist.append(x))\\n assert str(testlist) == str([0, 1, 2, 3, 4])\",\n \"def full_sort(orders: list or tuple) -> list:\\r\\n\\r\\n # We set up the topo sort to include the \\\"before\\\" and \\\"after\\\" objects\\r\\n # as another element in the sort. These are injected into the to-be\\r\\n # sorted list, and removed at the end.\\r\\n\\r\\n # Setup the topo sort.\\r\\n input_list = list(orders)\\r\\n\\r\\n def input_sorter(a, b) -> int:\\r\\n if isinstance(a, str):\\r\\n if isinstance(b, str):\\r\\n return (a == b and 0) or (a < b and -1) or 1\\r\\n else:\\r\\n # Orders always come before strings\\r\\n return 1\\r\\n elif isinstance(b, str):\\r\\n return -1\\r\\n else:\\r\\n assert isinstance(a, Order)\\r\\n assert isinstance(b, Order)\\r\\n return a - b\\r\\n\\r\\n is_visiting = {}\\r\\n visiting_stack = []\\r\\n\\r\\n depends = {}\\r\\n\\r\\n # Translate before / after into a strict dependency.\\r\\n for order in orders:\\r\\n assert isinstance(order, Order)\\r\\n depends[order] = list(order.occurs_after)\\r\\n # We don't need to capture the after in our dependency list -\\r\\n # if everything marks itself as being after something, but nothing\\r\\n # is before it, then it isn't necessary for the ordering.\\r\\n for name in order.occurs_before:\\r\\n if name in depends:\\r\\n depends[name].append(order)\\r\\n else:\\r\\n depends[name] = [order]\\r\\n input_list.append(name)\\r\\n\\r\\n # Sort the \\\"natural\\\" order (non-dependency check)\\r\\n input_list.sort(key=functools.cmp_to_key(input_sorter))\\r\\n for dep_list in depends.values():\\r\\n dep_list.sort(key=functools.cmp_to_key(input_sorter))\\r\\n\\r\\n # For each order item, run a Depth First Search-based sort\\r\\n # By putting the orders in a numbered sort first, we urge the\\r\\n # search to maintain the correct order.\\r\\n\\r\\n sorted_list = []\\r\\n for val in input_list:\\r\\n if val not in is_visiting:\\r\\n Order.__tsort(val, depends, is_visiting,\\r\\n visiting_stack, sorted_list)\\r\\n elif is_visiting[val]:\\r\\n # FIXME make this a warning? Definitely need better debugging.\\r\\n raise Exception(\\\"cyclic dependency in orders (FIXME add better debugging)\\\")\\r\\n\\r\\n ret = []\\r\\n for val in sorted_list:\\r\\n if isinstance(val, Order):\\r\\n ret.append(val)\\r\\n return ret\",\n \"def post_order_nodes(root):\\n if root.get_left():\\n for node in post_order_nodes(root.get_left()):\\n yield node\\n\\n if root.get_right():\\n for node in post_order_nodes(root.get_right()):\\n yield node\\n\\n yield root\",\n \"def pre_order(self, verbose=False):\\n if not self.root:\\n return []\\n alist = []\\n self.pre_order_helper(self.root, alist, verbose)\\n return alist\",\n \"def preorderTraversal(self, root: TreeNode) -> List[int]:\\n def preorder(root,seq):\\n if root is None:\\n return seq\\n seq.append(root.val)\\n preorder(root.left,seq)\\n preorder(root.right,seq)\\n return seq\\n \\n prelist= []\\n return preorder(root,prelist)\",\n \"def get_ordered_nodes(self):\\n nodes = []\\n self.build_nodes_list(self.root, nodes)\\n return nodes\",\n \"def get_order_from_tree(ids, tree_text):\\r\\n tree = parse_newick(tree_text, PhyloNode)\\r\\n ordered_ids = []\\r\\n for tip in tree.iterTips():\\r\\n if tip.Name in ids:\\r\\n ordered_ids.append(tip.Name)\\r\\n return names_to_indices(ids, ordered_ids)\",\n \"def pre_order(self):\\n stack = []\\n node = self\\n while stack or node:\\n if node:\\n yield node.val\\n stack.append(node)\\n node = node.left\\n else:\\n node = stack.pop()\\n node = node.right\",\n \"def post_order(self):\\n if self.left is not None:\\n for i in self.left.post_order():\\n yield i\\n if self.right is not None:\\n for i in self.right.post_order():\\n yield i\\n if self.val is not None:\\n yield self.val\",\n \"def postorderTraversal(self, root: TreeNode) -> List[int]:\\n stack = []\\n postorder = []\\n stack.append(root)\\n\\n while stack:\\n node = stack.pop()\\n if not node: continue\\n right = node.right\\n left = node.left\\n if not right and not left:\\n postorder.append(node.val)\\n else:\\n node.left = None\\n node.right = None\\n stack.append(node)\\n stack.append(right)\\n stack.append(left)\\n return postorder\",\n \"def _walk_ast_nodes_in_order(node):\\n # The implementation is basically the same as ``ast.walk``, but:\\n # 1. Use a stack instead of a deque. (I.e., depth-first search instead\\n # of breadth-first search.)\\n # 2. Use _iter_child_nodes_in_order instead of ``ast.iter_child_nodes``.\\n todo = [node]\\n while todo:\\n node = todo.pop()\\n yield node\\n todo.extend(reversed(list(_iter_child_nodes_in_order(node))))\",\n \"def post_order(self):\\n for node_data in self._post_order_helper(self._root):\\n yield node_data\"\n]","isConversational":false},"negative_scores":{"kind":"list like","value":["0.6355085","0.63519377","0.6300792","0.628017","0.62159055","0.61725616","0.6165398","0.61307913","0.6094408","0.60663015","0.60005105","0.5948831","0.594843","0.59415513","0.5918404","0.5880077","0.58614326","0.58589536","0.58558935","0.5847287","0.58438325","0.58097327","0.5800654","0.57961756","0.5784047","0.5779595","0.57790875","0.5769624","0.5762362","0.57501405","0.57329196","0.5698791","0.5688447","0.5686251","0.5679883","0.5679343","0.56642324","0.56444216","0.564431","0.56423324","0.5637619","0.56315166","0.56252426","0.56190145","0.5617865","0.5605612","0.55783105","0.5573567","0.5573486","0.55680513","0.5558768","0.55545586","0.5552693","0.55525583","0.55402863","0.5529364","0.55202895","0.5520021","0.5512201","0.55115175","0.55058485","0.54971814","0.5496706","0.5484051","0.5476939","0.5476766","0.54713833","0.5471026","0.5448635","0.544797","0.54469556","0.5441725","0.54405695","0.54250264","0.54156846","0.5414627","0.54142374","0.5405151","0.5403828","0.540109","0.54000276","0.5398069","0.53925806","0.5381918","0.536062","0.53596216","0.53350747","0.5332789","0.5314152","0.5313515","0.53123856","0.53122264","0.5308954","0.530176","0.5301199","0.5299157","0.52971584","0.5295842","0.5291969","0.5282311"],"string":"[\n \"0.6355085\",\n \"0.63519377\",\n \"0.6300792\",\n \"0.628017\",\n \"0.62159055\",\n \"0.61725616\",\n \"0.6165398\",\n \"0.61307913\",\n \"0.6094408\",\n \"0.60663015\",\n \"0.60005105\",\n \"0.5948831\",\n \"0.594843\",\n \"0.59415513\",\n \"0.5918404\",\n \"0.5880077\",\n \"0.58614326\",\n \"0.58589536\",\n \"0.58558935\",\n \"0.5847287\",\n \"0.58438325\",\n \"0.58097327\",\n \"0.5800654\",\n \"0.57961756\",\n \"0.5784047\",\n \"0.5779595\",\n \"0.57790875\",\n \"0.5769624\",\n \"0.5762362\",\n \"0.57501405\",\n \"0.57329196\",\n \"0.5698791\",\n \"0.5688447\",\n \"0.5686251\",\n \"0.5679883\",\n \"0.5679343\",\n \"0.56642324\",\n \"0.56444216\",\n \"0.564431\",\n \"0.56423324\",\n \"0.5637619\",\n \"0.56315166\",\n \"0.56252426\",\n \"0.56190145\",\n \"0.5617865\",\n \"0.5605612\",\n \"0.55783105\",\n \"0.5573567\",\n \"0.5573486\",\n \"0.55680513\",\n \"0.5558768\",\n \"0.55545586\",\n \"0.5552693\",\n \"0.55525583\",\n \"0.55402863\",\n \"0.5529364\",\n \"0.55202895\",\n \"0.5520021\",\n \"0.5512201\",\n \"0.55115175\",\n \"0.55058485\",\n \"0.54971814\",\n \"0.5496706\",\n \"0.5484051\",\n \"0.5476939\",\n \"0.5476766\",\n \"0.54713833\",\n \"0.5471026\",\n \"0.5448635\",\n \"0.544797\",\n \"0.54469556\",\n \"0.5441725\",\n \"0.54405695\",\n \"0.54250264\",\n \"0.54156846\",\n \"0.5414627\",\n \"0.54142374\",\n \"0.5405151\",\n \"0.5403828\",\n \"0.540109\",\n \"0.54000276\",\n \"0.5398069\",\n \"0.53925806\",\n \"0.5381918\",\n \"0.536062\",\n \"0.53596216\",\n \"0.53350747\",\n \"0.5332789\",\n \"0.5314152\",\n \"0.5313515\",\n \"0.53123856\",\n \"0.53122264\",\n \"0.5308954\",\n \"0.530176\",\n \"0.5301199\",\n \"0.5299157\",\n \"0.52971584\",\n \"0.5295842\",\n \"0.5291969\",\n \"0.5282311\"\n]","isConversational":false},"document_score":{"kind":"string","value":"0.695369"},"document_rank":{"kind":"string","value":"0"}}},{"rowIdx":75387,"cells":{"query":{"kind":"string","value":"S1 is like call stack s2 is the container nodes in s"},"document":{"kind":"string","value":"def StackBasedTraversal(self,root: TreeNode, strategy: str) -> List[int]:\n if(not root):\n return\n\n ans = []\n s1,s2 = [],[]\n s1.append(root)\n\n while(s1):\n x = s1[-1]\n s1.pop()\n s2.append(x)\n\n if(x.left):\n s1.append(x.left)\n\n if(x.right):\n s1.append(x.right)\n\n while(s2):\n y = s2[-1]\n s2.pop()\n ans.append(y.val)\n\n return ans"},"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__(self):\n self.s1 = Stack()\n self.s2 = Stack()","def pool_snps(parent1, parent2):","def __init__(self):\n # Use list to represent Stack \n self.s1 = []\n self.s2 = []\n # Note the first item to make peek() faster\n self.first = None","def __init__(self):\n self.sk1 = Stack()\n self.sk2 = Stack()","def copy_stacks(self, c1, c2):\n return (\n deque([n for n in self._s1][-c1:]),\n deque([n for n in self._s2][-c2:])\n )","def __init__(self):\n self.stack1 = []\n self.stack2 = []\n self.front = None","def __init__(self):\n self.stack1 = []\n self.stack2 = []\n\n # stack1[0] is the queue front\n # stack2[0] is the queue back","def __init__(self):\n self.stackIn = []\n self.stackOut = []","def run_stacker(self):\n \tself.iterator()\n \tself.stack()","def __init__(self):\n self.in_stack = []\n self.out_stack = []","def Stack():\n return []","def __init__(self):\n self.stack=[]\n self.top1=-1\n ","def peek(self):\n if not self.s2.isEmpty():\n return self.s2.peek()\n while not self.s1.isEmpty():\n self.s2.push(self.s1.pop())\n return self.s2.peek()","def containers(self):\n seen = set()\n return [l.from_segment for l in self.edges_to_containers \\\n if id(l) not in seen and not seen.add(id(l))]","def __init__(self):\n self.inStack = []\n self.outStack = []","def depth_first(self, xy1, xy2):\n tile_col1, tile_row1 = self.the_map.xy_to_cr(xy1[0], xy1[1])\n tile_col2, tile_row2 = self.the_map.xy_to_cr(xy2[0], xy2[1])\n \n successor_to_parent_map = {}\n start_state = (tile_col1, tile_row1)\n successor_to_parent_map[(start_state, None)] = None # (Successor, Action) -> (Parent, Action)\n \n open_list = Stack()\n open_list.push((start_state, None))\n closed = []\n \n while not open_list.isEmpty():\n current_state, action_to_current_state = open_list.pop()\n \n if current_state == (tile_col2, tile_row2):\n return self.__get_action_path((current_state, action_to_current_state), successor_to_parent_map)\n \n if current_state not in closed:\n for successor_state, action, step_cost in self.__get_successors(current_state):\n open_list.push((successor_state, action))\n \n if successor_state not in closed:\n successor_to_parent_map[(successor_state, action)] = (current_state, action_to_current_state)\n \n closed.append(current_state)\n return []","def __init__(self):\n self.s1 = list()\n self.s2 = list()","def __init__(self):\r\n self.stack = []\r\n self.minStack = []","def __init__(self):\r\n self.s1=[]\r\n self.s2=[]","def __init__(self):\r\n # 初始化两个列表,当作栈来使用\r\n self.stack = []","def __init__(self):\n\t\tself.s1 = []\n\t\tself.s2 = []","def test_sll_base(self):\n sll = SinglyLinkedList()\n a = Node('a')\n b = Node('b')\n sll.insert_beg(a)\n sll.insert_beg(b)","def __init__(self):\n self.s1=[]\n self.s2=[]","def sub_graph_merging(self):","def __init__(self):\n self.stack=[]","def __init__(self):\n self.stack = []","def __init__(self):\n self.stack = []","def __init__(self):\n self.stack = []","def __init__(self):\n self.stack = []","def __init__(self):\n self.stack = []","def __init__(self):\n self.stack = []","def __init__(self):\n self.stack = []","def __init__(self):\n self.stack = []","def __init__(self):\n self.stack = []","def __init__(self):\n self.stack = []","def __init__(self):\n self.stack = []","def shift(self): # shift\n span,stack_top=self\n pos=span[1]\n nex=self.lattice.begins.get(pos,None) # nex is the list of next items\n if not nex : return []\n # nex[0], nex[1] ...\n\n s0,s1,s2=stack_top\n\n print('shift==========', self)\n rtns=[]\n for ne in nex :\n item=self.lattice[ne]\n ns=((item[0],item[1]),((ne,None,None),s0,s1[0]if s1 else None))\n print(ne,ns)\n rtns.append((256+ne,pickle.dumps(ns)))\n return rtns","def __init__(self):\n self.stack = []\n self.minStack = []","def __init__(self):\n self.input_stack = []\n self.output_stack = []","def __init__(self):\n self.input_stack = []\n self.output_stack = []","def __init__(self):\n self.input_stack = []\n self.output_stack = []","def __call__(self, node):\n if node.children:\n if len(node.children) == 1:\n if self.TagEqual(node.children[0], node):\n #print node.ToString()\n node.tag = self.Tag(node, node.children[0]);\n lst = node.children[0].children;\n node.children = lst;","def part1(input_lines):\n # This is a DAG problem. We need to form a dependency graph.\n tower = get_tower(input_lines)\n return find_root(tower)","def peek(self):\r\n if len(self.s2)!=0:\r\n return self.s2[-1]\r\n while len(self.s1)!=0:\r\n self.s2.append(self.s1.pop()) \r\n return self.s2[-1]","def getSiblings():","def stack(*sources):\n return SearchSourceStack(*sources)","def __init__(self):\n self.stack = []\n self.stackMin = []","def rmdup2(sll):\n start = sll.head\n while start:\n node = start\n while node and node.next_:\n if node.next_.payload == start.payload:\n node.next_ = node.next_.next_\n node = node.next_\n start = start.next_\n return sll","def __init__(self):\n self.stack = []\n self.minStack = [] # 维护一个单调不增栈","def __init__(self):\n self._stack=[]","def __init__(self):\n self.min_stack = []\n self.stack = []","def fn(node):\n ans, stack = [], []\n while stack or node: \n if node: \n stack.append(node)\n node = node.left\n else: \n node = stack.pop()\n ans.append(node.val)\n node = node.right \n return ans","def __init__(self):\n self.stack = []\n self.ordered = []","def shortest_path(start, end):\n\troot1=node(start)\n\troot2=node(end)\n\tarr1=[[] for wqw in range(8)]\n\tarr2=[[] for qwq in range(8)]\n\tser1=[[] for lp in range(100000)]\n\tser2=[[] for lp in range(100000)]\n\tarr1[0].append(root1)\n\tarr2[0].append(root2)\n\tser1[(hash(start))%100000].append(start)\n\tser2[(hash(end))%100000].append(end)\n\tflag=0\n\tchk=None\n\tfor h in range(0,7):\n\t\tchk=check_match(arr1[h],arr2[h])\n\t\tif not chk is None:\n\t\t\tflag=1\n\t\t\tbreak\n\t\telse:\n\t\t\tfor w in range(len(arr1[h])):\n\t\t\t\tfin=insert_e(arr1[h][w].data,h+1,arr1,ser1)\n\t\t\t\tarr1=fin[0]\n\t\t\t\tser1=fin[1]\n\t\tchk=check_match(arr2[h],arr1[h+1])\n\t\tif not chk is None:\n\t\t\tflag=2\n\t\t\tbreak\n\t\telse:\n\t\t\tfor q in range(len(arr2[h])):\n\t\t\t\tfim=insert_e(arr2[h][w].data,h+1,arr2,ser2)\n\t\t\t\tarr2=fim[0]\n\t\t\t\tser2=fim[1]\n\tif flag==1:\n\t\twhile not chk[0].parent is None:\n\t\t\tres1.append(chk[0].parent.data)\n\t\t\tchk[0]=chk[0].parent\n\t\tres+=res1.reverse()\n\t\tres.append(chk[1])\n\t\twhile not chk[1].parent is None:\n\t\t\tres2.append(chk[1].parent.data)\n\t\t\tchk[1]=chk[1].parent\n\t\treturn res\n\telif flag==2:\n\t\twhile not chk[1].parent is None:\n\t\t\tres1.append(chk[1].parent.data)\n\t\t\tchk[1]=chk[1].parent\n\t\tif not res1 is None:\n\t\t\tres+=res1.reverse()\n\t\tres.append(chk[0])\n\t\twhile not chk[0].parent is None:\n\t\t\tres2.append(chk[0].parent.data)\n\t\t\tchk[0]=chk[0].parent\n\t\treturn res\n\telse:\n\t\tprint \"The given configuration is not solvable/wrong.\"","def _interconnect(self):\n self.clear_structure()\n self.structure.append(self.source)\n for i in range(len(self.stack)):\n self.structure.append(self.stack[i])\n self.structure.append(self.terminator)\n return","def stack(*bpfs) -> core.Stack:\n if len(bpfs) == 1 and isinstance(bpfs[0], (list, tuple)):\n bpfs = bpfs[0]\n return core.Stack(bpfs)","def resolve_stack(self):\n if not callable(self.alias):\n return\n # check that we actual need the stack\n sig = inspect.signature(self.alias)\n if len(sig.parameters) <= 5 and \"stack\" not in sig.parameters:\n return\n # compute the stack, and filter out these build methods\n # run_subproc() is the 4th command in the stack\n # we want to filter out one up, e.g. subproc_captured_hiddenobject()\n # after that the stack from the call site starts.\n stack = inspect.stack(context=0)\n assert stack[3][3] == \"run_subproc\", \"xonsh stack has changed!\"\n del stack[:5]\n self.stack = stack","def test_iter_sll_base(self):\n sll = SinglyLinkedList()\n a = Node('a')\n b = Node('b')\n sll.insert_beg(a)\n sll.insert_beg(b)\n for i in sll:\n print i","def stacklists(arg):\r\n if isinstance(arg, (tuple, list)):\r\n return stack(*map(stacklists, arg))\r\n else:\r\n return arg","def stack():\n return currentframe().f_back.f_locals.setdefault(SN, [])","def breadth_first(self, xy1, xy2):\n tile_col1, tile_row1 = self.the_map.xy_to_cr(xy1[0], xy1[1])\n tile_col2, tile_row2 = self.the_map.xy_to_cr(xy2[0], xy2[1])\n \n successor_to_parent_map = {}\n start_state = (tile_col1, tile_row1)\n successor_to_parent_map[(start_state, None)] = None # (Successor, Action) -> (Parent, Action)\n \n open_list = PriorityQueue()\n open_list.update((start_state, None), 0)\n closed = []\n \n while not open_list.isEmpty():\n current_state, action_to_current_state = open_list.pop()\n \n if current_state == (tile_col2, tile_row2):\n return self.__get_action_path((current_state, action_to_current_state), successor_to_parent_map)\n \n if current_state not in closed:\n for successor_state, action, step_cost in self.__get_successors(current_state):\n open_list.update((successor_state, action), 0)\n \n if successor_state not in closed:\n successor_to_parent_map[(successor_state, action)] = (current_state, action_to_current_state)\n \n closed.append(current_state)\n \n return []","def __init__(self):\r\n self.stack = deque()\r\n self.minEle = deque()","def __init__(self):\n self.head = None\n self.stack = list()","def pop(self):\r\n if len(self.s2)!=0:\r\n return self.s2.pop()\r\n while len(self.s1)!=0:\r\n self.s2.append(self.s1.pop())\r\n return self.s2.pop()","def sm_get_matching_blocks(s1,s2,min_length=1):\n anslist= list(SM(None, s1, s2).get_matching_blocks())\n\n\n anslist = [ l for l in anslist if l.size>=min_length]\n\n anslist=[ (s1[l.a:l.a+l.size], l.a, l.b, l.size) for l in anslist]\n return anslist","def symbol_execute2(self):\n start_s = self._create_start_state()\n root = NodeTreeNode(None)\n # self.symbol_execute2_single(root, start_s)\n self.symbol_execute2_single_tail([NodeStatePair(root, start_s)])\n return root","def peek(self):\n if len(self.stack2) != 0:\n return self.stack2[-1]\n else:\n while len(self.stack1) != 0:\n self.stack2.append(self.stack1.pop())\n return self.stack2[-1]","def __init__(self):\n self.stack = list()\n self.max_stack = list()\n self.sec_stack = list()","def __init__(self):\n self.min_stack = []","def __init__(self):\n self._input_stack = []\n self._output_stack = []","def __init__(self):\r\n self.stack=[]\r\n self.min=[]","def __init__(self):\n self.stack_top = DLL()\n self.sorted_stack = SortedDict() ## key: val; val: list of DLL nodes","def stack(context=1):\r\n return getouterframes(sys._getframe(1), context)","def DFS(graph, s, n):\n # Stack implemented using list. list.append() and list.pop() inherently have LIFO structure.\n visited = [False] * n\n stack = []\n \n stack.append(s)\n visited[s] = True\n while(stack):\n v = stack.pop()\n print(v, end= \" \")\n \n for i in range(len(graph[v])):\n if (not visited[graph[v][i]]):\n stack.append( graph[v][i] )\n visited[graph[v][i]] = True","def __call__(container, name, object):","def swap_stlcontainers(self, s):\n old = self.stlcontainers\n self.stlcontainers = s\n self.clearmemo()\n yield\n self.clearmemo()\n self.stlcontainers = old","def test_insert_node_singleton_content_2():\n first = 0\n second = first\n chain = N.Node(second)\n node = N.Node(first)\n\n result = A8.insert_node(node, chain)\n\n assert result.data == first, \"insert_node returned incorrect data value first given a node and singleton chain\"\n assert result.next.data == second, \"insert_node returned incorrect data value second given a node and singleton chain\"","def __init__(self):\r\n self.stack = []\r\n self.minEle = 2**31 - 1","def slice_graph_fwd( startea, reg ): \r\n\tgraph = vcg_Graph.vcgGraph({\"title\":'\"Slice for %s\"' % reg, \\\r\n\t\t\"manhattan_edges\":\"no\", \"layoutalgorithm\":\"maxdepth\"})\r\n\t#\r\n\t# Retrieve the name of the current basic block\r\n\t# \r\n\tworklist = []\r\n\tdata_bib = {}\r\n\tstartnode = slice_node( startea, 0, reg )\r\n\trootnode = graph.Add_Node( startnode.to_name() )\r\n\tdata_bib[ startnode.to_name() ] = startnode\r\n\tworklist.insert( 0, rootnode )\r\n\twhile len( worklist ) > 0:\r\n\t\tcurrnode = worklist.pop()\r\n\t\tcurrslice = data_bib[ currnode.get_name() ]\r\n\t\ttgt_reg = currslice.get_target_reg()\r\n\t\tif tgt_reg == \"END\":\r\n\t\t# Do not process this node any further\r\n\t\t\tpass\r\n\t\telif tgt_reg == \"\" or (( len( currslice.get_lines()) > 0) and \\\r\n\t\t\tcurrslice.endea != currslice.get_lines()[-1][0]):\r\n\t\t\t# Nothing much happening here, just proceed to parent bocks\r\n\t\t\tif ua_mnem( currslice.endea ) == \"call\":\r\n\t\t\t\txrefs = get_short_crefs_from( currslice.endea )\r\n\t\t\telse:\r\n\t\t\t\txrefs = get_crefs_from( currslice.endea )\r\n\t\t\tfor ref in xrefs:\r\n\t\t\t\tnewslice = slice_node( ref, 0, currslice.reg )\r\n\t\t\t\tif graph.Get_Node( newslice.to_name() ) == 0:\r\n\t\t\t\t\tnewnode = graph.Add_Node( newslice.to_name() )\r\n\t\t\t\t\tworklist.insert( 0, newnode )\r\n\t\t\t\t\tdata_bib[ newslice.to_name() ] = newslice\r\n\t\t\t\tgraph.Add_Link( currnode.get_name(), newslice.to_name())\r\n\t\telse:\r\n\t\t\t# Register was modified, use new register\r\n\t\t\txrefs = get_crefs_from( currslice.endea )\r\n\t\t\tfor ref in xrefs:\r\n\t\t\t\tnewslice = slice_node( ref, 0, tgt_reg )\r\n\t\t\t\tif graph.Get_Node( newslice.to_name() ) == 0:\r\n\t\t\t\t\tnewnode = graph.Add_Node( newslice.to_name() )\r\n\t\t\t\t\tworklist.insert( 0, newnode )\r\n\t\t\t\t\tdata_bib[ newslice.to_name() ] = newslice\r\n\t\t\t\tgraph.Add_Link( currnode.get_name(), newslice.to_name())\r\n\t\t\txrefs = get_crefs_from( currslice.endea )\r\n\t\t\tfor ref in xrefs:\r\n\t\t\t\tnewslice = slice_node( ref, 0, currslice.reg )\r\n\t\t\t\tif graph.Get_Node( newslice.to_name() ) == 0:\r\n\t\t\t\t\tnewnode = graph.Add_Node( newslice.to_name() )\r\n\t\t\t\t\tworklist.insert( 0, newnode )\r\n\t\t\t\t\tdata_bib[ newslice.to_name() ] = newslice\r\n\t\t\t\tgraph.Add_Link( currnode.get_name(), newslice.to_name())\r\n\treturn [ graph, data_bib ]","def __init__(self):\n self.stack = collections.deque([])","def Children(self) -> _n_1_t_2:","def forward(self, s):","def __init__(self):\n self.datastack = []\n self.minstack = []","def __init__(self):\n self.data_stack = []\n self.min_stack = []","def fn(node):\n if not node: return #null node\n stack.append(node.val)\n if node.left is node.right: ans.append(\"->\".join(map(str, stack))) #leaf node\n fn(node.left) or fn(node.right)\n stack.pop()","def double_stack(s: Stack) -> Stack:\n temp = Stack() # Stack to restore original values\n duplicated = Stack() # The stack to return.\n while not s.is_empty():\n val = s.pop()\n temp.push(val)\n # Add duplicates for each value.\n duplicated.push(val)\n duplicated.push(val)\n\n # Restore the original stack.\n while not temp.is_empty():\n s.push(temp.pop())\n\n return duplicated","def size(s: Stack) -> int:\n side_stack = Stack()\n count = 0\n # Pop everything off <s> and onto <side_stack>, counting as we go.\n while not s.is_empty():\n side_stack.push(s.pop())\n count += 1\n # Now pop everything off <side_stack> and back onto <s>.\n while not side_stack.is_empty():\n s.push(side_stack.pop())\n # <s> is restored to its state at the start of the function call.\n # We consider that it was not mutated.\n return count","def __init__(self):\n self.stack = list()\n self.count = 0","def stack(self, x, layers):\n if not layers:\n return x # Base case of the recursion is the just returning the input\n else:\n return layers[0]()(self.stack(x, layers[1:]))","def stack(self, x, layers):\n if not layers:\n return x # Base case of the recursion is the just returning the input\n else:\n return layers[0]()(self.stack(x, layers[1:]))","def __init__(self):\n self.stack = collections.deque()","def list_stack(st: Stack, lst: list) -> None:\n for element in lst:\n st.add(element)\n # for sth in st:\n # if type(sth) == list:\n # for e in sth:\n # st.add(e)\n while not st.is_empty():\n # new =[]\n top = st.remove()\n st.add(top)\n if type(top) == list:\n st.remove()\n for item in top:\n st.add(item)\n # st.remove()\n print(st.remove())","def __init__(self):\n self.stack = []\n self.min_elements = []","def rewrite(self, l1, l2):\n l1Stack = []\n l2Stack = []\n\n def fill(stack, li):\n while li:\n stack.append(li.val)\n li = li.next\n\n fill(l1Stack, l1)\n fill(l2Stack, l2)\n\n prev = None\n\n while l1Stack and l2Stack:\n if prev:\n cv = prev.val + l1Stack.pop() + l2Stack.pop()\n prev.val = cv % 10\n tmp = ListNode(cv / 10)\n tmp.next = prev\n prev = tmp\n else:\n cv = l1Stack.pop() + l2Stack.pop()\n tmp = ListNode(cv % 10)\n prev = ListNode(cv / 10)\n prev.next = tmp\n\n def single(stack, prev):\n while stack:\n if prev:\n cv = prev.val + stack.pop()\n prev.val = cv % 10\n tmp = ListNode(cv / 10)\n tmp.next = prev\n prev = tmp\n else:\n tmp = ListNode(stack.pop())\n prev = ListNode(None)\n prev.next = tmp\n return prev\n\n prev = single(l1Stack, prev)\n prev = single(l2Stack, prev)\n\n return prev if prev.val else prev.next","def __init__(self):\r\n self.popStack = []\r\n self.pushStack = []","def test_push_on_empty_stack(empty_stack): \n empty_stack.push([1, 'Good Boy'])\n assert empty_stack.top.val == [1, 'Good Boy']","def explore(self, *args):","def __init__(self):\n self.stack = []\n self.index_last = -1","def headsofunion(h1, h2):\n res = unfi.set(b'heads((%ln + %ln - null))', h1, h2)\n return {ctx.node() for ctx in res}","def __init__(self):\n\n self.container2 = []\n heapq.heapify(self.container2)","def recursion(self, l1,l2):\r\n c = l1.val + l2.val\r\n r = 0\r\n \r\n if c > 9:\r\n c = c - 10\r\n r = 1\r\n\r\n ans = ListNode(c)\r\n \r\n def goingIn(ans, l1, l2, r):\r\n one = l1\r\n two = l2\r\n c = 0\r\n \r\n if one != None:\r\n c += one.val\r\n \r\n if two != None:\r\n c+= two.val\r\n\r\n if r == 1:\r\n c += r\r\n r = 0\r\n\r\n if c > 9:\r\n c = c - 10\r\n r = 1\r\n\r\n ans.next = ListNode(c)\r\n \r\n if one== None and two == None:\r\n if r == 1:\r\n goingIn(ans.next, None, None, r)\r\n else:\r\n if c == 0:\r\n ans.next = None\r\n\r\n \r\n elif one != None and two != None:\r\n goingIn( ans.next, l1.next, l2.next, r)\r\n \r\n else:\r\n if one != None:\r\n goingIn(ans.next, l1.next, None, r)\r\n \r\n if two != None:\r\n goingIn(ans.next, None, l2.next, r)\r\n\r\n\r\n goingIn(ans, l1.next, l2.next, r)\r\n \r\n return ans"],"string":"[\n \"def __init__(self):\\n self.s1 = Stack()\\n self.s2 = Stack()\",\n \"def pool_snps(parent1, parent2):\",\n \"def __init__(self):\\n # Use list to represent Stack \\n self.s1 = []\\n self.s2 = []\\n # Note the first item to make peek() faster\\n self.first = None\",\n \"def __init__(self):\\n self.sk1 = Stack()\\n self.sk2 = Stack()\",\n \"def copy_stacks(self, c1, c2):\\n return (\\n deque([n for n in self._s1][-c1:]),\\n deque([n for n in self._s2][-c2:])\\n )\",\n \"def __init__(self):\\n self.stack1 = []\\n self.stack2 = []\\n self.front = None\",\n \"def __init__(self):\\n self.stack1 = []\\n self.stack2 = []\\n\\n # stack1[0] is the queue front\\n # stack2[0] is the queue back\",\n \"def __init__(self):\\n self.stackIn = []\\n self.stackOut = []\",\n \"def run_stacker(self):\\n \\tself.iterator()\\n \\tself.stack()\",\n \"def __init__(self):\\n self.in_stack = []\\n self.out_stack = []\",\n \"def Stack():\\n return []\",\n \"def __init__(self):\\n self.stack=[]\\n self.top1=-1\\n \",\n \"def peek(self):\\n if not self.s2.isEmpty():\\n return self.s2.peek()\\n while not self.s1.isEmpty():\\n self.s2.push(self.s1.pop())\\n return self.s2.peek()\",\n \"def containers(self):\\n seen = set()\\n return [l.from_segment for l in self.edges_to_containers \\\\\\n if id(l) not in seen and not seen.add(id(l))]\",\n \"def __init__(self):\\n self.inStack = []\\n self.outStack = []\",\n \"def depth_first(self, xy1, xy2):\\n tile_col1, tile_row1 = self.the_map.xy_to_cr(xy1[0], xy1[1])\\n tile_col2, tile_row2 = self.the_map.xy_to_cr(xy2[0], xy2[1])\\n \\n successor_to_parent_map = {}\\n start_state = (tile_col1, tile_row1)\\n successor_to_parent_map[(start_state, None)] = None # (Successor, Action) -> (Parent, Action)\\n \\n open_list = Stack()\\n open_list.push((start_state, None))\\n closed = []\\n \\n while not open_list.isEmpty():\\n current_state, action_to_current_state = open_list.pop()\\n \\n if current_state == (tile_col2, tile_row2):\\n return self.__get_action_path((current_state, action_to_current_state), successor_to_parent_map)\\n \\n if current_state not in closed:\\n for successor_state, action, step_cost in self.__get_successors(current_state):\\n open_list.push((successor_state, action))\\n \\n if successor_state not in closed:\\n successor_to_parent_map[(successor_state, action)] = (current_state, action_to_current_state)\\n \\n closed.append(current_state)\\n return []\",\n \"def __init__(self):\\n self.s1 = list()\\n self.s2 = list()\",\n \"def __init__(self):\\r\\n self.stack = []\\r\\n self.minStack = []\",\n \"def __init__(self):\\r\\n self.s1=[]\\r\\n self.s2=[]\",\n \"def __init__(self):\\r\\n # 初始化两个列表,当作栈来使用\\r\\n self.stack = []\",\n \"def __init__(self):\\n\\t\\tself.s1 = []\\n\\t\\tself.s2 = []\",\n \"def test_sll_base(self):\\n sll = SinglyLinkedList()\\n a = Node('a')\\n b = Node('b')\\n sll.insert_beg(a)\\n sll.insert_beg(b)\",\n \"def __init__(self):\\n self.s1=[]\\n self.s2=[]\",\n \"def sub_graph_merging(self):\",\n \"def __init__(self):\\n self.stack=[]\",\n \"def __init__(self):\\n self.stack = []\",\n \"def __init__(self):\\n self.stack = []\",\n \"def __init__(self):\\n self.stack = []\",\n \"def __init__(self):\\n self.stack = []\",\n \"def __init__(self):\\n self.stack = []\",\n \"def __init__(self):\\n self.stack = []\",\n \"def __init__(self):\\n self.stack = []\",\n \"def __init__(self):\\n self.stack = []\",\n \"def __init__(self):\\n self.stack = []\",\n \"def __init__(self):\\n self.stack = []\",\n \"def __init__(self):\\n self.stack = []\",\n \"def shift(self): # shift\\n span,stack_top=self\\n pos=span[1]\\n nex=self.lattice.begins.get(pos,None) # nex is the list of next items\\n if not nex : return []\\n # nex[0], nex[1] ...\\n\\n s0,s1,s2=stack_top\\n\\n print('shift==========', self)\\n rtns=[]\\n for ne in nex :\\n item=self.lattice[ne]\\n ns=((item[0],item[1]),((ne,None,None),s0,s1[0]if s1 else None))\\n print(ne,ns)\\n rtns.append((256+ne,pickle.dumps(ns)))\\n return rtns\",\n \"def __init__(self):\\n self.stack = []\\n self.minStack = []\",\n \"def __init__(self):\\n self.input_stack = []\\n self.output_stack = []\",\n \"def __init__(self):\\n self.input_stack = []\\n self.output_stack = []\",\n \"def __init__(self):\\n self.input_stack = []\\n self.output_stack = []\",\n \"def __call__(self, node):\\n if node.children:\\n if len(node.children) == 1:\\n if self.TagEqual(node.children[0], node):\\n #print node.ToString()\\n node.tag = self.Tag(node, node.children[0]);\\n lst = node.children[0].children;\\n node.children = lst;\",\n \"def part1(input_lines):\\n # This is a DAG problem. We need to form a dependency graph.\\n tower = get_tower(input_lines)\\n return find_root(tower)\",\n \"def peek(self):\\r\\n if len(self.s2)!=0:\\r\\n return self.s2[-1]\\r\\n while len(self.s1)!=0:\\r\\n self.s2.append(self.s1.pop()) \\r\\n return self.s2[-1]\",\n \"def getSiblings():\",\n \"def stack(*sources):\\n return SearchSourceStack(*sources)\",\n \"def __init__(self):\\n self.stack = []\\n self.stackMin = []\",\n \"def rmdup2(sll):\\n start = sll.head\\n while start:\\n node = start\\n while node and node.next_:\\n if node.next_.payload == start.payload:\\n node.next_ = node.next_.next_\\n node = node.next_\\n start = start.next_\\n return sll\",\n \"def __init__(self):\\n self.stack = []\\n self.minStack = [] # 维护一个单调不增栈\",\n \"def __init__(self):\\n self._stack=[]\",\n \"def __init__(self):\\n self.min_stack = []\\n self.stack = []\",\n \"def fn(node):\\n ans, stack = [], []\\n while stack or node: \\n if node: \\n stack.append(node)\\n node = node.left\\n else: \\n node = stack.pop()\\n ans.append(node.val)\\n node = node.right \\n return ans\",\n \"def __init__(self):\\n self.stack = []\\n self.ordered = []\",\n \"def shortest_path(start, end):\\n\\troot1=node(start)\\n\\troot2=node(end)\\n\\tarr1=[[] for wqw in range(8)]\\n\\tarr2=[[] for qwq in range(8)]\\n\\tser1=[[] for lp in range(100000)]\\n\\tser2=[[] for lp in range(100000)]\\n\\tarr1[0].append(root1)\\n\\tarr2[0].append(root2)\\n\\tser1[(hash(start))%100000].append(start)\\n\\tser2[(hash(end))%100000].append(end)\\n\\tflag=0\\n\\tchk=None\\n\\tfor h in range(0,7):\\n\\t\\tchk=check_match(arr1[h],arr2[h])\\n\\t\\tif not chk is None:\\n\\t\\t\\tflag=1\\n\\t\\t\\tbreak\\n\\t\\telse:\\n\\t\\t\\tfor w in range(len(arr1[h])):\\n\\t\\t\\t\\tfin=insert_e(arr1[h][w].data,h+1,arr1,ser1)\\n\\t\\t\\t\\tarr1=fin[0]\\n\\t\\t\\t\\tser1=fin[1]\\n\\t\\tchk=check_match(arr2[h],arr1[h+1])\\n\\t\\tif not chk is None:\\n\\t\\t\\tflag=2\\n\\t\\t\\tbreak\\n\\t\\telse:\\n\\t\\t\\tfor q in range(len(arr2[h])):\\n\\t\\t\\t\\tfim=insert_e(arr2[h][w].data,h+1,arr2,ser2)\\n\\t\\t\\t\\tarr2=fim[0]\\n\\t\\t\\t\\tser2=fim[1]\\n\\tif flag==1:\\n\\t\\twhile not chk[0].parent is None:\\n\\t\\t\\tres1.append(chk[0].parent.data)\\n\\t\\t\\tchk[0]=chk[0].parent\\n\\t\\tres+=res1.reverse()\\n\\t\\tres.append(chk[1])\\n\\t\\twhile not chk[1].parent is None:\\n\\t\\t\\tres2.append(chk[1].parent.data)\\n\\t\\t\\tchk[1]=chk[1].parent\\n\\t\\treturn res\\n\\telif flag==2:\\n\\t\\twhile not chk[1].parent is None:\\n\\t\\t\\tres1.append(chk[1].parent.data)\\n\\t\\t\\tchk[1]=chk[1].parent\\n\\t\\tif not res1 is None:\\n\\t\\t\\tres+=res1.reverse()\\n\\t\\tres.append(chk[0])\\n\\t\\twhile not chk[0].parent is None:\\n\\t\\t\\tres2.append(chk[0].parent.data)\\n\\t\\t\\tchk[0]=chk[0].parent\\n\\t\\treturn res\\n\\telse:\\n\\t\\tprint \\\"The given configuration is not solvable/wrong.\\\"\",\n \"def _interconnect(self):\\n self.clear_structure()\\n self.structure.append(self.source)\\n for i in range(len(self.stack)):\\n self.structure.append(self.stack[i])\\n self.structure.append(self.terminator)\\n return\",\n \"def stack(*bpfs) -> core.Stack:\\n if len(bpfs) == 1 and isinstance(bpfs[0], (list, tuple)):\\n bpfs = bpfs[0]\\n return core.Stack(bpfs)\",\n \"def resolve_stack(self):\\n if not callable(self.alias):\\n return\\n # check that we actual need the stack\\n sig = inspect.signature(self.alias)\\n if len(sig.parameters) <= 5 and \\\"stack\\\" not in sig.parameters:\\n return\\n # compute the stack, and filter out these build methods\\n # run_subproc() is the 4th command in the stack\\n # we want to filter out one up, e.g. subproc_captured_hiddenobject()\\n # after that the stack from the call site starts.\\n stack = inspect.stack(context=0)\\n assert stack[3][3] == \\\"run_subproc\\\", \\\"xonsh stack has changed!\\\"\\n del stack[:5]\\n self.stack = stack\",\n \"def test_iter_sll_base(self):\\n sll = SinglyLinkedList()\\n a = Node('a')\\n b = Node('b')\\n sll.insert_beg(a)\\n sll.insert_beg(b)\\n for i in sll:\\n print i\",\n \"def stacklists(arg):\\r\\n if isinstance(arg, (tuple, list)):\\r\\n return stack(*map(stacklists, arg))\\r\\n else:\\r\\n return arg\",\n \"def stack():\\n return currentframe().f_back.f_locals.setdefault(SN, [])\",\n \"def breadth_first(self, xy1, xy2):\\n tile_col1, tile_row1 = self.the_map.xy_to_cr(xy1[0], xy1[1])\\n tile_col2, tile_row2 = self.the_map.xy_to_cr(xy2[0], xy2[1])\\n \\n successor_to_parent_map = {}\\n start_state = (tile_col1, tile_row1)\\n successor_to_parent_map[(start_state, None)] = None # (Successor, Action) -> (Parent, Action)\\n \\n open_list = PriorityQueue()\\n open_list.update((start_state, None), 0)\\n closed = []\\n \\n while not open_list.isEmpty():\\n current_state, action_to_current_state = open_list.pop()\\n \\n if current_state == (tile_col2, tile_row2):\\n return self.__get_action_path((current_state, action_to_current_state), successor_to_parent_map)\\n \\n if current_state not in closed:\\n for successor_state, action, step_cost in self.__get_successors(current_state):\\n open_list.update((successor_state, action), 0)\\n \\n if successor_state not in closed:\\n successor_to_parent_map[(successor_state, action)] = (current_state, action_to_current_state)\\n \\n closed.append(current_state)\\n \\n return []\",\n \"def __init__(self):\\r\\n self.stack = deque()\\r\\n self.minEle = deque()\",\n \"def __init__(self):\\n self.head = None\\n self.stack = list()\",\n \"def pop(self):\\r\\n if len(self.s2)!=0:\\r\\n return self.s2.pop()\\r\\n while len(self.s1)!=0:\\r\\n self.s2.append(self.s1.pop())\\r\\n return self.s2.pop()\",\n \"def sm_get_matching_blocks(s1,s2,min_length=1):\\n anslist= list(SM(None, s1, s2).get_matching_blocks())\\n\\n\\n anslist = [ l for l in anslist if l.size>=min_length]\\n\\n anslist=[ (s1[l.a:l.a+l.size], l.a, l.b, l.size) for l in anslist]\\n return anslist\",\n \"def symbol_execute2(self):\\n start_s = self._create_start_state()\\n root = NodeTreeNode(None)\\n # self.symbol_execute2_single(root, start_s)\\n self.symbol_execute2_single_tail([NodeStatePair(root, start_s)])\\n return root\",\n \"def peek(self):\\n if len(self.stack2) != 0:\\n return self.stack2[-1]\\n else:\\n while len(self.stack1) != 0:\\n self.stack2.append(self.stack1.pop())\\n return self.stack2[-1]\",\n \"def __init__(self):\\n self.stack = list()\\n self.max_stack = list()\\n self.sec_stack = list()\",\n \"def __init__(self):\\n self.min_stack = []\",\n \"def __init__(self):\\n self._input_stack = []\\n self._output_stack = []\",\n \"def __init__(self):\\r\\n self.stack=[]\\r\\n self.min=[]\",\n \"def __init__(self):\\n self.stack_top = DLL()\\n self.sorted_stack = SortedDict() ## key: val; val: list of DLL nodes\",\n \"def stack(context=1):\\r\\n return getouterframes(sys._getframe(1), context)\",\n \"def DFS(graph, s, n):\\n # Stack implemented using list. list.append() and list.pop() inherently have LIFO structure.\\n visited = [False] * n\\n stack = []\\n \\n stack.append(s)\\n visited[s] = True\\n while(stack):\\n v = stack.pop()\\n print(v, end= \\\" \\\")\\n \\n for i in range(len(graph[v])):\\n if (not visited[graph[v][i]]):\\n stack.append( graph[v][i] )\\n visited[graph[v][i]] = True\",\n \"def __call__(container, name, object):\",\n \"def swap_stlcontainers(self, s):\\n old = self.stlcontainers\\n self.stlcontainers = s\\n self.clearmemo()\\n yield\\n self.clearmemo()\\n self.stlcontainers = old\",\n \"def test_insert_node_singleton_content_2():\\n first = 0\\n second = first\\n chain = N.Node(second)\\n node = N.Node(first)\\n\\n result = A8.insert_node(node, chain)\\n\\n assert result.data == first, \\\"insert_node returned incorrect data value first given a node and singleton chain\\\"\\n assert result.next.data == second, \\\"insert_node returned incorrect data value second given a node and singleton chain\\\"\",\n \"def __init__(self):\\r\\n self.stack = []\\r\\n self.minEle = 2**31 - 1\",\n \"def slice_graph_fwd( startea, reg ): \\r\\n\\tgraph = vcg_Graph.vcgGraph({\\\"title\\\":'\\\"Slice for %s\\\"' % reg, \\\\\\r\\n\\t\\t\\\"manhattan_edges\\\":\\\"no\\\", \\\"layoutalgorithm\\\":\\\"maxdepth\\\"})\\r\\n\\t#\\r\\n\\t# Retrieve the name of the current basic block\\r\\n\\t# \\r\\n\\tworklist = []\\r\\n\\tdata_bib = {}\\r\\n\\tstartnode = slice_node( startea, 0, reg )\\r\\n\\trootnode = graph.Add_Node( startnode.to_name() )\\r\\n\\tdata_bib[ startnode.to_name() ] = startnode\\r\\n\\tworklist.insert( 0, rootnode )\\r\\n\\twhile len( worklist ) > 0:\\r\\n\\t\\tcurrnode = worklist.pop()\\r\\n\\t\\tcurrslice = data_bib[ currnode.get_name() ]\\r\\n\\t\\ttgt_reg = currslice.get_target_reg()\\r\\n\\t\\tif tgt_reg == \\\"END\\\":\\r\\n\\t\\t# Do not process this node any further\\r\\n\\t\\t\\tpass\\r\\n\\t\\telif tgt_reg == \\\"\\\" or (( len( currslice.get_lines()) > 0) and \\\\\\r\\n\\t\\t\\tcurrslice.endea != currslice.get_lines()[-1][0]):\\r\\n\\t\\t\\t# Nothing much happening here, just proceed to parent bocks\\r\\n\\t\\t\\tif ua_mnem( currslice.endea ) == \\\"call\\\":\\r\\n\\t\\t\\t\\txrefs = get_short_crefs_from( currslice.endea )\\r\\n\\t\\t\\telse:\\r\\n\\t\\t\\t\\txrefs = get_crefs_from( currslice.endea )\\r\\n\\t\\t\\tfor ref in xrefs:\\r\\n\\t\\t\\t\\tnewslice = slice_node( ref, 0, currslice.reg )\\r\\n\\t\\t\\t\\tif graph.Get_Node( newslice.to_name() ) == 0:\\r\\n\\t\\t\\t\\t\\tnewnode = graph.Add_Node( newslice.to_name() )\\r\\n\\t\\t\\t\\t\\tworklist.insert( 0, newnode )\\r\\n\\t\\t\\t\\t\\tdata_bib[ newslice.to_name() ] = newslice\\r\\n\\t\\t\\t\\tgraph.Add_Link( currnode.get_name(), newslice.to_name())\\r\\n\\t\\telse:\\r\\n\\t\\t\\t# Register was modified, use new register\\r\\n\\t\\t\\txrefs = get_crefs_from( currslice.endea )\\r\\n\\t\\t\\tfor ref in xrefs:\\r\\n\\t\\t\\t\\tnewslice = slice_node( ref, 0, tgt_reg )\\r\\n\\t\\t\\t\\tif graph.Get_Node( newslice.to_name() ) == 0:\\r\\n\\t\\t\\t\\t\\tnewnode = graph.Add_Node( newslice.to_name() )\\r\\n\\t\\t\\t\\t\\tworklist.insert( 0, newnode )\\r\\n\\t\\t\\t\\t\\tdata_bib[ newslice.to_name() ] = newslice\\r\\n\\t\\t\\t\\tgraph.Add_Link( currnode.get_name(), newslice.to_name())\\r\\n\\t\\t\\txrefs = get_crefs_from( currslice.endea )\\r\\n\\t\\t\\tfor ref in xrefs:\\r\\n\\t\\t\\t\\tnewslice = slice_node( ref, 0, currslice.reg )\\r\\n\\t\\t\\t\\tif graph.Get_Node( newslice.to_name() ) == 0:\\r\\n\\t\\t\\t\\t\\tnewnode = graph.Add_Node( newslice.to_name() )\\r\\n\\t\\t\\t\\t\\tworklist.insert( 0, newnode )\\r\\n\\t\\t\\t\\t\\tdata_bib[ newslice.to_name() ] = newslice\\r\\n\\t\\t\\t\\tgraph.Add_Link( currnode.get_name(), newslice.to_name())\\r\\n\\treturn [ graph, data_bib ]\",\n \"def __init__(self):\\n self.stack = collections.deque([])\",\n \"def Children(self) -> _n_1_t_2:\",\n \"def forward(self, s):\",\n \"def __init__(self):\\n self.datastack = []\\n self.minstack = []\",\n \"def __init__(self):\\n self.data_stack = []\\n self.min_stack = []\",\n \"def fn(node):\\n if not node: return #null node\\n stack.append(node.val)\\n if node.left is node.right: ans.append(\\\"->\\\".join(map(str, stack))) #leaf node\\n fn(node.left) or fn(node.right)\\n stack.pop()\",\n \"def double_stack(s: Stack) -> Stack:\\n temp = Stack() # Stack to restore original values\\n duplicated = Stack() # The stack to return.\\n while not s.is_empty():\\n val = s.pop()\\n temp.push(val)\\n # Add duplicates for each value.\\n duplicated.push(val)\\n duplicated.push(val)\\n\\n # Restore the original stack.\\n while not temp.is_empty():\\n s.push(temp.pop())\\n\\n return duplicated\",\n \"def size(s: Stack) -> int:\\n side_stack = Stack()\\n count = 0\\n # Pop everything off <s> and onto <side_stack>, counting as we go.\\n while not s.is_empty():\\n side_stack.push(s.pop())\\n count += 1\\n # Now pop everything off <side_stack> and back onto <s>.\\n while not side_stack.is_empty():\\n s.push(side_stack.pop())\\n # <s> is restored to its state at the start of the function call.\\n # We consider that it was not mutated.\\n return count\",\n \"def __init__(self):\\n self.stack = list()\\n self.count = 0\",\n \"def stack(self, x, layers):\\n if not layers:\\n return x # Base case of the recursion is the just returning the input\\n else:\\n return layers[0]()(self.stack(x, layers[1:]))\",\n \"def stack(self, x, layers):\\n if not layers:\\n return x # Base case of the recursion is the just returning the input\\n else:\\n return layers[0]()(self.stack(x, layers[1:]))\",\n \"def __init__(self):\\n self.stack = collections.deque()\",\n \"def list_stack(st: Stack, lst: list) -> None:\\n for element in lst:\\n st.add(element)\\n # for sth in st:\\n # if type(sth) == list:\\n # for e in sth:\\n # st.add(e)\\n while not st.is_empty():\\n # new =[]\\n top = st.remove()\\n st.add(top)\\n if type(top) == list:\\n st.remove()\\n for item in top:\\n st.add(item)\\n # st.remove()\\n print(st.remove())\",\n \"def __init__(self):\\n self.stack = []\\n self.min_elements = []\",\n \"def rewrite(self, l1, l2):\\n l1Stack = []\\n l2Stack = []\\n\\n def fill(stack, li):\\n while li:\\n stack.append(li.val)\\n li = li.next\\n\\n fill(l1Stack, l1)\\n fill(l2Stack, l2)\\n\\n prev = None\\n\\n while l1Stack and l2Stack:\\n if prev:\\n cv = prev.val + l1Stack.pop() + l2Stack.pop()\\n prev.val = cv % 10\\n tmp = ListNode(cv / 10)\\n tmp.next = prev\\n prev = tmp\\n else:\\n cv = l1Stack.pop() + l2Stack.pop()\\n tmp = ListNode(cv % 10)\\n prev = ListNode(cv / 10)\\n prev.next = tmp\\n\\n def single(stack, prev):\\n while stack:\\n if prev:\\n cv = prev.val + stack.pop()\\n prev.val = cv % 10\\n tmp = ListNode(cv / 10)\\n tmp.next = prev\\n prev = tmp\\n else:\\n tmp = ListNode(stack.pop())\\n prev = ListNode(None)\\n prev.next = tmp\\n return prev\\n\\n prev = single(l1Stack, prev)\\n prev = single(l2Stack, prev)\\n\\n return prev if prev.val else prev.next\",\n \"def __init__(self):\\r\\n self.popStack = []\\r\\n self.pushStack = []\",\n \"def test_push_on_empty_stack(empty_stack): \\n empty_stack.push([1, 'Good Boy'])\\n assert empty_stack.top.val == [1, 'Good Boy']\",\n \"def explore(self, *args):\",\n \"def __init__(self):\\n self.stack = []\\n self.index_last = -1\",\n \"def headsofunion(h1, h2):\\n res = unfi.set(b'heads((%ln + %ln - null))', h1, h2)\\n return {ctx.node() for ctx in res}\",\n \"def __init__(self):\\n\\n self.container2 = []\\n heapq.heapify(self.container2)\",\n \"def recursion(self, l1,l2):\\r\\n c = l1.val + l2.val\\r\\n r = 0\\r\\n \\r\\n if c > 9:\\r\\n c = c - 10\\r\\n r = 1\\r\\n\\r\\n ans = ListNode(c)\\r\\n \\r\\n def goingIn(ans, l1, l2, r):\\r\\n one = l1\\r\\n two = l2\\r\\n c = 0\\r\\n \\r\\n if one != None:\\r\\n c += one.val\\r\\n \\r\\n if two != None:\\r\\n c+= two.val\\r\\n\\r\\n if r == 1:\\r\\n c += r\\r\\n r = 0\\r\\n\\r\\n if c > 9:\\r\\n c = c - 10\\r\\n r = 1\\r\\n\\r\\n ans.next = ListNode(c)\\r\\n \\r\\n if one== None and two == None:\\r\\n if r == 1:\\r\\n goingIn(ans.next, None, None, r)\\r\\n else:\\r\\n if c == 0:\\r\\n ans.next = None\\r\\n\\r\\n \\r\\n elif one != None and two != None:\\r\\n goingIn( ans.next, l1.next, l2.next, r)\\r\\n \\r\\n else:\\r\\n if one != None:\\r\\n goingIn(ans.next, l1.next, None, r)\\r\\n \\r\\n if two != None:\\r\\n goingIn(ans.next, None, l2.next, r)\\r\\n\\r\\n\\r\\n goingIn(ans, l1.next, l2.next, r)\\r\\n \\r\\n return ans\"\n]","isConversational":false},"negative_scores":{"kind":"list like","value":["0.6528504","0.6369448","0.62064385","0.6019769","0.5988017","0.5758984","0.56731415","0.5416152","0.5399732","0.5384572","0.53297","0.5311962","0.5303379","0.5283734","0.5267882","0.5263877","0.52613163","0.52601475","0.5260031","0.52584153","0.52553326","0.5250562","0.5206882","0.51929706","0.51844895","0.5181299","0.5181299","0.5181299","0.5181299","0.5181299","0.5181299","0.5181299","0.5181299","0.5181299","0.5181299","0.5181299","0.5177668","0.5174562","0.51541716","0.51541716","0.51541716","0.51472414","0.5145141","0.51364756","0.51288545","0.51264125","0.5116438","0.5106675","0.5105432","0.5104505","0.5080271","0.5059731","0.5033467","0.5029192","0.5005839","0.5004085","0.50006914","0.49992946","0.49949098","0.4993956","0.49844027","0.49691376","0.4963558","0.49635264","0.49529937","0.4946269","0.49412808","0.4938145","0.49311125","0.4920844","0.49150944","0.49082068","0.49053413","0.48961276","0.489047","0.48846075","0.48816052","0.4881352","0.48761564","0.4864291","0.48610964","0.48557454","0.48554027","0.4854841","0.4853883","0.4845903","0.48446435","0.48395512","0.48373196","0.48373196","0.48355663","0.48228636","0.48163018","0.4815868","0.48136568","0.48130664","0.4808675","0.48062292","0.48034856","0.47899178","0.47893435"],"string":"[\n \"0.6528504\",\n \"0.6369448\",\n \"0.62064385\",\n \"0.6019769\",\n \"0.5988017\",\n \"0.5758984\",\n \"0.56731415\",\n \"0.5416152\",\n \"0.5399732\",\n \"0.5384572\",\n \"0.53297\",\n \"0.5311962\",\n \"0.5303379\",\n \"0.5283734\",\n \"0.5267882\",\n \"0.5263877\",\n \"0.52613163\",\n \"0.52601475\",\n \"0.5260031\",\n \"0.52584153\",\n \"0.52553326\",\n \"0.5250562\",\n \"0.5206882\",\n \"0.51929706\",\n \"0.51844895\",\n \"0.5181299\",\n \"0.5181299\",\n \"0.5181299\",\n \"0.5181299\",\n \"0.5181299\",\n \"0.5181299\",\n \"0.5181299\",\n \"0.5181299\",\n \"0.5181299\",\n \"0.5181299\",\n \"0.5181299\",\n \"0.5177668\",\n \"0.5174562\",\n \"0.51541716\",\n \"0.51541716\",\n \"0.51541716\",\n \"0.51472414\",\n \"0.5145141\",\n \"0.51364756\",\n \"0.51288545\",\n \"0.51264125\",\n \"0.5116438\",\n \"0.5106675\",\n \"0.5105432\",\n \"0.5104505\",\n \"0.5080271\",\n \"0.5059731\",\n \"0.5033467\",\n \"0.5029192\",\n \"0.5005839\",\n \"0.5004085\",\n \"0.50006914\",\n \"0.49992946\",\n \"0.49949098\",\n \"0.4993956\",\n \"0.49844027\",\n \"0.49691376\",\n \"0.4963558\",\n \"0.49635264\",\n \"0.49529937\",\n \"0.4946269\",\n \"0.49412808\",\n \"0.4938145\",\n \"0.49311125\",\n \"0.4920844\",\n \"0.49150944\",\n \"0.49082068\",\n \"0.49053413\",\n \"0.48961276\",\n \"0.489047\",\n \"0.48846075\",\n \"0.48816052\",\n \"0.4881352\",\n \"0.48761564\",\n \"0.4864291\",\n \"0.48610964\",\n \"0.48557454\",\n \"0.48554027\",\n \"0.4854841\",\n \"0.4853883\",\n \"0.4845903\",\n \"0.48446435\",\n \"0.48395512\",\n \"0.48373196\",\n \"0.48373196\",\n \"0.48355663\",\n \"0.48228636\",\n \"0.48163018\",\n \"0.4815868\",\n \"0.48136568\",\n \"0.48130664\",\n \"0.4808675\",\n \"0.48062292\",\n \"0.48034856\",\n \"0.47899178\",\n \"0.47893435\"\n]","isConversational":false},"document_score":{"kind":"string","value":"0.0"},"document_rank":{"kind":"string","value":"-1"}}},{"rowIdx":75388,"cells":{"query":{"kind":"string","value":"Compute the GaussLobatto quadrature"},"document":{"kind":"string","value":"def jacobi_gauss_lobatto_nodes(alpha, beta, N, # noqa: N803\n backend=None, force_dim_axis=False):\n\n x = np.zeros((N + 1,))\n if N == 0:\n x[0] = 0\n return x\n\n x[0] = -1\n x[-1] = 1\n\n if N > 1:\n quad = JacobiGaussQuadrature(alpha + 1, beta + 1, N - 2,\n backend=backend, force_dim_axis=True)\n x[1:-1] = quad.nodes[0].real\n\n if force_dim_axis:\n x = x.reshape(1, -1)\n\n return x"},"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 make_quad_gauss(lmax,alm):\n return libcurvedsky.bispec.make_quad_gauss(lmax,alm)","def test_ln_gauss(self):\n Q = likelihood()\n test_object = test_values_for_likelihood()\n experimental_values = [0.9,2.1,3.2]\n errors = [0.5,0.4,0.3]\n reference_q_value = 0.27347222222222267\n reference_q_derivative = np.array([-3.75833333, -3.75833333])\n Q.add_observable(test_object,experimental_values,errors,scale=1.0)\n q_value, q_derivative = Q.compute_ln_gauss(np.array([1.0,1.0]))\n assert np.isclose(q_value,reference_q_value)\n assert np.all(np.isclose(q_derivative,reference_q_derivative))\n return","def gauss_seidel(self):\n for i in range(1,self.size[0]-1):\n for j in range(1,self.size[1]-1):\n for k in range(1,self.size[2]-1):\n self.A[(i,j,k)] = ((1/6)*(self.A[(i+1,j,k)] + self.A[(i-1,j,k)] + self.A[(i,j+1,k)] + self.A[(i,j-1,k)] + self.A[(i,j,k+1)] + self.A[(i,j,k-1)] + self.J[(i,j,k)]) - self.A[(i,j,k)])*self.omega + self.A_0[(i,j,k)]","def cost_gauss(self):\n J = np.mean(self.zvar1/self.rvar1 + np.log(2*np.pi*self.rvar1))\n J = J*np.product(self.shape1)\n if not self.is_complex:\n J = J / 2\n return J","def gauss(x,p):\n return np.exp((-(x - p[0])**2) / (2 * p[1]**2))","def fun_gauss_gauss(p,r):\n return p[1] * np.exp(-((r/p[0])**2)) + p[3] * np.exp(-((r/p[2])**2))","def phi_gauss(self,x,i):\n s = 0.1\n return np.exp(-(x-self.mu[i])**2/(2*s))","def kl_gauss(x, y, sig2=1.):\n return (x - y) ** 2 / (2 * sig2)","def gauss(x, gamma):\n return 1 / np.sqrt(2*np.pi) / gamma * np.exp(-(x/gamma)**2 / 2)","def gauss_quadrature(ab, N):\n\n from numpy.linalg import eigh\n\n n = ab.shape[0]\n if n+1 < N:\n raise IndexError('Require N+1 recurrence coefficients for an N-point rule.')\n\n J = np.diag(ab[1:N,1], k=1) + np.diag(ab[:N,0],k=0) + np.diag(ab[1:N,1], k=-1)\n lamb,v = eigh(J)\n\n return lamb, v[0,:]**2","def gauss(self, X, xm, amp, w):\n return amp * np.exp(-((X - xm) / w) ** 2)","def gauss_seidel(coeficientes, semilla, b, i, n):\n suma = 0\n for j in range(n):\n if j != i and coeficientes[j] != 0:\n suma += (coeficientes[j] * semilla[j]) / coeficientes[i]\n return (b / coeficientes[i]) - suma","def fun_lorentzian_p_gauss(p,r):\n return p[1] / ((r/p[0])**2 + 1) + p[2] * np.exp(-((r/p[0])**2)/2)","def gaussseidel_poissoneq(A, x0):\n return 1","def gauss(x, x0, gamma):\n sigma = gamma / sqrt(2.0)\n \n A = 1/ (sigma * sqrt(2*pi))\n return (A * exp (-0.5 * (x-x0)**2/sigma**2))","def fun_gauss(p,r):\n return p[1] * np.exp(-((r/p[0])**2))","def gauss(self, k, m, z):\n Rvir = self.U.rVir(m, z)\n result = np.exp(-0.5 * Rvir**2 * k**2)\n return result","def gauss(x, *p):\n mu, sigma = p\n return (1 / (sigma * np.sqrt(2 * np.pi)) *\n np.exp(-(x - mu) ** 2 / (2. * sigma ** 2)))","def fun_lorentzian_t_gauss(p,r):\n return p[2] / ((r/p[0])**2 + 1) * np.exp(-((r/p[1])**2)/2)","def kl_gauss_gauss(q_mean, q_logvar, p_mean, p_logvar, varmin=1e-16):\n q_logvar = np.log(np.exp(q_logvar) + varmin)\n p_logvar = np.log(np.exp(p_logvar) + varmin)\n return (0.5 * (p_logvar - q_logvar + np.exp(q_logvar - p_logvar)\n + np.square((q_mean - p_mean) / np.exp(0.5 * p_logvar)) - 1.0))","def g(self, X):\n\n return (X[0])**2 - 2*X[0] + X[1]**3 - 2","def gaussElimin(a,b):\n a=float64(a)\n b=float64(b)\n n=len(b)\n x=zeros((n,1),dtype=float)\n for k in range(n-1):\n for i in range(k+1,n):\n l=float(a[i][k])/a[k][k]\n\t a[i][k]=0\n\t for j in range(k+1,n):\n\t a[i][j]=a[i][j]-l*a[k][j]\n\t b[i]=b[i]-l*b[k]\n x[n-1]=float(b[n-1])/a[n-1][n-1]\n for i in range(n-2,-1,-1):\n sum=b[i]\n for j in range(i+1,n):\n sum=sum-a[i][j]*x[j]\n x[i]=float(sum)/a[i][i]\n return x","def gauss_seidel(L, U, S, x, k):\n\tn = len(x) - 1\n\tm = len(x)//2\n\t\n\t# Leftmost\n\tx[0] = (S[0]/k - U[0, 1]*x[1])/L[0, 0]\n\t# Interior\n\tfor i in range(1, m):\n\t\tx[i] = (S[i]/k - L[i, i - 1]*x[i - 1] - U[i, i + 1]*x[i + 1])/L[i, i]\n\tfor i in range(m, n):\n\t\tx[i] = (S[i]/k - L[i, i - 1]*x[i - 1] - U[i, i + 1]*x[i + 1] -\n\t\t L[i,i-m]*x[i-m]) / L[i,i]\n\t# Rightmost\n\tx[n] = (S[n]/k - L[n, n - 1]*x[n - 1] - L[n, n - m]*x[n - m])/L[n, n]\n\treturn x","def gauss(x, y, ax, ay, x0, y0, phase):\n g_x = ((ax / sqrt(pi)) ** 0.5\n * exp(-0.5 * ((x - x0) * ax) ** 2))\n g_y = ((ay / sqrt(pi)) ** 0.5\n * exp(-0.5 * ((y - y0) * ay) ** 2))\n\n gxy = np.zeros((len(x),len(y)), dtype=float)\n for i, _gx in enumerate(g_x):\n for j, _gy in enumerate(g_y):\n gxy[i,j] = _gx * _gy \n\n gxy2 = (1.0 / sqrt(1.0+abs(phase))) * np.array([gxy, phase*gxy], dtype=float) \n\n return gxy2","def Gauss(self, x, height, centre, width, b=0):\n\n return height * np.exp(-(x - centre)**2 / (2 * width**2)) - b","def G(U):\n n = U.shape[0]\n G_U = np.zeros([n,1])\n DELTA_x = float(2*L)/(n-1)\n for i in range(n):\n G_U[i][0] = U[(i+1)%n][0]\n G_U[i][0] -= U[(i-1)%n][0]\n G_U[i][0] /= (2* DELTA_x)\n G_U[i][0] += (float(epsilon) * (U[(i+1)%n][0]- U[(i-1)%n][0]) * (U[(i-1)%n][0]+U[(i+1)%n][0]+ U[i][0])/ (4* DELTA_x))\n G_U[i][0] += (float(epsilon) * (U[(i+2)%n][0]- 2*U[(i+1)%n][0]+ 2*U[(i-1)%n][0]- U[(i-2)%n][0]) / (12*( DELTA_x**3)))\n return G_U","def gauss_naive (M, b) -> list:\n dim = len(b)\n\n #Itero sulle Incognite da Trovare\n for i in range(dim):\n\n #Itero sulle righe su cui devo cancellare un elemento\n for j in range(i+1,dim):\n m__j_i = M[j][i] / M[i][i]\n M[j][i] = 0.0\n\n for k in range (i+1,dim):\n M[j][k] = M[j][k] - m__j_i * M[i][k]\n \n b[j] = b[j] - m__j_i * b[i]\n\n return M,b","def gauss(x, *p):\n A, mu, sigma = p\n\n return A*np.exp(-(x-mu)**2/(2.*sigma**2))","def legendreGauss (func, deg, a, b, ind, bsp, ind2=0):\n\n\tx, w = np.polynomial.legendre.leggauss(deg)\n\tt = 0.5*(x+1)*(b-a)+ a\n\t\n\tgauss = sum(w + func(t, bsp, ind, ind2))*( 0.5*(b-a))\n\n\treturn gauss","def gelu(x):\n return x * 0.5 * (1.0 + F.tanh(F.sqrt(2 / math.pi) * (x + 0.044715 * (x ** 3))))","def gauss_quad_1d(q, n=150):\n z, w = leggauss(n)\n theta = (THETA_HIGH-THETA_LOW)*(z + 1)/2 + THETA_LOW\n sin_theta = abs(sin(theta))\n Zq = kernel_1d(q=q, theta=theta)\n return np.sum(Zq*w*sin_theta)*(THETA_HIGH-THETA_LOW)/2","def gauss_rule(iel, elemType, normal_order, element):\n\n #sctr = element[iel, :] # element connectivity\n\n if ((elemType == 'Q4') and (normal_order <8)):\n W, Q = gpw.gauss_pt_wt(normal_order,'GAUSS',2)\n return W, Q","def gauss_func(x, wid, cen, amp):\n\n return np.exp(-((x-cen)**2.)/(2.*wid**2)) * amp","def gaussum(xdata,*params):\n\tamp = num.zeros(0)\n\tcen = num.zeros(0)\n\tstdv = num.zeros(0)\n\n\tfor i in range(0, len(params), 3): #This section is just unpacking the parameter array into amps, cens, and stdvs\n\t\tx = params[i]\n\t\tamp = num.append(amp,x)\n\t\ty = params[i+1]\n\t\tcen = num.append(cen,y)\n\t\tz = params[i+2]\n\t\tstdv = num.append(stdv,z)\n\tglobal storage #You may not need storage to be global so think about taking this part out. storage stores the data\n\tstorage = [[0 for x in range(1)] for x in range(len(params)/3)] #from each iteration of the gaussian equation into\n\tfor i in range(len(params)/3):#individual rows. So row one will be the gaussian solutions to the first peak and so on\n\t\tstorage[i] = gaus(xdata,amp[i],cen[i],stdv[i])\n\tstorage = num.asarray(storage)\n\treturn sum(storage)","def gauss_wp(self, delta, x0=0):\n constant = (delta/np.pi)**(1/4) * (1.+0.j) \n wp = constant * np.exp((-0.5 * delta * np.square(self.grid_x - x0)))\n return wp","def logGauss(self):\n #firstly initialise an array to store the values\n log = np.zeros([self.num])\n \n #now want to loop through each of the points in the collections\n for i in range(self.num):\n #get the point as an array\n point = self.pick(i)\n #key characteristic of standard normal: can treat as product of independent 1D normals\n log[i] = self.d - np.log(np.sqrt(2 * np.pi)) - 0.5 * np.sum(point**2)\n return log","def gaussKern(size):\n size = int(size)\n x, y = np.mgrid[-size:size + 1, -size:size + 1]\n g = np.exp(-(x**2/float(size) + y**2/float(size)))\n return g / g.sum()","def gauss_term_fn(iteration_count, v, z):\n return tf.math.square(z) / 4. / (\n (v + iteration_count - 1) * (v + iteration_count))","def gauss_hermite(dim=1, num_quad_pts=20):\n # sigma_pts, weights = hermgauss(num_quad_pts) # Gauss-Hermite sigma points and weights\n sigma_pts, weights = mvhermgauss(num_quad_pts, dim)\n sigma_pts = np.sqrt(2) * sigma_pts.T\n weights = weights.T * pi ** (-0.5 * dim) # scale weights by 1/√π\n return sigma_pts, weights","def GL(mu, wid, x, m = 0.5): \n return m * gaussian(mu, wid, x) + (1 - m) * lorentzian(mu, wid, x)","def klucb_gauss(x, d, sig2=1., precision=0.):\n return x + sqrt(2*sig2*d)","def doubling_gauss(f, xl: float, xr: float, n: int, tol: float, *params):\n # required local variables to return\n # S : computed value of the integral with required tolerance\n # N : number of steps for S\n # err : estimated error of S\n # iter : number of iterations (steps doubling)\n iter = 1\n N = 10\n m = (2 * n - 1) + 1\n\n # считаем интеграл с шагом h\n s1 = composite_gauss(f, xl, xr, N, n, *params)\n # цикл по итерациям\n while iter != MAXITER + 1:\n N *= 2\n # считаем интеграл с шагом h/2\n s2 = composite_gauss(f, xl, xr, N, n, *params)\n\n # берём погрешность из метода рунге\n estimate = np.abs(runge(s1, s2, 2, m))\n if estimate >= tol:\n s1 = s2\n else:\n return N, s2, estimate\n\n iter += 1\n\n if iter == MAXITER:\n print(\"Convergence not reached!\")\n return -1","def _malmquist_q_lngamma(self, gamma, f_bin):\n func = np.poly1d(self.malm_pars[::-1])\n const_factor = (1 - gamma) / (self.high_q ** (1 - gamma) - self.low_q ** (1 - gamma))\n integral = np.sum(\n [p * const_factor / (i + 1 - gamma) * (self.high_q ** (i + 1 - gamma) - self.low_q ** (i + 1 - gamma))\n for i, p in enumerate(self.malm_pars)])\n Pobs = integral if self.malm_pars.size == 1 and self.malm_pars[0] == 1 else self.Pobs\n denom = f_bin*integral + (1-f_bin)*Pobs\n logging.debug('Denominator = {}\\nIntegral = {}\\nPobs = {}\\n'.format(denom, integral, Pobs))\n return np.log(func(self.q)) + np.log(f_bin) + np.log(const_factor) - gamma*self.lnq - np.log(denom)","def gelu(x):\n return f_gelu(x)","def u(self, k, m, z):\n result = self.fGauss * self.gauss(k, m, z)\n result += (1. - self.fGauss) * self.nfw(k, m, z)\n result *= m / self.U.rho_m(z)\n return result","def gauss3(x,a1,c1,w1,a2,c2,w2,a3,c3,w3):\n return gaussian(x,a1,c1,w1)+gaussian(x,a2,c2,w2)+gaussian(x,a3,c3,w3)","def onedgauss(x,H,A,dx,w):\n #H,A,dx,w = params\n return H+A*np.exp(-(x-dx)**2/(2*w**2))","def g(x):\n return 5. - x[:, 1] - .5 * x[:, 0] ** 2.","def unitary_Gauss(x, center, FWHM):\n \n sigma = np.abs(FWHM) /( 2 * np.sqrt(2 * np.log(2)) );\n Amp = 1.0 / (sigma*np.sqrt(2*np.pi))\n tau = -((x - center)**2) / (2*(sigma**2))\n result = Amp * np.exp( tau );\n \n return result","def _malmquist(self, gamma):\n func = np.poly1d(self.malm_pars[::-1])\n const_factor = (1 - gamma) / (self.high_q ** (1 - gamma) - self.low_q ** (1 - gamma))\n denom = np.sum(\n [p * const_factor / (i + 1 - gamma) * (self.high_q ** (i + 1 - gamma) - self.low_q ** (i + 1 - gamma))\n for i, p in enumerate(self.malm_pars)])\n return func, denom","def _FSpecialGauss(size, sigma):\n radius = size // 2\n offset = 0.0\n start, stop = -radius, radius + 1\n if size % 2 == 0:\n offset = 0.5\n stop -= 1\n x, y = np.mgrid[offset + start:stop, offset + start:stop]\n assert len(x) == size\n g = np.exp(-((x**2 + y**2)/(2.0 * sigma**2)))\n return g / g.sum()","def _kld_gauss(self, mean_1, std_1, mean_2, std_2):\n kld_element = (2 * torch.log(std_2) - 2 * torch.log(std_1) + (std_1.pow(2) + (mean_1 - mean_2).pow(2)) / std_2.pow(2) - 1)\n return\t0.5 * torch.sum(kld_element)","def gauss2d(param, x, y):\n #2010-01-11 22:46 IJC: Created\n from numpy import array, abs, concatenate, exp\n x = array(x, dtype=float).copy()\n y = array(y, dtype=float).copy()\n p = array(param).copy()\n\n r = abs((x-p[2]) + 1j*(y-p[3]))\n\n if len(p)==4:\n p = concatenate((p, [0]))\n\n z = p[4] + p[0]/(p[1]*4*pi) * exp(-r**2 / (2*p[1]**2))\n \n return z","def _ucbpe_lcb(x):\n mu, sigma = gp.eval(x, uncert_form='std')\n return mu - beta_th * sigma","def _ucbpe_lcb(x):\n mu, sigma = gp.eval(x, uncert_form='std')\n return mu - beta_th * sigma","def Grundy(x):\n # n taille bianire max des xi; m longeur de x\n \n # Calcul de la longueur binaire utilisée\n # Complexité en O(m)\n \n n = 0\n \n for val in x :\n t = taille(val)\n if n < t :\n n = t\n \n \n \n # Ecriture de la liste x en binaire\n # Complexité en O(m*n) car binaire(x,n) en O(n)\n \n b = [binaire(val,n) for val in x ]\n \n # Calcul de la somme binaire mod 2 :\n # Complexité en O(m*n) : m valeur dans b; n tours de boucle\n \n a = []\n \n for i in range(n):\n \n s = 0\n \n for val in b :\n s+= val[i]\n \n a.append(s%2)\n \n return(decimal(a))","def gauss(x, mu, A, sigma):\n mu, A, sigma = np.atleast_2d(mu), np.atleast_2d(A), np.atleast_2d(sigma)\n val = (A / (sigma * np.sqrt(np.pi * 2)) *\n np.exp(-(x[:, None] - mu)**2 / (2 * sigma**2)))\n return val.sum(axis=-1)","def OF1_SumOfGauss(param_list, classNum, g_lvls):\n return sum([param_list[i] * norm.pdf(g_lvls, loc=param_list[i + classNum], \\\n scale=param_list[i + classNum * 2]) \\\n for i in range(classNum)])","def src_gauss(l, m, sigma_lm, A=1., i=0., pa=0., l0=0., m0=0.):\n l = np.atleast_1d(l)\n m = np.atleast_1d(m)\n sigma_x = sigma_lm\n sigma_y = sigma_lm * np.cos(i)\n a = 0.5 * ((np.cos(pa) / sigma_x)**2. + (np.sin(pa) / sigma_y)**2.)\n b = 0.5 * np.sin(2. * pa) * (sigma_x**-2. - sigma_y**-2.)\n c = 0.5 * ((np.sin(pa) / sigma_x)**2. + (np.cos(pa) / sigma_y)**2.)\n p = a * (l - l0)**2. + b * (l - l0) * (m - m0) + c * (m - m0)**2.\n I = A * np.exp(-p) / (2. * pi * sigma_x * sigma_y)\n return I","def geglu(x: Tensor) ->Tensor:\n assert x.shape[-1] % 2 == 0\n a, b = x.chunk(2, dim=-1)\n return a * F.gelu(b)","def lin_gaus(x,m,b,A,mu,sigma):\n return m*x+b + gaus(x,A,mu,sigma)","def gauss_sum_to(n):\n the_sum = n * (n + 1) / 2\n return the_sum","def fun_exp_p_gauss(p,r):\n return p[1] * np.exp(-((r**2/p[0]))) + p[3] * np.exp(-((np.abs(r)/p[2])))","def gauss2(x,a1,c1,w1,a2,c2,w2):\n return gaussian(x,a1,c1,w1)+gaussian(x,a2,c2,w2)","def Gauss_Seidel_Solve(A,b,tol=1.0e-6,max_iterations=100,LOUD=False):\n [Nrow, Ncol] = A.shape\n assert Nrow == Ncol\n N = Nrow\n converged = False\n iteration = 1\n x = np.random.rand(N) #random initial guess \n x_new = np.zeros(N)\n while not(converged):\n x = x_new.copy() #replace old value\n for row in range(N):\n x_new[row] = b[row]\n for column in range(N):\n if column != row:\n #only change from before is that I use x_new in the update\n x_new[row] -= A[row,column]*x_new[column]\n x_new[row] /= A[row,row]\n relative_change = np.linalg.norm(x_new-x)/np.linalg.norm(x_new)\n if (LOUD):\n print(\"Iteration\",iteration,\": Relative Change =\",relative_change)\n if (relative_change < tol) or (iteration >= max_iterations):\n converged = True\n iteration += 1\n return x_new","def gaussian_reduce(w, a, b):\n u = (0, 1)\n v = (1, 0)\n\n if dot(u, v, w, a, b) < 0:\n v = (-v[0], -v[1])\n\n if norm(u, w, a, b) < norm(v, w, a, b):\n u, v = v, u\n\n while norm(u, w, a, b) > norm(v, w, a, b):\n k = dot(u, v, w, a, b) // dot(v, v, w, a, b)\n u, v = v, (u[0]- k*v[0], u[1]- k*v[1])\n\n u, v = v, u\n\n if dot(u, v, w, a, b) < dot(v, v, w, a, b)/2 or norm((u[0]-v[0], u[1]-v[1]), w, a, b) > norm(v, w, a, b):\n c = v\n else:\n c = (u[0] - v[0], u[1] - v[1])\n\n return c[0]*w + b*c[1], c[0]","def Lq(self):\n if not self.isVaild():\n pass\n temp = ((self.r()**self.C)*self.Rho()) / \\\n (math.factorial(self.C)*((1 - self.Rho())**2))\n return temp*self.P0()","def g_rosenbrock(x, a=1, b=100):\n\n g = np.array(\n [\n -2 * a - 4 * b * x[0] * (-x[0] ** 2 + x[1]) + 2 * x[0],\n b * (-2 * x[0] ** 2 + 2 * x[1]),\n ]\n )\n\n return g","def gaussian_elimination(A, b):\n \n m, n = A.shape\n U = A.copy() \n b = b.copy()\n\n # forward sweep, reduce A to a upper triangular matrix\n for k in range(min(m, n)):\n swap = np.argmax(np.abs(U[k:, k])) + k\n if U[swap, k] == 0:\n raise ValueError('Singular matrix')\n U[[k, swap], :] = U[[swap, k], :]\n b[[k, swap]] = b[[swap, k]]\n \n for i in range(k + 1, m):\n factor = U[i, k] / U[k, k]\n b[i] = b[i] - factor*b[k]\n U[i, k+1:] = U[i, k+1:] - U[k, k+1:] * factor\n U[i, k] = 0\n \n # solve by back subistitution\n x = rbackwardsolve(U, b, m)\n\n return x","def der_gauss(shape,sigma):\r\n m,n = [(ss-1.)/2. for ss in shape]\r\n nx, ny = shape\r\n xv = np.linspace(-1, 1, nx)\r\n yv = np.linspace(-1, 1, ny)\r\n x1, y1 = np.meshgrid(xv, yv)\r\n \r\n \r\n h = np.exp( -(x1*x1 + y1*y1) / (2.*sigma*sigma) )\r\n h[ h < np.finfo(h.dtype).eps*h.max() ] = 0\r\n sumh = h.sum()\r\n if sumh != 0:\r\n h /= sumh\r\n gauss_x = -x1/sigma*sigma\r\n h1 = gauss_x * h\r\n return h1","def FormG():\n for i in range(2):\n for j in range(2):\n G[i, j] = 0.0\n for k in range(2):\n for l in range(2):\n G[i, j] = G[i, j] + P[k, l] * (TT[i, j, k, l] - 0.5 * TT[i, j, k, l])","def gaussint(x, mean=0.0, sigma=1.0):\n z = (x - mean) / (math.sqrt(2) * sigma)\n return (erf(z) + 1.0) / 2.0","def limdrift(g, tau, acyrus=0.25):\n tot = np.linalg.norm(g, axis=1) * acyrus\n mask = tot > 1e-8\n taueff = np.ones(tot.shape) * tau\n taueff[mask] = (np.sqrt(1 + 2 * tau * tot[mask]) - 1) / tot[mask]\n return g * taueff[:, np.newaxis]","def oned_gauss(rule):\n\n r = np.zeros(rule)\n w = np.zeros(rule)\n\n if rule == 1: # up to order 1 polynomials exact\n r[0] = 0\n w[0] = 2\n \n elif rule == 2: # up to order 3 polynomials exact\n r[0] =-1.0 / np.sqrt(3.0);\n r[1] =-r[0];\n w[0] = 1.0;\n w[1] = 1.0;\n \n elif rule == 3: # up to order 5 polynomials exact\n r[0] =-np.sqrt(3.0/5.0);\n r[1] = 0.0;\n r[2] =-r[0];\n w[0] = 5.0 / 9.0;\n w[1] = 8.0 / 9.0;\n w[2] = w[0];\n \n elif rule == 4: # up to order 7 polynomials exact\n r[0] =-np.sqrt((3.0+2.0*np.sqrt(6.0/5.0))/7.0);\n r[1] =-np.sqrt((3.0-2.0*np.sqrt(6.0/5.0))/7.0);\n r[2] =-r[1];\n r[3] =-r[0];\n w[0] = 0.5 - 1.0 / ( 6.0 * np.sqrt(6.0/5.0) );\n w[1] = 0.5 + 1.0 / ( 6.0 * np.sqrt(6.0/5.0) );\n w[2] = w[1];\n w[3] = w[0];\n \n elif rule == 5: # up to order 9 polynomials exact\n r[0] =-np.sqrt(5.0+4.0*np.sqrt(5.0/14.0)) / 3.0;\n r[1] =-np.sqrt(5.0-4.0*np.sqrt(5.0/14.0)) / 3.0;\n r[2] = 0.0;\n r[3] =-r[1];\n r[4] =-r[0];\n w[0] = 161.0/450.0-13.0/(180.*np.sqrt(5.0/14.0));\n w[1] = 161.0/450.0+13.0/(180.*np.sqrt(5.0/14.0));\n w[2] = 128.0/225.0;\n w[3] = w[1];\n w[4] = w[0];\n \n elif rule == 6:\n r[0] = -0.2386191860831969;\n r[1] = -0.6612093864662645;\n r[2] = -0.9324695142031521;\n r[3] = - r[0];\n r[4] = - r[1];\n r[5] = - r[2];\n w[0] = 0.4679139345726910;\n w[1] = 0.3607615730481386;\n w[2] = 0.1713244923791704;\n w[3] = w[0];\n w[4] = w[1];\n w[5] = w[2];\n \n elif rule == 7:\n r[0] = -0.9491079123427585;\n r[1] = -0.7415311855993945;\n r[2] = -0.4058451513773972;\n r[3] = 0.0000000000000000;\n r[4] = - r[2];\n r[5] = - r[1];\n r[6] = - r[0];\n w[0] = 0.1294849661688697;\n w[1] = 0.2797053914892766;\n w[2] = 0.3818300505051189;\n w[3] = 0.4179591836734694;\n w[4] = w[2];\n w[5] = w[1];\n w[6] = w[0];\n \n elif rule == 8:\n r[0] = -0.9602898564975363;\n r[1] = -0.7966664774136267;\n r[2] = -0.5255324099163290;\n r[3] = -0.1834346424956498;\n r[4] = - r[3];\n r[5] = - r[2];\n r[6] = - r[1];\n r[7] = - r[0];\n w[0] = 0.1012285362903763;\n w[1] = 0.2223810344533745;\n w[2] = 0.3137066458778873;\n w[3] = 0.3626837833783620;\n w[4] = w[3];\n w[5] = w[2];\n w[6] = w[1];\n w[7] = w[0];\n\n elif rule == 9:\n r[0] = -0.9681602395076261;\n r[1] = -0.8360311073266358;\n r[2] = -0.6133714327005904;\n r[3] = -0.3242534234038089;\n r[4] = 0.0000000000000000;\n r[5] = - r[3];\n r[6] = - r[2];\n r[7] = - r[1];\n r[8] = - r[0];\n w[0] = 0.0812743883615744;\n w[1] = 0.1806481606948574;\n w[2] = 0.2606106964029354;\n w[3] = 0.3123470770400029;\n w[4] = 0.3302393550012598;\n w[5] = w[3];\n w[6] = w[2];\n w[7] = w[1];\n w[8] = w[0];\n \n elif rule == 10:\n r[0] = -0.9739065285171717;\n r[1] = -0.8650633666889845;\n r[2] = -0.6794095682990244;\n r[3] = -0.4333953941292472;\n r[4] = -0.1488743389816312;\n r[5] = - r[4];\n r[6] = - r[3];\n r[7] = - r[2];\n r[8] = - r[1];\n r[9] = - r[0];\n w[0] = 0.0666713443086881;\n w[1] = 0.1494513491505806;\n w[2] = 0.2190863625159820;\n w[3] = 0.2692667193099963;\n w[4] = 0.2955242247147529;\n w[5] = w[4];\n w[6] = w[3];\n w[7] = w[2];\n w[8] = w[1];\n w[9] = w[0];\n\n elif rule == 11:\n r[0] = -0.9782286581460570;\n r[1] = -0.8870625997680953;\n r[2] = -0.7301520055740494;\n r[3] = -0.5190961292068118;\n r[4] = -0.2695431559523450;\n r[5] = 0.0000000000000000;\n r[6] = - r[4];\n r[7] = - r[3];\n r[8] = - r[2];\n r[9] = - r[1];\n r[10] = - r[0];\n w[0] = 0.0556685671161737;\n w[1] = 0.1255803694649046;\n w[2] = 0.1862902109277343;\n w[3] = 0.2331937645919905;\n w[4] = 0.2628045445102467;\n w[5] = 0.2729250867779006;\n w[6] = w[4];\n w[7] = w[3];\n w[8] = w[2];\n w[9] = w[1];\n w[10] = w[0];\n\n else:\n raise Exception('Quadrature rule not supported')\n #Return computed quantities\n \n return r,w","def getG(L, t, w=None, k=6, domain='time'):\n # evals1, U1 = eigs(L, k=k, which='LR')\n # evals1, U1 = sort(evals1, U1)\n\n # eigvals2, U2 = eigs(dag(L), k=k, which='LR')\n # eigvals2, U2 = sort(eigvals2, U2)\n evals1, U1 = eig(L.todense())\n # evals1, U1 = sort(evals1, U1)\n\n # U2 = U1.conj()\n # eigvals2, U2 = eig(dag(L).todense())\n # eigvals2, U2 = sort(eigvals2, U2)\n\n U2 = scipy.linalg.inv(U1)\n\n # norm = np.array([np.vdot(U2[:,n], U1[:,n]) for n in range(len(evals1))])\n\n # print('normalization', norm)\n\n if domain == 'time':\n\n U = -1j * np.exp(-1j * evals1[:,np.newaxis] * t[np.newaxis,:])\n # tmp = np.exp(-1j * evals1[:,np.newaxis] * t[np.newaxis,:])\n\n # U = np.einsum('ab, a -> ab', tmp, 1./norm)\n\n G = np.einsum('aj, jk, jb -> abk', U1, U, U2)\n\n elif domain == 'freq':\n\n W = 1./((w[:, np.newaxis] - evals1[np.newaxis, :]))\n G = np.einsum('an, nk, bn ->abk', U1, W, U2.conj())\n\n return G","def glorot(w):\n n_in, n_out = w.size()\n b = math.sqrt(6) / math.sqrt(n_in + n_out)\n return w.uniform_(-b, b)","def gauss_score(_r, _mu=0, _sigma=512):\n return np.exp(-(_r - _mu)**2 / (2*_sigma**2)) # / 2","def gelu(x):\r\n return x * 0.5 * (1.0 + torch.erf(x / math.sqrt(2.0)))","def gausspix(x, mean=0.0, sigma=1.0):\n edges = np.concatenate((x-0.5, x[-1:]+0.5))\n integrals = gaussint(edges, mean=mean, sigma=sigma)\n return integrals[1:] - integrals[0:-1]","def Gauss(self, center_x, center_y, width_x, width_y, height=1.0):\n width_x = float(width_x)\n width_y = float(width_y)\n return lambda x,y: height*math.exp(-(((center_x-x)/width_x)**2+((center_y-y)/width_y)**2)/2)","def gaussian_kld(mu_left, logvar_left, mu_right, logvar_right):\n gauss_klds = 0.5 * (logvar_right - logvar_left +\n (tf.exp(logvar_left) / tf.exp(logvar_right)) +\n ((mu_left - mu_right)**2.0 / tf.exp(logvar_right)) - 1.0)\n assert len(gauss_klds.shape) == 2\n return tf.reduce_sum(gauss_klds, axis=1)","def g(self, x):\n return x * (1 - x)","def agauss(self, X, xm, amp, w, a):\n # w(x) = 2 * w / (1 + np.exp(a * (X - xm)))\n return amp * np.exp(-((X - xm) / (2 * w / (1 + np.exp(a * (X - xm))))) ** 2)","def fun_exp_t_gauss(p,r):\n return p[2] * np.exp(-((r**2/p[0]))-((np.abs(r)/p[1])))","def qgset(x):\n return 0.2855*x - 0.8565","def g(x):\n if x[0]**2 + x[1]**2 + x[2]**2 <= 1:\n gx = 1\n else:\n gx = 0\n\n return gx","def gelu(x):\n return x * 0.5 * (1.0 + torch.erf(x / math.sqrt(2.0)))","def gelu(x):\n return x * 0.5 * (1.0 + torch.erf(x / math.sqrt(2.0)))","def gelu(x):\n return x * 0.5 * (1.0 + torch.erf(x / math.sqrt(2.0)))","def gelu(x):\n return x * 0.5 * (1.0 + torch.erf(x / math.sqrt(2.0)))","def gelu(x):\n return x * 0.5 * (1.0 + torch.erf(x / math.sqrt(2.0)))","def gelu(x):\n return x * 0.5 * (1.0 + torch.erf(x / math.sqrt(2.0)))","def gelu(x):\n return x * 0.5 * (1.0 + torch.erf(x / math.sqrt(2.0)))","def gelu(x):\n return x * 0.5 * (1.0 + torch.erf(x / math.sqrt(2.0)))","def get_vlb(self):\n vlb = 0\n\n # First term\n # E[LN p(g | \\gamma)]\n E_ln_g = self.expected_log_g()\n vlb += Dirichlet(self.gamma[None, None, :]).negentropy(E_ln_g=E_ln_g).sum()\n\n # Second term\n # E[LN q(g | \\tilde{gamma})]\n vlb -= Dirichlet(self.mf_gamma).negentropy().sum()\n\n return vlb","def gaussian_elimination_special_case(b):\n n = len(b)\n # init new (prime) arrays\n beta_prime = np.empty(n)\n beta_prime[0] = 2\n\n b_prime = np.empty(n)\n b_prime[0] = b[0]\n\n v = np.empty(n)\n i_array = np.arange(n)\n beta_prime = (i_array+2) / (i_array+1)\n\n for i in range(1,n):\n b_prime[i] = b[i] + (b_prime[i-1] / beta_prime[i-1])\n\n v[-1] = b_prime[-1] / beta_prime[-1]\n\n for i in range(n-2, -1, -1):\n v[i] = (b_prime[i] + v[i+1])/ beta_prime[i]\n\n return v","def lingauss(shape, w, cx = 0, cy = 0, theta = 0, cutoff = 0.05, norm = False):#, win = (-2, 2)):\n x = np.arange(0, shape[0])#linspace( win[0], win[1], shape[0] )\n y = np.arange(0, shape[1])#linspace( win[0], win[1], shape[1] )\n xx, yy = np.meshgrid(x, y, indexing='ij')\n xy = np.cos(theta)*(xx-cx) + np.sin(theta)*(yy-cy) # lin comb of x, y, to rotate gaussian\n h = np.exp( - ( xy*xy ) / (2*w*w) )\n if norm:\n h = h / (np.sqrt(two*np.pi)*w)\n h -= cutoff\n h[np.less(h, zero)] = zero\n return (h)","def matlab_style_gauss2D(shape,sigma):\n m,n = [(ss-1.)/2. for ss in shape]\n y,x = np.ogrid[-m:m+1,-n:n+1]\n h = np.exp( -(x*x + y*y) / (2.*sigma*sigma) )\n h[ h < np.finfo(h.dtype).eps*h.max() ] = 0\n sumh = h.sum()\n if sumh != 0:\n h /= sumh\n return h","def gelu(x):\n cdf = 0.5 * (1.0 + torch.tanh(\n (np.sqrt(2 / np.pi) * (x + 0.044715 * np.power(x, 3)))))\n return x * cdf","def get_vlb(self):\n raise NotImplementedError()\n vlb = 0\n\n # First term\n # E[LN p(g | \\gamma)]\n E_ln_g = self.expected_log_g()\n vlb += Dirichlet(self.gamma[None, None, :]).negentropy(E_ln_g=E_ln_g).sum()\n\n # Second term\n # E[LN q(g | \\tilde{gamma})]\n vlb -= Dirichlet(self.mf_gamma).negentropy().sum()\n\n return vlb","def grad(self, x):\n norm_beta = np.linalg.norm(x)\n if norm_beta > utils.TOLERANCE:\n return (self.l / norm_beta) * x\n else:\n D = x.shape[0]\n u = (self.rng(D, 1) * 2.0) - 1.0 # [-1, 1]^D\n norm_u = np.linalg.norm(u)\n a = self.rng() # [0, 1]\n\n return (self.l * (a / norm_u)) * u"],"string":"[\n \"def make_quad_gauss(lmax,alm):\\n return libcurvedsky.bispec.make_quad_gauss(lmax,alm)\",\n \"def test_ln_gauss(self):\\n Q = likelihood()\\n test_object = test_values_for_likelihood()\\n experimental_values = [0.9,2.1,3.2]\\n errors = [0.5,0.4,0.3]\\n reference_q_value = 0.27347222222222267\\n reference_q_derivative = np.array([-3.75833333, -3.75833333])\\n Q.add_observable(test_object,experimental_values,errors,scale=1.0)\\n q_value, q_derivative = Q.compute_ln_gauss(np.array([1.0,1.0]))\\n assert np.isclose(q_value,reference_q_value)\\n assert np.all(np.isclose(q_derivative,reference_q_derivative))\\n return\",\n \"def gauss_seidel(self):\\n for i in range(1,self.size[0]-1):\\n for j in range(1,self.size[1]-1):\\n for k in range(1,self.size[2]-1):\\n self.A[(i,j,k)] = ((1/6)*(self.A[(i+1,j,k)] + self.A[(i-1,j,k)] + self.A[(i,j+1,k)] + self.A[(i,j-1,k)] + self.A[(i,j,k+1)] + self.A[(i,j,k-1)] + self.J[(i,j,k)]) - self.A[(i,j,k)])*self.omega + self.A_0[(i,j,k)]\",\n \"def cost_gauss(self):\\n J = np.mean(self.zvar1/self.rvar1 + np.log(2*np.pi*self.rvar1))\\n J = J*np.product(self.shape1)\\n if not self.is_complex:\\n J = J / 2\\n return J\",\n \"def gauss(x,p):\\n return np.exp((-(x - p[0])**2) / (2 * p[1]**2))\",\n \"def fun_gauss_gauss(p,r):\\n return p[1] * np.exp(-((r/p[0])**2)) + p[3] * np.exp(-((r/p[2])**2))\",\n \"def phi_gauss(self,x,i):\\n s = 0.1\\n return np.exp(-(x-self.mu[i])**2/(2*s))\",\n \"def kl_gauss(x, y, sig2=1.):\\n return (x - y) ** 2 / (2 * sig2)\",\n \"def gauss(x, gamma):\\n return 1 / np.sqrt(2*np.pi) / gamma * np.exp(-(x/gamma)**2 / 2)\",\n \"def gauss_quadrature(ab, N):\\n\\n from numpy.linalg import eigh\\n\\n n = ab.shape[0]\\n if n+1 < N:\\n raise IndexError('Require N+1 recurrence coefficients for an N-point rule.')\\n\\n J = np.diag(ab[1:N,1], k=1) + np.diag(ab[:N,0],k=0) + np.diag(ab[1:N,1], k=-1)\\n lamb,v = eigh(J)\\n\\n return lamb, v[0,:]**2\",\n \"def gauss(self, X, xm, amp, w):\\n return amp * np.exp(-((X - xm) / w) ** 2)\",\n \"def gauss_seidel(coeficientes, semilla, b, i, n):\\n suma = 0\\n for j in range(n):\\n if j != i and coeficientes[j] != 0:\\n suma += (coeficientes[j] * semilla[j]) / coeficientes[i]\\n return (b / coeficientes[i]) - suma\",\n \"def fun_lorentzian_p_gauss(p,r):\\n return p[1] / ((r/p[0])**2 + 1) + p[2] * np.exp(-((r/p[0])**2)/2)\",\n \"def gaussseidel_poissoneq(A, x0):\\n return 1\",\n \"def gauss(x, x0, gamma):\\n sigma = gamma / sqrt(2.0)\\n \\n A = 1/ (sigma * sqrt(2*pi))\\n return (A * exp (-0.5 * (x-x0)**2/sigma**2))\",\n \"def fun_gauss(p,r):\\n return p[1] * np.exp(-((r/p[0])**2))\",\n \"def gauss(self, k, m, z):\\n Rvir = self.U.rVir(m, z)\\n result = np.exp(-0.5 * Rvir**2 * k**2)\\n return result\",\n \"def gauss(x, *p):\\n mu, sigma = p\\n return (1 / (sigma * np.sqrt(2 * np.pi)) *\\n np.exp(-(x - mu) ** 2 / (2. * sigma ** 2)))\",\n \"def fun_lorentzian_t_gauss(p,r):\\n return p[2] / ((r/p[0])**2 + 1) * np.exp(-((r/p[1])**2)/2)\",\n \"def kl_gauss_gauss(q_mean, q_logvar, p_mean, p_logvar, varmin=1e-16):\\n q_logvar = np.log(np.exp(q_logvar) + varmin)\\n p_logvar = np.log(np.exp(p_logvar) + varmin)\\n return (0.5 * (p_logvar - q_logvar + np.exp(q_logvar - p_logvar)\\n + np.square((q_mean - p_mean) / np.exp(0.5 * p_logvar)) - 1.0))\",\n \"def g(self, X):\\n\\n return (X[0])**2 - 2*X[0] + X[1]**3 - 2\",\n \"def gaussElimin(a,b):\\n a=float64(a)\\n b=float64(b)\\n n=len(b)\\n x=zeros((n,1),dtype=float)\\n for k in range(n-1):\\n for i in range(k+1,n):\\n l=float(a[i][k])/a[k][k]\\n\\t a[i][k]=0\\n\\t for j in range(k+1,n):\\n\\t a[i][j]=a[i][j]-l*a[k][j]\\n\\t b[i]=b[i]-l*b[k]\\n x[n-1]=float(b[n-1])/a[n-1][n-1]\\n for i in range(n-2,-1,-1):\\n sum=b[i]\\n for j in range(i+1,n):\\n sum=sum-a[i][j]*x[j]\\n x[i]=float(sum)/a[i][i]\\n return x\",\n \"def gauss_seidel(L, U, S, x, k):\\n\\tn = len(x) - 1\\n\\tm = len(x)//2\\n\\t\\n\\t# Leftmost\\n\\tx[0] = (S[0]/k - U[0, 1]*x[1])/L[0, 0]\\n\\t# Interior\\n\\tfor i in range(1, m):\\n\\t\\tx[i] = (S[i]/k - L[i, i - 1]*x[i - 1] - U[i, i + 1]*x[i + 1])/L[i, i]\\n\\tfor i in range(m, n):\\n\\t\\tx[i] = (S[i]/k - L[i, i - 1]*x[i - 1] - U[i, i + 1]*x[i + 1] -\\n\\t\\t L[i,i-m]*x[i-m]) / L[i,i]\\n\\t# Rightmost\\n\\tx[n] = (S[n]/k - L[n, n - 1]*x[n - 1] - L[n, n - m]*x[n - m])/L[n, n]\\n\\treturn x\",\n \"def gauss(x, y, ax, ay, x0, y0, phase):\\n g_x = ((ax / sqrt(pi)) ** 0.5\\n * exp(-0.5 * ((x - x0) * ax) ** 2))\\n g_y = ((ay / sqrt(pi)) ** 0.5\\n * exp(-0.5 * ((y - y0) * ay) ** 2))\\n\\n gxy = np.zeros((len(x),len(y)), dtype=float)\\n for i, _gx in enumerate(g_x):\\n for j, _gy in enumerate(g_y):\\n gxy[i,j] = _gx * _gy \\n\\n gxy2 = (1.0 / sqrt(1.0+abs(phase))) * np.array([gxy, phase*gxy], dtype=float) \\n\\n return gxy2\",\n \"def Gauss(self, x, height, centre, width, b=0):\\n\\n return height * np.exp(-(x - centre)**2 / (2 * width**2)) - b\",\n \"def G(U):\\n n = U.shape[0]\\n G_U = np.zeros([n,1])\\n DELTA_x = float(2*L)/(n-1)\\n for i in range(n):\\n G_U[i][0] = U[(i+1)%n][0]\\n G_U[i][0] -= U[(i-1)%n][0]\\n G_U[i][0] /= (2* DELTA_x)\\n G_U[i][0] += (float(epsilon) * (U[(i+1)%n][0]- U[(i-1)%n][0]) * (U[(i-1)%n][0]+U[(i+1)%n][0]+ U[i][0])/ (4* DELTA_x))\\n G_U[i][0] += (float(epsilon) * (U[(i+2)%n][0]- 2*U[(i+1)%n][0]+ 2*U[(i-1)%n][0]- U[(i-2)%n][0]) / (12*( DELTA_x**3)))\\n return G_U\",\n \"def gauss_naive (M, b) -> list:\\n dim = len(b)\\n\\n #Itero sulle Incognite da Trovare\\n for i in range(dim):\\n\\n #Itero sulle righe su cui devo cancellare un elemento\\n for j in range(i+1,dim):\\n m__j_i = M[j][i] / M[i][i]\\n M[j][i] = 0.0\\n\\n for k in range (i+1,dim):\\n M[j][k] = M[j][k] - m__j_i * M[i][k]\\n \\n b[j] = b[j] - m__j_i * b[i]\\n\\n return M,b\",\n \"def gauss(x, *p):\\n A, mu, sigma = p\\n\\n return A*np.exp(-(x-mu)**2/(2.*sigma**2))\",\n \"def legendreGauss (func, deg, a, b, ind, bsp, ind2=0):\\n\\n\\tx, w = np.polynomial.legendre.leggauss(deg)\\n\\tt = 0.5*(x+1)*(b-a)+ a\\n\\t\\n\\tgauss = sum(w + func(t, bsp, ind, ind2))*( 0.5*(b-a))\\n\\n\\treturn gauss\",\n \"def gelu(x):\\n return x * 0.5 * (1.0 + F.tanh(F.sqrt(2 / math.pi) * (x + 0.044715 * (x ** 3))))\",\n \"def gauss_quad_1d(q, n=150):\\n z, w = leggauss(n)\\n theta = (THETA_HIGH-THETA_LOW)*(z + 1)/2 + THETA_LOW\\n sin_theta = abs(sin(theta))\\n Zq = kernel_1d(q=q, theta=theta)\\n return np.sum(Zq*w*sin_theta)*(THETA_HIGH-THETA_LOW)/2\",\n \"def gauss_rule(iel, elemType, normal_order, element):\\n\\n #sctr = element[iel, :] # element connectivity\\n\\n if ((elemType == 'Q4') and (normal_order <8)):\\n W, Q = gpw.gauss_pt_wt(normal_order,'GAUSS',2)\\n return W, Q\",\n \"def gauss_func(x, wid, cen, amp):\\n\\n return np.exp(-((x-cen)**2.)/(2.*wid**2)) * amp\",\n \"def gaussum(xdata,*params):\\n\\tamp = num.zeros(0)\\n\\tcen = num.zeros(0)\\n\\tstdv = num.zeros(0)\\n\\n\\tfor i in range(0, len(params), 3): #This section is just unpacking the parameter array into amps, cens, and stdvs\\n\\t\\tx = params[i]\\n\\t\\tamp = num.append(amp,x)\\n\\t\\ty = params[i+1]\\n\\t\\tcen = num.append(cen,y)\\n\\t\\tz = params[i+2]\\n\\t\\tstdv = num.append(stdv,z)\\n\\tglobal storage #You may not need storage to be global so think about taking this part out. storage stores the data\\n\\tstorage = [[0 for x in range(1)] for x in range(len(params)/3)] #from each iteration of the gaussian equation into\\n\\tfor i in range(len(params)/3):#individual rows. So row one will be the gaussian solutions to the first peak and so on\\n\\t\\tstorage[i] = gaus(xdata,amp[i],cen[i],stdv[i])\\n\\tstorage = num.asarray(storage)\\n\\treturn sum(storage)\",\n \"def gauss_wp(self, delta, x0=0):\\n constant = (delta/np.pi)**(1/4) * (1.+0.j) \\n wp = constant * np.exp((-0.5 * delta * np.square(self.grid_x - x0)))\\n return wp\",\n \"def logGauss(self):\\n #firstly initialise an array to store the values\\n log = np.zeros([self.num])\\n \\n #now want to loop through each of the points in the collections\\n for i in range(self.num):\\n #get the point as an array\\n point = self.pick(i)\\n #key characteristic of standard normal: can treat as product of independent 1D normals\\n log[i] = self.d - np.log(np.sqrt(2 * np.pi)) - 0.5 * np.sum(point**2)\\n return log\",\n \"def gaussKern(size):\\n size = int(size)\\n x, y = np.mgrid[-size:size + 1, -size:size + 1]\\n g = np.exp(-(x**2/float(size) + y**2/float(size)))\\n return g / g.sum()\",\n \"def gauss_term_fn(iteration_count, v, z):\\n return tf.math.square(z) / 4. / (\\n (v + iteration_count - 1) * (v + iteration_count))\",\n \"def gauss_hermite(dim=1, num_quad_pts=20):\\n # sigma_pts, weights = hermgauss(num_quad_pts) # Gauss-Hermite sigma points and weights\\n sigma_pts, weights = mvhermgauss(num_quad_pts, dim)\\n sigma_pts = np.sqrt(2) * sigma_pts.T\\n weights = weights.T * pi ** (-0.5 * dim) # scale weights by 1/√π\\n return sigma_pts, weights\",\n \"def GL(mu, wid, x, m = 0.5): \\n return m * gaussian(mu, wid, x) + (1 - m) * lorentzian(mu, wid, x)\",\n \"def klucb_gauss(x, d, sig2=1., precision=0.):\\n return x + sqrt(2*sig2*d)\",\n \"def doubling_gauss(f, xl: float, xr: float, n: int, tol: float, *params):\\n # required local variables to return\\n # S : computed value of the integral with required tolerance\\n # N : number of steps for S\\n # err : estimated error of S\\n # iter : number of iterations (steps doubling)\\n iter = 1\\n N = 10\\n m = (2 * n - 1) + 1\\n\\n # считаем интеграл с шагом h\\n s1 = composite_gauss(f, xl, xr, N, n, *params)\\n # цикл по итерациям\\n while iter != MAXITER + 1:\\n N *= 2\\n # считаем интеграл с шагом h/2\\n s2 = composite_gauss(f, xl, xr, N, n, *params)\\n\\n # берём погрешность из метода рунге\\n estimate = np.abs(runge(s1, s2, 2, m))\\n if estimate >= tol:\\n s1 = s2\\n else:\\n return N, s2, estimate\\n\\n iter += 1\\n\\n if iter == MAXITER:\\n print(\\\"Convergence not reached!\\\")\\n return -1\",\n \"def _malmquist_q_lngamma(self, gamma, f_bin):\\n func = np.poly1d(self.malm_pars[::-1])\\n const_factor = (1 - gamma) / (self.high_q ** (1 - gamma) - self.low_q ** (1 - gamma))\\n integral = np.sum(\\n [p * const_factor / (i + 1 - gamma) * (self.high_q ** (i + 1 - gamma) - self.low_q ** (i + 1 - gamma))\\n for i, p in enumerate(self.malm_pars)])\\n Pobs = integral if self.malm_pars.size == 1 and self.malm_pars[0] == 1 else self.Pobs\\n denom = f_bin*integral + (1-f_bin)*Pobs\\n logging.debug('Denominator = {}\\\\nIntegral = {}\\\\nPobs = {}\\\\n'.format(denom, integral, Pobs))\\n return np.log(func(self.q)) + np.log(f_bin) + np.log(const_factor) - gamma*self.lnq - np.log(denom)\",\n \"def gelu(x):\\n return f_gelu(x)\",\n \"def u(self, k, m, z):\\n result = self.fGauss * self.gauss(k, m, z)\\n result += (1. - self.fGauss) * self.nfw(k, m, z)\\n result *= m / self.U.rho_m(z)\\n return result\",\n \"def gauss3(x,a1,c1,w1,a2,c2,w2,a3,c3,w3):\\n return gaussian(x,a1,c1,w1)+gaussian(x,a2,c2,w2)+gaussian(x,a3,c3,w3)\",\n \"def onedgauss(x,H,A,dx,w):\\n #H,A,dx,w = params\\n return H+A*np.exp(-(x-dx)**2/(2*w**2))\",\n \"def g(x):\\n return 5. - x[:, 1] - .5 * x[:, 0] ** 2.\",\n \"def unitary_Gauss(x, center, FWHM):\\n \\n sigma = np.abs(FWHM) /( 2 * np.sqrt(2 * np.log(2)) );\\n Amp = 1.0 / (sigma*np.sqrt(2*np.pi))\\n tau = -((x - center)**2) / (2*(sigma**2))\\n result = Amp * np.exp( tau );\\n \\n return result\",\n \"def _malmquist(self, gamma):\\n func = np.poly1d(self.malm_pars[::-1])\\n const_factor = (1 - gamma) / (self.high_q ** (1 - gamma) - self.low_q ** (1 - gamma))\\n denom = np.sum(\\n [p * const_factor / (i + 1 - gamma) * (self.high_q ** (i + 1 - gamma) - self.low_q ** (i + 1 - gamma))\\n for i, p in enumerate(self.malm_pars)])\\n return func, denom\",\n \"def _FSpecialGauss(size, sigma):\\n radius = size // 2\\n offset = 0.0\\n start, stop = -radius, radius + 1\\n if size % 2 == 0:\\n offset = 0.5\\n stop -= 1\\n x, y = np.mgrid[offset + start:stop, offset + start:stop]\\n assert len(x) == size\\n g = np.exp(-((x**2 + y**2)/(2.0 * sigma**2)))\\n return g / g.sum()\",\n \"def _kld_gauss(self, mean_1, std_1, mean_2, std_2):\\n kld_element = (2 * torch.log(std_2) - 2 * torch.log(std_1) + (std_1.pow(2) + (mean_1 - mean_2).pow(2)) / std_2.pow(2) - 1)\\n return\\t0.5 * torch.sum(kld_element)\",\n \"def gauss2d(param, x, y):\\n #2010-01-11 22:46 IJC: Created\\n from numpy import array, abs, concatenate, exp\\n x = array(x, dtype=float).copy()\\n y = array(y, dtype=float).copy()\\n p = array(param).copy()\\n\\n r = abs((x-p[2]) + 1j*(y-p[3]))\\n\\n if len(p)==4:\\n p = concatenate((p, [0]))\\n\\n z = p[4] + p[0]/(p[1]*4*pi) * exp(-r**2 / (2*p[1]**2))\\n \\n return z\",\n \"def _ucbpe_lcb(x):\\n mu, sigma = gp.eval(x, uncert_form='std')\\n return mu - beta_th * sigma\",\n \"def _ucbpe_lcb(x):\\n mu, sigma = gp.eval(x, uncert_form='std')\\n return mu - beta_th * sigma\",\n \"def Grundy(x):\\n # n taille bianire max des xi; m longeur de x\\n \\n # Calcul de la longueur binaire utilisée\\n # Complexité en O(m)\\n \\n n = 0\\n \\n for val in x :\\n t = taille(val)\\n if n < t :\\n n = t\\n \\n \\n \\n # Ecriture de la liste x en binaire\\n # Complexité en O(m*n) car binaire(x,n) en O(n)\\n \\n b = [binaire(val,n) for val in x ]\\n \\n # Calcul de la somme binaire mod 2 :\\n # Complexité en O(m*n) : m valeur dans b; n tours de boucle\\n \\n a = []\\n \\n for i in range(n):\\n \\n s = 0\\n \\n for val in b :\\n s+= val[i]\\n \\n a.append(s%2)\\n \\n return(decimal(a))\",\n \"def gauss(x, mu, A, sigma):\\n mu, A, sigma = np.atleast_2d(mu), np.atleast_2d(A), np.atleast_2d(sigma)\\n val = (A / (sigma * np.sqrt(np.pi * 2)) *\\n np.exp(-(x[:, None] - mu)**2 / (2 * sigma**2)))\\n return val.sum(axis=-1)\",\n \"def OF1_SumOfGauss(param_list, classNum, g_lvls):\\n return sum([param_list[i] * norm.pdf(g_lvls, loc=param_list[i + classNum], \\\\\\n scale=param_list[i + classNum * 2]) \\\\\\n for i in range(classNum)])\",\n \"def src_gauss(l, m, sigma_lm, A=1., i=0., pa=0., l0=0., m0=0.):\\n l = np.atleast_1d(l)\\n m = np.atleast_1d(m)\\n sigma_x = sigma_lm\\n sigma_y = sigma_lm * np.cos(i)\\n a = 0.5 * ((np.cos(pa) / sigma_x)**2. + (np.sin(pa) / sigma_y)**2.)\\n b = 0.5 * np.sin(2. * pa) * (sigma_x**-2. - sigma_y**-2.)\\n c = 0.5 * ((np.sin(pa) / sigma_x)**2. + (np.cos(pa) / sigma_y)**2.)\\n p = a * (l - l0)**2. + b * (l - l0) * (m - m0) + c * (m - m0)**2.\\n I = A * np.exp(-p) / (2. * pi * sigma_x * sigma_y)\\n return I\",\n \"def geglu(x: Tensor) ->Tensor:\\n assert x.shape[-1] % 2 == 0\\n a, b = x.chunk(2, dim=-1)\\n return a * F.gelu(b)\",\n \"def lin_gaus(x,m,b,A,mu,sigma):\\n return m*x+b + gaus(x,A,mu,sigma)\",\n \"def gauss_sum_to(n):\\n the_sum = n * (n + 1) / 2\\n return the_sum\",\n \"def fun_exp_p_gauss(p,r):\\n return p[1] * np.exp(-((r**2/p[0]))) + p[3] * np.exp(-((np.abs(r)/p[2])))\",\n \"def gauss2(x,a1,c1,w1,a2,c2,w2):\\n return gaussian(x,a1,c1,w1)+gaussian(x,a2,c2,w2)\",\n \"def Gauss_Seidel_Solve(A,b,tol=1.0e-6,max_iterations=100,LOUD=False):\\n [Nrow, Ncol] = A.shape\\n assert Nrow == Ncol\\n N = Nrow\\n converged = False\\n iteration = 1\\n x = np.random.rand(N) #random initial guess \\n x_new = np.zeros(N)\\n while not(converged):\\n x = x_new.copy() #replace old value\\n for row in range(N):\\n x_new[row] = b[row]\\n for column in range(N):\\n if column != row:\\n #only change from before is that I use x_new in the update\\n x_new[row] -= A[row,column]*x_new[column]\\n x_new[row] /= A[row,row]\\n relative_change = np.linalg.norm(x_new-x)/np.linalg.norm(x_new)\\n if (LOUD):\\n print(\\\"Iteration\\\",iteration,\\\": Relative Change =\\\",relative_change)\\n if (relative_change < tol) or (iteration >= max_iterations):\\n converged = True\\n iteration += 1\\n return x_new\",\n \"def gaussian_reduce(w, a, b):\\n u = (0, 1)\\n v = (1, 0)\\n\\n if dot(u, v, w, a, b) < 0:\\n v = (-v[0], -v[1])\\n\\n if norm(u, w, a, b) < norm(v, w, a, b):\\n u, v = v, u\\n\\n while norm(u, w, a, b) > norm(v, w, a, b):\\n k = dot(u, v, w, a, b) // dot(v, v, w, a, b)\\n u, v = v, (u[0]- k*v[0], u[1]- k*v[1])\\n\\n u, v = v, u\\n\\n if dot(u, v, w, a, b) < dot(v, v, w, a, b)/2 or norm((u[0]-v[0], u[1]-v[1]), w, a, b) > norm(v, w, a, b):\\n c = v\\n else:\\n c = (u[0] - v[0], u[1] - v[1])\\n\\n return c[0]*w + b*c[1], c[0]\",\n \"def Lq(self):\\n if not self.isVaild():\\n pass\\n temp = ((self.r()**self.C)*self.Rho()) / \\\\\\n (math.factorial(self.C)*((1 - self.Rho())**2))\\n return temp*self.P0()\",\n \"def g_rosenbrock(x, a=1, b=100):\\n\\n g = np.array(\\n [\\n -2 * a - 4 * b * x[0] * (-x[0] ** 2 + x[1]) + 2 * x[0],\\n b * (-2 * x[0] ** 2 + 2 * x[1]),\\n ]\\n )\\n\\n return g\",\n \"def gaussian_elimination(A, b):\\n \\n m, n = A.shape\\n U = A.copy() \\n b = b.copy()\\n\\n # forward sweep, reduce A to a upper triangular matrix\\n for k in range(min(m, n)):\\n swap = np.argmax(np.abs(U[k:, k])) + k\\n if U[swap, k] == 0:\\n raise ValueError('Singular matrix')\\n U[[k, swap], :] = U[[swap, k], :]\\n b[[k, swap]] = b[[swap, k]]\\n \\n for i in range(k + 1, m):\\n factor = U[i, k] / U[k, k]\\n b[i] = b[i] - factor*b[k]\\n U[i, k+1:] = U[i, k+1:] - U[k, k+1:] * factor\\n U[i, k] = 0\\n \\n # solve by back subistitution\\n x = rbackwardsolve(U, b, m)\\n\\n return x\",\n \"def der_gauss(shape,sigma):\\r\\n m,n = [(ss-1.)/2. for ss in shape]\\r\\n nx, ny = shape\\r\\n xv = np.linspace(-1, 1, nx)\\r\\n yv = np.linspace(-1, 1, ny)\\r\\n x1, y1 = np.meshgrid(xv, yv)\\r\\n \\r\\n \\r\\n h = np.exp( -(x1*x1 + y1*y1) / (2.*sigma*sigma) )\\r\\n h[ h < np.finfo(h.dtype).eps*h.max() ] = 0\\r\\n sumh = h.sum()\\r\\n if sumh != 0:\\r\\n h /= sumh\\r\\n gauss_x = -x1/sigma*sigma\\r\\n h1 = gauss_x * h\\r\\n return h1\",\n \"def FormG():\\n for i in range(2):\\n for j in range(2):\\n G[i, j] = 0.0\\n for k in range(2):\\n for l in range(2):\\n G[i, j] = G[i, j] + P[k, l] * (TT[i, j, k, l] - 0.5 * TT[i, j, k, l])\",\n \"def gaussint(x, mean=0.0, sigma=1.0):\\n z = (x - mean) / (math.sqrt(2) * sigma)\\n return (erf(z) + 1.0) / 2.0\",\n \"def limdrift(g, tau, acyrus=0.25):\\n tot = np.linalg.norm(g, axis=1) * acyrus\\n mask = tot > 1e-8\\n taueff = np.ones(tot.shape) * tau\\n taueff[mask] = (np.sqrt(1 + 2 * tau * tot[mask]) - 1) / tot[mask]\\n return g * taueff[:, np.newaxis]\",\n \"def oned_gauss(rule):\\n\\n r = np.zeros(rule)\\n w = np.zeros(rule)\\n\\n if rule == 1: # up to order 1 polynomials exact\\n r[0] = 0\\n w[0] = 2\\n \\n elif rule == 2: # up to order 3 polynomials exact\\n r[0] =-1.0 / np.sqrt(3.0);\\n r[1] =-r[0];\\n w[0] = 1.0;\\n w[1] = 1.0;\\n \\n elif rule == 3: # up to order 5 polynomials exact\\n r[0] =-np.sqrt(3.0/5.0);\\n r[1] = 0.0;\\n r[2] =-r[0];\\n w[0] = 5.0 / 9.0;\\n w[1] = 8.0 / 9.0;\\n w[2] = w[0];\\n \\n elif rule == 4: # up to order 7 polynomials exact\\n r[0] =-np.sqrt((3.0+2.0*np.sqrt(6.0/5.0))/7.0);\\n r[1] =-np.sqrt((3.0-2.0*np.sqrt(6.0/5.0))/7.0);\\n r[2] =-r[1];\\n r[3] =-r[0];\\n w[0] = 0.5 - 1.0 / ( 6.0 * np.sqrt(6.0/5.0) );\\n w[1] = 0.5 + 1.0 / ( 6.0 * np.sqrt(6.0/5.0) );\\n w[2] = w[1];\\n w[3] = w[0];\\n \\n elif rule == 5: # up to order 9 polynomials exact\\n r[0] =-np.sqrt(5.0+4.0*np.sqrt(5.0/14.0)) / 3.0;\\n r[1] =-np.sqrt(5.0-4.0*np.sqrt(5.0/14.0)) / 3.0;\\n r[2] = 0.0;\\n r[3] =-r[1];\\n r[4] =-r[0];\\n w[0] = 161.0/450.0-13.0/(180.*np.sqrt(5.0/14.0));\\n w[1] = 161.0/450.0+13.0/(180.*np.sqrt(5.0/14.0));\\n w[2] = 128.0/225.0;\\n w[3] = w[1];\\n w[4] = w[0];\\n \\n elif rule == 6:\\n r[0] = -0.2386191860831969;\\n r[1] = -0.6612093864662645;\\n r[2] = -0.9324695142031521;\\n r[3] = - r[0];\\n r[4] = - r[1];\\n r[5] = - r[2];\\n w[0] = 0.4679139345726910;\\n w[1] = 0.3607615730481386;\\n w[2] = 0.1713244923791704;\\n w[3] = w[0];\\n w[4] = w[1];\\n w[5] = w[2];\\n \\n elif rule == 7:\\n r[0] = -0.9491079123427585;\\n r[1] = -0.7415311855993945;\\n r[2] = -0.4058451513773972;\\n r[3] = 0.0000000000000000;\\n r[4] = - r[2];\\n r[5] = - r[1];\\n r[6] = - r[0];\\n w[0] = 0.1294849661688697;\\n w[1] = 0.2797053914892766;\\n w[2] = 0.3818300505051189;\\n w[3] = 0.4179591836734694;\\n w[4] = w[2];\\n w[5] = w[1];\\n w[6] = w[0];\\n \\n elif rule == 8:\\n r[0] = -0.9602898564975363;\\n r[1] = -0.7966664774136267;\\n r[2] = -0.5255324099163290;\\n r[3] = -0.1834346424956498;\\n r[4] = - r[3];\\n r[5] = - r[2];\\n r[6] = - r[1];\\n r[7] = - r[0];\\n w[0] = 0.1012285362903763;\\n w[1] = 0.2223810344533745;\\n w[2] = 0.3137066458778873;\\n w[3] = 0.3626837833783620;\\n w[4] = w[3];\\n w[5] = w[2];\\n w[6] = w[1];\\n w[7] = w[0];\\n\\n elif rule == 9:\\n r[0] = -0.9681602395076261;\\n r[1] = -0.8360311073266358;\\n r[2] = -0.6133714327005904;\\n r[3] = -0.3242534234038089;\\n r[4] = 0.0000000000000000;\\n r[5] = - r[3];\\n r[6] = - r[2];\\n r[7] = - r[1];\\n r[8] = - r[0];\\n w[0] = 0.0812743883615744;\\n w[1] = 0.1806481606948574;\\n w[2] = 0.2606106964029354;\\n w[3] = 0.3123470770400029;\\n w[4] = 0.3302393550012598;\\n w[5] = w[3];\\n w[6] = w[2];\\n w[7] = w[1];\\n w[8] = w[0];\\n \\n elif rule == 10:\\n r[0] = -0.9739065285171717;\\n r[1] = -0.8650633666889845;\\n r[2] = -0.6794095682990244;\\n r[3] = -0.4333953941292472;\\n r[4] = -0.1488743389816312;\\n r[5] = - r[4];\\n r[6] = - r[3];\\n r[7] = - r[2];\\n r[8] = - r[1];\\n r[9] = - r[0];\\n w[0] = 0.0666713443086881;\\n w[1] = 0.1494513491505806;\\n w[2] = 0.2190863625159820;\\n w[3] = 0.2692667193099963;\\n w[4] = 0.2955242247147529;\\n w[5] = w[4];\\n w[6] = w[3];\\n w[7] = w[2];\\n w[8] = w[1];\\n w[9] = w[0];\\n\\n elif rule == 11:\\n r[0] = -0.9782286581460570;\\n r[1] = -0.8870625997680953;\\n r[2] = -0.7301520055740494;\\n r[3] = -0.5190961292068118;\\n r[4] = -0.2695431559523450;\\n r[5] = 0.0000000000000000;\\n r[6] = - r[4];\\n r[7] = - r[3];\\n r[8] = - r[2];\\n r[9] = - r[1];\\n r[10] = - r[0];\\n w[0] = 0.0556685671161737;\\n w[1] = 0.1255803694649046;\\n w[2] = 0.1862902109277343;\\n w[3] = 0.2331937645919905;\\n w[4] = 0.2628045445102467;\\n w[5] = 0.2729250867779006;\\n w[6] = w[4];\\n w[7] = w[3];\\n w[8] = w[2];\\n w[9] = w[1];\\n w[10] = w[0];\\n\\n else:\\n raise Exception('Quadrature rule not supported')\\n #Return computed quantities\\n \\n return r,w\",\n \"def getG(L, t, w=None, k=6, domain='time'):\\n # evals1, U1 = eigs(L, k=k, which='LR')\\n # evals1, U1 = sort(evals1, U1)\\n\\n # eigvals2, U2 = eigs(dag(L), k=k, which='LR')\\n # eigvals2, U2 = sort(eigvals2, U2)\\n evals1, U1 = eig(L.todense())\\n # evals1, U1 = sort(evals1, U1)\\n\\n # U2 = U1.conj()\\n # eigvals2, U2 = eig(dag(L).todense())\\n # eigvals2, U2 = sort(eigvals2, U2)\\n\\n U2 = scipy.linalg.inv(U1)\\n\\n # norm = np.array([np.vdot(U2[:,n], U1[:,n]) for n in range(len(evals1))])\\n\\n # print('normalization', norm)\\n\\n if domain == 'time':\\n\\n U = -1j * np.exp(-1j * evals1[:,np.newaxis] * t[np.newaxis,:])\\n # tmp = np.exp(-1j * evals1[:,np.newaxis] * t[np.newaxis,:])\\n\\n # U = np.einsum('ab, a -> ab', tmp, 1./norm)\\n\\n G = np.einsum('aj, jk, jb -> abk', U1, U, U2)\\n\\n elif domain == 'freq':\\n\\n W = 1./((w[:, np.newaxis] - evals1[np.newaxis, :]))\\n G = np.einsum('an, nk, bn ->abk', U1, W, U2.conj())\\n\\n return G\",\n \"def glorot(w):\\n n_in, n_out = w.size()\\n b = math.sqrt(6) / math.sqrt(n_in + n_out)\\n return w.uniform_(-b, b)\",\n \"def gauss_score(_r, _mu=0, _sigma=512):\\n return np.exp(-(_r - _mu)**2 / (2*_sigma**2)) # / 2\",\n \"def gelu(x):\\r\\n return x * 0.5 * (1.0 + torch.erf(x / math.sqrt(2.0)))\",\n \"def gausspix(x, mean=0.0, sigma=1.0):\\n edges = np.concatenate((x-0.5, x[-1:]+0.5))\\n integrals = gaussint(edges, mean=mean, sigma=sigma)\\n return integrals[1:] - integrals[0:-1]\",\n \"def Gauss(self, center_x, center_y, width_x, width_y, height=1.0):\\n width_x = float(width_x)\\n width_y = float(width_y)\\n return lambda x,y: height*math.exp(-(((center_x-x)/width_x)**2+((center_y-y)/width_y)**2)/2)\",\n \"def gaussian_kld(mu_left, logvar_left, mu_right, logvar_right):\\n gauss_klds = 0.5 * (logvar_right - logvar_left +\\n (tf.exp(logvar_left) / tf.exp(logvar_right)) +\\n ((mu_left - mu_right)**2.0 / tf.exp(logvar_right)) - 1.0)\\n assert len(gauss_klds.shape) == 2\\n return tf.reduce_sum(gauss_klds, axis=1)\",\n \"def g(self, x):\\n return x * (1 - x)\",\n \"def agauss(self, X, xm, amp, w, a):\\n # w(x) = 2 * w / (1 + np.exp(a * (X - xm)))\\n return amp * np.exp(-((X - xm) / (2 * w / (1 + np.exp(a * (X - xm))))) ** 2)\",\n \"def fun_exp_t_gauss(p,r):\\n return p[2] * np.exp(-((r**2/p[0]))-((np.abs(r)/p[1])))\",\n \"def qgset(x):\\n return 0.2855*x - 0.8565\",\n \"def g(x):\\n if x[0]**2 + x[1]**2 + x[2]**2 <= 1:\\n gx = 1\\n else:\\n gx = 0\\n\\n return gx\",\n \"def gelu(x):\\n return x * 0.5 * (1.0 + torch.erf(x / math.sqrt(2.0)))\",\n \"def gelu(x):\\n return x * 0.5 * (1.0 + torch.erf(x / math.sqrt(2.0)))\",\n \"def gelu(x):\\n return x * 0.5 * (1.0 + torch.erf(x / math.sqrt(2.0)))\",\n \"def gelu(x):\\n return x * 0.5 * (1.0 + torch.erf(x / math.sqrt(2.0)))\",\n \"def gelu(x):\\n return x * 0.5 * (1.0 + torch.erf(x / math.sqrt(2.0)))\",\n \"def gelu(x):\\n return x * 0.5 * (1.0 + torch.erf(x / math.sqrt(2.0)))\",\n \"def gelu(x):\\n return x * 0.5 * (1.0 + torch.erf(x / math.sqrt(2.0)))\",\n \"def gelu(x):\\n return x * 0.5 * (1.0 + torch.erf(x / math.sqrt(2.0)))\",\n \"def get_vlb(self):\\n vlb = 0\\n\\n # First term\\n # E[LN p(g | \\\\gamma)]\\n E_ln_g = self.expected_log_g()\\n vlb += Dirichlet(self.gamma[None, None, :]).negentropy(E_ln_g=E_ln_g).sum()\\n\\n # Second term\\n # E[LN q(g | \\\\tilde{gamma})]\\n vlb -= Dirichlet(self.mf_gamma).negentropy().sum()\\n\\n return vlb\",\n \"def gaussian_elimination_special_case(b):\\n n = len(b)\\n # init new (prime) arrays\\n beta_prime = np.empty(n)\\n beta_prime[0] = 2\\n\\n b_prime = np.empty(n)\\n b_prime[0] = b[0]\\n\\n v = np.empty(n)\\n i_array = np.arange(n)\\n beta_prime = (i_array+2) / (i_array+1)\\n\\n for i in range(1,n):\\n b_prime[i] = b[i] + (b_prime[i-1] / beta_prime[i-1])\\n\\n v[-1] = b_prime[-1] / beta_prime[-1]\\n\\n for i in range(n-2, -1, -1):\\n v[i] = (b_prime[i] + v[i+1])/ beta_prime[i]\\n\\n return v\",\n \"def lingauss(shape, w, cx = 0, cy = 0, theta = 0, cutoff = 0.05, norm = False):#, win = (-2, 2)):\\n x = np.arange(0, shape[0])#linspace( win[0], win[1], shape[0] )\\n y = np.arange(0, shape[1])#linspace( win[0], win[1], shape[1] )\\n xx, yy = np.meshgrid(x, y, indexing='ij')\\n xy = np.cos(theta)*(xx-cx) + np.sin(theta)*(yy-cy) # lin comb of x, y, to rotate gaussian\\n h = np.exp( - ( xy*xy ) / (2*w*w) )\\n if norm:\\n h = h / (np.sqrt(two*np.pi)*w)\\n h -= cutoff\\n h[np.less(h, zero)] = zero\\n return (h)\",\n \"def matlab_style_gauss2D(shape,sigma):\\n m,n = [(ss-1.)/2. for ss in shape]\\n y,x = np.ogrid[-m:m+1,-n:n+1]\\n h = np.exp( -(x*x + y*y) / (2.*sigma*sigma) )\\n h[ h < np.finfo(h.dtype).eps*h.max() ] = 0\\n sumh = h.sum()\\n if sumh != 0:\\n h /= sumh\\n return h\",\n \"def gelu(x):\\n cdf = 0.5 * (1.0 + torch.tanh(\\n (np.sqrt(2 / np.pi) * (x + 0.044715 * np.power(x, 3)))))\\n return x * cdf\",\n \"def get_vlb(self):\\n raise NotImplementedError()\\n vlb = 0\\n\\n # First term\\n # E[LN p(g | \\\\gamma)]\\n E_ln_g = self.expected_log_g()\\n vlb += Dirichlet(self.gamma[None, None, :]).negentropy(E_ln_g=E_ln_g).sum()\\n\\n # Second term\\n # E[LN q(g | \\\\tilde{gamma})]\\n vlb -= Dirichlet(self.mf_gamma).negentropy().sum()\\n\\n return vlb\",\n \"def grad(self, x):\\n norm_beta = np.linalg.norm(x)\\n if norm_beta > utils.TOLERANCE:\\n return (self.l / norm_beta) * x\\n else:\\n D = x.shape[0]\\n u = (self.rng(D, 1) * 2.0) - 1.0 # [-1, 1]^D\\n norm_u = np.linalg.norm(u)\\n a = self.rng() # [0, 1]\\n\\n return (self.l * (a / norm_u)) * u\"\n]","isConversational":false},"negative_scores":{"kind":"list like","value":["0.71848667","0.70720714","0.68945396","0.67597324","0.6756922","0.65686566","0.6545775","0.6480314","0.647398","0.6451015","0.64494395","0.6436806","0.6406376","0.6401095","0.6389199","0.63581395","0.6356061","0.63102597","0.630343","0.63029367","0.624232","0.6238141","0.62028056","0.617805","0.6177437","0.615751","0.61469024","0.6144583","0.61191505","0.6114877","0.6108812","0.6034149","0.6029514","0.6029055","0.6025664","0.60192364","0.6015662","0.60103387","0.59914786","0.5982139","0.598116","0.59530795","0.5947983","0.59269065","0.5924985","0.5915966","0.5875808","0.58409655","0.5820992","0.58155245","0.58140635","0.5810506","0.58050835","0.5790589","0.5790589","0.5788872","0.57852024","0.5769339","0.575368","0.5740635","0.5716801","0.5704469","0.5696429","0.5682869","0.5682408","0.5673962","0.5664708","0.563752","0.56030273","0.56025237","0.55953866","0.5594682","0.55873996","0.5576705","0.5574208","0.55721986","0.55576307","0.5551254","0.5548096","0.5542841","0.5537162","0.5532395","0.5529696","0.5525907","0.5524442","0.55170846","0.5507336","0.5507336","0.5507336","0.5507336","0.5507336","0.5507336","0.5507336","0.5507336","0.550488","0.55047417","0.55044305","0.5504032","0.5493578","0.549067","0.54899335"],"string":"[\n \"0.71848667\",\n \"0.70720714\",\n \"0.68945396\",\n \"0.67597324\",\n \"0.6756922\",\n \"0.65686566\",\n \"0.6545775\",\n \"0.6480314\",\n \"0.647398\",\n \"0.6451015\",\n \"0.64494395\",\n \"0.6436806\",\n \"0.6406376\",\n \"0.6401095\",\n \"0.6389199\",\n \"0.63581395\",\n \"0.6356061\",\n \"0.63102597\",\n \"0.630343\",\n \"0.63029367\",\n \"0.624232\",\n \"0.6238141\",\n \"0.62028056\",\n \"0.617805\",\n \"0.6177437\",\n \"0.615751\",\n \"0.61469024\",\n \"0.6144583\",\n \"0.61191505\",\n \"0.6114877\",\n \"0.6108812\",\n \"0.6034149\",\n \"0.6029514\",\n \"0.6029055\",\n \"0.6025664\",\n \"0.60192364\",\n \"0.6015662\",\n \"0.60103387\",\n \"0.59914786\",\n \"0.5982139\",\n \"0.598116\",\n \"0.59530795\",\n \"0.5947983\",\n \"0.59269065\",\n \"0.5924985\",\n \"0.5915966\",\n \"0.5875808\",\n \"0.58409655\",\n \"0.5820992\",\n \"0.58155245\",\n \"0.58140635\",\n \"0.5810506\",\n \"0.58050835\",\n \"0.5790589\",\n \"0.5790589\",\n \"0.5788872\",\n \"0.57852024\",\n \"0.5769339\",\n \"0.575368\",\n \"0.5740635\",\n \"0.5716801\",\n \"0.5704469\",\n \"0.5696429\",\n \"0.5682869\",\n \"0.5682408\",\n \"0.5673962\",\n \"0.5664708\",\n \"0.563752\",\n \"0.56030273\",\n \"0.56025237\",\n \"0.55953866\",\n \"0.5594682\",\n \"0.55873996\",\n \"0.5576705\",\n \"0.5574208\",\n \"0.55721986\",\n \"0.55576307\",\n \"0.5551254\",\n \"0.5548096\",\n \"0.5542841\",\n \"0.5537162\",\n \"0.5532395\",\n \"0.5529696\",\n \"0.5525907\",\n \"0.5524442\",\n \"0.55170846\",\n \"0.5507336\",\n \"0.5507336\",\n \"0.5507336\",\n \"0.5507336\",\n \"0.5507336\",\n \"0.5507336\",\n \"0.5507336\",\n \"0.5507336\",\n \"0.550488\",\n \"0.55047417\",\n \"0.55044305\",\n \"0.5504032\",\n \"0.5493578\",\n \"0.549067\",\n \"0.54899335\"\n]","isConversational":false},"document_score":{"kind":"string","value":"0.0"},"document_rank":{"kind":"string","value":"-1"}}},{"rowIdx":75389,"cells":{"query":{"kind":"string","value":"Compute the LegendreGaussLobatto quadrature nodes."},"document":{"kind":"string","value":"def legendre_gauss_lobatto_nodes(N, # noqa: N803\n backend=None, force_dim_axis=False):\n return jacobi_gauss_lobatto_nodes(0, 0, N,\n backend=backend, force_dim_axis=force_dim_axis)"},"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 ReferenceElement(N):\n\n # Legendre polynomials are Jacobi(alpha,beta) polynomials\n alpha=0.; beta=0;\n\n # Gauss-Lobatto quadrature points for Legendre polynomials\n # According to Hesthaven+Warburton (p. 448, JacobGL.m) these\n # are the order N-2 Gauss quadrature points of Jacobi polynomials\n # with different alpha, beta, enlarged by end-points -1, +1.\n #\n # note len(r)=N, i.e. matrices defined below (V, Vr, etc) are square\n if(N==1):\n r=np.array([-1.,1.])\n else:\n # (N-2)-th order quadrature points are roots of (N-1)-st Jacobi polynomial\n inner_roots, inner_weights = scipy.special.roots_jacobi(N-1, alpha+1, beta+1)\n r = np.concatenate([ [-1.], inner_roots, [1.] ])\n\n # Vandermonde matrix for Legendre polynomials\n # V[i,j] = P_j(r_i), j=0,...,N, i=0,...,len(r)-1\n V = np.zeros( (len(r), N+1))\n for j in range(N+1):\n # scipy normalization determined by trial and error.\n # For **LAGRANGE POLY** ONLY, not general alpha, beta.\n # This makes the returned polynomials orthonormal\n normalization = np.sqrt((1.+2.*j)/2.)\n V[:,j] = scipy.special.eval_jacobi(j, alpha, beta, r)*normalization\n # or V[:,j] = scipy.special.legendre(j)(r)\n\n # check normalization\n # tmp_r, tmp_w = scipy.special.roots_jacobi(j+1, alpha, beta)\n # tmp_L=scipy.special.eval_jacobi(j, alpha, beta, tmp_r)*normalization\n # L_dot_L = sum(tmp_w*tmp_L*tmp_L)\n # print(\"j={}, (L,L)={}\".format(j, L_dot_L))\n\n\n Vinv=np.linalg.inv(V)\n\n # derivatives of Legendre polynomials, evaluated at quadrature points\n # Vr[i,j] = dP_j/dr(r_i), j=0,...,N, i=0,...,len(r)-1\n # use dP_j/dr = sqrt(j(j+1)) J^{alpha+1,beta+1}_{j-1} (H+W, Eq A2)\n #\n Vr = np.zeros( (len(r), N+1))\n for j in range(1,N+1):\n # scipy normalization determined by trial and error.\n # For **LAGRANGE POLY** ONLY, not general alpha, beta.\n # This makes the returned polynomials orthonormal, conforming\n # to H+W conventions\n scipy_normalization=np.sqrt((1.+2.*j)*(j+1.)/(8.*j))\n normed_J = scipy.special.jacobi(j-1, alpha+1, beta+1)(r)*scipy_normalization\n Vr[:,j] = np.sqrt(j*(j+alpha+beta+1.))*normed_J # H+W Eq. A2\n\n # - check normalization\n # - integrate by Legendre quadrature, to explicitly show weight-function in orthogonality\n # tmp_r, tmp_w = scipy.special.roots_jacobi(j+4, alpha, beta)\n # tmp_L=scipy.special.eval_jacobi(j-1, alpha+1, beta+1, tmp_r)*scipy_normalization\n # - evaluate orthogonality; note weight function (1-r)(1+r)\n # L_dot_L = sum(tmp_w*tmp_L*tmp_L*(1-tmp_r)*(1+tmp_r))\n # print(\"j={}, (L,L)={}\".format(j, L_dot_L))\n\n\n # derivatives of Lagrange interpolating polynomials\n # Dr(i,j) = dl_j/dr(r=r_i),\n # where l_j(r_i) = delta_{ij}\n # compute using P_j(r) = V[i,j]*l_i(r) => V[i,j] dl_i/dr = dP_j/dr (*)\n # => V^{-T} V^T[j,i] dl_i/dr = V^{-T} dP_j/dr\n Dr = np.matmul(Vr,Vinv)\n\n # inverse of mass-matrix\n # Using (*), one can show M = (V V^T)^(-1)\n # Turns out that the inverse of M is used in the DG algorithm,\n # and so we can directly compute M-inverse, without computing\n # matrix-inverses:\n Minv = np.matmul(V, V.transpose())\n\n # finally, M^{-1}S = Dr, and since we need S only multiplied by M^{-1},\n # we can just return Dr\n MinvS=Dr\n\n return r, Minv, MinvS","def Legendre( n, mu, checkXRange = True ) :\n\n if( n < 0 ) : raise ValueError( \"\\nError, n = %d < 0\" % n )\n if( checkXRange and ( abs( mu ) > 1 ) ) : raise ValueError( \"Legendre: |mu| > 1; mu = %g\" % mu )\n Pn = 0.\n Pnp1 = 1.\n n_ = 0\n twoNp1 = 1\n while( n_ < n ) :\n Pnm1 = Pn\n Pn = Pnp1\n n_p1 = n_ + 1\n Pnp1 = ( twoNp1 * mu * Pn - n_ * Pnm1 ) / n_p1\n twoNp1 += 2\n n_ = n_p1\n return( Pnp1 )","def leggauss(deg):\n ideg = int(deg)\n if ideg != deg or ideg < 1:\n raise ValueError(\"deg must be a non-negative integer\")\n\n # first approximation of roots. We use the fact that the companion\n # matrix is symmetric in this case in order to obtain better zeros.\n c = np.array([0]*deg + [1])\n m = np.polynomial.legendre.legcompanion(c)\n x = np.linalg.eigvalsh(m)\n\n # improve roots by one application of Newton\n dy = np.polynomial.legendre.legval(x, c)\n df = np.polynomial.legendre.legval(x, np.polynomial.legendre.legder(c))\n x -= dy/df\n\n # compute the weights. We scale the factor to avoid possible numerical\n # overflow.\n fm = np.polynomial.legendre.legval(x, c[1:])\n fm /= np.abs(fm).max()\n df /= np.abs(df).max()\n w = 1/(fm * df)\n\n # for Legendre we can also symmetrize\n w = (w + w[::-1])/2\n x = (x - x[::-1])/2\n\n # scale w to get the right value\n w *= 2. / w.sum()\n\n return x, w","def gelu(x):\n return f_gelu(x)","def gelu(x):\n return x * 0.5 * (1.0 + F.tanh(F.sqrt(2 / math.pi) * (x + 0.044715 * (x ** 3))))","def oned_shape(x,r,w):\n n_dof = len(x) # number of dofs\n n_g = len(r) # number of Gaussian quadrature points\n \n if n_dof==2:\n # Transform coordinates for linear elements\n c0 = ( x[-1]-x[0] )/2\n c1 = ( x[-1]+x[0] )/2\n \n # Gaussian nodes\n x_g = c0*r + c1\n\n # Evaluate basis function at Gauss points\n phi = np.empty((n_g,n_dof))\n phi[:,1] = ( 1+r )/2\n phi[:,0] = ( 1-r )/2\n \n # Evaluate first derivative at Gauss points\n p_x = np.empty((n_g,n_dof))\n p_x[:,1] = 0.5*np.ones(n_g)/c0\n p_x[:,0] =-p_x[:,1]\n\n # Jacobian\n djac = c0\n \n # Compute physical weights\n w_g = djac*w\n\n # Second derivative \n p_xx = np.zeros((n_g,n_dof))\n \n elif n_dof==3:\n \n # Transform coordinates for quadratic elements\n c0 = ( x[-1]-x[0] )/2\n c1 = ( x[-1]+x[0] )/2\n \n # Physical Gauss nodes\n x_g = c0*r + c1\n\n # defined backwards to help Matlab create the proper sized array\n phi = np.empty((n_g,n_dof))\n phi[:,2] = .5*r*( r+1 )\n phi[:,1] =-( r+1 )*( r-1 )\n phi[:,0] = .5*r*( r-1 )\n\n # First derivative \n p_x = np.empty((n_g,n_dof))\n p_x[:,2] = ( r+.5 )/c0\n p_x[:,1] =-2*r/c0\n p_x[:,0] = ( r-.5 )/c0\n\n # Jacobian \n djac = c0\n\n # Gaussian weight on physical element\n w_g = djac*w\n \n # Second derivative\n p_xx = np.empty((n_g,n_dof))\n p_xx[:,2] = np.ones(n_g)/c0**2\n p_xx[:,1] =-2*p_xx[:,2]\n p_xx[:,0] = p_xx[:,2]\n\n elif n_dof==4:\n # Transform coordinates for (nonconforming) cubic elements\n c0 = ( x[-1]-x[0] )/2\n c1 = ( x[-1]+x[0] )/2\n\n x_g = c0*r + c1\n\n r2 = r*r\n r3 = r*r2\n\n # defined backwards to help Matlab create the proper sized array\n phi = np.zeros((n_g,n_dof))\n phi[:,3] = 9*( r3+r2-r/9-1/9 )/16\n phi[:,2] =-27*( r3+r2/3-r-1/3 )/16\n phi[:,1] = 27*( r3-r2/3-r+1/3 )/16\n phi[:,0] =- 9*( r3-r2-r/9+1/9 )/16\n\n # Derivative on reference element\n p_r = np.zeros((n_g,n_dof))\n p_r[:,3] = 9*( 3*r2+2*r-1/9 )/16\n p_r[:,2] =-27*( 3*r2+2*r/3-1 )/16\n p_r[:,1] = 27*( 3*r2-2*r/3-1 )/16\n p_r[:,0] =- 9*( 3*r2-2*r-1/9 )/16\n \n # Second derivative on reference element\n p_rr = np.zeros((n_g,n_dof))\n p_rr[:,3] = 9*( 6*r+2 )/16\n p_rr[:,2] =-27*( 6*r+2/3 )/16\n p_rr[:,1] = 27*( 6*r-2/3 )/16\n p_rr[:,0] =- 9*( 6*r-2 )/16\n\n # Jacobian \n dxdr = p_r.dot(x)\n drdx = 1/dxdr\n \n # Quadrature weights on physical element\n w_g = dxdr*w\n \n # Derivative on physical element\n p_x = np.zeros((n_g,n_dof))\n p_x[:,3] = p_r[:,3]*drdx\n p_x[:,2] = p_r[:,2]*drdx\n p_x[:,1] = p_r[:,1]*drdx\n p_x[:,0] = p_r[:,0]*drdx\n \n # Second derivative on physical element\n p_xx = np.zeros((n_g,n_dof)) \n p_xx[:,3] = p_rr[:,3]*drdx**2\n p_xx[:,2] = p_rr[:,2]*drdx**2\n p_xx[:,1] = p_rr[:,1]*drdx**2\n p_xx[:,0] = p_rr[:,0]*drdx**2\n else:\n raise Exception('Elements higher than cubic not currently supported')\n \n # Return computed quantities\n return x_g, w_g, phi, p_x, p_xx","def local_gemm_to_ger(node):\r\n if node.op == gemm_no_inplace:\r\n z, a, x, y, b = node.inputs\r\n if x.broadcastable[1] and y.broadcastable[0]:\r\n # x and y are both vectors so this might qualifies for a GER\r\n xv = x.dimshuffle(0)\r\n yv = y.dimshuffle(1)\r\n try:\r\n bval = T.get_scalar_constant_value(b)\r\n except T.NotScalarConstantError:\r\n # b isn't a constant, GEMM is doing useful pre-scaling\r\n return\r\n\r\n if bval == 1: # best case a natural GER\r\n rval = ger(z, a, xv, yv)\r\n return [rval]\r\n elif bval == 0: # GER on zeros_like should be faster than GEMM\r\n zeros = T.zeros([x.shape[0], y.shape[1]], x.dtype)\r\n rval = ger(zeros, a, xv, yv)\r\n return [rval]\r\n else:\r\n # if bval is another constant, then z is being usefully\r\n # pre-scaled and GER isn't really the right tool for the job.\r\n return","def test_ln_gauss(self):\n Q = likelihood()\n test_object = test_values_for_likelihood()\n experimental_values = [0.9,2.1,3.2]\n errors = [0.5,0.4,0.3]\n reference_q_value = 0.27347222222222267\n reference_q_derivative = np.array([-3.75833333, -3.75833333])\n Q.add_observable(test_object,experimental_values,errors,scale=1.0)\n q_value, q_derivative = Q.compute_ln_gauss(np.array([1.0,1.0]))\n assert np.isclose(q_value,reference_q_value)\n assert np.all(np.isclose(q_derivative,reference_q_derivative))\n return","def g(self, X):\n\n return (X[0])**2 - 2*X[0] + X[1]**3 - 2","def G(U):\n n = U.shape[0]\n G_U = np.zeros([n,1])\n DELTA_x = float(2*L)/(n-1)\n for i in range(n):\n G_U[i][0] = U[(i+1)%n][0]\n G_U[i][0] -= U[(i-1)%n][0]\n G_U[i][0] /= (2* DELTA_x)\n G_U[i][0] += (float(epsilon) * (U[(i+1)%n][0]- U[(i-1)%n][0]) * (U[(i-1)%n][0]+U[(i+1)%n][0]+ U[i][0])/ (4* DELTA_x))\n G_U[i][0] += (float(epsilon) * (U[(i+2)%n][0]- 2*U[(i+1)%n][0]+ 2*U[(i-1)%n][0]- U[(i-2)%n][0]) / (12*( DELTA_x**3)))\n return G_U","def riemannian_grads(self):\n u = self.feature_embedding.weight.grad\n x = self.feature_embedding.weight.data\n u.narrow(-1, 0, 1).mul_(-1)\n u.addcmul_(ldot(x, u, keepdim=True).expand_as(x), x)\n return u # can be delete?","def jacobi_gauss_lobatto_nodes(alpha, beta, N, # noqa: N803\n backend=None, force_dim_axis=False):\n\n x = np.zeros((N + 1,))\n if N == 0:\n x[0] = 0\n return x\n\n x[0] = -1\n x[-1] = 1\n\n if N > 1:\n quad = JacobiGaussQuadrature(alpha + 1, beta + 1, N - 2,\n backend=backend, force_dim_axis=True)\n x[1:-1] = quad.nodes[0].real\n\n if force_dim_axis:\n x = x.reshape(1, -1)\n\n return x","def make_Gr(mlat, *J):\n\n if (len(J)!=3):\n print(\"Number of paramaters are not right, must be 5!\")\n \n NN = 2*mlat # # of sites in one super unitcell\n tau = -np.zeros((NN, NN),dtype=complex)\n h = np.zeros((NN,NN), dtype=complex)\n\n # translational cell's Hamiltonian\n for i in range(mlat-1):\n if (i%2==0):\n h[i,i+1] = J[0]\n h[mlat+i,mlat+i+1] = J[1]\n h[i,mlat+i] = J[2] # horizoltal connection\n elif (i%2==1):\n h[i,i+1] = J[1]\n h[mlat+i,mlat+i+1] = J[0]\n # longitudinal connection of the last sites\n if (mlat-1)%2 == 0:\n h[mlat-1,2*mlat-1] = J[2]\n \n h = h + h.conj().T # make it hermitian\n\n # Hopping matrix\n for i in range(1,mlat,2):\n tau[i+mlat,i] = J[2]\n\n return h, tau","def add_lp_qe_RBM_terms(K, lin_pot_mesh, quad_geo_mesh):\n pot_faces = lin_pot_mesh.get_faces()\n num_faces = pot_faces.shape[0]\n pot_nodes = lin_pot_mesh.get_nodes()\n num_nodes = pot_nodes.shape[0]\n S_D = quad_geo_mesh.get_surface_area()\n x_c = quad_geo_mesh.get_centroid()\n w = quad_geo_mesh.get_w()\n A_m = quad_geo_mesh.get_A_m()\n\n for face_num in range(num_faces):\n face_nodes = quad_geo_mesh.get_tri_nodes(face_num)\n face_hs = quad_geo_mesh.get_hs(face_num)\n for node_num in range(3): # face nodes\n face_node_global_num = pot_faces[face_num, node_num]\n v_sub_mat = (-4. * np.pi / S_D) * gq.int_over_tri_quad(\n make_lp_le_v_quad(node_num),\n face_nodes,\n face_hs,\n )\n j = 3 * face_node_global_num\n tmp_omega = gq.int_over_tri_quad(\n make_lp_le_omega_quad(node_num, x_c),\n face_nodes,\n face_hs,\n )\n tmp_arr = []\n for m in range(3):\n tmp_arr.append((1./ A_m[m]) * np.outer(w[m], np.einsum(\"l, ls\", w[m], tmp_omega)))\n tmp_omega_mat = -4. * np.pi * np.sum(tmp_arr, axis=0)\n for src_num in range(num_nodes):\n K[(3 * src_num):(3 * src_num + 3),\n j:j+3] += -1. / (4. * np.pi) * v_sub_mat\n src_pt = lin_pot_mesh.get_node(src_num)\n X_0 = src_pt - x_c\n omega_mat = np.einsum(\"ijk,js,k\", geo.LC_3, tmp_omega_mat, X_0)\n K[(3 * src_num):(3 * src_num + 3),\n j:j+3] += 1. / (4. * np.pi) * (omega_mat)","def legendreGauss (func, deg, a, b, ind, bsp, ind2=0):\n\n\tx, w = np.polynomial.legendre.leggauss(deg)\n\tt = 0.5*(x+1)*(b-a)+ a\n\t\n\tgauss = sum(w + func(t, bsp, ind, ind2))*( 0.5*(b-a))\n\n\treturn gauss","def geglu(x: Tensor) ->Tensor:\n assert x.shape[-1] % 2 == 0\n a, b = x.chunk(2, dim=-1)\n return a * F.gelu(b)","def gauss_legender_points(N=30):\n beta = np.array([(n + 1.0) / np.sqrt((2.0 * n + 1.0) * (2.0 * n + 3.0))\n for n in range(N - 1)], dtype=np.float32)\n M = np.diag(beta, -1) + np.diag(beta, 1)\n nodes, V = la.eigh(M, overwrite_a=True, overwrite_b=True)\n weight = 2 * V[0, :] ** 2\n return nodes, weight","def linearize_and_solve(g):\n\n # initialize the sparse H and the vector b\n H = np.zeros((len(g.x), len(g.x)), dtype='float')\n b = np.zeros(len(g.x), dtype='float')\n\n # set flag to fix gauge\n needToAddPrior = True\n Fx = 0\n\n # compute the addend term to H and b for each of our constraints\n print('linearize and build system')\n\n for edge in g.edges:\n\n # pose-pose constraint\n if edge.Type == 'P':\n\n # compute idx for nodes using lookup table\n fromIdx = g.lut[edge.fromNode]\n toIdx = g.lut[edge.toNode]\n\n # get node state for the current edge\n x_i = g.x[fromIdx:fromIdx + 3]\n x_j = g.x[toIdx:toIdx + 3]\n\n # (TODO) compute the error and the Jacobians\n e, A, B = linearize_pose_pose_constraint(\n x_i, x_j, edge.measurement)\n\n # # (TODO) compute the terms\n b_i = e.transpose() @ edge.information @ A\n b_j = e.transpose() @ edge.information @ B\n H_ii = A.transpose() @ edge.information @ A\n H_ij = A.transpose() @ edge.information @ B\n H_jj = B.transpose() @ edge.information @ B\n\n # (TODO) add the terms to H matrix and b\n H[fromIdx:fromIdx + 3, fromIdx:fromIdx + 3] += H_ii\n H[toIdx:toIdx + 3, toIdx:toIdx + 3] += H_jj\n H[fromIdx:fromIdx + 3, toIdx:toIdx + 3] += H_ij\n H[toIdx:toIdx + 3, fromIdx:fromIdx + 3, ] += H_ij.transpose()\n b[fromIdx:fromIdx + 3] += b_i[0, :]\n b[toIdx:toIdx + 3] += b_j[0, :]\n\n # Add the prior for one pose of this edge\n # This fixes one node to remain at its current location\n if needToAddPrior:\n H[fromIdx:fromIdx + 3, fromIdx:fromIdx +\n 3] = H[fromIdx:fromIdx + 3,\n fromIdx:fromIdx + 3] + 1000 * np.eye(3)\n needToAddPrior = False\n\n # pose-pose constraint\n elif edge.Type == 'L':\n print(\"you shouldn't be here...\")\n # compute idx for nodes using lookup table\n fromIdx = g.lut[edge.fromNode]\n toIdx = g.lut[edge.toNode]\n\n # get node states for the current edge\n x = g.x[fromIdx:fromIdx + 3]\n l = g.x[toIdx:toIdx + 2]\n\n # (TODO) compute the error and the Jacobians\n e, A, B = linearize_pose_landmark_constraint(\n x, l, edge.measurement)\n\n # (TODO) compute the terms\n b_i = e.transpose() @ edge.information @ A\n b_j = e.transpose() @ edge.information @ B\n H_ii = A.transpose() @ edge.information @ A\n H_ij = A.transpose() @ edge.information @ B\n H_jj = B.transpose() @ edge.information @ B\n\n # (TODO )add the terms to H matrix and b\n H[fromIdx:fromIdx + 3, fromIdx:fromIdx + 3] += H_ii\n H[toIdx:toIdx + 2, toIdx:toIdx + 2] += H_jj\n H[fromIdx:fromIdx + 3, toIdx:toIdx + 2] += H_ij\n H[toIdx:toIdx + 2, fromIdx:fromIdx + 3, ] += H_ij.transpose()\n b[fromIdx:fromIdx + 3] = b_i\n b[toIdx:toIdx + 2] = b_j\n # solve system\n dx = np.linalg.solve(H, b)\n\n return dx","def make_DL_reg_lp_le_quad_func(n, x_0, node_num):\n def quad_func(xi, eta, nodes):\n x = geo.linear_interp(xi, eta, nodes)\n S = geo.stresslet_n(x, x_0, n)\n phi = geo.shape_func_linear(xi, eta, node_num)\n return phi * S\n return quad_func","def getG(L, t, w=None, k=6, domain='time'):\n # evals1, U1 = eigs(L, k=k, which='LR')\n # evals1, U1 = sort(evals1, U1)\n\n # eigvals2, U2 = eigs(dag(L), k=k, which='LR')\n # eigvals2, U2 = sort(eigvals2, U2)\n evals1, U1 = eig(L.todense())\n # evals1, U1 = sort(evals1, U1)\n\n # U2 = U1.conj()\n # eigvals2, U2 = eig(dag(L).todense())\n # eigvals2, U2 = sort(eigvals2, U2)\n\n U2 = scipy.linalg.inv(U1)\n\n # norm = np.array([np.vdot(U2[:,n], U1[:,n]) for n in range(len(evals1))])\n\n # print('normalization', norm)\n\n if domain == 'time':\n\n U = -1j * np.exp(-1j * evals1[:,np.newaxis] * t[np.newaxis,:])\n # tmp = np.exp(-1j * evals1[:,np.newaxis] * t[np.newaxis,:])\n\n # U = np.einsum('ab, a -> ab', tmp, 1./norm)\n\n G = np.einsum('aj, jk, jb -> abk', U1, U, U2)\n\n elif domain == 'freq':\n\n W = 1./((w[:, np.newaxis] - evals1[np.newaxis, :]))\n G = np.einsum('an, nk, bn ->abk', U1, W, U2.conj())\n\n return G","def add_lp_le_RBM_terms(K, lin_pot_mesh, lin_geo_mesh):\n pot_faces = lin_pot_mesh.get_faces()\n num_faces = pot_faces.shape[0]\n pot_nodes = lin_pot_mesh.get_nodes()\n num_nodes = pot_nodes.shape[0]\n S_D = lin_geo_mesh.get_surface_area()\n x_c = lin_geo_mesh.get_centroid()\n w = lin_geo_mesh.get_w()\n A_m = lin_geo_mesh.get_A_m()\n\n for face_num in range(num_faces):\n face_nodes = lin_geo_mesh.get_tri_nodes(face_num)\n face_hs = lin_geo_mesh.get_hs(face_num)\n for node_num in range(3): # face nodes\n face_node_global_num = pot_faces[face_num, node_num]\n v_sub_mat = (-4. * np.pi / S_D) * gq.int_over_tri_lin(\n make_lp_le_v_quad(node_num),\n face_nodes,\n face_hs,\n )\n j = 3 * face_node_global_num\n tmp_omega = gq.int_over_tri_lin(\n make_lp_le_omega_quad(node_num, x_c),\n face_nodes,\n face_hs,\n )\n tmp_arr = []\n for m in range(3):\n tmp_arr.append((1./ A_m[m]) * np.outer(w[m], np.einsum(\"l, ls\", w[m], tmp_omega)))\n tmp_omega_mat = -4. * np.pi * np.sum(tmp_arr, axis=0)\n for src_num in range(num_nodes):\n K[(3 * src_num):(3 * src_num + 3),\n j:j+3] += -1. / (4. * np.pi) * v_sub_mat\n src_pt = lin_pot_mesh.get_node(src_num)\n X_0 = src_pt - x_c\n omega_mat = np.einsum(\"ijk,js,k\", geo.LC_3, tmp_omega_mat, X_0)\n K[(3 * src_num):(3 * src_num + 3),\n j:j+3] += 1. / (4. * np.pi) * (omega_mat)","def FormG():\n for i in range(2):\n for j in range(2):\n G[i, j] = 0.0\n for k in range(2):\n for l in range(2):\n G[i, j] = G[i, j] + P[k, l] * (TT[i, j, k, l] - 0.5 * TT[i, j, k, l])","def fun_lorentzian_p_gauss(p,r):\n return p[1] / ((r/p[0])**2 + 1) + p[2] * np.exp(-((r/p[0])**2)/2)","def fun_lorentzian_t_gauss(p,r):\n return p[2] / ((r/p[0])**2 + 1) * np.exp(-((r/p[1])**2)/2)","def _AffineGrothendieckPolynomial(self, la, m):\n return self._AffineGrothendieck(la.to_core(self.k).to_grassmannian(),m)","def calc_free_g(energies, temperatures):\n pass","def make_reg_lp_qe_quad_func(x_0, node_num):\n def quad_func(xi, eta, nodes):\n x = geo.quadratic_interp(xi, eta, nodes)\n S = geo.stresslet(x, x_0)\n phi = geo.shape_func_linear(xi, eta, node_num)\n return phi * S\n return quad_func","def calculateElementRightHandSideGaussLeg(self,triangleIndex,degree):\n\n transformMatrix,translateVector = self.calculateTransform(triangleIndex)\n determinant = abs(np.linalg.det(transformMatrix))\n\n \n trianglePoints =self.triangulation.points[self.triangulation.simplices[triangleIndex]]\n gPoints,gWeigths = leggauss(degree)\n elementRHS = []\n #calculate each entry per Gaussintegration\n for i in range(0,3):\n entry = 0.0\n #sum over weights evaluated at Gauss points,\n for indexX,pointX in enumerate(gPoints):\n for indexY,pointY in enumerate(gPoints):\n transformedPoint = np.dot(transformMatrix,np.array([(1+pointX)*0.5,(1-pointX)*(1+pointY)*0.25])) +translateVector\n entry+= determinant*0.5*(1-pointX)*0.125*self.functionRHS(transformedPoint)*gWeigths[indexX]*gWeigths[indexY]*self.linearBasis[i](np.array([(1+pointX)*0.5,(1-pointX)*(1+pointY)*0.25]))\n #0.5 comes from transformation of reference triangle to standard square [-1,1] x [-1,1]\n elementRHS.append(entry)\n return elementRHS","def local_energy_ueg(system, G, Ghalf=None, two_rdm=None):\n if (system.diagH1):\n ke = numpy.einsum('sii,sii->',system.H1,G)\n else:\n ke = numpy.einsum('sij,sij->',system.H1,G)\n\n Gkpq = numpy.zeros((2,len(system.qvecs)), dtype=numpy.complex128)\n Gpmq = numpy.zeros((2,len(system.qvecs)), dtype=numpy.complex128)\n Gprod = numpy.zeros((2,len(system.qvecs)), dtype=numpy.complex128)\n\n ne = [system.nup, system.ndown]\n nq = numpy.shape(system.qvecs)[0]\n\n for s in [0, 1]:\n # exchange_greens_function(nq, system.ikpq_i, system.ikpq_kpq, system.ipmq_i,system.ipmq_pmq, Gprod[s],G[s])\n # coulomb_greens_function(nq, system.ikpq_i, system.ikpq_kpq, system.ipmq_i, system.ipmq_pmq,Gkpq[s], Gpmq[s],G[s])\n for iq in range(nq):\n Gkpq[s,iq], Gpmq[s,iq] = coulomb_greens_function_per_qvec(system.ikpq_i[iq],\n system.ikpq_kpq[iq],\n system.ipmq_i[iq],\n system.ipmq_pmq[iq],\n G[s])\n Gprod[s,iq] = exchange_greens_function_per_qvec(system.ikpq_i[iq],\n system.ikpq_kpq[iq],\n system.ipmq_i[iq],\n system.ipmq_pmq[iq],\n G[s])\n\n if two_rdm is None:\n two_rdm = numpy.zeros((2,2,len(system.qvecs)), dtype=numpy.complex128)\n two_rdm[0,0] = numpy.multiply(Gkpq[0],Gpmq[0]) - Gprod[0]\n essa = (1.0/(2.0*system.vol))*system.vqvec.dot(two_rdm[0,0])\n\n two_rdm[1,1] = numpy.multiply(Gkpq[1],Gpmq[1]) - Gprod[1]\n essb = (1.0/(2.0*system.vol))*system.vqvec.dot(two_rdm[1,1])\n\n two_rdm[0,1] = numpy.multiply(Gkpq[0],Gpmq[1])\n two_rdm[1,0] = numpy.multiply(Gkpq[1],Gpmq[0])\n eos = (\n (1.0/(2.0*system.vol))*system.vqvec.dot(two_rdm[0,1])\n + (1.0/(2.0*system.vol))*system.vqvec.dot(two_rdm[1,0])\n )\n\n pe = essa + essb + eos\n\n return (ke+pe, ke, pe)","def evaluate( self, mu ) :\n\n P = 0.\n for l, c_l in enumerate( self.coefficients ) : P += ( l + 0.5 ) * c_l * Legendre( l, mu, checkXRange = False ) \n return( P )","def rosenbrock_grad(p, a=1.0, b=100.0):\n x, y = p\n gx = - 2*(a - x) - 2*b * (y - x**2)* 2*x\n gy = 2*b * (y - x**2)\n return np.array([gx, gy])","def _calc_g(self, lambdify=True):\n g = None\n g_func = None\n # check to see if we have our gravity term saved in file\n g, g_func = self._load_from_file('g', lambdify)\n\n if g is None and g_func is None:\n # if no saved file was loaded, generate function\n print('Generating gravity compensation function')\n\n # get the Jacobians for each link's COM\n J_links = [self._calc_J('link%s' % ii, x=self.x_zeros,\n lambdify=False)\n for ii in range(self.N_LINKS)]\n J_joints = [self._calc_J('joint%s' % ii, x=self.x_zeros,\n lambdify=False)\n for ii in range(self.N_JOINTS)]\n\n # sum together the effects of each arm segment's inertia\n g = sp.zeros(self.N_JOINTS, 1)\n for ii in range(self.N_LINKS):\n # transform each inertia matrix into joint space\n g += (J_links[ii].T * self._M_LINKS[ii] * self.gravity)\n # sum together the effects of each joint's inertia on each motor\n for ii in range(self.N_JOINTS):\n # transform each inertia matrix into joint space\n g += (J_joints[ii].T * self._M_JOINTS[ii] * self.gravity)\n g = sp.Matrix(g)\n\n # save to file\n abr_control.utils.os_utils.makedirs(\n '%s/g' % self.config_folder)\n cloudpickle.dump(g, open(\n '%s/g/g' % self.config_folder, 'wb'))\n\n if lambdify is False:\n # if should return expression not function\n return g\n\n if g_func is None:\n g_func = self._generate_and_save_function(\n filename='g', expression=g,\n parameters=self.q)\n return g_func","def gelu_new(x):\n\treturn 0.5 * x * (1 + torch.tanh(math.sqrt(2 / math.pi) * (x + 0.044715 * torch.pow(x, 3))))","def Grundy(x):\n # n taille bianire max des xi; m longeur de x\n \n # Calcul de la longueur binaire utilisée\n # Complexité en O(m)\n \n n = 0\n \n for val in x :\n t = taille(val)\n if n < t :\n n = t\n \n \n \n # Ecriture de la liste x en binaire\n # Complexité en O(m*n) car binaire(x,n) en O(n)\n \n b = [binaire(val,n) for val in x ]\n \n # Calcul de la somme binaire mod 2 :\n # Complexité en O(m*n) : m valeur dans b; n tours de boucle\n \n a = []\n \n for i in range(n):\n \n s = 0\n \n for val in b :\n s+= val[i]\n \n a.append(s%2)\n \n return(decimal(a))","def Gtilde(x):\n gt1 = 1.808 * cbrt(x) / np.sqrt(1 + 3.4 * cbrt(x) ** 2.)\n gt2 = 1 + 2.210 * cbrt(x) ** 2. + 0.347 * cbrt(x) ** 4.\n gt3 = 1 + 1.353 * cbrt(x) ** 2. + 0.217 * cbrt(x) ** 4.\n return gt1 * (gt2 / gt3) * np.exp(-x)","def cost_gauss(self):\n J = np.mean(self.zvar1/self.rvar1 + np.log(2*np.pi*self.rvar1))\n J = J*np.product(self.shape1)\n if not self.is_complex:\n J = J / 2\n return J","def HaarGOB(treeG):\n # number of level of the chain (or tree)\n Ntr = len(treeG)\n # reorder chain (optional) \n # reordering each level so that in each level the nodes are in the\n # descent order of degrees\n # compute u_l^c for level J_0 (top level)\n clusterJ0 = treeG[Ntr-1]['clusters']\n N0 = len(clusterJ0)\n # generate indicator function on G^c\n chic = np.identity(N0)\n uc = [None]*N0\n uc[0] = 1/np.sqrt(N0,dtype=np.float64)*np.ones(N0)\n for l in np.arange(1, N0):\n uc[l] = np.sqrt((N0-l)/(N0-l+1))*(chic[l-1,:]-1/(N0-l)*np.sum(chic[l:,:], axis=0))\n# u = copy.deepcopy(uc)\n treeG[Ntr-1]['u'] = uc\n # compute the next level orthonormal basis ulk and stored into u\n for j_tr in np.arange(0, Ntr-1)[::-1]:\n N1 = len(treeG[j_tr]['clusters'])\n u = [None]*N1\n i = N0\n for l in range(N0):\n clusterl = treeG[j_tr+1]['clusters'][l]\n kl = len(clusterl)\n #for k==1\n ucl = uc[l]\n ul1 = np.zeros(N1)\n for j in range(N0):\n idxj = treeG[j_tr+1]['clusters'][j]\n ul1[idxj] = ucl[j]/np.sqrt(len(idxj))\n u[l] = ul1\n if kl>1:\n chil = np.zeros((kl,N1))\n for k in range(kl):\n idxl = treeG[j_tr+1]['clusters'][l]\n chil[k,idxl[k]] = 1;\n \n for k in np.arange(1,kl):\n i = i+1\n ulk = np.sqrt((kl-k)/(kl-k+1))*(chil[k-1,:]-1/(kl-k)*np.sum(chil[k:,:],axis=0))\n u[i-1] = ulk\n treeG[j_tr]['u'] = u\n # update uc and N0\n# uc = copy.deepcopy(u)\n uc = u\n N0 = N1\n return treeG","def _g(X, g, n):\n if n == 3:\n n = 3.001 # for numerical stability\n xi = 1 + X**2\n hyp2f1_term = hyp2f1((n - 3) / 2, g / 2, n / 2, 1 / xi)\n beta_term_1 = beta((n - 3) / 2, (3-g)/2)\n beta_term_2 = beta((n-3)/2, 1.5)\n return 0.5 * (beta_term_1 - beta_term_2 * hyp2f1_term * xi ** ((3-n)/2))","def grad_step2(self, x, u_prime):\n v = 0.25 * (x @ self.w)\n w = u_prime + v\n z = w.t() @ x\n if self.debug:\n print(f\"-> v={v}\")\n print(f\"-> w={w}\")\n print(f\"-> z={z}\\n\\n\")\n return w, z","def convert_gelu(g, op, block):\n\n x = g.get_node(op.input(\"X\")[0])\n out = x * (\n _expr.const(0.5, dtype=\"float32\")\n + _op.erf(x * _expr.const(0.5**0.5, dtype=\"float32\")) * _expr.const(0.5, dtype=\"float32\")\n )\n g.add_node(op.output(\"Out\")[0], out)","def g3(self, nx, ny, x_des):\n\n # :evaluating constraints at the initial point\n g_3_0 = self.g3_unscaled(nx, ny, .5 * np.ones(4 * nx + 5 * ny))\n\n # :evaluating unscaled constraints:\n g_3 = self.g3_unscaled(nx, ny, x_des)\n\n # :define the threshold \"tow\" to translate the scaled constraint\n # :alpha determines to what extent the inactive constraints are satisfied\n tow, alpha = [], self.alpha_g3\n [tow.append(i) if self.mu_g3[list(g_3_0).index(i)] < self.p else tow.append(alpha + (1 - alpha) * i) for i in g_3_0]\n\n # define the translated constraint\n g_3_translated = []\n [g_3_translated.append(g_3[i] - tow[i]) for i in range(nx)]\n return g_3_translated","def precomp_GpGm(ells, thetas):\n n_ell, n_theta = len(ells), len(thetas)\n P_m_l = np.zeros((n_theta, n_ell))\n P_m_lminus1 = np.zeros_like(P_m_l)\n costs = np.cos(thetas)\n sints = np.sin(thetas)\n for it in range(n_theta):\n P_m_l[it] = P2l_rec_norm(ells, costs[it])\n # for il in range(n_ell):\n # P_m_l[it,il] = sp.lpmv(2, ells[il], costs[it])\n P_m_lminus1[:, 1:] = P_m_l[:, :-1]\n ELLS, THETAS = np.meshgrid(ells, thetas)\n COSTS, SINTS = np.cos(THETAS), np.sin(THETAS)\n G_plus = -((ELLS - 4) / SINTS**2 + 0.5 * ELLS * (ELLS - 1)) * \\\n P_m_l + (ELLS + 2) * COSTS * P_m_lminus1 / SINTS**2\n G_minus = 2 * ((ELLS - 1) * COSTS * P_m_l -\n (ELLS + 2) * P_m_lminus1) / SINTS**2\n G_plus[:, 0] = 0.\n G_minus[:, 0] = 0.\n return G_plus, G_minus","def quadratures(f, a=-1, b=1, n=30):\n nodes, weights = gauss_legender_points(n)\n w = to.tensor(weights.reshape(1, 1, -1))\n nodes = to.tensor(nodes.reshape(1, 1, -1))\n\n scale = (b - a) / 2.\n\n x = scale * nodes + (b + a) / 2.\n y = w * f(x)\n y = to.sum(scale * y, dim=-1)\n return y.type(dtype=to.float)","def grad_wrap_l_bfgs_b(linTH,X,Y,lda,dims):\n TH = roll(linTH,dims)\n\n # unravel\n G = grad(TH,X,Y,lda)\n linG = np.array(())\n for g in G:\n linG = np.concatenate((linG,np.array(g.ravel())[0]))\n return linG","def g_prime(z):\n return np.multiply(g(z), 1-g(z))","def gelu(x):\n cdf = 0.5 * (1.0 + torch.tanh(\n (np.sqrt(2 / np.pi) * (x + 0.044715 * np.power(x, 3)))))\n return x * cdf","def test_ggn_implementation(problem):\n problem.set_up()\n\n diag_ggn_from_full = AutogradExtensions(problem).diag_ggn_via_ggn()\n diag_ggn_from_block = AutogradExtensions(problem).diag_ggn()\n\n check_sizes_and_values(diag_ggn_from_full, diag_ggn_from_block)\n problem.tear_down()","def gauss_hermite_degree(self):\n return self._gauss_hermite_degree","def kl_div_prior_gradient(self, posterior_logits, posterior_binary_samples):\n #DVAE Eq11 - gradient of prior\n #gradient of the KLD between posterior and prior wrt to prior\n #parameters theta, i.e. generative model parameters.\n #logits to probabilities\n posterior_probs=torch.sigmoid(posterior_logits)\n positive_probs=posterior_probs.detach()\n \n #samples from posterior are labelled positive\n positive_samples=posterior_binary_samples.detach()\n\n n_split=positive_samples.size()[1]//2\n positive_samples_left,positive_samples_right=torch.split(positive_samples,split_size_or_sections=int(n_split),dim=1)\n \n #-z_left^t J z_right\n pos_first_term=torch.matmul(positive_samples_left,self.prior.get_weights())*positive_samples_right\n \n rbm_bias_left=self.prior.get_visible_bias()\n rbm_bias_right=self.prior.get_hidden_bias()\n rbm_bias=torch.cat([rbm_bias_left,rbm_bias_right])#self._h\n \n #this gives [42,400] size\n #- z^t h\n #TODO this uses positive probs. Should it not use positive samples?\n # FIXME an indication are the negative ones where samples are used! On\n #other hand this is the only place this this used\n pos_sec_term=positive_probs*rbm_bias\n # pos_sec_term=positive_samples*rbm_bias\n\n # Energy = -z_left^t J z_right - z^t h\n pos_kld_per_sample=-(torch.sum(pos_first_term,axis=1)+torch.sum(pos_sec_term,axis=1))\n #samples from rbm are labelled negative\n\n #rbm_samples Tensor(\"zeros:0\", shape=(200, 200), dtype=float32)\n #this returns the full RBM set: left and right nodes concatenated\n\n #TODO What are these samples here?\n #TODO what's the impact of doing gibbs sampling here? does this make\n #sense?\n rbm_samples=self.prior.get_samples_kld(approx_post_samples=positive_samples_left,n_gibbs_sampling_steps=1)\n negative_samples=rbm_samples.detach()\n\n # print(self.prior.get_weights())\n n_split=negative_samples.size()[1]//2\n negative_samples_left,negative_samples_right=torch.split(negative_samples,split_size_or_sections=int(n_split),dim=1)\n neg_first_term=torch.matmul(negative_samples_left,self.prior.get_weights())*negative_samples_right\n \n #FIXME see above, the positive case looks different. Why?\n neg_sec_term=negative_samples*rbm_bias\n neg_kld_per_sample=(torch.sum(neg_first_term,axis=1)+torch.sum(neg_sec_term,axis=1))\n \n kld_per_sample=pos_kld_per_sample+neg_kld_per_sample\n\n return kld_per_sample","def calculate_grad_B_tensor(self):\n s = self # Shorthand\n tensor = Struct()\n \n factor = s.spsi * s.B0 / s.d_l_d_varphi\n tensor.tn = s.sG * s.B0 * s.curvature\n tensor.nt = tensor.tn\n tensor.bb = factor * (s.X1c * s.d_Y1s_d_varphi - s.iotaN * s.X1c * s.Y1c)\n tensor.nn = factor * (s.d_X1c_d_varphi * s.Y1s + s.iotaN * s.X1c * s.Y1c)\n tensor.bn = factor * (-s.sG * s.spsi * s.d_l_d_varphi * s.torsion \\\n - s.iotaN * s.X1c * s.X1c)\n tensor.nb = factor * (s.d_Y1c_d_varphi * s.Y1s - s.d_Y1s_d_varphi * s.Y1c \\\n + s.sG * s.spsi * s.d_l_d_varphi * s.torsion \\\n + s.iotaN * (s.Y1s * s.Y1s + s.Y1c * s.Y1c))\n\n self.grad_B_tensor = tensor\n self.grad_B_colon_grad_B = tensor.tn * tensor.tn + tensor.nt * tensor.nt \\\n + tensor.bb * tensor.bb + tensor.nn * tensor.nn \\\n + tensor.nb * tensor.nb + tensor.bn * tensor.bn\n\n self.L_grad_B = s.B0 * np.sqrt(2 / self.grad_B_colon_grad_B)\n self.inv_L_grad_B = 1.0 / self.L_grad_B\n self.min_L_grad_B = fourier_minimum(self.L_grad_B)","def calculateExteriorElementBoundaryQuadrature(self):\n #\n #get physical locations of element boundary quadrature points\n #\n #assume all components live on the same mesh\n self.u[0].femSpace.elementMaps.getValuesGlobalExteriorTrace(self.elementBoundaryQuadraturePoints,\n self.ebqe['x'])\n #\n #get metric tensor and unit normals\n #\n if self.movingDomain:\n if self.tLast_mesh != None:\n self.ebqe['xt'][:]=self.ebqe['x']\n self.ebqe['xt']-=self.ebqe['x_last']\n alpha = 1.0/(self.t_mesh - self.tLast_mesh)\n self.ebqe['xt']*=alpha\n else:\n self.ebqe['xt'][:]=0.0\n self.ebqe['x_last'][:]=self.ebqe['x']\n self.u[0].femSpace.elementMaps.getJacobianValuesGlobalExteriorTrace_movingDomain(self.elementBoundaryQuadraturePoints,\n self.ebqe['xt'],\n self.ebqe['inverse(J)'],\n self.ebqe['g'],\n self.ebqe['sqrt(det(g))'],\n self.ebqe['n'])\n else:\n self.u[0].femSpace.elementMaps.getJacobianValuesGlobalExteriorTrace(self.elementBoundaryQuadraturePoints,\n self.ebqe['inverse(J)'],\n self.ebqe['g'],\n self.ebqe['sqrt(det(g))'],\n self.ebqe['n'])\n #now map the physical points back to the reference element\n #assume all components live on same mesh\n self.u[0].femSpace.elementMaps.getInverseValuesGlobalExteriorTrace(self.ebqe['inverse(J)'],self.ebqe['x'],self.ebqe['hat(x)'])\n #\n #since the points on the reference boundary may be reordered on many right element boundaries, we\n #have to use an array of reference boundary points on all element boundaries\n #first copy the left reference element boundary quadrature points from the reference element boundary\n self.testSpace[0].getBasisValuesGlobalExteriorTrace(self.elementBoundaryQuadraturePoints,\n self.ebqe[('w',0)])\n cfemIntegrals.calculateWeightedShapeGlobalExteriorTrace(self.mesh.exteriorElementBoundariesArray,\n self.mesh.elementBoundaryElementsArray,\n self.mesh.elementBoundaryLocalElementBoundariesArray,\n self.elementBoundaryQuadratureWeights[('f',0)],\n self.ebqe['sqrt(det(g))'],\n self.ebqe[('w',0)],\n self.ebqe[('w*dS_f',0)])\n self.u[0].femSpace.getBasisGradientValuesGlobalExteriorTrace(self.elementBoundaryQuadraturePoints,\n self.ebqe['inverse(J)'],\n self.ebqe[('grad(v)',0)])\n #setup flux boundary conditions\n self.fluxBoundaryConditionsObjectsDict = dict([(cj,FluxBoundaryConditions(self.mesh,\n self.nElementBoundaryQuadraturePoints_elementBoundary,\n self.ebqe[('x')],\n self.advectiveFluxBoundaryConditionsSetterDict[cj],\n self.diffusiveFluxBoundaryConditionsSetterDictDict[cj]))\n for cj in self.advectiveFluxBoundaryConditionsSetterDict.keys()])\n self.ebqe['dS'] = numpy.zeros((self.mesh.nExteriorElementBoundaries_global,self.nElementBoundaryQuadraturePoints_elementBoundary),'d')\n cfemIntegrals.calculateIntegrationWeights(self.ebqe['sqrt(det(g))'],\n self.elementBoundaryQuadratureWeights[('u',0)],\n self.ebqe['dS'])\n for ci in range(self.nc): self.ebqe[('dS',ci)] = self.ebqe['dS']\n #\n self.ellamDiscretization.calculateExteriorElementBoundaryQuadrature(self.ebqe)\n #\n self.coefficients.initializeGlobalExteriorElementBoundaryQuadrature(self.timeIntegration.t,self.ebqe)","def gelu(x):\n cdf = 0.5 * (1.0 + torch.tanh(\n (np.sqrt(2 / np.pi) * (x + 0.044715 * torch.pow(x, 3)))))\n return x * cdf","def gelu(input):\n return FunctionLib.apply('Gelu', input.device, [input], approximate=False)","def gelu(x):\n cdf = 0.5 * (1.0 + torch.tanh(\n (np.sqrt(2 / np.pi) * (x + 0.044715 * torch.pow(x, 3)))))\n return x * cdf","def g(x):\n if x[0]**2 + x[1]**2 + x[2]**2 <= 1:\n gx = 1\n else:\n gx = 0\n\n return gx","def gelu(self, x):\r\n cdf = 0.5 * (1.0 + tf.tanh(\r\n (np.sqrt(2 / np.pi) * (x + 0.044715 * tf.pow(x, 3)))))\r\n return x * cdf","def grad_ReLU(self):\n grad = np.where(self.x > 0, 1, 0)\n return grad","def H_G(X):\n n = X.shape[0]\n H = np.zeros([n,n])\n DELTA_x = float(2*L)/(n-1)\n for i in range(n):\n H[i][(i-2)%n] = - float(epsilon) / (12 * (DELTA_x**3))\n H[i][(i-1)%n] = (-2. - float(epsilon) * (X[i][0] + 2*X[(i-1)%n][0])) / (4 * DELTA_x) + 2. * (float(epsilon)/(12*(DELTA_x**3)))\n H[i][i] = (epsilon * (X[(i+1)%n][0] - X[(i-1)%n][0])) / (4 *DELTA_x)\n H[i][(i+1)%n] = (2. + float(epsilon) * (X[i][0] + 2*X[(i+1)%n][0])) / (4 * DELTA_x) - 2. * (float(epsilon)/(12*(DELTA_x**3)))\n H[i][(i+2)%n] = float(epsilon) / (12 * (DELTA_x)**3) \n return H","def gauss_seidel(self):\n for i in range(1,self.size[0]-1):\n for j in range(1,self.size[1]-1):\n for k in range(1,self.size[2]-1):\n self.A[(i,j,k)] = ((1/6)*(self.A[(i+1,j,k)] + self.A[(i-1,j,k)] + self.A[(i,j+1,k)] + self.A[(i,j-1,k)] + self.A[(i,j,k+1)] + self.A[(i,j,k-1)] + self.J[(i,j,k)]) - self.A[(i,j,k)])*self.omega + self.A_0[(i,j,k)]","def magma_zgels(trans, m, n, nrhs, A, lda, B, ldb, hwork, lwork):\n info = c_int_type()\n trans = _trans_conversion[trans]\n status = _libmagma.magma_zgels(trans, m, n, nrhs, int(A), lda,\n int(B), ldb, int(hwork), lwork,\n ctypes.byref(info))\n magmaCheckStatus(status)","def buildGrad(self):\r\n\r\n self.grads = T.grad(self.L_elbo_modif, self.params)","def derivative_matrix(g):\n\n def _(g):\n B = g.B[0].grad\n N = g.N[0]\n P = g.dec.P(1)\n H = np.vstack(P(B(i)) for i in range(N)).T\n return H\n\n return _(g), _(g.dual)","def compute_Rg(L):\n\treturn np.sqrt(np.dot(L,L))","def internal2external_grad(xi,bounds):\n \n ge = np.empty_like(xi)\n\n for i,(v,bound) in enumerate(zip(xi,bounds)):\n \n a = bound[0] # minimum\n b = bound[1] # maximum\n\n if a == None and b == None: # No constraints\n ge[i] = 1.0\n\n elif b == None: # only min\n ge[i] = v/np.sqrt(v**2+1)\n\n elif a == None: # only max\n ge[i] = -v/np.sqrt(v**2+1)\n\n else: # both min and max\n ge[i] = (b-a)*np.cos(v)/2.\n\n return ge","def gauss_naive (M, b) -> list:\n dim = len(b)\n\n #Itero sulle Incognite da Trovare\n for i in range(dim):\n\n #Itero sulle righe su cui devo cancellare un elemento\n for j in range(i+1,dim):\n m__j_i = M[j][i] / M[i][i]\n M[j][i] = 0.0\n\n for k in range (i+1,dim):\n M[j][k] = M[j][k] - m__j_i * M[i][k]\n \n b[j] = b[j] - m__j_i * b[i]\n\n return M,b","def calculate_grad_grad_B_tensor(self, two_ways=False):\n\n # Shortcuts\n s = self\n \n X1c = s.X1c\n Y1s = s.Y1s\n Y1c = s.Y1c\n\n X20 = s.X20\n X2s = s.X2s\n X2c = s.X2c\n\n Y20 = s.Y20\n Y2s = s.Y2s\n Y2c = s.Y2c\n\n Z20 = s.Z20\n Z2s = s.Z2s\n Z2c = s.Z2c\n\n iota_N0 = s.iotaN\n iota = s.iota\n lp = np.abs(s.G0) / s.B0\n\n curvature = s.curvature\n torsion = s.torsion\n\n sign_G = s.sG\n sign_psi = s.spsi\n B0 = s.B0\n G0 = s.G0\n I2 = s.I2\n G2 = s.G2\n p2 = s.p2\n\n B20 = s.B20\n B2s = s.B2s\n B2c = s.B2c\n\n d_X1c_d_varphi = s.d_X1c_d_varphi\n d_Y1s_d_varphi = s.d_Y1s_d_varphi\n d_Y1c_d_varphi = s.d_Y1c_d_varphi\n\n d_X20_d_varphi = s.d_X20_d_varphi\n d_X2s_d_varphi = s.d_X2s_d_varphi\n d_X2c_d_varphi = s.d_X2c_d_varphi\n\n d_Y20_d_varphi = s.d_Y20_d_varphi\n d_Y2s_d_varphi = s.d_Y2s_d_varphi\n d_Y2c_d_varphi = s.d_Y2c_d_varphi\n\n d_Z20_d_varphi = s.d_Z20_d_varphi\n d_Z2s_d_varphi = s.d_Z2s_d_varphi\n d_Z2c_d_varphi = s.d_Z2c_d_varphi\n\n d2_X1c_d_varphi2 = s.d2_X1c_d_varphi2\n d2_Y1s_d_varphi2 = s.d2_Y1s_d_varphi2\n d2_Y1c_d_varphi2 = s.d2_Y1c_d_varphi2\n d_curvature_d_varphi = s.d_curvature_d_varphi\n d_torsion_d_varphi = s.d_torsion_d_varphi\n\n grad_grad_B = np.zeros((s.nphi, 3, 3, 3))\n grad_grad_B_alt = np.zeros((s.nphi, 3, 3, 3))\n\n # The elements that follow are computed in the Mathematica notebook \"20200407-01 Grad grad B tensor near axis\"\n # and then formatted for fortran by the python script process_grad_grad_B_tensor_code\n\n # The order is (normal, binormal, tangent). So element 123 means nbt.