str:\\n components = [\\n self.attributee_html,\\n self.linked_title if self.title else 'untitled document',\\n self.date.string if self.date else '',\\n self.descriptive_phrase,\\n f'archived in {self.collection}' if self.collection else '',\\n ]\\n return self.components_to_html(components)\",\n \"def generate_html_template(self, news_list):\\n with open(self.template, \\\"r\\\") as fp:\\n template = Template(fp.read())\\n return template.render(news_list=news_list)\",\n \"def transformHtml(htmltext):\\n text_maker = ht.HTML2Text()\\n text_maker.bypass_tables = False\\n text_maker.ignore_tables = False\\n try:\\n markdown = text_maker.handle(str(htmltext))\\n except Exception as e:\\n print(\\\"[ERROR] can not transformer to markdown: \\\" + str(e))\\n return markdown\",\n \"def extract_news(parser):\\n news_list = []\\n\\n titles = parser.find_all(\\\"tr\\\", class_=\\\"athing\\\")\\n subtext = parser.find_all(\\\"td\\\", class_=\\\"subtext\\\")\\n\\n for i in range(len(titles)):\\n x = titles[i].find_all(\\\"td\\\", class_=\\\"title\\\")[1]\\n title = x.a.text\\n url = x.a[\\\"href\\\"]\\n c = subtext[i].find_all(\\\"a\\\")[4]\\n if c.text == \\\"discuss\\\":\\n comments = 0\\n else:\\n comments = c.text\\n author = subtext[i].find(\\\"a\\\", class_=\\\"hnuser\\\").get_text()\\n point = subtext[i].find(\\\"span\\\", class_=\\\"score\\\").text\\n points = point.split(' ')[0]\\n\\n news_list.append({\\\"author\\\": author, \\\"comments\\\": comments, \\\"points\\\": points, \\\"title\\\": title, \\\"url\\\": url})\\n\\n return news_list\",\n \"def parse_news(news):\\n default_value = '---'\\n\\n news_list = []\\n for entry in news:\\n title = entry.get('title', default_value)\\n link = entry.get('link', default_value)\\n published = entry.get('published', default_value)\\n source = entry.get('source', default_value)\\n description = entry.get('description', default_value)\\n media_content = entry.get('media_content', default_value)\\n\\n source_title = default_value\\n if source != default_value:\\n source_title = source['title']\\n\\n image = default_value\\n if media_content != image:\\n image = media_content[0]['url']\\n\\n article = Article(title, link, published, source_title, description, image)\\n news_list.append(article)\\n\\n return news_list\",\n \"def get_article(self, url):\\n response = self.opener.open(url)\\n doc = lxml.html.document_fromstring(response.read())\\n content = doc.find_class(\\\"post\\\")[0] # Select content by CSS class \\n cleaned_content = clean_html(content)\\n str_cleaned_content = lxml.html.tostring(cleaned_content)\\n # self.__save_article_to_file(str_cleaned_content)\\n return str_cleaned_content\",\n \"def update_news_to_model(self):\\n internet = 1\\n news_page_url_base = 'https://news.mydrivers.com/update/'\\n url_to_get_list = []\\n if self.dates_to_get[0] == 'Date entered error':\\n self.show_in_browser.emit(f'Date entered error: {self.dates_to_get[1]}')\\n self.show_in_browser.emit('Please check the entered date.')\\n self.thread.quit()\\n return\\n for date_to_get in self.dates_to_get:\\n if pd.to_datetime(date_to_get).date() > (datetime.date.today() + datetime.timedelta(days=1)):\\n break\\n url_to_get = news_page_url_base + date_to_get + '_1.htm'\\n url_to_get_list.append(url_to_get)\\n if url_to_get_list == []:\\n self.show_in_browser.emit(f'Date entered error: {self.dates_to_get}')\\n self.show_in_browser.emit('Please check the entered date.')\\n self.thread.quit()\\n return\\n\\n def get_url_content(url_to_get):\\n try:\\n r = requests.get(url_to_get)\\n except BaseException as e:\\n print('Cannot establish Internet connection with server.\\\\n', e)\\n self.show_in_browser.emit('Cannot establish Internet connection with server.\\\\n')\\n self.show_in_browser.emit(str(e))\\n nonlocal internet\\n internet = 0\\n return\\n status_code = r.status_code\\n if status_code == 200:\\n bs = BeautifulSoup(r.content, 'html.parser')\\n news_pages_list = bs.select('#newsleft > div > a')\\n news_summary_list = bs.select('#newsleft > li')\\n else:\\n news_pages_list = []\\n news_summary_list = []\\n return status_code, news_pages_list, news_summary_list\\n\\n def extract_news_pages(news_pages_list: list) -> list:\\n news_page_url_list = []\\n for item in news_pages_list:\\n news_page_url_tail = item.get('href')\\n if item.text.isnumeric() and news_page_url_tail:\\n news_page_url = news_page_url_base + news_page_url_tail\\n news_page_url_list.append(news_page_url)\\n return news_page_url_list\\n\\n def extract_news_summary(news_summary_list: list):\\n for item in news_summary_list:\\n news_title = item.find('h3').find('a').text\\n news_link = 'https:' + item.find('h3').find('a').get('href')\\n news_author = item.find(class_=\\\"newstiao4\\\").text\\n news_datetime_text = item.find(class_=\\\"news_plun hui2\\\") \\\\\\n .find('li').text\\n match = re.search('\\\\d{4}-\\\\d{2}-\\\\d{2} \\\\d{2}:\\\\d{2}',\\n news_datetime_text)\\n news_datetime = pd.to_datetime(match.group())\\n # Naive Bayes classifier\\n seg_list = jieba.cut(news_title)\\n p_news_like_product = 1 # p_1*p_2*...*p_n\\n p_news_like_1_product = 1 # (1-p_1)*(1-p_2)*...*(1-p_n)\\n p_news_nolike_product = 1\\n p_news_nolike_1_product = 1\\n for word in seg_list:\\n sql_query = f\\\"SELECT * FROM words WHERE word = '{word}';\\\"\\n rows = self.conn.execute(sql_query).fetchall()\\n if rows == []:\\n p_word_like = 0.4\\n p_word_nolike = 0.4\\n else:\\n p_word_like = rows[0][1] / rows[0][3]\\n p_word_nolike = rows[0][2] / rows[0][3]\\n p_news_like_product = p_news_like_product * p_word_like\\n p_news_like_1_product = p_news_like_1_product * (1 - p_word_like)\\n p_news_nolike_product = p_news_nolike_product * p_word_nolike\\n p_news_nolike_1_product = p_news_nolike_1_product * (1 - p_word_nolike)\\n p_news_like = 100 * p_news_like_product * self.p_like / (\\n p_news_like_product * self.p_like + p_news_like_1_product * (1 - self.p_like))\\n p_news_nolike = 100 * p_news_nolike_product * self.p_nolike / (\\n p_news_nolike_product * self.p_nolike + p_news_nolike_1_product * (1 - self.p_nolike))\\n p_news_like = round(p_news_like, 2)\\n p_news_nolike = round(p_news_nolike, 2)\\n\\n self.news_df = self.news_df.append(\\n pd.DataFrame([[news_datetime,\\n news_title,\\n news_link,\\n news_author,\\n p_news_like,\\n p_news_nolike]],\\n columns=self.news_df.columns),\\n ignore_index=True)\\n\\n self.news_df = pd.DataFrame({'datetime': [], 'title': [], 'link': [],\\n 'author': [], 'like': [], 'no_like': []})\\n\\n for url_to_get in url_to_get_list:\\n try:\\n status_code, news_pages_list, news_summary_list = \\\\\\n get_url_content(url_to_get)\\n except:\\n break\\n if status_code == 200:\\n print(url_to_get, ' loaded.')\\n self.show_in_browser.emit(url_to_get + ' loaded.')\\n extract_news_summary(news_summary_list)\\n else:\\n print(url_to_get, 'is not available. Status Code:', status_code)\\n self.show_in_browser.emit(url_to_get + ' is not available. Status Code: ' + str(status_code))\\n continue\\n\\n for news_page_url in extract_news_pages(news_pages_list):\\n status_code, news_pages_list, news_summary_list = \\\\\\n get_url_content(news_page_url)\\n if status_code == 200:\\n extract_news_summary(news_summary_list)\\n print(news_page_url, ' loaded.')\\n self.show_in_browser.emit(news_page_url + ' loaded.')\\n else:\\n print(url_to_get, 'is not available. Status Code:',\\n status_code)\\n self.show_in_browser.emit(url_to_get + ' is not available. Status Code: ' + str(status_code))\\n continue\\n\\n if internet == 0:\\n self.thread.quit()\\n return\\n\\n if (pd.to_datetime(self.dates_to_get[0]).date() == self.news_latest_datetime.date()):\\n self.news_df.drop(self.news_df[self.news_df.datetime < self.news_latest_datetime].index,\\n inplace=True)\\n try:\\n for link in self.news_latest_link:\\n self.news_df.drop(self.news_df[self.news_df.link == link].index, inplace=True)\\n except BaseException as e:\\n print(e)\\n else:\\n sql_query = \\\"\\\"\\\"SELECT datetime FROM news_unread\\\"\\\"\\\"\\n tmp_news_datetime = pd.read_sql_query(sql_query, self.conn,\\n parse_dates='datetime')\\n try: # Raise error when news_read is empty.\\n sql_query = \\\"\\\"\\\"SELECT datetime FROM news_read\\\"\\\"\\\"\\n tmp2_news_datetime = pd.read_sql_query(sql_query, self.conn,\\n parse_dates='datetime')\\n tmp_news_datetime = tmp_news_datetime.append(tmp2_news_datetime, ignore_index=True)\\n del tmp2_news_datetime\\n except:\\n pass\\n tmp_news_datetime.datetime = tmp_news_datetime.datetime.apply(lambda x: x.date())\\n for date in self.dates_to_get:\\n date = pd.to_datetime(date).date()\\n if (tmp_news_datetime.datetime == date).any():\\n sql_query = \\\"\\\"\\\"SELECT link FROM news_unread\\\"\\\"\\\"\\n links = pd.read_sql_query(sql_query, self.conn)\\n try:\\n sql_query = \\\"\\\"\\\"SELECT link FROM news_read\\\"\\\"\\\"\\n links2 = pd.read_sql_query(sql_query, self.conn)\\n links = links.append(links2, ignore_index=True)\\n except:\\n pass\\n links = links.link.tolist()\\n self.news_df = self.news_df.drop(self.news_df[self.news_df.link.isin(links)].index)\\n break\\n\\n self.news_df.sort_values('datetime', inplace=True)\\n self.news_df.to_sql('news_unread', self.conn, index=False,\\n if_exists='append')\\n sql_query = \\\"\\\"\\\"\\n SELECT * FROM news_unread ORDER BY datetime ASC\\n \\\"\\\"\\\"\\n self._data = pd.read_sql_query(sql_query, self.conn,\\n parse_dates='datetime')\\n self._data['datetime'] = self._data['datetime'].dt.strftime('%m-%d %H:%M')\\n self.update_finished.emit()\",\n \"def get_news(url):\\r\\n \\r\\n # parse RSS feed into list of dictionaries\\r\\n feed = feedparser.parse(url)\\r\\n\\r\\n # no RSS feed articles for url\\r\\n if len(feed['entries']) == 0:\\r\\n return []\\r\\n \\r\\n # get first ten articles from the RSS feed\\r\\n news = []\\r\\n i = 0\\r\\n while True:\\r\\n if i == len(feed['entries']) or i > 30:\\r\\n break\\r\\n \\r\\n try:\\r\\n # get link to article\\r\\n link = feed[\\\"entries\\\"][i][\\\"link\\\"]\\r\\n\\r\\n # get title of article\\r\\n title = feed[\\\"entries\\\"][i][\\\"title\\\"]\\r\\n \\r\\n try:\\r\\n # get raw summary of article\\r\\n summary_raw = feed[\\\"entries\\\"][i][\\\"summary\\\"]\\r\\n \\r\\n # format summary\\r\\n summary = \\\"\\\"\\r\\n for c in summary_raw:\\r\\n if c == \\\"<\\\":\\r\\n summary += \\\"...\\\"\\r\\n break\\r\\n summary += c\\r\\n except KeyError as e:\\r\\n logging.error(\\\"no summary for RSS feed article: {}\\\".format(link))\\r\\n summary = \\\"read more here...\\\"\\r\\n \\r\\n # get raw date \\r\\n date_raw = feed[\\\"entries\\\"][i][\\\"published_parsed\\\"]\\r\\n \\r\\n if date_raw is None:\\r\\n date = feed[\\\"entries\\\"][i][\\\"published\\\"]\\r\\n \\r\\n else:\\r\\n # format date\\r\\n year = str(date_raw.tm_year)\\r\\n months = [\\\"January\\\", \\\"February\\\", \\\"March\\\", \\\"April\\\", \\\"May\\\", \\\"June\\\", \\\"July\\\", \\\"August\\\", \\\"September\\\", \\\"October\\\", \\\"November\\\", \\\"December\\\"]\\r\\n month = months[date_raw.tm_mon - 1]\\r\\n day = str(date_raw.tm_mday)\\r\\n weekdays = [\\\"Monday\\\", \\\"Tuesday\\\", \\\"Wednesday\\\", \\\"Thursday\\\", \\\"Friday\\\", \\\"Saturday\\\", \\\"Sunday\\\"]\\r\\n wday = weekdays[date_raw.tm_wday]\\r\\n hour = str(date_raw.tm_hour)\\r\\n hour = \\\"{:2}\\\".format(hour).format(' ','0')\\r\\n min = str(date_raw.tm_min)\\r\\n min = \\\"{:2}\\\".format(min).replace(' ','0')\\r\\n date = hour + \\\":\\\" + min + \\\" - \\\" + wday + \\\" \\\" + month + \\\" \\\" + day + \\\", \\\" + year\\r\\n \\r\\n # compile entry and append to news list\\r\\n entry = {\\\"link\\\":link, \\\"title\\\":title, \\\"date\\\":date, \\\"summary\\\":summary}\\r\\n \\r\\n # sanitize entry\\r\\n for key in entry:\\r\\n # apostrophe\\r\\n entry[key] = entry[key].replace(\\\"'\\\", \\\"'\\\")\\r\\n # right single quotation mark\\r\\n entry[key] = entry[key].replace(\\\"’\\\", \\\"’\\\")\\r\\n # left single quotation mark\\r\\n entry[key] = entry[key].replace('\\\"', \\\"‘\\\")\\r\\n # right double quotation mark\\r\\n entry[key] = entry[key].replace(\\\"'\\\", \\\"”\\\")\\r\\n # left double quotation mark\\r\\n entry[key] = entry[key].replace(\\\"'\\\", \\\"“\\\")\\r\\n # Weird ampersand formatting\\r\\n entry[key] = entry[key].replace(\\\"&\\\", \\\"&\\\")\\r\\n \\r\\n # prepare entry for sqlite queries\\r\\n entry[key] = surround(entry[key])\\r\\n \\r\\n # add entry to news list\\r\\n news.append(entry)\\r\\n \\r\\n # max 10 entries\\r\\n if len(news) == 10:\\r\\n break\\r\\n i += 1\\r\\n \\r\\n except Exception as e:\\r\\n logging.error(e)\\r\\n i += 1\\r\\n pass\\r\\n \\r\\n # success\\r\\n return news\",\n \"def process(url):\\n feed = feedparser.parse(url)\\n entries = feed.entries\\n ret = []\\n for entry in entries:\\n print entry\\n guid = entry.guid\\n title = translate_html(entry.title)\\n link = entry.link\\n summary = translate_html(entry.summary)\\n try:\\n subject = translate_html(entry.tags[0]['term'])\\n except AttributeError:\\n subject = \\\"\\\"\\n newsStory = NewsStory(guid, title, subject, summary, link)\\n ret.append(newsStory)\\n return ret\",\n \"def get_html(self):\\r\\n pass\",\n \"def plain_to_html(plain_text):\\n if plain_text is None:\\n return \\\"\\\"\\n\\n # We remove trailing whitespace - most notably newlines - so we\\n # don't end up with unexpected vertical whitespace in the output.\\n html = str(plain_text).rstrip().replace(\\\"&\\\", \\\"&\\\"). \\\\\\n replace(\\\"<\\\", \\\"<\\\").replace(\\\">\\\", \\\">\\\")\\n\\n # *Sigh* see if we can work out what they wanted.\\n #\\n # If they use more than one space anywhere except at the start or\\n # end of a line, then they probably wanted fully preformatted text\\n # with a monospaced font. We'll also look for the line drawing\\n # character \\\"|\\\" and a common way tables are done (\\\" : \\\" after some\\n # text on the line).\\n #\\n # Note that if output_list() was used then the lines in the list will be\\n # joined together with newlines before getting here so they will be\\n # treated as preformatted.\\n #\\n # Otherwise if they've used spaces at the start of a line or they've\\n # used newlines then assume they want to control indentation and line\\n # breaks but they don't need a monospaced font.\\n #\\n # If neither of those are true, then we have a simple piece of HTML.\\n #\\n # Since all output goes through plain_to_html on entry, that provides a\\n # single entry point to change things later. For example, it could be\\n # adapted so that if it is given a dictionary then an html key gives the\\n # HTML rendering of the object or a pre key indicates that it's\\n # preformatted text. Similarly, passing in a object could defer the\\n # rendering to that object. This is the solution BlueCore's CoreTools\\n # uses. Other solutions are possible.\\n if re.search(r\\\"\\\\S {2,}\\\\S\\\", html) is not None or \\\"|\\\" in html or \\\\\\n re.search(r\\\"\\\\S.* : \\\", html) is not None:\\n return html_pre(html)\\n\\n if re.search(r\\\"^ \\\", html, flags=re.MULTILINE) or \\\"\\\\n\\\" in html:\\n return html_manual_format(html)\\n\\n return html\",\n \"def htmlForMarkdown(md):\\n return mdProcessor.convert(md)\",\n \"def markdown_to_html(s):\\n return markdown(s)\",\n \"def select_news(self):\\n data = self.soup.findAll('item')\\n for item in data:\\n news_data = dict()\\n for tag in ['title', 'link']:\\n news_data[tag] = item.find(tag).get_text()\\n\\n news_data['pubDate'] = parse(item.find('pubDate').get_text())\\n media = item.find('media:content')\\n\\n if media:\\n news_data['media'] = media.get('url')\\n else:\\n news_data['media'] = None\\n\\n yield news_data\",\n \"def to_html(self):\\n return self.serializer.render(self.formatter.formatMessage(self.oldmsg))\",\n \"def news(request):\\n articles = News.objects.all()\\n return render(request, 'news.html', {\\\"articles\\\": articles})\",\n \"def convert_text_to_rouge_format(text, title=\\\"dummy title\\\"):\\n sentences = text.split(\\\"\\\\n\\\")\\n sent_elems = [\\n \\\"<a name=\\\\\\\"{i}\\\\\\\">[{i}]</a> <a href=\\\\\\\"#{i}\\\\\\\" id={i}>\\\"\\n \\\"{text}</a>\\\".format(i=i, text=sent)\\n for i, sent in enumerate(sentences, start=1) if sent != '']\\n html = \\\"\\\"\\\"<html>\\n<head>\\n<title>{title}</title>\\n</head>\\n<body bgcolor=\\\"white\\\">\\n{elems}\\n</body>\\n</html>\\\"\\\"\\\".format(title=title, elems=\\\"\\\\n\\\".join(sent_elems))\\n\\n return html\",\n \"def news_article(id):\\n articles = get_articles(id)\\n return render_template('news_article.html', articles = articles)\",\n \"def norm_html_from_html(html):\\n if not isinstance(html, unicode):\\n html = html.decode('utf-8')\\n html = _markdown_email_link_re.sub(\\n _markdown_email_link_sub, html)\\n if sys.platform == \\\"win32\\\":\\n html = html.replace('\\\\r\\\\n', '\\\\n')\\n return html\",\n \"def __html__(self) -> str:\\n components = [\\n self.attributee_html,\\n f'\\\"{self.linked_title}\\\"',\\n self.date.string if self.date else '',\\n ]\\n return self.components_to_html(components)\",\n \"def get_html2text(html):\\n text_maker = html2text.HTML2Text()\\n text_maker.body_width = 0\\n return text_maker.handle(html)\",\n \"def format(self, content, link):\\n error = None\\n # Phase 1 - setting correct xpath -> what to include as text from html\\n tree = html.fromstring(content)\\n # TODO: They change this often!!!\\n texts = tree.xpath('//section[starts-with(@class,\\\"article-body\\\")]/div[starts-with(@id, \\\"node-\\\") and not(descendant::figcaption)]//text()')\\n if not texts:\\n texts = tree.xpath('//section[contains(@class,\\\"article-body\\\")]/div[starts-with(@id, \\\"node-\\\") and not(descendant::figcaption)]//text()')\\n if not texts:\\n texts = tree.xpath('//div[@class=\\\"classic-body\\\"]/p//text()')\\n # There are also articles that show 404\\n # This should be error message in Coindesk.article() as 404 articles\\n # does not have dates\\n if not texts:\\n try:\\n error = tree.xpath('//section[@class=\\\"error-module\\\"]')[0]\\n except:\\n error = None\\n # There are article that have no text\\n if not texts:\\n try:\\n empty_article = tree.xpath('//section[contains(@class,\\\"article-body\\\")]')[0]\\n except IndexError:\\n empty_article = None\\n # If article has no text, does not show 404 error it is time to update xpath\\n # This need to be tested on more articles !!!\\n if not texts and empty_article is None and error is None:\\n raise ContentError(f\\\"ERROR: Update xpath for text {link} !!!\\\")\\n elif not texts and empty_article is None and error is not None:\\n return \\\"404\\\"\\n else:\\n return 'No text available'\\n para = []\\n j = []\\n # Phase 2 - connecting appropriate items in paras list\\n text_list = [re.sub(r'[\\\\xa0]', '', i) for i in texts]\\n for i in text_list:\\n if i.endswith('.') or i.endswith('.\\\"') or i.endswith('. ') or i.endswith('.”') or i.endswith('.’”'):\\n if j:\\n j.append(i)\\n para.append(j)\\n j = []\\n else:\\n para.append(i)\\n else:\\n try:\\n next_space = text_list[text_list.index(i) + 1].startswith(' ')\\n next_capital = text_list[text_list.index(i) + 1][0].isupper()\\n except IndexError:\\n next_space = False\\n next_capital = False\\n if next_space and next_capital:\\n para.append(i)\\n else:\\n j.append(i)\\n # Phase 3 - final formatting corrections from list of paras to fluent text\\n paras = [' '.join(i) if isinstance(i, list) else i for i in para]\\n paras = [re.sub(r' +', ' ', i).strip() for i in paras]\\n paras = [str(i) + '\\\\n' for i in paras]\\n return ' '.join(paras)\",\n \"def parse_news(self, response):\\n \\n loader = NewsLoader(item=NewsItem(), response=response)\\n loader.add_xpath('title', '//header//h1/text()')\\n author = ''.join(response.xpath('//span[@class=\\\"byline\\\"]').extract())\\n author = remove_tags(author).replace(\\\"by\\\", '').replace(' and ', ', ')\\n loader.add_value('author', author)\\n timestamp = response.xpath('//meta[@name=\\\"DC.date.issued\\\"][1]/@content').extract()[0]\\n timestamp = du.normalize_timestamp(timestamp)\\n loader.add_value('date', timestamp.split(' ')[0])\\n loader.add_value('time', timestamp.split(' ')[1])\\n list_of_contents = response.xpath(\\n '//div[@id=\\\"storytext\\\"]/*[not(@class=\\\"cnnplayer\\\") and '\\n 'not(@class=\\\"storytimestamp\\\")]').extract()\\n content = ' '.join(list_of_contents)\\n loader.add_value('content', content)\\n loader.add_xpath('tags', '//meta[@name=\\\"keywords\\\"]/@content')\\n return loader.load_item()\",\n \"def news_handle(news_json):\\n #Sets all the variables to global space to be used later.\\n global title_1, title_2, author_1, author_2, source_1, source_2, url_1_final, url_2_final\\n #Handles data from API and assigns data to variables.\\n news_data = news_json['articles']\\n article_1 = news_data[0]\\n article_2 = news_data[1]\\n title_1 = article_1['title']\\n author_1 = article_1['author']\\n source_1 = article_1['source']['name']\\n url_1 = article_1['url']\\n url_1_final = '<a href=\\\"{}\\\">Read More</a>'.format(url_1)\\n title_2 = article_2['title']\\n author_2 = article_2['author']\\n source_2 = article_2['source']['name']\\n url_2 = article_2['url']\\n url_2_final = '<a href=\\\"{}\\\">Read More</a>'.format(url_2)\\n return\",\n \"def output(self):\\n entry = []\\n entry.append('''<entry>\\n <title mode=\\\"escaped\\\" type=\\\"text/html\\\">%(title)s</title>\\n <link rel=\\\"alternate\\\" type=\\\"text/html\\\" href=\\\"%(url)s\\\" />\\n <issued>%(issued)s</issued>\\n <modified>%(modified)s</modified>\\n ''' % self.__dict__)\\n \\n if self.feed:\\n entry.append('''<link rel=\\\"service.feed\\\" type=\\\"application/atom+xml\\\" href=\\\"%s\\\" title=\\\"%s\\\" />''' % (self.feed, self.feed_title))\\n if self.comments:\\n entry.append('''<link rel=\\\"comments\\\" type=\\\"application/atom+xml\\\" href=\\\"%s\\\" />''' % self.comments)\\n if self.author:\\n entry.append('''<author>%s</author>''' % self.author.output())\\n for person in self.contributors:\\n entry.append('''<contributor>%s</contributor>''' % person.output())\\n if self.id:\\n entry.append('''<id>%s</id>''' % self.id)\\n if self.created:\\n entry.append('''<created>%s</created>''' % self.created)\\n if self.summary:\\n entry.append('''<summary type=\\\"application/xhtml+xml\\\" xml:base=\\\"%s\\\" xml:space=\\\"preserve\\\">\\n <div xmlns=\\\"http://www.w3.org/1999/xhtml\\\">%s</div></summary>''' % (self.base_url, self.summary))\\n if self.content:\\n #entry.append('''<content type=\\\"application/xhtml+xml\\\" xml:base=\\\"%s\\\" xml:space=\\\"preserve\\\">\\n # <div xmlns=\\\"http://www.w3.org/1999/xhtml\\\">%s</div></content>''' % (self.base_url, self.content))\\n entry.append('''<content type=\\\"text/html\\\" mode=\\\"escaped\\\" xml:base=\\\"%s\\\" xml:space=\\\"preserve\\\">%s</content>''' % (self.base_url, self.content))\\n \\n entry.append('''</entry>''')\\n return '\\\\n'.join(entry)\",\n \"def articles(news_id):\\n #get news based on source id\\n articles = get_articles(news_id)\\n print(articles)\\n title = f'{news_id}'\\n return render_template(\\\"articles.html\\\", articles = articles, title = title)\",\n \"def html(input):\\n output=atpic.cleaner_alex.clean(input)\\n return output\",\n \"def get_article():\\n bbc_request = requests.get('https://www.bbc.com/ukrainian')\\n soup = BeautifulSoup(bbc_request.text, \\\"html.parser\\\")\\n raw_article = soup.find_all('div', {'class': \\\"bbc-1vo75s2-TextGridItem e19k1v2h0\\\"})[0].find_all(text=True, recursive=True)\\n\\n title = raw_article[0]\\n description = raw_article[1]\\n publish_time = raw_article[2]\\n href = soup.find_all('div', {'class': 'bbc-1vo75s2-TextGridItem e19k1v2h0'})[0].find('a', {'class': 'bbc-11m194t-Link evnt13t0'})['href']\\n link = f' https://www.bbc.com/{href}'\\n article = f\\\"\\\"\\\"⚠️ <b>Тема</b>: {title}\\n \\\\n📌 <b>Короткий опис</b>: {description}\\n \\\\n🕒 <b>Опубліковано</b>: {publish_time}\\n \\\\n➡️ <b>Повний текст</b>: {link}\\\"\\\"\\\"\\n return article\",\n \"def __html__(self, file_path:str) -> str:\\n with open(f\\\"{file_path}\\\", \\\"r\\\") as mdfile: # Parse markdown file\\n text = mdfile.read()\\n html = self.md.convert(text) # Convert the markdown content text to hmtl\\n return html\",\n \"async def news(self):\\n url = f\\\"https://newsapi.org/v2/top-headlines?country=nz&apiKey={self.bot.news_api_key}\\\"\\n async with ClientSession() as session:\\n async with session.get(url) as response:\\n r = await response.json()\\n firstArticle = r[\\\"articles\\\"][0]\\n nSource = firstArticle[\\\"source\\\"][\\\"name\\\"]\\n nTitle = firstArticle[\\\"title\\\"]\\n nTimestamp = firstArticle[\\\"publishedAt\\\"]\\n embed = discord.Embed(\\n title=f\\\"News Title: {nTitle}\\\", description=f\\\"News Source: {nSource}\\\"\\n )\\n embed.add_field(name=\\\"News Content\\\", value=firstArticle[\\\"description\\\"])\\n embed.set_image(url=firstArticle[\\\"urlToImage\\\"])\\n embed.set_footer(text=f\\\"News Timestamp: {nTimestamp}\\\")\\n\\n channel = self.bot.get_channel(self.bot.main_channel_id)\\n await channel.send(embed=embed)\",\n \"def txt_to_html(in_str):\\n replace_list = {\\n \\\">\\\": \\\">\\\",\\n \\\"<\\\": \\\"<\\\",\\n \\\"\\\\n\\\": \\\"<br/>\\\",\\n }\\n for i in replace_list:\\n in_str = re.sub(i, replace_list[i], in_str)\\n return in_str\",\n \"def newsfeed_en(request):\\n article_list = Article.objects.order_by('published_date')\\n context = {'article_list': article_list}\\n return render(request, 'sacms/newsfeed_en.html', context)\",\n \"def to_html(self, content, request, **parameters):\\n raise NotImplementedError\",\n \"def rest2html(s):\\n return core.publish_string(s, writer=html_fragment_writer)\",\n \"def ENMLToHTML(content, pretty=True, **kwargs):\\n soup = BeautifulSoup(content)\\n \\n todos = soup.find_all('en-todo')\\n for todo in todos:\\n checkbox = soup.new_tag('input')\\n checkbox['type'] = 'checkbox'\\n checkbox['disabled'] = 'true'\\n if todo.has_attr('checked'):\\n checkbox['checked'] = todo['checked']\\n todo.replace_with(checkbox)\\n\\n if 'media_store' in kwargs:\\n store = kwargs['media_store']\\n all_media = soup.find_all('en-media')\\n for media in all_media:\\n resource_url = store.save(media['hash'], media['type'])\\n # TODO: use different tags for different mime-types\\n new_tag = soup.new_tag('img')\\n new_tag['src'] = resource_url\\n if media.has_attr('alt'):\\n new_tag['alt'] = media['alt']\\n media.replace_with(new_tag)\\n \\n note = soup.find('en-note')\\n if note:\\n note.name = 'body'\\n html = soup.new_tag('html')\\n html.append(note)\\n\\n output = html.prettify().encode('utf-8') if pretty else str(html)\\n return output\\n \\n return content\",\n \"def to_html(self) -> str:\\n return f'''\\n <a href=\\\"{self.link}\\\"> ({self.source_name}, {self.timestamp.strftime('%Y')}) </a>\\n '''\",\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 test_format_html2text(self):\\n html = (\\n \\\"<div>Lorem Ipsum</div>\\\"\\n \\\"Lorem ipsum dolor sit amet, consectetur adipiscing elit.\\\"\\n \\\"Morbi eleifend magna sit amet sem gravida sollicitudin.\\\"\\n \\\"<br/>Vestibulum metus ipsum, varius in ultricies eget, vulputate eu felis.\\\"\\n )\\n text = format_html2text(self.report, html)\\n self.assertEqual(\\n text,\\n (\\n \\\"Lorem Ipsum\\\"\\n \\\"\\\\n\\\\n\\\"\\n \\\"Lorem ipsum dolor sit amet, consectetur adipiscing elit.\\\"\\n \\\"Morbi eleifend magna sit amet sem gravida sollicitudin. \\\\n\\\"\\n \\\"Vestibulum metus ipsum, varius in ultricies eget, vulputate eu felis.\\\\n\\\"\\n ),\\n )\",\n \"def __html__(self):\\n return self.html\",\n \"def unhtml(cls, text):\\n parser = cls()\\n parser.feed(text)\\n return parser\",\n \"def html(self) -> SafeString:\\n return format_html(self.__html__())\",\n \"def html(self) -> SafeString:\\n return format_html(self.__html__())\",\n \"def initFormat(self):\\n self.html = True\",\n \"def initFormat(self):\\n self.html = True\",\n \"def __repr__(self):\\r\\n\\r\\n return f\\\"<News: id = {self.news_id}, title = {self.title} summary = {self.summary}>\\\"\",\n \"def messageInHTML(aMessageTitle, aMessage):\\n return \\\"\\\"\\\"<html>\\n <head>\\n <meta http-equiv=\\\"content-type\\\" content=\\\"text/html; charset=UTF-8\\\">\\n <title>WebFilter</title>\\n </head>\\n <body> <h2>%s</h2> <br>%s </body>\\n </html>\\n \\\"\\\"\\\" %(aMessageTitle, aMessage)\",\n \"def from_html(self, content):\\r\\n pass\",\n \"def encode_html(self):\\n html_temp = self._html\\n try:\\n html_temp = html_temp.split('<div class=\\\"rich-text\\\">')[1].rsplit(\\n \\\"</div>\\\", 1\\n )[0]\\n except IndexError:\\n pass\\n return b64encode(html_temp.encode(\\\"utf-8\\\")).decode(\\\"utf-8\\\")\",\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 convertHTML(self, text):\\n return text.replace(''', \\\"'\\\")\",\n \"def get_html(self, *args, **kwargs):\\n return Text(self.get_data(*args, **kwargs), escape=False)\",\n \"def news(id):\\n\\tarticle = data.get_article(id)\\n\\tif not article:\\n\\t\\treturn \\\"article not found\\\", 404\\n\\treturn render_template(\\\"article.html\\\", article=article)\",\n \"def get_html_string(self, **kwargs):\\n ...\",\n \"def md2html():\\n # Get the markdown text from the request\\n data = json.loads(request.data)\\n md_text = data['md_text']\\n\\n # Convert the markdown text to html format\\n html_text = convert(md_text)\\n\\n # Generate response and send it to the front end\\n response_dict = {\\\"html_text\\\": html_text}\\n response = jsonify(response_dict)\\n response.headers.add('Access-Control-Allow-Origin', '*')\\n return response\",\n \"def get_university_news():\\n\\tresponse = requests.get('https://cumoodle.coventry.ac.uk')\\n\\tmoodleContent = BeautifulSoup(response.content, 'html.parser')\\n\\tpostLinks =[]\\n\\theadings = []\\n\\tdates = []\\n\\tdata = \\\"\\\"\\n\\tfor title in moodleContent.findAll('div',{'class':'subject'}):\\n\\t\\theadings.append(title.text+\\\"</a></p>\\\")\\n\\tfor link in moodleContent.findAll('div',{'class':'link'}):\\n\\t\\tpostLinks.append(\\\"<p style = 'font-size:120%;'> <a href = '\\\"+link.a['href']+\\\"'>\\\") \\n\\tfor date in moodleContent.findAll('div',{'class':'author'}):\\n\\t\\tdates.append(\\\"<p style='font-size:90%;'>\\\"+date.text[18:]+\\\"</p>\\\")\\n\\tresults = zip(postLinks, headings, dates)\\n\\tfor result in results:\\n\\t\\tdata+=(''.join(result))\\n\\treturn data\",\n \"async def news(self, ctx, typex):\\n if typex in options['news']['sport_type']:\\n output = news_output(get_sport_data())\\n elif typex in options['news']['world_type']:\\n output = news_output(get_news_data(\\\"https://www.novinky.cz/zahranicni\\\"))\\n elif typex in options['news']['home_type']:\\n output = news_output(get_news_data(\\\"https://www.novinky.cz/domaci\\\"))\\n else:\\n await ctx.send(\\\"Zadán nesprávný typ novinek\\\")\\n return\\n\\n for embed in output:\\n await ctx.send(embed=embed)\",\n \"def html_from_markdown(content): \\n\\n \\\"\\\"\\\"\\n Bold \\n \\\"\\\"\\\" \\n # Convert to <strong></strong>\\n regx = re.compile(r\\\"^\\\\*\\\\*(.*?)\\\\*\\\\*\\\", re.MULTILINE)\\n content = regx.sub(r\\\"<strong>\\\\1</strong>\\\",content) \\n\\n \\\"\\\"\\\"\\n Link \\n \\\"\\\"\\\" \\n # Convert to <a>\\n regx = re.compile(r\\\"\\\\[(.*)\\\\]\\\\((.*)\\\\)\\\", re.MULTILINE)\\n content = regx.sub(r\\\"<a href=\\\\2>\\\\1</a>\\\",content) \\n\\n \\\"\\\"\\\"\\n Paragraph \\n \\\"\\\"\\\" \\n new_content = \\\"\\\"\\n for line in content.splitlines():\\n line = re.sub(r'^(?!#|\\\\*)(.+)', r'<p>\\\\1</p>', line)\\n new_content = new_content + line + \\\"\\\\n\\\"\\n content = new_content\\n\\n \\\"\\\"\\\"\\n Unordered lists\\n \\\"\\\"\\\" \\n new_content = \\\"\\\" \\n u_list = False\\n for line in content.splitlines():\\n\\n if len(line) > 0: # Check the line is not empty\\n\\n l = line[:2]\\n if u_list and l!=\\\"* \\\": # check if there and unordered list to be closed.\\n new_content = new_content + \\\"</ul>\\\"\\n u_list = False # Flag indicates the unordered list has finished\\n\\n #if line[0]!=\\\"#\\\" and line[0]!=\\\"*\\\": # Add the paragraph to the line\\n # line = \\\"<p>\\\" + line + \\\"</p>\\\\n\\\"\\n\\n if line[:2]==\\\"* \\\": # Check if the lins is an unordered list\\n if not u_list: # Check if it´s the first item of the list\\n line = \\\"<ul><li>\\\" + line [2:] + \\\"</li>\\\"\\n u_list = True # Flag indicates the unordered list has started.\\n else:\\n line = \\\"<li>\\\" + line [2:] + \\\"</li>\\\"\\n\\n new_content = new_content + line + \\\"\\\\n\\\"\\n\\n if u_list : # in case still have an unordered list to be closed.\\n new_content = new_content + \\\"</ul>\\\"\\n\\n content = new_content\\n\\n \\\"\\\"\\\"\\n Headers \\n \\\"\\\"\\\" \\n # Convert to h1\\n regx = re.compile(r\\\"^#\\\\s(.*?)\\\\n\\\", re.MULTILINE)\\n content = regx.sub(r\\\"<h1>\\\\1</h1>\\\\n\\\",content) \\n\\n # Convert to h2\\n regx = re.compile(r\\\"^##\\\\s(.*?)\\\\n\\\", re.MULTILINE)\\n content = regx.sub(r\\\"<h2>\\\\1</h2>\\\\n\\\",content) \\n\\n # Convert to h3\\n regx = re.compile(r\\\"^###\\\\s(.*?)\\\\n\\\", re.MULTILINE)\\n content = regx.sub(r\\\"<h3>\\\\1</h3>\\\\n\\\",content) \\n\\n # Convert to h4\\n regx = re.compile(r\\\"^####\\\\s(.*?)\\\\n\\\", re.MULTILINE)\\n content = regx.sub(r\\\"<h4>\\\\1</h4>\\\\n\\\",content) \\n\\n # Convert to h5\\n regx = re.compile(r\\\"^#####\\\\s(.*?)\\\\n\\\", re.MULTILINE)\\n content = regx.sub(r\\\"<h5>\\\\1</h5>\\\\n\\\",content) \\n\\n # Convert to h6\\n regx = re.compile(r\\\"^######\\\\s(.*?)\\\\n\\\", re.MULTILINE) \\n content = regx.sub(r\\\"<h6>\\\\1</h6>\\\\n\\\",content) \\n\\n\\n return content\",\n \"def output(self): #1 这是在 BaseHTMLProcessor 中的一个方法，它永远不会被父类 SGMLParser 所调用。因为其它的处理器方法将它们重构的 HTML 保存在 self.pieces 中，这个函数需要将所有这些片段连接成一个字符串。正如前面提到的，Python 在处理列表方面非常出色，但对于字符串处理就逊色了。所以我们只有在某人确实需要它时才创建完整的字符串。\\n return \\\"\\\".join(self.pieces) #2 如果您愿意，也可以换成使用 string 模块的 join 方法：string.join(self.pieces, \\\"\\\")。 \",\n \"def analyze_news(self, soup_news) -> list:\\n logging.info(\\\"analyzing news collected\\\")\\n analyzed_news = []\\n for news in soup_news:\\n main_column = news.find(\\\"div\\\", {\\\"class\\\": \\\"column size-3-4 column-main\\\"})\\n tag_list = main_column.find_all(\\\"div\\\", {\\\"class\\\": re.compile(r\\\"etiqueta etiqueta-[0-9]\\\")})\\n fake = self.__is_fake(tag_list)\\n if fake:\\n analyzed_news.append(main_column)\\n\\n return analyzed_news\",\n \"def news_template_flex(news_list, events, key, id, isMore, alt_text='推薦新聞'):\\n news_box_components = []\\n for i, news in enumerate(news_list):\\n # img = \\\"https://cdn.pixabay.com/photo/2015/12/03/22/16/newspaper-1075795_1280.jpg\\\"\\n date = news['date']\\n date = re.sub(\\\"T.*Z\\\", \\\"\\\", date)\\n url = news['url']\\n\\n # Add separators between news.\\n if i > 0:\\n news_box_components.append(SeparatorComponent(margin='lg'))\\n # related_news_uri = f\\\"{RELATED_NEWS_LIFF_URI}?cid={news['cid']}&nid={news['nid']}\\\"\\n\\n TextComponents = []\\n TextComponents.append(TextComponent(\\n text=news['title'],\\n wrap=True,\\n size='md',\\n weight='bold',\\n action=URIAction(uri=url)\\n ))\\n TextComponents.append(TextComponent(\\n text='{} - {}'.format(news['source'], date),\\n wrap=True,\\n size='xxs',\\n color='#6c757d'\\n ))\\n words = []\\n event_v, event_o = events.split()\\n regex = \\\"({}|{})\\\".format(event_v, event_o)\\n tokenized_sentence = re.split(regex, news['sentences'][1])\\n tokenized_sentence = [t for t in tokenized_sentence if t != \\\"\\\"]\\n tokenized_sentence = [news['sentences'][0] + \\\" \\\"] + tokenized_sentence + [\\\" \\\" + news['sentences'][2]]\\n for i in tokenized_sentence:\\n if i == event_v or i == event_o:\\n words.append(SpanComponent(\\n text=i,\\n color=\\\"#e86d48\\\"\\n ))\\n else:\\n words.append(SpanComponent(\\n text=i,\\n ))\\n new_text_componet = TextComponent2(type=\\\"text\\\", text=\\\"hello, world\\\", contents=words, wrap=True)\\n TextComponents.append(new_text_componet)\\n TextComponents.append(BoxComponent(\\n layout='horizontal',\\n spacing='md',\\n contents=[\\n TextComponent(\\n text='查看這則新聞',\\n size='md',\\n color='#6E7DAB',\\n flex=1,\\n action=URIAction(uri=url),\\n ),\\n ]\\n ))\\n\\n news_box_components.append(\\n BoxComponent(\\n layout='horizontal',\\n spacing='md',\\n contents=[\\n BoxComponent(\\n layout='vertical',\\n flex=2,\\n spacing='sm',\\n contents=TextComponents\\n )\\n ]\\n )\\n )\\n if isMore:\\n news_box_components.append(\\n ButtonComponent(style=\\\"link\\\",\\n # margin=\\\"xs\\\",\\n action=MessageTemplateAction(\\n label=\\\"more\\\",\\n text=\\\"more:\\\" + str(id) + \\\":\\\" + key + \\\":\\\" + events\\n )))\\n\\n bubble = BubbleContainer(\\n body=BoxComponent(\\n layout='vertical',\\n spacing='lg',\\n contents=news_box_components\\n )\\n )\\n return FlexSendMessage(alt_text=alt_text, contents=bubble)\",\n \"def __get_local_g1_news(soup):\\n news = []\\n anchors = soup.find_all(\\n 'a', class_='feed-post-link gui-color-primary gui-color-hover')\\n\\n for a in anchors:\\n title = a.string\\n link = a['href']\\n news.append(dict(title=title, link=link))\\n return news\",\n \"def news(self):\\n\\n # Get articles with search term, if available, from each News API source\\n news_api_articles = pd.DataFrame()\\n\\n q = urllib.parse.quote(\\\" OR \\\".join(self.search_terms), safe='')\\n\\n response = requests.get(\\\"https://newsapi.org/v2/everything?q=\\\" + q + \\\"&from=\\\" + datetime.now().strftime(\\n \\\"%Y-%m-%d\\\") + \\\"&sortBy=popularity&pageSize=100&apiKey=\\\" + self.__news_api_key)\\n\\n if response.status_code == 200:\\n data = json.loads(response.text)\\n\\n source_articles = []\\n\\n for article in data['articles']:\\n source_articles.append([article['title'],\\n article['description'],\\n article['url'],\\n article['publishedAt']])\\n\\n source_articles = pd.DataFrame(source_articles, columns=['title', 'description', 'url', 'publishedAt'])\\n news_api_articles = pd.concat([news_api_articles, source_articles])\\n\\n news_api_articles = news_api_articles.reset_index(drop='True')\\n\\n news_api_articles['publishedAt'] = news_api_articles['publishedAt'].apply(pd.to_datetime)\\n\\n news_api_articles = news_api_articles.fillna(' ')\\n\\n term_in_title = news_api_articles['title'].apply(self.any_term)\\n\\n news_api_articles = news_api_articles[term_in_title]\\n\\n if (len(news_api_articles) > 10):\\n news_api_articles = news_api_articles[0:10]\\n\\n else:\\n print(\\\"News API failed to return any items\\\")\\n\\n # Create shortened links using bitly if access token is provided\\n if self.__bitly_access_token != '':\\n\\n bitly_urls = []\\n\\n for index, article in news_api_articles.iterrows():\\n url = article['url']\\n bitly_response = requests.get(\\\"https://api-ssl.bitly.com/v3/shorten\\\",\\n params={'longUrl': url, 'access_token': self.__bitly_access_token})\\n\\n if bitly_response.status_code == 200:\\n data = json.loads(bitly_response.text)\\n bitly_urls.append(data['data']['url'])\\n\\n news_api_articles['url'] = bitly_urls\\n\\n # Store final list to TwitterBot object\\n self.list = news_api_articles\\n\\n return\",\n \"def convertString(rst, filename=None, **kw):\\n\\n html = publish_string(source=rst,\\n source_path=filename,\\n writer_name='html',\\n settings_overrides={'input_encoding': 'utf-8',\\n 'output_encoding': 'utf-8',\\n })\\n tree = minidom.parseString(html)\\n title = xhtmlutil.getNodeContentsAsText(xhtmlutil.getTitle(tree))\\n\\n body = xhtmlutil.getBody(tree)\\n\\n doc = xhtmlutil.only(xhtmlutil.getElementsByClass(body, 'document'))\\n\\n # kill h1.title\\n for h1 in doc.getElementsByTagName('h1'):\\n if xhtmlutil.elementHasClass(h1, 'title'):\\n h1.parentNode.removeChild(h1)\\n break\\n\\n # kill .docinfo, but store the data\\n metadata = {}\\n for docinfo in xhtmlutil.getElementsByClass(doc, 'docinfo'):\\n for field in docinfo.getElementsByTagName('tr'):\\n nameElem = xhtmlutil.only(xhtmlutil.getElementsByClass(field, 'docinfo-name'))\\n name = xhtmlutil.getNodeContentsAsText(nameElem)\\n name = name.rstrip(':')\\n\\n bodyElem = xhtmlutil.only(field.getElementsByTagName('td'))\\n\\n # undo automatic link creation by reStructuredText\\n for node in bodyElem.childNodes:\\n if (node.nodeType == bodyElem.ELEMENT_NODE\\n and node.tagName == 'a'):\\n while node.hasChildNodes():\\n bodyElem.insertBefore(node.firstChild, node)\\n bodyElem.removeChild(node)\\n\\n body = xhtmlutil.getNodeContentsAsText(bodyElem)\\n\\n metadata[name.lower()] = body\\n docinfo.parentNode.removeChild(docinfo)\\n\\n for attr in ['updated', 'published']:\\n val = metadata.get(attr, None)\\n if val is not None:\\n val = datatypes.DateTime().coerce(val, None)\\n metadata[attr] = val\\n\\n metadata.setdefault('updated', metadata.get('published', None))\\n if not metadata.get('updated', None):\\n raise RuntimeError(\\\"Metadata field 'updated' not set.\\\")\\n\\n # split author to separate name and email\\n if 'author' in metadata:\\n realname, address = email.Utils.parseaddr(metadata['author'])\\n metadata['author'] = {'name': realname,\\n 'email': address,\\n }\\n\\n tags = metadata.pop('tags', '').split(None)\\n if tags:\\n metadata['category'] = tags\\n\\n xhtmlutil.removeClass(doc, 'document')\\n\\n newdoc = xml.dom.getDOMImplementation().createDocument(\\n 'http://www.w3.org/1999/xhtml',\\n 'div', None)\\n # kludge it to force xmlns to show up on output\\n ns = newdoc.createAttribute('xmlns')\\n ns.nodeValue = 'http://www.w3.org/1999/xhtml'\\n newdoc.firstChild.setAttributeNode(ns)\\n\\n xhtmlutil.stripLeadingWhitespace(doc)\\n xhtmlutil.stripTrailingWhitespace(doc)\\n\\n # if what is left is a single <p>, grab it's contents but not the <p>\\n if (len(doc.childNodes) == 1\\n and doc.firstChild.nodeType == doc.ELEMENT_NODE\\n and doc.firstChild.tagName == 'p'):\\n doc = doc.firstChild\\n\\n while doc.firstChild is not None:\\n newdoc.firstChild.appendChild(doc.firstChild)\\n\\n # fix the links; otherwise foo/bar.rst saying <a href=\\\"quux\\\">\\n # will link to quux, not foo/quux\\n for a in newdoc.firstChild.getElementsByTagName('a'):\\n href = a.getAttribute('href')\\n if href.startswith('#'):\\n continue\\n href = urlparse.urljoin(filename, href)\\n\\n a.setAttribute('href', href)\\n\\n content = atom.XHTMLContent(newdoc.firstChild)\\n\\n kw.update(metadata)\\n e = atom.Entry(title=title,\\n content=content,\\n **kw)\\n return e\",\n \"def getHTMLText(self, s):\\r\\n\\r\\n # Removes any \\\"<\\\" or \\\">\\\" from the text, and replaces line ends with <br> tags\\r\\n if s is not None:\\r\\n res = str(s)\\r\\n res = string.replace(res, \\\">\\\", \\\">\\\")\\r\\n res = string.replace(res, \\\"<\\\", \\\"<\\\")\\r\\n res = string.replace(s, \\\"\\\\n\\\", \\\"<br style='mso-data-placement:same-cell;'/>\\\")\\r\\n else:\\r\\n res = \\\"\\\"\\r\\n\\r\\n # Inserts formatting tag around text, if defined\\r\\n if self.formatBeginTag:\\r\\n res = self.formatBeginTag + res + self.formatEndTag\\r\\n\\r\\n return res\",\n \"def get_news_text(news_url: str):\\n print(\\\"Getting Article Content..\\\")\\n if news_url is None:\\n print(\\\"URL is missing\\\")\\n return None\\n else:\\n article = Article(news_url)\\n article.download()\\n article.parse()\\n return article.text\",\n \"def convert_to_pdf(self, news_list):\\n self.logger.info(\\\"Converting news to PDF...\\\")\\n self.prepare_storage()\\n self.process_news_list_with_images(news_list)\\n content = self.generate_html_template(news_list)\\n pdf = io.BytesIO()\\n pisa.pisaDocument(content, pdf)\\n self.write_to_file(pdf.getvalue())\",\n \"def _update_feeds_html():\\n with open(FEEDS_HTML_TEMPLATE) as hfd:\\n full_html = hfd.read()\\n full_trs = []\\n for username, display_name, timestamp in db.get_all_users():\\n name_td = '<td>{}</td>'.format(display_name)\\n twitter_td = '<td><a href=\\\"{}\\\">@{}</a></td>'.format(_get_user_url(username), username)\\n feed_td = '<td><a href=\\\"{}\\\">{}</a></td>'.format(_get_feed_url(username), _get_feed_name(username))\\n data_tstamp = _rss_time_format(timestamp)\\n timestamp_td = '<td class=\\\"rss_update\\\" data-tstamp=\\\"{}\\\">{}</td>'.format(data_tstamp, data_tstamp)\\n full_trs.append('<tr>{}{}{}{}</tr>'.format(name_td, twitter_td, feed_td, timestamp_td))\\n\\n full_html = full_html.replace('PLACEHOLDER', '\\\\n'.join(full_trs))\\n with open(Config.FEED_LIST_HTML, 'w') as hfd:\\n hfd.write(full_html)\",\n \"def html(self, first_date = \\\"\\\"):\\n\\n link = \\\"https://en.wikipedia.org/w/index.php?title=\\\" + self.title + \\\"&offset=\\\" + first_date + \\\"&limit=500&action=history\\\"\\n return bs(requests.get(link).text, 'lxml')\",\n \"def general_news():\\n\\n return general_scraper(['http://mesva.univaq.it/'])\",\n \"def html_to_markdown(self, content):\\n return html2text.html2text(content, bodywidth=0)\",\n \"def save_news(self, obj_list: List[Tag]) -> str:\\n for obj in obj_list:\\n header = obj.text.strip()\\n if not header:\\n if 'alt' in obj.__dict__:\\n header = obj.get('alt').strip()\\n # other if else\\n news = News(\\n origin=self.get_self_name(),\\n header=header\\n )\\n news.save()\\n return f'Data from service {self.get_self_name()} loaded successfully.'\",\n \"def pos_process_news(cls):\\n # get from db\\n df = Database.get_all_crawled_news(Database.db_news)\\n\\n # erase news without title or text\\n df = df.loc[df['news_site_title'] != '']\\n df = df.loc[df['news_site_text'] != '']\\n\\n # erase number words title\\n df['title_len'] = df.apply(lambda row: Utils.count_words(row['news_site_title']), axis=1)\\n df = df.loc[df['title_len'] > 3]\\n\\n # sort by date so that we keep the last news\\n df['arquivo_date'] = pd.to_datetime(df.arquivo_date)\\n df = df.sort_values(by=['arquivo_date'], ascending=False)\\n df = df.set_index(['arquivo_date'])\\n df['arquivo_date'] = df.index\\n\\n # drop news that have title repeated\\n df_title_text = df[['news_site_title']]\\n df_title_text = df_title_text.drop_duplicates()\\n df = df.loc[df.index.isin(df_title_text.index)]\\n\\n # insert in mongo db\\n Database.save_posprocessed_crawled_news(Database.db_pos_processed_news,df)\",\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_to_text(html):\\n s = TextExtractorHTMLParser()\\n s.feed(html)\\n return s.get_text()\",\n \"def testHTML(self):\\n\\n html = self.E.html()\",\n \"def news_web():\\n get_news()\",\n \"def promed_html_to_formatted_text(html):\\n # This is to fix some cases in malformed html where <s aren't esacaped.\\n # >s can be parsed without escaping.\\n normed_html = html.\\\\\\n replace(\\\"<<\\\", \\\"<<\\\").\\\\\\n replace(\\\"<http\\\", \\\"<http\\\").\\\\\\n replace(\\\"< \\\", \\\"< \\\")\\n return dom_tree_to_formatted_text(BeautifulSoup(normed_html))\",\n \"def __local_to(soup):\\n return __get_local_g1_news(soup)\"\n]","isConversational":false},"negative_scores":{"kind":"list like","value":["0.7749601","0.70121133","0.6875486","0.66495407","0.6365921","0.62624204","0.6246973","0.6110563","0.5929288","0.5882961","0.5868945","0.5861875","0.58461696","0.5827368","0.5789063","0.5754645","0.5731008","0.5714564","0.56914157","0.56863284","0.56724495","0.5665463","0.5651609","0.5641771","0.5635531","0.5605198","0.559418","0.55907536","0.55753756","0.55751103","0.55727184","0.55701435","0.5568115","0.5565003","0.5564966","0.55636865","0.55586547","0.5527891","0.5525436","0.55211097","0.5518154","0.5511134","0.54933095","0.5490884","0.5487334","0.5483726","0.5481424","0.5474273","0.54720557","0.5466641","0.54619676","0.54577637","0.54516387","0.54500186","0.54280525","0.54104525","0.54021543","0.54018396","0.53840965","0.5376311","0.5368733","0.5367416","0.5352094","0.5352094","0.5341399","0.5341399","0.5340217","0.53173417","0.53121376","0.5302701","0.5302675","0.5300908","0.5289053","0.5285903","0.5282101","0.5281677","0.52809185","0.52697086","0.52674747","0.52632666","0.526078","0.5255126","0.52511835","0.52412117","0.52308387","0.52294886","0.5226363","0.52083135","0.5207919","0.5203214","0.5203007","0.5198815","0.5195985","0.5186512","0.5182743","0.5182167","0.5178299","0.5176426","0.51758975","0.5171134"],"string":"[\n \"0.7749601\",\n \"0.70121133\",\n \"0.6875486\",\n \"0.66495407\",\n \"0.6365921\",\n \"0.62624204\",\n \"0.6246973\",\n \"0.6110563\",\n \"0.5929288\",\n \"0.5882961\",\n \"0.5868945\",\n \"0.5861875\",\n \"0.58461696\",\n \"0.5827368\",\n \"0.5789063\",\n \"0.5754645\",\n \"0.5731008\",\n \"0.5714564\",\n \"0.56914157\",\n \"0.56863284\",\n \"0.56724495\",\n \"0.5665463\",\n \"0.5651609\",\n \"0.5641771\",\n \"0.5635531\",\n \"0.5605198\",\n \"0.559418\",\n \"0.55907536\",\n \"0.55753756\",\n \"0.55751103\",\n \"0.55727184\",\n \"0.55701435\",\n \"0.5568115\",\n \"0.5565003\",\n \"0.5564966\",\n \"0.55636865\",\n \"0.55586547\",\n \"0.5527891\",\n \"0.5525436\",\n \"0.55211097\",\n \"0.5518154\",\n \"0.5511134\",\n \"0.54933095\",\n \"0.5490884\",\n \"0.5487334\",\n \"0.5483726\",\n \"0.5481424\",\n \"0.5474273\",\n \"0.54720557\",\n \"0.5466641\",\n \"0.54619676\",\n \"0.54577637\",\n \"0.54516387\",\n \"0.54500186\",\n \"0.54280525\",\n \"0.54104525\",\n \"0.54021543\",\n \"0.54018396\",\n \"0.53840965\",\n \"0.5376311\",\n \"0.5368733\",\n \"0.5367416\",\n \"0.5352094\",\n \"0.5352094\",\n \"0.5341399\",\n \"0.5341399\",\n \"0.5340217\",\n \"0.53173417\",\n \"0.53121376\",\n \"0.5302701\",\n \"0.5302675\",\n \"0.5300908\",\n \"0.5289053\",\n \"0.5285903\",\n \"0.5282101\",\n \"0.5281677\",\n \"0.52809185\",\n \"0.52697086\",\n \"0.52674747\",\n \"0.52632666\",\n \"0.526078\",\n \"0.5255126\",\n \"0.52511835\",\n \"0.52412117\",\n \"0.52308387\",\n \"0.52294886\",\n \"0.5226363\",\n \"0.52083135\",\n \"0.5207919\",\n \"0.5203214\",\n \"0.5203007\",\n \"0.5198815\",\n \"0.5195985\",\n \"0.5186512\",\n \"0.5182743\",\n \"0.5182167\",\n \"0.5178299\",\n \"0.5176426\",\n \"0.51758975\",\n \"0.5171134\"\n]","isConversational":false},"document_score":{"kind":"string","value":"0.65101534"},"document_rank":{"kind":"string","value":"4"}}},{"rowIdx":82389,"cells":{"query":{"kind":"string","value":"Method saves RSSfeed content to FB2file. If path is wrong returns exception."},"document":{"kind":"string","value":"def save_to_fb2(self, rss_feed, date=None):\n\n self.print_if_verbose(f\"Method 'save_to_fb2' is working: \\n\")\n\n if not os.path.exists(self.to_fb2_path):\n try:\n os.makedirs(self.to_fb2_path)\n except Exception as error:\n print(f\"Exception {error} - wrong path! File was not created!\")\n return\n\n os.chdir(self.to_fb2_path)\n\n with open(\"rss_news.fb2\", \"w\", encoding=\"utf-8\") as fb2_file:\n fb2_file.writelines(f\"<?xml version='1.0' encoding='UTF-8'?>\\n\")\n fb2_file.writelines(\n f\"<FictionBook xmlns='http://www.gribuser.ru/xml/fictionbook/2.0' \"\n f\"xmlns:l='http://www.w3.org/1999/xlink'>\\n\")\n fb2_file.writelines(f\" <body>\\n\")\n fb2_file.writelines(f\" <section>\\n\")\n\n if self.date:\n for news in rss_feed:\n fb2_file.writelines(self.add_to_fb2(news))\n else:\n for news in rss_feed[\"News\"]:\n fb2_file.writelines(self.add_to_fb2(news))\n\n fb2_file.writelines(f\" </section>\\n\")\n fb2_file.writelines(\" </body>\\n\")\n fb2_file.writelines(self.binaries)\n fb2_file.writelines(\"</FictionBook>\\n\")\n\n self.print_if_verbose(\n f\"File {self.to_fb2_path}{os.sep}rss_news.fb2 was created successfully. \\n\"\n f\"Method 'save_to_fb2' is finished.\\n\"\n )\n\n return True"},"metadata":{"kind":"string","value":"{\n \"objective\": {\n \"self\": [],\n \"paired\": [],\n \"triplet\": [\n [\n \"query\",\n \"document\",\n \"negatives\"\n ]\n ]\n }\n}"},"negatives":{"kind":"list like","value":["def write_to_file(self, content):\n try:\n with open(self.full_path_to_file, \"wb\") as fp:\n fp.write(content)\n except PermissionError:\n logging.error(\n \"Conversion cannot be performed. Permission denied for this directory\"\n )\n sys.exit()\n self.logger.info(\"News has been successfully converted\")","def save_channel(xml):\n overwrite = False\n path = config.BASE_PATH + '/' + config.SUBMISSION_RSS_URL\n if not os.path.exists(path):\n overwrite = True\n else:\n size = os.path.getsize(path)\n if size != len(xml):\n overwrite = True\n else:\n from hashlib import md5\n of = open(path, 'rb')\n old_xml = of.read()\n of.close()\n old_md5 = md5(old_xml).digest()\n new_md5 = md5(xml).digest()\n if old_md5 != new_md5:\n overwrite = True\n if overwrite:\n print \"Writing latest RSS feed to %s\" % path\n f = open(path, 'wb')\n f.write(xml)\n f.close()","def write_feed(self, elements, context, filename=None, feed_type='atom'):\n old_locale = locale.setlocale(locale.LC_ALL)\n locale.setlocale(locale.LC_ALL, 'C')\n try:\n self.site_url = context.get('SITEURL', get_relative_path(filename))\n self.feed_url= '%s/%s' % (self.site_url, filename)\n\n feed = self._create_new_feed(feed_type, context)\n\n for item in elements:\n self._add_item_to_the_feed(feed, item)\n\n if filename:\n complete_path = os.path.join(self.output_path, filename)\n try:\n os.makedirs(os.path.dirname(complete_path))\n except Exception:\n pass\n fp = open(complete_path, 'w')\n feed.write(fp, 'utf-8')\n print u' [ok] writing %s' % complete_path\n\n fp.close()\n return feed\n finally:\n locale.setlocale(locale.LC_ALL, old_locale)","def write_feed(self, elements, context, path=None, feed_type='atom'):\r\n if not is_selected_for_writing(self.settings, path):\r\n return\r\n old_locale = locale.setlocale(locale.LC_ALL)\r\n locale.setlocale(locale.LC_ALL, str('C'))\r\n try:\r\n self.site_url = context.get(\r\n 'SITEURL', path_to_url(get_relative_path(path)))\r\n\r\n self.feed_domain = context.get('FEED_DOMAIN')\r\n self.feed_url = '{}/{}'.format(self.feed_domain, path)\r\n\r\n feed = self._create_new_feed(feed_type, context)\r\n\r\n max_items = len(elements)\r\n if self.settings['FEED_MAX_ITEMS']:\r\n max_items = min(self.settings['FEED_MAX_ITEMS'], max_items)\r\n for i in range(max_items):\r\n self._add_item_to_the_feed(feed, elements[i])\r\n\r\n if path:\r\n complete_path = os.path.join(self.output_path, path)\r\n try:\r\n os.makedirs(os.path.dirname(complete_path))\r\n except Exception:\r\n pass\r\n\r\n encoding = 'utf-8' if six.PY3 else None\r\n with self._open_w(complete_path, encoding) as fp:\r\n feed.write(fp, 'utf-8')\r\n logger.info('writing %s' % complete_path)\r\n return feed\r\n finally:\r\n locale.setlocale(locale.LC_ALL, old_locale)","def save_to_html(self, rss_feed, date=None):\n\n self.print_if_verbose(f\"Method 'save_to_html' is working: \\n\")\n\n add_to_html_file = \"<!DOCTYPE html>\\n<html lang='en'>\\n<head>\\n<meta charset='UTF-8'>\\n</head>\\n<body>\\n\"\n\n if not os.path.exists(self.to_html_path):\n try:\n os.makedirs(self.to_html_path)\n except Exception as error:\n print(f\"Exception {error} - wrong path! File was not created!\")\n return\n\n os.chdir(self.to_html_path)\n\n with open(\"rss_news.html\", \"w\", encoding=\"utf-8\") as html_file:\n html_file.writelines(add_to_html_file)\n\n if self.date:\n for news in rss_feed:\n html_file.writelines(self.add_to_html(news))\n else:\n for news in rss_feed[\"News\"]:\n html_file.writelines(self.add_to_html(news))\n\n add_to_html_file = \"</body>\\n</html>\\n\"\n html_file.writelines(add_to_html_file)\n\n self.print_if_verbose(\n f\"File {self.to_html_path}{os.sep}rss_news.html was created successfully. \\n\"\n f\"Method 'save_to_html' is finished.\\n\"\n )\n\n return True","def writeXmlFile(config, rss):\n # Create a backup first\n xmlBackupFilepath = createBackup(config)\n\n # Overwrite the file\n xmlFilepath = config['xmlFilepath']\n try:\n with open(xmlFilepath, 'w') as f:\n f.write(prettifyXml(rss))\n except:\n # Delete the generated file\n try:\n os.remove(xmlFilepath)\n except:\n logger.warning(\"Could not remove the generated file \\`\" +\n xmlFilepath + \"\\`\")\n\n # If a backup exists, restore the original file\n if os.path.isfile(xmlBackupFilepath):\n try:\n shutil.copy(xmlBackupFilepath, xmlFilepath)\n except:\n logger.error(\"XML write and backup restore both failed.\")\n raise\n\n logger.error(\"XML write failed. Backup has been restored.\")\n else:\n logger.error(\"XML write failed. No backups to restore.\")\n\n raise\n\n logger.info(\"Saved new XML => \" + config['xmlFilepath'])","def __save_article_to_file(self, content):\n with open(\"article.txt\", 'w') as out:\n out.write(content)","def save_file(msl_data_path, filename, content):\n with open(msl_data_path + filename, 'wb') as (file_):\n file_.write(content)\n file_.flush()\n file_.close()","def __writeRSS2file(inputitems):\n feed_items = []\n for item in inputitems:\n print \"appending... \", item[1]\n feed_items.append(RSS2format(item[1]))\n rss = PyRSS2Gen.RSS2(\n title = \"A File-based RSS Feed Generator\",\n link = \"lphiri.cs.uct.ac.za/simplyct\",\n description = \"A File-based RSS Feed Generator\",\n lastBuildDate = datetime.utcnow(),\n items = feed_items\n )\n print feed_items\n rss.write_xml(open(\"new-simplyctrss2.xml\", \"w\"))\n print \"END: FEED GENERATOR[WRITING]: \", time.time()","def save(self, path):\n f = open(path, 'w')\n f.write(self.content().encode('utf-8'))\n f.close()","def writeRSS2file(inputitems):\n print \"START: FEED GENERATOR[WRITING]: \", time.time()\n feed_items = []\n for item in inputitems:\n print \"appending... \", item\n feed_items.append(RSS2format(item))\n rss = PyRSS2Gen.RSS2(\n title = \"A File-based RSS Feed Generator\",\n link = \"lphiri.cs.uct.ac.za/simplyct\",\n description = \"A File-based RSS Feed Generator\",\n lastBuildDate = datetime.utcnow(),\n items = feed_items\n )\n print \"DEBUGGING feed_items... -> \", feed_items\n for l in feed_items:\n pprint (vars(l))\n print \"DEBUGGING rss object... -> \", rss\n rss.write_xml(open(\"new-simplyctrss2.xml\", \"w\"))\n print \"END: FEED GENERATOR[WRITING]: \", time.time()","def write(self,path,content):\n file_path = os.path.join( self.directory, path)\n with open(file_path, \"w\") as file:\n file.write( content )","def save_news_to_cache(self, rss_feed):\n\n self.print_if_verbose(\n f\"Method 'save_news_to_cache' is working: \\n\"\n f\"Saving news to cache... \\n\"\n )\n\n rss_feed_to_cache_title = self.source\n\n if not os.path.exists(\"cache\"):\n os.mkdir(\"cache\")\n os.chdir(\"cache\")\n\n if not os.path.exists(\"image_cache\"):\n os.mkdir(\"image_cache\")\n os.chdir(\"image_cache\")\n self.full_path_to_image_cache = os.getcwd()\n os.chdir(\"..\")\n\n try:\n with open(\"rss_reader_cache.json\", \"r\", encoding=\"utf-8\") as cache_file:\n data_from_cache = json.load(cache_file)\n except:\n data_from_cache = [{rss_feed_to_cache_title: []}]\n self.print_if_verbose(f\"Cache file does not exist, a new one will be created. \\n\")\n\n is_append_flag = False\n\n for feed in data_from_cache:\n if rss_feed_to_cache_title in feed.keys():\n for news in rss_feed[\"News\"]:\n if news not in feed[rss_feed_to_cache_title]:\n feed[rss_feed_to_cache_title].append(news)\n self.save_image_to_image_cache(news[\"ImageLink\"], f\"{news['ImageCacheName']}\")\n is_append_flag = True\n\n if not is_append_flag:\n data_from_cache.append({rss_feed_to_cache_title: rss_feed[\"News\"]})\n for news in rss_feed[\"News\"]:\n self.save_image_to_image_cache(news[\"ImageLink\"], f\"{news['ImageCacheName']}\")\n\n with open(\"rss_reader_cache.json\", \"w\", encoding=\"utf-8\") as cache_file:\n json.dump(data_from_cache, cache_file, indent=3)\n\n os.chdir(\"..\")\n\n self.print_if_verbose(\n f\"News were added to cache successfully. \\n\"\n f\"Method 'save_news_to_cache' is finished. \\n\"\n )\n\n return data_from_cache","def save_content_to_file(content, url):\n try:\n save_content = eval(os.environ[\"URL_SAVE_CONTENT\"])\n save_dest = os.environ[\"URL_SAVE_DEST\"]\n debug(\"SAVING CONTENT TO DISK AT: {}\".format(save_dest))\n except Exception as e:\n save_content = False\n\n if not save_content: return\n\n target_dir = save_dest \n debug(\"saving content to {}/{}\".format(target_dir, url))\n try:\n with open(\"{}/{}\".format(target_dir, url), 'w') as url_content:\n url_content.write(content)\n except Exception as e:\n debug(\"Exception saving file to disk: {}\".format(e.message))","def export_to_file(self, path, graph_format):\n try:\n logging.info(\"Saving RDF data to \" + str(path))\n with open(path, \"wb\") as out_file:\n out_file.write(self.g.serialize(format=graph_format, encoding=\"UTF-8\"))\n except Exception as e:\n logging.error(\"Error while saving RDF results \"+str(e))","def save_content(content, dir_path, file_name):\r\n if not os.path.exists(dir_path):\r\n os.mkdir(dir_path)\r\n with open(f'{dir_path}\\\\{file_name}', 'w') as output_file:\r\n output_file.write(content)","def save_file_(msl_data_path, filename, content):\n with open(msl_data_path + filename, 'w') as (file_):\n file_.write(content)\n file_.flush()\n file_.close()","def save2File(self, contents, filename):\n self.setup()\n fullpath = os.path.join(self.output_path, filename)\n f = open(fullpath, 'w')\n f.write(contents) # python will convert \\n to os.linesep\n f.close() # you can omit in most cases as the destructor will call it\n url = \"file://\" + fullpath\n return url","def write(self, path, content):\n this_file = open(path, 'w')\n this_file.write(content)\n this_file.close()","def save(self, path):\n pass","def save(self, path):\n pass","def save(self, path):\n pass","def save(dest, content, encoding='utf-8'):\n #with open(dest, \"w\") as f:\n # f.write(content.encode(encoding))\n\n f = None\n try:\n head, tail = os.path.split(dest)\n if not os.path.exists(head):\n os.makedirs(head)\n f = open(dest, \"wb\")\n logging.info(\"Saving file: %s\", dest)\n f.write(content)\n except Exception as e:\n logging.error(\"Something nasty happend during wrote to %s, retrying\" % dest)\n logging.error(e)\n raise e\n finally:\n if f:\n f.close()","def save(self, path: str):\n pass","def save_file(self, filename):\r\n \r\n f = open(filename,'w')\r\n f.write(self.body)\r\n f.close","def save(self, path):\n (folder, filename) = os.path.split(path)\n (name, extension) = os.path.splitext(filename)\n\n if not name:\n raise ValueError, \"name is required\"\n\n path = os.path.join(folder, name + self.extension)\n f = open(path, \"wb\")\n f.write(self.contents)\n f.close()\n\n return path","def write_to_path(self, path):\n assert not path.exists()\n fout = path.open(\"wb\")\n fout.write(self.to_string())\n assert not fout.close()\n path.setdata()","def save(self, export_path: str):","def __write_file(path, link_name, context):\n file_name = ''\n for strng in ['/', 'http:']:\n if not file_name:\n file_name = link_name.replace(strng, '')\n else:\n file_name = file_name.replace(strng, '')\n\n full_path = os.path.join(path)\n if not os.path.exists(full_path):\n os.makedirs(full_path)\n open(full_path + '/' + file_name, 'w').write(context.encode('utf-8'))\n logging.info('File: %s Created', full_path + '/' + file_name)","def _save_feeds(self):\n\n def default(o: Feed | Folder):\n if type(o) is Feed:\n feed = copy(o)\n data = vars(feed)\n data[\"updated\"] = feed.updated.isoformat()\n data.pop(\"parent_folder\")\n return data\n elif type(o) is Folder:\n folder = copy(o)\n data = vars(folder)\n data.pop(\"parent_folder\")\n return data\n\n content = json.dumps(self.feed_cache.children, default=default, indent=4)\n with open(\"data/feeds.json\", \"w\") as feeds_file:\n feeds_file.write(content)","def saveOnFile(self, path, data):\n with open(path, \"w\") as f:\n f.write(data)","def output_attachment(self, path, content):\n\t\twith open(path, \"w+b\") as fd:\n\t\t\tfd.write(content)","def save_file(path, text):\n with path.open(mode='w') as f_stream:\n f_stream.write(text)","def save(self, content, address, file):\n full_address = \"/home/red/WAREHOUSE\" + address\n file_path = full_address + \"/\" + file\n\n try:\n os.makedirs(full_address, 0o777, True)\n except OSError:\n pass\n\n write_binary_file(content, file_path)","def create_file(self, content=\"\"):\n if (self.exists()):\n raise IOError(\"A file at '{}' already exists.\".format(self.location))\n with open(self.location, 'w') as f:\n f.write(content)","def import_opml(self, path):\n tree = ET.parse(path)\n root = tree.getroot()\n for feed_el in root.find('body').findall('outline'):\n if feed_el.get('type') == 'rss':\n self.add_by_url(feed_el.get('xmlUrl'), feed_el.get('text'))","def save_entry(title, content):\n filename = f\"entries/{title}.md\"\n if default_storage.exists(filename):\n default_storage.delete(filename)\n default_storage.save(filename, ContentFile(content))","def save_entry(title, content):\n filename = f\"entries/{title}.md\"\n if default_storage.exists(filename):\n default_storage.delete(filename)\n default_storage.save(filename, ContentFile(content))","def save_entry(title, content):\n\n title.strip # Remove the spaces from both sides.\n filename = f\"entries/{title}.md\"\n if default_storage.exists(filename):\n default_storage.delete(filename)\n default_storage.save(filename, ContentFile(content))\n return True","def saveToFile(self, filePath):\n d = self.save()\n with open(filePath, 'wb') as f:\n f.write(d)","def write_to_file(content, filename=\"default.txt\", filepath=\"default_path\"):\n\n fp = open_file(filename, filepath)\n\n try:\n for line in content:\n fp.write(str(line))\n except IOError:\n l.error(\"Writing to file \" + filename + \" was not possible\")\n except:\n l.error(\n \"Unknown error occured, while writing to file \" + str(filename),\n exc_info=True,\n )\n\n close_file(fp)","def create_file(path: Path, content: str) -> None:\n path.touch()\n with path.open(\"w\") as f:\n f.write(content)","def update_rss_feed(torrent_dir, suggested_name, url, download_url, tree_size, torrents):\n # Fetching the existing feed, if possible\n filepath = os.path.join(torrent_dir, '{}.rss'.format(suggested_name))\n try:\n with open(filepath, 'rb') as fd:\n doc = xml.dom.minidom.parse(fd)\n\n except IOError:\n # The RSS file does not exist; it is probably a first run\n doc = None\n\n # Fixing download URL, if need be, such that it ends with a slash\n if download_url[-1] != '/':\n download_url += '/'\n\n # Building/Verifying the XML structure\n try:\n chan = check_rss_dom_structure(doc)\n except:\n doc, chan = init_rss_dom_structure(url)\n\n for torrent_data in torrents:\n item = doc.createElement('item')\n chan.appendChild(item)\n\n title_elmt = doc.createElement('title')\n title_txt = doc.createTextNode('Package {} for tree_size {}'.format(torrent_data[2], tree_size))\n title_elmt.appendChild(title_txt)\n item.appendChild(title_elmt)\n\n desc_elmt = doc.createElement('description')\n desc_txt = doc.createTextNode(\n 'Comment: {} Creation Date: {}'.format(torrent_data[0]['comment'], torrent_data[0]['creation date'])\n )\n desc_elmt.appendChild(desc_txt)\n item.appendChild(desc_elmt)\n\n guid_elmt = doc.createElement('guid')\n fp = codecs.getencoder('hex')(torrent_data[1])[0]\n guid_txt = doc.createTextNode(fp.decode('UTF-8'))\n guid_elmt.appendChild(guid_txt)\n item.appendChild(guid_elmt)\n\n enclosure_elmt = doc.createElement('enclosure')\n enclosure_elmt.setAttribute('url', download_url + build_torrent_name(url, torrent_data[2], tree_size))\n enclosure_elmt.setAttribute('type', 'application/x-bittorrent')\n enclosure_elmt.setAttribute('len', str(torrent_data[3]))\n item.appendChild(enclosure_elmt)\n\n with open(filepath, 'wb') as fd:\n fd.write(doc.toxml('UTF-8'))","def save(self, file_path):\n with open(file_path, 'w') as file:\n file.write(self.text)\n file.close()","def write_file(self, directory, name, content):\n\n try:\n f = open(os.path.join(directory, name), 'w')\n f.write(content)\n f.close()\n except:\n print \"Content not written to file: %s\" % name","def wFile(path, content=None):\n try:\n with open(path, 'w') as f: \n if content == None:\n return f\n else:\n content = str(content)\n if content[-1] == \"\\n\":\n f.write(content)\n else:\n f.write(content + \"\\n\")\n return True\n except:\n print(\"Error: Could not write to a file.\")\n return False","def save_file(self, file_name, text):\n\n with open(file_name, 'w') as content_file:\n content = content_file.write(text)","def write(self, file_path, content):\n self._set_extension(file_path)\n\n logger.debug(\"writing to %s\", self._file_path)\n\n self._content = content\n\n if self._file_ext == 'json':\n self._write_json()","def add_rss(url):","def save_content(obj):\n filename, _, _, content = obj\n storage = get_storage(config['Instance']['content_dir'])\n return storage.save(filename, ContentFile(content))","def save_post(name, elements, status, current_section):\n\n try:\n\n # dealing with Posts\n if status == 4:\n extract_and_write_posts(elements, name)\n return\n\n except:\n print(\"Exception (save_to_file)\", \"Status =\",\n str(status), sys.exc_info()[0])\n\n return","def save_news_in_html_file(news, path_to_html, logger):\n check_path_to_directory(path_to_html, logger)\n html_file = tags.html(title='RSS news')\n html_file.add(tags.head(tags.meta(charset='utf-8')))\n\n logger.info('Converting news to html format...')\n for article in news:\n html_factory(article, html_file)\n\n path = os.path.join(path_to_html, 'rss_news.html')\n logger.info('Creating html-file...')\n with open(path, 'w', encoding='utf-8') as file_html:\n file_html.write(str(html_file))\n logger.info('Html-file is created successfully!')\n return file_html","def _save_tracker_uri_to_file(self):\r\n if not self.tracker_file_name:\r\n return\r\n f = None\r\n try:\r\n f = open(self.tracker_file_name, 'w')\r\n f.write(self.tracker_uri)\r\n except IOError, e:\r\n raise ResumableUploadException(\r\n 'Couldn\\'t write URI tracker file (%s): %s.\\nThis can happen'\r\n 'if you\\'re using an incorrectly configured upload tool\\n'\r\n '(e.g., gsutil configured to save tracker files to an '\r\n 'unwritable directory)' %\r\n (self.tracker_file_name, e.strerror),\r\n ResumableTransferDisposition.ABORT)\r\n finally:\r\n if f:\r\n f.close()","def write_file_content(path, file_name, content):\n if not os.path.exists(path):\n os.makedirs(path)\n f = io.open(path + file_name, \"w\", encoding = 'utf-8')\n f.write(content)\n f.close()","def add(self, content):\n if (not self.exists()):\n raise IOError(\"File at '{}' does not exist.\".format(self.location))\n with open(self.location, 'a') as f:\n f.write(content)","def saveFileTo(self, buf, encoding):\n if buf is None: buf__o = None\n else: buf__o = buf._o\n ret = libxml2mod.xmlSaveFileTo(buf__o, self._o, encoding)\n return ret","def append_to_file(filepath, content):\n file_path = os.path.join(Path.DOWNLOAD_DIR + filepath)\n\n with open(file_path, 'wb') as f:\n f.write(content)","def store(self, filename):","def create_or_update_file(\n file_path: str, file_content: str = \"\", file_content_encoding: str = \"utf-8\"\n) -> None:\n with open(file_path, \"wb+\") as file:\n file.write(file_content.encode(file_content_encoding))","def write(self, content, mode='wb'):\r\n self.localpath.write(content, mode)","def saveFile(self,newfile=None):\n if newfile == None:\n shutil.move(self.filename,self.filename+'~')\n self.handler = open(self.filename,'w')\n else:\n self.handler = open(newfile,'w')\n self.handler.writelines(self.content)\n self.handler.close()","def save_file(content: Any, filename: str, path: str):\n\n logging.info('Saving file: %s ' % filename)\n path_to_file = join(path, filename)\n if isfile(path_to_file):\n ctrl = input('%s exists already in\\n %s.\\n'\n ' Are you sure you want to overwrite it [y/N]: '\n % (filename, path))\n if ctrl.lower() == 'y' or ctrl.lower() == 'yes':\n with open(path_to_file, \"wb\") as f:\n pickle.dump(content, f)\n else:\n logging.warning(\"%s NOT saved..\" % filename)\n return\n else:\n with open(path_to_file, \"wb\") as f:\n pickle.dump(content, f)\n\n logging.info(\"File '%s' saved.\" % filename)","def write(self, content):\n ...","def save(self, fname):\n pass","def save(self, request, item, linked_item, linked_item_form):\n\t\ttry:\n\t\t\tform_file = request.FILES['file']\n\t\t\tdir_name = str(uuid.uuid1())\n\t\t\tplain_name = dir_name + '/' + form_file.name\n\t\t\tos.makedirs(os.path.join(settings.MEDIA_ROOT, 'documents', dir_name))\n\t\t\tfile_name = os.path.join(settings.MEDIA_ROOT, 'documents', plain_name)\n\t\t\tspool = open(file_name, 'wb')\n\t\t\tfor chunk in form_file.chunks():\n\t\t\t\tspool.write(chunk)\n\t\t\tspool.close()\n\t\t\tlinked_item.file = plain_name\n\n\t\t\tlinked_item.name = form_file.name\n\t\t\tif linked_item.name.find('.') != -1:\n\t\t\t\tlinked_item.filetype = linked_item.name[linked_item.name.rfind('.'):]\n\t\texcept:\n\t\t\tpass\n\n\t\titem.description = linked_item_form.cleaned_data['subject']\n\t\titem.tags = linked_item_form.cleaned_data['tags']\n\t\titem.save()\n\t\tlinked_item.save()\n\n\t\t#\n\t\t# Check for comments and add\n\t\t#\n\t\tcomment_text = linked_item_form.cleaned_data['comments'].strip()\n\t\tif comment_text != '':\n\t\t\tpost_comment(request, item, comment_text)","def write_file(content, file_path, mode='w', encoding='utf-8'):\n with codecs.open(file_path, mode, encoding=encoding) as fid:\n fid.write(content)","def _save(self, name, content):\n if not self.file_overwrite:\n return super(OverwriteFileSystemStorage, self)._save(name, content)\n\n full_path = self.path(name)\n\n # Create any intermediate directories that do not exist.\n # Note that there is a race between os.path.exists and os.makedirs:\n # if os.makedirs fails with EEXIST, the directory was created\n # concurrently, and we can continue normally. Refs #16082.\n directory = os.path.dirname(full_path)\n if not os.path.exists(directory):\n try:\n os.makedirs(directory)\n except OSError as e:\n if e.errno != errno.EEXIST:\n raise\n if not os.path.isdir(directory):\n raise IOError(\"%s exists and is not a directory.\" % directory)\n\n # This file has a file path that we can move.\n if hasattr(content, 'temporary_file_path'):\n file_move_safe(content.temporary_file_path(), full_path)\n content.close()\n\n # This is a normal uploadedfile that we can stream.\n else:\n flags = (os.O_WRONLY | os.O_CREAT | getattr(os, 'O_BINARY', 0))\n # The current umask value is masked out by os.open!\n fd = os.open(full_path, flags, 0o666)\n _file = None\n try:\n locks.lock(fd, locks.LOCK_EX)\n for chunk in content.chunks():\n if _file is None:\n mode = 'wb' if isinstance(chunk, bytes) else 'wt'\n _file = os.fdopen(fd, mode)\n _file.write(chunk)\n finally:\n locks.unlock(fd)\n if _file is not None:\n _file.close()\n else:\n os.close(fd)\n\n if settings.FILE_UPLOAD_PERMISSIONS is not None:\n os.chmod(full_path, settings.FILE_UPLOAD_PERMISSIONS)\n\n return name","def saveFileTo(self, cur, encoding):\n if cur is None: cur__o = None\n else: cur__o = cur._o\n ret = libxml2mod.xmlSaveFileTo(self._o, cur__o, encoding)\n return ret","def _save_file(self, file_path, data):\n self._ensure_directory(os.path.dirname(file_path))\n with open(file_path, \"wb\") as f:\n f.write(data)","def save(self, filename):\n pass","def write_content_file(file_content):\n\n f = open(workingfile, \"w\")\n if hasattr(file_content, \"decode\"):\n f.write(str(file_content.decode(\"utf-8\")))\n else:\n f.write(str(file_content))\n f.close()","def appendFeed(feed,filepath):\n print(\"::appending feed\")\n with open(filepath,mode='r', encoding='utf-8') as f:\n jconfig = json.load(f)\n\n with open(filepath,mode='w', encoding='utf-8') as feedsjson:\n feed = workAFeed(feed)\n entry = {'url':feed}\n print(\"::feeds=\",jconfig[1]['feeds'])\n jconfig[1]['feeds'].append(entry)\n\n print(json.dumps(jconfig,indent=2))\n json.dump(jconfig,feedsjson)","def saveToFile(html):\n #print(\"Saving to file.\")\n html += \"\\n\"\n #open necessary files to save\n logFile = open(\"postLog_{0}_{1}.txt\".format(os.path.splitext(path)[0], dateTimeNow), \"a\")\n logFile.write(html)\n logFile.close()\n #print(\"Check Point.\")","def _write_file(self, slug, folderpath, html):\n # check directories\n if not os.path.isdir(folderpath):\n try:\n os.makedirs(folderpath)\n self.info(\"Creating directory \" + folderpath)\n except Exception as e:\n self.err(e)\n return\n # construct file path\n filepath = folderpath + \"/\" + slug + \".html\"\n # write the file\n try:\n filex = open(filepath, \"w\")\n filex.write(html)\n filex.close()\n if self.notebook is False:\n self.ok(\"File written to\", filepath)\n else:\n html = '<a href=\"' + filepath + '\">' + filepath + '</a>'\n self.html(\"File written to\", html)\n except Exception as e:\n self.err(e)\n return filepath","def save(self, filename):\n pass","def write_raw(content, file_path):\n file = open(file_path, 'wb')\n file.write(content)\n file.close()","def saveFormatFileTo(self, buf, encoding, format):\n if buf is None: buf__o = None\n else: buf__o = buf._o\n ret = libxml2mod.xmlSaveFormatFileTo(buf__o, self._o, encoding, format)\n return ret","def _create_content_file(self, content):\n\n io = BytesIO()\n content.save(io, self._normalize_format(), quality=self.quality)\n return ContentFile(io.getvalue())","def _save(self, name, content):\n if not self.oauth_token:\n raise AuthenticationError(\"You must be authenticated with oAuth \"\n \"for upload files.\")\n params = {\n 'title': name,\n # 'description': description,\n # 'tags': tags,\n # 'is_public': is_public,\n 'is_public': 1,\n # 'is_friend': is_friend,\n # 'is_family': is_family,\n # 'safety_level': safety_level,\n # 'content_type': content_type,\n # 'hidden': hidden\n }\n response = self.oauth_session.post(self.API_POST_URL, params=params,\n files={'photo': content.file})\n xmldoc = minidom.parseString(response.content)\n rsp = xmldoc.getElementsByTagName('rsp')[0]\n if rsp.getAttribute('stat') == 'fail':\n msg = xmldoc.getElementsByTagName('err')[0].getAttribute('msg')\n raise FileSaveError(msg)\n photo_id = xmldoc.getElementsByTagName('photoid')[0].firstChild.nodeValue\n return photo_id","def save_to_file(name, elements, status, current_section):\n # status 3 = dealing with about section\n # status 4 = dealing with posts\n\n try:\n # dealing with About Section\n if status == 3:\n workEducation = elements[0].text.replace(\"\\n\", \"|\")\n influencerObject[\"Influencer\"][\"Description\"][\"Text\"] = workEducation\n\n # dealing with Posts\n # elif status == 4:\n # extract_and_write_posts(elements, name)\n # return\n\n except:\n print(\"Exception (save_to_file)\", \"Status =\",\n str(status), sys.exc_info()[0])\n\n return","def addNewFeed(feed):\n # config exist?\n configfile_path = confighome+\"config\"\n print(\"::checking for config\")\n if fileAccessible(configfile_path,'r'):\n print(\"::reading config\")\n appendFeed(feed,configfile_path)\n elif fileAccessible(configfile_path,'w'):\n createNewConfig(feed,configfile_path)\n else:\n print(\"::unable to read\")","def saving_file(xml):\r\n\r\n xml_string = etree.tostring(xml)\r\n parsed = minidom.parseString(xml_string)\r\n with open(self.app_path + \"\\\\temp_\\\\\" + file_name + \".xml\", \"w\") as file:\r\n file.write(parsed.toprettyxml(indent=\" \"))","def makeFile(self, path=None, content=b''):\n if path is None:\n path = self.mktemp()\n with open(path, 'wb') as file:\n file.write(content)\n return path","def create_file(self, name: str, content: str) -> None:\n file_path = self.path + os.path.sep + name\n with open(file_path, \"w+\") as file:\n file.write(content)\n file.close()","def save(self, content):\n raise NotImplemented()","def Feed(self, path):\n de = DesktopEntry(path)\n if not de.IsValid:\n print(\"File %s path is not valid!\\n\"%path)\n return\n if de.InVisiable:\n print(\"File :%s is invisible.\\n\"%path)\n return\n\n cat = self.cats.get(de.Category)\n if cat is None:\n cat = DE_Category(de.Category)\n self.cats[de.Category] = cat\n self.cats[de.Category].StoreEntry(de)","def download_filings(feedpath,args=None):\n\tlogger.info(\"Processing RSS feed %s\",feedpath)\n\n\tdir = filings_dir(feedpath)\n\tos.makedirs(dir,exist_ok=True)\n\n\tfiling_urls = []\n\tfor filing in feed_tools.read_feed(feedpath):\n\t\tif args:\n\t\t\tif args.company_re and not bool(args.company_re.match(filing['companyName'])):\n\t\t\t\tcontinue\n\t\t\tif args.cik and args.cik != filing['cikNumber']:\n\t\t\t\tcontinue\n\t\t\tif args.sic and args.sic != filing['assignedSic']:\n\t\t\t\tcontinue\n\t\t\tif args.form_type and args.form_type != filing['formType']:\n\t\t\t\tcontinue\n\t\tif 'enclosureUrl' in filing and not exists_filing(dir,filing['enclosureUrl'],filing['enclosureLength']):\n\t\t\tfiling_urls.append(filing['enclosureUrl'])\n\t\tif args and getattr(args,'with_exhibits',False):\n\t\t\tfiling_urls.extend( filing.get( 'exhibitList', [] ) )\n\n\tlogger.info(\"Start downloading %d new filings\",len(filing_urls))\n\twith concurrent.futures.ThreadPoolExecutor(max_workers=args.max_threads) as executor:\n\t\tfutures = [executor.submit(download_filing,dir,url,args.max_retries) for url in filing_urls]\n\t\tfor future in concurrent.futures.as_completed(futures):\n\t\t\ttry:\n\t\t\t\tfuture.result()\n\t\t\texcept Exception as e:\n\t\t\t\tprint(e)","def save(self):\n if PYTHON3:\n fileobj = open(self.filename, 'w', encoding=self.ENCODING, errors=\"replace\")\n else:\n fileobj = open(self.filename, 'w')\n self.save_to_fileobj(fileobj)\n fileobj.close()","def save_new_rss_subscription_task(feed_obj):\n save_new_rss_subscription(feed_obj)\n logger.info(\"Entries for new Feed subcription\")","def save_file(name, content):\n data = content.encode(\"utf8\").split(b\";base64,\")[1]\n with open(os.path.join(UPLOAD_DIRECTORY, name), \"wb\") as fp:\n fp.write(base64.decodebytes(data))","def save_file(name, content):\n data = content.encode(\"utf8\").split(b\";base64,\")[1]\n with open(os.path.join(UPLOAD_DIRECTORY, name), \"wb\") as fp:\n fp.write(base64.decodebytes(data))","def test_write_to_file():\n from scraper import write_to_file\n encoding = 'utf-8'\n write_to_file(TEST_FILE, TEST_CONTENT, encoding)\n assert True","def write_to_file(filename, content):\n with open(filename, 'w') as f:\n f.write(content)","def save(self):\n file = open(self.path, 'w')\n self.parser.write(file)\n file.close()","def save(self, save_path=None):\n if self._xml is None:\n raise IOError(\"There's nothing to save\")\n\n path = self._path_to_xml if save_path is None else save_path\n\n with open(path, 'w') as f:\n rough_string = Et.tostring(self._xml, 'utf-8')\n par = etree.XMLParser(remove_blank_text=True)\n elem = etree.XML(rough_string, parser=par)\n parsed = minidom.parseString(etree.tostring(elem))\n f.write(parsed.toprettyxml(indent=\" \"))","def save(self):\n return self.save_as(self.filename)","def savegraph(self, path):\n\n raise NotImplementedError","def save(datastream):","def save(self, path=None):\n if path is None:\n path = self.path\n try:\n with open(path, 'w') as fd:\n for entry in self:\n fd.write('{}\\n'.format(entry))\n except Exception as e:\n raise SSHKeyError('Error writing {}: {}'.format(path, e))","def write_file(self, contents):\n fd = open(os.path.join(os.path.dirname(__file__),\n 'data', 'test.html'), 'w')\n fd.write(contents)\n fd.close()"],"string":"[\n \"def write_to_file(self, content):\\n try:\\n with open(self.full_path_to_file, \\\"wb\\\") as fp:\\n fp.write(content)\\n except PermissionError:\\n logging.error(\\n \\\"Conversion cannot be performed. Permission denied for this directory\\\"\\n )\\n sys.exit()\\n self.logger.info(\\\"News has been successfully converted\\\")\",\n \"def save_channel(xml):\\n overwrite = False\\n path = config.BASE_PATH + '/' + config.SUBMISSION_RSS_URL\\n if not os.path.exists(path):\\n overwrite = True\\n else:\\n size = os.path.getsize(path)\\n if size != len(xml):\\n overwrite = True\\n else:\\n from hashlib import md5\\n of = open(path, 'rb')\\n old_xml = of.read()\\n of.close()\\n old_md5 = md5(old_xml).digest()\\n new_md5 = md5(xml).digest()\\n if old_md5 != new_md5:\\n overwrite = True\\n if overwrite:\\n print \\\"Writing latest RSS feed to %s\\\" % path\\n f = open(path, 'wb')\\n f.write(xml)\\n f.close()\",\n \"def write_feed(self, elements, context, filename=None, feed_type='atom'):\\n old_locale = locale.setlocale(locale.LC_ALL)\\n locale.setlocale(locale.LC_ALL, 'C')\\n try:\\n self.site_url = context.get('SITEURL', get_relative_path(filename))\\n self.feed_url= '%s/%s' % (self.site_url, filename)\\n\\n feed = self._create_new_feed(feed_type, context)\\n\\n for item in elements:\\n self._add_item_to_the_feed(feed, item)\\n\\n if filename:\\n complete_path = os.path.join(self.output_path, filename)\\n try:\\n os.makedirs(os.path.dirname(complete_path))\\n except Exception:\\n pass\\n fp = open(complete_path, 'w')\\n feed.write(fp, 'utf-8')\\n print u' [ok] writing %s' % complete_path\\n\\n fp.close()\\n return feed\\n finally:\\n locale.setlocale(locale.LC_ALL, old_locale)\",\n \"def write_feed(self, elements, context, path=None, feed_type='atom'):\\r\\n if not is_selected_for_writing(self.settings, path):\\r\\n return\\r\\n old_locale = locale.setlocale(locale.LC_ALL)\\r\\n locale.setlocale(locale.LC_ALL, str('C'))\\r\\n try:\\r\\n self.site_url = context.get(\\r\\n 'SITEURL', path_to_url(get_relative_path(path)))\\r\\n\\r\\n self.feed_domain = context.get('FEED_DOMAIN')\\r\\n self.feed_url = '{}/{}'.format(self.feed_domain, path)\\r\\n\\r\\n feed = self._create_new_feed(feed_type, context)\\r\\n\\r\\n max_items = len(elements)\\r\\n if self.settings['FEED_MAX_ITEMS']:\\r\\n max_items = min(self.settings['FEED_MAX_ITEMS'], max_items)\\r\\n for i in range(max_items):\\r\\n self._add_item_to_the_feed(feed, elements[i])\\r\\n\\r\\n if path:\\r\\n complete_path = os.path.join(self.output_path, path)\\r\\n try:\\r\\n os.makedirs(os.path.dirname(complete_path))\\r\\n except Exception:\\r\\n pass\\r\\n\\r\\n encoding = 'utf-8' if six.PY3 else None\\r\\n with self._open_w(complete_path, encoding) as fp:\\r\\n feed.write(fp, 'utf-8')\\r\\n logger.info('writing %s' % complete_path)\\r\\n return feed\\r\\n finally:\\r\\n locale.setlocale(locale.LC_ALL, old_locale)\",\n \"def save_to_html(self, rss_feed, date=None):\\n\\n self.print_if_verbose(f\\\"Method 'save_to_html' is working: \\\\n\\\")\\n\\n add_to_html_file = \\\"<!DOCTYPE html>\\\\n<html lang='en'>\\\\n<head>\\\\n<meta charset='UTF-8'>\\\\n</head>\\\\n<body>\\\\n\\\"\\n\\n if not os.path.exists(self.to_html_path):\\n try:\\n os.makedirs(self.to_html_path)\\n except Exception as error:\\n print(f\\\"Exception {error} - wrong path! File was not created!\\\")\\n return\\n\\n os.chdir(self.to_html_path)\\n\\n with open(\\\"rss_news.html\\\", \\\"w\\\", encoding=\\\"utf-8\\\") as html_file:\\n html_file.writelines(add_to_html_file)\\n\\n if self.date:\\n for news in rss_feed:\\n html_file.writelines(self.add_to_html(news))\\n else:\\n for news in rss_feed[\\\"News\\\"]:\\n html_file.writelines(self.add_to_html(news))\\n\\n add_to_html_file = \\\"</body>\\\\n</html>\\\\n\\\"\\n html_file.writelines(add_to_html_file)\\n\\n self.print_if_verbose(\\n f\\\"File {self.to_html_path}{os.sep}rss_news.html was created successfully. \\\\n\\\"\\n f\\\"Method 'save_to_html' is finished.\\\\n\\\"\\n )\\n\\n return True\",\n \"def writeXmlFile(config, rss):\\n # Create a backup first\\n xmlBackupFilepath = createBackup(config)\\n\\n # Overwrite the file\\n xmlFilepath = config['xmlFilepath']\\n try:\\n with open(xmlFilepath, 'w') as f:\\n f.write(prettifyXml(rss))\\n except:\\n # Delete the generated file\\n try:\\n os.remove(xmlFilepath)\\n except:\\n logger.warning(\\\"Could not remove the generated file \\\\`\\\" +\\n xmlFilepath + \\\"\\\\`\\\")\\n\\n # If a backup exists, restore the original file\\n if os.path.isfile(xmlBackupFilepath):\\n try:\\n shutil.copy(xmlBackupFilepath, xmlFilepath)\\n except:\\n logger.error(\\\"XML write and backup restore both failed.\\\")\\n raise\\n\\n logger.error(\\\"XML write failed. Backup has been restored.\\\")\\n else:\\n logger.error(\\\"XML write failed. No backups to restore.\\\")\\n\\n raise\\n\\n logger.info(\\\"Saved new XML => \\\" + config['xmlFilepath'])\",\n \"def __save_article_to_file(self, content):\\n with open(\\\"article.txt\\\", 'w') as out:\\n out.write(content)\",\n \"def save_file(msl_data_path, filename, content):\\n with open(msl_data_path + filename, 'wb') as (file_):\\n file_.write(content)\\n file_.flush()\\n file_.close()\",\n \"def __writeRSS2file(inputitems):\\n feed_items = []\\n for item in inputitems:\\n print \\\"appending... \\\", item[1]\\n feed_items.append(RSS2format(item[1]))\\n rss = PyRSS2Gen.RSS2(\\n title = \\\"A File-based RSS Feed Generator\\\",\\n link = \\\"lphiri.cs.uct.ac.za/simplyct\\\",\\n description = \\\"A File-based RSS Feed Generator\\\",\\n lastBuildDate = datetime.utcnow(),\\n items = feed_items\\n )\\n print feed_items\\n rss.write_xml(open(\\\"new-simplyctrss2.xml\\\", \\\"w\\\"))\\n print \\\"END: FEED GENERATOR[WRITING]: \\\", time.time()\",\n \"def save(self, path):\\n f = open(path, 'w')\\n f.write(self.content().encode('utf-8'))\\n f.close()\",\n \"def writeRSS2file(inputitems):\\n print \\\"START: FEED GENERATOR[WRITING]: \\\", time.time()\\n feed_items = []\\n for item in inputitems:\\n print \\\"appending... \\\", item\\n feed_items.append(RSS2format(item))\\n rss = PyRSS2Gen.RSS2(\\n title = \\\"A File-based RSS Feed Generator\\\",\\n link = \\\"lphiri.cs.uct.ac.za/simplyct\\\",\\n description = \\\"A File-based RSS Feed Generator\\\",\\n lastBuildDate = datetime.utcnow(),\\n items = feed_items\\n )\\n print \\\"DEBUGGING feed_items... -> \\\", feed_items\\n for l in feed_items:\\n pprint (vars(l))\\n print \\\"DEBUGGING rss object... -> \\\", rss\\n rss.write_xml(open(\\\"new-simplyctrss2.xml\\\", \\\"w\\\"))\\n print \\\"END: FEED GENERATOR[WRITING]: \\\", time.time()\",\n \"def write(self,path,content):\\n file_path = os.path.join( self.directory, path)\\n with open(file_path, \\\"w\\\") as file:\\n file.write( content )\",\n \"def save_news_to_cache(self, rss_feed):\\n\\n self.print_if_verbose(\\n f\\\"Method 'save_news_to_cache' is working: \\\\n\\\"\\n f\\\"Saving news to cache... \\\\n\\\"\\n )\\n\\n rss_feed_to_cache_title = self.source\\n\\n if not os.path.exists(\\\"cache\\\"):\\n os.mkdir(\\\"cache\\\")\\n os.chdir(\\\"cache\\\")\\n\\n if not os.path.exists(\\\"image_cache\\\"):\\n os.mkdir(\\\"image_cache\\\")\\n os.chdir(\\\"image_cache\\\")\\n self.full_path_to_image_cache = os.getcwd()\\n os.chdir(\\\"..\\\")\\n\\n try:\\n with open(\\\"rss_reader_cache.json\\\", \\\"r\\\", encoding=\\\"utf-8\\\") as cache_file:\\n data_from_cache = json.load(cache_file)\\n except:\\n data_from_cache = [{rss_feed_to_cache_title: []}]\\n self.print_if_verbose(f\\\"Cache file does not exist, a new one will be created. \\\\n\\\")\\n\\n is_append_flag = False\\n\\n for feed in data_from_cache:\\n if rss_feed_to_cache_title in feed.keys():\\n for news in rss_feed[\\\"News\\\"]:\\n if news not in feed[rss_feed_to_cache_title]:\\n feed[rss_feed_to_cache_title].append(news)\\n self.save_image_to_image_cache(news[\\\"ImageLink\\\"], f\\\"{news['ImageCacheName']}\\\")\\n is_append_flag = True\\n\\n if not is_append_flag:\\n data_from_cache.append({rss_feed_to_cache_title: rss_feed[\\\"News\\\"]})\\n for news in rss_feed[\\\"News\\\"]:\\n self.save_image_to_image_cache(news[\\\"ImageLink\\\"], f\\\"{news['ImageCacheName']}\\\")\\n\\n with open(\\\"rss_reader_cache.json\\\", \\\"w\\\", encoding=\\\"utf-8\\\") as cache_file:\\n json.dump(data_from_cache, cache_file, indent=3)\\n\\n os.chdir(\\\"..\\\")\\n\\n self.print_if_verbose(\\n f\\\"News were added to cache successfully. \\\\n\\\"\\n f\\\"Method 'save_news_to_cache' is finished. \\\\n\\\"\\n )\\n\\n return data_from_cache\",\n \"def save_content_to_file(content, url):\\n try:\\n save_content = eval(os.environ[\\\"URL_SAVE_CONTENT\\\"])\\n save_dest = os.environ[\\\"URL_SAVE_DEST\\\"]\\n debug(\\\"SAVING CONTENT TO DISK AT: {}\\\".format(save_dest))\\n except Exception as e:\\n save_content = False\\n\\n if not save_content: return\\n\\n target_dir = save_dest \\n debug(\\\"saving content to {}/{}\\\".format(target_dir, url))\\n try:\\n with open(\\\"{}/{}\\\".format(target_dir, url), 'w') as url_content:\\n url_content.write(content)\\n except Exception as e:\\n debug(\\\"Exception saving file to disk: {}\\\".format(e.message))\",\n \"def export_to_file(self, path, graph_format):\\n try:\\n logging.info(\\\"Saving RDF data to \\\" + str(path))\\n with open(path, \\\"wb\\\") as out_file:\\n out_file.write(self.g.serialize(format=graph_format, encoding=\\\"UTF-8\\\"))\\n except Exception as e:\\n logging.error(\\\"Error while saving RDF results \\\"+str(e))\",\n \"def save_content(content, dir_path, file_name):\\r\\n if not os.path.exists(dir_path):\\r\\n os.mkdir(dir_path)\\r\\n with open(f'{dir_path}\\\\\\\\{file_name}', 'w') as output_file:\\r\\n output_file.write(content)\",\n \"def save_file_(msl_data_path, filename, content):\\n with open(msl_data_path + filename, 'w') as (file_):\\n file_.write(content)\\n file_.flush()\\n file_.close()\",\n \"def save2File(self, contents, filename):\\n self.setup()\\n fullpath = os.path.join(self.output_path, filename)\\n f = open(fullpath, 'w')\\n f.write(contents) # python will convert \\\\n to os.linesep\\n f.close() # you can omit in most cases as the destructor will call it\\n url = \\\"file://\\\" + fullpath\\n return url\",\n \"def write(self, path, content):\\n this_file = open(path, 'w')\\n this_file.write(content)\\n this_file.close()\",\n \"def save(self, path):\\n pass\",\n \"def save(self, path):\\n pass\",\n \"def save(self, path):\\n pass\",\n \"def save(dest, content, encoding='utf-8'):\\n #with open(dest, \\\"w\\\") as f:\\n # f.write(content.encode(encoding))\\n\\n f = None\\n try:\\n head, tail = os.path.split(dest)\\n if not os.path.exists(head):\\n os.makedirs(head)\\n f = open(dest, \\\"wb\\\")\\n logging.info(\\\"Saving file: %s\\\", dest)\\n f.write(content)\\n except Exception as e:\\n logging.error(\\\"Something nasty happend during wrote to %s, retrying\\\" % dest)\\n logging.error(e)\\n raise e\\n finally:\\n if f:\\n f.close()\",\n \"def save(self, path: str):\\n pass\",\n \"def save_file(self, filename):\\r\\n \\r\\n f = open(filename,'w')\\r\\n f.write(self.body)\\r\\n f.close\",\n \"def save(self, path):\\n (folder, filename) = os.path.split(path)\\n (name, extension) = os.path.splitext(filename)\\n\\n if not name:\\n raise ValueError, \\\"name is required\\\"\\n\\n path = os.path.join(folder, name + self.extension)\\n f = open(path, \\\"wb\\\")\\n f.write(self.contents)\\n f.close()\\n\\n return path\",\n \"def write_to_path(self, path):\\n assert not path.exists()\\n fout = path.open(\\\"wb\\\")\\n fout.write(self.to_string())\\n assert not fout.close()\\n path.setdata()\",\n \"def save(self, export_path: str):\",\n \"def __write_file(path, link_name, context):\\n file_name = ''\\n for strng in ['/', 'http:']:\\n if not file_name:\\n file_name = link_name.replace(strng, '')\\n else:\\n file_name = file_name.replace(strng, '')\\n\\n full_path = os.path.join(path)\\n if not os.path.exists(full_path):\\n os.makedirs(full_path)\\n open(full_path + '/' + file_name, 'w').write(context.encode('utf-8'))\\n logging.info('File: %s Created', full_path + '/' + file_name)\",\n \"def _save_feeds(self):\\n\\n def default(o: Feed | Folder):\\n if type(o) is Feed:\\n feed = copy(o)\\n data = vars(feed)\\n data[\\\"updated\\\"] = feed.updated.isoformat()\\n data.pop(\\\"parent_folder\\\")\\n return data\\n elif type(o) is Folder:\\n folder = copy(o)\\n data = vars(folder)\\n data.pop(\\\"parent_folder\\\")\\n return data\\n\\n content = json.dumps(self.feed_cache.children, default=default, indent=4)\\n with open(\\\"data/feeds.json\\\", \\\"w\\\") as feeds_file:\\n feeds_file.write(content)\",\n \"def saveOnFile(self, path, data):\\n with open(path, \\\"w\\\") as f:\\n f.write(data)\",\n \"def output_attachment(self, path, content):\\n\\t\\twith open(path, \\\"w+b\\\") as fd:\\n\\t\\t\\tfd.write(content)\",\n \"def save_file(path, text):\\n with path.open(mode='w') as f_stream:\\n f_stream.write(text)\",\n \"def save(self, content, address, file):\\n full_address = \\\"/home/red/WAREHOUSE\\\" + address\\n file_path = full_address + \\\"/\\\" + file\\n\\n try:\\n os.makedirs(full_address, 0o777, True)\\n except OSError:\\n pass\\n\\n write_binary_file(content, file_path)\",\n \"def create_file(self, content=\\\"\\\"):\\n if (self.exists()):\\n raise IOError(\\\"A file at '{}' already exists.\\\".format(self.location))\\n with open(self.location, 'w') as f:\\n f.write(content)\",\n \"def import_opml(self, path):\\n tree = ET.parse(path)\\n root = tree.getroot()\\n for feed_el in root.find('body').findall('outline'):\\n if feed_el.get('type') == 'rss':\\n self.add_by_url(feed_el.get('xmlUrl'), feed_el.get('text'))\",\n \"def save_entry(title, content):\\n filename = f\\\"entries/{title}.md\\\"\\n if default_storage.exists(filename):\\n default_storage.delete(filename)\\n default_storage.save(filename, ContentFile(content))\",\n \"def save_entry(title, content):\\n filename = f\\\"entries/{title}.md\\\"\\n if default_storage.exists(filename):\\n default_storage.delete(filename)\\n default_storage.save(filename, ContentFile(content))\",\n \"def save_entry(title, content):\\n\\n title.strip # Remove the spaces from both sides.\\n filename = f\\\"entries/{title}.md\\\"\\n if default_storage.exists(filename):\\n default_storage.delete(filename)\\n default_storage.save(filename, ContentFile(content))\\n return True\",\n \"def saveToFile(self, filePath):\\n d = self.save()\\n with open(filePath, 'wb') as f:\\n f.write(d)\",\n \"def write_to_file(content, filename=\\\"default.txt\\\", filepath=\\\"default_path\\\"):\\n\\n fp = open_file(filename, filepath)\\n\\n try:\\n for line in content:\\n fp.write(str(line))\\n except IOError:\\n l.error(\\\"Writing to file \\\" + filename + \\\" was not possible\\\")\\n except:\\n l.error(\\n \\\"Unknown error occured, while writing to file \\\" + str(filename),\\n exc_info=True,\\n )\\n\\n close_file(fp)\",\n \"def create_file(path: Path, content: str) -> None:\\n path.touch()\\n with path.open(\\\"w\\\") as f:\\n f.write(content)\",\n \"def update_rss_feed(torrent_dir, suggested_name, url, download_url, tree_size, torrents):\\n # Fetching the existing feed, if possible\\n filepath = os.path.join(torrent_dir, '{}.rss'.format(suggested_name))\\n try:\\n with open(filepath, 'rb') as fd:\\n doc = xml.dom.minidom.parse(fd)\\n\\n except IOError:\\n # The RSS file does not exist; it is probably a first run\\n doc = None\\n\\n # Fixing download URL, if need be, such that it ends with a slash\\n if download_url[-1] != '/':\\n download_url += '/'\\n\\n # Building/Verifying the XML structure\\n try:\\n chan = check_rss_dom_structure(doc)\\n except:\\n doc, chan = init_rss_dom_structure(url)\\n\\n for torrent_data in torrents:\\n item = doc.createElement('item')\\n chan.appendChild(item)\\n\\n title_elmt = doc.createElement('title')\\n title_txt = doc.createTextNode('Package {} for tree_size {}'.format(torrent_data[2], tree_size))\\n title_elmt.appendChild(title_txt)\\n item.appendChild(title_elmt)\\n\\n desc_elmt = doc.createElement('description')\\n desc_txt = doc.createTextNode(\\n 'Comment: {} Creation Date: {}'.format(torrent_data[0]['comment'], torrent_data[0]['creation date'])\\n )\\n desc_elmt.appendChild(desc_txt)\\n item.appendChild(desc_elmt)\\n\\n guid_elmt = doc.createElement('guid')\\n fp = codecs.getencoder('hex')(torrent_data[1])[0]\\n guid_txt = doc.createTextNode(fp.decode('UTF-8'))\\n guid_elmt.appendChild(guid_txt)\\n item.appendChild(guid_elmt)\\n\\n enclosure_elmt = doc.createElement('enclosure')\\n enclosure_elmt.setAttribute('url', download_url + build_torrent_name(url, torrent_data[2], tree_size))\\n enclosure_elmt.setAttribute('type', 'application/x-bittorrent')\\n enclosure_elmt.setAttribute('len', str(torrent_data[3]))\\n item.appendChild(enclosure_elmt)\\n\\n with open(filepath, 'wb') as fd:\\n fd.write(doc.toxml('UTF-8'))\",\n \"def save(self, file_path):\\n with open(file_path, 'w') as file:\\n file.write(self.text)\\n file.close()\",\n \"def write_file(self, directory, name, content):\\n\\n try:\\n f = open(os.path.join(directory, name), 'w')\\n f.write(content)\\n f.close()\\n except:\\n print \\\"Content not written to file: %s\\\" % name\",\n \"def wFile(path, content=None):\\n try:\\n with open(path, 'w') as f: \\n if content == None:\\n return f\\n else:\\n content = str(content)\\n if content[-1] == \\\"\\\\n\\\":\\n f.write(content)\\n else:\\n f.write(content + \\\"\\\\n\\\")\\n return True\\n except:\\n print(\\\"Error: Could not write to a file.\\\")\\n return False\",\n \"def save_file(self, file_name, text):\\n\\n with open(file_name, 'w') as content_file:\\n content = content_file.write(text)\",\n \"def write(self, file_path, content):\\n self._set_extension(file_path)\\n\\n logger.debug(\\\"writing to %s\\\", self._file_path)\\n\\n self._content = content\\n\\n if self._file_ext == 'json':\\n self._write_json()\",\n \"def add_rss(url):\",\n \"def save_content(obj):\\n filename, _, _, content = obj\\n storage = get_storage(config['Instance']['content_dir'])\\n return storage.save(filename, ContentFile(content))\",\n \"def save_post(name, elements, status, current_section):\\n\\n try:\\n\\n # dealing with Posts\\n if status == 4:\\n extract_and_write_posts(elements, name)\\n return\\n\\n except:\\n print(\\\"Exception (save_to_file)\\\", \\\"Status =\\\",\\n str(status), sys.exc_info()[0])\\n\\n return\",\n \"def save_news_in_html_file(news, path_to_html, logger):\\n check_path_to_directory(path_to_html, logger)\\n html_file = tags.html(title='RSS news')\\n html_file.add(tags.head(tags.meta(charset='utf-8')))\\n\\n logger.info('Converting news to html format...')\\n for article in news:\\n html_factory(article, html_file)\\n\\n path = os.path.join(path_to_html, 'rss_news.html')\\n logger.info('Creating html-file...')\\n with open(path, 'w', encoding='utf-8') as file_html:\\n file_html.write(str(html_file))\\n logger.info('Html-file is created successfully!')\\n return file_html\",\n \"def _save_tracker_uri_to_file(self):\\r\\n if not self.tracker_file_name:\\r\\n return\\r\\n f = None\\r\\n try:\\r\\n f = open(self.tracker_file_name, 'w')\\r\\n f.write(self.tracker_uri)\\r\\n except IOError, e:\\r\\n raise ResumableUploadException(\\r\\n 'Couldn\\\\'t write URI tracker file (%s): %s.\\\\nThis can happen'\\r\\n 'if you\\\\'re using an incorrectly configured upload tool\\\\n'\\r\\n '(e.g., gsutil configured to save tracker files to an '\\r\\n 'unwritable directory)' %\\r\\n (self.tracker_file_name, e.strerror),\\r\\n ResumableTransferDisposition.ABORT)\\r\\n finally:\\r\\n if f:\\r\\n f.close()\",\n \"def write_file_content(path, file_name, content):\\n if not os.path.exists(path):\\n os.makedirs(path)\\n f = io.open(path + file_name, \\\"w\\\", encoding = 'utf-8')\\n f.write(content)\\n f.close()\",\n \"def add(self, content):\\n if (not self.exists()):\\n raise IOError(\\\"File at '{}' does not exist.\\\".format(self.location))\\n with open(self.location, 'a') as f:\\n f.write(content)\",\n \"def saveFileTo(self, buf, encoding):\\n if buf is None: buf__o = None\\n else: buf__o = buf._o\\n ret = libxml2mod.xmlSaveFileTo(buf__o, self._o, encoding)\\n return ret\",\n \"def append_to_file(filepath, content):\\n file_path = os.path.join(Path.DOWNLOAD_DIR + filepath)\\n\\n with open(file_path, 'wb') as f:\\n f.write(content)\",\n \"def store(self, filename):\",\n \"def create_or_update_file(\\n file_path: str, file_content: str = \\\"\\\", file_content_encoding: str = \\\"utf-8\\\"\\n) -> None:\\n with open(file_path, \\\"wb+\\\") as file:\\n file.write(file_content.encode(file_content_encoding))\",\n \"def write(self, content, mode='wb'):\\r\\n self.localpath.write(content, mode)\",\n \"def saveFile(self,newfile=None):\\n if newfile == None:\\n shutil.move(self.filename,self.filename+'~')\\n self.handler = open(self.filename,'w')\\n else:\\n self.handler = open(newfile,'w')\\n self.handler.writelines(self.content)\\n self.handler.close()\",\n \"def save_file(content: Any, filename: str, path: str):\\n\\n logging.info('Saving file: %s ' % filename)\\n path_to_file = join(path, filename)\\n if isfile(path_to_file):\\n ctrl = input('%s exists already in\\\\n %s.\\\\n'\\n ' Are you sure you want to overwrite it [y/N]: '\\n % (filename, path))\\n if ctrl.lower() == 'y' or ctrl.lower() == 'yes':\\n with open(path_to_file, \\\"wb\\\") as f:\\n pickle.dump(content, f)\\n else:\\n logging.warning(\\\"%s NOT saved..\\\" % filename)\\n return\\n else:\\n with open(path_to_file, \\\"wb\\\") as f:\\n pickle.dump(content, f)\\n\\n logging.info(\\\"File '%s' saved.\\\" % filename)\",\n \"def write(self, content):\\n ...\",\n \"def save(self, fname):\\n pass\",\n \"def save(self, request, item, linked_item, linked_item_form):\\n\\t\\ttry:\\n\\t\\t\\tform_file = request.FILES['file']\\n\\t\\t\\tdir_name = str(uuid.uuid1())\\n\\t\\t\\tplain_name = dir_name + '/' + form_file.name\\n\\t\\t\\tos.makedirs(os.path.join(settings.MEDIA_ROOT, 'documents', dir_name))\\n\\t\\t\\tfile_name = os.path.join(settings.MEDIA_ROOT, 'documents', plain_name)\\n\\t\\t\\tspool = open(file_name, 'wb')\\n\\t\\t\\tfor chunk in form_file.chunks():\\n\\t\\t\\t\\tspool.write(chunk)\\n\\t\\t\\tspool.close()\\n\\t\\t\\tlinked_item.file = plain_name\\n\\n\\t\\t\\tlinked_item.name = form_file.name\\n\\t\\t\\tif linked_item.name.find('.') != -1:\\n\\t\\t\\t\\tlinked_item.filetype = linked_item.name[linked_item.name.rfind('.'):]\\n\\t\\texcept:\\n\\t\\t\\tpass\\n\\n\\t\\titem.description = linked_item_form.cleaned_data['subject']\\n\\t\\titem.tags = linked_item_form.cleaned_data['tags']\\n\\t\\titem.save()\\n\\t\\tlinked_item.save()\\n\\n\\t\\t#\\n\\t\\t# Check for comments and add\\n\\t\\t#\\n\\t\\tcomment_text = linked_item_form.cleaned_data['comments'].strip()\\n\\t\\tif comment_text != '':\\n\\t\\t\\tpost_comment(request, item, comment_text)\",\n \"def write_file(content, file_path, mode='w', encoding='utf-8'):\\n with codecs.open(file_path, mode, encoding=encoding) as fid:\\n fid.write(content)\",\n \"def _save(self, name, content):\\n if not self.file_overwrite:\\n return super(OverwriteFileSystemStorage, self)._save(name, content)\\n\\n full_path = self.path(name)\\n\\n # Create any intermediate directories that do not exist.\\n # Note that there is a race between os.path.exists and os.makedirs:\\n # if os.makedirs fails with EEXIST, the directory was created\\n # concurrently, and we can continue normally. Refs #16082.\\n directory = os.path.dirname(full_path)\\n if not os.path.exists(directory):\\n try:\\n os.makedirs(directory)\\n except OSError as e:\\n if e.errno != errno.EEXIST:\\n raise\\n if not os.path.isdir(directory):\\n raise IOError(\\\"%s exists and is not a directory.\\\" % directory)\\n\\n # This file has a file path that we can move.\\n if hasattr(content, 'temporary_file_path'):\\n file_move_safe(content.temporary_file_path(), full_path)\\n content.close()\\n\\n # This is a normal uploadedfile that we can stream.\\n else:\\n flags = (os.O_WRONLY | os.O_CREAT | getattr(os, 'O_BINARY', 0))\\n # The current umask value is masked out by os.open!\\n fd = os.open(full_path, flags, 0o666)\\n _file = None\\n try:\\n locks.lock(fd, locks.LOCK_EX)\\n for chunk in content.chunks():\\n if _file is None:\\n mode = 'wb' if isinstance(chunk, bytes) else 'wt'\\n _file = os.fdopen(fd, mode)\\n _file.write(chunk)\\n finally:\\n locks.unlock(fd)\\n if _file is not None:\\n _file.close()\\n else:\\n os.close(fd)\\n\\n if settings.FILE_UPLOAD_PERMISSIONS is not None:\\n os.chmod(full_path, settings.FILE_UPLOAD_PERMISSIONS)\\n\\n return name\",\n \"def saveFileTo(self, cur, encoding):\\n if cur is None: cur__o = None\\n else: cur__o = cur._o\\n ret = libxml2mod.xmlSaveFileTo(self._o, cur__o, encoding)\\n return ret\",\n \"def _save_file(self, file_path, data):\\n self._ensure_directory(os.path.dirname(file_path))\\n with open(file_path, \\\"wb\\\") as f:\\n f.write(data)\",\n \"def save(self, filename):\\n pass\",\n \"def write_content_file(file_content):\\n\\n f = open(workingfile, \\\"w\\\")\\n if hasattr(file_content, \\\"decode\\\"):\\n f.write(str(file_content.decode(\\\"utf-8\\\")))\\n else:\\n f.write(str(file_content))\\n f.close()\",\n \"def appendFeed(feed,filepath):\\n print(\\\"::appending feed\\\")\\n with open(filepath,mode='r', encoding='utf-8') as f:\\n jconfig = json.load(f)\\n\\n with open(filepath,mode='w', encoding='utf-8') as feedsjson:\\n feed = workAFeed(feed)\\n entry = {'url':feed}\\n print(\\\"::feeds=\\\",jconfig[1]['feeds'])\\n jconfig[1]['feeds'].append(entry)\\n\\n print(json.dumps(jconfig,indent=2))\\n json.dump(jconfig,feedsjson)\",\n \"def saveToFile(html):\\n #print(\\\"Saving to file.\\\")\\n html += \\\"\\\\n\\\"\\n #open necessary files to save\\n logFile = open(\\\"postLog_{0}_{1}.txt\\\".format(os.path.splitext(path)[0], dateTimeNow), \\\"a\\\")\\n logFile.write(html)\\n logFile.close()\\n #print(\\\"Check Point.\\\")\",\n \"def _write_file(self, slug, folderpath, html):\\n # check directories\\n if not os.path.isdir(folderpath):\\n try:\\n os.makedirs(folderpath)\\n self.info(\\\"Creating directory \\\" + folderpath)\\n except Exception as e:\\n self.err(e)\\n return\\n # construct file path\\n filepath = folderpath + \\\"/\\\" + slug + \\\".html\\\"\\n # write the file\\n try:\\n filex = open(filepath, \\\"w\\\")\\n filex.write(html)\\n filex.close()\\n if self.notebook is False:\\n self.ok(\\\"File written to\\\", filepath)\\n else:\\n html = '<a href=\\\"' + filepath + '\\\">' + filepath + '</a>'\\n self.html(\\\"File written to\\\", html)\\n except Exception as e:\\n self.err(e)\\n return filepath\",\n \"def save(self, filename):\\n pass\",\n \"def write_raw(content, file_path):\\n file = open(file_path, 'wb')\\n file.write(content)\\n file.close()\",\n \"def saveFormatFileTo(self, buf, encoding, format):\\n if buf is None: buf__o = None\\n else: buf__o = buf._o\\n ret = libxml2mod.xmlSaveFormatFileTo(buf__o, self._o, encoding, format)\\n return ret\",\n \"def _create_content_file(self, content):\\n\\n io = BytesIO()\\n content.save(io, self._normalize_format(), quality=self.quality)\\n return ContentFile(io.getvalue())\",\n \"def _save(self, name, content):\\n if not self.oauth_token:\\n raise AuthenticationError(\\\"You must be authenticated with oAuth \\\"\\n \\\"for upload files.\\\")\\n params = {\\n 'title': name,\\n # 'description': description,\\n # 'tags': tags,\\n # 'is_public': is_public,\\n 'is_public': 1,\\n # 'is_friend': is_friend,\\n # 'is_family': is_family,\\n # 'safety_level': safety_level,\\n # 'content_type': content_type,\\n # 'hidden': hidden\\n }\\n response = self.oauth_session.post(self.API_POST_URL, params=params,\\n files={'photo': content.file})\\n xmldoc = minidom.parseString(response.content)\\n rsp = xmldoc.getElementsByTagName('rsp')[0]\\n if rsp.getAttribute('stat') == 'fail':\\n msg = xmldoc.getElementsByTagName('err')[0].getAttribute('msg')\\n raise FileSaveError(msg)\\n photo_id = xmldoc.getElementsByTagName('photoid')[0].firstChild.nodeValue\\n return photo_id\",\n \"def save_to_file(name, elements, status, current_section):\\n # status 3 = dealing with about section\\n # status 4 = dealing with posts\\n\\n try:\\n # dealing with About Section\\n if status == 3:\\n workEducation = elements[0].text.replace(\\\"\\\\n\\\", \\\"|\\\")\\n influencerObject[\\\"Influencer\\\"][\\\"Description\\\"][\\\"Text\\\"] = workEducation\\n\\n # dealing with Posts\\n # elif status == 4:\\n # extract_and_write_posts(elements, name)\\n # return\\n\\n except:\\n print(\\\"Exception (save_to_file)\\\", \\\"Status =\\\",\\n str(status), sys.exc_info()[0])\\n\\n return\",\n \"def addNewFeed(feed):\\n # config exist?\\n configfile_path = confighome+\\\"config\\\"\\n print(\\\"::checking for config\\\")\\n if fileAccessible(configfile_path,'r'):\\n print(\\\"::reading config\\\")\\n appendFeed(feed,configfile_path)\\n elif fileAccessible(configfile_path,'w'):\\n createNewConfig(feed,configfile_path)\\n else:\\n print(\\\"::unable to read\\\")\",\n \"def saving_file(xml):\\r\\n\\r\\n xml_string = etree.tostring(xml)\\r\\n parsed = minidom.parseString(xml_string)\\r\\n with open(self.app_path + \\\"\\\\\\\\temp_\\\\\\\\\\\" + file_name + \\\".xml\\\", \\\"w\\\") as file:\\r\\n file.write(parsed.toprettyxml(indent=\\\" \\\"))\",\n \"def makeFile(self, path=None, content=b''):\\n if path is None:\\n path = self.mktemp()\\n with open(path, 'wb') as file:\\n file.write(content)\\n return path\",\n \"def create_file(self, name: str, content: str) -> None:\\n file_path = self.path + os.path.sep + name\\n with open(file_path, \\\"w+\\\") as file:\\n file.write(content)\\n file.close()\",\n \"def save(self, content):\\n raise NotImplemented()\",\n \"def Feed(self, path):\\n de = DesktopEntry(path)\\n if not de.IsValid:\\n print(\\\"File %s path is not valid!\\\\n\\\"%path)\\n return\\n if de.InVisiable:\\n print(\\\"File :%s is invisible.\\\\n\\\"%path)\\n return\\n\\n cat = self.cats.get(de.Category)\\n if cat is None:\\n cat = DE_Category(de.Category)\\n self.cats[de.Category] = cat\\n self.cats[de.Category].StoreEntry(de)\",\n \"def download_filings(feedpath,args=None):\\n\\tlogger.info(\\\"Processing RSS feed %s\\\",feedpath)\\n\\n\\tdir = filings_dir(feedpath)\\n\\tos.makedirs(dir,exist_ok=True)\\n\\n\\tfiling_urls = []\\n\\tfor filing in feed_tools.read_feed(feedpath):\\n\\t\\tif args:\\n\\t\\t\\tif args.company_re and not bool(args.company_re.match(filing['companyName'])):\\n\\t\\t\\t\\tcontinue\\n\\t\\t\\tif args.cik and args.cik != filing['cikNumber']:\\n\\t\\t\\t\\tcontinue\\n\\t\\t\\tif args.sic and args.sic != filing['assignedSic']:\\n\\t\\t\\t\\tcontinue\\n\\t\\t\\tif args.form_type and args.form_type != filing['formType']:\\n\\t\\t\\t\\tcontinue\\n\\t\\tif 'enclosureUrl' in filing and not exists_filing(dir,filing['enclosureUrl'],filing['enclosureLength']):\\n\\t\\t\\tfiling_urls.append(filing['enclosureUrl'])\\n\\t\\tif args and getattr(args,'with_exhibits',False):\\n\\t\\t\\tfiling_urls.extend( filing.get( 'exhibitList', [] ) )\\n\\n\\tlogger.info(\\\"Start downloading %d new filings\\\",len(filing_urls))\\n\\twith concurrent.futures.ThreadPoolExecutor(max_workers=args.max_threads) as executor:\\n\\t\\tfutures = [executor.submit(download_filing,dir,url,args.max_retries) for url in filing_urls]\\n\\t\\tfor future in concurrent.futures.as_completed(futures):\\n\\t\\t\\ttry:\\n\\t\\t\\t\\tfuture.result()\\n\\t\\t\\texcept Exception as e:\\n\\t\\t\\t\\tprint(e)\",\n \"def save(self):\\n if PYTHON3:\\n fileobj = open(self.filename, 'w', encoding=self.ENCODING, errors=\\\"replace\\\")\\n else:\\n fileobj = open(self.filename, 'w')\\n self.save_to_fileobj(fileobj)\\n fileobj.close()\",\n \"def save_new_rss_subscription_task(feed_obj):\\n save_new_rss_subscription(feed_obj)\\n logger.info(\\\"Entries for new Feed subcription\\\")\",\n \"def save_file(name, content):\\n data = content.encode(\\\"utf8\\\").split(b\\\";base64,\\\")[1]\\n with open(os.path.join(UPLOAD_DIRECTORY, name), \\\"wb\\\") as fp:\\n fp.write(base64.decodebytes(data))\",\n \"def save_file(name, content):\\n data = content.encode(\\\"utf8\\\").split(b\\\";base64,\\\")[1]\\n with open(os.path.join(UPLOAD_DIRECTORY, name), \\\"wb\\\") as fp:\\n fp.write(base64.decodebytes(data))\",\n \"def test_write_to_file():\\n from scraper import write_to_file\\n encoding = 'utf-8'\\n write_to_file(TEST_FILE, TEST_CONTENT, encoding)\\n assert True\",\n \"def write_to_file(filename, content):\\n with open(filename, 'w') as f:\\n f.write(content)\",\n \"def save(self):\\n file = open(self.path, 'w')\\n self.parser.write(file)\\n file.close()\",\n \"def save(self, save_path=None):\\n if self._xml is None:\\n raise IOError(\\\"There's nothing to save\\\")\\n\\n path = self._path_to_xml if save_path is None else save_path\\n\\n with open(path, 'w') as f:\\n rough_string = Et.tostring(self._xml, 'utf-8')\\n par = etree.XMLParser(remove_blank_text=True)\\n elem = etree.XML(rough_string, parser=par)\\n parsed = minidom.parseString(etree.tostring(elem))\\n f.write(parsed.toprettyxml(indent=\\\" \\\"))\",\n \"def save(self):\\n return self.save_as(self.filename)\",\n \"def savegraph(self, path):\\n\\n raise NotImplementedError\",\n \"def save(datastream):\",\n \"def save(self, path=None):\\n if path is None:\\n path = self.path\\n try:\\n with open(path, 'w') as fd:\\n for entry in self:\\n fd.write('{}\\\\n'.format(entry))\\n except Exception as e:\\n raise SSHKeyError('Error writing {}: {}'.format(path, e))\",\n \"def write_file(self, contents):\\n fd = open(os.path.join(os.path.dirname(__file__),\\n 'data', 'test.html'), 'w')\\n fd.write(contents)\\n fd.close()\"\n]","isConversational":false},"negative_scores":{"kind":"list like","value":["0.6336935","0.6105859","0.5953287","0.59252447","0.58727354","0.5848965","0.5748638","0.57357615","0.57232815","0.57130784","0.5701286","0.56320465","0.5586502","0.5526823","0.5505389","0.55029744","0.54756254","0.54639256","0.5439159","0.5437611","0.5437611","0.5437611","0.5388603","0.536372","0.534672","0.53332394","0.5277731","0.5270043","0.526269","0.5212063","0.51845956","0.5184107","0.516087","0.5159967","0.5151197","0.5143721","0.51306784","0.51306784","0.51005256","0.5091913","0.5090892","0.50901026","0.5088001","0.50821614","0.50708956","0.50605404","0.50443465","0.50170094","0.5013805","0.5009637","0.50080067","0.49998233","0.49992684","0.49981043","0.49971846","0.49970198","0.49952257","0.49922115","0.49754015","0.49726668","0.4968767","0.49665552","0.4964727","0.49616355","0.49614346","0.49402115","0.4938142","0.49302456","0.49301803","0.49202895","0.49129623","0.4912861","0.49108014","0.48988792","0.48845276","0.48842388","0.48564613","0.48508006","0.48373353","0.48348415","0.48316705","0.4824815","0.48180467","0.4805602","0.4804627","0.4803073","0.47910237","0.47895288","0.47894597","0.47807023","0.47807023","0.47717687","0.47707963","0.47595546","0.47528714","0.47479203","0.47470587","0.47397622","0.4738345","0.47353458"],"string":"[\n \"0.6336935\",\n \"0.6105859\",\n \"0.5953287\",\n \"0.59252447\",\n \"0.58727354\",\n \"0.5848965\",\n \"0.5748638\",\n \"0.57357615\",\n \"0.57232815\",\n \"0.57130784\",\n \"0.5701286\",\n \"0.56320465\",\n \"0.5586502\",\n \"0.5526823\",\n \"0.5505389\",\n \"0.55029744\",\n \"0.54756254\",\n \"0.54639256\",\n \"0.5439159\",\n \"0.5437611\",\n \"0.5437611\",\n \"0.5437611\",\n \"0.5388603\",\n \"0.536372\",\n \"0.534672\",\n \"0.53332394\",\n \"0.5277731\",\n \"0.5270043\",\n \"0.526269\",\n \"0.5212063\",\n \"0.51845956\",\n \"0.5184107\",\n \"0.516087\",\n \"0.5159967\",\n \"0.5151197\",\n \"0.5143721\",\n \"0.51306784\",\n \"0.51306784\",\n \"0.51005256\",\n \"0.5091913\",\n \"0.5090892\",\n \"0.50901026\",\n \"0.5088001\",\n \"0.50821614\",\n \"0.50708956\",\n \"0.50605404\",\n \"0.50443465\",\n \"0.50170094\",\n \"0.5013805\",\n \"0.5009637\",\n \"0.50080067\",\n \"0.49998233\",\n \"0.49992684\",\n \"0.49981043\",\n \"0.49971846\",\n \"0.49970198\",\n \"0.49952257\",\n \"0.49922115\",\n \"0.49754015\",\n \"0.49726668\",\n \"0.4968767\",\n \"0.49665552\",\n \"0.4964727\",\n \"0.49616355\",\n \"0.49614346\",\n \"0.49402115\",\n \"0.4938142\",\n \"0.49302456\",\n \"0.49301803\",\n \"0.49202895\",\n \"0.49129623\",\n \"0.4912861\",\n \"0.49108014\",\n \"0.48988792\",\n \"0.48845276\",\n \"0.48842388\",\n \"0.48564613\",\n \"0.48508006\",\n \"0.48373353\",\n \"0.48348415\",\n \"0.48316705\",\n \"0.4824815\",\n \"0.48180467\",\n \"0.4805602\",\n \"0.4804627\",\n \"0.4803073\",\n \"0.47910237\",\n \"0.47895288\",\n \"0.47894597\",\n \"0.47807023\",\n \"0.47807023\",\n \"0.47717687\",\n \"0.47707963\",\n \"0.47595546\",\n \"0.47528714\",\n \"0.47479203\",\n \"0.47470587\",\n \"0.47397622\",\n \"0.4738345\",\n \"0.47353458\"\n]","isConversational":false},"document_score":{"kind":"string","value":"0.73413247"},"document_rank":{"kind":"string","value":"0"}}},{"rowIdx":82390,"cells":{"query":{"kind":"string","value":"Method converts a news to FB2format."},"document":{"kind":"string","value":"def add_to_fb2(self, news: dict) -> str:\n\n add_to_fb2_file = \"\"\n add_to_fb2_file += f\" <p>Title: {news['Title']}</p>\\n\"\n add_to_fb2_file += f\" <p><a l:href='{news['Link']}'> 'Link to news' </a></p>\\n\"\n add_to_fb2_file += f\" <p>PubDate: {news['PubDate']}</p>\\n\"\n add_to_fb2_file += f\" <p>Source: {news['Source']}</p>\\n\"\n\n if news['ImageCacheName']:\n add_to_fb2_file += f\" <p><image l:href='#{news['ImageCacheName']}'/></p>\\n\"\n with open(f\"{self.full_path_to_image_cache}{os.sep}{news['ImageCacheName']}\", \"rb\") as img_file:\n b64_string = base64.b64encode(img_file.read())\n self.binaries += f\"<binary id='{news['ImageCacheName']}' \" \\\n f\"content-type='image/jpeg'>{b64_string.decode('utf-8')}</binary>\\n\"\n\n add_to_fb2_file += f\" <empty-line/>\\n\"\n\n return add_to_fb2_file"},"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 format_news(news):\r\n news = re.sub(r\"[<>]|=+\", \" \", news)\r\n news = re.sub(r\"-{2,}|/{2,}\", \" \", news)\r\n news = re.sub(r\"\\(.+?\\)\", \" \", news)\r\n news = re.sub(r\"\\s+\", \" \", news)\r\n return news","def save_to_fb2(self, rss_feed, date=None):\n\n self.print_if_verbose(f\"Method 'save_to_fb2' is working: \\n\")\n\n if not os.path.exists(self.to_fb2_path):\n try:\n os.makedirs(self.to_fb2_path)\n except Exception as error:\n print(f\"Exception {error} - wrong path! File was not created!\")\n return\n\n os.chdir(self.to_fb2_path)\n\n with open(\"rss_news.fb2\", \"w\", encoding=\"utf-8\") as fb2_file:\n fb2_file.writelines(f\"<?xml version='1.0' encoding='UTF-8'?>\\n\")\n fb2_file.writelines(\n f\"<FictionBook xmlns='http://www.gribuser.ru/xml/fictionbook/2.0' \"\n f\"xmlns:l='http://www.w3.org/1999/xlink'>\\n\")\n fb2_file.writelines(f\" <body>\\n\")\n fb2_file.writelines(f\" <section>\\n\")\n\n if self.date:\n for news in rss_feed:\n fb2_file.writelines(self.add_to_fb2(news))\n else:\n for news in rss_feed[\"News\"]:\n fb2_file.writelines(self.add_to_fb2(news))\n\n fb2_file.writelines(f\" </section>\\n\")\n fb2_file.writelines(\" </body>\\n\")\n fb2_file.writelines(self.binaries)\n fb2_file.writelines(\"</FictionBook>\\n\")\n\n self.print_if_verbose(\n f\"File {self.to_fb2_path}{os.sep}rss_news.fb2 was created successfully. \\n\"\n f\"Method 'save_to_fb2' is finished.\\n\"\n )\n\n return True","def parse_news(news):\n default_value = '---'\n\n news_list = []\n for entry in news:\n title = entry.get('title', default_value)\n link = entry.get('link', default_value)\n published = entry.get('published', default_value)\n source = entry.get('source', default_value)\n description = entry.get('description', default_value)\n media_content = entry.get('media_content', default_value)\n\n source_title = default_value\n if source != default_value:\n source_title = source['title']\n\n image = default_value\n if media_content != image:\n image = media_content[0]['url']\n\n article = Article(title, link, published, source_title, description, image)\n news_list.append(article)\n\n return news_list","def extract_news(parser):\n news_list = []\n titles = []\n authors = []\n urls = []\n coms = []\n scores = []\n mya = parser.find_all('a', class_='storylink')\n for a in mya:\n titles.append(a.contents[0])\n author = parser.find_all('a', class_='hnuser')\n for a in author:\n authors.append(a.contents[0])\n myurl = parser.find_all('a', class_='storylink')\n for a in myurl:\n urls.append(a['href'])\n comments = parser.find_all('td', class_='subtext')\n for td in comments:\n coms.append(td.find_all('a')[-1].contents[0])\n score = parser.find_all('span', class_='score')\n for span in score:\n scores.append(span.contents[0])\n for i in range(len(titles)):\n new = {}\n new['title'] = titles[i]\n new['author'] = authors[i]\n new['urls'] = urls[i]\n new['comments'] = coms[i]\n new['score'] = scores[i]\n news_list.append(new)\n return news_list","def get_news(news_url):\n news_final = []\n try:\n news_handler = urllib.urlopen(news_url)\n news = news_handler.read()\n news = nl2br(news)\n news = string.split(news, '<br/>')\n\n news_array = {}\n value = {}\n for newsweb in news:\n value = string.split(newsweb, '|')\n if len(value[0]) > 1:\n news_array[value[0]] = value[1]\n\n info = {}\n for k in news_array:\n info = k[0:int(k.find(\"http://\") - 1)]\n info = string.split(k, ' - ')\n news_final.append((info[0], info[1], news_array[k]))\n\n news_handler.close()\n except IndexError:\n pass\n except IOError:\n pass\n\n return news_final","def load_article_into_newsitem_class(info_about_news_item):\n article = info_about_news_item.signalmedia_json\n identifier = article['id']\n \n a_news_item = semeval_classes.NewsItem(\n identifier = identifier,\n collection = 'SignalMedia',\n dct = article['published'],\n publisher = article['source'])\n \n for provenance, naf in info_about_news_item.preprocessing:\n iden2wf_el = {int(wf_el.get('id')[1:]): wf_el\n for wf_el in naf.iterfind('text/wf')}\n\n tid2lemma = {int(term_el.get('id')[1:]): term_el.get('lemma')\n for term_el in naf.iterfind('terms/term')}\n basename = ''\n if provenance == 'newsreader':\n basename = naf.find('nafHeader/fileDesc').get('filename')\n\n # add entity mentions\n for entity_el in naf.iterfind('entities/entity'):\n entity_mention_obj = create_entity_mention_obj(entity_el, provenance,\n iden2wf_el,\n debug=False)\n a_news_item.entity_mentions.add(entity_mention_obj)\n\n # add event mentions\n for predicate_el in naf.iterfind('srl/predicate'):\n an_event_mention_obj = create_event_mention_obj(predicate_el,\n provenance,\n iden2wf_el,\n tid2lemma,\n basename,\n debug=False)\n a_news_item.event_mentions.add(an_event_mention_obj)\n\n # add domain labels\n xpath_query = 'topics/topic[@method=\"JEX\"]'\n jex_domain_labels = {topic_el.text\n for topic_el in naf.iterfind(xpath_query)}\n a_news_item.domain = jex_domain_labels\n\n return a_news_item","def convert_to_html(self, news_list):\n self.logger.info(\"Converting news to HTML...\")\n self.prepare_storage()\n self.process_news_list_with_images(news_list)\n content = self.generate_html_template(news_list)\n self.write_to_file(content.encode(\"UTF-8\"))","def pos_process_news(cls):\n # get from db\n df = Database.get_all_crawled_news(Database.db_news)\n\n # erase news without title or text\n df = df.loc[df['news_site_title'] != '']\n df = df.loc[df['news_site_text'] != '']\n\n # erase number words title\n df['title_len'] = df.apply(lambda row: Utils.count_words(row['news_site_title']), axis=1)\n df = df.loc[df['title_len'] > 3]\n\n # sort by date so that we keep the last news\n df['arquivo_date'] = pd.to_datetime(df.arquivo_date)\n df = df.sort_values(by=['arquivo_date'], ascending=False)\n df = df.set_index(['arquivo_date'])\n df['arquivo_date'] = df.index\n\n # drop news that have title repeated\n df_title_text = df[['news_site_title']]\n df_title_text = df_title_text.drop_duplicates()\n df = df.loc[df.index.isin(df_title_text.index)]\n\n # insert in mongo db\n Database.save_posprocessed_crawled_news(Database.db_pos_processed_news,df)","def extract_news(parser):\r\n news_list = []\r\n\r\n tbl_list = parser.table.findAll('table')\r\n tr_list = tbl_list[1].findAll('tr')\r\n for i in range(0, 90, 3):\r\n new = dict()\r\n new['author'] = tr_list[i + 1].a.text\r\n new['points'] = tr_list[i + 1].span.text[:-6]\r\n comments = tr_list[i + 1].findAll('a')\r\n new['comments'] = comments[len(comments) - 1].text[:-9]\r\n if new['comments'] == '':\r\n new['comments'] = '0'\r\n new['title'] = tr_list[i].findAll('a')[1].text\r\n a_mas = List[str]\r\n a_mas = tr_list[i].findAll('a')\r\n new['url'] = a_mas[len(a_mas) - 1].text\r\n if new['url'] == new['title']:\r\n new['url'] = ''\r\n news_list.append(new)\r\n\r\n return news_list","def news():\n mesosite = iemdb.connect('mesosite', bypass=True)\n mcursor = mesosite.cursor(cursor_factory=psycopg2.extras.DictCursor)\n # Last dailyb delivery\n lastts = mx.DateTime.now() + mx.DateTime.RelativeDateTime(hour=11, days=-1)\n mcursor.execute(\"\"\"\n SELECT *, to_char(entered, 'DD Mon HH:MI AM') as nicedate \n from news WHERE entered > '%s' \n ORDER by entered DESC\"\"\" % (\n lastts.strftime(\"%Y-%m-%d %H:%M\"),) )\n\n textfmt = \"\"\"\n +----------------------------------------------\n | Title : %(title)s\n | Date : %(nicedate)s\n | Author: %(author)s\n | URL : %(url)s\n +----------------------------------------------\n\n%(body)s\n\n\"\"\"\n htmlfmt = \"\"\"\n<hr />\n<br /><strong>Title:</strong> <a href=\"http://mesonet.agron.iastate.edu/onsite/news.phtml?id=%(id)s\">%(title)s</a>\n<br /><strong>Date:</strong> %(nicedate)s\n<br /><strong>Author:</strong> %(author)s\n<br /><a href=\"%(url)s\">link</a>\n\n<p>%(body)s\n\n\"\"\"\n txt = \"> News\\n\"\n html = \"<h3>News</h3>\"\n\n for row in mcursor:\n txt += textfmt % row\n html += htmlfmt % row\n if mcursor.rowcount == 0:\n txt += \"\\n No news is good news\\n\\n\"\n html += \"<strong>No news is good news</strong>\"\n\n return txt, html","def rdfize_news_item(a_news_item, g):\n news_item_id = a_news_item.identifier\n # create URI for this news item\n newsItemURIString = \"%s%s\" % (cltlNewsPrefix, news_item_id)\n newsItem = URIRef(newsItemURIString)\n\n # Add the news item triples to the graph\n g.add(( newsItem, RDF.type, newsItemType))\n g.add(( newsItem, DCTERMS.source, Literal(news_item_id) ))\n g.add(( newsItem, DCTERMS.isPartOf, URIRef('%s%s' % (cltlDataPrefix, a_news_item.collection)) ))\n dct=datetime.datetime.strptime(a_news_item.dct, '%Y-%m-%dT%H:%M:%SZ') # 2015-09-04T10:43:03Z\n g.add(( newsItem, DCTERMS.created, Literal(dct)))\n g.add(( newsItem, DCTERMS.publisher, URIRef(create_publisher_uri(a_news_item.publisher))))\n if a_news_item.domain and len(a_news_item.domain):\n for domain in a_news_item.domain:\n if domain:\n g.add(( newsItem, DCTERMS.subject, URIRef(create_topic_uri(domain)) ))\n\n # iterate through the entity mentions\n for entity_mention_obj in a_news_item.entity_mentions:\n # create URI for this entity mention\n entityMentionURIString=\"%s#mention=%d,%d\" % (newsItemURIString,\n entity_mention_obj.begin_index,\n entity_mention_obj.end_index)\n instanceURI=URIRef(\"%s#entity=%d,%d\" % (newsItemURIString,\n entity_mention_obj.begin_index,\n entity_mention_obj.end_index))\n entityMentionURI=URIRef(entityMentionURIString)\n\n g.add((entityMentionURI, RDF.type, GAF.Mention))\n # add entity mention triples\n g.add((entityMentionURI, CLTLV.sent, Literal(int(entity_mention_obj.sentence))))\n g.add((entityMentionURI, NIF.anchorOf, Literal(entity_mention_obj.mention)))\n# if entity_mention_obj.lemma:\n# g.add((entityMentionURI, NIF.lemma, Literal(entity_mention.obj.lemma) ))\n g.add(( entityMentionURI, NIF.referenceContext, newsItem ))\n g.add(( entityMentionURI, GAF.denotes, instanceURI ))\n g.add((instanceURI, RDF.type, GAF.Instance))\n g.add((instanceURI, RDF.type, CLTLV.Entity))\n g.add((instanceURI, RDF.type, URIRef(makeType(entity_mention_obj.the_type))))\n g.add((entityMentionURI, NIF.beginIndex, Literal(entity_mention_obj.begin_index)))\n g.add((entityMentionURI, NIF.endIndex, Literal(entity_mention_obj.end_index)))\n g.add((entityMentionURI, PROV.wasAttributedTo, URIRef('%sprovenance/%s/entity' % (cltlPrefix, entity_mention_obj.provenance)) ))\n if entity_mention_obj.meaning:\n g.add(( instanceURI, OWL.sameAs, URIRef(entity_mention_obj.meaning) ))\n\n # Iterate through the event mentions\n for event_mention_obj in a_news_item.event_mentions:\n # create URI for this entity mention\n eventMentionURIString=\"%s#mention=%s\" % (newsItemURIString,\n hash_offsets(event_mention_obj.mention_offset_ranges))\n instanceURI=URIRef(\"%s#event=%s\" % (newsItemURIString,\n hash_offsets(event_mention_obj.mention_offset_ranges)))\n eventMentionURI=URIRef(eventMentionURIString)\n\n g.add((eventMentionURI, RDF.type, GAF.Mention))\n # add entity mention triples\n g.add((eventMentionURI, CLTLV.sent, Literal(int(event_mention_obj.sentence))))\n g.add((eventMentionURI, NIF.anchorOf, Literal(event_mention_obj.mention)))\n if event_mention_obj.lemma:\n g.add((eventMentionURI, NIF.lemma, Literal(event_mention_obj.lemma) ))\n g.add(( eventMentionURI, NIF.referenceContext, newsItem ))\n g.add(( eventMentionURI, GAF.denotes, instanceURI ))\n for offset_range in event_mention_obj.mention_offset_ranges: \n g.add((eventMentionURI, NIF.beginIndex, Literal(offset_range[0])))\n g.add((eventMentionURI, NIF.endIndex, Literal(offset_range[1])))\n g.add((instanceURI, RDF.type, GAF.Instance))\n g.add((instanceURI, RDF.type, CLTLV.Event))\n #g.add((instanceURI, RDF.type, Literal(entity_mention_obj.the_type)))\n g.add((eventMentionURI, PROV.wasAttributedTo, URIRef('%sprovenance/%s/event' % (cltlPrefix, event_mention_obj.provenance)) ))\n if event_mention_obj.meaning:\n g.add(( instanceURI, OWL.sameAs, URIRef(event_mention_obj.meaning) ))\n\n\n\n return g","def parse_gulde_news():\n # set url\n url = 'https://web-scraping-demo.zgulde.net/news' \n agent = 'codeup ds germain'\n # query\n response = requests.get(url, headers={'User-Agent': agent}) \n # soup\n soup = BeautifulSoup(response.text) \n # raw list of articles\n articles = soup.select('.grid.gap-y-12 > div') \n # list of dicts for dataframe\n article_list = [] \n # parse each article\n for article in articles: \n # grab title\n title = article.h2.text \n # grab date, author, contents of article\n date, author, contents = article.select('.py-3')[0]\\\n .find_all('p') \n # add dict of info to list\n article_list.append({'title':title, 'date':date.text,\n 'author':author.text, 'contents':contents.text}) \n # return dataframe\n return pd.DataFrame(article_list)","def extract_news(parser):\n news_list = []\n\n titles = parser.find_all(\"tr\", class_=\"athing\")\n subtext = parser.find_all(\"td\", class_=\"subtext\")\n\n for i in range(len(titles)):\n x = titles[i].find_all(\"td\", class_=\"title\")[1]\n title = x.a.text\n url = x.a[\"href\"]\n c = subtext[i].find_all(\"a\")[4]\n if c.text == \"discuss\":\n comments = 0\n else:\n comments = c.text\n author = subtext[i].find(\"a\", class_=\"hnuser\").get_text()\n point = subtext[i].find(\"span\", class_=\"score\").text\n points = point.split(' ')[0]\n\n news_list.append({\"author\": author, \"comments\": comments, \"points\": points, \"title\": title, \"url\": url})\n\n return news_list","def gnews(self):\n\t\tfeed_url = self.get_feed()\n\t\tfeed_data = feedparser.parse(feed_url)\n\t\tprint(\"\")\n\t\ttype_tiny = pyshorteners.Shortener()\n\t\tfor data in feed_data[\"items\"]:\n\t\t\ttiny_url = type_tiny.tinyurl.short(data[\"link\"])\n\t\t\t#tiny_url = tinyurl.create_one(data[\"link\"])\n\t\t\tprint('\\033[33m' + data[\"title\"] + \" : \" + Style.RESET_ALL + tiny_url)\n\t\t\tprint(\"\")","def news():\n mesosite = get_dbconn(\"mesosite\")\n mcursor = mesosite.cursor(cursor_factory=psycopg2.extras.DictCursor)\n # Last dailyb delivery\n lastts = datetime.datetime.now() + datetime.timedelta(days=-1)\n mcursor.execute(\n \"SELECT *, to_char(entered, 'DD Mon HH:MI AM') as nicedate \"\n \"from news WHERE entered > %s ORDER by entered DESC\",\n (lastts,),\n )\n\n textfmt = \"\"\"\n +----------------------------------------------\n | Title : %(title)s\n | Date : %(nicedate)s\n | Author: %(author)s\n | URL : %(url)s\n +----------------------------------------------\n\n%(body)s\n\n\"\"\"\n htmlfmt = (\n \"<hr />\\n\"\n \"<br /><strong>Title:</strong>\\n\"\n '<a href=\"https://mesonet.agron.iastate.edu/'\n 'onsite/news.phtml?id=%(id)s\">%(title)s</a>\\n'\n \"<br /><strong>Date:</strong> %(nicedate)s\\n\"\n \"<br /><strong>Author:</strong> %(author)s\\n\"\n '<br /><a href=\"%(url)s\">link</a>\\n\\n'\n \"<p>%(body)s\\n\"\n )\n txt = \"> News\\n\"\n html = \"<h3>News</h3>\"\n\n for row in mcursor:\n txt += textfmt % row\n html += htmlfmt % row\n if mcursor.rowcount == 0:\n txt += \"\\n No news is good news\\n\\n\"\n html += \"<strong>No news is good news</strong>\"\n\n return txt, html","def nfldotcomnews(self, irc, msg, args):\n \n url = self._b64decode('aHR0cDovL3MzLmFtYXpvbmF3cy5jb20vbmZsZ2MvYWxsX25ld3NMaXN0Lmpz')\n \n try:\n req = urllib2.Request(url)\n html = (urllib2.urlopen(req)).read()\n except:\n irc.reply(\"Failed to fetch: %s\" % url)\n return\n \n try:\n jsondata = json.loads(html)['content']\n except:\n irc.reply(\"Failed to parse article json from: %s\" % url)\n return\n \n for article in jsondata[0:6]:\n title = article.get('title', None)\n desc = article.get('description', None)\n link = article.get('linkURL', None)\n date = article.get('date_ago', None)\n \n output = \"{0} - {1}\".format(ircutils.bold(title), self._shortenUrl(link))\n irc.reply(output)","def __get_local_g1_news(soup):\n news = []\n anchors = soup.find_all(\n 'a', class_='feed-post-link gui-color-primary gui-color-hover')\n\n for a in anchors:\n title = a.string\n link = a['href']\n news.append(dict(title=title, link=link))\n return news","def __local_to(soup):\n return __get_local_g1_news(soup)","def __local_to(soup):\n return __get_local_g1_news(soup)","def get_news(url):\r\n \r\n # parse RSS feed into list of dictionaries\r\n feed = feedparser.parse(url)\r\n\r\n # no RSS feed articles for url\r\n if len(feed['entries']) == 0:\r\n return []\r\n \r\n # get first ten articles from the RSS feed\r\n news = []\r\n i = 0\r\n while True:\r\n if i == len(feed['entries']) or i > 30:\r\n break\r\n \r\n try:\r\n # get link to article\r\n link = feed[\"entries\"][i][\"link\"]\r\n\r\n # get title of article\r\n title = feed[\"entries\"][i][\"title\"]\r\n \r\n try:\r\n # get raw summary of article\r\n summary_raw = feed[\"entries\"][i][\"summary\"]\r\n \r\n # format summary\r\n summary = \"\"\r\n for c in summary_raw:\r\n if c == \"<\":\r\n summary += \"...\"\r\n break\r\n summary += c\r\n except KeyError as e:\r\n logging.error(\"no summary for RSS feed article: {}\".format(link))\r\n summary = \"read more here...\"\r\n \r\n # get raw date \r\n date_raw = feed[\"entries\"][i][\"published_parsed\"]\r\n \r\n if date_raw is None:\r\n date = feed[\"entries\"][i][\"published\"]\r\n \r\n else:\r\n # format date\r\n year = str(date_raw.tm_year)\r\n months = [\"January\", \"February\", \"March\", \"April\", \"May\", \"June\", \"July\", \"August\", \"September\", \"October\", \"November\", \"December\"]\r\n month = months[date_raw.tm_mon - 1]\r\n day = str(date_raw.tm_mday)\r\n weekdays = [\"Monday\", \"Tuesday\", \"Wednesday\", \"Thursday\", \"Friday\", \"Saturday\", \"Sunday\"]\r\n wday = weekdays[date_raw.tm_wday]\r\n hour = str(date_raw.tm_hour)\r\n hour = \"{:2}\".format(hour).format(' ','0')\r\n min = str(date_raw.tm_min)\r\n min = \"{:2}\".format(min).replace(' ','0')\r\n date = hour + \":\" + min + \" - \" + wday + \" \" + month + \" \" + day + \", \" + year\r\n \r\n # compile entry and append to news list\r\n entry = {\"link\":link, \"title\":title, \"date\":date, \"summary\":summary}\r\n \r\n # sanitize entry\r\n for key in entry:\r\n # apostrophe\r\n entry[key] = entry[key].replace(\"'\", \"'\")\r\n # right single quotation mark\r\n entry[key] = entry[key].replace(\"’\", \"’\")\r\n # left single quotation mark\r\n entry[key] = entry[key].replace('\"', \"‘\")\r\n # right double quotation mark\r\n entry[key] = entry[key].replace(\"'\", \"”\")\r\n # left double quotation mark\r\n entry[key] = entry[key].replace(\"'\", \"“\")\r\n # Weird ampersand formatting\r\n entry[key] = entry[key].replace(\"&\", \"&\")\r\n \r\n # prepare entry for sqlite queries\r\n entry[key] = surround(entry[key])\r\n \r\n # add entry to news list\r\n news.append(entry)\r\n \r\n # max 10 entries\r\n if len(news) == 10:\r\n break\r\n i += 1\r\n \r\n except Exception as e:\r\n logging.error(e)\r\n i += 1\r\n pass\r\n \r\n # success\r\n return news","def news()->str:#return array[news desc,news link]\n event_log(\"retrieve news data....\",\"\")\n c = 0\n location = read_json(\"news_api\")[0]\n main_url = \"https://newsapi.org/v2/top-headlines?country=\"+location+\"&apiKey=\"+read_json(\"news_api\")[1]+\"\"#add a country selection optin via json\n page = requests.get(main_url).json()\n article = page[\"articles\"]\n news_result = []\n for data in article:\n news_result.append([data[\"title\"],str(data[\"url\"]).replace('\"',\" \")])#exctracts the wanted data from api\n if c == 5:#add this to json file so scalibility\n break\n c+=1\n return news_result","def process(url):\n feed = feedparser.parse(url)\n entries = feed.entries\n ret = []\n for entry in entries:\n print entry\n guid = entry.guid\n title = translate_html(entry.title)\n link = entry.link\n summary = translate_html(entry.summary)\n try:\n subject = translate_html(entry.tags[0]['term'])\n except AttributeError:\n subject = \"\"\n newsStory = NewsStory(guid, title, subject, summary, link)\n ret.append(newsStory)\n return ret","def get_article():\n bbc_request = requests.get('https://www.bbc.com/ukrainian')\n soup = BeautifulSoup(bbc_request.text, \"html.parser\")\n raw_article = soup.find_all('div', {'class': \"bbc-1vo75s2-TextGridItem e19k1v2h0\"})[0].find_all(text=True, recursive=True)\n\n title = raw_article[0]\n description = raw_article[1]\n publish_time = raw_article[2]\n href = soup.find_all('div', {'class': 'bbc-1vo75s2-TextGridItem e19k1v2h0'})[0].find('a', {'class': 'bbc-11m194t-Link evnt13t0'})['href']\n link = f' https://www.bbc.com/{href}'\n article = f\"\"\"⚠️ <b>Тема</b>: {title}\n \\n📌 <b>Короткий опис</b>: {description}\n \\n🕒 <b>Опубліковано</b>: {publish_time}\n \\n➡️ <b>Повний текст</b>: {link}\"\"\"\n return article","def parse_news(self, response):\n \n loader = NewsLoader(item=NewsItem(), response=response)\n loader.add_xpath('title', '//header//h1/text()')\n author = ''.join(response.xpath('//span[@class=\"byline\"]').extract())\n author = remove_tags(author).replace(\"by\", '').replace(' and ', ', ')\n loader.add_value('author', author)\n timestamp = response.xpath('//meta[@name=\"DC.date.issued\"][1]/@content').extract()[0]\n timestamp = du.normalize_timestamp(timestamp)\n loader.add_value('date', timestamp.split(' ')[0])\n loader.add_value('time', timestamp.split(' ')[1])\n list_of_contents = response.xpath(\n '//div[@id=\"storytext\"]/*[not(@class=\"cnnplayer\") and '\n 'not(@class=\"storytimestamp\")]').extract()\n content = ' '.join(list_of_contents)\n loader.add_value('content', content)\n loader.add_xpath('tags', '//meta[@name=\"keywords\"]/@content')\n return loader.load_item()","def get_news(rss_feed):\r\n\r\n class _CurrentData(object):\r\n \"\"\"Class holding a set of current attributes.\"\"\"\r\n item = None\r\n text = None\r\n\r\n def _start_element_handler(name, attrs):\r\n \"\"\"Handle XML start-elements.\"\"\"\r\n if name == 'item':\r\n # Allocate a new item.\r\n current.item = NewsItem()\r\n\r\n def _end_element_handler(name):\r\n \"\"\"Handle XML end-elements.\"\"\"\r\n if name == 'item':\r\n news_items.append(current.item)\r\n elif name in ('title', 'description', 'link', 'category'):\r\n try:\r\n setattr(current.item, name, current.text)\r\n except AttributeError:\r\n # The parser has run into a non-news item.\r\n pass\r\n\r\n def _char_data_handler(data):\r\n \"\"\"Handle XML element character data.\"\"\"\r\n current.text = data\r\n\r\n news_items = list()\r\n current = _CurrentData()\r\n\r\n parser = expat.ParserCreate()\r\n parser.StartElementHandler = _start_element_handler\r\n parser.EndElementHandler = _end_element_handler\r\n parser.CharacterDataHandler = _char_data_handler\r\n\r\n news_handle = urllib2.urlopen(rss_feed)\r\n xml_data = news_handle.read()\r\n \r\n parser.Parse(xml_data)\r\n\r\n return news_items","def RSS2format(inputfile):\n print \"START: FEED GENERATOR[ITEM OBJECT CREATOR]: \", time.time()\n xmldocument = parse(inputfile)\n feed_title = \"\"\n try:\n feed_title = xmldocument.getElementsByTagName('dc:title')[0].firstChild.data\n except IndexError as details:\n print \"Handling IndexError: \", details\n feed_title = \"Handling IndexError...\"\n except AttributeError as details:\n print \"Handling AttributeError: \", details\n feed_title = \"Handling AttributeError...\"\n # only get first 100 characters.. RSS\n feed_description = \"\"\n try:\n feed_description = xmldocument.getElementsByTagName('dc:description')[0].firstChild.data[:100]\n except IndexError as details:\n print \"Handling IndexError: \"\n feed_description = \"Handling IndexError\"\n except AttributeError as details:\n\tfeed_description = \"Handling AttributeError\"\n feed_link = xmldocument.getElementsByTagName('identifier')[0].firstChild.data # get header identifier for link value\n feed_pubDate = xmldocument.getElementsByTagName('datestamp')[0].firstChild.data # get header datestamp for pubDate value\n feed_guid = xmldocument.getElementsByTagName('identifier')[0].firstChild.data # get header identifier for guid value\\\n # return a PyRSS2Gen object\n return PyRSS2Gen.RSSItem(\n title = feed_title,\n link = feed_link,\n description = feed_description,\n guid = feed_guid,\n pubDate = datetime.strptime(feed_pubDate.replace(\"T\", \" \").replace(\"Z\", \"\"), '%Y-%m-%d %H:%M:%S')\n )","def news_handle(news_json):\n #Sets all the variables to global space to be used later.\n global title_1, title_2, author_1, author_2, source_1, source_2, url_1_final, url_2_final\n #Handles data from API and assigns data to variables.\n news_data = news_json['articles']\n article_1 = news_data[0]\n article_2 = news_data[1]\n title_1 = article_1['title']\n author_1 = article_1['author']\n source_1 = article_1['source']['name']\n url_1 = article_1['url']\n url_1_final = '<a href=\"{}\">Read More</a>'.format(url_1)\n title_2 = article_2['title']\n author_2 = article_2['author']\n source_2 = article_2['source']['name']\n url_2 = article_2['url']\n url_2_final = '<a href=\"{}\">Read More</a>'.format(url_2)\n return","def analyse_news_article(url):\n if \"bbc\" in url:\n outlet = \"BBC\"\n parser = news_parser.BBC(\"https://www.bbc.co.uk/news\")\n elif \"dailymail\" in url:\n outlet = \"DailyMail\"\n parser = news_parser.DailyMail(\"https://www.dailymail.co.uk\")\n elif \"theguardian\" in url:\n outlet = \"Guardian\"\n parser = news_parser.Guardian(\"https://www.theguardian.com\")\n elif \"metro\" in url:\n outlet = \"Metro\"\n parser = news_parser.Metro(\"https://www.metro.co.uk\")\n elif \"mirror\" in url:\n outlet = \"Mirror\"\n parser = news_parser.Mirror(\"https://www.mirror.co.uk\")\n elif \"reuters\" in url:\n outlet = \"Reuters\"\n parser = news_parser.Reuters(\"https://uk.reuters.com\")\n elif \"thesun\" in url:\n outlet = \"Sun\"\n parser = news_parser.Sun(\"https://www.thesun.co.uk\")\n elif \"independent\" in url:\n outlet = \"Independent\"\n parser = news_parser.Independent(\"https://www.independent.co.uk\")\n else:\n outlet = \"BBC\"\n parser = news_parser.BBC(\"https://www.bbc.co.uk/news\")\n \n article_data = parser.get_article(url)\n article = parser.parse(article_data)\n response = {'title': article['title'], 'content': article['content'], 'url': article['url'], 'image_url':article['metadata']['meta_img']} \n return response, outlet","def analyze_news(self, soup_news) -> list:\n logging.info(\"analyzing news collected\")\n analyzed_news = []\n for news in soup_news:\n main_column = news.find(\"div\", {\"class\": \"column size-3-4 column-main\"})\n tag_list = main_column.find_all(\"div\", {\"class\": re.compile(r\"etiqueta etiqueta-[0-9]\")})\n fake = self.__is_fake(tag_list)\n if fake:\n analyzed_news.append(main_column)\n\n return analyzed_news","def save_news(self, obj_list: List[Tag]) -> str:\n for obj in obj_list:\n header = obj.text.strip()\n if not header:\n if 'alt' in obj.__dict__:\n header = obj.get('alt').strip()\n # other if else\n news = News(\n origin=self.get_self_name(),\n header=header\n )\n news.save()\n return f'Data from service {self.get_self_name()} loaded successfully.'","async def news(self):\n url = f\"https://newsapi.org/v2/top-headlines?country=nz&apiKey={self.bot.news_api_key}\"\n async with ClientSession() as session:\n async with session.get(url) as response:\n r = await response.json()\n firstArticle = r[\"articles\"][0]\n nSource = firstArticle[\"source\"][\"name\"]\n nTitle = firstArticle[\"title\"]\n nTimestamp = firstArticle[\"publishedAt\"]\n embed = discord.Embed(\n title=f\"News Title: {nTitle}\", description=f\"News Source: {nSource}\"\n )\n embed.add_field(name=\"News Content\", value=firstArticle[\"description\"])\n embed.set_image(url=firstArticle[\"urlToImage\"])\n embed.set_footer(text=f\"News Timestamp: {nTimestamp}\")\n\n channel = self.bot.get_channel(self.bot.main_channel_id)\n await channel.send(embed=embed)","def update_news_to_model(self):\n internet = 1\n news_page_url_base = 'https://news.mydrivers.com/update/'\n url_to_get_list = []\n if self.dates_to_get[0] == 'Date entered error':\n self.show_in_browser.emit(f'Date entered error: {self.dates_to_get[1]}')\n self.show_in_browser.emit('Please check the entered date.')\n self.thread.quit()\n return\n for date_to_get in self.dates_to_get:\n if pd.to_datetime(date_to_get).date() > (datetime.date.today() + datetime.timedelta(days=1)):\n break\n url_to_get = news_page_url_base + date_to_get + '_1.htm'\n url_to_get_list.append(url_to_get)\n if url_to_get_list == []:\n self.show_in_browser.emit(f'Date entered error: {self.dates_to_get}')\n self.show_in_browser.emit('Please check the entered date.')\n self.thread.quit()\n return\n\n def get_url_content(url_to_get):\n try:\n r = requests.get(url_to_get)\n except BaseException as e:\n print('Cannot establish Internet connection with server.\\n', e)\n self.show_in_browser.emit('Cannot establish Internet connection with server.\\n')\n self.show_in_browser.emit(str(e))\n nonlocal internet\n internet = 0\n return\n status_code = r.status_code\n if status_code == 200:\n bs = BeautifulSoup(r.content, 'html.parser')\n news_pages_list = bs.select('#newsleft > div > a')\n news_summary_list = bs.select('#newsleft > li')\n else:\n news_pages_list = []\n news_summary_list = []\n return status_code, news_pages_list, news_summary_list\n\n def extract_news_pages(news_pages_list: list) -> list:\n news_page_url_list = []\n for item in news_pages_list:\n news_page_url_tail = item.get('href')\n if item.text.isnumeric() and news_page_url_tail:\n news_page_url = news_page_url_base + news_page_url_tail\n news_page_url_list.append(news_page_url)\n return news_page_url_list\n\n def extract_news_summary(news_summary_list: list):\n for item in news_summary_list:\n news_title = item.find('h3').find('a').text\n news_link = 'https:' + item.find('h3').find('a').get('href')\n news_author = item.find(class_=\"newstiao4\").text\n news_datetime_text = item.find(class_=\"news_plun hui2\") \\\n .find('li').text\n match = re.search('\\d{4}-\\d{2}-\\d{2} \\d{2}:\\d{2}',\n news_datetime_text)\n news_datetime = pd.to_datetime(match.group())\n # Naive Bayes classifier\n seg_list = jieba.cut(news_title)\n p_news_like_product = 1 # p_1*p_2*...*p_n\n p_news_like_1_product = 1 # (1-p_1)*(1-p_2)*...*(1-p_n)\n p_news_nolike_product = 1\n p_news_nolike_1_product = 1\n for word in seg_list:\n sql_query = f\"SELECT * FROM words WHERE word = '{word}';\"\n rows = self.conn.execute(sql_query).fetchall()\n if rows == []:\n p_word_like = 0.4\n p_word_nolike = 0.4\n else:\n p_word_like = rows[0][1] / rows[0][3]\n p_word_nolike = rows[0][2] / rows[0][3]\n p_news_like_product = p_news_like_product * p_word_like\n p_news_like_1_product = p_news_like_1_product * (1 - p_word_like)\n p_news_nolike_product = p_news_nolike_product * p_word_nolike\n p_news_nolike_1_product = p_news_nolike_1_product * (1 - p_word_nolike)\n p_news_like = 100 * p_news_like_product * self.p_like / (\n p_news_like_product * self.p_like + p_news_like_1_product * (1 - self.p_like))\n p_news_nolike = 100 * p_news_nolike_product * self.p_nolike / (\n p_news_nolike_product * self.p_nolike + p_news_nolike_1_product * (1 - self.p_nolike))\n p_news_like = round(p_news_like, 2)\n p_news_nolike = round(p_news_nolike, 2)\n\n self.news_df = self.news_df.append(\n pd.DataFrame([[news_datetime,\n news_title,\n news_link,\n news_author,\n p_news_like,\n p_news_nolike]],\n columns=self.news_df.columns),\n ignore_index=True)\n\n self.news_df = pd.DataFrame({'datetime': [], 'title': [], 'link': [],\n 'author': [], 'like': [], 'no_like': []})\n\n for url_to_get in url_to_get_list:\n try:\n status_code, news_pages_list, news_summary_list = \\\n get_url_content(url_to_get)\n except:\n break\n if status_code == 200:\n print(url_to_get, ' loaded.')\n self.show_in_browser.emit(url_to_get + ' loaded.')\n extract_news_summary(news_summary_list)\n else:\n print(url_to_get, 'is not available. Status Code:', status_code)\n self.show_in_browser.emit(url_to_get + ' is not available. Status Code: ' + str(status_code))\n continue\n\n for news_page_url in extract_news_pages(news_pages_list):\n status_code, news_pages_list, news_summary_list = \\\n get_url_content(news_page_url)\n if status_code == 200:\n extract_news_summary(news_summary_list)\n print(news_page_url, ' loaded.')\n self.show_in_browser.emit(news_page_url + ' loaded.')\n else:\n print(url_to_get, 'is not available. Status Code:',\n status_code)\n self.show_in_browser.emit(url_to_get + ' is not available. Status Code: ' + str(status_code))\n continue\n\n if internet == 0:\n self.thread.quit()\n return\n\n if (pd.to_datetime(self.dates_to_get[0]).date() == self.news_latest_datetime.date()):\n self.news_df.drop(self.news_df[self.news_df.datetime < self.news_latest_datetime].index,\n inplace=True)\n try:\n for link in self.news_latest_link:\n self.news_df.drop(self.news_df[self.news_df.link == link].index, inplace=True)\n except BaseException as e:\n print(e)\n else:\n sql_query = \"\"\"SELECT datetime FROM news_unread\"\"\"\n tmp_news_datetime = pd.read_sql_query(sql_query, self.conn,\n parse_dates='datetime')\n try: # Raise error when news_read is empty.\n sql_query = \"\"\"SELECT datetime FROM news_read\"\"\"\n tmp2_news_datetime = pd.read_sql_query(sql_query, self.conn,\n parse_dates='datetime')\n tmp_news_datetime = tmp_news_datetime.append(tmp2_news_datetime, ignore_index=True)\n del tmp2_news_datetime\n except:\n pass\n tmp_news_datetime.datetime = tmp_news_datetime.datetime.apply(lambda x: x.date())\n for date in self.dates_to_get:\n date = pd.to_datetime(date).date()\n if (tmp_news_datetime.datetime == date).any():\n sql_query = \"\"\"SELECT link FROM news_unread\"\"\"\n links = pd.read_sql_query(sql_query, self.conn)\n try:\n sql_query = \"\"\"SELECT link FROM news_read\"\"\"\n links2 = pd.read_sql_query(sql_query, self.conn)\n links = links.append(links2, ignore_index=True)\n except:\n pass\n links = links.link.tolist()\n self.news_df = self.news_df.drop(self.news_df[self.news_df.link.isin(links)].index)\n break\n\n self.news_df.sort_values('datetime', inplace=True)\n self.news_df.to_sql('news_unread', self.conn, index=False,\n if_exists='append')\n sql_query = \"\"\"\n SELECT * FROM news_unread ORDER BY datetime ASC\n \"\"\"\n self._data = pd.read_sql_query(sql_query, self.conn,\n parse_dates='datetime')\n self._data['datetime'] = self._data['datetime'].dt.strftime('%m-%d %H:%M')\n self.update_finished.emit()","def __folha(soup):\n news = []\n\n items = soup.find('li', class_='c-most-popular__item')\n\n for item in items:\n div = item.find('div', class_='c-most-popular__content')\n link = div.a['href']\n title = div.a.string\n news.append(\n dict(title=title, link=replace_original_link_with_outline_call(link)))\n\n return news","def json2rdf(article, g):\n a_news_item = load_article_into_newsitem_class(article)\n g = rdfize_news_item(a_news_item, g)\n return g","def guidebook_news_feed(request):\n current_site = Site.objects.get_current()\n\n feed_title = 'DjangoCon US News Updates'\n feed_description = 'The latest updates and additions for DjangoCon US.'\n feed_mimetype = 'application/rss+xml'\n feed_template = 'pinax/blog/rss_feed.xml'\n\n blog_url = 'http://%s%s' % (current_site.domain, reverse('blog'))\n # feed_url = 'http://%s%s' % (current_site.domain, reverse(url_name, kwargs=kwargs))\n\n posts = Post.objects.published().exclude(title__endswith='Sponsor')\\\n .order_by('-published')\n\n if posts:\n feed_updated = posts[0].updated\n else:\n feed_updated = datetime(2009, 8, 1, 0, 0, 0)\n\n feed = render_to_string(feed_template, {\n # 'feed_id': feed_url,\n 'feed_title': feed_title,\n 'feed_description': feed_description,\n 'blog_url': blog_url,\n # 'feed_url': feed_url,\n 'feed_updated': feed_updated,\n 'entries': posts,\n 'current_site': current_site,\n })\n\n return HttpResponse(feed, content_type=feed_mimetype)","async def news(self, ctx, typex):\n if typex in options['news']['sport_type']:\n output = news_output(get_sport_data())\n elif typex in options['news']['world_type']:\n output = news_output(get_news_data(\"https://www.novinky.cz/zahranicni\"))\n elif typex in options['news']['home_type']:\n output = news_output(get_news_data(\"https://www.novinky.cz/domaci\"))\n else:\n await ctx.send(\"Zadán nesprávný typ novinek\")\n return\n\n for embed in output:\n await ctx.send(embed=embed)","def select_news(self):\n data = self.soup.findAll('item')\n for item in data:\n news_data = dict()\n for tag in ['title', 'link']:\n news_data[tag] = item.find(tag).get_text()\n\n news_data['pubDate'] = parse(item.find('pubDate').get_text())\n media = item.find('media:content')\n\n if media:\n news_data['media'] = media.get('url')\n else:\n news_data['media'] = None\n\n yield news_data","def get_news(self):\n if self.api_key_entry.get() == \"\":\n return None\n api = nac(api_key=self.api_key_entry.get())\n now = datetime.datetime.utcnow()\n two_weeks = (now-datetime.timedelta(days=14))\n #today = now.strftime()\n query = \"\"\n for cat in self.sorted_categories():\n query += f\"{cat},\"\n search = api.get_top_headlines(q=query,\n sources=\"bbc-news,the-verge\",\n language=\"en\")\n news = \"\"\n for article in search[\"articles\"]:\n news += f\"{search['articles'][article]['title']}\\n\"\n self.news_box.delete('1.0', tk.END)\n self.news_box.insert('1.0', news)","def news(self):\n\n # Get articles with search term, if available, from each News API source\n news_api_articles = pd.DataFrame()\n\n q = urllib.parse.quote(\" OR \".join(self.search_terms), safe='')\n\n response = requests.get(\"https://newsapi.org/v2/everything?q=\" + q + \"&from=\" + datetime.now().strftime(\n \"%Y-%m-%d\") + \"&sortBy=popularity&pageSize=100&apiKey=\" + self.__news_api_key)\n\n if response.status_code == 200:\n data = json.loads(response.text)\n\n source_articles = []\n\n for article in data['articles']:\n source_articles.append([article['title'],\n article['description'],\n article['url'],\n article['publishedAt']])\n\n source_articles = pd.DataFrame(source_articles, columns=['title', 'description', 'url', 'publishedAt'])\n news_api_articles = pd.concat([news_api_articles, source_articles])\n\n news_api_articles = news_api_articles.reset_index(drop='True')\n\n news_api_articles['publishedAt'] = news_api_articles['publishedAt'].apply(pd.to_datetime)\n\n news_api_articles = news_api_articles.fillna(' ')\n\n term_in_title = news_api_articles['title'].apply(self.any_term)\n\n news_api_articles = news_api_articles[term_in_title]\n\n if (len(news_api_articles) > 10):\n news_api_articles = news_api_articles[0:10]\n\n else:\n print(\"News API failed to return any items\")\n\n # Create shortened links using bitly if access token is provided\n if self.__bitly_access_token != '':\n\n bitly_urls = []\n\n for index, article in news_api_articles.iterrows():\n url = article['url']\n bitly_response = requests.get(\"https://api-ssl.bitly.com/v3/shorten\",\n params={'longUrl': url, 'access_token': self.__bitly_access_token})\n\n if bitly_response.status_code == 200:\n data = json.loads(bitly_response.text)\n bitly_urls.append(data['data']['url'])\n\n news_api_articles['url'] = bitly_urls\n\n # Store final list to TwitterBot object\n self.list = news_api_articles\n\n return","def feed2fields(file):\r\n import feedparser\r\n d = feedparser.parse(file)\r\n for entry in d.entries:\r\n date = (time.strftime(\"%Y-%m-%d %H:%M\", entry.updated_parsed)\r\n if hasattr(entry, \"updated_parsed\") else None)\r\n author = entry.author if hasattr(entry, \"author\") else None\r\n tags = [e['term'] for e in entry.tags] if hasattr(entry, \"tags\") else None\r\n\r\n slug = slugify(entry.title)\r\n kind = 'article'\r\n yield (entry.title, entry.description, slug, date, author, [], tags,\r\n kind, \"html\")","def convert_stories(self, language_code):\n self.logger.info(\"Converting stories...\")\n old_stories, current, total = self.sql.read_table_with_total(self.working_original, \"stories\")\n for old_story in old_stories:\n new_story = {\n 'id': old_story['sid'],\n 'title': key_find('title', old_story, '').strip(),\n 'summary': normalize(old_story['summary']),\n 'notes': key_find('storynotes', old_story, '').strip(),\n 'date': str(old_story['date']),\n 'updated': str(old_story['updated']),\n 'language_code': language_code\n }\n\n self.logger.debug(f\"Converting story metadata for '{new_story['title']}'\")\n query = f\"\"\"\n INSERT INTO stories (id, title, summary, notes, date, updated, language_code)\n VALUES {new_story['id'], new_story['title'], new_story['summary'],\n new_story['notes'], new_story['date'], new_story['updated'], new_story['language_code']};\n \"\"\"\n self.sql.execute(self.working_open_doors, query)\n\n self.logger.debug(f\" tags...\")\n tags = self._convert_story_tags(old_story)\n self._convert_tags_join(new_story, tags)\n\n self.logger.debug(f\" authors...\")\n self._convert_author_join(new_story, old_story['uid'])\n coauthors = []\n if key_find('coauthors', old_story):\n for authorid in old_story['coauthors'].split(\",\"):\n coauthors.append(authorid.strip())\n for coauthor in coauthors:\n self._convert_author_join(new_story, coauthor)\n\n current = print_progress(current, total, \"stories converted\")\n return self.sql.execute_and_fetchall(self.working_open_doors, \"SELECT * FROM stories\")","def parse_google_news_response(news_results, i=0):\n\n # get the value of the key 'status' from the response dictionary\n results_status = news_results['status']\n print(f'results_status: {results_status}')\n\n # get the value of the key 'totalResults' from the response dictionary\n num_results = news_results['totalResults']\n print(f'num_results: {num_results}')\n\n #save the response from the dictionary\n title = news_results['articles'][i]['title']\n source = news_results['articles'][i]['source']['name']\n published_time = news_results['articles'][i]['publishedAt']\n description = news_results['articles'][i]['description']\n article_url = news_results['articles'][i]['url']\n image_url = news_results['articles'][i]['urlToImage']\n return (title, source, published_time, description, article_url, image_url)","def newsfeed_en(request):\n article_list = Article.objects.order_by('published_date')\n context = {'article_list': article_list}\n return render(request, 'sacms/newsfeed_en.html', context)","def _transform_article(self, article):\n\n if \"_embedded\" in article:\n article[\"image\"] = article[\"_embedded\"].get(\n \"wp:featuredmedia\", [None]\n )[0]\n article[\"author\"] = article[\"_embedded\"].get(\"author\", [None])[0]\n\n if \"display_category\" not in article:\n categories = article[\"_embedded\"].get(\"wp:term\", [[]])[0]\n\n if categories:\n article[\"display_category\"] = categories[-1]\n\n if (\n \"wp:term\" in article[\"_embedded\"]\n and article[\"_embedded\"][\"wp:term\"][3]\n ):\n article[\"group\"] = article[\"_embedded\"][\"wp:term\"][3][0]\n\n if \"date_gmt\" in article:\n article_gmt = article[\"date_gmt\"]\n article_date = datetime.strptime(article_gmt, \"%Y-%m-%dT%H:%M:%S\")\n article[\"date\"] = article_date.strftime(\"%-d %B %Y\")\n\n if \"excerpt\" in article and \"rendered\" in article[\"excerpt\"]:\n article[\"excerpt\"][\"raw\"] = self._strip_excerpt(\n article[\"excerpt\"][\"rendered\"]\n )[:340]\n\n # If the excerpt doesn't end before 340 characters, add ellipsis\n raw_article = article[\"excerpt\"][\"raw\"]\n # split at the last 3 characters\n raw_article_start = raw_article[:-3]\n raw_article_end = raw_article[-3:]\n # for the last 3 characters replace any part of […]\n raw_article_end = raw_article_end.replace(\"[\", \"\")\n raw_article_end = raw_article_end.replace(\"…\", \"\")\n raw_article_end = raw_article_end.replace(\"]\", \"\")\n # join it back up\n article[\"excerpt\"][\"raw\"] = \"\".join(\n [raw_article_start, raw_article_end, \" […]\"]\n )\n\n if (\n article.get(\"_start_month\")\n and article.get(\"_start_year\")\n and article.get(\"_start_day\")\n ):\n start_month_name = self._get_month_name(\n int(article[\"_start_month\"])\n )\n article[\"start_date\"] = \"{} {} {}\".format(\n article[\"_start_day\"], start_month_name, article[\"_start_year\"]\n )\n\n if (\n article.get(\"_end_month\")\n and article.get(\"_end_year\")\n and article.get(\"_end_day\")\n ):\n end_month_name = self._get_month_name(int(article[\"_end_month\"]))\n article[\"end_date\"] = \"{} {} {}\".format(\n article[\"_end_day\"], end_month_name, article[\"_end_year\"]\n )\n\n if self.transform_links:\n # replace url on the blog article page\n article[\"content\"][\"rendered\"] = self._replace_url(\n article[\"content\"][\"rendered\"]\n )\n\n # replace url from the image thumbnail\n if (\n article[\"image\"] is not None\n and \"source_url\" in article[\"image\"]\n ):\n article[\"image\"][\"source_url\"] = self._replace_url(\n article[\"image\"][\"source_url\"]\n )\n\n return article","def __get_local_g1_news(soup):\n news = []\n anchors = soup.find('ul', class_='highlights').find_all('a')\n\n for a in anchors:\n title = a.span.string.strip()\n link = a['href']\n news.append(dict(title=title, link=link))\n return news","def process_content(self, channel) -> dict:\n\n self.print_if_verbose(f\"Method 'process_content' is working:\")\n\n if self.limit is None or self.limit >= self.news_amount:\n self.limit = self.news_amount\n\n rss_feed = {}\n rss_feed[\"Feed\"] = channel.findtext('title')\n rss_feed[\"Description\"] = channel.findtext('description')\n rss_feed[\"Link\"] = channel.findtext('link')\n rss_feed[\"Language\"] = channel.findtext('language')\n rss_feed[\"News\"] = []\n\n append_news_to_rss_feed = 0\n\n self.print_if_verbose(f\"Adding data to the work dict 'rss_feed'...\")\n\n POSSIBLE_IMAGE_TAGS = (\"content\", \"thumbnail\", \"image\")\n POSSIBLE_IMAGE_ATTR = (\"url\", \"href\")\n\n for item in channel.iterfind(\"item\"):\n child_news = {}\n child_news[\"Title\"] = item.findtext(\"title\")\n child_news[\"Link\"] = item.findtext(\"link\")\n child_news[\"PubDate\"] = self.get_formatted_date(item.findtext(\"pubDate\"))\n child_news[\"Source\"] = item.findtext(\"source\")\n child_news[\"ImageLink\"] = None\n child_news[\"ImageCacheName\"] = None\n\n for tag in POSSIBLE_IMAGE_TAGS:\n for item_field in item:\n if tag in item_field.tag:\n for attr in POSSIBLE_IMAGE_ATTR:\n if attr in item_field.attrib:\n child_news[\"ImageLink\"] = item_field.attrib[attr]\n child_news[\"ImageCacheName\"] = \\\n f\"{''.join(char for char in child_news['Link'] if char.isalnum())}.jpg\"\n break\n if child_news[\"ImageLink\"]:\n break\n if child_news[\"ImageLink\"]:\n break\n\n rss_feed[\"News\"].append(child_news)\n\n append_news_to_rss_feed += 1\n if append_news_to_rss_feed == self.limit:\n break\n\n self.print_if_verbose(\n f\"{append_news_to_rss_feed} news were added. \\n\"\n f\"Method 'process_content' is finished. \\n\"\n )\n\n return rss_feed","def adapt_tweet(feedpost):\n tweet = feedpost['title']\n for action in (_make_links, _clean_name, urlize):\n tweet = action(tweet)\n feedpost['title'] = _get_tweet_number(feedpost['link'])\n feedpost['body'] = u'<p>%s</p>' % tweet\n return feedpost","def wiki_to_kindle(article_urls, book_title, book_subtitle, \\\n kindle_email, from_email, smtp_server, smtp_username, smtp_password, \\\n convert_to_mobi=True):\n epub_filename = create_a_book(article_urls, book_title, book_subtitle)\n email_filename = epub_filename\n if convert_to_mobi:\n email_filename = convert_x_to_mobi(epub_filename)\n email_subject = book_title\n if len(book_subtitle):\n email_subject += u' - {0}'.format(book_subtitle)\n email_ebook(email_filename, from_email, kindle_email, email_subject, \\\n smtp_server, smtp_username, smtp_password)","def __repr__(self):\r\n\r\n return f\"<News: id = {self.news_id}, title = {self.title} summary = {self.summary}>\"","def parse(self, article: BeautifulSoup):\n # Need to find content in a different manner for each of the different sitetypes.\n # Read this as studio, video, article etc.\n # Assume that only articles ask for this functionality\n\n title = self.get_title(article)\n sub_title = self.get_sub_title(article)\n words = self.get_words(article)\n journalists = self.get_journalist(article)\n images = self.get_images(article)\n subscription = self.get_subscription(article)\n content_list = self.get_content(article)\n\n if not title:\n title = self.headline.revisions[0].title\n\n revision = Revision(timestamp=datetime.datetime.now(pytz.timezone(\"Europe/Oslo\")), title=title, sub_title=sub_title, words=words, subscription=subscription)\n\n article = Article(news_site=self.news_site, headline=self.headline)\n\n return revision, article, journalists, images, content_list","def _from_text_to_crf(self, message, entities=None):\n crf_format = []\n tokens = self.nlp(message[\"text\"]) \n for i, token in enumerate(tokens):\n pattern = {}\n entity = entities[i] if entities else \"N/A\"\n tag = None\n custom_ner_features = None\n crf_format.append((token.text, tag, entity, pattern, custom_ner_features))\n return crf_format","def get_feed(self):\n\t\turl=\"http://news.google.com/news?ned=%s&topic=%s&output=rss\"\n\t\tlinks=[{\"ned\":\"us\", \"type\":\"h\"},\n\t\t\t {\"ned\":\"us\", \"type\":\"w\"},\n\t\t\t {\"ned\":\"us\", \"type\":\"n\"},\n\t\t\t {\"ned\":\"us\", \"type\":\"n\"},\n\t\t\t {\"ned\":\"us\", \"type\":\"n\"},\n\t\t\t {\"ned\":\"us\", \"type\":\"n\"},\n\t\t\t {\"ned\":\"us\", \"type\":\"nz\"},\n\t\t\t {\"ned\":\"us\", \"type\":\"sa\"},\n\t\t\t {\"ned\":\"us\", \"type\":\"n\"},\n\t\t\t {\"ned\":\"us\", \"type\":\"n\"},\n\t\t\t {\"ned\":\"us\", \"type\":\"b\"},\n\t\t\t {\"ned\":\"us\", \"type\":\"t\"},\n\t\t\t {\"ned\":\"us\", \"type\":\"m\"},\n\t\t\t {\"ned\":\"us\", \"type\":\"s\"},\n\t\t\t {\"ned\":\"us\", \"type\":\"e\"},\n\t\t\t ]\n\t\tfeed = links[self.get_input()]\n\t\treturn url%(feed[\"ned\"],feed[\"type\"])","def __local_ms(soup):\n return __get_local_g1_news(soup)","def __local_ms(soup):\n return __get_local_g1_news(soup)","def __veja(soup):\n news = []\n\n links = soup.find(\n 'section', class_='block most-read dark').find_all('a', class_='card')\n\n for link in links:\n title = link.h2.text\n news.append(dict(title=title,\n link=replace_original_link_with_outline_call(link['href'])))\n return news","def _fetch_news(self, docid):\n detail_link = Fetch163.detail_link % docid\n try:\n resp = urllib2.urlopen(detail_link)\n except urllib2.URLError as e:\n urllib_error(e)\n else:\n try:\n doc_json = json.load(resp)\n content = doc_json[docid]\n title = content['title']\n except KeyError:\n print \"Error while Fetching docid: %s\" % docid\n except ValueError:\n print \"No JSON obj could be decoded\", resp.read()\n else:\n if self._filter_test(title):\n #print title\n c_num = content['replyCount']\n ptime = content['ptime']\n img_list = content['img']\n if img_list:\n list_pic = img_list[0]['src']\n else:\n list_pic = ''\n body = content['body']\n abstract = self._retrieve_abstract(body)\n body = convert_163(body, img_list)\n return abstract, body, c_num, ptime, list_pic\n return '', '', '', '', list_pic","def prepare_message(self, message, channel):\n\n if 'from' not in message:\n message['from'] = {\n 'id': channel.query,\n 'name': channel.name,\n }\n l = 'https://www.facebook.com/%s/posts/%s' \\\n % (message['from']['id'], message['id'].split('_')[1])\n else:\n l = 'http://www.facebook.com/permalink.php?id=%s&v=wall&story_fbid=%s' \\\n % (message['from']['id'], message['id'].split('_')[1])\n\n return PostParser(\n uid=message['id'],\n author=message['from']['name'],\n author_uid=message['from']['id'],\n avatar=message.get('picture', None),\n content=message.get('message', '') or message.get(\n 'description', ''),\n date=message['created_time'],\n image=message.get('picture', None),\n link=l\n )","def __local_es(soup):\n return __get_local_g1_news(soup)","def news(self) -> List[News]:\n return self._news","def fetch_news(n):\n\n # This is the list we will use the pass back the news information.\n data = []\n\n # Get news stories from the MEN RSS feed.\n response = feedparser.parse('https://www.manchestereveningnews.co.uk/?service=rss')\n\n # Loop through the news items, and the pull out the data we need.\n for news in response.entries[:n]:\n data.append({\n 'headline': news.title,\n 'content': news.description,\n })\n\n return data","def get_news() -> None:\r\n api_key = get_api_key()\r\n country = 'gb'\r\n url = 'https://newsapi.org/v2/top-headlines?country={}&apiKey={}' \\\r\n .format(country, api_key)\r\n new_news = requests.get(url).json()\r\n with open('news.json', 'r') as news_file:\r\n try:\r\n old_news = json.load(news_file)\r\n except Exception as error:\r\n log_warning(error)\r\n # Checks if the news is new or the same as the news already stored\r\n # in news.json.\r\n for i in range(5):\r\n if old_news['articles'][i] != new_news['articles'][i]:\r\n news_notification = ({'timestamp': \\\r\n time.strftime('%H:%M:%S'),\r\n 'type': 'News',\r\n 'title': new_news \\\r\n ['articles'][i]['title'],\r\n 'description': ''})\r\n news_log = ({'timestamp': time.strftime('%H:%M:%S'),\r\n 'type': 'news',\r\n 'description': 'New news articles' \\\r\n + new_news['articles'][i]['title'],\r\n 'error': ''})\r\n new_notification(news_notification)\r\n log_info(news_log)\r\n # RuntimeError caused when text to speech is already\r\n # currently playing something else.\r\n try:\r\n tts('New news story.' \\\r\n + new_news['articles'][i]['title'])\r\n except RuntimeError:\r\n log_error(RuntimeError)\r\n\r\n with open('news.json', 'w') as news_file:\r\n json.dump(new_news, news_file, indent=2)\r\n # Start the timer to run this function every 60 seconds.\r\n Timer(60, get_news).start()","def _convert_feed(self, json, since):\n\n items = []\n if json and json['response'].has_key('checkins'):\n for checkin in json['response']['checkins']['items']:\n created = datetime.fromtimestamp(checkin['createdAt'])\n\n if created.date() >= since:\n item = ServiceItem()\n item.location = {}\n item.link_back = 'http://foursquare.com/venue/%s' % (checkin['venue']['id'],)\n item.title = checkin['venue']['name']\n\n if checkin.has_key('shout') and checkin['shout']:\n item.body = checkin['shout']\n else:\n if len(checkin['venue']['categories']) > 0 and checkin['venue']['location'].has_key('city'):\n item.body = \"A %s in %s\" % (checkin['venue']['categories'][0]['name'], checkin['venue']['location']['city'])\n elif checkin['venue'].has_key('city'):\n item.body = \"In %s\" % (checkin['venue']['location']['city'])\n else:\n item.body = \"%s\" % (checkin['venue']['name'])\n\n if checkin['venue']['location'].has_key('lat') and checkin['venue']['location']['lng']:\n item.location['lat'] = checkin['venue']['location']['lat']\n item.location['long'] = checkin['venue']['location']['lng']\n\n item.created = created\n item.service = self.service\n \n if checkin.has_key('isMayor'):\n item.is_mayor = checkin['isMayor']\n else:\n pass\n\n if checkin['venue'].has_key('categories') and len(checkin['venue']['categories']) > 0:\n item.icon = checkin['venue']['categories'][0]['icon']\n item.categories = checkin['venue']['categories']\n\n items.append(item)\n del(item)\n\n return items","def article_cleanser(dirty_content):\n global id_\n\n articles_dict = {}\n source = dirty_content.find(\"title\").get_text()\n data_tuple_list = []\n\n for item in dirty_content.find_all(\"item\"):\n article_link = item.link.get_text()\n\n jj = item.pubDate.get_text().split(' ')\n article_pubdate = datetime(int(jj[3]), int(strptime(jj[2], '%b').tm_mon), int(jj[1]), int(jj[4][:2]),\n int(jj[4][3:5]),\n int(jj[4][6:])).isoformat(' ')\n\n if source == \"Sudans Post\":\n \"\"\" If the feed is for Sudans Post then it should scrap accordingly\"\"\"\n article_number = item.guid.get_text()\n guid_list = re.findall(\"(?<=p=)[0-9]{5}\", article_number)\n article_guid = int(guid_list[0] + '000')\n article_description = item.description.get_text().replace('[…]', '....')\n article_description = article_description.replace('’', \"'\")\n article_title = item.title.get_text().replace(u'\\xa0', ' ')\n article_text = item.encoded.get_text() # gives a not-navigable string\n\n \"\"\"\n So to make the article_content navigable I write it into a html file\n and retrieving it again below\n \"\"\"\n with open(\"article_content.html\", 'w') as fp:\n fp.write(article_text)\n\n with open(\"article_content.html\", \"r\") as fp:\n article_soup = BeautifulSoup(fp, \"lxml\")\n\n article_content = \"\"\n for c in article_soup.find_all('p'):\n article_content += \"\\n\" + c.get_text()\n\n image_link = article_soup.find('a')[\"href\"]\n\n categories = \"\"\n for cat in item.find_all(\"category\"):\n categories += f\"/{cat.get_text().lower()}\"\n\n elif source == \"Radio Tamazuj - Latest news\":\n \"\"\"If the feed is for Radio tamazuj, scrap accordingly \"\"\"\n\n article_title = item.title.get_text()\n article_description = item.description.get_text()\n article_guid = item.guid.get_text()[:8]\n categories = \"Not available\"\n\n def get_content_from_link(link):\n \"\"\" \n Gets the article from the link, because radio tamazuj doesn't \n post the article content on the rss feed\n \"\"\"\n\n page = requests.get(link)\n soup = BeautifulSoup(page.text, \"lxml\")\n return soup\n\n soup = get_content_from_link(article_link)\n image_link = \"https://radiotamazuj.org\" + soup.find(\"img\")[\"src\"]\n\n body = soup.select(\".body-text\")\n content = \"\"\n for i in body:\n content += i.get_text()\n article_content = content.replace(u'\\xa0', ' ')\n\n articles_dict[id_] = {\n \"id\": id_,\n \"source\": source,\n \"Title\": article_title,\n \"Link\": article_link,\n \"PubDate\": article_pubdate,\n \"guid\": article_guid,\n \"Description\": article_description,\n \"Content\": article_content,\n \"categories\": categories,\n \"image_link\": image_link,\n }\n data_tuple = (\n id_,\n source,\n article_title,\n article_link,\n article_pubdate,\n article_guid,\n article_description,\n article_content,\n categories,\n image_link,\n )\n id_ += 1 # counts number of articles\n data_tuple_list.append(data_tuple)\n return data_tuple_list","def convert_html():\n return","def news_template_flex(news_list, events, key, id, isMore, alt_text='推薦新聞'):\n news_box_components = []\n for i, news in enumerate(news_list):\n # img = \"https://cdn.pixabay.com/photo/2015/12/03/22/16/newspaper-1075795_1280.jpg\"\n date = news['date']\n date = re.sub(\"T.*Z\", \"\", date)\n url = news['url']\n\n # Add separators between news.\n if i > 0:\n news_box_components.append(SeparatorComponent(margin='lg'))\n # related_news_uri = f\"{RELATED_NEWS_LIFF_URI}?cid={news['cid']}&nid={news['nid']}\"\n\n TextComponents = []\n TextComponents.append(TextComponent(\n text=news['title'],\n wrap=True,\n size='md',\n weight='bold',\n action=URIAction(uri=url)\n ))\n TextComponents.append(TextComponent(\n text='{} - {}'.format(news['source'], date),\n wrap=True,\n size='xxs',\n color='#6c757d'\n ))\n words = []\n event_v, event_o = events.split()\n regex = \"({}|{})\".format(event_v, event_o)\n tokenized_sentence = re.split(regex, news['sentences'][1])\n tokenized_sentence = [t for t in tokenized_sentence if t != \"\"]\n tokenized_sentence = [news['sentences'][0] + \" \"] + tokenized_sentence + [\" \" + news['sentences'][2]]\n for i in tokenized_sentence:\n if i == event_v or i == event_o:\n words.append(SpanComponent(\n text=i,\n color=\"#e86d48\"\n ))\n else:\n words.append(SpanComponent(\n text=i,\n ))\n new_text_componet = TextComponent2(type=\"text\", text=\"hello, world\", contents=words, wrap=True)\n TextComponents.append(new_text_componet)\n TextComponents.append(BoxComponent(\n layout='horizontal',\n spacing='md',\n contents=[\n TextComponent(\n text='查看這則新聞',\n size='md',\n color='#6E7DAB',\n flex=1,\n action=URIAction(uri=url),\n ),\n ]\n ))\n\n news_box_components.append(\n BoxComponent(\n layout='horizontal',\n spacing='md',\n contents=[\n BoxComponent(\n layout='vertical',\n flex=2,\n spacing='sm',\n contents=TextComponents\n )\n ]\n )\n )\n if isMore:\n news_box_components.append(\n ButtonComponent(style=\"link\",\n # margin=\"xs\",\n action=MessageTemplateAction(\n label=\"more\",\n text=\"more:\" + str(id) + \":\" + key + \":\" + events\n )))\n\n bubble = BubbleContainer(\n body=BoxComponent(\n layout='vertical',\n spacing='lg',\n contents=news_box_components\n )\n )\n return FlexSendMessage(alt_text=alt_text, contents=bubble)","def create(self, validated_data):\n return News.objects.create(**validated_data)","def convert(text):\n return NewDocument.from_rst(text).format()","def convert(update, context):\n\n # remove \"/convert\" from the string\n user_string = update.message.text[8:].strip()\n send_converted_message(update, context, user_string)","def parse_details(ep_news):\n\n # instances that might be able to get from ep_news\n information = {\n 'price': None, # 价格：\n 'area': None, # 地区：*\n 'address': None, # 地址：\n 'contact': None, # 联系人：\n 'telephone': None, # 电话：*\n 'wechat': None, # 微信：\n 'qq': None, # QQ：\n }\n\n # price and location\n left = ep_news.find('div', {'class': 'l'})\n\n # loop through all left information\n for info in left.find_all('dl'):\n # get info name and value\n name, value = info.find('dt').get_text().encode('utf-8'), info.find('dd').contents[0].encode('utf-8')\n\n # match values\n if name == u'价格：'.encode('utf-8'):\n information['price'] = value if value != '面议' else None\n elif name == u'地区：'.encode('utf-8'):\n information['area'] = value\n elif name == u'地址：'.encode('utf-8'):\n information['address'] = value\n\n # contact information\n right = ep_news.find('div', {'class': 'r'})\n\n # loop through all right information\n for info in right.find_all('dl'):\n # get info name and value\n name, value = info.find('dt').get_text().encode('utf-8'), info.find('dd').contents[0].encode('utf-8')\n\n # match values\n if name == u'联系人：'.encode('utf-8'):\n information['contact'] = value\n elif name == u'电话：'.encode('utf-8'):\n information['telephone'] = value\n elif name == u'微信：'.encode('utf-8'):\n information['wechat'] = value\n elif name == u'QQ：'.encode('utf-8'):\n information['qq'] = value\n\n return information","def general_news():\n\n return general_scraper(['http://mesva.univaq.it/'])","def __cnn(soup): \n news = []\n headers = soup.find_all('h3', class_='most__read__title') \n for h3 in headers:\n title = h3.a['title']\n link = h3.a['href'] \n news.append(dict(title=title, link=link))\n \n return news","def updateNewsFeed(self):\n try:\n news, events, categories, eventCategories = self.requestData()\n for language in NEWSFEED_LANGUAGES:\n self.newsFeedModel.update(news[language], events[language], categories[language],\n eventCategories[language], language=language)\n except Exception as e:\n print(\"there was a problem while updating the news feed\")\n raise e","def newsFromList(request, new_db_id):\n MAX_NEWS = 10\n\n news_count = New.objects.count() # Pocet vsech zaznamu novinek\n news_list_all = New.objects.all().order_by(\"-date\") # Sort by date ...\n # Z celeho seznamu novinek vybereme jen 10 zacinajici pozadovanym 'new_db_id'\n news_list_part = []\n start_index = 0;\n new_db_id_num = string.atoi(new_db_id)\n for new in news_list_all:\n if new.id == new_db_id_num:\n news_list_part = news_list_all[start_index:start_index + MAX_NEWS]\n break\n start_index = start_index + 1\n\n # Vypocet prvniho ID z predchozi skupiny novinek (jedna skupina = MAX_NEWS)\n # Poradi je otocene! Posledne zadana novinka je prvni v poradi\n if (start_index + MAX_NEWS) < news_count:\n preview_start_id = start_index + MAX_NEWS\n else:\n preview_start_id = start_index\n\n # Vypocet prvniho ID z nasledujici skupiny novinek (jedna skupina = MAX_NEWS)\n # Poradi je otocene! Posledne zadana novinka je prvni v poradi\n next_start_id = start_index - MAX_NEWS # prvni ID nasledujicich novinek\n if next_start_id < 0:\n next_start_id = 0;\n\n pictureOfWeek = PhotoOfWeek.objects.last()\n context = {'news_list': news_list_part, 'news_count': news_count, 'pictureOfWeek': pictureOfWeek,\n 'start_id': start_index, 'preview_start_id': preview_start_id, 'next_start_id': next_start_id}\n return render(request, 'news/news.html', context)","def news_title_generator(keyword):\n keyword_quote = urllib.parse.quote(keyword)\n url = (\"https://news.google.com/news/rss/search/section/q/\" +\n keyword_quote + \"/\" + keyword_quote + \"?ned=jp&hl=ja&gl=JP\")\n if hasattr(ssl, '_create_unverified_context'):\n ssl._create_default_https_context = ssl._create_unverified_context\n news_dict = feedparser.parse(url)\n articles = list()\n for i, article in enumerate(news_dict.entries):\n tmp = {\n \"no\": i,\n \"title\": article.title,\n \"link\": article.link,\n \"published\": article.published\n }\n articles.append(tmp)\n title_list = [article['title'] for article in articles]\n return title_list","def save_news_in_html_file(news, path_to_html, logger):\n check_path_to_directory(path_to_html, logger)\n html_file = tags.html(title='RSS news')\n html_file.add(tags.head(tags.meta(charset='utf-8')))\n\n logger.info('Converting news to html format...')\n for article in news:\n html_factory(article, html_file)\n\n path = os.path.join(path_to_html, 'rss_news.html')\n logger.info('Creating html-file...')\n with open(path, 'w', encoding='utf-8') as file_html:\n file_html.write(str(html_file))\n logger.info('Html-file is created successfully!')\n return file_html","def rss_feed(rss_url):\n try:\n # Use feedparser to analyze given RSS feed, if it is valid RSS.\n d = feedparser.parse(rss_url)\n except:\n return \"Sorry, invalid RSS feed. Please check and try again later.\"\n \n total = len(d['entries'])\n updates = dict()\n for index, item in enumerate(d['entries']):\n # Convert publish time from ctime format to iso-time format.\n a_time = time_convert(item.published)\n # Set article url ad dictionary key, with publish date as value. \n updates[str(item.link)] = a_time \n return (total, updates)","def convert_to_pdf(self, news_list):\n self.logger.info(\"Converting news to PDF...\")\n self.prepare_storage()\n self.process_news_list_with_images(news_list)\n content = self.generate_html_template(news_list)\n pdf = io.BytesIO()\n pisa.pisaDocument(content, pdf)\n self.write_to_file(pdf.getvalue())","def __local_am(soup):\n return __get_local_g1_news(soup)","def convert(label, tags, categories, projects, view, featured):\n\n filename = f\"content/publication/{label}/index.md\"\n content = readfile(filename)\n if featured: \n content = content.replace(\"featured: false\", f'featured: true') \n if tags: \n content = content.replace(\"tags: []\", f'tags: [\"{tags}\"]') \n if categories: \n content = content.replace(\"categories: []\", f'categories: [\"{categories}\"]') \n if projects: \n content = content.replace(\"projects: []\", f'projects: [\"{projects}\"]')\n writefile(filename, content)\n if view:\n print(content)","def parse_rss(database, feed, depth=1):\n # Get the updates article count, and article urls and publish dates.\n rss_a = rss_feed(feed)\n \n # Get all (article urls, publish dates) pairs\n articles = []\n pairs = rss_a[1].items()\n for url, pubdate in pairs: \n articles += crawl_url(database, url, date=pubdate, depth=depth)\n \n return articles","def top_post(section: izi.types.one_of(('news', 'newest', 'show'))='news'):\n content = requests.get('https://news.ycombinator.com/{0}'.format(section)).content\n text = content.decode('utf-8')\n return text.split('<tr class=\\'athing\\'>')[1].split(\"<a href\")[1].split(\">\")[1].split(\"<\")[0]","def convert(self, *args):\n return _libsbml.SBMLDocument_convert(self, *args)","def __bol(soup):\n news = []\n anchors = soup.find(\n 'div', class_='mais-clicadas-lista link-primary').find_all('a')\n\n for a in anchors:\n title = a.find('span', class_='mais-clicadas-item-content').text\n link = a['href']\n news.append(dict(title=title, link=link))\n return news","def news_feed(request):\n\n all_friends = get_all_friends(request)\n news_feed = get_news_feed(request)\n user_profile = get_users_profile(request.user.id)\n\n context = {\n 'news_feed': news_feed,\n 'user_profile': user_profile,\n 'status_form': StatusForm,\n }\n\n return render(request, 'status/news_feed.html', context)","def __local_sc(soup):\n return __get_local_g1_news(soup)","def get_news(self, until_date=None):\r\n if not self.news:\r\n more_news = True\r\n headers = {\"Content-type\": \"application/x-www-form-urlencoded\", \"Accept\": \"text/plain\",\r\n 'Referer': 'http://' + self.domain + '/login.phtml', \"User-Agent\": user_agent}\r\n req = self.session.get('http://' + self.domain + '/team_news.phtml', headers=headers).content\r\n soup = BeautifulSoup(req, \"html.parser\")\r\n newsheader = soup.find_all('div', {'class', 'newsheader'})[1:]\r\n for index, i in enumerate(soup.find_all('div', {'class', 'article_content_text'})[1:]):\r\n news_date = datetime.strptime(newsheader[index].span['title'][0:8], \"%d.%m.%y\").date()\r\n news_title = newsheader[index].text.split(\">\")[1].strip()\r\n if news_date < until_date:\r\n more_news = None\r\n break\r\n self.news.append([news_date, news_title, i.text])\r\n first_news = 10\r\n while more_news and first_news < 200:\r\n other_news = BeautifulSoup(\r\n self.session.post('http://' + self.domain + '/team_news.phtml', headers=headers,\r\n data={'newsAction': 'reload', 'first_news': first_news}).content, \"html.parser\")\r\n newsheader = other_news.find_all('div', {'class', 'newsheader'})\r\n for index, i in enumerate(other_news.find_all('div', {'class', 'article_content_text'})):\r\n news_date = datetime.strptime(newsheader[index].span['title'][0:8], \"%d.%m.%y\").date()\r\n news_title = newsheader[index].text.split(\">\")[1].strip()\r\n if news_date < until_date:\r\n more_news = None\r\n break\r\n self.news.append([news_date, news_title, i.text])\r\n first_news += 10\r\n time.sleep(1)\r\n return self.news","def _parse_feed(self,feed): \n meta=[]\n for entry in feed:\n item_meta=self._parse_entry(entry)\n item_meta['video-id']='0'\n meta.append(item_meta)\n self._logger.info('%s videos were founded and parsed at Megavideo',len(meta)) \n return meta","def get_university_news():\n\tresponse = requests.get('https://cumoodle.coventry.ac.uk')\n\tmoodleContent = BeautifulSoup(response.content, 'html.parser')\n\tpostLinks =[]\n\theadings = []\n\tdates = []\n\tdata = \"\"\n\tfor title in moodleContent.findAll('div',{'class':'subject'}):\n\t\theadings.append(title.text+\"</a></p>\")\n\tfor link in moodleContent.findAll('div',{'class':'link'}):\n\t\tpostLinks.append(\"<p style = 'font-size:120%;'> <a href = '\"+link.a['href']+\"'>\") \n\tfor date in moodleContent.findAll('div',{'class':'author'}):\n\t\tdates.append(\"<p style='font-size:90%;'>\"+date.text[18:]+\"</p>\")\n\tresults = zip(postLinks, headings, dates)\n\tfor result in results:\n\t\tdata+=(''.join(result))\n\treturn data","def parse():\n G.go(SITE_URL)\n articles = []\n for article in G.doc.select(\"//li[@class='regularitem']\"):\n header = article.select('h4').text()\n text = article.select('div').text()\n url = article.select('h4/a/@href').text()\n dt_string = article.select('h5').text()\n # for date format \"1 Nov 2019 00:00:00\" or \"01 Nov 2019 00:00:00\"\n article_dt = re.search(r'\\d{1,2} [a-zA-Z]+ \\d{4} \\d{2}:\\d{2}:\\d{2}', dt_string)\n if article_dt is None:\n logging.exception('Datestring format is unknown: %s', dt_string)\n continue\n article_dt = article_dt.group(0)\n article_dt = datetime.datetime.strptime(article_dt, '%d %b %Y %H:%M:%S').strftime(\"%Y-%m-%d %H:%M:%S\")\n articles.append({'header': header, 'url': url, 'text': text, 'dt': article_dt})\n return articles","def activities_to_jsonfeed(activities, actor=None, title=None, feed_url=None,\n home_page_url=None):\n try:\n iter(activities)\n except TypeError:\n raise TypeError('activities must be iterable')\n\n if isinstance(activities, (dict, str)):\n raise TypeError('activities may not be a dict or string')\n\n def image_url(obj):\n return util.get_first(obj, 'image', {}).get('url')\n\n def actor_name(obj):\n return obj.get('displayName') or obj.get('username')\n\n if not actor:\n actor = {}\n\n items = []\n for activity in activities:\n obj = as1.get_object(activity) or activity\n if obj.get('objectType') == 'person':\n continue\n author = as1.get_object(obj, 'author')\n content = microformats2.render_content(\n obj, include_location=True, render_attachments=True,\n # Readers often obey CSS white-space: pre strictly and don't even line wrap,\n # so don't use it. https://github.com/snarfed/granary/issues/456\n white_space_pre=False)\n obj_title = obj.get('title') or obj.get('displayName')\n item = {\n 'id': obj.get('id') or obj.get('url'),\n 'url': obj.get('url'),\n 'image': image_url(obj),\n 'title': obj_title if mf2util.is_name_a_title(obj_title, content) else None,\n 'summary': obj.get('summary'),\n 'content_html': content,\n 'date_published': obj.get('published'),\n 'date_modified': obj.get('updated'),\n 'author': {\n 'name': actor_name(author),\n 'url': author.get('url'),\n 'avatar': image_url(author),\n },\n 'attachments': [],\n }\n\n for att in obj.get('attachments', []):\n url = util.get_url(att, 'stream') or util.get_url(att, 'image')\n mime = mimetypes.guess_type(url)[0] if url else None\n if (att.get('objectType') in ATTACHMENT_TYPES or\n mime and mime.split('/')[0] in ATTACHMENT_TYPES):\n item['attachments'].append({\n 'url': url or '',\n 'mime_type': mime,\n 'title': att.get('title'),\n })\n\n if not item['content_html']:\n item['content_text'] = ''\n items.append(item)\n\n return util.trim_nulls({\n 'version': 'https://jsonfeed.org/version/1',\n 'title': title or actor_name(actor) or 'JSON Feed',\n 'feed_url': feed_url,\n 'home_page_url': home_page_url or actor.get('url'),\n 'author': {\n 'name': actor_name(actor),\n 'url': actor.get('url'),\n 'avatar': image_url(actor),\n },\n 'items': items,\n }, ignore='content_text')","def _preprocess_feed(tweet: str):\n t = tweet.lower()\n t = re.sub(url_re, \" <URL> \", t)\n t = t.replace(\"\\n\", \"\")\n t = t.replace(\"#\", \" <HASHTAG> \")\n t = re.sub(mention_re, \" <USER> \", t)\n t = re.sub(smile_re, \" <EMOTICON> \", t)\n t = re.sub(emoji_re, \" <EMOJI> \", t)\n t = re.sub(time_re, \" <TIME> \", t)\n t = re.sub(numbers_re, \" <NUMBER> \", t)\n t = re.sub(not_ascii_re, \"\", t)\n t = re.sub(space_collapse_re, \" \", t)\n t = t.strip()\n return t","def _preprocess_feed(tweet: str):\n t = tweet.lower()\n t = re.sub(url_re, \" <URL> \", t)\n t = t.replace(\"\\n\", \"\")\n t = t.replace(\"#\", \" <HASHTAG> \")\n t = re.sub(mention_re, \" <USER> \", t)\n t = re.sub(smile_re, \" <EMOTICON> \", t)\n t = re.sub(emoji_re, \" <EMOJI> \", t)\n t = re.sub(time_re, \" <TIME> \", t)\n t = re.sub(numbers_re, \" <NUMBER> \", t)\n t = re.sub(not_ascii_re, \"\", t)\n t = re.sub(space_collapse_re, \" \", t)\n t = t.strip()\n return t","def send_news(self, msg):\n log.debug(\"Sending news: {news}\", news=msg)\n self.connection.send({'type': 'news', 'news': str(msg)})","def __uol(soup):\n news = []\n container = soup.find('ol', class_='mostRead')\n most_read = container.find_all('li')\n\n for item in most_read:\n title = item.a['title']\n link = item.a['href']\n if \"folha.uol\" in link:\n link = replace_original_link_with_outline_call(link)\n news.append(dict(title=title, link=link))\n return news","def get_information(article_link):\n\n if \"video\" in article_link or \"/apps/\" in article_link or \"checknews\" in\\\n article_link or not re.search(r\"\\d\\d\\d\\d/\\d\\d/\\d\\d\", article_link):\n return None\n\n else:\n\n date_article = re.search(r\"\\d{4}/\\d{2}/\\d{2}\", article_link)[0]\n date_article = date.datetime.strptime(date_article, \"%Y/%m/%d\")\n\n diff_date = date.datetime.now() - date_article\n\n if diff_date.days > 7:\n return None\n\n else:\n req = requests.get(article_link)\n req.encoding = \"utf-8\"\n data = req.text\n soup = BeautifulSoup(data, \"lxml\")\n\n if soup.find(\n \"div\",\n class_=\"direct-headband\") or article_link != req.url:\n return None\n else:\n balise_title = soup.find(\"h1\")\n balise_title = balise_title.get_text()\n balise_title = re.sub(r\"\\s\\s+\", \"\", balise_title)\n\n newspaper = \"Liberation\"\n title = unidecode.unidecode(balise_title)\n\n author = \"\"\n for span in soup.find_all('span'):\n if span.get(\"class\") == ['author']:\n if(span.a):\n author = span.a.string\n if span.get(\"class\") == ['date']:\n if(span.time):\n date_p = date.datetime.strptime(\n span.time.get(\"datetime\"), \"%Y-%m-%dT\" +\n \"%H:%M:%S\").date()\n date_p = date_p.strftime(\"%Y-%m-%d\")\n print(date_p)\n\n content = \"\"\n for div in soup.find_all('div'):\n for p in div.find_all('p'):\n content += p.get_text() + \" \"\n content = re.sub(\"<>\", \"\", content)\n content = unidecode.unidecode(content)\n\n new_article = utils.recovery_article(\n title, newspaper, [author], date_p, content, \" \")\n\n return new_article","def __uol(soup):\n news = []\n container = soup.select('.mais-lidas-container')[0]\n most_read = container.find_all('li')\n\n for item in most_read:\n title = item.find('span', class_='cor-transition').get_text()\n news.append(dict(title=title, link=item.a['href']))\n return news","def __repr__(self):\r\n\r\n return f\"<News: id = {self.news_id} >\"","def _fetch_latest_for_tag(self, tag, today):\n result = []\n url = Fetch163.search_link % urllib2.quote(tag.name.encode('utf8'))\n try:\n resp = urllib2.urlopen(url)\n except urllib2.URLError as e:\n urllib_error(e)\n else:\n doc = eval(resp.read())\n if doc and type(doc) is list:\n if today:\n news_today = self._today_filter(doc, delta=2)\n else:\n news_today = doc\n for d in news_today:\n docid = d.get('docid', '')\n #title = u'%s' % d.get('title', '')\n # the d.get('title') is a unicode string represent by\n # python str, so use unicode-escape to decode it.\n title = d.get('title', '')\n #print type(title)\n news_title = self._trans_title(title)\n if docid and title:\n news_exits = News.objects.filter(\n Q(docid=docid) | Q(title=news_title)\n )\n #print docid, news_title, news_exits\n intro, body, c_num, ptime, pic = self._fetch_news(docid)\n if not news_exits:\n print 'new news', news_title, docid\n news = News()\n news.docid = docid\n news.title = news_title\n news.content = body\n news.tag = tag\n news.comment_num = c_num\n news.list_pic = pic\n news.abstract = intro\n news.update_time = ptime\n news.save()\n import time\n time.sleep(2)\n if news:\n result.append(news)\n else:\n print 'update news', news_title\n n = news_exits[0]\n print 'old:', n.comment_num, 'new:', c_num\n n.comment_num = c_num\n n.save()\n else:\n print 'Fetch news for tag: %s, Error' % tag.name\n\n return result","def fetch(feed):\n # Fetch the feed data.\n data = feedparser.parse(feed.ext_url)\n new_articles = []\n\n # If the `bozo` value is anything\n # but 0, there was an error parsing (or connecting) to the feed.\n if data.bozo:\n # Some errors are ok.\n if not isinstance(data.bozo_exception, feedparser.CharacterEncodingOverride) and not isinstance(data.bozo_exception, feedparser.NonXMLContentType):\n raise data.bozo_exception\n\n for entry in data.entries:\n\n # URL for this entry.\n url = entry['links'][0]['href']\n\n # Check for an existing Article.\n # If one exists, skip.\n if Article.objects(ext_url=url).first():\n continue\n\n data = extractor.extract(url, existing_data=entry)\n\n if data is None:\n continue\n\n # Secondary check for an existing Article,\n # by checking the title and source.\n existing = Article.objects(title=data['title']).first()\n if existing and existing.feed.source == feed.source:\n continue\n\n data['feed'] = feed\n\n article = Article(**data)\n article.save()\n new_articles.append(article)\n\n return new_articles","def get_saved_news(user, origin=\"saved\"):\n \n saved_news_feed = []\n \n for entry in user.saved_news:\n article = entry.article\n print(article.article_id)\n article_item = {\n \"title\" : article.title,\n \"image\" : article.image,\n \"description\" : article.description,\n \"content\" : article.content,\n \"pub_date\" : article.pub_date.strftime(\"%m/%d/%Y\"),\n \"news_url\" : article.news_url,\n \"saved_news_id\" : entry.id,\n \"origin\" : origin,\n \"note\" : entry.notes,\n \"article_id\" : article.article_id\n }\n saved_news_feed.append(article_item)\n print(saved_news_feed)\n return saved_news_feed"],"string":"[\n \"def format_news(news):\\r\\n news = re.sub(r\\\"[<>]|=+\\\", \\\" \\\", news)\\r\\n news = re.sub(r\\\"-{2,}|/{2,}\\\", \\\" \\\", news)\\r\\n news = re.sub(r\\\"\\\\(.+?\\\\)\\\", \\\" \\\", news)\\r\\n news = re.sub(r\\\"\\\\s+\\\", \\\" \\\", news)\\r\\n return news\",\n \"def save_to_fb2(self, rss_feed, date=None):\\n\\n self.print_if_verbose(f\\\"Method 'save_to_fb2' is working: \\\\n\\\")\\n\\n if not os.path.exists(self.to_fb2_path):\\n try:\\n os.makedirs(self.to_fb2_path)\\n except Exception as error:\\n print(f\\\"Exception {error} - wrong path! File was not created!\\\")\\n return\\n\\n os.chdir(self.to_fb2_path)\\n\\n with open(\\\"rss_news.fb2\\\", \\\"w\\\", encoding=\\\"utf-8\\\") as fb2_file:\\n fb2_file.writelines(f\\\"<?xml version='1.0' encoding='UTF-8'?>\\\\n\\\")\\n fb2_file.writelines(\\n f\\\"<FictionBook xmlns='http://www.gribuser.ru/xml/fictionbook/2.0' \\\"\\n f\\\"xmlns:l='http://www.w3.org/1999/xlink'>\\\\n\\\")\\n fb2_file.writelines(f\\\" <body>\\\\n\\\")\\n fb2_file.writelines(f\\\" <section>\\\\n\\\")\\n\\n if self.date:\\n for news in rss_feed:\\n fb2_file.writelines(self.add_to_fb2(news))\\n else:\\n for news in rss_feed[\\\"News\\\"]:\\n fb2_file.writelines(self.add_to_fb2(news))\\n\\n fb2_file.writelines(f\\\" </section>\\\\n\\\")\\n fb2_file.writelines(\\\" </body>\\\\n\\\")\\n fb2_file.writelines(self.binaries)\\n fb2_file.writelines(\\\"</FictionBook>\\\\n\\\")\\n\\n self.print_if_verbose(\\n f\\\"File {self.to_fb2_path}{os.sep}rss_news.fb2 was created successfully. \\\\n\\\"\\n f\\\"Method 'save_to_fb2' is finished.\\\\n\\\"\\n )\\n\\n return True\",\n \"def parse_news(news):\\n default_value = '---'\\n\\n news_list = []\\n for entry in news:\\n title = entry.get('title', default_value)\\n link = entry.get('link', default_value)\\n published = entry.get('published', default_value)\\n source = entry.get('source', default_value)\\n description = entry.get('description', default_value)\\n media_content = entry.get('media_content', default_value)\\n\\n source_title = default_value\\n if source != default_value:\\n source_title = source['title']\\n\\n image = default_value\\n if media_content != image:\\n image = media_content[0]['url']\\n\\n article = Article(title, link, published, source_title, description, image)\\n news_list.append(article)\\n\\n return news_list\",\n \"def extract_news(parser):\\n news_list = []\\n titles = []\\n authors = []\\n urls = []\\n coms = []\\n scores = []\\n mya = parser.find_all('a', class_='storylink')\\n for a in mya:\\n titles.append(a.contents[0])\\n author = parser.find_all('a', class_='hnuser')\\n for a in author:\\n authors.append(a.contents[0])\\n myurl = parser.find_all('a', class_='storylink')\\n for a in myurl:\\n urls.append(a['href'])\\n comments = parser.find_all('td', class_='subtext')\\n for td in comments:\\n coms.append(td.find_all('a')[-1].contents[0])\\n score = parser.find_all('span', class_='score')\\n for span in score:\\n scores.append(span.contents[0])\\n for i in range(len(titles)):\\n new = {}\\n new['title'] = titles[i]\\n new['author'] = authors[i]\\n new['urls'] = urls[i]\\n new['comments'] = coms[i]\\n new['score'] = scores[i]\\n news_list.append(new)\\n return news_list\",\n \"def get_news(news_url):\\n news_final = []\\n try:\\n news_handler = urllib.urlopen(news_url)\\n news = news_handler.read()\\n news = nl2br(news)\\n news = string.split(news, '<br/>')\\n\\n news_array = {}\\n value = {}\\n for newsweb in news:\\n value = string.split(newsweb, '|')\\n if len(value[0]) > 1:\\n news_array[value[0]] = value[1]\\n\\n info = {}\\n for k in news_array:\\n info = k[0:int(k.find(\\\"http://\\\") - 1)]\\n info = string.split(k, ' - ')\\n news_final.append((info[0], info[1], news_array[k]))\\n\\n news_handler.close()\\n except IndexError:\\n pass\\n except IOError:\\n pass\\n\\n return news_final\",\n \"def load_article_into_newsitem_class(info_about_news_item):\\n article = info_about_news_item.signalmedia_json\\n identifier = article['id']\\n \\n a_news_item = semeval_classes.NewsItem(\\n identifier = identifier,\\n collection = 'SignalMedia',\\n dct = article['published'],\\n publisher = article['source'])\\n \\n for provenance, naf in info_about_news_item.preprocessing:\\n iden2wf_el = {int(wf_el.get('id')[1:]): wf_el\\n for wf_el in naf.iterfind('text/wf')}\\n\\n tid2lemma = {int(term_el.get('id')[1:]): term_el.get('lemma')\\n for term_el in naf.iterfind('terms/term')}\\n basename = ''\\n if provenance == 'newsreader':\\n basename = naf.find('nafHeader/fileDesc').get('filename')\\n\\n # add entity mentions\\n for entity_el in naf.iterfind('entities/entity'):\\n entity_mention_obj = create_entity_mention_obj(entity_el, provenance,\\n iden2wf_el,\\n debug=False)\\n a_news_item.entity_mentions.add(entity_mention_obj)\\n\\n # add event mentions\\n for predicate_el in naf.iterfind('srl/predicate'):\\n an_event_mention_obj = create_event_mention_obj(predicate_el,\\n provenance,\\n iden2wf_el,\\n tid2lemma,\\n basename,\\n debug=False)\\n a_news_item.event_mentions.add(an_event_mention_obj)\\n\\n # add domain labels\\n xpath_query = 'topics/topic[@method=\\\"JEX\\\"]'\\n jex_domain_labels = {topic_el.text\\n for topic_el in naf.iterfind(xpath_query)}\\n a_news_item.domain = jex_domain_labels\\n\\n return a_news_item\",\n \"def convert_to_html(self, news_list):\\n self.logger.info(\\\"Converting news to HTML...\\\")\\n self.prepare_storage()\\n self.process_news_list_with_images(news_list)\\n content = self.generate_html_template(news_list)\\n self.write_to_file(content.encode(\\\"UTF-8\\\"))\",\n \"def pos_process_news(cls):\\n # get from db\\n df = Database.get_all_crawled_news(Database.db_news)\\n\\n # erase news without title or text\\n df = df.loc[df['news_site_title'] != '']\\n df = df.loc[df['news_site_text'] != '']\\n\\n # erase number words title\\n df['title_len'] = df.apply(lambda row: Utils.count_words(row['news_site_title']), axis=1)\\n df = df.loc[df['title_len'] > 3]\\n\\n # sort by date so that we keep the last news\\n df['arquivo_date'] = pd.to_datetime(df.arquivo_date)\\n df = df.sort_values(by=['arquivo_date'], ascending=False)\\n df = df.set_index(['arquivo_date'])\\n df['arquivo_date'] = df.index\\n\\n # drop news that have title repeated\\n df_title_text = df[['news_site_title']]\\n df_title_text = df_title_text.drop_duplicates()\\n df = df.loc[df.index.isin(df_title_text.index)]\\n\\n # insert in mongo db\\n Database.save_posprocessed_crawled_news(Database.db_pos_processed_news,df)\",\n \"def extract_news(parser):\\r\\n news_list = []\\r\\n\\r\\n tbl_list = parser.table.findAll('table')\\r\\n tr_list = tbl_list[1].findAll('tr')\\r\\n for i in range(0, 90, 3):\\r\\n new = dict()\\r\\n new['author'] = tr_list[i + 1].a.text\\r\\n new['points'] = tr_list[i + 1].span.text[:-6]\\r\\n comments = tr_list[i + 1].findAll('a')\\r\\n new['comments'] = comments[len(comments) - 1].text[:-9]\\r\\n if new['comments'] == '':\\r\\n new['comments'] = '0'\\r\\n new['title'] = tr_list[i].findAll('a')[1].text\\r\\n a_mas = List[str]\\r\\n a_mas = tr_list[i].findAll('a')\\r\\n new['url'] = a_mas[len(a_mas) - 1].text\\r\\n if new['url'] == new['title']:\\r\\n new['url'] = ''\\r\\n news_list.append(new)\\r\\n\\r\\n return news_list\",\n \"def news():\\n mesosite = iemdb.connect('mesosite', bypass=True)\\n mcursor = mesosite.cursor(cursor_factory=psycopg2.extras.DictCursor)\\n # Last dailyb delivery\\n lastts = mx.DateTime.now() + mx.DateTime.RelativeDateTime(hour=11, days=-1)\\n mcursor.execute(\\\"\\\"\\\"\\n SELECT *, to_char(entered, 'DD Mon HH:MI AM') as nicedate \\n from news WHERE entered > '%s' \\n ORDER by entered DESC\\\"\\\"\\\" % (\\n lastts.strftime(\\\"%Y-%m-%d %H:%M\\\"),) )\\n\\n textfmt = \\\"\\\"\\\"\\n +----------------------------------------------\\n | Title : %(title)s\\n | Date : %(nicedate)s\\n | Author: %(author)s\\n | URL : %(url)s\\n +----------------------------------------------\\n\\n%(body)s\\n\\n\\\"\\\"\\\"\\n htmlfmt = \\\"\\\"\\\"\\n<hr />\\n<br /><strong>Title:</strong> <a href=\\\"http://mesonet.agron.iastate.edu/onsite/news.phtml?id=%(id)s\\\">%(title)s</a>\\n<br /><strong>Date:</strong> %(nicedate)s\\n<br /><strong>Author:</strong> %(author)s\\n<br /><a href=\\\"%(url)s\\\">link</a>\\n\\n<p>%(body)s\\n\\n\\\"\\\"\\\"\\n txt = \\\"> News\\\\n\\\"\\n html = \\\"<h3>News</h3>\\\"\\n\\n for row in mcursor:\\n txt += textfmt % row\\n html += htmlfmt % row\\n if mcursor.rowcount == 0:\\n txt += \\\"\\\\n No news is good news\\\\n\\\\n\\\"\\n html += \\\"<strong>No news is good news</strong>\\\"\\n\\n return txt, html\",\n \"def rdfize_news_item(a_news_item, g):\\n news_item_id = a_news_item.identifier\\n # create URI for this news item\\n newsItemURIString = \\\"%s%s\\\" % (cltlNewsPrefix, news_item_id)\\n newsItem = URIRef(newsItemURIString)\\n\\n # Add the news item triples to the graph\\n g.add(( newsItem, RDF.type, newsItemType))\\n g.add(( newsItem, DCTERMS.source, Literal(news_item_id) ))\\n g.add(( newsItem, DCTERMS.isPartOf, URIRef('%s%s' % (cltlDataPrefix, a_news_item.collection)) ))\\n dct=datetime.datetime.strptime(a_news_item.dct, '%Y-%m-%dT%H:%M:%SZ') # 2015-09-04T10:43:03Z\\n g.add(( newsItem, DCTERMS.created, Literal(dct)))\\n g.add(( newsItem, DCTERMS.publisher, URIRef(create_publisher_uri(a_news_item.publisher))))\\n if a_news_item.domain and len(a_news_item.domain):\\n for domain in a_news_item.domain:\\n if domain:\\n g.add(( newsItem, DCTERMS.subject, URIRef(create_topic_uri(domain)) ))\\n\\n # iterate through the entity mentions\\n for entity_mention_obj in a_news_item.entity_mentions:\\n # create URI for this entity mention\\n entityMentionURIString=\\\"%s#mention=%d,%d\\\" % (newsItemURIString,\\n entity_mention_obj.begin_index,\\n entity_mention_obj.end_index)\\n instanceURI=URIRef(\\\"%s#entity=%d,%d\\\" % (newsItemURIString,\\n entity_mention_obj.begin_index,\\n entity_mention_obj.end_index))\\n entityMentionURI=URIRef(entityMentionURIString)\\n\\n g.add((entityMentionURI, RDF.type, GAF.Mention))\\n # add entity mention triples\\n g.add((entityMentionURI, CLTLV.sent, Literal(int(entity_mention_obj.sentence))))\\n g.add((entityMentionURI, NIF.anchorOf, Literal(entity_mention_obj.mention)))\\n# if entity_mention_obj.lemma:\\n# g.add((entityMentionURI, NIF.lemma, Literal(entity_mention.obj.lemma) ))\\n g.add(( entityMentionURI, NIF.referenceContext, newsItem ))\\n g.add(( entityMentionURI, GAF.denotes, instanceURI ))\\n g.add((instanceURI, RDF.type, GAF.Instance))\\n g.add((instanceURI, RDF.type, CLTLV.Entity))\\n g.add((instanceURI, RDF.type, URIRef(makeType(entity_mention_obj.the_type))))\\n g.add((entityMentionURI, NIF.beginIndex, Literal(entity_mention_obj.begin_index)))\\n g.add((entityMentionURI, NIF.endIndex, Literal(entity_mention_obj.end_index)))\\n g.add((entityMentionURI, PROV.wasAttributedTo, URIRef('%sprovenance/%s/entity' % (cltlPrefix, entity_mention_obj.provenance)) ))\\n if entity_mention_obj.meaning:\\n g.add(( instanceURI, OWL.sameAs, URIRef(entity_mention_obj.meaning) ))\\n\\n # Iterate through the event mentions\\n for event_mention_obj in a_news_item.event_mentions:\\n # create URI for this entity mention\\n eventMentionURIString=\\\"%s#mention=%s\\\" % (newsItemURIString,\\n hash_offsets(event_mention_obj.mention_offset_ranges))\\n instanceURI=URIRef(\\\"%s#event=%s\\\" % (newsItemURIString,\\n hash_offsets(event_mention_obj.mention_offset_ranges)))\\n eventMentionURI=URIRef(eventMentionURIString)\\n\\n g.add((eventMentionURI, RDF.type, GAF.Mention))\\n # add entity mention triples\\n g.add((eventMentionURI, CLTLV.sent, Literal(int(event_mention_obj.sentence))))\\n g.add((eventMentionURI, NIF.anchorOf, Literal(event_mention_obj.mention)))\\n if event_mention_obj.lemma:\\n g.add((eventMentionURI, NIF.lemma, Literal(event_mention_obj.lemma) ))\\n g.add(( eventMentionURI, NIF.referenceContext, newsItem ))\\n g.add(( eventMentionURI, GAF.denotes, instanceURI ))\\n for offset_range in event_mention_obj.mention_offset_ranges: \\n g.add((eventMentionURI, NIF.beginIndex, Literal(offset_range[0])))\\n g.add((eventMentionURI, NIF.endIndex, Literal(offset_range[1])))\\n g.add((instanceURI, RDF.type, GAF.Instance))\\n g.add((instanceURI, RDF.type, CLTLV.Event))\\n #g.add((instanceURI, RDF.type, Literal(entity_mention_obj.the_type)))\\n g.add((eventMentionURI, PROV.wasAttributedTo, URIRef('%sprovenance/%s/event' % (cltlPrefix, event_mention_obj.provenance)) ))\\n if event_mention_obj.meaning:\\n g.add(( instanceURI, OWL.sameAs, URIRef(event_mention_obj.meaning) ))\\n\\n\\n\\n return g\",\n \"def parse_gulde_news():\\n # set url\\n url = 'https://web-scraping-demo.zgulde.net/news' \\n agent = 'codeup ds germain'\\n # query\\n response = requests.get(url, headers={'User-Agent': agent}) \\n # soup\\n soup = BeautifulSoup(response.text) \\n # raw list of articles\\n articles = soup.select('.grid.gap-y-12 > div') \\n # list of dicts for dataframe\\n article_list = [] \\n # parse each article\\n for article in articles: \\n # grab title\\n title = article.h2.text \\n # grab date, author, contents of article\\n date, author, contents = article.select('.py-3')[0]\\\\\\n .find_all('p') \\n # add dict of info to list\\n article_list.append({'title':title, 'date':date.text,\\n 'author':author.text, 'contents':contents.text}) \\n # return dataframe\\n return pd.DataFrame(article_list)\",\n \"def extract_news(parser):\\n news_list = []\\n\\n titles = parser.find_all(\\\"tr\\\", class_=\\\"athing\\\")\\n subtext = parser.find_all(\\\"td\\\", class_=\\\"subtext\\\")\\n\\n for i in range(len(titles)):\\n x = titles[i].find_all(\\\"td\\\", class_=\\\"title\\\")[1]\\n title = x.a.text\\n url = x.a[\\\"href\\\"]\\n c = subtext[i].find_all(\\\"a\\\")[4]\\n if c.text == \\\"discuss\\\":\\n comments = 0\\n else:\\n comments = c.text\\n author = subtext[i].find(\\\"a\\\", class_=\\\"hnuser\\\").get_text()\\n point = subtext[i].find(\\\"span\\\", class_=\\\"score\\\").text\\n points = point.split(' ')[0]\\n\\n news_list.append({\\\"author\\\": author, \\\"comments\\\": comments, \\\"points\\\": points, \\\"title\\\": title, \\\"url\\\": url})\\n\\n return news_list\",\n \"def gnews(self):\\n\\t\\tfeed_url = self.get_feed()\\n\\t\\tfeed_data = feedparser.parse(feed_url)\\n\\t\\tprint(\\\"\\\")\\n\\t\\ttype_tiny = pyshorteners.Shortener()\\n\\t\\tfor data in feed_data[\\\"items\\\"]:\\n\\t\\t\\ttiny_url = type_tiny.tinyurl.short(data[\\\"link\\\"])\\n\\t\\t\\t#tiny_url = tinyurl.create_one(data[\\\"link\\\"])\\n\\t\\t\\tprint('\\\\033[33m' + data[\\\"title\\\"] + \\\" : \\\" + Style.RESET_ALL + tiny_url)\\n\\t\\t\\tprint(\\\"\\\")\",\n \"def news():\\n mesosite = get_dbconn(\\\"mesosite\\\")\\n mcursor = mesosite.cursor(cursor_factory=psycopg2.extras.DictCursor)\\n # Last dailyb delivery\\n lastts = datetime.datetime.now() + datetime.timedelta(days=-1)\\n mcursor.execute(\\n \\\"SELECT *, to_char(entered, 'DD Mon HH:MI AM') as nicedate \\\"\\n \\\"from news WHERE entered > %s ORDER by entered DESC\\\",\\n (lastts,),\\n )\\n\\n textfmt = \\\"\\\"\\\"\\n +----------------------------------------------\\n | Title : %(title)s\\n | Date : %(nicedate)s\\n | Author: %(author)s\\n | URL : %(url)s\\n +----------------------------------------------\\n\\n%(body)s\\n\\n\\\"\\\"\\\"\\n htmlfmt = (\\n \\\"<hr />\\\\n\\\"\\n \\\"<br /><strong>Title:</strong>\\\\n\\\"\\n '<a href=\\\"https://mesonet.agron.iastate.edu/'\\n 'onsite/news.phtml?id=%(id)s\\\">%(title)s</a>\\\\n'\\n \\\"<br /><strong>Date:</strong> %(nicedate)s\\\\n\\\"\\n \\\"<br /><strong>Author:</strong> %(author)s\\\\n\\\"\\n '<br /><a href=\\\"%(url)s\\\">link</a>\\\\n\\\\n'\\n \\\"<p>%(body)s\\\\n\\\"\\n )\\n txt = \\\"> News\\\\n\\\"\\n html = \\\"<h3>News</h3>\\\"\\n\\n for row in mcursor:\\n txt += textfmt % row\\n html += htmlfmt % row\\n if mcursor.rowcount == 0:\\n txt += \\\"\\\\n No news is good news\\\\n\\\\n\\\"\\n html += \\\"<strong>No news is good news</strong>\\\"\\n\\n return txt, html\",\n \"def nfldotcomnews(self, irc, msg, args):\\n \\n url = self._b64decode('aHR0cDovL3MzLmFtYXpvbmF3cy5jb20vbmZsZ2MvYWxsX25ld3NMaXN0Lmpz')\\n \\n try:\\n req = urllib2.Request(url)\\n html = (urllib2.urlopen(req)).read()\\n except:\\n irc.reply(\\\"Failed to fetch: %s\\\" % url)\\n return\\n \\n try:\\n jsondata = json.loads(html)['content']\\n except:\\n irc.reply(\\\"Failed to parse article json from: %s\\\" % url)\\n return\\n \\n for article in jsondata[0:6]:\\n title = article.get('title', None)\\n desc = article.get('description', None)\\n link = article.get('linkURL', None)\\n date = article.get('date_ago', None)\\n \\n output = \\\"{0} - {1}\\\".format(ircutils.bold(title), self._shortenUrl(link))\\n irc.reply(output)\",\n \"def __get_local_g1_news(soup):\\n news = []\\n anchors = soup.find_all(\\n 'a', class_='feed-post-link gui-color-primary gui-color-hover')\\n\\n for a in anchors:\\n title = a.string\\n link = a['href']\\n news.append(dict(title=title, link=link))\\n return news\",\n \"def __local_to(soup):\\n return __get_local_g1_news(soup)\",\n \"def __local_to(soup):\\n return __get_local_g1_news(soup)\",\n \"def get_news(url):\\r\\n \\r\\n # parse RSS feed into list of dictionaries\\r\\n feed = feedparser.parse(url)\\r\\n\\r\\n # no RSS feed articles for url\\r\\n if len(feed['entries']) == 0:\\r\\n return []\\r\\n \\r\\n # get first ten articles from the RSS feed\\r\\n news = []\\r\\n i = 0\\r\\n while True:\\r\\n if i == len(feed['entries']) or i > 30:\\r\\n break\\r\\n \\r\\n try:\\r\\n # get link to article\\r\\n link = feed[\\\"entries\\\"][i][\\\"link\\\"]\\r\\n\\r\\n # get title of article\\r\\n title = feed[\\\"entries\\\"][i][\\\"title\\\"]\\r\\n \\r\\n try:\\r\\n # get raw summary of article\\r\\n summary_raw = feed[\\\"entries\\\"][i][\\\"summary\\\"]\\r\\n \\r\\n # format summary\\r\\n summary = \\\"\\\"\\r\\n for c in summary_raw:\\r\\n if c == \\\"<\\\":\\r\\n summary += \\\"...