\n\n # Element 111\n grad_grad_B[:,0,0,0] =(B0*B0*B0*B0*lp*lp*(8*iota_N0*X2c*Y1c*\\\n Y1s + 4*iota_N0*X2s*\\\n (-Y1c*Y1c + Y1s*Y1s) + \\\n 2*iota_N0*X1c*Y1s*Y20 + \\\n 2*iota_N0*X1c*Y1s*Y2c - \\\n 2*iota_N0*X1c*Y1c*Y2s + \\\n 5*iota_N0*X1c*X1c*Y1c*Y1s*\\\n curvature - \\\n 2*Y1c*Y20*d_X1c_d_varphi + \\\n 2*Y1c*Y2c*d_X1c_d_varphi + \\\n 2*Y1s*Y2s*d_X1c_d_varphi + \\\n 5*X1c*Y1s*Y1s*curvature*\\\n d_X1c_d_varphi + \\\n 2*Y1c*Y1c*d_X20_d_varphi + \\\n 2*Y1s*Y1s*d_X20_d_varphi - \\\n 2*Y1c*Y1c*d_X2c_d_varphi + \\\n 2*Y1s*Y1s*d_X2c_d_varphi - \\\n 4*Y1c*Y1s*d_X2s_d_varphi))/\\\n (G0*G0*G0)\n\n # Element 112\n grad_grad_B[:,0,0,1] =(B0*B0*B0*B0*lp*lp*(Y1c*Y1c*\\\n (-6*iota_N0*Y2s + \\\n 5*iota_N0*X1c*Y1s*\\\n curvature + \\\n 2*(lp*X20*torsion - \\\n lp*X2c*torsion + \\\n d_Y20_d_varphi - \\\n d_Y2c_d_varphi)) + \\\n Y1s*(5*iota_N0*X1c*Y1s*Y1s*\\\n curvature + \\\n 2*(lp*X1c*Y2s*torsion + \\\n Y2s*d_Y1c_d_varphi - \\\n (Y20 + Y2c)*\\\n d_Y1s_d_varphi) + \\\n Y1s*(6*iota_N0*Y2s + \\\n 2*lp*X20*torsion + \\\n 2*lp*X2c*torsion + \\\n 5*lp*X1c*X1c*curvature*\\\n torsion + \\\n 5*X1c*curvature*\\\n d_Y1c_d_varphi + \\\n 2*d_Y20_d_varphi + \\\n 2*d_Y2c_d_varphi)) + \\\n Y1c*(2*(lp*X1c*\\\n (-Y20 + Y2c)*torsion - \\\n Y20*d_Y1c_d_varphi + \\\n Y2c*d_Y1c_d_varphi + \\\n Y2s*d_Y1s_d_varphi) + \\\n Y1s*(12*iota_N0*Y2c - \\\n 4*lp*X2s*torsion - \\\n 5*X1c*curvature*\\\n d_Y1s_d_varphi - \\\n 4*d_Y2s_d_varphi))))/(G0*G0*G0)\n\n # Element 113\n grad_grad_B[:,0,0,2] =-((B0*B0*B0*lp*lp*(2*Y1c*Y1c*\\\n (2*B2c*G0*lp + B0*G2*lp + B0*I2*lp*iota - \\\n 2*G0*lp*B20 + 2*B0*G0*iota_N0*Z2s + \\\n B0*G0*lp*X20*curvature - \\\n B0*G0*lp*X2c*curvature - \\\n B0*G0*d_Z20_d_varphi + \\\n B0*G0*d_Z2c_d_varphi) + \\\n Y1s*(-2*B0*G0*lp*X1c*Y2s*\\\n curvature + \\\n Y1s*(-4*B2c*G0*lp + 2*B0*G2*lp + \\\n 2*B0*I2*lp*iota - 4*G0*lp*B20 - \\\n 4*B0*G0*iota_N0*Z2s + \\\n 2*B0*G0*lp*X20*curvature + \\\n 2*B0*G0*lp*X2c*curvature + \\\n B0*G0*lp*X1c*X1c*curvature*curvature - \\\n 2*B0*G0*d_Z20_d_varphi - \\\n 2*B0*G0*d_Z2c_d_varphi)) + \\\n 2*G0*Y1c*(B0*lp*X1c*\\\n (Y20 - Y2c)*curvature + \\\n 2*Y1s*(2*B2s*lp - 2*B0*iota_N0*Z2c - \\\n B0*lp*X2s*curvature + \\\n B0*d_Z2s_d_varphi))))/(G0*G0*G0*G0))\n\n # Element 121\n grad_grad_B[:,0,1,0] =-((B0*B0*B0*B0*lp*lp*(3*iota_N0*X1c*X1c*X1c*Y1s*\\\n curvature + \\\n 3*lp*X1c*X1c*Y1s*Y1s*curvature*\\\n torsion + \\\n 2*(X2s*Y1s*\\\n (-2*lp*Y1c*torsion + \\\n d_X1c_d_varphi) + \\\n X20*(lp*Y1c*Y1c*torsion + \\\n lp*Y1s*Y1s*torsion - \\\n Y1c*d_X1c_d_varphi) + \\\n X2c*(-(lp*Y1c*Y1c*\\\n torsion) + \\\n lp*Y1s*Y1s*torsion + \\\n Y1c*d_X1c_d_varphi)) - \\\n 2*X1c*(3*iota_N0*X2s*Y1c - \\\n iota_N0*X20*Y1s - \\\n 3*iota_N0*X2c*Y1s + \\\n lp*Y1c*Y20*torsion - \\\n lp*Y1c*Y2c*torsion - \\\n lp*Y1s*Y2s*torsion - \\\n Y1c*d_X20_d_varphi + \\\n Y1c*d_X2c_d_varphi + \\\n Y1s*d_X2s_d_varphi)))/\\\n (G0*G0*G0))\n\n # Element 122\n grad_grad_B[:,0,1,1] =(B0*B0*B0*B0*lp*lp*(-4*iota_N0*X1c*Y1s*\\\n Y2c + 4*iota_N0*X1c*Y1c*\\\n Y2s - 3*iota_N0*X1c*X1c*Y1c*\\\n Y1s*curvature + \\\n 2*X20*Y1c*d_Y1c_d_varphi + \\\n 2*X20*Y1s*d_Y1s_d_varphi + \\\n 3*X1c*X1c*Y1s*curvature*\\\n d_Y1s_d_varphi + \\\n 2*X2s*(iota_N0*Y1c*Y1c - \\\n Y1s*(iota_N0*Y1s + \\\n d_Y1c_d_varphi) - \\\n Y1c*d_Y1s_d_varphi) - \\\n 2*X2c*(Y1c*\\\n (2*iota_N0*Y1s + d_Y1c_d_varphi) \\\n - Y1s*d_Y1s_d_varphi) - \\\n 2*X1c*Y1c*d_Y20_d_varphi + \\\n 2*X1c*Y1c*d_Y2c_d_varphi + \\\n 2*X1c*Y1s*d_Y2s_d_varphi))/\\\n (G0*G0*G0)\n # (2*iota_N0*Y1s + d_Y1c_d_varphi) \\\\\n\n # Element 123\n grad_grad_B[:,0,1,2] =(2*B0*B0*B0*lp*lp*X1c*\\\n (Y1c*(2*B2c*G0*lp + B0*G2*lp + B0*I2*lp*iota - \\\n 2*G0*lp*B20 + 2*B0*G0*iota_N0*Z2s + \\\n 2*B0*G0*lp*X20*curvature - \\\n 2*B0*G0*lp*X2c*curvature - \\\n B0*G0*d_Z20_d_varphi + \\\n B0*G0*d_Z2c_d_varphi) + \\\n G0*Y1s*(2*B2s*lp - 2*B0*iota_N0*Z2c - \\\n 2*B0*lp*X2s*curvature + \\\n B0*d_Z2s_d_varphi)))/(G0*G0*G0*G0)\n\n # Element 131\n grad_grad_B[:,0,2,0] =(B0*B0*B0*B0*lp*(-4*lp*lp*X2s*Y1c*Y1s*\\\n curvature + \\\n 2*lp*lp*X2c*(-Y1c*Y1c + Y1s*Y1s)*\\\n curvature + \\\n 2*lp*lp*X20*(Y1c*Y1c + Y1s*Y1s)*\\\n curvature - \\\n 2*lp*lp*X1c*Y1c*Y20*\\\n curvature + \\\n 2*lp*lp*X1c*Y1c*Y2c*\\\n curvature + \\\n 2*lp*lp*X1c*Y1s*Y2s*\\\n curvature + \\\n 3*lp*lp*X1c*X1c*Y1s*Y1s*\\\n curvature*curvature + \\\n lp*iota_N0*X1c*X1c*X1c*Y1s*\\\n torsion - lp*iota_N0*X1c*\\\n Y1c*Y1c*Y1s*torsion - \\\n lp*iota_N0*X1c*Y1s*Y1s*Y1s*\\\n torsion - Y1s*Y1s*\\\n d_X1c_d_varphi*d_X1c_d_varphi + \\\n iota_N0*X1c*X1c*Y1s*\\\n d_Y1c_d_varphi - \\\n lp*X1c*Y1s*Y1s*torsion*\\\n d_Y1c_d_varphi - \\\n iota_N0*X1c*X1c*Y1c*\\\n d_Y1s_d_varphi + \\\n lp*X1c*Y1c*Y1s*\\\n torsion*d_Y1s_d_varphi + \\\n X1c*Y1s*Y1s*d2_X1c_d_varphi2))/\\\n (G0*G0*G0)\n\n # Element 132\n grad_grad_B[:,0,2,1] =(B0*B0*B0*B0*lp*(-(Y1s*d_X1c_d_varphi*\\\n (iota_N0*Y1c*Y1c + \\\n Y1s*(iota_N0*Y1s + \\\n d_Y1c_d_varphi) - \\\n Y1c*d_Y1s_d_varphi)) + \\\n lp*X1c*X1c*Y1s*\\\n (2*iota_N0*Y1c*torsion - \\\n torsion*d_Y1s_d_varphi + \\\n Y1s*d_torsion_d_varphi) + \\\n X1c*(Y1c*d_Y1s_d_varphi*\\\n (-(iota_N0*Y1c) + d_Y1s_d_varphi) \\\n + Y1s*Y1s*(lp*torsion*\\\n d_X1c_d_varphi + \\\n iota_N0*d_Y1s_d_varphi + \\\n d2_Y1c_d_varphi2) - \\\n Y1s*(d_Y1c_d_varphi*\\\n d_Y1s_d_varphi + \\\n Y1c*(-2*iota_N0*d_Y1c_d_varphi + \\\n d2_Y1s_d_varphi2)))))/(G0*G0*G0)\n # (-(iota_N0*Y1c) + d_Y1s_d_varphi) \\\\\n\n # Element 133\n grad_grad_B[:,0,2,2] =(B0*B0*B0*B0*lp*lp*X1c*Y1s*\\\n (-(Y1s*curvature*\\\n d_X1c_d_varphi) + \\\n X1c*(-(iota_N0*Y1c*\\\n curvature) + \\\n Y1s*d_curvature_d_varphi)))/\\\n (G0*G0*G0)\n\n # Element 211\n grad_grad_B[:,1,0,0] =(-2*B0*B0*B0*B0*lp*lp*X1c*\\\n (-2*iota_N0*X2s*Y1c + \\\n 2*iota_N0*X2c*Y1s - \\\n iota_N0*X1c*Y2s + \\\n iota_N0*X1c*X1c*Y1s*curvature + \\\n lp*X1c*Y1s*Y1s*curvature*\\\n torsion - Y20*\\\n d_X1c_d_varphi + \\\n Y2c*d_X1c_d_varphi + \\\n Y1c*d_X20_d_varphi - \\\n Y1c*d_X2c_d_varphi - \\\n Y1s*d_X2s_d_varphi))/(G0*G0*G0)\n\n # Element 212\n grad_grad_B[:,1,0,1] =(2*B0*B0*B0*B0*lp*lp*X1c*\\\n (lp*X1c*Y20*torsion - \\\n lp*X1c*Y2c*torsion + \\\n Y20*d_Y1c_d_varphi - \\\n Y2c*d_Y1c_d_varphi - \\\n Y2s*d_Y1s_d_varphi + \\\n Y1c*(3*iota_N0*Y2s - \\\n lp*X20*torsion + \\\n lp*X2c*torsion - \\\n d_Y20_d_varphi + d_Y2c_d_varphi) \\\n + Y1s*(iota_N0*Y20 - \\\n 3*iota_N0*Y2c - \\\n iota_N0*X1c*Y1c*curvature + \\\n lp*X2s*torsion + \\\n X1c*curvature*\\\n d_Y1s_d_varphi + d_Y2s_d_varphi))\\\n )/(G0*G0*G0)\n # d_Y20_d_varphi + d_Y2c_d_varphi) \\\\\n # d_Y1s_d_varphi + d_Y2s_d_varphi))\\\\\n\n # Element 213\n grad_grad_B[:,1,0,2] =(2*B0*B0*B0*lp*lp*X1c*\\\n (Y1c*(2*B2c*G0*lp + B0*G2*lp + B0*I2*lp*iota - \\\n 2*G0*lp*B20 + 2*B0*G0*iota_N0*Z2s + \\\n B0*G0*lp*X20*curvature - \\\n B0*G0*lp*X2c*curvature - \\\n B0*G0*d_Z20_d_varphi + \\\n B0*G0*d_Z2c_d_varphi) + \\\n G0*(B0*lp*X1c*(Y20 - Y2c)*\\\n curvature + \\\n Y1s*(2*B2s*lp - 2*B0*iota_N0*Z2c - \\\n B0*lp*X2s*curvature + \\\n B0*d_Z2s_d_varphi))))/(G0*G0*G0*G0)\n\n # Element 221\n grad_grad_B[:,1,1,0] =(-2*B0*B0*B0*B0*lp*lp*X1c*\\\n (lp*X2c*Y1c*torsion + \\\n lp*X2s*Y1s*torsion - \\\n X2c*d_X1c_d_varphi + \\\n X20*(-(lp*Y1c*torsion) + \\\n d_X1c_d_varphi) + \\\n X1c*(3*iota_N0*X2s + \\\n lp*Y20*torsion - \\\n lp*Y2c*torsion - \\\n d_X20_d_varphi + d_X2c_d_varphi)))/\\\n (G0*G0*G0)\n\n # Element 222\n grad_grad_B[:,1,1,1] =(-2*B0*B0*B0*B0*lp*lp*X1c*\\\n (-(iota_N0*X2c*Y1s) + \\\n 2*iota_N0*X1c*Y2s - \\\n X2c*d_Y1c_d_varphi + \\\n X20*(iota_N0*Y1s + \\\n d_Y1c_d_varphi) + \\\n X2s*(iota_N0*Y1c - \\\n d_Y1s_d_varphi) - \\\n X1c*d_Y20_d_varphi + \\\n X1c*d_Y2c_d_varphi))/(G0*G0*G0)\n\n # Element 223\n grad_grad_B[:,1,1,2] =(-2*B0*B0*B0*lp*lp*X1c*X1c*\\\n (2*B2c*G0*lp + B0*G2*lp + B0*I2*lp*iota - 2*G0*lp*B20 + \\\n 2*B0*G0*iota_N0*Z2s + \\\n 2*B0*G0*lp*X20*curvature - \\\n 2*B0*G0*lp*X2c*curvature - \\\n B0*G0*d_Z20_d_varphi + \\\n B0*G0*d_Z2c_d_varphi))/(G0*G0*G0*G0)\n\n # Element 231\n grad_grad_B[:,1,2,0] =(B0*B0*B0*B0*lp*X1c*(-2*lp*lp*X20*Y1c*\\\n curvature + \\\n 2*lp*lp*X2c*Y1c*curvature + \\\n 2*lp*lp*X2s*Y1s*curvature + \\\n 2*lp*lp*X1c*Y20*curvature - \\\n 2*lp*lp*X1c*Y2c*curvature + \\\n 2*lp*iota_N0*X1c*Y1c*Y1s*\\\n torsion - iota_N0*X1c*Y1s*\\\n d_X1c_d_varphi + \\\n lp*Y1s*Y1s*torsion*\\\n d_X1c_d_varphi + \\\n iota_N0*X1c*X1c*d_Y1s_d_varphi - \\\n lp*X1c*Y1s*torsion*\\\n d_Y1s_d_varphi - \\\n lp*X1c*Y1s*Y1s*\\\n d_torsion_d_varphi))/(G0*G0*G0)\n\n # Element 232\n grad_grad_B[:,1,2,1] =(B0*B0*B0*B0*lp*X1c*(-(lp*iota_N0*X1c*X1c*\\\n Y1s*torsion) + \\\n lp*Y1s*torsion*\\\n (iota_N0*Y1c*Y1c + \\\n Y1s*(iota_N0*Y1s + \\\n d_Y1c_d_varphi) - \\\n Y1c*d_Y1s_d_varphi) + \\\n X1c*((iota_N0*Y1c - \\\n d_Y1s_d_varphi)*d_Y1s_d_varphi \\\n + Y1s*(-(iota_N0*d_Y1c_d_varphi) + \\\n d2_Y1s_d_varphi2))))/(G0*G0*G0)\n # d_Y1s_d_varphi)*d_Y1s_d_varphi \\\\\n\n # Element 233\n grad_grad_B[:,1,2,2] =(B0*B0*B0*B0*lp*lp*X1c*X1c*Y1s*curvature*\\\n (iota_N0*X1c + 2*lp*Y1s*torsion))/\\\n (G0*G0*G0)\n\n # Element 311\n grad_grad_B[:,2,0,0] =(B0*B0*B0*B0*lp*X1c*Y1s*\\\n (lp*iota_N0*X1c*X1c*torsion - \\\n lp*iota_N0*Y1c*Y1c*torsion - \\\n lp*iota_N0*Y1s*Y1s*torsion - \\\n lp*Y1s*torsion*\\\n d_Y1c_d_varphi + \\\n X1c*(2*lp*lp*Y1s*curvature*curvature + \\\n iota_N0*d_Y1c_d_varphi) + \\\n d_X1c_d_varphi*d_Y1s_d_varphi + \\\n Y1c*(iota_N0*d_X1c_d_varphi + \\\n lp*torsion*d_Y1s_d_varphi) + \\\n Y1s*d2_X1c_d_varphi2))/(G0*G0*G0)\n\n # Element 312\n grad_grad_B[:,2,0,1] =(B0*B0*B0*B0*lp*X1c*Y1s*\\\n (lp*X1c*(2*iota_N0*Y1c*\\\n torsion + \\\n Y1s*d_torsion_d_varphi) + \\\n Y1s*(2*lp*torsion*\\\n d_X1c_d_varphi + \\\n 2*iota_N0*d_Y1s_d_varphi + \\\n d2_Y1c_d_varphi2) + \\\n Y1c*(2*iota_N0*d_Y1c_d_varphi - \\\n d2_Y1s_d_varphi2)))/(G0*G0*G0)\n\n # Element 313\n grad_grad_B[:,2,0,2] =(B0*B0*B0*B0*lp*lp*X1c*X1c*Y1s*\\\n (-(iota_N0*Y1c*curvature) + \\\n curvature*d_Y1s_d_varphi + \\\n Y1s*d_curvature_d_varphi))/\\\n (G0*G0*G0)\n\n # Element 321\n grad_grad_B[:,2,1,0] =-((B0*B0*B0*B0*lp*X1c*X1c*Y1s*\\\n (-2*lp*iota_N0*Y1c*torsion + \\\n 2*iota_N0*d_X1c_d_varphi + \\\n 2*lp*torsion*d_Y1s_d_varphi + \\\n lp*Y1s*d_torsion_d_varphi))/\\\n (G0*G0*G0))\n\n # Element 322\n grad_grad_B[:,2,1,1] =-((B0*B0*B0*B0*lp*X1c*Y1s*\\\n (lp*iota_N0*X1c*X1c*torsion - \\\n lp*iota_N0*Y1c*Y1c*torsion - \\\n lp*iota_N0*Y1s*Y1s*torsion - \\\n lp*Y1s*torsion*\\\n d_Y1c_d_varphi - \\\n d_X1c_d_varphi*d_Y1s_d_varphi + \\\n Y1c*(iota_N0*d_X1c_d_varphi + \\\n lp*torsion*d_Y1s_d_varphi) + \\\n X1c*(iota_N0*d_Y1c_d_varphi - \\\n d2_Y1s_d_varphi2)))/(G0*G0*G0))\n\n # Element 323\n grad_grad_B[:,2,1,2] =(B0*B0*B0*B0*lp*lp*X1c*X1c*Y1s*curvature*\\\n (iota_N0*X1c + 2*lp*Y1s*torsion))/\\\n (G0*G0*G0)\n\n # Element 331\n grad_grad_B[:,2,2,0] =(B0*B0*B0*B0*lp*lp*X1c*X1c*Y1s*\\\n (-(iota_N0*Y1c*curvature) + \\\n curvature*d_Y1s_d_varphi + \\\n Y1s*d_curvature_d_varphi))/\\\n (G0*G0*G0)\n\n # Element 332\n grad_grad_B[:,2,2,1] =-((B0*B0*B0*B0*lp*lp*X1c*Y1s*curvature*\\\n (iota_N0*Y1c*Y1c + \\\n Y1s*(iota_N0*Y1s + \\\n d_Y1c_d_varphi) - \\\n Y1c*d_Y1s_d_varphi))/(G0*G0*G0))\n\n # Element 333\n grad_grad_B[:,2,2,2] =(-2*B0*B0*B0*B0*lp*lp*lp*X1c*X1c*Y1s*Y1s*\\\n curvature*curvature)/(G0*G0*G0)\n\n\n self.grad_grad_B = grad_grad_B\n\n # Compute the (inverse) scale length\n squared = grad_grad_B * grad_grad_B\n norm_squared = np.sum(squared, axis=(1,2,3))\n self.grad_grad_B_inverse_scale_length_vs_varphi = np.sqrt(np.sqrt(norm_squared) / (4*B0))\n self.grad_grad_B_inverse_scale_length = np.max(self.grad_grad_B_inverse_scale_length_vs_varphi)\n\n if not two_ways:\n return\n\n # Build the whole tensor again using Rogerio's approach,\n # \"20200424-01 Rogerio's GradGradB calculation.nb\"\n # and verify the two calculations match.\n\n # Element 111\n grad_grad_B_alt[:,0,0,0] =(-2*B0*(-4*sign_G*sign_psi*iota_N0*X2c*Y1c*\\\n Y1s + iota_N0*X1c*X1c*\\\n Y1c*(Y1c*\\\n (-Y20 + Y2c) + \\\n Y1s*(Y2s - \\\n 2*sign_G*sign_psi*curvature)) + \\\n X20*Y1c*Y1c*Y1s*\\\n d_X1c_d_varphi - \\\n X2c*Y1c*Y1c*Y1s*\\\n d_X1c_d_varphi + \\\n X20*Y1s*Y1s*Y1s*\\\n d_X1c_d_varphi + \\\n X2c*Y1s*Y1s*Y1s*\\\n d_X1c_d_varphi + \\\n sign_G*sign_psi*Y1c*Y20*\\\n d_X1c_d_varphi - \\\n sign_G*sign_psi*Y1c*Y2c*\\\n d_X1c_d_varphi - \\\n sign_G*sign_psi*Y1s*Y2s*\\\n d_X1c_d_varphi - \\\n 2*X2s*(sign_G*sign_psi*iota_N0*Y1s*Y1s + \\\n Y1c*Y1c*\\\n (-(sign_G*sign_psi*iota_N0) + \\\n iota_N0*X1c*Y1s) + \\\n Y1c*Y1s*Y1s*\\\n d_X1c_d_varphi) + \\\n X1c*(iota_N0*X2c*Y1c*\\\n (-Y1c*Y1c + Y1s*Y1s) + \\\n iota_N0*X20*Y1c*\\\n (Y1c*Y1c + Y1s*Y1s) - \\\n sign_G*sign_psi*iota_N0*Y1s*Y20 - \\\n sign_G*sign_psi*iota_N0*Y1s*Y2c + \\\n sign_G*sign_psi*iota_N0*Y1c*Y2s - \\\n Y1c*Y1s*Y20*\\\n d_X1c_d_varphi + \\\n Y1c*Y1s*Y2c*\\\n d_X1c_d_varphi + \\\n Y1s*Y1s*Y2s*\\\n d_X1c_d_varphi - \\\n 2*sign_G*sign_psi*Y1s*Y1s*curvature*\\\n d_X1c_d_varphi) - \\\n sign_G*sign_psi*Y1c*Y1c*d_X20_d_varphi - \\\n sign_G*sign_psi*Y1s*Y1s*d_X20_d_varphi + \\\n sign_G*sign_psi*Y1c*Y1c*d_X2c_d_varphi - \\\n sign_G*sign_psi*Y1s*Y1s*d_X2c_d_varphi + \\\n 2*sign_G*sign_psi*Y1c*Y1s*\\\n d_X2s_d_varphi))/(lp*sign_psi)\n\n # Element 112\n grad_grad_B_alt[:,0,0,1] =(2*B0*(2*iota_N0*X2s*Y1c*Y1c*Y1c*\\\n Y1s + 2*iota_N0*X2s*\\\n Y1c*Y1s*Y1s*Y1s + \\\n iota_N0*X1c*Y1c*Y1c*Y1c*\\\n Y20 + iota_N0*X1c*Y1c*\\\n Y1s*Y1s*Y20 - \\\n iota_N0*X1c*Y1c*Y1c*Y1c*\\\n Y2c + 6*sign_G*sign_psi*iota_N0*Y1c*\\\n Y1s*Y2c - \\\n iota_N0*X1c*Y1c*Y1s*Y1s*\\\n Y2c - 3*sign_G*sign_psi*iota_N0*Y1c*Y1c*\\\n Y2s - iota_N0*X1c*\\\n Y1c*Y1c*Y1s*Y2s + \\\n 3*sign_G*sign_psi*iota_N0*Y1s*Y1s*Y2s - \\\n iota_N0*X1c*Y1s*Y1s*Y1s*\\\n Y2s + 2*sign_G*sign_psi*iota_N0*X1c*\\\n Y1c*Y1c*Y1s*curvature + \\\n 2*sign_G*sign_psi*iota_N0*X1c*Y1s*Y1s*Y1s*\\\n curvature - \\\n 2*lp*sign_G*sign_psi*X2s*Y1c*\\\n Y1s*torsion + \\\n 2*lp*X1c*X2s*Y1c*\\\n Y1s*Y1s*torsion - \\\n lp*sign_G*sign_psi*X1c*Y1c*\\\n Y20*torsion + \\\n lp*X1c*X1c*Y1c*Y1s*\\\n Y20*torsion + \\\n lp*sign_G*sign_psi*X1c*Y1c*\\\n Y2c*torsion - \\\n lp*X1c*X1c*Y1c*Y1s*\\\n Y2c*torsion + \\\n lp*sign_G*sign_psi*X1c*Y1s*\\\n Y2s*torsion - \\\n lp*X1c*X1c*Y1s*Y1s*Y2s*\\\n torsion + \\\n 2*lp*sign_G*sign_psi*X1c*X1c*Y1s*Y1s*\\\n curvature*torsion + \\\n 2*X2s*Y1c*Y1s*Y1s*\\\n d_Y1c_d_varphi - \\\n sign_G*sign_psi*Y1c*Y20*\\\n d_Y1c_d_varphi + \\\n X1c*Y1c*Y1s*\\\n Y20*d_Y1c_d_varphi + \\\n sign_G*sign_psi*Y1c*Y2c*\\\n d_Y1c_d_varphi - \\\n X1c*Y1c*Y1s*\\\n Y2c*d_Y1c_d_varphi + \\\n sign_G*sign_psi*Y1s*Y2s*\\\n d_Y1c_d_varphi - \\\n X1c*Y1s*Y1s*Y2s*\\\n d_Y1c_d_varphi + \\\n 2*sign_G*sign_psi*X1c*Y1s*Y1s*\\\n curvature*d_Y1c_d_varphi - \\\n 2*X2s*Y1c*Y1c*Y1s*\\\n d_Y1s_d_varphi - \\\n X1c*Y1c*Y1c*Y20*\\\n d_Y1s_d_varphi - \\\n sign_G*sign_psi*Y1s*Y20*\\\n d_Y1s_d_varphi + \\\n X1c*Y1c*Y1c*Y2c*\\\n d_Y1s_d_varphi - \\\n sign_G*sign_psi*Y1s*Y2c*\\\n d_Y1s_d_varphi + \\\n sign_G*sign_psi*Y1c*Y2s*\\\n d_Y1s_d_varphi + \\\n X1c*Y1c*Y1s*\\\n Y2s*d_Y1s_d_varphi - \\\n 2*sign_G*sign_psi*X1c*Y1c*Y1s*\\\n curvature*d_Y1s_d_varphi + \\\n X2c*(Y1c*Y1c - Y1s*Y1s)*\\\n (iota_N0*Y1c*Y1c + iota_N0*Y1s*Y1s - \\\n lp*sign_G*sign_psi*torsion + \\\n Y1s*(lp*X1c*torsion + \\\n d_Y1c_d_varphi) - \\\n Y1c*d_Y1s_d_varphi) - \\\n X20*(Y1c*Y1c + Y1s*Y1s)*\\\n (iota_N0*Y1c*Y1c + iota_N0*Y1s*Y1s - \\\n lp*sign_G*sign_psi*torsion + \\\n Y1s*(lp*X1c*torsion + \\\n d_Y1c_d_varphi) - \\\n Y1c*d_Y1s_d_varphi) + \\\n sign_G*sign_psi*Y1c*Y1c*d_Y20_d_varphi + \\\n sign_G*sign_psi*Y1s*Y1s*d_Y20_d_varphi - \\\n sign_G*sign_psi*Y1c*Y1c*d_Y2c_d_varphi + \\\n sign_G*sign_psi*Y1s*Y1s*d_Y2c_d_varphi - \\\n 2*sign_G*sign_psi*Y1c*Y1s*\\\n d_Y2s_d_varphi))/(lp*sign_psi)\n\n # Element 113\n grad_grad_B_alt[:,0,0,2] =(-2*(Y1c*Y1c*(G2*sign_psi + I2*sign_psi*iota - \\\n 2*lp*sign_G*sign_psi*B20 + \\\n 2*lp*sign_G*sign_psi*B2c + \\\n 2*B0*sign_G*sign_psi*iota_N0*Z2s + \\\n B0*lp*sign_G*sign_psi*X20*curvature - \\\n B0*lp*sign_G*sign_psi*X2c*curvature + \\\n B0*lp*X1c*X20*Y1s*\\\n curvature - \\\n B0*lp*X1c*X2c*Y1s*\\\n curvature - \\\n B0*sign_G*sign_psi*d_Z20_d_varphi + \\\n B0*sign_G*sign_psi*d_Z2c_d_varphi) + \\\n Y1s*(B0*lp*X1c*\\\n (X20 + X2c)*Y1s*Y1s*\\\n curvature - \\\n B0*lp*sign_G*sign_psi*X1c*Y2s*\\\n curvature + \\\n Y1s*(G2*sign_psi + I2*sign_psi*iota - \\\n 2*lp*sign_G*sign_psi*B20 - \\\n 2*lp*sign_G*sign_psi*B2c - \\\n 2*B0*sign_G*sign_psi*iota_N0*Z2s + \\\n B0*lp*sign_G*sign_psi*X20*curvature + \\\n B0*lp*sign_G*sign_psi*X2c*curvature + \\\n B0*lp*X1c*X1c*Y2s*\\\n curvature + \\\n B0*lp*sign_G*sign_psi*X1c*X1c*\\\n curvature*curvature - \\\n B0*sign_G*sign_psi*d_Z20_d_varphi - \\\n B0*sign_G*sign_psi*d_Z2c_d_varphi)) + \\\n Y1c*(4*lp*sign_G*sign_psi*B2s*\\\n Y1s - \\\n B0*(2*lp*X1c*X2s*\\\n Y1s*Y1s*curvature + \\\n lp*sign_G*sign_psi*X1c*\\\n (-Y20 + Y2c)*\\\n curvature + \\\n Y1s*\\\n (4*sign_G*sign_psi*iota_N0*Z2c + \\\n 2*lp*sign_G*sign_psi*X2s*curvature + \\\n lp*X1c*X1c*Y20*\\\n curvature - \\\n lp*X1c*X1c*Y2c*\\\n curvature - \\\n 2*sign_G*sign_psi*d_Z2s_d_varphi)))))/\\\n (lp*sign_psi)\n\n # Element 121\n grad_grad_B_alt[:,0,1,0] =(-2*B0*(iota_N0*X1c*X1c*X1c*\\\n (Y1c*(Y20 - Y2c) + \\\n Y1s*(-Y2s + \\\n sign_G*sign_psi*curvature)) - \\\n X1c*X1c*(iota_N0*X2c*\\\n (-Y1c*Y1c + Y1s*Y1s) + \\\n iota_N0*X20*\\\n (Y1c*Y1c + Y1s*Y1s) + \\\n Y1s*(-2*iota_N0*X2s*\\\n Y1c + \\\n lp*(Y1c*\\\n (-Y20 + Y2c) + \\\n Y1s*\\\n (Y2s - sign_G*sign_psi*curvature))*\\\n torsion)) + \\\n sign_G*sign_psi*(X2s*Y1s*\\\n (-2*lp*Y1c*torsion + \\\n d_X1c_d_varphi) + \\\n X20*(lp*Y1c*Y1c*\\\n torsion + \\\n lp*Y1s*Y1s*torsion - \\\n Y1c*d_X1c_d_varphi) + \\\n X2c*(-(lp*Y1c*Y1c*\\\n torsion) + \\\n lp*Y1s*Y1s*torsion + \\\n Y1c*d_X1c_d_varphi)) + \\\n X1c*(3*sign_G*sign_psi*iota_N0*X2c*\\\n Y1s + \\\n lp*X2c*Y1c*Y1c*Y1s*\\\n torsion - \\\n lp*X2c*Y1s*Y1s*Y1s*\\\n torsion - \\\n lp*sign_G*sign_psi*Y1c*Y20*\\\n torsion + \\\n lp*sign_G*sign_psi*Y1c*Y2c*\\\n torsion + \\\n lp*sign_G*sign_psi*Y1s*Y2s*\\\n torsion - \\\n X20*Y1s*\\\n (-(sign_G*sign_psi*iota_N0) + \\\n lp*Y1c*Y1c*torsion + \\\n lp*Y1s*Y1s*torsion) + \\\n X2s*Y1c*\\\n (-3*sign_G*sign_psi*iota_N0 + \\\n 2*lp*Y1s*Y1s*torsion) + \\\n sign_G*sign_psi*Y1c*d_X20_d_varphi - \\\n sign_G*sign_psi*Y1c*d_X2c_d_varphi - \\\n sign_G*sign_psi*Y1s*d_X2s_d_varphi)))/\\\n (lp*sign_psi)\n\n # Element 122\n grad_grad_B_alt[:,0,1,1] =(2*B0*(-(X1c*X1c*\\\n (Y1c*(Y20 - Y2c) + \\\n Y1s*(-Y2s + \\\n sign_G*sign_psi*curvature))*\\\n (iota_N0*Y1c - d_Y1s_d_varphi)) \\\n + X2s*(iota_N0*Y1c*Y1c*\\\n (sign_G*sign_psi - 2*X1c*Y1s) - \\\n sign_G*sign_psi*Y1s*\\\n (iota_N0*Y1s + \\\n d_Y1c_d_varphi) + \\\n Y1c*(-(sign_G*sign_psi) + \\\n 2*X1c*Y1s)*\\\n d_Y1s_d_varphi) + \\\n sign_G*sign_psi*(X20*\\\n (Y1c*d_Y1c_d_varphi + \\\n Y1s*d_Y1s_d_varphi) + \\\n X2c*(-(Y1c*\\\n (2*iota_N0*Y1s + \\\n d_Y1c_d_varphi)) + \\\n Y1s*d_Y1s_d_varphi)) + \\\n X1c*(-(X2c*\\\n (Y1c*Y1c - Y1s*Y1s)*\\\n (iota_N0*Y1c - \\\n d_Y1s_d_varphi)) + \\\n X20*(Y1c*Y1c + Y1s*Y1s)*\\\n (iota_N0*Y1c - d_Y1s_d_varphi) \\\n + sign_G*sign_psi*(Y1c*\\\n (2*iota_N0*Y2s - \\\n d_Y20_d_varphi + \\\n d_Y2c_d_varphi) + \\\n Y1s*\\\n (-2*iota_N0*Y2c + \\\n d_Y2s_d_varphi)))))/(lp*sign_psi)\n\n # Element 123\n grad_grad_B_alt[:,0,1,2] =(2*X1c*(Y1c*\\\n (G2 + I2*iota - 2*lp*sign_G*B20 + \\\n 2*lp*sign_G*B2c + \\\n 2*B0*sign_G*iota_N0*Z2s + \\\n 2*B0*lp*sign_G*X20*curvature - \\\n 2*B0*lp*sign_G*X2c*curvature - \\\n B0*sign_G*d_Z20_d_varphi + \\\n B0*sign_G*d_Z2c_d_varphi) + \\\n sign_G*Y1s*(2*lp*B2s + \\\n B0*(-2*iota_N0*Z2c - \\\n 2*lp*X2s*curvature + \\\n d_Z2s_d_varphi))))/(lp)\n\n # Element 131\n grad_grad_B_alt[:,0,2,0] =(B0*(-(lp*sign_G*sign_psi*iota_N0*Y1c*Y1c*\\\n torsion) + \\\n lp*iota_N0*X1c*X1c*X1c*Y1s*\\\n torsion + \\\n X1c*X1c*(lp*lp*Y1s*Y1s*\\\n torsion*torsion + \\\n iota_N0*Y1s*d_Y1c_d_varphi - \\\n iota_N0*Y1c*d_Y1s_d_varphi) + \\\n sign_G*sign_psi*Y1c*\\\n (iota_N0*d_X1c_d_varphi + \\\n 2*lp*torsion*d_Y1s_d_varphi) + \\\n X1c*Y1s*\\\n (2*lp*lp*sign_G*sign_psi*curvature*curvature - \\\n lp*lp*sign_G*sign_psi*torsion*torsion - \\\n iota_N0*Y1c*d_X1c_d_varphi + \\\n lp*torsion*\\\n (Y1s*d_Y1c_d_varphi - \\\n Y1c*d_Y1s_d_varphi)) - \\\n Y1s*(Y1s*\\\n (lp*sign_G*sign_psi*iota_N0*torsion + \\\n d_X1c_d_varphi*d_X1c_d_varphi) + \\\n sign_G*sign_psi*(2*lp*torsion*\\\n d_Y1c_d_varphi - \\\n d2_X1c_d_varphi2))))/(lp*lp*sign_G)\n\n # Element 132\n grad_grad_B_alt[:,0,2,1] =(B0*(-(iota_N0*Y1c*Y1c*Y1s*\\\n d_X1c_d_varphi) + \\\n lp*X1c*X1c*Y1s*torsion*\\\n (iota_N0*Y1c - d_Y1s_d_varphi) + \\\n X1c*(-(iota_N0*Y1c*Y1c*\\\n d_Y1s_d_varphi) + \\\n Y1c*(lp*sign_G*sign_psi*iota_N0*\\\n torsion + \\\n iota_N0*Y1s*\\\n d_Y1c_d_varphi + \\\n d_Y1s_d_varphi*d_Y1s_d_varphi) - \\\n Y1s*(lp*Y1s*torsion*\\\n d_X1c_d_varphi + \\\n d_Y1c_d_varphi*\\\n d_Y1s_d_varphi - \\\n lp*sign_G*sign_psi*d_torsion_d_varphi)) + \\\n Y1s*(-(iota_N0*Y1s*Y1s*\\\n d_X1c_d_varphi) - \\\n Y1s*d_X1c_d_varphi*\\\n d_Y1c_d_varphi + \\\n sign_G*sign_psi*(2*lp*torsion*\\\n d_X1c_d_varphi + \\\n iota_N0*d_Y1s_d_varphi + \\\n d2_Y1c_d_varphi2)) + \\\n Y1c*(sign_G*sign_psi*iota_N0*\\\n d_Y1c_d_varphi + \\\n Y1s*d_X1c_d_varphi*\\\n d_Y1s_d_varphi - \\\n sign_G*sign_psi*d2_Y1s_d_varphi2)))/\\\n (lp*lp*sign_G)\n\n # Element 133\n grad_grad_B_alt[:,0,2,2] =-((B0*(Y1s*curvature*\\\n d_X1c_d_varphi + \\\n X1c*(iota_N0*Y1c*\\\n curvature - \\\n Y1s*d_curvature_d_varphi)))/\\\n (lp*sign_psi))\n\n # Element 211\n grad_grad_B_alt[:,1,0,0] =(-2*B0*X1c*(2*sign_G*sign_psi*iota_N0*X2c*\\\n Y1s + iota_N0*X1c*X1c*\\\n (Y1c*(Y20 - Y2c) + \\\n sign_G*sign_psi*Y1s*curvature) - \\\n X20*Y1c*Y1s*\\\n d_X1c_d_varphi + \\\n X2c*Y1c*Y1s*\\\n d_X1c_d_varphi - \\\n sign_G*sign_psi*Y20*d_X1c_d_varphi + \\\n sign_G*sign_psi*Y2c*d_X1c_d_varphi + \\\n X2s*(Y1c*\\\n (-2*sign_G*sign_psi*iota_N0 + \\\n iota_N0*X1c*Y1s) + \\\n Y1s*Y1s*d_X1c_d_varphi) + \\\n X1c*(-(iota_N0*X20*\\\n Y1c*Y1c) + \\\n iota_N0*X2c*Y1c*Y1c - \\\n sign_G*sign_psi*iota_N0*Y2s + \\\n lp*sign_G*sign_psi*Y1s*Y1s*curvature*\\\n torsion + \\\n Y1s*Y20*\\\n d_X1c_d_varphi - \\\n Y1s*Y2c*\\\n d_X1c_d_varphi) + \\\n sign_G*sign_psi*Y1c*d_X20_d_varphi - \\\n sign_G*sign_psi*Y1c*d_X2c_d_varphi - \\\n sign_G*sign_psi*Y1s*d_X2s_d_varphi))/\\\n (lp*sign_psi)\n\n # Element 212\n grad_grad_B_alt[:,1,0,1] =(-2*B0*X1c*(iota_N0*X2s*\\\n Y1c*Y1c*Y1s + \\\n iota_N0*X2s*Y1s*Y1s*Y1s + \\\n iota_N0*X1c*Y1c*Y1c*\\\n Y20 - sign_G*sign_psi*iota_N0*Y1s*\\\n Y20 + iota_N0*X1c*\\\n Y1s*Y1s*Y20 - \\\n iota_N0*X1c*Y1c*Y1c*\\\n Y2c + 3*sign_G*sign_psi*iota_N0*Y1s*\\\n Y2c - iota_N0*X1c*\\\n Y1s*Y1s*Y2c - \\\n 3*sign_G*sign_psi*iota_N0*Y1c*Y2s + \\\n sign_G*sign_psi*iota_N0*X1c*Y1c*\\\n Y1s*curvature - \\\n lp*sign_G*sign_psi*X2s*Y1s*\\\n torsion + \\\n lp*X1c*X2s*Y1s*Y1s*\\\n torsion - \\\n lp*sign_G*sign_psi*X1c*Y20*\\\n torsion + \\\n lp*X1c*X1c*Y1s*Y20*\\\n torsion + \\\n lp*sign_G*sign_psi*X1c*Y2c*\\\n torsion - \\\n lp*X1c*X1c*Y1s*Y2c*\\\n torsion + \\\n X2s*Y1s*Y1s*\\\n d_Y1c_d_varphi - \\\n sign_G*sign_psi*Y20*d_Y1c_d_varphi + \\\n X1c*Y1s*Y20*\\\n d_Y1c_d_varphi + \\\n sign_G*sign_psi*Y2c*d_Y1c_d_varphi - \\\n X1c*Y1s*Y2c*\\\n d_Y1c_d_varphi - \\\n X2s*Y1c*Y1s*\\\n d_Y1s_d_varphi - \\\n X1c*Y1c*Y20*\\\n d_Y1s_d_varphi + \\\n X1c*Y1c*Y2c*\\\n d_Y1s_d_varphi + \\\n sign_G*sign_psi*Y2s*d_Y1s_d_varphi - \\\n sign_G*sign_psi*X1c*Y1s*\\\n curvature*d_Y1s_d_varphi - \\\n X20*Y1c*\\\n (iota_N0*Y1c*Y1c + iota_N0*Y1s*Y1s - \\\n lp*sign_G*sign_psi*torsion + \\\n Y1s*(lp*X1c*torsion + \\\n d_Y1c_d_varphi) - \\\n Y1c*d_Y1s_d_varphi) + \\\n X2c*Y1c*\\\n (iota_N0*Y1c*Y1c + iota_N0*Y1s*Y1s - \\\n lp*sign_G*sign_psi*torsion + \\\n Y1s*(lp*X1c*torsion + \\\n d_Y1c_d_varphi) - \\\n Y1c*d_Y1s_d_varphi) + \\\n sign_G*sign_psi*Y1c*d_Y20_d_varphi - \\\n sign_G*sign_psi*Y1c*d_Y2c_d_varphi - \\\n sign_G*sign_psi*Y1s*d_Y2s_d_varphi))/\\\n (lp*sign_psi)\n\n # Element 213\n grad_grad_B_alt[:,1,0,2] =(2*X1c*(2*lp*sign_G*sign_psi*B2s*\\\n Y1s + Y1c*\\\n (G2*sign_psi + I2*sign_psi*iota - \\\n 2*lp*sign_G*sign_psi*B20 + \\\n 2*lp*sign_G*sign_psi*B2c + \\\n 2*B0*sign_G*sign_psi*iota_N0*Z2s + \\\n B0*lp*sign_G*sign_psi*X20*curvature - \\\n B0*lp*sign_G*sign_psi*X2c*curvature + \\\n B0*lp*X1c*X20*Y1s*\\\n curvature - \\\n B0*lp*X1c*X2c*Y1s*\\\n curvature - \\\n B0*sign_G*sign_psi*d_Z20_d_varphi + \\\n B0*sign_G*sign_psi*d_Z2c_d_varphi) - \\\n B0*(lp*X1c*X2s*Y1s*Y1s*\\\n curvature + \\\n lp*sign_G*sign_psi*X1c*\\\n (-Y20 + Y2c)*\\\n curvature + \\\n Y1s*(2*sign_G*sign_psi*iota_N0*Z2c + \\\n lp*sign_G*sign_psi*X2s*curvature + \\\n lp*X1c*X1c*Y20*\\\n curvature - \\\n lp*X1c*X1c*Y2c*\\\n curvature - \\\n sign_G*sign_psi*d_Z2s_d_varphi))))/\\\n (lp*sign_psi)\n\n # Element 221\n grad_grad_B_alt[:,1,1,0] =(2*B0*X1c*(iota_N0*X1c*X1c*X1c*\\\n (Y20 - Y2c) + \\\n X1c*X1c*(-(iota_N0*X20*\\\n Y1c) + \\\n iota_N0*X2c*Y1c + \\\n Y1s*(iota_N0*X2s + \\\n lp*(Y20 - Y2c)*\\\n torsion)) - \\\n sign_G*sign_psi*(lp*X2s*Y1s*\\\n torsion + \\\n X2c*(lp*Y1c*\\\n torsion - d_X1c_d_varphi) + \\\n X20*(-(lp*Y1c*\\\n torsion) + d_X1c_d_varphi)) \\\n + X1c*(-(lp*X20*Y1c*\\\n Y1s*torsion) + \\\n lp*X2c*Y1c*Y1s*\\\n torsion - \\\n lp*sign_G*sign_psi*Y20*torsion + \\\n lp*sign_G*sign_psi*Y2c*torsion + \\\n X2s*(-3*sign_G*sign_psi*iota_N0 + \\\n lp*Y1s*Y1s*torsion) + \\\n sign_G*sign_psi*d_X20_d_varphi - \\\n sign_G*sign_psi*d_X2c_d_varphi)))/\\\n (lp*sign_psi)\n\n # Element 222\n grad_grad_B_alt[:,1,1,1] =(-2*B0*X1c*(sign_G*sign_psi*\\\n (X20 - X2c)*\\\n (iota_N0*Y1s + d_Y1c_d_varphi) + \\\n X2s*(sign_G*sign_psi - \\\n X1c*Y1s)*\\\n (iota_N0*Y1c - d_Y1s_d_varphi) - \\\n X1c*X1c*(Y20 - Y2c)*\\\n (iota_N0*Y1c - d_Y1s_d_varphi) + \\\n X1c*(X20*Y1c*\\\n (iota_N0*Y1c - d_Y1s_d_varphi) \\\n + X2c*Y1c*\\\n (-(iota_N0*Y1c) + \\\n d_Y1s_d_varphi) + \\\n sign_G*sign_psi*(2*iota_N0*Y2s - \\\n d_Y20_d_varphi + \\\n d_Y2c_d_varphi))))/(lp*sign_psi)\n\n # Element 223\n grad_grad_B_alt[:,1,1,2] =(-2*X1c*X1c*(G2 + I2*iota - 2*lp*sign_G*B20 + \\\n 2*lp*sign_G*B2c + 2*B0*sign_G*iota_N0*Z2s + \\\n 2*B0*lp*sign_G*X20*curvature - \\\n 2*B0*lp*sign_G*X2c*curvature - \\\n B0*sign_G*d_Z20_d_varphi + \\\n B0*sign_G*d_Z2c_d_varphi))/(lp)\n\n # Element 231\n grad_grad_B_alt[:,1,2,0] =(B0*X1c*(lp*iota_N0*Y1c*\\\n (sign_G*sign_psi + X1c*Y1s)*\\\n torsion + \\\n (-(sign_G*sign_psi*iota_N0) + \\\n lp*Y1s*Y1s*torsion)*\\\n d_X1c_d_varphi + \\\n iota_N0*X1c*X1c*d_Y1s_d_varphi - \\\n 2*lp*sign_G*sign_psi*torsion*\\\n d_Y1s_d_varphi + \\\n lp*X1c*Y1s*torsion*\\\n d_Y1s_d_varphi - \\\n lp*sign_G*sign_psi*Y1s*d_torsion_d_varphi))/\\\n (lp*lp*sign_G)\n\n # Element 232\n grad_grad_B_alt[:,1,2,1] =(B0*X1c*(lp*iota_N0*Y1c*Y1c*\\\n Y1s*torsion + \\\n lp*iota_N0*Y1s*Y1s*Y1s*torsion - \\\n lp*lp*sign_G*sign_psi*Y1s*torsion*torsion - \\\n sign_G*sign_psi*iota_N0*d_Y1c_d_varphi + \\\n lp*Y1s*Y1s*torsion*\\\n d_Y1c_d_varphi - \\\n lp*Y1c*Y1s*torsion*\\\n d_Y1s_d_varphi + \\\n X1c*(-(lp*sign_G*sign_psi*iota_N0*\\\n torsion) + \\\n lp*lp*Y1s*Y1s*torsion*torsion + \\\n (iota_N0*Y1c - d_Y1s_d_varphi)*\\\n d_Y1s_d_varphi) + \\\n sign_G*sign_psi*d2_Y1s_d_varphi2))/(lp*lp*sign_G)\n\n # Element 233\n grad_grad_B_alt[:,1,2,2] =(B0*X1c*curvature*\\\n (iota_N0*X1c + \\\n 2*lp*Y1s*torsion))/(lp*sign_psi)\n\n # Element 311\n grad_grad_B_alt[:,2,0,0] =(B0*(2*lp*lp*sign_G*sign_psi*curvature*curvature + \\\n lp*iota_N0*X1c*X1c*torsion - \\\n lp*iota_N0*Y1c*Y1c*torsion - \\\n lp*iota_N0*Y1s*Y1s*torsion + \\\n iota_N0*Y1c*d_X1c_d_varphi + \\\n iota_N0*X1c*d_Y1c_d_varphi - \\\n lp*Y1s*torsion*\\\n d_Y1c_d_varphi + \\\n lp*Y1c*torsion*\\\n d_Y1s_d_varphi + \\\n d_X1c_d_varphi*d_Y1s_d_varphi + \\\n Y1s*d2_X1c_d_varphi2))/\\\n (lp*lp*sign_psi)\n\n # Element 312\n grad_grad_B_alt[:,2,0,1] =(B0*(lp*X1c*(2*iota_N0*Y1c*\\\n torsion + \\\n Y1s*d_torsion_d_varphi) + \\\n Y1s*(2*lp*torsion*\\\n d_X1c_d_varphi + \\\n 2*iota_N0*d_Y1s_d_varphi + \\\n d2_Y1c_d_varphi2) + \\\n Y1c*(2*iota_N0*d_Y1c_d_varphi - \\\n d2_Y1s_d_varphi2)))/(lp*lp*sign_psi)\n\n # Element 313\n grad_grad_B_alt[:,2,0,2] =(B0*(-(iota_N0*X1c*Y1c*\\\n curvature) - \\\n Y1s*curvature*\\\n d_X1c_d_varphi + \\\n sign_G*sign_psi*d_curvature_d_varphi))/(lp*sign_psi)\n\n # Element 321\n grad_grad_B_alt[:,2,1,0] =(B0*X1c*(2*lp*iota_N0*Y1c*\\\n torsion - \\\n 2*iota_N0*d_X1c_d_varphi - \\\n lp*(2*torsion*d_Y1s_d_varphi + \\\n Y1s*d_torsion_d_varphi)))/\\\n (lp*lp*sign_psi)\n\n # Element 322\n grad_grad_B_alt[:,2,1,1] =-((B0*(iota_N0*Y1c*d_X1c_d_varphi + \\\n iota_N0*X1c*d_Y1c_d_varphi - \\\n d_X1c_d_varphi*\\\n d_Y1s_d_varphi + \\\n lp*torsion*\\\n (iota_N0*X1c*X1c - \\\n iota_N0*Y1c*Y1c - \\\n Y1s*(iota_N0*Y1s + \\\n d_Y1c_d_varphi) + \\\n Y1c*d_Y1s_d_varphi) - \\\n X1c*d2_Y1s_d_varphi2))/\\\n (lp*lp*sign_psi))\n\n # Element 323\n grad_grad_B_alt[:,2,1,2] =(B0*curvature*(iota_N0*X1c*X1c + \\\n lp*sign_G*sign_psi*torsion + \\\n lp*X1c*Y1s*torsion))/\\\n (lp*sign_psi)\n\n # Element 331\n grad_grad_B_alt[:,2,2,0] =(B0*(-(iota_N0*X1c*Y1c*\\\n curvature) - \\\n Y1s*curvature*\\\n d_X1c_d_varphi + \\\n sign_G*sign_psi*d_curvature_d_varphi))/(lp*sign_psi)\n\n # Element 332\n grad_grad_B_alt[:,2,2,1] =-((B0*curvature*(iota_N0*Y1c*Y1c + \\\n iota_N0*Y1s*Y1s - \\\n lp*sign_G*sign_psi*torsion + \\\n Y1s*(lp*X1c*torsion + \\\n d_Y1c_d_varphi) - \\\n Y1c*d_Y1s_d_varphi))/\\\n (lp*sign_psi))\n\n # Element 333\n grad_grad_B_alt[:,2,2,2] =(-2*B0*curvature*curvature)/sign_G\n\n self.grad_grad_B_alt = grad_grad_B_alt","def add_lp_qe_DL_terms(K, lin_pot_mesh, quad_geo_mesh):\n geo_faces = quad_geo_mesh.get_faces()\n pot_faces = lin_pot_mesh.get_faces()\n assert geo_faces.shape[0] == pot_faces.shape[0]\n num_faces = geo_faces.shape[0]\n pot_nodes = lin_pot_mesh.get_nodes()\n num_nodes = pot_nodes.shape[0]\n c_0 = 1. / (4. * np.pi)\n\n for face_num in range(num_faces): # integrate over faces\n face_nodes = quad_geo_mesh.get_tri_nodes(face_num)\n face_n = quad_geo_mesh.get_quad_n(face_num)\n face_hs = quad_geo_mesh.get_hs(face_num)\n for src_num in range(num_nodes): # source points\n src_pt = lin_pot_mesh.get_node(src_num)\n is_singular, local_singular_ind = lin_pot_mesh.check_in_face(src_num, face_num)\n\n if is_singular: # singular triangle\n for node_num in range(3):\n node_global_num = pot_faces[face_num, node_num] # global index for vert\n sub_mat = gq.int_over_tri_quad_n(\n make_sing_lp_qe_quad_func(\n src_pt, node_num, local_singular_ind\n ),\n face_nodes,\n face_n,\n face_hs,\n )\n K[(3 * src_num):(3 * src_num + 3),\n (3 * node_global_num):(3 * node_global_num + 3)] += sub_mat\n\n else: # regular triangle\n for node_num in range(3):\n node_global_num = pot_faces[face_num, node_num] # global index for vert\n sub_mat = gq.int_over_tri_quad_n(\n make_reg_lp_qe_quad_func(src_pt, node_num),\n face_nodes,\n face_n,\n face_hs,\n )\n K[(3 * src_num):(3 * src_num + 3),\n (3 * node_global_num):(3 * node_global_num + 3)] += sub_mat\n # subtracting the q(x_0) term\n sub_mat = gq.int_over_tri_quad_n(\n make_cp_qe_quad_func(src_pt),\n face_nodes,\n face_n,\n face_hs,\n )\n K[(3 * src_num):(3 * src_num + 3), (3 * src_num):(3 * src_num + 3)] -= sub_mat\n\n for src_num in range(num_nodes): # source points\n # whole surface q(x_0) term\n K[(3 * src_num):(3 * src_num + 3), (3 * src_num):(3 * src_num + 3)] -= (\n 4. * np.pi * np.identity(3)\n )\n\n K *= c_0","def rwg0_barycentric_function_space(coarse_space):\n from .space import SpaceBuilder\n from scipy.sparse import coo_matrix\n\n number_of_support_elements = coarse_space.number_of_support_elements\n bary_grid_number_of_elements = 6 * coarse_space.grid.number_of_elements\n\n bary_support_elements = 6 * _np.repeat(coarse_space.support_elements, 6) + _np.tile(\n _np.arange(6), number_of_support_elements\n )\n\n bary_support_size = len(bary_support_elements)\n\n support = _np.zeros(6 * coarse_space.grid.number_of_elements, dtype=_np.bool_)\n support[bary_support_elements] = True\n\n normal_multipliers = _np.repeat(coarse_space.normal_multipliers, 6)\n\n local_coords = _np.array(\n [[0, 0], [0.5, 0], [1, 0], [0.5, 0.5], [0, 1], [0, 0.5], [1.0 / 3, 1.0 / 3]]\n ).T\n\n coeffs = (\n _np.array(\n [\n [1, -1.0 / 3, 0],\n [-1.0 / 3, 1, 0],\n [0, 1.0 / 3, -1.0 / 6],\n [0, 0, 1.0 / 6],\n [0, 0, 1.0 / 6],\n [1.0 / 3, 0, -1.0 / 6],\n ]\n ),\n _np.array(\n [\n [0, 1.0 / 3, -1.0 / 6],\n [0, 0, 1.0 / 6],\n [0, 0, 1.0 / 6],\n [1.0 / 3, 0, -1.0 / 6],\n [1, -1.0 / 3, 0],\n [-1.0 / 3, 1, 0],\n ]\n ),\n _np.array(\n [\n [0, 0, 1.0 / 6],\n [1.0 / 3, 0, -1.0 / 6],\n [1, -1.0 / 3, 0],\n [-1.0 / 3, 1, 0],\n [0, 1.0 / 3, -1.0 / 6],\n [0, 0, 1.0 / 6],\n ]\n ),\n )\n\n coarse_dofs, bary_dofs, values = generate_rwg0_map(\n coarse_space.grid.data(), coarse_space.support_elements, local_coords, coeffs\n )\n\n local2global = _np.zeros((bary_grid_number_of_elements, 3), dtype=\"uint32\")\n local_multipliers = _np.zeros((bary_grid_number_of_elements, 3), dtype=\"uint32\")\n\n local2global[support] = _np.arange(3 * bary_support_size).reshape(\n bary_support_size, 3\n )\n\n local_multipliers[support] = 1\n\n transform = coo_matrix(\n (values, (bary_dofs, coarse_dofs)),\n shape=(3 * bary_support_size, 3 * number_of_support_elements),\n dtype=_np.float64,\n ).tocsr()\n\n dof_transformation = transform @ coarse_space.map_to_localised_space\n\n return (\n SpaceBuilder(coarse_space.grid.barycentric_refinement)\n .set_codomain_dimension(3)\n .set_support(support)\n .set_normal_multipliers(normal_multipliers)\n .set_order(0)\n .set_is_localised(True)\n .set_is_barycentric(True)\n .set_shapeset(\"rwg0\")\n .set_identifier(\"rwg0\")\n .set_local2global(local2global)\n .set_local_multipliers(local_multipliers)\n .set_dof_transformation(dof_transformation)\n .set_numba_evaluator(_numba_rwg0_evaluate)\n .build()\n )","def add_lp_le_DL_terms(K, lin_pot_mesh, lin_geo_mesh):\n c_0 = 1. / (4. * np.pi)\n num_faces = lin_pot_mesh.get_faces().shape[0]\n pot_faces = lin_pot_mesh.get_faces()\n pot_nodes = lin_pot_mesh.get_nodes()\n num_nodes = pot_nodes.shape[0]\n for face_num in range(num_faces): # integrate over faces\n face_nodes = lin_geo_mesh.get_tri_nodes(face_num)\n face_n = lin_geo_mesh.get_normal(face_num)\n face_hs = lin_geo_mesh.get_hs(face_num)\n for src_num in range(num_nodes): # source points\n src_pt = pot_nodes[src_num]\n is_singular, _local_singular_ind = lin_pot_mesh.check_in_face(src_num, face_num)\n if not is_singular: # regular triangle\n for node_num in range(3): # over 3 triangle nodes\n face_node_global_num = pot_faces[face_num, node_num] # global index for vert\n sub_mat = gq.int_over_tri_lin(\n make_DL_reg_lp_le_quad_func(\n face_n, src_pt, node_num\n ),\n face_nodes,\n face_hs\n )\n j = 3 * face_node_global_num\n K[(3 * src_num):(3 * src_num + 3),\n j:j + 3] += c_0 * sub_mat\n # subtracting the q(x_0) term\n sub_mat = gq.int_over_tri_lin(\n make_DL_cp_le_quad_func(face_n, src_pt),\n face_nodes,\n face_hs\n )\n K[(3 * src_num):(3 * src_num + 3), (3 * src_num):(3 * src_num + 3)] -= c_0 * sub_mat\n # for singular elements, \\hat{x} dotted with normal vector always zero\n # due to flat triangular elements\n\n for src_num in range(num_nodes): # source points\n # whole surface q(x_0) term\n j = 3 * src_num\n K[j:j+3, j:j+3] -= c_0 * (\n 4. * np.pi * np.identity(3)\n )","def gelu(x):\n cdf = 0.5 * (1.0 + tf.tanh(\n (np.sqrt(2 / np.pi) * (x + 0.044715 * tf.pow(x, 3)))))\n return x * cdf","def M_g(self):\n\n print(\"\", file=self.logfile)\n print(\"Updating g\", file=self.logfile)\n M_mu1 = np.lib.stride_tricks.as_strided(self.mu_pad,\n shape=[self.P+1, self.L_h],\n strides=[self.mu_pad.strides[-1], self.mu_pad.strides[-1]])\n\n M_mu1 = M_mu1[::-1,:]\n M_mu2 = np.transpose(M_mu1[1:,:])\n M_mu1 = M_mu1*self.e2\n\n M_mu = np.dot(M_mu1, M_mu2)\n v_mu = M_mu[0,:]\n M_mu = M_mu[1:,:]\n\n M_R = np.zeros((self.P,self.P+1))\n for p in range(1,self.P+1):\n for q in range(0,self.P+1):\n M_R[p-1,q] = np.sum(np.diag(self.R, q-p)[:self.L_h-max(p,q)]*self.e2[max(p,q):self.L_h])\n\n v_R = M_R[:,0]\n M_R = M_R[:,1:]\n\n self.alpha_g = np.dot(np.linalg.inv(M_mu + M_R), v_mu+v_R)\n self.A = np.concatenate([[1], -self.alpha_g])\n\n self._propagate_A()","def Intgrl(N, R, Zeta1, Zeta2, Za, Zb):\n\n global S12, T11, T12, T22, V11A, V12A, V22A, V11B, V12B, V22B, V1111, V2111, V2121, V2211, V2221, V2222\n\n S12 = 0.0\n T11 = 0.0\n T12 = 0.0\n T22 = 0.0\n V11A = 0.0\n V12A = 0.0\n V22A = 0.0\n V11B = 0.0\n V12B = 0.0\n V22B = 0.0\n V1111 = 0.0\n V2111 = 0.0\n V2121 = 0.0\n V2211 = 0.0\n V2221 = 0.0\n V2222 = 0.0\n\n R2 = R * R\n\n # The coefficients for the contracted Gaussian functions are below\n Coeff = np.array([[1.00000, 0.0000000, 0.000000],\n [0.678914, 0.430129, 0.000000],\n [0.444635, 0.535328, 0.154329]])\n\n Expon = np.array([[0.270950, 0.000000, 0.000000],\n [0.151623, 0.851819, 0.000000],\n [0.109818, 0.405771, 2.227660]])\n D1 = np.zeros([3])\n A1 = np.zeros([3])\n D2 = np.zeros([3])\n A2 = np.zeros([3])\n\n # This loop constructs the contracted Gaussian functions\n for i in range(N):\n A1[i] = Expon[N - 1, i] * (Zeta1 ** 2)\n D1[i] = Coeff[N - 1, i] * ((2.0 * A1[i] / np.pi) ** 0.75)\n A2[i] = Expon[N - 1, i] * (Zeta2 ** 2)\n D2[i] = Coeff[N - 1, i] * ((2.0 * A2[i] / np.pi) ** 0.75)\n\n # Calculate one electron integrals\n # Centre A is first atom centre B is second atom\n # Origin is on second atom\n # V12A - off diagonal nuclear attraction to centre A etc.\n for i in range(N):\n for j in range(N):\n # Rap2 - squared distance between centre A and centre P\n Rap = A2[j] * R / (A1[i] + A2[j])\n Rap2 = Rap ** 2\n Rbp2 = (R - Rap) ** 2\n S12 = S12 + S_int(A1[i], A2[j], R2) * D1[i] * D2[j]\n T11 = T11 + T_int(A1[i], A1[j], 0.0) * D1[i] * D1[j]\n T12 = T12 + T_int(A1[i], A2[j], R2) * D1[i] * D2[j]\n T22 = T22 + T_int(A2[i], A2[j], 0.0) * D2[i] * D2[j]\n V11A = V11A + V_int(A1[i], A1[j], 0.0, 0.0, Za) * D1[i] * D1[j]\n V12A = V12A + V_int(A1[i], A2[j], R2, Rap2, Za) * D1[i] * D2[j]\n V22A = V22A + V_int(A2[i], A2[j], 0.0, R2, Za) * D2[i] * D2[j]\n V11B = V11B + V_int(A1[i], A1[j], 0.0, R2, Zb) * D1[i] * D1[j]\n V12B = V12B + V_int(A1[i], A2[j], R2, Rbp2, Zb) * D1[i] * D2[j]\n V22B = V22B + V_int(A2[i], A2[j], 0.0, 0.0, Zb) * D2[i] * D2[j]\n\n # Calculate two electron integrals\n\n for i in range(N):\n for j in range(N):\n for k in range(N):\n for l in range(N):\n Rap = A2[i] * R / (A2[i] + A1[j])\n Rbp = R - Rap\n Raq = A2[k] * R / (A2[k] + A1[l])\n Rbq = R - Raq\n Rpq = Rap - Raq\n Rap2 = Rap * Rap\n Rbp2 = Rbp * Rbp\n Raq2 = Raq * Raq\n Rbq2 = Rbq * Rbq\n Rpq2 = Rpq * Rpq\n V1111 = V1111 + TwoE(A1[i], A1[j], A1[k], A1[l], 0.0, 0.0, 0.0) * D1[i] * D1[j] * D1[k] * D1[l]\n V2111 = V2111 + TwoE(A2[i], A1[j], A1[k], A1[l], R2, 0.0, Rap2) * D2[i] * D1[j] * D1[k] * D1[l]\n V2121 = V2121 + TwoE(A2[i], A1[j], A2[k], A1[l], R2, R2, Rpq2) * D2[i] * D1[j] * D2[k] * D1[l]\n V2211 = V2211 + TwoE(A2[i], A2[j], A1[k], A1[l], 0.0, 0.0, R2) * D2[i] * D2[j] * D1[k] * D1[l]\n V2221 = V2221 + TwoE(A2[i], A2[j], A2[k], A1[l], 0.0, R2, Rbq2) * D2[i] * D2[j] * D2[k] * D1[l]\n V2222 = V2222 + TwoE(A2[i], A2[j], A2[k], A2[l], 0.0, 0.0, 0.0) * D2[i] * D2[j] * D2[k] * D2[l]\n return","def G(self, (k,t), (j,x), **params):\n d = len(x)/2\n q,dq = x[:d],x[d:]\n J = (j == True)\n _J = np.logical_not(J)\n # number of constraints\n n = len(J) \n # number of active constraints\n m = np.sum(J) # = n - len(a)\n a = self.a( (k,t), (_J,q), **params)\n lambda_ = self.lambda_( (k,t), (J,q,dq), **params)\n # unilateral constraint forces\n lambda_ = lambda_[:m] \n g = np.nan*np.zeros(n)\n g[_J] = a\n g[J] = lambda_\n return g","def lagr(self, x):\n return","def bcL(self, rng=None):\n if rng is None:\n rng = random.PRNGKey(1)\n n = self.n\n x = onp.sin(self.bcmesh * np.pi)\n n_y = (np.floor((n + 1) / 2) - 1).astype(int)\n if rng is not None:\n coeffs = random.multivariate_normal(rng, np.zeros(16),\n np.diag(np.ones(16)))\n else:\n key = random.randint(random.PRNGKey(1), (1,), 1, 1000)\n coeffs = random.multivariate_normal(\n random.PRNGKey(key[0]), np.zeros(16), np.diag(np.ones(16)))\n left = coeffs[0] * x**3 + coeffs[1] * x**2 + coeffs[2] * x #+ coeffs[3]\n right = coeffs[4] * x**3 + coeffs[5] * x**2 + coeffs[6] * x #+ coeffs[7]\n lower = coeffs[8] * x**3 + coeffs[9] * x**2 + coeffs[10] * x #+ coeffs[11]\n upper = coeffs[12] * x**3 + coeffs[13] * x**2 + coeffs[14] * x #+ coeffs[15]\n shape = 2 * x.shape\n source = onp.zeros(shape)\n source[0, :] = upper\n source[n_y - 1, n_y - 1:] = lower[:n - n_y + 1]\n source[n_y - 1:, n_y - 1] = right[:n - n_y + 1]\n source[:, 0] = left\n source[-1, :n_y - 1] = right[n:n - n_y:-1]\n source[:n_y - 1, -1] = lower[n:n - n_y:-1]\n # because this makes the correct order of boundary conditions\n return source * (n + 1)**2","def kl_div_posterior_gradient(self, posterior_logits, posterior_binary_samples):\n #DVAE Eq12\n #gradient of the KLD between posterior and prior wrt to posterior\n #parameters phi\n \n logger.debug(\"kl_div_posterior_gradient\")\n posterior_upper_bound = 0.999*torch.ones_like(posterior_logits)\n #logits to probabilities\n posterior_probs=torch.min(posterior_upper_bound, torch.sigmoid(posterior_logits))\n \n n_split=int(posterior_binary_samples.size()[1]//2)\n #binarised samples from posterior to RBM layers\n rbm_samples_left,rbm_samples_right=torch.split(posterior_binary_samples,split_size_or_sections=n_split,dim=1)\n\n #the following prepares the variables in the calculation in tehir format\n rbm_bias_left=self.prior.get_visible_bias()\n rbm_bias_right=self.prior.get_hidden_bias()\n\n rbm_bias=torch.cat([rbm_bias_left,rbm_bias_right])#self._h\n rbm_weight=self.prior.get_weights()#self._J\n\n # this is transposed, so we multiply what we call \"right hand\" (\"hidden layer\")\n # samples with right rbm nodes\n # rbm_weight_t=torch.transpose(rbm_weight,0,1)#self._J\n \n rbm_activation_right=torch.matmul(rbm_samples_right,rbm_weight.t())\n rbm_activation_left=torch.matmul(rbm_samples_left,rbm_weight)\n\n #corresponds to samples_times_J\n rbm_activation=torch.cat([rbm_activation_right,rbm_activation_left],1)\n \n #TODO what is this scaling factor?\n #[400,400] \n hierarchy_scaling= (1.0 - posterior_binary_samples) / (1.0 - posterior_probs)\n hierarchy_scaling_left,hierarchy_scaling_right=torch.split(hierarchy_scaling, split_size_or_sections=int(n_split),dim=1)\n \n #TODO why does this happen? This seems to scale only the left side of\n #the RBM. Th right side is replaced with ones.\n hierarchy_scaling_with_ones=torch.cat([hierarchy_scaling_left,torch.ones(hierarchy_scaling_right.size())],axis=1)\n \n with torch.no_grad():\n undifferentiated_component=posterior_logits-rbm_bias-rbm_activation*hierarchy_scaling_with_ones\n undifferentiated_component=undifferentiated_component.detach()\n \n kld_per_sample = torch.sum(undifferentiated_component * posterior_probs, dim=1)\n\n return kld_per_sample","def gelu(x):\n cdf = 0.5 * (1.0 + tf.tanh(\n (np.sqrt(2 / np.pi) * (x + 0.044715 * tf.pow(x, 3)))))\n return x * cdf","def make_quad_gauss(lmax,alm):\n return libcurvedsky.bispec.make_quad_gauss(lmax,alm)","def graphe_regrets(rgt_alea, rgt_glou, rgt_glou_e, rgt_ucb):\n T = np.arange(rgt_alea.size)\n fig, ax = plt.subplots()\n ax.grid(True)\n plt.xlabel(\"T\")\n plt.ylabel(\"Regret\")\n \n ax.plot(T, rgt_alea, label = 'aléatoire') \n ax.plot(T, rgt_glou, label = 'glouton')\n ax.plot(T, rgt_glou_e, label = 'e-glouton')\n ax.plot(T, rgt_ucb, label = 'UCB')\n ax.legend(loc = \"upper left\")\n plt.title(\"Regrets des 4 algorithmes par rapport à T\")","def subgradient(self):\n \n UB_full = self.total_cost\n ufull = np.copy(self.u)\n\n # Update core: possible bottleneck\n (a_csr, a_csc) = self.update_core()\n mrows = a_csr.shape[0]\n ncols = a_csr.shape[1]\n u_this = self.u[~self.f_covered]\n # np.einsum is 20% faster than np.sum ...\n UB_fixed = self.fixed_cost\n UB = UB_full - UB_fixed\n cost = self.c[~self.f]\n\n # save nsteps calculations (Lagrangian multipliers and lower bounds)\n u_sequence = np.zeros((mrows, self._subg_nsteps)) \n Lu_sequence = np.zeros(self._subg_nsteps)\n # update u\n x = np.zeros(ncols, dtype=bool)\n niters_max = self._subg_maxiters\n maxfracchange = self._subg_maxfracchange\n maxabschange = self._subg_maxabschange\n\n # initialization\n f_change = _largenumber\n a_change = _largenumber\n niters = 0\n Lu_max0 = 0\n while ((f_change>maxfracchange) or (a_change>maxabschange)) and (niters<niters_max):\n u_this = (1.0+(np.random.rand(mrows)*2.-1)*self._u_perturb)*u_this\n u_sequence[:,0] = u_this\n cost_u = cost - a_csc.dot(u_sequence[:,0]) # Lagrangian cost\n # next lower bound of the Lagrangian subproblem\n Lu_sequence[0] = np.einsum('i->', cost_u[cost_u<0])+np.einsum('i->', u_sequence[:,0]) \n\n for i in np.arange(self._subg_nsteps-1):\n # current solution to the Lagrangian subproblem\n x[0:] = False\n x[cost_u<0] = True\n\n # subgradient; for two boolean arrays, multiplication seems to be the best way \n # (equivalent to logical_and)\n s_u = 1. - a_csr.dot(x.astype(int)) \n s_u_norm = np.einsum('i,i',s_u,s_u) # subgradient's norm squared\n\n # Update\n # next Lagrangian multiplier\n u_temp = u_sequence[:,i]+self._stepsize*(UB - Lu_sequence[i])/s_u_norm*s_u \n u_temp[u_temp<0] = 0\n\n u_sequence[:,i+1] = u_temp\n cost_u = cost - a_csc.dot(u_sequence[:,i+1]) # Lagrangian cost\n # next lower bound of the Lagrangian subproblem\n Lu_sequence[i+1] = np.einsum('i->', cost_u[cost_u<0])+np.einsum('i->', u_sequence[:,i+1]) \n \n #print(UB_full, UB, Lu_sequence[i+1])\n # Check the last nadaptive steps and see if the step size needs to be adapted\n if (np.mod(i+1,self._subg_nadaptive)==0):\n Lu_max_adapt = np.amax(Lu_sequence[i+1-self._subg_nadaptive:i+1])\n Lu_min_adapt = np.amin(Lu_sequence[i+1-self._subg_nadaptive:i+1])\n if (Lu_max_adapt <= 0.):\n Lu_max_adapt = _smallnumber\n f_change_adapt = (Lu_max_adapt-Lu_min_adapt)/np.fabs(Lu_max_adapt)\n if f_change_adapt > self._max_adapt:\n self._stepsize = self._stepsize*0.5\n elif (f_change_adapt < self._min_adapt) and (self._stepsize<1.5):\n self._stepsize = self._stepsize*1.5\n # swap the last multiplier with the optimal one\n i_optimal = np.argmax(Lu_sequence[i+1-self._subg_nadaptive:i+1])\n if (i_optimal != (self._subg_nadaptive-1)):\n u_temp = u_sequence[:,i]\n u_sequence[:,i] = u_sequence[:,i+1-self._subg_nadaptive+i_optimal]\n u_sequence[:,i+1-self._subg_nadaptive+i_optimal] = u_temp\n Lu_sequence[i+1-self._subg_nadaptive+i_optimal] = Lu_sequence[i]\n Lu_sequence[i] = Lu_max_adapt\n\n i_optimal = np.argmax(Lu_sequence)\n Lu_max = Lu_sequence[i_optimal]\n u_this = u_sequence[:,i_optimal]\n niters = niters + 1\n a_change = Lu_max - Lu_max0\n f_change = a_change/np.fabs(Lu_max)\n Lu_max0 = Lu_max # Just a copy. Not the reference (It's a number object)\n # save current u_this???\n\n if (niters == niters_max): \n warnings.warn(\"Iteration in subgradient reaches maximum = {0}\".format(niters))\n\n # update multipliers\n self.u[~self.f_covered] = u_this\n\n # return the last nsteps multipliers\n # save nsteps calculations (Lagrangian multipliers and lower bounds)\n u_sequence_full = np.zeros((self.mrows, self._subg_nsteps)) \n Lu_sequence_full = np.zeros(self._subg_nsteps)\n u_sequence_full[self.f_covered,:] = self.u[self.f_covered][:, np.newaxis]\n u_sequence_full[~self.f_covered,:] = u_sequence\n\n Lu_sequence_full = Lu_sequence + self.fixed_cost\n\n return (u_sequence_full, Lu_sequence_full)","def AffineGrothendieckPolynomial(self, la, m):\n if la == []:\n return self.a_realization().one()\n return self._AffineGrothendieckPolynomial(Partition(la),m)","def optg_xtb(self, acc='normal', nproc=1):\n for k in ['OMP_NUM_THREADS', 'MKL_NUM_THREADS']:\n os.environ[k] = '%d'%nproc\n uc = io2.Units()\n const = uc.h2e #/uc.b2a\n fmax = {'normal':1e-3, 'tight':8.e-4, 'vtight':2e-4}[acc] * const\n # 0.025 eV/A (ase force unit) ~ 0.001 Hartree/A (xtb force unit)\n print('acc = ', acc, 'fmax=', fmax)\n for cid in range(self.nconf):\n print('cid=', cid)\n c1 = self.mol.GetConformer(cid)\n zs = [ ai.GetAtomicNum() for ai in c1.GetAtoms() ]\n coords = []\n for ia in range(self.na):\n o = c1.GetAtomPosition(ia)\n coords.append( [o.x, o.y, o.z] )\n m1 = ase.Atoms(zs, coords)\n # create the calculator for GFN0-xTB under periodic boundary conditions\n calc = GFN2(print_level=2) # setting to 1 or 2 (the default) is ok, not so for 3\n m1.set_calculator(calc)\n e = m1.get_potential_energy()\n print(\"Initial energy: eV, Eh\", e, e/Hartree)\n relax = PreconFIRE(m1, precon=None, trajectory=None) # = 'tmp.traj')\n relax.run(fmax = fmax)\n for i in range(self.na):\n pt = Point3D()\n pt.x, pt.y, pt.z = coordsU[i]\n c1.SetAtomPosition(i, pt)","def grad_log_likelihood(g_t):\n # state vector [g1, g2, g3, ... , gn-1, gn]\n state_vector = np.zeros(N)\n\n # factor that will be added to one node and subtracted from another\n def alphaij(gj, gi, deltaij, varij):\n return ((gj - gi) - deltaij) / varij\n\n # indices of state vector\n # Iterate over all connections\n for r in self.graph.index:\n state1, state2, value, variance, ligand, standard_state = self.graph.iloc[r]\n i = self.states[self.states.name == state1].index[0]\n j = self.states[self.states.name == state2].index[0]\n\n gj = g_t[j]\n gi = g_t[i]\n\n edge_attr = self.cycle.edges()[(state1, state2)]\n deltaij = edge_attr['energy'] # measured difference\n varij = edge_attr['weight'] # measured variance\n\n shared_alpha = alphaij(gj, gi, deltaij, varij)\n\n state_vector[i] += shared_alpha\n state_vector[j] -= shared_alpha\n\n return state_vector","def grad_step3(self, x, w):\n z_prime = w.t() @ x\n if self.debug:\n print(f\"-> z_prime={z_prime}\\n\\n\")\n return z_prime","def g(self, p):\n re = self._re(p)\n Le = self._Le(p)\n wf = self._wf(p)\n rf = self._rf(p)\n A = Le @ np.einsum('...ij,...j', self.Ee, re)\n B = wf @ np.einsum('...ij,...j', self.Ff, rf)\n return (B - A) * G * self.d","def gauss_hermite(dim=1, num_quad_pts=20):\n # sigma_pts, weights = hermgauss(num_quad_pts) # Gauss-Hermite sigma points and weights\n sigma_pts, weights = mvhermgauss(num_quad_pts, dim)\n sigma_pts = np.sqrt(2) * sigma_pts.T\n weights = weights.T * pi ** (-0.5 * dim) # scale weights by 1/√π\n return sigma_pts, weights","def _gauss_newton_product(self):\n\n # Shortcuts.\n output_in = self.exprs['output_in']\n loss = self.exprs['loss']\n flat_pars = self.parameters.flat\n p = self.exprs['some-vector'] = T.vector('some-vector')\n\n Jp = T.Rop(output_in, flat_pars, p)\n HJp = T.grad(T.sum(T.grad(loss, output_in) * Jp),\n output_in, consider_constant=[Jp])\n Hp = T.grad(T.sum(HJp * output_in),\n flat_pars, consider_constant=[HJp, Jp])\n\n return Hp","def compute_lgcamp_grad(lgcamp, Z, Amatrices, sigma):\n\n i1 = np.dot(Amatrices[0], Z)\n i2 = np.dot(Amatrices[1], Z)\n i3 = np.dot(Amatrices[2], Z)\n i4 = np.dot(Amatrices[3], Z)\n lgcamp_samples = (np.log(np.abs(i1)) +\n np.log(np.abs(i2)) - \n np.log(np.abs(i3)) -\n np.log(np.abs(i4)))\n\n pp = (lgcamp - lgcamp_samples) / (sigma**2)\n pt1 = pp / i1\n pt2 = pp / i2\n pt3 = -pp / i3\n pt4 = -pp / i4\n out = (np.dot(pt1, Amatrices[0]) +\n np.dot(pt2, Amatrices[1]) +\n np.dot(pt3, Amatrices[2]) +\n np.dot(pt4, Amatrices[3]))\n\n return (-2.0/len(lgcamp)) * np.real(out)","def g_rosenbrock(x, a=1, b=100):\n\n g = np.array(\n [\n -2 * a - 4 * b * x[0] * (-x[0] ** 2 + x[1]) + 2 * x[0],\n b * (-2 * x[0] ** 2 + 2 * x[1]),\n ]\n )\n\n return g","def jacobian_g(self, x, out=None, **kwargs):\n return self._base_nlp.jacobian_g(x, out=out, **kwargs)","def gauss_rule(iel, elemType, normal_order, element):\n\n #sctr = element[iel, :] # element connectivity\n\n if ((elemType == 'Q4') and (normal_order <8)):\n W, Q = gpw.gauss_pt_wt(normal_order,'GAUSS',2)\n return W, Q","def gslb(self) :\n try :\n return self._gslb\n except Exception as e:\n raise e","def aggregation(lgca):\n newnodes = lgca.nodes.copy()\n relevant = (lgca.cell_density[lgca.nonborder] > 0) & \\\n (lgca.cell_density[lgca.nonborder] < lgca.K)\n coords = [a[relevant] for a in lgca.nonborder]\n\n g = np.asarray(lgca.gradient(lgca.cell_density))\n for coord in zip(*coords):\n n = lgca.cell_density[coord]\n permutations = lgca.permutations[n]\n j = lgca.j[n]\n weights = np.exp(lgca.beta * np.einsum('i,ij', g[coord], j)).cumsum()\n ind = bisect_left(weights, random() * weights[-1])\n newnodes[coord] = permutations[ind]\n\n lgca.nodes = newnodes","def lgammln(xx):\r\n\r\n coeff = [76.18009173, -86.50532033, 24.01409822, -1.231739516,\r\n 0.120858003e-2, -0.536382e-5]\r\n x = xx - 1.0\r\n tmp = x + 5.5\r\n tmp = tmp - (x+0.5)*math.log(tmp)\r\n ser = 1.0\r\n for j in range(len(coeff)):\r\n x = x + 1\r\n ser = ser + coeff[j]/x\r\n return -tmp + math.log(2.50662827465*ser)","def reconstruct_grad_domain( G, mask, bg ):\n sz = G.shape[:2] \n N = sz[0]*sz[1]\n\n\n # Gradient operators as sparse matrices\n o1 = np.ones((N,1))\n B = np.concatenate( (-o1, np.concatenate( (np.zeros((sz[0],1)), o1[:N-sz[0]]), 0 ) ), 1)\n B[N-sz[0]:N,0] = 0\n Ogx = sparse.spdiags(B.transpose(), [0 ,sz[0]], N, N ) # Forward difference operator along x\n\n B = np.concatenate( (-o1 ,np.concatenate((np.array([[0]]), o1[0:N-1]) ,0)), 1)\n B[sz[0]-1::sz[0], 0] = 0\n B[sz[0]::sz[0],1] = 0\n Ogy = sparse.spdiags( B.transpose(), [0, 1], N, N ) # Forward difference operator along y\n\n Ogx_prime = Ogx.transpose()\n Ogy_prime = Ogy.transpose()\n\n # Calculate pixels which are in the boundary\n boundary = []\n h, w = mask.shape\n for x in range(h):\n for y in range(w):\n if mask[x,y] == 1 and (x==0 or x==h-1 or y==0 or y==w-1 or min(mask[x,y-1], mask[x-1,y], mask[x+1,y], mask[x,y+1])==0):\n boundary.append((x,y))\n\n K = len(boundary)\n\n E = sparse.csc_matrix((K, N))\n T_E = np.zeros((K,))\n for i, (x,y) in enumerate(boundary):\n pos = y * sz[0] + x # Calulcate the pixel's position\n E[i, pos] = 1\n T_E[i] = bg[x,y]\n\n A = Ogx_prime @ Ogx + Ogy_prime @ Ogy + E.transpose() @ E\n\n Gx = G[:,:,0].flatten('F')\n Gy = G[:,:,1].flatten('F')\n\n b = Ogx_prime @ Gx + Ogy_prime @ Gy + E.transpose() @ T_E\n\n if USE_CHOLESKY:\n factor = cholesky(A)\n I = factor(b)\n\n else:\n I = sparse.linalg.spsolve(A, b)\n\n\n new_img = I.reshape(bg.shape, order='F')\n\n return new_img","def _g_prime(self, x):\n return self._g(x)*(1 - self._g(x))","def magma_zgels_gpu(trans, m, n, nrhs, A, lda, B, ldb, hwork, lwork):\n info = c_int_type()\n trans = _trans_conversion[trans]\n status = _libmagma.magma_zgels_gpu(trans, m, n, nrhs, int(A), lda,\n int(B), ldb, int(hwork), lwork,\n ctypes.byref(info))\n magmaCheckStatus(status)","def taylor_expansion(self,g_temp,g_step,var):\n A=np.zeros([self.n+1,self.n])\n for i in range(self.n):\n A[self.n][i]=1\n for j in range(self.n):\n if(i==j): A[i][j]=2.*var[i]+2.+g_temp*np.sum([self.XXZ.Z(k,i) for k in range(self.n) if k!=i])\n else: A[i][j]=-g_temp*self.XXZ.Z(j,i)\n #First derivative\n B1=np.zeros(self.n+1)\n for i in range(self.n): \n B1[i]=self.gamma*2.*g_temp*self.N*(self.n-self.N)+np.sum([self.XXZ.Z(k,i)*(var[k]-var[i]) for k in range(self.n) if k!=i])\n Ainv=np.linalg.pinv(A)\n der1=np.dot(Ainv,B1)\n #Second derivative\n B2=np.zeros(self.n+1)\n for k in range(self.n):\n B2[k]=self.gamma*2.*self.N*(self.n-self.N) -2.*der1[k]**2+2.*np.sum([self.XXZ.Z(l,k)*(der1[l]-der1[k]) for l in range(self.n) if k!=l])\n der2=np.dot(Ainv,B2)\n #Third derivative\n B3=np.zeros(self.n+1)\n for k in range(self.n):\n B3[k]=-6*der1[k]*der2[k]+3.*np.sum([self.XXZ.Z(l,k)*(der2[l]-der2[k]) for l in range(self.n) if k!=l])\n der3=np.dot(Ainv,B3)\n #Fourth derivative\n B4=np.zeros(self.n+1)\n for k in range(self.n):\n B4[k]=-8.*der3[k]*der1[k]-6.*der2[k]*der2[k]+4.*np.sum([self.XXZ.Z(l,k)*(der3[l]-der3[k]) for l in range(self.n) if k!=l])\n der4=np.dot(Ainv,B4)\n \n return var+g_step*der1+g_step**2*der2/2.+g_step**3*der3/6.+g_step**4*der4/24.","def gauss_quadrature(ab, N):\n\n from numpy.linalg import eigh\n\n n = ab.shape[0]\n if n+1 < N:\n raise IndexError('Require N+1 recurrence coefficients for an N-point rule.')\n\n J = np.diag(ab[1:N,1], k=1) + np.diag(ab[:N,0],k=0) + np.diag(ab[1:N,1], k=-1)\n lamb,v = eigh(J)\n\n return lamb, v[0,:]**2","def integrate_gausskronrod(f, a, b, args=()):\n\n assert b > a\n mid = 0.5*(b+a)\n dx = 0.5*(b-a)\n zi = mid+gausskronrod_nodes*dx\n integrand = f(zi)\n integral_G7 = np.sum(integrand[:7]*gauss_weights)\n integral_K15 = np.sum(integrand*kronrod_weights)\n\n error = (200*abs(integral_G7-integral_K15))**1.5\n\n return integral_K15*dx, dx*error"],"string":"[\n \"def ReferenceElement(N):\\n\\n # Legendre polynomials are Jacobi(alpha,beta) polynomials\\n alpha=0.; beta=0;\\n\\n # Gauss-Lobatto quadrature points for Legendre polynomials\\n # According to Hesthaven+Warburton (p. 448, JacobGL.m) these\\n # are the order N-2 Gauss quadrature points of Jacobi polynomials\\n # with different alpha, beta, enlarged by end-points -1, +1.\\n #\\n # note len(r)=N, i.e. matrices defined below (V, Vr, etc) are square\\n if(N==1):\\n r=np.array([-1.,1.])\\n else:\\n # (N-2)-th order quadrature points are roots of (N-1)-st Jacobi polynomial\\n inner_roots, inner_weights = scipy.special.roots_jacobi(N-1, alpha+1, beta+1)\\n r = np.concatenate([ [-1.], inner_roots, [1.] ])\\n\\n # Vandermonde matrix for Legendre polynomials\\n # V[i,j] = P_j(r_i), j=0,...,N, i=0,...,len(r)-1\\n V = np.zeros( (len(r), N+1))\\n for j in range(N+1):\\n # scipy normalization determined by trial and error.\\n # For **LAGRANGE POLY** ONLY, not general alpha, beta.\\n # This makes the returned polynomials orthonormal\\n normalization = np.sqrt((1.+2.*j)/2.)\\n V[:,j] = scipy.special.eval_jacobi(j, alpha, beta, r)*normalization\\n # or V[:,j] = scipy.special.legendre(j)(r)\\n\\n # check normalization\\n # tmp_r, tmp_w = scipy.special.roots_jacobi(j+1, alpha, beta)\\n # tmp_L=scipy.special.eval_jacobi(j, alpha, beta, tmp_r)*normalization\\n # L_dot_L = sum(tmp_w*tmp_L*tmp_L)\\n # print(\\\"j={}, (L,L)={}\\\".format(j, L_dot_L))\\n\\n\\n Vinv=np.linalg.inv(V)\\n\\n # derivatives of Legendre polynomials, evaluated at quadrature points\\n # Vr[i,j] = dP_j/dr(r_i), j=0,...,N, i=0,...,len(r)-1\\n # use dP_j/dr = sqrt(j(j+1)) J^{alpha+1,beta+1}_{j-1} (H+W, Eq A2)\\n #\\n Vr = np.zeros( (len(r), N+1))\\n for j in range(1,N+1):\\n # scipy normalization determined by trial and error.\\n # For **LAGRANGE POLY** ONLY, not general alpha, beta.\\n # This makes the returned polynomials orthonormal, conforming\\n # to H+W conventions\\n scipy_normalization=np.sqrt((1.+2.*j)*(j+1.)/(8.*j))\\n normed_J = scipy.special.jacobi(j-1, alpha+1, beta+1)(r)*scipy_normalization\\n Vr[:,j] = np.sqrt(j*(j+alpha+beta+1.))*normed_J # H+W Eq. A2\\n\\n # - check normalization\\n # - integrate by Legendre quadrature, to explicitly show weight-function in orthogonality\\n # tmp_r, tmp_w = scipy.special.roots_jacobi(j+4, alpha, beta)\\n # tmp_L=scipy.special.eval_jacobi(j-1, alpha+1, beta+1, tmp_r)*scipy_normalization\\n # - evaluate orthogonality; note weight function (1-r)(1+r)\\n # L_dot_L = sum(tmp_w*tmp_L*tmp_L*(1-tmp_r)*(1+tmp_r))\\n # print(\\\"j={}, (L,L)={}\\\".format(j, L_dot_L))\\n\\n\\n # derivatives of Lagrange interpolating polynomials\\n # Dr(i,j) = dl_j/dr(r=r_i),\\n # where l_j(r_i) = delta_{ij}\\n # compute using P_j(r) = V[i,j]*l_i(r) => V[i,j] dl_i/dr = dP_j/dr (*)\\n # => V^{-T} V^T[j,i] dl_i/dr = V^{-T} dP_j/dr\\n Dr = np.matmul(Vr,Vinv)\\n\\n # inverse of mass-matrix\\n # Using (*), one can show M = (V V^T)^(-1)\\n # Turns out that the inverse of M is used in the DG algorithm,\\n # and so we can directly compute M-inverse, without computing\\n # matrix-inverses:\\n Minv = np.matmul(V, V.transpose())\\n\\n # finally, M^{-1}S = Dr, and since we need S only multiplied by M^{-1},\\n # we can just return Dr\\n MinvS=Dr\\n\\n return r, Minv, MinvS\",\n \"def Legendre( n, mu, checkXRange = True ) :\\n\\n if( n < 0 ) : raise ValueError( \\\"\\\\nError, n = %d < 0\\\" % n )\\n if( checkXRange and ( abs( mu ) > 1 ) ) : raise ValueError( \\\"Legendre: |mu| > 1; mu = %g\\\" % mu )\\n Pn = 0.\\n Pnp1 = 1.\\n n_ = 0\\n twoNp1 = 1\\n while( n_ < n ) :\\n Pnm1 = Pn\\n Pn = Pnp1\\n n_p1 = n_ + 1\\n Pnp1 = ( twoNp1 * mu * Pn - n_ * Pnm1 ) / n_p1\\n twoNp1 += 2\\n n_ = n_p1\\n return( Pnp1 )\",\n \"def leggauss(deg):\\n ideg = int(deg)\\n if ideg != deg or ideg < 1:\\n raise ValueError(\\\"deg must be a non-negative integer\\\")\\n\\n # first approximation of roots. We use the fact that the companion\\n # matrix is symmetric in this case in order to obtain better zeros.\\n c = np.array([0]*deg + [1])\\n m = np.polynomial.legendre.legcompanion(c)\\n x = np.linalg.eigvalsh(m)\\n\\n # improve roots by one application of Newton\\n dy = np.polynomial.legendre.legval(x, c)\\n df = np.polynomial.legendre.legval(x, np.polynomial.legendre.legder(c))\\n x -= dy/df\\n\\n # compute the weights. We scale the factor to avoid possible numerical\\n # overflow.\\n fm = np.polynomial.legendre.legval(x, c[1:])\\n fm /= np.abs(fm).max()\\n df /= np.abs(df).max()\\n w = 1/(fm * df)\\n\\n # for Legendre we can also symmetrize\\n w = (w + w[::-1])/2\\n x = (x - x[::-1])/2\\n\\n # scale w to get the right value\\n w *= 2. / w.sum()\\n\\n return x, w\",\n \"def gelu(x):\\n return f_gelu(x)\",\n \"def gelu(x):\\n return x * 0.5 * (1.0 + F.tanh(F.sqrt(2 / math.pi) * (x + 0.044715 * (x ** 3))))\",\n \"def oned_shape(x,r,w):\\n n_dof = len(x) # number of dofs\\n n_g = len(r) # number of Gaussian quadrature points\\n \\n if n_dof==2:\\n # Transform coordinates for linear elements\\n c0 = ( x[-1]-x[0] )/2\\n c1 = ( x[-1]+x[0] )/2\\n \\n # Gaussian nodes\\n x_g = c0*r + c1\\n\\n # Evaluate basis function at Gauss points\\n phi = np.empty((n_g,n_dof))\\n phi[:,1] = ( 1+r )/2\\n phi[:,0] = ( 1-r )/2\\n \\n # Evaluate first derivative at Gauss points\\n p_x = np.empty((n_g,n_dof))\\n p_x[:,1] = 0.5*np.ones(n_g)/c0\\n p_x[:,0] =-p_x[:,1]\\n\\n # Jacobian\\n djac = c0\\n \\n # Compute physical weights\\n w_g = djac*w\\n\\n # Second derivative \\n p_xx = np.zeros((n_g,n_dof))\\n \\n elif n_dof==3:\\n \\n # Transform coordinates for quadratic elements\\n c0 = ( x[-1]-x[0] )/2\\n c1 = ( x[-1]+x[0] )/2\\n \\n # Physical Gauss nodes\\n x_g = c0*r + c1\\n\\n # defined backwards to help Matlab create the proper sized array\\n phi = np.empty((n_g,n_dof))\\n phi[:,2] = .5*r*( r+1 )\\n phi[:,1] =-( r+1 )*( r-1 )\\n phi[:,0] = .5*r*( r-1 )\\n\\n # First derivative \\n p_x = np.empty((n_g,n_dof))\\n p_x[:,2] = ( r+.5 )/c0\\n p_x[:,1] =-2*r/c0\\n p_x[:,0] = ( r-.5 )/c0\\n\\n # Jacobian \\n djac = c0\\n\\n # Gaussian weight on physical element\\n w_g = djac*w\\n \\n # Second derivative\\n p_xx = np.empty((n_g,n_dof))\\n p_xx[:,2] = np.ones(n_g)/c0**2\\n p_xx[:,1] =-2*p_xx[:,2]\\n p_xx[:,0] = p_xx[:,2]\\n\\n elif n_dof==4:\\n # Transform coordinates for (nonconforming) cubic elements\\n c0 = ( x[-1]-x[0] )/2\\n c1 = ( x[-1]+x[0] )/2\\n\\n x_g = c0*r + c1\\n\\n r2 = r*r\\n r3 = r*r2\\n\\n # defined backwards to help Matlab create the proper sized array\\n phi = np.zeros((n_g,n_dof))\\n phi[:,3] = 9*( r3+r2-r/9-1/9 )/16\\n phi[:,2] =-27*( r3+r2/3-r-1/3 )/16\\n phi[:,1] = 27*( r3-r2/3-r+1/3 )/16\\n phi[:,0] =- 9*( r3-r2-r/9+1/9 )/16\\n\\n # Derivative on reference element\\n p_r = np.zeros((n_g,n_dof))\\n p_r[:,3] = 9*( 3*r2+2*r-1/9 )/16\\n p_r[:,2] =-27*( 3*r2+2*r/3-1 )/16\\n p_r[:,1] = 27*( 3*r2-2*r/3-1 )/16\\n p_r[:,0] =- 9*( 3*r2-2*r-1/9 )/16\\n \\n # Second derivative on reference element\\n p_rr = np.zeros((n_g,n_dof))\\n p_rr[:,3] = 9*( 6*r+2 )/16\\n p_rr[:,2] =-27*( 6*r+2/3 )/16\\n p_rr[:,1] = 27*( 6*r-2/3 )/16\\n p_rr[:,0] =- 9*( 6*r-2 )/16\\n\\n # Jacobian \\n dxdr = p_r.dot(x)\\n drdx = 1/dxdr\\n \\n # Quadrature weights on physical element\\n w_g = dxdr*w\\n \\n # Derivative on physical element\\n p_x = np.zeros((n_g,n_dof))\\n p_x[:,3] = p_r[:,3]*drdx\\n p_x[:,2] = p_r[:,2]*drdx\\n p_x[:,1] = p_r[:,1]*drdx\\n p_x[:,0] = p_r[:,0]*drdx\\n \\n # Second derivative on physical element\\n p_xx = np.zeros((n_g,n_dof)) \\n p_xx[:,3] = p_rr[:,3]*drdx**2\\n p_xx[:,2] = p_rr[:,2]*drdx**2\\n p_xx[:,1] = p_rr[:,1]*drdx**2\\n p_xx[:,0] = p_rr[:,0]*drdx**2\\n else:\\n raise Exception('Elements higher than cubic not currently supported')\\n \\n # Return computed quantities\\n return x_g, w_g, phi, p_x, p_xx\",\n \"def local_gemm_to_ger(node):\\r\\n if node.op == gemm_no_inplace:\\r\\n z, a, x, y, b = node.inputs\\r\\n if x.broadcastable[1] and y.broadcastable[0]:\\r\\n # x and y are both vectors so this might qualifies for a GER\\r\\n xv = x.dimshuffle(0)\\r\\n yv = y.dimshuffle(1)\\r\\n try:\\r\\n bval = T.get_scalar_constant_value(b)\\r\\n except T.NotScalarConstantError:\\r\\n # b isn't a constant, GEMM is doing useful pre-scaling\\r\\n return\\r\\n\\r\\n if bval == 1: # best case a natural GER\\r\\n rval = ger(z, a, xv, yv)\\r\\n return [rval]\\r\\n elif bval == 0: # GER on zeros_like should be faster than GEMM\\r\\n zeros = T.zeros([x.shape[0], y.shape[1]], x.dtype)\\r\\n rval = ger(zeros, a, xv, yv)\\r\\n return [rval]\\r\\n else:\\r\\n # if bval is another constant, then z is being usefully\\r\\n # pre-scaled and GER isn't really the right tool for the job.\\r\\n return\",\n \"def test_ln_gauss(self):\\n Q = likelihood()\\n test_object = test_values_for_likelihood()\\n experimental_values = [0.9,2.1,3.2]\\n errors = [0.5,0.4,0.3]\\n reference_q_value = 0.27347222222222267\\n reference_q_derivative = np.array([-3.75833333, -3.75833333])\\n Q.add_observable(test_object,experimental_values,errors,scale=1.0)\\n q_value, q_derivative = Q.compute_ln_gauss(np.array([1.0,1.0]))\\n assert np.isclose(q_value,reference_q_value)\\n assert np.all(np.isclose(q_derivative,reference_q_derivative))\\n return\",\n \"def g(self, X):\\n\\n return (X[0])**2 - 2*X[0] + X[1]**3 - 2\",\n \"def G(U):\\n n = U.shape[0]\\n G_U = np.zeros([n,1])\\n DELTA_x = float(2*L)/(n-1)\\n for i in range(n):\\n G_U[i][0] = U[(i+1)%n][0]\\n G_U[i][0] -= U[(i-1)%n][0]\\n G_U[i][0] /= (2* DELTA_x)\\n G_U[i][0] += (float(epsilon) * (U[(i+1)%n][0]- U[(i-1)%n][0]) * (U[(i-1)%n][0]+U[(i+1)%n][0]+ U[i][0])/ (4* DELTA_x))\\n G_U[i][0] += (float(epsilon) * (U[(i+2)%n][0]- 2*U[(i+1)%n][0]+ 2*U[(i-1)%n][0]- U[(i-2)%n][0]) / (12*( DELTA_x**3)))\\n return G_U\",\n \"def riemannian_grads(self):\\n u = self.feature_embedding.weight.grad\\n x = self.feature_embedding.weight.data\\n u.narrow(-1, 0, 1).mul_(-1)\\n u.addcmul_(ldot(x, u, keepdim=True).expand_as(x), x)\\n return u # can be delete?\",\n \"def jacobi_gauss_lobatto_nodes(alpha, beta, N, # noqa: N803\\n backend=None, force_dim_axis=False):\\n\\n x = np.zeros((N + 1,))\\n if N == 0:\\n x[0] = 0\\n return x\\n\\n x[0] = -1\\n x[-1] = 1\\n\\n if N > 1:\\n quad = JacobiGaussQuadrature(alpha + 1, beta + 1, N - 2,\\n backend=backend, force_dim_axis=True)\\n x[1:-1] = quad.nodes[0].real\\n\\n if force_dim_axis:\\n x = x.reshape(1, -1)\\n\\n return x\",\n \"def make_Gr(mlat, *J):\\n\\n if (len(J)!=3):\\n print(\\\"Number of paramaters are not right, must be 5!\\\")\\n \\n NN = 2*mlat # # of sites in one super unitcell\\n tau = -np.zeros((NN, NN),dtype=complex)\\n h = np.zeros((NN,NN), dtype=complex)\\n\\n # translational cell's Hamiltonian\\n for i in range(mlat-1):\\n if (i%2==0):\\n h[i,i+1] = J[0]\\n h[mlat+i,mlat+i+1] = J[1]\\n h[i,mlat+i] = J[2] # horizoltal connection\\n elif (i%2==1):\\n h[i,i+1] = J[1]\\n h[mlat+i,mlat+i+1] = J[0]\\n # longitudinal connection of the last sites\\n if (mlat-1)%2 == 0:\\n h[mlat-1,2*mlat-1] = J[2]\\n \\n h = h + h.conj().T # make it hermitian\\n\\n # Hopping matrix\\n for i in range(1,mlat,2):\\n tau[i+mlat,i] = J[2]\\n\\n return h, tau\",\n \"def add_lp_qe_RBM_terms(K, lin_pot_mesh, quad_geo_mesh):\\n pot_faces = lin_pot_mesh.get_faces()\\n num_faces = pot_faces.shape[0]\\n pot_nodes = lin_pot_mesh.get_nodes()\\n num_nodes = pot_nodes.shape[0]\\n S_D = quad_geo_mesh.get_surface_area()\\n x_c = quad_geo_mesh.get_centroid()\\n w = quad_geo_mesh.get_w()\\n A_m = quad_geo_mesh.get_A_m()\\n\\n for face_num in range(num_faces):\\n face_nodes = quad_geo_mesh.get_tri_nodes(face_num)\\n face_hs = quad_geo_mesh.get_hs(face_num)\\n for node_num in range(3): # face nodes\\n face_node_global_num = pot_faces[face_num, node_num]\\n v_sub_mat = (-4. * np.pi / S_D) * gq.int_over_tri_quad(\\n make_lp_le_v_quad(node_num),\\n face_nodes,\\n face_hs,\\n )\\n j = 3 * face_node_global_num\\n tmp_omega = gq.int_over_tri_quad(\\n make_lp_le_omega_quad(node_num, x_c),\\n face_nodes,\\n face_hs,\\n )\\n tmp_arr = []\\n for m in range(3):\\n tmp_arr.append((1./ A_m[m]) * np.outer(w[m], np.einsum(\\\"l, ls\\\", w[m], tmp_omega)))\\n tmp_omega_mat = -4. * np.pi * np.sum(tmp_arr, axis=0)\\n for src_num in range(num_nodes):\\n K[(3 * src_num):(3 * src_num + 3),\\n j:j+3] += -1. / (4. * np.pi) * v_sub_mat\\n src_pt = lin_pot_mesh.get_node(src_num)\\n X_0 = src_pt - x_c\\n omega_mat = np.einsum(\\\"ijk,js,k\\\", geo.LC_3, tmp_omega_mat, X_0)\\n K[(3 * src_num):(3 * src_num + 3),\\n j:j+3] += 1. / (4. * np.pi) * (omega_mat)\",\n \"def legendreGauss (func, deg, a, b, ind, bsp, ind2=0):\\n\\n\\tx, w = np.polynomial.legendre.leggauss(deg)\\n\\tt = 0.5*(x+1)*(b-a)+ a\\n\\t\\n\\tgauss = sum(w + func(t, bsp, ind, ind2))*( 0.5*(b-a))\\n\\n\\treturn gauss\",\n \"def geglu(x: Tensor) ->Tensor:\\n assert x.shape[-1] % 2 == 0\\n a, b = x.chunk(2, dim=-1)\\n return a * F.gelu(b)\",\n \"def gauss_legender_points(N=30):\\n beta = np.array([(n + 1.0) / np.sqrt((2.0 * n + 1.0) * (2.0 * n + 3.0))\\n for n in range(N - 1)], dtype=np.float32)\\n M = np.diag(beta, -1) + np.diag(beta, 1)\\n nodes, V = la.eigh(M, overwrite_a=True, overwrite_b=True)\\n weight = 2 * V[0, :] ** 2\\n return nodes, weight\",\n \"def linearize_and_solve(g):\\n\\n # initialize the sparse H and the vector b\\n H = np.zeros((len(g.x), len(g.x)), dtype='float')\\n b = np.zeros(len(g.x), dtype='float')\\n\\n # set flag to fix gauge\\n needToAddPrior = True\\n Fx = 0\\n\\n # compute the addend term to H and b for each of our constraints\\n print('linearize and build system')\\n\\n for edge in g.edges:\\n\\n # pose-pose constraint\\n if edge.Type == 'P':\\n\\n # compute idx for nodes using lookup table\\n fromIdx = g.lut[edge.fromNode]\\n toIdx = g.lut[edge.toNode]\\n\\n # get node state for the current edge\\n x_i = g.x[fromIdx:fromIdx + 3]\\n x_j = g.x[toIdx:toIdx + 3]\\n\\n # (TODO) compute the error and the Jacobians\\n e, A, B = linearize_pose_pose_constraint(\\n x_i, x_j, edge.measurement)\\n\\n # # (TODO) compute the terms\\n b_i = e.transpose() @ edge.information @ A\\n b_j = e.transpose() @ edge.information @ B\\n H_ii = A.transpose() @ edge.information @ A\\n H_ij = A.transpose() @ edge.information @ B\\n H_jj = B.transpose() @ edge.information @ B\\n\\n # (TODO) add the terms to H matrix and b\\n H[fromIdx:fromIdx + 3, fromIdx:fromIdx + 3] += H_ii\\n H[toIdx:toIdx + 3, toIdx:toIdx + 3] += H_jj\\n H[fromIdx:fromIdx + 3, toIdx:toIdx + 3] += H_ij\\n H[toIdx:toIdx + 3, fromIdx:fromIdx + 3, ] += H_ij.transpose()\\n b[fromIdx:fromIdx + 3] += b_i[0, :]\\n b[toIdx:toIdx + 3] += b_j[0, :]\\n\\n # Add the prior for one pose of this edge\\n # This fixes one node to remain at its current location\\n if needToAddPrior:\\n H[fromIdx:fromIdx + 3, fromIdx:fromIdx +\\n 3] = H[fromIdx:fromIdx + 3,\\n fromIdx:fromIdx + 3] + 1000 * np.eye(3)\\n needToAddPrior = False\\n\\n # pose-pose constraint\\n elif edge.Type == 'L':\\n print(\\\"you shouldn't be here...\\\")\\n # compute idx for nodes using lookup table\\n fromIdx = g.lut[edge.fromNode]\\n toIdx = g.lut[edge.toNode]\\n\\n # get node states for the current edge\\n x = g.x[fromIdx:fromIdx + 3]\\n l = g.x[toIdx:toIdx + 2]\\n\\n # (TODO) compute the error and the Jacobians\\n e, A, B = linearize_pose_landmark_constraint(\\n x, l, edge.measurement)\\n\\n # (TODO) compute the terms\\n b_i = e.transpose() @ edge.information @ A\\n b_j = e.transpose() @ edge.information @ B\\n H_ii = A.transpose() @ edge.information @ A\\n H_ij = A.transpose() @ edge.information @ B\\n H_jj = B.transpose() @ edge.information @ B\\n\\n # (TODO )add the terms to H matrix and b\\n H[fromIdx:fromIdx + 3, fromIdx:fromIdx + 3] += H_ii\\n H[toIdx:toIdx + 2, toIdx:toIdx + 2] += H_jj\\n H[fromIdx:fromIdx + 3, toIdx:toIdx + 2] += H_ij\\n H[toIdx:toIdx + 2, fromIdx:fromIdx + 3, ] += H_ij.transpose()\\n b[fromIdx:fromIdx + 3] = b_i\\n b[toIdx:toIdx + 2] = b_j\\n # solve system\\n dx = np.linalg.solve(H, b)\\n\\n return dx\",\n \"def make_DL_reg_lp_le_quad_func(n, x_0, node_num):\\n def quad_func(xi, eta, nodes):\\n x = geo.linear_interp(xi, eta, nodes)\\n S = geo.stresslet_n(x, x_0, n)\\n phi = geo.shape_func_linear(xi, eta, node_num)\\n return phi * S\\n return quad_func\",\n \"def getG(L, t, w=None, k=6, domain='time'):\\n # evals1, U1 = eigs(L, k=k, which='LR')\\n # evals1, U1 = sort(evals1, U1)\\n\\n # eigvals2, U2 = eigs(dag(L), k=k, which='LR')\\n # eigvals2, U2 = sort(eigvals2, U2)\\n evals1, U1 = eig(L.todense())\\n # evals1, U1 = sort(evals1, U1)\\n\\n # U2 = U1.conj()\\n # eigvals2, U2 = eig(dag(L).todense())\\n # eigvals2, U2 = sort(eigvals2, U2)\\n\\n U2 = scipy.linalg.inv(U1)\\n\\n # norm = np.array([np.vdot(U2[:,n], U1[:,n]) for n in range(len(evals1))])\\n\\n # print('normalization', norm)\\n\\n if domain == 'time':\\n\\n U = -1j * np.exp(-1j * evals1[:,np.newaxis] * t[np.newaxis,:])\\n # tmp = np.exp(-1j * evals1[:,np.newaxis] * t[np.newaxis,:])\\n\\n # U = np.einsum('ab, a -> ab', tmp, 1./norm)\\n\\n G = np.einsum('aj, jk, jb -> abk', U1, U, U2)\\n\\n elif domain == 'freq':\\n\\n W = 1./((w[:, np.newaxis] - evals1[np.newaxis, :]))\\n G = np.einsum('an, nk, bn ->abk', U1, W, U2.conj())\\n\\n return G\",\n \"def add_lp_le_RBM_terms(K, lin_pot_mesh, lin_geo_mesh):\\n pot_faces = lin_pot_mesh.get_faces()\\n num_faces = pot_faces.shape[0]\\n pot_nodes = lin_pot_mesh.get_nodes()\\n num_nodes = pot_nodes.shape[0]\\n S_D = lin_geo_mesh.get_surface_area()\\n x_c = lin_geo_mesh.get_centroid()\\n w = lin_geo_mesh.get_w()\\n A_m = lin_geo_mesh.get_A_m()\\n\\n for face_num in range(num_faces):\\n face_nodes = lin_geo_mesh.get_tri_nodes(face_num)\\n face_hs = lin_geo_mesh.get_hs(face_num)\\n for node_num in range(3): # face nodes\\n face_node_global_num = pot_faces[face_num, node_num]\\n v_sub_mat = (-4. * np.pi / S_D) * gq.int_over_tri_lin(\\n make_lp_le_v_quad(node_num),\\n face_nodes,\\n face_hs,\\n )\\n j = 3 * face_node_global_num\\n tmp_omega = gq.int_over_tri_lin(\\n make_lp_le_omega_quad(node_num, x_c),\\n face_nodes,\\n face_hs,\\n )\\n tmp_arr = []\\n for m in range(3):\\n tmp_arr.append((1./ A_m[m]) * np.outer(w[m], np.einsum(\\\"l, ls\\\", w[m], tmp_omega)))\\n tmp_omega_mat = -4. * np.pi * np.sum(tmp_arr, axis=0)\\n for src_num in range(num_nodes):\\n K[(3 * src_num):(3 * src_num + 3),\\n j:j+3] += -1. / (4. * np.pi) * v_sub_mat\\n src_pt = lin_pot_mesh.get_node(src_num)\\n X_0 = src_pt - x_c\\n omega_mat = np.einsum(\\\"ijk,js,k\\\", geo.LC_3, tmp_omega_mat, X_0)\\n K[(3 * src_num):(3 * src_num + 3),\\n j:j+3] += 1. / (4. * np.pi) * (omega_mat)\",\n \"def FormG():\\n for i in range(2):\\n for j in range(2):\\n G[i, j] = 0.0\\n for k in range(2):\\n for l in range(2):\\n G[i, j] = G[i, j] + P[k, l] * (TT[i, j, k, l] - 0.5 * TT[i, j, k, l])\",\n \"def fun_lorentzian_p_gauss(p,r):\\n return p[1] / ((r/p[0])**2 + 1) + p[2] * np.exp(-((r/p[0])**2)/2)\",\n \"def fun_lorentzian_t_gauss(p,r):\\n return p[2] / ((r/p[0])**2 + 1) * np.exp(-((r/p[1])**2)/2)\",\n \"def _AffineGrothendieckPolynomial(self, la, m):\\n return self._AffineGrothendieck(la.to_core(self.k).to_grassmannian(),m)\",\n \"def calc_free_g(energies, temperatures):\\n pass\",\n \"def make_reg_lp_qe_quad_func(x_0, node_num):\\n def quad_func(xi, eta, nodes):\\n x = geo.quadratic_interp(xi, eta, nodes)\\n S = geo.stresslet(x, x_0)\\n phi = geo.shape_func_linear(xi, eta, node_num)\\n return phi * S\\n return quad_func\",\n \"def calculateElementRightHandSideGaussLeg(self,triangleIndex,degree):\\n\\n transformMatrix,translateVector = self.calculateTransform(triangleIndex)\\n determinant = abs(np.linalg.det(transformMatrix))\\n\\n \\n trianglePoints =self.triangulation.points[self.triangulation.simplices[triangleIndex]]\\n gPoints,gWeigths = leggauss(degree)\\n elementRHS = []\\n #calculate each entry per Gaussintegration\\n for i in range(0,3):\\n entry = 0.0\\n #sum over weights evaluated at Gauss points,\\n for indexX,pointX in enumerate(gPoints):\\n for indexY,pointY in enumerate(gPoints):\\n transformedPoint = np.dot(transformMatrix,np.array([(1+pointX)*0.5,(1-pointX)*(1+pointY)*0.25])) +translateVector\\n entry+= determinant*0.5*(1-pointX)*0.125*self.functionRHS(transformedPoint)*gWeigths[indexX]*gWeigths[indexY]*self.linearBasis[i](np.array([(1+pointX)*0.5,(1-pointX)*(1+pointY)*0.25]))\\n #0.5 comes from transformation of reference triangle to standard square [-1,1] x [-1,1]\\n elementRHS.append(entry)\\n return elementRHS\",\n \"def local_energy_ueg(system, G, Ghalf=None, two_rdm=None):\\n if (system.diagH1):\\n ke = numpy.einsum('sii,sii->',system.H1,G)\\n else:\\n ke = numpy.einsum('sij,sij->',system.H1,G)\\n\\n Gkpq = numpy.zeros((2,len(system.qvecs)), dtype=numpy.complex128)\\n Gpmq = numpy.zeros((2,len(system.qvecs)), dtype=numpy.complex128)\\n Gprod = numpy.zeros((2,len(system.qvecs)), dtype=numpy.complex128)\\n\\n ne = [system.nup, system.ndown]\\n nq = numpy.shape(system.qvecs)[0]\\n\\n for s in [0, 1]:\\n # exchange_greens_function(nq, system.ikpq_i, system.ikpq_kpq, system.ipmq_i,system.ipmq_pmq, Gprod[s],G[s])\\n # coulomb_greens_function(nq, system.ikpq_i, system.ikpq_kpq, system.ipmq_i, system.ipmq_pmq,Gkpq[s], Gpmq[s],G[s])\\n for iq in range(nq):\\n Gkpq[s,iq], Gpmq[s,iq] = coulomb_greens_function_per_qvec(system.ikpq_i[iq],\\n system.ikpq_kpq[iq],\\n system.ipmq_i[iq],\\n system.ipmq_pmq[iq],\\n G[s])\\n Gprod[s,iq] = exchange_greens_function_per_qvec(system.ikpq_i[iq],\\n system.ikpq_kpq[iq],\\n system.ipmq_i[iq],\\n system.ipmq_pmq[iq],\\n G[s])\\n\\n if two_rdm is None:\\n two_rdm = numpy.zeros((2,2,len(system.qvecs)), dtype=numpy.complex128)\\n two_rdm[0,0] = numpy.multiply(Gkpq[0],Gpmq[0]) - Gprod[0]\\n essa = (1.0/(2.0*system.vol))*system.vqvec.dot(two_rdm[0,0])\\n\\n two_rdm[1,1] = numpy.multiply(Gkpq[1],Gpmq[1]) - Gprod[1]\\n essb = (1.0/(2.0*system.vol))*system.vqvec.dot(two_rdm[1,1])\\n\\n two_rdm[0,1] = numpy.multiply(Gkpq[0],Gpmq[1])\\n two_rdm[1,0] = numpy.multiply(Gkpq[1],Gpmq[0])\\n eos = (\\n (1.0/(2.0*system.vol))*system.vqvec.dot(two_rdm[0,1])\\n + (1.0/(2.0*system.vol))*system.vqvec.dot(two_rdm[1,0])\\n )\\n\\n pe = essa + essb + eos\\n\\n return (ke+pe, ke, pe)\",\n \"def evaluate( self, mu ) :\\n\\n P = 0.\\n for l, c_l in enumerate( self.coefficients ) : P += ( l + 0.5 ) * c_l * Legendre( l, mu, checkXRange = False ) \\n return( P )\",\n \"def rosenbrock_grad(p, a=1.0, b=100.0):\\n x, y = p\\n gx = - 2*(a - x) - 2*b * (y - x**2)* 2*x\\n gy = 2*b * (y - x**2)\\n return np.array([gx, gy])\",\n \"def _calc_g(self, lambdify=True):\\n g = None\\n g_func = None\\n # check to see if we have our gravity term saved in file\\n g, g_func = self._load_from_file('g', lambdify)\\n\\n if g is None and g_func is None:\\n # if no saved file was loaded, generate function\\n print('Generating gravity compensation function')\\n\\n # get the Jacobians for each link's COM\\n J_links = [self._calc_J('link%s' % ii, x=self.x_zeros,\\n lambdify=False)\\n for ii in range(self.N_LINKS)]\\n J_joints = [self._calc_J('joint%s' % ii, x=self.x_zeros,\\n lambdify=False)\\n for ii in range(self.N_JOINTS)]\\n\\n # sum together the effects of each arm segment's inertia\\n g = sp.zeros(self.N_JOINTS, 1)\\n for ii in range(self.N_LINKS):\\n # transform each inertia matrix into joint space\\n g += (J_links[ii].T * self._M_LINKS[ii] * self.gravity)\\n # sum together the effects of each joint's inertia on each motor\\n for ii in range(self.N_JOINTS):\\n # transform each inertia matrix into joint space\\n g += (J_joints[ii].T * self._M_JOINTS[ii] * self.gravity)\\n g = sp.Matrix(g)\\n\\n # save to file\\n abr_control.utils.os_utils.makedirs(\\n '%s/g' % self.config_folder)\\n cloudpickle.dump(g, open(\\n '%s/g/g' % self.config_folder, 'wb'))\\n\\n if lambdify is False:\\n # if should return expression not function\\n return g\\n\\n if g_func is None:\\n g_func = self._generate_and_save_function(\\n filename='g', expression=g,\\n parameters=self.q)\\n return g_func\",\n \"def gelu_new(x):\\n\\treturn 0.5 * x * (1 + torch.tanh(math.sqrt(2 / math.pi) * (x + 0.044715 * torch.pow(x, 3))))\",\n \"def Grundy(x):\\n # n taille bianire max des xi; m longeur de x\\n \\n # Calcul de la longueur binaire utilisée\\n # Complexité en O(m)\\n \\n n = 0\\n \\n for val in x :\\n t = taille(val)\\n if n < t :\\n n = t\\n \\n \\n \\n # Ecriture de la liste x en binaire\\n # Complexité en O(m*n) car binaire(x,n) en O(n)\\n \\n b = [binaire(val,n) for val in x ]\\n \\n # Calcul de la somme binaire mod 2 :\\n # Complexité en O(m*n) : m valeur dans b; n tours de boucle\\n \\n a = []\\n \\n for i in range(n):\\n \\n s = 0\\n \\n for val in b :\\n s+= val[i]\\n \\n a.append(s%2)\\n \\n return(decimal(a))\",\n \"def Gtilde(x):\\n gt1 = 1.808 * cbrt(x) / np.sqrt(1 + 3.4 * cbrt(x) ** 2.)\\n gt2 = 1 + 2.210 * cbrt(x) ** 2. + 0.347 * cbrt(x) ** 4.\\n gt3 = 1 + 1.353 * cbrt(x) ** 2. + 0.217 * cbrt(x) ** 4.\\n return gt1 * (gt2 / gt3) * np.exp(-x)\",\n \"def cost_gauss(self):\\n J = np.mean(self.zvar1/self.rvar1 + np.log(2*np.pi*self.rvar1))\\n J = J*np.product(self.shape1)\\n if not self.is_complex:\\n J = J / 2\\n return J\",\n \"def HaarGOB(treeG):\\n # number of level of the chain (or tree)\\n Ntr = len(treeG)\\n # reorder chain (optional) \\n # reordering each level so that in each level the nodes are in the\\n # descent order of degrees\\n # compute u_l^c for level J_0 (top level)\\n clusterJ0 = treeG[Ntr-1]['clusters']\\n N0 = len(clusterJ0)\\n # generate indicator function on G^c\\n chic = np.identity(N0)\\n uc = [None]*N0\\n uc[0] = 1/np.sqrt(N0,dtype=np.float64)*np.ones(N0)\\n for l in np.arange(1, N0):\\n uc[l] = np.sqrt((N0-l)/(N0-l+1))*(chic[l-1,:]-1/(N0-l)*np.sum(chic[l:,:], axis=0))\\n# u = copy.deepcopy(uc)\\n treeG[Ntr-1]['u'] = uc\\n # compute the next level orthonormal basis ulk and stored into u\\n for j_tr in np.arange(0, Ntr-1)[::-1]:\\n N1 = len(treeG[j_tr]['clusters'])\\n u = [None]*N1\\n i = N0\\n for l in range(N0):\\n clusterl = treeG[j_tr+1]['clusters'][l]\\n kl = len(clusterl)\\n #for k==1\\n ucl = uc[l]\\n ul1 = np.zeros(N1)\\n for j in range(N0):\\n idxj = treeG[j_tr+1]['clusters'][j]\\n ul1[idxj] = ucl[j]/np.sqrt(len(idxj))\\n u[l] = ul1\\n if kl>1:\\n chil = np.zeros((kl,N1))\\n for k in range(kl):\\n idxl = treeG[j_tr+1]['clusters'][l]\\n chil[k,idxl[k]] = 1;\\n \\n for k in np.arange(1,kl):\\n i = i+1\\n ulk = np.sqrt((kl-k)/(kl-k+1))*(chil[k-1,:]-1/(kl-k)*np.sum(chil[k:,:],axis=0))\\n u[i-1] = ulk\\n treeG[j_tr]['u'] = u\\n # update uc and N0\\n# uc = copy.deepcopy(u)\\n uc = u\\n N0 = N1\\n return treeG\",\n \"def _g(X, g, n):\\n if n == 3:\\n n = 3.001 # for numerical stability\\n xi = 1 + X**2\\n hyp2f1_term = hyp2f1((n - 3) / 2, g / 2, n / 2, 1 / xi)\\n beta_term_1 = beta((n - 3) / 2, (3-g)/2)\\n beta_term_2 = beta((n-3)/2, 1.5)\\n return 0.5 * (beta_term_1 - beta_term_2 * hyp2f1_term * xi ** ((3-n)/2))\",\n \"def grad_step2(self, x, u_prime):\\n v = 0.25 * (x @ self.w)\\n w = u_prime + v\\n z = w.t() @ x\\n if self.debug:\\n print(f\\\"-> v={v}\\\")\\n print(f\\\"-> w={w}\\\")\\n print(f\\\"-> z={z}\\\\n\\\\n\\\")\\n return w, z\",\n \"def convert_gelu(g, op, block):\\n\\n x = g.get_node(op.input(\\\"X\\\")[0])\\n out = x * (\\n _expr.const(0.5, dtype=\\\"float32\\\")\\n + _op.erf(x * _expr.const(0.5**0.5, dtype=\\\"float32\\\")) * _expr.const(0.5, dtype=\\\"float32\\\")\\n )\\n g.add_node(op.output(\\\"Out\\\")[0], out)\",\n \"def g3(self, nx, ny, x_des):\\n\\n # :evaluating constraints at the initial point\\n g_3_0 = self.g3_unscaled(nx, ny, .5 * np.ones(4 * nx + 5 * ny))\\n\\n # :evaluating unscaled constraints:\\n g_3 = self.g3_unscaled(nx, ny, x_des)\\n\\n # :define the threshold \\\"tow\\\" to translate the scaled constraint\\n # :alpha determines to what extent the inactive constraints are satisfied\\n tow, alpha = [], self.alpha_g3\\n [tow.append(i) if self.mu_g3[list(g_3_0).index(i)] < self.p else tow.append(alpha + (1 - alpha) * i) for i in g_3_0]\\n\\n # define the translated constraint\\n g_3_translated = []\\n [g_3_translated.append(g_3[i] - tow[i]) for i in range(nx)]\\n return g_3_translated\",\n \"def precomp_GpGm(ells, thetas):\\n n_ell, n_theta = len(ells), len(thetas)\\n P_m_l = np.zeros((n_theta, n_ell))\\n P_m_lminus1 = np.zeros_like(P_m_l)\\n costs = np.cos(thetas)\\n sints = np.sin(thetas)\\n for it in range(n_theta):\\n P_m_l[it] = P2l_rec_norm(ells, costs[it])\\n # for il in range(n_ell):\\n # P_m_l[it,il] = sp.lpmv(2, ells[il], costs[it])\\n P_m_lminus1[:, 1:] = P_m_l[:, :-1]\\n ELLS, THETAS = np.meshgrid(ells, thetas)\\n COSTS, SINTS = np.cos(THETAS), np.sin(THETAS)\\n G_plus = -((ELLS - 4) / SINTS**2 + 0.5 * ELLS * (ELLS - 1)) * \\\\\\n P_m_l + (ELLS + 2) * COSTS * P_m_lminus1 / SINTS**2\\n G_minus = 2 * ((ELLS - 1) * COSTS * P_m_l -\\n (ELLS + 2) * P_m_lminus1) / SINTS**2\\n G_plus[:, 0] = 0.\\n G_minus[:, 0] = 0.\\n return G_plus, G_minus\",\n \"def quadratures(f, a=-1, b=1, n=30):\\n nodes, weights = gauss_legender_points(n)\\n w = to.tensor(weights.reshape(1, 1, -1))\\n nodes = to.tensor(nodes.reshape(1, 1, -1))\\n\\n scale = (b - a) / 2.\\n\\n x = scale * nodes + (b + a) / 2.\\n y = w * f(x)\\n y = to.sum(scale * y, dim=-1)\\n return y.type(dtype=to.float)\",\n \"def grad_wrap_l_bfgs_b(linTH,X,Y,lda,dims):\\n TH = roll(linTH,dims)\\n\\n # unravel\\n G = grad(TH,X,Y,lda)\\n linG = np.array(())\\n for g in G:\\n linG = np.concatenate((linG,np.array(g.ravel())[0]))\\n return linG\",\n \"def g_prime(z):\\n return np.multiply(g(z), 1-g(z))\",\n \"def gelu(x):\\n cdf = 0.5 * (1.0 + torch.tanh(\\n (np.sqrt(2 / np.pi) * (x + 0.044715 * np.power(x, 3)))))\\n return x * cdf\",\n \"def test_ggn_implementation(problem):\\n problem.set_up()\\n\\n diag_ggn_from_full = AutogradExtensions(problem).diag_ggn_via_ggn()\\n diag_ggn_from_block = AutogradExtensions(problem).diag_ggn()\\n\\n check_sizes_and_values(diag_ggn_from_full, diag_ggn_from_block)\\n problem.tear_down()\",\n \"def gauss_hermite_degree(self):\\n return self._gauss_hermite_degree\",\n \"def kl_div_prior_gradient(self, posterior_logits, posterior_binary_samples):\\n #DVAE Eq11 - gradient of prior\\n #gradient of the KLD between posterior and prior wrt to prior\\n #parameters theta, i.e. generative model parameters.\\n #logits to probabilities\\n posterior_probs=torch.sigmoid(posterior_logits)\\n positive_probs=posterior_probs.detach()\\n \\n #samples from posterior are labelled positive\\n positive_samples=posterior_binary_samples.detach()\\n\\n n_split=positive_samples.size()[1]//2\\n positive_samples_left,positive_samples_right=torch.split(positive_samples,split_size_or_sections=int(n_split),dim=1)\\n \\n #-z_left^t J z_right\\n pos_first_term=torch.matmul(positive_samples_left,self.prior.get_weights())*positive_samples_right\\n \\n rbm_bias_left=self.prior.get_visible_bias()\\n rbm_bias_right=self.prior.get_hidden_bias()\\n rbm_bias=torch.cat([rbm_bias_left,rbm_bias_right])#self._h\\n \\n #this gives [42,400] size\\n #- z^t h\\n #TODO this uses positive probs. Should it not use positive samples?\\n # FIXME an indication are the negative ones where samples are used! On\\n #other hand this is the only place this this used\\n pos_sec_term=positive_probs*rbm_bias\\n # pos_sec_term=positive_samples*rbm_bias\\n\\n # Energy = -z_left^t J z_right - z^t h\\n pos_kld_per_sample=-(torch.sum(pos_first_term,axis=1)+torch.sum(pos_sec_term,axis=1))\\n #samples from rbm are labelled negative\\n\\n #rbm_samples Tensor(\\\"zeros:0\\\", shape=(200, 200), dtype=float32)\\n #this returns the full RBM set: left and right nodes concatenated\\n\\n #TODO What are these samples here?\\n #TODO what's the impact of doing gibbs sampling here? does this make\\n #sense?\\n rbm_samples=self.prior.get_samples_kld(approx_post_samples=positive_samples_left,n_gibbs_sampling_steps=1)\\n negative_samples=rbm_samples.detach()\\n\\n # print(self.prior.get_weights())\\n n_split=negative_samples.size()[1]//2\\n negative_samples_left,negative_samples_right=torch.split(negative_samples,split_size_or_sections=int(n_split),dim=1)\\n neg_first_term=torch.matmul(negative_samples_left,self.prior.get_weights())*negative_samples_right\\n \\n #FIXME see above, the positive case looks different. Why?\\n neg_sec_term=negative_samples*rbm_bias\\n neg_kld_per_sample=(torch.sum(neg_first_term,axis=1)+torch.sum(neg_sec_term,axis=1))\\n \\n kld_per_sample=pos_kld_per_sample+neg_kld_per_sample\\n\\n return kld_per_sample\",\n \"def calculate_grad_B_tensor(self):\\n s = self # Shorthand\\n tensor = Struct()\\n \\n factor = s.spsi * s.B0 / s.d_l_d_varphi\\n tensor.tn = s.sG * s.B0 * s.curvature\\n tensor.nt = tensor.tn\\n tensor.bb = factor * (s.X1c * s.d_Y1s_d_varphi - s.iotaN * s.X1c * s.Y1c)\\n tensor.nn = factor * (s.d_X1c_d_varphi * s.Y1s + s.iotaN * s.X1c * s.Y1c)\\n tensor.bn = factor * (-s.sG * s.spsi * s.d_l_d_varphi * s.torsion \\\\\\n - s.iotaN * s.X1c * s.X1c)\\n tensor.nb = factor * (s.d_Y1c_d_varphi * s.Y1s - s.d_Y1s_d_varphi * s.Y1c \\\\\\n + s.sG * s.spsi * s.d_l_d_varphi * s.torsion \\\\\\n + s.iotaN * (s.Y1s * s.Y1s + s.Y1c * s.Y1c))\\n\\n self.grad_B_tensor = tensor\\n self.grad_B_colon_grad_B = tensor.tn * tensor.tn + tensor.nt * tensor.nt \\\\\\n + tensor.bb * tensor.bb + tensor.nn * tensor.nn \\\\\\n + tensor.nb * tensor.nb + tensor.bn * tensor.bn\\n\\n self.L_grad_B = s.B0 * np.sqrt(2 / self.grad_B_colon_grad_B)\\n self.inv_L_grad_B = 1.0 / self.L_grad_B\\n self.min_L_grad_B = fourier_minimum(self.L_grad_B)\",\n \"def calculateExteriorElementBoundaryQuadrature(self):\\n #\\n #get physical locations of element boundary quadrature points\\n #\\n #assume all components live on the same mesh\\n self.u[0].femSpace.elementMaps.getValuesGlobalExteriorTrace(self.elementBoundaryQuadraturePoints,\\n self.ebqe['x'])\\n #\\n #get metric tensor and unit normals\\n #\\n if self.movingDomain:\\n if self.tLast_mesh != None:\\n self.ebqe['xt'][:]=self.ebqe['x']\\n self.ebqe['xt']-=self.ebqe['x_last']\\n alpha = 1.0/(self.t_mesh - self.tLast_mesh)\\n self.ebqe['xt']*=alpha\\n else:\\n self.ebqe['xt'][:]=0.0\\n self.ebqe['x_last'][:]=self.ebqe['x']\\n self.u[0].femSpace.elementMaps.getJacobianValuesGlobalExteriorTrace_movingDomain(self.elementBoundaryQuadraturePoints,\\n self.ebqe['xt'],\\n self.ebqe['inverse(J)'],\\n self.ebqe['g'],\\n self.ebqe['sqrt(det(g))'],\\n self.ebqe['n'])\\n else:\\n self.u[0].femSpace.elementMaps.getJacobianValuesGlobalExteriorTrace(self.elementBoundaryQuadraturePoints,\\n self.ebqe['inverse(J)'],\\n self.ebqe['g'],\\n self.ebqe['sqrt(det(g))'],\\n self.ebqe['n'])\\n #now map the physical points back to the reference element\\n #assume all components live on same mesh\\n self.u[0].femSpace.elementMaps.getInverseValuesGlobalExteriorTrace(self.ebqe['inverse(J)'],self.ebqe['x'],self.ebqe['hat(x)'])\\n #\\n #since the points on the reference boundary may be reordered on many right element boundaries, we\\n #have to use an array of reference boundary points on all element boundaries\\n #first copy the left reference element boundary quadrature points from the reference element boundary\\n self.testSpace[0].getBasisValuesGlobalExteriorTrace(self.elementBoundaryQuadraturePoints,\\n self.ebqe[('w',0)])\\n cfemIntegrals.calculateWeightedShapeGlobalExteriorTrace(self.mesh.exteriorElementBoundariesArray,\\n self.mesh.elementBoundaryElementsArray,\\n self.mesh.elementBoundaryLocalElementBoundariesArray,\\n self.elementBoundaryQuadratureWeights[('f',0)],\\n self.ebqe['sqrt(det(g))'],\\n self.ebqe[('w',0)],\\n self.ebqe[('w*dS_f',0)])\\n self.u[0].femSpace.getBasisGradientValuesGlobalExteriorTrace(self.elementBoundaryQuadraturePoints,\\n self.ebqe['inverse(J)'],\\n self.ebqe[('grad(v)',0)])\\n #setup flux boundary conditions\\n self.fluxBoundaryConditionsObjectsDict = dict([(cj,FluxBoundaryConditions(self.mesh,\\n self.nElementBoundaryQuadraturePoints_elementBoundary,\\n self.ebqe[('x')],\\n self.advectiveFluxBoundaryConditionsSetterDict[cj],\\n self.diffusiveFluxBoundaryConditionsSetterDictDict[cj]))\\n for cj in self.advectiveFluxBoundaryConditionsSetterDict.keys()])\\n self.ebqe['dS'] = numpy.zeros((self.mesh.nExteriorElementBoundaries_global,self.nElementBoundaryQuadraturePoints_elementBoundary),'d')\\n cfemIntegrals.calculateIntegrationWeights(self.ebqe['sqrt(det(g))'],\\n self.elementBoundaryQuadratureWeights[('u',0)],\\n self.ebqe['dS'])\\n for ci in range(self.nc): self.ebqe[('dS',ci)] = self.ebqe['dS']\\n #\\n self.ellamDiscretization.calculateExteriorElementBoundaryQuadrature(self.ebqe)\\n #\\n self.coefficients.initializeGlobalExteriorElementBoundaryQuadrature(self.timeIntegration.t,self.ebqe)\",\n \"def gelu(x):\\n cdf = 0.5 * (1.0 + torch.tanh(\\n (np.sqrt(2 / np.pi) * (x + 0.044715 * torch.pow(x, 3)))))\\n return x * cdf\",\n \"def gelu(input):\\n return FunctionLib.apply('Gelu', input.device, [input], approximate=False)\",\n \"def gelu(x):\\n cdf = 0.5 * (1.0 + torch.tanh(\\n (np.sqrt(2 / np.pi) * (x + 0.044715 * torch.pow(x, 3)))))\\n return x * cdf\",\n \"def g(x):\\n if x[0]**2 + x[1]**2 + x[2]**2 <= 1:\\n gx = 1\\n else:\\n gx = 0\\n\\n return gx\",\n \"def gelu(self, x):\\r\\n cdf = 0.5 * (1.0 + tf.tanh(\\r\\n (np.sqrt(2 / np.pi) * (x + 0.044715 * tf.pow(x, 3)))))\\r\\n return x * cdf\",\n \"def grad_ReLU(self):\\n grad = np.where(self.x > 0, 1, 0)\\n return grad\",\n \"def H_G(X):\\n n = X.shape[0]\\n H = np.zeros([n,n])\\n DELTA_x = float(2*L)/(n-1)\\n for i in range(n):\\n H[i][(i-2)%n] = - float(epsilon) / (12 * (DELTA_x**3))\\n H[i][(i-1)%n] = (-2. - float(epsilon) * (X[i][0] + 2*X[(i-1)%n][0])) / (4 * DELTA_x) + 2. * (float(epsilon)/(12*(DELTA_x**3)))\\n H[i][i] = (epsilon * (X[(i+1)%n][0] - X[(i-1)%n][0])) / (4 *DELTA_x)\\n H[i][(i+1)%n] = (2. + float(epsilon) * (X[i][0] + 2*X[(i+1)%n][0])) / (4 * DELTA_x) - 2. * (float(epsilon)/(12*(DELTA_x**3)))\\n H[i][(i+2)%n] = float(epsilon) / (12 * (DELTA_x)**3) \\n return H\",\n \"def gauss_seidel(self):\\n for i in range(1,self.size[0]-1):\\n for j in range(1,self.size[1]-1):\\n for k in range(1,self.size[2]-1):\\n self.A[(i,j,k)] = ((1/6)*(self.A[(i+1,j,k)] + self.A[(i-1,j,k)] + self.A[(i,j+1,k)] + self.A[(i,j-1,k)] + self.A[(i,j,k+1)] + self.A[(i,j,k-1)] + self.J[(i,j,k)]) - self.A[(i,j,k)])*self.omega + self.A_0[(i,j,k)]\",\n \"def magma_zgels(trans, m, n, nrhs, A, lda, B, ldb, hwork, lwork):\\n info = c_int_type()\\n trans = _trans_conversion[trans]\\n status = _libmagma.magma_zgels(trans, m, n, nrhs, int(A), lda,\\n int(B), ldb, int(hwork), lwork,\\n ctypes.byref(info))\\n magmaCheckStatus(status)\",\n \"def buildGrad(self):\\r\\n\\r\\n self.grads = T.grad(self.L_elbo_modif, self.params)\",\n \"def derivative_matrix(g):\\n\\n def _(g):\\n B = g.B[0].grad\\n N = g.N[0]\\n P = g.dec.P(1)\\n H = np.vstack(P(B(i)) for i in range(N)).T\\n return H\\n\\n return _(g), _(g.dual)\",\n \"def compute_Rg(L):\\n\\treturn np.sqrt(np.dot(L,L))\",\n \"def internal2external_grad(xi,bounds):\\n \\n ge = np.empty_like(xi)\\n\\n for i,(v,bound) in enumerate(zip(xi,bounds)):\\n \\n a = bound[0] # minimum\\n b = bound[1] # maximum\\n\\n if a == None and b == None: # No constraints\\n ge[i] = 1.0\\n\\n elif b == None: # only min\\n ge[i] = v/np.sqrt(v**2+1)\\n\\n elif a == None: # only max\\n ge[i] = -v/np.sqrt(v**2+1)\\n\\n else: # both min and max\\n ge[i] = (b-a)*np.cos(v)/2.\\n\\n return ge\",\n \"def gauss_naive (M, b) -> list:\\n dim = len(b)\\n\\n #Itero sulle Incognite da Trovare\\n for i in range(dim):\\n\\n #Itero sulle righe su cui devo cancellare un elemento\\n for j in range(i+1,dim):\\n m__j_i = M[j][i] / M[i][i]\\n M[j][i] = 0.0\\n\\n for k in range (i+1,dim):\\n M[j][k] = M[j][k] - m__j_i * M[i][k]\\n \\n b[j] = b[j] - m__j_i * b[i]\\n\\n return M,b\",\n \"def calculate_grad_grad_B_tensor(self, two_ways=False):\\n\\n # Shortcuts\\n s = self\\n \\n X1c = s.X1c\\n Y1s = s.Y1s\\n Y1c = s.Y1c\\n\\n X20 = s.X20\\n X2s = s.X2s\\n X2c = s.X2c\\n\\n Y20 = s.Y20\\n Y2s = s.Y2s\\n Y2c = s.Y2c\\n\\n Z20 = s.Z20\\n Z2s = s.Z2s\\n Z2c = s.Z2c\\n\\n iota_N0 = s.iotaN\\n iota = s.iota\\n lp = np.abs(s.G0) / s.B0\\n\\n curvature = s.curvature\\n torsion = s.torsion\\n\\n sign_G = s.sG\\n sign_psi = s.spsi\\n B0 = s.B0\\n G0 = s.G0\\n I2 = s.I2\\n G2 = s.G2\\n p2 = s.p2\\n\\n B20 = s.B20\\n B2s = s.B2s\\n B2c = s.B2c\\n\\n d_X1c_d_varphi = s.d_X1c_d_varphi\\n d_Y1s_d_varphi = s.d_Y1s_d_varphi\\n d_Y1c_d_varphi = s.d_Y1c_d_varphi\\n\\n d_X20_d_varphi = s.d_X20_d_varphi\\n d_X2s_d_varphi = s.d_X2s_d_varphi\\n d_X2c_d_varphi = s.d_X2c_d_varphi\\n\\n d_Y20_d_varphi = s.d_Y20_d_varphi\\n d_Y2s_d_varphi = s.d_Y2s_d_varphi\\n d_Y2c_d_varphi = s.d_Y2c_d_varphi\\n\\n d_Z20_d_varphi = s.d_Z20_d_varphi\\n d_Z2s_d_varphi = s.d_Z2s_d_varphi\\n d_Z2c_d_varphi = s.d_Z2c_d_varphi\\n\\n d2_X1c_d_varphi2 = s.d2_X1c_d_varphi2\\n d2_Y1s_d_varphi2 = s.d2_Y1s_d_varphi2\\n d2_Y1c_d_varphi2 = s.d2_Y1c_d_varphi2\\n d_curvature_d_varphi = s.d_curvature_d_varphi\\n d_torsion_d_varphi = s.d_torsion_d_varphi\\n\\n grad_grad_B = np.zeros((s.nphi, 3, 3, 3))\\n grad_grad_B_alt = np.zeros((s.nphi, 3, 3, 3))\\n\\n # The elements that follow are computed in the Mathematica notebook \\\"20200407-01 Grad grad B tensor near axis\\\"\\n # and then formatted for fortran by the python script process_grad_grad_B_tensor_code\\n\\n # The order is (normal, binormal, tangent). So element 123 means nbt.\\n\\n # Element 111\\n grad_grad_B[:,0,0,0] =(B0*B0*B0*B0*lp*lp*(8*iota_N0*X2c*Y1c*\\\\\\n Y1s + 4*iota_N0*X2s*\\\\\\n (-Y1c*Y1c + Y1s*Y1s) + \\\\\\n 2*iota_N0*X1c*Y1s*Y20 + \\\\\\n 2*iota_N0*X1c*Y1s*Y2c - \\\\\\n 2*iota_N0*X1c*Y1c*Y2s + \\\\\\n 5*iota_N0*X1c*X1c*Y1c*Y1s*\\\\\\n curvature - \\\\\\n 2*Y1c*Y20*d_X1c_d_varphi + \\\\\\n 2*Y1c*Y2c*d_X1c_d_varphi + \\\\\\n 2*Y1s*Y2s*d_X1c_d_varphi + \\\\\\n 5*X1c*Y1s*Y1s*curvature*\\\\\\n d_X1c_d_varphi + \\\\\\n 2*Y1c*Y1c*d_X20_d_varphi + \\\\\\n 2*Y1s*Y1s*d_X20_d_varphi - \\\\\\n 2*Y1c*Y1c*d_X2c_d_varphi + \\\\\\n 2*Y1s*Y1s*d_X2c_d_varphi - \\\\\\n 4*Y1c*Y1s*d_X2s_d_varphi))/\\\\\\n (G0*G0*G0)\\n\\n # Element 112\\n grad_grad_B[:,0,0,1] =(B0*B0*B0*B0*lp*lp*(Y1c*Y1c*\\\\\\n (-6*iota_N0*Y2s + \\\\\\n 5*iota_N0*X1c*Y1s*\\\\\\n curvature + \\\\\\n 2*(lp*X20*torsion - \\\\\\n lp*X2c*torsion + \\\\\\n d_Y20_d_varphi - \\\\\\n d_Y2c_d_varphi)) + \\\\\\n Y1s*(5*iota_N0*X1c*Y1s*Y1s*\\\\\\n curvature + \\\\\\n 2*(lp*X1c*Y2s*torsion + \\\\\\n Y2s*d_Y1c_d_varphi - \\\\\\n (Y20 + Y2c)*\\\\\\n d_Y1s_d_varphi) + \\\\\\n Y1s*(6*iota_N0*Y2s + \\\\\\n 2*lp*X20*torsion + \\\\\\n 2*lp*X2c*torsion + \\\\\\n 5*lp*X1c*X1c*curvature*\\\\\\n torsion + \\\\\\n 5*X1c*curvature*\\\\\\n d_Y1c_d_varphi + \\\\\\n 2*d_Y20_d_varphi + \\\\\\n 2*d_Y2c_d_varphi)) + \\\\\\n Y1c*(2*(lp*X1c*\\\\\\n (-Y20 + Y2c)*torsion - \\\\\\n Y20*d_Y1c_d_varphi + \\\\\\n Y2c*d_Y1c_d_varphi + \\\\\\n Y2s*d_Y1s_d_varphi) + \\\\\\n Y1s*(12*iota_N0*Y2c - \\\\\\n 4*lp*X2s*torsion - \\\\\\n 5*X1c*curvature*\\\\\\n d_Y1s_d_varphi - \\\\\\n 4*d_Y2s_d_varphi))))/(G0*G0*G0)\\n\\n # Element 113\\n grad_grad_B[:,0,0,2] =-((B0*B0*B0*lp*lp*(2*Y1c*Y1c*\\\\\\n (2*B2c*G0*lp + B0*G2*lp + B0*I2*lp*iota - \\\\\\n 2*G0*lp*B20 + 2*B0*G0*iota_N0*Z2s + \\\\\\n B0*G0*lp*X20*curvature - \\\\\\n B0*G0*lp*X2c*curvature - \\\\\\n B0*G0*d_Z20_d_varphi + \\\\\\n B0*G0*d_Z2c_d_varphi) + \\\\\\n Y1s*(-2*B0*G0*lp*X1c*Y2s*\\\\\\n curvature + \\\\\\n Y1s*(-4*B2c*G0*lp + 2*B0*G2*lp + \\\\\\n 2*B0*I2*lp*iota - 4*G0*lp*B20 - \\\\\\n 4*B0*G0*iota_N0*Z2s + \\\\\\n 2*B0*G0*lp*X20*curvature + \\\\\\n 2*B0*G0*lp*X2c*curvature + \\\\\\n B0*G0*lp*X1c*X1c*curvature*curvature - \\\\\\n 2*B0*G0*d_Z20_d_varphi - \\\\\\n 2*B0*G0*d_Z2c_d_varphi)) + \\\\\\n 2*G0*Y1c*(B0*lp*X1c*\\\\\\n (Y20 - Y2c)*curvature + \\\\\\n 2*Y1s*(2*B2s*lp - 2*B0*iota_N0*Z2c - \\\\\\n B0*lp*X2s*curvature + \\\\\\n B0*d_Z2s_d_varphi))))/(G0*G0*G0*G0))\\n\\n # Element 121\\n grad_grad_B[:,0,1,0] =-((B0*B0*B0*B0*lp*lp*(3*iota_N0*X1c*X1c*X1c*Y1s*\\\\\\n curvature + \\\\\\n 3*lp*X1c*X1c*Y1s*Y1s*curvature*\\\\\\n torsion + \\\\\\n 2*(X2s*Y1s*\\\\\\n (-2*lp*Y1c*torsion + \\\\\\n d_X1c_d_varphi) + \\\\\\n X20*(lp*Y1c*Y1c*torsion + \\\\\\n lp*Y1s*Y1s*torsion - \\\\\\n Y1c*d_X1c_d_varphi) + \\\\\\n X2c*(-(lp*Y1c*Y1c*\\\\\\n torsion) + \\\\\\n lp*Y1s*Y1s*torsion + \\\\\\n Y1c*d_X1c_d_varphi)) - \\\\\\n 2*X1c*(3*iota_N0*X2s*Y1c - \\\\\\n iota_N0*X20*Y1s - \\\\\\n 3*iota_N0*X2c*Y1s + \\\\\\n lp*Y1c*Y20*torsion - \\\\\\n lp*Y1c*Y2c*torsion - \\\\\\n lp*Y1s*Y2s*torsion - \\\\\\n Y1c*d_X20_d_varphi + \\\\\\n Y1c*d_X2c_d_varphi + \\\\\\n Y1s*d_X2s_d_varphi)))/\\\\\\n (G0*G0*G0))\\n\\n # Element 122\\n grad_grad_B[:,0,1,1] =(B0*B0*B0*B0*lp*lp*(-4*iota_N0*X1c*Y1s*\\\\\\n Y2c + 4*iota_N0*X1c*Y1c*\\\\\\n Y2s - 3*iota_N0*X1c*X1c*Y1c*\\\\\\n Y1s*curvature + \\\\\\n 2*X20*Y1c*d_Y1c_d_varphi + \\\\\\n 2*X20*Y1s*d_Y1s_d_varphi + \\\\\\n 3*X1c*X1c*Y1s*curvature*\\\\\\n d_Y1s_d_varphi + \\\\\\n 2*X2s*(iota_N0*Y1c*Y1c - \\\\\\n Y1s*(iota_N0*Y1s + \\\\\\n d_Y1c_d_varphi) - \\\\\\n Y1c*d_Y1s_d_varphi) - \\\\\\n 2*X2c*(Y1c*\\\\\\n (2*iota_N0*Y1s + d_Y1c_d_varphi) \\\\\\n - Y1s*d_Y1s_d_varphi) - \\\\\\n 2*X1c*Y1c*d_Y20_d_varphi + \\\\\\n 2*X1c*Y1c*d_Y2c_d_varphi + \\\\\\n 2*X1c*Y1s*d_Y2s_d_varphi))/\\\\\\n (G0*G0*G0)\\n # (2*iota_N0*Y1s + d_Y1c_d_varphi) \\\\\\\\\\n\\n # Element 123\\n grad_grad_B[:,0,1,2] =(2*B0*B0*B0*lp*lp*X1c*\\\\\\n (Y1c*(2*B2c*G0*lp + B0*G2*lp + B0*I2*lp*iota - \\\\\\n 2*G0*lp*B20 + 2*B0*G0*iota_N0*Z2s + \\\\\\n 2*B0*G0*lp*X20*curvature - \\\\\\n 2*B0*G0*lp*X2c*curvature - \\\\\\n B0*G0*d_Z20_d_varphi + \\\\\\n B0*G0*d_Z2c_d_varphi) + \\\\\\n G0*Y1s*(2*B2s*lp - 2*B0*iota_N0*Z2c - \\\\\\n 2*B0*lp*X2s*curvature + \\\\\\n B0*d_Z2s_d_varphi)))/(G0*G0*G0*G0)\\n\\n # Element 131\\n grad_grad_B[:,0,2,0] =(B0*B0*B0*B0*lp*(-4*lp*lp*X2s*Y1c*Y1s*\\\\\\n curvature + \\\\\\n 2*lp*lp*X2c*(-Y1c*Y1c + Y1s*Y1s)*\\\\\\n curvature + \\\\\\n 2*lp*lp*X20*(Y1c*Y1c + Y1s*Y1s)*\\\\\\n curvature - \\\\\\n 2*lp*lp*X1c*Y1c*Y20*\\\\\\n curvature + \\\\\\n 2*lp*lp*X1c*Y1c*Y2c*\\\\\\n curvature + \\\\\\n 2*lp*lp*X1c*Y1s*Y2s*\\\\\\n curvature + \\\\\\n 3*lp*lp*X1c*X1c*Y1s*Y1s*\\\\\\n curvature*curvature + \\\\\\n lp*iota_N0*X1c*X1c*X1c*Y1s*\\\\\\n torsion - lp*iota_N0*X1c*\\\\\\n Y1c*Y1c*Y1s*torsion - \\\\\\n lp*iota_N0*X1c*Y1s*Y1s*Y1s*\\\\\\n torsion - Y1s*Y1s*\\\\\\n d_X1c_d_varphi*d_X1c_d_varphi + \\\\\\n iota_N0*X1c*X1c*Y1s*\\\\\\n d_Y1c_d_varphi - \\\\\\n lp*X1c*Y1s*Y1s*torsion*\\\\\\n d_Y1c_d_varphi - \\\\\\n iota_N0*X1c*X1c*Y1c*\\\\\\n d_Y1s_d_varphi + \\\\\\n lp*X1c*Y1c*Y1s*\\\\\\n torsion*d_Y1s_d_varphi + \\\\\\n X1c*Y1s*Y1s*d2_X1c_d_varphi2))/\\\\\\n (G0*G0*G0)\\n\\n # Element 132\\n grad_grad_B[:,0,2,1] =(B0*B0*B0*B0*lp*(-(Y1s*d_X1c_d_varphi*\\\\\\n (iota_N0*Y1c*Y1c + \\\\\\n Y1s*(iota_N0*Y1s + \\\\\\n d_Y1c_d_varphi) - \\\\\\n Y1c*d_Y1s_d_varphi)) + \\\\\\n lp*X1c*X1c*Y1s*\\\\\\n (2*iota_N0*Y1c*torsion - \\\\\\n torsion*d_Y1s_d_varphi + \\\\\\n Y1s*d_torsion_d_varphi) + \\\\\\n X1c*(Y1c*d_Y1s_d_varphi*\\\\\\n (-(iota_N0*Y1c) + d_Y1s_d_varphi) \\\\\\n + Y1s*Y1s*(lp*torsion*\\\\\\n d_X1c_d_varphi + \\\\\\n iota_N0*d_Y1s_d_varphi + \\\\\\n d2_Y1c_d_varphi2) - \\\\\\n Y1s*(d_Y1c_d_varphi*\\\\\\n d_Y1s_d_varphi + \\\\\\n Y1c*(-2*iota_N0*d_Y1c_d_varphi + \\\\\\n d2_Y1s_d_varphi2)))))/(G0*G0*G0)\\n # (-(iota_N0*Y1c) + d_Y1s_d_varphi) \\\\\\\\\\n\\n # Element 133\\n grad_grad_B[:,0,2,2] =(B0*B0*B0*B0*lp*lp*X1c*Y1s*\\\\\\n (-(Y1s*curvature*\\\\\\n d_X1c_d_varphi) + \\\\\\n X1c*(-(iota_N0*Y1c*\\\\\\n curvature) + \\\\\\n Y1s*d_curvature_d_varphi)))/\\\\\\n (G0*G0*G0)\\n\\n # Element 211\\n grad_grad_B[:,1,0,0] =(-2*B0*B0*B0*B0*lp*lp*X1c*\\\\\\n (-2*iota_N0*X2s*Y1c + \\\\\\n 2*iota_N0*X2c*Y1s - \\\\\\n iota_N0*X1c*Y2s + \\\\\\n iota_N0*X1c*X1c*Y1s*curvature + \\\\\\n lp*X1c*Y1s*Y1s*curvature*\\\\\\n torsion - Y20*\\\\\\n d_X1c_d_varphi + \\\\\\n Y2c*d_X1c_d_varphi + \\\\\\n Y1c*d_X20_d_varphi - \\\\\\n Y1c*d_X2c_d_varphi - \\\\\\n Y1s*d_X2s_d_varphi))/(G0*G0*G0)\\n\\n # Element 212\\n grad_grad_B[:,1,0,1] =(2*B0*B0*B0*B0*lp*lp*X1c*\\\\\\n (lp*X1c*Y20*torsion - \\\\\\n lp*X1c*Y2c*torsion + \\\\\\n Y20*d_Y1c_d_varphi - \\\\\\n Y2c*d_Y1c_d_varphi - \\\\\\n Y2s*d_Y1s_d_varphi + \\\\\\n Y1c*(3*iota_N0*Y2s - \\\\\\n lp*X20*torsion + \\\\\\n lp*X2c*torsion - \\\\\\n d_Y20_d_varphi + d_Y2c_d_varphi) \\\\\\n + Y1s*(iota_N0*Y20 - \\\\\\n 3*iota_N0*Y2c - \\\\\\n iota_N0*X1c*Y1c*curvature + \\\\\\n lp*X2s*torsion + \\\\\\n X1c*curvature*\\\\\\n d_Y1s_d_varphi + d_Y2s_d_varphi))\\\\\\n )/(G0*G0*G0)\\n # d_Y20_d_varphi + d_Y2c_d_varphi) \\\\\\\\\\n # d_Y1s_d_varphi + d_Y2s_d_varphi))\\\\\\\\\\n\\n # Element 213\\n grad_grad_B[:,1,0,2] =(2*B0*B0*B0*lp*lp*X1c*\\\\\\n (Y1c*(2*B2c*G0*lp + B0*G2*lp + B0*I2*lp*iota - \\\\\\n 2*G0*lp*B20 + 2*B0*G0*iota_N0*Z2s + \\\\\\n B0*G0*lp*X20*curvature - \\\\\\n B0*G0*lp*X2c*curvature - \\\\\\n B0*G0*d_Z20_d_varphi + \\\\\\n B0*G0*d_Z2c_d_varphi) + \\\\\\n G0*(B0*lp*X1c*(Y20 - Y2c)*\\\\\\n curvature + \\\\\\n Y1s*(2*B2s*lp - 2*B0*iota_N0*Z2c - \\\\\\n B0*lp*X2s*curvature + \\\\\\n B0*d_Z2s_d_varphi))))/(G0*G0*G0*G0)\\n\\n # Element 221\\n grad_grad_B[:,1,1,0] =(-2*B0*B0*B0*B0*lp*lp*X1c*\\\\\\n (lp*X2c*Y1c*torsion + \\\\\\n lp*X2s*Y1s*torsion - \\\\\\n X2c*d_X1c_d_varphi + \\\\\\n X20*(-(lp*Y1c*torsion) + \\\\\\n d_X1c_d_varphi) + \\\\\\n X1c*(3*iota_N0*X2s + \\\\\\n lp*Y20*torsion - \\\\\\n lp*Y2c*torsion - \\\\\\n d_X20_d_varphi + d_X2c_d_varphi)))/\\\\\\n (G0*G0*G0)\\n\\n # Element 222\\n grad_grad_B[:,1,1,1] =(-2*B0*B0*B0*B0*lp*lp*X1c*\\\\\\n (-(iota_N0*X2c*Y1s) + \\\\\\n 2*iota_N0*X1c*Y2s - \\\\\\n X2c*d_Y1c_d_varphi + \\\\\\n X20*(iota_N0*Y1s + \\\\\\n d_Y1c_d_varphi) + \\\\\\n X2s*(iota_N0*Y1c - \\\\\\n d_Y1s_d_varphi) - \\\\\\n X1c*d_Y20_d_varphi + \\\\\\n X1c*d_Y2c_d_varphi))/(G0*G0*G0)\\n\\n # Element 223\\n grad_grad_B[:,1,1,2] =(-2*B0*B0*B0*lp*lp*X1c*X1c*\\\\\\n (2*B2c*G0*lp + B0*G2*lp + B0*I2*lp*iota - 2*G0*lp*B20 + \\\\\\n 2*B0*G0*iota_N0*Z2s + \\\\\\n 2*B0*G0*lp*X20*curvature - \\\\\\n 2*B0*G0*lp*X2c*curvature - \\\\\\n B0*G0*d_Z20_d_varphi + \\\\\\n B0*G0*d_Z2c_d_varphi))/(G0*G0*G0*G0)\\n\\n # Element 231\\n grad_grad_B[:,1,2,0] =(B0*B0*B0*B0*lp*X1c*(-2*lp*lp*X20*Y1c*\\\\\\n curvature + \\\\\\n 2*lp*lp*X2c*Y1c*curvature + \\\\\\n 2*lp*lp*X2s*Y1s*curvature + \\\\\\n 2*lp*lp*X1c*Y20*curvature - \\\\\\n 2*lp*lp*X1c*Y2c*curvature + \\\\\\n 2*lp*iota_N0*X1c*Y1c*Y1s*\\\\\\n torsion - iota_N0*X1c*Y1s*\\\\\\n d_X1c_d_varphi + \\\\\\n lp*Y1s*Y1s*torsion*\\\\\\n d_X1c_d_varphi + \\\\\\n iota_N0*X1c*X1c*d_Y1s_d_varphi - \\\\\\n lp*X1c*Y1s*torsion*\\\\\\n d_Y1s_d_varphi - \\\\\\n lp*X1c*Y1s*Y1s*\\\\\\n d_torsion_d_varphi))/(G0*G0*G0)\\n\\n # Element 232\\n grad_grad_B[:,1,2,1] =(B0*B0*B0*B0*lp*X1c*(-(lp*iota_N0*X1c*X1c*\\\\\\n Y1s*torsion) + \\\\\\n lp*Y1s*torsion*\\\\\\n (iota_N0*Y1c*Y1c + \\\\\\n Y1s*(iota_N0*Y1s + \\\\\\n d_Y1c_d_varphi) - \\\\\\n Y1c*d_Y1s_d_varphi) + \\\\\\n X1c*((iota_N0*Y1c - \\\\\\n d_Y1s_d_varphi)*d_Y1s_d_varphi \\\\\\n + Y1s*(-(iota_N0*d_Y1c_d_varphi) + \\\\\\n d2_Y1s_d_varphi2))))/(G0*G0*G0)\\n # d_Y1s_d_varphi)*d_Y1s_d_varphi \\\\\\\\\\n\\n # Element 233\\n grad_grad_B[:,1,2,2] =(B0*B0*B0*B0*lp*lp*X1c*X1c*Y1s*curvature*\\\\\\n (iota_N0*X1c + 2*lp*Y1s*torsion))/\\\\\\n (G0*G0*G0)\\n\\n # Element 311\\n grad_grad_B[:,2,0,0] =(B0*B0*B0*B0*lp*X1c*Y1s*\\\\\\n (lp*iota_N0*X1c*X1c*torsion - \\\\\\n lp*iota_N0*Y1c*Y1c*torsion - \\\\\\n lp*iota_N0*Y1s*Y1s*torsion - \\\\\\n lp*Y1s*torsion*\\\\\\n d_Y1c_d_varphi + \\\\\\n X1c*(2*lp*lp*Y1s*curvature*curvature + \\\\\\n iota_N0*d_Y1c_d_varphi) + \\\\\\n d_X1c_d_varphi*d_Y1s_d_varphi + \\\\\\n Y1c*(iota_N0*d_X1c_d_varphi + \\\\\\n lp*torsion*d_Y1s_d_varphi) + \\\\\\n Y1s*d2_X1c_d_varphi2))/(G0*G0*G0)\\n\\n # Element 312\\n grad_grad_B[:,2,0,1] =(B0*B0*B0*B0*lp*X1c*Y1s*\\\\\\n (lp*X1c*(2*iota_N0*Y1c*\\\\\\n torsion + \\\\\\n Y1s*d_torsion_d_varphi) + \\\\\\n Y1s*(2*lp*torsion*\\\\\\n d_X1c_d_varphi + \\\\\\n 2*iota_N0*d_Y1s_d_varphi + \\\\\\n d2_Y1c_d_varphi2) + \\\\\\n Y1c*(2*iota_N0*d_Y1c_d_varphi - \\\\\\n d2_Y1s_d_varphi2)))/(G0*G0*G0)\\n\\n # Element 313\\n grad_grad_B[:,2,0,2] =(B0*B0*B0*B0*lp*lp*X1c*X1c*Y1s*\\\\\\n (-(iota_N0*Y1c*curvature) + \\\\\\n curvature*d_Y1s_d_varphi + \\\\\\n Y1s*d_curvature_d_varphi))/\\\\\\n (G0*G0*G0)\\n\\n # Element 321\\n grad_grad_B[:,2,1,0] =-((B0*B0*B0*B0*lp*X1c*X1c*Y1s*\\\\\\n (-2*lp*iota_N0*Y1c*torsion + \\\\\\n 2*iota_N0*d_X1c_d_varphi + \\\\\\n 2*lp*torsion*d_Y1s_d_varphi + \\\\\\n lp*Y1s*d_torsion_d_varphi))/\\\\\\n (G0*G0*G0))\\n\\n # Element 322\\n grad_grad_B[:,2,1,1] =-((B0*B0*B0*B0*lp*X1c*Y1s*\\\\\\n (lp*iota_N0*X1c*X1c*torsion - \\\\\\n lp*iota_N0*Y1c*Y1c*torsion - \\\\\\n lp*iota_N0*Y1s*Y1s*torsion - \\\\\\n lp*Y1s*torsion*\\\\\\n d_Y1c_d_varphi - \\\\\\n d_X1c_d_varphi*d_Y1s_d_varphi + \\\\\\n Y1c*(iota_N0*d_X1c_d_varphi + \\\\\\n lp*torsion*d_Y1s_d_varphi) + \\\\\\n X1c*(iota_N0*d_Y1c_d_varphi - \\\\\\n d2_Y1s_d_varphi2)))/(G0*G0*G0))\\n\\n # Element 323\\n grad_grad_B[:,2,1,2] =(B0*B0*B0*B0*lp*lp*X1c*X1c*Y1s*curvature*\\\\\\n (iota_N0*X1c + 2*lp*Y1s*torsion))/\\\\\\n (G0*G0*G0)\\n\\n # Element 331\\n grad_grad_B[:,2,2,0] =(B0*B0*B0*B0*lp*lp*X1c*X1c*Y1s*\\\\\\n (-(iota_N0*Y1c*curvature) + \\\\\\n curvature*d_Y1s_d_varphi + \\\\\\n Y1s*d_curvature_d_varphi))/\\\\\\n (G0*G0*G0)\\n\\n # Element 332\\n grad_grad_B[:,2,2,1] =-((B0*B0*B0*B0*lp*lp*X1c*Y1s*curvature*\\\\\\n (iota_N0*Y1c*Y1c + \\\\\\n Y1s*(iota_N0*Y1s + \\\\\\n d_Y1c_d_varphi) - \\\\\\n Y1c*d_Y1s_d_varphi))/(G0*G0*G0))\\n\\n # Element 333\\n grad_grad_B[:,2,2,2] =(-2*B0*B0*B0*B0*lp*lp*lp*X1c*X1c*Y1s*Y1s*\\\\\\n curvature*curvature)/(G0*G0*G0)\\n\\n\\n self.grad_grad_B = grad_grad_B\\n\\n # Compute the (inverse) scale length\\n squared = grad_grad_B * grad_grad_B\\n norm_squared = np.sum(squared, axis=(1,2,3))\\n self.grad_grad_B_inverse_scale_length_vs_varphi = np.sqrt(np.sqrt(norm_squared) / (4*B0))\\n self.grad_grad_B_inverse_scale_length = np.max(self.grad_grad_B_inverse_scale_length_vs_varphi)\\n\\n if not two_ways:\\n return\\n\\n # Build the whole tensor again using Rogerio's approach,\\n # \\\"20200424-01 Rogerio's GradGradB calculation.nb\\\"\\n # and verify the two calculations match.\\n\\n # Element 111\\n grad_grad_B_alt[:,0,0,0] =(-2*B0*(-4*sign_G*sign_psi*iota_N0*X2c*Y1c*\\\\\\n Y1s + iota_N0*X1c*X1c*\\\\\\n Y1c*(Y1c*\\\\\\n (-Y20 + Y2c) + \\\\\\n Y1s*(Y2s - \\\\\\n 2*sign_G*sign_psi*curvature)) + \\\\\\n X20*Y1c*Y1c*Y1s*\\\\\\n d_X1c_d_varphi - \\\\\\n X2c*Y1c*Y1c*Y1s*\\\\\\n d_X1c_d_varphi + \\\\\\n X20*Y1s*Y1s*Y1s*\\\\\\n d_X1c_d_varphi + \\\\\\n X2c*Y1s*Y1s*Y1s*\\\\\\n d_X1c_d_varphi + \\\\\\n sign_G*sign_psi*Y1c*Y20*\\\\\\n d_X1c_d_varphi - \\\\\\n sign_G*sign_psi*Y1c*Y2c*\\\\\\n d_X1c_d_varphi - \\\\\\n sign_G*sign_psi*Y1s*Y2s*\\\\\\n d_X1c_d_varphi - \\\\\\n 2*X2s*(sign_G*sign_psi*iota_N0*Y1s*Y1s + \\\\\\n Y1c*Y1c*\\\\\\n (-(sign_G*sign_psi*iota_N0) + \\\\\\n iota_N0*X1c*Y1s) + \\\\\\n Y1c*Y1s*Y1s*\\\\\\n d_X1c_d_varphi) + \\\\\\n X1c*(iota_N0*X2c*Y1c*\\\\\\n (-Y1c*Y1c + Y1s*Y1s) + \\\\\\n iota_N0*X20*Y1c*\\\\\\n (Y1c*Y1c + Y1s*Y1s) - \\\\\\n sign_G*sign_psi*iota_N0*Y1s*Y20 - \\\\\\n sign_G*sign_psi*iota_N0*Y1s*Y2c + \\\\\\n sign_G*sign_psi*iota_N0*Y1c*Y2s - \\\\\\n Y1c*Y1s*Y20*\\\\\\n d_X1c_d_varphi + \\\\\\n Y1c*Y1s*Y2c*\\\\\\n d_X1c_d_varphi + \\\\\\n Y1s*Y1s*Y2s*\\\\\\n d_X1c_d_varphi - \\\\\\n 2*sign_G*sign_psi*Y1s*Y1s*curvature*\\\\\\n d_X1c_d_varphi) - \\\\\\n sign_G*sign_psi*Y1c*Y1c*d_X20_d_varphi - \\\\\\n sign_G*sign_psi*Y1s*Y1s*d_X20_d_varphi + \\\\\\n sign_G*sign_psi*Y1c*Y1c*d_X2c_d_varphi - \\\\\\n sign_G*sign_psi*Y1s*Y1s*d_X2c_d_varphi + \\\\\\n 2*sign_G*sign_psi*Y1c*Y1s*\\\\\\n d_X2s_d_varphi))/(lp*sign_psi)\\n\\n # Element 112\\n grad_grad_B_alt[:,0,0,1] =(2*B0*(2*iota_N0*X2s*Y1c*Y1c*Y1c*\\\\\\n Y1s + 2*iota_N0*X2s*\\\\\\n Y1c*Y1s*Y1s*Y1s + \\\\\\n iota_N0*X1c*Y1c*Y1c*Y1c*\\\\\\n Y20 + iota_N0*X1c*Y1c*\\\\\\n Y1s*Y1s*Y20 - \\\\\\n iota_N0*X1c*Y1c*Y1c*Y1c*\\\\\\n Y2c + 6*sign_G*sign_psi*iota_N0*Y1c*\\\\\\n Y1s*Y2c - \\\\\\n iota_N0*X1c*Y1c*Y1s*Y1s*\\\\\\n Y2c - 3*sign_G*sign_psi*iota_N0*Y1c*Y1c*\\\\\\n Y2s - iota_N0*X1c*\\\\\\n Y1c*Y1c*Y1s*Y2s + \\\\\\n 3*sign_G*sign_psi*iota_N0*Y1s*Y1s*Y2s - \\\\\\n iota_N0*X1c*Y1s*Y1s*Y1s*\\\\\\n Y2s + 2*sign_G*sign_psi*iota_N0*X1c*\\\\\\n Y1c*Y1c*Y1s*curvature + \\\\\\n 2*sign_G*sign_psi*iota_N0*X1c*Y1s*Y1s*Y1s*\\\\\\n curvature - \\\\\\n 2*lp*sign_G*sign_psi*X2s*Y1c*\\\\\\n Y1s*torsion + \\\\\\n 2*lp*X1c*X2s*Y1c*\\\\\\n Y1s*Y1s*torsion - \\\\\\n lp*sign_G*sign_psi*X1c*Y1c*\\\\\\n Y20*torsion + \\\\\\n lp*X1c*X1c*Y1c*Y1s*\\\\\\n Y20*torsion + \\\\\\n lp*sign_G*sign_psi*X1c*Y1c*\\\\\\n Y2c*torsion - \\\\\\n lp*X1c*X1c*Y1c*Y1s*\\\\\\n Y2c*torsion + \\\\\\n lp*sign_G*sign_psi*X1c*Y1s*\\\\\\n Y2s*torsion - \\\\\\n lp*X1c*X1c*Y1s*Y1s*Y2s*\\\\\\n torsion + \\\\\\n 2*lp*sign_G*sign_psi*X1c*X1c*Y1s*Y1s*\\\\\\n curvature*torsion + \\\\\\n 2*X2s*Y1c*Y1s*Y1s*\\\\\\n d_Y1c_d_varphi - \\\\\\n sign_G*sign_psi*Y1c*Y20*\\\\\\n d_Y1c_d_varphi + \\\\\\n X1c*Y1c*Y1s*\\\\\\n Y20*d_Y1c_d_varphi + \\\\\\n sign_G*sign_psi*Y1c*Y2c*\\\\\\n d_Y1c_d_varphi - \\\\\\n X1c*Y1c*Y1s*\\\\\\n Y2c*d_Y1c_d_varphi + \\\\\\n sign_G*sign_psi*Y1s*Y2s*\\\\\\n d_Y1c_d_varphi - \\\\\\n X1c*Y1s*Y1s*Y2s*\\\\\\n d_Y1c_d_varphi + \\\\\\n 2*sign_G*sign_psi*X1c*Y1s*Y1s*\\\\\\n curvature*d_Y1c_d_varphi - \\\\\\n 2*X2s*Y1c*Y1c*Y1s*\\\\\\n d_Y1s_d_varphi - \\\\\\n X1c*Y1c*Y1c*Y20*\\\\\\n d_Y1s_d_varphi - \\\\\\n sign_G*sign_psi*Y1s*Y20*\\\\\\n d_Y1s_d_varphi + \\\\\\n X1c*Y1c*Y1c*Y2c*\\\\\\n d_Y1s_d_varphi - \\\\\\n sign_G*sign_psi*Y1s*Y2c*\\\\\\n d_Y1s_d_varphi + \\\\\\n sign_G*sign_psi*Y1c*Y2s*\\\\\\n d_Y1s_d_varphi + \\\\\\n X1c*Y1c*Y1s*\\\\\\n Y2s*d_Y1s_d_varphi - \\\\\\n 2*sign_G*sign_psi*X1c*Y1c*Y1s*\\\\\\n curvature*d_Y1s_d_varphi + \\\\\\n X2c*(Y1c*Y1c - Y1s*Y1s)*\\\\\\n (iota_N0*Y1c*Y1c + iota_N0*Y1s*Y1s - \\\\\\n lp*sign_G*sign_psi*torsion + \\\\\\n Y1s*(lp*X1c*torsion + \\\\\\n d_Y1c_d_varphi) - \\\\\\n Y1c*d_Y1s_d_varphi) - \\\\\\n X20*(Y1c*Y1c + Y1s*Y1s)*\\\\\\n (iota_N0*Y1c*Y1c + iota_N0*Y1s*Y1s - \\\\\\n lp*sign_G*sign_psi*torsion + \\\\\\n Y1s*(lp*X1c*torsion + \\\\\\n d_Y1c_d_varphi) - \\\\\\n Y1c*d_Y1s_d_varphi) + \\\\\\n sign_G*sign_psi*Y1c*Y1c*d_Y20_d_varphi + \\\\\\n sign_G*sign_psi*Y1s*Y1s*d_Y20_d_varphi - \\\\\\n sign_G*sign_psi*Y1c*Y1c*d_Y2c_d_varphi + \\\\\\n sign_G*sign_psi*Y1s*Y1s*d_Y2c_d_varphi - \\\\\\n 2*sign_G*sign_psi*Y1c*Y1s*\\\\\\n d_Y2s_d_varphi))/(lp*sign_psi)\\n\\n # Element 113\\n grad_grad_B_alt[:,0,0,2] =(-2*(Y1c*Y1c*(G2*sign_psi + I2*sign_psi*iota - \\\\\\n 2*lp*sign_G*sign_psi*B20 + \\\\\\n 2*lp*sign_G*sign_psi*B2c + \\\\\\n 2*B0*sign_G*sign_psi*iota_N0*Z2s + \\\\\\n B0*lp*sign_G*sign_psi*X20*curvature - \\\\\\n B0*lp*sign_G*sign_psi*X2c*curvature + \\\\\\n B0*lp*X1c*X20*Y1s*\\\\\\n curvature - \\\\\\n B0*lp*X1c*X2c*Y1s*\\\\\\n curvature - \\\\\\n B0*sign_G*sign_psi*d_Z20_d_varphi + \\\\\\n B0*sign_G*sign_psi*d_Z2c_d_varphi) + \\\\\\n Y1s*(B0*lp*X1c*\\\\\\n (X20 + X2c)*Y1s*Y1s*\\\\\\n curvature - \\\\\\n B0*lp*sign_G*sign_psi*X1c*Y2s*\\\\\\n curvature + \\\\\\n Y1s*(G2*sign_psi + I2*sign_psi*iota - \\\\\\n 2*lp*sign_G*sign_psi*B20 - \\\\\\n 2*lp*sign_G*sign_psi*B2c - \\\\\\n 2*B0*sign_G*sign_psi*iota_N0*Z2s + \\\\\\n B0*lp*sign_G*sign_psi*X20*curvature + \\\\\\n B0*lp*sign_G*sign_psi*X2c*curvature + \\\\\\n B0*lp*X1c*X1c*Y2s*\\\\\\n curvature + \\\\\\n B0*lp*sign_G*sign_psi*X1c*X1c*\\\\\\n curvature*curvature - \\\\\\n B0*sign_G*sign_psi*d_Z20_d_varphi - \\\\\\n B0*sign_G*sign_psi*d_Z2c_d_varphi)) + \\\\\\n Y1c*(4*lp*sign_G*sign_psi*B2s*\\\\\\n Y1s - \\\\\\n B0*(2*lp*X1c*X2s*\\\\\\n Y1s*Y1s*curvature + \\\\\\n lp*sign_G*sign_psi*X1c*\\\\\\n (-Y20 + Y2c)*\\\\\\n curvature + \\\\\\n Y1s*\\\\\\n (4*sign_G*sign_psi*iota_N0*Z2c + \\\\\\n 2*lp*sign_G*sign_psi*X2s*curvature + \\\\\\n lp*X1c*X1c*Y20*\\\\\\n curvature - \\\\\\n lp*X1c*X1c*Y2c*\\\\\\n curvature - \\\\\\n 2*sign_G*sign_psi*d_Z2s_d_varphi)))))/\\\\\\n (lp*sign_psi)\\n\\n # Element 121\\n grad_grad_B_alt[:,0,1,0] =(-2*B0*(iota_N0*X1c*X1c*X1c*\\\\\\n (Y1c*(Y20 - Y2c) + \\\\\\n Y1s*(-Y2s + \\\\\\n sign_G*sign_psi*curvature)) - \\\\\\n X1c*X1c*(iota_N0*X2c*\\\\\\n (-Y1c*Y1c + Y1s*Y1s) + \\\\\\n iota_N0*X20*\\\\\\n (Y1c*Y1c + Y1s*Y1s) + \\\\\\n Y1s*(-2*iota_N0*X2s*\\\\\\n Y1c + \\\\\\n lp*(Y1c*\\\\\\n (-Y20 + Y2c) + \\\\\\n Y1s*\\\\\\n (Y2s - sign_G*sign_psi*curvature))*\\\\\\n torsion)) + \\\\\\n sign_G*sign_psi*(X2s*Y1s*\\\\\\n (-2*lp*Y1c*torsion + \\\\\\n d_X1c_d_varphi) + \\\\\\n X20*(lp*Y1c*Y1c*\\\\\\n torsion + \\\\\\n lp*Y1s*Y1s*torsion - \\\\\\n Y1c*d_X1c_d_varphi) + \\\\\\n X2c*(-(lp*Y1c*Y1c*\\\\\\n torsion) + \\\\\\n lp*Y1s*Y1s*torsion + \\\\\\n Y1c*d_X1c_d_varphi)) + \\\\\\n X1c*(3*sign_G*sign_psi*iota_N0*X2c*\\\\\\n Y1s + \\\\\\n lp*X2c*Y1c*Y1c*Y1s*\\\\\\n torsion - \\\\\\n lp*X2c*Y1s*Y1s*Y1s*\\\\\\n torsion - \\\\\\n lp*sign_G*sign_psi*Y1c*Y20*\\\\\\n torsion + \\\\\\n lp*sign_G*sign_psi*Y1c*Y2c*\\\\\\n torsion + \\\\\\n lp*sign_G*sign_psi*Y1s*Y2s*\\\\\\n torsion - \\\\\\n X20*Y1s*\\\\\\n (-(sign_G*sign_psi*iota_N0) + \\\\\\n lp*Y1c*Y1c*torsion + \\\\\\n lp*Y1s*Y1s*torsion) + \\\\\\n X2s*Y1c*\\\\\\n (-3*sign_G*sign_psi*iota_N0 + \\\\\\n 2*lp*Y1s*Y1s*torsion) + \\\\\\n sign_G*sign_psi*Y1c*d_X20_d_varphi - \\\\\\n sign_G*sign_psi*Y1c*d_X2c_d_varphi - \\\\\\n sign_G*sign_psi*Y1s*d_X2s_d_varphi)))/\\\\\\n (lp*sign_psi)\\n\\n # Element 122\\n grad_grad_B_alt[:,0,1,1] =(2*B0*(-(X1c*X1c*\\\\\\n (Y1c*(Y20 - Y2c) + \\\\\\n Y1s*(-Y2s + \\\\\\n sign_G*sign_psi*curvature))*\\\\\\n (iota_N0*Y1c - d_Y1s_d_varphi)) \\\\\\n + X2s*(iota_N0*Y1c*Y1c*\\\\\\n (sign_G*sign_psi - 2*X1c*Y1s) - \\\\\\n sign_G*sign_psi*Y1s*\\\\\\n (iota_N0*Y1s + \\\\\\n d_Y1c_d_varphi) + \\\\\\n Y1c*(-(sign_G*sign_psi) + \\\\\\n 2*X1c*Y1s)*\\\\\\n d_Y1s_d_varphi) + \\\\\\n sign_G*sign_psi*(X20*\\\\\\n (Y1c*d_Y1c_d_varphi + \\\\\\n Y1s*d_Y1s_d_varphi) + \\\\\\n X2c*(-(Y1c*\\\\\\n (2*iota_N0*Y1s + \\\\\\n d_Y1c_d_varphi)) + \\\\\\n Y1s*d_Y1s_d_varphi)) + \\\\\\n X1c*(-(X2c*\\\\\\n (Y1c*Y1c - Y1s*Y1s)*\\\\\\n (iota_N0*Y1c - \\\\\\n d_Y1s_d_varphi)) + \\\\\\n X20*(Y1c*Y1c + Y1s*Y1s)*\\\\\\n (iota_N0*Y1c - d_Y1s_d_varphi) \\\\\\n + sign_G*sign_psi*(Y1c*\\\\\\n (2*iota_N0*Y2s - \\\\\\n d_Y20_d_varphi + \\\\\\n d_Y2c_d_varphi) + \\\\\\n Y1s*\\\\\\n (-2*iota_N0*Y2c + \\\\\\n d_Y2s_d_varphi)))))/(lp*sign_psi)\\n\\n # Element 123\\n grad_grad_B_alt[:,0,1,2] =(2*X1c*(Y1c*\\\\\\n (G2 + I2*iota - 2*lp*sign_G*B20 + \\\\\\n 2*lp*sign_G*B2c + \\\\\\n 2*B0*sign_G*iota_N0*Z2s + \\\\\\n 2*B0*lp*sign_G*X20*curvature - \\\\\\n 2*B0*lp*sign_G*X2c*curvature - \\\\\\n B0*sign_G*d_Z20_d_varphi + \\\\\\n B0*sign_G*d_Z2c_d_varphi) + \\\\\\n sign_G*Y1s*(2*lp*B2s + \\\\\\n B0*(-2*iota_N0*Z2c - \\\\\\n 2*lp*X2s*curvature + \\\\\\n d_Z2s_d_varphi))))/(lp)\\n\\n # Element 131\\n grad_grad_B_alt[:,0,2,0] =(B0*(-(lp*sign_G*sign_psi*iota_N0*Y1c*Y1c*\\\\\\n torsion) + \\\\\\n lp*iota_N0*X1c*X1c*X1c*Y1s*\\\\\\n torsion + \\\\\\n X1c*X1c*(lp*lp*Y1s*Y1s*\\\\\\n torsion*torsion + \\\\\\n iota_N0*Y1s*d_Y1c_d_varphi - \\\\\\n iota_N0*Y1c*d_Y1s_d_varphi) + \\\\\\n sign_G*sign_psi*Y1c*\\\\\\n (iota_N0*d_X1c_d_varphi + \\\\\\n 2*lp*torsion*d_Y1s_d_varphi) + \\\\\\n X1c*Y1s*\\\\\\n (2*lp*lp*sign_G*sign_psi*curvature*curvature - \\\\\\n lp*lp*sign_G*sign_psi*torsion*torsion - \\\\\\n iota_N0*Y1c*d_X1c_d_varphi + \\\\\\n lp*torsion*\\\\\\n (Y1s*d_Y1c_d_varphi - \\\\\\n Y1c*d_Y1s_d_varphi)) - \\\\\\n Y1s*(Y1s*\\\\\\n (lp*sign_G*sign_psi*iota_N0*torsion + \\\\\\n d_X1c_d_varphi*d_X1c_d_varphi) + \\\\\\n sign_G*sign_psi*(2*lp*torsion*\\\\\\n d_Y1c_d_varphi - \\\\\\n d2_X1c_d_varphi2))))/(lp*lp*sign_G)\\n\\n # Element 132\\n grad_grad_B_alt[:,0,2,1] =(B0*(-(iota_N0*Y1c*Y1c*Y1s*\\\\\\n d_X1c_d_varphi) + \\\\\\n lp*X1c*X1c*Y1s*torsion*\\\\\\n (iota_N0*Y1c - d_Y1s_d_varphi) + \\\\\\n X1c*(-(iota_N0*Y1c*Y1c*\\\\\\n d_Y1s_d_varphi) + \\\\\\n Y1c*(lp*sign_G*sign_psi*iota_N0*\\\\\\n torsion + \\\\\\n iota_N0*Y1s*\\\\\\n d_Y1c_d_varphi + \\\\\\n d_Y1s_d_varphi*d_Y1s_d_varphi) - \\\\\\n Y1s*(lp*Y1s*torsion*\\\\\\n d_X1c_d_varphi + \\\\\\n d_Y1c_d_varphi*\\\\\\n d_Y1s_d_varphi - \\\\\\n lp*sign_G*sign_psi*d_torsion_d_varphi)) + \\\\\\n Y1s*(-(iota_N0*Y1s*Y1s*\\\\\\n d_X1c_d_varphi) - \\\\\\n Y1s*d_X1c_d_varphi*\\\\\\n d_Y1c_d_varphi + \\\\\\n sign_G*sign_psi*(2*lp*torsion*\\\\\\n d_X1c_d_varphi + \\\\\\n iota_N0*d_Y1s_d_varphi + \\\\\\n d2_Y1c_d_varphi2)) + \\\\\\n Y1c*(sign_G*sign_psi*iota_N0*\\\\\\n d_Y1c_d_varphi + \\\\\\n Y1s*d_X1c_d_varphi*\\\\\\n d_Y1s_d_varphi - \\\\\\n sign_G*sign_psi*d2_Y1s_d_varphi2)))/\\\\\\n (lp*lp*sign_G)\\n\\n # Element 133\\n grad_grad_B_alt[:,0,2,2] =-((B0*(Y1s*curvature*\\\\\\n d_X1c_d_varphi + \\\\\\n X1c*(iota_N0*Y1c*\\\\\\n curvature - \\\\\\n Y1s*d_curvature_d_varphi)))/\\\\\\n (lp*sign_psi))\\n\\n # Element 211\\n grad_grad_B_alt[:,1,0,0] =(-2*B0*X1c*(2*sign_G*sign_psi*iota_N0*X2c*\\\\\\n Y1s + iota_N0*X1c*X1c*\\\\\\n (Y1c*(Y20 - Y2c) + \\\\\\n sign_G*sign_psi*Y1s*curvature) - \\\\\\n X20*Y1c*Y1s*\\\\\\n d_X1c_d_varphi + \\\\\\n X2c*Y1c*Y1s*\\\\\\n d_X1c_d_varphi - \\\\\\n sign_G*sign_psi*Y20*d_X1c_d_varphi + \\\\\\n sign_G*sign_psi*Y2c*d_X1c_d_varphi + \\\\\\n X2s*(Y1c*\\\\\\n (-2*sign_G*sign_psi*iota_N0 + \\\\\\n iota_N0*X1c*Y1s) + \\\\\\n Y1s*Y1s*d_X1c_d_varphi) + \\\\\\n X1c*(-(iota_N0*X20*\\\\\\n Y1c*Y1c) + \\\\\\n iota_N0*X2c*Y1c*Y1c - \\\\\\n sign_G*sign_psi*iota_N0*Y2s + \\\\\\n lp*sign_G*sign_psi*Y1s*Y1s*curvature*\\\\\\n torsion + \\\\\\n Y1s*Y20*\\\\\\n d_X1c_d_varphi - \\\\\\n Y1s*Y2c*\\\\\\n d_X1c_d_varphi) + \\\\\\n sign_G*sign_psi*Y1c*d_X20_d_varphi - \\\\\\n sign_G*sign_psi*Y1c*d_X2c_d_varphi - \\\\\\n sign_G*sign_psi*Y1s*d_X2s_d_varphi))/\\\\\\n (lp*sign_psi)\\n\\n # Element 212\\n grad_grad_B_alt[:,1,0,1] =(-2*B0*X1c*(iota_N0*X2s*\\\\\\n Y1c*Y1c*Y1s + \\\\\\n iota_N0*X2s*Y1s*Y1s*Y1s + \\\\\\n iota_N0*X1c*Y1c*Y1c*\\\\\\n Y20 - sign_G*sign_psi*iota_N0*Y1s*\\\\\\n Y20 + iota_N0*X1c*\\\\\\n Y1s*Y1s*Y20 - \\\\\\n iota_N0*X1c*Y1c*Y1c*\\\\\\n Y2c + 3*sign_G*sign_psi*iota_N0*Y1s*\\\\\\n Y2c - iota_N0*X1c*\\\\\\n Y1s*Y1s*Y2c - \\\\\\n 3*sign_G*sign_psi*iota_N0*Y1c*Y2s + \\\\\\n sign_G*sign_psi*iota_N0*X1c*Y1c*\\\\\\n Y1s*curvature - \\\\\\n lp*sign_G*sign_psi*X2s*Y1s*\\\\\\n torsion + \\\\\\n lp*X1c*X2s*Y1s*Y1s*\\\\\\n torsion - \\\\\\n lp*sign_G*sign_psi*X1c*Y20*\\\\\\n torsion + \\\\\\n lp*X1c*X1c*Y1s*Y20*\\\\\\n torsion + \\\\\\n lp*sign_G*sign_psi*X1c*Y2c*\\\\\\n torsion - \\\\\\n lp*X1c*X1c*Y1s*Y2c*\\\\\\n torsion + \\\\\\n X2s*Y1s*Y1s*\\\\\\n d_Y1c_d_varphi - \\\\\\n sign_G*sign_psi*Y20*d_Y1c_d_varphi + \\\\\\n X1c*Y1s*Y20*\\\\\\n d_Y1c_d_varphi + \\\\\\n sign_G*sign_psi*Y2c*d_Y1c_d_varphi - \\\\\\n X1c*Y1s*Y2c*\\\\\\n d_Y1c_d_varphi - \\\\\\n X2s*Y1c*Y1s*\\\\\\n d_Y1s_d_varphi - \\\\\\n X1c*Y1c*Y20*\\\\\\n d_Y1s_d_varphi + \\\\\\n X1c*Y1c*Y2c*\\\\\\n d_Y1s_d_varphi + \\\\\\n sign_G*sign_psi*Y2s*d_Y1s_d_varphi - \\\\\\n sign_G*sign_psi*X1c*Y1s*\\\\\\n curvature*d_Y1s_d_varphi - \\\\\\n X20*Y1c*\\\\\\n (iota_N0*Y1c*Y1c + iota_N0*Y1s*Y1s - \\\\\\n lp*sign_G*sign_psi*torsion + \\\\\\n Y1s*(lp*X1c*torsion + \\\\\\n d_Y1c_d_varphi) - \\\\\\n Y1c*d_Y1s_d_varphi) + \\\\\\n X2c*Y1c*\\\\\\n (iota_N0*Y1c*Y1c + iota_N0*Y1s*Y1s - \\\\\\n lp*sign_G*sign_psi*torsion + \\\\\\n Y1s*(lp*X1c*torsion + \\\\\\n d_Y1c_d_varphi) - \\\\\\n Y1c*d_Y1s_d_varphi) + \\\\\\n sign_G*sign_psi*Y1c*d_Y20_d_varphi - \\\\\\n sign_G*sign_psi*Y1c*d_Y2c_d_varphi - \\\\\\n sign_G*sign_psi*Y1s*d_Y2s_d_varphi))/\\\\\\n (lp*sign_psi)\\n\\n # Element 213\\n grad_grad_B_alt[:,1,0,2] =(2*X1c*(2*lp*sign_G*sign_psi*B2s*\\\\\\n Y1s + Y1c*\\\\\\n (G2*sign_psi + I2*sign_psi*iota - \\\\\\n 2*lp*sign_G*sign_psi*B20 + \\\\\\n 2*lp*sign_G*sign_psi*B2c + \\\\\\n 2*B0*sign_G*sign_psi*iota_N0*Z2s + \\\\\\n B0*lp*sign_G*sign_psi*X20*curvature - \\\\\\n B0*lp*sign_G*sign_psi*X2c*curvature + \\\\\\n B0*lp*X1c*X20*Y1s*\\\\\\n curvature - \\\\\\n B0*lp*X1c*X2c*Y1s*\\\\\\n curvature - \\\\\\n B0*sign_G*sign_psi*d_Z20_d_varphi + \\\\\\n B0*sign_G*sign_psi*d_Z2c_d_varphi) - \\\\\\n B0*(lp*X1c*X2s*Y1s*Y1s*\\\\\\n curvature + \\\\\\n lp*sign_G*sign_psi*X1c*\\\\\\n (-Y20 + Y2c)*\\\\\\n curvature + \\\\\\n Y1s*(2*sign_G*sign_psi*iota_N0*Z2c + \\\\\\n lp*sign_G*sign_psi*X2s*curvature + \\\\\\n lp*X1c*X1c*Y20*\\\\\\n curvature - \\\\\\n lp*X1c*X1c*Y2c*\\\\\\n curvature - \\\\\\n sign_G*sign_psi*d_Z2s_d_varphi))))/\\\\\\n (lp*sign_psi)\\n\\n # Element 221\\n grad_grad_B_alt[:,1,1,0] =(2*B0*X1c*(iota_N0*X1c*X1c*X1c*\\\\\\n (Y20 - Y2c) + \\\\\\n X1c*X1c*(-(iota_N0*X20*\\\\\\n Y1c) + \\\\\\n iota_N0*X2c*Y1c + \\\\\\n Y1s*(iota_N0*X2s + \\\\\\n lp*(Y20 - Y2c)*\\\\\\n torsion)) - \\\\\\n sign_G*sign_psi*(lp*X2s*Y1s*\\\\\\n torsion + \\\\\\n X2c*(lp*Y1c*\\\\\\n torsion - d_X1c_d_varphi) + \\\\\\n X20*(-(lp*Y1c*\\\\\\n torsion) + d_X1c_d_varphi)) \\\\\\n + X1c*(-(lp*X20*Y1c*\\\\\\n Y1s*torsion) + \\\\\\n lp*X2c*Y1c*Y1s*\\\\\\n torsion - \\\\\\n lp*sign_G*sign_psi*Y20*torsion + \\\\\\n lp*sign_G*sign_psi*Y2c*torsion + \\\\\\n X2s*(-3*sign_G*sign_psi*iota_N0 + \\\\\\n lp*Y1s*Y1s*torsion) + \\\\\\n sign_G*sign_psi*d_X20_d_varphi - \\\\\\n sign_G*sign_psi*d_X2c_d_varphi)))/\\\\\\n (lp*sign_psi)\\n\\n # Element 222\\n grad_grad_B_alt[:,1,1,1] =(-2*B0*X1c*(sign_G*sign_psi*\\\\\\n (X20 - X2c)*\\\\\\n (iota_N0*Y1s + d_Y1c_d_varphi) + \\\\\\n X2s*(sign_G*sign_psi - \\\\\\n X1c*Y1s)*\\\\\\n (iota_N0*Y1c - d_Y1s_d_varphi) - \\\\\\n X1c*X1c*(Y20 - Y2c)*\\\\\\n (iota_N0*Y1c - d_Y1s_d_varphi) + \\\\\\n X1c*(X20*Y1c*\\\\\\n (iota_N0*Y1c - d_Y1s_d_varphi) \\\\\\n + X2c*Y1c*\\\\\\n (-(iota_N0*Y1c) + \\\\\\n d_Y1s_d_varphi) + \\\\\\n sign_G*sign_psi*(2*iota_N0*Y2s - \\\\\\n d_Y20_d_varphi + \\\\\\n d_Y2c_d_varphi))))/(lp*sign_psi)\\n\\n # Element 223\\n grad_grad_B_alt[:,1,1,2] =(-2*X1c*X1c*(G2 + I2*iota - 2*lp*sign_G*B20 + \\\\\\n 2*lp*sign_G*B2c + 2*B0*sign_G*iota_N0*Z2s + \\\\\\n 2*B0*lp*sign_G*X20*curvature - \\\\\\n 2*B0*lp*sign_G*X2c*curvature - \\\\\\n B0*sign_G*d_Z20_d_varphi + \\\\\\n B0*sign_G*d_Z2c_d_varphi))/(lp)\\n\\n # Element 231\\n grad_grad_B_alt[:,1,2,0] =(B0*X1c*(lp*iota_N0*Y1c*\\\\\\n (sign_G*sign_psi + X1c*Y1s)*\\\\\\n torsion + \\\\\\n (-(sign_G*sign_psi*iota_N0) + \\\\\\n lp*Y1s*Y1s*torsion)*\\\\\\n d_X1c_d_varphi + \\\\\\n iota_N0*X1c*X1c*d_Y1s_d_varphi - \\\\\\n 2*lp*sign_G*sign_psi*torsion*\\\\\\n d_Y1s_d_varphi + \\\\\\n lp*X1c*Y1s*torsion*\\\\\\n d_Y1s_d_varphi - \\\\\\n lp*sign_G*sign_psi*Y1s*d_torsion_d_varphi))/\\\\\\n (lp*lp*sign_G)\\n\\n # Element 232\\n grad_grad_B_alt[:,1,2,1] =(B0*X1c*(lp*iota_N0*Y1c*Y1c*\\\\\\n Y1s*torsion + \\\\\\n lp*iota_N0*Y1s*Y1s*Y1s*torsion - \\\\\\n lp*lp*sign_G*sign_psi*Y1s*torsion*torsion - \\\\\\n sign_G*sign_psi*iota_N0*d_Y1c_d_varphi + \\\\\\n lp*Y1s*Y1s*torsion*\\\\\\n d_Y1c_d_varphi - \\\\\\n lp*Y1c*Y1s*torsion*\\\\\\n d_Y1s_d_varphi + \\\\\\n X1c*(-(lp*sign_G*sign_psi*iota_N0*\\\\\\n torsion) + \\\\\\n lp*lp*Y1s*Y1s*torsion*torsion + \\\\\\n (iota_N0*Y1c - d_Y1s_d_varphi)*\\\\\\n d_Y1s_d_varphi) + \\\\\\n sign_G*sign_psi*d2_Y1s_d_varphi2))/(lp*lp*sign_G)\\n\\n # Element 233\\n grad_grad_B_alt[:,1,2,2] =(B0*X1c*curvature*\\\\\\n (iota_N0*X1c + \\\\\\n 2*lp*Y1s*torsion))/(lp*sign_psi)\\n\\n # Element 311\\n grad_grad_B_alt[:,2,0,0] =(B0*(2*lp*lp*sign_G*sign_psi*curvature*curvature + \\\\\\n lp*iota_N0*X1c*X1c*torsion - \\\\\\n lp*iota_N0*Y1c*Y1c*torsion - \\\\\\n lp*iota_N0*Y1s*Y1s*torsion + \\\\\\n iota_N0*Y1c*d_X1c_d_varphi + \\\\\\n iota_N0*X1c*d_Y1c_d_varphi - \\\\\\n lp*Y1s*torsion*\\\\\\n d_Y1c_d_varphi + \\\\\\n lp*Y1c*torsion*\\\\\\n d_Y1s_d_varphi + \\\\\\n d_X1c_d_varphi*d_Y1s_d_varphi + \\\\\\n Y1s*d2_X1c_d_varphi2))/\\\\\\n (lp*lp*sign_psi)\\n\\n # Element 312\\n grad_grad_B_alt[:,2,0,1] =(B0*(lp*X1c*(2*iota_N0*Y1c*\\\\\\n torsion + \\\\\\n Y1s*d_torsion_d_varphi) + \\\\\\n Y1s*(2*lp*torsion*\\\\\\n d_X1c_d_varphi + \\\\\\n 2*iota_N0*d_Y1s_d_varphi + \\\\\\n d2_Y1c_d_varphi2) + \\\\\\n Y1c*(2*iota_N0*d_Y1c_d_varphi - \\\\\\n d2_Y1s_d_varphi2)))/(lp*lp*sign_psi)\\n\\n # Element 313\\n grad_grad_B_alt[:,2,0,2] =(B0*(-(iota_N0*X1c*Y1c*\\\\\\n curvature) - \\\\\\n Y1s*curvature*\\\\\\n d_X1c_d_varphi + \\\\\\n sign_G*sign_psi*d_curvature_d_varphi))/(lp*sign_psi)\\n\\n # Element 321\\n grad_grad_B_alt[:,2,1,0] =(B0*X1c*(2*lp*iota_N0*Y1c*\\\\\\n torsion - \\\\\\n 2*iota_N0*d_X1c_d_varphi - \\\\\\n lp*(2*torsion*d_Y1s_d_varphi + \\\\\\n Y1s*d_torsion_d_varphi)))/\\\\\\n (lp*lp*sign_psi)\\n\\n # Element 322\\n grad_grad_B_alt[:,2,1,1] =-((B0*(iota_N0*Y1c*d_X1c_d_varphi + \\\\\\n iota_N0*X1c*d_Y1c_d_varphi - \\\\\\n d_X1c_d_varphi*\\\\\\n d_Y1s_d_varphi + \\\\\\n lp*torsion*\\\\\\n (iota_N0*X1c*X1c - \\\\\\n iota_N0*Y1c*Y1c - \\\\\\n Y1s*(iota_N0*Y1s + \\\\\\n d_Y1c_d_varphi) + \\\\\\n Y1c*d_Y1s_d_varphi) - \\\\\\n X1c*d2_Y1s_d_varphi2))/\\\\\\n (lp*lp*sign_psi))\\n\\n # Element 323\\n grad_grad_B_alt[:,2,1,2] =(B0*curvature*(iota_N0*X1c*X1c + \\\\\\n lp*sign_G*sign_psi*torsion + \\\\\\n lp*X1c*Y1s*torsion))/\\\\\\n (lp*sign_psi)\\n\\n # Element 331\\n grad_grad_B_alt[:,2,2,0] =(B0*(-(iota_N0*X1c*Y1c*\\\\\\n curvature) - \\\\\\n Y1s*curvature*\\\\\\n d_X1c_d_varphi + \\\\\\n sign_G*sign_psi*d_curvature_d_varphi))/(lp*sign_psi)\\n\\n # Element 332\\n grad_grad_B_alt[:,2,2,1] =-((B0*curvature*(iota_N0*Y1c*Y1c + \\\\\\n iota_N0*Y1s*Y1s - \\\\\\n lp*sign_G*sign_psi*torsion + \\\\\\n Y1s*(lp*X1c*torsion + \\\\\\n d_Y1c_d_varphi) - \\\\\\n Y1c*d_Y1s_d_varphi))/\\\\\\n (lp*sign_psi))\\n\\n # Element 333\\n grad_grad_B_alt[:,2,2,2] =(-2*B0*curvature*curvature)/sign_G\\n\\n self.grad_grad_B_alt = grad_grad_B_alt\",\n \"def add_lp_qe_DL_terms(K, lin_pot_mesh, quad_geo_mesh):\\n geo_faces = quad_geo_mesh.get_faces()\\n pot_faces = lin_pot_mesh.get_faces()\\n assert geo_faces.shape[0] == pot_faces.shape[0]\\n num_faces = geo_faces.shape[0]\\n pot_nodes = lin_pot_mesh.get_nodes()\\n num_nodes = pot_nodes.shape[0]\\n c_0 = 1. / (4. * np.pi)\\n\\n for face_num in range(num_faces): # integrate over faces\\n face_nodes = quad_geo_mesh.get_tri_nodes(face_num)\\n face_n = quad_geo_mesh.get_quad_n(face_num)\\n face_hs = quad_geo_mesh.get_hs(face_num)\\n for src_num in range(num_nodes): # source points\\n src_pt = lin_pot_mesh.get_node(src_num)\\n is_singular, local_singular_ind = lin_pot_mesh.check_in_face(src_num, face_num)\\n\\n if is_singular: # singular triangle\\n for node_num in range(3):\\n node_global_num = pot_faces[face_num, node_num] # global index for vert\\n sub_mat = gq.int_over_tri_quad_n(\\n make_sing_lp_qe_quad_func(\\n src_pt, node_num, local_singular_ind\\n ),\\n face_nodes,\\n face_n,\\n face_hs,\\n )\\n K[(3 * src_num):(3 * src_num + 3),\\n (3 * node_global_num):(3 * node_global_num + 3)] += sub_mat\\n\\n else: # regular triangle\\n for node_num in range(3):\\n node_global_num = pot_faces[face_num, node_num] # global index for vert\\n sub_mat = gq.int_over_tri_quad_n(\\n make_reg_lp_qe_quad_func(src_pt, node_num),\\n face_nodes,\\n face_n,\\n face_hs,\\n )\\n K[(3 * src_num):(3 * src_num + 3),\\n (3 * node_global_num):(3 * node_global_num + 3)] += sub_mat\\n # subtracting the q(x_0) term\\n sub_mat = gq.int_over_tri_quad_n(\\n make_cp_qe_quad_func(src_pt),\\n face_nodes,\\n face_n,\\n face_hs,\\n )\\n K[(3 * src_num):(3 * src_num + 3), (3 * src_num):(3 * src_num + 3)] -= sub_mat\\n\\n for src_num in range(num_nodes): # source points\\n # whole surface q(x_0) term\\n K[(3 * src_num):(3 * src_num + 3), (3 * src_num):(3 * src_num + 3)] -= (\\n 4. * np.pi * np.identity(3)\\n )\\n\\n K *= c_0\",\n \"def rwg0_barycentric_function_space(coarse_space):\\n from .space import SpaceBuilder\\n from scipy.sparse import coo_matrix\\n\\n number_of_support_elements = coarse_space.number_of_support_elements\\n bary_grid_number_of_elements = 6 * coarse_space.grid.number_of_elements\\n\\n bary_support_elements = 6 * _np.repeat(coarse_space.support_elements, 6) + _np.tile(\\n _np.arange(6), number_of_support_elements\\n )\\n\\n bary_support_size = len(bary_support_elements)\\n\\n support = _np.zeros(6 * coarse_space.grid.number_of_elements, dtype=_np.bool_)\\n support[bary_support_elements] = True\\n\\n normal_multipliers = _np.repeat(coarse_space.normal_multipliers, 6)\\n\\n local_coords = _np.array(\\n [[0, 0], [0.5, 0], [1, 0], [0.5, 0.5], [0, 1], [0, 0.5], [1.0 / 3, 1.0 / 3]]\\n ).T\\n\\n coeffs = (\\n _np.array(\\n [\\n [1, -1.0 / 3, 0],\\n [-1.0 / 3, 1, 0],\\n [0, 1.0 / 3, -1.0 / 6],\\n [0, 0, 1.0 / 6],\\n [0, 0, 1.0 / 6],\\n [1.0 / 3, 0, -1.0 / 6],\\n ]\\n ),\\n _np.array(\\n [\\n [0, 1.0 / 3, -1.0 / 6],\\n [0, 0, 1.0 / 6],\\n [0, 0, 1.0 / 6],\\n [1.0 / 3, 0, -1.0 / 6],\\n [1, -1.0 / 3, 0],\\n [-1.0 / 3, 1, 0],\\n ]\\n ),\\n _np.array(\\n [\\n [0, 0, 1.0 / 6],\\n [1.0 / 3, 0, -1.0 / 6],\\n [1, -1.0 / 3, 0],\\n [-1.0 / 3, 1, 0],\\n [0, 1.0 / 3, -1.0 / 6],\\n [0, 0, 1.0 / 6],\\n ]\\n ),\\n )\\n\\n coarse_dofs, bary_dofs, values = generate_rwg0_map(\\n coarse_space.grid.data(), coarse_space.support_elements, local_coords, coeffs\\n )\\n\\n local2global = _np.zeros((bary_grid_number_of_elements, 3), dtype=\\\"uint32\\\")\\n local_multipliers = _np.zeros((bary_grid_number_of_elements, 3), dtype=\\\"uint32\\\")\\n\\n local2global[support] = _np.arange(3 * bary_support_size).reshape(\\n bary_support_size, 3\\n )\\n\\n local_multipliers[support] = 1\\n\\n transform = coo_matrix(\\n (values, (bary_dofs, coarse_dofs)),\\n shape=(3 * bary_support_size, 3 * number_of_support_elements),\\n dtype=_np.float64,\\n ).tocsr()\\n\\n dof_transformation = transform @ coarse_space.map_to_localised_space\\n\\n return (\\n SpaceBuilder(coarse_space.grid.barycentric_refinement)\\n .set_codomain_dimension(3)\\n .set_support(support)\\n .set_normal_multipliers(normal_multipliers)\\n .set_order(0)\\n .set_is_localised(True)\\n .set_is_barycentric(True)\\n .set_shapeset(\\\"rwg0\\\")\\n .set_identifier(\\\"rwg0\\\")\\n .set_local2global(local2global)\\n .set_local_multipliers(local_multipliers)\\n .set_dof_transformation(dof_transformation)\\n .set_numba_evaluator(_numba_rwg0_evaluate)\\n .build()\\n )\",\n \"def add_lp_le_DL_terms(K, lin_pot_mesh, lin_geo_mesh):\\n c_0 = 1. / (4. * np.pi)\\n num_faces = lin_pot_mesh.get_faces().shape[0]\\n pot_faces = lin_pot_mesh.get_faces()\\n pot_nodes = lin_pot_mesh.get_nodes()\\n num_nodes = pot_nodes.shape[0]\\n for face_num in range(num_faces): # integrate over faces\\n face_nodes = lin_geo_mesh.get_tri_nodes(face_num)\\n face_n = lin_geo_mesh.get_normal(face_num)\\n face_hs = lin_geo_mesh.get_hs(face_num)\\n for src_num in range(num_nodes): # source points\\n src_pt = pot_nodes[src_num]\\n is_singular, _local_singular_ind = lin_pot_mesh.check_in_face(src_num, face_num)\\n if not is_singular: # regular triangle\\n for node_num in range(3): # over 3 triangle nodes\\n face_node_global_num = pot_faces[face_num, node_num] # global index for vert\\n sub_mat = gq.int_over_tri_lin(\\n make_DL_reg_lp_le_quad_func(\\n face_n, src_pt, node_num\\n ),\\n face_nodes,\\n face_hs\\n )\\n j = 3 * face_node_global_num\\n K[(3 * src_num):(3 * src_num + 3),\\n j:j + 3] += c_0 * sub_mat\\n # subtracting the q(x_0) term\\n sub_mat = gq.int_over_tri_lin(\\n make_DL_cp_le_quad_func(face_n, src_pt),\\n face_nodes,\\n face_hs\\n )\\n K[(3 * src_num):(3 * src_num + 3), (3 * src_num):(3 * src_num + 3)] -= c_0 * sub_mat\\n # for singular elements, \\\\hat{x} dotted with normal vector always zero\\n # due to flat triangular elements\\n\\n for src_num in range(num_nodes): # source points\\n # whole surface q(x_0) term\\n j = 3 * src_num\\n K[j:j+3, j:j+3] -= c_0 * (\\n 4. * np.pi * np.identity(3)\\n )\",\n \"def gelu(x):\\n cdf = 0.5 * (1.0 + tf.tanh(\\n (np.sqrt(2 / np.pi) * (x + 0.044715 * tf.pow(x, 3)))))\\n return x * cdf\",\n \"def M_g(self):\\n\\n print(\\\"\\\", file=self.logfile)\\n print(\\\"Updating g\\\", file=self.logfile)\\n M_mu1 = np.lib.stride_tricks.as_strided(self.mu_pad,\\n shape=[self.P+1, self.L_h],\\n strides=[self.mu_pad.strides[-1], self.mu_pad.strides[-1]])\\n\\n M_mu1 = M_mu1[::-1,:]\\n M_mu2 = np.transpose(M_mu1[1:,:])\\n M_mu1 = M_mu1*self.e2\\n\\n M_mu = np.dot(M_mu1, M_mu2)\\n v_mu = M_mu[0,:]\\n M_mu = M_mu[1:,:]\\n\\n M_R = np.zeros((self.P,self.P+1))\\n for p in range(1,self.P+1):\\n for q in range(0,self.P+1):\\n M_R[p-1,q] = np.sum(np.diag(self.R, q-p)[:self.L_h-max(p,q)]*self.e2[max(p,q):self.L_h])\\n\\n v_R = M_R[:,0]\\n M_R = M_R[:,1:]\\n\\n self.alpha_g = np.dot(np.linalg.inv(M_mu + M_R), v_mu+v_R)\\n self.A = np.concatenate([[1], -self.alpha_g])\\n\\n self._propagate_A()\",\n \"def Intgrl(N, R, Zeta1, Zeta2, Za, Zb):\\n\\n global S12, T11, T12, T22, V11A, V12A, V22A, V11B, V12B, V22B, V1111, V2111, V2121, V2211, V2221, V2222\\n\\n S12 = 0.0\\n T11 = 0.0\\n T12 = 0.0\\n T22 = 0.0\\n V11A = 0.0\\n V12A = 0.0\\n V22A = 0.0\\n V11B = 0.0\\n V12B = 0.0\\n V22B = 0.0\\n V1111 = 0.0\\n V2111 = 0.0\\n V2121 = 0.0\\n V2211 = 0.0\\n V2221 = 0.0\\n V2222 = 0.0\\n\\n R2 = R * R\\n\\n # The coefficients for the contracted Gaussian functions are below\\n Coeff = np.array([[1.00000, 0.0000000, 0.000000],\\n [0.678914, 0.430129, 0.000000],\\n [0.444635, 0.535328, 0.154329]])\\n\\n Expon = np.array([[0.270950, 0.000000, 0.000000],\\n [0.151623, 0.851819, 0.000000],\\n [0.109818, 0.405771, 2.227660]])\\n D1 = np.zeros([3])\\n A1 = np.zeros([3])\\n D2 = np.zeros([3])\\n A2 = np.zeros([3])\\n\\n # This loop constructs the contracted Gaussian functions\\n for i in range(N):\\n A1[i] = Expon[N - 1, i] * (Zeta1 ** 2)\\n D1[i] = Coeff[N - 1, i] * ((2.0 * A1[i] / np.pi) ** 0.75)\\n A2[i] = Expon[N - 1, i] * (Zeta2 ** 2)\\n D2[i] = Coeff[N - 1, i] * ((2.0 * A2[i] / np.pi) ** 0.75)\\n\\n # Calculate one electron integrals\\n # Centre A is first atom centre B is second atom\\n # Origin is on second atom\\n # V12A - off diagonal nuclear attraction to centre A etc.\\n for i in range(N):\\n for j in range(N):\\n # Rap2 - squared distance between centre A and centre P\\n Rap = A2[j] * R / (A1[i] + A2[j])\\n Rap2 = Rap ** 2\\n Rbp2 = (R - Rap) ** 2\\n S12 = S12 + S_int(A1[i], A2[j], R2) * D1[i] * D2[j]\\n T11 = T11 + T_int(A1[i], A1[j], 0.0) * D1[i] * D1[j]\\n T12 = T12 + T_int(A1[i], A2[j], R2) * D1[i] * D2[j]\\n T22 = T22 + T_int(A2[i], A2[j], 0.0) * D2[i] * D2[j]\\n V11A = V11A + V_int(A1[i], A1[j], 0.0, 0.0, Za) * D1[i] * D1[j]\\n V12A = V12A + V_int(A1[i], A2[j], R2, Rap2, Za) * D1[i] * D2[j]\\n V22A = V22A + V_int(A2[i], A2[j], 0.0, R2, Za) * D2[i] * D2[j]\\n V11B = V11B + V_int(A1[i], A1[j], 0.0, R2, Zb) * D1[i] * D1[j]\\n V12B = V12B + V_int(A1[i], A2[j], R2, Rbp2, Zb) * D1[i] * D2[j]\\n V22B = V22B + V_int(A2[i], A2[j], 0.0, 0.0, Zb) * D2[i] * D2[j]\\n\\n # Calculate two electron integrals\\n\\n for i in range(N):\\n for j in range(N):\\n for k in range(N):\\n for l in range(N):\\n Rap = A2[i] * R / (A2[i] + A1[j])\\n Rbp = R - Rap\\n Raq = A2[k] * R / (A2[k] + A1[l])\\n Rbq = R - Raq\\n Rpq = Rap - Raq\\n Rap2 = Rap * Rap\\n Rbp2 = Rbp * Rbp\\n Raq2 = Raq * Raq\\n Rbq2 = Rbq * Rbq\\n Rpq2 = Rpq * Rpq\\n V1111 = V1111 + TwoE(A1[i], A1[j], A1[k], A1[l], 0.0, 0.0, 0.0) * D1[i] * D1[j] * D1[k] * D1[l]\\n V2111 = V2111 + TwoE(A2[i], A1[j], A1[k], A1[l], R2, 0.0, Rap2) * D2[i] * D1[j] * D1[k] * D1[l]\\n V2121 = V2121 + TwoE(A2[i], A1[j], A2[k], A1[l], R2, R2, Rpq2) * D2[i] * D1[j] * D2[k] * D1[l]\\n V2211 = V2211 + TwoE(A2[i], A2[j], A1[k], A1[l], 0.0, 0.0, R2) * D2[i] * D2[j] * D1[k] * D1[l]\\n V2221 = V2221 + TwoE(A2[i], A2[j], A2[k], A1[l], 0.0, R2, Rbq2) * D2[i] * D2[j] * D2[k] * D1[l]\\n V2222 = V2222 + TwoE(A2[i], A2[j], A2[k], A2[l], 0.0, 0.0, 0.0) * D2[i] * D2[j] * D2[k] * D2[l]\\n return\",\n \"def G(self, (k,t), (j,x), **params):\\n d = len(x)/2\\n q,dq = x[:d],x[d:]\\n J = (j == True)\\n _J = np.logical_not(J)\\n # number of constraints\\n n = len(J) \\n # number of active constraints\\n m = np.sum(J) # = n - len(a)\\n a = self.a( (k,t), (_J,q), **params)\\n lambda_ = self.lambda_( (k,t), (J,q,dq), **params)\\n # unilateral constraint forces\\n lambda_ = lambda_[:m] \\n g = np.nan*np.zeros(n)\\n g[_J] = a\\n g[J] = lambda_\\n return g\",\n \"def lagr(self, x):\\n return\",\n \"def bcL(self, rng=None):\\n if rng is None:\\n rng = random.PRNGKey(1)\\n n = self.n\\n x = onp.sin(self.bcmesh * np.pi)\\n n_y = (np.floor((n + 1) / 2) - 1).astype(int)\\n if rng is not None:\\n coeffs = random.multivariate_normal(rng, np.zeros(16),\\n np.diag(np.ones(16)))\\n else:\\n key = random.randint(random.PRNGKey(1), (1,), 1, 1000)\\n coeffs = random.multivariate_normal(\\n random.PRNGKey(key[0]), np.zeros(16), np.diag(np.ones(16)))\\n left = coeffs[0] * x**3 + coeffs[1] * x**2 + coeffs[2] * x #+ coeffs[3]\\n right = coeffs[4] * x**3 + coeffs[5] * x**2 + coeffs[6] * x #+ coeffs[7]\\n lower = coeffs[8] * x**3 + coeffs[9] * x**2 + coeffs[10] * x #+ coeffs[11]\\n upper = coeffs[12] * x**3 + coeffs[13] * x**2 + coeffs[14] * x #+ coeffs[15]\\n shape = 2 * x.shape\\n source = onp.zeros(shape)\\n source[0, :] = upper\\n source[n_y - 1, n_y - 1:] = lower[:n - n_y + 1]\\n source[n_y - 1:, n_y - 1] = right[:n - n_y + 1]\\n source[:, 0] = left\\n source[-1, :n_y - 1] = right[n:n - n_y:-1]\\n source[:n_y - 1, -1] = lower[n:n - n_y:-1]\\n # because this makes the correct order of boundary conditions\\n return source * (n + 1)**2\",\n \"def kl_div_posterior_gradient(self, posterior_logits, posterior_binary_samples):\\n #DVAE Eq12\\n #gradient of the KLD between posterior and prior wrt to posterior\\n #parameters phi\\n \\n logger.debug(\\\"kl_div_posterior_gradient\\\")\\n posterior_upper_bound = 0.999*torch.ones_like(posterior_logits)\\n #logits to probabilities\\n posterior_probs=torch.min(posterior_upper_bound, torch.sigmoid(posterior_logits))\\n \\n n_split=int(posterior_binary_samples.size()[1]//2)\\n #binarised samples from posterior to RBM layers\\n rbm_samples_left,rbm_samples_right=torch.split(posterior_binary_samples,split_size_or_sections=n_split,dim=1)\\n\\n #the following prepares the variables in the calculation in tehir format\\n rbm_bias_left=self.prior.get_visible_bias()\\n rbm_bias_right=self.prior.get_hidden_bias()\\n\\n rbm_bias=torch.cat([rbm_bias_left,rbm_bias_right])#self._h\\n rbm_weight=self.prior.get_weights()#self._J\\n\\n # this is transposed, so we multiply what we call \\\"right hand\\\" (\\\"hidden layer\\\")\\n # samples with right rbm nodes\\n # rbm_weight_t=torch.transpose(rbm_weight,0,1)#self._J\\n \\n rbm_activation_right=torch.matmul(rbm_samples_right,rbm_weight.t())\\n rbm_activation_left=torch.matmul(rbm_samples_left,rbm_weight)\\n\\n #corresponds to samples_times_J\\n rbm_activation=torch.cat([rbm_activation_right,rbm_activation_left],1)\\n \\n #TODO what is this scaling factor?\\n #[400,400] \\n hierarchy_scaling= (1.0 - posterior_binary_samples) / (1.0 - posterior_probs)\\n hierarchy_scaling_left,hierarchy_scaling_right=torch.split(hierarchy_scaling, split_size_or_sections=int(n_split),dim=1)\\n \\n #TODO why does this happen? This seems to scale only the left side of\\n #the RBM. Th right side is replaced with ones.\\n hierarchy_scaling_with_ones=torch.cat([hierarchy_scaling_left,torch.ones(hierarchy_scaling_right.size())],axis=1)\\n \\n with torch.no_grad():\\n undifferentiated_component=posterior_logits-rbm_bias-rbm_activation*hierarchy_scaling_with_ones\\n undifferentiated_component=undifferentiated_component.detach()\\n \\n kld_per_sample = torch.sum(undifferentiated_component * posterior_probs, dim=1)\\n\\n return kld_per_sample\",\n \"def gelu(x):\\n cdf = 0.5 * (1.0 + tf.tanh(\\n (np.sqrt(2 / np.pi) * (x + 0.044715 * tf.pow(x, 3)))))\\n return x * cdf\",\n \"def make_quad_gauss(lmax,alm):\\n return libcurvedsky.bispec.make_quad_gauss(lmax,alm)\",\n \"def graphe_regrets(rgt_alea, rgt_glou, rgt_glou_e, rgt_ucb):\\n T = np.arange(rgt_alea.size)\\n fig, ax = plt.subplots()\\n ax.grid(True)\\n plt.xlabel(\\\"T\\\")\\n plt.ylabel(\\\"Regret\\\")\\n \\n ax.plot(T, rgt_alea, label = 'aléatoire') \\n ax.plot(T, rgt_glou, label = 'glouton')\\n ax.plot(T, rgt_glou_e, label = 'e-glouton')\\n ax.plot(T, rgt_ucb, label = 'UCB')\\n ax.legend(loc = \\\"upper left\\\")\\n plt.title(\\\"Regrets des 4 algorithmes par rapport à T\\\")\",\n \"def subgradient(self):\\n \\n UB_full = self.total_cost\\n ufull = np.copy(self.u)\\n\\n # Update core: possible bottleneck\\n (a_csr, a_csc) = self.update_core()\\n mrows = a_csr.shape[0]\\n ncols = a_csr.shape[1]\\n u_this = self.u[~self.f_covered]\\n # np.einsum is 20% faster than np.sum ...\\n UB_fixed = self.fixed_cost\\n UB = UB_full - UB_fixed\\n cost = self.c[~self.f]\\n\\n # save nsteps calculations (Lagrangian multipliers and lower bounds)\\n u_sequence = np.zeros((mrows, self._subg_nsteps)) \\n Lu_sequence = np.zeros(self._subg_nsteps)\\n # update u\\n x = np.zeros(ncols, dtype=bool)\\n niters_max = self._subg_maxiters\\n maxfracchange = self._subg_maxfracchange\\n maxabschange = self._subg_maxabschange\\n\\n # initialization\\n f_change = _largenumber\\n a_change = _largenumber\\n niters = 0\\n Lu_max0 = 0\\n while ((f_change>maxfracchange) or (a_change>maxabschange)) and (niters<niters_max):\\n u_this = (1.0+(np.random.rand(mrows)*2.-1)*self._u_perturb)*u_this\\n u_sequence[:,0] = u_this\\n cost_u = cost - a_csc.dot(u_sequence[:,0]) # Lagrangian cost\\n # next lower bound of the Lagrangian subproblem\\n Lu_sequence[0] = np.einsum('i->', cost_u[cost_u<0])+np.einsum('i->', u_sequence[:,0]) \\n\\n for i in np.arange(self._subg_nsteps-1):\\n # current solution to the Lagrangian subproblem\\n x[0:] = False\\n x[cost_u<0] = True\\n\\n # subgradient; for two boolean arrays, multiplication seems to be the best way \\n # (equivalent to logical_and)\\n s_u = 1. - a_csr.dot(x.astype(int)) \\n s_u_norm = np.einsum('i,i',s_u,s_u) # subgradient's norm squared\\n\\n # Update\\n # next Lagrangian multiplier\\n u_temp = u_sequence[:,i]+self._stepsize*(UB - Lu_sequence[i])/s_u_norm*s_u \\n u_temp[u_temp<0] = 0\\n\\n u_sequence[:,i+1] = u_temp\\n cost_u = cost - a_csc.dot(u_sequence[:,i+1]) # Lagrangian cost\\n # next lower bound of the Lagrangian subproblem\\n Lu_sequence[i+1] = np.einsum('i->', cost_u[cost_u<0])+np.einsum('i->', u_sequence[:,i+1]) \\n \\n #print(UB_full, UB, Lu_sequence[i+1])\\n # Check the last nadaptive steps and see if the step size needs to be adapted\\n if (np.mod(i+1,self._subg_nadaptive)==0):\\n Lu_max_adapt = np.amax(Lu_sequence[i+1-self._subg_nadaptive:i+1])\\n Lu_min_adapt = np.amin(Lu_sequence[i+1-self._subg_nadaptive:i+1])\\n if (Lu_max_adapt <= 0.):\\n Lu_max_adapt = _smallnumber\\n f_change_adapt = (Lu_max_adapt-Lu_min_adapt)/np.fabs(Lu_max_adapt)\\n if f_change_adapt > self._max_adapt:\\n self._stepsize = self._stepsize*0.5\\n elif (f_change_adapt < self._min_adapt) and (self._stepsize<1.5):\\n self._stepsize = self._stepsize*1.5\\n # swap the last multiplier with the optimal one\\n i_optimal = np.argmax(Lu_sequence[i+1-self._subg_nadaptive:i+1])\\n if (i_optimal != (self._subg_nadaptive-1)):\\n u_temp = u_sequence[:,i]\\n u_sequence[:,i] = u_sequence[:,i+1-self._subg_nadaptive+i_optimal]\\n u_sequence[:,i+1-self._subg_nadaptive+i_optimal] = u_temp\\n Lu_sequence[i+1-self._subg_nadaptive+i_optimal] = Lu_sequence[i]\\n Lu_sequence[i] = Lu_max_adapt\\n\\n i_optimal = np.argmax(Lu_sequence)\\n Lu_max = Lu_sequence[i_optimal]\\n u_this = u_sequence[:,i_optimal]\\n niters = niters + 1\\n a_change = Lu_max - Lu_max0\\n f_change = a_change/np.fabs(Lu_max)\\n Lu_max0 = Lu_max # Just a copy. Not the reference (It's a number object)\\n # save current u_this???\\n\\n if (niters == niters_max): \\n warnings.warn(\\\"Iteration in subgradient reaches maximum = {0}\\\".format(niters))\\n\\n # update multipliers\\n self.u[~self.f_covered] = u_this\\n\\n # return the last nsteps multipliers\\n # save nsteps calculations (Lagrangian multipliers and lower bounds)\\n u_sequence_full = np.zeros((self.mrows, self._subg_nsteps)) \\n Lu_sequence_full = np.zeros(self._subg_nsteps)\\n u_sequence_full[self.f_covered,:] = self.u[self.f_covered][:, np.newaxis]\\n u_sequence_full[~self.f_covered,:] = u_sequence\\n\\n Lu_sequence_full = Lu_sequence + self.fixed_cost\\n\\n return (u_sequence_full, Lu_sequence_full)\",\n \"def AffineGrothendieckPolynomial(self, la, m):\\n if la == []:\\n return self.a_realization().one()\\n return self._AffineGrothendieckPolynomial(Partition(la),m)\",\n \"def optg_xtb(self, acc='normal', nproc=1):\\n for k in ['OMP_NUM_THREADS', 'MKL_NUM_THREADS']:\\n os.environ[k] = '%d'%nproc\\n uc = io2.Units()\\n const = uc.h2e #/uc.b2a\\n fmax = {'normal':1e-3, 'tight':8.e-4, 'vtight':2e-4}[acc] * const\\n # 0.025 eV/A (ase force unit) ~ 0.001 Hartree/A (xtb force unit)\\n print('acc = ', acc, 'fmax=', fmax)\\n for cid in range(self.nconf):\\n print('cid=', cid)\\n c1 = self.mol.GetConformer(cid)\\n zs = [ ai.GetAtomicNum() for ai in c1.GetAtoms() ]\\n coords = []\\n for ia in range(self.na):\\n o = c1.GetAtomPosition(ia)\\n coords.append( [o.x, o.y, o.z] )\\n m1 = ase.Atoms(zs, coords)\\n # create the calculator for GFN0-xTB under periodic boundary conditions\\n calc = GFN2(print_level=2) # setting to 1 or 2 (the default) is ok, not so for 3\\n m1.set_calculator(calc)\\n e = m1.get_potential_energy()\\n print(\\\"Initial energy: eV, Eh\\\", e, e/Hartree)\\n relax = PreconFIRE(m1, precon=None, trajectory=None) # = 'tmp.traj')\\n relax.run(fmax = fmax)\\n for i in range(self.na):\\n pt = Point3D()\\n pt.x, pt.y, pt.z = coordsU[i]\\n c1.SetAtomPosition(i, pt)\",\n \"def grad_log_likelihood(g_t):\\n # state vector [g1, g2, g3, ... , gn-1, gn]\\n state_vector = np.zeros(N)\\n\\n # factor that will be added to one node and subtracted from another\\n def alphaij(gj, gi, deltaij, varij):\\n return ((gj - gi) - deltaij) / varij\\n\\n # indices of state vector\\n # Iterate over all connections\\n for r in self.graph.index:\\n state1, state2, value, variance, ligand, standard_state = self.graph.iloc[r]\\n i = self.states[self.states.name == state1].index[0]\\n j = self.states[self.states.name == state2].index[0]\\n\\n gj = g_t[j]\\n gi = g_t[i]\\n\\n edge_attr = self.cycle.edges()[(state1, state2)]\\n deltaij = edge_attr['energy'] # measured difference\\n varij = edge_attr['weight'] # measured variance\\n\\n shared_alpha = alphaij(gj, gi, deltaij, varij)\\n\\n state_vector[i] += shared_alpha\\n state_vector[j] -= shared_alpha\\n\\n return state_vector\",\n \"def grad_step3(self, x, w):\\n z_prime = w.t() @ x\\n if self.debug:\\n print(f\\\"-> z_prime={z_prime}\\\\n\\\\n\\\")\\n return z_prime\",\n \"def g(self, p):\\n re = self._re(p)\\n Le = self._Le(p)\\n wf = self._wf(p)\\n rf = self._rf(p)\\n A = Le @ np.einsum('...ij,...j', self.Ee, re)\\n B = wf @ np.einsum('...ij,...j', self.Ff, rf)\\n return (B - A) * G * self.d\",\n \"def gauss_hermite(dim=1, num_quad_pts=20):\\n # sigma_pts, weights = hermgauss(num_quad_pts) # Gauss-Hermite sigma points and weights\\n sigma_pts, weights = mvhermgauss(num_quad_pts, dim)\\n sigma_pts = np.sqrt(2) * sigma_pts.T\\n weights = weights.T * pi ** (-0.5 * dim) # scale weights by 1/√π\\n return sigma_pts, weights\",\n \"def _gauss_newton_product(self):\\n\\n # Shortcuts.\\n output_in = self.exprs['output_in']\\n loss = self.exprs['loss']\\n flat_pars = self.parameters.flat\\n p = self.exprs['some-vector'] = T.vector('some-vector')\\n\\n Jp = T.Rop(output_in, flat_pars, p)\\n HJp = T.grad(T.sum(T.grad(loss, output_in) * Jp),\\n output_in, consider_constant=[Jp])\\n Hp = T.grad(T.sum(HJp * output_in),\\n flat_pars, consider_constant=[HJp, Jp])\\n\\n return Hp\",\n \"def compute_lgcamp_grad(lgcamp, Z, Amatrices, sigma):\\n\\n i1 = np.dot(Amatrices[0], Z)\\n i2 = np.dot(Amatrices[1], Z)\\n i3 = np.dot(Amatrices[2], Z)\\n i4 = np.dot(Amatrices[3], Z)\\n lgcamp_samples = (np.log(np.abs(i1)) +\\n np.log(np.abs(i2)) - \\n np.log(np.abs(i3)) -\\n np.log(np.abs(i4)))\\n\\n pp = (lgcamp - lgcamp_samples) / (sigma**2)\\n pt1 = pp / i1\\n pt2 = pp / i2\\n pt3 = -pp / i3\\n pt4 = -pp / i4\\n out = (np.dot(pt1, Amatrices[0]) +\\n np.dot(pt2, Amatrices[1]) +\\n np.dot(pt3, Amatrices[2]) +\\n np.dot(pt4, Amatrices[3]))\\n\\n return (-2.0/len(lgcamp)) * np.real(out)\",\n \"def g_rosenbrock(x, a=1, b=100):\\n\\n g = np.array(\\n [\\n -2 * a - 4 * b * x[0] * (-x[0] ** 2 + x[1]) + 2 * x[0],\\n b * (-2 * x[0] ** 2 + 2 * x[1]),\\n ]\\n )\\n\\n return g\",\n \"def jacobian_g(self, x, out=None, **kwargs):\\n return self._base_nlp.jacobian_g(x, out=out, **kwargs)\",\n \"def gauss_rule(iel, elemType, normal_order, element):\\n\\n #sctr = element[iel, :] # element connectivity\\n\\n if ((elemType == 'Q4') and (normal_order <8)):\\n W, Q = gpw.gauss_pt_wt(normal_order,'GAUSS',2)\\n return W, Q\",\n \"def gslb(self) :\\n try :\\n return self._gslb\\n except Exception as e:\\n raise e\",\n \"def aggregation(lgca):\\n newnodes = lgca.nodes.copy()\\n relevant = (lgca.cell_density[lgca.nonborder] > 0) & \\\\\\n (lgca.cell_density[lgca.nonborder] < lgca.K)\\n coords = [a[relevant] for a in lgca.nonborder]\\n\\n g = np.asarray(lgca.gradient(lgca.cell_density))\\n for coord in zip(*coords):\\n n = lgca.cell_density[coord]\\n permutations = lgca.permutations[n]\\n j = lgca.j[n]\\n weights = np.exp(lgca.beta * np.einsum('i,ij', g[coord], j)).cumsum()\\n ind = bisect_left(weights, random() * weights[-1])\\n newnodes[coord] = permutations[ind]\\n\\n lgca.nodes = newnodes\",\n \"def lgammln(xx):\\r\\n\\r\\n coeff = [76.18009173, -86.50532033, 24.01409822, -1.231739516,\\r\\n 0.120858003e-2, -0.536382e-5]\\r\\n x = xx - 1.0\\r\\n tmp = x + 5.5\\r\\n tmp = tmp - (x+0.5)*math.log(tmp)\\r\\n ser = 1.0\\r\\n for j in range(len(coeff)):\\r\\n x = x + 1\\r\\n ser = ser + coeff[j]/x\\r\\n return -tmp + math.log(2.50662827465*ser)\",\n \"def reconstruct_grad_domain( G, mask, bg ):\\n sz = G.shape[:2] \\n N = sz[0]*sz[1]\\n\\n\\n # Gradient operators as sparse matrices\\n o1 = np.ones((N,1))\\n B = np.concatenate( (-o1, np.concatenate( (np.zeros((sz[0],1)), o1[:N-sz[0]]), 0 ) ), 1)\\n B[N-sz[0]:N,0] = 0\\n Ogx = sparse.spdiags(B.transpose(), [0 ,sz[0]], N, N ) # Forward difference operator along x\\n\\n B = np.concatenate( (-o1 ,np.concatenate((np.array([[0]]), o1[0:N-1]) ,0)), 1)\\n B[sz[0]-1::sz[0], 0] = 0\\n B[sz[0]::sz[0],1] = 0\\n Ogy = sparse.spdiags( B.transpose(), [0, 1], N, N ) # Forward difference operator along y\\n\\n Ogx_prime = Ogx.transpose()\\n Ogy_prime = Ogy.transpose()\\n\\n # Calculate pixels which are in the boundary\\n boundary = []\\n h, w = mask.shape\\n for x in range(h):\\n for y in range(w):\\n if mask[x,y] == 1 and (x==0 or x==h-1 or y==0 or y==w-1 or min(mask[x,y-1], mask[x-1,y], mask[x+1,y], mask[x,y+1])==0):\\n boundary.append((x,y))\\n\\n K = len(boundary)\\n\\n E = sparse.csc_matrix((K, N))\\n T_E = np.zeros((K,))\\n for i, (x,y) in enumerate(boundary):\\n pos = y * sz[0] + x # Calulcate the pixel's position\\n E[i, pos] = 1\\n T_E[i] = bg[x,y]\\n\\n A = Ogx_prime @ Ogx + Ogy_prime @ Ogy + E.transpose() @ E\\n\\n Gx = G[:,:,0].flatten('F')\\n Gy = G[:,:,1].flatten('F')\\n\\n b = Ogx_prime @ Gx + Ogy_prime @ Gy + E.transpose() @ T_E\\n\\n if USE_CHOLESKY:\\n factor = cholesky(A)\\n I = factor(b)\\n\\n else:\\n I = sparse.linalg.spsolve(A, b)\\n\\n\\n new_img = I.reshape(bg.shape, order='F')\\n\\n return new_img\",\n \"def _g_prime(self, x):\\n return self._g(x)*(1 - self._g(x))\",\n \"def magma_zgels_gpu(trans, m, n, nrhs, A, lda, B, ldb, hwork, lwork):\\n info = c_int_type()\\n trans = _trans_conversion[trans]\\n status = _libmagma.magma_zgels_gpu(trans, m, n, nrhs, int(A), lda,\\n int(B), ldb, int(hwork), lwork,\\n ctypes.byref(info))\\n magmaCheckStatus(status)\",\n \"def taylor_expansion(self,g_temp,g_step,var):\\n A=np.zeros([self.n+1,self.n])\\n for i in range(self.n):\\n A[self.n][i]=1\\n for j in range(self.n):\\n if(i==j): A[i][j]=2.*var[i]+2.+g_temp*np.sum([self.XXZ.Z(k,i) for k in range(self.n) if k!=i])\\n else: A[i][j]=-g_temp*self.XXZ.Z(j,i)\\n #First derivative\\n B1=np.zeros(self.n+1)\\n for i in range(self.n): \\n B1[i]=self.gamma*2.*g_temp*self.N*(self.n-self.N)+np.sum([self.XXZ.Z(k,i)*(var[k]-var[i]) for k in range(self.n) if k!=i])\\n Ainv=np.linalg.pinv(A)\\n der1=np.dot(Ainv,B1)\\n #Second derivative\\n B2=np.zeros(self.n+1)\\n for k in range(self.n):\\n B2[k]=self.gamma*2.*self.N*(self.n-self.N) -2.*der1[k]**2+2.*np.sum([self.XXZ.Z(l,k)*(der1[l]-der1[k]) for l in range(self.n) if k!=l])\\n der2=np.dot(Ainv,B2)\\n #Third derivative\\n B3=np.zeros(self.n+1)\\n for k in range(self.n):\\n B3[k]=-6*der1[k]*der2[k]+3.*np.sum([self.XXZ.Z(l,k)*(der2[l]-der2[k]) for l in range(self.n) if k!=l])\\n der3=np.dot(Ainv,B3)\\n #Fourth derivative\\n B4=np.zeros(self.n+1)\\n for k in range(self.n):\\n B4[k]=-8.*der3[k]*der1[k]-6.*der2[k]*der2[k]+4.*np.sum([self.XXZ.Z(l,k)*(der3[l]-der3[k]) for l in range(self.n) if k!=l])\\n der4=np.dot(Ainv,B4)\\n \\n return var+g_step*der1+g_step**2*der2/2.+g_step**3*der3/6.+g_step**4*der4/24.\",\n \"def gauss_quadrature(ab, N):\\n\\n from numpy.linalg import eigh\\n\\n n = ab.shape[0]\\n if n+1 < N:\\n raise IndexError('Require N+1 recurrence coefficients for an N-point rule.')\\n\\n J = np.diag(ab[1:N,1], k=1) + np.diag(ab[:N,0],k=0) + np.diag(ab[1:N,1], k=-1)\\n lamb,v = eigh(J)\\n\\n return lamb, v[0,:]**2\",\n \"def integrate_gausskronrod(f, a, b, args=()):\\n\\n assert b > a\\n mid = 0.5*(b+a)\\n dx = 0.5*(b-a)\\n zi = mid+gausskronrod_nodes*dx\\n integrand = f(zi)\\n integral_G7 = np.sum(integrand[:7]*gauss_weights)\\n integral_K15 = np.sum(integrand*kronrod_weights)\\n\\n error = (200*abs(integral_G7-integral_K15))**1.5\\n\\n return integral_K15*dx, dx*error\"\n]","isConversational":false},"negative_scores":{"kind":"list like","value":["0.58593106","0.58049333","0.57061964","0.5585701","0.5494929","0.5449645","0.5404597","0.5396169","0.53935695","0.53853106","0.5384895","0.53766567","0.5354576","0.5340692","0.5332809","0.53303593","0.5283502","0.5264142","0.52536964","0.5246925","0.5240725","0.52155566","0.51669014","0.51598376","0.513356","0.5131827","0.5109728","0.50971836","0.5063893","0.5054534","0.50434035","0.5040795","0.50407934","0.5028627","0.5028115","0.5025504","0.50068563","0.5003184","0.499707","0.49726948","0.49700466","0.49538344","0.49521527","0.49450135","0.4940151","0.49263424","0.4924437","0.4921038","0.49065882","0.49044448","0.49022633","0.4902263","0.4898667","0.48909086","0.4865412","0.48653197","0.48543665","0.48414558","0.48399165","0.48382017","0.48371965","0.4830285","0.48266","0.48173946","0.4815758","0.48136786","0.48098847","0.4808593","0.4805968","0.48017484","0.47962657","0.47953004","0.47948208","0.4793891","0.4772009","0.4770261","0.47680792","0.4763454","0.47557127","0.4753133","0.47521362","0.47483847","0.47475097","0.47474232","0.47463438","0.47453788","0.47453162","0.47424844","0.47417396","0.47412524","0.47411504","0.47403875","0.47345132","0.4732807","0.47320414","0.47307512","0.47286603","0.47203952","0.471863","0.4717392"],"string":"[\n \"0.58593106\",\n \"0.58049333\",\n \"0.57061964\",\n \"0.5585701\",\n \"0.5494929\",\n \"0.5449645\",\n \"0.5404597\",\n \"0.5396169\",\n \"0.53935695\",\n \"0.53853106\",\n \"0.5384895\",\n \"0.53766567\",\n \"0.5354576\",\n \"0.5340692\",\n \"0.5332809\",\n \"0.53303593\",\n \"0.5283502\",\n \"0.5264142\",\n \"0.52536964\",\n \"0.5246925\",\n \"0.5240725\",\n \"0.52155566\",\n \"0.51669014\",\n \"0.51598376\",\n \"0.513356\",\n \"0.5131827\",\n \"0.5109728\",\n \"0.50971836\",\n \"0.5063893\",\n \"0.5054534\",\n \"0.50434035\",\n \"0.5040795\",\n \"0.50407934\",\n \"0.5028627\",\n \"0.5028115\",\n \"0.5025504\",\n \"0.50068563\",\n \"0.5003184\",\n \"0.499707\",\n \"0.49726948\",\n \"0.49700466\",\n \"0.49538344\",\n \"0.49521527\",\n \"0.49450135\",\n \"0.4940151\",\n \"0.49263424\",\n \"0.4924437\",\n \"0.4921038\",\n \"0.49065882\",\n \"0.49044448\",\n \"0.49022633\",\n \"0.4902263\",\n \"0.4898667\",\n \"0.48909086\",\n \"0.4865412\",\n \"0.48653197\",\n \"0.48543665\",\n \"0.48414558\",\n \"0.48399165\",\n \"0.48382017\",\n \"0.48371965\",\n \"0.4830285\",\n \"0.48266\",\n \"0.48173946\",\n \"0.4815758\",\n \"0.48136786\",\n \"0.48098847\",\n \"0.4808593\",\n \"0.4805968\",\n \"0.48017484\",\n \"0.47962657\",\n \"0.47953004\",\n \"0.47948208\",\n \"0.4793891\",\n \"0.4772009\",\n \"0.4770261\",\n \"0.47680792\",\n \"0.4763454\",\n \"0.47557127\",\n \"0.4753133\",\n \"0.47521362\",\n \"0.47483847\",\n \"0.47475097\",\n \"0.47474232\",\n \"0.47463438\",\n \"0.47453788\",\n \"0.47453162\",\n \"0.47424844\",\n \"0.47417396\",\n \"0.47412524\",\n \"0.47411504\",\n \"0.47403875\",\n \"0.47345132\",\n \"0.4732807\",\n \"0.47320414\",\n \"0.47307512\",\n \"0.47286603\",\n \"0.47203952\",\n \"0.471863\",\n \"0.4717392\"\n]","isConversational":false},"document_score":{"kind":"string","value":"0.62280107"},"document_rank":{"kind":"string","value":"0"}}},{"rowIdx":75390,"cells":{"query":{"kind":"string","value":"Sets the learning rate to the initial LR decayed by 10 every 30 epochs"},"document":{"kind":"string","value":"def adjust_learning_rate(initial_lr, optimizer, epoch, every_epoch):\n lr = initial_lr * (0.1 ** (epoch // every_epoch))\n for param_group in optimizer.param_groups:\n param_group['lr'] = lr"},"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 adjust_learning_rate(init_lr, optimizer, epoch, n=100):\n init_lr = init_lr * (0.1 ** (epoch // n))\n print('learning rate : ', init_lr)\n for param_group in optimizer.param_groups:\n param_group['lr'] = init_lr","def adjust_learning_rate(start_lr, optimizer, epoch, total_epoch_num):\n #lr = start_lr * (0.1 ** (epoch // 30))\n lr = start_lr * (0.3 ** (epoch // 5))\n if epoch==total_epoch_num:\n lr = lr * 0.3\n\n for param_group in optimizer.param_groups:\n param_group['lr'] = lr","def adjust_learning_rate(lr, optimizer, epoch):\n lr = lr_init * (0.1 ** (epoch // 10))\n for param_group in optimizer.param_groups:\n param_group['lr'] = lr\n return lr","def adjust_learning_rate(lr, optimizer, lr_decay, epoch):\n\n if epoch >= lr_decay[0]:\n lr = lr * 0.1\n if epoch >= lr_decay[1]:\n lr = lr * 0.01\n for param_group in optimizer.param_groups:\n param_group['lr'] = lr\n\n return lr","def initialize_learning_rate(self):\n\n if (self.FLAGS.learning_rate_decay is \"exponential\"):\n self.learning_rate = tf.train.exponential_decay(\n self.FLAGS.learning_rate,\n self.global_step,\n self.FLAGS.decay_steps,\n self.FLAGS.decay_rate)\n else :\n self.learning_rate = self.FLAGS.learning_rate","def adjust_learning_rate(lr, decay, optimizer, cur_epoch, every_n_epochs):\n new_lr = lr * (decay ** (cur_epoch // every_n_epochs))\n\n # if cur_epoch % every_n_epochs == 0:\n # new_lr = lr * 0.1\n\n for param_group in optimizer.param_groups:\n param_group['lr'] = new_lr","def adjust_learning_rate(optimizer, epoch, args):\r\n lr = args.lr * (0.1 ** (epoch // 30))\r\n for param_group in optimizer.param_groups:\r\n param_group['lr'] = lr","def adjust_learning_rate(self, optimizer, epoch, args):\n lr = args.learning_rate * (0.1 ** (epoch // 30))\n # print(lr)\n for param_group in optimizer.param_groups:\n param_group['lr'] = lr","def adjust_learning_rate(optimizer, epoch, args):\n lr = args.lr\n if 20 < epoch <= 30:\n lr = 0.0001\n elif 30 < epoch :\n lr = 0.00001\n for param_group in optimizer.param_groups:\n param_group['lr'] = lr\n print(\"learning rate -> {}\\n\".format(lr))","def adjust_learning_rate(optimizer, epoch, args):\r\n lr = args.lr * (0.1 ** (epoch // 30))\r\n for param_group in optimizer.param_groups:\r\n param_group['lr'] = lr","def adjust_learning_rate(lr, optimizer, epoch, decay_epoch=30):\n lr = lr * (0.1 ** (epoch // decay_epoch))\n for param_group in optimizer.param_groups:\n param_group['lr'] = lr","def adjust_learning_rate(optimizer, epoch, args):\n lr = args.lr * (0.1 ** (epoch // 30))\n for param_group in optimizer.param_groups:\n param_group['lr'] = lr","def adjust_learning_rate(optimizer, epoch, args):\n lr = args.lr * (0.1 ** (epoch // 30))\n for param_group in optimizer.param_groups:\n param_group['lr'] = lr","def adjust_learning_rate(optimizer, epoch, args):\n lr = args.lr * (0.1 ** (epoch // 30))\n for param_group in optimizer.param_groups:\n param_group['lr'] = lr","def adjust_lr(self):\n learning_rate = self.params.base_lr * (1 - float(self.epoch) / self.params.num_epoch) ** self.params.power\n for param_group in self.opt.param_groups:\n param_group['lr'] = learning_rate\n print('Change learning rate into %f' % (learning_rate))\n self.summary_writer.add_scalar('learning_rate', learning_rate, self.epoch)","def adjust_learning_rate(optimizer, epoch, args):\n lr = args.lr * (0.3 ** (epoch // args.lr_decay))\n for param_group in optimizer.param_groups:\n param_group['lr'] = lr","def adjust_learning_rate(self, epoch):\n lr = self.lr * (0.5 ** (epoch // 2))\n for param_group in self.optimizer.param_groups:\n param_group['lr'] = lr","def adjust_learning_rate(optimizer, epoch, args):\n lr = args.lr * (0.5 ** (epoch // 30))\n for param_group in optimizer.param_groups:\n param_group['lr'] = lr","def adjust_learning_rate(lr, optimizer, epoch):\n lr = lr * (0.1 ** (epoch // 10))\n for param_group in optimizer.param_groups:\n param_group['lr'] = lr","def adjust_learning_rate(optimizer, epoch):\n initial_lr = args.lr\n if epoch <= 150:\n lr = initial_lr\n elif epoch <=225:\n lr = initial_lr/10\n else:\n lr = initial_lr/100\n\n for param_group in optimizer.param_groups:\n param_group['lr'] = lr\n print(\"=\"*100)\n print('At epoch:',epoch,\" lr is:\",lr)","def adjust_learning_rate(optimizer, epoch):\r\n lr = 0.001 * (0.1 ** (epoch // 30))\r\n for param_group in optimizer.param_groups:\r\n param_group['lr'] = lr","def adjust_learning_rate(optimizer, epoch):\n lr = ln * (0.1 ** (epoch // 30))\n for param_group in optimizer.param_groups:\n param_group['lr'] = lr\n print(\"learning rate\",lr)","def learning_rate(epoch):\n self.lr = self.lr / 1.00000001\n return self.lr","def adjust_learning_rate(optimizer, epoch):\n lr = opt.lr * (0.5 ** (epoch // opt.step))\n return lr","def adjust_learning_rate(optimizer, epoch):\r\n lr = args.lr * (0.1 ** (epoch // 30))\r\n for param_group in optimizer.param_groups:\r\n param_group['lr'] = lr","def adjust_learning_rate(optimizer, epoch, args):\n \"\"\"Comes from pytorch demo\"\"\"\n lr = args.lr * (0.1 ** (epoch // 30))\n for param_group in optimizer.param_groups:\n param_group['lr'] = lr","def adjust_learning_rate(optimizer, epoch, power):\n lr = args.lr * (0.1 ** (power*(epoch // 30)))\n for param_group in optimizer.param_groups:\n param_group['lr'] = lr","def adjust_learning_rate(optimizer, epoch):\n lr = args.lr * (0.1**(epoch // 30))\n for param_group in optimizer.param_groups:\n param_group['lr'] = lr","def adjust_learning_rate(optimizer, epoch):\n lr = hyper.lr * (0.5 ** (epoch // 10))\n for param_group in optimizer.param_groups:\n param_group['lr'] = lr\n return lr","def adjust_learning_rate(optimizer, epoch):\n lr = args.lr * (0.1 ** (epoch // 30))\n for param_group in optimizer.param_groups:\n param_group['lr'] = lr","def adjust_learning_rate(optimizer, epoch):\n lr = args.lr * (0.1 ** (epoch // 30))\n for param_group in optimizer.param_groups:\n param_group['lr'] = lr","def adjust_learning_rate(optimizer, epoch):\n lr = args.lr * (0.1 ** (epoch // 30))\n for param_group in optimizer.param_groups:\n param_group['lr'] = lr","def adjust_learning_rate(optimizer, epoch):\n lr = args.lr\n if epoch >= 60:\n lr = args.lr * 0.1\n if epoch >= 90:\n lr = args.lr * 0.01\n for param_group in optimizer.param_groups:\n param_group['lr'] = lr","def adjust_learning_rate(optimizer, epoch,threshold,lr_init,lr_decay_rate):\n lr = lr_init * (lr_decay_rate ** (epoch // threshold))\n for param_group in optimizer.param_groups:\n param_group['lr'] = lr\n return lr","def adjust_learning_rate(optimizer, lr_factor, epoch):\n #lr = args.lr * (0.1 ** (epoch // 30))\n print('the learning rate is set to {0:.5f}'.format(lr_factor[epoch]*args.lr))\n for param_group in optimizer.param_groups:\n param_group['lr'] = lr_factor[epoch]*args.lr","def ft_adjust_learning_rate(optimizer, intial_lr, epoch, lr_steps):\n decay = 0.3 ** (sum(epoch >= np.array(lr_steps)))\n lr = intial_lr * decay\n for param_group in optimizer.param_groups:\n param_group['lr'] = lr","def adjust_learning_rate(optimizer, epoch, lr):\n lr = lr * (0.1 ** (epoch // 30))\n for param_group in optimizer.param_groups:\n param_group['lr'] = lr","def update_learning_rate(self) -> None:\n self.epsilon = self.initial_epsilon / (1. + self.rate_decay * self.n_it)\n return","def update_learning_rate(self) -> None:\n self.epsilon = self.initial_epsilon / (1. + self.rate_decay * self.n_it)\n return","def adjust_learning_rate(optimizer, args, epoch):\n\tlr = args.learning_rate * (0.1 ** (epoch // args.lr_decay_step))\n\tfor param_group in optimizer.param_groups:\n\t\tparam_group['lr'] = lr","def adjust_learning_rate_D(start_lr, optimizer, epoch):\n #lr = start_lr * (0.1 ** (epoch // 30))\n lr = start_lr * (0.3 ** (epoch // 5))\n for param_group in optimizer.param_groups:\n param_group['lr'] = lr","def adjust_learning_rate(self, optimizer, epoch):\n lr = self.lr\n if epoch >= 80:\n lr = self.lr * (0.1 ** ((epoch-80) // 40))\n\n for param_group in optimizer.param_groups:\n param_group['lr'] = lr","def adjust_learning_rate(args, optimizer, epoch):\n if (epoch*3==args.epochs) or (epoch*3==2*args.epochs):\n lr = args.lr * (0.1 ** (epoch*3//args.epochs))\n print(\"Changing Learning Rate to {}\".format(lr))\n for param_group in optimizer.param_groups:\n param_group['lr'] = lr","def assign_learning_rate(session, lr_update, lr_placeholder, new_lr):\n session.run(lr_update, feed_dict={lr_placeholder: new_lr})","def adjust_learning_rate(optimizer, epoch):\n lr = 0.5 * (0.1 ** (epoch // 100))\n for param_group in optimizer.param_groups:\n param_group['lr'] = lr","def adjust_learning_rate(optimizer, epoch, lr):\n lr = lr * ((1 - 0.015) ** epoch)\n print('learning rate : {}'.format(lr))\n for param_group in optimizer.param_groups:\n param_group['lr'] = lr","def adjust_learning_rate(jnet, optimizer, epoch):\n lr = args.lr * (0.5 ** (epoch // 10))\n for param_group in optimizer.param_groups:\n param_group['lr'] = lr","def adjust_learning_rate(optimizer, epoch, decay, lrate):\n lr = lrate * (0.1 ** (epoch // decay))\n for param_group in optimizer.param_groups:\n param_group['lr'] = lr","def adjust_learning_rate(lr, lr_decay_steps, optimizer, epoch, lr_decay_rate=0.1):\n steps = list(map(int, lr_decay_steps.split(',')))\n for milestone in steps:\n lr *= lr_decay_rate if epoch >= milestone else 1.\n for param_group in optimizer.param_groups:\n param_group['lr'] = lr","def adjust_learning_rate(optimizer, epoch):\n lr = args.lr * (0.1 ** (epoch // 15))\n for param_group in optimizer.param_groups:\n param_group['lr'] = lr","def adjust_learning_rate(optimizer, epoch):\r\n lr = args.lr\r\n if epoch >= 0.5 * args.epoch:\r\n lr /= 10\r\n if epoch >= 0.75 * args.epoch:\r\n lr /= 10\r\n for param_group in optimizer.param_groups:\r\n param_group['lr'] = lr","def adjust_learning_rate(optimizer, epoch):\n lr = args.lr * (0.1 ** (epoch // 100))\n for param_group in optimizer.param_groups:\n param_group['lr'] = lr","def adjust_learning_rate(opts, optimizer, epoch):\n lr = opts.lr * (0.1 ** (epoch // 30))\n for param_group in optimizer.param_groups:\n param_group['lr'] = lr","def adjust_learning_rate(optimizer, epoch):\n lr = args.lr\n if epoch >= 75:\n lr = args.lr * 0.1\n if epoch >= 90:\n lr = args.lr * 0.01\n if epoch >= 100:\n lr = args.lr * 0.001\n for param_group in optimizer.param_groups:\n param_group['lr'] = lr","def adjust_learning_rate(opt, epoch):\n d, e = args.lr_decay\n lr = args.lr * (d ** -(epoch // e))\n for param_group in opt.param_groups:\n param_group['lr'] = lr","def adjust_learning_rate(optimizer, epoch):\n lr = LEARNING_RATE * (0.1 ** (epoch // 10))\n for param_group in optimizer.param_groups:\n param_group['lr'] = lr","def adjust_learning_rate(learning_rate,optimizer, epoch):\n lr = learning_rate * (0.1 ** (epoch // 25))\n print(str(lr))\n for param_group in optimizer.param_groups:\n param_group['lr'] = lr","def adjust_learning_rate(optimizer, epoch):\n \n boundary = [args.epochs//2,args.epochs//4*3,args.epochs]\n lr = args.lr * 0.1 ** int(bisect.bisect_left(boundary, epoch))\n print('Learning rate: %f'%lr)\n #print(epoch, lr, bisect.bisect_left(boundary, epoch))\n # lr = args.lr * (0.1 ** (epoch // 30))\n for param_group in optimizer.param_groups:\n param_group['lr'] = lr\n\n return lr","def adjust_learning_rate(optimizer, epoch, args, step):\n lr = args.lr * (0.1 ** (epoch // step))\n for param_group in optimizer.param_groups:\n param_group['lr'] = lr","def adjust_learning_rate(optimizer, epoch):\n \n lr = learning_rate * (0.1 ** (epoch // 15))\n if lr >= 0.0001:\n lr = 0.0001\n for param_group in optimizer.param_groups:\n param_group['lr'] = lr","def adjust_learning_rate(optimizer, epoch, args):\n lr = args.lr\n if args.cos: # cosine lr schedule\n lr *= 0.5 * (1. + math.cos(math.pi * epoch / args.epochs))\n else: # stepwise lr schedule\n for milestone in args.schedule:\n lr *= 0.1 if epoch >= milestone else 1.\n for param_group in optimizer.param_groups:\n param_group['lr'] = lr","def adjust_learning_rate_schedule(optimizer, epoch, initial_lr, decay_factor, decay_epochs):\n\n # Find the index of the current interval:\n interval_index = len([mark for mark in decay_epochs if mark < epoch])\n\n lr = initial_lr * (decay_factor ** interval_index)\n for param_group in optimizer.param_groups:\n param_group['lr'] = lr","def adjust_learning_rate(epoch, learning_rate, lr_decay_epochs, optimizer):\r\n steps = np.sum(epoch > np.asarray(lr_decay_epochs))\r\n if steps > 0:\r\n new_lr = learning_rate * (lr_decay_rate ** steps)\r\n for param_group in optimizer.param_groups:\r\n param_group['lr'] = new_lr","def adjust_learning_rate(optimizer, epoch, lr):\n lr = lr * (0.5 ** (epoch // 20))\n for param_group in optimizer.param_groups:\n param_group['lr'] = lr\n return lr","def adjust_learning_rate(optimizer, epoch, learning_rate):\n\n if epoch >= 60 and epoch < 75:\n lr = learning_rate / 2\n elif epoch >= 75:\n lr = learning_rate / 4\n else:\n lr = learning_rate\n for param_group in optimizer.param_groups:\n param_group['lr'] = lr","def adjust_learning_rate(learning_rate, optimizer, epoch):\n lr = learning_rate * (0.1 ** (epoch // 30))\n for param_group in optimizer.param_groups:\n param_group['lr'] = lr","def adjust_learning_rate(self):\n out_base_lr = self.args.base_lr\n for param_group in self.optimizer.param_groups:\n in_lr = param_group[\"initial_lr\"]\n out_lr = in_lr\n if self.args.lr_decay_type == \"cos\": # cosine lr schedule\n out_lr *= 0.5 * (1.0 + np.cos(np.pi * self.epoch / self.args.epochs))\n else: # stepwise lr schedule\n for milestone in self.args.lr_step_schedule:\n out_lr *= 0.1 if self.epoch >= milestone else 1.0\n param_group[\"lr\"] = out_lr\n if in_lr == self.args.base_lr:\n out_base_lr = out_lr\n if self.train_logger is not None:\n self.train_logger.scalar_summary(\n \"metrics/%s/epoch\" % self.full_name, self.epoch, step=self.iteration, increment_counter=False\n )\n self.train_logger.scalar_summary(\n \"metrics/%s/lr\" % self.full_name, out_base_lr, step=self.iteration, increment_counter=False\n )\n print(\"Epoch\", self.epoch, \"Learning rate\", out_base_lr)\n return out_base_lr","def update_lr(optimizer, lr, epoch, max_epochs, exponent=0.9):\n optimizer.param_groups[0]['lr'] = lr * (1 - epoch / max_epochs)**exponent","def adjust_learning_rate(optimizer, epoch):\n lr = args.lr * ((1 - 0.015) ** epoch)\n for param_group in optimizer.param_groups:\n param_group['lr'] = lr","def adjust_learning_rate_adam(optimizer, epoch):\n \n boundary = [args.epochs//5*4]\n lr = args.lr * 0.2 ** int(bisect.bisect_left(boundary, epoch))\n print('Learning rate: %f'%lr)\n #print(epoch, lr)\n for param_group in optimizer.param_groups:\n param_group['lr'] = lr\n \n return lr","def adjust_learning_rate(optimizer, epoch):\n \n lr = opt.lr * (0.1 ** (epoch // 3)) # Original\n for param_group in optimizer.param_groups:\n param_group['lr'] = lr","def adjust_learning_rate(optimizer, epoch):\n lr = args.lr * (0.1 ** (epoch // args.lr_drop))\n print('lr= '+str(lr), flush=True)\n for param_group in optimizer.param_groups:\n param_group['lr'] = lr","def update_learning_rate(self):\r\n self.scheduler.step(self.clock.epoch)","def adjust_learning_rate(optimizer, epoch, n):\n lr = args.lr * (0.1 ** (epoch // n))\n for param_group in optimizer.param_groups:\n param_group['lr'] = lr","def adjust_learning_rate(optimizer, epoch, lr, schedule, gamma):\r\n if epoch in schedule:\r\n lr *= gamma\r\n for param_group in optimizer.param_groups:\r\n param_group['lr'] = lr\r\n return lr","def _update_learning_rate(self):\r\n\r\n self.n_steps += 1\r\n lr = self.factor * self._get_lr_scale()\r\n for param_group in self._optimizer.param_groups:\r\n param_group['lr'] = lr","def adjust_learning_rate(optimizer: torch.optim.SGD, epoch: int, args: Namespace):\n lr = args.lr * (0.1 ** (epoch // 90)) * (0.1 ** (epoch // 180)) * (0.1 ** (epoch // 270))\n # log to TensorBoard\n if args.tensorboard:\n log_value('learning_rate', lr, epoch)\n for param_group in optimizer.param_groups:\n param_group['lr'] = lr","def adjust_learning_rate(optimizer, lr, step, args):\n # decay = 0.1**(sum(epoch >= np.array(lr_steps)))\n lr = lr * (0.95**(step//args.lr_decay_every))\n print(\"current learning rate: {:.6f}\".format(lr))\n param_group = optimizer.param_groups\n for i in range(len(param_group)):\n param_group[i]['lr'] = lr\n\n return optimizer","def adjust_learning_rate(optimizer, epoch, lr=0.01):\n new_lr = lr * (0.1 ** (epoch // 5))\n for param_group in optimizer.param_groups:\n param_group[\"lr\"] = new_lr","def adjust_learning_rate(optimizer, epoch, lr, schedule, gamma):\n if epoch in schedule:\n lr *= gamma\n for param_group in optimizer.param_groups:\n param_group['lr'] = lr\n return lr","def adjust_learning_rate(optimizer, epoch, lr, schedule, gamma):\n if epoch in schedule:\n lr *= gamma\n for param_group in optimizer.param_groups:\n param_group['lr'] = lr\n return lr","def adjust_learning_rate(optimizer, epoch):\n lr = args.lr * (0.4 ** (epoch // 4))\n for param_group in optimizer.param_groups:\n param_group['lr'] = lr","def adjust_learning_rate(epoch, learn_rate, decay_step, decay_rate, optimizer):\n steps = np.sum(epoch > np.asarray(decay_step))\n if steps > 0:\n new_lr = learn_rate * (decay_rate ** steps)\n for param_group in optimizer.param_groups:\n param_group['lr'] = new_lr","def adjust_learning_rate(optimizer, epoch):\n lr = args.lr * (args.expo ** epoch)\n for param_group in optimizer.param_groups:\n param_group['lr'] = lr","def adjust_learning_rate(args,optimizer, epoch):\n \n args.epochs\n\n lr = args.lr * (\n (0.2 ** int(epoch >= args.epochs - 140))\n * (0.2 ** int(epoch >= args.epochs - 80))\n * (0.2 ** int(epoch >= args.epochs - 40))\n )\n\n ##lr = args.lr ##DELETE ME!\n\n if args.tensorboard:\n log_value(\"learning_rate\", lr, epoch)\n\n for param_group in optimizer.param_groups:\n param_group[\"lr\"] = lr","def adjust_learning_rate(self, opt, epoch):\n lr = opt.learning_rate * 0.1 ** (epoch // opt.lr_update)\n for param_group in self.optimizer.param_groups:\n param_group['lr'] = lr","def adjust_learning_rate(optimizer, cur_epoch, base_lr=0.1, lr_schedule=[4, 8, 12, 14, 16]):\n lr = 0\n for i, e in enumerate(lr_schedule):\n if cur_epoch < e:\n lr = base_lr * (0.1 ** i)\n break\n if lr == 0:\n lr = base_lr\n for param_group in optimizer.param_groups:\n param_group['lr'] = lr","def _update_initial_learning_rate(configs, learning_rate):\n\n optimizer_type = get_optimizer_type(configs[\"train_config\"])\n if optimizer_type == \"rms_prop_optimizer\":\n optimizer_config = configs[\"train_config\"].optimizer.rms_prop_optimizer\n elif optimizer_type == \"momentum_optimizer\":\n optimizer_config = configs[\"train_config\"].optimizer.momentum_optimizer\n elif optimizer_type == \"adam_optimizer\":\n optimizer_config = configs[\"train_config\"].optimizer.adam_optimizer\n else:\n raise TypeError(\"Optimizer %s is not supported.\" % optimizer_type)\n\n learning_rate_type = get_learning_rate_type(optimizer_config)\n if learning_rate_type == \"constant_learning_rate\":\n constant_lr = optimizer_config.learning_rate.constant_learning_rate\n constant_lr.learning_rate = learning_rate\n elif learning_rate_type == \"exponential_decay_learning_rate\":\n exponential_lr = (\n optimizer_config.learning_rate.exponential_decay_learning_rate)\n exponential_lr.initial_learning_rate = learning_rate\n elif learning_rate_type == \"manual_step_learning_rate\":\n manual_lr = optimizer_config.learning_rate.manual_step_learning_rate\n original_learning_rate = manual_lr.initial_learning_rate\n learning_rate_scaling = float(learning_rate) / original_learning_rate\n manual_lr.initial_learning_rate = learning_rate\n for schedule in manual_lr.schedule:\n schedule.learning_rate *= learning_rate_scaling\n elif learning_rate_type == \"cosine_decay_learning_rate\":\n cosine_lr = optimizer_config.learning_rate.cosine_decay_learning_rate\n learning_rate_base = cosine_lr.learning_rate_base\n warmup_learning_rate = cosine_lr.warmup_learning_rate\n warmup_scale_factor = warmup_learning_rate / learning_rate_base\n cosine_lr.learning_rate_base = learning_rate\n cosine_lr.warmup_learning_rate = warmup_scale_factor * learning_rate\n else:\n raise TypeError(\"Learning rate %s is not supported.\" % learning_rate_type)","def adjust_learning_rate(optimizer, epoch):\n lr = args.lr * (0.5 ** (epoch // args.low_lr))\n for param_group in optimizer.param_groups:\n param_group['lr'] = lr","def adjust_learning_rate(optimizer, epoch, config):\n if config.cos:\n lr_min = 0\n lr_max = config.lr\n lr = lr_min + 0.5 * (lr_max - lr_min) * (1 + math.cos(epoch / config.num_epochs * 3.1415926535))\n else:\n epoch = epoch + 1\n if epoch <= 5:\n lr = config.lr * epoch / 5\n elif epoch > 180:\n lr = config.lr * 0.01\n elif epoch > 160:\n lr = config.lr * 0.1\n else:\n lr = config.lr\n\n for param_group in optimizer.param_groups:\n param_group['lr'] = lr","def lr_decay(config, optimizer: optim.Optimizer, epoch: int) -> optim.Optimizer:\n lr = config.learning_rate / (1 + config.lr_decay * (epoch - 1))\n for param_group in optimizer.param_groups:\n param_group['lr'] = lr\n print('learning rate is set to: ', lr)\n return optimizer","def adjust_learning_rate(optimizer, epoch):\n lr = args.lr * (0.95 ** epoch)\n for param_group in optimizer.param_groups:\n param_group['lr'] = lr","def adjust_learning_rate(epoch, opt, optimizer):\n steps = np.sum(epoch >= np.asarray(opt.lr_decay_epochs))\n if steps > 0:\n new_lr = opt.learning_rate * (opt.lr_decay_rate ** steps)\n optimizer.set_learning_rate(new_lr)","def adjust_learning_rate(optimizer, epoch, base_lr):\n lr = max(base_lr * (0.5 ** (epoch // 20)), 1e-5)\n for param_group in optimizer.param_groups:\n param_group['lr'] = lr\n #if (epoch + 1) == 51 or (epoch + 1) == 101 or (epoch + 1) == 151:\n # #lr = lr * 0.1\n # for param_group in optimizer.param_groups:\n # param_group['lr'] *= 0.1","def step_decay(epoch):\n # initial_lrate = 1.0 # no longer needed\n #drop = 0.5\n #epochs_drop = number_epoch_drop\n #lrate = init_lr * math.pow(drop,\n #math.floor((1+epoch)/epochs_drop))\n lr = learning_rate\n if epoch >= 60:\n lr *= 1e-1\n print('Learning rate: ', lr)\n return lr","def adjust_learning_rate(cfg, optimizer):\n for idx, group in enumerate(optimizer.param_groups):\n init_lr = cfg.TRAINING.LR\n if 'step' not in group:\n group['step'] = 0.\n else:\n group['step'] += 1.\n\n group['lr'] = init_lr * (\n 1.0 - float(group['step']) * float(cfg.TRAINING.BATCH_SIZE) /\n (cfg.TRAINING.N_TRIPLETS * float(cfg.TRAINING.EPOCHS)))\n return","def adjust_learning_rate(self, optimizer, epoch, initial_lr, writer=None):\n lr = initial_lr * (0.98 ** epoch)\n for param_group in optimizer.param_groups:\n param_group['lr'] = lr\n if writer:\n writer.add_scalar(\"lr_G\", lr, epoch + 1)","def adjust_learning_rate_SR(optimizer, epoch):\r\n lr = opt.lr * (0.1 ** (epoch // opt.step))\r\n return lr","def adjust_learning_rate(self,itr, meta_lr, lr_decay_itr):\n if lr_decay_itr > 0:\n if int(itr % lr_decay_itr) == 0 and itr > 0:\n meta_lr = meta_lr / (10 ** int(itr / lr_decay_itr))\n self.fast_net.step_size = self.fast_net.step_size / 10\n for param_group in self.opt.param_groups:\n param_group['lr'] = meta_lr","def adjust_learning_rate(optimizer, epoch, num_epochs, lr_decay=0.5):\n step = num_epochs // 1\n\n if not epoch % step and epoch > 0:\n for param_group in optimizer.param_groups:\n param_group['lr'] *= lr_decay\n print('Learning rate sets to {}.'.format(param_group['lr']))\n else:\n for param_group in optimizer.param_groups:\n print('Learning rate sets to {}.'.format(param_group['lr']))"],"string":"[\n \"def adjust_learning_rate(init_lr, optimizer, epoch, n=100):\\n init_lr = init_lr * (0.1 ** (epoch // n))\\n print('learning rate : ', init_lr)\\n for param_group in optimizer.param_groups:\\n param_group['lr'] = init_lr\",\n \"def adjust_learning_rate(start_lr, optimizer, epoch, total_epoch_num):\\n #lr = start_lr * (0.1 ** (epoch // 30))\\n lr = start_lr * (0.3 ** (epoch // 5))\\n if epoch==total_epoch_num:\\n lr = lr * 0.3\\n\\n for param_group in optimizer.param_groups:\\n param_group['lr'] = lr\",\n \"def adjust_learning_rate(lr, optimizer, epoch):\\n lr = lr_init * (0.1 ** (epoch // 10))\\n for param_group in optimizer.param_groups:\\n param_group['lr'] = lr\\n return lr\",\n \"def adjust_learning_rate(lr, optimizer, lr_decay, epoch):\\n\\n if epoch >= lr_decay[0]:\\n lr = lr * 0.1\\n if epoch >= lr_decay[1]:\\n lr = lr * 0.01\\n for param_group in optimizer.param_groups:\\n param_group['lr'] = lr\\n\\n return lr\",\n \"def initialize_learning_rate(self):\\n\\n if (self.FLAGS.learning_rate_decay is \\\"exponential\\\"):\\n self.learning_rate = tf.train.exponential_decay(\\n self.FLAGS.learning_rate,\\n self.global_step,\\n self.FLAGS.decay_steps,\\n self.FLAGS.decay_rate)\\n else :\\n self.learning_rate = self.FLAGS.learning_rate\",\n \"def adjust_learning_rate(lr, decay, optimizer, cur_epoch, every_n_epochs):\\n new_lr = lr * (decay ** (cur_epoch // every_n_epochs))\\n\\n # if cur_epoch % every_n_epochs == 0:\\n # new_lr = lr * 0.1\\n\\n for param_group in optimizer.param_groups:\\n param_group['lr'] = new_lr\",\n \"def adjust_learning_rate(optimizer, epoch, args):\\r\\n lr = args.lr * (0.1 ** (epoch // 30))\\r\\n for param_group in optimizer.param_groups:\\r\\n param_group['lr'] = lr\",\n \"def adjust_learning_rate(self, optimizer, epoch, args):\\n lr = args.learning_rate * (0.1 ** (epoch // 30))\\n # print(lr)\\n for param_group in optimizer.param_groups:\\n param_group['lr'] = lr\",\n \"def adjust_learning_rate(optimizer, epoch, args):\\n lr = args.lr\\n if 20 < epoch <= 30:\\n lr = 0.0001\\n elif 30 < epoch :\\n lr = 0.00001\\n for param_group in optimizer.param_groups:\\n param_group['lr'] = lr\\n print(\\\"learning rate -> {}\\\\n\\\".format(lr))\",\n \"def adjust_learning_rate(optimizer, epoch, args):\\r\\n lr = args.lr * (0.1 ** (epoch // 30))\\r\\n for param_group in optimizer.param_groups:\\r\\n param_group['lr'] = lr\",\n \"def adjust_learning_rate(lr, optimizer, epoch, decay_epoch=30):\\n lr = lr * (0.1 ** (epoch // decay_epoch))\\n for param_group in optimizer.param_groups:\\n param_group['lr'] = lr\",\n \"def adjust_learning_rate(optimizer, epoch, args):\\n lr = args.lr * (0.1 ** (epoch // 30))\\n for param_group in optimizer.param_groups:\\n param_group['lr'] = lr\",\n \"def adjust_learning_rate(optimizer, epoch, args):\\n lr = args.lr * (0.1 ** (epoch // 30))\\n for param_group in optimizer.param_groups:\\n param_group['lr'] = lr\",\n \"def adjust_learning_rate(optimizer, epoch, args):\\n lr = args.lr * (0.1 ** (epoch // 30))\\n for param_group in optimizer.param_groups:\\n param_group['lr'] = lr\",\n \"def adjust_lr(self):\\n learning_rate = self.params.base_lr * (1 - float(self.epoch) / self.params.num_epoch) ** self.params.power\\n for param_group in self.opt.param_groups:\\n param_group['lr'] = learning_rate\\n print('Change learning rate into %f' % (learning_rate))\\n self.summary_writer.add_scalar('learning_rate', learning_rate, self.epoch)\",\n \"def adjust_learning_rate(optimizer, epoch, args):\\n lr = args.lr * (0.3 ** (epoch // args.lr_decay))\\n for param_group in optimizer.param_groups:\\n param_group['lr'] = lr\",\n \"def adjust_learning_rate(self, epoch):\\n lr = self.lr * (0.5 ** (epoch // 2))\\n for param_group in self.optimizer.param_groups:\\n param_group['lr'] = lr\",\n \"def adjust_learning_rate(optimizer, epoch, args):\\n lr = args.lr * (0.5 ** (epoch // 30))\\n for param_group in optimizer.param_groups:\\n param_group['lr'] = lr\",\n \"def adjust_learning_rate(lr, optimizer, epoch):\\n lr = lr * (0.1 ** (epoch // 10))\\n for param_group in optimizer.param_groups:\\n param_group['lr'] = lr\",\n \"def adjust_learning_rate(optimizer, epoch):\\n initial_lr = args.lr\\n if epoch <= 150:\\n lr = initial_lr\\n elif epoch <=225:\\n lr = initial_lr/10\\n else:\\n lr = initial_lr/100\\n\\n for param_group in optimizer.param_groups:\\n param_group['lr'] = lr\\n print(\\\"=\\\"*100)\\n print('At epoch:',epoch,\\\" lr is:\\\",lr)\",\n \"def adjust_learning_rate(optimizer, epoch):\\r\\n lr = 0.001 * (0.1 ** (epoch // 30))\\r\\n for param_group in optimizer.param_groups:\\r\\n param_group['lr'] = lr\",\n \"def adjust_learning_rate(optimizer, epoch):\\n lr = ln * (0.1 ** (epoch // 30))\\n for param_group in optimizer.param_groups:\\n param_group['lr'] = lr\\n print(\\\"learning rate\\\",lr)\",\n \"def learning_rate(epoch):\\n self.lr = self.lr / 1.00000001\\n return self.lr\",\n \"def adjust_learning_rate(optimizer, epoch):\\n lr = opt.lr * (0.5 ** (epoch // opt.step))\\n return lr\",\n \"def adjust_learning_rate(optimizer, epoch):\\r\\n lr = args.lr * (0.1 ** (epoch // 30))\\r\\n for param_group in optimizer.param_groups:\\r\\n param_group['lr'] = lr\",\n \"def adjust_learning_rate(optimizer, epoch, args):\\n \\\"\\\"\\\"Comes from pytorch demo\\\"\\\"\\\"\\n lr = args.lr * (0.1 ** (epoch // 30))\\n for param_group in optimizer.param_groups:\\n param_group['lr'] = lr\",\n \"def adjust_learning_rate(optimizer, epoch, power):\\n lr = args.lr * (0.1 ** (power*(epoch // 30)))\\n for param_group in optimizer.param_groups:\\n param_group['lr'] = lr\",\n \"def adjust_learning_rate(optimizer, epoch):\\n lr = args.lr * (0.1**(epoch // 30))\\n for param_group in optimizer.param_groups:\\n param_group['lr'] = lr\",\n \"def adjust_learning_rate(optimizer, epoch):\\n lr = hyper.lr * (0.5 ** (epoch // 10))\\n for param_group in optimizer.param_groups:\\n param_group['lr'] = lr\\n return lr\",\n \"def adjust_learning_rate(optimizer, epoch):\\n lr = args.lr * (0.1 ** (epoch // 30))\\n for param_group in optimizer.param_groups:\\n param_group['lr'] = lr\",\n \"def adjust_learning_rate(optimizer, epoch):\\n lr = args.lr * (0.1 ** (epoch // 30))\\n for param_group in optimizer.param_groups:\\n param_group['lr'] = lr\",\n \"def adjust_learning_rate(optimizer, epoch):\\n lr = args.lr * (0.1 ** (epoch // 30))\\n for param_group in optimizer.param_groups:\\n param_group['lr'] = lr\",\n \"def adjust_learning_rate(optimizer, epoch):\\n lr = args.lr\\n if epoch >= 60:\\n lr = args.lr * 0.1\\n if epoch >= 90:\\n lr = args.lr * 0.01\\n for param_group in optimizer.param_groups:\\n param_group['lr'] = lr\",\n \"def adjust_learning_rate(optimizer, epoch,threshold,lr_init,lr_decay_rate):\\n lr = lr_init * (lr_decay_rate ** (epoch // threshold))\\n for param_group in optimizer.param_groups:\\n param_group['lr'] = lr\\n return lr\",\n \"def adjust_learning_rate(optimizer, lr_factor, epoch):\\n #lr = args.lr * (0.1 ** (epoch // 30))\\n print('the learning rate is set to {0:.5f}'.format(lr_factor[epoch]*args.lr))\\n for param_group in optimizer.param_groups:\\n param_group['lr'] = lr_factor[epoch]*args.lr\",\n \"def ft_adjust_learning_rate(optimizer, intial_lr, epoch, lr_steps):\\n decay = 0.3 ** (sum(epoch >= np.array(lr_steps)))\\n lr = intial_lr * decay\\n for param_group in optimizer.param_groups:\\n param_group['lr'] = lr\",\n \"def adjust_learning_rate(optimizer, epoch, lr):\\n lr = lr * (0.1 ** (epoch // 30))\\n for param_group in optimizer.param_groups:\\n param_group['lr'] = lr\",\n \"def update_learning_rate(self) -> None:\\n self.epsilon = self.initial_epsilon / (1. + self.rate_decay * self.n_it)\\n return\",\n \"def update_learning_rate(self) -> None:\\n self.epsilon = self.initial_epsilon / (1. + self.rate_decay * self.n_it)\\n return\",\n \"def adjust_learning_rate(optimizer, args, epoch):\\n\\tlr = args.learning_rate * (0.1 ** (epoch // args.lr_decay_step))\\n\\tfor param_group in optimizer.param_groups:\\n\\t\\tparam_group['lr'] = lr\",\n \"def adjust_learning_rate_D(start_lr, optimizer, epoch):\\n #lr = start_lr * (0.1 ** (epoch // 30))\\n lr = start_lr * (0.3 ** (epoch // 5))\\n for param_group in optimizer.param_groups:\\n param_group['lr'] = lr\",\n \"def adjust_learning_rate(self, optimizer, epoch):\\n lr = self.lr\\n if epoch >= 80:\\n lr = self.lr * (0.1 ** ((epoch-80) // 40))\\n\\n for param_group in optimizer.param_groups:\\n param_group['lr'] = lr\",\n \"def adjust_learning_rate(args, optimizer, epoch):\\n if (epoch*3==args.epochs) or (epoch*3==2*args.epochs):\\n lr = args.lr * (0.1 ** (epoch*3//args.epochs))\\n print(\\\"Changing Learning Rate to {}\\\".format(lr))\\n for param_group in optimizer.param_groups:\\n param_group['lr'] = lr\",\n \"def assign_learning_rate(session, lr_update, lr_placeholder, new_lr):\\n session.run(lr_update, feed_dict={lr_placeholder: new_lr})\",\n \"def adjust_learning_rate(optimizer, epoch):\\n lr = 0.5 * (0.1 ** (epoch // 100))\\n for param_group in optimizer.param_groups:\\n param_group['lr'] = lr\",\n \"def adjust_learning_rate(optimizer, epoch, lr):\\n lr = lr * ((1 - 0.015) ** epoch)\\n print('learning rate : {}'.format(lr))\\n for param_group in optimizer.param_groups:\\n param_group['lr'] = lr\",\n \"def adjust_learning_rate(jnet, optimizer, epoch):\\n lr = args.lr * (0.5 ** (epoch // 10))\\n for param_group in optimizer.param_groups:\\n param_group['lr'] = lr\",\n \"def adjust_learning_rate(optimizer, epoch, decay, lrate):\\n lr = lrate * (0.1 ** (epoch // decay))\\n for param_group in optimizer.param_groups:\\n param_group['lr'] = lr\",\n \"def adjust_learning_rate(lr, lr_decay_steps, optimizer, epoch, lr_decay_rate=0.1):\\n steps = list(map(int, lr_decay_steps.split(',')))\\n for milestone in steps:\\n lr *= lr_decay_rate if epoch >= milestone else 1.\\n for param_group in optimizer.param_groups:\\n param_group['lr'] = lr\",\n \"def adjust_learning_rate(optimizer, epoch):\\n lr = args.lr * (0.1 ** (epoch // 15))\\n for param_group in optimizer.param_groups:\\n param_group['lr'] = lr\",\n \"def adjust_learning_rate(optimizer, epoch):\\r\\n lr = args.lr\\r\\n if epoch >= 0.5 * args.epoch:\\r\\n lr /= 10\\r\\n if epoch >= 0.75 * args.epoch:\\r\\n lr /= 10\\r\\n for param_group in optimizer.param_groups:\\r\\n param_group['lr'] = lr\",\n \"def adjust_learning_rate(optimizer, epoch):\\n lr = args.lr * (0.1 ** (epoch // 100))\\n for param_group in optimizer.param_groups:\\n param_group['lr'] = lr\",\n \"def adjust_learning_rate(opts, optimizer, epoch):\\n lr = opts.lr * (0.1 ** (epoch // 30))\\n for param_group in optimizer.param_groups:\\n param_group['lr'] = lr\",\n \"def adjust_learning_rate(optimizer, epoch):\\n lr = args.lr\\n if epoch >= 75:\\n lr = args.lr * 0.1\\n if epoch >= 90:\\n lr = args.lr * 0.01\\n if epoch >= 100:\\n lr = args.lr * 0.001\\n for param_group in optimizer.param_groups:\\n param_group['lr'] = lr\",\n \"def adjust_learning_rate(opt, epoch):\\n d, e = args.lr_decay\\n lr = args.lr * (d ** -(epoch // e))\\n for param_group in opt.param_groups:\\n param_group['lr'] = lr\",\n \"def adjust_learning_rate(optimizer, epoch):\\n lr = LEARNING_RATE * (0.1 ** (epoch // 10))\\n for param_group in optimizer.param_groups:\\n param_group['lr'] = lr\",\n \"def adjust_learning_rate(learning_rate,optimizer, epoch):\\n lr = learning_rate * (0.1 ** (epoch // 25))\\n print(str(lr))\\n for param_group in optimizer.param_groups:\\n param_group['lr'] = lr\",\n \"def adjust_learning_rate(optimizer, epoch):\\n \\n boundary = [args.epochs//2,args.epochs//4*3,args.epochs]\\n lr = args.lr * 0.1 ** int(bisect.bisect_left(boundary, epoch))\\n print('Learning rate: %f'%lr)\\n #print(epoch, lr, bisect.bisect_left(boundary, epoch))\\n # lr = args.lr * (0.1 ** (epoch // 30))\\n for param_group in optimizer.param_groups:\\n param_group['lr'] = lr\\n\\n return lr\",\n \"def adjust_learning_rate(optimizer, epoch, args, step):\\n lr = args.lr * (0.1 ** (epoch // step))\\n for param_group in optimizer.param_groups:\\n param_group['lr'] = lr\",\n \"def adjust_learning_rate(optimizer, epoch):\\n \\n lr = learning_rate * (0.1 ** (epoch // 15))\\n if lr >= 0.0001:\\n lr = 0.0001\\n for param_group in optimizer.param_groups:\\n param_group['lr'] = lr\",\n \"def adjust_learning_rate(optimizer, epoch, args):\\n lr = args.lr\\n if args.cos: # cosine lr schedule\\n lr *= 0.5 * (1. + math.cos(math.pi * epoch / args.epochs))\\n else: # stepwise lr schedule\\n for milestone in args.schedule:\\n lr *= 0.1 if epoch >= milestone else 1.\\n for param_group in optimizer.param_groups:\\n param_group['lr'] = lr\",\n \"def adjust_learning_rate_schedule(optimizer, epoch, initial_lr, decay_factor, decay_epochs):\\n\\n # Find the index of the current interval:\\n interval_index = len([mark for mark in decay_epochs if mark < epoch])\\n\\n lr = initial_lr * (decay_factor ** interval_index)\\n for param_group in optimizer.param_groups:\\n param_group['lr'] = lr\",\n \"def adjust_learning_rate(epoch, learning_rate, lr_decay_epochs, optimizer):\\r\\n steps = np.sum(epoch > np.asarray(lr_decay_epochs))\\r\\n if steps > 0:\\r\\n new_lr = learning_rate * (lr_decay_rate ** steps)\\r\\n for param_group in optimizer.param_groups:\\r\\n param_group['lr'] = new_lr\",\n \"def adjust_learning_rate(optimizer, epoch, lr):\\n lr = lr * (0.5 ** (epoch // 20))\\n for param_group in optimizer.param_groups:\\n param_group['lr'] = lr\\n return lr\",\n \"def adjust_learning_rate(optimizer, epoch, learning_rate):\\n\\n if epoch >= 60 and epoch < 75:\\n lr = learning_rate / 2\\n elif epoch >= 75:\\n lr = learning_rate / 4\\n else:\\n lr = learning_rate\\n for param_group in optimizer.param_groups:\\n param_group['lr'] = lr\",\n \"def adjust_learning_rate(learning_rate, optimizer, epoch):\\n lr = learning_rate * (0.1 ** (epoch // 30))\\n for param_group in optimizer.param_groups:\\n param_group['lr'] = lr\",\n \"def adjust_learning_rate(self):\\n out_base_lr = self.args.base_lr\\n for param_group in self.optimizer.param_groups:\\n in_lr = param_group[\\\"initial_lr\\\"]\\n out_lr = in_lr\\n if self.args.lr_decay_type == \\\"cos\\\": # cosine lr schedule\\n out_lr *= 0.5 * (1.0 + np.cos(np.pi * self.epoch / self.args.epochs))\\n else: # stepwise lr schedule\\n for milestone in self.args.lr_step_schedule:\\n out_lr *= 0.1 if self.epoch >= milestone else 1.0\\n param_group[\\\"lr\\\"] = out_lr\\n if in_lr == self.args.base_lr:\\n out_base_lr = out_lr\\n if self.train_logger is not None:\\n self.train_logger.scalar_summary(\\n \\\"metrics/%s/epoch\\\" % self.full_name, self.epoch, step=self.iteration, increment_counter=False\\n )\\n self.train_logger.scalar_summary(\\n \\\"metrics/%s/lr\\\" % self.full_name, out_base_lr, step=self.iteration, increment_counter=False\\n )\\n print(\\\"Epoch\\\", self.epoch, \\\"Learning rate\\\", out_base_lr)\\n return out_base_lr\",\n \"def update_lr(optimizer, lr, epoch, max_epochs, exponent=0.9):\\n optimizer.param_groups[0]['lr'] = lr * (1 - epoch / max_epochs)**exponent\",\n \"def adjust_learning_rate(optimizer, epoch):\\n lr = args.lr * ((1 - 0.015) ** epoch)\\n for param_group in optimizer.param_groups:\\n param_group['lr'] = lr\",\n \"def adjust_learning_rate_adam(optimizer, epoch):\\n \\n boundary = [args.epochs//5*4]\\n lr = args.lr * 0.2 ** int(bisect.bisect_left(boundary, epoch))\\n print('Learning rate: %f'%lr)\\n #print(epoch, lr)\\n for param_group in optimizer.param_groups:\\n param_group['lr'] = lr\\n \\n return lr\",\n \"def adjust_learning_rate(optimizer, epoch):\\n \\n lr = opt.lr * (0.1 ** (epoch // 3)) # Original\\n for param_group in optimizer.param_groups:\\n param_group['lr'] = lr\",\n \"def adjust_learning_rate(optimizer, epoch):\\n lr = args.lr * (0.1 ** (epoch // args.lr_drop))\\n print('lr= '+str(lr), flush=True)\\n for param_group in optimizer.param_groups:\\n param_group['lr'] = lr\",\n \"def update_learning_rate(self):\\r\\n self.scheduler.step(self.clock.epoch)\",\n \"def adjust_learning_rate(optimizer, epoch, n):\\n lr = args.lr * (0.1 ** (epoch // n))\\n for param_group in optimizer.param_groups:\\n param_group['lr'] = lr\",\n \"def adjust_learning_rate(optimizer, epoch, lr, schedule, gamma):\\r\\n if epoch in schedule:\\r\\n lr *= gamma\\r\\n for param_group in optimizer.param_groups:\\r\\n param_group['lr'] = lr\\r\\n return lr\",\n \"def _update_learning_rate(self):\\r\\n\\r\\n self.n_steps += 1\\r\\n lr = self.factor * self._get_lr_scale()\\r\\n for param_group in self._optimizer.param_groups:\\r\\n param_group['lr'] = lr\",\n \"def adjust_learning_rate(optimizer: torch.optim.SGD, epoch: int, args: Namespace):\\n lr = args.lr * (0.1 ** (epoch // 90)) * (0.1 ** (epoch // 180)) * (0.1 ** (epoch // 270))\\n # log to TensorBoard\\n if args.tensorboard:\\n log_value('learning_rate', lr, epoch)\\n for param_group in optimizer.param_groups:\\n param_group['lr'] = lr\",\n \"def adjust_learning_rate(optimizer, lr, step, args):\\n # decay = 0.1**(sum(epoch >= np.array(lr_steps)))\\n lr = lr * (0.95**(step//args.lr_decay_every))\\n print(\\\"current learning rate: {:.6f}\\\".format(lr))\\n param_group = optimizer.param_groups\\n for i in range(len(param_group)):\\n param_group[i]['lr'] = lr\\n\\n return optimizer\",\n \"def adjust_learning_rate(optimizer, epoch, lr=0.01):\\n new_lr = lr * (0.1 ** (epoch // 5))\\n for param_group in optimizer.param_groups:\\n param_group[\\\"lr\\\"] = new_lr\",\n \"def adjust_learning_rate(optimizer, epoch, lr, schedule, gamma):\\n if epoch in schedule:\\n lr *= gamma\\n for param_group in optimizer.param_groups:\\n param_group['lr'] = lr\\n return lr\",\n \"def adjust_learning_rate(optimizer, epoch, lr, schedule, gamma):\\n if epoch in schedule:\\n lr *= gamma\\n for param_group in optimizer.param_groups:\\n param_group['lr'] = lr\\n return lr\",\n \"def adjust_learning_rate(optimizer, epoch):\\n lr = args.lr * (0.4 ** (epoch // 4))\\n for param_group in optimizer.param_groups:\\n param_group['lr'] = lr\",\n \"def adjust_learning_rate(epoch, learn_rate, decay_step, decay_rate, optimizer):\\n steps = np.sum(epoch > np.asarray(decay_step))\\n if steps > 0:\\n new_lr = learn_rate * (decay_rate ** steps)\\n for param_group in optimizer.param_groups:\\n param_group['lr'] = new_lr\",\n \"def adjust_learning_rate(optimizer, epoch):\\n lr = args.lr * (args.expo ** epoch)\\n for param_group in optimizer.param_groups:\\n param_group['lr'] = lr\",\n \"def adjust_learning_rate(args,optimizer, epoch):\\n \\n args.epochs\\n\\n lr = args.lr * (\\n (0.2 ** int(epoch >= args.epochs - 140))\\n * (0.2 ** int(epoch >= args.epochs - 80))\\n * (0.2 ** int(epoch >= args.epochs - 40))\\n )\\n\\n ##lr = args.lr ##DELETE ME!\\n\\n if args.tensorboard:\\n log_value(\\\"learning_rate\\\", lr, epoch)\\n\\n for param_group in optimizer.param_groups:\\n param_group[\\\"lr\\\"] = lr\",\n \"def adjust_learning_rate(self, opt, epoch):\\n lr = opt.learning_rate * 0.1 ** (epoch // opt.lr_update)\\n for param_group in self.optimizer.param_groups:\\n param_group['lr'] = lr\",\n \"def adjust_learning_rate(optimizer, cur_epoch, base_lr=0.1, lr_schedule=[4, 8, 12, 14, 16]):\\n lr = 0\\n for i, e in enumerate(lr_schedule):\\n if cur_epoch < e:\\n lr = base_lr * (0.1 ** i)\\n break\\n if lr == 0:\\n lr = base_lr\\n for param_group in optimizer.param_groups:\\n param_group['lr'] = lr\",\n \"def _update_initial_learning_rate(configs, learning_rate):\\n\\n optimizer_type = get_optimizer_type(configs[\\\"train_config\\\"])\\n if optimizer_type == \\\"rms_prop_optimizer\\\":\\n optimizer_config = configs[\\\"train_config\\\"].optimizer.rms_prop_optimizer\\n elif optimizer_type == \\\"momentum_optimizer\\\":\\n optimizer_config = configs[\\\"train_config\\\"].optimizer.momentum_optimizer\\n elif optimizer_type == \\\"adam_optimizer\\\":\\n optimizer_config = configs[\\\"train_config\\\"].optimizer.adam_optimizer\\n else:\\n raise TypeError(\\\"Optimizer %s is not supported.\\\" % optimizer_type)\\n\\n learning_rate_type = get_learning_rate_type(optimizer_config)\\n if learning_rate_type == \\\"constant_learning_rate\\\":\\n constant_lr = optimizer_config.learning_rate.constant_learning_rate\\n constant_lr.learning_rate = learning_rate\\n elif learning_rate_type == \\\"exponential_decay_learning_rate\\\":\\n exponential_lr = (\\n optimizer_config.learning_rate.exponential_decay_learning_rate)\\n exponential_lr.initial_learning_rate = learning_rate\\n elif learning_rate_type == \\\"manual_step_learning_rate\\\":\\n manual_lr = optimizer_config.learning_rate.manual_step_learning_rate\\n original_learning_rate = manual_lr.initial_learning_rate\\n learning_rate_scaling = float(learning_rate) / original_learning_rate\\n manual_lr.initial_learning_rate = learning_rate\\n for schedule in manual_lr.schedule:\\n schedule.learning_rate *= learning_rate_scaling\\n elif learning_rate_type == \\\"cosine_decay_learning_rate\\\":\\n cosine_lr = optimizer_config.learning_rate.cosine_decay_learning_rate\\n learning_rate_base = cosine_lr.learning_rate_base\\n warmup_learning_rate = cosine_lr.warmup_learning_rate\\n warmup_scale_factor = warmup_learning_rate / learning_rate_base\\n cosine_lr.learning_rate_base = learning_rate\\n cosine_lr.warmup_learning_rate = warmup_scale_factor * learning_rate\\n else:\\n raise TypeError(\\\"Learning rate %s is not supported.\\\" % learning_rate_type)\",\n \"def adjust_learning_rate(optimizer, epoch):\\n lr = args.lr * (0.5 ** (epoch // args.low_lr))\\n for param_group in optimizer.param_groups:\\n param_group['lr'] = lr\",\n \"def adjust_learning_rate(optimizer, epoch, config):\\n if config.cos:\\n lr_min = 0\\n lr_max = config.lr\\n lr = lr_min + 0.5 * (lr_max - lr_min) * (1 + math.cos(epoch / config.num_epochs * 3.1415926535))\\n else:\\n epoch = epoch + 1\\n if epoch <= 5:\\n lr = config.lr * epoch / 5\\n elif epoch > 180:\\n lr = config.lr * 0.01\\n elif epoch > 160:\\n lr = config.lr * 0.1\\n else:\\n lr = config.lr\\n\\n for param_group in optimizer.param_groups:\\n param_group['lr'] = lr\",\n \"def lr_decay(config, optimizer: optim.Optimizer, epoch: int) -> optim.Optimizer:\\n lr = config.learning_rate / (1 + config.lr_decay * (epoch - 1))\\n for param_group in optimizer.param_groups:\\n param_group['lr'] = lr\\n print('learning rate is set to: ', lr)\\n return optimizer\",\n \"def adjust_learning_rate(optimizer, epoch):\\n lr = args.lr * (0.95 ** epoch)\\n for param_group in optimizer.param_groups:\\n param_group['lr'] = lr\",\n \"def adjust_learning_rate(epoch, opt, optimizer):\\n steps = np.sum(epoch >= np.asarray(opt.lr_decay_epochs))\\n if steps > 0:\\n new_lr = opt.learning_rate * (opt.lr_decay_rate ** steps)\\n optimizer.set_learning_rate(new_lr)\",\n \"def adjust_learning_rate(optimizer, epoch, base_lr):\\n lr = max(base_lr * (0.5 ** (epoch // 20)), 1e-5)\\n for param_group in optimizer.param_groups:\\n param_group['lr'] = lr\\n #if (epoch + 1) == 51 or (epoch + 1) == 101 or (epoch + 1) == 151:\\n # #lr = lr * 0.1\\n # for param_group in optimizer.param_groups:\\n # param_group['lr'] *= 0.1\",\n \"def step_decay(epoch):\\n # initial_lrate = 1.0 # no longer needed\\n #drop = 0.5\\n #epochs_drop = number_epoch_drop\\n #lrate = init_lr * math.pow(drop,\\n #math.floor((1+epoch)/epochs_drop))\\n lr = learning_rate\\n if epoch >= 60:\\n lr *= 1e-1\\n print('Learning rate: ', lr)\\n return lr\",\n \"def adjust_learning_rate(cfg, optimizer):\\n for idx, group in enumerate(optimizer.param_groups):\\n init_lr = cfg.TRAINING.LR\\n if 'step' not in group:\\n group['step'] = 0.\\n else:\\n group['step'] += 1.\\n\\n group['lr'] = init_lr * (\\n 1.0 - float(group['step']) * float(cfg.TRAINING.BATCH_SIZE) /\\n (cfg.TRAINING.N_TRIPLETS * float(cfg.TRAINING.EPOCHS)))\\n return\",\n \"def adjust_learning_rate(self, optimizer, epoch, initial_lr, writer=None):\\n lr = initial_lr * (0.98 ** epoch)\\n for param_group in optimizer.param_groups:\\n param_group['lr'] = lr\\n if writer:\\n writer.add_scalar(\\\"lr_G\\\", lr, epoch + 1)\",\n \"def adjust_learning_rate_SR(optimizer, epoch):\\r\\n lr = opt.lr * (0.1 ** (epoch // opt.step))\\r\\n return lr\",\n \"def adjust_learning_rate(self,itr, meta_lr, lr_decay_itr):\\n if lr_decay_itr > 0:\\n if int(itr % lr_decay_itr) == 0 and itr > 0:\\n meta_lr = meta_lr / (10 ** int(itr / lr_decay_itr))\\n self.fast_net.step_size = self.fast_net.step_size / 10\\n for param_group in self.opt.param_groups:\\n param_group['lr'] = meta_lr\",\n \"def adjust_learning_rate(optimizer, epoch, num_epochs, lr_decay=0.5):\\n step = num_epochs // 1\\n\\n if not epoch % step and epoch > 0:\\n for param_group in optimizer.param_groups:\\n param_group['lr'] *= lr_decay\\n print('Learning rate sets to {}.'.format(param_group['lr']))\\n else:\\n for param_group in optimizer.param_groups:\\n print('Learning rate sets to {}.'.format(param_group['lr']))\"\n]","isConversational":false},"negative_scores":{"kind":"list like","value":["0.79122996","0.79058665","0.77745456","0.77334857","0.7730185","0.7722212","0.7721426","0.771838","0.7708323","0.76928985","0.767362","0.7665062","0.7665062","0.7665062","0.76536506","0.7641248","0.7638249","0.7637116","0.7625168","0.7623629","0.7620543","0.76182115","0.76165646","0.76089275","0.7603141","0.7597323","0.7597047","0.7588299","0.758017","0.75794035","0.75794035","0.75794035","0.7578618","0.75727284","0.756621","0.75612444","0.756058","0.75542885","0.75542885","0.75528806","0.7546607","0.7542749","0.75386536","0.75376177","0.75264573","0.7522267","0.7515023","0.7514515","0.7514409","0.7513271","0.75104403","0.7504661","0.7501276","0.7498533","0.749738","0.7496126","0.74915016","0.7483672","0.74820125","0.74732804","0.7459435","0.7456911","0.745682","0.7422669","0.7406261","0.74045146","0.74033976","0.7399415","0.7397823","0.7382442","0.7381281","0.7378255","0.73770475","0.7375165","0.7373719","0.7372945","0.73728496","0.7370897","0.7355536","0.73538804","0.73538804","0.7352899","0.734928","0.7336721","0.7333202","0.73226666","0.73220986","0.73168737","0.7313164","0.7307959","0.7305941","0.73036695","0.7301138","0.729568","0.72924244","0.7290718","0.7287456","0.728681","0.72846115","0.7283224"],"string":"[\n \"0.79122996\",\n \"0.79058665\",\n \"0.77745456\",\n \"0.77334857\",\n \"0.7730185\",\n \"0.7722212\",\n \"0.7721426\",\n \"0.771838\",\n \"0.7708323\",\n \"0.76928985\",\n \"0.767362\",\n \"0.7665062\",\n \"0.7665062\",\n \"0.7665062\",\n \"0.76536506\",\n \"0.7641248\",\n \"0.7638249\",\n \"0.7637116\",\n \"0.7625168\",\n \"0.7623629\",\n \"0.7620543\",\n \"0.76182115\",\n \"0.76165646\",\n \"0.76089275\",\n \"0.7603141\",\n \"0.7597323\",\n \"0.7597047\",\n \"0.7588299\",\n \"0.758017\",\n \"0.75794035\",\n \"0.75794035\",\n \"0.75794035\",\n \"0.7578618\",\n \"0.75727284\",\n \"0.756621\",\n \"0.75612444\",\n \"0.756058\",\n \"0.75542885\",\n \"0.75542885\",\n \"0.75528806\",\n \"0.7546607\",\n \"0.7542749\",\n \"0.75386536\",\n \"0.75376177\",\n \"0.75264573\",\n \"0.7522267\",\n \"0.7515023\",\n \"0.7514515\",\n \"0.7514409\",\n \"0.7513271\",\n \"0.75104403\",\n \"0.7504661\",\n \"0.7501276\",\n \"0.7498533\",\n \"0.749738\",\n \"0.7496126\",\n \"0.74915016\",\n \"0.7483672\",\n \"0.74820125\",\n \"0.74732804\",\n \"0.7459435\",\n \"0.7456911\",\n \"0.745682\",\n \"0.7422669\",\n \"0.7406261\",\n \"0.74045146\",\n \"0.74033976\",\n \"0.7399415\",\n \"0.7397823\",\n \"0.7382442\",\n \"0.7381281\",\n \"0.7378255\",\n \"0.73770475\",\n \"0.7375165\",\n \"0.7373719\",\n \"0.7372945\",\n \"0.73728496\",\n \"0.7370897\",\n \"0.7355536\",\n \"0.73538804\",\n \"0.73538804\",\n \"0.7352899\",\n \"0.734928\",\n \"0.7336721\",\n \"0.7333202\",\n \"0.73226666\",\n \"0.73220986\",\n \"0.73168737\",\n \"0.7313164\",\n \"0.7307959\",\n \"0.7305941\",\n \"0.73036695\",\n \"0.7301138\",\n \"0.729568\",\n \"0.72924244\",\n \"0.7290718\",\n \"0.7287456\",\n \"0.728681\",\n \"0.72846115\",\n \"0.7283224\"\n]","isConversational":false},"document_score":{"kind":"string","value":"0.7848773"},"document_rank":{"kind":"string","value":"2"}}},{"rowIdx":75391,"cells":{"query":{"kind":"string","value":"Generate a base64 encoded reference to a given Django ORM instance."},"document":{"kind":"string","value":"def serialize_model_instance(instance):\n ref = [instance.__class__.__module__, instance.__class__.__name__, instance.pk]\n dumped_ref = json.dumps(ref).encode('utf-8')\n return b64encode(dumped_ref).decode('utf-8')"},"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 base64_string(self) -> global___Expression:","def serialize(obj):\n result = base64.urlsafe_b64encode(obj)\n # this workaround is needed because in case of python 3 the\n # urlsafe_b64encode method returns string of 'bytes' class.\n result = result.decode()\n return result","def serialized(self):\n return self.reference().Encode()","def urlsafe(self):\n # This is 3-4x faster than urlsafe_b64decode()\n urlsafe = base64.b64encode(self.reference().Encode())\n return urlsafe.rstrip('=').replace('+', '-').replace('/', '_')","def dumps(self) -> str:\n bits = dill.dumps(self)\n return base64.b64encode(bits).decode(\"ascii\")","def get_prep_value(self, value):\n if value is not None:\n return 'base64:' + base64.encodestring(json.dumps(value))","def fn_base64(self, value):\n if isinstance(value, str):\n value = value.encode()\n return base64.b64encode(value).decode()","def uuid(o):\n return sqlite3.Binary(o.bytes)","def get_Base64(self):\n\n return base64_with_linebreaks(self.get_DER())","def object_cache_key_adapter(obj):\n return hex(id(obj))[2:]","def encode(uuid_):\n return base64.urlsafe_b64encode(uuid_.bytes)[:-2] # Drop '==' padding","def persistent_cache_key_adapter(obj):\n # pylint: disable=protected-access\n if obj._p_oid:\n return hex(int.from_bytes(obj._p_oid, byteorder='big'))[2:]\n return hex(id(obj))[2:]","def gen_broadlink_base64_from_raw(data, repeat=0):\n return b64encode(bytes(gen_broadlink_from_raw(data, repeat)))","def encode(self):\n return base64.b64encode(self.content).decode('ascii')","def id_str(self):\n if hasattr(self, 'id'):\n return str(self.id)\n else:\n return 'obj%s' % id(self)","def base64_to_hex(apps, schema_editor):\n APNSDevice = apps.get_model('push_notifications', 'APNSDevice')\n for device in APNSDevice.objects.all():\n device.registration_id = b2a_hex(a2b_base64(device.registration_id))\n device.save()","def encode(self) -> str: \n\n # Encode the object\n b64 = base64.b64encode(zlib.compress(bytearray(json.dumps(self.data,sort_keys=True),'utf-8')))\n\n # Append the version code string and return it\n return f'{self.versionCode}{b64.decode(\"utf-8\")}'","def base64encode(self, item: str) -> bytes:\n b = self[item]\n b = b if isinstance(b, bytes) else b.encode()\n return base64.b64encode(b)","def base64_data(cls, val):\n return cls('base64_data', val)","def bytes_to_base64url(val: bytes) -> str:\n return urlsafe_b64encode(val).decode(\"utf-8\").rstrip(\"=\")","def make_absolute_object_link(db,id,bytes = []):\n href = db.locals[\"db_url\"] +\"/dispatcher.py/\" + \"/\".join(str(x) for x in id)\n if bytes:\n href += byte_query(bytes)\n return href","def stringKey(obj):\n unproxied = proxy.removeSecurityProxy(obj)\n mapper = orm.object_mapper(unproxied)\n #primary_key = mapper.primary_key_from_instance(unproxied)\n identity_values = [ getattr(unproxied, c.name) for c in mapper.primary_key ]\n identity_key = \"-\".join(map(str, identity_values))\n return \"obj-%s\" % (identity_key)","def __bytes__(self):\n return unicode(self).encode('utf-8')","def base64(self):\n image = self.png.getvalue()\n return base64.encodestring(image).decode('utf-8')","def object_to_param_str(obj):\n return b64encode(compress(cPickle.dumps(obj))).decode('utf8')","def serialize(self, obj):\n return dill.dumps(obj, 0).decode('latin-1')","def __bytes__(self):\n with BytesIO() as b:\n self.save(b)\n return b.getvalue()","def make(self, **kwargs):\n return bytes()","def generate_object_field(symbols, length, object, field):\n string = generate_random_string(symbols, length)\n try:\n instance = object.objects.get(**{field:string})\n if instance:\n string = generate_object_field(symbols, length, object, field)\n except object.DoesNotExist:\n pass\n return string","def obj_ref(self) -> str:\n return str(self._data[\"objRef\"])","def to_representation(self, value):\n if value:\n data = default_storage.open(value.path).read()\n encoded=base64.b64encode(data).decode(\"utf-8\")\n return encoded","def base64encode(self, value):\n\n return value.encode(\"base64\")[:-1].replace(\"\\n\", \"\")","def encode_base64(self, i):\n return base64.b64encode(struct.pack('!L', self.transcode(i)), self.extra_chars)[:6]","def _to_bytes(self):\n return self.to_uri().to_text().encode('ascii')","def _cache_key(cls, pk, db):\r\n key_parts = ('o', cls._meta, pk, db)\r\n return ':'.join(map(encoding.smart_unicode, key_parts))","def toHashBase(self) -> str:\r\n\r\n if self.hashBase != '':\r\n self_repr = '{}'.format(self.hashBase)\r\n else:\r\n self_repr = ''\r\n self_repr += '{}{}{}'.format(str(self.data), self.version,\r\n self.compatibilityLimit)\r\n if len(self.script) > 0:\r\n self_repr += ' '.join(self.script)\r\n if self.seriesSignature != '':\r\n self_repr += self.seriesSignature\r\n if self.pha != '':\r\n self_repr += self.pha\r\n for key, value in self.identityInfo.items():\r\n self_repr += '{}{}'.format(key, value)\r\n if self.message != '':\r\n self_repr += self.message\r\n\r\n return self_repr","def ref(self, attribute):\n attribute_to_field = {\n 'title': 'T',\n 'username': 'U',\n 'password': 'P',\n 'url': 'A',\n 'notes': 'N',\n 'uuid': 'I',\n }\n return '{{REF:{}@I:{}}}'.format(attribute_to_field[attribute], self.uuid.hex.upper())","def external_id_from_model_and_internal_id(model, internal_id):\n return get_hash_ids_source().encode(\n ContentType.objects.get_for_model(model).id, internal_id\n )","def obj_to_uid(obj):\n object_ser = pickle.dumps(obj)\n serialised_b64 = base64.encodebytes(object_ser)\n unique_id = serialised_b64.decode()\n return unique_id","def get_encoded(self):\n pass","def nice():\n rawBytes = uuid.uuid4().bytes\n rawBytes =bytes(chr((rawBytes[0]) & 0x7f),'ascii')+rawBytes[1:] # Ensure slug starts with [A-Za-f]\n return base64.urlsafe_b64encode(rawBytes)[:-2] # Drop '==' padding","def __str__(self):\n return str(self._id())","def get_publication_reference(self):\n return unicode(self.pk)","def Encode(cls, db, bom, device_info, version, is_rma_device):\n getter = _ConfiglessFieldGetter(\n db, bom, device_info, version, is_rma_device)\n return '-'.join(\n hex(getter(field)).upper().replace('0X', '') for field in cls.FIELDS)","def referent_to_bytes(referent):\n return pickle.dumps({\n 'left': referent.left_ref.address,\n 'key': referent.key,\n 'value': referent.value_ref.address,\n 'right': referent.right_ref.address,\n 'color': referent.color\n })","def _b64(b):\n return base64.urlsafe_b64encode(b).decode('utf8').replace(\"=\", \"\")","def __str__(self) -> str:\n return self.encode()","def _get_image(x):\n return b64encode(x).decode('ascii')","def full_id(schema_obj):\n\n return '0x%08x' % ((schema_obj.parent.number << 16) | schema_obj.number)","def unique_id() -> bytes:","def data64(self) -> str:\n return Image.encode64(self.data)","def toHashable(self) -> str:\r\n\r\n return self.toHashBase().encode('utf-8')","def getquoted(self):\n if self.is_geometry:\n # Psycopg will figure out whether to use E'\\\\000' or '\\000'.\n return b\"%s(%s)\" % (\n b\"ST_GeogFromWKB\" if self.geography else b\"ST_GeomFromEWKB\",\n sql.quote(self.ewkb).encode(),\n )\n else:\n # For rasters, add explicit type cast to WKB string.\n return b\"'%s'::raster\" % self.ewkb.hex().encode()","def adobe_base64_encode(cls, to_encode):\n if isinstance(to_encode, unicode):\n to_encode = to_encode.encode(\"utf8\")\n encoded = base64.encodestring(to_encode)\n return encoded.replace(b\"+\", b\":\").replace(b\"/\", b\";\").replace(b\"=\", b\"@\").strip()","def get_encoded_bytes_string(self, target):\n pass","def _encode_base64(data: str) -> str:\n ebytes = base64.b64encode(data.encode(\"utf-8\"))\n estring = str(ebytes, \"utf-8\")\n return estring","def b64encode(value, *args, **kwargs):\n return base64.b64encode(encode(value, *args, **kwargs).encode('ascii'))","def _hash_encoder(data: bytes) -> str:\n return base64.urlsafe_b64encode(data).rstrip(b\"=\").decode('ascii')","def serialize(self):\n return self.ref","def v4():\n return base64.urlsafe_b64encode(uuid.uuid4().bytes)[:-2] # Drop '==' padding","def __bytes__(self):\n return bytes([self.type * 2, len(self.value)]) + bytes(self.value, 'utf-8')","def __bytes__(self):\n return bytes([self.type * 2, len(self.value)]) + bytes(self.value, 'utf-8')","def __bytes__(self):\n return bytes([self.type * 2, len(self.value)]) + bytes(self.value, 'utf-8')","def __str__(self):\n return bytes_to_str(bytes(self))","def __unicode__(self):\n return str(self).decode('ascii')","def __bytes__(self):\n prm = self.package(self.p, LEN_PRIME)\n gen = self.package(self.g, LEN_GEN)\n pbk = self.package(self.pk, LEN_PK)\n return prm + gen + pbk","def unique_id() -> bytes:\n ...","def sql_encode(data: str) -> str:\n return base64.urlsafe_b64encode(gzip.compress(data.encode())).decode()","def encode(self, decoded):","def b64_encode(value: bytes) -> bytes:\n return base64.urlsafe_b64encode(value).strip(b\"=\")","def get_id(self):\n #return self.__str__().__hash__()\n object_type = self['object_type']\n shortname = self.get_description()\n object_name = self['name']\n filename = self['filename']\n id = \"%s-%s-%s-%s\" % ( object_type, shortname, object_name, filename)\n import md5\n return md5.new(id).hexdigest()\n return id","def hashable(obj):\n return bytes(str(obj), \"utf-8\")","def safe_repr(obj):\n return (\"%s@%x\" % (obj.__class__.__name__, id(obj)))","def to_sql(self):\n\n return self.get_DER()","def my_base64encode(s):\n return base64.b64encode(s).decode(\"utf-8\")","def encode(self) -> bytes:\n\n return bytes()","def encode(self) -> bytes:\n\n return bytes()","def base64_filter(val, indent=2):\n if isinstance(val, Undefined):\n return \"\"\n s = json.dumps(val).encode(\"utf-8\")\n return b64encode(s).decode(\"utf-8\")","def thumb64(self) -> str:\n return Image.encode64(self.thumb)","def makeid(cls):\n return str(uuid.uuid4().hex)","def jws_b64encode(source):\n return urlsafe_b64encode(source).decode('ascii').rstrip('=')","def base64(s):\n return b64encode(s,'[]').replace('=','_')","def string_raw(self):\n return \"x%x\" % self.encoded","def __str__(self):\n\n if compat.PY3:\n return self.__unicode__()\n return self.__bytes__()","def keyify_obj(o):\n return keyify(o.content_type.pk, o.pk)","def _get_encryption_key(self, obj, field_name: str):\n return hashlib.sha256(\n f'{obj.pk}::{self.get_encryption_key(obj)}::'\n f'{settings.GDPR_KEY if hasattr(settings, \"GDPR_KEY\") else settings.SECRET_KEY}::{field_name}'.encode(\n 'utf-8')).hexdigest()","def __str__(self):\n\n return '%s<%x>' % (self.__class__.__name__, self.id,)","def encode(self, longUrl):\n return str(base64.b64encode(longUrl.encode('utf-8')), 'utf-8')","def bytes(self) -> str:\n return b\"\".join(self)","def _encode_encrypted_part(self, value):\n\n return encoding_utils.bytes_to_base64(value)","def get_encoded(self):\n return self.key","def _generate_account_hash (self, account):\n return urlsafe_b64encode(account['email'])","def base64ify(bytes_or_str):\n if isinstance(bytes_or_str, str):\n input_bytes = bytes_or_str.encode(\"utf8\")\n else:\n input_bytes = bytes_or_str\n\n output_bytes = base64.urlsafe_b64encode(input_bytes)\n return output_bytes.decode(\"ascii\")","def real_encode(obj):\n if _debug: real_encode._debug(\"real_encode %r\", obj)\n\n tag = Tag()\n obj.encode(tag)\n if _debug: real_encode._debug(\" - tag: %r, %r\", tag, tag.tagData)\n\n return tag","def b64_image(self) -> bytes:\n buffer = BytesIO()\n self.image.save(buffer, \"PNG\") \n im_b64 = base64.b64encode(buffer.getvalue())\n im_b64 = b\"data:image/png;base64,\" + im_b64\n return im_b64","def to_bytes(self):\n pref = Utf8String(self.prefix)\n data = super(ElementRecord, self).to_bytes()\n type = data[0]\n return (type + pref.to_bytes() + data[1:])","def hash_str(self):\n return '___'.join([self.key.kind(), self.key.string_id(),\n self._Hash()])","def get_image(self, data_base):\n cursor = data_base.cursor(dictionary=True)\n cursor.execute(f\"SELECT data FROM image WHERE id = '{self.image_id}'\")\n image = cursor.fetchone()\n cursor.close()\n return b64encode(image['data']).decode('utf-8')","def __bytes__(self):\n return bytes(bytearray([int(self)]))","def serialize(cls, model_data: InvitationContent) -> str:\n model_w_short_aliases = cls.construct(**model_data.dict(exclude_unset=True))\n return model_w_short_aliases.json(exclude_unset=True, by_alias=True)"],"string":"[\n \"def base64_string(self) -> global___Expression:\",\n \"def serialize(obj):\\n result = base64.urlsafe_b64encode(obj)\\n # this workaround is needed because in case of python 3 the\\n # urlsafe_b64encode method returns string of 'bytes' class.\\n result = result.decode()\\n return result\",\n \"def serialized(self):\\n return self.reference().Encode()\",\n \"def urlsafe(self):\\n # This is 3-4x faster than urlsafe_b64decode()\\n urlsafe = base64.b64encode(self.reference().Encode())\\n return urlsafe.rstrip('=').replace('+', '-').replace('/', '_')\",\n \"def dumps(self) -> str:\\n bits = dill.dumps(self)\\n return base64.b64encode(bits).decode(\\\"ascii\\\")\",\n \"def get_prep_value(self, value):\\n if value is not None:\\n return 'base64:' + base64.encodestring(json.dumps(value))\",\n \"def fn_base64(self, value):\\n if isinstance(value, str):\\n value = value.encode()\\n return base64.b64encode(value).decode()\",\n \"def uuid(o):\\n return sqlite3.Binary(o.bytes)\",\n \"def get_Base64(self):\\n\\n return base64_with_linebreaks(self.get_DER())\",\n \"def object_cache_key_adapter(obj):\\n return hex(id(obj))[2:]\",\n \"def encode(uuid_):\\n return base64.urlsafe_b64encode(uuid_.bytes)[:-2] # Drop '==' padding\",\n \"def persistent_cache_key_adapter(obj):\\n # pylint: disable=protected-access\\n if obj._p_oid:\\n return hex(int.from_bytes(obj._p_oid, byteorder='big'))[2:]\\n return hex(id(obj))[2:]\",\n \"def gen_broadlink_base64_from_raw(data, repeat=0):\\n return b64encode(bytes(gen_broadlink_from_raw(data, repeat)))\",\n \"def encode(self):\\n return base64.b64encode(self.content).decode('ascii')\",\n \"def id_str(self):\\n if hasattr(self, 'id'):\\n return str(self.id)\\n else:\\n return 'obj%s' % id(self)\",\n \"def base64_to_hex(apps, schema_editor):\\n APNSDevice = apps.get_model('push_notifications', 'APNSDevice')\\n for device in APNSDevice.objects.all():\\n device.registration_id = b2a_hex(a2b_base64(device.registration_id))\\n device.save()\",\n \"def encode(self) -> str: \\n\\n # Encode the object\\n b64 = base64.b64encode(zlib.compress(bytearray(json.dumps(self.data,sort_keys=True),'utf-8')))\\n\\n # Append the version code string and return it\\n return f'{self.versionCode}{b64.decode(\\\"utf-8\\\")}'\",\n \"def base64encode(self, item: str) -> bytes:\\n b = self[item]\\n b = b if isinstance(b, bytes) else b.encode()\\n return base64.b64encode(b)\",\n \"def base64_data(cls, val):\\n return cls('base64_data', val)\",\n \"def bytes_to_base64url(val: bytes) -> str:\\n return urlsafe_b64encode(val).decode(\\\"utf-8\\\").rstrip(\\\"=\\\")\",\n \"def make_absolute_object_link(db,id,bytes = []):\\n href = db.locals[\\\"db_url\\\"] +\\\"/dispatcher.py/\\\" + \\\"/\\\".join(str(x) for x in id)\\n if bytes:\\n href += byte_query(bytes)\\n return href\",\n \"def stringKey(obj):\\n unproxied = proxy.removeSecurityProxy(obj)\\n mapper = orm.object_mapper(unproxied)\\n #primary_key = mapper.primary_key_from_instance(unproxied)\\n identity_values = [ getattr(unproxied, c.name) for c in mapper.primary_key ]\\n identity_key = \\\"-\\\".join(map(str, identity_values))\\n return \\\"obj-%s\\\" % (identity_key)\",\n \"def __bytes__(self):\\n return unicode(self).encode('utf-8')\",\n \"def base64(self):\\n image = self.png.getvalue()\\n return base64.encodestring(image).decode('utf-8')\",\n \"def object_to_param_str(obj):\\n return b64encode(compress(cPickle.dumps(obj))).decode('utf8')\",\n \"def serialize(self, obj):\\n return dill.dumps(obj, 0).decode('latin-1')\",\n \"def __bytes__(self):\\n with BytesIO() as b:\\n self.save(b)\\n return b.getvalue()\",\n \"def make(self, **kwargs):\\n return bytes()\",\n \"def generate_object_field(symbols, length, object, field):\\n string = generate_random_string(symbols, length)\\n try:\\n instance = object.objects.get(**{field:string})\\n if instance:\\n string = generate_object_field(symbols, length, object, field)\\n except object.DoesNotExist:\\n pass\\n return string\",\n \"def obj_ref(self) -> str:\\n return str(self._data[\\\"objRef\\\"])\",\n \"def to_representation(self, value):\\n if value:\\n data = default_storage.open(value.path).read()\\n encoded=base64.b64encode(data).decode(\\\"utf-8\\\")\\n return encoded\",\n \"def base64encode(self, value):\\n\\n return value.encode(\\\"base64\\\")[:-1].replace(\\\"\\\\n\\\", \\\"\\\")\",\n \"def encode_base64(self, i):\\n return base64.b64encode(struct.pack('!L', self.transcode(i)), self.extra_chars)[:6]\",\n \"def _to_bytes(self):\\n return self.to_uri().to_text().encode('ascii')\",\n \"def _cache_key(cls, pk, db):\\r\\n key_parts = ('o', cls._meta, pk, db)\\r\\n return ':'.join(map(encoding.smart_unicode, key_parts))\",\n \"def toHashBase(self) -> str:\\r\\n\\r\\n if self.hashBase != '':\\r\\n self_repr = '{}'.format(self.hashBase)\\r\\n else:\\r\\n self_repr = ''\\r\\n self_repr += '{}{}{}'.format(str(self.data), self.version,\\r\\n self.compatibilityLimit)\\r\\n if len(self.script) > 0:\\r\\n self_repr += ' '.join(self.script)\\r\\n if self.seriesSignature != '':\\r\\n self_repr += self.seriesSignature\\r\\n if self.pha != '':\\r\\n self_repr += self.pha\\r\\n for key, value in self.identityInfo.items():\\r\\n self_repr += '{}{}'.format(key, value)\\r\\n if self.message != '':\\r\\n self_repr += self.message\\r\\n\\r\\n return self_repr\",\n \"def ref(self, attribute):\\n attribute_to_field = {\\n 'title': 'T',\\n 'username': 'U',\\n 'password': 'P',\\n 'url': 'A',\\n 'notes': 'N',\\n 'uuid': 'I',\\n }\\n return '{{REF:{}@I:{}}}'.format(attribute_to_field[attribute], self.uuid.hex.upper())\",\n \"def external_id_from_model_and_internal_id(model, internal_id):\\n return get_hash_ids_source().encode(\\n ContentType.objects.get_for_model(model).id, internal_id\\n )\",\n \"def obj_to_uid(obj):\\n object_ser = pickle.dumps(obj)\\n serialised_b64 = base64.encodebytes(object_ser)\\n unique_id = serialised_b64.decode()\\n return unique_id\",\n \"def get_encoded(self):\\n pass\",\n \"def nice():\\n rawBytes = uuid.uuid4().bytes\\n rawBytes =bytes(chr((rawBytes[0]) & 0x7f),'ascii')+rawBytes[1:] # Ensure slug starts with [A-Za-f]\\n return base64.urlsafe_b64encode(rawBytes)[:-2] # Drop '==' padding\",\n \"def __str__(self):\\n return str(self._id())\",\n \"def get_publication_reference(self):\\n return unicode(self.pk)\",\n \"def Encode(cls, db, bom, device_info, version, is_rma_device):\\n getter = _ConfiglessFieldGetter(\\n db, bom, device_info, version, is_rma_device)\\n return '-'.join(\\n hex(getter(field)).upper().replace('0X', '') for field in cls.FIELDS)\",\n \"def referent_to_bytes(referent):\\n return pickle.dumps({\\n 'left': referent.left_ref.address,\\n 'key': referent.key,\\n 'value': referent.value_ref.address,\\n 'right': referent.right_ref.address,\\n 'color': referent.color\\n })\",\n \"def _b64(b):\\n return base64.urlsafe_b64encode(b).decode('utf8').replace(\\\"=\\\", \\\"\\\")\",\n \"def __str__(self) -> str:\\n return self.encode()\",\n \"def _get_image(x):\\n return b64encode(x).decode('ascii')\",\n \"def full_id(schema_obj):\\n\\n return '0x%08x' % ((schema_obj.parent.number << 16) | schema_obj.number)\",\n \"def unique_id() -> bytes:\",\n \"def data64(self) -> str:\\n return Image.encode64(self.data)\",\n \"def toHashable(self) -> str:\\r\\n\\r\\n return self.toHashBase().encode('utf-8')\",\n \"def getquoted(self):\\n if self.is_geometry:\\n # Psycopg will figure out whether to use E'\\\\\\\\000' or '\\\\000'.\\n return b\\\"%s(%s)\\\" % (\\n b\\\"ST_GeogFromWKB\\\" if self.geography else b\\\"ST_GeomFromEWKB\\\",\\n sql.quote(self.ewkb).encode(),\\n )\\n else:\\n # For rasters, add explicit type cast to WKB string.\\n return b\\\"'%s'::raster\\\" % self.ewkb.hex().encode()\",\n \"def adobe_base64_encode(cls, to_encode):\\n if isinstance(to_encode, unicode):\\n to_encode = to_encode.encode(\\\"utf8\\\")\\n encoded = base64.encodestring(to_encode)\\n return encoded.replace(b\\\"+\\\", b\\\":\\\").replace(b\\\"/\\\", b\\\";\\\").replace(b\\\"=\\\", b\\\"@\\\").strip()\",\n \"def get_encoded_bytes_string(self, target):\\n pass\",\n \"def _encode_base64(data: str) -> str:\\n ebytes = base64.b64encode(data.encode(\\\"utf-8\\\"))\\n estring = str(ebytes, \\\"utf-8\\\")\\n return estring\",\n \"def b64encode(value, *args, **kwargs):\\n return base64.b64encode(encode(value, *args, **kwargs).encode('ascii'))\",\n \"def _hash_encoder(data: bytes) -> str:\\n return base64.urlsafe_b64encode(data).rstrip(b\\\"=\\\").decode('ascii')\",\n \"def serialize(self):\\n return self.ref\",\n \"def v4():\\n return base64.urlsafe_b64encode(uuid.uuid4().bytes)[:-2] # Drop '==' padding\",\n \"def __bytes__(self):\\n return bytes([self.type * 2, len(self.value)]) + bytes(self.value, 'utf-8')\",\n \"def __bytes__(self):\\n return bytes([self.type * 2, len(self.value)]) + bytes(self.value, 'utf-8')\",\n \"def __bytes__(self):\\n return bytes([self.type * 2, len(self.value)]) + bytes(self.value, 'utf-8')\",\n \"def __str__(self):\\n return bytes_to_str(bytes(self))\",\n \"def __unicode__(self):\\n return str(self).decode('ascii')\",\n \"def __bytes__(self):\\n prm = self.package(self.p, LEN_PRIME)\\n gen = self.package(self.g, LEN_GEN)\\n pbk = self.package(self.pk, LEN_PK)\\n return prm + gen + pbk\",\n \"def unique_id() -> bytes:\\n ...\",\n \"def sql_encode(data: str) -> str:\\n return base64.urlsafe_b64encode(gzip.compress(data.encode())).decode()\",\n \"def encode(self, decoded):\",\n \"def b64_encode(value: bytes) -> bytes:\\n return base64.urlsafe_b64encode(value).strip(b\\\"=\\\")\",\n \"def get_id(self):\\n #return self.__str__().__hash__()\\n object_type = self['object_type']\\n shortname = self.get_description()\\n object_name = self['name']\\n filename = self['filename']\\n id = \\\"%s-%s-%s-%s\\\" % ( object_type, shortname, object_name, filename)\\n import md5\\n return md5.new(id).hexdigest()\\n return id\",\n \"def hashable(obj):\\n return bytes(str(obj), \\\"utf-8\\\")\",\n \"def safe_repr(obj):\\n return (\\\"%s@%x\\\" % (obj.__class__.__name__, id(obj)))\",\n \"def to_sql(self):\\n\\n return self.get_DER()\",\n \"def my_base64encode(s):\\n return base64.b64encode(s).decode(\\\"utf-8\\\")\",\n \"def encode(self) -> bytes:\\n\\n return bytes()\",\n \"def encode(self) -> bytes:\\n\\n return bytes()\",\n \"def base64_filter(val, indent=2):\\n if isinstance(val, Undefined):\\n return \\\"\\\"\\n s = json.dumps(val).encode(\\\"utf-8\\\")\\n return b64encode(s).decode(\\\"utf-8\\\")\",\n \"def thumb64(self) -> str:\\n return Image.encode64(self.thumb)\",\n \"def makeid(cls):\\n return str(uuid.uuid4().hex)\",\n \"def jws_b64encode(source):\\n return urlsafe_b64encode(source).decode('ascii').rstrip('=')\",\n \"def base64(s):\\n return b64encode(s,'[]').replace('=','_')\",\n \"def string_raw(self):\\n return \\\"x%x\\\" % self.encoded\",\n \"def __str__(self):\\n\\n if compat.PY3:\\n return self.__unicode__()\\n return self.__bytes__()\",\n \"def keyify_obj(o):\\n return keyify(o.content_type.pk, o.pk)\",\n \"def _get_encryption_key(self, obj, field_name: str):\\n return hashlib.sha256(\\n f'{obj.pk}::{self.get_encryption_key(obj)}::'\\n f'{settings.GDPR_KEY if hasattr(settings, \\\"GDPR_KEY\\\") else settings.SECRET_KEY}::{field_name}'.encode(\\n 'utf-8')).hexdigest()\",\n \"def __str__(self):\\n\\n return '%s<%x>' % (self.__class__.__name__, self.id,)\",\n \"def encode(self, longUrl):\\n return str(base64.b64encode(longUrl.encode('utf-8')), 'utf-8')\",\n \"def bytes(self) -> str:\\n return b\\\"\\\".join(self)\",\n \"def _encode_encrypted_part(self, value):\\n\\n return encoding_utils.bytes_to_base64(value)\",\n \"def get_encoded(self):\\n return self.key\",\n \"def _generate_account_hash (self, account):\\n return urlsafe_b64encode(account['email'])\",\n \"def base64ify(bytes_or_str):\\n if isinstance(bytes_or_str, str):\\n input_bytes = bytes_or_str.encode(\\\"utf8\\\")\\n else:\\n input_bytes = bytes_or_str\\n\\n output_bytes = base64.urlsafe_b64encode(input_bytes)\\n return output_bytes.decode(\\\"ascii\\\")\",\n \"def real_encode(obj):\\n if _debug: real_encode._debug(\\\"real_encode %r\\\", obj)\\n\\n tag = Tag()\\n obj.encode(tag)\\n if _debug: real_encode._debug(\\\" - tag: %r, %r\\\", tag, tag.tagData)\\n\\n return tag\",\n \"def b64_image(self) -> bytes:\\n buffer = BytesIO()\\n self.image.save(buffer, \\\"PNG\\\") \\n im_b64 = base64.b64encode(buffer.getvalue())\\n im_b64 = b\\\"data:image/png;base64,\\\" + im_b64\\n return im_b64\",\n \"def to_bytes(self):\\n pref = Utf8String(self.prefix)\\n data = super(ElementRecord, self).to_bytes()\\n type = data[0]\\n return (type + pref.to_bytes() + data[1:])\",\n \"def hash_str(self):\\n return '___'.join([self.key.kind(), self.key.string_id(),\\n self._Hash()])\",\n \"def get_image(self, data_base):\\n cursor = data_base.cursor(dictionary=True)\\n cursor.execute(f\\\"SELECT data FROM image WHERE id = '{self.image_id}'\\\")\\n image = cursor.fetchone()\\n cursor.close()\\n return b64encode(image['data']).decode('utf-8')\",\n \"def __bytes__(self):\\n return bytes(bytearray([int(self)]))\",\n \"def serialize(cls, model_data: InvitationContent) -> str:\\n model_w_short_aliases = cls.construct(**model_data.dict(exclude_unset=True))\\n return model_w_short_aliases.json(exclude_unset=True, by_alias=True)\"\n]","isConversational":false},"negative_scores":{"kind":"list like","value":["0.65252703","0.6164654","0.6147635","0.60311043","0.5965339","0.5942643","0.5856737","0.57892215","0.5784739","0.5777973","0.57017314","0.56621474","0.56593806","0.5639606","0.5626723","0.55980736","0.55938447","0.5560814","0.55501676","0.55286133","0.55277145","0.5514862","0.54684365","0.5462425","0.54247123","0.5415364","0.54134965","0.5360788","0.53564006","0.5352375","0.53364736","0.53348255","0.5333097","0.53245276","0.53076136","0.529992","0.52958924","0.5294191","0.5287846","0.52805454","0.52799153","0.527324","0.5272245","0.5270749","0.52661186","0.52605414","0.5252534","0.52512205","0.52470195","0.52391404","0.5233255","0.5211437","0.5196262","0.5190306","0.51872134","0.5160982","0.5145319","0.5141281","0.51260173","0.5125649","0.5124665","0.5124665","0.5124665","0.51220816","0.5101058","0.5096835","0.509521","0.50909376","0.50891876","0.5088298","0.50866616","0.50849307","0.5084339","0.50819397","0.5080707","0.5075882","0.5075882","0.50748795","0.50746477","0.50706553","0.50674295","0.50665754","0.50641483","0.5060867","0.5055572","0.5054959","0.5048743","0.5046009","0.5040152","0.5038273","0.50340575","0.5031976","0.50284535","0.5021684","0.5021149","0.50201136","0.50169915","0.5016323","0.50114655","0.50104433"],"string":"[\n \"0.65252703\",\n \"0.6164654\",\n \"0.6147635\",\n \"0.60311043\",\n \"0.5965339\",\n \"0.5942643\",\n \"0.5856737\",\n \"0.57892215\",\n \"0.5784739\",\n \"0.5777973\",\n \"0.57017314\",\n \"0.56621474\",\n \"0.56593806\",\n \"0.5639606\",\n \"0.5626723\",\n \"0.55980736\",\n \"0.55938447\",\n \"0.5560814\",\n \"0.55501676\",\n \"0.55286133\",\n \"0.55277145\",\n \"0.5514862\",\n \"0.54684365\",\n \"0.5462425\",\n \"0.54247123\",\n \"0.5415364\",\n \"0.54134965\",\n \"0.5360788\",\n \"0.53564006\",\n \"0.5352375\",\n \"0.53364736\",\n \"0.53348255\",\n \"0.5333097\",\n \"0.53245276\",\n \"0.53076136\",\n \"0.529992\",\n \"0.52958924\",\n \"0.5294191\",\n \"0.5287846\",\n \"0.52805454\",\n \"0.52799153\",\n \"0.527324\",\n \"0.5272245\",\n \"0.5270749\",\n \"0.52661186\",\n \"0.52605414\",\n \"0.5252534\",\n \"0.52512205\",\n \"0.52470195\",\n \"0.52391404\",\n \"0.5233255\",\n \"0.5211437\",\n \"0.5196262\",\n \"0.5190306\",\n \"0.51872134\",\n \"0.5160982\",\n \"0.5145319\",\n \"0.5141281\",\n \"0.51260173\",\n \"0.5125649\",\n \"0.5124665\",\n \"0.5124665\",\n \"0.5124665\",\n \"0.51220816\",\n \"0.5101058\",\n \"0.5096835\",\n \"0.509521\",\n \"0.50909376\",\n \"0.50891876\",\n \"0.5088298\",\n \"0.50866616\",\n \"0.50849307\",\n \"0.5084339\",\n \"0.50819397\",\n \"0.5080707\",\n \"0.5075882\",\n \"0.5075882\",\n \"0.50748795\",\n \"0.50746477\",\n \"0.50706553\",\n \"0.50674295\",\n \"0.50665754\",\n \"0.50641483\",\n \"0.5060867\",\n \"0.5055572\",\n \"0.5054959\",\n \"0.5048743\",\n \"0.5046009\",\n \"0.5040152\",\n \"0.5038273\",\n \"0.50340575\",\n \"0.5031976\",\n \"0.50284535\",\n \"0.5021684\",\n \"0.5021149\",\n \"0.50201136\",\n \"0.50169915\",\n \"0.5016323\",\n \"0.50114655\",\n \"0.50104433\"\n]","isConversational":false},"document_score":{"kind":"string","value":"0.67707986"},"document_rank":{"kind":"string","value":"0"}}},{"rowIdx":75392,"cells":{"query":{"kind":"string","value":"Load Django ORM instance from base64 encoded reference."},"document":{"kind":"string","value":"def deserialize_model_instance(encoded):\n dumped_ref = b64decode(encoded)\n ref = json.loads(dumped_ref.decode('utf-8'))\n\n model_module = import_module(ref[0])\n model_class = getattr(model_module, ref[1])\n return model_class.objects.get(pk=ref[2])"},"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 loads(cls, data: str) -> \"Serializable\":\n instance = dill.loads(base64.b64decode(data.encode(\"ascii\")))\n if not isinstance(instance, cls):\n raise TypeError(f\"Unpickled object is not of type {cls}\")\n return instance","def deserialize(obj):\n result = base64.urlsafe_b64decode(obj)\n # this workaround is needed because in case of python 3 the\n # urlsafe_b64decode method returns string of 'bytes' class\n result = result.decode()\n return result","def decode_content(self, raw_content):\n try:\n obj = pickle.loads(raw_content)\n return obj\n except Exception:\n raise IkatsException(\"Failed to load picked object. Context={}\".format(str(self)))","def deserialize(self, blob):\n return dill.loads(blob.encode('latin-1'))","def from_base64(cls, b64_str, content_type=None):\n if b64_str is None:\n raise ValueError(\"Base 64 string should not be None\")\n # The decode function does not like taking unicode strings.\n # Additionally, the encoding alphabet should not container any unicode\n # symbols, so this aught to be safe.\n b64_str = str(b64_str)\n return DataMemoryElement(base64.urlsafe_b64decode(b64_str),\n content_type)","def base64unpickle(self, value):\n if value:\n return pickle.loads(self.base64decode(value))","def uid_to_obj(uid):\n serialized_b64 = uid.encode()\n object_ser = base64.decodebytes(serialized_b64)\n obj = pickle.loads(object_ser)\n return obj","def load_blob(self, blob_uuid):\n raise NotImplementedError()","def from_db_value(self, value, *args):\n if value is not None:\n try:\n value = dbsafe_decode(value, self.compress)\n except Exception:\n # If the value is a definite pickle; and an error is raised in\n # de-pickling it should be allowed to propogate.\n if isinstance(value, PickledObject):\n raise\n else:\n if isinstance(value, _ObjectWrapper):\n return value._obj\n return value","def __init__(self, blob=None):\n if blob is None:\n self.versionCode = '0'\n self.data = {}\n else:\n self.versionCode = blob[0]\n encoded = blob[1:]\n compressed = base64.b64decode(encoded)\n self.data = json.loads(zlib.decompress(compressed))","def loads(self, data):\n return loads(data)","def decode(self, encoded):","def deserialize(self, blob):\n pass","def _feed_field_blobs_from_db_file(self, net):\n assert os.path.exists(self.db_path), \\\n 'db_path [{db_path}] does not exist'.format(db_path=self.db_path)\n net.Load(\n [],\n self.ds.get_blobs(),\n db=self.db_path,\n db_type=self.db_type,\n absolute_path=True,\n source_blob_names=self.ds.field_names(),\n )","def serialize_model_instance(instance):\n ref = [instance.__class__.__module__, instance.__class__.__name__, instance.pk]\n dumped_ref = json.dumps(ref).encode('utf-8')\n return b64encode(dumped_ref).decode('utf-8')","def retrieve_from_db(self):\n pass","def load_model(self, model_as_bytes: bytes) -> None:\n\n self.model = deserialize_from_zippy(model_as_bytes)","def decode_model_instance(hashid):\n try:\n return get_encoded_object_or_404(hashid).obj\n except Http404: # pragma: no cover\n return None","def sql_decode(data: str) -> str:\n return gzip.decompress(base64.urlsafe_b64decode(data.encode())).decode()","def load(cls, path):\n data = cls()\n data.set_params(pk.load(path))\n return data","def from_base64(x):\n\treturn numpy.fromstring(\n\t\tbinascii.a2b_base64(x),\n\t\tdtype=numpy.uint8\n\t)","def __init__(self, data, from_base64=False, **kwargs):\n super().__init__(data, **kwargs)\n self.type_name = 'base64'\n self.encoding = kwargs.get('encoding', 'original')\n # TODO data should be bytes only\n if from_base64:\n self.data = data\n else:\n self.data = base64.b64encode(data).decode()","def unserialize(self, data):\n self.ref = data\n return self","def base64_data(cls, val):\n return cls('base64_data', val)","def _blob(self):\n return self._load_blob","def from_sql(cls, x):\n\n return cls(DER = x)","def decode(eVal):\n return pickle.loads(zlib.decompress(base64.b64decode(eVal)))","def loads(data):\n return Decoder().decode(data)","def get_real_object(self):\n query_string = dedent(f\"\"\"\\\n import app.config.models_importer as models_importer\n\n class_ = models_importer.all_models['{self.ref_class}']\n \n class_.query.get({self.ref_id})\"\"\")\n\n return exec(query_string)","async def load_blob(elem, elem_type=None):\n if elem is None:\n return b''\n return bytearray(base64.b16decode(elem))","def from_pickle(cls, path_or_bytes):\n if isinstance(path_or_bytes, bytes):\n return dill.loads(path_or_bytes)\n with open(path_or_bytes, \"rb\") as f:\n return dill.load(f)","def from_bytes(self, ???):","def decode(binary):\n name, fields = JsonCoder.decode(binary)\n dataclass = dataclasses.make_dataclass(\n name, [(key, type(value)) for key, value in fields.items()]\n )\n instance = dataclass(**fields)\n return instance","def from_db_value(self, value, *args, **kwargs):\n return self.to_python(value)","def to_internal_value(self, data):\n if isinstance(data, str) and data.startswith('data:image'):\n # Found image is encoded, and must be decoded\n format, imgstr = data.split(';base64,')\n ext = format.split('/')[-1] # Extract file extension\n id = uuid.uuid4()\n data = ContentFile(base64.b64decode(imgstr), name = id.urn[9:] + '.' + ext)\n return super(Base64ImageField, self).to_internal_value(data)","def deserialize(serialized: str): \n try:\n decoded = b64decode(serialized)\n return loads(decoded)\n except Exception as e:\n # If we raise an Exception in a try/except, it includes the original exception.\n # So we include from None to prevent this (and only raise a single exception)\n raise Exception(f\"{type(e).__name__}: {e} raised on {serialized[:50]}\") from None","def _ReferenceFromSerialized(serialized):\n if not isinstance(serialized, basestring):\n raise TypeError('serialized must be a string; received %r' % serialized)\n elif isinstance(serialized, unicode):\n serialized = serialized.encode('utf8')\n return entity_pb.Reference(serialized)","def __bytes__(self):\n with BytesIO() as b:\n self.save(b)\n return b.getvalue()","def load(cls, id):\n key = cls.get_key_prefix()+\"#\"+str(id)\n src = dal_get(key)\n logger.debug( \"LOAD %s %s %s\", str(key), str(id), str(src))\n if src == None:\n raise cls.NotExist(\"No instance could be found with ID: \"+str(id))\n result = dal_retrieve(src)\n result = cls._from_dict(result)\n return result","def __getattr__(self, attr_name):\n\t\tobj = pickle.load(open('%s/%s'%(self.pg_dir,attr_name),'rb'))\n\t\tself.__setattr__(attr_name,obj)\n\t\treturn obj","def deserialize(self, blob):\n return json.loads(blob)","def _from_string(cls, serialized):\r\n parse = cls.URL_RE.match(serialized)\r\n if not parse:\r\n raise InvalidKeyError(cls, serialized)\r\n\r\n parse = parse.groupdict()\r\n if parse['definition_id']:\r\n parse['definition_id'] = cls.as_object_id(parse['definition_id'])\r\n\r\n return cls(**{key: parse.get(key) for key in cls.KEY_FIELDS})","def load_blob(self, blob_uuid):\n path = os.path.join(self.rootpath, self.OBJECTPATH, blob_uuid[:2], blob_uuid[-2:]) + os.sep\n logging.debug(\"load blob {}\".format(path))\n filepath = os.path.join(path, blob_uuid)\n data = Data.from_hdf5(filepath)\n return data","def serialize(obj):\n result = base64.urlsafe_b64encode(obj)\n # this workaround is needed because in case of python 3 the\n # urlsafe_b64encode method returns string of 'bytes' class.\n result = result.decode()\n return result","def open(self):\n if isinstance(self.raw_content, (bytes, str)):\n return BytesIO(self.content)\n fileobj = self.raw_content.open()\n\n # TODO remove when Django 1 is no longer supported\n if fileobj is None:\n assert not isinstance(self.raw_content, BlobMeta), repr(self)\n # work around Django 1.11 bug, fixed in 2.0\n # https://github.com/django/django/blob/1.11.15/django/core/files/base.py#L131-L137\n # https://github.com/django/django/blob/2.0/django/core/files/base.py#L128\n return self.raw_content\n\n return fileobj","def load_model(self) -> Any:","def param_str_to_object(param_str):\n return cPickle.loads(decompress(b64decode(param_str.encode('utf8'))))","def decode(self) -> D:\n if self.has_cached_data():\n return self._data\n\n # Dispatch decoding\n data = lookup_serializer(self.encoding).loads(self.blob)\n\n self._cache_data(data)\n return data","def to_internal_value(self, data):\n try: # ToDo penetrate in order test if it has any security flaws\n decoded = base64.b64decode(data)\n mime_type = magic.from_buffer(decoded, mime=True)\n file_ext = mimetypes.guess_extension(mime_type)\n except TypeError:\n raise serializers.ValidationError(\n _('Not a valid base64 file')\n )\n\n if file_ext not in settings.VALID_FILE_EXTENSIONS:\n raise serializers.ValidationError(\n _('Forbidden file extension')\n )\n\n file_name = \"{0}{1}\".format(uuid.uuid4(), file_ext)\n data = ContentFile(decoded, name=file_name)\n return data","def base64decode(self, value):\n\n return value.decode(\"base64\")","def bytes_to_referent(string):\n d = pickle.loads(string)\n return RedBlackNode(\n RedBlackNodeRef(address=d['left']),\n d['key'],\n ValueRef(address=d['value']),\n RedBlackNodeRef(address=d['right']),\n d['color']\n )","def decode(self): # pragma: no cover\n pass","def _b64decode(self, string):\n import base64\n return base64.b64decode(string)","def _b64decode(self, string):\n import base64\n return base64.b64decode(string)","def get_record(self, index, asbase64=False):\n data = self.bank.readrec(int(index))\n if asbase64:\n return base64.b64encode(data[1:]).decode('utf-8')\n else:\n return data[1:]","def dic_b64_and_pickle_loads(data):\n for i in data:\n data[i] = pickle.loads(base64.b64decode(data[i]))\n return data","def base64_string(self) -> global___Expression:","def _from_string(cls, serialized):\r\n parse = cls.parse_url(serialized)\r\n\r\n if parse['version_guid']:\r\n parse['version_guid'] = cls.as_object_id(parse['version_guid'])\r\n\r\n return cls(**{key: parse.get(key) for key in cls.KEY_FIELDS})","def gen_raw_from_broadlink_base64(data):\n yield from gen_raw_from_broadlink(b64decode(data))","def serialized(self):\n return self.reference().Encode()","def from_attr(self, session, resource_attr):\n # \"object_dict\" and \"tree\" uses LargeBinary sqlalchemy datatype.\n # LargeBinary sqlalchemy datatype needs \"bytes-like\" object\n # and in Py2, string are bytes-like objects while in Py3 they aren't.\n # So we need to store \"bytes-like\" object in DB objects.\n # For storing the DB model object,\n # we need to encode to utf-8 bytes format (for Py3 compatibility).\n # Since in Py2, string are bytes-like objects, encoding won't\n # make a difference.\n encoded_attr_dict = {}\n for k, v in list(resource_attr.items()):\n if k not in getattr(self, '_exclude_from', []):\n if k == 'object_dict':\n if isinstance(v, six.text_type):\n v = v.encode('utf-8')\n elif k == 'tree':\n if isinstance(v, six.text_type):\n v = v.encode('utf-8')\n encoded_attr_dict[k] = v\n self.set_attr(session, k, v, **encoded_attr_dict)","def serialize(self, obj):\n return dill.dumps(obj, 0).decode('latin-1')","def load_hickle_4_x_string(h_node,base_type,py_obj_type):\n if not 'object' in h_node.dtype.name or h_node.attrs.get('str_type',None) is not None:\n return load_list_dataset(h_node,base_type,py_obj_type)\n content = h_node[()]\n if py_obj_type is str:\n return content if isinstance(content,str) else content.decode('utf8')\n return py_obj_type(content) if content.__class__ is not py_obj_type else content","def to_representation(self, value):\n if value:\n data = default_storage.open(value.path).read()\n encoded=base64.b64encode(data).decode(\"utf-8\")\n return encoded","def set_pdf_from_base64(self, content_base64):\n try:\n raw = base64.standard_b64decode(str(content_base64))\n except (TypeError, binascii.Error):\n raise Exception('The provided PDF is not Base64-encoded')\n self.set_pdf_from_raw(raw)","def from_bytes(self, *args, **kwargs): # real signature unknown\n pass","def from_bytes(self, *args, **kwargs): # real signature unknown\n pass","def from_bytes(self, *args, **kwargs): # real signature unknown\n pass","def from_bytes(self, *args, **kwargs): # real signature unknown\n pass","def from_bytes(self, *args, **kwargs): # real signature unknown\n pass","def from_bytes(self, *args, **kwargs): # real signature unknown\n pass","def from_bytes(self, *args, **kwargs): # real signature unknown\n pass","def from_bytes(self, *args, **kwargs): # real signature unknown\n pass","def from_bytes(self, *args, **kwargs): # real signature unknown\n pass","def from_bytes(self, *args, **kwargs): # real signature unknown\n pass","def from_bytes(self, *args, **kwargs): # real signature unknown\n pass","def from_bytes(self, *args, **kwargs): # real signature unknown\n pass","def from_bytes(self, *args, **kwargs): # real signature unknown\n pass","def from_bytes(self, *args, **kwargs): # real signature unknown\n pass","def from_bytes(self, *args, **kwargs): # real signature unknown\n pass","def from_bytes(self, *args, **kwargs): # real signature unknown\n pass","def base64decode(self, item: str) -> bytes:\n b = self[item]\n b = b if isinstance(b, bytes) else b.encode()\n return base64.b64decode(b, validate=True)","def decode(self, base64: str) -> GeoLocation:\n\n longitude, latitude = shortlinkToGeoLoc(base64)\n\n # how to determine altitude from short links?\n return GeoLocation(latitude, longitude, altitude_in_meters=0)","def load(cls, strm):\n raise NotImplementedError(\n '\"load\" should be implemented in the' \" class derived from SurrogateBase\"\n )","def to_python(self, value):\n if value is not None and isinstance(value, basestring):\n value = str(value)\n if value.startswith('base64:'):\n value = value.split(':')[1]\n prepared_data = json.loads(base64.decodestring(value))\n return json.loads(prepared_data)\n elif value is not None and isinstance(value, dict):\n value = json.dumps(value)\n return value\n\n return value","def uuid(o):\n return sqlite3.Binary(o.bytes)","def test_the_unicode_in_data_base(self):\n\n model_instance = Contact.objects.first()\n model_instance.name = u'Олег'\n model_instance.surname = u'Сенишин'\n model_instance.bio = u'Працюю, після роботи, самостійно вивчаю Python, Django, JavaScript.'\n model_instance.save()\n\n response = self.client.get(reverse(\"my_info\"))\n\n model_instance = Contact.objects.get(id=1)\n\n self.assertIn(model_instance.name.encode('utf-8'), response.content)\n self.assertIn(model_instance.surname.encode('utf-8'), response.content)\n self.assertIn(model_instance.email.encode('utf-8'), response.content)\n self.assertIn(model_instance.bio.encode('utf-8'), response.content)\n self.assertIn(model_instance.skype.encode('utf-8'), response.content)\n self.assertIn(model_instance.contacts.encode('utf-8'), response.content)","def fromBytes(cls, inBytes):\n return cls.fromJson(inBytes.decode())","def fromBytes(cls, inBytes):\n return cls.fromJson(inBytes.decode())","def fromBytes(cls, inBytes):\n return cls.fromJson(inBytes.decode())","def load(self):\n\n raise NotImplementedError","def load(self):","def load(cls, data):\n return cls(**data)","def load_PIC(self, edron: Dict[str, str]) -> None:\n ...","def atob(b):\n return base64.b64decode(b.encode()).decode()","def decode_key_from_mongo(fieldname):\r\n return urllib.unquote(fieldname)","def decode_and_load_json(query):\n try:\n decoded_query = base64.b64decode(query)\n except binascii.Error:\n raise ParseError('Invalid base64 format')\n try:\n return json.loads(decoded_query)\n except json.JSONDecodeError:\n raise ParseError('Invalid json format')","def load(cls, data):\n if cls.Schema is None: # pragma: no cover\n msg = (\"Schema of this Model are not specified! For example: \"\n \"class User(BaseModel): ...; class UserSchema(Schema): ...; \"\n \"User.Schema = UserSchema\")\n raise NotImplementedError(msg)\n\n res = cls.Schema().load(data)\n if len(res.errors) == 0:\n return res.data\n else:\n raise Exception(\"Errors: {}\".format(res.errors))","def deserialize(cls, field_name, field_sig_dict, sig_version,\n database=DEFAULT_DB_ALIAS):\n validate_sig_version(sig_version)\n\n if sig_version == 2:\n field_sig_attrs = field_sig_dict.get('attrs', {})\n\n # Load the class for the referenced field type.\n field_type_module, field_type_name = \\\n field_sig_dict['type'].rsplit('.', 1)\n\n # If we have a field path in the signature that lives in\n # django.db.models.fields, update it to look in django.db.models\n # instead. This is for compatibility across all Django versions.\n if field_type_module.startswith('django.db.models.fields'):\n field_type_module = 'django.db.models'\n\n try:\n field_type = getattr(import_module(field_type_module),\n field_type_name)\n except (AttributeError, ImportError):\n raise ImportError('Unable to locate field type %s'\n % '%s.%s' % (field_type_module,\n field_type_name))\n elif sig_version == 1:\n field_sig_attrs = field_sig_dict\n field_type = field_sig_dict['field_type']\n\n field_attrs = {}\n\n for attr in cls._iter_attrs_for_field_type(field_type):\n if hasattr(cls, attr):\n # This is stored on the field signature class itself, so\n # it's not attribute data we want to load.\n continue\n\n alias = cls._ATTRIBUTE_ALIASES.get(attr)\n\n if alias and alias in field_sig_attrs:\n value = field_sig_attrs[alias]\n elif attr in field_sig_attrs:\n value = field_sig_attrs[attr]\n else:\n # The signature didn't contain a value for this attribute.\n continue\n\n field_attrs[attr] = value\n\n return cls(field_name=field_name,\n field_type=field_type,\n field_attrs=field_attrs,\n related_model=field_sig_dict.get('related_model'))","def load(self):\n return None"],"string":"[\n \"def loads(cls, data: str) -> \\\"Serializable\\\":\\n instance = dill.loads(base64.b64decode(data.encode(\\\"ascii\\\")))\\n if not isinstance(instance, cls):\\n raise TypeError(f\\\"Unpickled object is not of type {cls}\\\")\\n return instance\",\n \"def deserialize(obj):\\n result = base64.urlsafe_b64decode(obj)\\n # this workaround is needed because in case of python 3 the\\n # urlsafe_b64decode method returns string of 'bytes' class\\n result = result.decode()\\n return result\",\n \"def decode_content(self, raw_content):\\n try:\\n obj = pickle.loads(raw_content)\\n return obj\\n except Exception:\\n raise IkatsException(\\\"Failed to load picked object. Context={}\\\".format(str(self)))\",\n \"def deserialize(self, blob):\\n return dill.loads(blob.encode('latin-1'))\",\n \"def from_base64(cls, b64_str, content_type=None):\\n if b64_str is None:\\n raise ValueError(\\\"Base 64 string should not be None\\\")\\n # The decode function does not like taking unicode strings.\\n # Additionally, the encoding alphabet should not container any unicode\\n # symbols, so this aught to be safe.\\n b64_str = str(b64_str)\\n return DataMemoryElement(base64.urlsafe_b64decode(b64_str),\\n content_type)\",\n \"def base64unpickle(self, value):\\n if value:\\n return pickle.loads(self.base64decode(value))\",\n \"def uid_to_obj(uid):\\n serialized_b64 = uid.encode()\\n object_ser = base64.decodebytes(serialized_b64)\\n obj = pickle.loads(object_ser)\\n return obj\",\n \"def load_blob(self, blob_uuid):\\n raise NotImplementedError()\",\n \"def from_db_value(self, value, *args):\\n if value is not None:\\n try:\\n value = dbsafe_decode(value, self.compress)\\n except Exception:\\n # If the value is a definite pickle; and an error is raised in\\n # de-pickling it should be allowed to propogate.\\n if isinstance(value, PickledObject):\\n raise\\n else:\\n if isinstance(value, _ObjectWrapper):\\n return value._obj\\n return value\",\n \"def __init__(self, blob=None):\\n if blob is None:\\n self.versionCode = '0'\\n self.data = {}\\n else:\\n self.versionCode = blob[0]\\n encoded = blob[1:]\\n compressed = base64.b64decode(encoded)\\n self.data = json.loads(zlib.decompress(compressed))\",\n \"def loads(self, data):\\n return loads(data)\",\n \"def decode(self, encoded):\",\n \"def deserialize(self, blob):\\n pass\",\n \"def _feed_field_blobs_from_db_file(self, net):\\n assert os.path.exists(self.db_path), \\\\\\n 'db_path [{db_path}] does not exist'.format(db_path=self.db_path)\\n net.Load(\\n [],\\n self.ds.get_blobs(),\\n db=self.db_path,\\n db_type=self.db_type,\\n absolute_path=True,\\n source_blob_names=self.ds.field_names(),\\n )\",\n \"def serialize_model_instance(instance):\\n ref = [instance.__class__.__module__, instance.__class__.__name__, instance.pk]\\n dumped_ref = json.dumps(ref).encode('utf-8')\\n return b64encode(dumped_ref).decode('utf-8')\",\n \"def retrieve_from_db(self):\\n pass\",\n \"def load_model(self, model_as_bytes: bytes) -> None:\\n\\n self.model = deserialize_from_zippy(model_as_bytes)\",\n \"def decode_model_instance(hashid):\\n try:\\n return get_encoded_object_or_404(hashid).obj\\n except Http404: # pragma: no cover\\n return None\",\n \"def sql_decode(data: str) -> str:\\n return gzip.decompress(base64.urlsafe_b64decode(data.encode())).decode()\",\n \"def load(cls, path):\\n data = cls()\\n data.set_params(pk.load(path))\\n return data\",\n \"def from_base64(x):\\n\\treturn numpy.fromstring(\\n\\t\\tbinascii.a2b_base64(x),\\n\\t\\tdtype=numpy.uint8\\n\\t)\",\n \"def __init__(self, data, from_base64=False, **kwargs):\\n super().__init__(data, **kwargs)\\n self.type_name = 'base64'\\n self.encoding = kwargs.get('encoding', 'original')\\n # TODO data should be bytes only\\n if from_base64:\\n self.data = data\\n else:\\n self.data = base64.b64encode(data).decode()\",\n \"def unserialize(self, data):\\n self.ref = data\\n return self\",\n \"def base64_data(cls, val):\\n return cls('base64_data', val)\",\n \"def _blob(self):\\n return self._load_blob\",\n \"def from_sql(cls, x):\\n\\n return cls(DER = x)\",\n \"def decode(eVal):\\n return pickle.loads(zlib.decompress(base64.b64decode(eVal)))\",\n \"def loads(data):\\n return Decoder().decode(data)\",\n \"def get_real_object(self):\\n query_string = dedent(f\\\"\\\"\\\"\\\\\\n import app.config.models_importer as models_importer\\n\\n class_ = models_importer.all_models['{self.ref_class}']\\n \\n class_.query.get({self.ref_id})\\\"\\\"\\\")\\n\\n return exec(query_string)\",\n \"async def load_blob(elem, elem_type=None):\\n if elem is None:\\n return b''\\n return bytearray(base64.b16decode(elem))\",\n \"def from_pickle(cls, path_or_bytes):\\n if isinstance(path_or_bytes, bytes):\\n return dill.loads(path_or_bytes)\\n with open(path_or_bytes, \\\"rb\\\") as f:\\n return dill.load(f)\",\n \"def from_bytes(self, ???):\",\n \"def decode(binary):\\n name, fields = JsonCoder.decode(binary)\\n dataclass = dataclasses.make_dataclass(\\n name, [(key, type(value)) for key, value in fields.items()]\\n )\\n instance = dataclass(**fields)\\n return instance\",\n \"def from_db_value(self, value, *args, **kwargs):\\n return self.to_python(value)\",\n \"def to_internal_value(self, data):\\n if isinstance(data, str) and data.startswith('data:image'):\\n # Found image is encoded, and must be decoded\\n format, imgstr = data.split(';base64,')\\n ext = format.split('/')[-1] # Extract file extension\\n id = uuid.uuid4()\\n data = ContentFile(base64.b64decode(imgstr), name = id.urn[9:] + '.' + ext)\\n return super(Base64ImageField, self).to_internal_value(data)\",\n \"def deserialize(serialized: str): \\n try:\\n decoded = b64decode(serialized)\\n return loads(decoded)\\n except Exception as e:\\n # If we raise an Exception in a try/except, it includes the original exception.\\n # So we include from None to prevent this (and only raise a single exception)\\n raise Exception(f\\\"{type(e).__name__}: {e} raised on {serialized[:50]}\\\") from None\",\n \"def _ReferenceFromSerialized(serialized):\\n if not isinstance(serialized, basestring):\\n raise TypeError('serialized must be a string; received %r' % serialized)\\n elif isinstance(serialized, unicode):\\n serialized = serialized.encode('utf8')\\n return entity_pb.Reference(serialized)\",\n \"def __bytes__(self):\\n with BytesIO() as b:\\n self.save(b)\\n return b.getvalue()\",\n \"def load(cls, id):\\n key = cls.get_key_prefix()+\\\"#\\\"+str(id)\\n src = dal_get(key)\\n logger.debug( \\\"LOAD %s %s %s\\\", str(key), str(id), str(src))\\n if src == None:\\n raise cls.NotExist(\\\"No instance could be found with ID: \\\"+str(id))\\n result = dal_retrieve(src)\\n result = cls._from_dict(result)\\n return result\",\n \"def __getattr__(self, attr_name):\\n\\t\\tobj = pickle.load(open('%s/%s'%(self.pg_dir,attr_name),'rb'))\\n\\t\\tself.__setattr__(attr_name,obj)\\n\\t\\treturn obj\",\n \"def deserialize(self, blob):\\n return json.loads(blob)\",\n \"def _from_string(cls, serialized):\\r\\n parse = cls.URL_RE.match(serialized)\\r\\n if not parse:\\r\\n raise InvalidKeyError(cls, serialized)\\r\\n\\r\\n parse = parse.groupdict()\\r\\n if parse['definition_id']:\\r\\n parse['definition_id'] = cls.as_object_id(parse['definition_id'])\\r\\n\\r\\n return cls(**{key: parse.get(key) for key in cls.KEY_FIELDS})\",\n \"def load_blob(self, blob_uuid):\\n path = os.path.join(self.rootpath, self.OBJECTPATH, blob_uuid[:2], blob_uuid[-2:]) + os.sep\\n logging.debug(\\\"load blob {}\\\".format(path))\\n filepath = os.path.join(path, blob_uuid)\\n data = Data.from_hdf5(filepath)\\n return data\",\n \"def serialize(obj):\\n result = base64.urlsafe_b64encode(obj)\\n # this workaround is needed because in case of python 3 the\\n # urlsafe_b64encode method returns string of 'bytes' class.\\n result = result.decode()\\n return result\",\n \"def open(self):\\n if isinstance(self.raw_content, (bytes, str)):\\n return BytesIO(self.content)\\n fileobj = self.raw_content.open()\\n\\n # TODO remove when Django 1 is no longer supported\\n if fileobj is None:\\n assert not isinstance(self.raw_content, BlobMeta), repr(self)\\n # work around Django 1.11 bug, fixed in 2.0\\n # https://github.com/django/django/blob/1.11.15/django/core/files/base.py#L131-L137\\n # https://github.com/django/django/blob/2.0/django/core/files/base.py#L128\\n return self.raw_content\\n\\n return fileobj\",\n \"def load_model(self) -> Any:\",\n \"def param_str_to_object(param_str):\\n return cPickle.loads(decompress(b64decode(param_str.encode('utf8'))))\",\n \"def decode(self) -> D:\\n if self.has_cached_data():\\n return self._data\\n\\n # Dispatch decoding\\n data = lookup_serializer(self.encoding).loads(self.blob)\\n\\n self._cache_data(data)\\n return data\",\n \"def to_internal_value(self, data):\\n try: # ToDo penetrate in order test if it has any security flaws\\n decoded = base64.b64decode(data)\\n mime_type = magic.from_buffer(decoded, mime=True)\\n file_ext = mimetypes.guess_extension(mime_type)\\n except TypeError:\\n raise serializers.ValidationError(\\n _('Not a valid base64 file')\\n )\\n\\n if file_ext not in settings.VALID_FILE_EXTENSIONS:\\n raise serializers.ValidationError(\\n _('Forbidden file extension')\\n )\\n\\n file_name = \\\"{0}{1}\\\".format(uuid.uuid4(), file_ext)\\n data = ContentFile(decoded, name=file_name)\\n return data\",\n \"def base64decode(self, value):\\n\\n return value.decode(\\\"base64\\\")\",\n \"def bytes_to_referent(string):\\n d = pickle.loads(string)\\n return RedBlackNode(\\n RedBlackNodeRef(address=d['left']),\\n d['key'],\\n ValueRef(address=d['value']),\\n RedBlackNodeRef(address=d['right']),\\n d['color']\\n )\",\n \"def decode(self): # pragma: no cover\\n pass\",\n \"def _b64decode(self, string):\\n import base64\\n return base64.b64decode(string)\",\n \"def _b64decode(self, string):\\n import base64\\n return base64.b64decode(string)\",\n \"def get_record(self, index, asbase64=False):\\n data = self.bank.readrec(int(index))\\n if asbase64:\\n return base64.b64encode(data[1:]).decode('utf-8')\\n else:\\n return data[1:]\",\n \"def dic_b64_and_pickle_loads(data):\\n for i in data:\\n data[i] = pickle.loads(base64.b64decode(data[i]))\\n return data\",\n \"def base64_string(self) -> global___Expression:\",\n \"def _from_string(cls, serialized):\\r\\n parse = cls.parse_url(serialized)\\r\\n\\r\\n if parse['version_guid']:\\r\\n parse['version_guid'] = cls.as_object_id(parse['version_guid'])\\r\\n\\r\\n return cls(**{key: parse.get(key) for key in cls.KEY_FIELDS})\",\n \"def gen_raw_from_broadlink_base64(data):\\n yield from gen_raw_from_broadlink(b64decode(data))\",\n \"def serialized(self):\\n return self.reference().Encode()\",\n \"def from_attr(self, session, resource_attr):\\n # \\\"object_dict\\\" and \\\"tree\\\" uses LargeBinary sqlalchemy datatype.\\n # LargeBinary sqlalchemy datatype needs \\\"bytes-like\\\" object\\n # and in Py2, string are bytes-like objects while in Py3 they aren't.\\n # So we need to store \\\"bytes-like\\\" object in DB objects.\\n # For storing the DB model object,\\n # we need to encode to utf-8 bytes format (for Py3 compatibility).\\n # Since in Py2, string are bytes-like objects, encoding won't\\n # make a difference.\\n encoded_attr_dict = {}\\n for k, v in list(resource_attr.items()):\\n if k not in getattr(self, '_exclude_from', []):\\n if k == 'object_dict':\\n if isinstance(v, six.text_type):\\n v = v.encode('utf-8')\\n elif k == 'tree':\\n if isinstance(v, six.text_type):\\n v = v.encode('utf-8')\\n encoded_attr_dict[k] = v\\n self.set_attr(session, k, v, **encoded_attr_dict)\",\n \"def serialize(self, obj):\\n return dill.dumps(obj, 0).decode('latin-1')\",\n \"def load_hickle_4_x_string(h_node,base_type,py_obj_type):\\n if not 'object' in h_node.dtype.name or h_node.attrs.get('str_type',None) is not None:\\n return load_list_dataset(h_node,base_type,py_obj_type)\\n content = h_node[()]\\n if py_obj_type is str:\\n return content if isinstance(content,str) else content.decode('utf8')\\n return py_obj_type(content) if content.__class__ is not py_obj_type else content\",\n \"def to_representation(self, value):\\n if value:\\n data = default_storage.open(value.path).read()\\n encoded=base64.b64encode(data).decode(\\\"utf-8\\\")\\n return encoded\",\n \"def set_pdf_from_base64(self, content_base64):\\n try:\\n raw = base64.standard_b64decode(str(content_base64))\\n except (TypeError, binascii.Error):\\n raise Exception('The provided PDF is not Base64-encoded')\\n self.set_pdf_from_raw(raw)\",\n \"def from_bytes(self, *args, **kwargs): # real signature unknown\\n pass\",\n \"def from_bytes(self, *args, **kwargs): # real signature unknown\\n pass\",\n \"def from_bytes(self, *args, **kwargs): # real signature unknown\\n pass\",\n \"def from_bytes(self, *args, **kwargs): # real signature unknown\\n pass\",\n \"def from_bytes(self, *args, **kwargs): # real signature unknown\\n pass\",\n \"def from_bytes(self, *args, **kwargs): # real signature unknown\\n pass\",\n \"def from_bytes(self, *args, **kwargs): # real signature unknown\\n pass\",\n \"def from_bytes(self, *args, **kwargs): # real signature unknown\\n pass\",\n \"def from_bytes(self, *args, **kwargs): # real signature unknown\\n pass\",\n \"def from_bytes(self, *args, **kwargs): # real signature unknown\\n pass\",\n \"def from_bytes(self, *args, **kwargs): # real signature unknown\\n pass\",\n \"def from_bytes(self, *args, **kwargs): # real signature unknown\\n pass\",\n \"def from_bytes(self, *args, **kwargs): # real signature unknown\\n pass\",\n \"def from_bytes(self, *args, **kwargs): # real signature unknown\\n pass\",\n \"def from_bytes(self, *args, **kwargs): # real signature unknown\\n pass\",\n \"def from_bytes(self, *args, **kwargs): # real signature unknown\\n pass\",\n \"def base64decode(self, item: str) -> bytes:\\n b = self[item]\\n b = b if isinstance(b, bytes) else b.encode()\\n return base64.b64decode(b, validate=True)\",\n \"def decode(self, base64: str) -> GeoLocation:\\n\\n longitude, latitude = shortlinkToGeoLoc(base64)\\n\\n # how to determine altitude from short links?\\n return GeoLocation(latitude, longitude, altitude_in_meters=0)\",\n \"def load(cls, strm):\\n raise NotImplementedError(\\n '\\\"load\\\" should be implemented in the' \\\" class derived from SurrogateBase\\\"\\n )\",\n \"def to_python(self, value):\\n if value is not None and isinstance(value, basestring):\\n value = str(value)\\n if value.startswith('base64:'):\\n value = value.split(':')[1]\\n prepared_data = json.loads(base64.decodestring(value))\\n return json.loads(prepared_data)\\n elif value is not None and isinstance(value, dict):\\n value = json.dumps(value)\\n return value\\n\\n return value\",\n \"def uuid(o):\\n return sqlite3.Binary(o.bytes)\",\n \"def test_the_unicode_in_data_base(self):\\n\\n model_instance = Contact.objects.first()\\n model_instance.name = u'Олег'\\n model_instance.surname = u'Сенишин'\\n model_instance.bio = u'Працюю, після роботи, самостійно вивчаю Python, Django, JavaScript.'\\n model_instance.save()\\n\\n response = self.client.get(reverse(\\\"my_info\\\"))\\n\\n model_instance = Contact.objects.get(id=1)\\n\\n self.assertIn(model_instance.name.encode('utf-8'), response.content)\\n self.assertIn(model_instance.surname.encode('utf-8'), response.content)\\n self.assertIn(model_instance.email.encode('utf-8'), response.content)\\n self.assertIn(model_instance.bio.encode('utf-8'), response.content)\\n self.assertIn(model_instance.skype.encode('utf-8'), response.content)\\n self.assertIn(model_instance.contacts.encode('utf-8'), response.content)\",\n \"def fromBytes(cls, inBytes):\\n return cls.fromJson(inBytes.decode())\",\n \"def fromBytes(cls, inBytes):\\n return cls.fromJson(inBytes.decode())\",\n \"def fromBytes(cls, inBytes):\\n return cls.fromJson(inBytes.decode())\",\n \"def load(self):\\n\\n raise NotImplementedError\",\n \"def load(self):\",\n \"def load(cls, data):\\n return cls(**data)\",\n \"def load_PIC(self, edron: Dict[str, str]) -> None:\\n ...\",\n \"def atob(b):\\n return base64.b64decode(b.encode()).decode()\",\n \"def decode_key_from_mongo(fieldname):\\r\\n return urllib.unquote(fieldname)\",\n \"def decode_and_load_json(query):\\n try:\\n decoded_query = base64.b64decode(query)\\n except binascii.Error:\\n raise ParseError('Invalid base64 format')\\n try:\\n return json.loads(decoded_query)\\n except json.JSONDecodeError:\\n raise ParseError('Invalid json format')\",\n \"def load(cls, data):\\n if cls.Schema is None: # pragma: no cover\\n msg = (\\\"Schema of this Model are not specified! For example: \\\"\\n \\\"class User(BaseModel): ...; class UserSchema(Schema): ...; \\\"\\n \\\"User.Schema = UserSchema\\\")\\n raise NotImplementedError(msg)\\n\\n res = cls.Schema().load(data)\\n if len(res.errors) == 0:\\n return res.data\\n else:\\n raise Exception(\\\"Errors: {}\\\".format(res.errors))\",\n \"def deserialize(cls, field_name, field_sig_dict, sig_version,\\n database=DEFAULT_DB_ALIAS):\\n validate_sig_version(sig_version)\\n\\n if sig_version == 2:\\n field_sig_attrs = field_sig_dict.get('attrs', {})\\n\\n # Load the class for the referenced field type.\\n field_type_module, field_type_name = \\\\\\n field_sig_dict['type'].rsplit('.', 1)\\n\\n # If we have a field path in the signature that lives in\\n # django.db.models.fields, update it to look in django.db.models\\n # instead. This is for compatibility across all Django versions.\\n if field_type_module.startswith('django.db.models.fields'):\\n field_type_module = 'django.db.models'\\n\\n try:\\n field_type = getattr(import_module(field_type_module),\\n field_type_name)\\n except (AttributeError, ImportError):\\n raise ImportError('Unable to locate field type %s'\\n % '%s.%s' % (field_type_module,\\n field_type_name))\\n elif sig_version == 1:\\n field_sig_attrs = field_sig_dict\\n field_type = field_sig_dict['field_type']\\n\\n field_attrs = {}\\n\\n for attr in cls._iter_attrs_for_field_type(field_type):\\n if hasattr(cls, attr):\\n # This is stored on the field signature class itself, so\\n # it's not attribute data we want to load.\\n continue\\n\\n alias = cls._ATTRIBUTE_ALIASES.get(attr)\\n\\n if alias and alias in field_sig_attrs:\\n value = field_sig_attrs[alias]\\n elif attr in field_sig_attrs:\\n value = field_sig_attrs[attr]\\n else:\\n # The signature didn't contain a value for this attribute.\\n continue\\n\\n field_attrs[attr] = value\\n\\n return cls(field_name=field_name,\\n field_type=field_type,\\n field_attrs=field_attrs,\\n related_model=field_sig_dict.get('related_model'))\",\n \"def load(self):\\n return None\"\n]","isConversational":false},"negative_scores":{"kind":"list like","value":["0.60619754","0.59615856","0.5860249","0.5757516","0.5731678","0.5695101","0.5684532","0.56761885","0.5633992","0.54312295","0.5369191","0.5346863","0.5322224","0.5310522","0.5259935","0.5250897","0.5246336","0.5245134","0.5214885","0.5203839","0.51985216","0.5160335","0.51592475","0.51524204","0.51022315","0.50701505","0.50508004","0.50405115","0.50388956","0.50373226","0.5028102","0.50261027","0.5024681","0.49723467","0.49709496","0.49676615","0.49651793","0.4946815","0.49413726","0.49384275","0.49355313","0.49339947","0.49214476","0.49086604","0.49060273","0.49011335","0.49009112","0.48992014","0.4897634","0.48866564","0.48703843","0.4860937","0.48588353","0.48588353","0.48528272","0.48517245","0.48506787","0.4831522","0.4829883","0.48292083","0.48091218","0.48086485","0.48008794","0.47936615","0.47906038","0.47904524","0.47904524","0.47904524","0.47904524","0.47904524","0.47904524","0.47904524","0.47904524","0.47904524","0.47904524","0.47904524","0.47904524","0.47904524","0.47904524","0.47904524","0.47904524","0.47658783","0.47584","0.47507694","0.47493103","0.47491643","0.47379407","0.47330946","0.47330946","0.47330946","0.47280473","0.47263345","0.47257766","0.47197482","0.47190917","0.4716354","0.4716243","0.47142276","0.4684626","0.46743593"],"string":"[\n \"0.60619754\",\n \"0.59615856\",\n \"0.5860249\",\n \"0.5757516\",\n \"0.5731678\",\n \"0.5695101\",\n \"0.5684532\",\n \"0.56761885\",\n \"0.5633992\",\n \"0.54312295\",\n \"0.5369191\",\n \"0.5346863\",\n \"0.5322224\",\n \"0.5310522\",\n \"0.5259935\",\n \"0.5250897\",\n \"0.5246336\",\n \"0.5245134\",\n \"0.5214885\",\n \"0.5203839\",\n \"0.51985216\",\n \"0.5160335\",\n \"0.51592475\",\n \"0.51524204\",\n \"0.51022315\",\n \"0.50701505\",\n \"0.50508004\",\n \"0.50405115\",\n \"0.50388956\",\n \"0.50373226\",\n \"0.5028102\",\n \"0.50261027\",\n \"0.5024681\",\n \"0.49723467\",\n \"0.49709496\",\n \"0.49676615\",\n \"0.49651793\",\n \"0.4946815\",\n \"0.49413726\",\n \"0.49384275\",\n \"0.49355313\",\n \"0.49339947\",\n \"0.49214476\",\n \"0.49086604\",\n \"0.49060273\",\n \"0.49011335\",\n \"0.49009112\",\n \"0.48992014\",\n \"0.4897634\",\n \"0.48866564\",\n \"0.48703843\",\n \"0.4860937\",\n \"0.48588353\",\n \"0.48588353\",\n \"0.48528272\",\n \"0.48517245\",\n \"0.48506787\",\n \"0.4831522\",\n \"0.4829883\",\n \"0.48292083\",\n \"0.48091218\",\n \"0.48086485\",\n \"0.48008794\",\n \"0.47936615\",\n \"0.47906038\",\n \"0.47904524\",\n \"0.47904524\",\n \"0.47904524\",\n \"0.47904524\",\n \"0.47904524\",\n \"0.47904524\",\n \"0.47904524\",\n \"0.47904524\",\n \"0.47904524\",\n \"0.47904524\",\n \"0.47904524\",\n \"0.47904524\",\n \"0.47904524\",\n \"0.47904524\",\n \"0.47904524\",\n \"0.47904524\",\n \"0.47658783\",\n \"0.47584\",\n \"0.47507694\",\n \"0.47493103\",\n \"0.47491643\",\n \"0.47379407\",\n \"0.47330946\",\n \"0.47330946\",\n \"0.47330946\",\n \"0.47280473\",\n \"0.47263345\",\n \"0.47257766\",\n \"0.47197482\",\n \"0.47190917\",\n \"0.4716354\",\n \"0.4716243\",\n \"0.47142276\",\n \"0.4684626\",\n \"0.46743593\"\n]","isConversational":false},"document_score":{"kind":"string","value":"0.65203667"},"document_rank":{"kind":"string","value":"0"}}},{"rowIdx":75393,"cells":{"query":{"kind":"string","value":"Download data of one study from database"},"document":{"kind":"string","value":"def download_data(study, engine, has_trips=True, filter_coverage=False):\n\n def to_datetime(df):\n df[\"started_at\"] = pd.to_datetime(df[\"started_at\"], utc=True)\n df[\"finished_at\"] = pd.to_datetime(df[\"finished_at\"], utc=True)\n return df\n\n exclude_purpose_tist = [\n \"Light Rail\",\n \"Subway\",\n \"Platform\",\n \"Trail\",\n \"Road\",\n \"Train\",\n \"Bus Line\",\n ]\n\n print(\"\\t download staypoints\")\n sp = gpd.read_postgis(\n sql=\"select * from {}.{}\".format(study, sp_name_dict[study]),\n con=engine,\n geom_col=\"geom\",\n index_col=\"id\",\n )\n sp = to_datetime(sp)\n\n print(\"\\t download locations\")\n sql = f\"SELECT * FROM {study}.{locs_name_dict[study]}\"\n locs = ti.io.read_locations_postgis(sql, con=engine, index_col=\"id\")\n\n # studies with trips\n gap_treshold = 12\n if has_trips:\n print(\"\\t download trips\")\n trips = ti.io.read_trips_postgis(\n f\"select * from {study}.trips\", con=engine\n )\n if filter_coverage:\n # get_triplegs(study=study, engine=engine)\n print(\"\\t download triplegs\")\n tpls = pd.read_sql(\n sql=f\"select id, user_id, started_at, finished_at from {study}.triplegs\",\n con=engine,\n index_col=\"id\",\n )\n tpls = to_datetime(tpls)\n sp, locs, trips = filter_tracking_coverage(sp, locs, tpls, trips)\n\n # studies without trips (Foursquare)\n else:\n sp = sp[~sp[\"purpose\"].isin(exclude_purpose_tist)]\n trips = None\n return (sp, locs, trips, gap_treshold)"},"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 download_dataset(self):\n raise NotImplementedError","def download():\n return response.download(request,db)","def download():\n return response.download(request,db)","def download():\n return response.download(request,db)","def download():\n return response.download(request,db)","def download():\n return response.download(request,db)","def download():\n return response.download(request,db)","def fetch(self,url=URL):\n\t\tlog.info('downloading latest PHE case data')\n#\t\tself.data=lookup_json(url)\n\t\tself.fetch_csv() #JSON discontinued; switched back to CSV\n\t\tself.edition=self.latest_samples\n\t\tlog.info(f'Last samples from {self.edition}')","def download():\n\treturn response.download(request, db)","def download_hess_dr1_data():\n download_data_files(FILENAMES_HESS_DR1)","def download1():\n #t=request.vars.arg(0)\n response.flash=request\n #print request.wsgi.environ['HTTP_REFERER']\n #print 'yghklo=',request.args[0]\n a=db(db.Project.Project_File==request.args[0]).select(db.Project.ALL)\n #a=db(db.Project.id==38).select(db.Project.ALL)\n #if a == None:\n#\t print 'silent'\n # print 'a= aabhas download',a[0].no_of_download, a[0].Project_File\n # if a[0].no_of_download==None:\n#\t a[0].no_download=0\n db(db.Project.Project_File==a[0].Project_File).update(no_of_download=(a[0].no_of_download or 0)+1)\n print 'a.id=',a[0].id\n # print len(a),'\\n'\n #print \"\\n\\n\\n\\n\"\n return response.download(request, db)","def download_and_prepare_dmipy_example_dataset(self):\r\n subject_ID = 100307\r\n self.download_subject(subject_ID)\r\n self.prepare_example_slice(subject_ID)","def get_study_data(self, soup, url):\n pass","def download():\r\n reader = GSODDataReader()\r\n year_list = range(2001, 2012)\r\n austin = reader.collect_data(year_list, exact_station=True,\r\n station_name='AUSTIN CAMP MABRY', state='TX', country='US')\r\n houston = reader.collect_data(year_list, exact_station=True,\r\n station_name='HOUSTON/D.W. HOOKS', state='TX', country='US')\r\n new_york = reader.collect_data(year_list, exact_station=True,\r\n station_name='NEW YORK/LA GUARDIA', state='NY', country='US')\r\n newark = reader.collect_data(year_list, exact_station=True,\r\n station_name='NEWARK INTL AIRPORT', state='NJ', country='US')\r\n punta_arenas = reader.collect_data(year_list, exact_station=True,\r\n station_name='PUNTA ARENAS', country='CH')\r\n wellington = reader.collect_data(year_list, exact_station=True,\r\n station_name='WELLINGTON AIRPORT', country='NZ')\r\n store = HDFStore('weather.h5')\r\n store['austin'] = austin\r\n store['houston'] = houston\r\n store['nyc'] = new_york\r\n store['newark'] = newark\r\n store['punta_arenas'] = punta_arenas\r\n store['wellington'] = wellington\r\n store.close()","def download():\n if auth.has_membership(1):\n user = \"Admin\"\n elif auth.has_membership(2):\n user = \"Examiner\"\n elif auth.has_membership(3):\n user = \"student\"\n elif auth.has_membership(5):\n user = \"Managment\"\n\n db.activity_log.insert( Title_entry=\"Download assignment\", \n referance_id=auth.user.id,\n remarks=\"content downloaded by {}\".format(user))\n db.commit()\n return response.download(request, db)","def download():\n return response.download(request, db)","def download():\n return response.download(request, db)","def download():\n return response.download(request, db)","def download():\n return response.download(request, db)","def download():\n return response.download(request, db)","def download():\n return response.download(request, db)","def download():\n return response.download(request, db)","def download():\n return response.download(request, db)","def download():\n return response.download(request, db)","def download():\n return response.download(request, db)","def download():\n return response.download(request, db)","def download():\n return response.download(request, db)","def download():\n return response.download(request, db)","def download():\n return response.download(request, db)","def download():\n return response.download(request, db)","def download():\n return response.download(request, db)","def download():\n return response.download(request, db)","def download():\n return response.download(request, db)","def download():\n return response.download(request, db)","def download():\n return response.download(request, db)","def download():\n return response.download(request, db)","def download():\n return response.download(request, db)","def download():\n return response.download(request, db)","def download():\n return response.download(request, db)","def download():\n return response.download(request, db)","def download():\n return response.download(request, db)","def download():\n return response.download(request, db)","def download():\n return response.download(request, db)","def download():\n return response.download(request, db)","def download():\n return response.download(request, db)","def download():\n return response.download(request, db)","def download():\n return response.download(request, db)","def download():\n return response.download(request, db)","def download():\n return response.download(request, db)","def getData(constrain):\n\n dat_AGS = chunks(AGS, 100)\n for num, ags_c in enumerate(dat_AGS):\n to_download = DOWNLOAD_LINK.format(ags_id=ags_c, constrain=constrain)\n to_download = to_download.replace(\" \", \"\")\n download_name = \"../Data/Gemeinden/{}-{}.csv\".format(\n constrain, num)\n\n url.urlretrieve(to_download, filename=download_name)\n\n sleep(1) # be nice\n\n return(num)","def download(self, verbose):\n # Download datasets\n if verbose:\n print(\"Retrieving datasets from Our World In Data https://github.com/owid/covid-19-data/\")\n # Vaccinations\n v_rec_cols = [\n \"date\", \"location\", \"iso_code\", \"total_vaccinations\", \"people_vaccinated\", \"people_fully_vaccinated\"]\n v_rec_df = pd.read_csv(self.URL_V_REC, usecols=v_rec_cols)\n v_loc_df = pd.read_csv(self.URL_V_LOC, usecols=[\"location\", \"vaccines\"])\n v_df = v_rec_df.merge(v_loc_df, how=\"left\", on=\"location\")\n # Tests\n pcr_rec_cols = [\"ISO code\", \"Date\", \"Daily change in cumulative total\", \"Cumulative total\"]\n pcr_df = pd.read_csv(self.URL_P_REC, usecols=pcr_rec_cols)\n pcr_df = pcr_df.rename(columns={\"ISO code\": \"iso_code\", \"Date\": \"date\"})\n pcr_df[\"cumsum\"] = pcr_df.groupby(\"iso_code\")[\"Daily change in cumulative total\"].cumsum()\n pcr_df = pcr_df.assign(tests=lambda x: x[\"Cumulative total\"].fillna(x[\"cumsum\"]))\n # Combine data (vaccinations/tests)\n df = v_df.set_index([\"iso_code\", \"date\"])\n df = df.combine_first(pcr_df.set_index([\"iso_code\", \"date\"]).loc[:, [\"tests\"]])\n df = df.reset_index()\n # Location (country/province)\n df[\"location\"] = df[\"location\"].replace(\n {\n # COG\n \"Congo\": \"Republic of the Congo\",\n }\n )\n df = df.loc[~df[\"iso_code\"].str.contains(\"OWID_\")]\n df[\"location\"] = df.groupby(\"iso_code\")[\"location\"].bfill()\n df.loc[df[\"location\"] == df[\"iso_code\"], \"location\"] = None\n df.loc[df[\"location\"].isna(), \"location\"] = df.loc[df[\"location\"].isna(), \"iso_code\"].apply(\n lambda x: coco.convert(x, to=\"name_short\", not_found=None))\n df[self.PROVINCE] = self.UNKNOWN\n return df","def fetch_save(url):\n\n name = url.split(\"/\")[-1]\n response = requests.get(url, stream=True)\n if response.status_code == 200:\n with open(f\"{DATA_PATH}/{name}\", \"wb\") as f:\n f.write(response.raw.read())\n else:\n logging.info(f\"Failed {url} download\")","def fetch_inspect_data(filename, output, db_url=None):\n r2dt.write_training_data(filename, db_url, output)","def download_data_and_save():\n url = 'https://github.com/djay/covidthailand/wiki/combined.csv'\n s=requests.get(url).content\n global df\n global last_updated\n df=pd.read_csv(io.StringIO(s.decode('utf-8')), parse_dates= ['Date'])\n df.to_parquet(file_name, compression='UNCOMPRESSED')\n df.to_csv('jaydata.csv')\n last_updated = df['Date'][df.index[-1]].strftime(\"%d %B %Y\")\n\n url = 'https://raw.githubusercontent.com/wiki/djay/covidthailand/vaccinations.csv'\n s=requests.get(url).content\n global vac_df\n vac_df=pd.read_csv(io.StringIO(s.decode('utf-8')), parse_dates= ['Date'])\n vac_df.to_parquet('vaccination.parquet', compression='UNCOMPRESSED')\n\n print(\"Data downloaded and saved successfully. Data up to \" + last_updated)","def _download(self, path):\n self.logger.info('Getting Million Song Dataset...')\n self.logger.info('Downloading Echo Nest Taste Subprofile train data...')\n base_url = 'http://millionsongdataset.com/sites/default/files/challenge/'\n\n download_dataset(\n base_url + 'train_triplets.txt.zip',\n join(self.data_folder, 'train.zip')\n )\n rename(join(self.data_folder, 'train'), path)\n\n self.logger.info('Downloading evaluation data for MSD Challenge...')\n download_dataset(\n base_url + 'EvalDataYear1MSDWebsite.zip',\n join(path, 'eval.zip')\n )\n rename(\n join(path, 'EvalDataYear1MSDWebsite'),\n join(path, 'evaluation')\n )\n\n self.logger.info('Downloading list of matching errors...')\n url = 'http://millionsongdataset.com/sites/default/files/tasteprofile/sid_mismatches.txt'\n download_url(url, join(path, 'sid_mismatches.txt'))","def download_report():\n entities = get_names()\n save_csv(entities)","def download(args):\n with_dataset(args, Dataset._download)","def access_study(self, url):\n\n doc = requests.get(url)\n self.num_requests += 1\n if doc.status_code != 200:\n with open('runReport.txt', 'a') as report:\n report.write('\\n')\n report.write(str(doc.status_code) + \" for \" + url)\n return None\n else:\n soup = BeautifulSoup(doc.content, features=\"lxml\")\n self.num_hits += 1\n return self.get_study_data(soup, url)","def download(exam, out, name_question, sid_question, compact):\n exam_json, template_questions, email_to_data_map, total = examtool.api.download.download(exam)\n examtool.api.download.export(template_questions, email_to_data_map, total, exam, out, name_question, sid_question, compact)","def save_data(self):\n # Command to get the download data\n pass","def download_examples(request):\n\n file_required = request.GET.get('token',None)\n path = ''\n workpath = os.path.dirname(os.path.abspath(__file__)) # Returns the Path your .py file is in\n if file_required == 'reports':\n path = os.path.join(workpath, './static/examples/report.csv')\n\n elif file_required == 'concepts':\n path = os.path.join(workpath, './static/examples/concept.csv')\n\n elif file_required == 'labels':\n path = os.path.join(workpath, './static/examples/labels.csv')\n\n elif file_required == 'pubmed':\n path = os.path.join(workpath, './static/examples/pubmed.csv')\n\n content = open(path,'r')\n return HttpResponse(content, content_type='text/csv')","def fetch_data(self):","def downloadFile()-> None:\n logging.info(f\"Downloading current data set {getTime()}\")\n with open(DATA_FILE,\"wb\") as f:\n f.write(get(\"https://covid.ourworldindata.org/data/owid-covid-data.csv\").text.encode())\n logging.info(f\"Finished Downloading current data set {getTime()}\")","def downloadData(url : str, descriptor : str):\n assets = datapackage.Package(url).resources\n\n for data in filter(lambda x: x.tabular and x.descriptor['name'] == descriptor, assets):\n response = requests.get(data.descriptor['path'])\n return io.StringIO(response.content.decode('utf-8'))","def get_datafiles(self, url, survey_path):\n pass","def download(self, verbose):\n\n # Download datasets\n if verbose:\n print(\"Retrieving datasets from COVID-19 Open Data by Google Cloud Platform https://github.com/GoogleCloudPlatform/covid-19-open-data\")\n # Index\n i_cols = [\"location_key\", \"country_name\", \"subregion1_name\", \"subregion2_name\", \"iso_3166_1_alpha_3\"]\n i_df = pd.read_csv(self.URL_I, usecols=i_cols)\n # Mobility\n m_df = pd.read_csv(self.URL_M)\n m_df = (m_df.set_index([\"date\", \"location_key\"]) + 100).reset_index()\n # Combine data\n df = m_df.merge(i_df, how=\"left\", on=\"location_key\")\n # Location (country/province)\n df = df.loc[df[\"subregion2_name\"].isna()]\n df[self.PROVINCE] = df[\"subregion1_name\"].fillna(self.UNKNOWN).apply(unidecode)\n df[\"country_name\"] = df[\"country_name\"].replace(\n {\n # CIV\n \"Ivory Coast\": \"Cote d'Ivoire\",\n }\n )\n return df","def download_assignment_student(request, pk, i):\n evalassignment = Evalassignment.objects.\\\n filter(pk=pk, evaluator=request.user).first()\n if evalassignment:\n eval_name = '%s_%s' % (evalassignment.assignment.assignmentype.title.\n replace(\" \", \"\"), i)\n filename = 'assign_%s.%s' % (eval_name, evalassignment.assignment.\n document.name.split('.')[-1])\n response = HttpResponse(evalassignment.assignment.document,\n content_type='application/force_download')\n response['Content-Disposition'] = 'attachment; filename=%s' % filename\n return response\n else:\n return redirect('gradapp:dashboard_student')","def download(self):\n\n with open(self.dataset_path) as dataset_file:\n dataset = json.load(dataset_file)\n\n path = \"\".join([POST_HIT_PATH, dataset[\"dataset\"][\"data_path\"]])\n if not os.path.exists(path):\n os.makedirs(path)\n\n protocole = dataset[\"dataset\"][\"protocole\"]\n\n download_links = []\n\n for resource in dataset[\"dataset\"][\"resources\"]:\n file_path = \"\".join([path, resource[\"filename\"]])\n\n #Check if the the download link has not been used before (One download link for all)\n if resource[\"download_link\"] not in download_links:\n \n print(\"DOWNLOADING : {}\".format(resource[\"filename\"]))\n f = urllib.request.urlopen(resource[\"download_link\"])\n data = f.read()\n with open(file_path, \"wb\") as donwload_file:\n donwload_file.write(data)\n\n download_links.append(resource[\"download_link\"])\n\n \n #Extract all files from the tar archives if necessary\n if tarfile.is_tarfile(file_path):\n tf = tarfile.open(file_path)\n tf.exractall()","def download_models_and_data():\n\n for file in DATA_FILES:\n download_file(file[\"url\"], file[\"path\"])","def fetch_training_data(filename, output, db_url=None):\n r2dt.write_training_data(filename, db_url, output)","def dataset():\n\n # first, load the data into pd.DataFrames so that we can store it in a DB\n movements = pd.read_csv(bz2.open(request.files['movements']))\n vertices = pd.read_csv(request.files['vertices'])\n project = request.values['project']\n\n with sqlite3.connect('projects/%s.db'%project) as con:\n movements.to_sql('movements', con)\n vertices.to_sql('vertices', con)\n\n return redirect('/visualise.html?project=%s'%project, 200)","def download_data(session, date: datetime.date, sampling_feature: str):\n\n data = session.get_observation_by_date_and_feature(date=date, sampling_features=[sampling_feature])\n\n return data","def download(self,connector,condition):\n c= connector.cursor()\n\n\n snew = buildSelect(self,'LDA',condition)\n# print snew\n c.execute(snew)\n lnames=[]\n for name,val in sorted(self.__dict__.iteritems()):\n lnames.append(name)\n vobj=[]\n for row in c:\n# print row\n obj=LDADef()\n for i in range(len(lnames)):\n obj.__dict__[lnames[i]]=row[i]\n vobj.append(obj)\n return vobj","def generic_download(self, data_set, scene, output_dir, chunk_size=1024):\n\n ### LANDSAT DOWNLOAD ###\n if is_product_id(scene['displayId']):\n filename = self.download(scene['displayId'], output_dir)\n\n ### NON-LANDSAT ###\n else:\n filename = self.download(scene['displayId'], output_dir, data_set=data_set)\n\n return filename","def getPublicEMPDownloadLinks(self):\n try:\n studies = []\n con = self.getMetadataDatabaseConnection()\n results = con.cursor()\n con.cursor().callproc('get_public_emp_studies', [results])\n for row in results:\n # study_id, project_name, file_path, study_abstract\n studies.append((row[0], row[1], row[2], row[3]))\n return studies\n except Exception, e:\n print 'Exception caught: %s.\\nThe error is: %s' % (type(e), e)","def download():\n toydata = requests.get(DATA_URL).json()\n return toydata","def download():\n raise NotImplementedError","def download_data(dataset_name=None):\r\n\r\n dr = data_resources[dataset_name]\r\n if not authorize_download(dataset_name):\r\n raise Exception(\"Permission to download data set denied.\")\r\n\r\n if dr.has_key('suffices'):\r\n for url, files, suffices in zip(dr['urls'], dr['files'], dr['suffices']):\r\n for file, suffix in zip(files, suffices):\r\n download_url(os.path.join(url,file), dataset_name, dataset_name, suffix=suffix)\r\n else:\r\n for url, files in zip(dr['urls'], dr['files']):\r\n for file in files:\r\n download_url(os.path.join(url,file), dataset_name, dataset_name)\r\n return True","def get_drms_files(self):\n import drms\n client = drms.Client(email=self.email,verbose=True)\n fmt = '%Y.%m.%d_%H:%M'\n self.t_qstr = self.series+'[{0}_TAI-{1}_TAI@{2}]'.format(self.start.strftime(fmt),self.end.strftime(fmt),self.cadence) \n\n\n #create wavelength query string\n self.w_qstr = '[' \n for i in self.wav: self.w_qstr = self.w_qstr+'{0},'.format(int(i.value))\n #remove last , and add bracket\n self.w_qstr = self.w_qstr[:-1]+']'\n \n #make the series string\n self.s_qstr = '{'+self.segment+'}'\n\n #the full query\n self.qstr = self.t_qstr+self.w_qstr+self.s_qstr\n\n #IF ERRORS WITH URL ERROR IT IS BECAUSE THE DOWNLOAD FILE SIZE IS TOO LARGE\n #export the data file list \n self.expt = client.export(self.qstr)\n#create an array of indexes to download\n index = np.arange(np.size(self.expt.urls.url))\n# get file from JSOC\n #set directory to current if no path set\n outf = self.expt.download(self.odir,index,fname_from_rec=True)","def download_dataset():\n \n ID = \"1-3_oB5iSF-c_V65-uSdUlo024NzlgSYZ\"\n script1 = f\"\"\"\n wget --load-cookies /tmp/cookies.txt \"https://docs.google.com/uc?export=download&confirm=$(wget --quiet --save-cookies /tmp/cookies.txt --keep-session-cookies --no-check-certificate 'https://docs.google.com/uc?export=download&id='{ID} -O- | sed -rn 's/.*confirm=([0-9A-Za-z_]+).*/\\1\\n/p')&id=\"{ID} -O Data.zip && rm -rf /tmp/cookies.txt\n \"\"\"\n script2 = \"\"\"unzip Data.zip\"\"\"\n\n os.system(script1)\n os.system(script2)","def download_data(self):\n content = requests.get(self.TOP_250_LIST)\n soup = BeautifulSoup(content.content, 'lxml')\n movies = soup.select('tbody.lister-list tr')\n for m in movies:\n title_column = m.select('td.titleColumn')\n link = self.format_link(title_column[0].a['href'])\n title = self.format_title(title_column[0].a.string.encode('utf-8'))\n path = 'pages/{}.html'.format(title)\n if os.path.isfile(path):\n continue\n response = requests.get(link)\n with open(path, 'wr') as f:\n f.write(response.content)","def download_files(self):","def _downloadDataFile(self):\n config = SiteConfiguration.objects.get()\n\n with requests.Session() as s:\n # Authentication\n data = {\n 'identificationBean.identifiant': '{}'.format(config.login),\n 'identificationBean.mdp': '{}'.format(config.password),\n 'userName': '{}'.format(config.username)\n }\n url = 'http://extranet.ffbb.com/fbi/identification.do'\n s.post(url, data=data)\n\n # Create filters\n params = (\n ('action', 'executeCsv'),\n ('rechercherRencontreSaisieResultatBean.idDivision', ''),\n ('rechercherRencontreSaisieResultatBean.rechercherEquipe2', 'O'),\n ('rechercherRencontreSaisieResultatBean.dateDebutRencontre', ''),\n ('rechercherRencontreSaisieResultatBean.dateFinRencontre', ''),\n ('rechercherRencontreSaisieResultatBean.idPoule', ''),\n ('rechercherRencontreSaisieResultatBean.numeroEquipe', ''),\n )\n\n # Get Csv file\n url = 'http://extranet.ffbb.com/fbi/rechercherCompetitionRencontre.do'\n response = s.get(url, params=params)\n\n if(response.headers['content-type'] != 'application/ms-excel;charset=UTF-8'):\n return False\n\n # Create the file\n if response.status_code == 200:\n os.makedirs(os.path.dirname(settings.DATA_PATH), exist_ok=True)\n with open(settings.DATA_PATH, 'wb') as f:\n for chunk in response:\n f.write(chunk)\n\n return True","def download(self):\n pass","def download(self):\n pass","def download_scn(self, unq_id):\n if not os.path.exists(self.baseDownloadPath):\n raise EODataDownException(\"The download path does not exist, please create and run again.\")\n\n logger.debug(\"Creating Database Engine and Session.\")\n db_engine = sqlalchemy.create_engine(self.db_info_obj.dbConn)\n session_sqlalc = sqlalchemy.orm.sessionmaker(bind=db_engine)\n ses = session_sqlalc()\n\n logger.debug(\"Perform query to find scenes which need downloading.\")\n query_result = ses.query(EDDSentinel1ASF).filter(EDDSentinel1ASF.PID == unq_id,\n EDDSentinel1ASF.Downloaded == False).filter(\n EDDSentinel1ASF.Remote_URL is not None).all()\n ses.close()\n success = False\n if query_result is not None:\n if len(query_result) == 1:\n record = query_result[0]\n logger.debug(\"Building download info for '\" + record.Remote_URL + \"'\")\n scn_lcl_dwnld_path = os.path.join(self.baseDownloadPath,\n \"{}_{}\".format(record.Product_File_ID, record.PID))\n if not os.path.exists(scn_lcl_dwnld_path):\n os.mkdir(scn_lcl_dwnld_path)\n out_filename = record.Remote_FileName\n _download_scn_asf([record.PID, record.Product_File_ID, record.Remote_URL, self.db_info_obj,\n os.path.join(scn_lcl_dwnld_path, out_filename), self.asfUser, self.asfPass])\n success = True\n elif len(query_result) == 0:\n logger.info(\"PID {0} is either not available or already been downloaded.\".format(unq_id))\n else:\n logger.error(\"PID {0} has returned more than 1 scene - must be unique something really wrong.\".\n format(unq_id))\n raise EODataDownException(\"There was more than 1 scene which has been found - \"\n \"something has gone really wrong!\")\n else:\n logger.error(\"PID {0} has not returned a scene - check inputs.\".format(unq_id))\n raise EODataDownException(\"PID {0} has not returned a scene - check inputs.\".format(unq_id))\n return success","def get_dataset(request):\n from seed.models import obj_to_dict\n dataset_id = request.GET.get('dataset_id', '')\n orgs = request.user.orgs.all()\n # check if user has access to the dataset\n d = ImportRecord.objects.filter(\n super_organization__in=orgs, pk=dataset_id\n )\n if d.exists():\n d = d[0]\n else:\n return {\n 'status': 'success',\n 'dataset': {},\n }\n\n dataset = obj_to_dict(d)\n importfiles = []\n for f in d.files:\n importfile = obj_to_dict(f)\n importfile['name'] = f.filename_only\n importfiles.append(importfile)\n\n dataset['importfiles'] = importfiles\n if d.last_modified_by:\n dataset['last_modified_by'] = d.last_modified_by.email\n dataset['number_of_buildings'] = BuildingSnapshot.objects.filter(\n import_file__in=d.files\n ).count()\n dataset['updated_at'] = convert_to_js_timestamp(d.updated_at)\n\n return {\n 'status': 'success',\n 'dataset': dataset,\n }","def download_entire_db(storage_path=DEFAULT_STORAGE,\n remove_previous=True,\n return_df=False,\n return_latest_date=False,\n write=['feather']):\n # first check if we have the latest data\n if not os.path.exists(storage_path):\n splitpath = storage_path.split('/')[1:] # first entry is blank due to home dir /\n for i, p in enumerate(splitpath, 1):\n path = '/'.join(splitpath[:i])\n if not os.path.exists(path):\n os.mkdir(path)\n\n zip_file_url = 'https://www.quandl.com/api/v3/databases/EOD/data?api_key=' + Q_KEY\n s = req.Session()\n s.mount('https', HTTPAdapter(max_retries=10))\n r = s.get(zip_file_url)\n # another possible way to deal with retries\n # while True:\n # try:\n # r = req.get(zip_file_url, timeout=10)\n # break\n # except Exception as e:\n # print(e)\n\n z = zipfile.ZipFile(io.BytesIO(r.content))\n z.extractall(path=storage_path)\n df = pd.read_csv(storage_path + \\\n z.filelist[0].filename,\n names=HEADERS,\n index_col=1,\n parse_dates=True,\n infer_datetime_format=True)\n latest_date = df.index.max().date().strftime('%Y%m%d')\n\n if 'hdf5' in write:\n df.to_hdf(storage_path + 'EOD_' + latest_date + '.h5',\n key='data',\n complib='blosc',\n complevel=9)\n\n # also write feather file so can read into R\n # have to reset the index because feather can't handle non-default index (maybe non-unique?)\n df.reset_index(inplace=True)\n if 'feather' in write:\n df.to_feather(storage_path + 'EOD_' + latest_date + '.ft')\n\n if remove_previous:\n for ext in ['h5', 'ft']:\n files = glob.glob(storage_path + 'EOD_*.' + ext)\n files = [f for f in files if len(f.split('/')[-1]) == 15] # don't want any of the small files, only full DBs\n print(sorted(files, key=os.path.getctime))\n if len(files) > 1:\n previous_file = sorted(files, key=os.path.getctime)[-2]\n print('removing', previous_file)\n os.remove(previous_file)\n\n # delete downloaded zip file\n os.remove(storage_path + z.filelist[0].filename)\n\n if return_df:\n # set index back to normal for return_df\n df.set_index('Date', inplace=True)\n return df\n elif return_latest_date:\n return pd.to_datetime(df['Date'].max().date())","def get_dataset_details(name, analyst):\n\n template = None\n allowed_sources = user_sources(analyst)\n dataset_object = Dataset.objects(name = name,\n source__name__in=allowed_sources).first()\n if not dataset_object:\n error = (\"Either no data exists for this dataset\"\n \" or you do not have permission to view it.\")\n template = \"error.html\"\n args = {'error': error}\n return template, args\n\n dataset_object.sanitize_sources(username=\"%s\" % analyst,\n sources=allowed_sources)\n\n # remove pending notifications for user\n remove_user_from_notification(\"%s\" % analyst, dataset_object.id, 'Dataset')\n\n # subscription\n subscription = {\n 'type': 'Dataset',\n 'id': dataset_object.id,\n 'subscribed': is_user_subscribed(\"%s\" % analyst,\n 'Dataset',\n dataset_object.id),\n }\n\n #objects\n objects = dataset_object.sort_objects()\n\n #relationships\n relationships = dataset_object.sort_relationships(\"%s\" % analyst, meta=True)\n\n # relationship\n relationship = {\n 'type': 'Datset',\n 'value': dataset_object.id\n }\n\n #comments\n comments = {'comments': dataset_object.get_comments(),\n 'url_key':dataset_object.name}\n\n # favorites\n favorite = is_user_favorite(\"%s\" % analyst, 'Dataset', dataset_object.id)\n\n # services\n service_list = get_supported_services('Dataset')\n\n # analysis results\n service_results = dataset_object.get_analysis_results()\n\n args = {'dataset': dataset_object,\n 'objects': objects,\n 'relationships': relationships,\n 'comments': comments,\n 'favorite': favorite,\n 'relationship': relationship,\n 'subscription': subscription,\n 'name': dataset_object.name,\n 'service_list': service_list,\n 'service_results': service_results}\n\n return template, args","def download_dataset(request, dataset_key):\n try:\n dataset = models.OrganizerDataSet.objects.get(key=dataset_key)\n except ObjectDoesNotExist:\n raise Http404()\n\n try:\n if dataset.sub_data_files.count() > 0:\n # TODO: Could refactor this to only zip this stuff up one time, maybe after dataset creation?\n zip_buffer = io.ByteIO()\n\n zip_file = zipfile.ZipFile(zip_buffer, \"w\")\n file_name = \"\"\n\n for sub_dataset in dataset.sub_data_files.all():\n file_dir, file_name = os.path.split(sub_dataset.data_file.file.name)\n zip_file.writestr(file_name, sub_dataset.data_file.read())\n\n zip_file.close()\n\n resp = HttpResponse(zip_buffer.getvalue(), content_type=\"application/x-zip-compressed\")\n resp['Content-Disposition'] = 'attachment; filename=%s.zip' % dataset.name\n return resp\n else:\n return HttpResponseRedirect(_make_url_sassy(dataset.data_file.file.name))\n except:\n exc_type, exc_value, exc_traceback = sys.exc_info()\n logger.error(\"*** print_tb:\")\n traceback.print_tb(exc_traceback, limit=1, file=sys.stdout)\n logger.error(\"*** print_exception:\")\n traceback.print_exception(exc_type, exc_value, exc_traceback,\n limit=2, file=sys.stdout)\n logger.error(\"*** print_exc:\")\n traceback.print_exc()\n logger.error(\"*** format_exc, first and last line:\")\n formatted_lines = traceback.format_exc().splitlines()\n msg = \"There was an error retrieving the file. Please try again later or report the issue.