\\\"\\r\\n break\\r\\n summary += c\\r\\n except KeyError as e:\\r\\n logging.error(\\\"no summary for RSS feed article: {}\\\".format(link))\\r\\n summary = \\\"read more here...\\\"\\r\\n \\r\\n # get raw date \\r\\n date_raw = feed[\\\"entries\\\"][i][\\\"published_parsed\\\"]\\r\\n \\r\\n if date_raw is None:\\r\\n date = feed[\\\"entries\\\"][i][\\\"published\\\"]\\r\\n \\r\\n else:\\r\\n # format date\\r\\n year = str(date_raw.tm_year)\\r\\n months = [\\\"January\\\", \\\"February\\\", \\\"March\\\", \\\"April\\\", \\\"May\\\", \\\"June\\\", \\\"July\\\", \\\"August\\\", \\\"September\\\", \\\"October\\\", \\\"November\\\", \\\"December\\\"]\\r\\n month = months[date_raw.tm_mon - 1]\\r\\n day = str(date_raw.tm_mday)\\r\\n weekdays = [\\\"Monday\\\", \\\"Tuesday\\\", \\\"Wednesday\\\", \\\"Thursday\\\", \\\"Friday\\\", \\\"Saturday\\\", \\\"Sunday\\\"]\\r\\n wday = weekdays[date_raw.tm_wday]\\r\\n hour = str(date_raw.tm_hour)\\r\\n hour = \\\"{:2}\\\".format(hour).format(' ','0')\\r\\n min = str(date_raw.tm_min)\\r\\n min = \\\"{:2}\\\".format(min).replace(' ','0')\\r\\n date = hour + \\\":\\\" + min + \\\" - \\\" + wday + \\\" \\\" + month + \\\" \\\" + day + \\\", \\\" + year\\r\\n \\r\\n # compile entry and append to news list\\r\\n entry = {\\\"link\\\":link, \\\"title\\\":title, \\\"date\\\":date, \\\"summary\\\":summary}\\r\\n \\r\\n # sanitize entry\\r\\n for key in entry:\\r\\n # apostrophe\\r\\n entry[key] = entry[key].replace(\\\"'\\\", \\\"'\\\")\\r\\n # right single quotation mark\\r\\n entry[key] = entry[key].replace(\\\"’\\\", \\\"’\\\")\\r\\n # left single quotation mark\\r\\n entry[key] = entry[key].replace('\\\"', \\\"‘\\\")\\r\\n # right double quotation mark\\r\\n entry[key] = entry[key].replace(\\\"'\\\", \\\"”\\\")\\r\\n # left double quotation mark\\r\\n entry[key] = entry[key].replace(\\\"'\\\", \\\"“\\\")\\r\\n # Weird ampersand formatting\\r\\n entry[key] = entry[key].replace(\\\"&\\\", \\\"&\\\")\\r\\n \\r\\n # prepare entry for sqlite queries\\r\\n entry[key] = surround(entry[key])\\r\\n \\r\\n # add entry to news list\\r\\n news.append(entry)\\r\\n \\r\\n # max 10 entries\\r\\n if len(news) == 10:\\r\\n break\\r\\n i += 1\\r\\n \\r\\n except Exception as e:\\r\\n logging.error(e)\\r\\n i += 1\\r\\n pass\\r\\n \\r\\n # success\\r\\n return news\",\n \"def news()->str:#return array[news desc,news link]\\n event_log(\\\"retrieve news data....\\\",\\\"\\\")\\n c = 0\\n location = read_json(\\\"news_api\\\")[0]\\n main_url = \\\"https://newsapi.org/v2/top-headlines?country=\\\"+location+\\\"&apiKey=\\\"+read_json(\\\"news_api\\\")[1]+\\\"\\\"#add a country selection optin via json\\n page = requests.get(main_url).json()\\n article = page[\\\"articles\\\"]\\n news_result = []\\n for data in article:\\n news_result.append([data[\\\"title\\\"],str(data[\\\"url\\\"]).replace('\\\"',\\\" \\\")])#exctracts the wanted data from api\\n if c == 5:#add this to json file so scalibility\\n break\\n c+=1\\n return news_result\",\n \"def process(url):\\n feed = feedparser.parse(url)\\n entries = feed.entries\\n ret = []\\n for entry in entries:\\n print entry\\n guid = entry.guid\\n title = translate_html(entry.title)\\n link = entry.link\\n summary = translate_html(entry.summary)\\n try:\\n subject = translate_html(entry.tags[0]['term'])\\n except AttributeError:\\n subject = \\\"\\\"\\n newsStory = NewsStory(guid, title, subject, summary, link)\\n ret.append(newsStory)\\n return ret\",\n \"def get_article():\\n bbc_request = requests.get('https://www.bbc.com/ukrainian')\\n soup = BeautifulSoup(bbc_request.text, \\\"html.parser\\\")\\n raw_article = soup.find_all('div', {'class': \\\"bbc-1vo75s2-TextGridItem e19k1v2h0\\\"})[0].find_all(text=True, recursive=True)\\n\\n title = raw_article[0]\\n description = raw_article[1]\\n publish_time = raw_article[2]\\n href = soup.find_all('div', {'class': 'bbc-1vo75s2-TextGridItem e19k1v2h0'})[0].find('a', {'class': 'bbc-11m194t-Link evnt13t0'})['href']\\n link = f' https://www.bbc.com/{href}'\\n article = f\\\"\\\"\\\"⚠️ <b>Тема</b>: {title}\\n \\\\n📌 <b>Короткий опис</b>: {description}\\n \\\\n🕒 <b>Опубліковано</b>: {publish_time}\\n \\\\n➡️ <b>Повний текст</b>: {link}\\\"\\\"\\\"\\n return article\",\n \"def parse_news(self, response):\\n \\n loader = NewsLoader(item=NewsItem(), response=response)\\n loader.add_xpath('title', '//header//h1/text()')\\n author = ''.join(response.xpath('//span[@class=\\\"byline\\\"]').extract())\\n author = remove_tags(author).replace(\\\"by\\\", '').replace(' and ', ', ')\\n loader.add_value('author', author)\\n timestamp = response.xpath('//meta[@name=\\\"DC.date.issued\\\"][1]/@content').extract()[0]\\n timestamp = du.normalize_timestamp(timestamp)\\n loader.add_value('date', timestamp.split(' ')[0])\\n loader.add_value('time', timestamp.split(' ')[1])\\n list_of_contents = response.xpath(\\n '//div[@id=\\\"storytext\\\"]/*[not(@class=\\\"cnnplayer\\\") and '\\n 'not(@class=\\\"storytimestamp\\\")]').extract()\\n content = ' '.join(list_of_contents)\\n loader.add_value('content', content)\\n loader.add_xpath('tags', '//meta[@name=\\\"keywords\\\"]/@content')\\n return loader.load_item()\",\n \"def get_news(rss_feed):\\r\\n\\r\\n class _CurrentData(object):\\r\\n \\\"\\\"\\\"Class holding a set of current attributes.\\\"\\\"\\\"\\r\\n item = None\\r\\n text = None\\r\\n\\r\\n def _start_element_handler(name, attrs):\\r\\n \\\"\\\"\\\"Handle XML start-elements.\\\"\\\"\\\"\\r\\n if name == 'item':\\r\\n # Allocate a new item.\\r\\n current.item = NewsItem()\\r\\n\\r\\n def _end_element_handler(name):\\r\\n \\\"\\\"\\\"Handle XML end-elements.\\\"\\\"\\\"\\r\\n if name == 'item':\\r\\n news_items.append(current.item)\\r\\n elif name in ('title', 'description', 'link', 'category'):\\r\\n try:\\r\\n setattr(current.item, name, current.text)\\r\\n except AttributeError:\\r\\n # The parser has run into a non-news item.\\r\\n pass\\r\\n\\r\\n def _char_data_handler(data):\\r\\n \\\"\\\"\\\"Handle XML element character data.\\\"\\\"\\\"\\r\\n current.text = data\\r\\n\\r\\n news_items = list()\\r\\n current = _CurrentData()\\r\\n\\r\\n parser = expat.ParserCreate()\\r\\n parser.StartElementHandler = _start_element_handler\\r\\n parser.EndElementHandler = _end_element_handler\\r\\n parser.CharacterDataHandler = _char_data_handler\\r\\n\\r\\n news_handle = urllib2.urlopen(rss_feed)\\r\\n xml_data = news_handle.read()\\r\\n \\r\\n parser.Parse(xml_data)\\r\\n\\r\\n return news_items\",\n \"def RSS2format(inputfile):\\n print \\\"START: FEED GENERATOR[ITEM OBJECT CREATOR]: \\\", time.time()\\n xmldocument = parse(inputfile)\\n feed_title = \\\"\\\"\\n try:\\n feed_title = xmldocument.getElementsByTagName('dc:title')[0].firstChild.data\\n except IndexError as details:\\n print \\\"Handling IndexError: \\\", details\\n feed_title = \\\"Handling IndexError...\\\"\\n except AttributeError as details:\\n print \\\"Handling AttributeError: \\\", details\\n feed_title = \\\"Handling AttributeError...\\\"\\n # only get first 100 characters.. RSS\\n feed_description = \\\"\\\"\\n try:\\n feed_description = xmldocument.getElementsByTagName('dc:description')[0].firstChild.data[:100]\\n except IndexError as details:\\n print \\\"Handling IndexError: \\\"\\n feed_description = \\\"Handling IndexError\\\"\\n except AttributeError as details:\\n\\tfeed_description = \\\"Handling AttributeError\\\"\\n feed_link = xmldocument.getElementsByTagName('identifier')[0].firstChild.data # get header identifier for link value\\n feed_pubDate = xmldocument.getElementsByTagName('datestamp')[0].firstChild.data # get header datestamp for pubDate value\\n feed_guid = xmldocument.getElementsByTagName('identifier')[0].firstChild.data # get header identifier for guid value\\\\\\n # return a PyRSS2Gen object\\n return PyRSS2Gen.RSSItem(\\n title = feed_title,\\n link = feed_link,\\n description = feed_description,\\n guid = feed_guid,\\n pubDate = datetime.strptime(feed_pubDate.replace(\\\"T\\\", \\\" \\\").replace(\\\"Z\\\", \\\"\\\"), '%Y-%m-%d %H:%M:%S')\\n )\",\n \"def news_handle(news_json):\\n #Sets all the variables to global space to be used later.\\n global title_1, title_2, author_1, author_2, source_1, source_2, url_1_final, url_2_final\\n #Handles data from API and assigns data to variables.\\n news_data = news_json['articles']\\n article_1 = news_data[0]\\n article_2 = news_data[1]\\n title_1 = article_1['title']\\n author_1 = article_1['author']\\n source_1 = article_1['source']['name']\\n url_1 = article_1['url']\\n url_1_final = '<a href=\\\"{}\\\">Read More</a>'.format(url_1)\\n title_2 = article_2['title']\\n author_2 = article_2['author']\\n source_2 = article_2['source']['name']\\n url_2 = article_2['url']\\n url_2_final = '<a href=\\\"{}\\\">Read More</a>'.format(url_2)\\n return\",\n \"def analyse_news_article(url):\\n if \\\"bbc\\\" in url:\\n outlet = \\\"BBC\\\"\\n parser = news_parser.BBC(\\\"https://www.bbc.co.uk/news\\\")\\n elif \\\"dailymail\\\" in url:\\n outlet = \\\"DailyMail\\\"\\n parser = news_parser.DailyMail(\\\"https://www.dailymail.co.uk\\\")\\n elif \\\"theguardian\\\" in url:\\n outlet = \\\"Guardian\\\"\\n parser = news_parser.Guardian(\\\"https://www.theguardian.com\\\")\\n elif \\\"metro\\\" in url:\\n outlet = \\\"Metro\\\"\\n parser = news_parser.Metro(\\\"https://www.metro.co.uk\\\")\\n elif \\\"mirror\\\" in url:\\n outlet = \\\"Mirror\\\"\\n parser = news_parser.Mirror(\\\"https://www.mirror.co.uk\\\")\\n elif \\\"reuters\\\" in url:\\n outlet = \\\"Reuters\\\"\\n parser = news_parser.Reuters(\\\"https://uk.reuters.com\\\")\\n elif \\\"thesun\\\" in url:\\n outlet = \\\"Sun\\\"\\n parser = news_parser.Sun(\\\"https://www.thesun.co.uk\\\")\\n elif \\\"independent\\\" in url:\\n outlet = \\\"Independent\\\"\\n parser = news_parser.Independent(\\\"https://www.independent.co.uk\\\")\\n else:\\n outlet = \\\"BBC\\\"\\n parser = news_parser.BBC(\\\"https://www.bbc.co.uk/news\\\")\\n \\n article_data = parser.get_article(url)\\n article = parser.parse(article_data)\\n response = {'title': article['title'], 'content': article['content'], 'url': article['url'], 'image_url':article['metadata']['meta_img']} \\n return response, outlet\",\n \"def analyze_news(self, soup_news) -> list:\\n logging.info(\\\"analyzing news collected\\\")\\n analyzed_news = []\\n for news in soup_news:\\n main_column = news.find(\\\"div\\\", {\\\"class\\\": \\\"column size-3-4 column-main\\\"})\\n tag_list = main_column.find_all(\\\"div\\\", {\\\"class\\\": re.compile(r\\\"etiqueta etiqueta-[0-9]\\\")})\\n fake = self.__is_fake(tag_list)\\n if fake:\\n analyzed_news.append(main_column)\\n\\n return analyzed_news\",\n \"def save_news(self, obj_list: List[Tag]) -> str:\\n for obj in obj_list:\\n header = obj.text.strip()\\n if not header:\\n if 'alt' in obj.__dict__:\\n header = obj.get('alt').strip()\\n # other if else\\n news = News(\\n origin=self.get_self_name(),\\n header=header\\n )\\n news.save()\\n return f'Data from service {self.get_self_name()} loaded successfully.'\",\n \"async def news(self):\\n url = f\\\"https://newsapi.org/v2/top-headlines?country=nz&apiKey={self.bot.news_api_key}\\\"\\n async with ClientSession() as session:\\n async with session.get(url) as response:\\n r = await response.json()\\n firstArticle = r[\\\"articles\\\"][0]\\n nSource = firstArticle[\\\"source\\\"][\\\"name\\\"]\\n nTitle = firstArticle[\\\"title\\\"]\\n nTimestamp = firstArticle[\\\"publishedAt\\\"]\\n embed = discord.Embed(\\n title=f\\\"News Title: {nTitle}\\\", description=f\\\"News Source: {nSource}\\\"\\n )\\n embed.add_field(name=\\\"News Content\\\", value=firstArticle[\\\"description\\\"])\\n embed.set_image(url=firstArticle[\\\"urlToImage\\\"])\\n embed.set_footer(text=f\\\"News Timestamp: {nTimestamp}\\\")\\n\\n channel = self.bot.get_channel(self.bot.main_channel_id)\\n await channel.send(embed=embed)\",\n \"def update_news_to_model(self):\\n internet = 1\\n news_page_url_base = 'https://news.mydrivers.com/update/'\\n url_to_get_list = []\\n if self.dates_to_get[0] == 'Date entered error':\\n self.show_in_browser.emit(f'Date entered error: {self.dates_to_get[1]}')\\n self.show_in_browser.emit('Please check the entered date.')\\n self.thread.quit()\\n return\\n for date_to_get in self.dates_to_get:\\n if pd.to_datetime(date_to_get).date() > (datetime.date.today() + datetime.timedelta(days=1)):\\n break\\n url_to_get = news_page_url_base + date_to_get + '_1.htm'\\n url_to_get_list.append(url_to_get)\\n if url_to_get_list == []:\\n self.show_in_browser.emit(f'Date entered error: {self.dates_to_get}')\\n self.show_in_browser.emit('Please check the entered date.')\\n self.thread.quit()\\n return\\n\\n def get_url_content(url_to_get):\\n try:\\n r = requests.get(url_to_get)\\n except BaseException as e:\\n print('Cannot establish Internet connection with server.\\\\n', e)\\n self.show_in_browser.emit('Cannot establish Internet connection with server.\\\\n')\\n self.show_in_browser.emit(str(e))\\n nonlocal internet\\n internet = 0\\n return\\n status_code = r.status_code\\n if status_code == 200:\\n bs = BeautifulSoup(r.content, 'html.parser')\\n news_pages_list = bs.select('#newsleft > div > a')\\n news_summary_list = bs.select('#newsleft > li')\\n else:\\n news_pages_list = []\\n news_summary_list = []\\n return status_code, news_pages_list, news_summary_list\\n\\n def extract_news_pages(news_pages_list: list) -> list:\\n news_page_url_list = []\\n for item in news_pages_list:\\n news_page_url_tail = item.get('href')\\n if item.text.isnumeric() and news_page_url_tail:\\n news_page_url = news_page_url_base + news_page_url_tail\\n news_page_url_list.append(news_page_url)\\n return news_page_url_list\\n\\n def extract_news_summary(news_summary_list: list):\\n for item in news_summary_list:\\n news_title = item.find('h3').find('a').text\\n news_link = 'https:' + item.find('h3').find('a').get('href')\\n news_author = item.find(class_=\\\"newstiao4\\\").text\\n news_datetime_text = item.find(class_=\\\"news_plun hui2\\\") \\\\\\n .find('li').text\\n match = re.search('\\\\d{4}-\\\\d{2}-\\\\d{2} \\\\d{2}:\\\\d{2}',\\n news_datetime_text)\\n news_datetime = pd.to_datetime(match.group())\\n # Naive Bayes classifier\\n seg_list = jieba.cut(news_title)\\n p_news_like_product = 1 # p_1*p_2*...*p_n\\n p_news_like_1_product = 1 # (1-p_1)*(1-p_2)*...*(1-p_n)\\n p_news_nolike_product = 1\\n p_news_nolike_1_product = 1\\n for word in seg_list:\\n sql_query = f\\\"SELECT * FROM words WHERE word = '{word}';\\\"\\n rows = self.conn.execute(sql_query).fetchall()\\n if rows == []:\\n p_word_like = 0.4\\n p_word_nolike = 0.4\\n else:\\n p_word_like = rows[0][1] / rows[0][3]\\n p_word_nolike = rows[0][2] / rows[0][3]\\n p_news_like_product = p_news_like_product * p_word_like\\n p_news_like_1_product = p_news_like_1_product * (1 - p_word_like)\\n p_news_nolike_product = p_news_nolike_product * p_word_nolike\\n p_news_nolike_1_product = p_news_nolike_1_product * (1 - p_word_nolike)\\n p_news_like = 100 * p_news_like_product * self.p_like / (\\n p_news_like_product * self.p_like + p_news_like_1_product * (1 - self.p_like))\\n p_news_nolike = 100 * p_news_nolike_product * self.p_nolike / (\\n p_news_nolike_product * self.p_nolike + p_news_nolike_1_product * (1 - self.p_nolike))\\n p_news_like = round(p_news_like, 2)\\n p_news_nolike = round(p_news_nolike, 2)\\n\\n self.news_df = self.news_df.append(\\n pd.DataFrame([[news_datetime,\\n news_title,\\n news_link,\\n news_author,\\n p_news_like,\\n p_news_nolike]],\\n columns=self.news_df.columns),\\n ignore_index=True)\\n\\n self.news_df = pd.DataFrame({'datetime': [], 'title': [], 'link': [],\\n 'author': [], 'like': [], 'no_like': []})\\n\\n for url_to_get in url_to_get_list:\\n try:\\n status_code, news_pages_list, news_summary_list = \\\\\\n get_url_content(url_to_get)\\n except:\\n break\\n if status_code == 200:\\n print(url_to_get, ' loaded.')\\n self.show_in_browser.emit(url_to_get + ' loaded.')\\n extract_news_summary(news_summary_list)\\n else:\\n print(url_to_get, 'is not available. Status Code:', status_code)\\n self.show_in_browser.emit(url_to_get + ' is not available. Status Code: ' + str(status_code))\\n continue\\n\\n for news_page_url in extract_news_pages(news_pages_list):\\n status_code, news_pages_list, news_summary_list = \\\\\\n get_url_content(news_page_url)\\n if status_code == 200:\\n extract_news_summary(news_summary_list)\\n print(news_page_url, ' loaded.')\\n self.show_in_browser.emit(news_page_url + ' loaded.')\\n else:\\n print(url_to_get, 'is not available. Status Code:',\\n status_code)\\n self.show_in_browser.emit(url_to_get + ' is not available. Status Code: ' + str(status_code))\\n continue\\n\\n if internet == 0:\\n self.thread.quit()\\n return\\n\\n if (pd.to_datetime(self.dates_to_get[0]).date() == self.news_latest_datetime.date()):\\n self.news_df.drop(self.news_df[self.news_df.datetime < self.news_latest_datetime].index,\\n inplace=True)\\n try:\\n for link in self.news_latest_link:\\n self.news_df.drop(self.news_df[self.news_df.link == link].index, inplace=True)\\n except BaseException as e:\\n print(e)\\n else:\\n sql_query = \\\"\\\"\\\"SELECT datetime FROM news_unread\\\"\\\"\\\"\\n tmp_news_datetime = pd.read_sql_query(sql_query, self.conn,\\n parse_dates='datetime')\\n try: # Raise error when news_read is empty.\\n sql_query = \\\"\\\"\\\"SELECT datetime FROM news_read\\\"\\\"\\\"\\n tmp2_news_datetime = pd.read_sql_query(sql_query, self.conn,\\n parse_dates='datetime')\\n tmp_news_datetime = tmp_news_datetime.append(tmp2_news_datetime, ignore_index=True)\\n del tmp2_news_datetime\\n except:\\n pass\\n tmp_news_datetime.datetime = tmp_news_datetime.datetime.apply(lambda x: x.date())\\n for date in self.dates_to_get:\\n date = pd.to_datetime(date).date()\\n if (tmp_news_datetime.datetime == date).any():\\n sql_query = \\\"\\\"\\\"SELECT link FROM news_unread\\\"\\\"\\\"\\n links = pd.read_sql_query(sql_query, self.conn)\\n try:\\n sql_query = \\\"\\\"\\\"SELECT link FROM news_read\\\"\\\"\\\"\\n links2 = pd.read_sql_query(sql_query, self.conn)\\n links = links.append(links2, ignore_index=True)\\n except:\\n pass\\n links = links.link.tolist()\\n self.news_df = self.news_df.drop(self.news_df[self.news_df.link.isin(links)].index)\\n break\\n\\n self.news_df.sort_values('datetime', inplace=True)\\n self.news_df.to_sql('news_unread', self.conn, index=False,\\n if_exists='append')\\n sql_query = \\\"\\\"\\\"\\n SELECT * FROM news_unread ORDER BY datetime ASC\\n \\\"\\\"\\\"\\n self._data = pd.read_sql_query(sql_query, self.conn,\\n parse_dates='datetime')\\n self._data['datetime'] = self._data['datetime'].dt.strftime('%m-%d %H:%M')\\n self.update_finished.emit()\",\n \"def __folha(soup):\\n news = []\\n\\n items = soup.find('li', class_='c-most-popular__item')\\n\\n for item in items:\\n div = item.find('div', class_='c-most-popular__content')\\n link = div.a['href']\\n title = div.a.string\\n news.append(\\n dict(title=title, link=replace_original_link_with_outline_call(link)))\\n\\n return news\",\n \"def json2rdf(article, g):\\n a_news_item = load_article_into_newsitem_class(article)\\n g = rdfize_news_item(a_news_item, g)\\n return g\",\n \"def guidebook_news_feed(request):\\n current_site = Site.objects.get_current()\\n\\n feed_title = 'DjangoCon US News Updates'\\n feed_description = 'The latest updates and additions for DjangoCon US.'\\n feed_mimetype = 'application/rss+xml'\\n feed_template = 'pinax/blog/rss_feed.xml'\\n\\n blog_url = 'http://%s%s' % (current_site.domain, reverse('blog'))\\n # feed_url = 'http://%s%s' % (current_site.domain, reverse(url_name, kwargs=kwargs))\\n\\n posts = Post.objects.published().exclude(title__endswith='Sponsor')\\\\\\n .order_by('-published')\\n\\n if posts:\\n feed_updated = posts[0].updated\\n else:\\n feed_updated = datetime(2009, 8, 1, 0, 0, 0)\\n\\n feed = render_to_string(feed_template, {\\n # 'feed_id': feed_url,\\n 'feed_title': feed_title,\\n 'feed_description': feed_description,\\n 'blog_url': blog_url,\\n # 'feed_url': feed_url,\\n 'feed_updated': feed_updated,\\n 'entries': posts,\\n 'current_site': current_site,\\n })\\n\\n return HttpResponse(feed, content_type=feed_mimetype)\",\n \"async def news(self, ctx, typex):\\n if typex in options['news']['sport_type']:\\n output = news_output(get_sport_data())\\n elif typex in options['news']['world_type']:\\n output = news_output(get_news_data(\\\"https://www.novinky.cz/zahranicni\\\"))\\n elif typex in options['news']['home_type']:\\n output = news_output(get_news_data(\\\"https://www.novinky.cz/domaci\\\"))\\n else:\\n await ctx.send(\\\"Zadán nesprávný typ novinek\\\")\\n return\\n\\n for embed in output:\\n await ctx.send(embed=embed)\",\n \"def select_news(self):\\n data = self.soup.findAll('item')\\n for item in data:\\n news_data = dict()\\n for tag in ['title', 'link']:\\n news_data[tag] = item.find(tag).get_text()\\n\\n news_data['pubDate'] = parse(item.find('pubDate').get_text())\\n media = item.find('media:content')\\n\\n if media:\\n news_data['media'] = media.get('url')\\n else:\\n news_data['media'] = None\\n\\n yield news_data\",\n \"def get_news(self):\\n if self.api_key_entry.get() == \\\"\\\":\\n return None\\n api = nac(api_key=self.api_key_entry.get())\\n now = datetime.datetime.utcnow()\\n two_weeks = (now-datetime.timedelta(days=14))\\n #today = now.strftime()\\n query = \\\"\\\"\\n for cat in self.sorted_categories():\\n query += f\\\"{cat},\\\"\\n search = api.get_top_headlines(q=query,\\n sources=\\\"bbc-news,the-verge\\\",\\n language=\\\"en\\\")\\n news = \\\"\\\"\\n for article in search[\\\"articles\\\"]:\\n news += f\\\"{search['articles'][article]['title']}\\\\n\\\"\\n self.news_box.delete('1.0', tk.END)\\n self.news_box.insert('1.0', news)\",\n \"def news(self):\\n\\n # Get articles with search term, if available, from each News API source\\n news_api_articles = pd.DataFrame()\\n\\n q = urllib.parse.quote(\\\" OR \\\".join(self.search_terms), safe='')\\n\\n response = requests.get(\\\"https://newsapi.org/v2/everything?q=\\\" + q + \\\"&from=\\\" + datetime.now().strftime(\\n \\\"%Y-%m-%d\\\") + \\\"&sortBy=popularity&pageSize=100&apiKey=\\\" + self.__news_api_key)\\n\\n if response.status_code == 200:\\n data = json.loads(response.text)\\n\\n source_articles = []\\n\\n for article in data['articles']:\\n source_articles.append([article['title'],\\n article['description'],\\n article['url'],\\n article['publishedAt']])\\n\\n source_articles = pd.DataFrame(source_articles, columns=['title', 'description', 'url', 'publishedAt'])\\n news_api_articles = pd.concat([news_api_articles, source_articles])\\n\\n news_api_articles = news_api_articles.reset_index(drop='True')\\n\\n news_api_articles['publishedAt'] = news_api_articles['publishedAt'].apply(pd.to_datetime)\\n\\n news_api_articles = news_api_articles.fillna(' ')\\n\\n term_in_title = news_api_articles['title'].apply(self.any_term)\\n\\n news_api_articles = news_api_articles[term_in_title]\\n\\n if (len(news_api_articles) > 10):\\n news_api_articles = news_api_articles[0:10]\\n\\n else:\\n print(\\\"News API failed to return any items\\\")\\n\\n # Create shortened links using bitly if access token is provided\\n if self.__bitly_access_token != '':\\n\\n bitly_urls = []\\n\\n for index, article in news_api_articles.iterrows():\\n url = article['url']\\n bitly_response = requests.get(\\\"https://api-ssl.bitly.com/v3/shorten\\\",\\n params={'longUrl': url, 'access_token': self.__bitly_access_token})\\n\\n if bitly_response.status_code == 200:\\n data = json.loads(bitly_response.text)\\n bitly_urls.append(data['data']['url'])\\n\\n news_api_articles['url'] = bitly_urls\\n\\n # Store final list to TwitterBot object\\n self.list = news_api_articles\\n\\n return\",\n \"def feed2fields(file):\\r\\n import feedparser\\r\\n d = feedparser.parse(file)\\r\\n for entry in d.entries:\\r\\n date = (time.strftime(\\\"%Y-%m-%d %H:%M\\\", entry.updated_parsed)\\r\\n if hasattr(entry, \\\"updated_parsed\\\") else None)\\r\\n author = entry.author if hasattr(entry, \\\"author\\\") else None\\r\\n tags = [e['term'] for e in entry.tags] if hasattr(entry, \\\"tags\\\") else None\\r\\n\\r\\n slug = slugify(entry.title)\\r\\n kind = 'article'\\r\\n yield (entry.title, entry.description, slug, date, author, [], tags,\\r\\n kind, \\\"html\\\")\",\n \"def convert_stories(self, language_code):\\n self.logger.info(\\\"Converting stories...\\\")\\n old_stories, current, total = self.sql.read_table_with_total(self.working_original, \\\"stories\\\")\\n for old_story in old_stories:\\n new_story = {\\n 'id': old_story['sid'],\\n 'title': key_find('title', old_story, '').strip(),\\n 'summary': normalize(old_story['summary']),\\n 'notes': key_find('storynotes', old_story, '').strip(),\\n 'date': str(old_story['date']),\\n 'updated': str(old_story['updated']),\\n 'language_code': language_code\\n }\\n\\n self.logger.debug(f\\\"Converting story metadata for '{new_story['title']}'\\\")\\n query = f\\\"\\\"\\\"\\n INSERT INTO stories (id, title, summary, notes, date, updated, language_code)\\n VALUES {new_story['id'], new_story['title'], new_story['summary'],\\n new_story['notes'], new_story['date'], new_story['updated'], new_story['language_code']};\\n \\\"\\\"\\\"\\n self.sql.execute(self.working_open_doors, query)\\n\\n self.logger.debug(f\\\" tags...\\\")\\n tags = self._convert_story_tags(old_story)\\n self._convert_tags_join(new_story, tags)\\n\\n self.logger.debug(f\\\" authors...\\\")\\n self._convert_author_join(new_story, old_story['uid'])\\n coauthors = []\\n if key_find('coauthors', old_story):\\n for authorid in old_story['coauthors'].split(\\\",\\\"):\\n coauthors.append(authorid.strip())\\n for coauthor in coauthors:\\n self._convert_author_join(new_story, coauthor)\\n\\n current = print_progress(current, total, \\\"stories converted\\\")\\n return self.sql.execute_and_fetchall(self.working_open_doors, \\\"SELECT * FROM stories\\\")\",\n \"def parse_google_news_response(news_results, i=0):\\n\\n # get the value of the key 'status' from the response dictionary\\n results_status = news_results['status']\\n print(f'results_status: {results_status}')\\n\\n # get the value of the key 'totalResults' from the response dictionary\\n num_results = news_results['totalResults']\\n print(f'num_results: {num_results}')\\n\\n #save the response from the dictionary\\n title = news_results['articles'][i]['title']\\n source = news_results['articles'][i]['source']['name']\\n published_time = news_results['articles'][i]['publishedAt']\\n description = news_results['articles'][i]['description']\\n article_url = news_results['articles'][i]['url']\\n image_url = news_results['articles'][i]['urlToImage']\\n return (title, source, published_time, description, article_url, image_url)\",\n \"def newsfeed_en(request):\\n article_list = Article.objects.order_by('published_date')\\n context = {'article_list': article_list}\\n return render(request, 'sacms/newsfeed_en.html', context)\",\n \"def _transform_article(self, article):\\n\\n if \\\"_embedded\\\" in article:\\n article[\\\"image\\\"] = article[\\\"_embedded\\\"].get(\\n \\\"wp:featuredmedia\\\", [None]\\n )[0]\\n article[\\\"author\\\"] = article[\\\"_embedded\\\"].get(\\\"author\\\", [None])[0]\\n\\n if \\\"display_category\\\" not in article:\\n categories = article[\\\"_embedded\\\"].get(\\\"wp:term\\\", [[]])[0]\\n\\n if categories:\\n article[\\\"display_category\\\"] = categories[-1]\\n\\n if (\\n \\\"wp:term\\\" in article[\\\"_embedded\\\"]\\n and article[\\\"_embedded\\\"][\\\"wp:term\\\"][3]\\n ):\\n article[\\\"group\\\"] = article[\\\"_embedded\\\"][\\\"wp:term\\\"][3][0]\\n\\n if \\\"date_gmt\\\" in article:\\n article_gmt = article[\\\"date_gmt\\\"]\\n article_date = datetime.strptime(article_gmt, \\\"%Y-%m-%dT%H:%M:%S\\\")\\n article[\\\"date\\\"] = article_date.strftime(\\\"%-d %B %Y\\\")\\n\\n if \\\"excerpt\\\" in article and \\\"rendered\\\" in article[\\\"excerpt\\\"]:\\n article[\\\"excerpt\\\"][\\\"raw\\\"] = self._strip_excerpt(\\n article[\\\"excerpt\\\"][\\\"rendered\\\"]\\n )[:340]\\n\\n # If the excerpt doesn't end before 340 characters, add ellipsis\\n raw_article = article[\\\"excerpt\\\"][\\\"raw\\\"]\\n # split at the last 3 characters\\n raw_article_start = raw_article[:-3]\\n raw_article_end = raw_article[-3:]\\n # for the last 3 characters replace any part of […]\\n raw_article_end = raw_article_end.replace(\\\"[\\\", \\\"\\\")\\n raw_article_end = raw_article_end.replace(\\\"…\\\", \\\"\\\")\\n raw_article_end = raw_article_end.replace(\\\"]\\\", \\\"\\\")\\n # join it back up\\n article[\\\"excerpt\\\"][\\\"raw\\\"] = \\\"\\\".join(\\n [raw_article_start, raw_article_end, \\\" […]\\\"]\\n )\\n\\n if (\\n article.get(\\\"_start_month\\\")\\n and article.get(\\\"_start_year\\\")\\n and article.get(\\\"_start_day\\\")\\n ):\\n start_month_name = self._get_month_name(\\n int(article[\\\"_start_month\\\"])\\n )\\n article[\\\"start_date\\\"] = \\\"{} {} {}\\\".format(\\n article[\\\"_start_day\\\"], start_month_name, article[\\\"_start_year\\\"]\\n )\\n\\n if (\\n article.get(\\\"_end_month\\\")\\n and article.get(\\\"_end_year\\\")\\n and article.get(\\\"_end_day\\\")\\n ):\\n end_month_name = self._get_month_name(int(article[\\\"_end_month\\\"]))\\n article[\\\"end_date\\\"] = \\\"{} {} {}\\\".format(\\n article[\\\"_end_day\\\"], end_month_name, article[\\\"_end_year\\\"]\\n )\\n\\n if self.transform_links:\\n # replace url on the blog article page\\n article[\\\"content\\\"][\\\"rendered\\\"] = self._replace_url(\\n article[\\\"content\\\"][\\\"rendered\\\"]\\n )\\n\\n # replace url from the image thumbnail\\n if (\\n article[\\\"image\\\"] is not None\\n and \\\"source_url\\\" in article[\\\"image\\\"]\\n ):\\n article[\\\"image\\\"][\\\"source_url\\\"] = self._replace_url(\\n article[\\\"image\\\"][\\\"source_url\\\"]\\n )\\n\\n return article\",\n \"def __get_local_g1_news(soup):\\n news = []\\n anchors = soup.find('ul', class_='highlights').find_all('a')\\n\\n for a in anchors:\\n title = a.span.string.strip()\\n link = a['href']\\n news.append(dict(title=title, link=link))\\n return news\",\n \"def process_content(self, channel) -> dict:\\n\\n self.print_if_verbose(f\\\"Method 'process_content' is working:\\\")\\n\\n if self.limit is None or self.limit >= self.news_amount:\\n self.limit = self.news_amount\\n\\n rss_feed = {}\\n rss_feed[\\\"Feed\\\"] = channel.findtext('title')\\n rss_feed[\\\"Description\\\"] = channel.findtext('description')\\n rss_feed[\\\"Link\\\"] = channel.findtext('link')\\n rss_feed[\\\"Language\\\"] = channel.findtext('language')\\n rss_feed[\\\"News\\\"] = []\\n\\n append_news_to_rss_feed = 0\\n\\n self.print_if_verbose(f\\\"Adding data to the work dict 'rss_feed'...\\\")\\n\\n POSSIBLE_IMAGE_TAGS = (\\\"content\\\", \\\"thumbnail\\\", \\\"image\\\")\\n POSSIBLE_IMAGE_ATTR = (\\\"url\\\", \\\"href\\\")\\n\\n for item in channel.iterfind(\\\"item\\\"):\\n child_news = {}\\n child_news[\\\"Title\\\"] = item.findtext(\\\"title\\\")\\n child_news[\\\"Link\\\"] = item.findtext(\\\"link\\\")\\n child_news[\\\"PubDate\\\"] = self.get_formatted_date(item.findtext(\\\"pubDate\\\"))\\n child_news[\\\"Source\\\"] = item.findtext(\\\"source\\\")\\n child_news[\\\"ImageLink\\\"] = None\\n child_news[\\\"ImageCacheName\\\"] = None\\n\\n for tag in POSSIBLE_IMAGE_TAGS:\\n for item_field in item:\\n if tag in item_field.tag:\\n for attr in POSSIBLE_IMAGE_ATTR:\\n if attr in item_field.attrib:\\n child_news[\\\"ImageLink\\\"] = item_field.attrib[attr]\\n child_news[\\\"ImageCacheName\\\"] = \\\\\\n f\\\"{''.join(char for char in child_news['Link'] if char.isalnum())}.jpg\\\"\\n break\\n if child_news[\\\"ImageLink\\\"]:\\n break\\n if child_news[\\\"ImageLink\\\"]:\\n break\\n\\n rss_feed[\\\"News\\\"].append(child_news)\\n\\n append_news_to_rss_feed += 1\\n if append_news_to_rss_feed == self.limit:\\n break\\n\\n self.print_if_verbose(\\n f\\\"{append_news_to_rss_feed} news were added. \\\\n\\\"\\n f\\\"Method 'process_content' is finished. \\\\n\\\"\\n )\\n\\n return rss_feed\",\n \"def adapt_tweet(feedpost):\\n tweet = feedpost['title']\\n for action in (_make_links, _clean_name, urlize):\\n tweet = action(tweet)\\n feedpost['title'] = _get_tweet_number(feedpost['link'])\\n feedpost['body'] = u'<p>%s</p>' % tweet\\n return feedpost\",\n \"def wiki_to_kindle(article_urls, book_title, book_subtitle, \\\\\\n kindle_email, from_email, smtp_server, smtp_username, smtp_password, \\\\\\n convert_to_mobi=True):\\n epub_filename = create_a_book(article_urls, book_title, book_subtitle)\\n email_filename = epub_filename\\n if convert_to_mobi:\\n email_filename = convert_x_to_mobi(epub_filename)\\n email_subject = book_title\\n if len(book_subtitle):\\n email_subject += u' - {0}'.format(book_subtitle)\\n email_ebook(email_filename, from_email, kindle_email, email_subject, \\\\\\n smtp_server, smtp_username, smtp_password)\",\n \"def __repr__(self):\\r\\n\\r\\n return f\\\"<News: id = {self.news_id}, title = {self.title} summary = {self.summary}>\\\"\",\n \"def parse(self, article: BeautifulSoup):\\n # Need to find content in a different manner for each of the different sitetypes.\\n # Read this as studio, video, article etc.\\n # Assume that only articles ask for this functionality\\n\\n title = self.get_title(article)\\n sub_title = self.get_sub_title(article)\\n words = self.get_words(article)\\n journalists = self.get_journalist(article)\\n images = self.get_images(article)\\n subscription = self.get_subscription(article)\\n content_list = self.get_content(article)\\n\\n if not title:\\n title = self.headline.revisions[0].title\\n\\n revision = Revision(timestamp=datetime.datetime.now(pytz.timezone(\\\"Europe/Oslo\\\")), title=title, sub_title=sub_title, words=words, subscription=subscription)\\n\\n article = Article(news_site=self.news_site, headline=self.headline)\\n\\n return revision, article, journalists, images, content_list\",\n \"def _from_text_to_crf(self, message, entities=None):\\n crf_format = []\\n tokens = self.nlp(message[\\\"text\\\"]) \\n for i, token in enumerate(tokens):\\n pattern = {}\\n entity = entities[i] if entities else \\\"N/A\\\"\\n tag = None\\n custom_ner_features = None\\n crf_format.append((token.text, tag, entity, pattern, custom_ner_features))\\n return crf_format\",\n \"def get_feed(self):\\n\\t\\turl=\\\"http://news.google.com/news?ned=%s&topic=%s&output=rss\\\"\\n\\t\\tlinks=[{\\\"ned\\\":\\\"us\\\", \\\"type\\\":\\\"h\\\"},\\n\\t\\t\\t {\\\"ned\\\":\\\"us\\\", \\\"type\\\":\\\"w\\\"},\\n\\t\\t\\t {\\\"ned\\\":\\\"us\\\", \\\"type\\\":\\\"n\\\"},\\n\\t\\t\\t {\\\"ned\\\":\\\"us\\\", \\\"type\\\":\\\"n\\\"},\\n\\t\\t\\t {\\\"ned\\\":\\\"us\\\", \\\"type\\\":\\\"n\\\"},\\n\\t\\t\\t {\\\"ned\\\":\\\"us\\\", \\\"type\\\":\\\"n\\\"},\\n\\t\\t\\t {\\\"ned\\\":\\\"us\\\", \\\"type\\\":\\\"nz\\\"},\\n\\t\\t\\t {\\\"ned\\\":\\\"us\\\", \\\"type\\\":\\\"sa\\\"},\\n\\t\\t\\t {\\\"ned\\\":\\\"us\\\", \\\"type\\\":\\\"n\\\"},\\n\\t\\t\\t {\\\"ned\\\":\\\"us\\\", \\\"type\\\":\\\"n\\\"},\\n\\t\\t\\t {\\\"ned\\\":\\\"us\\\", \\\"type\\\":\\\"b\\\"},\\n\\t\\t\\t {\\\"ned\\\":\\\"us\\\", \\\"type\\\":\\\"t\\\"},\\n\\t\\t\\t {\\\"ned\\\":\\\"us\\\", \\\"type\\\":\\\"m\\\"},\\n\\t\\t\\t {\\\"ned\\\":\\\"us\\\", \\\"type\\\":\\\"s\\\"},\\n\\t\\t\\t {\\\"ned\\\":\\\"us\\\", \\\"type\\\":\\\"e\\\"},\\n\\t\\t\\t ]\\n\\t\\tfeed = links[self.get_input()]\\n\\t\\treturn url%(feed[\\\"ned\\\"],feed[\\\"type\\\"])\",\n \"def __local_ms(soup):\\n return __get_local_g1_news(soup)\",\n \"def __local_ms(soup):\\n return __get_local_g1_news(soup)\",\n \"def __veja(soup):\\n news = []\\n\\n links = soup.find(\\n 'section', class_='block most-read dark').find_all('a', class_='card')\\n\\n for link in links:\\n title = link.h2.text\\n news.append(dict(title=title,\\n link=replace_original_link_with_outline_call(link['href'])))\\n return news\",\n \"def _fetch_news(self, docid):\\n detail_link = Fetch163.detail_link % docid\\n try:\\n resp = urllib2.urlopen(detail_link)\\n except urllib2.URLError as e:\\n urllib_error(e)\\n else:\\n try:\\n doc_json = json.load(resp)\\n content = doc_json[docid]\\n title = content['title']\\n except KeyError:\\n print \\\"Error while Fetching docid: %s\\\" % docid\\n except ValueError:\\n print \\\"No JSON obj could be decoded\\\", resp.read()\\n else:\\n if self._filter_test(title):\\n #print title\\n c_num = content['replyCount']\\n ptime = content['ptime']\\n img_list = content['img']\\n if img_list:\\n list_pic = img_list[0]['src']\\n else:\\n list_pic = ''\\n body = content['body']\\n abstract = self._retrieve_abstract(body)\\n body = convert_163(body, img_list)\\n return abstract, body, c_num, ptime, list_pic\\n return '', '', '', '', list_pic\",\n \"def prepare_message(self, message, channel):\\n\\n if 'from' not in message:\\n message['from'] = {\\n 'id': channel.query,\\n 'name': channel.name,\\n }\\n l = 'https://www.facebook.com/%s/posts/%s' \\\\\\n % (message['from']['id'], message['id'].split('_')[1])\\n else:\\n l = 'http://www.facebook.com/permalink.php?id=%s&v=wall&story_fbid=%s' \\\\\\n % (message['from']['id'], message['id'].split('_')[1])\\n\\n return PostParser(\\n uid=message['id'],\\n author=message['from']['name'],\\n author_uid=message['from']['id'],\\n avatar=message.get('picture', None),\\n content=message.get('message', '') or message.get(\\n 'description', ''),\\n date=message['created_time'],\\n image=message.get('picture', None),\\n link=l\\n )\",\n \"def __local_es(soup):\\n return __get_local_g1_news(soup)\",\n \"def news(self) -> List[News]:\\n return self._news\",\n \"def fetch_news(n):\\n\\n # This is the list we will use the pass back the news information.\\n data = []\\n\\n # Get news stories from the MEN RSS feed.\\n response = feedparser.parse('https://www.manchestereveningnews.co.uk/?service=rss')\\n\\n # Loop through the news items, and the pull out the data we need.\\n for news in response.entries[:n]:\\n data.append({\\n 'headline': news.title,\\n 'content': news.description,\\n })\\n\\n return data\",\n \"def get_news() -> None:\\r\\n api_key = get_api_key()\\r\\n country = 'gb'\\r\\n url = 'https://newsapi.org/v2/top-headlines?country={}&apiKey={}' \\\\\\r\\n .format(country, api_key)\\r\\n new_news = requests.get(url).json()\\r\\n with open('news.json', 'r') as news_file:\\r\\n try:\\r\\n old_news = json.load(news_file)\\r\\n except Exception as error:\\r\\n log_warning(error)\\r\\n # Checks if the news is new or the same as the news already stored\\r\\n # in news.json.\\r\\n for i in range(5):\\r\\n if old_news['articles'][i] != new_news['articles'][i]:\\r\\n news_notification = ({'timestamp': \\\\\\r\\n time.strftime('%H:%M:%S'),\\r\\n 'type': 'News',\\r\\n 'title': new_news \\\\\\r\\n ['articles'][i]['title'],\\r\\n 'description': ''})\\r\\n news_log = ({'timestamp': time.strftime('%H:%M:%S'),\\r\\n 'type': 'news',\\r\\n 'description': 'New news articles' \\\\\\r\\n + new_news['articles'][i]['title'],\\r\\n 'error': ''})\\r\\n new_notification(news_notification)\\r\\n log_info(news_log)\\r\\n # RuntimeError caused when text to speech is already\\r\\n # currently playing something else.\\r\\n try:\\r\\n tts('New news story.' \\\\\\r\\n + new_news['articles'][i]['title'])\\r\\n except RuntimeError:\\r\\n log_error(RuntimeError)\\r\\n\\r\\n with open('news.json', 'w') as news_file:\\r\\n json.dump(new_news, news_file, indent=2)\\r\\n # Start the timer to run this function every 60 seconds.\\r\\n Timer(60, get_news).start()\",\n \"def _convert_feed(self, json, since):\\n\\n items = []\\n if json and json['response'].has_key('checkins'):\\n for checkin in json['response']['checkins']['items']:\\n created = datetime.fromtimestamp(checkin['createdAt'])\\n\\n if created.date() >= since:\\n item = ServiceItem()\\n item.location = {}\\n item.link_back = 'http://foursquare.com/venue/%s' % (checkin['venue']['id'],)\\n item.title = checkin['venue']['name']\\n\\n if checkin.has_key('shout') and checkin['shout']:\\n item.body = checkin['shout']\\n else:\\n if len(checkin['venue']['categories']) > 0 and checkin['venue']['location'].has_key('city'):\\n item.body = \\\"A %s in %s\\\" % (checkin['venue']['categories'][0]['name'], checkin['venue']['location']['city'])\\n elif checkin['venue'].has_key('city'):\\n item.body = \\\"In %s\\\" % (checkin['venue']['location']['city'])\\n else:\\n item.body = \\\"%s\\\" % (checkin['venue']['name'])\\n\\n if checkin['venue']['location'].has_key('lat') and checkin['venue']['location']['lng']:\\n item.location['lat'] = checkin['venue']['location']['lat']\\n item.location['long'] = checkin['venue']['location']['lng']\\n\\n item.created = created\\n item.service = self.service\\n \\n if checkin.has_key('isMayor'):\\n item.is_mayor = checkin['isMayor']\\n else:\\n pass\\n\\n if checkin['venue'].has_key('categories') and len(checkin['venue']['categories']) > 0:\\n item.icon = checkin['venue']['categories'][0]['icon']\\n item.categories = checkin['venue']['categories']\\n\\n items.append(item)\\n del(item)\\n\\n return items\",\n \"def article_cleanser(dirty_content):\\n global id_\\n\\n articles_dict = {}\\n source = dirty_content.find(\\\"title\\\").get_text()\\n data_tuple_list = []\\n\\n for item in dirty_content.find_all(\\\"item\\\"):\\n article_link = item.link.get_text()\\n\\n jj = item.pubDate.get_text().split(' ')\\n article_pubdate = datetime(int(jj[3]), int(strptime(jj[2], '%b').tm_mon), int(jj[1]), int(jj[4][:2]),\\n int(jj[4][3:5]),\\n int(jj[4][6:])).isoformat(' ')\\n\\n if source == \\\"Sudans Post\\\":\\n \\\"\\\"\\\" If the feed is for Sudans Post then it should scrap accordingly\\\"\\\"\\\"\\n article_number = item.guid.get_text()\\n guid_list = re.findall(\\\"(?<=p=)[0-9]{5}\\\", article_number)\\n article_guid = int(guid_list[0] + '000')\\n article_description = item.description.get_text().replace('[…]', '....')\\n article_description = article_description.replace('’', \\\"'\\\")\\n article_title = item.title.get_text().replace(u'\\\\xa0', ' ')\\n article_text = item.encoded.get_text() # gives a not-navigable string\\n\\n \\\"\\\"\\\"\\n So to make the article_content navigable I write it into a html file\\n and retrieving it again below\\n \\\"\\\"\\\"\\n with open(\\\"article_content.html\\\", 'w') as fp:\\n fp.write(article_text)\\n\\n with open(\\\"article_content.html\\\", \\\"r\\\") as fp:\\n article_soup = BeautifulSoup(fp, \\\"lxml\\\")\\n\\n article_content = \\\"\\\"\\n for c in article_soup.find_all('p'):\\n article_content += \\\"\\\\n\\\" + c.get_text()\\n\\n image_link = article_soup.find('a')[\\\"href\\\"]\\n\\n categories = \\\"\\\"\\n for cat in item.find_all(\\\"category\\\"):\\n categories += f\\\"/{cat.get_text().lower()}\\\"\\n\\n elif source == \\\"Radio Tamazuj - Latest news\\\":\\n \\\"\\\"\\\"If the feed is for Radio tamazuj, scrap accordingly \\\"\\\"\\\"\\n\\n article_title = item.title.get_text()\\n article_description = item.description.get_text()\\n article_guid = item.guid.get_text()[:8]\\n categories = \\\"Not available\\\"\\n\\n def get_content_from_link(link):\\n \\\"\\\"\\\" \\n Gets the article from the link, because radio tamazuj doesn't \\n post the article content on the rss feed\\n \\\"\\\"\\\"\\n\\n page = requests.get(link)\\n soup = BeautifulSoup(page.text, \\\"lxml\\\")\\n return soup\\n\\n soup = get_content_from_link(article_link)\\n image_link = \\\"https://radiotamazuj.org\\\" + soup.find(\\\"img\\\")[\\\"src\\\"]\\n\\n body = soup.select(\\\".body-text\\\")\\n content = \\\"\\\"\\n for i in body:\\n content += i.get_text()\\n article_content = content.replace(u'\\\\xa0', ' ')\\n\\n articles_dict[id_] = {\\n \\\"id\\\": id_,\\n \\\"source\\\": source,\\n \\\"Title\\\": article_title,\\n \\\"Link\\\": article_link,\\n \\\"PubDate\\\": article_pubdate,\\n \\\"guid\\\": article_guid,\\n \\\"Description\\\": article_description,\\n \\\"Content\\\": article_content,\\n \\\"categories\\\": categories,\\n \\\"image_link\\\": image_link,\\n }\\n data_tuple = (\\n id_,\\n source,\\n article_title,\\n article_link,\\n article_pubdate,\\n article_guid,\\n article_description,\\n article_content,\\n categories,\\n image_link,\\n )\\n id_ += 1 # counts number of articles\\n data_tuple_list.append(data_tuple)\\n return data_tuple_list\",\n \"def convert_html():\\n return\",\n \"def news_template_flex(news_list, events, key, id, isMore, alt_text='推薦新聞'):\\n news_box_components = []\\n for i, news in enumerate(news_list):\\n # img = \\\"https://cdn.pixabay.com/photo/2015/12/03/22/16/newspaper-1075795_1280.jpg\\\"\\n date = news['date']\\n date = re.sub(\\\"T.*Z\\\", \\\"\\\", date)\\n url = news['url']\\n\\n # Add separators between news.\\n if i > 0:\\n news_box_components.append(SeparatorComponent(margin='lg'))\\n # related_news_uri = f\\\"{RELATED_NEWS_LIFF_URI}?cid={news['cid']}&nid={news['nid']}\\\"\\n\\n TextComponents = []\\n TextComponents.append(TextComponent(\\n text=news['title'],\\n wrap=True,\\n size='md',\\n weight='bold',\\n action=URIAction(uri=url)\\n ))\\n TextComponents.append(TextComponent(\\n text='{} - {}'.format(news['source'], date),\\n wrap=True,\\n size='xxs',\\n color='#6c757d'\\n ))\\n words = []\\n event_v, event_o = events.split()\\n regex = \\\"({}|{})\\\".format(event_v, event_o)\\n tokenized_sentence = re.split(regex, news['sentences'][1])\\n tokenized_sentence = [t for t in tokenized_sentence if t != \\\"\\\"]\\n tokenized_sentence = [news['sentences'][0] + \\\" \\\"] + tokenized_sentence + [\\\" \\\" + news['sentences'][2]]\\n for i in tokenized_sentence:\\n if i == event_v or i == event_o:\\n words.append(SpanComponent(\\n text=i,\\n color=\\\"#e86d48\\\"\\n ))\\n else:\\n words.append(SpanComponent(\\n text=i,\\n ))\\n new_text_componet = TextComponent2(type=\\\"text\\\", text=\\\"hello, world\\\", contents=words, wrap=True)\\n TextComponents.append(new_text_componet)\\n TextComponents.append(BoxComponent(\\n layout='horizontal',\\n spacing='md',\\n contents=[\\n TextComponent(\\n text='查看這則新聞',\\n size='md',\\n color='#6E7DAB',\\n flex=1,\\n action=URIAction(uri=url),\\n ),\\n ]\\n ))\\n\\n news_box_components.append(\\n BoxComponent(\\n layout='horizontal',\\n spacing='md',\\n contents=[\\n BoxComponent(\\n layout='vertical',\\n flex=2,\\n spacing='sm',\\n contents=TextComponents\\n )\\n ]\\n )\\n )\\n if isMore:\\n news_box_components.append(\\n ButtonComponent(style=\\\"link\\\",\\n # margin=\\\"xs\\\",\\n action=MessageTemplateAction(\\n label=\\\"more\\\",\\n text=\\\"more:\\\" + str(id) + \\\":\\\" + key + \\\":\\\" + events\\n )))\\n\\n bubble = BubbleContainer(\\n body=BoxComponent(\\n layout='vertical',\\n spacing='lg',\\n contents=news_box_components\\n )\\n )\\n return FlexSendMessage(alt_text=alt_text, contents=bubble)\",\n \"def create(self, validated_data):\\n return News.objects.create(**validated_data)\",\n \"def convert(text):\\n return NewDocument.from_rst(text).format()\",\n \"def convert(update, context):\\n\\n # remove \\\"/convert\\\" from the string\\n user_string = update.message.text[8:].strip()\\n send_converted_message(update, context, user_string)\",\n \"def parse_details(ep_news):\\n\\n # instances that might be able to get from ep_news\\n information = {\\n 'price': None, # 价格：\\n 'area': None, # 地区：*\\n 'address': None, # 地址：\\n 'contact': None, # 联系人：\\n 'telephone': None, # 电话：*\\n 'wechat': None, # 微信：\\n 'qq': None, # QQ：\\n }\\n\\n # price and location\\n left = ep_news.find('div', {'class': 'l'})\\n\\n # loop through all left information\\n for info in left.find_all('dl'):\\n # get info name and value\\n name, value = info.find('dt').get_text().encode('utf-8'), info.find('dd').contents[0].encode('utf-8')\\n\\n # match values\\n if name == u'价格：'.encode('utf-8'):\\n information['price'] = value if value != '面议' else None\\n elif name == u'地区：'.encode('utf-8'):\\n information['area'] = value\\n elif name == u'地址：'.encode('utf-8'):\\n information['address'] = value\\n\\n # contact information\\n right = ep_news.find('div', {'class': 'r'})\\n\\n # loop through all right information\\n for info in right.find_all('dl'):\\n # get info name and value\\n name, value = info.find('dt').get_text().encode('utf-8'), info.find('dd').contents[0].encode('utf-8')\\n\\n # match values\\n if name == u'联系人：'.encode('utf-8'):\\n information['contact'] = value\\n elif name == u'电话：'.encode('utf-8'):\\n information['telephone'] = value\\n elif name == u'微信：'.encode('utf-8'):\\n information['wechat'] = value\\n elif name == u'QQ：'.encode('utf-8'):\\n information['qq'] = value\\n\\n return information\",\n \"def general_news():\\n\\n return general_scraper(['http://mesva.univaq.it/'])\",\n \"def __cnn(soup): \\n news = []\\n headers = soup.find_all('h3', class_='most__read__title') \\n for h3 in headers:\\n title = h3.a['title']\\n link = h3.a['href'] \\n news.append(dict(title=title, link=link))\\n \\n return news\",\n \"def updateNewsFeed(self):\\n try:\\n news, events, categories, eventCategories = self.requestData()\\n for language in NEWSFEED_LANGUAGES:\\n self.newsFeedModel.update(news[language], events[language], categories[language],\\n eventCategories[language], language=language)\\n except Exception as e:\\n print(\\\"there was a problem while updating the news feed\\\")\\n raise e\",\n \"def newsFromList(request, new_db_id):\\n MAX_NEWS = 10\\n\\n news_count = New.objects.count() # Pocet vsech zaznamu novinek\\n news_list_all = New.objects.all().order_by(\\\"-date\\\") # Sort by date ...\\n # Z celeho seznamu novinek vybereme jen 10 zacinajici pozadovanym 'new_db_id'\\n news_list_part = []\\n start_index = 0;\\n new_db_id_num = string.atoi(new_db_id)\\n for new in news_list_all:\\n if new.id == new_db_id_num:\\n news_list_part = news_list_all[start_index:start_index + MAX_NEWS]\\n break\\n start_index = start_index + 1\\n\\n # Vypocet prvniho ID z predchozi skupiny novinek (jedna skupina = MAX_NEWS)\\n # Poradi je otocene! Posledne zadana novinka je prvni v poradi\\n if (start_index + MAX_NEWS) < news_count:\\n preview_start_id = start_index + MAX_NEWS\\n else:\\n preview_start_id = start_index\\n\\n # Vypocet prvniho ID z nasledujici skupiny novinek (jedna skupina = MAX_NEWS)\\n # Poradi je otocene! Posledne zadana novinka je prvni v poradi\\n next_start_id = start_index - MAX_NEWS # prvni ID nasledujicich novinek\\n if next_start_id < 0:\\n next_start_id = 0;\\n\\n pictureOfWeek = PhotoOfWeek.objects.last()\\n context = {'news_list': news_list_part, 'news_count': news_count, 'pictureOfWeek': pictureOfWeek,\\n 'start_id': start_index, 'preview_start_id': preview_start_id, 'next_start_id': next_start_id}\\n return render(request, 'news/news.html', context)\",\n \"def news_title_generator(keyword):\\n keyword_quote = urllib.parse.quote(keyword)\\n url = (\\\"https://news.google.com/news/rss/search/section/q/\\\" +\\n keyword_quote + \\\"/\\\" + keyword_quote + \\\"?ned=jp&hl=ja&gl=JP\\\")\\n if hasattr(ssl, '_create_unverified_context'):\\n ssl._create_default_https_context = ssl._create_unverified_context\\n news_dict = feedparser.parse(url)\\n articles = list()\\n for i, article in enumerate(news_dict.entries):\\n tmp = {\\n \\\"no\\\": i,\\n \\\"title\\\": article.title,\\n \\\"link\\\": article.link,\\n \\\"published\\\": article.published\\n }\\n articles.append(tmp)\\n title_list = [article['title'] for article in articles]\\n return title_list\",\n \"def save_news_in_html_file(news, path_to_html, logger):\\n check_path_to_directory(path_to_html, logger)\\n html_file = tags.html(title='RSS news')\\n html_file.add(tags.head(tags.meta(charset='utf-8')))\\n\\n logger.info('Converting news to html format...')\\n for article in news:\\n html_factory(article, html_file)\\n\\n path = os.path.join(path_to_html, 'rss_news.html')\\n logger.info('Creating html-file...')\\n with open(path, 'w', encoding='utf-8') as file_html:\\n file_html.write(str(html_file))\\n logger.info('Html-file is created successfully!')\\n return file_html\",\n \"def rss_feed(rss_url):\\n try:\\n # Use feedparser to analyze given RSS feed, if it is valid RSS.\\n d = feedparser.parse(rss_url)\\n except:\\n return \\\"Sorry, invalid RSS feed. Please check and try again later.\\\"\\n \\n total = len(d['entries'])\\n updates = dict()\\n for index, item in enumerate(d['entries']):\\n # Convert publish time from ctime format to iso-time format.\\n a_time = time_convert(item.published)\\n # Set article url ad dictionary key, with publish date as value. \\n updates[str(item.link)] = a_time \\n return (total, updates)\",\n \"def convert_to_pdf(self, news_list):\\n self.logger.info(\\\"Converting news to PDF...\\\")\\n self.prepare_storage()\\n self.process_news_list_with_images(news_list)\\n content = self.generate_html_template(news_list)\\n pdf = io.BytesIO()\\n pisa.pisaDocument(content, pdf)\\n self.write_to_file(pdf.getvalue())\",\n \"def __local_am(soup):\\n return __get_local_g1_news(soup)\",\n \"def convert(label, tags, categories, projects, view, featured):\\n\\n filename = f\\\"content/publication/{label}/index.md\\\"\\n content = readfile(filename)\\n if featured: \\n content = content.replace(\\\"featured: false\\\", f'featured: true') \\n if tags: \\n content = content.replace(\\\"tags: []\\\", f'tags: [\\\"{tags}\\\"]') \\n if categories: \\n content = content.replace(\\\"categories: []\\\", f'categories: [\\\"{categories}\\\"]') \\n if projects: \\n content = content.replace(\\\"projects: []\\\", f'projects: [\\\"{projects}\\\"]')\\n writefile(filename, content)\\n if view:\\n print(content)\",\n \"def parse_rss(database, feed, depth=1):\\n # Get the updates article count, and article urls and publish dates.\\n rss_a = rss_feed(feed)\\n \\n # Get all (article urls, publish dates) pairs\\n articles = []\\n pairs = rss_a[1].items()\\n for url, pubdate in pairs: \\n articles += crawl_url(database, url, date=pubdate, depth=depth)\\n \\n return articles\",\n \"def top_post(section: izi.types.one_of(('news', 'newest', 'show'))='news'):\\n content = requests.get('https://news.ycombinator.com/{0}'.format(section)).content\\n text = content.decode('utf-8')\\n return text.split('<tr class=\\\\'athing\\\\'>')[1].split(\\\"<a href\\\")[1].split(\\\">\\\")[1].split(\\\"<\\\")[0]\",\n \"def convert(self, *args):\\n return _libsbml.SBMLDocument_convert(self, *args)\",\n \"def __bol(soup):\\n news = []\\n anchors = soup.find(\\n 'div', class_='mais-clicadas-lista link-primary').find_all('a')\\n\\n for a in anchors:\\n title = a.find('span', class_='mais-clicadas-item-content').text\\n link = a['href']\\n news.append(dict(title=title, link=link))\\n return news\",\n \"def news_feed(request):\\n\\n all_friends = get_all_friends(request)\\n news_feed = get_news_feed(request)\\n user_profile = get_users_profile(request.user.id)\\n\\n context = {\\n 'news_feed': news_feed,\\n 'user_profile': user_profile,\\n 'status_form': StatusForm,\\n }\\n\\n return render(request, 'status/news_feed.html', context)\",\n \"def __local_sc(soup):\\n return __get_local_g1_news(soup)\",\n \"def get_news(self, until_date=None):\\r\\n if not self.news:\\r\\n more_news = True\\r\\n headers = {\\\"Content-type\\\": \\\"application/x-www-form-urlencoded\\\", \\\"Accept\\\": \\\"text/plain\\\",\\r\\n 'Referer': 'http://' + self.domain + '/login.phtml', \\\"User-Agent\\\": user_agent}\\r\\n req = self.session.get('http://' + self.domain + '/team_news.phtml', headers=headers).content\\r\\n soup = BeautifulSoup(req, \\\"html.parser\\\")\\r\\n newsheader = soup.find_all('div', {'class', 'newsheader'})[1:]\\r\\n for index, i in enumerate(soup.find_all('div', {'class', 'article_content_text'})[1:]):\\r\\n news_date = datetime.strptime(newsheader[index].span['title'][0:8], \\\"%d.%m.%y\\\").date()\\r\\n news_title = newsheader[index].text.split(\\\">\\\")[1].strip()\\r\\n if news_date < until_date:\\r\\n more_news = None\\r\\n break\\r\\n self.news.append([news_date, news_title, i.text])\\r\\n first_news = 10\\r\\n while more_news and first_news < 200:\\r\\n other_news = BeautifulSoup(\\r\\n self.session.post('http://' + self.domain + '/team_news.phtml', headers=headers,\\r\\n data={'newsAction': 'reload', 'first_news': first_news}).content, \\\"html.parser\\\")\\r\\n newsheader = other_news.find_all('div', {'class', 'newsheader'})\\r\\n for index, i in enumerate(other_news.find_all('div', {'class', 'article_content_text'})):\\r\\n news_date = datetime.strptime(newsheader[index].span['title'][0:8], \\\"%d.%m.%y\\\").date()\\r\\n news_title = newsheader[index].text.split(\\\">\\\")[1].strip()\\r\\n if news_date < until_date:\\r\\n more_news = None\\r\\n break\\r\\n self.news.append([news_date, news_title, i.text])\\r\\n first_news += 10\\r\\n time.sleep(1)\\r\\n return self.news\",\n \"def _parse_feed(self,feed): \\n meta=[]\\n for entry in feed:\\n item_meta=self._parse_entry(entry)\\n item_meta['video-id']='0'\\n meta.append(item_meta)\\n self._logger.info('%s videos were founded and parsed at Megavideo',len(meta)) \\n return meta\",\n \"def get_university_news():\\n\\tresponse = requests.get('https://cumoodle.coventry.ac.uk')\\n\\tmoodleContent = BeautifulSoup(response.content, 'html.parser')\\n\\tpostLinks =[]\\n\\theadings = []\\n\\tdates = []\\n\\tdata = \\\"\\\"\\n\\tfor title in moodleContent.findAll('div',{'class':'subject'}):\\n\\t\\theadings.append(title.text+\\\"</a></p>\\\")\\n\\tfor link in moodleContent.findAll('div',{'class':'link'}):\\n\\t\\tpostLinks.append(\\\"<p style = 'font-size:120%;'> <a href = '\\\"+link.a['href']+\\\"'>\\\") \\n\\tfor date in moodleContent.findAll('div',{'class':'author'}):\\n\\t\\tdates.append(\\\"<p style='font-size:90%;'>\\\"+date.text[18:]+\\\"</p>\\\")\\n\\tresults = zip(postLinks, headings, dates)\\n\\tfor result in results:\\n\\t\\tdata+=(''.join(result))\\n\\treturn data\",\n \"def parse():\\n G.go(SITE_URL)\\n articles = []\\n for article in G.doc.select(\\\"//li[@class='regularitem']\\\"):\\n header = article.select('h4').text()\\n text = article.select('div').text()\\n url = article.select('h4/a/@href').text()\\n dt_string = article.select('h5').text()\\n # for date format \\\"1 Nov 2019 00:00:00\\\" or \\\"01 Nov 2019 00:00:00\\\"\\n article_dt = re.search(r'\\\\d{1,2} [a-zA-Z]+ \\\\d{4} \\\\d{2}:\\\\d{2}:\\\\d{2}', dt_string)\\n if article_dt is None:\\n logging.exception('Datestring format is unknown: %s', dt_string)\\n continue\\n article_dt = article_dt.group(0)\\n article_dt = datetime.datetime.strptime(article_dt, '%d %b %Y %H:%M:%S').strftime(\\\"%Y-%m-%d %H:%M:%S\\\")\\n articles.append({'header': header, 'url': url, 'text': text, 'dt': article_dt})\\n return articles\",\n \"def activities_to_jsonfeed(activities, actor=None, title=None, feed_url=None,\\n home_page_url=None):\\n try:\\n iter(activities)\\n except TypeError:\\n raise TypeError('activities must be iterable')\\n\\n if isinstance(activities, (dict, str)):\\n raise TypeError('activities may not be a dict or string')\\n\\n def image_url(obj):\\n return util.get_first(obj, 'image', {}).get('url')\\n\\n def actor_name(obj):\\n return obj.get('displayName') or obj.get('username')\\n\\n if not actor:\\n actor = {}\\n\\n items = []\\n for activity in activities:\\n obj = as1.get_object(activity) or activity\\n if obj.get('objectType') == 'person':\\n continue\\n author = as1.get_object(obj, 'author')\\n content = microformats2.render_content(\\n obj, include_location=True, render_attachments=True,\\n # Readers often obey CSS white-space: pre strictly and don't even line wrap,\\n # so don't use it. https://github.com/snarfed/granary/issues/456\\n white_space_pre=False)\\n obj_title = obj.get('title') or obj.get('displayName')\\n item = {\\n 'id': obj.get('id') or obj.get('url'),\\n 'url': obj.get('url'),\\n 'image': image_url(obj),\\n 'title': obj_title if mf2util.is_name_a_title(obj_title, content) else None,\\n 'summary': obj.get('summary'),\\n 'content_html': content,\\n 'date_published': obj.get('published'),\\n 'date_modified': obj.get('updated'),\\n 'author': {\\n 'name': actor_name(author),\\n 'url': author.get('url'),\\n 'avatar': image_url(author),\\n },\\n 'attachments': [],\\n }\\n\\n for att in obj.get('attachments', []):\\n url = util.get_url(att, 'stream') or util.get_url(att, 'image')\\n mime = mimetypes.guess_type(url)[0] if url else None\\n if (att.get('objectType') in ATTACHMENT_TYPES or\\n mime and mime.split('/')[0] in ATTACHMENT_TYPES):\\n item['attachments'].append({\\n 'url': url or '',\\n 'mime_type': mime,\\n 'title': att.get('title'),\\n })\\n\\n if not item['content_html']:\\n item['content_text'] = ''\\n items.append(item)\\n\\n return util.trim_nulls({\\n 'version': 'https://jsonfeed.org/version/1',\\n 'title': title or actor_name(actor) or 'JSON Feed',\\n 'feed_url': feed_url,\\n 'home_page_url': home_page_url or actor.get('url'),\\n 'author': {\\n 'name': actor_name(actor),\\n 'url': actor.get('url'),\\n 'avatar': image_url(actor),\\n },\\n 'items': items,\\n }, ignore='content_text')\",\n \"def _preprocess_feed(tweet: str):\\n t = tweet.lower()\\n t = re.sub(url_re, \\\" <URL> \\\", t)\\n t = t.replace(\\\"\\\\n\\\", \\\"\\\")\\n t = t.replace(\\\"#\\\", \\\" <HASHTAG> \\\")\\n t = re.sub(mention_re, \\\" <USER> \\\", t)\\n t = re.sub(smile_re, \\\" <EMOTICON> \\\", t)\\n t = re.sub(emoji_re, \\\" <EMOJI> \\\", t)\\n t = re.sub(time_re, \\\" <TIME> \\\", t)\\n t = re.sub(numbers_re, \\\" <NUMBER> \\\", t)\\n t = re.sub(not_ascii_re, \\\"\\\", t)\\n t = re.sub(space_collapse_re, \\\" \\\", t)\\n t = t.strip()\\n return t\",\n \"def _preprocess_feed(tweet: str):\\n t = tweet.lower()\\n t = re.sub(url_re, \\\" <URL> \\\", t)\\n t = t.replace(\\\"\\\\n\\\", \\\"\\\")\\n t = t.replace(\\\"#\\\", \\\" <HASHTAG> \\\")\\n t = re.sub(mention_re, \\\" <USER> \\\", t)\\n t = re.sub(smile_re, \\\" <EMOTICON> \\\", t)\\n t = re.sub(emoji_re, \\\" <EMOJI> \\\", t)\\n t = re.sub(time_re, \\\" <TIME> \\\", t)\\n t = re.sub(numbers_re, \\\" <NUMBER> \\\", t)\\n t = re.sub(not_ascii_re, \\\"\\\", t)\\n t = re.sub(space_collapse_re, \\\" \\\", t)\\n t = t.strip()\\n return t\",\n \"def send_news(self, msg):\\n log.debug(\\\"Sending news: {news}\\\", news=msg)\\n self.connection.send({'type': 'news', 'news': str(msg)})\",\n \"def __uol(soup):\\n news = []\\n container = soup.find('ol', class_='mostRead')\\n most_read = container.find_all('li')\\n\\n for item in most_read:\\n title = item.a['title']\\n link = item.a['href']\\n if \\\"folha.uol\\\" in link:\\n link = replace_original_link_with_outline_call(link)\\n news.append(dict(title=title, link=link))\\n return news\",\n \"def get_information(article_link):\\n\\n if \\\"video\\\" in article_link or \\\"/apps/\\\" in article_link or \\\"checknews\\\" in\\\\\\n article_link or not re.search(r\\\"\\\\d\\\\d\\\\d\\\\d/\\\\d\\\\d/\\\\d\\\\d\\\", article_link):\\n return None\\n\\n else:\\n\\n date_article = re.search(r\\\"\\\\d{4}/\\\\d{2}/\\\\d{2}\\\", article_link)[0]\\n date_article = date.datetime.strptime(date_article, \\\"%Y/%m/%d\\\")\\n\\n diff_date = date.datetime.now() - date_article\\n\\n if diff_date.days > 7:\\n return None\\n\\n else:\\n req = requests.get(article_link)\\n req.encoding = \\\"utf-8\\\"\\n data = req.text\\n soup = BeautifulSoup(data, \\\"lxml\\\")\\n\\n if soup.find(\\n \\\"div\\\",\\n class_=\\\"direct-headband\\\") or article_link != req.url:\\n return None\\n else:\\n balise_title = soup.find(\\\"h1\\\")\\n balise_title = balise_title.get_text()\\n balise_title = re.sub(r\\\"\\\\s\\\\s+\\\", \\\"\\\", balise_title)\\n\\n newspaper = \\\"Liberation\\\"\\n title = unidecode.unidecode(balise_title)\\n\\n author = \\\"\\\"\\n for span in soup.find_all('span'):\\n if span.get(\\\"class\\\") == ['author']:\\n if(span.a):\\n author = span.a.string\\n if span.get(\\\"class\\\") == ['date']:\\n if(span.time):\\n date_p = date.datetime.strptime(\\n span.time.get(\\\"datetime\\\"), \\\"%Y-%m-%dT\\\" +\\n \\\"%H:%M:%S\\\").date()\\n date_p = date_p.strftime(\\\"%Y-%m-%d\\\")\\n print(date_p)\\n\\n content = \\\"\\\"\\n for div in soup.find_all('div'):\\n for p in div.find_all('p'):\\n content += p.get_text() + \\\" \\\"\\n content = re.sub(\\\"<>\\\", \\\"\\\", content)\\n content = unidecode.unidecode(content)\\n\\n new_article = utils.recovery_article(\\n title, newspaper, [author], date_p, content, \\\" \\\")\\n\\n return new_article\",\n \"def __uol(soup):\\n news = []\\n container = soup.select('.mais-lidas-container')[0]\\n most_read = container.find_all('li')\\n\\n for item in most_read:\\n title = item.find('span', class_='cor-transition').get_text()\\n news.append(dict(title=title, link=item.a['href']))\\n return news\",\n \"def __repr__(self):\\r\\n\\r\\n return f\\\"<News: id = {self.news_id} >\\\"\",\n \"def _fetch_latest_for_tag(self, tag, today):\\n result = []\\n url = Fetch163.search_link % urllib2.quote(tag.name.encode('utf8'))\\n try:\\n resp = urllib2.urlopen(url)\\n except urllib2.URLError as e:\\n urllib_error(e)\\n else:\\n doc = eval(resp.read())\\n if doc and type(doc) is list:\\n if today:\\n news_today = self._today_filter(doc, delta=2)\\n else:\\n news_today = doc\\n for d in news_today:\\n docid = d.get('docid', '')\\n #title = u'%s' % d.get('title', '')\\n # the d.get('title') is a unicode string represent by\\n # python str, so use unicode-escape to decode it.\\n title = d.get('title', '')\\n #print type(title)\\n news_title = self._trans_title(title)\\n if docid and title:\\n news_exits = News.objects.filter(\\n Q(docid=docid) | Q(title=news_title)\\n )\\n #print docid, news_title, news_exits\\n intro, body, c_num, ptime, pic = self._fetch_news(docid)\\n if not news_exits:\\n print 'new news', news_title, docid\\n news = News()\\n news.docid = docid\\n news.title = news_title\\n news.content = body\\n news.tag = tag\\n news.comment_num = c_num\\n news.list_pic = pic\\n news.abstract = intro\\n news.update_time = ptime\\n news.save()\\n import time\\n time.sleep(2)\\n if news:\\n result.append(news)\\n else:\\n print 'update news', news_title\\n n = news_exits[0]\\n print 'old:', n.comment_num, 'new:', c_num\\n n.comment_num = c_num\\n n.save()\\n else:\\n print 'Fetch news for tag: %s, Error' % tag.name\\n\\n return result\",\n \"def fetch(feed):\\n # Fetch the feed data.\\n data = feedparser.parse(feed.ext_url)\\n new_articles = []\\n\\n # If the `bozo` value is anything\\n # but 0, there was an error parsing (or connecting) to the feed.\\n if data.bozo:\\n # Some errors are ok.\\n if not isinstance(data.bozo_exception, feedparser.CharacterEncodingOverride) and not isinstance(data.bozo_exception, feedparser.NonXMLContentType):\\n raise data.bozo_exception\\n\\n for entry in data.entries:\\n\\n # URL for this entry.\\n url = entry['links'][0]['href']\\n\\n # Check for an existing Article.\\n # If one exists, skip.\\n if Article.objects(ext_url=url).first():\\n continue\\n\\n data = extractor.extract(url, existing_data=entry)\\n\\n if data is None:\\n continue\\n\\n # Secondary check for an existing Article,\\n # by checking the title and source.\\n existing = Article.objects(title=data['title']).first()\\n if existing and existing.feed.source == feed.source:\\n continue\\n\\n data['feed'] = feed\\n\\n article = Article(**data)\\n article.save()\\n new_articles.append(article)\\n\\n return new_articles\",\n \"def get_saved_news(user, origin=\\\"saved\\\"):\\n \\n saved_news_feed = []\\n \\n for entry in user.saved_news:\\n article = entry.article\\n print(article.article_id)\\n article_item = {\\n \\\"title\\\" : article.title,\\n \\\"image\\\" : article.image,\\n \\\"description\\\" : article.description,\\n \\\"content\\\" : article.content,\\n \\\"pub_date\\\" : article.pub_date.strftime(\\\"%m/%d/%Y\\\"),\\n \\\"news_url\\\" : article.news_url,\\n \\\"saved_news_id\\\" : entry.id,\\n \\\"origin\\\" : origin,\\n \\\"note\\\" : entry.notes,\\n \\\"article_id\\\" : article.article_id\\n }\\n saved_news_feed.append(article_item)\\n print(saved_news_feed)\\n return saved_news_feed\"\n]","isConversational":false},"negative_scores":{"kind":"list like","value":["0.6300344","0.6065771","0.5977929","0.575112","0.57443106","0.57018936","0.5690105","0.56440276","0.56249213","0.56196296","0.56100756","0.55982596","0.55377096","0.55372685","0.5521784","0.5518923","0.55061096","0.54895186","0.54895186","0.5396291","0.5359799","0.5352498","0.534988","0.53484845","0.5348483","0.5289697","0.5260337","0.52427185","0.5218145","0.5211843","0.5188788","0.5184179","0.51599145","0.51565","0.5150597","0.51471215","0.51349044","0.5132267","0.50928825","0.5067635","0.50671023","0.50616616","0.5056778","0.504782","0.5046711","0.5027161","0.5016262","0.4997383","0.49870187","0.49838847","0.49677366","0.49518168","0.4934018","0.4934018","0.49198288","0.49014044","0.4894446","0.48619467","0.48596346","0.48554888","0.48520666","0.48460495","0.48426342","0.48185554","0.4810562","0.4803525","0.48008972","0.47991","0.47980604","0.47975948","0.47956908","0.47866538","0.47737437","0.4763137","0.4762547","0.47596604","0.47552457","0.47350743","0.47319803","0.47122803","0.46977937","0.46819112","0.46727067","0.46721467","0.46697444","0.4646871","0.46436623","0.46387064","0.46304837","0.46181178","0.46119756","0.46119756","0.46114177","0.46103105","0.46083736","0.46031713","0.45904195","0.4587209","0.4585196","0.45835128"],"string":"[\n \"0.6300344\",\n \"0.6065771\",\n \"0.5977929\",\n \"0.575112\",\n \"0.57443106\",\n \"0.57018936\",\n \"0.5690105\",\n \"0.56440276\",\n \"0.56249213\",\n \"0.56196296\",\n \"0.56100756\",\n \"0.55982596\",\n \"0.55377096\",\n \"0.55372685\",\n \"0.5521784\",\n \"0.5518923\",\n \"0.55061096\",\n \"0.54895186\",\n \"0.54895186\",\n \"0.5396291\",\n \"0.5359799\",\n \"0.5352498\",\n \"0.534988\",\n \"0.53484845\",\n \"0.5348483\",\n \"0.5289697\",\n \"0.5260337\",\n \"0.52427185\",\n \"0.5218145\",\n \"0.5211843\",\n \"0.5188788\",\n \"0.5184179\",\n \"0.51599145\",\n \"0.51565\",\n \"0.5150597\",\n \"0.51471215\",\n \"0.51349044\",\n \"0.5132267\",\n \"0.50928825\",\n \"0.5067635\",\n \"0.50671023\",\n \"0.50616616\",\n \"0.5056778\",\n \"0.504782\",\n \"0.5046711\",\n \"0.5027161\",\n \"0.5016262\",\n \"0.4997383\",\n \"0.49870187\",\n \"0.49838847\",\n \"0.49677366\",\n \"0.49518168\",\n \"0.4934018\",\n \"0.4934018\",\n \"0.49198288\",\n \"0.49014044\",\n \"0.4894446\",\n \"0.48619467\",\n \"0.48596346\",\n \"0.48554888\",\n \"0.48520666\",\n \"0.48460495\",\n \"0.48426342\",\n \"0.48185554\",\n \"0.4810562\",\n \"0.4803525\",\n \"0.48008972\",\n \"0.47991\",\n \"0.47980604\",\n \"0.47975948\",\n \"0.47956908\",\n \"0.47866538\",\n \"0.47737437\",\n \"0.4763137\",\n \"0.4762547\",\n \"0.47596604\",\n \"0.47552457\",\n \"0.47350743\",\n \"0.47319803\",\n \"0.47122803\",\n \"0.46977937\",\n \"0.46819112\",\n \"0.46727067\",\n \"0.46721467\",\n \"0.46697444\",\n \"0.4646871\",\n \"0.46436623\",\n \"0.46387064\",\n \"0.46304837\",\n \"0.46181178\",\n \"0.46119756\",\n \"0.46119756\",\n \"0.46114177\",\n \"0.46103105\",\n \"0.46083736\",\n \"0.46031713\",\n \"0.45904195\",\n \"0.4587209\",\n \"0.4585196\",\n \"0.45835128\"\n]","isConversational":false},"document_score":{"kind":"string","value":"0.6049749"},"document_rank":{"kind":"string","value":"2"}}},{"rowIdx":82391,"cells":{"query":{"kind":"string","value":"Returns the field names in the same order as str()"},"document":{"kind":"string","value":"def fieldNames():\n return (\n \"totalValue\", \"totalValueStd\",\n \"mean\", \"meanStd\",\n \"variance\", \"varianceStd\",\n \"skew\", \"skewStd\",\n \"kurtosis\", \"kurtosisStd\"\n )"},"metadata":{"kind":"string","value":"{\n \"objective\": {\n \"self\": [],\n \"paired\": [],\n \"triplet\": [\n [\n \"query\",\n \"document\",\n \"negatives\"\n ]\n ]\n }\n}"},"negatives":{"kind":"list like","value":["def get_field_names() -> Sequence[str]:\n raise NotImplementedError","def get_field_names(self):\n return {rv[0] for rv in self.iter_fields()}","def field_names(self):\n return self.base_field_names() + list(self.data.keys())","def field_names(self):\n ...","def field_names(self):\r\n return self._names","def _field_names(self):\n return [self._sanitize_field_name(field_name)\n for field_name in self._all_fields]","def _fields_names(cls) -> List:\n return list(field.name for field in dataclasses.fields(cls))","def get_field_names(self):\n return self._keys","def field_names(cls) -> tuple:\n return tuple((field.name for field in fields(cls)))","def fieldNames(self):\n return self.__fieldNames","def get_fieldnames(self):\n fieldnames = self._fields.keys()\n fieldnames.remove('time')\n fieldnames.remove('lon')\n fieldnames.remove('lat')\n return fieldnames","def field_names(self):\n if not self._field_names:\n self._field_names.update(self.properties.keys())\n\n self._field_names = [attr for attr in self._field_names if not attr.startswith(\"_\")]\n\n return self._field_names","def get_field_names(self):\n return self._parse_response(self.client.service.GetFieldNames())","def get_field_names(self, declared_fields, info):\n return self._requested_fields","def sortedFields(cls):\n return [\n i[0] for i in sorted(cls._nameToValue.items(), key=lambda item: item[1])\n ]","def getfieldnames(self, relType=None):\n if relType is None:\n names = [str(f.name) for f in self.info.fields]\n else:\n names = [str(f.name) for f in self.info.fields \\\n if f.relType == relType]\n return frozenset(names)","def _fields_as_string(self):\n return ', '.join([\n field.name for field in self.fields if field.required\n ] + [\n '[%s]' % field.name for field in self.fields if not field.required\n ])","def _get_fields(self):\n fields = self.table[0]\n fields = filter(None.__ne__, fields)\n return list(map(str.lower, fields))","def names(self):\r\n return self.get_field(self.name_field)","def get_field_names(cls):\n return cls._meta.get_field_names()","def get_fields(cls):\n return map(lambda x: getattr(cls, x), cls.get_field_names())","def fields(self):\n return [f[1] for f in sorted(self.dd.fields.items())]","def fields(self):\r\n return self._by_name.iteritems()","def fields(proto):\n return [x[0].name for x in proto.ListFields()]","def base_field_names(self):\n return self._base_field_names()","def test_get_field_names():\n assert set(get_field_names(AuditModel)) == {\n \"data_before\",\n \"data_after\",\n \"acting_user\",\n \"created_on\",\n \"updated_on\",\n }","def output_field_names(self):\r\n raise PendingDeprecationWarning\r\n return self.output_fields.names()","def stored_field_names(self):\r\n \r\n bn = self._by_name\r\n return [name for name in self._names if bn[name].stored]","def getFields(self):\n return sorted(self.schema.fields, key=lambda f: f.name)","def get_field_names(parsed):\n from ast import literal_eval\n field_names = set()\n for item in python_math_parser.walk(parsed):\n if isinstance(item[0], basestring):\n name, index, width = literal_eval(item[0])\n field_names.add(name)\n\n return list(field_names)","def names(self, indexes = None):\r\n\r\n if indexes:\r\n names = [self._field_names[i] for i in indexes]\r\n return names\r\n else:\r\n return self._field_names","def _getAttributeNames(self):\n return sorted(self._field_map.keys())","def get_result_field_names(table):\n field_names = []\n for th in table.find('thead').find_all('th'):\n field_names.append(th.string)\n\n return field_names\n # You could also use a list comprehension, e.g.\n #return [th.string for th in table.find('thead').find_all('th')]","def registrantFieldNames(self):\n fields = self.context.fgFields(self.request)\n field_names = []\n for field in fields:\n field_names.append(field.getName())\n return field_names","def list_fields(fc):\n return [f.name for f in arcpy.ListFields(fc)]","def gf_fieldnames(fn=\"forms-fields.txt\"):\n\tif fn is None: \n\t\treturn None\n\twith open(fn) as f:\n\t\tfieldnames = [fieldname.split(\":\")[0] for fieldname in f]\n\t\treturn fieldnames","def test_get_field_names(self):\n\n survey = self._create_test_survey()\n assert survey is not None\n\n survey.save_user_answers(self.student, self.student_answers, self.course_id)\n survey.save_user_answers(self.student2, self.student2_answers, self.course_id)\n\n names = survey.get_field_names()\n\n assert sorted(names) == ['ddl', 'field1', 'field2']","def fields(cls):\n return cls._nameToValue","def get_display_names(fields, capitalized=False):\n results = []\n for field in fields:\n field_title = get_display_name(field, capitalized)\n\n results.append(field_title)\n\n return results","def _get_fields(self):\n standard_fields = self._get_standards()\n fields = {\n \"id\": \"coalesce(address, id)\",\n \"blockNumber\": \"blockNumber\",\n \"address\": \"address\",\n \"owner\": \"from\",\n \"bytecode\": \"code\"\n }\n fields.update(standard_fields)\n fields_string = \", \".join([\n \"{} AS {}\".format(field, alias)\n for alias, field in fields.items()\n ])\n return fields_string","def get_default_field_names(self, declared_fields, model_info):\n return (\n list(declared_fields.keys()) +\n list(model_info.fields.keys())\n )","def defined_traj_field_names(self):\n\n return list(self.field_feature_shapes.keys())","def to_query(self):\n return [field.name for field in self.fields]","def get_fields_list(self):\n return self.description[\"fields\"][\"values\"].keys()","def field_names(self):\n\n entry_time_name = forms_builder.forms.models.FormEntry._meta.get_field('entry_time').verbose_name.title()\n document_title_name = Document._meta.get_field('name').verbose_name.title()\n document_url_name = Document._meta.get_field('url').verbose_name.title()\n\n form = self.form.all()[0]\n return ['user'] \\\n + [document_title_name, document_url_name] \\\n + [f.label\n for f in form.fields.all()] \\\n + [entry_time_name]","def get_fields(ds):\n\n # Get layer\n layer = ds.GetLayer(0)\n # feature.GetFieldCount()\n layer_defn = layer.GetLayerDefn()\n field_names = [layer_defn.GetFieldDefn(i).GetName() for i in range(layer_defn.GetFieldCount())]\n\n return field_names","def get_fields(self):\n field_list = []\n for field in self._meta.local_fields:\n if not field.primary_key:\n field_list.append([field.verbose_name.title(),\n self.__getattribute__(field.name),\n field.get_internal_type()])\n return field_list","def get_fields(self):\n \n fields = []\n for order in self.order_lst:\n fields += order.get_fields()\n \n fields = list(set(fields))\n \n out_fields = self.eod.sort_fields(fields)\n \n return out_fields","def get_fields(self):\n fields = []\n for items in self.order_items:\n fields += items.get_fields()\n \n fields = list(set(fields))\n \n field_order = ['recordId', 'orderId', 'itemId', 'collectionId']\n \n out_fields = field_order\n \n for f in fields:\n if f not in field_order:\n out_fields.append(f)\n \n return out_fields","def _fields_as_repr(self):\n return ', '.join([\n 'required(%s)' % repr(field.name)\n if field.required else\n repr(field.name)\n for field in self.fields\n ])","def _str_colnames(self):\n return ', '.join(self.colnames)","def names(self) -> list[str]:","def _query_fields(data):\n keys = data.keys() # Do this once to avoid any issue with dictionary order.\n column_fields = ', '.join(keys)\n value_fields = ', '.join('%({})s'.format(field) for field in keys)\n return column_fields, value_fields","def __fields(self):\n return [self.__class__.__dict__[f] for f in self.__class__._fields]","def get_field_names(self, product_name=None):\n if product_name is None:\n types = self.types.get_all()\n else:\n types = [self.types.get_by_name(product_name)]\n\n out = set()\n for type_ in types:\n out.update(type_.metadata_type.dataset_fields)\n return out","def names(self):\n if type(self.name) is types.StringType:\n return [self.name]\n else:\n return list(self.name)","def names(self):\n if self.dtype.fields:\n return list(self.dtype.names)\n elif getattr(self, \"_coldefs\", None) is not None:\n return self._coldefs.names\n else:\n return None","def __str__(self):\n return str([self.fields[col] for col in self.columns])","def get_renamed_input_fields(self):\n return self.renamed_input_fields","def select_fields_as_sql(self):\n fields = '*'\n if self._fields:\n fields = comma_join('`%s`' % field for field in self._fields)\n return fields","def _str_colnames(self):\n return ', '.join(self.galcat.colnames)","def names(cls) -> List[str]:","def _get_fields(self):\n table = self.ui.tableFields\n rows = table.rowCount()\n cols = table.columnCount()\n fields = []\n for i in range(rows):\n fields.append(\n tuple(map(lambda x: table.item(i, x).text(), range(cols)))\n )\n return fields","def raw_fields(self):\n list_fields = ['BAROR', None, None] + self.x.tolist() + [self.offt]\n return list_fields","def get_fields(self):\n\n\t\treturn self.__fields","def get_colnames(self, model):\n return [\n field.column \n for field in model._meta.get_fields() \n if getattr(field, 'di_show', False)\n ]","def get_fields(self):\n \n return self.metadata.keys()","def getattrnames(instance):\n return tuple(sorted([attr_name for attr_name in vars(instance).keys()\n if not attr_name.startswith('_')]))","def fields(self):\n for name, values in self.items():\n if name == \"set-cookie\":\n # Set-Cookie special case\n for value in values:\n yield (name, value)\n else:\n yield (name, \", \".join(values))","def names(self):\n if isinstance(self.name, string_types):\n return [self.name]\n else:\n return list(self.name)","def get_prev_field_names(self):\n if self.prev_description != None:\n return [i[0] for i in self.prev_description]\n else:\n return None","def _getFieldNameList(name):\n\n\t# check symbol set\n\tif not re.search(r\"^[\\w\\d_\\-\\.]+$\", name):\n\t\treturn None\n\n\telements = name.split('.')\n\n\tname_list = []\n\tfor element in elements:\n\n\t\tif len(element) == 0:\n\t\t\tname_list.append(None)\n\t\telif element[0].isdigit():\n\t\t\ttry:\n\t\t\t\tname_list.append(int(element))\n\t\t\texcept ValueError:\n\t\t\t\treturn None\n\t\telif element[0].isalpha() or element[0] == '_':\n\t\t\tname_list.append(element)\n\t\telse:\n\t\t\treturn None\n\n\treturn name_list","def get_fields(self):\n return list(self.metadata.keys())","def getFields(iface):\n return getFieldsInOrder(iface)","def getNames(self) -> List[unicode]:\n ...","def _list_fields(self):\n return list(self._state.keys())","def get_record_names(self, stripped=False):\n return _get_record_names(self._budget, stripped=stripped)","def fields(self, names = None):\r\n\r\n if not names:\r\n return self._fields\r\n\r\n fields = [self._field_dict[name] for name in names]\r\n\r\n return fields","def fields(self) -> Mapping[str, str]:\n return pulumi.get(self, \"fields\")","def get_field_names(prefix, msg, existing_names):\n if hasattr(msg, '__slots__'):\n for slot in msg.__slots__:\n get_field_names('_'.join([prefix, slot]), getattr(msg, slot), existing_names)\n elif isinstance(msg, list) and (len(msg) > 0) and hasattr(msg[0], '__slots__'):\n for slot in msg[0].__slots__:\n get_field_names('_'.join([prefix, slot]), getattr(msg[0], slot), existing_names)\n elif isinstance(msg, tuple):\n existing_names.append(prefix)\n else:\n existing_names.append(prefix)","def get_model_fields(self):\n fields = []\n\n for field in self.model._meta.get_fields():\n fields.append(field.name)\n\n return fields","def _get_fields(self):\n if not self._cursor.description:\n return {}\n\n results = {}\n column = 0\n\n for des in self._cursor.description:\n fieldname = des[0]\n results[column] = fieldname\n column = column + 1\n\n return results","def observable_field_names(self):\n\n n_trajs = self.num_trajs\n field_names = Counter()\n for traj in self.iter_trajs():\n for name in list(traj['observables']):\n field_names[name] += 1\n\n # if any of the field names has not occured for every\n # trajectory we raise an error\n for field_name, count in field_names.items():\n if count != n_trajs:\n raise TypeError(\"observable field names are inconsistent\")\n\n # otherwise return the field names for the observables\n return list(field_names.keys())","def all_fields(cls):\n return cls.__by_name.values()","def parse(self):\n result = []\n for field in self.get_fields():\n result.append(field.get_field())\n return result","def get_fields(params):\n field_str = params.pop('fields', None)\n if not field_str:\n return None\n return [f.strip() for f in field_str.split(',')]","def fields(self) -> List[str]:\n return FRAME_FIELDS[self.header.format] + CALCULATED_FIELDS","def fields(self) -> List[Field]: # pragma: no cover\n pass","def get_fields_by_names(form, field_names):\n fields = []\n for field_name in field_names.split(','):\n field = get_field_by_name(form, field_name)\n if field:\n fields.append(field)\n return fields","def fields(self):","def show_fields(self,\r\n ef_temp=None):\r\n\r\n returnstr = EMPTYCHAR\r\n temp_dict = {}\r\n returnset = set()\r\n for k_temp in self.default_dict['field']:\r\n k_temp = str(k_temp)\r\n\r\n if self.default_dict['field'][k_temp] in temp_dict:\r\n temp_dict[self.default_dict['field'][k_temp]].add(k_temp)\r\n else:\r\n temp_dict[self.default_dict['field'][k_temp]] = {k_temp}\r\n\r\n for k_temp in temp_dict:\r\n returnstr += (k_temp+' : '\r\n +str(rangelist.range_find([Index(a_temp)\r\n for a_temp\r\n in temp_dict[k_temp]],reduce=True)).replace(SLASH,LONGDASH)+EOL)\r\n if ef_temp is None:\r\n\r\n return returnstr\r\n\r\n for f_temp in ef_temp:\r\n returnset = returnset.union(temp_dict[f_temp])\r\n return returnset","def getAccessedFieldNames(self):\n pass","def get_fields(self, table_name):\n return self.get_table_meta(table_name)['fields']","def listFields(self):\n return self.get_json('/field')","def names(\n self\n ) -> Tuple[str, ...]:\n return self._names","def _get_fields(self):\n return self._fields","def list_fields(self):\n return list(self._state.keys())","def get_field_names_for_model(self, model):\n return [field.name for field in model._meta.fields if field.name != \"id\" and not\n (field.get_internal_type() == \"DateTimeField\" and\n (field.auto_now is True or field.auto_now_add is True))]","def get_names(self):\n return self.__names","def parameters_names(cls):\n return cls._Parameters._fields"],"string":"[\n \"def get_field_names() -> Sequence[str]:\\n raise NotImplementedError\",\n \"def get_field_names(self):\\n return {rv[0] for rv in self.iter_fields()}\",\n \"def field_names(self):\\n return self.base_field_names() + list(self.data.keys())\",\n \"def field_names(self):\\n ...\",\n \"def field_names(self):\\r\\n return self._names\",\n \"def _field_names(self):\\n return [self._sanitize_field_name(field_name)\\n for field_name in self._all_fields]\",\n \"def _fields_names(cls) -> List:\\n return list(field.name for field in dataclasses.fields(cls))\",\n \"def get_field_names(self):\\n return self._keys\",\n \"def field_names(cls) -> tuple:\\n return tuple((field.name for field in fields(cls)))\",\n \"def fieldNames(self):\\n return self.__fieldNames\",\n \"def get_fieldnames(self):\\n fieldnames = self._fields.keys()\\n fieldnames.remove('time')\\n fieldnames.remove('lon')\\n fieldnames.remove('lat')\\n return fieldnames\",\n \"def field_names(self):\\n if not self._field_names:\\n self._field_names.update(self.properties.keys())\\n\\n self._field_names = [attr for attr in self._field_names if not attr.startswith(\\\"_\\\")]\\n\\n return self._field_names\",\n \"def get_field_names(self):\\n return self._parse_response(self.client.service.GetFieldNames())\",\n \"def get_field_names(self, declared_fields, info):\\n return self._requested_fields\",\n \"def sortedFields(cls):\\n return [\\n i[0] for i in sorted(cls._nameToValue.items(), key=lambda item: item[1])\\n ]\",\n \"def getfieldnames(self, relType=None):\\n if relType is None:\\n names = [str(f.name) for f in self.info.fields]\\n else:\\n names = [str(f.name) for f in self.info.fields \\\\\\n if f.relType == relType]\\n return frozenset(names)\",\n \"def _fields_as_string(self):\\n return ', '.join([\\n field.name for field in self.fields if field.required\\n ] + [\\n '[%s]' % field.name for field in self.fields if not field.required\\n ])\",\n \"def _get_fields(self):\\n fields = self.table[0]\\n fields = filter(None.__ne__, fields)\\n return list(map(str.lower, fields))\",\n \"def names(self):\\r\\n return self.get_field(self.name_field)\",\n \"def get_field_names(cls):\\n return cls._meta.get_field_names()\",\n \"def get_fields(cls):\\n return map(lambda x: getattr(cls, x), cls.get_field_names())\",\n \"def fields(self):\\n return [f[1] for f in sorted(self.dd.fields.items())]\",\n \"def fields(self):\\r\\n return self._by_name.iteritems()\",\n \"def fields(proto):\\n return [x[0].name for x in proto.ListFields()]\",\n \"def base_field_names(self):\\n return self._base_field_names()\",\n \"def test_get_field_names():\\n assert set(get_field_names(AuditModel)) == {\\n \\\"data_before\\\",\\n \\\"data_after\\\",\\n \\\"acting_user\\\",\\n \\\"created_on\\\",\\n \\\"updated_on\\\",\\n }\",\n \"def output_field_names(self):\\r\\n raise PendingDeprecationWarning\\r\\n return self.output_fields.names()\",\n \"def stored_field_names(self):\\r\\n \\r\\n bn = self._by_name\\r\\n return [name for name in self._names if bn[name].stored]\",\n \"def getFields(self):\\n return sorted(self.schema.fields, key=lambda f: f.name)\",\n \"def get_field_names(parsed):\\n from ast import literal_eval\\n field_names = set()\\n for item in python_math_parser.walk(parsed):\\n if isinstance(item[0], basestring):\\n name, index, width = literal_eval(item[0])\\n field_names.add(name)\\n\\n return list(field_names)\",\n \"def names(self, indexes = None):\\r\\n\\r\\n if indexes:\\r\\n names = [self._field_names[i] for i in indexes]\\r\\n return names\\r\\n else:\\r\\n return self._field_names\",\n \"def _getAttributeNames(self):\\n return sorted(self._field_map.keys())\",\n \"def get_result_field_names(table):\\n field_names = []\\n for th in table.find('thead').find_all('th'):\\n field_names.append(th.string)\\n\\n return field_names\\n # You could also use a list comprehension, e.g.\\n #return [th.string for th in table.find('thead').find_all('th')]\",\n \"def registrantFieldNames(self):\\n fields = self.context.fgFields(self.request)\\n field_names = []\\n for field in fields:\\n field_names.append(field.getName())\\n return field_names\",\n \"def list_fields(fc):\\n return [f.name for f in arcpy.ListFields(fc)]\",\n \"def gf_fieldnames(fn=\\\"forms-fields.txt\\\"):\\n\\tif fn is None: \\n\\t\\treturn None\\n\\twith open(fn) as f:\\n\\t\\tfieldnames = [fieldname.split(\\\":\\\")[0] for fieldname in f]\\n\\t\\treturn fieldnames\",\n \"def test_get_field_names(self):\\n\\n survey = self._create_test_survey()\\n assert survey is not None\\n\\n survey.save_user_answers(self.student, self.student_answers, self.course_id)\\n survey.save_user_answers(self.student2, self.student2_answers, self.course_id)\\n\\n names = survey.get_field_names()\\n\\n assert sorted(names) == ['ddl', 'field1', 'field2']\",\n \"def fields(cls):\\n return cls._nameToValue\",\n \"def get_display_names(fields, capitalized=False):\\n results = []\\n for field in fields:\\n field_title = get_display_name(field, capitalized)\\n\\n results.append(field_title)\\n\\n return results\",\n \"def _get_fields(self):\\n standard_fields = self._get_standards()\\n fields = {\\n \\\"id\\\": \\\"coalesce(address, id)\\\",\\n \\\"blockNumber\\\": \\\"blockNumber\\\",\\n \\\"address\\\": \\\"address\\\",\\n \\\"owner\\\": \\\"from\\\",\\n \\\"bytecode\\\": \\\"code\\\"\\n }\\n fields.update(standard_fields)\\n fields_string = \\\", \\\".join([\\n \\\"{} AS {}\\\".format(field, alias)\\n for alias, field in fields.items()\\n ])\\n return fields_string\",\n \"def get_default_field_names(self, declared_fields, model_info):\\n return (\\n list(declared_fields.keys()) +\\n list(model_info.fields.keys())\\n )\",\n \"def defined_traj_field_names(self):\\n\\n return list(self.field_feature_shapes.keys())\",\n \"def to_query(self):\\n return [field.name for field in self.fields]\",\n \"def get_fields_list(self):\\n return self.description[\\\"fields\\\"][\\\"values\\\"].keys()\",\n \"def field_names(self):\\n\\n entry_time_name = forms_builder.forms.models.FormEntry._meta.get_field('entry_time').verbose_name.title()\\n document_title_name = Document._meta.get_field('name').verbose_name.title()\\n document_url_name = Document._meta.get_field('url').verbose_name.title()\\n\\n form = self.form.all()[0]\\n return ['user'] \\\\\\n + [document_title_name, document_url_name] \\\\\\n + [f.label\\n for f in form.fields.all()] \\\\\\n + [entry_time_name]\",\n \"def get_fields(ds):\\n\\n # Get layer\\n layer = ds.GetLayer(0)\\n # feature.GetFieldCount()\\n layer_defn = layer.GetLayerDefn()\\n field_names = [layer_defn.GetFieldDefn(i).GetName() for i in range(layer_defn.GetFieldCount())]\\n\\n return field_names\",\n \"def get_fields(self):\\n field_list = []\\n for field in self._meta.local_fields:\\n if not field.primary_key:\\n field_list.append([field.verbose_name.title(),\\n self.__getattribute__(field.name),\\n field.get_internal_type()])\\n return field_list\",\n \"def get_fields(self):\\n \\n fields = []\\n for order in self.order_lst:\\n fields += order.get_fields()\\n \\n fields = list(set(fields))\\n \\n out_fields = self.eod.sort_fields(fields)\\n \\n return out_fields\",\n \"def get_fields(self):\\n fields = []\\n for items in self.order_items:\\n fields += items.get_fields()\\n \\n fields = list(set(fields))\\n \\n field_order = ['recordId', 'orderId', 'itemId', 'collectionId']\\n \\n out_fields = field_order\\n \\n for f in fields:\\n if f not in field_order:\\n out_fields.append(f)\\n \\n return out_fields\",\n \"def _fields_as_repr(self):\\n return ', '.join([\\n 'required(%s)' % repr(field.name)\\n if field.required else\\n repr(field.name)\\n for field in self.fields\\n ])\",\n \"def _str_colnames(self):\\n return ', '.join(self.colnames)\",\n \"def names(self) -> list[str]:\",\n \"def _query_fields(data):\\n keys = data.keys() # Do this once to avoid any issue with dictionary order.\\n column_fields = ', '.join(keys)\\n value_fields = ', '.join('%({})s'.format(field) for field in keys)\\n return column_fields, value_fields\",\n \"def __fields(self):\\n return [self.__class__.__dict__[f] for f in self.__class__._fields]\",\n \"def get_field_names(self, product_name=None):\\n if product_name is None:\\n types = self.types.get_all()\\n else:\\n types = [self.types.get_by_name(product_name)]\\n\\n out = set()\\n for type_ in types:\\n out.update(type_.metadata_type.dataset_fields)\\n return out\",\n \"def names(self):\\n if type(self.name) is types.StringType:\\n return [self.name]\\n else:\\n return list(self.name)\",\n \"def names(self):\\n if self.dtype.fields:\\n return list(self.dtype.names)\\n elif getattr(self, \\\"_coldefs\\\", None) is not None:\\n return self._coldefs.names\\n else:\\n return None\",\n \"def __str__(self):\\n return str([self.fields[col] for col in self.columns])\",\n \"def get_renamed_input_fields(self):\\n return self.renamed_input_fields\",\n \"def select_fields_as_sql(self):\\n fields = '*'\\n if self._fields:\\n fields = comma_join('`%s`' % field for field in self._fields)\\n return fields\",\n \"def _str_colnames(self):\\n return ', '.join(self.galcat.colnames)\",\n \"def names(cls) -> List[str]:\",\n \"def _get_fields(self):\\n table = self.ui.tableFields\\n rows = table.rowCount()\\n cols = table.columnCount()\\n fields = []\\n for i in range(rows):\\n fields.append(\\n tuple(map(lambda x: table.item(i, x).text(), range(cols)))\\n )\\n return fields\",\n \"def raw_fields(self):\\n list_fields = ['BAROR', None, None] + self.x.tolist() + [self.offt]\\n return list_fields\",\n \"def get_fields(self):\\n\\n\\t\\treturn self.__fields\",\n \"def get_colnames(self, model):\\n return [\\n field.column \\n for field in model._meta.get_fields() \\n if getattr(field, 'di_show', False)\\n ]\",\n \"def get_fields(self):\\n \\n return self.metadata.keys()\",\n \"def getattrnames(instance):\\n return tuple(sorted([attr_name for attr_name in vars(instance).keys()\\n if not attr_name.startswith('_')]))\",\n \"def fields(self):\\n for name, values in self.items():\\n if name == \\\"set-cookie\\\":\\n # Set-Cookie special case\\n for value in values:\\n yield (name, value)\\n else:\\n yield (name, \\\", \\\".join(values))\",\n \"def names(self):\\n if isinstance(self.name, string_types):\\n return [self.name]\\n else:\\n return list(self.name)\",\n \"def get_prev_field_names(self):\\n if self.prev_description != None:\\n return [i[0] for i in self.prev_description]\\n else:\\n return None\",\n \"def _getFieldNameList(name):\\n\\n\\t# check symbol set\\n\\tif not re.search(r\\\"^[\\\\w\\\\d_\\\\-\\\\.]+$\\\", name):\\n\\t\\treturn None\\n\\n\\telements = name.split('.')\\n\\n\\tname_list = []\\n\\tfor element in elements:\\n\\n\\t\\tif len(element) == 0:\\n\\t\\t\\tname_list.append(None)\\n\\t\\telif element[0].isdigit():\\n\\t\\t\\ttry:\\n\\t\\t\\t\\tname_list.append(int(element))\\n\\t\\t\\texcept ValueError:\\n\\t\\t\\t\\treturn None\\n\\t\\telif element[0].isalpha() or element[0] == '_':\\n\\t\\t\\tname_list.append(element)\\n\\t\\telse:\\n\\t\\t\\treturn None\\n\\n\\treturn name_list\",\n \"def get_fields(self):\\n return list(self.metadata.keys())\",\n \"def getFields(iface):\\n return getFieldsInOrder(iface)\",\n \"def getNames(self) -> List[unicode]:\\n ...\",\n \"def _list_fields(self):\\n return list(self._state.keys())\",\n \"def get_record_names(self, stripped=False):\\n return _get_record_names(self._budget, stripped=stripped)\",\n \"def fields(self, names = None):\\r\\n\\r\\n if not names:\\r\\n return self._fields\\r\\n\\r\\n fields = [self._field_dict[name] for name in names]\\r\\n\\r\\n return fields\",\n \"def fields(self) -> Mapping[str, str]:\\n return pulumi.get(self, \\\"fields\\\")\",\n \"def get_field_names(prefix, msg, existing_names):\\n if hasattr(msg, '__slots__'):\\n for slot in msg.__slots__:\\n get_field_names('_'.join([prefix, slot]), getattr(msg, slot), existing_names)\\n elif isinstance(msg, list) and (len(msg) > 0) and hasattr(msg[0], '__slots__'):\\n for slot in msg[0].__slots__:\\n get_field_names('_'.join([prefix, slot]), getattr(msg[0], slot), existing_names)\\n elif isinstance(msg, tuple):\\n existing_names.append(prefix)\\n else:\\n existing_names.append(prefix)\",\n \"def get_model_fields(self):\\n fields = []\\n\\n for field in self.model._meta.get_fields():\\n fields.append(field.name)\\n\\n return fields\",\n \"def _get_fields(self):\\n if not self._cursor.description:\\n return {}\\n\\n results = {}\\n column = 0\\n\\n for des in self._cursor.description:\\n fieldname = des[0]\\n results[column] = fieldname\\n column = column + 1\\n\\n return results\",\n \"def observable_field_names(self):\\n\\n n_trajs = self.num_trajs\\n field_names = Counter()\\n for traj in self.iter_trajs():\\n for name in list(traj['observables']):\\n field_names[name] += 1\\n\\n # if any of the field names has not occured for every\\n # trajectory we raise an error\\n for field_name, count in field_names.items():\\n if count != n_trajs:\\n raise TypeError(\\\"observable field names are inconsistent\\\")\\n\\n # otherwise return the field names for the observables\\n return list(field_names.keys())\",\n \"def all_fields(cls):\\n return cls.__by_name.values()\",\n \"def parse(self):\\n result = []\\n for field in self.get_fields():\\n result.append(field.get_field())\\n return result\",\n \"def get_fields(params):\\n field_str = params.pop('fields', None)\\n if not field_str:\\n return None\\n return [f.strip() for f in field_str.split(',')]\",\n \"def fields(self) -> List[str]:\\n return FRAME_FIELDS[self.header.format] + CALCULATED_FIELDS\",\n \"def fields(self) -> List[Field]: # pragma: no cover\\n pass\",\n \"def get_fields_by_names(form, field_names):\\n fields = []\\n for field_name in field_names.split(','):\\n field = get_field_by_name(form, field_name)\\n if field:\\n fields.append(field)\\n return fields\",\n \"def fields(self):\",\n \"def show_fields(self,\\r\\n ef_temp=None):\\r\\n\\r\\n returnstr = EMPTYCHAR\\r\\n temp_dict = {}\\r\\n returnset = set()\\r\\n for k_temp in self.default_dict['field']:\\r\\n k_temp = str(k_temp)\\r\\n\\r\\n if self.default_dict['field'][k_temp] in temp_dict:\\r\\n temp_dict[self.default_dict['field'][k_temp]].add(k_temp)\\r\\n else:\\r\\n temp_dict[self.default_dict['field'][k_temp]] = {k_temp}\\r\\n\\r\\n for k_temp in temp_dict:\\r\\n returnstr += (k_temp+' : '\\r\\n +str(rangelist.range_find([Index(a_temp)\\r\\n for a_temp\\r\\n in temp_dict[k_temp]],reduce=True)).replace(SLASH,LONGDASH)+EOL)\\r\\n if ef_temp is None:\\r\\n\\r\\n return returnstr\\r\\n\\r\\n for f_temp in ef_temp:\\r\\n returnset = returnset.union(temp_dict[f_temp])\\r\\n return returnset\",\n \"def getAccessedFieldNames(self):\\n pass\",\n \"def get_fields(self, table_name):\\n return self.get_table_meta(table_name)['fields']\",\n \"def listFields(self):\\n return self.get_json('/field')\",\n \"def names(\\n self\\n ) -> Tuple[str, ...]:\\n return self._names\",\n \"def _get_fields(self):\\n return self._fields\",\n \"def list_fields(self):\\n return list(self._state.keys())\",\n \"def get_field_names_for_model(self, model):\\n return [field.name for field in model._meta.fields if field.name != \\\"id\\\" and not\\n (field.get_internal_type() == \\\"DateTimeField\\\" and\\n (field.auto_now is True or field.auto_now_add is True))]\",\n \"def get_names(self):\\n return self.__names\",\n \"def parameters_names(cls):\\n return cls._Parameters._fields\"\n]","isConversational":false},"negative_scores":{"kind":"list like","value":["0.82322943","0.8167811","0.8023562","0.80067897","0.7826049","0.7809036","0.77826476","0.7728658","0.7668442","0.76414555","0.7573801","0.7390122","0.7305378","0.7290598","0.7258827","0.7254","0.7249135","0.72328836","0.72208977","0.7136987","0.70926833","0.7043032","0.6977873","0.69729656","0.6949891","0.6858025","0.68467855","0.68209785","0.67909545","0.66615504","0.66608584","0.66576713","0.6622073","0.66129756","0.6573478","0.6548539","0.6512503","0.6493914","0.64446867","0.6444483","0.6430242","0.6427795","0.64219964","0.64105654","0.6407439","0.64019346","0.638239","0.63596225","0.6353821","0.6351755","0.628955","0.6267711","0.6259109","0.6239027","0.6229421","0.62245274","0.62196845","0.621906","0.62091804","0.6193881","0.6166669","0.61591226","0.6133215","0.6131709","0.6110938","0.610796","0.6104184","0.61027443","0.60926837","0.609146","0.60904","0.6084608","0.6078615","0.6077436","0.60727936","0.6072165","0.60637516","0.6062089","0.60533106","0.60500777","0.6048232","0.6035665","0.60329735","0.6022486","0.6020815","0.60186505","0.6018533","0.6018004","0.60023564","0.59993327","0.5995771","0.59769136","0.5970573","0.5969593","0.5961069","0.59556234","0.5942772","0.59375936","0.5934611","0.59289664"],"string":"[\n \"0.82322943\",\n \"0.8167811\",\n \"0.8023562\",\n \"0.80067897\",\n \"0.7826049\",\n \"0.7809036\",\n \"0.77826476\",\n \"0.7728658\",\n \"0.7668442\",\n \"0.76414555\",\n \"0.7573801\",\n \"0.7390122\",\n \"0.7305378\",\n \"0.7290598\",\n \"0.7258827\",\n \"0.7254\",\n \"0.7249135\",\n \"0.72328836\",\n \"0.72208977\",\n \"0.7136987\",\n \"0.70926833\",\n \"0.7043032\",\n \"0.6977873\",\n \"0.69729656\",\n \"0.6949891\",\n \"0.6858025\",\n \"0.68467855\",\n \"0.68209785\",\n \"0.67909545\",\n \"0.66615504\",\n \"0.66608584\",\n \"0.66576713\",\n \"0.6622073\",\n \"0.66129756\",\n \"0.6573478\",\n \"0.6548539\",\n \"0.6512503\",\n \"0.6493914\",\n \"0.64446867\",\n \"0.6444483\",\n \"0.6430242\",\n \"0.6427795\",\n \"0.64219964\",\n \"0.64105654\",\n \"0.6407439\",\n \"0.64019346\",\n \"0.638239\",\n \"0.63596225\",\n \"0.6353821\",\n \"0.6351755\",\n \"0.628955\",\n \"0.6267711\",\n \"0.6259109\",\n \"0.6239027\",\n \"0.6229421\",\n \"0.62245274\",\n \"0.62196845\",\n \"0.621906\",\n \"0.62091804\",\n \"0.6193881\",\n \"0.6166669\",\n \"0.61591226\",\n \"0.6133215\",\n \"0.6131709\",\n \"0.6110938\",\n \"0.610796\",\n \"0.6104184\",\n \"0.61027443\",\n \"0.60926837\",\n \"0.609146\",\n \"0.60904\",\n \"0.6084608\",\n \"0.6078615\",\n \"0.6077436\",\n \"0.60727936\",\n \"0.6072165\",\n \"0.60637516\",\n \"0.6062089\",\n \"0.60533106\",\n \"0.60500777\",\n \"0.6048232\",\n \"0.6035665\",\n \"0.60329735\",\n \"0.6022486\",\n \"0.6020815\",\n \"0.60186505\",\n \"0.6018533\",\n \"0.6018004\",\n \"0.60023564\",\n \"0.59993327\",\n \"0.5995771\",\n \"0.59769136\",\n \"0.5970573\",\n \"0.5969593\",\n \"0.5961069\",\n \"0.59556234\",\n \"0.5942772\",\n \"0.59375936\",\n \"0.5934611\",\n \"0.59289664\"\n]","isConversational":false},"document_score":{"kind":"string","value":"0.6830779"},"document_rank":{"kind":"string","value":"27"}}},{"rowIdx":82392,"cells":{"query":{"kind":"string","value":"Tuple of member data incl. uncertainty for export."},"document":{"kind":"string","value":"def fields(self):\n return (self._total + self._mean + self._variance\n + self._skew + self._kurtosis)"},"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 unpack(self) -> Tuple[list, list, list, list, float, int, list]:\n # (nice to have) todo:refactor --> as a namedtuple\n unpacked_super = super().unpack()\n\n observations, actions, rewards, Q_values, trajectory_return, _trajectory_lenght = unpacked_super\n\n return observations, actions, rewards, Q_values, trajectory_return, _trajectory_lenght, self.V_estimates","def get_data_attributes(self):\n if not self.transform_type:\n raise ValueError\n return ((self.mean_in, self.mean_out),\n (self.std_in, self.std_out),\n (self.max_in, self.max_out),\n (self.min_in, self.min_out))","def items(self):\n\t\treturn tuple(self.dist.items())","def values(self):\n\t\treturn tuple(self.dist.values())","def uncertainty(self):\n return self._uncertainty","def as_tuple(self):\n return self.value, self.name","def unpack_uncertainties(uarray):\n try:\n return un.nominal_values(uarray), un.std_devs(uarray)\n except:\n return uarray","def get_attributes(self):\n attrs = list()\n syms = list()\n for item in self.gradual_items:\n gi = item.as_integer()\n attrs.append(gi[0])\n syms.append(gi[1])\n return attrs, syms","def uncertainty(self):\n return self.uncertainty_per_deg * self.temp * self.n_atoms","def get_info_in_tuple(self):\r\n return self.key, self.value, self.get_color(), self.size_tree","def uncertainty(self):\n return self.uncertainty_per_atom * self.n_atoms","def retrieve_data_tuple(self):\n return ((42,))","def values(self) -> tuple[Any, ...]:\n return tuple(self.contents)","def get(self) -> tuple:","def serialize(self) -> typing.Tuple[str, bool, typing.Tuple[float, float]]:\r\n return (self.name, self.integral, self.domain)","def totuple(self):\n return (self.pageid, self.filename, self.uploader, self.width,\n self.height, self.size, self.quality_image,\n self.featured_picture, self.valued_image, self.timestamp)","def props(self):\n\n info = {'dtype': self.dtype, 'multivar': self.multivar, 'multiindex': self.multiindex}\n return info","def tuple(self) -> Tuple[float, float]:\n return (self.latitude, self.longitude)","def export_values(self):\n return (self.c0, self.rho0, self.p_ref, self.Ux, self.turb_intensity,\n self.length_scale, self.z_sl, self.Mach, self.beta,\n self.flow_param, self.dipole_axis)","def uncertainty(self) -> float:\n return self.