\"\n return HttpResponse(msg, status=400, content_type='text/plain')","def download_source():\n \n #if os.path.exists(UNFCC_FILE): \n # os.rename(UNFCC_FILE,'old_'+UNFCC_FILE)\n #if os.path.exists(EBAL_FILE):\n # os.rename(EBAL_FILE,'old_'+EBAL_FILE)\n\n try:\n unsd = sdmx.Request('UNSD')\n sdmx.logger.setLevel(logging.INFO)\n \n logger.info('Loading UNFCC Data')\n resp_unfcc = unsd.data('DF_UNData_UNFCC')\n\n logger.info('Loading UN Energy Balance Data')\n resp_ebal = unsd.data('DF_UNData_EnergyBalance')\n except Exception as e:\n logger.error('Error!! Please look at SDMX logs to troubleshoot' + str(e))\n traceback.print_exc(file = sys.stdout)\n\n try:\n df_ebal = resp_ebal.to_pandas()\n df_unfcc = resp_unfcc.to_pandas()\n\n df_unfcc.reset_index().to_csv(UNFCC_FILE,index=False)\n logger.info('UNFCC Greenhouse Data stored as {}'.format(UNFCC_FILE))\n\n df_ebal.reset_index().to_csv(EBAL_FILE,index=False)\n logger.info('UN Energy Balance Data stored as {}'.format(EBAL_FILE))\n except Exception as e:\n logger.error('Error!! While saving data from SDMX to CSV ' + str(e))\n traceback.print_exc(file = sys.stdout)","def download_ground_truths(request):\n\n workpath = os.path.dirname(os.path.abspath(__file__)) # Returns the Path your .py file is in\n path1 = os.path.join(workpath, './static/temp/temp.csv')\n path2 = os.path.join(workpath, './static/BioC/temp_files/to_download.csv')\n if os.path.exists(path1):\n os.remove(path1)\n if os.path.exists(path2):\n os.remove(path2)\n username = request.session['username']\n inst = request.GET.get('institute',None)\n if inst == '':\n inst = None\n else:\n inst = str(inst)\n use = request.GET.get('usec',None)\n if use == '':\n use = None\n else:\n use = str(use)\n report_type = request.GET.get('report_type',None)\n if report_type == '':\n report_type = None\n annotation_mode = request.GET.get('mode',None)\n if annotation_mode == '':\n annotation_mode = None\n lang = request.GET.get('lang',None)\n if lang == '':\n lang = None\n else:\n lang = str(lang)\n batch = request.GET.get('batch','') # added 22/10/2021\n if batch == '' or batch == 'all':\n batch = None\n else:\n batch = int(batch)\n\n all = request.GET.get('all_gt',None)\n action = request.GET.get('action',None)\n format = request.GET.get('format',None)\n json_resp = {}\n json_resp['ground_truth'] = []\n if format == 'json' or all =='all' :\n json_resp = create_json_to_download(report_type,action,username,use,annotation_mode,inst,lang,all,batch)\n return JsonResponse(json_resp)\n\n elif format == 'csv':\n response = HttpResponse(content_type='text/csv')\n resp = create_csv_to_download(report_type,annotation_mode,username,use,inst,lang,action,response,batch)\n return resp\n\n elif format == 'biocxml':\n json_keys_to_display = request.session['fields']\n json_keys_to_ann = request.session['fields_to_ann']\n if report_type == 'pubmed':\n json_keys_to_display = ['year','authors','volume','journal']\n json_keys_to_ann = ['title','abstract']\n json_keys = json_keys_to_display + json_keys_to_ann\n resp = generate_bioc(json_keys,json_keys_to_ann,username,action,lang,use,inst,'xml',annotation_mode,report_type,batch)\n return HttpResponse(resp,content_type='application/xml')\n\n elif format == 'biocjson':\n json_keys_to_display = request.session['fields']\n json_keys_to_ann = request.session['fields_to_ann']\n json_keys = json_keys_to_display + json_keys_to_ann\n resp = generate_bioc(json_keys,json_keys_to_ann,username,action,lang,use,inst,'json',annotation_mode,report_type,batch)\n return HttpResponse(resp,content_type='application/xml')","def dwn_rel_sup_csv(request):\n i = int(request.GET.get('i'))\n \n return FileResponse(open('temp/relation_support_datasets/relation_support_dataset_{}_{}.csv'.format(i, request.user.username),'rb'))","def _download_metafile(dataset, path=None):\n if not path:\n path = sunpy.config.get('downloads', 'sample_dir')\n base_url = 'https://spdf.gsfc.nasa.gov/pub/software/cdawlib/0MASTERS/'\n fname = dataset.lower() + '_00000000_v01.cdf'\n url = base_url + fname\n try:\n downloaded_file = pooch.retrieve(url=url, known_hash=None, fname=fname, path=path, progressbar=True)\n except ModuleNotFoundError:\n downloaded_file = pooch.retrieve(url=url, known_hash=None, fname=fname, path=path, progressbar=False)\n return downloaded_file","def download_dataset(self):\n dataset_name = ADE20K_URL.split(\"/\")[-1].split(\".\")[0]\n req = urllib.request.Request(ADE20K_URL, method=\"HEAD\")\n size_file = urllib.request.urlopen(req).headers[\"Content-Length\"]\n download = \"n\"\n while download != \"y\":\n if not self.yes_all:\n download = input(f\"You are about to download {dataset_name} ({size_file} bytes) to the temporary folder {self.tmp_path}. Do you want to continue? [y/n] \\n\")\n if self.yes_all or download == \"y\":\n logger.info(f\"Downloading dataset {dataset_name} at {ADE20K_URL} to temporary folder {self.tmp_path}...\")\n zip_path, hdrs = urllib.request.urlretrieve(ADE20K_URL, f\"{self.tmp_path}/{dataset_name}.zip\")\n logger.info(f\"Extracting {zip_path} to temporary folder {self.tmp_path}...\")\n with zipfile.ZipFile(f\"{zip_path}\", 'r') as z:\n z.extractall(f\"{self.tmp_path}\")\n self.input_data_path = zip_path[:-4]\n break\n elif download == \"n\":\n logger.error(f\"Cannot pursue without downloading the dataset.\")\n sys.exit()\n else:\n logger.error(\"Please enter a valid answer (y or n).\")","def download_sample():\n sample_id = demisto.getArg('id')\n r = req('GET', SUB_API + 'samples/' + sample_id + '/sample.zip')\n ec = {'ThreatGrid.DownloadedSamples.Id': sample_id}\n demisto.results([\n {\n 'Type': entryTypes['note'],\n 'EntryContext': ec,\n 'HumanReadable': '### ThreatGrid Sample Download - \\n'\n + 'Your download request has been completed successfully for ' + sample_id,\n 'Contents': ec,\n 'ContentsFormat': formats['json']\n },\n fileResult(sample_id + '-sample.zip', r.content)\n ])","def download_data(self):\n headers = {'User-Agent': 'Mozilla/5.0',}\n\n #Request for html data of url page\n r = requests.get(self.url, headers = headers, allow_redirects=True)\n soup = BeautifulSoup(r.text, \"html.parser\")\n\n #Checking if folder path exists, if not, creats it\n i=0\n while i<len(self.folder)-1:\n if self.folder[i] == '/':\n if not os.path.isdir(self.folder[:i]):\n os.mkdir(self.folder[:i])\n i+=1\n if i==len(self.folder)-1:\n if not os.path.isdir(self.folder):\n os.mkdir(self.folder)\n\n # if not os.path.isdir(self.folder):\n # os.mkdir(self.folder)\n\n #Gets every href to zip file with data\n entries = []\n for link in soup.find_all('a'):\n if re.search(\"^data/.*.zip\", link.get('href')):\n entries.append(link.get('href'))\n\n #Gets the newest dataset\n self.getCurrentData(entries)\n\n i=0\n #Saves each file in dataset\n for list in self.ListOfZipFiles:\n if not os.path.isfile(self.folder+list[4:]):\n r = requests.get(self.url+list)\n open(self.folder+list[4:], 'wb').write(r.content)\n #deletes prefix \"data/\"\n self.ListOfZipFiles[i] = list[4:]\n i+=1","def download_dataset(url=DATASET_URL):\n # disable insecure https warning\n urllib3.disable_warnings(urllib3.exceptions.InsecureRequestWarning)\n\n c = urllib3.PoolManager()\n with c.request(\"GET\", url, preload_content=False) as res, open(\n LOCAL_FILE_NAME, \"wb\"\n ) as out_file:\n shutil.copyfileobj(res, out_file)\n logging.info(\"Download completed.\")","def make_download_url(lender, metro):\n where = \"\"\n if lender:\n where = ''\n count = 0\n if type(lender) is QuerySet:\n for item in lender:\n query = '(agency_code=%s AND respondent_id=\"%s\" AND year=%s)'\n where += query % (item.institution.agency_id, item.institution.respondent_id, item.institution.year)\n count += 1\n if(count < len(lender)):\n where += \"OR\"\n else:\n query = '(agency_code=%s AND respondent_id=\"%s\" AND as_of_year=%s)'\n where += query % (lender.agency_id, lender.respondent_id, lender.year)\n if metro:\n divisions = [div.metdiv for div in\n Geo.objects.filter(\n geo_type=Geo.METDIV_TYPE, cbsa=metro.cbsa, year=metro.year\n ).order_by('cbsa')]\n if divisions:\n where += ' AND msamd IN (\"' + '\",\"'.join(divisions) + '\")'\n else: # no divisions, so just use the MSA\n where += ' AND msamd=\"' + metro.cbsa + '\"'\n\n query = urlencode({\n '$where': where,\n '$limit': 0\n })\n base_url = 'https://api.consumerfinance.gov/data/hmda/slice/'\n return base_url + 'hmda_lar.csv?' + query","def main(url, save_path, header):\n html = get_doctor_html(url)\n doctor_info = get_doctor_info(url,html) \n save_data(doctor_info, header, save_path)"],"string":"[\n \"def download_dataset(self):\\n raise NotImplementedError\",\n \"def download():\\n return response.download(request,db)\",\n \"def download():\\n return response.download(request,db)\",\n \"def download():\\n return response.download(request,db)\",\n \"def download():\\n return response.download(request,db)\",\n \"def download():\\n return response.download(request,db)\",\n \"def download():\\n return response.download(request,db)\",\n \"def fetch(self,url=URL):\\n\\t\\tlog.info('downloading latest PHE case data')\\n#\\t\\tself.data=lookup_json(url)\\n\\t\\tself.fetch_csv() #JSON discontinued; switched back to CSV\\n\\t\\tself.edition=self.latest_samples\\n\\t\\tlog.info(f'Last samples from {self.edition}')\",\n \"def download():\\n\\treturn response.download(request, db)\",\n \"def download_hess_dr1_data():\\n download_data_files(FILENAMES_HESS_DR1)\",\n \"def download1():\\n #t=request.vars.arg(0)\\n response.flash=request\\n #print request.wsgi.environ['HTTP_REFERER']\\n #print 'yghklo=',request.args[0]\\n a=db(db.Project.Project_File==request.args[0]).select(db.Project.ALL)\\n #a=db(db.Project.id==38).select(db.Project.ALL)\\n #if a == None:\\n#\\t print 'silent'\\n # print 'a= aabhas download',a[0].no_of_download, a[0].Project_File\\n # if a[0].no_of_download==None:\\n#\\t a[0].no_download=0\\n db(db.Project.Project_File==a[0].Project_File).update(no_of_download=(a[0].no_of_download or 0)+1)\\n print 'a.id=',a[0].id\\n # print len(a),'\\\\n'\\n #print \\\"\\\\n\\\\n\\\\n\\\\n\\\"\\n return response.download(request, db)\",\n \"def download_and_prepare_dmipy_example_dataset(self):\\r\\n subject_ID = 100307\\r\\n self.download_subject(subject_ID)\\r\\n self.prepare_example_slice(subject_ID)\",\n \"def get_study_data(self, soup, url):\\n pass\",\n \"def download():\\r\\n reader = GSODDataReader()\\r\\n year_list = range(2001, 2012)\\r\\n austin = reader.collect_data(year_list, exact_station=True,\\r\\n station_name='AUSTIN CAMP MABRY', state='TX', country='US')\\r\\n houston = reader.collect_data(year_list, exact_station=True,\\r\\n station_name='HOUSTON/D.W. HOOKS', state='TX', country='US')\\r\\n new_york = reader.collect_data(year_list, exact_station=True,\\r\\n station_name='NEW YORK/LA GUARDIA', state='NY', country='US')\\r\\n newark = reader.collect_data(year_list, exact_station=True,\\r\\n station_name='NEWARK INTL AIRPORT', state='NJ', country='US')\\r\\n punta_arenas = reader.collect_data(year_list, exact_station=True,\\r\\n station_name='PUNTA ARENAS', country='CH')\\r\\n wellington = reader.collect_data(year_list, exact_station=True,\\r\\n station_name='WELLINGTON AIRPORT', country='NZ')\\r\\n store = HDFStore('weather.h5')\\r\\n store['austin'] = austin\\r\\n store['houston'] = houston\\r\\n store['nyc'] = new_york\\r\\n store['newark'] = newark\\r\\n store['punta_arenas'] = punta_arenas\\r\\n store['wellington'] = wellington\\r\\n store.close()\",\n \"def download():\\n if auth.has_membership(1):\\n user = \\\"Admin\\\"\\n elif auth.has_membership(2):\\n user = \\\"Examiner\\\"\\n elif auth.has_membership(3):\\n user = \\\"student\\\"\\n elif auth.has_membership(5):\\n user = \\\"Managment\\\"\\n\\n db.activity_log.insert( Title_entry=\\\"Download assignment\\\", \\n referance_id=auth.user.id,\\n remarks=\\\"content downloaded by {}\\\".format(user))\\n db.commit()\\n return response.download(request, db)\",\n \"def download():\\n return response.download(request, db)\",\n \"def download():\\n return response.download(request, db)\",\n \"def download():\\n return response.download(request, db)\",\n \"def download():\\n return response.download(request, db)\",\n \"def download():\\n return response.download(request, db)\",\n \"def download():\\n return response.download(request, db)\",\n \"def download():\\n return response.download(request, db)\",\n \"def download():\\n return response.download(request, db)\",\n \"def download():\\n return response.download(request, db)\",\n \"def download():\\n return response.download(request, db)\",\n \"def download():\\n return response.download(request, db)\",\n \"def download():\\n return response.download(request, db)\",\n \"def download():\\n return response.download(request, db)\",\n \"def download():\\n return response.download(request, db)\",\n \"def download():\\n return response.download(request, db)\",\n \"def download():\\n return response.download(request, db)\",\n \"def download():\\n return response.download(request, db)\",\n \"def download():\\n return response.download(request, db)\",\n \"def download():\\n return response.download(request, db)\",\n \"def download():\\n return response.download(request, db)\",\n \"def download():\\n return response.download(request, db)\",\n \"def download():\\n return response.download(request, db)\",\n \"def download():\\n return response.download(request, db)\",\n \"def download():\\n return response.download(request, db)\",\n \"def download():\\n return response.download(request, db)\",\n \"def download():\\n return response.download(request, db)\",\n \"def download():\\n return response.download(request, db)\",\n \"def download():\\n return response.download(request, db)\",\n \"def download():\\n return response.download(request, db)\",\n \"def download():\\n return response.download(request, db)\",\n \"def download():\\n return response.download(request, db)\",\n \"def download():\\n return response.download(request, db)\",\n \"def download():\\n return response.download(request, db)\",\n \"def download():\\n return response.download(request, db)\",\n \"def getData(constrain):\\n\\n dat_AGS = chunks(AGS, 100)\\n for num, ags_c in enumerate(dat_AGS):\\n to_download = DOWNLOAD_LINK.format(ags_id=ags_c, constrain=constrain)\\n to_download = to_download.replace(\\\" \\\", \\\"\\\")\\n download_name = \\\"../Data/Gemeinden/{}-{}.csv\\\".format(\\n constrain, num)\\n\\n url.urlretrieve(to_download, filename=download_name)\\n\\n sleep(1) # be nice\\n\\n return(num)\",\n \"def download(self, verbose):\\n # Download datasets\\n if verbose:\\n print(\\\"Retrieving datasets from Our World In Data https://github.com/owid/covid-19-data/\\\")\\n # Vaccinations\\n v_rec_cols = [\\n \\\"date\\\", \\\"location\\\", \\\"iso_code\\\", \\\"total_vaccinations\\\", \\\"people_vaccinated\\\", \\\"people_fully_vaccinated\\\"]\\n v_rec_df = pd.read_csv(self.URL_V_REC, usecols=v_rec_cols)\\n v_loc_df = pd.read_csv(self.URL_V_LOC, usecols=[\\\"location\\\", \\\"vaccines\\\"])\\n v_df = v_rec_df.merge(v_loc_df, how=\\\"left\\\", on=\\\"location\\\")\\n # Tests\\n pcr_rec_cols = [\\\"ISO code\\\", \\\"Date\\\", \\\"Daily change in cumulative total\\\", \\\"Cumulative total\\\"]\\n pcr_df = pd.read_csv(self.URL_P_REC, usecols=pcr_rec_cols)\\n pcr_df = pcr_df.rename(columns={\\\"ISO code\\\": \\\"iso_code\\\", \\\"Date\\\": \\\"date\\\"})\\n pcr_df[\\\"cumsum\\\"] = pcr_df.groupby(\\\"iso_code\\\")[\\\"Daily change in cumulative total\\\"].cumsum()\\n pcr_df = pcr_df.assign(tests=lambda x: x[\\\"Cumulative total\\\"].fillna(x[\\\"cumsum\\\"]))\\n # Combine data (vaccinations/tests)\\n df = v_df.set_index([\\\"iso_code\\\", \\\"date\\\"])\\n df = df.combine_first(pcr_df.set_index([\\\"iso_code\\\", \\\"date\\\"]).loc[:, [\\\"tests\\\"]])\\n df = df.reset_index()\\n # Location (country/province)\\n df[\\\"location\\\"] = df[\\\"location\\\"].replace(\\n {\\n # COG\\n \\\"Congo\\\": \\\"Republic of the Congo\\\",\\n }\\n )\\n df = df.loc[~df[\\\"iso_code\\\"].str.contains(\\\"OWID_\\\")]\\n df[\\\"location\\\"] = df.groupby(\\\"iso_code\\\")[\\\"location\\\"].bfill()\\n df.loc[df[\\\"location\\\"] == df[\\\"iso_code\\\"], \\\"location\\\"] = None\\n df.loc[df[\\\"location\\\"].isna(), \\\"location\\\"] = df.loc[df[\\\"location\\\"].isna(), \\\"iso_code\\\"].apply(\\n lambda x: coco.convert(x, to=\\\"name_short\\\", not_found=None))\\n df[self.PROVINCE] = self.UNKNOWN\\n return df\",\n \"def fetch_save(url):\\n\\n name = url.split(\\\"/\\\")[-1]\\n response = requests.get(url, stream=True)\\n if response.status_code == 200:\\n with open(f\\\"{DATA_PATH}/{name}\\\", \\\"wb\\\") as f:\\n f.write(response.raw.read())\\n else:\\n logging.info(f\\\"Failed {url} download\\\")\",\n \"def fetch_inspect_data(filename, output, db_url=None):\\n r2dt.write_training_data(filename, db_url, output)\",\n \"def download_data_and_save():\\n url = 'https://github.com/djay/covidthailand/wiki/combined.csv'\\n s=requests.get(url).content\\n global df\\n global last_updated\\n df=pd.read_csv(io.StringIO(s.decode('utf-8')), parse_dates= ['Date'])\\n df.to_parquet(file_name, compression='UNCOMPRESSED')\\n df.to_csv('jaydata.csv')\\n last_updated = df['Date'][df.index[-1]].strftime(\\\"%d %B %Y\\\")\\n\\n url = 'https://raw.githubusercontent.com/wiki/djay/covidthailand/vaccinations.csv'\\n s=requests.get(url).content\\n global vac_df\\n vac_df=pd.read_csv(io.StringIO(s.decode('utf-8')), parse_dates= ['Date'])\\n vac_df.to_parquet('vaccination.parquet', compression='UNCOMPRESSED')\\n\\n print(\\\"Data downloaded and saved successfully. Data up to \\\" + last_updated)\",\n \"def _download(self, path):\\n self.logger.info('Getting Million Song Dataset...')\\n self.logger.info('Downloading Echo Nest Taste Subprofile train data...')\\n base_url = 'http://millionsongdataset.com/sites/default/files/challenge/'\\n\\n download_dataset(\\n base_url + 'train_triplets.txt.zip',\\n join(self.data_folder, 'train.zip')\\n )\\n rename(join(self.data_folder, 'train'), path)\\n\\n self.logger.info('Downloading evaluation data for MSD Challenge...')\\n download_dataset(\\n base_url + 'EvalDataYear1MSDWebsite.zip',\\n join(path, 'eval.zip')\\n )\\n rename(\\n join(path, 'EvalDataYear1MSDWebsite'),\\n join(path, 'evaluation')\\n )\\n\\n self.logger.info('Downloading list of matching errors...')\\n url = 'http://millionsongdataset.com/sites/default/files/tasteprofile/sid_mismatches.txt'\\n download_url(url, join(path, 'sid_mismatches.txt'))\",\n \"def download_report():\\n entities = get_names()\\n save_csv(entities)\",\n \"def download(args):\\n with_dataset(args, Dataset._download)\",\n \"def access_study(self, url):\\n\\n doc = requests.get(url)\\n self.num_requests += 1\\n if doc.status_code != 200:\\n with open('runReport.txt', 'a') as report:\\n report.write('\\\\n')\\n report.write(str(doc.status_code) + \\\" for \\\" + url)\\n return None\\n else:\\n soup = BeautifulSoup(doc.content, features=\\\"lxml\\\")\\n self.num_hits += 1\\n return self.get_study_data(soup, url)\",\n \"def download(exam, out, name_question, sid_question, compact):\\n exam_json, template_questions, email_to_data_map, total = examtool.api.download.download(exam)\\n examtool.api.download.export(template_questions, email_to_data_map, total, exam, out, name_question, sid_question, compact)\",\n \"def save_data(self):\\n # Command to get the download data\\n pass\",\n \"def download_examples(request):\\n\\n file_required = request.GET.get('token',None)\\n path = ''\\n workpath = os.path.dirname(os.path.abspath(__file__)) # Returns the Path your .py file is in\\n if file_required == 'reports':\\n path = os.path.join(workpath, './static/examples/report.csv')\\n\\n elif file_required == 'concepts':\\n path = os.path.join(workpath, './static/examples/concept.csv')\\n\\n elif file_required == 'labels':\\n path = os.path.join(workpath, './static/examples/labels.csv')\\n\\n elif file_required == 'pubmed':\\n path = os.path.join(workpath, './static/examples/pubmed.csv')\\n\\n content = open(path,'r')\\n return HttpResponse(content, content_type='text/csv')\",\n \"def fetch_data(self):\",\n \"def downloadFile()-> None:\\n logging.info(f\\\"Downloading current data set {getTime()}\\\")\\n with open(DATA_FILE,\\\"wb\\\") as f:\\n f.write(get(\\\"https://covid.ourworldindata.org/data/owid-covid-data.csv\\\").text.encode())\\n logging.info(f\\\"Finished Downloading current data set {getTime()}\\\")\",\n \"def downloadData(url : str, descriptor : str):\\n assets = datapackage.Package(url).resources\\n\\n for data in filter(lambda x: x.tabular and x.descriptor['name'] == descriptor, assets):\\n response = requests.get(data.descriptor['path'])\\n return io.StringIO(response.content.decode('utf-8'))\",\n \"def get_datafiles(self, url, survey_path):\\n pass\",\n \"def download(self, verbose):\\n\\n # Download datasets\\n if verbose:\\n print(\\\"Retrieving datasets from COVID-19 Open Data by Google Cloud Platform https://github.com/GoogleCloudPlatform/covid-19-open-data\\\")\\n # Index\\n i_cols = [\\\"location_key\\\", \\\"country_name\\\", \\\"subregion1_name\\\", \\\"subregion2_name\\\", \\\"iso_3166_1_alpha_3\\\"]\\n i_df = pd.read_csv(self.URL_I, usecols=i_cols)\\n # Mobility\\n m_df = pd.read_csv(self.URL_M)\\n m_df = (m_df.set_index([\\\"date\\\", \\\"location_key\\\"]) + 100).reset_index()\\n # Combine data\\n df = m_df.merge(i_df, how=\\\"left\\\", on=\\\"location_key\\\")\\n # Location (country/province)\\n df = df.loc[df[\\\"subregion2_name\\\"].isna()]\\n df[self.PROVINCE] = df[\\\"subregion1_name\\\"].fillna(self.UNKNOWN).apply(unidecode)\\n df[\\\"country_name\\\"] = df[\\\"country_name\\\"].replace(\\n {\\n # CIV\\n \\\"Ivory Coast\\\": \\\"Cote d'Ivoire\\\",\\n }\\n )\\n return df\",\n \"def download_assignment_student(request, pk, i):\\n evalassignment = Evalassignment.objects.\\\\\\n filter(pk=pk, evaluator=request.user).first()\\n if evalassignment:\\n eval_name = '%s_%s' % (evalassignment.assignment.assignmentype.title.\\n replace(\\\" \\\", \\\"\\\"), i)\\n filename = 'assign_%s.%s' % (eval_name, evalassignment.assignment.\\n document.name.split('.')[-1])\\n response = HttpResponse(evalassignment.assignment.document,\\n content_type='application/force_download')\\n response['Content-Disposition'] = 'attachment; filename=%s' % filename\\n return response\\n else:\\n return redirect('gradapp:dashboard_student')\",\n \"def download(self):\\n\\n with open(self.dataset_path) as dataset_file:\\n dataset = json.load(dataset_file)\\n\\n path = \\\"\\\".join([POST_HIT_PATH, dataset[\\\"dataset\\\"][\\\"data_path\\\"]])\\n if not os.path.exists(path):\\n os.makedirs(path)\\n\\n protocole = dataset[\\\"dataset\\\"][\\\"protocole\\\"]\\n\\n download_links = []\\n\\n for resource in dataset[\\\"dataset\\\"][\\\"resources\\\"]:\\n file_path = \\\"\\\".join([path, resource[\\\"filename\\\"]])\\n\\n #Check if the the download link has not been used before (One download link for all)\\n if resource[\\\"download_link\\\"] not in download_links:\\n \\n print(\\\"DOWNLOADING : {}\\\".format(resource[\\\"filename\\\"]))\\n f = urllib.request.urlopen(resource[\\\"download_link\\\"])\\n data = f.read()\\n with open(file_path, \\\"wb\\\") as donwload_file:\\n donwload_file.write(data)\\n\\n download_links.append(resource[\\\"download_link\\\"])\\n\\n \\n #Extract all files from the tar archives if necessary\\n if tarfile.is_tarfile(file_path):\\n tf = tarfile.open(file_path)\\n tf.exractall()\",\n \"def download_models_and_data():\\n\\n for file in DATA_FILES:\\n download_file(file[\\\"url\\\"], file[\\\"path\\\"])\",\n \"def fetch_training_data(filename, output, db_url=None):\\n r2dt.write_training_data(filename, db_url, output)\",\n \"def dataset():\\n\\n # first, load the data into pd.DataFrames so that we can store it in a DB\\n movements = pd.read_csv(bz2.open(request.files['movements']))\\n vertices = pd.read_csv(request.files['vertices'])\\n project = request.values['project']\\n\\n with sqlite3.connect('projects/%s.db'%project) as con:\\n movements.to_sql('movements', con)\\n vertices.to_sql('vertices', con)\\n\\n return redirect('/visualise.html?project=%s'%project, 200)\",\n \"def download_data(session, date: datetime.date, sampling_feature: str):\\n\\n data = session.get_observation_by_date_and_feature(date=date, sampling_features=[sampling_feature])\\n\\n return data\",\n \"def download(self,connector,condition):\\n c= connector.cursor()\\n\\n\\n snew = buildSelect(self,'LDA',condition)\\n# print snew\\n c.execute(snew)\\n lnames=[]\\n for name,val in sorted(self.__dict__.iteritems()):\\n lnames.append(name)\\n vobj=[]\\n for row in c:\\n# print row\\n obj=LDADef()\\n for i in range(len(lnames)):\\n obj.__dict__[lnames[i]]=row[i]\\n vobj.append(obj)\\n return vobj\",\n \"def generic_download(self, data_set, scene, output_dir, chunk_size=1024):\\n\\n ### LANDSAT DOWNLOAD ###\\n if is_product_id(scene['displayId']):\\n filename = self.download(scene['displayId'], output_dir)\\n\\n ### NON-LANDSAT ###\\n else:\\n filename = self.download(scene['displayId'], output_dir, data_set=data_set)\\n\\n return filename\",\n \"def getPublicEMPDownloadLinks(self):\\n try:\\n studies = []\\n con = self.getMetadataDatabaseConnection()\\n results = con.cursor()\\n con.cursor().callproc('get_public_emp_studies', [results])\\n for row in results:\\n # study_id, project_name, file_path, study_abstract\\n studies.append((row[0], row[1], row[2], row[3]))\\n return studies\\n except Exception, e:\\n print 'Exception caught: %s.\\\\nThe error is: %s' % (type(e), e)\",\n \"def download():\\n toydata = requests.get(DATA_URL).json()\\n return toydata\",\n \"def download():\\n raise NotImplementedError\",\n \"def download_data(dataset_name=None):\\r\\n\\r\\n dr = data_resources[dataset_name]\\r\\n if not authorize_download(dataset_name):\\r\\n raise Exception(\\\"Permission to download data set denied.\\\")\\r\\n\\r\\n if dr.has_key('suffices'):\\r\\n for url, files, suffices in zip(dr['urls'], dr['files'], dr['suffices']):\\r\\n for file, suffix in zip(files, suffices):\\r\\n download_url(os.path.join(url,file), dataset_name, dataset_name, suffix=suffix)\\r\\n else:\\r\\n for url, files in zip(dr['urls'], dr['files']):\\r\\n for file in files:\\r\\n download_url(os.path.join(url,file), dataset_name, dataset_name)\\r\\n return True\",\n \"def get_drms_files(self):\\n import drms\\n client = drms.Client(email=self.email,verbose=True)\\n fmt = '%Y.%m.%d_%H:%M'\\n self.t_qstr = self.series+'[{0}_TAI-{1}_TAI@{2}]'.format(self.start.strftime(fmt),self.end.strftime(fmt),self.cadence) \\n\\n\\n #create wavelength query string\\n self.w_qstr = '[' \\n for i in self.wav: self.w_qstr = self.w_qstr+'{0},'.format(int(i.value))\\n #remove last , and add bracket\\n self.w_qstr = self.w_qstr[:-1]+']'\\n \\n #make the series string\\n self.s_qstr = '{'+self.segment+'}'\\n\\n #the full query\\n self.qstr = self.t_qstr+self.w_qstr+self.s_qstr\\n\\n #IF ERRORS WITH URL ERROR IT IS BECAUSE THE DOWNLOAD FILE SIZE IS TOO LARGE\\n #export the data file list \\n self.expt = client.export(self.qstr)\\n#create an array of indexes to download\\n index = np.arange(np.size(self.expt.urls.url))\\n# get file from JSOC\\n #set directory to current if no path set\\n outf = self.expt.download(self.odir,index,fname_from_rec=True)\",\n \"def download_dataset():\\n \\n ID = \\\"1-3_oB5iSF-c_V65-uSdUlo024NzlgSYZ\\\"\\n script1 = f\\\"\\\"\\\"\\n wget --load-cookies /tmp/cookies.txt \\\"https://docs.google.com/uc?export=download&confirm=$(wget --quiet --save-cookies /tmp/cookies.txt --keep-session-cookies --no-check-certificate 'https://docs.google.com/uc?export=download&id='{ID} -O- | sed -rn 's/.*confirm=([0-9A-Za-z_]+).*/\\\\1\\\\n/p')&id=\\\"{ID} -O Data.zip && rm -rf /tmp/cookies.txt\\n \\\"\\\"\\\"\\n script2 = \\\"\\\"\\\"unzip Data.zip\\\"\\\"\\\"\\n\\n os.system(script1)\\n os.system(script2)\",\n \"def download_data(self):\\n content = requests.get(self.TOP_250_LIST)\\n soup = BeautifulSoup(content.content, 'lxml')\\n movies = soup.select('tbody.lister-list tr')\\n for m in movies:\\n title_column = m.select('td.titleColumn')\\n link = self.format_link(title_column[0].a['href'])\\n title = self.format_title(title_column[0].a.string.encode('utf-8'))\\n path = 'pages/{}.html'.format(title)\\n if os.path.isfile(path):\\n continue\\n response = requests.get(link)\\n with open(path, 'wr') as f:\\n f.write(response.content)\",\n \"def download_files(self):\",\n \"def _downloadDataFile(self):\\n config = SiteConfiguration.objects.get()\\n\\n with requests.Session() as s:\\n # Authentication\\n data = {\\n 'identificationBean.identifiant': '{}'.format(config.login),\\n 'identificationBean.mdp': '{}'.format(config.password),\\n 'userName': '{}'.format(config.username)\\n }\\n url = 'http://extranet.ffbb.com/fbi/identification.do'\\n s.post(url, data=data)\\n\\n # Create filters\\n params = (\\n ('action', 'executeCsv'),\\n ('rechercherRencontreSaisieResultatBean.idDivision', ''),\\n ('rechercherRencontreSaisieResultatBean.rechercherEquipe2', 'O'),\\n ('rechercherRencontreSaisieResultatBean.dateDebutRencontre', ''),\\n ('rechercherRencontreSaisieResultatBean.dateFinRencontre', ''),\\n ('rechercherRencontreSaisieResultatBean.idPoule', ''),\\n ('rechercherRencontreSaisieResultatBean.numeroEquipe', ''),\\n )\\n\\n # Get Csv file\\n url = 'http://extranet.ffbb.com/fbi/rechercherCompetitionRencontre.do'\\n response = s.get(url, params=params)\\n\\n if(response.headers['content-type'] != 'application/ms-excel;charset=UTF-8'):\\n return False\\n\\n # Create the file\\n if response.status_code == 200:\\n os.makedirs(os.path.dirname(settings.DATA_PATH), exist_ok=True)\\n with open(settings.DATA_PATH, 'wb') as f:\\n for chunk in response:\\n f.write(chunk)\\n\\n return True\",\n \"def download(self):\\n pass\",\n \"def download(self):\\n pass\",\n \"def download_scn(self, unq_id):\\n if not os.path.exists(self.baseDownloadPath):\\n raise EODataDownException(\\\"The download path does not exist, please create and run again.\\\")\\n\\n logger.debug(\\\"Creating Database Engine and Session.\\\")\\n db_engine = sqlalchemy.create_engine(self.db_info_obj.dbConn)\\n session_sqlalc = sqlalchemy.orm.sessionmaker(bind=db_engine)\\n ses = session_sqlalc()\\n\\n logger.debug(\\\"Perform query to find scenes which need downloading.\\\")\\n query_result = ses.query(EDDSentinel1ASF).filter(EDDSentinel1ASF.PID == unq_id,\\n EDDSentinel1ASF.Downloaded == False).filter(\\n EDDSentinel1ASF.Remote_URL is not None).all()\\n ses.close()\\n success = False\\n if query_result is not None:\\n if len(query_result) == 1:\\n record = query_result[0]\\n logger.debug(\\\"Building download info for '\\\" + record.Remote_URL + \\\"'\\\")\\n scn_lcl_dwnld_path = os.path.join(self.baseDownloadPath,\\n \\\"{}_{}\\\".format(record.Product_File_ID, record.PID))\\n if not os.path.exists(scn_lcl_dwnld_path):\\n os.mkdir(scn_lcl_dwnld_path)\\n out_filename = record.Remote_FileName\\n _download_scn_asf([record.PID, record.Product_File_ID, record.Remote_URL, self.db_info_obj,\\n os.path.join(scn_lcl_dwnld_path, out_filename), self.asfUser, self.asfPass])\\n success = True\\n elif len(query_result) == 0:\\n logger.info(\\\"PID {0} is either not available or already been downloaded.\\\".format(unq_id))\\n else:\\n logger.error(\\\"PID {0} has returned more than 1 scene - must be unique something really wrong.\\\".\\n format(unq_id))\\n raise EODataDownException(\\\"There was more than 1 scene which has been found - \\\"\\n \\\"something has gone really wrong!\\\")\\n else:\\n logger.error(\\\"PID {0} has not returned a scene - check inputs.\\\".format(unq_id))\\n raise EODataDownException(\\\"PID {0} has not returned a scene - check inputs.\\\".format(unq_id))\\n return success\",\n \"def get_dataset(request):\\n from seed.models import obj_to_dict\\n dataset_id = request.GET.get('dataset_id', '')\\n orgs = request.user.orgs.all()\\n # check if user has access to the dataset\\n d = ImportRecord.objects.filter(\\n super_organization__in=orgs, pk=dataset_id\\n )\\n if d.exists():\\n d = d[0]\\n else:\\n return {\\n 'status': 'success',\\n 'dataset': {},\\n }\\n\\n dataset = obj_to_dict(d)\\n importfiles = []\\n for f in d.files:\\n importfile = obj_to_dict(f)\\n importfile['name'] = f.filename_only\\n importfiles.append(importfile)\\n\\n dataset['importfiles'] = importfiles\\n if d.last_modified_by:\\n dataset['last_modified_by'] = d.last_modified_by.email\\n dataset['number_of_buildings'] = BuildingSnapshot.objects.filter(\\n import_file__in=d.files\\n ).count()\\n dataset['updated_at'] = convert_to_js_timestamp(d.updated_at)\\n\\n return {\\n 'status': 'success',\\n 'dataset': dataset,\\n }\",\n \"def download_entire_db(storage_path=DEFAULT_STORAGE,\\n remove_previous=True,\\n return_df=False,\\n return_latest_date=False,\\n write=['feather']):\\n # first check if we have the latest data\\n if not os.path.exists(storage_path):\\n splitpath = storage_path.split('/')[1:] # first entry is blank due to home dir /\\n for i, p in enumerate(splitpath, 1):\\n path = '/'.join(splitpath[:i])\\n if not os.path.exists(path):\\n os.mkdir(path)\\n\\n zip_file_url = 'https://www.quandl.com/api/v3/databases/EOD/data?api_key=' + Q_KEY\\n s = req.Session()\\n s.mount('https', HTTPAdapter(max_retries=10))\\n r = s.get(zip_file_url)\\n # another possible way to deal with retries\\n # while True:\\n # try:\\n # r = req.get(zip_file_url, timeout=10)\\n # break\\n # except Exception as e:\\n # print(e)\\n\\n z = zipfile.ZipFile(io.BytesIO(r.content))\\n z.extractall(path=storage_path)\\n df = pd.read_csv(storage_path + \\\\\\n z.filelist[0].filename,\\n names=HEADERS,\\n index_col=1,\\n parse_dates=True,\\n infer_datetime_format=True)\\n latest_date = df.index.max().date().strftime('%Y%m%d')\\n\\n if 'hdf5' in write:\\n df.to_hdf(storage_path + 'EOD_' + latest_date + '.h5',\\n key='data',\\n complib='blosc',\\n complevel=9)\\n\\n # also write feather file so can read into R\\n # have to reset the index because feather can't handle non-default index (maybe non-unique?)\\n df.reset_index(inplace=True)\\n if 'feather' in write:\\n df.to_feather(storage_path + 'EOD_' + latest_date + '.ft')\\n\\n if remove_previous:\\n for ext in ['h5', 'ft']:\\n files = glob.glob(storage_path + 'EOD_*.' + ext)\\n files = [f for f in files if len(f.split('/')[-1]) == 15] # don't want any of the small files, only full DBs\\n print(sorted(files, key=os.path.getctime))\\n if len(files) > 1:\\n previous_file = sorted(files, key=os.path.getctime)[-2]\\n print('removing', previous_file)\\n os.remove(previous_file)\\n\\n # delete downloaded zip file\\n os.remove(storage_path + z.filelist[0].filename)\\n\\n if return_df:\\n # set index back to normal for return_df\\n df.set_index('Date', inplace=True)\\n return df\\n elif return_latest_date:\\n return pd.to_datetime(df['Date'].max().date())\",\n \"def get_dataset_details(name, analyst):\\n\\n template = None\\n allowed_sources = user_sources(analyst)\\n dataset_object = Dataset.objects(name = name,\\n source__name__in=allowed_sources).first()\\n if not dataset_object:\\n error = (\\\"Either no data exists for this dataset\\\"\\n \\\" or you do not have permission to view it.\\\")\\n template = \\\"error.html\\\"\\n args = {'error': error}\\n return template, args\\n\\n dataset_object.sanitize_sources(username=\\\"%s\\\" % analyst,\\n sources=allowed_sources)\\n\\n # remove pending notifications for user\\n remove_user_from_notification(\\\"%s\\\" % analyst, dataset_object.id, 'Dataset')\\n\\n # subscription\\n subscription = {\\n 'type': 'Dataset',\\n 'id': dataset_object.id,\\n 'subscribed': is_user_subscribed(\\\"%s\\\" % analyst,\\n 'Dataset',\\n dataset_object.id),\\n }\\n\\n #objects\\n objects = dataset_object.sort_objects()\\n\\n #relationships\\n relationships = dataset_object.sort_relationships(\\\"%s\\\" % analyst, meta=True)\\n\\n # relationship\\n relationship = {\\n 'type': 'Datset',\\n 'value': dataset_object.id\\n }\\n\\n #comments\\n comments = {'comments': dataset_object.get_comments(),\\n 'url_key':dataset_object.name}\\n\\n # favorites\\n favorite = is_user_favorite(\\\"%s\\\" % analyst, 'Dataset', dataset_object.id)\\n\\n # services\\n service_list = get_supported_services('Dataset')\\n\\n # analysis results\\n service_results = dataset_object.get_analysis_results()\\n\\n args = {'dataset': dataset_object,\\n 'objects': objects,\\n 'relationships': relationships,\\n 'comments': comments,\\n 'favorite': favorite,\\n 'relationship': relationship,\\n 'subscription': subscription,\\n 'name': dataset_object.name,\\n 'service_list': service_list,\\n 'service_results': service_results}\\n\\n return template, args\",\n \"def download_dataset(request, dataset_key):\\n try:\\n dataset = models.OrganizerDataSet.objects.get(key=dataset_key)\\n except ObjectDoesNotExist:\\n raise Http404()\\n\\n try:\\n if dataset.sub_data_files.count() > 0:\\n # TODO: Could refactor this to only zip this stuff up one time, maybe after dataset creation?\\n zip_buffer = io.ByteIO()\\n\\n zip_file = zipfile.ZipFile(zip_buffer, \\\"w\\\")\\n file_name = \\\"\\\"\\n\\n for sub_dataset in dataset.sub_data_files.all():\\n file_dir, file_name = os.path.split(sub_dataset.data_file.file.name)\\n zip_file.writestr(file_name, sub_dataset.data_file.read())\\n\\n zip_file.close()\\n\\n resp = HttpResponse(zip_buffer.getvalue(), content_type=\\\"application/x-zip-compressed\\\")\\n resp['Content-Disposition'] = 'attachment; filename=%s.zip' % dataset.name\\n return resp\\n else:\\n return HttpResponseRedirect(_make_url_sassy(dataset.data_file.file.name))\\n except:\\n exc_type, exc_value, exc_traceback = sys.exc_info()\\n logger.error(\\\"*** print_tb:\\\")\\n traceback.print_tb(exc_traceback, limit=1, file=sys.stdout)\\n logger.error(\\\"*** print_exception:\\\")\\n traceback.print_exception(exc_type, exc_value, exc_traceback,\\n limit=2, file=sys.stdout)\\n logger.error(\\\"*** print_exc:\\\")\\n traceback.print_exc()\\n logger.error(\\\"*** format_exc, first and last line:\\\")\\n formatted_lines = traceback.format_exc().splitlines()\\n msg = \\\"There was an error retrieving the file. Please try again later or report the issue.\\\"\\n return HttpResponse(msg, status=400, content_type='text/plain')\",\n \"def download_source():\\n \\n #if os.path.exists(UNFCC_FILE): \\n # os.rename(UNFCC_FILE,'old_'+UNFCC_FILE)\\n #if os.path.exists(EBAL_FILE):\\n # os.rename(EBAL_FILE,'old_'+EBAL_FILE)\\n\\n try:\\n unsd = sdmx.Request('UNSD')\\n sdmx.logger.setLevel(logging.INFO)\\n \\n logger.info('Loading UNFCC Data')\\n resp_unfcc = unsd.data('DF_UNData_UNFCC')\\n\\n logger.info('Loading UN Energy Balance Data')\\n resp_ebal = unsd.data('DF_UNData_EnergyBalance')\\n except Exception as e:\\n logger.error('Error!! Please look at SDMX logs to troubleshoot' + str(e))\\n traceback.print_exc(file = sys.stdout)\\n\\n try:\\n df_ebal = resp_ebal.to_pandas()\\n df_unfcc = resp_unfcc.to_pandas()\\n\\n df_unfcc.reset_index().to_csv(UNFCC_FILE,index=False)\\n logger.info('UNFCC Greenhouse Data stored as {}'.format(UNFCC_FILE))\\n\\n df_ebal.reset_index().to_csv(EBAL_FILE,index=False)\\n logger.info('UN Energy Balance Data stored as {}'.format(EBAL_FILE))\\n except Exception as e:\\n logger.error('Error!! While saving data from SDMX to CSV ' + str(e))\\n traceback.print_exc(file = sys.stdout)\",\n \"def download_ground_truths(request):\\n\\n workpath = os.path.dirname(os.path.abspath(__file__)) # Returns the Path your .py file is in\\n path1 = os.path.join(workpath, './static/temp/temp.csv')\\n path2 = os.path.join(workpath, './static/BioC/temp_files/to_download.csv')\\n if os.path.exists(path1):\\n os.remove(path1)\\n if os.path.exists(path2):\\n os.remove(path2)\\n username = request.session['username']\\n inst = request.GET.get('institute',None)\\n if inst == '':\\n inst = None\\n else:\\n inst = str(inst)\\n use = request.GET.get('usec',None)\\n if use == '':\\n use = None\\n else:\\n use = str(use)\\n report_type = request.GET.get('report_type',None)\\n if report_type == '':\\n report_type = None\\n annotation_mode = request.GET.get('mode',None)\\n if annotation_mode == '':\\n annotation_mode = None\\n lang = request.GET.get('lang',None)\\n if lang == '':\\n lang = None\\n else:\\n lang = str(lang)\\n batch = request.GET.get('batch','') # added 22/10/2021\\n if batch == '' or batch == 'all':\\n batch = None\\n else:\\n batch = int(batch)\\n\\n all = request.GET.get('all_gt',None)\\n action = request.GET.get('action',None)\\n format = request.GET.get('format',None)\\n json_resp = {}\\n json_resp['ground_truth'] = []\\n if format == 'json' or all =='all' :\\n json_resp = create_json_to_download(report_type,action,username,use,annotation_mode,inst,lang,all,batch)\\n return JsonResponse(json_resp)\\n\\n elif format == 'csv':\\n response = HttpResponse(content_type='text/csv')\\n resp = create_csv_to_download(report_type,annotation_mode,username,use,inst,lang,action,response,batch)\\n return resp\\n\\n elif format == 'biocxml':\\n json_keys_to_display = request.session['fields']\\n json_keys_to_ann = request.session['fields_to_ann']\\n if report_type == 'pubmed':\\n json_keys_to_display = ['year','authors','volume','journal']\\n json_keys_to_ann = ['title','abstract']\\n json_keys = json_keys_to_display + json_keys_to_ann\\n resp = generate_bioc(json_keys,json_keys_to_ann,username,action,lang,use,inst,'xml',annotation_mode,report_type,batch)\\n return HttpResponse(resp,content_type='application/xml')\\n\\n elif format == 'biocjson':\\n json_keys_to_display = request.session['fields']\\n json_keys_to_ann = request.session['fields_to_ann']\\n json_keys = json_keys_to_display + json_keys_to_ann\\n resp = generate_bioc(json_keys,json_keys_to_ann,username,action,lang,use,inst,'json',annotation_mode,report_type,batch)\\n return HttpResponse(resp,content_type='application/xml')\",\n \"def dwn_rel_sup_csv(request):\\n i = int(request.GET.get('i'))\\n \\n return FileResponse(open('temp/relation_support_datasets/relation_support_dataset_{}_{}.csv'.format(i, request.user.username),'rb'))\",\n \"def _download_metafile(dataset, path=None):\\n if not path:\\n path = sunpy.config.get('downloads', 'sample_dir')\\n base_url = 'https://spdf.gsfc.nasa.gov/pub/software/cdawlib/0MASTERS/'\\n fname = dataset.lower() + '_00000000_v01.cdf'\\n url = base_url + fname\\n try:\\n downloaded_file = pooch.retrieve(url=url, known_hash=None, fname=fname, path=path, progressbar=True)\\n except ModuleNotFoundError:\\n downloaded_file = pooch.retrieve(url=url, known_hash=None, fname=fname, path=path, progressbar=False)\\n return downloaded_file\",\n \"def download_dataset(self):\\n dataset_name = ADE20K_URL.split(\\\"/\\\")[-1].split(\\\".\\\")[0]\\n req = urllib.request.Request(ADE20K_URL, method=\\\"HEAD\\\")\\n size_file = urllib.request.urlopen(req).headers[\\\"Content-Length\\\"]\\n download = \\\"n\\\"\\n while download != \\\"y\\\":\\n if not self.yes_all:\\n download = input(f\\\"You are about to download {dataset_name} ({size_file} bytes) to the temporary folder {self.tmp_path}. Do you want to continue? [y/n] \\\\n\\\")\\n if self.yes_all or download == \\\"y\\\":\\n logger.info(f\\\"Downloading dataset {dataset_name} at {ADE20K_URL} to temporary folder {self.tmp_path}...\\\")\\n zip_path, hdrs = urllib.request.urlretrieve(ADE20K_URL, f\\\"{self.tmp_path}/{dataset_name}.zip\\\")\\n logger.info(f\\\"Extracting {zip_path} to temporary folder {self.tmp_path}...\\\")\\n with zipfile.ZipFile(f\\\"{zip_path}\\\", 'r') as z:\\n z.extractall(f\\\"{self.tmp_path}\\\")\\n self.input_data_path = zip_path[:-4]\\n break\\n elif download == \\\"n\\\":\\n logger.error(f\\\"Cannot pursue without downloading the dataset.\\\")\\n sys.exit()\\n else:\\n logger.error(\\\"Please enter a valid answer (y or n).\\\")\",\n \"def download_sample():\\n sample_id = demisto.getArg('id')\\n r = req('GET', SUB_API + 'samples/' + sample_id + '/sample.zip')\\n ec = {'ThreatGrid.DownloadedSamples.Id': sample_id}\\n demisto.results([\\n {\\n 'Type': entryTypes['note'],\\n 'EntryContext': ec,\\n 'HumanReadable': '### ThreatGrid Sample Download - \\\\n'\\n + 'Your download request has been completed successfully for ' + sample_id,\\n 'Contents': ec,\\n 'ContentsFormat': formats['json']\\n },\\n fileResult(sample_id + '-sample.zip', r.content)\\n ])\",\n \"def download_data(self):\\n headers = {'User-Agent': 'Mozilla/5.0',}\\n\\n #Request for html data of url page\\n r = requests.get(self.url, headers = headers, allow_redirects=True)\\n soup = BeautifulSoup(r.text, \\\"html.parser\\\")\\n\\n #Checking if folder path exists, if not, creats it\\n i=0\\n while i<len(self.folder)-1:\\n if self.folder[i] == '/':\\n if not os.path.isdir(self.folder[:i]):\\n os.mkdir(self.folder[:i])\\n i+=1\\n if i==len(self.folder)-1:\\n if not os.path.isdir(self.folder):\\n os.mkdir(self.folder)\\n\\n # if not os.path.isdir(self.folder):\\n # os.mkdir(self.folder)\\n\\n #Gets every href to zip file with data\\n entries = []\\n for link in soup.find_all('a'):\\n if re.search(\\\"^data/.*.zip\\\", link.get('href')):\\n entries.append(link.get('href'))\\n\\n #Gets the newest dataset\\n self.getCurrentData(entries)\\n\\n i=0\\n #Saves each file in dataset\\n for list in self.ListOfZipFiles:\\n if not os.path.isfile(self.folder+list[4:]):\\n r = requests.get(self.url+list)\\n open(self.folder+list[4:], 'wb').write(r.content)\\n #deletes prefix \\\"data/\\\"\\n self.ListOfZipFiles[i] = list[4:]\\n i+=1\",\n \"def download_dataset(url=DATASET_URL):\\n # disable insecure https warning\\n urllib3.disable_warnings(urllib3.exceptions.InsecureRequestWarning)\\n\\n c = urllib3.PoolManager()\\n with c.request(\\\"GET\\\", url, preload_content=False) as res, open(\\n LOCAL_FILE_NAME, \\\"wb\\\"\\n ) as out_file:\\n shutil.copyfileobj(res, out_file)\\n logging.info(\\\"Download completed.\\\")\",\n \"def make_download_url(lender, metro):\\n where = \\\"\\\"\\n if lender:\\n where = ''\\n count = 0\\n if type(lender) is QuerySet:\\n for item in lender:\\n query = '(agency_code=%s AND respondent_id=\\\"%s\\\" AND year=%s)'\\n where += query % (item.institution.agency_id, item.institution.respondent_id, item.institution.year)\\n count += 1\\n if(count < len(lender)):\\n where += \\\"OR\\\"\\n else:\\n query = '(agency_code=%s AND respondent_id=\\\"%s\\\" AND as_of_year=%s)'\\n where += query % (lender.agency_id, lender.respondent_id, lender.year)\\n if metro:\\n divisions = [div.metdiv for div in\\n Geo.objects.filter(\\n geo_type=Geo.METDIV_TYPE, cbsa=metro.cbsa, year=metro.year\\n ).order_by('cbsa')]\\n if divisions:\\n where += ' AND msamd IN (\\\"' + '\\\",\\\"'.join(divisions) + '\\\")'\\n else: # no divisions, so just use the MSA\\n where += ' AND msamd=\\\"' + metro.cbsa + '\\\"'\\n\\n query = urlencode({\\n '$where': where,\\n '$limit': 0\\n })\\n base_url = 'https://api.consumerfinance.gov/data/hmda/slice/'\\n return base_url + 'hmda_lar.csv?' + query\",\n \"def main(url, save_path, header):\\n html = get_doctor_html(url)\\n doctor_info = get_doctor_info(url,html) \\n save_data(doctor_info, header, save_path)\"\n]","isConversational":false},"negative_scores":{"kind":"list like","value":["0.6781899","0.6684006","0.6684006","0.6684006","0.6684006","0.6684006","0.6684006","0.6534695","0.65255445","0.64752614","0.64643145","0.6462871","0.646285","0.64577276","0.6406183","0.6401371","0.6401371","0.6401371","0.6401371","0.6401371","0.6401371","0.6401371","0.6401371","0.6401371","0.6401371","0.6401371","0.6401371","0.6401371","0.6401371","0.6401371","0.6401371","0.6401371","0.6401371","0.6401371","0.6401371","0.6401371","0.6401371","0.6401371","0.6401371","0.6401371","0.6401371","0.6401371","0.6401371","0.6401371","0.6401371","0.6401371","0.6401371","0.6401371","0.6401371","0.6327544","0.622158","0.6197982","0.6194564","0.6194247","0.61688125","0.61509424","0.6134084","0.6124586","0.6116591","0.6115832","0.61024237","0.610012","0.6093137","0.6075935","0.6070294","0.6064644","0.604112","0.6039056","0.6034956","0.6011542","0.59825045","0.59437263","0.59058344","0.59057903","0.5903384","0.5873667","0.5858194","0.5857589","0.584361","0.5836152","0.58346045","0.58327824","0.5830381","0.5827596","0.5827596","0.5827291","0.5813742","0.5812356","0.57892555","0.5786854","0.576569","0.5757378","0.5754981","0.5748803","0.57275","0.57248884","0.5707021","0.5706425","0.57057387","0.5705314"],"string":"[\n \"0.6781899\",\n \"0.6684006\",\n \"0.6684006\",\n \"0.6684006\",\n \"0.6684006\",\n \"0.6684006\",\n \"0.6684006\",\n \"0.6534695\",\n \"0.65255445\",\n \"0.64752614\",\n \"0.64643145\",\n \"0.6462871\",\n \"0.646285\",\n \"0.64577276\",\n \"0.6406183\",\n \"0.6401371\",\n \"0.6401371\",\n \"0.6401371\",\n \"0.6401371\",\n \"0.6401371\",\n \"0.6401371\",\n \"0.6401371\",\n \"0.6401371\",\n \"0.6401371\",\n \"0.6401371\",\n \"0.6401371\",\n \"0.6401371\",\n \"0.6401371\",\n \"0.6401371\",\n \"0.6401371\",\n \"0.6401371\",\n \"0.6401371\",\n \"0.6401371\",\n \"0.6401371\",\n \"0.6401371\",\n \"0.6401371\",\n \"0.6401371\",\n \"0.6401371\",\n \"0.6401371\",\n \"0.6401371\",\n \"0.6401371\",\n \"0.6401371\",\n \"0.6401371\",\n \"0.6401371\",\n \"0.6401371\",\n \"0.6401371\",\n \"0.6401371\",\n \"0.6401371\",\n \"0.6401371\",\n \"0.6327544\",\n \"0.622158\",\n \"0.6197982\",\n \"0.6194564\",\n \"0.6194247\",\n \"0.61688125\",\n \"0.61509424\",\n \"0.6134084\",\n \"0.6124586\",\n \"0.6116591\",\n \"0.6115832\",\n \"0.61024237\",\n \"0.610012\",\n \"0.6093137\",\n \"0.6075935\",\n \"0.6070294\",\n \"0.6064644\",\n \"0.604112\",\n \"0.6039056\",\n \"0.6034956\",\n \"0.6011542\",\n \"0.59825045\",\n \"0.59437263\",\n \"0.59058344\",\n \"0.59057903\",\n \"0.5903384\",\n \"0.5873667\",\n \"0.5858194\",\n \"0.5857589\",\n \"0.584361\",\n \"0.5836152\",\n \"0.58346045\",\n \"0.58327824\",\n \"0.5830381\",\n \"0.5827596\",\n \"0.5827596\",\n \"0.5827291\",\n \"0.5813742\",\n \"0.5812356\",\n \"0.57892555\",\n \"0.5786854\",\n \"0.576569\",\n \"0.5757378\",\n \"0.5754981\",\n \"0.5748803\",\n \"0.57275\",\n \"0.57248884\",\n \"0.5707021\",\n \"0.5706425\",\n \"0.57057387\",\n \"0.5705314\"\n]","isConversational":false},"document_score":{"kind":"string","value":"0.6257936"},"document_rank":{"kind":"string","value":"50"}}},{"rowIdx":75394,"cells":{"query":{"kind":"string","value":"Filter out users where tracking coverage is too low"},"document":{"kind":"string","value":"def filter_tracking_coverage(sp, locs, tpls, trips):\n print(\"\\t filter by tracking coverage\")\n\n def filter_user_by_number_of_days(\n sp, tpls, coverage=0.7, min_nb_good_days=28, filter_sp=True\n ):\n nb_users = len(sp.user_id.unique())\n\n sp_tpls = sp.append(tpls).sort_values([\"user_id\", \"started_at\"])\n\n coverage_df = ti.analysis.tracking_quality.temporal_tracking_quality(\n sp_tpls, granularity=\"day\", max_iter=1000\n )\n\n good_days_count = (\n coverage_df[coverage_df[\"quality\"] >= coverage]\n .groupby(by=\"user_id\")[\"quality\"]\n .count()\n )\n good_users = good_days_count[good_days_count >= min_nb_good_days].index\n if filter_sp:\n sp = sp[sp.user_id.isin(good_users)]\n print(\n \"\\t\\t nb users now: \",\n len(sp.user_id.unique()),\n \"before: \",\n nb_users,\n )\n return sp, good_users\n\n if study == \"geolife\":\n sp, user_id_ix = filter_user_by_number_of_days(\n sp=sp, tpls=tpls, coverage=0.7, min_nb_good_days=14\n )\n else:\n sp, user_id_ix = filter_user_by_number_of_days(\n sp=sp, tpls=tpls, coverage=0.7, min_nb_good_days=14\n )\n print(\"\\t\\t drop users with bad coverage: before: \", len(locs))\n tpls = tpls[tpls.user_id.isin(user_id_ix)]\n trips = trips[trips.user_id.isin(user_id_ix)]\n locs = locs[locs.user_id.isin(user_id_ix)]\n print(\"after:\", len(locs))\n return sp, locs, trips"},"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 prune(self):\n target_user_ids = self.get_queryset().values_list('id', flat=True)\n exclude_user_ids = SentDrip.objects.filter(date__lt=conditional_now(),\n drip=self.drip_model,\n user__id__in=target_user_ids)\\\n .values_list('user_id', flat=True)\n self._queryset = self.get_queryset().exclude(id__in=exclude_user_ids)","def suppress_analyze(more_exclusions=None):\n return api.override_step_data(\n 'read filter exclusion spec',\n api.json.output({\n 'base': {\n 'exclusions': ['f.*'] + (more_exclusions or []),\n },\n 'chromium': {\n 'exclusions': [],\n },\n })\n )","def filter_user_interactions(df, min_value):\n # Number of users before filtering\n num_users_before = df['personId'].nunique()\n\n # Users with enough interactions\n filter_users = df['personId'].value_counts() > min_value\n filter_users = filter_users[filter_users].index.tolist()\n\n # New dataframe with only selected users\n df = df[(df['personId'].isin(filter_users))]\n df = df[['personId', 'contentId', 'virality']]\n\n # Number of users after filtering\n num_users_after = df['personId'].nunique()\n\n print('Number of users discarded: ', num_users_before - num_users_after)\n return df","def test_otoroshi_controllers_adminapi_analytics_controller_filterable_stats(self):\n pass","def reject_filter(self, x_hits, y_hits, *args):\n if self.verbose:\n print(\"max empty %d train %d predict\" % (self.max_train_empty, self.max_predict_empty))\n zeros_x = tf.reduce_sum(tf.to_int32(tf.equal(x_hits, 0.0)))\n keep = zeros_x <= self.max_train_empty\n return keep","def test_filter_user(self) -> None:\n\n channel = self.make_request(\n \"GET\",\n self.url + \"?user_id=%s\" % self.other_user,\n access_token=self.admin_user_tok,\n )\n\n self.assertEqual(200, channel.code, msg=channel.json_body)\n self.assertEqual(channel.json_body[\"total\"], 10)\n self.assertEqual(len(channel.json_body[\"event_reports\"]), 10)\n self.assertNotIn(\"next_token\", channel.json_body)\n self._check_fields(channel.json_body[\"event_reports\"])\n\n for report in channel.json_body[\"event_reports\"]:\n self.assertEqual(report[\"user_id\"], self.other_user)","def filter_criteria_met(current_gre, current_gpa, current_toefl):\r\n\r\n if int(current_gre) < global_constants['MINIMUM_GRE']:\r\n return False\r\n if float(current_gpa) < global_constants['MINIMUM_GPA']:\r\n return False\r\n if int(current_toefl) < global_constants['MINIMUM_TOEFL']:\r\n return False\r\n return True","def test_for_criteria(self):\n ignore = ['interpreter_method', 'average_by_sample_or_site', 'include_nrm']\n values = ([dic['value'] for dic in self.acceptance_criteria.values() if (dic['criterion_name'] not in ignore and dic['value'] != -999)])\n return values","def filter_by_cuisine(df, user):\n\n if len(user.cuisines) == 0:\n # User has selected no cuisines - do not need to remove recipes\n return df\n\n return df.loc[df.cuisines.isin(user.cuisines)]","def filter_data_on_complaince(folder_Path,complaince_rate):\n complaince_df=pd.read_csv(folder_Path+\"complaince.csv\")\n complaince_df['Percent'] = complaince_df['Percent'].apply(converters.ConvertPercent)\n complaince_df = complaince_df.loc[complaince_df.Percent >= complaince_rate]\n IDs= complaince_df.ID.unique()\n # print(IDs)\n df_apps=pd.read_csv(folder_Path+\"app_usage.csv\")\n df_apps = df_apps.loc[df_apps.user_id.isin(IDs)]\n df_apps = df_apps.reset_index(drop=True)\n df_apps.to_csv(folder_Path+\"Filtered/app_usage.csv\")\n\n\n df_battery= pd.read_csv(folder_Path+\"battery_events.csv\")\n df_battery= df_battery.loc[df_battery.user_id.isin(IDs)]\n df_battery = df_battery.reset_index(drop=True)\n df_battery.to_csv(folder_Path+\"Filtered/battery_events.csv\")\n\n\n df_bluetooth = pd.read_csv(folder_Path+\"bluetooth.csv\")\n df_bluetooth = df_bluetooth.loc[df_bluetooth.user_id.isin(IDs)]\n df_bluetooth = df_bluetooth.reset_index(drop=True)\n df_bluetooth.to_csv(folder_Path+\"Filtered/bluetooth.csv\")\n\n df_screen = pd.read_csv(folder_Path+\"screenstate.csv\")\n df_screen = df_screen.loc[df_screen.user_id.isin(IDs)]\n df_screen = df_screen.reset_index(drop=True)\n df_screen.to_csv(folder_Path+\"Filtered/screenstate.csv\")\n\n\n df_wifi = pd.read_csv(folder_Path+\"wifi.csv\")\n df_wifi = df_wifi.loc[df_wifi.user_id.isin(IDs)]\n df_wifi = df_wifi.reset_index(drop=True)\n df_wifi.to_csv(folder_Path+\"Filtered/wifi.csv\")","def test_profile_filtering(instr_task_workbench, instr_view):\n add_profile(instr_task_workbench, 'fp5', 'Dummy.dumb.002')\n p = instr_task_workbench.get_plugin('ecpy.instruments')\n filtered = instr_view.filter_profiles(p._profiles)\n assert 'fp5' not in filtered\n\n add_profile(instr_task_workbench, 'fp6', 'Dummy.dumb.003')\n p = instr_task_workbench.get_plugin('ecpy.instruments')\n filtered = instr_view.filter_profiles(p._profiles)\n assert 'fp6' in filtered","def filter_rare_node(users, businesses, reviews, user_thresh, business_thresh, friend_thresh):\n continue_filter = True\n filtered_users = set()\n filtered_businesses = set()\n while continue_filter:\n continue_filter = False\n # filter step 1\n users_interact_ind = {}\n business_interact_ind = {}\n for review in reviews:\n user_id = review['user_id'] # a list\n business_id = review['business_id'] # a list\n users_interact_ind[user_id] = users_interact_ind.get(user_id, 0) + 1\n business_interact_ind[business_id] = business_interact_ind.get(business_id, 0) + 1\n\n filtered_review_users = set(u for u in users_interact_ind.keys() if users_interact_ind[u]>=user_thresh)\n filtered_review_businesses = set(b for b in business_interact_ind.keys() if business_interact_ind[b]>=business_thresh)\n \n # loop until users' reviews equal to filtered reviews\n if (filtered_users != filtered_review_users) or (filtered_businesses != filtered_review_businesses):\n continue_filter = True\n\n # filter step 2\n # filter user and business\n # make user_friends_dict, only those users with lots of friends can be included\n user_friends_dict = {}\n for user in users:\n user_id = user['user_id']\n if user_id not in filtered_review_users:\n continue\n if not user['friends']:\n continue\n filtered_friends = [friend.strip() for friend in user['friends'].split(',') if friend.strip() in filtered_review_users]\n if len(filtered_friends) >= friend_thresh:\n user_friends_dict[user_id] = filtered_friends # users with friends larger than friend_thresh\n\n continue_inside = True\n while continue_inside:\n friends = {}\n continue_inside = False\n for user, user_friends in user_friends_dict.items():\n filtered_friends = [friend for friend in user_friends if friend in user_friends_dict] # friend in user_friends_dict's keys\n if len(filtered_friends) >= friend_thresh:\n friends[user] = filtered_friends\n else:\n continue_inside = True\n user_friends_dict = deepcopy(friends) # this takes time\n\n filtered_users = set(user_friends_dict.keys())\n filtered_businesses_list = []\n\n for business in businesses:\n business_id = business['business_id']\n if business_id not in filtered_review_businesses:\n continue\n if not business['categories']:\n continue\n if not business['city']:\n continue\n filtered_businesses_list.append(business_id)\n filtered_businesses = set(filtered_businesses_list)\n\n filtered_review = []\n for review in reviews:\n if (review['user_id'] in filtered_users) and (review['business_id'] in filtered_businesses):\n filtered_review.append(review)\n reviews = deepcopy(filtered_review) # this takes time\n\n print(len(list(filtered_users)))\n print(len(list(filtered_businesses)))\n print(len(reviews))\n print('filter loop')\n\n print('filter complete')\n return filtered_users, filtered_businesses, filtered_review","def filter_unknown_bases(self):\n self.failed[\"unknowns\"] = self.stats.index[\n self.stats[\"unknowns\"] > self.tolerance[\"unknowns\"]\n ]\n self.passed = self.stats.drop(self.failed[\"unknowns\"])","def filterTrackingCookies(self):\n return self.__filterTrackingCookies","def low_abundance(self, cutoff=10):\n data = json.loads(self.profile)\n\n checks = [mean(v) > cutoff for _, v in data[\"data\"].items()]\n\n return not any(checks)","def remove_less_than_treshold(potential_blockings, logger, dashboard_log):\n treshold = get_treshold(logger, dashboard_log)\n # create a new list containing all hosts with security value higher than treshold\n # inspired by https://stackoverflow.com/a/1207461\n return [host for host in potential_blockings if not get_average_security_value(host, logger) < treshold]","def test_filter_users_geq(app, add_ten_users):\n with app.app_context():\n add_ten_users()\n users = User.query\n users = apply_filter(users, User, {'column': 'id', 'type': 'geq',\n 'value': '5'})\n result = users.all()\n assert len(result) == 6","def tempfeeder_exp_nonzerotest_users():\n\n return [ user for user in tempfeeder_exp().user_ids if all(tempfeeder_exp()[user]['Load']['2005-10-01 00:00':]) ]","def filter_df(df):\n filtered_df = df[df['rtt_author_ids'].notnull()]\n filtered_df = filtered_df[filtered_df['user_country'] == 'FR']\n filtered_df = filtered_df[filtered_df['retweet_count'] > 1]\n filtered_df = filtered_df[filtered_df['favourites_count'] > 1000]\n return filtered_df","def filter_out_rare_points(points, threshold_pct=0.5):\n \n c = Counter(points)\n total = sum(c.values())\n l = []\n for p in points:\n v = c[p]\n if v/total * 100 <= threshold_pct:\n l.append(np.nan)\n else:\n l.append(p)\n \n return l","def purge_outlying_trials(self, trial_nums, thresh=5.0):\n for injkey in self.values.keys():\n for fit_key in self.values[injkey].keys():\n points = np.array(self.values[injkey][\n fit_key]['metric_val']['vals'])\n if len(points.shape) == 1:\n points = points[:, None]\n median = np.median(points, axis=0)\n diff = np.sum((points - median)**2, axis=-1)\n diff = np.sqrt(diff)\n med_abs_deviation = np.median(diff)\n modified_z_score = 0.6745 * diff / med_abs_deviation\n good_trials = modified_z_score < thresh\n if not np.all(good_trials):\n bad_trials = np.where(not good_trials)[0]\n logging.warning(\n 'Outlier(s) detected for %s in trial(s) %s. Will be '\n 'removed. If you think this should not happen, please '\n 'change the value of the threshold used for the '\n 'decision (currently set to %.2e).'%(\n fit_key, trial_nums[bad_trials], thresh\n )\n )\n for fitkey in self.values[injkey].keys():\n for param in self.values[injkey][fitkey].keys():\n new_vals = np.delete(\n np.array(self.values[injkey][\n fitkey][param]['vals']),\n bad_trials\n )\n self.values[injkey][\n fitkey][param]['vals'] = new_vals","def filter_safe_user_agents(self, filter_safe_user_agents: ConfigNodePropertyArray):\n\n self._filter_safe_user_agents = filter_safe_user_agents","def _remove_potential_false_positives_for_consent_version(self, df):\n\n # Pandas: find all the records we want to keep and make a new dataframe out of the result. Inverts the\n # \"and\" conditions above for the known false positives in order to find everything but those records\n filtered_df = df.loc[(df.sync_status != int(ConsentSyncStatus.NEEDS_CORRECTING)) |\\\n (df.hpo != 'VA') | (df.va_consent_for_non_va == 0) |\\\n (df.missing_file == 1) | (df.invalid_dob == 1) | (df.invalid_age_at_consent == 1) |\\\n (df.checkbox_unchecked == 1) | (df.non_va_consent_for_va == 1)]\n\n print(f'Filtered count for consent version false positives: {df.shape[0] - filtered_df.shape[0]}')\n\n return filtered_df","def coverage(self, user_list):\n all_recom_set = set()\n all_item = set(self.train['movieId'].values)\n print('\\nCalculated coverage: ')\n for user in tqdm(user_list):\n recom_data = self._get_recommend(user)\n recom_item = set([data[0] for data in recom_data])\n all_recom_set.update(recom_item)\n print('\\nCoverage is: ', len(all_recom_set) / (len(all_item) * 1.0))\n return len(all_recom_set) / (len(all_item) * 1.0)","def filter_point(x, y, xlower, xupper, ylower, yupper):\n ignore = False\n if (x < xlower or x > xupper or y < ylower or y > yupper):\n ignore = True\n return ignore","def filter_func(self, agents):\n return [\n agent for agent in agents\n if agent.energy < self.model.energy_threshold and not agent.pack\n ]","def filter_data(course_rating_df):\n users_counts = course_rating_df[\"user\"].value_counts()\n filtered_users_counts = users_counts[users_counts >= MIN_RATING_COUNT_FOR_USER]\n\n courses_counts = course_rating_df[\"course\"].value_counts()\n filtered_courses_counts = courses_counts[courses_counts >= MIN_RATING_COUNT_FOR_COURSE]\n\n filtered_ratings_df = course_rating_df[\n course_rating_df[\"user\"].isin(filtered_users_counts.index)\n ]\n filtered_ratings_df = filtered_ratings_df[\n filtered_ratings_df[\"course\"].isin(filtered_courses_counts.index)\n ]\n\n X = filtered_ratings_df[[\"user\", \"course\"]].to_numpy()\n y_diff = filtered_ratings_df[\"difficulty\"].to_numpy()\n y_wl = filtered_ratings_df[\"workload\"].to_numpy()\n\n return X, y_diff, y_wl","def _removeInsufficientTransformer(self, working_stats, params):\n\n for choice, subsets in working_stats.items():\n sufficient_values = [value for value in subsets if value > 0]\n if not sufficient_values:\n del working_stats[choice]\n\n return working_stats","def test_having_condition_with_preventing_aggregate_metrics_only(self):\n response = self.do_request(\n {\n \"field\": [\"transaction\", \"project\", \"p50(transaction.duration)\"],\n \"query\": \"event.type:transaction p50(transaction.duration):<50\",\n \"dataset\": \"metrics\",\n \"preventMetricAggregates\": \"1\",\n \"per_page\": 50,\n \"project\": self.project.id,\n }\n )\n assert response.status_code == 400, response.content","def unmark_all(range_days : int):\n logging.info(\"Unmarking users..\")\n now = datetime.now()\n time_limit = now - timedelta(days=range_days)\n users = User.query.filter_by(is_positive=True).\\\n filter(User.reported_positive_date <= time_limit).all()\n for user in users:\n if user != None and user.is_positive == True:\n user.is_positive = False\n user.reported_positive_date = None\n db.session.commit()\n\n return ''","def get_queryset(self):\n user_full = UserFullName.objects.get(id=self.request.user.id)\n queryset = CaseTrack.objects.filter(user_to=user_full, done=False)\n return queryset","def test_listing_from_wall_when_blocked_some_users(self):","def filter_deliveries(restriction):\n data = build_data([\n d for d in current_user.deliveries if restriction(d)])\n # Sort by date\n return sorted(data, key=lambda x: x['toDate'])","def filter_lowfreq(df, main_itype):\n df_purged = df.drop(['itype','Position','Position1','Position2'], 1)\n df['above_30perc'] = (df_purged > 50).sum(1)\n pos_topreserve = set(df['Position'][ (df['above_30perc'] > 1) & (df['itype'] == main_itype) ])\n df.drop('above_30perc', 1, inplace = True)\n df = df[df['Position'].isin(pos_topreserve)]\n \n return(df)","def filter_renters(data_df):\n return data_df[(data_df['sc116'] == 2) # Only renters\n & (data_df['uf17'] < 8000) # With a real rent provided\n ]","def test_user_rate_reached_perf_issues(self):\n for i in range(0, 10):\n event = self.store_transaction(\n environment=None,\n project_id=self.project.id,\n user_id=str(i),\n fingerprint=[f\"{GroupType.PERFORMANCE_N_PLUS_ONE_DB_QUERIES.value}-group1\"],\n )\n perf_group = event.groups[0]\n snooze = GroupSnooze.objects.create(group=perf_group, user_count=10, user_window=60)\n assert not snooze.is_valid(test_rates=True)","def filter_safe_user_agents(self) -> ConfigNodePropertyArray:\n return self._filter_safe_user_agents","def _filter_in_request(self):\n pass","def test_run_coverage_omit(self):\n cmd = GreenTestCommand(Distribution())\n cmd.coverage_omit = \"abc/*,*/def\"\n cmd.ensure_finalized()\n cmd.run()\n self.assertThat(_subprocess_call_args(),\n ContainsAll([\n \"-o\",\n cmd.coverage_omit\n ]))","def filter_number_units(self, scenarios):\n\n return (\n scenarios\n .groupby('scenario_id')\n .agg({'units':sum})\n .query('units <= 70')\n .drop('units', axis=1)\n .merge(scenarios, how='inner', on='scenario_id')\n )","def contributors_filter(self, **kwargs):\n return None","def filter_data(self):\n self.df = self.df[HeatStrokeDataFiller.important_features]","def no_filter(blast_subject_entry):\r\n return True","def filter_warnings():\n warnings.simplefilter(\"ignore\", category=UserWarning)\n warnings.simplefilter(\"ignore\", category=LightningDeprecationWarning)","def progress_filter(self, src, dst):\n log = logging.getLogger(self.name)\n for exclude in self.excludes:\n if fnmatch(src, exclude):\n if self.verbose:\n log.debug(\"Excluding %s\" % src.rstrip('/'))\n self.excluded_count = self.excluded_count + 1\n return\n return dst","def test_user_with_no_explorations_has_no_impact(self):\n user_a_id = self._sign_up_user(\n self.USER_A_EMAIL, self.USER_A_USERNAME)\n self._run_computation()\n user_stats_model = user_models.UserStatsModel.get(\n user_a_id, strict=False)\n self.assertIsNone(user_stats_model)","def filter_queryset(self, request, queryset, view):\n if str(request.query_params.get(\"orgs\")).lower() == \"false\":\n organization_user_ids = OrganizationProfile.objects.values_list(\n \"user__id\", flat=True\n )\n queryset = queryset.exclude(id__in=organization_user_ids)\n\n return queryset","def _check_dangerous_filter_wo_user(self):\n xml_files = self.filter_files_ext('xml')\n for xml_file in xml_files:\n ir_filter_records = self.get_xml_records(\n os.path.join(self.module_path, xml_file), model='ir.filters')\n for ir_filter_record in ir_filter_records:\n ir_filter_fields = ir_filter_record.xpath(\n \"field[@name='name' or @name='user_id']\")\n # if exists field=\"name\" then is a new record\n # then should be field=\"user_id\" too\n if ir_filter_fields and len(ir_filter_fields) == 1:\n # TODO: Add a list of msg_args before of return\n # TODO: Add source lineno in all xml checks\n self.msg_args = (\n \"%s:%d\" % (xml_file, ir_filter_record.sourceline),\n ir_filter_record.get('id'),)\n return False\n return True","def get_over(self, filter_dict, percentage):\n pass","def filter_out_reduced_healing(raw_heals):\n max_heal = max(raw_heals)\n threshold = 0.75 * max_heal\n\n selector = raw_heals > threshold\n\n return raw_heals[selector]","def filter_users_by_transaction_type(self, request):\n type = request.data[\"type\"] # type of transaction \"in\"- with +, out- with-\n if type == \"in\":\n users = User.objects.filter(transactions__amount__gt=0)\n else:\n users = User.objects.filter(transactions__amount__lt=0)\n serializer = UserSerializer(users, many=True)\n return Response(serializer.data)","def filter_for_user(self, user):\n if user.is_anonymous or user.is_staff or user.is_support:\n return self\n\n return self.filter(\n Q(\n project__permissions__user=user,\n project__permissions__is_active=True,\n )\n | Q(\n project__customer__permissions__user=user,\n project__customer__permissions__is_active=True,\n )\n | Q(\n offering__customer__permissions__user=user,\n offering__customer__permissions__is_active=True,\n )\n ).distinct()","def remove_outliers(lst):\n slst = sorted(lst)\n three_iqr = 3 * get_IQR(lst)\n low_boundary = float(np.percentile(lst, 25)) - three_iqr\n high_boundary = float(np.percentile(lst, 75)) + three_iqr\n\n return filter(lambda x: x >= low_boundary and x <= high_boundary, slst)","def filter_by_model(self, instance):\n content_type = ContentType.objects.get_for_model(instance.__class__)\n object_id = instance.id\n queryset = super(UserTrackerManager, self).filter(\n content_type=content_type, object_id=object_id)\n return queryset","def filter_to_verified(df, verified_df):\n return df[df.UID.isin(verified_df.UID.values)].reset_index(drop=True)","def filter_rare_genes(data, *extra_data, cutoff=0, min_cells=5):\n gene_sums = measure.gene_capture_count(data, cutoff=cutoff)\n keep_genes_idx = gene_sums >= min_cells\n data = select.select_cols(data, *extra_data, idx=keep_genes_idx)\n return data","def _exclude_visitor():\n return _is_robot() or _is_ignored_ip_address()","def adjust_filter(self, queryset):\n new_queryset = queryset.filter(points__gt=0.0).order_by('-points', 'rate')\n return self.validate_queryset(new_queryset)","def filterVarForWizard(self, v):\n return v.isMeasurement()","def not_test_without_user(self):\n # TODO","def filter_untracked(quant) -> bool:\n return quant.lot_id is None","def filter_de(stock, thresh = .5):\n \"\"\"ASSUMPTION: FILTER_ROE WAS RUN BEFORE THIS, OR GET_MORINGSTAR_RATIOS WAS CALLED\"\"\"\n \"\"\"TODO: ABSTRACT USING HOF\"\"\"\n return stock['DE'] <= thresh","def filterData(records):\n def isInteresting(record):\n if record[VO_ISSUER] in ('/DC=ch/DC=cern/OU=computers/CN=voms.cern.ch', '/DC=ch/DC=cern/OU=computers/CN=lcg-voms.cern.ch'):\n return True\n if record[VO_NAME] in ('atlas', 'cms', 'alice'):\n return True\n if record[USERSN] == '/C=SI/O=SiGNET/O=IJS/OU=F9/CN=Andrej Filipcic':\n return True\n if record[USERSN] in ('aliprod', '/aliprod'):\n return True\n if record[USERSN].startswith(ALIEN_USER_PREFIX):\n return True\n\n return False\n\n return [ r for r in records if isInteresting(r) ]","def set_restriction_filters(self):\n self.restriction_filters[\"pk__exact\"] = self.request.user.pk","def clean_customer_df(customer_df: pd.DataFrame) -> pd.DataFrame:\n \n # remove customers with more than 20 purchases\n if 'frequency' in customer_df.columns:\n customer_df = customer_df[customer_df.frequency < 20]\n \n return customer_df","def filter_outliers(data): \n \n idx_out = find_outliers_IQR(data)\n \n cleaned = data[~idx_out].copy()\n\n # print(f'There were {idx_out.sum()} outliers.')\n \n return cleaned","def filter_contigs(self, criteria):\n eligible_contigs = self.passed.contigs[self.passed.contigs > 10]\n not_enough_contigs = self.passed.contigs[self.passed.contigs <= 10]\n # TODO Define separate function for this\n med_abs_dev = abs(eligible_contigs - eligible_contigs.median()).mean()\n self.med_abs_devs[\"contigs\"] = med_abs_dev\n # Define separate function for this\n # The \"deviation reference\"\n dev_ref = med_abs_dev * self.contigs\n self.dev_refs[\"contigs\"] = dev_ref\n self.allowed[\"contigs\"] = eligible_contigs.median() + dev_ref\n self.failed[\"contigs\"] = eligible_contigs[\n abs(eligible_contigs - eligible_contigs.median()) > dev_ref\n ].index\n eligible_contigs = eligible_contigs[\n abs(eligible_contigs - eligible_contigs.median()) <= dev_ref\n ]\n eligible_contigs = pd.concat([eligible_contigs, not_enough_contigs])\n eligible_contigs = eligible_contigs.index\n self.passed = self.passed.loc[eligible_contigs]","def filter_for_user(self, user):\n query = Q(visibility=Document.PUBLIC) | \\\n Q(visibility=Document.PRIVATE, created_by=user) | \\\n Q(visibility=Document.ORG_ONLY,\n organization__memberships__user=user)\n\n if not user.external:\n query = query | Q(visibility=Document.ORG_ONLY_NO_EXTERNAL,\n organization__memberships__user=user)\n\n return super(DocumentManager, self).get_query_set().filter(query) \\\n .distinct()","def get_filtered_trials(self, include_pdb_hierarchies=False, log=None):\n if (log is None) : log = null_out()\n trials = sorted(self.get_trials(), key=operator.attrgetter(\"cc\"), reverse=True)\n filtered = []\n for k, trial in enumerate(trials):\n hierarchy = self.pdb_hierarchy.deep_copy()\n if (trial.min_fofc < self.params.min_fofc) or (trial.cc < self.params.cc_min):\n print(\" discarding trial %d [poor map quality]:\" % (k+1), file=log)\n trial.show_summary(out=log, prefix=\" \")\n continue\n sites = self.sites_start.deep_copy()\n sites.set_selected(self.iselection, trial.sites_cart)\n hierarchy.atoms().set_xyz(sites)\n if (self.params.filter_rotamer_outliers):\n n_outliers = alt_confs.score_rotamers(hierarchy=hierarchy,\n selection=self.iselection)\n if (n_outliers > 0):\n print(\" discarding trial %d [%d rotamer outlier(s)]:\" % \\\n (k+1, n_outliers), file=log)\n trial.show_summary(out=log, prefix=\" \")\n continue\n if (include_pdb_hierarchies):\n filtered.append((trial, hierarchy))\n else :\n filtered.append(trial)\n return filtered","def on_limit(self, track):\n print \"!!! Limitation notice received: %s\" % str(track)\n return","def filter_tracked(quant) -> bool:\n return quant.lot_id is not None","def filter_enable_safe_user_agents(self) -> ConfigNodePropertyBoolean:\n return self._filter_enable_safe_user_agents","def test_index_excludes_users(self):\n self.admin_api.create(\n 'User', email='',\n auth_id='',\n last_name='Doe', first_name='John')\n self.cron.index()\n result_dicts = [util.search_document_to_dict(doc)\n for doc in self.search_index.get_range()]\n result_kinds = [Model.get_kind(d['uid']) for d in result_dicts]\n self.assertNotIn('User', result_kinds)","def get_users_with_missing_data() -> Set[str]:\n users_data = {user[\"_source\"][\"VENDOR_UUID\"] for user in Handlers.elastic_handler.get_all_today_data(\n _type=\"status\",\n date_start=dt.date.today() + dt.timedelta(days=1),\n date_end=dt.date.today() + dt.timedelta(days=7),\n )}\n\n all_tokens = Handlers.token_handler.get_all_today_data(_type=\"token\")\n to_dict = {dict_[\"_source\"][\"VENDOR_UUID\"]: dict_[\"_source\"][\"TOKEN\"] for dict_ in all_tokens}\n\n return set(dict(filter(lambda item_tup: item_tup[0] not in users_data, to_dict.items())).values())","async def test_ignore_transaction(self):\n self.set_source_parameter(\"transactions_to_ignore\", [self.API2])\n response = await self.collect(get_request_json_return_value=self.JMETER_JSON)\n self.assert_measurement(response, value=\"123\", entities=[])","async def filter(self, **kwargs):\n\n pass","def filter_wolves(self, agents):\n return [\n agent for agent in agents\n if agent.energy < self.model.energy_threshold and not agent.pack\n ]","def filter_res(rows, res_class):\n keep = []\n match_logger.info('Limiting to sources in resolution class {} '\n '({}\" < BMIN <= {}\")'.format(res_class,\n res_dict[res_class][0],\n res_dict[res_class][1]))\n for row in rows:\n if row['res_class'] == res_class:\n keep.append(row)\n\n match_logger.info(' -- {} sources remaining'.format(len(keep)))\n\n return keep","def _filter(self):\n if self.properties['reason'] in PoliceReport.reason_filter:\n return False\n return True","def exclude_persons_without_registrations(request, filtered_queryset, statuses_that_disallow_empty_registrations=['A']):\n status_disallows_people_with_no_registrations = \\\n request.GET.get('status') in statuses_that_disallow_empty_registrations \n is_filtered_by_bbox = request.GET.get('sw_long') != None \\\n and request.GET.get('sw_lat') != None \\\n and request.GET.get('ne_long') != None\\\n and request.GET.get('ne_lat') != None\n is_filtered_by_city = request.GET.get('city', \"\") != \"\"\n if is_filtered_by_bbox or is_filtered_by_city or \\\n status_disallows_people_with_no_registrations:\n filtered_queryset = [f for f in filtered_queryset if len(f.registrations.all()) > 0]\n return filtered_queryset","def excluded_from_scan(self):\n return six.next(six.itervalues(self.zap._request(self.zap.base + 'spider/view/excludedFromScan/')))","def _check_for_noise(self) -> None:\n safety_stop = 5\n while self._has_noise() and safety_stop > 0:\n self.filter(size=3)\n safety_stop -= 1","def filter_negative(solver, sub_u_rate, threshold):\n classifier = joblib.load(join(join(_result_path, solver), str(sub_u_rate)) + '/logistic.pkl')\n\n sub_u = np.load(\"./processed_data/train/sub_u_\" + str(sub_u_rate) + \".npy\")\n sub_u_x = sub_u[:, :-1]\n result_sub_u = np.array(classifier.predict_proba(sub_u_x)[:, 1])\n\n sub_u_negative = sub_u[np.where(result_sub_u <= threshold)]\n print(sub_u_negative.shape)\n sub_u_negative_x = sub_u_negative[:, :-1]\n result_sub_u_negative = np.array(classifier.predict_proba(sub_u_negative_x)[:, 1])\n print(result_sub_u_negative.max())\n np.save(\"./processed_data/train/sub_u_negative.npy\", sub_u_negative)\n\n print(\"\\n\\n\\n\")\n unlabeled = np.load(\"./processed_data/train/raw/train_u.npy\")\n unlabeled_x = unlabeled[:, :-1]\n result_unlabeled = np.array(classifier.predict_proba(unlabeled_x)[:, 1])\n unlabeled_negative = unlabeled[np.where(result_unlabeled <= threshold)]\n print(unlabeled_negative.shape)\n result_unlabeled_negative = np.array(classifier.predict_proba(unlabeled_negative[:, :-1])[:, 1])\n print(result_unlabeled_negative.max())\n np.save(\"./processed_data/train/unlabeled_negative.npy\", unlabeled_negative)","def coverage(self):\r\n return 0, 1","def extra_users_groups(self, acs):\n return who_cannot_access(self.user, self.users_allowed.all(), self.groups_allowed.all(), acs)","def filter_cap(stock):\n return stock['Class'] == 'Small'","def get_filtered_pedigree_with_samples(self, _user=None):\n result = []\n seen = set()\n # Only select cases that have an active variant set.\n # TODO Perspectively, we need to distinguish between Small and Structural VariantSets.\n for case in self.get_active_smallvariant_cases():\n for line in case.get_filtered_pedigree_with_samples():\n if line[\"patient\"] not in seen:\n result.append(line)\n seen.add((case.name, line[\"patient\"]))\n return result","def unseen_count_for(self, user):\r\n return self.filter(user=user, unseen=True).count()","def test_not_calculates_risk_points_without_house(self):\n\n del self.user[\"houses\"]\n self.calculates_risk_points_by_houses.execute(self.user, self.risk_profile)\n self.assertEquals(self.risk_profile, MockRiskProfile.risk_profile)","def test_no_counterfactuals_found(self):\n threshold = 4.0\n self._config['Regression threshold'] = str(threshold)\n self._example = {'x_1': 1.0, 'x_2': 1.0}\n output = self._gen.generate(\n example=self._example,\n model=self._model,\n dataset=self._dataset,\n config=self._config)\n self.assertEmpty(output)","def reported_by(self, user):\n return Report.objects.filter(recipe=self, chef=user).exists()","def filter_passing_hits(self):\n self.create_fasta()\n self.blastn_commandline()\n\n hits = {}\n result_handle = open(generate_path(\"tmp/validate.xml\"))\n for record in NCBIXML.parse(result_handle):\n for entry in record.alignments:\n hit = entry.hit_def\n seqlen = entry.length\n hsp = entry.hsps[0]\n percent_ident = (float(hsp.positives) / float(seqlen)) * 100\n\n if 90 <= percent_ident <= 100:\n if hit in hits:\n if percent_ident > hits[hit]:\n hits[hit] = percent_ident\n else:\n hits[hit] = percent_ident\n del result_handle\n self.seqdata.hits = hits","def limited_infection(self, user, target_num, version):\n num_infected = 0\n\n # Checks to make sure we don't have a target_num greater than number of uninfected users\n uninfected = [u for u in self.users if u.version != version]\n if target_num > len(uninfected):\n self.infect_all(version)\n\n while num_infected < target_num:\n # Infect the given user first\n if user.version != version:\n user.version = version\n num_infected += 1\n\n # Infect the uninfected students of user\n uninfected_students = [s for s in user.students if s.version != version]\n for student in uninfected_students:\n student.version = version\n num_infected += 1\n\n # Find the next user to infect\n user = self.find_best_user(target_num - num_infected, version)\n\n # If there is no best user we have infected everyone\n if user is None:\n break","def remove_potential_false_positives_for_missing_signature(self, df):\n\n filter_date = date(year=2018,month=7,day=13)\n # Pandas: find all the records we want to keep and make a new dataframe out of the result. Inverts the\n # \"and\" conditions above for the known false positives in order to find everything but those records\n filtered_df = df.loc[(df.missing_file == 1) | (df.invalid_dob == 1) | (df.invalid_age_at_consent == 1) |\\\n (df.checkbox_unchecked == 1) | (df.non_va_consent_for_va == 1) |\\\n (df.expected_sign_date >= filter_date) | (df.signing_date.isnull() == False)].reset_index()\n\n print(f'Filtered count for signature_missing false positives: {df.shape[0] - filtered_df.shape[0]}')\n return filtered_df","def filter_enable_safe_user_agents(self, filter_enable_safe_user_agents: ConfigNodePropertyBoolean):\n\n self._filter_enable_safe_user_agents = filter_enable_safe_user_agents","def filterDataframeBySenSpeLimit(value_sen, value_spe, dataframe_values_models):\n\n datafram_values_filtered = dataframe_values_models.query('Sensitivity >= {0} and Specificity >= {1}'.format(value_sen, value_spe))\n return datafram_values_filtered","def filter_out_non_helpful_records(data_records):\n for r in data_records:\n if r.get_cruciality() is None:\n continue\n # We only want records that got exactly 3 votes\n if len(r.cruciality) != 3:\n continue\n else:\n yield r","def test_positive_electrode_potential_profile(self):\n\n # TODO: add these when have averages","def filterDataframeBySenSpeLimitContrary(value_sen, value_spe, dataframe_values_models):\n\n datafram_values_filtered = dataframe_values_models.query('Sensitivity < {0} or Specificity < {1}'.format(value_sen, value_spe))\n return datafram_values_filtered","def remove_outliers(data):\n upper_boundary = np.quantile(data, 0.992)\n lower_boundary = np.quantile(data, 0.008)\n selection = data[(data > lower_boundary) & (data < upper_boundary)]\n standard_dev = np.std(selection)\n median = np.median(selection)\n data[(median + 4.5 * standard_dev < data) | (data < median - 4.5 * standard_dev)] = median\n return data"],"string":"[\n \"def prune(self):\\n target_user_ids = self.get_queryset().values_list('id', flat=True)\\n exclude_user_ids = SentDrip.objects.filter(date__lt=conditional_now(),\\n drip=self.drip_model,\\n user__id__in=target_user_ids)\\\\\\n .values_list('user_id', flat=True)\\n self._queryset = self.get_queryset().exclude(id__in=exclude_user_ids)\",\n \"def suppress_analyze(more_exclusions=None):\\n return api.override_step_data(\\n 'read filter exclusion spec',\\n api.json.output({\\n 'base': {\\n 'exclusions': ['f.*'] + (more_exclusions or []),\\n },\\n 'chromium': {\\n 'exclusions': [],\\n },\\n })\\n )\",\n \"def filter_user_interactions(df, min_value):\\n # Number of users before filtering\\n num_users_before = df['personId'].nunique()\\n\\n # Users with enough interactions\\n filter_users = df['personId'].value_counts() > min_value\\n filter_users = filter_users[filter_users].index.tolist()\\n\\n # New dataframe with only selected users\\n df = df[(df['personId'].isin(filter_users))]\\n df = df[['personId', 'contentId', 'virality']]\\n\\n # Number of users after filtering\\n num_users_after = df['personId'].nunique()\\n\\n print('Number of users discarded: ', num_users_before - num_users_after)\\n return df\",\n \"def test_otoroshi_controllers_adminapi_analytics_controller_filterable_stats(self):\\n pass\",\n \"def reject_filter(self, x_hits, y_hits, *args):\\n if self.verbose:\\n print(\\\"max empty %d train %d predict\\\" % (self.max_train_empty, self.max_predict_empty))\\n zeros_x = tf.reduce_sum(tf.to_int32(tf.equal(x_hits, 0.0)))\\n keep = zeros_x <= self.max_train_empty\\n return keep\",\n \"def test_filter_user(self) -> None:\\n\\n channel = self.make_request(\\n \\\"GET\\\",\\n self.url + \\\"?user_id=%s\\\" % self.other_user,\\n access_token=self.admin_user_tok,\\n )\\n\\n self.assertEqual(200, channel.code, msg=channel.json_body)\\n self.assertEqual(channel.json_body[\\\"total\\\"], 10)\\n self.assertEqual(len(channel.json_body[\\\"event_reports\\\"]), 10)\\n self.assertNotIn(\\\"next_token\\\", channel.json_body)\\n self._check_fields(channel.json_body[\\\"event_reports\\\"])\\n\\n for report in channel.json_body[\\\"event_reports\\\"]:\\n self.assertEqual(report[\\\"user_id\\\"], self.other_user)\",\n \"def filter_criteria_met(current_gre, current_gpa, current_toefl):\\r\\n\\r\\n if int(current_gre) < global_constants['MINIMUM_GRE']:\\r\\n return False\\r\\n if float(current_gpa) < global_constants['MINIMUM_GPA']:\\r\\n return False\\r\\n if int(current_toefl) < global_constants['MINIMUM_TOEFL']:\\r\\n return False\\r\\n return True\",\n \"def test_for_criteria(self):\\n ignore = ['interpreter_method', 'average_by_sample_or_site', 'include_nrm']\\n values = ([dic['value'] for dic in self.acceptance_criteria.values() if (dic['criterion_name'] not in ignore and dic['value'] != -999)])\\n return values\",\n \"def filter_by_cuisine(df, user):\\n\\n if len(user.cuisines) == 0:\\n # User has selected no cuisines - do not need to remove recipes\\n return df\\n\\n return df.loc[df.cuisines.isin(user.cuisines)]\",\n \"def filter_data_on_complaince(folder_Path,complaince_rate):\\n complaince_df=pd.read_csv(folder_Path+\\\"complaince.csv\\\")\\n complaince_df['Percent'] = complaince_df['Percent'].apply(converters.ConvertPercent)\\n complaince_df = complaince_df.loc[complaince_df.Percent >= complaince_rate]\\n IDs= complaince_df.ID.unique()\\n # print(IDs)\\n df_apps=pd.read_csv(folder_Path+\\\"app_usage.csv\\\")\\n df_apps = df_apps.loc[df_apps.user_id.isin(IDs)]\\n df_apps = df_apps.reset_index(drop=True)\\n df_apps.to_csv(folder_Path+\\\"Filtered/app_usage.csv\\\")\\n\\n\\n df_battery= pd.read_csv(folder_Path+\\\"battery_events.csv\\\")\\n df_battery= df_battery.loc[df_battery.user_id.isin(IDs)]\\n df_battery = df_battery.reset_index(drop=True)\\n df_battery.to_csv(folder_Path+\\\"Filtered/battery_events.csv\\\")\\n\\n\\n df_bluetooth = pd.read_csv(folder_Path+\\\"bluetooth.csv\\\")\\n df_bluetooth = df_bluetooth.loc[df_bluetooth.user_id.isin(IDs)]\\n df_bluetooth = df_bluetooth.reset_index(drop=True)\\n df_bluetooth.to_csv(folder_Path+\\\"Filtered/bluetooth.csv\\\")\\n\\n df_screen = pd.read_csv(folder_Path+\\\"screenstate.csv\\\")\\n df_screen = df_screen.loc[df_screen.user_id.isin(IDs)]\\n df_screen = df_screen.reset_index(drop=True)\\n df_screen.to_csv(folder_Path+\\\"Filtered/screenstate.csv\\\")\\n\\n\\n df_wifi = pd.read_csv(folder_Path+\\\"wifi.csv\\\")\\n df_wifi = df_wifi.loc[df_wifi.user_id.isin(IDs)]\\n df_wifi = df_wifi.reset_index(drop=True)\\n df_wifi.to_csv(folder_Path+\\\"Filtered/wifi.csv\\\")\",\n \"def test_profile_filtering(instr_task_workbench, instr_view):\\n add_profile(instr_task_workbench, 'fp5', 'Dummy.dumb.002')\\n p = instr_task_workbench.get_plugin('ecpy.instruments')\\n filtered = instr_view.filter_profiles(p._profiles)\\n assert 'fp5' not in filtered\\n\\n add_profile(instr_task_workbench, 'fp6', 'Dummy.dumb.003')\\n p = instr_task_workbench.get_plugin('ecpy.instruments')\\n filtered = instr_view.filter_profiles(p._profiles)\\n assert 'fp6' in filtered\",\n \"def filter_rare_node(users, businesses, reviews, user_thresh, business_thresh, friend_thresh):\\n continue_filter = True\\n filtered_users = set()\\n filtered_businesses = set()\\n while continue_filter:\\n continue_filter = False\\n # filter step 1\\n users_interact_ind = {}\\n business_interact_ind = {}\\n for review in reviews:\\n user_id = review['user_id'] # a list\\n business_id = review['business_id'] # a list\\n users_interact_ind[user_id] = users_interact_ind.get(user_id, 0) + 1\\n business_interact_ind[business_id] = business_interact_ind.get(business_id, 0) + 1\\n\\n filtered_review_users = set(u for u in users_interact_ind.keys() if users_interact_ind[u]>=user_thresh)\\n filtered_review_businesses = set(b for b in business_interact_ind.keys() if business_interact_ind[b]>=business_thresh)\\n \\n # loop until users' reviews equal to filtered reviews\\n if (filtered_users != filtered_review_users) or (filtered_businesses != filtered_review_businesses):\\n continue_filter = True\\n\\n # filter step 2\\n # filter user and business\\n # make user_friends_dict, only those users with lots of friends can be included\\n user_friends_dict = {}\\n for user in users:\\n user_id = user['user_id']\\n if user_id not in filtered_review_users:\\n continue\\n if not user['friends']:\\n continue\\n filtered_friends = [friend.strip() for friend in user['friends'].split(',') if friend.strip() in filtered_review_users]\\n if len(filtered_friends) >= friend_thresh:\\n user_friends_dict[user_id] = filtered_friends # users with friends larger than friend_thresh\\n\\n continue_inside = True\\n while continue_inside:\\n friends = {}\\n continue_inside = False\\n for user, user_friends in user_friends_dict.items():\\n filtered_friends = [friend for friend in user_friends if friend in user_friends_dict] # friend in user_friends_dict's keys\\n if len(filtered_friends) >= friend_thresh:\\n friends[user] = filtered_friends\\n else:\\n continue_inside = True\\n user_friends_dict = deepcopy(friends) # this takes time\\n\\n filtered_users = set(user_friends_dict.keys())\\n filtered_businesses_list = []\\n\\n for business in businesses:\\n business_id = business['business_id']\\n if business_id not in filtered_review_businesses:\\n continue\\n if not business['categories']:\\n continue\\n if not business['city']:\\n continue\\n filtered_businesses_list.append(business_id)\\n filtered_businesses = set(filtered_businesses_list)\\n\\n filtered_review = []\\n for review in reviews:\\n if (review['user_id'] in filtered_users) and (review['business_id'] in filtered_businesses):\\n filtered_review.append(review)\\n reviews = deepcopy(filtered_review) # this takes time\\n\\n print(len(list(filtered_users)))\\n print(len(list(filtered_businesses)))\\n print(len(reviews))\\n print('filter loop')\\n\\n print('filter complete')\\n return filtered_users, filtered_businesses, filtered_review\",\n \"def filter_unknown_bases(self):\\n self.failed[\\\"unknowns\\\"] = self.stats.index[\\n self.stats[\\\"unknowns\\\"] > self.tolerance[\\\"unknowns\\\"]\\n ]\\n self.passed = self.stats.drop(self.failed[\\\"unknowns\\\"])\",\n \"def filterTrackingCookies(self):\\n return self.__filterTrackingCookies\",\n \"def low_abundance(self, cutoff=10):\\n data = json.loads(self.profile)\\n\\n checks = [mean(v) > cutoff for _, v in data[\\\"data\\\"].items()]\\n\\n return not any(checks)\",\n \"def remove_less_than_treshold(potential_blockings, logger, dashboard_log):\\n treshold = get_treshold(logger, dashboard_log)\\n # create a new list containing all hosts with security value higher than treshold\\n # inspired by https://stackoverflow.com/a/1207461\\n return [host for host in potential_blockings if not get_average_security_value(host, logger) < treshold]\",\n \"def test_filter_users_geq(app, add_ten_users):\\n with app.app_context():\\n add_ten_users()\\n users = User.query\\n users = apply_filter(users, User, {'column': 'id', 'type': 'geq',\\n 'value': '5'})\\n result = users.all()\\n assert len(result) == 6\",\n \"def tempfeeder_exp_nonzerotest_users():\\n\\n return [ user for user in tempfeeder_exp().user_ids if all(tempfeeder_exp()[user]['Load']['2005-10-01 00:00':]) ]\",\n \"def filter_df(df):\\n filtered_df = df[df['rtt_author_ids'].notnull()]\\n filtered_df = filtered_df[filtered_df['user_country'] == 'FR']\\n filtered_df = filtered_df[filtered_df['retweet_count'] > 1]\\n filtered_df = filtered_df[filtered_df['favourites_count'] > 1000]\\n return filtered_df\",\n \"def filter_out_rare_points(points, threshold_pct=0.5):\\n \\n c = Counter(points)\\n total = sum(c.values())\\n l = []\\n for p in points:\\n v = c[p]\\n if v/total * 100 <= threshold_pct:\\n l.append(np.nan)\\n else:\\n l.append(p)\\n \\n return l\",\n \"def purge_outlying_trials(self, trial_nums, thresh=5.0):\\n for injkey in self.values.keys():\\n for fit_key in self.values[injkey].keys():\\n points = np.array(self.values[injkey][\\n fit_key]['metric_val']['vals'])\\n if len(points.shape) == 1:\\n points = points[:, None]\\n median = np.median(points, axis=0)\\n diff = np.sum((points - median)**2, axis=-1)\\n diff = np.sqrt(diff)\\n med_abs_deviation = np.median(diff)\\n modified_z_score = 0.6745 * diff / med_abs_deviation\\n good_trials = modified_z_score < thresh\\n if not np.all(good_trials):\\n bad_trials = np.where(not good_trials)[0]\\n logging.warning(\\n 'Outlier(s) detected for %s in trial(s) %s. Will be '\\n 'removed. If you think this should not happen, please '\\n 'change the value of the threshold used for the '\\n 'decision (currently set to %.2e).'%(\\n fit_key, trial_nums[bad_trials], thresh\\n )\\n )\\n for fitkey in self.values[injkey].keys():\\n for param in self.values[injkey][fitkey].keys():\\n new_vals = np.delete(\\n np.array(self.values[injkey][\\n fitkey][param]['vals']),\\n bad_trials\\n )\\n self.values[injkey][\\n fitkey][param]['vals'] = new_vals\",\n \"def filter_safe_user_agents(self, filter_safe_user_agents: ConfigNodePropertyArray):\\n\\n self._filter_safe_user_agents = filter_safe_user_agents\",\n \"def _remove_potential_false_positives_for_consent_version(self, df):\\n\\n # Pandas: find all the records we want to keep and make a new dataframe out of the result. Inverts the\\n # \\\"and\\\" conditions above for the known false positives in order to find everything but those records\\n filtered_df = df.loc[(df.sync_status != int(ConsentSyncStatus.NEEDS_CORRECTING)) |\\\\\\n (df.hpo != 'VA') | (df.va_consent_for_non_va == 0) |\\\\\\n (df.missing_file == 1) | (df.invalid_dob == 1) | (df.invalid_age_at_consent == 1) |\\\\\\n (df.checkbox_unchecked == 1) | (df.non_va_consent_for_va == 1)]\\n\\n print(f'Filtered count for consent version false positives: {df.shape[0] - filtered_df.shape[0]}')\\n\\n return filtered_df\",\n \"def coverage(self, user_list):\\n all_recom_set = set()\\n all_item = set(self.train['movieId'].values)\\n print('\\\\nCalculated coverage: ')\\n for user in tqdm(user_list):\\n recom_data = self._get_recommend(user)\\n recom_item = set([data[0] for data in recom_data])\\n all_recom_set.update(recom_item)\\n print('\\\\nCoverage is: ', len(all_recom_set) / (len(all_item) * 1.0))\\n return len(all_recom_set) / (len(all_item) * 1.0)\",\n \"def filter_point(x, y, xlower, xupper, ylower, yupper):\\n ignore = False\\n if (x < xlower or x > xupper or y < ylower or y > yupper):\\n ignore = True\\n return ignore\",\n \"def filter_func(self, agents):\\n return [\\n agent for agent in agents\\n if agent.energy < self.model.energy_threshold and not agent.pack\\n ]\",\n \"def filter_data(course_rating_df):\\n users_counts = course_rating_df[\\\"user\\\"].value_counts()\\n filtered_users_counts = users_counts[users_counts >= MIN_RATING_COUNT_FOR_USER]\\n\\n courses_counts = course_rating_df[\\\"course\\\"].value_counts()\\n filtered_courses_counts = courses_counts[courses_counts >= MIN_RATING_COUNT_FOR_COURSE]\\n\\n filtered_ratings_df = course_rating_df[\\n course_rating_df[\\\"user\\\"].isin(filtered_users_counts.index)\\n ]\\n filtered_ratings_df = filtered_ratings_df[\\n filtered_ratings_df[\\\"course\\\"].isin(filtered_courses_counts.index)\\n ]\\n\\n X = filtered_ratings_df[[\\\"user\\\", \\\"course\\\"]].to_numpy()\\n y_diff = filtered_ratings_df[\\\"difficulty\\\"].to_numpy()\\n y_wl = filtered_ratings_df[\\\"workload\\\"].to_numpy()\\n\\n return X, y_diff, y_wl\",\n \"def _removeInsufficientTransformer(self, working_stats, params):\\n\\n for choice, subsets in working_stats.items():\\n sufficient_values = [value for value in subsets if value > 0]\\n if not sufficient_values:\\n del working_stats[choice]\\n\\n return working_stats\",\n \"def test_having_condition_with_preventing_aggregate_metrics_only(self):\\n response = self.do_request(\\n {\\n \\\"field\\\": [\\\"transaction\\\", \\\"project\\\", \\\"p50(transaction.duration)\\\"],\\n \\\"query\\\": \\\"event.type:transaction p50(transaction.duration):<50\\\",\\n \\\"dataset\\\": \\\"metrics\\\",\\n \\\"preventMetricAggregates\\\": \\\"1\\\",\\n \\\"per_page\\\": 50,\\n \\\"project\\\": self.project.id,\\n }\\n )\\n assert response.status_code == 400, response.content\",\n \"def unmark_all(range_days : int):\\n logging.info(\\\"Unmarking users..\\\")\\n now = datetime.now()\\n time_limit = now - timedelta(days=range_days)\\n users = User.query.filter_by(is_positive=True).\\\\\\n filter(User.reported_positive_date <= time_limit).all()\\n for user in users:\\n if user != None and user.is_positive == True:\\n user.is_positive = False\\n user.reported_positive_date = None\\n db.session.commit()\\n\\n return ''\",\n \"def get_queryset(self):\\n user_full = UserFullName.objects.get(id=self.request.user.id)\\n queryset = CaseTrack.objects.filter(user_to=user_full, done=False)\\n return queryset\",\n \"def test_listing_from_wall_when_blocked_some_users(self):\",\n \"def filter_deliveries(restriction):\\n data = build_data([\\n d for d in current_user.deliveries if restriction(d)])\\n # Sort by date\\n return sorted(data, key=lambda x: x['toDate'])\",\n \"def filter_lowfreq(df, main_itype):\\n df_purged = df.drop(['itype','Position','Position1','Position2'], 1)\\n df['above_30perc'] = (df_purged > 50).sum(1)\\n pos_topreserve = set(df['Position'][ (df['above_30perc'] > 1) & (df['itype'] == main_itype) ])\\n df.drop('above_30perc', 1, inplace = True)\\n df = df[df['Position'].isin(pos_topreserve)]\\n \\n return(df)\",\n \"def filter_renters(data_df):\\n return data_df[(data_df['sc116'] == 2) # Only renters\\n & (data_df['uf17'] < 8000) # With a real rent provided\\n ]\",\n \"def test_user_rate_reached_perf_issues(self):\\n for i in range(0, 10):\\n event = self.store_transaction(\\n environment=None,\\n project_id=self.project.id,\\n user_id=str(i),\\n fingerprint=[f\\\"{GroupType.PERFORMANCE_N_PLUS_ONE_DB_QUERIES.value}-group1\\\"],\\n )\\n perf_group = event.groups[0]\\n snooze = GroupSnooze.objects.create(group=perf_group, user_count=10, user_window=60)\\n assert not snooze.is_valid(test_rates=True)\",\n \"def filter_safe_user_agents(self) -> ConfigNodePropertyArray:\\n return self._filter_safe_user_agents\",\n \"def _filter_in_request(self):\\n pass\",\n \"def test_run_coverage_omit(self):\\n cmd = GreenTestCommand(Distribution())\\n cmd.coverage_omit = \\\"abc/*,*/def\\\"\\n cmd.ensure_finalized()\\n cmd.run()\\n self.assertThat(_subprocess_call_args(),\\n ContainsAll([\\n \\\"-o\\\",\\n cmd.coverage_omit\\n ]))\",\n \"def filter_number_units(self, scenarios):\\n\\n return (\\n scenarios\\n .groupby('scenario_id')\\n .agg({'units':sum})\\n .query('units <= 70')\\n .drop('units', axis=1)\\n .merge(scenarios, how='inner', on='scenario_id')\\n )\",\n \"def contributors_filter(self, **kwargs):\\n return None\",\n \"def filter_data(self):\\n self.df = self.df[HeatStrokeDataFiller.important_features]\",\n \"def no_filter(blast_subject_entry):\\r\\n return True\",\n \"def filter_warnings():\\n warnings.simplefilter(\\\"ignore\\\", category=UserWarning)\\n warnings.simplefilter(\\\"ignore\\\", category=LightningDeprecationWarning)\",\n \"def progress_filter(self, src, dst):\\n log = logging.getLogger(self.name)\\n for exclude in self.excludes:\\n if fnmatch(src, exclude):\\n if self.verbose:\\n log.debug(\\\"Excluding %s\\\" % src.rstrip('/'))\\n self.excluded_count = self.excluded_count + 1\\n return\\n return dst\",\n \"def test_user_with_no_explorations_has_no_impact(self):\\n user_a_id = self._sign_up_user(\\n self.USER_A_EMAIL, self.USER_A_USERNAME)\\n self._run_computation()\\n user_stats_model = user_models.UserStatsModel.get(\\n user_a_id, strict=False)\\n self.assertIsNone(user_stats_model)\",\n \"def filter_queryset(self, request, queryset, view):\\n if str(request.query_params.get(\\\"orgs\\\")).lower() == \\\"false\\\":\\n organization_user_ids = OrganizationProfile.objects.values_list(\\n \\\"user__id\\\", flat=True\\n )\\n queryset = queryset.exclude(id__in=organization_user_ids)\\n\\n return queryset\",\n \"def _check_dangerous_filter_wo_user(self):\\n xml_files = self.filter_files_ext('xml')\\n for xml_file in xml_files:\\n ir_filter_records = self.get_xml_records(\\n os.path.join(self.module_path, xml_file), model='ir.filters')\\n for ir_filter_record in ir_filter_records:\\n ir_filter_fields = ir_filter_record.xpath(\\n \\\"field[@name='name' or @name='user_id']\\\")\\n # if exists field=\\\"name\\\" then is a new record\\n # then should be field=\\\"user_id\\\" too\\n if ir_filter_fields and len(ir_filter_fields) == 1:\\n # TODO: Add a list of msg_args before of return\\n # TODO: Add source lineno in all xml checks\\n self.msg_args = (\\n \\\"%s:%d\\\" % (xml_file, ir_filter_record.sourceline),\\n ir_filter_record.get('id'),)\\n return False\\n return True\",\n \"def get_over(self, filter_dict, percentage):\\n pass\",\n \"def filter_out_reduced_healing(raw_heals):\\n max_heal = max(raw_heals)\\n threshold = 0.75 * max_heal\\n\\n selector = raw_heals > threshold\\n\\n return raw_heals[selector]\",\n \"def filter_users_by_transaction_type(self, request):\\n type = request.data[\\\"type\\\"] # type of transaction \\\"in\\\"- with +, out- with-\\n if type == \\\"in\\\":\\n users = User.objects.filter(transactions__amount__gt=0)\\n else:\\n users = User.objects.filter(transactions__amount__lt=0)\\n serializer = UserSerializer(users, many=True)\\n return Response(serializer.data)\",\n \"def filter_for_user(self, user):\\n if user.is_anonymous or user.is_staff or user.is_support:\\n return self\\n\\n return self.filter(\\n Q(\\n project__permissions__user=user,\\n project__permissions__is_active=True,\\n )\\n | Q(\\n project__customer__permissions__user=user,\\n project__customer__permissions__is_active=True,\\n )\\n | Q(\\n offering__customer__permissions__user=user,\\n offering__customer__permissions__is_active=True,\\n )\\n ).distinct()\",\n \"def remove_outliers(lst):\\n slst = sorted(lst)\\n three_iqr = 3 * get_IQR(lst)\\n low_boundary = float(np.percentile(lst, 25)) - three_iqr\\n high_boundary = float(np.percentile(lst, 75)) + three_iqr\\n\\n return filter(lambda x: x >= low_boundary and x <= high_boundary, slst)\",\n \"def filter_by_model(self, instance):\\n content_type = ContentType.objects.get_for_model(instance.__class__)\\n object_id = instance.id\\n queryset = super(UserTrackerManager, self).filter(\\n content_type=content_type, object_id=object_id)\\n return queryset\",\n \"def filter_to_verified(df, verified_df):\\n return df[df.UID.isin(verified_df.UID.values)].reset_index(drop=True)\",\n \"def filter_rare_genes(data, *extra_data, cutoff=0, min_cells=5):\\n gene_sums = measure.gene_capture_count(data, cutoff=cutoff)\\n keep_genes_idx = gene_sums >= min_cells\\n data = select.select_cols(data, *extra_data, idx=keep_genes_idx)\\n return data\",\n \"def _exclude_visitor():\\n return _is_robot() or _is_ignored_ip_address()\",\n \"def adjust_filter(self, queryset):\\n new_queryset = queryset.filter(points__gt=0.0).order_by('-points', 'rate')\\n return self.validate_queryset(new_queryset)\",\n \"def filterVarForWizard(self, v):\\n return v.isMeasurement()\",\n \"def not_test_without_user(self):\\n # TODO\",\n \"def filter_untracked(quant) -> bool:\\n return quant.lot_id is None\",\n \"def filter_de(stock, thresh = .5):\\n \\\"\\\"\\\"ASSUMPTION: FILTER_ROE WAS RUN BEFORE THIS, OR GET_MORINGSTAR_RATIOS WAS CALLED\\\"\\\"\\\"\\n \\\"\\\"\\\"TODO: ABSTRACT USING HOF\\\"\\\"\\\"\\n return stock['DE'] <= thresh\",\n \"def filterData(records):\\n def isInteresting(record):\\n if record[VO_ISSUER] in ('/DC=ch/DC=cern/OU=computers/CN=voms.cern.ch', '/DC=ch/DC=cern/OU=computers/CN=lcg-voms.cern.ch'):\\n return True\\n if record[VO_NAME] in ('atlas', 'cms', 'alice'):\\n return True\\n if record[USERSN] == '/C=SI/O=SiGNET/O=IJS/OU=F9/CN=Andrej Filipcic':\\n return True\\n if record[USERSN] in ('aliprod', '/aliprod'):\\n return True\\n if record[USERSN].startswith(ALIEN_USER_PREFIX):\\n return True\\n\\n return False\\n\\n return [ r for r in records if isInteresting(r) ]\",\n \"def set_restriction_filters(self):\\n self.restriction_filters[\\\"pk__exact\\\"] = self.request.user.pk\",\n \"def clean_customer_df(customer_df: pd.DataFrame) -> pd.DataFrame:\\n \\n # remove customers with more than 20 purchases\\n if 'frequency' in customer_df.columns:\\n customer_df = customer_df[customer_df.frequency < 20]\\n \\n return customer_df\",\n \"def filter_outliers(data): \\n \\n idx_out = find_outliers_IQR(data)\\n \\n cleaned = data[~idx_out].copy()\\n\\n # print(f'There were {idx_out.sum()} outliers.')\\n \\n return cleaned\",\n \"def filter_contigs(self, criteria):\\n eligible_contigs = self.passed.contigs[self.passed.contigs > 10]\\n not_enough_contigs = self.passed.contigs[self.passed.contigs <= 10]\\n # TODO Define separate function for this\\n med_abs_dev = abs(eligible_contigs - eligible_contigs.median()).mean()\\n self.med_abs_devs[\\\"contigs\\\"] = med_abs_dev\\n # Define separate function for this\\n # The \\\"deviation reference\\\"\\n dev_ref = med_abs_dev * self.contigs\\n self.dev_refs[\\\"contigs\\\"] = dev_ref\\n self.allowed[\\\"contigs\\\"] = eligible_contigs.median() + dev_ref\\n self.failed[\\\"contigs\\\"] = eligible_contigs[\\n abs(eligible_contigs - eligible_contigs.median()) > dev_ref\\n ].index\\n eligible_contigs = eligible_contigs[\\n abs(eligible_contigs - eligible_contigs.median()) <= dev_ref\\n ]\\n eligible_contigs = pd.concat([eligible_contigs, not_enough_contigs])\\n eligible_contigs = eligible_contigs.index\\n self.passed = self.passed.loc[eligible_contigs]\",\n \"def filter_for_user(self, user):\\n query = Q(visibility=Document.PUBLIC) | \\\\\\n Q(visibility=Document.PRIVATE, created_by=user) | \\\\\\n Q(visibility=Document.ORG_ONLY,\\n organization__memberships__user=user)\\n\\n if not user.external:\\n query = query | Q(visibility=Document.ORG_ONLY_NO_EXTERNAL,\\n organization__memberships__user=user)\\n\\n return super(DocumentManager, self).get_query_set().filter(query) \\\\\\n .distinct()\",\n \"def get_filtered_trials(self, include_pdb_hierarchies=False, log=None):\\n if (log is None) : log = null_out()\\n trials = sorted(self.get_trials(), key=operator.attrgetter(\\\"cc\\\"), reverse=True)\\n filtered = []\\n for k, trial in enumerate(trials):\\n hierarchy = self.pdb_hierarchy.deep_copy()\\n if (trial.min_fofc < self.params.min_fofc) or (trial.cc < self.params.cc_min):\\n print(\\\" discarding trial %d [poor map quality]:\\\" % (k+1), file=log)\\n trial.show_summary(out=log, prefix=\\\" \\\")\\n continue\\n sites = self.sites_start.deep_copy()\\n sites.set_selected(self.iselection, trial.sites_cart)\\n hierarchy.atoms().set_xyz(sites)\\n if (self.params.filter_rotamer_outliers):\\n n_outliers = alt_confs.score_rotamers(hierarchy=hierarchy,\\n selection=self.iselection)\\n if (n_outliers > 0):\\n print(\\\" discarding trial %d [%d rotamer outlier(s)]:\\\" % \\\\\\n (k+1, n_outliers), file=log)\\n trial.show_summary(out=log, prefix=\\\" \\\")\\n continue\\n if (include_pdb_hierarchies):\\n filtered.append((trial, hierarchy))\\n else :\\n filtered.append(trial)\\n return filtered\",\n \"def on_limit(self, track):\\n print \\\"!!! Limitation notice received: %s\\\" % str(track)\\n return\",\n \"def filter_tracked(quant) -> bool:\\n return quant.lot_id is not None\",\n \"def filter_enable_safe_user_agents(self) -> ConfigNodePropertyBoolean:\\n return self._filter_enable_safe_user_agents\",\n \"def test_index_excludes_users(self):\\n self.admin_api.create(\\n 'User', email='',\\n auth_id='',\\n last_name='Doe', first_name='John')\\n self.cron.index()\\n result_dicts = [util.search_document_to_dict(doc)\\n for doc in self.search_index.get_range()]\\n result_kinds = [Model.get_kind(d['uid']) for d in result_dicts]\\n self.assertNotIn('User', result_kinds)\",\n \"def get_users_with_missing_data() -> Set[str]:\\n users_data = {user[\\\"_source\\\"][\\\"VENDOR_UUID\\\"] for user in Handlers.elastic_handler.get_all_today_data(\\n _type=\\\"status\\\",\\n date_start=dt.date.today() + dt.timedelta(days=1),\\n date_end=dt.date.today() + dt.timedelta(days=7),\\n )}\\n\\n all_tokens = Handlers.token_handler.get_all_today_data(_type=\\\"token\\\")\\n to_dict = {dict_[\\\"_source\\\"][\\\"VENDOR_UUID\\\"]: dict_[\\\"_source\\\"][\\\"TOKEN\\\"] for dict_ in all_tokens}\\n\\n return set(dict(filter(lambda item_tup: item_tup[0] not in users_data, to_dict.items())).values())\",\n \"async def test_ignore_transaction(self):\\n self.set_source_parameter(\\\"transactions_to_ignore\\\", [self.API2])\\n response = await self.collect(get_request_json_return_value=self.JMETER_JSON)\\n self.assert_measurement(response, value=\\\"123\\\", entities=[])\",\n \"async def filter(self, **kwargs):\\n\\n pass\",\n \"def filter_wolves(self, agents):\\n return [\\n agent for agent in agents\\n if agent.energy < self.model.energy_threshold and not agent.pack\\n ]\",\n \"def filter_res(rows, res_class):\\n keep = []\\n match_logger.info('Limiting to sources in resolution class {} '\\n '({}\\\" < BMIN <= {}\\\")'.format(res_class,\\n res_dict[res_class][0],\\n res_dict[res_class][1]))\\n for row in rows:\\n if row['res_class'] == res_class:\\n keep.append(row)\\n\\n match_logger.info(' -- {} sources remaining'.format(len(keep)))\\n\\n return keep\",\n \"def _filter(self):\\n if self.properties['reason'] in PoliceReport.reason_filter:\\n return False\\n return True\",\n \"def exclude_persons_without_registrations(request, filtered_queryset, statuses_that_disallow_empty_registrations=['A']):\\n status_disallows_people_with_no_registrations = \\\\\\n request.GET.get('status') in statuses_that_disallow_empty_registrations \\n is_filtered_by_bbox = request.GET.get('sw_long') != None \\\\\\n and request.GET.get('sw_lat') != None \\\\\\n and request.GET.get('ne_long') != None\\\\\\n and request.GET.get('ne_lat') != None\\n is_filtered_by_city = request.GET.get('city', \\\"\\\") != \\\"\\\"\\n if is_filtered_by_bbox or is_filtered_by_city or \\\\\\n status_disallows_people_with_no_registrations:\\n filtered_queryset = [f for f in filtered_queryset if len(f.registrations.all()) > 0]\\n return filtered_queryset\",\n \"def excluded_from_scan(self):\\n return six.next(six.itervalues(self.zap._request(self.zap.base + 'spider/view/excludedFromScan/')))\",\n \"def _check_for_noise(self) -> None:\\n safety_stop = 5\\n while self._has_noise() and safety_stop > 0:\\n self.filter(size=3)\\n safety_stop -= 1\",\n \"def filter_negative(solver, sub_u_rate, threshold):\\n classifier = joblib.load(join(join(_result_path, solver), str(sub_u_rate)) + '/logistic.pkl')\\n\\n sub_u = np.load(\\\"./processed_data/train/sub_u_\\\" + str(sub_u_rate) + \\\".npy\\\")\\n sub_u_x = sub_u[:, :-1]\\n result_sub_u = np.array(classifier.predict_proba(sub_u_x)[:, 1])\\n\\n sub_u_negative = sub_u[np.where(result_sub_u <= threshold)]\\n print(sub_u_negative.shape)\\n sub_u_negative_x = sub_u_negative[:, :-1]\\n result_sub_u_negative = np.array(classifier.predict_proba(sub_u_negative_x)[:, 1])\\n print(result_sub_u_negative.max())\\n np.save(\\\"./processed_data/train/sub_u_negative.npy\\\", sub_u_negative)\\n\\n print(\\\"\\\\n\\\\n\\\\n\\\")\\n unlabeled = np.load(\\\"./processed_data/train/raw/train_u.npy\\\")\\n unlabeled_x = unlabeled[:, :-1]\\n result_unlabeled = np.array(classifier.predict_proba(unlabeled_x)[:, 1])\\n unlabeled_negative = unlabeled[np.where(result_unlabeled <= threshold)]\\n print(unlabeled_negative.shape)\\n result_unlabeled_negative = np.array(classifier.predict_proba(unlabeled_negative[:, :-1])[:, 1])\\n print(result_unlabeled_negative.max())\\n np.save(\\\"./processed_data/train/unlabeled_negative.npy\\\", unlabeled_negative)\",\n \"def coverage(self):\\r\\n return 0, 1\",\n \"def extra_users_groups(self, acs):\\n return who_cannot_access(self.user, self.users_allowed.all(), self.groups_allowed.all(), acs)\",\n \"def filter_cap(stock):\\n return stock['Class'] == 'Small'\",\n \"def get_filtered_pedigree_with_samples(self, _user=None):\\n result = []\\n seen = set()\\n # Only select cases that have an active variant set.\\n # TODO Perspectively, we need to distinguish between Small and Structural VariantSets.\\n for case in self.get_active_smallvariant_cases():\\n for line in case.get_filtered_pedigree_with_samples():\\n if line[\\\"patient\\\"] not in seen:\\n result.append(line)\\n seen.add((case.name, line[\\\"patient\\\"]))\\n return result\",\n \"def unseen_count_for(self, user):\\r\\n return self.filter(user=user, unseen=True).count()\",\n \"def test_not_calculates_risk_points_without_house(self):\\n\\n del self.user[\\\"houses\\\"]\\n self.calculates_risk_points_by_houses.execute(self.user, self.risk_profile)\\n self.assertEquals(self.risk_profile, MockRiskProfile.risk_profile)\",\n \"def test_no_counterfactuals_found(self):\\n threshold = 4.0\\n self._config['Regression threshold'] = str(threshold)\\n self._example = {'x_1': 1.0, 'x_2': 1.0}\\n output = self._gen.generate(\\n example=self._example,\\n model=self._model,\\n dataset=self._dataset,\\n config=self._config)\\n self.assertEmpty(output)\",\n \"def reported_by(self, user):\\n return Report.objects.filter(recipe=self, chef=user).exists()\",\n \"def filter_passing_hits(self):\\n self.create_fasta()\\n self.blastn_commandline()\\n\\n hits = {}\\n result_handle = open(generate_path(\\\"tmp/validate.xml\\\"))\\n for record in NCBIXML.parse(result_handle):\\n for entry in record.alignments:\\n hit = entry.hit_def\\n seqlen = entry.length\\n hsp = entry.hsps[0]\\n percent_ident = (float(hsp.positives) / float(seqlen)) * 100\\n\\n if 90 <= percent_ident <= 100:\\n if hit in hits:\\n if percent_ident > hits[hit]:\\n hits[hit] = percent_ident\\n else:\\n hits[hit] = percent_ident\\n del result_handle\\n self.seqdata.hits = hits\",\n \"def limited_infection(self, user, target_num, version):\\n num_infected = 0\\n\\n # Checks to make sure we don't have a target_num greater than number of uninfected users\\n uninfected = [u for u in self.users if u.version != version]\\n if target_num > len(uninfected):\\n self.infect_all(version)\\n\\n while num_infected < target_num:\\n # Infect the given user first\\n if user.version != version:\\n user.version = version\\n num_infected += 1\\n\\n # Infect the uninfected students of user\\n uninfected_students = [s for s in user.students if s.version != version]\\n for student in uninfected_students:\\n student.version = version\\n num_infected += 1\\n\\n # Find the next user to infect\\n user = self.find_best_user(target_num - num_infected, version)\\n\\n # If there is no best user we have infected everyone\\n if user is None:\\n break\",\n \"def remove_potential_false_positives_for_missing_signature(self, df):\\n\\n filter_date = date(year=2018,month=7,day=13)\\n # Pandas: find all the records we want to keep and make a new dataframe out of the result. Inverts the\\n # \\\"and\\\" conditions above for the known false positives in order to find everything but those records\\n filtered_df = df.loc[(df.missing_file == 1) | (df.invalid_dob == 1) | (df.invalid_age_at_consent == 1) |\\\\\\n (df.checkbox_unchecked == 1) | (df.non_va_consent_for_va == 1) |\\\\\\n (df.expected_sign_date >= filter_date) | (df.signing_date.isnull() == False)].reset_index()\\n\\n print(f'Filtered count for signature_missing false positives: {df.shape[0] - filtered_df.shape[0]}')\\n return filtered_df\",\n \"def filter_enable_safe_user_agents(self, filter_enable_safe_user_agents: ConfigNodePropertyBoolean):\\n\\n self._filter_enable_safe_user_agents = filter_enable_safe_user_agents\",\n \"def filterDataframeBySenSpeLimit(value_sen, value_spe, dataframe_values_models):\\n\\n datafram_values_filtered = dataframe_values_models.query('Sensitivity >= {0} and Specificity >= {1}'.format(value_sen, value_spe))\\n return datafram_values_filtered\",\n \"def filter_out_non_helpful_records(data_records):\\n for r in data_records:\\n if r.get_cruciality() is None:\\n continue\\n # We only want records that got exactly 3 votes\\n if len(r.cruciality) != 3:\\n continue\\n else:\\n yield r\",\n \"def test_positive_electrode_potential_profile(self):\\n\\n # TODO: add these when have averages\",\n \"def filterDataframeBySenSpeLimitContrary(value_sen, value_spe, dataframe_values_models):\\n\\n datafram_values_filtered = dataframe_values_models.query('Sensitivity < {0} or Specificity < {1}'.format(value_sen, value_spe))\\n return datafram_values_filtered\",\n \"def remove_outliers(data):\\n upper_boundary = np.quantile(data, 0.992)\\n lower_boundary = np.quantile(data, 0.008)\\n selection = data[(data > lower_boundary) & (data < upper_boundary)]\\n standard_dev = np.std(selection)\\n median = np.median(selection)\\n data[(median + 4.5 * standard_dev < data) | (data < median - 4.5 * standard_dev)] = median\\n return data\"\n]","isConversational":false},"negative_scores":{"kind":"list like","value":["0.56458056","0.5546361","0.5329408","0.5329177","0.52725947","0.526916","0.5250236","0.5200599","0.5181351","0.5166104","0.51466775","0.51419705","0.5112545","0.5094425","0.50508887","0.4991155","0.49397007","0.49257168","0.49173298","0.49149612","0.49059606","0.48847222","0.48797512","0.48780084","0.48685157","0.48551154","0.48529914","0.48489556","0.48406157","0.4839958","0.48368207","0.4805064","0.4802802","0.4795191","0.47901294","0.47879025","0.47863096","0.47718468","0.47600785","0.47561252","0.47512934","0.47270378","0.4725154","0.47248688","0.47207764","0.47058868","0.47018173","0.47003177","0.46936792","0.46882352","0.46854722","0.4684157","0.46821642","0.46818683","0.46813598","0.4678452","0.46771666","0.46753335","0.46728063","0.46726456","0.46717796","0.46621522","0.46613818","0.46572667","0.46560198","0.46453357","0.46415654","0.463862","0.4637671","0.46351087","0.46334806","0.46287796","0.46241572","0.46188337","0.4608059","0.46076366","0.46041158","0.4603039","0.4601025","0.4600068","0.4597649","0.45957774","0.45944956","0.45859215","0.45834488","0.4580519","0.45792562","0.4575911","0.4564016","0.45560962","0.4554933","0.45539877","0.4552239","0.45486128","0.45473602","0.45411947","0.45401224","0.453645","0.45289215","0.45240384"],"string":"[\n \"0.56458056\",\n \"0.5546361\",\n \"0.5329408\",\n \"0.5329177\",\n \"0.52725947\",\n \"0.526916\",\n \"0.5250236\",\n \"0.5200599\",\n \"0.5181351\",\n \"0.5166104\",\n \"0.51466775\",\n \"0.51419705\",\n \"0.5112545\",\n \"0.5094425\",\n \"0.50508887\",\n \"0.4991155\",\n \"0.49397007\",\n \"0.49257168\",\n \"0.49173298\",\n \"0.49149612\",\n \"0.49059606\",\n \"0.48847222\",\n \"0.48797512\",\n \"0.48780084\",\n \"0.48685157\",\n \"0.48551154\",\n \"0.48529914\",\n \"0.48489556\",\n \"0.48406157\",\n \"0.4839958\",\n \"0.48368207\",\n \"0.4805064\",\n \"0.4802802\",\n \"0.4795191\",\n \"0.47901294\",\n \"0.47879025\",\n \"0.47863096\",\n \"0.47718468\",\n \"0.47600785\",\n \"0.47561252\",\n \"0.47512934\",\n \"0.47270378\",\n \"0.4725154\",\n \"0.47248688\",\n \"0.47207764\",\n \"0.47058868\",\n \"0.47018173\",\n \"0.47003177\",\n \"0.46936792\",\n \"0.46882352\",\n \"0.46854722\",\n \"0.4684157\",\n \"0.46821642\",\n \"0.46818683\",\n \"0.46813598\",\n \"0.4678452\",\n \"0.46771666\",\n \"0.46753335\",\n \"0.46728063\",\n \"0.46726456\",\n \"0.46717796\",\n \"0.46621522\",\n \"0.46613818\",\n \"0.46572667\",\n \"0.46560198\",\n \"0.46453357\",\n \"0.46415654\",\n \"0.463862\",\n \"0.4637671\",\n \"0.46351087\",\n \"0.46334806\",\n \"0.46287796\",\n \"0.46241572\",\n \"0.46188337\",\n \"0.4608059\",\n \"0.46076366\",\n \"0.46041158\",\n \"0.4603039\",\n \"0.4601025\",\n \"0.4600068\",\n \"0.4597649\",\n \"0.45957774\",\n \"0.45944956\",\n \"0.45859215\",\n \"0.45834488\",\n \"0.4580519\",\n \"0.45792562\",\n \"0.4575911\",\n \"0.4564016\",\n \"0.45560962\",\n \"0.4554933\",\n \"0.45539877\",\n \"0.4552239\",\n \"0.45486128\",\n \"0.45473602\",\n \"0.45411947\",\n \"0.45401224\",\n \"0.453645\",\n \"0.45289215\",\n \"0.45240384\"\n]","isConversational":false},"document_score":{"kind":"string","value":"0.68626004"},"document_rank":{"kind":"string","value":"0"}}},{"rowIdx":75395,"cells":{"query":{"kind":"string","value":"Given the locations and staypoints OF ONE USER, generate the graph"},"document":{"kind":"string","value":"def generate_graph(\n locs_user, sp_user, study, trips_user=None, gap_threshold=None\n):\n # print(sp_user[\"location_id\"])\n # print(locs_user)\n AG = ActivityGraph(\n sp_user,\n locs_user,\n trips=trips_user,\n gap_threshold=gap_threshold,\n )\n # Add purpose feature\n if study == \"geolife\" or study == \"yumuv_graph_rep\":\n AG.add_node_features_from_staypoints(\n sp_user, agg_dict={\"started_at\": list, \"finished_at\": list}\n )\n else:\n AG.add_node_features_from_staypoints(\n sp_user,\n agg_dict={\n \"started_at\": list,\n \"finished_at\": list,\n \"purpose\": list,\n },\n )\n return AG"},"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 generate_locations(\n staypoints,\n method=\"dbscan\",\n epsilon=100,\n num_samples=1,\n distance_metric=\"haversine\",\n agg_level=\"user\",\n activities_only=False,\n print_progress=False,\n n_jobs=1,\n):\n if agg_level not in [\"user\", \"dataset\"]:\n raise AttributeError(f\"agg_level '{agg_level}' is unknown. Supported values are ['user', 'dataset'].\")\n if method not in [\"dbscan\"]:\n raise AttributeError(f\"method '{method}' is unknown. Supported value is ['dbscan'].\")\n\n # initialize the return GeoDataFrames\n sp = staypoints.copy()\n non_activities = None\n if activities_only:\n if \"activity\" not in sp.columns:\n raise KeyError('staypoints must contain column \"activity\" if \"activities_only\" flag is set.')\n non_activities = sp[~sp[\"activity\"]]\n sp = sp[sp[\"activity\"]]\n sp = sp.sort_values([\"user_id\", \"started_at\"])\n geo_col = sp.geometry.name\n\n if method == \"dbscan\":\n eps = epsilon / 6371000 if distance_metric == \"haversine\" else epsilon\n # scikit haversine_dist wants radian. (We assume that this is good enough)\n # https://scikit-learn.org/stable/modules/generated/sklearn.metrics.pairwise.haversine_distances.html\n db = DBSCAN(eps=eps, min_samples=num_samples, algorithm=\"ball_tree\", metric=distance_metric)\n\n if agg_level == \"user\":\n sp = applyParallel(\n sp.groupby(\"user_id\", as_index=False),\n _gen_locs_dbscan,\n n_jobs=n_jobs,\n print_progress=print_progress,\n distance_metric=distance_metric,\n db=db,\n )\n\n # keeping track of noise labels\n sp_non_noise_labels = sp[sp[\"location_id\"] != -1]\n sp_noise_labels = sp[sp[\"location_id\"] == -1]\n\n # sort so that the last location id of a user = max(location id)\n sp_non_noise_labels = sp_non_noise_labels.sort_values([\"user_id\", \"location_id\"])\n\n # identify start positions of new user_ids\n start_of_user_id = sp_non_noise_labels[\"user_id\"] != sp_non_noise_labels[\"user_id\"].shift(1)\n\n # calculate the offset (= last location id of the previous user)\n # multiplication is to mask all positions where no new user starts and addition is to have a +1 when a\n # new user starts\n loc_id_offset = sp_non_noise_labels[\"location_id\"].shift(1) * start_of_user_id + start_of_user_id\n\n # fill first nan with 0 and create the cumulative sum\n loc_id_offset = loc_id_offset.fillna(0).cumsum()\n\n sp_non_noise_labels[\"location_id\"] = sp_non_noise_labels[\"location_id\"] + loc_id_offset\n sp = gpd.GeoDataFrame(pd.concat([sp_non_noise_labels, sp_noise_labels]), geometry=geo_col)\n sp.sort_values([\"user_id\", \"started_at\"], inplace=True)\n\n else:\n _gen_locs_dbscan(sp, db=db, distance_metric=distance_metric)\n\n ### create locations as grouped staypoints\n temp_sp = sp[[\"user_id\", \"location_id\", sp.geometry.name]]\n if agg_level == \"user\":\n # directly dissolve by 'user_id' and 'location_id'\n locs = temp_sp.dissolve(by=[\"user_id\", \"location_id\"], as_index=False)\n else:\n ## generate user-location pairs with same geometries across users\n # get user-location pairs\n locs = temp_sp[[\"user_id\", \"location_id\"]].drop_duplicates(ignore_index=True)\n # get location geometries\n geom_gdf = temp_sp.dissolve(by=[\"location_id\"], as_index=False).drop(columns={\"user_id\"})\n # merge pairs with location geometries\n locs = geom_gdf.merge(locs, on=\"location_id\", how=\"right\")\n\n # filter staypoints not belonging to locations\n locs = locs.loc[locs[\"location_id\"] != -1]\n\n if check_gdf_planar(locs):\n locs[\"center\"] = locs.geometry.centroid\n else:\n # error of wrapping e.g. mean([-180, +180]) -> own function needed\n locs[\"center\"] = angle_centroid_multipoints(locs.geometry)\n\n # extent is the convex hull of the geometry\n locs[\"extent\"] = locs.geometry.convex_hull\n # convex_hull of Point is Point, and MultiPoint with two Points is a LineString\n # we change them into Polygon by creating a buffer of epsilon around them.\n pointLine_idx = (locs[\"extent\"].geom_type == \"LineString\") | (locs[\"extent\"].geom_type == \"Point\")\n\n # Perform meter to decimal conversion if the distance metric is haversine\n if distance_metric == \"haversine\":\n locs.loc[pointLine_idx, \"extent\"] = locs.loc[pointLine_idx].apply(\n lambda p: p[\"extent\"].buffer(meters_to_decimal_degrees(epsilon, p[\"center\"].y)), axis=1\n )\n else:\n locs.loc[pointLine_idx, \"extent\"] = locs.loc[pointLine_idx, \"extent\"].buffer(epsilon)\n\n locs = locs.set_geometry(\"center\", crs=sp.crs)\n locs = locs[[\"user_id\", \"location_id\", \"center\", \"extent\"]]\n\n # index management\n locs.rename(columns={\"location_id\": \"id\"}, inplace=True)\n locs.set_index(\"id\", inplace=True)\n\n # staypoints not linked to a location receive np.nan in 'location_id'\n sp.loc[sp[\"location_id\"] == -1, \"location_id\"] = np.nan\n\n # merge non_activities back if \"activities_only\" flag is set\n sp = pd.concat([sp, non_activities])\n\n if len(locs) > 0:\n locs.as_locations # empty location is not valid\n else:\n warnings.warn(\"No locations can be generated, returning empty locs.\")\n\n ## dtype consistency\n # locs id (generated by this function) should be int64\n locs.index = locs.index.astype(\"int64\")\n # location_id of staypoints can only be in Int64 (missing values)\n sp[\"location_id\"] = sp[\"location_id\"].astype(\"Int64\")\n # user_id of locs should be the same as sp\n locs[\"user_id\"] = locs[\"user_id\"].astype(sp[\"user_id\"].dtype)\n\n return sp, locs","def example_staypoints():\n p1 = Point(8.5067847, 47.4)\n p2 = Point(8.5067847, 47.5)\n p3 = Point(8.5067847, 47.6)\n p4 = Point(8.5067847, 47.7)\n\n t1 = pd.Timestamp(\"1971-01-01 00:00:00\", tz=\"utc\")\n t2 = pd.Timestamp(\"1971-01-01 05:00:00\", tz=\"utc\")\n t3 = pd.Timestamp(\"1971-01-02 07:00:00\", tz=\"utc\")\n t4 = pd.Timestamp(\"1971-01-02 08:00:00\", tz=\"utc\")\n t5 = pd.Timestamp(\"1971-01-02 09:00:00\", tz=\"utc\")\n t6 = pd.Timestamp(\"1971-01-02 10:00:00\", tz=\"utc\")\n\n list_dict = [\n {\"id\": 1, \"user_id\": 0, \"started_at\": t1, \"finished_at\": t2, \"geom\": p1},\n {\"id\": 5, \"user_id\": 0, \"started_at\": t2, \"finished_at\": t3, \"geom\": p2},\n {\"id\": 2, \"user_id\": 0, \"started_at\": t3, \"finished_at\": t4, \"geom\": p3},\n {\"id\": 6, \"user_id\": 0, \"started_at\": t4, \"finished_at\": t5, \"geom\": p2},\n {\"id\": 15, \"user_id\": 0, \"started_at\": t5, \"finished_at\": t6, \"geom\": p1},\n {\"id\": 7, \"user_id\": 1, \"started_at\": t3, \"finished_at\": t4, \"geom\": p4},\n {\"id\": 80, \"user_id\": 1, \"started_at\": t4, \"finished_at\": t5, \"geom\": p2},\n {\"id\": 3, \"user_id\": 1, \"started_at\": t5, \"finished_at\": t6, \"geom\": p2},\n ]\n sp = gpd.GeoDataFrame(data=list_dict, geometry=\"geom\", crs=\"EPSG:4326\")\n sp = sp.set_index(\"id\")\n sp.as_staypoints\n return sp","def create_graph(users, friend_counts):\n ###TODO-- Completed\n G = nx.Graph()\n\n #For Filtering the Nodes\n #print(friend_counts)\n friend_nodes = [friend for friend in friend_counts if friend_counts[friend] > 1]\n candidate_nodes = [user['screen_name'] for user in users]\n\n #print(\"Nodes: \",len(friend_nodes), len(candidate_nodes))\n #Adding Nodes to graph\n G.add_nodes_from(friend_nodes + candidate_nodes)\n\n #Connecting the Nodes with Edges\n for candidate in users:\n for friend in friend_nodes:\n if friend in candidate['friends']:\n G.add_edge(candidate['screen_name'], friend)\n\n return G","def NewHighRiskUsersGPS(list_infected):\n d=DISTANCE/6371000\n intervals_dates=getIntervals(T_START,T_END,INTERVAL)\n encounters=filterEvents(intervals_dates,list_infected,d)\n prob=dict()\n cont=1\n for h in range(len(encounters)):\n if(len(encuentros[h])!=0):\n map_infected=encounters[h].iloc[::,[0,2,1,3]]\n map_noinfected=encounters[h].iloc[:,[0,2,1,4,6,5,7,8]]\n map_infected=map_infected.groupby( [ \"user_i\"] ).size().to_frame(name = 'count').reset_index()\n map_infected['prob']=[1.0]*len(map_infected)\n map_infected['lat_i']=[None]*len(map_infected)\n map_infected['lon_i']=[None]*len(map_infected)\n map_infected['time_i']=[None]*len(map_infected)\n map_infected['user_infectado']=[None]*len(map_infected)\n map_infected['lat_infectado']=[None]*len(map_infected)\n map_infected['lon_infectado']=[None]*len(map_infected)\n map_infected = map_infected[[\"user_i\", \"lat_i\", \"lon_i\",\"time_i\",\"count\",\"prob\",\"user_infectado\",\"lat_infectado\",\"lon_infectado\"]]\n map_infected.columns=[\"user\",\"lat\",\"lon\",\"time\",\"datos\",\"prob\",\"user_infectado\",\"lat_infectado\",\"lon_infectado\"]\n users=map_noinfected['user_j'].unique()\n dict_probability=dict()\n probability_df=[]\n for u in users:\n try:\n dict_probability[u]=[prob[u]]\n except:\n dict_probability[u]=[0]\n user=[];probability=[]\n for e in range(len(encounters[h])):\n probcontagio=probaContagius(encounters[h]['lat_i'][e],encounters[h]['lon_i'][e],encounters[h]['lat_j'][e],encounters[h]['lon_j'][e],M)\n probability_df.append(probcontagio)\n dict_probability[encounters[h]['user_j'][e]].append(probcontagio)\n map_noinfected['prob']=probability_df\n for d in dict_probability:\n for i in range(len(dict_probability[d])-1):\n if(i==0):\n q=componeProbs(dict_probability[d][i],dict_probability[d][i+1])\n else:\n q=componeProbs(q,dict_probability[d][i+1])\n user.append(d)\n probability.append(q)\n list_df=[]\n for i in range(len(user)):\n df=map_noinfected[map_noinfected['user_j']==user[i]].reset_index(drop=True)\n df['prob']=probability[i]\n list_df.append(df)\n prob[user[i]]=probability[i]\n map_noinfected=pd.concat(list_df)\n map_noinfected = map_noinfected[[\"user_j\", \"lat_j\", \"lon_j\",\"time_j\",\"distance\",\"prob\",\"user_i\",\"lat_i\",\"lon_i\"]]\n map_noinfected.columns=[\"user\",\"lat\",\"lon\",\"time\",\"datos\",\"prob\",\"user_infectado\",\"lat_infectado\",\"lon_infectado\"]\n data=pd.concat([map_infected,map_noinfected])\n data.to_csv(OUTPUTPATH+\"eventos\"+str(cont)+\".csv\",index=False)\n cont=cont+1\n else:\n data=pd.DataFrame(columns=[\"user\",\"lat\",\"lon\",\"time\",\"datos\",\"prob\",\"user_infectado\",\"lat_infectado\",\"lon_infectado\"])\n data.to_csv(OUTPUTPATH+\"eventos\"+str(cont)+\".csv\",index=False)\n cont=cont+1\n data_probability=pd.DataFrame(prob.items(), columns=['user_id', 'Probability'])\n data_probability.to_csv(OUTPUTPATH+\"probability.csv\",index=False)","def create_graph(self, lat, lon):\n # Open connection to the database (nodes)\n cur = armaps.model.get_db()\n\n # Get the waypoints\n cur.execute(\n \"SELECT * FROM waypoints WHERE venue_id = %s\", \n (self.venue_id,)\n )\n waypoints = cur.fetchall()\n\n # Get the paths (edges)\n cur.execute(\n \"SELECT * FROM paths WHERE venue_id = %s\",\n (self.venue_id,)\n )\n paths = cur.fetchall()\n\n # Transform list of waypoints into dictionary with key = waypoint_id\n for waypoint in waypoints:\n self.waypoints[int(waypoint[\"waypoint_id\"])] = {\n \"lat\": float(waypoint[\"latitude\"]),\n \"lon\": float(waypoint[\"longitude\"]),\n \"waypoint_id\": int(waypoint[\"waypoint_id\"])\n }\n\n # Calculate weights of edges in graph\n for path in paths:\n # Get two nodes (waypoints) associated with edge\n inNode = int(path[\"innode\"])\n outNode = int(path[\"outnode\"])\n\n # Get the coordinates of nodes\n inNode_coords = (self.waypoints[inNode][\"lat\"], self.waypoints[inNode][\"lon\"])\n outNode_coords = (self.waypoints[outNode][\"lat\"], self.waypoints[outNode][\"lon\"])\n distance = geopy.distance.distance(inNode_coords, outNode_coords).miles\n\n # Add to graph (both ways for undirected)\n self.graph.add_edge(inNode, outNode, distance)\n self.graph.add_edge(outNode, inNode, distance)","def createGraph(self):\n \n for episode in self.episodes:\n listeSuccessors = [episode[episode[:,1] > episode[i,1]][:,0] # List of list of successors for each user\n for i in range(len(episode))] \n for i, successeur in enumerate(listeSuccessors): # for the list of successors of each user\n for v in successeur: # for every successor of a user\n u, proba = episode[i,0], np.random.random() # Generate a probability so within (0,1)\n self.successors[u][v] = proba # u ---(proba)---> v \n self.predecessors[v][u] = proba # v ---(proba)---> u","def example_staypoints_merge():\n p1 = Point(8.5067847, 47.4)\n\n t1 = pd.Timestamp(\"1971-01-01 00:00:00\", tz=\"utc\")\n t2 = pd.Timestamp(\"1971-01-02 05:00:00\", tz=\"utc\")\n t3 = pd.Timestamp(\"1971-01-02 06:45:00\", tz=\"utc\")\n t4 = pd.Timestamp(\"1971-01-02 08:55:00\", tz=\"utc\")\n t45 = pd.Timestamp(\"1971-01-02 08:57:00\", tz=\"utc\")\n t5 = pd.Timestamp(\"1971-01-02 09:00:00\", tz=\"utc\")\n t6 = pd.Timestamp(\"1971-01-02 09:20:00\", tz=\"utc\")\n\n list_dict = [\n {\"id\": 1, \"user_id\": 0, \"started_at\": t1, \"finished_at\": t2, \"geom\": p1, \"location_id\": 1},\n {\"id\": 5, \"user_id\": 0, \"started_at\": t2, \"finished_at\": t2, \"geom\": p1, \"location_id\": 2},\n {\"id\": 2, \"user_id\": 0, \"started_at\": t3, \"finished_at\": t4, \"geom\": p1, \"location_id\": 2},\n {\"id\": 6, \"user_id\": 0, \"started_at\": t4, \"finished_at\": t45, \"geom\": p1, \"location_id\": 2},\n {\"id\": 15, \"user_id\": 0, \"started_at\": t5, \"finished_at\": t6, \"geom\": p1, \"location_id\": 2},\n {\"id\": 7, \"user_id\": 1, \"started_at\": t3, \"finished_at\": t4, \"geom\": p1, \"location_id\": 2},\n {\"id\": 80, \"user_id\": 1, \"started_at\": t45, \"finished_at\": t5, \"geom\": p1, \"location_id\": 2},\n {\"id\": 3, \"user_id\": 1, \"started_at\": t5, \"finished_at\": t6, \"geom\": p1, \"location_id\": 4},\n ]\n sp = gpd.GeoDataFrame(data=list_dict, geometry=\"geom\", crs=\"EPSG:4326\")\n sp = sp.set_index(\"id\")\n sp.as_staypoints\n\n # generate empty triplegs for the merge function\n tpls = pd.DataFrame([], columns=[\"user_id\", \"started_at\", \"finished_at\"])\n return sp, tpls","def test_dbscan_user_dataset(self):\n sp_file = os.path.join(\"tests\", \"data\", \"geolife\", \"geolife_staypoints.csv\")\n sp = ti.read_staypoints_csv(sp_file, tz=\"utc\", index_col=\"id\", crs=\"epsg:4326\")\n # take the first row and duplicate once\n sp = sp.head(1)\n sp = pd.concat([sp, sp], ignore_index=True)\n # assign a different user_id to the second row\n sp.iloc[1, 4] = 1\n\n # duplicate for a certain number\n sp = pd.concat([sp] * 6, ignore_index=True)\n _, locs_ds = sp.as_staypoints.generate_locations(\n method=\"dbscan\", epsilon=10, num_samples=1, distance_metric=\"haversine\", agg_level=\"dataset\"\n )\n _, locs_us = sp.as_staypoints.generate_locations(\n method=\"dbscan\", epsilon=10, num_samples=1, distance_metric=\"haversine\", agg_level=\"user\"\n )\n loc_dataset_num = len(locs_ds.index.unique())\n loc_user_num = len(locs_us.index.unique())\n assert loc_dataset_num == 1\n assert loc_user_num == 2","def get_all_social_paths(self, user_id):\n visited = {} # Note that this is a dictionary, not a set\n # !!!! IMPLEMENT ME\n # graphs=Graph()\n # for i in self.users:\n # graphs.add_vertex(i)\n \n # for i in self.users:\n # for x in self.friendships[i]:\n # graphs.add_edge(i,x)\n\n # for i in graphs.vertices:\n # if graphs.bfs(i,user_id):\n # visited[i]=graphs.bfs(i,user_id)\n queue=Queue()\n queue.enqueue([user_id])\n while queue.size()>0:\n path=queue.dequeue()\n current_user = path[-1]\n if current_user not in visited:\n visited[current_user]=path\n for ID in self.friendships[current_user]:\n new_path=list(path)\n new_path.append(ID)\n queue.enqueue(new_path)\n return visited","def go_to_x_y(user_x, user_y, deltaball=1.0):\n global regions\n goal = Point()\n goal.x = user_x\n goal.y = user_y\n inc_x = user_x - x\n inc_y = user_y - y\n th = math.atan2(inc_y, inc_x)\n if th > 3.14:\n th = -(6.28 - th)\n elif th < -3.14:\n th = 6.28 + th\n go_to_angle(th)\n while math.sqrt(((user_x - x) * (user_x - x)) + ((user_y - y) * (user_y - y))) > deltaball:\n if regions['front'] > 2:\n if math.sqrt(((goal.x - x) * (goal.x - x)) + ((goal.y - y) * (goal.y - y))) > deltaball:\n speed.linear.x = 0.8\n speed.angular.z = 0\n pub.publish(speed)\n rate.sleep()\n else:\n speed.linear.x = 0\n speed.angular.z = 0\n pub.publish(speed)\n rate.sleep()\n # Let's find possible path\n if regions['right'] > 3:\n # Right path available\n obs_avoid(-0.5)\n inc_x = goal.x - x\n inc_y = goal.y - y\n th = math.atan2(inc_y, inc_x)\n go_to_angle(th)\n elif regions['left'] > 3:\n # front left path available\n obs_avoid(0.5)\n inc_x = goal.x - x\n inc_y = goal.y - y\n th = math.atan2(inc_y, inc_x)\n go_to_angle(th)\n elif regions['fright'] > 3:\n # Right path available\n obs_avoid(-0.5)\n inc_x = goal.x - x\n inc_y = goal.y - y\n th = math.atan2(inc_y, inc_x)\n go_to_angle(th)\n elif regions['fleft'] > 3:\n # front left path available\n obs_avoid(0.5)\n inc_x = goal.x - x\n inc_y = goal.y - y\n th = math.atan2(inc_y, inc_x)\n go_to_angle(th)\n else:\n obs_avoid(-0.5)\n inc_x = goal.x - x\n inc_y = goal.y - y\n th = math.atan2(inc_y, inc_x)\n go_to_angle(th)\n rate.sleep()\n speed.linear.x = 0\n speed.angular.z = 0\n pub.publish(speed)","def populate_graph(self, num_users, avg_friendships):\n # Reset graph\n self.last_id = 0\n self.users = {}\n self.friendships = {}\n # !!!! IMPLEMENT ME\n\n # add users\n for i in range(num_users):\n self.add_user(f'User {i}')\n\n # create friendships list\n possible_friendships = []\n\n # for each friend-user combination, place a tuple in possible_friendships \n for user_id in self.users:\n for friend_id in range(user_id + 1,self.last_id + 1):\n possible_friendships.append([user_id, friend_id])\n\n import random\n # shuffle the possible friendships so the order isn't biased towards towards the early users\n random.shuffle(possible_friendships)\n\n # add friendships\n # designate a certain number of friendships determined by avg friendships per total users\n for i in range(num_users * avg_friendships // 2):\n # this sets the friendship variable to a premade possible friendship\n friendship = possible_friendships[i]\n # then we add this friendship to the friendship dictionary that defines the graph\n # with the first tuple value as the key and the second as the value\n self.add_friendship(friendship[0],friendship[1])","def populate_graph(self, num_users, avg_friendships):\n # Reset graph\n self.last_id = 0\n self.users = {}\n self.friendships = {}\n # !!!! IMPLEMENT ME\n # Add users\n # numberOfUsers=num_users\n # while num_users:\n # self.add_user(num_users)\n # num_users-=1\n \n # DistributionOfFriendships=[]\n\n # while len(DistributionOfFriendships)<len(self.users):\n # if DistributionOfFriendships!=True:\n # DistributionOfFriendships.append(random.randrange(0,5))\n # if len(DistributionOfFriendships)==len(self.users):\n # break\n # while sum(DistributionOfFriendships)/len(DistributionOfFriendships)!=avg_friendships:\n # randomNum=random.randrange(0,5)\n # if (sum(DistributionOfFriendships)+randomNum)/(len(DistributionOfFriendships)+1) == avg_friendships:\n # DistributionOfFriendships.append(randomNum)\n # # Create friendships\n\n # print(DistributionOfFriendships)\n # for i in self.users:\n # # count=len(self.friendships[i])\n # # print(\"HELLLO\",i,self.friendships[i])\n # # print(\"COUNT\", count, self.friendships[i], len(self.friendships[i]),i) \n # if DistributionOfFriendships[i-1]>0: \n # if len(self.friendships[i]) <DistributionOfFriendships[i-1]:\n # while len(self.friendships[i])!=DistributionOfFriendships[i-1]:\n # randomNum=random.randrange(1,numberOfUsers+1)\n # print(DistributionOfFriendships,randomNum, i,len(self.friendships[randomNum]), DistributionOfFriendships[randomNum-1],self.friendships)\n # if len(self.friendships[randomNum])<DistributionOfFriendships[randomNum-1] and randomNum!=i:\n # # print(\"randomNum\",randomNum ,\"i\",i,\"friendships\",self.friendships[i], \"count\", count,DistributionOfFriendships[i-1], self.friendships)\n # if randomNum not in self.friendships[i]:\n # # count+=1\n # # print(\"III\",i,\"Distribution\",DistributionOfFriendships[i-1],\"randomNum\",randomNum,\"count\",count)\n # self.add_friendship(i,randomNum)\n # # print(\"after\")\n for i in range(num_users):\n self.add_user(i)\n\n possible_friendships=[]\n for user_id in self.users:\n for friend_id in range(user_id+1, self.last_id+1):\n possible_friendships.append((user_id, friend_id))\n\n random.shuffle(possible_friendships)\n\n for i in range(num_users*avg_friendships//2):\n friendship= possible_friendships[i]\n self.add_friendship(friendship[0],friendship[1])","def compute_pathways(users, job_graph, debug, min_likelihood_thr=0.2):\r\n start_time = time.time()\r\n __print_msg('Computing career pathways...', debug)\r\n user_pathways = {}\r\n tot_users = len(users)\r\n i = 0\r\n for user, user_jobs in users.items():\r\n user_pathway = compute_user_pathway(user_jobs, job_graph)\r\n recommended_pathway = recommend_pathway(user_jobs, job_graph, user_pathway[-1], min_likelihood_thr)\r\n user_pathways[user] = (user_pathway, recommended_pathway)\r\n i += 1\r\n if i % 1000 == 0:\r\n __print_msg('Num users processed: {}/{}'.format(i, tot_users), debug)\r\n end_time = time.time()\r\n __print_msg('Execution time: {} seconds'.format(end_time - start_time), debug)\r\n return user_pathways","def makeGraph(locations, distances):\n graph = dict() # maps (lat, lng) to Node\n for location,distance in zip(locations, distances):\n currLocation = location[0]\n neighbors = location[1:]\n makeNode(currLocation, neighbors, distance[1:], graph)\n return graph","def get_all_social_paths(self, user_id): #each user v v each path V V\n # output example- {1: [1], 8: [1, 8], 10: [1, 10], 5: [1, 5], 2: [1, 10, 2], 6: [1, 10, 6], 7: [1, 10, 2, 7]}\n visited = {} # Note that this is a dictionary, not a set\n # Need to do a bfs using the user id\n # first step is to traverse the graph and record all the vertices as keys in visited using bft\n # then take those keys and use bfs on each, using user_id as the starting node and and the key as\n # the destination node\n\n # Modification of BFT\n # create an empty dict\n # q = Queue()\n q = []\n\n # init enqueue the starting node\n q.append(user_id)\n\n while len(q) > 0:\n # Dequeue the first item\n v = q.pop(0)\n # If it's not been visited:\n if v not in visited:\n # Mark as visited (i.e. add to the visited set)\n visited[v] = []\n\n # Do something with the node\n print(f\"Visited {v}\")\n\n # Add all neighbors to the queue\n for next_vert in self.friendships[v]:\n q.append(next_vert)\n\n # once visited is filled, then we start the bfs\n #print('vv',visited)\n possible_paths = {}\n #run a bfs for each key in visited\n for v in visited:\n possible_paths[v] = []\n \n if v == user_id:\n visited[v] = [user_id]\n\n path = []\n while len(path) < len(visited):\n\n # Add all neighbors to the queue\n for next_vert in self.friendships[v]:\n print(possible_paths[v])\n # copy the path\n # temp_path = list(path)\n # temp_path.append(next_vert)\n # add path to possible_paths\n path.append(next_vert)\n\n possible_paths[v].append(path) # HAVE TO USE QUEUE OR STACK, THEY ENSURE THE NEIGHBORS\n # FOLLOW THE CORRECT ORDER WHEN LOOPING \n\n if v == path[-1]:\n \n # IF SO, RETURN PATH\n visited[v] = path\n break\n\n # for x in visited:\n # bfs(user_id, x)\n # visited[x].add(path)\n \n print('pct of total users in network', len(visited[1])/len(visited))\n print('degrees of separation', len(visited[1]) - 1)\n return visited","def compute_user_pathway(user_jobs, job_graph, debug=False):\r\n pathway = []\r\n for i, job in enumerate(user_jobs):\r\n if i == 0:\r\n continue\r\n cluster, _ = job_graph.assign_job_to_jobgraph_state(job)\r\n pathway.append(cluster)\r\n return pathway","def insert_user_location(self, lat, lon):\n # Waypoint id for user's location\n user_waypoint_id = 0\n\n # Keep track of closest waypoint to user\n closest_waypoint = None\n min_distance = float(\"inf\")\n\n # Find closest waypoint to user's location\n for key, val in self.waypoints.items():\n # Check to see if waypoint_id higher than user's\n if key > user_waypoint_id:\n user_waypoint_id = key\n\n # Calculate distance\n user_coords = (lat, lon)\n waypoint_coords = (val[\"lat\"], val[\"lon\"])\n distance = geopy.distance.distance(user_coords, waypoint_coords).miles\n\n if distance < min_distance:\n min_distance = distance\n closest_waypoint = key\n\n # Increment highest waypoint id seen to get user's starting waypoint id\n user_waypoint_id += 1\n\n # Add to graph\n self.graph.add_edge(user_waypoint_id, closest_waypoint, min_distance)\n self.graph.add_edge(closest_waypoint, user_waypoint_id, min_distance)\n\n # Return starting waypoint id\n return user_waypoint_id","def get_network(users_ids, as_edgelist=True):\r\n edges = []\r\n amount = 0\r\n for ind1 in range(len(users_ids)):\r\n for ind2 in range(len(users_ids)):\r\n try:\r\n if users_ids[ind2] in get_friends(users_ids[ind1])['response']['items']:\r\n edges += [(ind1, ind2)]\r\n amount += 1\r\n except KeyError:\r\n pass\r\n\r\n g = Graph(vertex_attrs={\"label\": users_ids},\r\n edges=edges, directed=False)\r\n\r\n plot_graph(g, amount)","def populate_graph(self, num_users, avg_friendships):\n # Reset graph\n self.last_id = 0\n self.users = {}\n self.friendships = {}\n # !!!! IMPLEMENT ME\n\n # Add users\n for i in range(num_users):\n self.add_user(f'User {i+1}')\n\n # Create friendships\n # Create a list with all possible friendships\n possible_friendships = []\n for user_id in self.users:\n for other_id in range(user_id +1, self.last_id + 1):\n possible_friendships.append((user_id, other_id))\n\n # Shuffle the list\n random.shuffle(possible_friendships)\n # Grab the first N pairs for the list and create those friendships\n for i in range(num_users * avg_friendships // 2):\n friendship = possible_friendships[i]\n self.add_friendship(friendship[0], friendship[1])","def populate_graph(self, num_users, avg_friendships):\n # Reset graph\n self.reset()\n\n for i in range(num_users):\n self.add_user(f\"User {i}\")\n\n target_friendships = num_users * avg_friendships\n total_friendships = 0\n collisions = 0\n\n while total_friendships < target_friendships:\n user_id = random.randint(1, self.last_id)\n friend_id = random.randint(1, self.last_id)\n\n if self.add_friendship(user_id, friend_id):\n total_friendships += 2\n else:\n collisions += 1","def populateGraph(self, num_users, avg_friendships):\n # Reset graph\n self.lastID = 0\n self.users = {}\n self.friendships = {}\n\n # Add users\n for user in range(num_users):\n self.addUser(\"User %s\" % user) # user 0, user 1, user 2\n\n # Create friendships\n\n # Generate all possible friendship combinations\n possible_friendships = []\n # avoid dups by making sure that the first id is higher than the 2nd id\n for user_id in self.users:\n for other_id in range(user_id + 1, self.lastID + 1):\n possible_friendships.append((user_id, other_id))\n\n random.shuffle(possible_friendships)\n\n # Create friendships(edges) for the first X tuples of the list\n # X is determined by the formula: numUsers * avgFriendships // 2\n # Need to divide by 2 since each addFriendship() creates 2 friendships\n for i in range((num_users * avg_friendships) // 2):\n friendship = possible_friendships[i]\n self.addFriendship(friendship[0], friendship[1])","def populate_graph_data(graph_data, whitelist, intervals, user_id):\n for detected_owner in whitelist:\n index = binsearch(intervals, detected_owner[\"timestamp\"])\n if index != -1:\n for profile in graph_data:\n if profile[\"aid\"] == detected_owner[\"aid\"]:\n profile[\"data\"][index][\"images\"].append(\n {\n \"link\": generate_presigned_link(detected_owner[\"key\"]),\n \"timestamp\": detected_owner[\"timestamp\"],\n \"location\": get_location_from_camera_id(\n user_id, detected_owner[\"metadata\"][\"camera_id\"]\n ),\n \"camera_id\": detected_owner[\"metadata\"][\"camera_id\"],\n }\n )\n profile[\"data\"][index][\"y\"] = profile[\"data\"][index][\"y\"] + 1\n break\n return graph_data","def populate_graph2(self, num_users, avg_friendships):\n # Reset graph\n self.reset()\n # random names to populate network with\n first_names = [\"John\", \"Frank\", \"George\", \"Jane\", \"Bruce\", \"Patrick\", \"James\",\n \"Superman\", \"Betty\", \"Wilma\", \"Michael\", \"Barney\", \"Spiderman\"]\n\n last_names = [\"Miller\", \"Wayne\", \"Rubble\", \"Doe\", \"McVey\",\n \"Gonzalez\", \"Replogle\", \"Franco\", \"Rae\", \"Espinosa\"]\n\n if num_users > avg_friendships:\n # Add users\n for _ in range(num_users):\n random_user_name = (random.choice(\n first_names) + \" \" + random.choice(last_names))\n\n self.add_user(random_user_name)\n\n # Create friendships\n for user_id in self.users:\n # repeat until user has at least avg_friendships\n while len(self.friendships[user_id]) < avg_friendships:\n random_id = random.randint(1, num_users)\n\n if random_id not in self.friendships[user_id] and user_id != random_id:\n self.add_friendship(user_id, random_id)\n else:\n print(\"Number of users must be larger that number of friendship links\")","def populateGraph(self, numUsers, avgFriendships):\n # Reset graph\n self.lastID = 0\n self.users = {}\n self.friendships = {}\n # !!!! IMPLEMENT ME\n # Add users\n #for i in range numUsers, call addUser and implement a random name\n # add their ID to a list, for building friendhips\n for i in range(1, numUsers + 1):\n self.addUser(\"Bob\") \n # Create friendships\n possible_friends = []\n\n total_friendships = numUsers * avgFriendships\n num_friendships = 0\n while num_friendships < total_friendships:\n userID = random.randint(1, self.lastID)\n friendID = random.randint(1, self.lastID)\n if self.addFriendship(userID, friendID):\n num_friendships += 2\n ''' \n for userID in self.users:\n for friendID in range(userID + 1, self.lastID + 1):\n possible_friends.append((userID, friendID))\n\n print(possible_friends)\n num_friendships = (numUsers * avgFriendships) // 2\n random.shuffle(possible_friends)\n friendships_to_create = possible_friends[:num_friendships]\n # or could use friendships_to_create = random.sample(possibleFriendships, (numUsers * avgFriendships) // 2)\n\n for friendship in friendships_to_create:\n self.addFriendship(friendship[0], friendship[1])\n\n '''","def main():\n users = {}\n\n with open('../the_data.json', 'r') as f:\n the_data = json.loads(f.read())\n\n user_data = the_data['data']\n the_user_ids = json.loads(the_data['user_ids_list'])\n \n def p(vector):\n vector = json.loads(vector)\n return {field: vector[i] for i,field in enumerate(the_data['vector_fields'])}\n\n result = {}\n for step in range(1, 20):\n step = str(step)\n\n users = {}\n the_user_ids_for_this_step = []\n for uid in the_user_ids:\n try:\n users[uid] = p(user_data[uid][step])\n the_user_ids_for_this_step.append(uid) \n except:\n pass\n\n for user_id in the_user_ids_for_this_step: \n nearest = computeNearestNeighbor(user_id,\n users,\n distance_algorithm='minkowski')\n # print user_id\n if user_id not in result:\n result[user_id] = {}\n\n result[user_id][step] = nearest[:3]\n\n\n\n # print result\n\n for u in result.keys():\n woha = []\n print '%s, step_count: %s' % (u, user_data[u]['step_count'])\n ls = result[u].keys()\n ls.sort()\n for s in ls: \n print s\n for near in result[u][s]:\n if near[1] in woha:\n ulala = '>'*woha.count(near[1])\n else:\n ulala = ''\n woha.append(near[1])\n print '\\t'*int(s), '%s %s, %s, step_count: %s' % (ulala, near[1], near[0], user_data[near[1]]['step_count'])\n\n print","def create_node2edges_on2freq_grid(self):\n trip_id2model = pickle.load(open('pickles/trip_id2model.pickle','rb'))\n old_trip_id = -1\n model = trip_id2model[1]\n sub_x = 5\n sub_y = 5\n node2edges_on2sub_grid2points = {}\n for line in self.lines:\n trip_id,lat,lon = normalize_simple(line)\n if trip_id != old_trip_id:\n #print trip_id\n model = trip_id2model[trip_id]\n old_trip_id = trip_id\n node = self.gps_to_node(lat,lon)\n if node == -1:\n continue\n #print \"pushed through\"\n incident_edges = self.incident_edges(node)\n edges_on = []\n for edge in incident_edges:\n if model[edge] == 1:\n edges_on.append(edge)\n edges_on.sort()\n edges_on = tuple(edges_on)\n min_lat,max_lat,min_lon,max_lon = self.coords_to_min_max_lat_lon(self.node_to_coords(node))\n\n sub_row,sub_col = gen_gps_to_coords(lat,lon,sub_x,sub_y,min_lat,max_lat,min_lon,max_lon)\n sub_tuple = (sub_row,sub_col)\n if node not in node2edges_on2sub_grid2points:\n node2edges_on2sub_grid2points[node] = {}\n edges_on2sub_grid2points = node2edges_on2sub_grid2points[node]\n if edges_on not in edges_on2sub_grid2points:\n edges_on2sub_grid2points[edges_on] = defaultdict(list)\n sub_grid2points = edges_on2sub_grid2points[edges_on]\n points = sub_grid2points[sub_tuple]\n node2edges_on2sub_grid2points[node][edges_on][sub_tuple].append([lat,lon])\n #points.append([lat,lon])\n\n print node2edges_on2sub_grid2points.keys()\n print node2edges_on2sub_grid2points[2].keys()\n print node2edges_on2sub_grid2points[2][(2,3)].keys()\n \n node2edges_on2median = {}\n for node in node2edges_on2sub_grid2points:\n print node\n edges_on2sub_grid2points = node2edges_on2sub_grid2points[node]\n node2edges_on2median[node] = {}\n for edges_on in edges_on2sub_grid2points:\n sub_grid2points = edges_on2sub_grid2points[edges_on]\n best_spot = (-1,-1)\n best_score = 0\n for spot in sub_grid2points:\n score = len(sub_grid2points[spot])\n if score > best_score:\n best_score = score\n best_spot = spot\n node2edges_on2median[node][edges_on] = list_median(sub_grid2points[spot])\n \n with open('pickles/node2edges_on2median-%d-%d.pickle' % (self.rows,self.cols),'wb') as output:\n pickle.dump(node2edges_on2median,output)","def create_graph(data, drone_altitude, safety_distance):\n # Find grid and offsets.\n grid, north_offset, east_offset = create_grid(data, drone_altitude, safety_distance)\n\n # Find object centers.\n centers = get_object_centers(data, north_offset, east_offset, drone_altitude, safety_distance)\n\n # Create Voronoid from centers\n voronoi = Voronoi(centers)\n\n # Find open edges\n edges = find_open_edges_voronoi(voronoi, grid)\n\n # Create graph.\n return (create_graph_from_edges(edges), north_offset, east_offset)","def gen_graph(self):","def populateGraph(self, numUsers, avgFriendships):\n # Reset graph\n self.lastID = 0\n self.users = {}\n self.friendships = {}\n\n # Add users\n for i in range(0, numUsers):\n self.addUser(\"Brian-\"+str(i+1))\n\n # Create friendships\n for i in range(0, avgFriendships):\n for u in self.users:\n self.friendships[u].add(random.choice([user for user in self.users]))","def populate_graph(self):","def create_graph(data, drone_altitude, safety_distance):\n grid, edges = environment.create_grid_and_edges(data, drone_altitude, safety_distance)\n print('Found %5d edges' % len(edges))\n\n # =====================================\n # create graph\n # =====================================\n # create the graph with the weight of the edges\n # set to the Euclidean distance between the points\n G = nx.Graph()\n\n for e in edges:\n dist = utils.euclidian_distance(e[0], e[1]) # LA.norm(np.array(e[0]) - np.array(e[1]))\n G.add_edge(e[0], e[1], weight=dist)\n\n return G, edges, grid","def draw_graph(self):\r\n G=nx.Graph()\r\n \r\n list_location1 = []\r\n list_location2 = []\r\n list_location3 = []\r\n list_location4 = []\r\n \r\n for citizen in self.citizens:\r\n G.add_node(citizen.id)\r\n if citizen.location == 1:\r\n list_location1.append(citizen.id)\r\n elif citizen.location == 2:\r\n list_location2.append(citizen.id)\r\n elif citizen.location == 3:\r\n list_location3.append(citizen.id)\r\n else: \r\n list_location4.append(citizen.id)\r\n\r\n for citizen in self.citizens:\r\n for friend in citizen.friends:\r\n G.add_edge(citizen.id,friend.id)\r\n\r\n pos = nx.random_layout(G)\r\n nx.draw_networkx_nodes(G,pos,node_size=60,nodelist=list_location1, node_color='r')\r\n nx.draw_networkx_nodes(G,pos,node_size=60,nodelist=list_location2, node_color='g')\r\n nx.draw_networkx_nodes(G,pos,node_size=60,nodelist=list_location3, node_color='b')\r\n nx.draw_networkx_nodes(G,pos,node_size=60,nodelist=list_location4, node_color='y')\r\n nx.draw_networkx_edges(G,pos, width=1)\r\n\r\n plt.show()","def draw_network(graph, users, filename):\n ###TODO-- Completed\n candidate_names = [user['screen_name'] for user in users]\n plt.figure(figsize=(12,12))\n candidate_labels = {node: node if node in candidate_names else '' for node in graph.nodes_iter()}\n #print(candidate_labels)\n nx.draw_networkx(graph, labels=candidate_labels, alpha=0.5, node_color='r', node_size=100, width=0.1)\n #plt.show()\n plt.axis('off')\n plt.savefig(filename)\n #pass","def getAllSocialPaths(self, userID):\n visited = {} # Note that this is a dictionary, not a set\n # !!!! IMPLEMENT ME\n pass","def test_dbscan_loc(self):\n sp_file = os.path.join(\"tests\", \"data\", \"geolife\", \"geolife_staypoints.csv\")\n sp = ti.read_staypoints_csv(sp_file, tz=\"utc\", index_col=\"id\", crs=\"epsg:4326\")\n sp, locs = sp.as_staypoints.generate_locations(\n method=\"dbscan\", epsilon=10, num_samples=1, distance_metric=\"haversine\", agg_level=\"dataset\"\n )\n\n # create locations as grouped staypoints, another way to create locations\n other_locs = []\n grouped_df = sp.groupby([\"user_id\", \"location_id\"])\n for combined_id, group in grouped_df:\n user_id, location_id = combined_id\n group.set_geometry(sp.geometry.name, inplace=True)\n\n if int(location_id) != -1:\n temp_loc = {}\n temp_loc[\"user_id\"] = user_id\n temp_loc[\"id\"] = location_id\n\n # point geometry of place\n temp_loc[\"center\"] = Point(group.geometry.x.mean(), group.geometry.y.mean())\n other_locs.append(temp_loc)\n\n other_locs = gpd.GeoDataFrame(other_locs, columns=[\"user_id\", \"id\", \"center\"], geometry=\"center\", crs=sp.crs)\n other_locs.set_index(\"id\", inplace=True)\n\n assert_geoseries_equal(other_locs[\"center\"], locs[\"center\"], check_less_precise=True)","def _make_graph(nodes, ways):\n graph = networkx.MultiDiGraph(crs=\"EPSG:4326\")\n ways_proj = ways.set_crs(\"EPSG:4326\").to_crs(\"EPSG:3395\")\n\n for node_id, node_attr in nodes.rename(columns={'longitude': 'x', 'latitude': 'y'}).iterrows():\n graph.add_node(node_id, **node_attr)\n\n for _, way in ways_proj.iterrows():\n\n osm_oneway_values = [\"yes\", \"true\", \"1\", \"-1\", \"T\", \"F\"]\n if \"oneway\" in way and way.oneway in osm_oneway_values:\n if way[\"oneway\"] == \"-1\" or way[\"oneway\"] == \"T\":\n # paths with a one-way value of -1 or T are one-way, but in the\n # reverse direction of the nodes' order, see osm documentation\n path_nodes = list(reversed(way.nodes))\n else:\n path_nodes = way.nodes\n # add this path (in only one direction) to the graph\n one_way = True\n\n elif \"junction\" in way and way.junction == \"roundabout\":\n # roundabout are also oneway but not tagged as is\n path_nodes = way.nodes\n one_way = True\n\n # else, this path is not tagged as one-way or it is a walking network\n # (you can walk both directions on a one-way street)\n else:\n # add this path (in both directions) to the graph and set its\n # 'oneway' attribute to False. if this is a walking network, this\n # may very well be a one-way street (as cars/bikes go), but in a\n # walking-only network it is a bi-directional edge\n path_nodes = way.nodes\n one_way = False\n\n # zip together the path nodes so you get tuples like (0,1), (1,2), (2,3)\n # and so on\n path_edges = list(zip(path_nodes[:-1], path_nodes[1:]))\n graph.add_edges_from(path_edges, **way[['id']])\n if not one_way:\n path_edges_reverse = [(v, u) for u, v in path_edges]\n graph.add_edges_from(path_edges_reverse, **way[['id']])\n\n graph = osmnx.utils_graph.add_edge_lengths(graph)\n return graph","def contact_tracing_users(past_reservations, user_id: int):\n # check which users were at the restaurant at the same time as the positive guy\n user_at_risk = []\n for reservation in past_reservations:\n et = reservation['entrance_time']\n if isinstance(et, str):\n et = datetime.strptime(reservation['entrance_time'], '%Y-%m-%dT%H:%M:%S.%f')\n # get average staying time of the restaurant and compute 'danger period'\n resp = requests.get(f\"http://{os.environ.get('GOS_RESTAURANT')}/restaurants/{reservation['restaurant_id']}\")\n if resp == 404:\n # restaurant does not exists/was deleted, carry on defining a standard avg time (prioritize reservations which are always kept)\n avg_stay_time = time(hour=1, minute=30)\n else:\n avg_stay_time = resp.json()['avg_stay_time']\n # convert time format back to object\n avg_stay_time = datetime.strptime(avg_stay_time, \"%H:%M:%S\").time()\n\n # avg_stay_time = Restaurant.query.filter_by(id=reservation['restaurant_id']).first().avg_stay_time\n staying_interval = timedelta(hours=avg_stay_time.hour, minutes=avg_stay_time.minute, seconds=avg_stay_time.second)\n start_time = et - staying_interval\n end_time = et + staying_interval\n\n user_reservation = Reservation.query.filter(Reservation.user_id != user_id).\\\n filter_by(restaurant_id=reservation['restaurant_id']).\\\n filter(Reservation.entrance_time.between(start_time, end_time)).all()\n # print(user_reservation)\n # preserve positive user reservation we're referring to, as to notify operator \n user_ids_args = 'or_(' + ', '.join(['User.id == ' + str(ur.user_id) for ur in user_reservation])+')'\n # request users information from User service\n resp = requests.get(f'http://{os.environ.get(\"GOS_USER\")}/users/filter?filter={user_ids_args}')\n if resp.status_code != 200:\n logging.error(\"Error in user service contact\")\n raise Exception\n user_at_risk += resp.json()\n return {'users': user_at_risk}, 200","def create_path_new(self):\n\n \n first_lasts = []\n first_lasts.append([0,0])\n matrices = []\n matrices.append([[[0 for i in range(self.graph.cols)] for i in range(self.graph.rows)],0])\n edge_sets = []\n edge_sets.append([0 for i in range(self.graph.num_edges)])\n cur_line = self.line_num\n nodes_visited = []\n nodes_visited.append([])\n normalized = normalize_simple(self.graph.lines[cur_line])\n matrices_index = 0\n prev_coords = (-1,-1)\n prev_gps = (-1.0,-1.0)\n while normalized[0] == self.trip_id:\n lat = normalized[1]\n lon = normalized[2]\n coords = self.graph.gps_to_coords(lat,lon)\n node = self.graph.coords_to_node(coords[0],coords[1])\n\n if prev_coords == (-1,-1) and coords[0] != -1:\n first_lasts[matrices_index][0] = node\n\n if coords[0] == -1 and prev_coords[0] != -1:\n prev_node = self.graph.coords_to_node(prev_coords[0],prev_coords[1])\n first_lasts[matrices_index][1] = prev_node\n\n if prev_coords != (-1,-1) and coords[0] != -1 and coords != prev_coords:\n edge_num = self.graph.edge_num(prev_coords[0],prev_coords[1],coords[0],coords[1])\n if edge_num == -1:\n new_edges = self.find_edges((lat,lon),prev_gps)\n for add_edge in new_edges:\n edge_sets[matrices_index][add_edge] = 1\n else:\n edge_sets[matrices_index][edge_num] = 1\n\n if coords[0] == -1:\n matrices.append([[[0 for i in range(self.graph.cols)] for i in range(self.graph.rows)],0])\n first_lasts.append([0,0])\n edge_sets.append([0 for i in range(self.graph.num_edges)])\n nodes_visited.append([])\n matrices_index += 1\n \n elif coords[0] < self.graph.rows and coords[1] < self.graph.cols and not matrices[matrices_index][0][coords[0]][coords[1]]:\n matrices[matrices_index][1] += 1\n matrices[matrices_index][0][coords[0]][coords[1]] = 1\n nodes_visited[matrices_index].append(coords)\n\n prev_coords = coords\n\n cur_line += 1\n if cur_line == len(self.graph.lines):\n break\n normalized = normalize_simple(self.graph.lines[cur_line])\n prev_gps = (lat,lon)\n\n prev_node = self.graph.coords_to_node(prev_coords[0],prev_coords[1])\n first_lasts[matrices_index][1] = prev_node\n self.next_line = cur_line\n best_index = 0\n best_score = 0\n for matrix_index in range(len(matrices)):\n if matrices[matrix_index][1] > best_score:\n best_score = matrices[matrix_index][1]\n best_index = matrix_index\n\n #for coords in nodes_visited[best_index]:\n # self.graph.node_visit(self.trip_id,coords)\n\n #if self.trip_id not in self.graph.trip_id2line_num:\n # self.graph.first_last2trip_ids[tuple(first_lasts[best_index])].append(self.trip_id)\n\n return matrices[best_index][0],edge_sets[best_index],first_lasts[best_index]","def graphformation(time_lower, time_upper):\n\tprm = param.Para()\n\ttry:\n\t\tdb_connection = mysql.connector.connect(\n\t\t host=prm.hostname,\n\t\t user=prm.username,\n\t\t passwd=prm.password,\n\t\t database= prm.dbname\n\t\t )\n\t\tdb_cursor = db_connection.cursor()\n\texcept:\n\t\tprint(\"Can't Connect to database, check credentials in parameter file\")\n\tquery = (\"SELECT * FROM identity \")\n\tdb_cursor.execute(query)\n\tdf1=pd.DataFrame(db_cursor.fetchall())\n\tdf1.columns= ['node','deviceid','student','rollno']\n\tdict_identity = dict(zip(df1.deviceid, df1.node))\n\trev_dict_identity = dict(zip(df1.node, df1.deviceid ))\n\tquery = (\"SELECT * FROM activity WHERE time BETWEEN '{}' AND '{}'\".format(time_lower,time_upper)) ## incomplete\n\tdb_cursor.execute(query)\n\tactivity_data = pd.DataFrame(db_cursor.fetchall())\n\tif activity_data.empty==False:\n\t\tactivity_data.columns=[\"sl_no\",\"time\",\"node\",\"latitude\",\"longitude\"]\n\telse:\n\t\tprint(\"No Activity in the selected Time Window\")\n\t\treturn\n\tnumnodes= len(df1)\n\tedges= []\n\tscore = {}\n\t#print(activity_data)\n\ttime_groups = activity_data.groupby('time')\n\twith open(r'C:\\Users\\HP\\Desktop\\project\\Contact_Graph\\bluetooth.txt') as json_file:\n\t\tdata1 = json.load(json_file)\n\tfor name, group in time_groups:\n\t\tscore_tmp = decayfunc(name,time_upper)\n\t\tgroup = group.sort_values('node')\n\t\tfor i in range(len(group)-1):\n\t\t\tnode1 = group.iloc[i,2]\n\t\t\t###########################\n\t\t\tlistnearby=[]\n\t\t\ttry:\n\t\t\t\tlistnearby = data1[rev_dict_identity[node1]][str(name)]\n\t\t\t\tlistnearby = [dict_identity[i] for i in listnearby if dict_identity[i]>node1]\n\t\t\t\tfor i in listnearby:\n\t\t\t\t\ttry:\n\t\t\t\t\t\tscore[(node1,i)]+=1\n\t\t\t\t\texcept:\n\t\t\t\t\t\tscore[(node1,i)]=1\n\t\t\texcept:\n\t\t\t\tpass\n\t\t\t###########################\n\t\t\tfor j in range(i+1,len(group)):\n\t\t\t\tnode2 =group.iloc[j,2]\n\t\t\t\tif proximityfunc(group.iloc[i,3],group.iloc[i,4],group.iloc[j,3],group.iloc[j,4]) and node2 not in listnearby:\n\t\t\t\t\ttry:\n\t\t\t\t\t\tscore[(group.iloc[i,2],group.iloc[j,2])]+=1\n\t\t\t\t\texcept:\n\t\t\t\t\t\tscore[(group.iloc[i,2],group.iloc[j,2])]=1\n\tnode_list = list(df1.node)\n\ttitle_list = list(df1.deviceid)\n\tedges_list = []\n\tfor edge,val in score.items():\n\t\tedges_list.append((int(edge[0]),int(edge[1]),float(val)))\n\n\treturn edges_list,node_list,title_list","def populateGraph(self, numUsers, avgFriendships):\n # Reset graph\n self.lastID = 0\n self.users = {}\n self.friendships = {}\n # !!!! IMPLEMENT ME\n alphabet = ['a','b','c','d','e','f','g','h','i','j','k','l','m','n','o','p','q','r','s','t','u','v','w','x','y','z']\n # Add users\n for i in range(numUsers):\n name = ''\n for i in range(randint(5,9)):\n name = name + alphabet[randint(0, len(alphabet) - 1)]\n self.addUser(name)\n\n # Create friendships\n for key in self.users:\n for _ in range(randint(0, avgFriendships)):\n new_friend = randint(1, len(self.users)-1)\n if new_friend not in self.friendships[key] and new_friend != key:\n self.addFriendship(key, new_friend)\n\n # get average friendships per user\n friendships = 0\n for key, item in self.friendships.items():\n friendships += len(item)\n \n print('average friendships', friendships/len(self.users))","def getAllSocialPaths(self, userID):\n visited = {} # Note that this is a dictionary, not a set\n # !!!! IMPLEMENT ME\n q = Queue()\n q.put([userID])\n\n while not q.empty():\n user_path = q.get()\n user = user_path[-1]\n\n if user not in visited.keys():\n visited[user] = user_path\n for friend in self.friendships[user]:\n new_path = user_path[::]\n new_path.append(friend)\n q.put(new_path)\n\n # get average degree of separation per user\n degrees = 0\n for key, item in visited.items():\n degrees += len(item)\n \n print('average degree of separation', degrees/len(self.users))\n\n return visited","def label_users(self):\n record_unit = 1000\n print self.friendship_graph.number_of_nodes()\n print self.friendship_graph.number_of_edges()\n\n for num, node in enumerate(self.friendship_graph.nodes()):\n fake_flag = self.determine_spammer_by_percentage(node)\n self.friendship_graph.node[node]['fake'] = fake_flag\n # print self.friendship_graph.node[node]\n if num % record_unit == 0:\n print num\n print time.strftime('%Y-%m-%d %H:%M:%S', time.localtime(time.time()))\n nx.write_gpickle(self.friendship_graph, \"graph/firendship_new_label%d.pickle\" % num)\n if num != 0:\n os.remove(\"graph/firendship_new_label%d.pickle\" % (num - record_unit))\n\n nx.write_gpickle(self.friendship_graph, \"graph/firendship_0.8fake_%d.pickle\" % num)","def example_triplegs_merge(example_staypoints_merge):\n # get staypoints from above\n sp, _ = example_staypoints_merge\n # create triplegs inbetween\n # tripleg between 5 and 2\n t21 = pd.Timestamp(\"1971-01-02 05:10:00\", tz=\"utc\")\n t22 = pd.Timestamp(\"1971-01-02 05:40:00\", tz=\"utc\")\n # tripleg between 6 and 15\n t45 = pd.Timestamp(\"1971-01-02 08:57:00\", tz=\"utc\")\n t5 = pd.Timestamp(\"1971-01-02 09:00:00\", tz=\"utc\")\n list_dict = [\n {\"id\": 0, \"user_id\": 0, \"started_at\": t45, \"finished_at\": t5},\n {\"id\": 1, \"user_id\": 0, \"started_at\": t21, \"finished_at\": t22},\n ]\n # geometry is not required for the merge operation, so we leave it away\n tpls = pd.DataFrame(data=list_dict)\n tpls = tpls.set_index(\"id\")\n return sp, tpls","def getAllSocialPaths(self, userID):\n visited = {} # Note that this is a dictionary, not a set\n print(f\"user ID {userID}\")\n\n for i in range(1, len(self.users)):\n visited[i] = self.bfs(userID, i)\n\n return visited","async def async_step_geography_by_coords(\n self, user_input: dict[str, str] | None = None\n ) -> FlowResult:\n if not user_input:\n return self.async_show_form(\n step_id=\"geography_by_coords\", data_schema=self.geography_coords_schema\n )\n\n return await self._async_init_geography(\n user_input, INTEGRATION_TYPE_GEOGRAPHY_COORDS\n )","def create_boston_graph(df, start, end):\n ports = pd.unique(df[['Source Port', 'Destination Port']].values.ravel('K'))\n ips = pd.unique(df[['Source IP', 'Destination IP']].values.ravel('K'))\n\n G = nx.Graph(mode=\"interval\", start=start, end=end)\n G.add_nodes_from(ports, type=\"port\")\n G.add_nodes_from(ips, type=\"ip\")\n\n interval_info = {}\n for index, row in df.iterrows():\n src_ip = row['Source IP']\n src_port = row['Source Port']\n dst_ip = row['Destination IP']\n dst_port = row['Destination Port']\n label = row['Label']\n interval_info['timestamp'] = row['Timestamp']\n\n # -1: malicious, 1: benign\n G.nodes[src_ip]['interval_ground_truth'] = convert_label_to_numerical(label)\n G.nodes[dst_ip]['interval_ground_truth'] = convert_label_to_numerical(label)\n G.nodes[src_port]['interval_ground_truth'] = convert_label_to_numerical(label)\n G.nodes[dst_port]['interval_ground_truth'] = convert_label_to_numerical(label)\n\n # Setting labels for nodes on the current window\n G.nodes[src_ip]['label'] = src_ip\n G.nodes[src_port]['label'] = src_port\n G.nodes[dst_ip]['label'] = dst_ip\n G.nodes[dst_port]['label'] = dst_port\n\n nodes = (src_ip, src_port, dst_ip, dst_port)\n\n for i in nodes:\n for j in nodes:\n if i != j:\n if i in (src_ip, dst_ip):\n frm = \"ip\"\n else:\n frm = \"port\"\n if j in (src_ip, dst_ip):\n to = \"ip\"\n else:\n to = \"port\"\n G.add_edge(i, j,\n label_class=label,\n weight=1,\n type=\"%s_to_%s\" % (frm, to))\n return G, interval_info","def get_all_social_paths(self, user_id):\n if len(self.friendships) > 0:\n visited = {}\n q = Queue()\n q.enqueue([user_id])\n\n while q.size() > 0:\n curr_path = q.dequeue()\n curr_vertex = curr_path[-1]\n\n if curr_vertex not in visited:\n visited[curr_vertex] = curr_path\n\n for friend in self.friendships[curr_vertex]:\n path_copy = curr_path[:]\n path_copy.append(friend)\n q.enqueue(path_copy)\n\n return visited\n\n else:\n print(\"There are currently no friendship paths in the network\")","def test_location_ids_and_noise(self, example_staypoints):\n sp = example_staypoints\n sp2, _ = sp.as_staypoints.generate_locations(\n method=\"dbscan\", epsilon=10, num_samples=2, distance_metric=\"haversine\", agg_level=\"user\"\n )\n assert sp2.loc[1, \"location_id\"] == sp2.loc[15, \"location_id\"]\n assert sp2.loc[5, \"location_id\"] == sp2.loc[6, \"location_id\"]\n assert sp2.loc[80, \"location_id\"] == sp2.loc[3, \"location_id\"]\n assert sp2.loc[1, \"location_id\"] != sp2.loc[6, \"location_id\"]\n assert sp2.loc[1, \"location_id\"] != sp2.loc[80, \"location_id\"]\n\n assert sp2.loc[[2, 7], \"location_id\"].isnull().all()","def get_friends_graph():\n total_users = 0\n count_bad_users = 0\n count_graph_users = 0\n\n result = \"was successful\"\n\n settings.print_message(\"Create VK session for application (app_id={})\".format(settings.VK_APPLICATION_ID))\n logger.debug(\"Create VK session for application (app_id={}).\".format(settings.VK_APPLICATION_ID))\n try:\n session = vk.Session(access_token=settings.VK_ACCESS_TOKEN)\n api = vk.API(session)\n except Exception as error:\n logger.warn(traceback.format_exc())\n settings.print_message(\"Can't create VK session for application with app_id={}\".format(settings.VK_APPLICATION_ID))\n return (\"with error.\", 0, 0, 0)\n # logger.debug(\"\".format())\n graph = yEdGraph.Graph()\n level_queue = [int(settings.PARAMS[\"user_id\"]), ]\n count_levels = int(settings.PARAMS[\"levels\"])\n all_level_ids = [list() for _ in range(count_levels)]\n\n for step in range(count_levels):\n settings.print_message(\"Process level #{} (total users {})\".format(step, level_counter))\n logger.debug(\"Process level #{} (total users {})\".format(step, level_counter))\n level_counter = len(level_queue)\n for user_index in range(level_counter):\n id = level_queue[user_index]\n total_users += 1\n settings.print_message(\"Process id {}. User #{} on level #{} (total {})\".format(id, user_index, step, level_counter), 2)\n logger.debug(\"Process id {}. User #{} on level #{} (total {}).\".format(id, user_index, step, level_counter))\n try:\n settings.print_message(\"Add user node in graph.\", 3)\n logger.debug(\"Check user (id={}) in graph\".format(id))\n if not id in graph.nodes.keys():\n logger.debug(\"Create user node in graph (id={}).\".format(id))\n logger.debug(\"Get info for user (id={}).\".format(id))\n try:\n user_info = api.users.get(user_ids=[id], fields = \"photo_200_orig\")\n except Exception as error:\n logger.warn(traceback.format_exc())\n logger.debug(\"Can not get info for user (id={}), skip.\".format(id))\n settings.print_message(\"Can not get info for user, skip.\", 3)\n count_bad_users += 1\n continue\n if not user_info: \n logger.debug(\"Can not get info for user (id={}), skip.\".format(id))\n settings.print_message(\"Can not get info for user, skip.\", 3)\n count_bad_users += 1\n continue\n user_info = user_info[0]\n logger.debug(\"User info='{}'.\".format(json.dumps(user_info)))\n logger.debug(\"Load user photo (id={}).\".format(id))\n photo = utils.get_request(user_info[\"photo_200_orig\"])\n if not photo:\n logger.debug(\"Can't loading user photo (id={}).\".format(id))\n info_label = \"ID: {} \\nФИГ: {} {} \\nНик: {} \\nПол: {} \\nДата рождения: {} \\nГород: {} \\nСтрана: {}\".format(\n id,\n user_info[\"first_name\"] if \"first_name\" in user_info else \"----\",\n user_info[\"last_name\"] if \"last_name\" in user_info else \"----\",\n user_info[\"nickname\"] if \"nickname\" in user_info else \"----\",\n utils.SEX[user_info[\"sex\"]] if \"sex\" in user_info else \"----\",\n user_info[\"bdate\"] if \"bdate\" in user_info else \"----\",\n user_info[\"city\"] if \"city\" in user_info else \"----\",\n user_info[\"country\"] if \"country\" in user_info else \"----\"\n )\n graph.add_node(id, check_existance=False, label=info_label, shape=\"roundrectangle\", font_style=\"italic\", underlined_text=\"false\", img=photo, width=\"200\", height=\"200\", border_has_color=\"false\")\n count_graph_users += 1\n else:\n logger.debug(\"Graph contains user node (id={}).\".format(id))\n settings.print_message(\"Graph already contains this user node.\", 3)\n settings.print_message(\"Get friendlist.\", 3)\n logger.debug(\"Get friend for user (id={}).\".format(id))\n try:\n friends = api.friends.get(user_id=id, count=1000000, fields = \"nickname, sex, bdate, city, country, photo_200_orig\")\n except Exception as error:\n logger.debug(\"Can not get friends, skip.\")\n settings.print_message(\"Can not get friendlist, skip.\", 3)\n count_bad_users += 1\n continue\n if not friends:\n logger.debug(\"Can not get friends, skip.\")\n settings.print_message(\"Can not get friendlist, skip.\", 3)\n count_bad_users += 1\n continue\n settings.print_message(\"Process friends (total {}, level #{}).\".format(len(friends), step + 1), 3)\n logger.debug(\"Friends count: {}\".format(len(friends)))\n _ = [level_queue.append(friend[\"user_id\"]) for friend in friends if not friend[\"user_id\"] in graph.nodes]\n logger.debug(\"Add node for each friend and create edges.\")\n for friend_index, friend in enumerate(friends):\n total_users += 1\n logger.debug(\"Process friend #{} (id={}).\".format(friend_index, friend[\"user_id\"]))\n settings.print_message(\"Process friends #{} id={} (total {}, level #{}).\".format(friend_index, friend[\"user_id\"], len(friends), step + 1), 4)\n #if friend_index > 10: break\n settings.print_message(\"Add user node in graph.\", 5)\n logger.debug(\"Check user (id={}) in graph\".format(friend[\"user_id\"]))\n if not friend[\"user_id\"] in graph.nodes.keys():\n logger.debug(\"Create user node in graph (id={}).\".format(friend[\"user_id\"]))\n logger.debug(\"User info='{}'.\".format(json.dumps(friend)))\n logger.debug(\"Load user photo (id={}).\".format(friend[\"user_id\"]))\n photo = utils.get_request(friend[\"photo_200_orig\"])\n if not photo:\n logger.debug(\"Can't loading user photo (id={}).\".format(friend[\"user_id\"]))\n info_label = \"ID: {} \\nФИГ: {} {} \\nНик: {} \\nПол: {} \\nДата рождения: {} \\nГород: {} \\nСтрана: {}\".format(\n friend[\"user_id\"],\n friend[\"first_name\"] if \"first_name\" in friend else \"----\",\n friend[\"last_name\"] if \"last_name\" in friend else \"----\",\n friend[\"nickname\"] if \"nickname\" in friend else \"----\",\n utils.SEX[friend[\"sex\"]] if \"sex\" in friend else \"----\",\n friend[\"bdate\"] if \"bdate\" in friend else \"----\",\n friend[\"city\"] if \"city\" in friend else \"----\",\n friend[\"country\"] if \"country\" in friend else \"----\"\n )\n graph.add_node(friend[\"user_id\"], check_existance=False, label=info_label, shape=\"roundrectangle\", font_style=\"italic\", underlined_text=\"false\", img=photo, width=\"200\", height=\"200\", border_has_color=\"false\")\n count_graph_users += 1\n else:\n logger.debug(\"Graph contains user node (id={}).\".format(friend[\"user_id\"]))\n settings.print_message(\"Graph already contains this user node.\", 5)\n logger.debug(\"Add adge {}-{} in graph.\".format(friend[\"user_id\"], id))\n # if ...\n graph.add_edge(friend[\"user_id\"], id, width=\"1.0\", color=\"#000000\", check_existance_nodes=False)\n except Exception as error:\n logger.warn(traceback.format_exc())\n result = \"with error\"\n level_queue = level_queue[level_counter:]\n logger.debug(\"Create graphml for graph.\")\n graph.construct_graphml()\n logger.debug(\"Save graphml in file {}.\".format(settings.OUTPUT_FILE))\n try:\n with open(settings.OUTPUT_FILE, \"w\", encoding=settings.OUTPUT_ENCODING) as f:\n f.write(graph.get_graph())\n except Exception as error:\n logger.warn(traceback.format_exc())\n result = \"with error\"\n return (result, total_users, count_graph_users, count_bad_users)","def echarts_Location(graph,\r\n h,\r\n x,\r\n y,\r\n symbol_size=5,\r\n line_width=1,\r\n width=\"1400px\",\r\n height=\"800px\",\r\n path='locationExample.html',\r\n title='Location_Graph',\r\n subtitle='',\r\n showName=True,\r\n font_size=15,\r\n edge_symbol_size=10,\r\n pageLayout=Page.SimplePageLayout,\r\n show_node_value=True,\r\n show_edge_value=True,\r\n render=True):\r\n\r\n # initialize nodes list\r\n nodes = []\r\n if show_node_value:\r\n for i in range(len(graph.nodes.aslist())):\r\n # calculate reachability for each nodes\r\n reachability = ot.calculate_reachability(graph, graph.nodes.labels()[i])\r\n if reachability > h:\r\n nodes.append(opts.GraphNode(name=graph.nodes.labels()[i],\r\n category=0,\r\n x=graph.nodes.aslist()[i].data[x],\r\n y=graph.nodes.aslist()[i].data[y],\r\n label_opts=opts.LabelOpts(is_show=showName, font_size=font_size),\r\n value=reachability\r\n\r\n ))\r\n else:\r\n nodes.append(opts.GraphNode(name=graph.nodes.labels()[i],\r\n category=1,\r\n x=graph.nodes.aslist()[i].data[x],\r\n y=graph.nodes.aslist()[i].data[y],\r\n label_opts=opts.LabelOpts(is_show=showName, font_size=font_size),\r\n value=reachability\r\n ))\r\n else:\r\n for i in range(len(graph.nodes.aslist())):\r\n # calculate reachability for each nodes\r\n reachability = ot.calculate_reachability(graph, graph.nodes.labels()[i])\r\n if reachability > h:\r\n nodes.append(opts.GraphNode(name=graph.nodes.labels()[i],\r\n category=0,\r\n x=graph.nodes.aslist()[i].data[x],\r\n y=graph.nodes.aslist()[i].data[y],\r\n label_opts=opts.LabelOpts(is_show=showName, font_size=font_size)\r\n\r\n ))\r\n else:\r\n nodes.append(opts.GraphNode(name=graph.nodes.labels()[i],\r\n category=1,\r\n x=graph.nodes.aslist()[i].data[x],\r\n y=graph.nodes.aslist()[i].data[y],\r\n label_opts=opts.LabelOpts(is_show=showName, font_size=font_size)\r\n\r\n ))\r\n\r\n # initialize links list\r\n links = []\r\n if show_edge_value:\r\n # initialize value list of edges\r\n edge_value = []\r\n # 1. compare all edge labels with unique edge labels to find which edges are duplicated\r\n # 2. edge value of duplicate edges will be added to the same list and shown together\r\n for i in range(len(graph.edges.labels())):\r\n for j in range(len(graph.edges.ulabels())):\r\n if graph.edges.labels()[i] == graph.edges.ulabels()[j]:\r\n if j < len(edge_value):\r\n edge_value[j].append('{start time: ' + str(graph.edges.start_times()[i]) + ', end time: ' + str(\r\n graph.edges.end_times()[i]) + '}')\r\n else:\r\n tmp_edge_value = []\r\n tmp_edge_value.append(\r\n '{start time: ' + str(graph.edges.start_times()[i]) + ', end time: ' + str(\r\n graph.edges.end_times()[i]) + '}')\r\n edge_value.append(tmp_edge_value)\r\n # print the rate of progress\r\n print('rate of progress: {}%'.format((i + 1) / len(graph.edges.labels()) * 100))\r\n\r\n for i in range(len(graph.edges.ulabels())):\r\n tmp = graph.edges.ulabels()[i].split('-')\r\n\r\n links.append(opts.GraphLink(source=tmp[0],\r\n target=tmp[1],\r\n value=edge_value[i]\r\n\r\n ))\r\n\r\n else:\r\n for i in range(len(graph.edges.ulabels())):\r\n tmp = graph.edges.ulabels()[i].split('-')\r\n\r\n links.append(opts.GraphLink(source=tmp[0],\r\n target=tmp[1]\r\n\r\n ))\r\n\r\n # initialize categories list\r\n categories = [\r\n opts.GraphCategory(name='nodes with reachability more than {}'.format(h)),\r\n opts.GraphCategory(name='nodes with reachability less than or equal to {}'.format(h))\r\n ]\r\n\r\n # generate an html file of the graph\r\n c = (\r\n Graph(init_opts=opts.InitOpts(width=width, height=height))\r\n .add(\r\n \"\",\r\n nodes=nodes,\r\n links=links,\r\n categories=categories,\r\n layout=\"none\",\r\n symbol_size=symbol_size,\r\n linestyle_opts=opts.LineStyleOpts(is_show=True, curve=0.1, width=line_width),\r\n label_opts=opts.LabelOpts(position=\"right\"),\r\n edge_symbol=['circle', 'arrow'],\r\n edge_symbol_size=symbol_size\r\n )\r\n .set_global_opts(\r\n title_opts=opts.TitleOpts(title=title, subtitle=subtitle,\r\n title_textstyle_opts=opts.TextStyleOpts(font_size=40),\r\n subtitle_textstyle_opts=opts.TextStyleOpts(font_size=20)),\r\n legend_opts=opts.LegendOpts(orient=\"vertical\", pos_left=\"2%\", pos_top=\"20%\",\r\n textstyle_opts=opts.TextStyleOpts(font_size=20)),\r\n )\r\n )\r\n\r\n if render:\r\n page = Page(layout=pageLayout)\r\n page.add(c)\r\n page.render(path)\r\n\r\n return c","def example_graphing(self) -> None:\n # Log the current amount of followers to our history of followers\n self.log_followers()\n\n # Graphs all followers / time\n self.graph_followers()","def plot_graph():\n name = request.args.get('instance')\n name = str(name)\n distance = request.args.get('distance')\n path = request.args.get('path')\n if name == 'Custom':\n coords = request.args.get('coords')\n coords = str(coords)\n nodes = custom_nodes(coords)\n else:\n nodes = create_nodes(name)\n fig = Figure()\n axis = fig.add_subplot(1, 1, 1)\n\n axis.set_title(name + \" - Distance: \"+ str(distance))\n path = str(path).split(',')\n path = [int(i) for i in path]\n for i in range(len(path) - 1):\n\n start_node = nodes[path[i]]\n x1, y1 = start_node.x, start_node.y\n axis.scatter(x1, y1, c = 'b', label = str(path[i]))\n axis.text(x1,y1, str(path[i]))\n end_node = nodes[path[i+1]]\n x2, y2 = end_node.x, end_node.y\n axis.plot([x1,x2], [y1, y2])\n\n last_node = nodes[path[len(path)-1]]\n x1, y1 = last_node.x, last_node.y\n axis.text(x1,y1, str(path[len(path)-1]))\n\n begin_node = nodes[path[0]]\n x2, y2 = begin_node.x, begin_node.y\n axis.scatter(x1, y1, c = 'b', label = str(path[len(path)-1]))\n axis.plot([x1,x2], [y1, y2])\n\n output = io.BytesIO()\n FigureCanvas(fig).print_png(output)\n return Response(output.getvalue(), mimetype=\"image/png\")","async def async_step_user(\n self, user_input: dict[str, Any] | None = None\n ) -> FlowResult:\n if not user_input:\n return self._show_form_user()\n\n await self.async_set_unique_id(\n f\"{user_input[CONF_LOCATION][CONF_LATITUDE]}_{user_input[CONF_LOCATION][CONF_LONGITUDE]}\"\n )\n self._abort_if_unique_id_configured()\n\n data = await async_get_nearby_stations(self.hass, user_input)\n if not data.get(\"ok\"):\n return self._show_form_user(\n user_input, errors={CONF_API_KEY: \"invalid_auth\"}\n )\n if len(stations := data.get(\"stations\", [])) == 0:\n return self._show_form_user(user_input, errors={CONF_RADIUS: \"no_stations\"})\n for station in stations:\n self._stations[station[\"id\"]] = (\n f\"{station['brand']} {station['street']} {station['houseNumber']} -\"\n f\" ({station['dist']}km)\"\n )\n\n self._data = user_input\n\n return await self.async_step_select_station()","def livingAreaFromPattern(business_list):\n print(\"livingAreaFromUser\")\n dataset_path = \"../dataset/yelp_dataset_challenge_academic_dataset/\"\n review_path = dataset_path + \"yelp_academic_dataset_review.json\"\n\n # make empty user list for each business\n business_user_list = {} # mine user set from business\n user_business_list = {} # mine business set from user\n for row in business_list:\n business_user_list[row[0]] = [] # business_id\n\n # get review by business\n # get user by business-review\n user_iter = FieldsIter(review_path, \"json\", [\"user_id\", \"business_id\"])\n for row in user_iter:\n user_id = row[0]\n business_id = row[1]\n if business_id in business_user_list:\n business_user_list[business_id].append(user_id)\n if user_id not in user_business_list:\n user_business_list[user_id] = [business_id]\n else:\n user_business_list[user_id].append(business_id)\n\n for business_id in business_user_list:\n print(\"business_id: \" + business_id + \" has \" + str(len(business_user_list[business_id])) + \" users' reviews\")\n # print(business_user_list[business_id])\n\n return business_user_list, user_business_list\n\n # reform user-categories(or business) association\n\n # fp-growth on user-categories(or business) set\n\n # observe the frequent category set, if it can form business cluster","def construct_graph(social_edges, spatial_edges, output_path=None):\n G = nx.DiGraph()\n with open(social_edges, 'r') as f:\n for l in f.read().splitlines():\n edge = l.split(\"\\t\")\n G.add_edge(USER_NODE_PREFIX + edge[0], USER_NODE_PREFIX + edge[-2], weight=float(edge[-1]))\n\n business_nodes = set([])\n with open(spatial_edges, 'r') as f:\n for l in f.read().splitlines():\n edge = l.split(\"\\t\")\n lat = float(edge[2])\n lng = float(edge[3])\n if edge[-2] not in business_nodes:\n G.add_node(BUSINESS_NODE_PREFIX + edge[-2], spatial={'lat': lat, 'lng': lng})\n business_nodes.add(edge[-2])\n\n with open(spatial_edges, 'r') as f:\n for l in f.read().splitlines():\n edge = l.split(\"\\t\")\n G.add_edge(USER_NODE_PREFIX + edge[0], BUSINESS_NODE_PREFIX + edge[-2], weight=float(edge[-1]))\n\n if output_path:\n pickle.dump(G, open(output_path, 'w'))\n return G","def plot_blocked_nodes(self, node_list, visited_set):\n points = Marker()\n #visualizations points and lines..\n points.header.frame_id = \"map\"\n points.header.stamp = rospy.get_rostime()\n points.ns = \"markers\"\n points.id = 0\n points.type = points.POINTS\n points.action = points.ADD\n points.pose.orientation.w = 1.0\n points.scale.x = 2*self.rviz_tuning_plt\n points.scale.y = 2*self.rviz_tuning_plt\n points.color.r = 1.0\n points.color.g = 0.0\n points.color.b = 0.0\n points.color.a = 1.0\n points.lifetime = rospy.Duration()\n\n # Nodes blocked by obstacles\n for node in node_list:\n if node.cost == float('Inf'):\n p1 = Point()\n p1.x = node.x\n p1.y = node.y\n p1.z = 0.011\n points.points.append(p1)\n\n # Nodes blocked by planner\n for node in visited_set:\n p1 = Point()\n p1.x = node.x\n p1.y = node.y\n p1.z = 0.03\n points.points.append(p1)\n \n self.pub_blocked_nodes.publish(points)","def generate_graph(self, calculate_rewards=False):\n g = nx.Graph()\n positions = {}\n\n # create nodes for all hotspots in list\n for hotspot in self.hotspot_list:\n if calculate_rewards:\n hotspot['rewards'] = get_hotspot_rewards(hotspot['address'], 5)\n else:\n hotspot['rewards'] = 0\n coords = (hotspot['lng'], hotspot['lat'])\n g.add_node(hotspot['address'], pos=coords, lat=hotspot['lat'], lng=hotspot['lng'],\n location_hex=hotspot['location_hex'], name=hotspot['name'], rewards=hotspot['rewards'],\n gain=hotspot['gain'], elevation=hotspot['elevation'])\n positions[hotspot['address']] = coords\n\n # create edges representing hotspots that witness each other\n i = 0\n for hotspot in self.hotspot_list:\n witness_list = list_witnesses_for_hotspot(hotspot['address'])\n hotspot['num_witnesses'] = len(witness_list)\n g.nodes[hotspot['address']]['num_witnesses'] = hotspot['num_witnesses']\n for witness in witness_list:\n\n # make sure that all witnesses are also in this subset\n if not any(witness['address'] == h['address'] for h in self.hotspot_list):\n continue\n\n # calculate distance between witness and challengee\n dist = math.sqrt((hotspot['lat'] - witness['lat']) ** 2 + (hotspot['lng'] - witness['lng']) ** 2)\n g.add_edge(hotspot['address'], witness['address'], weight=dist)\n\n i += 1\n if i % 10 == 0:\n print(f\"{str(i)} out of {str(len(self.hotspot_list))} hotspots complete...\")\n\n self.g = g\n self.positions = positions\n return g","def check_users(users: Dict[int, Point], places: Dict[int, List[Point]], writer_coroutine):\n for user_id, user_point in users.items():\n writer_coroutine.send(Point(user_id, sum({check_point(user_point, place) for place in places.values()})))","def make_flow_graph(interaction_logs):\n G = pgv.AGraph(directed=True)\n\n for module_id in interaction_logs['module_id'].unique():\n G.add_node(module_id)\n\n E = defaultdict(set)\n grouped = interaction_logs.groupby('user_id')\n for user_id, group in grouped:\n module_ids_in_student_path = group['module_id']\n for source_node, target_node in zip(module_ids_in_student_path[:-1], module_ids_in_student_path[1:]):\n if source_node != target_node: # stationary\n E[(source_node, target_node)] |= {user_id}\n\n for (source_node, target_node), students_that_made_transition in E.iteritems():\n G.add_edge(\n source_node,\n target_node,\n weight=len(students_that_made_transition))\n\n return G","def make_nodes_and_paths(friends_lst):\n\n # nodes = {}\n\n # for item in friends_lst:\n # friend1, friend2, group = item\n # for person in pair:\n # if not nodes.get(person):\n # nodes[person] = pair[1]\n\n # nodes = [{'name': person, 'friend': nodes[person]} for person in nodes.keys()]\n\n nodes = {}\n for item in friends_lst:\n friend1, friend2, group = item\n if not nodes.get(friend1):\n nodes[friend1] = group\n elif nodes.get(friend1) > group:\n nodes[friend1] = group\n\n nodes = [{'name': person, 'group': nodes[person]} for person in nodes.keys()]\n\n index_nodes = {}\n for idx, n in enumerate(nodes):\n index_nodes[n['name']] = (idx, n['group'])\n\n paths = []\n\n # paths.append({'source': item[1], 'target': item[0]})\n\n for item in friends_lst:\n # one = User.query.get(item.user_id)\n # two = User.query.get(item.friend_id)\n source, target, group = item\n paths.append({'source': index_nodes[source][0], 'target': index_nodes[target][0]})\n\n # print nodes\n # print index_nodes\n # print paths\n\n return nodes, paths","def Nodelocation(self, Tract_pop, Tractx, Tracty, longitude, latitude, cnum):\n import annealsimulation\n \n self.latl, self.lonl = [], []\n \n while(len(self.latl) != self.nodenum):\n lat = np.random.randint(len(self.Geoy) - 1)\n lon = np.random.randint(len(self.Geox) - 1)\n if(lat not in self.latl or lon not in self.lonl):\n self.latl.append(lat)\n self.lonl.append(lon) \n \n self.latl, self.lonl = np.array(self.latl), np.array(self.lonl)\n \n self.demandlat, self.demandlon = self.latl[self.demandseries], self.lonl[self.demandseries]\n self.tranlat, self.tranlon = self.latl[self.transeries], self.lonl[self.transeries]\n self.supplylat, self.supplylon = self.latl[self.supplyseries], self.lonl[self.supplyseries]\n \n self.demandloc = np.stack((self.demandlat, self.demandlon)).transpose()\n self.tranloc = np.stack((self.tranlat, self.tranlon)).transpose()\n self.supplyloc = np.stack((self.supplylat, self.supplylon)).transpose()\n \n #Demand node\n Geox1 = sf.FeatureScaling(self.Geox)\n Geoy1 = sf.FeatureScaling(self.Geoy)\n Tract_pop1 = sf.FeatureScaling(Tract_pop)\n Tractx1 = sf.FeatureScaling(Tractx)\n Tracty1 = sf.FeatureScaling(Tracty)\n \n self.demandloc, self.demandc, self.popuassign = ans.anneal2(self.demandloc, 'Population', Geox1, Geoy1, Tract_pop1, Tractx1, Tracty1, Tract_pop, cnum)\n self.demandy1 = Geoy1[self.demandloc[:, 0]]\n self.demandx1 = Geox1[self.demandloc[:, 1]]\n self.demandy = self.Geoy[self.demandloc[:, 0]]\n self.demandx = self.Geox[self.demandloc[:, 1]]\n #Transmission node\n self.tranloc, self.tranc, temp = ans.anneal2(self.tranloc, 'Facility', Geox1, Geoy1, Tract_pop1, self.demandx1, self.demandy1, Tract_pop, cnum)\n self.trany1 = Geoy1[self.tranloc[:, 0]]\n self.tranx1 = Geox1[self.tranloc[:, 1]]\n self.trany = self.Geoy[self.tranloc[:, 0]]\n self.tranx = self.Geox[self.tranloc[:, 1]]\n\n #Supply node\n self.supplyloc, self.supplyc, temp = ans.anneal2(self.supplyloc, 'Facility', Geox1, Geoy1, Tract_pop1, self.tranx1, self.trany1, Tract_pop, cnum)\n self.supplyy1 = Geoy1[self.supplyloc[:, 0]]\n self.supplyx1 = Geox1[self.supplyloc[:, 1]] \n self.supplyy = self.Geoy[self.supplyloc[:, 0]]\n self.supplyx = self.Geox[self.supplyloc[:, 1]]\n \n ##Coordinates of nodes\n self.y = np.concatenate((self.supplyy, self.trany, self.demandy))\n self.x = np.concatenate((self.supplyx, self.tranx, self.demandx))\n \n ##Latitudes and longitudes of nodes\n self.demandlatitude, self.demandlongitude = latitude[self.demandloc[:, 0]], longitude[self.demandloc[:, 1]]\n self.tranlatitude, self.tranlongitude = latitude[self.tranloc[:, 0]], longitude[self.tranloc[:, 1]]\n self.supplylatitude, self.supplylongitude = latitude[self.supplyloc[:, 0]], longitude[self.supplyloc[:, 1]]\n \n self.latitude = np.concatenate((self.supplylatitude, self.tranlatitude, self.demandlatitude))\n self.longitude = np.concatenate((self.supplylongitude, self.tranlongitude, self.demandlongitude))","def plot_graph(self) -> None:\n\n nodes_on_graph = self.dw_graph.get_all_v()\n for k, v in nodes_on_graph.items():\n if v.position is None:\n x_rand = random.uniform(0.5, self.dw_graph.v_size())\n y_rand = random.uniform(0.5, self.dw_graph.v_size())\n v.position = (x_rand, y_rand)\n x_vals = []\n y_vals = []\n n = list(nodes_on_graph.keys())\n for k, v in nodes_on_graph.items(): # draw nodes\n x_vals.append(v.position[0])\n y_vals.append(v.position[1])\n\n fig, ax = plt.subplots()\n plt.plot(x_vals, y_vals, 'ro', markersize=5, data=\"d\")\n\n for p, txt in enumerate(n):\n ax.annotate(n[p], (x_vals[p]+0.00003, y_vals[p]), color='g')\n\n for n in nodes_on_graph:\n n1 = self.dw_graph.get_nodes(n)\n x = n1.position[0]\n y = n1.position[1]\n for r in self.dw_graph.all_out_edges_of_node(n):\n dx = self.dw_graph.get_nodes(r).position[0]\n dy = self.dw_graph.get_nodes(r).position[1]\n ax.quiver(x, y, dx-x, dy-y, angles='xy', scale_units='xy', scale=1)\n #plt.arrow(x, y, dx - x, dy - y, head_width=0.0009, width=0.00005, length_includes_head=True)\n\n\n plt.xlabel(\"x axis \")\n plt.ylabel(\"y axis \")\n plt.title(\"The title of the graph\")\n plt.show()","def test_activity_flag(self, example_staypoints):\n # take out staypoint 6 that should have been merged with 2, 15\n sp = example_staypoints\n data = [True, True, True, True, False, True, True, True]\n idx = [1, 2, 3, 5, 6, 7, 15, 80]\n activities = pd.Series(data, index=idx)\n sp[\"activity\"] = activities\n sp, _ = sp.as_staypoints.generate_locations(\n method=\"dbscan\",\n epsilon=10,\n num_samples=2,\n distance_metric=\"haversine\",\n agg_level=\"user\",\n activities_only=True,\n )\n assert sp.loc[1, \"location_id\"] == sp.loc[15, \"location_id\"]\n assert sp.loc[2, \"location_id\"] is pd.NA","def generate(self):\n self.generate_points()\n self.generate_edges()","def two_user_route_statistics(i,j, source_data, destination_data, source_destination_data, delta=1.2):\n\toccupancy_ratio = 0.0\n\tminimum_distance_so_far = 0.0\n\tcommon_travel_distance = 0.0\n\n\ttry:\n\t\tif source_destination_data[j][i] + source_data[i][j] <= 1.2*source_destination_data[i][i] and source_destination_data[j][i] + destination_data[i][j] <= 1.2*source_destination_data[j][j]:\n\t\t\tfirst = ((source_destination_data[j][i] + source_data[i][j])/(source_destination_data[j][i] + source_data[i][j]+destination_data[i][j]))\n\t\t\tsecond = ((source_destination_data[j][i] + destination_data[i][j])/(source_destination_data[j][i] + source_data[i][j]+destination_data[i][j]))\n\t\t\toccupancy_ratio = (first+second)/2\n\t\t\tcommon_travel_distance = source_destination_data[j][i]\n\t\t\tminimum_distance_so_far = source_data[i][j] + source_destination_data[j][i] + destination_data[i][j]\n\n\t\tif source_destination_data[i][j] + destination_data[j][i] <= 1.2*source_destination_data[i][i] and source_destination_data[i][j] + source_data[j][i] <= 1.2*source_destination_data[j][j]:\n\t\t\tfirst = ((source_destination_data[i][j] + destination_data[j][i])/(source_destination_data[i][j] + destination_data[j][i]+source_data[j][i]))\t\t\n\t\t\tsecond = ((source_destination_data[i][j] + source_data[j][i])/(source_destination_data[i][j] + destination_data[j][i]+source_data[j][i]))\n\t\t\ttotal_distance = source_data[j][i] + source_destination_data[i][j] + destination_data[j][i]\n\n\t\t\tif total_distance < minimum_distance_so_far:\n\t\t\t\tminimum_distance_so_far = total_distance\n\t\t\t\tcommon_travel_distance = source_destination_data[i][j]\n\t\t\t\toccupancy_ratio = (first+second)/2\n\n\t\tif source_data[i][j]+source_destination_data[j][j]+destination_data[j][i] <= 1.2*source_destination_data[i][i]:\n\t\t\tfirst = (1)\n\t\t\tsecond = (source_destination_data[j][j]/(source_data[i][j]+source_destination_data[j][j]+destination_data[j][i]))\n\n\t\t\ttotal_distance = source_data[i][j] + source_destination_data[j][j] + destination_data[j][i]\n\n\t\t\tif total_distance < minimum_distance_so_far:\n\t\t\t\tminimum_distance_so_far = total_distance\n\t\t\t\tcommon_travel_distance = source_destination_data[j][j]\n\t\t\t\toccupancy_ratio = (first+second)/2\n\n\t\tif source_data[j][i]+source_destination_data[i][i]+destination_data[i][j] <= 1.2*source_destination_data[j][j]:\n\t\t\tfirst = (source_destination_data[i][i]/(source_data[j][i]+source_destination_data[i][i]+destination_data[i][j]))\n\t\t\tsecond = (1)\n\n\t\t\ttotal_distance = source_data[j][i]+source_destination_data[i][i]+destination_data[i][j]\n\n\t\t\tif total_distance < minimum_distance_so_far:\n\t\t\t\tminimum_distance_so_far = total_distance\n\t\t\t\tcommon_travel_distance = source_destination_data[i][i]\n\t\t\t\toccupancy_ratio = (first+second)/2\n\n\texcept Exception as e:\n\t\toccupancy_ratio = 1.0\n\t\tminimum_distance_so_far = 0.0\n\t\tcommon_travel_distance = 0.0\n\n\n\treturn occupancy_ratio, common_travel_distance, minimum_distance_so_far","def fake_destination_data():\n\n for _ in range(0, 35):\n user_id = random.randint(1, 8)\n\n print (str(user_id) + '|' + fake.company() + '|' + fake.street_address()\n + '|' + fake.city() + '|' + fake.state_abbr() + '|' +\n fake.postalcode())","def test_empty_generation(self, example_staypoints):\n # the pfs would not generate staypoints with the default parameters\n sp = example_staypoints\n # select subset of sp such that no locations can be generated\n sp = sp.iloc[:3]\n\n warn_string = \"No locations can be generated, returning empty locs.\"\n with pytest.warns(UserWarning, match=warn_string):\n sp, locs = sp.as_staypoints.generate_locations(\n method=\"dbscan\", epsilon=10, num_samples=2, distance_metric=\"haversine\", agg_level=\"user\"\n )\n assert len(locs) == 0","def graph(self):\n ...","def generate_coordinate():\n #TODO debug dat fucking code\n if not check_content_type():\n return jsonify(status=CONTENT_TYPE_ERROR)\n reqdata = request.json\n try:\n if not check_token(reqdata[\"token\"]):\n return jsonify(status=TOKEN_ERROR)\n except:\n return jsonify(status=REQ_ERROR)\n path_id = reqdata[\"path_id\"]\n path = db.session.query(Path).get_or_404(path_id)\n coordinate_data = []\n coordinate_data.append(path.start_coordinate)\n users_paths = path.users\n for e in users_paths:\n coordinate_data.append(e.coordinate)\n \n return jsonify(data=coordinate_data, status=OK_STATUS)\n\n #coordinate = (path.start_coordinate).split(\",\")\n #coordinate = (float(coordinate[0]), float(coordinate[1]))\n #coordinate_data.append(coordinate)\n #for user_path in path.users:\n # coordinate = (user_path.coordinate).split(\",\")\n # coordinate_data.append((float(coordinate[0]), float(coordinate[1])))\n #coordinate = (path.start_coordinate).split(\",\")\n #coordinate = (float(coordinate[0]), float(coordinate[1]))\n #try:\n # final_coordinates = calc_path(coordinate, coordinate_data)\n # final_str = \"\"\n # for coordinate in final_coordinates:\n # x, y = coordinate\n # final_str += str(x) + \",\" + str(y) + \"|\"\n # final_str = final_str[:-1]\n # return jsonify(data=final_str, status=OK_STATUS)\n #except Exception as err:\n # print(err)\n # return jsonify(status=REQ_ERROR)","def create_data():\n # Locations\n data = {}\n num_vehicles = 20\n depot = 0\n locations = loc1\n demands = popn\n\n num_locations = len(locations)\n dist_matrix = {}\n\n for from_node in range(0,num_locations):\n dist_matrix[from_node] = {}\n\n for to_node in range(0,num_locations):\n dist_matrix[from_node][to_node] = (\n haversine(\n locations[from_node],[to_node])\n #locations[to_node],[from_node])\n \"\"\"\n data[\"distances\"] =dist_matrix\n data[\"num_locations\"] = len(dist_matrix)\n data[\"num_vehicles\"] = 6\n data[\"depot\"] = 0\n data[\"demands\"] = demands\n #data[\"vehicle_capacities\"] = capacities\n data[\"time_per_demand_unit\"] = 0.05\n return data\n \"\"\"\n return [ num_vehicles, depot, locations, dist_matrix]","def get_all_social_paths(self, user_id):\n visited = {} # Note that this is a dictionary, not a set\n # !!!! IMPLEMENT ME\n # BFTs starting at user_id, return first path to every reachable person\n q = [[user_id]]\n while q:\n path = q.pop(0)\n person = path[-1]\n # add the person and the path to the person\n for friend in self.friendships[person]:\n if friend not in visited and friend != user_id:\n q.append(path + [friend])\n visited[friend] = path + [friend]\n\n return visited","def generate_weighted_users_traces(self):\n def generate_weighted_user_trace():\n towers_ids = np.arange(self.number_towers)\n\n trace = []\n direction = []\n for cycle in range(self.number_cycles):\n if cycle == 0:\n # For the first towers the chance of selecting a tower is equally distributed\n tower = np.random.choice(towers_ids)\n trace.append(tower)\n direction.append(self.towers[tower])\n elif cycle == 1:\n last_tower = trace[cycle - 1]\n tower = np.random.choice(\n towers_ids, p=self.probabilities[last_tower])\n trace.append(tower)\n direction.append(self.towers[tower])\n else:\n new_point = self.towers_manager.get_new_point(direction)\n nearest_tower = \\\n self.towers_manager.get_nearest_tower(new_point)\n tower = np.random.choice(\n towers_ids, p=self.probabilities[nearest_tower])\n trace.append(tower)\n direction = [direction[1], self.towers[tower]]\n\n return trace\n\n return np.array([\n generate_weighted_user_trace()\n for _ in range(self.number_users)\n ])","def getAllSocialPaths(self, userID):\n visited = {}\n # use a queue\n q = []\n q.append([userID])\n # add userID as its own key and value to visited\n visited[userID] = [userID]\n\n while len(q) > 0:\n path = q.pop(0)\n curr_friend = path[-1]\n\n # for all the userID keys inside self.friendships\n for friend in self.friendships[curr_friend]:\n # add neighbor as a key, if not visited, in visited with an empty list as value\n if friend not in visited:\n visited[friend] = list()\n # break out of loop if already in visited\n else: \n continue\n \n # create a new list that holds the path from userID to friend\n friend_path = list(path)\n # add the friend onto the end of the list\n friend_path.append(friend)\n # also add path to the queue\n q.append(friend_path) \n # add path as the value to the friend\n visited[friend].extend(friend_path)\n \n return visited","def create_graph(self):\n robot_pix = int(math.ceil(self.robot.size / self.resolution))\n ii = 0\n jj = 0\n for i in range(0, self.height, robot_pix):\n jj = 0\n for j in range(0, self.width, robot_pix):\n block = self.occ_grid[i:i+robot_pix, j:j+robot_pix].flatten()\n avg = np.mean(block)\n robot_block = self.tesselation_image[i:i+robot_pix, j:j+robot_pix].flatten()\n n_occur = np.bincount(robot_block)\n block_id = np.argmax(n_occur)\n \n p = Pose()\n p.position.x = self.resolution * j + self.resolution / 2.0 + self.origin.position.x\n p.position.y = self.height * self.resolution - (self.resolution * i + self.resolution / 2.0) + self.origin.position.y\n node = Node(ii, jj, p)\n idx = np.where(block > 20)\n if block_id == self.robot.robot_id:\n if 0 <= avg <= 20:\n print(\"Node in path\", node)\n node.valid = True\n else:\n node.valid = False\n elif block_id == 0:\n node.valid = False\n else:\n node.belongs = False\n self.nodes[ii,jj] = node\n jj += 1\n ii += 1\n\n\n height, width = self.nodes.shape\n print(\"Node shape: \", self.nodes.shape)\n for i in range(height):\n for j in range(width):\n min_i = max(0, i-1)\n max_i = min(height - 1, i+1) + 1\n min_j = max(0, j-1)\n max_j = min(width - 1, j+1) + 1\n\n node = self.nodes[i,j]\n neighbors = self.nodes[min_i:max_i, min_j:max_j].flatten()\n for n in neighbors:\n if not n or not node:\n print(\"None %d-%d\"%(i,j))\n continue\n if n != node:\n if n.valid:\n print(\"Neighbor appended\")\n self.nodes[i,j].neighbors.append(n)\n else:\n self.nodes[i,j].obstacle_neighbors.append(n)\n print(\"Graph is created!\")","def make_upa_graph(nodes, out_degree):\n graph = go.make_complete_graph(out_degree)\n trial = upa.UPATrial(out_degree)\n for new_node in range(out_degree, nodes):\n neighbors = trial.run_trial(out_degree)\n graph[new_node] = neighbors\n for neighbor in neighbors:\n graph[neighbor].add(new_node)\n return graph","def users_nearby(self, meters):\n location = Location.objects.get(id=self.most_recent_location_id)\n lng = location.position['coordinates'][0]\n lat = location.position['coordinates'][1]\n\n nearby_locations = Location.objects(position__near=[lng, lat], position__max_distance=meters)\n\n nearby_user_ids = []\n\n for loc in nearby_locations:\n nearby_user_ids.append(loc.uid)\n\n return SallasanaUser.objects.filter(id__in=nearby_user_ids)","def by_user(cls, *args, **kwargs):\n # Almost every user of UasTelemetry.by_user wants to use\n # the related AerialPosition and GpsPosition. To avoid excessive\n # database queries, we select these values from the database up front.\n return super(UasTelemetry, cls).by_user(*args, **kwargs) \\\n .select_related('uas_position__gps_position')","def test_parallel_computing(self, example_staypoints):\n sp = example_staypoints\n\n # without parallel computing code\n sp_ori, locs_ori = sp.as_staypoints.generate_locations(\n method=\"dbscan\", epsilon=10, num_samples=2, distance_metric=\"haversine\", agg_level=\"user\", n_jobs=1\n )\n # using two cores\n sp_para, locs_para = sp.as_staypoints.generate_locations(\n method=\"dbscan\", epsilon=10, num_samples=2, distance_metric=\"haversine\", agg_level=\"user\", n_jobs=2\n )\n\n # the result of parallel computing should be identical\n assert_geodataframe_equal(locs_ori, locs_para)\n assert_geodataframe_equal(sp_ori, sp_para)","def generate_graph(self):\n temp_graph = [[] for i in xrange(Parameters.num_peers)]\n unconnected = set([i for i in xrange(Parameters.num_peers)])\n while len(unconnected) > 1:\n node1 = random.sample(unconnected, 1)[0]\n unconnected.remove(node1)\n node2 = random.sample(unconnected, 1)[0]\n temp_graph[node2].append(self.nodes[node1])\n temp_graph[node1].append(self.nodes[node2])\n unconnected = set([i for i in xrange(Parameters.num_peers)])\n i = 0\n for i in xrange(Parameters.num_peers*Parameters.num_neighbours/2-Parameters.num_peers):\n a = random.sample(unconnected, 1)[0]\n b = random.sample(unconnected, 1)[0]\n while b == a:\n b = random.sample(unconnected, 1)[0]\n temp_graph[a].append(self.nodes[b])\n temp_graph[b].append(self.nodes[a])\n graph = {}\n for i in xrange(len(self.nodes)):\n graph[\"P_\" + str(i)] = list(set(temp_graph[i]))\n return graph","def save_friend_nodes(self):\n print \"Exporting to file tsv ...\"\n count_edge = 0\n count_node = 0\n with open('../data/yelp.tsv','w') as f:\n for user in self.df['user_id']:\n for friends in self.df['friends']:\n count_node += 1\n for friend in friends:\n f.write(\"%s\\t%s\\n\" % (user, friend))\n count_edge += 1\n print \"Graph Summary:\", count_node, \"nodes,\", count_edge, \"edges.\"","def get_clusters(self, base_user, user_friends_graph, method=\"\") -> Dict: # TODO:\n if method==\"modularity\":\n community_data = greedy_modularity_communities(user_friends_graph)\n else:\n community_data = [item for item in label_propagation_communities(user_friends_graph)]\n d = {}\n for i in range(len(community_data)):\n if base_user in community_data[i]:\n li = list(community_data[i])\n li.pop(li.index(base_user))\n li.insert(0, base_user)\n d[i] = li\n else:\n d[i] = [base_user] + list(community_data[i])\n\n # Flatten dict into cluster list\n cluster_list = [d[k] for k in d]\n print(\"Number of clusters \" + str(len(cluster_list)))\n\n return cluster_list","def data_graph():\n station_reference = request.args.get(\"stationReference\")\n station_name = request.args.get(\"stationName\")\n station_name = station_name.replace(\" \",\"+\")\n\n if station_name is not None:\n # station_data = station_data.replace(\" \", \"+\")\n station = station_data.loc[station_data.stationName == station_name]\n else:\n station = station_data.loc[station_data.stationReference == station_reference]\n result_station = station.iloc[0]\n\n # Get optional parameters\n time_from = request.args.get(\"from\")\n time_to = request.args.get(\"to\")\n if time_from:\n pass\n else:\n time_from = None\n if time_to:\n pass\n else:\n time_to = None\n # plot pic\n magic_trick= data.station_graph(result_station.stationName, time_from, time_to)\n # img_stream = io.BytesIO(img)\n # img = Image.open(img_stream)\n # imgByteArr = io.BytesIO()\n # img.save(imgByteArr,format='PNG')\n # imgByteArr = imgByteArr.getvalue()\n # return send_file(io.BytesIO(imgByteArr),\n # mimetype = 'image/png',\n # as_attachment = True,\n # attachment_filename = 'tmp.png')\n image_data = open(\"tmp.png\", \"rb\").read()\n response = make_response(image_data)\n response.headers['Content-Type'] = 'image/png'\n return response","def test_num_samples_high(self):\n sp_file = os.path.join(\"tests\", \"data\", \"geolife\", \"geolife_staypoints.csv\")\n sp = ti.read_staypoints_csv(sp_file, tz=\"utc\", index_col=\"id\", crs=\"epsg:4326\")\n sp_ns_5, _ = sp.as_staypoints.generate_locations(\n epsilon=50, distance_metric=\"haversine\", agg_level=\"user\", num_samples=2\n )\n non_noise_sp = sp_ns_5[sp_ns_5[\"location_id\"] != -1]\n\n # group_by_user_id and check that no two different user ids share a common location id\n grouped = list(non_noise_sp.groupby([\"user_id\"])[\"location_id\"].unique())\n loc_set = []\n for loc_list in grouped:\n loc_set.append(set(loc_list))\n\n # we assert that the count of overlaps is equal to the count of users\n # (each user has overlap with the same user)\n assert sum([int(len(p & q) > 0) for p in loc_set for q in loc_set]) == len(loc_set)","def read_file(network_filename, user_by_city_filename=None):\n graph = read_dictlist_from_file(network_filename)\n\n gg = Graph(directed=False) # new Graph object\n\n user_id_map = {} # storing new id info\n new_id = 0\n for user_id in graph:\n temp_users = []\n temp_users.append(user_id)\n for friend in graph[user_id]:\n temp_users.append(friend)\n for id1 in temp_users:\n if id1 not in user_id_map:\n user_id_map[id1] = new_id\n gg.add_vertex() # index for this vertex will be new_id\n new_id += 1\n if id1 > user_id:\n gg.add_edge(gg.vertex(user_id_map[user_id]),\n gg.vertex(user_id_map[id1]))\n print \"Done reading the graph.\"\n if user_by_city_filename is None:\n return (gg, None)\n if user_by_city_filename is not None:\n cities = read_dict_from_file(user_by_city_filename)\n # Adding vertex property as city\n city_prop = gg.new_vertex_property(\"int\")\n for user_id in cities:\n city_prop[gg.vertex(user_id_map[user_id])] = cities[user_id]\n print \"Done reading the city.\"\n return (gg, city_prop)","def do_graph( self, db_device_adapters, ts_begin, ts_end ):\n# self.graph_4( db_device_adapter, ts_begin, ts_end, min_points = 10 )\n# self.graph_many( db_device_adapters[0], ts_begin, ts_end, min_points = 10 )\n self.graph_many_really( db_device_adapters, ts_begin, ts_end, min_points = 10 )","def create_directly_follows_graph(log):\n dfg = dfg_discovery.apply(log)\n dfg = dict(dfg)\n return dfg","def get_locations(nodes, tl, br):\n \n # Base cases:\n if len(nodes) == 1: # for singleton, only choice is to place in the single spot in 1x1 square\n return {nodes[0]: tl}\n if len(nodes) == 2: # for two nodes, arbitrarily chose to place the first node in top left\n return {nodes[0]: tl, nodes[1]: br}\n\n # Recursive case, need to create and solve subproblems:\n ret = {}\n\n num_edges = count_num_edges(nodes)\n if num_edges == 0: # for empty graphs, no need to run METIS, just assign arbitrarily\n i = 0\n for x in range(tl.x, br.x+1): \n for y in range(tl.y, br.y+1):\n if i < len(nodes):\n ret.update({nodes[i]: Point(x,y)})\n i += 1\n return ret\n\n filename = splitext(basename(sys.argv[1]))[0] + '.p.' + sys.argv[2] + '.yx.' + sys.argv[3] + '.drop.' + sys.argv[4] + '.' +\\\n '_'.join(['delete', str(tl.x), str(tl.y), str(br.x), str(br.y)]) \n\n # special case for the very first call of get_locations. For example, suppose that there are\n # 97 nodes on a 10x10 grid. Instead of dividing the 97 nodes into 2 equal partitions, we should\n # divide them into a partition of 90 nodes and a partition of 7 nodes. The former should be\n # placed on a 10x9 grid and te latter should be placed on a 1x7 grid.\n if len(nodes) < (br.x - tl.x + 1) * (br.y - tl.y + 1):\n assert tl == Point(0, 0)\n size_tl_nodes = (br.x + 1) * int(len(nodes) / (br.x + 1))\n if size_tl_nodes == len(nodes):\n ret.update(get_locations(nodes, tl=Point(0, 0), br=Point(br.x, len(nodes) / (br.x + 1) - 1)))\n return ret\n\n nodes_tl, nodes_br = partition(nodes, size_tl_nodes, filename)\n # complicated indexing here. As an example, for the 97 into 10x10 case, we want to send 90 nodes\n # to a rectangle spanned by tl=Point(0, 0) and br=Point(9, 8) and we want to send 7 nodes to a \n # rectangle spanned by tl=Point(0, 9) and br=Point(6, 9)\n ret.update(get_locations(nodes_tl, tl=Point(0, 0), br=Point(br.x, len(nodes) / (br.x + 1) - 1)))\n ret.update(get_locations(nodes_br, tl=Point(0, len(nodes) / (br.x + 1)), br=Point(len(nodes) % (br.x + 1) - 1, len(nodes) / (br.x + 1))))\n return ret\n\n if br.x - tl.x > br.y - tl.y: # if rectangle is wider than tall, split on y axis\n half = tl.x + (br.x - tl.x - 1) / 2\n size_tl_nodes = (half - tl.x + 1) * (br.y - tl.y + 1)\n else: # split on x axis\n half = tl.y + (br.y - tl.y - 1) / 2\n size_tl_nodes = (br.x - tl.x + 1) * (half - tl.y + 1)\n\n nodes_tl, nodes_br = partition(nodes, size_tl_nodes, filename)\n\n if br.x - tl.x > br.y - tl.y: # if rectangle is wider than tall, split on y axis\n ret.update(get_locations(nodes_tl, tl=tl, br=Point(half, br.y)))\n ret.update(get_locations(nodes_br, tl=Point(half + 1,tl.y), br=br))\n else: # split on x axis\n ret.update(get_locations(nodes_tl, tl=tl, br=Point(br.x, half)))\n ret.update(get_locations(nodes_br, tl=Point(tl.x, half + 1), br=br))\n\n return ret","def test_location_ids_and_noise_dataset(self, example_staypoints):\n sp = example_staypoints\n sp2, _ = sp.as_staypoints.generate_locations(\n method=\"dbscan\", epsilon=10, num_samples=2, distance_metric=\"haversine\", agg_level=\"dataset\"\n )\n\n assert (sp2.loc[[5, 6, 80, 3], \"location_id\"] == sp2.loc[5, \"location_id\"]).all()\n assert sp2.loc[1, \"location_id\"] == sp2.loc[15, \"location_id\"]\n assert sp2.loc[1, \"location_id\"] != sp2.loc[5, \"location_id\"]\n\n assert sp2.loc[[2, 7], \"location_id\"].isnull().all()","def build_graph(self):\n pass","def chunk_user_route(detail_of_trip):\n\n # $$$$$$$$$$$$$$$$$$$$$$$$$$$$$$$$$$$$$$$$$$$$$$$$$$$$$$$$$$$$$$$$$$$$$$$$\n # since I can't get javascript to load, here's a hacky way of loading json\n # that details the route based on the user's point A and point B\n # detail_of_trip = api.directions(\n # (40.760350, -73.976209),\n # (40.754009, -73.981097),\n # mode=\"walking\"\n # )[0]\n # $$$$$$$$$$$$$$$$$$$$$$$$$$$$$$$$$$$$$$$$$$$$$$$$$$$$$$$$$$$$$$$$$$$$$$$$\n\n # now that I have javascript sending over the json, load json that details\n # the route based on the user's point A and point B\n\n # -------------- This section is for interpolation/splitting using shapely\n first = True # to see if this is the start position for the entire route\n line_points = [] # stores all the points to the route based on dict passed\n\n for leg in detail_of_trip['legs']:\n for step in leg['steps']:\n # Create a list of two element lists that represent points along the\n # route. via google. line_points = [ [lat1, lng1], [lat2, lng2],...]\n # Only add the starting point the first time. Every other iteration\n # we will just tack on the end points to our line.\n if first:\n line_points.append([step['start_location']['lat'], step['start_location']['lng']])\n first = False\n line_points.append([step['end_location']['lat'], step['end_location']['lng']])\n\n # Now load those points into a geometry, here shapely's LineString type.\n route_line = LineString(line_points)\n return (route_line, line_points)","def generate_points(self, userdata):\n # TODO: generate along multiple axes\n x, y, _ = userdata.initial_point\n x, y = 0, 0\n\n i = 0\n for _ in range(self.num_points):\n point = PointStamped()\n point.header.frame_id = \"map\"\n point.point.x = x + i * self.spacing\n point.point.y = y\n point.point.z = 0.0\n\n i += 1\n\n yield point","def generate_receivers(site_area, simulation_parameters, grid):\n receivers = []\n\n if grid == 1:\n\n geom = shape(site_area[0]['geometry'])\n geom_box = geom.bounds\n\n minx = geom_box[0]\n miny = geom_box[1]\n maxx = geom_box[2]\n maxy = geom_box[3]\n\n id_number = 0\n\n x_axis = np.linspace(\n minx, maxx, num=(\n int(math.sqrt(geom.area) / (math.sqrt(geom.area)/20))\n )\n )\n y_axis = np.linspace(\n miny, maxy, num=(\n int(math.sqrt(geom.area) / (math.sqrt(geom.area)/20))\n )\n )\n\n xv, yv = np.meshgrid(x_axis, y_axis, sparse=False, indexing='ij')\n for i in range(len(x_axis)):\n for j in range(len(y_axis)):\n receiver = Point((xv[i,j], yv[i,j]))\n indoor_outdoor_probability = np.random.rand(1,1)[0][0]\n if geom.contains(receiver):\n receivers.append({\n 'type': \"Feature\",\n 'geometry': {\n \"type\": \"Point\",\n \"coordinates\": [xv[i,j], yv[i,j]],\n },\n 'properties': {\n 'ue_id': \"id_{}\".format(id_number),\n \"misc_losses\": simulation_parameters['rx_misc_losses'],\n \"gain\": simulation_parameters['rx_gain'],\n \"losses\": simulation_parameters['rx_losses'],\n \"ue_height\": float(simulation_parameters['rx_height']),\n \"indoor\": (True if float(indoor_outdoor_probability) < \\\n float(0.5) else False),\n }\n })\n id_number += 1\n\n else:\n pass\n\n else:\n\n centroid = shape(site_area[0]['geometry']).centroid\n\n coord = site_area[0]['geometry']['coordinates'][0][0]\n path = LineString([(coord), (centroid)])\n length = int(path.length)\n increment = int(length / 20)\n\n indoor = simulation_parameters['indoor_users_percentage'] / 100\n\n id_number = 0\n for increment_value in range(1, 11):\n point = path.interpolate(increment * increment_value)\n indoor_outdoor_probability = np.random.rand(1,1)[0][0]\n receivers.append({\n 'type': \"Feature\",\n 'geometry': mapping(point),\n 'properties': {\n 'ue_id': \"id_{}\".format(id_number),\n \"misc_losses\": simulation_parameters['rx_misc_losses'],\n \"gain\": simulation_parameters['rx_gain'],\n \"losses\": simulation_parameters['rx_losses'],\n \"ue_height\": float(simulation_parameters['rx_height']),\n \"indoor\": (True if float(indoor_outdoor_probability) < \\\n float(indoor) else False),\n }\n })\n id_number += 1\n\n return receivers","def _CreateGraph(self):\n self.nodes = []\n self.edges = []\n for i, r in self.airports.set_index('airport_id').iterrows():\n self.nodes.append((i,r.to_dict()))\n for i, r in self.routes.set_index(['src_id','dst_id']).iterrows():\n self.edges.append((i[0],i[1],r.to_dict()))\n # print('node ex: {}'.format(self.nodes[0]))\n # print('edge ex: {}'.format(self.edges[0]))\n\n self.graph = self._CreateAdjacencyListGraph()","def graph_2hop(profile, recom, filename):\n g = Graph()\n\n # ! ids are raw -> strings\n clickedItems = set(map(lambda x: str(x[0]), profile)) # set of clicked items\n recomItems = set() # set of recommended items\n\n # get recommended items\n for click in range(0, len(profile)): # for all the clicks\n for rec in recom[click]: # for the topN recommendations\n recomItems.add(str(rec[0]))\n\n # write clicked item-nodes in an outter ring\n angleStep = 2*np.pi / float(len(clickedItems)) # polar coordinates angle step\n angle = 0 # polar coordinates angle [0, 2pi]\n R = 1000 # outter\n for item in clickedItems: # for all the clicks\n target = str(item)\n g.add_node(target)\n g.nodes[target]['color'] = [255,0,0,1] # RGBA format\n g.nodes[target]['pos'] = [R * np.cos(angle), R * np.sin(angle)]\n g.nodes[target]['text'] = target\n\n angle += angleStep\n \n # write the rest item-nodes in an inner ring\n angleStep = 2*np.pi / float(len(recomItems - clickedItems)) # polar coordinates angle step\n angle = 0 # polar coordinates angle [0, 2pi]\n R = 600 # outter\n for item in recomItems - clickedItems: # for the rest of the items\n target = str(item)\n g.add_node(target)\n g.nodes[target]['color'] = [0,0,255,1] # RGBA format\n g.nodes[target]['pos'] = [R * np.cos(angle), R * np.sin(angle)]\n g.nodes[target]['text'] = target\n\n angle += angleStep\n \n # construct edges\n edges = {} # dictionary: (source_iid, target_iid) -> Vertex object\n weight_prop = g.new_edge_property('float')\n \n for click in range(0, len(profile)): # for all the clicks\n for rec in recom[click]: # for the topN recommendations\n target= str(rec[0])\n source = str(profile[click][0])\n weight = rec[1]\n\n g.add_edge(source, target)\n g.edges(source, target)['weight'] = weight\n \n return g","def check_visited_places(userid: int, day_range: int):\n print(f\"Checking visited places by user {userid} in the last {day_range} days\")\n # get reservations in which user actually showed up from Reservation service\n range = datetime.now() - timedelta(days=day_range)\n range.replace(hour=0, minute=0, second=0, microsecond=0)\n\n reservations = Reservation.query.filter_by(user_id=userid).\\\n filter(Reservation.entrance_time != None).filter(Reservation.entrance_time >= range).all()\n # print(\"DB\", db)\n # also all results must be json serializable\n return [row.to_dict() for row in reservations]","def _build_graph(self):\n pass","def gen_graph():\n if config_pagination:\n gdata = tgraph.call_graph(offset=offset, limit=limit)\n else:\n gdata = tgraph.call_graph(start=start, end=end, contineous=contineous)\n\n for data in gdata:\n yield data","def get_time_slots_map_for_user(start_time, end_time, user):\n\n time_slots_map = TimeSlotsMap(start_time=start_time, end_time=end_time)\n\n user_query = Query()\n schedules = db.search(user_query.user == user)\n\n schedules.sort(key=lambda x:x['created_time'])\n\n for sc in schedules:\n day_sc_map = TimeHourSlotsInOneDay(initial_value=str(sc[\"available_hours_in_a_day\"]))\n\n sc_start_time = parse_datetime_str(sc.get(\"start_time\"))\n sc_end_time = parse_datetime_str(sc.get(\"end_time\"))\n\n time_slots_map.overlay_slots_map(time_slots_map=day_sc_map,start_time=sc_start_time,\n end_time=sc_end_time, repeat=True)\n\n return time_slots_map","def query_six(self, table_name_activities, table_name_trackpoints):\n\n query = (\n \"SELECT t1.user_id, t1.lat, t1.lon, t2.user_id, t2.lat, t2.lon \"\n \"FROM (SELECT user_id, lat, lon, date_time FROM %s inner join %s on Activity.id=TrackPoint.activity_id) as t1, \"\n \"(SELECT user_id, lat, lon, date_time FROM Activity inner join TrackPoint on Activity.id=TrackPoint.activity_id) as t2 \"\n \"where t1.user_id != t2.user_id \"\n \"AND ABS(TIMESTAMPDIFF(SECOND,t1.date_time, t2.date_time)) <= 60\"\n )\n\n self.cursor.execute(\n query % (table_name_activities, table_name_trackpoints))\n rows = self.cursor.fetchall()\n print(tabulate(rows, headers=self.cursor.column_names))\n\n user_dict = dict()\n for row in rows:\n if haversine((row[1], row[2]), (row[4], row[5]), unit=\"km\") <= 0.1:\n if row[0] in user_dict:\n user_dict[row[0]].append(row[3])\n else:\n user_dict[row[0]] = [row[3]]\n users = 0\n for value in users_dict.values():\n users += len(value)\n\n users = users / 2\n\n print(users)\n return users","def create_path(self):\n\n partials = []\n partials.append({})\n #print self.trip_id\n\n #this variable is true if we have not yet recorded the first edge of a path\n first_edge = True\n #this variable is false until we hit the midpoint\n hit_midpoint = False\n\n first_lasts = []\n first_lasts.append([0,0])\n matrices = []\n matrices.append([np.zeros((self.graph.rows,self.graph.cols)),0])\n edge_sets = []\n edge_sets.append([0 for i in range(self.graph.num_edges)])\n cur_line = self.line_num\n good_graphs = []\n good_graphs.append(True)\n nodes_visited = []\n nodes_visited.append([])\n #normalized = dg.normalize(self.graph.lines[cur_line])\n normalized = normalize_simple(self.graph.lines[cur_line])\n matrices_index = 0\n prev_coords = (-1,-1)\n while normalized[0] == self.trip_id:\n lat = normalized[1]\n lon = normalized[2]\n coords = self.graph.gps_to_coords(lat,lon)\n node = self.graph.coords_to_node(coords[0],coords[1])\n\n if prev_coords == (-1,-1) and coords[0] != -1:\n first_lasts[matrices_index][0] = node\n\n if coords[0] == -1 and prev_coords[0] != -1:\n prev_node = self.graph.coords_to_node(prev_coords[0],prev_coords[1])\n first_lasts[matrices_index][1] = prev_node\n\n if prev_coords != (-1,-1) and coords[0] != -1 and coords != prev_coords:\n edge_num = self.graph.edge_num(prev_coords[0],prev_coords[1],coords[0],coords[1])\n if edge_num == -1:\n good_graphs[matrices_index] = False\n else:\n edge_sets[matrices_index][edge_num] = 1\n if edge_num in partials[matrices_index] and partials[matrices_index][edge_num] == 0:\n del partials[matrices_index][edge_num]\n if not hit_midpoint:\n if first_edge:\n above = (prev_coords[0]-1,prev_coords[1])\n below = (prev_coords[0]+1,prev_coords[1])\n left = (prev_coords[0],prev_coords[1]-1)\n right = (prev_coords[0],prev_coords[1]+1)\n for next_coords in (above,below,left,right):\n other_edge = self.graph.edge_num(prev_coords[0],prev_coords[1],next_coords[0],next_coords[1])\n if other_edge != -1:\n partials[matrices_index][other_edge] = 0\n first_edge = False\n if self.graph.coords_to_node(prev_coords[0],prev_coords[1]) == self.midpoint:\n hit_midpoint = True\n partials[matrices_index][edge_num] = 1\n if self.graph.coords_to_node(coords[0],coords[1]) == self.midpoint:\n hit_midpoint = True\n\n\n\n if coords[0] == -1:\n matrices.append([np.zeros((self.graph.rows,self.graph.cols)),0])\n first_lasts.append([0,0])\n edge_sets.append([0 for i in range(self.graph.num_edges)])\n good_graphs.append(True)\n nodes_visited.append([])\n matrices_index += 1\n partials.append({})\n hit_midpoint = False\n first_edge = True\n \n elif coords[0] < self.graph.rows and coords[1] < self.graph.cols and not matrices[matrices_index][0][coords[0]][coords[1]]:\n matrices[matrices_index][1] += 1\n matrices[matrices_index][0][coords[0]][coords[1]] = 1\n nodes_visited[matrices_index].append(coords)\n\n prev_coords = coords\n\n cur_line += 1\n if cur_line == len(self.graph.lines):\n break\n #normalized = dg.normalize(self.graph.lines[cur_line])\n normalized = normalize_simple(self.graph.lines[cur_line])\n\n prev_node = self.graph.coords_to_node(prev_coords[0],prev_coords[1])\n first_lasts[matrices_index][1] = prev_node\n self.next_line = cur_line\n best_index = 0\n best_score = 0\n for matrix_index in range(len(matrices)):\n if matrices[matrix_index][1] > best_score:\n best_score = matrices[matrix_index][1]\n best_index = matrix_index\n\n for coords in nodes_visited[best_index]:\n self.graph.node_visit(self.trip_id,coords)\n \n\n if self.trip_id not in self.graph.trip_id2line_num:\n #if first_lasts[best_index] == [28,5]:\n # print \"a to b: %d\" % self.trip_id\n self.graph.first_last2trip_ids[tuple(first_lasts[best_index])].append(self.trip_id)\n\n return matrices[best_index][0],edge_sets[best_index],good_graphs[best_index],partials[best_index]"],"string":"[\n \"def generate_locations(\\n staypoints,\\n method=\\\"dbscan\\\",\\n epsilon=100,\\n num_samples=1,\\n distance_metric=\\\"haversine\\\",\\n agg_level=\\\"user\\\",\\n activities_only=False,\\n print_progress=False,\\n n_jobs=1,\\n):\\n if agg_level not in [\\\"user\\\", \\\"dataset\\\"]:\\n raise AttributeError(f\\\"agg_level '{agg_level}' is unknown. Supported values are ['user', 'dataset'].\\\")\\n if method not in [\\\"dbscan\\\"]:\\n raise AttributeError(f\\\"method '{method}' is unknown. Supported value is ['dbscan'].\\\")\\n\\n # initialize the return GeoDataFrames\\n sp = staypoints.copy()\\n non_activities = None\\n if activities_only:\\n if \\\"activity\\\" not in sp.columns:\\n raise KeyError('staypoints must contain column \\\"activity\\\" if \\\"activities_only\\\" flag is set.')\\n non_activities = sp[~sp[\\\"activity\\\"]]\\n sp = sp[sp[\\\"activity\\\"]]\\n sp = sp.sort_values([\\\"user_id\\\", \\\"started_at\\\"])\\n geo_col = sp.geometry.name\\n\\n if method == \\\"dbscan\\\":\\n eps = epsilon / 6371000 if distance_metric == \\\"haversine\\\" else epsilon\\n # scikit haversine_dist wants radian. (We assume that this is good enough)\\n # https://scikit-learn.org/stable/modules/generated/sklearn.metrics.pairwise.haversine_distances.html\\n db = DBSCAN(eps=eps, min_samples=num_samples, algorithm=\\\"ball_tree\\\", metric=distance_metric)\\n\\n if agg_level == \\\"user\\\":\\n sp = applyParallel(\\n sp.groupby(\\\"user_id\\\", as_index=False),\\n _gen_locs_dbscan,\\n n_jobs=n_jobs,\\n print_progress=print_progress,\\n distance_metric=distance_metric,\\n db=db,\\n )\\n\\n # keeping track of noise labels\\n sp_non_noise_labels = sp[sp[\\\"location_id\\\"] != -1]\\n sp_noise_labels = sp[sp[\\\"location_id\\\"] == -1]\\n\\n # sort so that the last location id of a user = max(location id)\\n sp_non_noise_labels = sp_non_noise_labels.sort_values([\\\"user_id\\\", \\\"location_id\\\"])\\n\\n # identify start positions of new user_ids\\n start_of_user_id = sp_non_noise_labels[\\\"user_id\\\"] != sp_non_noise_labels[\\\"user_id\\\"].shift(1)\\n\\n # calculate the offset (= last location id of the previous user)\\n # multiplication is to mask all positions where no new user starts and addition is to have a +1 when a\\n # new user starts\\n loc_id_offset = sp_non_noise_labels[\\\"location_id\\\"].shift(1) * start_of_user_id + start_of_user_id\\n\\n # fill first nan with 0 and create the cumulative sum\\n loc_id_offset = loc_id_offset.fillna(0).cumsum()\\n\\n sp_non_noise_labels[\\\"location_id\\\"] = sp_non_noise_labels[\\\"location_id\\\"] + loc_id_offset\\n sp = gpd.GeoDataFrame(pd.concat([sp_non_noise_labels, sp_noise_labels]), geometry=geo_col)\\n sp.sort_values([\\\"user_id\\\", \\\"started_at\\\"], inplace=True)\\n\\n else:\\n _gen_locs_dbscan(sp, db=db, distance_metric=distance_metric)\\n\\n ### create locations as grouped staypoints\\n temp_sp = sp[[\\\"user_id\\\", \\\"location_id\\\", sp.geometry.name]]\\n if agg_level == \\\"user\\\":\\n # directly dissolve by 'user_id' and 'location_id'\\n locs = temp_sp.dissolve(by=[\\\"user_id\\\", \\\"location_id\\\"], as_index=False)\\n else:\\n ## generate user-location pairs with same geometries across users\\n # get user-location pairs\\n locs = temp_sp[[\\\"user_id\\\", \\\"location_id\\\"]].drop_duplicates(ignore_index=True)\\n # get location geometries\\n geom_gdf = temp_sp.dissolve(by=[\\\"location_id\\\"], as_index=False).drop(columns={\\\"user_id\\\"})\\n # merge pairs with location geometries\\n locs = geom_gdf.merge(locs, on=\\\"location_id\\\", how=\\\"right\\\")\\n\\n # filter staypoints not belonging to locations\\n locs = locs.loc[locs[\\\"location_id\\\"] != -1]\\n\\n if check_gdf_planar(locs):\\n locs[\\\"center\\\"] = locs.geometry.centroid\\n else:\\n # error of wrapping e.g. mean([-180, +180]) -> own function needed\\n locs[\\\"center\\\"] = angle_centroid_multipoints(locs.geometry)\\n\\n # extent is the convex hull of the geometry\\n locs[\\\"extent\\\"] = locs.geometry.convex_hull\\n # convex_hull of Point is Point, and MultiPoint with two Points is a LineString\\n # we change them into Polygon by creating a buffer of epsilon around them.\\n pointLine_idx = (locs[\\\"extent\\\"].geom_type == \\\"LineString\\\") | (locs[\\\"extent\\\"].geom_type == \\\"Point\\\")\\n\\n # Perform meter to decimal conversion if the distance metric is haversine\\n if distance_metric == \\\"haversine\\\":\\n locs.loc[pointLine_idx, \\\"extent\\\"] = locs.loc[pointLine_idx].apply(\\n lambda p: p[\\\"extent\\\"].buffer(meters_to_decimal_degrees(epsilon, p[\\\"center\\\"].y)), axis=1\\n )\\n else:\\n locs.loc[pointLine_idx, \\\"extent\\\"] = locs.loc[pointLine_idx, \\\"extent\\\"].buffer(epsilon)\\n\\n locs = locs.set_geometry(\\\"center\\\", crs=sp.crs)\\n locs = locs[[\\\"user_id\\\", \\\"location_id\\\", \\\"center\\\", \\\"extent\\\"]]\\n\\n # index management\\n locs.rename(columns={\\\"location_id\\\": \\\"id\\\"}, inplace=True)\\n locs.set_index(\\\"id\\\", inplace=True)\\n\\n # staypoints not linked to a location receive np.nan in 'location_id'\\n sp.loc[sp[\\\"location_id\\\"] == -1, \\\"location_id\\\"] = np.nan\\n\\n # merge non_activities back if \\\"activities_only\\\" flag is set\\n sp = pd.concat([sp, non_activities])\\n\\n if len(locs) > 0:\\n locs.as_locations # empty location is not valid\\n else:\\n warnings.warn(\\\"No locations can be generated, returning empty locs.\\\")\\n\\n ## dtype consistency\\n # locs id (generated by this function) should be int64\\n locs.index = locs.index.astype(\\\"int64\\\")\\n # location_id of staypoints can only be in Int64 (missing values)\\n sp[\\\"location_id\\\"] = sp[\\\"location_id\\\"].astype(\\\"Int64\\\")\\n # user_id of locs should be the same as sp\\n locs[\\\"user_id\\\"] = locs[\\\"user_id\\\"].astype(sp[\\\"user_id\\\"].dtype)\\n\\n return sp, locs\",\n \"def example_staypoints():\\n p1 = Point(8.5067847, 47.4)\\n p2 = Point(8.5067847, 47.5)\\n p3 = Point(8.5067847, 47.6)\\n p4 = Point(8.5067847, 47.7)\\n\\n t1 = pd.Timestamp(\\\"1971-01-01 00:00:00\\\", tz=\\\"utc\\\")\\n t2 = pd.Timestamp(\\\"1971-01-01 05:00:00\\\", tz=\\\"utc\\\")\\n t3 = pd.Timestamp(\\\"1971-01-02 07:00:00\\\", tz=\\\"utc\\\")\\n t4 = pd.Timestamp(\\\"1971-01-02 08:00:00\\\", tz=\\\"utc\\\")\\n t5 = pd.Timestamp(\\\"1971-01-02 09:00:00\\\", tz=\\\"utc\\\")\\n t6 = pd.Timestamp(\\\"1971-01-02 10:00:00\\\", tz=\\\"utc\\\")\\n\\n list_dict = [\\n {\\\"id\\\": 1, \\\"user_id\\\": 0, \\\"started_at\\\": t1, \\\"finished_at\\\": t2, \\\"geom\\\": p1},\\n {\\\"id\\\": 5, \\\"user_id\\\": 0, \\\"started_at\\\": t2, \\\"finished_at\\\": t3, \\\"geom\\\": p2},\\n {\\\"id\\\": 2, \\\"user_id\\\": 0, \\\"started_at\\\": t3, \\\"finished_at\\\": t4, \\\"geom\\\": p3},\\n {\\\"id\\\": 6, \\\"user_id\\\": 0, \\\"started_at\\\": t4, \\\"finished_at\\\": t5, \\\"geom\\\": p2},\\n {\\\"id\\\": 15, \\\"user_id\\\": 0, \\\"started_at\\\": t5, \\\"finished_at\\\": t6, \\\"geom\\\": p1},\\n {\\\"id\\\": 7, \\\"user_id\\\": 1, \\\"started_at\\\": t3, \\\"finished_at\\\": t4, \\\"geom\\\": p4},\\n {\\\"id\\\": 80, \\\"user_id\\\": 1, \\\"started_at\\\": t4, \\\"finished_at\\\": t5, \\\"geom\\\": p2},\\n {\\\"id\\\": 3, \\\"user_id\\\": 1, \\\"started_at\\\": t5, \\\"finished_at\\\": t6, \\\"geom\\\": p2},\\n ]\\n sp = gpd.GeoDataFrame(data=list_dict, geometry=\\\"geom\\\", crs=\\\"EPSG:4326\\\")\\n sp = sp.set_index(\\\"id\\\")\\n sp.as_staypoints\\n return sp\",\n \"def create_graph(users, friend_counts):\\n ###TODO-- Completed\\n G = nx.Graph()\\n\\n #For Filtering the Nodes\\n #print(friend_counts)\\n friend_nodes = [friend for friend in friend_counts if friend_counts[friend] > 1]\\n candidate_nodes = [user['screen_name'] for user in users]\\n\\n #print(\\\"Nodes: \\\",len(friend_nodes), len(candidate_nodes))\\n #Adding Nodes to graph\\n G.add_nodes_from(friend_nodes + candidate_nodes)\\n\\n #Connecting the Nodes with Edges\\n for candidate in users:\\n for friend in friend_nodes:\\n if friend in candidate['friends']:\\n G.add_edge(candidate['screen_name'], friend)\\n\\n return G\",\n \"def NewHighRiskUsersGPS(list_infected):\\n d=DISTANCE/6371000\\n intervals_dates=getIntervals(T_START,T_END,INTERVAL)\\n encounters=filterEvents(intervals_dates,list_infected,d)\\n prob=dict()\\n cont=1\\n for h in range(len(encounters)):\\n if(len(encuentros[h])!=0):\\n map_infected=encounters[h].iloc[::,[0,2,1,3]]\\n map_noinfected=encounters[h].iloc[:,[0,2,1,4,6,5,7,8]]\\n map_infected=map_infected.groupby( [ \\\"user_i\\\"] ).size().to_frame(name = 'count').reset_index()\\n map_infected['prob']=[1.0]*len(map_infected)\\n map_infected['lat_i']=[None]*len(map_infected)\\n map_infected['lon_i']=[None]*len(map_infected)\\n map_infected['time_i']=[None]*len(map_infected)\\n map_infected['user_infectado']=[None]*len(map_infected)\\n map_infected['lat_infectado']=[None]*len(map_infected)\\n map_infected['lon_infectado']=[None]*len(map_infected)\\n map_infected = map_infected[[\\\"user_i\\\", \\\"lat_i\\\", \\\"lon_i\\\",\\\"time_i\\\",\\\"count\\\",\\\"prob\\\",\\\"user_infectado\\\",\\\"lat_infectado\\\",\\\"lon_infectado\\\"]]\\n map_infected.columns=[\\\"user\\\",\\\"lat\\\",\\\"lon\\\",\\\"time\\\",\\\"datos\\\",\\\"prob\\\",\\\"user_infectado\\\",\\\"lat_infectado\\\",\\\"lon_infectado\\\"]\\n users=map_noinfected['user_j'].unique()\\n dict_probability=dict()\\n probability_df=[]\\n for u in users:\\n try:\\n dict_probability[u]=[prob[u]]\\n except:\\n dict_probability[u]=[0]\\n user=[];probability=[]\\n for e in range(len(encounters[h])):\\n probcontagio=probaContagius(encounters[h]['lat_i'][e],encounters[h]['lon_i'][e],encounters[h]['lat_j'][e],encounters[h]['lon_j'][e],M)\\n probability_df.append(probcontagio)\\n dict_probability[encounters[h]['user_j'][e]].append(probcontagio)\\n map_noinfected['prob']=probability_df\\n for d in dict_probability:\\n for i in range(len(dict_probability[d])-1):\\n if(i==0):\\n q=componeProbs(dict_probability[d][i],dict_probability[d][i+1])\\n else:\\n q=componeProbs(q,dict_probability[d][i+1])\\n user.append(d)\\n probability.append(q)\\n list_df=[]\\n for i in range(len(user)):\\n df=map_noinfected[map_noinfected['user_j']==user[i]].reset_index(drop=True)\\n df['prob']=probability[i]\\n list_df.append(df)\\n prob[user[i]]=probability[i]\\n map_noinfected=pd.concat(list_df)\\n map_noinfected = map_noinfected[[\\\"user_j\\\", \\\"lat_j\\\", \\\"lon_j\\\",\\\"time_j\\\",\\\"distance\\\",\\\"prob\\\",\\\"user_i\\\",\\\"lat_i\\\",\\\"lon_i\\\"]]\\n map_noinfected.columns=[\\\"user\\\",\\\"lat\\\",\\\"lon\\\",\\\"time\\\",\\\"datos\\\",\\\"prob\\\",\\\"user_infectado\\\",\\\"lat_infectado\\\",\\\"lon_infectado\\\"]\\n data=pd.concat([map_infected,map_noinfected])\\n data.to_csv(OUTPUTPATH+\\\"eventos\\\"+str(cont)+\\\".csv\\\",index=False)\\n cont=cont+1\\n else:\\n data=pd.DataFrame(columns=[\\\"user\\\",\\\"lat\\\",\\\"lon\\\",\\\"time\\\",\\\"datos\\\",\\\"prob\\\",\\\"user_infectado\\\",\\\"lat_infectado\\\",\\\"lon_infectado\\\"])\\n data.to_csv(OUTPUTPATH+\\\"eventos\\\"+str(cont)+\\\".csv\\\",index=False)\\n cont=cont+1\\n data_probability=pd.DataFrame(prob.items(), columns=['user_id', 'Probability'])\\n data_probability.to_csv(OUTPUTPATH+\\\"probability.csv\\\",index=False)\",\n \"def create_graph(self, lat, lon):\\n # Open connection to the database (nodes)\\n cur = armaps.model.get_db()\\n\\n # Get the waypoints\\n cur.execute(\\n \\\"SELECT * FROM waypoints WHERE venue_id = %s\\\", \\n (self.venue_id,)\\n )\\n waypoints = cur.fetchall()\\n\\n # Get the paths (edges)\\n cur.execute(\\n \\\"SELECT * FROM paths WHERE venue_id = %s\\\",\\n (self.venue_id,)\\n )\\n paths = cur.fetchall()\\n\\n # Transform list of waypoints into dictionary with key = waypoint_id\\n for waypoint in waypoints:\\n self.waypoints[int(waypoint[\\\"waypoint_id\\\"])] = {\\n \\\"lat\\\": float(waypoint[\\\"latitude\\\"]),\\n \\\"lon\\\": float(waypoint[\\\"longitude\\\"]),\\n \\\"waypoint_id\\\": int(waypoint[\\\"waypoint_id\\\"])\\n }\\n\\n # Calculate weights of edges in graph\\n for path in paths:\\n # Get two nodes (waypoints) associated with edge\\n inNode = int(path[\\\"innode\\\"])\\n outNode = int(path[\\\"outnode\\\"])\\n\\n # Get the coordinates of nodes\\n inNode_coords = (self.waypoints[inNode][\\\"lat\\\"], self.waypoints[inNode][\\\"lon\\\"])\\n outNode_coords = (self.waypoints[outNode][\\\"lat\\\"], self.waypoints[outNode][\\\"lon\\\"])\\n distance = geopy.distance.distance(inNode_coords, outNode_coords).miles\\n\\n # Add to graph (both ways for undirected)\\n self.graph.add_edge(inNode, outNode, distance)\\n self.graph.add_edge(outNode, inNode, distance)\",\n \"def createGraph(self):\\n \\n for episode in self.episodes:\\n listeSuccessors = [episode[episode[:,1] > episode[i,1]][:,0] # List of list of successors for each user\\n for i in range(len(episode))] \\n for i, successeur in enumerate(listeSuccessors): # for the list of successors of each user\\n for v in successeur: # for every successor of a user\\n u, proba = episode[i,0], np.random.random() # Generate a probability so within (0,1)\\n self.successors[u][v] = proba # u ---(proba)---> v \\n self.predecessors[v][u] = proba # v ---(proba)---> u\",\n \"def example_staypoints_merge():\\n p1 = Point(8.5067847, 47.4)\\n\\n t1 = pd.Timestamp(\\\"1971-01-01 00:00:00\\\", tz=\\\"utc\\\")\\n t2 = pd.Timestamp(\\\"1971-01-02 05:00:00\\\", tz=\\\"utc\\\")\\n t3 = pd.Timestamp(\\\"1971-01-02 06:45:00\\\", tz=\\\"utc\\\")\\n t4 = pd.Timestamp(\\\"1971-01-02 08:55:00\\\", tz=\\\"utc\\\")\\n t45 = pd.Timestamp(\\\"1971-01-02 08:57:00\\\", tz=\\\"utc\\\")\\n t5 = pd.Timestamp(\\\"1971-01-02 09:00:00\\\", tz=\\\"utc\\\")\\n t6 = pd.Timestamp(\\\"1971-01-02 09:20:00\\\", tz=\\\"utc\\\")\\n\\n list_dict = [\\n {\\\"id\\\": 1, \\\"user_id\\\": 0, \\\"started_at\\\": t1, \\\"finished_at\\\": t2, \\\"geom\\\": p1, \\\"location_id\\\": 1},\\n {\\\"id\\\": 5, \\\"user_id\\\": 0, \\\"started_at\\\": t2, \\\"finished_at\\\": t2, \\\"geom\\\": p1, \\\"location_id\\\": 2},\\n {\\\"id\\\": 2, \\\"user_id\\\": 0, \\\"started_at\\\": t3, \\\"finished_at\\\": t4, \\\"geom\\\": p1, \\\"location_id\\\": 2},\\n {\\\"id\\\": 6, \\\"user_id\\\": 0, \\\"started_at\\\": t4, \\\"finished_at\\\": t45, \\\"geom\\\": p1, \\\"location_id\\\": 2},\\n {\\\"id\\\": 15, \\\"user_id\\\": 0, \\\"started_at\\\": t5, \\\"finished_at\\\": t6, \\\"geom\\\": p1, \\\"location_id\\\": 2},\\n {\\\"id\\\": 7, \\\"user_id\\\": 1, \\\"started_at\\\": t3, \\\"finished_at\\\": t4, \\\"geom\\\": p1, \\\"location_id\\\": 2},\\n {\\\"id\\\": 80, \\\"user_id\\\": 1, \\\"started_at\\\": t45, \\\"finished_at\\\": t5, \\\"geom\\\": p1, \\\"location_id\\\": 2},\\n {\\\"id\\\": 3, \\\"user_id\\\": 1, \\\"started_at\\\": t5, \\\"finished_at\\\": t6, \\\"geom\\\": p1, \\\"location_id\\\": 4},\\n ]\\n sp = gpd.GeoDataFrame(data=list_dict, geometry=\\\"geom\\\", crs=\\\"EPSG:4326\\\")\\n sp = sp.set_index(\\\"id\\\")\\n sp.as_staypoints\\n\\n # generate empty triplegs for the merge function\\n tpls = pd.DataFrame([], columns=[\\\"user_id\\\", \\\"started_at\\\", \\\"finished_at\\\"])\\n return sp, tpls\",\n \"def test_dbscan_user_dataset(self):\\n sp_file = os.path.join(\\\"tests\\\", \\\"data\\\", \\\"geolife\\\", \\\"geolife_staypoints.csv\\\")\\n sp = ti.read_staypoints_csv(sp_file, tz=\\\"utc\\\", index_col=\\\"id\\\", crs=\\\"epsg:4326\\\")\\n # take the first row and duplicate once\\n sp = sp.head(1)\\n sp = pd.concat([sp, sp], ignore_index=True)\\n # assign a different user_id to the second row\\n sp.iloc[1, 4] = 1\\n\\n # duplicate for a certain number\\n sp = pd.concat([sp] * 6, ignore_index=True)\\n _, locs_ds = sp.as_staypoints.generate_locations(\\n method=\\\"dbscan\\\", epsilon=10, num_samples=1, distance_metric=\\\"haversine\\\", agg_level=\\\"dataset\\\"\\n )\\n _, locs_us = sp.as_staypoints.generate_locations(\\n method=\\\"dbscan\\\", epsilon=10, num_samples=1, distance_metric=\\\"haversine\\\", agg_level=\\\"user\\\"\\n )\\n loc_dataset_num = len(locs_ds.index.unique())\\n loc_user_num = len(locs_us.index.unique())\\n assert loc_dataset_num == 1\\n assert loc_user_num == 2\",\n \"def get_all_social_paths(self, user_id):\\n visited = {} # Note that this is a dictionary, not a set\\n # !!!! IMPLEMENT ME\\n # graphs=Graph()\\n # for i in self.users:\\n # graphs.add_vertex(i)\\n \\n # for i in self.users:\\n # for x in self.friendships[i]:\\n # graphs.add_edge(i,x)\\n\\n # for i in graphs.vertices:\\n # if graphs.bfs(i,user_id):\\n # visited[i]=graphs.bfs(i,user_id)\\n queue=Queue()\\n queue.enqueue([user_id])\\n while queue.size()>0:\\n path=queue.dequeue()\\n current_user = path[-1]\\n if current_user not in visited:\\n visited[current_user]=path\\n for ID in self.friendships[current_user]:\\n new_path=list(path)\\n new_path.append(ID)\\n queue.enqueue(new_path)\\n return visited\",\n \"def go_to_x_y(user_x, user_y, deltaball=1.0):\\n global regions\\n goal = Point()\\n goal.x = user_x\\n goal.y = user_y\\n inc_x = user_x - x\\n inc_y = user_y - y\\n th = math.atan2(inc_y, inc_x)\\n if th > 3.14:\\n th = -(6.28 - th)\\n elif th < -3.14:\\n th = 6.28 + th\\n go_to_angle(th)\\n while math.sqrt(((user_x - x) * (user_x - x)) + ((user_y - y) * (user_y - y))) > deltaball:\\n if regions['front'] > 2:\\n if math.sqrt(((goal.x - x) * (goal.x - x)) + ((goal.y - y) * (goal.y - y))) > deltaball:\\n speed.linear.x = 0.8\\n speed.angular.z = 0\\n pub.publish(speed)\\n rate.sleep()\\n else:\\n speed.linear.x = 0\\n speed.angular.z = 0\\n pub.publish(speed)\\n rate.sleep()\\n # Let's find possible path\\n if regions['right'] > 3:\\n # Right path available\\n obs_avoid(-0.5)\\n inc_x = goal.x - x\\n inc_y = goal.y - y\\n th = math.atan2(inc_y, inc_x)\\n go_to_angle(th)\\n elif regions['left'] > 3:\\n # front left path available\\n obs_avoid(0.5)\\n inc_x = goal.x - x\\n inc_y = goal.y - y\\n th = math.atan2(inc_y, inc_x)\\n go_to_angle(th)\\n elif regions['fright'] > 3:\\n # Right path available\\n obs_avoid(-0.5)\\n inc_x = goal.x - x\\n inc_y = goal.y - y\\n th = math.atan2(inc_y, inc_x)\\n go_to_angle(th)\\n elif regions['fleft'] > 3:\\n # front left path available\\n obs_avoid(0.5)\\n inc_x = goal.x - x\\n inc_y = goal.y - y\\n th = math.atan2(inc_y, inc_x)\\n go_to_angle(th)\\n else:\\n obs_avoid(-0.5)\\n inc_x = goal.x - x\\n inc_y = goal.y - y\\n th = math.atan2(inc_y, inc_x)\\n go_to_angle(th)\\n rate.sleep()\\n speed.linear.x = 0\\n speed.angular.z = 0\\n pub.publish(speed)\",\n \"def populate_graph(self, num_users, avg_friendships):\\n # Reset graph\\n self.last_id = 0\\n self.users = {}\\n self.friendships = {}\\n # !!!! IMPLEMENT ME\\n\\n # add users\\n for i in range(num_users):\\n self.add_user(f'User {i}')\\n\\n # create friendships list\\n possible_friendships = []\\n\\n # for each friend-user combination, place a tuple in possible_friendships \\n for user_id in self.users:\\n for friend_id in range(user_id + 1,self.last_id + 1):\\n possible_friendships.append([user_id, friend_id])\\n\\n import random\\n # shuffle the possible friendships so the order isn't biased towards towards the early users\\n random.shuffle(possible_friendships)\\n\\n # add friendships\\n # designate a certain number of friendships determined by avg friendships per total users\\n for i in range(num_users * avg_friendships // 2):\\n # this sets the friendship variable to a premade possible friendship\\n friendship = possible_friendships[i]\\n # then we add this friendship to the friendship dictionary that defines the graph\\n # with the first tuple value as the key and the second as the value\\n self.add_friendship(friendship[0],friendship[1])\",\n \"def populate_graph(self, num_users, avg_friendships):\\n # Reset graph\\n self.last_id = 0\\n self.users = {}\\n self.friendships = {}\\n # !!!! IMPLEMENT ME\\n # Add users\\n # numberOfUsers=num_users\\n # while num_users:\\n # self.add_user(num_users)\\n # num_users-=1\\n \\n # DistributionOfFriendships=[]\\n\\n # while len(DistributionOfFriendships)<len(self.users):\\n # if DistributionOfFriendships!=True:\\n # DistributionOfFriendships.append(random.randrange(0,5))\\n # if len(DistributionOfFriendships)==len(self.users):\\n # break\\n # while sum(DistributionOfFriendships)/len(DistributionOfFriendships)!=avg_friendships:\\n # randomNum=random.randrange(0,5)\\n # if (sum(DistributionOfFriendships)+randomNum)/(len(DistributionOfFriendships)+1) == avg_friendships:\\n # DistributionOfFriendships.append(randomNum)\\n # # Create friendships\\n\\n # print(DistributionOfFriendships)\\n # for i in self.users:\\n # # count=len(self.friendships[i])\\n # # print(\\\"HELLLO\\\",i,self.friendships[i])\\n # # print(\\\"COUNT\\\", count, self.friendships[i], len(self.friendships[i]),i) \\n # if DistributionOfFriendships[i-1]>0: \\n # if len(self.friendships[i]) <DistributionOfFriendships[i-1]:\\n # while len(self.friendships[i])!=DistributionOfFriendships[i-1]:\\n # randomNum=random.randrange(1,numberOfUsers+1)\\n # print(DistributionOfFriendships,randomNum, i,len(self.friendships[randomNum]), DistributionOfFriendships[randomNum-1],self.friendships)\\n # if len(self.friendships[randomNum])<DistributionOfFriendships[randomNum-1] and randomNum!=i:\\n # # print(\\\"randomNum\\\",randomNum ,\\\"i\\\",i,\\\"friendships\\\",self.friendships[i], \\\"count\\\", count,DistributionOfFriendships[i-1], self.friendships)\\n # if randomNum not in self.friendships[i]:\\n # # count+=1\\n # # print(\\\"III\\\",i,\\\"Distribution\\\",DistributionOfFriendships[i-1],\\\"randomNum\\\",randomNum,\\\"count\\\",count)\\n # self.add_friendship(i,randomNum)\\n # # print(\\\"after\\\")\\n for i in range(num_users):\\n self.add_user(i)\\n\\n possible_friendships=[]\\n for user_id in self.users:\\n for friend_id in range(user_id+1, self.last_id+1):\\n possible_friendships.append((user_id, friend_id))\\n\\n random.shuffle(possible_friendships)\\n\\n for i in range(num_users*avg_friendships//2):\\n friendship= possible_friendships[i]\\n self.add_friendship(friendship[0],friendship[1])\",\n \"def compute_pathways(users, job_graph, debug, min_likelihood_thr=0.2):\\r\\n start_time = time.time()\\r\\n __print_msg('Computing career pathways...', debug)\\r\\n user_pathways = {}\\r\\n tot_users = len(users)\\r\\n i = 0\\r\\n for user, user_jobs in users.items():\\r\\n user_pathway = compute_user_pathway(user_jobs, job_graph)\\r\\n recommended_pathway = recommend_pathway(user_jobs, job_graph, user_pathway[-1], min_likelihood_thr)\\r\\n user_pathways[user] = (user_pathway, recommended_pathway)\\r\\n i += 1\\r\\n if i % 1000 == 0:\\r\\n __print_msg('Num users processed: {}/{}'.format(i, tot_users), debug)\\r\\n end_time = time.time()\\r\\n __print_msg('Execution time: {} seconds'.format(end_time - start_time), debug)\\r\\n return user_pathways\",\n \"def makeGraph(locations, distances):\\n graph = dict() # maps (lat, lng) to Node\\n for location,distance in zip(locations, distances):\\n currLocation = location[0]\\n neighbors = location[1:]\\n makeNode(currLocation, neighbors, distance[1:], graph)\\n return graph\",\n \"def get_all_social_paths(self, user_id): #each user v v each path V V\\n # output example- {1: [1], 8: [1, 8], 10: [1, 10], 5: [1, 5], 2: [1, 10, 2], 6: [1, 10, 6], 7: [1, 10, 2, 7]}\\n visited = {} # Note that this is a dictionary, not a set\\n # Need to do a bfs using the user id\\n # first step is to traverse the graph and record all the vertices as keys in visited using bft\\n # then take those keys and use bfs on each, using user_id as the starting node and and the key as\\n # the destination node\\n\\n # Modification of BFT\\n # create an empty dict\\n # q = Queue()\\n q = []\\n\\n # init enqueue the starting node\\n q.append(user_id)\\n\\n while len(q) > 0:\\n # Dequeue the first item\\n v = q.pop(0)\\n # If it's not been visited:\\n if v not in visited:\\n # Mark as visited (i.e. add to the visited set)\\n visited[v] = []\\n\\n # Do something with the node\\n print(f\\\"Visited {v}\\\")\\n\\n # Add all neighbors to the queue\\n for next_vert in self.friendships[v]:\\n q.append(next_vert)\\n\\n # once visited is filled, then we start the bfs\\n #print('vv',visited)\\n possible_paths = {}\\n #run a bfs for each key in visited\\n for v in visited:\\n possible_paths[v] = []\\n \\n if v == user_id:\\n visited[v] = [user_id]\\n\\n path = []\\n while len(path) < len(visited):\\n\\n # Add all neighbors to the queue\\n for next_vert in self.friendships[v]:\\n print(possible_paths[v])\\n # copy the path\\n # temp_path = list(path)\\n # temp_path.append(next_vert)\\n # add path to possible_paths\\n path.append(next_vert)\\n\\n possible_paths[v].append(path) # HAVE TO USE QUEUE OR STACK, THEY ENSURE THE NEIGHBORS\\n # FOLLOW THE CORRECT ORDER WHEN LOOPING \\n\\n if v == path[-1]:\\n \\n # IF SO, RETURN PATH\\n visited[v] = path\\n break\\n\\n # for x in visited:\\n # bfs(user_id, x)\\n # visited[x].add(path)\\n \\n print('pct of total users in network', len(visited[1])/len(visited))\\n print('degrees of separation', len(visited[1]) - 1)\\n return visited\",\n \"def compute_user_pathway(user_jobs, job_graph, debug=False):\\r\\n pathway = []\\r\\n for i, job in enumerate(user_jobs):\\r\\n if i == 0:\\r\\n continue\\r\\n cluster, _ = job_graph.assign_job_to_jobgraph_state(job)\\r\\n pathway.append(cluster)\\r\\n return pathway\",\n \"def insert_user_location(self, lat, lon):\\n # Waypoint id for user's location\\n user_waypoint_id = 0\\n\\n # Keep track of closest waypoint to user\\n closest_waypoint = None\\n min_distance = float(\\\"inf\\\")\\n\\n # Find closest waypoint to user's location\\n for key, val in self.waypoints.items():\\n # Check to see if waypoint_id higher than user's\\n if key > user_waypoint_id:\\n user_waypoint_id = key\\n\\n # Calculate distance\\n user_coords = (lat, lon)\\n waypoint_coords = (val[\\\"lat\\\"], val[\\\"lon\\\"])\\n distance = geopy.distance.distance(user_coords, waypoint_coords).miles\\n\\n if distance < min_distance:\\n min_distance = distance\\n closest_waypoint = key\\n\\n # Increment highest waypoint id seen to get user's starting waypoint id\\n user_waypoint_id += 1\\n\\n # Add to graph\\n self.graph.add_edge(user_waypoint_id, closest_waypoint, min_distance)\\n self.graph.add_edge(closest_waypoint, user_waypoint_id, min_distance)\\n\\n # Return starting waypoint id\\n return user_waypoint_id\",\n \"def get_network(users_ids, as_edgelist=True):\\r\\n edges = []\\r\\n amount = 0\\r\\n for ind1 in range(len(users_ids)):\\r\\n for ind2 in range(len(users_ids)):\\r\\n try:\\r\\n if users_ids[ind2] in get_friends(users_ids[ind1])['response']['items']:\\r\\n edges += [(ind1, ind2)]\\r\\n amount += 1\\r\\n except KeyError:\\r\\n pass\\r\\n\\r\\n g = Graph(vertex_attrs={\\\"label\\\": users_ids},\\r\\n edges=edges, directed=False)\\r\\n\\r\\n plot_graph(g, amount)\",\n \"def populate_graph(self, num_users, avg_friendships):\\n # Reset graph\\n self.last_id = 0\\n self.users = {}\\n self.friendships = {}\\n # !!!! IMPLEMENT ME\\n\\n # Add users\\n for i in range(num_users):\\n self.add_user(f'User {i+1}')\\n\\n # Create friendships\\n # Create a list with all possible friendships\\n possible_friendships = []\\n for user_id in self.users:\\n for other_id in range(user_id +1, self.last_id + 1):\\n possible_friendships.append((user_id, other_id))\\n\\n # Shuffle the list\\n random.shuffle(possible_friendships)\\n # Grab the first N pairs for the list and create those friendships\\n for i in range(num_users * avg_friendships // 2):\\n friendship = possible_friendships[i]\\n self.add_friendship(friendship[0], friendship[1])\",\n \"def populate_graph(self, num_users, avg_friendships):\\n # Reset graph\\n self.reset()\\n\\n for i in range(num_users):\\n self.add_user(f\\\"User {i}\\\")\\n\\n target_friendships = num_users * avg_friendships\\n total_friendships = 0\\n collisions = 0\\n\\n while total_friendships < target_friendships:\\n user_id = random.randint(1, self.last_id)\\n friend_id = random.randint(1, self.last_id)\\n\\n if self.add_friendship(user_id, friend_id):\\n total_friendships += 2\\n else:\\n collisions += 1\",\n \"def populateGraph(self, num_users, avg_friendships):\\n # Reset graph\\n self.lastID = 0\\n self.users = {}\\n self.friendships = {}\\n\\n # Add users\\n for user in range(num_users):\\n self.addUser(\\\"User %s\\\" % user) # user 0, user 1, user 2\\n\\n # Create friendships\\n\\n # Generate all possible friendship combinations\\n possible_friendships = []\\n # avoid dups by making sure that the first id is higher than the 2nd id\\n for user_id in self.users:\\n for other_id in range(user_id + 1, self.lastID + 1):\\n possible_friendships.append((user_id, other_id))\\n\\n random.shuffle(possible_friendships)\\n\\n # Create friendships(edges) for the first X tuples of the list\\n # X is determined by the formula: numUsers * avgFriendships // 2\\n # Need to divide by 2 since each addFriendship() creates 2 friendships\\n for i in range((num_users * avg_friendships) // 2):\\n friendship = possible_friendships[i]\\n self.addFriendship(friendship[0], friendship[1])\",\n \"def populate_graph_data(graph_data, whitelist, intervals, user_id):\\n for detected_owner in whitelist:\\n index = binsearch(intervals, detected_owner[\\\"timestamp\\\"])\\n if index != -1:\\n for profile in graph_data:\\n if profile[\\\"aid\\\"] == detected_owner[\\\"aid\\\"]:\\n profile[\\\"data\\\"][index][\\\"images\\\"].append(\\n {\\n \\\"link\\\": generate_presigned_link(detected_owner[\\\"key\\\"]),\\n \\\"timestamp\\\": detected_owner[\\\"timestamp\\\"],\\n \\\"location\\\": get_location_from_camera_id(\\n user_id, detected_owner[\\\"metadata\\\"][\\\"camera_id\\\"]\\n ),\\n \\\"camera_id\\\": detected_owner[\\\"metadata\\\"][\\\"camera_id\\\"],\\n }\\n )\\n profile[\\\"data\\\"][index][\\\"y\\\"] = profile[\\\"data\\\"][index][\\\"y\\\"] + 1\\n break\\n return graph_data\",\n \"def populate_graph2(self, num_users, avg_friendships):\\n # Reset graph\\n self.reset()\\n # random names to populate network with\\n first_names = [\\\"John\\\", \\\"Frank\\\", \\\"George\\\", \\\"Jane\\\", \\\"Bruce\\\", \\\"Patrick\\\", \\\"James\\\",\\n \\\"Superman\\\", \\\"Betty\\\", \\\"Wilma\\\", \\\"Michael\\\", \\\"Barney\\\", \\\"Spiderman\\\"]\\n\\n last_names = [\\\"Miller\\\", \\\"Wayne\\\", \\\"Rubble\\\", \\\"Doe\\\", \\\"McVey\\\",\\n \\\"Gonzalez\\\", \\\"Replogle\\\", \\\"Franco\\\", \\\"Rae\\\", \\\"Espinosa\\\"]\\n\\n if num_users > avg_friendships:\\n # Add users\\n for _ in range(num_users):\\n random_user_name = (random.choice(\\n first_names) + \\\" \\\" + random.choice(last_names))\\n\\n self.add_user(random_user_name)\\n\\n # Create friendships\\n for user_id in self.users:\\n # repeat until user has at least avg_friendships\\n while len(self.friendships[user_id]) < avg_friendships:\\n random_id = random.randint(1, num_users)\\n\\n if random_id not in self.friendships[user_id] and user_id != random_id:\\n self.add_friendship(user_id, random_id)\\n else:\\n print(\\\"Number of users must be larger that number of friendship links\\\")\",\n \"def populateGraph(self, numUsers, avgFriendships):\\n # Reset graph\\n self.lastID = 0\\n self.users = {}\\n self.friendships = {}\\n # !!!! IMPLEMENT ME\\n # Add users\\n #for i in range numUsers, call addUser and implement a random name\\n # add their ID to a list, for building friendhips\\n for i in range(1, numUsers + 1):\\n self.addUser(\\\"Bob\\\") \\n # Create friendships\\n possible_friends = []\\n\\n total_friendships = numUsers * avgFriendships\\n num_friendships = 0\\n while num_friendships < total_friendships:\\n userID = random.randint(1, self.lastID)\\n friendID = random.randint(1, self.lastID)\\n if self.addFriendship(userID, friendID):\\n num_friendships += 2\\n ''' \\n for userID in self.users:\\n for friendID in range(userID + 1, self.lastID + 1):\\n possible_friends.append((userID, friendID))\\n\\n print(possible_friends)\\n num_friendships = (numUsers * avgFriendships) // 2\\n random.shuffle(possible_friends)\\n friendships_to_create = possible_friends[:num_friendships]\\n # or could use friendships_to_create = random.sample(possibleFriendships, (numUsers * avgFriendships) // 2)\\n\\n for friendship in friendships_to_create:\\n self.addFriendship(friendship[0], friendship[1])\\n\\n '''\",\n \"def main():\\n users = {}\\n\\n with open('../the_data.json', 'r') as f:\\n the_data = json.loads(f.read())\\n\\n user_data = the_data['data']\\n the_user_ids = json.loads(the_data['user_ids_list'])\\n \\n def p(vector):\\n vector = json.loads(vector)\\n return {field: vector[i] for i,field in enumerate(the_data['vector_fields'])}\\n\\n result = {}\\n for step in range(1, 20):\\n step = str(step)\\n\\n users = {}\\n the_user_ids_for_this_step = []\\n for uid in the_user_ids:\\n try:\\n users[uid] = p(user_data[uid][step])\\n the_user_ids_for_this_step.append(uid) \\n except:\\n pass\\n\\n for user_id in the_user_ids_for_this_step: \\n nearest = computeNearestNeighbor(user_id,\\n users,\\n distance_algorithm='minkowski')\\n # print user_id\\n if user_id not in result:\\n result[user_id] = {}\\n\\n result[user_id][step] = nearest[:3]\\n\\n\\n\\n # print result\\n\\n for u in result.keys():\\n woha = []\\n print '%s, step_count: %s' % (u, user_data[u]['step_count'])\\n ls = result[u].keys()\\n ls.sort()\\n for s in ls: \\n print s\\n for near in result[u][s]:\\n if near[1] in woha:\\n ulala = '>'*woha.count(near[1])\\n else:\\n ulala = ''\\n woha.append(near[1])\\n print '\\\\t'*int(s), '%s %s, %s, step_count: %s' % (ulala, near[1], near[0], user_data[near[1]]['step_count'])\\n\\n print\",\n \"def create_node2edges_on2freq_grid(self):\\n trip_id2model = pickle.load(open('pickles/trip_id2model.pickle','rb'))\\n old_trip_id = -1\\n model = trip_id2model[1]\\n sub_x = 5\\n sub_y = 5\\n node2edges_on2sub_grid2points = {}\\n for line in self.lines:\\n trip_id,lat,lon = normalize_simple(line)\\n if trip_id != old_trip_id:\\n #print trip_id\\n model = trip_id2model[trip_id]\\n old_trip_id = trip_id\\n node = self.gps_to_node(lat,lon)\\n if node == -1:\\n continue\\n #print \\\"pushed through\\\"\\n incident_edges = self.incident_edges(node)\\n edges_on = []\\n for edge in incident_edges:\\n if model[edge] == 1:\\n edges_on.append(edge)\\n edges_on.sort()\\n edges_on = tuple(edges_on)\\n min_lat,max_lat,min_lon,max_lon = self.coords_to_min_max_lat_lon(self.node_to_coords(node))\\n\\n sub_row,sub_col = gen_gps_to_coords(lat,lon,sub_x,sub_y,min_lat,max_lat,min_lon,max_lon)\\n sub_tuple = (sub_row,sub_col)\\n if node not in node2edges_on2sub_grid2points:\\n node2edges_on2sub_grid2points[node] = {}\\n edges_on2sub_grid2points = node2edges_on2sub_grid2points[node]\\n if edges_on not in edges_on2sub_grid2points:\\n edges_on2sub_grid2points[edges_on] = defaultdict(list)\\n sub_grid2points = edges_on2sub_grid2points[edges_on]\\n points = sub_grid2points[sub_tuple]\\n node2edges_on2sub_grid2points[node][edges_on][sub_tuple].append([lat,lon])\\n #points.append([lat,lon])\\n\\n print node2edges_on2sub_grid2points.keys()\\n print node2edges_on2sub_grid2points[2].keys()\\n print node2edges_on2sub_grid2points[2][(2,3)].keys()\\n \\n node2edges_on2median = {}\\n for node in node2edges_on2sub_grid2points:\\n print node\\n edges_on2sub_grid2points = node2edges_on2sub_grid2points[node]\\n node2edges_on2median[node] = {}\\n for edges_on in edges_on2sub_grid2points:\\n sub_grid2points = edges_on2sub_grid2points[edges_on]\\n best_spot = (-1,-1)\\n best_score = 0\\n for spot in sub_grid2points:\\n score = len(sub_grid2points[spot])\\n if score > best_score:\\n best_score = score\\n best_spot = spot\\n node2edges_on2median[node][edges_on] = list_median(sub_grid2points[spot])\\n \\n with open('pickles/node2edges_on2median-%d-%d.pickle' % (self.rows,self.cols),'wb') as output:\\n pickle.dump(node2edges_on2median,output)\",\n \"def create_graph(data, drone_altitude, safety_distance):\\n # Find grid and offsets.\\n grid, north_offset, east_offset = create_grid(data, drone_altitude, safety_distance)\\n\\n # Find object centers.\\n centers = get_object_centers(data, north_offset, east_offset, drone_altitude, safety_distance)\\n\\n # Create Voronoid from centers\\n voronoi = Voronoi(centers)\\n\\n # Find open edges\\n edges = find_open_edges_voronoi(voronoi, grid)\\n\\n # Create graph.\\n return (create_graph_from_edges(edges), north_offset, east_offset)\",\n \"def gen_graph(self):\",\n \"def populateGraph(self, numUsers, avgFriendships):\\n # Reset graph\\n self.lastID = 0\\n self.users = {}\\n self.friendships = {}\\n\\n # Add users\\n for i in range(0, numUsers):\\n self.addUser(\\\"Brian-\\\"+str(i+1))\\n\\n # Create friendships\\n for i in range(0, avgFriendships):\\n for u in self.users:\\n self.friendships[u].add(random.choice([user for user in self.users]))\",\n \"def populate_graph(self):\",\n \"def create_graph(data, drone_altitude, safety_distance):\\n grid, edges = environment.create_grid_and_edges(data, drone_altitude, safety_distance)\\n print('Found %5d edges' % len(edges))\\n\\n # =====================================\\n # create graph\\n # =====================================\\n # create the graph with the weight of the edges\\n # set to the Euclidean distance between the points\\n G = nx.Graph()\\n\\n for e in edges:\\n dist = utils.euclidian_distance(e[0], e[1]) # LA.norm(np.array(e[0]) - np.array(e[1]))\\n G.add_edge(e[0], e[1], weight=dist)\\n\\n return G, edges, grid\",\n \"def draw_graph(self):\\r\\n G=nx.Graph()\\r\\n \\r\\n list_location1 = []\\r\\n list_location2 = []\\r\\n list_location3 = []\\r\\n list_location4 = []\\r\\n \\r\\n for citizen in self.citizens:\\r\\n G.add_node(citizen.id)\\r\\n if citizen.location == 1:\\r\\n list_location1.append(citizen.id)\\r\\n elif citizen.location == 2:\\r\\n list_location2.append(citizen.id)\\r\\n elif citizen.location == 3:\\r\\n list_location3.append(citizen.id)\\r\\n else: \\r\\n list_location4.append(citizen.id)\\r\\n\\r\\n for citizen in self.citizens:\\r\\n for friend in citizen.friends:\\r\\n G.add_edge(citizen.id,friend.id)\\r\\n\\r\\n pos = nx.random_layout(G)\\r\\n nx.draw_networkx_nodes(G,pos,node_size=60,nodelist=list_location1, node_color='r')\\r\\n nx.draw_networkx_nodes(G,pos,node_size=60,nodelist=list_location2, node_color='g')\\r\\n nx.draw_networkx_nodes(G,pos,node_size=60,nodelist=list_location3, node_color='b')\\r\\n nx.draw_networkx_nodes(G,pos,node_size=60,nodelist=list_location4, node_color='y')\\r\\n nx.draw_networkx_edges(G,pos, width=1)\\r\\n\\r\\n plt.show()\",\n \"def draw_network(graph, users, filename):\\n ###TODO-- Completed\\n candidate_names = [user['screen_name'] for user in users]\\n plt.figure(figsize=(12,12))\\n candidate_labels = {node: node if node in candidate_names else '' for node in graph.nodes_iter()}\\n #print(candidate_labels)\\n nx.draw_networkx(graph, labels=candidate_labels, alpha=0.5, node_color='r', node_size=100, width=0.1)\\n #plt.show()\\n plt.axis('off')\\n plt.savefig(filename)\\n #pass\",\n \"def getAllSocialPaths(self, userID):\\n visited = {} # Note that this is a dictionary, not a set\\n # !!!! IMPLEMENT ME\\n pass\",\n \"def test_dbscan_loc(self):\\n sp_file = os.path.join(\\\"tests\\\", \\\"data\\\", \\\"geolife\\\", \\\"geolife_staypoints.csv\\\")\\n sp = ti.read_staypoints_csv(sp_file, tz=\\\"utc\\\", index_col=\\\"id\\\", crs=\\\"epsg:4326\\\")\\n sp, locs = sp.as_staypoints.generate_locations(\\n method=\\\"dbscan\\\", epsilon=10, num_samples=1, distance_metric=\\\"haversine\\\", agg_level=\\\"dataset\\\"\\n )\\n\\n # create locations as grouped staypoints, another way to create locations\\n other_locs = []\\n grouped_df = sp.groupby([\\\"user_id\\\", \\\"location_id\\\"])\\n for combined_id, group in grouped_df:\\n user_id, location_id = combined_id\\n group.set_geometry(sp.geometry.name, inplace=True)\\n\\n if int(location_id) != -1:\\n temp_loc = {}\\n temp_loc[\\\"user_id\\\"] = user_id\\n temp_loc[\\\"id\\\"] = location_id\\n\\n # point geometry of place\\n temp_loc[\\\"center\\\"] = Point(group.geometry.x.mean(), group.geometry.y.mean())\\n other_locs.append(temp_loc)\\n\\n other_locs = gpd.GeoDataFrame(other_locs, columns=[\\\"user_id\\\", \\\"id\\\", \\\"center\\\"], geometry=\\\"center\\\", crs=sp.crs)\\n other_locs.set_index(\\\"id\\\", inplace=True)\\n\\n assert_geoseries_equal(other_locs[\\\"center\\\"], locs[\\\"center\\\"], check_less_precise=True)\",\n \"def _make_graph(nodes, ways):\\n graph = networkx.MultiDiGraph(crs=\\\"EPSG:4326\\\")\\n ways_proj = ways.set_crs(\\\"EPSG:4326\\\").to_crs(\\\"EPSG:3395\\\")\\n\\n for node_id, node_attr in nodes.rename(columns={'longitude': 'x', 'latitude': 'y'}).iterrows():\\n graph.add_node(node_id, **node_attr)\\n\\n for _, way in ways_proj.iterrows():\\n\\n osm_oneway_values = [\\\"yes\\\", \\\"true\\\", \\\"1\\\", \\\"-1\\\", \\\"T\\\", \\\"F\\\"]\\n if \\\"oneway\\\" in way and way.oneway in osm_oneway_values:\\n if way[\\\"oneway\\\"] == \\\"-1\\\" or way[\\\"oneway\\\"] == \\\"T\\\":\\n # paths with a one-way value of -1 or T are one-way, but in the\\n # reverse direction of the nodes' order, see osm documentation\\n path_nodes = list(reversed(way.nodes))\\n else:\\n path_nodes = way.nodes\\n # add this path (in only one direction) to the graph\\n one_way = True\\n\\n elif \\\"junction\\\" in way and way.junction == \\\"roundabout\\\":\\n # roundabout are also oneway but not tagged as is\\n path_nodes = way.nodes\\n one_way = True\\n\\n # else, this path is not tagged as one-way or it is a walking network\\n # (you can walk both directions on a one-way street)\\n else:\\n # add this path (in both directions) to the graph and set its\\n # 'oneway' attribute to False. if this is a walking network, this\\n # may very well be a one-way street (as cars/bikes go), but in a\\n # walking-only network it is a bi-directional edge\\n path_nodes = way.nodes\\n one_way = False\\n\\n # zip together the path nodes so you get tuples like (0,1), (1,2), (2,3)\\n # and so on\\n path_edges = list(zip(path_nodes[:-1], path_nodes[1:]))\\n graph.add_edges_from(path_edges, **way[['id']])\\n if not one_way:\\n path_edges_reverse = [(v, u) for u, v in path_edges]\\n graph.add_edges_from(path_edges_reverse, **way[['id']])\\n\\n graph = osmnx.utils_graph.add_edge_lengths(graph)\\n return graph\",\n \"def contact_tracing_users(past_reservations, user_id: int):\\n # check which users were at the restaurant at the same time as the positive guy\\n user_at_risk = []\\n for reservation in past_reservations:\\n et = reservation['entrance_time']\\n if isinstance(et, str):\\n et = datetime.strptime(reservation['entrance_time'], '%Y-%m-%dT%H:%M:%S.%f')\\n # get average staying time of the restaurant and compute 'danger period'\\n resp = requests.get(f\\\"http://{os.environ.get('GOS_RESTAURANT')}/restaurants/{reservation['restaurant_id']}\\\")\\n if resp == 404:\\n # restaurant does not exists/was deleted, carry on defining a standard avg time (prioritize reservations which are always kept)\\n avg_stay_time = time(hour=1, minute=30)\\n else:\\n avg_stay_time = resp.json()['avg_stay_time']\\n # convert time format back to object\\n avg_stay_time = datetime.strptime(avg_stay_time, \\\"%H:%M:%S\\\").time()\\n\\n # avg_stay_time = Restaurant.query.filter_by(id=reservation['restaurant_id']).first().avg_stay_time\\n staying_interval = timedelta(hours=avg_stay_time.hour, minutes=avg_stay_time.minute, seconds=avg_stay_time.second)\\n start_time = et - staying_interval\\n end_time = et + staying_interval\\n\\n user_reservation = Reservation.query.filter(Reservation.user_id != user_id).\\\\\\n filter_by(restaurant_id=reservation['restaurant_id']).\\\\\\n filter(Reservation.entrance_time.between(start_time, end_time)).all()\\n # print(user_reservation)\\n # preserve positive user reservation we're referring to, as to notify operator \\n user_ids_args = 'or_(' + ', '.join(['User.id == ' + str(ur.user_id) for ur in user_reservation])+')'\\n # request users information from User service\\n resp = requests.get(f'http://{os.environ.get(\\\"GOS_USER\\\")}/users/filter?filter={user_ids_args}')\\n if resp.status_code != 200:\\n logging.error(\\\"Error in user service contact\\\")\\n raise Exception\\n user_at_risk += resp.json()\\n return {'users': user_at_risk}, 200\",\n \"def create_path_new(self):\\n\\n \\n first_lasts = []\\n first_lasts.append([0,0])\\n matrices = []\\n matrices.append([[[0 for i in range(self.graph.cols)] for i in range(self.graph.rows)],0])\\n edge_sets = []\\n edge_sets.append([0 for i in range(self.graph.num_edges)])\\n cur_line = self.line_num\\n nodes_visited = []\\n nodes_visited.append([])\\n normalized = normalize_simple(self.graph.lines[cur_line])\\n matrices_index = 0\\n prev_coords = (-1,-1)\\n prev_gps = (-1.0,-1.0)\\n while normalized[0] == self.trip_id:\\n lat = normalized[1]\\n lon = normalized[2]\\n coords = self.graph.gps_to_coords(lat,lon)\\n node = self.graph.coords_to_node(coords[0],coords[1])\\n\\n if prev_coords == (-1,-1) and coords[0] != -1:\\n first_lasts[matrices_index][0] = node\\n\\n if coords[0] == -1 and prev_coords[0] != -1:\\n prev_node = self.graph.coords_to_node(prev_coords[0],prev_coords[1])\\n first_lasts[matrices_index][1] = prev_node\\n\\n if prev_coords != (-1,-1) and coords[0] != -1 and coords != prev_coords:\\n edge_num = self.graph.edge_num(prev_coords[0],prev_coords[1],coords[0],coords[1])\\n if edge_num == -1:\\n new_edges = self.find_edges((lat,lon),prev_gps)\\n for add_edge in new_edges:\\n edge_sets[matrices_index][add_edge] = 1\\n else:\\n edge_sets[matrices_index][edge_num] = 1\\n\\n if coords[0] == -1:\\n matrices.append([[[0 for i in range(self.graph.cols)] for i in range(self.graph.rows)],0])\\n first_lasts.append([0,0])\\n edge_sets.append([0 for i in range(self.graph.num_edges)])\\n nodes_visited.append([])\\n matrices_index += 1\\n \\n elif coords[0] < self.graph.rows and coords[1] < self.graph.cols and not matrices[matrices_index][0][coords[0]][coords[1]]:\\n matrices[matrices_index][1] += 1\\n matrices[matrices_index][0][coords[0]][coords[1]] = 1\\n nodes_visited[matrices_index].append(coords)\\n\\n prev_coords = coords\\n\\n cur_line += 1\\n if cur_line == len(self.graph.lines):\\n break\\n normalized = normalize_simple(self.graph.lines[cur_line])\\n prev_gps = (lat,lon)\\n\\n prev_node = self.graph.coords_to_node(prev_coords[0],prev_coords[1])\\n first_lasts[matrices_index][1] = prev_node\\n self.next_line = cur_line\\n best_index = 0\\n best_score = 0\\n for matrix_index in range(len(matrices)):\\n if matrices[matrix_index][1] > best_score:\\n best_score = matrices[matrix_index][1]\\n best_index = matrix_index\\n\\n #for coords in nodes_visited[best_index]:\\n # self.graph.node_visit(self.trip_id,coords)\\n\\n #if self.trip_id not in self.graph.trip_id2line_num:\\n # self.graph.first_last2trip_ids[tuple(first_lasts[best_index])].append(self.trip_id)\\n\\n return matrices[best_index][0],edge_sets[best_index],first_lasts[best_index]\",\n \"def graphformation(time_lower, time_upper):\\n\\tprm = param.Para()\\n\\ttry:\\n\\t\\tdb_connection = mysql.connector.connect(\\n\\t\\t host=prm.hostname,\\n\\t\\t user=prm.username,\\n\\t\\t passwd=prm.password,\\n\\t\\t database= prm.dbname\\n\\t\\t )\\n\\t\\tdb_cursor = db_connection.cursor()\\n\\texcept:\\n\\t\\tprint(\\\"Can't Connect to database, check credentials in parameter file\\\")\\n\\tquery = (\\\"SELECT * FROM identity \\\")\\n\\tdb_cursor.execute(query)\\n\\tdf1=pd.DataFrame(db_cursor.fetchall())\\n\\tdf1.columns= ['node','deviceid','student','rollno']\\n\\tdict_identity = dict(zip(df1.deviceid, df1.node))\\n\\trev_dict_identity = dict(zip(df1.node, df1.deviceid ))\\n\\tquery = (\\\"SELECT * FROM activity WHERE time BETWEEN '{}' AND '{}'\\\".format(time_lower,time_upper)) ## incomplete\\n\\tdb_cursor.execute(query)\\n\\tactivity_data = pd.DataFrame(db_cursor.fetchall())\\n\\tif activity_data.empty==False:\\n\\t\\tactivity_data.columns=[\\\"sl_no\\\",\\\"time\\\",\\\"node\\\",\\\"latitude\\\",\\\"longitude\\\"]\\n\\telse:\\n\\t\\tprint(\\\"No Activity in the selected Time Window\\\")\\n\\t\\treturn\\n\\tnumnodes= len(df1)\\n\\tedges= []\\n\\tscore = {}\\n\\t#print(activity_data)\\n\\ttime_groups = activity_data.groupby('time')\\n\\twith open(r'C:\\\\Users\\\\HP\\\\Desktop\\\\project\\\\Contact_Graph\\\\bluetooth.txt') as json_file:\\n\\t\\tdata1 = json.load(json_file)\\n\\tfor name, group in time_groups:\\n\\t\\tscore_tmp = decayfunc(name,time_upper)\\n\\t\\tgroup = group.sort_values('node')\\n\\t\\tfor i in range(len(group)-1):\\n\\t\\t\\tnode1 = group.iloc[i,2]\\n\\t\\t\\t###########################\\n\\t\\t\\tlistnearby=[]\\n\\t\\t\\ttry:\\n\\t\\t\\t\\tlistnearby = data1[rev_dict_identity[node1]][str(name)]\\n\\t\\t\\t\\tlistnearby = [dict_identity[i] for i in listnearby if dict_identity[i]>node1]\\n\\t\\t\\t\\tfor i in listnearby:\\n\\t\\t\\t\\t\\ttry:\\n\\t\\t\\t\\t\\t\\tscore[(node1,i)]+=1\\n\\t\\t\\t\\t\\texcept:\\n\\t\\t\\t\\t\\t\\tscore[(node1,i)]=1\\n\\t\\t\\texcept:\\n\\t\\t\\t\\tpass\\n\\t\\t\\t###########################\\n\\t\\t\\tfor j in range(i+1,len(group)):\\n\\t\\t\\t\\tnode2 =group.iloc[j,2]\\n\\t\\t\\t\\tif proximityfunc(group.iloc[i,3],group.iloc[i,4],group.iloc[j,3],group.iloc[j,4]) and node2 not in listnearby:\\n\\t\\t\\t\\t\\ttry:\\n\\t\\t\\t\\t\\t\\tscore[(group.iloc[i,2],group.iloc[j,2])]+=1\\n\\t\\t\\t\\t\\texcept:\\n\\t\\t\\t\\t\\t\\tscore[(group.iloc[i,2],group.iloc[j,2])]=1\\n\\tnode_list = list(df1.node)\\n\\ttitle_list = list(df1.deviceid)\\n\\tedges_list = []\\n\\tfor edge,val in score.items():\\n\\t\\tedges_list.append((int(edge[0]),int(edge[1]),float(val)))\\n\\n\\treturn edges_list,node_list,title_list\",\n \"def populateGraph(self, numUsers, avgFriendships):\\n # Reset graph\\n self.lastID = 0\\n self.users = {}\\n self.friendships = {}\\n # !!!! IMPLEMENT ME\\n alphabet = ['a','b','c','d','e','f','g','h','i','j','k','l','m','n','o','p','q','r','s','t','u','v','w','x','y','z']\\n # Add users\\n for i in range(numUsers):\\n name = ''\\n for i in range(randint(5,9)):\\n name = name + alphabet[randint(0, len(alphabet) - 1)]\\n self.addUser(name)\\n\\n # Create friendships\\n for key in self.users:\\n for _ in range(randint(0, avgFriendships)):\\n new_friend = randint(1, len(self.users)-1)\\n if new_friend not in self.friendships[key] and new_friend != key:\\n self.addFriendship(key, new_friend)\\n\\n # get average friendships per user\\n friendships = 0\\n for key, item in self.friendships.items():\\n friendships += len(item)\\n \\n print('average friendships', friendships/len(self.users))\",\n \"def getAllSocialPaths(self, userID):\\n visited = {} # Note that this is a dictionary, not a set\\n # !!!! IMPLEMENT ME\\n q = Queue()\\n q.put([userID])\\n\\n while not q.empty():\\n user_path = q.get()\\n user = user_path[-1]\\n\\n if user not in visited.keys():\\n visited[user] = user_path\\n for friend in self.friendships[user]:\\n new_path = user_path[::]\\n new_path.append(friend)\\n q.put(new_path)\\n\\n # get average degree of separation per user\\n degrees = 0\\n for key, item in visited.items():\\n degrees += len(item)\\n \\n print('average degree of separation', degrees/len(self.users))\\n\\n return visited\",\n \"def label_users(self):\\n record_unit = 1000\\n print self.friendship_graph.number_of_nodes()\\n print self.friendship_graph.number_of_edges()\\n\\n for num, node in enumerate(self.friendship_graph.nodes()):\\n fake_flag = self.determine_spammer_by_percentage(node)\\n self.friendship_graph.node[node]['fake'] = fake_flag\\n # print self.friendship_graph.node[node]\\n if num % record_unit == 0:\\n print num\\n print time.strftime('%Y-%m-%d %H:%M:%S', time.localtime(time.time()))\\n nx.write_gpickle(self.friendship_graph, \\\"graph/firendship_new_label%d.pickle\\\" % num)\\n if num != 0:\\n os.remove(\\\"graph/firendship_new_label%d.pickle\\\" % (num - record_unit))\\n\\n nx.write_gpickle(self.friendship_graph, \\\"graph/firendship_0.8fake_%d.pickle\\\" % num)\",\n \"def example_triplegs_merge(example_staypoints_merge):\\n # get staypoints from above\\n sp, _ = example_staypoints_merge\\n # create triplegs inbetween\\n # tripleg between 5 and 2\\n t21 = pd.Timestamp(\\\"1971-01-02 05:10:00\\\", tz=\\\"utc\\\")\\n t22 = pd.Timestamp(\\\"1971-01-02 05:40:00\\\", tz=\\\"utc\\\")\\n # tripleg between 6 and 15\\n t45 = pd.Timestamp(\\\"1971-01-02 08:57:00\\\", tz=\\\"utc\\\")\\n t5 = pd.Timestamp(\\\"1971-01-02 09:00:00\\\", tz=\\\"utc\\\")\\n list_dict = [\\n {\\\"id\\\": 0, \\\"user_id\\\": 0, \\\"started_at\\\": t45, \\\"finished_at\\\": t5},\\n {\\\"id\\\": 1, \\\"user_id\\\": 0, \\\"started_at\\\": t21, \\\"finished_at\\\": t22},\\n ]\\n # geometry is not required for the merge operation, so we leave it away\\n tpls = pd.DataFrame(data=list_dict)\\n tpls = tpls.set_index(\\\"id\\\")\\n return sp, tpls\",\n \"def getAllSocialPaths(self, userID):\\n visited = {} # Note that this is a dictionary, not a set\\n print(f\\\"user ID {userID}\\\")\\n\\n for i in range(1, len(self.users)):\\n visited[i] = self.bfs(userID, i)\\n\\n return visited\",\n \"async def async_step_geography_by_coords(\\n self, user_input: dict[str, str] | None = None\\n ) -> FlowResult:\\n if not user_input:\\n return self.async_show_form(\\n step_id=\\\"geography_by_coords\\\", data_schema=self.geography_coords_schema\\n )\\n\\n return await self._async_init_geography(\\n user_input, INTEGRATION_TYPE_GEOGRAPHY_COORDS\\n )\",\n \"def create_boston_graph(df, start, end):\\n ports = pd.unique(df[['Source Port', 'Destination Port']].values.ravel('K'))\\n ips = pd.unique(df[['Source IP', 'Destination IP']].values.ravel('K'))\\n\\n G = nx.Graph(mode=\\\"interval\\\", start=start, end=end)\\n G.add_nodes_from(ports, type=\\\"port\\\")\\n G.add_nodes_from(ips, type=\\\"ip\\\")\\n\\n interval_info = {}\\n for index, row in df.iterrows():\\n src_ip = row['Source IP']\\n src_port = row['Source Port']\\n dst_ip = row['Destination IP']\\n dst_port = row['Destination Port']\\n label = row['Label']\\n interval_info['timestamp'] = row['Timestamp']\\n\\n # -1: malicious, 1: benign\\n G.nodes[src_ip]['interval_ground_truth'] = convert_label_to_numerical(label)\\n G.nodes[dst_ip]['interval_ground_truth'] = convert_label_to_numerical(label)\\n G.nodes[src_port]['interval_ground_truth'] = convert_label_to_numerical(label)\\n G.nodes[dst_port]['interval_ground_truth'] = convert_label_to_numerical(label)\\n\\n # Setting labels for nodes on the current window\\n G.nodes[src_ip]['label'] = src_ip\\n G.nodes[src_port]['label'] = src_port\\n G.nodes[dst_ip]['label'] = dst_ip\\n G.nodes[dst_port]['label'] = dst_port\\n\\n nodes = (src_ip, src_port, dst_ip, dst_port)\\n\\n for i in nodes:\\n for j in nodes:\\n if i != j:\\n if i in (src_ip, dst_ip):\\n frm = \\\"ip\\\"\\n else:\\n frm = \\\"port\\\"\\n if j in (src_ip, dst_ip):\\n to = \\\"ip\\\"\\n else:\\n to = \\\"port\\\"\\n G.add_edge(i, j,\\n label_class=label,\\n weight=1,\\n type=\\\"%s_to_%s\\\" % (frm, to))\\n return G, interval_info\",\n \"def get_all_social_paths(self, user_id):\\n if len(self.friendships) > 0:\\n visited = {}\\n q = Queue()\\n q.enqueue([user_id])\\n\\n while q.size() > 0:\\n curr_path = q.dequeue()\\n curr_vertex = curr_path[-1]\\n\\n if curr_vertex not in visited:\\n visited[curr_vertex] = curr_path\\n\\n for friend in self.friendships[curr_vertex]:\\n path_copy = curr_path[:]\\n path_copy.append(friend)\\n q.enqueue(path_copy)\\n\\n return visited\\n\\n else:\\n print(\\\"There are currently no friendship paths in the network\\\")\",\n \"def test_location_ids_and_noise(self, example_staypoints):\\n sp = example_staypoints\\n sp2, _ = sp.as_staypoints.generate_locations(\\n method=\\\"dbscan\\\", epsilon=10, num_samples=2, distance_metric=\\\"haversine\\\", agg_level=\\\"user\\\"\\n )\\n assert sp2.loc[1, \\\"location_id\\\"] == sp2.loc[15, \\\"location_id\\\"]\\n assert sp2.loc[5, \\\"location_id\\\"] == sp2.loc[6, \\\"location_id\\\"]\\n assert sp2.loc[80, \\\"location_id\\\"] == sp2.loc[3, \\\"location_id\\\"]\\n assert sp2.loc[1, \\\"location_id\\\"] != sp2.loc[6, \\\"location_id\\\"]\\n assert sp2.loc[1, \\\"location_id\\\"] != sp2.loc[80, \\\"location_id\\\"]\\n\\n assert sp2.loc[[2, 7], \\\"location_id\\\"].isnull().all()\",\n \"def get_friends_graph():\\n total_users = 0\\n count_bad_users = 0\\n count_graph_users = 0\\n\\n result = \\\"was successful\\\"\\n\\n settings.print_message(\\\"Create VK session for application (app_id={})\\\".format(settings.VK_APPLICATION_ID))\\n logger.debug(\\\"Create VK session for application (app_id={}).\\\".format(settings.VK_APPLICATION_ID))\\n try:\\n session = vk.Session(access_token=settings.VK_ACCESS_TOKEN)\\n api = vk.API(session)\\n except Exception as error:\\n logger.warn(traceback.format_exc())\\n settings.print_message(\\\"Can't create VK session for application with app_id={}\\\".format(settings.VK_APPLICATION_ID))\\n return (\\\"with error.\\\", 0, 0, 0)\\n # logger.debug(\\\"\\\".format())\\n graph = yEdGraph.Graph()\\n level_queue = [int(settings.PARAMS[\\\"user_id\\\"]), ]\\n count_levels = int(settings.PARAMS[\\\"levels\\\"])\\n all_level_ids = [list() for _ in range(count_levels)]\\n\\n for step in range(count_levels):\\n settings.print_message(\\\"Process level #{} (total users {})\\\".format(step, level_counter))\\n logger.debug(\\\"Process level #{} (total users {})\\\".format(step, level_counter))\\n level_counter = len(level_queue)\\n for user_index in range(level_counter):\\n id = level_queue[user_index]\\n total_users += 1\\n settings.print_message(\\\"Process id {}. User #{} on level #{} (total {})\\\".format(id, user_index, step, level_counter), 2)\\n logger.debug(\\\"Process id {}. User #{} on level #{} (total {}).\\\".format(id, user_index, step, level_counter))\\n try:\\n settings.print_message(\\\"Add user node in graph.\\\", 3)\\n logger.debug(\\\"Check user (id={}) in graph\\\".format(id))\\n if not id in graph.nodes.keys():\\n logger.debug(\\\"Create user node in graph (id={}).\\\".format(id))\\n logger.debug(\\\"Get info for user (id={}).\\\".format(id))\\n try:\\n user_info = api.users.get(user_ids=[id], fields = \\\"photo_200_orig\\\")\\n except Exception as error:\\n logger.warn(traceback.format_exc())\\n logger.debug(\\\"Can not get info for user (id={}), skip.\\\".format(id))\\n settings.print_message(\\\"Can not get info for user, skip.\\\", 3)\\n count_bad_users += 1\\n continue\\n if not user_info: \\n logger.debug(\\\"Can not get info for user (id={}), skip.\\\".format(id))\\n settings.print_message(\\\"Can not get info for user, skip.\\\", 3)\\n count_bad_users += 1\\n continue\\n user_info = user_info[0]\\n logger.debug(\\\"User info='{}'.\\\".format(json.dumps(user_info)))\\n logger.debug(\\\"Load user photo (id={}).\\\".format(id))\\n photo = utils.get_request(user_info[\\\"photo_200_orig\\\"])\\n if not photo:\\n logger.debug(\\\"Can't loading user photo (id={}).\\\".format(id))\\n info_label = \\\"ID: {} \\\\nФИГ: {} {} \\\\nНик: {} \\\\nПол: {} \\\\nДата рождения: {} \\\\nГород: {} \\\\nСтрана: {}\\\".format(\\n id,\\n user_info[\\\"first_name\\\"] if \\\"first_name\\\" in user_info else \\\"----\\\",\\n user_info[\\\"last_name\\\"] if \\\"last_name\\\" in user_info else \\\"----\\\",\\n user_info[\\\"nickname\\\"] if \\\"nickname\\\" in user_info else \\\"----\\\",\\n utils.SEX[user_info[\\\"sex\\\"]] if \\\"sex\\\" in user_info else \\\"----\\\",\\n user_info[\\\"bdate\\\"] if \\\"bdate\\\" in user_info else \\\"----\\\",\\n user_info[\\\"city\\\"] if \\\"city\\\" in user_info else \\\"----\\\",\\n user_info[\\\"country\\\"] if \\\"country\\\" in user_info else \\\"----\\\"\\n )\\n graph.add_node(id, check_existance=False, label=info_label, shape=\\\"roundrectangle\\\", font_style=\\\"italic\\\", underlined_text=\\\"false\\\", img=photo, width=\\\"200\\\", height=\\\"200\\\", border_has_color=\\\"false\\\")\\n count_graph_users += 1\\n else:\\n logger.debug(\\\"Graph contains user node (id={}).\\\".format(id))\\n settings.print_message(\\\"Graph already contains this user node.\\\", 3)\\n settings.print_message(\\\"Get friendlist.\\\", 3)\\n logger.debug(\\\"Get friend for user (id={}).\\\".format(id))\\n try:\\n friends = api.friends.get(user_id=id, count=1000000, fields = \\\"nickname, sex, bdate, city, country, photo_200_orig\\\")\\n except Exception as error:\\n logger.debug(\\\"Can not get friends, skip.\\\")\\n settings.print_message(\\\"Can not get friendlist, skip.\\\", 3)\\n count_bad_users += 1\\n continue\\n if not friends:\\n logger.debug(\\\"Can not get friends, skip.\\\")\\n settings.print_message(\\\"Can not get friendlist, skip.\\\", 3)\\n count_bad_users += 1\\n continue\\n settings.print_message(\\\"Process friends (total {}, level #{}).\\\".format(len(friends), step + 1), 3)\\n logger.debug(\\\"Friends count: {}\\\".format(len(friends)))\\n _ = [level_queue.append(friend[\\\"user_id\\\"]) for friend in friends if not friend[\\\"user_id\\\"] in graph.nodes]\\n logger.debug(\\\"Add node for each friend and create edges.\\\")\\n for friend_index, friend in enumerate(friends):\\n total_users += 1\\n logger.debug(\\\"Process friend #{} (id={}).\\\".format(friend_index, friend[\\\"user_id\\\"]))\\n settings.print_message(\\\"Process friends #{} id={} (total {}, level #{}).\\\".format(friend_index, friend[\\\"user_id\\\"], len(friends), step + 1), 4)\\n #if friend_index > 10: break\\n settings.print_message(\\\"Add user node in graph.\\\", 5)\\n logger.debug(\\\"Check user (id={}) in graph\\\".format(friend[\\\"user_id\\\"]))\\n if not friend[\\\"user_id\\\"] in graph.nodes.keys():\\n logger.debug(\\\"Create user node in graph (id={}).\\\".format(friend[\\\"user_id\\\"]))\\n logger.debug(\\\"User info='{}'.\\\".format(json.dumps(friend)))\\n logger.debug(\\\"Load user photo (id={}).\\\".format(friend[\\\"user_id\\\"]))\\n photo = utils.get_request(friend[\\\"photo_200_orig\\\"])\\n if not photo:\\n logger.debug(\\\"Can't loading user photo (id={}).\\\".format(friend[\\\"user_id\\\"]))\\n info_label = \\\"ID: {} \\\\nФИГ: {} {} \\\\nНик: {} \\\\nПол: {} \\\\nДата рождения: {} \\\\nГород: {} \\\\nСтрана: {}\\\".format(\\n friend[\\\"user_id\\\"],\\n friend[\\\"first_name\\\"] if \\\"first_name\\\" in friend else \\\"----\\\",\\n friend[\\\"last_name\\\"] if \\\"last_name\\\" in friend else \\\"----\\\",\\n friend[\\\"nickname\\\"] if \\\"nickname\\\" in friend else \\\"----\\\",\\n utils.SEX[friend[\\\"sex\\\"]] if \\\"sex\\\" in friend else \\\"----\\\",\\n friend[\\\"bdate\\\"] if \\\"bdate\\\" in friend else \\\"----\\\",\\n friend[\\\"city\\\"] if \\\"city\\\" in friend else \\\"----\\\",\\n friend[\\\"country\\\"] if \\\"country\\\" in friend else \\\"----\\\"\\n )\\n graph.add_node(friend[\\\"user_id\\\"], check_existance=False, label=info_label, shape=\\\"roundrectangle\\\", font_style=\\\"italic\\\", underlined_text=\\\"false\\\", img=photo, width=\\\"200\\\", height=\\\"200\\\", border_has_color=\\\"false\\\")\\n count_graph_users += 1\\n else:\\n logger.debug(\\\"Graph contains user node (id={}).\\\".format(friend[\\\"user_id\\\"]))\\n settings.print_message(\\\"Graph already contains this user node.\\\", 5)\\n logger.debug(\\\"Add adge {}-{} in graph.\\\".format(friend[\\\"user_id\\\"], id))\\n # if ...\\n graph.add_edge(friend[\\\"user_id\\\"], id, width=\\\"1.0\\\", color=\\\"#000000\\\", check_existance_nodes=False)\\n except Exception as error:\\n logger.warn(traceback.format_exc())\\n result = \\\"with error\\\"\\n level_queue = level_queue[level_counter:]\\n logger.debug(\\\"Create graphml for graph.\\\")\\n graph.construct_graphml()\\n logger.debug(\\\"Save graphml in file {}.\\\".format(settings.OUTPUT_FILE))\\n try:\\n with open(settings.OUTPUT_FILE, \\\"w\\\", encoding=settings.OUTPUT_ENCODING) as f:\\n f.write(graph.get_graph())\\n except Exception as error:\\n logger.warn(traceback.format_exc())\\n result = \\\"with error\\\"\\n return (result, total_users, count_graph_users, count_bad_users)\",\n \"def echarts_Location(graph,\\r\\n h,\\r\\n x,\\r\\n y,\\r\\n symbol_size=5,\\r\\n line_width=1,\\r\\n width=\\\"1400px\\\",\\r\\n height=\\\"800px\\\",\\r\\n path='locationExample.html',\\r\\n title='Location_Graph',\\r\\n subtitle='',\\r\\n showName=True,\\r\\n font_size=15,\\r\\n edge_symbol_size=10,\\r\\n pageLayout=Page.SimplePageLayout,\\r\\n show_node_value=True,\\r\\n show_edge_value=True,\\r\\n render=True):\\r\\n\\r\\n # initialize nodes list\\r\\n nodes = []\\r\\n if show_node_value:\\r\\n for i in range(len(graph.nodes.aslist())):\\r\\n # calculate reachability for each nodes\\r\\n reachability = ot.calculate_reachability(graph, graph.nodes.labels()[i])\\r\\n if reachability > h:\\r\\n nodes.append(opts.GraphNode(name=graph.nodes.labels()[i],\\r\\n category=0,\\r\\n x=graph.nodes.aslist()[i].data[x],\\r\\n y=graph.nodes.aslist()[i].data[y],\\r\\n label_opts=opts.LabelOpts(is_show=showName, font_size=font_size),\\r\\n value=reachability\\r\\n\\r\\n ))\\r\\n else:\\r\\n nodes.append(opts.GraphNode(name=graph.nodes.labels()[i],\\r\\n category=1,\\r\\n x=graph.nodes.aslist()[i].data[x],\\r\\n y=graph.nodes.aslist()[i].data[y],\\r\\n label_opts=opts.LabelOpts(is_show=showName, font_size=font_size),\\r\\n value=reachability\\r\\n ))\\r\\n else:\\r\\n for i in range(len(graph.nodes.aslist())):\\r\\n # calculate reachability for each nodes\\r\\n reachability = ot.calculate_reachability(graph, graph.nodes.labels()[i])\\r\\n if reachability > h:\\r\\n nodes.append(opts.GraphNode(name=graph.nodes.labels()[i],\\r\\n category=0,\\r\\n x=graph.nodes.aslist()[i].data[x],\\r\\n y=graph.nodes.aslist()[i].data[y],\\r\\n label_opts=opts.LabelOpts(is_show=showName, font_size=font_size)\\r\\n\\r\\n ))\\r\\n else:\\r\\n nodes.append(opts.GraphNode(name=graph.nodes.labels()[i],\\r\\n category=1,\\r\\n x=graph.nodes.aslist()[i].data[x],\\r\\n y=graph.nodes.aslist()[i].data[y],\\r\\n label_opts=opts.LabelOpts(is_show=showName, font_size=font_size)\\r\\n\\r\\n ))\\r\\n\\r\\n # initialize links list\\r\\n links = []\\r\\n if show_edge_value:\\r\\n # initialize value list of edges\\r\\n edge_value = []\\r\\n # 1. compare all edge labels with unique edge labels to find which edges are duplicated\\r\\n # 2. edge value of duplicate edges will be added to the same list and shown together\\r\\n for i in range(len(graph.edges.labels())):\\r\\n for j in range(len(graph.edges.ulabels())):\\r\\n if graph.edges.labels()[i] == graph.edges.ulabels()[j]:\\r\\n if j < len(edge_value):\\r\\n edge_value[j].append('{start time: ' + str(graph.edges.start_times()[i]) + ', end time: ' + str(\\r\\n graph.edges.end_times()[i]) + '}')\\r\\n else:\\r\\n tmp_edge_value = []\\r\\n tmp_edge_value.append(\\r\\n '{start time: ' + str(graph.edges.start_times()[i]) + ', end time: ' + str(\\r\\n graph.edges.end_times()[i]) + '}')\\r\\n edge_value.append(tmp_edge_value)\\r\\n # print the rate of progress\\r\\n print('rate of progress: {}%'.format((i + 1) / len(graph.edges.labels()) * 100))\\r\\n\\r\\n for i in range(len(graph.edges.ulabels())):\\r\\n tmp = graph.edges.ulabels()[i].split('-')\\r\\n\\r\\n links.append(opts.GraphLink(source=tmp[0],\\r\\n target=tmp[1],\\r\\n value=edge_value[i]\\r\\n\\r\\n ))\\r\\n\\r\\n else:\\r\\n for i in range(len(graph.edges.ulabels())):\\r\\n tmp = graph.edges.ulabels()[i].split('-')\\r\\n\\r\\n links.append(opts.GraphLink(source=tmp[0],\\r\\n target=tmp[1]\\r\\n\\r\\n ))\\r\\n\\r\\n # initialize categories list\\r\\n categories = [\\r\\n opts.GraphCategory(name='nodes with reachability more than {}'.format(h)),\\r\\n opts.GraphCategory(name='nodes with reachability less than or equal to {}'.format(h))\\r\\n ]\\r\\n\\r\\n # generate an html file of the graph\\r\\n c = (\\r\\n Graph(init_opts=opts.InitOpts(width=width, height=height))\\r\\n .add(\\r\\n \\\"\\\",\\r\\n nodes=nodes,\\r\\n links=links,\\r\\n categories=categories,\\r\\n layout=\\\"none\\\",\\r\\n symbol_size=symbol_size,\\r\\n linestyle_opts=opts.LineStyleOpts(is_show=True, curve=0.1, width=line_width),\\r\\n label_opts=opts.LabelOpts(position=\\\"right\\\"),\\r\\n edge_symbol=['circle', 'arrow'],\\r\\n edge_symbol_size=symbol_size\\r\\n )\\r\\n .set_global_opts(\\r\\n title_opts=opts.TitleOpts(title=title, subtitle=subtitle,\\r\\n title_textstyle_opts=opts.TextStyleOpts(font_size=40),\\r\\n subtitle_textstyle_opts=opts.TextStyleOpts(font_size=20)),\\r\\n legend_opts=opts.LegendOpts(orient=\\\"vertical\\\", pos_left=\\\"2%\\\", pos_top=\\\"20%\\\",\\r\\n textstyle_opts=opts.TextStyleOpts(font_size=20)),\\r\\n )\\r\\n )\\r\\n\\r\\n if render:\\r\\n page = Page(layout=pageLayout)\\r\\n page.add(c)\\r\\n page.render(path)\\r\\n\\r\\n return c\",\n \"def example_graphing(self) -> None:\\n # Log the current amount of followers to our history of followers\\n self.log_followers()\\n\\n # Graphs all followers / time\\n self.graph_followers()\",\n \"def plot_graph():\\n name = request.args.get('instance')\\n name = str(name)\\n distance = request.args.get('distance')\\n path = request.args.get('path')\\n if name == 'Custom':\\n coords = request.args.get('coords')\\n coords = str(coords)\\n nodes = custom_nodes(coords)\\n else:\\n nodes = create_nodes(name)\\n fig = Figure()\\n axis = fig.add_subplot(1, 1, 1)\\n\\n axis.set_title(name + \\\" - Distance: \\\"+ str(distance))\\n path = str(path).split(',')\\n path = [int(i) for i in path]\\n for i in range(len(path) - 1):\\n\\n start_node = nodes[path[i]]\\n x1, y1 = start_node.x, start_node.y\\n axis.scatter(x1, y1, c = 'b', label = str(path[i]))\\n axis.text(x1,y1, str(path[i]))\\n end_node = nodes[path[i+1]]\\n x2, y2 = end_node.x, end_node.y\\n axis.plot([x1,x2], [y1, y2])\\n\\n last_node = nodes[path[len(path)-1]]\\n x1, y1 = last_node.x, last_node.y\\n axis.text(x1,y1, str(path[len(path)-1]))\\n\\n begin_node = nodes[path[0]]\\n x2, y2 = begin_node.x, begin_node.y\\n axis.scatter(x1, y1, c = 'b', label = str(path[len(path)-1]))\\n axis.plot([x1,x2], [y1, y2])\\n\\n output = io.BytesIO()\\n FigureCanvas(fig).print_png(output)\\n return Response(output.getvalue(), mimetype=\\\"image/png\\\")\",\n \"async def async_step_user(\\n self, user_input: dict[str, Any] | None = None\\n ) -> FlowResult:\\n if not user_input:\\n return self._show_form_user()\\n\\n await self.async_set_unique_id(\\n f\\\"{user_input[CONF_LOCATION][CONF_LATITUDE]}_{user_input[CONF_LOCATION][CONF_LONGITUDE]}\\\"\\n )\\n self._abort_if_unique_id_configured()\\n\\n data = await async_get_nearby_stations(self.hass, user_input)\\n if not data.get(\\\"ok\\\"):\\n return self._show_form_user(\\n user_input, errors={CONF_API_KEY: \\\"invalid_auth\\\"}\\n )\\n if len(stations := data.get(\\\"stations\\\", [])) == 0:\\n return self._show_form_user(user_input, errors={CONF_RADIUS: \\\"no_stations\\\"})\\n for station in stations:\\n self._stations[station[\\\"id\\\"]] = (\\n f\\\"{station['brand']} {station['street']} {station['houseNumber']} -\\\"\\n f\\\" ({station['dist']}km)\\\"\\n )\\n\\n self._data = user_input\\n\\n return await self.async_step_select_station()\",\n \"def livingAreaFromPattern(business_list):\\n print(\\\"livingAreaFromUser\\\")\\n dataset_path = \\\"../dataset/yelp_dataset_challenge_academic_dataset/\\\"\\n review_path = dataset_path + \\\"yelp_academic_dataset_review.json\\\"\\n\\n # make empty user list for each business\\n business_user_list = {} # mine user set from business\\n user_business_list = {} # mine business set from user\\n for row in business_list:\\n business_user_list[row[0]] = [] # business_id\\n\\n # get review by business\\n # get user by business-review\\n user_iter = FieldsIter(review_path, \\\"json\\\", [\\\"user_id\\\", \\\"business_id\\\"])\\n for row in user_iter:\\n user_id = row[0]\\n business_id = row[1]\\n if business_id in business_user_list:\\n business_user_list[business_id].append(user_id)\\n if user_id not in user_business_list:\\n user_business_list[user_id] = [business_id]\\n else:\\n user_business_list[user_id].append(business_id)\\n\\n for business_id in business_user_list:\\n print(\\\"business_id: \\\" + business_id + \\\" has \\\" + str(len(business_user_list[business_id])) + \\\" users' reviews\\\")\\n # print(business_user_list[business_id])\\n\\n return business_user_list, user_business_list\\n\\n # reform user-categories(or business) association\\n\\n # fp-growth on user-categories(or business) set\\n\\n # observe the frequent category set, if it can form business cluster\",\n \"def construct_graph(social_edges, spatial_edges, output_path=None):\\n G = nx.DiGraph()\\n with open(social_edges, 'r') as f:\\n for l in f.read().splitlines():\\n edge = l.split(\\\"\\\\t\\\")\\n G.add_edge(USER_NODE_PREFIX + edge[0], USER_NODE_PREFIX + edge[-2], weight=float(edge[-1]))\\n\\n business_nodes = set([])\\n with open(spatial_edges, 'r') as f:\\n for l in f.read().splitlines():\\n edge = l.split(\\\"\\\\t\\\")\\n lat = float(edge[2])\\n lng = float(edge[3])\\n if edge[-2] not in business_nodes:\\n G.add_node(BUSINESS_NODE_PREFIX + edge[-2], spatial={'lat': lat, 'lng': lng})\\n business_nodes.add(edge[-2])\\n\\n with open(spatial_edges, 'r') as f:\\n for l in f.read().splitlines():\\n edge = l.split(\\\"\\\\t\\\")\\n G.add_edge(USER_NODE_PREFIX + edge[0], BUSINESS_NODE_PREFIX + edge[-2], weight=float(edge[-1]))\\n\\n if output_path:\\n pickle.dump(G, open(output_path, 'w'))\\n return G\",\n \"def plot_blocked_nodes(self, node_list, visited_set):\\n points = Marker()\\n #visualizations points and lines..