__uncertainty","def uncertainty(self) -> float:\n return self.__uncertainty","def _deserialize(self):\n w = self.values[:, 0]\n x = self.values[:, 1]\n y = self.values[:, 2]\n z = self.values[:, 3]\n return (w, x, y, z)","def exportExtendedDT(self):\n return self.coords, list(self.triangles)","def to_plain(self) -> Tuple[List[List], List[str], List[str]]:\n\n return self.data, self.columns, self.dtypes","def apertures(self):\n return tuple(a for a in self._apertures if a is not None)","def unit_metadata(self):\n data = self.to_dict()\n metadata = [(k, v) for k, v in data.items() if k not in UNIT_KEYS]\n return metadata","def unfix(self) -> Union[tuple, None]:\n\n if hasattr(self, \"pred\"):\n return self.pred[\"class\"], (self.pred[\"score\"], False, self.pred[\"info\"])\n else:\n return None","def _unicode_members(self):\n return [(m.name or m._as_rhs()) for m in self.members]","def summary(self):\n return self.pfm","def items(self) -> List[Tuple[str, float]]:\n return self._loss_dict.items()","def get(self):\n if self.num_inst == 0:\n return (self.name, float('nan'))\n else:\n return (self.name, self.sum_metric / self.num_inst)","def house_attribs(self):\n return self.house_votes[4]","def summary(self):\n return [c.to_tuple() for c in self._components]","def attributes(self):\n\n attributes = []\n\n for member in self.members:\n if member.attribute:\n attributes.append(member)\n\n return attributes","def getDataAttributes(self):\n asRet = [];\n asAttrs = dir(self);\n for sAttr in asAttrs:\n if sAttr[0] == '_' or sAttr[0] == 'k':\n continue;\n if sAttr in self.kasInternalAttributes:\n continue;\n oValue = getattr(self, sAttr);\n if callable(oValue):\n continue;\n asRet.append(sAttr);\n return asRet;","def values(self) -> tuple[Any, ...]: # type: ignore\n return tuple(self.contents.values())","def filtered_xyz(self) -> tuple[int, int, int]:","def props(self):\r\n return ()","def props(self):\r\n return ()","def props(self):\r\n return ()","def xyz(self):\n return (self.x(), self.y(), self.z())","def extent(self) -> typing.Tuple[str, ...]:\n return self._extent.members()","def getTuple(self):\n return self.position.exportToTuple()","def data(self):\n return (self._full_name, self.total_donation, self.num_of_donations, self.avg_donation)","def _ps_fields(cls):\n return (\n ('exp_version', '', 'S16'),\n ('ad_observed', 0, 'b1'),\n ('is_stim', 0, 'b1')\n )","def umm_fields(item):\n return scom.umm_fields(item)","def info(self):\n return (self.kind, self.value)","def _get_fields(self):\n if self.data is not None:\n return self.begin, self.end, self.data\n else:\n return self.begin, self.end","def _get_fields(self):\n if self.data is not None:\n return self.begin, self.end, self.data\n else:\n return self.begin, self.end","def get_uvd(self):\n return {\"u\": self.__u,\n \"v\": self.__v,\n \"d\": self.__d}","def uniq_data(self):\n return (self.date, self.detection_chip_type, self.drop_psi,\n self.gain, self.instrument, self.laser_power, self.line_rate,\n self.oil_psi)","def _values(cls):\n assert inspect.isclass(cls)\n vals = tuple(val for val in _public_attributes(cls)\n if not inspect.isclass(val))\n return vals","def as_tuple(self):\n return (self.oid, self.type, self.value)","def get_data_tuple_names(self):\n return (('test_number',))","def get_varmeta(self):\n\n if self.ref_ds is not None:\n ref_meta = (self.ref_ds.id, self.ref_ds._names_from_attrs('all'))\n else:\n ref_meta = None\n if self.other_dss is not None:\n dss_meta = [(ds.id, ds._names_from_attrs('all')) for ds in self.other_dss]\n else:\n dss_meta = None\n if self.metric_ds is not None:\n mds_meta = (self.metric_ds.id, self.metric_ds._names_from_attrs('all'))\n else:\n mds_meta = None\n\n return ref_meta, dss_meta, mds_meta","def _get_std(self):\n return (0.229, 0.224, 0.225)","def attributes(self):\n _attrs = []\n if self.name:\n _attrs.append(\"name\")\n if self.label:\n _attrs.append(\"label\")\n if self.confidence:\n _attrs.append(\"confidence\")\n if self.index:\n _attrs.append(\"index\")\n if self.attrs:\n _attrs.append(\"attrs\")\n return _attrs + [\"points\"]","def z(self):\r\n return self.unif[2]","def to_tuple(self) -> tuple:\n return (self.transaction_number, self.date, self.description, self.memo, self.amount_debit,\n self.amount_credit, self.balance, self.check_number, self.fees, self.card_type,\n self.is_payment, self.is_transaction, self.user_id)","def data(self):\n tmp = self.acceleration()\n tmp.append(self.temperature())\n return tuple(tmp)","def as_tuple(self):\r\n return (self._real, self._imag)","def attrs(self):\n return self.size, self.propSuffix, self.specified","def getMetadataList(self):\n return [ x.toTuple() for x in self.metadata ]","def dataargs(self):\n return self.argsbytype(Data)","def get_result(self):\r\n return (self.x, self.lambd, self.z)","def get_data(self):\n return [self.weight]","def unpack_all(self) -> Tuple[Any, list]:\n unpack_super = super().unpack_all()\n return (*unpack_super, self.batch_Values_estimate.copy())","def itstat_extra(self):\n\n return ()","def xyz(self):\n return self._xyz","def values(self) -> tuple:\n\n return (\n self.left_pes,\n self.right_pes,\n self.left_manus,\n self.right_manus\n )","def print_data_members(self):\n keyVals = []\n for name in self.data_code['dataNames']:\n vals = getattr(self, name + 's')\n keyVals.append(vals)\n #print \"%ss = %s\" %(name,vals)\n\n msg = ''\n for name in self.data_code['dataNames']:\n msg += '%-10s ' % name\n msg += '\\n'\n\n nModes = len(keyVals[0])\n for i in xrange(nModes):\n for vals in keyVals:\n msg += '%-10g ' % vals[i]\n msg += '\\n'\n return msg + '\\n'","def test_export_attributes(self):\n\n v = Vector({ 'x': 3 }, { \"y\": True })\n e = v.to_array()\n self.assertEqual(v.attributes, Vector.from_array(e).attributes)\n self.assertEqual(v.__dict__, Vector.from_array(e).__dict__)","def record_to_tuple(record):\n return float(record[\"base_volume\"]), float(record[\"counter_volume\"]), int(record[\"trade_count\"])","def insert_tuple(self):\n\n return (self.raw_data.get('bill', {}).get(\"type\"),\n self.raw_data.get('chamber'),\n self.raw_data.get('session'),\n self.raw_data.get('date'),\n self._total_votes_cast(),\n self.passage_requirement,\n self.raw_data.get('result'),\n self.raw_data.get('congress'),\n self.raw_data.get('source_url'),\n self.raw_data.get('updated_at'),\n self.raw_data.get('vote_id'),\n self.yay_voter_cnt,\n self.nay_voter_cnt,\n self.abstain_voter_cnt,\n self.present_voter_cnt\n )","def attributes(self):","def metadata(self) -> Tuple[List[NodeType], List[EdgeType]]:\n return self.node_types, self.edge_types","def mem(self) -> List[float]:\n return list(map(attrgetter(\"mem\"), self.stats))","def get_perfect_information(self):\n raise NotImplementedError","def _prepare_for_stats(self):\n clean = self._discard_value(None)\n total = float(clean.total())\n return clean, total","def get_tup(self):\n\n tup = (\n self.key,\n self.headers,\n self.proxies,\n self.link,\n self.delta,\n self.size,\n self.status,\n datetime.now(),\n )\n return tup","def getFieldOfView(self): # pragma: no cover\n # to be overloaded by the child class.\n return 0, 0","def get(self) -> Tuple[str, Tuple]:\n # TODO:\n pass","def provide_data(self):\r\n # import pdb; pdb.set_trace()\r\n # for k, v in self.data:\r\n # print k,v\r\n return [(k, tuple([1] + list(v.shape[1:]))) for k, v in self.data]","def normalized_data(self):\n return self._data","def x_ub(self):\n pass","def missing(self):\n return tuple([f for f in self._required \\\n if f not in self or self[f] == None])","def get_highest_precision(self) -> Tuple[float, Dict[str, Any]]:\n highest_val = float('-inf')\n data = None\n for instance in self.stats:\n if self.stats[instance][\"Precision\"] > highest_val:\n highest_val = self.stats[instance][\"Precision\"]\n data = self.stats[instance]\n return highest_val, data","def getMeasures():","def getUserUnstakeInfo(self) -> list:\n unstake_info_list =[]\n for items in self._linked_list_var:\n unstake_info_list.append([items[1],items[2]])\n return unstake_info_list","def z(self):\n return self._data[2]","def get_tuples(self):\n pattern = list()\n for gi in self.gradual_items:\n temp = tuple([gi.attribute_col, gi.symbol])\n pattern.append(temp)\n return pattern","def items(self) -> tuple[tuple[Any, Any], ...]: # type: ignore\n return tuple(zip(self.keys(), self.values()))","def axis_member():\n return {\n \"type\": \"class\",\n \"base\": None,\n \"is_abstract\": False,\n \"properties\": [\n (\n \"description\",\n \"str\",\n \"0.1\",\n \"Description of the member (or name of parameter varied).\",\n ),\n (\n \"extra_detail\",\n \"str\",\n \"0.1\",\n \"If necessary: further information about ensemble member \"\n \"conformance.\",\n ),\n (\"index\", \"int\", \"1.1\", \"The ensemble member index.\"),\n (\n \"value\",\n \"float\",\n \"0.1\",\n \"If parameter varied; value for this member.\",\n ),\n (\n \"conformance\",\n \"linked_to(activity.conformance)\",\n \"0.1\",\n \"Conformance document for the target requirement that defines \"\n \"this member, if any.\",\n ),\n (\n \"axis\",\n \"linked_to(activity.ensemble_axis)\",\n \"1.1\",\n \"The parent axis of this ensemble member\",\n ),\n ],\n }","def getCoord(self):\n return (self.birth, self.death)","def data_privacy(self) -> pulumi.Output['outputs.DataPrivacyProperties']:\n return pulumi.get(self, \"data_privacy\")","def attributes(self) -> typing.Iterator[typing.Tuple[str]]:\n minimize = self.lattice._context._minimize(self._extent, self._intent)\n return (i.members() for i in minimize)","def _named_attrs(self, parts:dict) -> \\\n (QA4SMNamedAttributes, list, QA4SMNamedAttributes):\n\n if not self.ismetr():\n raise IOError(self.varname, '{} is not in form of a QA4SM metric variable.')\n\n if self.g == 0:\n a = QA4SMAttributes(self.attrs)\n ref_ds = QA4SMNamedAttributes(a.ref_dc - a._offset_id_dc,\n a.get_ref_names()['short_name'], self.attrs)\n return ref_ds, None, None\n else:\n dss = []\n ref_ds = QA4SMNamedAttributes(parts['ref_id'], parts['ref_ds'], self.attrs)\n ds = QA4SMNamedAttributes(parts['sat_id0'], parts['sat_ds0'], self.attrs)\n dss.append(ds)\n if self.g == 3:\n ds = QA4SMNamedAttributes(parts['sat_id1'], parts['sat_ds1'], self.attrs)\n dss.append(ds)\n mds = QA4SMNamedAttributes(parts['mds_id'], parts['mds'], self.attrs)\n else:\n mds = None\n return ref_ds, dss, mds","def tuple(self):\n return self.major, self.minor, self.patch","def values(self):\n # Account for events generated with different versions of code.\n values = [getattr(self, x, \"\") for x in self.base_field_names()]\n values += [self.data.get(x, \"\") for x in self.data]\n return values","def get_members():"],"string":"[\n \"def unpack(self) -> Tuple[list, list, list, list, float, int, list]:\\n # (nice to have) todo:refactor --> as a namedtuple\\n unpacked_super = super().unpack()\\n\\n observations, actions, rewards, Q_values, trajectory_return, _trajectory_lenght = unpacked_super\\n\\n return observations, actions, rewards, Q_values, trajectory_return, _trajectory_lenght, self.V_estimates\",\n \"def get_data_attributes(self):\\n if not self.transform_type:\\n raise ValueError\\n return ((self.mean_in, self.mean_out),\\n (self.std_in, self.std_out),\\n (self.max_in, self.max_out),\\n (self.min_in, self.min_out))\",\n \"def items(self):\\n\\t\\treturn tuple(self.dist.items())\",\n \"def values(self):\\n\\t\\treturn tuple(self.dist.values())\",\n \"def uncertainty(self):\\n return self._uncertainty\",\n \"def as_tuple(self):\\n return self.value, self.name\",\n \"def unpack_uncertainties(uarray):\\n try:\\n return un.nominal_values(uarray), un.std_devs(uarray)\\n except:\\n return uarray\",\n \"def get_attributes(self):\\n attrs = list()\\n syms = list()\\n for item in self.gradual_items:\\n gi = item.as_integer()\\n attrs.append(gi[0])\\n syms.append(gi[1])\\n return attrs, syms\",\n \"def uncertainty(self):\\n return self.uncertainty_per_deg * self.temp * self.n_atoms\",\n \"def get_info_in_tuple(self):\\r\\n return self.key, self.value, self.get_color(), self.size_tree\",\n \"def uncertainty(self):\\n return self.uncertainty_per_atom * self.n_atoms\",\n \"def retrieve_data_tuple(self):\\n return ((42,))\",\n \"def values(self) -> tuple[Any, ...]:\\n return tuple(self.contents)\",\n \"def get(self) -> tuple:\",\n \"def serialize(self) -> typing.Tuple[str, bool, typing.Tuple[float, float]]:\\r\\n return (self.name, self.integral, self.domain)\",\n \"def totuple(self):\\n return (self.pageid, self.filename, self.uploader, self.width,\\n self.height, self.size, self.quality_image,\\n self.featured_picture, self.valued_image, self.timestamp)\",\n \"def props(self):\\n\\n info = {'dtype': self.dtype, 'multivar': self.multivar, 'multiindex': self.multiindex}\\n return info\",\n \"def tuple(self) -> Tuple[float, float]:\\n return (self.latitude, self.longitude)\",\n \"def export_values(self):\\n return (self.c0, self.rho0, self.p_ref, self.Ux, self.turb_intensity,\\n self.length_scale, self.z_sl, self.Mach, self.beta,\\n self.flow_param, self.dipole_axis)\",\n \"def uncertainty(self) -> float:\\n return self.__uncertainty\",\n \"def uncertainty(self) -> float:\\n return self.__uncertainty\",\n \"def _deserialize(self):\\n w = self.values[:, 0]\\n x = self.values[:, 1]\\n y = self.values[:, 2]\\n z = self.values[:, 3]\\n return (w, x, y, z)\",\n \"def exportExtendedDT(self):\\n return self.coords, list(self.triangles)\",\n \"def to_plain(self) -> Tuple[List[List], List[str], List[str]]:\\n\\n return self.data, self.columns, self.dtypes\",\n \"def apertures(self):\\n return tuple(a for a in self._apertures if a is not None)\",\n \"def unit_metadata(self):\\n data = self.to_dict()\\n metadata = [(k, v) for k, v in data.items() if k not in UNIT_KEYS]\\n return metadata\",\n \"def unfix(self) -> Union[tuple, None]:\\n\\n if hasattr(self, \\\"pred\\\"):\\n return self.pred[\\\"class\\\"], (self.pred[\\\"score\\\"], False, self.pred[\\\"info\\\"])\\n else:\\n return None\",\n \"def _unicode_members(self):\\n return [(m.name or m._as_rhs()) for m in self.members]\",\n \"def summary(self):\\n return self.pfm\",\n \"def items(self) -> List[Tuple[str, float]]:\\n return self._loss_dict.items()\",\n \"def get(self):\\n if self.num_inst == 0:\\n return (self.name, float('nan'))\\n else:\\n return (self.name, self.sum_metric / self.num_inst)\",\n \"def house_attribs(self):\\n return self.house_votes[4]\",\n \"def summary(self):\\n return [c.to_tuple() for c in self._components]\",\n \"def attributes(self):\\n\\n attributes = []\\n\\n for member in self.members:\\n if member.attribute:\\n attributes.append(member)\\n\\n return attributes\",\n \"def getDataAttributes(self):\\n asRet = [];\\n asAttrs = dir(self);\\n for sAttr in asAttrs:\\n if sAttr[0] == '_' or sAttr[0] == 'k':\\n continue;\\n if sAttr in self.kasInternalAttributes:\\n continue;\\n oValue = getattr(self, sAttr);\\n if callable(oValue):\\n continue;\\n asRet.append(sAttr);\\n return asRet;\",\n \"def values(self) -> tuple[Any, ...]: # type: ignore\\n return tuple(self.contents.values())\",\n \"def filtered_xyz(self) -> tuple[int, int, int]:\",\n \"def props(self):\\r\\n return ()\",\n \"def props(self):\\r\\n return ()\",\n \"def props(self):\\r\\n return ()\",\n \"def xyz(self):\\n return (self.x(), self.y(), self.z())\",\n \"def extent(self) -> typing.Tuple[str, ...]:\\n return self._extent.members()\",\n \"def getTuple(self):\\n return self.position.exportToTuple()\",\n \"def data(self):\\n return (self._full_name, self.total_donation, self.num_of_donations, self.avg_donation)\",\n \"def _ps_fields(cls):\\n return (\\n ('exp_version', '', 'S16'),\\n ('ad_observed', 0, 'b1'),\\n ('is_stim', 0, 'b1')\\n )\",\n \"def umm_fields(item):\\n return scom.umm_fields(item)\",\n \"def info(self):\\n return (self.kind, self.value)\",\n \"def _get_fields(self):\\n if self.data is not None:\\n return self.begin, self.end, self.data\\n else:\\n return self.begin, self.end\",\n \"def _get_fields(self):\\n if self.data is not None:\\n return self.begin, self.end, self.data\\n else:\\n return self.begin, self.end\",\n \"def get_uvd(self):\\n return {\\\"u\\\": self.__u,\\n \\\"v\\\": self.__v,\\n \\\"d\\\": self.__d}\",\n \"def uniq_data(self):\\n return (self.date, self.detection_chip_type, self.drop_psi,\\n self.gain, self.instrument, self.laser_power, self.line_rate,\\n self.oil_psi)\",\n \"def _values(cls):\\n assert inspect.isclass(cls)\\n vals = tuple(val for val in _public_attributes(cls)\\n if not inspect.isclass(val))\\n return vals\",\n \"def as_tuple(self):\\n return (self.oid, self.type, self.value)\",\n \"def get_data_tuple_names(self):\\n return (('test_number',))\",\n \"def get_varmeta(self):\\n\\n if self.ref_ds is not None:\\n ref_meta = (self.ref_ds.id, self.ref_ds._names_from_attrs('all'))\\n else:\\n ref_meta = None\\n if self.other_dss is not None:\\n dss_meta = [(ds.id, ds._names_from_attrs('all')) for ds in self.other_dss]\\n else:\\n dss_meta = None\\n if self.metric_ds is not None:\\n mds_meta = (self.metric_ds.id, self.metric_ds._names_from_attrs('all'))\\n else:\\n mds_meta = None\\n\\n return ref_meta, dss_meta, mds_meta\",\n \"def _get_std(self):\\n return (0.229, 0.224, 0.225)\",\n \"def attributes(self):\\n _attrs = []\\n if self.name:\\n _attrs.append(\\\"name\\\")\\n if self.label:\\n _attrs.append(\\\"label\\\")\\n if self.confidence:\\n _attrs.append(\\\"confidence\\\")\\n if self.index:\\n _attrs.append(\\\"index\\\")\\n if self.attrs:\\n _attrs.append(\\\"attrs\\\")\\n return _attrs + [\\\"points\\\"]\",\n \"def z(self):\\r\\n return self.unif[2]\",\n \"def to_tuple(self) -> tuple:\\n return (self.transaction_number, self.date, self.description, self.memo, self.amount_debit,\\n self.amount_credit, self.balance, self.check_number, self.fees, self.card_type,\\n self.is_payment, self.is_transaction, self.user_id)\",\n \"def data(self):\\n tmp = self.acceleration()\\n tmp.append(self.temperature())\\n return tuple(tmp)\",\n \"def as_tuple(self):\\r\\n return (self._real, self._imag)\",\n \"def attrs(self):\\n return self.size, self.propSuffix, self.specified\",\n \"def getMetadataList(self):\\n return [ x.toTuple() for x in self.metadata ]\",\n \"def dataargs(self):\\n return self.argsbytype(Data)\",\n \"def get_result(self):\\r\\n return (self.x, self.lambd, self.z)\",\n \"def get_data(self):\\n return [self.weight]\",\n \"def unpack_all(self) -> Tuple[Any, list]:\\n unpack_super = super().unpack_all()\\n return (*unpack_super, self.batch_Values_estimate.copy())\",\n \"def itstat_extra(self):\\n\\n return ()\",\n \"def xyz(self):\\n return self._xyz\",\n \"def values(self) -> tuple:\\n\\n return (\\n self.left_pes,\\n self.right_pes,\\n self.left_manus,\\n self.right_manus\\n )\",\n \"def print_data_members(self):\\n keyVals = []\\n for name in self.data_code['dataNames']:\\n vals = getattr(self, name + 's')\\n keyVals.append(vals)\\n #print \\\"%ss = %s\\\" %(name,vals)\\n\\n msg = ''\\n for name in self.data_code['dataNames']:\\n msg += '%-10s ' % name\\n msg += '\\\\n'\\n\\n nModes = len(keyVals[0])\\n for i in xrange(nModes):\\n for vals in keyVals:\\n msg += '%-10g ' % vals[i]\\n msg += '\\\\n'\\n return msg + '\\\\n'\",\n \"def test_export_attributes(self):\\n\\n v = Vector({ 'x': 3 }, { \\\"y\\\": True })\\n e = v.to_array()\\n self.assertEqual(v.attributes, Vector.from_array(e).attributes)\\n self.assertEqual(v.__dict__, Vector.from_array(e).__dict__)\",\n \"def record_to_tuple(record):\\n return float(record[\\\"base_volume\\\"]), float(record[\\\"counter_volume\\\"]), int(record[\\\"trade_count\\\"])\",\n \"def insert_tuple(self):\\n\\n return (self.raw_data.get('bill', {}).get(\\\"type\\\"),\\n self.raw_data.get('chamber'),\\n self.raw_data.get('session'),\\n self.raw_data.get('date'),\\n self._total_votes_cast(),\\n self.passage_requirement,\\n self.raw_data.get('result'),\\n self.raw_data.get('congress'),\\n self.raw_data.get('source_url'),\\n self.raw_data.get('updated_at'),\\n self.raw_data.get('vote_id'),\\n self.yay_voter_cnt,\\n self.nay_voter_cnt,\\n self.abstain_voter_cnt,\\n self.present_voter_cnt\\n )\",\n \"def attributes(self):\",\n \"def metadata(self) -> Tuple[List[NodeType], List[EdgeType]]:\\n return self.node_types, self.edge_types\",\n \"def mem(self) -> List[float]:\\n return list(map(attrgetter(\\\"mem\\\"), self.stats))\",\n \"def get_perfect_information(self):\\n raise NotImplementedError\",\n \"def _prepare_for_stats(self):\\n clean = self._discard_value(None)\\n total = float(clean.total())\\n return clean, total\",\n \"def get_tup(self):\\n\\n tup = (\\n self.key,\\n self.headers,\\n self.proxies,\\n self.link,\\n self.delta,\\n self.size,\\n self.status,\\n datetime.now(),\\n )\\n return tup\",\n \"def getFieldOfView(self): # pragma: no cover\\n # to be overloaded by the child class.\\n return 0, 0\",\n \"def get(self) -> Tuple[str, Tuple]:\\n # TODO:\\n pass\",\n \"def provide_data(self):\\r\\n # import pdb; pdb.set_trace()\\r\\n # for k, v in self.data:\\r\\n # print k,v\\r\\n return [(k, tuple([1] + list(v.shape[1:]))) for k, v in self.data]\",\n \"def normalized_data(self):\\n return self._data\",\n \"def x_ub(self):\\n pass\",\n \"def missing(self):\\n return tuple([f for f in self._required \\\\\\n if f not in self or self[f] == None])\",\n \"def get_highest_precision(self) -> Tuple[float, Dict[str, Any]]:\\n highest_val = float('-inf')\\n data = None\\n for instance in self.stats:\\n if self.stats[instance][\\\"Precision\\\"] > highest_val:\\n highest_val = self.stats[instance][\\\"Precision\\\"]\\n data = self.stats[instance]\\n return highest_val, data\",\n \"def getMeasures():\",\n \"def getUserUnstakeInfo(self) -> list:\\n unstake_info_list =[]\\n for items in self._linked_list_var:\\n unstake_info_list.append([items[1],items[2]])\\n return unstake_info_list\",\n \"def z(self):\\n return self._data[2]\",\n \"def get_tuples(self):\\n pattern = list()\\n for gi in self.gradual_items:\\n temp = tuple([gi.attribute_col, gi.symbol])\\n pattern.append(temp)\\n return pattern\",\n \"def items(self) -> tuple[tuple[Any, Any], ...]: # type: ignore\\n return tuple(zip(self.keys(), self.values()))\",\n \"def axis_member():\\n return {\\n \\\"type\\\": \\\"class\\\",\\n \\\"base\\\": None,\\n \\\"is_abstract\\\": False,\\n \\\"properties\\\": [\\n (\\n \\\"description\\\",\\n \\\"str\\\",\\n \\\"0.1\\\",\\n \\\"Description of the member (or name of parameter varied).\\\",\\n ),\\n (\\n \\\"extra_detail\\\",\\n \\\"str\\\",\\n \\\"0.1\\\",\\n \\\"If necessary: further information about ensemble member \\\"\\n \\\"conformance.\\\",\\n ),\\n (\\\"index\\\", \\\"int\\\", \\\"1.1\\\", \\\"The ensemble member index.\\\"),\\n (\\n \\\"value\\\",\\n \\\"float\\\",\\n \\\"0.1\\\",\\n \\\"If parameter varied; value for this member.\\\",\\n ),\\n (\\n \\\"conformance\\\",\\n \\\"linked_to(activity.conformance)\\\",\\n \\\"0.1\\\",\\n \\\"Conformance document for the target requirement that defines \\\"\\n \\\"this member, if any.\\\",\\n ),\\n (\\n \\\"axis\\\",\\n \\\"linked_to(activity.ensemble_axis)\\\",\\n \\\"1.1\\\",\\n \\\"The parent axis of this ensemble member\\\",\\n ),\\n ],\\n }\",\n \"def getCoord(self):\\n return (self.birth, self.death)\",\n \"def data_privacy(self) -> pulumi.Output['outputs.DataPrivacyProperties']:\\n return pulumi.get(self, \\\"data_privacy\\\")\",\n \"def attributes(self) -> typing.Iterator[typing.Tuple[str]]:\\n minimize = self.lattice._context._minimize(self._extent, self._intent)\\n return (i.members() for i in minimize)\",\n \"def _named_attrs(self, parts:dict) -> \\\\\\n (QA4SMNamedAttributes, list, QA4SMNamedAttributes):\\n\\n if not self.ismetr():\\n raise IOError(self.varname, '{} is not in form of a QA4SM metric variable.')\\n\\n if self.g == 0:\\n a = QA4SMAttributes(self.attrs)\\n ref_ds = QA4SMNamedAttributes(a.ref_dc - a._offset_id_dc,\\n a.get_ref_names()['short_name'], self.attrs)\\n return ref_ds, None, None\\n else:\\n dss = []\\n ref_ds = QA4SMNamedAttributes(parts['ref_id'], parts['ref_ds'], self.attrs)\\n ds = QA4SMNamedAttributes(parts['sat_id0'], parts['sat_ds0'], self.attrs)\\n dss.append(ds)\\n if self.g == 3:\\n ds = QA4SMNamedAttributes(parts['sat_id1'], parts['sat_ds1'], self.attrs)\\n dss.append(ds)\\n mds = QA4SMNamedAttributes(parts['mds_id'], parts['mds'], self.attrs)\\n else:\\n mds = None\\n return ref_ds, dss, mds\",\n \"def tuple(self):\\n return self.major, self.minor, self.patch\",\n \"def values(self):\\n # Account for events generated with different versions of code.\\n values = [getattr(self, x, \\\"\\\") for x in self.base_field_names()]\\n values += [self.data.get(x, \\\"\\\") for x in self.data]\\n return values\",\n \"def get_members():\"\n]","isConversational":false},"negative_scores":{"kind":"list like","value":["0.6213964","0.6204447","0.59384376","0.57277507","0.5707521","0.5554493","0.55509335","0.5544605","0.5535825","0.55020016","0.5489144","0.5479862","0.5433677","0.5431568","0.5417379","0.5414188","0.54121655","0.5411566","0.53934836","0.5382212","0.5382212","0.53667146","0.5364346","0.53401995","0.530571","0.52794045","0.52637887","0.52612764","0.5253606","0.52379185","0.5237825","0.5211814","0.51885253","0.51851404","0.5181037","0.5166664","0.5159122","0.5157538","0.5157538","0.5157538","0.51527745","0.5147956","0.51370406","0.513676","0.51339704","0.5131229","0.5125237","0.5118849","0.5118849","0.51163507","0.5108643","0.51073825","0.50915104","0.50874895","0.5074483","0.50645864","0.5064425","0.50610423","0.5056007","0.5055086","0.50463843","0.5042161","0.50318474","0.50314313","0.5028854","0.50222254","0.5022021","0.5015508","0.5013362","0.5011073","0.50100374","0.50082785","0.49942482","0.49909666","0.4979132","0.4976285","0.49735284","0.49724078","0.49675813","0.49576685","0.4953223","0.49507418","0.49502727","0.4946624","0.49416196","0.49363208","0.4931424","0.49273038","0.49074435","0.49059045","0.4905109","0.4892678","0.4890164","0.48855454","0.48780528","0.48739105","0.48714438","0.487058","0.48640007","0.48637858"],"string":"[\n \"0.6213964\",\n \"0.6204447\",\n \"0.59384376\",\n \"0.57277507\",\n \"0.5707521\",\n \"0.5554493\",\n \"0.55509335\",\n \"0.5544605\",\n \"0.5535825\",\n \"0.55020016\",\n \"0.5489144\",\n \"0.5479862\",\n \"0.5433677\",\n \"0.5431568\",\n \"0.5417379\",\n \"0.5414188\",\n \"0.54121655\",\n \"0.5411566\",\n \"0.53934836\",\n \"0.5382212\",\n \"0.5382212\",\n \"0.53667146\",\n \"0.5364346\",\n \"0.53401995\",\n \"0.530571\",\n \"0.52794045\",\n \"0.52637887\",\n \"0.52612764\",\n \"0.5253606\",\n \"0.52379185\",\n \"0.5237825\",\n \"0.5211814\",\n \"0.51885253\",\n \"0.51851404\",\n \"0.5181037\",\n \"0.5166664\",\n \"0.5159122\",\n \"0.5157538\",\n \"0.5157538\",\n \"0.5157538\",\n \"0.51527745\",\n \"0.5147956\",\n \"0.51370406\",\n \"0.513676\",\n \"0.51339704\",\n \"0.5131229\",\n \"0.5125237\",\n \"0.5118849\",\n \"0.5118849\",\n \"0.51163507\",\n \"0.5108643\",\n \"0.51073825\",\n \"0.50915104\",\n \"0.50874895\",\n \"0.5074483\",\n \"0.50645864\",\n \"0.5064425\",\n \"0.50610423\",\n \"0.5056007\",\n \"0.5055086\",\n \"0.50463843\",\n \"0.5042161\",\n \"0.50318474\",\n \"0.50314313\",\n \"0.5028854\",\n \"0.50222254\",\n \"0.5022021\",\n \"0.5015508\",\n \"0.5013362\",\n \"0.5011073\",\n \"0.50100374\",\n \"0.50082785\",\n \"0.49942482\",\n \"0.49909666\",\n \"0.4979132\",\n \"0.4976285\",\n \"0.49735284\",\n \"0.49724078\",\n \"0.49675813\",\n \"0.49576685\",\n \"0.4953223\",\n \"0.49507418\",\n \"0.49502727\",\n \"0.4946624\",\n \"0.49416196\",\n \"0.49363208\",\n \"0.4931424\",\n \"0.49273038\",\n \"0.49074435\",\n \"0.49059045\",\n \"0.4905109\",\n \"0.4892678\",\n \"0.4890164\",\n \"0.48855454\",\n \"0.48780528\",\n \"0.48739105\",\n \"0.48714438\",\n \"0.487058\",\n \"0.48640007\",\n \"0.48637858\"\n]","isConversational":false},"document_score":{"kind":"string","value":"0.55766004"},"document_rank":{"kind":"string","value":"5"}}},{"rowIdx":82393,"cells":{"query":{"kind":"string","value":"Calculate contributions mask to be within the given value range."},"document":{"kind":"string","value":"def _setValidRange(self, contribs, valueRange):\n testfor(contribs.ndim == 2, ValueError)\n numContribs, numReps = contribs.shape\n self._validRange = np.zeros_like(contribs.T, dtype = bool)\n for ri in range(numReps):\n # the single set of R for this calculation\n rset = contribs[:, ri]\n self._validRange[ri] = ((rset > min(valueRange))\n * (rset < max(valueRange)))"},"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 _mask_for_values_between_exponent_limits(self):\n mask_between_exp_limits = None\n new_exp_lower = new_exp_upper = None\n if self.exp_lower is not None:\n new_exp_lower = self.exp_lower + 1\n if self.exp_upper is not None:\n new_exp_upper = self.exp_upper - 1\n if (new_exp_lower is not None) and (new_exp_upper is not None):\n if new_exp_lower <= new_exp_upper:\n mask_between_exp_limits = self.exponent.interval_mask(new_exp_lower, new_exp_upper)\n else:\n mask_between_exp_limits = self.exponent.interval_mask(new_exp_lower, new_exp_upper)\n\n return mask_between_exp_limits","def apply_mask(self, mask_band=None, mask_val=None):\n pass","def _make_mask(data, mask_bounds):\n # For each set of bounds add to the conditional.\n mask = False\n for lat_bounds, lon_bounds in mask_bounds:\n mask |= _add_to_mask(data, lat_bounds, lon_bounds)\n return mask","def mask_range(b, z, lam_em, lam_cent):\n dz = 2. * b * np.sqrt(np.log(2.)) / c\n del_lam = lam_cent * dz\n return lam_cent - del_lam, lam_cent + del_lam","def calcmask(self, *args, **kwargs):\n return _image.image_calcmask(self, *args, **kwargs)","def process_pain(x, lb, ub):\n x = x.abs()\n x.loc[(x > ub)] = 8\n x.loc[(x < lb) | (x > ub)] = np.nan\n return x","def _get_support_mask(self):\n mask = np.zeros(self.scores_.shape, dtype=bool)\n mask[self.scores_ >= self.min_count] = True\n return mask","def _mask_for_values_on_exponent_limit(self):\n lower_exponent = self.exponent.equality_mask(self.exp_lower)\n upper_exponent = self.exponent.equality_mask(self.exp_upper)\n\n mantissa_lower_limit = self.mantissa.get_lower_limit_mask(self.mantissa_lower)\n mantissa_upper_limit = self.mantissa.get_upper_limit_mask(self.mantissa_upper)\n\n lower_limit = logical_and_on_list_of_masks(\n [lower_exponent, mantissa_lower_limit])\n upper_limit = logical_and_on_list_of_masks(\n [upper_exponent, mantissa_upper_limit])\n return self._combine_limit_masks(lower_limit, upper_limit)","def _add_to_mask(data, lat_bounds, lon_bounds):\n mask_lat = ((data[internal_names.LAT_STR] > lat_bounds[0]) &\n (data[internal_names.LAT_STR] < lat_bounds[1]))\n return mask_lat & ((data[internal_names.LON_STR] > lon_bounds[0]) &\n (data[internal_names.LON_STR] < lon_bounds[1]))","def make_mask(data, min_value=None, max_value=None, is_array=False, **kwargs):\n\n # allow both min and min_value\n min_value = min_value if min_value is not None else kwargs.get('min')\n max_value = max_value if max_value is not None else kwargs.get('max')\n eps = kwargs.get('epsilon')\n\n if is_array:\n mask = np.empty(data.shape[0], dtype=bool)\n for i, array in enumerate(data):\n mask[i] = np.any(make_mask(array, min_value, max_value, False))\n\n return mask\n\n if isinstance(min_value, datetime):\n min_value = datetime_to_coda_time(min_value)\n eps = 0.0001 if eps is None else eps\n if isinstance(max_value, datetime):\n max_value = datetime_to_coda_time(max_value)\n eps = 0.0001 if eps is None else eps\n\n if min_value is not None and min_value == max_value:\n mask = data == min_value\n\n # special treatment when we get an epsilon value for accuracy\n elif eps is not None:\n if min_value is not None and max_value is not None:\n if min_value > max_value:\n mask = np.logical_or(\n (data - max_value) <= eps,\n (data - min_value) >= -eps,\n )\n else:\n mask = np.logical_and(\n (data - max_value) <= eps,\n (data - min_value) >= -eps,\n )\n elif min_value is not None:\n mask = (data - min_value) >= -eps\n elif max_value is not None:\n mask = (data - max_value) <= eps\n else:\n raise NotImplementedError\n\n # no epsilon, use fastest way possible\n elif min_value is not None and max_value is not None:\n if min_value > max_value:\n mask = np.logical_or(\n data <= max_value,\n data >= min_value\n )\n else:\n mask = np.logical_and(\n data <= max_value,\n data >= min_value\n )\n\n elif min_value is not None:\n mask = data >= min_value\n elif max_value is not None:\n mask = data <= max_value\n else:\n raise NotImplementedError\n\n if mask.ndim > 1:\n mask = np.any(mask, -1)\n\n return mask","def test_gross_range_check_masked(self):\n fail_span = (10, 50)\n suspect_span = (20, 40)\n vals = [\n None, # None\n 10, # Sensor range.\n 15, # User range.\n 20, 25, 30, 35, 40, # Valid\n np.nan, # np.nan\n 51, # Sensor range.\n np.ma.masked # np.ma.masked\n ]\n result = np.ma.array([\n 9,\n 3,\n 3,\n 1, 1, 1, 1, 1,\n 9,\n 4,\n 9\n ])\n\n with warnings.catch_warnings():\n warnings.simplefilter(\"ignore\")\n inputs = [\n vals,\n np.array(vals, dtype=np.floating),\n dask_arr(np.array(vals, dtype=np.floating))\n ]\n\n for i in inputs:\n npt.assert_array_equal(\n qartod.gross_range_test(\n vals,\n fail_span,\n suspect_span\n ),\n result\n\n )","def _compute_register_bounds(cls, num_values, probability):\n bits = np.arange(1, num_values + 1)\n probs = scipy.stats.geom.cdf(bits, probability)\n return probs / probs[-1]","def applyMaskOnValues(self, func):\r\n self._maskFunctions.append(func)\r\n pass","def test_returns_custom_range(self):\n img = np.arange(5)\n\n actual = util.apply_gamma(img, out_min=0, out_max=255, gamma=1)\n self.assertEqual(actual.min(), 0)\n self.assertEqual(actual.max(), 255)","def get_regions_mask(self, input):","def _perceive(self, p_range: Union[int, np.ndarray]) -> np.ndarray:\n dist = self.distance\n p_filter = np.where(((dist<p_range) & (dist>0)), 1, 0)\n inv_dist = 1 / (dist+EPSILON)\n inv_dist = np.multiply(inv_dist, p_filter)\n mut_influence = inv_dist / (np.sum(inv_dist, axis=-1, \n keepdims=True) + EPSILON)\n diags = 1 - np.sum(p_filter, axis=-1)\n np.fill_diagonal(mut_influence, diags)\n return mut_influence","def brightness_mask(cmb_map, synch_template, synch_freq, dust_template, dust_freq, mask_file, regions_file, threshold=10.0):\n\n cmb_fluctuation = np.std(cmb_map)\n synch70 = synch_template*(70.0/synch_freq)**(-3.0)\n gamma = h/(kb*19.4)\n dust70 = dust_template*(70.0/dust_freq)**2.6*(np.exp(gamma*dust_freq*1.0e9) - 1.0)/(np.exp(gamma*70.0e9) - 1.0)\n\n regions = hp.read_map(regions_file)\n region_nums = [i for i in range(1, int(np.amax(regions)) + 1)]\n\n mask = np.ones(hp.nside2npix(hp.get_nside(cmb_map)))\n\n synch_idx = np.where(synch70>=threshold*cmb_fluctuation)[0]\n dust_idx = np.where(dust70>=threshold*cmb_fluctuation)[0]\n mask[synch_idx] = 0\n mask[dust_idx] = 0\n\n hp.write_map(mask_file, mask, overwrite=True)\n\n return mask","def process_result_value(self, value, dialect):\n masks = list()\n if value:\n for e in enums.CryptographicUsageMask:\n if e.value & value:\n masks.append(e)\n return masks","def preprocess_mask(mask):\n # Project values interval on [0.0; 1.0]\n if mask.max() > 1:\n mask[mask <= 127.5] = 0.\n mask[mask > 127.5] = 1.\n else:\n mask[mask <= .5] = 0.\n mask[mask > .5] = 1.\n return mask","def get_granger_sig_mask(self):\n if not hasattr(self, 'percentile_granger'):\n self.calc_shuffle_threshold()\n if not hasattr(self, 'granger_actual'):\n self.calc_granger_actual()\n mean_granger_actual = np.mean(self.granger_actual, axis=0)\n self.masked_granger = np.ma.masked_where(\n mean_granger_actual < self.percentile_granger, mean_granger_actual)\n self.mask_array = np.ma.getmask(self.masked_granger)","def test_output_range(self):\n byt = bytscl(self.array1)\n outside = (byt < 0) | (byt > 255)\n total = numpy.sum(outside)\n self.assertEqual(total, 0)","def _value_in_bounds(self, val):\n val = self._stepped_value(val)\n\n if val <= self.valmin:\n if not self.closedmin:\n return\n val = self.valmin\n elif val >= self.valmax:\n if not self.closedmax:\n return\n val = self.valmax\n\n if self.slidermin is not None and val <= self.slidermin.val:\n if not self.closedmin:\n return\n val = self.slidermin.val\n\n if self.slidermax is not None and val >= self.slidermax.val:\n if not self.closedmax:\n return\n val = self.slidermax.val\n return val","def mask(self):","def mask(self):\n\n mask = self.freqs >= self.minimum_threshold\n mask = mask.astype(int)\n self.freqs = self.freqs * mask\n self.sums = self.sums * mask","def fit_to_range(val: float, a: float, b: float, a1: float, b1: float) -> float:\n new_value = ((val - a) / (b - a)) * (b1 - a1) + a1\n return new_value","def flag_absolute(data):\n data.mask = np.logical_or(data.mask, data > params.thr_max)\n data.mask = np.logical_or(data.mask, data < params.thr_min)\n return data.mask","def calcrange(a4lim,data):\r\n a4range=N.intersect1d(N.where(data>a4lim[0])[0],N.where(data<a4lim[1])[0])\r\n return a4range","def make_mask(cat_table, cut):\n cut_var = cut['cut_var']\n min_val = cut.get('min_val', None)\n max_val = cut.get('max_val', None)\n\n nsrc = len(cat_table)\n if min_val is None:\n min_mask = np.ones((nsrc), bool)\n else:\n min_mask = cat_table[cut_var] >= min_val\n if max_val is None:\n max_mask = np.ones((nsrc), bool)\n else:\n max_mask = cat_table[cut_var] <= max_val\n full_mask = min_mask * max_mask\n return full_mask","def compute_mask(self, inputs, mask=None):\n return None","def __validate(self, value: int, extend_range: bool):\n if extend_range:\n bottom, top = self.getRange()\n self.setRange(min(value, bottom), max(value, top))\n return numpy.clip(value, *self.getRange())","def get_continuum_in_range(w,s,low_low, low_high, high_low, high_high,\n pmin=12,pmax=88, only_correct_negative_values = False,\n fit_degree=2, plot = True, verbose = True, warnings=True) :\n s_low = s[np.where((w <= low_low))] \n s_high = s[np.where((w >= high_high))] \n \n w_fit = w[np.where((w > low_low) & (w < high_high))]\n w_fit_low = w[np.where((w > low_low) & (w < low_high))]\n w_fit_high = w[np.where((w > high_low) & (w < high_high))]\n\n y_fit = s[np.where((w > low_low) & (w < high_high))]\n y_fit_low = s[np.where((w > low_low) & (w < low_high))]\n y_fit_high = s[np.where((w > high_low) & (w < high_high))]\n\n # Remove outliers\n median_y_fit_low = np.nanmedian(y_fit_low)\n for i in range(len(y_fit_low)):\n if np.nanpercentile(y_fit_low,2) > y_fit_low[i] or y_fit_low[i] > np.nanpercentile(y_fit_low,98): y_fit_low[i] =median_y_fit_low\n\n median_y_fit_high = np.nanmedian(y_fit_high)\n for i in range(len(y_fit_high)):\n if np.nanpercentile(y_fit_high,2) > y_fit_high[i] or y_fit_high[i] > np.nanpercentile(y_fit_high,98): y_fit_high[i] =median_y_fit_high\n \n w_fit_cont = np.concatenate((w_fit_low,w_fit_high))\n y_fit_cont = np.concatenate((y_fit_low,y_fit_high))\n \n try:\n fit = np.polyfit(w_fit_cont,y_fit_cont, fit_degree)\n yfit = np.poly1d(fit)\n y_fitted = yfit(w_fit)\n \n y_fitted_low = yfit(w_fit_low)\n median_low = np.nanmedian(y_fit_low-y_fitted_low)\n rms=[]\n for i in range(len(y_fit_low)):\n rms.append(y_fit_low[i]-y_fitted_low[i]-median_low)\n \n # rms=y_fit-y_fitted\n lowlimit=np.nanpercentile(rms,pmin)\n highlimit=np.nanpercentile(rms,pmax)\n \n corrected_s_ =copy.deepcopy(y_fit)\n for i in range(len(w_fit)):\n if w_fit[i] >= low_high and w_fit[i] <= high_low: # ONLY CORRECT in [low_high,high_low] \n if only_correct_negative_values:\n if y_fit[i] <= 0 : \n corrected_s_[i] = y_fitted[i]\n else:\n if y_fit[i]-y_fitted[i] <= lowlimit or y_fit[i]-y_fitted[i] >= highlimit: corrected_s_[i] = y_fitted[i]\n \n \n corrected_s = np.concatenate((s_low,corrected_s_))\n corrected_s = np.concatenate((corrected_s,s_high))\n \n \n if plot:\n ptitle = \"CorrectionBase in range \"+np.str(np.round(low_low,2))+\" - [ \"+np.str(np.round(low_high,2))+\" - \"+np.str(np.round(high_low,2))+\" ] - \"+np.str(np.round(high_high,2))\n plot_plot(w_fit,[y_fit,y_fitted,y_fitted-highlimit,y_fitted-lowlimit,corrected_s_], color=[\"r\",\"b\", \"black\",\"black\",\"green\"], alpha=[0.3,0.7,0.2,0.2,0.5],xmin=low_low-40, xmax=high_high+40,vlines=[low_low,low_high,high_low,high_high],ptitle=ptitle, ylabel=\"Normalized flux\") \n #plot_plot(w,[s,corrected_s],xmin=low_low-40, xmax=high_high+40,vlines=[low_low,low_high,high_low,high_high])\n except Exception:\n if warnings: print(\" Fitting the continuum failed! Nothing done.\")\n corrected_s = s\n\n return corrected_s","def mask_percentage(self):\n return 100 - self.tissue_percentage","def _calc_range(self) -> np.ndarray:\n if self._is_ct25k():\n range_resolution = 30\n n_gates = 256\n else:\n n_gates = int(self.metadata[\"number_of_gates\"])\n range_resolution = int(self.metadata[\"range_resolution\"])\n return np.arange(n_gates) * range_resolution + range_resolution / 2","def sum_range(lower, upper):\n\n def copies(pmin, pmax):\n if lower <= pmin and pmax <= upper:\n return True\n elif pmax > upper:\n return False\n return copies(pmin+50, pmax+60)\n\n return copies(0, 0)","def _value_in_bounds(self, vals):\n return (self._min_in_bounds(vals[0]), self._max_in_bounds(vals[1]))","def _bound(x, min_value, max_value):\n return np.maximum(min_value, np.minimum(x, max_value))","def mask_value(self, ds, mask_value=0):\n xg, yg, mask = self._mask_grid()\n ds = ds.reshape(xg.shape)\n ds[mask == 0] = mask_value\n return xg, yg, ds","def normalise_modular_range(value, min, max):\n return numpy.mod(value-min, max-min)+min","def in_range(data, minval=-np.inf, maxval=np.inf):\n return (minval <= data) & (data <= maxval)","def source_annular(f, ctr, r_min, r_max, I0=1):\n\n f2 = (f[0] - ctr[0])**2 + (f[1] - ctr[1])**2\n mask = (f2 >= r_min**2) * (f2 <= r_max**2)\n return I0 * mask","def build_mask(sst, qual, qual_thresh=2, temp_bounds=(-2,33)):\n sst[np.isnan(sst)] = np.nan\n qual[np.isnan(qual)] = np.nan\n # Deal with NaN\n masks = np.logical_or(np.isnan(sst), np.isnan(qual))\n # Temperature bounds and quality\n qual_masks = np.zeros_like(masks)\n qual_masks[~masks] = (qual[~masks] > qual_thresh) | (sst[~masks] <= temp_bounds[0]) | (sst[~masks] > temp_bounds[1])\n masks = np.logical_or(masks, qual_masks)\n # Return\n return masks","def bounds(self) -> Tensor:\n return torch.cat([self.mins, self.mins + self.ranges], dim=-2)","def sat_in_range(arr, args):\n # find number of saturated pixels, args[0] is the pixel saturation limit\n sat_pixels = (arr > args[0]).sum()\n # args[1] is a limit of saturated pixels\n res = sat_pixels < args[1]\n result = ct.Result(res, 'saturation_in_range')\n return result","def mask_skymap(skymap, percent):\n masked_skymap = np.zeros_like(skymap)\n masked_skymap[ligo.skymap.postprocess.util.find_greedy_credible_levels(skymap) <= percent/100.] = 1.0\n return masked_skymap","def covers_overlaps(self, bounds):\n bounds = tuple(float(b) for b in bounds)\n return self.numba_rtree.covers_overlaps(bounds)","def sum_range(lower, upper):\n\n def copies(pmin, pmax):\n if lower <= pmin and pmax <= upper:\n return True\n elif pmax > upper:\n return False\n return copies(pmin + 50, pmax + 60) or copies(pmin + 130, pmax + 140)\n\n return copies(0, 0)","def mask_by(input_var, maskvar, low_limit=None, high_limit=None):\n var = copy.deepcopy(input_var)\n if low_limit is None and high_limit is None:\n return var\n if low_limit is None and high_limit is not None:\n maskvarmask = maskvar > high_limit\n elif low_limit is not None and high_limit is None:\n maskvarmask = maskvar < low_limit\n else:\n maskvarmask = (maskvar < low_limit) | (maskvar > high_limit)\n if var.mask is False:\n newmask = maskvarmask\n else:\n newmask = var.mask | maskvarmask\n var.mask = newmask\n return var","def SelectReduce(\n source_values: numpy.ndarray,\n mask: numpy.ndarray,\n) -> numpy.ndarray:\n return numpy.sum(source_values * mask, axis=mask.ndim - 1)","def applymask(self,mask):\n self.spec[mask==0]=np.nan","def Mask(self) -> int:","def mask(self, mask):\n return MaskedDistribution(self, mask)","def return_value_mask(actions):\n if hasattr(actions,'shape'):\n M = actions.shape[0]\n else:\n M = 1\n value_mask = torch.zeros(M,5)\n value_mask[torch.arange(M),actions] = 1\n value_mask = value_mask.bool()\n return value_mask.squeeze(0)","def create_region_mask(latitude_array, target_shape, lat_bounds):\n\n target_ndim = len(target_shape)\n\n southern_lat, northern_lat = lat_bounds\n mask_array = numpy.where((latitude_array >= southern_lat) & (latitude_array < northern_lat), False, True)\n\n mask = uconv.broadcast_array(mask_array, [target_ndim - 2, target_ndim - 1], target_shape)\n assert mask.shape == target_shape \n\n return mask","def scale_value(value, ip_range, domain=(0,1)):\n x1, x2 = domain\n y1, y2 = ip_range\n\n assert(y1 <= value <= y2)\n\n m = (x2 - x1)/(y2 - y1)\n b = y1 - m * x1\n return m * value - b","def filterInRange(frame, min, max, colorMode):\n\n tempFrame = cv2.cvtColor(frame, colorMode)\n\n mask = cv2.inRange(tempFrame, min, max)\n mask = cv2.bitwise_not(mask)\n\n filtered_frame = cv2.bitwise_and(frame, frame, mask=mask)\n\n return filtered_frame","def generateBounds(regionFilename, latitudeRange, longitudeRange): \n rastData = Dataset(regionFilename)\n\n #setting up values for raster data\n latsRast = np.array(rastData[\"lat\"][:])\n lonsRast = np.array(rastData[\"lon\"][:])\n regionOfInterest = np.array(rastData[\"Band1\"][:][:])\n\n\n regionArray = np.zeros((len(longitudeRange),len(latitudeRange)))\n\n\n for lat in latitudeRange:\n closestLatIndex = np.where( np.abs(latsRast-lat) == np.abs(latsRast-lat).min())[0][0]\n for lon in longitudeRange:\n closestLonIndex = np.where( np.abs(lonsRast-lon) == np.abs(lonsRast-lon).min())[0][0]\n\n #If lat long of MERRA data box is offshore or in region (values 1 in raster) set them equal to 1 for master Array, else they are left as zeros\n if (regionOfInterest[closestLatIndex][closestLonIndex] == 1):\n latIndex = np.where(latitudeRange == lat)[0][0]\n lonIndex = np.where(longitudeRange == lon)[0][0]\n regionArray[lonIndex][latIndex] = 1\n\n\n #for debugging\n ''' \n ax = sns.heatmap(regionArray)\n plt.show()\n '''\n return regionArray","def _local_mask(self, sparsity):\n for mask, param in self.masked_parameters:\n score = self.scores[id(param)]\n k = int((1.0 - sparsity) * score.numel())\n if not k < 1:\n threshold, _ = torch.kthvalue(torch.flatten(score), k)\n zero = torch.tensor([0.]).to(mask.device)\n one = torch.tensor([1.]).to(mask.device)\n mask.copy_(torch.where(score <= threshold, zero, one))","def threshold_mask(mask, threshold=0.5):\n mask[np.where(mask >= threshold)] = 1.\n mask[np.where(mask < threshold)] = 0.\n return mask","def _global_mask(self, sparsity):\n # # Set score for masked parameters to -inf \n # for mask, param in self.masked_parameters:\n # score = self.scores[id(param)]\n # score[mask == 0.0] = -np.inf\n\n # Threshold scores\n global_scores = torch.cat([torch.flatten(v) for v in self.scores.values()])\n k = int((1.0 - sparsity) * global_scores.numel())\n if not k < 1:\n threshold, _ = torch.kthvalue(global_scores, k)\n for mask, param in self.masked_parameters:\n score = self.scores[id(param)] \n zero = torch.tensor([0.]).to(mask.device)\n one = torch.tensor([1.]).to(mask.device)\n mask.copy_(torch.where(score <= threshold, zero, one))","def map_to_range(val, old_min, old_max, new_min, new_max):\n return new_max - (val - old_min) * (new_max - new_min) / (old_max - old_min)","def m_to_range(self, data):\n return (data - self._min_range_m) / self._total_range","def get_coverage_mask(self, coverages, covlim=0.2):\n if coverages.shape[0] != len(self):\n raise ValueError('Use QubicMultibandAcquisition.get_coverage method to create input')\n if len(self) == 1:\n cov = coverages\n return cov > covlim * np.max(cov)\n observed = [(coverages[i] > covlim * np.max(coverages[i])) for i in range(len(self))]\n obs = reduce(np.logical_and, tuple(observed[i] for i in range(len(self))))\n return obs","def fill_region(image,mask,value=1):\n\tim = image.copy().ravel()\n\tif image.ndim > 2:\n\t\tim_h, im_w, im_ch = image.shape\n\telse:\n\t\tim_ch = 1\n\t\tim_h, im_w = self.image.shape\n\t# linear indices of masked pixels\n\tind = masked_indices(mask)\n\tfor i in ind:\n\t\tfor ch in range(im_ch):\n\t\t\tim.data[i*im_ch+ch] = value\n\treturn im.reshape(image.shape)","def min_values(self, lower, upper): \n if not self.lower_bounds is None:\n return self.lower_bounds\n\n minus = np.clip(self.coeffs,-math.inf,0)\n plus = np.clip(self.coeffs,0,math.inf)\n self.lower_bounds = plus.dot(lower) + minus.dot(upper) + self.const\n \n return self.lower_bounds","def mapRange(num, min1, max1, min2, max2, clamp=True):\n if(clamp and num < min1):\n return min2\n if(clamp and num > max1):\n return max2\n\n num1 = (num - min1) / (max1 - min1)\n num2 = (num1 * (max2 - min2)) + min2\n return num2","def get_mask(total, begin, end):\n mask = np.zeros([total]).astype(np.float32)\n mask[begin:end] = 1\n return np.array(mask, dtype=np.bool)","def mask_var(self, data):\n return data.where(_make_mask(data, self.mask_bounds))","def nudged_min_max_compute(min_broadcast, max_broadcast, num_bits, narrow_range):\n\n\n dtype = min_broadcast.dtype\n quant_min = 1 if narrow_range else 0\n quant_max = (2 ** num_bits) - 1\n\n # because of need compute each channel, so quant_min and quant_max need to broadcast.\n quant_min_float = topi.full(min_broadcast.shape, dtype, tvm.const(quant_min, dtype))\n quant_max_float = topi.full(min_broadcast.shape, dtype, tvm.const(quant_max, dtype))\n\n # caculate each channel max and min difference.\n max_sub_min = topi.subtract(max_broadcast, min_broadcast)\n quant_max_sub_quant_min = topi.subtract(quant_max_float, quant_min_float)\n # compute scale = (max_broadcast - min_broadcast) / (quant_max - quant_min)\n # and min_div_scale = min_broadcast / scale\n if product_is_mini():\n scale = mul(max_sub_min, reciprocal(quant_max_sub_quant_min), target=utils.CCE)\n min_div_scale = Mul(min_broadcast, reciprocal(scale), target=utils.CCE)\n else:\n scale = divide(max_sub_min, quant_max_sub_quant_min, target=utils.CCE)\n min_div_scale = divide(min_broadcast, scale, target=utils.CCE)\n\n # zero_point_from_min = quant_min_float - min_broadcast / scale\n zero_point_from_min = topi.subtract(quant_min_float, min_div_scale)\n # if zero_point_from_min < quant_min_float, bool_less_quant_min_float = 1 else 0\n bool_less_quant_min_float = less_compare_float32(zero_point_from_min, quant_min_float)\n # if quant_max_float < zero_point_from_min, bool_more_quant_max_float = 1 else 0\n bool_more_quant_max_float = less_compare_float32(quant_max_float, zero_point_from_min)\n\n # according to above bool param to select effective value\n less_quant_min_float = topi.multiply(quant_min_float, bool_less_quant_min_float)\n more_quant_max_float = topi.multiply(quant_max_float, bool_more_quant_max_float)\n\n # compute which num is not less than quant_min_float and not large than quant_max_float\n tensor_one = topi.full(min_broadcast.shape, dtype, dc.one_const(dtype))\n bool_not_less_quant_min_float = topi.subtract(tensor_one, bool_less_quant_min_float)\n bool_not_more_quant_max_float = topi.subtract(tensor_one, bool_more_quant_max_float)\n bool_between_min_max = topi.multiply(bool_not_less_quant_min_float, bool_not_more_quant_max_float)\n between_min_max_float = topi.multiply(zero_point_from_min, bool_between_min_max)\n # add 0.5 to num which min <= num <= max and then floor them.\n between_min_max_add_half_one = topi.add(between_min_max_float, dc.half_const(dtype))\n between_min_max_round = akg.lang.ascend.floor(between_min_max_add_half_one)\n if product_is_mini():\n between_min_max_round = topi.cast(between_min_max_round, \"float16\")\n\n between_min_max_round = topi.cast(between_min_max_round, \"float32\")\n\n # calculate the maximum and minimum values of the quantization\n nudged_zero_point_tmp = topi.add(less_quant_min_float, more_quant_max_float)\n nudged_zero_point = topi.add(nudged_zero_point_tmp, between_min_max_round)\n\n nudged_min_tmp = topi.subtract(quant_min_float, nudged_zero_point)\n nudged_max_tmp = topi.subtract(quant_max_float, nudged_zero_point)\n nudged_min = topi.multiply(nudged_min_tmp, scale)\n nudged_max = topi.multiply(nudged_max_tmp, scale)\n res = [nudged_min, nudged_max, scale]\n\n return res","def in_range_bev(\n self, point_range: Union[Tensor, np.ndarray,\n Sequence[float]]) -> Tensor:\n in_range_flags = ((self.bev[:, 0] > point_range[0])\n & (self.bev[:, 1] > point_range[1])\n & (self.bev[:, 0] < point_range[2])\n & (self.bev[:, 1] < point_range[3]))\n return in_range_flags","def masked_mae_cal(inputs, target, mask):\n return torch.sum(torch.abs(inputs - target) * mask) / (torch.sum(mask) + 1e-9)","def mask_annular(m, bound):\n rmin, rmax, cmin, cmax = bound\n m[:rmin,:] = True\n m[rmax:,:] = True\n m[:,:cmin] = True\n m[:,cmax:] = True\n return m","def calculate_pixels(self, lower, upper):\n\n lower_range = np.array(lower)\n upper_range = np.array(upper)\n\n mask = cv2.inRange(self.hsv, lower_range, upper_range)\n return (mask == 255).sum()","def mask5(imagem, value, bandNames):\n mask = imagem.select(bandNames[0]).eq(value) \\\n .bitwiseAnd(imagem.select(bandNames[1]).neq(value)) \\\n .bitwiseAnd(imagem.select(bandNames[2]).neq(value)) \\\n .bitwiseAnd(imagem.select(bandNames[3]).neq(value)) \\\n .bitwiseAnd(imagem.select(bandNames[4]).eq(value))\n change_img = imagem.select(bandNames[1]).mask(mask.eq(1)).where(mask.eq(1), value) \n change_img1 = imagem.select(bandNames[2]).mask(mask.eq(1)).where(mask.eq(1), value) \n change_img2 = imagem.select(bandNames[3]).mask(mask.eq(1)).where(mask.eq(1), value) \n img_out = imagem.select(bandNames[1]).blend(change_img).blend(change_img1).blend(change_img2)\n return img_out","def discrete_potential(function, threshold):\n\n return np.where(function >= threshold, 1, 0)","def above_threshold(self, value):\n # We use floating point number here so we have to take care\n return finf(value,self.min) or finf(self.max,value)","def threshold(self, vmin, vmax=None, vcut=0.):\n if vmax == None:\n vmin = -np.abs(vmin)\n vmax = +np.abs(vmin)\n assert (vmin < vmax)\n\n ret = self.copy()\n for m in [ret.tmap, ret.qmap, ret.umap]:\n m[np.where(m < vmin)] = vcut\n m[np.where(m > vmax)] = vcut\n return ret","def map(value, in_low, in_high, out_low, out_high):\n # example from /animation_nodes/nodes/number/map_range.py\n # if inMin == inMax:\n # newValue = 0\n # # with clamping\n # if inMin < inMax:\n # _value = min(max(value, inMin), inMax)\n # else:\n # _value = min(max(value, inMax), inMin)\n # with interpolation\n # newValue = outMin + interpolation(\n # (_value - inMin) / (inMax - inMin)\n # ) * (outMax - outMin)\n # without interpolation\n # newValue = outMin + (\n # (_value - inMin) / (inMax - inMin)\n # ) * (outMax - outMin)\n # # without clamping\n # newValue = outMin + (\n # (value - inMin) / (inMax - inMin)\n # ) * (outMax - outMin)\n # # without clamping - reworded\n # result = (\n # (\n # ((value - in_low) / (in_high - in_low)) *\n # (out_high - out_low)\n # ) + out_low\n # )\n\n result = None\n\n # based on http://arduino.cc/en/Reference/Map\n # and http://stackoverflow.com/a/5650012/574981\n result = (\n (\n ((value - in_low) * (out_high - out_low)) /\n (in_high - in_low)\n ) + out_low\n )\n\n return result","def extract_single_color_range(image,hsv,lower,upper):\n mask = cv2.inRange(hsv, lower, upper)\n res = cv2.bitwise_and(image,image, mask= mask)\n return res","def _adjust_data_range_using_min_ratio(c_min, c_max, c_axis_range, *, min_ratio=0.01):\n c_range = c_max - c_min\n if c_range < c_axis_range * min_ratio:\n c_center = (c_max + c_min) / 2\n c_new_range = c_axis_range * min_ratio\n c_min = c_center - c_new_range / 2\n c_max = c_center + c_new_range / 2\n return c_min, c_max","def detect_range(self) -> Union[int, float]:\n return self.proto.detect_range","def _build_multiband_mask(data, tractor, filt2pixscale, fill_value=0.0,\n threshmask=0.01, r50mask=0.05, maxshift=10,\n relmaxshift=0.1,\n sigmamask=3.0, neighborfactor=1.0, verbose=False):\n import numpy.ma as ma\n from copy import copy\n from skimage.transform import resize\n from legacyhalos.mge import find_galaxy\n from legacyhalos.misc import srcs2image, ellipse_mask\n\n import matplotlib.pyplot as plt\n from astropy.visualization import simple_norm\n\n bands, refband = data['bands'], data['refband']\n #residual_mask = data['residual_mask']\n\n #nbox = 5\n #box = np.arange(nbox)-nbox // 2\n #box = np.meshgrid(np.arange(nbox), np.arange(nbox))[0]-nbox//2\n\n xobj, yobj = np.ogrid[0:data['refband_height'], 0:data['refband_width']]\n\n # If the row-index of the central galaxy is not provided, use the source\n # nearest to the center of the field.\n if 'galaxy_indx' in data.keys():\n galaxy_indx = np.atleast_1d(data['galaxy_indx'])\n else:\n galaxy_indx = np.array([np.argmin((tractor.bx - data['refband_height']/2)**2 +\n (tractor.by - data['refband_width']/2)**2)])\n data['galaxy_indx'] = np.atleast_1d(galaxy_indx)\n data['galaxy_id'] = ''\n\n #print('Import hack!')\n #norm = simple_norm(img, 'log', min_percent=0.05, clip=True)\n #import matplotlib.pyplot as plt ; from astropy.visualization import simple_norm\n\n ## Get the PSF sources.\n #psfindx = np.where(tractor.type == 'PSF')[0]\n #if len(psfindx) > 0:\n # psfsrcs = tractor.copy()\n # psfsrcs.cut(psfindx)\n #else:\n # psfsrcs = None\n\n def tractor2mge(indx, factor=1.0):\n # Convert a Tractor catalog entry to an MGE object.\n class MGEgalaxy(object):\n pass\n\n default_majoraxis = tractor.diam_init[indx] * 60 / 2 / filt2pixscale[refband] # [pixels]\n default_pa = tractor.pa_init[indx]\n default_ba = tractor.ba_init[indx]\n #default_theta = (270 - default_pa) % 180\n #default_eps = 1 - tractor.ba_init[indx]\n\n #if tractor.sga_id[indx] > -1:\n if tractor.type[indx] == 'PSF' or tractor.shape_r[indx] < 2:\n pa = tractor.pa_init[indx]\n ba = tractor.ba_init[indx]\n # take away the extra factor of 2 we put in in read_sample()\n r50 = tractor.diam_init[indx] * 60 / 2 / 2\n if r50 < 5:\n r50 = 5.0 # minimum size, arcsec\n majoraxis = factor * r50 / filt2pixscale[refband] # [pixels]\n #majoraxis = factor * tractor.diam_init[indx] * 60 / 2 / 2 / filt2pixscale[refband] # [pixels]\n else:\n ee = np.hypot(tractor.shape_e1[indx], tractor.shape_e2[indx])\n ba = (1 - ee) / (1 + ee)\n pa = 180 - (-np.rad2deg(np.arctan2(tractor.shape_e2[indx], tractor.shape_e1[indx]) / 2))\n pa = pa % 180\n\n # can be zero (or very small) if fit as a PSF or REX\n if tractor.shape_r[indx] > 1:\n majoraxis = factor * tractor.shape_r[indx] / filt2pixscale[refband] # [pixels]\n else:\n majoraxis = factor * tractor.diam_init[indx] * 60 / 2 / 2 / filt2pixscale[refband] # [pixels]\n\n mgegalaxy = MGEgalaxy()\n \n mgegalaxy.xmed = tractor.by[indx]\n mgegalaxy.ymed = tractor.bx[indx]\n mgegalaxy.xpeak = tractor.by[indx]\n mgegalaxy.ypeak = tractor.bx[indx]\n\n # never use the Tractor geometry (only the centroid)\n # https://portal.nersc.gov/project/cosmo/temp/ioannis/virgofilaments-html/215/NGC5584/NGC5584.html\n if True:\n mgegalaxy.eps = 1-ba\n mgegalaxy.pa = pa\n mgegalaxy.theta = (270 - pa) % 180\n mgegalaxy.majoraxis = majoraxis\n else:\n mgegalaxy.eps = 1 - default_ba\n mgegalaxy.pa = default_pa\n mgegalaxy.theta = (270 - default_pa) % 180\n mgegalaxy.majoraxis = default_majoraxis\n\n # always restore all pixels within the nominal / initial size of the galaxy\n #objmask = ellipse_mask(mgegalaxy.xmed, mgegalaxy.ymed, # object pixels are True\n # default_majoraxis,\n # default_majoraxis * (1-default_eps), \n # np.radians(default_theta-90), xobj, yobj)\n #objmask = ellipse_mask(mgegalaxy.xmed, mgegalaxy.ymed, # object pixels are True\n # default_majoraxis, default_majoraxis, 0.0, xobj, yobj)\n\n objmask = ellipse_mask(mgegalaxy.xmed, mgegalaxy.ymed, # object pixels are True\n mgegalaxy.majoraxis,\n mgegalaxy.majoraxis * (1-mgegalaxy.eps), \n np.radians(mgegalaxy.theta-90), xobj, yobj)\n\n # central 10% pixels can override the starmask\n objmask_center = ellipse_mask(mgegalaxy.xmed, mgegalaxy.ymed, # object pixels are True\n 0.1*mgegalaxy.majoraxis,\n 0.1*mgegalaxy.majoraxis * (1-mgegalaxy.eps), \n np.radians(mgegalaxy.theta-90), xobj, yobj)\n\n return mgegalaxy, objmask, objmask_center\n\n # Now, loop through each 'galaxy_indx' from bright to faint.\n data['mge'] = []\n for ii, central in enumerate(galaxy_indx):\n print('Determing the geometry for galaxy {}/{}.'.format(\n ii+1, len(galaxy_indx)))\n\n # [1] Determine the non-parametric geometry of the galaxy of interest\n # in the reference band. First, subtract all models except the galaxy\n # and galaxies \"near\" it. Also restore the original pixels of the\n # central in case there was a poor deblend.\n largeshift = False\n mge, centralmask, centralmask2 = tractor2mge(central, factor=1.0)\n #plt.clf() ; plt.imshow(centralmask, origin='lower') ; plt.savefig('desi-users/ioannis/tmp/junk-mask.png') ; pdb.set_trace()\n\n iclose = np.where([centralmask[np.int(by), np.int(bx)]\n for by, bx in zip(tractor.by, tractor.bx)])[0]\n \n srcs = tractor.copy()\n srcs.cut(np.delete(np.arange(len(tractor)), iclose))\n model = srcs2image(srcs, data['{}_wcs'.format(refband.lower())],\n band=refband.lower(),\n pixelized_psf=data['{}_psf'.format(refband.lower())])\n\n img = data[refband].data - model\n img[centralmask] = data[refband].data[centralmask]\n\n mask = np.logical_or(ma.getmask(data[refband]), data['residual_mask'])\n #mask = np.logical_or(data[refband].mask, data['residual_mask'])\n\n # restore the central pixels but not the masked stellar pixels\n centralmask[np.logical_and(data['starmask'], np.logical_not(centralmask2))] = False\n mask[centralmask] = False\n\n img = ma.masked_array(img, mask)\n ma.set_fill_value(img, fill_value)\n #if ii == 1:\n # pdb.set_trace()\n\n mgegalaxy = find_galaxy(img, nblob=1, binning=1, quiet=False)#, plot=True) ; plt.savefig('cosmo-www/tmp/junk-mge.png')\n #plt.clf() ; plt.imshow(mask, origin='lower') ; plt.savefig('junk-mask.png')\n ##plt.clf() ; plt.imshow(satmask, origin='lower') ; plt.savefig('/mnt/legacyhalos-data/debug.png')\n #pdb.set_trace()\n\n # Did the galaxy position move? If so, revert back to the Tractor geometry.\n if np.abs(mgegalaxy.xmed-mge.xmed) > maxshift or np.abs(mgegalaxy.ymed-mge.ymed) > maxshift:\n print('Large centroid shift (x,y)=({:.3f},{:.3f})-->({:.3f},{:.3f})'.format(\n mgegalaxy.xmed, mgegalaxy.ymed, mge.xmed, mge.ymed))\n print(' Reverting to the default geometry and the Tractor centroid.')\n largeshift = True\n mgegalaxy = copy(mge)\n\n radec_med = data['{}_wcs'.format(refband.lower())].pixelToPosition(\n mgegalaxy.ymed+1, mgegalaxy.xmed+1).vals\n radec_peak = data['{}_wcs'.format(refband.lower())].pixelToPosition(\n mgegalaxy.ypeak+1, mgegalaxy.xpeak+1).vals\n mge = {\n 'largeshift': largeshift,\n 'ra': tractor.ra[central], 'dec': tractor.dec[central],\n 'bx': tractor.bx[central], 'by': tractor.by[central],\n #'mw_transmission_g': tractor.mw_transmission_g[central],\n #'mw_transmission_r': tractor.mw_transmission_r[central],\n #'mw_transmission_z': tractor.mw_transmission_z[central],\n 'ra_moment': radec_med[0], 'dec_moment': radec_med[1],\n #'ra_peak': radec_med[0], 'dec_peak': radec_med[1]\n }\n for key in ('eps', 'majoraxis', 'pa', 'theta', 'xmed', 'ymed', 'xpeak', 'ypeak'):\n mge[key] = np.float32(getattr(mgegalaxy, key))\n if key == 'pa': # put into range [0-180]\n mge[key] = mge[key] % np.float32(180)\n data['mge'].append(mge)\n\n #if False:\n # #plt.clf() ; plt.imshow(mask, origin='lower') ; plt.savefig('/mnt/legacyhalos-data/debug.png')\n # plt.clf() ; mgegalaxy = find_galaxy(img, nblob=1, binning=1, quiet=True, plot=True)\n # plt.savefig('/mnt/legacyhalos-data/debug.png')\n\n # [2] Create the satellite mask in all the bandpasses. Use srcs here,\n # which has had the satellites nearest to the central galaxy trimmed\n # out.\n print('Building the satellite mask.')\n satmask = np.zeros(data[refband].shape, bool)\n for filt in bands:\n # do not let GALEX and WISE contribute to the satellite mask\n if data[filt].shape != satmask.shape:\n continue\n \n cenflux = getattr(tractor, 'flux_{}'.format(filt.lower()))[central]\n satflux = getattr(srcs, 'flux_{}'.format(filt.lower()))\n if cenflux <= 0.0:\n #raise ValueError('Central galaxy flux is negative!')\n print('Central galaxy flux is negative! Proceed with caution...')\n #pdb.set_trace()\n \n satindx = np.where(np.logical_or(\n (srcs.type != 'PSF') * (srcs.shape_r > r50mask) *\n (satflux > 0.0) * ((satflux / cenflux) > threshmask),\n srcs.ref_cat == 'R1'))[0]\n #satindx = np.where(srcs.ref_cat == 'R1')[0]\n #if np.isin(central, satindx):\n # satindx = satindx[np.logical_not(np.isin(satindx, central))]\n if len(satindx) == 0:\n #raise ValueError('All satellites have been dropped!')\n #print('Warning! All satellites have been dropped from band {}!'.format(filt))\n print('Note: no satellites to mask in band {}.'.format(filt))\n else:\n satsrcs = srcs.copy()\n #satsrcs = tractor.copy()\n satsrcs.cut(satindx)\n satimg = srcs2image(satsrcs, data['{}_wcs'.format(filt.lower())],\n band=filt.lower(),\n pixelized_psf=data['{}_psf'.format(filt.lower())])\n thissatmask = satimg > sigmamask*data['{}_sigma'.format(filt.lower())]\n #if filt == 'FUV':\n # plt.clf() ; plt.imshow(thissatmask, origin='lower') ; plt.savefig('junk-{}.png'.format(filt.lower()))\n # #plt.clf() ; plt.imshow(data[filt], origin='lower') ; plt.savefig('junk-{}.png'.format(filt.lower()))\n # pdb.set_trace()\n if satmask.shape != satimg.shape:\n thissatmask = resize(thissatmask*1.0, satmask.shape, mode='reflect') > 0\n\n satmask = np.logical_or(satmask, thissatmask)\n #if True:\n # import matplotlib.pyplot as plt\n # plt.clf() ; plt.imshow(np.log10(satimg), origin='lower') ; plt.savefig('debug.png')\n # plt.clf() ; plt.imshow(satmask, origin='lower') ; plt.savefig('debug.png')\n ## #plt.clf() ; plt.imshow(satmask, origin='lower') ; plt.savefig('/mnt/legacyhalos-data/debug.png')\n # pdb.set_trace()\n\n #print(filt, np.sum(satmask), np.sum(thissatmask))\n\n #plt.clf() ; plt.imshow(satmask, origin='lower') ; plt.savefig('desi-users/ioannis/tmp/junk-satmask.png')\n \n # [3] Build the final image (in each filter) for ellipse-fitting. First,\n # subtract out the PSF sources. Then update the mask (but ignore the\n # residual mask). Finally convert to surface brightness.\n #for filt in ['W1']:\n for filt in bands:\n thismask = ma.getmask(data[filt])\n if satmask.shape != thismask.shape:\n _satmask = (resize(satmask*1.0, thismask.shape, mode='reflect') > 0) == 1.0\n _centralmask = (resize(centralmask*1.0, thismask.shape, mode='reflect') > 0) == 1.0\n mask = np.logical_or(thismask, _satmask)\n mask[_centralmask] = False\n else:\n mask = np.logical_or(thismask, satmask)\n mask[centralmask] = False\n #if filt == 'r':\n # #plt.imshow(_satmask, origin='lower') ; plt.savefig('desi-users/ioannis/tmp/junk-satmask-{}.png'.format(filt))\n # plt.clf() ; plt.imshow(mask, origin='lower') ; plt.savefig('desi-users/ioannis/tmp/junk-mask-{}.png'.format(filt))\n # plt.clf() ; plt.imshow(img, origin='lower') ; plt.savefig('desi-users/ioannis/tmp/junk-img-{}.png'.format(filt))\n # pdb.set_trace()\n\n varkey = '{}_var'.format(filt.lower())\n imagekey = '{}_masked'.format(filt.lower())\n psfimgkey = '{}_psfimg'.format(filt.lower())\n thispixscale = filt2pixscale[filt]\n if imagekey not in data.keys():\n data[imagekey], data[varkey], data[psfimgkey] = [], [], []\n\n img = ma.getdata(data[filt]).copy()\n \n # Get the PSF sources.\n psfindx = np.where((tractor.type == 'PSF') * (getattr(tractor, 'flux_{}'.format(filt.lower())) / cenflux > threshmask))[0]\n if len(psfindx) > 0 and filt.upper() != 'W3' and filt.upper() != 'W4': \n #if len(psfindx) > 0 and filt.upper() != 'NUV' and filt.upper() != 'FUV' and filt.upper() != 'W3' and filt.upper() != 'W4':\n psfsrcs = tractor.copy()\n psfsrcs.cut(psfindx)\n else:\n psfsrcs = None\n \n if psfsrcs:\n psfimg = srcs2image(psfsrcs, data['{}_wcs'.format(filt.lower())],\n band=filt.lower(),\n pixelized_psf=data['{}_psf'.format(filt.lower())])\n if False:\n #import fitsio ; fitsio.write('junk-psf-{}.fits'.format(filt.lower()), data['{}_psf'.format(filt.lower())].img, clobber=True)\n fig, ((ax1, ax2), (ax3, ax4)) = plt.subplots(2, 2)\n im = ax1.imshow(np.log10(img), origin='lower') ; fig.colorbar(im, ax=ax1)\n im = ax2.imshow(np.log10(psfimg), origin='lower') ; fig.colorbar(im, ax=ax2)\n im = ax3.imshow(np.log10(data['{}_psf'.format(filt.lower())].img), origin='lower') ; fig.colorbar(im, ax=ax3)\n im = ax4.imshow(img-psfimg, origin='lower') ; fig.colorbar(im, ax=ax4)\n plt.savefig('desi-users/ioannis/tmp/qa-psf-{}.png'.format(filt.lower()))\n if filt == 'r':# or filt == 'r':\n pdb.set_trace()\n img -= psfimg\n else:\n psfimg = np.zeros((2, 2), 'f4')\n\n data[psfimgkey].append(psfimg)\n \n img = ma.masked_array((img / thispixscale**2).astype('f4'), mask) # [nanomaggies/arcsec**2]\n var = data['{}_var_'.format(filt.lower())] / thispixscale**4 # [nanomaggies**2/arcsec**4]\n\n # Fill with zeros, for fun--\n ma.set_fill_value(img, fill_value)\n #if ii == 0 and filt == 'r': #filt == 'W1' or \n # plt.clf() ; plt.imshow(img, origin='lower') ; plt.savefig('desi-users/ioannis/tmp/junk-img-{}.png'.format(filt.lower()))\n # plt.clf() ; plt.imshow(mask, origin='lower') ; plt.savefig('desi-users/ioannis/tmp/junk-mask-{}.png'.format(filt.lower()))\n ##### plt.clf() ; plt.imshow(thismask, origin='lower') ; plt.savefig('junk-thismask-{}.png'.format(filt.lower()))\n # pdb.set_trace()\n \n data[imagekey].append(img)\n data[varkey].append(var)\n\n #test = data['r_masked'][0]\n #plt.clf() ; plt.imshow(np.log(test.clip(test[mgegalaxy.xpeak, mgegalaxy.ypeak]/1e4)), origin='lower') ; plt.savefig('/mnt/legacyhalos-data/debug.png')\n #pdb.set_trace()\n\n # Cleanup?\n for filt in bands:\n del data[filt]\n del data['{}_var_'.format(filt.lower())]\n\n return data","def test_masked_data_below(self):\n\n expected_mask = np.full_like(self.cube.data, False, dtype=bool)\n expected_mask[:, 0, 0] = True\n expected_mask[1, 0, 2] = True\n expected_mask[2, 0] = True\n expected_mask[2, 1, 2] = True\n expected_mask[2, 1, 0] = True\n\n cube = set_up_probability_cube(\n 1 - self.cube.data,\n [200, 1000, 15000],\n variable_name=(\n \"cloud_base_height_assuming_only_consider_cloud_\"\n \"area_fraction_greater_than_4p5_oktas\"\n ),\n threshold_units=\"m\",\n spp__relative_to_threshold=\"below\",\n )\n\n result = Plugin(mask_percentiles=True)._probabilities_to_percentiles(\n cube, self.percentiles\n )\n self.assertArrayEqual(result.data.mask, expected_mask)","def query_range(tree, start_y, start_x, end_y, end_x):\n res = 0\n start_y -= 1\n\n while end_y > start_y:\n res += bit.query_range(tree[end_y], start_x, end_x)\n end_y -= (end_y & -end_y)\n\n while start_y > end_y:\n res -= bit.query_range(tree[start_y], start_x, end_x)\n start_y -= (start_y & -start_y)\n\n return res","def find_limits(self, \n label: str, \n limit: list,\n ) -> np.array :\n ci = np.array([])\n cdf = self.cdf[label]\n\n for l in limit:\n fintp = interp1d(cdf[0], cdf[1] - l, kind='linear')\n ci = np.append(ci, scipy.optimize.brentq(fintp, np.min(cdf[0]), \n np.max(cdf[0])))\n return ci","def _get_range_squared(self) -> np.ndarray:\n m2km = 0.001\n return (self.range * m2km) ** 2","def __limit_value(self, value, v_range):\n if np.isnan(value):\n print('Warning: trying to limit nan value in range {0}'.format(v_range))\n return value\n\n return np.min([v_range[1], np.max([value, v_range[0]])])","def mask_sonar_image(img, display_results=False):\n lower_color_bounds = (40, 80, 0) # Filter out lower values (ie blue)\n upper_color_bounds = (230, 250, 255) # Filter out too high values\n mask = cv2.inRange(img, lower_color_bounds, upper_color_bounds)\n\n if display_results:\n cv2.imshow(\"mask\", mask)\n cv2.waitKey(0)\n\n return mask","def masked_mre_cal(inputs, target, mask):\n return torch.sum(torch.abs(inputs - target) * mask) / (\n torch.sum(torch.abs(target * mask)) + 1e-9\n )","def __call__(self, mask):\n mapping = self.calculate_mapping(mask)\n return self.apply_mapping(mask, mapping)","def _full_value_range(self):\n min_value, max_value = self._raw_data.data_range\n return max_value - min_value","def calc(self,newValue):\n return np.sum(self.values<=newValue)/self.n","def False_positive_rate(result,start,end):\n Wider = 0.0002\n if start.loc[0,'lat'] > end.loc[0,'lat']:\n top = start.loc[0,'lat'] + Wider\n bottom = end.loc[0,'lat'] - Wider\n else:\n top = end.loc[0,'lat'] + Wider\n bottom = start.loc[0,'lat'] - Wider\n if start.loc[0,'lon'] > end.loc[0,'lon']:\n right = start.loc[0,'lon'] + Wider\n left = end.loc[0,'lon'] - Wider\n else:\n right = start.loc[0,'lon'] + Wider\n left = start.loc[0,'lon'] - Wider\n return (~(((result['lat'] >= bottom) & (result['lat'] <= top) \n & (result['lon'] >= left) & (result['lon'] <= right)))).sum()/result.shape[0]","def masked_mse_cal(inputs, target, mask):\n return torch.sum(torch.square(inputs - target) * mask) / (torch.sum(mask) + 1e-9)","def filter_labelmask(labelmask, func, above=0, below=1e6):\n labels = []\n for x in np.unique(labelmask):\n prop = func(labelmask, x)\n if (prop >= above and prop <= below):\n labels.append(x)\n labelmask_filtered = np.where(np.isin(labelmask, labels), labelmask, 0)\n return labelmask_filtered","def isShiftInRange(shiftValue, shiftRanges):\n\n for (minVal, maxVal) in shiftRanges:\n if shiftValue >= minVal:\n if shiftValue <= maxVal:\n return True\n \n return False","def get_acc_mask(cmfile, use_pt, min_acc):\n logging.info('Masking all bins of correction map with acceptance < {}'\n .format(min_acc))\n if use_pt:\n gen_dist = project(cmfile.Get(MAP_NAME.format('gen')), [0, 1, 2])\n acc_dist = project(cmfile.Get(MAP_NAME.format('acc')), [0, 1, 2])\n else:\n gen_dist = project(cmfile.Get(MAP_NAME.format('gen')), [1, 0])\n acc_dist = project(cmfile.Get(MAP_NAME.format('acc')), [1, 0])\n\n accmap = divide(acc_dist, gen_dist)\n\n mask = get_array(accmap) < min_acc\n return mask","def process_O2(x, lb, ub):\n x = x.abs()\n x.loc[(x <= 1) & (x > 0)] = x.loc[(x <= 1) & (x > 0)] * 100\n x.loc[(x <= 10) & (x > 1)] = x.loc[(x <= 10) & (x > 1)] * 10\n x.loc[(x <= lb ) | (x > ub)] = np.nan\n return x","def _in_range_op(spec):","def ignore_and_range(MAT, k=-9999.0):\n MAT[MAT==k]=0\n MAT = ((MAT-(MAT.min()))) / (MAT.max() - MAT.min())\n MAT *= 255\n \n return MAT","def test_set_clip_precentile_update(self):\n data = np.array([1., 2., 3.])\n self.mode.set_clip_percentile(10, 90)\n result = self.mode.get_bounds(data)\n self.mode.set_clip_percentile(20, 80)\n result = self.mode.get_bounds(data)\n np.testing.assert_array_almost_equal(result,\n np.percentile(data, [20, 80]))"],"string":"[\n \"def _mask_for_values_between_exponent_limits(self):\\n mask_between_exp_limits = None\\n new_exp_lower = new_exp_upper = None\\n if self.exp_lower is not None:\\n new_exp_lower = self.exp_lower + 1\\n if self.exp_upper is not None:\\n new_exp_upper = self.exp_upper - 1\\n if (new_exp_lower is not None) and (new_exp_upper is not None):\\n if new_exp_lower <= new_exp_upper:\\n mask_between_exp_limits = self.exponent.interval_mask(new_exp_lower, new_exp_upper)\\n else:\\n mask_between_exp_limits = self.exponent.interval_mask(new_exp_lower, new_exp_upper)\\n\\n return mask_between_exp_limits\",\n \"def apply_mask(self, mask_band=None, mask_val=None):\\n pass\",\n \"def _make_mask(data, mask_bounds):\\n # For each set of bounds add to the conditional.\\n mask = False\\n for lat_bounds, lon_bounds in mask_bounds:\\n mask |= _add_to_mask(data, lat_bounds, lon_bounds)\\n return mask\",\n \"def mask_range(b, z, lam_em, lam_cent):\\n dz = 2. * b * np.sqrt(np.log(2.)) / c\\n del_lam = lam_cent * dz\\n return lam_cent - del_lam, lam_cent + del_lam\",\n \"def calcmask(self, *args, **kwargs):\\n return _image.image_calcmask(self, *args, **kwargs)\",\n \"def process_pain(x, lb, ub):\\n x = x.abs()\\n x.loc[(x > ub)] = 8\\n x.loc[(x < lb) | (x > ub)] = np.nan\\n return x\",\n \"def _get_support_mask(self):\\n mask = np.zeros(self.scores_.shape, dtype=bool)\\n mask[self.scores_ >= self.min_count] = True\\n return mask\",\n \"def _mask_for_values_on_exponent_limit(self):\\n lower_exponent = self.exponent.equality_mask(self.exp_lower)\\n upper_exponent = self.exponent.equality_mask(self.exp_upper)\\n\\n mantissa_lower_limit = self.mantissa.get_lower_limit_mask(self.mantissa_lower)\\n mantissa_upper_limit = self.mantissa.get_upper_limit_mask(self.mantissa_upper)\\n\\n lower_limit = logical_and_on_list_of_masks(\\n [lower_exponent, mantissa_lower_limit])\\n upper_limit = logical_and_on_list_of_masks(\\n [upper_exponent, mantissa_upper_limit])\\n return self._combine_limit_masks(lower_limit, upper_limit)\",\n \"def _add_to_mask(data, lat_bounds, lon_bounds):\\n mask_lat = ((data[internal_names.LAT_STR] > lat_bounds[0]) &\\n (data[internal_names.LAT_STR] < lat_bounds[1]))\\n return mask_lat & ((data[internal_names.LON_STR] > lon_bounds[0]) &\\n (data[internal_names.LON_STR] < lon_bounds[1]))\",\n \"def make_mask(data, min_value=None, max_value=None, is_array=False, **kwargs):\\n\\n # allow both min and min_value\\n min_value = min_value if min_value is not None else kwargs.get('min')\\n max_value = max_value if max_value is not None else kwargs.get('max')\\n eps = kwargs.get('epsilon')\\n\\n if is_array:\\n mask = np.empty(data.shape[0], dtype=bool)\\n for i, array in enumerate(data):\\n mask[i] = np.any(make_mask(array, min_value, max_value, False))\\n\\n return mask\\n\\n if isinstance(min_value, datetime):\\n min_value = datetime_to_coda_time(min_value)\\n eps = 0.0001 if eps is None else eps\\n if isinstance(max_value, datetime):\\n max_value = datetime_to_coda_time(max_value)\\n eps = 0.0001 if eps is None else eps\\n\\n if min_value is not None and min_value == max_value:\\n mask = data == min_value\\n\\n # special treatment when we get an epsilon value for accuracy\\n elif eps is not None:\\n if min_value is not None and max_value is not None:\\n if min_value > max_value:\\n mask = np.logical_or(\\n (data - max_value) <= eps,\\n (data - min_value) >= -eps,\\n )\\n else:\\n mask = np.logical_and(\\n (data - max_value) <= eps,\\n (data - min_value) >= -eps,\\n )\\n elif min_value is not None:\\n mask = (data - min_value) >= -eps\\n elif max_value is not None:\\n mask = (data - max_value) <= eps\\n else:\\n raise NotImplementedError\\n\\n # no epsilon, use fastest way possible\\n elif min_value is not None and max_value is not None:\\n if min_value > max_value:\\n mask = np.logical_or(\\n data <= max_value,\\n data >= min_value\\n )\\n else:\\n mask = np.logical_and(\\n data <= max_value,\\n data >= min_value\\n )\\n\\n elif min_value is not None:\\n mask = data >= min_value\\n elif max_value is not None:\\n mask = data <= max_value\\n else:\\n raise NotImplementedError\\n\\n if mask.ndim > 1:\\n mask = np.any(mask, -1)\\n\\n return mask\",\n \"def test_gross_range_check_masked(self):\\n fail_span = (10, 50)\\n suspect_span = (20, 40)\\n vals = [\\n None, # None\\n 10, # Sensor range.\\n 15, # User range.\\n 20, 25, 30, 35, 40, # Valid\\n np.nan, # np.nan\\n 51, # Sensor range.\\n np.ma.masked # np.ma.masked\\n ]\\n result = np.ma.array([\\n 9,\\n 3,\\n 3,\\n 1, 1, 1, 1, 1,\\n 9,\\n 4,\\n 9\\n ])\\n\\n with warnings.catch_warnings():\\n warnings.simplefilter(\\\"ignore\\\")\\n inputs = [\\n vals,\\n np.array(vals, dtype=np.floating),\\n dask_arr(np.array(vals, dtype=np.floating))\\n ]\\n\\n for i in inputs:\\n npt.assert_array_equal(\\n qartod.gross_range_test(\\n vals,\\n fail_span,\\n suspect_span\\n ),\\n result\\n\\n )\",\n \"def _compute_register_bounds(cls, num_values, probability):\\n bits = np.arange(1, num_values + 1)\\n probs = scipy.stats.geom.cdf(bits, probability)\\n return probs / probs[-1]\",\n \"def applyMaskOnValues(self, func):\\r\\n self._maskFunctions.append(func)\\r\\n pass\",\n \"def test_returns_custom_range(self):\\n img = np.arange(5)\\n\\n actual = util.apply_gamma(img, out_min=0, out_max=255, gamma=1)\\n self.assertEqual(actual.min(), 0)\\n self.assertEqual(actual.max(), 255)\",\n \"def get_regions_mask(self, input):\",\n \"def _perceive(self, p_range: Union[int, np.ndarray]) -> np.ndarray:\\n dist = self.distance\\n p_filter = np.where(((dist<p_range) & (dist>0)), 1, 0)\\n inv_dist = 1 / (dist+EPSILON)\\n inv_dist = np.multiply(inv_dist, p_filter)\\n mut_influence = inv_dist / (np.sum(inv_dist, axis=-1, \\n keepdims=True) + EPSILON)\\n diags = 1 - np.sum(p_filter, axis=-1)\\n np.fill_diagonal(mut_influence, diags)\\n return mut_influence\",\n \"def brightness_mask(cmb_map, synch_template, synch_freq, dust_template, dust_freq, mask_file, regions_file, threshold=10.0):\\n\\n cmb_fluctuation = np.std(cmb_map)\\n synch70 = synch_template*(70.0/synch_freq)**(-3.0)\\n gamma = h/(kb*19.4)\\n dust70 = dust_template*(70.0/dust_freq)**2.6*(np.exp(gamma*dust_freq*1.0e9) - 1.0)/(np.exp(gamma*70.0e9) - 1.0)\\n\\n regions = hp.read_map(regions_file)\\n region_nums = [i for i in range(1, int(np.amax(regions)) + 1)]\\n\\n mask = np.ones(hp.nside2npix(hp.get_nside(cmb_map)))\\n\\n synch_idx = np.where(synch70>=threshold*cmb_fluctuation)[0]\\n dust_idx = np.where(dust70>=threshold*cmb_fluctuation)[0]\\n mask[synch_idx] = 0\\n mask[dust_idx] = 0\\n\\n hp.write_map(mask_file, mask, overwrite=True)\\n\\n return mask\",\n \"def process_result_value(self, value, dialect):\\n masks = list()\\n if value:\\n for e in enums.CryptographicUsageMask:\\n if e.value & value:\\n masks.append(e)\\n return masks\",\n \"def preprocess_mask(mask):\\n # Project values interval on [0.0; 1.0]\\n if mask.max() > 1:\\n mask[mask <= 127.5] = 0.\\n mask[mask > 127.5] = 1.\\n else:\\n mask[mask <= .5] = 0.\\n mask[mask > .5] = 1.\\n return mask\",\n \"def get_granger_sig_mask(self):\\n if not hasattr(self, 'percentile_granger'):\\n self.calc_shuffle_threshold()\\n if not hasattr(self, 'granger_actual'):\\n self.calc_granger_actual()\\n mean_granger_actual = np.mean(self.granger_actual, axis=0)\\n self.masked_granger = np.ma.masked_where(\\n mean_granger_actual < self.percentile_granger, mean_granger_actual)\\n self.mask_array = np.ma.getmask(self.masked_granger)\",\n \"def test_output_range(self):\\n byt = bytscl(self.array1)\\n outside = (byt < 0) | (byt > 255)\\n total = numpy.sum(outside)\\n self.assertEqual(total, 0)\",\n \"def _value_in_bounds(self, val):\\n val = self._stepped_value(val)\\n\\n if val <= self.valmin:\\n if not self.closedmin:\\n return\\n val = self.valmin\\n elif val >= self.valmax:\\n if not self.closedmax:\\n return\\n val = self.valmax\\n\\n if self.slidermin is not None and val <= self.slidermin.val:\\n if not self.closedmin:\\n return\\n val = self.slidermin.val\\n\\n if self.slidermax is not None and val >= self.slidermax.val:\\n if not self.closedmax:\\n return\\n val = self.slidermax.val\\n return val\",\n \"def mask(self):\",\n \"def mask(self):\\n\\n mask = self.freqs >= self.minimum_threshold\\n mask = mask.astype(int)\\n self.freqs = self.freqs * mask\\n self.sums = self.sums * mask\",\n \"def fit_to_range(val: float, a: float, b: float, a1: float, b1: float) -> float:\\n new_value = ((val - a) / (b - a)) * (b1 - a1) + a1\\n return new_value\",\n \"def flag_absolute(data):\\n data.mask = np.logical_or(data.mask, data > params.thr_max)\\n data.mask = np.logical_or(data.mask, data < params.thr_min)\\n return data.mask\",\n \"def calcrange(a4lim,data):\\r\\n a4range=N.intersect1d(N.where(data>a4lim[0])[0],N.where(data<a4lim[1])[0])\\r\\n return a4range\",\n \"def make_mask(cat_table, cut):\\n cut_var = cut['cut_var']\\n min_val = cut.get('min_val', None)\\n max_val = cut.get('max_val', None)\\n\\n nsrc = len(cat_table)\\n if min_val is None:\\n min_mask = np.ones((nsrc), bool)\\n else:\\n min_mask = cat_table[cut_var] >= min_val\\n if max_val is None:\\n max_mask = np.ones((nsrc), bool)\\n else:\\n max_mask = cat_table[cut_var] <= max_val\\n full_mask = min_mask * max_mask\\n return full_mask\",\n \"def compute_mask(self, inputs, mask=None):\\n return None\",\n \"def __validate(self, value: int, extend_range: bool):\\n if extend_range:\\n bottom, top = self.getRange()\\n self.setRange(min(value, bottom), max(value, top))\\n return numpy.clip(value, *self.getRange())\",\n \"def get_continuum_in_range(w,s,low_low, low_high, high_low, high_high,\\n pmin=12,pmax=88, only_correct_negative_values = False,\\n fit_degree=2, plot = True, verbose = True, warnings=True) :\\n s_low = s[np.where((w <= low_low))] \\n s_high = s[np.where((w >= high_high))] \\n \\n w_fit = w[np.where((w > low_low) & (w < high_high))]\\n w_fit_low = w[np.where((w > low_low) & (w < low_high))]\\n w_fit_high = w[np.where((w > high_low) & (w < high_high))]\\n\\n y_fit = s[np.where((w > low_low) & (w < high_high))]\\n y_fit_low = s[np.where((w > low_low) & (w < low_high))]\\n y_fit_high = s[np.where((w > high_low) & (w < high_high))]\\n\\n # Remove outliers\\n median_y_fit_low = np.nanmedian(y_fit_low)\\n for i in range(len(y_fit_low)):\\n if np.nanpercentile(y_fit_low,2) > y_fit_low[i] or y_fit_low[i] > np.nanpercentile(y_fit_low,98): y_fit_low[i] =median_y_fit_low\\n\\n median_y_fit_high = np.nanmedian(y_fit_high)\\n for i in range(len(y_fit_high)):\\n if np.nanpercentile(y_fit_high,2) > y_fit_high[i] or y_fit_high[i] > np.nanpercentile(y_fit_high,98): y_fit_high[i] =median_y_fit_high\\n \\n w_fit_cont = np.concatenate((w_fit_low,w_fit_high))\\n y_fit_cont = np.concatenate((y_fit_low,y_fit_high))\\n \\n try:\\n fit = np.polyfit(w_fit_cont,y_fit_cont, fit_degree)\\n yfit = np.poly1d(fit)\\n y_fitted = yfit(w_fit)\\n \\n y_fitted_low = yfit(w_fit_low)\\n median_low = np.nanmedian(y_fit_low-y_fitted_low)\\n rms=[]\\n for i in range(len(y_fit_low)):\\n rms.append(y_fit_low[i]-y_fitted_low[i]-median_low)\\n \\n # rms=y_fit-y_fitted\\n lowlimit=np.nanpercentile(rms,pmin)\\n highlimit=np.nanpercentile(rms,pmax)\\n \\n corrected_s_ =copy.deepcopy(y_fit)\\n for i in range(len(w_fit)):\\n if w_fit[i] >= low_high and w_fit[i] <= high_low: # ONLY CORRECT in [low_high,high_low] \\n if only_correct_negative_values:\\n if y_fit[i] <= 0 : \\n corrected_s_[i] = y_fitted[i]\\n else:\\n if y_fit[i]-y_fitted[i] <= lowlimit or y_fit[i]-y_fitted[i] >= highlimit: corrected_s_[i] = y_fitted[i]\\n \\n \\n corrected_s = np.concatenate((s_low,corrected_s_))\\n corrected_s = np.concatenate((corrected_s,s_high))\\n \\n \\n if plot:\\n ptitle = \\\"CorrectionBase in range \\\"+np.str(np.round(low_low,2))+\\\" - [ \\\"+np.str(np.round(low_high,2))+\\\" - \\\"+np.str(np.round(high_low,2))+\\\" ] - \\\"+np.str(np.round(high_high,2))\\n plot_plot(w_fit,[y_fit,y_fitted,y_fitted-highlimit,y_fitted-lowlimit,corrected_s_], color=[\\\"r\\\",\\\"b\\\", \\\"black\\\",\\\"black\\\",\\\"green\\\"], alpha=[0.3,0.7,0.2,0.2,0.5],xmin=low_low-40, xmax=high_high+40,vlines=[low_low,low_high,high_low,high_high],ptitle=ptitle, ylabel=\\\"Normalized flux\\\") \\n #plot_plot(w,[s,corrected_s],xmin=low_low-40, xmax=high_high+40,vlines=[low_low,low_high,high_low,high_high])\\n except Exception:\\n if warnings: print(\\\" Fitting the continuum failed! Nothing done.\\\")\\n corrected_s = s\\n\\n return corrected_s\",\n \"def mask_percentage(self):\\n return 100 - self.tissue_percentage\",\n \"def _calc_range(self) -> np.ndarray:\\n if self._is_ct25k():\\n range_resolution = 30\\n n_gates = 256\\n else:\\n n_gates = int(self.metadata[\\\"number_of_gates\\\"])\\n range_resolution = int(self.metadata[\\\"range_resolution\\\"])\\n return np.arange(n_gates) * range_resolution + range_resolution / 2\",\n \"def sum_range(lower, upper):\\n\\n def copies(pmin, pmax):\\n if lower <= pmin and pmax <= upper:\\n return True\\n elif pmax > upper:\\n return False\\n return copies(pmin+50, pmax+60)\\n\\n return copies(0, 0)\",\n \"def _value_in_bounds(self, vals):\\n return (self._min_in_bounds(vals[0]), self._max_in_bounds(vals[1]))\",\n \"def _bound(x, min_value, max_value):\\n return np.maximum(min_value, np.minimum(x, max_value))\",\n \"def mask_value(self, ds, mask_value=0):\\n xg, yg, mask = self._mask_grid()\\n ds = ds.reshape(xg.shape)\\n ds[mask == 0] = mask_value\\n return xg, yg, ds\",\n \"def normalise_modular_range(value, min, max):\\n return numpy.mod(value-min, max-min)+min\",\n \"def in_range(data, minval=-np.inf, maxval=np.inf):\\n return (minval <= data) & (data <= maxval)\",\n \"def source_annular(f, ctr, r_min, r_max, I0=1):\\n\\n f2 = (f[0] - ctr[0])**2 + (f[1] - ctr[1])**2\\n mask = (f2 >= r_min**2) * (f2 <= r_max**2)\\n return I0 * mask\",\n \"def build_mask(sst, qual, qual_thresh=2, temp_bounds=(-2,33)):\\n sst[np.isnan(sst)] = np.nan\\n qual[np.isnan(qual)] = np.nan\\n # Deal with NaN\\n masks = np.logical_or(np.isnan(sst), np.isnan(qual))\\n # Temperature bounds and quality\\n qual_masks = np.zeros_like(masks)\\n qual_masks[~masks] = (qual[~masks] > qual_thresh) | (sst[~masks] <= temp_bounds[0]) | (sst[~masks] > temp_bounds[1])\\n masks = np.logical_or(masks, qual_masks)\\n # Return\\n return masks\",\n \"def bounds(self) -> Tensor:\\n return torch.cat([self.mins, self.mins + self.ranges], dim=-2)\",\n \"def sat_in_range(arr, args):\\n # find number of saturated pixels, args[0] is the pixel saturation limit\\n sat_pixels = (arr > args[0]).sum()\\n # args[1] is a limit of saturated pixels\\n res = sat_pixels < args[1]\\n result = ct.Result(res, 'saturation_in_range')\\n return result\",\n \"def mask_skymap(skymap, percent):\\n masked_skymap = np.zeros_like(skymap)\\n masked_skymap[ligo.skymap.postprocess.util.find_greedy_credible_levels(skymap) <= percent/100.] = 1.0\\n return masked_skymap\",\n \"def covers_overlaps(self, bounds):\\n bounds = tuple(float(b) for b in bounds)\\n return self.numba_rtree.covers_overlaps(bounds)\",\n \"def sum_range(lower, upper):\\n\\n def copies(pmin, pmax):\\n if lower <= pmin and pmax <= upper:\\n return True\\n elif pmax > upper:\\n return False\\n return copies(pmin + 50, pmax + 60) or copies(pmin + 130, pmax + 140)\\n\\n return copies(0, 0)\",\n \"def mask_by(input_var, maskvar, low_limit=None, high_limit=None):\\n var = copy.deepcopy(input_var)\\n if low_limit is None and high_limit is None:\\n return var\\n if low_limit is None and high_limit is not None:\\n maskvarmask = maskvar > high_limit\\n elif low_limit is not None and high_limit is None:\\n maskvarmask = maskvar < low_limit\\n else:\\n maskvarmask = (maskvar < low_limit) | (maskvar > high_limit)\\n if var.mask is False:\\n newmask = maskvarmask\\n else:\\n newmask = var.mask | maskvarmask\\n var.mask = newmask\\n return var\",\n \"def SelectReduce(\\n source_values: numpy.ndarray,\\n mask: numpy.ndarray,\\n) -> numpy.ndarray:\\n return numpy.sum(source_values * mask, axis=mask.ndim - 1)\",\n \"def applymask(self,mask):\\n self.spec[mask==0]=np.nan\",\n \"def Mask(self) -> int:\",\n \"def mask(self, mask):\\n return MaskedDistribution(self, mask)\",\n \"def return_value_mask(actions):\\n if hasattr(actions,'shape'):\\n M = actions.shape[0]\\n else:\\n M = 1\\n value_mask = torch.zeros(M,5)\\n value_mask[torch.arange(M),actions] = 1\\n value_mask = value_mask.bool()\\n return value_mask.squeeze(0)\",\n \"def create_region_mask(latitude_array, target_shape, lat_bounds):\\n\\n target_ndim = len(target_shape)\\n\\n southern_lat, northern_lat = lat_bounds\\n mask_array = numpy.where((latitude_array >= southern_lat) & (latitude_array < northern_lat), False, True)\\n\\n mask = uconv.broadcast_array(mask_array, [target_ndim - 2, target_ndim - 1], target_shape)\\n assert mask.shape == target_shape \\n\\n return mask\",\n \"def scale_value(value, ip_range, domain=(0,1)):\\n x1, x2 = domain\\n y1, y2 = ip_range\\n\\n assert(y1 <= value <= y2)\\n\\n m = (x2 - x1)/(y2 - y1)\\n b = y1 - m * x1\\n return m * value - b\",\n \"def filterInRange(frame, min, max, colorMode):\\n\\n tempFrame = cv2.cvtColor(frame, colorMode)\\n\\n mask = cv2.inRange(tempFrame, min, max)\\n mask = cv2.bitwise_not(mask)\\n\\n filtered_frame = cv2.bitwise_and(frame, frame, mask=mask)\\n\\n return filtered_frame\",\n \"def generateBounds(regionFilename, latitudeRange, longitudeRange): \\n rastData = Dataset(regionFilename)\\n\\n #setting up values for raster data\\n latsRast = np.array(rastData[\\\"lat\\\"][:])\\n lonsRast = np.array(rastData[\\\"lon\\\"][:])\\n regionOfInterest = np.array(rastData[\\\"Band1\\\"][:][:])\\n\\n\\n regionArray = np.zeros((len(longitudeRange),len(latitudeRange)))\\n\\n\\n for lat in latitudeRange:\\n closestLatIndex = np.where( np.abs(latsRast-lat) == np.abs(latsRast-lat).min())[0][0]\\n for lon in longitudeRange:\\n closestLonIndex = np.where( np.abs(lonsRast-lon) == np.abs(lonsRast-lon).min())[0][0]\\n\\n #If lat long of MERRA data box is offshore or in region (values 1 in raster) set them equal to 1 for master Array, else they are left as zeros\\n if (regionOfInterest[closestLatIndex][closestLonIndex] == 1):\\n latIndex = np.where(latitudeRange == lat)[0][0]\\n lonIndex = np.where(longitudeRange == lon)[0][0]\\n regionArray[lonIndex][latIndex] = 1\\n\\n\\n #for debugging\\n ''' \\n ax = sns.heatmap(regionArray)\\n plt.show()\\n '''\\n return regionArray\",\n \"def _local_mask(self, sparsity):\\n for mask, param in self.masked_parameters:\\n score = self.scores[id(param)]\\n k = int((1.0 - sparsity) * score.numel())\\n if not k < 1:\\n threshold, _ = torch.kthvalue(torch.flatten(score), k)\\n zero = torch.tensor([0.]).to(mask.device)\\n one = torch.tensor([1.]).to(mask.device)\\n mask.copy_(torch.where(score <= threshold, zero, one))\",\n \"def threshold_mask(mask, threshold=0.5):\\n mask[np.where(mask >= threshold)] = 1.\\n mask[np.where(mask < threshold)] = 0.\\n return mask\",\n \"def _global_mask(self, sparsity):\\n # # Set score for masked parameters to -inf \\n # for mask, param in self.masked_parameters:\\n # score = self.scores[id(param)]\\n # score[mask == 0.0] = -np.inf\\n\\n # Threshold scores\\n global_scores = torch.cat([torch.flatten(v) for v in self.scores.values()])\\n k = int((1.0 - sparsity) * global_scores.numel())\\n if not k < 1:\\n threshold, _ = torch.kthvalue(global_scores, k)\\n for mask, param in self.masked_parameters:\\n score = self.scores[id(param)] \\n zero = torch.tensor([0.]).to(mask.device)\\n one = torch.tensor([1.]).to(mask.device)\\n mask.copy_(torch.where(score <= threshold, zero, one))\",\n \"def map_to_range(val, old_min, old_max, new_min, new_max):\\n return new_max - (val - old_min) * (new_max - new_min) / (old_max - old_min)\",\n \"def m_to_range(self, data):\\n return (data - self._min_range_m) / self._total_range\",\n \"def get_coverage_mask(self, coverages, covlim=0.2):\\n if coverages.shape[0] != len(self):\\n raise ValueError('Use QubicMultibandAcquisition.get_coverage method to create input')\\n if len(self) == 1:\\n cov = coverages\\n return cov > covlim * np.max(cov)\\n observed = [(coverages[i] > covlim * np.max(coverages[i])) for i in range(len(self))]\\n obs = reduce(np.logical_and, tuple(observed[i] for i in range(len(self))))\\n return obs\",\n \"def fill_region(image,mask,value=1):\\n\\tim = image.copy().ravel()\\n\\tif image.ndim > 2:\\n\\t\\tim_h, im_w, im_ch = image.shape\\n\\telse:\\n\\t\\tim_ch = 1\\n\\t\\tim_h, im_w = self.image.shape\\n\\t# linear indices of masked pixels\\n\\tind = masked_indices(mask)\\n\\tfor i in ind:\\n\\t\\tfor ch in range(im_ch):\\n\\t\\t\\tim.data[i*im_ch+ch] = value\\n\\treturn im.reshape(image.shape)\",\n \"def min_values(self, lower, upper): \\n if not self.lower_bounds is None:\\n return self.lower_bounds\\n\\n minus = np.clip(self.coeffs,-math.inf,0)\\n plus = np.clip(self.coeffs,0,math.inf)\\n self.lower_bounds = plus.dot(lower) + minus.dot(upper) + self.const\\n \\n return self.lower_bounds\",\n \"def mapRange(num, min1, max1, min2, max2, clamp=True):\\n if(clamp and num < min1):\\n return min2\\n if(clamp and num > max1):\\n return max2\\n\\n num1 = (num - min1) / (max1 - min1)\\n num2 = (num1 * (max2 - min2)) + min2\\n return num2\",\n \"def get_mask(total, begin, end):\\n mask = np.zeros([total]).astype(np.float32)\\n mask[begin:end] = 1\\n return np.array(mask, dtype=np.bool)\",\n \"def mask_var(self, data):\\n return data.where(_make_mask(data, self.mask_bounds))\",\n \"def nudged_min_max_compute(min_broadcast, max_broadcast, num_bits, narrow_range):\\n\\n\\n dtype = min_broadcast.dtype\\n quant_min = 1 if narrow_range else 0\\n quant_max = (2 ** num_bits) - 1\\n\\n # because of need compute each channel, so quant_min and quant_max need to broadcast.\\n quant_min_float = topi.full(min_broadcast.shape, dtype, tvm.const(quant_min, dtype))\\n quant_max_float = topi.full(min_broadcast.shape, dtype, tvm.const(quant_max, dtype))\\n\\n # caculate each channel max and min difference.\\n max_sub_min = topi.subtract(max_broadcast, min_broadcast)\\n quant_max_sub_quant_min = topi.subtract(quant_max_float, quant_min_float)\\n # compute scale = (max_broadcast - min_broadcast) / (quant_max - quant_min)\\n # and min_div_scale = min_broadcast / scale\\n if product_is_mini():\\n scale = mul(max_sub_min, reciprocal(quant_max_sub_quant_min), target=utils.CCE)\\n min_div_scale = Mul(min_broadcast, reciprocal(scale), target=utils.CCE)\\n else:\\n scale = divide(max_sub_min, quant_max_sub_quant_min, target=utils.CCE)\\n min_div_scale = divide(min_broadcast, scale, target=utils.CCE)\\n\\n # zero_point_from_min = quant_min_float - min_broadcast / scale\\n zero_point_from_min = topi.subtract(quant_min_float, min_div_scale)\\n # if zero_point_from_min < quant_min_float, bool_less_quant_min_float = 1 else 0\\n bool_less_quant_min_float = less_compare_float32(zero_point_from_min, quant_min_float)\\n # if quant_max_float < zero_point_from_min, bool_more_quant_max_float = 1 else 0\\n bool_more_quant_max_float = less_compare_float32(quant_max_float, zero_point_from_min)\\n\\n # according to above bool param to select effective value\\n less_quant_min_float = topi.multiply(quant_min_float, bool_less_quant_min_float)\\n more_quant_max_float = topi.multiply(quant_max_float, bool_more_quant_max_float)\\n\\n # compute which num is not less than quant_min_float and not large than quant_max_float\\n tensor_one = topi.full(min_broadcast.shape, dtype, dc.one_const(dtype))\\n bool_not_less_quant_min_float = topi.subtract(tensor_one, bool_less_quant_min_float)\\n bool_not_more_quant_max_float = topi.subtract(tensor_one, bool_more_quant_max_float)\\n bool_between_min_max = topi.multiply(bool_not_less_quant_min_float, bool_not_more_quant_max_float)\\n between_min_max_float = topi.multiply(zero_point_from_min, bool_between_min_max)\\n # add 0.5 to num which min <= num <= max and then floor them.\\n between_min_max_add_half_one = topi.add(between_min_max_float, dc.half_const(dtype))\\n between_min_max_round = akg.lang.ascend.floor(between_min_max_add_half_one)\\n if product_is_mini():\\n between_min_max_round = topi.cast(between_min_max_round, \\\"float16\\\")\\n\\n between_min_max_round = topi.cast(between_min_max_round, \\\"float32\\\")\\n\\n # calculate the maximum and minimum values of the quantization\\n nudged_zero_point_tmp = topi.add(less_quant_min_float, more_quant_max_float)\\n nudged_zero_point = topi.add(nudged_zero_point_tmp, between_min_max_round)\\n\\n nudged_min_tmp = topi.subtract(quant_min_float, nudged_zero_point)\\n nudged_max_tmp = topi.subtract(quant_max_float, nudged_zero_point)\\n nudged_min = topi.multiply(nudged_min_tmp, scale)\\n nudged_max = topi.multiply(nudged_max_tmp, scale)\\n res = [nudged_min, nudged_max, scale]\\n\\n return res\",\n \"def in_range_bev(\\n self, point_range: Union[Tensor, np.ndarray,\\n Sequence[float]]) -> Tensor:\\n in_range_flags = ((self.bev[:, 0] > point_range[0])\\n & (self.bev[:, 1] > point_range[1])\\n & (self.bev[:, 0] < point_range[2])\\n & (self.bev[:, 1] < point_range[3]))\\n return in_range_flags\",\n \"def masked_mae_cal(inputs, target, mask):\\n return torch.sum(torch.abs(inputs - target) * mask) / (torch.sum(mask) + 1e-9)\",\n \"def mask_annular(m, bound):\\n rmin, rmax, cmin, cmax = bound\\n m[:rmin,:] = True\\n m[rmax:,:] = True\\n m[:,:cmin] = True\\n m[:,cmax:] = True\\n return m\",\n \"def calculate_pixels(self, lower, upper):\\n\\n lower_range = np.array(lower)\\n upper_range = np.array(upper)\\n\\n mask = cv2.inRange(self.hsv, lower_range, upper_range)\\n return (mask == 255).sum()\",\n \"def mask5(imagem, value, bandNames):\\n mask = imagem.select(bandNames[0]).eq(value) \\\\\\n .bitwiseAnd(imagem.select(bandNames[1]).neq(value)) \\\\\\n .bitwiseAnd(imagem.select(bandNames[2]).neq(value)) \\\\\\n .bitwiseAnd(imagem.select(bandNames[3]).neq(value)) \\\\\\n .bitwiseAnd(imagem.select(bandNames[4]).eq(value))\\n change_img = imagem.select(bandNames[1]).mask(mask.eq(1)).where(mask.eq(1), value) \\n change_img1 = imagem.select(bandNames[2]).mask(mask.eq(1)).where(mask.eq(1), value) \\n change_img2 = imagem.select(bandNames[3]).mask(mask.eq(1)).where(mask.eq(1), value) \\n img_out = imagem.select(bandNames[1]).blend(change_img).blend(change_img1).blend(change_img2)\\n return img_out\",\n \"def discrete_potential(function, threshold):\\n\\n return np.where(function >= threshold, 1, 0)\",\n \"def above_threshold(self, value):\\n # We use floating point number here so we have to take care\\n return finf(value,self.min) or finf(self.max,value)\",\n \"def threshold(self, vmin, vmax=None, vcut=0.):\\n if vmax == None:\\n vmin = -np.abs(vmin)\\n vmax = +np.abs(vmin)\\n assert (vmin < vmax)\\n\\n ret = self.copy()\\n for m in [ret.tmap, ret.qmap, ret.umap]:\\n m[np.where(m < vmin)] = vcut\\n m[np.where(m > vmax)] = vcut\\n return ret\",\n \"def map(value, in_low, in_high, out_low, out_high):\\n # example from /animation_nodes/nodes/number/map_range.py\\n # if inMin == inMax:\\n # newValue = 0\\n # # with clamping\\n # if inMin < inMax:\\n # _value = min(max(value, inMin), inMax)\\n # else:\\n # _value = min(max(value, inMax), inMin)\\n # with interpolation\\n # newValue = outMin + interpolation(\\n # (_value - inMin) / (inMax - inMin)\\n # ) * (outMax - outMin)\\n # without interpolation\\n # newValue = outMin + (\\n # (_value - inMin) / (inMax - inMin)\\n # ) * (outMax - outMin)\\n # # without clamping\\n # newValue = outMin + (\\n # (value - inMin) / (inMax - inMin)\\n # ) * (outMax - outMin)\\n # # without clamping - reworded\\n # result = (\\n # (\\n # ((value - in_low) / (in_high - in_low)) *\\n # (out_high - out_low)\\n # ) + out_low\\n # )\\n\\n result = None\\n\\n # based on http://arduino.cc/en/Reference/Map\\n # and http://stackoverflow.com/a/5650012/574981\\n result = (\\n (\\n ((value - in_low) * (out_high - out_low)) /\\n (in_high - in_low)\\n ) + out_low\\n )\\n\\n return result\",\n \"def extract_single_color_range(image,hsv,lower,upper):\\n mask = cv2.inRange(hsv, lower, upper)\\n res = cv2.bitwise_and(image,image, mask= mask)\\n return res\",\n \"def _adjust_data_range_using_min_ratio(c_min, c_max, c_axis_range, *, min_ratio=0.01):\\n c_range = c_max - c_min\\n if c_range < c_axis_range * min_ratio:\\n c_center = (c_max + c_min) / 2\\n c_new_range = c_axis_range * min_ratio\\n c_min = c_center - c_new_range / 2\\n c_max = c_center + c_new_range / 2\\n return c_min, c_max\",\n \"def detect_range(self) -> Union[int, float]:\\n return self.proto.detect_range\",\n \"def _build_multiband_mask(data, tractor, filt2pixscale, fill_value=0.0,\\n threshmask=0.01, r50mask=0.05, maxshift=10,\\n relmaxshift=0.1,\\n sigmamask=3.0, neighborfactor=1.0, verbose=False):\\n import numpy.ma as ma\\n from copy import copy\\n from skimage.transform import resize\\n from legacyhalos.mge import find_galaxy\\n from legacyhalos.misc import srcs2image, ellipse_mask\\n\\n import matplotlib.pyplot as plt\\n from astropy.visualization import simple_norm\\n\\n bands, refband = data['bands'], data['refband']\\n #residual_mask = data['residual_mask']\\n\\n #nbox = 5\\n #box = np.arange(nbox)-nbox // 2\\n #box = np.meshgrid(np.arange(nbox), np.arange(nbox))[0]-nbox//2\\n\\n xobj, yobj = np.ogrid[0:data['refband_height'], 0:data['refband_width']]\\n\\n # If the row-index of the central galaxy is not provided, use the source\\n # nearest to the center of the field.\\n if 'galaxy_indx' in data.keys():\\n galaxy_indx = np.atleast_1d(data['galaxy_indx'])\\n else:\\n galaxy_indx = np.array([np.argmin((tractor.bx - data['refband_height']/2)**2 +\\n (tractor.by - data['refband_width']/2)**2)])\\n data['galaxy_indx'] = np.atleast_1d(galaxy_indx)\\n data['galaxy_id'] = ''\\n\\n #print('Import hack!')\\n #norm = simple_norm(img, 'log', min_percent=0.05, clip=True)\\n #import matplotlib.pyplot as plt ; from astropy.visualization import simple_norm\\n\\n ## Get the PSF sources.\\n #psfindx = np.where(tractor.type == 'PSF')[0]\\n #if len(psfindx) > 0:\\n # psfsrcs = tractor.copy()\\n # psfsrcs.cut(psfindx)\\n #else:\\n # psfsrcs = None\\n\\n def tractor2mge(indx, factor=1.0):\\n # Convert a Tractor catalog entry to an MGE object.\\n class MGEgalaxy(object):\\n pass\\n\\n default_majoraxis = tractor.diam_init[indx] * 60 / 2 / filt2pixscale[refband] # [pixels]\\n default_pa = tractor.pa_init[indx]\\n default_ba = tractor.ba_init[indx]\\n #default_theta = (270 - default_pa) % 180\\n #default_eps = 1 - tractor.ba_init[indx]\\n\\n #if tractor.sga_id[indx] > -1:\\n if tractor.type[indx] == 'PSF' or tractor.shape_r[indx] < 2:\\n pa = tractor.pa_init[indx]\\n ba = tractor.ba_init[indx]\\n # take away the extra factor of 2 we put in in read_sample()\\n r50 = tractor.diam_init[indx] * 60 / 2 / 2\\n if r50 < 5:\\n r50 = 5.0 # minimum size, arcsec\\n majoraxis = factor * r50 / filt2pixscale[refband] # [pixels]\\n #majoraxis = factor * tractor.diam_init[indx] * 60 / 2 / 2 / filt2pixscale[refband] # [pixels]\\n else:\\n ee = np.hypot(tractor.shape_e1[indx], tractor.shape_e2[indx])\\n ba = (1 - ee) / (1 + ee)\\n pa = 180 - (-np.rad2deg(np.arctan2(tractor.shape_e2[indx], tractor.shape_e1[indx]) / 2))\\n pa = pa % 180\\n\\n # can be zero (or very small) if fit as a PSF or REX\\n if tractor.shape_r[indx] > 1:\\n majoraxis = factor * tractor.shape_r[indx] / filt2pixscale[refband] # [pixels]\\n else:\\n majoraxis = factor * tractor.diam_init[indx] * 60 / 2 / 2 / filt2pixscale[refband] # [pixels]\\n\\n mgegalaxy = MGEgalaxy()\\n \\n mgegalaxy.xmed = tractor.by[indx]\\n mgegalaxy.ymed = tractor.bx[indx]\\n mgegalaxy.xpeak = tractor.by[indx]\\n mgegalaxy.ypeak = tractor.bx[indx]\\n\\n # never use the Tractor geometry (only the centroid)\\n # https://portal.nersc.gov/project/cosmo/temp/ioannis/virgofilaments-html/215/NGC5584/NGC5584.html\\n if True:\\n mgegalaxy.eps = 1-ba\\n mgegalaxy.pa = pa\\n mgegalaxy.theta = (270 - pa) % 180\\n mgegalaxy.majoraxis = majoraxis\\n else:\\n mgegalaxy.eps = 1 - default_ba\\n mgegalaxy.pa = default_pa\\n mgegalaxy.theta = (270 - default_pa) % 180\\n mgegalaxy.majoraxis = default_majoraxis\\n\\n # always restore all pixels within the nominal / initial size of the galaxy\\n #objmask = ellipse_mask(mgegalaxy.xmed, mgegalaxy.ymed, # object pixels are True\\n # default_majoraxis,\\n # default_majoraxis * (1-default_eps), \\n # np.radians(default_theta-90), xobj, yobj)\\n #objmask = ellipse_mask(mgegalaxy.xmed, mgegalaxy.ymed, # object pixels are True\\n # default_majoraxis, default_majoraxis, 0.0, xobj, yobj)\\n\\n objmask = ellipse_mask(mgegalaxy.xmed, mgegalaxy.ymed, # object pixels are True\\n mgegalaxy.majoraxis,\\n mgegalaxy.majoraxis * (1-mgegalaxy.eps), \\n np.radians(mgegalaxy.theta-90), xobj, yobj)\\n\\n # central 10% pixels can override the starmask\\n objmask_center = ellipse_mask(mgegalaxy.xmed, mgegalaxy.ymed, # object pixels are True\\n 0.1*mgegalaxy.majoraxis,\\n 0.1*mgegalaxy.majoraxis * (1-mgegalaxy.eps), \\n np.radians(mgegalaxy.theta-90), xobj, yobj)\\n\\n return mgegalaxy, objmask, objmask_center\\n\\n # Now, loop through each 'galaxy_indx' from bright to faint.\\n data['mge'] = []\\n for ii, central in enumerate(galaxy_indx):\\n print('Determing the geometry for galaxy {}/{}.'