\\n points.header.frame_id = \\\"map\\\"\\n points.header.stamp = rospy.get_rostime()\\n points.ns = \\\"markers\\\"\\n points.id = 0\\n points.type = points.POINTS\\n points.action = points.ADD\\n points.pose.orientation.w = 1.0\\n points.scale.x = 2*self.rviz_tuning_plt\\n points.scale.y = 2*self.rviz_tuning_plt\\n points.color.r = 1.0\\n points.color.g = 0.0\\n points.color.b = 0.0\\n points.color.a = 1.0\\n points.lifetime = rospy.Duration()\\n\\n # Nodes blocked by obstacles\\n for node in node_list:\\n if node.cost == float('Inf'):\\n p1 = Point()\\n p1.x = node.x\\n p1.y = node.y\\n p1.z = 0.011\\n points.points.append(p1)\\n\\n # Nodes blocked by planner\\n for node in visited_set:\\n p1 = Point()\\n p1.x = node.x\\n p1.y = node.y\\n p1.z = 0.03\\n points.points.append(p1)\\n \\n self.pub_blocked_nodes.publish(points)\",\n \"def generate_graph(self, calculate_rewards=False):\\n g = nx.Graph()\\n positions = {}\\n\\n # create nodes for all hotspots in list\\n for hotspot in self.hotspot_list:\\n if calculate_rewards:\\n hotspot['rewards'] = get_hotspot_rewards(hotspot['address'], 5)\\n else:\\n hotspot['rewards'] = 0\\n coords = (hotspot['lng'], hotspot['lat'])\\n g.add_node(hotspot['address'], pos=coords, lat=hotspot['lat'], lng=hotspot['lng'],\\n location_hex=hotspot['location_hex'], name=hotspot['name'], rewards=hotspot['rewards'],\\n gain=hotspot['gain'], elevation=hotspot['elevation'])\\n positions[hotspot['address']] = coords\\n\\n # create edges representing hotspots that witness each other\\n i = 0\\n for hotspot in self.hotspot_list:\\n witness_list = list_witnesses_for_hotspot(hotspot['address'])\\n hotspot['num_witnesses'] = len(witness_list)\\n g.nodes[hotspot['address']]['num_witnesses'] = hotspot['num_witnesses']\\n for witness in witness_list:\\n\\n # make sure that all witnesses are also in this subset\\n if not any(witness['address'] == h['address'] for h in self.hotspot_list):\\n continue\\n\\n # calculate distance between witness and challengee\\n dist = math.sqrt((hotspot['lat'] - witness['lat']) ** 2 + (hotspot['lng'] - witness['lng']) ** 2)\\n g.add_edge(hotspot['address'], witness['address'], weight=dist)\\n\\n i += 1\\n if i % 10 == 0:\\n print(f\\\"{str(i)} out of {str(len(self.hotspot_list))} hotspots complete...\\\")\\n\\n self.g = g\\n self.positions = positions\\n return g\",\n \"def check_users(users: Dict[int, Point], places: Dict[int, List[Point]], writer_coroutine):\\n for user_id, user_point in users.items():\\n writer_coroutine.send(Point(user_id, sum({check_point(user_point, place) for place in places.values()})))\",\n \"def make_flow_graph(interaction_logs):\\n G = pgv.AGraph(directed=True)\\n\\n for module_id in interaction_logs['module_id'].unique():\\n G.add_node(module_id)\\n\\n E = defaultdict(set)\\n grouped = interaction_logs.groupby('user_id')\\n for user_id, group in grouped:\\n module_ids_in_student_path = group['module_id']\\n for source_node, target_node in zip(module_ids_in_student_path[:-1], module_ids_in_student_path[1:]):\\n if source_node != target_node: # stationary\\n E[(source_node, target_node)] |= {user_id}\\n\\n for (source_node, target_node), students_that_made_transition in E.iteritems():\\n G.add_edge(\\n source_node,\\n target_node,\\n weight=len(students_that_made_transition))\\n\\n return G\",\n \"def make_nodes_and_paths(friends_lst):\\n\\n # nodes = {}\\n\\n # for item in friends_lst:\\n # friend1, friend2, group = item\\n # for person in pair:\\n # if not nodes.get(person):\\n # nodes[person] = pair[1]\\n\\n # nodes = [{'name': person, 'friend': nodes[person]} for person in nodes.keys()]\\n\\n nodes = {}\\n for item in friends_lst:\\n friend1, friend2, group = item\\n if not nodes.get(friend1):\\n nodes[friend1] = group\\n elif nodes.get(friend1) > group:\\n nodes[friend1] = group\\n\\n nodes = [{'name': person, 'group': nodes[person]} for person in nodes.keys()]\\n\\n index_nodes = {}\\n for idx, n in enumerate(nodes):\\n index_nodes[n['name']] = (idx, n['group'])\\n\\n paths = []\\n\\n # paths.append({'source': item[1], 'target': item[0]})\\n\\n for item in friends_lst:\\n # one = User.query.get(item.user_id)\\n # two = User.query.get(item.friend_id)\\n source, target, group = item\\n paths.append({'source': index_nodes[source][0], 'target': index_nodes[target][0]})\\n\\n # print nodes\\n # print index_nodes\\n # print paths\\n\\n return nodes, paths\",\n \"def Nodelocation(self, Tract_pop, Tractx, Tracty, longitude, latitude, cnum):\\n import annealsimulation\\n \\n self.latl, self.lonl = [], []\\n \\n while(len(self.latl) != self.nodenum):\\n lat = np.random.randint(len(self.Geoy) - 1)\\n lon = np.random.randint(len(self.Geox) - 1)\\n if(lat not in self.latl or lon not in self.lonl):\\n self.latl.append(lat)\\n self.lonl.append(lon) \\n \\n self.latl, self.lonl = np.array(self.latl), np.array(self.lonl)\\n \\n self.demandlat, self.demandlon = self.latl[self.demandseries], self.lonl[self.demandseries]\\n self.tranlat, self.tranlon = self.latl[self.transeries], self.lonl[self.transeries]\\n self.supplylat, self.supplylon = self.latl[self.supplyseries], self.lonl[self.supplyseries]\\n \\n self.demandloc = np.stack((self.demandlat, self.demandlon)).transpose()\\n self.tranloc = np.stack((self.tranlat, self.tranlon)).transpose()\\n self.supplyloc = np.stack((self.supplylat, self.supplylon)).transpose()\\n \\n #Demand node\\n Geox1 = sf.FeatureScaling(self.Geox)\\n Geoy1 = sf.FeatureScaling(self.Geoy)\\n Tract_pop1 = sf.FeatureScaling(Tract_pop)\\n Tractx1 = sf.FeatureScaling(Tractx)\\n Tracty1 = sf.FeatureScaling(Tracty)\\n \\n self.demandloc, self.demandc, self.popuassign = ans.anneal2(self.demandloc, 'Population', Geox1, Geoy1, Tract_pop1, Tractx1, Tracty1, Tract_pop, cnum)\\n self.demandy1 = Geoy1[self.demandloc[:, 0]]\\n self.demandx1 = Geox1[self.demandloc[:, 1]]\\n self.demandy = self.Geoy[self.demandloc[:, 0]]\\n self.demandx = self.Geox[self.demandloc[:, 1]]\\n #Transmission node\\n self.tranloc, self.tranc, temp = ans.anneal2(self.tranloc, 'Facility', Geox1, Geoy1, Tract_pop1, self.demandx1, self.demandy1, Tract_pop, cnum)\\n self.trany1 = Geoy1[self.tranloc[:, 0]]\\n self.tranx1 = Geox1[self.tranloc[:, 1]]\\n self.trany = self.Geoy[self.tranloc[:, 0]]\\n self.tranx = self.Geox[self.tranloc[:, 1]]\\n\\n #Supply node\\n self.supplyloc, self.supplyc, temp = ans.anneal2(self.supplyloc, 'Facility', Geox1, Geoy1, Tract_pop1, self.tranx1, self.trany1, Tract_pop, cnum)\\n self.supplyy1 = Geoy1[self.supplyloc[:, 0]]\\n self.supplyx1 = Geox1[self.supplyloc[:, 1]] \\n self.supplyy = self.Geoy[self.supplyloc[:, 0]]\\n self.supplyx = self.Geox[self.supplyloc[:, 1]]\\n \\n ##Coordinates of nodes\\n self.y = np.concatenate((self.supplyy, self.trany, self.demandy))\\n self.x = np.concatenate((self.supplyx, self.tranx, self.demandx))\\n \\n ##Latitudes and longitudes of nodes\\n self.demandlatitude, self.demandlongitude = latitude[self.demandloc[:, 0]], longitude[self.demandloc[:, 1]]\\n self.tranlatitude, self.tranlongitude = latitude[self.tranloc[:, 0]], longitude[self.tranloc[:, 1]]\\n self.supplylatitude, self.supplylongitude = latitude[self.supplyloc[:, 0]], longitude[self.supplyloc[:, 1]]\\n \\n self.latitude = np.concatenate((self.supplylatitude, self.tranlatitude, self.demandlatitude))\\n self.longitude = np.concatenate((self.supplylongitude, self.tranlongitude, self.demandlongitude))\",\n \"def plot_graph(self) -> None:\\n\\n nodes_on_graph = self.dw_graph.get_all_v()\\n for k, v in nodes_on_graph.items():\\n if v.position is None:\\n x_rand = random.uniform(0.5, self.dw_graph.v_size())\\n y_rand = random.uniform(0.5, self.dw_graph.v_size())\\n v.position = (x_rand, y_rand)\\n x_vals = []\\n y_vals = []\\n n = list(nodes_on_graph.keys())\\n for k, v in nodes_on_graph.items(): # draw nodes\\n x_vals.append(v.position[0])\\n y_vals.append(v.position[1])\\n\\n fig, ax = plt.subplots()\\n plt.plot(x_vals, y_vals, 'ro', markersize=5, data=\\\"d\\\")\\n\\n for p, txt in enumerate(n):\\n ax.annotate(n[p], (x_vals[p]+0.00003, y_vals[p]), color='g')\\n\\n for n in nodes_on_graph:\\n n1 = self.dw_graph.get_nodes(n)\\n x = n1.position[0]\\n y = n1.position[1]\\n for r in self.dw_graph.all_out_edges_of_node(n):\\n dx = self.dw_graph.get_nodes(r).position[0]\\n dy = self.dw_graph.get_nodes(r).position[1]\\n ax.quiver(x, y, dx-x, dy-y, angles='xy', scale_units='xy', scale=1)\\n #plt.arrow(x, y, dx - x, dy - y, head_width=0.0009, width=0.00005, length_includes_head=True)\\n\\n\\n plt.xlabel(\\\"x axis \\\")\\n plt.ylabel(\\\"y axis \\\")\\n plt.title(\\\"The title of the graph\\\")\\n plt.show()\",\n \"def test_activity_flag(self, example_staypoints):\\n # take out staypoint 6 that should have been merged with 2, 15\\n sp = example_staypoints\\n data = [True, True, True, True, False, True, True, True]\\n idx = [1, 2, 3, 5, 6, 7, 15, 80]\\n activities = pd.Series(data, index=idx)\\n sp[\\\"activity\\\"] = activities\\n sp, _ = sp.as_staypoints.generate_locations(\\n method=\\\"dbscan\\\",\\n epsilon=10,\\n num_samples=2,\\n distance_metric=\\\"haversine\\\",\\n agg_level=\\\"user\\\",\\n activities_only=True,\\n )\\n assert sp.loc[1, \\\"location_id\\\"] == sp.loc[15, \\\"location_id\\\"]\\n assert sp.loc[2, \\\"location_id\\\"] is pd.NA\",\n \"def generate(self):\\n self.generate_points()\\n self.generate_edges()\",\n \"def two_user_route_statistics(i,j, source_data, destination_data, source_destination_data, delta=1.2):\\n\\toccupancy_ratio = 0.0\\n\\tminimum_distance_so_far = 0.0\\n\\tcommon_travel_distance = 0.0\\n\\n\\ttry:\\n\\t\\tif source_destination_data[j][i] + source_data[i][j] <= 1.2*source_destination_data[i][i] and source_destination_data[j][i] + destination_data[i][j] <= 1.2*source_destination_data[j][j]:\\n\\t\\t\\tfirst = ((source_destination_data[j][i] + source_data[i][j])/(source_destination_data[j][i] + source_data[i][j]+destination_data[i][j]))\\n\\t\\t\\tsecond = ((source_destination_data[j][i] + destination_data[i][j])/(source_destination_data[j][i] + source_data[i][j]+destination_data[i][j]))\\n\\t\\t\\toccupancy_ratio = (first+second)/2\\n\\t\\t\\tcommon_travel_distance = source_destination_data[j][i]\\n\\t\\t\\tminimum_distance_so_far = source_data[i][j] + source_destination_data[j][i] + destination_data[i][j]\\n\\n\\t\\tif source_destination_data[i][j] + destination_data[j][i] <= 1.2*source_destination_data[i][i] and source_destination_data[i][j] + source_data[j][i] <= 1.2*source_destination_data[j][j]:\\n\\t\\t\\tfirst = ((source_destination_data[i][j] + destination_data[j][i])/(source_destination_data[i][j] + destination_data[j][i]+source_data[j][i]))\\t\\t\\n\\t\\t\\tsecond = ((source_destination_data[i][j] + source_data[j][i])/(source_destination_data[i][j] + destination_data[j][i]+source_data[j][i]))\\n\\t\\t\\ttotal_distance = source_data[j][i] + source_destination_data[i][j] + destination_data[j][i]\\n\\n\\t\\t\\tif total_distance < minimum_distance_so_far:\\n\\t\\t\\t\\tminimum_distance_so_far = total_distance\\n\\t\\t\\t\\tcommon_travel_distance = source_destination_data[i][j]\\n\\t\\t\\t\\toccupancy_ratio = (first+second)/2\\n\\n\\t\\tif source_data[i][j]+source_destination_data[j][j]+destination_data[j][i] <= 1.2*source_destination_data[i][i]:\\n\\t\\t\\tfirst = (1)\\n\\t\\t\\tsecond = (source_destination_data[j][j]/(source_data[i][j]+source_destination_data[j][j]+destination_data[j][i]))\\n\\n\\t\\t\\ttotal_distance = source_data[i][j] + source_destination_data[j][j] + destination_data[j][i]\\n\\n\\t\\t\\tif total_distance < minimum_distance_so_far:\\n\\t\\t\\t\\tminimum_distance_so_far = total_distance\\n\\t\\t\\t\\tcommon_travel_distance = source_destination_data[j][j]\\n\\t\\t\\t\\toccupancy_ratio = (first+second)/2\\n\\n\\t\\tif source_data[j][i]+source_destination_data[i][i]+destination_data[i][j] <= 1.2*source_destination_data[j][j]:\\n\\t\\t\\tfirst = (source_destination_data[i][i]/(source_data[j][i]+source_destination_data[i][i]+destination_data[i][j]))\\n\\t\\t\\tsecond = (1)\\n\\n\\t\\t\\ttotal_distance = source_data[j][i]+source_destination_data[i][i]+destination_data[i][j]\\n\\n\\t\\t\\tif total_distance < minimum_distance_so_far:\\n\\t\\t\\t\\tminimum_distance_so_far = total_distance\\n\\t\\t\\t\\tcommon_travel_distance = source_destination_data[i][i]\\n\\t\\t\\t\\toccupancy_ratio = (first+second)/2\\n\\n\\texcept Exception as e:\\n\\t\\toccupancy_ratio = 1.0\\n\\t\\tminimum_distance_so_far = 0.0\\n\\t\\tcommon_travel_distance = 0.0\\n\\n\\n\\treturn occupancy_ratio, common_travel_distance, minimum_distance_so_far\",\n \"def fake_destination_data():\\n\\n for _ in range(0, 35):\\n user_id = random.randint(1, 8)\\n\\n print (str(user_id) + '|' + fake.company() + '|' + fake.street_address()\\n + '|' + fake.city() + '|' + fake.state_abbr() + '|' +\\n fake.postalcode())\",\n \"def test_empty_generation(self, example_staypoints):\\n # the pfs would not generate staypoints with the default parameters\\n sp = example_staypoints\\n # select subset of sp such that no locations can be generated\\n sp = sp.iloc[:3]\\n\\n warn_string = \\\"No locations can be generated, returning empty locs.\\\"\\n with pytest.warns(UserWarning, match=warn_string):\\n sp, locs = sp.as_staypoints.generate_locations(\\n method=\\\"dbscan\\\", epsilon=10, num_samples=2, distance_metric=\\\"haversine\\\", agg_level=\\\"user\\\"\\n )\\n assert len(locs) == 0\",\n \"def graph(self):\\n ...\",\n \"def generate_coordinate():\\n #TODO debug dat fucking code\\n if not check_content_type():\\n return jsonify(status=CONTENT_TYPE_ERROR)\\n reqdata = request.json\\n try:\\n if not check_token(reqdata[\\\"token\\\"]):\\n return jsonify(status=TOKEN_ERROR)\\n except:\\n return jsonify(status=REQ_ERROR)\\n path_id = reqdata[\\\"path_id\\\"]\\n path = db.session.query(Path).get_or_404(path_id)\\n coordinate_data = []\\n coordinate_data.append(path.start_coordinate)\\n users_paths = path.users\\n for e in users_paths:\\n coordinate_data.append(e.coordinate)\\n \\n return jsonify(data=coordinate_data, status=OK_STATUS)\\n\\n #coordinate = (path.start_coordinate).split(\\\",\\\")\\n #coordinate = (float(coordinate[0]), float(coordinate[1]))\\n #coordinate_data.append(coordinate)\\n #for user_path in path.users:\\n # coordinate = (user_path.coordinate).split(\\\",\\\")\\n # coordinate_data.append((float(coordinate[0]), float(coordinate[1])))\\n #coordinate = (path.start_coordinate).split(\\\",\\\")\\n #coordinate = (float(coordinate[0]), float(coordinate[1]))\\n #try:\\n # final_coordinates = calc_path(coordinate, coordinate_data)\\n # final_str = \\\"\\\"\\n # for coordinate in final_coordinates:\\n # x, y = coordinate\\n # final_str += str(x) + \\\",\\\" + str(y) + \\\"|\\\"\\n # final_str = final_str[:-1]\\n # return jsonify(data=final_str, status=OK_STATUS)\\n #except Exception as err:\\n # print(err)\\n # return jsonify(status=REQ_ERROR)\",\n \"def create_data():\\n # Locations\\n data = {}\\n num_vehicles = 20\\n depot = 0\\n locations = loc1\\n demands = popn\\n\\n num_locations = len(locations)\\n dist_matrix = {}\\n\\n for from_node in range(0,num_locations):\\n dist_matrix[from_node] = {}\\n\\n for to_node in range(0,num_locations):\\n dist_matrix[from_node][to_node] = (\\n haversine(\\n locations[from_node],[to_node])\\n #locations[to_node],[from_node])\\n \\\"\\\"\\\"\\n data[\\\"distances\\\"] =dist_matrix\\n data[\\\"num_locations\\\"] = len(dist_matrix)\\n data[\\\"num_vehicles\\\"] = 6\\n data[\\\"depot\\\"] = 0\\n data[\\\"demands\\\"] = demands\\n #data[\\\"vehicle_capacities\\\"] = capacities\\n data[\\\"time_per_demand_unit\\\"] = 0.05\\n return data\\n \\\"\\\"\\\"\\n return [ num_vehicles, depot, locations, dist_matrix]\",\n \"def get_all_social_paths(self, user_id):\\n visited = {} # Note that this is a dictionary, not a set\\n # !!!! IMPLEMENT ME\\n # BFTs starting at user_id, return first path to every reachable person\\n q = [[user_id]]\\n while q:\\n path = q.pop(0)\\n person = path[-1]\\n # add the person and the path to the person\\n for friend in self.friendships[person]:\\n if friend not in visited and friend != user_id:\\n q.append(path + [friend])\\n visited[friend] = path + [friend]\\n\\n return visited\",\n \"def generate_weighted_users_traces(self):\\n def generate_weighted_user_trace():\\n towers_ids = np.arange(self.number_towers)\\n\\n trace = []\\n direction = []\\n for cycle in range(self.number_cycles):\\n if cycle == 0:\\n # For the first towers the chance of selecting a tower is equally distributed\\n tower = np.random.choice(towers_ids)\\n trace.append(tower)\\n direction.append(self.towers[tower])\\n elif cycle == 1:\\n last_tower = trace[cycle - 1]\\n tower = np.random.choice(\\n towers_ids, p=self.probabilities[last_tower])\\n trace.append(tower)\\n direction.append(self.towers[tower])\\n else:\\n new_point = self.towers_manager.get_new_point(direction)\\n nearest_tower = \\\\\\n self.towers_manager.get_nearest_tower(new_point)\\n tower = np.random.choice(\\n towers_ids, p=self.probabilities[nearest_tower])\\n trace.append(tower)\\n direction = [direction[1], self.towers[tower]]\\n\\n return trace\\n\\n return np.array([\\n generate_weighted_user_trace()\\n for _ in range(self.number_users)\\n ])\",\n \"def getAllSocialPaths(self, userID):\\n visited = {}\\n # use a queue\\n q = []\\n q.append([userID])\\n # add userID as its own key and value to visited\\n visited[userID] = [userID]\\n\\n while len(q) > 0:\\n path = q.pop(0)\\n curr_friend = path[-1]\\n\\n # for all the userID keys inside self.friendships\\n for friend in self.friendships[curr_friend]:\\n # add neighbor as a key, if not visited, in visited with an empty list as value\\n if friend not in visited:\\n visited[friend] = list()\\n # break out of loop if already in visited\\n else: \\n continue\\n \\n # create a new list that holds the path from userID to friend\\n friend_path = list(path)\\n # add the friend onto the end of the list\\n friend_path.append(friend)\\n # also add path to the queue\\n q.append(friend_path) \\n # add path as the value to the friend\\n visited[friend].extend(friend_path)\\n \\n return visited\",\n \"def create_graph(self):\\n robot_pix = int(math.ceil(self.robot.size / self.resolution))\\n ii = 0\\n jj = 0\\n for i in range(0, self.height, robot_pix):\\n jj = 0\\n for j in range(0, self.width, robot_pix):\\n block = self.occ_grid[i:i+robot_pix, j:j+robot_pix].flatten()\\n avg = np.mean(block)\\n robot_block = self.tesselation_image[i:i+robot_pix, j:j+robot_pix].flatten()\\n n_occur = np.bincount(robot_block)\\n block_id = np.argmax(n_occur)\\n \\n p = Pose()\\n p.position.x = self.resolution * j + self.resolution / 2.0 + self.origin.position.x\\n p.position.y = self.height * self.resolution - (self.resolution * i + self.resolution / 2.0) + self.origin.position.y\\n node = Node(ii, jj, p)\\n idx = np.where(block > 20)\\n if block_id == self.robot.robot_id:\\n if 0 <= avg <= 20:\\n print(\\\"Node in path\\\", node)\\n node.valid = True\\n else:\\n node.valid = False\\n elif block_id == 0:\\n node.valid = False\\n else:\\n node.belongs = False\\n self.nodes[ii,jj] = node\\n jj += 1\\n ii += 1\\n\\n\\n height, width = self.nodes.shape\\n print(\\\"Node shape: \\\", self.nodes.shape)\\n for i in range(height):\\n for j in range(width):\\n min_i = max(0, i-1)\\n max_i = min(height - 1, i+1) + 1\\n min_j = max(0, j-1)\\n max_j = min(width - 1, j+1) + 1\\n\\n node = self.nodes[i,j]\\n neighbors = self.nodes[min_i:max_i, min_j:max_j].flatten()\\n for n in neighbors:\\n if not n or not node:\\n print(\\\"None %d-%d\\\"%(i,j))\\n continue\\n if n != node:\\n if n.valid:\\n print(\\\"Neighbor appended\\\")\\n self.nodes[i,j].neighbors.append(n)\\n else:\\n self.nodes[i,j].obstacle_neighbors.append(n)\\n print(\\\"Graph is created!\\\")\",\n \"def make_upa_graph(nodes, out_degree):\\n graph = go.make_complete_graph(out_degree)\\n trial = upa.UPATrial(out_degree)\\n for new_node in range(out_degree, nodes):\\n neighbors = trial.run_trial(out_degree)\\n graph[new_node] = neighbors\\n for neighbor in neighbors:\\n graph[neighbor].add(new_node)\\n return graph\",\n \"def users_nearby(self, meters):\\n location = Location.objects.get(id=self.most_recent_location_id)\\n lng = location.position['coordinates'][0]\\n lat = location.position['coordinates'][1]\\n\\n nearby_locations = Location.objects(position__near=[lng, lat], position__max_distance=meters)\\n\\n nearby_user_ids = []\\n\\n for loc in nearby_locations:\\n nearby_user_ids.append(loc.uid)\\n\\n return SallasanaUser.objects.filter(id__in=nearby_user_ids)\",\n \"def by_user(cls, *args, **kwargs):\\n # Almost every user of UasTelemetry.by_user wants to use\\n # the related AerialPosition and GpsPosition. To avoid excessive\\n # database queries, we select these values from the database up front.\\n return super(UasTelemetry, cls).by_user(*args, **kwargs) \\\\\\n .select_related('uas_position__gps_position')\",\n \"def test_parallel_computing(self, example_staypoints):\\n sp = example_staypoints\\n\\n # without parallel computing code\\n sp_ori, locs_ori = sp.as_staypoints.generate_locations(\\n method=\\\"dbscan\\\", epsilon=10, num_samples=2, distance_metric=\\\"haversine\\\", agg_level=\\\"user\\\", n_jobs=1\\n )\\n # using two cores\\n sp_para, locs_para = sp.as_staypoints.generate_locations(\\n method=\\\"dbscan\\\", epsilon=10, num_samples=2, distance_metric=\\\"haversine\\\", agg_level=\\\"user\\\", n_jobs=2\\n )\\n\\n # the result of parallel computing should be identical\\n assert_geodataframe_equal(locs_ori, locs_para)\\n assert_geodataframe_equal(sp_ori, sp_para)\",\n \"def generate_graph(self):\\n temp_graph = [[] for i in xrange(Parameters.num_peers)]\\n unconnected = set([i for i in xrange(Parameters.num_peers)])\\n while len(unconnected) > 1:\\n node1 = random.sample(unconnected, 1)[0]\\n unconnected.remove(node1)\\n node2 = random.sample(unconnected, 1)[0]\\n temp_graph[node2].append(self.nodes[node1])\\n temp_graph[node1].append(self.nodes[node2])\\n unconnected = set([i for i in xrange(Parameters.num_peers)])\\n i = 0\\n for i in xrange(Parameters.num_peers*Parameters.num_neighbours/2-Parameters.num_peers):\\n a = random.sample(unconnected, 1)[0]\\n b = random.sample(unconnected, 1)[0]\\n while b == a:\\n b = random.sample(unconnected, 1)[0]\\n temp_graph[a].append(self.nodes[b])\\n temp_graph[b].append(self.nodes[a])\\n graph = {}\\n for i in xrange(len(self.nodes)):\\n graph[\\\"P_\\\" + str(i)] = list(set(temp_graph[i]))\\n return graph\",\n \"def save_friend_nodes(self):\\n print \\\"Exporting to file tsv ...\\\"\\n count_edge = 0\\n count_node = 0\\n with open('../data/yelp.tsv','w') as f:\\n for user in self.df['user_id']:\\n for friends in self.df['friends']:\\n count_node += 1\\n for friend in friends:\\n f.write(\\\"%s\\\\t%s\\\\n\\\" % (user, friend))\\n count_edge += 1\\n print \\\"Graph Summary:\\\", count_node, \\\"nodes,\\\", count_edge, \\\"edges.\\\"\",\n \"def get_clusters(self, base_user, user_friends_graph, method=\\\"\\\") -> Dict: # TODO:\\n if method==\\\"modularity\\\":\\n community_data = greedy_modularity_communities(user_friends_graph)\\n else:\\n community_data = [item for item in label_propagation_communities(user_friends_graph)]\\n d = {}\\n for i in range(len(community_data)):\\n if base_user in community_data[i]:\\n li = list(community_data[i])\\n li.pop(li.index(base_user))\\n li.insert(0, base_user)\\n d[i] = li\\n else:\\n d[i] = [base_user] + list(community_data[i])\\n\\n # Flatten dict into cluster list\\n cluster_list = [d[k] for k in d]\\n print(\\\"Number of clusters \\\" + str(len(cluster_list)))\\n\\n return cluster_list\",\n \"def data_graph():\\n station_reference = request.args.get(\\\"stationReference\\\")\\n station_name = request.args.get(\\\"stationName\\\")\\n station_name = station_name.replace(\\\" \\\",\\\"+\\\")\\n\\n if station_name is not None:\\n # station_data = station_data.replace(\\\" \\\", \\\"+\\\")\\n station = station_data.loc[station_data.stationName == station_name]\\n else:\\n station = station_data.loc[station_data.stationReference == station_reference]\\n result_station = station.iloc[0]\\n\\n # Get optional parameters\\n time_from = request.args.get(\\\"from\\\")\\n time_to = request.args.get(\\\"to\\\")\\n if time_from:\\n pass\\n else:\\n time_from = None\\n if time_to:\\n pass\\n else:\\n time_to = None\\n # plot pic\\n magic_trick= data.station_graph(result_station.stationName, time_from, time_to)\\n # img_stream = io.BytesIO(img)\\n # img = Image.open(img_stream)\\n # imgByteArr = io.BytesIO()\\n # img.save(imgByteArr,format='PNG')\\n # imgByteArr = imgByteArr.getvalue()\\n # return send_file(io.BytesIO(imgByteArr),\\n # mimetype = 'image/png',\\n # as_attachment = True,\\n # attachment_filename = 'tmp.png')\\n image_data = open(\\\"tmp.png\\\", \\\"rb\\\").read()\\n response = make_response(image_data)\\n response.headers['Content-Type'] = 'image/png'\\n return response\",\n \"def test_num_samples_high(self):\\n sp_file = os.path.join(\\\"tests\\\", \\\"data\\\", \\\"geolife\\\", \\\"geolife_staypoints.csv\\\")\\n sp = ti.read_staypoints_csv(sp_file, tz=\\\"utc\\\", index_col=\\\"id\\\", crs=\\\"epsg:4326\\\")\\n sp_ns_5, _ = sp.as_staypoints.generate_locations(\\n epsilon=50, distance_metric=\\\"haversine\\\", agg_level=\\\"user\\\", num_samples=2\\n )\\n non_noise_sp = sp_ns_5[sp_ns_5[\\\"location_id\\\"] != -1]\\n\\n # group_by_user_id and check that no two different user ids share a common location id\\n grouped = list(non_noise_sp.groupby([\\\"user_id\\\"])[\\\"location_id\\\"].unique())\\n loc_set = []\\n for loc_list in grouped:\\n loc_set.append(set(loc_list))\\n\\n # we assert that the count of overlaps is equal to the count of users\\n # (each user has overlap with the same user)\\n assert sum([int(len(p & q) > 0) for p in loc_set for q in loc_set]) == len(loc_set)\",\n \"def read_file(network_filename, user_by_city_filename=None):\\n graph = read_dictlist_from_file(network_filename)\\n\\n gg = Graph(directed=False) # new Graph object\\n\\n user_id_map = {} # storing new id info\\n new_id = 0\\n for user_id in graph:\\n temp_users = []\\n temp_users.append(user_id)\\n for friend in graph[user_id]:\\n temp_users.append(friend)\\n for id1 in temp_users:\\n if id1 not in user_id_map:\\n user_id_map[id1] = new_id\\n gg.add_vertex() # index for this vertex will be new_id\\n new_id += 1\\n if id1 > user_id:\\n gg.add_edge(gg.vertex(user_id_map[user_id]),\\n gg.vertex(user_id_map[id1]))\\n print \\\"Done reading the graph.\\\"\\n if user_by_city_filename is None:\\n return (gg, None)\\n if user_by_city_filename is not None:\\n cities = read_dict_from_file(user_by_city_filename)\\n # Adding vertex property as city\\n city_prop = gg.new_vertex_property(\\\"int\\\")\\n for user_id in cities:\\n city_prop[gg.vertex(user_id_map[user_id])] = cities[user_id]\\n print \\\"Done reading the city.\\\"\\n return (gg, city_prop)\",\n \"def do_graph( self, db_device_adapters, ts_begin, ts_end ):\\n# self.graph_4( db_device_adapter, ts_begin, ts_end, min_points = 10 )\\n# self.graph_many( db_device_adapters[0], ts_begin, ts_end, min_points = 10 )\\n self.graph_many_really( db_device_adapters, ts_begin, ts_end, min_points = 10 )\",\n \"def create_directly_follows_graph(log):\\n dfg = dfg_discovery.apply(log)\\n dfg = dict(dfg)\\n return dfg\",\n \"def get_locations(nodes, tl, br):\\n \\n # Base cases:\\n if len(nodes) == 1: # for singleton, only choice is to place in the single spot in 1x1 square\\n return {nodes[0]: tl}\\n if len(nodes) == 2: # for two nodes, arbitrarily chose to place the first node in top left\\n return {nodes[0]: tl, nodes[1]: br}\\n\\n # Recursive case, need to create and solve subproblems:\\n ret = {}\\n\\n num_edges = count_num_edges(nodes)\\n if num_edges == 0: # for empty graphs, no need to run METIS, just assign arbitrarily\\n i = 0\\n for x in range(tl.x, br.x+1): \\n for y in range(tl.y, br.y+1):\\n if i < len(nodes):\\n ret.update({nodes[i]: Point(x,y)})\\n i += 1\\n return ret\\n\\n filename = splitext(basename(sys.argv[1]))[0] + '.p.' + sys.argv[2] + '.yx.' + sys.argv[3] + '.drop.' + sys.argv[4] + '.' +\\\\\\n '_'.join(['delete', str(tl.x), str(tl.y), str(br.x), str(br.y)]) \\n\\n # special case for the very first call of get_locations. For example, suppose that there are\\n # 97 nodes on a 10x10 grid. Instead of dividing the 97 nodes into 2 equal partitions, we should\\n # divide them into a partition of 90 nodes and a partition of 7 nodes. The former should be\\n # placed on a 10x9 grid and te latter should be placed on a 1x7 grid.\\n if len(nodes) < (br.x - tl.x + 1) * (br.y - tl.y + 1):\\n assert tl == Point(0, 0)\\n size_tl_nodes = (br.x + 1) * int(len(nodes) / (br.x + 1))\\n if size_tl_nodes == len(nodes):\\n ret.update(get_locations(nodes, tl=Point(0, 0), br=Point(br.x, len(nodes) / (br.x + 1) - 1)))\\n return ret\\n\\n nodes_tl, nodes_br = partition(nodes, size_tl_nodes, filename)\\n # complicated indexing here. As an example, for the 97 into 10x10 case, we want to send 90 nodes\\n # to a rectangle spanned by tl=Point(0, 0) and br=Point(9, 8) and we want to send 7 nodes to a \\n # rectangle spanned by tl=Point(0, 9) and br=Point(6, 9)\\n ret.update(get_locations(nodes_tl, tl=Point(0, 0), br=Point(br.x, len(nodes) / (br.x + 1) - 1)))\\n ret.update(get_locations(nodes_br, tl=Point(0, len(nodes) / (br.x + 1)), br=Point(len(nodes) % (br.x + 1) - 1, len(nodes) / (br.x + 1))))\\n return ret\\n\\n if br.x - tl.x > br.y - tl.y: # if rectangle is wider than tall, split on y axis\\n half = tl.x + (br.x - tl.x - 1) / 2\\n size_tl_nodes = (half - tl.x + 1) * (br.y - tl.y + 1)\\n else: # split on x axis\\n half = tl.y + (br.y - tl.y - 1) / 2\\n size_tl_nodes = (br.x - tl.x + 1) * (half - tl.y + 1)\\n\\n nodes_tl, nodes_br = partition(nodes, size_tl_nodes, filename)\\n\\n if br.x - tl.x > br.y - tl.y: # if rectangle is wider than tall, split on y axis\\n ret.update(get_locations(nodes_tl, tl=tl, br=Point(half, br.y)))\\n ret.update(get_locations(nodes_br, tl=Point(half + 1,tl.y), br=br))\\n else: # split on x axis\\n ret.update(get_locations(nodes_tl, tl=tl, br=Point(br.x, half)))\\n ret.update(get_locations(nodes_br, tl=Point(tl.x, half + 1), br=br))\\n\\n return ret\",\n \"def test_location_ids_and_noise_dataset(self, example_staypoints):\\n sp = example_staypoints\\n sp2, _ = sp.as_staypoints.generate_locations(\\n method=\\\"dbscan\\\", epsilon=10, num_samples=2, distance_metric=\\\"haversine\\\", agg_level=\\\"dataset\\\"\\n )\\n\\n assert (sp2.loc[[5, 6, 80, 3], \\\"location_id\\\"] == sp2.loc[5, \\\"location_id\\\"]).all()\\n assert sp2.loc[1, \\\"location_id\\\"] == sp2.loc[15, \\\"location_id\\\"]\\n assert sp2.loc[1, \\\"location_id\\\"] != sp2.loc[5, \\\"location_id\\\"]\\n\\n assert sp2.loc[[2, 7], \\\"location_id\\\"].isnull().all()\",\n \"def build_graph(self):\\n pass\",\n \"def chunk_user_route(detail_of_trip):\\n\\n # $$$$$$$$$$$$$$$$$$$$$$$$$$$$$$$$$$$$$$$$$$$$$$$$$$$$$$$$$$$$$$$$$$$$$$$$\\n # since I can't get javascript to load, here's a hacky way of loading json\\n # that details the route based on the user's point A and point B\\n # detail_of_trip = api.directions(\\n # (40.760350, -73.976209),\\n # (40.754009, -73.981097),\\n # mode=\\\"walking\\\"\\n # )[0]\\n # $$$$$$$$$$$$$$$$$$$$$$$$$$$$$$$$$$$$$$$$$$$$$$$$$$$$$$$$$$$$$$$$$$$$$$$$\\n\\n # now that I have javascript sending over the json, load json that details\\n # the route based on the user's point A and point B\\n\\n # -------------- This section is for interpolation/splitting using shapely\\n first = True # to see if this is the start position for the entire route\\n line_points = [] # stores all the points to the route based on dict passed\\n\\n for leg in detail_of_trip['legs']:\\n for step in leg['steps']:\\n # Create a list of two element lists that represent points along the\\n # route. via google. line_points = [ [lat1, lng1], [lat2, lng2],...]\\n # Only add the starting point the first time. Every other iteration\\n # we will just tack on the end points to our line.\\n if first:\\n line_points.append([step['start_location']['lat'], step['start_location']['lng']])\\n first = False\\n line_points.append([step['end_location']['lat'], step['end_location']['lng']])\\n\\n # Now load those points into a geometry, here shapely's LineString type.\\n route_line = LineString(line_points)\\n return (route_line, line_points)\",\n \"def generate_points(self, userdata):\\n # TODO: generate along multiple axes\\n x, y, _ = userdata.initial_point\\n x, y = 0, 0\\n\\n i = 0\\n for _ in range(self.num_points):\\n point = PointStamped()\\n point.header.frame_id = \\\"map\\\"\\n point.point.x = x + i * self.spacing\\n point.point.y = y\\n point.point.z = 0.0\\n\\n i += 1\\n\\n yield point\",\n \"def generate_receivers(site_area, simulation_parameters, grid):\\n receivers = []\\n\\n if grid == 1:\\n\\n geom = shape(site_area[0]['geometry'])\\n geom_box = geom.bounds\\n\\n minx = geom_box[0]\\n miny = geom_box[1]\\n maxx = geom_box[2]\\n maxy = geom_box[3]\\n\\n id_number = 0\\n\\n x_axis = np.linspace(\\n minx, maxx, num=(\\n int(math.sqrt(geom.area) / (math.sqrt(geom.area)/20))\\n )\\n )\\n y_axis = np.linspace(\\n miny, maxy, num=(\\n int(math.sqrt(geom.area) / (math.sqrt(geom.area)/20))\\n )\\n )\\n\\n xv, yv = np.meshgrid(x_axis, y_axis, sparse=False, indexing='ij')\\n for i in range(len(x_axis)):\\n for j in range(len(y_axis)):\\n receiver = Point((xv[i,j], yv[i,j]))\\n indoor_outdoor_probability = np.random.rand(1,1)[0][0]\\n if geom.contains(receiver):\\n receivers.append({\\n 'type': \\\"Feature\\\",\\n 'geometry': {\\n \\\"type\\\": \\\"Point\\\",\\n \\\"coordinates\\\": [xv[i,j], yv[i,j]],\\n },\\n 'properties': {\\n 'ue_id': \\\"id_{}\\\".format(id_number),\\n \\\"misc_losses\\\": simulation_parameters['rx_misc_losses'],\\n \\\"gain\\\": simulation_parameters['rx_gain'],\\n \\\"losses\\\": simulation_parameters['rx_losses'],\\n \\\"ue_height\\\": float(simulation_parameters['rx_height']),\\n \\\"indoor\\\": (True if float(indoor_outdoor_probability) < \\\\\\n float(0.5) else False),\\n }\\n })\\n id_number += 1\\n\\n else:\\n pass\\n\\n else:\\n\\n centroid = shape(site_area[0]['geometry']).centroid\\n\\n coord = site_area[0]['geometry']['coordinates'][0][0]\\n path = LineString([(coord), (centroid)])\\n length = int(path.length)\\n increment = int(length / 20)\\n\\n indoor = simulation_parameters['indoor_users_percentage'] / 100\\n\\n id_number = 0\\n for increment_value in range(1, 11):\\n point = path.interpolate(increment * increment_value)\\n indoor_outdoor_probability = np.random.rand(1,1)[0][0]\\n receivers.append({\\n 'type': \\\"Feature\\\",\\n 'geometry': mapping(point),\\n 'properties': {\\n 'ue_id': \\\"id_{}\\\".format(id_number),\\n \\\"misc_losses\\\": simulation_parameters['rx_misc_losses'],\\n \\\"gain\\\": simulation_parameters['rx_gain'],\\n \\\"losses\\\": simulation_parameters['rx_losses'],\\n \\\"ue_height\\\": float(simulation_parameters['rx_height']),\\n \\\"indoor\\\": (True if float(indoor_outdoor_probability) < \\\\\\n float(indoor) else False),\\n }\\n })\\n id_number += 1\\n\\n return receivers\",\n \"def _CreateGraph(self):\\n self.nodes = []\\n self.edges = []\\n for i, r in self.airports.set_index('airport_id').iterrows():\\n self.nodes.append((i,r.to_dict()))\\n for i, r in self.routes.set_index(['src_id','dst_id']).iterrows():\\n self.edges.append((i[0],i[1],r.to_dict()))\\n # print('node ex: {}'.format(self.nodes[0]))\\n # print('edge ex: {}'.format(self.edges[0]))\\n\\n self.graph = self._CreateAdjacencyListGraph()\",\n \"def graph_2hop(profile, recom, filename):\\n g = Graph()\\n\\n # ! ids are raw -> strings\\n clickedItems = set(map(lambda x: str(x[0]), profile)) # set of clicked items\\n recomItems = set() # set of recommended items\\n\\n # get recommended items\\n for click in range(0, len(profile)): # for all the clicks\\n for rec in recom[click]: # for the topN recommendations\\n recomItems.add(str(rec[0]))\\n\\n # write clicked item-nodes in an outter ring\\n angleStep = 2*np.pi / float(len(clickedItems)) # polar coordinates angle step\\n angle = 0 # polar coordinates angle [0, 2pi]\\n R = 1000 # outter\\n for item in clickedItems: # for all the clicks\\n target = str(item)\\n g.add_node(target)\\n g.nodes[target]['color'] = [255,0,0,1] # RGBA format\\n g.nodes[target]['pos'] = [R * np.cos(angle), R * np.sin(angle)]\\n g.nodes[target]['text'] = target\\n\\n angle += angleStep\\n \\n # write the rest item-nodes in an inner ring\\n angleStep = 2*np.pi / float(len(recomItems - clickedItems)) # polar coordinates angle step\\n angle = 0 # polar coordinates angle [0, 2pi]\\n R = 600 # outter\\n for item in recomItems - clickedItems: # for the rest of the items\\n target = str(item)\\n g.add_node(target)\\n g.nodes[target]['color'] = [0,0,255,1] # RGBA format\\n g.nodes[target]['pos'] = [R * np.cos(angle), R * np.sin(angle)]\\n g.nodes[target]['text'] = target\\n\\n angle += angleStep\\n \\n # construct edges\\n edges = {} # dictionary: (source_iid, target_iid) -> Vertex object\\n weight_prop = g.new_edge_property('float')\\n \\n for click in range(0, len(profile)): # for all the clicks\\n for rec in recom[click]: # for the topN recommendations\\n target= str(rec[0])\\n source = str(profile[click][0])\\n weight = rec[1]\\n\\n g.add_edge(source, target)\\n g.edges(source, target)['weight'] = weight\\n \\n return g\",\n \"def check_visited_places(userid: int, day_range: int):\\n print(f\\\"Checking visited places by user {userid} in the last {day_range} days\\\")\\n # get reservations in which user actually showed up from Reservation service\\n range = datetime.now() - timedelta(days=day_range)\\n range.replace(hour=0, minute=0, second=0, microsecond=0)\\n\\n reservations = Reservation.query.filter_by(user_id=userid).\\\\\\n filter(Reservation.entrance_time != None).filter(Reservation.entrance_time >= range).all()\\n # print(\\\"DB\\\", db)\\n # also all results must be json serializable\\n return [row.to_dict() for row in reservations]\",\n \"def _build_graph(self):\\n pass\",\n \"def gen_graph():\\n if config_pagination:\\n gdata = tgraph.call_graph(offset=offset, limit=limit)\\n else:\\n gdata = tgraph.call_graph(start=start, end=end, contineous=contineous)\\n\\n for data in gdata:\\n yield data\",\n \"def get_time_slots_map_for_user(start_time, end_time, user):\\n\\n time_slots_map = TimeSlotsMap(start_time=start_time, end_time=end_time)\\n\\n user_query = Query()\\n schedules = db.search(user_query.user == user)\\n\\n schedules.sort(key=lambda x:x['created_time'])\\n\\n for sc in schedules:\\n day_sc_map = TimeHourSlotsInOneDay(initial_value=str(sc[\\\"available_hours_in_a_day\\\"]))\\n\\n sc_start_time = parse_datetime_str(sc.get(\\\"start_time\\\"))\\n sc_end_time = parse_datetime_str(sc.get(\\\"end_time\\\"))\\n\\n time_slots_map.overlay_slots_map(time_slots_map=day_sc_map,start_time=sc_start_time,\\n end_time=sc_end_time, repeat=True)\\n\\n return time_slots_map\",\n \"def query_six(self, table_name_activities, table_name_trackpoints):\\n\\n query = (\\n \\\"SELECT t1.user_id, t1.lat, t1.lon, t2.user_id, t2.lat, t2.lon \\\"\\n \\\"FROM (SELECT user_id, lat, lon, date_time FROM %s inner join %s on Activity.id=TrackPoint.activity_id) as t1, \\\"\\n \\\"(SELECT user_id, lat, lon, date_time FROM Activity inner join TrackPoint on Activity.id=TrackPoint.activity_id) as t2 \\\"\\n \\\"where t1.user_id != t2.user_id \\\"\\n \\\"AND ABS(TIMESTAMPDIFF(SECOND,t1.date_time, t2.date_time)) <= 60\\\"\\n )\\n\\n self.cursor.execute(\\n query % (table_name_activities, table_name_trackpoints))\\n rows = self.cursor.fetchall()\\n print(tabulate(rows, headers=self.cursor.column_names))\\n\\n user_dict = dict()\\n for row in rows:\\n if haversine((row[1], row[2]), (row[4], row[5]), unit=\\\"km\\\") <= 0.1:\\n if row[0] in user_dict:\\n user_dict[row[0]].append(row[3])\\n else:\\n user_dict[row[0]] = [row[3]]\\n users = 0\\n for value in users_dict.values():\\n users += len(value)\\n\\n users = users / 2\\n\\n print(users)\\n return users\",\n \"def create_path(self):\\n\\n partials = []\\n partials.append({})\\n #print self.trip_id\\n\\n #this variable is true if we have not yet recorded the first edge of a path\\n first_edge = True\\n #this variable is false until we hit the midpoint\\n hit_midpoint = False\\n\\n first_lasts = []\\n first_lasts.append([0,0])\\n matrices = []\\n matrices.append([np.zeros((self.graph.rows,self.graph.cols)),0])\\n edge_sets = []\\n edge_sets.append([0 for i in range(self.graph.num_edges)])\\n cur_line = self.line_num\\n good_graphs = []\\n good_graphs.append(True)\\n nodes_visited = []\\n nodes_visited.append([])\\n #normalized = dg.normalize(self.graph.lines[cur_line])\\n normalized = normalize_simple(self.graph.lines[cur_line])\\n matrices_index = 0\\n prev_coords = (-1,-1)\\n while normalized[0] == self.trip_id:\\n lat = normalized[1]\\n lon = normalized[2]\\n coords = self.graph.gps_to_coords(lat,lon)\\n node = self.graph.coords_to_node(coords[0],coords[1])\\n\\n if prev_coords == (-1,-1) and coords[0] != -1:\\n first_lasts[matrices_index][0] = node\\n\\n if coords[0] == -1 and prev_coords[0] != -1:\\n prev_node = self.graph.coords_to_node(prev_coords[0],prev_coords[1])\\n first_lasts[matrices_index][1] = prev_node\\n\\n if prev_coords != (-1,-1) and coords[0] != -1 and coords != prev_coords:\\n edge_num = self.graph.edge_num(prev_coords[0],prev_coords[1],coords[0],coords[1])\\n if edge_num == -1:\\n good_graphs[matrices_index] = False\\n else:\\n edge_sets[matrices_index][edge_num] = 1\\n if edge_num in partials[matrices_index] and partials[matrices_index][edge_num] == 0:\\n del partials[matrices_index][edge_num]\\n if not hit_midpoint:\\n if first_edge:\\n above = (prev_coords[0]-1,prev_coords[1])\\n below = (prev_coords[0]+1,prev_coords[1])\\n left = (prev_coords[0],prev_coords[1]-1)\\n right = (prev_coords[0],prev_coords[1]+1)\\n for next_coords in (above,below,left,right):\\n other_edge = self.graph.edge_num(prev_coords[0],prev_coords[1],next_coords[0],next_coords[1])\\n if other_edge != -1:\\n partials[matrices_index][other_edge] = 0\\n first_edge = False\\n if self.graph.coords_to_node(prev_coords[0],prev_coords[1]) == self.midpoint:\\n hit_midpoint = True\\n partials[matrices_index][edge_num] = 1\\n if self.graph.coords_to_node(coords[0],coords[1]) == self.midpoint:\\n hit_midpoint = True\\n\\n\\n\\n if coords[0] == -1:\\n matrices.append([np.zeros((self.graph.rows,self.graph.cols)),0])\\n first_lasts.append([0,0])\\n edge_sets.append([0 for i in range(self.graph.num_edges)])\\n good_graphs.append(True)\\n nodes_visited.append([])\\n matrices_index += 1\\n partials.append({})\\n hit_midpoint = False\\n first_edge = True\\n \\n elif coords[0] < self.graph.rows and coords[1] < self.graph.cols and not matrices[matrices_index][0][coords[0]][coords[1]]:\\n matrices[matrices_index][1] += 1\\n matrices[matrices_index][0][coords[0]][coords[1]] = 1\\n nodes_visited[matrices_index].append(coords)\\n\\n prev_coords = coords\\n\\n cur_line += 1\\n if cur_line == len(self.graph.lines):\\n break\\n #normalized = dg.normalize(self.graph.lines[cur_line])\\n normalized = normalize_simple(self.graph.lines[cur_line])\\n\\n prev_node = self.graph.coords_to_node(prev_coords[0],prev_coords[1])\\n first_lasts[matrices_index][1] = prev_node\\n self.next_line = cur_line\\n best_index = 0\\n best_score = 0\\n for matrix_index in range(len(matrices)):\\n if matrices[matrix_index][1] > best_score:\\n best_score = matrices[matrix_index][1]\\n best_index = matrix_index\\n\\n for coords in nodes_visited[best_index]:\\n self.graph.node_visit(self.trip_id,coords)\\n \\n\\n if self.trip_id not in self.graph.trip_id2line_num:\\n #if first_lasts[best_index] == [28,5]:\\n # print \\\"a to b: %d\\\" % self.trip_id\\n self.graph.first_last2trip_ids[tuple(first_lasts[best_index])].append(self.trip_id)\\n\\n return matrices[best_index][0],edge_sets[best_index],good_graphs[best_index],partials[best_index]\"\n]","isConversational":false},"negative_scores":{"kind":"list like","value":["0.659939","0.59695464","0.58203804","0.5736499","0.57258886","0.5721508","0.56901395","0.56083816","0.5594009","0.55866593","0.5553673","0.55434465","0.55286705","0.5508056","0.5475889","0.547315","0.54577315","0.5440168","0.5398705","0.53711665","0.53706187","0.53669477","0.53535527","0.5335062","0.53132886","0.52653545","0.5261905","0.52389765","0.5238165","0.5226132","0.52224094","0.5214971","0.5194379","0.51700073","0.516361","0.5156135","0.5144979","0.51429236","0.51417005","0.51376706","0.5115709","0.51039624","0.50704175","0.50691956","0.506648","0.50610346","0.5037069","0.5034235","0.5028411","0.5004762","0.49853426","0.49779946","0.4973033","0.49709916","0.49673718","0.49603453","0.49580774","0.4957086","0.49401805","0.49391833","0.49364436","0.49233693","0.4906022","0.49050665","0.48878747","0.48744464","0.48721695","0.48713827","0.48548496","0.48533964","0.48470408","0.484701","0.48464653","0.48445472","0.4842114","0.4833733","0.4830323","0.48280773","0.48096007","0.48076618","0.48044756","0.48027325","0.4801297","0.47976863","0.47957835","0.47863388","0.47816628","0.4781012","0.47801962","0.47730237","0.47728372","0.475975","0.47545505","0.47491816","0.47455513","0.4743482","0.4731838","0.47205967","0.47149995","0.47105336"],"string":"[\n \"0.659939\",\n \"0.59695464\",\n \"0.58203804\",\n \"0.5736499\",\n \"0.57258886\",\n \"0.5721508\",\n \"0.56901395\",\n \"0.56083816\",\n \"0.5594009\",\n \"0.55866593\",\n \"0.5553673\",\n \"0.55434465\",\n \"0.55286705\",\n \"0.5508056\",\n \"0.5475889\",\n \"0.547315\",\n \"0.54577315\",\n \"0.5440168\",\n \"0.5398705\",\n \"0.53711665\",\n \"0.53706187\",\n \"0.53669477\",\n \"0.53535527\",\n \"0.5335062\",\n \"0.53132886\",\n \"0.52653545\",\n \"0.5261905\",\n \"0.52389765\",\n \"0.5238165\",\n \"0.5226132\",\n \"0.52224094\",\n \"0.5214971\",\n \"0.5194379\",\n \"0.51700073\",\n \"0.516361\",\n \"0.5156135\",\n \"0.5144979\",\n \"0.51429236\",\n \"0.51417005\",\n \"0.51376706\",\n \"0.5115709\",\n \"0.51039624\",\n \"0.50704175\",\n \"0.50691956\",\n \"0.506648\",\n \"0.50610346\",\n \"0.5037069\",\n \"0.5034235\",\n \"0.5028411\",\n \"0.5004762\",\n \"0.49853426\",\n \"0.49779946\",\n \"0.4973033\",\n \"0.49709916\",\n \"0.49673718\",\n \"0.49603453\",\n \"0.49580774\",\n \"0.4957086\",\n \"0.49401805\",\n \"0.49391833\",\n \"0.49364436\",\n \"0.49233693\",\n \"0.4906022\",\n \"0.49050665\",\n \"0.48878747\",\n \"0.48744464\",\n \"0.48721695\",\n \"0.48713827\",\n \"0.48548496\",\n \"0.48533964\",\n \"0.48470408\",\n \"0.484701\",\n \"0.48464653\",\n \"0.48445472\",\n \"0.4842114\",\n \"0.4833733\",\n \"0.4830323\",\n \"0.48280773\",\n \"0.48096007\",\n \"0.48076618\",\n \"0.48044756\",\n \"0.48027325\",\n \"0.4801297\",\n \"0.47976863\",\n \"0.47957835\",\n \"0.47863388\",\n \"0.47816628\",\n \"0.4781012\",\n \"0.47801962\",\n \"0.47730237\",\n \"0.47728372\",\n \"0.475975\",\n \"0.47545505\",\n \"0.47491816\",\n \"0.47455513\",\n \"0.4743482\",\n \"0.4731838\",\n \"0.47205967\",\n \"0.47149995\",\n \"0.47105336\"\n]","isConversational":false},"document_score":{"kind":"string","value":"0.720365"},"document_rank":{"kind":"string","value":"0"}}},{"rowIdx":75396,"cells":{"query":{"kind":"string","value":"Reduce to the nodes with highest degree (in + out degree)"},"document":{"kind":"string","value":"def keep_important_nodes(graph, number_of_nodes):\n sorted_dict = np.array(\n [\n [k, v]\n for k, v in sorted(\n dict(graph.degree()).items(),\n key=lambda item: item[1],\n )\n ]\n )\n use_nodes = sorted_dict[-number_of_nodes:, 0]\n graph = graph.subgraph(use_nodes)\n return graph"},"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 degree_graph(g):\n return max(degree_node(g, node) for node in g)","def layer_out_degree(self, layer_id):\n ...","def max_degree_node(g, d, connected):\n if not connected:\n deg = d\n else:\n deg = {k: v for k, v in d.items() if k in connected}\n if not deg:\n return None\n n = max(deg.keys(), key=lambda k: deg[k])\n d.pop(n)\n for n_ in g.neighbors(n):\n connected.add(n_)\n return n","def _compute_node_degrees(self):\n mes = []\n args = []\n for metaedge, matrix in self.adj_matrices.items():\n mes.append(metaedge)\n args.append(matrix)\n res = parallel_process(array=args, function=mt.calculate_degrees, n_jobs=self.n_jobs, front_num=0)\n for metaedge, (out_degree, in_degree) in zip(mes, res):\n self.out_degree[metaedge] = out_degree\n self.in_degree[metaedge] = in_degree","def internal_outdegree(self, node_list, link_attribute=None):\n if link_attribute is None:\n return np.sum(self.internal_adjacency(node_list), axis=1)\n else:\n return np.sum(self.internal_link_attribute(link_attribute,\n node_list), axis=1)","def fn(graph, indeg):\n ans = []\n stack = [k for k in range(len(indeg)) if indeg[k] == 0]\n while stack: \n n = stack.pop()\n ans.append(n)\n for nn in graph.get(n, []):\n indeg[nn] -= 1\n if indeg[nn] == 0: stack.append(nn)\n return ans","def reduce(self, app, nodes, result):","def reduce(self):\n # initializing a hashtable for all the nodes in the tree\n hashtable = {}\n for node_it in self.nodes:\n # storing each node only once in the table\n if not node_it.__hash__() in hashtable:\n hashtable[node_it.__hash__()] = node_it\n\n def reduce_rec(node):\n \"\"\"\n The recursive method for the reduction.\n \"\"\"\n if node.is_leaf():\n return\n for edge in node.child_nodes:\n # replacing the subdiagram with a singular isomorphic one\n node.child_nodes[edge] = hashtable[node.child_nodes[edge].__hash__()]\n # and going down recursively along that subdiagram\n reduce_rec(node.child_nodes[edge])\n\n # calling the reduction method\n reduce_rec(self)\n # reinitializing the diagram\n self.reinitialize()\n return self","def countMaxDegree(self):\r\n max_degree = [0, 0] # le sommet, son nombre de connection \r\n for i_node, node_connections in enumerate(self.adjMatrix):\r\n connection = self.n - node_connections.count(0) # on compte le nombre de connections du sommet\r\n if connection > max_degree[1]:\r\n max_degree = max_degree[i_node, node_connections]\r\n return max_degree[0], max_degree[1] # C un tuple ! \r","def reduce(self) -> float:\n # Note: Reduction over segments not supported/needed for now.\n return self._tree[1]","def targeted_order(ugraph):\r\n # copy the graph\r\n new_graph = copy_graph(ugraph)\r\n \r\n order = [] \r\n while len(new_graph) > 0:\r\n max_degree = -1\r\n for node in new_graph:\r\n if len(new_graph[node]) > max_degree:\r\n max_degree = len(new_graph[node])\r\n max_degree_node = node\r\n \r\n neighbors = new_graph[max_degree_node]\r\n new_graph.pop(max_degree_node)\r\n for neighbor in neighbors:\r\n new_graph[neighbor].remove(max_degree_node)\r\n\r\n order.append(max_degree_node)\r\n return order","def targeted_order(ugraph):\n # copy the graph\n new_graph = copy_graph(ugraph)\n \n order = []\n while len(new_graph) > 0:\n max_degree = -1\n for node in new_graph:\n if len(new_graph[node]) > max_degree:\n max_degree = len(new_graph[node])\n max_degree_node = node\n \n neighbors = new_graph[max_degree_node]\n new_graph.pop(max_degree_node)\n for neighbor in neighbors:\n new_graph[neighbor].remove(max_degree_node)\n \n order.append(max_degree_node)\n return order","def reduce_triangle(to_reduce):\r\n last_row = to_reduce[-1]\r\n for index in xrange(len(to_reduce)-1):\r\n to_reduce[-2][index] += max(last_row[index:index+2])\r\n del to_reduce[-1]\r\n return to_reduce","def test_grow_maximal_degree(self, dim):\r\n graph = nx.lollipop_graph(dim, 1)\r\n graph.remove_edge(dim - 2, dim - 1)\r\n s = set(range(dim - 2))\r\n target = s | {dim - 1}\r\n assert set(clique.grow(s, graph, node_select=\"degree\")) == target","def targeted_order(ugraph):\n # copy the graph\n new_graph = copy_graph(ugraph)\n\n order = []\n while len(new_graph) > 0:\n max_degree = -1\n for node in new_graph:\n if len(new_graph[node]) > max_degree:\n max_degree = len(new_graph[node])\n max_degree_node = node\n\n neighbors = new_graph[max_degree_node]\n new_graph.pop(max_degree_node)\n for neighbor in neighbors:\n new_graph[neighbor].remove(max_degree_node)\n\n order.append(max_degree_node)\n return order","def targeted_order(ugraph):\n # copy the graph\n new_graph = copy_graph(ugraph)\n\n order = []\n while len(new_graph) > 0:\n max_degree = -1\n for node in new_graph:\n if len(new_graph[node]) > max_degree:\n max_degree = len(new_graph[node])\n max_degree_node = node\n\n neighbors = new_graph[max_degree_node]\n new_graph.pop(max_degree_node)\n for neighbor in neighbors:\n new_graph[neighbor].remove(max_degree_node)\n\n order.append(max_degree_node)\n return order","def kAnonymit(g,k):\n rlist=list()\n degreeH=nx.degree_histogram(g)\n## print degreeH\n li=[[] for i in range(len(degreeH))]\n uNode=dict(zip(range(len(degreeH)),li))\n \n gd=g.degree()\n for e in gd:\n uNode[gd[e]].append(e)\n ad=list()\n mi=list()\n su=list() \n sel3(g,3,ad,mi,su)\n nodeAd=list()\n nodeMi=list()\n nodeSu=list() \n for i in ad:\n nodeAd=nodeAd+uNode[i]\n #nodeAd store the graph ID\n for i in mi:\n nodeMi=nodeMi+uNode[i]\n for i in su:\n nodeSu=nodeSu+uNode[i]\n\n print nodeAd,nodeMi,nodeSu\n\n\n i=0#Add edge \n while i<len(nodeAd)-1:\n j=i+1\n while j<len(nodeAd):\n if not g.has_edge(nodeAd[i],nodeAd[j]):\n g.add_edge(nodeAd[i],nodeAd[j])\n rlist.append(nodeAd[i])\n rlist.append(nodeAd[j])\n nodeAd.pop(j)\n nodeAd.pop(i)\n i=i-1\n break\n else:\n j=j+1\n i=i+1\n print nodeAd\n \n i=0#delet edge\n while i<len(nodeMi)-1:\n j=i+1\n while j<len(nodeMi):\n if g.has_edge(nodeMi[i],nodeMi[j]):\n g.remove_edge(nodeMi[i],nodeMi[j])\n rlist.append(nodeMi[i])\n rlist.append(nodeMi[j])\n nodeMi.pop(j)\n nodeMi.pop(i)\n i=i-1\n break\n else:\n j=j+1\n i=i+1\n print nodeMi\n \n i=0#Add edge \n while i<len(nodeSu):\n j=0\n while j<len(nodeAd):\n if not g.has_edge(nodeSu[i],nodeAd[j]):\n g.add_edge(nodeSu[i],nodeAd[j])\n rlist.append(nodeSu[i])\n rlist.append(nodeAd[j])\n nodeAd.pop(j)\n nodeSu.pop(i)\n \n i=i-1\n break\n else:\n j=j+1\n i=i+1\n print nodeSu\n \n i=0#delet edge\n while i<len(nodeSu):\n j=0\n while j<len(nodeMi):\n if g.has_edge(nodeSu[i],nodeMi[j]):\n g.remove_edge(nodeSu[i],nodeMi[j])\n rlist.append(nodeSu[i])\n rlist.append(nodeMi[j])\n nodeMi.pop(j)\n nodeSu.pop(i)\n i=i-1\n break\n else:\n j=j+1\n i=i+1\n print nodeSu\n return rlist","def _reduce_triangle(to_reduce):\n last_row = to_reduce[-1]\n for index in xrange(len(to_reduce) - 1):\n to_reduce[-2][index] += max(last_row[index:index + 2])\n del to_reduce[-1]","def make_upa_graph(nodes, out_degree):\n graph = go.make_complete_graph(out_degree)\n trial = upa.UPATrial(out_degree)\n for new_node in range(out_degree, nodes):\n neighbors = trial.run_trial(out_degree)\n graph[new_node] = neighbors\n for neighbor in neighbors:\n graph[neighbor].add(new_node)\n return graph","def targeted_order(ugraph):\n # copy the graph\n new_graph = copy_graph(ugraph)\n \n counter = 0\n order = [] \n while len(new_graph) > 0:\n max_degree = -1\n for node in new_graph:\n counter += 1\n if len(new_graph[node]) > max_degree:\n max_degree = len(new_graph[node])\n max_degree_node = node\n \n \n neighbors = new_graph[max_degree_node]\n new_graph.pop(max_degree_node)\n for neighbor in neighbors:\n counter += 1\n new_graph[neighbor].remove(max_degree_node)\n\n order.append(max_degree_node)\n return order # uncomment to use graph_resilience_targeted_order","def reduce(nodes):\r\n accum = tc.mean(nodes.mailbox['m'], 1).cuda()\r\n return {'h': accum}","def _reduce(self, action):\n assert len(self.stack) >= 2, \"ERROR: Cannot reduce with stack length less than 2\"\n \n # STUDENT\n # hint: use list.pop()\n # END STUDENT\n rightarc = self.stack.pop()\n leftarc = self.stack.pop()\n head = rightarc if action == Actions.REDUCE_L else leftarc\n mod = leftarc if action == Actions.REDUCE_L else rightarc\n self.stack.append( StackEntry(head.headword, head.headword_pos, self.combiner(head.embedding,mod.embedding)) )\n return DepGraphEdge((head.headword, head.headword_pos),(mod.headword, mod.headword_pos))","def get_deg(nodes = 10000,edges=None):\n \n D= nx.MultiDiGraph()\n D.add_nodes_from(np.arange(1,nodes))\n D.add_edges_from(edges);\n return np.array(D.in_degree())[:,1],np.array(D.out_degree())[:,1],D","def reduce_rec(node):\n if node.is_leaf():\n return\n for edge in node.child_nodes:\n # replacing the subdiagram with a singular isomorphic one\n node.child_nodes[edge] = hashtable[node.child_nodes[edge].__hash__()]\n # and going down recursively along that subdiagram\n reduce_rec(node.child_nodes[edge])","def out_degree_sequence(self):\n return sorted(self.out_degree_iterator(), reverse=True)","def degree_node(g, node):\n return len(g[node])","def select_leaf(self):\n current = self\n best_child = None\n selected_nodes_R = 0\n while current.isExpanded:\n maxUCT = - float('inf')\n for child in current.children.values():\n UCT = child.compute_uct()\n if UCT > maxUCT:\n maxUCT = UCT\n best_child = child\n\n current = best_child\n selected_nodes_R += current.score\n return current, selected_nodes_R","def compute_perm(parents):\n # Function written by M. Defferrard, taken verbatim, from \n # https://github.com/mdeff/cnn_graph/blob/master/lib/coarsening.py#L167\n\n # Order of last layer is random (chosen by the clustering algorithm).\n indices = []\n if len(parents) > 0:\n M_last = max(parents[-1]) + 1\n indices.append(list(range(M_last)))\n\n for parent in parents[::-1]:\n #print('parent: {}'.format(parent))\n\n # Fake nodes go after real ones.\n pool_singeltons = len(parent)\n\n indices_layer = []\n for i in indices[-1]:\n indices_node = list(np.where(parent == i)[0])\n assert 0 <= len(indices_node) <= 2\n #print('indices_node: {}'.format(indices_node))\n\n # Add a node to go with a singelton.\n if len(indices_node) == 1:\n indices_node.append(pool_singeltons)\n pool_singeltons += 1\n #print('new singelton: {}'.format(indices_node))\n # Add two nodes as children of a singelton in the parent.\n elif len(indices_node) == 0:\n indices_node.append(pool_singeltons+0)\n indices_node.append(pool_singeltons+1)\n pool_singeltons += 2\n #print('singelton childrens: {}'.format(indices_node))\n\n indices_layer.extend(indices_node)\n indices.append(indices_layer)\n\n # Sanity checks.\n for i,indices_layer in enumerate(indices):\n M = M_last*2**i\n # Reduction by 2 at each layer (binary tree).\n assert len(indices[0] == M)\n # The new ordering does not omit an indice.\n assert sorted(indices_layer) == list(range(M))\n\n return indices[::-1]","def handle_leaf_nodes(self, nodes):\n max_node_id = -1\n for n in nodes:\n n.sum_hess = self.decrypt(n.sum_hess)\n n.sum_grad = self.decrypt(n.sum_grad)\n n.weight = self.splitter.node_weight(n.sum_grad, n.sum_hess)\n n.sitename = self.sitename\n if n.id > max_node_id:\n max_node_id = n.id\n new_nodes = [Node() for i in range(max_node_id + 1)]\n for n in nodes:\n new_nodes[n.id] = n\n return new_nodes","def largest_path_sum(T):\n return max(ft.reduce(fold_dist, T))","def reduce_inbound_connections(inbound_counts, nodes):\n nodes_without_inbound = []\n for node in nodes:\n inbound_counts[node] = inbound_counts[node] - 1\n if inbound_counts[node] == 0:\n nodes_without_inbound.append(node)\n return nodes_without_inbound","def fixedDiGraph(conn_,out_deg,target_degree,N):\n \n d_o = (out_deg[0:N]-target_degree) #outdegree difference\n # [1:NE] - ignores the first dummy element \n pos_ind = np.where(d_o>0)[0] # positive difference (remove connections)\n neg_ind = np.where(d_o<0)[0] # negative difference (add connection)\n\n add_ind= []\n for ind in pos_ind:\n while len(conn_[conn_[:,0]==ind])>target_degree: # +1 might need to go away\n add_ind.append(conn_[conn_[:,0]==ind][-1,1])\n # delete a raw\n ind_d = np.where(conn_[:,0]==ind)[0][-1]\n conn_ = np.delete(conn_,ind_d,0)\n ii = 0\n for ind in neg_ind:\n while len(conn_[conn_[:,0]==ind])<target_degree:\n conn_ = np.vstack((conn_,[ind, add_ind[ii]]))\n ii+=1\n\n return conn_","def reduce(self,\r\n noterange=None):\r\n\r\n if noterange is None:\r\n noterange = self.apply_limit([str(Index(a_temp))\r\n for a_temp in self.indexes()\r\n if Index(a_temp).is_top()])\r\n\r\n\r\n for tup in reduce_tupples([Index(x_temp) for x_temp in self.find_within(indexfrom=Index(0),orequal=True,withinrange=noterange)]):\r\n self.move(tup[0], tup[1], withchildren=True)","def calc_degree(graph_rdd):\n all_degree = graph_rdd \\\n .map(swap) \\\n .union(graph_rdd) \\\n .map(lambda (x, y): (x, 1)) \\\n .reduceByKey(add, numPartitions=40)\n return all_degree","def __init__(self, degree):\r\n self.root = Node([], [])\r\n self.min_num_keys = degree - 1 \r\n self.max_num_keys = 2*degree - 1","def equalize_node_density(self, maximum_distance, maximum_angle_delta, greedy=True):\n print('network: splitting long edges...')\n \"\"\" Split edges which are very long. \"\"\"\n self.split_edges(maximum_distance)\n\n print('network: merging short edges...')\n vertices_to_remove = []\n edges_to_add = []\n \"\"\" Merge edges which are close together, and collect vertices/edges which should be removed/added. \"\"\"\n for section_id in self.sections:\n utils.print_progress(len(self.sections), prefix='merging edges')\n new_edges, redundant_vertices = self.merge_edges(self.sections[section_id], maximum_distance,\n maximum_angle_delta, greedy)\n vertices_to_remove.extend(redundant_vertices)\n edges_to_add.extend(new_edges)\n # Maintain the section list\n self.sections[section_id] = list(filter(lambda v: v not in redundant_vertices, self.sections[section_id]))\n\n \"\"\" Add the new edges and edge weights into the graph. \"\"\"\n for edge, weight in edges_to_add:\n utils.print_progress(len(edges_to_add), prefix='adding edges')\n new_edge = self.graph.add_edge(edge[0], edge[1], add_missing=False)\n self.edge_weights[new_edge] = weight\n\n \"\"\" Removing vertices reindexes the vertices and edges of the graph. Need to maintain external data \n structures to prevent data corruption. \"\"\"\n original_indices = self.graph.vertex_index.copy() # Property map will correct for reindexing\n self.graph.remove_vertex(vertices_to_remove, fast=True)\n # Vertices have now been reindexed. Update each section with the new vertex IDs.\n for section_id in self.sections:\n utils.print_progress(len(self.sections), prefix='reindexing vertices')\n self.sections[section_id] = [find_vertex(self.graph, original_indices, v)[0] for v in\n self.sections[section_id]]\n\n return self.graph.num_vertices()","def get_subgraph(graph, min_degree):\n ###TODO\n pass","def connected((e,r)):\n \n # Deal with the middle case so we don't divide by zero\n if r==0: return [(1,1),(2,1),(3,1),(4,1),(5,1),(0,1)]\n # If the input is impossible, return nothing to prune the branch (shouldn't\n # happen)\n if e>=6*r: return []\n connected=[]\n mult=e//r\n rem=e % r\n #Going sideways\n toAdd=((6*r-1,r) if e==0 else (e-1,r))\n connected.append(toAdd)\n toAdd=((0,r) if e==6*r-1 else (e+1,r))\n connected.append(toAdd)\n #Going inward\n toAdd=( (0,r-1)if mult==5 and rem==r-1 else (mult*(r-1)+rem,r-1) )\n connected.append(toAdd)\n if rem!=0:\n connected.append((mult*(r-1)+rem-1,r-1))\n\n #Going outward\n if r<nLayers-1:\n connected.append((mult*(r+1)+rem,r+1))\n connected.append((mult*(r+1)+rem+1,r+1))\n if rem==0: # only case where negatives could result\n if mult>0: connected.append( (mult*(r+1)-1,r+1))\n else: connected.append( (6*(r+1)-1,r+1))\n \n return connected","def getReducedSubgraphU(self, G, PD, baseParams, bestParams, Lid):\n doshrink = True\n count_remove_nodes = 0\n FinalParams = bestParams\n while doshrink: #continue removing nodes one by one till no increase in IG\n doshrink = False\n for node in FinalParams['Pat'].NL:\n curParams = dict()\n curParams['Pat'] = FinalParams['Pat'].copy()\n curParams['Pat'].removeNode(node)\n curParams['codeLengthCprime'] = FinalParams['codeLengthCprime'] - self.computeCLgainRemoveNodeU(G, PD, curParams['Pat'].NL, node, [Lid])\n curParams['Pat'].setIC_dssg( baseParams['codeLengthC'] - curParams['codeLengthCprime'] )\n curParams['Pat'].setDL( computeDescriptionLength( dlmode=6, C=len(PD.lprevUpdate), gtype=self.gtype, WS=baseParams['Pat'].NCount, W=curParams['Pat'].NCount, kw=curParams['Pat'].ECount, isSimple=self.isSimple, kws=curParams['Pat'].kws ) )\n curParams['Pat'].setI( computeInterestingness( curParams['Pat'].IC_dssg, curParams['Pat'].DL, mode=self.imode ) )\n if curParams['Pat'].I > bestParams['Pat'].I:\n bestParams = curParams\n if bestParams['Pat'].I > FinalParams['Pat'].I:\n FinalParams = bestParams\n count_remove_nodes += 1\n doshrink = True\n if count_remove_nodes > 0 or FinalParams['Pat'].NCount < baseParams['Pat'].NCount:\n FinalParams['codeLengthC'] = baseParams['codeLengthC']\n FinalParams['Pat'].setLambda( PD.updateDistribution( pat=FinalParams['Pat'].G, idx=None, val_return='return', case=3, dropLidx=[Lid]) ) #// Todo: computeNewLambda\n FinalParams['codeLengthCprime'] = self.computeCodeLengthShrinkU(G, PD, 3, baseParams, FinalParams, Lid, FinalParams['Pat'].la) #// Todo computeNewCodeLength\n FinalParams['Pat'].setIC_dssg( FinalParams['codeLengthC'] - FinalParams['codeLengthCprime'] )\n FinalParams['Pat'].setDL( computeDescriptionLength( dlmode=6, C=len(PD.lprevUpdate), gtype=self.gtype, WS=baseParams['Pat'].NCount, W=FinalParams['Pat'].NCount, kw=FinalParams['Pat'].ECount, excActionType=False, l=self.l, isSimple=self.isSimple, kws=FinalParams['Pat'].kws ) )\n FinalParams['Pat'].setI( computeInterestingness( FinalParams['Pat'].IC_dssg, FinalParams['Pat'].DL, mode=self.imode ) )\n return FinalParams","def walk_sort(edges):\n g = nx.Graph()\n g.add_edges_from(edges)\n connected = set()\n degree = nx.degree(g)\n ordering = []\n while degree:\n next = max_degree_node(g, degree, connected)\n if next is not None:\n ordering.append(next)\n else:\n break\n return ordering","def fn(node):\n if not node: return 0\n return 1 + max(fn(node.left), fn(node.right))","def fn(node):\n if not node: return 0\n return 1 + max(fn(node.left), fn(node.right))","def fast_targeted_order(ugraph):\n degree_sets = {}\n num_nodes = len(ugraph)\n for node in range(0, num_nodes):\n degree_sets[node] = set([])\n\n for node_degree in range(0, num_nodes):\n degree = len(ugraph[node_degree])\n degree_sets[degree].add(node_degree)\n\n desc_node_degrees = []\n index = 0\n\n for degree in range(num_nodes - 1, -1, -1):\n while len(degree_sets[degree]) > 0:\n node_to_add = degree_sets[degree].pop()\n for neighbor in ugraph[node_to_add]:\n neighbor_degree = len(ugraph[neighbor])\n degree_sets[neighbor_degree].remove(neighbor)\n degree_sets[neighbor_degree - 1].add(neighbor)\n desc_node_degrees.insert(index, node_to_add)\n index += 1\n a.delete_node(ugraph, node_to_add)\n\n return desc_node_degrees","def convert_reduce(g, op, block):\n\n op_map = {\n \"reduce_all\": \"all\",\n \"reduce_any\": \"any\",\n \"reduce_max\": \"max\",\n \"reduce_min\": \"min\",\n \"reduce_prod\": \"prod\",\n \"reduce_sum\": \"sum\",\n \"reduce_mean\": \"mean\",\n }\n op_name = op_map[op.type]\n input_x = g.get_node(op.input(\"X\")[0])\n axis = op.attr(\"dim\")\n if op.attr(\"reduce_all\"):\n axis = None\n keepdims = op.attr(\"keep_dim\")\n out = get_relay_op(op_name)(input_x, axis=axis, keepdims=keepdims)\n if not axis and not keepdims:\n # use `expand_dims` to solve the following situation\n # for TVM, the shape of `out` will be (, )\n # for Paddle, the shape of `out` will be [1]\n out = _op.expand_dims(out, axis=0)\n g.add_node(op.output(\"Out\")[0], out)","def __call__(self, graph: Data, n_min: int, nodes_to_keep: List[int] = None, exhaustive: bool = False):\n nodes_to_keep = nodes_to_keep if nodes_to_keep is not None else []\n mcts = self._get_mcts(graph, n_min, nodes_to_keep, exhaustive)\n\n for iteration in range(self.m):\n mcts.search_one_iteration()\n\n explanation = mcts.best_leaf_node()\n\n return explanation.node_set, mcts","def internal_degree(self, node_list, link_attribute=None):\n if self.directed:\n return (self.internal_indegree(node_list, link_attribute)\n + self.internal_outdegree(node_list, link_attribute))\n else:\n return self.internal_outdegree(node_list, link_attribute)","def to_prufer(tree, n):\n d = defaultdict(int)\n L = []\n for edge in tree:\n # Increment the value of the corresponding\n # node in the degree list as we encounter an\n # edge involving it.\n d[edge[0]] += 1\n d[edge[1]] += 1\n for i in range(n - 2):\n # find the smallest leaf\n for x in range(n):\n if d[x] == 1:\n break\n # find the node it was connected to\n y = None\n for edge in tree:\n if x == edge[0]:\n y = edge[1]\n elif x == edge[1]:\n y = edge[0]\n if y is not None:\n break\n # record and update\n L.append(y)\n for j in (x, y):\n d[j] -= 1\n if not d[j]:\n d.pop(j)\n tree.remove(edge)\n return L","def select_final(self):\n best_qsa_star = -99999\n best_node = None\n for a, c in self.children.items():\n qsa = c.wins / c.visits\n if c.visits_amaf == 0:\n qsa_tilde = 0\n else:\n qsa_tilde = c.wins_amaf / c.visits_amaf\n bsa = sqrt(self.k / (self.visits + self.k))\n qsa_star = (1 - bsa) * qsa + bsa * qsa_tilde\n if qsa_star > best_qsa_star:\n best_qsa_star = qsa_star\n best_node = c\n return best_node.action","def reduce_element(self, elt):\n if not elt.module == self.parent:\n raise NotImplementedError\n if not self.is_sq_maxrank_HNF():\n msg = \"Reduction not implemented unless matrix square max-rank HNF\"\n raise StructureError(msg)\n B = self.basis_element_pullbacks()\n a = elt\n for i in range(self.n - 1, -1, -1):\n b = B[i]\n q = a.coeffs[i]*b.denom // (b.coeffs[i]*a.denom)\n a -= q*b\n return a","def max_degree(self, k):\n if k < 3:\n return (self.L.size() - 1) // (self.K.size() ** k)\n if k == 3:\n if self.t == 4:\n return 2 * self.q + 2 - int((self.q + 1) % 3)\n raise ValueError(\"The maximal degree of {}-vertex sets is unknown for this graph.\".format(k))","def get_rendezvous_node(nodes, key):\n # TODO\n \n highest_node = None\n weights = []\n for node in nodes:\n x = node + key\n x = x.encode()\n w = hashlib.md5(x).hexdigest()\n weights.append((w, node))\n\n _, highest_node = max(weights)\n return highest_node","def degree(self, node):\r\n if not 0 <= node < self.size:\r\n raise ValueError(\"Cannot find degree for a node not in the graph\")\r\n return len(self.edges[node])","def fn(node):\n if not node: return 0, 0 # null node \n if node.left is node.right: return node.val, 0 # leaf node \n left, right = fn(node.left), fn(node.right) # post-order traversal \n return node.val+left[1]+right[1], max(left)+max(right)","def degreeCentrality(graph, numberOfPoints):\n c_degree = nx.degree_centrality(graph)\n c_degree = heapq.nlargest(numberOfPoints, list(c_degree.values()))\n return c_degree","def reduce_parallel(edges):\n edges[:] = qsort(edges)\n logger.debug(\"Performing parallel optimization\\n\")\n e = edges # use shorter name for edges\n\n l = 0 # l stands for left and points to left side of comparison\n # r stands for right and points to the right side of comparison\n # items are sorted in the list, so parallel\n # edges are going to be next to each other\n for r in range(1, len(e)):\n logger.debug(\"CMP e[{}]:{} to e[{}]:{}\".format(l, e[l], r, e[r]))\n if (e[l][0] == e[r][0] and e[l][1] == e[r][1]):\n # for parallel edges - overwrite left delay with balanced delay\n # except if one of the edges is zero = set zero\n if(e[r][2] == 0):\n e[l][2] = 0\n else:\n e[l][2] = (e[l][2] * e[r][2]) / (e[l][2] + e[r][2])\n\n # keep right edge value unchanged as\n # it is going to be overwritten later,\n # when not parallel edges is going to be found\n logger.debug(\" Parallel: new value e[{}]:{}\".format(l, e[l]))\n else:\n # for not equal edges\n # copy right component to the place right after left.\n # e.g.: with l=0, r=1 this is going to be self assignment,\n # with l=3, r=5 this is going to overwrite one of old parallels\n # that were merged with their lefts\n l += 1\n e[l] = e[r]\n logger.debug(\" NOT Parallel: e[{}]:{} <= e[{}]:{}\".format(\n l, e[l], r, e[r]))\n\n # by the time this loop is ended l is going to point\n # to last meaningful edge in the list\n kept = l + 1\n removed = len(e) - kept\n logger.debug(\"\\n{} parallels removed\\n\".format(removed))\n\n e[:] = e[:kept]\n logger.debug(\"Parallel reduce result edges: {}\\n\".format(e))\n\n return removed","def degrees(self):\n A = self.adjacency()\n A.data = np.ones(A.nnz)\n right = np.array(A.sum(1)).ravel()\n left = np.array(A.sum(0)).ravel()\n return right, left","def zzx_reduce(f):\n if zzx_degree(f) <= 0:\n return 1, f\n\n g = INT_ZERO\n\n for i in xrange(len(f)):\n if not f[-i-1]:\n continue\n\n g = igcd(g, i)\n\n if g == 1:\n return 1, f\n\n return g, f[::g]","def bgll(self, graph, node_count, min_mod, max_pass):\n\n #the belonging of the node\n bl = [i for i in range(node_count)]\n #the node's weight in community\n _in = [0.0] * node_count\n #the node's weight in graph\n _tot = []\n #total weight of a node, just a copy of _tot\n k = []\n #the total weight of the graph\n m = []\n\n #inital the in-param\n network = [[0.0] * node_count for n in range(node_count)]\n for node, tag, weight in graph:\n network[node][tag] = weight\n for node in network:\n k.append(sum(node))\n _tot = k[:]\n m = sum(k)\n #inital the in-param\n\n def modularity():\n \"\"\"\n This function mainly computes the modularity of the network\n Return:\n mod->the modularity value\n \"\"\"\n\n q = 0.0\n for idx in range(0, node_count):\n if _tot[idx] > 0.0:\n q += (_in[idx] / m - math.pow(_tot[idx] / m, 2))\n return q\n\n def modularity_gain(n, c, dnodecomm):\n \"\"\"\n This function mainly computes the modularity gain of a node moving\n Param:\n n->node id\n c->community id\n dnodecomm->the weight between the node and the community\n Return:\n gain->modularity gain\n \"\"\"\n\n totc = _tot[c]\n degc = k[n]\n return dnodecomm - (totc * degc) / m\n\n def neigh_comm(n):\n \"\"\"\n This function mainly computes the weight between the node and it's neighbour community\n Param:\n n->node id\n Return:\n nc->the map of the weight between the node and it's neighbour community\n nc=>{cid,weight}\n \"\"\"\n\n nc = {bl[n]: 0.0}\n for idx in range(0, node_count):\n neigh = idx\n ncomm = bl[neigh]\n nei_weight = network[n][idx]\n if (neigh != n) & (nei_weight > 0.0):\n if ncomm in nc:\n nc[ncomm] += nei_weight\n else:\n nc[ncomm] = nei_weight\n return nc\n\n def insert(n, c, dnodecomm):\n \"\"\"\n This function mainly get the effect of insert the node into community\n Param:\n n->node id\n c->community id\n dnodecomm->the weight between the node and the community\n \"\"\"\n\n _tot[c] += k[n]\n _in[c] += 2 * dnodecomm + network[n][n]\n bl[n] = c\n\n def remove(n, c, dnodecomm):\n \"\"\"\n This function mainly get the effect of remove the node off community\n Param:\n n->node id\n c->community id\n dnodecomm->the weight between the node and the community\n \"\"\"\n\n _tot[c] -= k[n]\n _in[c] -= 2 * dnodecomm + network[n][n]\n bl[n] = -1\n\n def detect():\n \"\"\"\n This function mainly detect the community of the graph.\n \"\"\"\n\n _pass_done = 0\n _improve = True\n new_mod = modularity()\n cur_mod = -999999999.0\n rl = random.sample(range(0, node_count), node_count)\n while _improve & (_pass_done < max_pass) & (new_mod - cur_mod > min_mod):\n cur_mod = new_mod\n _improve = False\n _pass_done += 1\n for node_tmp in rl:\n n = node_tmp\n nc = bl[n]\n ncomm = neigh_comm(n)\n remove(n, nc, ncomm[nc])\n best_c = nc\n best_l = 0.0\n best_incre = 0.0\n for c in ncomm:\n incre = modularity_gain(n, c, ncomm[c])\n if incre > best_incre:\n best_incre = incre\n best_c = c\n best_l = ncomm[c]\n insert(n, best_c, best_l)\n if best_c != nc:\n _improve = True\n new_mod = modularity()\n print new_mod\n\n detect()\n return bl","def return_outdeg(self, vertex: np.int_):\n return len(self.__adj[vertex])","def _compute_longest_path_cut(self, src_uid, dst_uid):\n # NOTE: at the time being the reason why it was chosen to include edges\n # that belong to any path to the returned list, is unclear to me.\n to_visit_uids = [dst_uid]\n node_costs = {}\n node_succs = {}\n subgraph_edge_uids = []\n subgraph_node_uids = [src_uid]\n dsp = 0\n\n if (src_uid == dst_uid): # NOTE: deals with single-block graphs\n node = self._nodes[src_uid]\n return node.get_cost(), [src_uid], [], []\n\n # NOTE: the generated block graph might contain dead ends, thus\n # visited_uids must be pre-computed\n visited_uids = copy.deepcopy(self._nodes.keys())\n lvisited_uids = []\n while len(to_visit_uids) > 0:\n cur_uid = to_visit_uids[0]\n cur_node = self._nodes[cur_uid]\n\n if cur_uid in visited_uids:\n visited_uids.remove(cur_uid)\n\n for pred_uid in cur_node.get_predecessors():\n if pred_uid not in to_visit_uids and pred_uid not in lvisited_uids:\n to_visit_uids.append(pred_uid)\n\n to_visit_uids = to_visit_uids[1:]\n lvisited_uids.append(cur_uid)\n\n to_visit_uids = [dst_uid]\n\n # reverse graph exploration\n while 0 < len(to_visit_uids):\n assert(dsp < len(to_visit_uids))\t# dead ends or loops\n\n cur_uid = to_visit_uids[dsp]\n cur_node = self._nodes[cur_uid]\n\n assert(cur_uid != src_uid)\n assert(cur_uid not in visited_uids)\t# loop!\n\n # skip node for which there is still some unexplored successor\n skip_uid = False\n for succ_uid in cur_node.get_successors():\n if not succ_uid in visited_uids:\n skip_uid = True\n\n if skip_uid:\n dsp += 1\n continue\n else:\n dsp = 0\n\n if cur_uid == dst_uid:\n node_costs[cur_uid] = cur_node.get_cost()\n\n for pred_uid in cur_node.get_predecessors():\n pred_node = self._nodes[pred_uid]\n # get edge pred_uid ---> cur_uid\n edge_uid = Edge.get_edge_uid(pred_uid, cur_uid)\n edge = self._edges[edge_uid]\n edge_cost = edge.get_cost()\n if edge_uid not in subgraph_edge_uids:\n subgraph_edge_uids.append(edge_uid)\n\n # update node distance\n pred_node_cost = node_costs[cur_uid] + edge_cost + pred_node.get_cost()\n if pred_uid not in node_costs or pred_node_cost > node_costs[pred_uid]:\n node_costs[pred_uid] = pred_node_cost\n node_succs[pred_uid] = cur_uid\n\n # update to_visit_uids, a node cannot be visited more than once\n if (pred_uid != src_uid) and (pred_uid not in visited_uids) \\\n and (pred_uid not in to_visit_uids):\n to_visit_uids.append(pred_uid)\n\n to_visit_uids.remove(cur_uid)\n visited_uids.append(cur_uid)\n subgraph_node_uids.append(cur_uid)\n\n # construct path from src_node to dst_node\n cur_uid = src_uid\n maxpath_node_uids = [cur_uid]\n while cur_uid != dst_uid:\n cur_uid = node_succs[cur_uid]\n cur_node = self._nodes[cur_uid]\n if cur_uid not in maxpath_node_uids:\n maxpath_node_uids.append(cur_uid)\n else:\n raise Exception(\"invalid input source code graph: loop detected\")\n\n max_cost = node_costs[src_uid]\n return max_cost, maxpath_node_uids, subgraph_node_uids, subgraph_edge_uids","def out_degree_centrality(self):\n\n try:\n if not self.G.is_directed():\n self.logger.warning('G为无向网络！')\n else:\n self.logger.info('正在计算有向网络的出度中心性 ...')\n return self.order_dict(nx.out_degree_centrality(self.G), index=1)\n except Exception as e:\n self.logger.error(\"计算失败，原因：{0}\".format(e))","def fn(node, mn, mx):\n if not node: return mx - mn \n mn = min(mn, node.val)\n mx = max(mx, node.val)\n lv = fn(node.left, mn, mx)\n rv = fn(node.right, mn, mx)\n return max(lv, rv)","def degree(self):\n return sum(self)","def convert_relu6(g, op, block):\n\n x = g.get_node(op.input(\"X\")[0])\n out = _op.clip(x, 0.0, 6.0)\n g.add_node(op.output(\"Out\")[0], out)","def index_nodes(self):\n out = {}\n\n #avg = np.mean(list(self.rtype_vectors.values()),axis=0)\n\n\n #for name, node in self.nodes.items():\n # tmp1 = [self.rtype_vectors[rtype]\n # for rtype, dest in node.outgoing_relations] or [NULL_VEC()]\n # tmp2 = [permute_rtype_vector(self.rtype_vectors[rtype])\n # for rtype, prev in node.incoming_relations] or [NULL_VEC()]\n\n # net = tmp1 + tmp2\n\n # #out[name] = np.asarray(net).mean(axis=0)\n # #out[name] = np.asarray(net).sum(axis=0)\n # v = np.asarray(net).sum(axis=0)\n # if v.any():\n # out[name] = v/max(v)#softmax(v/max(v))\n # else:\n # out[name] = v\n\n\n #avg = np.mean(list(out.values()),axis=0)\n\n #maxm = np.max(list(out.values()),axis=0)\n\n ####normalize everything\n #for r,v in out.items():\n # if v.any():\n # #out[r] = v / sqrt(v.dot(v))\n # out[r] = softmax((v-avg)/maxm)\n\n\n\n # PCA method 0001701\n rmap = self.rtype_vectors\n data = np.zeros((len(self.nodes), JACCARD_DIMENSIONS), dtype=np.float)\n ix = 0\n for node in self.nodes.values():\n\n #compute weighted average of each relation type\n tmp = [rmap[rtype] for \n rtype, dest in node.outgoing_relations] + \\\n [permute_rtype_vector(rmap[rtype]) for \n rtype, prev in node.incoming_relations]\n\n v = np.asarray(tmp).mean(axis=0) if tmp else NULL_VEC()\n\n #normalize\n if v.any():\n data[ix] = v / sqrt(v.dot(v))\n else:\n data[ix] = v\n ix += 1\n\n #eliminate projection onto first 7 principal components\n d2 = data - PCA(data, 7)\n\n #order of nodes is preserved\n for i,v in enumerate(self.nodes):\n out[v] = softmax(d2[i])\n\n return out","def move_up(g,k): # g: graph; k: coefficient\n for i,_ in path(g): #i: node address\n if (i%k)!=0:\n move_up_node(g,i,k)","def internal_indegree(self, node_list, link_attribute=None):\n if link_attribute is None:\n return np.sum(self.internal_adjacency(node_list), axis=0)\n else:\n return np.sum(self.internal_link_attribute(link_attribute,\n node_list), axis=0)","def f(self,node):\r\n return (self.a*self.nodeDegree(node))/(1+self.b*self.nodeDegree(node))","def min_neighbor_node(g):\r\n return min(g.degree_iter(),key = lambda item:item[1])[0]","def exclude_nodes_degree(G, min_degree):\n remove = [node for (node, degree) in G.degree().items() if degree < min_degree]\n G.remove_nodes_from(remove)\n #remove new nodes without edges\n remove_zero_degree = [node for (node, degree) in G.degree().items() if degree == 0]\n G.remove_nodes_from(remove_zero_degree)\n return G","def optimize_graph( complete, desired, algo, simi_cutoff ) :\n if (algo == \"trim\") :\n return trim_cluster( desired, desired.nodes(), 2 )","def getNextNodeUsingTotalStepsToTravel(kGoalState):\n \n global fringe\n global solutions\n\n\n\n minNode = None\n minCost = 99999999999\n minNodeIndex = -1\n\n \n pnode = None\n pcost = None\n\n if len(solutions)>0 and solutions[0] != None:\n pnode = solutions[0];\n pcost = getTotalStepsToReachGoalState(pnode,kGoalState)\n # print pnode, pcost\n # raw_input()\n \n\n\n\n for idx,node in enumerate(fringe):\n #get the heu. function values\n g_value =getTotalStepsToReachGoalState(node,kGoalState)\n \n\n if g_value < minCost:\n minNode = node\n minNodeIndex = idx\n minCost = g_value\n\n\n fringe.pop(minNodeIndex)\n c = getTotalStepsToReachGoalState(minNode,kGoalState)\n if pnode != None:\n if c > pcost:\n minNode = None\n \n return minNode","def AGM_prim(mtr_adj, limited_nodes=[], raiz=1):\n\tnum_vertices = len(mtr_adj)\n\n\tfila = []\n\tvertices = [] # ordenados de acordo com a chave\n\t\n\t# adicionando os nodos na lista de vertices e ordenando pela key\n\tfor i in range( 1, num_vertices+1 ):\n\t\tvertices.append( vertice(i) )\n\n\tvertices.sort(key = lambda x: x.key)\n\tlog.debug('vertices: %s' % vertices)\n\t\n\t# fila a ser ordenada pela distancia\n\tfor nodo in vertices:\n\t\tfila.append(nodo)\n\n\t# se a raiz tiver grau máximo =1, seleciona o nodo mais próximo da raiz\n\t# para tornar ele a 'raiz'\n\tverificar_raiz(mtr_adj, limited_nodes, vertices, raiz)\n\t\n\tvertices[raiz-1].dist = 0\n\n\t# ordena a fila por ordem de distancia para o predecessor.\n\treordenar(fila)\n\tlog.debug('fila: %s' % fila)\n\n\t# criando arvore com os nodos que aceitam mais de 1 grau\n\twhile len(fila):\n\t\t# nodo a ser testado\n\t\tu = fila.pop(0)\n\t\t\n\t\t# evitar os nodos com grau máximo = 1 por enquanto\n\t\tif u.key in limited_nodes:\n\t\t\tcontinue\n\t\t\n\n\t\t# passando por todos os outros vértices, e adicionando\n\t\t# para selecionar os nodos que tem o nodo u como predecessor.\n\t\tfor v in range(1, num_vertices+1):\n\t\t\tif u.key != v and \\\n\t\t\tv not in limited_nodes and \\\n\t\t\tmtr_adj[ u.key-1 ][v-1] < vertices[v-1].dist and \\\n\t\t\tna_fila(v, fila):\n\t\t\t\tvertices[v-1].pred = u.key\n\t\t\t\tvertices[v-1].dist = mtr_adj[ u.key-1 ][v-1]\n\t\t\t\treordenar(fila)\n\t\tlog.debug('fila: %s' % fila)\t\n\t\n\t# conectando os nodos que aceitam grau maximo = 1 na arvore\n\tfor u in vertices:\n\t\tif u == raiz:\n\t\t\tcontinue\n\n\t\t# para cada nodo u de grau máximo = 1\n\t\tif u.key in limited_nodes:\n\t\t\t# verificar qual o nodo mais próximo, não limitado, diferente de u\n\t\t\tfor v in range(1, num_vertices+1):\n\t\t\t\tv_dist = mtr_adj[u.key-1][v-1] # distância de u até v\n\t\t\t\tif v != u.key and v_dist < u.dist and v not in limited_nodes:\n\t\t\t\t\tu.dist = v_dist\n\t\t\t\t\tu.pred = v\n\n\treturn vertices","def get_value(self):\r\n if len(self.walk) == 0:\r\n return 0\r\n value = 0\r\n start = 0\r\n end = len(self.walk) - 1\r\n while start < end:\r\n i_segment = self.get_segment(start+1)\r\n if i_segment.value == 'RUNG':\r\n break\r\n start += 2\r\n while end >= 2:\r\n i_segment = self.get_segment(end-1)\r\n if i_segment.value == 'RUNG':\r\n break\r\n end -= 2\r\n j = start\r\n while j < end:\r\n j_node = self.get_node(j)\r\n j += 1\r\n j_segment = self.get_segment(j)\r\n j += 1\r\n if j_segment.value != 'RUNG':\r\n # if the node connector is not critical, try to find and skip a loop\r\n k = j\r\n while k < end:\r\n k_node = self.get_node(k)\r\n k += 1\r\n k_segment = self.get_segment(k)\r\n k += 1\r\n if k_segment.value == 'RUNG':\r\n break\r\n if k_node == j_node:\r\n # Only skippable nodes existed before returned to original node, so skip that loop.\r\n value += (k - j) * 10\r\n j = k\r\n j_node = k_node\r\n j_segment = k_segment\r\n break\r\n if j_segment.value == 'SCAFFOLD':\r\n value -= j_segment.a.distance_sq(j_segment.b)\r\n elif j_segment.value == 'RUNG':\r\n value -= j_segment.a.distance_sq(j_segment.b)\r\n return value","def back_prop(nodes_in_path, playout_result):\n for temp_node in nodes_in_path:\n temp_node.visited += 1\n if str(playout_result) == str(temp_node.side):\n temp_node.winning += 1","def _update_max(self):\n tmp = self\n while tmp.right is not None:\n tmp = tmp.right\n return tmp.parent.key","def update(self, aggr_out):\r\n # Initial aggr_out = [#, heads, node_channels]\r\n aggr_out = aggr_out.view(-1, self.heads * self.node_channels)\r\n # Final aggr_out = [#, heads*node_channels]\r\n aggr_out = torch.matmul(aggr_out, self.weight_scale) #[#,node_channel]\r\n aggr_out = aggr_out + self.bias\r\n return aggr_out","def degree(self, node=None) -> int:\n if node:\n if node not in self._nodes:\n raise ValueError('node %s not in the graph' % node)\n else:\n return int(sum(self._adj[node]))\n else:\n return sum(len(e) for n, e in self._adj.items()) // 2","def update_flow(self):\n start_nodes = []\n end_nodes = []\n capacities = []\n # (1): add all edges (u, v) with capacity ub-lb\n B = self.get_max_lb()*(self.num_edges() - len(self) + 2)\n for arc in self.arc_info.keys():\n if self.arc_info[arc][\"upper_bound\"] == float('inf'):\n self.arc_info[arc][\"upper_bound\"] = B\n for arc in self.arc_info.keys():\n start_nodes.append(self.arc_info[arc][\"start\"])\n end_nodes.append(self.arc_info[arc][\"destin\"])\n capacities.append(int(self.arc_info[arc][\"upper_bound\"]\\\n - self.arc_info[arc][\"lower_bound\"]))\n # (2): add edge (t, s) with capacity B\n # B = max_lb * (m - n + 2)\n B = self.get_max_lb()*(self.num_edges() - len(self) + 2)\n if B == 0:\n #B = float('inf')\n B = 100000\n start_nodes.append(self.sink())\n end_nodes.append(self.source())\n capacities.append(int(B))\n # (3): for all verts, if exc > 0, add edge (s', v) with capacity exc(v),\n # and if exc < 0, add edge(s', v) with capacity -exc(v)\n s_prime = max(self.vertices) + 1\n t_prime = max(self.vertices) + 2\n print(\"s'={}, t'={}\".format(s_prime, t_prime))\n for v in self:\n #print(\"vert {} in arcs: {}\".format(v,\n # self.in_arcs_lists[v]))\n # compute exc: lower bounds of in - lower bounds of out\n sum_lb_in = 0\n for in_arc in self.in_arcs_lists[v]:\n sum_lb_in += self.arc_info[in_arc][\"lower_bound\"]\n sum_lb_out = 0\n #print(\"vert {} out arcs: {}\".format(v,\n # self.out_arcs_lists[v]))\n for out_arc in self.out_arcs_lists[v]:\n sum_lb_out += self.arc_info[out_arc][\"lower_bound\"]\n exc = sum_lb_in - sum_lb_out\n #print(\"exc is {}\".format(exc))\n if exc > 0:\n start_nodes.append(s_prime)\n end_nodes.append(v)\n capacities.append(int(exc))\n else:\n start_nodes.append(v)\n end_nodes.append(t_prime)\n capacities.append(int(-exc))\n # solve maxflow\n #print(\"s' is {} and t' is {}\".format(s_prime, t_prime))\n max_flow = pywrapgraph.SimpleMaxFlow()\n for u, v, cap in zip(start_nodes, end_nodes, capacities):\n #print(\"Adding edge {}, {} with cap {}\".format(u,v,cap))\n max_flow.AddArcWithCapacity(u, v, cap)\n success = True\n if max_flow.Solve(s_prime, t_prime) == max_flow.OPTIMAL:\n #print('Max flow: {}'.format( max_flow.OptimalFlow()))\n #print(' Arc Flow / Capacity')\n for i in range(max_flow.NumArcs()):\n # print('%1s -> %1s %3s / %3s' % (\n # max_flow.Tail(i),\n # max_flow.Head(i),\n # max_flow.Flow(i),\n # max_flow.Capacity(i)))\n # check that (s', v) edges are saturated (once we find a false,\n # stay false forever)\n if success:\n if max_flow.Tail(i) == s_prime:\n success = max_flow.Flow(i) == max_flow.Capacity(i)\n else:\n success = False\n print('There was an issue with the max flow input.')\n if success:\n # update the flows to be the flow found from maxflow problem\n for i in range(max_flow.NumArcs()):\n # if this is an original arc, update the flow\n if max_flow.Tail(i) != s_prime \\\n and max_flow.Head(i) != t_prime \\\n and not (max_flow.Tail(i) == self.sink() \\\n and max_flow.Head(i) == self.source()):\n # update arc\n start = max_flow.Tail(i)\n destin = max_flow.Head(i)\n arc = self.get_arc(start, destin)\n new_flow = self.arc_info[arc][\"lower_bound\"] + max_flow.Flow(i)\n old_flow = self.arc_info[arc][\"weight\"]\n self.arc_info[arc][\"weight\"] = new_flow\n #print(\"Edge {} {} adjusted from {} to {}\".format(\n # start,\n # destin,\n # old_flow,\n # new_flow\n # ))\n self.check_conservation_of_flow() # check that solution is valid\n return True\n else:\n return False","def calculate_ucb_max(self, node):\n pass","def solve(self):\n self.left -= len(self.nodes)\n \n def depths(x,depth = 0):\n depth+=1\n for y in self.graph[x]:\n if y in self.nodes:\n self.nodes.remove(y)\n depth = depths(y,depth)\n return depth\n \n while len(self.nodes):\n x = self.nodes.pop()\n self.firstGen.append(depths(x))\n #print self.graph\n #print self.nodes\n #print self.firstGen","def _reduce_max(self, input_tensor, reduction_indices, c):\n with self.session.graph.as_default():\n min_values = tf.reduce_min(input_tensor, reduction_indices, keep_dims=True)\n not_c = tf.abs(c - 1)\n\n return tf.reduce_max(input_tensor * c + not_c * min_values, reduction_indices)","def _get_full_graph(self):","def compute(self, node, input_vals):\r\n #start = time.time()\r\n\r\n #assert len(input_vals) == 1\r\n strides = node.const_attr[1]\r\n ksize = node.const_attr[0]\r\n ish = list(input_vals[0].shape)\r\n input = input_vals[0]\r\n output = np.zeros([ish[0],(ish[1]-ksize[1])//strides[1]+1,(ish[2]-ksize[2])//strides[2]+1,ish[3]])\r\n osh = output.shape\r\n #print(osh)\r\n for i in range(osh[1]):\r\n for j in range(osh[2]):\r\n output[:,i,j,:] = np.amax(input[:,i*strides[1]:(i+1)*strides[1],j*strides[1]:(j+1)*strides[1],:],axis=(1,2))\r\n #end = time.time() \r\n #print(\"max_pool\") \r\n #print(end - start) \r\n return output\r\n \r\n #assert False\r","def degree_on_basis(self, b):\n return sum(b)","def convert_max(node, **kwargs):\n name, input_nodes, attrs = get_inputs(node, kwargs)\n\n mx_axis = attrs.get(\"axis\", None)\n axes = convert_string_to_list(str(mx_axis)) if mx_axis is not None else None\n\n keepdims = get_boolean_attribute_value(attrs, \"keepdims\")\n\n if axes is not None:\n node = onnx.helper.make_node(\n 'ReduceMax',\n inputs=input_nodes,\n outputs=[name],\n axes=axes,\n keepdims=keepdims,\n name=name\n )\n\n return [node]\n else:\n node = onnx.helper.make_node(\n 'ReduceMax',\n inputs=input_nodes,\n outputs=[name],\n keepdims=keepdims,\n name=name\n )\n\n return [node]","def remove_leaves(self):\n result = 0\n for i in range(8):\n node = self.children[i]\n if node:\n self.color.red += node.color.red\n self.color.green += node.color.green\n self.color.blue += node.color.blue\n self.pixel_count += node.pixel_count\n result += 1\n return result - 1","def reduceAdjacency(adj):\n adj = checkArrayDim(adj,2)\n n = adj.shape[0]\n adj[adj == arange(n).reshape(n,-1)] = -1 # remove the item i\n adj = sortAdjacency(adj)\n adj[adj[:,:-1] == adj[:,1:]] = -1 #remove duplicate items\n adj = sortAdjacency(adj)\n return adj","def calculate_best_way(self) -> int:\n node = self._find_lowest_cost_node()\n while node:\n cost = self.costs[node]\n neighbors = self.graph[node]\n for neighbor in neighbors.keys():\n node_cost = cost + neighbors[neighbor]\n if self.costs[neighbor] > node_cost:\n self.costs[neighbor] = node_cost\n self.parents[neighbor] = node\n self.closed_nodes.append(node)\n node = self._find_lowest_cost_node()\n\n return self.costs[\"fin\"]","def reduce_sequential(edges, start, end):\n dd = get_degrees_dictionary(edges) # O(len(edges))\n tvs = get_transition_vertexes(dd, start, end) # O(len(dd))\n logger.debug(\"dd: {}\".format(dd))\n logger.debug(\"tvs: {}\".format(tvs))\n\n for v in tvs: # for each vertex in transitional vertexes\n # edges\n ei1 = tvs[v][0]\n ei2 = tvs[v][1]\n\n e1 = edges[ei1] # e1 is going to save resulted edge\n e2 = edges[ei2] # e2 is going to become cycled and then removed\n\n # vertexes\n # v - vertex to be removed\n # v1 - vertex, connected to v by e1 edge (unchanged)\n # v2 - vertex, connected to v by e2 edge\n # will be moved to e1 substituting v there\n # edges list in transitional vertex dictionary will be updated\n\n logger.debug(\"Substituted {}: {}:{}, {}:{} -> \".format(\n v, ei1, e1, ei2, e2))\n\n # v is going to be substituted in e1 by value of \"not v\" vertex in e2\n substitute_index_in_ei2 = 1 - e2.index(v) # if vi=0 s=1; v=1 s=0\n\n # replace v in ei1 by substitute from ei2\n v2 = e2[substitute_index_in_ei2]\n\n e1[e1.index(v)] = v2\n e2[substitute_index_in_ei2] = v\n\n # here we will have 2 edges\n # edges[ei1] -> ['v1', 'v2', ?] #\n # edges[ei2] -> ['v', 'v', 5] # delay not changed\n\n # updated edges for substituted vertex in tvs dict to point to\n # ei1 edge instead of ei2\n # e.g. 'v2' was connected by ei2, now is connected by ei1\n\n if v2 != start and v2 != end:\n # v2 is not present in tvi and shouldn't be updated\n v2ei = tvs[v2] # list of edges indexes for v2\n vei = tvs[v] # list of edges indexes for v\n v2ei[v2ei.index(ei2)] = ei1\n\n logger.debug(\"tvs[{}][2] = t[1] : {} = {}\".format(\n v2,\n tvs[v2][2],\n t[1]))\n\n # update weight\n new_weight = e1[2] + e2[2]\n e1[2] = new_weight\n\n # normalize result edge\n redirect_edge_alpabetically(e1)\n\n # here we will have 2 edges\n # edges[ei1] -> ['v1', 'v2', 8] #\n # edges[ei2] -> ['v', 'v', 5] # delay not changed\n\n # only thing left is to remove the ei2 edge, this will be done later\n # not to break iteration over edges\n\n logger.debug(\"{}:{}, {}:{}\".format(ei1, e1, ei2, e2))\n\n # get indexes of edges to be removed\n indexes = [i for i in reversed(sorted([tvs[v][1] for v in tvs]))]\n logger.debug(\"Edges index removed after sequential update: {}\".format(\n indexes))\n\n for i in indexes:\n edges.pop(i)\n\n return len(tvs) # amount of edges removed","def _create_reduceOp(cls, onnx_node, inputs, opset_version):\n axes = onnx_node.getattr(\"axes\", None)\n keepdims = onnx_node.getattr(\"keepdims\", 1)\n _, forward = cls._common_onnx_node_to_singa_op(onnx_node, inputs,\n opset_version)\n return _, forward(axes, keepdims)","def linkage(self):\n self.tree = {}\n un_linked = []\n for i in range(len(self.leaves)):\n leaf = self.leaves[i]\n un_linked.append({\n 'id': i,\n 'x': 0,\n 'y': 0,\n 'value': 0,\n 'set': leaf,\n 'children': []\n })\n pass\n while len(un_linked) > 1:\n # for i in tqdm(range(len(un_linked))):\n # print(\"Linking... {} nodes left\".format(len(un_linked)))\n for node in un_linked:\n for d in node['set']:\n node['x'] += d['x']\n node['y'] += d['y']\n node['value'] += d['value']\n pass\n node['x'] /= len(node['set'])\n node['y'] /= len(node['set'])\n node['value'] /= len(node['set'])\n pass\n # min_dif = ((un_linked[1]['x'] - un_linked[0]['x']) ** 2 + (un_linked[1]['y'] - un_linked[0]['y']) ** 2) \\\n # * self._alpha + (un_linked[1]['value'] - un_linked[0]['value']) * (1 - self._alpha)\n min_dif = ((un_linked[1]['x'] - un_linked[0]['x']) ** 2 + (un_linked[1]['y'] - un_linked[0]['y']) ** 2)\n min_cp = [0, 1]\n for i in range(len(un_linked) - 1):\n for j in range(i + 1, len(un_linked)):\n # dif = self._alpha * ((un_linked[j]['x'] - un_linked[i]['x']) ** 2\n # + (un_linked[j]['x'] - un_linked[i]['x']) ** 2) \\\n # + (1 - self._alpha) * (un_linked[j]['value'] - un_linked[i]['value'])\n dif = ((un_linked[j]['x'] - un_linked[i]['x']) ** 2\n + (un_linked[j]['x'] - un_linked[i]['x']) ** 2)\n if dif < min_dif:\n min_dif = dif\n min_cp = [i, j]\n pass\n pass\n pass\n set_a = []\n for each in un_linked[min_cp[0]]['set']:\n set_a.append(each)\n pass\n for each in un_linked[min_cp[1]]['set']:\n set_a.append(each)\n pass\n next_un_linked = []\n new_children = []\n if len(un_linked[min_cp[0]]['children']) != 0:\n new_children.append({'children': un_linked[min_cp[0]]['children'],\n 'value': len(un_linked[min_cp[0]]['set'])})\n pass\n else:\n new_children.append({'id': un_linked[min_cp[0]]['id'],\n 'value': len(un_linked[min_cp[0]]['set'])})\n if len(un_linked[min_cp[1]]['children']) != 0:\n new_children.append({'children': un_linked[min_cp[1]]['children'],\n 'value': len(un_linked[min_cp[1]]['set'])})\n pass\n else:\n new_children.append({'id': un_linked[min_cp[1]]['id'],\n 'value': len(un_linked[min_cp[1]]['set'])})\n pass\n next_un_linked.append({\n 'x': 0,\n 'y': 0,\n 'value': 0,\n 'set': set_a,\n 'children': new_children\n })\n del un_linked[min_cp[0]]['set']\n del un_linked[min_cp[0]]['x']\n del un_linked[min_cp[0]]['y']\n # del un_linked[min_cp[0]]['value']\n del un_linked[min_cp[1]]['set']\n del un_linked[min_cp[1]]['x']\n del un_linked[min_cp[1]]['y']\n # del un_linked[min_cp[1]]['value']\n for s in range(len(un_linked)):\n if s not in min_cp:\n next_un_linked.append(un_linked[s])\n pass\n pass\n un_linked = next_un_linked\n pass\n del un_linked[0]['set']\n del un_linked[0]['x']\n del un_linked[0]['y']\n # del un_linked[0]['value']\n self.tree = un_linked[0]\n self._count = 0\n\n self.tree = self._resolve(self.tree)\n return","def dfs(node):\n nonlocal ans \n if not node: return 0\n lx, rx = dfs(node.left), dfs(node.right) \n if not node.left or node.left.val != node.val: lx = 0\n if not node.right or node.right.val != node.val: rx = 0 \n ans = max(ans, 1 + lx + rx)\n return 1 + max(lx, rx)","def modularity():\n\n q = 0.0\n for idx in range(0, node_count):\n if _tot[idx] > 0.0:\n q += (_in[idx] / m - math.pow(_tot[idx] / m, 2))\n return q","def leaves(node, res):\n leaf = True\n if node.lesser:\n leaf = False\n leaves(node.lesser, res)\n if node.greater:\n leaf = False\n leaves(node.greater, res)\n if leaf:\n res.append(node.indices)","def getReducedSubgraphD(self, G, PD, baseParams, bestParams, Lid):\n doshrink = True\n count_remove_nodes = 0\n FinalParams = bestParams\n while doshrink: #continue removing nodes one by one till no increase in IG\n doshrink = False\n for node in FinalParams['Pat'].inNL:\n curParams = dict()\n curParams['Pat'] = FinalParams['Pat'].copy()\n curParams['Pat'].removeInNode(node)\n curParams['codeLengthCprime'] = FinalParams['codeLengthCprime'] - self.computeCLgainRemoveNodeD(G, PD, curParams['Pat'].outNL, node, [Lid], 1)\n curParams['Pat'].setIC_dssg( baseParams['codeLengthC'] - curParams['codeLengthCprime'] )\n curParams['Pat'].setDL( computeDescriptionLength( dlmode=6, C=len(PD.lprevUpdate), gtype=self.gtype, WIS=baseParams['Pat'].InNCount, WOS=baseParams['Pat'].OutNCount, WI=curParams['Pat'].InNL, WO=curParams['Pat'].OutNL, kw=curParams['Pat'].ECount, isSimple=self.isSimple, kws=curParams['Pat'].kws ) )\n curParams['Pat'].setI( computeInterestingness( curParams['Pat'].IC_dssg, curParams['Pat'].DL, mode=self.imode ) )\n if curParams['Pat'].I > bestParams['Pat'].I:\n bestParams = curParams\n for node in FinalParams['Pat'].outNL:\n curParams = dict()\n curParams['Pat'] = FinalParams['Pat'].copy()\n curParams['Pat'].removeOutNode(node)\n curParams['codeLengthCprime'] = FinalParams['codeLengthCprime'] - self.computeCLgainRemoveNodeD(G, PD, curParams['Pat'].inNL, node, [Lid], 2)\n curParams['Pat'].setIC_dssg( baseParams['codeLengthC'] - curParams['codeLengthCprime'] )\n curParams['Pat'].setDL( computeDescriptionLength( dlmode=6, C=len(PD.lprevUpdate), gtype=self.gtype, WIS=baseParams['Pat'].InNCount, WOS=baseParams['Pat'].OutNCount, WI=curParams['Pat'].InNL, WO=curParams['Pat'].OutNL, kw=curParams['Pat'].ECount, isSimple=self.isSimple, kws=curParams['Pat'].kws ) )\n curParams['Pat'].setI( computeInterestingness( curParams['Pat'].IC_dssg, curParams['Pat'].DL, mode=self.imode ) )\n if curParams['Pat'].I > bestParams['Pat'].I:\n bestParams = curParams\n if bestParams['Pat'].I > FinalParams['Pat'].I:\n FinalParams = bestParams\n count_remove_nodes += 1\n doshrink = True\n if count_remove_nodes > 0 or (FinalParams['Pat'].InNCount < baseParams['Pat'].InNCount and FinalParams['Pat'].OutNCount < baseParams['Pat'].OutNCount) :\n FinalParams['codeLengthC'] = baseParams['codeLengthC']\n FinalParams['Pat'].setLambda( PD.updateDistribution( FinalParams['Pat'].G, idx=None, val_return='return', case=3, dropLidx=[Lid]) ) #// Todo: computeNewLambda\n FinalParams['codeLengthCprime'] = self.computeCodeLengthShrinkD( G, PD, 3, baseParams, FinalParams, Lid, FinalParams['Pat'].la) #// Todo computeNewCodeLength\n FinalParams['Pat'].setIC_dssg( FinalParams['codeLengthC'] - FinalParams['codeLengthCprime'] )\n FinalParams['Pat'].setDL( computeDescriptionLength( dlmode=6, C=len(PD.lprevUpdate), gtype=self.gtype, WIS=baseParams['Pat'].InNCount, WOS=baseParams['Pat'].OutNCount, WI=FinalParams['Pat'].InNL, WO=FinalParams['Pat'].OutNL, kw=FinalParams['Pat'].ECount, excActionType=False, l=self.l, isSimple=self.isSimple, kws=FinalParams['Pat'].kws ) )\n FinalParams['Pat'].setI( computeInterestingness( FinalParams['Pat'].IC_dssg, FinalParams['Pat'].DL, mode=self.imode ) )\n return FinalParams","def find_max(self, node):\n current_node = node\n while current_node.right:\n current_node = current_node.right\n return current_node","def __opReduce2(self, elim, op, out=None): # assumes elim <= self.v\n if ((elim is None) or (len(elim)==len(self.v))):\n return op(self.t)\n else:\n if (out is None):\n out = Factor(VarSet(self.v - elim))\n else:\n assert (out.v == VarSet(self.v-elim) ), \"Cannot eliminate into an existing factor with incorrect scope\"\n ax = tuple(map(lambda x: self.v.index(x), elim)) #\n #t = np.apply_over_axes(op, self.t, ax) # bad: lots of intermediate memory?\n #t = op(self.t, axis=ax, out=None) # any better? TODO\n #return Factor().__build( VarSet(self.v - elim), t )\n op(self.t, axis=ax, out=out.t) # any better? TODO\n return out","def _drop_ground_node(self):\n self.laplace = np.concatenate((\n np.concatenate((\n self.laplace[:self.ground_node,:self.ground_node],\n self.laplace[:self.ground_node,self.ground_node+1:]),1),\n\n np.concatenate((\n self.laplace[self.ground_node+1:,:self.ground_node],\n self.laplace[self.ground_node+1:,self.ground_node+1:]), 1)))\n\n self.degree = np.concatenate((\n self.degree[:self.ground_node], \n self.degree[self.ground_node+1:]))","def agg_node(in_planes, out_planes):\n return nn.Sequential(\n nn.Conv2d(in_planes, in_planes, kernel_size=3, stride=1, padding=1),\n nn.ReLU(),\n nn.Conv2d(in_planes, out_planes, kernel_size=3, stride=1, padding=1),\n nn.ReLU(),\n )","def fn(node):\n if not node: return 0, -inf\n lh, lps = fn(node.left)\n rh, rps = fn(node.right)\n return node.val + max(0, lh, rh), max(lps, rps, node.val + max(0, lh) + max(0, rh))"],"string":"[\n \"def degree_graph(g):\\n return max(degree_node(g, node) for node in g)\",\n \"def layer_out_degree(self, layer_id):\\n ...\",\n \"def max_degree_node(g, d, connected):\\n if not connected:\\n deg = d\\n else:\\n deg = {k: v for k, v in d.items() if k in connected}\\n if not deg:\\n return None\\n n = max(deg.keys(), key=lambda k: deg[k])\\n d.pop(n)\\n for n_ in g.neighbors(n):\\n connected.add(n_)\\n return n\",\n \"def _compute_node_degrees(self):\\n mes = []\\n args = []\\n for metaedge, matrix in self.adj_matrices.items():\\n mes.append(metaedge)\\n args.append(matrix)\\n res = parallel_process(array=args, function=mt.calculate_degrees, n_jobs=self.n_jobs, front_num=0)\\n for metaedge, (out_degree, in_degree) in zip(mes, res):\\n self.out_degree[metaedge] = out_degree\\n self.in_degree[metaedge] = in_degree\",\n \"def internal_outdegree(self, node_list, link_attribute=None):\\n if link_attribute is None:\\n return np.sum(self.internal_adjacency(node_list), axis=1)\\n else:\\n return np.sum(self.internal_link_attribute(link_attribute,\\n node_list), axis=1)\",\n \"def fn(graph, indeg):\\n ans = []\\n stack = [k for k in range(len(indeg)) if indeg[k] == 0]\\n while stack: \\n n = stack.pop()\\n ans.append(n)\\n for nn in graph.get(n, []):\\n indeg[nn] -= 1\\n if indeg[nn] == 0: stack.append(nn)\\n return ans\",\n \"def reduce(self, app, nodes, result):\",\n \"def reduce(self):\\n # initializing a hashtable for all the nodes in the tree\\n hashtable = {}\\n for node_it in self.nodes:\\n # storing each node only once in the table\\n if not node_it.__hash__() in hashtable:\\n hashtable[node_it.__hash__()] = node_it\\n\\n def reduce_rec(node):\\n \\\"\\\"\\\"\\n The recursive method for the reduction.\\n \\\"\\\"\\\"\\n if node.is_leaf():\\n return\\n for edge in node.child_nodes:\\n # replacing the subdiagram with a singular isomorphic one\\n node.child_nodes[edge] = hashtable[node.child_nodes[edge].__hash__()]\\n # and going down recursively along that subdiagram\\n reduce_rec(node.child_nodes[edge])\\n\\n # calling the reduction method\\n reduce_rec(self)\\n # reinitializing the diagram\\n self.reinitialize()\\n return self\",\n \"def countMaxDegree(self):\\r\\n max_degree = [0, 0] # le sommet, son nombre de connection \\r\\n for i_node, node_connections in enumerate(self.adjMatrix):\\r\\n connection = self.n - node_connections.count(0) # on compte le nombre de connections du sommet\\r\\n if connection > max_degree[1]:\\r\\n max_degree = max_degree[i_node, node_connections]\\r\\n return max_degree[0], max_degree[1] # C un tuple ! \\r\",\n \"def reduce(self) -> float:\\n # Note: Reduction over segments not supported/needed for now.\\n return self._tree[1]\",\n \"def targeted_order(ugraph):\\r\\n # copy the graph\\r\\n new_graph = copy_graph(ugraph)\\r\\n \\r\\n order = [] \\r\\n while len(new_graph) > 0:\\r\\n max_degree = -1\\r\\n for node in new_graph:\\r\\n if len(new_graph[node]) > max_degree:\\r\\n max_degree = len(new_graph[node])\\r\\n max_degree_node = node\\r\\n \\r\\n neighbors = new_graph[max_degree_node]\\r\\n new_graph.pop(max_degree_node)\\r\\n for neighbor in neighbors:\\r\\n new_graph[neighbor].remove(max_degree_node)\\r\\n\\r\\n order.append(max_degree_node)\\r\\n return order\",\n \"def targeted_order(ugraph):\\n # copy the graph\\n new_graph = copy_graph(ugraph)\\n \\n order = []\\n while len(new_graph) > 0:\\n max_degree = -1\\n for node in new_graph:\\n if len(new_graph[node]) > max_degree:\\n max_degree = len(new_graph[node])\\n max_degree_node = node\\n \\n neighbors = new_graph[max_degree_node]\\n new_graph.pop(max_degree_node)\\n for neighbor in neighbors:\\n new_graph[neighbor].remove(max_degree_node)\\n \\n order.append(max_degree_node)\\n return order\",\n \"def reduce_triangle(to_reduce):\\r\\n last_row = to_reduce[-1]\\r\\n for index in xrange(len(to_reduce)-1):\\r\\n to_reduce[-2][index] += max(last_row[index:index+2])\\r\\n del to_reduce[-1]\\r\\n return to_reduce\",\n \"def test_grow_maximal_degree(self, dim):\\r\\n graph = nx.lollipop_graph(dim, 1)\\r\\n graph.remove_edge(dim - 2, dim - 1)\\r\\n s = set(range(dim - 2))\\r\\n target = s | {dim - 1}\\r\\n assert set(clique.grow(s, graph, node_select=\\\"degree\\\")) == target\",\n \"def targeted_order(ugraph):\\n # copy the graph\\n new_graph = copy_graph(ugraph)\\n\\n order = []\\n while len(new_graph) > 0:\\n max_degree = -1\\n for node in new_graph:\\n if len(new_graph[node]) > max_degree:\\n max_degree = len(new_graph[node])\\n max_degree_node = node\\n\\n neighbors = new_graph[max_degree_node]\\n new_graph.pop(max_degree_node)\\n for neighbor in neighbors:\\n new_graph[neighbor].remove(max_degree_node)\\n\\n order.append(max_degree_node)\\n return order\",\n \"def targeted_order(ugraph):\\n # copy the graph\\n new_graph = copy_graph(ugraph)\\n\\n order = []\\n while len(new_graph) > 0:\\n max_degree = -1\\n for node in new_graph:\\n if len(new_graph[node]) > max_degree:\\n max_degree = len(new_graph[node])\\n max_degree_node = node\\n\\n neighbors = new_graph[max_degree_node]\\n new_graph.pop(max_degree_node)\\n for neighbor in neighbors:\\n new_graph[neighbor].remove(max_degree_node)\\n\\n order.append(max_degree_node)\\n return order\",\n \"def kAnonymit(g,k):\\n rlist=list()\\n degreeH=nx.degree_histogram(g)\\n## print degreeH\\n li=[[] for i in range(len(degreeH))]\\n uNode=dict(zip(range(len(degreeH)),li))\\n \\n gd=g.degree()\\n for e in gd:\\n uNode[gd[e]].append(e)\\n ad=list()\\n mi=list()\\n su=list() \\n sel3(g,3,ad,mi,su)\\n nodeAd=list()\\n nodeMi=list()\\n nodeSu=list() \\n for i in ad:\\n nodeAd=nodeAd+uNode[i]\\n #nodeAd store the graph ID\\n for i in mi:\\n nodeMi=nodeMi+uNode[i]\\n for i in su:\\n nodeSu=nodeSu+uNode[i]\\n\\n print nodeAd,nodeMi,nodeSu\\n\\n\\n i=0#Add edge \\n while i<len(nodeAd)-1:\\n j=i+1\\n while j<len(nodeAd):\\n if not g.has_edge(nodeAd[i],nodeAd[j]):\\n g.add_edge(nodeAd[i],nodeAd[j])\\n rlist.append(nodeAd[i])\\n rlist.append(nodeAd[j])\\n nodeAd.pop(j)\\n nodeAd.pop(i)\\n i=i-1\\n break\\n else:\\n j=j+1\\n i=i+1\\n print nodeAd\\n \\n i=0#delet edge\\n while i<len(nodeMi)-1:\\n j=i+1\\n while j<len(nodeMi):\\n if g.has_edge(nodeMi[i],nodeMi[j]):\\n g.remove_edge(nodeMi[i],nodeMi[j])\\n rlist.append(nodeMi[i])\\n rlist.append(nodeMi[j])\\n nodeMi.pop(j)\\n nodeMi.pop(i)\\n i=i-1\\n break\\n else:\\n j=j+1\\n i=i+1\\n print nodeMi\\n \\n i=0#Add edge \\n while i<len(nodeSu):\\n j=0\\n while j<len(nodeAd):\\n if not g.has_edge(nodeSu[i],nodeAd[j]):\\n g.add_edge(nodeSu[i],nodeAd[j])\\n rlist.append(nodeSu[i])\\n rlist.append(nodeAd[j])\\n nodeAd.pop(j)\\n nodeSu.pop(i)\\n \\n i=i-1\\n break\\n else:\\n j=j+1\\n i=i+1\\n print nodeSu\\n \\n i=0#delet edge\\n while i<len(nodeSu):\\n j=0\\n while j<len(nodeMi):\\n if g.has_edge(nodeSu[i],nodeMi[j]):\\n g.remove_edge(nodeSu[i],nodeMi[j])\\n rlist.append(nodeSu[i])\\n rlist.append(nodeMi[j])\\n nodeMi.pop(j)\\n nodeSu.pop(i)\\n i=i-1\\n break\\n else:\\n j=j+1\\n i=i+1\\n print nodeSu\\n return rlist\",\n \"def _reduce_triangle(to_reduce):\\n last_row = to_reduce[-1]\\n for index in xrange(len(to_reduce) - 1):\\n to_reduce[-2][index] += max(last_row[index:index + 2])\\n del to_reduce[-1]\",\n \"def make_upa_graph(nodes, out_degree):\\n graph = go.make_complete_graph(out_degree)\\n trial = upa.UPATrial(out_degree)\\n for new_node in range(out_degree, nodes):\\n neighbors = trial.run_trial(out_degree)\\n graph[new_node] = neighbors\\n for neighbor in neighbors:\\n graph[neighbor].add(new_node)\\n return graph\",\n \"def targeted_order(ugraph):\\n # copy the graph\\n new_graph = copy_graph(ugraph)\\n \\n counter = 0\\n order = [] \\n while len(new_graph) > 0:\\n max_degree = -1\\n for node in new_graph:\\n counter += 1\\n if len(new_graph[node]) > max_degree:\\n max_degree = len(new_graph[node])\\n max_degree_node = node\\n \\n \\n neighbors = new_graph[max_degree_node]\\n new_graph.pop(max_degree_node)\\n for neighbor in neighbors:\\n counter += 1\\n new_graph[neighbor].remove(max_degree_node)\\n\\n order.append(max_degree_node)\\n return order # uncomment to use graph_resilience_targeted_order\",\n \"def reduce(nodes):\\r\\n accum = tc.mean(nodes.mailbox['m'], 1).cuda()\\r\\n return {'h': accum}\",\n \"def _reduce(self, action):\\n assert len(self.stack) >= 2, \\\"ERROR: Cannot reduce with stack length less than 2\\\"\\n \\n # STUDENT\\n # hint: use list.pop()\\n # END STUDENT\\n rightarc = self.stack.pop()\\n leftarc = self.stack.pop()\\n head = rightarc if action == Actions.REDUCE_L else leftarc\\n mod = leftarc if action == Actions.REDUCE_L else rightarc\\n self.stack.append( StackEntry(head.headword, head.headword_pos, self.combiner(head.embedding,mod.embedding)) )\\n return DepGraphEdge((head.headword, head.headword_pos),(mod.headword, mod.headword_pos))\",\n \"def get_deg(nodes = 10000,edges=None):\\n \\n D= nx.MultiDiGraph()\\n D.add_nodes_from(np.arange(1,nodes))\\n D.add_edges_from(edges);\\n return np.array(D.in_degree())[:,1],np.array(D.out_degree())[:,1],D\",\n \"def reduce_rec(node):\\n if node.is_leaf():\\n return\\n for edge in node.child_nodes:\\n # replacing the subdiagram with a singular isomorphic one\\n node.child_nodes[edge] = hashtable[node.child_nodes[edge].__hash__()]\\n # and going down recursively along that subdiagram\\n reduce_rec(node.child_nodes[edge])\",\n \"def out_degree_sequence(self):\\n return sorted(self.out_degree_iterator(), reverse=True)\",\n \"def degree_node(g, node):\\n return len(g[node])\",\n \"def select_leaf(self):\\n current = self\\n best_child = None\\n selected_nodes_R = 0\\n while current.isExpanded:\\n maxUCT = - float('inf')\\n for child in current.children.values():\\n UCT = child.compute_uct()\\n if UCT > maxUCT:\\n maxUCT = UCT\\n best_child = child\\n\\n current = best_child\\n selected_nodes_R += current.score\\n return current, selected_nodes_R\",\n \"def compute_perm(parents):\\n # Function written by M. Defferrard, taken verbatim, from \\n # https://github.com/mdeff/cnn_graph/blob/master/lib/coarsening.py#L167\\n\\n # Order of last layer is random (chosen by the clustering algorithm).\\n indices = []\\n if len(parents) > 0:\\n M_last = max(parents[-1]) + 1\\n indices.append(list(range(M_last)))\\n\\n for parent in parents[::-1]:\\n #print('parent: {}'.format(parent))\\n\\n # Fake nodes go after real ones.\\n pool_singeltons = len(parent)\\n\\n indices_layer = []\\n for i in indices[-1]:\\n indices_node = list(np.where(parent == i)[0])\\n assert 0 <= len(indices_node) <= 2\\n #print('indices_node: {}'.format(indices_node))\\n\\n # Add a node to go with a singelton.\\n if len(indices_node) == 1:\\n indices_node.append(pool_singeltons)\\n pool_singeltons += 1\\n #print('new singelton: {}'.format(indices_node))\\n # Add two nodes as children of a singelton in the parent.\\n elif len(indices_node) == 0:\\n indices_node.append(pool_singeltons+0)\\n indices_node.append(pool_singeltons+1)\\n pool_singeltons += 2\\n #print('singelton childrens: {}'.format(indices_node))\\n\\n indices_layer.extend(indices_node)\\n indices.append(indices_layer)\\n\\n # Sanity checks.\\n for i,indices_layer in enumerate(indices):\\n M = M_last*2**i\\n # Reduction by 2 at each layer (binary tree).\\n assert len(indices[0] == M)\\n # The new ordering does not omit an indice.\\n assert sorted(indices_layer) == list(range(M))\\n\\n return indices[::-1]\",\n \"def handle_leaf_nodes(self, nodes):\\n max_node_id = -1\\n for n in nodes:\\n n.sum_hess = self.decrypt(n.sum_hess)\\n n.sum_grad = self.decrypt(n.sum_grad)\\n n.weight = self.splitter.node_weight(n.sum_grad, n.sum_hess)\\n n.sitename = self.sitename\\n if n.id > max_node_id:\\n max_node_id = n.id\\n new_nodes = [Node() for i in range(max_node_id + 1)]\\n for n in nodes:\\n new_nodes[n.id] = n\\n return new_nodes\",\n \"def largest_path_sum(T):\\n return max(ft.reduce(fold_dist, T))\",\n \"def reduce_inbound_connections(inbound_counts, nodes):\\n nodes_without_inbound = []\\n for node in nodes:\\n inbound_counts[node] = inbound_counts[node] - 1\\n if inbound_counts[node] == 0:\\n nodes_without_inbound.append(node)\\n return nodes_without_inbound\",\n \"def fixedDiGraph(conn_,out_deg,target_degree,N):\\n \\n d_o = (out_deg[0:N]-target_degree) #outdegree difference\\n # [1:NE] - ignores the first dummy element \\n pos_ind = np.where(d_o>0)[0] # positive difference (remove connections)\\n neg_ind = np.where(d_o<0)[0] # negative difference (add connection)\\n\\n add_ind= []\\n for ind in pos_ind:\\n while len(conn_[conn_[:,0]==ind])>target_degree: # +1 might need to go away\\n add_ind.append(conn_[conn_[:,0]==ind][-1,1])\\n # delete a raw\\n ind_d = np.where(conn_[:,0]==ind)[0][-1]\\n conn_ = np.delete(conn_,ind_d,0)\\n ii = 0\\n for ind in neg_ind:\\n while len(conn_[conn_[:,0]==ind])<target_degree:\\n conn_ = np.vstack((conn_,[ind, add_ind[ii]]))\\n ii+=1\\n\\n return conn_\",\n \"def reduce(self,\\r\\n noterange=None):\\r\\n\\r\\n if noterange is None:\\r\\n noterange = self.apply_limit([str(Index(a_temp))\\r\\n for a_temp in self.indexes()\\r\\n if Index(a_temp).is_top()])\\r\\n\\r\\n\\r\\n for tup in reduce_tupples([Index(x_temp) for x_temp in self.find_within(indexfrom=Index(0),orequal=True,withinrange=noterange)]):\\r\\n self.move(tup[0], tup[1], withchildren=True)\",\n \"def calc_degree(graph_rdd):\\n all_degree = graph_rdd \\\\\\n .map(swap) \\\\\\n .union(graph_rdd) \\\\\\n .map(lambda (x, y): (x, 1)) \\\\\\n .reduceByKey(add, numPartitions=40)\\n return all_degree\",\n \"def __init__(self, degree):\\r\\n self.root = Node([], [])\\r\\n self.min_num_keys = degree - 1 \\r\\n self.max_num_keys = 2*degree - 1\",\n \"def equalize_node_density(self, maximum_distance, maximum_angle_delta, greedy=True):\\n print('network: splitting long edges...')\\n \\\"\\\"\\\" Split edges which are very long. \\\"\\\"\\\"\\n self.split_edges(maximum_distance)\\n\\n print('network: merging short edges...')\\n vertices_to_remove = []\\n edges_to_add = []\\n \\\"\\\"\\\" Merge edges which are close together, and collect vertices/edges which should be removed/added. \\\"\\\"\\\"\\n for section_id in self.sections:\\n utils.print_progress(len(self.sections), prefix='merging edges')\\n new_edges, redundant_vertices = self.merge_edges(self.sections[section_id], maximum_distance,\\n maximum_angle_delta, greedy)\\n vertices_to_remove.extend(redundant_vertices)\\n edges_to_add.extend(new_edges)\\n # Maintain the section list\\n self.sections[section_id] = list(filter(lambda v: v not in redundant_vertices, self.sections[section_id]))\\n\\n \\\"\\\"\\\" Add the new edges and edge weights into the graph. \\\"\\\"\\\"\\n for edge, weight in edges_to_add:\\n utils.print_progress(len(edges_to_add), prefix='adding edges')\\n new_edge = self.graph.add_edge(edge[0], edge[1], add_missing=False)\\n self.edge_weights[new_edge] = weight\\n\\n \\\"\\\"\\\" Removing vertices reindexes the vertices and edges of the graph. Need to maintain external data \\n structures to prevent data corruption. \\\"\\\"\\\"\\n original_indices = self.graph.vertex_index.copy() # Property map will correct for reindexing\\n self.graph.remove_vertex(vertices_to_remove, fast=True)\\n # Vertices have now been reindexed. Update each section with the new vertex IDs.\\n for section_id in self.sections:\\n utils.print_progress(len(self.sections), prefix='reindexing vertices')\\n self.sections[section_id] = [find_vertex(self.graph, original_indices, v)[0] for v in\\n self.sections[section_id]]\\n\\n return self.graph.num_vertices()\",\n \"def get_subgraph(graph, min_degree):\\n ###TODO\\n pass\",\n \"def connected((e,r)):\\n \\n # Deal with the middle case so we don't divide by zero\\n if r==0: return [(1,1),(2,1),(3,1),(4,1),(5,1),(0,1)]\\n # If the input is impossible, return nothing to prune the branch (shouldn't\\n # happen)\\n if e>=6*r: return []\\n connected=[]\\n mult=e//r\\n rem=e % r\\n #Going sideways\\n toAdd=((6*r-1,r) if e==0 else (e-1,r))\\n connected.append(toAdd)\\n toAdd=((0,r) if e==6*r-1 else (e+1,r))\\n connected.append(toAdd)\\n #Going inward\\n toAdd=( (0,r-1)if mult==5 and rem==r-1 else (mult*(r-1)+rem,r-1) )\\n connected.append(toAdd)\\n if rem!=0:\\n connected.append((mult*(r-1)+rem-1,r-1))\\n\\n #Going outward\\n if r<nLayers-1:\\n connected.append((mult*(r+1)+rem,r+1))\\n connected.append((mult*(r+1)+rem+1,r+1))\\n if rem==0: # only case where negatives could result\\n if mult>0: connected.append( (mult*(r+1)-1,r+1))\\n else: connected.append( (6*(r+1)-1,r+1))\\n \\n return connected\",\n \"def getReducedSubgraphU(self, G, PD, baseParams, bestParams, Lid):\\n doshrink = True\\n count_remove_nodes = 0\\n FinalParams = bestParams\\n while doshrink: #continue removing nodes one by one till no increase in IG\\n doshrink = False\\n for node in FinalParams['Pat'].NL:\\n curParams = dict()\\n curParams['Pat'] = FinalParams['Pat'].copy()\\n curParams['Pat'].removeNode(node)\\n curParams['codeLengthCprime'] = FinalParams['codeLengthCprime'] - self.computeCLgainRemoveNodeU(G, PD, curParams['Pat'].NL, node, [Lid])\\n curParams['Pat'].setIC_dssg( baseParams['codeLengthC'] - curParams['codeLengthCprime'] )\\n curParams['Pat'].setDL( computeDescriptionLength( dlmode=6, C=len(PD.lprevUpdate), gtype=self.gtype, WS=baseParams['Pat'].NCount, W=curParams['Pat'].NCount, kw=curParams['Pat'].ECount, isSimple=self.isSimple, kws=curParams['Pat'].kws ) )\\n curParams['Pat'].setI( computeInterestingness( curParams['Pat'].IC_dssg, curParams['Pat'].DL, mode=self.imode ) )\\n if curParams['Pat'].I > bestParams['Pat'].I:\\n bestParams = curParams\\n if bestParams['Pat'].I > FinalParams['Pat'].I:\\n FinalParams = bestParams\\n count_remove_nodes += 1\\n doshrink = True\\n if count_remove_nodes > 0 or FinalParams['Pat'].NCount < baseParams['Pat'].NCount:\\n FinalParams['codeLengthC'] = baseParams['codeLengthC']\\n FinalParams['Pat'].setLambda( PD.updateDistribution( pat=FinalParams['Pat'].G, idx=None, val_return='return', case=3, dropLidx=[Lid]) ) #// Todo: computeNewLambda\\n FinalParams['codeLengthCprime'] = self.computeCodeLengthShrinkU(G, PD, 3, baseParams, FinalParams, Lid, FinalParams['Pat'].la) #// Todo computeNewCodeLength\\n FinalParams['Pat'].setIC_dssg( FinalParams['codeLengthC'] - FinalParams['codeLengthCprime'] )\\n FinalParams['Pat'].setDL( computeDescriptionLength( dlmode=6, C=len(PD.lprevUpdate), gtype=self.gtype, WS=baseParams['Pat'].NCount, W=FinalParams['Pat'].NCount, kw=FinalParams['Pat'].ECount, excActionType=False, l=self.l, isSimple=self.isSimple, kws=FinalParams['Pat'].kws ) )\\n FinalParams['Pat'].setI( computeInterestingness( FinalParams['Pat'].IC_dssg, FinalParams['Pat'].DL, mode=self.imode ) )\\n return FinalParams\",\n \"def walk_sort(edges):\\n g = nx.Graph()\\n g.add_edges_from(edges)\\n connected = set()\\n degree = nx.degree(g)\\n ordering = []\\n while degree:\\n next = max_degree_node(g, degree, connected)\\n if next is not None:\\n ordering.append(next)\\n else:\\n break\\n return ordering\",\n \"def fn(node):\\n if not node: return 0\\n return 1 + max(fn(node.left), fn(node.right))\",\n \"def fn(node):\\n if not node: return 0\\n return 1 + max(fn(node.left), fn(node.right))\",\n \"def fast_targeted_order(ugraph):\\n degree_sets = {}\\n num_nodes = len(ugraph)\\n for node in range(0, num_nodes):\\n degree_sets[node] = set([])\\n\\n for node_degree in range(0, num_nodes):\\n degree = len(ugraph[node_degree])\\n degree_sets[degree].add(node_degree)\\n\\n desc_node_degrees = []\\n index = 0\\n\\n for degree in range(num_nodes - 1, -1, -1):\\n while len(degree_sets[degree]) > 0:\\n node_to_add = degree_sets[degree].pop()\\n for neighbor in ugraph[node_to_add]:\\n neighbor_degree = len(ugraph[neighbor])\\n degree_sets[neighbor_degree].remove(neighbor)\\n degree_sets[neighbor_degree - 1].add(neighbor)\\n desc_node_degrees.insert(index, node_to_add)\\n index += 1\\n a.delete_node(ugraph, node_to_add)\\n\\n return desc_node_degrees\",\n \"def convert_reduce(g, op, block):\\n\\n op_map = {\\n \\\"reduce_all\\\": \\\"all\\\",\\n \\\"reduce_any\\\": \\\"any\\\",\\n \\\"reduce_max\\\": \\\"max\\\",\\n \\\"reduce_min\\\": \\\"min\\\",\\n \\\"reduce_prod\\\": \\\"prod\\\",\\n \\\"reduce_sum\\\": \\\"sum\\\",\\n \\\"reduce_mean\\\": \\\"mean\\\",\\n }\\n op_name = op_map[op.type]\\n input_x = g.get_node(op.input(\\\"X\\\")[0])\\n axis = op.attr(\\\"dim\\\")\\n if op.attr(\\\"reduce_all\\\"):\\n axis = None\\n keepdims = op.attr(\\\"keep_dim\\\")\\n out = get_relay_op(op_name)(input_x, axis=axis, keepdims=keepdims)\\n if not axis and not keepdims:\\n # use `expand_dims` to solve the following situation\\n # for TVM, the shape of `out` will be (, )\\n # for Paddle, the shape of `out` will be [1]\\n out = _op.expand_dims(out, axis=0)\\n g.add_node(op.output(\\\"Out\\\")[0], out)\",\n \"def __call__(self, graph: Data, n_min: int, nodes_to_keep: List[int] = None, exhaustive: bool = False):\\n nodes_to_keep = nodes_to_keep if nodes_to_keep is not None else []\\n mcts = self._get_mcts(graph, n_min, nodes_to_keep, exhaustive)\\n\\n for iteration in range(self.m):\\n mcts.search_one_iteration()\\n\\n explanation = mcts.best_leaf_node()\\n\\n return explanation.node_set, mcts\",\n \"def internal_degree(self, node_list, link_attribute=None):\\n if self.directed:\\n return (self.internal_indegree(node_list, link_attribute)\\n + self.internal_outdegree(node_list, link_attribute))\\n else:\\n return self.internal_outdegree(node_list, link_attribute)\",\n \"def to_prufer(tree, n):\\n d = defaultdict(int)\\n L = []\\n for edge in tree:\\n # Increment the value of the corresponding\\n # node in the degree list as we encounter an\\n # edge involving it.\\n d[edge[0]] += 1\\n d[edge[1]] += 1\\n for i in range(n - 2):\\n # find the smallest leaf\\n for x in range(n):\\n if d[x] == 1:\\n break\\n # find the node it was connected to\\n y = None\\n for edge in tree:\\n if x == edge[0]:\\n y = edge[1]\\n elif x == edge[1]:\\n y = edge[0]\\n if y is not None:\\n break\\n # record and update\\n L.append(y)\\n for j in (x, y):\\n d[j] -= 1\\n if not d[j]:\\n d.pop(j)\\n tree.remove(edge)\\n return L\",\n \"def select_final(self):\\n best_qsa_star = -99999\\n best_node = None\\n for a, c in self.children.items():\\n qsa = c.wins / c.visits\\n if c.visits_amaf == 0:\\n qsa_tilde = 0\\n else:\\n qsa_tilde = c.wins_amaf / c.visits_amaf\\n bsa = sqrt(self.k / (self.visits + self.k))\\n qsa_star = (1 - bsa) * qsa + bsa * qsa_tilde\\n if qsa_star > best_qsa_star:\\n best_qsa_star = qsa_star\\n best_node = c\\n return best_node.action\",\n \"def reduce_element(self, elt):\\n if not elt.module == self.parent:\\n raise NotImplementedError\\n if not self.is_sq_maxrank_HNF():\\n msg = \\\"Reduction not implemented unless matrix square max-rank HNF\\\"\\n raise StructureError(msg)\\n B = self.basis_element_pullbacks()\\n a = elt\\n for i in range(self.n - 1, -1, -1):\\n b = B[i]\\n q = a.coeffs[i]*b.denom // (b.coeffs[i]*a.denom)\\n a -= q*b\\n return a\",\n \"def max_degree(self, k):\\n if k < 3:\\n return (self.L.size() - 1) // (self.K.size() ** k)\\n if k == 3:\\n if self.t == 4:\\n return 2 * self.q + 2 - int((self.q + 1) % 3)\\n raise ValueError(\\\"The maximal degree of {}-vertex sets is unknown for this graph.\\\".format(k))\",\n \"def get_rendezvous_node(nodes, key):\\n # TODO\\n \\n highest_node = None\\n weights = []\\n for node in nodes:\\n x = node + key\\n x = x.encode()\\n w = hashlib.md5(x).hexdigest()\\n weights.append((w, node))\\n\\n _, highest_node = max(weights)\\n return highest_node\",\n \"def degree(self, node):\\r\\n if not 0 <= node < self.size:\\r\\n raise ValueError(\\\"Cannot find degree for a node not in the graph\\\")\\r\\n return len(self.edges[node])\",\n \"def fn(node):\\n if not node: return 0, 0 # null node \\n if node.left is node.right: return node.val, 0 # leaf node \\n left, right = fn(node.left), fn(node.right) # post-order traversal \\n return node.val+left[1]+right[1], max(left)+max(right)\",\n \"def degreeCentrality(graph, numberOfPoints):\\n c_degree = nx.degree_centrality(graph)\\n c_degree = heapq.nlargest(numberOfPoints, list(c_degree.values()))\\n return c_degree\",\n \"def reduce_parallel(edges):\\n edges[:] = qsort(edges)\\n logger.debug(\\\"Performing parallel optimization\\\\n\\\")\\n e = edges # use shorter name for edges\\n\\n l = 0 # l stands for left and points to left side of comparison\\n # r stands for right and points to the right side of comparison\\n # items are sorted in the list, so parallel\\n # edges are going to be next to each other\\n for r in range(1, len(e)):\\n logger.debug(\\\"CMP e[{}]:{} to e[{}]:{}\\\".format(l, e[l], r, e[r]))\\n if (e[l][0] == e[r][0] and e[l][1] == e[r][1]):\\n # for parallel edges - overwrite left delay with balanced delay\\n # except if one of the edges is zero = set zero\\n if(e[r][2] == 0):\\n e[l][2] = 0\\n else:\\n e[l][2] = (e[l][2] * e[r][2]) / (e[l][2] + e[r][2])\\n\\n # keep right edge value unchanged as\\n # it is going to be overwritten later,\\n # when not parallel edges is going to be found\\n logger.debug(\\\" Parallel: new value e[{}]:{}\\\".format(l, e[l]))\\n else:\\n # for not equal edges\\n # copy right component to the place right after left.\\n # e.g.: with l=0, r=1 this is going to be self assignment,\\n # with l=3, r=5 this is going to overwrite one of old parallels\\n # that were merged with their lefts\\n l += 1\\n e[l] = e[r]\\n logger.debug(\\\" NOT Parallel: e[{}]:{} <= e[{}]:{}\\\".format(\\n l, e[l], r, e[r]))\\n\\n # by the time this loop is ended l is going to point\\n # to last meaningful edge in the list\\n kept = l + 1\\n removed = len(e) - kept\\n logger.debug(\\\"\\\\n{} parallels removed\\\\n\\\".format(removed))\\n\\n e[:] = e[:kept]\\n logger.debug(\\\"Parallel reduce result edges: {}\\\\n\\\".format(e))\\n\\n return removed\",\n \"def degrees(self):\\n A = self.adjacency()\\n A.data = np.ones(A.nnz)\\n right = np.array(A.sum(1)).ravel()\\n left = np.array(A.sum(0)).ravel()\\n return right, left\",\n \"def zzx_reduce(f):\\n if zzx_degree(f) <= 0:\\n return 1, f\\n\\n g = INT_ZERO\\n\\n for i in xrange(len(f)):\\n if not f[-i-1]:\\n continue\\n\\n g = igcd(g, i)\\n\\n if g == 1:\\n return 1, f\\n\\n return g, f[::g]\",\n \"def bgll(self, graph, node_count, min_mod, max_pass):\\n\\n #the belonging of the node\\n bl = [i for i in range(node_count)]\\n #the node's weight in community\\n _in = [0.0] * node_count\\n #the node's weight in graph\\n _tot = []\\n #total weight of a node, just a copy of _tot\\n k = []\\n #the total weight of the graph\\n m = []\\n\\n #inital the in-param\\n network = [[0.0] * node_count for n in range(node_count)]\\n for node, tag, weight in graph:\\n network[node][tag] = weight\\n for node in network:\\n k.append(sum(node))\\n _tot = k[:]\\n m = sum(k)\\n #inital the in-param\\n\\n def modularity():\\n \\\"\\\"\\\"\\n This function mainly computes the modularity of the network\\n Return:\\n mod->the modularity value\\n \\\"\\\"\\\"\\n\\n q = 0.0\\n for idx in range(0, node_count):\\n if _tot[idx] > 0.0:\\n q += (_in[idx] / m - math.pow(_tot[idx] / m, 2))\\n return q\\n\\n def modularity_gain(n, c, dnodecomm):\\n \\\"\\\"\\\"\\n This function mainly computes the modularity gain of a node moving\\n Param:\\n n->node id\\n c->community id\\n dnodecomm->the weight between the node and the community\\n Return:\\n gain->modularity gain\\n \\\"\\\"\\\"\\n\\n totc = _tot[c]\\n degc = k[n]\\n return dnodecomm - (totc * degc) / m\\n\\n def neigh_comm(n):\\n \\\"\\\"\\\"\\n This function mainly computes the weight between the node and it's neighbour community\\n Param:\\n n->node id\\n Return:\\n nc->the map of the weight between the node and it's neighbour community\\n nc=>{cid,weight}\\n \\\"\\\"\\\"\\n\\n nc = {bl[n]: 0.0}\\n for idx in range(0, node_count):\\n neigh = idx\\n ncomm = bl[neigh]\\n nei_weight = network[n][idx]\\n if (neigh != n) & (nei_weight > 0.0):\\n if ncomm in nc:\\n nc[ncomm] += nei_weight\\n else:\\n nc[ncomm] = nei_weight\\n return nc\\n\\n def insert(n, c, dnodecomm):\\n \\\"\\\"\\\"\\n This function mainly get the effect of insert the node into community\\n Param:\\n n->node id\\n c->community id\\n dnodecomm->the weight between the node and the community\\n \\\"\\\"\\\"\\n\\n _tot[c] += k[n]\\n _in[c] += 2 * dnodecomm + network[n][n]\\n bl[n] = c\\n\\n def remove(n, c, dnodecomm):\\n \\\"\\\"\\\"\\n This function mainly get the effect of remove the node off community\\n Param:\\n n->node id\\n c->community id\\n dnodecomm->the weight between the node and the community\\n \\\"\\\"\\\"\\n\\n _tot[c] -= k[n]\\n _in[c] -= 2 * dnodecomm + network[n][n]\\n bl[n] = -1\\n\\n def detect():\\n \\\"\\\"\\\"\\n This function mainly detect the community of the graph.\\n \\\"\\\"\\\"\\n\\n _pass_done = 0\\n _improve = True\\n new_mod = modularity()\\n cur_mod = -999999999.0\\n rl = random.sample(range(0, node_count), node_count)\\n while _improve & (_pass_done < max_pass) & (new_mod - cur_mod > min_mod):\\n cur_mod = new_mod\\n _improve = False\\n _pass_done += 1\\n for node_tmp in rl:\\n n = node_tmp\\n nc = bl[n]\\n ncomm = neigh_comm(n)\\n remove(n, nc, ncomm[nc])\\n best_c = nc\\n best_l = 0.0\\n best_incre = 0.0\\n for c in ncomm:\\n incre = modularity_gain(n, c, ncomm[c])\\n if incre > best_incre:\\n best_incre = incre\\n best_c = c\\n best_l = ncomm[c]\\n insert(n, best_c, best_l)\\n if best_c != nc:\\n _improve = True\\n new_mod = modularity()\\n print new_mod\\n\\n detect()\\n return bl\",\n \"def return_outdeg(self, vertex: np.int_):\\n return len(self.__adj[vertex])\",\n \"def _compute_longest_path_cut(self, src_uid, dst_uid):\\n # NOTE: at the time being the reason why it was chosen to include edges\\n # that belong to any path to the returned list, is unclear to me.\\n to_visit_uids = [dst_uid]\\n node_costs = {}\\n node_succs = {}\\n subgraph_edge_uids = []\\n subgraph_node_uids = [src_uid]\\n dsp = 0\\n\\n if (src_uid == dst_uid): # NOTE: deals with single-block graphs\\n node = self._nodes[src_uid]\\n return node.get_cost(), [src_uid], [], []\\n\\n # NOTE: the generated block graph might contain dead ends, thus\\n # visited_uids must be pre-computed\\n visited_uids = copy.deepcopy(self._nodes.keys())\\n lvisited_uids = []\\n while len(to_visit_uids) > 0:\\n cur_uid = to_visit_uids[0]\\n cur_node = self._nodes[cur_uid]\\n\\n if cur_uid in visited_uids:\\n visited_uids.remove(cur_uid)\\n\\n for pred_uid in cur_node.get_predecessors():\\n if pred_uid not in to_visit_uids and pred_uid not in lvisited_uids:\\n to_visit_uids.append(pred_uid)\\n\\n to_visit_uids = to_visit_uids[1:]\\n lvisited_uids.append(cur_uid)\\n\\n to_visit_uids = [dst_uid]\\n\\n # reverse graph exploration\\n while 0 < len(to_visit_uids):\\n assert(dsp < len(to_visit_uids))\\t# dead ends or loops\\n\\n cur_uid = to_visit_uids[dsp]\\n cur_node = self._nodes[cur_uid]\\n\\n assert(cur_uid != src_uid)\\n assert(cur_uid not in visited_uids)\\t# loop!\\n\\n # skip node for which there is still some unexplored successor\\n skip_uid = False\\n for succ_uid in cur_node.get_successors():\\n if not succ_uid in visited_uids:\\n skip_uid = True\\n\\n if skip_uid:\\n dsp += 1\\n continue\\n else:\\n dsp = 0\\n\\n if cur_uid == dst_uid:\\n node_costs[cur_uid] = cur_node.get_cost()\\n\\n for pred_uid in cur_node.get_predecessors():\\n pred_node = self._nodes[pred_uid]\\n # get edge pred_uid ---> cur_uid\\n edge_uid = Edge.get_edge_uid(pred_uid, cur_uid)\\n edge = self._edges[edge_uid]\\n edge_cost = edge.get_cost()\\n if edge_uid not in subgraph_edge_uids:\\n subgraph_edge_uids.append(edge_uid)\\n\\n # update node distance\\n pred_node_cost = node_costs[cur_uid] + edge_cost + pred_node.get_cost()\\n if pred_uid not in node_costs or pred_node_cost > node_costs[pred_uid]:\\n node_costs[pred_uid] = pred_node_cost\\n node_succs[pred_uid] = cur_uid\\n\\n # update to_visit_uids, a node cannot be visited more than once\\n if (pred_uid != src_uid) and (pred_uid not in visited_uids) \\\\\\n and (pred_uid not in to_visit_uids):\\n to_visit_uids.append(pred_uid)\\n\\n to_visit_uids.remove(cur_uid)\\n visited_uids.append(cur_uid)\\n subgraph_node_uids.append(cur_uid)\\n\\n # construct path from src_node to dst_node\\n cur_uid = src_uid\\n maxpath_node_uids = [cur_uid]\\n while cur_uid != dst_uid:\\n cur_uid = node_succs[cur_uid]\\n cur_node = self._nodes[cur_uid]\\n if cur_uid not in maxpath_node_uids:\\n maxpath_node_uids.append(cur_uid)\\n else:\\n raise Exception(\\\"invalid input source code graph: loop detected\\\")\\n\\n max_cost = node_costs[src_uid]\\n return max_cost, maxpath_node_uids, subgraph_node_uids, subgraph_edge_uids\",\n \"def out_degree_centrality(self):\\n\\n try:\\n if not self.G.is_directed():\\n self.logger.warning('G为无向网络！')\\n else:\\n self.logger.info('正在计算有向网络的出度中心性 ...')\\n return self.order_dict(nx.out_degree_centrality(self.G), index=1)\\n except Exception as e:\\n self.logger.error(\\\"计算失败，原因：{0}\\\".format(e))\",\n \"def fn(node, mn, mx):\\n if not node: return mx - mn \\n mn = min(mn, node.val)\\n mx = max(mx, node.val)\\n lv = fn(node.left, mn, mx)\\n rv = fn(node.right, mn, mx)\\n return max(lv, rv)\",\n \"def degree(self):\\n return sum(self)\",\n \"def convert_relu6(g, op, block):\\n\\n x = g.get_node(op.input(\\\"X\\\")[0])\\n out = _op.clip(x, 0.0, 6.0)\\n g.add_node(op.output(\\\"Out\\\")[0], out)\",\n \"def index_nodes(self):\\n out = {}\\n\\n #avg = np.mean(list(self.rtype_vectors.values()),axis=0)\\n\\n\\n #for name, node in self.nodes.items():\\n # tmp1 = [self.rtype_vectors[rtype]\\n # for rtype, dest in node.outgoing_relations] or [NULL_VEC()]\\n # tmp2 = [permute_rtype_vector(self.rtype_vectors[rtype])\\n # for rtype, prev in node.incoming_relations] or [NULL_VEC()]\\n\\n # net = tmp1 + tmp2\\n\\n # #out[name] = np.asarray(net).mean(axis=0)\\n # #out[name] = np.asarray(net).sum(axis=0)\\n # v = np.asarray(net).sum(axis=0)\\n # if v.any():\\n # out[name] = v/max(v)#softmax(v/max(v))\\n # else:\\n # out[name] = v\\n\\n\\n #avg = np.mean(list(out.values()),axis=0)\\n\\n #maxm = np.max(list(out.values()),axis=0)\\n\\n ####normalize everything\\n #for r,v in out.items():\\n # if v.any():\\n # #out[r] = v / sqrt(v.dot(v))\\n # out[r] = softmax((v-avg)/maxm)\\n\\n\\n\\n # PCA method 0001701\\n rmap = self.rtype_vectors\\n data = np.zeros((len(self.nodes), JACCARD_DIMENSIONS), dtype=np.float)\\n ix = 0\\n for node in self.nodes.values():\\n\\n #compute weighted average of each relation type\\n tmp = [rmap[rtype] for \\n rtype, dest in node.outgoing_relations] + \\\\\\n [permute_rtype_vector(rmap[rtype]) for \\n rtype, prev in node.incoming_relations]\\n\\n v = np.asarray(tmp).mean(axis=0) if tmp else NULL_VEC()\\n\\n #normalize\\n if v.any():\\n data[ix] = v / sqrt(v.dot(v))\\n else:\\n data[ix] = v\\n ix += 1\\n\\n #eliminate projection onto first 7 principal components\\n d2 = data - PCA(data, 7)\\n\\n #order of nodes is preserved\\n for i,v in enumerate(self.nodes):\\n out[v] = softmax(d2[i])\\n\\n return out\",\n \"def move_up(g,k): # g: graph; k: coefficient\\n for i,_ in path(g): #i: node address\\n if (i%k)!=0:\\n move_up_node(g,i,k)\",\n \"def internal_indegree(self, node_list, link_attribute=None):\\n if link_attribute is None:\\n return np.sum(self.internal_adjacency(node_list), axis=0)\\n else:\\n return np.sum(self.internal_link_attribute(link_attribute,\\n node_list), axis=0)\",\n \"def f(self,node):\\r\\n return (self.a*self.nodeDegree(node))/(1+self.b*self.nodeDegree(node))\",\n \"def min_neighbor_node(g):\\r\\n return min(g.degree_iter(),key = lambda item:item[1])[0]\",\n \"def exclude_nodes_degree(G, min_degree):\\n remove = [node for (node, degree) in G.degree().items() if degree < min_degree]\\n G.remove_nodes_from(remove)\\n #remove new nodes without edges\\n remove_zero_degree = [node for (node, degree) in G.degree().items() if degree == 0]\\n G.remove_nodes_from(remove_zero_degree)\\n return G\",\n \"def optimize_graph( complete, desired, algo, simi_cutoff ) :\\n if (algo == \\\"trim\\\") :\\n return trim_cluster( desired, desired.nodes(), 2 )\",\n \"def getNextNodeUsingTotalStepsToTravel(kGoalState):\\n \\n global fringe\\n global solutions\\n\\n\\n\\n minNode = None\\n minCost = 99999999999\\n minNodeIndex = -1\\n\\n \\n pnode = None\\n pcost = None\\n\\n if len(solutions)>0 and solutions[0] != None:\\n pnode = solutions[0];\\n pcost = getTotalStepsToReachGoalState(pnode,kGoalState)\\n # print pnode, pcost\\n # raw_input()\\n \\n\\n\\n\\n for idx,node in enumerate(fringe):\\n #get the heu. function values\\n g_value =getTotalStepsToReachGoalState(node,kGoalState)\\n \\n\\n if g_value < minCost:\\n minNode = node\\n minNodeIndex = idx\\n minCost = g_value\\n\\n\\n fringe.pop(minNodeIndex)\\n c = getTotalStepsToReachGoalState(minNode,kGoalState)\\n if pnode != None:\\n if c > pcost:\\n minNode = None\\n \\n return minNode\",\n \"def AGM_prim(mtr_adj, limited_nodes=[], raiz=1):\\n\\tnum_vertices = len(mtr_adj)\\n\\n\\tfila = []\\n\\tvertices = [] # ordenados de acordo com a chave\\n\\t\\n\\t# adicionando os nodos na lista de vertices e ordenando pela key\\n\\tfor i in range( 1, num_vertices+1 ):\\n\\t\\tvertices.append( vertice(i) )\\n\\n\\tvertices.sort(key = lambda x: x.key)\\n\\tlog.debug('vertices: %s' % vertices)\\n\\t\\n\\t# fila a ser ordenada pela distancia\\n\\tfor nodo in vertices:\\n\\t\\tfila.append(nodo)\\n\\n\\t# se a raiz tiver grau máximo =1, seleciona o nodo mais próximo da raiz\\n\\t# para tornar ele a 'raiz'\\n\\tverificar_raiz(mtr_adj, limited_nodes, vertices, raiz)\\n\\t\\n\\tvertices[raiz-1].dist = 0\\n\\n\\t# ordena a fila por ordem de distancia para o predecessor.\\n\\treordenar(fila)\\n\\tlog.debug('fila: %s' % fila)\\n\\n\\t# criando arvore com os nodos que aceitam mais de 1 grau\\n\\twhile len(fila):\\n\\t\\t# nodo a ser testado\\n\\t\\tu = fila.pop(0)\\n\\t\\t\\n\\t\\t# evitar os nodos com grau máximo = 1 por enquanto\\n\\t\\tif u.key in limited_nodes:\\n\\t\\t\\tcontinue\\n\\t\\t\\n\\n\\t\\t# passando por todos os outros vértices, e adicionando\\n\\t\\t# para selecionar os nodos que tem o nodo u como predecessor.\\n\\t\\tfor v in range(1, num_vertices+1):\\n\\t\\t\\tif u.key != v and \\\\\\n\\t\\t\\tv not in limited_nodes and \\\\\\n\\t\\t\\tmtr_adj[ u.key-1 ][v-1] < vertices[v-1].dist and \\\\\\n\\t\\t\\tna_fila(v, fila):\\n\\t\\t\\t\\tvertices[v-1].pred = u.key\\n\\t\\t\\t\\tvertices[v-1].dist = mtr_adj[ u.key-1 ][v-1]\\n\\t\\t\\t\\treordenar(fila)\\n\\t\\tlog.debug('fila: %s' % fila)\\t\\n\\t\\n\\t# conectando os nodos que aceitam grau maximo = 1 na arvore\\n\\tfor u in vertices:\\n\\t\\tif u == raiz:\\n\\t\\t\\tcontinue\\n\\n\\t\\t# para cada nodo u de grau máximo = 1\\n\\t\\tif u.key in limited_nodes:\\n\\t\\t\\t# verificar qual o nodo mais próximo, não limitado, diferente de u\\n\\t\\t\\tfor v in range(1, num_vertices+1):\\n\\t\\t\\t\\tv_dist = mtr_adj[u.key-1][v-1] # distância de u até v\\n\\t\\t\\t\\tif v != u.key and v_dist < u.dist and v not in limited_nodes:\\n\\t\\t\\t\\t\\tu.dist = v_dist\\n\\t\\t\\t\\t\\tu.pred = v\\n\\n\\treturn vertices\",\n \"def get_value(self):\\r\\n if len(self.walk) == 0:\\r\\n return 0\\r\\n value = 0\\r\\n start = 0\\r\\n end = len(self.walk) - 1\\r\\n while start < end:\\r\\n i_segment = self.get_segment(start+1)\\r\\n if i_segment.value == 'RUNG':\\r\\n break\\r\\n start += 2\\r\\n while end >= 2:\\r\\n i_segment = self.get_segment(end-1)\\r\\n if i_segment.value == 'RUNG':\\r\\n break\\r\\n end -= 2\\r\\n j = start\\r\\n while j < end:\\r\\n j_node = self.get_node(j)\\r\\n j += 1\\r\\n j_segment = self.get_segment(j)\\r\\n j += 1\\r\\n if j_segment.value != 'RUNG':\\r\\n # if the node connector is not critical, try to find and skip a loop\\r\\n k = j\\r\\n while k < end:\\r\\n k_node = self.get_node(k)\\r\\n k += 1\\r\\n k_segment = self.get_segment(k)\\r\\n k += 1\\r\\n if k_segment.value == 'RUNG':\\r\\n break\\r\\n if k_node == j_node:\\r\\n # Only skippable nodes existed before returned to original node, so skip that loop.\\r\\n value += (k - j) * 10\\r\\n j = k\\r\\n j_node = k_node\\r\\n j_segment = k_segment\\r\\n break\\r\\n if j_segment.value == 'SCAFFOLD':\\r\\n value -= j_segment.a.distance_sq(j_segment.b)\\r\\n elif j_segment.value == 'RUNG':\\r\\n value -= j_segment.a.distance_sq(j_segment.b)\\r\\n return value\",\n \"def back_prop(nodes_in_path, playout_result):\\n for temp_node in nodes_in_path:\\n temp_node.visited += 1\\n if str(playout_result) == str(temp_node.side):\\n temp_node.winning += 1\",\n \"def _update_max(self):\\n tmp = self\\n while tmp.right is not None:\\n tmp = tmp.right\\n return tmp.parent.key\",\n \"def update(self, aggr_out):\\r\\n # Initial aggr_out = [#, heads, node_channels]\\r\\n aggr_out = aggr_out.view(-1, self.heads * self.node_channels)\\r\\n # Final aggr_out = [#, heads*node_channels]\\r\\n aggr_out = torch.matmul(aggr_out, self.weight_scale) #[#,node_channel]\\r\\n aggr_out = aggr_out + self.bias\\r\\n return aggr_out\",\n \"def degree(self, node=None) -> int:\\n if node:\\n if node not in self._nodes:\\n raise ValueError('node %s not in the graph' % node)\\n else:\\n return int(sum(self._adj[node]))\\n else:\\n return sum(len(e) for n, e in self._adj.items()) // 2\",\n \"def update_flow(self):\\n start_nodes = []\\n end_nodes = []\\n capacities = []\\n # (1): add all edges (u, v) with capacity ub-lb\\n B = self.get_max_lb()*(self.num_edges() - len(self) + 2)\\n for arc in self.arc_info.keys():\\n if self.arc_info[arc][\\\"upper_bound\\\"] == float('inf'):\\n self.arc_info[arc][\\\"upper_bound\\\"] = B\\n for arc in self.arc_info.keys():\\n start_nodes.append(self.arc_info[arc][\\\"start\\\"])\\n end_nodes.append(self.arc_info[arc][\\\"destin\\\"])\\n capacities.append(int(self.arc_info[arc][\\\"upper_bound\\\"]\\\\\\n - self.arc_info[arc][\\\"lower_bound\\\"]))\\n # (2): add edge (t, s) with capacity B\\n # B = max_lb * (m - n + 2)\\n B = self.get_max_lb()*(self.num_edges() - len(self) + 2)\\n if B == 0:\\n #B = float('inf')\\n B = 100000\\n start_nodes.append(self.sink())\\n end_nodes.append(self.source())\\n capacities.append(int(B))\\n # (3): for all verts, if exc > 0, add edge (s', v) with capacity exc(v),\\n # and if exc < 0, add edge(s', v) with capacity -exc(v)\\n s_prime = max(self.vertices) + 1\\n t_prime = max(self.vertices) + 2\\n print(\\\"s'={}, t'={}\\\".format(s_prime, t_prime))\\n for v in self:\\n #print(\\\"vert {} in arcs: {}\\\".format(v,\\n # self.in_arcs_lists[v]))\\n # compute exc: lower bounds of in - lower bounds of out\\n sum_lb_in = 0\\n for in_arc in self.in_arcs_lists[v]:\\n sum_lb_in += self.arc_info[in_arc][\\\"lower_bound\\\"]\\n sum_lb_out = 0\\n #print(\\\"vert {} out arcs: {}\\\".format(v,\\n # self.out_arcs_lists[v]))\\n for out_arc in self.out_arcs_lists[v]:\\n sum_lb_out += self.arc_info[out_arc][\\\"lower_bound\\\"]\\n exc = sum_lb_in - sum_lb_out\\n #print(\\\"exc is {}\\\".format(exc))\\n if exc > 0:\\n start_nodes.append(s_prime)\\n end_nodes.append(v)\\n capacities.append(int(exc))\\n else:\\n start_nodes.append(v)\\n end_nodes.append(t_prime)\\n capacities.append(int(-exc))\\n # solve maxflow\\n #print(\\\"s' is {} and t' is {}\\\".format(s_prime, t_prime))\\n max_flow = pywrapgraph.SimpleMaxFlow()\\n for u, v, cap in zip(start_nodes, end_nodes, capacities):\\n #print(\\\"Adding edge {}, {} with cap {}\\\".format(u,v,cap))\\n max_flow.AddArcWithCapacity(u, v, cap)\\n success = True\\n if max_flow.Solve(s_prime, t_prime) == max_flow.OPTIMAL:\\n #print('Max flow: {}'.format( max_flow.OptimalFlow()))\\n #print(' Arc Flow / Capacity')\\n for i in range(max_flow.NumArcs()):\\n # print('%1s -> %1s %3s / %3s' % (\\n # max_flow.Tail(i),\\n # max_flow.Head(i),\\n # max_flow.Flow(i),\\n # max_flow.Capacity(i)))\\n # check that (s', v) edges are saturated (once we find a false,\\n # stay false forever)\\n if success:\\n if max_flow.Tail(i) == s_prime:\\n success = max_flow.Flow(i) == max_flow.Capacity(i)\\n else:\\n success = False\\n print('There was an issue with the max flow input.')