.format(\\n ii+1, len(galaxy_indx)))\\n\\n # [1] Determine the non-parametric geometry of the galaxy of interest\\n # in the reference band. First, subtract all models except the galaxy\\n # and galaxies \\\"near\\\" it. Also restore the original pixels of the\\n # central in case there was a poor deblend.\\n largeshift = False\\n mge, centralmask, centralmask2 = tractor2mge(central, factor=1.0)\\n #plt.clf() ; plt.imshow(centralmask, origin='lower') ; plt.savefig('desi-users/ioannis/tmp/junk-mask.png') ; pdb.set_trace()\\n\\n iclose = np.where([centralmask[np.int(by), np.int(bx)]\\n for by, bx in zip(tractor.by, tractor.bx)])[0]\\n \\n srcs = tractor.copy()\\n srcs.cut(np.delete(np.arange(len(tractor)), iclose))\\n model = srcs2image(srcs, data['{}_wcs'.format(refband.lower())],\\n band=refband.lower(),\\n pixelized_psf=data['{}_psf'.format(refband.lower())])\\n\\n img = data[refband].data - model\\n img[centralmask] = data[refband].data[centralmask]\\n\\n mask = np.logical_or(ma.getmask(data[refband]), data['residual_mask'])\\n #mask = np.logical_or(data[refband].mask, data['residual_mask'])\\n\\n # restore the central pixels but not the masked stellar pixels\\n centralmask[np.logical_and(data['starmask'], np.logical_not(centralmask2))] = False\\n mask[centralmask] = False\\n\\n img = ma.masked_array(img, mask)\\n ma.set_fill_value(img, fill_value)\\n #if ii == 1:\\n # pdb.set_trace()\\n\\n mgegalaxy = find_galaxy(img, nblob=1, binning=1, quiet=False)#, plot=True) ; plt.savefig('cosmo-www/tmp/junk-mge.png')\\n #plt.clf() ; plt.imshow(mask, origin='lower') ; plt.savefig('junk-mask.png')\\n ##plt.clf() ; plt.imshow(satmask, origin='lower') ; plt.savefig('/mnt/legacyhalos-data/debug.png')\\n #pdb.set_trace()\\n\\n # Did the galaxy position move? If so, revert back to the Tractor geometry.\\n if np.abs(mgegalaxy.xmed-mge.xmed) > maxshift or np.abs(mgegalaxy.ymed-mge.ymed) > maxshift:\\n print('Large centroid shift (x,y)=({:.3f},{:.3f})-->({:.3f},{:.3f})'.format(\\n mgegalaxy.xmed, mgegalaxy.ymed, mge.xmed, mge.ymed))\\n print(' Reverting to the default geometry and the Tractor centroid.')\\n largeshift = True\\n mgegalaxy = copy(mge)\\n\\n radec_med = data['{}_wcs'.format(refband.lower())].pixelToPosition(\\n mgegalaxy.ymed+1, mgegalaxy.xmed+1).vals\\n radec_peak = data['{}_wcs'.format(refband.lower())].pixelToPosition(\\n mgegalaxy.ypeak+1, mgegalaxy.xpeak+1).vals\\n mge = {\\n 'largeshift': largeshift,\\n 'ra': tractor.ra[central], 'dec': tractor.dec[central],\\n 'bx': tractor.bx[central], 'by': tractor.by[central],\\n #'mw_transmission_g': tractor.mw_transmission_g[central],\\n #'mw_transmission_r': tractor.mw_transmission_r[central],\\n #'mw_transmission_z': tractor.mw_transmission_z[central],\\n 'ra_moment': radec_med[0], 'dec_moment': radec_med[1],\\n #'ra_peak': radec_med[0], 'dec_peak': radec_med[1]\\n }\\n for key in ('eps', 'majoraxis', 'pa', 'theta', 'xmed', 'ymed', 'xpeak', 'ypeak'):\\n mge[key] = np.float32(getattr(mgegalaxy, key))\\n if key == 'pa': # put into range [0-180]\\n mge[key] = mge[key] % np.float32(180)\\n data['mge'].append(mge)\\n\\n #if False:\\n # #plt.clf() ; plt.imshow(mask, origin='lower') ; plt.savefig('/mnt/legacyhalos-data/debug.png')\\n # plt.clf() ; mgegalaxy = find_galaxy(img, nblob=1, binning=1, quiet=True, plot=True)\\n # plt.savefig('/mnt/legacyhalos-data/debug.png')\\n\\n # [2] Create the satellite mask in all the bandpasses. Use srcs here,\\n # which has had the satellites nearest to the central galaxy trimmed\\n # out.\\n print('Building the satellite mask.')\\n satmask = np.zeros(data[refband].shape, bool)\\n for filt in bands:\\n # do not let GALEX and WISE contribute to the satellite mask\\n if data[filt].shape != satmask.shape:\\n continue\\n \\n cenflux = getattr(tractor, 'flux_{}'.format(filt.lower()))[central]\\n satflux = getattr(srcs, 'flux_{}'.format(filt.lower()))\\n if cenflux <= 0.0:\\n #raise ValueError('Central galaxy flux is negative!')\\n print('Central galaxy flux is negative! Proceed with caution...')\\n #pdb.set_trace()\\n \\n satindx = np.where(np.logical_or(\\n (srcs.type != 'PSF') * (srcs.shape_r > r50mask) *\\n (satflux > 0.0) * ((satflux / cenflux) > threshmask),\\n srcs.ref_cat == 'R1'))[0]\\n #satindx = np.where(srcs.ref_cat == 'R1')[0]\\n #if np.isin(central, satindx):\\n # satindx = satindx[np.logical_not(np.isin(satindx, central))]\\n if len(satindx) == 0:\\n #raise ValueError('All satellites have been dropped!')\\n #print('Warning! All satellites have been dropped from band {}!'.format(filt))\\n print('Note: no satellites to mask in band {}.'.format(filt))\\n else:\\n satsrcs = srcs.copy()\\n #satsrcs = tractor.copy()\\n satsrcs.cut(satindx)\\n satimg = srcs2image(satsrcs, data['{}_wcs'.format(filt.lower())],\\n band=filt.lower(),\\n pixelized_psf=data['{}_psf'.format(filt.lower())])\\n thissatmask = satimg > sigmamask*data['{}_sigma'.format(filt.lower())]\\n #if filt == 'FUV':\\n # plt.clf() ; plt.imshow(thissatmask, origin='lower') ; plt.savefig('junk-{}.png'.format(filt.lower()))\\n # #plt.clf() ; plt.imshow(data[filt], origin='lower') ; plt.savefig('junk-{}.png'.format(filt.lower()))\\n # pdb.set_trace()\\n if satmask.shape != satimg.shape:\\n thissatmask = resize(thissatmask*1.0, satmask.shape, mode='reflect') > 0\\n\\n satmask = np.logical_or(satmask, thissatmask)\\n #if True:\\n # import matplotlib.pyplot as plt\\n # plt.clf() ; plt.imshow(np.log10(satimg), origin='lower') ; plt.savefig('debug.png')\\n # plt.clf() ; plt.imshow(satmask, origin='lower') ; plt.savefig('debug.png')\\n ## #plt.clf() ; plt.imshow(satmask, origin='lower') ; plt.savefig('/mnt/legacyhalos-data/debug.png')\\n # pdb.set_trace()\\n\\n #print(filt, np.sum(satmask), np.sum(thissatmask))\\n\\n #plt.clf() ; plt.imshow(satmask, origin='lower') ; plt.savefig('desi-users/ioannis/tmp/junk-satmask.png')\\n \\n # [3] Build the final image (in each filter) for ellipse-fitting. First,\\n # subtract out the PSF sources. Then update the mask (but ignore the\\n # residual mask). Finally convert to surface brightness.\\n #for filt in ['W1']:\\n for filt in bands:\\n thismask = ma.getmask(data[filt])\\n if satmask.shape != thismask.shape:\\n _satmask = (resize(satmask*1.0, thismask.shape, mode='reflect') > 0) == 1.0\\n _centralmask = (resize(centralmask*1.0, thismask.shape, mode='reflect') > 0) == 1.0\\n mask = np.logical_or(thismask, _satmask)\\n mask[_centralmask] = False\\n else:\\n mask = np.logical_or(thismask, satmask)\\n mask[centralmask] = False\\n #if filt == 'r':\\n # #plt.imshow(_satmask, origin='lower') ; plt.savefig('desi-users/ioannis/tmp/junk-satmask-{}.png'.format(filt))\\n # plt.clf() ; plt.imshow(mask, origin='lower') ; plt.savefig('desi-users/ioannis/tmp/junk-mask-{}.png'.format(filt))\\n # plt.clf() ; plt.imshow(img, origin='lower') ; plt.savefig('desi-users/ioannis/tmp/junk-img-{}.png'.format(filt))\\n # pdb.set_trace()\\n\\n varkey = '{}_var'.format(filt.lower())\\n imagekey = '{}_masked'.format(filt.lower())\\n psfimgkey = '{}_psfimg'.format(filt.lower())\\n thispixscale = filt2pixscale[filt]\\n if imagekey not in data.keys():\\n data[imagekey], data[varkey], data[psfimgkey] = [], [], []\\n\\n img = ma.getdata(data[filt]).copy()\\n \\n # Get the PSF sources.\\n psfindx = np.where((tractor.type == 'PSF') * (getattr(tractor, 'flux_{}'.format(filt.lower())) / cenflux > threshmask))[0]\\n if len(psfindx) > 0 and filt.upper() != 'W3' and filt.upper() != 'W4': \\n #if len(psfindx) > 0 and filt.upper() != 'NUV' and filt.upper() != 'FUV' and filt.upper() != 'W3' and filt.upper() != 'W4':\\n psfsrcs = tractor.copy()\\n psfsrcs.cut(psfindx)\\n else:\\n psfsrcs = None\\n \\n if psfsrcs:\\n psfimg = srcs2image(psfsrcs, data['{}_wcs'.format(filt.lower())],\\n band=filt.lower(),\\n pixelized_psf=data['{}_psf'.format(filt.lower())])\\n if False:\\n #import fitsio ; fitsio.write('junk-psf-{}.fits'.format(filt.lower()), data['{}_psf'.format(filt.lower())].img, clobber=True)\\n fig, ((ax1, ax2), (ax3, ax4)) = plt.subplots(2, 2)\\n im = ax1.imshow(np.log10(img), origin='lower') ; fig.colorbar(im, ax=ax1)\\n im = ax2.imshow(np.log10(psfimg), origin='lower') ; fig.colorbar(im, ax=ax2)\\n im = ax3.imshow(np.log10(data['{}_psf'.format(filt.lower())].img), origin='lower') ; fig.colorbar(im, ax=ax3)\\n im = ax4.imshow(img-psfimg, origin='lower') ; fig.colorbar(im, ax=ax4)\\n plt.savefig('desi-users/ioannis/tmp/qa-psf-{}.png'.format(filt.lower()))\\n if filt == 'r':# or filt == 'r':\\n pdb.set_trace()\\n img -= psfimg\\n else:\\n psfimg = np.zeros((2, 2), 'f4')\\n\\n data[psfimgkey].append(psfimg)\\n \\n img = ma.masked_array((img / thispixscale**2).astype('f4'), mask) # [nanomaggies/arcsec**2]\\n var = data['{}_var_'.format(filt.lower())] / thispixscale**4 # [nanomaggies**2/arcsec**4]\\n\\n # Fill with zeros, for fun--\\n ma.set_fill_value(img, fill_value)\\n #if ii == 0 and filt == 'r': #filt == 'W1' or \\n # plt.clf() ; plt.imshow(img, origin='lower') ; plt.savefig('desi-users/ioannis/tmp/junk-img-{}.png'.format(filt.lower()))\\n # plt.clf() ; plt.imshow(mask, origin='lower') ; plt.savefig('desi-users/ioannis/tmp/junk-mask-{}.png'.format(filt.lower()))\\n ##### plt.clf() ; plt.imshow(thismask, origin='lower') ; plt.savefig('junk-thismask-{}.png'.format(filt.lower()))\\n # pdb.set_trace()\\n \\n data[imagekey].append(img)\\n data[varkey].append(var)\\n\\n #test = data['r_masked'][0]\\n #plt.clf() ; plt.imshow(np.log(test.clip(test[mgegalaxy.xpeak, mgegalaxy.ypeak]/1e4)), origin='lower') ; plt.savefig('/mnt/legacyhalos-data/debug.png')\\n #pdb.set_trace()\\n\\n # Cleanup?\\n for filt in bands:\\n del data[filt]\\n del data['{}_var_'.format(filt.lower())]\\n\\n return data\",\n \"def test_masked_data_below(self):\\n\\n expected_mask = np.full_like(self.cube.data, False, dtype=bool)\\n expected_mask[:, 0, 0] = True\\n expected_mask[1, 0, 2] = True\\n expected_mask[2, 0] = True\\n expected_mask[2, 1, 2] = True\\n expected_mask[2, 1, 0] = True\\n\\n cube = set_up_probability_cube(\\n 1 - self.cube.data,\\n [200, 1000, 15000],\\n variable_name=(\\n \\\"cloud_base_height_assuming_only_consider_cloud_\\\"\\n \\\"area_fraction_greater_than_4p5_oktas\\\"\\n ),\\n threshold_units=\\\"m\\\",\\n spp__relative_to_threshold=\\\"below\\\",\\n )\\n\\n result = Plugin(mask_percentiles=True)._probabilities_to_percentiles(\\n cube, self.percentiles\\n )\\n self.assertArrayEqual(result.data.mask, expected_mask)\",\n \"def query_range(tree, start_y, start_x, end_y, end_x):\\n res = 0\\n start_y -= 1\\n\\n while end_y > start_y:\\n res += bit.query_range(tree[end_y], start_x, end_x)\\n end_y -= (end_y & -end_y)\\n\\n while start_y > end_y:\\n res -= bit.query_range(tree[start_y], start_x, end_x)\\n start_y -= (start_y & -start_y)\\n\\n return res\",\n \"def find_limits(self, \\n label: str, \\n limit: list,\\n ) -> np.array :\\n ci = np.array([])\\n cdf = self.cdf[label]\\n\\n for l in limit:\\n fintp = interp1d(cdf[0], cdf[1] - l, kind='linear')\\n ci = np.append(ci, scipy.optimize.brentq(fintp, np.min(cdf[0]), \\n np.max(cdf[0])))\\n return ci\",\n \"def _get_range_squared(self) -> np.ndarray:\\n m2km = 0.001\\n return (self.range * m2km) ** 2\",\n \"def __limit_value(self, value, v_range):\\n if np.isnan(value):\\n print('Warning: trying to limit nan value in range {0}'.format(v_range))\\n return value\\n\\n return np.min([v_range[1], np.max([value, v_range[0]])])\",\n \"def mask_sonar_image(img, display_results=False):\\n lower_color_bounds = (40, 80, 0) # Filter out lower values (ie blue)\\n upper_color_bounds = (230, 250, 255) # Filter out too high values\\n mask = cv2.inRange(img, lower_color_bounds, upper_color_bounds)\\n\\n if display_results:\\n cv2.imshow(\\\"mask\\\", mask)\\n cv2.waitKey(0)\\n\\n return mask\",\n \"def masked_mre_cal(inputs, target, mask):\\n return torch.sum(torch.abs(inputs - target) * mask) / (\\n torch.sum(torch.abs(target * mask)) + 1e-9\\n )\",\n \"def __call__(self, mask):\\n mapping = self.calculate_mapping(mask)\\n return self.apply_mapping(mask, mapping)\",\n \"def _full_value_range(self):\\n min_value, max_value = self._raw_data.data_range\\n return max_value - min_value\",\n \"def calc(self,newValue):\\n return np.sum(self.values<=newValue)/self.n\",\n \"def False_positive_rate(result,start,end):\\n Wider = 0.0002\\n if start.loc[0,'lat'] > end.loc[0,'lat']:\\n top = start.loc[0,'lat'] + Wider\\n bottom = end.loc[0,'lat'] - Wider\\n else:\\n top = end.loc[0,'lat'] + Wider\\n bottom = start.loc[0,'lat'] - Wider\\n if start.loc[0,'lon'] > end.loc[0,'lon']:\\n right = start.loc[0,'lon'] + Wider\\n left = end.loc[0,'lon'] - Wider\\n else:\\n right = start.loc[0,'lon'] + Wider\\n left = start.loc[0,'lon'] - Wider\\n return (~(((result['lat'] >= bottom) & (result['lat'] <= top) \\n & (result['lon'] >= left) & (result['lon'] <= right)))).sum()/result.shape[0]\",\n \"def masked_mse_cal(inputs, target, mask):\\n return torch.sum(torch.square(inputs - target) * mask) / (torch.sum(mask) + 1e-9)\",\n \"def filter_labelmask(labelmask, func, above=0, below=1e6):\\n labels = []\\n for x in np.unique(labelmask):\\n prop = func(labelmask, x)\\n if (prop >= above and prop <= below):\\n labels.append(x)\\n labelmask_filtered = np.where(np.isin(labelmask, labels), labelmask, 0)\\n return labelmask_filtered\",\n \"def isShiftInRange(shiftValue, shiftRanges):\\n\\n for (minVal, maxVal) in shiftRanges:\\n if shiftValue >= minVal:\\n if shiftValue <= maxVal:\\n return True\\n \\n return False\",\n \"def get_acc_mask(cmfile, use_pt, min_acc):\\n logging.info('Masking all bins of correction map with acceptance < {}'\\n .format(min_acc))\\n if use_pt:\\n gen_dist = project(cmfile.Get(MAP_NAME.format('gen')), [0, 1, 2])\\n acc_dist = project(cmfile.Get(MAP_NAME.format('acc')), [0, 1, 2])\\n else:\\n gen_dist = project(cmfile.Get(MAP_NAME.format('gen')), [1, 0])\\n acc_dist = project(cmfile.Get(MAP_NAME.format('acc')), [1, 0])\\n\\n accmap = divide(acc_dist, gen_dist)\\n\\n mask = get_array(accmap) < min_acc\\n return mask\",\n \"def process_O2(x, lb, ub):\\n x = x.abs()\\n x.loc[(x <= 1) & (x > 0)] = x.loc[(x <= 1) & (x > 0)] * 100\\n x.loc[(x <= 10) & (x > 1)] = x.loc[(x <= 10) & (x > 1)] * 10\\n x.loc[(x <= lb ) | (x > ub)] = np.nan\\n return x\",\n \"def _in_range_op(spec):\",\n \"def ignore_and_range(MAT, k=-9999.0):\\n MAT[MAT==k]=0\\n MAT = ((MAT-(MAT.min()))) / (MAT.max() - MAT.min())\\n MAT *= 255\\n \\n return MAT\",\n \"def test_set_clip_precentile_update(self):\\n data = np.array([1., 2., 3.])\\n self.mode.set_clip_percentile(10, 90)\\n result = self.mode.get_bounds(data)\\n self.mode.set_clip_percentile(20, 80)\\n result = self.mode.get_bounds(data)\\n np.testing.assert_array_almost_equal(result,\\n np.percentile(data, [20, 80]))\"\n]","isConversational":false},"negative_scores":{"kind":"list like","value":["0.6139728","0.5778516","0.5743647","0.5667564","0.5649735","0.5611406","0.5567767","0.55613536","0.5501029","0.54364896","0.54216534","0.5400132","0.5389187","0.5379457","0.53776085","0.5347557","0.5343009","0.53155017","0.53128374","0.5302718","0.52962077","0.5240116","0.52240044","0.52085024","0.51951325","0.51928806","0.5184483","0.51767457","0.516311","0.5146565","0.5139372","0.51276803","0.5127359","0.51265806","0.5125085","0.5119931","0.5119555","0.5110698","0.5102513","0.5095314","0.50913376","0.5084662","0.5084656","0.50821936","0.5078251","0.50726265","0.5060313","0.50597656","0.5057197","0.50380456","0.5033367","0.5032373","0.5032271","0.5027874","0.5027376","0.50268924","0.5023091","0.5022106","0.50185513","0.5017041","0.50052047","0.500458","0.499008","0.49892893","0.49890602","0.49871394","0.49802342","0.49769092","0.4962353","0.4957776","0.4948114","0.494161","0.4940159","0.49366248","0.49357682","0.49344638","0.4928699","0.4926178","0.49213853","0.49205762","0.4913284","0.49118504","0.4908732","0.49041274","0.49032536","0.48970628","0.48943868","0.48928744","0.4888188","0.48858055","0.4884773","0.488413","0.48827538","0.48826092","0.48789784","0.4878556","0.48755527","0.48749194","0.4871006","0.48704413"],"string":"[\n \"0.6139728\",\n \"0.5778516\",\n \"0.5743647\",\n \"0.5667564\",\n \"0.5649735\",\n \"0.5611406\",\n \"0.5567767\",\n \"0.55613536\",\n \"0.5501029\",\n \"0.54364896\",\n \"0.54216534\",\n \"0.5400132\",\n \"0.5389187\",\n \"0.5379457\",\n \"0.53776085\",\n \"0.5347557\",\n \"0.5343009\",\n \"0.53155017\",\n \"0.53128374\",\n \"0.5302718\",\n \"0.52962077\",\n \"0.5240116\",\n \"0.52240044\",\n \"0.52085024\",\n \"0.51951325\",\n \"0.51928806\",\n \"0.5184483\",\n \"0.51767457\",\n \"0.516311\",\n \"0.5146565\",\n \"0.5139372\",\n \"0.51276803\",\n \"0.5127359\",\n \"0.51265806\",\n \"0.5125085\",\n \"0.5119931\",\n \"0.5119555\",\n \"0.5110698\",\n \"0.5102513\",\n \"0.5095314\",\n \"0.50913376\",\n \"0.5084662\",\n \"0.5084656\",\n \"0.50821936\",\n \"0.5078251\",\n \"0.50726265\",\n \"0.5060313\",\n \"0.50597656\",\n \"0.5057197\",\n \"0.50380456\",\n \"0.5033367\",\n \"0.5032373\",\n \"0.5032271\",\n \"0.5027874\",\n \"0.5027376\",\n \"0.50268924\",\n \"0.5023091\",\n \"0.5022106\",\n \"0.50185513\",\n \"0.5017041\",\n \"0.50052047\",\n \"0.500458\",\n \"0.499008\",\n \"0.49892893\",\n \"0.49890602\",\n \"0.49871394\",\n \"0.49802342\",\n \"0.49769092\",\n \"0.4962353\",\n \"0.4957776\",\n \"0.4948114\",\n \"0.494161\",\n \"0.4940159\",\n \"0.49366248\",\n \"0.49357682\",\n \"0.49344638\",\n \"0.4928699\",\n \"0.4926178\",\n \"0.49213853\",\n \"0.49205762\",\n \"0.4913284\",\n \"0.49118504\",\n \"0.4908732\",\n \"0.49041274\",\n \"0.49032536\",\n \"0.48970628\",\n \"0.48943868\",\n \"0.48928744\",\n \"0.4888188\",\n \"0.48858055\",\n \"0.4884773\",\n \"0.488413\",\n \"0.48827538\",\n \"0.48826092\",\n \"0.48789784\",\n \"0.4878556\",\n \"0.48755527\",\n \"0.48749194\",\n \"0.4871006\",\n \"0.48704413\"\n]","isConversational":false},"document_score":{"kind":"string","value":"0.60455686"},"document_rank":{"kind":"string","value":"1"}}},{"rowIdx":82394,"cells":{"query":{"kind":"string","value":"Calculates the moments of the distribution of the current particular (implied) parameter."},"document":{"kind":"string","value":"def _calcMoments(self, contribs, fraction):\n numContribs, numReps = contribs.shape\n val = np.zeros(numReps)\n mu = np.zeros(numReps)\n var = np.zeros(numReps)\n skw = np.zeros(numReps)\n krt = np.zeros(numReps)\n # loop over each repetition\n for ri in range(numReps):\n # the single set of R for this calculation\n if not any(self._validRange[ri]):\n continue # what to do if validRange is empty?\n rset = contribs[self._validRange[ri], ri]\n frac = fraction[self._validRange[ri], ri]\n val[ri] = sum(frac)\n mu[ri] = sum(rset * frac)\n if 0 != sum(frac):\n mu[ri] /= sum(frac)\n var[ri] = sum( (rset-mu[ri])**2 * frac )/sum(frac)\n sigma = np.sqrt(abs(var[ri]))\n skw[ri] = ( sum( (rset-mu[ri])**3 * frac )\n / (sum(frac) * sigma**3))\n krt[ri] = ( sum( (rset-mu[ri])**4 * frac )\n / (sum(frac) * sigma**4))\n\n DDoF = 0\n if numReps > 1: # prevent division by zero in numpy.std()\n DDoF = 1\n self._total = (val.mean(), val.std(ddof = DDoF))\n self._mean = ( mu.mean(), mu.std(ddof = DDoF))\n self._variance = (var.mean(), var.std(ddof = DDoF))\n self._skew = (skw.mean(), skw.std(ddof = DDoF))\n self._kurtosis = (krt.mean(), krt.std(ddof = DDoF))"},"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 moments(self):","def calc_moments(distribution):\n x = torch.linspace(2, 22, 31)\n d_mean = torch.sum(x * distribution)\n d_var = torch.sum(distribution * (x - d_mean) ** 2) \n \n return d_mean, torch.sqrt(d_var)","def moment(self, n, mu, sigma):\n return scipy_norm.moment(n, mu, sigma)","def _moments_match_numerical(self,obs,tau,v):\r\n #Compute first integral for zeroth moment.\r\n #NOTE constant np.sqrt(2*pi/tau) added at the end of the function\r\n mu = v/tau\r\n def int_1(f):\r\n return self.pdf(f, obs)*np.exp(-0.5*tau*np.square(mu-f))\r\n z_scaled, accuracy = quad(int_1, -np.inf, np.inf)\r\n\r\n #Compute second integral for first moment\r\n def int_2(f):\r\n return f*self.pdf(f, obs)*np.exp(-0.5*tau*np.square(mu-f))\r\n mean, accuracy = quad(int_2, -np.inf, np.inf)\r\n mean /= z_scaled\r\n\r\n #Compute integral for variance\r\n def int_3(f):\r\n return (f**2)*self.pdf(f, obs)*np.exp(-0.5*tau*np.square(mu-f))\r\n Ef2, accuracy = quad(int_3, -np.inf, np.inf)\r\n Ef2 /= z_scaled\r\n variance = Ef2 - mean**2\r\n\r\n #Add constant to the zeroth moment\r\n #NOTE: this constant is not needed in the other moments because it cancells out.\r\n z = z_scaled/np.sqrt(2*np.pi/tau)\r\n\r\n return z, mean, variance","def _compute_pooled_moments(x, alpha, batch_mean, batch_var, augment_moment_fn):\n augment_mean, augment_var = augment_moment_fn(x)\n pooled_mean = alpha * batch_mean + (1.0 - alpha) * augment_mean\n batch_mean_diff = batch_mean - pooled_mean\n augment_mean_diff = augment_mean - pooled_mean\n pooled_var = alpha * (batch_var + (batch_mean_diff * batch_mean_diff)) +\\\n (1.0 - alpha) * (augment_var + (augment_mean_diff * augment_mean_diff))\n return pooled_mean, pooled_var","def A(param):\n return (param.delta + param.nu + param.mu0) * (param.beta - param.nu)","def posterior_mean(self, parameter):\n if parameter not in self:\n raise ConjugateParameterException('Parameter not recognized!')\n else:\n i = parameter.split('_')[1]\n ai = self.prior_hyperparameters['a_{}'.format(i)]\n A = sum(self.prior_hyperparameters.values())\n ni = self.data[i]\n N = sum(self.data.values())\n\n return (ai+ni)/(A+N)","def _compute_instance_moments(x):\n return torch.mean(x, dim=(2, 3), keepdim=True), torch.var(x, dim=(2, 3), keepdim=True)","def _compute_layer_moments(x):\n return torch.mean(x, dim=(1, 2, 3), keepdim=True), torch.var(x, dim=(1, 2, 3), keepdim=True)","def parameters_to_marginal_moments(prob, distmu, distsigma):\n good = np.isfinite(prob) & np.isfinite(distmu) & np.isfinite(distsigma)\n prob = prob[good]\n distmu = distmu[good]\n distsigma = distsigma[good]\n distmean, diststd, _ = parameters_to_moments(distmu, distsigma)\n rbar = (prob * distmean).sum()\n r2bar = (prob * (np.square(diststd) + np.square(distmean))).sum()\n return rbar, np.sqrt(r2bar - np.square(rbar))","def _prior_marginal_scipy(self, parameter):\n letter = parameter.strip().split('_')[1]\n A = sum(self._prior_hyperparameters.values())\n ai = self.prior_hyperparameters['a_{}'.format(letter)]\n\n return _scipy_beta(ai, A-ai)","def prior_mean(self, parameter):\n if parameter not in self:\n raise ConjugateParameterException('Parameter not recognized!')\n else:\n i = parameter.split('_')[1]\n ai = self.prior_hyperparameters['a_{}'.format(i)]\n A = sum(self.prior_hyperparameters.values())\n\n return ai/A","def moments(data):\n height = data.max()\n background = data.min()\n data = data - np.min(data)\n total = data.sum()\n x, y = np.indices(data.shape)\n x = (x * data).sum() / total\n y = (y * data).sum() / total\n col = data[:, int(y)]\n width_x = np.sqrt(abs((np.arange(col.size) - y) ** 2 * col).sum() / col.sum())\n row = data[int(x), :]\n width_y = np.sqrt(abs((np.arange(row.size) - x) ** 2 * row).sum() / row.sum())\n width_x /= gaussian_sigma_to_fwhm\n width_y /= gaussian_sigma_to_fwhm\n return {\n \"amplitude\": height,\n \"x\": x,\n \"y\": y,\n \"sigma_x\": width_x,\n \"sigma_y\": width_y,\n \"background\": background,\n \"theta\": 0.0,\n }","def _posterior_marginal_scipy(self, parameter):\n letter = parameter.strip().split('_')[1]\n A = sum(self._prior_hyperparameters.values())\n ai = self.prior_hyperparameters['a_{}'.format(letter)]\n N = sum(self._data.values())\n ni = self._data[letter]\n\n return _scipy_beta(ai+ni, A-ai+N-ni)","def _etaM_cool(self,x):\n return self._eta_sfr_scaling(x,'M_cool')","def simple_moments(x):\n\n mean = x.mean()\n std = x.std()\n sterr = std / np.sqrt(len(x))\n\n return mean, std, sterr","def moments_match_ep(self,obs,tau,v,Y_metadata_i=None):\n #Compute first integral for zeroth moment.\n #NOTE constant np.sqrt(2*pi/tau) added at the end of the function\n mu = v/tau\n sigma2 = 1./tau\n #Lets do these for now based on the same idea as Gaussian quadrature\n # i.e. multiply anything by close to zero, and its zero.\n f_min = mu - 20*np.sqrt(sigma2)\n f_max = mu + 20*np.sqrt(sigma2)\n\n def int_1(f):\n return self.pdf(f, obs, Y_metadata=Y_metadata_i)*np.exp(-0.5*tau*np.square(mu-f))\n z_scaled, accuracy = quad(int_1, f_min, f_max)\n\n #Compute second integral for first moment\n def int_2(f):\n return f*self.pdf(f, obs, Y_metadata=Y_metadata_i)*np.exp(-0.5*tau*np.square(mu-f))\n mean, accuracy = quad(int_2, f_min, f_max)\n mean /= z_scaled\n\n #Compute integral for variance\n def int_3(f):\n return (f**2)*self.pdf(f, obs, Y_metadata=Y_metadata_i)*np.exp(-0.5*tau*np.square(mu-f))\n Ef2, accuracy = quad(int_3, f_min, f_max)\n Ef2 /= z_scaled\n variance = Ef2 - mean**2\n\n #Add constant to the zeroth moment\n #NOTE: this constant is not needed in the other moments because it cancells out.\n z = z_scaled/np.sqrt(2*np.pi/tau)\n\n return z, mean, variance","def estimate_moment(self):\n # Due to the optimization, we may store more than k elements in the\n # sample. The following removes excessive elements if needed.\n if len(self.elements) > self.k:\n self._remove_additional_elements()\n\n # The inclusion threshold (highest seed of element in the sample) is\n # used to compute the inclusion probabilities for the other elements\n # in the sample.\n max_in_sample = max(self.elements.items(), key=lambda x: x[1][0])\n threshold = max_in_sample[1][0]\n\n # Computes and sums the inverse-probability estimator for all keys\n # in the sample.\n sum_estimator = 0.0\n for key, (seed, count) in self.elements.items():\n if key != max_in_sample[0]:\n weight = self.func_of_freq(self.advice_obj.predict(key))\n # Warns us if we may run into float precision issues.\n # TODO(ofirg): change this warning to something more robust than\n # a print (and maybe use other approximations of exp() that are\n # better for this case).\n if weight * threshold < 2.0**(-24):\n print(\"weight * threshold < 2^{-24}\")\n print(weight * threshold)\n inc_pr = 1.0 - np.exp(-1.0 * weight * threshold)\n estimator = self.func_of_freq(count) / inc_pr\n sum_estimator += estimator\n\n return sum_estimator","def estimate_moment(self):\n # Due to the optimization, we may store more than k elements in the\n # sample. The following removes excessive elements if needed.\n if len(self.elements) > self.k:\n self._remove_additional_elements()\n\n # The inclusion threshold (highest seed of element in the sample) is\n # used to compute the inclusion probabilities for the other elements\n # in the sample.\n max_in_sample = max(self.elements.items(), key=lambda x: x[1][0])\n threshold = max_in_sample[1][0]\n\n # Computes and sums the inverse-probability estimator for all keys\n # in the sample.\n sum_estimator = 0.0\n for key, (seed, count) in self.elements.items():\n if key != max_in_sample[0]:\n # Warns us if we may run into float precision issues.\n # TODO(ofirg): change this warning to something more robust than\n # a print (and maybe use other approximations of exp() that are\n # better for this case).\n if (count**self.sample_p) * threshold < 2.0**(-24):\n print(\"(count**self.sample_p) * threshold < 2^{-24}\")\n print((count**self.sample_p) * threshold)\n inc_pr = 1.0 - np.exp(-1.0 * (count**self.sample_p) * threshold)\n estimator = self.func_of_freq(count) / inc_pr\n sum_estimator += estimator\n\n return sum_estimator","def demo_indef():\n n_dim = 2\n A = np.eye(n_dim)\n A[1, 1] = -1.\n covar = np.eye(n_dim)\n mean = np.zeros(n_dim)\n approx = approx_quad_form(mean, covar, A)\n\n # Sample from true dist\n n_sample = 10000\n x = np.random.multivariate_normal(mean, covar, n_sample)\n q_samples = np.zeros(n_sample)\n for i in range(n_sample):\n q_samples[i] = x[i] @ A @ x[i]\n\n q = np.linspace(-10, 10)\n\n plt.plot(\n q, approx(q), label='Approx.',\n color='tab:blue', linestyle='--')\n bins = np.linspace(-8, 8, 81)\n bins[0] = -np.inf\n bins[-1] = np.inf\n plt.hist(\n q_samples, density=True, histtype='stepfilled',\n bins=bins,\n alpha=0.5, color='black', label='Samples')\n plt.xlabel('q')\n plt.ylabel('pdf(q) [-]')\n plt.legend()\n\n central_moments_sample = scipy.stats.moment(\n q_samples, moment=[0, 1, 2, 3, 4])\n print(central_moments_sample)","def _compute_batch_moments(x):\n return torch.mean(x, dim=(0, 2, 3), keepdim=True), torch.var(x, dim=(0, 2, 3), keepdim=True)","def amoment(a,moment=1,dimension=None):\r\n if dimension == None:\r\n a = N.ravel(a)\r\n dimension = 0\r\n if moment == 1:\r\n return 0.0\r\n else:\r\n mn = amean(a,dimension,1) # 1=keepdims\r\n s = N.power((a-mn),moment)\r\n return amean(s,dimension)","def getMomentum(self):\n return self.p","def update_moments_r(self):\n denominator = np.exp(self.gamma_r[:, None] * self.constellation\n - self.Sigma_r[:, None] * np.power(self.constellation, 2) /2 )\n nominator1 = np.exp(self.gamma_r[:, None] * self.constellation\n - self.Sigma_r[:, None] * np.power(self.constellation, 2) /2 ) * self.constellation\n \n nominator2 = np.exp(self.gamma_r[:, None] * self.constellation\n - self.Sigma_r[:, None] * np.power(self.constellation, 2) /2) * np.power(self.constellation, 2)\n try:\n \n moment1 = nominator1.sum(axis=1) / denominator.sum(axis=1)\n moment2 = nominator2.sum(axis=1) / denominator.sum(axis=1)\n assert np.all(np.logical_not(np.isnan(moment1))) and np.all(np.logical_not(np.isnan(moment2)))\n except:\n print(\"Oops! That was no valid number. Try again...\")\n\n \n self.mu = moment1\n return moment1, moment2","def GetCurrentMoments(self, theta):\n p = self.params\n if p.use_moving_avg_in_training:\n beta = 0.0\n gamma = 1.0\n else:\n beta = theta.beta\n gamma = theta.gamma\n if not p.gamma_zero_init and not p.gamma_one_init:\n # Note, The real gamma to use is 1 + gamma.\n gamma = 1.0 + gamma\n return theta.moving_mean, theta.moving_variance, beta, gamma","def moments(values):\n\n meanValue = numpy.mean(values)\n return (meanValue,\n numpy.sqrt(moment(values, meanValue, 2)),\n moment(values, meanValue, 3),\n moment(values, meanValue, 4))","def GetCurrentMoments(self, theta):\n p = self.params\n if p.use_moving_avg_in_training:\n return self._moving_mean, self._moving_variance, 0.0, 1.0\n else:\n return self._moving_mean, self._moving_variance, theta.beta, theta.gamma","def get_means_and_scales(self):\n return self.optim.parameters[::2], np.exp(self.optim.parameters[1::2])","def moments(data):\n# =============================================================================\n# total = data.sum()\n# X, Y = np.indices(data.shape)\n# x = (X*data).sum()/total\n# y = (Y*data).sum()/total\n# col = data[:, int(y)]\n# \n# width_x = np.sqrt(np.abs((np.arange(col.size)-y)**2*col).sum()/col.sum())\n# \n# row = data[int(x), :]\n# width_y = np.sqrt(np.abs((np.arange(row.size)-x)**2*row).sum()/row.sum())\n# height = data.max()\n# height1 = height\n# =============================================================================\n return(1, 15, 14, 3, 3, 1, 14, 16, 3, 2)","def estimate(self):\n mu = self.mean()\n var = np.average((self.particles - mu) ** 2, weights=self.weights, axis=0)\n\n return mu, var","def _momentum_update(self):\n for param_ol, param_tgt in zip(self.online_net.parameters(),\n self.target_net.parameters()):\n param_tgt.data = param_tgt.data * self.momentum + \\\n param_ol.data * (1. - self.momentum)","def estimate_gaussian_params(X):\n mu = X.mean(axis=0)\n var = X.std(axis=0)**2.0\n return mu,var","def first_moment(self, mass, z=None):\n return 1.0","def fit_normal(distogram):\n L = distogram.shape[1]\n params = torch.empty((3, L, L))\n \n for i in range(L):\n for j in range(L):\n m, s = calc_moments(distogram[:, i, j])\n scalar = torch.max(distogram[:, i, j]) / normal_distr(m, m, s)\n params[0, i, j], params[1, i, j], params[2, i, j] = m, s, scalar\n \n return params","def mom(x):\n with mp.extradps(5):\n x = _validate_x_bounds(x, low=0, high=1,\n strict_low=True, strict_high=True)\n M1 = _mean(x)\n M2 = _mean([t**2 for t in x])\n c = (M1 - M2) / (M2 - M1**2)\n a = M1*c\n b = (1 - M1)*c\n return a, b","def update_moments(self, X: np.ndarray) -> \"FeatureGenerator\":\n self._scaler.partial_fit(X)\n self._total_samples += X.shape[0]\n\n return self","def moment_update(model, model_ema, m):\n for p1, p2 in zip(model.parameters(), model_ema.parameters()):\n p2.data.mul_(m).add_(1-m, p1.detach().data)","def moment_update(model, model_ema, m):\n for p1, p2 in zip(model.parameters(), model_ema.parameters()):\n p2.data.mul_(m).add_(1-m, p1.detach().data)","def get_optimizer_momentum(optimizer: Optimizer) -> float:\n beta = safitty.get(optimizer.param_groups, 0, \"betas\", 0)\n momentum = safitty.get(optimizer.param_groups, 0, \"momentum\")\n return beta if beta is not None else momentum","def computeMoments(x):\n return (abs(stats.skew(x)),abs(stats.kurtosis(x,None,True)))","def avg_func(self, averaged_param: Tensor, source_param: Tensor,\n steps: int) -> None:\n averaged_param.mul_(1 - self.momentum).add_(\n source_param, alpha=self.momentum)","def _compute_moments(self, u):\n\n # Get the moments from the parent Gaussian Markov Chain\n #u = self.parents[0].get_moments() #message_to_child()\n\n # Send only moments <X(n)> and <X(n)X(n)> but not <X(n-1)X(n)>\n return u[:2]","def MyBaseMoments(p,q,img,gauss_sigma,gauss_centroid=None, gauss_g1=0., gauss_g2=0.):\n weight = galsim.Image(np.zeros_like(img.array))\n gauss = galsim.Gaussian(sigma=gauss_sigma*pixel_scale).shear(g1=gauss_g1,g2=gauss_g2)\n if gauss_centroid is None:\n gauss_centroid = img.true_center\n weight = gauss.drawImage(image=weight, scale=pixel_scale, method='no_pixel', use_true_center=True, offset=(gauss_centroid-img.true_center)*(1))\n x = np.linspace(img.xmin-img.center.x*0-gauss_centroid.x*1, img.xmax-img.center.x*0-gauss_centroid.x*1, img.xmax-img.xmin+1)+0.*0.5\n y = np.linspace(img.ymin-img.center.y*0-gauss_centroid.y*1, img.ymax-img.center.y*0-gauss_centroid.y*1, img.ymax-img.ymin+1)+0.*0.5\n X, Y = np.meshgrid(x,y)\n\n Q00 = np.sum(weight.array*img.array)\n Q10 = gauss_centroid.x + np.sum(X*weight.array*img.array)/Q00\n Q01 = gauss_centroid.y + np.sum(Y*weight.array*img.array)/Q00\n Q20 = np.sum((X**2)*weight.array*img.array)\n Q02 = np.sum((Y**2)*weight.array*img.array)\n\n monomial = 1.\n for pp in xrange(p):\n monomial *= X\n for qq in xrange(q):\n monomial *= Y\n Qpq = np.sum(monomial*weight.array*img.array) #/Q00\n\n return Qpq","def omega(self):\n return self._data.train_X @ self._thetas","def objective_function(params):\n cirq_circuit = variational_state_evolve(params)\n mean_value = self.expectation(\n all_qubits_in_circuit, cirq_circuit, hamiltonian)\n self._current_expectation = mean_value\n return mean_value","def measure_image_moments(image):\n data = image.quantity\n\n coords = image.geom.get_coord().skycoord\n x, y = coords.data.lon.wrap_at(\"180d\"), coords.data.lat\n\n A = data[np.isfinite(data)].sum()\n\n # Center of mass\n x_cms = (x * data)[np.isfinite(data)].sum() / A\n y_cms = (y * data)[np.isfinite(data)].sum() / A\n\n # Second moments\n x_var = ((x - x_cms) ** 2 * data)[np.isfinite(data)].sum() / A\n y_var = ((y - y_cms) ** 2 * data)[np.isfinite(data)].sum() / A\n x_sigma = np.sqrt(x_var)\n y_sigma = np.sqrt(y_var)\n\n return A, x_cms, y_cms, x_sigma, y_sigma, np.sqrt(x_sigma * y_sigma)","def calcMomentumFromVelocity(self):\n if self.mass is None:\n raise CoordinateVector(\"The particle mass needs to be specified to calculate the particle momentum from velocity.\")\n values = {}\n for direction in self.v.order:\n gamma = self.calcLorentzGammaFromVelocity(direction)\n values[direction] = getattr(self.v,direction)*gamma*self.mass\n self.setMomentum(Cartesian3DVector(**values))\n return self.getMomentum()","def _moments_match_analytical(self,data_i,tau_i,v_i):\r\n sigma2_hat = 1./(1./self.variance + tau_i)\r\n mu_hat = sigma2_hat*(data_i/self.variance + v_i)\r\n sum_var = self.variance + 1./tau_i\r\n Z_hat = 1./np.sqrt(2.*np.pi*sum_var)*np.exp(-.5*(data_i - v_i/tau_i)**2./sum_var)\r\n return Z_hat, mu_hat, sigma2_hat","def _construct_mom_stuff(self):\n a = self.mom_mix_rate\n dist_mean = self.GN.dist_mean\n dist_cov = self.GN.dist_cov\n # Get the generated sample observations for this batch, transformed\n # linearly into the desired space for moment matching...\n X_b = T.dot(self.GN.output, self.mom_match_proj)\n # Get their mean\n batch_mean = T.mean(X_b, axis=0)\n # Get the updated generator distribution mean\n new_mean = ((1.0 - a[0]) * self.GN.dist_mean) + (a[0] * batch_mean)\n # Use the mean to get the updated generator distribution covariance\n X_b_minus_mean = X_b - new_mean\n # Whelp, I guess this line needs the cast... for some reason...\n batch_cov = T.dot(X_b_minus_mean.T, X_b_minus_mean) / T.cast(X_b.shape[0], 'floatX')\n new_cov = ((1.0 - a[0]) * self.GN.dist_cov) + (a[0] * batch_cov)\n # Get the cost for deviation from the target distribution's moments\n mean_err = new_mean - self.target_mean\n cov_err = (new_cov - self.target_cov)\n mm_cost = self.mom_match_weight[0] * \\\n (T.sum(mean_err**2.0) + T.sum(cov_err**2.0))\n # Construct the updates for the running estimates of the generator\n # distribution's first and second-order moments.\n mom_updates = OrderedDict()\n mom_updates[self.GN.dist_mean] = new_mean\n mom_updates[self.GN.dist_cov] = new_cov\n return [mm_cost, mom_updates]","def normalDist(x, mean, var):\n\n if (var != 0):\n return -(x - mean)**2/(2*var) - 0.5*np.log(2*np.pi*var)\n else:\n return 0","def totalmomentum (self):\n tot_p=0.\n for planet in self.planets: #this loop takes each 'planet' momentum in 'self.planets' and sums them.\n tot_p += planet.momentum #'tot_p' is the resulting vector of all momenta vectors.\n total_mom = np.linalg.norm(tot_p) #the 'total_mom' is the total linear momentum's magnitude, which is conserved.\n return (total_mom)","def normal_pmf(x: np.array, mean: float, sigma: float) -> np.array:\n x = np.exp(-1 / 2 * ((x - mean) / sigma) ** 2)\n x /= np.sqrt(2 * np.pi * sigma ** 2)\n x /= x.sum()\n return x","def parade_mean(p):\n return [noneneg(mj.get('mean')) for mj in p['moments']]","def moment_update(model, model_ema, m):\r\n for p1, p2 in zip(model.parameters(), model_ema.parameters()):\r\n p2.data.mul_(m).add_(1 - m, p1.detach().data)\r\n # p2.data.mul_(m).add_(1 - m, p1.data)","def prior_distribution(self):\n out = self.model.forward(self.inducing_points)\n return MultivariateNormal(out.mean, out.lazy_covariance_matrix.evaluate_kernel())","def _get_augment_moment_fn(self):\n return self._compute_instance_moments","def Delta_0(param):\n delta_0 = param.delta + param.nu + param.mu0\n delta_1 = param.delta + param.nu + param.mu1\n return (param.beta - param.nu)**2 * delta_1**2 * (param.b)**2 - 2*param.A*(param.beta - param.nu) * (param.beta*(param.mu1 - param.mu0) + delta_0*(delta_1-param.beta)) * param.b + param.A**2*(param.beta - delta_0)**2","def update_momentum(self, factor):\n if not self.gpu:\n self.psi *= np.exp(-1j*factor*self.m*self.V)\n else:\n self.g_psi_hat[...] = -1.0j*factor*self.m*self.g_V\n cumath.exp(self.g_psi_hat, out=self.g_psi_hat)\n self.g_psi *= self.g_psi_hat","def __calcMomenta(self, kinPara):\n self.physArea,self.__allMomenta,self.PreFac = self.__kinLib(kinPara)\n return 0","def empirical_erm(self):\n return lambda samples: np.mean(samples) - 0.5 * self.alpha * np.var(samples)","def gaussian_prior(self):\n self.prior = sps.multivariate_normal(self.m0,self.S0)","def marginal(self):\n m = np.zeros(len(self.domain))\n for fnode in self.neighbors:\n m += self.received[fnode]\n return np.exp(normalize(m))","def _em_autoregressive(self, result, betas, tmp=None):\n if tmp is None:\n tmp = np.sqrt(result.smoothed_marginal_probabilities)\n\n resid = np.zeros((self.k_regimes, self.nobs + self.order))\n resid[:] = self.orig_endog\n if self._k_exog > 0:\n for i in range(self.k_regimes):\n resid[i] -= np.dot(self.orig_exog, betas[i])\n\n # The difference between this and `_em_exog` is that here we have a\n # different endog and exog for each regime\n coeffs = np.zeros((self.k_regimes,) + (self.order,))\n variance = np.zeros((self.k_regimes,))\n exog = np.zeros((self.nobs, self.order))\n for i in range(self.k_regimes):\n endog = resid[i, self.order:]\n exog = lagmat(resid[i], self.order)[self.order:]\n tmp_endog = tmp[i] * endog\n tmp_exog = tmp[i][:, None] * exog\n\n coeffs[i] = np.dot(np.linalg.pinv(tmp_exog), tmp_endog)\n\n if self.switching_variance:\n tmp_resid = endog - np.dot(exog, coeffs[i])\n variance[i] = (np.sum(\n tmp_resid ** 2 * result.smoothed_marginal_probabilities[i]) /\n np.sum(result.smoothed_marginal_probabilities[i]))\n else:\n tmp_resid = tmp_endog - np.dot(tmp_exog, coeffs[i])\n variance[i] = np.sum(tmp_resid ** 2)\n # Variances\n if not self.switching_variance:\n variance = variance.sum() / self.nobs\n return coeffs, variance","def gaussianDist(self, x, mu, var):\n val = 1/(math.sqrt(2 * math.pi * var)) * math.exp(-1 * (x - mu)**2 / (2*var))\n return val","def generate_moments(hyper, params):\n\n k, d = hyper['k'], hyper['d']\n\n p = params # Shorthand, don't judge\n m = {} # Moments\n for x1 in xrange(1,d+1):\n m[(x1,)] = sum( p[(h,x1)] * p[(h,)] for h in xrange(1,k+1) )\n for x2 in xrange(1,d+1):\n m[(x1,x2)] = sum( p[(h,x1)] * p[(h,x2)] * p[(h,)] for h in xrange(1,k+1) )\n for x3 in xrange(1,d+1):\n m[(x1,x2,x3)] = sum( p[(h,x1)] * p[(h,x2)] * p[(h,x3)] * p[(h,)] for h in xrange(1,k+1) )\n return m","def f(x):\n return delta_eps_mu(x, mu) - delta","def compute_fixed_moments(self, x):\n return [x]","def update_variables_momentum(alpha, beta1, var, grad, v):\n v = beta1 * v + (1-beta1) * grad\n var = var - alpha * v\n return var, v","def approximate_gamma_mom(mean, variance):\n assert mean > 0\n assert variance > 0\n alpha = mean**2 / variance\n beta = mean / variance\n return alpha, beta","def stdProbabilityNorm(self):\n return 1./factorial(self.alpha-1)","def gaussian(amp, fwhm, mean, x):\n return amp * np.exp(-4. * np.log(2) * (x-mean)**2 / fwhm**2)","def M(self,v):\n assert len(self.d_moments)==1, \"For now only supporting 1 dimension at a time\"\n d = int(list(self.d_moments.keys())[0])\n *start,n,a = v.shape \n N = n//(d+1) # number of extended bodies\n v_reshaped = v.reshape(*start,N,d+1,a) # (*, # separate bodies, # internal body nodes, a) \n moments = torch.exp(self.d_moments[str(d)])\n masses = moments[:,:1]\n if d==0: return (masses.unsqueeze(-1)*v_reshaped).reshape(*v.shape) # no inertia for point masses\n a00 = (masses + moments[:,1:].sum(-1,keepdims=True)).unsqueeze(-1) #(N,1,1)\n ai0 = a0i = -moments[:,1:].unsqueeze(-1) #(N,d,1)\n p0 = a00*v[...,:1,:] + (a0i*v[...,1:,:]).sum(-2,keepdims=True)\n aii = moments[:,1:].unsqueeze(-1) # (N,d,1)\n \n pi = ai0*v[...,:1,:] +aii*v[...,1:,:]\n return torch.cat([p0,pi],dim=-2).reshape(*v.shape)","def moments(self, x, y):\n # Let's attempt to remove noise by axing points\n # below 5% of the peak\n threshold = y.max() * 0.05\n clean = np.array(y)\n clean[clean < threshold] = 0\n zero = clean.sum()\n one = np.sum(x * clean)\n two = np.sum(x * x * clean)\n mean = one / zero\n var = two / zero - mean**2\n stdev = np.sqrt(var)\n return (mean, stdev)","def log_marginal(self):\n #\n # Predictive covariance of x is sum of covariance of phi a and covariance of x|a\n x_Sigma = self.phi @ self.phi.T + np.diag(self.sigma_n**2 * np.ones(self.M))\n #\n # Predictive mean is 0 by symmetry\n # so given that x is distributed as a MVN, the exact marginal is\n lp_exact = st.multivariate_normal.logpdf(self.x, cov=x_Sigma)\n #\n return lp_exact","def log_prior(self, params):\n # log likelihood function, see:\n # https://en.wikipedia.org/wiki/Multivariate_normal_distribution#Likelihood_function\n variance = self.std ** 2\n ndim = params.ndim\n mean_diff = params - self.mean\n scaled_sq_err = jnp.dot(mean_diff, mean_diff) / variance\n # log determinant of covariance matrix\n log_det_cov = 2 * ndim * jnp.log(self.std)\n norm_term = ndim * jnp.log(2 * jnp.pi)\n return -0.5 * (log_det_cov + scaled_sq_err + norm_term)","def pvalue_gaussian(self):\n \n pv = 2 * stats.norm.sf(abs(self.TS_prime_obs), loc=0, scale=1)\n return(pv)","def get_center_of_mass_allies(self,obs):","def normal(x, mean, dispersion):\n return np.exp(\n -1 * squared_norm(x - mean) / (2 * dispersion)\n ) / (\n np.sqrt(2 * np.pi * dispersion)\n )","def prop_dist_form_params(*arg):\n return np.random.multivariate_normal(*arg)","def calculate_posterior_mean_var(self, context_arm_grid):\n # all points played so far\n data = self.played_points_hist[:self.t]\n # kernels between all possible context-arms and the previous rounds\n kernel_vectors = self.kernel_fn(context_arm_grid, data, self.best_ard_params)\n # kernel matrix of data\n kernel_matrix = self.kernel_fn(data, data, self.best_ard_params)\n c_matrix = kernel_matrix + (self.noise_sigma ** 2) * np.eye(data.shape[0])\n c_matrix_inv = np.linalg.inv(c_matrix)\n mu_ests_vector = np.matmul(kernel_vectors, np.matmul(c_matrix_inv, self.rews_hist[:self.t])) # mean estimation\n sigma_ests_first_term = np.diag(self.kernel_fn(context_arm_grid, context_arm_grid, self.best_ard_params))\n sigma_ests_second_term = np.diag(np.matmul(kernel_vectors, np.matmul(c_matrix_inv, kernel_vectors.T)))\n sigma_ests_vector = sigma_ests_first_term - sigma_ests_second_term # variance estimation\n return mu_ests_vector, sigma_ests_vector","def var_momentum(self):\n\n if isinstance(self.momentum, MomentumNesterov):\n return self.t\n return self.k","def _moments_match_analytical(self,obs,tau,v):\r\n raise NotImplementedError","def init_moments(self, sample_count):\n # Compute outputs for the input latent noise in X_noise\n X = self.GN.sample_from_model(sample_count)\n # Get the transform to apply prior to moment matching\n P = self.mom_match_proj.get_value(borrow=False)\n # Compute post-transform mean and covariance of the outputs\n mu, sigma = projected_moments(X, P, ary_type='numpy')\n # Initialize the generator network's running moment estimates \n self.GN.dist_cov.set_value(sigma.astype(theano.config.floatX))\n self.GN.dist_mean.set_value(mu.astype(theano.config.floatX))\n return","def update_params(x, prior, posterior):\r\n mu0, kappa0, alpha0, beta0 = prior\r\n mu_t, kappa_t, alpha_t, beta_t = posterior\r\n return np.r_[mu0, (kappa_t*mu_t + x)/(kappa_t + 1)], \\\r\n np.r_[kappa0, kappa_t + 1], \\\r\n np.r_[alpha0, alpha_t + 0.5], \\\r\n np.r_[beta0, beta_t + 0.5*kappa_t*(x - mu_t)**2/(kappa_t + 1)]","def moments(data,x0=None,y0=None):\n total = data.sum()\n X, Y = np.indices(data.shape)\n x = (X*data).sum()/total\n y = (Y*data).sum()/total\n col = data[:, int(y)]\n width_x = np.sqrt(abs((np.arange(col.size)-y)**2*col).sum()/col.sum())\n row = data[int(x), :]\n width_y = np.sqrt(abs((np.arange(row.size)-x)**2*row).sum()/row.sum())\n height = data.max()\n if x0 is None:\n return height, x, y, width_x, width_y, 0.0, 0.0\n else:\n xstep = x0[1] - x0[0]\n ystep = y0[1] - y0[0]\n return height, x*xstep+x0[0], y*ystep+y0[0], width_x*xstep, width_y*ystep, 0.0, 0.0","def calcVelocityFromMomentum(self):\n if self.mass is None:\n raise CoordinateVector(\"The particle mass needs to be specified to calculate the particle velocity from momentum.\")\n values = {}\n for direction in self.p.order:\n gamma = self.calcLorentzGammaFromMomentum(direction)\n values[direction] = getattr(self.p,direction)/(gamma*self.mass)\n self.setVelocity(Cartesian3DVector(**values))\n return self.getVelocity()","def _etaM(self,x):\n return self._etaM_cool(x) + self._etaM_hot(x)","def reduced_normal_pmf(x: np.array, mean: float, sigma: float) -> np.array:\n x = np.exp(-1 / 2 * ((x - mean) / sigma) ** 2)\n x /= np.sqrt(2 * np.pi * sigma ** 2)\n x[mean] = 0.\n x /= x.sum()\n return x","def like_one(theta,dt,dmag,sigma):\n\n gamma, A = theta\n aux=(1/np.sqrt(2*np.pi*Veff2(dt,sigma,A,gamma)))*np.exp(-1.0*(dmag**2)/(2.0*Veff2(dt,sigma,A,gamma)))\n\n return aux","def calculate_moment(p, N, limits, dx=0.01):\n # Make a grid from the limits and resolution\n grid = _calculate_grid_parameters(limits, dx)\n\n # Evaluate the functions on the grid\n pe = p.gridded(grid.grid_values)[1]\n\n # calculate the moment\n grid_to_N = grid.grid_values ** N\n M = array_metrics.quick_moment(pe, grid_to_N, grid.resolution)\n\n return M","def arg_per(self):\n h_mom = self.sp_ang_mom()\n n_vec = np.cross([0, 0, 1], h_mom)\n e_vec = self.get_ecc()\n if np.linalg.norm(e_vec) == 0:\n small_omega = 0.0\n else:\n small_omega = np.arccos(np.dot(n_vec, e_vec) /\n (np.linalg.norm(n_vec) *\n np.linalg.norm(e_vec)))\n if e_vec[2] < 0:\n small_omega = 2*np.pi - small_omega\n return small_omega","def lmoment(inlist,moment=1):\r\n if moment == 1:\r\n return 0.0\r\n else:\r\n mn = mean(inlist)\r\n n = len(inlist)\r\n s = 0\r\n for x in inlist:\r\n s = s + (x-mn)**moment\r\n return s/float(n)","def linear_momentum(self):\r\n return self.mass * self.vel","def _compute_internals(self, moments):\n\n [mean, stdv] = moments\n internals = {}\n internals['k'] = mean ** 2. / stdv ** 2.\n internals['LAMBDA'] = mean / stdv ** 2.\n\n return internals","def _compute_log_moment(self, num_examples=5040):\n q = tf.cast(num_examples, tf.float64) * 1.0 / self._total_examples\n mu_1, sigma_1 = 0, 4 # mean and standard deviation\n s_1 = np.random.normal(mu_1, sigma_1, 1000)\n mu_2, sigma_2 = 1, 4 # mean and standard deviation\n s_2 = np.random.normal(mu_2, sigma_2, 1000)\n s = (1-q)*s_1 + q*s_2\n moment_1 =[0]*len(self._log_moments)\n moment_2 = [0]*len(self._log_moments)\n log_moment = [0] * len(self._log_moments)\n for i in range(len(self._log_moments)):\n for j in range(len(s_1)):\n moment_1[i] += ((s_1[j]/s[j])**self._moment_orders[i])/len(s_1)\n moment_2[i] += ((s[j] / s_1[j]) ** self._moment_orders[i]) / len(s_1)\n for i in range(len(self._log_moments)):\n log_moment[i] = math.log(abs(max(moment_1[i],moment_2[i])))\n return log_moment","def moments(data):\n total = data.sum()\n if total != 0.:\n X, Y = np.indices(data.shape)\n x = (X*data).sum()/total\n y = (Y*data).sum()/total\n col = data[:, int(y)]\n width_x = np.sqrt(abs((np.arange(col.size)-y)**2*col).sum()/col.sum())\n row = data[int(x), :]\n width_y = np.sqrt(abs((np.arange(row.size)-x)**2*row).sum()/row.sum())\n height = data.max()\n else:\n height=0\n x=0\n y=0\n width_x=0\n width_y=0\n return height,np.sqrt(width_x**2 + width_y**2)","def _compute_internals(self, moments):\n\n [mean, stdv] = moments\n internals = {}\n internals['mu'] = mean\n internals['sigma'] = stdv\n\n return internals","def _predictive_mean_analytical(self, mu, sigma):\r\n #FIXME: Not correct\r\n return mu","def temper_momentum(self, epoch):\n if epoch == 0:\n return\n self.momentums = [\n x + y for x, y in zip(self.momentums, self.momentum_temper_rates)\n ]\n for i, param_group in enumerate(self.optimizer.param_groups):\n param_group['momentum'] = self.momentums[i]","def _get_distribution_variables(self, R):\n domain, Domain = self.domain_Domain\n phase_name = self.phase_name\n\n R_typ = self.phase_param.R_typ # [m]\n # Particle-size distribution (area-weighted)\n f_a_dist = self.phase_param.f_a_dist(R) # [m-1]\n\n # Ensure the distribution is normalised, irrespective of discretisation\n # or user input\n f_a_dist = f_a_dist / pybamm.Integral(f_a_dist, R) # [m-1]\n\n # Volume-weighted particle-size distribution\n f_v_dist = R * f_a_dist / pybamm.Integral(R * f_a_dist, R) # [m-1]\n\n # Number-based particle-size distribution\n f_num_dist = (f_a_dist / R**2) / pybamm.Integral(\n f_a_dist / R**2, R\n ) # [m-1]\n\n # True mean radii and standard deviations, calculated from the f_a_dist that\n # was given, all have units [m]\n R_num_mean = pybamm.Integral(R * f_num_dist, R)\n R_a_mean = pybamm.Integral(R * f_a_dist, R)\n R_v_mean = pybamm.Integral(R * f_v_dist, R)\n sd_num = pybamm.sqrt(pybamm.Integral((R - R_num_mean) ** 2 * f_num_dist, R))\n sd_a = pybamm.sqrt(pybamm.Integral((R - R_a_mean) ** 2 * f_a_dist, R))\n sd_v = pybamm.sqrt(pybamm.Integral((R - R_v_mean) ** 2 * f_v_dist, R))\n\n # X-average the means and standard deviations to give scalars\n # (to remove the \"electrode\" domain, if present)\n R_num_mean = pybamm.x_average(R_num_mean)\n R_a_mean = pybamm.x_average(R_a_mean)\n R_v_mean = pybamm.x_average(R_v_mean)\n sd_num = pybamm.x_average(sd_num)\n sd_a = pybamm.x_average(sd_a)\n sd_v = pybamm.x_average(sd_v)\n\n # X-averaged distributions, or broadcast\n if R.domains[\"secondary\"] == [f\"{domain} electrode\"]:\n f_a_dist_xav = pybamm.x_average(f_a_dist)\n f_v_dist_xav = pybamm.x_average(f_v_dist)\n f_num_dist_xav = pybamm.x_average(f_num_dist)\n else:\n f_a_dist_xav = f_a_dist\n f_v_dist_xav = f_v_dist\n f_num_dist_xav = f_num_dist\n\n # broadcast\n f_a_dist = pybamm.SecondaryBroadcast(f_a_dist_xav, [f\"{domain} electrode\"])\n f_v_dist = pybamm.SecondaryBroadcast(f_v_dist_xav, [f\"{domain} electrode\"])\n f_num_dist = pybamm.SecondaryBroadcast(\n f_num_dist_xav, [f\"{domain} electrode\"]\n )\n\n variables = {\n f\"{Domain} {phase_name}particle sizes\": R / R_typ,\n f\"{Domain} {phase_name}particle sizes [m]\": R,\n f\"{Domain} area-weighted {phase_name}particle-size\"\n \" distribution [m-1]\": f_a_dist,\n f\"{Domain} volume-weighted {phase_name}particle-size\"\n \" distribution [m-1]\": f_v_dist,\n f\"{Domain} number-based {phase_name}particle-size\"\n \" distribution [m-1]\": f_num_dist,\n f\"{Domain} area-weighted mean particle radius [m]\": R_a_mean,\n f\"{Domain} volume-weighted mean particle radius [m]\": R_v_mean,\n f\"{Domain} number-based mean particle radius [m]\": R_num_mean,\n f\"{Domain} area-weighted {phase_name}particle-size\"\n \" standard deviation [m]\": sd_a,\n f\"{Domain} volume-weighted {phase_name}particle-size\"\n \" standard deviation [m]\": sd_v,\n f\"{Domain} number-based {phase_name}particle-size\"\n \" standard deviation [m]\": sd_num,\n # X-averaged sizes and distributions\n f\"X-averaged {domain} {phase_name}particle sizes [m]\": pybamm.x_average(R),\n f\"X-averaged {domain} area-weighted {phase_name}particle-size \"\n \"distribution [m-1]\": f_a_dist_xav,\n f\"X-averaged {domain} volume-weighted {phase_name}particle-size \"\n \"distribution [m-1]\": f_v_dist_xav,\n f\"X-averaged {domain} number-based {phase_name}particle-size \"\n \"distribution [m-1]\": f_num_dist_xav,\n }\n\n return variables","def update(self, es, **kwargs):\n es.sigma = self.coefficient * es.sp.weights.mueff * _norm(es.mean) / es.N / es.sp.cmean"],"string":"[\n \"def moments(self):\",\n \"def calc_moments(distribution):\\n x = torch.linspace(2, 22, 31)\\n d_mean = torch.sum(x * distribution)\\n d_var = torch.sum(distribution * (x - d_mean) ** 2) \\n \\n return d_mean, torch.sqrt(d_var)\",\n \"def moment(self, n, mu, sigma):\\n return scipy_norm.moment(n, mu, sigma)\",\n \"def _moments_match_numerical(self,obs,tau,v):\\r\\n #Compute first integral for zeroth moment.\\r\\n #NOTE constant np.sqrt(2*pi/tau) added at the end of the function\\r\\n mu = v/tau\\r\\n def int_1(f):\\r\\n return self.pdf(f, obs)*np.exp(-0.5*tau*np.square(mu-f))\\r\\n z_scaled, accuracy = quad(int_1, -np.inf, np.inf)\\r\\n\\r\\n #Compute second integral for first moment\\r\\n def int_2(f):\\r\\n return f*self.pdf(f, obs)*np.exp(-0.5*tau*np.square(mu-f))\\r\\n mean, accuracy = quad(int_2, -np.inf, np.inf)\\r\\n mean /= z_scaled\\r\\n\\r\\n #Compute integral for variance\\r\\n def int_3(f):\\r\\n return (f**2)*self.pdf(f, obs)*np.exp(-0.5*tau*np.square(mu-f))\\r\\n Ef2, accuracy = quad(int_3, -np.inf, np.inf)\\r\\n Ef2 /= z_scaled\\r\\n variance = Ef2 - mean**2\\r\\n\\r\\n #Add constant to the zeroth moment\\r\\n #NOTE: this constant is not needed in the other moments because it cancells out.\\r\\n z = z_scaled/np.sqrt(2*np.pi/tau)\\r\\n\\r\\n return z, mean, variance\",\n \"def _compute_pooled_moments(x, alpha, batch_mean, batch_var, augment_moment_fn):\\n augment_mean, augment_var = augment_moment_fn(x)\\n pooled_mean = alpha * batch_mean + (1.0 - alpha) * augment_mean\\n batch_mean_diff = batch_mean - pooled_mean\\n augment_mean_diff = augment_mean - pooled_mean\\n pooled_var = alpha * (batch_var + (batch_mean_diff * batch_mean_diff)) +\\\\\\n (1.0 - alpha) * (augment_var + (augment_mean_diff * augment_mean_diff))\\n return pooled_mean, pooled_var\",\n \"def A(param):\\n return (param.delta + param.nu + param.mu0) * (param.beta - param.nu)\",\n \"def posterior_mean(self, parameter):\\n if parameter not in self:\\n raise ConjugateParameterException('Parameter not recognized!')\\n else:\\n i = parameter.split('_')[1]\\n ai = self.prior_hyperparameters['a_{}'.format(i)]\\n A = sum(self.prior_hyperparameters.values())\\n ni = self.data[i]\\n N = sum(self.data.values())\\n\\n return (ai+ni)/(A+N)\",\n \"def _compute_instance_moments(x):\\n return torch.mean(x, dim=(2, 3), keepdim=True), torch.var(x, dim=(2, 3), keepdim=True)\",\n \"def _compute_layer_moments(x):\\n return torch.mean(x, dim=(1, 2, 3), keepdim=True), torch.var(x, dim=(1, 2, 3), keepdim=True)\",\n \"def parameters_to_marginal_moments(prob, distmu, distsigma):\\n good = np.isfinite(prob) & np.isfinite(distmu) & np.isfinite(distsigma)\\n prob = prob[good]\\n distmu = distmu[good]\\n distsigma = distsigma[good]\\n distmean, diststd, _ = parameters_to_moments(distmu, distsigma)\\n rbar = (prob * distmean).sum()\\n r2bar = (prob * (np.square(diststd) + np.square(distmean))).sum()\\n return rbar, np.sqrt(r2bar - np.square(rbar))\",\n \"def _prior_marginal_scipy(self, parameter):\\n letter = parameter.strip().split('_')[1]\\n A = sum(self._prior_hyperparameters.values())\\n ai = self.prior_hyperparameters['a_{}'.format(letter)]\\n\\n return _scipy_beta(ai, A-ai)\",\n \"def prior_mean(self, parameter):\\n if parameter not in self:\\n raise ConjugateParameterException('Parameter not recognized!')\\n else:\\n i = parameter.split('_')[1]\\n ai = self.prior_hyperparameters['a_{}'.format(i)]\\n A = sum(self.prior_hyperparameters.values())\\n\\n return ai/A\",\n \"def moments(data):\\n height = data.max()\\n background = data.min()\\n data = data - np.min(data)\\n total = data.sum()\\n x, y = np.indices(data.shape)\\n x = (x * data).sum() / total\\n y = (y * data).sum() / total\\n col = data[:, int(y)]\\n width_x = np.sqrt(abs((np.arange(col.size) - y) ** 2 * col).sum() / col.sum())\\n row = data[int(x), :]\\n width_y = np.sqrt(abs((np.arange(row.size) - x) ** 2 * row).sum() / row.sum())\\n width_x /= gaussian_sigma_to_fwhm\\n width_y /= gaussian_sigma_to_fwhm\\n return {\\n \\\"amplitude\\\": height,\\n \\\"x\\\": x,\\n \\\"y\\\": y,\\n \\\"sigma_x\\\": width_x,\\n \\\"sigma_y\\\": width_y,\\n \\\"background\\\": background,\\n \\\"theta\\\": 0.0,\\n }\",\n \"def _posterior_marginal_scipy(self, parameter):\\n letter = parameter.strip().split('_')[1]\\n A = sum(self._prior_hyperparameters.values())\\n ai = self.prior_hyperparameters['a_{}'.format(letter)]\\n N = sum(self._data.values())\\n ni = self._data[letter]\\n\\n return _scipy_beta(ai+ni, A-ai+N-ni)\",\n \"def _etaM_cool(self,x):\\n return self._eta_sfr_scaling(x,'M_cool')\",\n \"def simple_moments(x):\\n\\n mean = x.mean()\\n std = x.std()\\n sterr = std / np.sqrt(len(x))\\n\\n return mean, std, sterr\",\n \"def moments_match_ep(self,obs,tau,v,Y_metadata_i=None):\\n #Compute first integral for zeroth moment.\\n #NOTE constant np.sqrt(2*pi/tau) added at the end of the function\\n mu = v/tau\\n sigma2 = 1./tau\\n #Lets do these for now based on the same idea as Gaussian quadrature\\n # i.e. multiply anything by close to zero, and its zero.\\n f_min = mu - 20*np.sqrt(sigma2)\\n f_max = mu + 20*np.sqrt(sigma2)\\n\\n def int_1(f):\\n return self.pdf(f, obs, Y_metadata=Y_metadata_i)*np.exp(-0.5*tau*np.square(mu-f))\\n z_scaled, accuracy = quad(int_1, f_min, f_max)\\n\\n #Compute second integral for first moment\\n def int_2(f):\\n return f*self.pdf(f, obs, Y_metadata=Y_metadata_i)*np.exp(-0.5*tau*np.square(mu-f))\\n mean, accuracy = quad(int_2, f_min, f_max)\\n mean /= z_scaled\\n\\n #Compute integral for variance\\n def int_3(f):\\n return (f**2)*self.pdf(f, obs, Y_metadata=Y_metadata_i)*np.exp(-0.5*tau*np.square(mu-f))\\n Ef2, accuracy = quad(int_3, f_min, f_max)\\n Ef2 /= z_scaled\\n variance = Ef2 - mean**2\\n\\n #Add constant to the zeroth moment\\n #NOTE: this constant is not needed in the other moments because it cancells out.\\n z = z_scaled/np.sqrt(2*np.pi/tau)\\n\\n return z, mean, variance\",\n \"def estimate_moment(self):\\n # Due to the optimization, we may store more than k elements in the\\n # sample. The following removes excessive elements if needed.\\n if len(self.elements) > self.k:\\n self._remove_additional_elements()\\n\\n # The inclusion threshold (highest seed of element in the sample) is\\n # used to compute the inclusion probabilities for the other elements\\n # in the sample.\\n max_in_sample = max(self.elements.items(), key=lambda x: x[1][0])\\n threshold = max_in_sample[1][0]\\n\\n # Computes and sums the inverse-probability estimator for all keys\\n # in the sample.\\n sum_estimator = 0.0\\n for key, (seed, count) in self.elements.items():\\n if key != max_in_sample[0]:\\n weight = self.func_of_freq(self.advice_obj.predict(key))\\n # Warns us if we may run into float precision issues.\\n # TODO(ofirg): change this warning to something more robust than\\n # a print (and maybe use other approximations of exp() that are\\n # better for this case).\\n if weight * threshold < 2.0**(-24):\\n print(\\\"weight * threshold < 2^{-24}\\\")\\n print(weight * threshold)\\n inc_pr = 1.0 - np.exp(-1.0 * weight * threshold)\\n estimator = self.func_of_freq(count) / inc_pr\\n sum_estimator += estimator\\n\\n return sum_estimator\",\n \"def estimate_moment(self):\\n # Due to the optimization, we may store more than k elements in the\\n # sample. The following removes excessive elements if needed.\\n if len(self.elements) > self.k:\\n self._remove_additional_elements()\\n\\n # The inclusion threshold (highest seed of element in the sample) is\\n # used to compute the inclusion probabilities for the other elements\\n # in the sample.\\n max_in_sample = max(self.elements.items(), key=lambda x: x[1][0])\\n threshold = max_in_sample[1][0]\\n\\n # Computes and sums the inverse-probability estimator for all keys\\n # in the sample.\\n sum_estimator = 0.0\\n for key, (seed, count) in self.elements.items():\\n if key != max_in_sample[0]:\\n # Warns us if we may run into float precision issues.\\n # TODO(ofirg): change this warning to something more robust than\\n # a print (and maybe use other approximations of exp() that are\\n # better for this case).\\n if (count**self.sample_p) * threshold < 2.0**(-24):\\n print(\\\"(count**self.sample_p) * threshold < 2^{-24}\\\")\\n print((count**self.sample_p) * threshold)\\n inc_pr = 1.0 - np.exp(-1.0 * (count**self.sample_p) * threshold)\\n estimator = self.func_of_freq(count) / inc_pr\\n sum_estimator += estimator\\n\\n return sum_estimator\",\n \"def demo_indef():\\n n_dim = 2\\n A = np.eye(n_dim)\\n A[1, 1] = -1.\\n covar = np.eye(n_dim)\\n mean = np.zeros(n_dim)\\n approx = approx_quad_form(mean, covar, A)\\n\\n # Sample from true dist\\n n_sample = 10000\\n x = np.random.multivariate_normal(mean, covar, n_sample)\\n q_samples = np.zeros(n_sample)\\n for i in range(n_sample):\\n q_samples[i] = x[i] @ A @ x[i]\\n\\n q = np.linspace(-10, 10)\\n\\n plt.plot(\\n q, approx(q), label='Approx.',\\n color='tab:blue', linestyle='--')\\n bins = np.linspace(-8, 8, 81)\\n bins[0] = -np.inf\\n bins[-1] = np.inf\\n plt.hist(\\n q_samples, density=True, histtype='stepfilled',\\n bins=bins,\\n alpha=0.5, color='black', label='Samples')\\n plt.xlabel('q')\\n plt.ylabel('pdf(q) [-]')\\n plt.legend()\\n\\n central_moments_sample = scipy.stats.moment(\\n q_samples, moment=[0, 1, 2, 3, 4])\\n print(central_moments_sample)\",\n \"def _compute_batch_moments(x):\\n return torch.mean(x, dim=(0, 2, 3), keepdim=True), torch.var(x, dim=(0, 2, 3), keepdim=True)\",\n \"def amoment(a,moment=1,dimension=None):\\r\\n if dimension == None:\\r\\n a = N.ravel(a)\\r\\n dimension = 0\\r\\n if moment == 1:\\r\\n return 0.0\\r\\n else:\\r\\n mn = amean(a,dimension,1) # 1=keepdims\\r\\n s = N.power((a-mn),moment)\\r\\n return amean(s,dimension)\",\n \"def getMomentum(self):\\n return self.p\",\n \"def update_moments_r(self):\\n denominator = np.exp(self.gamma_r[:, None] * self.constellation\\n - self.Sigma_r[:, None] * np.power(self.constellation, 2) /2 )\\n nominator1 = np.exp(self.gamma_r[:, None] * self.constellation\\n - self.Sigma_r[:, None] * np.power(self.constellation, 2) /2 ) * self.constellation\\n \\n nominator2 = np.exp(self.gamma_r[:, None] * self.constellation\\n - self.Sigma_r[:, None] * np.power(self.constellation, 2) /2) * np.power(self.constellation, 2)\\n try:\\n \\n moment1 = nominator1.sum(axis=1) / denominator.sum(axis=1)\\n moment2 = nominator2.sum(axis=1) / denominator.sum(axis=1)\\n assert np.all(np.logical_not(np.isnan(moment1))) and np.all(np.logical_not(np.isnan(moment2)))\\n except:\\n print(\\\"Oops! That was no valid number. Try again...\\\")\\n\\n \\n self.mu = moment1\\n return moment1, moment2\",\n \"def GetCurrentMoments(self, theta):\\n p = self.params\\n if p.use_moving_avg_in_training:\\n beta = 0.0\\n gamma = 1.0\\n else:\\n beta = theta.beta\\n gamma = theta.gamma\\n if not p.gamma_zero_init and not p.gamma_one_init:\\n # Note, The real gamma to use is 1 + gamma.\\n gamma = 1.0 + gamma\\n return theta.moving_mean, theta.moving_variance, beta, gamma\",\n \"def moments(values):\\n\\n meanValue = numpy.mean(values)\\n return (meanValue,\\n numpy.sqrt(moment(values, meanValue, 2)),\\n moment(values, meanValue, 3),\\n moment(values, meanValue, 4))\",\n \"def GetCurrentMoments(self, theta):\\n p = self.params\\n if p.use_moving_avg_in_training:\\n return self._moving_mean, self._moving_variance, 0.0, 1.0\\n else:\\n return self._moving_mean, self._moving_variance, theta.beta, theta.gamma\",\n \"def get_means_and_scales(self):\\n return self.optim.parameters[::2], np.exp(self.optim.parameters[1::2])\",\n \"def moments(data):\\n# =============================================================================\\n# total = data.sum()\\n# X, Y = np.indices(data.shape)\\n# x = (X*data).sum()/total\\n# y = (Y*data).sum()/total\\n# col = data[:, int(y)]\\n# \\n# width_x = np.sqrt(np.abs((np.arange(col.size)-y)**2*col).sum()/col.sum())\\n# \\n# row = data[int(x), :]\\n# width_y = np.sqrt(np.abs((np.arange(row.size)-x)**2*row).sum()/row.sum())\\n# height = data.max()\\n# height1 = height\\n# =============================================================================\\n return(1, 15, 14, 3, 3, 1, 14, 16, 3, 2)\",\n \"def estimate(self):\\n mu = self.mean()\\n var = np.average((self.particles - mu) ** 2, weights=self.weights, axis=0)\\n\\n return mu, var\",\n \"def _momentum_update(self):\\n for param_ol, param_tgt in zip(self.online_net.parameters(),\\n self.target_net.parameters()):\\n param_tgt.data = param_tgt.data * self.momentum + \\\\\\n param_ol.data * (1. - self.momentum)\",\n \"def estimate_gaussian_params(X):\\n mu = X.mean(axis=0)\\n var = X.std(axis=0)**2.0\\n return mu,var\",\n \"def first_moment(self, mass, z=None):\\n return 1.0\",\n \"def fit_normal(distogram):\\n L = distogram.shape[1]\\n params = torch.empty((3, L, L))\\n \\n for i in range(L):\\n for j in range(L):\\n m, s = calc_moments(distogram[:, i, j])\\n scalar = torch.max(distogram[:, i, j]) / normal_distr(m, m, s)\\n params[0, i, j], params[1, i, j], params[2, i, j] = m, s, scalar\\n \\n return params\",\n \"def mom(x):\\n with mp.extradps(5):\\n x = _validate_x_bounds(x, low=0, high=1,\\n strict_low=True, strict_high=True)\\n M1 = _mean(x)\\n M2 = _mean([t**2 for t in x])\\n c = (M1 - M2) / (M2 - M1**2)\\n a = M1*c\\n b = (1 - M1)*c\\n return a, b\",\n \"def update_moments(self, X: np.ndarray) -> \\\"FeatureGenerator\\\":\\n self._scaler.partial_fit(X)\\n self._total_samples += X.shape[0]\\n\\n return self\",\n \"def moment_update(model, model_ema, m):\\n for p1, p2 in zip(model.parameters(), model_ema.parameters()):\\n p2.data.mul_(m).add_(1-m, p1.detach().data)\",\n \"def moment_update(model, model_ema, m):\\n for p1, p2 in zip(model.parameters(), model_ema.parameters()):\\n p2.data.mul_(m).add_(1-m, p1.detach().data)\",\n \"def get_optimizer_momentum(optimizer: Optimizer) -> float:\\n beta = safitty.get(optimizer.param_groups, 0, \\\"betas\\\", 0)\\n momentum = safitty.get(optimizer.param_groups, 0, \\\"momentum\\\")\\n return beta if beta is not None else momentum\",\n \"def computeMoments(x):\\n return (abs(stats.skew(x)),abs(stats.kurtosis(x,None,True)))\",\n \"def avg_func(self, averaged_param: Tensor, source_param: Tensor,\\n steps: int) -> None:\\n averaged_param.mul_(1 - self.momentum).add_(\\n source_param, alpha=self.momentum)\",\n \"def _compute_moments(self, u):\\n\\n # Get the moments from the parent Gaussian Markov Chain\\n #u = self.parents[0].get_moments() #message_to_child()\\n\\n # Send only moments <X(n)> and <X(n)X(n)> but not <X(n-1)X(n)>\\n return u[:2]\",\n \"def MyBaseMoments(p,q,img,gauss_sigma,gauss_centroid=None, gauss_g1=0., gauss_g2=0.):\\n weight = galsim.Image(np.zeros_like(img.array))\\n gauss = galsim.Gaussian(sigma=gauss_sigma*pixel_scale).shear(g1=gauss_g1,g2=gauss_g2)\\n if gauss_centroid is None:\\n gauss_centroid = img.true_center\\n weight = gauss.drawImage(image=weight, scale=pixel_scale, method='no_pixel', use_true_center=True, offset=(gauss_centroid-img.true_center)*(1))\\n x = np.linspace(img.xmin-img.center.x*0-gauss_centroid.x*1, img.xmax-img.center.x*0-gauss_centroid.x*1, img.xmax-img.xmin+1)+0.*0.5\\n y = np.linspace(img.ymin-img.center.y*0-gauss_centroid.y*1, img.ymax-img.center.y*0-gauss_centroid.y*1, img.ymax-img.ymin+1)+0.*0.5\\n X, Y = np.meshgrid(x,y)\\n\\n Q00 = np.sum(weight.array*img.array)\\n Q10 = gauss_centroid.x + np.sum(X*weight.array*img.array)/Q00\\n Q01 = gauss_centroid.y + np.sum(Y*weight.array*img.array)/Q00\\n Q20 = np.sum((X**2)*weight.array*img.array)\\n Q02 = np.sum((Y**2)*weight.array*img.array)\\n\\n monomial = 1.\\n for pp in xrange(p):\\n monomial *= X\\n for qq in xrange(q):\\n monomial *= Y\\n Qpq = np.sum(monomial*weight.array*img.array) #/Q00\\n\\n return Qpq\",\n \"def omega(self):\\n return self._data.train_X @ self._thetas\",\n \"def objective_function(params):\\n cirq_circuit = variational_state_evolve(params)\\n mean_value = self.expectation(\\n all_qubits_in_circuit, cirq_circuit, hamiltonian)\\n self._current_expectation = mean_value\\n return mean_value\",\n \"def measure_image_moments(image):\\n data = image.quantity\\n\\n coords = image.geom.get_coord().skycoord\\n x, y = coords.data.lon.wrap_at(\\\"180d\\\"), coords.data.lat\\n\\n A = data[np.isfinite(data)].sum()\\n\\n # Center of mass\\n x_cms = (x * data)[np.isfinite(data)].sum() / A\\n y_cms = (y * data)[np.isfinite(data)].sum() / A\\n\\n # Second moments\\n x_var = ((x - x_cms) ** 2 * data)[np.isfinite(data)].sum() / A\\n y_var = ((y - y_cms) ** 2 * data)[np.isfinite(data)].sum() / A\\n x_sigma = np.sqrt(x_var)\\n y_sigma = np.sqrt(y_var)\\n\\n return A, x_cms, y_cms, x_sigma, y_sigma, np.sqrt(x_sigma * y_sigma)\",\n \"def calcMomentumFromVelocity(self):\\n if self.mass is None:\\n raise CoordinateVector(\\\"The particle mass needs to be specified to calculate the particle momentum from velocity.\\\")\\n values = {}\\n for direction in self.v.order:\\n gamma = self.calcLorentzGammaFromVelocity(direction)\\n values[direction] = getattr(self.v,direction)*gamma*self.mass\\n self.setMomentum(Cartesian3DVector(**values))\\n return self.getMomentum()\",\n \"def _moments_match_analytical(self,data_i,tau_i,v_i):\\r\\n sigma2_hat = 1./(1./self.variance + tau_i)\\r\\n mu_hat = sigma2_hat*(data_i/self.variance + v_i)\\r\\n sum_var = self.variance + 1./tau_i\\r\\n Z_hat = 1./np.sqrt(2.*np.pi*sum_var)*np.exp(-.5*(data_i - v_i/tau_i)**2./sum_var)\\r\\n return Z_hat, mu_hat, sigma2_hat\",\n \"def _construct_mom_stuff(self):\\n a = self.mom_mix_rate\\n dist_mean = self.GN.dist_mean\\n dist_cov = self.GN.dist_cov\\n # Get the generated sample observations for this batch, transformed\\n # linearly into the desired space for moment matching...\\n X_b = T.dot(self.GN.output, self.mom_match_proj)\\n # Get their mean\\n batch_mean = T.mean(X_b, axis=0)\\n # Get the updated generator distribution mean\\n new_mean = ((1.0 - a[0]) * self.GN.dist_mean) + (a[0] * batch_mean)\\n # Use the mean to get the updated generator distribution covariance\\n X_b_minus_mean = X_b - new_mean\\n # Whelp, I guess this line needs the cast... for some reason...\\n batch_cov = T.dot(X_b_minus_mean.T, X_b_minus_mean) / T.cast(X_b.shape[0], 'floatX')\\n new_cov = ((1.0 - a[0]) * self.GN.dist_cov) + (a[0] * batch_cov)\\n # Get the cost for deviation from the target distribution's moments\\n mean_err = new_mean - self.target_mean\\n cov_err = (new_cov - self.target_cov)\\n mm_cost = self.mom_match_weight[0] * \\\\\\n (T.sum(mean_err**2.0) + T.sum(cov_err**2.0))\\n # Construct the updates for the running estimates of the generator\\n # distribution's first and second-order moments.\\n mom_updates = OrderedDict()\\n mom_updates[self.GN.dist_mean] = new_mean\\n mom_updates[self.GN.dist_cov] = new_cov\\n return [mm_cost, mom_updates]\",\n \"def normalDist(x, mean, var):\\n\\n if (var != 0):\\n return -(x - mean)**2/(2*var) - 0.5*np.log(2*np.pi*var)\\n else:\\n return 0\",\n \"def totalmomentum (self):\\n tot_p=0.\\n for planet in self.planets: #this loop takes each 'planet' momentum in 'self.planets' and sums them.\\n tot_p += planet.momentum #'tot_p' is the resulting vector of all momenta vectors.\\n total_mom = np.linalg.norm(tot_p) #the 'total_mom' is the total linear momentum's magnitude, which is conserved.\\n return (total_mom)\",\n \"def normal_pmf(x: np.array, mean: float, sigma: float) -> np.array:\\n x = np.exp(-1 / 2 * ((x - mean) / sigma) ** 2)\\n x /= np.sqrt(2 * np.pi * sigma ** 2)\\n x /= x.sum()\\n return x\",\n \"def parade_mean(p):\\n return [noneneg(mj.get('mean')) for mj in p['moments']]\",\n \"def moment_update(model, model_ema, m):\\r\\n for p1, p2 in zip(model.parameters(), model_ema.parameters()):\\r\\n p2.data.mul_(m).add_(1 - m, p1.detach().data)\\r\\n # p2.data.mul_(m).add_(1 - m, p1.data)\",\n \"def prior_distribution(self):\\n out = self.model.forward(self.inducing_points)\\n return MultivariateNormal(out.mean, out.lazy_covariance_matrix.evaluate_kernel())\",\n \"def _get_augment_moment_fn(self):\\n return self._compute_instance_moments\",\n \"def Delta_0(param):\\n delta_0 = param.delta + param.nu + param.mu0\\n delta_1 = param.delta + param.nu + param.mu1\\n return (param.beta - param.nu)**2 * delta_1**2 * (param.b)**2 - 2*param.A*(param.beta - param.nu) * (param.beta*(param.mu1 - param.mu0) + delta_0*(delta_1-param.beta)) * param.b + param.A**2*(param.beta - delta_0)**2\",\n \"def update_momentum(self, factor):\\n if not self.gpu:\\n self.psi *= np.exp(-1j*factor*self.m*self.V)\\n else:\\n self.g_psi_hat[...] = -1.0j*factor*self.m*self.g_V\\n cumath.exp(self.g_psi_hat, out=self.g_psi_hat)\\n self.g_psi *= self.g_psi_hat\",\n \"def __calcMomenta(self, kinPara):\\n self.physArea,self.__allMomenta,self.PreFac = self.__kinLib(kinPara)\\n return 0\",\n \"def empirical_erm(self):\\n return lambda samples: np.mean(samples) - 0.5 * self.alpha * np.var(samples)\",\n \"def gaussian_prior(self):\\n self.prior = sps.multivariate_normal(self.m0,self.S0)\",\n \"def marginal(self):\\n m = np.zeros(len(self.domain))\\n for fnode in self.neighbors:\\n m += self.received[fnode]\\n return np.exp(normalize(m))\",\n \"def _em_autoregressive(self, result, betas, tmp=None):\\n if tmp is None:\\n tmp = np.sqrt(result.smoothed_marginal_probabilities)\\n\\n resid = np.zeros((self.k_regimes, self.nobs + self.order))\\n resid[:] = self.orig_endog\\n if self._k_exog > 0:\\n for i in range(self.k_regimes):\\n resid[i] -= np.dot(self.orig_exog, betas[i])\\n\\n # The difference between this and `_em_exog` is that here we have a\\n # different endog and exog for each regime\\n coeffs = np.zeros((self.k_regimes,) + (self.order,))\\n variance = np.zeros((self.k_regimes,))\\n exog = np.zeros((self.nobs, self.order))\\n for i in range(self.k_regimes):\\n endog = resid[i, self.order:]\\n exog = lagmat(resid[i], self.order)[self.order:]\\n tmp_endog = tmp[i] * endog\\n tmp_exog = tmp[i][:, None] * exog\\n\\n coeffs[i] = np.dot(np.linalg.pinv(tmp_exog), tmp_endog)\\n\\n if self.switching_variance:\\n tmp_resid = endog - np.dot(exog, coeffs[i])\\n variance[i] = (np.sum(\\n tmp_resid ** 2 * result.smoothed_marginal_probabilities[i]) /\\n np.sum(result.smoothed_marginal_probabilities[i]))\\n else:\\n tmp_resid = tmp_endog - np.dot(tmp_exog, coeffs[i])\\n variance[i] = np.sum(tmp_resid ** 2)\\n # Variances\\n if not self.switching_variance:\\n variance = variance.sum() / self.nobs\\n return coeffs, variance\",\n \"def gaussianDist(self, x, mu, var):\\n val = 1/(math.sqrt(2 * math.pi * var)) * math.exp(-1 * (x - mu)**2 / (2*var))\\n return val\",\n \"def generate_moments(hyper, params):\\n\\n k, d = hyper['k'], hyper['d']\\n\\n p = params # Shorthand, don't judge\\n m = {} # Moments\\n for x1 in xrange(1,d+1):\\n m[(x1,)] = sum( p[(h,x1)] * p[(h,)] for h in xrange(1,k+1) )\\n for x2 in xrange(1,d+1):\\n m[(x1,x2)] = sum( p[(h,x1)] * p[(h,x2)] * p[(h,)] for h in xrange(1,k+1) )\\n for x3 in xrange(1,d+1):\\n m[(x1,x2,x3)] = sum( p[(h,x1)] * p[(h,x2)] * p[(h,x3)] * p[(h,)] for h in xrange(1,k+1) )\\n return m\",\n \"def f(x):\\n return delta_eps_mu(x, mu) - delta\",\n \"def compute_fixed_moments(self, x):\\n return [x]\",\n \"def update_variables_momentum(alpha, beta1, var, grad, v):\\n v = beta1 * v + (1-beta1) * grad\\n var = var - alpha * v\\n return var, v\",\n \"def approximate_gamma_mom(mean, variance):\\n assert mean > 0\\n assert variance > 0\\n alpha = mean**2 / variance\\n beta = mean / variance\\n return alpha, beta\",\n \"def stdProbabilityNorm(self):\\n return 1./factorial(self.alpha-1)\",\n \"def gaussian(amp, fwhm, mean, x):\\n return amp * np.exp(-4. * np.log(2) * (x-mean)**2 / fwhm**2)\",\n \"def M(self,v):\\n assert len(self.d_moments)==1, \\\"For now only supporting 1 dimension at a time\\\"\\n d = int(list(self.d_moments.keys())[0])\\n *start,n,a = v.shape \\n N = n//(d+1) # number of extended bodies\\n v_reshaped = v.reshape(*start,N,d+1,a) # (*, # separate bodies, # internal body nodes, a) \\n moments = torch.exp(self.d_moments[str(d)])\\n masses = moments[:,:1]\\n if d==0: return (masses.unsqueeze(-1)*v_reshaped).reshape(*v.shape) # no inertia for point masses\\n a00 = (masses + moments[:,1:].sum(-1,keepdims=True)).unsqueeze(-1) #(N,1,1)\\n ai0 = a0i = -moments[:,1:].unsqueeze(-1) #(N,d,1)\\n p0 = a00*v[...,:1,:] + (a0i*v[...,1:,:]).sum(-2,keepdims=True)\\n aii = moments[:,1:].unsqueeze(-1) # (N,d,1)\\n \\n pi = ai0*v[...,:1,:] +aii*v[...,1:,:]\\n return torch.cat([p0,pi],dim=-2).reshape(*v.shape)\",\n \"def moments(self, x, y):\\n # Let's attempt to remove noise by axing points\\n # below 5% of the peak\\n threshold = y.max() * 0.05\\n clean = np.array(y)\\n clean[clean < threshold] = 0\\n zero = clean.sum()\\n one = np.sum(x * clean)\\n two = np.sum(x * x * clean)\\n mean = one / zero\\n var = two / zero - mean**2\\n stdev = np.sqrt(var)\\n return (mean, stdev)\",\n \"def log_marginal(self):\\n #\\n # Predictive covariance of x is sum of covariance of phi a and covariance of x|a\\n x_Sigma = self.phi @ self.phi.T + np.diag(self.sigma_n**2 * np.ones(self.M))\\n #\\n # Predictive mean is 0 by symmetry\\n # so given that x is distributed as a MVN, the exact marginal is\\n lp_exact = st.multivariate_normal.logpdf(self.x, cov=x_Sigma)\\n #\\n return lp_exact\",\n \"def log_prior(self, params):\\n # log likelihood function, see:\\n # https://en.wikipedia.org/wiki/Multivariate_normal_distribution#Likelihood_function\\n variance = self.std ** 2\\n ndim = params.ndim\\n mean_diff = params - self.mean\\n scaled_sq_err = jnp.dot(mean_diff, mean_diff) / variance\\n # log determinant of covariance matrix\\n log_det_cov = 2 * ndim * jnp.log(self.std)\\n norm_term = ndim * jnp.log(2 * jnp.pi)\\n return -0.5 * (log_det_cov + scaled_sq_err + norm_term)\",\n \"def pvalue_gaussian(self):\\n \\n pv = 2 * stats.norm.sf(abs(self.TS_prime_obs), loc=0, scale=1)\\n return(pv)\",\n \"def get_center_of_mass_allies(self,obs):\",\n \"def normal(x, mean, dispersion):\\n return np.exp(\\n -1 * squared_norm(x - mean) / (2 * dispersion)\\n ) / (\\n np.sqrt(2 * np.pi * dispersion)\\n )\",\n \"def prop_dist_form_params(*arg):\\n return np.random.multivariate_normal(*arg)\",\n \"def calculate_posterior_mean_var(self, context_arm_grid):\\n # all points played so far\\n data = self.played_points_hist[:self.t]\\n # kernels between all possible context-arms and the previous rounds\\n kernel_vectors = self.kernel_fn(context_arm_grid, data, self.best_ard_params)\\n # kernel matrix of data\\n kernel_matrix = self.kernel_fn(data, data, self.best_ard_params)\\n c_matrix = kernel_matrix + (self.noise_sigma ** 2) * np.eye(data.shape[0])\\n c_matrix_inv = np.linalg.inv(c_matrix)\\n mu_ests_vector = np.matmul(kernel_vectors, np.matmul(c_matrix_inv, self.rews_hist[:self.t])) # mean estimation\\n sigma_ests_first_term = np.diag(self.kernel_fn(context_arm_grid, context_arm_grid, self.best_ard_params))\\n sigma_ests_second_term = np.diag(np.matmul(kernel_vectors, np.matmul(c_matrix_inv, kernel_vectors.T)))\\n sigma_ests_vector = sigma_ests_first_term - sigma_ests_second_term # variance estimation\\n return mu_ests_vector, sigma_ests_vector\",\n \"def var_momentum(self):\\n\\n if isinstance(self.momentum, MomentumNesterov):\\n return self.t\\n return self.k\",\n \"def _moments_match_analytical(self,obs,tau,v):\\r\\n raise NotImplementedError\",\n \"def init_moments(self, sample_count):\\n # Compute outputs for the input latent noise in X_noise\\n X = self.GN.sample_from_model(sample_count)\\n # Get the transform to apply prior to moment matching\\n P = self.mom_match_proj.get_value(borrow=False)\\n # Compute post-transform mean and covariance of the outputs\\n mu, sigma = projected_moments(X, P, ary_type='numpy')\\n # Initialize the generator network's running moment estimates \\n self.GN.dist_cov.set_value(sigma.astype(theano.config.floatX))\\n self.GN.dist_mean.set_value(mu.astype(theano.config.floatX))\\n return\",\n \"def update_params(x, prior, posterior):\\r\\n mu0, kappa0, alpha0, beta0 = prior\\r\\n mu_t, kappa_t, alpha_t, beta_t = posterior\\r\\n return np.r_[mu0, (kappa_t*mu_t + x)/(kappa_t + 1)], \\\\\\r\\n np.r_[kappa0, kappa_t + 1], \\\\\\r\\n np.r_[alpha0, alpha_t + 0.5], \\\\\\r\\n np.r_[beta0, beta_t + 0.5*kappa_t*(x - mu_t)**2/(kappa_t + 1)]\",\n \"def moments(data,x0=None,y0=None):\\n total = data.sum()\\n X, Y = np.indices(data.shape)\\n x = (X*data).sum()/total\\n y = (Y*data).sum()/total\\n col = data[:, int(y)]\\n width_x = np.sqrt(abs((np.arange(col.size)-y)**2*col).sum()/col.sum())\\n row = data[int(x), :]\\n width_y = np.sqrt(abs((np.arange(row.size)-x)**2*row).sum()/row.sum())\\n height = data.max()\\n if x0 is None:\\n return height, x, y, width_x, width_y, 0.0, 0.0\\n else:\\n xstep = x0[1] - x0[0]\\n ystep = y0[1] - y0[0]\\n return height, x*xstep+x0[0], y*ystep+y0[0], width_x*xstep, width_y*ystep, 0.0, 0.0\",\n \"def calcVelocityFromMomentum(self):\\n if self.mass is None:\\n raise CoordinateVector(\\\"The particle mass needs to be specified to calculate the particle velocity from momentum.\\\")\\n values = {}\\n for direction in self.p.order:\\n gamma = self.calcLorentzGammaFromMomentum(direction)\\n values[direction] = getattr(self.p,direction)/(gamma*self.mass)\\n self.setVelocity(Cartesian3DVector(**values))\\n return self.getVelocity()\",\n \"def _etaM(self,x):\\n return self._etaM_cool(x) + self._etaM_hot(x)\",\n \"def reduced_normal_pmf(x: np.array, mean: float, sigma: float) -> np.array:\\n x = np.exp(-1 / 2 * ((x - mean) / sigma) ** 2)\\n x /= np.sqrt(2 * np.pi * sigma ** 2)\\n x[mean] = 0.\\n x /= x.sum()\\n return x\",\n \"def like_one(theta,dt,dmag,sigma):\\n\\n gamma, A = theta\\n aux=(1/np.sqrt(2*np.pi*Veff2(dt,sigma,A,gamma)))*np.exp(-1.0*(dmag**2)/(2.0*Veff2(dt,sigma,A,gamma)))\\n\\n return aux\",\n \"def calculate_moment(p, N, limits, dx=0.01):\\n # Make a grid from the limits and resolution\\n grid = _calculate_grid_parameters(limits, dx)\\n\\n # Evaluate the functions on the grid\\n pe = p.gridded(grid.grid_values)[1]\\n\\n # calculate the moment\\n grid_to_N = grid.grid_values ** N\\n M = array_metrics.quick_moment(pe, grid_to_N, grid.resolution)\\n\\n return M\",\n \"def arg_per(self):\\n h_mom = self.sp_ang_mom()\\n n_vec = np.cross([0, 0, 1], h_mom)\\n e_vec = self.get_ecc()\\n if np.linalg.norm(e_vec) == 0:\\n small_omega = 0.0\\n else:\\n small_omega = np.arccos(np.dot(n_vec, e_vec) /\\n (np.linalg.norm(n_vec) *\\n np.linalg.norm(e_vec)))\\n if e_vec[2] < 0:\\n small_omega = 2*np.pi - small_omega\\n return small_omega\",\n \"def lmoment(inlist,moment=1):\\r\\n if moment == 1:\\r\\n return 0.0\\r\\n else:\\r\\n mn = mean(inlist)\\r\\n n = len(inlist)\\r\\n s = 0\\r\\n for x in inlist:\\r\\n s = s + (x-mn)**moment\\r\\n return s/float(n)\",\n \"def linear_momentum(self):\\r\\n return self.mass * self.vel\",\n \"def _compute_internals(self, moments):\\n\\n [mean, stdv] = moments\\n internals = {}\\n internals['k'] = mean ** 2. / stdv ** 2.\\n internals['LAMBDA'] = mean / stdv ** 2.\\n\\n return internals\",\n \"def _compute_log_moment(self, num_examples=5040):\\n q = tf.cast(num_examples, tf.float64) * 1.0 / self._total_examples\\n mu_1, sigma_1 = 0, 4 # mean and standard deviation\\n s_1 = np.random.normal(mu_1, sigma_1, 1000)\\n mu_2, sigma_2 = 1, 4 # mean and standard deviation\\n s_2 = np.random.normal(mu_2, sigma_2, 1000)\\n s = (1-q)*s_1 + q*s_2\\n moment_1 =[0]*len(self._log_moments)\\n moment_2 = [0]*len(self._log_moments)\\n log_moment = [0] * len(self._log_moments)\\n for i in range(len(self._log_moments)):\\n for j in range(len(s_1)):\\n moment_1[i] += ((s_1[j]/s[j])**self._moment_orders[i])/len(s_1)\\n moment_2[i] += ((s[j] / s_1[j]) ** self._moment_orders[i]) / len(s_1)\\n for i in range(len(self._log_moments)):\\n log_moment[i] = math.log(abs(max(moment_1[i],moment_2[i])))\\n return log_moment\",\n \"def moments(data):\\n total = data.sum()\\n if total != 0.:\\n X, Y = np.indices(data.shape)\\n x = (X*data).sum()/total\\n y = (Y*data).sum()/total\\n col = data[:, int(y)]\\n width_x = np.sqrt(abs((np.arange(col.size)-y)**2*col).sum()/col.sum())\\n row = data[int(x), :]\\n width_y = np.sqrt(abs((np.arange(row.size)-x)**2*row).sum()/row.sum())\\n height = data.max()\\n else:\\n height=0\\n x=0\\n y=0\\n width_x=0\\n width_y=0\\n return height,np.sqrt(width_x**2 + width_y**2)\",\n \"def _compute_internals(self, moments):\\n\\n [mean, stdv] = moments\\n internals = {}\\n internals['mu'] = mean\\n internals['sigma'] = stdv\\n\\n return internals\",\n \"def _predictive_mean_analytical(self, mu, sigma):\\r\\n #FIXME: Not correct\\r\\n return mu\",\n \"def temper_momentum(self, epoch):\\n if epoch == 0:\\n return\\n self.momentums = [\\n x + y for x, y in zip(self.momentums, self.momentum_temper_rates)\\n ]\\n for i, param_group in enumerate(self.optimizer.param_groups):\\n param_group['momentum'] = self.momentums[i]\",\n \"def _get_distribution_variables(self, R):\\n domain, Domain = self.domain_Domain\\n phase_name = self.phase_name\\n\\n R_typ = self.phase_param.R_typ # [m]\\n # Particle-size distribution (area-weighted)\\n f_a_dist = self.phase_param.f_a_dist(R) # [m-1]\\n\\n # Ensure the distribution is normalised, irrespective of discretisation\\n # or user input\\n f_a_dist = f_a_dist / pybamm.Integral(f_a_dist, R) # [m-1]\\n\\n # Volume-weighted particle-size distribution\\n f_v_dist = R * f_a_dist / pybamm.Integral(R * f_a_dist, R) # [m-1]\\n\\n # Number-based particle-size distribution\\n f_num_dist = (f_a_dist / R**2) / pybamm.Integral(\\n f_a_dist / R**2, R\\n ) # [m-1]\\n\\n # True mean radii and standard deviations, calculated from the f_a_dist that\\n # was given, all have units [m]\\n R_num_mean = pybamm.Integral(R * f_num_dist, R)\\n R_a_mean = pybamm.Integral(R * f_a_dist, R)\\n R_v_mean = pybamm.Integral(R * f_v_dist, R)\\n sd_num = pybamm.sqrt(pybamm.Integral((R - R_num_mean) ** 2 * f_num_dist, R))\\n sd_a = pybamm.sqrt(pybamm.Integral((R - R_a_mean) ** 2 * f_a_dist, R))\\n sd_v = pybamm.sqrt(pybamm.Integral((R - R_v_mean) ** 2 * f_v_dist, R))\\n\\n # X-average the means and standard deviations to give scalars\\n # (to remove the \\\"electrode\\\" domain, if present)\\n R_num_mean = pybamm.x_average(R_num_mean)\\n R_a_mean = pybamm.x_average(R_a_mean)\\n R_v_mean = pybamm.x_average(R_v_mean)\\n sd_num = pybamm.x_average(sd_num)\\n sd_a = pybamm.x_average(sd_a)\\n sd_v = pybamm.x_average(sd_v)\\n\\n # X-averaged distributions, or broadcast\\n if R.domains[\\\"secondary\\\"] == [f\\\"{domain} electrode\\\"]:\\n f_a_dist_xav = pybamm.x_average(f_a_dist)\\n f_v_dist_xav = pybamm.x_average(f_v_dist)\\n f_num_dist_xav = pybamm.x_average(f_num_dist)\\n else:\\n f_a_dist_xav = f_a_dist\\n f_v_dist_xav = f_v_dist\\n f_num_dist_xav = f_num_dist\\n\\n # broadcast\\n f_a_dist = pybamm.SecondaryBroadcast(f_a_dist_xav, [f\\\"{domain} electrode\\\"])\\n f_v_dist = pybamm.SecondaryBroadcast(f_v_dist_xav, [f\\\"{domain} electrode\\\"])\\n f_num_dist = pybamm.SecondaryBroadcast(\\n f_num_dist_xav, [f\\\"{domain} electrode\\\"]\\n )\\n\\n variables = {\\n f\\\"{Domain} {phase_name}particle sizes\\\": R / R_typ,\\n f\\\"{Domain} {phase_name}particle sizes [m]\\\": R,\\n f\\\"{Domain} area-weighted {phase_name}particle-size\\\"\\n \\\" distribution [m-1]\\\": f_a_dist,\\n f\\\"{Domain} volume-weighted {phase_name}particle-size\\\"\\n \\\" distribution [m-1]\\\": f_v_dist,\\n f\\\"{Domain} number-based {phase_name}particle-size\\\"\\n \\\" distribution [m-1]\\\": f_num_dist,\\n f\\\"{Domain} area-weighted mean particle radius [m]\\\": R_a_mean,\\n f\\\"{Domain} volume-weighted mean particle radius [m]\\\": R_v_mean,\\n f\\\"{Domain} number-based mean particle radius [m]\\\": R_num_mean,\\n f\\\"{Domain} area-weighted {phase_name}particle-size\\\"\\n \\\" standard deviation [m]\\\": sd_a,\\n f\\\"{Domain} volume-weighted {phase_name}particle-size\\\"\\n \\\" standard deviation [m]\\\": sd_v,\\n f\\\"{Domain} number-based {phase_name}particle-size\\\"\\n \\\" standard deviation [m]\\\": sd_num,\\n # X-averaged sizes and distributions\\n f\\\"X-averaged {domain} {phase_name}particle sizes [m]\\\": pybamm.x_average(R),\\n f\\\"X-averaged {domain} area-weighted {phase_name}particle-size \\\"\\n \\\"distribution [m-1]\\\": f_a_dist_xav,\\n f\\\"X-averaged {domain} volume-weighted {phase_name}particle-size \\\"\\n \\\"distribution [m-1]\\\": f_v_dist_xav,\\n f\\\"X-averaged {domain} number-based {phase_name}particle-size \\\"\\n \\\"distribution [m-1]\\\": f_num_dist_xav,\\n }\\n\\n return variables\",\n \"def update(self, es, **kwargs):\\n es.sigma = self.coefficient * es.sp.weights.mueff * _norm(es.mean) / es.N / es.sp.cmean\"\n]","isConversational":false},"negative_scores":{"kind":"list like","value":["0.68563664","0.6402003","0.6158102","0.6069788","0.60613734","0.5964357","0.5932727","0.5914467","0.59122914","0.58807665","0.58597636","0.5813083","0.581118","0.58006424","0.57764125","0.57536584","0.57454926","0.57426","0.5733544","0.5727761","0.5726019","0.5698233","0.56964","0.56758094","0.5629441","0.5617482","0.5611085","0.56052256","0.5604859","0.56024003","0.55916435","0.55873805","0.5584042","0.55752057","0.5560382","0.5558598","0.5551418","0.5551418","0.554093","0.5539232","0.5534639","0.55144566","0.55050665","0.55001885","0.5482818","0.54819876","0.5474364","0.54691416","0.5455255","0.54538727","0.5447436","0.54428285","0.544045","0.54398316","0.54340017","0.54326147","0.5407704","0.5405355","0.5394504","0.53938186","0.5390518","0.5382631","0.5381185","0.5379764","0.53766304","0.53577423","0.53562236","0.53504664","0.5347092","0.5337481","0.53354686","0.53189224","0.53164524","0.53104913","0.5306045","0.53012043","0.52969176","0.52875465","0.5282356","0.5270544","0.5270229","0.5242398","0.5241467","0.5230794","0.5229787","0.52290857","0.5227526","0.5223154","0.52230215","0.521657","0.521596","0.5212967","0.5212964","0.5212692","0.52096534","0.520799","0.52071214","0.5204142","0.518799","0.5185018","0.51839453"],"string":"[\n \"0.68563664\",\n \"0.6402003\",\n \"0.6158102\",\n \"0.6069788\",\n \"0.60613734\",\n \"0.5964357\",\n \"0.5932727\",\n \"0.5914467\",\n \"0.59122914\",\n \"0.58807665\",\n \"0.58597636\",\n \"0.5813083\",\n \"0.581118\",\n \"0.58006424\",\n \"0.57764125\",\n \"0.57536584\",\n \"0.57454926\",\n \"0.57426\",\n \"0.5733544\",\n \"0.5727761\",\n \"0.5726019\",\n \"0.5698233\",\n \"0.56964\",\n \"0.56758094\",\n \"0.5629441\",\n \"0.5617482\",\n \"0.5611085\",\n \"0.56052256\",\n \"0.5604859\",\n \"0.56024003\",\n \"0.55916435\",\n \"0.55873805\",\n \"0.5584042\",\n \"0.55752057\",\n \"0.5560382\",\n \"0.5558598\",\n \"0.5551418\",\n \"0.5551418\",\n \"0.554093\",\n \"0.5539232\",\n \"0.5534639\",\n \"0.55144566\",\n \"0.55050665\",\n \"0.55001885\",\n \"0.5482818\",\n \"0.54819876\",\n \"0.5474364\",\n \"0.54691416\",\n \"0.5455255\",\n \"0.54538727\",\n \"0.5447436\",\n \"0.54428285\",\n \"0.544045\",\n \"0.54398316\",\n \"0.54340017\",\n \"0.54326147\",\n \"0.5407704\",\n \"0.5405355\",\n \"0.5394504\",\n \"0.53938186\",\n \"0.5390518\",\n \"0.5382631\",\n \"0.5381185\",\n \"0.5379764\",\n \"0.53766304\",\n \"0.53577423\",\n \"0.53562236\",\n \"0.53504664\",\n \"0.5347092\",\n \"0.5337481\",\n \"0.53354686\",\n \"0.53189224\",\n \"0.53164524\",\n \"0.53104913\",\n \"0.5306045\",\n \"0.53012043\",\n \"0.52969176\",\n \"0.52875465\",\n \"0.5282356\",\n \"0.5270544\",\n \"0.5270229\",\n \"0.5242398\",\n \"0.5241467\",\n \"0.5230794\",\n \"0.5229787\",\n \"0.52290857\",\n \"0.5227526\",\n \"0.5223154\",\n \"0.52230215\",\n \"0.521657\",\n \"0.521596\",\n \"0.5212967\",\n \"0.5212964\",\n \"0.5212692\",\n \"0.52096534\",\n \"0.520799\",\n \"0.52071214\",\n \"0.5204142\",\n \"0.518799\",\n \"0.5185018\",\n \"0.51839453\"\n]","isConversational":false},"document_score":{"kind":"string","value":"0.0"},"document_rank":{"kind":"string","value":"-1"}}},{"rowIdx":82395,"cells":{"query":{"kind":"string","value":"Sets it to the first available option by default."},"document":{"kind":"string","value":"def xscale(self, kind):\n self._xscale = str(kind).strip() # remove whitespace eventually\n if self._xscale not in self.xscaling():\n self._xscale = self.xscaling(1)"},"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":["async def _opt_default(self, ctx, option):\n try:\n guild_options = self.database.get_guild_options(ctx.guild.id)\n setattr(guild_options, option, None)\n self.database.save_item(guild_options)\n await ctx.send(f\"Option {option} set to default\")\n except AttributeError:\n await ctx.send(\"I don't recognize that option.\")","def set_defaults(self):\r\n for name, option in self.options.iteritems():\r\n if not option.is_required():\r\n self.set_value(name, option, option.default)","def set_default_option(self, name: str):\n\n if (name not in self.parent.options) and (self.is_available(name)):\n self.parent.options[name] = self.get_app_cfg_by_name(name)","def set_first(self, value):\n if value not in (\"player\", \"computer\", \"random\"):\n raise SettingsError(\"Invalid choice\")\n self._parser.set(\"settings\", \"first\", value)\n self._save()","def getopt_default(self, option):\n if option not in self.conf:\n print(\"Unrecognized option %r\" % option)\n return\n print(\"%s: %s\" % (option, self.conf[option]))","def test_default(self):\r\n self.assertEqual(self.option.default, 1234)","def test_default(self):\r\n self.assertEqual(self.option.default, False)","def default(self):\n raise Error(\"Missing mandatory setting:\", self.name)","def init_opt(self):\n raise NotImplementedError","def init_opt(self):\n raise NotImplementedError","def init_opt(self):\n raise NotImplementedError","def init_opt(self):\n raise NotImplementedError","def _default(self, section, option, default):\r\n if not self.has_section(section):\r\n self.add_section(section)\r\n if not self.has_option(section, option):\r\n self.set(section, option, default)\r\n self.save()","def _choose_best_option(self):","def test_default(self):\r\n self.assertEqual(self.option.default, 'testing')","def opt(self, key, default=False):\n if key not in self.options:\n return default\n return self.options.get(key)","def force_option(args, run):\n run.force = True","def set_is_default(self):\n self.is_default = True","def set_is_default(self):\n self.is_default = True","def _backend_opts_set(self, val):\n self._dflt_backend_opts = val\n return","def set_default(self, name, default, group=None):\n opt_info = self._get_opt_info(name, group)\n opt_info['default'] = self._get_enforced_type_value(\n opt_info['opt'], default)\n opt_info['location'] = LocationInfo(\n Locations.set_default,\n _get_caller_detail(3), # this function has a decorator to skip\n )","def set_log_option_default(self, value):\n self._log_option_default = value","def set_default(self, section, option, value=None):\r\n self.defaults[section, option] = value","def set_option(self, dest, value, force=True):\r\n if hasattr(self._option_values, dest) and not force:\r\n return\r\n setattr(self._option_values, dest, value)","def initialize_options(self):\n self.all = None","def test_default(self):\r\n self.assertEqual(self.option.default, 'hello')","def set_option(self, key, value):\r\n return self.set_options([(key, value)])","def setDefaultOpts(self):\n\t\tself.getopt.s = ['h']\n\t\tself.getopt.l = ['help']\n\t\tself.getopt.s.extend([('x:', 'screen')])\n\t\tself.getopt.l.extend([('xml=', 'screen')])\n\t\treturn","def default(self, default):\n\n self._default = default","def option(number, default='no'):\n return answer(number).get('options', default)","def set_value_to_default(self):\n self.setValue(self.default_value)","def _coalesceOption(self, name, default = ''):\n return self.view.settings().get(name, self.options.get(name, default))","def setdefault(self, k, d=None): # real signature unknown; restored from __doc__\n pass","def visit_option(self, state, opt_name, opt_value):\n pass","def opt_option(self, option):\n if \"=\" in option:\n path, value = option.split(\"=\")\n self.setOverride(\n DEFAULT_CONFIG,\n path.split(\"/\"),\n value,\n self.overrides\n )\n else:\n self.opt_option(\"{}=True\".format(option))","def option_is_default(self, opt):\n return opt in self.results and self.results[opt][1] is self._is_default","def initialize_options(self):","def test_default(self):\r\n self.assertEqual(self.option.default, '/tmp')","def default(self, value):\n # also easy\n self._default = value\n # all done\n return","def set_default_value(self, default_value, option_type):\n if default_value is not None:\n self.defaultValue = default_value\n if option_type is not None:\n self.optionType = option_type","def default(self, default):\n self._default = default\n return self","def first_selected_option(self):\n try:\n return self.all_selected_options[0]\n except IndexError:\n raise ValueError(\"No options are selected\")","def pick(self):\n self._val = False","def set_default_protein_options(treebuilder):\n treebuilder.options = get_default_options()","def default(self, value):\n # save {value} as the default\n self._default = value\n # all done\n return","def set_option(self, field):\n self.lnp.set_option(field, self.controls[field].get())\n self.update_displays()","def setOption(self, name, value):\n petsc.optionsSetValue(name, value)\n return","def add_default_options(self):\n\n options = getattr(self.parent, \"pyautodoc_set_default_option\", [])\n for option in options:\n self.set_default_option(option)","def SetSelection(self, index):\n \n self.choices[index].SetValue(True)","def setInitDefault(self, editText):\n self.initDefault = self.storedText(editText)[0]","def setdefault(self, key):\n pass","def form_SelectChoiceDefault(request):\n schema = schemaish.Structure()\n schema.add('mySelect', schemaish.Integer())\n options = [(1,'a'),(2,'b'),(3,'c')]\n\n form = formish.Form(schema, 'form')\n form['mySelect'].widget = formish.SelectChoice(options)\n form['mySelect'].default = 2\n return form","def get_one_menu_option():","def set_default_dna_options(treebuilder):\n treebuilder.options = get_default_options()","def set_option(self, name, val, action=Empty, opts=Empty):\n if action is Empty and opts is Empty:\n self.specified.append(name)\n super(SpecRegister, self).set_option(name, val)\n else:\n super(SpecRegister, self).set_option(name, val, action, opts)","def setOption(self, pluginOptionDict):\n return True","def choose_option(self, state):\n options = [o for o in self.options if o.initiation_set[state] == 1]\n return random.choice(options)","def opt_option(self, fuse_option):\n self.fuse_options.append(fuse_option)","def set_option(self, name: str, value: str, section: str = None):\n # TODO: implement\n pass","def defaultPreset (self):\n assert False, \"To be implemented by child\"","def clear_default(self, name, group=None):\n opt_info = self._get_opt_info(name, group)\n opt_info.pop('default', None)","def default(self, option: str, default: Any = None) -> Any:\n if option == 'pull':\n return self.get('force-pull', default=default)\n\n return super().default(option, default=default)","def initialize_options(self):\n pass","def initialize_options(self):\n pass","def initialize_options(self):\n pass","def pop_default(self, option: str) -> Optional[Any]:\n index = self._get_index(option)\n assert index is not None\n value = self._options[index]\n del self._options[index]\n default = value[1] if isinstance(value, tuple) else None\n return default","def select(self):\n\t\tif not (self.setting_key):\n\t\t\tSelectScreen.select(self)\n\t\t\treturn","def set_option(config, key, interactive, logger, message=''):\n message = message or \"\\nSet %s to (%s) \\nor enter new option: \"\n value = config.get(key)\n name = key.upper()\n if interactive:\n value = raw_input(message % (name, value)) or value\n \n config[key] = value\n return config","def opt_option(self, option):\n\n if \"=\" in option:\n path, value = option.split('=')\n self._setOverride(\n DEFAULT_CONFIG,\n path.split('/'),\n value,\n self.overrides\n )\n else:\n self.opt_option('%s=True' % (option,))","def set_option(self, key, value):\n self.options[key] = value","def set_select(self, val):\n self.select = val\n return self","def initial(self):\n from setman import settings\n return getattr(settings, self.name, self.default)","def f_default(self, default = 1) :\n pass","def setup(self, optparser):\n\t\tpass","def Option(self):\n return self._Option","def set_option(self, option, value):\n for option_dict in (\"_general_options\", \"_specific_options\"):\n option_dict = getattr(self, option_dict)\n if option in option_dict:\n if option_dict[option][\"divider\"] != 1:\n value /= float(option_dict[option][\"divider\"])\n setattr(self, \"_\" + option, value)\n break","def optval(self, option, default=None):\n return getattr(self._options_, option, default)","def test_set_defaults(self):\r\n self.assertEqual(self.config.values['option1'], 1337)\r\n self.assertNotIn('option2', self.config.values)","def _set_kql_option(self, option, value):\n if self._ip is None:\n return None\n set_txt = f\"{option}={value}\"\n return self._ip.run_line_magic(\"config\", line=set_txt)","def setdefault(self, value: Any) -> None:\n self.default_factory = value \n return","def set_options(self, options):\n self.options = options","def default(path: str = 'setings.INI'):\n Setings._delete_setings(path)\n Setings._create_default_setting(path)","def setDefaultCapability(self, b):\n self.defaultAllow = b","def set_pede_option(process, option, drop = False):\n\n existing_options = process.AlignmentProducer.algoConfig.pedeSteerer.options\n\n exists = False\n for i in range(len(existing_options)):\n if existing_options[i].split()[0] == option.split()[0]:\n existing_options[i] = option.strip()\n exists = True\n if drop: existing_options.pop(i)\n break\n\n if not exists and not drop: existing_options.append(option.strip())","def initialize_options(self):\n #Each user option must be listed here with their default value.\n self.pylint_rcfile = ''","def setDefault(self):\n for attr in self._filter():\n if attr.attrName() in ('tx', 'ty', 'tz', 'rx', 'ry', 'rz'):\n defaultValue = 0\n elif attr.attrName() in ('sx', 'sy', 'sz', 'v'):\n defaultValue = 1\n else:\n defaultValue = pm.addAttr(attr, query=True, defaultValue=True)\n\n if not (defaultValue is None):\n try:\n attr.set(defaultValue)\n except RuntimeError as message:\n pm.warning(message.message[:-1])","def __init__(self, **user_options):\n self.options = config.default_options.copy()\n self.configure(**user_options)","def options(self, value):\n self._options = value\n if self._options.get(\"legacy\"):\n self._options[\"extended\"] = False","def update_option(**options):\n api_options = default_options\n\n for k,v in option.iteritems():\n if k in default_options:\n api_options[k] = v\n\n return api_options","def setOption(self, name, value):\n name = name.lower()\n try:\n self.defaultOptions[name]\n except KeyError:\n Error(\"Option \\'%-30s\\' is not a valid %s option.\" % (\n name, self.name))\n\n # Make sure we are not trying to change an immutable option if\n # we are not allowed to.\n if self.solverCreated and name in self.imOptions:\n raise Error(\"Option '%-35s' cannot be modified after the solver \"\n \"is created.\" % name)\n\n # Now we know the option exists, lets check if the type is ok:\n if isinstance(value, self.defaultOptions[name][0]):\n # Just set:\n self.options[name] = [type(value), value]\n else:\n raise Error(\"Datatype for Option %-35s was not valid \\n \"\n \"Expected data type is %-47s \\n \"\n \"Received data type is %-47s\" % (\n name, self.defaultOptions[name][0], type(value)))","def select(self, _: int = 0) -> None:\n if not self.all_items:\n self._exit()\n return\n self.selected_option = self.current_option\n\n assert self.selected_item is not None\n self.selected_item.set_up()\n self.selected_item.action()\n self.selected_item.clean_up()\n\n self.returned_value = self.selected_item.get_return()\n self.should_exit = self.selected_item.should_exit\n\n if not self.should_exit:\n self.draw()","def test_exactly_explicit_default_no_args_optional():\n class TestCmdLine(CmdLine):\n yaml_def = '''\n supported_options:\n - category:\n options:\n - name : test_opt\n long : test-opt\n opt : param\n default : default-value\n multi_type: exactly\n count : 1\n required : false\n '''\n test_opt = None\n args = \"util-name\"\n parse_result = TestCmdLine.parse(args)\n assert parse_result.value == ParseResultEnum.SUCCESS.value\n assert TestCmdLine.test_opt == \"default-value\"","def find_option(self, name, namespace=...):\n ...","def setdefault(self, value: Any) -> None: # type: ignore\n self.default_factory = value \n return","def _additional_option(self):\n pass","def test_config_option_not_required_default():\n class Config(config.Config):\n a = config.option(int, default=12, required=False, help=\"\")\n\n c = config.structure({\"a\": None}, Config)\n assert c.a is None","def is_default(self, is_default):\n\n self._is_default = is_default","def default_sortoption(sort_options):\n return sorted_options(sort_options)[0]['value']","def getDefault():","def set_option(self, **kwargs):\n option = self.dismod_file.option\n unknowns = list()\n for name in kwargs.keys():\n if not (option.option_name == name).any():\n unknowns.append(name)\n if unknowns:\n raise KeyError(f\"Unknown options {unknowns}\")\n\n for name, value in kwargs.items():\n if isinstance(value, str):\n str_value = value\n elif isinstance(value, Iterable):\n str_value = \" \".join(str(x) for x in value)\n elif isinstance(value, bool):\n str_value = str(value).lower()\n elif value is None or isnan(value):\n str_value = None\n else:\n str_value = str(value)\n if str_value is not None:\n option.loc[option.option_name == name, \"option_value\"] = str_value\n else:\n option.loc[option.option_name == name, \"option_value\"] = nan\n option = option.reset_index(drop=True)\n option = option.assign(option_id=option.index)\n self.dismod_file.option = option","def get(self, **kws):\n assert len (kws)==1,`kws`\n key, default = kws.items()[0]\n if key not in self.__dict__:\n if VERBOSE:\n print 'Options.get: adding new option: %s=%r' % (key, default)\n self.__dict__[key] = default\n value = self.__dict__[key]\n if value is None:\n value = self.