\\n if success:\\n # update the flows to be the flow found from maxflow problem\\n for i in range(max_flow.NumArcs()):\\n # if this is an original arc, update the flow\\n if max_flow.Tail(i) != s_prime \\\\\\n and max_flow.Head(i) != t_prime \\\\\\n and not (max_flow.Tail(i) == self.sink() \\\\\\n and max_flow.Head(i) == self.source()):\\n # update arc\\n start = max_flow.Tail(i)\\n destin = max_flow.Head(i)\\n arc = self.get_arc(start, destin)\\n new_flow = self.arc_info[arc][\\\"lower_bound\\\"] + max_flow.Flow(i)\\n old_flow = self.arc_info[arc][\\\"weight\\\"]\\n self.arc_info[arc][\\\"weight\\\"] = new_flow\\n #print(\\\"Edge {} {} adjusted from {} to {}\\\".format(\\n # start,\\n # destin,\\n # old_flow,\\n # new_flow\\n # ))\\n self.check_conservation_of_flow() # check that solution is valid\\n return True\\n else:\\n return False\",\n \"def calculate_ucb_max(self, node):\\n pass\",\n \"def solve(self):\\n self.left -= len(self.nodes)\\n \\n def depths(x,depth = 0):\\n depth+=1\\n for y in self.graph[x]:\\n if y in self.nodes:\\n self.nodes.remove(y)\\n depth = depths(y,depth)\\n return depth\\n \\n while len(self.nodes):\\n x = self.nodes.pop()\\n self.firstGen.append(depths(x))\\n #print self.graph\\n #print self.nodes\\n #print self.firstGen\",\n \"def _reduce_max(self, input_tensor, reduction_indices, c):\\n with self.session.graph.as_default():\\n min_values = tf.reduce_min(input_tensor, reduction_indices, keep_dims=True)\\n not_c = tf.abs(c - 1)\\n\\n return tf.reduce_max(input_tensor * c + not_c * min_values, reduction_indices)\",\n \"def _get_full_graph(self):\",\n \"def compute(self, node, input_vals):\\r\\n #start = time.time()\\r\\n\\r\\n #assert len(input_vals) == 1\\r\\n strides = node.const_attr[1]\\r\\n ksize = node.const_attr[0]\\r\\n ish = list(input_vals[0].shape)\\r\\n input = input_vals[0]\\r\\n output = np.zeros([ish[0],(ish[1]-ksize[1])//strides[1]+1,(ish[2]-ksize[2])//strides[2]+1,ish[3]])\\r\\n osh = output.shape\\r\\n #print(osh)\\r\\n for i in range(osh[1]):\\r\\n for j in range(osh[2]):\\r\\n output[:,i,j,:] = np.amax(input[:,i*strides[1]:(i+1)*strides[1],j*strides[1]:(j+1)*strides[1],:],axis=(1,2))\\r\\n #end = time.time() \\r\\n #print(\\\"max_pool\\\") \\r\\n #print(end - start) \\r\\n return output\\r\\n \\r\\n #assert False\\r\",\n \"def degree_on_basis(self, b):\\n return sum(b)\",\n \"def convert_max(node, **kwargs):\\n name, input_nodes, attrs = get_inputs(node, kwargs)\\n\\n mx_axis = attrs.get(\\\"axis\\\", None)\\n axes = convert_string_to_list(str(mx_axis)) if mx_axis is not None else None\\n\\n keepdims = get_boolean_attribute_value(attrs, \\\"keepdims\\\")\\n\\n if axes is not None:\\n node = onnx.helper.make_node(\\n 'ReduceMax',\\n inputs=input_nodes,\\n outputs=[name],\\n axes=axes,\\n keepdims=keepdims,\\n name=name\\n )\\n\\n return [node]\\n else:\\n node = onnx.helper.make_node(\\n 'ReduceMax',\\n inputs=input_nodes,\\n outputs=[name],\\n keepdims=keepdims,\\n name=name\\n )\\n\\n return [node]\",\n \"def remove_leaves(self):\\n result = 0\\n for i in range(8):\\n node = self.children[i]\\n if node:\\n self.color.red += node.color.red\\n self.color.green += node.color.green\\n self.color.blue += node.color.blue\\n self.pixel_count += node.pixel_count\\n result += 1\\n return result - 1\",\n \"def reduceAdjacency(adj):\\n adj = checkArrayDim(adj,2)\\n n = adj.shape[0]\\n adj[adj == arange(n).reshape(n,-1)] = -1 # remove the item i\\n adj = sortAdjacency(adj)\\n adj[adj[:,:-1] == adj[:,1:]] = -1 #remove duplicate items\\n adj = sortAdjacency(adj)\\n return adj\",\n \"def calculate_best_way(self) -> int:\\n node = self._find_lowest_cost_node()\\n while node:\\n cost = self.costs[node]\\n neighbors = self.graph[node]\\n for neighbor in neighbors.keys():\\n node_cost = cost + neighbors[neighbor]\\n if self.costs[neighbor] > node_cost:\\n self.costs[neighbor] = node_cost\\n self.parents[neighbor] = node\\n self.closed_nodes.append(node)\\n node = self._find_lowest_cost_node()\\n\\n return self.costs[\\\"fin\\\"]\",\n \"def reduce_sequential(edges, start, end):\\n dd = get_degrees_dictionary(edges) # O(len(edges))\\n tvs = get_transition_vertexes(dd, start, end) # O(len(dd))\\n logger.debug(\\\"dd: {}\\\".format(dd))\\n logger.debug(\\\"tvs: {}\\\".format(tvs))\\n\\n for v in tvs: # for each vertex in transitional vertexes\\n # edges\\n ei1 = tvs[v][0]\\n ei2 = tvs[v][1]\\n\\n e1 = edges[ei1] # e1 is going to save resulted edge\\n e2 = edges[ei2] # e2 is going to become cycled and then removed\\n\\n # vertexes\\n # v - vertex to be removed\\n # v1 - vertex, connected to v by e1 edge (unchanged)\\n # v2 - vertex, connected to v by e2 edge\\n # will be moved to e1 substituting v there\\n # edges list in transitional vertex dictionary will be updated\\n\\n logger.debug(\\\"Substituted {}: {}:{}, {}:{} -> \\\".format(\\n v, ei1, e1, ei2, e2))\\n\\n # v is going to be substituted in e1 by value of \\\"not v\\\" vertex in e2\\n substitute_index_in_ei2 = 1 - e2.index(v) # if vi=0 s=1; v=1 s=0\\n\\n # replace v in ei1 by substitute from ei2\\n v2 = e2[substitute_index_in_ei2]\\n\\n e1[e1.index(v)] = v2\\n e2[substitute_index_in_ei2] = v\\n\\n # here we will have 2 edges\\n # edges[ei1] -> ['v1', 'v2', ?] #\\n # edges[ei2] -> ['v', 'v', 5] # delay not changed\\n\\n # updated edges for substituted vertex in tvs dict to point to\\n # ei1 edge instead of ei2\\n # e.g. 'v2' was connected by ei2, now is connected by ei1\\n\\n if v2 != start and v2 != end:\\n # v2 is not present in tvi and shouldn't be updated\\n v2ei = tvs[v2] # list of edges indexes for v2\\n vei = tvs[v] # list of edges indexes for v\\n v2ei[v2ei.index(ei2)] = ei1\\n\\n logger.debug(\\\"tvs[{}][2] = t[1] : {} = {}\\\".format(\\n v2,\\n tvs[v2][2],\\n t[1]))\\n\\n # update weight\\n new_weight = e1[2] + e2[2]\\n e1[2] = new_weight\\n\\n # normalize result edge\\n redirect_edge_alpabetically(e1)\\n\\n # here we will have 2 edges\\n # edges[ei1] -> ['v1', 'v2', 8] #\\n # edges[ei2] -> ['v', 'v', 5] # delay not changed\\n\\n # only thing left is to remove the ei2 edge, this will be done later\\n # not to break iteration over edges\\n\\n logger.debug(\\\"{}:{}, {}:{}\\\".format(ei1, e1, ei2, e2))\\n\\n # get indexes of edges to be removed\\n indexes = [i for i in reversed(sorted([tvs[v][1] for v in tvs]))]\\n logger.debug(\\\"Edges index removed after sequential update: {}\\\".format(\\n indexes))\\n\\n for i in indexes:\\n edges.pop(i)\\n\\n return len(tvs) # amount of edges removed\",\n \"def _create_reduceOp(cls, onnx_node, inputs, opset_version):\\n axes = onnx_node.getattr(\\\"axes\\\", None)\\n keepdims = onnx_node.getattr(\\\"keepdims\\\", 1)\\n _, forward = cls._common_onnx_node_to_singa_op(onnx_node, inputs,\\n opset_version)\\n return _, forward(axes, keepdims)\",\n \"def linkage(self):\\n self.tree = {}\\n un_linked = []\\n for i in range(len(self.leaves)):\\n leaf = self.leaves[i]\\n un_linked.append({\\n 'id': i,\\n 'x': 0,\\n 'y': 0,\\n 'value': 0,\\n 'set': leaf,\\n 'children': []\\n })\\n pass\\n while len(un_linked) > 1:\\n # for i in tqdm(range(len(un_linked))):\\n # print(\\\"Linking... {} nodes left\\\".format(len(un_linked)))\\n for node in un_linked:\\n for d in node['set']:\\n node['x'] += d['x']\\n node['y'] += d['y']\\n node['value'] += d['value']\\n pass\\n node['x'] /= len(node['set'])\\n node['y'] /= len(node['set'])\\n node['value'] /= len(node['set'])\\n pass\\n # min_dif = ((un_linked[1]['x'] - un_linked[0]['x']) ** 2 + (un_linked[1]['y'] - un_linked[0]['y']) ** 2) \\\\\\n # * self._alpha + (un_linked[1]['value'] - un_linked[0]['value']) * (1 - self._alpha)\\n min_dif = ((un_linked[1]['x'] - un_linked[0]['x']) ** 2 + (un_linked[1]['y'] - un_linked[0]['y']) ** 2)\\n min_cp = [0, 1]\\n for i in range(len(un_linked) - 1):\\n for j in range(i + 1, len(un_linked)):\\n # dif = self._alpha * ((un_linked[j]['x'] - un_linked[i]['x']) ** 2\\n # + (un_linked[j]['x'] - un_linked[i]['x']) ** 2) \\\\\\n # + (1 - self._alpha) * (un_linked[j]['value'] - un_linked[i]['value'])\\n dif = ((un_linked[j]['x'] - un_linked[i]['x']) ** 2\\n + (un_linked[j]['x'] - un_linked[i]['x']) ** 2)\\n if dif < min_dif:\\n min_dif = dif\\n min_cp = [i, j]\\n pass\\n pass\\n pass\\n set_a = []\\n for each in un_linked[min_cp[0]]['set']:\\n set_a.append(each)\\n pass\\n for each in un_linked[min_cp[1]]['set']:\\n set_a.append(each)\\n pass\\n next_un_linked = []\\n new_children = []\\n if len(un_linked[min_cp[0]]['children']) != 0:\\n new_children.append({'children': un_linked[min_cp[0]]['children'],\\n 'value': len(un_linked[min_cp[0]]['set'])})\\n pass\\n else:\\n new_children.append({'id': un_linked[min_cp[0]]['id'],\\n 'value': len(un_linked[min_cp[0]]['set'])})\\n if len(un_linked[min_cp[1]]['children']) != 0:\\n new_children.append({'children': un_linked[min_cp[1]]['children'],\\n 'value': len(un_linked[min_cp[1]]['set'])})\\n pass\\n else:\\n new_children.append({'id': un_linked[min_cp[1]]['id'],\\n 'value': len(un_linked[min_cp[1]]['set'])})\\n pass\\n next_un_linked.append({\\n 'x': 0,\\n 'y': 0,\\n 'value': 0,\\n 'set': set_a,\\n 'children': new_children\\n })\\n del un_linked[min_cp[0]]['set']\\n del un_linked[min_cp[0]]['x']\\n del un_linked[min_cp[0]]['y']\\n # del un_linked[min_cp[0]]['value']\\n del un_linked[min_cp[1]]['set']\\n del un_linked[min_cp[1]]['x']\\n del un_linked[min_cp[1]]['y']\\n # del un_linked[min_cp[1]]['value']\\n for s in range(len(un_linked)):\\n if s not in min_cp:\\n next_un_linked.append(un_linked[s])\\n pass\\n pass\\n un_linked = next_un_linked\\n pass\\n del un_linked[0]['set']\\n del un_linked[0]['x']\\n del un_linked[0]['y']\\n # del un_linked[0]['value']\\n self.tree = un_linked[0]\\n self._count = 0\\n\\n self.tree = self._resolve(self.tree)\\n return\",\n \"def dfs(node):\\n nonlocal ans \\n if not node: return 0\\n lx, rx = dfs(node.left), dfs(node.right) \\n if not node.left or node.left.val != node.val: lx = 0\\n if not node.right or node.right.val != node.val: rx = 0 \\n ans = max(ans, 1 + lx + rx)\\n return 1 + max(lx, rx)\",\n \"def modularity():\\n\\n q = 0.0\\n for idx in range(0, node_count):\\n if _tot[idx] > 0.0:\\n q += (_in[idx] / m - math.pow(_tot[idx] / m, 2))\\n return q\",\n \"def leaves(node, res):\\n leaf = True\\n if node.lesser:\\n leaf = False\\n leaves(node.lesser, res)\\n if node.greater:\\n leaf = False\\n leaves(node.greater, res)\\n if leaf:\\n res.append(node.indices)\",\n \"def getReducedSubgraphD(self, G, PD, baseParams, bestParams, Lid):\\n doshrink = True\\n count_remove_nodes = 0\\n FinalParams = bestParams\\n while doshrink: #continue removing nodes one by one till no increase in IG\\n doshrink = False\\n for node in FinalParams['Pat'].inNL:\\n curParams = dict()\\n curParams['Pat'] = FinalParams['Pat'].copy()\\n curParams['Pat'].removeInNode(node)\\n curParams['codeLengthCprime'] = FinalParams['codeLengthCprime'] - self.computeCLgainRemoveNodeD(G, PD, curParams['Pat'].outNL, node, [Lid], 1)\\n curParams['Pat'].setIC_dssg( baseParams['codeLengthC'] - curParams['codeLengthCprime'] )\\n curParams['Pat'].setDL( computeDescriptionLength( dlmode=6, C=len(PD.lprevUpdate), gtype=self.gtype, WIS=baseParams['Pat'].InNCount, WOS=baseParams['Pat'].OutNCount, WI=curParams['Pat'].InNL, WO=curParams['Pat'].OutNL, kw=curParams['Pat'].ECount, isSimple=self.isSimple, kws=curParams['Pat'].kws ) )\\n curParams['Pat'].setI( computeInterestingness( curParams['Pat'].IC_dssg, curParams['Pat'].DL, mode=self.imode ) )\\n if curParams['Pat'].I > bestParams['Pat'].I:\\n bestParams = curParams\\n for node in FinalParams['Pat'].outNL:\\n curParams = dict()\\n curParams['Pat'] = FinalParams['Pat'].copy()\\n curParams['Pat'].removeOutNode(node)\\n curParams['codeLengthCprime'] = FinalParams['codeLengthCprime'] - self.computeCLgainRemoveNodeD(G, PD, curParams['Pat'].inNL, node, [Lid], 2)\\n curParams['Pat'].setIC_dssg( baseParams['codeLengthC'] - curParams['codeLengthCprime'] )\\n curParams['Pat'].setDL( computeDescriptionLength( dlmode=6, C=len(PD.lprevUpdate), gtype=self.gtype, WIS=baseParams['Pat'].InNCount, WOS=baseParams['Pat'].OutNCount, WI=curParams['Pat'].InNL, WO=curParams['Pat'].OutNL, kw=curParams['Pat'].ECount, isSimple=self.isSimple, kws=curParams['Pat'].kws ) )\\n curParams['Pat'].setI( computeInterestingness( curParams['Pat'].IC_dssg, curParams['Pat'].DL, mode=self.imode ) )\\n if curParams['Pat'].I > bestParams['Pat'].I:\\n bestParams = curParams\\n if bestParams['Pat'].I > FinalParams['Pat'].I:\\n FinalParams = bestParams\\n count_remove_nodes += 1\\n doshrink = True\\n if count_remove_nodes > 0 or (FinalParams['Pat'].InNCount < baseParams['Pat'].InNCount and FinalParams['Pat'].OutNCount < baseParams['Pat'].OutNCount) :\\n FinalParams['codeLengthC'] = baseParams['codeLengthC']\\n FinalParams['Pat'].setLambda( PD.updateDistribution( FinalParams['Pat'].G, idx=None, val_return='return', case=3, dropLidx=[Lid]) ) #// Todo: computeNewLambda\\n FinalParams['codeLengthCprime'] = self.computeCodeLengthShrinkD( G, PD, 3, baseParams, FinalParams, Lid, FinalParams['Pat'].la) #// Todo computeNewCodeLength\\n FinalParams['Pat'].setIC_dssg( FinalParams['codeLengthC'] - FinalParams['codeLengthCprime'] )\\n FinalParams['Pat'].setDL( computeDescriptionLength( dlmode=6, C=len(PD.lprevUpdate), gtype=self.gtype, WIS=baseParams['Pat'].InNCount, WOS=baseParams['Pat'].OutNCount, WI=FinalParams['Pat'].InNL, WO=FinalParams['Pat'].OutNL, kw=FinalParams['Pat'].ECount, excActionType=False, l=self.l, isSimple=self.isSimple, kws=FinalParams['Pat'].kws ) )\\n FinalParams['Pat'].setI( computeInterestingness( FinalParams['Pat'].IC_dssg, FinalParams['Pat'].DL, mode=self.imode ) )\\n return FinalParams\",\n \"def find_max(self, node):\\n current_node = node\\n while current_node.right:\\n current_node = current_node.right\\n return current_node\",\n \"def __opReduce2(self, elim, op, out=None): # assumes elim <= self.v\\n if ((elim is None) or (len(elim)==len(self.v))):\\n return op(self.t)\\n else:\\n if (out is None):\\n out = Factor(VarSet(self.v - elim))\\n else:\\n assert (out.v == VarSet(self.v-elim) ), \\\"Cannot eliminate into an existing factor with incorrect scope\\\"\\n ax = tuple(map(lambda x: self.v.index(x), elim)) #\\n #t = np.apply_over_axes(op, self.t, ax) # bad: lots of intermediate memory?\\n #t = op(self.t, axis=ax, out=None) # any better? TODO\\n #return Factor().__build( VarSet(self.v - elim), t )\\n op(self.t, axis=ax, out=out.t) # any better? TODO\\n return out\",\n \"def _drop_ground_node(self):\\n self.laplace = np.concatenate((\\n np.concatenate((\\n self.laplace[:self.ground_node,:self.ground_node],\\n self.laplace[:self.ground_node,self.ground_node+1:]),1),\\n\\n np.concatenate((\\n self.laplace[self.ground_node+1:,:self.ground_node],\\n self.laplace[self.ground_node+1:,self.ground_node+1:]), 1)))\\n\\n self.degree = np.concatenate((\\n self.degree[:self.ground_node], \\n self.degree[self.ground_node+1:]))\",\n \"def agg_node(in_planes, out_planes):\\n return nn.Sequential(\\n nn.Conv2d(in_planes, in_planes, kernel_size=3, stride=1, padding=1),\\n nn.ReLU(),\\n nn.Conv2d(in_planes, out_planes, kernel_size=3, stride=1, padding=1),\\n nn.ReLU(),\\n )\",\n \"def fn(node):\\n if not node: return 0, -inf\\n lh, lps = fn(node.left)\\n rh, rps = fn(node.right)\\n return node.val + max(0, lh, rh), max(lps, rps, node.val + max(0, lh) + max(0, rh))\"\n]","isConversational":false},"negative_scores":{"kind":"list like","value":["0.6181302","0.5884981","0.5822757","0.5800841","0.5687816","0.5639941","0.54734296","0.5471626","0.5468302","0.546607","0.544309","0.5424738","0.5419244","0.54152405","0.54112226","0.54112226","0.5405578","0.5396399","0.53594226","0.5346556","0.5326148","0.5302676","0.5293267","0.5280803","0.5279524","0.5232205","0.52256095","0.51995796","0.51986474","0.5179815","0.51786137","0.5167149","0.51631063","0.51627517","0.5156789","0.51505244","0.51457375","0.5145015","0.51447415","0.5143199","0.5134374","0.5134374","0.5128276","0.5125426","0.51045144","0.50892365","0.50873184","0.50860876","0.50743574","0.50722045","0.5067357","0.5064768","0.5061425","0.50610936","0.5054356","0.5051218","0.5050117","0.504929","0.5011145","0.50060105","0.4999156","0.49976942","0.4995512","0.49913892","0.49886787","0.49813813","0.4978709","0.49681723","0.49677715","0.49593586","0.4946316","0.49447945","0.49400583","0.49392483","0.4936675","0.49182764","0.4916494","0.49135244","0.49116832","0.49084866","0.49070567","0.4901646","0.4893265","0.48925442","0.4892513","0.48909658","0.48837057","0.4881456","0.487796","0.4873509","0.4872189","0.48688212","0.48673025","0.48506632","0.48483828","0.48469666","0.4846733","0.48463663","0.4843895","0.4843092","0.48274532"],"string":"[\n \"0.6181302\",\n \"0.5884981\",\n \"0.5822757\",\n \"0.5800841\",\n \"0.5687816\",\n \"0.5639941\",\n \"0.54734296\",\n \"0.5471626\",\n \"0.5468302\",\n \"0.546607\",\n \"0.544309\",\n \"0.5424738\",\n \"0.5419244\",\n \"0.54152405\",\n \"0.54112226\",\n \"0.54112226\",\n \"0.5405578\",\n \"0.5396399\",\n \"0.53594226\",\n \"0.5346556\",\n \"0.5326148\",\n \"0.5302676\",\n \"0.5293267\",\n \"0.5280803\",\n \"0.5279524\",\n \"0.5232205\",\n \"0.52256095\",\n \"0.51995796\",\n \"0.51986474\",\n \"0.5179815\",\n \"0.51786137\",\n \"0.5167149\",\n \"0.51631063\",\n \"0.51627517\",\n \"0.5156789\",\n \"0.51505244\",\n \"0.51457375\",\n \"0.5145015\",\n \"0.51447415\",\n \"0.5143199\",\n \"0.5134374\",\n \"0.5134374\",\n \"0.5128276\",\n \"0.5125426\",\n \"0.51045144\",\n \"0.50892365\",\n \"0.50873184\",\n \"0.50860876\",\n \"0.50743574\",\n \"0.50722045\",\n \"0.5067357\",\n \"0.5064768\",\n \"0.5061425\",\n \"0.50610936\",\n \"0.5054356\",\n \"0.5051218\",\n \"0.5050117\",\n \"0.504929\",\n \"0.5011145\",\n \"0.50060105\",\n \"0.4999156\",\n \"0.49976942\",\n \"0.4995512\",\n \"0.49913892\",\n \"0.49886787\",\n \"0.49813813\",\n \"0.4978709\",\n \"0.49681723\",\n \"0.49677715\",\n \"0.49593586\",\n \"0.4946316\",\n \"0.49447945\",\n \"0.49400583\",\n \"0.49392483\",\n \"0.4936675\",\n \"0.49182764\",\n \"0.4916494\",\n \"0.49135244\",\n \"0.49116832\",\n \"0.49084866\",\n \"0.49070567\",\n \"0.4901646\",\n \"0.4893265\",\n \"0.48925442\",\n \"0.4892513\",\n \"0.48909658\",\n \"0.48837057\",\n \"0.4881456\",\n \"0.487796\",\n \"0.4873509\",\n \"0.4872189\",\n \"0.48688212\",\n \"0.48673025\",\n \"0.48506632\",\n \"0.48483828\",\n \"0.48469666\",\n \"0.4846733\",\n \"0.48463663\",\n \"0.4843895\",\n \"0.4843092\",\n \"0.48274532\"\n]","isConversational":false},"document_score":{"kind":"string","value":"0.0"},"document_rank":{"kind":"string","value":"-1"}}},{"rowIdx":75397,"cells":{"query":{"kind":"string","value":"Decorator to transform tuple into a series"},"document":{"kind":"string","value":"def to_series(func):\n\n @wraps(func)\n def add_series(center, home_center):\n normed_center = func(center.x, center.y, home_center)\n return pd.Series(normed_center, index=[\"x_normed\", \"y_normed\"])\n\n return add_series"},"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 transform_series(obj):\n vals = obj.values\n return transform_array(vals)","def fit_transform(self,\n series: Union[TimeSeries, Sequence[TimeSeries]],\n *args,\n **kwargs) -> Union[TimeSeries, List[TimeSeries]]:\n return self.fit(series).transform(series, *args, **kwargs)","def _fit_iterator(self, series: Sequence[TimeSeries]) -> Iterator[Tuple[TimeSeries]]:\n return zip(series)","def dec_tupl(fnc,*data):\n def wrapper(*args, **kwargs):\n return (fnc(*args,**kwargs),) + data\n return wrapper","def apply_on_series(self, func, *args, **kwargs):\n assert self.is_series_like()\n\n return SeriesDefault.register(pandas.Series.apply)(\n self,\n func=func,\n *args,\n **kwargs,\n )","def _to_list(series: Union[TimeSeries, Sequence[TimeSeries]]) -> Sequence[TimeSeries]:\n\n return [series] if not isinstance(series, Sequence) else series","def as_series(self, arraylike: Iterable) -> pd.Series:\n return pd.Series(arraylike, index=self.data.index)","def __call__(self) -> pd.Series:\n\n converter = {\"datetime\": self._convert_datetime,\n \"int\": self._convert_int,\n \"bool\": self._convert_bool}\n\n func = converter.get(self.parent.dtype, self._convert)\n\n return func()","def _apply_as_series_or_frame(self, func: Callable[[Column], Column]) -> FrameLike:\n pass","def _apply_as_series_or_frame(self, func: Callable[[Column], Column]) -> FrameLike:\n pass","def generate_from_tuple(t):\n\n data_generator(*t)","def tuple_from_sequence(*args):\n return tuple(args)","def n_ary(func):\n def wrapper(x, *args):\n return x if not args else func(x, wrapper(*args))\n return wrapper","def tuple(x):\n pass","def to_real_series(self, data: pd.Series) -> pd.Series:\n ...","def map_series(self, fn, dt_index = None):\n if dt_index == None:\n dt_index = self.index()\n return TimeSeriesRDD(dt_index, self.map(fn))","def to_pandas_series_rdd(self):\n pd_index = self.index().to_pandas_index()\n return self.map(lambda x: (x[0], pd.Series(x[1], pd_index)))","def etuple(*args, **kwargs):\n return ExpressionTuple(args, **kwargs)","def series(*resistors):\n return sum(resistors)","def to_series(self) -> pd.Series:\n df = self.to_dataframe(\"* values *\")\n dims = self.dims_list\n if len(dims) == 1:\n dims = dims[0]\n return df.set_index(dims)[\"* values *\"]","def wrap_result(data: interpreter.TupleValue):\n return tuple(handle(d) for d in data)","def SweepSeries(*args, **kwargs):\n if args or kwargs:\n underride(kwargs, dtype=float)\n series = pd.Series(*args, **kwargs)\n else:\n series = pd.Series([], dtype=np.float64)\n\n series.index.name = 'Parameter'\n if 'name' not in kwargs:\n series.name = 'Metric'\n return series","def __wrap_with_tuple(self) -> tuple:\r\n l = list()\r\n length = len(self.data)\r\n while self.idx < length:\r\n l.append(self.__parse())\r\n return tuple(l)","def parse(x):\n if isinstance(x, container_abcs.Iterable):\n return x\n return tuple(repeat(x, n))","def tuples_to_df(ts):\n return pd.DataFrame.from_records(ts, columns=['user', 'item', 'rating'])","def parse_result_series(result):\n if isinstance(result, np.ndarray):\n return result\n\n if result is None or not len(result):\n return None\n\n dates, values = result\n return pd.DataFrame({0:dates.astype(int)/1000,1:values})","def _xml_to_series(self, xml):\n d = self._xml_to_dict(xml)\n d = d['Data']\n if isinstance(d, basestring):\n raise EmptyXMLError('Provided xml has no content.')\n shows = d['Series'] #list of dicts\n if self.get_first:\n if isinstance(shows, list):\n return Series(**shows[0])\n else:\n return Series(**shows)\n else:\n if isinstance(shows, list): \n return [Series(**v) for v in shows]\n else:\n return [Series(**shows)]","def TimeSeries(*args, **kwargs):\n if args or kwargs:\n underride(kwargs, dtype=float)\n series = pd.Series(*args, **kwargs)\n else:\n series = pd.Series([], dtype=float)\n\n series.index.name = 'Time'\n if 'name' not in kwargs:\n series.name = 'Quantity'\n return series","def make_series(x, y, **options):\n underride(options, name='values')\n if isinstance(y, pd.Series):\n y = y.values\n series = pd.Series(y, index=x, **options)\n series.index.name = 'index'\n return series","def tuple_grapher(arr):\n # Sets the column titles\n cols = [\"x\", \"y\"]\n # Creates a pandas DB using the list of tuples\n db = pandas.DataFrame.from_records(arr, columns=cols)\n # Plots the DB\n db.plot(kind='scatter', x=cols[0], y=cols[1])\n # Displays the graph\n return plt.show()","def to_tuple(\n value: Union[TypeNumber, Iterable[TypeNumber]],\n length: int = 1,\n ) -> Union[TypeTripletFloat, Tuple[TypeNumber, ...]]:\n try:\n iter(value)\n value = tuple(value)\n except TypeError:\n value = length * (value,)\n return value","def __call__(self, batch: iter) -> torch.Tensor or list(torch.Tensor):\n if torch.is_tensor(batch[0]):\n return torch.cat(tuple(t.unsqueeze(0) for t in batch), 0).view(-1, self.batch_size, *batch[0].size())\n elif isinstance(batch[0], int):\n return torch.LongTensor(batch).view(-1, self.batch_size)\n elif isinstance(batch[0], collections.Iterable):\n # if each batch element is not a tensor, then it should be a tuple\n # of tensors; in that case we collate each element in the tuple\n transposed = zip(*batch)\n return tuple(self.__call__(samples) for samples in transposed)\n\n raise TypeError((\"batch must contain tensors, numbers, or lists; found {}\"\n .format(type(batch[0]))))","def pandas_series(arr, nan_to_null=False):\n import pandas as pd\n return pd.Series(arr, copy=False)","def apply_log(a: tuple, func: Callable[[Any], Tuple[Any, Log]]) -> Tuple[Any, Log]:\n value, log = a\n new, entry = func(value)\n return new, log + entry","def lucas():\n return sum_series(a=2, b=1)","def _convert_fn(dataset: tf.data.Dataset) -> tf.data.Dataset:\n spec = dataset.element_spec\n if isinstance(spec, collections.abc.Mapping):\n return dataset.map(lambda observation: (observation[\"x\"], observation[\"y\"]))\n else:\n return dataset.map(lambda x, y: (x, y))","def CombineSeries(*args):\r\n df = pd.concat([*args], axis=1)\r\n\r\n return df","def series_view(self, **kwargs): # noqa: PR02\n return SeriesDefault.register(pandas.Series.view)(self, **kwargs)","def scalar(f):\n def g(X: tuple()):\n return Scalar(f(Scalar(X)))\n return g","def at(self, x):\n x = array(x)\n if x.ndim == 1:\n return self._method(x)\n elif x.ndim == 2:\n return Series(\n index=MultiIndex.from_arrays(\n arrays=x.T,\n names=[f'x{num}' for num in range(1, self._num_dims + 1)]\n ), data=self._method(x), name=f'{self._name}({self._parent})'\n )","def hinted_tuple_hook(obj):\n if '__tuple__' in obj:\n return tuple(obj['items'])\n return obj","def as_series(request: Any) -> Any:\n return request.param","def as_series(request: Any) -> Any:\n return request.param","def float_series() -> pd.Series:\n series = pd.Series([(n/1000) for n in range(1001)])\n return series","def convert_series(self, column: str, dtype: str) -> TYPE_ROW:\n\n series = self.df[column]\n\n if dtype != \"float\":\n series = series.fillna(NULL)\n\n values = self.force_dtype(series, dtype)\n\n return values","def astuple(self):\n try:\n return tuple([x.astuple() for x in self])\n except Exception:\n pass\n return tuple([x for x in self])","def tuple_map(x):\n return x * 2","def _create_batch(self, series):\n import pandas as pd\n import pyarrow as pa\n\n # Make input conform to [(series1, type1), (series2, type2), ...]\n if not isinstance(series, (list, tuple)) or (\n len(series) == 2 and isinstance(series[1], pa.DataType)\n ):\n series = [series]\n series = ((s, None) if not isinstance(s, (list, tuple)) else s for s in series)\n\n arrs = []\n for s, t in series:\n if self._struct_in_pandas == \"dict\" and t is not None and pa.types.is_struct(t):\n # A pandas UDF should return pd.DataFrame when the return type is a struct type.\n # If it returns a pd.Series, it should throw an error.\n if not isinstance(s, pd.DataFrame):\n raise PySparkValueError(\n \"A field of type StructType expects a pandas.DataFrame, \"\n \"but got: %s\" % str(type(s))\n )\n arrs.append(self._create_struct_array(s, t))\n else:\n arrs.append(self._create_array(s, t, arrow_cast=self._arrow_cast))\n\n return pa.RecordBatch.from_arrays(arrs, [\"_%d\" % i for i in range(len(arrs))])","def to_work_series(self, data: pd.Series) -> pd.Series:\n ...","def visit_Tuple(self, node):\n self.generic_visit(node)\n if isinstance(node.ctx, ast.Load):\n return to_call(to_attribute(self.operator, '__tuple__'), node.elts)\n return node","def agg(self, func: Callable[[List[float]], float]) -> \"Stream[float]\":\n return ExpandingNode(func)(self).astype(\"float\")","def triplet(value):\n return tuplet(value, 3, 2)","def decorator(arg):\n return lambda: list(arg)","def astuple(v: Any) -> Tuple[Any, ...]:\n return to_tuple(v, reuse_instances=False, convert_sets=False)","def _series_handler(self, values, style, caller, *args):\n\n behaviors = {\"over\": values.ge,\n \"under\": values.lt}\n\n evaluated = values[behaviors.get(caller)(self.margin)]\n\n if style == \"values\":\n return evaluated\n else:\n return list(evaluated.index)","def next_tuple(self):\n raise NotImplementedError(\"Spout not implementing next_tuple() method\")","def _to_tuple(values: Union[int, Iterable[int]]) -> Tuple[int, ...]:\n try:\n return tuple(values)\n except TypeError:\n return (values,)","def scale_tuple(t: Tuple[float, ...], v: float) -> Tuple[float, ...]:\n # dbg(f'scaleTuple: t={t} v={v}')\n return tuple(i for i in map(lambda p: p * v, t))","def persistence_model(series):\n return [x for x in series]","def as_args(function):\n return lambda x: function(*x)","def tuple(self, spec):\n return tuple(self(inner_spec) for inner_spec in spec)","def _sfn(x):\n if len(x) == 1:\n return x[0]\n return fn(*x)","def eval_func_tuple(f_args):\n return f_args[0](*f_args[1:])","def wrap(func, *args, unsqueeze=False):\n\n # Convert input types where applicable\n args = list(args)\n for i, arg in enumerate(args):\n if type(arg) == np.ndarray:\n args[i] = torch.from_numpy(arg)\n if unsqueeze:\n args[i] = args[i].unsqueeze(0)\n\n result = func(*args)\n\n # Convert output types where applicable\n if isinstance(result, tuple):\n result = list(result)\n for i, res in enumerate(result):\n if type(res) == torch.Tensor:\n if unsqueeze:\n res = res.squeeze(0)\n result[i] = res.numpy()\n return tuple(result)\n elif type(result) == torch.Tensor:\n if unsqueeze:\n result = result.squeeze(0)\n return result.numpy()\n else:\n return result","def convert_input_to_tuple(fn):\n @wraps(fn)\n def wrapper(*args, **kwargs):\n data = args[0].api.payload\n try:\n kwargs['tupled_output'] = json.loads(data,\n object_hook=_json_object_hook)\n return fn(*args, **kwargs)\n except Exception:\n data = json.dumps(data)\n kwargs['tupled_output'] = json.loads(data,\n object_hook=_json_object_hook)\n return fn(*args, **kwargs)\n\n return wrapper","def convert_tuple(self, v, t):\n return tuple(self(v, t)\n for v, t in zip(v, t.elements))","def get_series(gval, series):\n minlen = min([len(d[series]) for f, d in gval])\n return np.stack([d[series][:minlen] for f, d in gval])","def ts_fit(series: TimeSeries) -> TimeSeries:\n pass","def add_collection(s: ObjectStream[T], a: ast.Call) -> Tuple[ObjectStream[T], ast.Call]:\n assert isinstance(a.func, ast.Attribute)\n if a.func.attr == \"Jets\":\n s_update = s.MetaData({\"j\": \"stuff\"})\n return s_update, a\n elif a.func.attr == \"EventNumber\":\n new_call = copy.copy(a)\n new_call.args = [ast_lambda(\"20\")]\n return s, new_call\n else:\n return s, a","def as_series(self) -> \"pd.Series\":\n import pandas as pd\n\n data = {\"_row_id\": self.id, \"_row_num\": self.num, **self.as_dict()}\n series = pd.Series(data)\n return series","def _separate(xs):\n if isinstance(xs, typing.Sequence):\n return tuple(_single_separate(x) for x in xs)\n else:\n return _single_separate(xs)","def infer(self, values):\n raise NotImplementedError","def to_variable(f):\n\n def variable_wrapper(*args, **kwargs):\n rez = f(*args, **kwargs)\n if isinstance(rez, tuple):\n return tuple([Variable(x) for x in rez])\n return Variable(rez)\n return variable_wrapper","def _create_batch(self, series):\n import pandas as pd\n import pyarrow as pa\n\n # Make input conform to [(series1, type1), (series2, type2), ...]\n if not isinstance(series, (list, tuple)) or (\n len(series) == 2 and isinstance(series[1], pa.DataType)\n ):\n series = [series]\n series = ((s, None) if not isinstance(s, (list, tuple)) else s for s in series)\n\n arrs = []\n for s, t in series:\n if not isinstance(s, pd.DataFrame):\n raise PySparkValueError(\n \"Output of an arrow-optimized Python UDTFs expects \"\n f\"a pandas.DataFrame but got: {type(s)}\"\n )\n\n arrs.append(self._create_struct_array(s, t))\n\n return pa.RecordBatch.from_arrays(arrs, [\"_%d\" % i for i in range(len(arrs))])","def tuple_cast(inputs, dst_type):\n return iter_cast(inputs, dst_type, return_type=tuple)","def tuple2func(func1, func2):\n return lambda e: (func1(e), func2(e))","def decorate(name, f=noOp):\n\tdef df(s, loc, tok):\n\t\trules = []\n\t\tundec_tok = []\n\t\tfor t in tok:\n\t\t\tif isinstance(t, tuple):\n\t\t\t\trules += [t[0]]\n\t\t\t\tundec_tok += [t[1]]\n\t\t\telse:\n\t\t\t\tundec_tok += [t]\n\t\tif 0 == len(rules):\n\t\t\trules = \"\".join(undec_tok)\n\t\treturn ((name, rules), f(s, loc, undec_tok))\n\treturn df","def _silent_ntuple_ ( s , *args ) :\n from Ostap.Utils import mute_py \n with mute_py() : \n tup = _TU.__nTuple__(s , *args )\n logger.info ( 'Booked n-tuple %s' % tup )\n return tup","def from_tuple(cls, coords):\n return cls(*coords)","def sum_series(n, first_value=0, second_value=1):\n for i in range(n):\n first_value, second_value = second_value, first_value + second_value\n return first_value","def cast_to_list(position):\n\n\t@wrapt.decorator\n\tdef wrapper(function, instance, args, kwargs):\n\t\tif not isinstance(args[position], list):\n\t\t\targs = list(args)\n\t\t\targs[position] = [args[position]]\n\t\t\targs = tuple(args)\n\n\t\treturn function(*args, **kwargs)\n\n\treturn wrapper","def augment_column(self, col: pd.Series,) -> pd.Series:","def add_to_series_dict(func):\n def add(self, *args, **kwargs):\n s = func(self, *args, **kwargs)\n self.sid_series[s['id']] = s\n return s\n return add","def totuple(self, arr):\n\n\t\ttry:\n\t\t return tuple(self.totuple(i) for i in arr)\n\t\texcept TypeError:\n\t\t return arr","def _metatize_Sequence(obj):\n return type(obj)([metatize(o) for o in obj])","def _unpack_tuple(x):\n if len(x) == 1:\n return x[0]\n else:\n return x","def _unpack_tuple(x):\n if len(x) == 1:\n return x[0]\n else:\n return x","def n_ary(f):\n def n_ary_fn(x, *args):\n return x if not args else f(x, n_ary_fn(*args))\n return n_ary_fn","def _stack_serieses(serieses: List[pd.Series]) -> pd.DataFrame:\n return pd.concat(serieses, axis=\"columns\").T","def __call__(self, index: pd.Index) -> pd.DataFrame:\n if not isinstance(self.values, (list, tuple)):\n values = pd.Series(\n self.values,\n index=index,\n name=self.value_columns[0] if self.value_columns else None,\n )\n else:\n values = dict(\n zip(self.value_columns, [pd.Series(v, index=index) for v in self.values])\n )\n return pd.DataFrame(values)","def reprocessSeries(self, tiltseriesdata):\n\t\treturn None","def aind(x):\n\treturn tuple(x.T)","def collect(tp, *args):\n\n def decorator(f):\n add_triple_pattern(tp, f, args)\n\n return decorator","def _dummy_func(a: int) -> Tuple[int, int]:\n return a, a**2","async def infer_shape_tuple_setitem(track, seq, idx, value):\n seq_sh = await seq['shape']\n idx_v = await idx['value']\n value_sh = await value['shape']\n new_sh = list(seq_sh.shape)\n new_sh[idx_v] = value_sh\n return TupleShape(new_sh)","def expand_series(ser, columns):\n return ser.to_frame(columns[0]).reindex(columns=columns).ffill(axis=1)","def getAxisTuple(axis):","def map(self, func: Callable[[Trajectory, Any], Tuple[Trajectory, Any]]) -> Data:\n trajs, labels = [], []\n for traj, label in zip(self.trajs, self.labels):\n traj, label = func(traj, label)\n trajs.append(traj)\n labels.append(label)\n return Data(trajs, labels)","def Scalar(obj):\n return Sequence([obj])","def from_series(cls, s: pd.Series, schema: Schema) -> \"Flat\":\n if isinstance(s.index, pd.MultiIndex):\n dims = s.index.names\n else:\n if not s.index.name:\n err_msg = (\n \"Series index does not have a name. Unable to infer dimension.\\n\"\n \"When creating the series, ensure the index has a name:\\n\"\n \"s = pd.Series([1, 2, 3], index=pd.Index(['S', 'M', 'L'], name='size'))\"\n )\n raise TypeError(err_msg)\n dims = [s.index.name]\n df = s.to_frame(\"* value *\").reset_index()\n return cls.from_dataframe(df, schema, dims, \"* value *\")"],"string":"[\n \"def transform_series(obj):\\n vals = obj.values\\n return transform_array(vals)\",\n \"def fit_transform(self,\\n series: Union[TimeSeries, Sequence[TimeSeries]],\\n *args,\\n **kwargs) -> Union[TimeSeries, List[TimeSeries]]:\\n return self.fit(series).transform(series, *args, **kwargs)\",\n \"def _fit_iterator(self, series: Sequence[TimeSeries]) -> Iterator[Tuple[TimeSeries]]:\\n return zip(series)\",\n \"def dec_tupl(fnc,*data):\\n def wrapper(*args, **kwargs):\\n return (fnc(*args,**kwargs),) + data\\n return wrapper\",\n \"def apply_on_series(self, func, *args, **kwargs):\\n assert self.is_series_like()\\n\\n return SeriesDefault.register(pandas.Series.apply)(\\n self,\\n func=func,\\n *args,\\n **kwargs,\\n )\",\n \"def _to_list(series: Union[TimeSeries, Sequence[TimeSeries]]) -> Sequence[TimeSeries]:\\n\\n return [series] if not isinstance(series, Sequence) else series\",\n \"def as_series(self, arraylike: Iterable) -> pd.Series:\\n return pd.Series(arraylike, index=self.data.index)\",\n \"def __call__(self) -> pd.Series:\\n\\n converter = {\\\"datetime\\\": self._convert_datetime,\\n \\\"int\\\": self._convert_int,\\n \\\"bool\\\": self._convert_bool}\\n\\n func = converter.get(self.parent.dtype, self._convert)\\n\\n return func()\",\n \"def _apply_as_series_or_frame(self, func: Callable[[Column], Column]) -> FrameLike:\\n pass\",\n \"def _apply_as_series_or_frame(self, func: Callable[[Column], Column]) -> FrameLike:\\n pass\",\n \"def generate_from_tuple(t):\\n\\n data_generator(*t)\",\n \"def tuple_from_sequence(*args):\\n return tuple(args)\",\n \"def n_ary(func):\\n def wrapper(x, *args):\\n return x if not args else func(x, wrapper(*args))\\n return wrapper\",\n \"def tuple(x):\\n pass\",\n \"def to_real_series(self, data: pd.Series) -> pd.Series:\\n ...\",\n \"def map_series(self, fn, dt_index = None):\\n if dt_index == None:\\n dt_index = self.index()\\n return TimeSeriesRDD(dt_index, self.map(fn))\",\n \"def to_pandas_series_rdd(self):\\n pd_index = self.index().to_pandas_index()\\n return self.map(lambda x: (x[0], pd.Series(x[1], pd_index)))\",\n \"def etuple(*args, **kwargs):\\n return ExpressionTuple(args, **kwargs)\",\n \"def series(*resistors):\\n return sum(resistors)\",\n \"def to_series(self) -> pd.Series:\\n df = self.to_dataframe(\\\"* values *\\\")\\n dims = self.dims_list\\n if len(dims) == 1:\\n dims = dims[0]\\n return df.set_index(dims)[\\\"* values *\\\"]\",\n \"def wrap_result(data: interpreter.TupleValue):\\n return tuple(handle(d) for d in data)\",\n \"def SweepSeries(*args, **kwargs):\\n if args or kwargs:\\n underride(kwargs, dtype=float)\\n series = pd.Series(*args, **kwargs)\\n else:\\n series = pd.Series([], dtype=np.float64)\\n\\n series.index.name = 'Parameter'\\n if 'name' not in kwargs:\\n series.name = 'Metric'\\n return series\",\n \"def __wrap_with_tuple(self) -> tuple:\\r\\n l = list()\\r\\n length = len(self.data)\\r\\n while self.idx < length:\\r\\n l.append(self.__parse())\\r\\n return tuple(l)\",\n \"def parse(x):\\n if isinstance(x, container_abcs.Iterable):\\n return x\\n return tuple(repeat(x, n))\",\n \"def tuples_to_df(ts):\\n return pd.DataFrame.from_records(ts, columns=['user', 'item', 'rating'])\",\n \"def parse_result_series(result):\\n if isinstance(result, np.ndarray):\\n return result\\n\\n if result is None or not len(result):\\n return None\\n\\n dates, values = result\\n return pd.DataFrame({0:dates.astype(int)/1000,1:values})\",\n \"def _xml_to_series(self, xml):\\n d = self._xml_to_dict(xml)\\n d = d['Data']\\n if isinstance(d, basestring):\\n raise EmptyXMLError('Provided xml has no content.')\\n shows = d['Series'] #list of dicts\\n if self.get_first:\\n if isinstance(shows, list):\\n return Series(**shows[0])\\n else:\\n return Series(**shows)\\n else:\\n if isinstance(shows, list): \\n return [Series(**v) for v in shows]\\n else:\\n return [Series(**shows)]\",\n \"def TimeSeries(*args, **kwargs):\\n if args or kwargs:\\n underride(kwargs, dtype=float)\\n series = pd.Series(*args, **kwargs)\\n else:\\n series = pd.Series([], dtype=float)\\n\\n series.index.name = 'Time'\\n if 'name' not in kwargs:\\n series.name = 'Quantity'\\n return series\",\n \"def make_series(x, y, **options):\\n underride(options, name='values')\\n if isinstance(y, pd.Series):\\n y = y.values\\n series = pd.Series(y, index=x, **options)\\n series.index.name = 'index'\\n return series\",\n \"def tuple_grapher(arr):\\n # Sets the column titles\\n cols = [\\\"x\\\", \\\"y\\\"]\\n # Creates a pandas DB using the list of tuples\\n db = pandas.DataFrame.from_records(arr, columns=cols)\\n # Plots the DB\\n db.plot(kind='scatter', x=cols[0], y=cols[1])\\n # Displays the graph\\n return plt.show()\",\n \"def to_tuple(\\n value: Union[TypeNumber, Iterable[TypeNumber]],\\n length: int = 1,\\n ) -> Union[TypeTripletFloat, Tuple[TypeNumber, ...]]:\\n try:\\n iter(value)\\n value = tuple(value)\\n except TypeError:\\n value = length * (value,)\\n return value\",\n \"def __call__(self, batch: iter) -> torch.Tensor or list(torch.Tensor):\\n if torch.is_tensor(batch[0]):\\n return torch.cat(tuple(t.unsqueeze(0) for t in batch), 0).view(-1, self.batch_size, *batch[0].size())\\n elif isinstance(batch[0], int):\\n return torch.LongTensor(batch).view(-1, self.batch_size)\\n elif isinstance(batch[0], collections.Iterable):\\n # if each batch element is not a tensor, then it should be a tuple\\n # of tensors; in that case we collate each element in the tuple\\n transposed = zip(*batch)\\n return tuple(self.__call__(samples) for samples in transposed)\\n\\n raise TypeError((\\\"batch must contain tensors, numbers, or lists; found {}\\\"\\n .format(type(batch[0]))))\",\n \"def pandas_series(arr, nan_to_null=False):\\n import pandas as pd\\n return pd.Series(arr, copy=False)\",\n \"def apply_log(a: tuple, func: Callable[[Any], Tuple[Any, Log]]) -> Tuple[Any, Log]:\\n value, log = a\\n new, entry = func(value)\\n return new, log + entry\",\n \"def lucas():\\n return sum_series(a=2, b=1)\",\n \"def _convert_fn(dataset: tf.data.Dataset) -> tf.data.Dataset:\\n spec = dataset.element_spec\\n if isinstance(spec, collections.abc.Mapping):\\n return dataset.map(lambda observation: (observation[\\\"x\\\"], observation[\\\"y\\\"]))\\n else:\\n return dataset.map(lambda x, y: (x, y))\",\n \"def CombineSeries(*args):\\r\\n df = pd.concat([*args], axis=1)\\r\\n\\r\\n return df\",\n \"def series_view(self, **kwargs): # noqa: PR02\\n return SeriesDefault.register(pandas.Series.view)(self, **kwargs)\",\n \"def scalar(f):\\n def g(X: tuple()):\\n return Scalar(f(Scalar(X)))\\n return g\",\n \"def at(self, x):\\n x = array(x)\\n if x.ndim == 1:\\n return self._method(x)\\n elif x.ndim == 2:\\n return Series(\\n index=MultiIndex.from_arrays(\\n arrays=x.T,\\n names=[f'x{num}' for num in range(1, self._num_dims + 1)]\\n ), data=self._method(x), name=f'{self._name}({self._parent})'\\n )\",\n \"def hinted_tuple_hook(obj):\\n if '__tuple__' in obj:\\n return tuple(obj['items'])\\n return obj\",\n \"def as_series(request: Any) -> Any:\\n return request.param\",\n \"def as_series(request: Any) -> Any:\\n return request.param\",\n \"def float_series() -> pd.Series:\\n series = pd.Series([(n/1000) for n in range(1001)])\\n return series\",\n \"def convert_series(self, column: str, dtype: str) -> TYPE_ROW:\\n\\n series = self.df[column]\\n\\n if dtype != \\\"float\\\":\\n series = series.fillna(NULL)\\n\\n values = self.force_dtype(series, dtype)\\n\\n return values\",\n \"def astuple(self):\\n try:\\n return tuple([x.astuple() for x in self])\\n except Exception:\\n pass\\n return tuple([x for x in self])\",\n \"def tuple_map(x):\\n return x * 2\",\n \"def _create_batch(self, series):\\n import pandas as pd\\n import pyarrow as pa\\n\\n # Make input conform to [(series1, type1), (series2, type2), ...]\\n if not isinstance(series, (list, tuple)) or (\\n len(series) == 2 and isinstance(series[1], pa.DataType)\\n ):\\n series = [series]\\n series = ((s, None) if not isinstance(s, (list, tuple)) else s for s in series)\\n\\n arrs = []\\n for s, t in series:\\n if self._struct_in_pandas == \\\"dict\\\" and t is not None and pa.types.is_struct(t):\\n # A pandas UDF should return pd.DataFrame when the return type is a struct type.\\n # If it returns a pd.Series, it should throw an error.\\n if not isinstance(s, pd.DataFrame):\\n raise PySparkValueError(\\n \\\"A field of type StructType expects a pandas.DataFrame, \\\"\\n \\\"but got: %s\\\" % str(type(s))\\n )\\n arrs.append(self._create_struct_array(s, t))\\n else:\\n arrs.append(self._create_array(s, t, arrow_cast=self._arrow_cast))\\n\\n return pa.RecordBatch.from_arrays(arrs, [\\\"_%d\\\" % i for i in range(len(arrs))])\",\n \"def to_work_series(self, data: pd.Series) -> pd.Series:\\n ...\",\n \"def visit_Tuple(self, node):\\n self.generic_visit(node)\\n if isinstance(node.ctx, ast.Load):\\n return to_call(to_attribute(self.operator, '__tuple__'), node.elts)\\n return node\",\n \"def agg(self, func: Callable[[List[float]], float]) -> \\\"Stream[float]\\\":\\n return ExpandingNode(func)(self).astype(\\\"float\\\")\",\n \"def triplet(value):\\n return tuplet(value, 3, 2)\",\n \"def decorator(arg):\\n return lambda: list(arg)\",\n \"def astuple(v: Any) -> Tuple[Any, ...]:\\n return to_tuple(v, reuse_instances=False, convert_sets=False)\",\n \"def _series_handler(self, values, style, caller, *args):\\n\\n behaviors = {\\\"over\\\": values.ge,\\n \\\"under\\\": values.lt}\\n\\n evaluated = values[behaviors.get(caller)(self.margin)]\\n\\n if style == \\\"values\\\":\\n return evaluated\\n else:\\n return list(evaluated.index)\",\n \"def next_tuple(self):\\n raise NotImplementedError(\\\"Spout not implementing next_tuple() method\\\")\",\n \"def _to_tuple(values: Union[int, Iterable[int]]) -> Tuple[int, ...]:\\n try:\\n return tuple(values)\\n except TypeError:\\n return (values,)\",\n \"def scale_tuple(t: Tuple[float, ...], v: float) -> Tuple[float, ...]:\\n # dbg(f'scaleTuple: t={t} v={v}')\\n return tuple(i for i in map(lambda p: p * v, t))\",\n \"def persistence_model(series):\\n return [x for x in series]\",\n \"def as_args(function):\\n return lambda x: function(*x)\",\n \"def tuple(self, spec):\\n return tuple(self(inner_spec) for inner_spec in spec)\",\n \"def _sfn(x):\\n if len(x) == 1:\\n return x[0]\\n return fn(*x)\",\n \"def eval_func_tuple(f_args):\\n return f_args[0](*f_args[1:])\",\n \"def wrap(func, *args, unsqueeze=False):\\n\\n # Convert input types where applicable\\n args = list(args)\\n for i, arg in enumerate(args):\\n if type(arg) == np.ndarray:\\n args[i] = torch.from_numpy(arg)\\n if unsqueeze:\\n args[i] = args[i].unsqueeze(0)\\n\\n result = func(*args)\\n\\n # Convert output types where applicable\\n if isinstance(result, tuple):\\n result = list(result)\\n for i, res in enumerate(result):\\n if type(res) == torch.Tensor:\\n if unsqueeze:\\n res = res.squeeze(0)\\n result[i] = res.numpy()\\n return tuple(result)\\n elif type(result) == torch.Tensor:\\n if unsqueeze:\\n result = result.squeeze(0)\\n return result.numpy()\\n else:\\n return result\",\n \"def convert_input_to_tuple(fn):\\n @wraps(fn)\\n def wrapper(*args, **kwargs):\\n data = args[0].api.payload\\n try:\\n kwargs['tupled_output'] = json.loads(data,\\n object_hook=_json_object_hook)\\n return fn(*args, **kwargs)\\n except Exception:\\n data = json.dumps(data)\\n kwargs['tupled_output'] = json.loads(data,\\n object_hook=_json_object_hook)\\n return fn(*args, **kwargs)\\n\\n return wrapper\",\n \"def convert_tuple(self, v, t):\\n return tuple(self(v, t)\\n for v, t in zip(v, t.elements))\",\n \"def get_series(gval, series):\\n minlen = min([len(d[series]) for f, d in gval])\\n return np.stack([d[series][:minlen] for f, d in gval])\",\n \"def ts_fit(series: TimeSeries) -> TimeSeries:\\n pass\",\n \"def add_collection(s: ObjectStream[T], a: ast.Call) -> Tuple[ObjectStream[T], ast.Call]:\\n assert isinstance(a.func, ast.Attribute)\\n if a.func.attr == \\\"Jets\\\":\\n s_update = s.MetaData({\\\"j\\\": \\\"stuff\\\"})\\n return s_update, a\\n elif a.func.attr == \\\"EventNumber\\\":\\n new_call = copy.copy(a)\\n new_call.args = [ast_lambda(\\\"20\\\")]\\n return s, new_call\\n else:\\n return s, a\",\n \"def as_series(self) -> \\\"pd.Series\\\":\\n import pandas as pd\\n\\n data = {\\\"_row_id\\\": self.id, \\\"_row_num\\\": self.num, **self.as_dict()}\\n series = pd.Series(data)\\n return series\",\n \"def _separate(xs):\\n if isinstance(xs, typing.Sequence):\\n return tuple(_single_separate(x) for x in xs)\\n else:\\n return _single_separate(xs)\",\n \"def infer(self, values):\\n raise NotImplementedError\",\n \"def to_variable(f):\\n\\n def variable_wrapper(*args, **kwargs):\\n rez = f(*args, **kwargs)\\n if isinstance(rez, tuple):\\n return tuple([Variable(x) for x in rez])\\n return Variable(rez)\\n return variable_wrapper\",\n \"def _create_batch(self, series):\\n import pandas as pd\\n import pyarrow as pa\\n\\n # Make input conform to [(series1, type1), (series2, type2), ...]\\n if not isinstance(series, (list, tuple)) or (\\n len(series) == 2 and isinstance(series[1], pa.DataType)\\n ):\\n series = [series]\\n series = ((s, None) if not isinstance(s, (list, tuple)) else s for s in series)\\n\\n arrs = []\\n for s, t in series:\\n if not isinstance(s, pd.DataFrame):\\n raise PySparkValueError(\\n \\\"Output of an arrow-optimized Python UDTFs expects \\\"\\n f\\\"a pandas.DataFrame but got: {type(s)}\\\"\\n )\\n\\n arrs.append(self._create_struct_array(s, t))\\n\\n return pa.RecordBatch.from_arrays(arrs, [\\\"_%d\\\" % i for i in range(len(arrs))])\",\n \"def tuple_cast(inputs, dst_type):\\n return iter_cast(inputs, dst_type, return_type=tuple)\",\n \"def tuple2func(func1, func2):\\n return lambda e: (func1(e), func2(e))\",\n \"def decorate(name, f=noOp):\\n\\tdef df(s, loc, tok):\\n\\t\\trules = []\\n\\t\\tundec_tok = []\\n\\t\\tfor t in tok:\\n\\t\\t\\tif isinstance(t, tuple):\\n\\t\\t\\t\\trules += [t[0]]\\n\\t\\t\\t\\tundec_tok += [t[1]]\\n\\t\\t\\telse:\\n\\t\\t\\t\\tundec_tok += [t]\\n\\t\\tif 0 == len(rules):\\n\\t\\t\\trules = \\\"\\\".join(undec_tok)\\n\\t\\treturn ((name, rules), f(s, loc, undec_tok))\\n\\treturn df\",\n \"def _silent_ntuple_ ( s , *args ) :\\n from Ostap.Utils import mute_py \\n with mute_py() : \\n tup = _TU.__nTuple__(s , *args )\\n logger.info ( 'Booked n-tuple %s' % tup )\\n return tup\",\n \"def from_tuple(cls, coords):\\n return cls(*coords)\",\n \"def sum_series(n, first_value=0, second_value=1):\\n for i in range(n):\\n first_value, second_value = second_value, first_value + second_value\\n return first_value\",\n \"def cast_to_list(position):\\n\\n\\t@wrapt.decorator\\n\\tdef wrapper(function, instance, args, kwargs):\\n\\t\\tif not isinstance(args[position], list):\\n\\t\\t\\targs = list(args)\\n\\t\\t\\targs[position] = [args[position]]\\n\\t\\t\\targs = tuple(args)\\n\\n\\t\\treturn function(*args, **kwargs)\\n\\n\\treturn wrapper\",\n \"def augment_column(self, col: pd.Series,) -> pd.Series:\",\n \"def add_to_series_dict(func):\\n def add(self, *args, **kwargs):\\n s = func(self, *args, **kwargs)\\n self.sid_series[s['id']] = s\\n return s\\n return add\",\n \"def totuple(self, arr):\\n\\n\\t\\ttry:\\n\\t\\t return tuple(self.totuple(i) for i in arr)\\n\\t\\texcept TypeError:\\n\\t\\t return arr\",\n \"def _metatize_Sequence(obj):\\n return type(obj)([metatize(o) for o in obj])\",\n \"def _unpack_tuple(x):\\n if len(x) == 1:\\n return x[0]\\n else:\\n return x\",\n \"def _unpack_tuple(x):\\n if len(x) == 1:\\n return x[0]\\n else:\\n return x\",\n \"def n_ary(f):\\n def n_ary_fn(x, *args):\\n return x if not args else f(x, n_ary_fn(*args))\\n return n_ary_fn\",\n \"def _stack_serieses(serieses: List[pd.Series]) -> pd.DataFrame:\\n return pd.concat(serieses, axis=\\\"columns\\\").T\",\n \"def __call__(self, index: pd.Index) -> pd.DataFrame:\\n if not isinstance(self.values, (list, tuple)):\\n values = pd.Series(\\n self.values,\\n index=index,\\n name=self.value_columns[0] if self.value_columns else None,\\n )\\n else:\\n values = dict(\\n zip(self.value_columns, [pd.Series(v, index=index) for v in self.values])\\n )\\n return pd.DataFrame(values)\",\n \"def reprocessSeries(self, tiltseriesdata):\\n\\t\\treturn None\",\n \"def aind(x):\\n\\treturn tuple(x.T)\",\n \"def collect(tp, *args):\\n\\n def decorator(f):\\n add_triple_pattern(tp, f, args)\\n\\n return decorator\",\n \"def _dummy_func(a: int) -> Tuple[int, int]:\\n return a, a**2\",\n \"async def infer_shape_tuple_setitem(track, seq, idx, value):\\n seq_sh = await seq['shape']\\n idx_v = await idx['value']\\n value_sh = await value['shape']\\n new_sh = list(seq_sh.shape)\\n new_sh[idx_v] = value_sh\\n return TupleShape(new_sh)\",\n \"def expand_series(ser, columns):\\n return ser.to_frame(columns[0]).reindex(columns=columns).ffill(axis=1)\",\n \"def getAxisTuple(axis):\",\n \"def map(self, func: Callable[[Trajectory, Any], Tuple[Trajectory, Any]]) -> Data:\\n trajs, labels = [], []\\n for traj, label in zip(self.trajs, self.labels):\\n traj, label = func(traj, label)\\n trajs.append(traj)\\n labels.append(label)\\n return Data(trajs, labels)\",\n \"def Scalar(obj):\\n return Sequence([obj])\",\n \"def from_series(cls, s: pd.Series, schema: Schema) -> \\\"Flat\\\":\\n if isinstance(s.index, pd.MultiIndex):\\n dims = s.index.names\\n else:\\n if not s.index.name:\\n err_msg = (\\n \\\"Series index does not have a name. Unable to infer dimension.\\\\n\\\"\\n \\\"When creating the series, ensure the index has a name:\\\\n\\\"\\n \\\"s = pd.Series([1, 2, 3], index=pd.Index(['S', 'M', 'L'], name='size'))\\\"\\n )\\n raise TypeError(err_msg)\\n dims = [s.index.name]\\n df = s.to_frame(\\\"* value *\\\").reset_index()\\n return cls.from_dataframe(df, schema, dims, \\\"* value *\\\")\"\n]","isConversational":false},"negative_scores":{"kind":"list like","value":["0.6347814","0.5983925","0.59830374","0.57251686","0.5697443","0.55903757","0.5556683","0.5526106","0.55077857","0.55077857","0.54631025","0.54327905","0.5429192","0.53740853","0.5326982","0.5293809","0.5269717","0.52383643","0.5229252","0.5213238","0.5200948","0.51864535","0.51845586","0.51840824","0.5154374","0.5124524","0.5109539","0.51023006","0.50978523","0.50891954","0.508768","0.50819105","0.5072701","0.5070661","0.50546736","0.50434554","0.504334","0.50417286","0.5039696","0.5035013","0.49981225","0.49966875","0.49966875","0.49964347","0.49837682","0.497228","0.49657872","0.49393603","0.4938437","0.49378678","0.49326065","0.4932129","0.4926208","0.49217814","0.4903891","0.48986408","0.48959017","0.48920852","0.48669764","0.48643643","0.4857244","0.48440874","0.48392534","0.48389775","0.4832803","0.48292485","0.48249885","0.48209715","0.4818137","0.48141736","0.4814099","0.48138154","0.47992092","0.47896582","0.47894993","0.478864","0.47885793","0.4788101","0.47878343","0.4784864","0.47815263","0.47784746","0.4776519","0.4776153","0.47705692","0.47667137","0.47667137","0.47663715","0.47490475","0.47465077","0.47405446","0.4740064","0.47395068","0.4724647","0.47211477","0.4719958","0.47158104","0.4712069","0.47090146","0.4695462"],"string":"[\n \"0.6347814\",\n \"0.5983925\",\n \"0.59830374\",\n \"0.57251686\",\n \"0.5697443\",\n \"0.55903757\",\n \"0.5556683\",\n \"0.5526106\",\n \"0.55077857\",\n \"0.55077857\",\n \"0.54631025\",\n \"0.54327905\",\n \"0.5429192\",\n \"0.53740853\",\n \"0.5326982\",\n \"0.5293809\",\n \"0.5269717\",\n \"0.52383643\",\n \"0.5229252\",\n \"0.5213238\",\n \"0.5200948\",\n \"0.51864535\",\n \"0.51845586\",\n \"0.51840824\",\n \"0.5154374\",\n \"0.5124524\",\n \"0.5109539\",\n \"0.51023006\",\n \"0.50978523\",\n \"0.50891954\",\n \"0.508768\",\n \"0.50819105\",\n \"0.5072701\",\n \"0.5070661\",\n \"0.50546736\",\n \"0.50434554\",\n \"0.504334\",\n \"0.50417286\",\n \"0.5039696\",\n \"0.5035013\",\n \"0.49981225\",\n \"0.49966875\",\n \"0.49966875\",\n \"0.49964347\",\n \"0.49837682\",\n \"0.497228\",\n \"0.49657872\",\n \"0.49393603\",\n \"0.4938437\",\n \"0.49378678\",\n \"0.49326065\",\n \"0.4932129\",\n \"0.4926208\",\n \"0.49217814\",\n \"0.4903891\",\n \"0.48986408\",\n \"0.48959017\",\n \"0.48920852\",\n \"0.48669764\",\n \"0.48643643\",\n \"0.4857244\",\n \"0.48440874\",\n \"0.48392534\",\n \"0.48389775\",\n \"0.4832803\",\n \"0.48292485\",\n \"0.48249885\",\n \"0.48209715\",\n \"0.4818137\",\n \"0.48141736\",\n \"0.4814099\",\n \"0.48138154\",\n \"0.47992092\",\n \"0.47896582\",\n \"0.47894993\",\n \"0.478864\",\n \"0.47885793\",\n \"0.4788101\",\n \"0.47878343\",\n \"0.4784864\",\n \"0.47815263\",\n \"0.47784746\",\n \"0.4776519\",\n \"0.4776153\",\n \"0.47705692\",\n \"0.47667137\",\n \"0.47667137\",\n \"0.47663715\",\n \"0.47490475\",\n \"0.47465077\",\n \"0.47405446\",\n \"0.4740064\",\n \"0.47395068\",\n \"0.4724647\",\n \"0.47211477\",\n \"0.4719958\",\n \"0.47158104\",\n \"0.4712069\",\n \"0.47090146\",\n \"0.4695462\"\n]","isConversational":false},"document_score":{"kind":"string","value":"0.61962026"},"document_rank":{"kind":"string","value":"1"}}},{"rowIdx":75398,"cells":{"query":{"kind":"string","value":"Normalize (x, y) point by home center with haversine distance"},"document":{"kind":"string","value":"def get_haversine_displacement(x, y, home_center):\n sign_x = 1 if x > home_center.x else -1\n displacement_x = ti.geogr.point_distances.haversine_dist(\n x, home_center.y, home_center.x, home_center.y\n )[0]\n sign_y = 1 if y > home_center.y else -1\n displacement_y = ti.geogr.point_distances.haversine_dist(\n home_center.x, y, home_center.x, home_center.y\n )[0]\n return displacement_x * sign_x, displacement_y * sign_y"},"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 center_normalize(x):\n return (x - K.mean(x)) / K.std(x)","def center_normalize(x):\n return (x - K.mean(x)) / K.std(x)","def center(x):\n return x - x.mean()","def project_normalize_coordinates(node_feats, transformer=None, crs=None):\n # get home node:\n home_node = node_feats.iloc[\n (node_feats[\"in_degree\"] + node_feats[\"out_degree\"]).argmax()\n ]\n home_center = home_node[\"center\"]\n\n @to_series\n def get_projected_displacement(x, y, home_center):\n if (x_min < x < x_max) and (y_min < y < y_max):\n proj_x, proj_y = transformer.transform(x, y)\n home_x, home_y = transformer.transform(home_center.x, home_center.y)\n return (proj_x - home_x, proj_y - home_y)\n else: # fall back to haversine\n return get_haversine_displacement.__wrapped__(x, y, home_center)\n\n if transformer is not None:\n # get bounds\n x_min, y_min, x_max, y_max = crs.area_of_use.bounds\n normed_coords = node_feats[\"center\"].apply(\n get_projected_displacement, args=[home_center]\n )\n else:\n normed_coords = node_feats[\"center\"].apply(\n get_haversine_displacement, args=[home_center]\n )\n\n # add_distance\n normed_coords[\"distance\"] = normed_coords.apply(\n lambda x: np.sqrt(x[0] ** 2 + x[1] ** 2), axis=1\n )\n # TODO: add as a TEST! compare haversine dist to fake-projected coordinates\n # print(normed_coords[\"distance\"])\n # test_distance = node_feats[\"center\"].apply(\n # lambda point: ti.geogr.point_distances.haversine_dist(\n # point.x, point.y, home_center.x, home_center.y\n # )[0]\n # )\n # print(\n # pd.merge(\n # normed_coords, test_distance, left_index=True, right_index=True\n # )\n # )\n\n return pd.merge(\n node_feats, normed_coords, left_index=True, right_index=True\n )","def dist_from_center_to(x):\n pass","def _normalize_point(self, p):\n segment_right_index = 1\n while p.x > self._hull_points[segment_right_index].x and segment_right_index < len(self._hull_points) - 1:\n segment_right_index += 1\n p1, p2 = self._hull_points[segment_right_index - 1], self._hull_points[segment_right_index]\n k = (p2.y - p1.y) / (p2.x - p1.x)\n b = p1.y - k * p1.x\n return Point(p.x, p.y / (k * p.x + b))","def normalize(self,x,xmin,xmax):\n return (x-xmin)/(xmax-xmin)","def calculateCenter(self):\n y_avg = int(sum(self.points[:,0])/float(len(self.points)))\n x_avg = int(sum(self.points[:,1])/float(len(self.points)))\n self.center = (x_avg, y_avg)\n return(x_avg,y_avg)","def get_center(self):\n\n x = np.array(self.x)\n y = np.array(self.y)\n return np.mean(x), np.mean(y)","def center(self, obj):\n mn0 = self.master.xy >= obj.center\n mn1 = self.master.xy <= obj.center\n\n point_list = [self.master.xy[mn0], self.master.xy[mn1], self.master.xy[mn0[0], mn1[1]], self.master.xy[mn1[0], mn0[1]]] # 4 physical points near the center coordinate.\n dist_list = []\n idx = 0\n for point in point_list:\n dist_list.append([idx, np.linalg.norm(point - obj.center)]) # Calculate Euclidean distances.\n idx += 1\n dist_sorted = sorted(dist_list, key=lambda distance : distance[1]) # Sort distances in ascending order.\n return self.master.mn(point_list[dist_sorted[0][0]]) # Convert the closest point to abstract coordinate and then return.","def normalize(dist):\n return np.array(dist, 'double') / np.sum(dist)","def normalize_face_landmarks(face_landmarks):\r\n\tface_landmarks_norm = np.zeros(face_landmarks.shape)\r\n\t\r\n\tfor (i, lm) in enumerate(face_landmarks):\r\n\t\tface_landmarks_norm[i] = lm - lm[nose_center_idx]\r\n\t\t\t\r\n\tstd_x = np.std(face_landmarks_norm[:,:,0].reshape((-1,)))\r\n\tstd_y = np.std(face_landmarks_norm[:,:,1].reshape((-1,)))\r\n\t\r\n\tface_landmarks_norm[:,:,0] = np.multiply(face_landmarks_norm[:,:,0], 1./std_x)\r\n\tface_landmarks_norm[:,:,1] = np.multiply(face_landmarks_norm[:,:,1], 1./std_y)\r\n\t\r\n\treturn face_landmarks_norm","def xy_norm(self):\n center_xy = self.to_xyah()[0:2]\n center_xy[0] = center_xy[0] / self.im_shape[1]\n center_xy[1] = center_xy[1] / self.im_shape[0]\n\n #assert 0 <= center_xy[0] <= 1 and 0 <= center_xy[1] <= 1\n return center_xy","def center(self):\n if not hasattr(self, '_center'):\n self._center = np.unique(self.points, axis=0).mean(axis=0)\n return self._center","def center(self) -> Point:\n return Point(*np.sum(self.normalized_array[:, :-1], axis=0))","def _center_distance(self):\n # Split positions in segments of two points :\n cut = np.vsplit(self.a_position, int(self.a_position.shape[0]/2))\n # Get center position and starting line position :\n center = np.mean(cut, axis=1)\n\n # ============ EUCLIDIAN DISTANCE ============\n diff = np.sqrt(np.square(center[:, np.newaxis, :] - center).sum(2))\n diff[np.tril_indices_from(diff)] = np.inf\n\n return center, diff","def getCenter(self):\n return Point.average(self.points)","def centerMeanAndNormalize(df):\n return minMax(df - df.mean(axis=0))","def calculate_laser_center(self):\n image = np.copy(self.camera_fiber.temp_image)\n brightest = np.unravel_index(image.argmax(), image.shape)\n self.laser_center = self.calculate_gaussian_centroid(image, brightest[0], brightest[1], crop_size=25)","def _normalize_location(lat: float, lon: float):\n latitude = \"{0:.3f}\".format(round(lat, 3))\n longitude = \"{0:.3f}\".format(round(lon, 3))\n return latitude + \":\" + longitude","def normalize(X, m, s):\n return ((X - m) / s)","def Light_Spot_Centroid(self,Amp,x,y,Amp_flag=True):\r\n \r\n if Amp_flag:\r\n I = (Amp*np.conjugate(Amp)).real\r\n else:\r\n I = Amp\r\n dx = x[0,1]-x[0,0]\r\n Nominator_x = self.double_trapz(I*x,dx=dx,dy=dx)\r\n Nominator_y = self.double_trapz(I*y,dx=dx,dy=dx)\r\n Denominator = self.double_trapz(I,dx=dx,dy=dx)\r\n \r\n x_c = Nominator_x/Denominator\r\n y_c = Nominator_y/Denominator\r\n \r\n return x_c,y_c","def centre(self):\n n = len(self.point)\n return Point(\n sum(map(lambda p: p.x, self.point)) / n,\n sum(map(lambda p: p.y, self.point)) / n\n )","def pointcenter(x):\n return point(x)","def _normalize_measure(value, maximum=1.0, center=0.0):\n if isiterable(value):\n value = np.asarray(value)\n if isiterable(center):\n center = np.asarray(center)\n if isiterable(maximum):\n maximum = np.asarray(maximum)\n return np.divide(value - center, maximum - center)","def normalize_wrt_x(self):\n\n x_min = min(self.x)\n x_max = max(self.x)\n y_min = min(self.y)\n\n x_range = x_max - x_min\n\n x = np.array(self.x)\n y = np.array(self.y)\n x -= x_min\n y -= y_min\n x = x / float(x_range)\n y = y / float(x_range)\n\n self.x = x.tolist()\n self.y = y.tolist()","def normalize(x, min_x, max_x):\n\treturn (x - min_x) / (max_x - min_x)","def calculate_H(s_lat,s_lon,e_lat,e_lon):\n R = 6371.0\n snlat = radians(s_lat)\n snlon = radians(s_lon)\n elat = radians(e_lat)\n elon = radians(e_lon)\n actual_dist = 6371.01 * acos(sin(snlat) * sin(elat) + cos(snlat) * cos(elat) * cos(snlon - elon))\n actual_dist = actual_dist * 1000\n return actual_dist","def _center_data(X, y, fit_intercept, normalize=False, copy=True):\n X = as_float_array(X, copy)\n\n if fit_intercept:\n if sp.issparse(X):\n X_mean = np.zeros(X.shape[1])\n X_std = np.ones(X.shape[1])\n else:\n X_mean = X.mean(axis=0)\n X -= X_mean\n if normalize:\n X_std = np.sqrt(np.sum(X ** 2, axis=0))\n X_std[X_std == 0] = 1\n X /= X_std\n else:\n X_std = np.ones(X.shape[1])\n y_mean = y.mean()\n y = y - y_mean\n else:\n X_mean = np.zeros(X.shape[1])\n X_std = np.ones(X.shape[1])\n y_mean = 0.\n return X, y, X_mean, y_mean, X_std","def normalize(X, m, s):\n return (X - m) / s","def vincenty_direct_solution(begin_lat, begin_lon, begin_azimuth, distance, a, b, f):\n # Convert latitude, longitude, azimuth of the begining point to radians\n lat1 = math.radians(begin_lat)\n lon1 = math.radians(begin_lon)\n alfa1 = math.radians(begin_azimuth)\n\n sinAlfa1 = math.sin(alfa1)\n cosAlfa1 = math.cos(alfa1)\n\n # U1 - reduced latitude\n tanU1 = (1 - f) * math.tan(lat1)\n cosU1 = 1 / math.sqrt(1 + tanU1 * tanU1)\n sinU1 = tanU1 * cosU1\n\n # sigma1 - angular distance on the sphere from the equator to begining point\n sigma1 = math.atan2(tanU1, math.cos(alfa1))\n\n # sinAlfa - azimuth of the geodesic at the equator\n sinAlfa = cosU1 * sinAlfa1\n cosSqAlfa = 1 - sinAlfa * sinAlfa\n uSq = cosSqAlfa * (a * a - b * b) / (b * b)\n A = 1 + uSq / 16384 * (4096 + uSq * (-768 + uSq * (320 - 175 * uSq)))\n B = uSq / 1024 * (256 + uSq * (-128 + uSq * (74 - 47 * uSq)))\n\n sigma = distance / (b * A)\n sigmap = 1\n\n while (math.fabs(sigma - sigmap) > 1e-12):\n cos2sigmaM = math.cos(2 * sigma1 + sigma)\n sinSigma = math.sin(sigma)\n cosSigma = math.cos(sigma)\n dSigma = B * sinSigma * (cos2sigmaM + B / 4 * (\n cosSigma * (-1 + 2 * cos2sigmaM * cos2sigmaM) - B / 6 * cos2sigmaM * (\n -3 + 4 * sinSigma * sinSigma) * (-3 + 4 * cos2sigmaM * cos2sigmaM)))\n sigmap = sigma\n sigma = distance / (b * A) + dSigma\n\n var_aux = sinU1 * sinSigma - cosU1 * cosSigma * cosAlfa1 # Auxiliary variable\n\n # Latitude of the end point in radians\n lat2 = math.atan2(sinU1 * cosSigma + cosU1 * sinSigma * cosAlfa1,\n (1 - f) * math.sqrt(sinAlfa * sinAlfa + var_aux * var_aux))\n\n lamb = math.atan2(sinSigma * sinAlfa1, cosU1 * cosSigma - sinU1 * sinSigma * cosAlfa1)\n C = f / 16 * cosSqAlfa * (4 + f * (4 - 3 * cosSqAlfa))\n L = lamb - (1 - C) * f * sinAlfa * (\n sigma + C * sinSigma * (cos2sigmaM + C * cosSigma * (-1 + 2 * cos2sigmaM * cos2sigmaM)))\n # Longitude of the second point in radians\n lon2 = (lon1 + L + 3 * math.pi) % (2 * math.pi) - math.pi\n\n # Convert to decimal degrees\n lat2_dd = math.degrees(lat2)\n lon2_dd = math.degrees(lon2)\n\n return lat2_dd, lon2_dd","def calculate_center(self):\n return [(self.startX + self.endX) / 2., (self.startY + self.endY) / 2.]","def normalize(x, x_max, x_min):\n return (x - x_min) / (x_max - x_min)","def normalize(X, m, s):\n return (X - m)/s","def center_point(self) -> tuple:\n return (self.min_lat + self.max_lat) / 2, (self.min_lon + self.max_lon) / 2","def haversine(test_point, training_locations):\n test_point = test_point.reshape(1, 2)\n difference = (test_point - training_locations) * np.pi / 180\n test_point_lat = test_point[:, 1] * np.pi / 180\n training_locations_lat = training_locations[:, 1] * np.pi / 180\n \n a = np.sin(difference[:, 0] / 2)**2 * np.cos(test_point_lat) * np.cos(training_locations_lat) +\\\n np.sin(difference[:, 1] / 2)**2 \n radius = 6371\n c = 2 * np.arcsin(np.sqrt(a))\n return radius * c","def vincenty_direct_solution(begin_lat, begin_lon, begin_azimuth, distance, a, b, f):\n # Convert latitude, longitude, azimuth of the begining point to radians\n lat1 = math.radians(begin_lat)\n lon1 = math.radians(begin_lon)\n alfa1 = math.radians(begin_azimuth)\n\n sinAlfa1 = math.sin(alfa1)\n cosAlfa1 = math.cos(alfa1)\n \n # U1 - reduced latitude\n tanU1 = (1 - f) * math.tan(lat1)\n cosU1 = 1 / math.sqrt(1 + tanU1 * tanU1)\n sinU1 = tanU1 * cosU1\n \n # sigma1 - angular distance on the sphere from the equator to begining point\n sigma1 = math.atan2(tanU1, math.cos(alfa1))\n \n # sinAlfa - azimuth of the geodesic at the equator\n sinAlfa = cosU1 * sinAlfa1\n cosSqAlfa = 1 - sinAlfa * sinAlfa\n uSq = cosSqAlfa * (a * a - b * b) / (b * b)\n A = 1 + uSq/16384 * (4096 + uSq * (-768 + uSq * (320 - 175 * uSq)))\n B = uSq/1024 * (256 + uSq * (-128 + uSq * (74 - 47 * uSq)))\n \n sigma = distance / (b * A)\n sigmap = 1\n \n while (math.fabs(sigma - sigmap) > 1e-12):\n cos2sigmaM = math.cos(2 * sigma1 + sigma)\n sinSigma = math.sin(sigma)\n cosSigma = math.cos(sigma)\n dSigma = B*sinSigma*(cos2sigmaM+B/4*(cosSigma*(-1+2*cos2sigmaM*cos2sigmaM)-B/6*cos2sigmaM*(-3+4*sinSigma*sinSigma)*(-3+4*cos2sigmaM*cos2sigmaM))) \n sigmap = sigma\n sigma = distance / (b * A) + dSigma\n \n var_aux = sinU1 * sinSigma - cosU1 * cosSigma * cosAlfa1\n \n # Latitude of the end point in radians\n lat2 = math.atan2(sinU1 * cosSigma + cosU1 * sinSigma*cosAlfa1, (1 - f)*math.sqrt(sinAlfa * sinAlfa + var_aux*var_aux))\n \n lamb = math.atan2 (sinSigma * sinAlfa1, cosU1 * cosSigma - sinU1 * sinSigma * cosAlfa1)\n C = f / 16 * cosSqAlfa * (4 + f * (4 - 3 * cosSqAlfa))\n L = lamb - (1 - C) * f * sinAlfa *(sigma + C * sinSigma * (cos2sigmaM + C * cosSigma * (-1 + 2 * cos2sigmaM * cos2sigmaM)))\n # Longitude of the second point in radians\n lon2 = (lon1 + L +3*math.pi)%(2*math.pi) - math.pi\n \n # Convert to decimal degrees\n lat2_dd = math.degrees(lat2) \n lon2_dd = math.degrees(lon2)\n \n return lat2_dd, lon2_dd","def calcApproxDist(lon1, lat1, lon2, lat2):\n\n import math\n from shapely.geometry import Point\n\n if lat1 == lat2 and lon1 == lon2:\n return 0.0\n\n point1 = Point(lon1,lat1)\n point2 = Point(lon2, lat2)\n\n return math.acos(math.sin(math.radians(point1.y))*math.sin(math.radians(point2.y))+math.cos(math.radians(\n point1.y))*math.cos(math.radians(point2.y))*math.cos(math.radians(point2.x)-math.radians(point1.x)))*6371","def center(self):\n points = set()\n for face in self._points:\n points.update(face)\n x_points = [point[0] for point in points]\n y_points = [point[1] for point in points]\n z_points = [point[2] for point in points]\n return \\\n (np.average(x_points), np.average(y_points), np.average(z_points))","def centroid(self): # -> BaseGeometry:\n ...","def dist2cax(self) -> float:\n # TODO: see about using line and built-in dist to point.\n center_fit = np.poly1d(self.fit)\n if self.orientation == Orientation.UP_DOWN:\n length = self.image.shape[0]\n else:\n length = self.image.shape[1]\n x_data = np.arange(length)\n y_data = center_fit(x_data)\n idx = int(round(len(x_data) / 2))\n if self.orientation == Orientation.UP_DOWN:\n axis = \"x\"\n p1 = Point(y_data[idx], x_data[idx])\n else:\n axis = \"y\"\n p1 = Point(x_data[idx], y_data[idx])\n return (getattr(self.image.center, axis) - getattr(p1, axis)) / self.image.dpmm","def center(self):\n return self._lower + 0.5 * (self._upper - self._lower)","def calculate_spot_center(source_position, light_dir):\n x_spot = source_position[0] + light_dir[0]*source_position[2]/light_dir[2]\n y_spot = source_position[1] + light_dir[1]*source_position[2]/light_dir[2]\n return x_spot, y_spot, 0","def normalized(self):\n d = self.magnitude()\n if d:\n return type(self)(self.x / d, self.y / d)\n return self.copy()","def normalize(self):\n total = 0.0\n for i in range(0,self.npoints):\n total+=self.y[i]*self._dx\n for i in range(0,self.npoints):\n self.y[i]/=total\n return","def normalize_coords(xx, yy, width, height):\n xx = (2.0 / (width - 1.0)) * xx.float() - 1.0\n yy = (2.0 / (height - 1.0)) * yy.float() - 1.0\n return xx, yy","def normalize(x):\r\n return x/norm(x)","def normalize(x):\n return (x + 1e-10) / (K.sqrt(K.mean(K.square(x))) + 1e-10)","def normalise(self):\n for at in self.atoms:\n if at.x < 0. :\n at.x = self.coordx + at.x\n if at.y < 0. :\n at.y = self.coordy + at.y\n if at.z < 0. :\n at.z = self.coordz + at.z","def center_point(polyline):\n\tpts = unique(polyline.points)\n\treturn sum(pts) / len(pts)","def recenter(self):\n self.centroid = self.consensus()\n return self.centroid","def center(emg_data: np.ndarray, center_value: float = None) -> np.ndarray:\n center_value = center_value if center_value else emg_data.mean(axis=1)\n emg_centered = np.copy(emg_data)\n for i in range(emg_data.shape[0]):\n emg_centered[i, :] = emg_data[i, :] - center_value[i]\n return emg_centered","def _normalize(self, x):\n # TODO: imagenet normalization\n\n return x","def compute_centroid(data):\n return sum(data[:]) / len(data)","def getCentroid(self):\n centroid = 0.0\n sumMagnitude = 0.0\n\n for i in range(0,self.nUniquePoints):\n freq,magnitude = self.fDomain[i]\n\n centroid += freq*magnitude\n sumMagnitude += magnitude\n \n centroid /= sumMagnitude\n return centroid","def center(coords):\n for c in coords:\n if 'avg' not in locals():\n avg = c\n else:\n avg += c\n return avg / len(coords)","def vector_to(self, latlng, normalize=False):\n vec = np.array([latlng.lat, latlng.lng]) - np.array([self.lat, self.lng])\n\n if normalize and np.linalg.norm(vec) != 0:\n vec /= np.linalg.norm(vec)\n return vec","def normalize(coords, radius):\n co = coords.copy()\n # calculate current distance\n dist = (coords[0] ** 2 + coords[1] ** 2 + coords[2] ** 2) ** 0.5\n # normalize\n for axis in range(3):\n co[axis] = coords[axis] / dist * radius\n return co","def norm(point):\n return np.sqrt(norm2(point))","def _normalize_pose_landmarks(self, landmarks):\n landmarks = np.copy(landmarks)\n\n # Normalize translation.\n pose_center = self._get_pose_center(landmarks)\n landmarks -= pose_center\n\n # Normalize scale.\n pose_size = self._get_pose_size(landmarks, self._torso_size_multiplier)\n landmarks /= pose_size\n # Multiplication by 100 is not required, but makes it eaasier to debug.\n landmarks *= 100\n\n return landmarks","def egim_hesapla(x1, y1, x2, y2):\n\tsonuc = (y2 - y1) / (x2 - x1)\n\tprint float(sonuc)","def normalize(x):\n # TODO: Implement Function\n data_max = np.max(x)\n data_min = np.min(x)\n x = (x - data_min) / (data_max - data_min)\n return x","def _set_center(self):\n sl_center = np.array(\n [self.sl_list[k].mean_pos for k in range(self.nb_sl)]\n )\n sl_nb_pts = np.array(\n [self.sl_list[k].nb_points for k in range(self.nb_sl)]\n )\n sl_wcenter = [sl_center[k] * sl_nb_pts[k] for k in range(self.nb_sl)]\n self.center = np.sum(sl_wcenter, axis=0) / np.sum(sl_nb_pts)","def center(data, desired=0.0):\n\n return (data -data.mean()) + desired","def dist_sf_alt( lon1, lon2, lat1, lat2 ):\n\n dist = Geodesic.WGS84.Inverse( lat1, lon1, lat2, lon2 )[ \"s12\" ]\n \n return dist","def compute_center(self, mole_object):\r\n if mole_object.plugin_type == \"PyMOL\":\r\n sel = PymolPlugin.PymolPlugin().get_model('all')\r\n cnt = len(sel.atom)\r\n\r\n else:\r\n sel = ChimeraPlugin.ChimeraPlugin().select()\r\n cnt = len(ChimeraPlugin.ChimeraPlugin().current_atoms())\r\n\r\n cent_x = 0\r\n cent_y = 0\r\n cent_z = 0\r\n\r\n if cnt == 0:\r\n return 0, 0, 0\r\n\r\n if mole_object.plugin_type == \"PyMOL\":\r\n\r\n for a in sel.atom:\r\n cent_x += a.coord[0]\r\n cent_y += a.coord[1]\r\n cent_z += a.coord[2]\r\n\r\n else:\r\n\r\n for a in ChimeraPlugin.ChimeraPlugin().current_atoms():\r\n cent_x += a.coord()[0]\r\n cent_y += a.coord()[1]\r\n cent_z += a.coord()[2]\r\n\r\n cent_x /= cnt\r\n cent_y /= cnt\r\n cent_z /= cnt\r\n\r\n self.point_x.component('entryfield').setentry(cent_x)\r\n self.point_y.component('entryfield').setentry(cent_y)\r\n self.point_z.component('entryfield').setentry(cent_z)\r\n\r\n self.show_crisscross(mole_object)","def normalize(shape):\n s = shape\n matrix = Shape.get_matrix(s.get_vector())\n norm_x = math.sqrt(sum(matrix[:, 0] ** 2))\n norm_y = math.sqrt(sum(matrix[:, 1] ** 2))\n for pt in s.pts:\n pt.x /= norm_x\n pt.y /= norm_y\n return s","def GetCentroid(self, p_float=..., p_float=..., p_float=...):\n ...","def _centered_coords(self):\n\n array = self.detection_base.height_model.array\n height, width = self.detection_base.height_model.cell_size_x * array.shape[0], \\\n self.detection_base.height_model.cell_size_y * array.shape[1]\n min_x, min_y = self.detection_base.height_model._bounding_box[0], \\\n self.detection_base.height_model._bounding_box[2]\n\n coords = self.detection_base._coords_array_single\n coords[:, 0], coords[:,1] = coords[:, 0] - (min_x + (width / 2)), coords[:,1] - (min_y + (height / 2))\n return coords","def estimate_center(self, center_accuracy):\n estimated_center = numpy.random.rand(3) * center_accuracy + self._x\n estimated_center[2] = 0.0\n return estimated_center","def fd_center(f, h, x0):\n return (f(x0 + h) - 2*f(x0) + f(x0 - h))/(h**2)","def _dist(x, y):\n return np.sqrt(np.mean(np.square(x - y)))","def meanHaversineDistance(lat_sub, lon_sub, lat_real, lon_real):\n return np.mean(HaversineDistance(lat_sub, lon_sub, lat_real, lon_real))","def distance_to_origin(self):\n return np.sqrt(self.x ** 2 + self.y ** 2)","def centers_displacement(p1, p2):\n # geometric center\n c1 = np.mean(np.array(p1), axis=0)\n c2 = np.mean(np.array(p2), axis=0)\n return c2 - c1","def getBoundingBoxCenter(self, shell=False, *args, **kwargs):\n if shell:\n self.grabShell()\n uvBB = pm.polyEvaluate(boundingBoxComponent2d=True)\n uvCenter = [((uvBB[0][1] + uvBB[0][0]) / 2), ((uvBB[1][1] + uvBB[1][0]) / 2)]\n return uvCenter","def centroid(self) -> Point:\n points = self.normalized_array\n centroids = [np.average(points[[0, i, i + 1], :-1], axis=0) for i in range(1, points.shape[0] - 1)]\n weights = [det(self._normalized_projection()[[0, i, i + 1]]) / 2 for i in range(1, points.shape[0] - 1)]\n return Point(*np.average(centroids, weights=weights, axis=0))","def find_centroid_for_each(self):","def normal(self, point):\n point = self._center - np.array(point)\n # if abs(point.dot(point) - self._radius**2) > 1e-15:\n # raise RayTraceError(\n # 'Cannot compute normal. Point is too far from surface ({}).'.format(\n # (abs(point.dot(point) - self._radius**2))))\n return normalize(point / self._radius)","def center(self):\n return np.sum(self.bbox, 0) / 2","def _center_scale_xy(X, Y, scale=True):\n # center\n x_mean = torch.mean(X, axis=0)\n X -= x_mean\n y_mean = torch.mean(Y, axis=0)\n Y -= y_mean\n # scale\n if scale:\n x_std = torch.std(X, dim = 0)\n x_std[x_std == 0.0] = 1.0\n X = X/x_std\n y_std = torch.std(Y, dim = 0)\n y_std[y_std == 0.0] = 1.0\n Y = Y/y_std\n else:\n x_std = torch.ones(X.size()[1])\n y_std = torch.ones(Y.size()[1])\n return X, Y, x_mean, y_mean, x_std, y_std","def centerness(game, player):\n w, h = game.width / 2., game.height / 2.\n y, x = game.get_player_location(player)\n return float((h - y)**2 + (w - x)**2)","def centroid(func, step=0.1):\n points = func.points(step)\n num, den = 0, 0\n\n for x, y in points:\n num += x * y\n den += y\n\n return num / den","def center(self):\n try: \n return self._center\n except AttributeError:\n self._center = vector(ZZ, [0]*self.ambient_dim())\n for v in self.vertex_generator(): self._center += v.vector()\n self._center /= self.n_vertices()\n return self._center","def center_data(x: npt.NDArray, y: npt.NDArray) -> Tuple[npt.NDArray, ...]:\n centroid = np.array([x.mean(), y.mean()])\n xc = x - centroid[0]\n yc = y - centroid[1]\n return xc, yc, centroid","def get_center_location(self):\n latitude = 0\n longitude = 0\n for centroid in self.centroids:\n latitude += centroid[0]\n longitude += centroid[1]\n return [latitude / len(self.centroids), longitude / len(self.centroids)]","def _center_scale_xy(X, Y, scale=True):\n # center\n x_mean = X.mean(axis=0)\n X -= x_mean\n y_mean = Y.mean(axis=0)\n Y -= y_mean\n # scale\n if scale:\n x_std = X.std(axis=0, ddof=1)\n x_std[x_std == 0.0] = 1.0\n X /= x_std\n y_std = Y.std(axis=0, ddof=1)\n y_std[y_std == 0.0] = 1.0\n Y /= y_std\n else:\n x_std = np.ones(X.shape[1])\n y_std = np.ones(Y.shape[1])\n return X, Y, x_mean, y_mean, x_std, y_std","def center(self):\n return (self.upper_right + self.lower_left) * 0.5","def calc_centroid(self):\n num = 0\n centroid = numpy.zeros(3, float)\n for atm in self:\n if atm.position is not None:\n centroid += atm.position\n num += 1\n return centroid / num","def __abs__(self):\n return Vector.createFromPoint(self).norm","def normalize(X, m, s):\n normX = (X - m) / s\n return (normX)","def normalize(img):\r\n min = img.min()\r\n max = img.max()\r\n x = 2.0 * (img - min) / (max - min) - 1.0\r\n return x","def haversine(lon1, lat1, lon2, lat2): \r\n lon1, lat1, lon2, lat2 = map(radians, [lon1, lat1, lon2, lat2]) \r\n #print 34\r\n dlon = lon2 - lon1 \r\n dlat = lat2 - lat1 \r\n a = sin(dlat/2)**2 + cos(lat1) * cos(lat2) * sin(dlon/2)**2 \r\n c = 2 * atan(sqrt(a)/sqrt(1-a)) \r\n r = 6371 \r\n d=c * r\r\n #print type(d)\r\n return d","def center_abs(self, source):\n if self.relative:\n return source.center + self.center\n else:\n return self.center","def centroid(self):\n return _property_geo(arctern.ST_Centroid, self)","def haversine_distance(self, end_point):\n \n dlng = math.radians(end_point.lng - self.lng) \n dlat = math.radians(end_point.lat - self.lat)\n # a is the square of half the chord length between the points.'''\n a = math.sin(dlat/2) * math.sin(dlat/2) + math.cos( math.radians(self.lat) ) * math.cos( math.radians(end_point.lat) ) * math.sin(dlng/2) * math.sin(dlng/2)\n # Account for rounding errors. If a is very close to 1 set it to one to avoid domain exception.'''\n if math.fabs(1-a) < 1e-10:\n a = 1\n # c is the angular distance in radians between the points.'''\n x = math.sqrt(1-a)\n y = math.sqrt(a)\n c = 2 * math.atan2(y, x)\n return A_WGS84 * c","def center(self, obj):\n return self.phy2abs.center(obj)","def get_center_point(self):\n raise NotImplementedError()","def __normalize(self, value, lower_bound, upper_bound):\n\n min_max_diff = self.max - self.min\n bound_diff = upper_bound - lower_bound\n return (value - self.min) / min_max_diff * bound_diff + lower_bound","def normalize(x: float, y: float, z: float) -> Point3D:\n mag = math.sqrt(x*x + y*y + z*z)\n return x/mag, y/mag, z/mag"],"string":"[\n \"def center_normalize(x):\\n return (x - K.mean(x)) / K.std(x)\",\n \"def center_normalize(x):\\n return (x - K.mean(x)) / K.std(x)\",\n \"def center(x):\\n return x - x.mean()\",\n \"def project_normalize_coordinates(node_feats, transformer=None, crs=None):\\n # get home node:\\n home_node = node_feats.iloc[\\n (node_feats[\\\"in_degree\\\"] + node_feats[\\\"out_degree\\\"]).argmax()\\n ]\\n home_center = home_node[\\\"center\\\"]\\n\\n @to_series\\n def get_projected_displacement(x, y, home_center):\\n if (x_min < x < x_max) and (y_min < y < y_max):\\n proj_x, proj_y = transformer.transform(x, y)\\n home_x, home_y = transformer.transform(home_center.x, home_center.y)\\n return (proj_x - home_x, proj_y - home_y)\\n else: # fall back to haversine\\n return get_haversine_displacement.__wrapped__(x, y, home_center)\\n\\n if transformer is not None:\\n # get bounds\\n x_min, y_min, x_max, y_max = crs.area_of_use.bounds\\n normed_coords = node_feats[\\\"center\\\"].apply(\\n get_projected_displacement, args=[home_center]\\n )\\n else:\\n normed_coords = node_feats[\\\"center\\\"].apply(\\n get_haversine_displacement, args=[home_center]\\n )\\n\\n # add_distance\\n normed_coords[\\\"distance\\\"] = normed_coords.apply(\\n lambda x: np.sqrt(x[0] ** 2 + x[1] ** 2), axis=1\\n )\\n # TODO: add as a TEST! compare haversine dist to fake-projected coordinates\\n # print(normed_coords[\\\"distance\\\"])\\n # test_distance = node_feats[\\\"center\\\"].apply(\\n # lambda point: ti.geogr.point_distances.haversine_dist(\\n # point.x, point.y, home_center.x, home_center.y\\n # )[0]\\n # )\\n # print(\\n # pd.merge(\\n # normed_coords, test_distance, left_index=True, right_index=True\\n # )\\n # )\\n\\n return pd.merge(\\n node_feats, normed_coords, left_index=True, right_index=True\\n )\",\n \"def dist_from_center_to(x):\\n pass\",\n \"def _normalize_point(self, p):\\n segment_right_index = 1\\n while p.x > self._hull_points[segment_right_index].x and segment_right_index < len(self._hull_points) - 1:\\n segment_right_index += 1\\n p1, p2 = self._hull_points[segment_right_index - 1], self._hull_points[segment_right_index]\\n k = (p2.y - p1.y) / (p2.x - p1.x)\\n b = p1.y - k * p1.x\\n return Point(p.x, p.y / (k * p.x + b))\",\n \"def normalize(self,x,xmin,xmax):\\n return (x-xmin)/(xmax-xmin)\",\n \"def calculateCenter(self):\\n y_avg = int(sum(self.points[:,0])/float(len(self.points)))\\n x_avg = int(sum(self.points[:,1])/float(len(self.points)))\\n self.center = (x_avg, y_avg)\\n return(x_avg,y_avg)\",\n \"def get_center(self):\\n\\n x = np.array(self.x)\\n y = np.array(self.y)\\n return np.mean(x), np.mean(y)\",\n \"def center(self, obj):\\n mn0 = self.master.xy >= obj.center\\n mn1 = self.master.xy <= obj.center\\n\\n point_list = [self.master.xy[mn0], self.master.xy[mn1], self.master.xy[mn0[0], mn1[1]], self.master.xy[mn1[0], mn0[1]]] # 4 physical points near the center coordinate.\\n dist_list = []\\n idx = 0\\n for point in point_list:\\n dist_list.append([idx, np.linalg.norm(point - obj.center)]) # Calculate Euclidean distances.\\n idx += 1\\n dist_sorted = sorted(dist_list, key=lambda distance : distance[1]) # Sort distances in ascending order.\\n return self.master.mn(point_list[dist_sorted[0][0]]) # Convert the closest point to abstract coordinate and then return.\",\n \"def normalize(dist):\\n return np.array(dist, 'double') / np.sum(dist)\",\n \"def normalize_face_landmarks(face_landmarks):\\r\\n\\tface_landmarks_norm = np.zeros(face_landmarks.shape)\\r\\n\\t\\r\\n\\tfor (i, lm) in enumerate(face_landmarks):\\r\\n\\t\\tface_landmarks_norm[i] = lm - lm[nose_center_idx]\\r\\n\\t\\t\\t\\r\\n\\tstd_x = np.std(face_landmarks_norm[:,:,0].reshape((-1,)))\\r\\n\\tstd_y = np.std(face_landmarks_norm[:,:,1].reshape((-1,)))\\r\\n\\t\\r\\n\\tface_landmarks_norm[:,:,0] = np.multiply(face_landmarks_norm[:,:,0], 1./std_x)\\r\\n\\tface_landmarks_norm[:,:,1] = np.multiply(face_landmarks_norm[:,:,1], 1./std_y)\\r\\n\\t\\r\\n\\treturn face_landmarks_norm\",\n \"def xy_norm(self):\\n center_xy = self.to_xyah()[0:2]\\n center_xy[0] = center_xy[0] / self.im_shape[1]\\n center_xy[1] = center_xy[1] / self.im_shape[0]\\n\\n #assert 0 <= center_xy[0] <= 1 and 0 <= center_xy[1] <= 1\\n return center_xy\",\n \"def center(self):\\n if not hasattr(self, '_center'):\\n self._center = np.unique(self.points, axis=0).mean(axis=0)\\n return self._center\",\n \"def center(self) -> Point:\\n return Point(*np.sum(self.normalized_array[:, :-1], axis=0))\",\n \"def _center_distance(self):\\n # Split positions in segments of two points :\\n cut = np.vsplit(self.a_position, int(self.a_position.shape[0]/2))\\n # Get center position and starting line position :\\n center = np.mean(cut, axis=1)\\n\\n # ============ EUCLIDIAN DISTANCE ============\\n diff = np.sqrt(np.square(center[:, np.newaxis, :] - center).sum(2))\\n diff[np.tril_indices_from(diff)] = np.inf\\n\\n return center, diff\",\n \"def getCenter(self):\\n return Point.average(self.points)\",\n \"def centerMeanAndNormalize(df):\\n return minMax(df - df.mean(axis=0))\",\n \"def calculate_laser_center(self):\\n image = np.copy(self.camera_fiber.temp_image)\\n brightest = np.unravel_index(image.argmax(), image.shape)\\n self.laser_center = self.calculate_gaussian_centroid(image, brightest[0], brightest[1], crop_size=25)\",\n \"def _normalize_location(lat: float, lon: float):\\n latitude = \\\"{0:.3f}\\\".format(round(lat, 3))\\n longitude = \\\"{0:.3f}\\\".format(round(lon, 3))\\n return latitude + \\\":\\\" + longitude\",\n \"def normalize(X, m, s):\\n return ((X - m) / s)\",\n \"def Light_Spot_Centroid(self,Amp,x,y,Amp_flag=True):\\r\\n \\r\\n if Amp_flag:\\r\\n I = (Amp*np.conjugate(Amp)).real\\r\\n else:\\r\\n I = Amp\\r\\n dx = x[0,1]-x[0,0]\\r\\n Nominator_x = self.double_trapz(I*x,dx=dx,dy=dx)\\r\\n Nominator_y = self.double_trapz(I*y,dx=dx,dy=dx)\\r\\n Denominator = self.double_trapz(I,dx=dx,dy=dx)\\r\\n \\r\\n x_c = Nominator_x/Denominator\\r\\n y_c = Nominator_y/Denominator\\r\\n \\r\\n return x_c,y_c\",\n \"def centre(self):\\n n = len(self.point)\\n return Point(\\n sum(map(lambda p: p.x, self.point)) / n,\\n sum(map(lambda p: p.y, self.point)) / n\\n )\",\n \"def pointcenter(x):\\n return point(x)\",\n \"def _normalize_measure(value, maximum=1.0, center=0.0):\\n if isiterable(value):\\n value = np.asarray(value)\\n if isiterable(center):\\n center = np.asarray(center)\\n if isiterable(maximum):\\n maximum = np.asarray(maximum)\\n return np.divide(value - center, maximum - center)\",\n \"def normalize_wrt_x(self):\\n\\n x_min = min(self.x)\\n x_max = max(self.x)\\n y_min = min(self.y)\\n\\n x_range = x_max - x_min\\n\\n x = np.array(self.x)\\n y = np.array(self.y)\\n x -= x_min\\n y -= y_min\\n x = x / float(x_range)\\n y = y / float(x_range)\\n\\n self.x = x.tolist()\\n self.y = y.tolist()\",\n \"def normalize(x, min_x, max_x):\\n\\treturn (x - min_x) / (max_x - min_x)\",\n \"def calculate_H(s_lat,s_lon,e_lat,e_lon):\\n R = 6371.0\\n snlat = radians(s_lat)\\n snlon = radians(s_lon)\\n elat = radians(e_lat)\\n elon = radians(e_lon)\\n actual_dist = 6371.01 * acos(sin(snlat) * sin(elat) + cos(snlat) * cos(elat) * cos(snlon - elon))\\n actual_dist = actual_dist * 1000\\n return actual_dist\",\n \"def _center_data(X, y, fit_intercept, normalize=False, copy=True):\\n X = as_float_array(X, copy)\\n\\n if fit_intercept:\\n if sp.issparse(X):\\n X_mean = np.zeros(X.shape[1])\\n X_std = np.ones(X.shape[1])\\n else:\\n X_mean = X.mean(axis=0)\\n X -= X_mean\\n if normalize:\\n X_std = np.sqrt(np.sum(X ** 2, axis=0))\\n X_std[X_std == 0] = 1\\n X /= X_std\\n else:\\n X_std = np.ones(X.shape[1])\\n y_mean = y.mean()\\n y = y - y_mean\\n else:\\n X_mean = np.zeros(X.shape[1])\\n X_std = np.ones(X.shape[1])\\n y_mean = 0.\\n return X, y, X_mean, y_mean, X_std\",\n \"def normalize(X, m, s):\\n return (X - m) / s\",\n \"def vincenty_direct_solution(begin_lat, begin_lon, begin_azimuth, distance, a, b, f):\\n # Convert latitude, longitude, azimuth of the begining point to radians\\n lat1 = math.radians(begin_lat)\\n lon1 = math.radians(begin_lon)\\n alfa1 = math.radians(begin_azimuth)\\n\\n sinAlfa1 = math.sin(alfa1)\\n cosAlfa1 = math.cos(alfa1)\\n\\n # U1 - reduced latitude\\n tanU1 = (1 - f) * math.tan(lat1)\\n cosU1 = 1 / math.sqrt(1 + tanU1 * tanU1)\\n sinU1 = tanU1 * cosU1\\n\\n # sigma1 - angular distance on the sphere from the equator to begining point\\n sigma1 = math.atan2(tanU1, math.cos(alfa1))\\n\\n # sinAlfa - azimuth of the geodesic at the equator\\n sinAlfa = cosU1 * sinAlfa1\\n cosSqAlfa = 1 - sinAlfa * sinAlfa\\n uSq = cosSqAlfa * (a * a - b * b) / (b * b)\\n A = 1 + uSq / 16384 * (4096 + uSq * (-768 + uSq * (320 - 175 * uSq)))\\n B = uSq / 1024 * (256 + uSq * (-128 + uSq * (74 - 47 * uSq)))\\n\\n sigma = distance / (b * A)\\n sigmap = 1\\n\\n while (math.fabs(sigma - sigmap) > 1e-12):\\n cos2sigmaM = math.cos(2 * sigma1 + sigma)\\n sinSigma = math.sin(sigma)\\n cosSigma = math.cos(sigma)\\n dSigma = B * sinSigma * (cos2sigmaM + B / 4 * (\\n cosSigma * (-1 + 2 * cos2sigmaM * cos2sigmaM) - B / 6 * cos2sigmaM * (\\n -3 + 4 * sinSigma * sinSigma) * (-3 + 4 * cos2sigmaM * cos2sigmaM)))\\n sigmap = sigma\\n sigma = distance / (b * A) + dSigma\\n\\n var_aux = sinU1 * sinSigma - cosU1 * cosSigma * cosAlfa1 # Auxiliary variable\\n\\n # Latitude of the end point in radians\\n lat2 = math.atan2(sinU1 * cosSigma + cosU1 * sinSigma * cosAlfa1,\\n (1 - f) * math.sqrt(sinAlfa * sinAlfa + var_aux * var_aux))\\n\\n lamb = math.atan2(sinSigma * sinAlfa1, cosU1 * cosSigma - sinU1 * sinSigma * cosAlfa1)\\n C = f / 16 * cosSqAlfa * (4 + f * (4 - 3 * cosSqAlfa))\\n L = lamb - (1 - C) * f * sinAlfa * (\\n sigma + C * sinSigma * (cos2sigmaM + C * cosSigma * (-1 + 2 * cos2sigmaM * cos2sigmaM)))\\n # Longitude of the second point in radians\\n lon2 = (lon1 + L + 3 * math.pi) % (2 * math.pi) - math.pi\\n\\n # Convert to decimal degrees\\n lat2_dd = math.degrees(lat2)\\n lon2_dd = math.degrees(lon2)\\n\\n return lat2_dd, lon2_dd\",\n \"def calculate_center(self):\\n return [(self.startX + self.endX) / 2., (self.startY + self.endY) / 2.]\",\n \"def normalize(x, x_max, x_min):\\n return (x - x_min) / (x_max - x_min)\",\n \"def normalize(X, m, s):\\n return (X - m)/s\",\n \"def center_point(self) -> tuple:\\n return (self.min_lat + self.max_lat) / 2, (self.min_lon + self.max_lon) / 2\",\n \"def haversine(test_point, training_locations):\\n test_point = test_point.reshape(1, 2)\\n difference = (test_point - training_locations) * np.pi / 180\\n test_point_lat = test_point[:, 1] * np.pi / 180\\n training_locations_lat = training_locations[:, 1] * np.pi / 180\\n \\n a = np.sin(difference[:, 0] / 2)**2 * np.cos(test_point_lat) * np.cos(training_locations_lat) +\\\\\\n np.sin(difference[:, 1] / 2)**2 \\n radius = 6371\\n c = 2 * np.arcsin(np.sqrt(a))\\n return radius * c\",\n \"def vincenty_direct_solution(begin_lat, begin_lon, begin_azimuth, distance, a, b, f):\\n # Convert latitude, longitude, azimuth of the begining point to radians\\n lat1 = math.radians(begin_lat)\\n lon1 = math.radians(begin_lon)\\n alfa1 = math.radians(begin_azimuth)\\n\\n sinAlfa1 = math.sin(alfa1)\\n cosAlfa1 = math.cos(alfa1)\\n \\n # U1 - reduced latitude\\n tanU1 = (1 - f) * math.tan(lat1)\\n cosU1 = 1 / math.sqrt(1 + tanU1 * tanU1)\\n sinU1 = tanU1 * cosU1\\n \\n # sigma1 - angular distance on the sphere from the equator to begining point\\n sigma1 = math.atan2(tanU1, math.cos(alfa1))\\n \\n # sinAlfa - azimuth of the geodesic at the equator\\n sinAlfa = cosU1 * sinAlfa1\\n cosSqAlfa = 1 - sinAlfa * sinAlfa\\n uSq = cosSqAlfa * (a * a - b * b) / (b * b)\\n A = 1 + uSq/16384 * (4096 + uSq * (-768 + uSq * (320 - 175 * uSq)))\\n B = uSq/1024 * (256 + uSq * (-128 + uSq * (74 - 47 * uSq)))\\n \\n sigma = distance / (b * A)\\n sigmap = 1\\n \\n while (math.fabs(sigma - sigmap) > 1e-12):\\n cos2sigmaM = math.cos(2 * sigma1 + sigma)\\n sinSigma = math.sin(sigma)\\n cosSigma = math.cos(sigma)\\n dSigma = B*sinSigma*(cos2sigmaM+B/4*(cosSigma*(-1+2*cos2sigmaM*cos2sigmaM)-B/6*cos2sigmaM*(-3+4*sinSigma*sinSigma)*(-3+4*cos2sigmaM*cos2sigmaM))) \\n sigmap = sigma\\n sigma = distance / (b * A) + dSigma\\n \\n var_aux = sinU1 * sinSigma - cosU1 * cosSigma * cosAlfa1\\n \\n # Latitude of the end point in radians\\n lat2 = math.atan2(sinU1 * cosSigma + cosU1 * sinSigma*cosAlfa1, (1 - f)*math.sqrt(sinAlfa * sinAlfa + var_aux*var_aux))\\n \\n lamb = math.atan2 (sinSigma * sinAlfa1, cosU1 * cosSigma - sinU1 * sinSigma * cosAlfa1)\\n C = f / 16 * cosSqAlfa * (4 + f * (4 - 3 * cosSqAlfa))\\n L = lamb - (1 - C) * f * sinAlfa *(sigma + C * sinSigma * (cos2sigmaM + C * cosSigma * (-1 + 2 * cos2sigmaM * cos2sigmaM)))\\n # Longitude of the second point in radians\\n lon2 = (lon1 + L +3*math.pi)%(2*math.pi) - math.pi\\n \\n # Convert to decimal degrees\\n lat2_dd = math.degrees(lat2) \\n lon2_dd = math.degrees(lon2)\\n \\n return lat2_dd, lon2_dd\",\n \"def calcApproxDist(lon1, lat1, lon2, lat2):\\n\\n import math\\n from shapely.geometry import Point\\n\\n if lat1 == lat2 and lon1 == lon2:\\n return 0.0\\n\\n point1 = Point(lon1,lat1)\\n point2 = Point(lon2, lat2)\\n\\n return math.acos(math.sin(math.radians(point1.y))*math.sin(math.radians(point2.y))+math.cos(math.radians(\\n point1.y))*math.cos(math.radians(point2.y))*math.cos(math.radians(point2.x)-math.radians(point1.x)))*6371\",\n \"def center(self):\\n points = set()\\n for face in self._points:\\n points.update(face)\\n x_points = [point[0] for point in points]\\n y_points = [point[1] for point in points]\\n z_points = [point[2] for point in points]\\n return \\\\\\n (np.average(x_points), np.average(y_points), np.average(z_points))\",\n \"def centroid(self): # -> BaseGeometry:\\n ...\",\n \"def dist2cax(self) -> float:\\n # TODO: see about using line and built-in dist to point.\\n center_fit = np.poly1d(self.fit)\\n if self.orientation == Orientation.UP_DOWN:\\n length = self.image.shape[0]\\n else:\\n length = self.image.shape[1]\\n x_data = np.arange(length)\\n y_data = center_fit(x_data)\\n idx = int(round(len(x_data) / 2))\\n if self.orientation == Orientation.UP_DOWN:\\n axis = \\\"x\\\"\\n p1 = Point(y_data[idx], x_data[idx])\\n else:\\n axis = \\\"y\\\"\\n p1 = Point(x_data[idx], y_data[idx])\\n return (getattr(self.image.center, axis) - getattr(p1, axis)) / self.image.dpmm\",\n \"def center(self):\\n return self._lower + 0.5 * (self._upper - self._lower)\",\n \"def calculate_spot_center(source_position, light_dir):\\n x_spot = source_position[0] + light_dir[0]*source_position[2]/light_dir[2]\\n y_spot = source_position[1] + light_dir[1]*source_position[2]/light_dir[2]\\n return x_spot, y_spot, 0\",\n \"def normalized(self):\\n d = self.magnitude()\\n if d:\\n return type(self)(self.x / d, self.y / d)\\n return self.copy()\",\n \"def normalize(self):\\n total = 0.0\\n for i in range(0,self.npoints):\\n total+=self.y[i]*self._dx\\n for i in range(0,self.npoints):\\n self.y[i]/=total\\n return\",\n \"def normalize_coords(xx, yy, width, height):\\n xx = (2.0 / (width - 1.0)) * xx.float() - 1.0\\n yy = (2.0 / (height - 1.0)) * yy.float() - 1.0\\n return xx, yy\",\n \"def normalize(x):\\r\\n return x/norm(x)\",\n \"def normalize(x):\\n return (x + 1e-10) / (K.sqrt(K.mean(K.square(x))) + 1e-10)\",\n \"def normalise(self):\\n for at in self.atoms:\\n if at.x < 0. :\\n at.x = self.coordx + at.x\\n if at.y < 0. :\\n at.y = self.coordy + at.y\\n if at.z < 0. :\\n at.z = self.coordz + at.z\",\n \"def center_point(polyline):\\n\\tpts = unique(polyline.points)\\n\\treturn sum(pts) / len(pts)\",\n \"def recenter(self):\\n self.centroid = self.consensus()\\n return self.centroid\",\n \"def center(emg_data: np.ndarray, center_value: float = None) -> np.ndarray:\\n center_value = center_value if center_value else emg_data.mean(axis=1)\\n emg_centered = np.copy(emg_data)\\n for i in range(emg_data.shape[0]):\\n emg_centered[i, :] = emg_data[i, :] - center_value[i]\\n return emg_centered\",\n \"def _normalize(self, x):\\n # TODO: imagenet normalization\\n\\n return x\",\n \"def compute_centroid(data):\\n return sum(data[:]) / len(data)\",\n \"def getCentroid(self):\\n centroid = 0.0\\n sumMagnitude = 0.0\\n\\n for i in range(0,self.nUniquePoints):\\n freq,magnitude = self.fDomain[i]\\n\\n centroid += freq*magnitude\\n sumMagnitude += magnitude\\n \\n centroid /= sumMagnitude\\n return centroid\",\n \"def center(coords):\\n for c in coords:\\n if 'avg' not in locals():\\n avg = c\\n else:\\n avg += c\\n return avg / len(coords)\",\n \"def vector_to(self, latlng, normalize=False):\\n vec = np.array([latlng.lat, latlng.lng]) - np.array([self.lat, self.lng])\\n\\n if normalize and np.linalg.norm(vec) != 0:\\n vec /= np.linalg.norm(vec)\\n return vec\",\n \"def normalize(coords, radius):\\n co = coords.copy()\\n # calculate current distance\\n dist = (coords[0] ** 2 + coords[1] ** 2 + coords[2] ** 2) ** 0.5\\n # normalize\\n for axis in range(3):\\n co[axis] = coords[axis] / dist * radius\\n return co\",\n \"def norm(point):\\n return np.sqrt(norm2(point))\",\n \"def _normalize_pose_landmarks(self, landmarks):\\n landmarks = np.copy(landmarks)\\n\\n # Normalize translation.\\n pose_center = self._get_pose_center(landmarks)\\n landmarks -= pose_center\\n\\n # Normalize scale.\\n pose_size = self._get_pose_size(landmarks, self._torso_size_multiplier)\\n landmarks /= pose_size\\n # Multiplication by 100 is not required, but makes it eaasier to debug.\\n landmarks *= 100\\n\\n return landmarks\",\n \"def egim_hesapla(x1, y1, x2, y2):\\n\\tsonuc = (y2 - y1) / (x2 - x1)\\n\\tprint float(sonuc)\",\n \"def normalize(x):\\n # TODO: Implement Function\\n data_max = np.max(x)\\n data_min = np.min(x)\\n x = (x - data_min) / (data_max - data_min)\\n return x\",\n \"def _set_center(self):\\n sl_center = np.array(\\n [self.sl_list[k].mean_pos for k in range(self.nb_sl)]\\n )\\n sl_nb_pts = np.array(\\n [self.sl_list[k].nb_points for k in range(self.nb_sl)]\\n )\\n sl_wcenter = [sl_center[k] * sl_nb_pts[k] for k in range(self.nb_sl)]\\n self.center = np.sum(sl_wcenter, axis=0) / np.sum(sl_nb_pts)\",\n \"def center(data, desired=0.0):\\n\\n return (data -data.mean()) + desired\",\n \"def dist_sf_alt( lon1, lon2, lat1, lat2 ):\\n\\n dist = Geodesic.WGS84.Inverse( lat1, lon1, lat2, lon2 )[ \\\"s12\\\" ]\\n \\n return dist\",\n \"def compute_center(self, mole_object):\\r\\n if mole_object.plugin_type == \\\"PyMOL\\\":\\r\\n sel = PymolPlugin.PymolPlugin().get_model('all')\\r\\n cnt = len(sel.atom)\\r\\n\\r\\n else:\\r\\n sel = ChimeraPlugin.ChimeraPlugin().select()\\r\\n cnt = len(ChimeraPlugin.ChimeraPlugin().current_atoms())\\r\\n\\r\\n cent_x = 0\\r\\n cent_y = 0\\r\\n cent_z = 0\\r\\n\\r\\n if cnt == 0:\\r\\n return 0, 0, 0\\r\\n\\r\\n if mole_object.plugin_type == \\\"PyMOL\\\":\\r\\n\\r\\n for a in sel.atom:\\r\\n cent_x += a.coord[0]\\r\\n cent_y += a.coord[1]\\r\\n cent_z += a.coord[2]\\r\\n\\r\\n else:\\r\\n\\r\\n for a in ChimeraPlugin.ChimeraPlugin().current_atoms():\\r\\n cent_x += a.coord()[0]\\r\\n cent_y += a.coord()[1]\\r\\n cent_z += a.coord()[2]\\r\\n\\r\\n cent_x /= cnt\\r\\n cent_y /= cnt\\r\\n cent_z /= cnt\\r\\n\\r\\n self.point_x.component('entryfield').setentry(cent_x)\\r\\n self.point_y.component('entryfield').setentry(cent_y)\\r\\n self.point_z.component('entryfield').setentry(cent_z)\\r\\n\\r\\n self.show_crisscross(mole_object)\",\n \"def normalize(shape):\\n s = shape\\n matrix = Shape.get_matrix(s.get_vector())\\n norm_x = math.sqrt(sum(matrix[:, 0] ** 2))\\n norm_y = math.sqrt(sum(matrix[:, 1] ** 2))\\n for pt in s.pts:\\n pt.x /= norm_x\\n pt.y /= norm_y\\n return s\",\n \"def GetCentroid(self, p_float=..., p_float=..., p_float=...):\\n ...\",\n \"def _centered_coords(self):\\n\\n array = self.detection_base.height_model.array\\n height, width = self.detection_base.height_model.cell_size_x * array.shape[0], \\\\\\n self.detection_base.height_model.cell_size_y * array.shape[1]\\n min_x, min_y = self.detection_base.height_model._bounding_box[0], \\\\\\n self.detection_base.height_model._bounding_box[2]\\n\\n coords = self.detection_base._coords_array_single\\n coords[:, 0], coords[:,1] = coords[:, 0] - (min_x + (width / 2)), coords[:,1] - (min_y + (height / 2))\\n return coords\",\n \"def estimate_center(self, center_accuracy):\\n estimated_center = numpy.random.rand(3) * center_accuracy + self._x\\n estimated_center[2] = 0.0\\n return estimated_center\",\n \"def fd_center(f, h, x0):\\n return (f(x0 + h) - 2*f(x0) + f(x0 - h))/(h**2)\",\n \"def _dist(x, y):\\n return np.sqrt(np.mean(np.square(x - y)))\",\n \"def meanHaversineDistance(lat_sub, lon_sub, lat_real, lon_real):\\n return np.mean(HaversineDistance(lat_sub, lon_sub, lat_real, lon_real))\",\n \"def distance_to_origin(self):\\n return np.sqrt(self.x ** 2 + self.y ** 2)\",\n \"def centers_displacement(p1, p2):\\n # geometric center\\n c1 = np.mean(np.array(p1), axis=0)\\n c2 = np.mean(np.array(p2), axis=0)\\n return c2 - c1\",\n \"def getBoundingBoxCenter(self, shell=False, *args, **kwargs):\\n if shell:\\n self.grabShell()\\n uvBB = pm.polyEvaluate(boundingBoxComponent2d=True)\\n uvCenter = [((uvBB[0][1] + uvBB[0][0]) / 2), ((uvBB[1][1] + uvBB[1][0]) / 2)]\\n return uvCenter\",\n \"def centroid(self) -> Point:\\n points = self.normalized_array\\n centroids = [np.average(points[[0, i, i + 1], :-1], axis=0) for i in range(1, points.shape[0] - 1)]\\n weights = [det(self._normalized_projection()[[0, i, i + 1]]) / 2 for i in range(1, points.shape[0] - 1)]\\n return Point(*np.average(centroids, weights=weights, axis=0))\",\n \"def find_centroid_for_each(self):\",\n \"def normal(self, point):\\n point = self._center - np.array(point)\\n # if abs(point.dot(point) - self._radius**2) > 1e-15:\\n # raise RayTraceError(\\n # 'Cannot compute normal. Point is too far from surface ({}).'.format(\\n # (abs(point.dot(point) - self._radius**2))))\\n return normalize(point / self._radius)\",\n \"def center(self):\\n return np.sum(self.bbox, 0) / 2\",\n \"def _center_scale_xy(X, Y, scale=True):\\n # center\\n x_mean = torch.mean(X, axis=0)\\n X -= x_mean\\n y_mean = torch.mean(Y, axis=0)\\n Y -= y_mean\\n # scale\\n if scale:\\n x_std = torch.std(X, dim = 0)\\n x_std[x_std == 0.0] = 1.0\\n X = X/x_std\\n y_std = torch.std(Y, dim = 0)\\n y_std[y_std == 0.0] = 1.0\\n Y = Y/y_std\\n else:\\n x_std = torch.ones(X.size()[1])\\n y_std = torch.ones(Y.size()[1])\\n return X, Y, x_mean, y_mean, x_std, y_std\",\n \"def centerness(game, player):\\n w, h = game.width / 2., game.height / 2.\\n y, x = game.get_player_location(player)\\n return float((h - y)**2 + (w - x)**2)\",\n \"def centroid(func, step=0.1):\\n points = func.points(step)\\n num, den = 0, 0\\n\\n for x, y in points:\\n num += x * y\\n den += y\\n\\n return num / den\",\n \"def center(self):\\n try: \\n return self._center\\n except AttributeError:\\n self._center = vector(ZZ, [0]*self.ambient_dim())\\n for v in self.vertex_generator(): self._center += v.vector()\\n self._center /= self.n_vertices()\\n return self._center\",\n \"def center_data(x: npt.NDArray, y: npt.NDArray) -> Tuple[npt.NDArray, ...]:\\n centroid = np.array([x.mean(), y.mean()])\\n xc = x - centroid[0]\\n yc = y - centroid[1]\\n return xc, yc, centroid\",\n \"def get_center_location(self):\\n latitude = 0\\n longitude = 0\\n for centroid in self.centroids:\\n latitude += centroid[0]\\n longitude += centroid[1]\\n return [latitude / len(self.centroids), longitude / len(self.centroids)]\",\n \"def _center_scale_xy(X, Y, scale=True):\\n # center\\n x_mean = X.mean(axis=0)\\n X -= x_mean\\n y_mean = Y.mean(axis=0)\\n Y -= y_mean\\n # scale\\n if scale:\\n x_std = X.std(axis=0, ddof=1)\\n x_std[x_std == 0.0] = 1.0\\n X /= x_std\\n y_std = Y.std(axis=0, ddof=1)\\n y_std[y_std == 0.0] = 1.0\\n Y /= y_std\\n else:\\n x_std = np.ones(X.shape[1])\\n y_std = np.ones(Y.shape[1])\\n return X, Y, x_mean, y_mean, x_std, y_std\",\n \"def center(self):\\n return (self.upper_right + self.lower_left) * 0.5\",\n \"def calc_centroid(self):\\n num = 0\\n centroid = numpy.zeros(3, float)\\n for atm in self:\\n if atm.position is not None:\\n centroid += atm.position\\n num += 1\\n return centroid / num\",\n \"def __abs__(self):\\n return Vector.createFromPoint(self).norm\",\n \"def normalize(X, m, s):\\n normX = (X - m) / s\\n return (normX)\",\n \"def normalize(img):\\r\\n min = img.min()\\r\\n max = img.max()\\r\\n x = 2.0 * (img - min) / (max - min) - 1.0\\r\\n return x\",\n \"def haversine(lon1, lat1, lon2, lat2): \\r\\n lon1, lat1, lon2, lat2 = map(radians, [lon1, lat1, lon2, lat2]) \\r\\n #print 34\\r\\n dlon = lon2 - lon1 \\r\\n dlat = lat2 - lat1 \\r\\n a = sin(dlat/2)**2 + cos(lat1) * cos(lat2) * sin(dlon/2)**2 \\r\\n c = 2 * atan(sqrt(a)/sqrt(1-a)) \\r\\n r = 6371 \\r\\n d=c * r\\r\\n #print type(d)\\r\\n return d\",\n \"def center_abs(self, source):\\n if self.relative:\\n return source.center + self.center\\n else:\\n return self.center\",\n \"def centroid(self):\\n return _property_geo(arctern.ST_Centroid, self)\",\n \"def haversine_distance(self, end_point):\\n \\n dlng = math.radians(end_point.lng - self.lng) \\n dlat = math.radians(end_point.lat - self.lat)\\n # a is the square of half the chord length between the points.'''\\n a = math.sin(dlat/2) * math.sin(dlat/2) + math.cos( math.radians(self.lat) ) * math.cos( math.radians(end_point.lat) ) * math.sin(dlng/2) * math.sin(dlng/2)\\n # Account for rounding errors. If a is very close to 1 set it to one to avoid domain exception.'''\\n if math.fabs(1-a) < 1e-10:\\n a = 1\\n # c is the angular distance in radians between the points.'''\\n x = math.sqrt(1-a)\\n y = math.sqrt(a)\\n c = 2 * math.atan2(y, x)\\n return A_WGS84 * c\",\n \"def center(self, obj):\\n return self.phy2abs.center(obj)\",\n \"def get_center_point(self):\\n raise NotImplementedError()\",\n \"def __normalize(self, value, lower_bound, upper_bound):\\n\\n min_max_diff = self.max - self.min\\n bound_diff = upper_bound - lower_bound\\n return (value - self.min) / min_max_diff * bound_diff + lower_bound\",\n \"def normalize(x: float, y: float, z: float) -> Point3D:\\n mag = math.sqrt(x*x + y*y + z*z)\\n return x/mag, y/mag, z/mag\"\n]","isConversational":false},"negative_scores":{"kind":"list like","value":["0.6706094","0.6706094","0.6531805","0.6390352","0.6324326","0.6195097","0.61200726","0.608594","0.59820485","0.59815353","0.59811616","0.59621364","0.59285176","0.5893189","0.58789176","0.5869087","0.5867697","0.5864994","0.58597124","0.5850982","0.5841252","0.58277625","0.5826769","0.58176756","0.5816077","0.5810844","0.58053994","0.5802518","0.57974696","0.5794419","0.5789104","0.57837385","0.5767119","0.5765933","0.5760263","0.5753492","0.57171017","0.57097334","0.5702769","0.56986004","0.5696674","0.56869876","0.56813097","0.56658316","0.56615794","0.5650698","0.5634212","0.5620501","0.5612902","0.5609949","0.55988634","0.55966383","0.5581015","0.5573782","0.5573459","0.55627173","0.555422","0.5551825","0.55452335","0.55447316","0.5541323","0.553105","0.55242234","0.55061835","0.5505984","0.5497203","0.5495628","0.5489856","0.5480373","0.5478706","0.5475755","0.5473747","0.5472922","0.5471055","0.54660386","0.5463556","0.54627395","0.5458334","0.5456286","0.5452676","0.54476523","0.544223","0.5435928","0.5431407","0.54311883","0.54271734","0.5427166","0.54263437","0.5425298","0.5419283","0.5413671","0.54104257","0.5406339","0.5404945","0.5401594","0.5401528","0.53996474","0.5398667","0.5397236","0.5396771"],"string":"[\n \"0.6706094\",\n \"0.6706094\",\n \"0.6531805\",\n \"0.6390352\",\n \"0.6324326\",\n \"0.6195097\",\n \"0.61200726\",\n \"0.608594\",\n \"0.59820485\",\n \"0.59815353\",\n \"0.59811616\",\n \"0.59621364\",\n \"0.59285176\",\n \"0.5893189\",\n \"0.58789176\",\n \"0.5869087\",\n \"0.5867697\",\n \"0.5864994\",\n \"0.58597124\",\n \"0.5850982\",\n \"0.5841252\",\n \"0.58277625\",\n \"0.5826769\",\n \"0.58176756\",\n \"0.5816077\",\n \"0.5810844\",\n \"0.58053994\",\n \"0.5802518\",\n \"0.57974696\",\n \"0.5794419\",\n \"0.5789104\",\n \"0.57837385\",\n \"0.5767119\",\n \"0.5765933\",\n \"0.5760263\",\n \"0.5753492\",\n \"0.57171017\",\n \"0.57097334\",\n \"0.5702769\",\n \"0.56986004\",\n \"0.5696674\",\n \"0.56869876\",\n \"0.56813097\",\n \"0.56658316\",\n \"0.56615794\",\n \"0.5650698\",\n \"0.5634212\",\n \"0.5620501\",\n \"0.5612902\",\n \"0.5609949\",\n \"0.55988634\",\n \"0.55966383\",\n \"0.5581015\",\n \"0.5573782\",\n \"0.5573459\",\n \"0.55627173\",\n \"0.555422\",\n \"0.5551825\",\n \"0.55452335\",\n \"0.55447316\",\n \"0.5541323\",\n \"0.553105\",\n \"0.55242234\",\n \"0.55061835\",\n \"0.5505984\",\n \"0.5497203\",\n \"0.5495628\",\n \"0.5489856\",\n \"0.5480373\",\n \"0.5478706\",\n \"0.5475755\",\n \"0.5473747\",\n \"0.5472922\",\n \"0.5471055\",\n \"0.54660386\",\n \"0.5463556\",\n \"0.54627395\",\n \"0.5458334\",\n \"0.5456286\",\n \"0.5452676\",\n \"0.54476523\",\n \"0.544223\",\n \"0.5435928\",\n \"0.5431407\",\n \"0.54311883\",\n \"0.54271734\",\n \"0.5427166\",\n \"0.54263437\",\n \"0.5425298\",\n \"0.5419283\",\n \"0.5413671\",\n \"0.54104257\",\n \"0.5406339\",\n \"0.5404945\",\n \"0.5401594\",\n \"0.5401528\",\n \"0.53996474\",\n \"0.5398667\",\n \"0.5397236\",\n \"0.5396771\"\n]","isConversational":false},"document_score":{"kind":"string","value":"0.67650664"},"document_rank":{"kind":"string","value":"0"}}},{"rowIdx":75399,"cells":{"query":{"kind":"string","value":"As input to the DL model, we want coordinates relative to home. To do so, we project the coordinates if possible or use the haversine distance."},"document":{"kind":"string","value":"def project_normalize_coordinates(node_feats, transformer=None, crs=None):\n # get home node:\n home_node = node_feats.iloc[\n (node_feats[\"in_degree\"] + node_feats[\"out_degree\"]).argmax()\n ]\n home_center = home_node[\"center\"]\n\n @to_series\n def get_projected_displacement(x, y, home_center):\n if (x_min < x < x_max) and (y_min < y < y_max):\n proj_x, proj_y = transformer.transform(x, y)\n home_x, home_y = transformer.transform(home_center.x, home_center.y)\n return (proj_x - home_x, proj_y - home_y)\n else: # fall back to haversine\n return get_haversine_displacement.__wrapped__(x, y, home_center)\n\n if transformer is not None:\n # get bounds\n x_min, y_min, x_max, y_max = crs.area_of_use.bounds\n normed_coords = node_feats[\"center\"].apply(\n get_projected_displacement, args=[home_center]\n )\n else:\n normed_coords = node_feats[\"center\"].apply(\n get_haversine_displacement, args=[home_center]\n )\n\n # add_distance\n normed_coords[\"distance\"] = normed_coords.apply(\n lambda x: np.sqrt(x[0] ** 2 + x[1] ** 2), axis=1\n )\n # TODO: add as a TEST! compare haversine dist to fake-projected coordinates\n # print(normed_coords[\"distance\"])\n # test_distance = node_feats[\"center\"].apply(\n # lambda point: ti.geogr.point_distances.haversine_dist(\n # point.x, point.y, home_center.x, home_center.y\n # )[0]\n # )\n # print(\n # pd.merge(\n # normed_coords, test_distance, left_index=True, right_index=True\n # )\n # )\n\n return pd.merge(\n node_feats, normed_coords, left_index=True, right_index=True\n )"},"metadata":{"kind":"string","value":"{\n \"objective\": {\n \"self\": [],\n \"paired\": [],\n \"triplet\": [\n [\n \"query\",\n \"document\",\n \"negatives\"\n ]\n ]\n }\n}"},"negatives":{"kind":"list like","value":["def project(self, (lng, lat)):\n x = lng * DEG_TO_RAD\n lat = max(min(MAX_LATITUDE, lat), -MAX_LATITUDE)\n y = lat * DEG_TO_RAD\n y = math.log(math.tan((math.pi / 4) + (y / 2)))\n return (x*EARTH_RADIUS, y*EARTH_RADIUS)","def get_haversine_displacement(x, y, home_center):\n sign_x = 1 if x > home_center.x else -1\n displacement_x = ti.geogr.point_distances.haversine_dist(\n x, home_center.y, home_center.x, home_center.y\n )[0]\n sign_y = 1 if y > home_center.y else -1\n displacement_y = ti.geogr.point_distances.haversine_dist(\n home_center.x, y, home_center.x, home_center.y\n )[0]\n return displacement_x * sign_x, displacement_y * sign_y","def lat_lons(self):","def compute_loc(self, loc, dist, bearing):\n lat = self.nwsli_provider[loc][\"lat\"]\n lon = self.nwsli_provider[loc][\"lon\"]\n # shortcut\n if dist == 0:\n return lon, lat\n meters = distance(float(dist), \"MI\").value(\"M\")\n northing = meters * math.cos(math.radians(bearing)) / 111111.0\n easting = (\n meters\n * math.sin(math.radians(bearing))\n / math.cos(math.radians(lat))\n / 111111.0\n )\n return lon + easting, lat + northing","def initialCoordinates():\r\n return (-250,-250)","def get_location_offset_meters(self, dNorth, dEast, alt):\n earth_radius=6378137.0 #Radius of \"spherical\" earth\n #Coordinate offsets in radians\n dLat = dNorth/earth_radius\n dLon = dEast/(earth_radius*math.cos(math.pi*self.home.lat/180))\n\n #New position in decimal degrees\n newlat = self.home.lat + (dLat * 180/math.pi)\n newlon = self.home.lon + (dLon * 180/math.pi)\n return LocationGlobal(newlat, newlon,self.home.alt+alt)","def get_distance_from_hq(self, lat, lon):\n coords_1 = (40.646860, -8.642999) # Coords to the office\n coords_2 = (lat, lon)\n return distance.distance(coords_1, coords_2).km","def nearlonlat_zl(lon,lat,lonp,latp): # needed for the next function get_FVCOM_bottom_temp \r\n # approximation for small distance \r\n cp=np.cos(latp*np.pi/180.) \r\n dx=(lon-lonp)*cp\r\n dy=lat-latp \r\n xi=np.argmin(abs(dx)) \r\n yi=np.argmin(abs(dy))\r\n min_dist=111*np.sqrt(dx[xi]**2+dy[yi]**2)\r\n return xi,yi,min_dist","def lidar_relative(self):\n return self.distance","def add_on_land_position(self) -> Point2:\n return self.position.offset(Point2((-2.5, 0.5)))","def getSearchSpaceCoords(self):","def coordinates(self):","def getFeaturedLocation(guide):\n photos = guide.photos.all()\n\n x = 0\n y = 0\n z = 0\n\n size = 0\n\n for photo in photos:\n if photo.latitude:\n lat = radians(float(photo.latitude))\n lon = radians(float(photo.longitude))\n x += cos(lat) * cos(lon)\n y += cos(lat) * sin(lon)\n z += sin(lat)\n size+=1\n\n if size is 0:\n return None\n\n x = float(x / size)\n y = float(y / size)\n z = float(z / size)\n\n return {\n 'latitude': degrees(atan2(z, sqrt(x * x + y * y))),\n 'longitude': degrees(atan2(y, x))\n }\n # return atan2(z, sqrt(x * x + y * y)), atan2(y, x)\n\n\n\n # for photo in photos:\n # if photo.latitude:\n # return {\n # 'latitude': photo.latitude,\n # 'longitude': photo.longitude\n # }\n\n # return None","def calculate_target_location(self, alphas, epsilons, data_collected):\n if len(alphas) == 1:\n \tfor i in range(0, self.number_sampling_points-1):\n \t\talphas.append(alphas[0])\n \t\tepsilons.append(epsilons[0])\n\n # if self.target_location == None:\n # # dBm_list = []\n # # for sample in data_collected[0][3]:\n # # dBm_list.append(sample)\n\n # # average_dBm = sum(dBm_list) / float(len(dBm_list))\n # # radius_target_position = 10 ** ((average_dBm - self.epsilon) / self.alpha)\n # # ###TODO: fix radius_target_position\n # # if radius_target_position > self.altitude:\n # # horizontal_distance = sqrt((radius_target_position**2) - (self.altitude**2))\n # # else:\n # # horizontal_distance = 0\n\n # local_position = self.dc.read_gps()\n # local_coord = Coordinate(local_position.lat, local_position.lon)\n\n # first_emulated_target = local_coord.offset_toward_target(self.region.center(), DISTANCE_TO_TARGET)\n\n # self.log.debug('=========================================================================')\n # self.log.debug('Calculated emulated target at location: {}'.format(first_emulated_target))\n # self.log.debug('=========================================================================')\n\n # return first_emulated_target\n\n # else:\n prediction = predict(dronenum=self.number_sampling_points,\n maxRun=1,\n numIterations=GDParameters.NUM_ITERATIONS,\n numEpoch=GDParameters.NUM_EPOCH,\n threshold=GDParameters.THRESHOLD,\n learning_rate=GDParameters.LEARNING_RATE,\n numberBatch=1,\n data_length=NUMBER_SAMPLES*self.number_sampling_points)\n\n try:\n target = prediction.swarm(drone_data=data_collected,\n alphas=alphas,\n epsilons=epsilons)\n except IndexError:\n self.log.warn('Target localization failed. Data not good enough.')\n return False\n\n computed_target_position = Coordinate(target[0], target[1])\n\n self.log.debug('=========================================================================')\n self.log.debug('Calculated new target at location: {}'.format(computed_target_position))\n\n if IS_SIMULATION:\n error = computed_target_position.distance_to(self.current_simulated_target)\n self.log.debug('Simulated error: {err}, Simulated target has moved {dist} meters to: {loc}'.format(\n err=error,\n dist=self.target_meters_moved,\n loc=self.current_simulated_target\n ))\n self.log.debug('=========================================================================')\n\n if not self.region.contains(computed_target_position) and not IS_SIMULATION:\n self.log.debug('New target is out of region')\n self.log.debug('Setting new target location as the latest one calculated')\n return self.target_location\n\n return computed_target_position","def ll_coordinates(self):\n\n x, y = self.xy_coordinates\n proj_out = check_crs('EPSG:4326')\n\n return transform_proj(self.proj, proj_out, x, y)","def get_point_on_rhumb_line(self, distance, bearing):\n\n # bearing in assumed to be in degrees\n bearing_in_radians = math.radians(bearing)\n # ad is the angular distance (in radians) traveled.\n ad = distance/A_WGS84\n # dlat is the net change in latitude\n dlat = ad*math.cos(bearing_in_radians)\n # lat1 is the latitude of the starting point in radians.\n lat1 = math.radians(self.lat)\n # lng1 is the longitude of the starting point in radians.\n lng1 = math.radians(self.lng)\n # The new latitude, in radians, is the starting latitude plus the change in latitude\n lat2 = lat1 + dlat\n \n dPhi = math.log(math.tan(lat2/2+math.pi/4)/math.tan(lat1/2+math.pi/4))\n if dPhi == 0:\n q = math.cos(lat1)\n else:\n q = dlat/dPhi\n # The change in longitude\n dlng = ad*math.sin(bearing_in_radians)/q\n # If the latitude happens to go beyond the pole\n if math.fabs(lat2) > math.pi/2:\n # if the latitude is beyond 90 degrees north\n if lat2>0:\n # latitude should be 90 degree north\n lat2 = math.pi/2\n else:\n # latidude is beyond 90 degree south\n # latitude should be 90 degrees south\n lat2 = -1*math.pi/2\n # The new longitude, in radians \n lng2 = (lng1+dlng+math.pi)%(2*math.pi)-math.pi\n \n lng2d = math.degrees(lng2)\n lat2d = math.degrees(lat2)\n return Point(float(truncate(lng2d,DEGREE_DIGITS)), float(truncate(lat2d,DEGREE_DIGITS)))","def to_location(self) -> EarthLocation:\n # Height is not in some MegaPrime files. Use the value from\n # EarthLocation.of_site(\"CFHT\")\n # Some data uses OBS-LONG, OBS-LAT, other data uses LONGITUD and\n # LATITUDE\n for long_key, lat_key in ((\"LONGITUD\", \"LATITUDE\"), (\"OBS-LONG\", \"OBS-LAT\")):\n if self.are_keys_ok([long_key, lat_key]):\n value = EarthLocation.from_geodetic(self._header[long_key], self._header[lat_key], 4215.0)\n self._used_these_cards(long_key, lat_key)\n break\n else:\n value = EarthLocation.of_site(\"CFHT\")\n return value","def unproject(self, (x, y)):\n lng = x/EARTH_RADIUS * RAD_TO_DEG\n lat = 2 * math.atan(math.exp(y/EARTH_RADIUS)) - math.pi/2 * RAD_TO_DEG\n return (lng, lat)","def get_point_from_distance_along_great_circle(self, distance, bearing):\n \n # ad is the angular distance (in radians) traveled.\n ad = distance/A_WGS84\n # lat1 is the latitude of the starting point in radians.\n lat1 = math.radians(self.lat)\n # lng1 is the longitude of the starting point in radians.\n lng1 = math.radians(self.lng)\n # b is the bearing direction in radians.\n b = math.radians(bearing)\n # lat2 is the latitude of the end point in radians.\n lat2 = math.asin( math.sin(lat1) * math.cos(ad) + math.cos(lat1) * math.sin(ad) * math.cos(b) )\n y = math.sin(b) * math.sin(ad) * math.cos(lat1)\n x = math.cos(ad) - math.sin(lat1) * math.sin(lat2)\n \n \"\"\"Account for rounding errors. If x is very close to 0, set it to 0 to avoid \n incorrect hemisphere determination.\n For example, if x = -1.1e-16, atan2(0,x) will be -math.pi when it should be 0.\"\"\"\n if math.fabs(x) < 1e-10:\n x = 0\n # lng2 is the longitude of the end point in radians.\n lng2 = lng1 + math.atan2(y, x)\n lng2d = math.degrees(lng2)\n lat2d = math.degrees(lat2)\n return Point(float(truncate(lng2d,DEGREE_DIGITS)), float(truncate(lat2d,DEGREE_DIGITS)))","def haversine(test_point, training_locations):\n test_point = test_point.reshape(1, 2)\n difference = (test_point - training_locations) * np.pi / 180\n test_point_lat = test_point[:, 1] * np.pi / 180\n training_locations_lat = training_locations[:, 1] * np.pi / 180\n \n a = np.sin(difference[:, 0] / 2)**2 * np.cos(test_point_lat) * np.cos(training_locations_lat) +\\\n np.sin(difference[:, 1] / 2)**2 \n radius = 6371\n c = 2 * np.arcsin(np.sqrt(a))\n return radius * c","def nancay():\n return coord.EarthLocation(lat=47.376511*u.deg, lon=2.1924002*u.deg)","def haversine(lon1, lat1, lon2, lat2): \r\n lon1, lat1, lon2, lat2 = map(radians, [lon1, lat1, lon2, lat2]) \r\n #print 34\r\n dlon = lon2 - lon1 \r\n dlat = lat2 - lat1 \r\n a = sin(dlat/2)**2 + cos(lat1) * cos(lat2) * sin(dlon/2)**2 \r\n c = 2 * atan(sqrt(a)/sqrt(1-a)) \r\n r = 6371 \r\n d=c * r\r\n #print type(d)\r\n return d","def set_home_position(self, lat, lon, alt):\n pass","def calculate_H(s_lat,s_lon,e_lat,e_lon):\n R = 6371.0\n snlat = radians(s_lat)\n snlon = radians(s_lon)\n elat = radians(e_lat)\n elon = radians(e_lon)\n actual_dist = 6371.01 * acos(sin(snlat) * sin(elat) + cos(snlat) * cos(elat) * cos(snlon - elon))\n actual_dist = actual_dist * 1000\n return actual_dist","def getPosition(self, request, context): \n \n # The latitude and longitude are relative to the WGS84 coordinate system. \n # The altitude is relative to mean sea-level (MSL)\n globalLocation = str(self.vehicle.location.global_frame)\n \n # LocationGlobalRelative:lat=0.0,lon=0.0,alt=6.6\n # Altitude relative to the home location. The lat and long values for this and the above\n # variable will be the same.\n relativeLocation = str(self.vehicle.location.global_relative_frame)\n \n latitude, longitude, altGPS, altHome = None, None, None, None\n \n match = re.search(r\"lat=(.*),lon=(.*),alt=(.*)$\", globalLocation)\n if match:\n latitude = match.group(1)\n longitude = match.group(2)\n altGPS = match.group(3)\n \n match = re.search(r\"lat=(.*),lon=(.*),alt=(.*)$\", relativeLocation)\n if match:\n altHome = match.group(3)\n\t\t\t\n return droneconnect_pb2.Position(lat = float(latitude),\n lon = float(longitude),\n gpsAltitude = float(altGPS),\n relativeAltitude = float(altHome))","def find_hypocenter(self):\n ps_hypo = min(self.pointsources, key=lambda x: x.time_shift)\n self.hypocenter_longitude = ps_hypo.longitude\n self.hypocenter_latitude = ps_hypo.latitude\n self.hypocenter_depth_in_m = ps_hypo.depth_in_m","def calc_distance(self, observation):\n actual_obs = observation[0]\n scrn_player = actual_obs.observation.feature_screen.player_relative\n scrn_select = actual_obs.observation.feature_screen.selected\n scrn_density = actual_obs.observation.feature_screen.unit_density\n\n state_added = scrn_select + scrn_density\n\n marine_center = np.mean(self.xy_locs(scrn_player == 1), axis=0).round()\n\n # first step\n if np.sum(scrn_select) == 0:\n marine_center = np.mean(self.xy_locs(scrn_player == 1), axis=0).round()\n # marine behind beacon\n if isinstance(marine_center, float):\n marine_center = np.mean(self.xy_locs(state_added == 2), axis=0).round()\n else:\n # normal navigation\n marine_center = np.mean(self.xy_locs(state_added == 2), axis=0).round()\n if isinstance(marine_center, float):\n marine_center = np.mean(self.xy_locs(state_added == 3), axis=0).round()\n\n beacon_center = np.mean(self.xy_locs(scrn_player == 3), axis=0).round()\n #\n # print(state_added)\n # print(\"---- Marine {} | {} Beacon ----\".format(marine_center, beacon_center))\n # time.sleep(0.2)\n distance = math.hypot(beacon_center[0] - marine_center[0],\n beacon_center[1] - marine_center[1])\n\n return beacon_center, marine_center, distance","def distance_to(self, p1):\n # due to rounding errors the actual function can return non-zero distance for the same point!\n if self.round() == p1.round():\n return 0\n from_theta = float(self.lat) / 360.0 * 2.0 * math.pi\n from_landa = float(self.long) / 360.0 * 2.0 * math.pi\n to_theta = float(p1.lat) / 360.0 * 2.0 * math.pi\n to_landa = float(p1.long) / 360.0 * 2.0 * math.pi\n tmp = math.sin(from_theta) * math.sin(to_theta) + math.cos(from_theta) * math.cos(to_theta) * math.cos(\n to_landa - from_landa)\n # if I don't round the number, ValueError: math domain error may occur\n return math.acos(round(tmp, 15)) * R_EARTH","def set_home_locations(self):\n self.swarmie.set_home_gps_location(self.swarmie.get_gps_location())\n\n current_location = self.swarmie.get_odom_location()\n current_pose = current_location.get_pose()\n home_odom = Location(current_location.Odometry)\n\n detections = self.swarmie.get_latest_targets().detections\n try:\n for detection in detections:\n if detection.id == 256:\n see_home_tag = True\n home_detection = self._transform_to_odom(detection)\n\n quat = [home_detection.pose.orientation.x,\n home_detection.pose.orientation.y,\n home_detection.pose.orientation.z,\n home_detection.pose.orientation.w]\n _r, _p, yaw = tf.transformations.euler_from_quaternion(\n quat\n )\n yaw += math.pi / 2\n\n home_odom.Odometry.pose.pose.position.x = float(\n home_detection.pose.position.x + 0.5 * math.cos(yaw)\n )\n home_odom.Odometry.pose.pose.position.y = float(\n home_detection.pose.position.y + 0.5 * math.sin(yaw)\n )\n self.swarmie.set_home_odom_location(home_odom)\n return\n\n except tf.Exception:\n pass # use backup below\n\n # project home_odom location 50cm in front of rover's current location\n home_odom.Odometry.pose.pose.position.x = (\n current_pose.x + 0.5 * math.cos(current_pose.theta)\n )\n home_odom.Odometry.pose.pose.position.y = (\n current_pose.y + 0.5 * math.sin(current_pose.theta)\n )\n self.swarmie.set_home_odom_location(home_odom)\n return","def distance_from(self, location_obj):\n \n distance = math.sqrt( (self.east - location_obj.east) ** 2 +\n (self.north - location_obj.north) ** 2 )\n return distance / 1000.0","def calculate_waypoints(global_start, global_goal, global_home, data, drone_altitude, safety_distance):\n # Calculate graph and offsets\n graph, north_offset, east_offset = create_graph(data, drone_altitude, safety_distance)\n\n map_offset = np.array([north_offset, east_offset, .0])\n\n # Convert start position from global to local.\n local_position = global_to_local(global_start, global_home) - map_offset\n\n # Find closest point to the graph for start\n graph_start = closest_point(graph, local_position)\n\n # Convert goal postion from global to local\n local_goal = global_to_local(global_goal, global_home) - map_offset\n\n # Find closest point to the graph for goal\n graph_goal = closest_point(graph, local_goal)\n\n # Find path\n path, _ = a_star(graph, graph_start, graph_goal)\n path.append(local_goal)\n\n # Prune path\n path = collinearity_prune(path, epsilon=1e-3)\n\n # Calculate waypoints\n return [[int(p[0] + north_offset), int(p[1] + east_offset), drone_altitude, 0] for p in path]","def lat_run_step(self, target_location):\n v_begin = self.current_transform.location\n v_end = v_begin + carla.Location(x=math.cos(math.radians(self.current_transform.rotation.yaw)),\n y=math.sin(math.radians(self.current_transform.rotation.yaw)))\n v_vec = np.array([v_end.x - v_begin.x, v_end.y - v_begin.y, 0.0])\n w_vec = np.array([target_location.x -\n v_begin.x, target_location.y -\n v_begin.y, 0.0])\n _dot = math.acos(np.clip(np.dot(w_vec, v_vec) /\n (np.linalg.norm(w_vec) * np.linalg.norm(v_vec)), -1.0, 1.0))\n _cross = np.cross(v_vec, w_vec)\n\n if _cross[2] < 0:\n _dot *= -1.0\n\n self._lon_ebuffer.append(_dot)\n if len(self._lon_ebuffer) >= 2:\n _de = (self._lon_ebuffer[-1] - self._lon_ebuffer[-2]) / self.dt\n _ie = sum(self._lon_ebuffer) * self.dt\n else:\n _de = 0.0\n _ie = 0.0\n\n return np.clip((self._lat_k_p * _dot) + (self._lat_k_d * _de) + (self._lat_k_i * _ie), -1.0, 1.0)","def project(self, latitude, longitude):\n longitude = longitude if longitude > 0 else 360 + longitude\n\n lon_array = self.ecmwf_data.longitude\n lat_array = self.ecmwf_data.latitude\n\n # Find in the lon_array / lat_array the index interval\n # Including lon_ul and lat_ul\n a_lon = np.where((lon_array < longitude))[0][-1]\n if longitude > lon_array.max():\n # lon is between 359.6 and 0 ...\n b_lon = 0\n else:\n b_lon = np.where((lon_array >= longitude))[0][0]\n\n a_lat = np.where((lat_array < latitude))[0][0]\n b_lat = np.where((lat_array >= latitude))[0][-1]\n\n # Compute geo extent around the point :\n # => extent definition : LR,LL,UL,UR\n extent = [lon_array[a_lon], lat_array[a_lat],\n lon_array[b_lon], lat_array[a_lat],\n lon_array[b_lon], lat_array[b_lat],\n lon_array[a_lon], lat_array[b_lat]]\n\n extent_index = [a_lon, a_lat,\n b_lon, a_lat,\n b_lon, b_lat,\n a_lon, b_lat]\n\n log.info(' - Selected vertex : ')\n log.info('LL (px,ln) / (lon,lat) : (%s, %s) / (%s dd , %s dd)' % (\n str(extent_index[0]), str(extent_index[1]), str(extent[0]), str(extent[1])))\n log.info('LR (px,ln) / (lon,lat) : (%s, %s) / (%s dd , %s dd)' % (\n str(extent_index[2]), str(extent_index[3]), str(extent[2]), str(extent[3])))\n log.info('UR (px,ln) / (lon,lat) : (%s, %s) / (%s dd , %s dd)' % (\n str(extent_index[4]), str(extent_index[5]), str(extent[4]), str(extent[5])))\n log.info('UL (px,ln) / (lon,lat) : (%s, %s) / (%s dd , %s dd)' % (\n str(extent_index[6]), str(extent_index[7]), str(extent[6]), str(extent[7])))\n\n # TIE Point grid defined - compute linear transformation\n # to estimate value at the lat/lon location\n # origin : extent_ul[0], extent_ul[1]\n delta_lon = 0.4 # extent[4] - extent[6] # UR - UL\n delta_lat = -0.4 # extent[1] - extent[7] # LL - UL\n\n lambda_lat = latitude - extent[7]\n lambda_lon = longitude - extent[6]\n\n beta_longitude = lambda_lon / delta_lon\n beta_latitude = lambda_lat / delta_lat\n\n # Processing of all keys\n for key in self.ecmwf_data.mandatory_attributes:\n M = getattr(self.ecmwf_data, key)\n v = self.linear_estimate(M,\n beta_latitude,\n beta_longitude,\n extent_index)\n setattr(self, key, v)","def augmentDistance(self):\n\n for key,value in self._models.iteritems():\n src=[float(i) for i in value['src'].replace('#',' ').split()]\n tgt=[float(i) for i in value['tgt'].replace('#',' ').split()]\n\n dist = haversine((np.mean(src[0:2]),np.mean(src[2:])),\n (np.mean(tgt[0:2]),np.mean(tgt[2:])),\n miles=True)\n self._models[key]['distance'] = dist\n\n return","def desiredENU2localSp(self, xd, yd, zd):\n\n\t\tif self.INDOOR:\n\t\t\tself.mavros_sp.position.x = xd\n\t\t\tself.mavros_sp.position.y = yd\n\t\t\tself.mavros_sp.position.z = zd\n\t\telse:\n\n\t\t\tlat, lon, alt = self.desiredENU2geo(xd, yd, zd)\n\t\t\t# converts to differences in robot's ENU\n\t\t\tx,y,z = pm.geodetic2enu(lat, lon, alt, self.curr_lat, self.curr_lon, self.gpsAlt)\n\n\t\t\t# Add the differences to robot's current location\n\t\t\tself.mavros_sp.position.x = self.local_pose.pose.position.x + x\n\t\t\tself.mavros_sp.position.y = self.local_pose.pose.position.y + y\n\t\t\tself.mavros_sp.position.z = zd + self.GND_ALT","def distance_to_origin(self):\n\n self.D = edist(self.isomap,\n np.zeros([1, self.isomap.shape[1]])).flatten()","def coordinates_abs(self, source):\n if not hasattr(self, 'azimuth'):\n return self.center_abs(source)\n else:\n return (*self.center_abs(source), self.azimuth, self.elevation)","def Distance_orthonormique(lon1, lat1, lon2, lat2):\r\n \r\n #Convert position in radians\r\n lon1, lat1, lon2, lat2 = map(radians, [lon1, lat1, lon2, lat2])\r\n #rvmT = Earth radius [km]\r\n rvmT = 6371 \r\n #Project the position on\r\n a = sin((lat2 - lat1)/2)**2 + cos(lat1) * cos(lat2) * sin((lon2 - lon1)/2)**2\r\n c = 2 * asin(sqrt(a)) \r\n \r\n d = c * rvmT\r\n return d","def _calculate_location(self, map_4d):\n\n # MARGINALISE: Determining the 3-D coalescence map\n self.coa_map = np.log(np.sum(np.exp(map_4d), axis=-1))\n\n # Normalise\n self.coa_map = self.coa_map/np.max(self.coa_map)\n\n # Fit 3-D spline function to small window around max coalescence\n # location and interpolate to determine sub-grid maximum coalescence\n # location.\n loc_spline = self._splineloc(np.copy(self.coa_map))\n\n # Apply gaussian smoothing to small window around max coalescence\n # location and fit 3-D gaussian function to determine local\n # expectation location and uncertainty\n smoothed_coa_map = self._gaufilt3d(np.copy(self.coa_map))\n loc_gau, loc_gau_err = self._gaufit3d(np.copy(smoothed_coa_map),\n thresh=0.)\n\n # Calculate global covariance expected location and uncertainty\n loc_cov, loc_cov_err = self._covfit3d(np.copy(self.coa_map))\n\n return loc_spline, loc_gau, loc_gau_err, loc_cov, loc_cov_err","def _normalize_location(lat: float, lon: float):\n latitude = \"{0:.3f}\".format(round(lat, 3))\n longitude = \"{0:.3f}\".format(round(lon, 3))\n return latitude + \":\" + longitude","def predict_qth(self):\n return (self.latitude, -self.longitude, self.altitude)","def calc_vehicle_offset(undist, left_fit, right_fit):\n\n bottom_y = undist.shape[0] - 1\n bottom_x_left = left_fit[0]*(bottom_y**2) + left_fit[1]*bottom_y + left_fit[2]\n bottom_x_right = right_fit[0]*(bottom_y**2) + right_fit[1]*bottom_y + right_fit[2]\n vehicle_offset = undist.shape[1]/2 - (bottom_x_left + bottom_x_right)/2\n\n # Convert pixel offset to meters\n vehicle_offset *= xm_per_pix\n\n return vehicle_offset","def test_locate():\n x, y = hyperbolic_location.locate(-.290955, -.08254229)\n print(x, y)","def easting(self):\r\n x, y = self.lonlat2xy(self.longitude, self.latitude)\r\n return x","def distance_to_origin(self):\n return np.sqrt(self.x ** 2 + self.y ** 2)","def calcApproxDist(lon1, lat1, lon2, lat2):\n\n import math\n from shapely.geometry import Point\n\n if lat1 == lat2 and lon1 == lon2:\n return 0.0\n\n point1 = Point(lon1,lat1)\n point2 = Point(lon2, lat2)\n\n return math.acos(math.sin(math.radians(point1.y))*math.sin(math.radians(point2.y))+math.cos(math.radians(\n point1.y))*math.cos(math.radians(point2.y))*math.cos(math.radians(point2.x)-math.radians(point1.x)))*6371","def calc_distance(user_loc, space):\n geocode_result = gmaps.geocode(space['_location'])\n dest_loc = geocode_result[0]['geometry']['location']\n direction = gmaps.distance_matrix(user_loc, dest_loc, mode=\"walking\")\n distance = direction['rows'][0]['elements'][0]['distance']['value']\n # convert to mile\n distance = distance * 0.000621371\n return distance","def ecliptic_position(self):\n vector = _ECLIPJ2000.dot(self.position.au)\n return Distance(vector)","def CreateLocationByProximityQuery(self):\n self.CenterPoint = self.randomGenerator.CreateRandomLattitudeLongitude()\n self.SearchDistanceMiles = self.randomGenerator.CreateRandomNumber(self.minDistanceMiles,self.maxDistanceMiles)\n self.SearchDistanceMeters = GeoConversion.ConvertMilesToMeters(self.SearchDistanceMiles)\n QueryParameterDic = {}\n QueryParameterDic['SearchType'] ='LocationByProximity'\n QueryParameterDic['MaxResults'] = self.maxSearchResults\n QueryParameterDic['MaxDistanceMeters'] = GeoConversion.ConvertMilesToMeters(self.SearchDistanceMiles)\n QueryParameterDic['CenterPt'] = self.CenterPoint\n QueryParameterDic['HasShopsOnly'] = 'True'\n QueryParameterDic['FilterByTrueDistance'] = 'True'\n\n # Get a list of GpGridCells to search\n gpSearch = GpSearch()\n gpSearch.MaxSearchCellCount = self.MaxSearchCellCount\n centerGpPoint = GpPoint(self.CenterPoint[0],self.CenterPoint[1])\n gpSearch.ComputeSearchListForMetersProximity(centerGpPoint,self.SearchDistanceMeters)\n self.FinalSearchResolution = gpSearch.FinalSearchResolution\n self.SearchGpGridCells = gpSearch.SearchCellList\n\n QueryParameterDic['SearchGridCells'] = self.SearchGpGridCells\n return QueryParameterDic","def get_location_metres(original_location, dNorth, dEast):\n earth_radius=6378137.0 #Radius of \"spherical\" earth\n #Coordinate offsets in radians\n dLat = dNorth/earth_radius\n dLon = dEast/(earth_radius*math.cos(math.pi*original_location.lat/180))\n\n #New position in decimal degrees\n newlat = original_location.lat + (dLat * 180/math.pi)\n newlon = original_location.lon + (dLon * 180/math.pi)\n\n return dk.LocationGlobal(newlat, newlon,original_location.alt)","def test_dist_itslef(self):\n X = [[0, 10], [4, 2]] # Just some points. I've no idea where on globe.\n c = cdist(X[0], X[1])\n string_geopy = '{}'.format(great_circle(X[0], X[1]))\n float_geopy = float(string_geopy[:-3])\n self.assertTrue(np.round(c) == np.round(float_geopy))\n\n X = [[34.0522, 118.2437], # Lon Angeles\n [37.7749, 122.4194]] # San Francisco\n c = cdist(X[0], X[1])\n string_geopy = '{}'.format(great_circle(X[0], X[1]))\n float_geopy = float(string_geopy[:-3])\n self.assertTrue(np.round(c) == np.round(float_geopy))","def desiredENU2geo(self, x_L, y_L, z):\n\t\tx = cos(self.local_rot)*x_L - sin(self.local_rot)*y_L\n\t\ty = sin(self.local_rot)*x_L + cos(self.local_rot)*y_L\n\n\t\tlat0 = self.origin[0]\n\t\tlon0 = self.origin[1]\n\n\t\tlat, lon, alt = pm.enu2geodetic(x, y, z, lat0, lon0, self.h0)\n\t\treturn lat, lon, alt","def save(self, *args, **kwargs):\n self.distance_from_hq = self.get_distance_from_hq(self.lat, self.lon)\n super().save(*args, **kwargs)","def test_coord_preceding_fs(self):","def closest_on_screen_point(trajectory, viewpoint, yaw, gaze_on_screen):\n\n traj_angles = dp.world_to_angles_through_screen(trajectory, viewpoint, yaw) \n #pprint(traj_angles)\n\n #onscreen_idx, dists, *_ = find_closest_index(traj_angles, gaze_on_screen)\n #idx = closest_node(traj_angles, gaze_on_screen)\n idx = find_closest_index(traj_angles, gaze_on_screen)\n # print(idx)\n\n #traj_ref = trajectory[idx, :]\n screen_ref = traj_angles[idx, :]\n world_ref = trajectory[idx, :]\n\n path_dist = ab_path_length(trajectory, viewpoint, world_ref)\n path_dist /= 8.0 #time headway\n\n #plot_traj(screen_ref, gaze_on_screen, traj_angles)\n\n return(idx, screen_ref, world_ref, path_dist)#, traj_angles)","def _get_dlon_dlat_km(self):\n self.dlon_km=np.array([])\n self.dlat_km=np.array([])\n for lat in self.lat:\n dist_lon, az, baz = obspy.geodetics.gps2dist_azimuth(lat, 0., lat, self.dlon)\n dist_lat, az, baz = obspy.geodetics.gps2dist_azimuth(lat, 0., lat+self.dlat, 0.)\n self.dlon_km=np.append(self.dlon_km, dist_lon/1000.)\n self.dlat_km=np.append(self.dlat_km, dist_lat/1000.)\n self.dlon_kmArr=(np.tile(self.dlon_km, self.Nlon).reshape(self.Nlon, self.Nlat)).T\n self.dlat_kmArr=(np.tile(self.dlat_km, self.Nlon).reshape(self.Nlon, self.Nlat)).T\n return","def __calculateOffset(self):\n #if len(self.__XValue) > 0:\n # print(\"GPSBearing: \"+str(round(self.__GPSBearing[-1]))+\", heading: \"+str(round(self.value))+\n # \", x: \"+str(round(self.__XValue[-1]))+\", stdev: \"+str(round(np.std(self.__GPSBearing),5))+\n # \", offset: \"+str(self.__offset))\n if len(self.__GPSBearing) == self.__GPSBearing.maxlen and np.std(self.__GPSBearing) < 0.2 and self.speed > 7:\n self.__offset = (np.mean(self.__GPSBearing) + 360 - np.mean(self.__XValue)) % 360\n #print(\"new offset: \"+str(self.__offset)) ","def calculate_vars(data, lat, lon):\n # Keep track of running distance and time calculations\n distance_to_dest = 0.0\n time_estimate = 0.0\n\n # Calculate from starting dest to first point in data\n user_coords = (lat, lon)\n first_path_coords = (data[0][\"lat\"], data[0][\"lon\"])\n first_distance = geopy.distance.distance(user_coords, first_path_coords).miles\n distance_to_dest += first_distance\n time_estimate += first_distance * 20 # 3mph walking speed\n\n # Calculate for all other points\n for i in range(1, len(data) - 1):\n this_coords = (data[i][\"lat\"], data[i][\"lon\"])\n next_coords = (data[i + 1][\"lat\"], data[i + 1][\"lon\"])\n\n distance = geopy.distance.distance(this_coords, next_coords).miles\n distance_to_dest += distance\n time_estimate += distance * 20 # 3mph walking speed\n\n # Round distance and time estimates\n distance_to_dest = round(distance_to_dest, 1)\n time_estimate = round(time_estimate)\n\n return distance_to_dest, time_estimate","def calculate_spot_center(source_position, light_dir):\n x_spot = source_position[0] + light_dir[0]*source_position[2]/light_dir[2]\n y_spot = source_position[1] + light_dir[1]*source_position[2]/light_dir[2]\n return x_spot, y_spot, 0","def calculate_latitude(source_lat, distance, heading):\n return math.asin(math.sin(source_lat)*math.cos(distance) + \\\n math.cos(source_lat)*math.sin(distance)*math.cos(heading))","def northing(self):\r\n x, y = self.lonlat2xy(self.longitude, self.latitude)\r\n return y","def getPoint(self, a):\n lng = self.source.center.lng + (self.target.center.lng - self.source.center.lng) * min(a, 1)\n lat = self.source.center.lat + (self.target.center.lat - self.source.center.lat) * min(a, 1)\n return lng, lat","def distance(self, lat: float, long: float) -> float:\n # Initial euclidian formula below\n # diff_lat = self.lat - lat\n # diff_long = self.long - long\n # euclidian = math.sqrt((diff_lat ** 2 + diff_long ** 2 + self.altitude ** 2))\n\n return self._haversine(lat, long) + self.altitude / 1000","def dd2dm(lat,lon):\r\n lat_d = int(abs(lat)) #calculate latitude degrees\r\n lat_m = (abs(lat) - lat_d) * 60. #calculate latitude minutes\r\n\r\n lon_d = int(abs(lon))\r\n lon_m = (abs(lon) - lon_d) * 60.\r\n \r\n la=lat_d*100.+lat_m\r\n lo=lon_d*100.+lon_m\r\n return la,lo","def calc_dist_to_poi(self,agent):\n mini_dist = 100000 \n for poi in self.poi_pos_list:\n mini_dist = np.linalg.norm(agent.get_pos() - poi)\n\n return mini_dist","def edwin_location(self, res):\n if res.data[0:5] == \"WHERE\":\n #gets edwin's location\n where = res.data[5:]\n\n #massive string formatting - takes the string, splits by a formatter, then takes the array index that holds the XYZ,\n #then strips that string and splits it by spacing, and then takes the XYZ\n #reason for this massive formatting is b/c sent data format is not consistent\n where = where.split(\"\\r\\n\")[2].strip().split(' ')\n\n #gets rid of empty strings that result from formatting\n where = filter(None, where)[0:3]\n\n #makes everything numbers that can be used as coordinates for Edwin\n where = [int(float(coord) * 10) for coord in where]\n\n self.edwinx = where[0]\n self.edwiny = where[1]\n self.edwinz = where[2]","def top_coords_absolute(self):\n pass","def closest_point(\n self, lx: float, ly: float, reference: Output | None = None\n ) -> tuple[float, float]:\n if reference:\n reference_ptr = reference._ptr\n else:\n reference_ptr = ffi.NULL\n\n dest_lx = ffi.new(\"double *\")\n dest_ly = ffi.new(\"double *\")\n lib.wlr_output_layout_closest_point(\n self._ptr, reference_ptr, lx, ly, dest_lx, dest_ly\n )\n return dest_lx[0], dest_ly[0]","def get_nearest(infected_coordinates, uninfected_coordinates, d):\n # Create tree from the GPS coordinates of uninfected users\n tree = BallTree(uninfected_coordinates, leaf_size=15, metric='haversine')\n indices,distances=tree.query_radius(infected_coordinates, r=d,return_distance=True)\n indices=indices.transpose()\n distances=distances.transpose()\n return indices,distances","def location(bot, update):\n\n bot.send_message(chat_id=update.message.chat_id, text=\"OK you wait ah...\")\n latitude = update.message.location.latitude\n longitude = update.message.location.longitude\n bot.send_message(chat_id=update.message.chat_id, text=\"Just let you know for fun lol - your latitude is {0}, and your longitude is {1}\".format(latitude,longitude))\n try:\n # Read carpark csv as dataframe\n df = pd.read_csv('Parking_withcoords.csv')\n \n # Calculate distance between each carpark and postal code and append it to dataframe\n distance = []\n for coord in df['Coord_rad']: \n carpark = haversine((radians(latitude),radians(longitude)), ast.literal_eval(coord)) #converts string to tuple\n distance.append(carpark)\n df['Distance_km'] = distance\n\n # Sort in ascending order and extract top 5\n top_five = df.sort_values('Distance_km').head(5)\n\n for row in top_five['Info']:\n bot.send_message(chat_id=update.message.chat_id, parse_mode='HTML', text=row.replace(\"\\$\", \"$\"))\n\n bot.send_message(chat_id=update.message.chat_id, text=\"Fast hor! If you want to check other places, type /start again ok :P\")\n except:\n bot.send_message(chat_id=update.message.chat_id, text=\"Jialat liao got error...try again with /start and then use the postal code method can? Paiseh!\")","def calculate(self):\n\n x_wamv = self.location[0]\n y_wamv = self.location[1]\n theta_wamv = self.heading\n\n x_wpt = self.waypoint_loc[0]\n y_wpt = self.waypoint_loc[1]\n\n dy = y_wpt - y_wamv\n dx = x_wpt - x_wamv\n\n # Could add in a 'round' if required.\n theta_wpt = math.degrees(math.atan2(dx,dy))\n\n self.heading_publisher.publish(theta_wpt)","def lane_waypt_to_query_dist(\n query_xy_city_coords: np.ndarray, nearby_lane_objs: List[LaneSegment]\n) -> Tuple[np.ndarray, np.ndarray, np.ndarray]:\n per_lane_dists: List[float] = []\n dense_centerlines: List[np.ndarray] = []\n for nn_idx, lane_obj in enumerate(nearby_lane_objs):\n centerline = lane_obj.centerline\n # densely sample more points\n sample_num = 50\n centerline = interp_arc(sample_num, centerline[:, 0], centerline[:, 1])\n dense_centerlines += [centerline]\n # compute norms to waypoints\n waypoint_dist = np.linalg.norm(centerline - query_xy_city_coords, axis=1).min()\n per_lane_dists += [waypoint_dist]\n per_lane_dists = np.array(per_lane_dists)\n min_dist_nn_indices = np.argsort(per_lane_dists)\n return per_lane_dists, min_dist_nn_indices, dense_centerlines","def _internal_get_upnp_coord(self):\n return self._upnp_coord","def location_input():\n \n # location for OPV greenhouse at UA CEAC in Tucson, AZ\n \n latitude = 32.28 # OPV greenhouse latitude (deg)\n longitude = -110.94 # OPV greenhouse longitude (deg)\n timezone = -7 # Tucson, AZ timezone (UTC)\n elevation = 718 # OPV greenhouse elevation (m)\n \n return latitude, longitude, timezone, elevation","def geo2desiredENU(self, lat, lon, h):\n\t\tlat0 = self.origin[0]\n\t\tlon0 = self.origin[1]\n\t\tx,y,z = pm.geodetic2enu(lat, lon, h, lat0, lon0, self.h0)\n\n\t\tx_L = cos(self.local_rot)*x + sin(self.local_rot)*y\n\t\ty_L = -1*sin(self.local_rot)*x + cos(self.local_rot)*y\n\n\t\tz = self.curr_z_enu - self.GND_ALT\n\t\treturn x_L, y_L, z","def _distance_to(self, coordinates):\n\n return abs(coordinates[0]) + abs(coordinates[1])","def get_location_metres(original_location, dNorth, dEast): \n [r_center, r_level] = eclipse_compensate(original_location)\n \n # coordinate offsets in radians\n dLat = dNorth / r_center\n dLon = dEast / r_level\n \n # new position in decimal degrees\n newlat = original_location.lat + math.degrees(dLat)\n newlon = original_location.lon + math.degrees(dLon)\n \n # return according to the input coordinate Class\n if isinstance(original_location,LocationGlobal):\n targetlocation = LocationGlobal(newlat, newlon,original_location.alt)\n \n elif isinstance(original_location,LocationGlobalRelative):\n targetlocation = LocationGlobalRelative(newlat, newlon,original_location.alt)\n \n else:\n raise Exception(\"Invalid Location object passed\")\n \n return targetlocation","def global_coords(self) -> GlobalCoordsABC:","def __init__(self):\n self.x = 0.0\n self.y = 0.0\n self.theta = 0.0\n self.total_distance_covered = 0.0","def create_pseudo_epsg4326_coordinates(self):\n self.create_3d_coord_on_sphere(on_sphere=True)\n self.df_attributes['lat'] = 180*(pi/2 - np.arccos(self.df_attributes['coord_z']))/pi\n self.df_attributes['lon'] = 180*np.arctan2(self.df_attributes['coord_y'], self.df_attributes['coord_x'])/pi","def distance(data):\n p = pi/180\n a = 0.5 - np.cos((data['end_lat']-data['lat'])*p)/2 + np.cos(data['lat']*p) * np.cos(data['end_lat']*p) * (1-np.cos((data['end_lon']-data['lon'])*p))/2\n data['distance_to_end'] = 12742 * np.arcsin(np.sqrt(a)) * 1000 \n b = 0.5 - np.cos((data['start_lat']-data['lat'])*p)/2 + np.cos(data['lat']*p) * np.cos(data['start_lat']*p) * (1-np.cos((data['start_lon']-data['lon'])*p))/2\n data['distance_from_start'] = 12742 * np.arcsin(np.sqrt(b)) * 1000 \n return data","def calculate_distance(x: float, y: float) -> float:\n # return geopy.distance.vincenty(x, y).km\n R = 6370\n lat1 = radians(x[0]) #insert value\n lon1 = radians(x[1])\n lat2 = radians(y[0])\n lon2 = radians(y[1])\n\n dlon = lon2 - lon1\n dlat = lat2- lat1\n\n a = sin(dlat / 2)**2 + cos(lat1) * cos(lat2) * sin(dlon / 2)**2\n c = 2 * atan2(sqrt(a), sqrt(1-a))\n distance = R * c\n return distance","def calculate_longitude(prior_lat, source_long, distance, heading):\n\n return ((source_long - \\\n math.asin(math.sin(heading)*math.sin(distance)/math.cos(prior_lat))\n + math.pi) % (2*math.pi)) - math.pi","def latlon_dist(input_lat, input_lon):\n eudist_un = []\n for dis in newgeo:\n slat = radians(float(input_lat)) # Input static Latitudefrom user\n slon = radians(float(input_lon)) # Input static Lontitude from user\n elat = radians(float(dis['lat']))\n elon = radians(float(dis['lon']))\n dist = round(6371.01 * acos(sin(slat)*sin(elat) +\n cos(slat)*cos(elat) *\n cos(slon - elon)), 2)\n eudist_un.append({\n \"ipv4\": dis[\"ipv4\"],\n \"lat\": dis[\"lat\"],\n \"lon\": dis[\"lon\"],\n \"dist\": dist\n })\n return eudist_un","def guess_anchor(self):\n return self.nearest(\"VP\")","def coord(self):\r\n return self.model.coord","def recalc_view_lonlat_limits(self):\n\n self.view_llon = self.map_llon + self.view_offset_x / self.ppd_x\n self.view_rlon = self.view_llon + self.view_width / self.ppd_x\n\n self.view_tlat = self.map_tlat - self.view_offset_y / self.ppd_y\n self.view_blat = self.view_tlat - self.view_height / self.ppd_y","def getDist(lat1,long1,lat2,long2):\n\tlat1 = math.radians(lat1)\n\tlong1 = math.radians(long1)\n\tlat2 = math.radians(lat2)\n\tlong2 = math.radians(long2)\n\tR = 6371 # km\n\td = cmath.acos(cmath.sin(lat1) * cmath.sin(lat2) + \\\n\tcmath.cos(lat1) * cmath.cos(lat2) *\n\tcmath.cos(long2 - long1)) * R\n\treturn abs(d) # cast to float","def _get_pose_distance_embedding(self, landmarks):\n embedding = np.array([\n # One joint.\n\n self._get_distance(\n self._get_average_by_names(landmarks, 'left_hip', 'right_hip'),\n self._get_average_by_names(landmarks, 'left_shoulder', 'right_shoulder')),\n\n self._get_distance_by_names(landmarks, 'left_shoulder', 'left_elbow'),\n self._get_distance_by_names(landmarks, 'right_shoulder', 'right_elbow'),\n\n self._get_distance_by_names(landmarks, 'left_elbow', 'left_wrist'),\n self._get_distance_by_names(landmarks, 'right_elbow', 'right_wrist'),\n\n self._get_distance_by_names(landmarks, 'left_hip', 'left_knee'),\n self._get_distance_by_names(landmarks, 'right_hip', 'right_knee'),\n\n self._get_distance_by_names(landmarks, 'left_knee', 'left_ankle'),\n self._get_distance_by_names(landmarks, 'right_knee', 'right_ankle'),\n\n # Two joints.\n\n self._get_distance_by_names(landmarks, 'left_shoulder', 'left_wrist'),\n self._get_distance_by_names(landmarks, 'right_shoulder', 'right_wrist'),\n\n self._get_distance_by_names(landmarks, 'left_hip', 'left_ankle'),\n self._get_distance_by_names(landmarks, 'right_hip', 'right_ankle'),\n\n # Four joints.\n\n self._get_distance_by_names(landmarks, 'left_hip', 'left_wrist'),\n self._get_distance_by_names(landmarks, 'right_hip', 'right_wrist'),\n\n # Five joints.\n\n self._get_distance_by_names(landmarks, 'left_shoulder', 'left_ankle'),\n self._get_distance_by_names(landmarks, 'right_shoulder', 'right_ankle'),\n \n self._get_distance_by_names(landmarks, 'left_hip', 'left_wrist'),\n self._get_distance_by_names(landmarks, 'right_hip', 'right_wrist'),\n\n # Cross body.\n\n self._get_distance_by_names(landmarks, 'left_elbow', 'right_elbow'),\n self._get_distance_by_names(landmarks, 'left_knee', 'right_knee'),\n\n self._get_distance_by_names(landmarks, 'left_wrist', 'right_wrist'),\n self._get_distance_by_names(landmarks, 'left_ankle', 'right_ankle'),\n\n # Body bent direction.\n\n # self._get_distance(\n # self._get_average_by_names(landmarks, 'left_wrist', 'left_ankle'),\n # landmarks[self._landmark_names.index('left_hip')]),\n # self._get_distance(\n # self._get_average_by_names(landmarks, 'right_wrist', 'right_ankle'),\n # landmarks[self._landmark_names.index('right_hip')]),\n ])\n\n return embedding","def set_coordinates(self):\n client = Client(api_key=settings.YANDEX_GEOCODER_KEY)\n address = f'Россия, {self.state}, {self.city}, {self.street}'\n self.longitude, self.latitude = client.coordinates(address)","def get_longitude0(self):\n return self.L","def lat_lng_approximation(homes, destination, lat_lng_dest, average_speed):\n\n for home in homes:\n # Stores the lat and lng points for the home\n lat_lng_home = (home.home.latitude, home.home.longitude)\n\n # Returns the distance from the two lat lng points in miles\n distance = geopy.distance.geodesic(lat_lng_home, lat_lng_dest).miles\n\n # If the distance is less than a mile then don't add any distance since it is already so close\n if distance > 1:\n # Extra distance is determined by giving more distance to homes farther away\n extra_distance = EXTRA_DISTANCE_LAT_LNG_APPROX * (1 - 1.0/distance)\n # This normalizes the value since walking needs less of a weight than biking since homes\n # are more direct when walking.\n distance += extra_distance * average_speed/AVERAGE_BICYCLING_SPEED\n if average_speed is not 0:\n # If the speed is not zero (to prevent divide by zero, then add the commute time to\n # the home\n commute_time_hours = distance / average_speed\n commute_time = commute_time_hours * 60\n home.approx_commute_times[destination] = commute_time\n else:\n # If there was a divide by zero then just eliminate the home\n home.eliminate_home()","def approach_gps(g_lat,g_lon,emily_lat_start, emily_lon_start, pose_rad, Parameters): #approach a gps position using potential fields\r\n\tx_goal,y_goal = latlongtoxy(g_lat,g_lon,g_lat)\r\n\tx_e_start,y_e_start = latlongtoxy(emily_lat_start,emily_lon_start,g_lat)\r\n\r\n\tprint (\"\\n HERE I AM\\n\\n\")\r\n\r\n\tdist = haver_distance(g_lat, g_lon, emily_lat_start, emily_lon_start)\r\n\tinitial_dist = dist\r\n\r\n\tprint ('Distance: ',dist)\r\n\theading = get_heading(emily_lat_start, emily_lon_start, g_lat, g_lon)\r\n print ('After get heading')\r\n\t# Eric: I'm not sure if turn_towards is necessary for a successful run.\r\n\t#turn_towards(heading)\r\n\tprint ('After Turn towards')\r\n\t#turn towards the goal initially\r\n\r\n\tstart_time = time.time()\r\n\tcurrent_time = 0\r\n\tdstore = []\r\n\thstore = []\r\n\twhile(dist >= goal_radius):\r\n\r\n\t\t#------------ code for reading gps location of emily and its orientation ------\r\n\t\te_lat = vehicle.location.global_frame.lat\r\n\t\te_lon = vehicle.location.global_frame.lon\r\n\t\te_heading = vehicle.heading * pi/180\t\t# convert heading to radians\r\n\t\t#------------------ get e_lat,e_lon, e_orient ---------------------\r\n\r\n\r\n\t\tx_e,y_e = latlongtoxy(e_lat,e_lon,g_lat)\t\t\t#change latitude and longitude to xy\r\n\r\n\t\t#x,y are given to approach victim function as y,x to algin the north heading and direction in x,y\r\n\r\n\t\tdx,dy = approach_victim_behaviour(y_goal,x_goal, y_e,x_e, pose_rad, Parameters)\t#get potential field vector\r\n\t\trc1, rc3 = dxdytorc(dx,dy, e_heading,g_lon)\t\t\t\t\t#get rc parameters\r\n\t\tdist = haver_distance(g_lat, g_lon, e_lat, e_lon)\t\t\t\t#haversine distance\r\n\r\n\t\tcurrent_time = time.time() - start_time\r\n\t\tprint (\"Time, Heading, Distance\")\r\n\t\tprint (current_time, e_heading*180/pi, dist)\r\n\t\tdstore.append(dist)\r\n\t\thstore.append(e_heading*180/pi)\r\n\t\t#code for sending the writing the rc commands\r\n\t\t# 3 is the thrust control\r\n\t\t#vehicle.channels.overrides = {'3':rc3}\r\n\t\tsendThrottleCommand(rc3, enableThrottle)\r\n\t\ttime.sleep(0.5)\r\n\t\tvehicle.channels.overrides = {'1':rc1}\r\n\t\tprint (\"Rudder: \",rc1)\r\n\t\tprint (\"Throttle: \",rc3)\r\n\t\tsaveToLog(e_lat, e_lon,dist,rc1,rc3)\r\n\t\ttime.sleep(0.5)\r\n\tprint(initial_dist)\r\n\tprint(\"intial \", emily_lat_start,emily_lon_start)\r\n\tprint(\"final \",e_lat,e_lon)\r\n\tplt.plot(dstore)\r\n\t#plt.title('Distance form home vs time')\r\n\tplt.xlabel(\"Time\")\r\n\tplt.ylabel('Distance')\r\n\tplt.show()\r\n\tplt.plot(hstore)\r\n\tplt.show()","def test_distance_between_points_near_0_longitude(self) -> None:\n distance = mod_geo.distance(latitude_1=0, longitude_1=0.1, elevation_1=0, latitude_2=0, longitude_2=-0.1, elevation_2=0, haversine=True)\n print(distance)\n self.assertTrue(distance < 230000)\n distance = mod_geo.distance(latitude_1=0, longitude_1=0.1, elevation_1=0, latitude_2=0, longitude_2=-0.1, elevation_2=0, haversine=False)\n print(distance)\n self.assertTrue(distance < 230000)\n distance = mod_geo.distance(latitude_1=0, longitude_1=0.1, elevation_1=0, latitude_2=0, longitude_2=360-0.1, elevation_2=0, haversine=True)\n print(distance)\n self.assertTrue(distance < 230000)\n distance = mod_geo.distance(latitude_1=0, longitude_1=0.1, elevation_1=0, latitude_2=0, longitude_2=360-0.1, elevation_2=0, haversine=False)\n print(distance)\n self.assertTrue(distance < 230000)","def main():\r\n location = gen_Location(\"0.856901647439813,14.08447265625\")\r\n add_location(location)","def distance_from_origin(self):\n return ((self.x ** 2) + (self.y ** 2)) **0.5","def closest_point(self, l):\n cos = np.dot(self.direction, l.direction)\n n = 1 - cos ** 2\n if n < sys.float_info.epsilon:\n # Lines are parallel.\n return self.zero\n\n d0 = l.zero - self.zero\n a = np.dot(d0, self.direction)\n b = np.dot(d0, l.direction)\n return self.zero + self.direction * ( a - b * cos) / n","def llh(self):\n return Station._ellipsoid.geodetic(self.xyz())","def update_position_direction(self, l):\n\n x = np.sqrt(self.x**2 + l**2 + 2 * l * self.x * self.mu)\n mu = (l + self.x * self.mu) / x\n\n return x, mu","def gps_to_coords(self,lat,lon):\n\n if (lat <= self.min_lat or lat >= self.max_lat or lon <= self.min_lon or lon >= self.max_lon):\n return (-1,-1)\n\n lat_spot = int((self.max_lat-lat)/self.lat_step)\n lon_spot = int((lon-self.min_lon)/self.lon_step)\n #print \"lat: %f lon: %f lat_spot: %f lon_spot: %f\" % (lat,lon,lat_spot,lon_spot)\n return (lat_spot,lon_spot)"],"string":"[\n \"def project(self, (lng, lat)):\\n x = lng * DEG_TO_RAD\\n lat = max(min(MAX_LATITUDE, lat), -MAX_LATITUDE)\\n y = lat * DEG_TO_RAD\\n y = math.log(math.tan((math.pi / 4) + (y / 2)))\\n return (x*EARTH_RADIUS, y*EARTH_RADIUS)\",\n \"def get_haversine_displacement(x, y, home_center):\\n sign_x = 1 if x > home_center.x else -1\\n displacement_x = ti.geogr.point_distances.haversine_dist(\\n x, home_center.y, home_center.x, home_center.y\\n )[0]\\n sign_y = 1 if y > home_center.y else -1\\n displacement_y = ti.geogr.point_distances.haversine_dist(\\n home_center.x, y, home_center.x, home_center.y\\n )[0]\\n return displacement_x * sign_x, displacement_y * sign_y\",\n \"def lat_lons(self):\",\n \"def compute_loc(self, loc, dist, bearing):\\n lat = self.nwsli_provider[loc][\\\"lat\\\"]\\n lon = self.nwsli_provider[loc][\\\"lon\\\"]\\n # shortcut\\n if dist == 0:\\n return lon, lat\\n meters = distance(float(dist), \\\"MI\\\").value(\\\"M\\\")\\n northing = meters * math.cos(math.radians(bearing)) / 111111.0\\n easting = (\\n meters\\n * math.sin(math.radians(bearing))\\n / math.cos(math.radians(lat))\\n / 111111.0\\n )\\n return lon + easting, lat + northing\",\n \"def initialCoordinates():\\r\\n return (-250,-250)\",\n \"def get_location_offset_meters(self, dNorth, dEast, alt):\\n earth_radius=6378137.0 #Radius of \\\"spherical\\\" earth\\n #Coordinate offsets in radians\\n dLat = dNorth/earth_radius\\n dLon = dEast/(earth_radius*math.cos(math.pi*self.home.lat/180))\\n\\n #New position in decimal degrees\\n newlat = self.home.lat + (dLat * 180/math.pi)\\n newlon = self.home.lon + (dLon * 180/math.pi)\\n return LocationGlobal(newlat, newlon,self.home.alt+alt)\",\n \"def get_distance_from_hq(self, lat, lon):\\n coords_1 = (40.646860, -8.642999) # Coords to the office\\n coords_2 = (lat, lon)\\n return distance.distance(coords_1, coords_2).km\",\n \"def nearlonlat_zl(lon,lat,lonp,latp): # needed for the next function get_FVCOM_bottom_temp \\r\\n # approximation for small distance \\r\\n cp=np.cos(latp*np.pi/180.) \\r\\n dx=(lon-lonp)*cp\\r\\n dy=lat-latp \\r\\n xi=np.argmin(abs(dx)) \\r\\n yi=np.argmin(abs(dy))\\r\\n min_dist=111*np.sqrt(dx[xi]**2+dy[yi]**2)\\r\\n return xi,yi,min_dist\",\n \"def lidar_relative(self):\\n return self.distance\",\n \"def add_on_land_position(self) -> Point2:\\n return self.position.offset(Point2((-2.5, 0.5)))\",\n \"def getSearchSpaceCoords(self):\",\n \"def coordinates(self):\",\n \"def getFeaturedLocation(guide):\\n photos = guide.photos.all()\\n\\n x = 0\\n y = 0\\n z = 0\\n\\n size = 0\\n\\n for photo in photos:\\n if photo.latitude:\\n lat = radians(float(photo.latitude))\\n lon = radians(float(photo.longitude))\\n x += cos(lat) * cos(lon)\\n y += cos(lat) * sin(lon)\\n z += sin(lat)\\n size+=1\\n\\n if size is 0:\\n return None\\n\\n x = float(x / size)\\n y = float(y / size)\\n z = float(z / size)\\n\\n return {\\n 'latitude': degrees(atan2(z, sqrt(x * x + y * y))),\\n 'longitude': degrees(atan2(y, x))\\n }\\n # return atan2(z, sqrt(x * x + y * y)), atan2(y, x)\\n\\n\\n\\n # for photo in photos:\\n # if photo.latitude:\\n # return {\\n # 'latitude': photo.latitude,\\n # 'longitude': photo.longitude\\n # }\\n\\n # return None\",\n \"def calculate_target_location(self, alphas, epsilons, data_collected):\\n if len(alphas) == 1:\\n \\tfor i in range(0, self.number_sampling_points-1):\\n \\t\\talphas.append(alphas[0])\\n \\t\\tepsilons.append(epsilons[0])\\n\\n # if self.target_location == None:\\n # # dBm_list = []\\n # # for sample in data_collected[0][3]:\\n # # dBm_list.append(sample)\\n\\n # # average_dBm = sum(dBm_list) / float(len(dBm_list))\\n # # radius_target_position = 10 ** ((average_dBm - self.epsilon) / self.alpha)\\n # # ###TODO: fix radius_target_position\\n # # if radius_target_position > self.altitude:\\n # # horizontal_distance = sqrt((radius_target_position**2) - (self.altitude**2))\\n # # else:\\n # # horizontal_distance = 0\\n\\n # local_position = self.dc.read_gps()\\n # local_coord = Coordinate(local_position.lat, local_position.lon)\\n\\n # first_emulated_target = local_coord.offset_toward_target(self.region.center(), DISTANCE_TO_TARGET)\\n\\n # self.log.debug('=========================================================================')\\n # self.log.debug('Calculated emulated target at location: {}'.format(first_emulated_target))\\n # self.log.debug('=========================================================================')\\n\\n # return first_emulated_target\\n\\n # else:\\n prediction = predict(dronenum=self.number_sampling_points,\\n maxRun=1,\\n numIterations=GDParameters.NUM_ITERATIONS,\\n numEpoch=GDParameters.NUM_EPOCH,\\n threshold=GDParameters.THRESHOLD,\\n learning_rate=GDParameters.LEARNING_RATE,\\n numberBatch=1,\\n data_length=NUMBER_SAMPLES*self.number_sampling_points)\\n\\n try:\\n target = prediction.swarm(drone_data=data_collected,\\n alphas=alphas,\\n epsilons=epsilons)\\n except IndexError:\\n self.log.warn('Target localization failed. Data not good enough.')\\n return False\\n\\n computed_target_position = Coordinate(target[0], target[1])\\n\\n self.log.debug('=========================================================================')\\n self.log.debug('Calculated new target at location: {}'.format(computed_target_position))\\n\\n if IS_SIMULATION:\\n error = computed_target_position.distance_to(self.current_simulated_target)\\n self.log.debug('Simulated error: {err}, Simulated target has moved {dist} meters to: {loc}'.format(\\n err=error,\\n dist=self.target_meters_moved,\\n loc=self.current_simulated_target\\n ))\\n self.log.debug('=========================================================================')\\n\\n if not self.region.contains(computed_target_position) and not IS_SIMULATION:\\n self.log.debug('New target is out of region')\\n self.log.debug('Setting new target location as the latest one calculated')\\n return self.target_location\\n\\n return computed_target_position\",\n \"def ll_coordinates(self):\\n\\n x, y = self.xy_coordinates\\n proj_out = check_crs('EPSG:4326')\\n\\n return transform_proj(self.proj, proj_out, x, y)\",\n \"def get_point_on_rhumb_line(self, distance, bearing):\\n\\n # bearing in assumed to be in degrees\\n bearing_in_radians = math.radians(bearing)\\n # ad is the angular distance (in radians) traveled.\\n ad = distance/A_WGS84\\n # dlat is the net change in latitude\\n dlat = ad*math.cos(bearing_in_radians)\\n # lat1 is the latitude of the starting point in radians.\\n lat1 = math.radians(self.lat)\\n # lng1 is the longitude of the starting point in radians.\\n lng1 = math.radians(self.lng)\\n # The new latitude, in radians, is the starting latitude plus the change in latitude\\n lat2 = lat1 + dlat\\n \\n dPhi = math.log(math.tan(lat2/2+math.pi/4)/math.tan(lat1/2+math.pi/4))\\n if dPhi == 0:\\n q = math.cos(lat1)\\n else:\\n q = dlat/dPhi\\n # The change in longitude\\n dlng = ad*math.sin(bearing_in_radians)/q\\n # If the latitude happens to go beyond the pole\\n if math.fabs(lat2) > math.pi/2:\\n # if the latitude is beyond 90 degrees north\\n if lat2>0:\\n # latitude should be 90 degree north\\n lat2 = math.pi/2\\n else:\\n # latidude is beyond 90 degree south\\n # latitude should be 90 degrees south\\n lat2 = -1*math.pi/2\\n # The new longitude, in radians \\n lng2 = (lng1+dlng+math.pi)%(2*math.pi)-math.pi\\n \\n lng2d = math.degrees(lng2)\\n lat2d = math.degrees(lat2)\\n return Point(float(truncate(lng2d,DEGREE_DIGITS)), float(truncate(lat2d,DEGREE_DIGITS)))\",\n \"def to_location(self) -> EarthLocation:\\n # Height is not in some MegaPrime files. Use the value from\\n # EarthLocation.of_site(\\\"CFHT\\\")\\n # Some data uses OBS-LONG, OBS-LAT, other data uses LONGITUD and\\n # LATITUDE\\n for long_key, lat_key in ((\\\"LONGITUD\\\", \\\"LATITUDE\\\"), (\\\"OBS-LONG\\\", \\\"OBS-LAT\\\")):\\n if self.are_keys_ok([long_key, lat_key]):\\n value = EarthLocation.from_geodetic(self._header[long_key], self._header[lat_key], 4215.0)\\n self._used_these_cards(long_key, lat_key)\\n break\\n else:\\n value = EarthLocation.of_site(\\\"CFHT\\\")\\n return value\",\n \"def unproject(self, (x, y)):\\n lng = x/EARTH_RADIUS * RAD_TO_DEG\\n lat = 2 * math.atan(math.exp(y/EARTH_RADIUS)) - math.pi/2 * RAD_TO_DEG\\n return (lng, lat)\",\n \"def get_point_from_distance_along_great_circle(self, distance, bearing):\\n \\n # ad is the angular distance (in radians) traveled.\\n ad = distance/A_WGS84\\n # lat1 is the latitude of the starting point in radians.\\n lat1 = math.radians(self.lat)\\n # lng1 is the longitude of the starting point in radians.\\n lng1 = math.radians(self.lng)\\n # b is the bearing direction in radians.\\n b = math.radians(bearing)\\n # lat2 is the latitude of the end point in radians.\\n lat2 = math.asin( math.sin(lat1) * math.cos(ad) + math.cos(lat1) * math.sin(ad) * math.cos(b) )\\n y = math.sin(b) * math.sin(ad) * math.cos(lat1)\\n x = math.cos(ad) - math.sin(lat1) * math.sin(lat2)\\n \\n \\\"\\\"\\\"Account for rounding errors. If x is very close to 0, set it to 0 to avoid \\n incorrect hemisphere determination.\\n For example, if x = -1.1e-16, atan2(0,x) will be -math.pi when it should be 0.\\\"\\\"\\\"\\n if math.fabs(x) < 1e-10:\\n x = 0\\n # lng2 is the longitude of the end point in radians.\\n lng2 = lng1 + math.atan2(y, x)\\n lng2d = math.degrees(lng2)\\n lat2d = math.degrees(lat2)\\n return Point(float(truncate(lng2d,DEGREE_DIGITS)), float(truncate(lat2d,DEGREE_DIGITS)))\",\n \"def haversine(test_point, training_locations):\\n test_point = test_point.reshape(1, 2)\\n difference = (test_point - training_locations) * np.pi / 180\\n test_point_lat = test_point[:, 1] * np.pi / 180\\n training_locations_lat = training_locations[:, 1] * np.pi / 180\\n \\n a = np.sin(difference[:, 0] / 2)**2 * np.cos(test_point_lat) * np.cos(training_locations_lat) +\\\\\\n np.sin(difference[:, 1] / 2)**2 \\n radius = 6371\\n c = 2 * np.arcsin(np.sqrt(a))\\n return radius * c\",\n \"def nancay():\\n return coord.EarthLocation(lat=47.376511*u.deg, lon=2.1924002*u.deg)\",\n \"def haversine(lon1, lat1, lon2, lat2): \\r\\n lon1, lat1, lon2, lat2 = map(radians, [lon1, lat1, lon2, lat2]) \\r\\n #print 34\\r\\n dlon = lon2 - lon1 \\r\\n dlat = lat2 - lat1 \\r\\n a = sin(dlat/2)**2 + cos(lat1) * cos(lat2) * sin(dlon/2)**2 \\r\\n c = 2 * atan(sqrt(a)/sqrt(1-a)) \\r\\n r = 6371 \\r\\n d=c * r\\r\\n #print type(d)\\r\\n return d\",\n \"def set_home_position(self, lat, lon, alt):\\n pass\",\n \"def calculate_H(s_lat,s_lon,e_lat,e_lon):\\n R = 6371.0\\n snlat = radians(s_lat)\\n snlon = radians(s_lon)\\n elat = radians(e_lat)\\n elon = radians(e_lon)\\n actual_dist = 6371.01 * acos(sin(snlat) * sin(elat) + cos(snlat) * cos(elat) * cos(snlon - elon))\\n actual_dist = actual_dist * 1000\\n return actual_dist\",\n \"def getPosition(self, request, context): \\n \\n # The latitude and longitude are relative to the WGS84 coordinate system. \\n # The altitude is relative to mean sea-level (MSL)\\n globalLocation = str(self.vehicle.location.global_frame)\\n \\n # LocationGlobalRelative:lat=0.0,lon=0.0,alt=6.6\\n # Altitude relative to the home location. The lat and long values for this and the above\\n # variable will be the same.\\n relativeLocation = str(self.vehicle.location.global_relative_frame)\\n \\n latitude, longitude, altGPS, altHome = None, None, None, None\\n \\n match = re.search(r\\\"lat=(.*),lon=(.*),alt=(.*)$\\\", globalLocation)\\n if match:\\n latitude = match.group(1)\\n longitude = match.group(2)\\n altGPS = match.group(3)\\n \\n match = re.search(r\\\"lat=(.*),lon=(.*),alt=(.*)$\\\", relativeLocation)\\n if match:\\n altHome = match.group(3)\\n\\t\\t\\t\\n return droneconnect_pb2.Position(lat = float(latitude),\\n lon = float(longitude),\\n gpsAltitude = float(altGPS),\\n relativeAltitude = float(altHome))\",\n \"def find_hypocenter(self):\\n ps_hypo = min(self.pointsources, key=lambda x: x.time_shift)\\n self.hypocenter_longitude = ps_hypo.longitude\\n self.hypocenter_latitude = ps_hypo.latitude\\n self.hypocenter_depth_in_m = ps_hypo.depth_in_m\",\n \"def calc_distance(self, observation):\\n actual_obs = observation[0]\\n scrn_player = actual_obs.observation.feature_screen.player_relative\\n scrn_select = actual_obs.observation.feature_screen.selected\\n scrn_density = actual_obs.observation.feature_screen.unit_density\\n\\n state_added = scrn_select + scrn_density\\n\\n marine_center = np.mean(self.xy_locs(scrn_player == 1), axis=0).round()\\n\\n # first step\\n if np.sum(scrn_select) == 0:\\n marine_center = np.mean(self.xy_locs(scrn_player == 1), axis=0).round()\\n # marine behind beacon\\n if isinstance(marine_center, float):\\n marine_center = np.mean(self.xy_locs(state_added == 2), axis=0).round()\\n else:\\n # normal navigation\\n marine_center = np.mean(self.xy_locs(state_added == 2), axis=0).round()\\n if isinstance(marine_center, float):\\n marine_center = np.mean(self.xy_locs(state_added == 3), axis=0).round()\\n\\n beacon_center = np.mean(self.xy_locs(scrn_player == 3), axis=0).round()\\n #\\n # print(state_added)\\n # print(\\\"---- Marine {} | {} Beacon ----\\\".format(marine_center, beacon_center))\\n # time.sleep(0.2)\\n distance = math.hypot(beacon_center[0] - marine_center[0],\\n beacon_center[1] - marine_center[1])\\n\\n return beacon_center, marine_center, distance\",\n \"def distance_to(self, p1):\\n # due to rounding errors the actual function can return non-zero distance for the same point!\\n if self.round() == p1.round():\\n return 0\\n from_theta = float(self.lat) / 360.0 * 2.0 * math.pi\\n from_landa = float(self.long) / 360.0 * 2.0 * math.pi\\n to_theta = float(p1.lat) / 360.0 * 2.0 * math.pi\\n to_landa = float(p1.long) / 360.0 * 2.0 * math.pi\\n tmp = math.sin(from_theta) * math.sin(to_theta) + math.cos(from_theta) * math.cos(to_theta) * math.cos(\\n to_landa - from_landa)\\n # if I don't round the number, ValueError: math domain error may occur\\n return math.acos(round(tmp, 15)) * R_EARTH\",\n \"def set_home_locations(self):\\n self.swarmie.set_home_gps_location(self.swarmie.get_gps_location())\\n\\n current_location = self.swarmie.get_odom_location()\\n current_pose = current_location.get_pose()\\n home_odom = Location(current_location.Odometry)\\n\\n detections = self.swarmie.get_latest_targets().detections\\n try:\\n for detection in detections:\\n if detection.id == 256:\\n see_home_tag = True\\n home_detection = self._transform_to_odom(detection)\\n\\n quat = [home_detection.pose.orientation.x,\\n home_detection.pose.orientation.y,\\n home_detection.pose.orientation.z,\\n home_detection.pose.orientation.w]\\n _r, _p, yaw = tf.transformations.euler_from_quaternion(\\n quat\\n )\\n yaw += math.pi / 2\\n\\n home_odom.Odometry.pose.pose.position.x = float(\\n home_detection.pose.position.x + 0.5 * math.cos(yaw)\\n )\\n home_odom.Odometry.pose.pose.position.y = float(\\n home_detection.pose.position.y + 0.5 * math.sin(yaw)\\n )\\n self.swarmie.set_home_odom_location(home_odom)\\n return\\n\\n except tf.Exception:\\n pass # use backup below\\n\\n # project home_odom location 50cm in front of rover's current location\\n home_odom.Odometry.pose.pose.position.x = (\\n current_pose.x + 0.5 * math.cos(current_pose.theta)\\n )\\n home_odom.Odometry.pose.pose.position.y = (\\n current_pose.y + 0.5 * math.sin(current_pose.theta)\\n )\\n self.swarmie.set_home_odom_location(home_odom)\\n return\",\n \"def distance_from(self, location_obj):\\n \\n distance = math.sqrt( (self.east - location_obj.east) ** 2 +\\n (self.north - location_obj.north) ** 2 )\\n return distance / 1000.0\",\n \"def calculate_waypoints(global_start, global_goal, global_home, data, drone_altitude, safety_distance):\\n # Calculate graph and offsets\\n graph, north_offset, east_offset = create_graph(data, drone_altitude, safety_distance)\\n\\n map_offset = np.array([north_offset, east_offset, .0])\\n\\n # Convert start position from global to local.\\n local_position = global_to_local(global_start, global_home) - map_offset\\n\\n # Find closest point to the graph for start\\n graph_start = closest_point(graph, local_position)\\n\\n # Convert goal postion from global to local\\n local_goal = global_to_local(global_goal, global_home) - map_offset\\n\\n # Find closest point to the graph for goal\\n graph_goal = closest_point(graph, local_goal)\\n\\n # Find path\\n path, _ = a_star(graph, graph_start, graph_goal)\\n path.append(local_goal)\\n\\n # Prune path\\n path = collinearity_prune(path, epsilon=1e-3)\\n\\n # Calculate waypoints\\n return [[int(p[0] + north_offset), int(p[1] + east_offset), drone_altitude, 0] for p in path]\",\n \"def lat_run_step(self, target_location):\\n v_begin = self.current_transform.location\\n v_end = v_begin + carla.Location(x=math.cos(math.radians(self.current_transform.rotation.yaw)),\\n y=math.sin(math.radians(self.current_transform.rotation.yaw)))\\n v_vec = np.array([v_end.x - v_begin.x, v_end.y - v_begin.y, 0.0])\\n w_vec = np.array([target_location.x -\\n v_begin.x, target_location.y -\\n v_begin.y, 0.0])\\n _dot = math.acos(np.clip(np.dot(w_vec, v_vec) /\\n (np.linalg.norm(w_vec) * np.linalg.norm(v_vec)), -1.0, 1.0))\\n _cross = np.cross(v_vec, w_vec)\\n\\n if _cross[2] < 0:\\n _dot *= -1.0\\n\\n self._lon_ebuffer.append(_dot)\\n if len(self._lon_ebuffer) >= 2:\\n _de = (self._lon_ebuffer[-1] - self._lon_ebuffer[-2]) / self.dt\\n _ie = sum(self._lon_ebuffer) * self.dt\\n else:\\n _de = 0.0\\n _ie = 0.0\\n\\n return np.clip((self._lat_k_p * _dot) + (self._lat_k_d * _de) + (self._lat_k_i * _ie), -1.0, 1.0)\",\n \"def project(self, latitude, longitude):\\n longitude = longitude if longitude > 0 else 360 + longitude\\n\\n lon_array = self.ecmwf_data.longitude\\n lat_array = self.ecmwf_data.latitude\\n\\n # Find in the lon_array / lat_array the index interval\\n # Including lon_ul and lat_ul\\n a_lon = np.where((lon_array < longitude))[0][-1]\\n if longitude > lon_array.max():\\n # lon is between 359.6 and 0 ...\\n b_lon = 0\\n else:\\n b_lon = np.where((lon_array >= longitude))[0][0]\\n\\n a_lat = np.where((lat_array < latitude))[0][0]\\n b_lat = np.where((lat_array >= latitude))[0][-1]\\n\\n # Compute geo extent around the point :\\n # => extent definition : LR,LL,UL,UR\\n extent = [lon_array[a_lon], lat_array[a_lat],\\n lon_array[b_lon], lat_array[a_lat],\\n lon_array[b_lon], lat_array[b_lat],\\n lon_array[a_lon], lat_array[b_lat]]\\n\\n extent_index = [a_lon, a_lat,\\n b_lon, a_lat,\\n b_lon, b_lat,\\n a_lon, b_lat]\\n\\n log.info(' - Selected vertex : ')\\n log.info('LL (px,ln) / (lon,lat) : (%s, %s) / (%s dd , %s dd)' % (\\n str(extent_index[0]), str(extent_index[1]), str(extent[0]), str(extent[1])))\\n log.info('LR (px,ln) / (lon,lat) : (%s, %s) / (%s dd , %s dd)' % (\\n str(extent_index[2]), str(extent_index[3]), str(extent[2]), str(extent[3])))\\n log.info('UR (px,ln) / (lon,lat) : (%s, %s) / (%s dd , %s dd)' % (\\n str(extent_index[4]), str(extent_index[5]), str(extent[4]), str(extent[5])))\\n log.info('UL (px,ln) / (lon,lat) : (%s, %s) / (%s dd , %s dd)' % (\\n str(extent_index[6]), str(extent_index[7]), str(extent[6]), str(extent[7])))\\n\\n # TIE Point grid defined - compute linear transformation\\n # to estimate value at the lat/lon location\\n # origin : extent_ul[0], extent_ul[1]\\n delta_lon = 0.4 # extent[4] - extent[6] # UR - UL\\n delta_lat = -0.4 # extent[1] - extent[7] # LL - UL\\n\\n lambda_lat = latitude - extent[7]\\n lambda_lon = longitude - extent[6]\\n\\n beta_longitude = lambda_lon / delta_lon\\n beta_latitude = lambda_lat / delta_lat\\n\\n # Processing of all keys\\n for key in self.ecmwf_data.mandatory_attributes:\\n M = getattr(self.ecmwf_data, key)\\n v = self.linear_estimate(M,\\n beta_latitude,\\n beta_longitude,\\n extent_index)\\n setattr(self, key, v)\",\n \"def augmentDistance(self):\\n\\n for key,value in self._models.iteritems():\\n src=[float(i) for i in value['src'].replace('#',' ').split()]\\n tgt=[float(i) for i in value['tgt'].replace('#',' ').split()]\\n\\n dist = haversine((np.mean(src[0:2]),np.mean(src[2:])),\\n (np.mean(tgt[0:2]),np.mean(tgt[2:])),\\n miles=True)\\n self._models[key]['distance'] = dist\\n\\n return\",\n \"def desiredENU2localSp(self, xd, yd, zd):\\n\\n\\t\\tif self.INDOOR:\\n\\t\\t\\tself.mavros_sp.position.x = xd\\n\\t\\t\\tself.mavros_sp.position.y = yd\\n\\t\\t\\tself.mavros_sp.position.z = zd\\n\\t\\telse:\\n\\n\\t\\t\\tlat, lon, alt = self.desiredENU2geo(xd, yd, zd)\\n\\t\\t\\t# converts to differences in robot's ENU\\n\\t\\t\\tx,y,z = pm.geodetic2enu(lat, lon, alt, self.curr_lat, self.curr_lon, self.gpsAlt)\\n\\n\\t\\t\\t# Add the differences to robot's current location\\n\\t\\t\\tself.mavros_sp.position.x = self.local_pose.pose.position.x + x\\n\\t\\t\\tself.mavros_sp.position.y = self.local_pose.pose.position.y + y\\n\\t\\t\\tself.mavros_sp.position.z = zd + self.GND_ALT\",\n \"def distance_to_origin(self):\\n\\n self.D = edist(self.isomap,\\n np.zeros([1, self.isomap.shape[1]])).flatten()\",\n \"def coordinates_abs(self, source):\\n if not hasattr(self, 'azimuth'):\\n return self.center_abs(source)\\n else:\\n return (*self.center_abs(source), self.azimuth, self.elevation)\",\n \"def Distance_orthonormique(lon1, lat1, lon2, lat2):\\r\\n \\r\\n #Convert position in radians\\r\\n lon1, lat1, lon2, lat2 = map(radians, [lon1, lat1, lon2, lat2])\\r\\n #rvmT = Earth radius [km]\\r\\n rvmT = 6371 \\r\\n #Project the position on\\r\\n a = sin((lat2 - lat1)/2)**2 + cos(lat1) * cos(lat2) * sin((lon2 - lon1)/2)**2\\r\\n c = 2 * asin(sqrt(a)) \\r\\n \\r\\n d = c * rvmT\\r\\n return d\",\n \"def _calculate_location(self, map_4d):\\n\\n # MARGINALISE: Determining the 3-D coalescence map\\n self.coa_map = np.log(np.sum(np.exp(map_4d), axis=-1))\\n\\n # Normalise\\n self.coa_map = self.coa_map/np.max(self.coa_map)\\n\\n # Fit 3-D spline function to small window around max coalescence\\n # location and interpolate to determine sub-grid maximum coalescence\\n # location.\\n loc_spline = self._splineloc(np.copy(self.coa_map))\\n\\n # Apply gaussian smoothing to small window around max coalescence\\n # location and fit 3-D gaussian function to determine local\\n # expectation location and uncertainty\\n smoothed_coa_map = self._gaufilt3d(np.copy(self.coa_map))\\n loc_gau, loc_gau_err = self._gaufit3d(np.copy(smoothed_coa_map),\\n thresh=0.)\\n\\n # Calculate global covariance expected location and uncertainty\\n loc_cov, loc_cov_err = self._covfit3d(np.copy(self.coa_map))\\n\\n return loc_spline, loc_gau, loc_gau_err, loc_cov, loc_cov_err\",\n \"def _normalize_location(lat: float, lon: float):\\n latitude = \\\"{0:.3f}\\\".format(round(lat, 3))\\n longitude = \\\"{0:.3f}\\\".format(round(lon, 3))\\n return latitude + \\\":\\\" + longitude\",\n \"def predict_qth(self):\\n return (self.latitude, -self.longitude, self.altitude)\",\n \"def calc_vehicle_offset(undist, left_fit, right_fit):\\n\\n bottom_y = undist.shape[0] - 1\\n bottom_x_left = left_fit[0]*(bottom_y**2) + left_fit[1]*bottom_y + left_fit[2]\\n bottom_x_right = right_fit[0]*(bottom_y**2) + right_fit[1]*bottom_y + right_fit[2]\\n vehicle_offset = undist.shape[1]/2 - (bottom_x_left + bottom_x_right)/2\\n\\n # Convert pixel offset to meters\\n vehicle_offset *= xm_per_pix\\n\\n return vehicle_offset\",\n \"def test_locate():\\n x, y = hyperbolic_location.locate(-.290955, -.08254229)\\n print(x, y)\",\n \"def easting(self):\\r\\n x, y = self.lonlat2xy(self.longitude, self.latitude)\\r\\n return x\",\n \"def distance_to_origin(self):\\n return np.sqrt(self.x ** 2 + self.y ** 2)\",\n \"def calcApproxDist(lon1, lat1, lon2, lat2):\\n\\n import math\\n from shapely.geometry import Point\\n\\n if lat1 == lat2 and lon1 == lon2:\\n return 0.0\\n\\n point1 = Point(lon1,lat1)\\n point2 = Point(lon2, lat2)\\n\\n return math.acos(math.sin(math.radians(point1.y))*math.sin(math.radians(point2.y))+math.cos(math.radians(\\n point1.y))*math.cos(math.radians(point2.y))*math.cos(math.radians(point2.x)-math.radians(point1.x)))*6371\",\n \"def calc_distance(user_loc, space):\\n geocode_result = gmaps.geocode(space['_location'])\\n dest_loc = geocode_result[0]['geometry']['location']\\n direction = gmaps.distance_matrix(user_loc, dest_loc, mode=\\\"walking\\\")\\n distance = direction['rows'][0]['elements'][0]['distance']['value']\\n # convert to mile\\n distance = distance * 0.000621371\\n return distance\",\n \"def ecliptic_position(self):\\n vector = _ECLIPJ2000.dot(self.position.au)\\n return Distance(vector)\",\n \"def CreateLocationByProximityQuery(self):\\n self.CenterPoint = self.randomGenerator.CreateRandomLattitudeLongitude()\\n self.SearchDistanceMiles = self.randomGenerator.CreateRandomNumber(self.minDistanceMiles,self.maxDistanceMiles)\\n self.SearchDistanceMeters = GeoConversion.ConvertMilesToMeters(self.SearchDistanceMiles)\\n QueryParameterDic = {}\\n QueryParameterDic['SearchType'] ='LocationByProximity'\\n QueryParameterDic['MaxResults'] = self.maxSearchResults\\n QueryParameterDic['MaxDistanceMeters'] = GeoConversion.ConvertMilesToMeters(self.SearchDistanceMiles)\\n QueryParameterDic['CenterPt'] = self.CenterPoint\\n QueryParameterDic['HasShopsOnly'] = 'True'\\n QueryParameterDic['FilterByTrueDistance'] = 'True'\\n\\n # Get a list of GpGridCells to search\\n gpSearch = GpSearch()\\n gpSearch.MaxSearchCellCount = self.MaxSearchCellCount\\n centerGpPoint = GpPoint(self.CenterPoint[0],self.CenterPoint[1])\\n gpSearch.ComputeSearchListForMetersProximity(centerGpPoint,self.SearchDistanceMeters)\\n self.FinalSearchResolution = gpSearch.FinalSearchResolution\\n self.SearchGpGridCells = gpSearch.SearchCellList\\n\\n QueryParameterDic['SearchGridCells'] = self.SearchGpGridCells\\n return QueryParameterDic\",\n \"def get_location_metres(original_location, dNorth, dEast):\\n earth_radius=6378137.0 #Radius of \\\"spherical\\\" earth\\n #Coordinate offsets in radians\\n dLat = dNorth/earth_radius\\n dLon = dEast/(earth_radius*math.cos(math.pi*original_location.lat/180))\\n\\n #New position in decimal degrees\\n newlat = original_location.lat + (dLat * 180/math.pi)\\n newlon = original_location.lon + (dLon * 180/math.pi)\\n\\n return dk.LocationGlobal(newlat, newlon,original_location.alt)\",\n \"def test_dist_itslef(self):\\n X = [[0, 10], [4, 2]] # Just some points. I've no idea where on globe.\\n c = cdist(X[0], X[1])\\n string_geopy = '{}'.format(great_circle(X[0], X[1]))\\n float_geopy = float(string_geopy[:-3])\\n self.assertTrue(np.round(c) == np.round(float_geopy))\\n\\n X = [[34.0522, 118.2437], # Lon Angeles\\n [37.7749, 122.4194]] # San Francisco\\n c = cdist(X[0], X[1])\\n string_geopy = '{}'.format(great_circle(X[0], X[1]))\\n float_geopy = float(string_geopy[:-3])\\n self.assertTrue(np.round(c) == np.round(float_geopy))\",\n \"def desiredENU2geo(self, x_L, y_L, z):\\n\\t\\tx = cos(self.local_rot)*x_L - sin(self.local_rot)*y_L\\n\\t\\ty = sin(self.local_rot)*x_L + cos(self.local_rot)*y_L\\n\\n\\t\\tlat0 = self.origin[0]\\n\\t\\tlon0 = self.origin[1]\\n\\n\\t\\tlat, lon, alt = pm.enu2geodetic(x, y, z, lat0, lon0, self.h0)\\n\\t\\treturn lat, lon, alt\",\n \"def save(self, *args, **kwargs):\\n self.distance_from_hq = self.get_distance_from_hq(self.lat, self.lon)\\n super().save(*args, **kwargs)\",\n \"def test_coord_preceding_fs(self):\",\n \"def closest_on_screen_point(trajectory, viewpoint, yaw, gaze_on_screen):\\n\\n traj_angles = dp.world_to_angles_through_screen(trajectory, viewpoint, yaw) \\n #pprint(traj_angles)\\n\\n #onscreen_idx, dists, *_ = find_closest_index(traj_angles, gaze_on_screen)\\n #idx = closest_node(traj_angles, gaze_on_screen)\\n idx = find_closest_index(traj_angles, gaze_on_screen)\\n # print(idx)\\n\\n #traj_ref = trajectory[idx, :]\\n screen_ref = traj_angles[idx, :]\\n world_ref = trajectory[idx, :]\\n\\n path_dist = ab_path_length(trajectory, viewpoint, world_ref)\\n path_dist /= 8.0 #time headway\\n\\n #plot_traj(screen_ref, gaze_on_screen, traj_angles)\\n\\n return(idx, screen_ref, world_ref, path_dist)#, traj_angles)\",\n \"def _get_dlon_dlat_km(self):\\n self.dlon_km=np.array([])\\n self.dlat_km=np.array([])\\n for lat in self.lat:\\n dist_lon, az, baz = obspy.geodetics.gps2dist_azimuth(lat, 0., lat, self.dlon)\\n dist_lat, az, baz = obspy.geodetics.gps2dist_azimuth(lat, 0., lat+self.dlat, 0.)\\n self.dlon_km=np.append(self.dlon_km, dist_lon/1000.)\\n self.dlat_km=np.append(self.dlat_km, dist_lat/1000.)\\n self.dlon_kmArr=(np.tile(self.dlon_km, self.Nlon).reshape(self.Nlon, self.Nlat)).T\\n self.dlat_kmArr=(np.tile(self.dlat_km, self.Nlon).reshape(self.Nlon, self.Nlat)).T\\n return\",\n \"def __calculateOffset(self):\\n #if len(self.__XValue) > 0:\\n # print(\\\"GPSBearing: \\\"+str(round(self.__GPSBearing[-1]))+\\\", heading: \\\"+str(round(self.value))+\\n # \\\", x: \\\"+str(round(self.__XValue[-1]))+\\\", stdev: \\\"+str(round(np.std(self.__GPSBearing),5))+\\n # \\\", offset: \\\"+str(self.__offset))\\n if len(self.__GPSBearing) == self.__GPSBearing.maxlen and np.std(self.__GPSBearing) < 0.2 and self.speed > 7:\\n self.__offset = (np.mean(self.__GPSBearing) + 360 - np.mean(self.__XValue)) % 360\\n #print(\\\"new offset: \\\"+str(self.__offset)) \",\n \"def calculate_vars(data, lat, lon):\\n # Keep track of running distance and time calculations\\n distance_to_dest = 0.0\\n time_estimate = 0.0\\n\\n # Calculate from starting dest to first point in data\\n user_coords = (lat, lon)\\n first_path_coords = (data[0][\\\"lat\\\"], data[0][\\\"lon\\\"])\\n first_distance = geopy.distance.distance(user_coords, first_path_coords).miles\\n distance_to_dest += first_distance\\n time_estimate += first_distance * 20 # 3mph walking speed\\n\\n # Calculate for all other points\\n for i in range(1, len(data) - 1):\\n this_coords = (data[i][\\\"lat\\\"], data[i][\\\"lon\\\"])\\n next_coords = (data[i + 1][\\\"lat\\\"], data[i + 1][\\\"lon\\\"])\\n\\n distance = geopy.distance.distance(this_coords, next_coords).miles\\n distance_to_dest += distance\\n time_estimate += distance * 20 # 3mph walking speed\\n\\n # Round distance and time estimates\\n distance_to_dest = round(distance_to_dest, 1)\\n time_estimate = round(time_estimate)\\n\\n return distance_to_dest, time_estimate\",\n \"def calculate_spot_center(source_position, light_dir):\\n x_spot = source_position[0] + light_dir[0]*source_position[2]/light_dir[2]\\n y_spot = source_position[1] + light_dir[1]*source_position[2]/light_dir[2]\\n return x_spot, y_spot, 0\",\n \"def calculate_latitude(source_lat, distance, heading):\\n return math.asin(math.sin(source_lat)*math.cos(distance) + \\\\\\n math.cos(source_lat)*math.sin(distance)*math.cos(heading))\",\n \"def northing(self):\\r\\n x, y = self.lonlat2xy(self.longitude, self.latitude)\\r\\n return y\",\n \"def getPoint(self, a):\\n lng = self.source.center.lng + (self.target.center.lng - self.source.center.lng) * min(a, 1)\\n lat = self.source.center.lat + (self.target.center.lat - self.source.center.lat) * min(a, 1)\\n return lng, lat\",\n \"def distance(self, lat: float, long: float) -> float:\\n # Initial euclidian formula below\\n # diff_lat = self.lat - lat\\n # diff_long = self.long - long\\n # euclidian = math.sqrt((diff_lat ** 2 + diff_long ** 2 + self.altitude ** 2))\\n\\n return self._haversine(lat, long) + self.altitude / 1000\",\n \"def dd2dm(lat,lon):\\r\\n lat_d = int(abs(lat)) #calculate latitude degrees\\r\\n lat_m = (abs(lat) - lat_d) * 60. #calculate latitude minutes\\r\\n\\r\\n lon_d = int(abs(lon))\\r\\n lon_m = (abs(lon) - lon_d) * 60.\\r\\n \\r\\n la=lat_d*100.+lat_m\\r\\n lo=lon_d*100.+lon_m\\r\\n return la,lo\",\n \"def calc_dist_to_poi(self,agent):\\n mini_dist = 100000 \\n for poi in self.poi_pos_list:\\n mini_dist = np.linalg.norm(agent.get_pos() - poi)\\n\\n return mini_dist\",\n \"def edwin_location(self, res):\\n if res.data[0:5] == \\\"WHERE\\\":\\n #gets edwin's location\\n where = res.data[5:]\\n\\n #massive string formatting - takes the string, splits by a formatter, then takes the array index that holds the XYZ,\\n #then strips that string and splits it by spacing, and then takes the XYZ\\n #reason for this massive formatting is b/c sent data format is not consistent\\n where = where.split(\\\"\\\\r\\\\n\\\")[2].strip().split(' ')\\n\\n #gets rid of empty strings that result from formatting\\n where = filter(None, where)[0:3]\\n\\n #makes everything numbers that can be used as coordinates for Edwin\\n where = [int(float(coord) * 10) for coord in where]\\n\\n self.edwinx = where[0]\\n self.edwiny = where[1]\\n self.edwinz = where[2]\",\n \"def top_coords_absolute(self):\\n pass\",\n \"def closest_point(\\n self, lx: float, ly: float, reference: Output | None = None\\n ) -> tuple[float, float]:\\n if reference:\\n reference_ptr = reference._ptr\\n else:\\n reference_ptr = ffi.NULL\\n\\n dest_lx = ffi.new(\\\"double *\\\")\\n dest_ly = ffi.new(\\\"double *\\\")\\n lib.wlr_output_layout_closest_point(\\n self._ptr, reference_ptr, lx, ly, dest_lx, dest_ly\\n )\\n return dest_lx[0], dest_ly[0]\",\n \"def get_nearest(infected_coordinates, uninfected_coordinates, d):\\n # Create tree from the GPS coordinates of uninfected users\\n tree = BallTree(uninfected_coordinates, leaf_size=15, metric='haversine')\\n indices,distances=tree.query_radius(infected_coordinates, r=d,return_distance=True)\\n indices=indices.transpose()\\n distances=distances.transpose()\\n return indices,distances\",\n \"def location(bot, update):\\n\\n bot.send_message(chat_id=update.message.chat_id, text=\\\"OK you wait ah...\\\")\\n latitude = update.message.location.latitude\\n longitude = update.message.location.longitude\\n bot.send_message(chat_id=update.message.chat_id, text=\\\"Just let you know for fun lol - your latitude is {0}, and your longitude is {1}\\\".format(latitude,longitude))\\n try:\\n # Read carpark csv as dataframe\\n df = pd.read_csv('Parking_withcoords.csv')\\n \\n # Calculate distance between each carpark and postal code and append it to dataframe\\n distance = []\\n for coord in df['Coord_rad']: \\n carpark = haversine((radians(latitude),radians(longitude)), ast.literal_eval(coord)) #converts string to tuple\\n distance.append(carpark)\\n df['Distance_km'] = distance\\n\\n # Sort in ascending order and extract top 5\\n top_five = df.sort_values('Distance_km').head(5)\\n\\n for row in top_five['Info']:\\n bot.send_message(chat_id=update.message.chat_id, parse_mode='HTML', text=row.replace(\\\"\\\\$\\\", \\\"$\\\"))\\n\\n bot.send_message(chat_id=update.message.chat_id, text=\\\"Fast hor! If you want to check other places, type /start again ok :P\\\")\\n except:\\n bot.send_message(chat_id=update.message.chat_id, text=\\\"Jialat liao got error...try again with /start and then use the postal code method can? Paiseh!\\\")\",\n \"def calculate(self):\\n\\n x_wamv = self.location[0]\\n y_wamv = self.location[1]\\n theta_wamv = self.heading\\n\\n x_wpt = self.waypoint_loc[0]\\n y_wpt = self.waypoint_loc[1]\\n\\n dy = y_wpt - y_wamv\\n dx = x_wpt - x_wamv\\n\\n # Could add in a 'round' if required.\\n theta_wpt = math.degrees(math.atan2(dx,dy))\\n\\n self.heading_publisher.publish(theta_wpt)\",\n \"def lane_waypt_to_query_dist(\\n query_xy_city_coords: np.ndarray, nearby_lane_objs: List[LaneSegment]\\n) -> Tuple[np.ndarray, np.ndarray, np.ndarray]:\\n per_lane_dists: List[float] = []\\n dense_centerlines: List[np.ndarray] = []\\n for nn_idx, lane_obj in enumerate(nearby_lane_objs):\\n centerline = lane_obj.centerline\\n # densely sample more points\\n sample_num = 50\\n centerline = interp_arc(sample_num, centerline[:, 0], centerline[:, 1])\\n dense_centerlines += [centerline]\\n # compute norms to waypoints\\n waypoint_dist = np.linalg.norm(centerline - query_xy_city_coords, axis=1).min()\\n per_lane_dists += [waypoint_dist]\\n per_lane_dists = np.array(per_lane_dists)\\n min_dist_nn_indices = np.argsort(per_lane_dists)\\n return per_lane_dists, min_dist_nn_indices, dense_centerlines\",\n \"def _internal_get_upnp_coord(self):\\n return self._upnp_coord\",\n \"def location_input():\\n \\n # location for OPV greenhouse at UA CEAC in Tucson, AZ\\n \\n latitude = 32.28 # OPV greenhouse latitude (deg)\\n longitude = -110.94 # OPV greenhouse longitude (deg)\\n timezone = -7 # Tucson, AZ timezone (UTC)\\n elevation = 718 # OPV greenhouse elevation (m)\\n \\n return latitude, longitude, timezone, elevation\",\n \"def geo2desiredENU(self, lat, lon, h):\\n\\t\\tlat0 = self.origin[0]\\n\\t\\tlon0 = self.origin[1]\\n\\t\\tx,y,z = pm.geodetic2enu(lat, lon, h, lat0, lon0, self.h0)\\n\\n\\t\\tx_L = cos(self.local_rot)*x + sin(self.local_rot)*y\\n\\t\\ty_L = -1*sin(self.local_rot)*x + cos(self.local_rot)*y\\n\\n\\t\\tz = self.curr_z_enu - self.GND_ALT\\n\\t\\treturn x_L, y_L, z\",\n \"def _distance_to(self, coordinates):\\n\\n return abs(coordinates[0]) + abs(coordinates[1])\",\n \"def get_location_metres(original_location, dNorth, dEast): \\n [r_center, r_level] = eclipse_compensate(original_location)\\n \\n # coordinate offsets in radians\\n dLat = dNorth / r_center\\n dLon = dEast / r_level\\n \\n # new position in decimal degrees\\n newlat = original_location.lat + math.degrees(dLat)\\n newlon = original_location.lon + math.degrees(dLon)\\n \\n # return according to the input coordinate Class\\n if isinstance(original_location,LocationGlobal):\\n targetlocation = LocationGlobal(newlat, newlon,original_location.alt)\\n \\n elif isinstance(original_location,LocationGlobalRelative):\\n targetlocation = LocationGlobalRelative(newlat, newlon,original_location.alt)\\n \\n else:\\n raise Exception(\\\"Invalid Location object passed\\\")\\n \\n return targetlocation\",\n \"def global_coords(self) -> GlobalCoordsABC:\",\n \"def __init__(self):\\n self.x = 0.0\\n self.y = 0.0\\n self.theta = 0.0\\n self.total_distance_covered = 0.0\",\n \"def create_pseudo_epsg4326_coordinates(self):\\n self.create_3d_coord_on_sphere(on_sphere=True)\\n self.df_attributes['lat'] = 180*(pi/2 - np.arccos(self.df_attributes['coord_z']))/pi\\n self.df_attributes['lon'] = 180*np.arctan2(self.df_attributes['coord_y'], self.df_attributes['coord_x'])/pi\",\n \"def distance(data):\\n p = pi/180\\n a = 0.5 - np.cos((data['end_lat']-data['lat'])*p)/2 + np.cos(data['lat']*p) * np.cos(data['end_lat']*p) * (1-np.cos((data['end_lon']-data['lon'])*p))/2\\n data['distance_to_end'] = 12742 * np.arcsin(np.sqrt(a)) * 1000 \\n b = 0.5 - np.cos((data['start_lat']-data['lat'])*p)/2 + np.cos(data['lat']*p) * np.cos(data['start_lat']*p) * (1-np.cos((data['start_lon']-data['lon'])*p))/2\\n data['distance_from_start'] = 12742 * np.arcsin(np.sqrt(b)) * 1000 \\n return data\",\n \"def calculate_distance(x: float, y: float) -> float:\\n # return geopy.distance.vincenty(x, y).km\\n R = 6370\\n lat1 = radians(x[0]) #insert value\\n lon1 = radians(x[1])\\n lat2 = radians(y[0])\\n lon2 = radians(y[1])\\n\\n dlon = lon2 - lon1\\n dlat = lat2- lat1\\n\\n a = sin(dlat / 2)**2 + cos(lat1) * cos(lat2) * sin(dlon / 2)**2\\n c = 2 * atan2(sqrt(a), sqrt(1-a))\\n distance = R * c\\n return distance\",\n \"def calculate_longitude(prior_lat, source_long, distance, heading):\\n\\n return ((source_long - \\\\\\n math.asin(math.sin(heading)*math.sin(distance)/math.cos(prior_lat))\\n + math.pi) % (2*math.pi)) - math.pi\",\n \"def latlon_dist(input_lat, input_lon):\\n eudist_un = []\\n for dis in newgeo:\\n slat = radians(float(input_lat)) # Input static Latitudefrom user\\n slon = radians(float(input_lon)) # Input static Lontitude from user\\n elat = radians(float(dis['lat']))\\n elon = radians(float(dis['lon']))\\n dist = round(6371.01 * acos(sin(slat)*sin(elat) +\\n cos(slat)*cos(elat) *\\n cos(slon - elon)), 2)\\n eudist_un.append({\\n \\\"ipv4\\\": dis[\\\"ipv4\\\"],\\n \\\"lat\\\": dis[\\\"lat\\\"],\\n \\\"lon\\\": dis[\\\"lon\\\"],\\n \\\"dist\\\": dist\\n })\\n return eudist_un\",\n \"def guess_anchor(self):\\n return self.nearest(\\\"VP\\\")\",\n \"def coord(self):\\r\\n return self.model.coord\",\n \"def recalc_view_lonlat_limits(self):\\n\\n self.view_llon = self.map_llon + self.view_offset_x / self.ppd_x\\n self.view_rlon = self.view_llon + self.view_width / self.ppd_x\\n\\n self.view_tlat = self.map_tlat - self.view_offset_y / self.ppd_y\\n self.view_blat = self.view_tlat - self.view_height / self.ppd_y\",\n \"def getDist(lat1,long1,lat2,long2):\\n\\tlat1 = math.radians(lat1)\\n\\tlong1 = math.radians(long1)\\n\\tlat2 = math.radians(lat2)\\n\\tlong2 = math.radians(long2)\\n\\tR = 6371 # km\\n\\td = cmath.acos(cmath.sin(lat1) * cmath.sin(lat2) + \\\\\\n\\tcmath.cos(lat1) * cmath.cos(lat2) *\\n\\tcmath.cos(long2 - long1)) * R\\n\\treturn abs(d) # cast to float\",\n \"def _get_pose_distance_embedding(self, landmarks):\\n embedding = np.array([\\n # One joint.\\n\\n self._get_distance(\\n self._get_average_by_names(landmarks, 'left_hip', 'right_hip'),\\n self._get_average_by_names(landmarks, 'left_shoulder', 'right_shoulder')),\\n\\n self._get_distance_by_names(landmarks, 'left_shoulder', 'left_elbow'),\\n self._get_distance_by_names(landmarks, 'right_shoulder', 'right_elbow'),\\n\\n self._get_distance_by_names(landmarks, 'left_elbow', 'left_wrist'),\\n self._get_distance_by_names(landmarks, 'right_elbow', 'right_wrist'),\\n\\n self._get_distance_by_names(landmarks, 'left_hip', 'left_knee'),\\n self._get_distance_by_names(landmarks, 'right_hip', 'right_knee'),\\n\\n self._get_distance_by_names(landmarks, 'left_knee', 'left_ankle'),\\n self._get_distance_by_names(landmarks, 'right_knee', 'right_ankle'),\\n\\n # Two joints.\\n\\n self._get_distance_by_names(landmarks, 'left_shoulder', 'left_wrist'),\\n self._get_distance_by_names(landmarks, 'right_shoulder', 'right_wrist'),\\n\\n self._get_distance_by_names(landmarks, 'left_hip', 'left_ankle'),\\n self._get_distance_by_names(landmarks, 'right_hip', 'right_ankle'),\\n\\n # Four joints.\\n\\n self._get_distance_by_names(landmarks, 'left_hip', 'left_wrist'),\\n self._get_distance_by_names(landmarks, 'right_hip', 'right_wrist'),\\n\\n # Five joints.\\n\\n self._get_distance_by_names(landmarks, 'left_shoulder', 'left_ankle'),\\n self._get_distance_by_names(landmarks, 'right_shoulder', 'right_ankle'),\\n \\n self._get_distance_by_names(landmarks, 'left_hip', 'left_wrist'),\\n self._get_distance_by_names(landmarks, 'right_hip', 'right_wrist'),\\n\\n # Cross body.\\n\\n self._get_distance_by_names(landmarks, 'left_elbow', 'right_elbow'),\\n self._get_distance_by_names(landmarks, 'left_knee', 'right_knee'),\\n\\n self._get_distance_by_names(landmarks, 'left_wrist', 'right_wrist'),\\n self._get_distance_by_names(landmarks, 'left_ankle', 'right_ankle'),\\n\\n # Body bent direction.\\n\\n # self._get_distance(\\n # self._get_average_by_names(landmarks, 'left_wrist', 'left_ankle'),\\n # landmarks[self._landmark_names.index('left_hip')]),\\n # self._get_distance(\\n # self._get_average_by_names(landmarks, 'right_wrist', 'right_ankle'),\\n # landmarks[self._landmark_names.index('right_hip')]),\\n ])\\n\\n return embedding\",\n \"def set_coordinates(self):\\n client = Client(api_key=settings.YANDEX_GEOCODER_KEY)\\n address = f'Россия, {self.state}, {self.city}, {self.street}'\\n self.longitude, self.latitude = client.coordinates(address)\",\n \"def get_longitude0(self):\\n return self.L\",\n \"def lat_lng_approximation(homes, destination, lat_lng_dest, average_speed):\\n\\n for home in homes:\\n # Stores the lat and lng points for the home\\n lat_lng_home = (home.home.latitude, home.home.longitude)\\n\\n # Returns the distance from the two lat lng points in miles\\n distance = geopy.distance.geodesic(lat_lng_home, lat_lng_dest).miles\\n\\n # If the distance is less than a mile then don't add any distance since it is already so close\\n if distance > 1:\\n # Extra distance is determined by giving more distance to homes farther away\\n extra_distance = EXTRA_DISTANCE_LAT_LNG_APPROX * (1 - 1.0/distance)\\n # This normalizes the value since walking needs less of a weight than biking since homes\\n # are more direct when walking.\\n distance += extra_distance * average_speed/AVERAGE_BICYCLING_SPEED\\n if average_speed is not 0:\\n # If the speed is not zero (to prevent divide by zero, then add the commute time to\\n # the home\\n commute_time_hours = distance / average_speed\\n commute_time = commute_time_hours * 60\\n home.approx_commute_times[destination] = commute_time\\n else:\\n # If there was a divide by zero then just eliminate the home\\n home.eliminate_home()\",\n \"def approach_gps(g_lat,g_lon,emily_lat_start, emily_lon_start, pose_rad, Parameters): #approach a gps position using potential fields\\r\\n\\tx_goal,y_goal = latlongtoxy(g_lat,g_lon,g_lat)\\r\\n\\tx_e_start,y_e_start = latlongtoxy(emily_lat_start,emily_lon_start,g_lat)\\r\\n\\r\\n\\tprint (\\\"\\\\n HERE I AM\\\\n\\\\n\\\")\\r\\n\\r\\n\\tdist = haver_distance(g_lat, g_lon, emily_lat_start, emily_lon_start)\\r\\n\\tinitial_dist = dist\\r\\n\\r\\n\\tprint ('Distance: ',dist)\\r\\n\\theading = get_heading(emily_lat_start, emily_lon_start, g_lat, g_lon)\\r\\n print ('After get heading')\\r\\n\\t# Eric: I'm not sure if turn_towards is necessary for a successful run.\\r\\n\\t#turn_towards(heading)\\r\\n\\tprint ('After Turn towards')\\r\\n\\t#turn towards the goal initially\\r\\n\\r\\n\\tstart_time = time.time()\\r\\n\\tcurrent_time = 0\\r\\n\\tdstore = []\\r\\n\\thstore = []\\r\\n\\twhile(dist >= goal_radius):\\r\\n\\r\\n\\t\\t#------------ code for reading gps location of emily and its orientation ------\\r\\n\\t\\te_lat = vehicle.location.global_frame.lat\\r\\n\\t\\te_lon = vehicle.location.global_frame.lon\\r\\n\\t\\te_heading = vehicle.heading * pi/180\\t\\t# convert heading to radians\\r\\n\\t\\t#------------------ get e_lat,e_lon, e_orient ---------------------\\r\\n\\r\\n\\r\\n\\t\\tx_e,y_e = latlongtoxy(e_lat,e_lon,g_lat)\\t\\t\\t#change latitude and longitude to xy\\r\\n\\r\\n\\t\\t#x,y are given to approach victim function as y,x to algin the north heading and direction in x,y\\r\\n\\r\\n\\t\\tdx,dy = approach_victim_behaviour(y_goal,x_goal, y_e,x_e, pose_rad, Parameters)\\t#get potential field vector\\r\\n\\t\\trc1, rc3 = dxdytorc(dx,dy, e_heading,g_lon)\\t\\t\\t\\t\\t#get rc parameters\\r\\n\\t\\tdist = haver_distance(g_lat, g_lon, e_lat, e_lon)\\t\\t\\t\\t#haversine distance\\r\\n\\r\\n\\t\\tcurrent_time = time.time() - start_time\\r\\n\\t\\tprint (\\\"Time, Heading, Distance\\\")\\r\\n\\t\\tprint (current_time, e_heading*180/pi, dist)\\r\\n\\t\\tdstore.append(dist)\\r\\n\\t\\thstore.append(e_heading*180/pi)\\r\\n\\t\\t#code for sending the writing the rc commands\\r\\n\\t\\t# 3 is the thrust control\\r\\n\\t\\t#vehicle.channels.overrides = {'3':rc3}\\r\\n\\t\\tsendThrottleCommand(rc3, enableThrottle)\\r\\n\\t\\ttime.sleep(0.5)\\r\\n\\t\\tvehicle.channels.overrides = {'1':rc1}\\r\\n\\t\\tprint (\\\"Rudder: \\\",rc1)\\r\\n\\t\\tprint (\\\"Throttle: \\\",rc3)\\r\\n\\t\\tsaveToLog(e_lat, e_lon,dist,rc1,rc3)\\r\\n\\t\\ttime.sleep(0.5)\\r\\n\\tprint(initial_dist)\\r\\n\\tprint(\\\"intial \\\", emily_lat_start,emily_lon_start)\\r\\n\\tprint(\\\"final \\\",e_lat,e_lon)\\r\\n\\tplt.plot(dstore)\\r\\n\\t#plt.title('Distance form home vs time')\\r\\n\\tplt.xlabel(\\\"Time\\\")\\r\\n\\tplt.ylabel('Distance')\\r\\n\\tplt.show()\\r\\n\\tplt.plot(hstore)\\r\\n\\tplt.show()\",\n \"def test_distance_between_points_near_0_longitude(self) -> None:\\n distance = mod_geo.distance(latitude_1=0, longitude_1=0.1, elevation_1=0, latitude_2=0, longitude_2=-0.1, elevation_2=0, haversine=True)\\n print(distance)\\n self.assertTrue(distance < 230000)\\n distance = mod_geo.distance(latitude_1=0, longitude_1=0.1, elevation_1=0, latitude_2=0, longitude_2=-0.1, elevation_2=0, haversine=False)\\n print(distance)\\n self.assertTrue(distance < 230000)\\n distance = mod_geo.distance(latitude_1=0, longitude_1=0.1, elevation_1=0, latitude_2=0, longitude_2=360-0.1, elevation_2=0, haversine=True)\\n print(distance)\\n self.assertTrue(distance < 230000)\\n distance = mod_geo.distance(latitude_1=0, longitude_1=0.1, elevation_1=0, latitude_2=0, longitude_2=360-0.1, elevation_2=0, haversine=False)\\n print(distance)\\n self.assertTrue(distance < 230000)\",\n \"def main():\\r\\n location = gen_Location(\\\"0.856901647439813,14.08447265625\\\")\\r\\n add_location(location)\",\n \"def distance_from_origin(self):\\n return ((self.x ** 2) + (self.y ** 2)) **0.5\",\n \"def closest_point(self, l):\\n cos = np.dot(self.direction, l.direction)\\n n = 1 - cos ** 2\\n if n < sys.float_info.epsilon:\\n # Lines are parallel.\\n return self.zero\\n\\n d0 = l.zero - self.zero\\n a = np.dot(d0, self.direction)\\n b = np.dot(d0, l.direction)\\n return self.zero + self.direction * ( a - b * cos) / n\",\n \"def llh(self):\\n return Station._ellipsoid.geodetic(self.xyz())\",\n \"def update_position_direction(self, l):\\n\\n x = np.sqrt(self.x**2 + l**2 + 2 * l * self.x * self.mu)\\n mu = (l + self.x * self.mu) / x\\n\\n return x, mu\",\n \"def gps_to_coords(self,lat,lon):\\n\\n if (lat <= self.min_lat or lat >= self.max_lat or lon <= self.min_lon or lon >= self.max_lon):\\n return (-1,-1)\\n\\n lat_spot = int((self.max_lat-lat)/self.lat_step)\\n lon_spot = int((lon-self.min_lon)/self.lon_step)\\n #print \\\"lat: %f lon: %f lat_spot: %f lon_spot: %f\\\" % (lat,lon,lat_spot,lon_spot)\\n return (lat_spot,lon_spot)\"\n]","isConversational":false},"negative_scores":{"kind":"list like","value":["0.6284721","0.6001774","0.5923108","0.59172964","0.5848251","0.58461255","0.5788823","0.5744928","0.5701934","0.56556606","0.56522423","0.56383795","0.5605939","0.556082","0.5559464","0.55476177","0.5541876","0.55329305","0.55219483","0.5511411","0.54956084","0.546798","0.54617983","0.54408294","0.5414723","0.5408058","0.5400798","0.5386142","0.53854847","0.5379186","0.5375146","0.53507906","0.5342446","0.5337335","0.53331536","0.5330718","0.53220373","0.53218657","0.5320891","0.53189987","0.5312347","0.5312035","0.5303219","0.5300909","0.5298059","0.5290681","0.52831304","0.52811956","0.5275971","0.52746785","0.5271925","0.5271867","0.5269647","0.52671087","0.5265802","0.52611214","0.5255117","0.52499044","0.524786","0.52467746","0.52446234","0.5239651","0.52343017","0.5227257","0.5222987","0.52217245","0.5218522","0.5217782","0.5216367","0.5215079","0.5210948","0.521046","0.52100706","0.5206635","0.5205477","0.5202924","0.5200142","0.51962733","0.51923317","0.5192043","0.5180402","0.5178292","0.51753336","0.5171434","0.5164145","0.51612383","0.5157838","0.5156455","0.51548725","0.5153302","0.5147254","0.5146061","0.5144882","0.5142979","0.51357704","0.51340246","0.51334274","0.513297","0.5132892","0.51328003"],"string":"[\n \"0.6284721\",\n \"0.6001774\",\n \"0.5923108\",\n \"0.59172964\",\n \"0.5848251\",\n \"0.58461255\",\n \"0.5788823\",\n \"0.5744928\",\n \"0.5701934\",\n \"0.56556606\",\n \"0.56522423\",\n \"0.56383795\",\n \"0.5605939\",\n \"0.556082\",\n \"0.5559464\",\n \"0.55476177\",\n \"0.5541876\",\n \"0.55329305\",\n \"0.55219483\",\n \"0.5511411\",\n \"0.54956084\",\n \"0.546798\",\n \"0.54617983\",\n \"0.54408294\",\n \"0.5414723\",\n \"0.5408058\",\n \"0.5400798\",\n \"0.5386142\",\n \"0.53854847\",\n \"0.5379186\",\n \"0.5375146\",\n \"0.53507906\",\n \"0.5342446\",\n \"0.5337335\",\n \"0.53331536\",\n \"0.5330718\",\n \"0.53220373\",\n \"0.53218657\",\n \"0.5320891\",\n \"0.53189987\",\n \"0.5312347\",\n \"0.5312035\",\n \"0.5303219\",\n \"0.5300909\",\n \"0.5298059\",\n \"0.5290681\",\n \"0.52831304\",\n \"0.52811956\",\n \"0.5275971\",\n \"0.52746785\",\n \"0.5271925\",\n \"0.5271867\",\n \"0.5269647\",\n \"0.52671087\",\n \"0.5265802\",\n \"0.52611214\",\n \"0.5255117\",\n \"0.52499044\",\n \"0.524786\",\n \"0.52467746\",\n \"0.52446234\",\n \"0.5239651\",\n \"0.52343017\",\n \"0.5227257\",\n \"0.5222987\",\n \"0.52217245\",\n \"0.5218522\",\n \"0.5217782\",\n \"0.5216367\",\n \"0.5215079\",\n \"0.5210948\",\n \"0.521046\",\n \"0.52100706\",\n \"0.5206635\",\n \"0.5205477\",\n \"0.5202924\",\n \"0.5200142\",\n \"0.51962733\",\n \"0.51923317\",\n \"0.5192043\",\n \"0.5180402\",\n \"0.5178292\",\n \"0.51753336\",\n \"0.5171434\",\n \"0.5164145\",\n \"0.51612383\",\n \"0.5157838\",\n \"0.5156455\",\n \"0.51548725\",\n \"0.5153302\",\n \"0.5147254\",\n \"0.5146061\",\n \"0.5144882\",\n \"0.5142979\",\n \"0.51357704\",\n \"0.51340246\",\n \"0.51334274\",\n \"0.513297\",\n \"0.5132892\",\n \"0.51328003\"\n]","isConversational":false},"document_score":{"kind":"string","value":"0.6179198"},"document_rank":{"kind":"string","value":"1"}}}],"truncated":false,"partial":true},"paginationData":{"pageIndex":753,"numItemsPerPage":100,"numTotalItems":95772,"offset":75300,"length":100}},"jwt":"eyJhbGciOiJFZERTQSIsImtpZCI6IjVHZDBvd0g5MTM2eDZjc1FvbE1zcktNWUZoRVFoUm5PVVVybEpjOUhaUEEifQ.eyJyZWFkIjp0cnVlLCJwZXJtaXNzaW9ucyI6eyJyZXBvLmNvbnRlbnQucmVhZCI6dHJ1ZX0sImlhdCI6MTc4NjY0MTU5MywianRpIjoiNzJiZTNiYTUtZjBiNy00M2ZmLWIyMTYtODQxMTQ1MGFjODcwIiwic3ViIjoiL2RhdGFzZXRzL25vbWljLWFpL2Nvcm5zdGFjay1weXRob24tdjEiLCJleHAiOjE3ODY2NDUxOTMsImlzcyI6Imh0dHBzOi8vaHVnZ2luZ2ZhY2UuY28ifQ.NeRNADzJw3sCgvXbEzKGCXL0vHjzXcehaCyjbVQddX-dXOQ7XazpoCFtYumrTPHkZgJkPKF26mQVeqq_kVxrCA","displayUrls":true,"splitSizeSummaries":[{"config":"default","split":"train","numRows":95772,"numBytesParquet":1928375891}]},"discussionsStats":{"closed":1,"open":1,"total":2},"fullWidth":true,"hasGatedAccess":true,"hasFullAccess":true,"isEmbedded":false,"savedQueries":{"community":[],"user":[]}}">
query stringlengths 9 3.4k | document stringlengths 9 87.4k | metadata dict | negatives listlengths 4 101 | negative_scores listlengths 4 101 | document_score stringlengths 3 10 | document_rank stringclasses 102
values |
|---|---|---|---|---|---|---|
Updates tree with new infections and stores information about new | def measure(self, recommender):
self._manage_new_infections(recommender.users_hat.value, recommender.infection_state)
self.observe(self.diffusion_tree.number_of_nodes(), copy=False)
self._old_infection_state = np.copy(recommender.infection_state.value) | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def updateTree(self):\n self.reset()\n self.resetTree() \n self.read()",
"def _tree_update(self, new_tree: Tree, event: Event):\n raise NotImplementedError()",
"def updatetree(self):\n if self.node:\n self.node.update()\n self.draw()",
"def _auxRefreshTree(self, tree_... | [
"0.7097137",
"0.6863075",
"0.6561149",
"0.63185143",
"0.6186158",
"0.6152338",
"0.61417896",
"0.6036651",
"0.6019624",
"0.6018832",
"0.5895768",
"0.58878404",
"0.5868002",
"0.58640766",
"0.5825007",
"0.57873863",
"0.5766704",
"0.57249945",
"0.5709253",
"0.5633454",
"0.5605295... | 0.0 | -1 |
Plots the tree using the Networkx library API. | def draw_tree(self):
nx.draw(self.diffusion_tree, with_labels=True) | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def plotTree(self):\n t = self.make(self.tree)\n t.draw()",
"def plot_tree(tree, name):\n graph = pydot.Dot(graph_type='graph')\n tree_graph = pydot.Cluster(\n graph_name=\"Learner Tree\",\n label=\"Learner Tree\",\n fontsize=\"15\",\n )\n graphlegend = pydot.Cluste... | [
"0.84178543",
"0.7434519",
"0.70544666",
"0.70324796",
"0.70190734",
"0.68298215",
"0.6808153",
"0.6801817",
"0.6801509",
"0.678496",
"0.67640483",
"0.67397344",
"0.66852444",
"0.66803414",
"0.667726",
"0.6659991",
"0.66083467",
"0.6587126",
"0.65706104",
"0.6558903",
"0.6558... | 0.7235767 | 2 |
Returns a measure of structural virality. Returns | def get_structural_virality(self):
num_nodes = self.diffusion_tree.number_of_nodes()
return wiener_index(self.diffusion_tree) / (num_nodes * (num_nodes - 1)) | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def fraction_of_infectious_virus(self) -> _VectorisedFloat:\n return self.virus.viable_to_RNA_ratio * (1 - self.host_immunity)",
"def fraction_of_infectious_virus(self) -> _VectorisedFloat:\n return 1.",
"def getViscosityLaw(self):\n return self.viscosityLaw",
"def compute_volume(self) -... | [
"0.62442523",
"0.61286414",
"0.60827076",
"0.60565484",
"0.6045784",
"0.60139126",
"0.5982267",
"0.5955694",
"0.58917797",
"0.5873444",
"0.584239",
"0.5830845",
"0.5823968",
"0.5816936",
"0.5791665",
"0.57834685",
"0.5768636",
"0.5765892",
"0.5763708",
"0.57599145",
"0.573346... | 0.7318086 | 0 |
Measures the average range (across users) of item attributes for items users were recommended at a time step. Used as a measure of within list recommendation diversity | def measure(self, recommender):
items_shown = recommender.items_shown
if items_shown.size == 0:
# at beginning of simulation, there are no recommendations,
# so we log a `None` value
self.observe(None)
return
recommended_item_attr = recommender.it... | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def find_predictions(actives, train_rdd_gbitem_dict, train_rdd_gbuser_dict, num_items):\n active_user = actives[0][0]\n active_item = actives[0][1]\n\n # -----------------------------------\n # train_rdd_gbitem_dict = (item, ([(user,r),(user,r)...],avg_of_item))\n # train_rdd_gbuser_dict = (user, [(... | [
"0.63024384",
"0.5934979",
"0.58648264",
"0.5863556",
"0.58444667",
"0.5841134",
"0.580924",
"0.57839996",
"0.5736375",
"0.57359916",
"0.56896806",
"0.56890374",
"0.5681781",
"0.5677384",
"0.56624997",
"0.5658554",
"0.5627349",
"0.561665",
"0.56071526",
"0.5561088",
"0.555942... | 0.7054138 | 0 |
Split the last dimension into (num_heads, depth). Transpose the result such that the shape is (batch_size, num_heads, seq_len, depth) | def split_heads(self, x, batch_size):
x = tf.reshape(x, (batch_size, -1, self.num_heads, self.depth))
return tf.transpose(x, perm=[0, 2, 1, 3]) | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def split_heads(x, batch_size, num_heads, depth):\n x = tf.reshape(x, (batch_size, -1, num_heads, depth))\n return tf.transpose(x, perm=[0, 2, 1, 3])",
"def _split_heads(x, num_heads):\n\tshape_lst = bert_utils.get_shape_list(x)\n\tdepth = shape_lst[-1]\n\tbatch = shape_lst[0]\n\tseq = shap... | [
"0.79167247",
"0.7599264",
"0.7481323",
"0.73289317",
"0.7253544",
"0.7161259",
"0.705449",
"0.6593404",
"0.6588756",
"0.6588756",
"0.6527045",
"0.63732386",
"0.62700814",
"0.60761714",
"0.60019857",
"0.5990543",
"0.5914784",
"0.5881012",
"0.5855259",
"0.58284926",
"0.5773926... | 0.7250492 | 11 |
Inits Movie with parameters movie_title, poster_image, trailer_youtube and review_imdb. | def __init__(self, movie_title, poster_image, trailer_youtube, review_imdb):
self.title = movie_title
self.poster_image_url = poster_image
self.trailer_youtube_url = trailer_youtube
self.imdb_url = review_imdb | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def __init__(self, movie_title, movie_storyline, movie_poster_image_url, movie_trailer):\n\t\tself.title = movie_title\n\t\tself.storyline = movie_storyline\n\t\tself.poster_image_url = movie_poster_image_url\n\t\tself.trailer_youtube_url = movie_trailer",
"def __init__(self, movie_title, movie_storyline, poster... | [
"0.7487349",
"0.73171014",
"0.7302299",
"0.728411",
"0.7268446",
"0.72576356",
"0.723372",
"0.7217655",
"0.7213409",
"0.7186125",
"0.71557635",
"0.715567",
"0.7118879",
"0.7104062",
"0.70794713",
"0.69712996",
"0.6897454",
"0.68907535",
"0.6795655",
"0.6761041",
"0.6745814",
... | 0.7561743 | 0 |
Assert `mean` aggfunc returns the same values as `average` with equal weights. | def test_avg_mean(forecasters):
y = make_forecasting_problem()
forecaster = EnsembleForecaster(forecasters)
forecaster.fit(y, fh=[1, 2, 3])
mean_pred = forecaster.predict()
forecaster_1 = EnsembleForecaster(forecasters, aggfunc="mean", weights=[1, 1])
forecaster_1.fit(y, fh=[1, 2, 3])
avg_p... | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def test_multiple_equal_weights(self):\n df = self.df.copy()\n weights = np.array([1.0 / len(df.index)] * len(df.index))\n out = nan_weighted_mean(df.values, weights=weights)\n self.assertTrue(\n np.allclose(out, np.average(df.values, weights=weights, axis=0))\n )",
... | [
"0.80318874",
"0.7917933",
"0.7491748",
"0.7478012",
"0.7416002",
"0.7332966",
"0.71670896",
"0.68208194",
"0.67587596",
"0.6553819",
"0.64296246",
"0.6391525",
"0.636191",
"0.6341783",
"0.63208413",
"0.6271881",
"0.6256403",
"0.62454754",
"0.6226937",
"0.62077683",
"0.619926... | 0.6780406 | 8 |
Assert aggfunc returns the correct values. | def test_aggregation_unweighted(forecasters, y, aggfunc):
forecaster = EnsembleForecaster(forecasters=forecasters, aggfunc=aggfunc)
forecaster.fit(y, fh=[1, 2, 3])
actual_pred = forecaster.predict()
predictions = []
_aggfunc = VALID_AGG_FUNCS[aggfunc]["unweighted"]
for _, forecaster in forecas... | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def agg(self, values, agg_func):\n assert len(values) > 0, \"Empty list of values\"\n f = agg_func.strip().lower()\n assert f in self.__AGGREGATIONS, \"Aggregation function \" + agg_func + \" is not valid\"\n\n ret = 0 # just to avoid \"Local variable might be referenced before assignm... | [
"0.6288633",
"0.6043896",
"0.59823674",
"0.5838905",
"0.5815745",
"0.57400197",
"0.57151836",
"0.56909937",
"0.5671473",
"0.566555",
"0.5657786",
"0.5644742",
"0.563922",
"0.56219685",
"0.56038195",
"0.55829823",
"0.55639493",
"0.55471826",
"0.5496884",
"0.54636925",
"0.54589... | 0.55470717 | 18 |
Assert weighted aggfunc returns the correct values. | def test_aggregation_weighted(forecasters, y, aggfunc, weights):
forecaster = EnsembleForecaster(
forecasters=forecasters, aggfunc=aggfunc, weights=weights
)
forecaster.fit(y, fh=[1, 2, 3])
actual_pred = forecaster.predict()
predictions = []
for _, forecaster in forecasters:
f =... | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def calculate_weighted_results():\n pass",
"def test_aggregation_unweighted(forecasters, y, aggfunc):\n forecaster = EnsembleForecaster(forecasters=forecasters, aggfunc=aggfunc)\n forecaster.fit(y, fh=[1, 2, 3])\n actual_pred = forecaster.predict()\n\n predictions = []\n\n _aggfunc = VALID_AGG_... | [
"0.69678855",
"0.69622105",
"0.68174344",
"0.64943796",
"0.62034446",
"0.61748505",
"0.61683464",
"0.610991",
"0.6063537",
"0.60567755",
"0.60419095",
"0.6034405",
"0.60315365",
"0.6015006",
"0.59978324",
"0.59898674",
"0.5972689",
"0.5967299",
"0.59628946",
"0.5956217",
"0.5... | 0.72802424 | 0 |
Check if invalid aggregation functions return Error. | def test_invalid_aggfuncs(forecasters, aggfunc):
y = make_forecasting_problem()
forecaster = EnsembleForecaster(forecasters=forecasters, aggfunc=aggfunc)
forecaster.fit(y, fh=[1, 2])
with pytest.raises(ValueError, match=r"not recognized"):
forecaster.predict() | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def test_forbid_aggregation(self):\n\n bad_examples = \"\"\"\nsum([score]) ->\nAggregations are not allowed in this field.\n\nsum([score])\n^\n===\nsum(score) ->\nAggregations are not allowed in this field.\n\nsum(score)\n^\n===\nsum(department) ->\nA str can not be aggregated using sum.\n\nsum(department)\... | [
"0.6858648",
"0.6550308",
"0.61447114",
"0.60995835",
"0.609905",
"0.59968966",
"0.59887826",
"0.57757694",
"0.57358176",
"0.5663834",
"0.55953944",
"0.5573108",
"0.5552472",
"0.5536463",
"0.55199814",
"0.5469119",
"0.54419756",
"0.541829",
"0.53880364",
"0.5378703",
"0.53744... | 0.5788905 | 7 |
Convert a Python list (xy list of a shape) into a Numpy matrix of vertices | def list2matrix_homog(xy_shape):
vertex_array = []
for i in range(0, len(xy_shape), 2):
vertex_x = xy_shape[ i]
vertex_y = xy_shape[i + 1]
extra_element = 1.0
vertex = [vertex_x, vertex_y, extra_element]
vertex_array.append(vertex)
vertex_matrix = num... | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def vertices(tri, vertex_list):\n dim = len(vertex_list[0])\n p = numpy.zeros((3, dim))\n for j in range(3):\n p[j] = vertex_list[tri[j]]\n return p",
"def _vertex_arrays_to_list(x_coords_metres, y_coords_metres):\n\n _check_polyline(\n x_coords_metres=x_coords_metres, y_coords_metre... | [
"0.69606847",
"0.6807603",
"0.66569346",
"0.6394897",
"0.62601906",
"0.6189537",
"0.61764187",
"0.6162913",
"0.61064583",
"0.60833645",
"0.607453",
"0.60307765",
"0.5982219",
"0.59796184",
"0.59634584",
"0.5934088",
"0.5903811",
"0.5898226",
"0.58964306",
"0.588976",
"0.58892... | 0.76167476 | 0 |
Convert Numpy matrix of vertices into a Python list (xy list of a shape) | def matrix2list(vertex_matrix):
flat_array = vertex_matrix.tolist()
xy_list = []
for i in range(0, len(flat_array)):
xy_list.append( flat_array[i][0] )
xy_list.append( flat_array[i][1] )
return xy_list | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def _vertex_arrays_to_list(x_coords_metres, y_coords_metres):\n\n _check_polyline(\n x_coords_metres=x_coords_metres, y_coords_metres=y_coords_metres)\n\n num_vertices = len(x_coords_metres)\n vertex_list_xy_metres = []\n for i in range(num_vertices):\n vertex_list_xy_metres.append((x_coo... | [
"0.7413975",
"0.7025411",
"0.68816763",
"0.6750809",
"0.6745582",
"0.66855454",
"0.65625846",
"0.6517452",
"0.64930856",
"0.6437436",
"0.64248884",
"0.6411927",
"0.6367023",
"0.63419783",
"0.6332802",
"0.6315702",
"0.63048035",
"0.62930965",
"0.6277304",
"0.62731403",
"0.6272... | 0.8255958 | 0 |
Shift the shape in the xdirection and ydirection by shift_x and shift_y respectively. | def Translate(shift_x, shift_y):
shifted = numpy.matrix([[1.0, 0.0, 1.0 ],
[0.0, 1.0, 1.0 ],
[shift_x, shift_y, 1.0 ]])
return shifted | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def translate_shape(shape, x_shift, y_shift):",
"def translate_shape(shape, x_shift, y_shift): \n new_shape = [] \n for i in range(int(len(shape)/2 )): \n x_new = shape[2*i] + x_shift \n y_new = shape[2*i+1] + y_shift \n new_shape.append(x_new) \n new_shape.append(y_new) \n ... | [
"0.71501184",
"0.6926701",
"0.68378925",
"0.67621315",
"0.6599585",
"0.6575673",
"0.655384",
"0.6439177",
"0.6411115",
"0.63591564",
"0.62583876",
"0.61242646",
"0.6087045",
"0.59914124",
"0.5948126",
"0.59432817",
"0.5911263",
"0.58916354",
"0.58784395",
"0.58392805",
"0.583... | 0.59613466 | 14 |
Copy the worldview and its immediate neighbours. | def locality_copy(self):
new = copy(self)
new.north = copy(self.north)
new.south = copy(self.south)
new.east = copy(self.east)
new.west = copy(self.west)
return new | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def copy(self):\n return _coordsys.coordsys_copy(self)",
"def copy(self):\n\t\tb = Board(self.size, self.end_count)\n\t\tfor x in range(self.size):\n\t\t\tfor y in range(self.size):\n\t\t\t\tb.tiles[x][y] = self.tiles[x][y]\n\t\treturn b",
"def copy(self):\n return self.__class__(*self.maps[:-1],... | [
"0.61862814",
"0.57994807",
"0.5732002",
"0.5688422",
"0.5686226",
"0.5660167",
"0.5655698",
"0.56276125",
"0.56194663",
"0.56163204",
"0.56155884",
"0.56155884",
"0.5602962",
"0.56004703",
"0.55852944",
"0.5538871",
"0.5536237",
"0.5529703",
"0.5507877",
"0.5504208",
"0.5503... | 0.6385162 | 0 |
Test whether this location is the last place the current person has visited. | def is_most_recent_location(self, person): | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def has_current_location(self):\n return self.location_set.current_location is not None",
"def is_finish(self,location):\n return location[0] == self.columns - 1 and location[1] == self.rows - 1",
"def is_last_position(self):\r\n return self.position >= len(self.rule.rightside)",
"def is... | [
"0.7002344",
"0.6673924",
"0.660607",
"0.65718144",
"0.65527457",
"0.6518544",
"0.6288451",
"0.62500423",
"0.6199464",
"0.6172359",
"0.6069799",
"0.60570204",
"0.6023481",
"0.5978436",
"0.5900725",
"0.58284974",
"0.5796673",
"0.5783083",
"0.5769586",
"0.5756217",
"0.57546085"... | 0.7422763 | 0 |
Allow sorting of people. | def __lt__(self, other):
return self.name < other.name | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def sortby(self):\n ...",
"def people(persons):\n sorted_list = sorted(persons, key=lambda k: k['age'])\n return sorted_list",
"def sorted(self): \n pass",
"def _place_list_for_person(self, person):\n list = []\n for event in self.sort:\n if ( even... | [
"0.728682",
"0.69563615",
"0.6764732",
"0.6616113",
"0.64332837",
"0.6432068",
"0.63607216",
"0.62676686",
"0.62516946",
"0.6250053",
"0.62266594",
"0.6220339",
"0.6183714",
"0.6153633",
"0.6141533",
"0.6140396",
"0.6140396",
"0.6107826",
"0.6089885",
"0.60787123",
"0.6062166... | 0.0 | -1 |
Injure this person by a certain amount (amount<=1.0) amount may be negative to heal | def injure(self, amount):
self.health -= amount
if self.health < 0.0:
self.health = 0.0
if self.health > 1.0:
self.health = 1.0 | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def give_raise(self,amount=5000):\n self.salary += amount",
"def give_raise(self, amount=5000):\n self.salary += amount",
"def heal(self, amount: int) -> str:\n if self.hp + amount > MAX_HP:\n amount = MAX_HP - self.hp\n new_hp = self.hp + amount\n self.hp = new_h... | [
"0.6924608",
"0.69075024",
"0.69006264",
"0.6801079",
"0.67490596",
"0.6691288",
"0.66403013",
"0.6638185",
"0.65639347",
"0.64924115",
"0.64563555",
"0.6416064",
"0.630171",
"0.6301572",
"0.62866527",
"0.62706417",
"0.62615544",
"0.6211703",
"0.6164884",
"0.6131726",
"0.6104... | 0.7328159 | 0 |
Convenience method for self.diary.log(...) | def log(self, event_cls, *args, **kw):
args = list(args)
args.append(self.time)
args.append(self)
args.append(self.worldview.locality_copy())
self.diary.log(self.time, event_cls(*args, **kw)) | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def log(self, message):",
"def _log(self, message):\n pass",
"def log(self, message: str):",
"def log_message(self, format, *args):",
"def logger(self, value):\n pass",
"def log(self, level, msg, *args, **kwargs):\n pass",
"def do_log(self, arg):\n arg = \" %s :custom l... | [
"0.77647996",
"0.76023424",
"0.7586511",
"0.73101985",
"0.7211697",
"0.718583",
"0.71120936",
"0.7088803",
"0.7069826",
"0.7067038",
"0.7059218",
"0.7045978",
"0.70359576",
"0.70248723",
"0.70237345",
"0.7021706",
"0.70182997",
"0.69959927",
"0.69920385",
"0.6989792",
"0.6963... | 0.6743905 | 35 |
Swap suits to remove most symmetries. Modifies cards in place Fails to remove some in the case where there is a lowerranked pair or triple that shares a suit with a higherranked card. | def swap_suits(cards):
cards_need_swap = cards
new_suit = 5
while cards_need_swap.shape[0] > 0:
suit = cards_need_swap[0,1]
cards[cards[:,1] == suit, 1] = new_suit
new_suit += 1
cards_need_swap = cards[cards[:,1] < 5]
cards[:,1] = cards[:,1] - 4
return cards | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def swap_suits(cards):\n cards_need_swap = cards\n new_suit = 5\n while cards_need_swap.shape[0] > 0:\n suit = cards_need_swap[0,1]\n cards[cards[:,1] == suit, 1] = new_suit\n new_suit += 1\n cards_need_swap = cards[cards[:,1] < 5]\n cards[:,1] = cards[:,1] - 4\n return c... | [
"0.731475",
"0.6521344",
"0.64044553",
"0.5974011",
"0.58290917",
"0.57423127",
"0.56717587",
"0.55904925",
"0.55786467",
"0.5472148",
"0.5469577",
"0.54659194",
"0.5464322",
"0.5456538",
"0.54394937",
"0.54057246",
"0.5331853",
"0.5318002",
"0.5312691",
"0.530591",
"0.530110... | 0.73551816 | 0 |
to learn the attention of one channel and the rest channels | def channelAttention(self,x):
b, c, h, w = x.shape
pre_x = x.contiguous().view(b, c, h * w)
att_x, relations = self.forward_unified(pre_x, pre_x, pre_x)
att_x = att_x.contiguous().view(b, c, h, w)
return att_x | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def _attention(self, inputs):\n attn_weights = K.batch_dot(x=inputs,\n y=K.permute_dimensions(inputs,\n pattern=(0, 2, 1)))\n return K.permute_dimensions(attn_weights, (0, 2, 1))",
"def attention(self, decode... | [
"0.6191988",
"0.6182749",
"0.61674166",
"0.6110955",
"0.60878927",
"0.6033937",
"0.60321414",
"0.6014058",
"0.60054046",
"0.5966352",
"0.5961111",
"0.59551805",
"0.5920994",
"0.59022796",
"0.5893938",
"0.58766156",
"0.5868471",
"0.5847925",
"0.58348364",
"0.5827881",
"0.58140... | 0.62221354 | 0 |
to learn the attention of one position and the other position in spicial scale. | def pixelAttention(self,x):
b, c, w, h = x.shape
pre_x = x.permute(0, 2, 3, 1)
pre_x = pre_x.contiguous().view(b, h * w, c)
att_x, relations = self.forward_unified(pre_x, pre_x, pre_x)
att_x = att_x.contiguous().view(b, h, w, c)
att_x = att_x.permute(0, 3, 1, 2)
r... | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def compute_attention(t1, t2):\n dim = t1.shape.as_list()[2]\n init = tf.constant_initializer(1.0 / dim)\n\n t1_logits = ops.last_dim_weighted_sum(t1, \"t1_w\")\n t2_logits = ops.last_dim_weighted_sum(t2, \"t2_w\")\n\n dot_w = tf.get_variable(\n \"dot_w\", shape=dim, initializer=init, dtype=tf.float32)\n... | [
"0.59121007",
"0.5880821",
"0.58658934",
"0.57155955",
"0.56898075",
"0.5676364",
"0.5617764",
"0.54645956",
"0.5413231",
"0.53985935",
"0.5354993",
"0.5340669",
"0.53163207",
"0.53115535",
"0.53035134",
"0.5289783",
"0.5286561",
"0.5261909",
"0.52617943",
"0.5252311",
"0.525... | 0.51107275 | 38 |
to learn the self_attention of n objects | def blockAttention(self,x):
b, n, c, w, h = x.shape
pre_x = x.contiguous().view(b, n, -1)
att_x, relations = self.forward_unified(pre_x, pre_x, pre_x)
att_x = att_x.contiguous().view(b, n, c, w, h)
return att_x | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def learn(self,n):\n for i in range(n):\n self.class_counts,self.feature_counts = self.em_step(self.class_counts,\n self.feature_counts)",
"def learn(self):\n pass",
"def learn(self):\n pass",
"def learn(self):\n ... | [
"0.72311056",
"0.66329956",
"0.66329956",
"0.6434422",
"0.6313986",
"0.6202342",
"0.6121104",
"0.6064416",
"0.6046662",
"0.5997235",
"0.5904722",
"0.58928293",
"0.58772796",
"0.5854052",
"0.5809342",
"0.576956",
"0.5754304",
"0.5736277",
"0.5731372",
"0.57274973",
"0.57257706... | 0.0 | -1 |
Create test client, add sample data. | def setUp(self):
db.drop_all()
db.create_all()
self.client = app.test_client()
User.query.delete()
Team.query.delete()
# SET DATE ##
curr_date = datetime.datetime.now().strftime('%Y-%m-%d')
## ADD USERS ##
coach1 = User(username="coach_E", pass... | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def test_create_client(self):\n pass",
"def test_client_create(self):\n pass",
"def setUp(self):\n self.client = DummyClient()",
"def testclient():\n base_url = PARAMS.get(\"url\") + \"/v2\"\n client = Client(\n base_url=base_url,\n headers={\n \"Authorizat... | [
"0.7501162",
"0.720549",
"0.7062802",
"0.6803163",
"0.6757587",
"0.6726198",
"0.6722994",
"0.668832",
"0.668832",
"0.668832",
"0.668832",
"0.66681767",
"0.6666075",
"0.6655439",
"0.6596407",
"0.65942925",
"0.6584745",
"0.6527722",
"0.65173876",
"0.65142703",
"0.64355755",
"... | 0.0 | -1 |
List all naf tournaments from members.thenaf.net | def load_tournaments(renew_time=3600, force=False):
filename = "data/naf_tourneys.html"
LOG.debug("Loading tournaments")
last_modified = round((time.time() - os.path.getmtime(filename)))
LOG.debug("data/naf_tourneys.html modified %ss ago", last_modified)
if force or last_modified > renew_time:
... | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def list_tournaments(renew_time=False, force=None):\n filename = \"data/naf_tourneys.html\"\n LOG.debug(\"Listing tournaments\")\n\n if renew_time or force:\n return load_tournaments(renew_time=renew_time, force=force)\n tournaments = load_cached(nafparser.parse_tournaments, filename)\n\n ret... | [
"0.7149776",
"0.62914014",
"0.6109366",
"0.5846553",
"0.57829034",
"0.56696373",
"0.5601255",
"0.5601255",
"0.5601255",
"0.5601255",
"0.55882907",
"0.55572516",
"0.55448544",
"0.55409384",
"0.54748046",
"0.54364485",
"0.53815234",
"0.53773314",
"0.5302785",
"0.52871",
"0.5272... | 0.5313395 | 18 |
List all naf tournaments from members.thenaf.net | def list_tournaments(renew_time=False, force=None):
filename = "data/naf_tourneys.html"
LOG.debug("Listing tournaments")
if renew_time or force:
return load_tournaments(renew_time=renew_time, force=force)
tournaments = load_cached(nafparser.parse_tournaments, filename)
return tournaments | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def get_tournament_list():\n database = TinyDB('db.json')\n tournament_list = database.table('tournaments').all()\n return tournament_list",
"def display_tournament_list():\r\n for tournament in tournaments_table:\r\n print(tournament['Nom'])",
"def house_members(*, verbose=False):\n... | [
"0.62914014",
"0.6109366",
"0.5846553",
"0.57829034",
"0.56696373",
"0.5601255",
"0.5601255",
"0.5601255",
"0.5601255",
"0.55882907",
"0.55572516",
"0.55448544",
"0.55409384",
"0.54748046",
"0.54364485",
"0.53815234",
"0.53773314",
"0.5313395",
"0.5302785",
"0.52871",
"0.5272... | 0.7149776 | 0 |
addone function takes a number as an array with an element for each digit. It adds one to the number and returns the result as a similar array. Any leading zeroes will be removed. | def addone(arg):
for s in arg[:]:
if s == 0: # Remove any leading zeroes
arg.remove(s)
if arg[len(arg) - 1] != 9:
arg[len(arg) - 1] += 1 # Add one to the last element.
else: # If the last number is nine then the second last
# element must also be changed.
if le... | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def plusOne(self, digits: List[int]) -> List[int]:\n n = len(digits)\n for i in reversed(range(n)):\n # set all the nines at the end of array to zeros\n if digits[i] == 9:\n digits[i] = 0\n\n # here we have the rightmost not-nine \n else:\n # increase this... | [
"0.7688615",
"0.7510205",
"0.64064455",
"0.63816565",
"0.6377803",
"0.6266959",
"0.6123707",
"0.6068747",
"0.5995501",
"0.59779876",
"0.5936203",
"0.58488744",
"0.5805329",
"0.56270254",
"0.558196",
"0.5570155",
"0.5533241",
"0.5513251",
"0.55027306",
"0.54867077",
"0.5482474... | 0.86135465 | 0 |
Saves score to db | def create(cls, data):
doc = {
"type": cls.type,
**data
}
db_utils.set_created_at(doc)
LOG.debug("Adding score {} with data: {}".format(cls.type, data))
return DB.save(doc) | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def write_to_db(name: str, score: float):\n score = float(score)\n\n with open('db.json') as fo:\n data = loads(fo.read())\n\n data[name] = score\n\n with open('db.json', 'w') as fo:\n fo.write(dumps(data))",
"def save_scores(self):\n\n with open('.scores.pickle', 'wb') as f:\n ... | [
"0.715416",
"0.69668126",
"0.69310766",
"0.6872937",
"0.6851604",
"0.6812061",
"0.6766803",
"0.6747991",
"0.6744735",
"0.6700026",
"0.6619312",
"0.65273136",
"0.6497375",
"0.640503",
"0.6403405",
"0.6376395",
"0.63477",
"0.63327163",
"0.6325372",
"0.62925833",
"0.62912065",
... | 0.57981735 | 91 |
Gets score counts per player in a date scope | def counts_per_players(cls, date_scope):
return cls._counts_per("player_name", date_scope) | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def counts_per_teams(cls, date_scope):\n return cls._counts_per(\"player_team\", date_scope)",
"def games_count(self, start_date, end_date): # noqa\n if start_date > end_date:\n raise RuntimeError(\"End date must be beyond start\")\n cur_date = start_date\n tot_gc = default... | [
"0.70805335",
"0.61034626",
"0.5999346",
"0.59791017",
"0.5965352",
"0.5873043",
"0.582727",
"0.5764276",
"0.5752803",
"0.57515305",
"0.56772786",
"0.5654991",
"0.5558223",
"0.55491227",
"0.5534203",
"0.55316144",
"0.5500438",
"0.5496959",
"0.5493157",
"0.54864126",
"0.547416... | 0.7679463 | 0 |
Gets score counts per team in a date scope | def counts_per_teams(cls, date_scope):
return cls._counts_per("player_team", date_scope) | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def games_count(self, start_date, end_date): # noqa\n if start_date > end_date:\n raise RuntimeError(\"End date must be beyond start\")\n cur_date = start_date\n tot_gc = defaultdict(int)\n while cur_date <= end_date:\n teams_playing = self._teams_playing_one_day(c... | [
"0.6440161",
"0.6400013",
"0.6081967",
"0.593549",
"0.5931942",
"0.5877388",
"0.58412147",
"0.5751432",
"0.57329845",
"0.5712143",
"0.5702498",
"0.5694906",
"0.5685514",
"0.56634647",
"0.56083405",
"0.55377275",
"0.54963267",
"0.54125893",
"0.54095155",
"0.54077744",
"0.53844... | 0.7475803 | 0 |
Creates a root device object. | def __init__(self):
super(UpnpEmbeddedDevice, self).__init__()
return | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def create_root_device_instance(self, manufacturer:str, modelNumber: str, modelDescription: str) -> UpnpRootDevice:\n deviceClass = UpnpRootDevice\n if manufacturer is not None and modelNumber is not None and modelDescription is not None:\n extkey = generate_extension_key(manufacturer, mod... | [
"0.7622642",
"0.6808447",
"0.650578",
"0.650578",
"0.6462673",
"0.6353511",
"0.61486673",
"0.60996056",
"0.60711485",
"0.6047455",
"0.59923583",
"0.59328663",
"0.5872992",
"0.5866362",
"0.5858065",
"0.58089036",
"0.5802254",
"0.5801608",
"0.5791029",
"0.5775517",
"0.5775132",... | 0.5388762 | 39 |
Updates the embedded devices description. | def update_description(self, host, baseUrl, description):
self._host = host
self._urlBase = baseUrl
self._description = description
return | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def _populate_embedded_devices(self, factory, description):\n # pylint: disable=no-self-use,unused-argument\n raise AKitSemanticError(\"Embedded devices inside an embedded device is currently not supported.\")",
"def update(self):\n self.device = self._api.device_query(self._hardware_address... | [
"0.7054676",
"0.60299516",
"0.5912329",
"0.58961916",
"0.5896006",
"0.58923954",
"0.5791732",
"0.57783717",
"0.5647076",
"0.5647076",
"0.5566036",
"0.5546734",
"0.5529445",
"0.55041945",
"0.5477634",
"0.5455053",
"0.544172",
"0.5414883",
"0.5405733",
"0.5384925",
"0.53810143"... | 0.5044277 | 57 |
This method is overloaded to prohibit the poplulation of embedded devices insided embedded devices. | def _populate_embedded_devices(self, factory, description):
# pylint: disable=no-self-use,unused-argument
raise AKitSemanticError("Embedded devices inside an embedded device is currently not supported.") | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def pop_adv_devices(self):\r\n if self.localSDK.devList:\r\n del self.localSDK.devList[:]\r\n try:\r\n self.localSDK.get_devices() # Get list of boards from KSDK manifest file\r\n except IOError:\r\n self.localSDK.devList = ['None']\r\n retu... | [
"0.58297354",
"0.5809984",
"0.57054234",
"0.565818",
"0.56520927",
"0.56433535",
"0.56425285",
"0.5591485",
"0.55774033",
"0.557727",
"0.54898685",
"0.54536617",
"0.54339343",
"0.5404499",
"0.5364736",
"0.5357825",
"0.5357825",
"0.5336527",
"0.5275434",
"0.52752054",
"0.52625... | 0.49261054 | 79 |
Compute matches when text is a simple name. Return a list of all keywords, builtin functions and names currently defined in self.namespace that match. | def global_matches(self, text):
matches = []
n = len(text)
for list in [keyword.kwlist,
__builtin__.__dict__,
self.namespace]:
for word in list:
if word[:n] == text:
matches.append(word)
return matc... | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def global_matches(self, text):\n import keyword\n matches = []\n n = len(text)\n for word in keyword.kwlist:\n if word[:n] == text:\n matches.append(word)\n for nspace in [builtins.__dict__, self.namespace]:\n for word, val in nspace.items():... | [
"0.81320006",
"0.7065873",
"0.7050401",
"0.69584954",
"0.6639191",
"0.65908337",
"0.64813334",
"0.61590165",
"0.60079956",
"0.55892473",
"0.5545319",
"0.5419989",
"0.53929675",
"0.53920263",
"0.53830427",
"0.53503126",
"0.5301055",
"0.5280724",
"0.52775556",
"0.52568513",
"0.... | 0.7634869 | 1 |
Compute matches when text contains a dot. Assuming the text is of the form NAME.NAME....[NAME], and is evaluatable in self.namespace, it will be evaluated and its attributes (as revealed by dir()) are used as possible completions. (For class instances, class members are also considered.) | def attr_matches(self, text):
m = re.match(r"(\w+(\.\w+)*)\.(\w*)", text)
if not m:
return []
expr, attr = m.group(1, 3)
object = eval(expr, self.namespace)
words = dir(object)
if hasattr(object,'__class__'):
words.append('__class__')
w... | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def python_matches(self,text):\n #print 'Completer->python_matches' # dbg\n if \".\" in text:\n try:\n matches = self.attr_matches(text)\n if text.endswith('.') and self.omit__names:\n if self.omit__names == 1:\n ... | [
"0.67350745",
"0.6467589",
"0.5659091",
"0.56373996",
"0.55783284",
"0.54272413",
"0.53458554",
"0.528147",
"0.5278319",
"0.52603084",
"0.5194667",
"0.51900584",
"0.51714355",
"0.5169853",
"0.5109693",
"0.5102715",
"0.50885576",
"0.50000334",
"0.49939442",
"0.4941889",
"0.491... | 0.6702154 | 1 |
Construct a layernorm module in the TF style (epsilon inside the square root). | def __init__(self, hidden_size, eps=1e-12):
super(BertLayerNorm, self).__init__()
self.weight = nn.Parameter(torch.ones(hidden_size))
self.bias = nn.Parameter(torch.zeros(hidden_size))
self.variance_epsilon = eps | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def verhulst(nb_init, t0, tf, eps, methode, gamma, K) :\n f=lambda y,t : gamma*y*(1-y/K)\n Y=meth_epsilon(nb_init, t0, tf, eps, f, methode)\n return Y",
"def malthusiens(nb_init, t0, tf, eps, methode, gamma ) :\n\n f=lambda y, t : gamma*y\n Y=meth_epsilon(nb_init, t0, tf, eps, f, methode)\n ... | [
"0.62758327",
"0.60142326",
"0.5795031",
"0.5740771",
"0.55372477",
"0.5517929",
"0.54223156",
"0.54113936",
"0.5386929",
"0.5368306",
"0.53505456",
"0.52857476",
"0.52559835",
"0.52327746",
"0.5227854",
"0.5224699",
"0.5219319",
"0.5218825",
"0.51930606",
"0.51930606",
"0.51... | 0.0 | -1 |
Allow input of data on event | def view_event(request, event_id):
event = get_object_or_404(Event, pk=event_id)
context = {'event': event }
return render_to_response('event_view.html',
context,
context_instance=RequestContext(request)) | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def handle_input(self, event):\n pass",
"def ev_textinput(self, event: TextInput) -> None:",
"def handle_keyboard_data(data):\n pass",
"def handle_input(data: dict):",
"def ev_textinput(self, event: tcod.event.TextInput) -> T | None:",
"def on_user_input(self, dut_address, reply_boolean, expect... | [
"0.75046116",
"0.69239",
"0.6504716",
"0.6503113",
"0.64280546",
"0.636753",
"0.63566643",
"0.62977934",
"0.6292795",
"0.62885463",
"0.623799",
"0.62167597",
"0.62059057",
"0.61748546",
"0.6164931",
"0.6164055",
"0.61352056",
"0.61321485",
"0.6082178",
"0.60712785",
"0.606794... | 0.0 | -1 |
Display a list of Event paginated | def list_event(request):
event_list = Event.objects.all()
paginator = Paginator(event_list, 5)
try:
page = int(request.GET.get('page', '1'))
except ValueError:
page = 1
# If page request (9999) is out of range, deliver last page of results.
try:
event_list = paginator.p... | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def show_events_list():\r\n\tevents_list = Page.objects.filter(tags='events').order_by('-created')\r\n\treturn {'events_list': events_list}",
"def event_list(request):\n if request.method == 'GET':\n if request.GET.get('search'):\n request_terms = request.GET.get('search')\n searc... | [
"0.83030045",
"0.7657986",
"0.7629149",
"0.7394973",
"0.7273532",
"0.71547526",
"0.6988922",
"0.6988497",
"0.6924161",
"0.6869403",
"0.68199164",
"0.6779448",
"0.67320573",
"0.671834",
"0.67172897",
"0.6673681",
"0.66686934",
"0.65960234",
"0.6561609",
"0.65346944",
"0.651391... | 0.849096 | 0 |
Allow input of data on event | def form_event(request, event_id):
event = get_object_or_404(Event ,pk=event_id)
if request.method == "POST":
form = EventForm(request.POST, instance=event)
if form.is_valid():
form.save()
else:
form = EventForm(instance=event)
context = {'event': event,
... | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def handle_input(self, event):\n pass",
"def ev_textinput(self, event: TextInput) -> None:",
"def handle_keyboard_data(data):\n pass",
"def handle_input(data: dict):",
"def ev_textinput(self, event: tcod.event.TextInput) -> T | None:",
"def on_user_input(self, dut_address, reply_boolean, expect... | [
"0.75049454",
"0.69242203",
"0.6504036",
"0.6501898",
"0.6428295",
"0.6366693",
"0.6354882",
"0.62957245",
"0.6294693",
"0.629008",
"0.62417084",
"0.62185454",
"0.6202413",
"0.6175176",
"0.6166408",
"0.61663496",
"0.61377245",
"0.61337566",
"0.6082846",
"0.6071894",
"0.607018... | 0.0 | -1 |
Allow input of data on member | def view_member(request, member_id):
member = get_object_or_404(Member, pk=member_id)
context = {'member': member }
return render_to_response('member_view.html',
context,
context_instance=RequestContext(request)) | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def __call__(self, value):\n self.data_validator(value)",
"def d_input(self):\n pass",
"def validate_input(self, *args):\n return",
"def input(self):\r\n pass",
"def _validate(self, instance, value):",
"def pass_data(self, data):\n self.data = data\n self.load_in... | [
"0.6091474",
"0.60770625",
"0.5984453",
"0.59829456",
"0.59364",
"0.5923605",
"0.5848573",
"0.5848573",
"0.578865",
"0.5777267",
"0.57750106",
"0.57236516",
"0.57038593",
"0.5688617",
"0.5682563",
"0.5680961",
"0.56385183",
"0.5597286",
"0.5586429",
"0.55672604",
"0.55592513"... | 0.0 | -1 |
Display a list of Member paginated | def list_member(request):
member_list = Member.objects.all()
paginator = Paginator(member_list, 5)
try:
page = int(request.GET.get('page', '1'))
except ValueError:
page = 1
# If page request (9999) is out of range, deliver last page of results.
try:
member_list = pagina... | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def list(request):\n assert isinstance(request, HttpRequest)\n\n form = SearchForm(request.GET or None)\n if form.is_valid():\n search_name = form.data['search_name']\n member_list = Member.objects.filter( Q(name__icontains=search_name) | Q(full_name__icontains=search_nam... | [
"0.78345263",
"0.70588547",
"0.7022545",
"0.6863475",
"0.67834175",
"0.6747191",
"0.6563027",
"0.6514594",
"0.64230216",
"0.637318",
"0.6352936",
"0.6283344",
"0.62146246",
"0.6173804",
"0.6141176",
"0.61208135",
"0.61201227",
"0.610306",
"0.6090148",
"0.6090148",
"0.60892296... | 0.83615065 | 0 |
Allow input of data on member | def form_member(request, member_id):
member = get_object_or_404(Member ,pk=member_id)
if request.method == "POST":
form = MemberForm(request.POST, instance=member)
if form.is_valid():
form.save()
else:
form = MemberForm(instance=member)
context = {'member': member,
... | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def __call__(self, value):\n self.data_validator(value)",
"def d_input(self):\n pass",
"def validate_input(self, *args):\n return",
"def input(self):\r\n pass",
"def _validate(self, instance, value):",
"def pass_data(self, data):\n self.data = data\n self.load_in... | [
"0.60913104",
"0.60763896",
"0.59840703",
"0.5982266",
"0.5936191",
"0.5923617",
"0.58483535",
"0.58483535",
"0.5788462",
"0.57769746",
"0.5774602",
"0.5723344",
"0.5703288",
"0.5688608",
"0.5682276",
"0.5680635",
"0.56381243",
"0.55968136",
"0.5586484",
"0.5567039",
"0.55590... | 0.0 | -1 |
Compile the regexes in grammar | def __init__(self):
for item in grammar:
item['matches_compiled'] = {}
for name,pattern in item['matches'].items():
item['matches_compiled'][name] = \
re.compile(pattern, re.IGNORECASE)
item['semantics_compiled'] = {}
for name,... | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def _compile_regex(self):\n self.raw = self._compile('rawLevel')\n self.run = self._compile('runLevel')\n self.sample = self._compile('sampleLevel')\n self.agg = self._compile('aggLevel')",
"def _compileRegex(self):\n\t\tself.__punctuationRegex = re.compile(\"|\".join(self._punctuatio... | [
"0.72584164",
"0.67762935",
"0.67014307",
"0.66918826",
"0.6617731",
"0.6525257",
"0.6477457",
"0.63297504",
"0.6174383",
"0.60777885",
"0.60444814",
"0.6012791",
"0.6005029",
"0.6004373",
"0.59888434",
"0.5980766",
"0.5979322",
"0.59718186",
"0.5966301",
"0.5963155",
"0.5953... | 0.6124754 | 9 |
Parse input using NLTK parser+chunker | def nlp_parse(self, input):
resp = {}
resp['type'] = 'nomatch'
VDB_set = {}
WP_set = {}
tagset = self.build_tagset(input)
resp['words'] = self.build_keywords(tagset)
w = resp['words']
if not w:
if constants.DEBUG:
log.debug("No... | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def parse(self, tokenizer):\n pass",
"def parser(sent_list): #input: list of sentences",
"def parse(raw_query, EXPAND_SET = False):\r\n\t\r\n\t# tokenize and tag the query using nltk tools, use .lower() to standardize the input\r\n\ttokenized_query = nltk.word_tokenize(raw_query.lower())\r\n\ttagged_que... | [
"0.676532",
"0.6750108",
"0.6618776",
"0.65109926",
"0.649654",
"0.6220005",
"0.6132676",
"0.6083811",
"0.60810405",
"0.6036656",
"0.60250545",
"0.5985231",
"0.5939019",
"0.5930759",
"0.5887026",
"0.5864591",
"0.58607024",
"0.58260447",
"0.5798554",
"0.5759019",
"0.57415426",... | 0.61024725 | 7 |
Get the internal registry. | def internal_registry(self) -> Dict[str, Resource]:
return self.manager.registry | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def registry(self):\n return self.__registry",
"def registry(self):\n return self._registry",
"def get_unit_registry():\n\n global _unit_registry\n\n # Cache the unit registry for speedier access\n if _unit_registry is None:\n return reload_unit_registry()\n else:\n retu... | [
"0.838872",
"0.8387603",
"0.73312694",
"0.70455664",
"0.691805",
"0.6821618",
"0.6715011",
"0.66693926",
"0.66524637",
"0.6613443",
"0.6613443",
"0.6500425",
"0.64740384",
"0.6458545",
"0.6450646",
"0.6366988",
"0.6355886",
"0.6339974",
"0.6274995",
"0.62644315",
"0.62457114"... | 0.8258994 | 2 |
Get the mapping prefixes that should be skipped to their reasons (strings). | def get_skip(self) -> Mapping[str, str]:
return {} | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def get_prefixes(context: str = \"go\"):\n context = load_context(context)\n extended_prefix_map = context.as_extended_prefix_map()\n converter = Converter.from_extended_prefix_map(extended_prefix_map)\n cmaps = converter.prefix_map\n # hacky solution to: https://github.com/geneontology/go-site/issu... | [
"0.6458126",
"0.6146187",
"0.5821375",
"0.5761392",
"0.5677433",
"0.56264687",
"0.5600088",
"0.5573373",
"0.54468733",
"0.5427342",
"0.53815764",
"0.5332524",
"0.53015023",
"0.5299096",
"0.5292142",
"0.5283038",
"0.5278991",
"0.52788407",
"0.5266334",
"0.5259487",
"0.5258129"... | 0.59197617 | 2 |
Align the external registry. | def _align(self):
for external_id, external_entry in sorted(self.external_registry.items()):
if external_id in self.skip_external:
continue
bioregistry_id = self.external_id_to_bioregistry_id.get(external_id)
# There's already a mapping for this external ID t... | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def align(self):\n ...",
"def temporally_align_data_interfaces(self):\n pass",
"def AlignFieldmaps(self):\n for entry in self.entry_map['fmap']:\n info = self.info[entry]\n\n# Register the magnitude image at the shortest TR to the T1-IR\n# structural image.\n... | [
"0.6081261",
"0.557019",
"0.5147257",
"0.49545655",
"0.49139827",
"0.48331964",
"0.48314154",
"0.46982148",
"0.45978466",
"0.45773122",
"0.45580065",
"0.45571685",
"0.45098197",
"0.45003244",
"0.4498881",
"0.44842976",
"0.44819683",
"0.44467154",
"0.44428003",
"0.44403112",
"... | 0.6750257 | 0 |
Prepare a dictionary to be added to the bioregistry for each external registry entry. The default implementation returns `external_entry` unchanged. If you need more than that, override this method. | def prepare_external(self, external_id: str, external_entry: Dict[str, Any]) -> Dict[str, Any]:
return external_entry | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def _align(self):\n for external_id, external_entry in sorted(self.external_registry.items()):\n if external_id in self.skip_external:\n continue\n\n bioregistry_id = self.external_id_to_bioregistry_id.get(external_id)\n # There's already a mapping for this ex... | [
"0.5661854",
"0.52508706",
"0.52203363",
"0.4919496",
"0.48606208",
"0.48559207",
"0.48190767",
"0.48090026",
"0.47993204",
"0.4797935",
"0.47642413",
"0.47563672",
"0.47513482",
"0.4740633",
"0.47319844",
"0.4727857",
"0.4723997",
"0.47188598",
"0.47101957",
"0.47030905",
"0... | 0.77220005 | 0 |
Write the internal registry. | def write_registry(self) -> None:
self.manager.write_registry() | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def __write(self, registry_data: list) -> None:\n with open(self.__registry_filepath, 'w+') as registry_file:\n registry_file.seek(0)\n registry_file.write(json.dumps(registry_data))\n registry_file.truncate()",
"def write(self):\n\t\traise NotImplementedError('%s: No writ... | [
"0.71736217",
"0.6395241",
"0.6308655",
"0.6276962",
"0.6276962",
"0.6197535",
"0.6197535",
"0.6181082",
"0.61420774",
"0.60741246",
"0.60397553",
"0.60311437",
"0.59387153",
"0.5858807",
"0.58051056",
"0.5795055",
"0.5783678",
"0.5652679",
"0.56347007",
"0.562523",
"0.562328... | 0.8478858 | 0 |
Align and output the curation sheet. | def align(
cls,
dry: bool = False,
show: bool = False,
force_download: Optional[bool] = None,
) -> None:
instance = cls(force_download=force_download)
if not dry:
instance.write_registry()
if show:
instance.print_curation_table()
... | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def format_sheet(sheet_to_be_formatted, output_name):\n\n wb = load_workbook(sheet_to_be_formatted)\n # ws = book['Sheet1']\n ws = wb.active\n\n columns_to_keep_unhidden = ['index', 'company_name','price_90_days_ago', 'price_30_days_ago', 'price_21_days_ago', \n 'price_7_... | [
"0.56579214",
"0.5517449",
"0.5478366",
"0.54658437",
"0.52604353",
"0.5230534",
"0.5222758",
"0.52020246",
"0.51637006",
"0.5143857",
"0.51387686",
"0.5129854",
"0.51248664",
"0.5093642",
"0.5041381",
"0.5038929",
"0.50342596",
"0.49654126",
"0.49563086",
"0.49545103",
"0.49... | 0.466461 | 78 |
Construct a CLI for the aligner. | def cli(cls):
@click.command()
@click.option("--dry", is_flag=True, help="if set, don't write changes to the registry")
@click.option("--show", is_flag=True, help="if set, print a curation table")
@click.option(
"--no-force", is_flag=True, help="if set, do not force re-downl... | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def cmdline_parser():\n parser = argparse.ArgumentParser(description=\"\"\" \"\"\")\n parser.add_argument(\"-i1\",\n help=\"\"\"viral alignments\"\"\",\n dest=\"viral\",\n required=True)\n parser.add_argument(\"-i2\",\n ... | [
"0.6725033",
"0.6607274",
"0.6418465",
"0.6382979",
"0.6382979",
"0.6382979",
"0.6382979",
"0.6382979",
"0.6382979",
"0.6382979",
"0.6382979",
"0.6382979",
"0.6382979",
"0.6382979",
"0.6382979",
"0.6382979",
"0.6382979",
"0.6382979",
"0.6382979",
"0.6382979",
"0.6382979",
"... | 0.5967073 | 84 |
Get a sequence of items that will be ech row in the curation table. | def get_curation_row(self, external_id, external_entry) -> Sequence[str]: # noqa:DAR202
rv = []
for k in self.curation_header:
value = external_entry.get(k)
if value is None:
rv.append("")
elif isinstance(value, str):
rv.append(value.s... | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def sequence_items(self):\r\n seq_css = 'ol#sequence-list>li>a>p'\r\n return self.q(css=seq_css).map(self._clean_seq_titles).results",
"def items(self):\r\n for column in self.table.columns:\r\n yield (column, self[column.name])",
"def items(self):\n return zip(self._sequ... | [
"0.6003368",
"0.58419013",
"0.5718137",
"0.5499399",
"0.54639125",
"0.5459525",
"0.54581946",
"0.5413328",
"0.53819495",
"0.5363526",
"0.5357809",
"0.53151834",
"0.53015083",
"0.5299001",
"0.5241888",
"0.5216109",
"0.5214752",
"0.52066666",
"0.52004117",
"0.5199581",
"0.51903... | 0.477345 | 100 |
Write the curation table to a TSV. | def write_curation_table(self) -> None:
path = EXTERNAL.joinpath(self.key, "curation.tsv")
rows = list(self._iter_curation_rows())
if not rows:
if path.is_file():
path.unlink()
return
path.parent.mkdir(exist_ok=True, parents=True)
with pat... | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def write_tsv(self, filename):\n f = open(filename,'wb')\n wr = csv.writer(f,delimiter='\\t',quoting=csv.QUOTE_ALL)\n colrow = []\n for col in self.cols:\n colrow.append('<undefined>' if len(col) == 0 else unicode(iter(col).next()).encode('unicode-escape'))\n wr.writerow(colrow)\n for row in... | [
"0.6911387",
"0.68106085",
"0.65260524",
"0.64977854",
"0.64346373",
"0.61961037",
"0.6176493",
"0.61177903",
"0.6073387",
"0.6044617",
"0.58639824",
"0.58490986",
"0.582473",
"0.5821321",
"0.5815019",
"0.58101106",
"0.5785907",
"0.5764009",
"0.570739",
"0.5705173",
"0.567227... | 0.589101 | 10 |
Print the curation table. | def print_curation_table(self, **kwargs) -> None:
s = self.get_curation_table(**kwargs)
if s:
print(s) # noqa:T201 | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def print_table(self):\n print(\"%-12s%-12s%-12s%-12s%-12s\" % (\"index\",\"balance\",\"payment\",\"interest\",\"amortization\"))\n print(\"-------------------------------------------------------------\")\n for i in self.table[\"index\"]:\n print(\"%-12i%-12i%-12i%-12i%-12i\" % (sel... | [
"0.7252084",
"0.7043366",
"0.6997914",
"0.6970662",
"0.68268204",
"0.6667863",
"0.65658396",
"0.6513143",
"0.64058745",
"0.6379583",
"0.6373463",
"0.6369926",
"0.633189",
"0.63122463",
"0.627458",
"0.6262004",
"0.6254405",
"0.62401485",
"0.6236742",
"0.62190664",
"0.6151593",... | 0.72845334 | 0 |
Prepare and start the sequence acquisition. Write frame in an tiff file during acquisition. This function use the labjack to detect a camera trigger. | def sequenceAcqLabjackTrig(mmc, nbImages, maxFrames, expRatio, deviceLabel, ledList, tiffWriterList, textFile, labjack, window, app, exit):
#readOutFrame = 10 #ms ##Minimal time between 2 frames (cf page 45 zyla hardware guide)
#timeStamps.append(time()) #Useless to have a timestamp here
exp = flo... | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def _frameSaving(self):\n self.mmc.clearCircularBuffer()\n imageCount=0\n self.mmc.startContinuousSequenceAcquisition(1)\n while(imageCount<(self.nbFrames) and self.acqRunning and self.loopRunning):\n if self.mmc.getRemainingImageCount() > 0: #Returns number of image in circu... | [
"0.66560525",
"0.66160214",
"0.59900504",
"0.597327",
"0.5946283",
"0.592411",
"0.59203595",
"0.58946085",
"0.58694327",
"0.58440864",
"0.5773642",
"0.57569706",
"0.5754705",
"0.57376325",
"0.57085884",
"0.5704029",
"0.56933516",
"0.5685275",
"0.56782025",
"0.5663697",
"0.566... | 0.58092505 | 10 |
Prepare and start the sequence acquisition. Write frame in an tiff file during acquisition. This function use the labjack to detect a camera trigger. | def sequenceAcqLabjackTrig2(mmc, nbImages, maxFrames, expRatio, deviceLabel, ledList, tiffWriterList, textFile, labjack, window, app, exit):
#readOutFrame = 10 #ms ##Minimal time between 2 frames (cf page 45 zyla hardware guide)
#timeStamps.append(time()) #Useless to have a timestamp here
exp = fl... | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def _frameSaving(self):\n self.mmc.clearCircularBuffer()\n imageCount=0\n self.mmc.startContinuousSequenceAcquisition(1)\n while(imageCount<(self.nbFrames) and self.acqRunning and self.loopRunning):\n if self.mmc.getRemainingImageCount() > 0: #Returns number of image in circu... | [
"0.6655961",
"0.661718",
"0.5988536",
"0.597382",
"0.59228516",
"0.59199524",
"0.58925843",
"0.58694386",
"0.584409",
"0.58088",
"0.5771378",
"0.5757235",
"0.57543904",
"0.57374334",
"0.5708323",
"0.57027847",
"0.5692867",
"0.5683424",
"0.56785035",
"0.5662035",
"0.56600606",... | 0.5945944 | 4 |
Make turtle t draw a square of with side sz. | def drawSquare(t, sz):
t.shape("turtle")
while 1:
if sz > 200:
break
for j in range (36):
t.left(10)
sz = sz + 1
if j%2 == 1:
t.color("red")
else:
t.color("blue")
for i in range(4):
t.forward(sz)
t.left(90)
sz = sz + 1 | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def draw_square(turtle, size):\n\n square = range(4)\n\n for line in square:\n alexa.forward(size)\n alexa.left(90)",
"def make_square(turt,sz):\n for i in range(4):\n turt.forward(sz)\n turt.left(90)",
"def draw_square():\r\n turtle.forward(100)\r\n turtle.le... | [
"0.8070417",
"0.8039957",
"0.774589",
"0.7098677",
"0.6976722",
"0.6861428",
"0.6785766",
"0.6780308",
"0.6778455",
"0.6728188",
"0.6636653",
"0.65735304",
"0.6547466",
"0.6542052",
"0.6403489",
"0.63923377",
"0.63704145",
"0.6338713",
"0.6337131",
"0.6320703",
"0.6318662",
... | 0.85142684 | 0 |
Used for automatic time based ending of quests. | def force_done(self):
if self.can_done():
return self.done()
else:
# we can not set that quest to done regularly, so we force it
# nobody gets any experience and we might need a special notification for this
self.quest.done = True
self.quest.s... | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def end(self, won, reason):\n pass\n # replace with your end logic",
"def end(self, time, reward):\n return True",
"def end_time(self):\n pass",
"def end_time(self) -> float:\r\n ...",
"def finish(self, finish_time=None):\n pass",
"def done_action(self) -> None:\... | [
"0.656753",
"0.65287447",
"0.64753294",
"0.64367896",
"0.6436146",
"0.6408996",
"0.62925214",
"0.62605107",
"0.6227713",
"0.6177872",
"0.61477166",
"0.6123459",
"0.6116317",
"0.60349846",
"0.60272604",
"0.6016484",
"0.60000616",
"0.59916973",
"0.5985124",
"0.596463",
"0.59600... | 0.0 | -1 |
Gets the data from a given slug, returns a JSON respone from the Helix API endpoint | def get_data(slug: str) -> dict:
response = get("data", slug=slug)
try:
return response["data"][0]
except KeyError as e:
log.error(f"Ran into exception: {e}")
log.error(f"Response: {response}")
return response | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"async def getProductDetailBySlug(self, slug=None):\n payload = {}\n \n if slug:\n payload[\"slug\"] = slug\n \n\n # Parameter validation\n schema = CatalogValidator.getProductDetailBySlug()\n schema.dump(schema.load(payload))\n \n\n url_with... | [
"0.68774015",
"0.6794511",
"0.66812634",
"0.6649883",
"0.65714777",
"0.65092707",
"0.64786947",
"0.6264475",
"0.6155149",
"0.60536057",
"0.60405624",
"0.59062344",
"0.5894154",
"0.585762",
"0.58429706",
"0.5822873",
"0.5815605",
"0.5812659",
"0.5802165",
"0.5779416",
"0.57777... | 0.77147025 | 0 |
Gets the data for given slug, returns a tuple first entry being the mp4_url used to download the clip, second entry being the title of the clip to be used as filename. | def get_clip_data(slug: str) -> tuple:
clip_info = get_data(slug)
if "thumbnail_url" in clip_info and "title" in clip_info:
# All to get what we need to return
# the mp4_url and title of the clip
thumb_url = clip_info["thumbnail_url"]
slice_point = thumb_url.index("-previ... | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def get_data(slug: str) -> dict:\r\n response = get(\"data\", slug=slug)\r\n\r\n try:\r\n return response[\"data\"][0]\r\n except KeyError as e:\r\n log.error(f\"Ran into exception: {e}\")\r\n log.error(f\"Response: {response}\")\r\n return response",
"def download_clip(clip:... | [
"0.62556016",
"0.62428874",
"0.55988955",
"0.54840624",
"0.54813236",
"0.54214275",
"0.540999",
"0.538477",
"0.53262895",
"0.53129536",
"0.52874196",
"0.5253897",
"0.5250813",
"0.5233298",
"0.5196757",
"0.5182033",
"0.51498044",
"0.5142984",
"0.512994",
"0.5127548",
"0.511784... | 0.8549872 | 0 |
Used for printing the download progress | def get_progress(count, block_size, total_size) -> None:
percent = int(count * block_size * 100 / total_size)
print(f"Downloading clip... {percent}%", end="\r", flush=True) | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def _print_progress(self):\n \n print 'Completed %d of %d' %(self.progress_id, self.total_work)\n self.progress_id += 1",
"def _print_progress(self):\n if self.current_training_size % 1000 == 0:\n print(self.current_training_size, end='')\n elif self.current_training... | [
"0.7822493",
"0.7672975",
"0.75927395",
"0.75803006",
"0.7568847",
"0.75567335",
"0.7505376",
"0.74961805",
"0.74940753",
"0.74914426",
"0.74773335",
"0.73411936",
"0.7307862",
"0.72979075",
"0.72837406",
"0.72772455",
"0.7248799",
"0.71521616",
"0.7150122",
"0.7094859",
"0.7... | 0.7450158 | 11 |
Splits up the URL given and returns the slug of the clip. | def get_slug(clip: str) -> str:
slug = clip.split("/")
return slug[len(slug) - 1] | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def slug_from_url(url):\n\n find_replaces = [\n ('http(s)?://', ''),\n ('\\.', '-'),\n ('/', '-'),\n ]\n\n for find, replace in find_replaces:\n url = re.sub(find, replace, url)\n\n return url",
"def _split_url(url):\n return url[1:].split('/')",
"def get_slug(sel... | [
"0.6573873",
"0.6397004",
"0.6325671",
"0.62193835",
"0.61308074",
"0.60969",
"0.60584766",
"0.6047683",
"0.60388464",
"0.5998331",
"0.5956057",
"0.5953348",
"0.5937267",
"0.5903982",
"0.58827615",
"0.5872457",
"0.58718574",
"0.5865004",
"0.5849255",
"0.57959837",
"0.57903177... | 0.78426516 | 0 |
Downloads the clip, does not return anything. | def download_clip(clip: str, basepath: str) -> None:
slug = get_slug(clip)
mp4_url, clip_title = get_clip_data(slug)
# Remove special characters so we can save the video
regex = re.compile("[^a-zA-Z0-9_]")
clip_title = clip_title.replace(" ", "_") if CLIP_TITLE == "title" else slug
out_fil... | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def fetch_cover(self) -> None:\n self.cover_path = None\n if self.cover_url is None:\n Logger.Logger.log('No cover picture found for this song.')\n return\n Logger.Logger.log('Retrieving cover picture from iTunes...')\n url_hash: str = md5(self.cover_url.encode('ut... | [
"0.5969689",
"0.576693",
"0.5721179",
"0.561436",
"0.561436",
"0.5589499",
"0.5550476",
"0.553288",
"0.55146897",
"0.54741114",
"0.5457423",
"0.5455695",
"0.54392725",
"0.54390466",
"0.54390466",
"0.5427849",
"0.5362654",
"0.53607637",
"0.53044415",
"0.52991045",
"0.52852416"... | 0.6272273 | 0 |
Gets the top clips for given game, returns JSON response from the Kraken API endpoint. | def get_clips(category: str, path: str) -> dict:
data = {}
PARAMS[MODE] = category
response = get("top_clips", headers=HEADERS, params=PARAMS)
if "clips" in response:
for clip in response["clips"]:
data[clip["tracking_id"]] = {
"url": "https://clips.twi... | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"async def get_all_top_10(self) -> 'Response':\n headers = {\n 'Content-Type': 'application/json',\n 'Accept': 'application/vnd.lichess.v3+json'\n }\n response = await self._client.request(method=RequestMethods.GET, url=USERS_PLAYER_URL, headers=headers)\n return re... | [
"0.649867",
"0.6287386",
"0.61398864",
"0.61083853",
"0.6087414",
"0.6057744",
"0.5892684",
"0.587624",
"0.57806104",
"0.5774665",
"0.57705295",
"0.570377",
"0.56656986",
"0.55479366",
"0.55076",
"0.5493204",
"0.5483413",
"0.5470106",
"0.54600656",
"0.5455603",
"0.54535437",
... | 0.5818894 | 8 |
Downloads clips, returns a list of streamer names. | def download_clips(data: dict, length: float, path: str) -> list:
amount = 0
length *= 60
names = []
for clip in data:
download_clip(data[clip]["url"], path)
length -= data[clip]["duration"]
name = data[clip]["display_name"]
amount += 1
if name no... | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"async def get_live_streamers(self) -> 'Response':\n headers = {\n 'Content-Type': 'application/json',\n }\n response = await self._client.request(method=RequestMethods.GET,\n url=USERS_LIVE_STREAMERS_URL,\n ... | [
"0.57559013",
"0.53566355",
"0.53493756",
"0.53475106",
"0.52516747",
"0.5216363",
"0.5199266",
"0.51281935",
"0.51072365",
"0.50970334",
"0.5091703",
"0.5090108",
"0.5083827",
"0.5082633",
"0.5065399",
"0.50641793",
"0.5059439",
"0.5045423",
"0.5039622",
"0.50376827",
"0.503... | 0.6597001 | 0 |
Concretize a spec with nondefault options in an environment. Make sure that when we ask for a matching spec when the environment is active that we get the instance concretized in the environment. | def test_match_spec_env(mock_packages, mutable_mock_env_path):
# Initial sanity check: we are planning on choosing a non-default
# value, so make sure that is in fact not the default.
check_defaults = spack.cmd.parse_specs(["a"], concretize=True)[0]
assert not check_defaults.satisfies("foobar=baz")
... | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def test_env_spec_make_workaround(env: LlvmEnv):\n env.reset(benchmark=\"cbench-v1/bitcount\")\n env.spec._kwargs[ # pylint: disable=protected-access\n \"benchmark\"\n ] = \"cbench-v1/bitcount\"\n with env.spec.make() as new_env:\n assert new_env.benchmark == env.benchmark",
"def __ini... | [
"0.52778894",
"0.5273832",
"0.5237516",
"0.52121687",
"0.5153508",
"0.51231545",
"0.48871702",
"0.48617077",
"0.48567748",
"0.47990334",
"0.47969693",
"0.4748159",
"0.47394314",
"0.47262514",
"0.4722297",
"0.47121826",
"0.4707871",
"0.47060472",
"0.4698635",
"0.4697722",
"0.4... | 0.5025732 | 6 |
Ensure that ConfigSetAction is doing the right thing. | def test_concretizer_arguments(mutable_config, mock_packages):
spec = spack.main.SpackCommand("spec")
assert spack.config.get("concretizer:reuse", None) is None
spec("--reuse", "zlib")
assert spack.config.get("concretizer:reuse", None) is True
spec("--fresh", "zlib")
assert spack.config.get... | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def _check_config(self):",
"def test_conflicting_actions(self):\n\n err = pyramid_config.ConfigurationError\n self.assertRaises(err, self.factory) # calls the includeme",
"def test_safe(self):\n\n conf = configuration(\n category(\n 'test',\n Parame... | [
"0.6267934",
"0.6074447",
"0.6070502",
"0.6009013",
"0.58821166",
"0.58627784",
"0.5845754",
"0.570631",
"0.570631",
"0.5699852",
"0.5699852",
"0.5686381",
"0.5678665",
"0.5676008",
"0.5599686",
"0.5584741",
"0.5535828",
"0.5507596",
"0.5479177",
"0.5474924",
"0.5466765",
"... | 0.0 | -1 |
Plot the skeleton based on joint angle data | def visualise_from_joints(self, ax):
ax.set_xlim(-2,2)
ax.set_ylim(-2,2)
ax.set_zlim(-1,1)
self.joint2pos()
skeleton_points = self.skeleton_param['list_points']
skeleton_segments = self.skeleton_param['list_segments']
for seg in skeleton_segments:
point_ini = skeleton_segments[seg]['points'][0]
x_... | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def draw_full_skeleton(input_image, joints, all_joints=None, draw_edges=True, vis=None, all_vis=None, radius=None):\n\n \n joints = joints + 112\n print(joints)\n\n if radius is None:\n radius = max(4, (np.mean(input_image.shape[:2]) * 0.01).astype(int))\n\n colors = {\n 'pink': np.arr... | [
"0.6712119",
"0.66732395",
"0.6326688",
"0.6149413",
"0.6103602",
"0.60394126",
"0.57644",
"0.57560295",
"0.57036847",
"0.5674877",
"0.56541306",
"0.55659026",
"0.5548639",
"0.5521969",
"0.5489853",
"0.547457",
"0.5472636",
"0.5417708",
"0.539933",
"0.5385514",
"0.5375",
"0... | 0.6926737 | 0 |
Returns latitude and longitude. | async def lat_long(zip_code: str, country: str) -> Sequence[float]:
key: str = f"{zip_code}, {country}"
url: str = f'http://www.datasciencetoolkit.org/street2coordinates/{key.replace(" ", "+")}'
async with aiohttp.ClientSession() as session:
async with session.get(url) as response:
resp... | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def geolocation(self):\n if self.latitude and self.longitude:\n return self.longitude, self.latitude",
"def coordinates(self):\n return self.latitude, self.longitude",
"def getLngLat(self):\n lat = None\n lng = None\n exif_data = self.get_exif_data()\n if \"... | [
"0.80100036",
"0.7491967",
"0.7471748",
"0.7453064",
"0.74518454",
"0.74210477",
"0.7373584",
"0.73724455",
"0.7307737",
"0.72870207",
"0.7231296",
"0.7096985",
"0.7032187",
"0.6950929",
"0.6939933",
"0.68792295",
"0.6872782",
"0.6870527",
"0.68537533",
"0.68281883",
"0.68262... | 0.0 | -1 |
Register call records namespace. | def register(blueprint):
blueprint.add_route(post, "/call-records", methods=['POST']) | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def register_recorder(self, groupname, tablename, recorder,\n timeseries=False):\n self.open_db()\n tab = self.get_table(groupname, tablename)\n if timeseries is False:\n self.add_row(tab, recorder())\n else:\n self.recorders.append((tab, r... | [
"0.51874465",
"0.5077612",
"0.5027467",
"0.49655706",
"0.49356636",
"0.4900239",
"0.4900239",
"0.4899169",
"0.48648772",
"0.48585153",
"0.48410738",
"0.48243338",
"0.4807487",
"0.48033518",
"0.4797804",
"0.47961318",
"0.47794095",
"0.4770171",
"0.47700715",
"0.47573164",
"0.4... | 0.52914554 | 0 |
Read filefooter metadata from each individual piece. Since this operation can be painfully slow in some cases, abort if any metadata or statistics are missing | def _get_md_row_groups(pieces):
row_groups = []
for piece in pieces:
for rg in range(piece.get_metadata().num_row_groups):
row_group = piece.get_metadata().row_group(rg)
for c in range(row_group.num_columns):
if not row_group.column(c).statistics:
... | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def extract_metadata():\n\n create_output(ARGS.out)\n index = pre.pixz.read_index(ARGS.traffic)\n\n try:\n tmp = tempfile.mkdtemp(prefix=\"ictf2017_cache_\")\n print(\"Using temporary cache for extracted files at {}\".format(tmp))\n\n file_indexes = [i for i in range(len(index))\n ... | [
"0.60882914",
"0.55803096",
"0.5514509",
"0.55087686",
"0.55087686",
"0.54893374",
"0.5471986",
"0.54566586",
"0.545227",
"0.5429195",
"0.5382588",
"0.5363607",
"0.534647",
"0.5325232",
"0.5310612",
"0.53077656",
"0.5306999",
"0.5297029",
"0.529208",
"0.5279255",
"0.527717",
... | 0.0 | -1 |
Determine how to access metadata and break read into ``parts`` This logic is mostly to handle `gather_statistics=False` cases, because this also means we should avoid scanning every file in the dataset. | def _determine_dataset_parts(fs, paths, gather_statistics, filters, dataset_kwargs):
parts = []
if len(paths) > 1:
if gather_statistics is not False:
# This scans all the files
dataset = pq.ParquetDataset(
paths, filesystem=fs, filters=filters, **dataset_kwargs
... | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def _extract(self):\r\n self._data = []\r\n for fname in self.files:\r\n meta = dict(filename=fname)\r\n\r\n # Perform the actual metadata extraction\r\n fname = os.path.splitext(self.filter_filename(fname))[0]\r\n values = fname.split(self.sep)\r\n\r\n ... | [
"0.6003094",
"0.5807587",
"0.5770916",
"0.5663388",
"0.5620173",
"0.5604538",
"0.5548953",
"0.55457515",
"0.55446166",
"0.55306935",
"0.55277216",
"0.5524769",
"0.55243605",
"0.5494944",
"0.5478514",
"0.546195",
"0.5459989",
"0.54192567",
"0.5416553",
"0.53937715",
"0.5377363... | 0.6683663 | 0 |
Format a report in HTML | def get_html_report(cfg_file=None, cfg=None):
rval = ""
if cfg is not None:
# use it
pass
elif cfg_file is not None:
cfg = CrawlConfig.add_config(filename=cfg_file)
else:
cfg = CrawlConfig.add_config()
db = CrawlDBI.DBI(dbtype="crawler")
last_rpt_time = rpt_lib.... | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def display_html_report():\n display(HTML('report_page.html'))",
"def pretty_html_report(self, report, header):\n\t\t# Use a nice style for the table view\n\n\t\t# Build an html table from the report and it's header\n\t\thtml_table = \"<table id=\\\"t01\\\">\"\n\t\thtml_table += \"<caption><b>Tracking Sta... | [
"0.72927934",
"0.71423423",
"0.69298834",
"0.69140023",
"0.6805506",
"0.67813575",
"0.67666703",
"0.67604965",
"0.6707439",
"0.6675773",
"0.65653753",
"0.6501293",
"0.64967006",
"0.6393157",
"0.6388295",
"0.6316796",
"0.6277492",
"0.62656116",
"0.62604576",
"0.6252544",
"0.62... | 0.5737316 | 67 |
Format the CV report in HTML | def get_html_cv_report(db, last_rpt_time):
rval = ""
if not db.table_exists(table="checkables"):
return rval
rval += ("<h2>%s</h2>\n" % cv_sublib.report_title())
diml = [{'name': 'cos',
'pop': "type = 'f' and ? < last_check",
'samp': "type = 'f' and checksum = 1 and ? ... | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def preview():\r\n html = create_html_report()\r\n return html",
"def save_html(self, report_summary, file_name, folder):\n myfile = open(file_name, \"w\")\n myfile.write(t('! DOCTYPE html') + nl())\n myfile.write(t('html') + nl())\n myfile.write(t('head') + nl())\n myfil... | [
"0.6857024",
"0.6329301",
"0.6285875",
"0.6268089",
"0.6226494",
"0.62075233",
"0.61447465",
"0.6047048",
"0.60389507",
"0.59905636",
"0.595511",
"0.5953522",
"0.5907026",
"0.58661455",
"0.5827086",
"0.5820692",
"0.5806999",
"0.57924706",
"0.5770915",
"0.5728266",
"0.5708893"... | 0.63381964 | 1 |
Format the MPRA report in HTML | def get_html_mpra_report(db, last_rpt_time):
if not db.table_exists(table="mpra"):
return ""
rval = ("<h2>%s</h2>\n" % mpra_sublib.report_title())
body = ''
hfmt = " %-5s %-20s %-20s %-20s %8s\n"
bfmt = " %-5s %-20s %-20s %-20s %8d\n"
mdelta = 0
pdelta = 0
rows = mpr... | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def display_html_report():\n display(HTML('report_page.html'))",
"def format_report_header(self):",
"def report_processing_qc(analysis_dir,html_file):\n # Initialise the HTML report\n processing_qc = Document(\"Processing report for %s\" %\n os.path.basename(analysis_di... | [
"0.6548287",
"0.6382216",
"0.6350493",
"0.63070124",
"0.6249452",
"0.6193579",
"0.61822295",
"0.6177774",
"0.6018906",
"0.59797597",
"0.59589267",
"0.5935615",
"0.5932245",
"0.5914714",
"0.5901026",
"0.5877641",
"0.58640754",
"0.58531994",
"0.5836908",
"0.5832936",
"0.5818078... | 0.6652169 | 0 |
Format the TCC report in HTML | def get_html_tcc_report(db, last_rpt_time):
rval = ""
if not db.table_exists(table='tcc_data'):
return rval
rval = ("<h2>%s</h2>\n" % tcc_sublib.report_title())
checks = correct = error = 0
rows = tcc_sublib.recent_records(last_rpt_time, db=db)
for (t, l, h, c, e) in rows:
che... | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def to_html(self) -> str:\n source_name = escape(self.source_name)\n (covered, lines) = self.coverage_stats()\n lines_stats = \"{} / {} ({} lines of code)\".format(covered, lines, len(self.source_code))\n (br_covered, br_count, calls_covered, calls_count) = self.branch_stats()\n ... | [
"0.61556923",
"0.60551417",
"0.6044294",
"0.6040636",
"0.60342383",
"0.5991108",
"0.5984696",
"0.5921795",
"0.5864797",
"0.5859054",
"0.58359385",
"0.57773423",
"0.57677716",
"0.5733748",
"0.57317203",
"0.57093257",
"0.56609064",
"0.5646776",
"0.5618573",
"0.55744916",
"0.557... | 0.68484527 | 0 |
Extracts url from html | def extract_url(td):
url = td.find('a',href=True)['href']
return url | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def process_url(self, url, html):\n html = self.getHtmlContent(html, 'content')\n new_urls = self.getValidUrlsFromHtml(html)\n return new_urls",
"def extractUrl(self, href):\n url = ''\n pattern = re.compile(r'(http[s]?://[^&]+)&', re.U | re.M)\n url_match = pattern.sear... | [
"0.6942664",
"0.694266",
"0.67256683",
"0.6684412",
"0.66800857",
"0.65427136",
"0.65086895",
"0.64643157",
"0.6385671",
"0.63537264",
"0.6315027",
"0.630541",
"0.62315476",
"0.61999756",
"0.6182553",
"0.6172405",
"0.6132446",
"0.6130918",
"0.6104548",
"0.60942876",
"0.607704... | 0.7369381 | 0 |
Extracts text from html | def extract_text(td):
text = td.find('a',href=True).text
return text | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def extract_text(html, guess_punct_space=True):\n sel = cleaned_selector(html)\n return selector_to_text(sel, guess_punct_space=guess_punct_space)",
"def html_to_text(html):\n s = TextExtractorHTMLParser()\n s.feed(html)\n return s.get_text()",
"def extract_page_text(html):\n soup = bs4.Beaut... | [
"0.7791069",
"0.7684028",
"0.7683524",
"0.7471255",
"0.7464041",
"0.74003136",
"0.7259061",
"0.7134363",
"0.7097741",
"0.7085013",
"0.7047965",
"0.69694406",
"0.69121873",
"0.69020706",
"0.6863355",
"0.6851142",
"0.680404",
"0.6749167",
"0.6742216",
"0.67198294",
"0.6711281",... | 0.69887584 | 11 |
Extract unique ID from url | def extract_id(url):
trail_id = url.replace('https://www.trailforks.com/trails/','').replace('/','')
return trail_id | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def get_id(self, url):\n return url.split('/')[-1]",
"def id_from_url(url):\n return url.split('-')[-1].split('.html')[0]",
"def _id_from_url(url):\n url = re.sub(r'\\?.*', '', url)\n video_id = url.split('/')[-2]\n return video_id",
"def get_clean_url(url, unique_id):\n sea... | [
"0.8380673",
"0.81195986",
"0.80006224",
"0.7679931",
"0.75390005",
"0.75193775",
"0.73941654",
"0.73696965",
"0.7283127",
"0.7201107",
"0.7174527",
"0.71031886",
"0.7055173",
"0.70551497",
"0.70354474",
"0.6988878",
"0.6982627",
"0.69719243",
"0.6964515",
"0.6903353",
"0.688... | 0.79425615 | 3 |
1 sh 0 sz | def _select_market_code(code):
code = str(code)
if code[0] in ['5', '6', '9'] or code[:3] in ["009", "126", "110", "201", "202", "203", "204"]:
return 1
return 0 | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def cmd_size(args):",
"def shar():\n pass",
"def do_sh(self, none):\n print(\"**** Not Implemented\")",
"def size(*args):",
"def make_square(turt,sz):\n for i in range(4):\n turt.forward(sz)\n turt.left(90)",
"def shopen():\n\tsh('o')",
"def _s2bl(size):\n return size*... | [
"0.63248545",
"0.5898294",
"0.58236194",
"0.5499801",
"0.52991474",
"0.52804255",
"0.51600564",
"0.5145358",
"0.51127124",
"0.5105524",
"0.50900805",
"0.5079548",
"0.50585145",
"0.5053135",
"0.5038149",
"0.4947009",
"0.494617",
"0.49350992",
"0.49325883",
"0.4879849",
"0.4873... | 0.0 | -1 |
Allows resource methods to return models, lists of models, and nested dicts including models; calls public_dict() on all objects that provide it. | def return_dict(f):
@wraps(f)
def wrapper(*args, **kwargs):
return dictify(f(*args, **kwargs))
return wrapper | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def opt_get_all_models_rest_api():\n return retrieve_all_models()",
"def on_get_resource(self, req, resp, **params):\n instance = self.get_object(**params)\n resp.json(**instance.as_resource)",
"def handle_models_request():\n # TODO: add sort and filter by creation/modification date\n re... | [
"0.6702457",
"0.65570825",
"0.6285609",
"0.6211514",
"0.6006195",
"0.5948634",
"0.59445965",
"0.5880195",
"0.5846682",
"0.5846682",
"0.5846682",
"0.58263266",
"0.58077884",
"0.57976645",
"0.5784321",
"0.5751144",
"0.5739015",
"0.57262045",
"0.5678348",
"0.56772375",
"0.567575... | 0.0 | -1 |
Moves get/post arguments into the arg list. | def extend_with_args(f):
@wraps(f)
def wrapper(*args, **kwargs):
if request.get_json():
kwargs.update(request.get_json())
# WARNING: This may get us into trouble when it comes to
# list-style "get" args.
kwargs.update({k: v for k, v in request.args.iteritems()})
... | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def _transform_args(self) -> None:\n self.args = None if self.args == [] else self.args",
"def _set_arguments(self):\n self._arguments = []",
"def _set_args(self, args):\n if not args:\n self.arg = None\n elif len(args) == 1:\n self.arg = args[0]\n else:... | [
"0.7024696",
"0.62392586",
"0.6223757",
"0.61863637",
"0.6133016",
"0.6104633",
"0.6030061",
"0.599919",
"0.59782875",
"0.5964543",
"0.59543717",
"0.5943862",
"0.58861876",
"0.58676785",
"0.58403295",
"0.5811097",
"0.57581645",
"0.5723659",
"0.57110935",
"0.5691672",
"0.56877... | 0.0 | -1 |
Assignment of users to queues | def cli() -> None:
pass # pragma: no cover | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def update_user(self, queue: SubnetQueue, *args):",
"def task_queue_users():\n users = firebase_admin.auth.list_users().iterate_all()\n for user in users:\n if user.disabled: # skip disabled users\n continue\n # add user task\n add_task(url_for(\"task_queue_podcasts\"), {\"... | [
"0.7112401",
"0.65540063",
"0.6523784",
"0.6046179",
"0.5905534",
"0.58399224",
"0.5835143",
"0.57973343",
"0.57812953",
"0.5747986",
"0.570831",
"0.5543441",
"0.5543441",
"0.5543441",
"0.5516011",
"0.5516011",
"0.551184",
"0.55012774",
"0.54841727",
"0.54561526",
"0.5411173"... | 0.0 | -1 |
List all users and their assignments to queues. | def list_command(ctx: click.Context, user_ids: Tuple[int], queue_ids: Tuple[int]) -> None:
with RossumClient(context=ctx.obj) as rossum:
queue_users = rossum.get_queues((USERS,), users=user_ids)
user_queues: Dict[int, List[List[Optional[str]]]] = {}
for queue in queue_users:
if queue_ids an... | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def task_queue_users():\n users = firebase_admin.auth.list_users().iterate_all()\n for user in users:\n if user.disabled: # skip disabled users\n continue\n # add user task\n add_task(url_for(\"task_queue_podcasts\"), {\"user_uid\": user.uid})\n\n return OK_RESPONSE",
"d... | [
"0.6420194",
"0.62280095",
"0.61567736",
"0.6114229",
"0.6111416",
"0.60904527",
"0.6056923",
"0.59933186",
"0.59414256",
"0.5924326",
"0.5871818",
"0.58335733",
"0.5831743",
"0.58315027",
"0.5815715",
"0.5800357",
"0.5722068",
"0.57204676",
"0.5710141",
"0.57079506",
"0.5701... | 0.71933156 | 0 |
Helper Function for isvalid, Checks if s1, s2 are a valid bracket pair | def isValidPair(self,s1,s2):
if (s1 == '(' and s2 == ')'):
return True
if (s1 == '[' and s2 == ']'):
return True
if (s1 == '{' and s2 == '}'):
return True
return False | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def match(str1, str2):\n if ( (str2 == '[' and str1 == ']')\n or (str2 == '{' and str1 == '}')\n or (str2 == '(' and str1 == ')')):\n return True\n return False",
"def multi_bracket_validation(input):\n brackets = Stack()\n for i in input:\n if i == '(' or i == '[' or... | [
"0.6902342",
"0.6578745",
"0.64985824",
"0.6201826",
"0.6157199",
"0.61534166",
"0.6116992",
"0.60981786",
"0.60610473",
"0.5993438",
"0.5981416",
"0.5980033",
"0.5964657",
"0.59297395",
"0.58786607",
"0.5873677",
"0.5793385",
"0.57868797",
"0.57779795",
"0.5727495",
"0.57272... | 0.86355895 | 0 |
Checks if s contains valid ({[]}) pair in correct order | def isValid(self, s: str) -> bool:
st = []
for char in s:
if (len(st) != 0):
e = st[-1]
if (self.isValidPair(e,char)):
st.pop()
continue
st.append(char)
return (len(st)==0) | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def isValid(self, s):\n # 1\n if not s:\n return True\n\n # 2\n if not len(s) % 2 == 0:\n return False\n\n comp = {\n ')': '(',\n ']': '[',\n '}': '{',\n }\n stack = []\n\n for char in s:\n # 3... | [
"0.6825853",
"0.6750236",
"0.6749478",
"0.66877943",
"0.6573299",
"0.6547619",
"0.6510734",
"0.6484433",
"0.62763965",
"0.6209836",
"0.61905116",
"0.61400807",
"0.60200226",
"0.59876984",
"0.5978047",
"0.5973066",
"0.5904061",
"0.58395123",
"0.5775908",
"0.5752234",
"0.563264... | 0.69817543 | 0 |
Runs a BFS Traversal, Returning a 2D Array representing notes at each level of the tree. One dimension representing each level, and it's array representing nodes at that level. q is a queue of child nodes, starting from the root, down to the end. | def levelOrder(self, root: TreeNode) -> List[List[int]]:
result = []
if(root is None):
return result
q = deque([root])
while(q):
n = len(q)
level = []
for i in range(0,n):
f = q.popleft()
level.append(f.val... | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def bfs_traversal_queue(self):\n queue = ArrayQueue()\n if not self.root:\n print(\"Tree is empty\")\n return\n queue.enqueue(self.root)\n\n while (queue.length() > 0):\n node = queue.dequeue()\n print(node.data, end=\" \")\n if nod... | [
"0.7717397",
"0.71512514",
"0.7137357",
"0.68200314",
"0.6652208",
"0.6628352",
"0.66126645",
"0.6381496",
"0.6362987",
"0.6343037",
"0.62802523",
"0.6268586",
"0.6243742",
"0.6185299",
"0.6096152",
"0.60347956",
"0.60323125",
"0.59980357",
"0.59462214",
"0.5933634",
"0.59022... | 0.0 | -1 |
Returns a list with the order traversal of elements in the tree Uses a strategy type pattern with parameters strategy = {PRE,POST,IN} For respective order traversals | def RecursiveTraversal(self, root: TreeNode, strategy: str) -> List[int]:
result = []
if (root):
if strategy == 'POST':
if (root.left is not None):
result = result + self.RecursiveTraversal(root.left,strategy)
if (root.right is not None):
... | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def traverse_postorder(operation):\n\n nodes_postorder = []\n def recurse(node):\n if isinstance(node, Operation):\n for input_node in node.input_nodes:\n recurse(input_node)\n nodes_postorder.append(node)\n\n recurse(operation)\n return nodes_postorder",
"... | [
"0.6355085",
"0.63519377",
"0.6300792",
"0.628017",
"0.62159055",
"0.61725616",
"0.6165398",
"0.61307913",
"0.6094408",
"0.60663015",
"0.60005105",
"0.5948831",
"0.594843",
"0.59415513",
"0.5918404",
"0.5880077",
"0.58614326",
"0.58589536",
"0.58558935",
"0.5847287",
"0.58438... | 0.695369 | 0 |
S1 is like call stack s2 is the container nodes in s | def StackBasedTraversal(self,root: TreeNode, strategy: str) -> List[int]:
if(not root):
return
ans = []
s1,s2 = [],[]
s1.append(root)
while(s1):
x = s1[-1]
s1.pop()
s2.append(x)
if(x.left):
s1.append(x... | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def __init__(self):\n self.s1 = Stack()\n self.s2 = Stack()",
"def pool_snps(parent1, parent2):",
"def __init__(self):\n # Use list to represent Stack \n self.s1 = []\n self.s2 = []\n # Note the first item to make peek() faster\n self.first = None",
"def __ini... | [
"0.6528504",
"0.6369448",
"0.62064385",
"0.6019769",
"0.5988017",
"0.5758984",
"0.56731415",
"0.5416152",
"0.5399732",
"0.5384572",
"0.53297",
"0.5311962",
"0.5303379",
"0.5283734",
"0.5267882",
"0.5263877",
"0.52613163",
"0.52601475",
"0.5260031",
"0.52584153",
"0.52553326",... | 0.0 | -1 |
Compute the GaussLobatto quadrature | def jacobi_gauss_lobatto_nodes(alpha, beta, N, # noqa: N803
backend=None, force_dim_axis=False):
x = np.zeros((N + 1,))
if N == 0:
x[0] = 0
return x
x[0] = -1
x[-1] = 1
if N > 1:
quad = JacobiGaussQuadrature(alpha + 1, beta + 1, N - 2,
back... | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def make_quad_gauss(lmax,alm):\n return libcurvedsky.bispec.make_quad_gauss(lmax,alm)",
"def test_ln_gauss(self):\n Q = likelihood()\n test_object = test_values_for_likelihood()\n experimental_values = [0.9,2.1,3.2]\n errors = [0.5,0.4,0.3]\n reference_q_value = 0.273472222222... | [
"0.71848667",
"0.70720714",
"0.68945396",
"0.67597324",
"0.6756922",
"0.65686566",
"0.6545775",
"0.6480314",
"0.647398",
"0.6451015",
"0.64494395",
"0.6436806",
"0.6406376",
"0.6401095",
"0.6389199",
"0.63581395",
"0.6356061",
"0.63102597",
"0.630343",
"0.63029367",
"0.624232... | 0.0 | -1 |
Compute the LegendreGaussLobatto quadrature nodes. | def legendre_gauss_lobatto_nodes(N, # noqa: N803
backend=None, force_dim_axis=False):
return jacobi_gauss_lobatto_nodes(0, 0, N,
backend=backend, force_dim_axis=force_dim_axis) | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def ReferenceElement(N):\n\n # Legendre polynomials are Jacobi(alpha,beta) polynomials\n alpha=0.; beta=0;\n\n # Gauss-Lobatto quadrature points for Legendre polynomials\n # According to Hesthaven+Warburton (p. 448, JacobGL.m) these\n # are the order N-2 Gauss quadrature points of Jacobi polynomials... | [
"0.58593106",
"0.58049333",
"0.57061964",
"0.5585701",
"0.5494929",
"0.5449645",
"0.5404597",
"0.5396169",
"0.53935695",
"0.53853106",
"0.5384895",
"0.53766567",
"0.5354576",
"0.5340692",
"0.5332809",
"0.53303593",
"0.5283502",
"0.5264142",
"0.52536964",
"0.5246925",
"0.52407... | 0.62280107 | 0 |
Sets the learning rate to the initial LR decayed by 10 every 30 epochs | def adjust_learning_rate(initial_lr, optimizer, epoch, every_epoch):
lr = initial_lr * (0.1 ** (epoch // every_epoch))
for param_group in optimizer.param_groups:
param_group['lr'] = lr | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def adjust_learning_rate(init_lr, optimizer, epoch, n=100):\n init_lr = init_lr * (0.1 ** (epoch // n))\n print('learning rate : ', init_lr)\n for param_group in optimizer.param_groups:\n param_group['lr'] = init_lr",
"def adjust_learning_rate(start_lr, optimizer, epoch, total_epoch_num):\n #l... | [
"0.79122996",
"0.79058665",
"0.77745456",
"0.77334857",
"0.7730185",
"0.7722212",
"0.7721426",
"0.771838",
"0.7708323",
"0.76928985",
"0.767362",
"0.7665062",
"0.7665062",
"0.7665062",
"0.76536506",
"0.7641248",
"0.7638249",
"0.7637116",
"0.7625168",
"0.7623629",
"0.7620543",... | 0.7848773 | 2 |
Generate a base64 encoded reference to a given Django ORM instance. | def serialize_model_instance(instance):
ref = [instance.__class__.__module__, instance.__class__.__name__, instance.pk]
dumped_ref = json.dumps(ref).encode('utf-8')
return b64encode(dumped_ref).decode('utf-8') | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def base64_string(self) -> global___Expression:",
"def serialize(obj):\n result = base64.urlsafe_b64encode(obj)\n # this workaround is needed because in case of python 3 the\n # urlsafe_b64encode method returns string of 'bytes' class.\n result = result.decode()\n return result",
"def serialized... | [
"0.65252703",
"0.6164654",
"0.6147635",
"0.60311043",
"0.5965339",
"0.5942643",
"0.5856737",
"0.57892215",
"0.5784739",
"0.5777973",
"0.57017314",
"0.56621474",
"0.56593806",
"0.5639606",
"0.5626723",
"0.55980736",
"0.55938447",
"0.5560814",
"0.55501676",
"0.55286133",
"0.552... | 0.67707986 | 0 |
Load Django ORM instance from base64 encoded reference. | def deserialize_model_instance(encoded):
dumped_ref = b64decode(encoded)
ref = json.loads(dumped_ref.decode('utf-8'))
model_module = import_module(ref[0])
model_class = getattr(model_module, ref[1])
return model_class.objects.get(pk=ref[2]) | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def loads(cls, data: str) -> \"Serializable\":\n instance = dill.loads(base64.b64decode(data.encode(\"ascii\")))\n if not isinstance(instance, cls):\n raise TypeError(f\"Unpickled object is not of type {cls}\")\n return instance",
"def deserialize(obj):\n result = base64.urlsaf... | [
"0.60619754",
"0.59615856",
"0.5860249",
"0.5757516",
"0.5731678",
"0.5695101",
"0.5684532",
"0.56761885",
"0.5633992",
"0.54312295",
"0.5369191",
"0.5346863",
"0.5322224",
"0.5310522",
"0.5259935",
"0.5250897",
"0.5246336",
"0.5245134",
"0.5214885",
"0.5203839",
"0.51985216"... | 0.65203667 | 0 |
Download data of one study from database | def download_data(study, engine, has_trips=True, filter_coverage=False):
def to_datetime(df):
df["started_at"] = pd.to_datetime(df["started_at"], utc=True)
df["finished_at"] = pd.to_datetime(df["finished_at"], utc=True)
return df
exclude_purpose_tist = [
"Light Rail",
"... | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def download_dataset(self):\n raise NotImplementedError",
"def download():\n return response.download(request,db)",
"def download():\n return response.download(request,db)",
"def download():\n return response.download(request,db)",
"def download():\n return response.download(request,db)"... | [
"0.6781899",
"0.6684006",
"0.6684006",
"0.6684006",
"0.6684006",
"0.6684006",
"0.6684006",
"0.6534695",
"0.65255445",
"0.64752614",
"0.64643145",
"0.6462871",
"0.646285",
"0.64577276",
"0.6406183",
"0.6401371",
"0.6401371",
"0.6401371",
"0.6401371",
"0.6401371",
"0.6401371",
... | 0.6257936 | 50 |
Filter out users where tracking coverage is too low | def filter_tracking_coverage(sp, locs, tpls, trips):
print("\t filter by tracking coverage")
def filter_user_by_number_of_days(
sp, tpls, coverage=0.7, min_nb_good_days=28, filter_sp=True
):
nb_users = len(sp.user_id.unique())
sp_tpls = sp.append(tpls).sort_values(["user_id", "star... | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def prune(self):\n target_user_ids = self.get_queryset().values_list('id', flat=True)\n exclude_user_ids = SentDrip.objects.filter(date__lt=conditional_now(),\n drip=self.drip_model,\n user__id__in=tar... | [
"0.56458056",
"0.5546361",
"0.5329408",
"0.5329177",
"0.52725947",
"0.526916",
"0.5250236",
"0.5200599",
"0.5181351",
"0.5166104",
"0.51466775",
"0.51419705",
"0.5112545",
"0.5094425",
"0.50508887",
"0.4991155",
"0.49397007",
"0.49257168",
"0.49173298",
"0.49149612",
"0.49059... | 0.68626004 | 0 |
Given the locations and staypoints OF ONE USER, generate the graph | def generate_graph(
locs_user, sp_user, study, trips_user=None, gap_threshold=None
):
# print(sp_user["location_id"])
# print(locs_user)
AG = ActivityGraph(
sp_user,
locs_user,
trips=trips_user,
gap_threshold=gap_threshold,
)
# Add purpose feature
if study == ... | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def generate_locations(\n staypoints,\n method=\"dbscan\",\n epsilon=100,\n num_samples=1,\n distance_metric=\"haversine\",\n agg_level=\"user\",\n activities_only=False,\n print_progress=False,\n n_jobs=1,\n):\n if agg_level not in [\"user\", \"dataset\"]:\n raise AttributeErr... | [
"0.659939",
"0.59695464",
"0.58203804",
"0.5736499",
"0.57258886",
"0.5721508",
"0.56901395",
"0.56083816",
"0.5594009",
"0.55866593",
"0.5553673",
"0.55434465",
"0.55286705",
"0.5508056",
"0.5475889",
"0.547315",
"0.54577315",
"0.5440168",
"0.5398705",
"0.53711665",
"0.53706... | 0.720365 | 0 |
Reduce to the nodes with highest degree (in + out degree) | def keep_important_nodes(graph, number_of_nodes):
sorted_dict = np.array(
[
[k, v]
for k, v in sorted(
dict(graph.degree()).items(),
key=lambda item: item[1],
)
]
)
use_nodes = sorted_dict[-number_of_nodes:, 0]
graph = g... | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def degree_graph(g):\n return max(degree_node(g, node) for node in g)",
"def layer_out_degree(self, layer_id):\n ...",
"def max_degree_node(g, d, connected):\n if not connected:\n deg = d\n else:\n deg = {k: v for k, v in d.items() if k in connected}\n if not deg:\n retu... | [
"0.6181302",
"0.5884981",
"0.5822757",
"0.5800841",
"0.5687816",
"0.5639941",
"0.54734296",
"0.5471626",
"0.5468302",
"0.546607",
"0.544309",
"0.5424738",
"0.5419244",
"0.54152405",
"0.54112226",
"0.54112226",
"0.5405578",
"0.5396399",
"0.53594226",
"0.5346556",
"0.5326148",
... | 0.0 | -1 |
Decorator to transform tuple into a series | def to_series(func):
@wraps(func)
def add_series(center, home_center):
normed_center = func(center.x, center.y, home_center)
return pd.Series(normed_center, index=["x_normed", "y_normed"])
return add_series | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def transform_series(obj):\n vals = obj.values\n return transform_array(vals)",
"def fit_transform(self,\n series: Union[TimeSeries, Sequence[TimeSeries]],\n *args,\n **kwargs) -> Union[TimeSeries, List[TimeSeries]]:\n return self.fit(se... | [
"0.6347814",
"0.5983925",
"0.59830374",
"0.57251686",
"0.5697443",
"0.55903757",
"0.5556683",
"0.5526106",
"0.55077857",
"0.55077857",
"0.54631025",
"0.54327905",
"0.5429192",
"0.53740853",
"0.5326982",
"0.5293809",
"0.5269717",
"0.52383643",
"0.5229252",
"0.5213238",
"0.5200... | 0.61962026 | 1 |
Normalize (x, y) point by home center with haversine distance | def get_haversine_displacement(x, y, home_center):
sign_x = 1 if x > home_center.x else -1
displacement_x = ti.geogr.point_distances.haversine_dist(
x, home_center.y, home_center.x, home_center.y
)[0]
sign_y = 1 if y > home_center.y else -1
displacement_y = ti.geogr.point_distances.haversine... | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def center_normalize(x):\n return (x - K.mean(x)) / K.std(x)",
"def center_normalize(x):\n return (x - K.mean(x)) / K.std(x)",
"def center(x):\n return x - x.mean()",
"def project_normalize_coordinates(node_feats, transformer=None, crs=None):\n # get home node:\n home_node = node_feats.iloc[\n... | [
"0.6706094",
"0.6706094",
"0.6531805",
"0.6390352",
"0.6324326",
"0.6195097",
"0.61200726",
"0.608594",
"0.59820485",
"0.59815353",
"0.59811616",
"0.59621364",
"0.59285176",
"0.5893189",
"0.58789176",
"0.5869087",
"0.5867697",
"0.5864994",
"0.58597124",
"0.5850982",
"0.584125... | 0.67650664 | 0 |
As input to the DL model, we want coordinates relative to home. To do so, we project the coordinates if possible or use the haversine distance. | def project_normalize_coordinates(node_feats, transformer=None, crs=None):
# get home node:
home_node = node_feats.iloc[
(node_feats["in_degree"] + node_feats["out_degree"]).argmax()
]
home_center = home_node["center"]
@to_series
def get_projected_displacement(x, y, home_center):
... | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def project(self, (lng, lat)):\n x = lng * DEG_TO_RAD\n lat = max(min(MAX_LATITUDE, lat), -MAX_LATITUDE)\n y = lat * DEG_TO_RAD\n y = math.log(math.tan((math.pi / 4) + (y / 2)))\n return (x*EARTH_RADIUS, y*EARTH_RADIUS)",
"def get_haversine_displacement(x, y, home_center):\n sign_x = 1 if x > home_center... | [
"0.6284721",
"0.6001774",
"0.5923108",
"0.59172964",
"0.5848251",
"0.58461255",
"0.5788823",
"0.5744928",
"0.5701934",
"0.56556606",
"0.56522423",
"0.56383795",
"0.5605939",
"0.556082",
"0.5559464",
"0.55476177",
"0.5541876",
"0.55329305",
"0.55219483",
"0.5511411",
"0.549560... | 0.6179198 | 1 |
Subsets and Splits
No community queries yet
The top public SQL queries from the community will appear here once available.