__dict__[key] = default\n return value"],"string":"[\n \"async def _opt_default(self, ctx, option):\\n try:\\n guild_options = self.database.get_guild_options(ctx.guild.id)\\n setattr(guild_options, option, None)\\n self.database.save_item(guild_options)\\n await ctx.send(f\\\"Option {option} set to default\\\")\\n except AttributeError:\\n await ctx.send(\\\"I don't recognize that option.\\\")\",\n \"def set_defaults(self):\\r\\n for name, option in self.options.iteritems():\\r\\n if not option.is_required():\\r\\n self.set_value(name, option, option.default)\",\n \"def set_default_option(self, name: str):\\n\\n if (name not in self.parent.options) and (self.is_available(name)):\\n self.parent.options[name] = self.get_app_cfg_by_name(name)\",\n \"def set_first(self, value):\\n if value not in (\\\"player\\\", \\\"computer\\\", \\\"random\\\"):\\n raise SettingsError(\\\"Invalid choice\\\")\\n self._parser.set(\\\"settings\\\", \\\"first\\\", value)\\n self._save()\",\n \"def getopt_default(self, option):\\n if option not in self.conf:\\n print(\\\"Unrecognized option %r\\\" % option)\\n return\\n print(\\\"%s: %s\\\" % (option, self.conf[option]))\",\n \"def test_default(self):\\r\\n self.assertEqual(self.option.default, 1234)\",\n \"def test_default(self):\\r\\n self.assertEqual(self.option.default, False)\",\n \"def default(self):\\n raise Error(\\\"Missing mandatory setting:\\\", self.name)\",\n \"def init_opt(self):\\n raise NotImplementedError\",\n \"def init_opt(self):\\n raise NotImplementedError\",\n \"def init_opt(self):\\n raise NotImplementedError\",\n \"def init_opt(self):\\n raise NotImplementedError\",\n \"def _default(self, section, option, default):\\r\\n if not self.has_section(section):\\r\\n self.add_section(section)\\r\\n if not self.has_option(section, option):\\r\\n self.set(section, option, default)\\r\\n self.save()\",\n \"def _choose_best_option(self):\",\n \"def test_default(self):\\r\\n self.assertEqual(self.option.default, 'testing')\",\n \"def opt(self, key, default=False):\\n if key not in self.options:\\n return default\\n return self.options.get(key)\",\n \"def force_option(args, run):\\n run.force = True\",\n \"def set_is_default(self):\\n self.is_default = True\",\n \"def set_is_default(self):\\n self.is_default = True\",\n \"def _backend_opts_set(self, val):\\n self._dflt_backend_opts = val\\n return\",\n \"def set_default(self, name, default, group=None):\\n opt_info = self._get_opt_info(name, group)\\n opt_info['default'] = self._get_enforced_type_value(\\n opt_info['opt'], default)\\n opt_info['location'] = LocationInfo(\\n Locations.set_default,\\n _get_caller_detail(3), # this function has a decorator to skip\\n )\",\n \"def set_log_option_default(self, value):\\n self._log_option_default = value\",\n \"def set_default(self, section, option, value=None):\\r\\n self.defaults[section, option] = value\",\n \"def set_option(self, dest, value, force=True):\\r\\n if hasattr(self._option_values, dest) and not force:\\r\\n return\\r\\n setattr(self._option_values, dest, value)\",\n \"def initialize_options(self):\\n self.all = None\",\n \"def test_default(self):\\r\\n self.assertEqual(self.option.default, 'hello')\",\n \"def set_option(self, key, value):\\r\\n return self.set_options([(key, value)])\",\n \"def setDefaultOpts(self):\\n\\t\\tself.getopt.s = ['h']\\n\\t\\tself.getopt.l = ['help']\\n\\t\\tself.getopt.s.extend([('x:', 'screen')])\\n\\t\\tself.getopt.l.extend([('xml=', 'screen')])\\n\\t\\treturn\",\n \"def default(self, default):\\n\\n self._default = default\",\n \"def option(number, default='no'):\\n return answer(number).get('options', default)\",\n \"def set_value_to_default(self):\\n self.setValue(self.default_value)\",\n \"def _coalesceOption(self, name, default = ''):\\n return self.view.settings().get(name, self.options.get(name, default))\",\n \"def setdefault(self, k, d=None): # real signature unknown; restored from __doc__\\n pass\",\n \"def visit_option(self, state, opt_name, opt_value):\\n pass\",\n \"def opt_option(self, option):\\n if \\\"=\\\" in option:\\n path, value = option.split(\\\"=\\\")\\n self.setOverride(\\n DEFAULT_CONFIG,\\n path.split(\\\"/\\\"),\\n value,\\n self.overrides\\n )\\n else:\\n self.opt_option(\\\"{}=True\\\".format(option))\",\n \"def option_is_default(self, opt):\\n return opt in self.results and self.results[opt][1] is self._is_default\",\n \"def initialize_options(self):\",\n \"def test_default(self):\\r\\n self.assertEqual(self.option.default, '/tmp')\",\n \"def default(self, value):\\n # also easy\\n self._default = value\\n # all done\\n return\",\n \"def set_default_value(self, default_value, option_type):\\n if default_value is not None:\\n self.defaultValue = default_value\\n if option_type is not None:\\n self.optionType = option_type\",\n \"def default(self, default):\\n self._default = default\\n return self\",\n \"def first_selected_option(self):\\n try:\\n return self.all_selected_options[0]\\n except IndexError:\\n raise ValueError(\\\"No options are selected\\\")\",\n \"def pick(self):\\n self._val = False\",\n \"def set_default_protein_options(treebuilder):\\n treebuilder.options = get_default_options()\",\n \"def default(self, value):\\n # save {value} as the default\\n self._default = value\\n # all done\\n return\",\n \"def set_option(self, field):\\n self.lnp.set_option(field, self.controls[field].get())\\n self.update_displays()\",\n \"def setOption(self, name, value):\\n petsc.optionsSetValue(name, value)\\n return\",\n \"def add_default_options(self):\\n\\n options = getattr(self.parent, \\\"pyautodoc_set_default_option\\\", [])\\n for option in options:\\n self.set_default_option(option)\",\n \"def SetSelection(self, index):\\n \\n self.choices[index].SetValue(True)\",\n \"def setInitDefault(self, editText):\\n self.initDefault = self.storedText(editText)[0]\",\n \"def setdefault(self, key):\\n pass\",\n \"def form_SelectChoiceDefault(request):\\n schema = schemaish.Structure()\\n schema.add('mySelect', schemaish.Integer())\\n options = [(1,'a'),(2,'b'),(3,'c')]\\n\\n form = formish.Form(schema, 'form')\\n form['mySelect'].widget = formish.SelectChoice(options)\\n form['mySelect'].default = 2\\n return form\",\n \"def get_one_menu_option():\",\n \"def set_default_dna_options(treebuilder):\\n treebuilder.options = get_default_options()\",\n \"def set_option(self, name, val, action=Empty, opts=Empty):\\n if action is Empty and opts is Empty:\\n self.specified.append(name)\\n super(SpecRegister, self).set_option(name, val)\\n else:\\n super(SpecRegister, self).set_option(name, val, action, opts)\",\n \"def setOption(self, pluginOptionDict):\\n return True\",\n \"def choose_option(self, state):\\n options = [o for o in self.options if o.initiation_set[state] == 1]\\n return random.choice(options)\",\n \"def opt_option(self, fuse_option):\\n self.fuse_options.append(fuse_option)\",\n \"def set_option(self, name: str, value: str, section: str = None):\\n # TODO: implement\\n pass\",\n \"def defaultPreset (self):\\n assert False, \\\"To be implemented by child\\\"\",\n \"def clear_default(self, name, group=None):\\n opt_info = self._get_opt_info(name, group)\\n opt_info.pop('default', None)\",\n \"def default(self, option: str, default: Any = None) -> Any:\\n if option == 'pull':\\n return self.get('force-pull', default=default)\\n\\n return super().default(option, default=default)\",\n \"def initialize_options(self):\\n pass\",\n \"def initialize_options(self):\\n pass\",\n \"def initialize_options(self):\\n pass\",\n \"def pop_default(self, option: str) -> Optional[Any]:\\n index = self._get_index(option)\\n assert index is not None\\n value = self._options[index]\\n del self._options[index]\\n default = value[1] if isinstance(value, tuple) else None\\n return default\",\n \"def select(self):\\n\\t\\tif not (self.setting_key):\\n\\t\\t\\tSelectScreen.select(self)\\n\\t\\t\\treturn\",\n \"def set_option(config, key, interactive, logger, message=''):\\n message = message or \\\"\\\\nSet %s to (%s) \\\\nor enter new option: \\\"\\n value = config.get(key)\\n name = key.upper()\\n if interactive:\\n value = raw_input(message % (name, value)) or value\\n \\n config[key] = value\\n return config\",\n \"def opt_option(self, option):\\n\\n if \\\"=\\\" in option:\\n path, value = option.split('=')\\n self._setOverride(\\n DEFAULT_CONFIG,\\n path.split('/'),\\n value,\\n self.overrides\\n )\\n else:\\n self.opt_option('%s=True' % (option,))\",\n \"def set_option(self, key, value):\\n self.options[key] = value\",\n \"def set_select(self, val):\\n self.select = val\\n return self\",\n \"def initial(self):\\n from setman import settings\\n return getattr(settings, self.name, self.default)\",\n \"def f_default(self, default = 1) :\\n pass\",\n \"def setup(self, optparser):\\n\\t\\tpass\",\n \"def Option(self):\\n return self._Option\",\n \"def set_option(self, option, value):\\n for option_dict in (\\\"_general_options\\\", \\\"_specific_options\\\"):\\n option_dict = getattr(self, option_dict)\\n if option in option_dict:\\n if option_dict[option][\\\"divider\\\"] != 1:\\n value /= float(option_dict[option][\\\"divider\\\"])\\n setattr(self, \\\"_\\\" + option, value)\\n break\",\n \"def optval(self, option, default=None):\\n return getattr(self._options_, option, default)\",\n \"def test_set_defaults(self):\\r\\n self.assertEqual(self.config.values['option1'], 1337)\\r\\n self.assertNotIn('option2', self.config.values)\",\n \"def _set_kql_option(self, option, value):\\n if self._ip is None:\\n return None\\n set_txt = f\\\"{option}={value}\\\"\\n return self._ip.run_line_magic(\\\"config\\\", line=set_txt)\",\n \"def setdefault(self, value: Any) -> None:\\n self.default_factory = value \\n return\",\n \"def set_options(self, options):\\n self.options = options\",\n \"def default(path: str = 'setings.INI'):\\n Setings._delete_setings(path)\\n Setings._create_default_setting(path)\",\n \"def setDefaultCapability(self, b):\\n self.defaultAllow = b\",\n \"def set_pede_option(process, option, drop = False):\\n\\n existing_options = process.AlignmentProducer.algoConfig.pedeSteerer.options\\n\\n exists = False\\n for i in range(len(existing_options)):\\n if existing_options[i].split()[0] == option.split()[0]:\\n existing_options[i] = option.strip()\\n exists = True\\n if drop: existing_options.pop(i)\\n break\\n\\n if not exists and not drop: existing_options.append(option.strip())\",\n \"def initialize_options(self):\\n #Each user option must be listed here with their default value.\\n self.pylint_rcfile = ''\",\n \"def setDefault(self):\\n for attr in self._filter():\\n if attr.attrName() in ('tx', 'ty', 'tz', 'rx', 'ry', 'rz'):\\n defaultValue = 0\\n elif attr.attrName() in ('sx', 'sy', 'sz', 'v'):\\n defaultValue = 1\\n else:\\n defaultValue = pm.addAttr(attr, query=True, defaultValue=True)\\n\\n if not (defaultValue is None):\\n try:\\n attr.set(defaultValue)\\n except RuntimeError as message:\\n pm.warning(message.message[:-1])\",\n \"def __init__(self, **user_options):\\n self.options = config.default_options.copy()\\n self.configure(**user_options)\",\n \"def options(self, value):\\n self._options = value\\n if self._options.get(\\\"legacy\\\"):\\n self._options[\\\"extended\\\"] = False\",\n \"def update_option(**options):\\n api_options = default_options\\n\\n for k,v in option.iteritems():\\n if k in default_options:\\n api_options[k] = v\\n\\n return api_options\",\n \"def setOption(self, name, value):\\n name = name.lower()\\n try:\\n self.defaultOptions[name]\\n except KeyError:\\n Error(\\\"Option \\\\'%-30s\\\\' is not a valid %s option.\\\" % (\\n name, self.name))\\n\\n # Make sure we are not trying to change an immutable option if\\n # we are not allowed to.\\n if self.solverCreated and name in self.imOptions:\\n raise Error(\\\"Option '%-35s' cannot be modified after the solver \\\"\\n \\\"is created.\\\" % name)\\n\\n # Now we know the option exists, lets check if the type is ok:\\n if isinstance(value, self.defaultOptions[name][0]):\\n # Just set:\\n self.options[name] = [type(value), value]\\n else:\\n raise Error(\\\"Datatype for Option %-35s was not valid \\\\n \\\"\\n \\\"Expected data type is %-47s \\\\n \\\"\\n \\\"Received data type is %-47s\\\" % (\\n name, self.defaultOptions[name][0], type(value)))\",\n \"def select(self, _: int = 0) -> None:\\n if not self.all_items:\\n self._exit()\\n return\\n self.selected_option = self.current_option\\n\\n assert self.selected_item is not None\\n self.selected_item.set_up()\\n self.selected_item.action()\\n self.selected_item.clean_up()\\n\\n self.returned_value = self.selected_item.get_return()\\n self.should_exit = self.selected_item.should_exit\\n\\n if not self.should_exit:\\n self.draw()\",\n \"def test_exactly_explicit_default_no_args_optional():\\n class TestCmdLine(CmdLine):\\n yaml_def = '''\\n supported_options:\\n - category:\\n options:\\n - name : test_opt\\n long : test-opt\\n opt : param\\n default : default-value\\n multi_type: exactly\\n count : 1\\n required : false\\n '''\\n test_opt = None\\n args = \\\"util-name\\\"\\n parse_result = TestCmdLine.parse(args)\\n assert parse_result.value == ParseResultEnum.SUCCESS.value\\n assert TestCmdLine.test_opt == \\\"default-value\\\"\",\n \"def find_option(self, name, namespace=...):\\n ...\",\n \"def setdefault(self, value: Any) -> None: # type: ignore\\n self.default_factory = value \\n return\",\n \"def _additional_option(self):\\n pass\",\n \"def test_config_option_not_required_default():\\n class Config(config.Config):\\n a = config.option(int, default=12, required=False, help=\\\"\\\")\\n\\n c = config.structure({\\\"a\\\": None}, Config)\\n assert c.a is None\",\n \"def is_default(self, is_default):\\n\\n self._is_default = is_default\",\n \"def default_sortoption(sort_options):\\n return sorted_options(sort_options)[0]['value']\",\n \"def getDefault():\",\n \"def set_option(self, **kwargs):\\n option = self.dismod_file.option\\n unknowns = list()\\n for name in kwargs.keys():\\n if not (option.option_name == name).any():\\n unknowns.append(name)\\n if unknowns:\\n raise KeyError(f\\\"Unknown options {unknowns}\\\")\\n\\n for name, value in kwargs.items():\\n if isinstance(value, str):\\n str_value = value\\n elif isinstance(value, Iterable):\\n str_value = \\\" \\\".join(str(x) for x in value)\\n elif isinstance(value, bool):\\n str_value = str(value).lower()\\n elif value is None or isnan(value):\\n str_value = None\\n else:\\n str_value = str(value)\\n if str_value is not None:\\n option.loc[option.option_name == name, \\\"option_value\\\"] = str_value\\n else:\\n option.loc[option.option_name == name, \\\"option_value\\\"] = nan\\n option = option.reset_index(drop=True)\\n option = option.assign(option_id=option.index)\\n self.dismod_file.option = option\",\n \"def get(self, **kws):\\n assert len (kws)==1,`kws`\\n key, default = kws.items()[0]\\n if key not in self.__dict__:\\n if VERBOSE:\\n print 'Options.get: adding new option: %s=%r' % (key, default)\\n self.__dict__[key] = default\\n value = self.__dict__[key]\\n if value is None:\\n value = self.__dict__[key] = default\\n return value\"\n]","isConversational":false},"negative_scores":{"kind":"list like","value":["0.71261716","0.66823363","0.6678832","0.6494152","0.638087","0.63636804","0.628238","0.62773716","0.62408084","0.62408084","0.62408084","0.62408084","0.6227838","0.6217609","0.62013143","0.6193903","0.6130184","0.61205834","0.61205834","0.6114318","0.61084294","0.60534555","0.60517144","0.6030905","0.60033643","0.5982202","0.59760886","0.5972838","0.59454995","0.5942656","0.5941016","0.5916708","0.5898755","0.5897424","0.5896757","0.58792365","0.5879055","0.5870172","0.5839519","0.58269364","0.5823666","0.5817193","0.58136714","0.58072656","0.58071434","0.58063364","0.5794545","0.57770234","0.57710844","0.57645935","0.57612044","0.57567894","0.57514495","0.57477635","0.5745635","0.57423127","0.5726746","0.5726008","0.5708739","0.5701016","0.5683146","0.56789166","0.5668779","0.56680554","0.56680554","0.5662618","0.5649253","0.5631739","0.56267464","0.5615936","0.55888575","0.5585102","0.55732435","0.5570087","0.55688953","0.5523776","0.5523027","0.5518416","0.5516072","0.5514163","0.5512957","0.5512228","0.55008006","0.54982316","0.5497153","0.549562","0.5492705","0.54868376","0.5481842","0.5481499","0.54769397","0.54695874","0.54676014","0.54671645","0.54656154","0.54634315","0.5459294","0.54566747","0.5456162","0.54559916","0.5452068"],"string":"[\n \"0.71261716\",\n \"0.66823363\",\n \"0.6678832\",\n \"0.6494152\",\n \"0.638087\",\n \"0.63636804\",\n \"0.628238\",\n \"0.62773716\",\n \"0.62408084\",\n \"0.62408084\",\n \"0.62408084\",\n \"0.62408084\",\n \"0.6227838\",\n \"0.6217609\",\n \"0.62013143\",\n \"0.6193903\",\n \"0.6130184\",\n \"0.61205834\",\n \"0.61205834\",\n \"0.6114318\",\n \"0.61084294\",\n \"0.60534555\",\n \"0.60517144\",\n \"0.6030905\",\n \"0.60033643\",\n \"0.5982202\",\n \"0.59760886\",\n \"0.5972838\",\n \"0.59454995\",\n \"0.5942656\",\n \"0.5941016\",\n \"0.5916708\",\n \"0.5898755\",\n \"0.5897424\",\n \"0.5896757\",\n \"0.58792365\",\n \"0.5879055\",\n \"0.5870172\",\n \"0.5839519\",\n \"0.58269364\",\n \"0.5823666\",\n \"0.5817193\",\n \"0.58136714\",\n \"0.58072656\",\n \"0.58071434\",\n \"0.58063364\",\n \"0.5794545\",\n \"0.57770234\",\n \"0.57710844\",\n \"0.57645935\",\n \"0.57612044\",\n \"0.57567894\",\n \"0.57514495\",\n \"0.57477635\",\n \"0.5745635\",\n \"0.57423127\",\n \"0.5726746\",\n \"0.5726008\",\n \"0.5708739\",\n \"0.5701016\",\n \"0.5683146\",\n \"0.56789166\",\n \"0.5668779\",\n \"0.56680554\",\n \"0.56680554\",\n \"0.5662618\",\n \"0.5649253\",\n \"0.5631739\",\n \"0.56267464\",\n \"0.5615936\",\n \"0.55888575\",\n \"0.5585102\",\n \"0.55732435\",\n \"0.5570087\",\n \"0.55688953\",\n \"0.5523776\",\n \"0.5523027\",\n \"0.5518416\",\n \"0.5516072\",\n \"0.5514163\",\n \"0.5512957\",\n \"0.5512228\",\n \"0.55008006\",\n \"0.54982316\",\n \"0.5497153\",\n \"0.549562\",\n \"0.5492705\",\n \"0.54868376\",\n \"0.5481842\",\n \"0.5481499\",\n \"0.54769397\",\n \"0.54695874\",\n \"0.54676014\",\n \"0.54671645\",\n \"0.54656154\",\n \"0.54634315\",\n \"0.5459294\",\n \"0.54566747\",\n \"0.5456162\",\n \"0.54559916\",\n \"0.5452068\"\n]","isConversational":false},"document_score":{"kind":"string","value":"0.0"},"document_rank":{"kind":"string","value":"-1"}}},{"rowIdx":82396,"cells":{"query":{"kind":"string","value":"Lower limit in display units including the unit text."},"document":{"kind":"string","value":"def lowerDisplay(self):\n return \"{0:g} ({1})\".format(self._param.toDisplay(self.lower),\n self._param.displayMagnitudeName())"},"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_display_size(self):\n return '%d MB' % self.size if self.size < 1024 else '%.2f GB' % (self.size / 1024.0)","def unit_of_measurement(self):\n return \"%\"","def units(self):\n pass","def unit_of_measurement(self):\n return '%'","def unit_of_measurement(self) -> str:\n return \"%\"","def unit_of_measurement(self) -> str:\n return \"%\"","def unit(self) -> str:","def unit_of_measurement(self):\n return 'shows'","def set_hm_unit_display(self):\r\n units = str(self.entries['units'].combobox.currentText())\r\n self.ui.is_unitL1.setText(units)\r\n self.ui.is_unitL2.setText(units)\r\n self.ui.is_unitL3.setText(units)\r\n self.ui.is_unitL4.setText(units)","def unit_of_measurement(self):\n return LENGTH_METERS","def unit_number(self) -> int:\n return self._unit * -1","def unit_of_measurement(self) -> str:\n return self._unit","def unit_of_measurement(self):\n return None","def __human_limit(limit, used):\n if limit == 0: return \"none\"\n\n percentage = str(int(used*100/limit)) + \"%\"\n percentage = \" \"*(3-len(percentage)) + percentage\n return __human_size(limit, False) + \" (\" + percentage + \")\"","def useUnits():","def unit_of_measurement(self):\n return self.display_currency","def unit_of_measurement(self) -> str:\n return self._unit_of_measurement","def unit_of_measurement(self) -> str:\n return MS","def limit_bandwidth_unit(self):\n return self._limit_bandwidth_unit","def unitsize(self, unit):\n\t\treturn self._unitsize[unit]","def native_unit_of_measurement(self) -> str:\n return f\"{CURRENCY_CENT}/{UnitOfVolume.LITERS}\"","def clamped(self, magnitude):\n return self.unit * magnitude","def _unit_min(self):\n return self.time_base","def get_printable_size(self):\n size = self.size\n prefix = ''\n for (s, l) in [(1024*1024*1024, 'GB'), (1024*1024, 'MB'), (1024, 'KB')]:\n if (size>s):\n size = float(size)/s\n prefix = l\n\n return '%10.2f %s' % (size, prefix)","def upperDisplay(self):\n return \"{0:g} ({1})\".format(self._param.toDisplay(self.upper),\n self._param.displayMagnitudeName())","def _get_FIELD_humanized_display(self, field):\n value = getattr(self, field.attname)\n if value is None:\n return\n power = max([i for i in utils.POWERS if value // i > 0 and i > 1])\n value /= power\n template = ''\n template += '{:.%sf}' % field.humanized_decimals\n template += ' ' if field.spaced_display else ''\n template += utils.POWERS[power]\n template += '{!s:s}'\n return template.format(value, field.unit)","def min_ems(self, value) -> 'Size':\n raise_not_number(value)\n self.minimum = '{}em'.format(value)\n return self","def humanvalue(self, value):\n if value > 1024 * 1024 * 1024:\n return \"%d\" % (value / 1024 / 1024 / 1024 / 1024)\n if value > 1024 * 1024:\n return \"%d\" % (value / 1024 / 1024)\n if value > 1024:\n return \"%d\" % (value / 1024 / 1024)","def humanize_size_range(size_from, size_to, unit='ft'):\n size_from = int(size_from) if size_from else 0\n size_to = int(size_to) if size_to else 0\n\n if size_to == 99:\n return str(_('from {size_from} {unit}')).format(size_from=size_from, unit=unit)\n\n if size_from == 0:\n return str(_('up to {size_to} {unit}')).format(size_to=size_to, unit=unit)\n\n return str(_('{size_from}-{size_to} {unit}')).format(\n size_from=size_from, size_to=size_to, unit=unit\n )","def set_units(self, units):\n self.units = units","def unit_min(self):\n return self.time_base","def unit(self):\n return self._unit","def minimum(self):\n return self.cleaning('Mínimo')","def unit_of_measurement(self):\n return self._unit","def unit_of_measurement(self):\n return self._unit","def unit_of_measurement(self):\n return self._unit","def unit_of_measurement(self):\n return self._unit","def unit_of_measurement(self):\n return self._unit","def unit_of_measurement(self):\n return self._unit","def unit_of_measurement(self):\n return self._unit","def human_m(v):\n if v < 1e-2:\n return (v*1.0e3, 'mm')\n if v < 1:\n return (v*1.0e2, 'cm')\n if v < 1000:\n return (v, 'm')\n return (v/1.0e3, 'km')","def min_width(self):\n ...","def unit_of_measurement(self):\n if self._xfinity_data.unit is not None:\n return self._xfinity_data.unit","def get_unit(self):\n return self.unit","def unit_of_measurement(self):\n return self.device.unit()","def set_unit(self,unit):\n self.unit = unit","def unit_of_measure(self):\n try:\n uom = self.metadata['geosoft']['dataset']['geo:unitofmeasurement']['#text']\n except KeyError:\n uom = ''\n return uom","def get_units(self):\n return str(self._modeler.GetModelUnits())","def tranfer_unit(number):\n count = 0\n unit_name = \"\"\n if 2 ** 20 > number > 2 ** 10:\n unit_name = \"Kb\"\n count = 1\n elif 2 ** 30 > number > 2 ** 20:\n unit_name = \"Mb\"\n count = 2\n elif number > 2 ** 30:\n unit_name = \"Gb\"\n count = 3\n else:\n unit_name = \"b\"\n if count != 0:\n unit_number = round(number / ((2 ** 10) ** count), 2)\n else:\n unit_number = round(number, 2)\n unit_str = \"{num}{name}\".format(num=unit_number, name=unit_name)\n return unit_str","def to_units(number):\n unit = 0\n while number >= 1024.:\n unit += 1\n number = number / 1024.\n if unit == len(UNITS) - 1:\n break\n if unit:\n return '%.2f%s' % (number, UNITS[unit])\n return '%d' % number","def getUmidadeArMin(self):\n return str(self.getWeather('umid-min')[:2]) + '%'","def unit(self):\n return self.__unit","def get_threshold_str(self):\n if self.unit == ThresholdUnit.PERCENTAGE:\n return f\"{self.threshold}%\"\n return self.threshold","def _display_unit(unit):\r\n name = getattr(unit, 'display_name', None)\r\n if name:\r\n return u'{0} ({1})'.format(name, unit.location.to_deprecated_string())\r\n else:\r\n return unit.location.to_deprecated_string()","def pprint_size(value):\n for postfix, limit in [(\"G\", 1e9), (\"M\", 1e6), (\"K\", 1e3), (\"\", 1)]:\n if value >= limit:\n return \"{}{}\".format(int(value/limit), postfix)","def __str__(self):\n quota = self.id + \" = \" + str(self.usage)\n if self.units != 'integer':\n quota += \"\" + self.units\n return quota","def meter(illustrate, size, value):\n display = illustrate(int(value))\n display += ' '*(size - len(display)/3)\n return display","def humanize_imperial_size(size): # noqa: D301, WPS342\n if not size:\n return None\n\n total_inches = round(size / 25.4)\n feet = int(total_inches / 12)\n inches = int(total_inches % 12)\n\n if feet > 10:\n # Round inches for long boats\n if inches == 1:\n inches = 0\n elif inches == 11:\n inches = 0\n feet += 1\n\n size = \"{0}'\".format(feet) if feet else ''\n if inches:\n return '{0} {1}\"'.format(size, inches).strip()\n return size","def get_default_item_label_maxlength(self):\n return 15","def unit_of_measurement(self):\n return self._unit_of_measurement","def unit_of_measurement(self):\n return self._unit_of_measurement","def unit_of_measurement(self):\n return self._unit_of_measurement","def unit_of_measurement(self):\n return self._unit_of_measurement","def unit_of_measurement(self):\n return self._unit_of_measurement","def unit_of_measurement(self):\n return self._unit_of_measurement","def unit_of_measurement(self):\n return self._unit_of_measurement","def unit_of_measurement(self):\n return self._unit_of_measurement","def unit_of_measurement(self):\n return self._unit_of_measurement","def unit_of_measurement(self):\n return self._unit_of_measurement","def unit_of_measurement(self):\n return self._unit_of_measurement","def unit_of_measurement(self):\n return self._unit_of_measurement","def unit_of_measurement(self):\n return self._unit_of_measurement","def unit_of_measurement(self):\n return self._unit_of_measurement","def set_min_sentence_length(self):\n new_min = int(self.set_min_sentence.get())\n cur_max = self.max_sentence_length\n\n if new_min < cur_max:\n self.min_sentence_length = new_min\n else:\n old_min = self.min_sentence_length\n old_min_var = tk.StringVar(self.master)\n old_min_var.set(str(old_min))\n self.set_min_sentence.config(textvariable=old_min_var)","def humanize_metric_size(size):\n if not size:\n return None\n\n meters = '{0:.2f}'.format(size / 1000.0).rstrip('0').rstrip('.')\n return str(_('{meters} m')).format(meters=meters)","def small_text(self):\n pass","def unit(self):\n return self._unit","def unit(self):\n return self._unit","def get_width(self):\n return \"%s\" % self.width","def min_width(self) -> float:\n ws = self.get_widths()\n return ws[0] + ws[1] * self.factor","def units(self):\n return self._units","def units(self):\n return self._units","def formatUnits(value, units, precision=1, useUnits=True):\n\tif value is None:\n\t\treturn None\n\n\t#No need to attach units to infinities\n\telif math.isinf(value):\n\t\treturn str(value)\n\n\tscaledValue = value\n\tunit = ''\n\tif useUnits:\n\t\tfor name, multiplier in units:\n\t\t\tif value < multiplier:\n\t\t\t\tbreak\n\n\t\t\tscaledValue = float(value) / multiplier\n\t\t\tunit = name\n\n\tformatString = '%%.%df%%s' % precision\n\treturn formatString % (float(scaledValue), unit)","def width(self):\n\t\tpass","def unit_of_measurement(self):\n return self._var_unit","def unit_of_measurement(self):\n return self._var_unit","def unitsofmeasure(self):\n return self._unitsofmeasure","def quantity_unit(self) -> str:\n return self.__quantity_unit","def sizeHint(self):\n return self.minimumSize() * 3","def shorten_number(self, number):\n if number < 1000:\n return number\n elif number >= 1000 and number < 1000000:\n num = self.rounded_number(number, 1000)\n val = \"1M\" if num == \"1000\" else num + \"K\"\n return val\n elif number >= 1000000 and number < 1000000000:\n num = self.rounded_number(number, 1000000)\n val = \"1B\" if num==\"1000\" else num + \"M\"\n return val\n elif number >= 1000000000 and number < 1000000000000:\n num = self.rounded_number(number, 1000000000)\n val = \"1T\" if num==\"1000\" else num + \"B\"\n return val\n else:\n num = self.rounded_number(number, 1000000000000)\n return num + \"T\"","def lowerLimit(self):\r\n if len(self.elements) > 0:\r\n return self.elements[0].lowerLimit\r\n else:\r\n raise KeyError('No elements in Computation object')","def __str__(self):\n return self.limit","def getSize(self, precision=1):\n kilo = 1000.0\n if self.size < kilo:\n return str(self.size) + \"B\"\n elif self.size < kilo ** 2:\n return str(round(self.size / kilo, precision)) + \"K\"\n elif self.size < kilo ** 3:\n return str(round(self.size / (kilo ** 2), precision)) + \"M\"\n elif self.size < kilo ** 4:\n return str(round(self.size / (kilo ** 3), precision)) + \"G\"\n else:\n return str(round(self.size / (kilo ** 4), precision)) + \"T\"","def _unit_ms(self):\n return (self.time_base / 1000.0) / 60.0","def unit_of_measurement(self):\n return self.values.primary.units","def _get_display_duration(self):\n duration = self.duration\n return '%dmn' % duration if duration < 60 else '%dh%dmn' % (duration / 60, duration % 60)","def get_display_plan_limit(self, source):\n commitment = getattr(self.get_subscription(), 'commitment', {})\n return self.get_plan().get_display_price_data(source, commitment)[1]","def units(self):\n return self.__class__.get_setting_units(self.key, **self.get_kwargs())","def width(self):\n xx = self.xx\n return max(xx) - min(xx)","def units(self) -> Optional[pulumi.Input[Sequence[pulumi.Input['OneDashboardPageWidgetLineUnitArgs']]]]:\n return pulumi.get(self, \"units\")"],"string":"[\n \"def _get_display_size(self):\\n return '%d MB' % self.size if self.size < 1024 else '%.2f GB' % (self.size / 1024.0)\",\n \"def unit_of_measurement(self):\\n return \\\"%\\\"\",\n \"def units(self):\\n pass\",\n \"def unit_of_measurement(self):\\n return '%'\",\n \"def unit_of_measurement(self) -> str:\\n return \\\"%\\\"\",\n \"def unit_of_measurement(self) -> str:\\n return \\\"%\\\"\",\n \"def unit(self) -> str:\",\n \"def unit_of_measurement(self):\\n return 'shows'\",\n \"def set_hm_unit_display(self):\\r\\n units = str(self.entries['units'].combobox.currentText())\\r\\n self.ui.is_unitL1.setText(units)\\r\\n self.ui.is_unitL2.setText(units)\\r\\n self.ui.is_unitL3.setText(units)\\r\\n self.ui.is_unitL4.setText(units)\",\n \"def unit_of_measurement(self):\\n return LENGTH_METERS\",\n \"def unit_number(self) -> int:\\n return self._unit * -1\",\n \"def unit_of_measurement(self) -> str:\\n return self._unit\",\n \"def unit_of_measurement(self):\\n return None\",\n \"def __human_limit(limit, used):\\n if limit == 0: return \\\"none\\\"\\n\\n percentage = str(int(used*100/limit)) + \\\"%\\\"\\n percentage = \\\" \\\"*(3-len(percentage)) + percentage\\n return __human_size(limit, False) + \\\" (\\\" + percentage + \\\")\\\"\",\n \"def useUnits():\",\n \"def unit_of_measurement(self):\\n return self.display_currency\",\n \"def unit_of_measurement(self) -> str:\\n return self._unit_of_measurement\",\n \"def unit_of_measurement(self) -> str:\\n return MS\",\n \"def limit_bandwidth_unit(self):\\n return self._limit_bandwidth_unit\",\n \"def unitsize(self, unit):\\n\\t\\treturn self._unitsize[unit]\",\n \"def native_unit_of_measurement(self) -> str:\\n return f\\\"{CURRENCY_CENT}/{UnitOfVolume.LITERS}\\\"\",\n \"def clamped(self, magnitude):\\n return self.unit * magnitude\",\n \"def _unit_min(self):\\n return self.time_base\",\n \"def get_printable_size(self):\\n size = self.size\\n prefix = ''\\n for (s, l) in [(1024*1024*1024, 'GB'), (1024*1024, 'MB'), (1024, 'KB')]:\\n if (size>s):\\n size = float(size)/s\\n prefix = l\\n\\n return '%10.2f %s' % (size, prefix)\",\n \"def upperDisplay(self):\\n return \\\"{0:g} ({1})\\\".format(self._param.toDisplay(self.upper),\\n self._param.displayMagnitudeName())\",\n \"def _get_FIELD_humanized_display(self, field):\\n value = getattr(self, field.attname)\\n if value is None:\\n return\\n power = max([i for i in utils.POWERS if value // i > 0 and i > 1])\\n value /= power\\n template = ''\\n template += '{:.%sf}' % field.humanized_decimals\\n template += ' ' if field.spaced_display else ''\\n template += utils.POWERS[power]\\n template += '{!s:s}'\\n return template.format(value, field.unit)\",\n \"def min_ems(self, value) -> 'Size':\\n raise_not_number(value)\\n self.minimum = '{}em'.format(value)\\n return self\",\n \"def humanvalue(self, value):\\n if value > 1024 * 1024 * 1024:\\n return \\\"%d\\\" % (value / 1024 / 1024 / 1024 / 1024)\\n if value > 1024 * 1024:\\n return \\\"%d\\\" % (value / 1024 / 1024)\\n if value > 1024:\\n return \\\"%d\\\" % (value / 1024 / 1024)\",\n \"def humanize_size_range(size_from, size_to, unit='ft'):\\n size_from = int(size_from) if size_from else 0\\n size_to = int(size_to) if size_to else 0\\n\\n if size_to == 99:\\n return str(_('from {size_from} {unit}')).format(size_from=size_from, unit=unit)\\n\\n if size_from == 0:\\n return str(_('up to {size_to} {unit}')).format(size_to=size_to, unit=unit)\\n\\n return str(_('{size_from}-{size_to} {unit}')).format(\\n size_from=size_from, size_to=size_to, unit=unit\\n )\",\n \"def set_units(self, units):\\n self.units = units\",\n \"def unit_min(self):\\n return self.time_base\",\n \"def unit(self):\\n return self._unit\",\n \"def minimum(self):\\n return self.cleaning('Mínimo')\",\n \"def unit_of_measurement(self):\\n return self._unit\",\n \"def unit_of_measurement(self):\\n return self._unit\",\n \"def unit_of_measurement(self):\\n return self._unit\",\n \"def unit_of_measurement(self):\\n return self._unit\",\n \"def unit_of_measurement(self):\\n return self._unit\",\n \"def unit_of_measurement(self):\\n return self._unit\",\n \"def unit_of_measurement(self):\\n return self._unit\",\n \"def human_m(v):\\n if v < 1e-2:\\n return (v*1.0e3, 'mm')\\n if v < 1:\\n return (v*1.0e2, 'cm')\\n if v < 1000:\\n return (v, 'm')\\n return (v/1.0e3, 'km')\",\n \"def min_width(self):\\n ...\",\n \"def unit_of_measurement(self):\\n if self._xfinity_data.unit is not None:\\n return self._xfinity_data.unit\",\n \"def get_unit(self):\\n return self.unit\",\n \"def unit_of_measurement(self):\\n return self.device.unit()\",\n \"def set_unit(self,unit):\\n self.unit = unit\",\n \"def unit_of_measure(self):\\n try:\\n uom = self.metadata['geosoft']['dataset']['geo:unitofmeasurement']['#text']\\n except KeyError:\\n uom = ''\\n return uom\",\n \"def get_units(self):\\n return str(self._modeler.GetModelUnits())\",\n \"def tranfer_unit(number):\\n count = 0\\n unit_name = \\\"\\\"\\n if 2 ** 20 > number > 2 ** 10:\\n unit_name = \\\"Kb\\\"\\n count = 1\\n elif 2 ** 30 > number > 2 ** 20:\\n unit_name = \\\"Mb\\\"\\n count = 2\\n elif number > 2 ** 30:\\n unit_name = \\\"Gb\\\"\\n count = 3\\n else:\\n unit_name = \\\"b\\\"\\n if count != 0:\\n unit_number = round(number / ((2 ** 10) ** count), 2)\\n else:\\n unit_number = round(number, 2)\\n unit_str = \\\"{num}{name}\\\".format(num=unit_number, name=unit_name)\\n return unit_str\",\n \"def to_units(number):\\n unit = 0\\n while number >= 1024.:\\n unit += 1\\n number = number / 1024.\\n if unit == len(UNITS) - 1:\\n break\\n if unit:\\n return '%.2f%s' % (number, UNITS[unit])\\n return '%d' % number\",\n \"def getUmidadeArMin(self):\\n return str(self.getWeather('umid-min')[:2]) + '%'\",\n \"def unit(self):\\n return self.__unit\",\n \"def get_threshold_str(self):\\n if self.unit == ThresholdUnit.PERCENTAGE:\\n return f\\\"{self.threshold}%\\\"\\n return self.threshold\",\n \"def _display_unit(unit):\\r\\n name = getattr(unit, 'display_name', None)\\r\\n if name:\\r\\n return u'{0} ({1})'.format(name, unit.location.to_deprecated_string())\\r\\n else:\\r\\n return unit.location.to_deprecated_string()\",\n \"def pprint_size(value):\\n for postfix, limit in [(\\\"G\\\", 1e9), (\\\"M\\\", 1e6), (\\\"K\\\", 1e3), (\\\"\\\", 1)]:\\n if value >= limit:\\n return \\\"{}{}\\\".format(int(value/limit), postfix)\",\n \"def __str__(self):\\n quota = self.id + \\\" = \\\" + str(self.usage)\\n if self.units != 'integer':\\n quota += \\\"\\\" + self.units\\n return quota\",\n \"def meter(illustrate, size, value):\\n display = illustrate(int(value))\\n display += ' '*(size - len(display)/3)\\n return display\",\n \"def humanize_imperial_size(size): # noqa: D301, WPS342\\n if not size:\\n return None\\n\\n total_inches = round(size / 25.4)\\n feet = int(total_inches / 12)\\n inches = int(total_inches % 12)\\n\\n if feet > 10:\\n # Round inches for long boats\\n if inches == 1:\\n inches = 0\\n elif inches == 11:\\n inches = 0\\n feet += 1\\n\\n size = \\\"{0}'\\\".format(feet) if feet else ''\\n if inches:\\n return '{0} {1}\\\"'.format(size, inches).strip()\\n return size\",\n \"def get_default_item_label_maxlength(self):\\n return 15\",\n \"def unit_of_measurement(self):\\n return self._unit_of_measurement\",\n \"def unit_of_measurement(self):\\n return self._unit_of_measurement\",\n \"def unit_of_measurement(self):\\n return self._unit_of_measurement\",\n \"def unit_of_measurement(self):\\n return self._unit_of_measurement\",\n \"def unit_of_measurement(self):\\n return self._unit_of_measurement\",\n \"def unit_of_measurement(self):\\n return self._unit_of_measurement\",\n \"def unit_of_measurement(self):\\n return self._unit_of_measurement\",\n \"def unit_of_measurement(self):\\n return self._unit_of_measurement\",\n \"def unit_of_measurement(self):\\n return self._unit_of_measurement\",\n \"def unit_of_measurement(self):\\n return self._unit_of_measurement\",\n \"def unit_of_measurement(self):\\n return self._unit_of_measurement\",\n \"def unit_of_measurement(self):\\n return self._unit_of_measurement\",\n \"def unit_of_measurement(self):\\n return self._unit_of_measurement\",\n \"def unit_of_measurement(self):\\n return self._unit_of_measurement\",\n \"def set_min_sentence_length(self):\\n new_min = int(self.set_min_sentence.get())\\n cur_max = self.max_sentence_length\\n\\n if new_min < cur_max:\\n self.min_sentence_length = new_min\\n else:\\n old_min = self.min_sentence_length\\n old_min_var = tk.StringVar(self.master)\\n old_min_var.set(str(old_min))\\n self.set_min_sentence.config(textvariable=old_min_var)\",\n \"def humanize_metric_size(size):\\n if not size:\\n return None\\n\\n meters = '{0:.2f}'.format(size / 1000.0).rstrip('0').rstrip('.')\\n return str(_('{meters} m')).format(meters=meters)\",\n \"def small_text(self):\\n pass\",\n \"def unit(self):\\n return self._unit\",\n \"def unit(self):\\n return self._unit\",\n \"def get_width(self):\\n return \\\"%s\\\" % self.width\",\n \"def min_width(self) -> float:\\n ws = self.get_widths()\\n return ws[0] + ws[1] * self.factor\",\n \"def units(self):\\n return self._units\",\n \"def units(self):\\n return self._units\",\n \"def formatUnits(value, units, precision=1, useUnits=True):\\n\\tif value is None:\\n\\t\\treturn None\\n\\n\\t#No need to attach units to infinities\\n\\telif math.isinf(value):\\n\\t\\treturn str(value)\\n\\n\\tscaledValue = value\\n\\tunit = ''\\n\\tif useUnits:\\n\\t\\tfor name, multiplier in units:\\n\\t\\t\\tif value < multiplier:\\n\\t\\t\\t\\tbreak\\n\\n\\t\\t\\tscaledValue = float(value) / multiplier\\n\\t\\t\\tunit = name\\n\\n\\tformatString = '%%.%df%%s' % precision\\n\\treturn formatString % (float(scaledValue), unit)\",\n \"def width(self):\\n\\t\\tpass\",\n \"def unit_of_measurement(self):\\n return self._var_unit\",\n \"def unit_of_measurement(self):\\n return self._var_unit\",\n \"def unitsofmeasure(self):\\n return self._unitsofmeasure\",\n \"def quantity_unit(self) -> str:\\n return self.__quantity_unit\",\n \"def sizeHint(self):\\n return self.minimumSize() * 3\",\n \"def shorten_number(self, number):\\n if number < 1000:\\n return number\\n elif number >= 1000 and number < 1000000:\\n num = self.rounded_number(number, 1000)\\n val = \\\"1M\\\" if num == \\\"1000\\\" else num + \\\"K\\\"\\n return val\\n elif number >= 1000000 and number < 1000000000:\\n num = self.rounded_number(number, 1000000)\\n val = \\\"1B\\\" if num==\\\"1000\\\" else num + \\\"M\\\"\\n return val\\n elif number >= 1000000000 and number < 1000000000000:\\n num = self.rounded_number(number, 1000000000)\\n val = \\\"1T\\\" if num==\\\"1000\\\" else num + \\\"B\\\"\\n return val\\n else:\\n num = self.rounded_number(number, 1000000000000)\\n return num + \\\"T\\\"\",\n \"def lowerLimit(self):\\r\\n if len(self.elements) > 0:\\r\\n return self.elements[0].lowerLimit\\r\\n else:\\r\\n raise KeyError('No elements in Computation object')\",\n \"def __str__(self):\\n return self.limit\",\n \"def getSize(self, precision=1):\\n kilo = 1000.0\\n if self.size < kilo:\\n return str(self.size) + \\\"B\\\"\\n elif self.size < kilo ** 2:\\n return str(round(self.size / kilo, precision)) + \\\"K\\\"\\n elif self.size < kilo ** 3:\\n return str(round(self.size / (kilo ** 2), precision)) + \\\"M\\\"\\n elif self.size < kilo ** 4:\\n return str(round(self.size / (kilo ** 3), precision)) + \\\"G\\\"\\n else:\\n return str(round(self.size / (kilo ** 4), precision)) + \\\"T\\\"\",\n \"def _unit_ms(self):\\n return (self.time_base / 1000.0) / 60.0\",\n \"def unit_of_measurement(self):\\n return self.values.primary.units\",\n \"def _get_display_duration(self):\\n duration = self.duration\\n return '%dmn' % duration if duration < 60 else '%dh%dmn' % (duration / 60, duration % 60)\",\n \"def get_display_plan_limit(self, source):\\n commitment = getattr(self.get_subscription(), 'commitment', {})\\n return self.get_plan().get_display_price_data(source, commitment)[1]\",\n \"def units(self):\\n return self.__class__.get_setting_units(self.key, **self.get_kwargs())\",\n \"def width(self):\\n xx = self.xx\\n return max(xx) - min(xx)\",\n \"def units(self) -> Optional[pulumi.Input[Sequence[pulumi.Input['OneDashboardPageWidgetLineUnitArgs']]]]:\\n return pulumi.get(self, \\\"units\\\")\"\n]","isConversational":false},"negative_scores":{"kind":"list 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\"0.68737787\",\n \"0.6705744\",\n \"0.6617617\",\n \"0.65782964\",\n \"0.6403219\",\n \"0.6403219\",\n \"0.6349418\",\n \"0.62952155\",\n \"0.62344605\",\n \"0.6159931\",\n \"0.6157702\",\n \"0.61567926\",\n \"0.6149927\",\n \"0.6146399\",\n \"0.6140932\",\n \"0.61266\",\n \"0.61061746\",\n \"0.6068677\",\n \"0.605865\",\n \"0.5996165\",\n \"0.5980514\",\n \"0.5980035\",\n \"0.59707224\",\n \"0.59606045\",\n \"0.59603274\",\n \"0.5959472\",\n \"0.59016895\",\n \"0.58914024\",\n \"0.5891398\",\n \"0.58882517\",\n \"0.5876914\",\n \"0.58657616\",\n \"0.58498114\",\n \"0.5846621\",\n \"0.5846621\",\n \"0.5846621\",\n \"0.5846621\",\n \"0.5846621\",\n \"0.5846621\",\n \"0.5846621\",\n \"0.5833652\",\n \"0.5826394\",\n \"0.578062\",\n \"0.5780583\",\n \"0.5779858\",\n \"0.5776899\",\n \"0.577543\",\n \"0.5752095\",\n \"0.57496977\",\n \"0.5728904\",\n \"0.57189465\",\n \"0.57076126\",\n \"0.57075995\",\n \"0.5707336\",\n \"0.57020324\",\n \"0.57018244\",\n \"0.5690334\",\n \"0.5687458\",\n \"0.5683902\",\n \"0.56774926\",\n \"0.56774926\",\n \"0.56774926\",\n \"0.56774926\",\n \"0.56774926\",\n \"0.56774926\",\n \"0.56774926\",\n \"0.56774926\",\n \"0.56774926\",\n \"0.56774926\",\n \"0.56774926\",\n \"0.56774926\",\n \"0.56774926\",\n \"0.56774926\",\n \"0.56729954\",\n \"0.56690997\",\n \"0.5660297\",\n \"0.56592727\",\n \"0.56592727\",\n \"0.565555\",\n \"0.5655369\",\n \"0.5653897\",\n \"0.5653897\",\n \"0.563394\",\n \"0.5629981\",\n \"0.561747\",\n \"0.561747\",\n \"0.56164163\",\n \"0.5613695\",\n \"0.5610865\",\n \"0.56013745\",\n \"0.5598777\",\n \"0.5598629\",\n \"0.55984354\",\n \"0.5594076\",\n \"0.55862176\",\n \"0.5581396\",\n \"0.55806375\",\n \"0.55777365\",\n \"0.5567402\",\n \"0.55644006\"\n]","isConversational":false},"document_score":{"kind":"string","value":"0.6856167"},"document_rank":{"kind":"string","value":"1"}}},{"rowIdx":82397,"cells":{"query":{"kind":"string","value":"Upper limit in display units including the unit text."},"document":{"kind":"string","value":"def upperDisplay(self):\n return \"{0:g} ({1})\".format(self._param.toDisplay(self.upper),\n self._param.displayMagnitudeName())"},"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_display_size(self):\n return '%d MB' % self.size if self.size < 1024 else '%.2f GB' % (self.size / 1024.0)","def unit_of_measurement(self):\n return \"%\"","def unit_of_measurement(self):\n return '%'","def units(self):\n pass","def unit_of_measurement(self) -> str:\n return \"%\"","def unit_of_measurement(self) -> str:\n return \"%\"","def unit_number(self) -> int:\n return self._unit * -1","def unit(self) -> str:","def set_hm_unit_display(self):\r\n units = str(self.entries['units'].combobox.currentText())\r\n self.ui.is_unitL1.setText(units)\r\n self.ui.is_unitL2.setText(units)\r\n self.ui.is_unitL3.setText(units)\r\n self.ui.is_unitL4.setText(units)","def unitsize(self, unit):\n\t\treturn self._unitsize[unit]","def unit_of_measurement(self):\n return self.display_currency","def unit_of_measurement(self) -> str:\n return self._unit","def getUmidadeArMax(self):\n return str(self.getWeather('umid-max')[:2]) + '%'","def __human_limit(limit, used):\n if limit == 0: return \"none\"\n\n percentage = str(int(used*100/limit)) + \"%\"\n percentage = \" \"*(3-len(percentage)) + percentage\n return __human_size(limit, False) + \" (\" + percentage + \")\"","def unit_of_measurement(self) -> str:\n return self._unit_of_measurement","def unit_of_measurement(self):\n return LENGTH_METERS","def unit_of_measurement(self):\n return None","def limit_bandwidth_unit(self):\n return self._limit_bandwidth_unit","def unit(self):\n return self._unit","def get_unit(self):\n return self.unit","def unit_of_measure(self):\n try:\n uom = self.metadata['geosoft']['dataset']['geo:unitofmeasurement']['#text']\n except KeyError:\n uom = ''\n return uom","def unit_of_measurement(self):\n return self._unit","def unit_of_measurement(self):\n return self._unit","def unit_of_measurement(self):\n return self._unit","def unit_of_measurement(self):\n return self._unit","def unit_of_measurement(self):\n return self._unit","def unit_of_measurement(self):\n return self._unit","def unit_of_measurement(self):\n return self._unit","def unit_of_measurement(self):\n return 'shows'","def unit_of_measurement(self):\n return self.device.unit()","def _get_display_duration(self):\n duration = self.duration\n return '%dmn' % duration if duration < 60 else '%dh%dmn' % (duration / 60, duration % 60)","def unit(self):\n return self.__unit","def unit_of_measurement(self):\n return self._var_unit","def unit_of_measurement(self):\n return self._var_unit","def upperLimit(self):\r\n if len(self.elements) > 0:\r\n return self.elements[-1].upperLimit\r\n else:\r\n raise KeyError('No elements in Computation object')","def unit(self):\n return self._unit","def unit(self):\n return self._unit","def unit_of_measurement(self) -> Any:\n return PERCENTAGE","def unit(cls, val):\n raise NotImplementedError","def trim_display_field(self, value, max_length):\n if not value:\n return ''\n if len(value) > max_length:\n return value[:max_length - 3] + '...'\n return value","def useUnits():","def unit_of_measurement(self) -> str:\n return MS","def unit_of_measurement(self):\n if self._xfinity_data.unit is not None:\n return self._xfinity_data.unit","def get_printable_size(self):\n size = self.size\n prefix = ''\n for (s, l) in [(1024*1024*1024, 'GB'), (1024*1024, 'MB'), (1024, 'KB')]:\n if (size>s):\n size = float(size)/s\n prefix = l\n\n return '%10.2f %s' % (size, prefix)","def get_units(self):\n return str(self._modeler.GetModelUnits())","def unit_of_measurement(self):\n return self._unit_of_measurement","def unit_of_measurement(self):\n return self._unit_of_measurement","def unit_of_measurement(self):\n return self._unit_of_measurement","def unit_of_measurement(self):\n return self._unit_of_measurement","def unit_of_measurement(self):\n return self._unit_of_measurement","def unit_of_measurement(self):\n return self._unit_of_measurement","def unit_of_measurement(self):\n return self._unit_of_measurement","def unit_of_measurement(self):\n return self._unit_of_measurement","def unit_of_measurement(self):\n return self._unit_of_measurement","def unit_of_measurement(self):\n return self._unit_of_measurement","def unit_of_measurement(self):\n return self._unit_of_measurement","def unit_of_measurement(self):\n return self._unit_of_measurement","def unit_of_measurement(self):\n return self._unit_of_measurement","def unit_of_measurement(self):\n return self._unit_of_measurement","def upperLimit(lenFact1):\n\tfact2 = lenFact1*fact9\n\tlenfact2 = len(str(fact2))\n\tmaxFact = lenfact2*fact9\n\tlenMax = len(str(maxFact))\n\tif lenMax == lenfact2: \n\t\treturn maxFact\n\telse:\n\t\treturn upperLimit(lenMax)","def unit_of_measurement(self):\n return self._tasmota_entity.unit","def set_unit(self,unit):\n self.unit = unit","def clamped(self, magnitude):\n return self.unit * magnitude","def units(self) -> Optional[pulumi.Input[Sequence[pulumi.Input['OneDashboardPageWidgetHeatmapUnitArgs']]]]:\n return pulumi.get(self, \"units\")","def set_units(self, units):\n self.units = units","def calc_unit_factor(self, ui_unit):\n # namedView = self.document.getroot().find(inkex.addNS('namedview', 'sodipodi'))\n # doc_units = self.getunittouu(str(1.0) + namedView.get(inkex.addNS('document-units', 'inkscape')))\n unit_factor = self.getunittouu(str(1.0) + ui_unit)\n return unit_factor","def unit_of_measurement(self):\n return self.var_units","def to_units(number):\n unit = 0\n while number >= 1024.:\n unit += 1\n number = number / 1024.\n if unit == len(UNITS) - 1:\n break\n if unit:\n return '%.2f%s' % (number, UNITS[unit])\n return '%d' % number","def unit_of_measurement(self):\n return self._sensor.unit","def unit_of_measurement(self):\n return self.sensor_type[\"unit\"]","def units(self) -> Optional[pulumi.Input[Sequence[pulumi.Input['OneDashboardPageWidgetBarUnitArgs']]]]:\n return pulumi.get(self, \"units\")","def humanvalue(self, value):\n if value > 1024 * 1024 * 1024:\n return \"%d\" % (value / 1024 / 1024 / 1024 / 1024)\n if value > 1024 * 1024:\n return \"%d\" % (value / 1024 / 1024)\n if value > 1024:\n return \"%d\" % (value / 1024 / 1024)","def unit_of_measurement(self):\n\n if self._sensor_class == DEVICE_CLASS_TEMPERATURE:\n return TEMP_CELSIUS\n\n elif self._sensor_class == DEVICE_CLASS_HUMIDITY:\n return '%'","def humanize_size_range(size_from, size_to, unit='ft'):\n size_from = int(size_from) if size_from else 0\n size_to = int(size_to) if size_to else 0\n\n if size_to == 99:\n return str(_('from {size_from} {unit}')).format(size_from=size_from, unit=unit)\n\n if size_from == 0:\n return str(_('up to {size_to} {unit}')).format(size_to=size_to, unit=unit)\n\n return str(_('{size_from}-{size_to} {unit}')).format(\n size_from=size_from, size_to=size_to, unit=unit\n )","def upper_bound(self) -> float:\n ...","def units(self) -> Optional[pulumi.Input[Sequence[pulumi.Input['OneDashboardPageWidgetMarkdownUnitArgs']]]]:\n return pulumi.get(self, \"units\")","def units(self):\n return self._units","def units(self):\n return self._units","def quantity_unit(self) -> str:\n return self.__quantity_unit","def upper_earning_limit(self):\n\t\treturn self._upper_earning_limit","def unit_of_measurement(self):\n return self.values.primary.units","def units(self) -> Optional[pulumi.Input[Sequence[pulumi.Input['OneDashboardPageWidgetBulletUnitArgs']]]]:\n return pulumi.get(self, \"units\")","def units(self) -> Optional[pulumi.Input[Sequence[pulumi.Input['OneDashboardPageWidgetPyUnitArgs']]]]:\n return pulumi.get(self, \"units\")","def units(self) -> Optional[pulumi.Input[Sequence[pulumi.Input['OneDashboardPageWidgetAreaUnitArgs']]]]:\n return pulumi.get(self, \"units\")","def native_unit_of_measurement(self) -> str:\n return f\"{CURRENCY_CENT}/{UnitOfVolume.LITERS}\"","def units(self):\n return self.__class__.get_setting_units(self.key, **self.get_kwargs())","def _get_FIELD_humanized_display(self, field):\n value = getattr(self, field.attname)\n if value is None:\n return\n power = max([i for i in utils.POWERS if value // i > 0 and i > 1])\n value /= power\n template = ''\n template += '{:.%sf}' % field.humanized_decimals\n template += ' ' if field.spaced_display else ''\n template += utils.POWERS[power]\n template += '{!s:s}'\n return template.format(value, field.unit)","def _display_unit(unit):\r\n name = getattr(unit, 'display_name', None)\r\n if name:\r\n return u'{0} ({1})'.format(name, unit.location.to_deprecated_string())\r\n else:\r\n return unit.location.to_deprecated_string()","def _uppLim(self):\n if self.getResult(param='TS value')[0] >= self.tsmin:\n print(\"\\t=== TS value {} is above TSmin {}, no need to compute an upperlimit ===\"\n .format(self.getResult(param='TS value')[0], self.tsmin))\n return\n\n from UpperLimits import UpperLimits\n import UnbinnedAnalysis as UA\n \n like = UA.unbinnedAnalysis(evfile=self.outmktime, scfile=self.ft2, expmap=self.outexpmap,\n expcube=self.outltcube, irfs=self.irf, optimizer=\"NewMinuit\", srcmdl=self.model)\n like.fit(0)\n ul = UpperLimits(like)\n\n try:\n upp, norm=ul['TARGET'].bayesianUL(emin=self.emin, emax=self.emax, cl=0.95) \n except:\n upp = -1\n wf = open(self.outgtlike, 'a')\n wf.write(\"\\nUpper limit on source 'TARGET': {} ph/cm2/s.\".format(upp))\n wf.close()\n return","def _unit_ms(self):\n return (self.time_base / 1000.0) / 60.0","def units(self) -> Optional[pulumi.Input[Sequence[pulumi.Input['OneDashboardPageWidgetLineUnitArgs']]]]:\n return pulumi.get(self, \"units\")","def verticalScaleDecrease(self):\n scaleFac = float(self.qline4.text())\n self.qline4.setText(str(scaleFac / 2))\n self.model.refreshScreen()","def tranfer_unit(number):\n count = 0\n unit_name = \"\"\n if 2 ** 20 > number > 2 ** 10:\n unit_name = \"Kb\"\n count = 1\n elif 2 ** 30 > number > 2 ** 20:\n unit_name = \"Mb\"\n count = 2\n elif number > 2 ** 30:\n unit_name = \"Gb\"\n count = 3\n else:\n unit_name = \"b\"\n if count != 0:\n unit_number = round(number / ((2 ** 10) ** count), 2)\n else:\n unit_number = round(number, 2)\n unit_str = \"{num}{name}\".format(num=unit_number, name=unit_name)\n return unit_str","def unit_of_measurement(self):\n return self._metadata[1]","def unit_of_measurement(self):\n return self._sensor_type.unit","def getUnits(self):\n\n return len(self.units)","def unitsofmeasure(self):\n return self._unitsofmeasure","def amount_ui(self) -> str:\n return \"{:,.2f}\".format(self.amount)","def totalUnits(self):\n\t\treturn self.units","def units(self) -> Optional[pulumi.Input[Sequence[pulumi.Input['OneDashboardPageWidgetStackedBarUnitArgs']]]]:\n return pulumi.get(self, \"units\")"],"string":"[\n \"def _get_display_size(self):\\n return '%d MB' % self.size if self.size < 1024 else '%.2f GB' % (self.size / 1024.0)\",\n \"def unit_of_measurement(self):\\n return \\\"%\\\"\",\n \"def unit_of_measurement(self):\\n return '%'\",\n \"def units(self):\\n pass\",\n \"def unit_of_measurement(self) -> str:\\n return \\\"%\\\"\",\n \"def unit_of_measurement(self) -> str:\\n return \\\"%\\\"\",\n \"def unit_number(self) -> int:\\n return self._unit * -1\",\n \"def unit(self) -> str:\",\n \"def set_hm_unit_display(self):\\r\\n units = str(self.entries['units'].combobox.currentText())\\r\\n self.ui.is_unitL1.setText(units)\\r\\n self.ui.is_unitL2.setText(units)\\r\\n self.ui.is_unitL3.setText(units)\\r\\n self.ui.is_unitL4.setText(units)\",\n \"def unitsize(self, unit):\\n\\t\\treturn self._unitsize[unit]\",\n \"def unit_of_measurement(self):\\n return self.display_currency\",\n \"def unit_of_measurement(self) -> str:\\n return self._unit\",\n \"def getUmidadeArMax(self):\\n return str(self.getWeather('umid-max')[:2]) + '%'\",\n \"def __human_limit(limit, used):\\n if limit == 0: return \\\"none\\\"\\n\\n percentage = str(int(used*100/limit)) + \\\"%\\\"\\n percentage = \\\" \\\"*(3-len(percentage)) + percentage\\n return __human_size(limit, False) + \\\" (\\\" + percentage + \\\")\\\"\",\n \"def unit_of_measurement(self) -> str:\\n return self._unit_of_measurement\",\n \"def unit_of_measurement(self):\\n return LENGTH_METERS\",\n \"def unit_of_measurement(self):\\n return None\",\n \"def limit_bandwidth_unit(self):\\n return self._limit_bandwidth_unit\",\n \"def unit(self):\\n return self._unit\",\n \"def get_unit(self):\\n return self.unit\",\n \"def unit_of_measure(self):\\n try:\\n uom = self.metadata['geosoft']['dataset']['geo:unitofmeasurement']['#text']\\n except KeyError:\\n uom = ''\\n return uom\",\n \"def unit_of_measurement(self):\\n return self._unit\",\n \"def unit_of_measurement(self):\\n return self._unit\",\n \"def unit_of_measurement(self):\\n return self._unit\",\n \"def unit_of_measurement(self):\\n return self._unit\",\n \"def unit_of_measurement(self):\\n return self._unit\",\n \"def unit_of_measurement(self):\\n return self._unit\",\n \"def unit_of_measurement(self):\\n return self._unit\",\n \"def unit_of_measurement(self):\\n return 'shows'\",\n \"def unit_of_measurement(self):\\n return self.device.unit()\",\n \"def _get_display_duration(self):\\n duration = self.duration\\n return '%dmn' % duration if duration < 60 else '%dh%dmn' % (duration / 60, duration % 60)\",\n \"def unit(self):\\n return self.__unit\",\n \"def unit_of_measurement(self):\\n return self._var_unit\",\n \"def unit_of_measurement(self):\\n return self._var_unit\",\n \"def upperLimit(self):\\r\\n if len(self.elements) > 0:\\r\\n return self.elements[-1].upperLimit\\r\\n else:\\r\\n raise KeyError('No elements in Computation object')\",\n \"def unit(self):\\n return self._unit\",\n \"def unit(self):\\n return self._unit\",\n \"def unit_of_measurement(self) -> Any:\\n return PERCENTAGE\",\n \"def unit(cls, val):\\n raise NotImplementedError\",\n \"def trim_display_field(self, value, max_length):\\n if not value:\\n return ''\\n if len(value) > max_length:\\n return value[:max_length - 3] + '...'\\n return value\",\n \"def useUnits():\",\n \"def unit_of_measurement(self) -> str:\\n return MS\",\n \"def unit_of_measurement(self):\\n if self._xfinity_data.unit is not None:\\n return self._xfinity_data.unit\",\n \"def get_printable_size(self):\\n size = self.size\\n prefix = ''\\n for (s, l) in [(1024*1024*1024, 'GB'), (1024*1024, 'MB'), (1024, 'KB')]:\\n if (size>s):\\n size = float(size)/s\\n prefix = l\\n\\n return '%10.2f %s' % (size, prefix)\",\n \"def get_units(self):\\n return str(self._modeler.GetModelUnits())\",\n \"def unit_of_measurement(self):\\n return self._unit_of_measurement\",\n \"def unit_of_measurement(self):\\n return self._unit_of_measurement\",\n \"def unit_of_measurement(self):\\n return self._unit_of_measurement\",\n \"def unit_of_measurement(self):\\n return self._unit_of_measurement\",\n \"def unit_of_measurement(self):\\n return self._unit_of_measurement\",\n \"def unit_of_measurement(self):\\n return self._unit_of_measurement\",\n \"def unit_of_measurement(self):\\n return self._unit_of_measurement\",\n \"def unit_of_measurement(self):\\n return self._unit_of_measurement\",\n \"def unit_of_measurement(self):\\n return self._unit_of_measurement\",\n \"def unit_of_measurement(self):\\n return self._unit_of_measurement\",\n \"def unit_of_measurement(self):\\n return self._unit_of_measurement\",\n \"def unit_of_measurement(self):\\n return self._unit_of_measurement\",\n \"def unit_of_measurement(self):\\n return self._unit_of_measurement\",\n \"def unit_of_measurement(self):\\n return self._unit_of_measurement\",\n \"def upperLimit(lenFact1):\\n\\tfact2 = lenFact1*fact9\\n\\tlenfact2 = len(str(fact2))\\n\\tmaxFact = lenfact2*fact9\\n\\tlenMax = len(str(maxFact))\\n\\tif lenMax == lenfact2: \\n\\t\\treturn maxFact\\n\\telse:\\n\\t\\treturn upperLimit(lenMax)\",\n \"def unit_of_measurement(self):\\n return self._tasmota_entity.unit\",\n \"def set_unit(self,unit):\\n self.unit = unit\",\n \"def clamped(self, magnitude):\\n return self.unit * magnitude\",\n \"def units(self) -> Optional[pulumi.Input[Sequence[pulumi.Input['OneDashboardPageWidgetHeatmapUnitArgs']]]]:\\n return pulumi.get(self, \\\"units\\\")\",\n \"def set_units(self, units):\\n self.units = units\",\n \"def calc_unit_factor(self, ui_unit):\\n # namedView = self.document.getroot().find(inkex.addNS('namedview', 'sodipodi'))\\n # doc_units = self.getunittouu(str(1.0) + namedView.get(inkex.addNS('document-units', 'inkscape')))\\n unit_factor = self.getunittouu(str(1.0) + ui_unit)\\n return unit_factor\",\n \"def unit_of_measurement(self):\\n return self.var_units\",\n \"def to_units(number):\\n unit = 0\\n while number >= 1024.:\\n unit += 1\\n number = number / 1024.\\n if unit == len(UNITS) - 1:\\n break\\n if unit:\\n return '%.2f%s' % (number, UNITS[unit])\\n return '%d' % number\",\n \"def unit_of_measurement(self):\\n return self._sensor.unit\",\n \"def unit_of_measurement(self):\\n return self.sensor_type[\\\"unit\\\"]\",\n \"def units(self) -> Optional[pulumi.Input[Sequence[pulumi.Input['OneDashboardPageWidgetBarUnitArgs']]]]:\\n return pulumi.get(self, \\\"units\\\")\",\n \"def humanvalue(self, value):\\n if value > 1024 * 1024 * 1024:\\n return \\\"%d\\\" % (value / 1024 / 1024 / 1024 / 1024)\\n if value > 1024 * 1024:\\n return \\\"%d\\\" % (value / 1024 / 1024)\\n if value > 1024:\\n return \\\"%d\\\" % (value / 1024 / 1024)\",\n \"def unit_of_measurement(self):\\n\\n if self._sensor_class == DEVICE_CLASS_TEMPERATURE:\\n return TEMP_CELSIUS\\n\\n elif self._sensor_class == DEVICE_CLASS_HUMIDITY:\\n return '%'\",\n \"def humanize_size_range(size_from, size_to, unit='ft'):\\n size_from = int(size_from) if size_from else 0\\n size_to = int(size_to) if size_to else 0\\n\\n if size_to == 99:\\n return str(_('from {size_from} {unit}')).format(size_from=size_from, unit=unit)\\n\\n if size_from == 0:\\n return str(_('up to {size_to} {unit}')).format(size_to=size_to, unit=unit)\\n\\n return str(_('{size_from}-{size_to} {unit}')).format(\\n size_from=size_from, size_to=size_to, unit=unit\\n )\",\n \"def upper_bound(self) -> float:\\n ...\",\n \"def units(self) -> Optional[pulumi.Input[Sequence[pulumi.Input['OneDashboardPageWidgetMarkdownUnitArgs']]]]:\\n return pulumi.get(self, \\\"units\\\")\",\n \"def units(self):\\n return self._units\",\n \"def units(self):\\n return self._units\",\n \"def quantity_unit(self) -> str:\\n return self.__quantity_unit\",\n \"def upper_earning_limit(self):\\n\\t\\treturn self._upper_earning_limit\",\n \"def unit_of_measurement(self):\\n return self.values.primary.units\",\n \"def units(self) -> Optional[pulumi.Input[Sequence[pulumi.Input['OneDashboardPageWidgetBulletUnitArgs']]]]:\\n return pulumi.get(self, \\\"units\\\")\",\n \"def units(self) -> Optional[pulumi.Input[Sequence[pulumi.Input['OneDashboardPageWidgetPyUnitArgs']]]]:\\n return pulumi.get(self, \\\"units\\\")\",\n \"def units(self) -> Optional[pulumi.Input[Sequence[pulumi.Input['OneDashboardPageWidgetAreaUnitArgs']]]]:\\n return pulumi.get(self, \\\"units\\\")\",\n \"def native_unit_of_measurement(self) -> str:\\n return f\\\"{CURRENCY_CENT}/{UnitOfVolume.LITERS}\\\"\",\n \"def units(self):\\n return self.__class__.get_setting_units(self.key, **self.get_kwargs())\",\n \"def _get_FIELD_humanized_display(self, field):\\n value = getattr(self, field.attname)\\n if value is None:\\n return\\n power = max([i for i in utils.POWERS if value // i > 0 and i > 1])\\n value /= power\\n template = ''\\n template += '{:.%sf}' % field.humanized_decimals\\n template += ' ' if field.spaced_display else ''\\n template += utils.POWERS[power]\\n template += '{!s:s}'\\n return template.format(value, field.unit)\",\n \"def _display_unit(unit):\\r\\n name = getattr(unit, 'display_name', None)\\r\\n if name:\\r\\n return u'{0} ({1})'.format(name, unit.location.to_deprecated_string())\\r\\n else:\\r\\n return unit.location.to_deprecated_string()\",\n \"def _uppLim(self):\\n if self.getResult(param='TS value')[0] >= self.tsmin:\\n print(\\\"\\\\t=== TS value {} is above TSmin {}, no need to compute an upperlimit ===\\\"\\n .format(self.getResult(param='TS value')[0], self.tsmin))\\n return\\n\\n from UpperLimits import UpperLimits\\n import UnbinnedAnalysis as UA\\n \\n like = UA.unbinnedAnalysis(evfile=self.outmktime, scfile=self.ft2, expmap=self.outexpmap,\\n expcube=self.outltcube, irfs=self.irf, optimizer=\\\"NewMinuit\\\", srcmdl=self.model)\\n like.fit(0)\\n ul = UpperLimits(like)\\n\\n try:\\n upp, norm=ul['TARGET'].bayesianUL(emin=self.emin, emax=self.emax, cl=0.95) \\n except:\\n upp = -1\\n wf = open(self.outgtlike, 'a')\\n wf.write(\\\"\\\\nUpper limit on source 'TARGET': {} ph/cm2/s.\\\".format(upp))\\n wf.close()\\n return\",\n \"def _unit_ms(self):\\n return (self.time_base / 1000.0) / 60.0\",\n \"def units(self) -> Optional[pulumi.Input[Sequence[pulumi.Input['OneDashboardPageWidgetLineUnitArgs']]]]:\\n return pulumi.get(self, \\\"units\\\")\",\n \"def verticalScaleDecrease(self):\\n scaleFac = float(self.qline4.text())\\n self.qline4.setText(str(scaleFac / 2))\\n self.model.refreshScreen()\",\n \"def tranfer_unit(number):\\n count = 0\\n unit_name = \\\"\\\"\\n if 2 ** 20 > number > 2 ** 10:\\n unit_name = \\\"Kb\\\"\\n count = 1\\n elif 2 ** 30 > number > 2 ** 20:\\n unit_name = \\\"Mb\\\"\\n count = 2\\n elif number > 2 ** 30:\\n unit_name = \\\"Gb\\\"\\n count = 3\\n else:\\n unit_name = \\\"b\\\"\\n if count != 0:\\n unit_number = round(number / ((2 ** 10) ** count), 2)\\n else:\\n unit_number = round(number, 2)\\n unit_str = \\\"{num}{name}\\\".format(num=unit_number, name=unit_name)\\n return unit_str\",\n \"def unit_of_measurement(self):\\n return self._metadata[1]\",\n \"def unit_of_measurement(self):\\n return self._sensor_type.unit\",\n \"def getUnits(self):\\n\\n return len(self.units)\",\n \"def unitsofmeasure(self):\\n return self._unitsofmeasure\",\n \"def amount_ui(self) -> str:\\n return \\\"{:,.2f}\\\".format(self.amount)\",\n \"def totalUnits(self):\\n\\t\\treturn self.units\",\n \"def units(self) -> Optional[pulumi.Input[Sequence[pulumi.Input['OneDashboardPageWidgetStackedBarUnitArgs']]]]:\\n return pulumi.get(self, \\\"units\\\")\"\n]","isConversational":false},"negative_scores":{"kind":"list like","value":["0.70054436","0.6769809","0.6708141","0.6669131","0.65528774","0.65528774","0.65095055","0.6420288","0.6403162","0.63651204","0.63568985","0.6297604","0.62844765","0.62185395","0.6201521","0.6183936","0.61839217","0.6120024","0.611906","0.6106219","0.6085906","0.6050985","0.6050985","0.6050985","0.6050985","0.6050985","0.6050985","0.6050985","0.6025021","0.6021392","0.5994075","0.5992875","0.59557897","0.59557897","0.5951165","0.59505945","0.59505945","0.5946959","0.5932994","0.59230816","0.5898791","0.58774036","0.58695734","0.58638823","0.58606386","0.584905","0.584905","0.584905","0.584905","0.584905","0.584905","0.584905","0.584905","0.584905","0.584905","0.584905","0.584905","0.584905","0.584905","0.584331","0.5842159","0.5837492","0.58371645","0.5831562","0.5826635","0.58111453","0.5808725","0.57997507","0.5798771","0.57709026","0.57700497","0.5759791","0.57588667","0.57583135","0.5758064","0.57546365","0.57449496","0.57449496","0.5739421","0.5736253","0.5726165","0.57185125","0.57077706","0.57046264","0.56969005","0.5695315","0.5693338","0.56870675","0.5674706","0.5665489","0.56636554","0.5662524","0.5649793","0.5648276","0.56465125","0.56462","0.56410384","0.56304675","0.5629709","0.56233585"],"string":"[\n \"0.70054436\",\n \"0.6769809\",\n \"0.6708141\",\n \"0.6669131\",\n \"0.65528774\",\n \"0.65528774\",\n \"0.65095055\",\n \"0.6420288\",\n \"0.6403162\",\n \"0.63651204\",\n \"0.63568985\",\n \"0.6297604\",\n \"0.62844765\",\n \"0.62185395\",\n \"0.6201521\",\n \"0.6183936\",\n \"0.61839217\",\n \"0.6120024\",\n \"0.611906\",\n \"0.6106219\",\n \"0.6085906\",\n \"0.6050985\",\n \"0.6050985\",\n \"0.6050985\",\n \"0.6050985\",\n \"0.6050985\",\n \"0.6050985\",\n \"0.6050985\",\n \"0.6025021\",\n \"0.6021392\",\n \"0.5994075\",\n \"0.5992875\",\n \"0.59557897\",\n \"0.59557897\",\n \"0.5951165\",\n \"0.59505945\",\n \"0.59505945\",\n \"0.5946959\",\n \"0.5932994\",\n \"0.59230816\",\n \"0.5898791\",\n \"0.58774036\",\n \"0.58695734\",\n \"0.58638823\",\n \"0.58606386\",\n \"0.584905\",\n \"0.584905\",\n \"0.584905\",\n \"0.584905\",\n \"0.584905\",\n \"0.584905\",\n \"0.584905\",\n \"0.584905\",\n \"0.584905\",\n \"0.584905\",\n \"0.584905\",\n \"0.584905\",\n \"0.584905\",\n \"0.584905\",\n \"0.584331\",\n \"0.5842159\",\n \"0.5837492\",\n \"0.58371645\",\n \"0.5831562\",\n \"0.5826635\",\n \"0.58111453\",\n \"0.5808725\",\n \"0.57997507\",\n \"0.5798771\",\n \"0.57709026\",\n \"0.57700497\",\n \"0.5759791\",\n \"0.57588667\",\n \"0.57583135\",\n \"0.5758064\",\n \"0.57546365\",\n \"0.57449496\",\n \"0.57449496\",\n \"0.5739421\",\n \"0.5736253\",\n \"0.5726165\",\n \"0.57185125\",\n \"0.57077706\",\n \"0.57046264\",\n \"0.56969005\",\n \"0.5695315\",\n \"0.5693338\",\n \"0.56870675\",\n \"0.5674706\",\n \"0.5665489\",\n \"0.56636554\",\n \"0.5662524\",\n \"0.5649793\",\n \"0.5648276\",\n \"0.56465125\",\n \"0.56462\",\n \"0.56410384\",\n \"0.56304675\",\n \"0.5629709\",\n \"0.56233585\"\n]","isConversational":false},"document_score":{"kind":"string","value":"0.7146413"},"document_rank":{"kind":"string","value":"0"}}},{"rowIdx":82398,"cells":{"query":{"kind":"string","value":"Restricts histogram range according to changed parameter range if needed. Checks histogram range against parameter limits."},"document":{"kind":"string","value":"def updateRange(self):\n if self.autoFollow:\n self.xrange = self.param.activeRange()\n self.xrange = self.xrange # call getter & setter again to verify limits"},"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 autoHistogramRange(self):\n self.vb.enableAutoRange(self.vb.XAxis, True)\n self.vb.enableAutoRange(self.vb.YAxis, True)\n # self.range = None\n # self.updateRange()\n # self.vb.setMouseEnabled(False, False)\n\n # def updateRange(self):\n # self.vb.autoRange()\n # if self.range is not None:\n # self.vb.setYRange(*self.range)\n # vr = self.vb.viewRect()\n\n # self.region.setBounds([vr.top(), vr.bottom()])","def test_range_argument_ignored(self):\n bins_range = (1, 2)\n\n bin_edges, hist, _, _ = hist_w_unc(\n self.input,\n bins=self.bin_edges,\n bins_range=bins_range,\n normed=False,\n )\n\n # check if we end up with the same bin edges anyway\n np.testing.assert_array_almost_equal(self.bin_edges, bin_edges)\n np.testing.assert_array_almost_equal(self.hist, hist)","def setHistogramRange(self, mn, mx, padding=0.1):\n self.vb.enableAutoRange(self.vb.YAxis, False)\n if self.orientation == 'horizontal':\n self.vb.setXRange(mn, mx, padding)\n elif self.orientation == 'vertical':\n self.vb.setYrange(mn, mx, padding)\n # mn -= d*padding\n # mx += d*padding\n # self.range = [mn,mx]\n # self.updateRange()\n # self.vb.setMouseEnabled(False, True)\n # self.region.setBounds([mn,mx])","def test_range_argument(self):\n # we test with range from 0 to 2, with 3 bins -> [0, 0.66, 1.33, 2] exp. bins\n bins_range = (0, 2)\n bins_exp = np.array([0, 2 / 3, 1 + 1 / 3, 2])\n hist_exp = np.array([1, 2, 0])\n\n bin_edges, hist, _, _ = hist_w_unc(\n self.input,\n bins=self.n_bins,\n bins_range=bins_range,\n normed=False,\n )\n\n # check if we end up with the same bin edges anyway\n np.testing.assert_array_almost_equal(bins_exp, bin_edges)\n np.testing.assert_array_almost_equal(hist_exp, hist)","def update_bins_to_view(self, *args):\n with delay_callback(self, 'hist_x_min', 'hist_x_max'):\n if self.x_max > self.x_min:\n self.hist_x_min = self.x_min\n self.hist_x_max = self.x_max\n else:\n self.hist_x_min = self.x_max\n self.hist_x_max = self.x_min","def update_bins_to_view(self, *args):\n with delay_callback(self, 'hist_x_min', 'hist_x_max'):\n if self.x_max > self.x_min:\n self.hist_x_min = self.x_min\n self.hist_x_max = self.x_max\n else:\n self.hist_x_min = self.x_max\n self.hist_x_max = self.x_min","def _find_limits(hist, treated, untreated):\n # Treated Sample\n # Set the lowest frequency observed in the treated subsample\n # as the default for the lower limit of the common support\n lower_limit = np.min(treated)\n\n # The following algorithm checks for any empty histogram bins\n # (starting from 0 going up to 0.5).\n # If an empty histogram bin is found, the lower_limit is set to\n # the corresponding P(Z) value of the next bin above.\n for low in range(len(hist[0][0])):\n\n # Only consider values in the interval [0, 0.5)\n if hist[1][low] > 0.5:\n break\n\n # If the algorithm starts below the sample minimum,\n # move on to the next bin\n elif hist[1][low] < np.min(treated):\n continue\n\n else:\n # If the current bin is non-empty, we have still continuous\n # support and the sample minimum remains our lower limit\n if hist[0][0][low] > 0:\n pass\n\n # If an empty bin is found, set the lower limit to the next bin above\n # and move on to the next bin until P(Z) = 0.5 is reached\n else:\n lower_limit = hist[1][low + 1]\n\n # Untreated Sample\n # Set the highest frequency observed in the untreated subsample\n # as the default for the upper limit of the common support\n upper_limit = np.max(untreated)\n\n # The following algorithm checks for any empty histogram bins\n # (starting from 1 going down to 0.5).\n # If an empty histogram bin is found, the upper_limit is set to the\n # current next bin.\n for up in reversed(range(len(hist[0][1]))):\n\n # Only consider values in the interval (0.5, 1]\n if hist[1][up] < 0.5:\n break\n\n # If the algorithm starts above the sample maximum, move on to the next bin\n elif hist[1][up] > np.max(untreated):\n continue\n\n else:\n # If the current bin is non-empty, we have still continuous support and\n # the sample maximum remains our upper limit\n if hist[0][1][up] > 0:\n pass\n\n # If an empty bin is found, set the upper limit to the next bin below\n # and move on to the next bin until P(Z) = 0.5 is reached\n else:\n upper_limit = hist[1][up]\n\n return lower_limit, upper_limit","def check_binning_parameter_range(x_min, x_max, ws_unit):\n if ws_unit == 'dSpacing' and not 0 < x_min < x_max < 20:\n # dspacing within (0, 20)\n x_range_is_wrong = True\n elif ws_unit == 'TOF' and not 1000 < x_min < x_max < 1000000:\n # TOF within (1000, 1000000)\n x_range_is_wrong = True\n elif ws_unit != 'dSpacing' and ws_unit != 'TOF':\n raise NotImplementedError('Impossible case for unit {}'.format(ws_unit))\n else:\n # good cases\n x_range_is_wrong = False\n\n if x_range_is_wrong:\n ero_msg = 'For {0}, X range ({1}, {2}) does not make sense' \\\n ''.format(ws_unit, x_min, x_max)\n print('[ERROR CAUSING CRASH] {}'.format(ero_msg))\n raise RuntimeError(ero_msg)\n\n return","def update_view_to_bins(self, *args):\n with delay_callback(self, 'x_min', 'x_max'):\n self.x_min = self.hist_x_min\n self.x_max = self.hist_x_max","def update_view_to_bins(self, *args):\n with delay_callback(self, 'x_min', 'x_max'):\n self.x_min = self.hist_x_min\n self.x_max = self.hist_x_max","def set_range(self, **rangekwargs):\n\n if 'xrange' in rangekwargs.keys(): \n xrange = rangekwargs['xrange']\n else: \n xrange = [-50.0, 50.0] # (default)\n\n if 'yrange' in rangekwargs.keys(): \n yrange = rangekwargs['yrange']\n else: \n yrange = [0.0, 1.25 * self.hist_max]\n\n self.sub.set_xlim(xrange) \n self.sub.set_ylim(yrange) \n\n self.sub.set_xlabel(r\"$\\mathtt{d_{LOS}}$ (Mpc/h)\", fontsize=20)\n\n return None","def lhistogram (inlist,numbins=10,defaultreallimits=None,printextras=0):\r\n if (defaultreallimits <> None):\r\n if type(defaultreallimits) not in [ListType,TupleType] or len(defaultreallimits)==1: # only one limit given, assumed to be lower one & upper is calc'd\r\n lowerreallimit = defaultreallimits\r\n upperreallimit = 1.000001 * max(inlist)\r\n else: # assume both limits given\r\n lowerreallimit = defaultreallimits[0]\r\n upperreallimit = defaultreallimits[1]\r\n binsize = (upperreallimit-lowerreallimit)/float(numbins)\r\n else: # no limits given for histogram, both must be calc'd\r\n estbinwidth=(max(inlist)-min(inlist))/float(numbins) +1e-6 #1=>cover all\r\n binsize = ((max(inlist)-min(inlist)+estbinwidth))/float(numbins)\r\n lowerreallimit = min(inlist) - binsize/2 #lower real limit,1st bin\r\n bins = [0]*(numbins)\r\n extrapoints = 0\r\n for num in inlist:\r\n try:\r\n if (num-lowerreallimit) < 0:\r\n extrapoints = extrapoints + 1\r\n else:\r\n bintoincrement = int((num-lowerreallimit)/float(binsize))\r\n bins[bintoincrement] = bins[bintoincrement] + 1\r\n except:\r\n extrapoints = extrapoints + 1\r\n if (extrapoints > 0 and printextras == 1):\r\n print '\\nPoints outside given histogram range =',extrapoints\r\n return (bins, lowerreallimit, binsize, extrapoints)","def set_slider_bounds(self,lower,upper,inclusive_bounds=None):\n self.bounds = (lower,upper)\n\n if inclusive_bounds is not None:\n self.inclusive_bounds = inclusive_bounds\n\n epsilon = max(self.slider['resolution'],0.00000000001)\n\n if self.inclusive_bounds[0] is False:\n lower+=epsilon\n if self.inclusive_bounds[1] is False:\n upper-=epsilon\n self.slider.config(from_=lower,to=upper)","def _set_axes_limits(ax, parameter, axis=\"x\"):\n\n lims = list(ax.get_xlim()) if axis == \"x\" else list(ax.get_ylim())\n\n if \"low\" in DEFAULT_BOUNDS[parameter]:\n low = DEFAULT_BOUNDS[parameter][\"low\"]\n if lims[0] < low:\n lims[0] = DEFAULT_BOUNDS[parameter][\"low\"]\n if \"high\" in DEFAULT_BOUNDS[parameter]:\n high = DEFAULT_BOUNDS[parameter][\"high\"]\n if lims[1] > high:\n lims[1] = DEFAULT_BOUNDS[parameter][\"high\"]\n\n if axis == \"x\":\n ax.set_xlim(lims)\n else:\n ax.set_ylim(lims)","def __rangeChanged(self, first, second):\n tooltip = \"Histogram range:\\n[%g, %g]\" % (first, second)\n self.__rangeSlider.setToolTip(tooltip)\n self.__rangeLabel.setToolTip(tooltip)","def remove_invalid_values(self, lower_bound=float('-inf'), upper_bound=float('inf')) :\n valid_indices = (lower_bound < self.signal) * (self.signal < upper_bound)\n self.time_scale = self.time_scale[valid_indices]\n self.signal = self.signal[valid_indices]","def apply_bound(x, var_min, var_max):\n x.position = np.maximum(x.position, var_min)\n x.position = np.minimum(x.position, var_max)","def rangeLimit(val, minv, maxv):\n\treturn range_limit(val, minv, maxv)","def update_histo_frame():\n min_histo.text = str(MIN_RANGE_F) # Display the legend\n max_histo.text = str(MAX_RANGE_F)\n\n histogram = np.zeros(GRID_AXIS) # Clear histogram accumulation array\n # Collect camera data and calculate the histogram\n for _row in range(0, GRID_AXIS):\n for _col in range(0, GRID_AXIS):\n histo_index = int(map_range(GRID_DATA[_col, _row], 0, 1, 0, GRID_AXIS - 1))\n histogram[histo_index] = histogram[histo_index] + 1\n\n histo_scale = np.max(histogram) / (GRID_AXIS - 1)\n if histo_scale <= 0:\n histo_scale = 1\n\n # Display the histogram\n for _col in range(0, GRID_AXIS):\n for _row in range(0, GRID_AXIS):\n if histogram[_col] / histo_scale > GRID_AXIS - 1 - _row:\n image_group[((_row * GRID_AXIS) + _col)].fill = index_to_rgb(\n round((_col / GRID_AXIS), 3)\n )\n else:\n image_group[((_row * GRID_AXIS) + _col)].fill = BLACK","def _setBound(self, value):\n if self._colormap is not None:\n if self._index == 0:\n min_ = value\n max_ = self._colormap.getVMax()\n else: # self._index == 1\n min_ = self._colormap.getVMin()\n max_ = value\n\n if max_ is not None and min_ is not None and min_ > max_:\n min_, max_ = max_, min_\n self._colormap.setVRange(min_, max_)","def updateRanges(self):\n # work directly on the histograms, no copy\n for i in range(len(self)):\n self[i].updateRange()","def set_colormap_range(self):\n cmin = self.settingsWidget.ui.colormap_min\n cmax = self.settingsWidget.ui.colormap_max\n region = self.plot.getHistogramWidget().region\n\n if(self.sender() == region):\n cmin.setText(str(region.getRegion()[0]))\n cmax.setText(str(region.getRegion()[1]))\n return\n\n # Sometimes the values in the lineEdits are\n # not proper floats so we get ValueErrors\n try:\n # If necessary swap min and max\n if(float(cmin.text()) > float(cmax.text())):\n _tmp = cmin.text()\n cmin.setText(cmax.text())\n cmax.setText(_tmp)\n\n region = [float(cmin.text()), float(cmax.text())]\n self.plot.getHistogramWidget().region.setRegion(region)\n except ValueError:\n return","def set_output_limits(self, min_value, max_value):\n self.out_min = min_value\n self.out_max = max_value\n if self.out_min > self.out_max:\n print(\"set_output_limits(): min must be smaller than max.\")\n self.iterm = self.clip_to_output_limits(self.iterm)\n self.output = self.clip_to_output_limits(self.output)","def get_limits(hist, xaxis=True):\n if xaxis:\n return (hist.GetXaxis().GetXmin(), hist.GetXaxis().GetXmax())\n else:\n return (hist.GetMinimum(), hist.GetMaximum())","def _handleInput(self, paramInput):\n super()._handleInput(paramInput)\n if not self.upperBoundUsed or not self.lowerBoundUsed:\n self.raiseAnError(IOError,'the Uniform distribution needs both upperBound and lowerBound attributes. Got upperBound? '+ str(self.upperBoundUsed) + '. Got lowerBound? '+str(self.lowerBoundUsed))\n self.range = self.upperBound - self.lowerBound\n self.initializeDistribution()","def __check_range(self, range_type, name):\n\n # Before we really do anything, make sure a warning or critical \n # threshold were even set.\n\n self.__verbose_print('range was ' + range_type)\n\n range_list = self.__warning\n if range_type == 'critical':\n range_list = self.__critical\n\n if len(range_list) < 1:\n return\n\n threshold = self.__perf[name]['threshold']\n val = self.__perf[name]['val']\n\n # Even if the warning/critical thresholds are required, a user may\n # forget to set all necessary ranges. If the ranges are required,\n # exit with a complaint, otherwise just skip the range test.\n\n if len(range_list) < threshold:\n if range_type not in self.__req_option:\n return\n raise UserWarning (\"Please set part %s of the %s threshold!\" % (threshold, range_type))\n\n # The option parser should have already split these into proper\n # bottom, top, and match inversion, so long as the array element\n # is defined Perform our range test and set the exit status.\n\n self.__verbose_print(range_list[threshold-1])\n (bottom, top, invert, raw) = range_list[threshold-1]\n\n self.__perf[name][\"raw_%s\" % range_type] = raw\n\n if ((not invert and (val < bottom or val > top)) or\n (invert and val >= bottom and val <= top)):\n self.__exit_status = range_type\n self.__perf[name]['state'] = range_type\n\n self.__verbose_print(\"%s:%s:%s = %s\" % (val, bottom, top, ((val < bottom) or (val > top))))","def write_bounds(self):\n optimized_par_df = \\\n self.parameter_df.loc[self.parameter_df.estimate == 1\n & (~self.parameter_df.index.isin(\n self.amici_model.getFixedParameterIds())), :]\n self.f.require_dataset('/parameters/lowerBound',\n shape=optimized_par_df.lowerBound.shape,\n data=optimized_par_df.lowerBound, dtype='f8')\n self.f.require_dataset('/parameters/upperBound',\n shape=optimized_par_df.upperBound.shape,\n data=optimized_par_df.upperBound, dtype='f8')","def set_bounds(\n self: A,\n lower: BoundValue = None,\n upper: BoundValue = None,\n method: str = \"clipping\",\n full_range_sampling: bool = False,\n a_min: BoundValue = None,\n a_max: BoundValue = None,\n ) -> A: # TODO improve description of methods\n lower, upper = _a_min_max_deprecation(**locals())\n bounds = tuple(a if isinstance(a, np.ndarray) or a is None else np.array([a], dtype=float) for a in (lower, upper))\n both_bounds = all(b is not None for b in bounds)\n # preliminary checks\n if self.bound_transform is not None:\n raise RuntimeError(\"A bounding method has already been set\")\n if full_range_sampling and not both_bounds:\n raise ValueError(\"Cannot use full range sampling if both bounds are not set\")\n checker = BoundChecker(*bounds)\n if not checker(self.value):\n raise ValueError(\"Current value is not within bounds, please update it first\")\n if not (lower is None or upper is None):\n if (bounds[0] >= bounds[1]).any(): # type: ignore\n raise ValueError(f\"Lower bounds {lower} should be strictly smaller than upper bounds {upper}\")\n # update instance\n transforms = dict(clipping=trans.Clipping, arctan=trans.ArctanBound, tanh=trans.TanhBound)\n if method in transforms:\n if self.exponent is not None and method != \"clipping\":\n raise ValueError(f'Cannot use method \"{method}\" in logarithmic mode')\n self.bound_transform = transforms[method](*bounds)\n elif method == \"constraint\":\n self.register_cheap_constraint(checker)\n else:\n raise ValueError(f\"Unknown method {method}\")\n self.bounds = bounds # type: ignore\n self.full_range_sampling = full_range_sampling\n # warn if sigma is too large for range\n if both_bounds and method != \"tanh\": # tanh goes to infinity anyway\n std_bounds = tuple(self._to_reduced_space(b) for b in self.bounds) # type: ignore\n min_dist = np.min(np.abs(std_bounds[0] - std_bounds[1]).ravel())\n if min_dist < 3.0:\n warnings.warn(f\"Bounds are {min_dist} sigma away from each other at the closest, \"\n \"you should aim for at least 3 for better quality.\")\n return self","def check_ranges(self, param_name, param_value):\n\n if np.isscalar(param_value):\n if param_value < 0. or param_value > 1.:\n raise ValueError((param_name, ' should be within range [0,1]'))\n else:\n if param_name == 'zoom_range' or param_name == 'illumination_range':\n if param_value == 1:\n self.dict[param_name] = [1, 1]\n else:\n self.dict[param_name] = [1 - param_value, 1 + param_value]\n else:\n self.dict[param_name] = [0., param_value]\n\n elif len(param_value) == 2:\n\n if param_name != 'zoom_range' and param_name != 'illumination_range' \\\n and (param_value[0] < 0. or param_value[0] > 1. or param_value[1] < 0. or param_value[1] > 1.):\n raise ValueError((param_name, ' should be within range [0,1]'))\n else:\n self.dict[param_name] = [param_value[0], param_value[1]]\n else:\n raise ValueError((param_name, ' should be a float or '\n 'a tuple or list of two floats. '\n 'Received arg: '), param_value)","def __updateHistogramFromControls(self, *args):\n\n hist = self.getHistogram(copy=False)\n if hist is not None:\n count, edges = hist\n if (len(count) == self.__nbinsLineEdit.getValue() and\n (edges[0], edges[-1]) == self.__rangeSlider.getValues()):\n return # Nothing has changed\n\n self._updateFromItem()","def _setValidRange(self, contribs, valueRange):\n testfor(contribs.ndim == 2, ValueError)\n numContribs, numReps = contribs.shape\n self._validRange = np.zeros_like(contribs.T, dtype = bool)\n for ri in range(numReps):\n # the single set of R for this calculation\n rset = contribs[:, ri]\n self._validRange[ri] = ((rset > min(valueRange))\n * (rset < max(valueRange)))","def ahistogram (inarray,numbins=10,defaultlimits=None,printextras=1):\r\n inarray = N.ravel(inarray) # flatten any >1D arrays\r\n if (defaultlimits <> None):\r\n lowerreallimit = defaultlimits[0]\r\n upperreallimit = defaultlimits[1]\r\n binsize = (upperreallimit-lowerreallimit) / float(numbins)\r\n else:\r\n Min = N.minimum.reduce(inarray)\r\n Max = N.maximum.reduce(inarray)\r\n estbinwidth = float(Max - Min)/float(numbins) + 1e-6\r\n binsize = (Max-Min+estbinwidth)/float(numbins)\r\n lowerreallimit = Min - binsize/2.0 #lower real limit,1st bin\r\n bins = N.zeros(numbins)\r\n extrapoints = 0\r\n for num in inarray:\r\n try:\r\n if (num-lowerreallimit) < 0:\r\n extrapoints = extrapoints + 1\r\n else:\r\n bintoincrement = int((num-lowerreallimit) / float(binsize))\r\n bins[bintoincrement] = bins[bintoincrement] + 1\r\n except: # point outside lower/upper limits\r\n extrapoints = extrapoints + 1\r\n if (extrapoints > 0 and printextras == 1):\r\n print '\\nPoints outside given histogram range =',extrapoints\r\n return (bins, lowerreallimit, binsize, extrapoints)","def _check_paramrange(value, parameter):\n\n if parameter not in PARAMETER_RANGES.keys():\n raise ValueError('parameter {} not found in dictonary {}'\n .format(parameter, PARAMETER_RANGES))\n ranges = PARAMETER_RANGES[parameter]\n lo = ranges[0]\n hi = ranges[1]\n INRANGE = True\n if not (lo <= value < hi):\n INRANGE = False\n\n return INRANGE, lo, hi","def output_limits(self, limits):\n if limits is None:\n self._min_output, self._max_output = None, None\n return\n\n min_output, max_output = limits\n\n if None not in limits and max_output < min_output:\n raise ValueError('lower limit must be less than upper limit')\n\n self._min_output = min_output\n self._max_output = max_output\n\n self._integral = self._clamp(self._integral, self.output_limits)\n self._last_output = self._clamp(self._last_output, self.output_limits)","def _is_out_of_range(self, signal, y_range, threshold):\n out_of_range = [s for s in signal if s < y_range.min or s > y_range.max]\n out_of_range_percentage = len(out_of_range) / len(signal)\n\n return out_of_range_percentage > threshold","def cb_bounds(self, variable, results_dict, keys, fixed_bounds):\n tas_bound, pr_bound = fixed_bounds\n if variable == \"tas\":\n if tas_bound:\n bound_limit = tas_bound\n else:\n bound_limit = self.find_abs_bound_range(results_dict, keys)\n cmap = plt.cm.RdBu_r\n else:\n if pr_bound:\n bound_limit = pr_bound\n else:\n bound_limit = self.find_abs_bound_range(results_dict,\n keys,\n avg_over=25)\n cmap = plt.cm.BrBG\n bounds = np.linspace(-1 * bound_limit, bound_limit, 11)\n return [bounds, cmap]","def _changeDisplayRange(self):\n try:\n newrange = float(str(self._wmin.text())), float(str(self._wmax.text()))\n except ValueError:\n return\n self._rc.setDisplayRange(*newrange)","def updateThresholdValues (self, DoubleSlider, Node, ThMax ):\n DoubleSlider.Slider.minimum = 0\n DoubleSlider.SpinBoxL.setRange(0,ThMax)\n DoubleSlider.Slider.maximum = ThMax\n DoubleSlider.SpinBoxR.setRange(0,ThMax)\n if ThMax!=0:\n DisplayNode = Node.GetScalarVolumeDisplayNode()\n LowerThreshold = DisplayNode.GetLowerThreshold()\n UpperThreshold = DisplayNode.GetUpperThreshold()\n DoubleSlider.Slider.minimumValue = LowerThreshold\n DoubleSlider.Slider.maximumValue = UpperThreshold \n DoubleSlider.SpinBoxL.blockSignals(True)\n DoubleSlider.SpinBoxR.blockSignals(True)\n DoubleSlider.SpinBoxL.value = LowerThreshold\n DoubleSlider.SpinBoxR.value = UpperThreshold\n DoubleSlider.SpinBoxL.blockSignals(False)\n DoubleSlider.SpinBoxR.blockSignals(False)","def angle_limit_validation(self, lower_limit, upper_limit):\n self._angle_limit_validation(\n self.max_angle,\n self.min_angle,\n self.sample_count,\n lower_limit,\n upper_limit,\n self._name,\n )","def constrain(inputVal, lower_limit, upper_limit):\n \n if (inputVal < lower_limit):\n return lower_limit\n elif (inputVal > upper_limit):\n return upper_limit\n else:\n return inputVal","def bounds(x, xMin, xMax):\n if (x < xMin):\n x = xMin\n elif (x > xMax):\n x = xMax\n return(x)","def _bound(x, min_value, max_value):\n return np.maximum(min_value, np.minimum(x, max_value))","def initializeDistribution(self):\n self.minVal = min(math.exp(self.upperBound),math.exp(self.lowerBound))\n self.maxVal = max(math.exp(self.upperBound),math.exp(self.lowerBound))","def range_limit(val, minv, maxv):\n\tif (val < minv):\n\t\tval = minv\n\telif (val > maxv):\n\t\tval = maxv\n\treturn val","def SetHistogramParameters(self, numBins: 'int const', lowerBound: 'double', upperBound: 'double') -> \"void\":\n return _itkLabelStatisticsImageFilterPython.itkLabelStatisticsImageFilterIF2IUS2_SetHistogramParameters(self, numBins, lowerBound, upperBound)","def set_visualization_range(self, start: int, end: int):\n self.__range = (start, end)","def _setup(self, h: histogram.Histogram1D) -> Tuple[histogram.Histogram1D, pachyderm.fit.T_FitArguments]:\n fit_range = self.fit_options[\"range\"]\n # Restrict the range so that we only fit within the desired input.\n restricted_range = (h.x > fit_range.min) & (h.x < fit_range.max)\n restricted_hist = histogram.Histogram1D(\n # We need the bin edges to be inclusive.\n # Need the +/- epsilons here to be extra safe, because apparently some of the <= and >= can fail\n # (as might be guessed with floats, but I hadn't observed until now). We don't do this above\n # because we don't want to be inclusive on the edges.\n bin_edges = h.bin_edges[(h.bin_edges >= (fit_range.min - utils.epsilon)) & (h.bin_edges <= (fit_range.max + utils.epsilon))],\n y = h.y[restricted_range],\n errors_squared = h.errors_squared[restricted_range]\n )\n\n # Default arguments\n # Use the minimum of the histogram as the starting value.\n arguments: pachyderm.fit.T_FitArguments = {\n \"slope\": 0, \"error_slope\": 0.005,\n \"const\": 1, \"error_const\": 0.005,\n \"limit_slope\": (-100, 100),\n \"limit_const\": (-10, 10),\n }\n\n return restricted_hist, arguments","def test_threshold_range_b(self):\n code, out, err = self.t.runError(\"--threshold --min 3.2 --max 3.1\")\n self.assertIn(\"The max value must be higher than the min value.\", out)","def check_out_range(value, lim_1, lim_2):\n lo_lim = min(lim_1, lim_2)\n hi_lim = max(lim_1, lim_2)\n \n if (abs(value) > abs(hi_lim)) or (abs(value) < abs(lo_lim)):\n return True\n else:\n return False","def check_limits(data, tables, max_percent=1., replace_with='interpolation'):\n from . import trend as pmtrend\n import numpy as np\n from . import algs\n import pandas as pd\n\n df = data.copy()\n max_count = int(len(df)*max_percent/100.)\n low_count = pd.Series(0, index=tables.columns)\n upp_count = pd.Series(0, index=tables.columns)\n fault_count = pd.Series(0, index=tables.columns)\n\n #-----------\n # First we check the lower values\n if 'lower_limits' in tables.index.values:\n faulty = df < tables.loc['lower_limits']\n low_count = df[ faulty ].count() \n df[ faulty ] = np.nan\n #-------------------------------\n \n #-------------------------------\n # Now we check the upper values\n if 'upper_limits' in tables.index.values:\n faulty = df > tables.loc['upper_limits']\n upp_count = df[ faulty ].count() \n df[ faulty ] = np.nan\n #-------------------------------\n\n fault_count = low_count + upp_count\n valid = fault_count < max_count\n\n #------------\n # Replace data with either its trend or by interpolating\n if replace_with=='trend':\n trend = pmdata.trend(df, how='linear')\n df = df.fillna(trend)\n elif replace_with=='interpolation':\n df = df.interpolate(method='index', limit_direction='both')\n #------------\n\n #-------------------------------\n # Substitute faulty points by the linear trend\n #trend = data.polyfit()\n #df = df.fillna(trend)\n #-------------------------------\n\n return df, valid, fault_count","def view_limits(self, dmin, dmax):\n base = self._select_base(dmin, dmax)\n if mpl.rcParams['axes.autolimit_mode'] == 'round_numbers':\n vmin = base.le(dmin)\n vmax = base.ge(dmax)\n if vmin == vmax:\n vmin -= 1\n vmax += 1\n else:\n vmin = dmin\n vmax = dmax\n\n return mtransforms.nonsingular(vmin, vmax)","def _onSetParameterLower(self, value):\n self._parameters['lower'] = min(value, self._parameters['upper']) # Limit at upper\n self._logger.info(\"Parameter 'lower' of function '{}' changed to {}\".format(self._function, value))\n self.functionChanged.emit(self._dim, self._function, self._parameters.copy())","def setRange(self, x_range, y_range):\n pass","def bandpassFilter (self, lowerFreq, upperFreq):\n self.bandpassLimits = (lowerFreq, upperFreq)\n # stuff to do","def act_limits(self, act_limits):\n\n # Validate input\n missing_keys = [key for key in [\"low\", \"high\"] if key not in act_limits.keys()]\n if missing_keys:\n warn_string = \"WARN: act_limits could not be set as {} not found.\".format(\n f\"keys {missing_keys} were\"\n if len(missing_keys) > 1\n else f\"key {missing_keys} was\"\n )\n print(colorize(warn_string, \"yellow\"))\n invalid_length = [\n key for key, val in act_limits.items() if len(val) != self._a_dim\n ]\n if invalid_length:\n warn_string = (\n f\"WARN: act_limits could not be set as the length of {invalid_length} \"\n + \"{}\".format(\"were\" if len(invalid_length) > 1 else \"was\")\n + f\" unequal to the dimension of the action space (dim={self._a_dim}).\"\n )\n print(colorize(warn_string, \"yellow\"))\n\n # Set action limits\n self._act_limits = {\"low\": act_limits[\"low\"], \"high\": act_limits[\"high\"]}\n self.ga.act_limits = self._act_limits","def SetHistogramParameters(self, numBins: 'int const', lowerBound: 'double', upperBound: 'double') -> \"void\":\n return _itkLabelStatisticsImageFilterPython.itkLabelStatisticsImageFilterIF2ISS2_SetHistogramParameters(self, numBins, lowerBound, upperBound)","def validate_subset_of_schema(self, schema):\n super(NumericAttributeSchema, self).validate_subset_of_schema(schema)\n\n if self.range and (\n not schema.range\n or self.range[0] < schema.range[0]\n or self.range[1] > schema.range[1]\n ):\n raise AttributeSchemaError(\n \"Range %s is not a subset of %s\" % (self.range, schema.range)\n )","def test_returns_custom_range(self):\n img = np.arange(5)\n\n actual = util.apply_gamma(img, out_min=0, out_max=255, gamma=1)\n self.assertEqual(actual.min(), 0)\n self.assertEqual(actual.max(), 255)","def set_period_limits(self): # function[Tmin, Tmax] = setTlim(obj)\n\n x_min = self.tf.minimum_period\n x_max = self.tf.maximum_period\n\n Tmin = 10 ** (np.floor(np.log10(x_min) * 2) / 2)\n if (np.log10(x_min) - np.log10(Tmin)) < 0.15:\n Tmin = 10 ** (np.log10(Tmin) - 0.3)\n\n Tmax = 10 ** (np.ceil(np.log10(x_max) * 2) / 2)\n if (np.log10(Tmax) - np.log10(x_max)) < 0.15:\n Tmax = 10 ** (np.log10(Tmax) + 0.3)\n return Tmin, Tmax","def trim_range(self, low_bound, hi_bound, full_bound=True):\n low_bound_int = int(low_bound[:self.place+1])\n hi_bound_int = int(hi_bound[:self.place+1])\n\n # Remove keys outside of range\n # modifying dict during loop caused lots of problems - del after loop\n keys_to_del = []\n for key in self.Poss_Tree:\n if key < int(low_bound[:self.place]):\n keys_to_del.append(key)\n continue\n elif key > int(hi_bound[:self.place]):\n keys_to_del.append(key)\n continue\n for key in keys_to_del:\n del self.Poss_Tree[key]\n\n # Remove values outside of range\n vals_to_del = defaultdict(list)\n for key in self.Poss_Tree:\n for choice in self.Poss_Tree[key]:\n password = int(construct_pass(key, choice))\n if password > hi_bound_int or password < low_bound_int:\n vals_to_del[key].append(choice)\n for key in vals_to_del:\n for val in vals_to_del[key]:\n self.Poss_Tree[key].remove(val)","def SetHistogramParameters(self, numBins: 'int const', lowerBound: 'double', upperBound: 'double') -> \"void\":\n return _itkLabelStatisticsImageFilterPython.itkLabelStatisticsImageFilterIUS2IUS2_SetHistogramParameters(self, numBins, lowerBound, upperBound)","def set_limits_percent(self, percent=95):\n low = np.nanmin(self.datasource.data[\"values\"])\n high = np.nanmax(self.datasource.data[\"values\"])\n\n frac = percent / 100.0\n self.set_limits_minmax(low, high - (1.0 - frac) * (high - low))","def adjust_parameter_validator(self, param):\n if not isinstance(param.vals, Numbers):\n raise Exception('Only the Numbers validator is supported.')\n min_val = param.vals._min_value\n max_val = param.vals._max_value\n\n min_val_upd = self.round_dac(min_val, param.name)\n max_val_upd = self.round_dac(max_val, param.name)\n\n param.vals = Numbers(min_val_upd, max_val_upd)","def _withinRangeCheckerWrapper(self, args):\n\n constraints = args['constraints']\n\n def _withinRangeChecker(entity, params):\n \"\"\"Checks if certain properties are within given constrains. \n \"\"\"\n\n for constraint in constraints:\n type = constraint.get('type')\n field = constraint.get('field')\n\n if not type or not field:\n raise ProtocolError()\n\n min_value = constraint.get('min_value', 0)\n max_value = constraint.get('max_value', 1)\n\n if type == 'size':\n value = entity.__getattribute__(field)\n if len(value) < min_value or len(value) > max_value:\n return False\n else:\n raise ProtocolError()\n \n return True\n \n return _withinRangeChecker","def SetHistogramParameters(self, numBins: 'int const', lowerBound: 'double', upperBound: 'double') -> \"void\":\n return _itkLabelStatisticsImageFilterPython.itkLabelStatisticsImageFilterISS2IUS2_SetHistogramParameters(self, numBins, lowerBound, upperBound)","def SetHistogramParameters(self, numBins: 'int const', lowerBound: 'double', upperBound: 'double') -> \"void\":\n return _itkLabelStatisticsImageFilterPython.itkLabelStatisticsImageFilterIF2IUC2_SetHistogramParameters(self, numBins, lowerBound, upperBound)","def _verticalLimit_changed(self):\n self.masterContainer.range2d.y_range.high = self.verticalLimit","def pass_selection_val(self, val, val_min=None, val_max=None):\n if (val_min is not None) and (val_max is not None):\n return True if (val > val_min) and (val < val_max) else False\n elif (val_min is None) and (val_max is not None):\n # Check against val_max only. \n return True if (val < val_max) else False\n elif (val_min is not None) and (val_max is None):\n # Check against val_min only. \n return True if (val > val_min) else False\n else:\n msg = \"[WARNING] You performed a cut, but didn't specify any bounds.\"\n raise RuntimeWarning(msg)","def exclude_zero_bins(f,fitmin,fitmax,hs=[],limit=0,sumall=False):\n if len(hs)==0:\n return\n nbins = fitmax - fitmin + 1\n nexcl = 0\n exclist = []\n for ibin in xrange(fitmin,fitmax+1):\n do_exc = any( [h.GetBinContent(ibin)<limit for h in hs] ) if sumall==False else (sum([h.GetBinContent(ibin) for h in hs])<limit)\n if do_exc:\n f.ExcludeBin(ibin)\n exclist.append(ibin)\n nexcl += 1\n if nexcl>0:\n print 'WARNING: TFractionFitter excluded %d out of %d bins due to low statistics (nentries<%d)'%(nexcl,nbins,limit)\n print ' bins: ' + ' '.join([str(xx) for xx in exclist])\n else:\n print 'INFO: TFractionFitter does not need to exclude any bins'\n return True","def __check_args_val(self):\n if self.__min_range < 0:\n error_msg = \"min_range must be greater than or equal to zero\"\n raise ValueError(error_msg)\n elif self.__max_range < 0:\n error_msg = \"max_range must be greater than or equal to zero\"\n raise ValueError(error_msg)\n elif self.__max_range < self.__min_range:\n error_msg = \"max_range must be greater than or equal to min_range\"\n raise ValueError(error_msg)","def clip_range(x, xlim):\n return min([max([x, xlim[0]]), xlim[1]])","def py_apply_limits(self, plot):\n if any(x is not None for x in self.x_lim):\n if self.x_lim[0] is not None: # at least left?\n if self.x_lim[1] is not None: # left and right?\n plot.set_xlim(left=self.x_lim[0], right=self.x_lim[1])\n else:\n plot.set_xlim(left=self.x_lim[0])\n else: # just right\n plot.set_xlim(rigt=self.x_lim[1])\n if any(y is not None for y in self.y_lim):\n if self.y_lim[0] is not None: # at least bottom?\n if self.y_lim[1] is not None:\n plot.set_ylim(bottom=self.y_lim[0], top=self.y_lim[1])\n else:\n plot.set_ylim(bottom=self.y_lim[0])\n else:\n plot.set_ylim(top=self.y_lim[1])","def lim(cls, lower=PWM_MIN, upper=PWM_MAX, value=None, less_than_lower_default=None, greater_than_upper_default=None):\n #Sanitise inputs\n if less_than_lower_default is None:\n less_than_lower_default = lower\n if greater_than_upper_default is None:\n greater_than_upper_default = upper\n if not (less_than_lower_default >= lower and greater_than_upper_default <= upper):\n raise Exception(\"LEDStrip.lim(): Defaults %s,%s are not within %s - %s\" % (less_than_lower_default, greater_than_upper_default, lower, upper)) \n if value is None:\n return less_than_lower_default\n \n #Test values\n try:\n if value < lower:\n logging.warn(\" LEDStrip.lim(): Value %s is less than lower limit %s. Setting to %s.\" % (value, lower, less_than_lower_default))\n return float(less_than_lower_default)\n if value > upper:\n logging.warn(\" LEDStrip.lim(): Value %s is greater than upper limit %s. Setting to %s\" % (value, upper, greater_than_upper_default))\n return float(greater_than_upper_default)\n except (ValueError, TypeError, AttributeError):\n return float(less_than_lower_default)\n return float(value)","def adjust_dynamic_range(data, drange_in=(-1, 1), drange_out=(0, 1)):\r\n if drange_in != drange_out:\r\n scale = (np.float32(drange_out[1]) - np.float32(drange_out[0])) / (\r\n np.float32(drange_in[1]) - np.float32(drange_in[0]))\r\n bias = (np.float32(drange_out[0]) - np.float32(drange_in[0]) * scale)\r\n data = data * scale + bias\r\n return torch.clamp(data, min=0, max=1)","def collect_absolute_value(self, name_to_arr):\n for tensor, data_arr in name_to_arr.items():\n data_arr = np.asarray(data_arr) # noqa: PLW2901\n data_arr = data_arr.flatten() # noqa: PLW2901\n if data_arr.size > 0:\n min_value = np.min(data_arr)\n max_value = np.max(data_arr)\n else:\n min_value = 0\n max_value = 0\n\n data_arr = np.absolute(data_arr) # only consider absolute value # noqa: PLW2901\n\n if tensor not in self.histogram_dict:\n # first time it uses num_bins to compute histogram.\n hist, hist_edges = np.histogram(data_arr, bins=self.num_bins)\n self.histogram_dict[tensor] = (hist, hist_edges, min_value, max_value)\n else:\n old_histogram = self.histogram_dict[tensor]\n old_min = old_histogram[2]\n old_max = old_histogram[3]\n old_hist = old_histogram[0]\n old_hist_edges = old_histogram[1]\n temp_amax = np.max(data_arr)\n if temp_amax > old_hist_edges[-1]:\n # increase the number of bins\n width = old_hist_edges[1] - old_hist_edges[0]\n # NOTE: np.arange may create an extra bin after the one containing temp_amax\n new_bin_edges = np.arange(old_hist_edges[-1] + width, temp_amax + width, width)\n old_hist_edges = np.hstack((old_hist_edges, new_bin_edges))\n hist, hist_edges = np.histogram(data_arr, bins=old_hist_edges)\n hist[: len(old_hist)] += old_hist\n self.histogram_dict[tensor] = (hist, hist_edges, min(old_min, min_value), max(old_max, max_value))","def SetHistogramParameters(self, numBins: 'int const', lowerBound: 'double', upperBound: 'double') -> \"void\":\n return _itkLabelStatisticsImageFilterPython.itkLabelStatisticsImageFilterIUS2ISS2_SetHistogramParameters(self, numBins, lowerBound, upperBound)","def set_shader_range_ui(*args):\n shader_range = pm.floatField(\"f_shaderRange\", value=True, query=True)\n set_shader_range(shader_range)","def setlimits(self, Xlim=[], Ylim=[]):\n self.data['Xmin'] = Xlim[0]\n self.data['Xmax'] = Xlim[1]\n self.data['Ymin'] = Ylim[0]\n self.data['Ymax'] = Ylim[1]","def set_progress_range(self, maximum):\r\n\r\n pass","def set_bounds(self, **kwargs):\n for name, bounds in kwargs.items():\n if name not in self._parameters:\n raise AttributeError('Unknown parameter %s for %s' % (name, self.__class__.__name__))\n param = self._parameters[name]\n # Set bounds\n lower_bound, upper_bound = bounds\n if torch.is_tensor(lower_bound) and torch.is_tensor(upper_bound):\n if lower_bound.size() != upper_bound.size() or \\\n lower_bound.size() != param.size():\n raise AttributeError('Lower bound, upper bound, and param should have the same size')\n elif not (isinstance(lower_bound, int) or isinstance(lower_bound, float)) or \\\n not (isinstance(upper_bound, int) or isinstance(upper_bound, float)):\n raise AttributeError('Unsupported argument types for parameter %s' % name)\n\n if name not in self._bounds:\n self._bounds[name] = [None, None]\n self._bounds[name][0] = lower_bound\n self._bounds[name][1] = upper_bound\n return self","def SetHistogramParameters(self, numBins: 'int const', lowerBound: 'double', upperBound: 'double') -> \"void\":\n return _itkLabelStatisticsImageFilterPython.itkLabelStatisticsImageFilterISS2ISS2_SetHistogramParameters(self, numBins, lowerBound, upperBound)","def normalize_bounds(self, bounds):\n scaled_bounds = []\n scalings = []\n intercepts = []\n \n non_fixed_params = []\n \n print(self.device)\n \n for name, domain in self.bounds.items():\n # Get any fixed parmeters\n if type(domain) == int or type(domain) == float:\n # Take note\n self.fixed_parameters.append(name)\n\n # Free parameters\n elif type(domain) == tuple:\n # Bookkeeping\n self.free_parameters.append(name)\n\n # Get scaling\n lower_bound = min(domain)\n upper_bound = max(domain)\n scale = upper_bound - lower_bound\n\n # Transform to [0, 1] domain\n #scaled_bound = {'name': name, 'type': 'continuous', 'domain': (0., 1.)} #torch.adjustment required\n non_fixed_params.append(name)\n \n # Store\n #scaled_bounds.append(scaled_bound)\n scalings.append(scale)\n intercepts.append(lower_bound)\n else:\n raise ValueError(\"Domain bounds not understood\")\n \n n_hyperparams = len(non_fixed_params)\n \n scaled_bounds = cat([zeros(1,n_hyperparams, device = self.device), \n ones(1, n_hyperparams, device = self.device)], 0)\n return scaled_bounds, tensor(scalings, device = self.device, requires_grad = False), tensor(intercepts, device = self.device, requires_grad = False) #torch.adjustment required","def set_lim(x, y, **kws):\n per = kws['per']\n min_per = 50 - per/2\n max_per = per/2 + 50\n xper = np.nanpercentile(x,[min_per,max_per])\n yper = np.nanpercentile(y,[min_per,max_per])\n ax = plt.gca()\n ax.set_xlim(xper)\n ax.set_ylim(yper)","def test_threshold_range_a(self):\n code, out, err = self.t.runError(\"--threshold --max 3.1 --min 3.2\")\n self.assertIn(\"The min value must be lower than the max value.\", out)","def _init_optimizer_bounds(self):\n bounds = []\n for filt in self.filters:\n if filt.optimize_fc:\n bounds.append((np.log10(filt.min_fc), np.log10(filt.max_fc)))\n if filt.optimize_q:\n bounds.append((filt.min_q, filt.max_q))\n if filt.optimize_gain:\n bounds.append((filt.min_gain, filt.max_gain))\n return bounds","def ctrl_limit_changed(self, which, new_limit):\n super(PyDMSpinbox, self).ctrl_limit_changed(which, new_limit)\n if not self.userDefinedLimits:\n if which == \"UPPER\":\n self.setMaximum(new_limit)\n else:\n self.setMinimum(new_limit)","def histogram(hist_data, bins, minimum, maximum):\r\n hist_data2 = [x for x in hist_data if x <= maximum and x >= minimum]\r\n y, x = np.histogram(hist_data2, bins=bins)\r\n x_new = [((x[i] + x[i - 1]) / 2) for i in range(1, len(x))]\r\n max_freq = max(y)\r\n mean = x_new[y.argmax()]\r\n sigma = (x[1] - x[0]) * 4\r\n\r\n gauss = models.GaussianModel()\r\n fit = gauss.fit(y, x=x_new, center = mean, amplitude = max_freq, sigma = sigma, nan_policy= 'omit')\r\n\r\n return x, y, x_new, fit.best_fit","def _handleInput(self, paramInput):\n upperBound = paramInput.findFirst('upperBound')\n if upperBound !=None:\n self.upperBound = upperBound.value\n self.upperBoundUsed = True\n lowerBound = paramInput.findFirst('lowerBound')\n if lowerBound !=None:\n self.lowerBound = lowerBound.value\n self.lowerBoundUsed = True\n if self.lowerBoundUsed and self.upperBoundUsed:\n if self.lowerBound == self.upperBound:\n self.raiseAnError(IOError, 'Lower bound for Distribution \"'+self.name+'\" is equal to the upper bound!')\n if self.lowerBound > self.upperBound:\n self.raiseAnError(IOError, 'Lower bound for Distribution \"'+self.name+'\" is greater than the upper bound!')","def _normalize_range():\n clipped = tf.clip_by_value(inputs, self.minimum, self.maximum)\n return -1 + 2 * (clipped - self.minimum) / length","def clip(x, min, max):\r\n # see decorator for function body\r\n # for grep: clamp, bound\r","def histogram(a, bins=10, range=None, normed=False, weights=None, new=False):\n # Old behavior\n if new is False:\n warnings.warn(\"\"\"\n The semantics of histogram will be modified in\n release 1.2 to improve outlier handling. The new behavior can be\n obtained using new=True. Note that the new version accepts/returns\n the bin edges instead of the left bin edges.\n Please read the docstring for more information.\"\"\", FutureWarning)\n a = asarray(a).ravel()\n\n if (range is not None):\n mn, mx = range\n if (mn > mx):\n raise AttributeError, \\\n 'max must be larger than min in range parameter.'\n\n if not iterable(bins):\n if range is None:\n range = (a.min(), a.max())\n else:\n warnings.warn(\"\"\"\n Outliers handling will change in version 1.2.\n Please read the docstring for details.\"\"\", FutureWarning)\n mn, mx = [mi+0.0 for mi in range]\n if mn == mx:\n mn -= 0.5\n mx += 0.5\n bins = linspace(mn, mx, bins, endpoint=False)\n else:\n if normed:\n raise ValueError, 'Use new=True to pass bin edges explicitly.'\n warnings.warn(\"\"\"\n The semantic for bins will change in version 1.2.\n The bins will become the bin edges, instead of the left bin edges.\n \"\"\", FutureWarning)\n bins = asarray(bins)\n if (np.diff(bins) < 0).any():\n raise AttributeError, 'bins must increase monotonically.'\n\n\n if weights is not None:\n raise ValueError, 'weights are only available with new=True.'\n\n # best block size probably depends on processor cache size\n block = 65536\n n = sort(a[:block]).searchsorted(bins)\n for i in xrange(block, a.size, block):\n n += sort(a[i:i+block]).searchsorted(bins)\n n = concatenate([n, [len(a)]])\n n = n[1:]-n[:-1]\n\n if normed:\n db = bins[1] - bins[0]\n return 1.0/(a.size*db) * n, bins\n else:\n return n, bins\n\n\n\n # New behavior\n elif new is True:\n a = asarray(a)\n if weights is not None:\n weights = asarray(weights)\n if np.any(weights.shape != a.shape):\n raise ValueError, 'weights should have the same shape as a.'\n weights = weights.ravel()\n a = a.ravel()\n\n if (range is not None):\n mn, mx = range\n if (mn > mx):\n raise AttributeError, \\\n 'max must be larger than min in range parameter.'\n\n if not iterable(bins):\n if range is None:\n range = (a.min(), a.max())\n mn, mx = [mi+0.0 for mi in range]\n if mn == mx:\n mn -= 0.5\n mx += 0.5\n bins = linspace(mn, mx, bins+1, endpoint=True)\n else:\n bins = asarray(bins)\n if (np.diff(bins) < 0).any():\n raise AttributeError, 'bins must increase monotonically.'\n\n # Histogram is an integer or a float array depending on the weights.\n if weights is None:\n ntype = int\n else:\n ntype = weights.dtype\n n = np.zeros(bins.shape, ntype)\n\n block = 65536\n if weights is None:\n for i in arange(0, len(a), block):\n sa = sort(a[i:i+block])\n n += np.r_[sa.searchsorted(bins[:-1], 'left'), \\\n sa.searchsorted(bins[-1], 'right')]\n else:\n zero = array(0, dtype=ntype)\n for i in arange(0, len(a), block):\n tmp_a = a[i:i+block]\n tmp_w = weights[i:i+block]\n sorting_index = np.argsort(tmp_a)\n sa = tmp_a[sorting_index]\n sw = tmp_w[sorting_index]\n cw = np.concatenate(([zero,], sw.cumsum()))\n bin_index = np.r_[sa.searchsorted(bins[:-1], 'left'), \\\n sa.searchsorted(bins[-1], 'right')]\n n += cw[bin_index]\n\n n = np.diff(n)\n\n if normed is False:\n return n, bins\n elif normed is True:\n db = array(np.diff(bins), float)\n return n/(n*db).sum(), bins","def _update_limits(self):\n if self.pos_x > self.max_x:\n self.max_x = self.pos_x\n if self.pos_y > self.max_y:\n self.max_y = self.pos_y\n if self.pos_x < self.min_x:\n self.min_x = self.pos_x\n if self.pos_y < self.min_y:\n self.min_y = self.pos_y","def set_range(self, full_range, abs_range=None, suppress_feedback=False):\n self._enabled = full_range[0] != full_range[1]\n if not self._enabled:\n full_range = (\n full_range[0] - 1, full_range[0])\n self._suppress_feedback = suppress_feedback\n super(MarkerProperty, self).set_range(full_range, abs_range=abs_range)","def filter_range(self, name, field, start, end):\n self.__names[name] = {\n 'filter': {\n 'range': {\n field: {\n 'gt': start,\n 'lt': end\n }\n }\n }\n }\n self.__last_name = name\n return self","def norm_hist(self, hist_lower, hist_upper, lower, upper, value) :\n norm = 1.\n if upper>lower :\n norm = hist_lower.Integral()+(hist_upper.Integral()-hist_lower.Integral())/abs(upper-lower)*(value-lower)\n return norm","def hist_normalize_linear(data, new_min, new_max):\n data_min = np.ma.min(data)\n data_max = np.ma.max(data)\n scaled = (data - data_min) * ((new_max - new_min) / (data_max - data_min))\n scaled.mask = data.mask\n return scaled","def _get_colorbar_limits(self):\n if self.boundaries is not None:\n C = self.boundaries\n if self.extend in [\"min\", \"both\"]:\n C = C[1:]\n\n if self.extend in [\"max\", \"both\"]:\n C = C[:-1]\n return min(C), max(C)\n else:\n return self.get_clim()","def SetHistogramParameters(self, numBins: 'int const', lowerBound: 'double', upperBound: 'double') -> \"void\":\n return _itkLabelStatisticsImageFilterPython.itkLabelStatisticsImageFilterIUC2IUS2_SetHistogramParameters(self, numBins, lowerBound, upperBound)","def __validate(self, value: int, extend_range: bool):\n if extend_range:\n bottom, top = self.getRange()\n self.setRange(min(value, bottom), max(value, top))\n return numpy.clip(value, *self.getRange())","def limit(self, lim: float):\n if self.mag() > lim:\n self.values = tuple(self.norm()*lim)\n return self"],"string":"[\n \"def autoHistogramRange(self):\\n self.vb.enableAutoRange(self.vb.XAxis, True)\\n self.vb.enableAutoRange(self.vb.YAxis, True)\\n # self.range = None\\n # self.updateRange()\\n # self.vb.setMouseEnabled(False, False)\\n\\n # def updateRange(self):\\n # self.vb.autoRange()\\n # if self.range is not None:\\n # self.vb.setYRange(*self.range)\\n # vr = self.vb.viewRect()\\n\\n # self.region.setBounds([vr.top(), vr.bottom()])\",\n \"def test_range_argument_ignored(self):\\n bins_range = (1, 2)\\n\\n bin_edges, hist, _, _ = hist_w_unc(\\n self.input,\\n bins=self.bin_edges,\\n bins_range=bins_range,\\n normed=False,\\n )\\n\\n # check if we end up with the same bin edges anyway\\n np.testing.assert_array_almost_equal(self.bin_edges, bin_edges)\\n np.testing.assert_array_almost_equal(self.hist, hist)\",\n \"def setHistogramRange(self, mn, mx, padding=0.1):\\n self.vb.enableAutoRange(self.vb.YAxis, False)\\n if self.orientation == 'horizontal':\\n self.vb.setXRange(mn, mx, padding)\\n elif self.orientation == 'vertical':\\n self.vb.setYrange(mn, mx, padding)\\n # mn -= d*padding\\n # mx += d*padding\\n # self.range = [mn,mx]\\n # self.updateRange()\\n # self.vb.setMouseEnabled(False, True)\\n # self.region.setBounds([mn,mx])\",\n \"def test_range_argument(self):\\n # we test with range from 0 to 2, with 3 bins -> [0, 0.66, 1.33, 2] exp. bins\\n bins_range = (0, 2)\\n bins_exp = np.array([0, 2 / 3, 1 + 1 / 3, 2])\\n hist_exp = np.array([1, 2, 0])\\n\\n bin_edges, hist, _, _ = hist_w_unc(\\n self.input,\\n bins=self.n_bins,\\n bins_range=bins_range,\\n normed=False,\\n )\\n\\n # check if we end up with the same bin edges anyway\\n np.testing.assert_array_almost_equal(bins_exp, bin_edges)\\n np.testing.assert_array_almost_equal(hist_exp, hist)\",\n \"def update_bins_to_view(self, *args):\\n with delay_callback(self, 'hist_x_min', 'hist_x_max'):\\n if self.x_max > self.x_min:\\n self.hist_x_min = self.x_min\\n self.hist_x_max = self.x_max\\n else:\\n self.hist_x_min = self.x_max\\n self.hist_x_max = self.x_min\",\n \"def update_bins_to_view(self, *args):\\n with delay_callback(self, 'hist_x_min', 'hist_x_max'):\\n if self.x_max > self.x_min:\\n self.hist_x_min = self.x_min\\n self.hist_x_max = self.x_max\\n else:\\n self.hist_x_min = self.x_max\\n self.hist_x_max = self.x_min\",\n \"def _find_limits(hist, treated, untreated):\\n # Treated Sample\\n # Set the lowest frequency observed in the treated subsample\\n # as the default for the lower limit of the common support\\n lower_limit = np.min(treated)\\n\\n # The following algorithm checks for any empty histogram bins\\n # (starting from 0 going up to 0.5).\\n # If an empty histogram bin is found, the lower_limit is set to\\n # the corresponding P(Z) value of the next bin above.\\n for low in range(len(hist[0][0])):\\n\\n # Only consider values in the interval [0, 0.5)\\n if hist[1][low] > 0.5:\\n break\\n\\n # If the algorithm starts below the sample minimum,\\n # move on to the next bin\\n elif hist[1][low] < np.min(treated):\\n continue\\n\\n else:\\n # If the current bin is non-empty, we have still continuous\\n # support and the sample minimum remains our lower limit\\n if hist[0][0][low] > 0:\\n pass\\n\\n # If an empty bin is found, set the lower limit to the next bin above\\n # and move on to the next bin until P(Z) = 0.5 is reached\\n else:\\n lower_limit = hist[1][low + 1]\\n\\n # Untreated Sample\\n # Set the highest frequency observed in the untreated subsample\\n # as the default for the upper limit of the common support\\n upper_limit = np.max(untreated)\\n\\n # The following algorithm checks for any empty histogram bins\\n # (starting from 1 going down to 0.5).\\n # If an empty histogram bin is found, the upper_limit is set to the\\n # current next bin.\\n for up in reversed(range(len(hist[0][1]))):\\n\\n # Only consider values in the interval (0.5, 1]\\n if hist[1][up] < 0.5:\\n break\\n\\n # If the algorithm starts above the sample maximum, move on to the next bin\\n elif hist[1][up] > np.max(untreated):\\n continue\\n\\n else:\\n # If the current bin is non-empty, we have still continuous support and\\n # the sample maximum remains our upper limit\\n if hist[0][1][up] > 0:\\n pass\\n\\n # If an empty bin is found, set the upper limit to the next bin below\\n # and move on to the next bin until P(Z) = 0.5 is reached\\n else:\\n upper_limit = hist[1][up]\\n\\n return lower_limit, upper_limit\",\n \"def check_binning_parameter_range(x_min, x_max, ws_unit):\\n if ws_unit == 'dSpacing' and not 0 < x_min < x_max < 20:\\n # dspacing within (0, 20)\\n x_range_is_wrong = True\\n elif ws_unit == 'TOF' and not 1000 < x_min < x_max < 1000000:\\n # TOF within (1000, 1000000)\\n x_range_is_wrong = True\\n elif ws_unit != 'dSpacing' and ws_unit != 'TOF':\\n raise NotImplementedError('Impossible case for unit {}'.format(ws_unit))\\n else:\\n # good cases\\n x_range_is_wrong = False\\n\\n if x_range_is_wrong:\\n ero_msg = 'For {0}, X range ({1}, {2}) does not make sense' \\\\\\n ''.format(ws_unit, x_min, x_max)\\n print('[ERROR CAUSING CRASH] {}'.format(ero_msg))\\n raise RuntimeError(ero_msg)\\n\\n return\",\n \"def update_view_to_bins(self, *args):\\n with delay_callback(self, 'x_min', 'x_max'):\\n self.x_min = self.hist_x_min\\n self.x_max = self.hist_x_max\",\n \"def update_view_to_bins(self, *args):\\n with delay_callback(self, 'x_min', 'x_max'):\\n self.x_min = self.hist_x_min\\n self.x_max = self.hist_x_max\",\n \"def set_range(self, **rangekwargs):\\n\\n if 'xrange' in rangekwargs.keys(): \\n xrange = rangekwargs['xrange']\\n else: \\n xrange = [-50.0, 50.0] # (default)\\n\\n if 'yrange' in rangekwargs.keys(): \\n yrange = rangekwargs['yrange']\\n else: \\n yrange = [0.0, 1.25 * self.hist_max]\\n\\n self.sub.set_xlim(xrange) \\n self.sub.set_ylim(yrange) \\n\\n self.sub.set_xlabel(r\\\"$\\\\mathtt{d_{LOS}}$ (Mpc/h)\\\", fontsize=20)\\n\\n return None\",\n \"def lhistogram (inlist,numbins=10,defaultreallimits=None,printextras=0):\\r\\n if (defaultreallimits <> None):\\r\\n if type(defaultreallimits) not in [ListType,TupleType] or len(defaultreallimits)==1: # only one limit given, assumed to be lower one & upper is calc'd\\r\\n lowerreallimit = defaultreallimits\\r\\n upperreallimit = 1.000001 * max(inlist)\\r\\n else: # assume both limits given\\r\\n lowerreallimit = defaultreallimits[0]\\r\\n upperreallimit = defaultreallimits[1]\\r\\n binsize = (upperreallimit-lowerreallimit)/float(numbins)\\r\\n else: # no limits given for histogram, both must be calc'd\\r\\n estbinwidth=(max(inlist)-min(inlist))/float(numbins) +1e-6 #1=>cover all\\r\\n binsize = ((max(inlist)-min(inlist)+estbinwidth))/float(numbins)\\r\\n lowerreallimit = min(inlist) - binsize/2 #lower real limit,1st bin\\r\\n bins = [0]*(numbins)\\r\\n extrapoints = 0\\r\\n for num in inlist:\\r\\n try:\\r\\n if (num-lowerreallimit) < 0:\\r\\n extrapoints = extrapoints + 1\\r\\n else:\\r\\n bintoincrement = int((num-lowerreallimit)/float(binsize))\\r\\n bins[bintoincrement] = bins[bintoincrement] + 1\\r\\n except:\\r\\n extrapoints = extrapoints + 1\\r\\n if (extrapoints > 0 and printextras == 1):\\r\\n print '\\\\nPoints outside given histogram range =',extrapoints\\r\\n return (bins, lowerreallimit, binsize, extrapoints)\",\n \"def set_slider_bounds(self,lower,upper,inclusive_bounds=None):\\n self.bounds = (lower,upper)\\n\\n if inclusive_bounds is not None:\\n self.inclusive_bounds = inclusive_bounds\\n\\n epsilon = max(self.slider['resolution'],0.00000000001)\\n\\n if self.inclusive_bounds[0] is False:\\n lower+=epsilon\\n if self.inclusive_bounds[1] is False:\\n upper-=epsilon\\n self.slider.config(from_=lower,to=upper)\",\n \"def _set_axes_limits(ax, parameter, axis=\\\"x\\\"):\\n\\n lims = list(ax.get_xlim()) if axis == \\\"x\\\" else list(ax.get_ylim())\\n\\n if \\\"low\\\" in DEFAULT_BOUNDS[parameter]:\\n low = DEFAULT_BOUNDS[parameter][\\\"low\\\"]\\n if lims[0] < low:\\n lims[0] = DEFAULT_BOUNDS[parameter][\\\"low\\\"]\\n if \\\"high\\\" in DEFAULT_BOUNDS[parameter]:\\n high = DEFAULT_BOUNDS[parameter][\\\"high\\\"]\\n if lims[1] > high:\\n lims[1] = DEFAULT_BOUNDS[parameter][\\\"high\\\"]\\n\\n if axis == \\\"x\\\":\\n ax.set_xlim(lims)\\n else:\\n ax.set_ylim(lims)\",\n \"def __rangeChanged(self, first, second):\\n tooltip = \\\"Histogram range:\\\\n[%g, %g]\\\" % (first, second)\\n self.__rangeSlider.setToolTip(tooltip)\\n self.__rangeLabel.setToolTip(tooltip)\",\n \"def remove_invalid_values(self, lower_bound=float('-inf'), upper_bound=float('inf')) :\\n valid_indices = (lower_bound < self.signal) * (self.signal < upper_bound)\\n self.time_scale = self.time_scale[valid_indices]\\n self.signal = self.signal[valid_indices]\",\n \"def apply_bound(x, var_min, var_max):\\n x.position = np.maximum(x.position, var_min)\\n x.position = np.minimum(x.position, var_max)\",\n \"def rangeLimit(val, minv, maxv):\\n\\treturn range_limit(val, minv, maxv)\",\n \"def update_histo_frame():\\n min_histo.text = str(MIN_RANGE_F) # Display the legend\\n max_histo.text = str(MAX_RANGE_F)\\n\\n histogram = np.zeros(GRID_AXIS) # Clear histogram accumulation array\\n # Collect camera data and calculate the histogram\\n for _row in range(0, GRID_AXIS):\\n for _col in range(0, GRID_AXIS):\\n histo_index = int(map_range(GRID_DATA[_col, _row], 0, 1, 0, GRID_AXIS - 1))\\n histogram[histo_index] = histogram[histo_index] + 1\\n\\n histo_scale = np.max(histogram) / (GRID_AXIS - 1)\\n if histo_scale <= 0:\\n histo_scale = 1\\n\\n # Display the histogram\\n for _col in range(0, GRID_AXIS):\\n for _row in range(0, GRID_AXIS):\\n if histogram[_col] / histo_scale > GRID_AXIS - 1 - _row:\\n image_group[((_row * GRID_AXIS) + _col)].fill = index_to_rgb(\\n round((_col / GRID_AXIS), 3)\\n )\\n else:\\n image_group[((_row * GRID_AXIS) + _col)].fill = BLACK\",\n \"def _setBound(self, value):\\n if self._colormap is not None:\\n if self._index == 0:\\n min_ = value\\n max_ = self._colormap.getVMax()\\n else: # self._index == 1\\n min_ = self._colormap.getVMin()\\n max_ = value\\n\\n if max_ is not None and min_ is not None and min_ > max_:\\n min_, max_ = max_, min_\\n self._colormap.setVRange(min_, max_)\",\n \"def updateRanges(self):\\n # work directly on the histograms, no copy\\n for i in range(len(self)):\\n self[i].updateRange()\",\n \"def set_colormap_range(self):\\n cmin = self.settingsWidget.ui.colormap_min\\n cmax = self.settingsWidget.ui.colormap_max\\n region = self.plot.getHistogramWidget().region\\n\\n if(self.sender() == region):\\n cmin.setText(str(region.getRegion()[0]))\\n cmax.setText(str(region.getRegion()[1]))\\n return\\n\\n # Sometimes the values in the lineEdits are\\n # not proper floats so we get ValueErrors\\n try:\\n # If necessary swap min and max\\n if(float(cmin.text()) > float(cmax.text())):\\n _tmp = cmin.text()\\n cmin.setText(cmax.text())\\n cmax.setText(_tmp)\\n\\n region = [float(cmin.text()), float(cmax.text())]\\n self.plot.getHistogramWidget().region.setRegion(region)\\n except ValueError:\\n return\",\n \"def set_output_limits(self, min_value, max_value):\\n self.out_min = min_value\\n self.out_max = max_value\\n if self.out_min > self.out_max:\\n print(\\\"set_output_limits(): min must be smaller than max.\\\")\\n self.iterm = self.clip_to_output_limits(self.iterm)\\n self.output = self.clip_to_output_limits(self.output)\",\n \"def get_limits(hist, xaxis=True):\\n if xaxis:\\n return (hist.GetXaxis().GetXmin(), hist.GetXaxis().GetXmax())\\n else:\\n return (hist.GetMinimum(), hist.GetMaximum())\",\n \"def _handleInput(self, paramInput):\\n super()._handleInput(paramInput)\\n if not self.upperBoundUsed or not self.lowerBoundUsed:\\n self.raiseAnError(IOError,'the Uniform distribution needs both upperBound and lowerBound attributes. Got upperBound? '+ str(self.upperBoundUsed) + '. Got lowerBound? '+str(self.lowerBoundUsed))\\n self.range = self.upperBound - self.lowerBound\\n self.initializeDistribution()\",\n \"def __check_range(self, range_type, name):\\n\\n # Before we really do anything, make sure a warning or critical \\n # threshold were even set.\\n\\n self.__verbose_print('range was ' + range_type)\\n\\n range_list = self.__warning\\n if range_type == 'critical':\\n range_list = self.__critical\\n\\n if len(range_list) < 1:\\n return\\n\\n threshold = self.__perf[name]['threshold']\\n val = self.__perf[name]['val']\\n\\n # Even if the warning/critical thresholds are required, a user may\\n # forget to set all necessary ranges. If the ranges are required,\\n # exit with a complaint, otherwise just skip the range test.\\n\\n if len(range_list) < threshold:\\n if range_type not in self.__req_option:\\n return\\n raise UserWarning (\\\"Please set part %s of the %s threshold!\\\" % (threshold, range_type))\\n\\n # The option parser should have already split these into proper\\n # bottom, top, and match inversion, so long as the array element\\n # is defined Perform our range test and set the exit status.\\n\\n self.__verbose_print(range_list[threshold-1])\\n (bottom, top, invert, raw) = range_list[threshold-1]\\n\\n self.__perf[name][\\\"raw_%s\\\" % range_type] = raw\\n\\n if ((not invert and (val < bottom or val > top)) or\\n (invert and val >= bottom and val <= top)):\\n self.__exit_status = range_type\\n self.__perf[name]['state'] = range_type\\n\\n self.__verbose_print(\\\"%s:%s:%s = %s\\\" % (val, bottom, top, ((val < bottom) or (val > top))))\",\n \"def write_bounds(self):\\n optimized_par_df = \\\\\\n self.parameter_df.loc[self.parameter_df.estimate == 1\\n & (~self.parameter_df.index.isin(\\n self.amici_model.getFixedParameterIds())), :]\\n self.f.require_dataset('/parameters/lowerBound',\\n shape=optimized_par_df.lowerBound.shape,\\n data=optimized_par_df.lowerBound, dtype='f8')\\n self.f.require_dataset('/parameters/upperBound',\\n shape=optimized_par_df.upperBound.shape,\\n data=optimized_par_df.upperBound, dtype='f8')\",\n \"def set_bounds(\\n self: A,\\n lower: BoundValue = None,\\n upper: BoundValue = None,\\n method: str = \\\"clipping\\\",\\n full_range_sampling: bool = False,\\n a_min: BoundValue = None,\\n a_max: BoundValue = None,\\n ) -> A: # TODO improve description of methods\\n lower, upper = _a_min_max_deprecation(**locals())\\n bounds = tuple(a if isinstance(a, np.ndarray) or a is None else np.array([a], dtype=float) for a in (lower, upper))\\n both_bounds = all(b is not None for b in bounds)\\n # preliminary checks\\n if self.bound_transform is not None:\\n raise RuntimeError(\\\"A bounding method has already been set\\\")\\n if full_range_sampling and not both_bounds:\\n raise ValueError(\\\"Cannot use full range sampling if both bounds are not set\\\")\\n checker = BoundChecker(*bounds)\\n if not checker(self.value):\\n raise ValueError(\\\"Current value is not within bounds, please update it first\\\")\\n if not (lower is None or upper is None):\\n if (bounds[0] >= bounds[1]).any(): # type: ignore\\n raise ValueError(f\\\"Lower bounds {lower} should be strictly smaller than upper bounds {upper}\\\")\\n # update instance\\n transforms = dict(clipping=trans.Clipping, arctan=trans.ArctanBound, tanh=trans.TanhBound)\\n if method in transforms:\\n if self.exponent is not None and method != \\\"clipping\\\":\\n raise ValueError(f'Cannot use method \\\"{method}\\\" in logarithmic mode')\\n self.bound_transform = transforms[method](*bounds)\\n elif method == \\\"constraint\\\":\\n self.register_cheap_constraint(checker)\\n else:\\n raise ValueError(f\\\"Unknown method {method}\\\")\\n self.bounds = bounds # type: ignore\\n self.full_range_sampling = full_range_sampling\\n # warn if sigma is too large for range\\n if both_bounds and method != \\\"tanh\\\": # tanh goes to infinity anyway\\n std_bounds = tuple(self._to_reduced_space(b) for b in self.bounds) # type: ignore\\n min_dist = np.min(np.abs(std_bounds[0] - std_bounds[1]).ravel())\\n if min_dist < 3.0:\\n warnings.warn(f\\\"Bounds are {min_dist} sigma away from each other at the closest, \\\"\\n \\\"you should aim for at least 3 for better quality.\\\")\\n return self\",\n \"def check_ranges(self, param_name, param_value):\\n\\n if np.isscalar(param_value):\\n if param_value < 0. or param_value > 1.:\\n raise ValueError((param_name, ' should be within range [0,1]'))\\n else:\\n if param_name == 'zoom_range' or param_name == 'illumination_range':\\n if param_value == 1:\\n self.dict[param_name] = [1, 1]\\n else:\\n self.dict[param_name] = [1 - param_value, 1 + param_value]\\n else:\\n self.dict[param_name] = [0., param_value]\\n\\n elif len(param_value) == 2:\\n\\n if param_name != 'zoom_range' and param_name != 'illumination_range' \\\\\\n and (param_value[0] < 0. or param_value[0] > 1. or param_value[1] < 0. or param_value[1] > 1.):\\n raise ValueError((param_name, ' should be within range [0,1]'))\\n else:\\n self.dict[param_name] = [param_value[0], param_value[1]]\\n else:\\n raise ValueError((param_name, ' should be a float or '\\n 'a tuple or list of two floats. '\\n 'Received arg: '), param_value)\",\n \"def __updateHistogramFromControls(self, *args):\\n\\n hist = self.getHistogram(copy=False)\\n if hist is not None:\\n count, edges = hist\\n if (len(count) == self.__nbinsLineEdit.getValue() and\\n (edges[0], edges[-1]) == self.__rangeSlider.getValues()):\\n return # Nothing has changed\\n\\n self._updateFromItem()\",\n \"def _setValidRange(self, contribs, valueRange):\\n testfor(contribs.ndim == 2, ValueError)\\n numContribs, numReps = contribs.shape\\n self._validRange = np.zeros_like(contribs.T, dtype = bool)\\n for ri in range(numReps):\\n # the single set of R for this calculation\\n rset = contribs[:, ri]\\n self._validRange[ri] = ((rset > min(valueRange))\\n * (rset < max(valueRange)))\",\n \"def ahistogram (inarray,numbins=10,defaultlimits=None,printextras=1):\\r\\n inarray = N.ravel(inarray) # flatten any >1D arrays\\r\\n if (defaultlimits <> None):\\r\\n lowerreallimit = defaultlimits[0]\\r\\n upperreallimit = defaultlimits[1]\\r\\n binsize = (upperreallimit-lowerreallimit) / float(numbins)\\r\\n else:\\r\\n Min = N.minimum.reduce(inarray)\\r\\n Max = N.maximum.reduce(inarray)\\r\\n estbinwidth = float(Max - Min)/float(numbins) + 1e-6\\r\\n binsize = (Max-Min+estbinwidth)/float(numbins)\\r\\n lowerreallimit = Min - binsize/2.0 #lower real limit,1st bin\\r\\n bins = N.zeros(numbins)\\r\\n extrapoints = 0\\r\\n for num in inarray:\\r\\n try:\\r\\n if (num-lowerreallimit) < 0:\\r\\n extrapoints = extrapoints + 1\\r\\n else:\\r\\n bintoincrement = int((num-lowerreallimit) / float(binsize))\\r\\n bins[bintoincrement] = bins[bintoincrement] + 1\\r\\n except: # point outside lower/upper limits\\r\\n extrapoints = extrapoints + 1\\r\\n if (extrapoints > 0 and printextras == 1):\\r\\n print '\\\\nPoints outside given histogram range =',extrapoints\\r\\n return (bins, lowerreallimit, binsize, extrapoints)\",\n \"def _check_paramrange(value, parameter):\\n\\n if parameter not in PARAMETER_RANGES.keys():\\n raise ValueError('parameter {} not found in dictonary {}'\\n .format(parameter, PARAMETER_RANGES))\\n ranges = PARAMETER_RANGES[parameter]\\n lo = ranges[0]\\n hi = ranges[1]\\n INRANGE = True\\n if not (lo <= value < hi):\\n INRANGE = False\\n\\n return INRANGE, lo, hi\",\n \"def output_limits(self, limits):\\n if limits is None:\\n self._min_output, self._max_output = None, None\\n return\\n\\n min_output, max_output = limits\\n\\n if None not in limits and max_output < min_output:\\n raise ValueError('lower limit must be less than upper limit')\\n\\n self._min_output = min_output\\n self._max_output = max_output\\n\\n self._integral = self._clamp(self._integral, self.output_limits)\\n self._last_output = self._clamp(self._last_output, self.output_limits)\",\n \"def _is_out_of_range(self, signal, y_range, threshold):\\n out_of_range = [s for s in signal if s < y_range.min or s > y_range.max]\\n out_of_range_percentage = len(out_of_range) / len(signal)\\n\\n return out_of_range_percentage > threshold\",\n \"def cb_bounds(self, variable, results_dict, keys, fixed_bounds):\\n tas_bound, pr_bound = fixed_bounds\\n if variable == \\\"tas\\\":\\n if tas_bound:\\n bound_limit = tas_bound\\n else:\\n bound_limit = self.find_abs_bound_range(results_dict, keys)\\n cmap = plt.cm.RdBu_r\\n else:\\n if pr_bound:\\n bound_limit = pr_bound\\n else:\\n bound_limit = self.find_abs_bound_range(results_dict,\\n keys,\\n avg_over=25)\\n cmap = plt.cm.BrBG\\n bounds = np.linspace(-1 * bound_limit, bound_limit, 11)\\n return [bounds, cmap]\",\n \"def _changeDisplayRange(self):\\n try:\\n newrange = float(str(self._wmin.text())), float(str(self._wmax.text()))\\n except ValueError:\\n return\\n self._rc.setDisplayRange(*newrange)\",\n \"def updateThresholdValues (self, DoubleSlider, Node, ThMax ):\\n DoubleSlider.Slider.minimum = 0\\n DoubleSlider.SpinBoxL.setRange(0,ThMax)\\n DoubleSlider.Slider.maximum = ThMax\\n DoubleSlider.SpinBoxR.setRange(0,ThMax)\\n if ThMax!=0:\\n DisplayNode = Node.GetScalarVolumeDisplayNode()\\n LowerThreshold = DisplayNode.GetLowerThreshold()\\n UpperThreshold = DisplayNode.GetUpperThreshold()\\n DoubleSlider.Slider.minimumValue = LowerThreshold\\n DoubleSlider.Slider.maximumValue = UpperThreshold \\n DoubleSlider.SpinBoxL.blockSignals(True)\\n DoubleSlider.SpinBoxR.blockSignals(True)\\n DoubleSlider.SpinBoxL.value = LowerThreshold\\n DoubleSlider.SpinBoxR.value = UpperThreshold\\n DoubleSlider.SpinBoxL.blockSignals(False)\\n DoubleSlider.SpinBoxR.blockSignals(False)\",\n \"def angle_limit_validation(self, lower_limit, upper_limit):\\n self._angle_limit_validation(\\n self.max_angle,\\n self.min_angle,\\n self.sample_count,\\n lower_limit,\\n upper_limit,\\n self._name,\\n )\",\n \"def constrain(inputVal, lower_limit, upper_limit):\\n \\n if (inputVal < lower_limit):\\n return lower_limit\\n elif (inputVal > upper_limit):\\n return upper_limit\\n else:\\n return inputVal\",\n \"def bounds(x, xMin, xMax):\\n if (x < xMin):\\n x = xMin\\n elif (x > xMax):\\n x = xMax\\n return(x)\",\n \"def _bound(x, min_value, max_value):\\n return np.maximum(min_value, np.minimum(x, max_value))\",\n \"def initializeDistribution(self):\\n self.minVal = min(math.exp(self.upperBound),math.exp(self.lowerBound))\\n self.maxVal = max(math.exp(self.upperBound),math.exp(self.lowerBound))\",\n \"def range_limit(val, minv, maxv):\\n\\tif (val < minv):\\n\\t\\tval = minv\\n\\telif (val > maxv):\\n\\t\\tval = maxv\\n\\treturn val\",\n \"def SetHistogramParameters(self, numBins: 'int const', lowerBound: 'double', upperBound: 'double') -> \\\"void\\\":\\n return _itkLabelStatisticsImageFilterPython.itkLabelStatisticsImageFilterIF2IUS2_SetHistogramParameters(self, numBins, lowerBound, upperBound)\",\n \"def set_visualization_range(self, start: int, end: int):\\n self.__range = (start, end)\",\n \"def _setup(self, h: histogram.Histogram1D) -> Tuple[histogram.Histogram1D, pachyderm.fit.T_FitArguments]:\\n fit_range = self.fit_options[\\\"range\\\"]\\n # Restrict the range so that we only fit within the desired input.\\n restricted_range = (h.x > fit_range.min) & (h.x < fit_range.max)\\n restricted_hist = histogram.Histogram1D(\\n # We need the bin edges to be inclusive.\\n # Need the +/- epsilons here to be extra safe, because apparently some of the <= and >= can fail\\n # (as might be guessed with floats, but I hadn't observed until now). We don't do this above\\n # because we don't want to be inclusive on the edges.\\n bin_edges = h.bin_edges[(h.bin_edges >= (fit_range.min - utils.epsilon)) & (h.bin_edges <= (fit_range.max + utils.epsilon))],\\n y = h.y[restricted_range],\\n errors_squared = h.errors_squared[restricted_range]\\n )\\n\\n # Default arguments\\n # Use the minimum of the histogram as the starting value.\\n arguments: pachyderm.fit.T_FitArguments = {\\n \\\"slope\\\": 0, \\\"error_slope\\\": 0.005,\\n \\\"const\\\": 1, \\\"error_const\\\": 0.005,\\n \\\"limit_slope\\\": (-100, 100),\\n \\\"limit_const\\\": (-10, 10),\\n }\\n\\n return restricted_hist, arguments\",\n \"def test_threshold_range_b(self):\\n code, out, err = self.t.runError(\\\"--threshold --min 3.2 --max 3.1\\\")\\n self.assertIn(\\\"The max value must be higher than the min value.\\\", out)\",\n \"def check_out_range(value, lim_1, lim_2):\\n lo_lim = min(lim_1, lim_2)\\n hi_lim = max(lim_1, lim_2)\\n \\n if (abs(value) > abs(hi_lim)) or (abs(value) < abs(lo_lim)):\\n return True\\n else:\\n return False\",\n \"def check_limits(data, tables, max_percent=1., replace_with='interpolation'):\\n from . import trend as pmtrend\\n import numpy as np\\n from . import algs\\n import pandas as pd\\n\\n df = data.copy()\\n max_count = int(len(df)*max_percent/100.)\\n low_count = pd.Series(0, index=tables.columns)\\n upp_count = pd.Series(0, index=tables.columns)\\n fault_count = pd.Series(0, index=tables.columns)\\n\\n #-----------\\n # First we check the lower values\\n if 'lower_limits' in tables.index.values:\\n faulty = df < tables.loc['lower_limits']\\n low_count = df[ faulty ].count() \\n df[ faulty ] = np.nan\\n #-------------------------------\\n \\n #-------------------------------\\n # Now we check the upper values\\n if 'upper_limits' in tables.index.values:\\n faulty = df > tables.loc['upper_limits']\\n upp_count = df[ faulty ].count() \\n df[ faulty ] = np.nan\\n #-------------------------------\\n\\n fault_count = low_count + upp_count\\n valid = fault_count < max_count\\n\\n #------------\\n # Replace data with either its trend or by interpolating\\n if replace_with=='trend':\\n trend = pmdata.trend(df, how='linear')\\n df = df.fillna(trend)\\n elif replace_with=='interpolation':\\n df = df.interpolate(method='index', limit_direction='both')\\n #------------\\n\\n #-------------------------------\\n # Substitute faulty points by the linear trend\\n #trend = data.polyfit()\\n #df = df.fillna(trend)\\n #-------------------------------\\n\\n return df, valid, fault_count\",\n \"def view_limits(self, dmin, dmax):\\n base = self._select_base(dmin, dmax)\\n if mpl.rcParams['axes.autolimit_mode'] == 'round_numbers':\\n vmin = base.le(dmin)\\n vmax = base.ge(dmax)\\n if vmin == vmax:\\n vmin -= 1\\n vmax += 1\\n else:\\n vmin = dmin\\n vmax = dmax\\n\\n return mtransforms.nonsingular(vmin, vmax)\",\n \"def _onSetParameterLower(self, value):\\n self._parameters['lower'] = min(value, self._parameters['upper']) # Limit at upper\\n self._logger.info(\\\"Parameter 'lower' of function '{}' changed to {}\\\".format(self._function, value))\\n self.functionChanged.emit(self._dim, self._function, self._parameters.copy())\",\n \"def setRange(self, x_range, y_range):\\n pass\",\n \"def bandpassFilter (self, lowerFreq, upperFreq):\\n self.bandpassLimits = (lowerFreq, upperFreq)\\n # stuff to do\",\n \"def act_limits(self, act_limits):\\n\\n # Validate input\\n missing_keys = [key for key in [\\\"low\\\", \\\"high\\\"] if key not in act_limits.keys()]\\n if missing_keys:\\n warn_string = \\\"WARN: act_limits could not be set as {} not found.\\\".format(\\n f\\\"keys {missing_keys} were\\\"\\n if len(missing_keys) > 1\\n else f\\\"key {missing_keys} was\\\"\\n )\\n print(colorize(warn_string, \\\"yellow\\\"))\\n invalid_length = [\\n key for key, val in act_limits.items() if len(val) != self._a_dim\\n ]\\n if invalid_length:\\n warn_string = (\\n f\\\"WARN: act_limits could not be set as the length of {invalid_length} \\\"\\n + \\\"{}\\\".format(\\\"were\\\" if len(invalid_length) > 1 else \\\"was\\\")\\n + f\\\" unequal to the dimension of the action space (dim={self._a_dim}).\\\"\\n )\\n print(colorize(warn_string, \\\"yellow\\\"))\\n\\n # Set action limits\\n self._act_limits = {\\\"low\\\": act_limits[\\\"low\\\"], \\\"high\\\": act_limits[\\\"high\\\"]}\\n self.ga.act_limits = self._act_limits\",\n \"def SetHistogramParameters(self, numBins: 'int const', lowerBound: 'double', upperBound: 'double') -> \\\"void\\\":\\n return _itkLabelStatisticsImageFilterPython.itkLabelStatisticsImageFilterIF2ISS2_SetHistogramParameters(self, numBins, lowerBound, upperBound)\",\n \"def validate_subset_of_schema(self, schema):\\n super(NumericAttributeSchema, self).validate_subset_of_schema(schema)\\n\\n if self.range and (\\n not schema.range\\n or self.range[0] < schema.range[0]\\n or self.range[1] > schema.range[1]\\n ):\\n raise AttributeSchemaError(\\n \\\"Range %s is not a subset of %s\\\" % (self.range, schema.range)\\n )\",\n \"def test_returns_custom_range(self):\\n img = np.arange(5)\\n\\n actual = util.apply_gamma(img, out_min=0, out_max=255, gamma=1)\\n self.assertEqual(actual.min(), 0)\\n self.assertEqual(actual.max(), 255)\",\n \"def set_period_limits(self): # function[Tmin, Tmax] = setTlim(obj)\\n\\n x_min = self.tf.minimum_period\\n x_max = self.tf.maximum_period\\n\\n Tmin = 10 ** (np.floor(np.log10(x_min) * 2) / 2)\\n if (np.log10(x_min) - np.log10(Tmin)) < 0.15:\\n Tmin = 10 ** (np.log10(Tmin) - 0.3)\\n\\n Tmax = 10 ** (np.ceil(np.log10(x_max) * 2) / 2)\\n if (np.log10(Tmax) - np.log10(x_max)) < 0.15:\\n Tmax = 10 ** (np.log10(Tmax) + 0.3)\\n return Tmin, Tmax\",\n \"def trim_range(self, low_bound, hi_bound, full_bound=True):\\n low_bound_int = int(low_bound[:self.place+1])\\n hi_bound_int = int(hi_bound[:self.place+1])\\n\\n # Remove keys outside of range\\n # modifying dict during loop caused lots of problems - del after loop\\n keys_to_del = []\\n for key in self.Poss_Tree:\\n if key < int(low_bound[:self.place]):\\n keys_to_del.append(key)\\n continue\\n elif key > int(hi_bound[:self.place]):\\n keys_to_del.append(key)\\n continue\\n for key in keys_to_del:\\n del self.Poss_Tree[key]\\n\\n # Remove values outside of range\\n vals_to_del = defaultdict(list)\\n for key in self.Poss_Tree:\\n for choice in self.Poss_Tree[key]:\\n password = int(construct_pass(key, choice))\\n if password > hi_bound_int or password < low_bound_int:\\n vals_to_del[key].append(choice)\\n for key in vals_to_del:\\n for val in vals_to_del[key]:\\n self.Poss_Tree[key].remove(val)\",\n \"def SetHistogramParameters(self, numBins: 'int const', lowerBound: 'double', upperBound: 'double') -> \\\"void\\\":\\n return _itkLabelStatisticsImageFilterPython.itkLabelStatisticsImageFilterIUS2IUS2_SetHistogramParameters(self, numBins, lowerBound, upperBound)\",\n \"def set_limits_percent(self, percent=95):\\n low = np.nanmin(self.datasource.data[\\\"values\\\"])\\n high = np.nanmax(self.datasource.data[\\\"values\\\"])\\n\\n frac = percent / 100.0\\n self.set_limits_minmax(low, high - (1.0 - frac) * (high - low))\",\n \"def adjust_parameter_validator(self, param):\\n if not isinstance(param.vals, Numbers):\\n raise Exception('Only the Numbers validator is supported.')\\n min_val = param.vals._min_value\\n max_val = param.vals._max_value\\n\\n min_val_upd = self.round_dac(min_val, param.name)\\n max_val_upd = self.round_dac(max_val, param.name)\\n\\n param.vals = Numbers(min_val_upd, max_val_upd)\",\n \"def _withinRangeCheckerWrapper(self, args):\\n\\n constraints = args['constraints']\\n\\n def _withinRangeChecker(entity, params):\\n \\\"\\\"\\\"Checks if certain properties are within given constrains. \\n \\\"\\\"\\\"\\n\\n for constraint in constraints:\\n type = constraint.get('type')\\n field = constraint.get('field')\\n\\n if not type or not field:\\n raise ProtocolError()\\n\\n min_value = constraint.get('min_value', 0)\\n max_value = constraint.get('max_value', 1)\\n\\n if type == 'size':\\n value = entity.__getattribute__(field)\\n if len(value) < min_value or len(value) > max_value:\\n return False\\n else:\\n raise ProtocolError()\\n \\n return True\\n \\n return _withinRangeChecker\",\n \"def SetHistogramParameters(self, numBins: 'int const', lowerBound: 'double', upperBound: 'double') -> \\\"void\\\":\\n return _itkLabelStatisticsImageFilterPython.itkLabelStatisticsImageFilterISS2IUS2_SetHistogramParameters(self, numBins, lowerBound, upperBound)\",\n \"def SetHistogramParameters(self, numBins: 'int const', lowerBound: 'double', upperBound: 'double') -> \\\"void\\\":\\n return _itkLabelStatisticsImageFilterPython.itkLabelStatisticsImageFilterIF2IUC2_SetHistogramParameters(self, numBins, lowerBound, upperBound)\",\n \"def _verticalLimit_changed(self):\\n self.masterContainer.range2d.y_range.high = self.verticalLimit\",\n \"def pass_selection_val(self, val, val_min=None, val_max=None):\\n if (val_min is not None) and (val_max is not None):\\n return True if (val > val_min) and (val < val_max) else False\\n elif (val_min is None) and (val_max is not None):\\n # Check against val_max only. \\n return True if (val < val_max) else False\\n elif (val_min is not None) and (val_max is None):\\n # Check against val_min only. \\n return True if (val > val_min) else False\\n else:\\n msg = \\\"[WARNING] You performed a cut, but didn't specify any bounds.\\\"\\n raise RuntimeWarning(msg)\",\n \"def exclude_zero_bins(f,fitmin,fitmax,hs=[],limit=0,sumall=False):\\n if len(hs)==0:\\n return\\n nbins = fitmax - fitmin + 1\\n nexcl = 0\\n exclist = []\\n for ibin in xrange(fitmin,fitmax+1):\\n do_exc = any( [h.GetBinContent(ibin)<limit for h in hs] ) if sumall==False else (sum([h.GetBinContent(ibin) for h in hs])<limit)\\n if do_exc:\\n f.ExcludeBin(ibin)\\n exclist.append(ibin)\\n nexcl += 1\\n if nexcl>0:\\n print 'WARNING: TFractionFitter excluded %d out of %d bins due to low statistics (nentries<%d)'%(nexcl,nbins,limit)\\n print ' bins: ' + ' '.join([str(xx) for xx in exclist])\\n else:\\n print 'INFO: TFractionFitter does not need to exclude any bins'\\n return True\",\n \"def __check_args_val(self):\\n if self.__min_range < 0:\\n error_msg = \\\"min_range must be greater than or equal to zero\\\"\\n raise ValueError(error_msg)\\n elif self.__max_range < 0:\\n error_msg = \\\"max_range must be greater than or equal to zero\\\"\\n raise ValueError(error_msg)\\n elif self.__max_range < self.__min_range:\\n error_msg = \\\"max_range must be greater than or equal to min_range\\\"\\n raise ValueError(error_msg)\",\n \"def clip_range(x, xlim):\\n return min([max([x, xlim[0]]), xlim[1]])\",\n \"def py_apply_limits(self, plot):\\n if any(x is not None for x in self.x_lim):\\n if self.x_lim[0] is not None: # at least left?\\n if self.x_lim[1] is not None: # left and right?\\n plot.set_xlim(left=self.x_lim[0], right=self.x_lim[1])\\n else:\\n plot.set_xlim(left=self.x_lim[0])\\n else: # just right\\n plot.set_xlim(rigt=self.x_lim[1])\\n if any(y is not None for y in self.y_lim):\\n if self.y_lim[0] is not None: # at least bottom?\\n if self.y_lim[1] is not None:\\n plot.set_ylim(bottom=self.y_lim[0], top=self.y_lim[1])\\n else:\\n plot.set_ylim(bottom=self.y_lim[0])\\n else:\\n plot.set_ylim(top=self.y_lim[1])\",\n \"def lim(cls, lower=PWM_MIN, upper=PWM_MAX, value=None, less_than_lower_default=None, greater_than_upper_default=None):\\n #Sanitise inputs\\n if less_than_lower_default is None:\\n less_than_lower_default = lower\\n if greater_than_upper_default is None:\\n greater_than_upper_default = upper\\n if not (less_than_lower_default >= lower and greater_than_upper_default <= upper):\\n raise Exception(\\\"LEDStrip.lim(): Defaults %s,%s are not within %s - %s\\\" % (less_than_lower_default, greater_than_upper_default, lower, upper)) \\n if value is None:\\n return less_than_lower_default\\n \\n #Test values\\n try:\\n if value < lower:\\n logging.warn(\\\" LEDStrip.lim(): Value %s is less than lower limit %s. Setting to %s.\\\" % (value, lower, less_than_lower_default))\\n return float(less_than_lower_default)\\n if value > upper:\\n logging.warn(\\\" LEDStrip.lim(): Value %s is greater than upper limit %s. Setting to %s\\\" % (value, upper, greater_than_upper_default))\\n return float(greater_than_upper_default)\\n except (ValueError, TypeError, AttributeError):\\n return float(less_than_lower_default)\\n return float(value)\",\n \"def adjust_dynamic_range(data, drange_in=(-1, 1), drange_out=(0, 1)):\\r\\n if drange_in != drange_out:\\r\\n scale = (np.float32(drange_out[1]) - np.float32(drange_out[0])) / (\\r\\n np.float32(drange_in[1]) - np.float32(drange_in[0]))\\r\\n bias = (np.float32(drange_out[0]) - np.float32(drange_in[0]) * scale)\\r\\n data = data * scale + bias\\r\\n return torch.clamp(data, min=0, max=1)\",\n \"def collect_absolute_value(self, name_to_arr):\\n for tensor, data_arr in name_to_arr.items():\\n data_arr = np.asarray(data_arr) # noqa: PLW2901\\n data_arr = data_arr.flatten() # noqa: PLW2901\\n if data_arr.size > 0:\\n min_value = np.min(data_arr)\\n max_value = np.max(data_arr)\\n else:\\n min_value = 0\\n max_value = 0\\n\\n data_arr = np.absolute(data_arr) # only consider absolute value # noqa: PLW2901\\n\\n if tensor not in self.histogram_dict:\\n # first time it uses num_bins to compute histogram.\\n hist, hist_edges = np.histogram(data_arr, bins=self.num_bins)\\n self.histogram_dict[tensor] = (hist, hist_edges, min_value, max_value)\\n else:\\n old_histogram = self.histogram_dict[tensor]\\n old_min = old_histogram[2]\\n old_max = old_histogram[3]\\n old_hist = old_histogram[0]\\n old_hist_edges = old_histogram[1]\\n temp_amax = np.max(data_arr)\\n if temp_amax > old_hist_edges[-1]:\\n # increase the number of bins\\n width = old_hist_edges[1] - old_hist_edges[0]\\n # NOTE: np.arange may create an extra bin after the one containing temp_amax\\n new_bin_edges = np.arange(old_hist_edges[-1] + width, temp_amax + width, width)\\n old_hist_edges = np.hstack((old_hist_edges, new_bin_edges))\\n hist, hist_edges = np.histogram(data_arr, bins=old_hist_edges)\\n hist[: len(old_hist)] += old_hist\\n self.histogram_dict[tensor] = (hist, hist_edges, min(old_min, min_value), max(old_max, max_value))\",\n \"def SetHistogramParameters(self, numBins: 'int const', lowerBound: 'double', upperBound: 'double') -> \\\"void\\\":\\n return _itkLabelStatisticsImageFilterPython.itkLabelStatisticsImageFilterIUS2ISS2_SetHistogramParameters(self, numBins, lowerBound, upperBound)\",\n \"def set_shader_range_ui(*args):\\n shader_range = pm.floatField(\\\"f_shaderRange\\\", value=True, query=True)\\n set_shader_range(shader_range)\",\n \"def setlimits(self, Xlim=[], Ylim=[]):\\n self.data['Xmin'] = Xlim[0]\\n self.data['Xmax'] = Xlim[1]\\n self.data['Ymin'] = Ylim[0]\\n self.data['Ymax'] = Ylim[1]\",\n \"def set_progress_range(self, maximum):\\r\\n\\r\\n pass\",\n \"def set_bounds(self, **kwargs):\\n for name, bounds in kwargs.items():\\n if name not in self._parameters:\\n raise AttributeError('Unknown parameter %s for %s' % (name, self.__class__.__name__))\\n param = self._parameters[name]\\n # Set bounds\\n lower_bound, upper_bound = bounds\\n if torch.is_tensor(lower_bound) and torch.is_tensor(upper_bound):\\n if lower_bound.size() != upper_bound.size() or \\\\\\n lower_bound.size() != param.size():\\n raise AttributeError('Lower bound, upper bound, and param should have the same size')\\n elif not (isinstance(lower_bound, int) or isinstance(lower_bound, float)) or \\\\\\n not (isinstance(upper_bound, int) or isinstance(upper_bound, float)):\\n raise AttributeError('Unsupported argument types for parameter %s' % name)\\n\\n if name not in self._bounds:\\n self._bounds[name] = [None, None]\\n self._bounds[name][0] = lower_bound\\n self._bounds[name][1] = upper_bound\\n return self\",\n \"def SetHistogramParameters(self, numBins: 'int const', lowerBound: 'double', upperBound: 'double') -> \\\"void\\\":\\n return _itkLabelStatisticsImageFilterPython.itkLabelStatisticsImageFilterISS2ISS2_SetHistogramParameters(self, numBins, lowerBound, upperBound)\",\n \"def normalize_bounds(self, bounds):\\n scaled_bounds = []\\n scalings = []\\n intercepts = []\\n \\n non_fixed_params = []\\n \\n print(self.device)\\n \\n for name, domain in self.bounds.items():\\n # Get any fixed parmeters\\n if type(domain) == int or type(domain) == float:\\n # Take note\\n self.fixed_parameters.append(name)\\n\\n # Free parameters\\n elif type(domain) == tuple:\\n # Bookkeeping\\n self.free_parameters.append(name)\\n\\n # Get scaling\\n lower_bound = min(domain)\\n upper_bound = max(domain)\\n scale = upper_bound - lower_bound\\n\\n # Transform to [0, 1] domain\\n #scaled_bound = {'name': name, 'type': 'continuous', 'domain': (0., 1.)} #torch.adjustment required\\n non_fixed_params.append(name)\\n \\n # Store\\n #scaled_bounds.append(scaled_bound)\\n scalings.append(scale)\\n intercepts.append(lower_bound)\\n else:\\n raise ValueError(\\\"Domain bounds not understood\\\")\\n \\n n_hyperparams = len(non_fixed_params)\\n \\n scaled_bounds = cat([zeros(1,n_hyperparams, device = self.device), \\n ones(1, n_hyperparams, device = self.device)], 0)\\n return scaled_bounds, tensor(scalings, device = self.device, requires_grad = False), tensor(intercepts, device = self.device, requires_grad = False) #torch.adjustment required\",\n \"def set_lim(x, y, **kws):\\n per = kws['per']\\n min_per = 50 - per/2\\n max_per = per/2 + 50\\n xper = np.nanpercentile(x,[min_per,max_per])\\n yper = np.nanpercentile(y,[min_per,max_per])\\n ax = plt.gca()\\n ax.set_xlim(xper)\\n ax.set_ylim(yper)\",\n \"def test_threshold_range_a(self):\\n code, out, err = self.t.runError(\\\"--threshold --max 3.1 --min 3.2\\\")\\n self.assertIn(\\\"The min value must be lower than the max value.\\\", out)\",\n \"def _init_optimizer_bounds(self):\\n bounds = []\\n for filt in self.filters:\\n if filt.optimize_fc:\\n bounds.append((np.log10(filt.min_fc), np.log10(filt.max_fc)))\\n if filt.optimize_q:\\n bounds.append((filt.min_q, filt.max_q))\\n if filt.optimize_gain:\\n bounds.append((filt.min_gain, filt.max_gain))\\n return bounds\",\n \"def ctrl_limit_changed(self, which, new_limit):\\n super(PyDMSpinbox, self).ctrl_limit_changed(which, new_limit)\\n if not self.userDefinedLimits:\\n if which == \\\"UPPER\\\":\\n self.setMaximum(new_limit)\\n else:\\n self.setMinimum(new_limit)\",\n \"def histogram(hist_data, bins, minimum, maximum):\\r\\n hist_data2 = [x for x in hist_data if x <= maximum and x >= minimum]\\r\\n y, x = np.histogram(hist_data2, bins=bins)\\r\\n x_new = [((x[i] + x[i - 1]) / 2) for i in range(1, len(x))]\\r\\n max_freq = max(y)\\r\\n mean = x_new[y.argmax()]\\r\\n sigma = (x[1] - x[0]) * 4\\r\\n\\r\\n gauss = models.GaussianModel()\\r\\n fit = gauss.fit(y, x=x_new, center = mean, amplitude = max_freq, sigma = sigma, nan_policy= 'omit')\\r\\n\\r\\n return x, y, x_new, fit.best_fit\",\n \"def _handleInput(self, paramInput):\\n upperBound = paramInput.findFirst('upperBound')\\n if upperBound !=None:\\n self.upperBound = upperBound.value\\n self.upperBoundUsed = True\\n lowerBound = paramInput.findFirst('lowerBound')\\n if lowerBound !=None:\\n self.lowerBound = lowerBound.value\\n self.lowerBoundUsed = True\\n if self.lowerBoundUsed and self.upperBoundUsed:\\n if self.lowerBound == self.upperBound:\\n self.raiseAnError(IOError, 'Lower bound for Distribution \\\"'+self.name+'\\\" is equal to the upper bound!')\\n if self.lowerBound > self.upperBound:\\n self.raiseAnError(IOError, 'Lower bound for Distribution \\\"'+self.name+'\\\" is greater than the upper bound!')\",\n \"def _normalize_range():\\n clipped = tf.clip_by_value(inputs, self.minimum, self.maximum)\\n return -1 + 2 * (clipped - self.minimum) / length\",\n \"def clip(x, min, max):\\r\\n # see decorator for function body\\r\\n # for grep: clamp, bound\\r\",\n \"def histogram(a, bins=10, range=None, normed=False, weights=None, new=False):\\n # Old behavior\\n if new is False:\\n warnings.warn(\\\"\\\"\\\"\\n The semantics of histogram will be modified in\\n release 1.2 to improve outlier handling. The new behavior can be\\n obtained using new=True. Note that the new version accepts/returns\\n the bin edges instead of the left bin edges.\\n Please read the docstring for more information.\\\"\\\"\\\", FutureWarning)\\n a = asarray(a).ravel()\\n\\n if (range is not None):\\n mn, mx = range\\n if (mn > mx):\\n raise AttributeError, \\\\\\n 'max must be larger than min in range parameter.'\\n\\n if not iterable(bins):\\n if range is None:\\n range = (a.min(), a.max())\\n else:\\n warnings.warn(\\\"\\\"\\\"\\n Outliers handling will change in version 1.2.\\n Please read the docstring for details.\\\"\\\"\\\", FutureWarning)\\n mn, mx = [mi+0.0 for mi in range]\\n if mn == mx:\\n mn -= 0.5\\n mx += 0.5\\n bins = linspace(mn, mx, bins, endpoint=False)\\n else:\\n if normed:\\n raise ValueError, 'Use new=True to pass bin edges explicitly.'\\n warnings.warn(\\\"\\\"\\\"\\n The semantic for bins will change in version 1.2.\\n The bins will become the bin edges, instead of the left bin edges.\\n \\\"\\\"\\\", FutureWarning)\\n bins = asarray(bins)\\n if (np.diff(bins) < 0).any():\\n raise AttributeError, 'bins must increase monotonically.'\\n\\n\\n if weights is not None:\\n raise ValueError, 'weights are only available with new=True.'\\n\\n # best block size probably depends on processor cache size\\n block = 65536\\n n = sort(a[:block]).searchsorted(bins)\\n for i in xrange(block, a.size, block):\\n n += sort(a[i:i+block]).searchsorted(bins)\\n n = concatenate([n, [len(a)]])\\n n = n[1:]-n[:-1]\\n\\n if normed:\\n db = bins[1] - bins[0]\\n return 1.0/(a.size*db) * n, bins\\n else:\\n return n, bins\\n\\n\\n\\n # New behavior\\n elif new is True:\\n a = asarray(a)\\n if weights is not None:\\n weights = asarray(weights)\\n if np.any(weights.shape != a.shape):\\n raise ValueError, 'weights should have the same shape as a.'\\n weights = weights.ravel()\\n a = a.ravel()\\n\\n if (range is not None):\\n mn, mx = range\\n if (mn > mx):\\n raise AttributeError, \\\\\\n 'max must be larger than min in range parameter.'\\n\\n if not iterable(bins):\\n if range is None:\\n range = (a.min(), a.max())\\n mn, mx = [mi+0.0 for mi in range]\\n if mn == mx:\\n mn -= 0.5\\n mx += 0.5\\n bins = linspace(mn, mx, bins+1, endpoint=True)\\n else:\\n bins = asarray(bins)\\n if (np.diff(bins) < 0).any():\\n raise AttributeError, 'bins must increase monotonically.'\\n\\n # Histogram is an integer or a float array depending on the weights.\\n if weights is None:\\n ntype = int\\n else:\\n ntype = weights.dtype\\n n = np.zeros(bins.shape, ntype)\\n\\n block = 65536\\n if weights is None:\\n for i in arange(0, len(a), block):\\n sa = sort(a[i:i+block])\\n n += np.r_[sa.searchsorted(bins[:-1], 'left'), \\\\\\n sa.searchsorted(bins[-1], 'right')]\\n else:\\n zero = array(0, dtype=ntype)\\n for i in arange(0, len(a), block):\\n tmp_a = a[i:i+block]\\n tmp_w = weights[i:i+block]\\n sorting_index = np.argsort(tmp_a)\\n sa = tmp_a[sorting_index]\\n sw = tmp_w[sorting_index]\\n cw = np.concatenate(([zero,], sw.cumsum()))\\n bin_index = np.r_[sa.searchsorted(bins[:-1], 'left'), \\\\\\n sa.searchsorted(bins[-1], 'right')]\\n n += cw[bin_index]\\n\\n n = np.diff(n)\\n\\n if normed is False:\\n return n, bins\\n elif normed is True:\\n db = array(np.diff(bins), float)\\n return n/(n*db).sum(), bins\",\n \"def _update_limits(self):\\n if self.pos_x > self.max_x:\\n self.max_x = self.pos_x\\n if self.pos_y > self.max_y:\\n self.max_y = self.pos_y\\n if self.pos_x < self.min_x:\\n self.min_x = self.pos_x\\n if self.pos_y < self.min_y:\\n self.min_y = self.pos_y\",\n \"def set_range(self, full_range, abs_range=None, suppress_feedback=False):\\n self._enabled = full_range[0] != full_range[1]\\n if not self._enabled:\\n full_range = (\\n full_range[0] - 1, full_range[0])\\n self._suppress_feedback = suppress_feedback\\n super(MarkerProperty, self).set_range(full_range, abs_range=abs_range)\",\n \"def filter_range(self, name, field, start, end):\\n self.__names[name] = {\\n 'filter': {\\n 'range': {\\n field: {\\n 'gt': start,\\n 'lt': end\\n }\\n }\\n }\\n }\\n self.__last_name = name\\n return self\",\n \"def norm_hist(self, hist_lower, hist_upper, lower, upper, value) :\\n norm = 1.\\n if upper>lower :\\n norm = hist_lower.Integral()+(hist_upper.Integral()-hist_lower.Integral())/abs(upper-lower)*(value-lower)\\n return norm\",\n \"def hist_normalize_linear(data, new_min, new_max):\\n data_min = np.ma.min(data)\\n data_max = np.ma.max(data)\\n scaled = (data - data_min) * ((new_max - new_min) / (data_max - data_min))\\n scaled.mask = data.mask\\n return scaled\",\n \"def _get_colorbar_limits(self):\\n if self.boundaries is not None:\\n C = self.boundaries\\n if self.extend in [\\\"min\\\", \\\"both\\\"]:\\n C = C[1:]\\n\\n if self.extend in [\\\"max\\\", \\\"both\\\"]:\\n C = C[:-1]\\n return min(C), max(C)\\n else:\\n return self.get_clim()\",\n \"def SetHistogramParameters(self, numBins: 'int const', lowerBound: 'double', upperBound: 'double') -> \\\"void\\\":\\n return _itkLabelStatisticsImageFilterPython.itkLabelStatisticsImageFilterIUC2IUS2_SetHistogramParameters(self, numBins, lowerBound, upperBound)\",\n \"def __validate(self, value: int, extend_range: bool):\\n if extend_range:\\n bottom, top = self.getRange()\\n self.setRange(min(value, bottom), max(value, top))\\n return numpy.clip(value, *self.getRange())\",\n \"def limit(self, lim: float):\\n if self.mag() > lim:\\n self.values = tuple(self.norm()*lim)\\n return self\"\n]","isConversational":false},"negative_scores":{"kind":"list like","value":["0.68070877","0.663788","0.6523232","0.6398338","0.63696873","0.63696873","0.63291216","0.6299463","0.60977095","0.60977095","0.6090064","0.60784125","0.5962637","0.5954221","0.5932417","0.5870318","0.586771","0.5864656","0.5857545","0.5852148","0.5813075","0.5804698","0.5767122","0.5730996","0.57275325","0.5689645","0.5687849","0.568662","0.56817687","0.5669869","0.5665311","0.5660677","0.56541574","0.5635038","0.56297636","0.5615472","0.56153893","0.5578091","0.5576175","0.5573766","0.55713964","0.55586886","0.5556694","0.554437","0.55359465","0.5524956","0.55243915","0.5497544","0.5496868","0.5496754","0.5493804","0.54884744","0.5477628","0.54722947","0.54666877","0.54611295","0.5451296","0.54495484","0.54408586","0.5437805","0.5435629","0.54333895","0.5431286","0.54287934","0.5421974","0.54191256","0.541283","0.54111797","0.54081523","0.54062915","0.5404739","0.54041815","0.54033417","0.53987396","0.53987163","0.5390269","0.538317","0.53830075","0.53813887","0.53770375","0.53737336","0.53718483","0.53685045","0.5362615","0.5362172","0.53616387","0.5359742","0.53593063","0.5358694","0.53563243","0.5351541","0.5350321","0.5347604","0.5340948","0.5333363","0.53311443","0.53304607","0.53270876","0.53265786","0.53241634"],"string":"[\n \"0.68070877\",\n \"0.663788\",\n \"0.6523232\",\n \"0.6398338\",\n \"0.63696873\",\n \"0.63696873\",\n \"0.63291216\",\n \"0.6299463\",\n \"0.60977095\",\n \"0.60977095\",\n \"0.6090064\",\n \"0.60784125\",\n \"0.5962637\",\n \"0.5954221\",\n \"0.5932417\",\n \"0.5870318\",\n \"0.586771\",\n \"0.5864656\",\n \"0.5857545\",\n \"0.5852148\",\n \"0.5813075\",\n \"0.5804698\",\n \"0.5767122\",\n \"0.5730996\",\n \"0.57275325\",\n \"0.5689645\",\n \"0.5687849\",\n \"0.568662\",\n \"0.56817687\",\n \"0.5669869\",\n \"0.5665311\",\n \"0.5660677\",\n \"0.56541574\",\n \"0.5635038\",\n \"0.56297636\",\n \"0.5615472\",\n \"0.56153893\",\n \"0.5578091\",\n \"0.5576175\",\n \"0.5573766\",\n \"0.55713964\",\n \"0.55586886\",\n \"0.5556694\",\n \"0.554437\",\n \"0.55359465\",\n \"0.5524956\",\n \"0.55243915\",\n \"0.5497544\",\n \"0.5496868\",\n \"0.5496754\",\n \"0.5493804\",\n \"0.54884744\",\n \"0.5477628\",\n \"0.54722947\",\n \"0.54666877\",\n \"0.54611295\",\n \"0.5451296\",\n \"0.54495484\",\n \"0.54408586\",\n \"0.5437805\",\n \"0.5435629\",\n \"0.54333895\",\n \"0.5431286\",\n \"0.54287934\",\n \"0.5421974\",\n \"0.54191256\",\n \"0.541283\",\n \"0.54111797\",\n \"0.54081523\",\n \"0.54062915\",\n \"0.5404739\",\n \"0.54041815\",\n \"0.54033417\",\n \"0.53987396\",\n \"0.53987163\",\n \"0.5390269\",\n \"0.538317\",\n \"0.53830075\",\n \"0.53813887\",\n \"0.53770375\",\n \"0.53737336\",\n \"0.53718483\",\n \"0.53685045\",\n \"0.5362615\",\n \"0.5362172\",\n \"0.53616387\",\n \"0.5359742\",\n \"0.53593063\",\n \"0.5358694\",\n \"0.53563243\",\n \"0.5351541\",\n \"0.5350321\",\n \"0.5347604\",\n \"0.5340948\",\n \"0.5333363\",\n \"0.53311443\",\n \"0.53304607\",\n \"0.53270876\",\n \"0.53265786\",\n \"0.53241634\"\n]","isConversational":false},"document_score":{"kind":"string","value":"0.5693101"},"document_rank":{"kind":"string","value":"25"}}},{"rowIdx":82399,"cells":{"query":{"kind":"string","value":"Descriptive text of fields for UI display."},"document":{"kind":"string","value":"def displayDataDescr(cls):\n return (\n \"Parameter\",\n \"Auto range\",\n \"Lower\",\n \"Upper\",\n \"Number of bins\",\n \"X-axis scaling\",\n \"Y-axis weighting\"\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 description(self):","def displayField(self):\n print(\"Field :\")\n for i in range(len(self.field)):\n currentSuit = Suit(i + 1)\n print(Bcolor.BOLD + Suit.toColor(currentSuit) + \"\\t\" + str(currentSuit), self.field[i], end=\"\\t\" + Bcolor.END)\n print()","def description():","def Description(self) -> str:","def Description(self) -> str:","def desc_user(self):\n print(f\"\\tF.N: {self.first_name.title()}\\n\\tL.N:{self.last_name.title()}\\n\\tEmail: {self.email}\\n\\tage:{self.age}\")","def get_description(self):\n text = \"is a student's t distribution; characterised by its degrees of freedom, which here is\"+str(self.dofs)+\".\"\n return text","def info(level):\n if level == 'basic':\n string = _(\"Basic markup\")\n text = _(\"Only basic text tags are available in this input field.\")\n elif level == 'rich':\n string = _(\"Rich markup\")\n text = _(\"Rich and basic text tags are available in this input field.\") \n elif level == 'full':\n string = _(\"Full markup\")\n text = _(\"Every tags are available in this input field.\") \n elif level == 'none':\n string = _(\"No markup\")\n text = _(\"No tags are available in this input field.\") \n\n if level != 'none':\n text = text + \" \" + _(\"Check the markup reminder in related documentation for a description of these tags.\")\n\n return '<span class=\"help\" title=' + quoteattr(text) \\\n + '><img src=\"' + settings.STATIC_MEDIA_URL \\\n + 'images/savane/common/misc.default/edit.png' \\\n + ' border=\"0\" class=\"icon\" alt=\"\" />' \\\n + string + '</span>'","def description(self):\n pass","def description(self):\n pass","def show_fields(*fields):\n\n fields = filter( lambda x: x, fields )\n target_len = max( len(name) for name, value in fields ) + 2\n for name, value in fields:\n line = name + ':' + \" \" * (target_len - len(name))\n if type(value) == bool:\n line += color_text(\"Yes\", 'green') if value else color_text(\"No\", 'red')\n else:\n line += str(value)\n print line","def field_display(obj, field):\n return get_field_value(obj, field)","def text(self) -> None:\n label_space = tk.Label(self)\n label_space.grid(row=0)\n label_book_number = tk.Label(self, text=f'Номер книги:')\n label_book_number.grid(row=1, column=0, ipady=5)\n label_title = tk.Label(self, text='Название книги:')\n label_title.grid(row=2, column=0, padx=5)\n label_author = tk.Label(self, text='Автор:')\n label_author.grid(row=3, column=0, pady=5)\n label_genre = tk.Label(self, text='Жанр:')\n label_genre.grid(row=4, column=0)","def logic_program_form(self):\r\n return '% ASP{f} Translation of System Description ' + self.name + '\\n\\n'","def get_description(self):","def describe(self):\n return ''","def describe_user(self):\r\n print('\\nFirst Name: ' + self.first_name.title(), end='\\n',)\r\n print('Last Name: ' + self.last_name.title(), end='\\n')\r\n print('Address: ' + self.address.title(), end='\\n',)\r\n print('State: ' + self.state.title(), end='\\n',)\r\n print('Country: ' + self.country.title())","def display(self):\n return f'{self._last_name},{self._first_name}:({self._student_id}) {self._major} gpa:{self._gpa}'","def describe_user(self):\n print(\"\\n Name: \" + self.f_name.title() + ' ' + self.l_name.title())\n print(\"Age: \" + str(self.age)) \n print(\"Birthplace: \" + self.birthplace.title())","def describe(self) -> Text:\n return self.__repr__()","def test_form_help_text_is_correct(self):\n # https://stackoverflow.com/questions/24344981/how-to-change-help-\n # text-of-a-django-form-field\n\n # Above link helped figure out how to access help_text.\n self.assertEqual(\n self.form.fields[\"texture\"].help_text,\n \"One word descriptions seperated by commas.\",\n )","def fieldhelp2(self, fieldid):\n txt = []\n dd_desc = M.Globals[\"^DD\"][self.fileid][fieldid][21]\n for k,v in dd_desc.keys_with_decendants():\n txt.append(dd_desc[k][0].value)\n return '\\n'.join(txt)","def description(self):\n\t\treturn \"%s, %s\" % (self.name, self.country)","def describe(self):\n print(self.description)","def describe(self):\n print(self.description)","def describe(self):\r\n print( self.name + \" is here!\" )\r\n print( self.description )","def Label(self) -> str:","def details(self) -> str:\n return f\"- **language**: [{self.language}]\\n\" \\\n f\"- **opengame**: [{self.opengame}]\\n\" \\\n f\"- **system**: [{self.system}]\\n\" \\\n f\"- **mode**: [{self.mode}]\\n\" \\\n f\"- **attributes**: [{self.attributes}]\\n \" \\\n f\"- **score_threshold**: [{self.score_threshold}]\\n \" \\\n f\"- **monsters**: [{self.monsters}]\\n\"","def get_description(self):\n return DisplayText(self._description)","def get_descriptive_name(self):\n description = (f\"{self.year} {self.manufacturer.title()} \"\n f\"{self.model.title()}\")\n\n return description","def display_name(self):","def __str__(self):\n return \"Description(values={},data_model={})\".format(\n self._values, self.data_model\n )","def getText(self):","def __str__(self):\n text = []\n for varname, attrname, valuetype, label, widget, group, options in self.entries:\n value = getattr(self, varname, \"\")\n text.append(\"%s: %s\" % (attrname, value))\n return \"\\n\".join(text)","def get_text(self):\n inp = \" \"\n if self.link_id:\n inp += \"LINK \" + self.link_id\n inp += self.status + ' '\n if self.node_id:\n inp += \"NODE \" + self.node_id + ' '\n if self.value:\n inp += self.control_type.name + ' ' + str(self.value) + ' '\n if self.time:\n inp += self.time + ' '\n if self.clock_time:\n inp += self.clock_time + ' '\n # TODO: research correct formatting of time, clock_time options\n return inp","def description(self) -> str:\n pass","def show_info(self):\n txt = \"Brand: %s\\nModel: %s\\nHostname: %s\\n\"%(self.brand, self.model, self.hostname)\n return txt","def __str__(self):\n return \"{0} : {1}\".format(self.name, self.description)","def field_names(self):\n\n entry_time_name = forms_builder.forms.models.FormEntry._meta.get_field('entry_time').verbose_name.title()\n document_title_name = Document._meta.get_field('name').verbose_name.title()\n document_url_name = Document._meta.get_field('url').verbose_name.title()\n\n form = self.form.all()[0]\n return ['user'] \\\n + [document_title_name, document_url_name] \\\n + [f.label\n for f in form.fields.all()] \\\n + [entry_time_name]","def show(self) -> str:\n return f'[{self.font}]{self.text}[{self.font}]' if self.font else self.text","def display_fields(self):\r\n\r\n field_text = self.show_fields()\r\n field_text_list = field_text.split(EOL)[0:-1]\r\n\r\n def fld_format (x_temp):\r\n\r\n x_temp = x_temp.split(COLON)[0], x_temp.split(COLON)[1]\r\n\r\n \"\"\"formats output of the list of search results\"\"\"\r\n\r\n if not isinstance(x_temp[1],str):\r\n shown_indexes = rangelist.range_find([int(Index(a_temp))\r\n for a_temp in x_temp[1]],reduce=True)\r\n else:\r\n shown_indexes = x_temp[1]\r\n\r\n if len(shown_indexes) < 20:\r\n return (abridge(x_temp[0]).replace(VERTLINE,SLASH)\r\n +VERTLINE\r\n +shown_indexes)\r\n\r\n\r\n returnlist = []\r\n sp_temp = rangelist.split_up_range(shown_indexes)\r\n\r\n\r\n returnlist.append(x_temp[0].replace(VERTLINE,SLASH)[0:min([60,len(x_temp[0])])]\r\n +VERTLINE+sp_temp[0])\r\n for s_temp in sp_temp[1:]:\r\n returnlist.append(VERTLINE+s_temp)\r\n\r\n return returnlist\r\n\r\n show_list(field_text_list,\r\n alerts.FIELDS[3:],0,40,\r\n func=fld_format,\r\n present=True,\r\n display=display)","def __str__(self):\n\n return (self._display_name + \"\\n\\t\" + self._description + \"\\n\")","def describe_user(self):\n message = f\"This user's full name is {self.first_name.title()} \"\n message += f\"{self.last_name.title()}. {self.first_name.title()} is \"\n message += f\"{self.age} years old, lives in \"\n message += f\"{self.location.title()} and uses {self.username} \"\n message += f\"as username.\"\n print(message)","def describe(self) -> str:","def get_field_display_info(self, field_dict, field_name):\n raise NotImplementedError","def describe_user(self):\n\t\tprint(self.first_name.title() + \" \" + self.last_name.title() + \" - \" + str(self.age))","def __str__(self):\n txt = (self.name, self.description)\n return txt","def get_description(self):\n pass","def getText():","def getText():","def getText():","def getText():","def getText():","def full_alert_text(self):\n if self.label:\n return '%s: %s' % (self.label, self.description)\n\n return self.description","def abbrev_help_more_text(self):\n pass","def help_description():\n pass","def __str__(self):\n return self.description","def __str__(self):\n return self.description","def __str__(self) -> str:\n return self.description","def _description(self):\n return None","def get_description(self, field_name='DESCRIPTION'):\n return self.get_html(field_name)","def describe(self) -> str:\n return (\n \"{name} {surname} è nata/o a {birth_municipality} ({birth_province_code}) il {birthdate}.\"\n \" Ora vive a {municipality} ({province_code}) in {address} {house_number}.\"\n ).format(**self._data)","def helptext(self):\n return \"\"","def textual(self):\n return [self.version_label, self.timestamp_label,\n self.message_label, self.battery_label,\n self.battery_icon, self.record_sensors]","def displayInput(self):\n\n ## Before changes are committed the user can see all changes made\n print(\"\\nCurrent Record:\\n===============\\nName: \" + self.first_name.title() + \" \" + self.last_name.title() + \"\\nCompany: \" + self.company_name.title() + \"\\nAddress: \" + self.address.title() + \"\\nCity: \" + self.city.title() + \"\\nState: \" + self.state_code.upper() + \"\\nZip Code: \" + str(self.zip_code) + \"\\nPrimary Phone: \" + self.phone_number + \"\\nSecondary Phone: \" + self.phone_number_2 + \"\\nEmail: \" + self.email_address)","def description(self):\n\n\t\treturn \"%d %s %s\" % (self.vintage, self.winery.name, self.name)","def __str__(self) -> str:\n\n return self.display_form()","def _get_FIELD_humanized_display(self, field):\n value = getattr(self, field.attname)\n if value is None:\n return\n power = max([i for i in utils.POWERS if value // i > 0 and i > 1])\n value /= power\n template = ''\n template += '{:.%sf}' % field.humanized_decimals\n template += ' ' if field.spaced_display else ''\n template += utils.POWERS[power]\n template += '{!s:s}'\n return template.format(value, field.unit)","def __str__(self):\n #Format data from default fields\n template = \"{number:4}|{rep:4}|{time:5}{priority:+2}|\" \\\n \"{record_type:8}|{name:17}\"\n default_fields = template.format(**self)\n \n #Format data from custom fields\n custom_field_list = []\n for label in self.custom_labels:\n custom_field_list.append(\"|{:17}:{!s:<5}\".format(label,\n self[label]))\n custom_fields = \"\".join(custom_field_list)\n \n return default_fields + custom_fields","def get_description(self) -> str:\n pass","def describe_user(self):\n print(f'\\nName of the user is: {self.name}')\n print(f'Age of the user: {self.age}')\n print(f'Location of user is: {self.location}')","def get_describe_name(self):\n long_name = str(self.year)+ ' ' + self.make.title()+ ' ' +self.model.title()\n return long_name","def summary(self):\n if self.intact and self.valid:\n return 'INTACT:' + ','.join(self.summary_fields())\n else:\n return 'INVALID'","def label(self):\n return ''","def properties(cls) -> str:\n with cls.prime_subfield.repr(\"int\"):\n irreducible_poly_str = str(cls._irreducible_poly)\n primitive_element_str = poly_to_str(integer_to_poly(int(cls.primitive_element), cls.characteristic))\n\n string = \"Galois Field:\"\n string += f\"\\n name: {cls.name}\"\n string += f\"\\n characteristic: {cls.characteristic}\"\n string += f\"\\n degree: {cls.degree}\"\n string += f\"\\n order: {cls.order}\"\n string += f\"\\n irreducible_poly: {irreducible_poly_str}\"\n string += f\"\\n is_primitive_poly: {cls.is_primitive_poly}\"\n string += f\"\\n primitive_element: {primitive_element_str}\"\n\n return string","def description(self) -> str:\n raise NotImplementedError","def description(self) -> str:\n raise NotImplementedError","def description(self) -> str:\n raise NotImplementedError","def description(self) -> str:\r\n description = \"The player must aim to put the most possible units \" + \\\r\n \"of \" + colour_name(self.colour) + \" on the outer\" +\\\r\n \" perimeter.\"\r\n return description","def description(self) -> str:\r\n raise NotImplementedError","def description(self) -> str:\r\n raise NotImplementedError","def description(self) -> str:\r\n raise NotImplementedError","def description(self):\n\t\tif self._record is not None:\n\t\t return self._record.description\n\t\telse:\n\t\t return \"\"","def __str__(self):\n if len(self.label) > 0:\n descr = [\"'%s', target='%s' [%s]\" % (self.label, self.target.name, self.target.body_type)]\n else:\n descr = [\"target='%s' [%s]\" % (self.target.name, self.target.body_type)]\n if self.baseline:\n descr[0] += ', initial baseline offset=%f' % (self.baseline.poly[-1],)\n if self.beam:\n descr[0] += ', beam height=%f' % (self.beam.height,)\n for scan_ind, scan in enumerate(self.scans):\n descr.append('%4d: %s' % (scan_ind, str(scan)))\n return '\\n'.join(descr)","def description(self):\n return \"Le célèbre \" + self._nom + \" se trouve devant vous.\"","def __str__(self):\n description = \"-------- %s --------\" % (self.name)\n description += \"\\nnmax = %i\" % (self.nmax)\n description += \"\\nnslots = %i\" % (self.nslots)\n description += \"\\nbonus_power = %i\" % (self.bonus_power)\n description += \"\\nbonus_initiative = %.1f\" % (self.bonus_initiative)\n description += \"\\nneeds_drive = %i\" % (self.needs_drive)\n description += \"\\nis_mobile = %i\" % (self.is_mobile)\n description += \"\\n----- Default Parts -----\"\n for i in range(len(self.default_parts)):\n description += \"\\n%i) %s\" % (i + 1, self.default_parts[i].name)\n return description","def info(self):\n return f\"{self.get_first_name}, {self.get_last_name}. {self.get_age} y.o. #{self.get_id_number}\"","def __str__(self) -> str:\n\t\treturn \"{}{}; {}\".format(TAB, self.get_typestring(force_regen=True), self.get_description())","def __str__(self) -> str:\n return f'label: {self.label}, freq: {self.freq}, code: {self.code}'","def get_descriptive_name(self):\n return f\"{self.year} {self.make} {self.model}\".title()","def show_stat_desc(self):\n qtw.QMessageBox.information(\n self, \"Stat Descriptions\", '*This list is not comprehensive, and '\n 'will vary depending on race/(sub)class.*\\n\\nStrength\\n\\t-damage '\n 'and attack bonuses for most melee and thrown weapons\\n\\t-carry '\n 'capacity\\n\\t-Athletics checks\\nDexterity\\n\\t-damage and attack '\n 'bonuses for ranged/finesse weapons\\n\\t-Acrobatics, Stealth, '\n 'Sleight of Hand, and Initiative checks\\nConstitution\\n\\t-hit '\n 'points\\n\\t-resistance to poisons, etc.\\nIntelligence\\n\\t-spell '\n 'save DC/attack bonus for Wizards\\n\\t-Arcana, History, '\n 'Investigation, Nature, and Religion checks\\nWisdom\\n\\t-spell save'\n ' DC/attack bonus for Druid, Cleric, and Ranger\\n\\t-Animal '\n 'Handling, Insight, Medicine, Perception, and Survival checks\\n'\n 'Charisma\\n\\t-spell save DC/attack bonus for Bard, Paladin, '\n 'Warlock and Sorceror\\n\\t-Deception, Intimidation, Performance and'\n ' Persuasion checks'\n )","def describe(self):\r\n return dict((field.name, dict(\r\n id=field.name,\r\n name=field.label,\r\n validators=ValidatorSerializer(\r\n field.requires if isSequenceType(field.requires) else [field.requires])(),\r\n comment=field.comment,\r\n readable=field.readable,\r\n writable=field.writable,\r\n type=getattr(field, 'wtype',\r\n field.type.type if isinstance(field.type, SQLCustomType) else field.type.split('(')[0]),\r\n # w2pwidget=field.widget,\r\n )) for field in self.descibe_columns)","def describe_user(self):\n print(self.first_name + \"\\n\" + self.last_name + \"\\n\" + self.Screen_name)","def summary_line_and_description():","def help_text(cls):\n ret = (\"I currently answer these burning questions, \" +\n \"but only when you address me by name:\\n\" +\n \"\\\"tell me about server `(server_id|server_name)`\\\"\\n\" +\n \"\\\"tell me about ip `ip_address`\\\"\\n\" +\n \"\\\"tell me about group `(group_id|group_name)`\\\"\\n\" +\n \"\\\"list all servers\\\"\\n\" +\n \"\\\"list server groups\\\"\\n\" +\n \"\\\"servers with CVE `cve_id`\\\"\\n\" +\n \"\\\"servers in group `(group_id|group_name)`\\\"\\n\" +\n \"\\\"group firewall `(group_id|group_name)`\\\"\\n\" +\n \"\\\"ec2 halo footprint csv\\\"\\n\" +\n \"\\\"version\\\"\\n\" +\n \"\\\"tasks\\\"\\n\" +\n \"\\\"config\\\"\\n\")\n return ret","def summary(self):\r\n return '%s%s: %s%s %s%s' % (BLUE, self.title,\r\n GREEN, self.description,\r\n NORMAL, self.link)","def get_help_text(self, field_name):\n try:\n if self.view.serializer_class:\n serializer = self.view.serializer_class()\n\n if field_name in serializer.fields and serializer.fields[field_name].help_text:\n return serializer.fields[field_name].help_text\n\n except AttributeError:\n pass\n\n return None","def __str__(self):\n return \"choice_text: \" + self.choice_text","def text(self) -> str:","def description(self):\n return (self.__doc__ or \"\").strip()"],"string":"[\n \"def description(self):\",\n \"def displayField(self):\\n print(\\\"Field :\\\")\\n for i in range(len(self.field)):\\n currentSuit = Suit(i + 1)\\n print(Bcolor.BOLD + Suit.toColor(currentSuit) + \\\"\\\\t\\\" + str(currentSuit), self.field[i], end=\\\"\\\\t\\\" + Bcolor.END)\\n print()\",\n \"def description():\",\n \"def Description(self) -> str:\",\n \"def Description(self) -> str:\",\n \"def desc_user(self):\\n print(f\\\"\\\\tF.N: {self.first_name.title()}\\\\n\\\\tL.N:{self.last_name.title()}\\\\n\\\\tEmail: {self.email}\\\\n\\\\tage:{self.age}\\\")\",\n \"def get_description(self):\\n text = \\\"is a student's t distribution; characterised by its degrees of freedom, which here is\\\"+str(self.dofs)+\\\".\\\"\\n return text\",\n \"def info(level):\\n if level == 'basic':\\n string = _(\\\"Basic markup\\\")\\n text = _(\\\"Only basic text tags are available in this input field.\\\")\\n elif level == 'rich':\\n string = _(\\\"Rich markup\\\")\\n text = _(\\\"Rich and basic text tags are available in this input field.\\\") \\n elif level == 'full':\\n string = _(\\\"Full markup\\\")\\n text = _(\\\"Every tags are available in this input field.\\\") \\n elif level == 'none':\\n string = _(\\\"No markup\\\")\\n text = _(\\\"No tags are available in this input field.\\\") \\n\\n if level != 'none':\\n text = text + \\\" \\\" + _(\\\"Check the markup reminder in related documentation for a description of these tags.\\\")\\n\\n return '<span class=\\\"help\\\" title=' + quoteattr(text) \\\\\\n + '><img src=\\\"' + settings.STATIC_MEDIA_URL \\\\\\n + 'images/savane/common/misc.default/edit.png' \\\\\\n + ' border=\\\"0\\\" class=\\\"icon\\\" alt=\\\"\\\" />' \\\\\\n + string + '</span>'\",\n \"def description(self):\\n pass\",\n \"def description(self):\\n pass\",\n \"def show_fields(*fields):\\n\\n fields = filter( lambda x: x, fields )\\n target_len = max( len(name) for name, value in fields ) + 2\\n for name, value in fields:\\n line = name + ':' + \\\" \\\" * (target_len - len(name))\\n if type(value) == bool:\\n line += color_text(\\\"Yes\\\", 'green') if value else color_text(\\\"No\\\", 'red')\\n else:\\n line += str(value)\\n print line\",\n \"def field_display(obj, field):\\n return get_field_value(obj, field)\",\n \"def text(self) -> None:\\n label_space = tk.Label(self)\\n label_space.grid(row=0)\\n label_book_number = tk.Label(self, text=f'Номер книги:')\\n label_book_number.grid(row=1, column=0, ipady=5)\\n label_title = tk.Label(self, text='Название книги:')\\n label_title.grid(row=2, column=0, padx=5)\\n label_author = tk.Label(self, text='Автор:')\\n label_author.grid(row=3, column=0, pady=5)\\n label_genre = tk.Label(self, text='Жанр:')\\n label_genre.grid(row=4, column=0)\",\n \"def logic_program_form(self):\\r\\n return '% ASP{f} Translation of System Description ' + self.name + '\\\\n\\\\n'\",\n \"def get_description(self):\",\n \"def describe(self):\\n return ''\",\n \"def describe_user(self):\\r\\n print('\\\\nFirst Name: ' + self.first_name.title(), end='\\\\n',)\\r\\n print('Last Name: ' + self.last_name.title(), end='\\\\n')\\r\\n print('Address: ' + self.address.title(), end='\\\\n',)\\r\\n print('State: ' + self.state.title(), end='\\\\n',)\\r\\n print('Country: ' + self.country.title())\",\n \"def display(self):\\n return f'{self._last_name},{self._first_name}:({self._student_id}) {self._major} gpa:{self._gpa}'\",\n \"def describe_user(self):\\n print(\\\"\\\\n Name: \\\" + self.f_name.title() + ' ' + self.l_name.title())\\n print(\\\"Age: \\\" + str(self.age)) \\n print(\\\"Birthplace: \\\" + self.birthplace.title())\",\n \"def describe(self) -> Text:\\n return self.__repr__()\",\n \"def test_form_help_text_is_correct(self):\\n # https://stackoverflow.com/questions/24344981/how-to-change-help-\\n # text-of-a-django-form-field\\n\\n # Above link helped figure out how to access help_text.\\n self.assertEqual(\\n self.form.fields[\\\"texture\\\"].help_text,\\n \\\"One word descriptions seperated by commas.\\\",\\n )\",\n \"def fieldhelp2(self, fieldid):\\n txt = []\\n dd_desc = M.Globals[\\\"^DD\\\"][self.fileid][fieldid][21]\\n for k,v in dd_desc.keys_with_decendants():\\n txt.append(dd_desc[k][0].value)\\n return '\\\\n'.join(txt)\",\n \"def description(self):\\n\\t\\treturn \\\"%s, %s\\\" % (self.name, self.country)\",\n \"def describe(self):\\n print(self.description)\",\n \"def describe(self):\\n print(self.description)\",\n \"def describe(self):\\r\\n print( self.name + \\\" is here!\\\" )\\r\\n print( self.description )\",\n \"def Label(self) -> str:\",\n \"def details(self) -> str:\\n return f\\\"- **language**: [{self.language}]\\\\n\\\" \\\\\\n f\\\"- **opengame**: [{self.opengame}]\\\\n\\\" \\\\\\n f\\\"- **system**: [{self.system}]\\\\n\\\" \\\\\\n f\\\"- **mode**: [{self.mode}]\\\\n\\\" \\\\\\n f\\\"- **attributes**: [{self.attributes}]\\\\n \\\" \\\\\\n f\\\"- **score_threshold**: [{self.score_threshold}]\\\\n \\\" \\\\\\n f\\\"- **monsters**: [{self.monsters}]\\\\n\\\"\",\n \"def get_description(self):\\n return DisplayText(self._description)\",\n \"def get_descriptive_name(self):\\n description = (f\\\"{self.year} {self.manufacturer.title()} \\\"\\n f\\\"{self.model.title()}\\\")\\n\\n return description\",\n \"def display_name(self):\",\n \"def __str__(self):\\n return \\\"Description(values={},data_model={})\\\".format(\\n self._values, self.data_model\\n )\",\n \"def getText(self):\",\n \"def __str__(self):\\n text = []\\n for varname, attrname, valuetype, label, widget, group, options in self.entries:\\n value = getattr(self, varname, \\\"\\\")\\n text.append(\\\"%s: %s\\\" % (attrname, value))\\n return \\\"\\\\n\\\".join(text)\",\n \"def get_text(self):\\n inp = \\\" \\\"\\n if self.link_id:\\n inp += \\\"LINK \\\" + self.link_id\\n inp += self.status + ' '\\n if self.node_id:\\n inp += \\\"NODE \\\" + self.node_id + ' '\\n if self.value:\\n inp += self.control_type.name + ' ' + str(self.value) + ' '\\n if self.time:\\n inp += self.time + ' '\\n if self.clock_time:\\n inp += self.clock_time + ' '\\n # TODO: research correct formatting of time, clock_time options\\n return inp\",\n \"def description(self) -> str:\\n pass\",\n \"def show_info(self):\\n txt = \\\"Brand: %s\\\\nModel: %s\\\\nHostname: %s\\\\n\\\"%(self.brand, self.model, self.hostname)\\n return txt\",\n \"def __str__(self):\\n return \\\"{0} : {1}\\\".format(self.name, self.description)\",\n \"def field_names(self):\\n\\n entry_time_name = forms_builder.forms.models.FormEntry._meta.get_field('entry_time').verbose_name.title()\\n document_title_name = Document._meta.get_field('name').verbose_name.title()\\n document_url_name = Document._meta.get_field('url').verbose_name.title()\\n\\n form = self.form.all()[0]\\n return ['user'] \\\\\\n + [document_title_name, document_url_name] \\\\\\n + [f.label\\n for f in form.fields.all()] \\\\\\n + [entry_time_name]\",\n \"def show(self) -> str:\\n return f'[{self.font}]{self.text}[{self.font}]' if self.font else self.text\",\n \"def display_fields(self):\\r\\n\\r\\n field_text = self.show_fields()\\r\\n field_text_list = field_text.split(EOL)[0:-1]\\r\\n\\r\\n def fld_format (x_temp):\\r\\n\\r\\n x_temp = x_temp.split(COLON)[0], x_temp.split(COLON)[1]\\r\\n\\r\\n \\\"\\\"\\\"formats output of the list of search results\\\"\\\"\\\"\\r\\n\\r\\n if not isinstance(x_temp[1],str):\\r\\n shown_indexes = rangelist.range_find([int(Index(a_temp))\\r\\n for a_temp in x_temp[1]],reduce=True)\\r\\n else:\\r\\n shown_indexes = x_temp[1]\\r\\n\\r\\n if len(shown_indexes) < 20:\\r\\n return (abridge(x_temp[0]).replace(VERTLINE,SLASH)\\r\\n +VERTLINE\\r\\n +shown_indexes)\\r\\n\\r\\n\\r\\n returnlist = []\\r\\n sp_temp = rangelist.split_up_range(shown_indexes)\\r\\n\\r\\n\\r\\n returnlist.append(x_temp[0].replace(VERTLINE,SLASH)[0:min([60,len(x_temp[0])])]\\r\\n +VERTLINE+sp_temp[0])\\r\\n for s_temp in sp_temp[1:]:\\r\\n returnlist.append(VERTLINE+s_temp)\\r\\n\\r\\n return returnlist\\r\\n\\r\\n show_list(field_text_list,\\r\\n alerts.FIELDS[3:],0,40,\\r\\n func=fld_format,\\r\\n present=True,\\r\\n display=display)\",\n \"def __str__(self):\\n\\n return (self._display_name + \\\"\\\\n\\\\t\\\" + self._description + \\\"\\\\n\\\")\",\n \"def describe_user(self):\\n message = f\\\"This user's full name is {self.first_name.title()} \\\"\\n message += f\\\"{self.last_name.title()}. {self.first_name.title()} is \\\"\\n message += f\\\"{self.age} years old, lives in \\\"\\n message += f\\\"{self.location.title()} and uses {self.username} \\\"\\n message += f\\\"as username.\\\"\\n print(message)\",\n \"def describe(self) -> str:\",\n \"def get_field_display_info(self, field_dict, field_name):\\n raise NotImplementedError\",\n \"def describe_user(self):\\n\\t\\tprint(self.first_name.title() + \\\" \\\" + self.last_name.title() + \\\" - \\\" + str(self.age))\",\n \"def __str__(self):\\n txt = (self.name, self.description)\\n return txt\",\n \"def get_description(self):\\n pass\",\n \"def getText():\",\n \"def getText():\",\n \"def getText():\",\n \"def getText():\",\n \"def getText():\",\n \"def full_alert_text(self):\\n if self.label:\\n return '%s: %s' % (self.label, self.description)\\n\\n return self.description\",\n \"def abbrev_help_more_text(self):\\n pass\",\n \"def help_description():\\n pass\",\n \"def __str__(self):\\n return self.description\",\n \"def __str__(self):\\n return self.description\",\n \"def __str__(self) -> str:\\n return self.description\",\n \"def _description(self):\\n return None\",\n \"def get_description(self, field_name='DESCRIPTION'):\\n return self.get_html(field_name)\",\n \"def describe(self) -> str:\\n return (\\n \\\"{name} {surname} è nata/o a {birth_municipality} ({birth_province_code}) il {birthdate}.\\\"\\n \\\" Ora vive a {municipality} ({province_code}) in {address} {house_number}.\\\"\\n ).format(**self._data)\",\n \"def helptext(self):\\n return \\\"\\\"\",\n \"def textual(self):\\n return [self.version_label, self.timestamp_label,\\n self.message_label, self.battery_label,\\n self.battery_icon, self.record_sensors]\",\n \"def displayInput(self):\\n\\n ## Before changes are committed the user can see all changes made\\n print(\\\"\\\\nCurrent Record:\\\\n===============\\\\nName: \\\" + self.first_name.title() + \\\" \\\" + self.last_name.title() + \\\"\\\\nCompany: \\\" + self.company_name.title() + \\\"\\\\nAddress: \\\" + self.address.title() + \\\"\\\\nCity: \\\" + self.city.title() + \\\"\\\\nState: \\\" + self.state_code.upper() + \\\"\\\\nZip Code: \\\" + str(self.zip_code) + \\\"\\\\nPrimary Phone: \\\" + self.phone_number + \\\"\\\\nSecondary Phone: \\\" + self.phone_number_2 + \\\"\\\\nEmail: \\\" + self.email_address)\",\n \"def description(self):\\n\\n\\t\\treturn \\\"%d %s %s\\\" % (self.vintage, self.winery.name, self.name)\",\n \"def __str__(self) -> str:\\n\\n return self.display_form()\",\n \"def _get_FIELD_humanized_display(self, field):\\n value = getattr(self, field.attname)\\n if value is None:\\n return\\n power = max([i for i in utils.POWERS if value // i > 0 and i > 1])\\n value /= power\\n template = ''\\n template += '{:.%sf}' % field.humanized_decimals\\n template += ' ' if field.spaced_display else ''\\n template += utils.POWERS[power]\\n template += '{!s:s}'\\n return template.format(value, field.unit)\",\n \"def __str__(self):\\n #Format data from default fields\\n template = \\\"{number:4}|{rep:4}|{time:5}{priority:+2}|\\\" \\\\\\n \\\"{record_type:8}|{name:17}\\\"\\n default_fields = template.format(**self)\\n \\n #Format data from custom fields\\n custom_field_list = []\\n for label in self.custom_labels:\\n custom_field_list.append(\\\"|{:17}:{!s:<5}\\\".format(label,\\n self[label]))\\n custom_fields = \\\"\\\".join(custom_field_list)\\n \\n return default_fields + custom_fields\",\n \"def get_description(self) -> str:\\n pass\",\n \"def describe_user(self):\\n print(f'\\\\nName of the user is: {self.name}')\\n print(f'Age of the user: {self.age}')\\n print(f'Location of user is: {self.location}')\",\n \"def get_describe_name(self):\\n long_name = str(self.year)+ ' ' + self.make.title()+ ' ' +self.model.title()\\n return long_name\",\n \"def summary(self):\\n if self.intact and self.valid:\\n return 'INTACT:' + ','.join(self.summary_fields())\\n else:\\n return 'INVALID'\",\n \"def label(self):\\n return ''\",\n \"def properties(cls) -> str:\\n with cls.prime_subfield.repr(\\\"int\\\"):\\n irreducible_poly_str = str(cls._irreducible_poly)\\n primitive_element_str = poly_to_str(integer_to_poly(int(cls.primitive_element), cls.characteristic))\\n\\n string = \\\"Galois Field:\\\"\\n string += f\\\"\\\\n name: {cls.name}\\\"\\n string += f\\\"\\\\n characteristic: {cls.characteristic}\\\"\\n string += f\\\"\\\\n degree: {cls.degree}\\\"\\n string += f\\\"\\\\n order: {cls.order}\\\"\\n string += f\\\"\\\\n irreducible_poly: {irreducible_poly_str}\\\"\\n string += f\\\"\\\\n is_primitive_poly: {cls.is_primitive_poly}\\\"\\n string += f\\\"\\\\n primitive_element: {primitive_element_str}\\\"\\n\\n return string\",\n \"def description(self) -> str:\\n raise NotImplementedError\",\n \"def description(self) -> str:\\n raise NotImplementedError\",\n \"def description(self) -> str:\\n raise NotImplementedError\",\n \"def description(self) -> str:\\r\\n description = \\\"The player must aim to put the most possible units \\\" + \\\\\\r\\n \\\"of \\\" + colour_name(self.colour) + \\\" on the outer\\\" +\\\\\\r\\n \\\" perimeter.\\\"\\r\\n return description\",\n \"def description(self) -> str:\\r\\n raise NotImplementedError\",\n \"def description(self) -> str:\\r\\n raise NotImplementedError\",\n \"def description(self) -> str:\\r\\n raise NotImplementedError\",\n \"def description(self):\\n\\t\\tif self._record is not None:\\n\\t\\t return self._record.description\\n\\t\\telse:\\n\\t\\t return \\\"\\\"\",\n \"def __str__(self):\\n if len(self.label) > 0:\\n descr = [\\\"'%s', target='%s' [%s]\\\" % (self.label, self.target.name, self.target.body_type)]\\n else:\\n descr = [\\\"target='%s' [%s]\\\" % (self.target.name, self.target.body_type)]\\n if self.baseline:\\n descr[0] += ', initial baseline offset=%f' % (self.baseline.poly[-1],)\\n if self.beam:\\n descr[0] += ', beam height=%f' % (self.beam.height,)\\n for scan_ind, scan in enumerate(self.scans):\\n descr.append('%4d: %s' % (scan_ind, str(scan)))\\n return '\\\\n'.join(descr)\",\n \"def description(self):\\n return \\\"Le célèbre \\\" + self._nom + \\\" se trouve devant vous.\\\"\",\n \"def __str__(self):\\n description = \\\"-------- %s --------\\\" % (self.name)\\n description += \\\"\\\\nnmax = %i\\\" % (self.nmax)\\n description += \\\"\\\\nnslots = %i\\\" % (self.nslots)\\n description += \\\"\\\\nbonus_power = %i\\\" % (self.bonus_power)\\n description += \\\"\\\\nbonus_initiative = %.1f\\\" % (self.bonus_initiative)\\n description += \\\"\\\\nneeds_drive = %i\\\" % (self.needs_drive)\\n description += \\\"\\\\nis_mobile = %i\\\" % (self.is_mobile)\\n description += \\\"\\\\n----- Default Parts -----\\\"\\n for i in range(len(self.default_parts)):\\n description += \\\"\\\\n%i) %s\\\" % (i + 1, self.default_parts[i].name)\\n return description\",\n \"def info(self):\\n return f\\\"{self.get_first_name}, {self.get_last_name}. {self.get_age} y.o. #{self.get_id_number}\\\"\",\n \"def __str__(self) -> str:\\n\\t\\treturn \\\"{}{}; {}\\\".format(TAB, self.get_typestring(force_regen=True), self.get_description())\",\n \"def __str__(self) -> str:\\n return f'label: {self.label}, freq: {self.freq}, code: {self.code}'\",\n \"def get_descriptive_name(self):\\n return f\\\"{self.year} {self.make} {self.model}\\\".title()\",\n \"def show_stat_desc(self):\\n qtw.QMessageBox.information(\\n self, \\\"Stat Descriptions\\\", '*This list is not comprehensive, and '\\n 'will vary depending on race/(sub)class.*\\\\n\\\\nStrength\\\\n\\\\t-damage '\\n 'and attack bonuses for most melee and thrown weapons\\\\n\\\\t-carry '\\n 'capacity\\\\n\\\\t-Athletics checks\\\\nDexterity\\\\n\\\\t-damage and attack '\\n 'bonuses for ranged/finesse weapons\\\\n\\\\t-Acrobatics, Stealth, '\\n 'Sleight of Hand, and Initiative checks\\\\nConstitution\\\\n\\\\t-hit '\\n 'points\\\\n\\\\t-resistance to poisons, etc.\\\\nIntelligence\\\\n\\\\t-spell '\\n 'save DC/attack bonus for Wizards\\\\n\\\\t-Arcana, History, '\\n 'Investigation, Nature, and Religion checks\\\\nWisdom\\\\n\\\\t-spell save'\\n ' DC/attack bonus for Druid, Cleric, and Ranger\\\\n\\\\t-Animal '\\n 'Handling, Insight, Medicine, Perception, and Survival checks\\\\n'\\n 'Charisma\\\\n\\\\t-spell save DC/attack bonus for Bard, Paladin, '\\n 'Warlock and Sorceror\\\\n\\\\t-Deception, Intimidation, Performance and'\\n ' Persuasion checks'\\n )\",\n \"def describe(self):\\r\\n return dict((field.name, dict(\\r\\n id=field.name,\\r\\n name=field.label,\\r\\n validators=ValidatorSerializer(\\r\\n field.requires if isSequenceType(field.requires) else [field.requires])(),\\r\\n comment=field.comment,\\r\\n readable=field.readable,\\r\\n writable=field.writable,\\r\\n type=getattr(field, 'wtype',\\r\\n field.type.type if isinstance(field.type, SQLCustomType) else field.type.split('(')[0]),\\r\\n # w2pwidget=field.widget,\\r\\n )) for field in self.descibe_columns)\",\n \"def describe_user(self):\\n print(self.first_name + \\\"\\\\n\\\" + self.last_name + \\\"\\\\n\\\" + self.Screen_name)\",\n \"def summary_line_and_description():\",\n \"def help_text(cls):\\n ret = (\\\"I currently answer these burning questions, \\\" +\\n \\\"but only when you address me by name:\\\\n\\\" +\\n \\\"\\\\\\\"tell me about server `(server_id|server_name)`\\\\\\\"\\\\n\\\" +\\n \\\"\\\\\\\"tell me about ip `ip_address`\\\\\\\"\\\\n\\\" +\\n \\\"\\\\\\\"tell me about group `(group_id|group_name)`\\\\\\\"\\\\n\\\" +\\n \\\"\\\\\\\"list all servers\\\\\\\"\\\\n\\\" +\\n \\\"\\\\\\\"list server groups\\\\\\\"\\\\n\\\" +\\n \\\"\\\\\\\"servers with CVE `cve_id`\\\\\\\"\\\\n\\\" +\\n \\\"\\\\\\\"servers in group `(group_id|group_name)`\\\\\\\"\\\\n\\\" +\\n \\\"\\\\\\\"group firewall `(group_id|group_name)`\\\\\\\"\\\\n\\\" +\\n \\\"\\\\\\\"ec2 halo footprint csv\\\\\\\"\\\\n\\\" +\\n \\\"\\\\\\\"version\\\\\\\"\\\\n\\\" +\\n \\\"\\\\\\\"tasks\\\\\\\"\\\\n\\\" +\\n \\\"\\\\\\\"config\\\\\\\"\\\\n\\\")\\n return ret\",\n \"def summary(self):\\r\\n return '%s%s: %s%s %s%s' % (BLUE, self.title,\\r\\n GREEN, self.description,\\r\\n NORMAL, self.link)\",\n \"def get_help_text(self, field_name):\\n try:\\n if self.view.serializer_class:\\n serializer = self.view.serializer_class()\\n\\n if field_name in serializer.fields and serializer.fields[field_name].help_text:\\n return serializer.fields[field_name].help_text\\n\\n except AttributeError:\\n pass\\n\\n return None\",\n \"def __str__(self):\\n return \\\"choice_text: \\\" + self.choice_text\",\n \"def text(self) -> str:\",\n \"def description(self):\\n return (self.__doc__ or \\\"\\\").strip()\"\n]","isConversational":false},"negative_scores":{"kind":"list like","value":["0.6875522","0.67876977","0.6738545","0.67317694","0.67317694","0.6706509","0.6705255","0.6658271","0.65845865","0.65845865","0.6545622","0.649939","0.64937603","0.6486334","0.6432132","0.64196366","0.63851905","0.6357764","0.6353186","0.6350117","0.6344004","0.62834835","0.62828624","0.6260835","0.6260835","0.62495834","0.62435365","0.62429416","0.6223997","0.6219985","0.6215097","0.6207674","0.61965084","0.61949486","0.6189602","0.6185365","0.6184711","0.61789584","0.6175831","0.61728144","0.61597246","0.6155285","0.61470723","0.6145075","0.614369","0.6141447","0.6139969","0.6113935","0.611304","0.611304","0.611304","0.611304","0.611304","0.61041653","0.6103224","0.61017257","0.6097289","0.6097289","0.6097271","0.6092938","0.608442","0.6069945","0.60602546","0.60568345","0.6051374","0.6049249","0.6046136","0.60420114","0.6039418","0.6038854","0.6035613","0.60354865","0.6032393","0.6030859","0.60234207","0.6019902","0.6019902","0.6019902","0.60185313","0.6016787","0.6016787","0.6016787","0.6000664","0.59933424","0.5992686","0.5992411","0.5991186","0.59900373","0.59878224","0.5984634","0.5981671","0.5963939","0.595954","0.59573805","0.5956293","0.5954162","0.5950506","0.5949658","0.5948804","0.59480476"],"string":"[\n \"0.6875522\",\n \"0.67876977\",\n \"0.6738545\",\n \"0.67317694\",\n \"0.67317694\",\n \"0.6706509\",\n \"0.6705255\",\n \"0.6658271\",\n \"0.65845865\",\n \"0.65845865\",\n \"0.6545622\",\n \"0.649939\",\n \"0.64937603\",\n \"0.6486334\",\n \"0.6432132\",\n \"0.64196366\",\n \"0.63851905\",\n \"0.6357764\",\n \"0.6353186\",\n \"0.6350117\",\n \"0.6344004\",\n \"0.62834835\",\n \"0.62828624\",\n \"0.6260835\",\n \"0.6260835\",\n \"0.62495834\",\n \"0.62435365\",\n \"0.62429416\",\n \"0.6223997\",\n \"0.6219985\",\n \"0.6215097\",\n \"0.6207674\",\n \"0.61965084\",\n \"0.61949486\",\n \"0.6189602\",\n \"0.6185365\",\n \"0.6184711\",\n \"0.61789584\",\n \"0.6175831\",\n \"0.61728144\",\n \"0.61597246\",\n \"0.6155285\",\n \"0.61470723\",\n \"0.6145075\",\n \"0.614369\",\n \"0.6141447\",\n \"0.6139969\",\n \"0.6113935\",\n \"0.611304\",\n \"0.611304\",\n \"0.611304\",\n \"0.611304\",\n \"0.611304\",\n \"0.61041653\",\n \"0.6103224\",\n \"0.61017257\",\n \"0.6097289\",\n \"0.6097289\",\n \"0.6097271\",\n \"0.6092938\",\n \"0.608442\",\n \"0.6069945\",\n \"0.60602546\",\n \"0.60568345\",\n \"0.6051374\",\n \"0.6049249\",\n \"0.6046136\",\n \"0.60420114\",\n \"0.6039418\",\n \"0.6038854\",\n \"0.6035613\",\n \"0.60354865\",\n \"0.6032393\",\n \"0.6030859\",\n \"0.60234207\",\n \"0.6019902\",\n \"0.6019902\",\n \"0.6019902\",\n \"0.60185313\",\n \"0.6016787\",\n \"0.6016787\",\n \"0.6016787\",\n \"0.6000664\",\n \"0.59933424\",\n \"0.5992686\",\n \"0.5992411\",\n \"0.5991186\",\n \"0.59900373\",\n \"0.59878224\",\n \"0.5984634\",\n \"0.5981671\",\n \"0.5963939\",\n \"0.595954\",\n \"0.59573805\",\n \"0.5956293\",\n \"0.5954162\",\n \"0.5950506\",\n \"0.5949658\",\n \"0.5948804\",\n \"0.59480476\"\n]","isConversational":false},"document_score":{"kind":"string","value":"0.616597"},"document_rank":{"kind":"string","value":"40"}}}],"truncated":false,"partial":true},"paginationData":{"pageIndex":823,"numItemsPerPage":100,"numTotalItems":95772,"offset":82300,"length":100}},"jwt":"eyJhbGciOiJFZERTQSIsImtpZCI6IjVHZDBvd0g5MTM2eDZjc1FvbE1zcktNWUZoRVFoUm5PVVVybEpjOUhaUEEifQ.eyJyZWFkIjp0cnVlLCJwZXJtaXNzaW9ucyI6eyJyZXBvLmNvbnRlbnQucmVhZCI6dHJ1ZX0sImlhdCI6MTc4NjExOTYzMSwianRpIjoiZmM5NDRmNzYtMTdlMi00NDNlLTliYzUtMWY2YzAxYjgyZGRmIiwic3ViIjoiL2RhdGFzZXRzL25vbWljLWFpL2Nvcm5zdGFjay1weXRob24tdjEiLCJleHAiOjE3ODYxMjMyMzEsImlzcyI6Imh0dHBzOi8vaHVnZ2luZ2ZhY2UuY28ifQ.Cnt5VOlTPMaiLoObLp8gay4xstRJEwtuJ_DyrasIJs6MayicneNLFZHr54aPGFmD7535FlKZ29WJ845IyOLQDg","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 |
|---|---|---|---|---|---|---|
Your value iteration agent should take an mdp on construction, run the indicated number of iterations and then act according to the resulting policy. | def __init__(self, mdp, discount = 0.9, iterations = 100):
self.mdp = mdp
self.discount = discount
self.iterations = iterations
self.values = util.Counter() # A Counter is a dict with default 0
"*** YOUR CODE HERE ***"
# OUR CODE HERE
#Note: I think we should use the util.Counter thing... | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def __init__(self, mdp, discount = 0.9, iterations = 1000):\n ValueIterationAgent.__init__(self, mdp, discount, iterations)",
"def __init__(self, mdp, discount = 0.9, iterations = 1000):\n ValueIterationAgent.__init__(self, mdp, discount, iterations)",
"def __init__(self, mdp, discount = 0.9, ite... | [
"0.7559497",
"0.7559497",
"0.7559497",
"0.7559497",
"0.7559497",
"0.7255983",
"0.7130516",
"0.6889592",
"0.6889592",
"0.6889592",
"0.6889592",
"0.6889592",
"0.68525124",
"0.68525124",
"0.68525124",
"0.68525124",
"0.68525124",
"0.6847414",
"0.67304295",
"0.6688111",
"0.6649843... | 0.6877906 | 12 |
Return the value of the state (computed in __init__). | def getValue(self, state):
return self.values[state] | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def value(self, state):\n raise NotImplementedError",
"def value(self, state):\n raise NotImplementedError",
"def value(self, state):\n\t\traise NotImplementedError",
"def getValue(self, state):\n util.raiseNotDefined()",
"def getValue(self, state):\n util.raiseNotDefined()",
... | [
"0.830136",
"0.830136",
"0.82602024",
"0.8086008",
"0.8086008",
"0.7958413",
"0.78017086",
"0.7737145",
"0.77342933",
"0.7733657",
"0.7733657",
"0.7733657",
"0.7733657",
"0.7733657",
"0.7733657",
"0.7733657",
"0.7733657",
"0.7733657",
"0.7733657",
"0.7733657",
"0.7733657",
... | 0.7929993 | 6 |
The qvalue of the state action pair (after the indicated number of value iteration passes). Note that value iteration does not necessarily create this quantity and you may have to derive it on the fly. | def getQValue(self, state, action):
"*** YOUR CODE HERE ***"
# OUR CODE HERE
#get the value of the state
qVal = self.values[state]
#iterate through the MDP transition states from the current state
for transitionState, probability in self.mdp.getTransitionStatesAndProbs(state, action):
#q v... | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def get_Q_value(self, state, action):\n return self.Q.get((state, action), 0.0) # Return 0.0 if state-action pair does not exist",
"def q_value(self, state_action):\n state, action = state_action\n hashable_state = self.represent_state(state)\n if not (hashable_state, action) in self... | [
"0.8044818",
"0.8034924",
"0.79408586",
"0.79031444",
"0.7877577",
"0.786727",
"0.77985466",
"0.77936316",
"0.7789955",
"0.77891916",
"0.774716",
"0.7724164",
"0.7708895",
"0.7675049",
"0.76234305",
"0.75912863",
"0.7588857",
"0.7552034",
"0.7504086",
"0.74793303",
"0.745265"... | 0.79513013 | 2 |
The policy is the best action in the given state according to the values computed by value iteration. You may break ties any way you see fit. Note that if there are no legal actions, which is the case at the terminal state, you should return None. | def getPolicy(self, state):
"*** YOUR CODE HERE ***"
# OUR CODE HERE
possibleActions = self.mdp.getPossibleActions(state)
#checking for terminal state (no possible actions)
if len(possibleActions) is 0:
return None
#attempt at using the Counter
eValsActions = util.Counter()
... | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def computeActionFromQValues(self, state):\n actions = self.getLegalActions(state)\n if len(actions) == 0:\n return None\n qVals = [self.getQValue(state, a) for a in actions]\n bestActions = []\n bestVal = max(qVals)\n ... | [
"0.76518464",
"0.7514951",
"0.750535",
"0.7422988",
"0.7374947",
"0.7333288",
"0.73329633",
"0.7313349",
"0.727814",
"0.72609013",
"0.7233381",
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"0.70963556",
"0.70804584",
"0.70765764",
"0.70520097",
"0.7025357",
"0.70238316",
"0.7017081",
"0.70063794",
"0.700637... | 0.7954697 | 0 |
Loads an image in the shape expected by other functions in this module. Doesn't Tensor it, in case you need to do further work with it. | def image_load(path) -> numpy.ndarray:
# file
na = numpy.array(Image.open(path))
# fix shape
na = numpy.moveaxis(na, [2,0,1], [0,1,2])
# shape is now (3,h,w), add 1
na = na.reshape(1,3,na.shape[1],na.shape[2])
# change type
na = na.astype("float32") / 255.0
return na | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def load_and_prep_image(img, img_shape=224):\n\n # Decode the read file into a tensor & ensure 3 colour channels \n # (our model is trained on images with 3 colour channels and sometimes images have 4 colour channels)\n img = tf.image.decode_image(img, channels=3)\n\n # Resize the image (to the same size our m... | [
"0.7699778",
"0.74027497",
"0.73622835",
"0.7289454",
"0.7263987",
"0.7171054",
"0.7155694",
"0.7111653",
"0.70905066",
"0.70701224",
"0.70668185",
"0.7058207",
"0.69781375",
"0.6976194",
"0.6969333",
"0.6964644",
"0.695557",
"0.69107634",
"0.69051933",
"0.6844532",
"0.681885... | 0.6454209 | 57 |
Saves an image of the shape expected by other functions in this module. However, note this expects a numpy array. | def image_save(path, na: numpy.ndarray):
# change type
na = numpy.fmax(numpy.fmin(na * 255.0, 255), 0).astype("uint8")
# shape is now (1,3,h,w), remove 1
na = na.reshape(3,na.shape[2],na.shape[3])
# fix shape
na = numpy.moveaxis(na, [0,1,2], [2,0,1])
# shape is now (h,w,3)
# file
Image.fromarray(na).s... | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def save_img(img: np.ndarray, path: str) -> None:\n\n img_obj = Image.fromarray(img)\n img_obj.save(path)",
"def save_npimg(array: np.ndarray, path: str) -> None:\r\n img = Image.fromarray(array.squeeze())\r\n img.save(path)",
"def imwrite(image, path):\n\n if image.ndim == 3 and image.shape[2] ... | [
"0.7397582",
"0.72214085",
"0.7198307",
"0.71386844",
"0.71040505",
"0.70708066",
"0.7057253",
"0.7015668",
"0.698637",
"0.6963281",
"0.69594914",
"0.68962365",
"0.6883985",
"0.68804735",
"0.6848746",
"0.6804764",
"0.67976993",
"0.6780601",
"0.6767705",
"0.6767581",
"0.672906... | 0.7051175 | 7 |
Loads weights from one of the waifu2x JSON files, i.e. waifu2x/models/vgg_7/art/noise0_model.json data (passed in) is assumed to be the output of json.load or some similar on such a file | def load_waifu2x_json(self, data: list):
self.conv1.load_waifu2x_json(data[0])
self.conv2.load_waifu2x_json(data[1])
self.conv3.load_waifu2x_json(data[2])
self.conv4.load_waifu2x_json(data[3])
self.conv5.load_waifu2x_json(data[4])
self.conv6.load_waifu2x_json(data[5])
self.conv7.load_waifu2x... | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def _load_weights(self):\n self.npz_weights = np.load(self._weight_file)\n self._load_byte_embedding()\n self._load_cnn_weights()\n self._load_highway()\n self._load_projection()",
"def load_weight(model):\n file = h5py.File(WEIGHT_SAVE, 'r')\n weight = []\n for i in r... | [
"0.6488443",
"0.62604135",
"0.61644244",
"0.60816514",
"0.604148",
"0.6019102",
"0.60177386",
"0.60040563",
"0.60030246",
"0.5998064",
"0.599155",
"0.5979306",
"0.59469867",
"0.5919713",
"0.5865932",
"0.5829013",
"0.5796365",
"0.578166",
"0.5680778",
"0.5673663",
"0.56730056"... | 0.6863052 | 0 |
Given an ndarray image as loaded by image_load (NOT a tensor), scales it, pads it, splits it up, forwards the pieces, and reconstitutes it. Note that you really shouldn't try to run anything not (1, 3, , ) through this. | def forward_tiled(self, image: numpy.ndarray, tile_size: int) -> numpy.ndarray:
# Constant that only really gets repeated a ton here.
context = 7
context2 = context + context
# Notably, numpy is used here because it makes this fine manipulation a lot simpler.
# Scaling first - repeat on axis 2 and ... | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def dimension_postprocess(self, chunked_data, original_data, scale=1, padding=True):\r\n\r\n assert len(original_data.shape) == 2, \"data dimension expected to be (xline ,samp_point)\"\r\n assert len(chunked_data.shape) == 3, \"Chunked data dimension expected to be (batch_size, xline, samp_point)\"\r... | [
"0.68369544",
"0.6804232",
"0.63060457",
"0.6298447",
"0.62349844",
"0.62218934",
"0.6182368",
"0.6164234",
"0.6161654",
"0.6146714",
"0.6141564",
"0.61321",
"0.61149937",
"0.6093621",
"0.6093426",
"0.6044871",
"0.5970364",
"0.59532017",
"0.59203714",
"0.59078735",
"0.5903252... | 0.0 | -1 |
Create lookup tables for vocabulary | def create_lookup_tables(text):
word_counts = Counter(text)
sorted_vocab = sorted(word_counts, key=word_counts.get, reverse=True)
int_to_vocab = {ii: word for ii, word in enumerate(sorted_vocab)}
vocab_to_int = {word: ii for ii, word in int_to_vocab.items()}
return vocab_to_int, int_to_vocab | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def create_lookup_tables(text):\n # todo 需要编程：\n words = sorted(list(set(text)))\n vocab_to_int = {word:idx for idx, word in enumerate(words)}\n int_to_vocab = dict(enumerate(words))\n return vocab_to_int, int_to_vocab",
"def create_lookup_tables(text):\n vocab = set(text.split())\n vocab_to... | [
"0.7771384",
"0.77559125",
"0.767672",
"0.75280625",
"0.7388749",
"0.7338626",
"0.6986911",
"0.6866718",
"0.6844249",
"0.6324012",
"0.62225556",
"0.6083606",
"0.5979698",
"0.59622264",
"0.5914716",
"0.590712",
"0.58832353",
"0.58711576",
"0.5857526",
"0.58014566",
"0.5794954"... | 0.75501454 | 3 |
Generate a dict to turn punctuation into a token. | def token_lookup():
token_dict = {}
token_dict['.'] = "||Period||"
token_dict[','] = "||Comma||"
token_dict['"'] = "||Quotation_Mark||"
token_dict[';'] = "||Semicolon||"
token_dict['!'] = "||Exclamation_Mark||"
token_dict['?'] = "||Question_Mark||"
token_dict['('] = "||Left_Parentheses||... | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def token_lookup():\n token_dict = {'.':'||Period||', ', ':'||Comma||', '\"':'||Quotation_Mark||', ';':'||Semicolon||',\n '!':'||Exclamation_mark||', '?':'||Question_mark||', '(':'||Left_Parentheses||',\n ')':'||Right_Parentheses||', '--':'||Dash||', '\\n':'||Return||'}\n re... | [
"0.66179246",
"0.6473263",
"0.63673395",
"0.63541335",
"0.6318267",
"0.6195351",
"0.6170302",
"0.61027676",
"0.61009026",
"0.6056571",
"0.583608",
"0.580536",
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"0.5717652",
"0.5713248",
"0.5703686",
"0.56706595",
"0.56492156",
"0.56279296",
"0.562042... | 0.6736538 | 0 |
Create TF Placeholders for input, targets, and learning rate. | def get_inputs():
inputs = tf.placeholder(tf.int32, [None, None], name='input')
targets = tf.placeholder(tf.int32,[None, None], name='targets')
learning_rate = tf.placeholder(tf.float32, name='learning_rate')
return inputs, targets, learning_rate | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def _create_placeholders(self):\n\n\t\twith tf.name_scope(\"input_data\"):\n\t\t\tself.input_words=tf.placeholder(shape=(None,self.look_back), dtype=tf.int32,name='input_tokens')\n\t\twith tf.name_scope(\"output_data\"):\t\n\t\t\tself.output_words=tf.placeholder(shape=(None,self.look_back),dtype=tf.int32,name='out... | [
"0.7744293",
"0.7662721",
"0.7621616",
"0.76179385",
"0.75356156",
"0.75137645",
"0.7418147",
"0.7405449",
"0.7404622",
"0.73941994",
"0.73695993",
"0.73649645",
"0.7349455",
"0.73242545",
"0.72866756",
"0.72504604",
"0.7224488",
"0.72090685",
"0.7191729",
"0.71353704",
"0.71... | 0.6295598 | 47 |
Create an RNN Cell and initialize it. | def get_init_cell(batch_size, rnn_size):
lstm = tf.contrib.rnn.BasicLSTMCell(rnn_size)
#drop = tf.contrib.rnn.DropoutWrapper(lstm, output_keep_prob=0.5)
cell = tf.contrib.rnn.MultiRNNCell([lstm] * 3)
initial_state = cell.zero_state(batch_size, tf.float32)
initial_state = tf.identity(initial_state, n... | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def __init__(self, rnn_cell, batch_size,\n input_size, num_outputs):\n super().__init__(name='RNNCellStateModel')\n self._rnn_cell = rnn_cell\n self._batch_size = batch_size\n self._input_size = input_size\n self._num_outputs = num_outputs",
"def constructCell():\n\t\tself.weightGene... | [
"0.71328205",
"0.6902493",
"0.6705795",
"0.6509429",
"0.6456088",
"0.6427877",
"0.64073706",
"0.63708305",
"0.6354739",
"0.6346675",
"0.6290552",
"0.62503237",
"0.62420845",
"0.6240313",
"0.6204581",
"0.61756074",
"0.6160576",
"0.61548066",
"0.6071491",
"0.5992168",
"0.59629"... | 0.6565958 | 3 |
Create embedding for . | def get_embed(input_data, vocab_size, embed_dim):
embedding = tf.Variable(tf.random_uniform((vocab_size, embed_dim), -1, 1))
embed = tf.nn.embedding_lookup(embedding, input_data)
return embed | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def create_embedding(skills):\n corpus = list(skills[\"description\"].values)\n embedder = SentenceTransformer(config[\"sentence_transformer\"][\"model\"])\n embedding = embedder.encode(corpus, show_progress_bar=True)\n return embedding",
"def construct_embedding(self):\n i = 0\n self.l... | [
"0.7729022",
"0.70906544",
"0.7015876",
"0.6964542",
"0.6931766",
"0.6912709",
"0.68787014",
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"0.6829165",
"0.678458",
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"0.6654593",
"0.6634601",
"0.65902793",
"0.65855086",
"0.65748143",
"0.6549607",... | 0.61894983 | 56 |
Create a RNN using a RNN Cell | def build_rnn(cell, inputs):
outputs, final_state = tf.nn.dynamic_rnn(cell, inputs, dtype=tf.float32)
final_state = tf.identity(final_state, name="final_state")
return outputs, final_state | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def build_rnn(cell, inputs):\n #_,initial_state = get_init_cell(batch_size, rnn_size)\n \n output, final_state = tf.nn.dynamic_rnn(cell, inputs, dtype=tf.float32)\n final_state = tf.identity(final_state, name='final_state')\n\n return (output, final_state)",
"def _rnn_layer(input_data, rnn_cell, r... | [
"0.7033094",
"0.69850665",
"0.68633723",
"0.6852711",
"0.67769927",
"0.66758764",
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"0.6567434",
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"0.6469102",
"0.6350825",
"0.6320496",
"0.6306484",
"0.6303071",
"0.62986463",
"0.62471"... | 0.680208 | 4 |
Build part of the neural network | def build_nn(cell, rnn_size, input_data, vocab_size, embed_dim):
embedding = get_embed(input_data, vocab_size, rnn_size)
outputs, final_state = build_rnn(cell, embedding)
logits = tf.contrib.layers.fully_connected(outputs, vocab_size, activation_fn=None)
return logits, final_state | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def _build_networks(self):\n self.online_convnet = self._create_network(name='Online')\n self.target_convnet = self._create_network(name='Target')\n self._net_outputs = self.online_convnet(self.state_ph, training=True)\n self._q_argmax = tf.argmax(self._net_outputs.q_values, axis=1)[0]\n self._repla... | [
"0.766122",
"0.7452656",
"0.7433276",
"0.7394448",
"0.7365365",
"0.7333781",
"0.7314295",
"0.7269099",
"0.7231311",
"0.72150916",
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"0.7060453",
"0.7048502",
"0.70391226",
"0.70122766",
"0.699026",
"0.6972236",
... | 0.0 | -1 |
Return batches of input and target | def get_batches(int_text, batch_size, seq_length):
slice_size = batch_size * seq_length
n_batches = int(len(int_text) / slice_size)
result = []
for i in range(n_batches):
start, stop = i * slice_size, (i + 1) * slice_size
x = int_text[start: stop]
x = np.pad(x, (0, slice_size - ... | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def get_batch(source, i):\n data = source[i]\n target = source[i + 1]\n return data.reshape((1, len(data))), target.reshape((-1,))",
"def batch_data(source, target, batch_size):\n for batch_i in range(0, len(source)//batch_size):\n start_i = batch_i * batch_size\n source_batch = source[... | [
"0.7363033",
"0.71290106",
"0.70627284",
"0.703322",
"0.6986728",
"0.69704914",
"0.69097173",
"0.69094884",
"0.68605214",
"0.68146205",
"0.681418",
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"0.6777295",
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"0.66930664",
"0.6679705",
"0.6668017",
"0.6667492",
"0.66516435",
"0.664385",
"0.66402... | 0.0 | -1 |
We use addiional id to make it possible to run multiple instances of the same code We use the neputne id for an easy reference. piotr.milos | def get_config(ctx):
global HISTORY_LOGS, EXPERIMENT_ID #Ugly hack, make it better at some point, may be ;)
id = ctx.job.id
EXPERIMENT_ID = hash(id)
import montezuma_env
ctx.job.register_action("Set starting point procssor:",
lambda str: set_motezuma_env_options(str, mo... | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def __init__(self):\n self.id = id(self)",
"def _id(self):\n pass",
"def getID():",
"def pid(self):",
"def __init__(self, id: int = 0, /):",
"def __init__(self, id: str):\n self.id = id",
"def __init__(self, id: int, /):",
"def __init__(self, id: int, /):",
"def __init__(self, ... | [
"0.64650697",
"0.64362",
"0.63527906",
"0.6186919",
"0.6167249",
"0.5979204",
"0.59692824",
"0.59692824",
"0.59692824",
"0.59692824",
"0.5918313",
"0.5918313",
"0.5908174",
"0.58874714",
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"0.586581",
"0.584896",
"0.5848244",
"0.584183",
"0.584183",
"0.584183",
"... | 0.0 | -1 |
Create a simulated image for testing. | def setup_class(self):
from scipy.spatial import cKDTree
shape = (500, 500)
# define random star positions
nstars = 50
from astropy.utils.misc import NumpyRNGContext
with NumpyRNGContext(12345): # seed for repeatability
xx = np.random.uniform(low=0, high... | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def test_create_image(self):\n pass",
"def new_test_image():\n warnings.warn(DeprecationWarning(\n \"new_test_image() is deprecated in favour of the get_sample_image() \"\n \"context manager.\"), stacklevel=2)\n image_name = 'test-{}.png'.format(uuid.uuid4())\n image = Image.new('RG... | [
"0.71623504",
"0.68563354",
"0.67887807",
"0.67866874",
"0.66272223",
"0.65794975",
"0.65335965",
"0.6525982",
"0.649347",
"0.6438166",
"0.6362632",
"0.63506156",
"0.62497157",
"0.6224325",
"0.62128174",
"0.61762375",
"0.609001",
"0.6079063",
"0.60769093",
"0.6073406",
"0.607... | 0.0 | -1 |
This is an endtoend test of EPSFBuilder on a simulated image. | def test_epsf_build(self):
size = 25
oversampling = 4.
stars = extract_stars(self.nddata, self.init_stars, size=size)
epsf_builder = EPSFBuilder(oversampling=oversampling, maxiters=20,
progress_bar=False)
epsf, fitted_stars = epsf_builder(stars... | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def expose_test(self):\n with self.lock:\n self.dark = 1\n self.tstart = time.time()\n self.timestamp = time.strftime(\"%Y-%m-%dT%H:%M:%S\", time.localtime(self.tstart))\n imagesize = (self.expArea[3] - self.expArea[1],\n self.expArea[2] - ... | [
"0.6532734",
"0.65219575",
"0.61546344",
"0.6055118",
"0.59790444",
"0.5955276",
"0.58926064",
"0.58453953",
"0.5843574",
"0.5825598",
"0.5800128",
"0.57825714",
"0.5774205",
"0.57699555",
"0.57476026",
"0.5737863",
"0.573536",
"0.5732193",
"0.5694142",
"0.56920683",
"0.56847... | 0.6694861 | 0 |
Test that the input fitter is an EPSFFitter instance. | def test_epsf_build_invalid_fitter(self):
with pytest.raises(TypeError):
EPSFBuilder(fitter=EPSFFitter, maxiters=3)
with pytest.raises(TypeError):
EPSFBuilder(fitter=LevMarLSQFitter(), maxiters=3)
with pytest.raises(TypeError):
EPSFBuilder(fitter=LevMarLSQF... | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def test_training_is_training_instance(self):\n self.assertIsInstance(self.weekly_training, Training)\n self.assertIsInstance(self.one_off_training, Training)",
"def test_fitters_interface(fitter):\n fitter = fitter()\n\n model = models.Gaussian1D(10, 4, 0.3)\n x = np.arange(21)\n y = m... | [
"0.58193254",
"0.576989",
"0.5444093",
"0.5416641",
"0.54027134",
"0.53487724",
"0.5334198",
"0.52976006",
"0.5282834",
"0.52802503",
"0.5256052",
"0.5253613",
"0.51621646",
"0.5144735",
"0.5130423",
"0.5122691",
"0.51144564",
"0.5113895",
"0.5088265",
"0.5082022",
"0.5029815... | 0.6341368 | 0 |
An instance of a `FitInterferometer` corresponding to the maximum log likelihood model inferred by the nonlinear search. | def max_log_likelihood_fit(self) -> FitInterferometer:
return self.analysis.fit_interferometer_via_instance_from(
instance=self.instance
) | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def max_log_likelihood_fit(self) -> ag.FitQuantity:\r\n\r\n return self.analysis.fit_quantity_for_instance(instance=self.instance)",
"def log_likelihood_function(self, instance):\r\n\r\n try:\r\n return self.fit_interferometer_for_instance(\r\n instance=instance\r\n ... | [
"0.69463223",
"0.63066924",
"0.6028813",
"0.5956261",
"0.5869772",
"0.5844398",
"0.5813573",
"0.57820946",
"0.5766396",
"0.5750532",
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"0.55123526",
"0.55059314",
"0.5490913",
"0.5480697",
"0.54476845",
"0.54429275",
"0.543328... | 0.81059855 | 0 |
An instance of a `Tracer` corresponding to the maximum log likelihood model inferred by the nonlinear search. The `Tracer` is computed from the `max_log_likelihood_fit`, as this ensures that all linear light profiles are converted to normal light profiles with their `intensity` values updated. | def max_log_likelihood_tracer(self) -> Tracer:
return (
self.max_log_likelihood_fit.model_obj_linear_light_profiles_to_light_profiles
) | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def max_log_likelihood_tracer(self) -> Tracer:\r\n return self.analysis.tracer_via_instance_from(instance=self.instance)",
"def max_log_likelihood_fit(self) -> FitInterferometer:\r\n return self.analysis.fit_interferometer_via_instance_from(\r\n instance=self.instance\r\n )",
"d... | [
"0.74774915",
"0.66032386",
"0.57672596",
"0.5737945",
"0.558012",
"0.53950375",
"0.53794014",
"0.53068775",
"0.53021854",
"0.528267",
"0.5255574",
"0.5232161",
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"0.5137233",
"0.5123503",
"0.510592",
"0.50647867",
"0.506299",
"0.50606036",
"0.5060061"... | 0.81920236 | 0 |
The real space mask used by this modelfit. | def real_space_mask(self) -> aa.Mask2D:
return self.max_log_likelihood_fit.dataset.real_space_mask | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def mask(self):\n return self._mask",
"def mask(self):\n return self._mask",
"def mask(self):\n return self._mask",
"def mask(self):\n return self._mask",
"def mask(self):\n return self.mask_index",
"def medicalMask(self) -> float:\n return self._coreEstimation.m... | [
"0.71758425",
"0.71758425",
"0.71758425",
"0.71758425",
"0.6801398",
"0.67848504",
"0.6766158",
"0.65531385",
"0.65167063",
"0.64373904",
"0.6400794",
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"0.61990255",
"0.61990255",
"0.6129481",
"0.60355794",
"0.60135835",
"0... | 0.820525 | 0 |
Calculate the annualised sample covariance matrix of (daily) asset returns. | def sample_cov(prices, frequency=252):
if not isinstance(prices, pd.DataFrame):
warnings.warn("prices are not in a dataframe", RuntimeWarning)
prices = pd.DataFrame(prices)
daily_returns = daily_price_returns(prices)
return daily_returns.cov() * frequency | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def cov(self):\n E_x = Sample.mean(self)\n Std_x = Sample.std(self)\n cov = Std_x/E_x\n return(cov)",
"def covariance(mtrx):\r\n\r\n # Average column of matrix\r\n T = np.transpose(mtrx)\r\n ave = np.zeros(len(mtrx))\r\n mtrx = np.asarray(mtrx)\r\n\r\n if isinstance(mtr... | [
"0.61024827",
"0.60882634",
"0.60061836",
"0.60022545",
"0.5951774",
"0.5946839",
"0.5942755",
"0.593687",
"0.5928027",
"0.5898082",
"0.58764166",
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"0.5812346",
"0.5749281",
"0.5738869",
"0.57069844",
"0.5693732",
"0.56880194",
"0.567809",
"0.5676851"... | 0.60892475 | 1 |
Estimate the semicovariance matrix, i.e the covariance given that the returns are less than the benchmark. .. semicov = E([min(r_i B, 0)] . [min(r_j B, 0)]) | def semicovariance(prices, benchmark=0, frequency=252):
if not isinstance(prices, pd.DataFrame):
warnings.warn("prices are not in a dataframe", RuntimeWarning)
prices = pd.DataFrame(prices)
daily_returns = daily_price_returns(prices)
drops = np.fmin(daily_returns - benchmark, 0)
return d... | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def decomposeCov(M):\n U,s,V = linalg.svd(M)\n R = mat(U)\n if R[1,0] > 0: R = -1.*R\n if linalg.det(R) < 0:\n R[:,1] = -1.*R[:,1]\n\n S = mat(diag(s))\n return R,S",
"def sigma_from_cov(params, cov):\n rands = np.random.multivariate_normal(params, cov, 10000)\n breakdowns = -1*ran... | [
"0.6204444",
"0.5936843",
"0.58973277",
"0.5895796",
"0.58810544",
"0.58502036",
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"0.57432806",
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"0.571641",
"0.56612056",
"0.56434214",
"0.56434214",
"0.56368524",
"0.56346697",
"0.5591559",
"0.55855596",
"0.5577742",
"0.5570818",
"0.5570406",
"0.556534... | 0.6246111 | 0 |
Calculate the exponential covariance between two timeseries of returns. | def _pair_exp_cov(X, Y, span=180):
covariation = (X - X.mean()) * (Y - Y.mean())
# Exponentially weight the covariation and take the mean
if span < 10:
warnings.warn("it is recommended to use a higher span, e.g 30 days")
return covariation.ewm(span=span).mean()[-1] | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def covariance(self, x1, x2, lengths):\n z = self.dist(x1, x2, lengths)\n return (self.a**2) * exp(-0.5*z)",
"def covariance (x, y):\n n = len(x)\n return dot(de_mean(x), de_mean(y))/(n-1)",
"def calculate_covariance(column1: pd.Series, column2: pd.Series) -> np.float64:\n\n cov = column... | [
"0.663079",
"0.64257526",
"0.6424279",
"0.64234096",
"0.6411297",
"0.6403209",
"0.63896066",
"0.6319481",
"0.6286836",
"0.614348",
"0.61390924",
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"0.6087634",
"0.5945993",
"0.5933623",
"0.58242244",
"0.5799875",
"0.57965803",
"0.57709515",... | 0.64166164 | 4 |
Estimate the exponentiallyweighted covariance matrix, which gives greater weight to more recent data. | def exp_cov(prices, span=180, frequency=252):
if not isinstance(prices, pd.DataFrame):
warnings.warn("prices are not in a dataframe", RuntimeWarning)
prices = pd.DataFrame(prices)
assets = prices.columns
daily_returns = daily_price_returns(prices)
N = len(assets)
# Loop over matrix,... | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def cov(self):\n cov_ = np.dot(self.weights * self.demeaned.T, self.demeaned)\n cov_ /= self.sum_weights - self.ddof\n return cov_",
"def _mn_cov_ ( self , size = -1 , root = False ) :\n #\n if size <= 0 : size = len ( self )\n size = min ( size , len ( self ) ) \n #\n from ar... | [
"0.67914",
"0.6656926",
"0.66080594",
"0.64771646",
"0.63540035",
"0.6342093",
"0.6293979",
"0.6258623",
"0.6247504",
"0.609174",
"0.60618913",
"0.606088",
"0.6040714",
"0.6021034",
"0.5987279",
"0.59834814",
"0.5938113",
"0.58909416",
"0.5872555",
"0.58216465",
"0.5801764",
... | 0.0 | -1 |
Calculate the minimum covariance determinant, an estimator of the covariance matrix that is more robust to noise. | def min_cov_determinant(prices, frequency=252, random_state=None):
if not isinstance(prices, pd.DataFrame):
warnings.warn("prices are not in a dataframe", RuntimeWarning)
prices = pd.DataFrame(prices)
assets = prices.columns
X = prices.pct_change().dropna(how="all")
X = np.nan_to_num(X.v... | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def covariance(data_matrix):\n return np.asmatrix(np.cov(data_matrix, rowvar=0))",
"def cov_matrix(X, mu):\n m, n = X.shape\n X_minus_mu = X - mu\n sigma = (1 / m) * (X_minus_mu.T).dot(X_minus_mu)\n\n return sigma",
"def _mn_cov_ ( self , size = -1 , root = False ) :\n #\n if size <= 0 : s... | [
"0.6320921",
"0.63008386",
"0.62460333",
"0.6208877",
"0.6198202",
"0.61210555",
"0.6080598",
"0.606703",
"0.6057687",
"0.603914",
"0.5986421",
"0.59207064",
"0.59205496",
"0.58831877",
"0.5879957",
"0.5871225",
"0.5817185",
"0.57943755",
"0.5783197",
"0.5736097",
"0.573593",... | 0.6314101 | 1 |
Helper method which annualises the output of shrinkage calculations, and formats the result into a dataframe | def format_and_annualise(self, raw_cov_array):
assets = self.X.columns
return (
pd.DataFrame(raw_cov_array, index=assets, columns=assets) * self.frequency
) | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def __str__(self):\n df = pd.DataFrame([self.Xa.mean(),self.Xa.std()])\n df.index = ['E[R]','SD[R]']\n header = '\\nAnnual Returns (%):\\n\\n'\n return header + df.__str__()",
"def compute (self):\r\n #obtain and validate the inputs\r\n startBalance = self.amount.getNumb... | [
"0.6088556",
"0.5884749",
"0.5837706",
"0.580568",
"0.5794596",
"0.5783352",
"0.57674056",
"0.57600665",
"0.56907064",
"0.5690144",
"0.564202",
"0.56202173",
"0.55736405",
"0.55640435",
"0.5552903",
"0.55230665",
"0.55076694",
"0.55026937",
"0.5494513",
"0.5490592",
"0.548795... | 0.52572143 | 50 |
Shrink a sample covariance matrix to the identity matrix (scaled by the average sample variance). This method does not estimate an optimal shrinkage parameter, it requires manual input. | def shrunk_covariance(self, delta=0.2):
self.delta = delta
N = self.S.shape[1]
# Shrinkage target
mu = np.trace(self.S) / N
F = np.identity(N) * mu
# Shrinkage
shrunk_cov = delta * F + (1 - delta) * self.S
return self.format_and_annualise(shrunk_cov) | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def shrink(self):\n x = np.nan_to_num(self.X.values)\n\n # de-mean returns\n t, n = np.shape(x)\n meanx = x.mean(axis=0)\n x = x - np.tile(meanx, (t, 1))\n xmkt = x.mean(axis=1).reshape(t, 1)\n\n # compute sample covariance matrix\n sample = np.cov(np.append(... | [
"0.686523",
"0.5708117",
"0.56834924",
"0.5621674",
"0.54906625",
"0.54182756",
"0.533052",
"0.5224842",
"0.51816285",
"0.5174759",
"0.5155567",
"0.51307696",
"0.5077133",
"0.5030297",
"0.5008513",
"0.49890906",
"0.498165",
"0.49331114",
"0.489998",
"0.48944172",
"0.4869452",... | 0.52941155 | 7 |
Calculate the LedoitWolf shrinkage estimate. | def ledoit_wolf(self):
X = np.nan_to_num(self.X.values)
shrunk_cov, self.delta = covariance.ledoit_wolf(X)
return self.format_and_annualise(shrunk_cov) | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def lapse(self):\n pass",
"def sweep50W(self):\n return 25.9",
"def eady_growth_rate(data):\n N2 = ixr.brunt_vaisala(data)\n f = 2.0*omega*xruf.sin(xruf.deg2rad(data.lat))\n\n dz = ixr.domain.calculate_dz(data)\n du = ixr.domain.diff_pfull(data.ucomp, data)\n\n N = xruf.sqrt(N2.whe... | [
"0.60400766",
"0.59365445",
"0.5810836",
"0.5762381",
"0.5751515",
"0.5725546",
"0.5681198",
"0.56707937",
"0.5667252",
"0.56470907",
"0.5641813",
"0.56293994",
"0.5625519",
"0.56059796",
"0.560109",
"0.5598705",
"0.5592972",
"0.5587249",
"0.55850375",
"0.5578529",
"0.5576009... | 0.6030858 | 1 |
Calculate the Oracle Approximating Shrinkage estimate | def oracle_approximating(self):
X = np.nan_to_num(self.X.values)
shrunk_cov, self.delta = covariance.oas(X)
return self.format_and_annualise(shrunk_cov) | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def new_sga(self) -> float:\n increase_factor = (\n self.total_demand() / self.operations.productivity.total_m3_collected - 1\n )\n increase_factor *= 0.75\n return self.income_statement.opex.sga * (1 + increase_factor)",
"def __calc_s(self, df):\n df.loc[:, \"avg_nu... | [
"0.57590866",
"0.5601078",
"0.55657876",
"0.5491326",
"0.5476864",
"0.5450543",
"0.5446301",
"0.5414561",
"0.54031104",
"0.5387726",
"0.5379592",
"0.5351469",
"0.5332968",
"0.53287953",
"0.53218544",
"0.52865034",
"0.5273125",
"0.5265359",
"0.525904",
"0.5256719",
"0.52269393... | 0.51361924 | 31 |
Calculate the ConstantCorrelation covariance matrix. | def shrink(self):
x = np.nan_to_num(self.X.values)
# de-mean returns
t, n = np.shape(x)
meanx = x.mean(axis=0)
x = x - np.tile(meanx, (t, 1))
# compute sample covariance matrix
sample = (1.0 / t) * np.dot(x.T, x)
# compute prior
var = np.diag(sa... | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def correlation_matrix(self):\n correlation_matrix = self.model.covariance.copy()\n sigmaD = np.sqrt(np.diag(correlation_matrix))\n for ii in range(correlation_matrix.shape[0]):\n for jj in range(correlation_matrix.shape[1]):\n correlation_matrix[ii, jj] /= sigmaD[ii]... | [
"0.77284074",
"0.7547228",
"0.7090517",
"0.7039451",
"0.6994395",
"0.69679624",
"0.6934814",
"0.68709993",
"0.68600374",
"0.6836816",
"0.6824904",
"0.6757267",
"0.6741739",
"0.66652405",
"0.66569704",
"0.6645018",
"0.65971345",
"0.65955824",
"0.65839565",
"0.6551665",
"0.6527... | 0.0 | -1 |
Calculate the ConstantCorrelation covariance matrix. | def shrink(self):
x = np.nan_to_num(self.X.values)
# de-mean returns
t, n = np.shape(x)
meanx = x.mean(axis=0)
x = x - np.tile(meanx, (t, 1))
xmkt = x.mean(axis=1).reshape(t, 1)
# compute sample covariance matrix
sample = np.cov(np.append(x, xmkt, axis=1... | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def correlation_matrix(self):\n correlation_matrix = self.model.covariance.copy()\n sigmaD = np.sqrt(np.diag(correlation_matrix))\n for ii in range(correlation_matrix.shape[0]):\n for jj in range(correlation_matrix.shape[1]):\n correlation_matrix[ii, jj] /= sigmaD[ii]... | [
"0.77284074",
"0.7547228",
"0.7090517",
"0.7039451",
"0.6994395",
"0.69679624",
"0.6934814",
"0.68709993",
"0.68600374",
"0.6836816",
"0.6824904",
"0.6757267",
"0.6741739",
"0.66652405",
"0.66569704",
"0.6645018",
"0.65971345",
"0.65955824",
"0.65839565",
"0.6551665",
"0.6527... | 0.0 | -1 |
Add video tutorial link | def add_video(request):
if request.method == "POST":
form = AddForms(request.POST)
if form.is_valid():
form.save()
messages.info(request, 'New video added')
else:
logger = logging.getLogger(__name__)
messages.error(request, form.errors)
... | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def show_trailer(self):\r\n webbrowser.open(self.trailer_youtube_url)",
"async def mathlesson(self, ctx):\r\n await ctx.send('https://www.youtube.com/watch?v=WFoC3TR5rzI')",
"def getYouTubeLink(self):\n \n return self.link.replace('watch?v=', 'v/')",
"def show_trailer(self):\n\t\twebb... | [
"0.61660105",
"0.6136898",
"0.6028367",
"0.6017572",
"0.6017572",
"0.6017572",
"0.60162205",
"0.6013646",
"0.59756875",
"0.5960356",
"0.5960356",
"0.5960356",
"0.59319365",
"0.5921021",
"0.5905654",
"0.5895406",
"0.58787245",
"0.58760995",
"0.58713305",
"0.58693415",
"0.58693... | 0.0 | -1 |
Serve book, take notes etc | def book(request, slug):
if request.method == 'POST':
book = Book.objects.get(slug=slug)
form = AddForms(request.POST, instance=book)
if form.is_valid():
form.save()
messages.info(request, "Info updated")
else:
logger = logging.getLogger(__name__)
... | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def download_a_book():\n try:\n book_name = request.args.get('book_name')\n record = search_a_book(replica_one, book_name)\n if 'success' in record:\n blob = replica_one.blob(book_name)\n size = sys.getsizeof(blob.download_as_string())\n response = Response(... | [
"0.62519765",
"0.6162564",
"0.60040337",
"0.58135223",
"0.57725567",
"0.5688528",
"0.568447",
"0.56799257",
"0.5678683",
"0.5664258",
"0.564394",
"0.5622881",
"0.55608475",
"0.55499345",
"0.55365074",
"0.55188197",
"0.551556",
"0.5500022",
"0.5494903",
"0.5486445",
"0.5486445... | 0.55682933 | 12 |
handle book "finish" in model | def book_finished(request, slug):
book = Book.objects.get(slug=slug)
book.finished = True
book.finished_at = timezone.datetime.today()
book.save()
messages.info(request, "Book marked as finished")
return redirect('/book/' + slug + "/") | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def finish(self):",
"def finish(self):",
"def finish(self):\n pass",
"def finish(self):\n pass",
"def finish():\n pass",
"def finish(self) -> None:",
"def finish(self) -> None:",
"def done(self):",
"def done(self):",
"def back(self):\n self.book.back()\n self.bo... | [
"0.651837",
"0.651837",
"0.6296331",
"0.6296331",
"0.6232587",
"0.62136847",
"0.62136847",
"0.60935384",
"0.60935384",
"0.6090043",
"0.6085858",
"0.6074087",
"0.60662776",
"0.6063426",
"0.6063426",
"0.6063426",
"0.6063426",
"0.6054718",
"0.6011309",
"0.58680695",
"0.5864044",... | 0.7256532 | 0 |
Mask an item or a list of items, so it can not be searched. | def mask(self, item_or_items: Union[str, list]) -> None:
if isinstance(item_or_items, str):
self._masked_items.add(item_or_items)
elif isinstance(item_or_items, list):
for item in item_or_items:
assert isinstance(item, str)
self._masked_items.add(i... | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def _mask_list(self, items):\n\n result_type = type(items)\n results = []\n for item in items:\n results.append(self._mask(item))\n\n return result_type(results)",
"def clear_mask(self, item_or_items: Union[str, list]) -> None:\n if isinstance(item_or_items, str) and... | [
"0.71734196",
"0.6949816",
"0.6528739",
"0.62340534",
"0.5941682",
"0.5838568",
"0.56591773",
"0.5654658",
"0.54791373",
"0.54755586",
"0.53369683",
"0.52959144",
"0.52852184",
"0.5266971",
"0.5258228",
"0.52347344",
"0.5234729",
"0.5205539",
"0.520498",
"0.51970905",
"0.5194... | 0.7793145 | 0 |
Recover a masked item or a list of maksed items, so it can be searched again. | def clear_mask(self, item_or_items: Union[str, list]) -> None:
if isinstance(item_or_items, str) and item_or_items in self._masked_items:
self._masked_items.remove(item_or_items)
elif isinstance(item_or_items, list):
for item in item_or_items:
if item in self._mas... | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def mask(self, item_or_items: Union[str, list]) -> None:\n if isinstance(item_or_items, str):\n self._masked_items.add(item_or_items)\n elif isinstance(item_or_items, list):\n for item in item_or_items:\n assert isinstance(item, str)\n self._masked_... | [
"0.66658765",
"0.6491505",
"0.5480818",
"0.5453097",
"0.5444256",
"0.5365729",
"0.5221703",
"0.51793486",
"0.51662123",
"0.5162058",
"0.515265",
"0.5151804",
"0.5125398",
"0.50725377",
"0.5067496",
"0.5029297",
"0.5011799",
"0.4962683",
"0.49376792",
"0.49319416",
"0.48999804... | 0.6037998 | 2 |
Insert a term into the tree. A term can be a word or phrase. | def insert(self, tokens: List[str]):
cur = self._root
for token in tokens:
if token not in cur.children:
cur.children[token] = _TrieNode(token)
cur = cur.children[token]
cur.is_term = True
self._len += 1 | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def insert(self, word):\n if not word:\n return\n if word[0] in self.trie:\n cur = self.trie[word[0]]\n else:\n cur = TrieNode(word[0])\n for char in word[1:]:\n if char not in cur.nexts:\n cur.nexts[char] = TrieNode(char)\n ... | [
"0.73594046",
"0.7153792",
"0.71109015",
"0.7109018",
"0.7107041",
"0.7096108",
"0.7021035",
"0.701429",
"0.7012358",
"0.70056224",
"0.69533575",
"0.6944374",
"0.6943619",
"0.69425434",
"0.689561",
"0.6865354",
"0.68383473",
"0.68182147",
"0.68131256",
"0.6804924",
"0.6795131... | 0.64872843 | 40 |
Search a term int the tree. A term can be a word or phrase. | def search(self, tokens: List[str]) -> bool:
item = "".join(tokens)
if item in self._masked_items:
return False
cur = self._root
for token in tokens:
if token not in cur.children:
return False
cur = cur.children[token]
return ... | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def search(self, term):\n return self._search(self._root, term, 0)",
"def search(self, term):",
"def search(self, word):\n node = self.root\n return self.searchHelper(node, word)",
"def search(self, word):\n return self.find(self.root,word)",
"def search(self, word):\n re... | [
"0.80329716",
"0.7918371",
"0.7470943",
"0.73325664",
"0.73010314",
"0.7292465",
"0.7216557",
"0.71666795",
"0.7162614",
"0.7153656",
"0.71518344",
"0.71518344",
"0.7117164",
"0.7061479",
"0.6989512",
"0.69825965",
"0.6951326",
"0.69268405",
"0.69040906",
"0.68754804",
"0.687... | 0.62226194 | 72 |
Enumerate all matched terms according to the prefix. | def enumerate_match(self, prefix: List[str]) -> List[str]:
matched_terms = []
cur = self._root
for i, token in enumerate(prefix):
if token not in cur.children:
break
cur = cur.children[token]
if cur.is_term:
item = "".join(prefi... | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def prefixSearch(self, prefix: str, _prec=\"\"):\n if prefix == \"\":\n # prefix exhasuted, match all\n yield from self.keys(_prec)\n else:\n try:\n # prefix not exhausted, traverse further\n chld = self.children[prefix[0]]\n ... | [
"0.6639456",
"0.6516617",
"0.61553776",
"0.61436945",
"0.6130935",
"0.60279405",
"0.60198647",
"0.5976832",
"0.5889858",
"0.57862514",
"0.5730367",
"0.56938714",
"0.56853217",
"0.5670705",
"0.5616336",
"0.5614195",
"0.5613336",
"0.5585791",
"0.55320466",
"0.5512074",
"0.55025... | 0.824299 | 0 |
Load all lexicons into the trie tree. | def load_lexicons(self, folder: str, file_name: str):
file_path = os.path.join(folder, file_name)
with open(file_path, "r", encoding="utf-8") as f_in:
for line in f_in.readlines():
line = line.replace("\n", "")
tokens = list(line)
self.insert(t... | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def loadTrie(self):\n for file in self._gram_files:\n trie_file = getTrieFile(os.path.basename(file), self._pickle_dir)\n with open(trie_file, 'rb') as fd:\n self._tries.append(pickle.load(fd))",
"def _add_all_to_tree(elms, trie):\n for elm in elms:\n ... | [
"0.70103794",
"0.60826737",
"0.5833833",
"0.58041453",
"0.58041453",
"0.57136434",
"0.5500055",
"0.544029",
"0.5410637",
"0.541009",
"0.53844976",
"0.53844976",
"0.53844976",
"0.53844976",
"0.5345329",
"0.5302791",
"0.52921546",
"0.52657026",
"0.5254543",
"0.524532",
"0.52442... | 0.6468606 | 1 |
Return the total number of terms. | def __len__(self):
return self._len | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def getNumTerms(self):\n\n return self._numTerms",
"def get_num_terms(self, documents=None):\n terms = []\n if documents == None:\n docs = self.vocab\n else:\n docs = [term for term in self.vocab if term['name'] in documents]\n for doc in docs:\n ... | [
"0.7713267",
"0.7317305",
"0.7209781",
"0.707415",
"0.7074131",
"0.6786542",
"0.67702496",
"0.66621053",
"0.66484886",
"0.6547783",
"0.6531839",
"0.6531839",
"0.6531839",
"0.6483369",
"0.6482668",
"0.6460727",
"0.64578813",
"0.6441393",
"0.64279175",
"0.6408187",
"0.6394199",... | 0.0 | -1 |
Calls super and then redefines the order in which the fields appear. for parameters see BaseForm.__init__() | def __init__(self, *args, **kwargs):
super(CreateForm, self).__init__(*args, **kwargs)
# set form fields order
self.fields.keyOrder = ['to_user', 'subject', 'message'] | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def __init__(self, *args, **kw):\n super(SignupFormExtra, self).__init__(*args, **kw)\n # Put the first and last name at the top\n new_order = self.fields.keyOrder[:-2]\n new_order.insert(0, 'first_name')\n new_order.insert(1, 'last_name')\n self.fields.keyOrder = new_orde... | [
"0.72668296",
"0.6936237",
"0.6508285",
"0.6419419",
"0.6311311",
"0.6287516",
"0.62845707",
"0.6266717",
"0.62274545",
"0.62013614",
"0.6138659",
"0.61382353",
"0.6132888",
"0.61148196",
"0.6064425",
"0.60594225",
"0.6054909",
"0.6046325",
"0.6040513",
"0.60086805",
"0.60006... | 0.6542123 | 2 |
Defines the fields and methods required for the base View class to provide the user with list, public, create, edit and delete views. | def __init__(self, params=None):
rights = access.Checker(params)
rights['unspecified'] = ['deny']
rights['edit'] = ['deny']
rights['show'] = [('checkIsMyEntity', [notification_logic, 'scope_path'])]
rights['delete'] = [('checkIsMyEntity', [notification_logic, 'scope_path'])]
rights['list'] = ['... | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def admin_views(admin):\n\n # student no delete\n admin.add_view(CTextsView(Texts, db.session, name='Тексты'))\n\n admin.add_view(UserView(User, db.session, category=\"Люди\", name='Пользователи'))\n\n # chief upper full\n admin.add_view(CKeywordsView(Keywords, db.session, category=\"Жанры, слова\",... | [
"0.6588185",
"0.64474666",
"0.6445904",
"0.6151662",
"0.6102565",
"0.6067544",
"0.59592795",
"0.59253454",
"0.5914338",
"0.5893061",
"0.58813554",
"0.5878796",
"0.57946974",
"0.57932574",
"0.5790788",
"0.57866484",
"0.5786224",
"0.57761693",
"0.57538766",
"0.57471484",
"0.571... | 0.5388812 | 38 |
Lists all notifications that the current logged in user has stored. for parameters see base.list() | def list(self, request, access_type,
page_name=None, params=None, filter=None, order=None, **kwargs):
# get the current user
user_entity = user_logic.getForCurrentAccount()
# only select the notifications for this user so construct a filter
filter = {
'scope': user_entity,
'... | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def list_notifications():\n token = request.args.get('token')\n user = User.query.filter_by(token=token).first()\n\n if user is None:\n return jsonify({\"error\": \"Access Denied!\"})\n\n # Filter Posts so the user doesn't have to filter it\n notifications = Notifications.query.filter_by(user... | [
"0.78857595",
"0.746229",
"0.7321766",
"0.7266858",
"0.72493035",
"0.7199395",
"0.71889263",
"0.6943352",
"0.69205034",
"0.6903298",
"0.6903298",
"0.68649685",
"0.68214977",
"0.681583",
"0.68128014",
"0.6807877",
"0.68037546",
"0.6783332",
"0.6765252",
"0.6734677",
"0.6674633... | 0.68386793 | 12 |
Checks if scope_path is seeded and puts it into to_user. for parameters see base._editSeed() | def _editSeed(self, request, seed):
# if scope_path is present
if 'scope_path' in seed.keys():
# fill the to_user field with the scope path
seed['to_user'] = seed['scope_path'] | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def initial(self, request, *args, **kwargs):\n try:\n request.data[\"user\"] = request.auth.user\n except:\n pass\n return super(BoundToUserMixin, self).initial(request, *args, **kwargs)",
"def before_request ():\n try:\n g.user = current_user\n except Name... | [
"0.5439406",
"0.5422712",
"0.52258366",
"0.5164079",
"0.50828594",
"0.50826675",
"0.5003571",
"0.4918171",
"0.4914299",
"0.4907016",
"0.49020693",
"0.48807842",
"0.4876331",
"0.48537108",
"0.48348698",
"0.48314634",
"0.48105988",
"0.47979325",
"0.47977874",
"0.47941908",
"0.4... | 0.7813154 | 0 |
Marks the Notification as read if that hasn't happened yet. for parameters see base._public() | def _public(self, request, entity, context):
# if the user viewing is the user for which this notification is meant
# and the notification has not been read yet
if entity.unread:
# get the current user
user = user_logic.getForCurrentAccount()
# if the message is meant for the user ... | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def mark_read(self):\n # Obviously remove the exception when Kippt says the support it.\n raise NotImplementedError(\n \"The Kippt API does not yet support marking notifications as read.\"\n )\n\n data = json.dumps({\"action\": \"mark_seen\"})\n r = requests.post(\n ... | [
"0.7378796",
"0.71452856",
"0.70881987",
"0.69997036",
"0.6812067",
"0.67487156",
"0.66420895",
"0.6324497",
"0.629524",
"0.6247599",
"0.62287843",
"0.6210138",
"0.6176907",
"0.61076736",
"0.6079115",
"0.6078349",
"0.6051828",
"0.60317713",
"0.60317713",
"0.60030264",
"0.5937... | 0.6149425 | 13 |
Set the global end timer, call at very end of algorithm. | def returnGlobalTimer(self):
self.globalTime = (time.time() - self.globalStartRef) + self.addedTime #Reports time in minutes, addedTime is for population reboot.
return self.globalTime/ 60.0 | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def stop_timer(self):\n self.end_time = datetime.now()",
"def _end_of_loop(self):\n\n ### LIVE FRAMERATE ###\n self.timer_ms.run(current_time=time.time())\n if self.timer_ms.isComplete():\n t_end = time.time()\n t_elapsed = np.round((t_end-self.timer_ms.startTime... | [
"0.7395899",
"0.69717705",
"0.6800864",
"0.66952986",
"0.66952986",
"0.6679938",
"0.65479946",
"0.65479946",
"0.65479946",
"0.6544798",
"0.64001036",
"0.63770664",
"0.6320661",
"0.6230333",
"0.61251456",
"0.6105645",
"0.61051744",
"0.6089474",
"0.60740453",
"0.6064848",
"0.60... | 0.0 | -1 |
Sets all time values to the those previously evolved in the loaded popFile. | def setTimerRestart(self, remakeFile):
try:
fileObject = open(remakeFile+"_PopStats.txt", 'r') # opens each datafile to read.
except Exception as inst:
print(type(inst))
print(inst.args)
print(inst)
print('cannot open', remakeFile+"_PopStats.t... | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def refreshMTimes(self):\n del self.mtimesReset[:]\n for fileName, fileInfo in self.data.items():\n oldMTime = self.mtimes.get(fileName,fileInfo.mtime)\n self.mtimes[fileName] = oldMTime\n #--Reset mtime?\n if fileInfo.mtime != oldMTime and oldMTime != -1:\... | [
"0.6186886",
"0.6107426",
"0.5825859",
"0.58206654",
"0.57823503",
"0.5752457",
"0.57256806",
"0.5710723",
"0.56567633",
"0.5641898",
"0.561546",
"0.561546",
"0.561546",
"0.561546",
"0.561546",
"0.55910146",
"0.55683637",
"0.55649704",
"0.55640715",
"0.55567926",
"0.5555497",... | 0.5639504 | 10 |
Reports the time summaries for this run. Returns a string ready to be printed out. | def reportTimes(self):
outputTime = "Global Time\t"+str(self.globalTime/ 60.0)+ \
"\nMatching Time\t" + str(self.globalMatching/ 60.0)+ \
"\nDeletion Time\t" + str(self.globalDeletion/ 60.0)+ \
"\nSubsumption Time\t" + str(self.globalSubsumption/ 60.0)+ \
"\nSelection Time\t"+str... | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def report(self) -> str:\n return REPORT_TIMEFRAME.format(self.name,\n self.times_run,\n self.average_runtime)",
"def render_timing_report(self):\r\n report = ('Timing report\\n'\r\n '=============\\n')\r\n for ... | [
"0.76224065",
"0.7170328",
"0.6984836",
"0.6865964",
"0.68170786",
"0.6785364",
"0.6738881",
"0.6686491",
"0.6552146",
"0.65373576",
"0.6534475",
"0.6499587",
"0.64938",
"0.6482341",
"0.64753103",
"0.64553666",
"0.64503497",
"0.63862836",
"0.6380132",
"0.6368545",
"0.63616604... | 0.7542045 | 1 |
Establish a connection with the given XenServer using XMLRPC | def getXenConnection(server, logger):
server = server.lower()
#read the global 'locations' file with all information
if os.path.isfile(LOCATIONS_CONF):
try:
locationConf = ConfigObj(LOCATIONS_CONF)
#check if the server is specified
if server in... | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def xs_connection(password, url, user='root'):\n \n conn = xmlrpclib.Server(url)\n connection = conn.session.login_with_password(user, password)\n \n if connection['Status'] == 'Success':\n token = connection['Value']\n sys.stderr.write (\"\\n Connection unique ref: %s\\n\" %token)\n ... | [
"0.6573482",
"0.6204108",
"0.5981148",
"0.59743536",
"0.589587",
"0.58379835",
"0.5822125",
"0.5776857",
"0.5766201",
"0.575728",
"0.5750974",
"0.56980014",
"0.56352746",
"0.55725807",
"0.55596584",
"0.5543674",
"0.552844",
"0.5514942",
"0.5501016",
"0.54789513",
"0.5441803",... | 0.71508884 | 0 |
Function to make a new, random ball. | def make_ball():
ball = Ball()
# Starting position of the ball.
# Take into account the ball size so we don't spawn on the edge.
ball.x = random.randrange(BALL_SIZE, SCREEN_WIDTH - BALL_SIZE)
ball.y = random.randrange(BALL_SIZE, SCREEN_HEIGHT - BALL_SIZE)
# Speed and direction of rectangle
... | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def make_ball(id):\n ball = Ball()\n\n ball.id = id\n\n # Size of the ball\n # ball.size = random.randrange(10, 30)\n ball.size = 10\n\n # Starting position of the ball.\n # Take into account the ball size so we don't spawn on the edge.\n ball.x = random.randrange(ball.size, WINDOW_WIDTH - ... | [
"0.7997919",
"0.7979903",
"0.7828779",
"0.770357",
"0.75933796",
"0.72658503",
"0.71938133",
"0.716515",
"0.6854851",
"0.6770598",
"0.66657805",
"0.6590058",
"0.6589296",
"0.65399325",
"0.65335727",
"0.6460613",
"0.64353716",
"0.6428926",
"0.6326836",
"0.63059175",
"0.6277418... | 0.85226744 | 0 |
This is our main program. | def main():
pygame.init()
# Set the height and width of the screen
size = [SCREEN_WIDTH, SCREEN_HEIGHT]
screen = pygame.display.set_mode(size)
pygame.display.set_caption("Bouncing Balls")
# Loop until the user clicks the close button.
done = False
# Used to manage how fast the screen... | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def main():",
"def main():",
"def main():",
"def main():",
"def main():",
"def main():",
"def main():",
"def main():",
"def main():",
"def main():",
"def main():",
"def main():",
"def main():",
"def main():",
"def main():",
"def main():",
"def main():",
"def main():",
"def main(... | [
"0.89765817",
"0.89765817",
"0.89765817",
"0.89765817",
"0.89765817",
"0.89765817",
"0.89765817",
"0.89765817",
"0.89765817",
"0.89765817",
"0.89765817",
"0.89765817",
"0.89765817",
"0.89765817",
"0.89765817",
"0.89765817",
"0.89765817",
"0.89765817",
"0.89765817",
"0.89765817"... | 0.0 | -1 |
These arguments should be helpful in tweaking the outputs to better fit the hardware or specific decoder requirements. I.e. if your hardware shapes the signal on longer/shorter intervals than the actual value, it can be adjusted here. Currently only bit_X_part_duration and packet_separation are used. `n. Performance sh... | def __init__(self,
bit_one_part_min_duration=55, # microseconds
bit_one_part_max_duration=61,
bit_one_part_duration=58,
bit_zero_part_min_duration=95,
bit_zero_part_max_duration=9900,
bit_zero_part_duration=100,
... | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def generate_synth_data(n):",
"def measure_all_1(n, state):\n state = state.copy()\n\n outs = ''\n for i in range(n):\n out = measure_single(n, state,\n i) # After measuring bit0, bit0 collapses. It affects the subsequent bit1 measurement, but does not affect the 1000... | [
"0.5669672",
"0.55470574",
"0.55303466",
"0.5242294",
"0.52390707",
"0.51292825",
"0.51087517",
"0.5086169",
"0.50713027",
"0.5057984",
"0.5040431",
"0.49897262",
"0.4978931",
"0.4975838",
"0.49727336",
"0.48955587",
"0.48590678",
"0.48550522",
"0.48550522",
"0.48501915",
"0.... | 0.59223044 | 0 |
Executes a command and waits for it to finish. If args are provided, then they will be used. If args are not provided, and arguments were used to run this program, then those arguments will be used. If args are not provided, and no arguments were used to run this program, and default args are provided, then they will b... | def execute(
file: str,
args: Sequence[str],
cwd: Optional[Union[Path, str]] = None,
env: Optional[dict] = None,
capture: bool = False,
verbose: Optional[bool] = None,
) -> CompletedProcess:
if env is None:
env = os.environ.copy()
if is_verbose(verbose):
log.ok(f"run: {... | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def do_command(): # pragma: no cover\n args = parse_args(sys.argv[1:])\n status = run(args)\n sys.exit(status)",
"def run_command(*args):\n\n # Want to print the commmand before running it, like `set -x`. This is very niave, but at least gets the idea\n # across. A more accurate command line wi... | [
"0.71821827",
"0.6948193",
"0.6944926",
"0.682519",
"0.676949",
"0.6722863",
"0.6686224",
"0.66784173",
"0.66211605",
"0.65788233",
"0.6488399",
"0.6342324",
"0.6300042",
"0.62756133",
"0.626245",
"0.6260843",
"0.6245185",
"0.6204857",
"0.6172158",
"0.6147972",
"0.6116247",
... | 0.5562702 | 96 |
Imports a Python module from any local filesystem path. Temporarily alters sys.path to allow the imported module to import other modules in the same directory. | def import_file(path: Union[PurePath, str]) -> Generator[ModuleType, None, None]:
pathdir = os.path.dirname(path)
if pathdir in sys.path:
added_to_sys_path = False
else:
sys.path.insert(0, pathdir)
added_to_sys_path = True
try:
name = os.path.basename(path).split(".")[0]... | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def import_module_from(mod_path):\n if '.' in mod_path:\n bits = mod_path.split('.')\n mod_name = bits.pop()\n mod_path = '.'.join(bits)\n return import_module(mod_path, mod_name)\n else:\n return import_module(mod_path)",
"def import_module(name, path):\n spec = impor... | [
"0.7278681",
"0.7132707",
"0.7093543",
"0.7034862",
"0.699624",
"0.6958857",
"0.6950275",
"0.69009155",
"0.68416375",
"0.67911935",
"0.67736846",
"0.67294097",
"0.6699479",
"0.663062",
"0.663062",
"0.663062",
"0.66145456",
"0.66145456",
"0.66067624",
"0.6605258",
"0.6592462",... | 0.61376876 | 36 |
create a graph with the partition R of size n1 and partition H of size n2 | def seat_model_generator(n1, n2, k_low, k_up, flag=0):
def order_by_master_list(l, master_list):
return sorted(l, key=master_list.index)
possible_credits = [5, 10, 15, 20]
# set up geometric distribution among above possible hospital credits
probs = np.random.geometric(p=0.10, size=len(possible... | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def GenDumbbellGraph(n1, n2):\n G = nx.complete_graph(n1)\n H = nx.complete_graph(n2)\n\n mapping = {}\n for i in range(n2):\n mapping[i] = i+n1\n H = nx.relabel_nodes(H, mapping=mapping)\n\n I = nx.union(G,H)\n I.add_edge(n1-1,n1)\n I.weighted = False\n #set weight to 1\n for ... | [
"0.63051015",
"0.6041714",
"0.5770795",
"0.5697603",
"0.56216264",
"0.5607458",
"0.5580612",
"0.5548421",
"0.5535614",
"0.54805267",
"0.5478538",
"0.54504657",
"0.5440092",
"0.5411817",
"0.5397303",
"0.5375728",
"0.5331656",
"0.5326185",
"0.53207487",
"0.53173274",
"0.5306093... | 0.49827084 | 67 |
delete not exist pipeline | def test_delete_pipeline_not_exist(self):
pipeline_name = 'pipeline_name_not_exist'
try:
self.client.delete_pipeline(pipeline_name)
except BceHttpClientError as e:
if isinstance(e.last_error, BceServerError):
assert e.last_error.message.startswith('The req... | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def test_list_pipeline_delete_one(self):\n response = self.client.delete_pipeline(self.pipeline_name)\n nose.tools.assert_is_not_none(response)\n\n response = self.client.list_pipelines()\n exsit = False\n for pipeline in response.pipelines:\n if pipeline.pipeline_name... | [
"0.6537438",
"0.5996526",
"0.59681296",
"0.59105635",
"0.57883143",
"0.5757299",
"0.56991524",
"0.56369615",
"0.5517205",
"0.5428441",
"0.5411213",
"0.53958535",
"0.5372841",
"0.53464174",
"0.5332597",
"0.5326581",
"0.5326282",
"0.53257066",
"0.53243315",
"0.53197414",
"0.529... | 0.59694725 | 2 |
delete pipeline with name is empty | def test_delete_pipeline_with_name_is_empty(self):
pipeline_name = ''
with nose.tools.assert_raises_regexp(BceClientError,
'pipeline_name can\'t be empty string'):
self.client.delete_pipeline(pipeline_name) | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def test_delete_pipeline_with_name_is_none(self):\n with nose.tools.assert_raises_regexp(ValueError,\n 'arg \"pipeline_name\" should not be None'):\n self.client.delete_pipeline(None)",
"def test_list_pipeline_delete_one(self):\n response = self.client.delete_pipeline(self... | [
"0.7732262",
"0.76229423",
"0.64914685",
"0.64858747",
"0.6106427",
"0.6091376",
"0.5948275",
"0.588508",
"0.5849737",
"0.571674",
"0.5584181",
"0.5460383",
"0.5422234",
"0.5408701",
"0.54028195",
"0.53799385",
"0.5361476",
"0.5329178",
"0.52891815",
"0.52666765",
"0.5263914"... | 0.7667069 | 1 |
delete pipeline with name is none | def test_delete_pipeline_with_name_is_none(self):
with nose.tools.assert_raises_regexp(ValueError,
'arg "pipeline_name" should not be None'):
self.client.delete_pipeline(None) | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def test_list_pipeline_delete_one(self):\n response = self.client.delete_pipeline(self.pipeline_name)\n nose.tools.assert_is_not_none(response)\n\n response = self.client.list_pipelines()\n exsit = False\n for pipeline in response.pipelines:\n if pipeline.pipeline_name... | [
"0.763168",
"0.71707374",
"0.66285604",
"0.64226484",
"0.6412838",
"0.63978565",
"0.61271435",
"0.59604853",
"0.5898413",
"0.58966225",
"0.5781897",
"0.5680162",
"0.5664345",
"0.561427",
"0.555643",
"0.5535108",
"0.5525379",
"0.55244136",
"0.5465732",
"0.54537594",
"0.5414938... | 0.7625798 | 1 |
Returns the model properties as a dict | def to_dict(self):
result = {}
for attr, _ in six.iteritems(self.openapi_types):
value = getattr(self, attr)
if isinstance(value, list):
result[attr] = list(map(
lambda x: x.to_dict() if hasattr(x, "to_dict") else x,
value
... | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def to_dict(self):\n return self.properties",
"def to_dict(self):\n return self.properties",
"def get_properties(self):\n return self.properties",
"def asdict(self):\n return self._prop_dict",
"def json(self):\n rv = {\n prop: getattr(self, prop)\n f... | [
"0.7751993",
"0.7751993",
"0.73391134",
"0.7334895",
"0.7297356",
"0.727818",
"0.7159078",
"0.71578115",
"0.71494967",
"0.71494967",
"0.71283495",
"0.71275014",
"0.7122587",
"0.71079814",
"0.7060394",
"0.7043251",
"0.7034103",
"0.70233124",
"0.69635814",
"0.69586295",
"0.6900... | 0.0 | -1 |
Returns the string representation of the model | def to_str(self):
import simplejson as json
if six.PY2:
import sys
reload(sys)
sys.setdefaultencoding("utf-8")
return json.dumps(sanitize_for_serialization(self), ensure_ascii=False) | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def __str__(self):\n return super().__str__() + self.model.__str__()",
"def __str__(self) -> str:\n # noinspection PyUnresolvedReferences\n opts = self._meta\n if self.name_field:\n result = str(opts.get_field(self.name_field).value_from_object(self))\n else:\n ... | [
"0.85856473",
"0.7816244",
"0.77898955",
"0.77511245",
"0.77511245",
"0.7712556",
"0.76984036",
"0.766997",
"0.7650706",
"0.7601334",
"0.7583777",
"0.7571045",
"0.75404567",
"0.7523676",
"0.7515677",
"0.7501507",
"0.7488033",
"0.7488033",
"0.74696296",
"0.7452353",
"0.7446052... | 0.0 | -1 |
Returns true if both objects are equal | def __eq__(self, other):
if not isinstance(other, ListDevicesRequest):
return False
return self.__dict__ == other.__dict__ | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def __eq__(self, other):\n return are_equal(self, other)",
"def __eq__(self, other):\n return are_equal(self, other)",
"def __eq__(self,other):\n try: return self.object==other.object and isinstance(self,type(other))\n except: return False",
"def __eq__(self, other):\n if i... | [
"0.8088132",
"0.8088132",
"0.8054589",
"0.7982687",
"0.79670393",
"0.79670393",
"0.79670393",
"0.79670393",
"0.79670393",
"0.79670393",
"0.79670393",
"0.79670393",
"0.79670393",
"0.79670393",
"0.79670393",
"0.79670393",
"0.79670393",
"0.79670393",
"0.79670393",
"0.79670393",
... | 0.0 | -1 |
Returns true if both objects are not equal | def __ne__(self, other):
return not self == other | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def __ne__(self, other: object) -> bool:\n if self.__eq__(other):\n return False\n return True",
"def __ne__(self, other: object) -> bool:\n return not self.__eq__(other)",
"def __ne__(self, other) -> bool:\n return not self.__eq__(other)",
"def __eq__(self, other):\n ... | [
"0.845611",
"0.8391477",
"0.8144138",
"0.81410587",
"0.8132492",
"0.8093973",
"0.80920255",
"0.80920255",
"0.80920255",
"0.8085325",
"0.8085325",
"0.8076365",
"0.8076365",
"0.8065748"
] | 0.0 | -1 |
Shuffle (random out of order) | def shuffle(self):
self._current = 0
random.shuffle(self._cards) | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def shuffle(self):\n for i in xrange(self.n - 1):\n pos = random.randint(i, self.n - 1)\n self.to[i], self.to[pos] = self.to[pos], self.to[i]\n self.a[i], self.a[pos] = self.a[pos], self.a[i]\n return self.a",
"def _shuffle():\n\n random.shuffle(deck)",
... | [
"0.79199487",
"0.78305924",
"0.7767727",
"0.7755848",
"0.775454",
"0.77540964",
"0.77540964",
"0.77540964",
"0.77540964",
"0.77540964",
"0.77485484",
"0.7747164",
"0.7740806",
"0.7716279",
"0.7658358",
"0.76217234",
"0.7616497",
"0.7616497",
"0.7579704",
"0.75728077",
"0.7549... | 0.7156094 | 49 |
The player sorts the cards in his hand | def arrange(self, card_key):
self._cards_on_hand.sort(key=card_key) | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def sort(self):\n self.cards.sort()",
"def sort(self):\n self.cards.sort()",
"def sort(self):\n self.deckcards.sort()",
"def sort_cards(self):\n self.cards.sort(key=operator.attrgetter('persona', 'rank'))\n self.update_position()",
"def deal_cards(self, players):\n ... | [
"0.80259424",
"0.80259424",
"0.7755631",
"0.7686",
"0.74837816",
"0.7480401",
"0.7467405",
"0.7019499",
"0.6767792",
"0.6756765",
"0.6609087",
"0.65876836",
"0.65850675",
"0.6544533",
"0.6536881",
"0.65107507",
"0.6496739",
"0.6467788",
"0.6414254",
"0.6405045",
"0.639959",
... | 0.7612255 | 4 |
Return the points of the player's hand type and the points of the largest card | def score_on_hands(cards_on_hand):
score = 0
straightCount = 0
max_card = 0
suite_dict = {}
face_dict = {}
transfer_dict = {'A':1,'J':11,'Q':12,'K':13}
card_face = []
'''Circulate the player's hand, build a list of points and a suit dict'''
for index in range(len(cards_on_ha... | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def hand_points(hand):\n points = [[]]\n branch = 1\n for card in hand:\n if not card[\"is_hidden\"]:\n if card[\"value\"].isnumeric():\n for possibility in range(branch):\n points[possibility].append(int(card[\"value\"]))\n elif card[\"value\"] == \"A\":\n for possibility in... | [
"0.7345715",
"0.6820116",
"0.6792824",
"0.67184216",
"0.6671203",
"0.6506451",
"0.648527",
"0.6457946",
"0.64482373",
"0.63901895",
"0.63653654",
"0.6356537",
"0.63349295",
"0.63116616",
"0.63022965",
"0.63019645",
"0.62861043",
"0.6262797",
"0.62608933",
"0.6257797",
"0.6253... | 0.7179427 | 1 |
Convert to jsonifyable dictionary. | def to_dict(self):
print("\n\nSTARTING...")
ea = db.session.query(entity_assets).filter(entity_assets.c.entity_id == self.id).all()
print("\n\nmade it", ea)
em = db.session.query(entity_meters).filter(entity_meters.c.entity_id == self.id).all()
est = db.session.query(entity_statu... | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def to_dict(self) -> dict:",
"def to_json(self) -> Dict[str, Any]:\n return self.__dict__",
"def json(self) -> Dict[str, Union[List, Dict, str, int, float]]:",
"def convert_to_json(self):\n return self.__dict__",
"def to_dict(self, data):\n return json.loads(json.dumps(data))",
"def ... | [
"0.749095",
"0.7483248",
"0.7391134",
"0.73811585",
"0.7361108",
"0.72996116",
"0.7164284",
"0.7152736",
"0.71295935",
"0.70000273",
"0.6992031",
"0.69914335",
"0.69574475",
"0.6900836",
"0.68706214",
"0.68675035",
"0.6857552",
"0.68555397",
"0.68414974",
"0.6832031",
"0.6832... | 0.0 | -1 |
this fnc made for test because we need user input but intest it cant typing itself os it need to call patch module but it need to create a sperate function and call it in function that we want to use | def inask(question: str) -> str:
answer = input(question)
return answer | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def test_main_gc_2(test):\n answers = (i for i in (test, '1 1 1 1', 'q'))\n with mock.patch.object(builtins, 'input', lambda _: next(answers)):\n g_c.main()",
"def test_111(self):\n user_input = [\"1\",\"1\",\"1\"]\n with patch(\"builtins.input\", side_effect=user_input) as input_call:... | [
"0.60000163",
"0.596667",
"0.5931788",
"0.5911655",
"0.58867043",
"0.5871372",
"0.57529557",
"0.5751793",
"0.57436305",
"0.57372427",
"0.57297534",
"0.5723434",
"0.5711433",
"0.5695959",
"0.5677015",
"0.56619185",
"0.56531984",
"0.55036503",
"0.5501072",
"0.549215",
"0.548908... | 0.0 | -1 |
init draft draft_file (str) draft_work (list) if user did not have draft.json it will return None | def __init__(self, path: str, filename: str, draft: dict) -> None:
self.draft_file = draft["fileDraft"]
self.draft_out = draft["outputDraft"]
self.pre_data = None
self.filename = filename | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def test_save_draft():\n with open(basedir + \"fixture/7149593_formatted.json\", \"r\") as f:\n storage.save_draft(user_id, \"bib\", \"7149593\", f.read(), \"1362044230872\")\n with open(basedir + \"some/path/\" + user_id + \"/bib/7149593\", \"r\") as f:\n json_data = json.loads(f.read())\n ... | [
"0.5677488",
"0.5291713",
"0.5274986",
"0.5244381",
"0.5198095",
"0.51400155",
"0.50597966",
"0.5025025",
"0.49975803",
"0.49552223",
"0.49455118",
"0.4918419",
"0.49164775",
"0.49087638",
"0.49034452",
"0.48699167",
"0.4825065",
"0.4804959",
"0.47942033",
"0.47536626",
"0.47... | 0.6959813 | 0 |
prepare filename to dict | def _filename_pre_data(self) -> dict:
key = []
remainder = ""
prework = {}
for i in self.draft_file:
if i == "{":
remainder = ""
elif i == "}":
key.append(remainder)
else:
remainder += i
list_file... | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def file_to_dictionary():\n\n return;",
"def make_file_dict():\r\n fileDict = {'pageUrls': [],\r\n 'pageFileNames': [],\r\n 'pageIds': [],\r\n 'fileUrls': [],\r\n 'fileIds': [],\r\n 'fileNames': [],\r\n 'cssUrls': [],... | [
"0.71812004",
"0.6705322",
"0.6690661",
"0.66186893",
"0.6445358",
"0.64392036",
"0.64164746",
"0.6411736",
"0.63395435",
"0.6329124",
"0.6299308",
"0.61634296",
"0.6087172",
"0.6072993",
"0.60555255",
"0.60460156",
"0.60145646",
"0.59737724",
"0.5971008",
"0.59170955",
"0.59... | 0.6893264 | 1 |
make that studect_data(dict) ready for the next step by get the output draft and set it into student_data and have its value is "N/"A | def prepare_student_data(self) -> dict:
self._filename_pre_data()
empty_student = {}
empty_student["scoreTimestamp"] = "N/A"
for i in self.draft_out:
empty_student[i] = "N/A"
for i in self.pre_data:
empty_student[i] = self.pre_data[i]
self.pre_data... | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def _build_student_data(self, data, csvwriter):\n url_base = data['base_url']\n course_id = data['course']\n is_resumen = data['format']\n course_key = CourseKey.from_string(course_id)\n if is_resumen:\n header = ['Username', 'Email', 'Run', 'Seccion', 'SubSeccion', 'U... | [
"0.6489561",
"0.6420505",
"0.62448007",
"0.5916584",
"0.577582",
"0.5774251",
"0.56840724",
"0.5663926",
"0.5551127",
"0.5542213",
"0.54767984",
"0.5294155",
"0.5282048",
"0.5276514",
"0.5263904",
"0.5198079",
"0.51527464",
"0.51271755",
"0.5115177",
"0.5107831",
"0.5105366",... | 0.7730551 | 0 |
get data form user and set into student data(dict) | def data_input(self, post_student_data: dict) -> dict:
for i in post_student_data:
if post_student_data[i] == "N/A":
while True:
if i == "scoreTimestamp":
post_student_data[i] = int(round(time.time() * 1000))
break... | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def student_view_data(self):\n def get_student_profile_data():\n # pylint: disable=no-member\n \"\"\"\n Returns profile data for all students on the course.\n \"\"\"\n try:\n regexp_string = self.regexp_from_users_included_email(self.user... | [
"0.67464226",
"0.66404426",
"0.6622056",
"0.6347945",
"0.6245057",
"0.62142384",
"0.6205935",
"0.615984",
"0.61378896",
"0.61116576",
"0.6104827",
"0.6083482",
"0.6057624",
"0.5952726",
"0.5943629",
"0.5928082",
"0.5885253",
"0.5879775",
"0.58410686",
"0.58036226",
"0.5772137... | 0.6305316 | 4 |
ask user for student data | def ask(self) -> data_input:
print("===========================")
post_student_data = self.pre_data
for i in post_student_data:
if post_student_data[i] != "N/A":
print(f"{i}: {post_student_data[i]}")
print("===========================")
post_data = sel... | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def main():\n student_info = prompt_student()\n display_student(student_info)",
"def prompt_student():\n # user prompts\n f_name = input('Please enter your first name: ')\n l_name = input('Please enter your last name: ')\n s_id = int(input('Please enter your id number: '))\n\n # creates a ne... | [
"0.71365875",
"0.68289113",
"0.67440414",
"0.6611434",
"0.657059",
"0.65320116",
"0.6474956",
"0.64589435",
"0.6430051",
"0.63646716",
"0.6351014",
"0.6342025",
"0.63141805",
"0.63013244",
"0.62784135",
"0.62527335",
"0.62441015",
"0.6243171",
"0.62387955",
"0.6225735",
"0.61... | 0.66810966 | 3 |
Runs classmethods according to the input parameters. | def run_parsing(self):
if self.version:
print(f'"{VERSION}"')
return VERSION
elif self.limit is not None and self.limit <= 0:
print("Limit must be greater than 0!")
return "Limit must be greater than 0!"
elif self.date:
if len(str(self... | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def run_test_class(self, ClassName):\n tests = ClassName(\n self.model,\n self.parameter_values,\n self.disc,\n self.solution,\n self.operating_condition,\n )\n tests.test_all()",
"def run_methods(self):\n results = {}\n me... | [
"0.64773834",
"0.6194458",
"0.612699",
"0.6097567",
"0.6065442",
"0.60520273",
"0.60313284",
"0.59927815",
"0.59875256",
"0.59539783",
"0.58946216",
"0.58850116",
"0.58268774",
"0.58260775",
"0.58240694",
"0.5815849",
"0.5781631",
"0.5753776",
"0.5736039",
"0.57344794",
"0.57... | 0.0 | -1 |
Method gets content of the RSS feed and converts it to XML. If URL is wrong returns exception. | def get_content(self):
try:
self.print_if_verbose(
f"Method 'get_content' is working: \n"
f"Trying to get content from RSS source: {self.source} ..."
)
rss_xml = urlopen(self.source).read().decode("utf-8")
self.news_amount = rss_xm... | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def request_rss(self, url):\n return feedparser.parse(url)",
"def get_feed(self):\n possible_endings = ('rss', 'rss/')\n if not self.url or not self.url.endswith(possible_endings):\n print('Please check URL(is RSS?) and Internet connection')\n sys.exit()\n try:\n ... | [
"0.7589636",
"0.7390871",
"0.73857987",
"0.69300675",
"0.681616",
"0.65743273",
"0.6510668",
"0.6483486",
"0.64016354",
"0.6348268",
"0.6312308",
"0.6298152",
"0.6253646",
"0.6192138",
"0.6180068",
"0.61718124",
"0.6167248",
"0.61640453",
"0.6153321",
"0.6140984",
"0.61041224... | 0.684713 | 4 |
Method gets XML element and converts it to dict. | def process_content(self, channel) -> dict:
self.print_if_verbose(f"Method 'process_content' is working:")
if self.limit is None or self.limit >= self.news_amount:
self.limit = self.news_amount
rss_feed = {}
rss_feed["Feed"] = channel.findtext('title')
rss_feed["De... | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def get_el_dict(activity_name, element):\n set_xml()\n element_dict = activity.get(activity_name).get(element)\n print(element_dict)\n return element_dict",
"def __get_el_dict(activity_name, element_name):\n __set_xml()\n element_dict = activity.get(activity_name).get(element_name)\n return ... | [
"0.75600344",
"0.75184923",
"0.6895443",
"0.6887687",
"0.6611917",
"0.63355374",
"0.6308879",
"0.62345254",
"0.61984074",
"0.6177131",
"0.60915977",
"0.607099",
"0.6038045",
"0.5996926",
"0.5996239",
"0.59638584",
"0.5933457",
"0.5915614",
"0.5885475",
"0.58365715",
"0.583657... | 0.0 | -1 |
Method saves RSSfeed content to cache. | def save_news_to_cache(self, rss_feed):
self.print_if_verbose(
f"Method 'save_news_to_cache' is working: \n"
f"Saving news to cache... \n"
)
rss_feed_to_cache_title = self.source
if not os.path.exists("cache"):
os.mkdir("cache")
os.chdir("ca... | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def write_cache(feed):\n if ARGV.get(NOCACHE_OPT):\n return\n CACHE['feed'] = feed\n CACHE['last-request'] = str(time.time())\n CACHE['max-age'] = feed.headers['Cache-Control'].split('=')[1]\n save_datfile()",
"def add_to_cache(self, content: Content):\n cache = self.cache\n c... | [
"0.67586154",
"0.6627388",
"0.6459616",
"0.63490593",
"0.62718034",
"0.62651795",
"0.6241656",
"0.6175605",
"0.617538",
"0.61674666",
"0.61565787",
"0.58985907",
"0.5897402",
"0.58390415",
"0.5832458",
"0.58263427",
"0.5811275",
"0.58038193",
"0.57993054",
"0.5782869",
"0.574... | 0.73235667 | 0 |
Method saves images to local image_cache | def save_image_to_image_cache(self, image_url, image_name):
self.print_if_verbose(
f"Method 'save_image_to_image_cache' is working: \n"
f"Saving image to image_cache... \n"
)
os.chdir("image_cache")
try:
urlretrieve(image_url, image_name)
... | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def cache(self):\n\t\tprint self.url\n\t\tif self.url and not self.streetimage:\n\t\t\tresult = urllib.urlretrieve(self.url)\n\t\t\tfname = os.path.basename(self.url).split('&')[-1]+\".jpg\"\n\t\t\tprint 'fname = ', fname, 'result = ', result\n\t\t\tself.streetimage.save(fname, File(open(result[0])))\n\t\t\tself.s... | [
"0.7162924",
"0.696713",
"0.6893315",
"0.68605286",
"0.6816649",
"0.6638263",
"0.66211474",
"0.6558931",
"0.6538121",
"0.6499858",
"0.64770514",
"0.6462724",
"0.6439817",
"0.64209604",
"0.6399955",
"0.6393154",
"0.63581383",
"0.6332679",
"0.63150334",
"0.62993383",
"0.629635"... | 0.7229129 | 0 |
Method gets content from cache and converts it to list. | def get_content_from_cache(self):
rss_feed = []
news_to_show = 0
try:
self.print_if_verbose(
f"Method 'get_content_from_cache' is working: \n"
f"Trying to get content from cache..."
)
os.chdir("cache")
except Exception... | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"async def get() -> list:\n if _cache is None:\n await _update()\n return _cache",
"def get(self):\n if path.exists(self.cachefile):\n self.invalidion()\n full_cache = self._get_all()\n return full_cache\n else:\n return []",
"def getCacheCo... | [
"0.7287074",
"0.7173358",
"0.70179826",
"0.67280906",
"0.6566055",
"0.6531295",
"0.634033",
"0.6329253",
"0.6320799",
"0.6245494",
"0.6172922",
"0.6124554",
"0.6120082",
"0.61168504",
"0.60797656",
"0.60768974",
"0.60223424",
"0.60223424",
"0.60223424",
"0.60223424",
"0.60222... | 0.6955499 | 3 |
Method prints RSSfeed content in standard format. | def print_content(self, rss_feed):
self.print_if_verbose(
f"Method 'print_content' is working: \n"
f"Print information about RSS-feed: \n"
)
for key in rss_feed:
if type(rss_feed[key]) != list:
print(f"{key}: {rss_feed[key]}")
print(... | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def output(self):\n feed = []\n feed.append('''<?xml version=\"1.0\" encoding=\"UTF-8\" standalone=\"yes\"?>\n <?xml-stylesheet href=\"http://www.blogger.com/styles/atom.css\" type=\"text/css\"?>\n <feed version=\"%(version)s\" xmlns=\"http://purl.org/atom/ns#\" xml:lang=\"%(lan... | [
"0.69217837",
"0.6848304",
"0.67503965",
"0.6540545",
"0.6479408",
"0.63609755",
"0.62872815",
"0.604937",
"0.60263556",
"0.60063654",
"0.5867962",
"0.57046604",
"0.5669589",
"0.56494915",
"0.55631423",
"0.5525209",
"0.55078566",
"0.5471238",
"0.54546905",
"0.5432877",
"0.541... | 0.79999995 | 0 |
Method prints RSSfeed content in JSON format. | def print_json_content(self, rss_feed):
self.print_if_verbose(
f"Method 'print_json_content' is working: \n"
f"RSS feed will be printed in JSON format: \n"
)
json_content = json.dumps(rss_feed, indent=3)
print(json_content)
self.print_if_verbose(f"Metho... | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def print_content(self, rss_feed):\n\n self.print_if_verbose(\n f\"Method 'print_content' is working: \\n\"\n f\"Print information about RSS-feed: \\n\"\n )\n\n for key in rss_feed:\n if type(rss_feed[key]) != list:\n print(f\"{key}: {rss_feed[ke... | [
"0.70555997",
"0.64175785",
"0.64175785",
"0.6284882",
"0.6267104",
"0.6229609",
"0.61915755",
"0.6137583",
"0.58659434",
"0.5838001",
"0.5723957",
"0.57200825",
"0.5677733",
"0.5626589",
"0.5596967",
"0.5479984",
"0.5475434",
"0.543073",
"0.54114175",
"0.5400201",
"0.5398685... | 0.8396723 | 0 |
Method prints RSSfeed content from cache. | def print_content_from_cache(self, rss_feed):
self.print_if_verbose(f"Method 'print_content_from_cache' is working: \n")
for news in rss_feed:
for key in news.keys():
print(f"{key}: {news[key]}")
print()
self.print_if_verbose(f"Method 'print_content_fro... | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def get_content_from_cache(self):\n\n rss_feed = []\n news_to_show = 0\n\n try:\n self.print_if_verbose(\n f\"Method 'get_content_from_cache' is working: \\n\"\n f\"Trying to get content from cache...\"\n )\n os.chdir(\"cache\")\n ... | [
"0.6982981",
"0.64359665",
"0.6297086",
"0.61537176",
"0.5946821",
"0.5885559",
"0.58853716",
"0.5868752",
"0.58248276",
"0.57419926",
"0.5715743",
"0.5712399",
"0.57096833",
"0.56506985",
"0.5642677",
"0.5574007",
"0.55680573",
"0.55316573",
"0.55299604",
"0.55256534",
"0.54... | 0.83626264 | 0 |
Method prints logs to stdout | def print_if_verbose(self, log):
if self.verbose:
print(log)
return log | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def log(self, *args):\n self.log_stdout(*args)\n print(*args, file=self.general_log_file.file)\n self.general_log_file.flush()",
"def PrintLogs(self) -> None:\n assert self.Finished()\n for f, stream_name in (\n (self.stdout, \"STDOUT\"), (self.stderr, \"STDERR\")):\n f.flu... | [
"0.76321125",
"0.7469357",
"0.7286969",
"0.7198565",
"0.71786773",
"0.7098051",
"0.6855569",
"0.68315417",
"0.6798937",
"0.6730275",
"0.67166114",
"0.6708592",
"0.6638024",
"0.6633471",
"0.6632932",
"0.6629533",
"0.662067",
"0.66190934",
"0.66034806",
"0.65940773",
"0.6569787... | 0.0 | -1 |
Method converts date to format "%Y%m%d" if format of the date is in "possible_datetime_formats", else returns date unchanged. | def get_formatted_date(self, date):
formatted_date = date
possible_datetime_formats = [
"%Y-%m-%dT%H:%M:%S%z", # "2021-10-19T16:46:02Z"
"%a, %d %b %Y %H:%M:%S %z", # "Tue, 19 Oct 2021 21:00:13 +0300"
"%a, %d %b %Y %H:%M:%S %Z", # "Tue, 19 Oct 2021 18:54:00 GMT"
... | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def date_format_correct(self):\n valid_format = True\n try:\n new_val = self.date_edit.text()\n datetime_object = datetime.strptime(new_val, \"%Y-%m-%d\")\n except BaseException:\n valid_format = False\n return valid_format",
"def convert_date(self, da... | [
"0.6769846",
"0.6541767",
"0.6534601",
"0.63497317",
"0.6287422",
"0.62583554",
"0.6258047",
"0.62580156",
"0.62022954",
"0.6196835",
"0.6192632",
"0.61566573",
"0.61417395",
"0.6123462",
"0.6105762",
"0.6104587",
"0.60866493",
"0.6068869",
"0.6066987",
"0.6055134",
"0.604416... | 0.6154808 | 12 |
Method saves RSSfeed content to HTMLfile. If path is wrong returns exception. | def save_to_html(self, rss_feed, date=None):
self.print_if_verbose(f"Method 'save_to_html' is working: \n")
add_to_html_file = "<!DOCTYPE html>\n<html lang='en'>\n<head>\n<meta charset='UTF-8'>\n</head>\n<body>\n"
if not os.path.exists(self.to_html_path):
try:
os.m... | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def save_news_in_html_file(news, path_to_html, logger):\n check_path_to_directory(path_to_html, logger)\n html_file = tags.html(title='RSS news')\n html_file.add(tags.head(tags.meta(charset='utf-8')))\n\n logger.info('Converting news to html format...')\n for article in news:\n html_factory(a... | [
"0.70882803",
"0.6659082",
"0.6444682",
"0.6379773",
"0.6294752",
"0.6192147",
"0.610183",
"0.60757816",
"0.60023487",
"0.5964989",
"0.59016937",
"0.5819117",
"0.58073294",
"0.5782866",
"0.5758187",
"0.57215035",
"0.56917626",
"0.56676036",
"0.56220895",
"0.56053066",
"0.5592... | 0.75413376 | 0 |
Method converts a news to HTMLformat. | def add_to_html(self, news: dict) -> str:
add_to_html_file = ""
add_to_html_file += f"<h2>Title: {news['Title']}</h2>\n"
add_to_html_file += f"<a href={news['Link']}>Link to news</a><br>\n"
add_to_html_file += f"<p>PubDate: {news['PubDate']}</p>\n"
add_to_html_file += f"<p>Sourc... | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def convert_to_html(self, news_list):\n self.logger.info(\"Converting news to HTML...\")\n self.prepare_storage()\n self.process_news_list_with_images(news_list)\n content = self.generate_html_template(news_list)\n self.write_to_file(content.encode(\"UTF-8\"))",
"def convert_ht... | [
"0.7749601",
"0.70121133",
"0.6875486",
"0.66495407",
"0.6365921",
"0.62624204",
"0.6246973",
"0.6110563",
"0.5929288",
"0.5882961",
"0.5868945",
"0.5861875",
"0.58461696",
"0.5827368",
"0.5789063",
"0.5754645",
"0.5731008",
"0.5714564",
"0.56914157",
"0.56863284",
"0.5672449... | 0.65101534 | 4 |
Method saves RSSfeed content to FB2file. If path is wrong returns exception. | def save_to_fb2(self, rss_feed, date=None):
self.print_if_verbose(f"Method 'save_to_fb2' is working: \n")
if not os.path.exists(self.to_fb2_path):
try:
os.makedirs(self.to_fb2_path)
except Exception as error:
print(f"Exception {error} - wrong pat... | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def write_to_file(self, content):\n try:\n with open(self.full_path_to_file, \"wb\") as fp:\n fp.write(content)\n except PermissionError:\n logging.error(\n \"Conversion cannot be performed. Permission denied for this directory\"\n )\n ... | [
"0.6336935",
"0.6105859",
"0.5953287",
"0.59252447",
"0.58727354",
"0.5848965",
"0.5748638",
"0.57357615",
"0.57232815",
"0.57130784",
"0.5701286",
"0.56320465",
"0.5586502",
"0.5526823",
"0.5505389",
"0.55029744",
"0.54756254",
"0.54639256",
"0.5439159",
"0.5437611",
"0.5437... | 0.73413247 | 0 |
Method converts a news to FB2format. | def add_to_fb2(self, news: dict) -> str:
add_to_fb2_file = ""
add_to_fb2_file += f" <p>Title: {news['Title']}</p>\n"
add_to_fb2_file += f" <p><a l:href='{news['Link']}'> 'Link to news' </a></p>\n"
add_to_fb2_file += f" <p>PubDate: {news['PubDate']}</p>\n"
add_to_f... | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def format_news(news):\r\n news = re.sub(r\"[<>]|=+\", \" \", news)\r\n news = re.sub(r\"-{2,}|/{2,}\", \" \", news)\r\n news = re.sub(r\"\\(.+?\\)\", \" \", news)\r\n news = re.sub(r\"\\s+\", \" \", news)\r\n return news",
"def save_to_fb2(self, rss_feed, date=None):\n\n self.print_if_verb... | [
"0.6300344",
"0.6065771",
"0.5977929",
"0.575112",
"0.57443106",
"0.57018936",
"0.5690105",
"0.56440276",
"0.56249213",
"0.56196296",
"0.56100756",
"0.55982596",
"0.55377096",
"0.55372685",
"0.5521784",
"0.5518923",
"0.55061096",
"0.54895186",
"0.54895186",
"0.5396291",
"0.53... | 0.6049749 | 2 |
Returns the field names in the same order as str() | def fieldNames():
return (
"totalValue", "totalValueStd",
"mean", "meanStd",
"variance", "varianceStd",
"skew", "skewStd",
"kurtosis", "kurtosisStd"
) | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def get_field_names() -> Sequence[str]:\n raise NotImplementedError",
"def get_field_names(self):\n return {rv[0] for rv in self.iter_fields()}",
"def field_names(self):\n return self.base_field_names() + list(self.data.keys())",
"def field_names(self):\n ...",
"def field_names(... | [
"0.82322943",
"0.8167811",
"0.8023562",
"0.80067897",
"0.7826049",
"0.7809036",
"0.77826476",
"0.7728658",
"0.7668442",
"0.76414555",
"0.7573801",
"0.7390122",
"0.7305378",
"0.7290598",
"0.7258827",
"0.7254",
"0.7249135",
"0.72328836",
"0.72208977",
"0.7136987",
"0.70926833",... | 0.6830779 | 27 |
Tuple of member data incl. uncertainty for export. | def fields(self):
return (self._total + self._mean + self._variance
+ self._skew + self._kurtosis) | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def unpack(self) -> Tuple[list, list, list, list, float, int, list]:\n # (nice to have) todo:refactor --> as a namedtuple\n unpacked_super = super().unpack()\n\n observations, actions, rewards, Q_values, trajectory_return, _trajectory_lenght = unpacked_super\n\n return observations, act... | [
"0.6213964",
"0.6204447",
"0.59384376",
"0.57277507",
"0.5707521",
"0.5554493",
"0.55509335",
"0.5544605",
"0.5535825",
"0.55020016",
"0.5489144",
"0.5479862",
"0.5433677",
"0.5431568",
"0.5417379",
"0.5414188",
"0.54121655",
"0.5411566",
"0.53934836",
"0.5382212",
"0.5382212... | 0.55766004 | 5 |
Calculate contributions mask to be within the given value range. | def _setValidRange(self, contribs, valueRange):
testfor(contribs.ndim == 2, ValueError)
numContribs, numReps = contribs.shape
self._validRange = np.zeros_like(contribs.T, dtype = bool)
for ri in range(numReps):
# the single set of R for this calculation
rset = con... | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def _mask_for_values_between_exponent_limits(self):\n mask_between_exp_limits = None\n new_exp_lower = new_exp_upper = None\n if self.exp_lower is not None:\n new_exp_lower = self.exp_lower + 1\n if self.exp_upper is not None:\n new_exp_upper = self.exp_upper - 1\n... | [
"0.6139728",
"0.5778516",
"0.5743647",
"0.5667564",
"0.5649735",
"0.5611406",
"0.5567767",
"0.55613536",
"0.5501029",
"0.54364896",
"0.54216534",
"0.5400132",
"0.5389187",
"0.5379457",
"0.53776085",
"0.5347557",
"0.5343009",
"0.53155017",
"0.53128374",
"0.5302718",
"0.5296207... | 0.60455686 | 1 |
Calculates the moments of the distribution of the current particular (implied) parameter. | def _calcMoments(self, contribs, fraction):
numContribs, numReps = contribs.shape
val = np.zeros(numReps)
mu = np.zeros(numReps)
var = np.zeros(numReps)
skw = np.zeros(numReps)
krt = np.zeros(numReps)
# loop over each repetition
for ri in range(numReps):
... | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def moments(self):",
"def calc_moments(distribution):\n x = torch.linspace(2, 22, 31)\n d_mean = torch.sum(x * distribution)\n d_var = torch.sum(distribution * (x - d_mean) ** 2) \n \n return d_mean, torch.sqrt(d_var)",
"def moment(self, n, mu, sigma):\n return scipy_norm.moment(n, mu, si... | [
"0.68563664",
"0.6402003",
"0.6158102",
"0.6069788",
"0.60613734",
"0.5964357",
"0.5932727",
"0.5914467",
"0.59122914",
"0.58807665",
"0.58597636",
"0.5813083",
"0.581118",
"0.58006424",
"0.57764125",
"0.57536584",
"0.57454926",
"0.57426",
"0.5733544",
"0.5727761",
"0.5726019... | 0.0 | -1 |
Sets it to the first available option by default. | def xscale(self, kind):
self._xscale = str(kind).strip() # remove whitespace eventually
if self._xscale not in self.xscaling():
self._xscale = self.xscaling(1) | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"async def _opt_default(self, ctx, option):\n try:\n guild_options = self.database.get_guild_options(ctx.guild.id)\n setattr(guild_options, option, None)\n self.database.save_item(guild_options)\n await ctx.send(f\"Option {option} set to default\")\n except ... | [
"0.71261716",
"0.66823363",
"0.6678832",
"0.6494152",
"0.638087",
"0.63636804",
"0.628238",
"0.62773716",
"0.62408084",
"0.62408084",
"0.62408084",
"0.62408084",
"0.6227838",
"0.6217609",
"0.62013143",
"0.6193903",
"0.6130184",
"0.61205834",
"0.61205834",
"0.6114318",
"0.6108... | 0.0 | -1 |
Lower limit in display units including the unit text. | def lowerDisplay(self):
return "{0:g} ({1})".format(self._param.toDisplay(self.lower),
self._param.displayMagnitudeName()) | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def _get_display_size(self):\n return '%d MB' % self.size if self.size < 1024 else '%.2f GB' % (self.size / 1024.0)",
"def unit_of_measurement(self):\n return \"%\"",
"def units(self):\n pass",
"def unit_of_measurement(self):\n return '%'",
"def unit_of_measurement(self) -> str:\n ... | [
"0.68737787",
"0.6705744",
"0.6617617",
"0.65782964",
"0.6403219",
"0.6403219",
"0.6349418",
"0.62952155",
"0.62344605",
"0.6159931",
"0.6157702",
"0.61567926",
"0.6149927",
"0.6146399",
"0.6140932",
"0.61266",
"0.61061746",
"0.6068677",
"0.605865",
"0.5996165",
"0.5980514",
... | 0.6856167 | 1 |
Upper limit in display units including the unit text. | def upperDisplay(self):
return "{0:g} ({1})".format(self._param.toDisplay(self.upper),
self._param.displayMagnitudeName()) | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def _get_display_size(self):\n return '%d MB' % self.size if self.size < 1024 else '%.2f GB' % (self.size / 1024.0)",
"def unit_of_measurement(self):\n return \"%\"",
"def unit_of_measurement(self):\n return '%'",
"def units(self):\n pass",
"def unit_of_measurement(self) -> str:\n ... | [
"0.70054436",
"0.6769809",
"0.6708141",
"0.6669131",
"0.65528774",
"0.65528774",
"0.65095055",
"0.6420288",
"0.6403162",
"0.63651204",
"0.63568985",
"0.6297604",
"0.62844765",
"0.62185395",
"0.6201521",
"0.6183936",
"0.61839217",
"0.6120024",
"0.611906",
"0.6106219",
"0.60859... | 0.7146413 | 0 |
Restricts histogram range according to changed parameter range if needed. Checks histogram range against parameter limits. | def updateRange(self):
if self.autoFollow:
self.xrange = self.param.activeRange()
self.xrange = self.xrange # call getter & setter again to verify limits | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def autoHistogramRange(self):\n self.vb.enableAutoRange(self.vb.XAxis, True)\n self.vb.enableAutoRange(self.vb.YAxis, True)\n # self.range = None\n # self.updateRange()\n # self.vb.setMouseEnabled(False, False)\n\n # def updateRange(self):\n # self.vb.autoRange()\n ... | [
"0.68070877",
"0.663788",
"0.6523232",
"0.6398338",
"0.63696873",
"0.63696873",
"0.63291216",
"0.6299463",
"0.60977095",
"0.60977095",
"0.6090064",
"0.60784125",
"0.5962637",
"0.5954221",
"0.5932417",
"0.5870318",
"0.586771",
"0.5864656",
"0.5857545",
"0.5852148",
"0.5813075"... | 0.5693101 | 25 |
Descriptive text of fields for UI display. | def displayDataDescr(cls):
return (
"Parameter",
"Auto range",
"Lower",
"Upper",
"Number of bins",
"X-axis scaling",
"Y-axis weighting"
) | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def description(self):",
"def displayField(self):\n print(\"Field :\")\n for i in range(len(self.field)):\n currentSuit = Suit(i + 1)\n print(Bcolor.BOLD + Suit.toColor(currentSuit) + \"\\t\" + str(currentSuit), self.field[i], end=\"\\t\" + Bcolor.END)\n print()",
"de... | [
"0.6875522",
"0.67876977",
"0.6738545",
"0.67317694",
"0.67317694",
"0.6706509",
"0.6705255",
"0.6658271",
"0.65845865",
"0.65845865",
"0.6545622",
"0.649939",
"0.64937603",
"0.6486334",
"0.6432132",
"0.64196366",
"0.63851905",
"0.6357764",
"0.6353186",
"0.6350117",
"0.634400... | 0.616597 | 40 |
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